From 0091cd7649dba602b735d6e269318ef5b71a43c5 Mon Sep 17 00:00:00 2001
From: "shengquan.nian" <shengquan.nian@iluvatar.ai>
Date: Mon, 22 Apr 2024 12:18:56 +0800
Subject: [PATCH] add model pointrcnn_iou

---
 .../pointrcnn-iou/pytorch/README.md           |    69 +
 .../pointrcnn-iou/pytorch/numba/.coveragerc   |    24 +
 .../pointrcnn-iou/pytorch/numba/.flake8       |   263 +
 .../pytorch/numba/.gitattributes              |     1 +
 .../pytorch/numba/.github/CODEOWNERS          |    98 +
 .../.github/ISSUE_TEMPLATE/Bug_report.md      |    34 +
 .../.github/ISSUE_TEMPLATE/Feature_request.md |    23 +
 .../numba/.github/ISSUE_TEMPLATE/config.yml   |    11 +
 .../ISSUE_TEMPLATE/first_rc_checklist.md      |    41 +
 .../ISSUE_TEMPLATE/sub_rc_checklist.md        |    37 +
 .../numba/.github/PULL_REQUEST_TEMPLATE.md    |    39 +
 .../pytorch/numba/.github/workflows/stale.yml |    20 +
 .../pointrcnn-iou/pytorch/numba/.gitignore    |    28 +
 .../pytorch/numba/.pre-commit-config.yaml     |     5 +
 .../pytorch/numba/.readthedocs.yml            |    15 +
 .../pointrcnn-iou/pytorch/numba/CHANGE_LOG    |  5717 +++++++
 .../pytorch/numba/CONTRIBUTING.md             |    53 +
 .../pointrcnn-iou/pytorch/numba/LICENSE       |    24 +
 .../pytorch/numba/LICENSES.third-party        |   493 +
 .../pointrcnn-iou/pytorch/numba/MANIFEST.in   |    10 +
 .../pointrcnn-iou/pytorch/numba/README.rst    |    61 +
 .../pytorch/numba/azure-pipelines.yml         |   129 +
 .../pointrcnn-iou/pytorch/numba/bin/numba     |     8 +
 .../pytorch/numba/build_numba.sh              |    21 +
 .../buildscripts/appveyor/run_with_env.cmd    |    90 +
 .../buildscripts/azure/azure-linux-macos.yml  |    47 +
 .../buildscripts/azure/azure-windows.yml      |    62 +
 .../buildscripts/condarecipe.local/bld.bat    |     3 +
 .../buildscripts/condarecipe.local/build.sh   |    16 +
 .../condarecipe.local/conda_build_config.yaml |    12 +
 .../condarecipe.local/license.txt             |    24 +
 .../buildscripts/condarecipe.local/meta.yaml  |    94 +
 .../condarecipe.local/run_test.bat            |    19 +
 .../condarecipe.local/run_test.sh             |    53 +
 .../condarecipe_clone_icc_rt/bld.bat          |     3 +
 .../condarecipe_clone_icc_rt/build.sh         |     5 +
 .../condarecipe_clone_icc_rt/meta.yaml        |    38 +
 .../condarecipe_clone_icc_rt/scripts/build.py |    33 +
 .../numba/buildscripts/gpuci/axis.yaml        |    19 +
 .../pytorch/numba/buildscripts/gpuci/build.sh |    74 +
 .../incremental/MacOSX10.10.sdk.checksum      |     1 +
 .../buildscripts/incremental/after_success.sh |    11 +
 .../numba/buildscripts/incremental/build.cmd  |    10 +
 .../numba/buildscripts/incremental/build.sh   |    28 +
 .../incremental/install_miniconda.sh          |    19 +
 .../incremental/setup_conda_environment.cmd   |    45 +
 .../incremental/setup_conda_environment.sh    |   117 +
 .../numba/buildscripts/incremental/test.cmd   |    34 +
 .../numba/buildscripts/incremental/test.sh    |   131 +
 .../pytorch/numba/clean_numba.sh              |     9 +
 .../pointrcnn-iou/pytorch/numba/codecov.yml   |    22 +
 .../pytorch/numba/contrib/valgrind-numba.supp |    21 +
 .../pointrcnn-iou/pytorch/numba/docs/Makefile |   177 +
 .../numba/docs/_static/js/modernizr.min.js    |     7 +
 .../docs/_static/numba-blue-icon-rgb.svg      |    27 +
 .../docs/_static/numba-white-icon-rgb.svg     |    27 +
 .../numba/docs/_static/rtd-overrides.css      |     3 +
 .../pytorch/numba/docs/_templates/EMPTY       |     0
 .../pytorch/numba/docs/dagmap/README.md       |    57 +
 .../pytorch/numba/docs/dagmap/dagmap.yaml     |   195 +
 .../numba/docs/dagmap/jquery.graphviz.svg.js  |   537 +
 .../pytorch/numba/docs/dagmap/render.py       |    85 +
 .../pytorch/numba/docs/dagmap/template.html   |   110 +
 .../pytorch/numba/docs/environment.yml        |    16 +
 .../pytorch/numba/docs/gh-pages.py            |   150 +
 .../pointrcnn-iou/pytorch/numba/docs/make.bat |   242 +
 .../pytorch/numba/docs/requirements.txt       |     1 +
 .../pytorch/numba/docs/source/_ext/ghfiles.py |    75 +
 .../pytorch/numba/docs/source/conf.py         |   343 +
 .../numba/docs/source/cuda-reference/host.rst |   232 +
 .../docs/source/cuda-reference/index.rst      |    10 +
 .../docs/source/cuda-reference/kernel.rst     |   586 +
 .../docs/source/cuda-reference/libdevice.rst  |    16 +
 .../docs/source/cuda-reference/memory.rst     |    24 +
 .../docs/source/cuda-reference/types.rst      |    56 +
 .../numba/docs/source/cuda/bindings.rst       |    43 +
 .../numba/docs/source/cuda/caching.rst        |    35 +
 .../docs/source/cuda/cooperative_groups.rst   |   111 +
 .../docs/source/cuda/cuda_array_interface.rst |   531 +
 .../numba/docs/source/cuda/cuda_ffi.rst       |   158 +
 .../docs/source/cuda/cudapysupported.rst      |   296 +
 .../docs/source/cuda/device-functions.rst     |    15 +
 .../docs/source/cuda/device-management.rst    |    92 +
 .../numba/docs/source/cuda/examples.rst       |   527 +
 .../docs/source/cuda/external-memory.rst      |   320 +
 .../pytorch/numba/docs/source/cuda/faq.rst    |    20 +
 .../numba/docs/source/cuda/fastmath.rst       |    36 +
 .../pytorch/numba/docs/source/cuda/index.rst  |    29 +
 .../numba/docs/source/cuda/intrinsics.rst     |    58 +
 .../pytorch/numba/docs/source/cuda/ipc.rst    |    36 +
 .../numba/docs/source/cuda/kernels.rst        |   233 +
 .../numba/docs/source/cuda/laplace_final.svg  |  1953 +++
 .../docs/source/cuda/laplace_initial.svg      |  1838 ++
 .../pytorch/numba/docs/source/cuda/memory.rst |   257 +
 .../numba/docs/source/cuda/overview.rst       |   133 +
 .../pytorch/numba/docs/source/cuda/random.rst |    90 +
 .../numba/docs/source/cuda/reduction.rst      |    33 +
 .../numba/docs/source/cuda/simulator.rst      |   104 +
 .../pytorch/numba/docs/source/cuda/ufunc.rst  |   154 +
 .../docs/source/developer/architecture.rst    |   909 +
 .../numba/docs/source/developer/caching.rst   |   111 +
 .../source/developer/compiler_pass_example.py |    78 +
 .../docs/source/developer/contributing.rst    |   491 +
 .../docs/source/developer/custom_pipeline.rst |   173 +
 .../numba/docs/source/developer/debugging.rst |   138 +
 .../docs/source/developer/dispatching.rst     |   267 +
 .../docs/source/developer/environment.rst     |    59 +
 .../numba/docs/source/developer/event_api.rst |     5 +
 .../docs/source/developer/generators.rst      |   307 +
 .../numba/docs/source/developer/hashing.rst   |    54 +
 .../numba/docs/source/developer/index.rst     |    32 +
 .../docs/source/developer/inline_example.py   |    82 +
 .../developer/inline_overload_example.py      |    61 +
 .../numba/docs/source/developer/inlining.rst  |   281 +
 .../numba/docs/source/developer/listings.rst  |    29 +
 .../numba/docs/source/developer/literal.rst   |    82 +
 .../developer/live_variable_analysis.rst      |    86 +
 .../docs/source/developer/llvm_timings.rst    |   107 +
 .../numba/docs/source/developer/mission.rst   |    82 +
 .../docs/source/developer/numba-runtime.rst   |   200 +
 .../numba/docs/source/developer/release.rst   |    49 +
 .../numba/docs/source/developer/repomap.rst   |   582 +
 .../numba/docs/source/developer/rewrites.rst  |   397 +
 .../numba/docs/source/developer/stencil.rst   |   170 +
 .../source/developer/target_extension.rst     |    61 +
 .../developer/threading_implementation.rst    |   249 +
 .../docs/source/extending/entrypoints.rst     |    65 +
 .../docs/source/extending/high-level.rst      |   254 +
 .../numba/docs/source/extending/index.rst     |    30 +
 .../source/extending/interval-example.rst     |   345 +
 .../numba/docs/source/extending/low-level.rst |   194 +
 .../numba/docs/source/extending/mynorm.py     |    72 +
 .../source/extending/overloading-guide.rst    |   192 +
 .../numba/docs/source/extending/template.py   |    21 +
 .../pytorch/numba/docs/source/glossary.rst    |   106 +
 .../pytorch/numba/docs/source/index.rst       |    37 +
 .../numba/docs/source/proposals/cfunc.rst     |   147 +
 .../source/proposals/extension-points.rst     |   414 +
 .../proposals/external-memory-management.rst  |   922 +
 .../numba/docs/source/proposals/index.rst     |    35 +
 .../docs/source/proposals/integer-typing.rst  |   186 +
 .../docs/source/proposals/jit-classes.rst     |   231 +
 .../source/proposals/np-where-override.py     |    51 +
 .../source/proposals/recursion_callstack.svg  |     4 +
 .../docs/source/proposals/type-inference.rst  |   124 +
 .../source/proposals/typing_recursion.rst     |   129 +
 .../docs/source/reference/aot-compilation.rst |    76 +
 .../docs/source/reference/deprecation.rst     |   312 +
 .../numba/docs/source/reference/envvars.rst   |   589 +
 .../docs/source/reference/fpsemantics.rst     |    81 +
 .../numba/docs/source/reference/index.rst     |    16 +
 .../docs/source/reference/jit-compilation.rst |   572 +
 .../docs/source/reference/numpysupported.rst  |   925 +
 .../docs/source/reference/pysemantics.rst     |    88 +
 .../docs/source/reference/pysupported.rst     |  1284 ++
 .../numba/docs/source/reference/types.rst     |   375 +
 .../numba/docs/source/reference/utils.rst     |    34 +
 .../numba/docs/source/release-notes.rst       |     5 +
 .../numba/docs/source/user/5minguide.rst      |   223 +
 .../pytorch/numba/docs/source/user/cfunc.rst  |   218 +
 .../pytorch/numba/docs/source/user/cli.rst    |   165 +
 .../numba/docs/source/user/examples.rst       |    52 +
 .../pytorch/numba/docs/source/user/faq.rst    |   413 +
 .../numba/docs/source/user/generated-jit.rst  |    71 +
 .../pytorch/numba/docs/source/user/index.rst  |    26 +
 .../numba/docs/source/user/installing.rst     |   373 +
 .../numba/docs/source/user/jit-module.rst     |   103 +
 .../pytorch/numba/docs/source/user/jit.rst    |   201 +
 .../numba/docs/source/user/jitclass.rst       |   270 +
 .../numba/docs/source/user/overview.rst       |    34 +
 .../numba/docs/source/user/parallel.rst       |   719 +
 .../docs/source/user/performance-tips.rst     |   248 +
 .../pytorch/numba/docs/source/user/pycc.rst   |   140 +
 .../numba/docs/source/user/stencil.rst        |   255 +
 .../pytorch/numba/docs/source/user/talks.rst  |    36 +
 .../docs/source/user/threading-layer.rst      |   313 +
 .../numba/docs/source/user/troubleshoot.rst   |  1179 ++
 .../numba/docs/source/user/vectorize.rst      |   423 +
 .../numba/docs/source/user/withobjmode.rst    |    34 +
 .../pytorch/numba/install_numba.sh            |    42 +
 .../pointrcnn-iou/pytorch/numba/mypy.ini      |    52 +
 .../pytorch/numba/numba/__init__.py           |   224 +
 .../pytorch/numba/numba/__main__.py           |     6 +
 .../pytorch/numba/numba/_arraystruct.h        |    21 +
 .../pytorch/numba/numba/_devicearray.cpp      |   142 +
 .../pytorch/numba/numba/_devicearray.h        |    25 +
 .../pytorch/numba/numba/_dispatcher.cpp       |  1223 ++
 .../pytorch/numba/numba/_dynfunc.c            |   507 +
 .../pytorch/numba/numba/_dynfuncmod.c         |    93 +
 .../pytorch/numba/numba/_hashtable.c          |   530 +
 .../pytorch/numba/numba/_hashtable.h          |   132 +
 .../pytorch/numba/numba/_helperlib.c          |  1186 ++
 .../pytorch/numba/numba/_helpermod.c          |   307 +
 .../pytorch/numba/numba/_lapack.c             |  1946 +++
 .../pytorch/numba/numba/_npymath_exports.c    |    46 +
 .../pytorch/numba/numba/_numba_common.h       |    39 +
 .../pytorch/numba/numba/_pymodule.h           |    32 +
 .../pytorch/numba/numba/_random.c             |   492 +
 .../pytorch/numba/numba/_typeof.c             |  1133 ++
 .../pytorch/numba/numba/_typeof.h             |    16 +
 .../pytorch/numba/numba/_unicodetype_db.h     |  6091 +++++++
 .../pytorch/numba/numba/_version.py           |   238 +
 .../pytorch/numba/numba/capsulethunk.h        |   108 +
 .../pytorch/numba/numba/cext/__init__.py      |    23 +
 .../pytorch/numba/numba/cext/cext.h           |    18 +
 .../pytorch/numba/numba/cext/dictobject.c     |  1191 ++
 .../pytorch/numba/numba/cext/dictobject.h     |   222 +
 .../pytorch/numba/numba/cext/listobject.c     |   977 ++
 .../pytorch/numba/numba/cext/listobject.h     |   135 +
 .../pytorch/numba/numba/cext/utils.c          |     8 +
 .../numba/numba/cloudpickle/__init__.py       |    12 +
 .../numba/numba/cloudpickle/cloudpickle.py    |   848 +
 .../numba/cloudpickle/cloudpickle_fast.py     |   775 +
 .../pytorch/numba/numba/cloudpickle/compat.py |    13 +
 .../pytorch/numba/numba/core/__init__.py      |     0
 .../pytorch/numba/numba/core/analysis.py      |   722 +
 .../numba/numba/core/annotations/__init__.py  |     0
 .../numba/core/annotations/pretty_annotate.py |   283 +
 .../numba/core/annotations/template.html      |   144 +
 .../core/annotations/type_annotations.py      |   283 +
 .../pytorch/numba/numba/core/base.py          |  1255 ++
 .../pytorch/numba/numba/core/boxing.py        |  1317 ++
 .../pytorch/numba/numba/core/bytecode.py      |   369 +
 .../pytorch/numba/numba/core/byteflow.py      |  1555 ++
 .../pytorch/numba/numba/core/caching.py       |   731 +
 .../pytorch/numba/numba/core/callconv.py      |   650 +
 .../pytorch/numba/numba/core/callwrapper.py   |   226 +
 .../pytorch/numba/numba/core/ccallback.py     |   134 +
 .../pytorch/numba/numba/core/cgutils.py       |  1194 ++
 .../pytorch/numba/numba/core/codegen.py       |  1437 ++
 .../pytorch/numba/numba/core/compiler.py      |   790 +
 .../pytorch/numba/numba/core/compiler_lock.py |    56 +
 .../numba/numba/core/compiler_machinery.py    |   463 +
 .../pytorch/numba/numba/core/config.py        |   527 +
 .../pytorch/numba/numba/core/consts.py        |   118 +
 .../pytorch/numba/numba/core/controlflow.py   |   954 ++
 .../pytorch/numba/numba/core/cpu.py           |   379 +
 .../pytorch/numba/numba/core/cpu_options.py   |   181 +
 .../pytorch/numba/numba/core/dataflow.py      |   914 +
 .../numba/numba/core/datamodel/__init__.py    |     4 +
 .../numba/numba/core/datamodel/manager.py     |    47 +
 .../numba/numba/core/datamodel/models.py      |  1384 ++
 .../numba/numba/core/datamodel/packer.py      |   213 +
 .../numba/numba/core/datamodel/registry.py    |    18 +
 .../numba/numba/core/datamodel/testing.py     |   150 +
 .../pytorch/numba/numba/core/debuginfo.py     |   609 +
 .../pytorch/numba/numba/core/decorators.py    |   309 +
 .../pytorch/numba/numba/core/descriptors.py   |    21 +
 .../pytorch/numba/numba/core/dispatcher.py    |  1322 ++
 .../pytorch/numba/numba/core/entrypoints.py   |    58 +
 .../pytorch/numba/numba/core/environment.py   |    64 +
 .../pytorch/numba/numba/core/errors.py        |   848 +
 .../pytorch/numba/numba/core/event.py         |   491 +
 .../pytorch/numba/numba/core/extending.py     |   583 +
 .../pytorch/numba/numba/core/externals.py     |   155 +
 .../pytorch/numba/numba/core/fastmathpass.py  |    44 +
 .../pytorch/numba/numba/core/funcdesc.py      |   230 +
 .../pytorch/numba/numba/core/generators.py    |   356 +
 .../pytorch/numba/numba/core/imputils.py      |   469 +
 .../numba/numba/core/inline_closurecall.py    |  1557 ++
 .../pytorch/numba/numba/core/interpreter.py   |  2876 ++++
 .../pytorch/numba/numba/core/intrinsics.py    |   101 +
 .../pytorch/numba/numba/core/ir.py            |  1630 ++
 .../pytorch/numba/numba/core/ir_utils.py      |  2350 +++
 .../numba/numba/core/itanium_mangler.py       |   205 +
 .../pytorch/numba/numba/core/llvm_bindings.py |    46 +
 .../pytorch/numba/numba/core/lowering.py      |  1536 ++
 .../numba/numba/core/object_mode_passes.py    |   169 +
 .../pytorch/numba/numba/core/optional.py      |   121 +
 .../pytorch/numba/numba/core/options.py       |   109 +
 .../pytorch/numba/numba/core/overload_glue.py |   308 +
 .../pytorch/numba/numba/core/postproc.py      |   238 +
 .../pytorch/numba/numba/core/pylowering.py    |   655 +
 .../pytorch/numba/numba/core/pythonapi.py     |  1686 ++
 .../pytorch/numba/numba/core/registry.py      |   112 +
 .../numba/numba/core/removerefctpass.py       |   120 +
 .../pytorch/numba/numba/core/retarget.py      |   135 +
 .../numba/numba/core/rewrites/__init__.py     |     8 +
 .../numba/numba/core/rewrites/ir_print.py     |    82 +
 .../numba/numba/core/rewrites/registry.py     |    98 +
 .../numba/numba/core/rewrites/static_binop.py |    35 +
 .../numba/core/rewrites/static_getitem.py     |   175 +
 .../numba/numba/core/rewrites/static_raise.py |    79 +
 .../numba/numba/core/runtime/__init__.py      |     1 +
 .../numba/numba/core/runtime/_nrt_python.c    |   459 +
 .../numba/numba/core/runtime/_nrt_pythonmod.c |   207 +
 .../numba/numba/core/runtime/context.py       |   401 +
 .../pytorch/numba/numba/core/runtime/nrt.c    |   595 +
 .../pytorch/numba/numba/core/runtime/nrt.h    |   272 +
 .../pytorch/numba/numba/core/runtime/nrt.py   |   136 +
 .../numba/numba/core/runtime/nrt_external.h   |    65 +
 .../numba/numba/core/runtime/nrtdynmod.py     |   215 +
 .../numba/numba/core/runtime/nrtopt.py        |   182 +
 .../pytorch/numba/numba/core/serialize.py     |   248 +
 .../pytorch/numba/numba/core/sigutils.py      |    53 +
 .../pytorch/numba/numba/core/ssa.py           |   457 +
 .../numba/numba/core/target_extension.py      |   168 +
 .../pytorch/numba/numba/core/targetconfig.py  |   321 +
 .../pytorch/numba/numba/core/tracing.py       |   216 +
 .../pytorch/numba/numba/core/transforms.py    |   918 +
 .../numba/numba/core/typeconv/__init__.py     |     1 +
 .../numba/numba/core/typeconv/_typeconv.cpp   |   205 +
 .../numba/numba/core/typeconv/castgraph.py    |   133 +
 .../numba/numba/core/typeconv/rules.py        |    60 +
 .../numba/numba/core/typeconv/test.cpp        |    39 +
 .../numba/numba/core/typeconv/typeconv.cpp    |   210 +
 .../numba/numba/core/typeconv/typeconv.hpp    |    98 +
 .../numba/numba/core/typeconv/typeconv.py     |   128 +
 .../pytorch/numba/numba/core/typed_passes.py  |   864 +
 .../pytorch/numba/numba/core/typeinfer.py     |  1780 ++
 .../numba/numba/core/types/__init__.py        |   178 +
 .../numba/numba/core/types/abstract.py        |   507 +
 .../pytorch/numba/numba/core/types/common.py  |   104 +
 .../numba/numba/core/types/containers.py      |   958 ++
 .../numba/numba/core/types/function_type.py   |   211 +
 .../numba/numba/core/types/functions.py       |   745 +
 .../numba/numba/core/types/iterators.py       |   108 +
 .../pytorch/numba/numba/core/types/misc.py    |   545 +
 .../numba/numba/core/types/npytypes.py        |   604 +
 .../pytorch/numba/numba/core/types/scalars.py |   270 +
 .../numba/numba/core/typing/__init__.py       |     3 +
 .../numba/numba/core/typing/arraydecl.py      |   849 +
 .../numba/numba/core/typing/asnumbatype.py    |   139 +
 .../numba/numba/core/typing/bufproto.py       |    67 +
 .../numba/numba/core/typing/builtins.py       |  1157 ++
 .../numba/numba/core/typing/cffi_utils.py     |   229 +
 .../numba/numba/core/typing/cmathdecl.py      |    72 +
 .../numba/numba/core/typing/collections.py    |   121 +
 .../numba/numba/core/typing/context.py        |   720 +
 .../numba/numba/core/typing/ctypes_utils.py   |   128 +
 .../numba/numba/core/typing/dictdecl.py       |    29 +
 .../numba/numba/core/typing/enumdecl.py       |    64 +
 .../numba/numba/core/typing/listdecl.py       |   198 +
 .../numba/numba/core/typing/mathdecl.py       |   141 +
 .../numba/numba/core/typing/npdatetime.py     |   285 +
 .../numba/numba/core/typing/npydecl.py        |  1239 ++
 .../numba/numba/core/typing/randomdecl.py     |   300 +
 .../numba/numba/core/typing/setdecl.py        |   192 +
 .../numba/numba/core/typing/templates.py      |  1322 ++
 .../pytorch/numba/numba/core/typing/typeof.py |   284 +
 .../numba/numba/core/unsafe/__init__.py       |     0
 .../pytorch/numba/numba/core/unsafe/bytes.py  |    49 +
 .../pytorch/numba/numba/core/unsafe/eh.py     |    62 +
 .../pytorch/numba/numba/core/unsafe/nrt.py    |    20 +
 .../numba/numba/core/unsafe/refcount.py       |    80 +
 .../numba/numba/core/untyped_passes.py        |  1722 ++
 .../pytorch/numba/numba/core/utils.py         |   722 +
 .../pytorch/numba/numba/core/withcontexts.py  |   535 +
 .../pytorch/numba/numba/cpython/__init__.py   |     0
 .../pytorch/numba/numba/cpython/builtins.py   |   825 +
 .../pytorch/numba/numba/cpython/charseq.py    |  1020 ++
 .../pytorch/numba/numba/cpython/cmathimpl.py  |   528 +
 .../pytorch/numba/numba/cpython/enumimpl.py   |    89 +
 .../pytorch/numba/numba/cpython/hashing.py    |   759 +
 .../pytorch/numba/numba/cpython/heapq.py      |   266 +
 .../pytorch/numba/numba/cpython/iterators.py  |   140 +
 .../pytorch/numba/numba/cpython/listobj.py    |  1260 ++
 .../pytorch/numba/numba/cpython/mathimpl.py   |   453 +
 .../pytorch/numba/numba/cpython/numbers.py    |  1363 ++
 .../pytorch/numba/numba/cpython/printimpl.py  |    82 +
 .../pytorch/numba/numba/cpython/randomimpl.py |  1644 ++
 .../pytorch/numba/numba/cpython/rangeobj.py   |   299 +
 .../pytorch/numba/numba/cpython/setobj.py     |  1543 ++
 .../pytorch/numba/numba/cpython/slicing.py    |   302 +
 .../pytorch/numba/numba/cpython/tupleobj.py   |   412 +
 .../pytorch/numba/numba/cpython/unicode.py    |  2498 +++
 .../numba/numba/cpython/unicode_support.py    |   765 +
 .../numba/numba/cpython/unsafe/__init__.py    |     0
 .../numba/numba/cpython/unsafe/numbers.py     |    53 +
 .../numba/numba/cpython/unsafe/tuple.py       |    84 +
 .../pytorch/numba/numba/cuda/__init__.py      |    16 +
 .../pytorch/numba/numba/cuda/api.py           |   517 +
 .../pytorch/numba/numba/cuda/api_util.py      |    30 +
 .../pytorch/numba/numba/cuda/args.py          |    77 +
 .../pytorch/numba/numba/cuda/codegen.py       |   443 +
 .../pytorch/numba/numba/cuda/compiler.py      |   319 +
 .../pytorch/numba/numba/cuda/cuda_paths.py    |   165 +
 .../pytorch/numba/numba/cuda/cudadecl.py      |   689 +
 .../numba/numba/cuda/cudadrv/__init__.py      |     9 +
 .../numba/numba/cuda/cudadrv/_extras.c        |    45 +
 .../numba/numba/cuda/cudadrv/devicearray.py   |   902 +
 .../numba/numba/cuda/cudadrv/devices.py       |   248 +
 .../numba/numba/cuda/cudadrv/driver.py        |  3191 ++++
 .../numba/numba/cuda/cudadrv/drvapi.py        |   394 +
 .../pytorch/numba/numba/cuda/cudadrv/enums.py |   444 +
 .../pytorch/numba/numba/cuda/cudadrv/error.py |    19 +
 .../pytorch/numba/numba/cuda/cudadrv/libs.py  |   127 +
 .../numba/numba/cuda/cudadrv/ndarray.py       |    20 +
 .../pytorch/numba/numba/cuda/cudadrv/nvvm.py  |   909 +
 .../pytorch/numba/numba/cuda/cudadrv/rtapi.py |    10 +
 .../numba/numba/cuda/cudadrv/runtime.py       |   143 +
 .../pytorch/numba/numba/cuda/cudaimpl.py      |  1116 ++
 .../pytorch/numba/numba/cuda/cudamath.py      |   130 +
 .../pytorch/numba/numba/cuda/decorators.py    |   170 +
 .../pytorch/numba/numba/cuda/descriptor.py    |    33 +
 .../pytorch/numba/numba/cuda/device_init.py   |    88 +
 .../pytorch/numba/numba/cuda/dispatcher.py    |   902 +
 .../pytorch/numba/numba/cuda/errors.py        |    59 +
 .../pytorch/numba/numba/cuda/initialize.py    |    17 +
 .../numba/numba/cuda/intrinsic_wrapper.py     |    77 +
 .../numba/numba/cuda/kernels/__init__.py      |     0
 .../numba/numba/cuda/kernels/reduction.py     |   262 +
 .../numba/numba/cuda/kernels/transpose.py     |    65 +
 .../pytorch/numba/numba/cuda/libdevice.py     |  3382 ++++
 .../pytorch/numba/numba/cuda/libdevicedecl.py |    17 +
 .../numba/numba/cuda/libdevicefuncs.py        |  1057 ++
 .../pytorch/numba/numba/cuda/libdeviceimpl.py |    83 +
 .../pytorch/numba/numba/cuda/mathimpl.py      |   287 +
 .../pytorch/numba/numba/cuda/models.py        |    41 +
 .../pytorch/numba/numba/cuda/nvvmutils.py     |   265 +
 .../pytorch/numba/numba/cuda/printimpl.py     |    86 +
 .../pytorch/numba/numba/cuda/random.py        |   291 +
 .../numba/numba/cuda/simulator/__init__.py    |    38 +
 .../pytorch/numba/numba/cuda/simulator/api.py |   103 +
 .../numba/numba/cuda/simulator/compiler.py    |     7 +
 .../numba/cuda/simulator/cudadrv/__init__.py  |     2 +
 .../cuda/simulator/cudadrv/devicearray.py     |   407 +
 .../numba/cuda/simulator/cudadrv/devices.py   |   111 +
 .../numba/cuda/simulator/cudadrv/driver.py    |    54 +
 .../numba/cuda/simulator/cudadrv/drvapi.py    |     4 +
 .../numba/cuda/simulator/cudadrv/error.py     |     2 +
 .../numba/cuda/simulator/cudadrv/libs.py      |     2 +
 .../numba/cuda/simulator/cudadrv/nvvm.py      |    30 +
 .../numba/cuda/simulator/cudadrv/runtime.py   |    19 +
 .../numba/numba/cuda/simulator/kernel.py      |   307 +
 .../numba/numba/cuda/simulator/kernelapi.py   |   439 +
 .../numba/numba/cuda/simulator/reduction.py   |    15 +
 .../numba/cuda/simulator/vector_types.py      |    58 +
 .../numba/numba/cuda/simulator_init.py        |    17 +
 .../pytorch/numba/numba/cuda/stubs.py         |   827 +
 .../pytorch/numba/numba/cuda/target.py        |   374 +
 .../pytorch/numba/numba/cuda/testing.py       |   170 +
 .../numba/numba/cuda/tests/__init__.py        |    24 +
 .../numba/cuda/tests/cudadrv/__init__.py      |     8 +
 .../numba/cuda/tests/cudadrv/data/__init__.py |     0
 .../cuda/tests/cudadrv/data/cuda_include.cu   |     5 +
 .../numba/cuda/tests/cudadrv/data/error.cu    |     7 +
 .../numba/cuda/tests/cudadrv/data/jitlink.cu  |    13 +
 .../numba/cuda/tests/cudadrv/data/jitlink.ptx |    30 +
 .../numba/cuda/tests/cudadrv/data/warn.cu     |     7 +
 .../cuda/tests/cudadrv/test_array_attr.py     |    92 +
 .../cuda/tests/cudadrv/test_context_stack.py  |   145 +
 .../tests/cudadrv/test_cuda_array_slicing.py  |   379 +
 .../tests/cudadrv/test_cuda_auto_context.py   |    21 +
 .../tests/cudadrv/test_cuda_devicerecord.py   |   179 +
 .../cuda/tests/cudadrv/test_cuda_driver.py    |   235 +
 .../cuda/tests/cudadrv/test_cuda_libraries.py |    22 +
 .../cuda/tests/cudadrv/test_cuda_memory.py    |   193 +
 .../cuda/tests/cudadrv/test_cuda_ndarray.py   |   547 +
 .../cuda/tests/cudadrv/test_deallocations.py  |   249 +
 .../numba/cuda/tests/cudadrv/test_detect.py   |    81 +
 .../cuda/tests/cudadrv/test_emm_plugins.py    |   192 +
 .../numba/cuda/tests/cudadrv/test_events.py   |    38 +
 .../cuda/tests/cudadrv/test_host_alloc.py     |    65 +
 .../numba/cuda/tests/cudadrv/test_init.py     |   139 +
 .../cuda/tests/cudadrv/test_inline_ptx.py     |    35 +
 .../numba/cuda/tests/cudadrv/test_ir_patch.py |    26 +
 .../numba/cuda/tests/cudadrv/test_linker.py   |   230 +
 .../cuda/tests/cudadrv/test_managed_alloc.py  |   127 +
 .../cuda/tests/cudadrv/test_nvvm_driver.py    |   147 +
 .../numba/cuda/tests/cudadrv/test_pinned.py   |    37 +
 .../numba/cuda/tests/cudadrv/test_profiler.py |    20 +
 .../numba/cuda/tests/cudadrv/test_ptds.py     |   145 +
 .../cuda/tests/cudadrv/test_reset_device.py   |    36 +
 .../numba/cuda/tests/cudadrv/test_runtime.py  |    86 +
 .../cuda/tests/cudadrv/test_select_device.py  |    41 +
 .../numba/cuda/tests/cudadrv/test_streams.py  |   111 +
 .../numba/numba/cuda/tests/cudapy/__init__.py |     8 +
 .../numba/cuda/tests/cudapy/cache_usecases.py |   223 +
 .../tests/cudapy/cache_with_cpu_usecases.py   |    41 +
 .../cuda/tests/cudapy/extensions_usecases.py  |    58 +
 .../numba/numba/cuda/tests/cudapy/jitlink.ptx |    30 +
 .../cuda/tests/cudapy/recursion_usecases.py   |   100 +
 .../numba/cuda/tests/cudapy/test_alignment.py |    42 +
 .../numba/cuda/tests/cudapy/test_array.py     |   260 +
 .../cuda/tests/cudapy/test_array_args.py      |   201 +
 .../cuda/tests/cudapy/test_array_methods.py   |    35 +
 .../numba/cuda/tests/cudapy/test_atomics.py   |  1559 ++
 .../cuda/tests/cudapy/test_blackscholes.py    |   120 +
 .../numba/cuda/tests/cudapy/test_boolean.py   |    24 +
 .../numba/cuda/tests/cudapy/test_caching.py   |   493 +
 .../numba/cuda/tests/cudapy/test_casting.py   |   239 +
 .../numba/cuda/tests/cudapy/test_compiler.py  |   163 +
 .../numba/cuda/tests/cudapy/test_complex.py   |   296 +
 .../cuda/tests/cudapy/test_complex_kernel.py  |    20 +
 .../cuda/tests/cudapy/test_const_string.py    |   216 +
 .../numba/cuda/tests/cudapy/test_constmem.py  |   185 +
 .../tests/cudapy/test_cooperative_groups.py   |   145 +
 .../tests/cudapy/test_cuda_array_interface.py |   435 +
 .../tests/cudapy/test_cuda_jit_no_types.py    |    90 +
 .../numba/cuda/tests/cudapy/test_datetime.py  |    94 +
 .../numba/cuda/tests/cudapy/test_debug.py     |    93 +
 .../numba/cuda/tests/cudapy/test_debuginfo.py |   274 +
 .../cuda/tests/cudapy/test_device_func.py     |   222 +
 .../cuda/tests/cudapy/test_dispatcher.py      |   292 +
 .../numba/cuda/tests/cudapy/test_enums.py     |   121 +
 .../numba/cuda/tests/cudapy/test_errors.py    |    79 +
 .../numba/cuda/tests/cudapy/test_exception.py |   168 +
 .../numba/cuda/tests/cudapy/test_extending.py |   155 +
 .../numba/cuda/tests/cudapy/test_fastmath.py  |   204 +
 .../numba/cuda/tests/cudapy/test_forall.py    |    52 +
 .../numba/cuda/tests/cudapy/test_freevar.py   |    29 +
 .../cuda/tests/cudapy/test_frexp_ldexp.py     |    66 +
 .../numba/cuda/tests/cudapy/test_globals.py   |    60 +
 .../numba/cuda/tests/cudapy/test_gufunc.py    |   309 +
 .../cuda/tests/cudapy/test_gufunc_scalar.py   |   159 +
 .../tests/cudapy/test_gufunc_scheduling.py    |    95 +
 .../numba/cuda/tests/cudapy/test_idiv.py      |    37 +
 .../numba/cuda/tests/cudapy/test_inspect.py   |   133 +
 .../cuda/tests/cudapy/test_intrinsics.py      |   870 +
 .../numba/numba/cuda/tests/cudapy/test_ipc.py |   314 +
 .../numba/cuda/tests/cudapy/test_iterators.py |    99 +
 .../numba/cuda/tests/cudapy/test_lang.py      |    64 +
 .../numba/cuda/tests/cudapy/test_laplace.py   |   119 +
 .../numba/cuda/tests/cudapy/test_libdevice.py |   186 +
 .../numba/cuda/tests/cudapy/test_lineinfo.py  |    43 +
 .../numba/cuda/tests/cudapy/test_localmem.py  |   164 +
 .../numba/cuda/tests/cudapy/test_mandel.py    |    33 +
 .../numba/cuda/tests/cudapy/test_math.py      |   739 +
 .../numba/cuda/tests/cudapy/test_matmul.py    |    74 +
 .../numba/cuda/tests/cudapy/test_minmax.py    |   113 +
 .../cuda/tests/cudapy/test_montecarlo.py      |    22 +
 .../numba/cuda/tests/cudapy/test_multigpu.py  |   140 +
 .../cuda/tests/cudapy/test_multiprocessing.py |    46 +
 .../cuda/tests/cudapy/test_multithreads.py    |   101 +
 .../numba/cuda/tests/cudapy/test_nondet.py    |    49 +
 .../numba/cuda/tests/cudapy/test_operator.py  |    41 +
 .../cuda/tests/cudapy/test_optimization.py    |    86 +
 .../numba/cuda/tests/cudapy/test_overload.py  |   300 +
 .../numba/cuda/tests/cudapy/test_powi.py      |   124 +
 .../numba/cuda/tests/cudapy/test_print.py     |   128 +
 .../cuda/tests/cudapy/test_py2_div_issue.py   |    33 +
 .../numba/cuda/tests/cudapy/test_random.py    |   104 +
 .../cuda/tests/cudapy/test_record_dtype.py    |   610 +
 .../numba/cuda/tests/cudapy/test_recursion.py |   125 +
 .../numba/cuda/tests/cudapy/test_reduction.py |    76 +
 .../test_retrieve_autoconverted_arrays.py     |    83 +
 .../numba/cuda/tests/cudapy/test_serialize.py |    85 +
 .../numba/cuda/tests/cudapy/test_slicing.py   |    37 +
 .../numba/numba/cuda/tests/cudapy/test_sm.py  |   444 +
 .../cuda/tests/cudapy/test_sm_creation.py     |   205 +
 .../numba/cuda/tests/cudapy/test_sync.py      |   244 +
 .../numba/cuda/tests/cudapy/test_transpose.py |    80 +
 .../numba/cuda/tests/cudapy/test_userexc.py   |    40 +
 .../cuda/tests/cudapy/test_vector_type.py     |   307 +
 .../numba/cuda/tests/cudapy/test_vectorize.py |   226 +
 .../tests/cudapy/test_vectorize_complex.py    |    20 +
 .../cuda/tests/cudapy/test_vectorize_decor.py |    69 +
 .../tests/cudapy/test_vectorize_device.py     |    36 +
 .../tests/cudapy/test_vectorize_scalar_arg.py |    37 +
 .../numba/cuda/tests/cudapy/test_warning.py   |   139 +
 .../numba/cuda/tests/cudapy/test_warp_ops.py  |   276 +
 .../numba/cuda/tests/cudasim/__init__.py      |     6 +
 .../numba/numba/cuda/tests/cudasim/support.py |     6 +
 .../cuda/tests/cudasim/test_cudasim_issues.py |   102 +
 .../numba/cuda/tests/doc_examples/__init__.py |     6 +
 .../cuda/tests/doc_examples/ffi/__init__.py   |     0
 .../cuda/tests/doc_examples/ffi/functions.cu  |    15 +
 .../numba/cuda/tests/doc_examples/test_cg.py  |    75 +
 .../tests/doc_examples/test_cpu_gpu_compat.py |    76 +
 .../numba/cuda/tests/doc_examples/test_ffi.py |    50 +
 .../cuda/tests/doc_examples/test_laplace.py   |   153 +
 .../cuda/tests/doc_examples/test_matmul.py    |   173 +
 .../tests/doc_examples/test_montecarlo.py     |   109 +
 .../cuda/tests/doc_examples/test_random.py    |    59 +
 .../cuda/tests/doc_examples/test_reduction.py |    76 +
 .../tests/doc_examples/test_sessionize.py     |   128 +
 .../cuda/tests/doc_examples/test_vecadd.py    |    73 +
 .../numba/numba/cuda/tests/nocuda/__init__.py |     8 +
 .../numba/cuda/tests/nocuda/test_import.py    |    49 +
 .../cuda/tests/nocuda/test_library_lookup.py  |   238 +
 .../numba/cuda/tests/nocuda/test_nvvm.py      |    80 +
 .../pytorch/numba/numba/cuda/types.py         |    37 +
 .../pytorch/numba/numba/cuda/vector_types.py  |   209 +
 .../pytorch/numba/numba/cuda/vectorizers.py   |   253 +
 .../numba/numba/experimental/__init__.py      |     1 +
 .../numba/numba/experimental/function_type.py |   265 +
 .../numba/experimental/jitclass/__init__.py   |     3 +
 .../numba/numba/experimental/jitclass/_box.c  |   179 +
 .../numba/numba/experimental/jitclass/base.py |   592 +
 .../numba/experimental/jitclass/boxing.py     |   256 +
 .../numba/experimental/jitclass/decorators.py |    82 +
 .../numba/experimental/jitclass/overloads.py  |   227 +
 .../numba/numba/experimental/structref.py     |   384 +
 .../pytorch/numba/numba/extending.py          |     3 +
 .../pytorch/numba/numba/mathnames.h           |    77 +
 .../pytorch/numba/numba/misc/__init__.py      |     0
 .../pytorch/numba/numba/misc/appdirs.py       |   551 +
 .../pytorch/numba/numba/misc/cffiimpl.py      |    22 +
 .../pytorch/numba/numba/misc/cmdlang.gdb      |     5 +
 .../pytorch/numba/numba/misc/dummyarray.py    |   456 +
 .../pytorch/numba/numba/misc/dump_style.py    |    84 +
 .../pytorch/numba/numba/misc/findlib.py       |    60 +
 .../numba/numba/misc/firstlinefinder.py       |    96 +
 .../pytorch/numba/numba/misc/gdb_hook.py      |   228 +
 .../numba/numba/misc/gdb_print_extension.py   |   212 +
 .../pytorch/numba/numba/misc/help/__init__.py |     0
 .../numba/numba/misc/help/inspector.py        |   433 +
 .../pytorch/numba/numba/misc/init_utils.py    |    44 +
 .../pytorch/numba/numba/misc/inspection.py    |   103 +
 .../pytorch/numba/numba/misc/literal.py       |    24 +
 .../numba/numba/misc/llvm_pass_timings.py     |   402 +
 .../pytorch/numba/numba/misc/mergesort.py     |   126 +
 .../pytorch/numba/numba/misc/numba_entry.py   |    72 +
 .../pytorch/numba/numba/misc/numba_gdbinfo.py |   161 +
 .../pytorch/numba/numba/misc/numba_sysinfo.py |   667 +
 .../pytorch/numba/numba/misc/quicksort.py     |   258 +
 .../pytorch/numba/numba/misc/special.py       |   104 +
 .../pytorch/numba/numba/misc/timsort.py       |   943 ++
 .../pytorch/numba/numba/mviewbuf.c            |   370 +
 .../pytorch/numba/numba/np/__init__.py        |     0
 .../pytorch/numba/numba/np/arraymath.py       |  4881 ++++++
 .../pytorch/numba/numba/np/arrayobj.py        |  6075 +++++++
 .../pytorch/numba/numba/np/extensions.py      |    10 +
 .../pytorch/numba/numba/np/linalg.py          |  2763 +++
 .../pytorch/numba/numba/np/npdatetime.py      |   855 +
 .../numba/numba/np/npdatetime_helpers.py      |   212 +
 .../pytorch/numba/numba/np/npyfuncs.py        |  1624 ++
 .../pytorch/numba/numba/np/npyimpl.py         |   613 +
 .../pytorch/numba/numba/np/numpy_support.py   |   720 +
 .../pytorch/numba/numba/np/polynomial.py      |    59 +
 .../pytorch/numba/numba/np/random/__init__.py |     0
 .../numba/numba/np/random/generator_core.py   |   124 +
 .../numba/np/random/generator_methods.py      |    73 +
 .../pytorch/numba/numba/np/ufunc/__init__.py  |    32 +
 .../pytorch/numba/numba/np/ufunc/_internal.c  |   804 +
 .../pytorch/numba/numba/np/ufunc/_internal.h  |    27 +
 .../numba/numba/np/ufunc/_num_threads.c       |    36 +
 .../pytorch/numba/numba/np/ufunc/_ufunc.c     |   222 +
 .../numba/numba/np/ufunc/array_exprs.py       |   406 +
 .../numba/numba/np/ufunc/decorators.py        |   199 +
 .../numba/numba/np/ufunc/deviceufunc.py       |   841 +
 .../pytorch/numba/numba/np/ufunc/dufunc.py    |   323 +
 .../pytorch/numba/numba/np/ufunc/gufunc.py    |   192 +
 .../numba/numba/np/ufunc/gufunc_scheduler.cpp |   441 +
 .../numba/numba/np/ufunc/gufunc_scheduler.h   |    51 +
 .../pytorch/numba/numba/np/ufunc/omppool.cpp  |   265 +
 .../pytorch/numba/numba/np/ufunc/parallel.py  |   758 +
 .../pytorch/numba/numba/np/ufunc/sigparse.py  |    63 +
 .../pytorch/numba/numba/np/ufunc/tbbpool.cpp  |   352 +
 .../numba/numba/np/ufunc/ufuncbuilder.py      |   394 +
 .../pytorch/numba/numba/np/ufunc/workqueue.c  |   656 +
 .../pytorch/numba/numba/np/ufunc/workqueue.h  |    65 +
 .../pytorch/numba/numba/np/ufunc/wrappers.py  |   743 +
 .../pytorch/numba/numba/np/ufunc_db.py        |  1153 ++
 .../pytorch/numba/numba/np/unsafe/__init__.py |     0
 .../pytorch/numba/numba/np/unsafe/ndarray.py  |    80 +
 .../pytorch/numba/numba/parfors/__init__.py   |     1 +
 .../numba/numba/parfors/array_analysis.py     |  3260 ++++
 .../pytorch/numba/numba/parfors/parfor.py     |  5016 ++++++
 .../numba/numba/parfors/parfor_lowering.py    |  1923 +++
 .../numba/parfors/parfor_lowering_utils.py    |   213 +
 .../pytorch/numba/numba/pycc/__init__.py      |   100 +
 .../pytorch/numba/numba/pycc/cc.py            |   306 +
 .../pytorch/numba/numba/pycc/compiler.py      |   471 +
 .../pytorch/numba/numba/pycc/decorators.py    |    72 +
 .../pytorch/numba/numba/pycc/llvm_types.py    |    37 +
 .../pytorch/numba/numba/pycc/modulemixin.c    |   205 +
 .../pytorch/numba/numba/pycc/platform.py      |   273 +
 .../pytorch/numba/numba/pycc/pycc             |     3 +
 .../pytorch/numba/numba/runtests.py           |     9 +
 .../pytorch/numba/numba/scripts/__init__.py   |     0
 .../numba/scripts/generate_lower_listing.py   |   169 +
 .../pytorch/numba/numba/stencils/__init__.py  |     0
 .../pytorch/numba/numba/stencils/stencil.py   |   837 +
 .../numba/numba/stencils/stencilparfor.py     |   944 ++
 .../pytorch/numba/numba/testing/__init__.py   |    61 +
 .../pytorch/numba/numba/testing/__main__.py   |     4 +
 .../pytorch/numba/numba/testing/_runtests.py  |   114 +
 .../pytorch/numba/numba/testing/loader.py     |    26 +
 .../pytorch/numba/numba/testing/main.py       |   796 +
 .../pytorch/numba/numba/testing/notebook.py   |   171 +
 .../pytorch/numba/numba/tests/__init__.py     |    33 +
 .../numba/numba/tests/annotation_usecases.py  |    16 +
 .../numba/numba/tests/cache_usecases.py       |   184 +
 .../numba/numba/tests/cffi_usecases.py        |   197 +
 .../numba/numba/tests/cfunc_cache_usecases.py |    70 +
 .../numba/tests/cloudpickle_main_class.py     |     6 +
 .../numba/numba/tests/compile_with_pycc.py    |   134 +
 .../numba/numba/tests/complex_usecases.py     |    93 +
 .../numba/numba/tests/ctypes_usecases.py      |   114 +
 .../numba/tests/doc_examples/__init__.py      |    10 +
 .../numba/tests/doc_examples/test_examples.py |   202 +
 .../numba/tests/doc_examples/test_jitclass.py |    97 +
 .../test_literal_container_usage.py           |   161 +
 .../doc_examples/test_literally_usage.py      |    59 +
 .../doc_examples/test_llvm_pass_timings.py    |    31 +
 .../doc_examples/test_numpy_generators.py     |    38 +
 .../doc_examples/test_parallel_chunksize.py   |   122 +
 .../tests/doc_examples/test_rec_array.py      |    46 +
 .../doc_examples/test_structref_usage.py      |   149 +
 .../doc_examples/test_typed_dict_usage.py     |   111 +
 .../doc_examples/test_typed_list_usage.py     |    95 +
 .../pytorch/numba/numba/tests/dummy_module.py |     4 +
 .../numba/numba/tests/enum_usecases.py        |    55 +
 .../numba/numba/tests/error_usecases.py       |     6 +
 .../pytorch/numba/numba/tests/gdb/__init__.py |    10 +
 .../numba/numba/tests/gdb/test_array_arg.py   |    51 +
 .../numba/numba/tests/gdb/test_basic.py       |    39 +
 .../numba/tests/gdb/test_break_on_symbol.py   |    34 +
 .../tests/gdb/test_break_on_symbol_version.py |    65 +
 .../tests/gdb/test_conditional_breakpoint.py  |    45 +
 .../numba/tests/gdb/test_pretty_print.py      |    69 +
 .../pytorch/numba/numba/tests/gdb_support.py  |   197 +
 .../numba/numba/tests/inlining_usecases.py    |    45 +
 .../numba/numba/tests/matmul_usecase.py       |    24 +
 .../numba/numba/tests/npyufunc/__init__.py    |    10 +
 .../numba/tests/npyufunc/cache_usecases.py    |    76 +
 .../numba/tests/npyufunc/test_caching.py      |   228 +
 .../numba/numba/tests/npyufunc/test_dufunc.py |   125 +
 .../numba/numba/tests/npyufunc/test_errors.py |   174 +
 .../numba/numba/tests/npyufunc/test_gufunc.py |   481 +
 .../npyufunc/test_parallel_env_variable.py    |    38 +
 .../tests/npyufunc/test_parallel_low_work.py  |    44 +
 .../npyufunc/test_parallel_ufunc_issues.py    |   128 +
 .../numba/numba/tests/npyufunc/test_ufunc.py  |   145 +
 .../tests/npyufunc/test_ufuncbuilding.py      |   369 +
 .../tests/npyufunc/test_vectorize_decor.py    |   151 +
 .../numba/tests/orphaned_semaphore_usecase.py |    24 +
 .../numba/numba/tests/overload_usecases.py    |    28 +
 .../numba/tests/parfors_cache_usecases.py     |    66 +
 .../numba/tests/parfors_max_label_error.py    |    68 +
 .../numba/numba/tests/pdlike_usecase.py       |   306 +
 .../tests/pycc_distutils_usecase/__init__.py  |     0
 .../pycc_distutils_usecase/nested/__init__.py |     0
 .../nested/source_module.py                   |    20 +
 .../pycc_distutils_usecase/setup_distutils.py |    15 +
 .../setup_distutils_nested.py                 |    15 +
 .../setup_setuptools.py                       |    15 +
 .../setup_setuptools_nested.py                |    15 +
 .../pycc_distutils_usecase/source_module.py   |    18 +
 .../numba/numba/tests/recursion_usecases.py   |   228 +
 .../numba/numba/tests/serialize_usecases.py   |   114 +
 .../pytorch/numba/numba/tests/support.py      |  1200 ++
 .../numba/numba/tests/test_alignment.py       |    40 +
 .../numba/numba/tests/test_analysis.py        |  1050 ++
 .../numba/numba/tests/test_annotations.py     |   251 +
 .../pytorch/numba/numba/tests/test_api.py     |    90 +
 .../numba/numba/tests/test_array_analysis.py  |  1140 ++
 .../numba/numba/tests/test_array_attr.py      |   389 +
 .../numba/numba/tests/test_array_constants.py |   193 +
 .../numba/numba/tests/test_array_exprs.py     |   742 +
 .../numba/numba/tests/test_array_iterators.py |   549 +
 .../numba/tests/test_array_manipulation.py    |  1256 ++
 .../numba/numba/tests/test_array_methods.py   |  1646 ++
 .../numba/tests/test_array_reductions.py      |  1214 ++
 .../numba/numba/tests/test_array_return.py    |    41 +
 .../numba/numba/tests/test_asnumbatype.py     |   170 +
 .../numba/numba/tests/test_auto_constants.py  |    40 +
 .../numba/numba/tests/test_blackscholes.py    |   125 +
 .../numba/numba/tests/test_boundscheck.py     |   266 +
 .../numba/numba/tests/test_buffer_protocol.py |   289 +
 .../numba/numba/tests/test_builtins.py        |  1404 ++
 .../numba/numba/tests/test_byteflow.py        |    94 +
 .../pytorch/numba/numba/tests/test_caching.py |  1076 ++
 .../pytorch/numba/numba/tests/test_casting.py |   140 +
 .../pytorch/numba/numba/tests/test_cffi.py    |   194 +
 .../pytorch/numba/numba/tests/test_cfunc.py   |   412 +
 .../pytorch/numba/numba/tests/test_cgutils.py |   152 +
 .../numba/numba/tests/test_chained_assign.py  |   145 +
 .../numba/numba/tests/test_chrome_trace.py    |    51 +
 .../pytorch/numba/numba/tests/test_cli.py     |   281 +
 .../pytorch/numba/numba/tests/test_closure.py |   575 +
 .../pytorch/numba/numba/tests/test_codegen.py |   261 +
 .../numba/numba/tests/test_compile_cache.py   |   153 +
 .../numba/numba/tests/test_compiler_flags.py  |   111 +
 .../numba/numba/tests/test_compiler_lock.py   |    23 +
 .../pytorch/numba/numba/tests/test_complex.py |   315 +
 .../numba/numba/tests/test_comprehension.py   |   527 +
 .../tests/test_conditions_as_predicates.py    |   193 +
 .../pytorch/numba/numba/tests/test_config.py  |   123 +
 .../numba/numba/tests/test_conversion.py      |   233 +
 .../numba/numba/tests/test_copy_propagate.py  |   171 +
 .../pytorch/numba/numba/tests/test_ctypes.py  |   261 +
 .../numba/numba/tests/test_dataflow.py        |   192 +
 .../numba/numba/tests/test_datamodel.py       |   219 +
 .../pytorch/numba/numba/tests/test_debug.py   |   314 +
 .../numba/numba/tests/test_debuginfo.py       |   751 +
 .../numba/numba/tests/test_deprecations.py    |    59 +
 .../numba/numba/tests/test_dictimpl.py        |   651 +
 .../numba/numba/tests/test_dictobject.py      |  2429 +++
 .../pytorch/numba/numba/tests/test_dicts.py   |   172 +
 .../numba/numba/tests/test_dispatcher.py      |  1227 ++
 .../numba/numba/tests/test_dummyarray.py      |   350 +
 .../numba/numba/tests/test_dyn_array.py       |  1805 ++
 .../numba/numba/tests/test_dyn_func.py        |    43 +
 .../numba/numba/tests/test_entrypoints.py     |   230 +
 .../pytorch/numba/numba/tests/test_enums.py   |   180 +
 .../numba/numba/tests/test_errorhandling.py   |   496 +
 .../numba/numba/tests/test_errormodels.py     |    28 +
 .../pytorch/numba/numba/tests/test_event.py   |   217 +
 .../numba/numba/tests/test_exceptions.py      |   330 +
 .../numba/numba/tests/test_extended_arg.py    |    42 +
 .../numba/numba/tests/test_extending.py       |  2077 +++
 .../numba/numba/tests/test_extending_types.py |   170 +
 .../numba/numba/tests/test_fancy_indexing.py  |   261 +
 .../numba/numba/tests/test_fastmath.py        |   123 +
 .../numba/numba/tests/test_firstlinefinder.py |   119 +
 .../numba/numba/tests/test_flow_control.py    |  1311 ++
 .../numba/numba/tests/test_func_interface.py  |    43 +
 .../numba/numba/tests/test_func_lifetime.py   |   164 +
 .../numba/numba/tests/test_funcdesc.py        |    61 +
 .../numba/numba/tests/test_function_type.py   |  1261 ++
 .../numba/numba/tests/test_gdb_bindings.py    |   271 +
 .../numba/numba/tests/test_gdb_dwarf.py       |   261 +
 .../numba/numba/tests/test_generators.py      |   682 +
 .../pytorch/numba/numba/tests/test_gil.py     |   183 +
 .../pytorch/numba/numba/tests/test_globals.py |   266 +
 .../pytorch/numba/numba/tests/test_hashing.py |   383 +
 .../pytorch/numba/numba/tests/test_heapq.py   |   489 +
 .../pytorch/numba/numba/tests/test_help.py    |    92 +
 .../pytorch/numba/numba/tests/test_import.py  |   112 +
 .../numba/numba/tests/test_indexing.py        |  1140 ++
 .../numba/numba/tests/test_init_utils.py      |    42 +
 .../numba/numba/tests/test_inlining.py        |   286 +
 .../numba/numba/tests/test_interpreter.py     |  1150 ++
 .../numba/numba/tests/test_interproc.py       |    47 +
 .../numba/numba/tests/test_intwidth.py        |    90 +
 .../pytorch/numba/numba/tests/test_ir.py      |   560 +
 .../numba/numba/tests/test_ir_inlining.py     |  1544 ++
 .../numba/numba/tests/test_ir_utils.py        |   273 +
 .../numba/numba/tests/test_itanium_mangler.py |    80 +
 .../numba/numba/tests/test_iteration.py       |   254 +
 .../numba/numba/tests/test_jit_module.py      |   145 +
 .../numba/numba/tests/test_jitclasses.py      |  1878 +++
 .../numba/numba/tests/test_jitmethod.py       |    72 +
 .../pytorch/numba/numba/tests/test_linalg.py  |  2691 +++
 .../numba/numba/tests/test_listimpl.py        |   527 +
 .../numba/numba/tests/test_listobject.py      |  1638 ++
 .../pytorch/numba/numba/tests/test_lists.py   |  1832 ++
 .../numba/tests/test_literal_dispatch.py      |   400 +
 .../numba/tests/test_llvm_pass_timings.py     |   124 +
 .../numba/tests/test_llvm_version_check.py    |    42 +
 .../pytorch/numba/numba/tests/test_locals.py  |    18 +
 .../numba/numba/tests/test_looplifting.py     |   604 +
 .../test_make_function_to_jit_function.py     |   284 +
 .../numba/numba/tests/test_mandelbrot.py      |    35 +
 .../numba/numba/tests/test_mangling.py        |    41 +
 .../numba/tests/test_map_filter_reduce.py     |    91 +
 .../pytorch/numba/numba/tests/test_mathlib.py |   655 +
 .../pytorch/numba/numba/tests/test_maxmin.py  |    41 +
 .../numba/tests/test_mixed_tuple_unroller.py  |  1984 +++
 .../numba/numba/tests/test_moved_modules.py   |    30 +
 .../pytorch/numba/numba/tests/test_multi3.py  |    43 +
 .../pytorch/numba/numba/tests/test_nan.py     |    40 +
 .../numba/tests/test_ndarray_subclasses.py    |   346 +
 .../numba/numba/tests/test_nested_calls.py    |   146 +
 .../numba/numba/tests/test_np_functions.py    |  4841 ++++++
 .../numba/numba/tests/test_np_randomgen.py    |   153 +
 .../numba/numba/tests/test_npdatetime.py      |  1089 ++
 .../pytorch/numba/numba/tests/test_nrt.py     |   696 +
 .../numba/numba/tests/test_nrt_refct.py       |   114 +
 .../numba/numba/tests/test_num_threads.py     |   630 +
 .../numba/numba/tests/test_numberctor.py      |   250 +
 .../pytorch/numba/numba/tests/test_numbers.py |    90 +
 .../pytorch/numba/numba/tests/test_numconv.py |    38 +
 .../numba/numba/tests/test_numpy_support.py   |   437 +
 .../numba/numba/tests/test_numpyadapt.py      |    43 +
 .../numba/numba/tests/test_obj_lifetime.py    |   503 +
 .../numba/numba/tests/test_object_mode.py     |   192 +
 .../pytorch/numba/numba/tests/test_objects.py |    69 +
 .../numba/numba/tests/test_operators.py       |  1612 ++
 .../numba/numba/tests/test_optional.py        |   264 +
 .../pytorch/numba/numba/tests/test_overlap.py |   134 +
 .../numba/tests/test_parallel_backend.py      |  1245 ++
 .../pytorch/numba/numba/tests/test_parfors.py |  4623 +++++
 .../numba/numba/tests/test_parfors_caching.py |    83 +
 .../numba/numba/tests/test_parfors_passes.py  |   678 +
 .../numba/numba/tests/test_pipeline.py        |   158 +
 .../numba/numba/tests/test_polynomial.py      |   116 +
 .../tests/test_practical_lowering_issues.py   |   212 +
 .../pytorch/numba/numba/tests/test_print.py   |   197 +
 .../numba/numba/tests/test_profiler.py        |    98 +
 .../pytorch/numba/numba/tests/test_pycc.py    |   429 +
 .../numba/numba/tests/test_python_int.py      |    54 +
 .../pytorch/numba/numba/tests/test_random.py  |  1677 ++
 .../pytorch/numba/numba/tests/test_range.py   |   196 +
 .../numba/tests/test_recarray_usecases.py     |   148 +
 .../numba/numba/tests/test_record_dtype.py    |  1737 ++
 .../numba/numba/tests/test_recursion.py       |   124 +
 .../numba/numba/tests/test_refop_pruning.py   |   160 +
 .../numba/numba/tests/test_remove_dead.py     |   304 +
 .../numba/numba/tests/test_retargeting.py     |   291 +
 .../numba/numba/tests/test_return_values.py   |    78 +
 .../numba/numba/tests/test_runtests.py        |   205 +
 .../numba/numba/tests/test_serialize.py       |   325 +
 .../pytorch/numba/numba/tests/test_sets.py    |   785 +
 .../pytorch/numba/numba/tests/test_slices.py  |   259 +
 .../pytorch/numba/numba/tests/test_sort.py    |  1199 ++
 .../pytorch/numba/numba/tests/test_ssa.py     |   577 +
 .../numba/numba/tests/test_stencils.py        |  2838 ++++
 .../numba/numba/tests/test_storeslice.py      |    71 +
 .../numba/numba/tests/test_struct_ref.py      |   429 +
 .../pytorch/numba/numba/tests/test_support.py |   346 +
 .../pytorch/numba/numba/tests/test_svml.py    |   454 +
 .../numba/tests/test_sys_stdin_assignment.py  |    67 +
 .../pytorch/numba/numba/tests/test_sysinfo.py |   189 +
 .../numba/tests/test_target_extension.py      |   852 +
 .../tests/test_target_overloadselector.py     |   148 +
 .../numba/numba/tests/test_threadsafety.py    |    97 +
 .../pytorch/numba/numba/tests/test_tracing.py |   182 +
 .../numba/numba/tests/test_try_except.py      |   851 +
 .../pytorch/numba/numba/tests/test_tuples.py  |   777 +
 .../numba/numba/tests/test_typeconv.py        |   295 +
 .../numba/numba/tests/test_typedlist.py       |  1660 ++
 .../numba/tests/test_typedobjectutils.py      |    68 +
 .../numba/numba/tests/test_typeguard.py       |    35 +
 .../numba/numba/tests/test_typeinfer.py       |   837 +
 .../numba/numba/tests/test_typenames.py       |    17 +
 .../pytorch/numba/numba/tests/test_typeof.py  |   569 +
 .../pytorch/numba/numba/tests/test_types.py   |   879 +
 .../numba/numba/tests/test_typingerror.py     |   237 +
 .../pytorch/numba/numba/tests/test_ufuncs.py  |  1831 ++
 .../pytorch/numba/numba/tests/test_unicode.py |  2615 +++
 .../numba/numba/tests/test_unicode_array.py   |   892 +
 .../numba/numba/tests/test_unicode_names.py   |    61 +
 .../numba/numba/tests/test_unpack_sequence.py |   237 +
 .../tests/test_unpickle_without_module.py     |    49 +
 .../numba/tests/test_unsafe_intrinsics.py     |   230 +
 .../numba/numba/tests/test_usecases.py        |   225 +
 .../numba/numba/tests/test_vectorization.py   |    73 +
 .../test_vectorization_type_inference.py      |    43 +
 .../numba/numba/tests/test_warnings.py        |   222 +
 .../numba/numba/tests/test_withlifting.py     |  1256 ++
 .../pytorch/numba/numba/tests/test_wrapper.py |   104 +
 .../numba/tests/threading_backend_usecases.py |    29 +
 .../pytorch/numba/numba/tests/usecases.py     |    91 +
 .../pytorch/numba/numba/typed/__init__.py     |    20 +
 .../pytorch/numba/numba/typed/dictimpl.py     |    20 +
 .../pytorch/numba/numba/typed/dictobject.py   |  1349 ++
 .../pytorch/numba/numba/typed/listobject.py   |  1531 ++
 .../pytorch/numba/numba/typed/py.typed        |     0
 .../pytorch/numba/numba/typed/typeddict.py    |   355 +
 .../pytorch/numba/numba/typed/typedlist.py    |   687 +
 .../numba/numba/typed/typedobjectutils.py     |   199 +
 .../pytorch/numba/numba/types/__init__.py     |     3 +
 .../pytorch/numba/requirements.txt            |     1 +
 .../pointrcnn-iou/pytorch/numba/runtests.py   |    16 +
 .../pointrcnn-iou/pytorch/numba/setup.py      |   439 +
 .../pointrcnn-iou/pytorch/numba/version.txt   |     1 +
 .../pointrcnn-iou/pytorch/numba/versioneer.py |  1046 ++
 .../.github/workflows/close_stale_issues.yml  |    27 +
 .../pytorch/openpcdet/.gitignore              |    31 +
 .../pointrcnn-iou/pytorch/openpcdet/LICENSE   |   201 +
 .../pytorch/openpcdet/README-ILUVATAR.md      |     6 +
 .../pointrcnn-iou/pytorch/openpcdet/README.md |   291 +
 .../pytorch/openpcdet/build_openpcdet.sh      |    16 +
 .../pytorch/openpcdet/clean_openpcdet.sh      |    10 +
 .../pytorch/openpcdet/docker/Dockerfile       |    55 +
 .../pytorch/openpcdet/docker/README.md        |    25 +
 .../pytorch/openpcdet/docker/cu116.Dockerfile |    83 +
 .../openpcdet/docs/CUSTOM_DATASET_TUTORIAL.md |   108 +
 .../pytorch/openpcdet/docs/DEMO.md            |    51 +
 .../pytorch/openpcdet/docs/GETTING_STARTED.md |   273 +
 .../pytorch/openpcdet/docs/INSTALL.md         |    38 +
 .../pytorch/openpcdet/docs/changelog.md       |    40 +
 .../openpcdet/docs/dataset_vs_model.png       |   Bin 0 -> 126450 bytes
 .../pytorch/openpcdet/docs/demo.png           |   Bin 0 -> 620088 bytes
 .../guidelines_of_approaches/bevfusion.md     |    35 +
 .../docs/guidelines_of_approaches/mppnet.md   |    73 +
 .../openpcdet/docs/model_framework.png        |   Bin 0 -> 104083 bytes
 .../openpcdet/docs/multiple_models_demo.png   |   Bin 0 -> 235379 bytes
 .../pytorch/openpcdet/docs/open_mmlab.png     |   Bin 0 -> 262300 bytes
 .../pytorch/openpcdet/install_openpcdet.sh    |    33 +
 .../pytorch/openpcdet/pcdet/__init__.py       |    24 +
 .../pytorch/openpcdet/pcdet/config.py         |    85 +
 .../openpcdet/pcdet/datasets/__init__.py      |    82 +
 .../pcdet/datasets/argo2/__init__.py          |     2 +
 .../pcdet/datasets/argo2/argo2_dataset.py     |   537 +
 .../datasets/argo2/argo2_utils/constants.py   |    12 +
 .../pcdet/datasets/argo2/argo2_utils/so3.py   |   141 +
 .../pcdet/datasets/augmentor/__init__.py      |     0
 .../datasets/augmentor/augmentor_utils.py     |   658 +
 .../datasets/augmentor/data_augmentor.py      |   319 +
 .../datasets/augmentor/database_sampler.py    |   502 +
 .../pcdet/datasets/custom/__init__.py         |     0
 .../pcdet/datasets/custom/custom_dataset.py   |   283 +
 .../openpcdet/pcdet/datasets/dataset.py       |   325 +
 .../pcdet/datasets/kitti/__init__.py          |     0
 .../pcdet/datasets/kitti/kitti_dataset.py     |   484 +
 .../kitti/kitti_object_eval_python/LICENSE    |    21 +
 .../kitti/kitti_object_eval_python/README.md  |    32 +
 .../kitti_object_eval_python/__init__.py      |     0
 .../kitti/kitti_object_eval_python/eval.py    |   808 +
 .../kitti_object_eval_python/evaluate.py      |    33 +
 .../kitti_object_eval_python/kitti_common.py  |   412 +
 .../kitti_object_eval_python/rotate_iou.py    |   330 +
 .../pcdet/datasets/kitti/kitti_utils.py       |    66 +
 .../openpcdet/pcdet/datasets/lyft/__init__.py |     0
 .../pcdet/datasets/lyft/lyft_dataset.py       |   303 +
 .../datasets/lyft/lyft_mAP_eval/__init__.py   |     0
 .../datasets/lyft/lyft_mAP_eval/lyft_eval.py  |   435 +
 .../pcdet/datasets/lyft/lyft_utils.py         |   332 +
 .../pcdet/datasets/nuscenes/__init__.py       |     0
 .../datasets/nuscenes/nuscenes_dataset.py     |   434 +
 .../pcdet/datasets/nuscenes/nuscenes_utils.py |   588 +
 .../openpcdet/pcdet/datasets/once/__init__.py |     0
 .../pcdet/datasets/once/once_dataset.py       |   444 +
 .../datasets/once/once_eval/eval_utils.py     |    53 +
 .../datasets/once/once_eval/evaluation.py     |   420 +
 .../datasets/once/once_eval/iou_utils.py      |   344 +
 .../pcdet/datasets/once/once_toolkits.py      |   125 +
 .../pcdet/datasets/pandaset/__init__.py       |     0
 .../datasets/pandaset/pandaset_dataset.py     |   489 +
 .../pcdet/datasets/processor/__init__.py      |     0
 .../datasets/processor/data_processor.py      |   298 +
 .../processor/point_feature_encoder.py        |    57 +
 .../pcdet/datasets/waymo/__init__.py          |     0
 .../pcdet/datasets/waymo/waymo_dataset.py     |   827 +
 .../pcdet/datasets/waymo/waymo_eval.py        |   251 +
 .../pcdet/datasets/waymo/waymo_utils.py       |   268 +
 .../openpcdet/pcdet/models/__init__.py        |    54 +
 .../pcdet/models/backbones_2d/__init__.py     |     7 +
 .../models/backbones_2d/base_bev_backbone.py  |   351 +
 .../models/backbones_2d/fuser/__init__.py     |     4 +
 .../models/backbones_2d/fuser/convfuser.py    |    33 +
 .../backbones_2d/map_to_bev/__init__.py       |    10 +
 .../map_to_bev/conv2d_collapse.py             |    38 +
 .../map_to_bev/height_compression.py          |    26 +
 .../map_to_bev/pointpillar_scatter.py         |    73 +
 .../pcdet/models/backbones_3d/__init__.py     |    22 +
 .../pcdet/models/backbones_3d/dsvt.py         |   616 +
 .../focal_sparse_conv/SemanticSeg/__init__.py |     0
 .../SemanticSeg/basic_blocks.py               |    65 +
 .../SemanticSeg/pyramid_ffn.py                |    77 +
 .../SemanticSeg/sem_deeplabv3.py              |   160 +
 .../focal_sparse_conv/__init__.py             |     0
 .../focal_sparse_conv/focal_sparse_conv.py    |   224 +
 .../focal_sparse_conv/focal_sparse_utils.py   |   147 +
 .../pcdet/models/backbones_3d/pfe/__init__.py |     5 +
 .../backbones_3d/pfe/voxel_set_abstraction.py |   411 +
 .../models/backbones_3d/pointnet2_backbone.py |   206 +
 .../models/backbones_3d/spconv_backbone.py    |   295 +
 .../models/backbones_3d/spconv_backbone_2d.py |   300 +
 .../backbones_3d/spconv_backbone_focal.py     |   269 +
 .../backbones_3d/spconv_backbone_voxelnext.py |   225 +
 .../spconv_backbone_voxelnext2d.py            |   219 +
 .../pcdet/models/backbones_3d/spconv_unet.py  |   212 +
 .../pcdet/models/backbones_3d/vfe/__init__.py |    18 +
 .../backbones_3d/vfe/dynamic_mean_vfe.py      |    76 +
 .../backbones_3d/vfe/dynamic_pillar_vfe.py    |   240 +
 .../backbones_3d/vfe/dynamic_voxel_vfe.py     |   106 +
 .../models/backbones_3d/vfe/image_vfe.py      |    85 +
 .../vfe/image_vfe_modules/__init__.py         |     0
 .../vfe/image_vfe_modules/f2v/__init__.py     |     5 +
 .../f2v/frustum_grid_generator.py             |   145 +
 .../image_vfe_modules/f2v/frustum_to_voxel.py |    54 +
 .../vfe/image_vfe_modules/f2v/sampler.py      |    37 +
 .../vfe/image_vfe_modules/ffn/__init__.py     |     5 +
 .../vfe/image_vfe_modules/ffn/ddn/__init__.py |     5 +
 .../ffn/ddn/ddn_deeplabv3.py                  |    24 +
 .../image_vfe_modules/ffn/ddn/ddn_template.py |   162 +
 .../ffn/ddn_loss/__init__.py                  |     5 +
 .../ffn/ddn_loss/balancer.py                  |    50 +
 .../ffn/ddn_loss/ddn_loss.py                  |    75 +
 .../vfe/image_vfe_modules/ffn/depth_ffn.py    |   103 +
 .../pcdet/models/backbones_3d/vfe/mean_vfe.py |    31 +
 .../models/backbones_3d/vfe/pillar_vfe.py     |   123 +
 .../models/backbones_3d/vfe/vfe_template.py   |    22 +
 .../pcdet/models/backbones_image/__init__.py  |     4 +
 .../backbones_image/img_neck/__init__.py      |     4 +
 .../img_neck/generalized_lss.py               |    76 +
 .../pcdet/models/backbones_image/swin.py      |   736 +
 .../pcdet/models/dense_heads/__init__.py      |    21 +
 .../models/dense_heads/anchor_head_multi.py   |   373 +
 .../models/dense_heads/anchor_head_single.py  |    75 +
 .../dense_heads/anchor_head_template.py       |   275 +
 .../pcdet/models/dense_heads/center_head.py   |   416 +
 .../models/dense_heads/point_head_box.py      |   115 +
 .../models/dense_heads/point_head_simple.py   |    91 +
 .../models/dense_heads/point_head_template.py |   210 +
 .../dense_heads/point_intra_part_head.py      |   127 +
 .../dense_heads/target_assigner/__init__.py   |     0
 .../target_assigner/anchor_generator.py       |    79 +
 .../target_assigner/atss_target_assigner.py   |   141 +
 .../axis_aligned_target_assigner.py           |   210 +
 .../target_assigner/hungarian_assigner.py     |   131 +
 .../models/dense_heads/transfusion_head.py    |   479 +
 .../models/dense_heads/voxelnext_head.py      |   559 +
 .../pcdet/models/detectors/PartA2_net.py      |    31 +
 .../pcdet/models/detectors/__init__.py        |    46 +
 .../pcdet/models/detectors/bevfusion.py       |   101 +
 .../openpcdet/pcdet/models/detectors/caddn.py |    38 +
 .../pcdet/models/detectors/centerpoint.py     |    50 +
 .../models/detectors/detector3d_template.py   |   415 +
 .../pcdet/models/detectors/mppnet.py          |   181 +
 .../pcdet/models/detectors/mppnet_e2e.py      |   222 +
 .../pcdet/models/detectors/pillarnet.py       |    50 +
 .../pcdet/models/detectors/point_rcnn.py      |    30 +
 .../pcdet/models/detectors/pointpillar.py     |    34 +
 .../pcdet/models/detectors/pv_rcnn.py         |    36 +
 .../models/detectors/pv_rcnn_plusplus.py      |    53 +
 .../pcdet/models/detectors/second_net.py      |    34 +
 .../pcdet/models/detectors/second_net_iou.py  |   177 +
 .../pcdet/models/detectors/transfusion.py     |    50 +
 .../pcdet/models/detectors/voxel_rcnn.py      |    37 +
 .../pcdet/models/detectors/voxelnext.py       |    44 +
 .../pcdet/models/model_utils/__init__.py      |     0
 .../models/model_utils/basic_block_2d.py      |    34 +
 .../models/model_utils/centernet_utils.py     |   385 +
 .../pcdet/models/model_utils/dsvt_utils.py    |   150 +
 .../models/model_utils/model_nms_utils.py     |   107 +
 .../pcdet/models/model_utils/mppnet_utils.py  |   420 +
 .../pcdet/models/model_utils/swin_utils.py    |   659 +
 .../models/model_utils/transfusion_utils.py   |   102 +
 .../pcdet/models/roi_heads/__init__.py        |    19 +
 .../pcdet/models/roi_heads/mppnet_head.py     |   992 ++
 .../roi_heads/mppnet_memory_bank_e2e.py       |   581 +
 .../pcdet/models/roi_heads/partA2_head.py     |   224 +
 .../pcdet/models/roi_heads/pointrcnn_head.py  |   179 +
 .../pcdet/models/roi_heads/pvrcnn_head.py     |   175 +
 .../models/roi_heads/roi_head_template.py     |   261 +
 .../pcdet/models/roi_heads/second_head.py     |   188 +
 .../roi_heads/target_assigner/__init__.py     |     0
 .../target_assigner/proposal_target_layer.py  |   228 +
 .../pcdet/models/roi_heads/voxelrcnn_head.py  |   262 +
 .../pcdet/models/view_transforms/__init__.py  |     4 +
 .../pcdet/models/view_transforms/depth_lss.py |   258 +
 .../pytorch/openpcdet/pcdet/ops/__init__.py   |     0
 .../openpcdet/pcdet/ops/bev_pool/__init__.py  |     1 +
 .../openpcdet/pcdet/ops/bev_pool/bev_pool.py  |    97 +
 .../pcdet/ops/bev_pool/src/bev_pool.cpp       |    94 +
 .../pcdet/ops/bev_pool/src/bev_pool_cuda.cu   |    98 +
 .../pcdet/ops/ingroup_inds/__init__.py        |     0
 .../pcdet/ops/ingroup_inds/ingroup_inds_op.py |    31 +
 .../pcdet/ops/ingroup_inds/src/error.cuh      |    18 +
 .../ops/ingroup_inds/src/ingroup_inds.cpp     |    54 +
 .../ingroup_inds/src/ingroup_inds_kernel.cu   |    77 +
 .../openpcdet/pcdet/ops/iou3d_nms/__init__.py |     0
 .../pcdet/ops/iou3d_nms/iou3d_nms_utils.py    |   189 +
 .../pcdet/ops/iou3d_nms/src/iou3d_cpu.cpp     |   273 +
 .../pcdet/ops/iou3d_nms/src/iou3d_cpu.h       |    11 +
 .../pcdet/ops/iou3d_nms/src/iou3d_nms.cpp     |   235 +
 .../pcdet/ops/iou3d_nms/src/iou3d_nms.h       |    17 +
 .../pcdet/ops/iou3d_nms/src/iou3d_nms_api.cpp |    20 +
 .../ops/iou3d_nms/src/iou3d_nms_kernel.cu     |   464 +
 .../openpcdet/pcdet/ops/pointnet2/__init__.py |     0
 .../ops/pointnet2/pointnet2_batch/__init__.py |     0
 .../pointnet2_batch/pointnet2_modules.py      |   174 +
 .../pointnet2_batch/pointnet2_utils.py        |   290 +
 .../pointnet2_batch/src/ball_query.cpp        |    39 +
 .../pointnet2_batch/src/ball_query_gpu.cu     |    73 +
 .../pointnet2_batch/src/ball_query_gpu.h      |    15 +
 .../pointnet2_batch/src/cuda_utils.h          |    16 +
 .../pointnet2_batch/src/group_points.cpp      |    36 +
 .../pointnet2_batch/src/group_points_gpu.cu   |    92 +
 .../pointnet2_batch/src/group_points_gpu.h    |    22 +
 .../pointnet2_batch/src/interpolate.cpp       |    56 +
 .../pointnet2_batch/src/interpolate_gpu.cu    |   169 +
 .../pointnet2_batch/src/interpolate_gpu.h     |    30 +
 .../pointnet2_batch/src/pointnet2_api.cpp     |    24 +
 .../pointnet2_batch/src/sampling.cpp          |    46 +
 .../pointnet2_batch/src/sampling_gpu.cu       |   260 +
 .../pointnet2_batch/src/sampling_gpu.h        |    29 +
 .../ops/pointnet2/pointnet2_stack/__init__.py |     0
 .../pointnet2_stack/pointnet2_modules.py      |   470 +
 .../pointnet2_stack/pointnet2_utils.py        |   457 +
 .../pointnet2_stack/src/ball_query.cpp        |    45 +
 .../pointnet2_stack/src/ball_query_gpu.cu     |    90 +
 .../pointnet2_stack/src/ball_query_gpu.h      |    25 +
 .../pointnet2_stack/src/cuda_utils.h          |     9 +
 .../pointnet2_stack/src/group_points.cpp      |    68 +
 .../pointnet2_stack/src/group_points_gpu.cu   |   125 +
 .../pointnet2_stack/src/group_points_gpu.h    |    31 +
 .../pointnet2_stack/src/interpolate.cpp       |   107 +
 .../pointnet2_stack/src/interpolate_gpu.cu    |   196 +
 .../pointnet2_stack/src/interpolate_gpu.h     |    39 +
 .../pointnet2_stack/src/pointnet2_api.cpp     |    31 +
 .../pointnet2_stack/src/sampling.cpp          |    57 +
 .../pointnet2_stack/src/sampling_gpu.cu       |   350 +
 .../pointnet2_stack/src/sampling_gpu.h        |    23 +
 .../pointnet2_stack/src/vector_pool.cpp       |   200 +
 .../pointnet2_stack/src/vector_pool_gpu.cu    |   487 +
 .../pointnet2_stack/src/vector_pool_gpu.h     |    71 +
 .../pointnet2_stack/src/voxel_query.cpp       |    41 +
 .../pointnet2_stack/src/voxel_query_gpu.cu    |   113 +
 .../pointnet2_stack/src/voxel_query_gpu.h     |    19 +
 .../pointnet2_stack/voxel_pool_modules.py     |   131 +
 .../pointnet2_stack/voxel_query_utils.py      |   100 +
 .../pcdet/ops/roiaware_pool3d/__init__.py     |     0
 .../roiaware_pool3d/roiaware_pool3d_utils.py  |   111 +
 .../roiaware_pool3d/src/roiaware_pool3d.cpp   |   177 +
 .../src/roiaware_pool3d_kernel.cu             |   359 +
 .../pcdet/ops/roipoint_pool3d/__init__.py     |     0
 .../roipoint_pool3d/roipoint_pool3d_utils.py  |    67 +
 .../roipoint_pool3d/src/roipoint_pool3d.cpp   |    60 +
 .../src/roipoint_pool3d_kernel.cu             |   165 +
 .../pytorch/openpcdet/pcdet/utils/__init__.py |     0
 .../openpcdet/pcdet/utils/box_coder_utils.py  |   222 +
 .../openpcdet/pcdet/utils/box_utils.py        |   440 +
 .../pcdet/utils/calibration_kitti.py          |   125 +
 .../openpcdet/pcdet/utils/common_utils.py     |   295 +
 .../openpcdet/pcdet/utils/commu_utils.py      |   182 +
 .../openpcdet/pcdet/utils/loss_utils.py       |   649 +
 .../openpcdet/pcdet/utils/object3d_custom.py  |    83 +
 .../openpcdet/pcdet/utils/object3d_kitti.py   |    83 +
 .../openpcdet/pcdet/utils/spconv_utils.py     |    38 +
 .../openpcdet/pcdet/utils/transform_utils.py  |    91 +
 .../pytorch/openpcdet/requirements.txt        |     4 +
 .../pointrcnn-iou/pytorch/openpcdet/setup.py  |   137 +
 .../pytorch/openpcdet/tools/_init_path.py     |     2 +
 .../pytorch/openpcdet/tools/demo.py           |   112 +
 .../openpcdet/tools/eval_utils/eval_utils.py  |   140 +
 .../create_integrated_database.py             |    86 +
 .../tools/scripts/slurm_test_mgpu.sh          |    30 +
 .../tools/scripts/slurm_test_single.sh        |    19 +
 .../openpcdet/tools/scripts/slurm_train.sh    |    32 +
 .../openpcdet/tools/scripts/slurm_train_v2.sh |    30 +
 .../openpcdet/tools/scripts/torch_train.sh    |    18 +
 .../pytorch/openpcdet/tools/test.py           |   207 +
 .../pytorch/openpcdet/tools/train.py          |   230 +
 .../train_utils/optimization/__init__.py      |    68 +
 .../train_utils/optimization/fastai_optim.py  |   264 +
 .../optimization/learning_schedules_fastai.py |   162 +
 .../tools/train_utils/train_utils.py          |   272 +
 .../tools/visual_utils/open3d_vis_utils.py    |   116 +
 .../tools/visual_utils/visualize_utils.py     |   215 +
 .../pointrcnn-iou/pytorch/spconv/.gitignore   |   109 +
 .../pointrcnn-iou/pytorch/spconv/.gitmodules  |     3 +
 .../pytorch/spconv/CMakeLists.txt             |    50 +
 .../pointrcnn-iou/pytorch/spconv/LICENSE      |   201 +
 .../pytorch/spconv/README-ILUVATAR.md         |    16 +
 .../pointrcnn-iou/pytorch/spconv/README.md    |   142 +
 .../pytorch/spconv/build_spconv.sh            |    33 +
 .../pytorch/spconv/clean_spconv.sh            |     9 +
 .../pytorch/spconv/include/paramsgrid.h       |    62 +
 .../pytorch/spconv/include/prettyprint.h      |   445 +
 .../pytorch/spconv/include/pybind11_utils.h   |    61 +
 .../pytorch/spconv/include/spconv/box_iou.h   |   103 +
 .../pytorch/spconv/include/spconv/geometry.h  |   297 +
 .../pytorch/spconv/include/spconv/indice.cu.h |   244 +
 .../pytorch/spconv/include/spconv/indice.h    |    79 +
 .../pytorch/spconv/include/spconv/maxpool.h   |    44 +
 .../pytorch/spconv/include/spconv/mp_helper.h |    47 +
 .../pytorch/spconv/include/spconv/nms.h       |   201 +
 .../pytorch/spconv/include/spconv/nms_gpu.h   |    18 +
 .../spconv/include/spconv/point2voxel.h       |    94 +
 .../pytorch/spconv/include/spconv/pool_ops.h  |    97 +
 .../spconv/include/spconv/reordering.cu.h     |   161 +
 .../spconv/include/spconv/reordering.h        |    40 +
 .../spconv/include/spconv/spconv_ops.h        |   561 +
 .../include/tensorview/helper_kernel.cu.h     |    81 +
 .../spconv/include/tensorview/helper_launch.h |    21 +
 .../spconv/include/tensorview/tensorview.h    |  1146 ++
 .../pytorch/spconv/include/torch_utils.h      |    66 +
 .../pytorch/spconv/include/utility/timer.h    |    54 +
 .../pytorch/spconv/install_spconv.sh          |    35 +
 .../pointrcnn-iou/pytorch/spconv/setup.py     |    89 +
 .../pytorch/spconv/spconv/__init__.py         |    97 +
 .../pytorch/spconv/spconv/conv.py             |   355 +
 .../pytorch/spconv/spconv/functional.py       |   118 +
 .../pytorch/spconv/spconv/modules.py          |   130 +
 .../pytorch/spconv/spconv/ops.py              |   157 +
 .../pytorch/spconv/spconv/pool.py             |   105 +
 .../pytorch/spconv/spconv/test_utils.py       |   190 +
 .../pytorch/spconv/spconv/utils/__init__.py   |   112 +
 .../pytorch/spconv/src/spconv/CMakeLists.txt  |     9 +
 .../pytorch/spconv/src/spconv/all.cc          |    34 +
 .../pytorch/spconv/src/spconv/indice.cc       |    90 +
 .../pytorch/spconv/src/spconv/indice.cu       |   158 +
 .../pytorch/spconv/src/spconv/maxpool.cc      |    82 +
 .../pytorch/spconv/src/spconv/maxpool.cu      |   472 +
 .../pytorch/spconv/src/spconv/reordering.cc   |    70 +
 .../pytorch/spconv/src/spconv/reordering.cu   |   155 +
 .../pytorch/spconv/src/utils/CMakeLists.txt   |    22 +
 .../pytorch/spconv/src/utils/all.cc           |    54 +
 .../pytorch/spconv/src/utils/nms.cu           |   165 +
 .../pytorch/spconv/test/CMakeLists.txt        |    27 +
 .../pytorch/spconv/test/src/catch_main.cpp    |    15 +
 .../spconv/test/src/test_conv_rule.cpp        |   127 +
 .../pytorch/spconv/test/test_SparseConv2d.py  |   125 +
 .../pytorch/spconv/test/test_SparseConv3d.py  |   160 +
 .../spconv/test/test_SparseConvTensor.py      |    20 +
 .../spconv/test/test_SparseInverseConv2d.py   |    90 +
 .../spconv/test/test_SparseInverseConv3d.py   |    91 +
 .../pytorch/spconv/test/test_SubMConv2d.py    |   126 +
 .../pytorch/spconv/test/test_SubMConv3d.py    |   123 +
 .../pytorch/spconv/test/test_conv.py          |   618 +
 .../spconv/third_party/catch2/catch.hpp       | 14020 ++++++++++++++++
 .../spconv/third_party/pybind11/.appveyor.yml |    35 +
 .../spconv/third_party/pybind11/.clang-format |    38 +
 .../spconv/third_party/pybind11/.clang-tidy   |    75 +
 .../third_party/pybind11/.cmake-format.yaml   |    73 +
 .../third_party/pybind11/.gitattributes       |     1 +
 .../third_party/pybind11/.github/CODEOWNERS   |     9 +
 .../pybind11/.github/CONTRIBUTING.md          |   388 +
 .../.github/ISSUE_TEMPLATE/bug-report.yml     |    45 +
 .../.github/ISSUE_TEMPLATE/config.yml         |     8 +
 .../pybind11/.github/dependabot.yml           |     7 +
 .../third_party/pybind11/.github/labeler.yml  |     8 +
 .../pybind11/.github/labeler_merged.yml       |     3 +
 .../pybind11/.github/matchers/pylint.json     |    32 +
 .../pybind11/.github/pull_request_template.md |    19 +
 .../pybind11/.github/workflows/ci.yml         |   956 ++
 .../pybind11/.github/workflows/configure.yml  |    80 +
 .../pybind11/.github/workflows/format.yml     |    55 +
 .../pybind11/.github/workflows/labeler.yml    |    16 +
 .../pybind11/.github/workflows/pip.yml        |   110 +
 .../pybind11/.github/workflows/upstream.yml   |   112 +
 .../spconv/third_party/pybind11/.gitignore    |    45 +
 .../pybind11/.pre-commit-config.yaml          |   170 +
 .../third_party/pybind11/.readthedocs.yml     |     3 +
 .../third_party/pybind11/CMakeLists.txt       |   299 +
 .../spconv/third_party/pybind11/LICENSE       |    29 +
 .../spconv/third_party/pybind11/MANIFEST.in   |     5 +
 .../spconv/third_party/pybind11/README.rst    |   180 +
 .../spconv/third_party/pybind11/docs/Doxyfile |    21 +
 .../pybind11/docs/_static/css/custom.css      |     3 +
 .../pybind11/docs/advanced/cast/chrono.rst    |    81 +
 .../pybind11/docs/advanced/cast/custom.rst    |    93 +
 .../pybind11/docs/advanced/cast/eigen.rst     |   310 +
 .../docs/advanced/cast/functional.rst         |   109 +
 .../pybind11/docs/advanced/cast/index.rst     |    43 +
 .../pybind11/docs/advanced/cast/overview.rst  |   170 +
 .../pybind11/docs/advanced/cast/stl.rst       |   249 +
 .../pybind11/docs/advanced/cast/strings.rst   |   292 +
 .../pybind11/docs/advanced/classes.rst        |  1335 ++
 .../pybind11/docs/advanced/embedding.rst      |   262 +
 .../pybind11/docs/advanced/exceptions.rst     |   398 +
 .../pybind11/docs/advanced/functions.rst      |   614 +
 .../pybind11/docs/advanced/misc.rst           |   337 +
 .../pybind11/docs/advanced/pycpp/index.rst    |    13 +
 .../pybind11/docs/advanced/pycpp/numpy.rst    |   455 +
 .../pybind11/docs/advanced/pycpp/object.rst   |   286 +
 .../docs/advanced/pycpp/utilities.rst         |   155 +
 .../pybind11/docs/advanced/smart_ptrs.rst     |   174 +
 .../third_party/pybind11/docs/basics.rst      |   307 +
 .../third_party/pybind11/docs/benchmark.py    |    87 +
 .../third_party/pybind11/docs/benchmark.rst   |    95 +
 .../third_party/pybind11/docs/changelog.rst   |  2468 +++
 .../third_party/pybind11/docs/classes.rst     |   541 +
 .../third_party/pybind11/docs/cmake/index.rst |     8 +
 .../third_party/pybind11/docs/compiling.rst   |   638 +
 .../spconv/third_party/pybind11/docs/conf.py  |   369 +
 .../spconv/third_party/pybind11/docs/faq.rst  |   307 +
 .../third_party/pybind11/docs/index.rst       |    48 +
 .../third_party/pybind11/docs/installing.rst  |   105 +
 .../third_party/pybind11/docs/limitations.rst |    72 +
 .../pybind11/docs/pybind11-logo.png           |   Bin 0 -> 61034 bytes
 .../docs/pybind11_vs_boost_python1.png        |   Bin 0 -> 44653 bytes
 .../docs/pybind11_vs_boost_python1.svg        |   427 +
 .../docs/pybind11_vs_boost_python2.png        |   Bin 0 -> 41121 bytes
 .../docs/pybind11_vs_boost_python2.svg        |   427 +
 .../third_party/pybind11/docs/reference.rst   |   130 +
 .../third_party/pybind11/docs/release.rst     |    97 +
 .../pybind11/docs/requirements.txt            |     6 +
 .../third_party/pybind11/docs/upgrade.rst     |   552 +
 .../pybind11/include/pybind11/attr.h          |   678 +
 .../pybind11/include/pybind11/buffer_info.h   |   193 +
 .../pybind11/include/pybind11/cast.h          |  1665 ++
 .../pybind11/include/pybind11/chrono.h        |   225 +
 .../pybind11/include/pybind11/common.h        |     2 +
 .../pybind11/include/pybind11/complex.h       |    74 +
 .../pybind11/include/pybind11/detail/class.h  |   742 +
 .../pybind11/include/pybind11/detail/common.h |  1169 ++
 .../pybind11/include/pybind11/detail/descr.h  |   158 +
 .../pybind11/include/pybind11/detail/init.h   |   428 +
 .../include/pybind11/detail/internals.h       |   562 +
 .../pybind11/detail/type_caster_base.h        |  1010 ++
 .../pybind11/include/pybind11/detail/typeid.h |    65 +
 .../pybind11/include/pybind11/eigen.h         |   708 +
 .../pybind11/include/pybind11/embed.h         |   277 +
 .../pybind11/include/pybind11/eval.h          |   156 +
 .../pybind11/include/pybind11/functional.h    |   130 +
 .../pybind11/include/pybind11/gil.h           |   202 +
 .../pybind11/include/pybind11/iostream.h      |   265 +
 .../pybind11/include/pybind11/numpy.h         |  1984 +++
 .../pybind11/include/pybind11/operators.h     |   201 +
 .../pybind11/include/pybind11/options.h       |    76 +
 .../pybind11/include/pybind11/pybind11.h      |  2864 ++++
 .../pybind11/include/pybind11/pytypes.h       |  2392 +++
 .../pybind11/include/pybind11/stl.h           |   425 +
 .../include/pybind11/stl/filesystem.h         |   116 +
 .../pybind11/include/pybind11/stl_bind.h      |   785 +
 .../spconv/third_party/pybind11/noxfile.py    |    97 +
 .../third_party/pybind11/pybind11/__init__.py |    16 +
 .../third_party/pybind11/pybind11/__main__.py |    49 +
 .../third_party/pybind11/pybind11/_version.py |    12 +
 .../third_party/pybind11/pybind11/commands.py |    25 +
 .../third_party/pybind11/pybind11/py.typed    |     0
 .../pybind11/pybind11/setup_helpers.py        |   504 +
 .../third_party/pybind11/pyproject.toml       |    61 +
 .../spconv/third_party/pybind11/setup.cfg     |    50 +
 .../spconv/third_party/pybind11/setup.py      |   149 +
 .../third_party/pybind11/tests/CMakeLists.txt |   558 +
 .../third_party/pybind11/tests/conftest.py    |   213 +
 .../pybind11/tests/constructor_stats.h        |   322 +
 .../pybind11/tests/cross_module_gil_utils.cpp |    45 +
 ...s_module_interleaved_error_already_set.cpp |    51 +
 .../spconv/third_party/pybind11/tests/env.py  |    28 +
 .../tests/extra_python_package/pytest.ini     |     0
 .../tests/extra_python_package/test_files.py  |   274 +
 .../tests/extra_setuptools/pytest.ini         |     0
 .../extra_setuptools/test_setuphelper.py      |   151 +
 .../pybind11/tests/local_bindings.h           |    92 +
 .../third_party/pybind11/tests/object.h       |   205 +
 .../tests/pybind11_cross_module_tests.cpp     |   149 +
 .../pybind11/tests/pybind11_tests.cpp         |   117 +
 .../pybind11/tests/pybind11_tests.h           |    85 +
 .../third_party/pybind11/tests/pytest.ini     |    22 +
 .../pybind11/tests/requirements.txt           |     9 +
 .../third_party/pybind11/tests/test_async.cpp |    25 +
 .../third_party/pybind11/tests/test_async.py  |    24 +
 .../pybind11/tests/test_buffers.cpp           |   224 +
 .../pybind11/tests/test_buffers.py            |   163 +
 .../pybind11/tests/test_builtin_casters.cpp   |   382 +
 .../pybind11/tests/test_builtin_casters.py    |   526 +
 .../pybind11/tests/test_call_policies.cpp     |   115 +
 .../pybind11/tests/test_call_policies.py      |   247 +
 .../pybind11/tests/test_callbacks.cpp         |   243 +
 .../pybind11/tests/test_callbacks.py          |   195 +
 .../pybind11/tests/test_chrono.cpp            |    81 +
 .../third_party/pybind11/tests/test_chrono.py |   209 +
 .../third_party/pybind11/tests/test_class.cpp |   619 +
 .../third_party/pybind11/tests/test_class.py  |   471 +
 .../tests/test_cmake_build/CMakeLists.txt     |    84 +
 .../pybind11/tests/test_cmake_build/embed.cpp |    23 +
 .../installed_embed/CMakeLists.txt            |    28 +
 .../installed_function/CMakeLists.txt         |    39 +
 .../installed_target/CMakeLists.txt           |    46 +
 .../pybind11/tests/test_cmake_build/main.cpp  |     6 +
 .../subdirectory_embed/CMakeLists.txt         |    41 +
 .../subdirectory_function/CMakeLists.txt      |    35 +
 .../subdirectory_target/CMakeLists.txt        |    41 +
 .../pybind11/tests/test_cmake_build/test.py   |     8 +
 .../pybind11/tests/test_const_name.cpp        |    55 +
 .../pybind11/tests/test_const_name.py         |    29 +
 .../tests/test_constants_and_functions.cpp    |   159 +
 .../tests/test_constants_and_functions.py     |    52 +
 .../pybind11/tests/test_copy_move.cpp         |   295 +
 .../pybind11/tests/test_copy_move.py          |   132 +
 .../tests/test_custom_type_casters.cpp        |   209 +
 .../tests/test_custom_type_casters.py         |   120 +
 .../pybind11/tests/test_custom_type_setup.cpp |    41 +
 .../pybind11/tests/test_custom_type_setup.py  |    48 +
 .../pybind11/tests/test_docstring_options.cpp |    88 +
 .../pybind11/tests/test_docstring_options.py  |    41 +
 .../third_party/pybind11/tests/test_eigen.cpp |   401 +
 .../third_party/pybind11/tests/test_eigen.py  |   775 +
 .../pybind11/tests/test_embed/CMakeLists.txt  |    47 +
 .../pybind11/tests/test_embed/catch.cpp       |    27 +
 .../tests/test_embed/external_module.cpp      |    20 +
 .../tests/test_embed/test_interpreter.cpp     |   395 +
 .../tests/test_embed/test_interpreter.py      |    14 +
 .../tests/test_embed/test_trampoline.py       |    16 +
 .../third_party/pybind11/tests/test_enum.cpp  |   133 +
 .../third_party/pybind11/tests/test_enum.py   |   264 +
 .../third_party/pybind11/tests/test_eval.cpp  |   118 +
 .../third_party/pybind11/tests/test_eval.py   |    50 +
 .../pybind11/tests/test_eval_call.py          |     4 +
 .../pybind11/tests/test_exceptions.cpp        |   337 +
 .../pybind11/tests/test_exceptions.h          |    13 +
 .../pybind11/tests/test_exceptions.py         |   362 +
 .../tests/test_factory_constructors.cpp       |   430 +
 .../tests/test_factory_constructors.py        |   516 +
 .../pybind11/tests/test_gil_scoped.cpp        |    47 +
 .../pybind11/tests/test_gil_scoped.py         |    93 +
 .../pybind11/tests/test_iostream.cpp          |   126 +
 .../pybind11/tests/test_iostream.py           |   295 +
 .../tests/test_kwargs_and_defaults.cpp        |   273 +
 .../tests/test_kwargs_and_defaults.py         |   390 +
 .../pybind11/tests/test_local_bindings.cpp    |   106 +
 .../pybind11/tests/test_local_bindings.py     |   256 +
 .../tests/test_methods_and_attributes.cpp     |   459 +
 .../tests/test_methods_and_attributes.py      |   527 +
 .../pybind11/tests/test_modules.cpp           |   125 +
 .../pybind11/tests/test_modules.py            |   121 +
 .../tests/test_multiple_inheritance.cpp       |   341 +
 .../tests/test_multiple_inheritance.py        |   493 +
 .../pybind11/tests/test_numpy_array.cpp       |   524 +
 .../pybind11/tests/test_numpy_array.py        |   587 +
 .../pybind11/tests/test_numpy_dtypes.cpp      |   614 +
 .../pybind11/tests/test_numpy_dtypes.py       |   446 +
 .../pybind11/tests/test_numpy_vectorize.cpp   |   107 +
 .../pybind11/tests/test_numpy_vectorize.py    |   266 +
 .../pybind11/tests/test_opaque_types.cpp      |    77 +
 .../pybind11/tests/test_opaque_types.py       |    58 +
 .../tests/test_operator_overloading.cpp       |   288 +
 .../tests/test_operator_overloading.py        |   152 +
 .../pybind11/tests/test_pickling.cpp          |   194 +
 .../pybind11/tests/test_pickling.py           |    93 +
 .../pybind11/tests/test_pytypes.cpp           |   759 +
 .../pybind11/tests/test_pytypes.py            |   741 +
 .../tests/test_sequences_and_iterators.cpp    |   562 +
 .../tests/test_sequences_and_iterators.py     |   243 +
 .../pybind11/tests/test_smart_ptr.cpp         |   470 +
 .../pybind11/tests/test_smart_ptr.py          |   315 +
 .../third_party/pybind11/tests/test_stl.cpp   |   545 +
 .../third_party/pybind11/tests/test_stl.py    |   377 +
 .../pybind11/tests/test_stl_binders.cpp       |   152 +
 .../pybind11/tests/test_stl_binders.py        |   311 +
 .../tests/test_tagbased_polymorphic.cpp       |   147 +
 .../tests/test_tagbased_polymorphic.py        |    28 +
 .../pybind11/tests/test_thread.cpp            |    66 +
 .../third_party/pybind11/tests/test_thread.py |    42 +
 .../third_party/pybind11/tests/test_union.cpp |    22 +
 .../third_party/pybind11/tests/test_union.py  |     8 +
 .../pybind11/tests/test_virtual_functions.cpp |   591 +
 .../pybind11/tests/test_virtual_functions.py  |   459 +
 .../pybind11/tests/valgrind-numpy-scipy.supp  |   140 +
 .../pybind11/tests/valgrind-python.supp       |   117 +
 .../pybind11/tools/FindCatch.cmake            |    72 +
 .../pybind11/tools/FindEigen3.cmake           |    86 +
 .../pybind11/tools/FindPythonLibsNew.cmake    |   281 +
 .../third_party/pybind11/tools/check-style.sh |    44 +
 .../pybind11/tools/cmake_uninstall.cmake.in   |    23 +
 .../third_party/pybind11/tools/libsize.py     |    36 +
 .../pybind11/tools/make_changelog.py          |    63 +
 .../pybind11/tools/pybind11Common.cmake       |   385 +
 .../pybind11/tools/pybind11Config.cmake.in    |   231 +
 .../pybind11/tools/pybind11NewTools.cmake     |   254 +
 .../pybind11/tools/pybind11Tools.cmake        |   227 +
 .../third_party/pybind11/tools/pyproject.toml |     3 +
 .../pybind11/tools/setup_global.py.in         |    59 +
 .../pybind11/tools/setup_main.py.in           |    40 +
 1515 files changed, 454893 insertions(+)
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/README.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.coveragerc
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.flake8
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.gitattributes
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/CODEOWNERS
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/Bug_report.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/Feature_request.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/config.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/first_rc_checklist.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/sub_rc_checklist.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/PULL_REQUEST_TEMPLATE.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/workflows/stale.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.gitignore
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.pre-commit-config.yaml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/.readthedocs.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/CHANGE_LOG
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/CONTRIBUTING.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/LICENSE
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/LICENSES.third-party
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/MANIFEST.in
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/README.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/azure-pipelines.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/bin/numba
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/build_numba.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/appveyor/run_with_env.cmd
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/azure/azure-linux-macos.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/azure/azure-windows.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/bld.bat
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/build.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/conda_build_config.yaml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/license.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/meta.yaml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/run_test.bat
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/run_test.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/bld.bat
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/build.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/meta.yaml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/scripts/build.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/gpuci/axis.yaml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/gpuci/build.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/MacOSX10.10.sdk.checksum
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/after_success.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/build.cmd
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/build.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/install_miniconda.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/setup_conda_environment.cmd
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/setup_conda_environment.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/test.cmd
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/test.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/clean_numba.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/codecov.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/contrib/valgrind-numba.supp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/Makefile
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/js/modernizr.min.js
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/numba-blue-icon-rgb.svg
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/numba-white-icon-rgb.svg
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/rtd-overrides.css
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_templates/EMPTY
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/README.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/dagmap.yaml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/jquery.graphviz.svg.js
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/render.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/template.html
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/environment.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/gh-pages.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/make.bat
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/requirements.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/_ext/ghfiles.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/conf.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/host.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/kernel.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/libdevice.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/memory.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/types.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/bindings.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/caching.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cooperative_groups.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cuda_array_interface.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cuda_ffi.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cudapysupported.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/device-functions.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/device-management.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/examples.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/external-memory.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/faq.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/fastmath.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/intrinsics.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/ipc.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/kernels.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/laplace_final.svg
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/laplace_initial.svg
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/memory.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/overview.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/random.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/reduction.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/simulator.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/ufunc.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/architecture.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/caching.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/compiler_pass_example.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/contributing.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/custom_pipeline.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/debugging.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/dispatching.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/environment.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/event_api.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/generators.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/hashing.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inline_example.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inline_overload_example.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inlining.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/listings.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/literal.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/live_variable_analysis.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/llvm_timings.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/mission.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/numba-runtime.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/release.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/repomap.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/rewrites.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/stencil.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/target_extension.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/threading_implementation.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/entrypoints.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/high-level.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/interval-example.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/low-level.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/mynorm.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/overloading-guide.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/template.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/glossary.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/cfunc.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/extension-points.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/external-memory-management.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/integer-typing.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/jit-classes.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/np-where-override.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/recursion_callstack.svg
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/type-inference.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/typing_recursion.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/aot-compilation.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/deprecation.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/envvars.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/fpsemantics.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/jit-compilation.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/numpysupported.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/pysemantics.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/pysupported.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/types.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/utils.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/release-notes.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/5minguide.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/cfunc.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/cli.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/examples.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/faq.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/generated-jit.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/installing.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jit-module.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jit.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jitclass.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/overview.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/parallel.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/performance-tips.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/pycc.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/stencil.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/talks.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/threading-layer.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/troubleshoot.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/vectorize.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/withobjmode.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/install_numba.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/mypy.ini
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/__main__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_arraystruct.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_devicearray.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_devicearray.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dispatcher.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dynfunc.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dynfuncmod.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_hashtable.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_hashtable.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_helperlib.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_helpermod.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_lapack.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_npymath_exports.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_numba_common.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_pymodule.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_random.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_typeof.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_typeof.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_unicodetype_db.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_version.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/capsulethunk.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/cext.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/dictobject.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/dictobject.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/listobject.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/listobject.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/utils.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/cloudpickle.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/cloudpickle_fast.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/compat.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/analysis.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/pretty_annotate.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/template.html
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/type_annotations.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/base.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/boxing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/bytecode.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/byteflow.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/caching.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/callconv.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/callwrapper.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ccallback.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cgutils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/codegen.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler_lock.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler_machinery.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/config.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/consts.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/controlflow.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cpu.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cpu_options.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/dataflow.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/manager.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/models.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/packer.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/registry.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/testing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/debuginfo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/decorators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/descriptors.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/dispatcher.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/entrypoints.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/environment.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/errors.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/event.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/extending.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/externals.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/fastmathpass.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/funcdesc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/generators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/imputils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/inline_closurecall.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/interpreter.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/intrinsics.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ir.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ir_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/itanium_mangler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/llvm_bindings.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/lowering.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/object_mode_passes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/optional.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/options.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/overload_glue.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/postproc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/pylowering.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/pythonapi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/registry.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/removerefctpass.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/retarget.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/ir_print.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/registry.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_binop.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_getitem.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_raise.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/_nrt_python.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/_nrt_pythonmod.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/context.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt_external.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrtdynmod.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrtopt.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/serialize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/sigutils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ssa.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/target_extension.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/targetconfig.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/tracing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/transforms.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/_typeconv.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/castgraph.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/rules.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/test.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.hpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typed_passes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeinfer.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/abstract.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/common.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/containers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/function_type.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/functions.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/iterators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/misc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/npytypes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/scalars.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/arraydecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/asnumbatype.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/bufproto.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/builtins.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/cffi_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/cmathdecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/collections.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/context.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/ctypes_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/dictdecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/enumdecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/listdecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/mathdecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/npdatetime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/npydecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/randomdecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/setdecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/templates.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/typeof.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/bytes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/eh.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/nrt.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/refcount.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/untyped_passes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/withcontexts.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/builtins.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/charseq.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/cmathimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/enumimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/hashing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/heapq.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/iterators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/listobj.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/mathimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/numbers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/printimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/randomimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/rangeobj.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/setobj.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/slicing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/tupleobj.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unicode.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unicode_support.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/numbers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/tuple.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/api.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/api_util.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/args.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/codegen.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/compiler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cuda_paths.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/_extras.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/devicearray.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/devices.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/driver.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/drvapi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/enums.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/error.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/libs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/ndarray.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/nvvm.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/rtapi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/runtime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudaimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudamath.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/decorators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/descriptor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/device_init.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/dispatcher.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/errors.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/initialize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/intrinsic_wrapper.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/reduction.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/transpose.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevice.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevicedecl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevicefuncs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdeviceimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/mathimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/models.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/nvvmutils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/printimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/random.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/api.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/compiler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/devicearray.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/devices.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/driver.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/drvapi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/error.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/libs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/nvvm.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/runtime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/kernel.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/kernelapi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/reduction.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/vector_types.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator_init.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/stubs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/target.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/testing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/cuda_include.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/error.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/jitlink.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/jitlink.ptx
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/warn.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_array_attr.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_context_stack.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_array_slicing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_auto_context.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_devicerecord.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_driver.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_libraries.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_memory.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_ndarray.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_deallocations.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_detect.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_emm_plugins.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_events.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_host_alloc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_init.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_inline_ptx.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_ir_patch.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_linker.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_managed_alloc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_nvvm_driver.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_pinned.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_profiler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_ptds.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_reset_device.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_runtime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_select_device.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_streams.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/cache_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/cache_with_cpu_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/extensions_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/jitlink.ptx
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/recursion_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_alignment.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array_args.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array_methods.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_atomics.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_blackscholes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_boolean.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_caching.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_casting.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_compiler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_complex.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_complex_kernel.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_const_string.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_constmem.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cooperative_groups.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cuda_array_interface.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cuda_jit_no_types.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_datetime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_debug.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_debuginfo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_device_func.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_dispatcher.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_enums.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_errors.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_exception.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_extending.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_fastmath.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_forall.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_freevar.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_frexp_ldexp.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_globals.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc_scalar.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc_scheduling.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_idiv.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_inspect.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_intrinsics.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_ipc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_iterators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_lang.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_laplace.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_libdevice.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_lineinfo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_localmem.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_mandel.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_math.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_matmul.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_minmax.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_montecarlo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multigpu.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multiprocessing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multithreads.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_nondet.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_operator.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_optimization.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_overload.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_powi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_print.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_py2_div_issue.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_random.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_record_dtype.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_recursion.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_reduction.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_retrieve_autoconverted_arrays.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_serialize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_slicing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sm.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sm_creation.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sync.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_transpose.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_userexc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vector_type.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_complex.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_decor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_device.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_scalar_arg.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_warning.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_warp_ops.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/support.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/test_cudasim_issues.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/ffi/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/ffi/functions.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_cg.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_ffi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_laplace.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_matmul.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_montecarlo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_random.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_reduction.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_sessionize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_vecadd.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_import.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_library_lookup.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_nvvm.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/types.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/vector_types.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/vectorizers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/function_type.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/_box.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/base.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/boxing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/decorators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/overloads.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/structref.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/extending.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/mathnames.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/appdirs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/cffiimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/cmdlang.gdb
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/dummyarray.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/dump_style.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/findlib.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/firstlinefinder.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/gdb_hook.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/gdb_print_extension.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/help/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/help/inspector.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/init_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/inspection.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/literal.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/llvm_pass_timings.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/mergesort.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_entry.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_gdbinfo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_sysinfo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/quicksort.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/special.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/timsort.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/mviewbuf.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/arraymath.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/arrayobj.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/extensions.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/linalg.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npdatetime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npdatetime_helpers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npyfuncs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npyimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/numpy_support.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/polynomial.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/generator_core.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/generator_methods.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_internal.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_internal.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_num_threads.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_ufunc.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/array_exprs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/decorators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/deviceufunc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/dufunc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc_scheduler.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc_scheduler.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/omppool.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/parallel.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/sigparse.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/tbbpool.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/ufuncbuilder.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/workqueue.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/workqueue.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/wrappers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc_db.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/unsafe/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/unsafe/ndarray.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/array_analysis.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor_lowering.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor_lowering_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/cc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/compiler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/decorators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/llvm_types.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/modulemixin.c
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/platform.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/pycc
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/runtests.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/scripts/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/scripts/generate_lower_listing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/stencil.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/stencilparfor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/__main__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/_runtests.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/loader.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/main.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/notebook.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/annotation_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cache_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cffi_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cfunc_cache_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cloudpickle_main_class.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/compile_with_pycc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/complex_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/ctypes_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_examples.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_jitclass.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_literal_container_usage.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_literally_usage.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_llvm_pass_timings.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_numpy_generators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_parallel_chunksize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_rec_array.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_structref_usage.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_typed_dict_usage.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_typed_list_usage.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/dummy_module.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/enum_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/error_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_array_arg.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_basic.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_break_on_symbol.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_break_on_symbol_version.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_conditional_breakpoint.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_pretty_print.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb_support.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/inlining_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/matmul_usecase.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/cache_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_caching.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_dufunc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_errors.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_gufunc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_env_variable.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_low_work.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_ufunc_issues.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_ufunc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_ufuncbuilding.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_vectorize_decor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/orphaned_semaphore_usecase.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/overload_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/parfors_cache_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/parfors_max_label_error.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pdlike_usecase.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/nested/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/nested/source_module.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_distutils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_distutils_nested.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_setuptools.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_setuptools_nested.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/source_module.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/recursion_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/serialize_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/support.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_alignment.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_analysis.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_annotations.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_api.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_analysis.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_attr.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_constants.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_exprs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_iterators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_manipulation.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_methods.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_reductions.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_return.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_asnumbatype.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_auto_constants.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_blackscholes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_boundscheck.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_buffer_protocol.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_builtins.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_byteflow.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_caching.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_casting.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cffi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cfunc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cgutils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_chained_assign.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_chrome_trace.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cli.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_closure.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_codegen.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compile_cache.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compiler_flags.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compiler_lock.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_complex.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_comprehension.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_conditions_as_predicates.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_config.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_conversion.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_copy_propagate.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ctypes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dataflow.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_datamodel.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_debug.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_debuginfo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_deprecations.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dictimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dictobject.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dicts.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dispatcher.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dummyarray.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dyn_array.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dyn_func.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_entrypoints.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_enums.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_errorhandling.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_errormodels.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_event.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_exceptions.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extended_arg.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extending.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extending_types.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_fancy_indexing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_fastmath.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_firstlinefinder.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_flow_control.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_func_interface.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_func_lifetime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_funcdesc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_function_type.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gdb_bindings.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gdb_dwarf.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_generators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gil.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_globals.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_hashing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_heapq.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_help.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_import.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_indexing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_init_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_inlining.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_interpreter.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_interproc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_intwidth.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir_inlining.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_itanium_mangler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_iteration.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jit_module.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jitclasses.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jitmethod.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_linalg.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_listimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_listobject.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_lists.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_literal_dispatch.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_llvm_pass_timings.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_llvm_version_check.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_locals.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_looplifting.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_make_function_to_jit_function.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mandelbrot.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mangling.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_map_filter_reduce.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mathlib.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_maxmin.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mixed_tuple_unroller.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_moved_modules.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_multi3.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nan.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ndarray_subclasses.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nested_calls.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_np_functions.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_np_randomgen.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_npdatetime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nrt.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nrt_refct.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_num_threads.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numberctor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numbers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numconv.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numpy_support.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numpyadapt.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_obj_lifetime.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_object_mode.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_objects.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_operators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_optional.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_overlap.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parallel_backend.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors_caching.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors_passes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_pipeline.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_polynomial.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_practical_lowering_issues.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_print.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_profiler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_pycc.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_python_int.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_random.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_range.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_recarray_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_record_dtype.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_recursion.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_refop_pruning.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_remove_dead.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_retargeting.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_return_values.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_runtests.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_serialize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sets.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_slices.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sort.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ssa.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_stencils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_storeslice.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_struct_ref.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_support.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_svml.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sys_stdin_assignment.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sysinfo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_target_extension.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_target_overloadselector.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_threadsafety.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_tracing.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_try_except.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_tuples.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeconv.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typedlist.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typedobjectutils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeguard.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeinfer.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typenames.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeof.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_types.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typingerror.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ufuncs.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode_array.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode_names.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unpack_sequence.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unpickle_without_module.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unsafe_intrinsics.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_vectorization.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_vectorization_type_inference.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_warnings.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_withlifting.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_wrapper.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/threading_backend_usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/usecases.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/dictimpl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/dictobject.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/listobject.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/py.typed
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typeddict.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typedlist.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typedobjectutils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/types/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/requirements.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/runtests.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/setup.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/version.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/numba/versioneer.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/.github/workflows/close_stale_issues.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/.gitignore
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/LICENSE
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/README-ILUVATAR.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/README.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/build_openpcdet.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/clean_openpcdet.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/Dockerfile
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/README.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/cu116.Dockerfile
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/CUSTOM_DATASET_TUTORIAL.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/DEMO.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/GETTING_STARTED.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/INSTALL.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/changelog.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/dataset_vs_model.png
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/demo.png
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/guidelines_of_approaches/bevfusion.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/guidelines_of_approaches/mppnet.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/model_framework.png
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/multiple_models_demo.png
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/open_mmlab.png
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/install_openpcdet.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/config.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_utils/constants.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_utils/so3.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/augmentor_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/data_augmentor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/database_sampler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/custom/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/custom/custom_dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/LICENSE
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/README.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/eval.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/evaluate.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/kitti_common.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/rotate_iou.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_mAP_eval/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_mAP_eval/lyft_eval.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/nuscenes_dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/nuscenes_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/eval_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/evaluation.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/iou_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_toolkits.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/pandaset/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/pandaset/pandaset_dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/data_processor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/point_feature_encoder.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_dataset.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_eval.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/base_bev_backbone.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/fuser/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/fuser/convfuser.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/conv2d_collapse.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/height_compression.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/pointpillar_scatter.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/dsvt.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/basic_blocks.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/pyramid_ffn.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/sem_deeplabv3.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/focal_sparse_conv.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/focal_sparse_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pfe/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pointnet2_backbone.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_2d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_focal.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_voxelnext.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_voxelnext2d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_unet.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_mean_vfe.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_pillar_vfe.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_voxel_vfe.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_grid_generator.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_to_voxel.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/sampler.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_deeplabv3.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_template.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/balancer.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/ddn_loss.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/depth_ffn.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/mean_vfe.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/pillar_vfe.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/vfe_template.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/img_neck/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/img_neck/generalized_lss.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/swin.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_multi.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_single.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_template.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/center_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_box.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_simple.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_template.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_intra_part_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/anchor_generator.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/atss_target_assigner.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/axis_aligned_target_assigner.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/hungarian_assigner.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/transfusion_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/voxelnext_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/PartA2_net.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/bevfusion.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/caddn.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/centerpoint.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/detector3d_template.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/mppnet.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/mppnet_e2e.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pillarnet.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/point_rcnn.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pointpillar.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pv_rcnn.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pv_rcnn_plusplus.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/second_net.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/second_net_iou.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/transfusion.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/voxel_rcnn.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/voxelnext.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/basic_block_2d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/centernet_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/dsvt_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/model_nms_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/mppnet_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/swin_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/transfusion_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/mppnet_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/mppnet_memory_bank_e2e.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/partA2_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/pointrcnn_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/pvrcnn_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/roi_head_template.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/second_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/target_assigner/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/target_assigner/proposal_target_layer.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/voxelrcnn_head.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/view_transforms/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/view_transforms/depth_lss.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/bev_pool.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/src/bev_pool.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/src/bev_pool_cuda.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/ingroup_inds_op.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/error.cuh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/ingroup_inds.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/ingroup_inds_kernel.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/iou3d_nms_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_cpu.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_cpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms_api.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_modules.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/cuda_utils.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/pointnet2_api.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/cuda_utils.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/pointnet2_api.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query_gpu.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/voxel_pool_modules.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/voxel_query_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/roiaware_pool3d_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/src/roiaware_pool3d.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/src/roiaware_pool3d_kernel.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/roipoint_pool3d_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/src/roipoint_pool3d.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/src/roipoint_pool3d_kernel.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/box_coder_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/box_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/calibration_kitti.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/common_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/commu_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/loss_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/object3d_custom.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/object3d_kitti.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/spconv_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/transform_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/requirements.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/setup.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/_init_path.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/demo.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/eval_utils/eval_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/process_tools/create_integrated_database.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_test_mgpu.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_test_single.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_train.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_train_v2.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/torch_train.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/test.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/fastai_optim.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/learning_schedules_fastai.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/train_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/visual_utils/open3d_vis_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/visual_utils/visualize_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/.gitignore
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/.gitmodules
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/LICENSE
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/README-ILUVATAR.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/README.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/build_spconv.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/clean_spconv.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/paramsgrid.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/prettyprint.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/pybind11_utils.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/box_iou.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/geometry.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/indice.cu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/indice.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/maxpool.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/mp_helper.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/nms.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/nms_gpu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/point2voxel.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/pool_ops.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/reordering.cu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/reordering.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/spconv_ops.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/helper_kernel.cu.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/helper_launch.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/tensorview.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/torch_utils.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/utility/timer.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/install_spconv.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/setup.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/conv.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/functional.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/modules.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/ops.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/pool.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/test_utils.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/utils/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/all.cc
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/indice.cc
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/indice.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/maxpool.cc
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/maxpool.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/reordering.cc
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/reordering.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/all.cc
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/nms.cu
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/src/catch_main.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/src/test_conv_rule.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConv2d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConv3d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConvTensor.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseInverseConv2d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseInverseConv3d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SubMConv2d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SubMConv3d.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_conv.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/catch2/catch.hpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.appveyor.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.clang-format
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.clang-tidy
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.cmake-format.yaml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.gitattributes
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/CODEOWNERS
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/CONTRIBUTING.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/ISSUE_TEMPLATE/bug-report.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/ISSUE_TEMPLATE/config.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/dependabot.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/labeler.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/labeler_merged.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/matchers/pylint.json
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/pull_request_template.md
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/ci.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/configure.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/format.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/labeler.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/pip.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/upstream.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.gitignore
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.pre-commit-config.yaml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.readthedocs.yml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/LICENSE
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/MANIFEST.in
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/README.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/Doxyfile
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/_static/css/custom.css
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/chrono.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/custom.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/eigen.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/functional.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/overview.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/stl.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/strings.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/classes.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/embedding.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/exceptions.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/functions.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/misc.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/numpy.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/object.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/utilities.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/smart_ptrs.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/basics.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/benchmark.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/benchmark.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/changelog.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/classes.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/cmake/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/compiling.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/conf.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/faq.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/index.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/installing.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/limitations.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11-logo.png
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python1.png
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python1.svg
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python2.png
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python2.svg
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/reference.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/release.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/requirements.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/upgrade.rst
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/attr.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/buffer_info.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/cast.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/chrono.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/common.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/complex.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/class.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/common.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/descr.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/init.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/internals.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/type_caster_base.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/typeid.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/eigen.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/embed.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/eval.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/functional.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/gil.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/iostream.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/numpy.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/operators.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/options.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/pybind11.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/pytypes.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl/filesystem.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl_bind.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/noxfile.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/__init__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/__main__.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/_version.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/commands.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/py.typed
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/setup_helpers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pyproject.toml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/setup.cfg
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/setup.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/conftest.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/constructor_stats.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/cross_module_gil_utils.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/cross_module_interleaved_error_already_set.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/env.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_python_package/pytest.ini
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_python_package/test_files.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_setuptools/pytest.ini
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_setuptools/test_setuphelper.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/local_bindings.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/object.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_cross_module_tests.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_tests.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_tests.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pytest.ini
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/requirements.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_async.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_async.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_buffers.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_buffers.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_builtin_casters.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_builtin_casters.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_call_policies.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_call_policies.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_callbacks.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_callbacks.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_chrono.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_chrono.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_class.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_class.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/embed.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_embed/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_function/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_target/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/main.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_embed/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_function/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_target/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/test.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_const_name.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_const_name.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_constants_and_functions.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_constants_and_functions.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_copy_move.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_copy_move.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_casters.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_casters.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_setup.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_setup.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_docstring_options.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_docstring_options.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eigen.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eigen.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/CMakeLists.txt
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/catch.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/external_module.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_interpreter.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_interpreter.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_trampoline.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_enum.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_enum.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval_call.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.h
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_factory_constructors.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_factory_constructors.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_gil_scoped.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_gil_scoped.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_iostream.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_iostream.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_kwargs_and_defaults.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_kwargs_and_defaults.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_local_bindings.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_local_bindings.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_methods_and_attributes.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_methods_and_attributes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_modules.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_modules.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_multiple_inheritance.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_multiple_inheritance.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_array.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_array.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_dtypes.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_dtypes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_vectorize.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_vectorize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_opaque_types.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_opaque_types.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_operator_overloading.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_operator_overloading.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pickling.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pickling.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pytypes.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pytypes.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_sequences_and_iterators.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_sequences_and_iterators.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_smart_ptr.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_smart_ptr.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl_binders.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl_binders.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_tagbased_polymorphic.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_tagbased_polymorphic.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_thread.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_thread.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_union.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_union.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_virtual_functions.cpp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_virtual_functions.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/valgrind-numpy-scipy.supp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/valgrind-python.supp
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindCatch.cmake
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindEigen3.cmake
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindPythonLibsNew.cmake
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/check-style.sh
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/cmake_uninstall.cmake.in
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/libsize.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/make_changelog.py
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Common.cmake
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Config.cmake.in
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11NewTools.cmake
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Tools.cmake
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pyproject.toml
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/setup_global.py.in
 create mode 100644 cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/setup_main.py.in

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/README.md b/cv/3d_detection/pointrcnn-iou/pytorch/README.md
new file mode 100644
index 000000000..6f689f170
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/README.md
@@ -0,0 +1,69 @@
+# PointRCNN-IoU
+
+## Model description
+PointRCNN-IoU is an extension of the PointRCNN object detection framework that incorporates Intersection over Union (IoU) as a metric for evaluation. IoU is a common metric used in object detection tasks to measure the overlap between predicted bounding boxes and ground truth bounding boxes.
+
+## Step 1: Installation
+```
+## install libGL and libboost
+yum install mesa-libGL
+yum install boost-devel
+
+# Install numba
+cd numba
+python3 setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log
+bash install_numba.sh
+cd ..
+
+# Install spconv
+cd spconv
+bash clean_spconv.sh
+bash build_spconv.sh
+bash install_spconv.sh
+cd ..
+
+# Install openpcdet
+cd openpcdet
+pip3 install -r requirements.txt
+bash build_openpcdet.sh
+bash install_openpcdet.sh
+cd ..
+```
+
+## Step 2: Preparing datasets
+Download the kitti dataset from <http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d>
+
+Download the "planes" subdataset from <https://drive.google.com/file/d/1d5mq0RXRnvHPVeKx6Q612z0YRO1t2wAp/view?usp=sharing>
+```
+OpenPCDet
+├── data
+│   ├── kitti
+│   │   │── ImageSets
+│   │   │── training
+│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
+│   │   │── testing
+│   │   │   ├──calib & velodyne & image_2
+├── pcdet
+├── tools
+```
+
+```
+# Modify the `DATA_PATH` in the kitti_dataset.yaml to your own
+cd openpcdet
+python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
+```
+
+## Step 3: Training
+
+### Single GPU training
+
+```bash
+cd tools
+python3 train.py --cfg_file cfgs/kitti_models/pointrcnn_iou.yaml
+```
+
+### Multiple GPU training
+
+```bash
+bash scripts/dist_train.sh 16 --cfg_file cfgs/kitti_models/pointrcnn_iou.yaml
+```
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.coveragerc b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.coveragerc
new file mode 100644
index 000000000..e9eda9644
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.coveragerc
@@ -0,0 +1,24 @@
+# configuration file used by run_coverage.py
+[run]
+branch = True
+source = numba
+concurrency = multiprocessing
+parallel = True
+
+[report]
+
+omit =
+    */__main__.py
+    # Vendored packages
+    numba/misc/appdirs.py
+    numba/cloudpickle/__init__.py
+    numba/cloudpickle/cloudpickle.py
+    numba/cloudpickle/cloudpickle_fast.py
+    numba/cloudpickle/compat.py
+    numba/_version.py
+
+exclude_lines =
+    pragma: no cover
+    if __name__ == .__main__.:
+
+[html]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.flake8 b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.flake8
new file mode 100644
index 000000000..9d77a1d5e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.flake8
@@ -0,0 +1,263 @@
+[flake8]
+ignore =
+    E20,   # Extra space in brackets
+    E231,E241,  # Multiple spaces around ","
+    E26,   # Comments
+    E731,  # Assigning lambda expression
+    E741,  # Ambiguous variable names
+    W503,  # line break before binary operator
+    W504,  # line break after binary operator
+max-line-length = 80
+
+exclude =
+    __pycache__
+    .git
+    *.pyc
+    *~
+    *.o
+    *.so
+    *.cpp
+    *.c
+    *.h
+    __init__.py
+    # Ignore vendored files
+    numba/cloudpickle/*
+    # Grandfather in existing failing files.  This list should shrink over time
+    numba/stencils/stencil.py
+    numba/core/transforms.py
+    numba/core/tracing.py
+    numba/core/withcontexts.py
+    numba/_version.py
+    numba/core/inline_closurecall.py
+    numba/core/ir_utils.py
+    numba/core/pylowering.py
+    numba/python_utils.py
+    numba/parfors/parfor.py
+    numba/misc/numba_entry.py
+    numba/stencils/stencilparfor.py
+    numba/core/ir.py
+    numba/core/generators.py
+    numba/misc/appdirs.py
+    numba/core/caching.py
+    numba/core/debuginfo.py
+    numba/core/annotations/pretty_annotate.py
+    numba/misc/dummyarray.py
+    numba/core/dataflow.py
+    numba/core/pythonapi.py
+    numba/core/decorators.py
+    numba/core/typeconv/rules.py
+    numba/core/typeconv/castgraph.py
+    numba/core/rewrites/registry.py
+    numba/core/rewrites/macros.py
+    numba/core/rewrites/static_binop.py
+    numba/core/rewrites/ir_print.py
+    numba/core/types/abstract.py
+    numba/core/types/misc.py
+    numba/core/types/npytypes.py
+    numba/core/types/common.py
+    numba/core/types/iterators.py
+    numba/core/types/scalars.py
+    numba/core/fastmathpass.py
+    numba/cpython/setobj.py
+    numba/core/options.py
+    numba/cpython/printimpl.py
+    numba/cpython/cmathimpl.py
+    numba/cpython/tupleobj.py
+    numba/cpython/mathimpl.py
+    numba/core/registry.py
+    numba/core/imputils.py
+    numba/cpython/builtins.py
+    numba/core/cpu.py
+    numba/misc/quicksort.py
+    numba/core/callconv.py
+    numba/cpython/randomimpl.py
+    numba/np/npyimpl.py
+    numba/cpython/slicing.py
+    numba/cpython/numbers.py
+    numba/cpython/listobj.py
+    numba/core/removerefctpass.py
+    numba/core/boxing.py
+    numba/misc/cffiimpl.py
+    numba/np/linalg.py
+    numba/cpython/rangeobj.py
+    numba/np/npyfuncs.py
+    numba/cpython/iterators.py
+    numba/core/codegen.py
+    numba/np/polynomial.py
+    numba/misc/mergesort.py
+    numba/core/base.py
+    numba/np/npdatetime.py
+    numba/pycc/cc.py
+    numba/pycc/compiler.py
+    numba/pycc/llvm_types.py
+    numba/pycc/platform.py
+    numba/pycc/decorators.py
+    numba/core/runtime/nrtdynmod.py
+    numba/core/runtime/context.py
+    numba/tests/test_support.py
+    numba/tests/test_llvm_version_check.py
+    numba/tests/test_builtins.py
+    numba/tests/test_jitmethod.py
+    numba/tests/test_inlining.py
+    numba/tests/test_array_manipulation.py
+    numba/tests/test_dummyarray.py
+    numba/tests/test_smart_array.py
+    numba/tests/test_linalg.py
+    numba/tests/test_threadsafety.py
+    numba/tests/test_utils.py
+    numba/tests/cfunc_cache_usecases.py
+    numba/tests/enum_usecases.py
+    numba/tests/test_func_lifetime.py
+    numba/tests/test_typeinfer.py
+    numba/tests/test_return_values.py
+    numba/tests/test_npdatetime.py
+    numba/tests/test_fancy_indexing.py
+    numba/tests/support.py
+    numba/tests/test_print.py
+    numba/tests/test_debug.py
+    numba/tests/test_interproc.py
+    numba/tests/test_typeconv.py
+    numba/tests/test_tracing.py
+    numba/tests/usecases.py
+    numba/tests/test_vectorization_type_inference.py
+    numba/tests/matmul_usecase.py
+    numba/tests/complex_usecases.py
+    numba/tests/test_array_exprs.py
+    numba/tests/test_polynomial.py
+    numba/tests/test_wrapper.py
+    numba/tests/test_obj_lifetime.py
+    numba/tests/test_intwidth.py
+    numba/tests/test_remove_dead.py
+    numba/tests/serialize_usecases.py
+    numba/tests/test_del.py
+    numba/tests/test_gil.py
+    numba/tests/cffi_usecases.py
+    numba/tests/test_slices.py
+    numba/tests/test_mandelbrot.py
+    numba/tests/compile_with_pycc.py
+    numba/tests/test_deprecations.py
+    numba/tests/test_looplifting.py
+    numba/tests/test_storeslice.py
+    numba/tests/recursion_usecases.py
+    numba/tests/dummy_module.py
+    numba/tests/test_operators.py
+    numba/tests/test_comprehension.py
+    numba/tests/ctypes_usecases.py
+    numba/tests/test_locals.py
+    numba/tests/test_dicts.py
+    numba/tests/test_optional.py
+    numba/tests/test_mathlib.py
+    numba/tests/test_numberctor.py
+    numba/tests/test_globals.py
+    numba/tests/test_typingerror.py
+    numba/tests/test_copy_propagate.py
+    numba/tests/test_ctypes.py
+    numba/tests/test_typeof.py
+    numba/tests/test_usecases.py
+    numba/tests/test_auto_constants.py
+    numba/tests/test_cffi.py
+    numba/tests/test_sort.py
+    numba/tests/test_cfunc.py
+    numba/tests/test_conversion.py
+    numba/tests/test_indexing.py
+    numba/tests/test_pycc.py
+    numba/tests/annotation_usecases.py
+    numba/tests/test_extended_arg.py
+    numba/tests/test_alignment.py
+    numba/tests/test_multi3.py
+    numba/tests/test_overlap.py
+    numba/tests/test_array_attr.py
+    numba/tests/test_array_methods.py
+    numba/tests/test_enums.py
+    numba/tests/test_profiler.py
+    numba/tests/test_numpyadapt.py
+    numba/tests/test_stencils.py
+    numba/tests/cache_usecases.py
+    numba/tests/true_div_usecase.py
+    numba/tests/test_dataflow.py
+    numba/tests/test_tuples.py
+    numba/tests/test_svml.py
+    numba/tests/test_array_iterators.py
+    numba/tests/test_buffer_protocol.py
+    numba/tests/test_casting.py
+    numba/tests/test_lists.py
+    numba/tests/test_array_analysis.py
+    numba/tests/test_serialize.py
+    numba/tests/test_iteration.py
+    numba/tests/test_recarray_usecases.py
+    numba/tests/test_target_overloadselector.py
+    numba/tests/test_compile_cache.py
+    numba/tests/test_array_reductions.py
+    numba/tests/test_dyn_func.py
+    numba/tests/test_unpack_sequence.py
+    numba/tests/test_cgutils.py
+    numba/tests/test_complex.py
+    numba/tests/test_hashing.py
+    numba/tests/test_sys_stdin_assignment.py
+    numba/tests/test_ufuncs.py
+    numba/tests/pdlike_usecase.py
+    numba/tests/test_range.py
+    numba/tests/test_nrt_refct.py
+    numba/misc/timsort.py
+    numba/tests/test_nested_calls.py
+    numba/tests/test_chained_assign.py
+    numba/tests/test_withlifting.py
+    numba/tests/test_parfors.py
+    numba/tests/test_sets.py
+    numba/tests/test_dyn_array.py
+    numba/tests/test_objects.py
+    numba/tests/test_random.py
+    numba/tests/test_nan.py
+    numba/tests/pycc_distutils_usecase/source_module.py
+    numba/tests/npyufunc/test_ufuncbuilding.py
+    numba/tests/npyufunc/test_errors.py
+    numba/tests/npyufunc/test_vectorize_decor.py
+    numba/tests/npyufunc/test_parallel_ufunc_issues.py
+    numba/tests/npyufunc/test_parallel_env_variable.py
+    numba/tests/npyufunc/test_gufunc.py
+    numba/core/typing/cmathdecl.py
+    numba/core/typing/bufproto.py
+    numba/core/typing/mathdecl.py
+    numba/core/typing/listdecl.py
+    numba/core/typing/builtins.py
+    numba/core/typing/randomdecl.py
+    numba/core/typing/setdecl.py
+    numba/core/typing/npydecl.py
+    numba/core/typing/arraydecl.py
+    numba/core/typing/collections.py
+    numba/core/typing/ctypes_utils.py
+    numba/core/typing/enumdecl.py
+    numba/core/typing/cffi_utils.py
+    numba/core/typing/npdatetime.py
+    numba/core/annotations/type_annotations.py
+    numba/testing/ddt.py
+    numba/testing/loader.py
+    numba/testing/notebook.py
+    numba/testing/main.py
+    numba/np/unsafe/ndarray.py
+    numba/np/ufunc/deviceufunc.py
+    numba/np/ufunc/sigparse.py
+    numba/parfors/parfor_lowering.py
+    numba/np/ufunc/array_exprs.py
+    numba/np/ufunc/decorators.py
+    numba/core/datamodel/models.py
+    numba/core/datamodel/packer.py
+    numba/core/datamodel/testing.py
+    numba/core/datamodel/manager.py
+
+per-file-ignores =
+    # Ignore star imports, unused imports, and "may be defined by star imports"
+    # errors in device_init because its purpose is to bring together a lot of
+    # the public API to be star-imported in numba.cuda.__init__
+    numba/cuda/device_init.py:F401,F403,F405
+    # libdevice.py is an autogenerated file containing stubs for all the device
+    # functions. Some of the lines in docstrings are a little over-long, as they
+    # contain the URLs of the reference pages in the online libdevice
+    # documentation.
+    numba/cuda/libdevice.py:E501
+    # Ignore too-long lines in the CUDA doc examples, prioritising readability
+    # in the docs over line length in the example source (especially given that
+    # the test code is already indented by 8 spaces)
+    numba/cuda/tests/doc_examples/test_random.py:E501
+    numba/cuda/tests/doc_examples/test_cg.py:E501
+    numba/cuda/tests/doc_examples/test_matmul.py:E501
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.gitattributes b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.gitattributes
new file mode 100644
index 000000000..972ba2b7f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.gitattributes
@@ -0,0 +1 @@
+numba/_version.py export-subst
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/CODEOWNERS b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/CODEOWNERS
new file mode 100644
index 000000000..a9d8b4265
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/CODEOWNERS
@@ -0,0 +1,98 @@
+# Numba's codeowners file is dual purpose, it:
+#
+# 1. Provides information to github about who should be requested to review a PR
+# 2. Provides contributors/czars general information about who to contact
+#    first about various parts of the code base. A lot of concepts in Numba are
+#    necessarily spread throughout the code base, consequently some of the
+#    "code ownership"/first contact is concept based opposed to file/directory
+#    based.
+#
+# ------------------------------------------------------------------------------
+# Information for github
+# ------------------------------------------------------------------------------
+# These people are the default "owners" for everything in the repo unless a
+# later match is made, they will automatically be requested to review PRs.
+* @sklam @stuartarchibald @esc
+
+# Owners of specific parts of the code, will be requested to review if a PR
+# touches code in the matched pattern
+/numba/cuda/ @gmarkall
+/numba/parfors/ @DrTodd13
+/numba/stencils/ @DrTodd13
+
+# ------------------------------------------------------------------------------
+# Information for contributors
+# ------------------------------------------------------------------------------
+# This section provides a rough list of who to contact first for help with
+# various parts/concepts in the code base, first contact does not imply
+# ownership!
+#
+# Parts of the code base:
+#
+# * Parfors/Parallel Accelerator (@DrTodd13)
+#       - Array Analysis (@DrTodd13)
+#       - Parfors transforms (@DrTodd13)
+# * Stencils (@DrTodd13)
+# * Experimental:
+#       - Jitclasses (@sklam)
+#       - StructRef (@sklam)
+# * Typed containers:
+#       - Typed.List (@esc)
+#       - Typed.Dict (@sklam)
+# * Documentation (Needs first contact/owner)
+# * NumPy (Needs first contact/owner)
+#       - ufuncs (Needs first contact/owner)
+#       - linalg (@stuartarchibald)
+#       - Implementation of specific functions (Needs first contact/owner)
+#       - Parallel backends/threading layers (@stuartarchibald)
+# * CPython implementation (Needs first contact/owner)
+# * Extension API (Needs first contact/owner)
+# * AOT (Needs first contact/owner)
+# * Compiler:
+#       - Type inference (@sklam)
+#       - Bytecode analysis/CFA/DFA (@sklam)
+#       - Compiler Pipeline infrastructure (@stuartarchibald)
+#       - Compiler passes:
+#               - Rewrites (Needs first contact/owner)
+#               - Branch pruning (@stuartarchibald)
+#               - Literal unroll (@stuartarchibald)
+#               - Rewrite Semantic Constants (@stuartarchibald)
+#               - MakeFunction To Jit function (@stuartarchibald)
+#               - Overload and function inlining (@stuartarchibald)
+#               - With Lifting (@sklam)
+#               - Exception handling (@sklam)
+#               - Literally (@sklam)
+#               - SSA (@sklam)
+#               - lowering.py, codegen.py (@sklam)
+#       - Datamodels/call conventions (@sklam)
+#       - Inlining in general (@stuartarchibald)
+#
+# Additional Concepts:
+#
+# * Reference counting and NRT (@sklam)
+# * Testing (Needs first contact/owner)
+# * CI:
+#       - public CI (azure) (Needs first contact/owner)
+#       - Numba build farm (@esc)
+# * Integration testing (https://github.com/numba/numba-integration-testing)
+#   (@esc)
+# * ASV profiling (@esc)
+# * Type Annotations (@luk-f-a and @EPronovost)
+# * Ufunc/GUfunc (Needs first contact/owner)
+# * Profiling (Needs first contact/owner (and code!))
+# * Debugging:
+#       - DWARF (@sklam)
+#       - gdb support (@stuartarchibald)
+# * Hardware targets:
+#       - The CUDA target (@gmarkall)
+#       - The ROCm target (@stuartarchibald)
+#       - ARM* (@stuartarchibald)
+#       - POWER (Needs first contact/owner)
+#       - X86* (Needs first contact/owner)
+# * OS:
+#       - Linux (@stuartarchibald)
+#       - OSX
+#       - Windows
+#       - BSD (@stuartarchibald)
+#
+# Anything not covered by someone else... ping @sklam and @stuartarchibald
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/Bug_report.md b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/Bug_report.md
new file mode 100644
index 000000000..db91bced7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/Bug_report.md
@@ -0,0 +1,34 @@
+---
+name: Bug Report
+about: Report a bug. Not for asking general questions - see below.
+
+---
+
+<!--
+
+Thanks for opening an issue! To help the Numba team handle your information
+efficiently, please first ensure that there is no other issue present that
+already describes the issue you have
+(search at https://github.com/numba/numba/issues?&q=is%3Aissue).
+
+-->
+
+## Reporting a bug
+
+<!--
+
+Before submitting a bug report please ensure that you can check off these boxes:
+
+-->
+
+- [ ] I have tried using the latest released version of Numba (most recent is
+ visible in the change log (https://github.com/numba/numba/blob/main/CHANGE_LOG).
+- [ ] I have included a self contained code sample to reproduce the problem.
+  i.e. it's possible to run as 'python bug.py'.
+
+<!--
+
+Please include details of the bug here, including, if applicable, what you
+expected to happen!
+
+-->
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/Feature_request.md b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/Feature_request.md
new file mode 100644
index 000000000..398c277a9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/Feature_request.md
@@ -0,0 +1,23 @@
+---
+name: Feature Request
+about: Tell us about something in the Python language/NumPy you'd like Numba to support. Not for asking general questions - see below.
+
+---
+
+<!--
+
+Thanks for opening an issue! To help the Numba team handle your information
+efficiently, please first ensure that there is no other issue present that
+already describes the issue you have
+(search at https://github.com/numba/numba/issues?&q=is%3Aissue).
+
+-->
+
+## Feature request
+
+<!--
+
+Please include details of the feature you would like to see, why you would
+like to see it/the use case.
+
+-->
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/config.yml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 000000000..08310d750
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,11 @@
+blank_issues_enabled: false
+contact_links:
+  - name: General Question
+    url: https://numba.discourse.group/c/numba/community-support/
+    about: "If you have a general question (not a bug report or feature request) then please ask on Numba's discourse instance."
+  - name: Quick Question/Just want to say Hi!
+    url: https://gitter.im/numba/numba
+    about: "If you have a quick question or want chat to users/developers in real time then please use gitter.im/numba/numba"
+  - name: Discuss an involved feature
+    url: https://numba.discourse.group/c/numba/development/
+    about: "If you would like to suggest a more involved feature like *Can a new compiler pass be added to do X* then please start a discussion on Numba's discourse instance."
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/first_rc_checklist.md b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/first_rc_checklist.md
new file mode 100644
index 000000000..be572dfec
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/first_rc_checklist.md
@@ -0,0 +1,41 @@
+---
+name: First Release Candidate Checklist (maintainer only)
+about: Checklist template for the first release of every series
+title: Numba X.Y.Zrc1 Checklist (FIXME)
+labels: task
+
+---
+
+
+## Numba X.Y.Z
+
+* [ ] Merge to main.
+    - [ ] "remaining Pull-Requests from milestone".
+* [ ] Check Numba's version support table documentation. Update via PR if
+      needed.
+* [ ] Review deprecation schedule and notices. Make PRs if need be.
+* [ ] Merge change log changes.
+    - [ ] "PR with changelog entries".
+* [ ] Create X.Y release branch.
+* [ ] Dependency version pinning on release branch
+  * [ ] Pin llvmlite to `>=0.A.0rc1,<0.A+1.0`.
+  * [ ] Pin NumPy if needed
+  * [ ] Pin TBB if needed
+* [ ] Annotated tag X.Y.Zrc1 on release branch (no `v` prefix).
+* [ ] Build and upload conda packages on buildfarm (check "upload").
+* [ ] Build wheels and sdist on the buildfarm (check "upload").
+* [ ] Verify packages uploaded to Anaconda Cloud and move to `numba/label/main`.
+* [ ] Upload wheels and sdist to PyPI (upload from `ci_artifacts`).
+* [ ] Verify wheels for all platforms arrived on PyPi.
+* [ ] Initialize and verify ReadTheDocs build.
+* [ ] Send RC announcement email / post announcement to discourse group.
+* [ ] Post link to Twitter.
+
+### Post Release:
+
+* [ ] Clean up `ci_artifacts` by moving files to sub-directories
+* [ ] Tag X.Y+1.0dev0 to start new development cycle on `main`.
+* [ ] Update llvmlite dependency spec to match next version via PR to `main`.
+* [ ] Update release checklist template with any additional bullet points that
+      may have arisen during the release.
+* [ ] Close milestone (and then close this release issue).
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/sub_rc_checklist.md b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/sub_rc_checklist.md
new file mode 100644
index 000000000..5874922a6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/ISSUE_TEMPLATE/sub_rc_checklist.md
@@ -0,0 +1,37 @@
+---
+name: Subsequent Release Candidate Checklist (maintainer only)
+about: Checklist template for all subsequent releases (RC 2-N, FINAL and PATCH) of every series
+title: Numba X.Y.Zrc1 Checklist (FIXME)
+labels: task
+
+---
+
+
+## numba X.Y.Z
+
+* [ ] Cherry-pick items from the X.Y.Z milestone into a PR.
+* [ ] Approve change log modifications and cherry-pick.
+* [ ] Merge change log modifications and cherry-picks to X.Y release branch.
+  * [ ] https://github.com/numba/numba/pull/XXXX
+* [ ] Review, merge and check execution of release notebook. (FINAL ONLY)
+* [ ] Annotated tag X.Y.Z on release branch (no `v` prefix).
+* [ ] Build and upload conda packages on buildfarm (check `upload`).
+* [ ] Build wheels and sdist on the buildfarm (check "upload").
+* [ ] Verify packages uploaded to Anaconda Cloud and move to
+  `numba/label/main`.
+* [ ] Upload wheels and sdist to PyPI (upload from `ci_artifacts`).
+* [ ] Verify wheels for all platforms arrived on PyPi.
+* [ ] Verify ReadTheDocs build.
+* [ ] Send RC/FINAL announcement email / post announcement to discourse group.
+* [ ] Post link to Twitter.
+* [ ] Post link to python-announce-list@python.org.
+
+### Post release
+
+* [ ] Snapshot Build Farm config
+* [ ] Clean up `ci_artifacts` by moving files to subdirectories
+* [ ] Update release checklist template with any additional bullet points that
+      may have arisen during the release.
+* [ ] Ping Anaconda Distro team to trigger a build for `defaults` (FINAL ONLY).
+* [ ] Create a release on Github at https://github.com/numba/numba/releases (FINAL ONLY).
+* [ ] Close milestone (and then close this release issue).
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/PULL_REQUEST_TEMPLATE.md b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 000000000..18d4b105f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,39 @@
+<!--
+
+Thanks for wanting to contribute to Numba :)
+
+First, if you need some help or want to chat to the core developers, please
+visit https://gitter.im/numba/numba for real time chat or post to the Numba
+forum https://numba.discourse.group/.
+
+Here's some guidelines to help the review process go smoothly.
+
+0. Please write a description in this text box of the changes that are being
+   made.
+
+1. Please ensure that you have written units tests for the changes made/features
+   added.
+
+2. If you are closing an issue please use one of the automatic closing words as
+   noted here: https://help.github.com/articles/closing-issues-using-keywords/
+
+3. If your pull request is not ready for review but you want to make use of the
+   continuous integration testing facilities here, please click the arrow besides
+   "Create Pull Request" and choose "Create Draft Pull Request".
+   When it's ready for review, you can click the button "ready to review" near
+   the end of the pull request
+   (besides "This pull request is still a work in progress".)
+   The maintainers will then be automatically notified to review it.
+
+4. Once review has taken place please do not add features or make changes out of
+   the scope of those requested by the reviewer (doing this just add delays as
+   already reviewed code ends up having to be re-reviewed/it is hard to tell
+   what is new etc!). Further, please do not rebase your branch on main/force
+   push/rewrite history, doing any of these causes the context of any comments
+   made by reviewers to be lost. If conflicts occur against main they should
+   be resolved by merging main into the branch used for making the pull
+   request.
+
+Many thanks in advance for your cooperation!
+
+-->
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/workflows/stale.yml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/workflows/stale.yml
new file mode 100644
index 000000000..6415ada98
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.github/workflows/stale.yml
@@ -0,0 +1,20 @@
+name: 'Mark stale issues'
+on:
+  schedule:
+    - cron: '30 1 * * *'
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v3
+        with:
+          stale-issue-message: >
+            This issue is marked as stale as it has had no activity in the past
+            30 days. Please close this issue if no further response or action is
+            needed. Otherwise, please respond with any updates and confirm that
+            this issue still needs to be addressed.
+          stale-issue-label: 'stale'
+          any-of-labels: 'question,needtriage,more info needed'
+          days-before-issue-stale: 30
+          days-before-issue-close: -1
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.gitignore b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.gitignore
new file mode 100644
index 000000000..f4a687d88
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.gitignore
@@ -0,0 +1,28 @@
+*.pyc
+*.o
+*.so
+*.dylib
+*.pyd
+*.pdb
+*.egg-info
+*.sw[po]
+*.out
+*.ll
+.coverage
+.nfs*
+tags
+MANIFEST
+
+build/
+docs/_build/
+docs/gh-pages/
+dist/
+htmlcov/
+.idea/
+.vscode/
+.ycm_extra_conf.py
+.mypy_cache/
+.ipynb_checkpoints/
+__pycache__/
+
+docs/source/developer/autogen*
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.pre-commit-config.yaml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.pre-commit-config.yaml
new file mode 100644
index 000000000..bf2fb6423
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.pre-commit-config.yaml
@@ -0,0 +1,5 @@
+repos:
+- repo: https://gitlab.com/pycqa/flake8
+  rev: 3.7.8
+  hooks:
+  - id: flake8
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/.readthedocs.yml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.readthedocs.yml
new file mode 100644
index 000000000..f74302f7e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/.readthedocs.yml
@@ -0,0 +1,15 @@
+version: 2
+build:
+  os: ubuntu-20.04
+  tools:
+    python: mambaforge-4.10
+sphinx:
+  configuration: docs/source/conf.py
+python:
+  install:
+  - method: setuptools
+    path: .
+conda:
+  environment: docs/environment.yml
+formats:
+- pdf
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/CHANGE_LOG b/cv/3d_detection/pointrcnn-iou/pytorch/numba/CHANGE_LOG
new file mode 100644
index 000000000..e94fa6673
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/CHANGE_LOG
@@ -0,0 +1,5717 @@
+Version 0.56.4 (3 November, 2022)
+---------------------------------
+
+This is a bugfix release to fix a regression in the CUDA target in relation to
+the ``.view()`` method on CUDA device arrays that is present when using NumPy
+version 1.23.0 or later.
+
+Pull-Requests:
+
+* PR `#8537 <https://github.com/numba/numba/pull/8537>`_: Make ol_compatible_view accessible on all targets (`gmarkall <https://github.com/gmarkall>`_)
+* PR `#8552 <https://github.com/numba/numba/pull/8552>`_: Update version support table for 0.56.4. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8553 <https://github.com/numba/numba/pull/8553>`_: Update CHANGE_LOG for 0.56.4 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8570 <https://github.com/numba/numba/pull/8570>`_: Release 0.56 branch: Fix overloads with ``target="generic"`` for CUDA (`gmarkall <https://github.com/gmarkall>`_)
+* PR `#8571 <https://github.com/numba/numba/pull/8571>`_: Additional update to CHANGE_LOG for 0.56.4 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+
+Authors:
+
+* `gmarkall <https://github.com/gmarkall>`_
+* `stuartarchibald <https://github.com/stuartarchibald>`_
+
+Version 0.56.3 (13 October, 2022)
+---------------------------------
+
+This is a bugfix release to remove the version restriction applied to the
+``setuptools`` package and to fix a bug in the CUDA target in relation to
+copying zero length device arrays to zero length host arrays.
+
+Pull-Requests:
+
+* PR `#8475 <https://github.com/numba/numba/pull/8475>`_: Remove setuptools version pin (`gmarkall <https://github.com/gmarkall>`_)
+* PR `#8482 <https://github.com/numba/numba/pull/8482>`_: Fix #8477: Allow copies with different strides for 0-length data (`gmarkall <https://github.com/gmarkall>`_)
+* PR `#8486 <https://github.com/numba/numba/pull/8486>`_: Restrict the TBB development package to supported version in Azure. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8503 <https://github.com/numba/numba/pull/8503>`_: Update version support table for 0.56.3 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8504 <https://github.com/numba/numba/pull/8504>`_: Update CHANGE_LOG for 0.56.3 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+
+Authors:
+
+* `gmarkall <https://github.com/gmarkall>`_
+* `stuartarchibald <https://github.com/stuartarchibald>`_
+
+Version 0.56.2 (1 September, 2022)
+----------------------------------
+
+This is a bugfix release that supports NumPy 1.23 and fixes CUDA function
+caching.
+
+Pull-Requests:
+
+* PR `#8239 <https://github.com/numba/numba/pull/8239>`_: Add decorator to run a test in a subprocess (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8276 <https://github.com/numba/numba/pull/8276>`_: Move Azure to use macos-11 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8310 <https://github.com/numba/numba/pull/8310>`_: CUDA: Fix Issue #8309 - atomics don't work on complex components (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8342 <https://github.com/numba/numba/pull/8342>`_: Upgrade to ubuntu-20.04 for azure pipeline CI (`jamesobutler <https://github.com/jamesobutler>`_)
+* PR `#8356 <https://github.com/numba/numba/pull/8356>`_: Update setup.py, buildscripts, CI and docs to require setuptools<60 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8374 <https://github.com/numba/numba/pull/8374>`_: Don't pickle LLVM IR for CUDA code libraries (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8377 <https://github.com/numba/numba/pull/8377>`_: Add support for NumPy 1.23 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8384 <https://github.com/numba/numba/pull/8384>`_: Move strace() check into tests that actually need it (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8386 <https://github.com/numba/numba/pull/8386>`_: Fix the docs for numba.get_thread_id (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8407 <https://github.com/numba/numba/pull/8407>`_: Pin NumPy version to 1.18-1.24 (`Andre Masella <https://github.com/apmasell>`_)
+* PR `#8411 <https://github.com/numba/numba/pull/8411>`_: update version support table for 0.56.1 (`esc <https://github.com/esc>`_)
+* PR `#8412 <https://github.com/numba/numba/pull/8412>`_: Create changelog for 0.56.1 (`Andre Masella <https://github.com/apmasell>`_)
+* PR `#8413 <https://github.com/numba/numba/pull/8413>`_: Fix Azure CI for NumPy 1.23 and use conda-forge scipy (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8414 <https://github.com/numba/numba/pull/8413>`_: Hotfix for 0.56.2 (`Siu Kwan Lam <https://github.com/sklam>`_)
+
+Version 0.56.1 (1 September, 2022)
+----------------------------------
+
+The release was skipped due to issues during the release process.
+
+Version 0.56.0 (25 July, 2022)
+------------------------------
+
+This release continues to add new features, bug fixes and stability improvements
+to Numba. Please note that this will be the last release that has support for
+Python 3.7 as the next release series (Numba 0.57) will support Python 3.11!
+Also note that, this will be the last release to support linux-32 packages
+produced by the Numba team.
+
+Python language support enhancements:
+
+* Previously missing support for large, in-line dictionaries and internal calls
+  to functions with large numbers of keyword arguments in Python 3.10 has been
+  added.
+* ``operator.mul`` now works for ``list`` s.
+* Literal slices, e.g. ``slice(1, 10, 2)`` can be returned from ``nopython``
+  mode functions.
+* The ``len`` function now works on ``dict_keys``, ``dict_values`` and
+  ``dict_items`` .
+* Numba's ``set`` implementation now supports reference counted items e.g.
+  strings.
+
+Numba specific feature enhancements:
+
+* The experimental ``jitclass`` feature gains support for a large number of
+  ``builtin`` methods e.g. declaring ``__hash__`` or ``__getitem__`` for a
+  ``jitclass`` type.
+* It's now possible to use ``@vectorize`` on an already ``@jit`` family
+  decorated function.
+* Name mangling has been updated to emit compiled function names that exactly
+  match the function name in Python. This means debuggers, like GDB, can be set
+  to break directly on Python function names.
+* A GDB "pretty printing" support module has been added, when loaded into GDB
+  Numba's internal representations of Python/NumPy types are rendered inside GDB
+  as they would be in Python.
+* An experimental option is added to the ``@jit`` family decorators to entirely
+  turn off LLVM's optimisation passes for a given function (see
+  ``_dbg_optnone`` kwarg in the ``@jit`` decorator family).
+* A new environment variable is added ``NUMBA_EXTEND_VARIABLE_LIFETIMES``, which
+  if set will extend the lifetime of variables to the end of their basic block,
+  this to permit a debugging experience in GDB similar to that found in compiled
+  C/C++/Fortran code.
+
+NumPy features/enhancements:
+
+* Initial support for passing, using and returning ``numpy.random.Generator``
+  instances has been added, this currently includes support for the ``random``
+  distribution.
+* The broadcasting functions ``np.broadcast_shapes`` and ``np.broadcast_arrays``
+  are now supported.
+* The ``min`` and ``max`` functions now work with ``np.timedelta64`` and
+  ``np.datetime64`` types.
+* Sorting multi-dimensional arrays along the last axis is now supported in
+  ``np.sort()``.
+* The ``np.clip`` function is updated to accept NumPy arrays for the ``a_min``
+  and ``a_max`` arguments.
+* The NumPy allocation routines (``np.empty`` , ``np.ones`` etc.) support shape
+  arguments specified using members of ``enum.IntEnum`` s.
+* The function ``np.random.noncentral_chisquare`` is now supported.
+* The performance of functions ``np.full`` and ``np.ones`` has been improved.
+
+Parallel Accelerator enhancements:
+
+* The ``parallel=True`` functionality is enhanced through the addition of the
+  functions ``numba.set_parallel_chunksize`` and
+  ``numba.get_parallel_chunksize`` to permit a more fine grained scheduling of
+  work defined in a parallel region. There is also support for adjusting the
+  ``chunksize`` via a context manager.
+* The ``ID`` of a thread is now defined to be predictable and within a known
+  range, it is available through calling the function ``numba.get_thread_id``.
+* The performance of ``@stencil`` s has been improved in both serial and
+  parallel execution.
+
+CUDA enhancements:
+
+* New functionality:
+
+  * Self-recursive device functions.
+  * Vector type support (``float4``, ``int2``, etc.).
+  * Shared / local arrays of extension types can now be created.
+  * Support for linking CUDA C / C++ device functions into Python kernels.
+  * PTX generation for Compute Capabilities 8.6 and 8.7 - e.g. RTX A series,
+    GTX 3000 series.
+  * Comparison operations for ``float16`` types.
+
+* Performance improvements:
+
+  * Context queries are no longer made during launch configuration.
+  * Launch configurations are now LRU cached.
+  * On-disk caching of CUDA kernels is now supported.
+
+* Documentation: many new examples added.
+
+Docs:
+
+* Numba now has an official "mission statement".
+* There's now a "version support table" in the documentation to act as an easy
+  to use, single reference point, for looking up information about Numba
+  releases and their required/supported dependencies.
+
+General Enhancements:
+
+* Numba imports more quickly in environments with large numbers of packages as
+  it now uses ``importlib-metadata`` for querying other packages.
+* Emission of chrome tracing output is now supported for the internal
+  compilation event handling system.
+* This release is tested and known to work when using the
+  `Pyston <https://www.pyston.org/>`_ Python interpreter.
+
+Pull-Requests:
+
+* PR `#5209 <https://github.com/numba/numba/pull/5209>`_: Use importlib to load numba extensions (`Stepan Rakitin <https://github.com/svrakitin>`_ `Graham Markall <https://github.com/gmarkall>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#5877 <https://github.com/numba/numba/pull/5877>`_: Jitclass builtin methods (`Ethan Pronovost <https://github.com/EPronovost>`_ `Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6490 <https://github.com/numba/numba/pull/6490>`_: Stencil output allocated with np.empty now and new code to initialize the borders. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7005 <https://github.com/numba/numba/pull/7005>`_: Make `numpy.searchsorted` match NumPy when first argument is unsorted (`Brandon T. Willard <https://github.com/brandonwillard>`_)
+* PR `#7363 <https://github.com/numba/numba/pull/7363>`_: Update cuda.local.array to clarify "simple constant expression" (e.g. no NumPy ints) (`Sterling Baird <https://github.com/sgbaird>`_)
+* PR `#7364 <https://github.com/numba/numba/pull/7364>`_: Removes an instance of signed integer overflow undefined behaviour. (`Tobias Sargeant <https://github.com/folded>`_)
+* PR `#7537 <https://github.com/numba/numba/pull/7537>`_: Add chrome tracing (`Hadia Ahmed <https://github.com/hadia206>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7556 <https://github.com/numba/numba/pull/7556>`_: Testhound/fp16 comparison (`Michael Collison <https://github.com/testhound>`_ `Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7586 <https://github.com/numba/numba/pull/7586>`_: Support for len on dict.keys, dict.values, and dict.items (`Nick Riasanovsky <https://github.com/njriasan>`_)
+* PR `#7617 <https://github.com/numba/numba/pull/7617>`_: Numba gdb-python extension for printing (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7619 <https://github.com/numba/numba/pull/7619>`_: CUDA: Fix linking with PTX when compiling lazily (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7621 <https://github.com/numba/numba/pull/7621>`_: Add support for linking CUDA C / C++ with `@cuda.jit` kernels (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7625 <https://github.com/numba/numba/pull/7625>`_: Combined parfor chunking and caching PRs. (`stuartarchibald <https://github.com/stuartarchibald>`_ `Todd A. Anderson <https://github.com/DrTodd13>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7651 <https://github.com/numba/numba/pull/7651>`_: DOC: pypi and conda-forge badges (`Ray Bell <https://github.com/raybellwaves>`_)
+* PR `#7660 <https://github.com/numba/numba/pull/7660>`_: Add support for np.broadcast_arrays (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#7664 <https://github.com/numba/numba/pull/7664>`_: Flatten mangling dicts into a single dict (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7680 <https://github.com/numba/numba/pull/7680>`_: CUDA Docs: include example calling slow matmul (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7682 <https://github.com/numba/numba/pull/7682>`_: performance improvements to np.full and np.ones (`Rishi Kulkarni <https://github.com/rishi-kulkarni>`_)
+* PR `#7684 <https://github.com/numba/numba/pull/7684>`_: DOC: remove incorrect warning in np.random reference (`Rishi Kulkarni <https://github.com/rishi-kulkarni>`_)
+* PR `#7685 <https://github.com/numba/numba/pull/7685>`_: Don't convert setitems that have dimension mismatches to parfors. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7690 <https://github.com/numba/numba/pull/7690>`_: Implemented np.random.noncentral_chisquare for all size arguments (`Rishi Kulkarni <https://github.com/rishi-kulkarni>`_)
+* PR `#7695 <https://github.com/numba/numba/pull/7695>`_: `IntEnumMember` support for  `np.empty`, `np.zeros`, and `np.ones` (`Benjamin Graham <https://github.com/benwilliamgraham>`_)
+* PR `#7699 <https://github.com/numba/numba/pull/7699>`_: CUDA: Provide helpful error if the return type is missing for `declare_device` (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7700 <https://github.com/numba/numba/pull/7700>`_: Support for scalar arguments in Np.ascontiguousarray  (`Dhruv Patel <https://github.com/DhruvPatel01>`_)
+* PR `#7703 <https://github.com/numba/numba/pull/7703>`_: Ignore unsupported types in `ShapeEquivSet._getnames()` (`Benjamin Graham <https://github.com/benwilliamgraham>`_)
+* PR `#7704 <https://github.com/numba/numba/pull/7704>`_: Move the type annotation pass to post legalization. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7709 <https://github.com/numba/numba/pull/7709>`_: CUDA: Fixes missing type annotation pass following #7704 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7712 <https://github.com/numba/numba/pull/7712>`_: Fixing issue 7693 (`stuartarchibald <https://github.com/stuartarchibald>`_ `Graham Markall <https://github.com/gmarkall>`_ `luk-f-a <https://github.com/luk-f-a>`_)
+* PR `#7714 <https://github.com/numba/numba/pull/7714>`_: Support for boxing SliceLiteral type (`Nick Riasanovsky <https://github.com/njriasan>`_)
+* PR `#7718 <https://github.com/numba/numba/pull/7718>`_: Bump llvmlite dependency to 0.39.0dev0 for Numba 0.56.0dev0 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7724 <https://github.com/numba/numba/pull/7724>`_: Update URLs in error messages to refer to RTD docs. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7728 <https://github.com/numba/numba/pull/7728>`_: Document that AOT-compiled functions do not check arg types (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7729 <https://github.com/numba/numba/pull/7729>`_: Handle Omitted/OmittedArgDataModel in DI generation. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7732 <https://github.com/numba/numba/pull/7732>`_: update release checklist following 0.55.0 RC1 (`esc <https://github.com/esc>`_)
+* PR `#7736 <https://github.com/numba/numba/pull/7736>`_: Update CHANGE_LOG for 0.55.0 final. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7740 <https://github.com/numba/numba/pull/7740>`_: CUDA Python 11.6 support (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7744 <https://github.com/numba/numba/pull/7744>`_: Fix issues with locating/parsing source during DebugInfo emission. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7745 <https://github.com/numba/numba/pull/7745>`_: Fix the release year for Numba 0.55 change log entry. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7748 <https://github.com/numba/numba/pull/7748>`_: Fix #7713: Ensure _prng_random_hash return has correct bitwidth (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7749 <https://github.com/numba/numba/pull/7749>`_: Refactor threading layer priority tests to not use stdout/stderr (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7752 <https://github.com/numba/numba/pull/7752>`_: Fix #7751: Use original filename for array exprs (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7755 <https://github.com/numba/numba/pull/7755>`_: CUDA: Deprecate support for CC < 5.3 and CTK < 10.2 (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7763 <https://github.com/numba/numba/pull/7763>`_: Update Read the Docs configuration (automatic) (`readthedocs-assistant <https://github.com/readthedocs-assistant>`_)
+* PR `#7764 <https://github.com/numba/numba/pull/7764>`_: Add dbg_optnone and dbg_extend_lifetimes flags (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7771 <https://github.com/numba/numba/pull/7771>`_: Move function unique ID to abi-tags (`stuartarchibald <https://github.com/stuartarchibald>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7772 <https://github.com/numba/numba/pull/7772>`_: CUDA: Add Support to Creating `StructModel` Array (`Michael Wang <https://github.com/isVoid>`_)
+* PR `#7776 <https://github.com/numba/numba/pull/7776>`_: Updates coverage.py config (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7777 <https://github.com/numba/numba/pull/7777>`_: Remove reference existing issue from GH template. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7778 <https://github.com/numba/numba/pull/7778>`_: Remove long deprecated flags from the CLI. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7780 <https://github.com/numba/numba/pull/7780>`_: Fix sets with reference counted items (`Benjamin Graham <https://github.com/benwilliamgraham>`_)
+* PR `#7782 <https://github.com/numba/numba/pull/7782>`_: adding reminder to check on deprecations (`esc <https://github.com/esc>`_)
+* PR `#7783 <https://github.com/numba/numba/pull/7783>`_: remove upper limit on Python version (`esc <https://github.com/esc>`_)
+* PR `#7786 <https://github.com/numba/numba/pull/7786>`_: Remove dependency on intel-openmp for OSX (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7788 <https://github.com/numba/numba/pull/7788>`_: Avoid issue with DI gen for arrayexprs. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7796 <https://github.com/numba/numba/pull/7796>`_: update change-log for 0.55.1 (`esc <https://github.com/esc>`_)
+* PR `#7797 <https://github.com/numba/numba/pull/7797>`_: prune README (`esc <https://github.com/esc>`_)
+* PR `#7799 <https://github.com/numba/numba/pull/7799>`_: update the release checklist post 0.55.1 (`esc <https://github.com/esc>`_)
+* PR `#7801 <https://github.com/numba/numba/pull/7801>`_: add sdist command and umask reminder (`esc <https://github.com/esc>`_)
+* PR `#7804 <https://github.com/numba/numba/pull/7804>`_: update local references from master -> main (`esc <https://github.com/esc>`_)
+* PR `#7805 <https://github.com/numba/numba/pull/7805>`_: Enhance source line finding logic for debuginfo (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7809 <https://github.com/numba/numba/pull/7809>`_: Updates the gdb configuration to accept a binary name or a path. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7813 <https://github.com/numba/numba/pull/7813>`_: Extend parfors test timeout for aarch64. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7814 <https://github.com/numba/numba/pull/7814>`_: CUDA Dispatcher refactor (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7815 <https://github.com/numba/numba/pull/7815>`_: CUDA Dispatcher refactor 2: inherit from `dispatcher.Dispatcher` (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7817 <https://github.com/numba/numba/pull/7817>`_: Update intersphinx URLs for NumPy and llvmlite. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7823 <https://github.com/numba/numba/pull/7823>`_: Add renamed vars to callee scope such that it is self consistent. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7829 <https://github.com/numba/numba/pull/7829>`_: CUDA: Support `Enum/IntEnum` in Kernel (`Michael Wang <https://github.com/isVoid>`_)
+* PR `#7833 <https://github.com/numba/numba/pull/7833>`_: Add version support information table to docs. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7835 <https://github.com/numba/numba/pull/7835>`_: Fix pickling error when module cannot be imported (`idorrington <https://github.com/idorrington>`_)
+* PR `#7836 <https://github.com/numba/numba/pull/7836>`_: min() and max() support for np.datetime and np.timedelta (`Benjamin Graham <https://github.com/benwilliamgraham>`_)
+* PR `#7837 <https://github.com/numba/numba/pull/7837>`_: Initial refactoring of parfor reduction lowering  (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7845 <https://github.com/numba/numba/pull/7845>`_: change time.time() to time.perf_counter() in docs (`Nopileos2 <https://github.com/Nopileos2>`_)
+* PR `#7846 <https://github.com/numba/numba/pull/7846>`_: Fix CUDA enum vectorize test on Windows (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7848 <https://github.com/numba/numba/pull/7848>`_: Support for int * list (`Nick Riasanovsky <https://github.com/njriasan>`_)
+* PR `#7850 <https://github.com/numba/numba/pull/7850>`_: CUDA: Pass `fastmath` compiler flag down to `compile_ptx` and `compile_device`; Improve `fastmath` tests (`Michael Wang <https://github.com/isVoid>`_)
+* PR `#7855 <https://github.com/numba/numba/pull/7855>`_: Ensure np.argmin/no.argmax return type is intp (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7858 <https://github.com/numba/numba/pull/7858>`_: CUDA: Deprecate `ptx` Attribute and Update Tests (`Graham Markall <https://github.com/gmarkall>`_ `Michael Wang <https://github.com/isVoid>`_)
+* PR `#7861 <https://github.com/numba/numba/pull/7861>`_: Fix a spelling mistake in README (`Zizheng Guo <https://github.com/gzz2000>`_)
+* PR `#7864 <https://github.com/numba/numba/pull/7864>`_: Fix cross_iter_dep check. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7865 <https://github.com/numba/numba/pull/7865>`_: Remove add_user_function (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7866 <https://github.com/numba/numba/pull/7866>`_: Support for large numbers of args/kws with Python 3.10 (`Nick Riasanovsky <https://github.com/njriasan>`_)
+* PR `#7878 <https://github.com/numba/numba/pull/7878>`_: CUDA: Remove some deprecated support, add CC 8.6 and 8.7 (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7893 <https://github.com/numba/numba/pull/7893>`_: Use uuid.uuid4() as the key in serialization. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7895 <https://github.com/numba/numba/pull/7895>`_: Remove use of `llvmlite.llvmpy` (`Andre Masella <https://github.com/apmasell>`_)
+* PR `#7898 <https://github.com/numba/numba/pull/7898>`_: Skip test_ptds under cuda-memcheck (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7901 <https://github.com/numba/numba/pull/7901>`_: Pyston compatibility for the test suite (`Kevin Modzelewski <https://github.com/kmod>`_)
+* PR `#7904 <https://github.com/numba/numba/pull/7904>`_: Support m1 (`esc <https://github.com/esc>`_)
+* PR `#7911 <https://github.com/numba/numba/pull/7911>`_: added sys import (`Nightfurex <https://github.com/Nightfurex>`_)
+* PR `#7915 <https://github.com/numba/numba/pull/7915>`_: CUDA: Fix test checking debug info rendering. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7918 <https://github.com/numba/numba/pull/7918>`_: Add JIT examples to CUDA docs (`brandon-b-miller <https://github.com/brandon-b-miller>`_ `Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7919 <https://github.com/numba/numba/pull/7919>`_: Disallow //= reductions in pranges. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7924 <https://github.com/numba/numba/pull/7924>`_: Retain non-modified index tuple components. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7939 <https://github.com/numba/numba/pull/7939>`_: Fix rendering in feature request template. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7940 <https://github.com/numba/numba/pull/7940>`_: Implemented `np.allclose` in `numba/np/arraymath.py` (`Gagandeep Singh <https://github.com/czgdp1807>`_)
+* PR `#7941 <https://github.com/numba/numba/pull/7941>`_: Remove debug dump output from closure inlining pass. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7946 <https://github.com/numba/numba/pull/7946>`_: instructions for creating a build environment were outdated (`esc <https://github.com/esc>`_)
+* PR `#7949 <https://github.com/numba/numba/pull/7949>`_: Add Cuda Vector Types (`Michael Wang <https://github.com/isVoid>`_)
+* PR `#7950 <https://github.com/numba/numba/pull/7950>`_: mission statement (`esc <https://github.com/esc>`_)
+* PR `#7956 <https://github.com/numba/numba/pull/7956>`_: Stop using pip for 3.10 on public ci (Revert "start testing Python 3.10 on public CI") (`esc <https://github.com/esc>`_)
+* PR `#7957 <https://github.com/numba/numba/pull/7957>`_: Use cloudpickle for disk caches (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7958 <https://github.com/numba/numba/pull/7958>`_: `numpy.clip` accept `numpy.array` for `a_min`, `a_max` (`Gagandeep Singh <https://github.com/czgdp1807>`_)
+* PR `#7959 <https://github.com/numba/numba/pull/7959>`_: Permit a new array model to have a super set of array model fields. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7961 <https://github.com/numba/numba/pull/7961>`_: `numba.typed.typeddict.Dict.get` uses `castedkey` to avoid returning default value even if the key is present (`Gagandeep Singh <https://github.com/czgdp1807>`_)
+* PR `#7963 <https://github.com/numba/numba/pull/7963>`_: remove the roadmap from the sphinx based docs (`esc <https://github.com/esc>`_)
+* PR `#7964 <https://github.com/numba/numba/pull/7964>`_: Support for large constant dictionaries in Python 3.10 (`Nick Riasanovsky <https://github.com/njriasan>`_)
+* PR `#7965 <https://github.com/numba/numba/pull/7965>`_: Use uuid4 instead of PID in cache temp name to prevent collisions. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7971 <https://github.com/numba/numba/pull/7971>`_: lru cache for configure call (`Tingkai Liu <https://github.com/TK-21st>`_)
+* PR `#7972 <https://github.com/numba/numba/pull/7972>`_: Fix fp16 support for cuda shared array (`Michael Collison <https://github.com/testhound>`_ `Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7986 <https://github.com/numba/numba/pull/7986>`_: Small caching refactor to support target cache implementations (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7994 <https://github.com/numba/numba/pull/7994>`_: Supporting multidimensional arrays in quick sort (`Gagandeep Singh <https://github.com/czgdp1807>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7996 <https://github.com/numba/numba/pull/7996>`_: Fix binding logic in `@overload_glue`. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7999 <https://github.com/numba/numba/pull/7999>`_: Remove `@overload_glue` for NumPy allocators. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8003 <https://github.com/numba/numba/pull/8003>`_: Add np.broadcast_shapes (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#8004 <https://github.com/numba/numba/pull/8004>`_: CUDA fixes for Windows (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8014 <https://github.com/numba/numba/pull/8014>`_: Fix support for {real,imag} array attrs in Parfors. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8016 <https://github.com/numba/numba/pull/8016>`_: [Docs] [Very Minor] Make `numba.jit` boundscheck doc line consistent (`Kyle Martin <https://github.com/martinky24>`_)
+* PR `#8017 <https://github.com/numba/numba/pull/8017>`_: Update FAQ to include details about using debug-only option (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#8027 <https://github.com/numba/numba/pull/8027>`_: Support for NumPy 1.22 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8031 <https://github.com/numba/numba/pull/8031>`_: Support for Numpy BitGenerators PR#1 - Core Generator Support (`Kaustubh <https://github.com/kc611>`_)
+* PR `#8035 <https://github.com/numba/numba/pull/8035>`_: Fix a couple of typos RE implementation (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8037 <https://github.com/numba/numba/pull/8037>`_: CUDA self-recursion tests (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8044 <https://github.com/numba/numba/pull/8044>`_: Make Python 3.10 kwarg peephole less restrictive (`Nick Riasanovsky <https://github.com/njriasan>`_)
+* PR `#8046 <https://github.com/numba/numba/pull/8046>`_: Fix caching test failures (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8049 <https://github.com/numba/numba/pull/8049>`_: support str(bool) syntax (`LI Da <https://github.com/dlee992>`_)
+* PR `#8052 <https://github.com/numba/numba/pull/8052>`_: Ensure pthread is linked in when building for ppc64le. (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8056 <https://github.com/numba/numba/pull/8056>`_: Move caching tests from test_dispatcher to test_caching (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8057 <https://github.com/numba/numba/pull/8057>`_: Fix coverage checking (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8064 <https://github.com/numba/numba/pull/8064>`_: Rename "nb:run_pass" to "numba:run_pass" and document it. (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8065 <https://github.com/numba/numba/pull/8065>`_: Fix PyLowering mishandling starargs (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8068 <https://github.com/numba/numba/pull/8068>`_: update changelog for 0.55.2 (`esc <https://github.com/esc>`_)
+* PR `#8077 <https://github.com/numba/numba/pull/8077>`_: change return type of np.broadcast_shapes to a tuple (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#8080 <https://github.com/numba/numba/pull/8080>`_: Fix windows test failure due to timeout when the machine is slow poss… (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8081 <https://github.com/numba/numba/pull/8081>`_: Fix erroneous array count in parallel gufunc kernel generation. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8089 <https://github.com/numba/numba/pull/8089>`_: Support on-disk caching in the CUDA target (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8097 <https://github.com/numba/numba/pull/8097>`_: Exclude libopenblas 0.3.20 on osx-arm64 (`esc <https://github.com/esc>`_)
+* PR `#8099 <https://github.com/numba/numba/pull/8099>`_: Fix Py_DECREF use in case of error state (for devicearray). (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8102 <https://github.com/numba/numba/pull/8102>`_: Combine numpy run_constrained in meta.yaml to the run requirements (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8109 <https://github.com/numba/numba/pull/8109>`_: Pin TBB support with respect to incompatible 2021.6 API. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8118 <https://github.com/numba/numba/pull/8118>`_: Update release checklists post 0.55.2 (`esc <https://github.com/esc>`_)
+* PR `#8123 <https://github.com/numba/numba/pull/8123>`_: Fix CUDA print tests on Windows (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8124 <https://github.com/numba/numba/pull/8124>`_: Add explicit checks to all allocators in the NRT. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8126 <https://github.com/numba/numba/pull/8126>`_: Mark gufuncs as having mutable outputs (`Andre Masella <https://github.com/apmasell>`_)
+* PR `#8133 <https://github.com/numba/numba/pull/8133>`_: Fix #8132. Regression in Record.make_c_struct for handling nestedarray (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8137 <https://github.com/numba/numba/pull/8137>`_: CUDA: Fix #7806, Division by zero stops the kernel (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8142 <https://github.com/numba/numba/pull/8142>`_: CUDA: Fix some missed changes from dropping 9.2 (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8144 <https://github.com/numba/numba/pull/8144>`_: Fix NumPy capitalisation in docs. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8145 <https://github.com/numba/numba/pull/8145>`_: Allow ufunc builder to use previously JITed function (`Andre Masella <https://github.com/apmasell>`_)
+* PR `#8151 <https://github.com/numba/numba/pull/8151>`_: pin NumPy to build 0 of 1.19.2 on public CI (`esc <https://github.com/esc>`_)
+* PR `#8163 <https://github.com/numba/numba/pull/8163>`_: CUDA: Remove context query in launch config (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8165 <https://github.com/numba/numba/pull/8165>`_: Restrict strace based tests to be linux only via support feature. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8170 <https://github.com/numba/numba/pull/8170>`_: CUDA: Fix missing space in low occupancy warning (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8175 <https://github.com/numba/numba/pull/8175>`_: make build and upload order consistent (`esc <https://github.com/esc>`_)
+* PR `#8181 <https://github.com/numba/numba/pull/8181>`_: Fix various typos (`luzpaz <https://github.com/luzpaz>`_)
+* PR `#8187 <https://github.com/numba/numba/pull/8187>`_: Update CHANGE_LOG for 0.55.2 (`stuartarchibald <https://github.com/stuartarchibald>`_ `esc <https://github.com/esc>`_)
+* PR `#8189 <https://github.com/numba/numba/pull/8189>`_: updated version support information for 0.55.2/0.57 (`esc <https://github.com/esc>`_)
+* PR `#8191 <https://github.com/numba/numba/pull/8191>`_: CUDA: Update deprecation notes for 0.56. (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8192 <https://github.com/numba/numba/pull/8192>`_: Update CHANGE_LOG for 0.56.0 (`stuartarchibald <https://github.com/stuartarchibald>`_ `esc <https://github.com/esc>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8195 <https://github.com/numba/numba/pull/8195>`_: Make the workqueue threading backend once again fork safe. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8196 <https://github.com/numba/numba/pull/8196>`_: Fix numerical tolerance in parfors caching test. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8197 <https://github.com/numba/numba/pull/8197>`_: Fix `isinstance` warning check test. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8203 <https://github.com/numba/numba/pull/8203>`_: pin llvmlite 0.39 for public CI builds (`esc <https://github.com/esc>`_)
+* PR `#8205 <https://github.com/numba/numba/pull/8205>`_: Pin llvmlite and NumPy on release branch.(`esc <https://github.com/esc>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8255 <https://github.com/numba/numba/pull/8255>`_: CUDA: Make numba.cuda.tests.doc_examples.ffi a module to fix #8252 (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#8274 <https://github.com/numba/numba/pull/8274>`_: Update version support table doc for 0.56. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8275 <https://github.com/numba/numba/pull/8275>`_: Update CHANGE_LOG for 0.56.0 final (`stuartarchibald <https://github.com/stuartarchibald>`_)
+
+Authors:
+
+* `Andre Masella <https://github.com/apmasell>`_
+* `Benjamin Graham <https://github.com/benwilliamgraham>`_
+* `brandon-b-miller <https://github.com/brandon-b-miller>`_
+* `Brandon T. Willard <https://github.com/brandonwillard>`_
+* `Gagandeep Singh <https://github.com/czgdp1807>`_
+* `Dhruv Patel <https://github.com/DhruvPatel01>`_
+* `LI Da <https://github.com/dlee992>`_
+* `Todd A. Anderson <https://github.com/DrTodd13>`_
+* `Ethan Pronovost <https://github.com/EPronovost>`_
+* `esc <https://github.com/esc>`_
+* `Tobias Sargeant <https://github.com/folded>`_
+* `Graham Markall <https://github.com/gmarkall>`_
+* `Guilherme Leobas <https://github.com/guilhermeleobas>`_
+* `Zizheng Guo <https://github.com/gzz2000>`_
+* `Hadia Ahmed <https://github.com/hadia206>`_
+* `idorrington <https://github.com/idorrington>`_
+* `Michael Wang <https://github.com/isVoid>`_
+* `Kaustubh <https://github.com/kc611>`_
+* `Kevin Modzelewski <https://github.com/kmod>`_
+* `luk-f-a <https://github.com/luk-f-a>`_
+* `luzpaz <https://github.com/luzpaz>`_
+* `Kyle Martin <https://github.com/martinky24>`_
+* `Nightfurex <https://github.com/Nightfurex>`_
+* `Nick Riasanovsky <https://github.com/njriasan>`_
+* `Nopileos2 <https://github.com/Nopileos2>`_
+* `Ray Bell <https://github.com/raybellwaves>`_
+* `readthedocs-assistant <https://github.com/readthedocs-assistant>`_
+* `Rishi Kulkarni <https://github.com/rishi-kulkarni>`_
+* `Sterling Baird <https://github.com/sgbaird>`_
+* `Siu Kwan Lam <https://github.com/sklam>`_
+* `stuartarchibald <https://github.com/stuartarchibald>`_
+* `Stepan Rakitin <https://github.com/svrakitin>`_
+* `Michael Collison <https://github.com/testhound>`_
+* `Tingkai Liu <https://github.com/TK-21st>`_
+
+Version 0.55.2 (25 May, 2022)
+-----------------------------
+
+This is a maintenance release to support NumPy 1.22 and Apple M1.
+
+Pull-Requests:
+
+* PR `#8067 <https://github.com/numba/numba/pull/8067>`_: Backport #8027: Support for NumPy 1.22 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8069 <https://github.com/numba/numba/pull/8069>`_: Install llvmlite 0.38 for Numba 0.55.* (`esc <https://github.com/esc>`_)
+* PR `#8075 <https://github.com/numba/numba/pull/8075>`_: update max NumPy for 0.55.2 (`esc <https://github.com/esc>`_)
+* PR `#8078 <https://github.com/numba/numba/pull/8078>`_: Backport #7804: update local references from master -> main (`esc <https://github.com/esc>`_)
+* PR `#8082 <https://github.com/numba/numba/pull/8082>`_: Backport #8080: fix windows failure due to timeout (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8084 <https://github.com/numba/numba/pull/8084>`_: Pin meta.yaml to llvmlite 0.38 series (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8093 <https://github.com/numba/numba/pull/8093>`_: Backport #7904: Support m1 (`esc <https://github.com/esc>`_)
+* PR `#8094 <https://github.com/numba/numba/pull/8094>`_: Backport #8052 Ensure pthread is linked in when building for ppc64le. (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8098 <https://github.com/numba/numba/pull/8098>`_: Backport #8097: Exclude libopenblas 0.3.20 on osx-arm64 (`esc <https://github.com/esc>`_)
+* PR `#8100 <https://github.com/numba/numba/pull/8100>`_: Backport #7786 for 0.55.2: Remove dependency on intel-openmp for OSX  (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#8103 <https://github.com/numba/numba/pull/8103>`_: Backport #8102 to fix numpy requirements (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#8114 <https://github.com/numba/numba/pull/8114>`_: Backport #8109 Pin TBB support with respect to incompatible 2021.6 API. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+
+Total PRs: 12
+
+Authors:
+
+* `esc <https://github.com/esc>`_
+* `Siu Kwan Lam <https://github.com/sklam>`_
+* `stuartarchibald <https://github.com/stuartarchibald>`_
+
+Total authors: 3
+
+Version 0.55.1 (27 January, 2022)
+---------------------------------
+
+This is a bugfix release that closes all the remaining issues from the
+accelerated release of 0.55.0 and also any release critical regressions
+discovered since then.
+
+CUDA target deprecation notices:
+
+* Support for CUDA toolkits < 10.2 is deprecated and will be removed in Numba
+  0.56.
+* Support for devices with Compute Capability < 5.3 is deprecated and will be
+  removed in Numba 0.56.
+
+
+Pull-Requests:
+
+* PR `#7755 <https://github.com/numba/numba/pull/7755>`_: CUDA: Deprecate support for CC < 5.3 and CTK < 10.2 (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7749 <https://github.com/numba/numba/pull/7749>`_: Refactor threading layer priority tests to not use stdout/stderr (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7744 <https://github.com/numba/numba/pull/7744>`_: Fix issues with locating/parsing source during DebugInfo emission. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7712 <https://github.com/numba/numba/pull/7712>`_: Fixing issue 7693 (`Graham Markall <https://github.com/gmarkall>`_ `luk-f-a <https://github.com/luk-f-a>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7729 <https://github.com/numba/numba/pull/7729>`_: Handle Omitted/OmittedArgDataModel in DI generation. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7788 <https://github.com/numba/numba/pull/7788>`_: Avoid issue with DI gen for arrayexprs. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7752 <https://github.com/numba/numba/pull/7752>`_: Fix #7751: Use original filename for array exprs (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7748 <https://github.com/numba/numba/pull/7748>`_: Fix #7713: Ensure _prng_random_hash return has correct bitwidth (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7745 <https://github.com/numba/numba/pull/7745>`_: Fix the release year for Numba 0.55 change log entry. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7740 <https://github.com/numba/numba/pull/7740>`_: CUDA Python 11.6 support (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7724 <https://github.com/numba/numba/pull/7724>`_: Update URLs in error messages to refer to RTD docs. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7709 <https://github.com/numba/numba/pull/7709>`_: CUDA: Fixes missing type annotation pass following #7704 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7704 <https://github.com/numba/numba/pull/7704>`_: Move the type annotation pass to post legalization. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7619 <https://github.com/numba/numba/pull/7619>`_: CUDA: Fix linking with PTX when compiling lazily (`Graham Markall <https://github.com/gmarkall>`_)
+
+Authors:
+
+* `Graham Markall <https://github.com/gmarkall>`_
+* `luk-f-a <https://github.com/luk-f-a>`_
+* `stuartarchibald <https://github.com/stuartarchibald>`_
+
+Version 0.55.0 (13 January, 2022)
+---------------------------------
+
+This release includes a significant number important dependency upgrades along
+with a number of new features and bug fixes.
+
+NOTE: Due to NumPy CVE-2021-33430 this release has bypassed the usual release
+process so as to promptly provide a Numba release that supports NumPy 1.21. A
+single release candidate (RC1) was made and a few issues were reported, these
+are summarised as follows and will be fixed in a subsequent 0.55.1 release.
+
+Known issues with this release:
+
+* Incorrect result copying array-typed field of structured array (`#7693 <https://github.com/numba/numba/pull/7693>`_)
+* Two issues in DebugInfo generation (`#7726 <https://github.com/numba/numba/pull/7726>`_, `#7730 <https://github.com/numba/numba/pull/7730>`_)
+* Compilation failure for ``hash`` of floating point values on 32 bit Windows
+  when using Python 3.10 (`#7713 <https://github.com/numba/numba/pull/7713>`_).
+
+Highlights of core dependency upgrades:
+
+* Support for Python 3.10
+* Support for NumPy 1.21
+
+Python language support enhancements:
+
+* Experimental support for ``isinstance``.
+
+NumPy features/enhancements:
+
+The following functions are now supported:
+
+* ``np.broadcast_to``
+* ``np.float_power``
+* ``np.cbrt``
+* ``np.logspace``
+* ``np.take_along_axis``
+* ``np.average``
+* ``np.argmin`` gains support for the ``axis`` kwarg.
+* ``np.ndarray.astype`` gains support for types expressed as literal strings.
+
+Highlights of core changes:
+
+* For users of the Numba extension API, Numba now has a new error handling mode
+  whereby it will treat all exceptions that do not inherit from
+  ``numba.errors.NumbaException`` as a "hard error" and immediately unwind the
+  stack. This makes it much easier to debug when writing ``@overload``\s etc
+  from the extension API as there's now no confusion between Python errors and
+  Numba errors. This feature can be enabled by setting the environment
+  variable: ``NUMBA_CAPTURED_ERRORS='new_style'``.
+* The threading layer selection priority can now be changed via the environment
+  variable ``NUMBA_THREADING_LAYER_PRIORITY``.
+
+Highlights of changes for the CUDA target:
+
+* Support for NVIDIA's CUDA Python bindings.
+* Support for 16-bit floating point numbers and their basic operations via
+  intrinsics.
+* Streams are provided in the ``Stream.async_done`` result, making it easier to
+  implement asynchronous work queues.
+* Support for structured types in device arrays, character sequences in NumPy
+  arrays, and some array operations on nested arrays.
+* Much underlying refactoring to align the CUDA target more closely with the
+  CPU target, which lays the groudwork for supporting the high level extension
+  API in CUDA in future releases.
+
+Intel also kindly sponsored research and development into native debug (DWARF)
+support and handling per-function compilation flags:
+
+* Line number/location tracking is much improved.
+* Numba's internal representation of containers (e.g. tuples, arrays) are now
+  encoded as structures.
+* Numba's per-function compilation flags are encoded into the ABI field of the
+  mangled name of the function such that it's possible to compile and
+  differentiate between versions of the same function with different flags set.
+
+General deprecation notices:
+
+* There are no new general deprecations.
+
+CUDA target deprecation notices:
+
+* There are no new CUDA target deprecations.
+
+Version support/dependency changes:
+
+* Python 3.10 is supported.
+* NumPy version 1.21 is supported.
+* The minimum supported NumPy version is raised to 1.18 for runtime (compilation
+  however remains compatible with NumPy 1.11).
+
+
+Pull-Requests:
+
+* PR `#6075 <https://github.com/numba/numba/pull/6075>`_: add np.float_power and np.cbrt (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#7047 <https://github.com/numba/numba/pull/7047>`_: Support __hash__ for numpy.datetime64 (`Guilherme Leobas <https://github.com/guilhermeleobas>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7057 <https://github.com/numba/numba/pull/7057>`_: Fix #7041: Add charseq registry to CUDA target (`Graham Markall <https://github.com/gmarkall>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7082 <https://github.com/numba/numba/pull/7082>`_: Added Add/Sub between datetime64 array and timedelta64 scalar (`Nick Riasanovsky <https://github.com/njriasan>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7119 <https://github.com/numba/numba/pull/7119>`_: Add support for `np.broadcast_to` (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#7129 <https://github.com/numba/numba/pull/7129>`_: Add support for axis keyword argument to np.argmin() (`Itamar Turner-Trauring <https://github.com/itamarst>`_)
+* PR `#7132 <https://github.com/numba/numba/pull/7132>`_: gh #7131 Support for astype with literal strings (`Nick Riasanovsky <https://github.com/njriasan>`_)
+* PR `#7177 <https://github.com/numba/numba/pull/7177>`_: Add debug infomation support based on datamodel. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7185 <https://github.com/numba/numba/pull/7185>`_: Add get_impl_key as abstract method to types.Callable (`Alexey Kozlov <https://github.com/kozlov-alexey>`_)
+* PR `#7186 <https://github.com/numba/numba/pull/7186>`_: Add support for np.logspace. (`Guoqiang QI <https://github.com/guoqiangqi>`_)
+* PR `#7189 <https://github.com/numba/numba/pull/7189>`_: CUDA: Skip IPC tests on ARM (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7190 <https://github.com/numba/numba/pull/7190>`_: CUDA: Fix test_pinned on Jetson (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7192 <https://github.com/numba/numba/pull/7192>`_: Fix missing import in array.argsort impl and add more tests. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7196 <https://github.com/numba/numba/pull/7196>`_: Fixes for lineinfo emission (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7197 <https://github.com/numba/numba/pull/7197>`_: don't post to python announce on the first RC (`esc <https://github.com/esc>`_)
+* PR `#7202 <https://github.com/numba/numba/pull/7202>`_: Initial implementation of np.take_along_axis (`Itamar Turner-Trauring <https://github.com/itamarst>`_)
+* PR `#7203 <https://github.com/numba/numba/pull/7203>`_: remove duplicate changelog entries (`esc <https://github.com/esc>`_)
+* PR `#7216 <https://github.com/numba/numba/pull/7216>`_: Update CHANGE_LOG for 0.54.0rc2 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7219 <https://github.com/numba/numba/pull/7219>`_: bump llvmlite dependency to 0.38.0dev0 for Numba 0.55.0dev0 (`esc <https://github.com/esc>`_)
+* PR `#7220 <https://github.com/numba/numba/pull/7220>`_: update release checklist post 0.54rc1+2 (`esc <https://github.com/esc>`_)
+* PR `#7221 <https://github.com/numba/numba/pull/7221>`_: Show GPU UUIDs in cuda.detect() output (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7222 <https://github.com/numba/numba/pull/7222>`_: CUDA: Warn when debug=True and opt=True (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7223 <https://github.com/numba/numba/pull/7223>`_: Replace assertion errors on IR assumption violation (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7226 <https://github.com/numba/numba/pull/7226>`_: Add support for structured types in Device Arrays (`Michael Collison <https://github.com/testhound>`_)
+* PR `#7227 <https://github.com/numba/numba/pull/7227>`_: FIX: Typo (`Srinath Kailasa <https://github.com/skailasa>`_)
+* PR `#7230 <https://github.com/numba/numba/pull/7230>`_: PR #7171 bugfix only (`stuartarchibald <https://github.com/stuartarchibald>`_ `Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7234 <https://github.com/numba/numba/pull/7234>`_: add THREADING_LAYER_PRIORITY & NUMBA_THREADING_LAYER_PRIORITY (`Kolen Cheung <https://github.com/ickc>`_)
+* PR `#7235 <https://github.com/numba/numba/pull/7235>`_: replace wordings of WIP by draft PR (`Kolen Cheung <https://github.com/ickc>`_)
+* PR `#7236 <https://github.com/numba/numba/pull/7236>`_: CUDA: Skip managed alloc tests on ARM (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7237 <https://github.com/numba/numba/pull/7237>`_: fix a typo in a string (`Kolen Cheung <https://github.com/ickc>`_)
+* PR `#7241 <https://github.com/numba/numba/pull/7241>`_: Set aliasing information for inplace_binops.. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7242 <https://github.com/numba/numba/pull/7242>`_: FIX: typo (`Srinath Kailasa <https://github.com/skailasa>`_)
+* PR `#7244 <https://github.com/numba/numba/pull/7244>`_: Implement partial literal propagation pass (support 'isinstance') (`Guilherme Leobas <https://github.com/guilhermeleobas>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7247 <https://github.com/numba/numba/pull/7247>`_: Solve memory leak to fix issue #7210  (`Siu Kwan Lam <https://github.com/sklam>`_ `Graham Markall <https://github.com/gmarkall>`_ `ysheffer <https://github.com/ysheffer>`_)
+* PR `#7251 <https://github.com/numba/numba/pull/7251>`_: Fix #6001: typed.List ignores ctor arguments with JIT disabled (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7256 <https://github.com/numba/numba/pull/7256>`_: Fix link to the discourse forum in README (`Kenichi Maehashi <https://github.com/kmaehashi>`_)
+* PR `#7257 <https://github.com/numba/numba/pull/7257>`_: Use normal list constructor in List.__new__() (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7260 <https://github.com/numba/numba/pull/7260>`_: Support typed lists in `heapq` (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7263 <https://github.com/numba/numba/pull/7263>`_: Updated issue URL for error messages #7261 (`DeviousLab <https://github.com/DeviousLab>`_)
+* PR `#7265 <https://github.com/numba/numba/pull/7265>`_: Fix linspace to use np.divide and clamp to stop. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7266 <https://github.com/numba/numba/pull/7266>`_: CUDA: Skip multi-GPU copy test with peer access disabled (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7267 <https://github.com/numba/numba/pull/7267>`_: Fix #7258. Bug in SROA optimization (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7271 <https://github.com/numba/numba/pull/7271>`_: Update 3rd party license text. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7272 <https://github.com/numba/numba/pull/7272>`_: Allow annotations in njit-ed functions (`LunarLanding <https://github.com/LunarLanding>`_)
+* PR `#7273 <https://github.com/numba/numba/pull/7273>`_: Update CHANGE_LOG for 0.54.0rc3. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7283 <https://github.com/numba/numba/pull/7283>`_: Added NPM to Glossary and linked to mentions (`Nihal Shetty <https://github.com/nihalshetty-boop>`_)
+* PR `#7285 <https://github.com/numba/numba/pull/7285>`_: CUDA: Fix OOB in test_kernel_arg (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7288 <https://github.com/numba/numba/pull/7288>`_: Handle cval as a np attr in stencil generation. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7294 <https://github.com/numba/numba/pull/7294>`_: Continuation of PR #7280, fixing lifetime of TBB task_scheduler_handle (`Sergey Pokhodenko <https://github.com/PokhodenkoSA>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7296 <https://github.com/numba/numba/pull/7296>`_: Fix generator lowering not casting to the actual yielded type (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7298 <https://github.com/numba/numba/pull/7298>`_: Use CBC to pin GCC to 7 on most linux and 9 on aarch64. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7304 <https://github.com/numba/numba/pull/7304>`_: Continue PR#3655: add support for np.average (`Hadia Ahmed <https://github.com/hadia206>`_ `slnguyen <https://github.com/slnguyen>`_)
+* PR `#7307 <https://github.com/numba/numba/pull/7307>`_: Prevent mutation of arrays in global tuples. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7309 <https://github.com/numba/numba/pull/7309>`_: Update MapConstraint to handle type coercion for typed.Dict correctly. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7312 <https://github.com/numba/numba/pull/7312>`_: Fix #7302. Workaround missing pthread problem on ppc64le (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7315 <https://github.com/numba/numba/pull/7315>`_: Link ELF obj as DSO for radare2 disassembly CFG (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7316 <https://github.com/numba/numba/pull/7316>`_: Use float64 for consistent typing in heapq tests. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7317 <https://github.com/numba/numba/pull/7317>`_: In TBB tsh test switch os.fork for mp fork ctx (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7319 <https://github.com/numba/numba/pull/7319>`_: Update CHANGE_LOG for 0.54.0 final. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7329 <https://github.com/numba/numba/pull/7329>`_: Improve documentation in reference to CUDA local memory (`Sterling Baird <https://github.com/sgbaird>`_)
+* PR `#7330 <https://github.com/numba/numba/pull/7330>`_: Cuda matmul docs (`Sterling Baird <https://github.com/sgbaird>`_)
+* PR `#7340 <https://github.com/numba/numba/pull/7340>`_: Add size_t and ssize_t types (`Bruce Merry <https://github.com/bmerry>`_)
+* PR `#7345 <https://github.com/numba/numba/pull/7345>`_: Add check for ipykernel file in IPython cache locator (`Sahil Gupta <https://github.com/sahil1105>`_)
+* PR `#7347 <https://github.com/numba/numba/pull/7347>`_: fix:updated url for error report and feature rquest using issue template (`DEBARGHA SAHA <https://github.com/Stark-developer01>`_)
+* PR `#7349 <https://github.com/numba/numba/pull/7349>`_: Allow arbitrary walk-back in reduction nodes to find inplace_binop. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7359 <https://github.com/numba/numba/pull/7359>`_: Extend support for nested arrays inside numpy records (`Graham Markall <https://github.com/gmarkall>`_ `luk-f-a <https://github.com/luk-f-a>`_)
+* PR `#7375 <https://github.com/numba/numba/pull/7375>`_: CUDA: Run doctests as part of numba.cuda.tests and fix test_cg (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7395 <https://github.com/numba/numba/pull/7395>`_: Fix #7394 and #6550 & Added test & improved error message (`MegaIng <https://github.com/MegaIng>`_)
+* PR `#7397 <https://github.com/numba/numba/pull/7397>`_: Add option to catch only Numba `numba.core.errors` derived exceptions. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7398 <https://github.com/numba/numba/pull/7398>`_: Add support for arrayanalysis of tuple args. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7403 <https://github.com/numba/numba/pull/7403>`_: Fix for issue 7402: implement missing numpy ufunc interface (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#7404 <https://github.com/numba/numba/pull/7404>`_: fix typo in literal_unroll docs (`esc <https://github.com/esc>`_)
+* PR `#7419 <https://github.com/numba/numba/pull/7419>`_: insert missing backtick in comment (`esc <https://github.com/esc>`_)
+* PR `#7422 <https://github.com/numba/numba/pull/7422>`_: Update Omitted Type to use Hashable Values as Keys for Caching (`Nick Riasanovsky <https://github.com/njriasan>`_)
+* PR `#7429 <https://github.com/numba/numba/pull/7429>`_: Update CHANGE_LOG for 0.54.1 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7432 <https://github.com/numba/numba/pull/7432>`_: add github release task to checklist (`esc <https://github.com/esc>`_)
+* PR `#7440 <https://github.com/numba/numba/pull/7440>`_: Refactor TargetConfig naming. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7441 <https://github.com/numba/numba/pull/7441>`_: Permit any string as a key in literalstrkeydict type. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7442 <https://github.com/numba/numba/pull/7442>`_: Add some diagnostics to SVML test failures. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7443 <https://github.com/numba/numba/pull/7443>`_: Refactor template selection logic for targets. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7444 <https://github.com/numba/numba/pull/7444>`_: use correct variable name in closure (`esc <https://github.com/esc>`_)
+* PR `#7447 <https://github.com/numba/numba/pull/7447>`_: cleanup Numba metadata (`esc <https://github.com/esc>`_)
+* PR `#7453 <https://github.com/numba/numba/pull/7453>`_: CUDA: Provide stream in async_done result (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7456 <https://github.com/numba/numba/pull/7456>`_: Fix invalid codegen for #7451. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7457 <https://github.com/numba/numba/pull/7457>`_: Factor out target registry selection logic (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7459 <https://github.com/numba/numba/pull/7459>`_: Include compiler flags in symbol mangling (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7460 <https://github.com/numba/numba/pull/7460>`_: Add FP16 support for CUDA (`Michael Collison <https://github.com/testhound>`_ `Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7461 <https://github.com/numba/numba/pull/7461>`_: Support NVIDIA's CUDA Python bindings (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7465 <https://github.com/numba/numba/pull/7465>`_: Update changelog for 0.54.1 release (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7477 <https://github.com/numba/numba/pull/7477>`_: Fix unicode operator.eq handling of Optional types. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7479 <https://github.com/numba/numba/pull/7479>`_: CUDA: Print format string and warn for > 32 print() args (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7483 <https://github.com/numba/numba/pull/7483>`_: NumPy 1.21 support (`Sebastian Berg <https://github.com/seberg>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7484 <https://github.com/numba/numba/pull/7484>`_: Fixed outgoing link to nvidia documentation. (`Dhruv Patel <https://github.com/DhruvPatel01>`_)
+* PR `#7493 <https://github.com/numba/numba/pull/7493>`_: Consolidate TLS stacks in target configuration (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7496 <https://github.com/numba/numba/pull/7496>`_: CUDA: Use a single dispatcher class for all kinds of functions (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7498 <https://github.com/numba/numba/pull/7498>`_: refactor  with-detection logic (`stuartarchibald <https://github.com/stuartarchibald>`_ `esc <https://github.com/esc>`_)
+* PR `#7499 <https://github.com/numba/numba/pull/7499>`_: Add build scripts for CUDA testing on gpuCI  (`Charles Blackmon-Luca <https://github.com/charlesbluca>`_ `Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7500 <https://github.com/numba/numba/pull/7500>`_: Update parallel.rst (`Julius Bier Kirkegaard <https://github.com/juliusbierk>`_)
+* PR `#7506 <https://github.com/numba/numba/pull/7506>`_: Enhance Flags mangling/demangling (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7514 <https://github.com/numba/numba/pull/7514>`_: Fixup cuda debuginfo emission for 7177 (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7525 <https://github.com/numba/numba/pull/7525>`_: Make sure` demangle()` returns `str` type. (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7538 <https://github.com/numba/numba/pull/7538>`_: Fix `@overload_glue` performance regression. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7539 <https://github.com/numba/numba/pull/7539>`_: Fix str decode issue from merge #7525/#7506 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7546 <https://github.com/numba/numba/pull/7546>`_: Fix handling of missing const key in LiteralStrKeyDict (`Siu Kwan Lam <https://github.com/sklam>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7547 <https://github.com/numba/numba/pull/7547>`_: Remove 32bit linux scipy installation. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7548 <https://github.com/numba/numba/pull/7548>`_: Correct evaluation order in assert statement (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7552 <https://github.com/numba/numba/pull/7552>`_: Prepend the inlined function name to inlined variables. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7557 <https://github.com/numba/numba/pull/7557>`_: Python3.10 v2 (`stuartarchibald <https://github.com/stuartarchibald>`_ `esc <https://github.com/esc>`_)
+* PR `#7560 <https://github.com/numba/numba/pull/7560>`_: Refactor with detection py310 (`Siu Kwan Lam <https://github.com/sklam>`_ `esc <https://github.com/esc>`_)
+* PR `#7561 <https://github.com/numba/numba/pull/7561>`_: fix a typo (`Kolen Cheung <https://github.com/ickc>`_)
+* PR `#7567 <https://github.com/numba/numba/pull/7567>`_: Update docs to note meetings are public. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7570 <https://github.com/numba/numba/pull/7570>`_: Update the docs and error message for errors when importing Numba. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7580 <https://github.com/numba/numba/pull/7580>`_: Fix #7507. catch `NotImplementedError` in `.get_function()`  (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7581 <https://github.com/numba/numba/pull/7581>`_: Add support for casting from int enums (`Michael Collison <https://github.com/testhound>`_)
+* PR `#7583 <https://github.com/numba/numba/pull/7583>`_: Make numba.types.Optional __str__ less verbose. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7588 <https://github.com/numba/numba/pull/7588>`_: Fix casting of start/stop in linspace (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7591 <https://github.com/numba/numba/pull/7591>`_: Remove deprecations (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7596 <https://github.com/numba/numba/pull/7596>`_: Fix max symbol match length for r2 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7597 <https://github.com/numba/numba/pull/7597>`_: Update gdb docs for new DWARF enhancements. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7603 <https://github.com/numba/numba/pull/7603>`_: Fix list.insert() for refcounted values (`Ehsan Totoni <https://github.com/ehsantn>`_)
+* PR `#7605 <https://github.com/numba/numba/pull/7605>`_: Fix TBB 2021 DSO names on OSX/Win and make TBB reporting consistent (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7606 <https://github.com/numba/numba/pull/7606>`_: Ensure a prescribed threading layer can load in CI. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7610 <https://github.com/numba/numba/pull/7610>`_: Fix #7609. Type should not be mutated. (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7618 <https://github.com/numba/numba/pull/7618>`_: Fix the doc build: docutils 0.18 not compatible with pinned sphinx (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7626 <https://github.com/numba/numba/pull/7626>`_: Fix issues with package dependencies. (`stuartarchibald <https://github.com/stuartarchibald>`_ `esc <https://github.com/esc>`_)
+* PR `#7627 <https://github.com/numba/numba/pull/7627>`_: PR 7321 continued (`stuartarchibald <https://github.com/stuartarchibald>`_ `Eric Wieser <https://github.com/eric-wieser>`_)
+* PR `#7628 <https://github.com/numba/numba/pull/7628>`_: Move to using windows-2019 images in Azure (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7632 <https://github.com/numba/numba/pull/7632>`_: Capture output in CUDA matmul doctest (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7636 <https://github.com/numba/numba/pull/7636>`_: Copy prange loop header to after the parfor. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7637 <https://github.com/numba/numba/pull/7637>`_: Increase the timeout on the SVML tests for loaded machines. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7645 <https://github.com/numba/numba/pull/7645>`_: In debuginfo, do not add noinline to functions marked alwaysinline (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7650 <https://github.com/numba/numba/pull/7650>`_: Move Azure builds to OSX 10.15 (`stuartarchibald <https://github.com/stuartarchibald>`_ `esc <https://github.com/esc>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+
+Authors:
+
+* `Bruce Merry <https://github.com/bmerry>`_
+* `Charles Blackmon-Luca <https://github.com/charlesbluca>`_
+* `DeviousLab <https://github.com/DeviousLab>`_
+* `Dhruv Patel <https://github.com/DhruvPatel01>`_
+* `Todd A. Anderson <https://github.com/DrTodd13>`_
+* `Ehsan Totoni <https://github.com/ehsantn>`_
+* `Eric Wieser <https://github.com/eric-wieser>`_
+* `esc <https://github.com/esc>`_
+* `Graham Markall <https://github.com/gmarkall>`_
+* `Guilherme Leobas <https://github.com/guilhermeleobas>`_
+* `Guoqiang QI <https://github.com/guoqiangqi>`_
+* `Hadia Ahmed <https://github.com/hadia206>`_
+* `Kolen Cheung <https://github.com/ickc>`_
+* `Itamar Turner-Trauring <https://github.com/itamarst>`_
+* `Julius Bier Kirkegaard <https://github.com/juliusbierk>`_
+* `Kenichi Maehashi <https://github.com/kmaehashi>`_
+* `Alexey Kozlov <https://github.com/kozlov-alexey>`_
+* `luk-f-a <https://github.com/luk-f-a>`_
+* `LunarLanding <https://github.com/LunarLanding>`_
+* `MegaIng <https://github.com/MegaIng>`_
+* `Nihal Shetty <https://github.com/nihalshetty-boop>`_
+* `Nick Riasanovsky <https://github.com/njriasan>`_
+* `Sergey Pokhodenko <https://github.com/PokhodenkoSA>`_
+* `Sahil Gupta <https://github.com/sahil1105>`_
+* `Sebastian Berg <https://github.com/seberg>`_
+* `Sterling Baird <https://github.com/sgbaird>`_
+* `Srinath Kailasa <https://github.com/skailasa>`_
+* `Siu Kwan Lam <https://github.com/sklam>`_
+* `slnguyen <https://github.com/slnguyen>`_
+* `DEBARGHA SAHA <https://github.com/Stark-developer01>`_
+* `stuartarchibald <https://github.com/stuartarchibald>`_
+* `Michael Collison <https://github.com/testhound>`_
+* `ysheffer <https://github.com/ysheffer>`_
+
+Version 0.54.1 (7 October, 2021)
+--------------------------------
+
+This is a bugfix release for 0.54.0. It fixes a regression in structured array
+type handling, a potential leak on initialization failure in the CUDA target, a
+regression caused by Numba's vendored cloudpickle module resetting dynamic
+classes and a few minor testing/infrastructure related problems.
+
+* PR `#7348 <https://github.com/numba/numba/pull/7348>`_: test_inspect_cli: Decode exception with default (utf-8) codec (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7360 <https://github.com/numba/numba/pull/7360>`_: CUDA: Fix potential leaks when initialization fails (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7386 <https://github.com/numba/numba/pull/7386>`_: Ensure the NRT is initialized prior to use in external NRT tests. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7388 <https://github.com/numba/numba/pull/7388>`_: Patch cloudpickle to not reset dynamic class each time it is unpickled (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7393 <https://github.com/numba/numba/pull/7393>`_: skip azure pipeline test if file not present (`esc <https://github.com/esc>`_)
+* PR `#7428 <https://github.com/numba/numba/pull/7428>`_: Fix regression #7355: cannot set items in structured array data types (`Siu Kwan Lam <https://github.com/sklam>`_)
+
+Authors:
+
+* `esc <https://github.com/esc>`_
+* `Graham Markall <https://github.com/gmarkall>`_
+* `Siu Kwan Lam <https://github.com/sklam>`_
+* `stuartarchibald <https://github.com/stuartarchibald>`_
+
+
+Version 0.54.0 (19 August, 2021)
+--------------------------------
+
+This release includes a significant number of new features, important
+refactoring, critical bug fixes and a number of dependency upgrades.
+
+Python language support enhancements:
+
+* Basic support for ``f-strings``.
+* ``dict`` comprehensions are now supported.
+* The ``sum`` built-in function is implemented.
+
+NumPy features/enhancements:
+
+The following functions are now supported:
+
+  * ``np.clip``
+  * ``np.iscomplex``
+  * ``np.iscomplexobj``
+  * ``np.isneginf``
+  * ``np.isposinf``
+  * ``np.isreal``
+  * ``np.isrealobj``
+  * ``np.isscalar``
+  * ``np.random.dirichlet``
+  * ``np.rot90``
+  * ``np.swapaxes``
+
+Also ``np.argmax`` has gained support for the ``axis`` keyword argument and it's
+now possible to use ``0d`` NumPy arrays as scalars in ``__setitem__`` calls.
+
+Internal changes:
+
+* Debugging support through DWARF has been fixed and enhanced.
+* Numba now optimises the way in which locals are emitted to help reduce time
+  spent in LLVM's SROA passes.
+
+CUDA target changes:
+
+* Support for emitting ``lineinfo`` to be consumed by profiling tools such as
+  Nsight Compute
+* Improved fastmath code generation for various trig, division, and other
+  functions
+* Faster compilation using lazy addition of libdevice to compiled units
+* Support for IPC on Windows
+* Support for passing tuples to CUDA ufuncs
+* Performance warnings:
+
+  * When making implicit copies by calling a kernel on arrays in host memory
+  * When occupancy is poor due to kernel or ufunc/gufunc configuration
+
+* Support for implementing warp-aggregated intrinsics:
+
+  * Using support for more CUDA functions: ``activemask()``, ``lanemask_lt()``
+  * The ``ffs()`` function now works correctly!
+
+* Support for ``@overload`` in the CUDA target
+
+Intel kindly sponsored research and development that lead to a number of new
+features and internal support changes:
+
+* Dispatchers can now be retargetted to a new target via a user defined context
+  manager.
+* Support for custom NumPy array subclasses has been added (including an
+  overloadable memory allocator).
+* An inheritance based model for targets that permits targets to share
+  ``@overload`` implementations.
+* Per function compiler flags with inheritance behaviours.
+* The extension API now has support for overloading class methods via the
+  ``@overload_classmethod`` decorator.
+
+Deprecations:
+
+* The ``ROCm`` target (for AMD ROC GPUs) has been moved to an "unmaintained"
+  status and a seperate repository stub has been created for it at:
+  https://github.com/numba/numba-rocm
+
+CUDA target deprecations and breaking changes:
+
+* Relaxed strides checking is now the default when computing the contiguity of
+  device arrays.
+* The ``inspect_ptx()`` method is deprecated. For use cases that obtain PTX for
+  further compilation outside of Numba, use ``compile_ptx()`` instead.
+* Eager compilation of device functions (the case when ``device=True`` and a
+  signature is provided) is deprecated.
+
+Version support/dependency changes:
+
+* LLVM 11 is now supported on all platforms via llvmlite.
+* The minimum supported Python version is raised to 3.7.
+* NumPy version 1.20 is supported.
+* The minimum supported NumPy version is raised to 1.17 for runtime (compilation
+  however remains compatible with NumPy 1.11).
+* Vendor `cloudpickle <https://github.com/cloudpipe/cloudpickle>`_ `v1.6.0` --
+  now used for all ``pickle`` operations.
+* TBB >= 2021 is now supported and all prior versions are unsupported (not
+  easily possible to maintain the ABI breaking changes).
+
+Pull-Requests:
+
+* PR `#4516 <https://github.com/numba/numba/pull/4516>`_: Make setitem accept 0d np-arrays (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#4610 <https://github.com/numba/numba/pull/4610>`_: Implement np.is* functions (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#5984 <https://github.com/numba/numba/pull/5984>`_: Handle idx and size unification in wrap_index manually. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#6468 <https://github.com/numba/numba/pull/6468>`_: Access ``replace_functions_map`` via PreParforPass instance (`Sergey Pokhodenko <https://github.com/PokhodenkoSA>`_ `Reazul Hoque <https://github.com/reazulhoque>`_)
+* PR `#6469 <https://github.com/numba/numba/pull/6469>`_: Add address space in pointer type (`Sergey Pokhodenko <https://github.com/PokhodenkoSA>`_ `Reazul Hoque <https://github.com/reazulhoque>`_)
+* PR `#6608 <https://github.com/numba/numba/pull/6608>`_: Support f-strings for common cases (`Ehsan Totoni <https://github.com/ehsantn>`_)
+* PR `#6619 <https://github.com/numba/numba/pull/6619>`_: Improved fastmath code generation for trig, log, and exp/pow. (`Graham Markall <https://github.com/gmarkall>`_ `Michael Collison <https://github.com/testhound>`_)
+* PR `#6681 <https://github.com/numba/numba/pull/6681>`_: Explicitly catch ``with..as`` and raise error. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6689 <https://github.com/numba/numba/pull/6689>`_: Fix setup.py build command detection (`Hannes Pahl <https://github.com/HPLegion>`_)
+* PR `#6695 <https://github.com/numba/numba/pull/6695>`_: Enable negative indexing for cuda atomic operations (`Ashutosh Varma <https://github.com/ashutoshvarma>`_)
+* PR `#6696 <https://github.com/numba/numba/pull/6696>`_: flake8: made more files flake8 compliant (`Ashutosh Varma <https://github.com/ashutoshvarma>`_)
+* PR `#6698 <https://github.com/numba/numba/pull/6698>`_: Fix #6697: Wrong dtype when using np.asarray on DeviceNDArray (`Ashutosh Varma <https://github.com/ashutoshvarma>`_)
+* PR `#6700 <https://github.com/numba/numba/pull/6700>`_: Add UUID to CUDA devices (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6709 <https://github.com/numba/numba/pull/6709>`_: Block matplotlib in test examples (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6718 <https://github.com/numba/numba/pull/6718>`_: doc: fix typo in rewrites.rst (extra iterates) (`Alexander-Makaryev <https://github.com/Alexander-Makaryev>`_)
+* PR `#6720 <https://github.com/numba/numba/pull/6720>`_: Faster compile (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6730 <https://github.com/numba/numba/pull/6730>`_: Fix Typeguard error (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6731 <https://github.com/numba/numba/pull/6731>`_: Add CUDA-specific pipeline (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6735 <https://github.com/numba/numba/pull/6735>`_: CUDA: Don't parse IR for modules with llvmlite (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6736 <https://github.com/numba/numba/pull/6736>`_: Support for dict comprehension (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6742 <https://github.com/numba/numba/pull/6742>`_: Do not add overload function definitions to index. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6750 <https://github.com/numba/numba/pull/6750>`_: Bump to llvmlite 0.37 series (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6751 <https://github.com/numba/numba/pull/6751>`_: Suppress typeguard warnings that affect testing. (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6753 <https://github.com/numba/numba/pull/6753>`_: The check for internal types in RewriteArrayExprs (`Alexander-Makaryev <https://github.com/Alexander-Makaryev>`_)
+* PR `#6755 <https://github.com/numba/numba/pull/6755>`_: install llvmlite from numba/label/dev (`esc <https://github.com/esc>`_)
+* PR `#6758 <https://github.com/numba/numba/pull/6758>`_: patch to compile _devicearray.cpp with c++11 (`esc <https://github.com/esc>`_)
+* PR `#6760 <https://github.com/numba/numba/pull/6760>`_: Fix scheduler bug where it rounds to 0 divisions for a chunk. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#6762 <https://github.com/numba/numba/pull/6762>`_: Glue wrappers to create @overload from split typing and lowering. (`stuartarchibald <https://github.com/stuartarchibald>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6766 <https://github.com/numba/numba/pull/6766>`_: Fix DeviceNDArray null shape issue (`Michael Collison <https://github.com/testhound>`_)
+* PR `#6769 <https://github.com/numba/numba/pull/6769>`_: CUDA: Replace ``CachedPTX`` and ``CachedCUFunction`` with ``CUDACodeLibrary`` functionality (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6776 <https://github.com/numba/numba/pull/6776>`_: Fix issue with TBB interface causing warnings and parfors counting them (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6779 <https://github.com/numba/numba/pull/6779>`_: Fix wrap_index type unification. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#6786 <https://github.com/numba/numba/pull/6786>`_: Fix gufunc kwargs support (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6788 <https://github.com/numba/numba/pull/6788>`_: Add support for fastmath 32-bit floating point divide (`Michael Collison <https://github.com/testhound>`_)
+* PR `#6789 <https://github.com/numba/numba/pull/6789>`_: Fix warnings struct ref typeguard (`stuartarchibald <https://github.com/stuartarchibald>`_ `Siu Kwan Lam <https://github.com/sklam>`_ `esc <https://github.com/esc>`_)
+* PR `#6794 <https://github.com/numba/numba/pull/6794>`_: refactor and move create_temp_module into numba.tests.support (`Alexander-Makaryev <https://github.com/Alexander-Makaryev>`_)
+* PR `#6795 <https://github.com/numba/numba/pull/6795>`_: CUDA: Lazily add libdevice to compilation units  (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6798 <https://github.com/numba/numba/pull/6798>`_: CUDA: Add optional Driver API argument logging (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6799 <https://github.com/numba/numba/pull/6799>`_: Print Numba and llvmlite versions in sysinfo (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6800 <https://github.com/numba/numba/pull/6800>`_: Make a common standard API for querying ufunc impl (`Sergey Pokhodenko <https://github.com/PokhodenkoSA>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6801 <https://github.com/numba/numba/pull/6801>`_: ParallelAccelerator no long will convert StaticSetItem to SetItem because record arrays require StaticSetItems. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#6802 <https://github.com/numba/numba/pull/6802>`_: Add lineinfo flag to PTX and SASS compilation (`Graham Markall <https://github.com/gmarkall>`_ `Max Katz <https://github.com/maxpkatz>`_)
+* PR `#6804 <https://github.com/numba/numba/pull/6804>`_: added runtime version to ``numba -s`` (`Kalyan <https://github.com/rawwar>`_)
+* PR `#6808 <https://github.com/numba/numba/pull/6808>`_: #3468 continued: Add support for ``np.clip`` (`Graham Markall <https://github.com/gmarkall>`_ `Aaron Russell Voelker <https://github.com/arvoelke>`_)
+* PR `#6809 <https://github.com/numba/numba/pull/6809>`_: #3203 additional info in cuda detect (`Kalyan <https://github.com/rawwar>`_)
+* PR `#6810 <https://github.com/numba/numba/pull/6810>`_: Fix tiny formatting error in ROC kernel docs (`Felix Divo <https://github.com/felixdivo>`_)
+* PR `#6811 <https://github.com/numba/numba/pull/6811>`_: CUDA: Remove test of runtime being a supported version (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6813 <https://github.com/numba/numba/pull/6813>`_: Mostly CUDA: Replace llvmpy API usage with llvmlite APIs (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6814 <https://github.com/numba/numba/pull/6814>`_: Improving context stack (`stuartarchibald <https://github.com/stuartarchibald>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6818 <https://github.com/numba/numba/pull/6818>`_: CUDA: Support IPC on Windows (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6822 <https://github.com/numba/numba/pull/6822>`_: Add support for np.rot90 (`stuartarchibald <https://github.com/stuartarchibald>`_ `Daniel Nagel <https://github.com/braniii>`_)
+* PR `#6829 <https://github.com/numba/numba/pull/6829>`_: Fix accuracy of np.arange and np.linspace (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6830 <https://github.com/numba/numba/pull/6830>`_: CUDA: Use relaxed strides checking to compute contiguity (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6833 <https://github.com/numba/numba/pull/6833>`_: Raise TypeError exception if numpy array is cast to scalar (`Michael Collison <https://github.com/testhound>`_)
+* PR `#6834 <https://github.com/numba/numba/pull/6834>`_: Remove illegal "debug" kw argument (`Shaun Cutts <https://github.com/shaunc>`_)
+* PR `#6836 <https://github.com/numba/numba/pull/6836>`_: CUDA: Documentation updates (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6840 <https://github.com/numba/numba/pull/6840>`_: CUDA: Remove items deprecated in 0.53 + simulator test fixes (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6841 <https://github.com/numba/numba/pull/6841>`_: CUDA: Fix source location on kernel entry and enable breakpoints to be set on kernels by mangled name (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6843 <https://github.com/numba/numba/pull/6843>`_: cross-referenced Array type in docs (`Kalyan <https://github.com/rawwar>`_)
+* PR `#6844 <https://github.com/numba/numba/pull/6844>`_: CUDA: Remove NUMBAPRO env var warnings, envvars.py + other small tidy-ups (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6848 <https://github.com/numba/numba/pull/6848>`_: Ignore .ycm_extra_conf.py (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6849 <https://github.com/numba/numba/pull/6849>`_: Add __hash__ for IntEnum (`Hannes Pahl <https://github.com/HPLegion>`_)
+* PR `#6850 <https://github.com/numba/numba/pull/6850>`_: Fix up more internal warnings (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6854 <https://github.com/numba/numba/pull/6854>`_: PR 6096 continued (`stuartarchibald <https://github.com/stuartarchibald>`_ `Ivan Butygin <https://github.com/Hardcode84>`_)
+* PR `#6861 <https://github.com/numba/numba/pull/6861>`_: updated reference to hsa with roc (`Kalyan <https://github.com/rawwar>`_)
+* PR `#6867 <https://github.com/numba/numba/pull/6867>`_: Update changelog for 0.53.1 (`esc <https://github.com/esc>`_)
+* PR `#6869 <https://github.com/numba/numba/pull/6869>`_: Implement builtin sum() (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6870 <https://github.com/numba/numba/pull/6870>`_: Add support for dispatcher retargeting using with-context (`stuartarchibald <https://github.com/stuartarchibald>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6871 <https://github.com/numba/numba/pull/6871>`_: Force text-align:left when using Annotate (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#6873 <https://github.com/numba/numba/pull/6873>`_: docs: Update reference to @jitclass location (`David Nadlinger <https://github.com/dnadlinger>`_)
+* PR `#6876 <https://github.com/numba/numba/pull/6876>`_: Add trailing slashes to dir paths in CODEOWNERS (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6877 <https://github.com/numba/numba/pull/6877>`_: Add doc for recent target extension features (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6878 <https://github.com/numba/numba/pull/6878>`_: CUDA: Support passing tuples to ufuncs (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6879 <https://github.com/numba/numba/pull/6879>`_: CUDA: NumPy and string dtypes for local and shared arrays (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6880 <https://github.com/numba/numba/pull/6880>`_: Add attribute lower_extension to CPUContext (`Reazul Hoque <https://github.com/reazulhoque>`_)
+* PR `#6883 <https://github.com/numba/numba/pull/6883>`_: Add support of np.swapaxes #4074 (`Daniel Nagel <https://github.com/braniii>`_)
+* PR `#6885 <https://github.com/numba/numba/pull/6885>`_: CUDA: Explicitly specify objmode + looplifting for jit functions in cuda.random (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6886 <https://github.com/numba/numba/pull/6886>`_: CUDA: Fix parallel testing for all testsuite submodules (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6888 <https://github.com/numba/numba/pull/6888>`_: Get overload to consider compiler flags in cache lookup (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6889 <https://github.com/numba/numba/pull/6889>`_: Address guvectorize too slow for cuda target (`Michael Collison <https://github.com/testhound>`_)
+* PR `#6890 <https://github.com/numba/numba/pull/6890>`_: fixes #6884  (`Kalyan <https://github.com/rawwar>`_)
+* PR `#6898 <https://github.com/numba/numba/pull/6898>`_: Work on overloading by hardware target. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6911 <https://github.com/numba/numba/pull/6911>`_: CUDA: Add support for activemask(), lanemask_lt(), and nanosleep() (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6912 <https://github.com/numba/numba/pull/6912>`_: Prevent use of varargs in closure calls. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6913 <https://github.com/numba/numba/pull/6913>`_: Add runtests option to gitdiff on the common ancestor (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6915 <https://github.com/numba/numba/pull/6915>`_: Update _Intrinsic for sphinx to capture the inner docstring (`Guilherme Leobas <https://github.com/guilhermeleobas>`_)
+* PR `#6917 <https://github.com/numba/numba/pull/6917>`_: Add type conversion for StringLiteral to unicode_type and test. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6918 <https://github.com/numba/numba/pull/6918>`_: Start section on commonly encounted unsupported parfors code. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6924 <https://github.com/numba/numba/pull/6924>`_: CUDA: Fix ``ffs`` (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6928 <https://github.com/numba/numba/pull/6928>`_: Add support for axis keyword arg to numpy.argmax() (`stuartarchibald <https://github.com/stuartarchibald>`_ `Itamar Turner-Trauring <https://github.com/itamarst>`_)
+* PR `#6929 <https://github.com/numba/numba/pull/6929>`_: Fix CI failure when gitpython is missing. (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6935 <https://github.com/numba/numba/pull/6935>`_: fixes broken link in numba-runtime.rst (`Kalyan <https://github.com/rawwar>`_)
+* PR `#6936 <https://github.com/numba/numba/pull/6936>`_: CUDA: Implement support for PTDS globally (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6937 <https://github.com/numba/numba/pull/6937>`_: Fix memory leak in bytes boxing (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6940 <https://github.com/numba/numba/pull/6940>`_: Fix function resolution for intrinsics across hardware. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6941 <https://github.com/numba/numba/pull/6941>`_: ABC the target descriptor and make consistent throughout. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6944 <https://github.com/numba/numba/pull/6944>`_: CUDA: Support for ``@overload`` (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6945 <https://github.com/numba/numba/pull/6945>`_: Fix issue with array analysis tests needing scipy. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6948 <https://github.com/numba/numba/pull/6948>`_: Refactor registry init. (`stuartarchibald <https://github.com/stuartarchibald>`_ `Graham Markall <https://github.com/gmarkall>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6953 <https://github.com/numba/numba/pull/6953>`_: CUDA: Fix and deprecate ``inspect_ptx()``, fix NVVM option setup for device functions (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6958 <https://github.com/numba/numba/pull/6958>`_: Inconsistent behavior of reshape between numpy and numba/cuda device array (`Lauren Arnett <https://github.com/laurenarnett>`_)
+* PR `#6961 <https://github.com/numba/numba/pull/6961>`_: Update overload glue to deal with typing_key (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6964 <https://github.com/numba/numba/pull/6964>`_: Move minimum supported Python version to 3.7 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6966 <https://github.com/numba/numba/pull/6966>`_: Fix issue with TBB test detecting forks from incorrect state. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6971 <https://github.com/numba/numba/pull/6971>`_: Fix CUDA ``@intrinsic`` use (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6977 <https://github.com/numba/numba/pull/6977>`_: Vendor cloudpickle (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#6978 <https://github.com/numba/numba/pull/6978>`_: Implement operator.contains for empty Tuples (`Brandon T. Willard <https://github.com/brandonwillard>`_)
+* PR `#6981 <https://github.com/numba/numba/pull/6981>`_: Fix LLVM IR parsing error on use of ``np.bool_`` in globals (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6983 <https://github.com/numba/numba/pull/6983>`_: Support Optional types in ufuncs. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6985 <https://github.com/numba/numba/pull/6985>`_: Implement static set/get items on records with integer index (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6986 <https://github.com/numba/numba/pull/6986>`_: document release checklist (`esc <https://github.com/esc>`_)
+* PR `#6989 <https://github.com/numba/numba/pull/6989>`_: update threading docs for function loading (`esc <https://github.com/esc>`_)
+* PR `#6990 <https://github.com/numba/numba/pull/6990>`_: Refactor hardware extension API to refer to "target" instead. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6991 <https://github.com/numba/numba/pull/6991>`_: Move ROCm target status to "unmaintained". (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#6995 <https://github.com/numba/numba/pull/6995>`_: Resolve issue where nan was being assigned to int type numpy array (`Michael Collison <https://github.com/testhound>`_)
+* PR `#6996 <https://github.com/numba/numba/pull/6996>`_: Add constant lowering support for `SliceType`s (`Brandon T. Willard <https://github.com/brandonwillard>`_)
+* PR `#6997 <https://github.com/numba/numba/pull/6997>`_: CUDA: Remove catch of NotImplementedError in target.py (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#6999 <https://github.com/numba/numba/pull/6999>`_: Fix errors introduced by the cloudpickle patch (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7003 <https://github.com/numba/numba/pull/7003>`_: More mainline fixes (`stuartarchibald <https://github.com/stuartarchibald>`_ `Graham Markall <https://github.com/gmarkall>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7004 <https://github.com/numba/numba/pull/7004>`_: Test extending the CUDA target (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7007 <https://github.com/numba/numba/pull/7007>`_: Made stencil compilation not fail for arrays of conflicting types. (`MegaIng <https://github.com/MegaIng>`_)
+* PR `#7008 <https://github.com/numba/numba/pull/7008>`_: Added support for np.random.dirichlet with all size arguments (`Rishi Kulkarni <https://github.com/rishi-kulkarni>`_)
+* PR `#7016 <https://github.com/numba/numba/pull/7016>`_: Docs: Add DALI to list of CAI-supporting libraries (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7018 <https://github.com/numba/numba/pull/7018>`_: Remove cu{blas,sparse,rand,fft} from library checks (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7019 <https://github.com/numba/numba/pull/7019>`_: Support NumPy 1.20 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7020 <https://github.com/numba/numba/pull/7020>`_: Fix #7017. Adds util class PickleCallableByPath  (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7024 <https://github.com/numba/numba/pull/7024>`_: fixed llvmir usage in create_module method (`stuartarchibald <https://github.com/stuartarchibald>`_ `Kalyan <https://github.com/rawwar>`_)
+* PR `#7027 <https://github.com/numba/numba/pull/7027>`_: Fix nrt debug print (`MegaIng <https://github.com/MegaIng>`_)
+* PR `#7031 <https://github.com/numba/numba/pull/7031>`_: Fix inliner to use a single scope for all blocks (`Alexey Kozlov <https://github.com/kozlov-alexey>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7040 <https://github.com/numba/numba/pull/7040>`_: Add Github action to mark issues as stale (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7044 <https://github.com/numba/numba/pull/7044>`_: Fixes for LLVM 11 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7049 <https://github.com/numba/numba/pull/7049>`_: Make NumPy random module use @overload_glue (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7050 <https://github.com/numba/numba/pull/7050>`_: Add overload_classmethod (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7052 <https://github.com/numba/numba/pull/7052>`_: Fix string support in CUDA target (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7056 <https://github.com/numba/numba/pull/7056>`_: Change prange conversion approach to reuse header block. (`Todd A. Anderson <https://github.com/DrTodd13>`_)
+* PR `#7061 <https://github.com/numba/numba/pull/7061>`_: Add ndarray allocator classmethod (`stuartarchibald <https://github.com/stuartarchibald>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7064 <https://github.com/numba/numba/pull/7064>`_: Testhound/host array performance warning (`Michael Collison <https://github.com/testhound>`_)
+* PR `#7066 <https://github.com/numba/numba/pull/7066>`_: Fix #7065: Add expected exception messages for NumPy 1.20 to tests (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7068 <https://github.com/numba/numba/pull/7068>`_: Enhancing docs about PRNG seeding (`Jérome Eertmans <https://github.com/jeertmans>`_)
+* PR `#7070 <https://github.com/numba/numba/pull/7070>`_: Improve the issue templates and pull request template. (`Guoqiang QI <https://github.com/guoqiangqi>`_)
+* PR `#7080 <https://github.com/numba/numba/pull/7080>`_: Fix ``__eq__`` for Flags and cpu_options classes (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7087 <https://github.com/numba/numba/pull/7087>`_: Add note to docs about zero-initialization of variables. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7088 <https://github.com/numba/numba/pull/7088>`_: Initialize NUMBA_DEFAULT_NUM_THREADS with a batch scheduler aware value (`Thomas VINCENT <https://github.com/t20100>`_)
+* PR `#7100 <https://github.com/numba/numba/pull/7100>`_: Replace deprecated call to cuDeviceComputeCapability (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7113 <https://github.com/numba/numba/pull/7113>`_: Temporarily disable debug env export. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7114 <https://github.com/numba/numba/pull/7114>`_: CUDA: Deprecate eager compilation of device functions (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7116 <https://github.com/numba/numba/pull/7116>`_: Fix various issues with dwarf emission: (`stuartarchibald <https://github.com/stuartarchibald>`_ `vlad-perevezentsev <https://github.com/vlad-perevezentsev>`_)
+* PR `#7118 <https://github.com/numba/numba/pull/7118>`_: Remove print to stdout (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7121 <https://github.com/numba/numba/pull/7121>`_: Continue work on numpy subclasses (`Todd A. Anderson <https://github.com/DrTodd13>`_ `Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7122 <https://github.com/numba/numba/pull/7122>`_: Rtd/sphinx compat (`esc <https://github.com/esc>`_)
+* PR `#7134 <https://github.com/numba/numba/pull/7134>`_: Move minimum LLVM version to 11. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7137 <https://github.com/numba/numba/pull/7137>`_: skip pycc test on Python 3.7 + macOS because of distutils issue (`esc <https://github.com/esc>`_)
+* PR `#7138 <https://github.com/numba/numba/pull/7138>`_: Update the Azure default linux image to Ubuntu 18.04 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7141 <https://github.com/numba/numba/pull/7141>`_: Require llvmlite 0.37 as minimum supported. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7143 <https://github.com/numba/numba/pull/7143>`_: Update version checks in __init__ for np 1.17 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7145 <https://github.com/numba/numba/pull/7145>`_: Fix mainline (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7146 <https://github.com/numba/numba/pull/7146>`_: Fix ``inline_closurecall`` may not be imported (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7147 <https://github.com/numba/numba/pull/7147>`_: Revert "Workaround gitpython 3.1.18 dependency issue" (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7149 <https://github.com/numba/numba/pull/7149>`_: Fix issue in bytecode analysis where target and next are same. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7152 <https://github.com/numba/numba/pull/7152>`_: Fix iterators in CUDA (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7156 <https://github.com/numba/numba/pull/7156>`_: Fix ``ir_utils._max_label`` being updated incorrectly (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7160 <https://github.com/numba/numba/pull/7160>`_: Split parfors tests (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7161 <https://github.com/numba/numba/pull/7161>`_: Update README for 0.54 (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7162 <https://github.com/numba/numba/pull/7162>`_: CUDA: Fix linkage of device functions when compiling for debug (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7163 <https://github.com/numba/numba/pull/7163>`_: Split legalization pass to consider IR and features separately. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7165 <https://github.com/numba/numba/pull/7165>`_: Fix use of np.clip where out is not provided. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7189 <https://github.com/numba/numba/pull/7189>`_: CUDA: Skip IPC tests on ARM (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7190 <https://github.com/numba/numba/pull/7190>`_: CUDA: Fix test_pinned on Jetson (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7192 <https://github.com/numba/numba/pull/7192>`_: Fix missing import in array.argsort impl and add more tests. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7196 <https://github.com/numba/numba/pull/7196>`_: Fixes for lineinfo emission. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7203 <https://github.com/numba/numba/pull/7203>`_: remove duplicate changelog entries (`esc <https://github.com/esc>`_)
+* PR `#7209 <https://github.com/numba/numba/pull/7209>`_: Clamp numpy (`esc <https://github.com/esc>`_)
+* PR `#7216 <https://github.com/numba/numba/pull/7216>`_: Update CHANGE_LOG for 0.54.0rc2. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7223 <https://github.com/numba/numba/pull/7223>`_: Replace assertion errors on IR assumption violation (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7230 <https://github.com/numba/numba/pull/7230>`_: PR #7171 bugfix only (`Todd A. Anderson <https://github.com/DrTodd13>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7236 <https://github.com/numba/numba/pull/7236>`_: CUDA: Skip managed alloc tests on ARM (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7267 <https://github.com/numba/numba/pull/7267>`_: Fix #7258. Bug in SROA optimization (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7271 <https://github.com/numba/numba/pull/7271>`_: Update 3rd party license text. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7272 <https://github.com/numba/numba/pull/7272>`_: Allow annotations in njit-ed functions (`LunarLanding <https://github.com/LunarLanding>`_)
+* PR `#7273 <https://github.com/numba/numba/pull/7273>`_: Update CHANGE_LOG for 0.54.0rc3. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7285 <https://github.com/numba/numba/pull/7285>`_: CUDA: Fix OOB in test_kernel_arg (`Graham Markall <https://github.com/gmarkall>`_)
+* PR `#7294 <https://github.com/numba/numba/pull/7294>`_: Continuation of PR #7280, fixing lifetime of TBB task_scheduler_handle (`Sergey Pokhodenko <https://github.com/PokhodenkoSA>`_ `stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7298 <https://github.com/numba/numba/pull/7298>`_: Use CBC to pin GCC to 7 on most linux and 9 on aarch64. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7312 <https://github.com/numba/numba/pull/7312>`_: Fix #7302. Workaround missing pthread problem on ppc64le (`Siu Kwan Lam <https://github.com/sklam>`_)
+* PR `#7317 <https://github.com/numba/numba/pull/7317>`_: In TBB tsh test switch os.fork for mp fork ctx (`stuartarchibald <https://github.com/stuartarchibald>`_)
+* PR `#7319 <https://github.com/numba/numba/pull/7319>`_: Update CHANGE_LOG for 0.54.0 final. (`stuartarchibald <https://github.com/stuartarchibald>`_)
+
+Authors:
+
+* `Alexander-Makaryev <https://github.com/Alexander-Makaryev>`_
+* `Todd A. Anderson <https://github.com/DrTodd13>`_
+* `Hannes Pahl <https://github.com/HPLegion>`_
+* `Ivan Butygin <https://github.com/Hardcode84>`_
+* `MegaIng <https://github.com/MegaIng>`_
+* `Sergey Pokhodenko <https://github.com/PokhodenkoSA>`_
+* `Aaron Russell Voelker <https://github.com/arvoelke>`_
+* `Ashutosh Varma <https://github.com/ashutoshvarma>`_
+* `Ben Greiner <https://github.com/bnavigator>`_
+* `Brandon T. Willard <https://github.com/brandonwillard>`_
+* `Daniel Nagel <https://github.com/braniii>`_
+* `David Nadlinger <https://github.com/dnadlinger>`_
+* `Ehsan Totoni <https://github.com/ehsantn>`_
+* `esc <https://github.com/esc>`_
+* `Felix Divo <https://github.com/felixdivo>`_
+* `Graham Markall <https://github.com/gmarkall>`_
+* `Guilherme Leobas <https://github.com/guilhermeleobas>`_
+* `Guoqiang QI <https://github.com/guoqiangqi>`_
+* `Itamar Turner-Trauring <https://github.com/itamarst>`_
+* `Jérome Eertmans <https://github.com/jeertmans>`_
+* `Alexey Kozlov <https://github.com/kozlov-alexey>`_
+* `Lauren Arnett <https://github.com/laurenarnett>`_
+* `LunarLanding <https://github.com/LunarLanding>`_
+* `Max Katz <https://github.com/maxpkatz>`_
+* `Kalyan <https://github.com/rawwar>`_
+* `Reazul Hoque <https://github.com/reazulhoque>`_
+* `Rishi Kulkarni <https://github.com/rishi-kulkarni>`_
+* `Shaun Cutts <https://github.com/shaunc>`_
+* `Siu Kwan Lam <https://github.com/sklam>`_
+* `stuartarchibald <https://github.com/stuartarchibald>`_
+* `Thomas VINCENT <https://github.com/t20100>`_
+* `Michael Collison <https://github.com/testhound>`_
+* `vlad-perevezentsev <https://github.com/vlad-perevezentsev>`_
+
+
+Version 0.53.1 (25 March, 2021)
+-------------------------------
+
+This is a bugfix release for 0.53.0. It contains the following four
+pull-requests which fix two critical regressions and two build failures
+reported by the openSuSe team:
+
+* PR #6826 Fix regression on gufunc serialization
+* PR #6828 Fix regression in CUDA: Set stream in mapped and managed array
+  device_setup
+* PR #6837 Ignore warnings from packaging module when testing import behaviour.
+* PR #6851 set non-reported llvm timing values to 0.0
+
+Authors:
+
+* Ben Greiner
+* Graham Markall
+* Siu Kwan Lam
+* Stuart Archibald
+
+Version 0.53.0 (11 March, 2021)
+-------------------------------
+
+This release continues to add new features, bug fixes and stability improvements
+to Numba.
+
+Highlights of core changes:
+
+* Support for Python 3.9 (Stuart Archibald).
+* Function sub-typing (Lucio Fernandez-Arjona).
+* Initial support for dynamic ``gufuncs`` (i.e. from ``@guvectorize``)
+  (Guilherme Leobas).
+* Parallel Accelerator (``@njit(parallel=True)`` now supports Fortran ordered
+  arrays (Todd A. Anderson and Siu Kwan Lam).
+
+Intel also kindly sponsored research and development that lead to two new
+features:
+
+  * Exposing LLVM compilation pass timings for diagnostic purposes (Siu Kwan
+    Lam).
+  * An event system for broadcasting compiler events (Siu Kwan Lam).
+
+Highlights of changes for the CUDA target:
+
+* CUDA 11.2 onwards (versions of the toolkit using NVVM IR 1.6 / LLVM IR 7.0.1)
+  are now supported (Graham Markall).
+* A fast cube root function is added (Michael Collison).
+* Support for atomic ``xor``, increment, decrement, exchange, are added, and
+  compare-and-swap is extended to support 64-bit integers (Michael Collison).
+* Addition of ``cuda.is_supported_version()`` to check if the CUDA runtime
+  version is supported (Graham Markall).
+* The CUDA dispatcher now shares infrastructure with the CPU dispatcher,
+  improving launch times for lazily-compiled kernels (Graham Markall).
+* The CUDA Array Interface is updated to version 3, with support for streams
+  added (Graham Markall).
+* Tuples and ``namedtuples`` can now be passed to kernels (Graham Markall).
+* Initial support for Cooperative Groups is added, with support for Grid Groups
+  and Grid Sync (Graham Markall and Nick White).
+* Support for ``math.log2`` and ``math.remainder`` is added (Guilherme Leobas).
+
+General deprecation notices:
+
+* There are no new general deprecations.
+
+CUDA target deprecation notices:
+
+* CUDA support on macOS is deprecated with this release (it still works, it is
+  just unsupported).
+* The ``argtypes``, ``restypes``, and ``bind`` keyword arguments to the
+  ``cuda.jit`` decorator, deprecated since 0.51.0, are removed
+* The ``Device.COMPUTE_CAPABILITY`` property, deprecated since 2014, has been
+  removed (use ``compute_capability`` instead).
+* The ``to_host`` method of device arrays is removed (use ``copy_to_host``
+  instead).
+
+General Enhancements:
+
+* PR #4769: objmode complex type spelling (Siu Kwan Lam)
+* PR #5579: Function subtyping (Lucio Fernandez-Arjona)
+* PR #5659: Add support for parfors creating 'F'ortran layout Numpy arrays.
+  (Todd A. Anderson)
+* PR #5936: Improve array analysis for user-defined data types. (Todd A.
+  Anderson)
+* PR #5938: Initial support for dynamic gufuncs (Guilherme Leobas)
+* PR #5958: Making typed.List a typing Generic (Lucio Fernandez-Arjona)
+* PR #6334: Support attribute access from other modules (Farah Hariri)
+* PR #6373: Allow Dispatchers to be cached (Eric Wieser)
+* PR #6519: Avoid unnecessary ir.Del generation and removal (Ehsan Totoni)
+* PR #6545: Refactoring ParforDiagnostics (Elena Totmenina)
+* PR #6560: Add LLVM pass timer (Siu Kwan Lam)
+* PR #6573: Improve ``__str__`` for typed.List when invoked from IPython shell
+  (Amin Sadeghi)
+* PR #6575: Avoid temp variable assignments (Ehsan Totoni)
+* PR #6578: Add support for numpy ``intersect1d`` and basic test cases
+  (``@caljrobe``)
+* PR #6579: Python 3.9 support. (Stuart Archibald)
+* PR #6580: Store partial typing errors in compiler state (Ehsan Totoni)
+* PR #6626: A simple event system to broadcast compiler events (Siu Kwan Lam)
+* PR #6635: Try to resolve dynamic getitems as static post unroll transform.
+  (Stuart Archibald)
+* PR #6636: Adds llvm_lock event (Siu Kwan Lam)
+* PR #6664: Adds tests for PR 5659 (Siu Kwan Lam)
+* PR #6680: Allow getattr to work in objmode output type spec (Siu Kwan Lam)
+
+Fixes:
+
+* PR #6176: Remove references to deprecated numpy globals (Eric Wieser)
+* PR #6374: Use Python 3 style OSError handling (Eric Wieser)
+* PR #6402: Fix ``typed.Dict`` and ``typed.List`` crashing on parametrized types
+  (Andreas Sodeur)
+* PR #6403: Add ``types.ListType.key`` (Andreas Sodeur)
+* PR #6410: Fixes issue #6386 (Danny Weitekamp)
+* PR #6425: Fix unicode join for issue #6405 (Teugea Ioan-Teodor)
+* PR #6437: Don't pass reduction variables known in an outer parfor to inner
+  parfors when analyzing reductions. (Todd A. Anderson)
+* PR #6453: Keep original variable names in metadata to improve diagnostics
+  (Ehsan Totoni)
+* PR #6454: FIX: Fixes for literals (Eric Larson)
+* PR #6463: Bump llvmlite to 0.36 series (Stuart Archibald)
+* PR #6466: Remove the misspelling of finalize_dynamic_globals (Sergey
+  Pokhodenko)
+* PR #6489: Improve the error message for unsupported Buffer in Buffer
+  situation. (Stuart Archibald)
+* PR #6503: Add test to ensure Numba imports without warnings. (Stuart
+  Archibald)
+* PR #6508: Defer requirements to setup.py (Siu Kwan Lam)
+* PR #6521: Skip annotated jitclass test if typeguard is running. (Stuart
+  Archibald)
+* PR #6524: Fix typed.List return value (Lucio Fernandez-Arjona)
+* PR #6562: Correcting typo in numba sysinfo output (Nick Sutcliffe)
+* PR #6574: Run parfor fusion if 2 or more parfors (Ehsan Totoni)
+* PR #6582: Fix typed dict error with uninitialized padding bytes  (Siu Kwan
+  Lam)
+* PR #6584: Remove jitclass from ``__init__`` ``__all__``. (Stuart Archibald)
+* PR #6586: Run closure inlining ahead of branch pruning in case of nonlocal
+  (Stuart Archibald)
+* PR #6591: Fix inlineasm test failure. (Siu Kwan Lam)
+* PR #6622: Fix 6534, handle unpack of assign-like tuples. (Stuart Archibald)
+* PR #6652: Simplify PR-6334 (Siu Kwan Lam)
+* PR #6653: Fix get_numba_envvar (Siu Kwan Lam)
+* PR #6654: Fix #6632 support alternative dtype string spellings (Stuart
+  Archibald)
+* PR #6685: Add Python 3.9 to classifiers. (Stuart Archibald)
+* PR #6693: patch to compile _devicearray.cpp with c++11 (Valentin Haenel)
+* PR #6716: Consider assignment lhs live if used in rhs (Fixes #6715) (Ehsan
+  Totoni)
+* PR #6727: Avoid errors in array analysis for global tuples with non-int
+  (Ehsan Totoni)
+* PR #6733: Fix segfault and errors in #6668 (Siu Kwan Lam)
+* PR #6741: Enable SSA in IR inliner (Ehsan Totoni)
+* PR #6763: use an alternative constraint for the conda packages (Valentin
+  Haenel)
+* PR #6786: Fix gufunc kwargs support (Siu Kwan Lam)
+
+CUDA Enhancements/Fixes:
+
+* PR #5162: Specify synchronization semantics of CUDA Array Interface (Graham
+  Markall)
+* PR #6245: CUDA Cooperative grid groups (Graham Markall and Nick White)
+* PR #6333: Remove dead ``_Kernel.__call__`` (Graham Markall)
+* PR #6343: CUDA: Add support for passing tuples and namedtuples to kernels
+  (Graham Markall)
+* PR #6349: Refactor Dispatcher to remove unnecessary indirection (Graham
+  Markall)
+* PR #6358: Add log2 and remainder implementations for cuda (Guilherme Leobas)
+* PR #6376: Added a fixed seed in test_atomics.py for issue #6370 (Teugea
+  Ioan-Teodor)
+* PR #6377: CUDA: Fix various issues in test suite (Graham Markall)
+* PR #6409: Implement cuda atomic xor (Michael Collison)
+* PR #6422: CUDA: Remove deprecated items, expect CUDA 11.1 (Graham Markall)
+* PR #6427: Remove duplicate repeated definition of gufunc (Amit Kumar)
+* PR #6432: CUDA: Use ``_dispatcher.Dispatcher`` as base Dispatcher class
+  (Graham Markall)
+* PR #6447: CUDA: Add get_regs_per_thread method to Dispatcher (Graham Markall)
+* PR #6499: CUDA atomic increment, decrement, exchange and compare and swap
+  (Michael Collison)
+* PR #6510: CUDA: Make device array assignment synchronous where necessary
+  (Graham Markall)
+* PR #6517: CUDA: Add NVVM test of all 8-bit characters (Graham Markall)
+* PR #6567: Refactor llvm replacement code into separate function (Michael
+  Collison)
+* PR #6642: Testhound/cuda cuberoot (Michael Collison)
+* PR #6661: CUDA: Support NVVM70 / CUDA 11.2 (Graham Markall)
+* PR #6663: Fix error caused by missing "-static" libraries defined for some
+  platforms (Siu Kwan Lam)
+* PR #6666: CUDA: Add a function to query whether the runtime version is
+  supported. (Graham Markall)
+* PR #6725: CUDA: Fix compile to PTX with debug for CUDA 11.2 (Graham Markall)
+
+Documentation Updates:
+
+* PR #5740: Add FAQ entry on how to create a MWR. (Stuart Archibald)
+* PR #6346: DOC: add where to get dev builds from to FAQ  (Eyal Trabelsi)
+* PR #6418: docs: use https for homepage (``@imba-tjd``)
+* PR #6430: CUDA docs: Add RNG example with 3D grid and strided loops (Graham
+  Markall)
+* PR #6436: docs: remove typo in Deprecation Notices (Thibault Ballier)
+* PR #6440: Add note about performance of typed containers from the interpreter.
+  (Stuart Archibald)
+* PR #6457: Link to read the docs instead of numba homepage (Hannes Pahl)
+* PR #6470: Adding PyCon Sweden 2020 talk on numba (Ankit Mahato)
+* PR #6472: Document ``numba.extending.is_jitted`` (Stuart Archibald)
+* PR #6495: Fix typo in literal list docs. (Stuart Archibald)
+* PR #6501: Add doc entry on Numba's limited resources and how to help. (Stuart
+  Archibald)
+* PR #6502: Add CODEOWNERS file. (Stuart Archibald)
+* PR #6531: Update canonical URL. (Stuart Archibald)
+* PR #6544: Minor typo / grammar fixes to 5 minute guide (Ollin Boer Bohan)
+* PR #6599: docs: fix simple typo, consevatively -> conservatively (Tim Gates)
+* PR #6609: Recommend miniforge instead of c4aarch64 (Isuru Fernando)
+* PR #6671: Update environment creation example to python 3.8 (Lucio
+  Fernandez-Arjona)
+* PR #6676: Update hardware and software versions in various docs. (Stuart
+  Archibald)
+* PR #6682: Update deprecation notices for 0.53 (Stuart Archibald)
+
+CI/Infrastructure Updates:
+
+* PR #6458: Enable typeguard in CI (Siu Kwan Lam)
+* PR #6500: Update bug and feature request templates. (Stuart Archibald)
+* PR #6516: Fix RTD build by using conda. (Stuart Archibald)
+* PR #6587: Add zenodo badge (Siu Kwan Lam)
+
+Authors:
+
+* Amin Sadeghi
+* Amit Kumar
+* Andreas Sodeur
+* Ankit Mahato
+* Chris Barnes
+* Danny Weitekamp
+* Ehsan Totoni (core dev)
+* Eric Larson
+* Eric Wieser
+* Eyal Trabelsi
+* Farah Hariri
+* Graham Markall
+* Guilherme Leobas
+* Hannes Pahl
+* Isuru Fernando
+* Lucio Fernandez-Arjona
+* Michael Collison
+* Nick Sutcliffe
+* Nick White
+* Ollin Boer Bohan
+* Sergey Pokhodenko
+* Siu Kwan Lam (core dev)
+* Stuart Archibald (core dev)
+* Teugea Ioan-Teodor
+* Thibault Ballier
+* Tim Gates
+* Todd A. Anderson (core dev)
+* Valentin Haenel (core dev)
+* ``@caljrobe``
+* ``@imba-tjd``
+
+
+Version 0.52.0 (30 November, 2020)
+----------------------------------
+
+This release focuses on performance improvements, but also adds some new
+features and contains numerous bug fixes and stability improvements.
+
+Highlights of core performance improvements include:
+
+* Intel kindly sponsored research and development into producing a new reference
+  count pruning pass. This pass operates at the LLVM level and can prune a
+  number of common reference counting patterns. This will improve performance
+  for two primary reasons:
+
+  * There will be less pressure on the atomic locks used to do the reference
+    counting.
+  * Removal of reference counting operations permits more inlining and the
+    optimisation passes can in general do more with what is present.
+
+  (Siu Kwan Lam).
+* Intel also sponsored work to improve the performance of the
+  ``numba.typed.List`` container, particularly in the case of ``__getitem__``
+  and iteration (Stuart Archibald).
+* Superword-level parallelism vectorization is now switched on and the
+  optimisation pipeline has been lightly analysed and tuned so as to be able to
+  vectorize more and more often (Stuart Archibald).
+
+Highlights of core feature changes include:
+
+* The ``inspect_cfg`` method on the JIT dispatcher object has been
+  significantly enhanced and now includes highlighted output and interleaved
+  line markers and Python source (Stuart Archibald).
+* The BSD operating system is now unofficially supported (Stuart Archibald).
+* Numerous features/functionality improvements to NumPy support, including
+  support for:
+
+  * ``np.asfarray`` (Guilherme Leobas)
+  * "subtyping" in record arrays (Lucio Fernandez-Arjona)
+  * ``np.split`` and ``np.array_split`` (Isaac Virshup)
+  * ``operator.contains`` with ``ndarray`` (``@mugoh``).
+  * ``np.asarray_chkfinite`` (Rishabh Varshney).
+  * NumPy 1.19 (Stuart Archibald).
+  * the ``ndarray`` allocators, ``empty``, ``ones`` and ``zeros``, accepting a
+    ``dtype`` specified as a string literal (Stuart Archibald).
+
+* Booleans are now supported as literal types (Alexey Kozlov).
+* On the CUDA target:
+
+  * CUDA 9.0 is now the minimum supported version (Graham Markall).
+  * Support for Unified Memory has been added (Max Katz).
+  * Kernel launch overhead is reduced (Graham Markall).
+  * Cudasim support for mapped array, memcopies and memset has been added (Mike
+    Williams).
+  * Access has been wired in to all libdevice functions (Graham Markall).
+  * Additional CUDA atomic operations have been added (Michael Collison).
+  * Additional math library functions (``frexp``, ``ldexp``, ``isfinite``)
+    (Zhihao Yuan).
+  * Support for ``power`` on complex numbers (Graham Markall).
+
+Deprecations to note:
+
+There are no new deprecations. However, note that "compatibility" mode, which
+was added some 40 releases ago to help transition from 0.11 to 0.12+, has been
+removed! Also, the shim to permit the import of ``jitclass`` from Numba's top
+level namespace has now been removed as per the deprecation schedule.
+
+General Enhancements:
+
+* PR #5418: Add np.asfarray impl (Guilherme Leobas)
+* PR #5560: Record subtyping (Lucio Fernandez-Arjona)
+* PR #5609: Jitclass Infer Spec from Type Annotations (Ethan Pronovost)
+* PR #5699: Implement np.split and np.array_split (Isaac Virshup)
+* PR #6015: Adding BooleanLiteral type (Alexey Kozlov)
+* PR #6027: Support operators inlining in InlineOverloads (Alexey Kozlov)
+* PR #6038: Closes #6037, fixing FreeBSD compilation (László Károlyi)
+* PR #6086: Add more accessible version information (Stuart Archibald)
+* PR #6157: Add pipeline_class argument to @cfunc as supported by @jit. (Arthur
+  Peters)
+* PR #6262: Support dtype from str literal. (Stuart Archibald)
+* PR #6271: Support ``ndarray`` contains (``@mugoh``)
+* PR #6295: Enhance inspect_cfg (Stuart Archibald)
+* PR #6304: Support NumPy 1.19 (Stuart Archibald)
+* PR #6309: Add suitable file search path for BSDs. (Stuart Archibald)
+* PR #6341: Re roll 6279 (Rishabh Varshney and Valentin Haenel)
+
+Performance Enhancements:
+
+* PR #6145: Patch to fingerprint namedtuples. (Stuart Archibald)
+* PR #6202: Speed up str(int) (Stuart Archibald)
+* PR #6261: Add np.ndarray.ptp() support. (Stuart Archibald)
+* PR #6266: Use custom LLVM refcount pruning pass (Siu Kwan Lam)
+* PR #6275: Switch on SLP vectorize. (Stuart Archibald)
+* PR #6278: Improve typed list performance. (Stuart Archibald)
+* PR #6335: Split optimisation passes. (Stuart Archibald)
+* PR #6455: Fix refprune on obfuscated refs and stabilize optimisation WRT
+  wrappers. (Stuart Archibald)
+
+Fixes:
+
+* PR #5639: Make UnicodeType inherit from Hashable (Stuart Archibald)
+* PR #6006: Resolves incorrectly hoisted list in parfor. (Todd A. Anderson)
+* PR #6126: fix version_info if version can not be determined (Valentin Haenel)
+* PR #6137: Remove references to Python 2's long (Eric Wieser)
+* PR #6139: Use direct syntax instead of the ``add_metaclass`` decorator (Eric
+  Wieser)
+* PR #6140: Replace calls to utils.iteritems(d) with d.items() (Eric Wieser)
+* PR #6141: Fix #6130 objmode cache segfault (Siu Kwan Lam)
+* PR #6156: Remove callers of ``reraise`` in favor of using ``with_traceback``
+  directly (Eric Wieser)
+* PR #6162: Move charseq support out of init (Stuart Archibald)
+* PR #6165: #5425 continued (Amos Bird and Stuart Archibald)
+* PR #6166: Remove Python 2 compatibility from numba.core.utils (Eric Wieser)
+* PR #6185: Better error message on NotDefinedError (Luiz Almeida)
+* PR #6194: Remove recursion from traverse_types (Radu Popovici)
+* PR #6200: Workaround #5973 (Stuart Archibald)
+* PR #6203: Make find_callname only lookup functions that are likely part of
+  NumPy. (Stuart Archibald)
+* PR #6204: Fix unicode kind selection for getitem. (Stuart Archibald)
+* PR #6206: Build all extension modules with -g -Wall -Werror on Linux x86,
+  provide -O0 flag option (Graham Markall)
+* PR #6212: Fix for objmode recompilation issue (Alexey Kozlov)
+* PR #6213: Fix #6177. Remove AOT dependency on the Numba package (Siu Kwan Lam)
+* PR #6224: Add support for tuple concatenation to array analysis. (#5396
+  continued) (Todd A. Anderson)
+* PR #6231: Remove compatibility mode (Graham Markall)
+* PR #6254: Fix win-32 hashing bug (from Stuart Archibald) (Ray Donnelly)
+* PR #6265: Fix #6260 (Stuart Archibald)
+* PR #6267: speed up a couple of really slow unittests (Stuart Archibald)
+* PR #6281: Remove numba.jitclass shim as per deprecation schedule. (Stuart
+  Archibald)
+* PR #6294: Make return type propagate to all return variables (Andreas Sodeur)
+* PR #6300: Un-skip tests that were skipped because of #4026. (Owen Anderson)
+* PR #6307: Remove restrictions on SVML version due to bug in LLVM SVML CC
+  (Stuart Archibald)
+* PR #6316: Make IR inliner tests not self mutating. (Stuart Archibald)
+* PR #6318: PR #5892 continued (Todd A. Anderson, via Stuart Archibald)
+* PR #6319: Permit switching off boundschecking when debug is on. (Stuart
+  Archibald)
+* PR #6324: PR 6208 continued (Ivan Butygin and Stuart Archibald)
+* PR #6337: Implements ``key`` on ``types.TypeRef`` (Andreas Sodeur)
+* PR #6354: Bump llvmlite to 0.35. series. (Stuart Archibald)
+* PR #6357: Fix enumerate invalid decref (Siu Kwan Lam)
+* PR #6359: Fixes typed list indexing on 32bit (Stuart Archibald)
+* PR #6378: Fix incorrect CPU override in vectorization test. (Stuart Archibald)
+* PR #6379: Use O0 to enable inline and not affect loop-vectorization by later
+  O3... (Siu Kwan Lam)
+* PR #6384: Fix failing tests to match on platform invariant int spelling.
+  (Stuart Archibald)
+* PR #6390: Updates inspect_cfg (Stuart Archibald)
+* PR #6396: Remove hard dependency on tbb package. (Stuart Archibald)
+* PR #6408: Don't do array analysis for tuples that contain arrays. (Todd A.
+  Anderson)
+* PR #6441: Fix ASCII flag in Unicode slicing (0.52.0rc2 regression) (Ehsan
+  Totoni)
+* PR #6442: Fix array analysis regression in 0.52 RC2 for tuple of 1D arrays
+  (Ehsan Totoni)
+* PR #6446: Fix #6444: pruner issues with reference stealing functions (Siu
+  Kwan Lam)
+* PR #6450: Fix asfarray kwarg default handling. (Stuart Archibald)
+* PR #6486: fix abstract base class import (Valentin Haenel)
+* PR #6487: Restrict maximum version of python (Siu Kwan Lam)
+* PR #6527: setup.py: fix py version guard (Chris Barnes)
+
+CUDA Enhancements/Fixes:
+
+* PR #5465: Remove macro expansion and replace uses with FE typing + BE lowering
+  (Graham Markall)
+* PR #5741: CUDA: Add two-argument implementation of round() (Graham Markall)
+* PR #5900: Enable CUDA Unified Memory (Max Katz)
+* PR #6042: CUDA: Lower launch overhead by launching kernel directly (Graham
+  Markall)
+* PR #6064: Lower math.frexp and math.ldexp in numba.cuda (Zhihao Yuan)
+* PR #6066: Lower math.isfinite in numba.cuda (Zhihao Yuan)
+* PR #6092: CUDA: Add mapped_array_like and pinned_array_like (Graham Markall)
+* PR #6127: Fix race in reduction kernels on Volta, require CUDA 9, add syncwarp
+  with default mask (Graham Markall)
+* PR #6129: Extend Cudasim to support most of the memory functionality. (Mike
+  Williams)
+* PR #6150: CUDA: Turn on flake8 for cudadrv and fix errors (Graham Markall)
+* PR #6152: CUDA: Provide wrappers for all libdevice functions, and fix typing
+  of math function (#4618) (Graham Markall)
+* PR #6227: Raise exception when no supported architectures are found (Jacob
+  Tomlinson)
+* PR #6244: CUDA Docs: Make workflow using simulator more explicit (Graham
+  Markall)
+* PR #6248: Add support for CUDA atomic subtract operations (Michael Collison)
+* PR #6289: Refactor atomic test cases to reduce code duplication (Michael
+  Collison)
+* PR #6290: CUDA: Add support for complex power (Graham Markall)
+* PR #6296: Fix flake8 violations in numba.cuda module (Graham Markall)
+* PR #6297: Fix flake8 violations in numba.cuda.tests.cudapy module (Graham
+  Markall)
+* PR #6298: Fix flake8 violations in numba.cuda.tests.cudadrv (Graham Markall)
+* PR #6299: Fix flake8 violations in numba.cuda.simulator (Graham Markall)
+* PR #6306: Fix flake8 in cuda atomic test from merge. (Stuart Archibald)
+* PR #6325: Refactor code for atomic operations (Michael Collison)
+* PR #6329: Flake8 fix for a CUDA test (Stuart Archibald)
+* PR #6331: Explicitly state that NUMBA_ENABLE_CUDASIM needs to be set before
+  import (Graham Markall)
+* PR #6340: CUDA: Fix #6339, performance regression launching specialized
+  kernels (Graham Markall)
+* PR #6380: Only test managed allocations on Linux (Graham Markall)
+
+Documentation Updates:
+
+* PR #6090: doc: Add doc on direct creation of Numba typed-list (``@rht``)
+* PR #6110: Update CONTRIBUTING.md (Stuart Archibald)
+* PR #6128: CUDA Docs: Restore Dispatcher.forall() docs (Graham Markall)
+* PR #6277: fix: cross2d wrong doc. reference (issue #6276) (``@jeertmans``)
+* PR #6282: Remove docs on Python 2(.7) EOL. (Stuart Archibald)
+* PR #6283: Add note on how public CI is impl and what users can do to help.
+  (Stuart Archibald)
+* PR #6292: Document support for structured array attribute access
+  (Graham Markall)
+* PR #6310: Declare unofficial \*BSD support (Stuart Archibald)
+* PR #6342: Fix docs on literally usage. (Stuart Archibald)
+* PR #6348: doc: fix typo in jitclass.rst ("initilising" -> "initialising")
+  (``@muxator``)
+* PR #6362: Move llvmlite support in README to 0.35 (Stuart Archibald)
+* PR #6363: Note that reference counted types are not permitted in set().
+  (Stuart Archibald)
+* PR #6364: Move deprecation schedules for 0.52 (Stuart Archibald)
+
+CI/Infrastructure Updates:
+
+* PR #6252: Show channel URLs (Siu Kwan Lam)
+* PR #6338: Direct user questions to Discourse instead of the Google Group.
+  (Stan Seibert)
+* PR #6474: Add skip on PPC64LE for tests causing SIGABRT in LLVM. (Stuart
+  Archibald)
+
+Authors:
+
+* Alexey Kozlov
+* Amos Bird
+* Andreas Sodeur
+* Arthur Peters
+* Chris Barnes
+* Ehsan Totoni (core dev)
+* Eric Wieser
+* Ethan Pronovost
+* Graham Markall
+* Guilherme Leobas
+* Isaac Virshup
+* Ivan Butygin
+* Jacob Tomlinson
+* Luiz Almeida
+* László Károlyi
+* Lucio Fernandez-Arjona
+* Max Katz
+* Michael Collison
+* Mike Williams
+* Owen Anderson
+* Radu Popovici
+* Ray Donnelly
+* Rishabh Varshney
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* Valentin Haenel (core dev)
+* Zhihao Yuan
+* ``@jeertmans``
+* ``@mugoh``
+* ``@muxator``
+* ``@rht``
+
+
+
+Version 0.51.2 (September 2, 2020)
+----------------------------------
+
+This is a bugfix release for 0.51.1. It fixes a critical performance bug in the
+CFG back edge computation algorithm that leads to exponential time complexity
+arising in compilation for use cases with certain pathological properties.
+
+* PR #6195: PR 6187 Continue. Don't visit already checked successors
+
+Authors:
+
+* Graham Markall
+* Siu Kwan Lam (core dev)
+
+
+Version 0.51.1 (August 26, 2020)
+--------------------------------
+
+This is a bugfix release for 0.51.0, it fixes a critical bug in caching, another
+critical bug in the CUDA target initialisation sequence and also fixes some
+compile time performance regressions:
+
+* PR #6141: Fix #6130 objmode cache segfault
+* PR #6146: Fix compilation slowdown due to controlflow analysis
+* PR #6147: CUDA: Don't make a runtime call on import
+* PR #6153: Fix for #6151. Make UnicodeCharSeq into str for comparison.
+* PR #6168: Fix Issue #6167: Failure in test_cuda_submodules
+
+Authors:
+
+* Graham Markall
+* Siu Kwan Lam (core dev)
+* Stuart Archibald (core dev)
+
+
+Version 0.51.0 (August 12, 2020)
+--------------------------------
+
+This release continues to add new features to Numba and also contains a
+significant number of bug fixes and stability improvements.
+
+Highlights of core feature changes include:
+
+* The compilation chain is now based on LLVM 10 (Valentin Haenel).
+* Numba has internally switched to prefer non-literal types over literal ones so
+  as to reduce function over-specialisation, this with view of speeding up
+  compile times (Siu Kwan Lam).
+* On the CUDA target: Support for CUDA Toolkit 11, Ampere, and Compute
+  Capability 8.0; Printing of ``SASS`` code for kernels; Callbacks to Python
+  functions can be inserted into CUDA streams, and streams are async awaitable;
+  Atomic ``nanmin`` and ``nanmax`` functions are added; Fixes for various
+  miscompilations and segfaults. (mostly Graham Markall; call backs on
+  streams by Peter Würtz).
+
+Intel also kindly sponsored research and development that lead to some exciting
+new features:
+
+* Support for heterogeneous immutable lists and heterogeneous immutable string
+  key dictionaries. Also optional initial/construction value capturing for all
+  lists and dictionaries containing literal values (Stuart Archibald).
+* A new pass-by-reference mutable structure extension type ``StructRef`` (Siu
+  Kwan Lam).
+* Object mode blocks are now cacheable, with the side effect of numerous bug
+  fixes and performance improvements in caching. This also permits caching of
+  functions defined in closures (Siu Kwan Lam).
+
+Deprecations to note:
+
+To align with other targets, the ``argtypes`` and ``restypes`` kwargs to
+``@cuda.jit`` are now deprecated, the ``bind`` kwarg is also deprecated.
+Further the ``target`` kwarg to the ``numba.jit`` decorator family is
+deprecated.
+
+General Enhancements:
+
+* PR #5463: Add str(int) impl
+* PR #5526: Impl. np.asarray(literal)
+* PR #5619: Add support for multi-output ufuncs
+* PR #5711: Division with timedelta input
+* PR #5763: Support minlength argument to np.bincount
+* PR #5779: Return zero array from np.dot when the arguments are empty.
+* PR #5796: Add implementation for np.positive
+* PR #5849: Setitem for records when index is StringLiteral, including literal
+  unroll
+* PR #5856: Add support for conversion of inplace_binop to parfor.
+* PR #5893: Allocate 1D iteration space one at a time for more even
+  distribution.
+* PR #5922: Reduce objmode and unpickling overhead
+* PR #5944: re-enable OpenMP in wheels
+* PR #5946: Implement literal dictionaries and lists.
+* PR #5956: Update numba_sysinfo.py
+* PR #5978: Add structref as a mutable struct that is pass-by-ref
+* PR #5980: Deprecate target kwarg for numba.jit.
+* PR #6058: Add prefer_literal option to overload API
+
+Fixes:
+
+* PR #5674: Fix #3955. Allow `with objmode` to be cached
+* PR #5724: Initialize process lock lazily to prevent multiprocessing issue
+* PR #5783: Make np.divide and np.remainder code more similar
+* PR #5808: Fix 5665 Block jit(nopython=True, forceobj=True) and suppress
+  njit(forceobj=True)
+* PR #5834: Fix the is operator on Ellipsis
+* PR #5838: Ensure ``Dispatcher.__eq__`` always returns a bool
+* PR #5841: cleanup: Use PythonAPI.bool_from_bool in more places
+* PR #5862: Do not leak loop iteration variables into the numba.np.npyimpl
+  namespace
+* PR #5869: Update repomap
+* PR #5879: Fix erroneous input mutation in linalg routines
+* PR #5882: Type check function in jit decorator
+* PR #5925: Use np.inf and -np.inf for max and min float values respectively.
+* PR #5935: Fix default arguments with multiprocessing
+* PR #5952: Fix "Internal error ... local variable 'errstr' referenced before
+  assignment during BoundFunction(...)"
+* PR #5962: Fix SVML tests with LLVM 10 and AVX512
+* PR #5972: fix flake8 for numba/runtests.py
+* PR #5995: Update setup.py with new llvmlite versions
+* PR #5996: Set lower bound for llvmlite to 0.33
+* PR #6004: Fix problem in branch pruning with LiteralStrKeyDict
+* PR #6017: Fixing up numba_do_raise
+* PR #6028: Fix #6023
+* PR #6031: Continue 5821
+* PR #6035: Fix overspecialize of literal
+* PR #6046: Fixes statement reordering bug in maximize fusion step.
+* PR #6056: Fix issue on invalid inlining of non-empty build_list by
+  inline_arraycall
+* PR #6057: fix aarch64/python_3.8 failure on master
+* PR #6070: Fix overspecialized containers
+* PR #6071: Remove f-strings in setup.py
+* PR #6072: Fix for #6005
+* PR #6073: Fixes invalid C prototype in helper function.
+* PR #6078: Duplicate NumPy's PyArray_DescrCheck macro
+* PR #6081: Fix issue with cross drive use and relpath.
+* PR #6083: Fix bug in initial value unify.
+* PR #6087: remove invalid sanity check from randrange tests
+* PR #6089: Fix invalid reference to TypingError
+* PR #6097: Add function code and closure bytes into cache key
+* PR #6099: Restrict upper limit of TBB version due to ABI changes.
+* PR #6101: Restrict lower limit of icc_rt version due to assumed SVML bug.
+* PR #6107: Fix and test #6095
+* PR #6109: Fixes an issue reported in #6094
+* PR #6111: Decouple LiteralList and LiteralStrKeyDict from tuple
+* PR #6116: Fix #6102. Problem with non-unique label.
+
+CUDA Enhancements/Fixes:
+
+* PR #5359: Remove special-casing of 0d arrays
+* PR #5709: CUDA: Refactoring of cuda.jit and kernel / dispatcher abstractions
+* PR #5732: CUDA Docs: document ``forall`` method of kernels
+* PR #5745: CUDA stream callbacks and async awaitable streams
+* PR #5761: Add implmentation for int types for isnan and isinf for CUDA
+* PR #5819: Add support for CUDA 11 and Ampere / CC 8.0
+* PR #5826: CUDA: Add function to get SASS for kernels
+* PR #5846: CUDA: Allow disabling NVVM optimizations, and fix debug issues
+* PR #5851: CUDA EMM enhancements - add default get_ipc_handle implementation,
+  skip a test conditionally
+* PR #5852: CUDA: Fix ``cuda.test()``
+* PR #5857: CUDA docs: Add notes on resetting the EMM plugin
+* PR #5859: CUDA: Fix reduce docs and style improvements
+* PR #6016: Fixes change of list spelling in a cuda test.
+* PR #6020: CUDA: Fix #5820, adding atomic nanmin / nanmax
+* PR #6030: CUDA: Don't optimize IR before sending it to NVVM
+* PR #6052: Fix dtype for atomic_add_double testsuite
+* PR #6080: CUDA: Prevent auto-upgrade of atomic intrinsics
+* PR #6123: Fix #6121
+
+Documentation Updates:
+
+* PR #5782: Host docs on Read the Docs
+* PR #5830: doc: Mention that caching uses pickle
+* PR #5963: Fix broken link to numpy ufunc signature docs
+* PR #5975: restructure communication section
+* PR #5981: Document bounds-checking behavior in python deviations page
+* PR #5993: Docs for structref
+* PR #6008: Small fix so bullet points are rendered by sphinx
+* PR #6013: emphasize cuda kernel functions are asynchronous
+* PR #6036: Update deprecation doc from numba.errors to numba.core.errors
+* PR #6062: Change references to numba.pydata.org to https
+
+CI updates:
+
+* PR #5850: Updates the "New Issue" behaviour to better redirect users.
+* PR #5940: Add discourse badge
+* PR #5960: Setting mypy on CI
+
+Enhancements from user contributed PRs (with thanks!):
+
+* Aisha Tammy added the ability to switch off TBB support at compile time in
+  #5821 (continued in #6031 by Stuart Archibald).
+* Alexander Stiebing fixed a reference before assignment bug in #5952.
+* Alexey Kozlov fixed a bug in tuple getitem for literals in #6028.
+* Andrew Eckart updated the repomap in #5869, added support for Read the Docs
+  in #5782, fixed a bug in the ``np.dot`` implementation to correctly handle
+  empty arrays in #5779 and added support for ``minlength`` to ``np.bincount``
+  in #5763.
+* ``@bitsisbits`` updated ``numba_sysinfo.py`` to handle HSA agents correctly in
+  #5956.
+* Daichi Suzuo Fixed a bug in the threading backend initialisation sequence such
+  that it is now correctly a lazy lock in #5724.
+* Eric Wieser contributed a number of patches, particularly in enhancing and
+  improving the ``ufunc`` capabilities:
+
+  * #5359: Remove special-casing of 0d arrays
+  * #5834: Fix the is operator on Ellipsis
+  * #5619: Add support for multi-output ufuncs
+  * #5841: cleanup: Use PythonAPI.bool_from_bool in more places
+  * #5862: Do not leak loop iteration variables into the numba.np.npyimpl
+    namespace
+  * #5838: Ensure ``Dispatcher.__eq__`` always returns a bool
+  * #5830: doc: Mention that caching uses pickle
+  * #5783: Make np.divide and np.remainder code more similar
+
+* Ethan Pronovost added a guard to prevent the common mistake of applying a jit
+  decorator to the same function twice in #5881.
+* Graham Markall contributed many patches to the CUDA target, as follows:
+
+  * #6052: Fix dtype for atomic_add_double tests
+  * #6030: CUDA: Don't optimize IR before sending it to NVVM
+  * #5846: CUDA: Allow disabling NVVM optimizations, and fix debug issues
+  * #5826: CUDA: Add function to get SASS for kernels
+  * #5851: CUDA EMM enhancements - add default get_ipc_handle implementation,
+    skip a test conditionally
+  * #5709: CUDA: Refactoring of cuda.jit and kernel / dispatcher abstractions
+  * #5819: Add support for CUDA 11 and Ampere / CC 8.0
+  * #6020: CUDA: Fix #5820, adding atomic nanmin / nanmax
+  * #5857: CUDA docs: Add notes on resetting the EMM plugin
+  * #5859: CUDA: Fix reduce docs and style improvements
+  * #5852: CUDA: Fix ``cuda.test()``
+  * #5732: CUDA Docs: document ``forall`` method of kernels
+
+* Guilherme Leobas added support for ``str(int)`` in #5463 and
+  ``np.asarray(literal value)``` in #5526.
+* Hameer Abbasi deprecated the ``target`` kwarg for ``numba.jit`` in #5980.
+* Hannes Pahl added a badge to the Numba github page linking to the new
+  discourse forum in #5940 and also fixed a bug that permitted illegal
+  combinations of flags to be passed into ``@jit`` in #5808.
+* Kayran Schmidt emphasized that CUDA kernel functions are asynchronous in the
+  documentation in #6013.
+* Leonardo Uieda fixed a broken link to the NumPy ufunc signature docs in #5963.
+* Lucio Fernandez-Arjona added mypy to CI and started adding type annotations to
+  the code base in #5960, also fixed a (de)serialization problem on the
+  dispatcher in #5935, improved the undefined variable error message in #5876,
+  added support for division with timedelta input in #5711 and implemented
+  ``setitem`` for records when the index is a ``StringLiteral`` in #5849.
+* Ludovic Tiako documented Numba's bounds-checking behavior in the python
+  deviations page in #5981.
+* Matt Roeschke changed all ``http`` references ``https`` in #6062.
+* ``@niteya-shah`` implemented ``isnan`` and ``isinf`` for integer types on the
+  CUDA target in #5761 and implemented ``np.positive`` in #5796.
+* Peter Würtz added CUDA stream callbacks and async awaitable streams in #5745.
+* ``@rht`` fixed an invalid import referred to in the deprecation documentation
+  in #6036.
+* Sergey Pokhodenko updated the SVML tests for LLVM 10 in #5962.
+* Shyam Saladi fixed a Sphinx rendering bug in #6008.
+
+Authors:
+
+* Aisha Tammy
+* Alexander Stiebing
+* Alexey Kozlov
+* Andrew Eckart
+* ``@bitsisbits``
+* Daichi Suzuo
+* Eric Wieser
+* Ethan Pronovost
+* Graham Markall
+* Guilherme Leobas
+* Hameer Abbasi
+* Hannes Pahl
+* Kayran Schmidt
+* Kozlov, Alexey
+* Leonardo Uieda
+* Lucio Fernandez-Arjona
+* Ludovic Tiako
+* Matt Roeschke
+* ``@niteya-shah``
+* Peter Würtz
+* Sergey Pokhodenko
+* Shyam Saladi
+* ``@rht``
+* Siu Kwan Lam (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* Valentin Haenel (core dev)
+
+
+Version 0.50.1 (Jun 24, 2020)
+-----------------------------
+
+This is a bugfix release for 0.50.0, it fixes a critical bug in error reporting
+and a number of other smaller issues:
+
+* PR #5861: Added except for possible Windows get_terminal_size exception
+* PR #5876: Improve undefined variable error message
+* PR #5884: Update the deprecation notices for 0.50.1
+* PR #5889: Fixes literally not forcing re-dispatch for inline='always'
+* PR #5912: Fix bad attr access on certain typing templates breaking exceptions.
+* PR #5918: Fix cuda test due to #5876
+
+Authors:
+
+* ``@pepping_dore``
+* Lucio Fernandez-Arjona
+* Siu Kwan Lam (core dev)
+* Stuart Archibald (core dev)
+
+
+Version 0.50.0 (Jun 10, 2020)
+-----------------------------
+
+This is a more usual release in comparison to the others that have been made in
+the last six months. It comprises the result of a number of maintenance tasks
+along with some new features and a lot of bug fixes.
+
+Highlights of core feature changes include:
+
+* The compilation chain is now based on LLVM 9.
+* The error handling and reporting system has been improved to reduce the size
+  of error messages, and also improve quality and specificity.
+* The CUDA target has more stream constructors available and a new function for
+  compiling to PTX without linking and loading the code to a device. Further,
+  the macro-based system for describing CUDA threads and blocks has been
+  replaced with standard typing and lowering implementations, for improved
+  debugging and extensibility.
+
+IMPORTANT: The backwards compatibility shim, that was present in 0.49.x to
+accommodate the refactoring of Numba's internals, has been removed. If a module
+is imported from a moved location an ``ImportError`` will occur.
+
+General Enhancements:
+
+* PR #5060: Enables np.sum for timedelta64
+* PR #5225: Adjust interpreter to make conditionals predicates via bool() call.
+* PR #5506: Jitclass static methods
+* PR #5580: Revert shim
+* PR #5591: Fix #5525 Add figure for total memory to ``numba -s`` output.
+* PR #5616: Simplify the ufunc kernel registration
+* PR #5617: Remove /examples from the Numba repo.
+* PR #5673: Fix inliners to run all passes on IR and clean up correctly.
+* PR #5700: Make it easier to understand type inference: add SSA dump, use for
+  ``DEBUG_TYPEINFER``
+* PR #5702: Fixes for LLVM 9
+* PR #5722: Improve error messages.
+* PR #5758: Support NumPy 1.18
+
+Fixes:
+
+* PR #5390: add error handling for lookup_module
+* PR #5464: Jitclass drops annotations to avoid error
+* PR #5478: Fix #5471. Issue with omitted type not recognized as literal value.
+* PR #5517: Fix numba.typed.List extend for singleton and empty iterable
+* PR #5549: Check type getitem
+* PR #5568: Add skip to entrypoint test on windows
+* PR #5581: Revert #5568
+* PR #5602: Fix segfault caused by pop from numba.typed.List
+* PR #5645: Fix SSA redundant CFG computation
+* PR #5686: Fix issue with SSA not minimal
+* PR #5689: Fix bug in unified_function_type (issue 5685)
+* PR #5694: Skip part of slice array analysis if any part is not analyzable.
+* PR #5697: Fix usedef issue with parfor loopnest variables.
+* PR #5705: A fix for cases where SSA looks like a reduction variable.
+* PR #5714: Fix bug in test
+* PR #5717: Initialise Numba extensions ahead of any compilation starting.
+* PR #5721: Fix array iterator layout.
+* PR #5738: Unbreak master on buildfarm
+* PR #5757: Force LLVM to use ZMM registers for vectorization.
+* PR #5764: fix flake8 errors
+* PR #5768: Interval example: fix import
+* PR #5781: Moving record array examples to a test module
+* PR #5791: Fix up no cgroups problem
+* PR #5795: Restore refct removal pass and make it strict
+* PR #5807: Skip failing test on POWER8 due to PPC CTR Loop problem.
+* PR #5812: Fix side issue from #5792, @overload inliner cached IR being
+  mutated.
+* PR #5815: Pin llvmlite to 0.33
+* PR #5833: Fixes the source location appearing incorrectly in error messages.
+
+CUDA Enhancements/Fixes:
+
+* PR #5347: CUDA: Provide more stream constructors
+* PR #5388: CUDA: Fix OOB write in test_round{f4,f8}
+* PR #5437: Fix #5429: Exception using ``.get_ipc_handle(...)`` on array from
+  ``as_cuda_array(...)``
+* PR #5481: CUDA: Replace macros with typing and lowering implementations
+* PR #5556: CUDA: Make atomic semantics match Python / NumPy, and fix #5458
+* PR #5558: CUDA: Only release primary ctx if retained
+* PR #5561: CUDA: Add function for compiling to PTX (+ other small fixes)
+* PR #5573: CUDA: Skip tests under cuda-memcheck that hang it
+* PR #5578: Implement math.modf for CUDA target
+* PR #5704: CUDA Eager compilation: Fix max_registers kwarg
+* PR #5718: CUDA lib path tests: unset CUDA_PATH when CUDA_HOME unset
+* PR #5800: Fix LLVM 9 IR for NVVM
+* PR #5803: CUDA Update expected error messages to fix #5797
+
+Documentation Updates:
+
+* PR #5546: DOC: Add documentation about cost model to inlining notes.
+* PR #5653: Update doc with respect to try-finally case
+
+Enhancements from user contributed PRs (with thanks!):
+
+* Elias Kuthe fixed in issue with imports in the Interval example in #5768
+* Eric Wieser Simplified the ufunc kernel registration mechanism in #5616
+* Ethan Pronovost patched a problem with ``__annotations__`` in ``jitclass`` in
+  #5464, fixed a bug that lead to infinite loops in Numba's ``Type.__getitem__``
+  in #5549, fixed a bug in ``np.arange`` testing in #5714 and added support for
+  ``@staticmethod`` to ``jitclass`` in #5506.
+* Gabriele Gemmi implemented ``math.modf`` for the CUDA target in #5578
+* Graham Markall contributed many patches, largely to the CUDA target, as
+  follows:
+
+  * #5347: CUDA: Provide more stream constructors
+  * #5388: CUDA: Fix OOB write in test_round{f4,f8}
+  * #5437: Fix #5429: Exception using ``.get_ipc_handle(...)`` on array from
+    ``as_cuda_array(...)``
+  * #5481: CUDA: Replace macros with typing and lowering implementations
+  * #5556: CUDA: Make atomic semantics match Python / NumPy, and fix #5458
+  * #5558: CUDA: Only release primary ctx if retained
+  * #5561: CUDA: Add function for compiling to PTX (+ other small fixes)
+  * #5573: CUDA: Skip tests under cuda-memcheck that hang it
+  * #5648: Unset the memory manager after EMM Plugin tests
+  * #5700: Make it easier to understand type inference: add SSA dump, use for
+    ``DEBUG_TYPEINFER``
+  * #5704: CUDA Eager compilation: Fix max_registers kwarg
+  * #5718: CUDA lib path tests: unset CUDA_PATH when CUDA_HOME unset
+  * #5800: Fix LLVM 9 IR for NVVM
+  * #5803: CUDA Update expected error messages to fix #5797
+
+* Guilherme Leobas updated the documentation surrounding try-finally in #5653
+* Hameer Abbasi added documentation about the cost model to the notes on
+  inlining in #5546
+* Jacques Gaudin rewrote ``numba -s`` to produce and consume a dictionary of
+  output about the current system in #5591
+* James Bourbeau Updated min/argmin and max/argmax to handle non-leading nans
+  (via #5758)
+* Lucio Fernandez-Arjona moved the record array examples to a test module in
+  #5781 and added ``np.timedelta64`` handling to ``np.sum`` in #5060
+* Pearu Peterson Fixed a bug in unified_function_type in #5689
+* Sergey Pokhodenko fixed an issue impacting LLVM 10 regarding vectorization
+  widths on Intel SkyLake processors in #5757
+* Shan Sikdar added error handling for ``lookup_module`` in #5390
+* @toddrme2178 add CI testing for NumPy 1.18 (via #5758)
+
+Authors:
+
+* Elias Kuthe
+* Eric Wieser
+* Ethan Pronovost
+* Gabriele Gemmi
+* Graham Markall
+* Guilherme Leobas
+* Hameer Abbasi
+* Jacques Gaudin
+* James Bourbeau
+* Lucio Fernandez-Arjona
+* Pearu Peterson
+* Sergey Pokhodenko
+* Shan Sikdar
+* Siu Kwan Lam (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* ``@toddrme2178``
+* Valentin Haenel (core dev)
+
+
+Version 0.49.1 (May 7, 2020)
+----------------------------
+
+This is a bugfix release for 0.49.0, it fixes some residual issues with SSA
+form, a critical bug in the branch pruning logic and a number of other smaller
+issues:
+
+* PR #5587: Fixed #5586 Threading Implementation Typos
+* PR #5592: Fixes #5583 Remove references to cffi_support from docs and examples
+* PR #5614: Fix invalid type in resolve for comparison expr in parfors.
+* PR #5624: Fix erroneous rewrite of predicate to bit const on prune.
+* PR #5627: Fixes #5623, SSA local def scan based on invalid equality
+  assumption.
+* PR #5629: Fixes naming error in array_exprs
+* PR #5630: Fix #5570. Incorrect race variable detection due to SSA naming.
+* PR #5638: Make literal_unroll function work as a freevar.
+* PR #5648: Unset the memory manager after EMM Plugin tests
+* PR #5651: Fix some SSA issues
+* PR #5652: Pin to sphinx=2.4.4 to avoid problem with C declaration
+* PR #5658: Fix unifying undefined first class function types issue
+* PR #5669: Update example in 5m guide WRT SSA type stability.
+* PR #5676: Restore ``numba.types`` as public API
+
+Authors:
+
+* Graham Markall
+* Juan Manuel Cruz Martinez
+* Pearu Peterson
+* Sean Law
+* Stuart Archibald (core dev)
+* Siu Kwan Lam (core dev)
+
+
+Version 0.49.0 (Apr 16, 2020)
+-----------------------------
+
+This release is very large in terms of code changes. Large scale removal of
+unsupported Python and NumPy versions has taken place along with a significant
+amount of refactoring to simplify the Numba code base to make it easier for
+contributors. Numba's intermediate representation has also undergone some
+important changes to solve a number of long standing issues. In addition some
+new features have been added and a large number of bugs have been fixed!
+
+IMPORTANT: In this release Numba's internals have moved about a lot. A backwards
+compatibility "shim" is provided for this release so as to not immediately break
+projects using Numba's internals. If a module is imported from a moved location
+the shim will issue a deprecation warning and suggest how to update the import
+statement for the new location. The shim will be removed in 0.50.0!
+
+Highlights of core feature changes include:
+
+* Removal of all Python 2 related code and also updating the minimum supported
+  Python version to 3.6, the minimum supported NumPy version to 1.15 and the
+  minimum supported SciPy version to 1.0. (Stuart Archibald).
+* Refactoring of the Numba code base. The code is now organised into submodules
+  by functionality. This cleans up Numba's top level namespace.
+  (Stuart Archibald).
+* Introduction of an ``ir.Del`` free static single assignment form for Numba's
+  intermediate representation (Siu Kwan Lam and Stuart Archibald).
+* An OpenMP-like thread masking API has been added for use with code using the
+  parallel CPU backends (Aaron Meurer and Stuart Archibald).
+* For the CUDA target, all kernel launches now require a configuration, this
+  preventing accidental launches of kernels with the old default of a single
+  thread in a single block. The hard-coded autotuner is also now removed, such
+  tuning is deferred to CUDA API calls that provide the same functionality
+  (Graham Markall).
+* The CUDA target also gained an External Memory Management plugin interface to
+  allow Numba to use another CUDA-aware library for all memory allocations and
+  deallocations (Graham Markall).
+* The Numba Typed List container gained support for construction from iterables
+  (Valentin Haenel).
+* Experimental support was added for first-class function types
+  (Pearu Peterson).
+
+Enhancements from user contributed PRs (with thanks!):
+
+* Aaron Meurer added support for thread masking at runtime in #4615.
+* Andreas Sodeur fixed a long standing bug that was preventing ``cProfile`` from
+  working with Numba JIT compiled functions in #4476.
+* Arik Funke fixed error messages in ``test_array_reductions`` (#5278), fixed
+  an issue with test discovery (#5239), made it so the documentation would build
+  again on windows (#5453) and fixed a nested list problem in the docs in #5489.
+* Antonio Russo fixed a SyntaxWarning in #5252.
+* Eric Wieser added support for inferring the types of object arrays (#5348) and
+  iterating over 2D arrays (#5115), also fixed some compiler warnings due to
+  missing (void) in #5222. Also helped improved the "shim" and associated
+  warnings in #5485, #5488, #5498 and partly #5532.
+* Ethan Pronovost fixed a problem with the shim erroneously warning for jitclass
+  use in #5454 and also prevented illegal return values in jitclass ``__init__``
+  in #5505.
+* Gabriel Majeri added SciPy 2019 talks to the docs in #5106.
+* Graham Markall changed the Numba HTML documentation theme to resolve a number
+  of long standing issues in #5346. Also contributed were a large number of CUDA
+  enhancements and fixes, namely:
+
+  * #5519: CUDA: Silence the test suite - Fix #4809, remove autojit, delete
+    prints
+  * #5443: Fix #5196: Docs: assert in CUDA only enabled for debug
+  * #5436: Fix #5408: test_set_registers_57 fails on Maxwell
+  * #5423: Fix #5421: Add notes on printing in CUDA kernels
+  * #5400: Fix #4954, and some other small CUDA testsuite fixes
+  * #5328: NBEP 7: External Memory Management Plugin Interface
+  * #5144: Fix #4875: Make #2655 test with debug expect to pass
+  * #5323: Document lifetime semantics of CUDA Array Interface
+  * #5061: Prevent kernel launch with no configuration, remove autotuner
+  * #5099: Fix #5073: Slices of dynamic shared memory all alias
+  * #5136: CUDA: Enable asynchronous operations on the default stream
+  * #5085: Support other itemsizes with view
+  * #5059: Docs: Explain how to use Memcheck with Numba, fixups in CUDA
+    documentation
+  * #4957: Add notes on overwriting gufunc inputs to docs
+
+* Greg Jennings fixed an issue with ``np.random.choice`` not acknowledging the
+  RNG seed correctly in #3897/#5310.
+* Guilherme Leobas added support for ``np.isnat`` in #5293.
+* Henry Schreiner made the llvmlite requirements more explicit in
+  requirements.txt in #5150.
+* Ivan Butygin helped fix an issue with parfors sequential lowering in
+  #5114/#5250.
+* Jacques Gaudin fixed a bug for Python >= 3.8 in ``numba -s`` in #5548.
+* Jim Pivarski added some hints for debugging entry points in #5280.
+* John Kirkham added ``numpy.dtype`` coercion for the ``dtype`` argument to CUDA
+  device arrays in #5252.
+* Leo Fang added a list of libraries that support ``__cuda_array_interface__``
+  in #5104.
+* Lucio Fernandez-Arjona added ``getitem`` for the NumPy record type when the
+  index is a ``StringLiteral`` type in #5182 and improved the documentation
+  rendering via additions to the TOC and removal of numbering in #5450.
+* Mads R. B. Kristensen fixed an issue with ``__cuda_array_interface__`` not
+  requiring the context in #5189.
+* Marcin Tolysz added support for nested modules in AOT compilation in #5174.
+* Mike Williams fixed some issues with NumPy records and ``getitem`` in the CUDA
+  simulator in #5343.
+* Pearu Peterson added experimental support for first-class function types in
+  #5287 (and fixes in #5459, #5473/#5429, and #5557).
+* Ravi Teja Gutta added support for ``np.flip`` in #4376/#5313.
+* Rohit Sanjay fixed an issue with type refinement for unicode input supplied to
+  typed-list ``extend()`` (#5295) and fixed unicode ``.strip()`` to strip all
+  whitespace characters in #5213.
+* Vladimir Lukyanov fixed an awkward bug in ``typed.dict`` in #5361, added a fix
+  to ensure the LLVM and assembly dumps are highlighted correctly in #5357 and
+  implemented a Numba IR Lexer and added highlighting to Numba IR dumps in
+  #5333.
+* hdf fixed an issue with the ``boundscheck`` flag in the CUDA jit target in
+  #5257.
+
+General Enhancements:
+
+* PR #4615: Allow masking threads out at runtime
+* PR #4798: Add branch pruning based on raw predicates.
+* PR #5115: Add support for iterating over 2D arrays
+* PR #5117: Implement ord()/chr()
+* PR #5122: Remove Python 2.
+* PR #5127: Calling convention adaptor for boxer/unboxer to call jitcode
+* PR #5151: implement None-typed typed-list
+* PR #5174: Nested modules https://github.com/numba/numba/issues/4739
+* PR #5182: Add getitem for Record type when index is StringLiteral
+* PR #5185: extract code-gen utilities from closures
+* PR #5197: Refactor Numba, part I
+* PR #5210: Remove more unsupported Python versions from build tooling.
+* PR #5212: Adds support for viewing the CFG of the ELF disassembly.
+* PR #5227: Immutable typed-list
+* PR #5231: Added support for ``np.asarray`` to be used with
+  ``numba.typed.List``
+* PR #5235: Added property ``dtype`` to ``numba.typed.List``
+* PR #5272: Refactor parfor: split up ParforPass
+* PR #5281: Make IR ir.Del free until legalized.
+* PR #5287: First-class function type
+* PR #5293: np.isnat
+* PR #5294: Create typed-list from iterable
+* PR #5295: refine typed-list on unicode input to extend
+* PR #5296: Refactor parfor: better exception from passes
+* PR #5308: Provide ``numba.extending.is_jitted``
+* PR #5320: refactor array_analysis
+* PR #5325: Let literal_unroll accept types.Named*Tuple
+* PR #5330: refactor common operation in parfor lowering into a new util
+* PR #5333: Add: highlight Numba IR dump
+* PR #5342: Support for tuples passed to parfors.
+* PR #5348: Add support for inferring the types of object arrays
+* PR #5351: SSA again
+* PR #5352: Add shim to accommodate refactoring.
+* PR #5356: implement allocated parameter in njit
+* PR #5369: Make test ordering more consistent across feature availability
+* PR #5428: Wip/deprecate jitclass location
+* PR #5441: Additional changes to first class function
+* PR #5455: Move to llvmlite 0.32.*
+* PR #5457: implement repr for untyped lists
+
+Fixes:
+
+* PR #4476: Another attempt at fixing frame injection in the dispatcher tracing
+  path
+* PR #4942: Prevent some parfor aliasing.  Rename copied function var to prevent
+  recursive type locking.
+* PR #5092: Fix 5087
+* PR #5150: More explicit llvmlite requirement in requirements.txt
+* PR #5172: fix version spec for llvmlite
+* PR #5176: Normalize kws going into fold_arguments.
+* PR #5183: pass 'inline' explicitly to overload
+* PR #5193: Fix CI failure due to missing files when installed
+* PR #5213: Fix ``.strip()`` to strip all whitespace characters
+* PR #5216: Fix namedtuple mistreated by dispatcher as simple tuple
+* PR #5222: Fix compiler warnings due to missing (void)
+* PR #5232: Fixes a bad import that breaks master
+* PR #5239: fix test discovery for unittest
+* PR #5247: Continue PR #5126
+* PR #5250: Part fix/5098
+* PR #5252: Trivially fix SyntaxWarning
+* PR #5276: Add prange variant to has_no_side_effect.
+* PR #5278: fix error messages in test_array_reductions
+* PR #5310: PR #3897 continued
+* PR #5313: Continues PR #4376
+* PR #5318: Remove AUTHORS file reference from MANIFEST.in
+* PR #5327: Add warning if FNV hashing is found as the default for CPython.
+* PR #5338: Remove refcount pruning pass
+* PR #5345: Disable test failing due to removed pass.
+* PR #5357: Small fix to have llvm and asm highlighted properly
+* PR #5361: 5081 typed.dict
+* PR #5431: Add tolerance to numba extension module entrypoints.
+* PR #5432: Fix code causing compiler warnings.
+* PR #5445: Remove undefined variable
+* PR #5454: Don't warn for numba.experimental.jitclass
+* PR #5459: Fixes issue 5448
+* PR #5480: Fix for #5477, literal_unroll KeyError searching for getitems
+* PR #5485: Show the offending module in "no direct replacement" error message
+* PR #5488: Add missing ``numba.config`` shim
+* PR #5495: Fix missing null initializer for variable after phi strip
+* PR #5498: Make the shim deprecation warnings work on python 3.6 too
+* PR #5505: Better error message if __init__ returns value
+* PR #5527: Attempt to fix #5518
+* PR #5529: PR #5473 continued
+* PR #5532: Make ``numba.<mod>`` available without an import
+* PR #5542: Fixes RC2 module shim bug
+* PR #5548: Fix #5537 Removed reference to ``platform.linux_distribution``
+* PR #5555: Fix #5515 by reverting changes to ArrayAnalysis
+* PR #5557: First-class function call cannot use keyword arguments
+* PR #5569: Fix RewriteConstGetitems not registering calltype for new expr
+* PR #5571: Pin down llvmlite requirement
+
+CUDA Enhancements/Fixes:
+
+* PR #5061: Prevent kernel launch with no configuration, remove autotuner
+* PR #5085: Support other itemsizes with view
+* PR #5099: Fix #5073: Slices of dynamic shared memory all alias
+* PR #5104: Add a list of libraries that support __cuda_array_interface__
+* PR #5136: CUDA: Enable asynchronous operations on the default stream
+* PR #5144: Fix #4875: Make #2655 test with debug expect to pass
+* PR #5189: __cuda_array_interface__ not requiring context
+* PR #5253: Coerce ``dtype`` to ``numpy.dtype``
+* PR #5257: boundscheck fix
+* PR #5319: Make user facing error string use abs path not rel.
+* PR #5323: Document lifetime semantics of CUDA Array Interface
+* PR #5328: NBEP 7: External Memory Management Plugin Interface
+* PR #5343: Fix cuda spoof
+* PR #5400: Fix #4954, and some other small CUDA testsuite fixes
+* PR #5436: Fix #5408: test_set_registers_57 fails on Maxwell
+* PR #5519: CUDA: Silence the test suite - Fix #4809, remove autojit, delete
+  prints
+
+Documentation Updates:
+
+* PR #4957: Add notes on overwriting gufunc inputs to docs
+* PR #5059: Docs: Explain how to use Memcheck with Numba, fixups in CUDA
+  documentation
+* PR #5106: Add SciPy 2019 talks to docs
+* PR #5147: Update master for 0.48.0 updates
+* PR #5155: Explain what inlining at Numba IR level will do
+* PR #5161: Fix README.rst formatting
+* PR #5207: Remove AUTHORS list
+* PR #5249: fix target path for See also
+* PR #5262: fix typo in inlining docs
+* PR #5270: fix 'see also' in typeddict docs
+* PR #5280: Added some hints for debugging entry points.
+* PR #5297: Update docs with intro to {g,}ufuncs.
+* PR #5326: Update installation docs with OpenMP requirements.
+* PR #5346: Docs: use sphinx_rtd_theme
+* PR #5366: Remove reference to Python 2.7 in install check output
+* PR #5423: Fix #5421: Add notes on printing in CUDA kernels
+* PR #5438: Update package deps for doc building.
+* PR #5440: Bump deprecation notices.
+* PR #5443: Fix #5196: Docs: assert in CUDA only enabled for debug
+* PR #5450: Docs: remove numbers and add titles to TOC
+* PR #5453: fix building docs on windows
+* PR #5489: docs: fix rendering of nested bulleted list
+
+CI updates:
+
+* PR #5314: Update the image used in Azure CI for OSX.
+* PR #5360: Remove Travis CI badge.
+
+Authors:
+
+* Aaron Meurer
+* Andreas Sodeur
+* Antonio Russo
+* Arik Funke
+* Eric Wieser
+* Ethan Pronovost
+* Gabriel Majeri
+* Graham Markall
+* Greg Jennings
+* Guilherme Leobas
+* hdf
+* Henry Schreiner
+* Ivan Butygin
+* Jacques Gaudin
+* Jim Pivarski
+* John Kirkham
+* Leo Fang
+* Lucio Fernandez-Arjona
+* Mads R. B. Kristensen
+* Marcin Tolysz
+* Mike Williams
+* Pearu Peterson
+* Ravi Teja Gutta
+* Rohit Sanjay
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* Valentin Haenel (core dev)
+* Vladimir Lukyanov
+
+
+Version 0.48.0 (Jan 27, 2020)
+-----------------------------
+
+This release is particularly small as it was present to catch anything that
+missed the 0.47.0 deadline (the deadline deliberately coincided with the end of
+support for Python 2.7). The next release will be considerably larger.
+
+The core changes in this release are dominated by the start of the clean up
+needed for the end of Python 2.7 support, improvements to the CUDA target and
+support for numerous additional unicode string methods.
+
+Enhancements from user contributed PRs (with thanks!):
+
+* Brian Wignall fixed more spelling typos in #4998.
+* Denis Smirnov added support for string methods ``capitalize`` (#4823),
+  ``casefold`` (#4824), ``swapcase`` (#4825), ``rsplit`` (#4834), ``partition``
+  (#4845) and ``splitlines`` (#4849).
+* Elena Totmenina extended support for string methods ``startswith`` (#4867) and
+  added ``endswith`` (#4868).
+* Eric Wieser made ``type_callable`` return the decorated function itself in
+  #4760
+* Ethan Pronovost added support for ``np.argwhere`` in #4617
+* Graham Markall contributed a large number of CUDA enhancements and fixes,
+  namely:
+
+  * #5068: Remove Python 3.4 backports from utils
+  * #4975: Make ``device_array_like`` create contiguous arrays (Fixes #4832)
+  * #5023: Don't launch ForAll kernels with 0 elements (Fixes #5017)
+  * #5016: Fix various issues in CUDA library search (Fixes #4979)
+  * #5014: Enable use of records and bools for shared memory, remove ddt, add
+    additional transpose tests
+  * #4964: Fix #4628: Add more appropriate typing for CUDA device arrays
+  * #5007: test_consuming_strides: Keep dev array alive
+  * #4997: State that CUDA Toolkit 8.0 required in docs
+
+* James Bourbeau added the Python 3.8 classifier to setup.py in #5027.
+* John Kirkham added a clarification to the ``__cuda_array_interface__``
+  documentation in #5049.
+* Leo Fang Fixed an indexing problem in ``dummyarray`` in #5012.
+* Marcel Bargull fixed a build and test issue for Python 3.8 in #5029.
+* Maria Rubtsov added support for string methods ``isdecimal`` (#4842),
+  ``isdigit`` (#4843), ``isnumeric`` (#4844) and ``replace`` (#4865).
+
+General Enhancements:
+
+* PR #4760: Make type_callable return the decorated function
+* PR #5010: merge string prs
+
+  This merge PR included the following:
+
+  * PR #4823: Implement str.capitalize() based on CPython
+  * PR #4824: Implement str.casefold() based on CPython
+  * PR #4825: Implement str.swapcase() based on CPython
+  * PR #4834: Implement str.rsplit() based on CPython
+  * PR #4842: Implement str.isdecimal
+  * PR #4843: Implement str.isdigit
+  * PR #4844: Implement str.isnumeric
+  * PR #4845: Implement str.partition() based on CPython
+  * PR #4849: Implement str.splitlines() based on CPython
+  * PR #4865: Implement str.replace
+  * PR #4867: Functionality extension str.startswith() based on CPython
+  * PR #4868: Add functionality for str.endswith()
+
+* PR #5039: Disable help messages.
+* PR #4617: Add coverage for ``np.argwhere``
+
+Fixes:
+
+* PR #4724: Only use lives (and not aliases) to create post parfor live set.
+* PR #4998: Fix more spelling typos
+* PR #5024: Propagate semantic constants ahead of static rewrites.
+* PR #5027: Add Python 3.8 classifier to setup.py
+* PR #5046: Update setup.py and buildscripts for dependency requirements
+* PR #5053: Convert from arrays to names in define() and don't invalidate for
+  multiple consistent defines.
+* PR #5058: Permit mixed int types in wrap_index
+* PR #5078: Catch the use of global typed-list in JITed functions
+* PR #5092: Fix #5087, bug in bytecode analysis.
+
+CUDA Enhancements/Fixes:
+
+* PR #4964: Fix #4628: Add more appropriate typing for CUDA device arrays
+* PR #4975: Make ``device_array_like`` create contiguous arrays (Fixes #4832)
+* PR #4997: State that CUDA Toolkit 8.0 required in docs
+* PR #5007: test_consuming_strides: Keep dev array alive
+* PR #5012: Fix IndexError when accessing the "-1" element of dummyarray
+* PR #5014: Enable use of records and bools for shared memory, remove ddt, add
+  additional transpose tests
+* PR #5016: Fix various issues in CUDA library search (Fixes #4979)
+* PR #5023: Don't launch ForAll kernels with 0 elements (Fixes #5017)
+* PR #5068: Remove Python 3.4 backports from utils
+
+Documentation Updates:
+
+* PR #5049: Clarify what dictionary means
+* PR #5062: Update docs for updated version requirements
+* PR #5090: Update deprecation notices for 0.48.0
+
+CI updates:
+
+* PR #5029: Install optional dependencies for Python 3.8 tests
+* PR #5040: Drop Py2.7 and Py3.5 from public CI
+* PR #5048: Fix CI py38
+
+Authors:
+
+* Brian Wignall
+* Denis Smirnov
+* Elena Totmenina
+* Eric Wieser
+* Ethan Pronovost
+* Graham Markall
+* James Bourbeau
+* John Kirkham
+* Leo Fang
+* Marcel Bargull
+* Maria Rubtsov
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* Valentin Haenel (core dev)
+
+
+Version 0.47.0  (Jan 2, 2020)
+-----------------------------
+
+This release expands the capability of Numba in a number of important areas and
+is also significant as it is the last major point release with support for
+Python 2 and Python 3.5 included. The next release (0.48.0) will be for Python
+3.6+ only!  (This follows NumPy's deprecation schedule as specified in
+`NEP 29 <https://numpy.org/neps/nep-0029-deprecation_policy.html>`_.)
+
+Highlights of core feature changes include:
+
+* Full support for Python 3.8 (Siu Kwan Lam)
+* Opt-in bounds checking (Aaron Meurer)
+* Support for ``map``, ``filter`` and ``reduce`` (Stuart Archibald)
+
+Intel also kindly sponsored research and development that lead to some exciting
+new features:
+
+* Initial support for basic ``try``/``except`` use (Siu Kwan Lam)
+* The ability to pass functions created from closures/lambdas as arguments
+  (Stuart Archibald)
+* ``sorted`` and ``list.sort()`` now accept the ``key`` argument (Stuart
+  Archibald and Siu Kwan Lam)
+* A new compiler pass triggered through the use of the function
+  ``numba.literal_unroll`` which permits iteration over heterogeneous tuples
+  and constant lists of constants. (Stuart Archibald)
+
+Enhancements from user contributed PRs (with thanks!):
+
+* Ankit Mahato added a reference to a new talk on Numba at PyCon India 2019 in
+  #4862
+* Brian Wignall kindly fixed some spelling mistakes and typos in #4909
+* Denis Smirnov wrote numerous methods to considerable enhance string support
+  including:
+
+  * ``str.rindex()`` in #4861
+  * ``str.isprintable()`` in #4836
+  * ``str.index()`` in #4860
+  * ``start/end`` parameters for ``str.find()`` in #4866
+  * ``str.isspace()`` in #4835
+  * ``str.isidentifier()`` #4837
+  * ``str.rpartition()`` in #4841
+  * ``str.lower()`` and ``str.islower()`` in #4651
+
+* Elena Totmenina implemented both ``str.isalnum()``, ``str.isalpha()`` and
+  ``str.isascii`` in #4839, #4840 and #4847 respectively.
+* Eric Larson fixed a bug in literal comparison in #4710
+* Ethan Pronovost updated the ``np.arange`` implementation in #4770 to allow
+  the use of the ``dtype`` key word argument and also added ``bool``
+  implementations for several types in #4715.
+* Graham Markall fixed some issues with the CUDA target, namely:
+
+  * #4931: Added physical limits for CC 7.0 / 7.5 to CUDA autotune
+  * #4934: Fixed bugs in TestCudaWarpOperations
+  * #4938: Improved errors / warnings for the CUDA vectorize decorator
+
+* Guilherme Leobas fixed a typo in the ``urem`` implementation in #4667
+* Isaac Virshup contributed a number of patches that fixed bugs, added support
+  for more NumPy functions and enhanced Python feature support. These
+  contributions included:
+
+  * #4729: Allow array construction with mixed type shape tuples
+  * #4904: Implementing ``np.lcm``
+  * #4780: Implement np.gcd and math.gcd
+  * #4779: Make slice constructor more similar to python.
+  * #4707: Added support for slice.indices
+  * #4578: Clarify numba ufunc supported features
+
+* James Bourbeau fixed some issues with tooling, #4794 add ``setuptools`` as a
+  dependency and #4501 add pre-commit hooks for ``flake8`` compliance.
+* Leo Fang made ``numba.dummyarray.Array`` iterable in #4629
+* Marc Garcia fixed the ``numba.jit`` parameter name signature_or_function in
+  #4703
+* Marcelo Duarte Trevisani patched the llvmlite requirement to ``>=0.30.0`` in
+  #4725
+* Matt Cooper fixed a long standing CI problem in #4737 by remove maxParallel
+* Matti Picus fixed an issue with ``collections.abc`` in #4734
+  from Azure Pipelines.
+* Rob Ennis patched a bug in ``np.interp`` ``float32`` handling in #4911
+* VDimir fixed a bug in array transposition layouts in #4777 and re-enabled and
+  fixed some idle tests in #4776.
+* Vyacheslav Smirnov Enable support for `str.istitle()`` in #4645
+
+General Enhancements:
+
+* PR #4432: Bounds checking
+* PR #4501: Add pre-commit hooks
+* PR #4536: Handle kw args in inliner when callee is a function
+* PR #4599: Permits closures to become functions, enables map(), filter()
+* PR #4611: Implement method title() for unicode based on Cpython
+* PR #4645: Enable support for istitle() method for unicode string
+* PR #4651: Implement str.lower() and str.islower()
+* PR #4652: Implement str.rfind()
+* PR #4695: Refactor `overload*` and support `jit_options` and `inline`
+* PR #4707: Added support for slice.indices
+* PR #4715: Add `bool` overload for several types
+* PR #4729: Allow array construction with mixed type shape tuples
+* PR #4755: Python3.8 support
+* PR #4756: Add parfor support for ndarray.fill.
+* PR #4768: Update typeconv error message to ask for sys.executable.
+* PR #4770: Update `np.arange` implementation with `@overload`
+* PR #4779: Make slice constructor more similar to python.
+* PR #4780: Implement np.gcd and math.gcd
+* PR #4794: Add setuptools as a dependency
+* PR #4802: put git hash into build string
+* PR #4803: Better compiler error messages for improperly used reduction
+  variables.
+* PR #4817: Typed list implement and expose allocation
+* PR #4818: Typed list faster copy
+* PR #4835: Implement str.isspace() based on CPython
+* PR #4836: Implement str.isprintable() based on CPython
+* PR #4837: Implement str.isidentifier() based on CPython
+* PR #4839: Implement str.isalnum() based on CPython
+* PR #4840: Implement str.isalpha() based on CPython
+* PR #4841: Implement str.rpartition() based on CPython
+* PR #4847: Implement str.isascii() based on CPython
+* PR #4851: Add graphviz output for FunctionIR
+* PR #4854: Python3.8 looplifting
+* PR #4858: Implement str.expandtabs() based on CPython
+* PR #4860: Implement str.index() based on CPython
+* PR #4861: Implement str.rindex() based on CPython
+* PR #4866: Support params start/end for str.find()
+* PR #4874: Bump to llvmlite 0.31
+* PR #4896: Specialise arange dtype on arch + python version.
+* PR #4902: basic support for try except
+* PR #4904: Implement np.lcm
+* PR #4910: loop canonicalisation and type aware tuple unroller/loop body
+  versioning passes
+* PR #4961: Update hash(tuple) for Python 3.8.
+* PR #4977: Implement sort/sorted with key.
+* PR #4987: Add `is_internal` property to all Type classes.
+
+Fixes:
+
+* PR #4090: Update to LLVM8 memset/memcpy intrinsic
+* PR #4582: Convert sub to add and div to mul when doing the reduction across
+  the per-thread reduction array.
+* PR #4648: Handle 0 correctly as slice parameter.
+* PR #4660: Remove multiply defined variables from all blocks' equivalence sets.
+* PR #4672: Fix pickling of dufunc
+* PR #4710: BUG: Comparison for literal
+* PR #4718: Change get_call_table to support intermediate Vars.
+* PR #4725: Requires  llvmlite >=0.30.0
+* PR #4734: prefer to import from collections.abc
+* PR #4736: fix flake8 errors
+* PR #4776: Fix and enable idle tests from test_array_manipulation
+* PR #4777: Fix transpose output array layout
+* PR #4782: Fix issue with SVML (and knock-on function resolution effects).
+* PR #4785: Treat 0d arrays like scalars.
+* PR #4787: fix missing incref on flags
+* PR #4789: fix typos in numba/targets/base.py
+* PR #4791: fix typos
+* PR #4811: fix spelling in now-failing tests
+* PR #4852: windowing test should check equality only up to double precision
+  errors
+* PR #4881: fix refining list by using extend on an iterator
+* PR #4882: Fix return type in arange and zero step size handling.
+* PR #4885: suppress spurious RuntimeWarning about ufunc sizes
+* PR #4891: skip the xfail test for now.  Py3.8 CFG refactor seems to have
+  changed the test case
+* PR #4892: regex needs to accept singular form of "argument"
+* PR #4901: fix typed list equals
+* PR #4909: Fix some spelling typos
+* PR #4911: np.interp bugfix for float32 handling
+* PR #4920: fix creating list with JIT disabled
+* PR #4921: fix creating dict with JIT disabled
+* PR #4935: Better handling of prange with multiple reductions on the same
+  variable.
+* PR #4946: Improve the error message for `raise <string>`.
+* PR #4955: Move overload of literal_unroll to avoid circular dependency that
+  breaks Python 2.7
+* PR #4962: Fix test error on windows
+* PR #4973: Fixes a bug in the relabelling logic in literal_unroll.
+* PR #4978: Fix overload_method problem with stararg
+* PR #4981: Add ind_to_const to enable fewer equivalence classes.
+* PR #4991: Continuation of #4588 (Let dead code removal handle removing more of
+  the unneeded code after prange conversion to parfor)
+* PR #4994: Remove xfail for test which has since had underlying issue fixed.
+* PR #5018: Fix #5011.
+* PR #5019: skip pycc test on Python 3.8 + macOS because of distutils issue
+
+CUDA Enhancements/Fixes:
+
+* PR #4629: Make numba.dummyarray.Array iterable
+* PR #4675: Bump cuda array interface to version 2
+* PR #4741: Update choosing the "CUDA_PATH" for windows
+* PR #4838: Permit ravel('A') for contig device arrays in CUDA target
+* PR #4931: Add physical limits for CC 7.0 / 7.5 to autotune
+* PR #4934: Fix fails in TestCudaWarpOperations
+* PR #4938: Improve errors / warnings for cuda vectorize decorator
+
+Documentation Updates:
+
+* PR #4418: Directed graph task roadmap
+* PR #4578: Clarify numba ufunc supported features
+* PR #4655: fix sphinx build warning
+* PR #4667: Fix typo on urem implementation
+* PR #4669: Add link to ParallelAccelerator paper.
+* PR #4703: Fix numba.jit parameter name signature_or_function
+* PR #4862: Addition of PyCon India 2019 talk on Numba
+* PR #4947: Document jitclass with numba.typed use.
+* PR #4958: Add docs for `try..except`
+* PR #4993: Update deprecations for 0.47
+
+CI Updates:
+
+* PR #4737: remove maxParallel from Azure Pipelines
+* PR #4767: pin to 2.7.16 for py27 on osx
+* PR #4781: WIP/runtest cf pytest
+
+Authors:
+
+* Aaron Meurer
+* Ankit Mahato
+* Brian Wignall
+* Denis Smirnov
+* Ehsan Totoni (core dev)
+* Elena Totmenina
+* Eric Larson
+* Ethan Pronovost
+* Giovanni Cavallin
+* Graham Markall
+* Guilherme Leobas
+* Isaac Virshup
+* James Bourbeau
+* Leo Fang
+* Marc Garcia
+* Marcelo Duarte Trevisani
+* Matt Cooper
+* Matti Picus
+* Rob Ennis
+* Rujal Desai
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* VDimir
+* Valentin Haenel (core dev)
+* Vyacheslav Smirnov
+
+
+Version 0.46.0
+--------------
+
+This release significantly reworked one of the main parts of Numba, the compiler
+pipeline, to make it more extensible and easier to use. The purpose of this was
+to continue enhancing Numba's ability for use as a compiler toolkit. In a
+similar vein, Numba now has an extension registration mechanism to allow other
+Numba-using projects to automatically have their Numba JIT compilable functions
+discovered. There were also a number of other related compiler toolkit
+enhancement added along with some more NumPy features and a lot of bug fixes.
+
+This release has updated the CUDA Array Interface specification to version 2,
+which clarifies the `strides` attribute for C-contiguous arrays and specifies
+the treatment for zero-size arrays. The implementation in Numba has been
+changed and may affect downstream packages relying on the old behavior
+(see issue #4661).
+
+Enhancements from user contributed PRs (with thanks!):
+
+* Aaron Meurer fixed some Python issues in the code base in #4345 and #4341.
+* Ashwin Srinath fixed a CUDA performance bug via #4576.
+* Ethan Pronovost added support for triangular indices functions in #4601 (the
+  NumPy functions ``tril_indices``, ``tril_indices_from``, ``triu_indices``, and
+  ``triu_indices_from``).
+* Gerald Dalley fixed a tear down race occurring in Python 2.
+* Gregory R. Lee fixed the use of deprecated ``inspect.getargspec``.
+* Guilherme Leobas contributed five PRs, adding support for ``np.append`` and
+  ``np.count_nonzero`` in #4518 and #4386. The typed List was fixed to accept
+  unsigned integers in #4510. #4463 made a fix to NamedTuple internals and #4397
+  updated the docs for ``np.sum``.
+* James Bourbeau added a new feature to permit the automatic application of the
+  `jit` decorator to a whole module in #4331. Also some small fixes to the docs
+  and the code base were made in #4447 and #4433, and a fix to inplace array
+  operation in #4228.
+* Jim Crist fixed a bug in the rendering of patched errors in #4464.
+* Leo Fang updated the CUDA Array Interface contract in #4609.
+* Pearu Peterson added support for Unicode based NumPy arrays in #4425.
+* Peter Andreas Entschev fixed a CUDA concurrency bug in #4581.
+* Lucio Fernandez-Arjona extended Numba's ``np.sum`` support to now accept the
+  ``dtype`` kwarg in #4472.
+* Pedro A. Morales Maries added support for ``np.cross`` in #4128 and also added
+  the necessary extension ``numba.numpy_extensions.cross2d`` in #4595.
+* David Hoese, Eric Firing, Joshua Adelman, and Juan Nunez-Iglesias all made
+  documentation fixes in #4565, #4482, #4455, #4375 respectively.
+* Vyacheslav Smirnov and Rujal Desai enabled support for ``count()`` on unicode
+  strings in #4606.
+
+General Enhancements:
+
+* PR #4113: Add rewrite for semantic constants.
+* PR #4128: Add np.cross support
+* PR #4162: Make IR comparable and legalize it.
+* PR #4208: R&D inlining, jitted and overloaded.
+* PR #4331: Automatic JIT of called functions
+* PR #4353: Inspection tool to check what numba supports
+* PR #4386: Implement np.count_nonzero
+* PR #4425: Unicode array support
+* PR #4427: Entrypoints for numba extensions
+* PR #4467: Literal dispatch
+* PR #4472: Allow dtype input argument in np.sum
+* PR #4513: New compiler.
+* PR #4518: add support for np.append
+* PR #4554: Refactor NRT C-API
+* PR #4556: 0.46 scheduled deprecations
+* PR #4567: Add env var to disable performance warnings.
+* PR #4568: add np.array_equal support
+* PR #4595: Implement numba.cross2d
+* PR #4601: Add triangular indices functions
+* PR #4606: Enable support for count() method for unicode string
+
+Fixes:
+
+* PR #4228: Fix inplace operator error for arrays
+* PR #4282: Detect and raise unsupported on generator expressions
+* PR #4305: Don't allow the allocation of mutable objects written into a
+  container to be hoisted.
+* PR #4311: Avoid deprecated use of inspect.getargspec
+* PR #4328:  Replace GC macro with function call
+* PR #4330: Loosen up typed container casting checks
+* PR #4341: Fix some coding lines at the top of some files (utf8 -> utf-8)
+* PR #4345: Replace "import \*" with explicit imports in numba/types
+* PR #4346: Fix incorrect alg in isupper for ascii strings.
+* PR #4349: test using jitclass in typed-list
+* PR #4361: Add allocation hoisting info to LICM section at diagnostic L4
+* PR #4366: Offset search box to avoid wrapping on some pages with Safari.
+  Fixes #4365.
+* PR #4372: Replace all "except BaseException" with "except Exception".
+* PR #4407: Restore the "free" conda channel for NumPy 1.10 support.
+* PR #4408: Add lowering for constant bytes.
+* PR #4409: Add exception chaining for better error context
+* PR #4411: Name of type should not contain user facing description for debug.
+* PR #4412: Fix #4387. Limit the number of return types for recursive functions
+* PR #4426: Fixed two module teardown races in py2.
+* PR #4431: Fix and test numpy.random.random_sample(n) for np117
+* PR #4463: NamedTuple - Raises an error on non-iterable elements
+* PR #4464: Add a newline in patched errors
+* PR #4474: Fix liveness for remove dead of parfors (and other IR extensions)
+* PR #4510: Make List.__getitem__ accept unsigned parameters
+* PR #4512: Raise specific error at typing time for iteration on >1D array.
+* PR #4532: Fix static_getitem with Literal type as index
+* PR #4547: Update to inliner cost model information.
+* PR #4557: Use specific random number seed when generating arbitrary test data
+* PR #4559: Adjust test timeouts
+* PR #4564: Skip unicode array tests on ppc64le that trigger an LLVM bug
+* PR #4621: Fix packaging issue due to missing numba/cext
+* PR #4623: Fix issue 4520 due to storage model mismatch
+* PR #4644: Updates for llvmlite 0.30.0
+
+CUDA Enhancements/Fixes:
+
+* PR #4410: Fix #4111. cudasim mishandling recarray
+* PR #4576: Replace use of `np.prod` with `functools.reduce` for computing size
+  from shape
+* PR #4581: Prevent taking the GIL in ForAll
+* PR #4592: Fix #4589.  Just pass NULL for b2d_func for constant dynamic
+  sharedmem
+* PR #4609: Update CUDA Array Interface & Enforce Numba compliance
+* PR #4619: Implement math.{degrees, radians} for the CUDA target.
+* PR #4675: Bump cuda array interface to version 2
+
+Documentation Updates:
+
+* PR #4317: Add docs for ARMv8/AArch64
+* PR #4318: Add supported platforms to the docs.  Closes #4316
+* PR #4375: Add docstrings to inspect methods
+* PR #4388: Update Python 2.7 EOL statement
+* PR #4397: Add note about np.sum
+* PR #4447: Minor parallel performance tips edits
+* PR #4455: Clarify docs for typed dict with regard to arrays
+* PR #4482: Fix example in guvectorize docstring.
+* PR #4541: fix two typos in architecture.rst
+* PR #4548: Document numba.extending.intrinsic and inlining.
+* PR #4565: Fix typo in jit-compilation docs
+* PR #4607: add dependency list to docs
+* PR #4614: Add documentation for implementing new compiler passes.
+
+CI Updates:
+
+* PR #4415: Make 32bit incremental builds on linux not use free channel
+* PR #4433: Removes stale azure comment
+* PR #4493: Fix Overload Inliner wrt CUDA Intrinsics
+* PR #4593: Enable Azure CI batching
+
+Contributors:
+
+* Aaron Meurer
+* Ashwin Srinath
+* David Hoese
+* Ehsan Totoni (core dev)
+* Eric Firing
+* Ethan Pronovost
+* Gerald Dalley
+* Gregory R. Lee
+* Guilherme Leobas
+* James Bourbeau
+* Jim Crist
+* Joshua Adelman
+* Juan Nunez-Iglesias
+* Leo Fang
+* Lucio Fernandez-Arjona
+* Pearu Peterson
+* Pedro A. Morales Marie
+* Peter Andreas Entschev
+* Rujal Desai
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* Valentin Haenel (core dev)
+* Vyacheslav Smirnov
+
+
+Version 0.45.1
+--------------
+
+This patch release addresses some regressions reported in the 0.45.0 release and
+adds support for NumPy 1.17:
+
+* PR #4325: accept scalar/0d-arrays
+* PR #4338: Fix #4299. Parfors reduction vars not deleted.
+* PR #4350: Use process level locks for fork() only.
+* PR #4354: Try to fix #4352.
+* PR #4357: Fix np1.17 isnan, isinf, isfinite ufuncs
+* PR #4363: Fix np.interp for np1.17 nan handling
+* PR #4371: Fix nump1.17 random function non-aliasing
+
+Contributors:
+
+* Siu Kwan Lam (core dev)
+* Stuart Archibald (core dev)
+* Valentin Haenel (core dev)
+
+
+Version 0.45.0
+--------------
+
+In this release, Numba gained an experimental :ref:`numba.typed.List
+<feature-typed-list>` container as a future replacement of the :ref:`reflected
+list <feature-reflected-list>`. In addition, functions decorated with
+``parallel=True`` can now be cached to reduce compilation overhead associated
+with the auto-parallelization.
+
+
+Enhancements from user contributed PRs (with thanks!):
+
+* James Bourbeau added the Numba version to reportable error messages in #4227,
+  added the ``signature`` parameter to ``inspect_types`` in #4200, improved the
+  docstring of ``normalize_signature`` in #4205, and fixed #3658 by adding
+  reference counting to ``register_dispatcher`` in #4254
+
+* Guilherme Leobas implemented the dominator tree and dominance frontier
+  algorithms in #4216 and #4149, respectively.
+
+* Nick White fixed the issue with ``round`` in the CUDA target in #4137.
+
+* Joshua Adelman added support for determining if a value is in a `range`
+  (i.e.  ``x in range(...)``) in #4129, and added windowing functions
+  (``np.bartlett``, ``np.hamming``, ``np.blackman``, ``np.hanning``,
+  ``np.kaiser``) from NumPy in #4076.
+
+* Lucio Fernandez-Arjona added support for ``np.select`` in #4077
+
+* Rob Ennis added support for ``np.flatnonzero`` in #4157
+
+* Keith Kraus extended the ``__cuda_array_interface__`` with an optional mask
+  attribute in #4199.
+
+* Gregory R. Lee replaced deprecated use of ``inspect.getargspec`` in #4311.
+
+
+General Enhancements:
+
+* PR #4328: Replace GC macro with function call
+* PR #4311: Avoid deprecated use of inspect.getargspec
+* PR #4296: Slacken window function testing tol on ppc64le
+* PR #4254: Add reference counting to register_dispatcher
+* PR #4239: Support len() of multi-dim arrays in array analysis
+* PR #4234: Raise informative error for np.kron array order
+* PR #4232: Add unicodetype db, low level str functions and examples.
+* PR #4229: Make hashing cacheable
+* PR #4227: Include numba version in reportable error message
+* PR #4216: Add dominator tree
+* PR #4200: Add signature parameter to inspect_types
+* PR #4196: Catch missing imports of internal functions.
+* PR #4180: Update use of unlowerable global message.
+* PR #4166: Add tests for PR #4149
+* PR #4157: Support for np.flatnonzero
+* PR #4149: Implement dominance frontier for SSA for the Numba IR
+* PR #4148: Call branch pruning in inline_closure_call()
+* PR #4132: Reduce usage of inttoptr
+* PR #4129: Support contains for range
+* PR #4112: better error messages for np.transpose and tuples
+* PR #4110: Add range attrs, start, stop, step
+* PR #4077: Add np select
+* PR #4076: Add numpy windowing functions support (np.bartlett, np.hamming,
+  np.blackman, np.hanning, np.kaiser)
+* PR #4095: Support ir.Global/FreeVar in find_const()
+* PR #3691: Make TypingError abort compiling earlier
+* PR #3646: Log internal errors encountered in typeinfer
+
+Fixes:
+
+* PR #4303: Work around scipy bug 10206
+* PR #4302: Fix flake8 issue on master
+* PR #4301: Fix integer literal bug in np.select impl
+* PR #4291: Fix pickling of jitclass type
+* PR #4262: Resolves #4251 - Fix bug in reshape analysis.
+* PR #4233: Fixes issue revealed by #4215
+* PR #4224: Fix #4223. Looplifting error due to StaticSetItem in objectmode
+* PR #4222: Fix bad python path.
+* PR #4178: Fix unary operator overload, check with unicode impl
+* PR #4173: Fix return type in np.bincount with weights
+* PR #4153: Fix slice shape assignment in array analysis
+* PR #4152: fix status check in dict lookup
+* PR #4145: Use callable instead of checking __module__
+* PR #4118: Fix inline assembly support on CPU.
+* PR #4088: Resolves #4075 - parfors array_analysis bug.
+* PR #4085: Resolves #3314 - parfors array_analysis bug with reshape.
+
+CUDA Enhancements/Fixes:
+
+* PR #4199: Extend `__cuda_array_interface__` with optional mask attribute,
+  bump version to 1
+* PR #4137: CUDA - Fix round Builtin
+* PR #4114: Support 3rd party activated CUDA context
+
+Documentation Updates:
+
+* PR #4317: Add docs for ARMv8/AArch64
+* PR #4318: Add supported platforms to the docs. Closes #4316
+* PR #4295: Alter deprecation schedules
+* PR #4253: fix typo in pysupported docs
+* PR #4252: fix typo on repomap
+* PR #4241: remove unused import
+* PR #4240: fix typo in jitclass docs
+* PR #4205: Update return value order in normalize_signature docstring
+* PR #4237: Update doc links to point to latest not dev docs.
+* PR #4197: hyperlink repomap
+* PR #4170: Clarify docs on accumulating into arrays in prange
+* PR #4147: fix docstring for DictType iterables
+* PR #3951: A guide to overloading
+
+CI Updates:
+
+* PR #4300: AArch64 has no faulthandler package
+* PR #4273: pin to MKL BLAS for testing to get consistent results
+* PR #4209: Revert previous network tol patch and try with conda config
+* PR #4138: Remove tbb before Azure test only on Python 3, since it was already
+  removed for Python 2
+
+Contributors:
+
+* Ehsan Totoni (core dev)
+* Gregory R. Lee
+* Guilherme Leobas
+* James Bourbeau
+* Joshua L. Adelman
+* Keith Kraus
+* Lucio Fernandez-Arjona
+* Nick White
+* Rob Ennis
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* Valentin Haenel (core dev)
+
+
+Version 0.44.1
+--------------
+
+This patch release addresses some regressions reported in the 0.44.0 release:
+
+- PR #4165: Fix #4164 issue with NUMBAPRO_NVVM.
+- PR #4172: Abandon branch pruning if an arg name is redefined. (Fixes #4163)
+- PR #4183: Fix #4156. Problem with defining in-loop variables.
+
+
+Version 0.44.0
+--------------
+
+IMPORTANT: In this release a few significant deprecations (and some less
+significant ones) are being made, users are encouraged to read the related
+documentation.
+
+General enhancements in this release include:
+
+- Numba is backed by LLVM 8 on all platforms apart from ppc64le, which, due to
+  bugs, remains on the LLVM 7.x series.
+- Numba's dictionary support now includes type inference for keys and values.
+- The .view() method now works for NumPy scalar types.
+- Newly supported NumPy functions added: np.delete, np.nanquantile, np.quantile,
+  np.repeat, np.shape.
+
+In addition considerable effort has been made to fix some long standing bugs and
+a large number of other bugs, the "Fixes" section is very large this time!
+
+Enhancements from user contributed PRs (with thanks!):
+
+- Max Bolingbroke added support for the selective use of ``fastmath`` flags in
+  #3847.
+- Rob Ennis made min() and max() work on iterables in #3820 and added
+  np.quantile and np.nanquantile in #3899.
+- Sergey Shalnov added numerous unicode string related features, zfill in #3978,
+  ljust in #4001, rjust and center in #4044 and strip, lstrip and rstrip in
+  #4048.
+- Guilherme Leobas added support for np.delete in #3890
+- Christoph Deil exposed the Numba CLI via ``python -m numba`` in #4066 and made
+  numerous documentation fixes.
+- Leo Schwarz wrote the bulk of the code for jitclass default constructor
+  arguments in #3852.
+- Nick White enhanced the CUDA backend to use min/max PTX instructions where
+  possible in #4054.
+- Lucio Fernandez-Arjona implemented the unicode string ``__mul__`` function in
+  #3952.
+- Dimitri Vorona wrote the bulk of the code to implement getitem and setitem for
+  jitclass in #3861.
+
+General Enhancements:
+
+* PR #3820: Min max on iterables
+* PR #3842: Unicode type iteration
+* PR #3847: Allow fine-grained control of fastmath flags to partially address #2923
+* PR #3852: Continuation of PR #2894
+* PR #3861: Continuation of PR #3730
+* PR #3890: Add support for np.delete
+* PR #3899: Support for np.quantile and np.nanquantile
+* PR #3900: Fix 3457 :: Implements np.repeat
+* PR #3928: Add .view() method for NumPy scalars
+* PR #3939: Update icc_rt clone recipe.
+* PR #3952: __mul__ for strings, initial implementation and tests
+* PR #3956: Type-inferred dictionary
+* PR #3959: Create a view for string slicing to avoid extra allocations
+* PR #3978: zfill operation implementation
+* PR #4001: ljust operation implementation
+* PR #4010: Support `dict()` and `{}`
+* PR #4022: Support for llvm 8
+* PR #4034: Make type.Optional str more representative
+* PR #4041: Deprecation warnings
+* PR #4044: rjust and center operations implementation
+* PR #4048: strip, lstrip and rstrip operations implementation
+* PR #4066: Expose numba CLI via python -m numba
+* PR #4081: Impl `np.shape` and support function for `asarray`.
+* PR #4091: Deprecate the use of iternext_impl without RefType
+
+CUDA Enhancements/Fixes:
+
+* PR #3933: Adds `.nbytes` property to CUDA device array objects.
+* PR #4011: Add .inspect_ptx() to cuda device function
+* PR #4054: CUDA: Use min/max PTX Instructions
+* PR #4096: Update env-vars for CUDA libraries lookup
+
+Documentation Updates:
+
+* PR #3867: Code repository map
+* PR #3918: adding Joris' Fosdem 2019 presentation
+* PR #3926: order talks on applications of Numba by date
+* PR #3943: fix two small typos in vectorize docs
+* PR #3944: Fixup jitclass docs
+* PR #3990: mention preprint repo in FAQ. Fixes #3981
+* PR #4012: Correct runtests command in contributing.rst
+* PR #4043: fix typo
+* PR #4047: Ambiguous Documentation fix for guvectorize.
+* PR #4060: Remove remaining mentions of autojit in docs
+* PR #4063: Fix annotate example in docstring
+* PR #4065: Add FAQ entry explaining Numba project name
+* PR #4079: Add Documentation for atomicity of typed.Dict
+* PR #4105: Remove info about CUDA ENVVAR potential replacement
+
+Fixes:
+
+* PR #3719: Resolves issue #3528.  Adds support for slices when not using parallel=True.
+* PR #3727: Remove dels for known dead vars.
+* PR #3845: Fix mutable flag transmission in .astype
+* PR #3853: Fix some minor issues in the C source.
+* PR #3862: Correct boolean reinterpretation of data
+* PR #3863: Comments out the appveyor badge
+* PR #3869: fixes flake8 after merge
+* PR #3871: Add assert to ir.py to help enforce correct structuring
+* PR #3881: fix preparfor dtype transform for datetime64
+* PR #3884: Prevent mutation of objmode fallback IR.
+* PR #3885: Updates for llvmlite 0.29
+* PR #3886: Use `safe_load` from pyyaml.
+* PR #3887: Add tolerance to network errors by permitting conda to retry
+* PR #3893: Fix casting in namedtuple ctor.
+* PR #3894: Fix array inliner for multiple array definition.
+* PR #3905: Cherrypick #3903 to main
+* PR #3920: Raise better error if unsupported jump opcode found.
+* PR #3927: Apply flake8 to the numpy related files
+* PR #3935: Silence DeprecationWarning
+* PR #3938: Better error message for unknown opcode
+* PR #3941: Fix typing of ufuncs in parfor conversion
+* PR #3946: Return variable renaming dict from inline_closurecall
+* PR #3962: Fix bug in alignment computation of `Record.make_c_struct`
+* PR #3967: Fix error with pickling unicode
+* PR #3964: Unicode split algo versioning
+* PR #3975: Add handler for unknown locale to numba -s
+* PR #3991: Permit Optionals in ufunc machinery
+* PR #3995: Remove assert in type inference causing poor error message.
+* PR #3996: add is_ascii flag to UnicodeType
+* PR #4009: Prevent zero division error in np.linalg.cond
+* PR #4014: Resolves #4007.
+* PR #4021: Add a more specific error message for invalid write to a global.
+* PR #4023: Fix handling of titles in record dtype
+* PR #4024: Do a check if a call is const before saying that an object is multiply defined.
+* PR #4027: Fix issue #4020.  Turn off no_cpython_wrapper flag when compiling for…
+* PR #4033: [WIP] Fixing wrong dtype of array inside reflected list #4028
+* PR #4061: Change IPython cache dir name to numba_cache
+* PR #4067: Delete examples/notebooks/LinearRegr.py
+* PR #4070: Catch writes to global typed.Dict and raise.
+* PR #4078: Check tuple length
+* PR #4084: Fix missing incref on optional return None
+* PR #4089: Make the warnings fixer flush work for warning comparing on type.
+* PR #4094: Fix function definition finding logic for commented def
+* PR #4100: Fix alignment check on 32-bit.
+* PR #4104: Use PEP 508 compliant env markers for install deps
+
+Contributors:
+
+* Benjamin Zaitlen
+* Christoph Deil
+* David Hirschfeld
+* Dimitri Vorona
+* Ehsan Totoni (core dev)
+* Guilherme Leobas
+* Leo Schwarz
+* Lucio Fernandez-Arjona
+* Max Bolingbroke
+* NanduTej
+* Nick White
+* Ravi Teja Gutta
+* Rob Ennis
+* Sergey Shalnov
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+* Valentin Haenel (core dev)
+
+
+Version 0.43.1
+--------------
+
+This is a bugfix release that provides minor changes to fix: a bug in branch
+pruning, bugs in `np.interp` functionality, and also fully accommodate the
+NumPy 1.16 release series.
+
+* PR #3826: NumPy 1.16 support
+* PR #3850: Refactor np.interp
+* PR #3883: Rewrite pruned conditionals as their evaluated constants.
+
+Contributors:
+
+* Rob Ennis
+* Siu Kwan Lam (core dev)
+* Stuart Archibald (core dev)
+
+
+Version 0.43.0
+--------------
+
+In this release, the major new features are:
+
+- Initial support for statically typed dictionaries
+- Improvements to `hash()` to match Python 3 behavior
+- Support for the heapq module
+- Ability to pass C structs to Numba
+- More NumPy functions: asarray, trapz, roll, ptp, extract
+
+
+NOTE:
+
+The vast majority of NumPy 1.16 behaviour is supported, however
+``datetime`` and ``timedelta`` use involving ``NaT`` matches the behaviour
+present in earlier release. The ufunc suite has not been extending to
+accommodate the two new time computation related additions present in NumPy
+1.16. In addition the functions ``ediff1d`` and ``interp`` have known minor
+issues in replicating outputs exactly when ``NaN``'s occur in certain input
+patterns.
+
+General Enhancements:
+
+* PR #3563: Support for np.roll
+* PR #3572: Support for np.ptp
+* PR #3592: Add dead branch prune before type inference.
+* PR #3598: Implement np.asarray()
+* PR #3604: Support for np.interp
+* PR #3607: Some simplication to lowering
+* PR #3612: Exact match flag in dispatcher
+* PR #3627: Support for np.trapz
+* PR #3630: np.where with broadcasting
+* PR #3633: Support for np.extract
+* PR #3657: np.max, np.min, np.nanmax, np.nanmin - support for complex dtypes
+* PR #3661: Access C Struct as Numpy Structured Array
+* PR #3678: Support for str.split and str.join
+* PR #3684: Support C array in C struct
+* PR #3696: Add intrinsic to help debug refcount
+* PR #3703: Implementations of type hashing.
+* PR #3715: Port CPython3.7 dictionary for numba internal use
+* PR #3716: Support inplace concat of strings
+* PR #3718: Add location to ConstantInferenceError exceptions.
+* PR #3720: improve error msg about invalid signature
+* PR #3731: Support for heapq
+* PR #3754: Updates for llvmlite 0.28
+* PR #3760: Overloadable operator.setitem
+* PR #3775: Support overloading operator.delitem
+* PR #3777: Implement compiler support for dictionary
+* PR #3791: Implement interpreter-side interface for numba dict
+* PR #3799: Support refcount'ed types in numba dict
+
+CUDA Enhancements/Fixes:
+
+* PR #3713: Fix the NvvmSupportError message when CC too low
+* PR #3722: Fix #3705: slicing error with negative strides
+* PR #3755: Make cuda.to_device accept readonly host array
+* PR #3773: Adapt library search to accommodate multiple locations
+
+Documentation Updates:
+
+* PR #3651: fix link to berryconda in docs
+* PR #3668: Add Azure Pipelines build badge
+* PR #3749: DOC: Clarify when prange is different from range
+* PR #3771: fix a few typos
+* PR #3785: Clarify use of range as function only.
+* PR #3829: Add docs for typed-dict
+
+Fixes:
+
+* PR #3614: Resolve #3586
+* PR #3618: Skip gdb tests on ARM.
+* PR #3643: Remove support_literals usage
+* PR #3645: Enforce and fix that AbstractTemplate.generic must be returning a Signature
+* PR #3648: Fail on @overload signature mismatch.
+* PR #3660: Added Ignore message to test numba.tests.test_lists.TestLists.test_mul_error
+* PR #3662: Replace six with numba.six
+* PR #3663: Removes coverage computation from travisci builds
+* PR #3672: Avoid leaking memory when iterating over uniform tuple
+* PR #3676: Fixes constant string lowering inside tuples
+* PR #3677: Ensure all referenced compiled functions are linked properly
+* PR #3692: Fix test failure due to overly strict test on floating point values.
+* PR #3693: Intercept failed import to help users.
+* PR #3694: Fix memory leak in enumerate iterator
+* PR #3695: Convert return of None from intrinsic implementation to dummy value
+* PR #3697: Fix for issue #3687
+* PR #3701: Fix array.T analysis (fixes #3700)
+* PR #3704: Fixes for overload_method
+* PR #3706: Don't push call vars recursively into nested parfors. Resolves #3686.
+* PR #3710: Set as non-hoistable if a mutable variable is passed to a function in a loop. Resolves #3699.
+* PR #3712: parallel=True to use better builtin mechanism to resolve call types. Resolves issue #3671
+* PR #3725: Fix invalid removal of dead empty list
+* PR #3740: add uintp as a valid type to the tuple operator.getitem
+* PR #3758: Fix target definition update in inlining
+* PR #3782: Raise typing error on yield optional.
+* PR #3792: Fix non-module object used as the module of a function.
+* PR #3800: Bugfix for np.interp
+* PR #3808: Bump macro to include VS2014 to fix py3.5 build
+* PR #3809: Add debug guard to debug only C function.
+* PR #3816: Fix array.sum(axis) 1d input return type.
+* PR #3821: Replace PySys_WriteStdout with PySys_FormatStdout to ensure no truncation.
+* PR #3830: Getitem should not return optional type
+* PR #3832: Handle single string as path in find_file()
+
+Contributors:
+
+* Ehsan Totoni
+* Gryllos Prokopis
+* Jonathan J. Helmus
+* Kayla Ngan
+* lalitparate
+* luk-f-a
+* Matyt
+* Max Bolingbroke
+* Michael Seifert
+* Rob Ennis
+* Siu Kwan Lam
+* Stan Seibert
+* Stuart Archibald
+* Todd A. Anderson
+* Tao He
+* Valentin Haenel
+
+
+Version 0.42.1
+--------------
+
+Bugfix release to fix the incorrect hash in OSX wheel packages.
+No change in source code.
+
+
+Version 0.42.0
+--------------
+
+In this release the major features are:
+
+- The capability to launch and attach the GDB debugger from within a jitted
+  function.
+- The upgrading of LLVM to version 7.0.0.
+
+We added a draft of the project roadmap to the developer manual. The roadmap is
+for informational purposes only as priorities and resources may change.
+
+Here are some enhancements from contributed PRs:
+
+- #3532. Daniel Wennberg improved the ``cuda.{pinned, mapped}`` API so that
+  the associated memory is released immediately at the exit of the context
+  manager.
+- #3531. Dimitri Vorona enabled the inlining of jitclass methods.
+- #3516. Simon Perkins added the support for passing numpy dtypes (i.e.
+  ``np.dtype("int32")``) and their type constructor (i.e. ``np.int32``) into
+  a jitted function.
+- #3509. Rob Ennis added support for ``np.corrcoef``.
+
+A regression issue (#3554, #3461) relating to making an empty slice in parallel
+mode is resolved by #3558.
+
+General Enhancements:
+
+* PR #3392: Launch and attach gdb directly from Numba.
+* PR #3437: Changes to accommodate LLVM 7.0.x
+* PR #3509: Support for np.corrcoef
+* PR #3516: Typeof dtype values
+* PR #3520: Fix @stencil ignoring cval if out kwarg supplied.
+* PR #3531: Fix jitclass method inlining and avoid unnecessary increfs
+* PR #3538: Avoid future C-level assertion error due to invalid visibility
+* PR #3543: Avoid implementation error being hidden by the try-except
+* PR #3544: Add `long_running` test flag and feature to exclude tests.
+* PR #3549: ParallelAccelerator caching improvements
+* PR #3558: Fixes array analysis for inplace binary operators.
+* PR #3566: Skip alignment tests on armv7l.
+* PR #3567: Fix unifying literal types in namedtuple
+* PR #3576: Add special copy routine for NumPy out arrays
+* PR #3577: Fix example and docs typos for `objmode` context manager.
+  reorder statements.
+* PR #3580: Use alias information when determining whether it is safe to
+* PR #3583: Use `ir.unknown_loc` for unknown `Loc`, as #3390 with tests
+* PR #3587: Fix llvm.memset usage changes in llvm7
+* PR #3596: Fix Array Analysis for Global Namedtuples
+* PR #3597: Warn users if threading backend init unsafe.
+* PR #3605: Add guard for writing to read only arrays from ufunc calls
+* PR #3606: Improve the accuracy of error message wording for undefined type.
+* PR #3611: gdb test guard needs to ack ptrace permissions
+* PR #3616: Skip gdb tests on ARM.
+
+CUDA Enhancements:
+
+* PR #3532: Unregister temporarily pinned host arrays at once
+* PR #3552: Handle broadcast arrays correctly in host->device transfer.
+* PR #3578: Align cuda and cuda simulator kwarg names.
+
+Documentation Updates:
+
+* PR #3545: Fix @njit description in 5 min guide
+* PR #3570: Minor documentation fixes for numba.cuda
+* PR #3581: Fixing minor typo in `reference/types.rst`
+* PR #3594: Changing `@stencil` docs to correctly reflect `func_or_mode` param
+* PR #3617: Draft roadmap as of Dec 2018
+
+Contributors:
+
+* Aaron Critchley
+* Daniel Wennberg
+* Dimitri Vorona
+* Dominik Stańczak
+* Ehsan Totoni (core dev)
+* Iskander Sharipov
+* Rob Ennis
+* Simon Muller
+* Simon Perkins
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+
+
+Version 0.41.0
+--------------
+
+This release adds the following major features:
+
+* Diagnostics showing the optimizations done by ParallelAccelerator
+* Support for profiling Numba-compiled functions in Intel VTune
+* Additional NumPy functions: partition, nancumsum, nancumprod, ediff1d, cov,
+  conj, conjugate, tri, tril, triu
+* Initial support for Python 3 Unicode strings
+
+General Enhancements:
+
+* PR #1968: armv7 support
+* PR #2983: invert mapping b/w binop operators and the operator module #2297
+* PR #3160: First attempt at parallel diagnostics
+* PR #3307: Adding NUMBA_ENABLE_PROFILING envvar, enabling jit event
+* PR #3320: Support for np.partition
+* PR #3324: Support for np.nancumsum and np.nancumprod
+* PR #3325: Add location information to exceptions.
+* PR #3337: Support for np.ediff1d
+* PR #3345: Support for np.cov
+* PR #3348: Support user pipeline class in with lifting
+* PR #3363: string support
+* PR #3373: Improve error message for empty imprecise lists.
+* PR #3375: Enable overload(operator.getitem)
+* PR #3402: Support negative indexing in tuple.
+* PR #3414: Refactor Const type
+* PR #3416: Optimized usage of alloca out of the loop
+* PR #3424: Updates for llvmlite 0.26
+* PR #3462: Add support for `np.conj/np.conjugate`.
+* PR #3480: np.tri, np.tril, np.triu - default optional args
+* PR #3481: Permit dtype argument as sole kwarg in np.eye
+
+CUDA Enhancements:
+
+* PR #3399: Add max_registers Option to cuda.jit
+
+Continuous Integration / Testing:
+
+* PR #3303: CI with Azure Pipelines
+* PR #3309: Workaround race condition with apt
+* PR #3371: Fix issues with Azure Pipelines
+* PR #3362: Fix #3360: `RuntimeWarning: 'numba.runtests' found in sys.modules`
+* PR #3374: Disable openmp in wheel building
+* PR #3404: Azure Pipelines templates
+* PR #3419: Fix cuda tests and error reporting in test discovery
+* PR #3491: Prevent faulthandler installation on armv7l
+* PR #3493: Fix CUDA test that used negative indexing behaviour that's fixed.
+* PR #3495: Start Flake8 checking of Numba source
+
+Fixes:
+
+* PR #2950: Fix dispatcher to only consider contiguous-ness.
+* PR #3124: Fix 3119, raise for 0d arrays in reductions
+* PR #3228: Reduce redundant module linking
+* PR #3329: Fix AOT on windows.
+* PR #3335: Fix memory management of __cuda_array_interface__ views.
+* PR #3340: Fix typo in error name.
+* PR #3365: Fix the default unboxing logic
+* PR #3367: Allow non-global reference to objmode() context-manager
+* PR #3381: Fix global reference in objmode for dynamically created function
+* PR #3382: CUDA_ERROR_MISALIGNED_ADDRESS Using Multiple Const Arrays
+* PR #3384: Correctly handle very old versions of colorama
+* PR #3394: Add 32bit package guard for non-32bit installs
+* PR #3397: Fix with-objmode warning
+* PR #3403 Fix label offset in call inline after parfor pass
+* PR #3429: Fixes raising of user defined exceptions for exec(<string>).
+* PR #3432: Fix error due to function naming in CI in py2.7
+* PR #3444: Fixed TBB's single thread execution and test added for #3440
+* PR #3449: Allow matching non-array objects in find_callname()
+* PR #3455: Change getiter and iternext to not be pure. Resolves #3425
+* PR #3467: Make ir.UndefinedType singleton class.
+* PR #3478: Fix np.random.shuffle sideeffect
+* PR #3487: Raise unsupported for kwargs given to `print()`
+* PR #3488: Remove dead script.
+* PR #3498: Fix stencil support for boolean as return type
+* PR #3511: Fix handling make_function literals (regression of #3414)
+* PR #3514: Add missing unicode != unicode
+* PR #3527: Fix complex math sqrt implementation for large -ve values
+* PR #3530: This adds arg an check for the pattern supplied to Parfors.
+* PR #3536: Sets list dtor linkage to `linkonce_odr` to fix visibility in AOT
+
+Documentation Updates:
+
+* PR #3316: Update 0.40 changelog with additional PRs
+* PR #3318: Tweak spacing to avoid search box wrapping onto second line
+* PR #3321: Add note about memory leaks with exceptions to docs. Fixes #3263
+* PR #3322: Add FAQ on CUDA + fork issue. Fixes #3315.
+* PR #3343: Update docs for argsort, kind kwarg partially supported.
+* PR #3357: Added mention of njit in 5minguide.rst
+* PR #3434: Fix parallel reduction example in docs.
+* PR #3452: Fix broken link and mark up problem.
+* PR #3484: Size Numba logo in docs in em units. Fixes #3313
+* PR #3502: just two typos
+* PR #3506: Document string support
+* PR #3513: Documentation for parallel diagnostics.
+* PR #3526: Fix 5 min guide with respect to @njit decl
+
+Contributors:
+
+* Alex Ford
+* Andreas Sodeur
+* Anton Malakhov
+* Daniel Stender
+* Ehsan Totoni (core dev)
+* Henry Schreiner
+* Marcel Bargull
+* Matt Cooper
+* Nick White
+* Nicolas Hug
+* rjenc29
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Todd A. Anderson (core dev)
+
+
+Version 0.40.1
+--------------
+
+This is a PyPI-only patch release to ensure that PyPI wheels can enable the
+TBB threading backend, and to disable the OpenMP backend in the wheels.
+Limitations of manylinux1 and variation in user environments can cause
+segfaults when OpenMP is enabled on wheel builds.  Note that this release has
+no functional changes for users who obtained Numba 0.40.0 via conda.
+
+Patches:
+
+* PR #3338: Accidentally left Anton off contributor list for 0.40.0
+* PR #3374: Disable OpenMP in wheel building
+* PR #3376: Update 0.40.1 changelog and docs on OpenMP backend
+
+Version 0.40.0
+--------------
+
+This release adds a number of major features:
+
+* A new GPU backend: kernels for AMD GPUs can now be compiled using the ROCm
+  driver on Linux.
+* The thread pool implementation used by Numba for automatic multithreading
+  is configurable to use TBB, OpenMP, or the old "workqueue" implementation.
+  (TBB is likely to become the preferred default in a future release.)
+* New documentation on thread and fork-safety with Numba, along with overall
+  improvements in thread-safety.
+* Experimental support for executing a block of code inside a nopython mode
+  function in object mode.
+* Parallel loops now allow arrays as reduction variables
+* CUDA improvements: FMA, faster float64 atomics on supporting hardware,
+  records in const memory, and improved datatime dtype support
+* More NumPy functions: vander, tri, triu, tril, fill_diagonal
+
+
+General Enhancements:
+
+* PR #3017: Add facility to support with-contexts
+* PR #3033: Add support for multidimensional CFFI arrays
+* PR #3122: Add inliner to object mode pipeline
+* PR #3127: Support for reductions on arrays.
+* PR #3145: Support for np.fill_diagonal
+* PR #3151: Keep a queue of references to last N deserialized functions.  Fixes #3026
+* PR #3154: Support use of list() if typeable.
+* PR #3166: Objmode with-block
+* PR #3179: Updates for llvmlite 0.25
+* PR #3181: Support function extension in alias analysis
+* PR #3189: Support literal constants in typing of object methods
+* PR #3190: Support passing closures as literal values in typing
+* PR #3199: Support inferring stencil index as constant in simple unary expressions
+* PR #3202: Threading layer backend refactor/rewrite/reinvention!
+* PR #3209: Support for np.tri, np.tril and np.triu
+* PR #3211: Handle unpacking in building tuple (BUILD_TUPLE_UNPACK opcode)
+* PR #3212: Support for np.vander
+* PR #3227: Add NumPy 1.15 support
+* PR #3272: Add MemInfo_data to runtime._nrt_python.c_helpers
+* PR #3273: Refactor. Removing thread-local-storage based context nesting.
+* PR #3278: compiler threadsafety lockdown
+* PR #3291: Add CPU count and CFS restrictions info to numba -s.
+
+CUDA Enhancements:
+
+* PR #3152: Use cuda driver api to get best blocksize for best occupancy
+* PR #3165: Add FMA intrinsic support
+* PR #3172: Use float64 add Atomics, Where Available
+* PR #3186: Support Records in CUDA Const Memory
+* PR #3191: CUDA: fix log size
+* PR #3198: Fix GPU datetime timedelta types usage
+* PR #3221: Support datetime/timedelta scalar argument to a CUDA kernel.
+* PR #3259: Add DeviceNDArray.view method to reinterpret data as a different type.
+* PR #3310: Fix IPC handling of sliced cuda array.
+
+ROCm Enhancements:
+
+* PR #3023: Support for AMDGCN/ROCm.
+* PR #3108: Add ROC info to `numba -s` output.
+* PR #3176: Move ROC vectorize init to npyufunc
+* PR #3177: Add auto_synchronize support to ROC stream
+* PR #3178: Update ROC target documentation.
+* PR #3294: Add compiler lock to ROC compilation path.
+* PR #3280: Add wavebits property to the HSA Agent.
+* PR #3281: Fix ds_permute types and add tests
+
+Continuous Integration / Testing:
+
+* PR #3091: Remove old recipes, switch to test config based on env var.
+* PR #3094: Add higher ULP tolerance for products in complex space.
+* PR #3096: Set exit on error in incremental scripts
+* PR #3109: Add skip to test needing jinja2 if no jinja2.
+* PR #3125: Skip cudasim only tests
+* PR #3126: add slack, drop flowdock
+* PR #3147: Improve error message for arg type unsupported during typing.
+* PR #3128: Fix recipe/build for jetson tx2/ARM
+* PR #3167: In build script activate env before installing.
+* PR #3180: Add skip to broken test.
+* PR #3216: Fix libcuda.so loading in some container setup
+* PR #3224: Switch to new Gitter notification webhook URL and encrypt it
+* PR #3235: Add 32bit Travis CI jobs
+* PR #3257: This adds scipy/ipython back into windows conda test phase.
+
+Fixes:
+
+* PR #3038: Fix random integer generation to match results from NumPy.
+* PR #3045: Fix #3027 - Numba reassigns sys.stdout
+* PR #3059: Handler for known LoweringErrors.
+* PR #3060: Adjust attribute error for NumPy functions.
+* PR #3067: Abort simulator threads on exception in thread block.
+* PR #3079: Implement +/-(types.boolean) Fix #2624
+* PR #3080: Compute np.var and np.std correctly for complex types.
+* PR #3088: Fix #3066 (array.dtype.type in prange)
+* PR #3089: Fix invalid ParallelAccelerator hoisting issue.
+* PR #3136: Fix #3135 (lowering error)
+* PR #3137: Fix for issue3103 (race condition detection)
+* PR #3142: Fix Issue #3139 (parfors reuse of reduction variable across prange blocks)
+* PR #3148: Remove dead array equal @infer code
+* PR #3153: Fix canonicalize_array_math typing for calls with kw args
+* PR #3156: Fixes issue with missing pygments in testing and adds guards.
+* PR #3168: Py37 bytes output fix.
+* PR #3171: Fix #3146.  Fix CFUNCTYPE void* return-type handling
+* PR #3193: Fix setitem/getitem resolvers
+* PR #3222: Fix #3214.  Mishandling of POP_BLOCK in while True loop.
+* PR #3230: Fixes liveness analysis issue in looplifting
+* PR #3233: Fix return type difference for 32bit ctypes.c_void_p
+* PR #3234: Fix types and layout for `np.where`.
+* PR #3237: Fix DeprecationWarning about imp module
+* PR #3241: Fix #3225.  Normalize 0nd array to scalar in typing of indexing code.
+* PR #3256: Fix #3251: Move imports of ABCs to collections.abc for Python >= 3.3
+* PR #3292: Fix issue3279.
+* PR #3302: Fix error due to mismatching dtype
+
+Documentation Updates:
+
+* PR #3104: Workaround for #3098 (test_optional_unpack Heisenbug)
+* PR #3132: Adds an ~5 minute guide to Numba.
+* PR #3194: Fix docs RE: np.random generator fork/thread safety
+* PR #3242: Page with Numba talks and tutorial links
+* PR #3258: Allow users to choose the type of issue they are reporting.
+* PR #3260: Fixed broken link
+* PR #3266: Fix cuda pointer ownership problem with user/externally allocated pointer
+* PR #3269: Tweak typography with CSS
+* PR #3270: Update FAQ for functions passed as arguments
+* PR #3274: Update installation instructions
+* PR #3275: Note pyobject and voidptr are types in docs
+* PR #3288: Do not need to call parallel optimizations "experimental" anymore
+* PR #3318: Tweak spacing to avoid search box wrapping onto second line
+
+Contributors:
+
+* Anton Malakhov
+* Alex Ford
+* Anthony Bisulco
+* Ehsan Totoni (core dev)
+* Leonard Lausen
+* Matthew Petroff
+* Nick White
+* Ray Donnelly
+* rjenc29
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Stuart Reynolds
+* Todd A. Anderson (core dev)
+
+
+Version 0.39.0
+--------------
+
+Here are the highlights for the Numba 0.39.0 release.
+
+* This is the first version that supports Python 3.7.
+* With help from Intel, we have fixed the issues with SVML support (related
+  issues #2938, #2998, #3006).
+* List has gained support for containing reference-counted types like NumPy
+  arrays and `list`.  Note, list still cannot hold heterogeneous types.
+* We have made a significant change to the internal calling-convention,
+  which should be transparent to most users, to allow for a future feature that
+  will permitting jumping back into python-mode from a nopython-mode function.
+  This also fixes a limitation to `print` that disabled its use from nopython
+  functions that were deep in the call-stack.
+* For CUDA GPU support, we added a `__cuda_array_interface__` following the
+  NumPy array interface specification to allow Numba to consume externally
+  defined device arrays.  We have opened a corresponding pull request to CuPy to
+  test out the concept and be able to use a CuPy GPU array.
+* The Numba dispatcher `inspect_types()` method now supports the kwarg `pretty`
+  which if set to `True` will produce ANSI/HTML output, showing the annotated
+  types, when invoked from ipython/jupyter-notebook respectively.
+* The NumPy functions `ndarray.dot`, `np.percentile` and `np.nanpercentile`, and
+  `np.unique` are now supported.
+* Numba now supports the use of a per-project configuration file to permanently
+  set behaviours typically set via `NUMBA_*` family environment variables.
+* Support for the `ppc64le` architecture has been added.
+
+Enhancements:
+
+* PR #2793: Simplify and remove javascript from html_annotate templates.
+* PR #2840: Support list of refcounted types
+* PR #2902: Support for np.unique
+* PR #2926: Enable fence for all architecture and add developer notes
+* PR #2928: Making error about untyped list more informative.
+* PR #2930: Add configuration file and color schemes.
+* PR #2932: Fix encoding to 'UTF-8' in `check_output` decode.
+* PR #2938: Python 3.7 compat: _Py_Finalizing becomes _Py_IsFinalizing()
+* PR #2939: Comprehensive SVML unit test
+* PR #2946: Add support for `ndarray.dot` method and tests.
+* PR #2953: percentile and nanpercentile
+* PR #2957: Add new 3.7 opcode support.
+* PR #2963: Improve alias analysis to be more comprehensive
+* PR #2984: Support for namedtuples in array analysis
+* PR #2986: Fix environment propagation
+* PR #2990: Improve function call matching for intrinsics
+* PR #3002: Second pass at error rewrites (interpreter errors).
+* PR #3004: Add numpy.empty to the list of pure functions.
+* PR #3008: Augment SVML detection with llvmlite SVML patch detection.
+* PR #3012: Make use of the common spelling of heterogeneous/homogeneous.
+* PR #3032: Fix pycc ctypes test due to mismatch in calling-convention
+* PR #3039: Add SVML detection to Numba environment diagnostic tool.
+* PR #3041: This adds @needs_blas to tests that use BLAS
+* PR #3056: Require llvmlite>=0.24.0
+
+CUDA Enhancements:
+
+* PR #2860: __cuda_array_interface__
+* PR #2910: More CUDA intrinsics
+* PR #2929: Add Flag To Prevent Unneccessary D->H Copies
+* PR #3037: Add CUDA IPC support on non-peer-accessible devices
+
+CI Enhancements:
+
+* PR #3021: Update appveyor config.
+* PR #3040: Add fault handler to all builds
+* PR #3042: Add catchsegv
+* PR #3077: Adds optional number of processes for `-m` in testing
+
+Fixes:
+
+* PR #2897: Fix line position of delete statement in numba ir
+* PR #2905: Fix for #2862
+* PR #3009: Fix optional type returning in recursive call
+* PR #3019: workaround and unittest for issue #3016
+* PR #3035: [TESTING] Attempt delayed removal of Env
+* PR #3048: [WIP] Fix cuda tests failure on buildfarm
+* PR #3054: Make test work on 32-bit
+* PR #3062: Fix cuda.In freeing devary before the kernel launch
+* PR #3073: Workaround #3072
+* PR #3076: Avoid ignored exception due to missing globals at interpreter teardown
+
+Documentation Updates:
+
+* PR #2966: Fix syntax in env var docs.
+* PR #2967: Fix typo in CUDA kernel layout example.
+* PR #2970: Fix docstring copy paste error.
+
+Contributors:
+
+The following people contributed to this release.
+
+* Anton Malakhov
+* Ehsan Totoni  (core dev)
+* Julia Tatz
+* Matthias Bussonnier
+* Nick White
+* Ray Donnelly
+* Siu Kwan Lam  (core dev)
+* Stan Seibert  (core dev)
+* Stuart Archibald  (core dev)
+* Todd A. Anderson  (core dev)
+* Rik-de-Kort
+* rjenc29
+
+
+Version 0.38.1
+--------------
+
+This is a critical bug fix release addressing:
+https://github.com/numba/numba/issues/3006
+
+The bug does not impact users using conda packages from Anaconda or Intel Python
+Distribution (but it does impact conda-forge). It does not impact users of pip
+using wheels from PyPI.
+
+This only impacts a small number of users where:
+
+ * The ICC runtime (specifically libsvml) is present in the user's environment.
+ * The user is using an llvmlite statically linked against a version of LLVM
+   that has not been patched with SVML support.
+ * The platform is 64-bit.
+
+The release fixes a code generation path that could lead to the production of
+incorrect results under the above situation.
+
+Fixes:
+
+* PR #3007: Augment SVML detection with llvmlite SVML patch detection.
+
+Contributors:
+
+The following people contributed to this release.
+
+* Stuart Archibald (core dev)
+
+
+Version 0.38.0
+--------------
+
+Following on from the bug fix focus of the last release, this release swings
+back towards the addition of new features and usability improvements based on
+community feedback. This release is comparatively large! Three key features/
+changes to note are:
+
+ * Numba (via llvmlite) is now backed by LLVM 6.0, general vectorization is
+   improved as a result. A significant long standing LLVM bug that was causing
+   corruption was also found and fixed.
+ * Further considerable improvements in vectorization are made available as
+   Numba now supports Intel's short vector math library (SVML).
+   Try it out with `conda install -c numba icc_rt`.
+ * CUDA 8.0 is now the minimum supported CUDA version.
+
+Other highlights include:
+
+ * Bug fixes to `parallel=True` have enabled more vectorization opportunities
+   when using the ParallelAccelerator technology.
+ * Much effort has gone into improving error reporting and the general usability
+   of Numba. This includes highlighted error messages and performance tips
+   documentation. Try it out with `conda install colorama`.
+ * A number of new NumPy functions are supported, `np.convolve`, `np.correlate`
+   `np.reshape`, `np.transpose`, `np.permutation`, `np.real`, `np.imag`, and
+   `np.searchsorted` now supports the`side` kwarg. Further, `np.argsort` now
+   supports the `kind` kwarg with `quicksort` and `mergesort` available.
+ * The Numba extension API has gained the ability operate more easily with
+   functions from Cython modules through the use of
+   `numba.extending.get_cython_function_address` to obtain function addresses
+   for direct use in `ctypes.CFUNCTYPE`.
+ * Numba now allows the passing of jitted functions (and containers of jitted
+   functions) as arguments to other jitted functions.
+ * The CUDA functionality has gained support for a larger selection of bit
+   manipulation intrinsics, also SELP, and has had a number of bugs fixed.
+ * Initial work to support the PPC64LE platform has been added, full support is
+   however waiting on the LLVM 6.0.1 release as it contains critical patches
+   not present in 6.0.0.
+   It is hoped that any remaining issues will be fixed in the next release.
+ * The capacity for advanced users/compiler engineers to define their own
+   compilation pipelines.
+
+Enhancements:
+
+* PR #2660: Support bools from cffi in nopython.
+* PR #2741: Enhance error message for undefined variables.
+* PR #2744: Add diagnostic error message to test suite discovery failure.
+* PR #2748: Added Intel SVML optimizations as opt-out choice working by default
+* PR #2762: Support transpose with axes arguments.
+* PR #2777: Add support for np.correlate and np.convolve
+* PR #2779: Implement np.random.permutation
+* PR #2801: Passing jitted functions as args
+* PR #2802: Support np.real() and np.imag()
+* PR #2807: Expose `import_cython_function`
+* PR #2821: Add kwarg 'side' to np.searchsorted
+* PR #2822: Adds stable argsort
+* PR #2832: Fixups for llvmlite 0.23/llvm 6
+* PR #2836: Support `index` method on tuples
+* PR #2839: Support for np.transpose and np.reshape.
+* PR #2843: Custom pipeline
+* PR #2847: Replace signed array access indices in unsiged prange loop body
+* PR #2859: Add support for improved error reporting.
+* PR #2880: This adds a github issue template.
+* PR #2881: Build recipe to clone Intel ICC runtime.
+* PR #2882: Update TravisCI to test SVML
+* PR #2893: Add reference to the data buffer in array.ctypes object
+* PR #2895: Move to CUDA 8.0
+
+Fixes:
+
+* PR #2737: Fix #2007 (part 1). Empty array handling in np.linalg.
+* PR #2738: Fix install_requires to allow pip getting pre-release version
+* PR #2740: Fix 2208. Generate better error message.
+* PR #2765: Fix Bit-ness
+* PR #2780: PowerPC reference counting memory fences
+* PR #2805: Fix six imports.
+* PR #2813: Fix #2812: gufunc scalar output bug.
+* PR #2814: Fix the build post #2727
+* PR #2831: Attempt to fix #2473
+* PR #2842: Fix issue with test discovery and broken CUDA drivers.
+* PR #2850: Add rtsys init guard and test.
+* PR #2852: Skip vectorization test with targets that are not x86
+* PR #2856: Prevent printing to stdout in `test_extending.py`
+* PR #2864: Correct C code to prevent compiler warnings.
+* PR #2889: Attempt to fix #2386.
+* PR #2891: Removed test skipping for inspect_cfg
+* PR #2898: Add guard to parallel test on unsupported platforms
+* PR #2907: Update change log for PPC64LE LLVM dependency.
+* PR #2911: Move build requirement to llvmlite>=0.23.0dev0
+* PR #2912: Fix random permutation test.
+* PR #2914: Fix MD list syntax in issue template.
+
+Documentation Updates:
+
+* PR #2739: Explicitly state default value of error_model in docstring
+* PR #2803: DOC: parallel vectorize requires signatures
+* PR #2829: Add Python 2.7 EOL plan to docs
+* PR #2838: Use automatic numbering syntax in list.
+* PR #2877: Add performance tips documentation.
+* PR #2883: Fix #2872: update rng doc about thread/fork-safety
+* PR #2908: Add missing link and ref to docs.
+* PR #2909: Tiny typo correction
+
+ParallelAccelerator enhancements/fixes:
+
+* PR #2727: Changes to enable vectorization in ParallelAccelerator.
+* PR #2816: Array analysis for transpose with arbitrary arguments
+* PR #2874: Fix dead code eliminator not to remove a call with side-effect
+* PR #2886: Fix ParallelAccelerator arrayexpr repr
+
+CUDA enhancements:
+
+* PR #2734: More Constants From cuda.h
+* PR #2767: Add len(..) Support to DeviceNDArray
+* PR #2778: Add More Device Array API Functions to CUDA Simulator
+* PR #2824: Add CUDA Primitives for Population Count
+* PR #2835: Emit selp Instructions to Avoid Branching
+* PR #2867: Full support for CUDA device attributes
+
+CUDA fixes:
+* PR #2768: Don't Compile Code on Every Assignment
+* PR #2878: Fixes a Win64 issue with the test in Pr/2865
+
+Contributors:
+
+The following people contributed to this release.
+
+* Abutalib Aghayev
+* Alex Olivas
+* Anton Malakhov
+* Dong-hee Na
+* Ehsan Totoni (core dev)
+* John Zwinck
+* Josh Wilson
+* Kelsey Jordahl
+* Nick White
+* Olexa Bilaniuk
+* Rik-de-Kort
+* Siu Kwan Lam (core dev)
+* Stan Seibert (core dev)
+* Stuart Archibald (core dev)
+* Thomas Arildsen
+* Todd A. Anderson (core dev)
+
+
+Version 0.37.0
+--------------
+
+This release focuses on bug fixing and stability but also adds a few new
+features including support for Numpy 1.14. The key change for Numba core was the
+long awaited addition of the final tranche of thread safety improvements that
+allow Numba to be run concurrently on multiple threads without hitting known
+thread safety issues inside LLVM itself. Further, a number of fixes and
+enhancements went into the CUDA implementation and ParallelAccelerator gained
+some new features and underwent some internal refactoring.
+
+Misc enhancements:
+
+* PR #2627: Remove hacks to make llvmlite threadsafe
+* PR #2672: Add ascontiguousarray
+* PR #2678: Add Gitter badge
+* PR #2691: Fix #2690: add intrinsic to convert array to tuple
+* PR #2703: Test runner feature: failed-first and last-failed
+* PR #2708: Patch for issue #1907
+* PR #2732: Add support for array.fill
+
+Misc Fixes:
+
+* PR #2610: Fix #2606 lowering of optional.setattr
+* PR #2650: Remove skip for win32 cosine test
+* PR #2668: Fix empty_like from readonly arrays.
+* PR #2682: Fixes 2210, remove _DisableJitWrapper
+* PR #2684: Fix #2340, generator error yielding bool
+* PR #2693: Add travis-ci testing of NumPy 1.14, and also check on Python 2.7
+* PR #2694: Avoid type inference failure due to a typing template rejection
+* PR #2695: Update llvmlite version dependency.
+* PR #2696: Fix tuple indexing codegeneration for empty tuple
+* PR #2698: Fix #2697 by deferring deletion in the simplify_CFG loop.
+* PR #2701: Small fix to avoid tempfiles being created in the current directory
+* PR #2725: Fix 2481, LLVM IR parsing error due to mutated IR
+* PR #2726: Fix #2673: incorrect fork error msg.
+* PR #2728: Alternative to #2620.  Remove dead code ByteCodeInst.get.
+* PR #2730: Add guard for test needing SciPy/BLAS
+
+Documentation updates:
+
+* PR #2670: Update communication channels
+* PR #2671: Add docs about diagnosing loop vectorizer
+* PR #2683: Add docs on const arg requirements and on const mem alloc
+* PR #2722: Add docs on numpy support in cuda
+* PR #2724: Update doc: warning about unsupported arguments
+
+ParallelAccelerator enhancements/fixes:
+
+Parallel support for `np.arange` and `np.linspace`, also `np.mean`, `np.std`
+and `np.var` are added. This was performed as part of a general refactor and
+cleanup of the core ParallelAccelerator code.
+
+* PR #2674: Core pa
+* PR #2704: Generate Dels after parfor sequential lowering
+* PR #2716: Handle matching directly supported functions
+
+CUDA enhancements:
+
+* PR #2665: CUDA DeviceNDArray: Support numpy tranpose API
+* PR #2681: Allow Assigning to DeviceNDArrays
+* PR #2702: Make DummyArray do High Dimensional Reshapes
+* PR #2714: Use CFFI to Reuse Code
+
+CUDA fixes:
+
+* PR #2667: Fix CUDA DeviceNDArray slicing
+* PR #2686: Fix #2663: incorrect offset when indexing cuda array.
+* PR #2687: Ensure Constructed Stream Bound
+* PR #2706: Workaround for unexpected warp divergence due to exception raising
+  code
+* PR #2707: Fix regression: cuda test submodules not loading properly in
+  runtests
+* PR #2731: Use more challenging values in slice tests.
+* PR #2720: A quick testsuite fix to not run the new cuda testcase in the
+  multiprocess pool
+
+Contributors:
+
+The following people contributed to this release.
+
+* Coutinho Menezes Nilo
+* Daniel
+* Ehsan Totoni
+* Nick White
+* Paul H. Liu
+* Siu Kwan Lam
+* Stan Seibert
+* Stuart Archibald
+* Todd A. Anderson
+
+
+Version 0.36.2
+--------------
+
+This is a bugfix release that provides minor changes to address:
+
+* PR #2645: Avoid CPython bug with ``exec`` in older 2.7.x.
+* PR #2652: Add support for CUDA 9.
+
+
+Version 0.36.1
+--------------
+
+This release continues to add new features to the work undertaken in partnership
+with Intel on ParallelAccelerator technology. Other changes of note include the
+compilation chain being updated to use LLVM 5.0 and the production of conda
+packages using conda-build 3 and the new compilers that ship with it.
+
+NOTE: A version 0.36.0 was tagged for internal use but not released.
+
+ParallelAccelerator:
+
+NOTE: The ParallelAccelerator technology is under active development and should
+be considered experimental.
+
+New features relating to ParallelAccelerator, from work undertaken with Intel,
+include the addition of the `@stencil` decorator for ease of implementation of
+stencil-like computations, support for general reductions, and slice and
+range fusion for parallel slice/bit-array assignments. Documentation on both the
+use and implementation of the above has been added. Further, a new debug
+environment variable `NUMBA_DEBUG_ARRAY_OPT_STATS` is made available to give
+information about which operators/calls are converted to parallel for-loops.
+
+ParallelAccelerator features:
+
+* PR #2457: Stencil Computations in ParallelAccelerator
+* PR #2548: Slice and range fusion, parallelizing bitarray and slice assignment
+* PR #2516: Support general reductions in ParallelAccelerator
+
+ParallelAccelerator fixes:
+
+* PR #2540: Fix bug #2537
+* PR #2566: Fix issue #2564.
+* PR #2599: Fix nested multi-dimensional parfor type inference issue
+* PR #2604: Fixes for stencil tests and cmath sin().
+* PR #2605: Fixes issue #2603.
+
+Additional features of note:
+
+This release of Numba (and llvmlite) is updated to use LLVM version 5.0 as the
+compiler back end, the main change to Numba to support this was the addition of
+a custom symbol tracker to avoid the calls to LLVM's `ExecutionEngine` that was
+crashing when asking for non-existent symbol addresses. Further, the conda
+packages for this release of Numba are built using conda build version 3 and the
+new compilers/recipe grammar that are present in that release.
+
+* PR #2568: Update for LLVM 5
+* PR #2607: Fixes abort when getting address to "nrt_unresolved_abort"
+* PR #2615: Working towards conda build 3
+
+Thanks to community feedback and bug reports, the following fixes were also
+made.
+
+Misc fixes/enhancements:
+
+* PR #2534: Add tuple support to np.take.
+* PR #2551: Rebranding fix
+* PR #2552: relative doc links
+* PR #2570: Fix issue #2561, handle missing successor on loop exit
+* PR #2588: Fix #2555. Disable libpython.so linking on linux
+* PR #2601: Update llvmlite version dependency.
+* PR #2608: Fix potential cache file collision
+* PR #2612: Fix NRT test failure due to increased overhead when running in coverage
+* PR #2619: Fix dubious pthread_cond_signal not in lock
+* PR #2622: Fix `np.nanmedian` for all NaN case.
+* PR #2633: Fix markdown in CONTRIBUTING.md
+* PR #2635: Make the dependency on compilers for AOT optional.
+
+CUDA support fixes:
+
+* PR #2523: Fix invalid cuda context in memory transfer calls in another thread
+* PR #2575: Use CPU to initialize xoroshiro states for GPU RNG. Fixes #2573
+* PR #2581: Fix cuda gufunc mishandling of scalar arg as array and out argument
+
+
+Version 0.35.0
+--------------
+
+This release includes some exciting new features as part of the work
+performed in partnership with Intel on ParallelAccelerator technology.
+There are also some additions made to Numpy support and small but
+significant fixes made as a result of considerable effort spent chasing bugs
+and implementing stability improvements.
+
+
+ParallelAccelerator:
+
+NOTE: The ParallelAccelerator technology is under active development and should
+be considered experimental.
+
+New features relating to ParallelAccelerator, from work undertaken with Intel,
+include support for a larger range of `np.random` functions in `parallel`
+mode, printing Numpy arrays in no Python mode, the capacity to initialize Numpy
+arrays directly from list comprehensions, and the axis argument to `.sum()`.
+Documentation on the ParallelAccelerator technology implementation has also
+been added. Further, a large amount of work on equivalence relations was
+undertaken to enable runtime checks of broadcasting behaviours in parallel mode.
+
+ParallelAccelerator features:
+
+* PR #2400: Array comprehension
+* PR #2405: Support printing Numpy arrays
+* PR #2438: from Support more np.random functions in ParallelAccelerator
+* PR #2482: Support for sum with axis in nopython mode.
+* PR #2487: Adding developer documentation for ParallelAccelerator technology.
+* PR #2492: Core PA refactor adds assertions for broadcast semantics
+
+ParallelAccelerator fixes:
+
+* PR #2478: Rename cfg before parfor translation (#2477)
+* PR #2479: Fix broken array comprehension tests on unsupported platforms
+* PR #2484: Fix array comprehension test on win64
+* PR #2506: Fix for 32-bit machines.
+
+
+Additional features of note:
+
+Support for `np.take`, `np.finfo`, `np.iinfo` and `np.MachAr` in no Python
+mode is added. Further, three new environment variables are added, two for
+overriding CPU target/features and another to warn if `parallel=True` was set
+no such transform was possible.
+
+* PR #2490: Implement np.take and ndarray.take
+* PR #2493: Display a warning if parallel=True is set but not possible.
+* PR #2513: Add np.MachAr, np.finfo, np.iinfo
+* PR #2515: Allow environ overriding of cpu target and cpu features.
+
+
+Due to expansion of the test farm and a focus on fixing bugs, the following
+fixes were also made.
+
+Misc fixes/enhancements:
+
+* PR #2455: add contextual information to runtime errors
+* PR #2470: Fixes #2458, poor performance in np.median
+* PR #2471: Ensure LLVM threadsafety in {g,}ufunc building.
+* PR #2494: Update doc theme
+* PR #2503: Remove hacky code added in 2482 and feature enhancement
+* PR #2505: Serialise env mutation tests during multithreaded testing.
+* PR #2520: Fix failing cpu-target override tests
+
+CUDA support fixes:
+
+* PR #2504: Enable CUDA toolkit version testing
+* PR #2509: Disable tests generating code unavailable in lower CC versions.
+* PR #2511: Fix Windows 64 bit CUDA tests.
+
+
+Version 0.34.0
+--------------
+
+This release adds a significant set of new features arising from combined work
+with Intel on ParallelAccelerator technology. It also adds list comprehension
+and closure support, support for Numpy 1.13 and a new, faster, CUDA reduction
+algorithm. For Linux users this release is the first to be built on Centos 6,
+which will be the new base platform for future releases. Finally a number of
+thread-safety, type inference and other smaller enhancements and bugs have been
+fixed.
+
+
+ParallelAccelerator features:
+
+NOTE: The ParallelAccelerator technology is under active development and should
+be considered experimental.
+
+The ParallelAccelerator technology is accessed via a new "nopython" mode option
+"parallel". The ParallelAccelerator technology attempts to identify operations
+which have parallel semantics (for instance adding a scalar to a vector), fuse
+together adjacent such operations, and then parallelize their execution across
+a number of CPU cores. This is essentially auto-parallelization.
+
+In addition to the auto-parallelization feature, explicit loop based
+parallelism is made available through the use of `prange` in place of `range`
+as a loop iterator.
+
+More information and examples on both auto-parallelization and `prange` are
+available in the documentation and examples directory respectively.
+
+As part of the necessary work for ParallelAccelerator, support for closures
+and list comprehensions is added:
+
+* PR #2318: Transfer ParallelAccelerator technology to Numba
+* PR #2379: ParallelAccelerator Core Improvements
+* PR #2367: Add support for len(range(...))
+* PR #2369: List comprehension
+* PR #2391: Explicit Parallel Loop Support (prange)
+
+The ParallelAccelerator features are available on all supported platforms and
+Python versions with the exceptions of (with view of supporting in a future
+release):
+
+* The combination of Windows operating systems with Python 2.7.
+* Systems running 32 bit Python.
+
+
+CUDA support enhancements:
+
+* PR #2377: New GPU reduction algorithm
+
+
+CUDA support fixes:
+
+* PR #2397: Fix #2393, always set alignment of cuda static memory regions
+
+
+Misc Fixes:
+
+* PR #2373, Issue #2372: 32-bit compatibility fix for parfor related code
+* PR #2376: Fix #2375 missing stdint.h for py2.7 vc9
+* PR #2378: Fix deadlock in parallel gufunc when kernel acquires the GIL.
+* PR #2382: Forbid unsafe casting in bitwise operation
+* PR #2385: docs: fix Sphinx errors
+* PR #2396: Use 64-bit RHS operand for shift
+* PR #2404: Fix threadsafety logic issue in ufunc compilation cache.
+* PR #2424: Ensure consistent iteration order of blocks for type inference.
+* PR #2425: Guard code to prevent the use of 'parallel' on win32 + py27
+* PR #2426: Basic test for Enum member type recovery.
+* PR #2433: Fix up the parfors tests with respect to windows py2.7
+* PR #2442: Skip tests that need BLAS/LAPACK if scipy is not available.
+* PR #2444: Add test for invalid array setitem
+* PR #2449: Make the runtime initialiser threadsafe
+* PR #2452: Skip CFG test on 64bit windows
+
+
+Misc Enhancements:
+
+* PR #2366: Improvements to IR utils
+* PR #2388: Update README.rst to indicate the proper version of LLVM
+* PR #2394: Upgrade to llvmlite 0.19.*
+* PR #2395: Update llvmlite version to 0.19
+* PR #2406: Expose environment object to ufuncs
+* PR #2407: Expose environment object to target-context inside lowerer
+* PR #2413: Add flags to pass through to conda build for buildbot
+* PR #2414: Add cross compile flags to local recipe
+* PR #2415: A few cleanups for rewrites
+* PR #2418: Add getitem support for Enum classes
+* PR #2419: Add support for returning enums in vectorize
+* PR #2421: Add copyright notice for Intel contributed files.
+* PR #2422: Patch code base to work with np 1.13 release
+* PR #2448: Adds in warning message when using 'parallel' if cache=True
+* PR #2450: Add test for keyword arg on .sum-like and .cumsum-like array
+  methods
+
+
+Version 0.33.0
+--------------
+
+This release resolved several performance issues caused by atomic
+reference counting operations inside loop bodies.  New optimization
+passes have been added to reduce the impact of these operations.  We
+observe speed improvements between 2x-10x in affected programs due to
+the removal of unnecessary reference counting operations.
+
+There are also several enhancements to the CUDA GPU support:
+
+* A GPU random number generator based on `xoroshiro128+ algorithm <http://xoroshiro.di.unimi.it/>`_ is added.
+  See details and examples in :ref:`documentation <cuda-random>`.
+* ``@cuda.jit`` CUDA kernels can now call ``@jit`` and ``@njit``
+  CPU functions and they will automatically be compiled as CUDA device
+  functions.
+* CUDA IPC memory API is exposed for sharing memory between proceses.
+  See usage details in :ref:`documentation <cuda-ipc-memory>`.
+
+Reference counting enhancements:
+
+* PR #2346, Issue #2345, #2248: Add extra refcount pruning after inlining
+* PR #2349: Fix refct pruning not removing refct op with tail call.
+* PR #2352, Issue #2350: Add refcount pruning pass for function that does not need refcount
+
+CUDA support enhancements:
+
+* PR #2023: Supports CUDA IPC for device array
+* PR #2343, Issue #2335: Allow CPU jit decorated function to be used as cuda device function
+* PR #2347: Add random number generator support for CUDA device code
+* PR #2361: Update autotune table for CC: 5.3, 6.0, 6.1, 6.2
+
+Misc fixes:
+
+* PR #2362: Avoid test failure due to typing to int32 on 32-bit platforms
+* PR #2359: Fixed nogil example that threw a TypeError when executed.
+* PR #2357, Issue #2356: Fix fragile test that depends on how the script is executed.
+* PR #2355: Fix cpu dispatcher referenced as attribute of another module
+* PR #2354: Fixes an issue with caching when function needs NRT and refcount pruning
+* PR #2342, Issue #2339: Add warnings to inspection when it is used on unserialized cached code
+* PR #2329, Issue #2250: Better handling of missing op codes
+
+Misc enhancements:
+
+* PR #2360: Adds missing values in error mesasge interp.
+* PR #2353: Handle when get_host_cpu_features() raises RuntimeError
+* PR #2351: Enable SVML for erf/erfc/gamma/lgamma/log2
+* PR #2344: Expose error_model setting in jit decorator
+* PR #2337: Align blocking terminate support for fork() with new TBB version
+* PR #2336: Bump llvmlite version to 0.18
+* PR #2330: Core changes in PR #2318
+
+
+Version 0.32.0
+--------------
+
+In this release, we are upgrading to LLVM 4.0.  A lot of work has been done
+to fix many race-condition issues inside LLVM when the compiler is
+used concurrently, which is likely when Numba is used with Dask.
+
+Improvements:
+
+* PR #2322: Suppress test error due to unknown but consistent error with tgamma
+* PR #2320: Update llvmlite dependency to 0.17
+* PR #2308: Add details to error message on why cuda support is disabled.
+* PR #2302: Add os x to travis
+* PR #2294: Disable remove_module on MCJIT due to memory leak inside LLVM
+* PR #2291: Split parallel tests and recycle workers to tame memory usage
+* PR #2253: Remove the pointer-stuffing hack for storing meminfos in lists
+
+Fixes:
+
+* PR #2331: Fix a bug in the GPU array indexing
+* PR #2326: Fix #2321 docs referring to non-existing function.
+* PR #2316: Fixing more race-condition problems
+* PR #2315: Fix #2314.  Relax strict type check to allow optional type.
+* PR #2310: Fix race condition due to concurrent compilation and cache loading
+* PR #2304: Fix intrinsic 1st arg not a typing.Context as stated by the docs.
+* PR #2287: Fix int64 atomic min-max
+* PR #2286: Fix #2285 `@overload_method` not linking dependent libs
+* PR #2303: Missing import statements to interval-example.rst
+
+
+Version 0.31.0
+--------------
+
+In this release, we added preliminary support for debugging with GDB
+version >= 7.0. The feature is enabled by setting the ``debug=True`` compiler
+option, which causes GDB compatible debug info to be generated.
+The CUDA backend also gained limited debugging support so that source locations
+are showed in memory-checking and profiling tools.
+For details, see :ref:`numba-troubleshooting`.
+
+Also, we added the ``fastmath=True`` compiler option to enable unsafe
+floating-point transformations, which allows LLVM to auto-vectorize more code.
+
+Other important changes include upgrading to LLVM 3.9.1 and adding support for
+Numpy 1.12.
+
+Improvements:
+
+* PR #2281: Update for numpy1.12
+* PR #2278: Add CUDA atomic.{max, min, compare_and_swap}
+* PR #2277: Add about section to conda recipies to identify license and other
+  metadata in Anaconda Cloud
+* PR #2271: Adopt itanium C++-style mangling for CPU and CUDA targets
+* PR #2267: Add fastmath flags
+* PR #2261: Support dtype.type
+* PR #2249: Changes for llvm3.9
+* PR #2234: Bump llvmlite requirement to 0.16 and add install_name_tool_fixer to
+  mviewbuf for OS X
+* PR #2230: Add python3.6 to TravisCi
+* PR #2227: Enable caching for gufunc wrapper
+* PR #2170: Add debugging support
+* PR #2037: inspect_cfg() for easier visualization of the function operation
+
+Fixes:
+
+* PR #2274: Fix nvvm ir patch in mishandling "load"
+* PR #2272: Fix breakage to cuda7.5
+* PR #2269: Fix caching of copy_strides kernel in cuda.reduce
+* PR #2265: Fix #2263: error when linking two modules with dynamic globals
+* PR #2252: Fix path separator in test
+* PR #2246: Fix overuse of memory in some system with fork
+* PR #2241: Fix #2240: __module__ in dynamically created function not a str
+* PR #2239: Fix fingerprint computation failure preventing fallback
+
+
+Version 0.30.1
+--------------
+
+This is a bug-fix release to enable Python 3.6 support.  In addition,
+there is now early Intel TBB support for parallel ufuncs when building from
+source with TBBROOT defined.  The TBB feature is not enabled in our official
+builds.
+
+Fixes:
+
+* PR #2232: Fix name clashes with _Py_hashtable_xxx in Python 3.6.
+
+Improvements:
+
+* PR #2217: Add Intel TBB threadpool implementation for parallel ufunc.
+
+
+Version 0.30.0
+--------------
+
+This release adds preliminary support for Python 3.6, but no official build is
+available yet.  A new system reporting tool (``numba --sysinfo``) is added to
+provide system information to help core developers in replication and debugging.
+See below for other improvements and bug fixes.
+
+Improvements:
+
+* PR #2209: Support Python 3.6.
+* PR #2175: Support ``np.trace()``, ``np.outer()`` and ``np.kron()``.
+* PR #2197: Support ``np.nanprod()``.
+* PR #2190: Support caching for ufunc.
+* PR #2186: Add system reporting tool.
+
+Fixes:
+
+* PR #2214, Issue #2212: Fix memory error with ndenumerate and flat iterators.
+* PR #2206, Issue #2163: Fix ``zip()`` consuming extra elements in early
+  exhaustion.
+* PR #2185, Issue #2159, #2169: Fix rewrite pass affecting objmode fallback.
+* PR #2204, Issue #2178: Fix annotation for liftedloop.
+* PR #2203: Fix Appveyor segfault with Python 3.5.
+* PR #2202, Issue #2198: Fix target context not initialized when loading from
+  ufunc cache.
+* PR #2172, Issue #2171: Fix optional type unpacking.
+* PR #2189, Issue #2188: Disable freezing of big (>1MB) global arrays.
+* PR #2180, Issue #2179: Fix invalid variable version in looplifting.
+* PR #2156, Issue #2155: Fix divmod, floordiv segfault on CUDA.
+
+
+Version 0.29.0
+--------------
+
+This release extends the support of recursive functions to include direct and
+indirect recursion without explicit function type annotations.  See new example
+in `examples/mergesort.py`.  Newly supported numpy features include array
+stacking functions, np.linalg.eig* functions, np.linalg.matrix_power, np.roots
+and array to array broadcasting in assignments.
+
+This release depends on llvmlite 0.14.0 and supports CUDA 8 but it is not
+required.
+
+Improvements:
+
+* PR #2130, #2137: Add type-inferred recursion with docs and examples.
+* PR #2134: Add ``np.linalg.matrix_power``.
+* PR #2125: Add ``np.roots``.
+* PR #2129: Add ``np.linalg.{eigvals,eigh,eigvalsh}``.
+* PR #2126: Add array-to-array broadcasting.
+* PR #2069: Add hstack and related functions.
+* PR #2128: Allow for vectorizing a jitted function. (thanks to @dhirschfeld)
+* PR #2117: Update examples and make them test-able.
+* PR #2127: Refactor interpreter class and its results.
+
+Fixes:
+
+* PR #2149: Workaround MSVC9.0 SP1 fmod bug kb982107.
+* PR #2145, Issue #2009: Fixes kwargs for jitclass ``__init__`` method.
+* PR #2150: Fix slowdown in objmode fallback.
+* PR #2050, Issue #1259: Fix liveness problem with some generator loops.
+* PR #2072, Issue #1995: Right shift of unsigned LHS should be logical.
+* PR #2115, Issue #1466: Fix inspect_types() error due to mangled variable name.
+* PR #2119, Issue #2118: Fix array type created from record-dtype.
+* PR #2122, Issue #1808: Fix returning a generator due to datamodel error.
+
+
+Version 0.28.1
+--------------
+
+This is a bug-fix release to resolve packaging issues with setuptools
+dependency.
+
+
+Version 0.28.0
+--------------
+
+Amongst other improvements, this version improves again the level of
+support for linear algebra -- functions from the :mod:`numpy.linalg`
+module.  Also, our random generator is now guaranteed to be thread-safe
+and fork-safe.
+
+Improvements:
+
+* PR #2019: Add the ``@intrinsic`` decorator to define low-level
+  subroutines callable from JIT functions (this is considered
+  a private API for now).
+* PR #2059: Implement ``np.concatenate`` and ``np.stack``.
+* PR #2048: Make random generation fork-safe and thread-safe, producing
+  independent streams of random numbers for each thread or process.
+* PR #2031: Add documentation of floating-point pitfalls.
+* Issue #2053: Avoid polling in parallel CPU target (fixes severe performance
+  regression on Windows).
+* Issue #2029: Make default arguments fast.
+* PR #2052: Add logging to the CUDA driver.
+* PR #2049: Implement the built-in ``divmod()`` function.
+* PR #2036: Implement the ``argsort()`` method on arrays.
+* PR #2046: Improving CUDA memory management by deferring deallocations
+  until certain thresholds are reached, so as to avoid breaking asynchronous
+  execution.
+* PR #2040: Switch the CUDA driver implementation to use CUDA's
+  "primary context" API.
+* PR #2017: Allow ``min(tuple)`` and ``max(tuple)``.
+* PR #2039: Reduce fork() detection overhead in CUDA.
+* PR #2021: Handle structured dtypes with titles.
+* PR #1996: Rewrite looplifting as a transformation on Numba IR.
+* PR #2014: Implement ``np.linalg.matrix_rank``.
+* PR #2012: Implement ``np.linalg.cond``.
+* PR #1985: Rewrite even trivial array expressions, which opens the door
+  for other optimizations (for example, ``array ** 2`` can be converted
+  into ``array * array``).
+* PR #1950: Have ``typeof()`` always raise ValueError on failure.
+  Previously, it would either raise or return None, depending on the input.
+* PR #1994: Implement ``np.linalg.norm``.
+* PR #1987: Implement ``np.linalg.det`` and ``np.linalg.slogdet``.
+* Issue #1979: Document integer width inference and how to workaround.
+* PR #1938: Numba is now compatible with LLVM 3.8.
+* PR #1967: Restrict ``np.linalg`` functions to homogeneous dtypes.  Users
+  wanting to pass mixed-typed inputs have to convert explicitly, which
+  makes the performance implications more obvious.
+
+Fixes:
+
+* PR #2006: ``array(float32) ** int`` should return ``array(float32)``.
+* PR #2044: Allow reshaping empty arrays.
+* Issue #2051: Fix refcounting issue when concatenating tuples.
+* Issue #2000: Make Numpy optional for setup.py, to allow ``pip install``
+  to work without Numpy pre-installed.
+* PR #1989: Fix assertion in ``Dispatcher.disable_compile()``.
+* Issue #2028: Ignore filesystem errors when caching from multiple processes.
+* Issue #2003: Allow unicode variable and function names (on Python 3).
+* Issue #1998: Fix deadlock in parallel ufuncs that reacquire the GIL.
+* PR #1997: Fix random crashes when AOT compiling on certain Windows platforms.
+* Issue #1988: Propagate jitclass docstring.
+* Issue #1933: Ensure array constants are emitted with the right alignment.
+
+
+Version 0.27.0
+--------------
+
+Improvements:
+
+* Issue #1976: improve error message when non-integral dimensions are given
+  to a CUDA kernel.
+* PR #1970: Optimize the power operator with a static exponent.
+* PR #1710: Improve contextual information for compiler errors.
+* PR #1961: Support printing constant strings.
+* PR #1959: Support more types in the print() function.
+* PR #1823: Support ``compute_50`` in CUDA backend.
+* PR #1955: Support ``np.linalg.pinv``.
+* PR #1896: Improve the ``SmartArray`` API.
+* PR #1947: Support ``np.linalg.solve``.
+* Issue #1943: Improve error message when an argument fails typing.4
+* PR #1927: Support ``np.linalg.lstsq``.
+* PR #1934: Use system functions for hypot() where possible, instead of our
+  own implementation.
+* PR #1929: Add cffi support to ``@cfunc`` objects.
+* PR #1932: Add user-controllable thread pool limits for parallel CPU target.
+* PR #1928: Support self-recursion when the signature is explicit.
+* PR #1890: List all lowering implementations in the developer docs.
+* Issue #1884: Support ``np.lib.stride_tricks.as_strided()``.
+
+Fixes:
+
+* Issue #1960: Fix sliced assignment when source and destination areas are
+  overlapping.
+* PR #1963: Make CUDA print() atomic.
+* PR #1956: Allow 0d array constants.
+* Issue #1945: Allow using Numpy ufuncs in AOT compiled code.
+* Issue #1916: Fix documentation example for ``@generated_jit``.
+* Issue #1926: Fix regression when caching functions in an IPython session.
+* Issue #1923: Allow non-intp integer arguments to carray() and farray().
+* Issue #1908: Accept non-ASCII unicode docstrings on Python 2.
+* Issue #1874: Allow ``del container[key]`` in object mode.
+* Issue #1913: Fix set insertion bug when the lookup chain contains deleted
+  entries.
+* Issue #1911: Allow function annotations on jitclass methods.
+
+
+Version 0.26.0
+--------------
+
+This release adds support for ``cfunc`` decorator for exporting numba jitted
+functions to 3rd party API that takes C callbacks.  Most of the overhead of
+using jitclasses inside the interpreter are eliminated.  Support for
+decompositions in ``numpy.linalg`` are added.  Finally, Numpy 1.11 is
+supported.
+
+Improvements:
+
+* PR #1889: Export BLAS and LAPACK wrappers for pycc.
+* PR #1888: Faster array power.
+* Issue #1867: Allow "out" keyword arg for dufuncs.
+* PR #1871: ``carray()`` and ``farray()`` for creating arrays from pointers.
+* PR #1855: ``@cfunc`` decorator for exporting as ctypes function.
+* PR #1862: Add support for ``numpy.linalg.qr``.
+* PR #1851: jitclass support for '_' and '__' prefixed attributes.
+* PR #1842: Optimize jitclass in Python interpreter.
+* Issue #1837: Fix CUDA simulator issues with device function.
+* PR #1839: Add support for decompositions from ``numpy.linalg``.
+* PR #1829: Support Python enums.
+* PR #1828: Add support for ``numpy.random.rand()``` and
+  ``numpy.random.randn()``
+* Issue #1825: Use of 0-darray in place of scalar index.
+* Issue #1824: Scalar arguments to object mode gufuncs.
+* Issue #1813: Let bitwise bool operators return booleans, not integers.
+* Issue #1760: Optional arguments in generators.
+* PR #1780: Numpy 1.11 support.
+
+
+Version 0.25.0
+--------------
+
+This release adds support for ``set`` objects in nopython mode.  It also
+adds support for many missing Numpy features and functions.  It improves
+Numba's compatibility and performance when using a distributed execution
+framework such as dask, distributed or Spark.  Finally, it removes
+compatibility with Python 2.6, Python 3.3 and Numpy 1.6.
+
+Improvements:
+
+* Issue #1800: Add erf(), erfc(), gamma() and lgamma() to CUDA targets.
+* PR #1793: Implement more Numpy functions: np.bincount(), np.diff(),
+  np.digitize(), np.histogram(), np.searchsorted() as well as NaN-aware
+  reduction functions (np.nansum(), np.nanmedian(), etc.)
+* PR #1789: Optimize some reduction functions such as np.sum(), np.prod(),
+  np.median(), etc.
+* PR #1752: Make CUDA features work in dask, distributed and Spark.
+* PR #1787: Support np.nditer() for fast multi-array indexing with
+  broadcasting.
+* PR #1799: Report JIT-compiled functions as regular Python functions
+  when profiling (allowing to see the filename and line number where a
+  function is defined).
+* PR #1782: Support np.any() and np.all().
+* Issue #1788: Support the iter() and next() built-in functions.
+* PR #1778: Support array.astype().
+* Issue #1775: Allow the user to set the target CPU model for AOT compilation.
+* PR #1758: Support creating random arrays using the ``size`` parameter
+  to the np.random APIs.
+* PR #1757: Support len() on array.flat objects.
+* PR #1749: Remove Numpy 1.6 compatibility.
+* PR #1748: Remove Python 2.6 and 3.3 compatibility.
+* PR #1735: Support the ``not in`` operator as well as operator.contains().
+* PR #1724: Support homogeneous sets in nopython mode.
+* Issue #875: make compilation of array constants faster.
+
+Fixes:
+
+* PR #1795: Fix a massive performance issue when calling Numba functions
+  with distributed, Spark or a similar mechanism using serialization.
+* Issue #1784: Make jitclasses usable with NUMBA_DISABLE_JIT=1.
+* Issue #1786: Allow using linear algebra functions when profiling.
+* Issue #1796: Fix np.dot() memory leak on non-contiguous inputs.
+* PR #1792: Fix static negative indexing of tuples.
+* Issue #1771: Use fallback cache directory when __pycache__ isn't writable,
+  such as when user code is installed in a system location.
+* Issue #1223: Use Numpy error model in array expressions (e.g. division
+  by zero returns ``inf`` or ``nan`` instead of raising an error).
+* Issue #1640: Fix np.random.binomial() for large n values.
+* Issue #1643: Improve error reporting when passing an invalid spec to
+  ``@jitclass``.
+* PR #1756: Fix slicing with a negative step and an omitted start.
+
+
+Version 0.24.0
+--------------
+
+This release introduces several major changes, including the ``@generated_jit``
+decorator for flexible specializations as with Julia's "``@generated``" macro,
+or the SmartArray array wrapper type that allows seamless transfer of array
+data between the CPU and the GPU.
+
+This will be the last version to support Python 2.6, Python 3.3 and Numpy 1.6.
+
+Improvements:
+
+* PR #1723: Improve compatibility of JIT functions with the Python profiler.
+* PR #1509: Support array.ravel() and array.flatten().
+* PR #1676: Add SmartArray type to support transparent data management in
+  multiple address spaces (host & GPU).
+* PR #1689: Reduce startup overhead of importing Numba.
+* PR #1705: Support registration of CFFI types as corresponding to known
+  Numba types.
+* PR #1686: Document the extension API.
+* PR #1698: Improve warnings raised during type inference.
+* PR #1697: Support np.dot() and friends on non-contiguous arrays.
+* PR #1692: cffi.from_buffer() improvements (allow more pointer types,
+  allow non-Numpy buffer objects).
+* PR #1648: Add the ``@generated_jit`` decorator.
+* PR #1651: Implementation of np.linalg.inv using LAPACK.  Thanks to
+  Matthieu Dartiailh.
+* PR #1674: Support np.diag().
+* PR #1673: Improve error message when looking up an attribute on an
+  unknown global.
+* Issue #1569: Implement runtime check for the LLVM locale bug.
+* PR #1612: Switch to LLVM 3.7 in sync with llvmlite.
+* PR #1624: Allow slice assignment of sequence to array.
+* PR #1622: Support slicing tuples with a constant slice.
+
+Fixes:
+
+* Issue #1722: Fix returning an optional boolean (bool or None).
+* Issue #1734: NRT decref bug when variable is del'ed before being defined,
+  leading to a possible memory leak.
+* PR #1732: Fix tuple getitem regression for CUDA target.
+* PR #1718: Mishandling of optional to optional casting.
+* PR #1714: Fix .compile() on a JIT function not respecting ._can_compile.
+* Issue #1667: Fix np.angle() on arrays.
+* Issue #1690: Fix slicing with an omitted stop and a negative step value.
+* PR #1693: Fix gufunc bug in handling scalar formal arg with non-scalar
+  input value.
+* PR #1683: Fix parallel testing under Windows.
+* Issue #1616: Use system-provided versions of C99 math where possible.
+* Issue #1652: Reductions of bool arrays (e.g. sum() or mean()) should
+  return integers or floats, not bools.
+* Issue #1664: Fix regression when indexing a record array with a constant
+  index.
+* PR #1661: Disable AVX on old Linux kernels.
+* Issue #1636: Allow raising an exception looked up on a module.
+
+
+Version 0.23.1
+--------------
+
+This is a bug-fix release to address several regressions introduced
+in the 0.23.0 release, and a couple other issues.
+
+Fixes:
+
+* Issue #1645: CUDA ufuncs were broken in 0.23.0.
+* Issue #1638: Check tuple sizes when passing a list of tuples.
+* Issue #1630: Parallel ufunc would keep eating CPU even after finishing
+  under Windows.
+* Issue #1628: Fix ctypes and cffi tests under Windows with Python 3.5.
+* Issue #1627: Fix xrange() support.
+* PR #1611: Rewrite variable liveness analysis.
+* Issue #1610: Allow nested calls between explicitly-typed ufuncs.
+* Issue #1593: Fix `*args` in object mode.
+
+
+Version 0.23.0
+--------------
+
+This release introduces JIT classes using the new ``@jitclass`` decorator,
+allowing user-defined structures for nopython mode.  Other improvements
+and bug fixes are listed below.
+
+Improvements:
+
+* PR #1609: Speed up some simple math functions by inlining them
+  in their caller
+* PR #1571: Implement JIT classes
+* PR #1584: Improve typing of array indexing
+* PR #1583: Allow printing booleans
+* PR #1542: Allow negative values in np.reshape()
+* PR #1560: Support vector and matrix dot product, including ``np.dot()``
+  and the ``@`` operator in Python 3.5
+* PR #1546: Support field lookup on record arrays and scalars (i.e.
+  ``array['field']`` in addition to ``array.field``)
+* PR #1440: Support the HSA wavebarrier() and activelanepermute_wavewidth()
+  intrinsics
+* PR #1540: Support np.angle()
+* PR #1543: Implement CPU multithreaded gufuncs (target="parallel")
+* PR #1551: Allow scalar arguments in np.where(), np.empty_like().
+* PR #1516: Add some more examples from NumbaPro
+* PR #1517: Support np.sinc()
+
+Fixes:
+
+* Issue #1603: Fix calling a non-cached function from a cached function
+* Issue #1594: Ensure a list is homogeneous when unboxing
+* Issue #1595: Replace deprecated use of get_pointer_to_function()
+* Issue #1586: Allow tests to be run by different users on the same machine
+* Issue #1587: Make CudaAPIError picklable
+* Issue #1568: Fix using Numba from inside Visual Studio 2015
+* Issue #1559: Fix serializing a jit function referring a renamed module
+* PR #1508: Let reshape() accept integer argument(s), not just a tuple
+* Issue #1545: Improve error checking when unboxing list objects
+* Issue #1538: Fix array broadcasting in CUDA gufuncs
+* Issue #1526: Fix a reference count handling bug
+
+
+Version 0.22.1
+--------------
+
+This is a bug-fix release to resolve some packaging issues and other
+problems found in the 0.22.0 release.
+
+Fixes:
+
+* PR #1515: Include MANIFEST.in in MANIFEST.in so that sdist still works from
+  source tar files.
+* PR #1518: Fix reference counting bug caused by hidden alias
+* PR #1519: Fix erroneous assert when passing nopython=True to guvectorize.
+* PR #1521: Fix cuda.test()
+
+Version 0.22.0
+--------------
+
+This release features several highlights: Python 3.5 support, Numpy 1.10
+support, Ahead-of-Time compilation of extension modules, additional
+vectorization features that were previously only available with the
+proprietary extension NumbaPro, improvements in array indexing.
+
+Improvements:
+
+* PR #1497: Allow scalar input type instead of size-1 array to @guvectorize
+* PR #1480: Add distutils support for AOT compilation
+* PR #1460: Create a new API for Ahead-of-Time (AOT) compilation
+* PR #1451: Allow passing Python lists to JIT-compiled functions, and
+  reflect mutations on function return
+* PR #1387: Numpy 1.10 support
+* PR #1464: Support cffi.FFI.from_buffer()
+* PR #1437: Propagate errors raised from Numba-compiled ufuncs; also,
+  let "division by zero" and other math errors produce a warning instead
+  of exiting the function early
+* PR #1445: Support a subset of fancy indexing
+* PR #1454: Support "out-of-line" CFFI modules
+* PR #1442: Improve array indexing to support more kinds of basic slicing
+* PR #1409: Support explicit CUDA memory fences
+* PR #1435: Add support for vectorize() and guvectorize() with HSA
+* PR #1432: Implement numpy.nonzero() and numpy.where()
+* PR #1416: Add support for vectorize() and guvectorize() with CUDA,
+  as originally provided in NumbaPro
+* PR #1424: Support in-place array operators
+* PR #1414: Python 3.5 support
+* PR #1404: Add the parallel ufunc functionality originally provided in
+  NumbaPro
+* PR #1393: Implement sorting on arrays and lists
+* PR #1415: Add functions to estimate the occupancy of a CUDA kernel
+* PR #1360: The JIT cache now stores the compiled object code, yielding
+  even larger speedups.
+* PR #1402: Fixes for the ARMv7 (armv7l) architecture under Linux
+* PR #1400: Add the cuda.reduce() decorator originally provided in NumbaPro
+
+Fixes:
+
+* PR #1483: Allow np.empty_like() and friends on non-contiguous arrays
+* Issue #1471: Allow caching JIT functions defined in IPython
+* PR #1457: Fix flat indexing of boolean arrays
+* PR #1421: Allow calling Numpy ufuncs, without an explicit output, on
+  non-contiguous arrays
+* Issue #1411: Fix crash when unpacking a tuple containing a Numba-allocated array
+* Issue #1394: Allow unifying range_state32 and range_state64
+* Issue #1373: Fix code generation error on lists of bools
+
+
+Version 0.21.0
+--------------
+
+This release introduces support for AMD's Heterogeneous System Architecture,
+which allows memory to be shared directly between the CPU and the GPU.
+Other major enhancements are support for lists and the introduction of
+an opt-in compilation cache.
+
+Improvements:
+
+* PR #1391: Implement print() for CUDA code
+* PR #1366: Implement integer typing enhancement proposal (NBEP 1)
+* PR #1380: Support the one-argument type() builtin
+* PR #1375: Allow boolean evaluation of lists and tuples
+* PR #1371: Support array.view() in CUDA mode
+* PR #1369: Support named tuples in nopython mode
+* PR #1250: Implement numpy.median().
+* PR #1289: Make dispatching faster when calling a JIT-compiled function
+  from regular Python
+* Issue #1226: Improve performance of integer power
+* PR #1321: Document features supported with CUDA
+* PR #1345: HSA support
+* PR #1343: Support lists in nopython mode
+* PR #1356: Make Numba-allocated memory visible to tracemalloc
+* PR #1363: Add an environment variable NUMBA_DEBUG_TYPEINFER
+* PR #1051: Add an opt-in, per-function compilation cache
+
+Fixes:
+
+* Issue #1372: Some array expressions would fail rewriting when involved
+  the same variable more than once, or a unary operator
+* Issue #1385: Allow CUDA local arrays to be declared anywhere in a function
+* Issue #1285: Support datetime64 and timedelta64 in Numpy reduction functions
+* Issue #1332: Handle the EXTENDED_ARG opcode.
+* PR #1329: Handle the ``in`` operator in object mode
+* Issue #1322: Fix augmented slice assignment on Python 2
+* PR #1357: Fix slicing with some negative bounds or step values.
+
+
+Version 0.20.0
+--------------
+
+This release updates Numba to use LLVM 3.6 and CUDA 7 for CUDA support.
+Following the platform deprecation in CUDA 7, Numba's CUDA feature is no
+longer supported on 32-bit platforms.  The oldest supported version of
+Windows is Windows 7.
+
+Improvements:
+
+* Issue #1203: Support indexing ndarray.flat
+* PR #1200: Migrate cgutils to llvmlite
+* PR #1190: Support more array methods: .transpose(), .T, .copy(), .reshape(), .view()
+* PR #1214: Simplify setup.py and avoid manual maintenance
+* PR #1217: Support datetime64 and timedelta64 constants
+* PR #1236: Reload environment variables when compiling
+* PR #1225: Various speed improvements in generated code
+* PR #1252: Support cmath module in CUDA
+* PR #1238: Use 32-byte aligned allocator to optimize for AVX
+* PR #1258: Support numpy.frombuffer()
+* PR #1274: Use TravisCI container infrastructure for lower wait time
+* PR #1279: Micro-optimize overload resolution in call dispatch
+* Issue #1248: Improve error message when return type unification fails
+
+Fixes:
+
+* Issue #1131: Handling of negative zeros in np.conjugate() and np.arccos()
+* Issue #1188: Fix slow array return
+* Issue #1164: Avoid warnings from CUDA context at shutdown
+* Issue #1229: Respect the writeable flag in arrays
+* Issue #1244: Fix bug in refcount pruning pass
+* Issue #1251: Fix partial left-indexing of Fortran contiguous array
+* Issue #1264: Fix compilation error in array expression
+* Issue #1254: Fix error when yielding array objects
+* Issue #1276: Fix nested generator use
+
+
+Version 0.19.2
+--------------
+
+This release fixes the source distribution on pypi.  The only change is in the
+setup.py file.  We do not plan to provide a conda package as this release is
+essentially the same as 0.19.1 for conda users.
+
+
+Version 0.19.1
+--------------
+
+* Issue #1196:
+
+  * fix double-free segfault due to redundant variable deletion in the
+    Numba IR (#1195)
+  * fix use-after-delete in array expression rewrite pass
+
+
+Version 0.19.0
+--------------
+
+This version introduces memory management in the Numba runtime, allowing to
+allocate new arrays inside Numba-compiled functions.  There is also a rework
+of the ufunc infrastructure, and an optimization pass to collapse cascading
+array operations into a single efficient loop.
+
+.. warning::
+   Support for Windows XP and Vista with all compiler targets and support
+   for 32-bit platforms (Win/Mac/Linux) with the CUDA compiler target are
+   deprecated.  In the next release of Numba, the oldest version of Windows
+   supported will be Windows 7.  CPU compilation will remain supported
+   on 32-bit Linux and Windows platforms.
+
+Known issues:
+
+* There are some performance regressions in very short running ``nopython``
+  functions due to the additional overhead incurred by memory management.
+  We will work to reduce this overhead in future releases.
+
+Features:
+
+* Issue #1181: Add a Frequently Asked Questions section to the documentation.
+* Issue #1162: Support the ``cumsum()`` and ``cumprod()`` methods on Numpy
+  arrays.
+* Issue #1152: Support the ``*args`` argument-passing style.
+* Issue #1147: Allow passing character sequences as arguments to
+  JIT-compiled functions.
+* Issue #1110: Shortcut deforestation and loop fusion for array expressions.
+* Issue #1136: Support various Numpy array constructors, for example
+  numpy.zeros() and numpy.zeros_like().
+* Issue #1127: Add a CUDA simulator running on the CPU, enabled with the
+  NUMBA_ENABLE_CUDASIM environment variable.
+* Issue #1086: Allow calling standard Numpy ufuncs without an explicit
+  output array from ``nopython`` functions.
+* Issue #1113: Support keyword arguments when calling numpy.empty()
+  and related functions.
+* Issue #1108: Support the ``ctypes.data`` attribute of Numpy arrays.
+* Issue #1077: Memory management for array allocations in ``nopython`` mode.
+* Issue #1105: Support calling a ctypes function that takes ctypes.py_object
+  parameters.
+* Issue #1084: Environment variable NUMBA_DISABLE_JIT disables compilation
+  of ``@jit`` functions, instead calling into the Python interpreter
+  when called.  This allows easier debugging of multiple jitted functions.
+* Issue #927: Allow gufuncs with no output array.
+* Issue #1097: Support comparisons between tuples.
+* Issue #1075: Numba-generated ufuncs can now be called from ``nopython``
+  functions.
+* Issue #1062: ``@vectorize`` now allows omitting the signatures, and will
+  compile the required specializations on the fly (like ``@jit`` does).
+* Issue #1027: Support numpy.round().
+* Issue #1085: Allow returning a character sequence (as fetched from a
+  structured array) from a JIT-compiled function.
+
+Fixes:
+
+* Issue #1170: Ensure ``ndindex()``, ``ndenumerate()`` and ``ndarray.flat``
+  work properly inside generators.
+* Issue #1151: Disallow unpacking of tuples with the wrong size.
+* Issue #1141: Specify install dependencies in setup.py.
+* Issue #1106: Loop-lifting would fail when the lifted loop does not
+  produce any output values for the function tail.
+* Issue #1103: Fix mishandling of some inputs when a JIT-compiled function
+  is called with multiple array layouts.
+* Issue #1089: Fix range() with large unsigned integers.
+* Issue #1088: Install entry-point scripts (numba, pycc) from the conda
+  build recipe.
+* Issue #1081: Constant structured scalars now work properly.
+* Issue #1080: Fix automatic promotion of booleans to integers.
+
+
+Version 0.18.2
+--------------
+
+Bug fixes:
+
+* Issue #1073: Fixes missing template file for HTML annotation
+* Issue #1074: Fixes CUDA support on Windows machine due to NVVM API mismatch
+
+
+Version 0.18.1
+--------------
+
+Version 0.18.0 is not officially released.
+
+This version removes the old deprecated and undocumented ``argtypes`` and
+``restype`` arguments to the ``@jit`` decorator.  Function signatures
+should always be passed as the first argument to ``@jit``.
+
+Features:
+
+* Issue #960: Add inspect_llvm() and inspect_asm() methods to JIT-compiled
+  functions: they output the LLVM IR and the native assembler source of the
+  compiled function, respectively.
+* Issue #990: Allow passing tuples as arguments to JIT-compiled functions
+  in ``nopython`` mode.
+* Issue #774: Support two-argument round() in ``nopython`` mode.
+* Issue #987: Support missing functions from the math module in nopython
+  mode: frexp(), ldexp(), gamma(), lgamma(), erf(), erfc().
+* Issue #995: Improve code generation for round() on Python 3.
+* Issue #981: Support functions from the random and numpy.random modules
+  in ``nopython`` mode.
+* Issue #979: Add cuda.atomic.max().
+* Issue #1006: Improve exception raising and reporting.  It is now allowed
+  to raise an exception with an error message in ``nopython`` mode.
+* Issue #821: Allow ctypes- and cffi-defined functions as arguments to
+  ``nopython`` functions.
+* Issue #901: Allow multiple explicit signatures with ``@jit``.  The
+  signatures must be passed in a list, as with ``@vectorize``.
+* Issue #884: Better error message when a JIT-compiled function is called
+  with the wrong types.
+* Issue #1010: Simpler and faster CUDA argument marshalling thanks to a
+  refactoring of the data model.
+* Issue #1018: Support arrays of scalars inside Numpy structured types.
+* Issue #808: Reduce Numba import time by half.
+* Issue #1021: Support the buffer protocol in ``nopython`` mode.
+  Buffer-providing objects, such as ``bytearray``, ``array.array`` or
+  ``memoryview`` support array-like operations such as indexing and iterating.
+  Furthermore, some standard attributes on the ``memoryview`` object are
+  supported.
+* Issue #1030: Support nested arrays in Numpy structured arrays.
+* Issue #1033: Implement the inspect_types(), inspect_llvm() and inspect_asm()
+  methods for CUDA kernels.
+* Issue #1029: Support Numpy structured arrays with CUDA as well.
+* Issue #1034: Support for generators in nopython and object mode.
+* Issue #1044: Support default argument values when calling Numba-compiled
+  functions.
+* Issue #1048: Allow calling Numpy scalar constructors from CUDA functions.
+* Issue #1047: Allow indexing a multi-dimensional array with a single integer,
+  to take a view.
+* Issue #1050: Support len() on tuples.
+* Issue #1011: Revive HTML annotation.
+
+Fixes:
+
+* Issue #977: Assignment optimization was too aggressive.
+* Issue #561: One-argument round() now returns an int on Python 3.
+* Issue #1001: Fix an unlikely bug where two closures with the same name
+  and id() would compile to the same LLVM function name, despite different
+  closure values.
+* Issue #1006: Fix reference leak when a JIT-compiled function is disposed of.
+* Issue #1017: Update instructions for CUDA in the README.
+* Issue #1008: Generate shorter LLVM type names to avoid segfaults with CUDA.
+* Issue #1005: Properly clean up references when raising an exception from
+  object mode.
+* Issue #1041: Fix incompatibility between Numba and the third-party
+  library "future".
+* Issue #1053: Fix the size attribute of CUDA shared arrays.
+
+
+Version 0.17.0
+--------------
+
+The major focus in this release has been a rewrite of the documentation.
+The new documentation is better structured and has more detailed coverage
+of Numba features and APIs.  It can be found online at
+https://numba.pydata.org/numba-doc/dev/index.html
+
+Features:
+
+* Issue #895: LLVM can now inline nested function calls in ``nopython`` mode.
+* Issue #863: CUDA kernels can now infer the types of their arguments
+  ("autojit"-like).
+* Issue #833: Support numpy.{min,max,argmin,argmax,sum,mean,var,std}
+  in ``nopython`` mode.
+* Issue #905: Add a ``nogil`` argument to the ``@jit`` decorator, to
+  release the GIL in ``nopython`` mode.
+* Issue #829: Add a ``identity`` argument to ``@vectorize`` and
+  ``@guvectorize``, to set the identity value of the ufunc.
+* Issue #843: Allow indexing 0-d arrays with the empty tuple.
+* Issue #933: Allow named arguments, not only positional arguments, when
+  calling a Numba-compiled function.
+* Issue #902: Support numpy.ndenumerate() in ``nopython`` mode.
+* Issue #950: AVX is now enabled by default except on Sandy Bridge and
+  Ivy Bridge CPUs, where it can produce slower code than SSE.
+* Issue #956: Support constant arrays of structured type.
+* Issue #959: Indexing arrays with floating-point numbers isn't allowed
+  anymore.
+* Issue #955: Add support for 3D CUDA grids and thread blocks.
+* Issue #902: Support numpy.ndindex() in ``nopython`` mode.
+* Issue #951: Numpy number types (``numpy.int8``, etc.) can be used as
+  constructors for type conversion in ``nopython`` mode.
+
+Fixes:
+
+* Issue #889: Fix ``NUMBA_DUMP_ASSEMBLY`` for the CUDA backend.
+* Issue #903: Fix calling of stdcall functions with ctypes under Windows.
+* Issue #908: Allow lazy-compiling from several threads at once.
+* Issue #868: Wrong error message when multiplying a scalar by a non-scalar.
+* Issue #917: Allow vectorizing with datetime64 and timedelta64 in the
+  signature (only with unit-less values, though, because of a Numpy limitation).
+* Issue #431: Allow overloading of cuda device function.
+* Issue #917: Print out errors occurred in object mode ufuncs.
+* Issue #923: Numba-compiled ufuncs now inherit the name and doc of the
+  original Python function.
+* Issue #928: Fix boolean return value in nested calls.
+* Issue #915: ``@jit`` called with an explicit signature with a mismatching
+  type of arguments now raises an error.
+* Issue #784: Fix the truth value of NaNs.
+* Issue #953: Fix using shared memory in more than one function (kernel or
+  device).
+* Issue #970: Fix an uncommon double to uint64 conversion bug on CentOS5
+  32-bit (C compiler issue).
+
+
+Version 0.16.0
+--------------
+
+This release contains a major refactor to switch from llvmpy to `llvmlite <https://github.com/numba/llvmlite>`_
+as our code generation backend.  The switch is necessary to reconcile
+different compiler requirements for LLVM 3.5 (needs C++11) and Python
+extensions (need specific compiler versions on Windows). As a bonus, we have
+found the use of llvmlite speeds up compilation by a factor of 2!
+
+Other Major Changes:
+
+* Faster dispatch for numpy structured arrays
+* Optimized array.flat()
+* Improved CPU feature selection
+* Fix constant tuple regression in macro expansion code
+
+Known Issues:
+
+* AVX code generation is still disabled by default due to performance
+  regressions when operating on misaligned NumPy arrays.  We hope to have a
+  workaround in the future.
+* In *extremely* rare circumstances, a `known issue with LLVM 3.5 <http://llvm.org/bugs/show_bug.cgi?id=21423>`_
+  code generation can cause an ELF relocation error on 64-bit Linux systems.
+
+
+Version 0.15.1
+--------------
+
+(This was a bug-fix release that superceded version 0.15 before it was
+announced.)
+
+Fixes:
+
+* Workaround for missing __ftol2 on Windows XP.
+* Do not lift loops for compilation that contain break statements.
+* Fix a bug in loop-lifting when multiple values need to be returned to
+  the enclosing scope.
+* Handle the loop-lifting case where an accumulator needs to be updated when
+  the loop count is zero.
+
+Version 0.15
+------------
+
+Features:
+
+* Support for the Python ``cmath`` module.  (NumPy complex functions were
+  already supported.)
+* Support for ``.real``, ``.imag``, and `.conjugate()`` on non-complex
+  numbers.
+* Add support for ``math.isfinite()`` and ``math.copysign()``.
+* Compatibility mode: If enabled (off by default), a failure to compile in
+  object mode will fall back to using the pure Python implementation of the
+  function.
+* *Experimental* support for serializing JIT functions with cloudpickle.
+* Loop-jitting in object mode now works with loops that modify scalars that
+  are accessed after the loop, such as accumulators.
+* ``@vectorize`` functions can be compiled in object mode.
+* Numba can now be built using the `Visual C++ Compiler for Python 2.7 <http://aka.ms/vcpython27>`_
+  on Windows platforms.
+* CUDA JIT functions can be returned by factory functions with variables in
+  the closure frozen as constants.
+* Support for "optional" types in nopython mode, which allow ``None`` to be a
+  valid value.
+
+Fixes:
+
+* If nopython mode compilation fails for any reason, automatically fall back
+  to object mode (unless nopython=True is passed to @jit) rather than raise
+  an exeception.
+* Allow function objects to be returned from a function compiled in object
+  mode.
+* Fix a linking problem that caused slower platform math functions (such as
+  ``exp()``) to be used on Windows, leading to performance regressions against
+  NumPy.
+* ``min()`` and ``max()`` no longer accept scalars arguments in nopython mode.
+* Fix handling of ambigous type promotion among several compiled versions of a
+  JIT function.  The dispatcher will now compile a new version to resolve the
+  problem.  (issue #776)
+* Fix float32 to uint64 casting bug on 32-bit Linux.
+* Fix type inference to allow forced casting of return types.
+* Allow the shape of a 1D ``cuda.shared.array`` and ``cuda.local.array`` to be
+  a one-element tuple.
+* More correct handling of signed zeros.
+* Add custom implementation of ``atan2()`` on Windows to handle special cases
+  properly.
+* Eliminated race condition in the handling of the pagelocked staging area
+  used when transferring CUDA arrays.
+* Fix non-deterministic type unification leading to varying performance.
+  (issue #797)
+
+
+Version 0.14
+------------
+
+Features:
+
+* Support for nearly all the Numpy math functions (including comparison,
+  logical, bitwise and some previously missing float functions) in nopython mode.
+* The Numpy datetime64 and timedelta64 dtypes are supported in nopython mode
+  with Numpy 1.7 and later.
+* Support for Numpy math functions on complex numbers in nopython mode.
+* ndarray.sum() is supported in nopython mode.
+* Better error messages when unsupported types are used in Numpy math functions.
+* Set NUMBA_WARNINGS=1 in the environment to see which functions are compiled
+  in object mode vs. nopython mode.
+* Add support for the two-argument pow() builtin function in nopython mode.
+* New developer documentation describing how Numba works, and how to
+  add new types.
+* Support for Numpy record arrays on the GPU. (Note: Improper alignment of dtype
+  fields will cause an exception to be raised.)
+* Slices on GPU device arrays.
+* GPU objects can be used as Python context managers to select the active
+  device in a block.
+* GPU device arrays can be bound to a CUDA stream.  All subsequent operations
+  (such as memory copies) will be queued on that stream instead of the default.
+  This can prevent unnecessary synchronization with other streams.
+
+Fixes:
+
+* Generation of AVX instructions has been disabled to avoid performance bugs
+  when calling external math functions that may use SSE instructions,
+  especially on OS X.
+* JIT functions can be removed by the garbage collector when they are no
+  longer accessible.
+* Various other reference counting fixes to prevent memory leaks.
+* Fixed handling of exception when input argument is out of range.
+* Prevent autojit functions from making unsafe numeric conversions when
+  called with different numeric types.
+* Fix a compilation error when an unhashable global value is accessed.
+* Gracefully handle failure to enable faulthandler in the IPython Notebook.
+* Fix a bug that caused loop lifting to fail if the loop was inside an
+  ``else`` block.
+* Fixed a problem with selecting CUDA devices in multithreaded programs on
+  Linux.
+* The ``pow()`` function (and ``**`` operation) applied to two integers now
+  returns an integer rather than a float.
+* Numpy arrays using the object dtype no longer cause an exception in the
+  autojit.
+* Attempts to write to a global array will cause compilation to fall back
+  to object mode, rather than attempt and fail at nopython mode.
+* ``range()`` works with all negative arguments (ex: ``range(-10, -12, -1)``)
+
+Version 0.13.4
+--------------
+
+Features:
+
+* Setting and deleting attributes in object mode
+* Added documentation of supported and currently unsupported numpy ufuncs
+* Assignment to 1-D numpy array slices
+* Closure variables and functions can be used in object mode
+* All numeric global values in modules can be used as constants in JIT
+  compiled code
+* Support for the start argument in enumerate()
+* Inplace arithmetic operations (+=, -=, etc.)
+* Direct iteration over a 1D numpy array (e.g. "for x in array: ...")
+  in nopython mode
+
+Fixes:
+
+* Support for NVIDIA compute capability 5.0 devices (such as the GTX 750)
+* Vectorize no longer crashes/gives an error when bool\_ is used as return type
+* Return the correct dictionary when globals() is used in JIT functions
+* Fix crash bug when creating dictionary literals in object
+* Report more informative error message on import if llvmpy is too old
+* Temporarily disable pycc --header, which generates incorrect function
+  signatures.
+
+Version 0.13.3
+--------------
+
+Features:
+
+* Support for enumerate() and zip() in nopython mode
+* Increased LLVM optimization of JIT functions to -O1, enabling automatic
+  vectorization of compiled code in some cases
+* Iteration over tuples and unpacking of tuples in nopython mode
+* Support for dict and set (Python >= 2.7) literals in object mode
+
+Fixes:
+
+* JIT functions have the same __name__ and __doc__ as the original function.
+* Numerous improvements to better match the data types and behavior of Python
+  math functions in JIT compiled code on different platforms.
+* Importing Numba will no longer throw an exception if the CUDA driver is
+  present, but cannot be initialized.
+* guvectorize now properly supports functions with scalar arguments.
+* CUDA driver is lazily initialized
+
+Version 0.13.2
+--------------
+
+Features:
+
+* @vectorize ufunc now can generate SIMD fast path for unit strided array
+* Added cuda.gridsize
+* Added preliminary exception handling (raise exception class)
+
+Fixes:
+
+* UNARY_POSITIVE
+* Handling of closures and dynamically generated functions
+* Global None value
+
+Version 0.13.1
+--------------
+
+Features:
+
+* Initial support for CUDA array slicing
+
+Fixes:
+
+* Indirectly fixes numbapro when the system has a incompatible CUDA driver
+* Fix numba.cuda.detect
+* Export numba.intp and numba.intc
+
+Version 0.13
+------------
+
+Features:
+
+* Opensourcing NumbaPro CUDA python support in `numba.cuda`
+* Add support for ufunc array broadcasting
+* Add support for mixed input types for ufuncs
+* Add support for returning tuple from jitted function
+
+Fixes:
+
+* Fix store slice bytecode handling for Python2
+* Fix inplace subtract
+* Fix pycc so that correct header is emitted
+* Allow vectorize to work on functions with jit decorator
+
+
+Version 0.12.2
+--------------
+
+Fixes:
+
+* Improved NumPy ufunc support in nopython mode
+* Misc bug fixes
+
+
+Version 0.12.1
+--------------
+
+This version fixed many regressions reported by user for the 0.12 release.
+This release contains a new loop-lifting mechanism that specializes certains
+loop patterns for nopython mode compilation.  This avoid direct support
+for heap-allocating and other very dynamic operations.
+
+Improvements:
+
+* Add loop-lifting--jit-ing loops in nopython for object mode code. This allows
+  functions to allocate NumPy arrays and use Python objects, while the tight
+  loops in the function can still be compiled in nopython mode. Any arrays that
+  the tight loop uses should be created before the loop is entered.
+
+Fixes:
+
+* Add support for majority of "math" module functions
+* Fix for...else handling
+* Add support for builtin round()
+* Fix tenary if...else support
+* Revive "numba" script
+* Fix problems with some boolean expressions
+* Add support for more NumPy ufuncs
+
+
+Version 0.12
+------------
+
+Version 0.12 contains a big refactor of the compiler. The main objective for
+this refactor was to simplify the code base to create a better foundation for
+further work. A secondary objective was to improve the worst case performance
+to ensure that compiled functions in object mode never run slower than pure
+Python code (this was a problem in several cases with the old code base). This
+refactor is still a work in progress and further testing is needed.
+
+Main improvements:
+
+* Major refactor of compiler for performance and maintenance reasons
+* Better fallback to object mode when native mode fails
+* Improved worst case performance in object mode
+
+The public interface of numba has been slightly changed. The idea is to
+make it cleaner and more rational:
+
+* jit decorator has been modified, so that it can be called without a signature.
+  When called without a signature, it behaves as the old autojit. Autojit
+  has been deprecated in favour of this approach.
+* Jitted functions can now be overloaded.
+* Added a "njit" decorator that behaves like "jit" decorator with nopython=True.
+* The numba.vectorize namespace is gone. The vectorize decorator will
+  be in the main numba namespace.
+* Added a guvectorize decorator in the main numba namespace. It is
+  similar to numba.vectorize, but takes a dimension signature. It
+  generates gufuncs. This is a replacement for the GUVectorize gufunc
+  factory which has been deprecated.
+
+Main regressions (will be fixed in a future release):
+
+* Creating new NumPy arrays is not supported in nopython mode
+* Returning NumPy arrays is not supported in nopython mode
+* NumPy array slicing is not supported in nopython mode
+* lists and tuples are not supported in nopython mode
+* string, datetime, cdecimal, and struct types are not implemented yet
+* Extension types (classes) are not supported in nopython mode
+* Closures are not supported
+* Raise keyword is not supported
+* Recursion is not support in nopython mode
+
+Version 0.11
+------------
+* Experimental support for NumPy datetime type
+
+Version 0.10
+------------
+* Annotation tool (./bin/numba --annotate --fancy) (thanks to Jay Bourque)
+* Open sourced prange
+* Support for raise statement
+* Pluggable array representation
+* Support for enumerate and zip (thanks to Eugene Toder)
+* Better string formatting support (thanks to Eugene Toder)
+* Builtins min(), max() and bool() (thanks to Eugene Toder)
+* Fix some code reloading issues (thanks to Björn Linse)
+* Recognize NumPy scalar objects (thanks to Björn Linse)
+
+
+Version 0.9
+-----------
+* Improved math support
+* Open sourced generalized ufuncs
+* Improved array expressions
+
+Version 0.8
+-----------
+* Support for autojit classes
+    * Inheritance not yet supported
+* Python 3 support for pycc
+* Allow retrieval of ctypes function wrapper
+    * And hence support retrieval of a pointer to the function
+* Fixed a memory leak of array slicing views
+
+Version 0.7.2
+-------------
+* Official Python 3 support (python 3.2 and 3.3)
+* Support for intrinsics and instructions
+* Various bug fixes (see https://github.com/numba/numba/issues?milestone=7&state=closed)
+
+Version 0.7.1
+-------------
+* Various bug fixes
+
+Version 0.7
+-----------
+* Open sourced single-threaded ufunc vectorizer
+* Open sourced NumPy array expression compilation
+* Open sourced fast NumPy array slicing
+* Experimental Python 3 support
+* Support for typed containers
+    * typed lists and tuples
+* Support for iteration over objects
+* Support object comparisons
+* Preliminary CFFI support
+    * Jit calls to CFFI functions (passed into autojit functions)
+    * TODO: Recognize ffi_lib.my_func attributes
+* Improved support for ctypes
+* Allow declaring extension attribute types as through class attributes
+* Support for type casting in Python
+    * Get the same semantics with or without numba compilation
+* Support for recursion
+    * For jit methods and extension classes
+* Allow jit functions as C callbacks
+* Friendlier error reporting
+* Internal improvements
+* A variety of bug fixes
+
+Version 0.6.1
+--------------
+* Support for bitwise operations
+
+Version 0.6
+--------------
+* Python 2.6 support
+* Programmable typing
+    * Allow users to add type inference for external code
+* Better NumPy type inference
+    * outer, inner, dot, vdot, tensordot, nonzero, where,
+      binary ufuncs + methods (reduce, accumulate, reduceat, outer)
+* Type based alias analysis
+    * Support for strict aliasing
+* Much faster autojit dispatch when calling from Python
+* Faster numerical loops through data and stride pre-loading
+* Integral overflow and underflow checking for conversions from objects
+* Make Meta dependency optional
+
+Version 0.5
+--------------
+* SSA-based type inference
+    * Allows variable reuse
+    * Allow referring to variables before lexical definition
+* Support multiple comparisons
+* Support for template types
+* List comprehensions
+* Support for pointers
+* Many bug fixes
+* Added user documentation
+
+Version 0.4
+--------------
+
+Version 0.3.2
+--------------
+
+* Add support for object arithmetic (issue 56).
+* Bug fixes (issue 55).
+
+Version 0.3
+--------------
+* Changed default compilation approach to ast
+* Added support for cross-module linking
+* Added support for closures (can jit inner functions and return them) (see examples/closure.py)
+* Added support for dtype structures (can access elements of structure with attribute access) (see examples/structures.py)
+* Added support for extension types (numba classes) (see examples/numbaclasses.py)
+* Added support for general Python code (use nopython to raise an error if Python C-API is used to avoid unexpected slowness because of lack of implementation defaulting to generic Python)
+* Fixed many bugs
+* Added support to detect math operations.
+* Added with python and with nopython contexts
+* Added more examples
+
+Many features need to be documented still.  Look at examples and tests for more information.
+
+
+Version 0.2
+--------------
+* Added an ast approach to compilation
+* Removed d, f, i, b from numba namespace (use f8, f4, i4, b1)
+* Changed function to autojit2
+* Added autojit function to decorate calls to the function and use types of the variable to create compiled versions.
+* changed keyword arguments to jit and autojit functions to restype and argtypes to be consistent with ctypes module.
+* Added pycc -- a python to shared library compiler
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/CONTRIBUTING.md b/cv/3d_detection/pointrcnn-iou/pytorch/numba/CONTRIBUTING.md
new file mode 100644
index 000000000..59d6b807c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/CONTRIBUTING.md
@@ -0,0 +1,53 @@
+
+We welcome people who want to make contributions to Numba, big or small!
+Even simple documentation improvements are encouraged.
+
+# Asking questions
+
+Numba has a [discourse forum](https://numba.discourse.group/) for longer/more
+involved questions and an IRC channel on
+[gitter.im](https://gitter.im/numba/numba) for quick questions and interactive
+help.
+
+# Ways to help:
+
+There's lots of ways to help improve Numba, some of these require creating code
+changes, see **contributing patches** below.
+
+## Quick things:
+
+* Answer a question asked on [discourse](https://numba.discourse.group/) or
+  [gitter.im](https://gitter.im/numba/numba).
+* Review a page of documentation, check it makes sense, that it's clear and
+  still relevant, that the examples are present, good and working. Fix anything
+  that needs updating in a pull request.
+* Make a file that is not `flake8` compliant meet the standard, a list of all
+  failing files is in the `exclude` section of the [`.flake8` config](https://github.com/numba/numba/blob/main/.flake8),
+  then create a pull request with the change.
+
+## More involved things:
+
+* Review a pull request, you don't need to be a compiler engineer to do an
+  initial review of a pull request. It's incredibly helpful to have pull
+  requests go through a review to just make sure the code change is well formed,
+  documented, efficient and clear. Further, if the code is fixing a bug, making
+  sure that tests are present demonstrating it is fixed! Look out for PRs with
+  the [`needs initial review`](https://github.com/numba/numba/labels/needs%20initial%20review)
+  label.
+* Work on fixing or implementing something in the code base, there are a lot of
+  [`good first issue's`](https://github.com/numba/numba/labels/good%20first%20issue)
+  and [`good second issue's`](https://github.com/numba/numba/labels/good%20first%20issue).
+  For implementing new features/functionality, the extension API is the best
+  thing to use and a guide to using `@overload` in particular is
+  [here](https://numba.pydata.org/numba-doc/dev/extending/overloading-guide.html)
+  and the API documentation is [here](https://numba.pydata.org/numba-doc/latest/extending/high-level.html#implementing-functions).
+
+## Contributing patches
+
+Please fork the Numba repository on Github, and create a new branch
+containing your work.  When you are done, open a pull request.
+
+# Further reading
+
+Please read the [contributing guide](
+https://numba.pydata.org/numba-doc/dev/developer/contributing.html).
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/LICENSE b/cv/3d_detection/pointrcnn-iou/pytorch/numba/LICENSE
new file mode 100644
index 000000000..7d19426e7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/LICENSE
@@ -0,0 +1,24 @@
+Copyright (c) 2012, Anaconda, Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+Redistributions of source code must retain the above copyright notice,
+this list of conditions and the following disclaimer.
+
+Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/LICENSES.third-party b/cv/3d_detection/pointrcnn-iou/pytorch/numba/LICENSES.third-party
new file mode 100644
index 000000000..056142b01
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/LICENSES.third-party
@@ -0,0 +1,493 @@
+The Numba source tree includes vendored libraries governed by the following
+licenses.
+
+
+appdirs
+-------
+
+# This is the MIT license
+
+Copyright (c) 2010 ActiveState Software Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be included
+in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+
+NetworkX
+--------
+The dominance frontier algorithm is from a pull request
+https://github.com/numba/numba/pull/4149/files which is based
+on the implementation of NetworkX of dominance. NetworkX has the following
+license:
+
+NetworkX is distributed with the 3-clause BSD license.
+
+::
+
+  Copyright (C) 2004-2019, NetworkX Developers
+  Aric Hagberg <hagberg@lanl.gov>
+  Dan Schult <dschult@colgate.edu>
+  Pieter Swart <swart@lanl.gov>
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are
+  met:
+
+   * Redistributions of source code must retain the above copyright
+     notice, this list of conditions and the following disclaimer.
+
+   * Redistributions in binary form must reproduce the above
+     copyright notice, this list of conditions and the following
+     disclaimer in the documentation and/or other materials provided
+     with the distribution.
+
+   * Neither the name of the NetworkX Developers nor the names of its
+     contributors may be used to endorse or promote products derived
+     from this software without specific prior written permission.
+
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+jquery.graphviz.svg (https://github.com/mountainstorm/jquery.graphviz.svg/)
+---------------------------------------------------------------------------
+The DAG roadmap rendering code in docs/dagmap/ uses Javascript from this 
+package to draw graphs in HTML.
+
+Copyright (c) 2015 Mountainstorm
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+
+
+CPython (https://github.com/python/cpython)
+-------------------------------------------
+Numba source code that references URLs starting with:
+
+https://github.com/python/cpython/
+
+relates to use/inclusion of CPython source code which has the following license:
+
+A. HISTORY OF THE SOFTWARE
+==========================
+
+Python was created in the early 1990s by Guido van Rossum at Stichting
+Mathematisch Centrum (CWI, see http://www.cwi.nl) in the Netherlands
+as a successor of a language called ABC.  Guido remains Python's
+principal author, although it includes many contributions from others.
+
+In 1995, Guido continued his work on Python at the Corporation for
+National Research Initiatives (CNRI, see http://www.cnri.reston.va.us)
+in Reston, Virginia where he released several versions of the
+software.
+
+In May 2000, Guido and the Python core development team moved to
+BeOpen.com to form the BeOpen PythonLabs team.  In October of the same
+year, the PythonLabs team moved to Digital Creations, which became
+Zope Corporation.  In 2001, the Python Software Foundation (PSF, see
+https://www.python.org/psf/) was formed, a non-profit organization
+created specifically to own Python-related Intellectual Property.
+Zope Corporation was a sponsoring member of the PSF.
+
+All Python releases are Open Source (see http://www.opensource.org for
+the Open Source Definition).  Historically, most, but not all, Python
+releases have also been GPL-compatible; the table below summarizes
+the various releases.
+
+    Release         Derived     Year        Owner       GPL-
+                    from                                compatible? (1)
+
+    0.9.0 thru 1.2              1991-1995   CWI         yes
+    1.3 thru 1.5.2  1.2         1995-1999   CNRI        yes
+    1.6             1.5.2       2000        CNRI        no
+    2.0             1.6         2000        BeOpen.com  no
+    1.6.1           1.6         2001        CNRI        yes (2)
+    2.1             2.0+1.6.1   2001        PSF         no
+    2.0.1           2.0+1.6.1   2001        PSF         yes
+    2.1.1           2.1+2.0.1   2001        PSF         yes
+    2.1.2           2.1.1       2002        PSF         yes
+    2.1.3           2.1.2       2002        PSF         yes
+    2.2 and above   2.1.1       2001-now    PSF         yes
+
+Footnotes:
+
+(1) GPL-compatible doesn't mean that we're distributing Python under
+    the GPL.  All Python licenses, unlike the GPL, let you distribute
+    a modified version without making your changes open source.  The
+    GPL-compatible licenses make it possible to combine Python with
+    other software that is released under the GPL; the others don't.
+
+(2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
+    because its license has a choice of law clause.  According to
+    CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
+    is "not incompatible" with the GPL.
+
+Thanks to the many outside volunteers who have worked under Guido's
+direction to make these releases possible.
+
+
+B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
+===============================================================
+
+PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+--------------------------------------------
+
+1. This LICENSE AGREEMENT is between the Python Software Foundation
+("PSF"), and the Individual or Organization ("Licensee") accessing and
+otherwise using this software ("Python") in source or binary form and
+its associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, PSF hereby
+grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+analyze, test, perform and/or display publicly, prepare derivative works,
+distribute, and otherwise use Python alone or in any derivative version,
+provided, however, that PSF's License Agreement and PSF's notice of copyright,
+i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
+2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 Python Software Foundation;
+All Rights Reserved" are retained in Python alone or in any derivative version
+prepared by Licensee.
+
+3. In the event Licensee prepares a derivative work that is based on
+or incorporates Python or any part thereof, and wants to make
+the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to Python.
+
+4. PSF is making Python available to Licensee on an "AS IS"
+basis.  PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
+OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. Nothing in this License Agreement shall be deemed to create any
+relationship of agency, partnership, or joint venture between PSF and
+Licensee.  This License Agreement does not grant permission to use PSF
+trademarks or trade name in a trademark sense to endorse or promote
+products or services of Licensee, or any third party.
+
+8. By copying, installing or otherwise using Python, Licensee
+agrees to be bound by the terms and conditions of this License
+Agreement.
+
+
+BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
+-------------------------------------------
+
+BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
+
+1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
+office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
+Individual or Organization ("Licensee") accessing and otherwise using
+this software in source or binary form and its associated
+documentation ("the Software").
+
+2. Subject to the terms and conditions of this BeOpen Python License
+Agreement, BeOpen hereby grants Licensee a non-exclusive,
+royalty-free, world-wide license to reproduce, analyze, test, perform
+and/or display publicly, prepare derivative works, distribute, and
+otherwise use the Software alone or in any derivative version,
+provided, however, that the BeOpen Python License is retained in the
+Software, alone or in any derivative version prepared by Licensee.
+
+3. BeOpen is making the Software available to Licensee on an "AS IS"
+basis.  BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
+SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
+AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
+DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+5. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+6. This License Agreement shall be governed by and interpreted in all
+respects by the law of the State of California, excluding conflict of
+law provisions.  Nothing in this License Agreement shall be deemed to
+create any relationship of agency, partnership, or joint venture
+between BeOpen and Licensee.  This License Agreement does not grant
+permission to use BeOpen trademarks or trade names in a trademark
+sense to endorse or promote products or services of Licensee, or any
+third party.  As an exception, the "BeOpen Python" logos available at
+http://www.pythonlabs.com/logos.html may be used according to the
+permissions granted on that web page.
+
+7. By copying, installing or otherwise using the software, Licensee
+agrees to be bound by the terms and conditions of this License
+Agreement.
+
+
+CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
+---------------------------------------
+
+1. This LICENSE AGREEMENT is between the Corporation for National
+Research Initiatives, having an office at 1895 Preston White Drive,
+Reston, VA 20191 ("CNRI"), and the Individual or Organization
+("Licensee") accessing and otherwise using Python 1.6.1 software in
+source or binary form and its associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, CNRI
+hereby grants Licensee a nonexclusive, royalty-free, world-wide
+license to reproduce, analyze, test, perform and/or display publicly,
+prepare derivative works, distribute, and otherwise use Python 1.6.1
+alone or in any derivative version, provided, however, that CNRI's
+License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
+1995-2001 Corporation for National Research Initiatives; All Rights
+Reserved" are retained in Python 1.6.1 alone or in any derivative
+version prepared by Licensee.  Alternately, in lieu of CNRI's License
+Agreement, Licensee may substitute the following text (omitting the
+quotes): "Python 1.6.1 is made available subject to the terms and
+conditions in CNRI's License Agreement.  This Agreement together with
+Python 1.6.1 may be located on the Internet using the following
+unique, persistent identifier (known as a handle): 1895.22/1013.  This
+Agreement may also be obtained from a proxy server on the Internet
+using the following URL: http://hdl.handle.net/1895.22/1013".
+
+3. In the event Licensee prepares a derivative work that is based on
+or incorporates Python 1.6.1 or any part thereof, and wants to make
+the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to Python 1.6.1.
+
+4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
+basis.  CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1,
+OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. This License Agreement shall be governed by the federal
+intellectual property law of the United States, including without
+limitation the federal copyright law, and, to the extent such
+U.S. federal law does not apply, by the law of the Commonwealth of
+Virginia, excluding Virginia's conflict of law provisions.
+Notwithstanding the foregoing, with regard to derivative works based
+on Python 1.6.1 that incorporate non-separable material that was
+previously distributed under the GNU General Public License (GPL), the
+law of the Commonwealth of Virginia shall govern this License
+Agreement only as to issues arising under or with respect to
+Paragraphs 4, 5, and 7 of this License Agreement.  Nothing in this
+License Agreement shall be deemed to create any relationship of
+agency, partnership, or joint venture between CNRI and Licensee.  This
+License Agreement does not grant permission to use CNRI trademarks or
+trade name in a trademark sense to endorse or promote products or
+services of Licensee, or any third party.
+
+8. By clicking on the "ACCEPT" button where indicated, or by copying,
+installing or otherwise using Python 1.6.1, Licensee agrees to be
+bound by the terms and conditions of this License Agreement.
+
+        ACCEPT
+
+
+CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
+--------------------------------------------------
+
+Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
+The Netherlands.  All rights reserved.
+
+Permission to use, copy, modify, and distribute this software and its
+documentation for any purpose and without fee is hereby granted,
+provided that the above copyright notice appear in all copies and that
+both that copyright notice and this permission notice appear in
+supporting documentation, and that the name of Stichting Mathematisch
+Centrum or CWI not be used in advertising or publicity pertaining to
+distribution of the software without specific, written prior
+permission.
+
+STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
+THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
+FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+
+CPython unicode (https://github.com/python/cpython)
+---------------------------------------------------
+Numba's unicode support includes source code/algorithms from CPython's unicode
+implementation, Numba source code that has a reference starting with:
+
+https://github.com/python/cpython/
+
+and contains in the path "Objects/unicodeobject.c" relates to use/inclusion of
+CPython source code which has the following license along with the standard
+CPython license:
+
+
+Unicode implementation based on original code by Fredrik Lundh,
+modified by Marc-Andre Lemburg <mal@lemburg.com>.
+
+Major speed upgrades to the method implementations at the Reykjavik
+NeedForSpeed sprint, by Fredrik Lundh and Andrew Dalke.
+
+Copyright (c) Corporation for National Research Initiatives.
+
+--------------------------------------------------------------------
+The original string type implementation is:
+
+  Copyright (c) 1999 by Secret Labs AB
+  Copyright (c) 1999 by Fredrik Lundh
+
+By obtaining, using, and/or copying this software and/or its
+associated documentation, you agree that you have read, understood,
+and will comply with the following terms and conditions:
+
+Permission to use, copy, modify, and distribute this software and its
+associated documentation for any purpose and without fee is hereby
+granted, provided that the above copyright notice appears in all
+copies, and that both that copyright notice and this permission notice
+appear in supporting documentation, and that the name of Secret Labs
+AB or the author not be used in advertising or publicity pertaining to
+distribution of the software without specific, written prior
+permission.
+
+SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO
+THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS.  IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR BE LIABLE FOR
+ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+--------------------------------------------------------------------
+
+
+cloudpickle
+-----------
+
+This module was extracted from the `cloud` package, developed by
+PiCloud, Inc.
+
+Copyright (c) 2015, Cloudpickle contributors.
+Copyright (c) 2012, Regents of the University of California.
+Copyright (c) 2009 PiCloud, Inc. http://www.picloud.com.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the University of California, Berkeley nor the
+      names of its contributors may be used to endorse or promote
+      products derived from this software without specific prior written
+      permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+© 2020 GitHub, Inc.
+
+
+NumPy (https://github.com/numpy/numpy)
+--------------------------------------
+Numba source code that references URLs starting with:
+
+https://github.com/numpy/numpy
+
+relates to use of/inclusion of/derivate work based on NumPy source code which
+has the following license:
+
+
+Copyright (c) 2005-2021, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/MANIFEST.in b/cv/3d_detection/pointrcnn-iou/pytorch/numba/MANIFEST.in
new file mode 100644
index 000000000..449dab31c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/MANIFEST.in
@@ -0,0 +1,10 @@
+include MANIFEST.in
+include README.rst setup.py runtests.py versioneer.py CHANGE_LOG LICENSE
+
+recursive-include numba *.c *.cpp *.h *.hpp *.inc
+recursive-include docs *.ipynb *.txt *.py Makefile *.rst
+recursive-include examples *.py
+
+prune docs/_build
+prune docs/gh-pages
+include numba/_version.py
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/README.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/README.rst
new file mode 100644
index 000000000..48b2855b6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/README.rst
@@ -0,0 +1,61 @@
+*****
+Numba
+*****
+
+.. image:: https://badges.gitter.im/numba/numba.svg
+   :target: https://gitter.im/numba/numba?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
+   :alt: Gitter
+
+.. image:: https://img.shields.io/badge/discuss-on%20discourse-blue
+   :target: https://numba.discourse.group/
+   :alt: Discourse
+
+.. image:: https://zenodo.org/badge/3659275.svg
+   :target: https://zenodo.org/badge/latestdoi/3659275
+   :alt: Zenodo DOI
+
+.. image:: https://img.shields.io/pypi/v/numba.svg
+   :target: https://pypi.python.org/pypi/numba/
+   :alt: PyPI
+
+.. image:: https://dev.azure.com/numba/numba/_apis/build/status/numba.numba?branchName=main
+    :target: https://dev.azure.com/numba/numba/_build/latest?definitionId=1?branchName=main
+    :alt: Azure Pipelines
+
+A Just-In-Time Compiler for Numerical Functions in Python
+#########################################################
+
+Numba is an open source, NumPy-aware optimizing compiler for Python sponsored
+by Anaconda, Inc.  It uses the LLVM compiler project to generate machine code
+from Python syntax.
+
+Numba can compile a large subset of numerically-focused Python, including many
+NumPy functions.  Additionally, Numba has support for automatic
+parallelization of loops, generation of GPU-accelerated code, and creation of
+ufuncs and C callbacks.
+
+For more information about Numba, see the Numba homepage:
+https://numba.pydata.org and the online documentation:
+https://numba.readthedocs.io/en/stable/index.html
+
+Installation
+============
+
+Please follow the instructions:
+
+https://numba.readthedocs.io/en/stable/user/installing.html
+
+Demo
+====
+
+Please have a look and the demo notebooks via the mybinder service:
+
+https://mybinder.org/v2/gh/numba/numba-examples/master?filepath=notebooks
+
+Contact
+=======
+
+Numba has a discourse forum for discussions:
+
+* https://numba.discourse.group
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/azure-pipelines.yml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/azure-pipelines.yml
new file mode 100644
index 000000000..79980d31b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/azure-pipelines.yml
@@ -0,0 +1,129 @@
+trigger:
+  batch: true
+
+variables:
+  # Change the following along with adding new TEST_START_INDEX.
+  TEST_COUNT: 20
+
+jobs:
+# Mac and Linux use the same template with different matrixes
+- template: buildscripts/azure/azure-linux-macos.yml
+  parameters:
+    name: macOS
+    vmImage: macos-11
+    matrix:
+      py37_np118:
+        PYTHON: '3.7'
+        NUMPY: '1.18'
+        CONDA_ENV: 'azure_ci'
+        TEST_START_INDEX: 0
+      py310_np123:
+        PYTHON: '3.10'
+        NUMPY: '1.23'
+        CONDA_ENV: 'azure_ci'
+        TEST_THREADING: 'tbb'
+        TEST_START_INDEX: 1
+
+- template: buildscripts/azure/azure-linux-macos.yml
+  parameters:
+    name: Linux
+    vmImage: ubuntu-20.04
+    matrix:
+      py37_np118_32bit:
+        # 32 bit linux only has np 1.15
+        PYTHON: '3.7'
+        NUMPY: '1.18'
+        CONDA_ENV: azure_ci
+        BITS32: yes
+        TEST_START_INDEX: 2
+      py37_np118_vanilla:
+        PYTHON: '3.7'
+        NUMPY: '1.18'
+        CONDA_ENV: azure_ci
+        VANILLA_INSTALL: yes
+        TEST_START_INDEX: 3
+      py38_np118_cov:
+        PYTHON: '3.8'
+        NUMPY: '1.18'
+        CONDA_ENV: azure_ci
+        RUN_COVERAGE: yes
+        RUN_FLAKE8: yes
+        RUN_MYPY: yes
+        TEST_START_INDEX: 4
+      py38_np119_tbb:
+        PYTHON: '3.8'
+        NUMPY: '1.19.2=*_0'
+        CONDA_ENV: azure_ci
+        TEST_THREADING: 'tbb'
+        TEST_START_INDEX: 5
+      py38_np119_omp:
+        PYTHON: '3.8'
+        NUMPY: '1.19.2=*_0'
+        CONDA_ENV: azure_ci
+        TEST_THREADING: omp
+        TEST_START_INDEX: 6
+      py38_np119_workqueue:
+        PYTHON: '3.8'
+        NUMPY: '1.19.2=*_0'
+        CONDA_ENV: azure_ci
+        TEST_THREADING: workqueue
+        TEST_START_INDEX: 7
+      py38_np120_doc:
+        PYTHON: '3.8'
+        NUMPY: '1.20'
+        CONDA_ENV: azure_ci
+        BUILD_DOC: yes
+        TEST_START_INDEX: 8
+      py38_np120_pickle5:
+        PYTHON: '3.8'
+        NUMPY: '1.20'
+        CONDA_ENV: azure_ci
+        TEST_PICKLE5: yes
+        TEST_START_INDEX: 9
+      py38_np120_svml:
+        PYTHON: '3.8'
+        NUMPY: '1.20'
+        CONDA_ENV: azure_ci
+        TEST_SVML: yes
+        TEST_START_INDEX: 10
+      py38_np122:
+        PYTHON: '3.8'
+        NUMPY: '1.22'
+        CONDA_ENV: azure_ci
+        TEST_START_INDEX: 11
+      py39_np119:
+        PYTHON: '3.9'
+        NUMPY: '1.19.2=*_0'
+        CONDA_ENV: azure_ci
+        TEST_START_INDEX: 12
+      py39_np120_typeguard:
+        PYTHON: '3.9'
+        NUMPY: '1.20'
+        CONDA_ENV: azure_ci
+        RUN_TYPEGUARD: yes
+        TEST_START_INDEX: 13
+      py39_np121:
+        PYTHON: '3.9'
+        NUMPY: '1.21'
+        CONDA_ENV: azure_ci
+        TEST_START_INDEX: 14
+      py39_np123:
+        PYTHON: '3.9'
+        NUMPY: '1.23'
+        CONDA_ENV: azure_ci
+        TEST_START_INDEX: 15
+      py310_np121:
+        PYTHON: '3.10'
+        NUMPY: '1.21'
+        CONDA_ENV: azure_ci
+        TEST_START_INDEX: 16
+      py310_np123:
+        PYTHON: '3.10'
+        NUMPY: '1.23'
+        CONDA_ENV: azure_ci
+        TEST_START_INDEX: 17
+
+- template: buildscripts/azure/azure-windows.yml
+  parameters:
+    name: Windows
+    vmImage: windows-2019
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/bin/numba b/cv/3d_detection/pointrcnn-iou/pytorch/numba/bin/numba
new file mode 100644
index 000000000..32e7180df
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/bin/numba
@@ -0,0 +1,8 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+from __future__ import print_function, division, absolute_import
+
+from numba.misc.numba_entry import main
+
+if __name__ == "__main__":
+    main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/build_numba.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/build_numba.sh
new file mode 100644
index 000000000..2e4dda01e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/build_numba.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+
+clang_version=`clang --version | grep "clang version 16."`
+if [[ "${clang_version}" != "" ]]; then
+  echo "Not support LLVM16 now!"
+  exit 0
+fi
+
+COREX_VERSION=${COREX_VERSION:-latest}
+
+PYTHON_PATH=$(which python3)
+
+if [[ "${COREX_VERSION}" == "latest" ]]; then
+  COREX_VERSION=`date --utc +%Y%m%d%H%M%S`
+fi
+export NUMBA_LOCAL_IDENTIFIER="corex.${COREX_VERSION}"
+
+${PYTHON_PATH} setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log; [[ ${PIPESTATUS[0]} == 0 ]] || exit
+
+# Return 0 status if all finished
+exit 0
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/appveyor/run_with_env.cmd b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/appveyor/run_with_env.cmd
new file mode 100644
index 000000000..3a56e3e84
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/appveyor/run_with_env.cmd
@@ -0,0 +1,90 @@
+:: From https://github.com/ogrisel/python-appveyor-demo
+::
+:: To build extensions for 64 bit Python 3, we need to configure environment
+:: variables to use the MSVC 2010 C++ compilers from GRMSDKX_EN_DVD.iso of:
+:: MS Windows SDK for Windows 7 and .NET Framework 4 (SDK v7.1)
+::
+:: To build extensions for 64 bit Python 2, we need to configure environment
+:: variables to use the MSVC 2008 C++ compilers from GRMSDKX_EN_DVD.iso of:
+:: MS Windows SDK for Windows 7 and .NET Framework 3.5 (SDK v7.0)
+::
+:: 32 bit builds, and 64-bit builds for 3.5 and beyond, do not require specific
+:: environment configurations.
+::
+:: Note: this script needs to be run with the /E:ON and /V:ON flags for the
+:: cmd interpreter, at least for (SDK v7.0)
+::
+:: More details at:
+:: https://github.com/cython/cython/wiki/64BitCythonExtensionsOnWindows
+:: http://stackoverflow.com/a/13751649/163740
+::
+:: Author: Olivier Grisel
+:: License: CC0 1.0 Universal: http://creativecommons.org/publicdomain/zero/1.0/
+::
+:: Notes about batch files for Python people:
+::
+:: Quotes in values are literally part of the values:
+::      SET FOO="bar"
+:: FOO is now five characters long: " b a r "
+:: If you don't want quotes, don't include them on the right-hand side.
+::
+:: The CALL lines at the end of this file look redundant, but if you move them
+:: outside of the IF clauses, they do not run properly in the SET_SDK_64==Y
+:: case, I don't know why.
+@ECHO OFF
+
+SET COMMAND_TO_RUN=%*
+SET WIN_SDK_ROOT=C:\Program Files\Microsoft SDKs\Windows
+SET WIN_WDK=c:\Program Files (x86)\Windows Kits\10\Include\wdf
+
+:: Extract the major and minor versions, and allow for the minor version to be
+:: more than 9.  This requires the version number to have two dots in it.
+SET MAJOR_PYTHON_VERSION=%PYTHON:~0,1%
+IF "%PYTHON:~3,1%" == "." (
+    SET MINOR_PYTHON_VERSION=%PYTHON:~2,1%
+) ELSE (
+    SET MINOR_PYTHON_VERSION=%PYTHON:~2,2%
+)
+
+:: Based on the Python version, determine what SDK version to use, and whether
+:: to set the SDK for 64-bit.
+IF %MAJOR_PYTHON_VERSION% == 2 (
+    SET WINDOWS_SDK_VERSION="v7.0"
+    SET SET_SDK_64=Y
+) ELSE (
+    IF %MAJOR_PYTHON_VERSION% == 3 (
+        SET WINDOWS_SDK_VERSION="v7.1"
+        IF %MINOR_PYTHON_VERSION% LEQ 4 (
+            SET SET_SDK_64=Y
+        ) ELSE (
+            SET SET_SDK_64=N
+            IF EXIST "%WIN_WDK%" (
+                :: See: https://connect.microsoft.com/VisualStudio/feedback/details/1610302/
+                REN "%WIN_WDK%" 0wdf
+            )
+        )
+    ) ELSE (
+        ECHO Unsupported Python version: "%MAJOR_PYTHON_VERSION%"
+        EXIT 1
+    )
+)
+
+IF %ARCH% == 64 (
+    IF %SET_SDK_64% == Y (
+        ECHO Configuring Windows SDK %WINDOWS_SDK_VERSION% for Python %MAJOR_PYTHON_VERSION% on a 64 bit architecture
+        SET DISTUTILS_USE_SDK=1
+        SET MSSdk=1
+        "%WIN_SDK_ROOT%\%WINDOWS_SDK_VERSION%\Setup\WindowsSdkVer.exe" -q -version:%WINDOWS_SDK_VERSION%
+        "%WIN_SDK_ROOT%\%WINDOWS_SDK_VERSION%\Bin\SetEnv.cmd" /x64 /release
+        ECHO Executing: %COMMAND_TO_RUN%
+        call %COMMAND_TO_RUN% || EXIT 1
+    ) ELSE (
+        ECHO Using default MSVC build environment for 64 bit architecture
+        ECHO Executing: %COMMAND_TO_RUN%
+        call %COMMAND_TO_RUN% || EXIT 1
+    )
+) ELSE (
+    ECHO Using default MSVC build environment for 32 bit architecture
+    ECHO Executing: %COMMAND_TO_RUN%
+    call %COMMAND_TO_RUN% || EXIT 1
+)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/azure/azure-linux-macos.yml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/azure/azure-linux-macos.yml
new file mode 100644
index 000000000..74cd37f41
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/azure/azure-linux-macos.yml
@@ -0,0 +1,47 @@
+parameters:
+  name: ''
+  vmImage: ''
+  matrix: []
+
+jobs:
+- job: ${{ parameters.name }}
+  pool:
+    vmImage: ${{ parameters.vmImage }}
+  strategy:
+    matrix:
+      ${{ insert }}: ${{ parameters.matrix }}
+
+  steps:
+    - script: |
+        if [ "$(uname)" == "Linux" ] && [[ "$CONDA_SUBDIR" == "linux-32" || "$BITS32" == "yes" ]]; then sudo apt-get install -y libc6-dev-i386; fi
+        if [ "$(uname)" == "Linux" ] && [[ "$CONDA_SUBDIR" != "linux-32" && "$BITS32" != "yes" ]]; then sudo apt-get install -y gdb; fi
+        echo "Installing Miniconda"
+        buildscripts/incremental/install_miniconda.sh
+        export PATH=$HOME/miniconda3/bin:$PATH
+        echo "Setting up Conda environment"
+        buildscripts/incremental/setup_conda_environment.sh
+      displayName: 'Before Install'
+
+    - script: |
+        export PATH=$HOME/miniconda3/bin:$PATH
+        buildscripts/incremental/build.sh
+      displayName: 'Build'
+
+    - script: |
+        export PATH=$HOME/miniconda3/bin:$PATH
+        conda install -y flake8
+        flake8 numba
+      displayName: 'Flake8'
+      condition: eq(variables['RUN_FLAKE8'], 'yes')
+
+    - script: |
+        export PATH=$HOME/miniconda3/bin:$PATH
+        conda install -y mypy
+        mypy
+      displayName: 'Mypy'
+      condition: eq(variables['RUN_MYPY'], 'yes')
+
+    - script: |
+        export PATH=$HOME/miniconda3/bin:$PATH
+        buildscripts/incremental/test.sh
+      displayName: 'Test'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/azure/azure-windows.yml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/azure/azure-windows.yml
new file mode 100644
index 000000000..aa38d8b32
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/azure/azure-windows.yml
@@ -0,0 +1,62 @@
+parameters:
+  name: ''
+  vmImage: ''
+
+jobs:
+- job: ${{ parameters.name }}
+  pool:
+    vmImage: ${{ parameters.vmImage }}
+  strategy:
+    matrix:
+      py310_np123:
+        PYTHON: '3.10'
+        NUMPY: '1.23'
+        CONDA_ENV: 'testenv'
+        TEST_START_INDEX: 18
+      py37_np118:
+        PYTHON: '3.7'
+        NUMPY: '1.18'
+        CONDA_ENV: 'testenv'
+        TEST_START_INDEX: 19
+
+  steps:
+    - task: CondaEnvironment@1
+      inputs:
+        updateConda: no
+        packageSpecs: ''
+
+    - script: |
+        buildscripts\\incremental\\setup_conda_environment.cmd
+      displayName: 'Before Install'
+
+    - script: |
+        buildscripts\\incremental\\build.cmd
+      displayName: 'Build'
+
+    - script: |
+        call activate %CONDA_ENV%
+        python -m numba -s
+      displayName: 'Display numba system information'
+
+    - script: |
+        call activate %CONDA_ENV%
+        python -m numba.tests.test_runtests
+      displayName: 'Verify runtests'
+
+    - script: |
+        call activate %CONDA_ENV%
+        python -m numba.runtests -l
+      displayName: 'List discovered tests'
+
+    - script: |
+        call activate %CONDA_ENV%
+        set NUMBA_CAPTURED_ERRORS=new_style
+        echo "Running slice of discovered tests: %TEST_START_INDEX%,None,%TEST_COUNT%"
+        python -m numba.runtests -b -v -g -m 2 -- numba.tests
+      displayName: 'Test modified test files'
+
+    - script: |
+        call activate %CONDA_ENV%
+        set NUMBA_CAPTURED_ERRORS=new_style
+        python runtests.py -m 2 -b -j "%TEST_START_INDEX%,None,%TEST_COUNT%"  --exclude-tags='long_running'  -- numba.tests
+      displayName: 'Test slice of test files'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/bld.bat b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/bld.bat
new file mode 100644
index 000000000..6372f3a4d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/bld.bat
@@ -0,0 +1,3 @@
+%PYTHON% setup.py build install --single-version-externally-managed --record=record.txt
+
+exit /b %errorlevel%
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/build.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/build.sh
new file mode 100644
index 000000000..f08a73391
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/build.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+if [[ "$(uname -s)" == *"Linux"* ]] && [[ "$(uname -p)" == *"86"* ]]; then
+    EXTRA_BUILD_EXT_FLAGS="--werror --wall"
+else
+    EXTRA_BUILD_EXT_FLAGS=""
+fi
+
+if [[ "$(uname -s)" == *"Linux"* ]] && [[ "$(uname -p)" == *"ppc64le"* ]]; then
+    # To workaround https://github.com/numba/numba/issues/7302 
+    # because of a python build problem that the -pthread could be stripped.
+    export CC="$CC -pthread"
+    export CXX="$CXX -pthread"
+fi
+
+MACOSX_DEPLOYMENT_TARGET=10.10 $PYTHON setup.py build_ext $EXTRA_BUILD_EXT_FLAGS build install --single-version-externally-managed --record=record.txt
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/conda_build_config.yaml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/conda_build_config.yaml
new file mode 100644
index 000000000..9798e4b69
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/conda_build_config.yaml
@@ -0,0 +1,12 @@
+# Numba/llvmlite stack needs an older compiler for backwards compatibility.
+c_compiler_version:         # [linux]
+  - 7                       # [linux and (x86_64 or ppc64le)]
+  - 9                       # [linux and aarch64]
+
+cxx_compiler_version:       # [linux]
+  - 7                       # [linux and (x86_64 or ppc64le)]
+  - 9                       # [linux and aarch64]
+
+fortran_compiler_version:   # [linux]
+  - 7                       # [linux and (x86_64 or ppc64le)]
+  - 9                       # [linux and aarch64]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/license.txt b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/license.txt
new file mode 100644
index 000000000..7d19426e7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/license.txt
@@ -0,0 +1,24 @@
+Copyright (c) 2012, Anaconda, Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+Redistributions of source code must retain the above copyright notice,
+this list of conditions and the following disclaimer.
+
+Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/meta.yaml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/meta.yaml
new file mode 100644
index 000000000..ad94a6896
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/meta.yaml
@@ -0,0 +1,94 @@
+package:
+   name: numba
+   version: {{ GIT_DESCRIBE_TAG }}
+
+source:
+   path: ../..
+
+build:
+  number: {{ GIT_DESCRIBE_NUMBER|int }}
+  string: np{{ NPY_VER }}py{{ PY_VER }}h{{ PKG_HASH }}_{{GIT_DESCRIBE_HASH}}_{{ GIT_DESCRIBE_NUMBER }}
+  entry_points:
+    - pycc = numba.pycc:main
+    - numba = numba.misc.numba_entry:main
+  script_env:
+    - PY_VCRUNTIME_REDIST
+  missing_dso_whitelist: # [osx]
+    # optional dependency: required only when omp is chosen as the backend for
+    # the threading layer
+    - lib/libiomp5.dylib # [osx]
+  ignore_run_exports:
+    # tbb-devel triggers hard dependency on tbb, this is not the case.
+    - tbb     # [not (armv6l or armv7l or aarch64 or linux32)]
+
+requirements:
+  # build and run dependencies are duplicated to avoid setuptools issues
+  # when we also set install_requires in setup.py
+  build:
+    - {{ compiler('c') }}      # [not (armv6l or armv7l or aarch64)]
+    - {{ compiler('cxx') }}    # [not (armv6l or armv7l or aarch64)]
+    # OpenMP headers from llvm needed for OSX.
+    - llvm-openmp              # [osx]
+  host:
+    - python
+    - numpy
+    - setuptools
+    - importlib_metadata       # [py<39]
+    # On channel https://anaconda.org/numba/
+    - llvmlite 0.39.*
+    # TBB devel version is to match TBB libs.
+    # 2020.3 is the last version with the "old" ABI
+    # NOTE: 2021.1..2021.5 are API compatible for Numba's purposes.
+    # NOTE: ppc64le exclusion is temporary until packages are more generally
+    #       available.
+    - tbb-devel >=2021,<2021.6       # [not (armv6l or armv7l or aarch64 or linux32 or ppc64le)]
+  run:
+    - python >=3.7
+    # NumPy 1.22.0, 1.22.1, 1.22.2 are all broken for ufuncs, see #7756
+    - numpy >=1.18, !=1.22.0, !=1.22.1, !=1.22.2, <1.24
+    - setuptools
+    - importlib_metadata       # [py<39]
+    # On channel https://anaconda.org/numba/
+    - llvmlite 0.39.*
+  run_constrained:
+    # If TBB is present it must be at least version 2021
+    - tbb >=2021    # [not (armv6l or armv7l or aarch64 or linux32 or ppc64le)]
+    # avoid confusion from openblas bugs
+    - libopenblas !=0.3.6      # [x86_64]
+    # 0.3.17 buggy on M1 silicon
+    # https://github.com/xianyi/OpenBLAS/blob/v0.3.20/Changelog.txt#L118
+    # https://github.com/numba/numba/issues/7822#issuecomment-1063229855
+    # Exclude 0.3.20 too
+    # https://github.com/numba/numba/issues/8096
+    - libopenblas >=0.3.18, !=0.3.20     # [arm64]
+    # CUDA 10.2 or later is required for CUDA support
+    - cudatoolkit >=10.2
+    # scipy 1.0 or later
+    - scipy >=1.0
+    # CUDA Python 11.6 or later
+    - cuda-python >=11.6
+
+test:
+  requires:
+    - jinja2
+    # Required to test optional Numba features
+    - cffi
+    # temporarily disable scipy testing on ARM, need to build out more packages
+    - scipy                    # [not (armv6l or armv7l)]
+    - ipython                  # [not (armv6l or armv7l or aarch64)]
+    - setuptools
+    - tbb  >=2021              # [not (armv6l or armv7l or aarch64 or linux32 or ppc64le)]
+    - llvm-openmp              # [osx]
+    # This is for driving gdb tests
+    - pexpect                  # [linux64]
+    # For testing ipython
+    - ipykernel
+    # Need these for AOT. Do not init msvc as it may not be present
+    - {{ compiler('c') }}      # [not (win or armv6l or armv7l or aarch64)]
+    - {{ compiler('cxx') }}    # [not (win or armv6l or armv7l or aarch64)]
+
+about:
+  home: https://numba.pydata.org/
+  license: BSD
+  license_file: LICENSE
+  summary: a just-in-time Python function compiler based on LLVM
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/run_test.bat b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/run_test.bat
new file mode 100644
index 000000000..077067e06
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/run_test.bat
@@ -0,0 +1,19 @@
+set NUMBA_DEVELOPER_MODE=1
+set NUMBA_DISABLE_ERROR_MESSAGE_HIGHLIGHTING=1
+set NUMBA_CAPTURED_ERRORS=new_style
+set PYTHONFAULTHANDLER=1
+
+@rem Check Numba executables are there
+pycc -h
+numba -h
+
+@rem Run system info tool
+numba -s
+
+@rem Check test discovery works
+python -m numba.tests.test_runtests
+
+@rem Run the whole test suite
+python -m numba.runtests -b -m -- %TESTS_TO_RUN%
+
+if errorlevel 1 exit 1
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/run_test.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/run_test.sh
new file mode 100644
index 000000000..69cc46008
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe.local/run_test.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+set -e
+
+export NUMBA_DEVELOPER_MODE=1
+export NUMBA_DISABLE_ERROR_MESSAGE_HIGHLIGHTING=1
+export NUMBA_CAPTURED_ERRORS="new_style"
+export PYTHONFAULTHANDLER=1
+
+# Disable NumPy dispatching to AVX512_SKX feature extensions if the chip is
+# reported to support the feature and NumPy >= 1.22 as this results in the use
+# of low accuracy SVML libm replacements in ufunc loops.
+_NPY_CMD='from numba.misc import numba_sysinfo;\
+          sysinfo=numba_sysinfo.get_sysinfo();\
+          print(sysinfo["NumPy AVX512_SKX detected"] and
+                sysinfo["NumPy Version"]>="1.22")'
+NUMPY_DETECTS_AVX512_SKX_NP_GT_122=$(python -c "$_NPY_CMD")
+echo "NumPy >= 1.22 with AVX512_SKX detected: $NUMPY_DETECTS_AVX512_SKX_NP_GT_122"
+
+if [[ "$NUMPY_DETECTS_AVX512_SKX_NP_GT_122" == "True" ]]; then
+    export NPY_DISABLE_CPU_FEATURES="AVX512_SKX"
+fi
+
+
+unamestr=`uname`
+if [[ "$unamestr" == 'Linux' ]]; then
+  SEGVCATCH=catchsegv
+elif [[ "$unamestr" == 'Darwin' ]]; then
+  SEGVCATCH=""
+else
+  echo Error
+fi
+
+# limit CPUs in use on PPC64LE, fork() issues
+# occur on high core count systems
+archstr=`uname -m`
+if [[ "$archstr" == 'ppc64le' ]]; then
+    TEST_NPROCS=16
+fi
+
+# Check Numba executables are there
+pycc -h
+numba -h
+
+# run system info tool
+numba -s
+
+# Check test discovery works
+python -m numba.tests.test_runtests
+
+# Run the whole test suite
+echo "Running: $SEGVCATCH python -m numba.runtests -b -m $TEST_NPROCS -- $TESTS_TO_RUN"
+$SEGVCATCH python -m numba.runtests -b -m $TEST_NPROCS -- $TESTS_TO_RUN
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/bld.bat b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/bld.bat
new file mode 100644
index 000000000..e2fd587c5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/bld.bat
@@ -0,0 +1,3 @@
+%PYTHON% build.py
+
+exit /b %errorlevel%
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/build.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/build.sh
new file mode 100644
index 000000000..9ed12875c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/build.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+
+set -x
+
+${PYTHON} build.py
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/meta.yaml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/meta.yaml
new file mode 100644
index 000000000..76f64a6b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/meta.yaml
@@ -0,0 +1,38 @@
+{% set version = "2019.3" %} # this is the intel version to get
+{% set win_build_number = "203" %}   # the build number from the intel windows version
+{% set osx_build_number = "199" %}   # the build number from the intel osx version
+{% set lnx_build_number = "199" %}   # the build number from the intel linux version
+
+package:
+  name: icc_rt
+  version: {{ version }}
+
+build:
+  number: {{ win_build_number }}  # [win]
+  number: {{ osx_build_number }}  # [osx]
+  number: {{ lnx_build_number }}  # [linux]
+
+source:
+    - url: https://anaconda.org/intel/icc_rt/{{ version }}/download/win-64/icc_rt-{{version}}-intel_{{win_build_number}}.tar.bz2 # [win]
+    - md5: d39bae3218457a4ea045763fdcfc1562 # [win]
+    - sha256: 2c55b8af1dea35ee4648b671050899a93b7eba1b26acad019bf569ca777a944e # [win]
+
+    - url: https://anaconda.org/intel/icc_rt/{{ version }}/download/osx-64/icc_rt-{{version}}-intel_{{osx_build_number}}.tar.bz2 # [osx]
+    - md5: 064566ac53e729d3f008e32b1f73d1fa # [osx]
+    - sha256: 54a372b0d8d5b4d750c28ea122851b52ec9aa3cccb8d4cf4a2999494dfda6656 # [osx]
+
+    - url: https://anaconda.org/intel/icc_rt/{{ version }}/download/linux-64/icc_rt-{{version}}-intel_{{lnx_build_number}}.tar.bz2 # [linux]
+    - md5: 306c3ee9491577715dbd76c838147078 # [linux]
+    - sha256: 4cedd10343d1ab4403af2ff080b47afe5399be550f1c215e5a7c7eceec672516 # [linux]
+
+    - path: scripts
+
+requirements:
+  build:
+    - python>=3
+
+about:
+  license: "Intel"
+  license_family: "Proprietary"
+  license_file: LICENSE.txt
+summary: Intel ICC runtime.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/scripts/build.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/scripts/build.py
new file mode 100644
index 000000000..11eac6282
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/condarecipe_clone_icc_rt/scripts/build.py
@@ -0,0 +1,33 @@
+import sys
+import os
+import shutil
+
+libdir = {'w': 'Library',
+          'l': 'lib',
+          'd': 'lib'}
+
+
+def run():
+    src_dir = os.environ.get('SRC_DIR')
+    prefix = os.environ.get('PREFIX')
+
+    libd = libdir.get(sys.platform[0], None)
+    assert libd is not None
+
+    # remove 'lib' from the prefix so a direct copy from the original
+    # package can be made
+    lib_dir = os.path.join(prefix, libd)
+    shutil.rmtree(lib_dir)
+    # copy in the original package lib dir
+    shutil.copytree(os.path.join(src_dir, libd), lib_dir)
+
+    # and copy the license
+    info_dir = os.path.join(src_dir, 'info')
+    shutil.copy(os.path.join(info_dir, 'LICENSE.txt'), src_dir)
+    shutil.rmtree(info_dir)
+
+
+if __name__ == "__main__":
+    args = sys.argv
+    assert len(args) == 1
+    run()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/gpuci/axis.yaml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/gpuci/axis.yaml
new file mode 100644
index 000000000..417971411
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/gpuci/axis.yaml
@@ -0,0 +1,19 @@
+PYTHON_VER:
+- "3.8"
+
+CUDA_VER:
+- "11.2"
+
+CUDA_TOOLKIT_VER:
+- "10.2"
+- "11.1"
+- "11.2"
+- "11.5"
+
+LINUX_VER:
+- ubuntu18.04
+
+RAPIDS_VER:
+- "21.12"
+
+excludes:
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/gpuci/build.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/gpuci/build.sh
new file mode 100644
index 000000000..1088b1eeb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/gpuci/build.sh
@@ -0,0 +1,74 @@
+##############################################
+# Numba GPU build and test script for CI     #
+##############################################
+set -e
+
+# Set path and build parallel level
+export PATH=/opt/conda/bin:/usr/local/cuda/bin:$PATH
+export PARALLEL_LEVEL=${PARALLEL_LEVEL:-4}
+
+# Set home to the job's workspace
+export HOME="$WORKSPACE"
+
+# Switch to project root; also root of repo checkout
+cd "$WORKSPACE"
+
+# Determine CUDA release version
+export CUDA_REL=${CUDA_VERSION%.*}
+
+# Test with NVIDIA Bindings on CUDA 11.5
+if [ $CUDA_TOOLKIT_VER == "11.5" ]
+then
+  export NUMBA_CUDA_USE_NVIDIA_BINDING=1;
+else
+  export NUMBA_CUDA_USE_NVIDIA_BINDING=0;
+fi;
+
+################################################################################
+# SETUP - Check environment
+################################################################################
+
+gpuci_logger "Check environment variables"
+env
+
+gpuci_logger "Check GPU usage"
+nvidia-smi
+
+gpuci_logger "Create testing env"
+. /opt/conda/etc/profile.d/conda.sh
+gpuci_mamba_retry create -n numba_ci -y \
+                  "python=${PYTHON_VER}" \
+                  "cudatoolkit=${CUDA_TOOLKIT_VER}" \
+                  "numba/label/dev::llvmlite" \
+                  "numpy=1.21" \
+                  "scipy" \
+                  "cffi" \
+                  "psutil" \
+                  "gcc_linux-64=7" \
+                  "gxx_linux-64=7" \
+                  "setuptools"
+
+conda activate numba_ci
+
+if [ $NUMBA_CUDA_USE_NVIDIA_BINDING == "1" ]
+then
+  gpuci_logger "Install NVIDIA CUDA Python bindings";
+  gpuci_mamba_retry install nvidia::cuda-python=11.7.0;
+fi;
+
+gpuci_logger "Install numba"
+python setup.py develop
+
+gpuci_logger "Check Compiler versions"
+$CC --version
+$CXX --version
+
+gpuci_logger "Check conda environment"
+conda info
+conda config --show-sources
+
+gpuci_logger "Dump system information from Numba"
+python -m numba -s
+
+gpuci_logger "Run tests in numba.cuda.tests"
+python -m numba.runtests numba.cuda.tests -v -m
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/MacOSX10.10.sdk.checksum b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/MacOSX10.10.sdk.checksum
new file mode 100644
index 000000000..0a82e4317
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/MacOSX10.10.sdk.checksum
@@ -0,0 +1 @@
+ea40a3b9dc48cd3593628490f2738b89282f00ab  ./MacOSX10.10.sdk.tar.xz
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/after_success.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/after_success.sh
new file mode 100644
index 000000000..63c55b63d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/after_success.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+source activate $CONDA_ENV
+
+# Make sure any error below is reported as such
+set -v -e
+
+if [ "$RUN_COVERAGE" == "yes" ]; then
+    coverage combine
+    codecov
+fi
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/build.cmd b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/build.cmd
new file mode 100644
index 000000000..645a15625
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/build.cmd
@@ -0,0 +1,10 @@
+
+call activate %CONDA_ENV%
+
+@rem Build numba extensions without silencing compile errors
+python setup.py build_ext -q --inplace
+
+@rem Install numba locally for use in `numba -s` sys info tool at test time
+python -m pip install -e .
+
+if %errorlevel% neq 0 exit /b %errorlevel%
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/build.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/build.sh
new file mode 100644
index 000000000..4c696d69c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/build.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+
+source  activate
+conda activate $CONDA_ENV
+
+# Make sure any error below is reported as such
+set -v -e
+
+# Build numba extensions without silencing compile errors
+if [[ "$(uname -s)" == *"Linux"* ]] && [[ "$(uname -p)" == *"86"* ]]; then
+    EXTRA_BUILD_EXT_FLAGS="--werror --wall"
+else
+    EXTRA_BUILD_EXT_FLAGS=""
+fi
+
+if [[ $(uname) == "Darwin" ]]; then
+    # The following is suggested in https://docs.conda.io/projects/conda-build/en/latest/resources/compiler-tools.html?highlight=SDK#macos-sdk
+    wget -q https://github.com/phracker/MacOSX-SDKs/releases/download/11.3/MacOSX10.10.sdk.tar.xz
+    shasum -c ./buildscripts/incremental/MacOSX10.10.sdk.checksum
+    tar -xf ./MacOSX10.10.sdk.tar.xz
+    export SDKROOT=`pwd`/MacOSX10.10.sdk
+fi
+python setup.py build_ext -q --inplace --debug $EXTRA_BUILD_EXT_FLAGS --verbose
+# (note we don't install to avoid problems with extra long Windows paths
+#  during distutils-dependent tests -- e.g. test_pycc)
+
+# Install numba locally for use in `numba -s` sys info tool at test time
+python -m pip install --no-deps -e .
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/install_miniconda.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/install_miniconda.sh
new file mode 100644
index 000000000..4aa73bac6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/install_miniconda.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+set -v -e
+
+# Install Miniconda
+unamestr=`uname`
+if [[ "$unamestr" == 'Linux' ]]; then
+    if [[ "$BITS32" == "yes" ]]; then
+        wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86.sh -O miniconda.sh
+    else
+        wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+    fi
+elif [[ "$unamestr" == 'Darwin' ]]; then
+    wget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh
+else
+  echo Error
+fi
+chmod +x miniconda.sh
+./miniconda.sh -b
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/setup_conda_environment.cmd b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/setup_conda_environment.cmd
new file mode 100644
index 000000000..1a4ecb7c7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/setup_conda_environment.cmd
@@ -0,0 +1,45 @@
+@rem first configure conda to have more tolerance of network problems, these
+@rem numbers are not scientifically chosen, just merely larger than defaults
+set CONDA_CONFIG=cmd /C conda config
+%CONDA_CONFIG% --write-default
+%CONDA_CONFIG% --set remote_connect_timeout_secs 30.15
+%CONDA_CONFIG% --set remote_max_retries 10
+%CONDA_CONFIG% --set remote_read_timeout_secs 120.2
+%CONDA_CONFIG% --set restore_free_channel true
+%CONDA_CONFIG% --set show_channel_urls true
+cmd /C conda info
+%CONDA_CONFIG% --show
+
+@rem The cmd /C hack circumvents a regression where conda installs a conda.bat
+@rem script in non-root environments.
+set CONDA_INSTALL=cmd /C conda install -q -y
+set PIP_INSTALL=pip install -q
+
+@echo on
+
+@rem Deactivate any environment
+call deactivate
+@rem Display root environment (for debugging)
+conda list
+@rem Scipy, CFFI, jinja2 and IPython are optional dependencies, but exercised in the test suite
+conda create -n %CONDA_ENV% -q -y python=%PYTHON% numpy=%NUMPY% cffi pip jinja2 ipython gitpython pyyaml
+
+call activate %CONDA_ENV%
+@rem Scipy comes from conda-forge for NumPy 1.23
+if %NUMPY% == "1.23" (%CONDA_INSTALL% conda-forge::scipy) else (%CONDA_INSTALL% scipy)
+@rem Install latest llvmlite build
+%CONDA_INSTALL% -c numba/label/dev llvmlite=0.39
+@rem Install required backports for older Pythons
+if %PYTHON% LSS 3.9 (%CONDA_INSTALL% importlib_metadata)
+@rem Install dependencies for building the documentation
+if "%BUILD_DOC%" == "yes" (%CONDA_INSTALL% sphinx sphinx_rtd_theme pygments)
+@rem Install dependencies for code coverage (codecov.io)
+if "%RUN_COVERAGE%" == "yes" (%PIP_INSTALL% codecov)
+@rem Install TBB
+%CONDA_INSTALL% -c numba tbb=2021 "tbb-devel>=2021,<2021.6"
+if %errorlevel% neq 0 exit /b %errorlevel%
+
+echo "DEBUG ENV:"
+echo "-------------------------------------------------------------------------"
+conda env export
+echo "-------------------------------------------------------------------------"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/setup_conda_environment.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/setup_conda_environment.sh
new file mode 100644
index 000000000..943c428ad
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/setup_conda_environment.sh
@@ -0,0 +1,117 @@
+#!/bin/bash
+
+set -v -e
+
+# first configure conda to have more tolerance of network problems, these
+# numbers are not scientifically chosen, just merely larger than defaults
+conda config --write-default
+conda config --set remote_connect_timeout_secs 30.15
+conda config --set remote_max_retries 10
+conda config --set remote_read_timeout_secs 120.2
+conda config --set show_channel_urls true
+if [[ $(uname) == Linux ]]; then
+    if [[ "$CONDA_SUBDIR" != "linux-32" && "$BITS32" != "yes" ]] ; then
+        conda config --set restore_free_channel true
+    fi
+fi
+conda info
+conda config --show
+
+CONDA_INSTALL="conda install -q -y"
+PIP_INSTALL="pip install -q"
+
+
+EXTRA_CHANNELS=""
+if [ "${USE_C3I_TEST_CHANNEL}" == "yes" ]; then
+    EXTRA_CHANNELS="${EXTRA_CHANNELS} -c c3i_test"
+fi
+
+
+# Deactivate any environment
+source deactivate
+# Display root environment (for debugging)
+conda list
+
+# If VANILLA_INSTALL is yes, then only Python, NumPy and pip are installed, this
+# is to catch tests/code paths that require an optional package and are not
+# guarding against the possibility that it does not exist in the environment.
+# Create a base env first and then add to it...
+# NOTE: gitpython is needed for CI testing to do the test slicing
+# NOTE: pyyaml is used to ensure that the Azure CI config is valid
+# NOTE: 32 bit linux... do not install NumPy, there's no conda package for >1.15
+# so it has to come from pip later
+if [[ "$CONDA_SUBDIR" == "linux-32" || "$BITS32" == "yes" ]]; then
+    conda create -n $CONDA_ENV -q -y ${EXTRA_CHANNELS} python=$PYTHON pip gitpython pyyaml
+else
+    conda create -n $CONDA_ENV -q -y ${EXTRA_CHANNELS} python=$PYTHON numpy=$NUMPY pip gitpython pyyaml
+fi
+
+# Activate first
+set +v
+source activate $CONDA_ENV
+set -v
+
+# Install optional packages into activated env
+echo "PYTHON=$PYTHON"
+echo "VANILLA_INSTALL=$VANILLA_INSTALL"
+if [ "${VANILLA_INSTALL}" != "yes" ]; then
+    # Scipy, CFFI, jinja2, IPython and pygments are optional
+    # dependencies, but exercised in the test suite
+    # pexpect is used to run the gdb tests.
+    # ipykernel is used for testing ipython behaviours.
+    $CONDA_INSTALL ${EXTRA_CHANNELS} cffi jinja2 ipython ipykernel pygments pexpect
+    # Only install scipy on 64bit, else it'll pull in NumPy, 32bit linux needs
+    # to get scipy from pip
+    if [[ "$CONDA_SUBDIR" != "linux-32" && "$BITS32" != "yes" ]] ; then
+        if [[ "$NUMPY" == "1.23" ]] ; then
+            $CONDA_INSTALL ${EXTRA_CHANNELS} conda-forge::scipy
+        else
+            $CONDA_INSTALL ${EXTRA_CHANNELS} scipy
+        fi
+    fi
+fi
+
+# Install the compiler toolchain
+if [[ $(uname) == Linux ]]; then
+    if [[ "$CONDA_SUBDIR" == "linux-32" || "$BITS32" == "yes" ]] ; then
+        $CONDA_INSTALL gcc_linux-32 gxx_linux-32
+    else
+        $CONDA_INSTALL gcc_linux-64 gxx_linux-64
+    fi
+elif  [[ $(uname) == Darwin ]]; then
+    $CONDA_INSTALL clang_osx-64 clangxx_osx-64
+    # Install llvm-openmp on OSX for headers during build and runtime during
+    # testing
+    $CONDA_INSTALL llvm-openmp
+fi
+
+# If on 32bit linux, now pip install NumPy (no conda package), SciPy is broken?!
+if [[ "$CONDA_SUBDIR" == "linux-32" || "$BITS32" == "yes" ]] ; then
+    $PIP_INSTALL numpy==$NUMPY
+fi
+
+# Install latest correct build
+$CONDA_INSTALL -c numba/label/dev llvmlite=0.39
+
+# Install importlib-metadata for Python < 3.9
+if [ $PYTHON \< "3.9" ]; then $CONDA_INSTALL importlib_metadata; fi
+
+# Install dependencies for building the documentation
+if [ "$BUILD_DOC" == "yes" ]; then $CONDA_INSTALL sphinx=2.4.4 docutils=0.17 sphinx_rtd_theme pygments numpydoc; fi
+if [ "$BUILD_DOC" == "yes" ]; then $PIP_INSTALL rstcheck; fi
+# Install dependencies for code coverage (codecov.io)
+if [ "$RUN_COVERAGE" == "yes" ]; then $PIP_INSTALL codecov; fi
+# Install SVML
+if [ "$TEST_SVML" == "yes" ]; then $CONDA_INSTALL -c numba icc_rt; fi
+# Install Intel TBB parallel backend
+if [ "$TEST_THREADING" == "tbb" ]; then $CONDA_INSTALL -c numba tbb=2021 "tbb-devel>=2021,<2021.6"; fi
+# Install pickle5
+if [ "$TEST_PICKLE5" == "yes" ]; then $PIP_INSTALL pickle5; fi
+# Install typeguard
+if [ "$RUN_TYPEGUARD" == "yes" ]; then $CONDA_INSTALL conda-forge::typeguard; fi
+
+# environment dump for debug
+# echo "DEBUG ENV:"
+# echo "-------------------------------------------------------------------------"
+# conda env export
+# echo "-------------------------------------------------------------------------"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/test.cmd b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/test.cmd
new file mode 100644
index 000000000..c35278d2b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/test.cmd
@@ -0,0 +1,34 @@
+
+call activate %CONDA_ENV%
+
+@rem Ensure that the documentation builds without warnings
+if "%BUILD_DOC%" == "yes" python setup.py build_doc
+@rem Run system info tool
+pushd bin
+numba -s
+popd
+
+@rem switch off color messages
+set NUMBA_DISABLE_ERROR_MESSAGE_HIGHLIGHTING=1
+@rem switch on developer mode
+set NUMBA_DEVELOPER_MODE=1
+@rem enable the faulthandler
+set PYTHONFAULTHANDLER=1
+@rem enable new style error handling
+set NUMBA_CAPTURED_ERRORS=new_style
+
+@rem First check that the test discovery works
+python -m numba.tests.test_runtests
+@rem Now run the Numba test suite
+@rem Note that coverage is run from the checkout dir to match the "source"
+@rem directive in .coveragerc
+if "%RUN_COVERAGE%" == "yes" (
+    set PYTHONPATH=.
+    coverage erase
+    coverage run runtests.py -b --exclude-tags='long_running' -m -- numba.tests
+) else (
+    set NUMBA_ENABLE_CUDASIM=1
+    python -m numba.runtests -b --exclude-tags='long_running' -m -- numba.tests
+)
+
+if %errorlevel% neq 0 exit /b %errorlevel%
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/test.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/test.sh
new file mode 100644
index 000000000..a3e32bd4a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/buildscripts/incremental/test.sh
@@ -0,0 +1,131 @@
+#!/bin/bash
+
+source activate $CONDA_ENV
+
+# Make sure any error below is reported as such
+set -v -e
+
+# Ensure the README is correctly formatted
+if [ "$BUILD_DOC" == "yes" ]; then rstcheck README.rst; fi
+# Ensure that the documentation builds without warnings
+pushd docs
+if [ "$BUILD_DOC" == "yes" ]; then make SPHINXOPTS=-W clean html; fi
+popd
+# Run system info tool
+pushd bin
+numba -s
+popd
+
+# switch off color messages
+export NUMBA_DISABLE_ERROR_MESSAGE_HIGHLIGHTING=1
+# switch on developer mode
+export NUMBA_DEVELOPER_MODE=1
+# enable the fault handler
+export PYTHONFAULTHANDLER=1
+
+# enable new style error handling
+export NUMBA_CAPTURED_ERRORS="new_style"
+
+# Disable NumPy dispatching to AVX512_SKX feature extensions if the chip is
+# reported to support the feature and NumPy >= 1.22 as this results in the use
+# of low accuracy SVML libm replacements in ufunc loops.
+_NPY_CMD='from numba.misc import numba_sysinfo;\
+          sysinfo=numba_sysinfo.get_sysinfo();\
+          print(sysinfo["NumPy AVX512_SKX detected"] and
+                sysinfo["NumPy Version"]>="1.22")'
+NUMPY_DETECTS_AVX512_SKX_NP_GT_122=$(python -c "$_NPY_CMD")
+echo "NumPy >= 1.22 with AVX512_SKX detected: $NUMPY_DETECTS_AVX512_SKX_NP_GT_122"
+
+if [[ "$NUMPY_DETECTS_AVX512_SKX_NP_GT_122" == "True" ]]; then
+    export NPY_DISABLE_CPU_FEATURES="AVX512_SKX"
+fi
+
+# deal with threading layers
+if [ -z ${TEST_THREADING+x} ]; then
+    echo "INFO: Threading layer not explicitly set."
+else
+    case "${TEST_THREADING}" in "workqueue"|"omp"|"tbb")
+        export NUMBA_THREADING_LAYER="$TEST_THREADING"
+        echo "INFO: Threading layer set as: $TEST_THREADING"
+        ;;
+        *)
+        echo "INFO: Threading layer explicitly set to bad value: $TEST_THREADING."
+        exit 1
+        ;;
+    esac
+fi
+
+# If TEST_THREADING is set in the env, then check that Numba agrees that the
+# environment can support the requested threading.
+function check_sysinfo() {
+    cmd="import os;\
+         from numba.misc.numba_sysinfo import get_sysinfo;\
+         assert get_sysinfo()['$1 Threading'] is True, 'Threading layer $1 '\
+         'is not supported';\
+         print('Threading layer $1 is supported')"
+    python -c "$cmd"
+}
+
+if [[ "$TEST_THREADING" ]]; then
+    if [[ "$TEST_THREADING" == "tbb" ]]; then
+        check_sysinfo "TBB"
+    elif [[ "$TEST_THREADING" == "omp" ]]; then
+        check_sysinfo "OpenMP"
+    elif [[ "$TEST_THREADING" == "workqueue" ]]; then
+        check_sysinfo "Workqueue"
+    else
+        echo "Unknown threading layer requested: $TEST_THREADING"
+        exit 1
+    fi
+fi
+
+# Find catchsegv
+unamestr=`uname`
+if [[ "$unamestr" == 'Linux' ]]; then
+    if [[ "${BITS32}" == "yes" ]]; then
+        SEGVCATCH=""
+    else
+        SEGVCATCH=catchsegv
+    fi
+elif [[ "$unamestr" == 'Darwin' ]]; then
+  SEGVCATCH=""
+else
+  echo Error
+fi
+
+# limit CPUs in use on PPC64LE, fork() issues
+# occur on high core count systems
+archstr=`uname -m`
+if [[ "$archstr" == 'ppc64le' ]]; then
+    TEST_NPROCS=16
+fi
+
+# setup SDKROOT on Mac
+if [[ $(uname) == "Darwin" ]]; then
+    export SDKROOT=`pwd`/MacOSX10.10.sdk
+fi
+
+# First check that the test discovery works
+python -m numba.tests.test_runtests
+
+# Now run tests based on the changes identified via git
+NUMBA_ENABLE_CUDASIM=1 $SEGVCATCH python -m numba.runtests -b -v -g -m $TEST_NPROCS -- numba.tests
+
+# List the tests found
+echo "INFO: All discovered tests:"
+python -m numba.runtests -l
+
+# Now run the Numba test suite with slicing
+# Note that coverage is run from the checkout dir to match the "source"
+# directive in .coveragerc
+echo "INFO: Running slice of discovered tests: ($TEST_START_INDEX,None,$TEST_COUNT)"
+if [ "$RUN_COVERAGE" == "yes" ]; then
+    export PYTHONPATH=.
+    coverage erase
+    $SEGVCATCH coverage run runtests.py -b -j "$TEST_START_INDEX,None,$TEST_COUNT" --exclude-tags='long_running' -m $TEST_NPROCS -- numba.tests
+elif [ "$RUN_TYPEGUARD" == "yes" ]; then
+    echo "INFO: Running with typeguard"
+    NUMBA_USE_TYPEGUARD=1 NUMBA_ENABLE_CUDASIM=1 PYTHONWARNINGS="ignore:::typeguard" $SEGVCATCH python runtests.py -b -j "$TEST_START_INDEX,None,$TEST_COUNT" --exclude-tags='long_running' -m $TEST_NPROCS -- numba.tests
+else
+    NUMBA_ENABLE_CUDASIM=1 $SEGVCATCH python -m numba.runtests -b -j "$TEST_START_INDEX,None,$TEST_COUNT" --exclude-tags='long_running' -m $TEST_NPROCS -- numba.tests
+fi
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/clean_numba.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/clean_numba.sh
new file mode 100644
index 000000000..7d8d2e487
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/clean_numba.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+PYTHON_PATH=$(which python3)
+
+${PYTHON_PATH} setup.py clean || true
+rm -rf build build_pip
+
+# Return 0 status if all finished
+exit 0
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/codecov.yml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/codecov.yml
new file mode 100644
index 000000000..c293219ba
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/codecov.yml
@@ -0,0 +1,22 @@
+# Configuration for codecov.io
+# When editing this file, please validate its contents using:
+#   curl -X POST --data-binary @- https://codecov.io/validate < codecov.yml
+
+comment:
+    layout: "header, diff, changes, uncovered"
+
+coverage:
+    ignore:
+        - "numba/cuda/.*"
+        - "numba/hsa/.*"
+
+    status:
+        project:
+            default:
+                # The build fails if total project coverage drops by more than 3%
+                target: auto
+                threshold: "3%"
+        # These checks can mark a build failed if too much new code
+        # is not covered (which happens often with JITted functions).
+        changes: false
+        patch: false
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/contrib/valgrind-numba.supp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/contrib/valgrind-numba.supp
new file mode 100644
index 000000000..26271eb4e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/contrib/valgrind-numba.supp
@@ -0,0 +1,21 @@
+{
+   <llvmpy_get_cpu_name_cond>
+   Memcheck:Cond
+   fun:_ZN4llvm3sys14getHostCPUNameEv
+   fun:LLVMPY_GetHostCPUName
+}
+
+{
+   <llvmpy_get_cpu_name_value8>
+   Memcheck:Value8
+   fun:_ZN4llvm3sys14getHostCPUNameEv
+   fun:LLVMPY_GetHostCPUName
+}
+
+{
+   <openmp_init_cond>
+   Memcheck:Cond
+   fun:__intel_sse2_strrchr
+   fun:_ZN67_INTERNAL_45_______src_thirdparty_tbb_omp_dynamic_link_cpp_c306cade5__kmp12init_dl_dataEv
+   fun:__sti__$E
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/Makefile b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/Makefile
new file mode 100644
index 000000000..b60d7c1d6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/Makefile
@@ -0,0 +1,177 @@
+# Makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    = -j1
+SPHINXBUILD   = sphinx-build
+PAPER         =
+BUILDDIR      = _build
+
+# User-friendly check for sphinx-build
+ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
+$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
+endif
+
+# Internal variables.
+PAPEROPT_a4     = -D latex_paper_size=a4
+PAPEROPT_letter = -D latex_paper_size=letter
+ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
+# the i18n builder cannot share the environment and doctrees with the others
+I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
+
+.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
+
+help:
+	@echo "Please use \`make <target>' where <target> is one of"
+	@echo "  html       to make standalone HTML files"
+	@echo "  dirhtml    to make HTML files named index.html in directories"
+	@echo "  singlehtml to make a single large HTML file"
+	@echo "  pickle     to make pickle files"
+	@echo "  json       to make JSON files"
+	@echo "  htmlhelp   to make HTML files and a HTML help project"
+	@echo "  qthelp     to make HTML files and a qthelp project"
+	@echo "  devhelp    to make HTML files and a Devhelp project"
+	@echo "  epub       to make an epub"
+	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
+	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
+	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
+	@echo "  text       to make text files"
+	@echo "  man        to make manual pages"
+	@echo "  texinfo    to make Texinfo files"
+	@echo "  info       to make Texinfo files and run them through makeinfo"
+	@echo "  gettext    to make PO message catalogs"
+	@echo "  changes    to make an overview of all changed/added/deprecated items"
+	@echo "  xml        to make Docutils-native XML files"
+	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
+	@echo "  linkcheck  to check all external links for integrity"
+	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
+
+clean:
+	rm -rf $(BUILDDIR)/*
+
+html:
+	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+dirhtml:
+	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
+
+singlehtml:
+	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
+	@echo
+	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
+
+pickle:
+	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
+	@echo
+	@echo "Build finished; now you can process the pickle files."
+
+json:
+	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
+	@echo
+	@echo "Build finished; now you can process the JSON files."
+
+htmlhelp:
+	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
+	@echo
+	@echo "Build finished; now you can run HTML Help Workshop with the" \
+	      ".hhp project file in $(BUILDDIR)/htmlhelp."
+
+qthelp:
+	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
+	@echo
+	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
+	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
+	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/Numba.qhcp"
+	@echo "To view the help file:"
+	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/Numba.qhc"
+
+devhelp:
+	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
+	@echo
+	@echo "Build finished."
+	@echo "To view the help file:"
+	@echo "# mkdir -p $$HOME/.local/share/devhelp/Numba"
+	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/Numba"
+	@echo "# devhelp"
+
+epub:
+	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
+	@echo
+	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
+
+latex:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo
+	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
+	@echo "Run \`make' in that directory to run these through (pdf)latex" \
+	      "(use \`make latexpdf' here to do that automatically)."
+
+latexpdf:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through pdflatex..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+latexpdfja:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through platex and dvipdfmx..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+text:
+	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
+	@echo
+	@echo "Build finished. The text files are in $(BUILDDIR)/text."
+
+man:
+	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
+	@echo
+	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
+
+texinfo:
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo
+	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
+	@echo "Run \`make' in that directory to run these through makeinfo" \
+	      "(use \`make info' here to do that automatically)."
+
+info:
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo "Running Texinfo files through makeinfo..."
+	make -C $(BUILDDIR)/texinfo info
+	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
+
+gettext:
+	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
+	@echo
+	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
+
+changes:
+	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
+	@echo
+	@echo "The overview file is in $(BUILDDIR)/changes."
+
+linkcheck:
+	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
+	@echo
+	@echo "Link check complete; look for any errors in the above output " \
+	      "or in $(BUILDDIR)/linkcheck/output.txt."
+
+doctest:
+	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
+	@echo "Testing of doctests in the sources finished, look at the " \
+	      "results in $(BUILDDIR)/doctest/output.txt."
+
+xml:
+	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
+	@echo
+	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
+
+pseudoxml:
+	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
+	@echo
+	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/js/modernizr.min.js b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/js/modernizr.min.js
new file mode 100644
index 000000000..939eaaf03
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/js/modernizr.min.js
@@ -0,0 +1,7 @@
+/* modernizr.min.js is unused but causes a reflow on load. In firefox, this
+ * manifests as the Numba logo flashing up across the whole browser window for a
+ * split second every time the page is loaded or a documentation link is
+ * clicked. This empty file overrides the version included by the theme.
+ *
+ * Reference: https://github.com/readthedocs/sphinx_rtd_theme/issues/724
+ */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/numba-blue-icon-rgb.svg b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/numba-blue-icon-rgb.svg
new file mode 100644
index 000000000..0df9c042e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/numba-blue-icon-rgb.svg
@@ -0,0 +1,27 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 20.1.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#00A3E0;}
+	.st1{fill:none;}
+	.st2{fill:url(#SVGID_1_);}
+</style>
+<g>
+	<path class="st0" d="M333.2,310.3c0,0,136.3-100.5,81.1-165.5c-64-75.4-243.4,84.5-263,52S310.5,86.4,329.9,73.4
+		c2.1-1.4,2.7-4.1,2.1-7.2l20.7-2.4l-12.9-6.5l3.3-12.9l-15,11.7c-5.6-10.1-15.2-21-20.7-24.8c-19.5-12.9-55.2-9.8-107.1,16.2
+		S28,154.6,89.6,245.5c55.2,81.1,213.8-58.3,240.3-35.8c22.7,19.5-152.5,139.6-152.5,139.6s74.4,0,74.7,0
+		C239.2,381.8,177.4,476,177.4,476l224.8-162.4C378.6,310.1,333.2,310.3,333.2,310.3z M272.1,41c8-0.6,14.9,5.6,15.5,13.5
+		c0.6,8-5.6,14.9-13.5,15.5c-8,0.6-14.9-5.6-15.5-13.5C257.9,48.5,264.1,41.6,272.1,41z"/>
+</g>
+<rect x="22.5" y="22.5" class="st1" width="455" height="455"/>
+<linearGradient id="SVGID_1_" gradientUnits="userSpaceOnUse" x1="301.6" y1="435.7" x2="301.6" y2="435.7" gradientTransform="matrix(1 0 0 -1 0 500)">
+	<stop  offset="0" style="stop-color:#243746"/>
+	<stop  offset="9.525055e-02" style="stop-color:#223442"/>
+	<stop  offset="0.1897" style="stop-color:#1C2B36"/>
+	<stop  offset="0.2839" style="stop-color:#121B22"/>
+	<stop  offset="0.3773" style="stop-color:#030507"/>
+	<stop  offset="0.3961" style="stop-color:#000000"/>
+</linearGradient>
+<path class="st2" d="M301.6,64.3"/>
+</svg>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/numba-white-icon-rgb.svg b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/numba-white-icon-rgb.svg
new file mode 100644
index 000000000..904b61c13
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/numba-white-icon-rgb.svg
@@ -0,0 +1,27 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 20.1.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#FFFFFF;}
+	.st1{fill:none;}
+	.st2{fill:url(#SVGID_1_);}
+</style>
+<g>
+	<path class="st0" d="M333.2,310.3c0,0,136.3-100.5,81.1-165.5c-64-75.4-243.4,84.5-263,52S310.5,86.4,329.9,73.4
+		c2.1-1.4,2.7-4.1,2.1-7.2l20.7-2.4l-12.9-6.5l3.3-12.9l-15,11.7c-5.6-10.1-15.2-21-20.7-24.8c-19.5-12.9-55.2-9.8-107.1,16.2
+		S28,154.6,89.6,245.5c55.2,81.1,213.8-58.3,240.3-35.8c22.7,19.5-152.5,139.6-152.5,139.6s74.4,0,74.7,0
+		C239.2,381.8,177.4,476,177.4,476l224.8-162.4C378.6,310.1,333.2,310.3,333.2,310.3z M272.1,41c8-0.6,14.9,5.6,15.5,13.5
+		c0.6,8-5.6,14.9-13.5,15.5c-8,0.6-14.9-5.6-15.5-13.5C257.9,48.5,264.1,41.6,272.1,41z"/>
+</g>
+<rect x="22.5" y="22.5" class="st1" width="455" height="455"/>
+<linearGradient id="SVGID_1_" gradientUnits="userSpaceOnUse" x1="301.6" y1="435.7" x2="301.6" y2="435.7" gradientTransform="matrix(1 0 0 -1 0 500)">
+	<stop  offset="0" style="stop-color:#243746"/>
+	<stop  offset="9.525055e-02" style="stop-color:#223442"/>
+	<stop  offset="0.1897" style="stop-color:#1C2B36"/>
+	<stop  offset="0.2839" style="stop-color:#121B22"/>
+	<stop  offset="0.3773" style="stop-color:#030507"/>
+	<stop  offset="0.3961" style="stop-color:#000000"/>
+</linearGradient>
+<path class="st2" d="M301.6,64.3"/>
+</svg>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/rtd-overrides.css b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/rtd-overrides.css
new file mode 100644
index 000000000..50c71cdb9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_static/rtd-overrides.css
@@ -0,0 +1,3 @@
+.wy-nav-content {
+   max-width: 1200px
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_templates/EMPTY b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/_templates/EMPTY
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/README.md b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/README.md
new file mode 100644
index 000000000..66fd85853
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/README.md
@@ -0,0 +1,57 @@
+# DAG Roadmap
+
+This directory includes a representation of the Numba roadmap in the form of a
+DAG.  We have done this to enable a highly granular display of enhancements to
+Numba that also shows the relationships between these tasks. Many tasks have
+prerequisites, and we've found that issue trackers, Kanban boards, and
+time-bucketed roadmap documentation all fail to represent this information in
+different ways.
+
+## Requirements
+
+```
+conda install jinja2 python-graphviz pyyaml
+```
+
+## Usage
+
+```
+./render.py -o dagmap.html dagmap.yaml
+```
+
+The generated HTML file will look for `jquery.graphviz.svg.js` in the same
+directory.
+
+## Updating the DAG
+
+Copy one of the existing tasks and edit:
+  * `label`: text appears on the node.  Embed `\n` for line breaks.
+  * `id`: Referenced to indicate a dependency
+  * `description`: Shown in the tooltip.  Automatically word-wrapped.
+  * `depends_on`: Optional list of task IDs which this task depends on.
+
+The `style` section of the file is not used yet.
+
+## Notes
+
+The HTML rendering of the graph is based on a slightly modified version of
+(jquery.graphviz.svg)[https://github.com/mountainstorm/jquery.graphviz.svg/].
+Its license is:
+```
+Copyright (c) 2015 Mountainstorm
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/dagmap.yaml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/dagmap.yaml
new file mode 100644
index 000000000..57df7b755
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/dagmap.yaml
@@ -0,0 +1,195 @@
+meta:
+  version: 1
+style:
+  tags:
+    performance:
+      border: red
+tasks:
+  - label: Track allocations in functions
+    id: track_alloc
+    description: |
+      Maintain a list of allocations inside each function which can be used
+      for freeing things on return, and also for debugging memory usage.
+
+  - label: Catch exceptions
+    id: catch_exceptions
+    description: |
+      Allow exceptions raised in nopython mode to be caught in nopython mode.
+    depends_on:
+      - track_alloc
+
+
+  - label: New IR
+    id: new_ir
+    description: |
+      New intermediate representation for Numba that is backed by a dictionary
+
+  - label: New Type Matching DSL
+    id: type_matching
+    description: |
+      Replace the current DSL for Numba types with something more expressive
+      that can match type patterns
+
+  - label: Declarative type signatures\nfor @overload/@overload_method
+    id: declarative_overload
+    description: |
+      Replace the current DSL for Numba types with something more expressive\n
+      that can match type patterns
+    depends_on:
+      - type_matching
+
+  - label: Rewrite "old-style" implementations
+    id: rewrite_old_impls
+    description: |
+      Rewrite implementations of functions that use the old extension API that
+      separates typing from implementation, and often uses the LLVM builder
+      unnecessarily.
+    depends_on:
+      - declarative_overload
+      - improve_test_suite_tooling
+      - faster_pr_testing
+
+  - label: Unify and add more test suite tooling
+    id: improve_test_suite_tooling
+    description: |
+      Add tools to help with common patterns in testing and unify the ones we
+      have, there's no need for 12 spellings of "is this Python 3" Also decide
+      on "what to test", do all types need testing if inputs are being
+      "as_array"'d?
+
+  - label: Pipeline pass formalisation
+    id: pass_formalisation
+    description: |
+      Decide on a formal description of a compiler pass and create supporting
+      code for it
+
+  - label: Array expression fusion pass
+    id: new_array_expr_fusion_pass
+    description:
+        From parfors extract out the array expression fusion pass
+    depends_on:
+      - parfors_clean_up
+      - pass_formalisation
+
+  - label: LICM Pass
+    id: new_licm_pass
+    description: |
+      Create a LICM Pass
+    depends_on:
+      - parfors_clean_up
+      - pass_formalisation
+
+  - label: Clean up Parfors
+    id: parfors_clean_up
+    description: |
+      General clean up and refactoring of parfors ahead of any additional work
+
+  - label: Mode based pipeline
+    id: mode_based_pipeline
+    description: |
+      Switch the jit decorator to use a mode based pipeline with
+      `nopython=True` equivalent as default.
+
+  - label: Remove object mode fallback
+    id: remove_objmode_fallback
+    description: |
+      Remove the deprecated object mode fallback
+    depends_on:
+        - mode_based_pipeline
+
+  - label: Switch to ORC JIT
+    id: orc_jit
+    description: |
+      MCJIT has been deprecated for some time.  Need to switch to the newer
+      ORC JIT class.
+
+  - label: Performance analysis suite
+    id: perform_analysis_suite
+    description: |
+      Meta task for all performance analysis related functionality
+    depends_on:
+      - line_profiling
+      - assembly_analysis_tooling
+      - vectorisation_analysis
+
+  - label: Vectorisation analysis
+    id: vectorisation_analysis
+    description: |
+      Obtain LLVMs vectorisation reports and present these in a user friendly
+      manner
+
+  - label: Line profiling
+    id: line_profiling
+    description: |
+      Support collection of profiling statistics from compiled machine code
+      and map back to lines of Python.
+    depends_on:
+      - orc_jit
+      - assembly_analysis_tooling
+
+  - label: Assembly analysis tooling
+    id: assembly_analysis_tooling
+    description: |
+      Tie generated assembly back to python lines and annotate instruction
+      quality
+    depends_on:
+      - capstone
+
+  - label: Build capstone against llvmdev
+    id: capstone
+    description: |
+      Build capstone against llvmdev and create conda packages/wheels
+
+  - label: Increase JIT class method performance
+    id: jit_class_method_performance
+    description: |
+      Increase the performance of jitclass methods
+    depends_on:
+      - llvm_ref_count_pruning
+      - new_licm_pass
+
+  - label: LLVM level ref count pruning
+    id: llvm_ref_count_pruning
+    description: |
+      Add a LLVM compiler pass to prune refcounts across entire functions
+
+  - label: JITted coverage information
+    id: jitted_coverage_info
+    description: |
+      Work out how to leverage gcov support in LLVM to enable coverage
+      information
+    depends_on:
+      - compiler_rt
+
+  - label: LLVM compiler_rt support
+    id: compiler_rt
+    description: |
+      Work out how to build compiler_rt into LLVM and how to use it in Numba
+
+  - label: Switch to pytest
+    id: pytest
+    description: |
+      Make it possible to use pytest as test runner for Numba
+
+  - label: Option to run modified tests only
+    id: run_new_tests
+    description: |
+      Use / make pytest plugin to detect all test files which are new /
+      changed relative to a given branch, and run only those tests
+    depends_on:
+      - pytest
+
+  - label: Option to run 1/N slice of tests
+    id: run_test_slice
+    description: |
+      Use / make pytest plugin to run 1/N of enumerated tests.
+    depends_on:
+      - pytest
+
+  - label: Faster PR testing
+    id: faster_pr_testing
+    description: |
+      Make automated PR testing with public CI services give faster feedback.
+    depends_on:
+      - run_new_tests
+      - run_test_slice
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/jquery.graphviz.svg.js b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/jquery.graphviz.svg.js
new file mode 100644
index 000000000..15bea27bd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/jquery.graphviz.svg.js
@@ -0,0 +1,537 @@
+/*
+ * Copyright (c) 2015 Mountainstorm
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+
+ +function ($) {
+  'use strict'
+
+  // Cross Browser starts/endsWith support
+  // =====================================
+  String.prototype.startsWith = function(prefix) {
+    return this.indexOf(prefix) == 0;
+  };
+
+  String.prototype.endsWith = function(suffix) {
+    return this.indexOf(suffix, this.length - suffix.length) !== -1;
+  };
+
+  // GRAPHVIZSVG PUBLIC CLASS DEFINITION
+  // ===================================
+
+  var GraphvizSvg = function (element, options) {
+    this.type       = null
+    this.options    = null
+    this.enabled    = null
+    this.$element   = null
+
+    this.init('graphviz.svg', element, options)
+  }
+
+  GraphvizSvg.VERSION  = '1.0.1'
+
+  GraphvizSvg.GVPT_2_PX = 32.5 // used to ease removal of extra space
+
+  GraphvizSvg.DEFAULTS = {
+    url: null,
+    svg: null,
+    shrink: '0.125pt',
+    tooltips: {
+      init: function ($graph) {
+        var $a = $(this)
+        $a.tooltip({
+          container: $graph,
+          placement: 'auto left',
+          animation: false,
+          viewport: null
+        }).on('hide.bs.tooltip', function() {
+          // keep them visible even if you accidentally mouse over
+          if ($a.attr('data-tooltip-keepvisible')) {
+            return false
+          }
+        })
+      },
+      show: function () {
+        var $a = $(this)
+        $a.attr('data-tooltip-keepvisible', true)
+        $a.tooltip('show')
+      },
+      hide: function () {
+        var $a = $(this)
+        $a.removeAttr('data-tooltip-keepvisible')
+        $a.tooltip('hide')
+      },
+      update: function () {
+        var $this = $(this)
+        if ($this.attr('data-tooltip-keepvisible')) {
+          $this.tooltip('show')
+          return
+        }
+      }
+    },
+    zoom: true,
+    highlight: {
+      selected: function (col, bg) {
+        return col
+      },
+      unselected: function (col, bg) {
+        return jQuery.Color(col).transition(bg, 0.9)
+      }
+    },
+    ready: null
+  }
+
+  GraphvizSvg.prototype.init = function (type, element, options) {
+    this.enabled   = true
+    this.type      = type
+    this.$element  = $(element)
+    this.options   = this.getOptions(options)
+
+    if (options.url) {
+      var that = this
+      $.get(options.url, null, function(data) {
+        var svg = $("svg", data)
+        that.$element.html(document.adoptNode(svg[0]))
+        that.setup()
+      }, "xml")
+    } else {
+      if (options.svg) {
+        this.$element.html(options.svg)
+      }
+      this.setup()
+    }
+  }
+
+  GraphvizSvg.prototype.getDefaults = function () {
+    return GraphvizSvg.DEFAULTS
+  }
+
+  GraphvizSvg.prototype.getOptions = function (options) {
+    options = $.extend({}, this.getDefaults(), this.$element.data(), options)
+
+    if (options.shrink) {
+      if (typeof options.shrink != 'object') {
+        options.shrink = {
+          x: options.shrink,
+          y: options.shrink
+        }
+      }
+      options.shrink.x = this.convertToPx(options.shrink.x)
+      options.shrink.y = this.convertToPx(options.shrink.y)
+    }
+    return options
+  }
+
+  GraphvizSvg.prototype.setup = function () {
+    var options = this.options
+
+    // save key elements in the graph for easy access
+    var $svg = $(this.$element.children('svg'))
+    var $graph = $svg.children('g:first')
+    this.$svg = $svg
+    this.$graph = $graph
+    this.$background = $graph.children('polygon:first') // might not exist
+    this.$nodes = $graph.children('.node')
+    this.$edges = $graph.children('.edge')
+    this._nodesByName = {}
+    this._edgesByName = {}
+
+    // add top level class and copy background color to element
+    this.$element.addClass('graphviz-svg')
+    if (this.$background.length) {
+      this.$element.css('background', this.$background.attr('fill'))
+    }
+
+    // setup all the nodes and edges
+    var that = this
+    this.$nodes.each(function () { that.setupNodesEdges($(this), true) })
+    this.$edges.each(function () { that.setupNodesEdges($(this), false) })
+
+    // remove the graph title element
+    var $title = this.$graph.children('title')
+    this.$graph.attr('data-name', $title.text())
+    $title.remove()
+
+    if (options.zoom) {
+      this.setupZoom()
+    }
+
+    // tell people we're done
+    if (options.ready) {
+      options.ready.call(this)
+    }
+  }
+
+  GraphvizSvg.prototype.setupNodesEdges = function ($el, isNode) {
+    var that = this
+    var options = this.options
+
+    // save the colors of the paths, ellipses and polygons
+    $el.find('polygon, ellipse, path').each(function () {
+      var $this = $(this)
+      // save original colors
+      $this.data('graphviz.svg.color', {
+        fill: $this.attr('fill'),
+        stroke: $this.attr('stroke')
+
+      })
+
+      // shrink it if it's a node
+      if (isNode && options.shrink) {
+        that.scaleNode($this)
+      }
+    })
+
+    // save the node name and check if theres a comment above; save it
+    var $title = $el.children('title')
+    if ($title[0]) {
+      // remove any compass points:
+      var title = $title.text().replace(/:[snew][ew]?/g,'')
+      $el.attr('data-name', title)
+      $title.remove()
+      if (isNode) {
+        this._nodesByName[title] = $el[0]
+      } else {
+        this._edgesByName[title] = $el[0]
+      }
+      // without a title we can't tell if its a user comment or not
+      var previousSibling = $el[0].previousSibling
+      while (previousSibling && previousSibling.nodeType != 8) {
+        previousSibling = previousSibling.previousSibling
+      }
+      if (previousSibling != null && previousSibling.nodeType == 8) {
+        var htmlDecode = function (input) {
+          var e = document.createElement('div')
+          e.innerHTML = input
+          return e.childNodes[0].nodeValue
+        }
+        var value = htmlDecode(previousSibling.nodeValue.trim())
+        if (value != title) {
+          // user added comment
+          $el.attr('data-comment', value)
+        }
+      }
+    }
+
+    // remove namespace from a[xlink:title]
+    $el.find('a').filter(function () { 
+      return $(this).attr('xlink:title') }).each(function () {
+        var $a = $(this)
+        $a.attr('title', $a.attr('xlink:title'))
+        $a.removeAttr('xlink:title')
+        if (options.tooltips) {
+          options.tooltips.init.call(this, that.$element)
+        }
+      })
+  }
+
+  GraphvizSvg.prototype.setupZoom = function() {
+    var that = this
+    var $element = this.$element
+    var $svg = this.$svg
+    this.zoom = {width: $svg.attr('width'), height: $svg.attr('height'), percentage: null }
+    this.scaleView(100.0)
+    $element.mousewheel(function (evt) {
+        if (evt.shiftKey) {
+          var percentage = that.zoom.percentage
+          percentage -= evt.deltaY * evt.deltaFactor
+          if (percentage < 100.0) {
+            percentage = 100.0
+          }
+          // get pointer offset in view
+          // ratio offset within svg
+          var dx = evt.pageX - $svg.offset().left
+          var dy = evt.pageY - $svg.offset().top
+          var rx = dx / $svg.width()
+          var ry = dy / $svg.height()
+
+          // offset within frame ($element)
+          var px = evt.pageX - $element.offset().left
+          var py = evt.pageY - $element.offset().top
+
+          that.scaleView(percentage)
+          // scroll so pointer is still in same place
+          $element.scrollLeft((rx * $svg.width()) + 0.5 - px)
+          $element.scrollTop((ry * $svg.height()) + 0.5 - py)
+          return false // stop propagation
+        }
+      })
+  }
+
+  GraphvizSvg.prototype.scaleView = function(percentage) {
+    var that = this
+    var $svg = this.$svg
+    $svg.attr('width', percentage + '%')
+    $svg.attr('height', percentage + '%')
+    this.zoom.percentage = percentage
+    // now callback to update tooltip position
+    var $everything = this.$nodes.add(this.$edges)
+    $everything.children('a[title]').each(function () {
+      that.options.tooltips.update.call(this)
+    })
+  }
+
+  GraphvizSvg.prototype.scaleNode = function($node) {
+    var dx = this.options.shrink.x
+    var dy = this.options.shrink.y
+    var tagName = $node.prop('tagName')
+    if (tagName == 'ellipse') {
+      $node.attr('rx', parseFloat($node.attr('rx')) - dx)
+      $node.attr('ry', parseFloat($node.attr('ry')) - dy)
+    } else if (tagName == 'polygon') {
+      // this is more complex - we need to scale it manually
+      var bbox = $node[0].getBBox()
+      var cx = bbox.x + (bbox.width / 2)
+      var cy = bbox.y + (bbox.height / 2)
+      var pts = $node.attr('points').split(' ')
+      var points = '' // new value
+      for (var i in pts) {
+        var xy = pts[i].split(',')
+        var ox = parseFloat(xy[0])
+        var oy = parseFloat(xy[1])
+        points += (((cx - ox) / (bbox.width / 2) * dx) + ox) +
+          ',' +
+          (((cy - oy) / (bbox.height / 2) * dy) + oy) +
+          ' '
+      }
+      $node.attr('points', points)
+    }
+  }
+
+  GraphvizSvg.prototype.convertToPx = function (val) {
+    var retval = val
+    if (typeof val == 'string') {
+      var end = val.length
+      var factor = 1.0
+      if (val.endsWith('px')) {
+        end -= 2
+      } else if (val.endsWith('pt')) {
+        end -= 2
+        factor = GraphvizSvg.GVPT_2_PX
+      }
+      retval = parseFloat(val.substring(0, end)) * factor
+    }
+    return retval
+  }
+
+  GraphvizSvg.prototype.findEdge = function (nodeName, testEdge, $retval) {
+    var retval = []
+    for (var name in this._edgesByName) {
+      var match = testEdge(nodeName, name)
+      if (match) {
+        if ($retval) {
+          $retval.push(this._edgesByName[name])
+        }
+        retval.push(match)
+      }
+    }
+    return retval
+  }
+
+  GraphvizSvg.prototype.findLinked = function (node, includeEdges, testEdge, $retval) {
+    var that = this
+    var $node = $(node)
+    var $edges = null
+    if (includeEdges) {
+      $edges = $retval
+    }
+    var names = this.findEdge($node.attr('data-name'), testEdge, $edges)
+    for (var i in names) {
+      var n = this._nodesByName[names[i]]
+      if (!$retval.is(n)) {
+        $retval.push(n)
+        that.findLinked(n, includeEdges, testEdge, $retval)
+      }
+    }
+  }
+
+  GraphvizSvg.prototype.colorElement = function ($el, getColor) {
+    var bg = this.$element.css('background')
+    $el.find('polygon, ellipse, path').each(function() {
+      var $this = $(this)
+      var color = $this.data('graphviz.svg.color')
+      if (color.fill && $this.prop('tagName') != 'path') {
+        $this.attr('fill', getColor(color.fill, bg)) // don't set  fill if it's a path
+      }
+      if (color.stroke) {
+        $this.attr('stroke', getColor(color.stroke, bg))
+      }
+    })
+  }
+
+  GraphvizSvg.prototype.restoreElement = function ($el) {
+    $el.find('polygon, ellipse, path').each(function() {
+      var $this = $(this)
+      var color = $this.data('graphviz.svg.color')
+      if (color.fill) {
+        $this.attr('fill', color.fill) // don't set  fill if it's a path
+      }
+      if (color.stroke) {
+        $this.attr('stroke', color.stroke)
+      }
+    })
+  }
+
+
+  // methods users can actually call
+  GraphvizSvg.prototype.nodes = function () {
+    return this.$nodes
+  }
+
+  GraphvizSvg.prototype.edges = function () {
+    return this.$edges
+  }
+
+  GraphvizSvg.prototype.nodesByName = function () {
+    return this._nodesByName
+  }
+
+  GraphvizSvg.prototype.edgesByName = function () {
+    return this._edgesByName
+  }
+
+  GraphvizSvg.prototype.linkedTo = function (node, includeEdges) {
+    var $retval = $()
+    this.findLinked(node, includeEdges, function (nodeName, edgeName) {
+      var other = null;
+      var match = '->' + nodeName
+      if (edgeName.endsWith(match)) {
+        other = edgeName.substring(0, edgeName.length - match.length);
+      }
+      return other;
+    }, $retval)
+    return $retval
+  }
+
+  GraphvizSvg.prototype.linkedFrom = function (node, includeEdges) {
+    var $retval = $()
+    this.findLinked(node, includeEdges, function (nodeName, edgeName) {
+      var other = null;
+      var match = nodeName + '->'
+      if (edgeName.startsWith(match)) {
+        other = edgeName.substring(match.length);
+      }
+      return other;
+    }, $retval)
+    return $retval
+  }
+
+  GraphvizSvg.prototype.linked = function (node, includeEdges) {
+    var $retval = $()
+    this.findLinked(node, includeEdges, function (nodeName, edgeName) {
+      return '^' + name + '--(.*)$'
+    }, $retval)
+    this.findLinked(node, includeEdges, function (nodeName, edgeName) {
+      return '^(.*)--' + name + '$'
+    }, $retval)
+    return $retval
+  }
+
+  GraphvizSvg.prototype.tooltip = function ($elements, show) {
+    var that = this
+    var options = this.options
+    $elements.each(function () {
+      $(this).find('a[title]').each(function () {
+        if (show) {
+          options.tooltips.show.call(this)
+        } else {
+          options.tooltips.hide.call(this)
+        }
+      })
+    })
+  }
+
+  GraphvizSvg.prototype.bringToFront = function ($elements) {
+    $elements.detach().appendTo(this.$graph)
+  }
+
+  GraphvizSvg.prototype.sendToBack = function ($elements) {
+    if (this.$background.length) {
+      $element.insertAfter(this.$background)
+    } else {
+      $elements.detach().prependTo(this.$graph)
+    }
+  }
+
+  GraphvizSvg.prototype.highlight = function ($nodesEdges, tooltips) {
+    var that = this
+    var options = this.options
+    var $everything = this.$nodes.add(this.$edges)
+    if ($nodesEdges && $nodesEdges.length > 0) {
+      // create set of all other elements and dim them
+      $everything.not($nodesEdges).each(function () {
+        that.colorElement($(this), options.highlight.unselected)
+        $(this).css('font-weight', 'normal')
+        that.tooltip($(this))
+      })
+      $nodesEdges.each(function () {
+        that.colorElement($(this), options.highlight.selected)
+        $(this).css('font-weight', 'normal')
+      })
+
+      this.tooltip($nodesEdges, tooltips)
+    } else {
+      $everything.each(function () {
+        that.restoreElement($(this))
+        $(this).css('font-weight', 'normal')
+      })
+      this.tooltip($everything)
+    }
+  }
+
+  GraphvizSvg.prototype.destroy = function () {
+    var that = this
+    this.hide(function () {
+      that.$element.off('.' + that.type).removeData(that.type)
+    })
+  }
+
+
+  // GRAPHVIZSVG PLUGIN DEFINITION
+  // =============================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this   = $(this)
+      var data    = $this.data('graphviz.svg')
+      var options = typeof option == 'object' && option
+
+      if (!data && /destroy/.test(option)) return
+      if (!data) $this.data('graphviz.svg', (data = new GraphvizSvg(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  var old = $.fn.graphviz
+
+  $.fn.graphviz             = Plugin
+  $.fn.graphviz.Constructor = GraphvizSvg
+
+
+  // GRAPHVIZ NO CONFLICT
+  // ====================
+
+  $.fn.graphviz.noConflict = function () {
+    $.fn.graphviz = old
+    return this
+  }
+
+}(jQuery)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/render.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/render.py
new file mode 100644
index 000000000..28791e0b7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/render.py
@@ -0,0 +1,85 @@
+#!/usr/bin/env python
+
+import os.path
+import json
+import collections
+import yaml
+import graphviz
+from jinja2 import Environment, FileSystemLoader
+
+
+Dagmap = collections.namedtuple('Dagmap',
+             ['version', 'meta', 'style', 'tasks'])
+
+
+def parse_yaml(filename):
+    with open(filename, 'r') as f:
+        contents = yaml.safe_load(f)
+    
+    meta = contents['meta']
+    version = meta['version']
+    if version > 1:
+        raise Exception('Unsupported version %d' % version)
+    del meta['version']
+
+    style = contents['style']
+    tasks = contents['tasks']
+    if not isinstance(tasks, list):
+        raise Exception('"tasks" must be a list')
+
+    return Dagmap(version=version, meta=meta, style=style, tasks=tasks)
+
+
+def to_graphviz(dagmap):
+    G = graphviz.Digraph(format='svg', engine='neato',
+        graph_attr=dict(bgcolor="#f4f4f4", pad="0.5", overlap="false"),
+        node_attr=dict(width="0.6", style="filled",
+                       fillcolor="#83c6de", color="#83c6de", penwidth="3", label="",
+                       fontname="helvetica Neue Ultra Light", fontsize="28"),
+        edge_attr=dict(color="#616a72", arrowsize="2.0", penwidth="4", fontname="helvetica Neue Ultra Light"))
+
+    G.node(name='_nothing', label='', style='invis')
+
+    for task in dagmap.tasks:
+        G.node(name=task['id'], label=task['label'],
+               tooltip=task['description'].strip())
+        depends_on = task.get('depends_on', ['_nothing'])
+        for dep in depends_on:
+            if dep == '_nothing':
+                attrs = {
+                    'style': 'invis',
+                }
+            else:
+                attrs = {}
+            G.edge(dep, task['id'], **attrs)
+
+    return G
+
+
+def main(argv):
+    import argparse
+    parser = argparse.ArgumentParser(description='Render Dagmap to Graphviz')
+    parser.add_argument('-o', '--output', required=True, help='output svg filename')
+    parser.add_argument('-t', '--template', default='template.html', help='HTML rendering template')
+    parser.add_argument('input', metavar='INPUT', type=str,
+                        help='YAML input filename')
+
+    args = parser.parse_args(argv[1:])
+
+    dagmap = parse_yaml(args.input)
+    graph = to_graphviz(dagmap)
+    svg = graph.pipe().decode('utf-8')
+
+    template_env = Environment(loader=FileSystemLoader(os.path.dirname(__file__)))
+    template = template_env.get_template(args.template)
+    html = template.render(svg=json.dumps(svg))
+
+    with open(args.output, 'w') as f:
+        f.write(html)
+
+    return 0
+
+
+if __name__ == '__main__':
+    import sys
+    sys.exit(main(sys.argv))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/template.html b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/template.html
new file mode 100644
index 000000000..0a634757c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/dagmap/template.html
@@ -0,0 +1,110 @@
+<!--
+ * Copyright (c) 2015 Mountainstorm
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ -->
+<html>
+
+<head>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.4/css/bootstrap.min.css">
+</head>
+
+<style>
+    #instructions {
+        color: #0a0a0a;
+        position: absolute;
+        z-index: 100;
+        bottom: 0px;
+        left: 0px;
+    }
+
+    /* this element needs tooltip positioning to work */
+    .graphviz-svg {
+        position: relative;
+    }
+
+    /* stop people selecting text on nodes */
+    .graphviz-svg text {
+        -webkit-touch-callout: none;
+        -webkit-user-select: none;
+        -khtml-user-select: none;
+        -moz-user-select: none;
+        -ms-user-select: none;
+        user-select: none;
+        cursor: default;
+    }
+</style>
+
+<body>
+    <h4 id="instructions">Click node to highlight; Shift-scroll to zoom; Esc to unhighlight</h4>
+
+    <div id="details", style="float: left; width: 20%; height: 100%; background-color:lightgray; font-color: black; overflow: scroll; padding: 1em;">
+        <h5>Details</h5>
+        <p>(Click on a node for details)</p>
+    </div>
+
+    <div id="graph" style="float: right; width: 80%; height: 100%; overflow: scroll;"></div>
+
+    <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.3.min.js"></script>
+    <script type="text/javascript"
+        src="https://cdn.rawgit.com/jquery/jquery-mousewheel/master/jquery.mousewheel.min.js"></script>
+    <script type="text/javascript" src="https://cdn.rawgit.com/jquery/jquery-color/master/jquery.color.js"></script>
+    <script type="text/javascript"
+        src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.4/js/bootstrap.min.js"></script>
+    <script type="text/javascript" src="jquery.graphviz.svg.js"></script>
+    <script type="text/javascript">
+        var svg = {{ svg }}
+    </script>
+    <script type="text/javascript">
+        $(document).ready(function () {
+            $("#graph").graphviz({
+                svg: svg,
+                ready: function () {
+                    var gv = this
+                    gv.nodes().click(function () {
+                        var $set = $()
+                        $set.push(this)
+                        $set = $set.add(gv.linkedFrom(this, true))
+                        $set = $set.add(gv.linkedTo(this, true))
+                        gv.highlight($set, false)
+                        gv.tooltip($(this), true)
+                        gv.bringToFront($set)
+
+                        // copy tooltip text to pane
+                        var tooltip_id = $(this).find("a").attr("aria-describedby")
+                        var tooltip_box = $("#"+tooltip_id).find(".tooltip-inner")
+                        var tooltip_data = tooltip_box.html()
+                        $("#details p").html(tooltip_data)
+                        gv.tooltip($(this), false)
+                        $(this).css('font-weight', 'bold')
+                    })
+                    $(document).keydown(function (evt) {
+                        if (evt.keyCode == 27) {
+                            gv.highlight()
+                        }
+                    })
+                }
+            });
+        });
+    </script>
+
+
+</body>
+
+</html>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/environment.yml b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/environment.yml
new file mode 100644
index 000000000..8028a3366
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/environment.yml
@@ -0,0 +1,16 @@
+# This environment is used by the RTD config for PR builds. RTD uses this as the
+# base environment and then adds in the sphinx etc tools on top.
+# See: https://docs.readthedocs.io/en/stable/guides/conda.html
+name: rtd
+channels:
+  - numba/label/dev
+dependencies:
+  - python=3.7
+  - llvmlite=0.39
+  - numpy
+  - numpydoc
+  - setuptools
+  # https://stackoverflow.com/questions/67542699/readthedocs-sphinx-not-rendering-bullet-list-from-rst-fileA
+  - docutils==0.16
+  # The following is needed to fix RTD.
+  - conda
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/gh-pages.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/gh-pages.py
new file mode 100644
index 000000000..3c8093d06
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/gh-pages.py
@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""Script to commit the doc build outputs into the github-pages repo.
+
+Use:
+
+  gh-pages.py [tag]
+
+If no tag is given, the current output of 'git describe' is used.  If given,
+that is how the resulting directory will be named.
+
+In practice, you should use either actual clean tags from a current build or
+something like 'current' as a stable URL for the most current version of the """
+from __future__ import print_function, division, absolute_import
+
+#-----------------------------------------------------------------------------
+# Imports
+#-----------------------------------------------------------------------------
+import os
+import re
+import shutil
+import sys
+from os import chdir as cd
+from os.path import join as pjoin
+
+from subprocess import Popen, PIPE, CalledProcessError, check_call
+
+#-----------------------------------------------------------------------------
+# Globals
+#-----------------------------------------------------------------------------
+
+pages_dir = 'gh-pages'
+html_dir = '_build/html'
+pdf_dir = '_build/latex'
+pages_repo = 'git@github.com:numba/numba-doc.git'
+
+#-----------------------------------------------------------------------------
+# Functions
+#-----------------------------------------------------------------------------
+def sub_environment():
+    """Return an environment dict for executing subcommands in."""
+    env = os.environ.copy()
+    # Force untranslated messages for regex matching
+    env['LANG'] = 'C'
+    return env
+
+
+def sh(cmd):
+    """Execute command in a subshell, return status code."""
+    return check_call(cmd, shell=True, env=sub_environment())
+
+
+def sh2(cmd):
+    """Execute command in a subshell, return stdout.
+
+    Stderr is unbuffered from the subshell.x"""
+    p = Popen(cmd, stdout=PIPE, shell=True, env=sub_environment())
+    out = p.communicate()[0]
+    retcode = p.returncode
+    if retcode:
+        raise CalledProcessError(retcode, cmd)
+    else:
+        return out.rstrip()
+
+
+def sh3(cmd):
+    """Execute command in a subshell, return stdout, stderr
+
+    If anything appears in stderr, print it out to sys.stderr"""
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE, shell=True,
+              env=sub_environment())
+    out, err = p.communicate()
+    retcode = p.returncode
+    if retcode:
+        raise CalledProcessError(retcode, cmd)
+    else:
+        return out.rstrip(), err.rstrip()
+
+
+def init_repo(path):
+    """clone the gh-pages repo if we haven't already."""
+    sh("git clone %s %s"%(pages_repo, path))
+    here = os.getcwd()
+    cd(path)
+    sh('git checkout gh-pages')
+    cd(here)
+
+#-----------------------------------------------------------------------------
+# Script starts
+#-----------------------------------------------------------------------------
+if __name__ == '__main__':
+    # The tag can be given as a positional argument
+    try:
+        tag = sys.argv[1]
+    except IndexError:
+        try:
+            tag = sh2('git describe --exact-match').decode()
+        except CalledProcessError:
+            tag = "dev"   # Fallback
+            print("Using dev")
+
+    startdir = os.getcwd()
+    if not os.path.exists(pages_dir):
+        # init the repo
+        init_repo(pages_dir)
+    else:
+        # ensure up-to-date before operating
+        cd(pages_dir)
+        sh('git checkout gh-pages')
+        sh('git pull')
+        cd(startdir)
+
+    dest = pjoin(pages_dir, tag)
+
+    # don't `make html` here, because gh-pages already depends on html in Makefile
+    # sh('make html')
+    if tag != 'dev':
+        # only build pdf for non-dev targets
+        #sh2('make pdf')
+        pass
+
+    # This is pretty unforgiving: we unconditionally nuke the destination
+    # directory, and then copy the html tree in there
+    shutil.rmtree(dest, ignore_errors=True)
+    shutil.copytree(html_dir, dest)
+    if tag != 'dev':
+        #shutil.copy(pjoin(pdf_dir, 'ipython.pdf'), pjoin(dest, 'ipython.pdf'))
+        pass
+
+    try:
+        cd(pages_dir)
+        status = sh2('git status | head -1').decode()
+        branch = re.match('\#?\s*On branch (.*)$', status).group(1)
+        if branch != 'gh-pages':
+            e = 'On %r, git branch is %r, MUST be "gh-pages"' % (pages_dir,
+                                                                 branch)
+            raise RuntimeError(e)
+
+        sh('git add -A %s' % tag)
+        sh('git commit -m"Updated doc release: %s"' % tag)
+        print()
+        print('Most recent 3 commits:')
+        sys.stdout.flush()
+        sh('git --no-pager log --oneline HEAD~3..')
+    finally:
+        cd(startdir)
+
+    print()
+    print('Now verify the build in: %r' % dest)
+    print("If everything looks good, 'git push'")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/make.bat b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/make.bat
new file mode 100644
index 000000000..29b481d88
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/make.bat
@@ -0,0 +1,242 @@
+@ECHO OFF
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set BUILDDIR=_build
+set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source
+set I18NSPHINXOPTS=%SPHINXOPTS% source
+if NOT "%PAPER%" == "" (
+	set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
+	set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
+)
+
+if "%1" == "" goto help
+
+if "%1" == "help" (
+	:help
+	echo.Please use `make ^<target^>` where ^<target^> is one of
+	echo.  html       to make standalone HTML files
+	echo.  dirhtml    to make HTML files named index.html in directories
+	echo.  singlehtml to make a single large HTML file
+	echo.  pickle     to make pickle files
+	echo.  json       to make JSON files
+	echo.  htmlhelp   to make HTML files and a HTML help project
+	echo.  qthelp     to make HTML files and a qthelp project
+	echo.  devhelp    to make HTML files and a Devhelp project
+	echo.  epub       to make an epub
+	echo.  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter
+	echo.  text       to make text files
+	echo.  man        to make manual pages
+	echo.  texinfo    to make Texinfo files
+	echo.  gettext    to make PO message catalogs
+	echo.  changes    to make an overview over all changed/added/deprecated items
+	echo.  xml        to make Docutils-native XML files
+	echo.  pseudoxml  to make pseudoxml-XML files for display purposes
+	echo.  linkcheck  to check all external links for integrity
+	echo.  doctest    to run all doctests embedded in the documentation if enabled
+	goto end
+)
+
+if "%1" == "clean" (
+	for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
+	del /q /s %BUILDDIR%\*
+	goto end
+)
+
+
+%SPHINXBUILD% 2> nul
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "html" (
+	%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/html.
+	goto end
+)
+
+if "%1" == "dirhtml" (
+	%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
+	goto end
+)
+
+if "%1" == "singlehtml" (
+	%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
+	goto end
+)
+
+if "%1" == "pickle" (
+	%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can process the pickle files.
+	goto end
+)
+
+if "%1" == "json" (
+	%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can process the JSON files.
+	goto end
+)
+
+if "%1" == "htmlhelp" (
+	%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can run HTML Help Workshop with the ^
+.hhp project file in %BUILDDIR%/htmlhelp.
+	goto end
+)
+
+if "%1" == "qthelp" (
+	%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can run "qcollectiongenerator" with the ^
+.qhcp project file in %BUILDDIR%/qthelp, like this:
+	echo.^> qcollectiongenerator %BUILDDIR%\qthelp\Numba.qhcp
+	echo.To view the help file:
+	echo.^> assistant -collectionFile %BUILDDIR%\qthelp\Numba.ghc
+	goto end
+)
+
+if "%1" == "devhelp" (
+	%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished.
+	goto end
+)
+
+if "%1" == "epub" (
+	%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The epub file is in %BUILDDIR%/epub.
+	goto end
+)
+
+if "%1" == "latex" (
+	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
+	goto end
+)
+
+if "%1" == "latexpdf" (
+	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+	cd %BUILDDIR%/latex
+	make all-pdf
+	cd %BUILDDIR%/..
+	echo.
+	echo.Build finished; the PDF files are in %BUILDDIR%/latex.
+	goto end
+)
+
+if "%1" == "latexpdfja" (
+	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+	cd %BUILDDIR%/latex
+	make all-pdf-ja
+	cd %BUILDDIR%/..
+	echo.
+	echo.Build finished; the PDF files are in %BUILDDIR%/latex.
+	goto end
+)
+
+if "%1" == "text" (
+	%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The text files are in %BUILDDIR%/text.
+	goto end
+)
+
+if "%1" == "man" (
+	%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The manual pages are in %BUILDDIR%/man.
+	goto end
+)
+
+if "%1" == "texinfo" (
+	%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
+	goto end
+)
+
+if "%1" == "gettext" (
+	%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
+	goto end
+)
+
+if "%1" == "changes" (
+	%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.The overview file is in %BUILDDIR%/changes.
+	goto end
+)
+
+if "%1" == "linkcheck" (
+	%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Link check complete; look for any errors in the above output ^
+or in %BUILDDIR%/linkcheck/output.txt.
+	goto end
+)
+
+if "%1" == "doctest" (
+	%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Testing of doctests in the sources finished, look at the ^
+results in %BUILDDIR%/doctest/output.txt.
+	goto end
+)
+
+if "%1" == "xml" (
+	%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The XML files are in %BUILDDIR%/xml.
+	goto end
+)
+
+if "%1" == "pseudoxml" (
+	%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
+	goto end
+)
+
+:end
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/requirements.txt b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/requirements.txt
new file mode 100644
index 000000000..66f88aae4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/requirements.txt
@@ -0,0 +1 @@
+numpydoc
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/_ext/ghfiles.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/_ext/ghfiles.py
new file mode 100644
index 000000000..d0320cb3a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/_ext/ghfiles.py
@@ -0,0 +1,75 @@
+import os.path as path
+import subprocess
+import shlex
+from sphinx.util import logging
+from docutils import nodes
+logger = logging.getLogger(__name__)
+
+
+# use an old git trick, to get the top-level, could have used ../ etc.. but
+# this will be fine..
+top = subprocess.check_output(shlex.split(
+    "git rev-parse --show-toplevel")).strip().decode("utf-8")
+
+
+def make_ref(text):
+    """ Make hyperlink to Github """
+    full_path = path.join(top, text)
+    if path.isfile(full_path):
+        ref = "https://www.github.com/numba/numba/blob/main/" + text
+    elif path.isdir(full_path):
+        ref = "https://www.github.com/numba/numba/tree/main/" + text
+    else:
+        logger.warn("Failed to find file in repomap: " + text)
+        ref = "https://www.github.com/numba/numba"
+    return ref
+
+
+def intersperse(lst, item):
+    """ Insert item between each item in lst.
+
+    Copied under CC-BY-SA from stackoverflow at:
+
+    https://stackoverflow.com/questions/5920643/
+    add-an-item-between-each-item-already-in-the-list
+
+    """
+    result = [item] * (len(lst) * 2 - 1)
+    result[0::2] = lst
+    return result
+
+
+def ghfile_role(name, rawtext, text, lineno, inliner, options={}, content=[]):
+    """ Emit hyperlink nodes for a given file in repomap. """
+    my_nodes = []
+    if "{" in text:  # myfile.{c,h} - make two nodes
+        # could have used regexes, but this will be fine..
+        base = text[:text.find(".") + 1]
+        exts = text[text.find("{") + 1:text.find("}")].split(",")
+        for e in exts:
+            node = nodes.reference(rawtext,
+                                   base + e,
+                                   refuri=make_ref(base + e),
+                                   **options)
+            my_nodes.append(node)
+    elif "*" in text:  # path/*_files.py - link to directory
+        # Could have used something from os.path, but this will be fine..
+        ref = path.dirname(text) + path.sep
+        node = nodes.reference(rawtext, text, refuri=make_ref(ref), **options)
+        my_nodes.append(node)
+    else:  # everything else is taken verbatim
+        node = nodes.reference(rawtext, text, refuri=make_ref(text), **options)
+        my_nodes.append(node)
+
+    # insert separators if needed
+    if len(my_nodes) > 1:
+        my_nodes = intersperse(my_nodes, nodes.Text(" | "))
+    return my_nodes, []
+
+
+def setup(app):
+    logger.info('Initializing ghfiles plugin')
+    app.add_role('ghfile', ghfile_role)
+
+    metadata = {'parallel_read_safe': True, 'parallel_write_safe': True}
+    return metadata
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/conf.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/conf.py
new file mode 100644
index 000000000..f81d55abd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/conf.py
@@ -0,0 +1,343 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# Numba documentation build configuration file, created by
+# sphinx-quickstart on Tue Dec 30 11:55:40 2014.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+try:
+    # Numba is installed
+    import numba
+except ImportError:
+    # Numba is run from its source checkout
+    sys.path.insert(0, os.path.abspath('../..'))
+    import numba
+
+
+on_rtd = os.environ.get('READTHEDOCS') == 'True'
+
+if on_rtd:
+    # The following is needed to fix RTD issue with numpydoc
+    # https://github.com/readthedocs/sphinx_rtd_theme/issues/766
+    from conda.cli.python_api import run_command as conda_cmd
+
+    conda_cmd("install", "-c", "conda-forge", "sphinx_rtd_theme>=0.5.1", "-y")
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.todo',
+    #'sphinx.ext.mathjax',
+    'sphinx.ext.autodoc',
+    #'sphinx.ext.graphviz',
+    'numpydoc',
+]
+
+# Adding the github files extension
+sys.path.append(os.path.abspath(os.path.join(".", "_ext")))
+extensions.append('ghfiles')
+
+todo_include_todos = True
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['../_templates']
+
+# The suffix of source filenames.
+source_suffix = '.rst'
+
+# The encoding of source files.
+#source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'Numba'
+copyright = u'2012-2020, Anaconda, Inc. and others'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+version = '.'.join(numba.__version__.split('.')[:2])
+# The full version, including alpha/beta/rc tags.
+release = numba.__version__
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = []
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+#add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+#show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+#keep_warnings = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+html_theme = 'sphinx_rtd_theme'
+
+# All sphinx_rtd_theme options. Default values commented out; uncomment to
+# change.
+html_theme_options = {
+    'canonical_url': 'https://numba.readthedocs.io/en/stable/',
+    # 'logo_only': False,
+    # 'display_version': True,
+    # 'prev_next_buttons_location': 'bottom',
+    'style_external_links': True,
+    # 'vcs_pageview_mode': '',
+    'style_nav_header_background': '#00A3E0',
+    # Toc options
+    'collapse_navigation': False,
+    # 'sticky_navigation': True,
+    # 'navigation_depth': 4,
+    # 'includehidden': True,
+    # 'titles_only': False
+}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = None
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+html_logo = "../_static/numba-white-icon-rgb.svg"
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+html_favicon = '../_static/numba-blue-icon-rgb.svg'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['../_static']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+#html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'Numbadoc'
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #'papersize': 'letterpaper',
+
+    # The font size ('10pt', '11pt' or '12pt').
+    #'pointsize': '10pt',
+
+    # Additional stuff for the LaTeX preamble.
+    #'preamble': '',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    ('index', 'numba.tex', u'Numba Documentation',
+     u'Anaconda', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+#latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# If true, show page references after internal links.
+#latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+#latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    ('index', 'numba', 'Numba Documentation',
+     ['Anaconda'], 1)
+]
+
+# If true, show URL addresses after external links.
+#man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    ('index', 'Numba', 'Numba Documentation',
+     'Anaconda', 'Numba', 'One line description of project.',
+     'Miscellaneous'),
+]
+
+# Documents to append as an appendix to all manuals.
+#texinfo_appendices = []
+
+# If false, no module index is generated.
+#texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+#texinfo_no_detailmenu = False
+
+
+# Configuration for intersphinx: refer to the Python standard library
+# and the Numpy documentation.
+intersphinx_mapping = {
+    'python': ('https://docs.python.org/3', None),
+    'numpy': ('https://numpy.org/doc/stable/', None),
+    'llvmlite': ('https://llvmlite.readthedocs.io/en/latest/', None),
+}
+
+
+# numpydoc options
+
+# To silence "WARNING: toctree contains reference to nonexisting document"
+numpydoc_show_class_members = False
+
+# -- Custom autogeneration ------------------------------------------------
+
+
+def _autogenerate():
+    from numba.scripts.generate_lower_listing import gen_lower_listing
+    from numba.misc.help.inspector import write_listings
+
+    basedir = os.path.dirname(__file__)
+    gen_lower_listing(os.path.join(basedir,
+                                   'developer/autogen_lower_listing.rst'))
+
+    # Run inspector on supported packages
+    for package in ['builtins', 'math', 'cmath', 'numpy']:
+        write_listings(
+            package_name=package,
+            filename=os.path.join(
+                basedir, 'developer', 'autogen_{}_listing'.format(package),
+            ),
+            output_format='rst',
+        )
+
+
+_autogenerate()
+
+
+def setup(app):
+    app.add_css_file('rtd-overrides.css')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/host.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/host.rst
new file mode 100644
index 000000000..4c2dd0cfb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/host.rst
@@ -0,0 +1,232 @@
+CUDA Host API
+=============
+
+Device Management
+-----------------
+
+Device detection and enquiry
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following functions are available for querying the available hardware:
+
+.. autofunction:: numba.cuda.is_available
+
+.. autofunction:: numba.cuda.detect
+
+Context management
+~~~~~~~~~~~~~~~~~~
+
+CUDA Python functions execute within a CUDA context. Each CUDA device in a
+system has an associated CUDA context, and Numba presently allows only one context
+per thread. For further details on CUDA Contexts, refer to the `CUDA Driver API
+Documentation on Context Management
+<http://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__CTX.html>`_ and the
+`CUDA C Programming Guide Context Documentation
+<http://docs.nvidia.com/cuda/cuda-c-programming-guide/#context>`_. CUDA Contexts
+are instances of the :class:`~numba.cuda.cudadrv.driver.Context` class:
+
+.. autoclass:: numba.cuda.cudadrv.driver.Context
+   :members: reset, get_memory_info, push, pop
+
+The following functions can be used to get or select the context:
+
+.. autofunction:: numba.cuda.current_context
+.. autofunction:: numba.cuda.require_context
+
+The following functions affect the current context:
+
+.. autofunction:: numba.cuda.synchronize
+.. autofunction:: numba.cuda.close
+
+Device management
+~~~~~~~~~~~~~~~~~
+
+Numba maintains a list of supported CUDA-capable devices:
+
+.. attribute:: numba.cuda.gpus
+
+   An indexable list of supported CUDA devices. This list is indexed by integer
+   device ID.
+
+Alternatively, the current device can be obtained:
+
+.. function:: numba.cuda.gpus.current
+
+   Return the currently-selected device.
+
+Getting a device through :attr:`numba.cuda.gpus` always provides an instance of
+:class:`numba.cuda.cudadrv.devices._DeviceContextManager`, which acts as a
+context manager for the selected device:
+
+.. autoclass:: numba.cuda.cudadrv.devices._DeviceContextManager
+
+One may also select a context and device or get the current device using the
+following three functions:
+
+.. autofunction:: numba.cuda.select_device
+.. autofunction:: numba.cuda.get_current_device
+.. autofunction:: numba.cuda.list_devices
+
+The :class:`numba.cuda.cudadrv.driver.Device` class can be used to enquire about
+the functionality of the selected device:
+
+.. class:: numba.cuda.cudadrv.driver.Device
+
+   The device associated with a particular context.
+
+   .. attribute:: compute_capability
+
+      A tuple, *(major, minor)* indicating the supported compute capability.
+
+   .. attribute:: id
+
+      The integer ID of the device.
+
+   .. attribute:: name
+
+      The name of the device (e.g. "GeForce GTX 970").
+
+   .. attribute:: uuid
+
+      The UUID of the device (e.g. "GPU-e6489c45-5b68-3b03-bab7-0e7c8e809643").
+
+   .. method:: reset
+
+      Delete the context for the device. This will destroy all memory
+      allocations, events, and streams created within the context.
+
+
+Compilation
+-----------
+
+Numba provides an entry point for compiling a Python function to PTX without
+invoking any of the driver API. This can be useful for:
+
+- Generating PTX that is to be inlined into other PTX code (e.g. from outside
+  the Numba / Python ecosystem).
+- Generating code when there is no device present.
+- Generating code prior to a fork without initializing CUDA.
+
+.. note:: It is the user's responsibility to manage any ABI issues arising from
+   the use of compilation to PTX.
+
+.. autofunction:: numba.cuda.compile_ptx
+
+
+The environment variable ``NUMBA_CUDA_DEFAULT_PTX_CC`` can be set to control
+the default compute capability targeted by ``compile_ptx`` - see
+:ref:`numba-envvars-gpu-support`. If PTX for the compute capability of the
+current device is required, the ``compile_ptx_for_current_device`` function can
+be used:
+
+.. autofunction:: numba.cuda.compile_ptx_for_current_device
+
+
+
+Measurement
+-----------
+
+.. _cuda-profiling:
+
+Profiling
+~~~~~~~~~
+
+The NVidia Visual Profiler can be used directly on executing CUDA Python code -
+it is not a requirement to insert calls to these functions into user code.
+However, these functions can be used to allow profiling to be performed
+selectively on specific portions of the code. For further information on
+profiling, see the `NVidia Profiler User's Guide
+<https://docs.nvidia.com/cuda/profiler-users-guide/>`_.
+
+.. autofunction:: numba.cuda.profile_start
+.. autofunction:: numba.cuda.profile_stop
+.. autofunction:: numba.cuda.profiling
+
+
+.. _events:
+
+Events
+~~~~~~
+
+Events can be used to monitor the progress of execution and to record the
+timestamps of specific points being reached. Event creation returns immediately,
+and the created event can be queried to determine if it has been reached. For
+further information, see the `CUDA C Programming Guide Events section
+<http://docs.nvidia.com/cuda/cuda-c-programming-guide/#events>`_.
+
+The following functions are used for creating and measuring the time between
+events:
+
+.. autofunction:: numba.cuda.event
+.. autofunction:: numba.cuda.event_elapsed_time
+
+Events are instances of the :class:`numba.cuda.cudadrv.driver.Event` class:
+
+.. autoclass:: numba.cuda.cudadrv.driver.Event
+   :members: query, record, synchronize, wait
+
+
+.. _streams:
+
+Stream Management
+-----------------
+
+Streams allow concurrency of execution on a single device within a given
+context. Queued work items in the same stream execute sequentially, but work
+items in different streams may execute concurrently. Most operations involving a
+CUDA device can be performed asynchronously using streams, including data
+transfers and kernel execution. For further details on streams, see the `CUDA C
+Programming Guide Streams section
+<http://docs.nvidia.com/cuda/cuda-c-programming-guide/#streams>`_.
+
+Numba defaults to using the legacy default stream as the default stream. The
+per-thread default stream can be made the default stream by setting the
+environment variable ``NUMBA_CUDA_PER_THREAD_DEFAULT_STREAM`` to ``1`` (see the
+:ref:`CUDA Environment Variables section <numba-envvars-gpu-support>`).
+Regardless of this setting, the objects representing the legacy and per-thread
+default streams can be constructed using the functions below.
+
+Streams are instances of :class:`numba.cuda.cudadrv.driver.Stream`:
+
+.. autoclass:: numba.cuda.cudadrv.driver.Stream
+   :members: synchronize, auto_synchronize, add_callback, async_done
+
+To create a new stream:
+
+.. autofunction:: numba.cuda.stream
+
+To get the default stream:
+
+.. autofunction:: numba.cuda.default_stream
+
+To get the default stream with an explicit choice of whether it is the legacy
+or per-thread default stream:
+
+.. autofunction:: numba.cuda.legacy_default_stream
+
+.. autofunction:: numba.cuda.per_thread_default_stream
+
+To construct a Numba ``Stream`` object using a stream allocated elsewhere, the
+``external_stream`` function is provided. Note that the lifetime of external
+streams must be managed by the user - Numba will not deallocate an external
+stream, and the stream must remain valid whilst the Numba ``Stream`` object is
+in use.
+
+.. autofunction:: numba.cuda.external_stream
+
+
+Runtime
+-------
+
+Numba generally uses the Driver API, but it provides a simple wrapper to the
+Runtime API so that the version of the runtime in use can be queried. This is
+accessed through ``cuda.runtime``, which is an instance of the
+:class:`numba.cuda.cudadrv.runtime.Runtime` class:
+
+.. autoclass:: numba.cuda.cudadrv.runtime.Runtime
+   :members: get_version, is_supported_version, supported_versions
+
+Whether the current runtime is officially supported and tested with the current
+version of Numba can also be queried:
+
+.. autofunction:: numba.cuda.is_supported_version
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/index.rst
new file mode 100644
index 000000000..771afea19
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/index.rst
@@ -0,0 +1,10 @@
+CUDA Python Reference
+=====================
+
+.. toctree::
+
+   host.rst
+   kernel.rst
+   types.rst
+   memory.rst
+   libdevice.rst
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/kernel.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/kernel.rst
new file mode 100644
index 000000000..d23b2eed4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/kernel.rst
@@ -0,0 +1,586 @@
+CUDA Kernel API
+===============
+
+Kernel declaration
+------------------
+
+The ``@cuda.jit`` decorator is used to create a CUDA dispatcher object that can
+be configured and launched:
+
+.. autofunction:: numba.cuda.jit
+
+
+Dispatcher objects
+------------------
+
+The usual syntax for configuring a Dispatcher with a launch configuration uses
+subscripting, with the arguments being as in the following:
+
+.. code-block:: python
+
+   # func is some function decorated with @cuda.jit
+   func[griddim, blockdim, stream, sharedmem]
+
+
+The ``griddim`` and ``blockdim`` arguments specify the size of the grid and
+thread blocks, and may be either integers or tuples of length up to 3. The
+``stream`` parameter is an optional stream on which the kernel will be launched,
+and the ``sharedmem`` parameter specifies the size of dynamic shared memory in
+bytes.
+
+Subscripting the Dispatcher returns a configuration object that can be called
+with the kernel arguments:
+
+.. code-block:: python
+
+   configured = func[griddim, blockdim, stream, sharedmem]
+   configured(x, y, z)
+
+
+However, it is more idiomatic to configure and call the kernel within a single
+statement:
+
+.. code-block:: python
+
+   func[griddim, blockdim, stream, sharedmem](x, y, z)
+
+This is similar to launch configuration in CUDA C/C++:
+
+.. code-block:: cuda
+
+   func<<<griddim, blockdim, sharedmem, stream>>>(x, y, z)
+
+.. note:: The order of ``stream`` and ``sharedmem`` are reversed in Numba
+   compared to in CUDA C/C++.
+
+Dispatcher objects also provide several utility methods for inspection and
+creating a specialized instance:
+
+.. autoclass:: numba.cuda.dispatcher.CUDADispatcher
+   :members: inspect_asm, inspect_llvm, inspect_sass, inspect_types,
+             get_regs_per_thread, specialize, specialized, extensions, forall
+
+
+Intrinsic Attributes and Functions
+----------------------------------
+
+The remainder of the attributes and functions in this section may only be called
+from within a CUDA Kernel.
+
+Thread Indexing
+~~~~~~~~~~~~~~~
+
+.. attribute:: numba.cuda.threadIdx
+
+    The thread indices in the current thread block, accessed through the
+    attributes ``x``, ``y``, and ``z``. Each index is an integer spanning the
+    range from 0 inclusive to the corresponding value of the attribute in
+    :attr:`numba.cuda.blockDim` exclusive.
+
+.. attribute:: numba.cuda.blockIdx
+
+    The block indices in the grid of thread blocks, accessed through the
+    attributes ``x``, ``y``, and ``z``. Each index is an integer spanning the
+    range from 0 inclusive to the corresponding value of the attribute in
+    :attr:`numba.cuda.gridDim` exclusive.
+
+.. attribute:: numba.cuda.blockDim
+
+    The shape of a block of threads, as declared when instantiating the
+    kernel.  This value is the same for all threads in a given kernel, even
+    if they belong to different blocks (i.e. each block is "full").
+
+.. attribute:: numba.cuda.gridDim
+
+    The shape of the grid of blocks, accessed through the attributes ``x``,
+    ``y``, and ``z``.
+
+.. attribute:: numba.cuda.laneid
+
+    The thread index in the current warp, as an integer spanning the range
+    from 0 inclusive to the :attr:`numba.cuda.warpsize` exclusive.
+
+.. attribute:: numba.cuda.warpsize
+
+    The size in threads of a warp on the GPU. Currently this is always 32.
+
+.. function:: numba.cuda.grid(ndim)
+
+   Return the absolute position of the current thread in the entire
+   grid of blocks.  *ndim* should correspond to the number of dimensions
+   declared when instantiating the kernel.  If *ndim* is 1, a single integer
+   is returned.  If *ndim* is 2 or 3, a tuple of the given number of
+   integers is returned.
+
+   Computation of the first integer is as follows::
+
+      cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
+
+   and is similar for the other two indices, but using the ``y`` and ``z``
+   attributes.
+
+.. function:: numba.cuda.gridsize(ndim)
+
+   Return the absolute size (or shape) in threads of the entire grid of
+   blocks. *ndim* should correspond to the number of dimensions declared when
+   instantiating the kernel.
+
+   Computation of the first integer is as follows::
+
+       cuda.blockDim.x * cuda.gridDim.x
+
+   and is similar for the other two indices, but using the ``y`` and ``z``
+   attributes.
+
+Memory Management
+~~~~~~~~~~~~~~~~~
+
+.. function:: numba.cuda.shared.array(shape, dtype)
+
+   Creates an array in the local memory space of the CUDA kernel with
+   the given ``shape`` and ``dtype``.
+
+   Returns an array with its content uninitialized.
+
+   .. note:: All threads in the same thread block sees the same array.
+
+.. function:: numba.cuda.local.array(shape, dtype)
+
+   Creates an array in the local memory space of the CUDA kernel with the
+   given ``shape`` and ``dtype``.
+
+   Returns an array with its content uninitialized.
+
+   .. note:: Each thread sees a unique array.
+
+.. function:: numba.cuda.const.array_like(ary)
+
+   Copies the ``ary`` into constant memory space on the CUDA kernel at compile
+   time.
+
+   Returns an array like the ``ary`` argument.
+
+   .. note:: All threads and blocks see the same array.
+
+Synchronization and Atomic Operations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. function:: numba.cuda.atomic.add(array, idx, value)
+
+    Perform ``array[idx] += value``. Support int32, int64, float32 and
+    float64 only. The ``idx`` argument can be an integer or a tuple of integer
+    indices for indexing into multiple dimensional arrays. The number of element
+    in ``idx`` must match the number of dimension of ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+.. function:: numba.cuda.atomic.sub(array, idx, value)
+
+    Perform ``array[idx] -= value``. Supports int32, int64, float32 and
+    float64 only. The ``idx`` argument can be an integer or a tuple of integer
+    indices for indexing into multi-dimensional arrays. The number of elements
+    in ``idx`` must match the number of dimensions of ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+.. function:: numba.cuda.atomic.and_(array, idx, value)
+
+    Perform ``array[idx] &= value``. Supports int32, uint32, int64,
+    and uint64 only. The ``idx`` argument can be an integer or a tuple of
+    integer indices for indexing into multi-dimensional arrays. The number
+    of elements in ``idx`` must match the number of dimensions of ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+.. function:: numba.cuda.atomic.or_(array, idx, value)
+
+    Perform ``array[idx] |= value``. Supports int32, uint32, int64,
+    and uint64 only. The ``idx`` argument can be an integer or a tuple of
+    integer indices for indexing into multi-dimensional arrays. The number
+    of elements in ``idx`` must match the number of dimensions of ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+.. function:: numba.cuda.atomic.xor(array, idx, value)
+
+    Perform ``array[idx] ^= value``. Supports int32, uint32, int64,
+    and uint64 only. The ``idx`` argument can be an integer or a tuple of
+    integer indices for indexing into multi-dimensional arrays. The number
+    of elements in ``idx`` must match the number of dimensions of ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+.. function:: numba.cuda.atomic.exch(array, idx, value)
+
+    Perform ``array[idx] = value``. Supports int32, uint32, int64,
+    and uint64 only. The ``idx`` argument can be an integer or a tuple of
+    integer indices for indexing into multi-dimensional arrays. The number
+    of elements in ``idx`` must match the number of dimensions of ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+.. function:: numba.cuda.atomic.inc(array, idx, value)
+
+    Perform ``array[idx] = (0 if array[idx] >= value else array[idx] + 1)``.
+    Supports uint32, and uint64 only. The ``idx`` argument can be an integer
+    or a tuple of integer indices for indexing into multi-dimensional arrays.
+    The number of elements in ``idx`` must match the number of dimensions of
+    ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+.. function:: numba.cuda.atomic.dec(array, idx, value)
+
+    Perform ``array[idx] =
+    (value if (array[idx] == 0) or (array[idx] > value) else array[idx] - 1)``.
+    Supports uint32, and uint64 only. The ``idx`` argument can be an integer
+    or a tuple of integer indices for indexing into multi-dimensional arrays.
+    The number of elements in ``idx`` must match the number of dimensions of
+    ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+.. function:: numba.cuda.atomic.max(array, idx, value)
+
+    Perform ``array[idx] = max(array[idx], value)``. Support int32, int64,
+    float32 and float64 only. The ``idx`` argument can be an integer or a
+    tuple of integer indices for indexing into multiple dimensional arrays.
+    The number of element in ``idx`` must match the number of dimension of
+    ``array``.
+
+    Returns the value of ``array[idx]`` before the storing the new value.
+    Behaves like an atomic load.
+
+
+.. function:: numba.cuda.syncthreads
+
+    Synchronize all threads in the same thread block.  This function implements
+    the same pattern as barriers in traditional multi-threaded programming: this
+    function waits until all threads in the block call it, at which point it
+    returns control to all its callers.
+
+.. function:: numba.cuda.syncthreads_count(predicate)
+
+    An extension to :attr:`numba.cuda.syncthreads` where the return value is a count
+    of the threads where ``predicate`` is true.
+
+.. function:: numba.cuda.syncthreads_and(predicate)
+
+    An extension to :attr:`numba.cuda.syncthreads` where 1 is returned if ``predicate`` is
+    true for all threads or 0 otherwise.
+
+.. function:: numba.cuda.syncthreads_or(predicate)
+
+    An extension to :attr:`numba.cuda.syncthreads` where 1 is returned if ``predicate`` is
+    true for any thread or 0 otherwise.
+
+    .. warning:: All syncthreads functions must be called by every thread in the
+                 thread-block. Falling to do so may result in undefined behavior.
+
+
+Cooperative Groups
+~~~~~~~~~~~~~~~~~~
+
+.. function:: numba.cuda.cg.this_grid()
+
+   Get the current grid group.
+
+   :return: The current grid group
+   :rtype: numba.cuda.cg.GridGroup
+
+.. class:: numba.cuda.cg.GridGroup
+
+   A grid group. Users should not construct a GridGroup directly - instead, get
+   the current grid group using :func:`cg.this_grid() <numba.cuda.cg.this_grid>`.
+
+   .. method:: sync()
+
+      Synchronize the current grid group.
+
+
+Memory Fences
+~~~~~~~~~~~~~
+
+The memory fences are used to guarantee the effect of memory operations
+are visible by other threads within the same thread-block, the same GPU device,
+and the same system (across GPUs on global memory). Memory loads and stores
+are guaranteed to not move across the memory fences by optimization passes.
+
+.. warning:: The memory fences are considered to be advanced API and most
+             usercases should use the thread barrier (e.g. ``syncthreads()``).
+
+
+
+.. function:: numba.cuda.threadfence
+
+   A memory fence at device level (within the GPU).
+
+.. function:: numba.cuda.threadfence_block
+
+   A memory fence at thread block level.
+
+.. function:: numba.cuda.threadfence_system
+
+
+   A memory fence at system level (across GPUs).
+
+Warp Intrinsics
+~~~~~~~~~~~~~~~
+
+The argument ``membermask`` is a 32 bit integer mask with each bit
+corresponding to a thread in the warp, with 1 meaning the thread is in the
+subset of threads within the function call. The ``membermask`` must be all 1 if
+the GPU compute capability is below 7.x.
+
+.. function:: numba.cuda.syncwarp(membermask)
+
+   Synchronize a masked subset of the threads in a warp.
+
+.. function:: numba.cuda.all_sync(membermask, predicate)
+
+    If the ``predicate`` is true for all threads in the masked warp, then
+    a non-zero value is returned, otherwise 0 is returned.
+
+.. function:: numba.cuda.any_sync(membermask, predicate)
+
+    If the ``predicate`` is true for any thread in the masked warp, then
+    a non-zero value is returned, otherwise 0 is returned.
+
+.. function:: numba.cuda.eq_sync(membermask, predicate)
+
+    If the boolean ``predicate`` is the same for all threads in the masked warp,
+    then a non-zero value is returned, otherwise 0 is returned.
+
+.. function:: numba.cuda.ballot_sync(membermask, predicate)
+
+    Returns a mask of all threads in the warp whose ``predicate`` is true,
+    and are within the given mask.
+
+.. function:: numba.cuda.shfl_sync(membermask, value, src_lane)
+
+    Shuffles ``value`` across the masked warp and returns the ``value``
+    from ``src_lane``. If this is outside the warp, then the
+    given ``value`` is returned.
+
+.. function:: numba.cuda.shfl_up_sync(membermask, value, delta)
+
+    Shuffles ``value`` across the masked warp and returns the ``value``
+    from ``laneid - delta``. If this is outside the warp, then the
+    given ``value`` is returned.
+
+.. function:: numba.cuda.shfl_down_sync(membermask, value, delta)
+
+    Shuffles ``value`` across the masked warp and returns the ``value``
+    from ``laneid + delta``. If this is outside the warp, then the
+    given ``value`` is returned.
+
+.. function:: numba.cuda.shfl_xor_sync(membermask, value, lane_mask)
+
+    Shuffles ``value`` across the masked warp and returns the ``value``
+    from ``laneid ^ lane_mask``.
+
+.. function:: numba.cuda.match_any_sync(membermask, value, lane_mask)
+
+    Returns a mask of threads that have same ``value`` as the given ``value``
+    from within the masked warp.
+
+.. function:: numba.cuda.match_all_sync(membermask, value, lane_mask)
+
+    Returns a tuple of (mask, pred), where mask is a mask of threads that have
+    same ``value`` as the given ``value`` from within the masked warp, if they
+    all have the same value, otherwise it is 0. And pred is a boolean of whether
+    or not all threads in the mask warp have the same warp.
+
+.. function:: numba.cuda.activemask()
+
+    Returns a 32-bit integer mask of all currently active threads in the
+    calling warp. The Nth bit is set if the Nth lane in the warp is active when
+    activemask() is called. Inactive threads are represented by 0 bits in the
+    returned mask. Threads which have exited the kernel are always marked as
+    inactive.
+
+.. function:: numba.cuda.lanemask_lt()
+
+    Returns a 32-bit integer mask of all lanes (including inactive ones) with
+    ID less than the current lane.
+
+
+Integer Intrinsics
+~~~~~~~~~~~~~~~~~~
+
+A subset of the CUDA Math API's integer intrinsics are available. For further
+documentation, including semantics, please refer to the `CUDA Toolkit
+documentation
+<https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH__INTRINSIC__INT.html>`_.
+
+
+.. function:: numba.cuda.popc(x)
+
+   Returns the number of bits set in ``x``.
+
+.. function:: numba.cuda.brev(x)
+
+   Returns the reverse of the bit pattern of ``x``. For example, ``0b10110110``
+   becomes ``0b01101101``.
+
+.. function:: numba.cuda.clz(x)
+
+   Returns the number of leading zeros in ``x``.
+
+.. function:: numba.cuda.ffs(x)
+
+   Returns the position of the first (least significant) bit set to 1 in ``x``,
+   where the least significant bit position is 1. ``ffs(0)`` returns 0.
+
+
+Floating Point Intrinsics
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A subset of the CUDA Math API's floating point intrinsics are available. For further
+documentation, including semantics, please refer to the `single
+<https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH__SINGLE.html>`_ and
+`double <https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH__DOUBLE.html>`_
+precision parts of the CUDA Toolkit documentation.
+
+
+.. function:: numba.cuda.fma
+
+   Perform the fused multiply-add operation. Named after the ``fma`` and ``fmaf`` in
+   the C api, but maps to the ``fma.rn.f32`` and ``fma.rn.f64`` (round-to-nearest-even)
+   PTX instructions.
+
+.. function:: numba.cuda.cbrt (x)
+
+   Perform the cube root operation, x ** (1/3). Named after the functions
+   ``cbrt`` and ``cbrtf`` in the C api. Supports float32, and float64 arguments
+   only.
+
+16-bit Floating Point Intrinsics
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following functions are used to operate on 16-bit floating point operands.
+These functions return a 16-bit floating point result.
+
+
+.. function:: numba.cuda.fp16.hfma (a, b, c)
+
+   Perform the fused multiply-add operation ``(a * b) + c`` on 16-bit
+   floating point arguments in round to nearest mode. Maps to the ``fma.rn.f16``
+   PTX instruction.
+
+   Returns the 16-bit floating point result of the fused multiply-add.
+
+.. function:: numba.cuda.fp16.hadd (a, b)
+
+   Perform the add operation ``a + b`` on 16-bit floating point arguments in
+   round to nearest mode. Maps to the ``add.f16`` PTX instruction.
+
+   Returns the 16-bit floating point result of the addition.
+
+.. function:: numba.cuda.fp16.hsub (a, b)
+
+   Perform the subtract operation ``a - b`` on 16-bit floating point arguments in
+   round to nearest mode. Maps to the ``sub.f16`` PTX instruction.
+
+   Returns the 16-bit floating point result of the subtraction.
+
+.. function:: numba.cuda.fp16.hmul (a, b)
+
+   Perform the multiply operation ``a * b`` on 16-bit floating point arguments in
+   round to nearest mode. Maps to the ``mul.f16`` PTX instruction.
+
+   Returns the 16-bit floating point result of the multiplication.
+
+.. function:: numba.cuda.fp16.hneg (a)
+
+   Perform the negation operation ``-a`` on the 16-bit floating point argument.
+   Maps to the ``neg.f16`` PTX instruction.
+
+   Returns the 16-bit floating point result of the negation.
+
+.. function:: numba.cuda.fp16.habs (a)
+
+   Perform the absolute value operation ``|a|`` on the 16-bit floating point argument.
+
+   Returns the 16-bit floating point result of the absolute value operation.
+
+.. function:: numba.cuda.fp16.heq (a, b)
+
+   Perform the comparison operation ``a == b`` on 16-bit floating point arguments.
+
+   Returns a boolean.
+
+.. function:: numba.cuda.fp16.hne (a, b)
+
+   Perform the comparison operation ``a != b`` on 16-bit floating point arguments.
+
+   Returns a boolean.
+
+.. function:: numba.cuda.fp16.hgt (a, b)
+
+   Perform the comparison operation ``a > b`` on 16-bit floating point arguments.
+
+   Returns a boolean.
+
+.. function:: numba.cuda.fp16.hge (a, b)
+
+   Perform the comparison operation ``a >= b`` on 16-bit floating point arguments.
+
+   Returns a boolean.
+
+.. function:: numba.cuda.fp16.hlt (a, b)
+
+   Perform the comparison operation ``a < b`` on 16-bit floating point arguments.
+
+   Returns a boolean.
+
+.. function:: numba.cuda.fp16.hle (a, b)
+
+   Perform the comparison operation ``a <= b`` on 16-bit floating point arguments.
+
+   Returns a boolean.
+
+.. function:: numba.cuda.fp16.hmax (a, b)
+
+   Perform the operation ``a if a > b else b.``
+
+   Returns a 16-bit floating point value.
+
+.. function:: numba.cuda.fp16.hmin (a, b)
+
+   Perform the operation ``a if a < b else b.``
+
+   Returns a 16-bit floating point value.
+
+Control Flow Instructions
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A subset of the CUDA's control flow instructions are directly available as
+intrinsics. Avoiding branches is a key way to improve CUDA performance, and
+using these intrinsics mean you don't have to rely on the ``nvcc`` optimizer
+identifying and removing branches. For further documentation, including
+semantics, please refer to the `relevant CUDA Toolkit documentation
+<https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#comparison-and-selection-instructions>`_.
+
+
+.. function:: numba.cuda.selp
+
+    Select between two expressions, depending on the value of the first
+    argument. Similar to LLVM's ``select`` instruction.
+
+
+Timer Intrinsics
+~~~~~~~~~~~~~~~~
+
+.. function:: numba.cuda.nanosleep(ns)
+
+    Suspends the thread for a sleep duration approximately close to the delay
+    ``ns``, specified in nanoseconds.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/libdevice.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/libdevice.rst
new file mode 100644
index 000000000..be3cf2080
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/libdevice.rst
@@ -0,0 +1,16 @@
+Libdevice functions
+===================
+
+All wrapped libdevice functions are listed in this section. All functions in
+libdevice are wrapped, with the exception of ``__nv_nan`` and ``__nv_nanf``.
+These functions return a representation of a quiet NaN, but the argument they
+take (a pointer to an object specifying the representation) is undocumented, and
+follows an unusual form compared to the rest of libdevice - it is not an output
+like every other pointer argument. If a NaN is required, one can be obtained in
+CUDA Python by other means, e.g. ``math.nan``.
+
+Wrapped functions
+-----------------
+
+.. automodule:: numba.cuda.libdevice
+   :members:
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/memory.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/memory.rst
new file mode 100644
index 000000000..b70995e96
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/memory.rst
@@ -0,0 +1,24 @@
+Memory Management
+=================
+
+.. autofunction:: numba.cuda.to_device
+.. autofunction:: numba.cuda.device_array
+.. autofunction:: numba.cuda.device_array_like
+.. autofunction:: numba.cuda.pinned_array
+.. autofunction:: numba.cuda.pinned_array_like
+.. autofunction:: numba.cuda.mapped_array
+.. autofunction:: numba.cuda.mapped_array_like
+.. autofunction:: numba.cuda.managed_array
+.. autofunction:: numba.cuda.pinned
+.. autofunction:: numba.cuda.mapped
+
+Device Objects
+--------------
+
+.. autoclass:: numba.cuda.cudadrv.devicearray.DeviceNDArray
+   :members: copy_to_device, copy_to_host, is_c_contiguous, is_f_contiguous,
+              ravel, reshape, split
+.. autoclass:: numba.cuda.cudadrv.devicearray.DeviceRecord
+   :members: copy_to_device, copy_to_host
+.. autoclass:: numba.cuda.cudadrv.devicearray.MappedNDArray
+   :members: copy_to_device, copy_to_host, split
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/types.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/types.rst
new file mode 100644
index 000000000..31197241e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda-reference/types.rst
@@ -0,0 +1,56 @@
+CUDA-Specific Types
+====================
+
+.. note::
+
+    This page is about types specific to CUDA targets. Many other types are also
+    available in the CUDA target - see :ref:`cuda-built-in-types`.
+
+Vector Types
+~~~~~~~~~~~~
+
+`CUDA Vector Types <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#built-in-vector-types>`_
+are usable in kernels. There are two important distinctions from vector types in CUDA C/C++:
+
+First, the recommended names for vector types in Numba CUDA is formatted as ``<base_type>x<N>``,
+where ``base_type`` is the base type of the vector, and ``N`` is the number of elements in the vector.
+Examples include ``int64x3``, ``uint16x4``, ``float32x4``, etc. For new Numba CUDA kernels,
+this is the recommended way to instantiate vector types.
+
+For convenience, users adapting existing kernels from CUDA C/C++ to Python may use
+aliases consistent with the C/C++ namings. For example, ``float3`` aliases ``float32x3``,
+``long3`` aliases ``int32x3`` or ``int64x3`` (depending on the platform), etc. 
+
+Second, unlike CUDA C/C++ where factory functions are used, vector types are constructed directly
+with their constructor. For example, to construct a ``float32x3``:
+
+.. code-block:: python3
+
+    from numba.cuda import float32x3
+
+    # In kernel
+    f3 = float32x3(0.0, -1.0, 1.0)
+
+Additionally, vector types can be constructed from a combination of vector and
+primitive types, as long as the total number of components matches the result
+vector type. For example, all of the following constructions are valid:
+
+.. code-block:: python3
+
+    zero = uint32(0)
+    u2 = uint32x2(1, 2)
+    # Construct a 3-component vector with primitive type and a 2-component vector
+    u3 = uint32x3(zero, u2)
+    # Construct a 4-component vector with 2 2-component vectors
+    u4 = uint32x4(u2, u2)
+
+The 1st, 2nd, 3rd and 4th component of the vector type can be accessed through fields 
+``x``, ``y``, ``z``, and ``w`` respectively. The components are immutable after
+construction in the present version of Numba; it is expected that support for
+mutating vector components will be added in a future release.
+
+.. code-block:: python3
+
+    v1 = float32x2(1.0, 1.0)
+    v2 = float32x2(1.0, -1.0)
+    dotprod = v1.x * v2.x + v1.y * v2.y
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/bindings.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/bindings.rst
new file mode 100644
index 000000000..d8425a91f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/bindings.rst
@@ -0,0 +1,43 @@
+CUDA Bindings
+=============
+
+Numba supports two bindings to the CUDA Driver APIs: its own internal bindings
+based on ctypes, and the official `NVIDIA CUDA Python bindings
+<https://nvidia.github.io/cuda-python/>`_. Functionality is equivalent between
+the two bindings.
+
+The internal bindings are used by default. If the NVIDIA bindings are installed,
+then they can be used by setting the environment variable
+``NUMBA_CUDA_USE_NVIDIA_BINDING`` to ``1`` prior to the import of Numba. Once
+Numba has been imported, the selected binding cannot be changed.
+
+
+Per-Thread Default Streams
+--------------------------
+
+Responsibility for handling Per-Thread Default Streams (PTDS) is delegated to
+the NVIDIA bindings when they are in use. To use PTDS with the NVIDIA bindings,
+set the environment variable ``CUDA_PYTHON_CUDA_PER_THREAD_DEFAULT_STREAM`` to
+``1`` instead of Numba's environmnent variable
+:envvar:`NUMBA_CUDA_PER_THREAD_DEFAULT_STREAM`.
+
+.. seealso::
+
+   The `Default Stream section
+   <https://nvidia.github.io/cuda-python/release/11.6.0-notes.html#default-stream>`_
+   in the NVIDIA Bindings documentation.
+
+
+Roadmap
+-------
+
+In Numba 0.56, the NVIDIA Bindings will be used by default, if they are
+installed.
+
+In future versions of Numba:
+
+- The internal bindings will be deprecated.
+- The internal bindings will be removed.
+
+At present, no specific release is planned for the deprecation or removal of
+the internal bindings.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/caching.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/caching.rst
new file mode 100644
index 000000000..06c84ff77
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/caching.rst
@@ -0,0 +1,35 @@
+On-disk Kernel Caching
+======================
+
+When the ``cache`` keyword argument of the :func:`@cuda.jit <numba.cuda.jit>`
+decorator is ``True``, a file-based cache is enabled. This shortens compilation
+times when the function was already compiled in a previous invocation.
+
+The cache is maintained in the ``__pycache__`` subdirectory of the directory
+containing the source file; if the current user is not allowed to write to it,
+the cache implementation falls back to a platform-specific user-wide cache
+directory (such as ``$HOME/.cache/numba`` on Unix platforms).
+
+
+Compute capability considerations
+---------------------------------
+
+Separate cache files are maintained for each compute capability. When a cached
+kernel is loaded, the compute capability of the device the kernel is first
+launched on in the current run is used to determine which version to load.
+Therefore, on systems that have multiple GPUs with differing compute
+capabilities, the cached versions of kernels are only used for one compute
+capability, and recompilation will occur for other compute capabilities.
+
+For example: if a system has two GPUs, one of compute capability 7.5 and one of
+8.0, then:
+
+* If a cached kernel is first launched on the CC 7.5 device, then the cached
+  version for CC 7.5 is used. If it is subsequently launched on the CC 8.0
+  device, a recompilation will occur.
+* If in a subsequent run the cached kernel is first launched on the CC 8.0
+  device, then the cached version for CC 8.0 is used. A subsequent launch on
+  the CC 7.5 device will require a recompilation.
+
+This limitation is not expected to present issues in most practical scenarios,
+as multi-GPU production systems tend to have identical GPUs within each node.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cooperative_groups.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cooperative_groups.rst
new file mode 100644
index 000000000..a51e8ffcb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cooperative_groups.rst
@@ -0,0 +1,111 @@
+==================
+Cooperative Groups
+==================
+
+Supported features
+------------------
+
+Numba's Cooperative Groups support presently provides grid groups and grid
+synchronization, along with cooperative kernel launches.
+
+Cooperative groups are supported on Linux, and Windows for devices in `TCC
+mode
+<https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#tesla-compute-cluster-mode-for-windows>`_.
+Cooperative Groups also require the CUDA Device Runtime library, ``cudadevrt``,
+to be available - for conda default channel-installed CUDA toolkit packages, it
+is only available in versions 10.2 onwards. System-installed toolkits (e.g. from
+NVIDIA distribution packages or runfiles) all include ``cudadevrt``.
+
+Using Grid Groups
+-----------------
+
+To get the current grid group, use the :meth:`cg.this_grid()
+<numba.cuda.cg.this_grid>` function:
+
+.. code-block:: python
+
+   g = cuda.cg.this_grid()
+
+Synchronizing the grid is done with the :meth:`sync()
+<numba.cuda.cg.GridGroup.sync>` method of the grid group:
+
+.. code-block:: python
+
+   g.sync()
+
+
+Cooperative Launches
+--------------------
+
+Unlike the CUDA C/C++ API, a cooperative launch is invoked using the same syntax
+as a normal kernel launch - Numba automatically determines whether a cooperative
+launch is required based on whether a grid group is synchronized in the kernel.
+
+The grid size limit for a cooperative launch is more restrictive than for a
+normal launch - the grid must be no larger than the maximum number of active
+blocks on the device on which it is launched. To get maximum grid size for a
+cooperative launch of a kernel with a given block size and dynamic shared
+memory requirement, use the ``max_cooperative_grid_blocks()`` method of kernel
+overloads:
+
+.. automethod:: numba.cuda.dispatcher._Kernel.max_cooperative_grid_blocks
+
+This can be used to ensure that the kernel is launched with no more than the
+maximum number of blocks. Exceeding the maximum number of blocks for the
+cooperative launch will result in a ``CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE``
+error. 
+
+
+Applications and Example
+------------------------
+
+Grid group synchronization can be used to implement a global barrier across all
+threads in the grid - applications of this include a global reduction to a
+single value, or looping over rows of a large matrix sequentially using the
+entire grid to operate on column elements in parallel.
+
+In the following example, rows are written sequentially by the grid. Each thread
+in the grid reads a value from the previous row written by it's *opposite*
+thread. A grid sync is needed to ensure that threads in the grid don't run ahead
+of threads in other blocks, or fail to see updates from their opposite thread.
+
+First we'll define our kernel:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_cg.py
+   :language: python
+   :caption: from ``test_grid_sync`` of ``numba/cuda/tests/doc_example/test_cg.py``
+   :start-after: magictoken.ex_grid_sync_kernel.begin
+   :end-before: magictoken.ex_grid_sync_kernel.end
+   :dedent: 8
+   :linenos:
+
+Then create some empty input data and determine the grid and block sizes:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_cg.py
+   :language: python
+   :caption: from ``test_grid_sync`` of ``numba/cuda/tests/doc_example/test_cg.py``
+   :start-after: magictoken.ex_grid_sync_data.begin
+   :end-before: magictoken.ex_grid_sync_data.end
+   :dedent: 8
+   :linenos:
+
+Finally we launch the kernel and print the result:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_cg.py
+   :language: python
+   :caption: from ``test_grid_sync`` of ``numba/cuda/tests/doc_example/test_cg.py``
+   :start-after: magictoken.ex_grid_sync_launch.begin
+   :end-before: magictoken.ex_grid_sync_launch.end
+   :dedent: 8
+   :linenos:
+
+
+The maximum grid size for ``sequential_rows`` can be enquired using:
+
+
+.. code-block:: python
+
+   overload = sequential_rows.overloads[(int32[:,::1],)
+   max_blocks = overload.max_cooperative_grid_blocks(blockdim)
+   print(max_blocks)
+   # 1152 (e.g. on Quadro RTX 8000 with Numba 0.52.1 and CUDA 11.0)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cuda_array_interface.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cuda_array_interface.rst
new file mode 100644
index 000000000..304f4ecab
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cuda_array_interface.rst
@@ -0,0 +1,531 @@
+.. _cuda-array-interface:
+
+================================
+CUDA Array Interface (Version 3)
+================================
+
+The *CUDA Array Interface* (or CAI) is created for interoperability between
+different implementations of CUDA array-like objects in various projects. The
+idea is borrowed from the `NumPy array interface`_.
+
+
+.. note::
+    Currently, we only define the Python-side interface.  In the future, we may
+    add a C-side interface for efficient exchange of the information in
+    compiled code.
+
+
+Python Interface Specification
+==============================
+
+.. note:: Experimental feature.  Specification may change.
+
+The ``__cuda_array_interface__`` attribute returns a dictionary (``dict``)
+that must contain the following entries:
+
+- **shape**: ``(integer, ...)``
+
+  A tuple of ``int`` (or ``long``) representing the size of each dimension.
+
+- **typestr**: ``str``
+
+  The type string.  This has the same definition as ``typestr`` in the
+  `NumPy array interface`_.
+
+- **data**: ``(integer, boolean)``
+
+  The **data** is a 2-tuple.  The first element is the data pointer
+  as a Python ``int`` (or ``long``).  The data must be device-accessible.
+  For zero-size arrays, use ``0`` here.
+  The second element is the read-only flag as a Python ``bool``.
+
+  Because the user of the interface may or may not be in the same context,
+  the most common case is to use ``cuPointerGetAttribute`` with
+  ``CU_POINTER_ATTRIBUTE_DEVICE_POINTER`` in the CUDA driver API (or the
+  equivalent CUDA Runtime API) to retrieve a device pointer that
+  is usable in the currently active context.
+
+- **version**: ``integer``
+
+  An integer for the version of the interface being exported.
+  The current version is *3*.
+
+
+The following are optional entries:
+
+- **strides**: ``None`` or ``(integer, ...)``
+
+  If **strides** is not given, or it is ``None``, the array is in
+  C-contiguous layout. Otherwise, a tuple of ``int`` (or ``long``) is explicitly
+  given for representing the number of bytes to skip to access the next
+  element at each dimension.
+
+- **descr**
+
+  This is for describing more complicated types.  This follows the same
+  specification as in the `NumPy array interface`_.
+
+- **mask**: ``None`` or object exposing the ``__cuda_array_interface__``
+
+  If ``None`` then all values in **data** are valid. All elements of the mask
+  array should be interpreted only as true or not true indicating which
+  elements of this array are valid. This has the same definition as ``mask``
+  in the `NumPy array interface`_.
+
+  .. note:: Numba does not currently support working with masked CUDA arrays
+            and will raise a ``NotImplementedError`` exception if one is passed
+            to a GPU function.
+
+- **stream**: ``None`` or ``integer``
+
+  An optional stream upon which synchronization must take place at the point of
+  consumption, either by synchronizing on the stream or enqueuing operations on
+  the data on the given stream. Integer values in this entry are as follows:
+
+  - ``0``: This is disallowed as it would be ambiguous between ``None`` and the
+    default stream, and also between the legacy and per-thread default streams.
+    Any use case where ``0`` might be given should either use ``None``, ``1``,
+    or ``2`` instead for clarity.
+  - ``1``: The legacy default stream.
+  - ``2``: The per-thread default stream.
+  - Any other integer: a ``cudaStream_t`` represented as a Python integer.
+
+  When ``None``, no synchronization is required. See the
+  :ref:`cuda-array-interface-synchronization` section below for further details.
+
+  In a future revision of the interface, this entry may be expanded (or another
+  entry added) so that an event to synchronize on can be specified instead of a
+  stream.
+
+
+.. _cuda-array-interface-synchronization:
+
+Synchronization
+---------------
+
+Definitions
+~~~~~~~~~~~
+
+When discussing synchronization, the following definitions are used:
+
+- *Producer*: The library / object on which ``__cuda_array_interface__`` is
+  accessed.
+- *Consumer*: The library / function that accesses the
+  ``__cuda_array_interface__`` of the Producer.
+- *User Code*: Code that induces a Producer and Consumer to share data through
+  the CAI.
+- *User*: The person writing or maintaining the User Code. The User may
+  implement User Code without knowledge of the CAI, since the CAI accesses can
+  be hidden from their view.
+
+In the following example:
+
+.. code-block:: python
+
+   import cupy
+   from numba import cuda
+
+   @cuda.jit
+   def add(x, y, out):
+       start = cuda.grid(1)
+       stride = cuda.gridsize(1)
+       for i in range(start, x.shape[0], stride):
+           out[i] = x[i] + y[i]
+
+   a = cupy.arange(10)
+   b = a * 2
+   out = cupy.zeros_like(a)
+
+   add[1, 32](a, b, out)
+
+When the ``add`` kernel is launched:
+
+- ``a``, ``b``, ``out`` are Producers.
+- The ``add`` kernel is the Consumer.
+- The User Code is specifically ``add[1, 32](a, b, out)``.
+- The author of the code is the User.
+
+
+Design Motivations
+~~~~~~~~~~~~~~~~~~
+
+Elements of the CAI design related to synchronization seek to fulfill these
+requirements:
+
+1. Producers and Consumers that exchange data through the CAI must be able to do
+   so without data races.
+2. Requirement 1 should be met without requiring the user to be
+   aware of any particulars of the CAI - in other words, exchanging data between
+   Producers and Consumers that operate on data asynchronously should be correct
+   by default.
+
+   - An exception to this requirement is made for Producers and Consumers that
+     explicitly document that the User is required to take additional steps to
+     ensure correctness with respect to synchronization. In this case, Users
+     are required to understand the details of the CUDA Array Interface, and
+     the Producer/Consumer library documentation must specify the steps that
+     Users are required to take.
+
+     Use of this exception should be avoided where possible, as it is provided
+     for libraries that cannot implement the synchronization semantics without
+     the involvement of the User - for example, those interfacing with
+     third-party libraries oblivious to the CUDA Array Interface.
+
+3. Where the User is aware of the particulars of the CAI and implementation
+   details of the Producer and Consumer, they should be able to, at their
+   discretion, override some of the synchronization semantics of the interface
+   to reduce the synchronization overhead. Overriding synchronization semantics
+   implies that:
+
+   - The CAI design, and the design and implementation of the Producer and
+     Consumer do not specify or guarantee correctness with respect to data
+     races.
+   - Instead, the User is responsible for ensuring correctness with respect to
+     data races.
+
+
+Interface Requirements
+~~~~~~~~~~~~~~~~~~~~~~
+
+The ``stream`` entry enables Producers and Consumers to avoid hazards when
+exchanging data. Expected behaviour of the Consumer is as follows:
+
+* When ``stream`` is not present or is ``None``:
+
+  - No synchronization is required on the part of the Consumer.
+  - The Consumer may enqueue operations on the underlying data immediately on
+    any stream.
+
+* When ``stream`` is an integer, its value indicates the stream on which the
+  Producer may have in-progress operations on the data, and which the Consumer
+  is expected to either:
+
+  - Synchronize on before accessing the data, or
+  - Enqueue operations in when accessing the data.
+
+  The Consumer can choose which mechanism to use, with the following
+  considerations:
+
+  - If the Consumer synchronizes on the provided stream prior to accessing the
+    data, then it must ensure that no computation can take place in the provided
+    stream until its operations in its own choice of stream have taken place.
+    This could be achieved by either:
+
+    - Placing a wait on an event in the provided stream that occurs once all
+      of the Consumer's operations on the data are completed, or
+    - Avoiding returning control to the user code until after its operations
+      on its own stream have completed.
+
+  - If the consumer chooses to only enqueue operations on the data in the
+    provided stream, then it may return control to the User code immediately
+    after enqueueing its work, as the work will all be serialized on the
+    exported array's stream. This is sufficient to ensure correctness even if
+    the User code were to induce the Producer to subsequently start enqueueing
+    more work on the same stream.
+
+* If the User has set the Consumer to ignore CAI synchronization semantics, the
+  Consumer may assume it can operate on the data immediately in any stream with
+  no further synchronization, even if the ``stream`` member has an integer
+  value.
+
+
+When exporting an array through the CAI, Producers must ensure that:
+
+* If there is work on the data enqueued in one or more streams, then
+  synchronization on the provided ``stream`` is sufficient to ensure
+  synchronization with all pending work.
+
+  - If the Producer has no enqueued work, or work only enqueued on the stream
+    identified by ``stream``, then this condition is met.
+  - If the Producer has enqueued work on the data on multiple streams, then it
+    must enqueue events on those streams that follow the enqueued work, and
+    then wait on those events in the provided ``stream``. For example:
+
+    1. Work is enqueued by the Producer on streams ``7``, ``9``, and ``15``.
+    2. Events are then enqueued on each of streams ``7``, ``9``, and ``15``.
+    3. Producer then tells stream ``3`` to wait on the events from Step 2, and
+       the ``stream`` entry is set to ``3``.
+
+* If there is no work enqueued on the data, then the ``stream`` entry may be
+  either ``None``, or not provided.
+
+Optionally, to facilitate the User relaxing conformance to synchronization
+semantics:
+
+* Producers may provide a configuration option to always set ``stream`` to
+  ``None``.
+* Consumers may provide a configuration option to ignore the value of ``stream``
+  and act as if it were ``None`` or not provided.  This elides synchronization
+  on the Producer-provided streams, and allows enqueuing work on streams other
+  than that provided by the Producer.
+
+These options should not be set by default in either a Producer or a Consumer.
+The CAI specification does not prescribe the exact mechanism by which these
+options are set, or related options that Producers or Consumers might provide
+to allow the user further control over synchronization behavior.
+
+
+Synchronization in Numba
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+Numba is neither strictly a Producer nor a Consumer - it may be used to
+implement either by a User. In order to facilitate the correct implementation of
+synchronization semantics, Numba exhibits the following behaviors related to
+synchronization of the interface:
+
+- When Numba acts as a Consumer (for example when an array-like object is passed
+  to a kernel launch): If ``stream`` is an integer, then Numba will immediately
+  synchronize on the provided ``stream``. A Numba :class:`Device Array
+  <numba.cuda.cudadrv.devicearray.DeviceNDArray>` created from an array-like
+  object has its *default stream* set to the provided stream.
+
+- When Numba acts as a Producer (when the ``__cuda_array_interface__`` property
+  of a Numba CUDA Array is accessed): If the exported CUDA Array has a
+  *default stream*, then it is given as the ``stream`` entry. Otherwise,
+  ``stream`` is set to ``None``.
+
+.. note:: In Numba's terminology, an array's *default stream* is a property
+          specifying the stream that Numba will enqueue asynchronous
+          transfers in if no other stream is provided as an argument to the
+          function invoking the transfer. It is not the same as the `Default
+          Stream
+          <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#default-stream>`_
+          in normal CUDA terminology.
+
+Numba's synchronization behavior results in the following intended
+consequences:
+
+- Exchanging data either as a Producer or a Consumer will be correct without
+  the need for any further action from the User, provided that the other side
+  of the interaction also follows the CAI synchronization semantics.
+- The User is expected to either:
+
+  - Avoid launching kernels or other operations on streams that
+    are not the default stream for their parameters, or
+  - When launching operations on a stream that is not the default stream for
+    a given parameter, they should then insert an event into the stream that
+    they are operating in, and wait on that event in the default stream for
+    the parameter. For an example of this, :ref:`see below
+    <example-multi-streams>`.
+
+The User may override Numba's synchronization behavior by setting the
+environment variable ``NUMBA_CUDA_ARRAY_INTERFACE_SYNC`` or the config variable
+``CUDA_ARRAY_INTERFACE_SYNC`` to ``0`` (see :ref:`GPU Support Environment
+Variables <numba-envvars-gpu-support>`).  When set, Numba will not synchronize
+on the streams of imported arrays, and it is the responsibility of the user to
+ensure correctness with respect to stream synchronization. Synchronization when
+creating a Numba CUDA Array from an object exporting the CUDA Array Interface
+may also be elided by passing ``sync=False`` when creating the Numba CUDA
+Array with :func:`numba.cuda.as_cuda_array` or
+:func:`numba.cuda.from_cuda_array_interface`.
+
+There is scope for Numba's synchronization implementation to be optimized in
+the future, by eliding synchronizations when a kernel or driver API operation
+(e.g.  a memcopy or memset) is launched on the same stream as an imported
+array.
+
+
+.. _example-multi-streams:
+
+An example launching on an array's non-default stream
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This example shows how to ensure that a Consumer can safely consume an array
+with a default stream when it is passed to a kernel launched in a different
+stream.
+
+First we need to import Numba and a consumer library (a fictitious library named
+``other_cai_library`` for this example):
+
+.. code-block:: python
+
+   from numba import cuda, int32, void
+   import other_cai_library
+
+Now we'll define a kernel - this initializes the elements of the array, setting
+each entry to its index:
+
+.. code-block:: python
+
+   @cuda.jit(void, int32[::1])
+   def initialize_array(x):
+       i = cuda.grid(1)
+       if i < len(x):
+           x[i] = i
+
+Next we will create two streams:
+
+.. code-block:: python
+
+   array_stream = cuda.stream()
+   kernel_stream = cuda.stream()
+
+Then create an array with one of the streams as its default stream:
+
+.. code-block:: python
+
+   N = 16384
+   x = cuda.device_array(N, stream=array_stream)
+
+Now we launch the kernel in the other stream:
+
+.. code-block:: python
+
+   nthreads = 256
+   nblocks = N // nthreads
+
+   initialize_array[nthreads, nblocks, kernel_stream](x)
+
+If we were to pass ``x`` to a Consumer now, there is a risk that it may operate on
+it in ``array_stream`` whilst the kernel is still running in ``kernel_stream``.
+To prevent operations in ``array_stream`` starting before the kernel launch is
+finished, we create an event and wait on it:
+
+.. code-block:: python
+
+   # Create event
+   evt = cuda.event()
+   # Record the event after the kernel launch in kernel_stream
+   evt.record(kernel_stream)
+   # Wait for the event in array_stream
+   evt.wait(array_stream)
+
+It is now safe for ``other_cai_library`` to consume ``x``:
+
+.. code-block:: python
+
+   other_cai_library.consume(x)
+
+
+Lifetime management
+-------------------
+
+Data
+~~~~
+
+Obtaining the value of the ``__cuda_array_interface__`` property of any object
+has no effect on the lifetime of the object from which it was created. In
+particular, note that the interface has no slot for the owner of the data.
+
+The User code must preserve the lifetime of the object owning the data for as
+long as the Consumer might use it.
+
+
+Streams
+~~~~~~~
+
+Like data, CUDA streams also have a finite lifetime. It is therefore required
+that a Producer exporting data on the interface with an associated stream
+ensures that the exported stream's lifetime is equal to or surpasses the
+lifetime of the object from which the interface was exported.
+
+
+Lifetime management in Numba
+----------------------------
+
+Producing Arrays
+~~~~~~~~~~~~~~~~
+
+Numba takes no steps to maintain the lifetime of an object from which the
+interface is exported - it is the user's responsibility to ensure that the
+underlying object is kept alive for the duration that the exported interface
+might be used.
+
+The lifetime of any Numba-managed stream exported on the interface is guaranteed
+to equal or surpass the lifetime of the underlying object, because the
+underlying object holds a reference to the stream.
+
+.. note:: Numba-managed streams are those created with
+          ``cuda.default_stream()``, ``cuda.legacy_default_stream()``, or
+          ``cuda.per_thread_default_stream()``. Streams not managed by Numba
+          are created from an external stream with ``cuda.external_stream()``.
+
+
+Consuming Arrays
+~~~~~~~~~~~~~~~~
+
+Numba provides two mechanisms for creating device arrays from objects exporting
+the CUDA Array Interface. Which to use depends on whether the created device
+array should maintain the life of the object from which it is created:
+
+- ``as_cuda_array``: This creates a device array that holds a reference to the
+  owning object. As long as a reference to the device array is held, its
+  underlying data will also be kept alive, even if all other references to the
+  original owning object have been dropped.
+- ``from_cuda_array_interface``: This creates a device array with no reference
+  to the owning object by default. The owning object, or some other object to
+  be considered the owner can be passed in the ``owner`` parameter.
+
+The interfaces of these functions are:
+
+.. automethod:: numba.cuda.as_cuda_array
+
+.. automethod:: numba.cuda.from_cuda_array_interface
+
+
+Pointer Attributes
+------------------
+
+Additional information about the data pointer can be retrieved using
+``cuPointerGetAttribute`` or ``cudaPointerGetAttributes``.  Such information
+include:
+
+- the CUDA context that owns the pointer;
+- is the pointer host-accessible?
+- is the pointer a managed memory?
+
+
+.. _NumPy array interface: https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.interface.html#__array_interface__
+
+
+Differences with CUDA Array Interface (Version 0)
+-------------------------------------------------
+
+Version 0 of the CUDA Array Interface did not have the optional **mask**
+attribute to support masked arrays.
+
+
+Differences with CUDA Array Interface (Version 1)
+-------------------------------------------------
+
+Versions 0 and 1 of the CUDA Array Interface neither clarified the
+**strides** attribute for C-contiguous arrays nor specified the treatment for
+zero-size arrays.
+
+
+Differences with CUDA Array Interface (Version 2)
+-------------------------------------------------
+
+Prior versions of the CUDA Array Interface made no statement about
+synchronization.
+
+
+Interoperability
+----------------
+
+The following Python libraries have adopted the CUDA Array Interface:
+
+- Numba
+- `CuPy <https://docs-cupy.chainer.org/en/stable/reference/interoperability.html>`_
+- `PyTorch <https://pytorch.org>`_
+- `PyArrow <https://arrow.apache.org/docs/python/generated/pyarrow.cuda.Context.html#pyarrow.cuda.Context.buffer_from_object>`_
+- `mpi4py <https://mpi4py.readthedocs.io/en/latest/overview.html#support-for-cuda-aware-mpi>`_
+- `ArrayViews <https://github.com/xnd-project/arrayviews>`_
+- `JAX <https://jax.readthedocs.io/en/latest/index.html>`_
+- `PyCUDA <https://documen.tician.de/pycuda/tutorial.html#interoperability-with-other-libraries-using-the-cuda-array-interface>`_
+- `DALI: the NVIDIA Data Loading Library <https://github.com/NVIDIA/DALI>`_ :
+
+    - `TensorGPU objects
+      <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/data_types.html#nvidia.dali.backend.TensorGPU>`_
+      expose the CUDA Array Interface.
+    - `The External Source operator
+      <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/supported_ops.html#nvidia.dali.fn.external_source>`_
+      consumes objects exporting the CUDA Array Interface.
+- The RAPIDS stack:
+
+    - `cuDF <https://rapidsai.github.io/projects/cudf/en/0.11.0/10min-cudf-cupy.html>`_
+    - `cuML <https://docs.rapids.ai/api/cuml/nightly/>`_
+    - `cuSignal <https://github.com/rapidsai/cusignal>`_
+    - `RMM <https://docs.rapids.ai/api/rmm/stable/>`_
+
+If your project is not on this list, please feel free to report it on the `Numba issue tracker <https://github.com/numba/numba/issues>`_.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cuda_ffi.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cuda_ffi.rst
new file mode 100644
index 000000000..1ee441254
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cuda_ffi.rst
@@ -0,0 +1,158 @@
+
+.. _cuda_ffi:
+
+Calling foreign functions from Python kernels
+=============================================
+
+Python kernels can call device functions written in other languages. CUDA C/C++,
+PTX, and binary objects (cubins, fat binaries, etc.) are directly supported;
+sources in other languages must be compiled to PTX first. The constituent parts
+of a Python kernel call to a foreign device function are:
+
+- The device function implementation in a foreign language (e.g. CUDA C).
+- A declaration of the device function in Python.
+- A kernel that links with and calls the foreign function.
+
+
+Device function ABI
+-------------------
+
+Numba's ABI for calling device functions defines the following prototype in
+C/C++:
+
+.. code:: C
+
+   extern "C"
+   __device__ int
+   function(
+     T* return_value,
+     ...
+   );
+
+
+Components of the prototype are as follows:
+
+- ``extern "C"`` is used to prevent name-mangling so that it is easy to declare
+  the function in Python. It can be removed, but then the mangled name must be
+  used in the declaration of the function in Python.
+- ``__device__`` is required to define the function as a device function.
+- The return value is always of type ``int``, and is used to signal whether a
+  Python exception occurred. Since Python exceptions don't occur in foreign
+  functions, this should always be set to 0 by the callee.
+- The first argument is a pointer to the return value of type ``T``, which is
+  allocated in the local address space [#f1]_ and passed in by the caller. If
+  the function returns a value, the pointee should be set by the callee to
+  store the return value.
+- Subsequent arguments should match the types and order of arguments passed to
+  the function from the Python kernel.
+
+Functions written in other languages must compile to PTX that conforms to this
+prototype specification.
+
+A function that accepts two floats and returns a float would have the following
+prototype:
+
+.. code:: C
+
+   extern "C"
+   __device__ int
+   mul_f32_f32(
+     float* return_value,
+     float x,
+     float y
+   )
+
+.. rubric:: Notes
+
+.. [#f1] Care must be taken to ensure that any operations on the return value
+         are applicable to data in the local address space.  Some operations,
+         such as atomics, cannot be performed on data in the local address
+         space.
+
+Declaration in Python
+---------------------
+
+To declare a foreign device function in Python, use :func:`declare_device()
+<numba.cuda.declare_device>`:
+
+.. autofunction:: numba.cuda.declare_device
+
+The returned descriptor name need not match the name of the foreign function.
+For example, when:
+
+.. code::
+
+   mul = cuda.declare_device('mul_f32_f32', 'float32(float32, float32)')
+
+is declared, calling ``mul(a, b)`` inside a kernel will translate into a call to
+``mul_f32_f32(a, b)`` in the compiled code.
+
+
+Linking and Calling functions
+-----------------------------
+
+The ``link`` keyword argument of the :func:`@cuda.jit <numba.cuda.jit>`
+decorator accepts a list of file names specified by absolute path or a path
+relative to the current working directory. Files whose name ends in ``.cu``
+will be compiled with the `NVIDIA Runtime Compiler (NVRTC)
+<https://docs.nvidia.com/cuda/nvrtc/index.html>`_ and linked into the kernel as
+PTX; other files will be passed directly to the CUDA Linker.
+
+For example, the following kernel calls the ``mul()`` function declared above
+with the implementation ``mul_f32_f32()`` in a file called ``functions.cu``:
+
+.. code::
+
+   @cuda.jit(link=['functions.cu'])
+   def multiply_vectors(r, x, y):
+       i = cuda.grid(1)
+
+       if i < len(r):
+           r[i] = mul(x[i], y[i])
+
+
+C/C++ Support
+-------------
+
+Support for compiling and linking of CUDA C/C++ code is provided through the use
+of NVRTC subject to the following considerations:
+
+- It is only available when using the NVIDIA Bindings. See
+  :envvar:`NUMBA_CUDA_USE_NVIDIA_BINDING`.
+- A suitable version of the NVRTC library for the installed version of the
+  NVIDIA CUDA Bindings must be available.
+- The CUDA include path is assumed by default to be ``/usr/local/cuda/include``
+  on Linux and ``$env:CUDA_PATH\include`` on Windows. It can be modified using
+  the environment variable :envvar:`NUMBA_CUDA_INCLUDE_PATH`.
+- The CUDA include directory will be made available to NVRTC on the include
+  path; additional includes are not supported.
+
+
+Complete Example
+----------------
+
+This example demonstrates calling a foreign function written in CUDA C to
+multiply pairs of numbers from two arrays.
+
+The foreign function is written as follows:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/ffi/functions.cu
+   :language: C
+   :caption: ``numba/cuda/tests/doc_examples/ffi/functions.cu``
+   :linenos:
+
+The Python code and kernel are:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_ffi.py
+   :language: python
+   :caption: from ``test_ex_linking_cu`` in ``numba/cuda/tests/doc_examples/test_ffi.py``
+   :start-after: magictoken.ex_linking_cu.begin
+   :end-before: magictoken.ex_linking_cu.end
+   :dedent: 8
+   :linenos:
+
+.. note::
+
+  The example above is minimal in order to illustrate a foreign function call -
+  it would not be expected to be particularly performant due to the small grid
+  and light workload of the foreign function.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cudapysupported.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cudapysupported.rst
new file mode 100644
index 000000000..d5dc5a790
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/cudapysupported.rst
@@ -0,0 +1,296 @@
+========================================
+Supported Python features in CUDA Python
+========================================
+
+This page lists the Python features supported in the CUDA Python.  This includes
+all kernel and device functions compiled with ``@cuda.jit`` and other higher
+level Numba decorators that targets the CUDA GPU.
+
+Language
+========
+
+Execution Model
+---------------
+
+CUDA Python maps directly to the *single-instruction multiple-thread*
+execution (SIMT) model of CUDA.  Each instruction is implicitly
+executed by multiple threads in parallel.  With this execution model, array
+expressions are less useful because we don't want multiple threads to perform
+the same task.  Instead, we want threads to perform a task in a cooperative
+fashion.
+
+For details please consult the
+`CUDA Programming Guide
+<http://docs.nvidia.com/cuda/cuda-c-programming-guide/#programming-model>`_.
+
+Floating Point Error Model
+--------------------------
+
+By default, CUDA Python kernels execute with the NumPy error model. In this
+model, division by zero raises no exception and instead produces a result of
+``inf``, ``-inf`` or ``nan``. This differs from the normal Python error model,
+in which division by zero raises a ``ZeroDivisionError``.
+
+When debug is enabled (by passing ``debug=True`` to the
+:func:`@cuda.jit <numba.cuda.jit>` decorator), the Python error model is used.
+This allows division-by-zero errors during kernel execution to be identified.
+
+Constructs
+----------
+
+The following Python constructs are not supported:
+
+* Exception handling (``try .. except``, ``try .. finally``)
+* Context management (the ``with`` statement)
+* Comprehensions (either list, dict, set or generator comprehensions)
+* Generator (any ``yield`` statements)
+
+The ``raise`` and ``assert`` statements are supported, with the following
+constraints:
+
+- They can only be used in kernels, not in device functions.
+- They only have an effect when ``debug=True`` is passed to the
+  :func:`@cuda.jit <numba.cuda.jit>` decorator. This is similar to the behavior
+  of the ``assert`` keyword in CUDA C/C++, which is ignored unless compiling
+  with device debug turned on.
+
+
+Printing of strings, integers, and floats is supported, but printing is an
+asynchronous operation - in order to ensure that all output is printed after a
+kernel launch, it is necessary to call :func:`numba.cuda.synchronize`. Eliding
+the call to ``synchronize`` is acceptable, but output from a kernel may appear
+during other later driver operations (e.g. subsequent kernel launches, memory
+transfers, etc.), or fail to appear before the program execution completes. Up
+to 32 arguments may be passed to the ``print`` function - if more are passed
+then a format string will be emitted instead and a warning will be produced.
+This is due to a general limitation in CUDA printing, as outlined in the
+`section on limitations in printing
+<https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#limitations>`_
+in the CUDA C++ Programming Guide.
+
+
+Recursion
+---------
+
+Self-recursive device functions are supported, with the constraint that
+recursive calls must have the same argument types as the initial call to
+the function. For example, the following form of recursion is supported:
+
+.. code:: python
+
+   @cuda.jit("int64(int64)", device=True)
+   def fib(n):
+       if n < 2:
+           return n
+       return fib(n - 1) + fib(n - 2)
+
+(the ``fib`` function always has an ``int64`` argument), whereas the following
+is unsupported:
+
+.. code:: python
+
+   # Called with x := int64, y := float64
+   @cuda.jit
+   def type_change_self(x, y):
+       if x > 1 and y > 0:
+           return x + type_change_self(x - y, y)
+       else:
+           return y
+
+The outer call to ``type_change_self`` provides ``(int64, float64)`` arguments,
+but the inner call uses ``(float64, float64)`` arguments (because ``x - y`` /
+``int64 - float64`` results in a ``float64`` type). Therefore, this function is
+unsupported.
+
+Mutual recursion between functions (e.g. where a function ``func1()`` calls
+``func2()`` which again calls ``func1()``) is unsupported.
+
+.. note::
+
+   The call stack in CUDA is typically quite limited in size, so it is easier
+   to overflow it with recursive calls on CUDA devices than it is on CPUs.
+
+   Stack overflow will result in an Unspecified Launch Failure (ULF) during
+   kernel execution.  In order to identify whether a ULF is due to stack
+   overflow, programs can be run under `Compute Sanitizer
+   <https://docs.nvidia.com/compute-sanitizer/ComputeSanitizer/index.html>`_,
+   which explicitly states when stack overflow has occurred.
+
+.. _cuda-built-in-types:
+
+Built-in types
+===============
+
+The following built-in types support are inherited from CPU nopython mode.
+
+* int
+* float
+* complex
+* bool
+* None
+* tuple
+* Enum, IntEnum
+
+See :ref:`nopython built-in types <pysupported-builtin-types>`.
+
+There is also some very limited support for character sequences (bytes and
+unicode strings) used in NumPy arrays. Note that this support can only be used
+with CUDA 11.2 onwards.
+
+Built-in functions
+==================
+
+The following built-in functions are supported:
+
+* :func:`abs`
+* :class:`bool`
+* :class:`complex`
+* :func:`enumerate`
+* :class:`float`
+* :class:`int`: only the one-argument form
+* :func:`len`
+* :func:`min`: only the multiple-argument form
+* :func:`max`: only the multiple-argument form
+* :func:`pow`
+* :class:`range`
+* :func:`round`
+* :func:`zip`
+
+
+Standard library modules
+========================
+
+
+``cmath``
+---------
+
+The following functions from the :mod:`cmath` module are supported:
+
+* :func:`cmath.acos`
+* :func:`cmath.acosh`
+* :func:`cmath.asin`
+* :func:`cmath.asinh`
+* :func:`cmath.atan`
+* :func:`cmath.atanh`
+* :func:`cmath.cos`
+* :func:`cmath.cosh`
+* :func:`cmath.exp`
+* :func:`cmath.isfinite`
+* :func:`cmath.isinf`
+* :func:`cmath.isnan`
+* :func:`cmath.log`
+* :func:`cmath.log10`
+* :func:`cmath.phase`
+* :func:`cmath.polar`
+* :func:`cmath.rect`
+* :func:`cmath.sin`
+* :func:`cmath.sinh`
+* :func:`cmath.sqrt`
+* :func:`cmath.tan`
+* :func:`cmath.tanh`
+
+``math``
+--------
+
+The following functions from the :mod:`math` module are supported:
+
+* :func:`math.acos`
+* :func:`math.asin`
+* :func:`math.atan`
+* :func:`math.acosh`
+* :func:`math.asinh`
+* :func:`math.atanh`
+* :func:`math.cos`
+* :func:`math.sin`
+* :func:`math.tan`
+* :func:`math.hypot`
+* :func:`math.cosh`
+* :func:`math.sinh`
+* :func:`math.tanh`
+* :func:`math.atan2`
+* :func:`math.erf`
+* :func:`math.erfc`
+* :func:`math.exp`
+* :func:`math.expm1`
+* :func:`math.fabs`
+* :func:`math.frexp`
+* :func:`math.ldexp`
+* :func:`math.gamma`
+* :func:`math.lgamma`
+* :func:`math.log`
+* :func:`math.log2`
+* :func:`math.log10`
+* :func:`math.log1p`
+* :func:`math.sqrt`
+* :func:`math.remainder`: Python 3.7+
+* :func:`math.pow`
+* :func:`math.ceil`
+* :func:`math.floor`
+* :func:`math.copysign`
+* :func:`math.fmod`
+* :func:`math.modf`
+* :func:`math.isnan`
+* :func:`math.isinf`
+* :func:`math.isfinite`
+
+
+``operator``
+------------
+
+The following functions from the :mod:`operator` module are supported:
+
+* :func:`operator.add`
+* :func:`operator.and_`
+* :func:`operator.eq`
+* :func:`operator.floordiv`
+* :func:`operator.ge`
+* :func:`operator.gt`
+* :func:`operator.iadd`
+* :func:`operator.iand`
+* :func:`operator.ifloordiv`
+* :func:`operator.ilshift`
+* :func:`operator.imod`
+* :func:`operator.imul`
+* :func:`operator.invert`
+* :func:`operator.ior`
+* :func:`operator.ipow`
+* :func:`operator.irshift`
+* :func:`operator.isub`
+* :func:`operator.itruediv`
+* :func:`operator.ixor`
+* :func:`operator.le`
+* :func:`operator.lshift`
+* :func:`operator.lt`
+* :func:`operator.mod`
+* :func:`operator.mul`
+* :func:`operator.ne`
+* :func:`operator.neg`
+* :func:`operator.not_`
+* :func:`operator.or_`
+* :func:`operator.pos`
+* :func:`operator.pow`
+* :func:`operator.rshift`
+* :func:`operator.sub`
+* :func:`operator.truediv`
+* :func:`operator.xor`
+
+
+NumPy support
+=============
+
+Due to the CUDA programming model, dynamic memory allocation inside a kernel is
+inefficient and is often not needed.  Numba disallows any memory allocating features.
+This disables a large number of NumPy APIs.  For best performance, users should write
+code such that each thread is dealing with a single element at a time.
+
+Supported NumPy features:
+
+* accessing `ndarray` attributes `.shape`, `.strides`, `.ndim`, `.size`, etc..
+* scalar ufuncs that have equivalents in the `math` module; i.e. ``np.sin(x[0])``, where x is a 1D array.
+* indexing and slicing works.
+
+Unsupported NumPy features:
+
+* array creation APIs.
+* array methods.
+* functions that returns a new array.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/device-functions.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/device-functions.rst
new file mode 100644
index 000000000..4fba8c66f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/device-functions.rst
@@ -0,0 +1,15 @@
+
+Writing Device Functions
+========================
+
+CUDA device functions can only be invoked from within the device (by a kernel
+or another device function).  To define a device function::
+
+    from numba import cuda
+
+    @cuda.jit(device=True)
+    def a_device_function(a, b):
+        return a + b
+
+Unlike a kernel function, a device function can return a value like normal
+functions.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/device-management.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/device-management.rst
new file mode 100644
index 000000000..8f9beb4db
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/device-management.rst
@@ -0,0 +1,92 @@
+
+Device management
+=================
+
+For multi-GPU machines, users may want to select which GPU to use.
+By default the CUDA driver selects the fastest GPU as the device 0,
+which is the default device used by Numba.
+
+The features introduced on this page are generally not of interest
+unless working with systems hosting/offering more than one CUDA-capable GPU.
+
+Device Selection
+----------------
+
+If at all required, device selection must be done before any CUDA feature is
+used.
+
+::
+
+    from numba import cuda
+    cuda.select_device(0)
+
+The device can be closed by:
+
+::
+
+    cuda.close()
+
+Users can then create a new context with another device.
+
+::
+
+    cuda.select_device(1)  # assuming we have 2 GPUs
+
+
+.. function:: numba.cuda.select_device(device_id)
+   :noindex:
+
+   Create a new CUDA context for the selected *device_id*.  *device_id*
+   should be the number of the device (starting from 0; the device order
+   is determined by the CUDA libraries).  The context is associated with
+   the current thread.  Numba currently allows only one context per thread.
+
+   If successful, this function returns a device instance.
+
+   .. XXX document device instances?
+
+
+.. function:: numba.cuda.close
+   :noindex:
+
+   Explicitly close all contexts in the current thread.
+
+   .. note::
+      Compiled functions are associated with the CUDA context.
+      This makes it not very useful to close and create new devices, though it
+      is certainly useful for choosing which device to use when the machine
+      has multiple GPUs.
+
+The Device List
+===============
+
+The Device List is a list of all the GPUs in the system, and can be indexed to
+obtain a context manager that ensures execution on the selected GPU.
+
+.. attribute:: numba.cuda.gpus
+   :noindex:
+.. attribute:: numba.cuda.cudadrv.devices.gpus
+
+:py:data:`numba.cuda.gpus` is an instance of the ``_DeviceList`` class, from
+which the current GPU context can also be retrieved:
+
+.. autoclass:: numba.cuda.cudadrv.devices._DeviceList
+    :members: current
+    :noindex:
+
+
+Device UUIDs
+============
+
+The UUID of a device (equal to that returned by ``nvidia-smi -L``) is available
+in the :attr:`uuid <numba.cuda.cudadrv.driver.Device.uuid>` attribute of a CUDA
+device object.
+
+For example, to obtain the UUID of the current device:
+
+.. code-block:: python
+
+   dev = cuda.current_context().device
+   # prints e.g. "GPU-e6489c45-5b68-3b03-bab7-0e7c8e809643"
+   print(dev.uuid)
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/examples.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/examples.rst
new file mode 100644
index 000000000..793d13ba2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/examples.rst
@@ -0,0 +1,527 @@
+
+========
+Examples
+========
+
+.. _cuda-vecadd:
+
+Vector Addition
+===============
+This example uses Numba to create on-device arrays and a vector addition kernel;
+it is a warmup for learning how to write GPU kernels using Numba. We'll begin
+with some required imports:
+
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_vecadd.py
+   :language: python
+   :caption: from ``test_ex_vecadd`` in ``numba/cuda/tests/doc_examples/test_vecadd.py``
+   :start-after: ex_vecadd.import.begin
+   :end-before: ex_vecadd.import.end
+   :dedent: 8
+   :linenos:
+
+The following function is the kernel. Note that it is defined in terms of Python
+variables with unspecified types. When the kernel is launched, Numba will
+examine the types of the arguments that are passed at runtime and generate a
+CUDA kernel specialized for them.
+
+Note that Numba kernels do not return values and must write any output into
+arrays passed in as parameters (this is similar to the requirement that CUDA
+C/C++ kernels have ``void`` return type). Here we pass in ``c`` for the results
+to be written into.
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_vecadd.py
+   :language: python
+   :caption: from ``test_ex_vecadd`` in ``numba/cuda/tests/doc_examples/test_vecadd.py``
+   :start-after: ex_vecadd.kernel.begin
+   :end-before: ex_vecadd.kernel.end
+   :dedent: 8
+   :linenos:
+
+:func:`cuda.to_device() <numba.cuda.to_device>` can be used create device-side
+copies of arrays.  :func:`cuda.device_array_like()
+<numba.cuda.device_array_like>` creates an uninitialized array of the same shape
+and type as an existing array.  Here we transfer two vectors and create an empty
+vector to hold our results:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_vecadd.py
+   :language: python
+   :caption: from ``test_ex_vecadd`` in ``numba/cuda/tests/doc_examples/test_vecadd.py``
+   :start-after: ex_vecadd.allocate.begin
+   :end-before: ex_vecadd.allocate.end
+   :dedent: 8
+   :linenos:
+
+A call to :meth:`forall() <numba.cuda.dispatcher.Dispatcher.forall>` generates
+an appropriate launch configuration with a 1D grid (see
+:ref:`cuda-kernel-invocation`) for a given data size and is often the simplest
+way of launching a kernel:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_vecadd.py
+   :language: python
+   :caption: from ``test_ex_vecadd`` in ``numba/cuda/tests/doc_examples/test_vecadd.py``
+   :start-after: ex_vecadd.forall.begin
+   :end-before: ex_vecadd.forall.end
+   :dedent: 8
+   :linenos:
+
+This prints:
+
+.. code-block:: none
+
+   [0.73548323 1.32061059 0.12582968 ... 1.25925809 1.49335059 1.59315414]
+
+One can also configure the grid manually using the subscripting syntax. The
+following example launches a grid with sufficient threads to operate on every
+vector element:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_vecadd.py
+   :language: python
+   :caption: from ``test_ex_vecadd`` in ``numba/cuda/tests/doc_examples/test_vecadd.py``
+   :start-after: ex_vecadd.launch.begin
+   :end-before: ex_vecadd.launch.end
+   :dedent: 8
+   :linenos:
+
+This also prints:
+
+.. code-block:: none
+
+   [0.73548323 1.32061059 0.12582968 ... 1.25925809 1.49335059 1.59315414]
+
+.. _cuda-laplace:
+
+1D Heat Equation
+=====================
+This example solves Laplace's equation in one dimension for a certain set of initial
+conditions and boundary conditions. A full discussion of Laplace's equation is out of
+scope for this documentation, but it will suffice to say that it describes how heat
+propagates through an object over time. It works by discretizing the problem in two ways:
+
+1. The domain is partitioned into a mesh of points that each have an individual temperature.
+2. Time is partitioned into discrete intervals that are advanced forward sequentially.
+
+Then, the following assumption is applied: The temperature of a point after some interval 
+has passed is some weighted average of the temperature of the points that are directly
+adjacent to it. Intuitively, if all the points in the domain are very hot
+and a single point in the middle is very cold, as time passes, the hot points will cause
+the cold one to heat up and the cold point will cause the surrounding hot pieces to cool
+slightly. Simply put, the heat spreads throughout the object.
+
+We can implement this simulation using a Numba kernel. Let's start simple by assuming
+we have a one dimensional object which we'll represent with an array of values. The position 
+of the element in the array is the position of a point within the object, and the value
+of the element represents the temperature. 
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_laplace.py
+   :language: python
+   :caption: from ``test_ex_laplace`` in ``numba/cuda/tests/doc_examples/test_laplace.py``
+   :start-after: ex_laplace.import.begin
+   :end-before: ex_laplace.import.end
+   :dedent: 8
+   :linenos:
+
+
+Some initial setup here. Let's make one point in the center of the object very hot.
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_laplace.py
+   :language: python
+   :caption: from ``test_ex_laplace`` in ``numba/cuda/tests/doc_examples/test_laplace.py``
+   :start-after: ex_laplace.allocate.begin
+   :end-before: ex_laplace.allocate.end
+   :dedent: 8
+   :linenos:
+
+The initial state of the problem can be visualized as:
+
+.. image:: laplace_initial.svg
+
+In our kernel each thread will be responsible for managing the temperature update for a single element
+in a loop over the desired number of timesteps. The kernel is below. Note the use of cooperative group
+synchronization and the use of two buffers swapped at each iteration to avoid race conditions. See 
+:func:`numba.cuda.cg.this_grid() <numba.cuda.cg.this_grid>` for details.
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_laplace.py
+   :language: python
+   :caption: from ``test_ex_laplace`` in ``numba/cuda/tests/doc_examples/test_laplace.py``
+   :start-after: ex_laplace.kernel.begin
+   :end-before: ex_laplace.kernel.end
+   :dedent: 8
+   :linenos:
+
+
+Calling the kernel:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_laplace.py
+   :language: python
+   :caption: from ``test_ex_laplace`` in ``numba/cuda/tests/doc_examples/test_laplace.py``
+   :start-after: ex_laplace.launch.begin
+   :end-before: ex_laplace.launch.end
+   :dedent: 8
+   :linenos:
+
+
+Plotting the final data shows an arc that is highest where
+the object was hot initially and gradually sloping down to zero towards the
+edges where the temperature is fixed at zero. In the limit of infinite time,
+the arc will flatten out completely.
+
+.. image:: laplace_final.svg
+
+.. _cuda_reduction_shared:
+
+Shared Memory Reduction
+=======================
+Numba exposes many CUDA features, including :ref:`shared memory
+<cuda-shared-memory>`. To demonstrate shared memory, let's reimplement a
+famous CUDA solution for summing a vector which works by "folding" the data up
+using a successively smaller number of threads.
+
+
+Note that this is a fairly naive implementation, and there are more efficient ways of implementing reductions
+using Numba - see :ref:`cuda_montecarlo` for an example.
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_reduction.py
+   :language: python
+   :caption: from ``test_ex_reduction`` in ``numba/cuda/tests/doc_examples/test_reduction.py``
+   :start-after: ex_reduction.import.begin
+   :end-before: ex_reduction.import.end
+   :dedent: 8
+   :linenos:
+
+Let's create some one dimensional data that we'll use to demonstrate the
+kernel itself:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_reduction.py
+   :language: python
+   :caption: from ``test_ex_reduction`` in ``numba/cuda/tests/doc_examples/test_reduction.py``
+   :start-after: ex_reduction.allocate.begin
+   :end-before: ex_reduction.allocate.end
+   :dedent: 8
+   :linenos:
+
+
+Here is a version of the kernel implemented using Numba:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_reduction.py
+   :language: python
+   :caption: from ``test_ex_reduction`` in ``numba/cuda/tests/doc_examples/test_reduction.py``
+   :start-after: ex_reduction.kernel.begin
+   :end-before: ex_reduction.kernel.end
+   :dedent: 8
+   :linenos:
+
+We can run kernel and verify that the same result is obtained through
+summing data on the host as follows:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_reduction.py
+   :language: python
+   :caption: from ``test_ex_reduction`` in ``numba/cuda/tests/doc_examples/test_reduction.py``
+   :start-after: ex_reduction.launch.begin
+   :end-before: ex_reduction.launch.end
+   :dedent: 8
+   :linenos:
+
+This algorithm can be greatly improved upon by redesigning the inner loop
+to use sequential memory accesses, and even further by using strategies that
+keep more threads active and working, since in this example most threads quickly
+become idle.
+
+.. _cuda_sessionization:
+
+Dividing Click Data into Sessions
+=================================
+
+
+A common problem in business analytics is that of grouping the activity of users of an online platform into
+sessions, called "sessionization". The idea is that users generally traverse through a website and perform
+various actions (clicking something, filling out a form, etc.) in discrete groups. Perhaps a customer spends
+some time shopping for an item in the morning and then again at night - often the business is interested in
+treating these periods as separate interactions with their service, and this creates the problem of 
+programmatically splitting up activity in some agreed-upon way.
+
+Here we'll illustrate how to write a Numba kernel to solve this problem. We'll start with data 
+containing two fields: let ``user_id`` represent a unique ID corresponding to an individual customer, and let 
+``action_time`` be a time that some unknown action was taken on the service. Right now, we'll assume there's 
+only one type of action, so all there is to know is when it happened.
+
+Our goal will be to create a new column called ``session_id``, which contains a label corresponding to a unique 
+session. We'll define the boundary between sessions as when there has been at least one hour between clicks.
+
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_sessionize.py
+   :language: python
+   :caption: from ``test_ex_sessionize`` in ``numba/cuda/tests/doc_examples/test_sessionize.py``
+   :start-after: ex_sessionize.import.begin
+   :end-before: ex_sessionize.import.end
+   :dedent: 8
+   :linenos:
+   
+Here is a solution using Numba:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_sessionize.py
+   :language: python
+   :caption: from ``test_ex_sessionize`` in ``numba/cuda/tests/doc_examples/test_sessionize.py``
+   :start-after: ex_sessionize.kernel.begin
+   :end-before: ex_sessionize.kernel.end
+   :dedent: 8
+   :linenos:
+
+Let's generate some data and try out the kernel:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_sessionize.py
+   :language: python
+   :caption: from ``test_ex_sessionize`` in ``numba/cuda/tests/doc_examples/test_sessionize.py``
+   :start-after: ex_sessionize.allocate.begin
+   :end-before: ex_sessionize.allocate.end
+   :dedent: 8
+   :linenos:
+
+As can be seen above, the kernel successfully divided the first three datapoints from the second three for the first user ID,
+and a similar pattern is seen throughout.
+
+.. _cuda_reuse_function:
+
+JIT Function CPU-GPU Compatibility
+==================================
+
+This example demonstrates how ``numba.jit`` can be used to jit compile a function for the CPU, while at the same time making 
+it available for use inside CUDA kernels. This can be very useful for users that are migrating workflows from CPU to GPU as 
+they can directly reuse potential business logic with fewer code changes.
+
+Take the following example function:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py
+   :language: python
+   :caption: from ``test_ex_cpu_gpu_compat`` in ``numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py``
+   :start-after: ex_cpu_gpu_compat.define.begin
+   :end-before: ex_cpu_gpu_compat.define.end
+   :dedent: 8
+   :linenos:
+
+The function ``business_logic`` can be run standalone in compiled form on the CPU:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py
+   :language: python
+   :caption: from ``test_ex_cpu_gpu_compat`` in ``numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py``
+   :start-after: ex_cpu_gpu_compat.cpurun.begin
+   :end-before: ex_cpu_gpu_compat.cpurun.end
+   :dedent: 8
+   :linenos:
+
+It can also be directly reused threadwise inside a GPU kernel. For example one may 
+generate some vectors to represent ``x``, ``y``, and ``z``:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py
+   :language: python
+   :caption: from ``test_ex_cpu_gpu_compat`` in ``numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py``
+   :start-after: ex_cpu_gpu_compat.allocate.begin
+   :end-before: ex_cpu_gpu_compat.allocate.end
+   :dedent: 8
+   :linenos:
+
+And a numba kernel referencing the decorated function:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py
+   :language: python
+   :caption: from ``test_ex_cpu_gpu_compat`` in ``numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py``
+   :start-after: ex_cpu_gpu_compat.usegpu.begin
+   :end-before: ex_cpu_gpu_compat.usegpu.end
+   :dedent: 8
+   :linenos:
+
+This kernel can be invoked in the normal way:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py
+   :language: python
+   :caption: from ``test_ex_cpu_gpu_compat`` in ``numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py``
+   :start-after: ex_cpu_gpu_compat.launch.begin
+   :end-before: ex_cpu_gpu_compat.launch.end
+   :dedent: 8
+   :linenos:
+
+.. _cuda_montecarlo:
+
+Monte Carlo Integration
+=======================
+
+This example shows how to use Numba to approximate the value of a definite integral by rapidly generating 
+random numbers on the GPU. A detailed description of the mathematical mechanics of Monte Carlo integeration 
+is out of the scope of the example, but it can briefly be described as an averaging process where the area 
+under the curve is approximated by taking the average of many rectangles formed by its function values.
+
+In addition, this example shows how to perform reductions in numba using the 
+:func:`cuda.reduce() <numba.cuda.Reduce>` API.
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_montecarlo.py
+   :language: python
+   :caption: from ``test_ex_montecarlo`` in ``numba/cuda/tests/doc_examples/test_montecarlo.py``
+   :start-after: ex_montecarlo.import.begin
+   :end-before: ex_montecarlo.import.end
+   :dedent: 8
+   :linenos:
+
+Let's create a variable to control the number of samples drawn:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_montecarlo.py
+   :language: python
+   :caption: from ``test_ex_montecarlo`` in ``numba/cuda/tests/doc_examples/test_montecarlo.py``
+   :start-after: ex_montecarlo.define.begin
+   :end-before: ex_montecarlo.define.end
+   :dedent: 8
+   :linenos:
+
+
+The following kernel implements the main integration routine:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_montecarlo.py
+   :language: python
+   :caption: from ``test_ex_montecarlo`` in ``numba/cuda/tests/doc_examples/test_montecarlo.py``
+   :start-after: ex_montecarlo.kernel.begin
+   :end-before: ex_montecarlo.kernel.end
+   :dedent: 8
+   :linenos:
+
+This convenience function calls the kernel performs some
+preprocessing and post processing steps. Note the use of Numba's reduction API to
+take sum of the array and compute the final result:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_montecarlo.py
+   :language: python
+   :caption: from ``test_ex_montecarlo`` in ``numba/cuda/tests/doc_examples/test_montecarlo.py``
+   :start-after: ex_montecarlo.callfunc.begin
+   :end-before: ex_montecarlo.callfunc.end
+   :dedent: 8
+   :linenos:
+
+
+We can now use ``mc_integrate`` to compute the definite integral of this function between
+two limits:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_montecarlo.py
+   :language: python
+   :caption: from ``test_ex_montecarlo`` in ``numba/cuda/tests/doc_examples/test_montecarlo.py``
+   :start-after: ex_montecarlo.launch.begin
+   :end-before: ex_montecarlo.launch.end
+   :dedent: 8
+   :linenos:
+
+
+.. _cuda-matmul:
+
+Matrix multiplication
+=====================
+First, import the modules needed for this example:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_matmul.py
+   :language: python
+   :caption: from ``test_ex_matmul`` in ``numba/cuda/tests/doc_examples/test_matmul.py``
+   :start-after: magictoken.ex_import.begin
+   :end-before: magictoken.ex_import.end
+   :dedent: 8
+   :linenos:
+
+Here is a naïve implementation of matrix multiplication using a CUDA kernel:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_matmul.py
+   :language: python
+   :caption: from ``test_ex_matmul`` in ``numba/cuda/tests/doc_examples/test_matmul.py``
+   :start-after: magictoken.ex_matmul.begin
+   :end-before: magictoken.ex_matmul.end
+   :dedent: 8
+   :linenos:
+
+An example usage of this function is as follows:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_matmul.py
+   :language: python
+   :caption: from ``test_ex_matmul`` in ``numba/cuda/tests/doc_examples/test_matmul.py``
+   :start-after: magictoken.ex_run_matmul.begin
+   :end-before: magictoken.ex_run_matmul.end
+   :dedent: 8
+   :linenos:
+
+This implementation is straightforward and intuitive but performs poorly,
+because the same matrix elements will be loaded multiple times from device
+memory, which is slow (some devices may have transparent data caches, but
+they may not be large enough to hold the entire inputs at once).
+
+It will be faster if we use a blocked algorithm to reduce accesses to the
+device memory.  CUDA provides a fast :ref:`shared memory <cuda-shared-memory>`
+for threads in a block to cooperatively compute on a task.  The following
+implements a faster version of the square matrix multiplication using shared
+memory:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_matmul.py
+   :language: python
+   :caption: from ``test_ex_matmul`` in ``numba/cuda/tests/doc_examples/test_matmul.py``
+   :start-after: magictoken.ex_fast_matmul.begin
+   :end-before: magictoken.ex_fast_matmul.end
+   :dedent: 8
+   :linenos:
+
+
+Because the shared memory is a limited resource, the code preloads a small
+block at a time from the input arrays.  Then, it calls
+:func:`~numba.cuda.syncthreads` to wait until all threads have finished
+preloading and before doing the computation on the shared memory.
+It synchronizes again after the computation to ensure all threads
+have finished with the data in shared memory before overwriting it
+in the next loop iteration.
+
+An example usage of the ``fast_matmul`` function is as follows:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_matmul.py
+   :language: python
+   :caption: from ``test_ex_matmul`` in ``numba/cuda/tests/doc_examples/test_matmul.py``
+   :start-after: magictoken.ex_run_fast_matmul.begin
+   :end-before: magictoken.ex_run_fast_matmul.end
+   :dedent: 8
+   :linenos:
+
+
+This passes a :ref:`CUDA memory check test <debugging-cuda-python-code>`, which
+can help with debugging. Running the code above produces the following output:
+
+.. code-block:: none
+
+    $ python fast_matmul.py
+    [[ 6.  6.  6.  6.]
+    [22. 22. 22. 22.]
+    [38. 38. 38. 38.]
+    [54. 54. 54. 54.]]
+    [[ 6.  6.  6.  6.]
+    [22. 22. 22. 22.]
+    [38. 38. 38. 38.]
+    [54. 54. 54. 54.]]
+
+.. note:: For high performance matrix multiplication in CUDA, see also the `CuPy implementation <https://docs.cupy.dev/en/stable/reference/generated/cupy.matmul.html>`_.
+
+The approach outlined here generalizes to non-square matrix multiplication as
+follows by adjusting the ``blockspergrid`` variable:
+
+Again, here is an example usage:
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_matmul.py
+   :language: python
+   :caption: from ``test_ex_matmul`` in ``numba/cuda/tests/doc_examples/test_matmul.py``
+   :start-after: magictoken.ex_run_nonsquare.begin
+   :end-before: magictoken.ex_run_nonsquare.end
+   :dedent: 8
+   :linenos:
+
+and the corresponding output:
+
+.. code-block:: none
+
+  $ python nonsquare_matmul.py
+  [[ 253.  253.  253.  253.  253.  253.  253.]
+  [ 782.  782.  782.  782.  782.  782.  782.]
+  [1311. 1311. 1311. 1311. 1311. 1311. 1311.]
+  [1840. 1840. 1840. 1840. 1840. 1840. 1840.]
+  [2369. 2369. 2369. 2369. 2369. 2369. 2369.]]
+  [[ 253.  253.  253.  253.  253.  253.  253.]
+  [ 782.  782.  782.  782.  782.  782.  782.]
+  [1311. 1311. 1311. 1311. 1311. 1311. 1311.]
+  [1840. 1840. 1840. 1840. 1840. 1840. 1840.]
+  [2369. 2369. 2369. 2369. 2369. 2369. 2369.]]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/external-memory.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/external-memory.rst
new file mode 100644
index 000000000..28a8f59f0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/external-memory.rst
@@ -0,0 +1,320 @@
+.. _cuda-emm-plugin:
+
+=================================================
+External Memory Management (EMM) Plugin interface
+=================================================
+
+The :ref:`CUDA Array Interface <cuda-array-interface>` enables sharing of data
+between different Python libraries that access CUDA devices. However, each
+library manages its own memory distinctly from the others. For example:
+
+- By default, Numba allocates memory on CUDA devices by interacting with the
+  CUDA driver API to call functions such as ``cuMemAlloc`` and ``cuMemFree``,
+  which is suitable for many use cases.
+- The RAPIDS libraries (cuDF, cuML, etc.) use the `RAPIDS Memory Manager (RMM)
+  <https://github.com/rapidsai/rmm>`_ for allocating device memory.
+- `CuPy <https://cupy.chainer.org/>`_ includes a `memory pool implementation
+  <https://docs-cupy.chainer.org/en/stable/reference/memory.html>`_ for both
+  device and pinned memory.
+
+When multiple CUDA-aware libraries are used together, it may be preferable for
+Numba to defer to another library for memory management. The EMM Plugin
+interface facilitates this, by enabling Numba to use another CUDA-aware library
+for all allocations and deallocations.
+
+An EMM Plugin is used to facilitate the use of an external library for memory
+management. An EMM Plugin can be a part of an external library, or could be
+implemented as a separate library.
+
+
+Overview of External Memory Management
+======================================
+
+When an EMM Plugin is in use (see :ref:`setting-emm-plugin`), Numba will make
+memory allocations and deallocations through the Plugin. It will never directly call
+functions such as ``cuMemAlloc``, ``cuMemFree``, etc.
+
+EMM Plugins always take responsibility for the management of device memory.
+However, not all CUDA-aware libraries also support managing host memory, so a
+facility for Numba to continue the management of host memory whilst ceding
+control of device memory to the EMM is provided (see
+:ref:`host-only-cuda-memory-manager`).
+
+
+Effects on Deallocation Strategies
+----------------------------------
+
+Numba's internal :ref:`deallocation-behavior` is designed to increase efficiency
+by deferring deallocations until a significant quantity are pending. It also
+provides a mechanism for preventing deallocations entirely during critical
+sections, using the :func:`~numba.cuda.defer_cleanup` context manager.
+
+When an EMM Plugin is in use, the deallocation strategy is implemented by the
+EMM, and Numba's internal deallocation mechanism is not used. The EMM
+Plugin could implement:
+  
+- A similar strategy to the Numba deallocation behaviour, or
+- Something more appropriate to the plugin - for example, deallocated memory
+  might immediately be returned to a memory pool.
+
+The ``defer_cleanup`` context manager may behave differently with an EMM Plugin
+- an EMM Plugin should be accompanied by documentation of the behaviour of the
+``defer_cleanup`` context manager when it is in use. For example, a pool
+allocator could always immediately return memory to a pool even when the
+context manager is in use, but could choose not to free empty pools until
+``defer_cleanup`` is not in use.
+
+
+Management of other objects
+---------------------------
+
+In addition to memory, Numba manages the allocation and deallocation of
+:ref:`events <events>`, :ref:`streams <streams>`, and modules (a module is a
+compiled object, which is generated from ``@cuda.jit``\ -ted functions). The
+management of events, streams, and modules is unchanged by the use of an EMM
+Plugin.
+
+
+Asynchronous allocation and deallocation
+----------------------------------------
+
+The present EMM Plugin interface does not provide support for asynchronous
+allocation and deallocation. This may be added to a future version of the
+interface.
+
+
+Implementing an EMM Plugin
+==========================
+
+An EMM Plugin is implemented by deriving from
+:class:`~numba.cuda.BaseCUDAMemoryManager`. A summary of considerations for the
+implementation follows:
+
+- Numba instantiates one instance of the EMM Plugin class per context. The
+  context that owns an EMM Plugin object is accessible through ``self.context``,
+  if required.
+- The EMM Plugin is transparent to any code that uses Numba - all its methods
+  are invoked by Numba, and never need to be called by code that uses Numba.
+- The allocation methods ``memalloc``, ``memhostalloc``, and ``mempin``, should
+  use the underlying library to allocate and/or pin device or host memory, and
+  construct an instance of a :ref:`memory pointer <memory-pointers>`
+  representing the memory to return back to Numba. These methods are always
+  called when the current CUDA context is the context that owns the EMM Plugin
+  instance.
+- The ``initialize`` method is called by Numba prior to the first use of the EMM
+  Plugin object for a context. This method should do anything required to
+  prepare the underlying library for allocations in the current context. This
+  method may be called multiple times, and must not invalidate previous state
+  when it is called.
+- The ``reset`` method is called when all allocations in the context are to be
+  cleaned up. It may be called even prior to ``initialize``, and an EMM Plugin
+  implementation needs to guard against this.
+- To support inter-GPU communication, the ``get_ipc_handle`` method should
+  provide an :class:`~numba.cuda.IpcHandle` for a given
+  :class:`~numba.cuda.MemoryPointer` instance. This method is part of the EMM
+  interface (rather than being handled within Numba) because the base address of
+  the allocation is only known by the underlying library. Closing an IPC handle
+  is handled internally within Numba.
+- It is optional to provide memory info from the ``get_memory_info`` method, which
+  provides a count of the total and free memory on the device for the context.
+  It is preferable to implement the method, but this may not be practical for
+  all allocators. If memory info is not provided, this method should raise a
+  :class:`RuntimeError`.
+- The ``defer_cleanup`` method should return a context manager that ensures that
+  expensive cleanup operations are avoided whilst it is active. The nuances of
+  this will vary between plugins, so the plugin documentation should include an
+  explanation of how deferring cleanup affects deallocations, and performance in
+  general.
+- The ``interface_version`` property is used to ensure that the plugin version
+  matches the interface provided by the version of Numba. At present, this
+  should always be 1.
+
+Full documentation for the base class follows:
+
+.. autoclass:: numba.cuda.BaseCUDAMemoryManager
+   :members: memalloc, memhostalloc, mempin, initialize, get_ipc_handle,
+             get_memory_info, reset, defer_cleanup, interface_version
+   :member-order: bysource
+
+
+.. _host-only-cuda-memory-manager:
+
+The Host-Only CUDA Memory Manager
+---------------------------------
+
+Some external memory managers will support management of on-device memory but
+not host memory. For implementing EMM Plugins using one of these memory
+managers, a partial implementation of a plugin that implements host-side
+allocation and pinning is provided. To use it, derive from
+:class:`~numba.cuda.HostOnlyCUDAMemoryManager` instead of
+:class:`~numba.cuda.BaseCUDAMemoryManager`. Guidelines for using this class
+are:
+
+- The host-only memory manager implements ``memhostalloc`` and ``mempin`` - the
+  EMM Plugin should still implement ``memalloc``.
+- If ``reset`` is overridden, it must also call ``super().reset()`` to allow the
+  host allocations to be cleaned up.
+- If ``defer_cleanup`` is overridden, it must hold an active context manager
+  from ``super().defer_cleanup()`` to ensure that host-side cleanup is also
+  deferred.
+
+Documentation for the methods of :class:`~numba.cuda.HostOnlyCUDAMemoryManager`
+follows:
+
+.. autoclass:: numba.cuda.HostOnlyCUDAMemoryManager
+   :members: memhostalloc, mempin, reset, defer_cleanup
+   :member-order: bysource
+
+
+The IPC Handle Mixin
+--------------------
+
+An implementation of the ``get_ipc_handle()`` function is is provided in the
+``GetIpcHandleMixin`` class. This uses the driver API to determine the base
+address of an allocation for opening an IPC handle. If this implementation is
+appropriate for an EMM plugin, it can be added by mixing in the
+``GetIpcHandleMixin`` class:
+
+.. autoclass:: numba.cuda.GetIpcHandleMixin
+   :members: get_ipc_handle
+
+
+Classes and structures of returned objects
+==========================================
+
+This section provides an overview of the classes and structures that need to be
+constructed by an EMM Plugin.
+
+.. _memory-pointers:
+
+Memory Pointers
+---------------
+
+EMM Plugins should construct memory pointer instances that represent their
+allocations, for return to Numba. The appropriate memory pointer class to use in
+each method is:
+
+- :class:`~numba.cuda.MemoryPointer`: returned from ``memalloc``
+- :class:`~numba.cuda.MappedMemory`: returned from ``memhostalloc`` or
+  ``mempin`` when the host memory is mapped into the device memory space.
+- :class:`~numba.cuda.PinnedMemory`: return from ``memhostalloc`` or ``mempin``
+  when the host memory is not mapped into the device memory space.
+
+Memory pointers can take a finalizer, which is a function that is called when
+the buffer is no longer needed. Usually the finalizer will make a call to the
+memory management library (either internal to Numba, or external if allocated
+by an EMM Plugin) to inform it that the memory is no longer required, and that
+it could potentially be freed and/or unpinned. The memory manager may choose to
+defer actually cleaning up the memory to any later time after the finalizer
+runs - it is not required to free the buffer immediately.
+
+Documentation for the memory pointer classes follows.
+
+.. autoclass:: numba.cuda.MemoryPointer
+
+The ``AutoFreePointer`` class need not be used directly, but is documented here
+as it is subclassed by :class:`numba.cuda.MappedMemory`:
+
+.. autoclass:: numba.cuda.cudadrv.driver.AutoFreePointer
+
+.. autoclass:: numba.cuda.MappedMemory
+
+.. autoclass:: numba.cuda.PinnedMemory
+
+
+Memory Info
+-----------
+
+If an implementation of
+:meth:`~numba.cuda.BaseCUDAMemoryManager.get_memory_info` is to provide a
+result, then it should return an instance of the ``MemoryInfo`` named tuple:
+
+.. autoclass:: numba.cuda.MemoryInfo
+
+
+IPC
+---
+
+An instance of ``IpcHandle`` is required to be returned from an implementation
+of :meth:`~numba.cuda.BaseCUDAMemoryManager.get_ipc_handle`:
+
+.. autoclass:: numba.cuda.IpcHandle
+
+Guidance for constructing an IPC handle in the context of implementing an EMM
+Plugin:
+
+- The ``memory`` parameter passed to the ``get_ipc_handle`` method of an EMM
+  Plugin can be passed as the ``base`` parameter.
+- A suitable type for the ``handle`` can be constructed as ``ctypes.c_byte *
+  64``. The data for ``handle`` must be populated using a method for obtaining a
+  CUDA IPC handle appropriate to the underlying library.
+- ``size`` should match the size of the original allocation, which can be
+  obtained with ``memory.size`` in ``get_ipc_handle``.
+- An appropriate value for ``source_info`` can be created by calling
+  ``self.context.device.get_device_identity()``.
+- If the underlying memory does not point to the base of an allocation returned
+  by the CUDA driver or runtime API (e.g. if a pool allocator is in use) then
+  the ``offset`` from the base must be provided.
+
+
+.. _setting-emm-plugin:
+
+Setting the EMM Plugin
+======================
+
+By default, Numba uses its internal memory management - if an EMM Plugin is to
+be used, it must be configured. There are two mechanisms for configuring the use
+of an EMM Plugin: an environment variable, and a function.
+
+
+Environment variable
+--------------------
+
+A module name can be provided in the environment variable,
+``NUMBA_CUDA_MEMORY_MANAGER``. If this environment variable is set, Numba will
+attempt to import the module, and and use its ``_numba_memory_manager`` global
+variable as the memory manager class. This is primarily useful for running the
+Numba test suite with an EMM Plugin, e.g.:
+
+.. code::
+
+   $ NUMBA_CUDA_MEMORY_MANAGER=rmm python -m numba.runtests numba.cuda.tests
+
+
+Function
+--------
+
+The :func:`~numba.cuda.set_memory_manager` function can be used to set the
+memory manager at runtime. This should be called prior to the initialization of
+any contexts, as EMM Plugin instances are instantiated along with contexts.
+
+.. autofunction:: numba.cuda.set_memory_manager
+
+
+Resetting the memory manager
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+It is recommended that the memory manager is set once prior to using any CUDA
+functionality, and left unchanged for the remainder of execution. It is possible
+to set the memory manager multiple times, noting the following:
+
+* At the time of their creation, contexts are bound to an instance of a memory
+  manager for their lifetime.
+* Changing the memory manager will have no effect on existing contexts - only
+  contexts created after the memory manager was updated will use instances of
+  the new memory manager.
+* :func:`numba.cuda.close` can be used to destroy contexts after setting the
+  memory manager so that they get re-created with the new memory manager.
+
+  - This will invalidate any arrays, streams, events, and modules owned by the
+    context.
+  - Attempting to use invalid arrays, streams, or events will likely fail with
+    an exception being raised due to a ``CUDA_ERROR_INVALID_CONTEXT`` or
+    ``CUDA_ERROR_CONTEXT_IS_DESTROYED`` return code from a Driver API function.
+  - Attempting to use an invalid module will result in similar, or in some
+    cases a segmentation fault / access violation.
+
+.. note:: The invalidation of modules means that all functions compiled with
+          ``@cuda.jit`` prior to context destruction will need to be
+          redefined, as the code underlying them will also have been unloaded
+          from the GPU.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/faq.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/faq.rst
new file mode 100644
index 000000000..b4392e6ab
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/faq.rst
@@ -0,0 +1,20 @@
+
+.. _cudafaq:
+
+=================================================
+CUDA Frequently Asked Questions
+=================================================
+
+nvprof reports "No kernels were profiled"
+-----------------------------------------
+
+When using the ``nvprof`` tool to profile Numba jitted code for the CUDA
+target, the output contains ``No kernels were profiled`` but there are clearly
+running kernels present, what is going on?
+
+This is quite likely due to the profiling data not being flushed on program
+exit, see the `NVIDIA CUDA documentation
+<http://docs.nvidia.com/cuda/profiler-users-guide/#flush-profile-data>`_ for
+details. To fix this simply add a call to ``numba.cuda.profile_stop()`` prior
+to the exit point in your program (or wherever you want to stop profiling).
+For more on CUDA profiling support in Numba, see :ref:`cuda-profiling`.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/fastmath.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/fastmath.rst
new file mode 100644
index 000000000..fb9de10ea
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/fastmath.rst
@@ -0,0 +1,36 @@
+
+.. _cuda-fast-math:
+
+CUDA Fast Math
+==============
+
+As noted in :ref:`fast-math`, for certain classes of applications that utilize
+floating point, strict IEEE-754 conformance is not required. For this subset of
+applications, performance speedups may be possible.
+
+The CUDA target implements :ref:`fast-math` behavior with two differences.
+
+* First, the ``fastmath`` argument to the :func:`@jit decorator
+  <numba.cuda.jit>` is limited to the values ``True`` and ``False``.
+  When ``True``, the following optimizations are enabled:
+
+  - Flushing of denormals to zero.
+  - Use of a fast approximation to the square root function.
+  - Use of a fast approximation to the division operation.
+  - Contraction of multiply and add operations into single fused multiply-add
+    operations.
+
+  See the `documentation for nvvmCompileProgram <https://docs.nvidia.com/cuda/libnvvm-api/group__compilation.html#group__compilation_1g76ac1e23f5d0e2240e78be0e63450346>`_ for more details of these optimizations.
+
+* Secondly, calls to a subset of math module functions on ``float32`` operands
+  will be implemented using fast approximate implementations from the libdevice
+  library.
+
+  - :func:`math.cos`: Implemented using `__nv_fast_cosf <https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_cosf.html>`_.
+  - :func:`math.sin`: Implemented using `__nv_fast_sinf <https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_sinf.html>`_.
+  - :func:`math.tan`: Implemented using `__nv_fast_tanf <https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_tanf.html>`_.
+  - :func:`math.exp`: Implemented using `__nv_fast_expf <https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_expf.html>`_.
+  - :func:`math.log2`: Implemented using `__nv_fast_log2f <https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_log2f.html>`_.
+  - :func:`math.log10`: Implemented using `__nv_fast_log10f <https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_log10f.html>`_.
+  - :func:`math.log`: Implemented using `__nv_fast_logf <https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_logf.html>`_.
+  - :func:`math.pow`: Implemented using `__nv_fast_powf <https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_powf.html>`_.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/index.rst
new file mode 100644
index 000000000..686b4ad0b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/index.rst
@@ -0,0 +1,29 @@
+
+.. _cuda-index:
+
+Numba for CUDA GPUs
+===================
+
+.. toctree::
+
+   overview.rst
+   kernels.rst
+   memory.rst
+   device-functions.rst
+   cudapysupported.rst
+   fastmath.rst
+   intrinsics.rst
+   cooperative_groups.rst
+   random.rst
+   device-management.rst
+   examples.rst
+   simulator.rst
+   reduction.rst
+   ufunc.rst
+   ipc.rst
+   cuda_array_interface.rst
+   external-memory.rst
+   bindings.rst
+   cuda_ffi.rst
+   caching.rst
+   faq.rst
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/intrinsics.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/intrinsics.rst
new file mode 100644
index 000000000..521c1d918
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/intrinsics.rst
@@ -0,0 +1,58 @@
+
+Supported Atomic Operations
+===========================
+
+Numba provides access to some of the atomic operations supported in CUDA. Those
+that are presently implemented are as follows:
+
+.. automodule:: numba.cuda
+    :members: atomic
+    :noindex:
+
+Example
+'''''''
+
+The following code demonstrates the use of :class:`numba.cuda.atomic.max` to
+find the maximum value in an array. Note that this is not the most efficient way
+of finding a maximum in this case, but that it serves as an example::
+
+    from numba import cuda
+    import numpy as np
+
+    @cuda.jit
+    def max_example(result, values):
+        """Find the maximum value in values and store in result[0]"""
+        tid = cuda.threadIdx.x
+        bid = cuda.blockIdx.x
+        bdim = cuda.blockDim.x
+        i = (bid * bdim) + tid
+        cuda.atomic.max(result, 0, values[i])
+
+
+    arr = np.random.rand(16384)
+    result = np.zeros(1, dtype=np.float64)
+
+    max_example[256,64](result, arr)
+    print(result[0]) # Found using cuda.atomic.max
+    print(max(arr))  # Print max(arr) for comparison (should be equal!)
+
+
+Multiple dimension arrays are supported by using a tuple of ints for the index::
+
+
+    @cuda.jit
+    def max_example_3d(result, values):
+        """
+        Find the maximum value in values and store in result[0].
+        Both result and values are 3d arrays.
+        """
+        i, j, k = cuda.grid(3)
+        # Atomically store to result[0,1,2] from values[i, j, k]
+        cuda.atomic.max(result, (0, 1, 2), values[i, j, k])
+
+    arr = np.random.rand(1000).reshape(10,10,10)
+    result = np.zeros((3, 3, 3), dtype=np.float64)
+    max_example_3d[(2, 2, 2), (5, 5, 5)](result, arr)
+    print(result[0, 1, 2], '==', np.max(arr))
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/ipc.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/ipc.rst
new file mode 100644
index 000000000..ce0f508e5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/ipc.rst
@@ -0,0 +1,36 @@
+===================
+Sharing CUDA Memory
+===================
+
+.. _cuda-ipc-memory:
+
+Sharing between process
+=======================
+
+Sharing between processes is implemented using the Legacy CUDA IPC API
+(functions whose names begin with ``cuIpc``), and is supported only on Linux.
+
+
+Export device array to another process
+--------------------------------------
+
+A device array can be shared with another process in the same machine using
+the CUDA IPC API.  To do so, use the ``.get_ipc_handle()`` method on the device
+array to get a ``IpcArrayHandle`` object, which can be transferred to another
+process.
+
+
+.. automethod:: numba.cuda.cudadrv.devicearray.DeviceNDArray.get_ipc_handle
+    :noindex:
+
+.. autoclass:: numba.cuda.cudadrv.devicearray.IpcArrayHandle
+    :members: open, close
+
+
+Import IPC memory from another process
+--------------------------------------
+
+The following function is used to open IPC handle from another process
+as a device array.
+
+.. automethod:: numba.cuda.open_ipc_array
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/kernels.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/kernels.rst
new file mode 100644
index 000000000..b4af2ccf8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/kernels.rst
@@ -0,0 +1,233 @@
+
+====================
+Writing CUDA Kernels
+====================
+
+Introduction
+============
+
+CUDA has an execution model unlike the traditional sequential model used
+for programming CPUs.  In CUDA, the code you write will be executed by
+multiple threads at once (often hundreds or thousands).  Your solution will
+be modeled by defining a thread hierarchy of *grid*, *blocks* and *threads*.
+
+Numba's CUDA support exposes facilities to declare and manage this
+hierarchy of threads.  The facilities are largely similar to those
+exposed by NVidia's CUDA C language.
+
+Numba also exposes three kinds of GPU memory: global :ref:`device memory
+<cuda-device-memory>` (the large, relatively slow
+off-chip memory that's connected to the GPU itself), on-chip
+:ref:`shared memory <cuda-shared-memory>` and :ref:`local memory <cuda-local-memory>`.
+For all but the simplest algorithms, it is important that you carefully
+consider how to use and access memory in order to minimize bandwidth
+requirements and contention.
+
+
+Kernel declaration
+==================
+
+A *kernel function* is a GPU function that is meant to be called from CPU
+code (*).  It gives it two fundamental characteristics:
+
+* kernels cannot explicitly return a value; all result data must be written
+  to an array passed to the function (if computing a scalar, you will
+  probably pass a one-element array);
+
+* kernels explicitly declare their thread hierarchy when called: i.e.
+  the number of thread blocks and the number of threads per block
+  (note that while a kernel is compiled once, it can be called multiple
+  times with different block sizes or grid sizes).
+
+At first sight, writing a CUDA kernel with Numba looks very much like
+writing a :term:`JIT function` for the CPU::
+
+    @cuda.jit
+    def increment_by_one(an_array):
+        """
+        Increment all array elements by one.
+        """
+        # code elided here; read further for different implementations
+
+(*) Note: newer CUDA devices support device-side kernel launching; this feature
+is called *dynamic parallelism* but Numba does not support it currently)
+
+
+.. _cuda-kernel-invocation:
+
+Kernel invocation
+=================
+
+A kernel is typically launched in the following way::
+
+    threadsperblock = 32
+    blockspergrid = (an_array.size + (threadsperblock - 1)) // threadsperblock
+    increment_by_one[blockspergrid, threadsperblock](an_array)
+
+We notice two steps here:
+
+* Instantiate the kernel proper, by specifying a number of blocks
+  (or "blocks per grid"), and a number of threads per block.  The product
+  of the two will give the total number of threads launched.  Kernel
+  instantiation is done by taking the compiled kernel function
+  (here ``increment_by_one``) and indexing it with a tuple of integers.
+
+* Running the kernel, by passing it the input array (and any separate
+  output arrays if necessary). Kernels run asynchronously: launches queue their
+  execution on the device and then return immediately.  You can use
+  :func:`cuda.synchronize() <numba.cuda.synchronize>` to wait for all previous
+  kernel launches to finish executing.
+
+.. note:: Passing an array that resides in host memory will implicitly cause a
+   copy back to the host, which will be synchronous. In this case, the kernel
+   launch will not return until the data is copied back, and therefore appears
+   to execute synchronously.
+
+Choosing the block size
+-----------------------
+
+It might seem curious to have a two-level hierarchy when declaring the
+number of threads needed by a kernel.  The block size (i.e. number of
+threads per block) is often crucial:
+
+* On the software side, the block size determines how many threads
+  share a given area of :ref:`shared memory <cuda-shared-memory>`.
+
+* On the hardware side, the block size must be large enough for full
+  occupation of execution units; recommendations can be found in the
+  `CUDA C Programming Guide`_.
+
+Multi-dimensional blocks and grids
+----------------------------------
+
+To help deal with multi-dimensional arrays, CUDA allows you to specify
+multi-dimensional blocks and grids.  In the example above, you could
+make ``blockspergrid`` and ``threadsperblock`` tuples of one, two
+or three integers.  Compared to 1D declarations of equivalent sizes,
+this doesn't change anything to the efficiency or behaviour of generated
+code, but can help you write your algorithms in a more natural way.
+
+
+Thread positioning
+==================
+
+When running a kernel, the kernel function's code is executed by every
+thread once.  It therefore has to know which thread it is in, in order
+to know which array element(s) it is responsible for (complex algorithms
+may define more complex responsibilities, but the underlying principle
+is the same).
+
+One way is for the thread to determine its position in the grid and block
+and manually compute the corresponding array position::
+
+    @cuda.jit
+    def increment_by_one(an_array):
+        # Thread id in a 1D block
+        tx = cuda.threadIdx.x
+        # Block id in a 1D grid
+        ty = cuda.blockIdx.x
+        # Block width, i.e. number of threads per block
+        bw = cuda.blockDim.x
+        # Compute flattened index inside the array
+        pos = tx + ty * bw
+        if pos < an_array.size:  # Check array boundaries
+            an_array[pos] += 1
+
+.. note:: Unless you are sure the block size and grid size is a divisor
+   of your array size, you **must** check boundaries as shown above.
+
+:attr:`.threadIdx`, :attr:`.blockIdx`, :attr:`.blockDim` and :attr:`.gridDim`
+are special objects provided by the CUDA backend for the sole purpose of
+knowing the geometry of the thread hierarchy and the position of the
+current thread within that geometry.
+
+These objects can be 1D, 2D or 3D, depending on how the kernel was
+:ref:`invoked <cuda-kernel-invocation>`.  To access the value at each
+dimension, use the ``x``, ``y`` and ``z`` attributes of these objects,
+respectively.
+
+.. attribute:: numba.cuda.threadIdx
+   :noindex:
+
+   The thread indices in the current thread block.  For 1D blocks, the index
+   (given by the ``x`` attribute) is an integer spanning the range from 0
+   inclusive to :attr:`numba.cuda.blockDim` exclusive.  A similar rule
+   exists for each dimension when more than one dimension is used.
+
+.. attribute:: numba.cuda.blockDim
+   :noindex:
+
+   The shape of the block of threads, as declared when instantiating the
+   kernel.  This value is the same for all threads in a given kernel, even
+   if they belong to different blocks (i.e. each block is "full").
+
+.. attribute:: numba.cuda.blockIdx
+   :noindex:
+
+   The block indices in the grid of threads launched a kernel.  For a 1D grid,
+   the index (given by the ``x`` attribute) is an integer spanning the range
+   from 0 inclusive to :attr:`numba.cuda.gridDim` exclusive.  A similar rule
+   exists for each dimension when more than one dimension is used.
+
+.. attribute:: numba.cuda.gridDim
+   :noindex:
+
+   The shape of the grid of blocks, i.e. the total number of blocks launched
+   by this kernel invocation, as declared when instantiating the kernel.
+
+Absolute positions
+------------------
+
+Simple algorithms will tend to always use thread indices in the
+same way as shown in the example above.  Numba provides additional facilities
+to automate such calculations:
+
+.. function:: numba.cuda.grid(ndim)
+   :noindex:
+
+   Return the absolute position of the current thread in the entire
+   grid of blocks.  *ndim* should correspond to the number of dimensions
+   declared when instantiating the kernel.  If *ndim* is 1, a single integer
+   is returned.  If *ndim* is 2 or 3, a tuple of the given number of
+   integers is returned.
+
+.. function:: numba.cuda.gridsize(ndim)
+   :noindex:
+
+   Return the absolute size (or shape) in threads of the entire grid of
+   blocks.  *ndim* has the same meaning as in :func:`.grid` above.
+
+With these functions, the incrementation example can become::
+
+    @cuda.jit
+    def increment_by_one(an_array):
+        pos = cuda.grid(1)
+        if pos < an_array.size:
+            an_array[pos] += 1
+
+The same example for a 2D array and grid of threads would be::
+
+    @cuda.jit
+    def increment_a_2D_array(an_array):
+        x, y = cuda.grid(2)
+        if x < an_array.shape[0] and y < an_array.shape[1]:
+           an_array[x, y] += 1
+
+Note the grid computation when instantiating the kernel must still be
+done manually, for example::
+
+    threadsperblock = (16, 16)
+    blockspergrid_x = math.ceil(an_array.shape[0] / threadsperblock[0])
+    blockspergrid_y = math.ceil(an_array.shape[1] / threadsperblock[1])
+    blockspergrid = (blockspergrid_x, blockspergrid_y)
+    increment_a_2D_array[blockspergrid, threadsperblock](an_array)
+
+
+Further Reading
+----------------
+
+Please refer to the the `CUDA C Programming Guide`_ for a detailed discussion
+of CUDA programming.
+
+
+.. _CUDA C Programming Guide: http://docs.nvidia.com/cuda/cuda-c-programming-guide
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/laplace_final.svg b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/laplace_final.svg
new file mode 100644
index 000000000..4f3b197fb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/laplace_final.svg
@@ -0,0 +1,1953 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="760.32pt" height="427.68pt" viewBox="0 0 760.32 427.68" xmlns="http://www.w3.org/2000/svg" version="1.1">
+ <metadata>
+  <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+   <cc:Work>
+    <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+    <dc:date>2022-04-18T06:58:19.244680</dc:date>
+    <dc:format>image/svg+xml</dc:format>
+    <dc:creator>
+     <cc:Agent>
+      <dc:title>Matplotlib v3.5.1, https://matplotlib.org/</dc:title>
+     </cc:Agent>
+    </dc:creator>
+   </cc:Work>
+  </rdf:RDF>
+ </metadata>
+ <defs>
+  <style type="text/css">*{stroke-linejoin: round; stroke-linecap: butt}</style>
+ </defs>
+ <g id="figure_1">
+  <g id="patch_1">
+   <path d="M 0 427.68 
+L 760.32 427.68 
+L 760.32 0 
+L 0 0 
+z
+" style="fill: #ffffff"/>
+  </g>
+  <g id="axes_1">
+   <g id="patch_2">
+    <path d="M 95.04 380.6352 
+L 684.288 380.6352 
+L 684.288 51.3216 
+L 95.04 51.3216 
+z
+" style="fill: #ffffff"/>
+   </g>
+   <g id="matplotlib.axis_1">
+    <g id="xtick_1">
+     <g id="line2d_1">
+      <defs>
+       <path id="mdea99e8948" d="M 0 0 
+L 0 3.5 
+" style="stroke: #000000; stroke-width: 0.8"/>
+      </defs>
+      <g>
+       <use xlink:href="#mdea99e8948" x="95.04" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_1">
+      <!-- 0 -->
+      <g transform="translate(91.85875 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-30" d="M 2034 4250 
+Q 1547 4250 1301 3770 
+Q 1056 3291 1056 2328 
+Q 1056 1369 1301 889 
+Q 1547 409 2034 409 
+Q 2525 409 2770 889 
+Q 3016 1369 3016 2328 
+Q 3016 3291 2770 3770 
+Q 2525 4250 2034 4250 
+z
+M 2034 4750 
+Q 2819 4750 3233 4129 
+Q 3647 3509 3647 2328 
+Q 3647 1150 3233 529 
+Q 2819 -91 2034 -91 
+Q 1250 -91 836 529 
+Q 422 1150 422 2328 
+Q 422 3509 836 4129 
+Q 1250 4750 2034 4750 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_2">
+     <g id="line2d_2">
+      <g>
+       <use xlink:href="#mdea99e8948" x="212.771868" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_2">
+      <!-- 200 -->
+      <g transform="translate(203.228118 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-32" d="M 1228 531 
+L 3431 531 
+L 3431 0 
+L 469 0 
+L 469 531 
+Q 828 903 1448 1529 
+Q 2069 2156 2228 2338 
+Q 2531 2678 2651 2914 
+Q 2772 3150 2772 3378 
+Q 2772 3750 2511 3984 
+Q 2250 4219 1831 4219 
+Q 1534 4219 1204 4116 
+Q 875 4013 500 3803 
+L 500 4441 
+Q 881 4594 1212 4672 
+Q 1544 4750 1819 4750 
+Q 2544 4750 2975 4387 
+Q 3406 4025 3406 3419 
+Q 3406 3131 3298 2873 
+Q 3191 2616 2906 2266 
+Q 2828 2175 2409 1742 
+Q 1991 1309 1228 531 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_3">
+     <g id="line2d_3">
+      <g>
+       <use xlink:href="#mdea99e8948" x="330.503736" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_3">
+      <!-- 400 -->
+      <g transform="translate(320.959986 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-34" d="M 2419 4116 
+L 825 1625 
+L 2419 1625 
+L 2419 4116 
+z
+M 2253 4666 
+L 3047 4666 
+L 3047 1625 
+L 3713 1625 
+L 3713 1100 
+L 3047 1100 
+L 3047 0 
+L 2419 0 
+L 2419 1100 
+L 313 1100 
+L 313 1709 
+L 2253 4666 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_4">
+     <g id="line2d_4">
+      <g>
+       <use xlink:href="#mdea99e8948" x="448.235604" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_4">
+      <!-- 600 -->
+      <g transform="translate(438.691854 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-36" d="M 2113 2584 
+Q 1688 2584 1439 2293 
+Q 1191 2003 1191 1497 
+Q 1191 994 1439 701 
+Q 1688 409 2113 409 
+Q 2538 409 2786 701 
+Q 3034 994 3034 1497 
+Q 3034 2003 2786 2293 
+Q 2538 2584 2113 2584 
+z
+M 3366 4563 
+L 3366 3988 
+Q 3128 4100 2886 4159 
+Q 2644 4219 2406 4219 
+Q 1781 4219 1451 3797 
+Q 1122 3375 1075 2522 
+Q 1259 2794 1537 2939 
+Q 1816 3084 2150 3084 
+Q 2853 3084 3261 2657 
+Q 3669 2231 3669 1497 
+Q 3669 778 3244 343 
+Q 2819 -91 2113 -91 
+Q 1303 -91 875 529 
+Q 447 1150 447 2328 
+Q 447 3434 972 4092 
+Q 1497 4750 2381 4750 
+Q 2619 4750 2861 4703 
+Q 3103 4656 3366 4563 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_5">
+     <g id="line2d_5">
+      <g>
+       <use xlink:href="#mdea99e8948" x="565.967473" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_5">
+      <!-- 800 -->
+      <g transform="translate(556.423723 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-38" d="M 2034 2216 
+Q 1584 2216 1326 1975 
+Q 1069 1734 1069 1313 
+Q 1069 891 1326 650 
+Q 1584 409 2034 409 
+Q 2484 409 2743 651 
+Q 3003 894 3003 1313 
+Q 3003 1734 2745 1975 
+Q 2488 2216 2034 2216 
+z
+M 1403 2484 
+Q 997 2584 770 2862 
+Q 544 3141 544 3541 
+Q 544 4100 942 4425 
+Q 1341 4750 2034 4750 
+Q 2731 4750 3128 4425 
+Q 3525 4100 3525 3541 
+Q 3525 3141 3298 2862 
+Q 3072 2584 2669 2484 
+Q 3125 2378 3379 2068 
+Q 3634 1759 3634 1313 
+Q 3634 634 3220 271 
+Q 2806 -91 2034 -91 
+Q 1263 -91 848 271 
+Q 434 634 434 1313 
+Q 434 1759 690 2068 
+Q 947 2378 1403 2484 
+z
+M 1172 3481 
+Q 1172 3119 1398 2916 
+Q 1625 2713 2034 2713 
+Q 2441 2713 2670 2916 
+Q 2900 3119 2900 3481 
+Q 2900 3844 2670 4047 
+Q 2441 4250 2034 4250 
+Q 1625 4250 1398 4047 
+Q 1172 3844 1172 3481 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-38"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_6">
+     <g id="line2d_6">
+      <g>
+       <use xlink:href="#mdea99e8948" x="683.699341" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_6">
+      <!-- 1000 -->
+      <g transform="translate(670.974341 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-31" d="M 794 531 
+L 1825 531 
+L 1825 4091 
+L 703 3866 
+L 703 4441 
+L 1819 4666 
+L 2450 4666 
+L 2450 531 
+L 3481 531 
+L 3481 0 
+L 794 0 
+L 794 531 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+       <use xlink:href="#DejaVuSans-30" x="190.869141"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_7">
+     <!-- Position -->
+     <g transform="translate(342.95025 419.549575)scale(0.24 -0.24)">
+      <defs>
+       <path id="DejaVuSans-50" d="M 1259 4147 
+L 1259 2394 
+L 2053 2394 
+Q 2494 2394 2734 2622 
+Q 2975 2850 2975 3272 
+Q 2975 3691 2734 3919 
+Q 2494 4147 2053 4147 
+L 1259 4147 
+z
+M 628 4666 
+L 2053 4666 
+Q 2838 4666 3239 4311 
+Q 3641 3956 3641 3272 
+Q 3641 2581 3239 2228 
+Q 2838 1875 2053 1875 
+L 1259 1875 
+L 1259 0 
+L 628 0 
+L 628 4666 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-6f" d="M 1959 3097 
+Q 1497 3097 1228 2736 
+Q 959 2375 959 1747 
+Q 959 1119 1226 758 
+Q 1494 397 1959 397 
+Q 2419 397 2687 759 
+Q 2956 1122 2956 1747 
+Q 2956 2369 2687 2733 
+Q 2419 3097 1959 3097 
+z
+M 1959 3584 
+Q 2709 3584 3137 3096 
+Q 3566 2609 3566 1747 
+Q 3566 888 3137 398 
+Q 2709 -91 1959 -91 
+Q 1206 -91 779 398 
+Q 353 888 353 1747 
+Q 353 2609 779 3096 
+Q 1206 3584 1959 3584 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-73" d="M 2834 3397 
+L 2834 2853 
+Q 2591 2978 2328 3040 
+Q 2066 3103 1784 3103 
+Q 1356 3103 1142 2972 
+Q 928 2841 928 2578 
+Q 928 2378 1081 2264 
+Q 1234 2150 1697 2047 
+L 1894 2003 
+Q 2506 1872 2764 1633 
+Q 3022 1394 3022 966 
+Q 3022 478 2636 193 
+Q 2250 -91 1575 -91 
+Q 1294 -91 989 -36 
+Q 684 19 347 128 
+L 347 722 
+Q 666 556 975 473 
+Q 1284 391 1588 391 
+Q 1994 391 2212 530 
+Q 2431 669 2431 922 
+Q 2431 1156 2273 1281 
+Q 2116 1406 1581 1522 
+L 1381 1569 
+Q 847 1681 609 1914 
+Q 372 2147 372 2553 
+Q 372 3047 722 3315 
+Q 1072 3584 1716 3584 
+Q 2034 3584 2315 3537 
+Q 2597 3491 2834 3397 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-69" d="M 603 3500 
+L 1178 3500 
+L 1178 0 
+L 603 0 
+L 603 3500 
+z
+M 603 4863 
+L 1178 4863 
+L 1178 4134 
+L 603 4134 
+L 603 4863 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-74" d="M 1172 4494 
+L 1172 3500 
+L 2356 3500 
+L 2356 3053 
+L 1172 3053 
+L 1172 1153 
+Q 1172 725 1289 603 
+Q 1406 481 1766 481 
+L 2356 481 
+L 2356 0 
+L 1766 0 
+Q 1100 0 847 248 
+Q 594 497 594 1153 
+L 594 3053 
+L 172 3053 
+L 172 3500 
+L 594 3500 
+L 594 4494 
+L 1172 4494 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-6e" d="M 3513 2113 
+L 3513 0 
+L 2938 0 
+L 2938 2094 
+Q 2938 2591 2744 2837 
+Q 2550 3084 2163 3084 
+Q 1697 3084 1428 2787 
+Q 1159 2491 1159 1978 
+L 1159 0 
+L 581 0 
+L 581 3500 
+L 1159 3500 
+L 1159 2956 
+Q 1366 3272 1645 3428 
+Q 1925 3584 2291 3584 
+Q 2894 3584 3203 3211 
+Q 3513 2838 3513 2113 
+z
+" transform="scale(0.015625)"/>
+      </defs>
+      <use xlink:href="#DejaVuSans-50"/>
+      <use xlink:href="#DejaVuSans-6f" x="56.677734"/>
+      <use xlink:href="#DejaVuSans-73" x="117.859375"/>
+      <use xlink:href="#DejaVuSans-69" x="169.958984"/>
+      <use xlink:href="#DejaVuSans-74" x="197.742188"/>
+      <use xlink:href="#DejaVuSans-69" x="236.951172"/>
+      <use xlink:href="#DejaVuSans-6f" x="264.734375"/>
+      <use xlink:href="#DejaVuSans-6e" x="325.916016"/>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_2">
+    <g id="ytick_1">
+     <g id="line2d_7">
+      <defs>
+       <path id="mded8b8a71f" d="M 0 0 
+L -3.5 0 
+" style="stroke: #000000; stroke-width: 0.8"/>
+      </defs>
+      <g>
+       <use xlink:href="#mded8b8a71f" x="95.04" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_8">
+      <!-- 0 -->
+      <g transform="translate(81.6775 384.434419)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_2">
+     <g id="line2d_8">
+      <g>
+       <use xlink:href="#mded8b8a71f" x="95.04" y="322.158479" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_9">
+      <!-- 10 -->
+      <g transform="translate(75.315 325.957697)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_3">
+     <g id="line2d_9">
+      <g>
+       <use xlink:href="#mded8b8a71f" x="95.04" y="263.681757" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_10">
+      <!-- 20 -->
+      <g transform="translate(75.315 267.480976)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_4">
+     <g id="line2d_10">
+      <g>
+       <use xlink:href="#mded8b8a71f" x="95.04" y="205.205036" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_11">
+      <!-- 30 -->
+      <g transform="translate(75.315 209.004255)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-33" d="M 2597 2516 
+Q 3050 2419 3304 2112 
+Q 3559 1806 3559 1356 
+Q 3559 666 3084 287 
+Q 2609 -91 1734 -91 
+Q 1441 -91 1130 -33 
+Q 819 25 488 141 
+L 488 750 
+Q 750 597 1062 519 
+Q 1375 441 1716 441 
+Q 2309 441 2620 675 
+Q 2931 909 2931 1356 
+Q 2931 1769 2642 2001 
+Q 2353 2234 1838 2234 
+L 1294 2234 
+L 1294 2753 
+L 1863 2753 
+Q 2328 2753 2575 2939 
+Q 2822 3125 2822 3475 
+Q 2822 3834 2567 4026 
+Q 2313 4219 1838 4219 
+Q 1578 4219 1281 4162 
+Q 984 4106 628 3988 
+L 628 4550 
+Q 988 4650 1302 4700 
+Q 1616 4750 1894 4750 
+Q 2613 4750 3031 4423 
+Q 3450 4097 3450 3541 
+Q 3450 3153 3228 2886 
+Q 3006 2619 2597 2516 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_5">
+     <g id="line2d_11">
+      <g>
+       <use xlink:href="#mded8b8a71f" x="95.04" y="146.728315" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_12">
+      <!-- 40 -->
+      <g transform="translate(75.315 150.527533)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_6">
+     <g id="line2d_12">
+      <g>
+       <use xlink:href="#mded8b8a71f" x="95.04" y="88.251593" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_13">
+      <!-- 50 -->
+      <g transform="translate(75.315 92.050812)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-35" d="M 691 4666 
+L 3169 4666 
+L 3169 4134 
+L 1269 4134 
+L 1269 2991 
+Q 1406 3038 1543 3061 
+Q 1681 3084 1819 3084 
+Q 2600 3084 3056 2656 
+Q 3513 2228 3513 1497 
+Q 3513 744 3044 326 
+Q 2575 -91 1722 -91 
+Q 1428 -91 1123 -41 
+Q 819 9 494 109 
+L 494 744 
+Q 775 591 1075 516 
+Q 1375 441 1709 441 
+Q 2250 441 2565 725 
+Q 2881 1009 2881 1497 
+Q 2881 1984 2565 2268 
+Q 2250 2553 1709 2553 
+Q 1456 2553 1204 2497 
+Q 953 2441 691 2322 
+L 691 4666 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_14">
+     <!-- Temperature -->
+     <g transform="translate(66.32375 291.985275)rotate(-90)scale(0.24 -0.24)">
+      <defs>
+       <path id="DejaVuSans-54" d="M -19 4666 
+L 3928 4666 
+L 3928 4134 
+L 2272 4134 
+L 2272 0 
+L 1638 0 
+L 1638 4134 
+L -19 4134 
+L -19 4666 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-65" d="M 3597 1894 
+L 3597 1613 
+L 953 1613 
+Q 991 1019 1311 708 
+Q 1631 397 2203 397 
+Q 2534 397 2845 478 
+Q 3156 559 3463 722 
+L 3463 178 
+Q 3153 47 2828 -22 
+Q 2503 -91 2169 -91 
+Q 1331 -91 842 396 
+Q 353 884 353 1716 
+Q 353 2575 817 3079 
+Q 1281 3584 2069 3584 
+Q 2775 3584 3186 3129 
+Q 3597 2675 3597 1894 
+z
+M 3022 2063 
+Q 3016 2534 2758 2815 
+Q 2500 3097 2075 3097 
+Q 1594 3097 1305 2825 
+Q 1016 2553 972 2059 
+L 3022 2063 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-6d" d="M 3328 2828 
+Q 3544 3216 3844 3400 
+Q 4144 3584 4550 3584 
+Q 5097 3584 5394 3201 
+Q 5691 2819 5691 2113 
+L 5691 0 
+L 5113 0 
+L 5113 2094 
+Q 5113 2597 4934 2840 
+Q 4756 3084 4391 3084 
+Q 3944 3084 3684 2787 
+Q 3425 2491 3425 1978 
+L 3425 0 
+L 2847 0 
+L 2847 2094 
+Q 2847 2600 2669 2842 
+Q 2491 3084 2119 3084 
+Q 1678 3084 1418 2786 
+Q 1159 2488 1159 1978 
+L 1159 0 
+L 581 0 
+L 581 3500 
+L 1159 3500 
+L 1159 2956 
+Q 1356 3278 1631 3431 
+Q 1906 3584 2284 3584 
+Q 2666 3584 2933 3390 
+Q 3200 3197 3328 2828 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-70" d="M 1159 525 
+L 1159 -1331 
+L 581 -1331 
+L 581 3500 
+L 1159 3500 
+L 1159 2969 
+Q 1341 3281 1617 3432 
+Q 1894 3584 2278 3584 
+Q 2916 3584 3314 3078 
+Q 3713 2572 3713 1747 
+Q 3713 922 3314 415 
+Q 2916 -91 2278 -91 
+Q 1894 -91 1617 61 
+Q 1341 213 1159 525 
+z
+M 3116 1747 
+Q 3116 2381 2855 2742 
+Q 2594 3103 2138 3103 
+Q 1681 3103 1420 2742 
+Q 1159 2381 1159 1747 
+Q 1159 1113 1420 752 
+Q 1681 391 2138 391 
+Q 2594 391 2855 752 
+Q 3116 1113 3116 1747 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-72" d="M 2631 2963 
+Q 2534 3019 2420 3045 
+Q 2306 3072 2169 3072 
+Q 1681 3072 1420 2755 
+Q 1159 2438 1159 1844 
+L 1159 0 
+L 581 0 
+L 581 3500 
+L 1159 3500 
+L 1159 2956 
+Q 1341 3275 1631 3429 
+Q 1922 3584 2338 3584 
+Q 2397 3584 2469 3576 
+Q 2541 3569 2628 3553 
+L 2631 2963 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-61" d="M 2194 1759 
+Q 1497 1759 1228 1600 
+Q 959 1441 959 1056 
+Q 959 750 1161 570 
+Q 1363 391 1709 391 
+Q 2188 391 2477 730 
+Q 2766 1069 2766 1631 
+L 2766 1759 
+L 2194 1759 
+z
+M 3341 1997 
+L 3341 0 
+L 2766 0 
+L 2766 531 
+Q 2569 213 2275 61 
+Q 1981 -91 1556 -91 
+Q 1019 -91 701 211 
+Q 384 513 384 1019 
+Q 384 1609 779 1909 
+Q 1175 2209 1959 2209 
+L 2766 2209 
+L 2766 2266 
+Q 2766 2663 2505 2880 
+Q 2244 3097 1772 3097 
+Q 1472 3097 1187 3025 
+Q 903 2953 641 2809 
+L 641 3341 
+Q 956 3463 1253 3523 
+Q 1550 3584 1831 3584 
+Q 2591 3584 2966 3190 
+Q 3341 2797 3341 1997 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-75" d="M 544 1381 
+L 544 3500 
+L 1119 3500 
+L 1119 1403 
+Q 1119 906 1312 657 
+Q 1506 409 1894 409 
+Q 2359 409 2629 706 
+Q 2900 1003 2900 1516 
+L 2900 3500 
+L 3475 3500 
+L 3475 0 
+L 2900 0 
+L 2900 538 
+Q 2691 219 2414 64 
+Q 2138 -91 1772 -91 
+Q 1169 -91 856 284 
+Q 544 659 544 1381 
+z
+M 1991 3584 
+L 1991 3584 
+z
+" transform="scale(0.015625)"/>
+      </defs>
+      <use xlink:href="#DejaVuSans-54"/>
+      <use xlink:href="#DejaVuSans-65" x="44.083984"/>
+      <use xlink:href="#DejaVuSans-6d" x="105.607422"/>
+      <use xlink:href="#DejaVuSans-70" x="203.019531"/>
+      <use xlink:href="#DejaVuSans-65" x="266.496094"/>
+      <use xlink:href="#DejaVuSans-72" x="328.019531"/>
+      <use xlink:href="#DejaVuSans-61" x="369.132812"/>
+      <use xlink:href="#DejaVuSans-74" x="430.412109"/>
+      <use xlink:href="#DejaVuSans-75" x="469.621094"/>
+      <use xlink:href="#DejaVuSans-72" x="533"/>
+      <use xlink:href="#DejaVuSans-65" x="571.863281"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_13">
+    <path d="M 95.04 380.6352 
+L 222.779077 380.529075 
+L 235.729582 380.282199 
+L 243.970813 379.915133 
+L 250.446066 379.406046 
+L 255.744 378.766883 
+L 260.453275 377.962709 
+L 264.57389 377.018844 
+L 268.105846 375.978796 
+L 271.637802 374.686465 
+L 274.581099 373.381548 
+L 277.524396 371.835995 
+L 280.467692 370.015621 
+L 283.410989 367.883451 
+L 285.765626 365.921436 
+L 288.120264 363.713694 
+L 290.474901 361.238701 
+L 292.829538 358.474235 
+L 295.184176 355.397908 
+L 297.538813 351.987431 
+L 299.893451 348.220835 
+L 302.248088 344.077154 
+L 304.602725 339.526635 
+L 306.957363 334.561291 
+L 309.312 329.163589 
+L 312.255297 321.78728 
+L 315.198593 313.692617 
+L 318.14189 304.866826 
+L 321.085187 295.305618 
+L 324.028484 285.00832 
+L 327.56044 271.724356 
+L 331.092396 257.491814 
+L 335.213011 239.815134 
+L 339.922286 218.452801 
+L 345.808879 190.573925 
+L 356.404747 140.249826 
+L 360.525363 121.846751 
+L 364.057319 107.108755 
+L 367.000615 95.761174 
+L 369.355253 87.403982 
+L 371.70989 79.767014 
+L 374.064527 72.918457 
+L 375.830505 68.33741 
+L 377.596484 64.262089 
+L 379.362462 60.725229 
+L 381.12844 57.732218 
+L 382.894418 55.297498 
+L 384.071736 53.990912 
+L 385.249055 52.941263 
+L 386.426374 52.151061 
+L 387.603692 51.622212 
+L 388.781011 51.355994 
+L 389.95833 51.353058 
+L 391.135648 51.61342 
+L 392.312967 52.136471 
+L 393.490286 52.920959 
+L 394.667604 53.96499 
+L 395.844923 55.266207 
+L 397.022242 56.828276 
+L 398.78822 59.649475 
+L 400.554198 63.018479 
+L 402.320176 66.917588 
+L 404.086154 71.326333 
+L 406.440791 77.95742 
+L 408.795429 85.392994 
+L 411.150066 93.566486 
+L 414.093363 104.71072 
+L 417.036659 116.760021 
+L 420.568615 132.184312 
+L 425.27789 153.916868 
+L 434.10778 196.168455 
+L 440.583033 226.545841 
+L 445.292308 247.498702 
+L 449.412923 264.715831 
+L 452.944879 278.497039 
+L 456.476835 291.278339 
+L 459.420132 301.142925 
+L 462.363429 310.276618 
+L 465.306725 318.677631 
+L 468.250022 326.354584 
+L 471.193319 333.324327 
+L 473.547956 338.392009 
+L 475.902593 343.036068 
+L 478.257231 347.278437 
+L 480.611868 351.138941 
+L 482.966505 354.63858 
+L 485.321143 357.799137 
+L 487.67578 360.642848 
+L 490.030418 363.192205 
+L 492.385055 365.456926 
+L 494.739692 367.467195 
+L 497.682989 369.660056 
+L 500.626286 371.534221 
+L 503.569582 373.127028 
+L 506.512879 374.473205 
+L 509.456176 375.604492 
+L 512.988132 376.714066 
+L 516.520088 377.600877 
+L 520.640703 378.406028 
+L 525.349978 379.087343 
+L 530.647912 379.624016 
+L 537.123165 380.047007 
+L 545.364396 380.350594 
+L 557.137582 380.540892 
+L 578.917978 380.625338 
+L 683.699341 380.6352 
+L 683.699341 380.6352 
+" clip-path="url(#p24d58054f1)" style="fill: none; stroke: #000000; stroke-width: 3; stroke-linecap: square"/>
+    <defs>
+     <path id="m7b5e1d1292" d="M 0 -3 
+L -0.673542 -0.927051 
+L -2.85317 -0.927051 
+L -1.089814 0.354102 
+L -1.763356 2.427051 
+L -0 1.145898 
+L 1.763356 2.427051 
+L 1.089814 0.354102 
+L 2.85317 -0.927051 
+L 0.673542 -0.927051 
+z
+" style="stroke: #000000; stroke-linejoin: bevel"/>
+    </defs>
+    <g clip-path="url(#p24d58054f1)">
+     <use xlink:href="#m7b5e1d1292" x="95.04" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="95.628659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="96.217319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="96.805978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="97.394637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="97.983297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="98.571956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="99.160615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="99.749275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="100.337934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="100.926593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="101.515253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="102.103912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="102.692571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="103.281231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="103.86989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="104.458549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="105.047209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="105.635868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="106.224527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="106.813187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="107.401846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="107.990505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="108.579165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="109.167824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="109.756484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="110.345143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="110.933802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="111.522462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="112.111121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="112.69978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="113.28844" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="113.877099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="114.465758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="115.054418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="115.643077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="116.231736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="116.820396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="117.409055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="117.997714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="118.586374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="119.175033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="119.763692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="120.352352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="120.941011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="121.52967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="122.11833" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="122.706989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="123.295648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="123.884308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="124.472967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="125.061626" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="125.650286" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="126.238945" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="126.827604" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="127.416264" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="128.004923" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="128.593582" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="129.182242" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="129.770901" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="130.35956" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="130.94822" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="131.536879" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="132.125538" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="132.714198" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="133.302857" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="133.891516" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="134.480176" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="135.068835" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="135.657495" y="380.635197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="136.246154" y="380.635197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="136.834813" y="380.635197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="137.423473" y="380.635197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="138.012132" y="380.635196" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="138.600791" y="380.635196" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="139.189451" y="380.635196" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="139.77811" y="380.635195" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="140.366769" y="380.635195" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="140.955429" y="380.635194" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="141.544088" y="380.635194" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="142.132747" y="380.635193" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="142.721407" y="380.635193" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="143.310066" y="380.635192" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="143.898725" y="380.635191" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="144.487385" y="380.635191" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="145.076044" y="380.63519" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="145.664703" y="380.635189" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="146.253363" y="380.635188" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="146.842022" y="380.635187" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="147.430681" y="380.635186" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="148.019341" y="380.635184" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="148.608" y="380.635183" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="149.196659" y="380.635182" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="149.785319" y="380.63518" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="150.373978" y="380.635178" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="150.962637" y="380.635177" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="151.551297" y="380.635175" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="152.139956" y="380.635172" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="152.728615" y="380.63517" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="153.317275" y="380.635168" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="153.905934" y="380.635165" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="154.494593" y="380.635162" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="155.083253" y="380.635159" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="155.671912" y="380.635155" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="156.260571" y="380.635152" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="156.849231" y="380.635148" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="157.43789" y="380.635143" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="158.026549" y="380.635139" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="158.615209" y="380.635134" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="159.203868" y="380.635128" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="159.792527" y="380.635123" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="160.381187" y="380.635116" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="160.969846" y="380.63511" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="161.558505" y="380.635102" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="162.147165" y="380.635094" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="162.735824" y="380.635086" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="163.324484" y="380.635077" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="163.913143" y="380.635067" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="164.501802" y="380.635056" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="165.090462" y="380.635045" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="165.679121" y="380.635033" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="166.26778" y="380.635019" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="166.85644" y="380.635005" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="167.445099" y="380.63499" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="168.033758" y="380.634973" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="168.622418" y="380.634956" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="169.211077" y="380.634937" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="169.799736" y="380.634916" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="170.388396" y="380.634894" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="170.977055" y="380.634871" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="171.565714" y="380.634845" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="172.154374" y="380.634818" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="172.743033" y="380.634788" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="173.331692" y="380.634757" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="173.920352" y="380.634723" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="174.509011" y="380.634687" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="175.09767" y="380.634648" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="175.68633" y="380.634606" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="176.274989" y="380.634561" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="176.863648" y="380.634513" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="177.452308" y="380.634462" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="178.040967" y="380.634406" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="178.629626" y="380.634347" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="179.218286" y="380.634284" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="179.806945" y="380.634216" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="180.395604" y="380.634143" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="180.984264" y="380.634065" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="181.572923" y="380.633982" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="182.161582" y="380.633893" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="182.750242" y="380.633798" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="183.338901" y="380.633695" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="183.92756" y="380.633586" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="184.51622" y="380.633469" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="185.104879" y="380.633345" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="185.693538" y="380.633211" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="186.282198" y="380.633068" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="186.870857" y="380.632916" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="187.459516" y="380.632753" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="188.048176" y="380.63258" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="188.636835" y="380.632394" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="189.225495" y="380.632195" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="189.814154" y="380.631982" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="190.402813" y="380.631755" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="190.991473" y="380.631513" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="191.580132" y="380.631255" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="192.168791" y="380.63098" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="192.757451" y="380.630686" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="193.34611" y="380.630373" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="193.934769" y="380.63004" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="194.523429" y="380.629685" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="195.112088" y="380.629306" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="195.700747" y="380.628903" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="196.289407" y="380.628474" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="196.878066" y="380.628018" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="197.466725" y="380.627531" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="198.055385" y="380.627014" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="198.644044" y="380.626464" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="199.232703" y="380.625878" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="199.821363" y="380.625255" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="200.410022" y="380.624593" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="200.998681" y="380.623889" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="201.587341" y="380.623141" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="202.176" y="380.622346" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="202.764659" y="380.621501" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="203.353319" y="380.620604" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="203.941978" y="380.619651" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="204.530637" y="380.618639" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="205.119297" y="380.617565" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="205.707956" y="380.616424" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="206.296615" y="380.615214" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="206.885275" y="380.61393" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="207.473934" y="380.612566" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="208.062593" y="380.611117" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="208.651253" y="380.60958" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="209.239912" y="380.607949" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="209.828571" y="380.606222" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="210.417231" y="380.604392" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="211.00589" y="380.602453" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="211.594549" y="380.6004" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="212.183209" y="380.598227" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="212.771868" y="380.595926" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="213.360527" y="380.593491" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="213.949187" y="380.590915" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="214.537846" y="380.58819" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="215.126505" y="380.585307" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="215.715165" y="380.582259" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="216.303824" y="380.579037" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="216.892484" y="380.575631" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="217.481143" y="380.572031" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="218.069802" y="380.568228" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="218.658462" y="380.56421" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="219.247121" y="380.559967" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="219.83578" y="380.555487" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="220.42444" y="380.550756" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="221.013099" y="380.545764" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="221.601758" y="380.540496" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="222.190418" y="380.534937" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="222.779077" y="380.529075" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="223.367736" y="380.522892" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="223.956396" y="380.516373" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="224.545055" y="380.509501" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="225.133714" y="380.502259" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="225.722374" y="380.494626" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="226.311033" y="380.486591" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="226.899692" y="380.478067" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="227.488352" y="380.4691" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="228.077011" y="380.45966" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="228.66567" y="380.449726" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="229.25433" y="380.439273" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="229.842989" y="380.428279" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="230.431648" y="380.416715" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="231.020308" y="380.404556" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="231.608967" y="380.391773" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="232.197626" y="380.378336" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="232.786286" y="380.364216" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="233.374945" y="380.34938" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="233.963604" y="380.333796" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="234.552264" y="380.317428" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="235.140923" y="380.300242" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="235.729582" y="380.282199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="236.318242" y="380.263262" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="236.906901" y="380.243389" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="237.49556" y="380.22254" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="238.08422" y="380.200671" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="238.672879" y="380.177736" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="239.261538" y="380.15369" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="239.850198" y="380.128484" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="240.438857" y="380.102067" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="241.027516" y="380.07439" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="241.616176" y="380.045396" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="242.204835" y="380.015032" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="242.793495" y="379.983241" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="243.382154" y="379.949961" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="243.970813" y="379.915133" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="244.559473" y="379.8787" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="245.148132" y="379.840587" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="245.736791" y="379.800663" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="246.325451" y="379.758628" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="246.91411" y="379.714616" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="247.502769" y="379.668611" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="248.091429" y="379.62055" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="248.680088" y="379.57035" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="249.268747" y="379.517927" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="249.857407" y="379.463189" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="250.446066" y="379.406046" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="251.034725" y="379.346402" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="251.623385" y="379.28416" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="252.212044" y="379.219219" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="252.800703" y="379.151476" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="253.389363" y="379.080825" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="253.978022" y="379.007154" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="254.566681" y="378.930352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="255.155341" y="378.850302" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="255.744" y="378.766883" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="256.332659" y="378.679972" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="256.921319" y="378.589443" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="257.509978" y="378.495165" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="258.098637" y="378.397003" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="258.687297" y="378.29482" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="259.275956" y="378.188474" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="259.864615" y="378.07782" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="260.453275" y="377.962709" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="261.041934" y="377.842985" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="261.630593" y="377.71849" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="262.219253" y="377.58906" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="262.807912" y="377.45453" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="263.396571" y="377.314746" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="263.985231" y="377.169565" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="264.57389" y="377.018844" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="265.162549" y="376.861033" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="265.751209" y="376.697311" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="266.339868" y="376.527448" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="266.928527" y="376.351219" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="267.517187" y="376.168404" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="268.105846" y="375.978796" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="268.694505" y="375.782187" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="269.283165" y="375.578363" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="269.871824" y="375.367105" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="270.460484" y="375.14819" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="271.049143" y="374.921388" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="271.637802" y="374.686465" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="272.226462" y="374.443184" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="272.815121" y="374.1913" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="273.40378" y="373.930569" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="273.99244" y="373.660737" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="274.581099" y="373.381548" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="275.169758" y="373.092744" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="275.758418" y="372.794057" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="276.347077" y="372.48522" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="276.935736" y="372.165959" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="277.524396" y="371.835995" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="278.113055" y="371.495046" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="278.701714" y="371.142825" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="279.290374" y="370.779044" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="279.879033" y="370.403408" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="280.467692" y="370.015621" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="281.056352" y="369.615385" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="281.645011" y="369.202399" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="282.23367" y="368.776397" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="282.82233" y="368.336997" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="283.410989" y="367.883451" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="283.999648" y="367.415122" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="284.588308" y="366.932002" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="285.176967" y="366.434194" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="285.765626" y="365.921436" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="286.354286" y="365.393349" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="286.942945" y="364.849592" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="287.531604" y="364.289822" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="288.120264" y="363.713694" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="288.708923" y="363.120865" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="289.297582" y="362.510987" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="289.886242" y="361.883715" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="290.474901" y="361.238701" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="291.06356" y="360.575595" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="291.65222" y="359.894049" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="292.240879" y="359.193713" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="292.829538" y="358.474235" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="293.418198" y="357.735265" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="294.006857" y="356.976453" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="294.595516" y="356.197451" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="295.184176" y="355.397908" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="295.772835" y="354.577479" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="296.361495" y="353.735819" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="296.950154" y="352.872583" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="297.538813" y="351.987431" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="298.127473" y="351.080018" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="298.716132" y="350.150006" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="299.304791" y="349.197056" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="299.893451" y="348.220835" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="300.48211" y="347.22101" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="301.070769" y="346.197621" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="301.659429" y="345.149753" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="302.248088" y="344.077154" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="302.836747" y="342.976741" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="303.425407" y="341.852318" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="304.014066" y="340.702326" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="304.602725" y="339.526635" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="305.191385" y="338.324968" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="305.780044" y="337.097031" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="306.368703" y="335.842556" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="306.957363" y="334.561291" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="307.546022" y="333.252993" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="308.134681" y="331.917426" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="308.723341" y="330.554364" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="309.312" y="329.163589" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="309.900659" y="327.744898" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="310.489319" y="326.298097" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="311.077978" y="324.823005" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="311.666637" y="323.319451" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="312.255297" y="321.78728" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="312.843956" y="320.226348" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="313.432615" y="318.636525" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="314.021275" y="317.017695" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="314.609934" y="315.369755" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="315.198593" y="313.692617" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="315.787253" y="311.986204" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="316.375912" y="310.250455" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="316.964571" y="308.485326" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="317.553231" y="306.690786" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="318.14189" y="304.866826" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="318.730549" y="303.013449" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="319.319209" y="301.130689" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="319.907868" y="299.218588" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="320.496527" y="297.27702" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="321.085187" y="295.305618" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="321.673846" y="293.304238" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="322.262505" y="291.273326" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="322.851165" y="289.213507" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="323.439824" y="287.125119" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="324.028484" y="285.00832" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="324.617143" y="282.863303" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="325.205802" y="280.690313" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="325.794462" y="278.489622" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="326.383121" y="276.26152" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="326.97178" y="274.006321" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="327.56044" y="271.724356" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="328.149099" y="269.415978" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="328.737758" y="267.081562" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="329.326418" y="264.721504" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="329.915077" y="262.336227" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="330.503736" y="259.926175" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="331.092396" y="257.491814" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="331.681055" y="255.033639" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="332.269714" y="252.552166" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="332.858374" y="250.047935" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="333.447033" y="247.521514" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="334.035692" y="244.973492" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="334.624352" y="242.404485" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="335.213011" y="239.815134" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="335.80167" y="237.206103" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="336.39033" y="234.57808" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="336.978989" y="231.931775" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="337.567648" y="229.267917" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="338.156308" y="226.587251" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="338.744967" y="223.890552" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="339.333626" y="221.178725" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="339.922286" y="218.452801" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="340.510945" y="215.710209" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="341.099604" y="212.955256" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="341.688264" y="210.188644" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="342.276923" y="207.4112" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="342.865582" y="204.62388" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="343.454242" y="201.827662" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="344.042901" y="199.02354" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="344.63156" y="196.212522" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="345.22022" y="193.395635" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="345.808879" y="190.573925" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="346.397538" y="187.748455" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="346.986198" y="184.920308" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="347.574857" y="182.090586" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="348.163516" y="179.260406" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="348.752176" y="176.430903" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="349.340835" y="173.603227" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="349.929495" y="170.778542" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="350.518154" y="167.958026" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="351.106813" y="165.142873" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="351.695473" y="162.334287" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="352.284132" y="159.533484" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="352.872791" y="156.74169" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="353.461451" y="153.960143" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="354.05011" y="151.190087" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="354.638769" y="148.432772" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="355.227429" y="145.689452" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="355.816088" y="142.961381" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="356.404747" y="140.249826" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="356.993407" y="137.556082" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="357.582066" y="134.881526" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="358.170725" y="132.227646" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="358.759385" y="129.594563" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="359.348044" y="126.987828" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="359.936703" y="124.404411" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="360.525363" y="121.846751" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="361.114022" y="119.315961" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="361.702681" y="116.813261" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="362.291341" y="114.33992" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="362.88" y="111.897212" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="363.468659" y="109.486404" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="364.057319" y="107.108755" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="364.645978" y="104.765513" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="365.234637" y="102.457914" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="365.823297" y="100.187186" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="366.411956" y="97.954539" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="367.000615" y="95.761174" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="367.589275" y="93.608275" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="368.177934" y="91.497012" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="368.766593" y="89.428536" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="369.355253" y="87.403982" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="369.943912" y="85.424467" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="370.532571" y="83.491086" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="371.121231" y="81.604917" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="371.70989" y="79.767014" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="372.298549" y="77.978415" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="372.887209" y="76.240133" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="373.475868" y="74.553157" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="374.064527" y="72.918457" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="374.653187" y="71.336983" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="375.241846" y="69.809702" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="375.830505" y="68.33741" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="376.419165" y="66.921011" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="377.007824" y="65.561731" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="377.596484" y="64.262089" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="378.185143" y="63.023603" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="378.773802" y="61.844703" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="379.362462" y="60.725229" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="379.951121" y="59.666379" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="380.53978" y="58.668544" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="381.12844" y="57.732218" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="381.717099" y="56.857931" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="382.305758" y="56.046199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="382.894418" y="55.297498" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="383.483077" y="54.612267" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="384.071736" y="53.990912" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="384.660396" y="53.433799" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="385.249055" y="52.941263" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="385.837714" y="52.513597" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="386.426374" y="52.151061" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="387.015033" y="51.853873" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="387.603692" y="51.622212" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="388.192352" y="51.456219" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="388.781011" y="51.355994" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="389.36967" y="51.3216" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="389.95833" y="51.353058" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="390.546989" y="51.45035" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="391.135648" y="51.61342" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="391.724308" y="51.842172" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="392.312967" y="52.136471" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="392.901626" y="52.49614" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="393.490286" y="52.920959" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="394.078945" y="53.410669" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="394.667604" y="53.96499" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="395.256264" y="54.583518" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="395.844923" y="55.266207" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="396.433582" y="56.01038" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="397.022242" y="56.828276" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="397.610901" y="57.706704" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="398.19956" y="58.647396" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="398.78822" y="59.649475" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="399.376879" y="60.712435" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="399.965538" y="61.835651" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="400.554198" y="63.018479" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="401.142857" y="64.260234" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="401.731516" y="65.560191" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="402.320176" y="66.917588" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="402.908835" y="68.33163" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="403.497495" y="69.801495" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="404.086154" y="71.326333" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="404.674813" y="72.905264" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="405.263473" y="74.537382" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="405.852132" y="76.221754" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="406.440791" y="77.95742" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="407.029451" y="79.743396" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="407.61811" y="81.578675" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="408.206769" y="83.462225" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="408.795429" y="85.392994" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="409.384088" y="87.369909" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="409.972747" y="89.391875" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="410.561407" y="91.457779" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="411.150066" y="93.566486" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="411.738725" y="95.716843" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="412.327385" y="97.907671" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="412.916044" y="100.137762" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="413.504703" y="102.405852" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="414.093363" y="104.71072" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="414.682022" y="107.051049" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="415.270681" y="109.427081" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="415.859341" y="111.83916" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="416.448" y="114.284395" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="417.036659" y="116.760021" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="417.625319" y="119.264866" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="418.213978" y="121.797687" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="418.802637" y="124.357297" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="419.391297" y="126.942453" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="419.979956" y="129.551886" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="420.568615" y="132.184312" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="421.157275" y="134.838439" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="421.745934" y="137.512977" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="422.334593" y="140.206635" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="422.923253" y="142.918126" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="423.511912" y="145.646168" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="424.100571" y="148.389485" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="424.689231" y="151.146806" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="425.27789" y="153.916868" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="425.866549" y="156.698416" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="426.455209" y="159.490203" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="427.043868" y="162.290992" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="427.632527" y="165.099557" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="428.221187" y="167.914683" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="428.809846" y="170.735168" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="429.398505" y="173.559819" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="429.987165" y="176.38746" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="430.575824" y="179.216923" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="431.164484" y="182.047052" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="431.753143" y="184.876704" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="432.341802" y="187.704746" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="432.930462" y="190.530062" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="433.519121" y="193.351553" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="434.10778" y="196.168455" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="434.69644" y="198.978801" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="435.285099" y="201.782497" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="435.873758" y="204.578244" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="436.462418" y="207.365077" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="437.051077" y="210.142047" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="437.639736" y="212.908222" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="438.228396" y="215.662688" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="438.817055" y="218.404548" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="439.405714" y="221.132926" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="439.994374" y="223.846968" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="440.583033" y="226.545841" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="441.171692" y="229.228736" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="441.760352" y="231.894869" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="442.349011" y="234.543478" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="442.93767" y="237.173825" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="443.52633" y="239.785198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="444.114989" y="242.376908" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="444.703648" y="244.948289" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="445.292308" y="247.498702" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="445.880967" y="250.027529" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="446.469626" y="252.53418" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="447.058286" y="255.018086" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="447.646945" y="257.478706" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="448.235604" y="259.915523" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="448.824264" y="262.328049" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="449.412923" y="264.715831" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="450.001582" y="267.078443" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="450.590242" y="269.415509" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="451.178901" y="271.72674" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="451.76756" y="274.011574" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="452.35622" y="276.268685" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="452.944879" y="278.497039" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="453.533538" y="280.697036" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="454.122198" y="282.869371" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="454.710857" y="285.013975" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="455.299516" y="287.1305" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="455.888176" y="289.218693" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="456.476835" y="291.278339" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="457.065495" y="293.309257" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="457.654154" y="295.311293" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="458.242813" y="297.284318" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="458.831473" y="299.228224" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="459.420132" y="301.142925" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="460.008791" y="303.028353" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="460.597451" y="304.884458" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="461.18611" y="306.71121" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="461.774769" y="308.508595" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="462.363429" y="310.276618" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="462.952088" y="312.015301" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="463.540747" y="313.724681" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="464.129407" y="315.404813" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="464.718066" y="317.055768" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="465.306725" y="318.677631" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="465.895385" y="320.270503" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="466.484044" y="321.834498" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="467.072703" y="323.369748" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="467.661363" y="324.876392" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="468.250022" y="326.354584" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="468.838681" y="327.80449" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="469.427341" y="329.226273" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="470.016" y="330.620141" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="470.604659" y="331.986198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="471.193319" y="333.324327" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="471.781978" y="334.635537" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="472.370637" y="335.913578" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="472.959297" y="337.166" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="473.547956" y="338.392009" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="474.136615" y="339.591721" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="474.725275" y="340.765442" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="475.313934" y="341.913458" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="475.902593" y="343.036068" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="476.491253" y="344.133567" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="477.079912" y="345.206256" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="477.668571" y="346.254442" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="478.257231" y="347.278437" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="478.84589" y="348.27856" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="479.434549" y="349.255133" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="480.023209" y="350.208483" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="480.611868" y="351.138941" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="481.200527" y="352.046842" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="481.789187" y="352.932521" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="482.377846" y="353.79632" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="482.966505" y="354.63858" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="483.555165" y="355.459645" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="484.143824" y="356.259861" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="484.732484" y="357.039576" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="485.321143" y="357.799137" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="485.909802" y="358.538894" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="486.498462" y="359.259197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="487.087121" y="359.960398" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="487.67578" y="360.642848" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="488.26444" y="361.3069" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="488.853099" y="361.952908" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="489.441758" y="362.581226" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="490.030418" y="363.192205" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="490.619077" y="363.786196" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="491.207736" y="364.359297" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="491.796396" y="364.916095" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="492.385055" y="365.456926" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="492.973714" y="365.982122" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="493.562374" y="366.492014" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="494.151033" y="366.98693" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="494.739692" y="367.467195" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="495.328352" y="367.933135" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="495.917011" y="368.385072" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="496.50567" y="368.823327" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="497.09433" y="369.248217" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="497.682989" y="369.660056" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="498.271648" y="370.059154" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="498.860308" y="370.445819" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="499.448967" y="370.820353" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="500.037626" y="371.183056" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="500.626286" y="371.534221" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="501.214945" y="371.874141" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="501.803604" y="372.203101" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="502.392264" y="372.521385" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="502.980923" y="372.829269" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="503.569582" y="373.127028" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="504.158242" y="373.41493" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="504.746901" y="373.69324" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="505.33556" y="373.962219" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="505.92422" y="374.222123" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="506.512879" y="374.473205" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="507.101538" y="374.715715" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="507.690198" y="374.949898" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="508.278857" y="375.176002" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="508.867516" y="375.394199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="509.456176" y="375.604492" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="510.044835" y="375.806942" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="510.633495" y="376.00193" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="511.222154" y="376.189896" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="511.810813" y="376.37111" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="512.399473" y="376.545768" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="512.988132" y="376.714066" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="513.576791" y="376.876198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="514.165451" y="377.032352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="514.75411" y="377.182717" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="515.342769" y="377.327474" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="515.931429" y="377.466803" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="516.520088" y="377.600877" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="517.108747" y="377.729866" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="517.697407" y="377.853938" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="518.286066" y="377.973252" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="518.874725" y="378.087967" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="519.463385" y="378.198236" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="520.052044" y="378.304208" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="520.640703" y="378.406028" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="521.229363" y="378.503838" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="521.818022" y="378.597777" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="522.406681" y="378.687977" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="522.995341" y="378.774569" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="523.584" y="378.85768" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="524.172659" y="378.937434" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="524.761319" y="379.013949" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="525.349978" y="379.087343" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="525.938637" y="379.157726" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="526.527297" y="379.225209" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="527.115956" y="379.289898" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="527.704615" y="379.35188" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="528.293275" y="379.411215" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="528.881934" y="379.467976" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="529.470593" y="379.52228" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="530.059253" y="379.574259" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="530.647912" y="379.624016" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="531.236571" y="379.671635" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="531.825231" y="379.717198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="532.41389" y="379.760785" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="533.002549" y="379.802472" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="533.591209" y="379.842333" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="534.179868" y="379.88044" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="534.768527" y="379.916862" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="535.357187" y="379.951666" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="535.945846" y="379.984917" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="536.534505" y="380.016677" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="537.123165" y="380.047007" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="537.711824" y="380.075965" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="538.300484" y="380.103607" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="538.889143" y="380.129987" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="539.477802" y="380.155158" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="540.066462" y="380.179171" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="540.655121" y="380.202073" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="541.24378" y="380.223913" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="541.83244" y="380.244734" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="542.421099" y="380.26458" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="543.009758" y="380.283493" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="543.598418" y="380.301513" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="544.187077" y="380.318678" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="544.775736" y="380.335026" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="545.364396" y="380.350594" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="545.953055" y="380.365414" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="546.541714" y="380.379519" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="547.130374" y="380.392965" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="547.719033" y="380.405647" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="548.307692" y="380.41773" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="548.896352" y="380.429217" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="549.485011" y="380.440136" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="550.07367" y="380.450515" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="550.66233" y="380.460377" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="551.250989" y="380.469745" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="551.839648" y="380.478644" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="552.428308" y="380.487094" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="553.016967" y="380.495117" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="553.605626" y="380.502732" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="554.194286" y="380.50996" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="554.782945" y="380.516817" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="555.371604" y="380.523323" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="555.960264" y="380.529493" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="556.548923" y="380.535345" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="557.137582" y="380.540892" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="557.726242" y="380.54615" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="558.314901" y="380.551134" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="558.90356" y="380.555855" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="559.49222" y="380.560328" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="560.080879" y="380.564565" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="560.669538" y="380.568577" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="561.258198" y="380.572375" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="561.846857" y="380.575971" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="562.435516" y="380.579374" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="563.024176" y="380.582594" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="563.612835" y="380.585641" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="564.201495" y="380.588523" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="564.790154" y="380.591249" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="565.378813" y="380.593827" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="565.967473" y="380.596259" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="566.556132" y="380.598542" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="567.144791" y="380.600692" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="567.733451" y="380.602724" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="568.32211" y="380.604642" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="568.910769" y="380.606453" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="569.499429" y="380.608162" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="570.088088" y="380.609774" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="570.676747" y="380.611295" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="571.265407" y="380.61273" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="571.854066" y="380.614083" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="572.442725" y="380.615359" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="573.031385" y="380.616561" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="573.620044" y="380.617694" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="574.208703" y="380.618761" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="574.797363" y="380.619767" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="575.386022" y="380.620714" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="575.974681" y="380.621606" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="576.563341" y="380.622446" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="577.152" y="380.623236" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="577.740659" y="380.62398" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="578.329319" y="380.624679" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="578.917978" y="380.625338" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="579.506637" y="380.625957" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="580.095297" y="380.626539" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="580.683956" y="380.627086" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="581.272615" y="380.627601" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="581.861275" y="380.628084" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="582.449934" y="380.628539" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="583.038593" y="380.628965" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="583.627253" y="380.629366" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="584.215912" y="380.629743" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="584.804571" y="380.630096" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="585.393231" y="380.630426" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="585.98189" y="380.630735" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="586.570549" y="380.631024" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="587.159209" y="380.631296" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="587.747868" y="380.631551" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="588.336527" y="380.63179" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="588.925187" y="380.632014" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="589.513846" y="380.632223" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="590.102505" y="380.63242" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="590.691165" y="380.632604" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="591.279824" y="380.632776" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="591.868484" y="380.632938" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="592.457143" y="380.633089" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="593.045802" y="380.63323" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="593.634462" y="380.633362" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="594.223121" y="380.633486" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="594.81178" y="380.633602" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="595.40044" y="380.63371" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="595.989099" y="380.633811" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="596.577758" y="380.633906" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="597.166418" y="380.633995" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="597.755077" y="380.634077" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="598.343736" y="380.634154" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="598.932396" y="380.634226" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="599.521055" y="380.634294" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="600.109714" y="380.634357" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="600.698374" y="380.634415" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="601.287033" y="380.63447" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="601.875692" y="380.634521" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="602.464352" y="380.634569" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="603.053011" y="380.634613" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="603.64167" y="380.634655" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="604.23033" y="380.634693" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="604.818989" y="380.634729" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="605.407648" y="380.634762" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="605.996308" y="380.634794" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="606.584967" y="380.634823" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="607.173626" y="380.634849" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="607.762286" y="380.634875" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="608.350945" y="380.634898" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="608.939604" y="380.63492" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="609.528264" y="380.63494" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="610.116923" y="380.634959" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="610.705582" y="380.634976" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="611.294242" y="380.634993" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="611.882901" y="380.635008" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="612.47156" y="380.635022" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="613.06022" y="380.635035" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="613.648879" y="380.635047" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="614.237538" y="380.635058" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="614.826198" y="380.635069" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="615.414857" y="380.635078" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="616.003516" y="380.635088" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="616.592176" y="380.635096" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="617.180835" y="380.635104" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="617.769495" y="380.635111" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="618.358154" y="380.635118" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="618.946813" y="380.635124" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="619.535473" y="380.63513" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="620.124132" y="380.635135" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="620.712791" y="380.63514" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="621.301451" y="380.635144" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="621.89011" y="380.635149" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="622.478769" y="380.635153" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="623.067429" y="380.635156" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="623.656088" y="380.63516" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="624.244747" y="380.635163" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="624.833407" y="380.635166" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="625.422066" y="380.635168" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="626.010725" y="380.635171" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="626.599385" y="380.635173" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="627.188044" y="380.635175" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="627.776703" y="380.635177" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="628.365363" y="380.635179" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="628.954022" y="380.63518" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="629.542681" y="380.635182" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="630.131341" y="380.635183" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="630.72" y="380.635185" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="631.308659" y="380.635186" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="631.897319" y="380.635187" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="632.485978" y="380.635188" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="633.074637" y="380.635189" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="633.663297" y="380.63519" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="634.251956" y="380.635191" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="634.840615" y="380.635191" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="635.429275" y="380.635192" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="636.017934" y="380.635193" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="636.606593" y="380.635193" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="637.195253" y="380.635194" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="637.783912" y="380.635194" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="638.372571" y="380.635195" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="638.961231" y="380.635195" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="639.54989" y="380.635196" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="640.138549" y="380.635196" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="640.727209" y="380.635196" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="641.315868" y="380.635197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="641.904527" y="380.635197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="642.493187" y="380.635197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="643.081846" y="380.635197" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="643.670505" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="644.259165" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="644.847824" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="645.436484" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="646.025143" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="646.613802" y="380.635198" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="647.202462" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="647.791121" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="648.37978" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="648.96844" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="649.557099" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="650.145758" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="650.734418" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="651.323077" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="651.911736" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="652.500396" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="653.089055" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="653.677714" y="380.635199" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="654.266374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="654.855033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="655.443692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="656.032352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="656.621011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="657.20967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="657.79833" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="658.386989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="658.975648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="659.564308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="660.152967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="660.741626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="661.330286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="661.918945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="662.507604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="663.096264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="663.684923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="664.273582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="664.862242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="665.450901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="666.03956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="666.62822" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="667.216879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="667.805538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="668.394198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="668.982857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="669.571516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="670.160176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="670.748835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="671.337495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="671.926154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="672.514813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="673.103473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="673.692132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="674.280791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="674.869451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="675.45811" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="676.046769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="676.635429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="677.224088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="677.812747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="678.401407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="678.990066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="679.578725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="680.167385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="680.756044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="681.344703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="681.933363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="682.522022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="683.110681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m7b5e1d1292" x="683.699341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+    </g>
+   </g>
+   <g id="patch_3">
+    <path d="M 95.04 380.6352 
+L 95.04 51.3216 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_4">
+    <path d="M 684.288 380.6352 
+L 684.288 51.3216 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_5">
+    <path d="M 95.04 380.6352 
+L 684.288 380.6352 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_6">
+    <path d="M 95.04 51.3216 
+L 684.288 51.3216 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="text_15">
+    <!-- T = 10000 -->
+    <g transform="translate(326.4765 45.3216)scale(0.24 -0.24)">
+     <defs>
+      <path id="DejaVuSans-20" transform="scale(0.015625)"/>
+      <path id="DejaVuSans-3d" d="M 678 2906 
+L 4684 2906 
+L 4684 2381 
+L 678 2381 
+L 678 2906 
+z
+M 678 1631 
+L 4684 1631 
+L 4684 1100 
+L 678 1100 
+L 678 1631 
+z
+" transform="scale(0.015625)"/>
+     </defs>
+     <use xlink:href="#DejaVuSans-54"/>
+     <use xlink:href="#DejaVuSans-20" x="61.083984"/>
+     <use xlink:href="#DejaVuSans-3d" x="92.871094"/>
+     <use xlink:href="#DejaVuSans-20" x="176.660156"/>
+     <use xlink:href="#DejaVuSans-31" x="208.447266"/>
+     <use xlink:href="#DejaVuSans-30" x="272.070312"/>
+     <use xlink:href="#DejaVuSans-30" x="335.693359"/>
+     <use xlink:href="#DejaVuSans-30" x="399.316406"/>
+     <use xlink:href="#DejaVuSans-30" x="462.939453"/>
+    </g>
+   </g>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="p24d58054f1">
+   <rect x="95.04" y="51.3216" width="589.248" height="329.3136"/>
+  </clipPath>
+ </defs>
+</svg>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/laplace_initial.svg b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/laplace_initial.svg
new file mode 100644
index 000000000..dbede3687
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/laplace_initial.svg
@@ -0,0 +1,1838 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="760.32pt" height="427.68pt" viewBox="0 0 760.32 427.68" xmlns="http://www.w3.org/2000/svg" version="1.1">
+ <metadata>
+  <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+   <cc:Work>
+    <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+    <dc:date>2022-04-18T06:58:18.768147</dc:date>
+    <dc:format>image/svg+xml</dc:format>
+    <dc:creator>
+     <cc:Agent>
+      <dc:title>Matplotlib v3.5.1, https://matplotlib.org/</dc:title>
+     </cc:Agent>
+    </dc:creator>
+   </cc:Work>
+  </rdf:RDF>
+ </metadata>
+ <defs>
+  <style type="text/css">*{stroke-linejoin: round; stroke-linecap: butt}</style>
+ </defs>
+ <g id="figure_1">
+  <g id="patch_1">
+   <path d="M 0 427.68 
+L 760.32 427.68 
+L 760.32 0 
+L 0 0 
+z
+" style="fill: #ffffff"/>
+  </g>
+  <g id="axes_1">
+   <g id="patch_2">
+    <path d="M 95.04 380.6352 
+L 684.288 380.6352 
+L 684.288 51.3216 
+L 95.04 51.3216 
+z
+" style="fill: #ffffff"/>
+   </g>
+   <g id="matplotlib.axis_1">
+    <g id="xtick_1">
+     <g id="line2d_1">
+      <defs>
+       <path id="mdbd2280614" d="M 0 0 
+L 0 3.5 
+" style="stroke: #000000; stroke-width: 0.8"/>
+      </defs>
+      <g>
+       <use xlink:href="#mdbd2280614" x="95.04" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_1">
+      <!-- 0 -->
+      <g transform="translate(91.85875 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-30" d="M 2034 4250 
+Q 1547 4250 1301 3770 
+Q 1056 3291 1056 2328 
+Q 1056 1369 1301 889 
+Q 1547 409 2034 409 
+Q 2525 409 2770 889 
+Q 3016 1369 3016 2328 
+Q 3016 3291 2770 3770 
+Q 2525 4250 2034 4250 
+z
+M 2034 4750 
+Q 2819 4750 3233 4129 
+Q 3647 3509 3647 2328 
+Q 3647 1150 3233 529 
+Q 2819 -91 2034 -91 
+Q 1250 -91 836 529 
+Q 422 1150 422 2328 
+Q 422 3509 836 4129 
+Q 1250 4750 2034 4750 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_2">
+     <g id="line2d_2">
+      <g>
+       <use xlink:href="#mdbd2280614" x="212.771868" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_2">
+      <!-- 200 -->
+      <g transform="translate(203.228118 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-32" d="M 1228 531 
+L 3431 531 
+L 3431 0 
+L 469 0 
+L 469 531 
+Q 828 903 1448 1529 
+Q 2069 2156 2228 2338 
+Q 2531 2678 2651 2914 
+Q 2772 3150 2772 3378 
+Q 2772 3750 2511 3984 
+Q 2250 4219 1831 4219 
+Q 1534 4219 1204 4116 
+Q 875 4013 500 3803 
+L 500 4441 
+Q 881 4594 1212 4672 
+Q 1544 4750 1819 4750 
+Q 2544 4750 2975 4387 
+Q 3406 4025 3406 3419 
+Q 3406 3131 3298 2873 
+Q 3191 2616 2906 2266 
+Q 2828 2175 2409 1742 
+Q 1991 1309 1228 531 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_3">
+     <g id="line2d_3">
+      <g>
+       <use xlink:href="#mdbd2280614" x="330.503736" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_3">
+      <!-- 400 -->
+      <g transform="translate(320.959986 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-34" d="M 2419 4116 
+L 825 1625 
+L 2419 1625 
+L 2419 4116 
+z
+M 2253 4666 
+L 3047 4666 
+L 3047 1625 
+L 3713 1625 
+L 3713 1100 
+L 3047 1100 
+L 3047 0 
+L 2419 0 
+L 2419 1100 
+L 313 1100 
+L 313 1709 
+L 2253 4666 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_4">
+     <g id="line2d_4">
+      <g>
+       <use xlink:href="#mdbd2280614" x="448.235604" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_4">
+      <!-- 600 -->
+      <g transform="translate(438.691854 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-36" d="M 2113 2584 
+Q 1688 2584 1439 2293 
+Q 1191 2003 1191 1497 
+Q 1191 994 1439 701 
+Q 1688 409 2113 409 
+Q 2538 409 2786 701 
+Q 3034 994 3034 1497 
+Q 3034 2003 2786 2293 
+Q 2538 2584 2113 2584 
+z
+M 3366 4563 
+L 3366 3988 
+Q 3128 4100 2886 4159 
+Q 2644 4219 2406 4219 
+Q 1781 4219 1451 3797 
+Q 1122 3375 1075 2522 
+Q 1259 2794 1537 2939 
+Q 1816 3084 2150 3084 
+Q 2853 3084 3261 2657 
+Q 3669 2231 3669 1497 
+Q 3669 778 3244 343 
+Q 2819 -91 2113 -91 
+Q 1303 -91 875 529 
+Q 447 1150 447 2328 
+Q 447 3434 972 4092 
+Q 1497 4750 2381 4750 
+Q 2619 4750 2861 4703 
+Q 3103 4656 3366 4563 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_5">
+     <g id="line2d_5">
+      <g>
+       <use xlink:href="#mdbd2280614" x="565.967473" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_5">
+      <!-- 800 -->
+      <g transform="translate(556.423723 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-38" d="M 2034 2216 
+Q 1584 2216 1326 1975 
+Q 1069 1734 1069 1313 
+Q 1069 891 1326 650 
+Q 1584 409 2034 409 
+Q 2484 409 2743 651 
+Q 3003 894 3003 1313 
+Q 3003 1734 2745 1975 
+Q 2488 2216 2034 2216 
+z
+M 1403 2484 
+Q 997 2584 770 2862 
+Q 544 3141 544 3541 
+Q 544 4100 942 4425 
+Q 1341 4750 2034 4750 
+Q 2731 4750 3128 4425 
+Q 3525 4100 3525 3541 
+Q 3525 3141 3298 2862 
+Q 3072 2584 2669 2484 
+Q 3125 2378 3379 2068 
+Q 3634 1759 3634 1313 
+Q 3634 634 3220 271 
+Q 2806 -91 2034 -91 
+Q 1263 -91 848 271 
+Q 434 634 434 1313 
+Q 434 1759 690 2068 
+Q 947 2378 1403 2484 
+z
+M 1172 3481 
+Q 1172 3119 1398 2916 
+Q 1625 2713 2034 2713 
+Q 2441 2713 2670 2916 
+Q 2900 3119 2900 3481 
+Q 2900 3844 2670 4047 
+Q 2441 4250 2034 4250 
+Q 1625 4250 1398 4047 
+Q 1172 3844 1172 3481 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-38"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_6">
+     <g id="line2d_6">
+      <g>
+       <use xlink:href="#mdbd2280614" x="683.699341" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_6">
+      <!-- 1000 -->
+      <g transform="translate(670.974341 395.233637)scale(0.1 -0.1)">
+       <defs>
+        <path id="DejaVuSans-31" d="M 794 531 
+L 1825 531 
+L 1825 4091 
+L 703 3866 
+L 703 4441 
+L 1819 4666 
+L 2450 4666 
+L 2450 531 
+L 3481 531 
+L 3481 0 
+L 794 0 
+L 794 531 
+z
+" transform="scale(0.015625)"/>
+       </defs>
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+       <use xlink:href="#DejaVuSans-30" x="190.869141"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_7">
+     <!-- Position -->
+     <g transform="translate(342.95025 419.549575)scale(0.24 -0.24)">
+      <defs>
+       <path id="DejaVuSans-50" d="M 1259 4147 
+L 1259 2394 
+L 2053 2394 
+Q 2494 2394 2734 2622 
+Q 2975 2850 2975 3272 
+Q 2975 3691 2734 3919 
+Q 2494 4147 2053 4147 
+L 1259 4147 
+z
+M 628 4666 
+L 2053 4666 
+Q 2838 4666 3239 4311 
+Q 3641 3956 3641 3272 
+Q 3641 2581 3239 2228 
+Q 2838 1875 2053 1875 
+L 1259 1875 
+L 1259 0 
+L 628 0 
+L 628 4666 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-6f" d="M 1959 3097 
+Q 1497 3097 1228 2736 
+Q 959 2375 959 1747 
+Q 959 1119 1226 758 
+Q 1494 397 1959 397 
+Q 2419 397 2687 759 
+Q 2956 1122 2956 1747 
+Q 2956 2369 2687 2733 
+Q 2419 3097 1959 3097 
+z
+M 1959 3584 
+Q 2709 3584 3137 3096 
+Q 3566 2609 3566 1747 
+Q 3566 888 3137 398 
+Q 2709 -91 1959 -91 
+Q 1206 -91 779 398 
+Q 353 888 353 1747 
+Q 353 2609 779 3096 
+Q 1206 3584 1959 3584 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-73" d="M 2834 3397 
+L 2834 2853 
+Q 2591 2978 2328 3040 
+Q 2066 3103 1784 3103 
+Q 1356 3103 1142 2972 
+Q 928 2841 928 2578 
+Q 928 2378 1081 2264 
+Q 1234 2150 1697 2047 
+L 1894 2003 
+Q 2506 1872 2764 1633 
+Q 3022 1394 3022 966 
+Q 3022 478 2636 193 
+Q 2250 -91 1575 -91 
+Q 1294 -91 989 -36 
+Q 684 19 347 128 
+L 347 722 
+Q 666 556 975 473 
+Q 1284 391 1588 391 
+Q 1994 391 2212 530 
+Q 2431 669 2431 922 
+Q 2431 1156 2273 1281 
+Q 2116 1406 1581 1522 
+L 1381 1569 
+Q 847 1681 609 1914 
+Q 372 2147 372 2553 
+Q 372 3047 722 3315 
+Q 1072 3584 1716 3584 
+Q 2034 3584 2315 3537 
+Q 2597 3491 2834 3397 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-69" d="M 603 3500 
+L 1178 3500 
+L 1178 0 
+L 603 0 
+L 603 3500 
+z
+M 603 4863 
+L 1178 4863 
+L 1178 4134 
+L 603 4134 
+L 603 4863 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-74" d="M 1172 4494 
+L 1172 3500 
+L 2356 3500 
+L 2356 3053 
+L 1172 3053 
+L 1172 1153 
+Q 1172 725 1289 603 
+Q 1406 481 1766 481 
+L 2356 481 
+L 2356 0 
+L 1766 0 
+Q 1100 0 847 248 
+Q 594 497 594 1153 
+L 594 3053 
+L 172 3053 
+L 172 3500 
+L 594 3500 
+L 594 4494 
+L 1172 4494 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-6e" d="M 3513 2113 
+L 3513 0 
+L 2938 0 
+L 2938 2094 
+Q 2938 2591 2744 2837 
+Q 2550 3084 2163 3084 
+Q 1697 3084 1428 2787 
+Q 1159 2491 1159 1978 
+L 1159 0 
+L 581 0 
+L 581 3500 
+L 1159 3500 
+L 1159 2956 
+Q 1366 3272 1645 3428 
+Q 1925 3584 2291 3584 
+Q 2894 3584 3203 3211 
+Q 3513 2838 3513 2113 
+z
+" transform="scale(0.015625)"/>
+      </defs>
+      <use xlink:href="#DejaVuSans-50"/>
+      <use xlink:href="#DejaVuSans-6f" x="56.677734"/>
+      <use xlink:href="#DejaVuSans-73" x="117.859375"/>
+      <use xlink:href="#DejaVuSans-69" x="169.958984"/>
+      <use xlink:href="#DejaVuSans-74" x="197.742188"/>
+      <use xlink:href="#DejaVuSans-69" x="236.951172"/>
+      <use xlink:href="#DejaVuSans-6f" x="264.734375"/>
+      <use xlink:href="#DejaVuSans-6e" x="325.916016"/>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_2">
+    <g id="ytick_1">
+     <g id="line2d_7">
+      <defs>
+       <path id="maf970daf46" d="M 0 0 
+L -3.5 0 
+" style="stroke: #000000; stroke-width: 0.8"/>
+      </defs>
+      <g>
+       <use xlink:href="#maf970daf46" x="95.04" y="380.6352" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_8">
+      <!-- 0 -->
+      <g transform="translate(81.6775 384.434419)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_2">
+     <g id="line2d_8">
+      <g>
+       <use xlink:href="#maf970daf46" x="95.04" y="314.779066" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_9">
+      <!-- 2000 -->
+      <g transform="translate(62.59 318.578284)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+       <use xlink:href="#DejaVuSans-30" x="190.869141"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_3">
+     <g id="line2d_9">
+      <g>
+       <use xlink:href="#maf970daf46" x="95.04" y="248.922931" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_10">
+      <!-- 4000 -->
+      <g transform="translate(62.59 252.72215)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+       <use xlink:href="#DejaVuSans-30" x="190.869141"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_4">
+     <g id="line2d_10">
+      <g>
+       <use xlink:href="#maf970daf46" x="95.04" y="183.066797" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_11">
+      <!-- 6000 -->
+      <g transform="translate(62.59 186.866016)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+       <use xlink:href="#DejaVuSans-30" x="190.869141"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_5">
+     <g id="line2d_11">
+      <g>
+       <use xlink:href="#maf970daf46" x="95.04" y="117.210662" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_12">
+      <!-- 8000 -->
+      <g transform="translate(62.59 121.009881)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-38"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+       <use xlink:href="#DejaVuSans-30" x="190.869141"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_6">
+     <g id="line2d_12">
+      <g>
+       <use xlink:href="#maf970daf46" x="95.04" y="51.354528" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_13">
+      <!-- 10000 -->
+      <g transform="translate(56.2275 55.153747)scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" x="63.623047"/>
+       <use xlink:href="#DejaVuSans-30" x="127.246094"/>
+       <use xlink:href="#DejaVuSans-30" x="190.869141"/>
+       <use xlink:href="#DejaVuSans-30" x="254.492188"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_14">
+     <!-- Temperature -->
+     <g transform="translate(47.23625 291.985275)rotate(-90)scale(0.24 -0.24)">
+      <defs>
+       <path id="DejaVuSans-54" d="M -19 4666 
+L 3928 4666 
+L 3928 4134 
+L 2272 4134 
+L 2272 0 
+L 1638 0 
+L 1638 4134 
+L -19 4134 
+L -19 4666 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-65" d="M 3597 1894 
+L 3597 1613 
+L 953 1613 
+Q 991 1019 1311 708 
+Q 1631 397 2203 397 
+Q 2534 397 2845 478 
+Q 3156 559 3463 722 
+L 3463 178 
+Q 3153 47 2828 -22 
+Q 2503 -91 2169 -91 
+Q 1331 -91 842 396 
+Q 353 884 353 1716 
+Q 353 2575 817 3079 
+Q 1281 3584 2069 3584 
+Q 2775 3584 3186 3129 
+Q 3597 2675 3597 1894 
+z
+M 3022 2063 
+Q 3016 2534 2758 2815 
+Q 2500 3097 2075 3097 
+Q 1594 3097 1305 2825 
+Q 1016 2553 972 2059 
+L 3022 2063 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-6d" d="M 3328 2828 
+Q 3544 3216 3844 3400 
+Q 4144 3584 4550 3584 
+Q 5097 3584 5394 3201 
+Q 5691 2819 5691 2113 
+L 5691 0 
+L 5113 0 
+L 5113 2094 
+Q 5113 2597 4934 2840 
+Q 4756 3084 4391 3084 
+Q 3944 3084 3684 2787 
+Q 3425 2491 3425 1978 
+L 3425 0 
+L 2847 0 
+L 2847 2094 
+Q 2847 2600 2669 2842 
+Q 2491 3084 2119 3084 
+Q 1678 3084 1418 2786 
+Q 1159 2488 1159 1978 
+L 1159 0 
+L 581 0 
+L 581 3500 
+L 1159 3500 
+L 1159 2956 
+Q 1356 3278 1631 3431 
+Q 1906 3584 2284 3584 
+Q 2666 3584 2933 3390 
+Q 3200 3197 3328 2828 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-70" d="M 1159 525 
+L 1159 -1331 
+L 581 -1331 
+L 581 3500 
+L 1159 3500 
+L 1159 2969 
+Q 1341 3281 1617 3432 
+Q 1894 3584 2278 3584 
+Q 2916 3584 3314 3078 
+Q 3713 2572 3713 1747 
+Q 3713 922 3314 415 
+Q 2916 -91 2278 -91 
+Q 1894 -91 1617 61 
+Q 1341 213 1159 525 
+z
+M 3116 1747 
+Q 3116 2381 2855 2742 
+Q 2594 3103 2138 3103 
+Q 1681 3103 1420 2742 
+Q 1159 2381 1159 1747 
+Q 1159 1113 1420 752 
+Q 1681 391 2138 391 
+Q 2594 391 2855 752 
+Q 3116 1113 3116 1747 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-72" d="M 2631 2963 
+Q 2534 3019 2420 3045 
+Q 2306 3072 2169 3072 
+Q 1681 3072 1420 2755 
+Q 1159 2438 1159 1844 
+L 1159 0 
+L 581 0 
+L 581 3500 
+L 1159 3500 
+L 1159 2956 
+Q 1341 3275 1631 3429 
+Q 1922 3584 2338 3584 
+Q 2397 3584 2469 3576 
+Q 2541 3569 2628 3553 
+L 2631 2963 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-61" d="M 2194 1759 
+Q 1497 1759 1228 1600 
+Q 959 1441 959 1056 
+Q 959 750 1161 570 
+Q 1363 391 1709 391 
+Q 2188 391 2477 730 
+Q 2766 1069 2766 1631 
+L 2766 1759 
+L 2194 1759 
+z
+M 3341 1997 
+L 3341 0 
+L 2766 0 
+L 2766 531 
+Q 2569 213 2275 61 
+Q 1981 -91 1556 -91 
+Q 1019 -91 701 211 
+Q 384 513 384 1019 
+Q 384 1609 779 1909 
+Q 1175 2209 1959 2209 
+L 2766 2209 
+L 2766 2266 
+Q 2766 2663 2505 2880 
+Q 2244 3097 1772 3097 
+Q 1472 3097 1187 3025 
+Q 903 2953 641 2809 
+L 641 3341 
+Q 956 3463 1253 3523 
+Q 1550 3584 1831 3584 
+Q 2591 3584 2966 3190 
+Q 3341 2797 3341 1997 
+z
+" transform="scale(0.015625)"/>
+       <path id="DejaVuSans-75" d="M 544 1381 
+L 544 3500 
+L 1119 3500 
+L 1119 1403 
+Q 1119 906 1312 657 
+Q 1506 409 1894 409 
+Q 2359 409 2629 706 
+Q 2900 1003 2900 1516 
+L 2900 3500 
+L 3475 3500 
+L 3475 0 
+L 2900 0 
+L 2900 538 
+Q 2691 219 2414 64 
+Q 2138 -91 1772 -91 
+Q 1169 -91 856 284 
+Q 544 659 544 1381 
+z
+M 1991 3584 
+L 1991 3584 
+z
+" transform="scale(0.015625)"/>
+      </defs>
+      <use xlink:href="#DejaVuSans-54"/>
+      <use xlink:href="#DejaVuSans-65" x="44.083984"/>
+      <use xlink:href="#DejaVuSans-6d" x="105.607422"/>
+      <use xlink:href="#DejaVuSans-70" x="203.019531"/>
+      <use xlink:href="#DejaVuSans-65" x="266.496094"/>
+      <use xlink:href="#DejaVuSans-72" x="328.019531"/>
+      <use xlink:href="#DejaVuSans-61" x="369.132812"/>
+      <use xlink:href="#DejaVuSans-74" x="430.412109"/>
+      <use xlink:href="#DejaVuSans-75" x="469.621094"/>
+      <use xlink:href="#DejaVuSans-72" x="533"/>
+      <use xlink:href="#DejaVuSans-65" x="571.863281"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_13">
+    <path d="M 95.04 380.6352 
+L 388.781011 380.6352 
+L 389.36967 51.354528 
+L 389.95833 380.6352 
+L 683.699341 380.6352 
+L 683.699341 380.6352 
+" clip-path="url(#pe338a5f14f)" style="fill: none; stroke: #000000; stroke-width: 3; stroke-linecap: square"/>
+    <defs>
+     <path id="m9f78a702ea" d="M 0 -3 
+L -0.673542 -0.927051 
+L -2.85317 -0.927051 
+L -1.089814 0.354102 
+L -1.763356 2.427051 
+L -0 1.145898 
+L 1.763356 2.427051 
+L 1.089814 0.354102 
+L 2.85317 -0.927051 
+L 0.673542 -0.927051 
+z
+" style="stroke: #000000; stroke-linejoin: bevel"/>
+    </defs>
+    <g clip-path="url(#pe338a5f14f)">
+     <use xlink:href="#m9f78a702ea" x="95.04" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="95.628659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="96.217319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="96.805978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="97.394637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="97.983297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="98.571956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="99.160615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="99.749275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="100.337934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="100.926593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="101.515253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="102.103912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="102.692571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="103.281231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="103.86989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="104.458549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="105.047209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="105.635868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="106.224527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="106.813187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="107.401846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="107.990505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="108.579165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="109.167824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="109.756484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="110.345143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="110.933802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="111.522462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="112.111121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="112.69978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="113.28844" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="113.877099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="114.465758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="115.054418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="115.643077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="116.231736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="116.820396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="117.409055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="117.997714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="118.586374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="119.175033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="119.763692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="120.352352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="120.941011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="121.52967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="122.11833" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="122.706989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="123.295648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="123.884308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="124.472967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="125.061626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="125.650286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="126.238945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="126.827604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="127.416264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="128.004923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="128.593582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="129.182242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="129.770901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="130.35956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="130.94822" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="131.536879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="132.125538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="132.714198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="133.302857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="133.891516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="134.480176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="135.068835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="135.657495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="136.246154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="136.834813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="137.423473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="138.012132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="138.600791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="139.189451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="139.77811" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="140.366769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="140.955429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="141.544088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="142.132747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="142.721407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="143.310066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="143.898725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="144.487385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="145.076044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="145.664703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="146.253363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="146.842022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="147.430681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="148.019341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="148.608" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="149.196659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="149.785319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="150.373978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="150.962637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="151.551297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="152.139956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="152.728615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="153.317275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="153.905934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="154.494593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="155.083253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="155.671912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="156.260571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="156.849231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="157.43789" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="158.026549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="158.615209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="159.203868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="159.792527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="160.381187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="160.969846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="161.558505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="162.147165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="162.735824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="163.324484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="163.913143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="164.501802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="165.090462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="165.679121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="166.26778" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="166.85644" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="167.445099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="168.033758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="168.622418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="169.211077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="169.799736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="170.388396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="170.977055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="171.565714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="172.154374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="172.743033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="173.331692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="173.920352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="174.509011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="175.09767" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="175.68633" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="176.274989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="176.863648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="177.452308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="178.040967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="178.629626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="179.218286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="179.806945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="180.395604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="180.984264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="181.572923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="182.161582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="182.750242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="183.338901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="183.92756" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="184.51622" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="185.104879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="185.693538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="186.282198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="186.870857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="187.459516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="188.048176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="188.636835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="189.225495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="189.814154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="190.402813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="190.991473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="191.580132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="192.168791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="192.757451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="193.34611" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="193.934769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="194.523429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="195.112088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="195.700747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="196.289407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="196.878066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="197.466725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="198.055385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="198.644044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="199.232703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="199.821363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="200.410022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="200.998681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="201.587341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="202.176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="202.764659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="203.353319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="203.941978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="204.530637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="205.119297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="205.707956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="206.296615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="206.885275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="207.473934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="208.062593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="208.651253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="209.239912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="209.828571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="210.417231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="211.00589" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="211.594549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="212.183209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="212.771868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="213.360527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="213.949187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="214.537846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="215.126505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="215.715165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="216.303824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="216.892484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="217.481143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="218.069802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="218.658462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="219.247121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="219.83578" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="220.42444" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="221.013099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="221.601758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="222.190418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="222.779077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="223.367736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="223.956396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="224.545055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="225.133714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="225.722374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="226.311033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="226.899692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="227.488352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="228.077011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="228.66567" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="229.25433" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="229.842989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="230.431648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="231.020308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="231.608967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="232.197626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="232.786286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="233.374945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="233.963604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="234.552264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="235.140923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="235.729582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="236.318242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="236.906901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="237.49556" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="238.08422" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="238.672879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="239.261538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="239.850198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="240.438857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="241.027516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="241.616176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="242.204835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="242.793495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="243.382154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="243.970813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="244.559473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="245.148132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="245.736791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="246.325451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="246.91411" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="247.502769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="248.091429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="248.680088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="249.268747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="249.857407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="250.446066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="251.034725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="251.623385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="252.212044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="252.800703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="253.389363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="253.978022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="254.566681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="255.155341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="255.744" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="256.332659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="256.921319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="257.509978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="258.098637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="258.687297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="259.275956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="259.864615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="260.453275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="261.041934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="261.630593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="262.219253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="262.807912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="263.396571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="263.985231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="264.57389" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="265.162549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="265.751209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="266.339868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="266.928527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="267.517187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="268.105846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="268.694505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="269.283165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="269.871824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="270.460484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="271.049143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="271.637802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="272.226462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="272.815121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="273.40378" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="273.99244" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="274.581099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="275.169758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="275.758418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="276.347077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="276.935736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="277.524396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="278.113055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="278.701714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="279.290374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="279.879033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="280.467692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="281.056352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="281.645011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="282.23367" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="282.82233" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="283.410989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="283.999648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="284.588308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="285.176967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="285.765626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="286.354286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="286.942945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="287.531604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="288.120264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="288.708923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="289.297582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="289.886242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="290.474901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="291.06356" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="291.65222" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="292.240879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="292.829538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="293.418198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="294.006857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="294.595516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="295.184176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="295.772835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="296.361495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="296.950154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="297.538813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="298.127473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="298.716132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="299.304791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="299.893451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="300.48211" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="301.070769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="301.659429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="302.248088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="302.836747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="303.425407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="304.014066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="304.602725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="305.191385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="305.780044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="306.368703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="306.957363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="307.546022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="308.134681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="308.723341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="309.312" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="309.900659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="310.489319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="311.077978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="311.666637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="312.255297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="312.843956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="313.432615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="314.021275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="314.609934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="315.198593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="315.787253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="316.375912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="316.964571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="317.553231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="318.14189" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="318.730549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="319.319209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="319.907868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="320.496527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="321.085187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="321.673846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="322.262505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="322.851165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="323.439824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="324.028484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="324.617143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="325.205802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="325.794462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="326.383121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="326.97178" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="327.56044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="328.149099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="328.737758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="329.326418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="329.915077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="330.503736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="331.092396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="331.681055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="332.269714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="332.858374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="333.447033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="334.035692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="334.624352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="335.213011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="335.80167" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="336.39033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="336.978989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="337.567648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="338.156308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="338.744967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="339.333626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="339.922286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="340.510945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="341.099604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="341.688264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="342.276923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="342.865582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="343.454242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="344.042901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="344.63156" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="345.22022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="345.808879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="346.397538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="346.986198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="347.574857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="348.163516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="348.752176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="349.340835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="349.929495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="350.518154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="351.106813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="351.695473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="352.284132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="352.872791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="353.461451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="354.05011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="354.638769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="355.227429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="355.816088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="356.404747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="356.993407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="357.582066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="358.170725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="358.759385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="359.348044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="359.936703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="360.525363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="361.114022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="361.702681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="362.291341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="362.88" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="363.468659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="364.057319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="364.645978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="365.234637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="365.823297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="366.411956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="367.000615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="367.589275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="368.177934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="368.766593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="369.355253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="369.943912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="370.532571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="371.121231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="371.70989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="372.298549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="372.887209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="373.475868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="374.064527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="374.653187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="375.241846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="375.830505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="376.419165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="377.007824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="377.596484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="378.185143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="378.773802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="379.362462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="379.951121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="380.53978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="381.12844" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="381.717099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="382.305758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="382.894418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="383.483077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="384.071736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="384.660396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="385.249055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="385.837714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="386.426374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="387.015033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="387.603692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="388.192352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="388.781011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="389.36967" y="51.354528" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="389.95833" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="390.546989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="391.135648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="391.724308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="392.312967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="392.901626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="393.490286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="394.078945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="394.667604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="395.256264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="395.844923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="396.433582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="397.022242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="397.610901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="398.19956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="398.78822" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="399.376879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="399.965538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="400.554198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="401.142857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="401.731516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="402.320176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="402.908835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="403.497495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="404.086154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="404.674813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="405.263473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="405.852132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="406.440791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="407.029451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="407.61811" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="408.206769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="408.795429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="409.384088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="409.972747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="410.561407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="411.150066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="411.738725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="412.327385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="412.916044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="413.504703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="414.093363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="414.682022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="415.270681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="415.859341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="416.448" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="417.036659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="417.625319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="418.213978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="418.802637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="419.391297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="419.979956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="420.568615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="421.157275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="421.745934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="422.334593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="422.923253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="423.511912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="424.100571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="424.689231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="425.27789" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="425.866549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="426.455209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="427.043868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="427.632527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="428.221187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="428.809846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="429.398505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="429.987165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="430.575824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="431.164484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="431.753143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="432.341802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="432.930462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="433.519121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="434.10778" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="434.69644" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="435.285099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="435.873758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="436.462418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="437.051077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="437.639736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="438.228396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="438.817055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="439.405714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="439.994374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="440.583033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="441.171692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="441.760352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="442.349011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="442.93767" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="443.52633" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="444.114989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="444.703648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="445.292308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="445.880967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="446.469626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="447.058286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="447.646945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="448.235604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="448.824264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="449.412923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="450.001582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="450.590242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="451.178901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="451.76756" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="452.35622" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="452.944879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="453.533538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="454.122198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="454.710857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="455.299516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="455.888176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="456.476835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="457.065495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="457.654154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="458.242813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="458.831473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="459.420132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="460.008791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="460.597451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="461.18611" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="461.774769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="462.363429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="462.952088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="463.540747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="464.129407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="464.718066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="465.306725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="465.895385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="466.484044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="467.072703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="467.661363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="468.250022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="468.838681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="469.427341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="470.016" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="470.604659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="471.193319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="471.781978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="472.370637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="472.959297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="473.547956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="474.136615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="474.725275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="475.313934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="475.902593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="476.491253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="477.079912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="477.668571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="478.257231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="478.84589" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="479.434549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="480.023209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="480.611868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="481.200527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="481.789187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="482.377846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="482.966505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="483.555165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="484.143824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="484.732484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="485.321143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="485.909802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="486.498462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="487.087121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="487.67578" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="488.26444" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="488.853099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="489.441758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="490.030418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="490.619077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="491.207736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="491.796396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="492.385055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="492.973714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="493.562374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="494.151033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="494.739692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="495.328352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="495.917011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="496.50567" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="497.09433" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="497.682989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="498.271648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="498.860308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="499.448967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="500.037626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="500.626286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="501.214945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="501.803604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="502.392264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="502.980923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="503.569582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="504.158242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="504.746901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="505.33556" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="505.92422" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="506.512879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="507.101538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="507.690198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="508.278857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="508.867516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="509.456176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="510.044835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="510.633495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="511.222154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="511.810813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="512.399473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="512.988132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="513.576791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="514.165451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="514.75411" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="515.342769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="515.931429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="516.520088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="517.108747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="517.697407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="518.286066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="518.874725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="519.463385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="520.052044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="520.640703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="521.229363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="521.818022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="522.406681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="522.995341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="523.584" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="524.172659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="524.761319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="525.349978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="525.938637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="526.527297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="527.115956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="527.704615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="528.293275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="528.881934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="529.470593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="530.059253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="530.647912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="531.236571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="531.825231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="532.41389" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="533.002549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="533.591209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="534.179868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="534.768527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="535.357187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="535.945846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="536.534505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="537.123165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="537.711824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="538.300484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="538.889143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="539.477802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="540.066462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="540.655121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="541.24378" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="541.83244" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="542.421099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="543.009758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="543.598418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="544.187077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="544.775736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="545.364396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="545.953055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="546.541714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="547.130374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="547.719033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="548.307692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="548.896352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="549.485011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="550.07367" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="550.66233" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="551.250989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="551.839648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="552.428308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="553.016967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="553.605626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="554.194286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="554.782945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="555.371604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="555.960264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="556.548923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="557.137582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="557.726242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="558.314901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="558.90356" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="559.49222" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="560.080879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="560.669538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="561.258198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="561.846857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="562.435516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="563.024176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="563.612835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="564.201495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="564.790154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="565.378813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="565.967473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="566.556132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="567.144791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="567.733451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="568.32211" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="568.910769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="569.499429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="570.088088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="570.676747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="571.265407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="571.854066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="572.442725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="573.031385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="573.620044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="574.208703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="574.797363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="575.386022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="575.974681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="576.563341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="577.152" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="577.740659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="578.329319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="578.917978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="579.506637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="580.095297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="580.683956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="581.272615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="581.861275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="582.449934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="583.038593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="583.627253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="584.215912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="584.804571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="585.393231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="585.98189" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="586.570549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="587.159209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="587.747868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="588.336527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="588.925187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="589.513846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="590.102505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="590.691165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="591.279824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="591.868484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="592.457143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="593.045802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="593.634462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="594.223121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="594.81178" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="595.40044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="595.989099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="596.577758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="597.166418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="597.755077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="598.343736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="598.932396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="599.521055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="600.109714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="600.698374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="601.287033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="601.875692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="602.464352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="603.053011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="603.64167" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="604.23033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="604.818989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="605.407648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="605.996308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="606.584967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="607.173626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="607.762286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="608.350945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="608.939604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="609.528264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="610.116923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="610.705582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="611.294242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="611.882901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="612.47156" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="613.06022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="613.648879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="614.237538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="614.826198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="615.414857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="616.003516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="616.592176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="617.180835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="617.769495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="618.358154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="618.946813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="619.535473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="620.124132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="620.712791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="621.301451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="621.89011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="622.478769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="623.067429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="623.656088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="624.244747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="624.833407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="625.422066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="626.010725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="626.599385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="627.188044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="627.776703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="628.365363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="628.954022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="629.542681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="630.131341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="630.72" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="631.308659" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="631.897319" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="632.485978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="633.074637" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="633.663297" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="634.251956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="634.840615" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="635.429275" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="636.017934" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="636.606593" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="637.195253" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="637.783912" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="638.372571" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="638.961231" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="639.54989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="640.138549" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="640.727209" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="641.315868" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="641.904527" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="642.493187" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="643.081846" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="643.670505" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="644.259165" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="644.847824" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="645.436484" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="646.025143" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="646.613802" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="647.202462" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="647.791121" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="648.37978" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="648.96844" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="649.557099" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="650.145758" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="650.734418" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="651.323077" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="651.911736" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="652.500396" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="653.089055" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="653.677714" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="654.266374" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="654.855033" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="655.443692" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="656.032352" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="656.621011" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="657.20967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="657.79833" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="658.386989" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="658.975648" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="659.564308" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="660.152967" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="660.741626" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="661.330286" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="661.918945" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="662.507604" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="663.096264" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="663.684923" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="664.273582" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="664.862242" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="665.450901" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="666.03956" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="666.62822" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="667.216879" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="667.805538" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="668.394198" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="668.982857" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="669.571516" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="670.160176" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="670.748835" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="671.337495" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="671.926154" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="672.514813" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="673.103473" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="673.692132" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="674.280791" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="674.869451" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="675.45811" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="676.046769" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="676.635429" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="677.224088" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="677.812747" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="678.401407" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="678.990066" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="679.578725" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="680.167385" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="680.756044" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="681.344703" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="681.933363" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="682.522022" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="683.110681" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+     <use xlink:href="#m9f78a702ea" x="683.699341" y="380.6352" style="stroke: #000000; stroke-linejoin: bevel"/>
+    </g>
+   </g>
+   <g id="patch_3">
+    <path d="M 95.04 380.6352 
+L 95.04 51.3216 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_4">
+    <path d="M 684.288 380.6352 
+L 684.288 51.3216 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_5">
+    <path d="M 95.04 380.6352 
+L 684.288 380.6352 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_6">
+    <path d="M 95.04 51.3216 
+L 684.288 51.3216 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="text_15">
+    <!-- Initial State -->
+    <g transform="translate(320.8815 45.3216)scale(0.24 -0.24)">
+     <defs>
+      <path id="DejaVuSans-49" d="M 628 4666 
+L 1259 4666 
+L 1259 0 
+L 628 0 
+L 628 4666 
+z
+" transform="scale(0.015625)"/>
+      <path id="DejaVuSans-6c" d="M 603 4863 
+L 1178 4863 
+L 1178 0 
+L 603 0 
+L 603 4863 
+z
+" transform="scale(0.015625)"/>
+      <path id="DejaVuSans-20" transform="scale(0.015625)"/>
+      <path id="DejaVuSans-53" d="M 3425 4513 
+L 3425 3897 
+Q 3066 4069 2747 4153 
+Q 2428 4238 2131 4238 
+Q 1616 4238 1336 4038 
+Q 1056 3838 1056 3469 
+Q 1056 3159 1242 3001 
+Q 1428 2844 1947 2747 
+L 2328 2669 
+Q 3034 2534 3370 2195 
+Q 3706 1856 3706 1288 
+Q 3706 609 3251 259 
+Q 2797 -91 1919 -91 
+Q 1588 -91 1214 -16 
+Q 841 59 441 206 
+L 441 856 
+Q 825 641 1194 531 
+Q 1563 422 1919 422 
+Q 2459 422 2753 634 
+Q 3047 847 3047 1241 
+Q 3047 1584 2836 1778 
+Q 2625 1972 2144 2069 
+L 1759 2144 
+Q 1053 2284 737 2584 
+Q 422 2884 422 3419 
+Q 422 4038 858 4394 
+Q 1294 4750 2059 4750 
+Q 2388 4750 2728 4690 
+Q 3069 4631 3425 4513 
+z
+" transform="scale(0.015625)"/>
+     </defs>
+     <use xlink:href="#DejaVuSans-49"/>
+     <use xlink:href="#DejaVuSans-6e" x="29.492188"/>
+     <use xlink:href="#DejaVuSans-69" x="92.871094"/>
+     <use xlink:href="#DejaVuSans-74" x="120.654297"/>
+     <use xlink:href="#DejaVuSans-69" x="159.863281"/>
+     <use xlink:href="#DejaVuSans-61" x="187.646484"/>
+     <use xlink:href="#DejaVuSans-6c" x="248.925781"/>
+     <use xlink:href="#DejaVuSans-20" x="276.708984"/>
+     <use xlink:href="#DejaVuSans-53" x="308.496094"/>
+     <use xlink:href="#DejaVuSans-74" x="371.972656"/>
+     <use xlink:href="#DejaVuSans-61" x="411.181641"/>
+     <use xlink:href="#DejaVuSans-74" x="472.460938"/>
+     <use xlink:href="#DejaVuSans-65" x="511.669922"/>
+    </g>
+   </g>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="pe338a5f14f">
+   <rect x="95.04" y="51.3216" width="589.248" height="329.3136"/>
+  </clipPath>
+ </defs>
+</svg>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/memory.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/memory.rst
new file mode 100644
index 000000000..fa6fe6a97
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/memory.rst
@@ -0,0 +1,257 @@
+=================
+Memory management
+=================
+
+.. _cuda-device-memory:
+
+Data transfer
+=============
+
+Even though Numba can automatically transfer NumPy arrays to the device,
+it can only do so conservatively by always transferring device memory back to
+the host when a kernel finishes. To avoid the unnecessary transfer for
+read-only arrays, you can use the following APIs to manually control the
+transfer:
+
+.. autofunction:: numba.cuda.device_array
+   :noindex:
+.. autofunction:: numba.cuda.device_array_like
+   :noindex:
+.. autofunction:: numba.cuda.to_device
+   :noindex:
+
+In addition to the device arrays, Numba can consume any object that implements
+:ref:`cuda array interface <cuda-array-interface>`.  These objects also can be
+manually converted into a Numba device array by creating a view of the GPU
+buffer using the following APIs:
+
+.. autofunction:: numba.cuda.as_cuda_array
+  :noindex:
+.. autofunction:: numba.cuda.is_cuda_array
+  :noindex:
+
+
+Device arrays
+-------------
+
+Device array references have the following methods.  These methods are to be
+called in host code, not within CUDA-jitted functions.
+
+.. autoclass:: numba.cuda.cudadrv.devicearray.DeviceNDArray
+    :members: copy_to_host, is_c_contiguous, is_f_contiguous, ravel, reshape
+    :noindex:
+
+
+.. note:: DeviceNDArray defines the :ref:`cuda array interface <cuda-array-interface>`.
+
+
+Pinned memory
+=============
+
+.. autofunction:: numba.cuda.pinned
+   :noindex:
+.. autofunction:: numba.cuda.pinned_array
+   :noindex:
+.. autofunction:: numba.cuda.pinned_array_like
+   :noindex:
+
+
+Mapped memory
+=============
+
+.. autofunction:: numba.cuda.mapped
+   :noindex:
+.. autofunction:: numba.cuda.mapped_array
+   :noindex:
+.. autofunction:: numba.cuda.mapped_array_like
+   :noindex:
+
+
+
+Managed memory
+==============
+
+.. autofunction:: numba.cuda.managed_array
+   :noindex:
+
+
+Streams
+=======
+
+Streams can be passed to functions that accept them (e.g. copies between the
+host and device) and into kernel launch configurations so that the operations
+are executed asynchronously.
+
+.. autofunction:: numba.cuda.stream
+   :noindex:
+
+.. autofunction:: numba.cuda.default_stream
+   :noindex:
+
+.. autofunction:: numba.cuda.legacy_default_stream
+   :noindex:
+
+.. autofunction:: numba.cuda.per_thread_default_stream
+   :noindex:
+
+.. autofunction:: numba.cuda.external_stream
+   :noindex:
+
+CUDA streams have the following methods:
+
+.. autoclass:: numba.cuda.cudadrv.driver.Stream
+    :members: synchronize, auto_synchronize
+    :noindex:
+
+.. _cuda-shared-memory:
+
+Shared memory and thread synchronization
+========================================
+
+A limited amount of shared memory can be allocated on the device to speed
+up access to data, when necessary.  That memory will be shared (i.e. both
+readable and writable) amongst all threads belonging to a given block
+and has faster access times than regular device memory.  It also allows
+threads to cooperate on a given solution.  You can think of it as a
+manually-managed data cache.
+
+The memory is allocated once for the duration of the kernel, unlike
+traditional dynamic memory management.
+
+.. function:: numba.cuda.shared.array(shape, type)
+   :noindex:
+
+   Allocate a shared array of the given *shape* and *type* on the device.
+   This function must be called on the device (i.e. from a kernel or
+   device function). *shape* is either an integer or a tuple of integers
+   representing the array's dimensions and must be a simple constant
+   expression. A "simple constant expression" includes, but is not limited to:
+   
+      #. A literal (e.g. ``10``)
+      #. A local variable whose right-hand side is a literal or a simple constant
+         expression (e.g. ``shape``, where ``shape`` is defined earlier in the function 
+         as ``shape = 10``)
+      #. A global variable that is defined in the jitted function's globals by the time
+         of compilation (e.g. ``shape``, where ``shape`` is defined using any expression
+         at global scope).
+
+   The definition must result in a Python ``int`` (i.e. not a NumPy scalar or other
+   scalar / integer-like type). *type* is a :ref:`Numba type <numba-types>` of the
+   elements needing to be stored in the array. The returned array-like object can be
+   read and written to like any normal device array (e.g. through indexing).
+
+   A common pattern is to have each thread populate one element in the
+   shared array and then wait for all threads to finish using :func:`.syncthreads`.
+
+
+.. function:: numba.cuda.syncthreads()
+   :noindex:
+
+   Synchronize all threads in the same thread block.  This function
+   implements the same pattern as `barriers <http://en.wikipedia.org/wiki/Barrier_%28computer_science%29>`_
+   in traditional multi-threaded programming: this function waits
+   until all threads in the block call it, at which point it returns
+   control to all its callers.
+
+.. seealso::
+   :ref:`Matrix multiplication example <cuda-matmul>`.
+
+.. _cuda-local-memory:
+
+Local memory
+============
+
+Local memory is an area of memory private to each thread.  Using local
+memory helps allocate some scratchpad area when scalar local variables
+are not enough.  The memory is allocated once for the duration of the kernel,
+unlike traditional dynamic memory management.
+
+.. function:: numba.cuda.local.array(shape, type)
+   :noindex:
+
+   Allocate a local array of the given *shape* and *type* on the device.
+   *shape* is either an integer or a tuple of integers representing the array's
+   dimensions and must be a simple constant expression. A "simple constant expression" 
+   includes, but is not limited to:
+
+      #. A literal (e.g. ``10``)
+      #. A local variable whose right-hand side is a literal or a simple constant
+         expression (e.g. ``shape``, where ``shape`` is defined earlier in the function
+         as ``shape = 10``)
+      #. A global variable that is defined in the jitted function's globals by the time 
+         of compilation (e.g. ``shape``, where ``shape`` is defined using any expression
+         at global scope).
+
+   The definition must result in a Python ``int`` (i.e. not a NumPy scalar or other
+   scalar / integer-like type). *type* is a :ref:`Numba type <numba-types>`
+   of the elements needing to be stored in the array. The array is private to
+   the current thread. An array-like object is returned which can be read and
+   written to like any standard array (e.g. through indexing).
+
+   .. seealso:: The Local Memory section of `Device Memory Accesses
+      <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#device-memory-accesses>`_
+      in the CUDA programming guide.
+
+Constant memory
+===============
+
+Constant memory is an area of memory that is read only, cached and off-chip, it
+is accessible by all threads and is host allocated. A method of
+creating an array in constant memory is through the use of:
+
+.. function:: numba.cuda.const.array_like(arr)
+   :noindex:
+
+   Allocate and make accessible an array in constant memory based on array-like
+   *arr*.
+
+
+.. _deallocation-behavior:
+
+Deallocation Behavior
+=====================
+
+This section describes the deallocation behaviour of Numba's internal memory
+management. If an External Memory Management Plugin is in use (see
+:ref:`cuda-emm-plugin`), then deallocation behaviour may differ; you may refer to the
+documentation for the EMM Plugin to understand its deallocation behaviour.
+
+Deallocation of all CUDA resources are tracked on a per-context basis.
+When the last reference to a device memory is dropped, the underlying memory
+is scheduled to be deallocated.  The deallocation does not occur immediately.
+It is added to a queue of pending deallocations.  This design has two benefits:
+
+1. Resource deallocation API may cause the device to synchronize; thus, breaking
+   any asynchronous execution.  Deferring the deallocation could avoid latency
+   in performance critical code section.
+2. Some deallocation errors may cause all the remaining deallocations to fail.
+   Continued deallocation errors can cause critical errors at the CUDA driver
+   level.  In some cases, this could mean a segmentation fault in the CUDA
+   driver. In the worst case, this could cause the system GUI to freeze and
+   could only recover with a system reset.  When an error occurs during a
+   deallocation, the remaining pending deallocations are cancelled.  Any
+   deallocation error will be reported.  When the process is terminated, the
+   CUDA driver is able to release all allocated resources by the terminated
+   process.
+
+The deallocation queue is flushed automatically as soon as the following events
+occur:
+
+- An allocation failed due to out-of-memory error.  Allocation is retried after
+  flushing all deallocations.
+- The deallocation queue has reached its maximum size, which is default to 10.
+  User can override by setting the environment variable
+  `NUMBA_CUDA_MAX_PENDING_DEALLOCS_COUNT`.  For example,
+  `NUMBA_CUDA_MAX_PENDING_DEALLOCS_COUNT=20`, increases the limit to 20.
+- The maximum accumulated byte size of resources that are pending deallocation
+  is reached.  This is default to 20% of the device memory capacity.
+  User can override by setting the environment variable
+  `NUMBA_CUDA_MAX_PENDING_DEALLOCS_RATIO`. For example,
+  `NUMBA_CUDA_MAX_PENDING_DEALLOCS_RATIO=0.5` sets the limit to 50% of the
+  capacity.
+
+Sometimes, it is desired to defer resource deallocation until a code section
+ends.  Most often, users want to avoid any implicit synchronization due to
+deallocation.  This can be done by using the following context manager:
+
+.. autofunction:: numba.cuda.defer_cleanup
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/overview.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/overview.rst
new file mode 100644
index 000000000..4d5f7d1c7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/overview.rst
@@ -0,0 +1,133 @@
+========
+Overview
+========
+
+Numba supports CUDA GPU programming by directly compiling a restricted subset
+of Python code into CUDA kernels and device functions following the CUDA
+execution model.  Kernels written in Numba appear to have direct access
+to NumPy arrays.  NumPy arrays are transferred between the CPU and the
+GPU automatically.
+
+
+Terminology
+===========
+
+Several important terms in the topic of CUDA programming are listed here:
+
+- *host*: the CPU
+- *device*: the GPU
+- *host memory*: the system main memory
+- *device memory*: onboard memory on a GPU card
+- *kernels*: a GPU function launched by the host and executed on the device
+- *device function*: a GPU function executed on the device which can only be
+  called from the device (i.e. from a kernel or another device function)
+
+
+Programming model
+=================
+
+Most CUDA programming facilities exposed by Numba map directly to the CUDA
+C language offered by NVidia.  Therefore, it is recommended you read the
+official `CUDA C programming guide <http://docs.nvidia.com/cuda/cuda-c-programming-guide>`_.
+
+
+Requirements
+============
+
+Supported GPUs
+--------------
+
+Numba supports CUDA-enabled GPUs with Compute Capability 3.5 or greater.
+Support for devices with Compute Capability less than 5.3 is deprecated, and
+will be removed in a future Numba release.
+
+Devices with Compute Capability 5.3 or greater include (but are not limited to):
+
+- Embedded platforms: NVIDIA Jetson Nano, TX1, TX2, Xavier NX, AGX Xavier, AGX
+  Orin.
+- Desktop / Server GPUs: All GPUs with Pascal microarchitecture or later. E.g.
+  GTX 10 / 16 series, RTX 20 / 30 series, Quadro P / V / RTX series, RTX A
+  series, H100.
+- Laptop GPUs: All GPUs with Pascal microarchitecture or later. E.g. MX series,
+  Quadro P / T series (mobile), RTX 20 / 30 series (mobile), RTX A series (mobile).
+
+Software
+--------
+
+Numba aims to support CUDA Toolkit versions released within the last 3 years.
+An NVIDIA driver sufficient for the toolkit version is also required.
+Presently:
+
+* 10.2 is the minimum required toolkit version.
+* 11.2 or later is recommended, as it uses an NVVM version based on LLVM 7 (as
+  opposed to 3.4 in earlier releases).
+
+CUDA is supported on 64-bit Linux and Windows.
+
+If you are using Conda, you can install the CUDA toolkit with::
+
+   $ conda install cudatoolkit
+
+If you are not using Conda or if you want to use a different version of CUDA
+toolkit, the following describes how Numba searches for a CUDA toolkit
+installation.
+
+.. _cuda-bindings:
+
+CUDA Bindings
+~~~~~~~~~~~~~
+
+Numba supports interacting with the CUDA Driver API via the `NVIDIA CUDA Python
+bindings <https://nvidia.github.io/cuda-python/>`_ and its own ctypes-based
+bindings. Functionality is equivalent between the two bindings. The
+ctypes-based bindings are presently the default, but the NVIDIA bindings will
+be used by default (if they are available in the environment) in a future Numba
+release.
+
+You can install the NVIDIA bindings with::
+
+   $ conda install nvidia::cuda-python
+
+if you are using Conda, or::
+
+   $ pip install cuda-python
+
+if you are using pip.
+
+The use of the NVIDIA bindings is enabled by setting the environment variable
+:envvar:`NUMBA_CUDA_USE_NVIDIA_BINDING` to ``"1"``.
+
+.. _cudatoolkit-lookup:
+
+Setting CUDA Installation Path
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Numba searches for a CUDA toolkit installation in the following order:
+
+1. Conda installed `cudatoolkit` package.
+2. Environment variable ``CUDA_HOME``, which points to the directory of the
+   installed CUDA toolkit (i.e. ``/home/user/cuda-10``)
+3. System-wide installation at exactly ``/usr/local/cuda`` on Linux platforms.
+   Versioned installation paths (i.e. ``/usr/local/cuda-10.0``) are intentionally
+   ignored.  Users can use ``CUDA_HOME`` to select specific versions.
+
+In addition to the CUDA toolkit libraries, which can be installed by conda into
+an environment or installed system-wide by the `CUDA SDK installer
+<(https://developer.nvidia.com/cuda-downloads)>`_, the CUDA target in Numba
+also requires an up-to-date NVIDIA graphics driver.  Updated graphics drivers
+are also installed by the CUDA SDK installer, so there is no need to do both.
+Note that on macOS, the CUDA SDK must be installed to get the required driver,
+and the driver is only supported on macOS prior to 10.14 (Mojave).  If the
+``libcuda`` library is in a non-standard location, users can set environment
+variable ``NUMBA_CUDA_DRIVER`` to the file path (not the directory path) of the
+shared library file.
+
+
+Missing CUDA Features
+=====================
+
+Numba does not implement all features of CUDA, yet.  Some missing features
+are listed below:
+
+* dynamic parallelism
+* texture memory
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/random.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/random.rst
new file mode 100644
index 000000000..6f3ebd85a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/random.rst
@@ -0,0 +1,90 @@
+
+.. _cuda-random:
+
+Random Number Generation
+========================
+
+Numba provides a random number generation algorithm that can be executed on
+the GPU.  Due to technical issues with how NVIDIA implemented cuRAND, however,
+Numba's GPU random number generator is not based on cuRAND.  Instead, Numba's
+GPU RNG is an implementation of the `xoroshiro128+ algorithm
+<http://xoroshiro.di.unimi.it/>`_. The xoroshiro128+ algorithm has a period of
+``2**128 - 1``, which is shorter than the period of the XORWOW algorithm
+used by default in cuRAND, but xoroshiro128+ still passes the BigCrush tests
+of random number generator quality.
+
+When using any RNG on the GPU, it is important to make sure that each thread
+has its own RNG state, and they have been initialized to produce non-overlapping
+sequences.  The  numba.cuda.random module provides a host function to do this,
+as well as CUDA device functions to obtain uniformly or normally distributed
+random numbers.
+
+.. note:: Numba (like cuRAND) uses the
+    `Box-Muller transform <https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform>`
+    to generate normally distributed random numbers from a uniform generator.
+    However, Box-Muller generates pairs of random numbers, and the current
+    implementation only returns one of them.  As a result, generating normally
+    distributed values is half the speed of uniformly distributed values.
+
+.. automodule:: numba.cuda.random
+    :members: create_xoroshiro128p_states, init_xoroshiro128p_states, xoroshiro128p_uniform_float32, xoroshiro128p_uniform_float64, xoroshiro128p_normal_float32, xoroshiro128p_normal_float64
+    :noindex:
+
+A simple example
+''''''''''''''''
+
+Here is a sample program that uses the random number generator::
+
+    from __future__ import print_function, absolute_import
+
+    from numba import cuda
+    from numba.cuda.random import create_xoroshiro128p_states, xoroshiro128p_uniform_float32
+    import numpy as np
+
+    @cuda.jit
+    def compute_pi(rng_states, iterations, out):
+        """Find the maximum value in values and store in result[0]"""
+        thread_id = cuda.grid(1)
+
+        # Compute pi by drawing random (x, y) points and finding what
+        # fraction lie inside a unit circle
+        inside = 0
+        for i in range(iterations):
+            x = xoroshiro128p_uniform_float32(rng_states, thread_id)
+            y = xoroshiro128p_uniform_float32(rng_states, thread_id)
+            if x**2 + y**2 <= 1.0:
+                inside += 1
+
+        out[thread_id] = 4.0 * inside / iterations
+
+    threads_per_block = 64
+    blocks = 24
+    rng_states = create_xoroshiro128p_states(threads_per_block * blocks, seed=1)
+    out = np.zeros(threads_per_block * blocks, dtype=np.float32)
+
+    compute_pi[blocks, threads_per_block](rng_states, 10000, out)
+    print('pi:', out.mean())
+
+An example of managing RNG state size and using a 3D grid
+'''''''''''''''''''''''''''''''''''''''''''''''''''''''''
+
+The number of RNG states scales with the number of threads using the RNG, so it
+is often better to use strided loops in conjunction with the RNG in order to
+keep the state size manageable.
+
+In the following example, which initializes a large 3D array with random
+numbers, using one thread per output element would result in 453,617,100 RNG
+states.  This would take a long time to initialize and poorly utilize the GPU.
+Instead, it uses a fixed size 3D grid with a total of 2,097,152 (``(16 ** 3) *
+(8 ** 3)``) threads striding over the output array. The 3D thread indices
+``startx``, ``starty``, and ``startz``  are linearized into a 1D index,
+``tid``, to index into the 2,097,152 RNG states.
+
+
+.. literalinclude:: ../../../numba/cuda/tests/doc_examples/test_random.py
+   :language: python
+   :caption: from ``test_ex_3d_grid of ``numba/cuda/tests/doc_example/test_random.py``
+   :start-after: magictoken.ex_3d_grid.begin
+   :end-before: magictoken.ex_3d_grid.end
+   :dedent: 8
+   :linenos:
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/reduction.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/reduction.rst
new file mode 100644
index 000000000..674728408
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/reduction.rst
@@ -0,0 +1,33 @@
+GPU Reduction
+==============
+
+Writing a reduction algorithm for CUDA GPU can be tricky.  Numba provides a
+``@reduce`` decorator for converting a simple binary operation into a reduction
+kernel. An example follows::
+
+    import numpy
+    from numba import cuda
+
+    @cuda.reduce
+    def sum_reduce(a, b):
+        return a + b
+
+    A = (numpy.arange(1234, dtype=numpy.float64)) + 1
+    expect = A.sum()      # NumPy sum reduction
+    got = sum_reduce(A)   # cuda sum reduction
+    assert expect == got
+
+Lambda functions can also be used here::
+
+    sum_reduce = cuda.reduce(lambda a, b: a + b)
+
+The Reduce class
+----------------
+
+The ``reduce`` decorator creates an instance of the ``Reduce`` class.
+Currently, ``reduce`` is an alias to ``Reduce``, but this behavior is not
+guaranteed.
+
+.. autoclass:: numba.cuda.Reduce
+   :members: __init__, __call__
+   :member-order: bysource
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/simulator.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/simulator.rst
new file mode 100644
index 000000000..099ffc347
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/simulator.rst
@@ -0,0 +1,104 @@
+
+.. _simulator:
+
+=================================================
+Debugging CUDA Python with the the CUDA Simulator
+=================================================
+
+Numba includes a CUDA Simulator that implements most of the semantics in CUDA
+Python using the Python interpreter and some additional Python code. This can
+be used to debug CUDA Python code, either by adding print statements to your
+code, or by using the debugger to step through the execution of an individual
+thread.
+
+The simulator deliberately allows running non-CUDA code like starting a debugger 
+and printing arbitrary expressions for debugging purposes. Therefore, it is
+best to start from code that compiles for the CUDA target, and then move over to
+the simulator to investigate issues.
+
+Execution of kernels is performed by the simulator one block at a time. One
+thread is spawned for each thread in the block, and scheduling of the execution
+of these threads is left up to the operating system.
+
+Using the simulator
+===================
+
+The simulator is enabled by setting the environment variable
+:envvar:`NUMBA_ENABLE_CUDASIM` to 1 prior to importing Numba. CUDA Python code 
+may then be executed as normal. The easiest way to use the debugger inside a
+kernel is to only stop a single thread, otherwise the interaction with the
+debugger is difficult to handle. For example, the kernel below will stop in
+the thread ``<<<(3,0,0), (1, 0, 0)>>>``::
+
+    @cuda.jit
+    def vec_add(A, B, out):
+        x = cuda.threadIdx.x
+        bx = cuda.blockIdx.x
+        bdx = cuda.blockDim.x
+        if x == 1 and bx == 3:
+            from pdb import set_trace; set_trace()
+        i = bx * bdx + x
+        out[i] = A[i] + B[i]
+
+when invoked with a one-dimensional grid and one-dimensional blocks.
+
+Supported features
+==================
+
+The simulator aims to provide as complete a simulation of execution on a real
+GPU as possible - in particular, the following are supported:
+
+* Atomic operations
+* Constant memory
+* Local memory
+* Shared memory: declarations of shared memory arrays must be on separate source
+  lines, since the simulator uses source line information to keep track of
+  allocations of shared memory across threads.
+* Mapped arrays.
+* Host and device memory operations: copying and setting memory.
+* :func:`.syncthreads` is supported - however, in the case where divergent
+  threads enter different :func:`.syncthreads` calls, the launch will not fail,
+  but unexpected behaviour will occur. A future version of the simulator may
+  detect this condition.
+* The stream API is supported, but all operations occur sequentially and
+  synchronously, unlike on a real device. Synchronising on a stream is therefore
+  a no-op.
+* The event API is also supported, but provides no meaningful timing
+  information.
+* Data transfer to and from the GPU - in particular, creating array objects with
+  :func:`.device_array` and :func:`.device_array_like`. The APIs for pinned memory
+  :func:`.pinned` and :func:`.pinned_array` are also supported, but no pinning
+  takes place.
+* The driver API implementation of the list of GPU contexts (``cuda.gpus`` and
+  ``cuda.cudadrv.devices.gpus``) is supported, and reports a single GPU context.
+  This context can be closed and reset as the real one would.
+* The :func:`.detect` function is supported, and reports one device called
+  `SIMULATOR`.
+* Cooperative grids: A cooperative kernel can be launched, but with only one
+  block - the simulator always returns ``1`` from a kernel overload's
+  :meth:`~numba.cuda.dispatcher._Kernel.max_cooperative_grid_blocks` method.
+
+Some limitations of the simulator include:
+
+* It does not perform type checking/type inference. If any argument types to a
+  jitted function are incorrect, or if the specification of the type of any
+  local variables are incorrect, this will not be detected by the simulator.
+* Only one GPU is simulated.
+* Multithreaded accesses to a single GPU are not supported, and will result in
+  unexpected behaviour.
+* Most of the driver API is unimplemented.
+* It is not possible to link PTX code with CUDA Python functions.
+* Warps and warp-level operations are not yet implemented.
+* Because the simulator executes kernels using the Python interpreter,
+  structured array access by attribute that works with the hardware target may
+  fail in the simulator - see :ref:`structured-array-access`.
+* Operations directly against device arrays are only partially supported, that
+  is, testing equality, less than, greater than, and basic mathematical 
+  operations are supported, but many other operations, such as the in-place 
+  operators and bit operators are not.
+* The :func:`ffs() <numba.cuda.ffs>` function only works correctly for values
+  that can be represented using 32-bit integers.
+
+Obviously, the speed of the simulator is also much lower than that of a real
+device. It may be necessary to reduce the size of input data and the size of the
+CUDA grid in order to make debugging with the simulator tractable.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/ufunc.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/ufunc.rst
new file mode 100644
index 000000000..c690557fc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/cuda/ufunc.rst
@@ -0,0 +1,154 @@
+CUDA Ufuncs and Generalized Ufuncs
+==================================
+
+This page describes the CUDA ufunc-like object.
+
+To support the programming pattern of CUDA programs, CUDA Vectorize and
+GUVectorize cannot produce a conventional ufunc.  Instead, a ufunc-like
+object is returned.  This object is a close analog but not fully
+compatible with a regular NumPy ufunc.  The CUDA ufunc adds support for
+passing intra-device arrays (already on the GPU device) to reduce
+traffic over the PCI-express bus.  It also accepts a `stream` keyword
+for launching in asynchronous mode.
+
+Example: Basic Example
+------------------------
+
+::
+
+    import math
+    from numba import vectorize, cuda
+    import numpy as np
+
+    @vectorize(['float32(float32, float32, float32)',
+                'float64(float64, float64, float64)'],
+               target='cuda')
+    def cu_discriminant(a, b, c):
+        return math.sqrt(b ** 2 - 4 * a * c)
+
+    N = 10000
+    dtype = np.float32
+
+    # prepare the input
+    A = np.array(np.random.sample(N), dtype=dtype)
+    B = np.array(np.random.sample(N) + 10, dtype=dtype)
+    C = np.array(np.random.sample(N), dtype=dtype)
+
+    D = cu_discriminant(A, B, C)
+
+    print(D)  # print result
+
+Example: Calling Device Functions
+----------------------------------
+
+All CUDA ufunc kernels have the ability to call other CUDA device functions::
+
+    from numba import vectorize, cuda
+
+    # define a device function
+    @cuda.jit('float32(float32, float32, float32)', device=True, inline=True)
+    def cu_device_fn(x, y, z):
+        return x ** y / z
+
+    # define a ufunc that calls our device function
+    @vectorize(['float32(float32, float32, float32)'], target='cuda')
+    def cu_ufunc(x, y, z):
+        return cu_device_fn(x, y, z)
+
+
+Generalized CUDA ufuncs
+-----------------------
+
+Generalized ufuncs may be executed on the GPU using CUDA, analogous to
+the CUDA ufunc functionality.  This may be accomplished as follows::
+
+    from numba import guvectorize
+
+    @guvectorize(['void(float32[:,:], float32[:,:], float32[:,:])'], 
+                 '(m,n),(n,p)->(m,p)', target='cuda')
+    def matmulcore(A, B, C):
+        ...
+
+There are times when the gufunc kernel uses too many of a GPU's
+resources, which can cause the kernel launch to fail.  The user can
+explicitly control the maximum size of the thread block by setting
+the `max_blocksize` attribute on the compiled gufunc object.
+
+::
+
+    from numba import guvectorize
+
+    @guvectorize(..., target='cuda')
+    def very_complex_kernel(A, B, C):
+        ...
+
+    very_complex_kernel.max_blocksize = 32  # limits to 32 threads per block
+
+.. comment
+
+    Example: A Chunk at a Time
+    ---------------------------
+
+    Partitioning your data into chunks allows computation and memory transfer
+    to be overlapped.  This can increase the throughput of your ufunc and
+    enables your ufunc to operate on data that is larger than the memory
+    capacity of your GPU.  For example:
+
+    ::
+
+        import math
+        from numba import vectorize, cuda
+        import numpy as np
+
+        # the ufunc kernel
+        def discriminant(a, b, c):
+            return math.sqrt(b ** 2 - 4 * a * c)
+
+        cu_discriminant = vectorize(['float32(float32, float32, float32)',
+                                     'float64(float64, float64, float64)'],
+                                    target='cuda')(discriminant)
+
+        N = int(1e+8)
+        dtype = np.float32
+
+        # prepare the input
+        A = np.array(np.random.sample(N), dtype=dtype)
+        B = np.array(np.random.sample(N) + 10, dtype=dtype)
+        C = np.array(np.random.sample(N), dtype=dtype)
+        D = np.empty(A.shape, dtype=A.dtype)
+
+        # create a CUDA stream
+        stream = cuda.stream()
+
+        chunksize = 1e+6
+        chunkcount = N // chunksize
+
+        # partition NumPy arrays into chunks
+        # no copying is performed
+        sA = np.split(A, chunkcount)
+        sB = np.split(B, chunkcount)
+        sC = np.split(C, chunkcount)
+        sD = np.split(D, chunkcount)
+
+        device_ptrs = []
+
+        with stream.auto_synchronize():
+            # every operation in this context with be launched asynchronously
+            # by using the CUDA stream
+
+            # for each chunk
+            for a, b, c, d in zip(sA, sB, sC, sD):
+                # transfer to device
+                dA = cuda.to_device(a, stream)
+                dB = cuda.to_device(b, stream)
+                dC = cuda.to_device(c, stream)
+                dD = cuda.to_device(d, stream, copy=False) # no copying
+                # launch kernel
+                cu_discriminant(dA, dB, dC, out=dD, stream=stream)
+                # retrieve result
+                dD.copy_to_host(d, stream)
+                # store device pointers to prevent them from freeing before
+                # the kernel is scheduled
+                device_ptrs.extend([dA, dB, dC, dD])
+
+        # data is ready at this point inside D
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/architecture.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/architecture.rst
new file mode 100644
index 000000000..3e9dffe1b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/architecture.rst
@@ -0,0 +1,909 @@
+
+.. _architecture:
+
+==================
+Numba architecture
+==================
+
+Introduction
+============
+
+Numba is a compiler for Python bytecode with optional type-specialization.
+
+Suppose you enter a function like this into the standard Python interpreter
+(henceforward referred to as "CPython")::
+
+    def add(a, b):
+        return a + b
+
+The interpreter will immediately parse the function and convert it into a
+bytecode representation that describes how the CPython interpreter should
+execute the function at a low level.  For the example above, it looks
+something like this::
+
+    >>> import dis
+    >>> dis.dis(add)
+    2           0 LOAD_FAST                0 (a)
+                3 LOAD_FAST                1 (b)
+                6 BINARY_ADD
+                7 RETURN_VALUE
+
+
+CPython uses a stack-based interpreter (much like an HP calculator), so the
+code first pushes two local variables onto the stack.  The ``BINARY_ADD``
+opcode pops the top two arguments off the stack and makes a Python C API
+function call that is equivalent to calling ``a.__add__(b)``.  The result is
+then pushed onto the top of the interpreter stack.  Finally, the
+``RETURN_VALUE`` opcode returns value on the top of the stack as the result of
+the function call.
+
+Numba can take this bytecode and compile it to machine code that performs the
+same operations as the CPython interpreter, treating ``a`` and ``b`` as
+generic Python objects.  The full semantics of Python are preserved, and the
+compiled function can be used with any kind of objects that have the add
+operator defined.  When a Numba function is compiled this way, we say that it
+has been compiled in :term:`object mode`, because the code still manipulates
+Python objects.
+
+Numba code compiled in object mode is not much faster than executing the
+original Python function in the CPython interpreter.  However, if we
+specialize the function to only run with certain data types, Numba can
+generate much shorter and more efficient code that manipulates the data
+natively without any calls into the Python C API.  When code has been compiled
+for specific data types so that the function body no longer relies on the
+Python runtime, we say the function has been compiled in :term:`nopython mode`.
+Numeric code compiled in nopython mode can be hundreds of times faster
+than the original Python.
+
+
+Compiler architecture
+=====================
+
+Like many compilers, Numba can be conceptually divided into a
+*frontend* and a *backend*.
+
+The Numba *frontend* comprises the stages which analyze the Python bytecode,
+translate it to :term:`Numba IR` and perform various transformations and
+analysis steps on the IR.  One of the key steps is :term:`type inference`.
+The frontend must succeed in typing all variables unambiguously in order
+for the backend to generate code in :term:`nopython mode`, because the
+backend uses type information to match appropriate code generators with
+the values they operate on.
+
+The Numba *backend* walks the Numba IR resulting from the frontend analyses
+and exploits the type information deduced by the type inference phase to
+produce the right LLVM code for each encountered operation.  After LLVM
+code is produced, the LLVM library is asked to optimize it and generate
+native processor code for the final, native function.
+
+There are other pieces besides the compiler frontend and backend, such
+as the caching machinery for JIT functions.  Those pieces are not considered
+in this document.
+
+
+Contexts
+========
+
+Numba is quite flexible, allowing it to generate code for different hardware
+architectures like CPUs and GPUs.  In order to support these different
+applications, Numba uses a *typing context* and a *target context*.
+
+A *typing context* is used in the compiler frontend to perform type inference
+on operations and values in the function.  Similar typing contexts could be
+used for many architectures because for nearly all cases, typing inference
+is hardware-independent.  However, Numba currently has a different typing
+context for each target.
+
+A *target context* is used to generate the specific instruction sequence
+required to operate on the Numba types identified during type inference.
+Target contexts are architecture-specific and are flexible in defining
+the execution model and available Python APIs.  For example, Numba has a "cpu"
+and a "cuda" context for those two kinds of architecture, and a "parallel"
+context which produces multithreaded CPU code.
+
+
+Compiler stages
+===============
+
+The :func:`~numba.jit` decorator in Numba ultimately calls
+``numba.compiler.compile_extra()`` which compiles the Python function in a
+multi-stage process, described below.
+
+Stage 1: Analyze bytecode
+-------------------------
+
+At the start of compilation, the function bytecode is passed to an instance of
+the Numba interpreter (``numba.interpreter``).  The interpreter object
+analyzes the bytecode to find the control flow graph (``numba.controlflow``).
+The control flow graph (CFG) describes the ways that execution can move from one
+block to the next inside the function as a result of loops and branches.
+
+The data flow analysis (``numba.dataflow``) takes the control flow graph and
+traces how values get pushed and popped off the Python interpreter stack for
+different code paths.  This is important to understand the lifetimes of
+variables on the stack, which are needed in Stage 2.
+
+If you set the environment variable ``NUMBA_DUMP_CFG`` to 1, Numba will dump
+the results of the control flow graph analysis to the screen.  Our ``add()``
+example is pretty boring, since there is only one statement block::
+
+    CFG adjacency lists:
+    {0: []}
+    CFG dominators:
+    {0: set([0])}
+    CFG post-dominators:
+    {0: set([0])}
+    CFG back edges: []
+    CFG loops:
+    {}
+    CFG node-to-loops:
+    {0: []}
+
+A function with more complex flow control will have a more interesting
+control flow graph.  This function::
+
+    def doloops(n):
+        acc = 0
+        for i in range(n):
+            acc += 1
+            if n == 10:
+                break
+        return acc
+
+compiles to this bytecode::
+
+      9           0 LOAD_CONST               1 (0)
+                  3 STORE_FAST               1 (acc)
+
+     10           6 SETUP_LOOP              46 (to 55)
+                  9 LOAD_GLOBAL              0 (range)
+                 12 LOAD_FAST                0 (n)
+                 15 CALL_FUNCTION            1
+                 18 GET_ITER
+            >>   19 FOR_ITER                32 (to 54)
+                 22 STORE_FAST               2 (i)
+
+     11          25 LOAD_FAST                1 (acc)
+                 28 LOAD_CONST               2 (1)
+                 31 INPLACE_ADD
+                 32 STORE_FAST               1 (acc)
+
+     12          35 LOAD_FAST                0 (n)
+                 38 LOAD_CONST               3 (10)
+                 41 COMPARE_OP               2 (==)
+                 44 POP_JUMP_IF_FALSE       19
+
+     13          47 BREAK_LOOP
+                 48 JUMP_ABSOLUTE           19
+                 51 JUMP_ABSOLUTE           19
+            >>   54 POP_BLOCK
+
+     14     >>   55 LOAD_FAST                1 (acc)
+                 58 RETURN_VALUE
+
+The corresponding CFG for this bytecode is::
+
+    CFG adjacency lists:
+    {0: [6], 6: [19], 19: [54, 22], 22: [19, 47], 47: [55], 54: [55], 55: []}
+    CFG dominators:
+    {0: set([0]),
+     6: set([0, 6]),
+     19: set([0, 6, 19]),
+     22: set([0, 6, 19, 22]),
+     47: set([0, 6, 19, 22, 47]),
+     54: set([0, 6, 19, 54]),
+     55: set([0, 6, 19, 55])}
+    CFG post-dominators:
+    {0: set([0, 6, 19, 55]),
+     6: set([6, 19, 55]),
+     19: set([19, 55]),
+     22: set([22, 55]),
+     47: set([47, 55]),
+     54: set([54, 55]),
+     55: set([55])}
+    CFG back edges: [(22, 19)]
+    CFG loops:
+    {19: Loop(entries=set([6]), exits=set([54, 47]), header=19, body=set([19, 22]))}
+    CFG node-to-loops:
+    {0: [], 6: [], 19: [19], 22: [19], 47: [], 54: [], 55: []}
+
+The numbers in the CFG refer to the bytecode offsets shown just to the left
+of the opcode names above.
+
+.. _arch_generate_numba_ir:
+
+Stage 2: Generate the Numba IR
+------------------------------
+
+Once the control flow and data analyses are complete, the Numba interpreter
+can step through the bytecode and translate it into an Numba-internal
+intermediate representation.  This translation process changes the function
+from a stack machine representation (used by the Python interpreter) to a
+register machine representation (used by LLVM).
+
+Although the IR is stored in memory as a tree of objects, it can be serialized
+to a string for debugging.  If you set the environment variable
+``NUMBA_DUMP_IR`` equal to 1, the Numba IR will be dumped to the screen.  For
+the ``add()`` function described above, the Numba IR looks like::
+
+   label 0:
+       a = arg(0, name=a)                       ['a']
+       b = arg(1, name=b)                       ['b']
+       $0.3 = a + b                             ['$0.3', 'a', 'b']
+       del b                                    []
+       del a                                    []
+       $0.4 = cast(value=$0.3)                  ['$0.3', '$0.4']
+       del $0.3                                 []
+       return $0.4                              ['$0.4']
+
+The ``del`` instructions are produced by :ref:`live variable analysis`.
+Those instructions ensure references are not leaked.
+In :term:`nopython mode`, some objects are tracked by the Numba runtime and
+some are not.  For tracked objects, a dereference operation is emitted;
+otherwise, the instruction is an no-op.
+In :term:`object mode` each variable contains an owned reference to a PyObject.
+
+
+.. _`rewrite-untyped-ir`:
+
+Stage 3: Rewrite untyped IR
+---------------------------
+
+Before running type inference, it may be desired to run certain
+transformations on the Numba IR.  One such example is to detect ``raise``
+statements which have an implicitly constant argument, so as to
+support them in :term:`nopython mode`.  Let's say you compile the
+following function with Numba::
+
+   def f(x):
+      if x == 0:
+         raise ValueError("x cannot be zero")
+
+If you set the :envvar:`NUMBA_DUMP_IR` environment variable to ``1``,
+you'll see the IR being rewritten before the type inference phase::
+
+   REWRITING:
+       del $0.3                                 []
+       $12.1 = global(ValueError: <class 'ValueError'>) ['$12.1']
+       $const12.2 = const(str, x cannot be zero) ['$const12.2']
+       $12.3 = call $12.1($const12.2)           ['$12.1', '$12.3', '$const12.2']
+       del $const12.2                           []
+       del $12.1                                []
+       raise $12.3                              ['$12.3']
+   ____________________________________________________________
+       del $0.3                                 []
+       $12.1 = global(ValueError: <class 'ValueError'>) ['$12.1']
+       $const12.2 = const(str, x cannot be zero) ['$const12.2']
+       $12.3 = call $12.1($const12.2)           ['$12.1', '$12.3', '$const12.2']
+       del $const12.2                           []
+       del $12.1                                []
+       raise <class 'ValueError'>('x cannot be zero') []
+
+
+.. _arch_type_inference:
+
+Stage 4: Infer types
+--------------------
+
+Now that the Numba IR has been generated, type analysis can be performed.  The
+types of the function arguments can be taken either from the explicit function
+signature given in the ``@jit`` decorator (such as ``@jit('float64(float64,
+float64)')``), or they can be taken from the types of the actual function
+arguments if compilation is happening when the function is first called.
+
+The type inference engine is found in ``numba.typeinfer``.  Its job is to
+assign a type to every intermediate variable in the Numba IR.  The result of
+this pass can be seen by setting the :envvar:`NUMBA_DUMP_ANNOTATION`
+environment variable to 1:
+
+.. code-block:: python
+
+   -----------------------------------ANNOTATION-----------------------------------
+   # File: archex.py
+   # --- LINE 4 ---
+
+   @jit(nopython=True)
+
+   # --- LINE 5 ---
+
+   def add(a, b):
+
+       # --- LINE 6 ---
+       # label 0
+       #   a = arg(0, name=a)  :: int64
+       #   b = arg(1, name=b)  :: int64
+       #   $0.3 = a + b  :: int64
+       #   del b
+       #   del a
+       #   $0.4 = cast(value=$0.3)  :: int64
+       #   del $0.3
+       #   return $0.4
+
+       return a + b
+
+
+If type inference fails to find a consistent type assignment for all the
+intermediate variables, it will label every variable as type ``pyobject`` and
+fall back to object mode.  Type inference can fail when unsupported Python
+types, language features, or functions are used in the function body.
+
+
+.. _`rewrite-typed-ir`:
+
+Stage 5a: Rewrite typed IR
+--------------------------
+
+This pass's purpose is to perform any high-level optimizations that still
+require, or could at least benefit from, Numba IR type information.
+
+One example of a problem domain that isn't as easily optimized once
+lowered is the domain of multidimensional array operations.  When
+Numba lowers an array operation, Numba treats the operation like a
+full ufunc kernel.  During lowering a single array operation, Numba
+generates an inline broadcasting loop that creates a new result array.
+Then Numba generates an application loop that applies the operator
+over the array inputs.  Recognizing and rewriting these loops once
+they are lowered into LLVM is hard, if not impossible.
+
+An example pair of optimizations in the domain of array operators is
+loop fusion and shortcut deforestation.  When the optimizer
+recognizes that the output of one array operator is being fed into
+another array operator, and only to that array operator, it can fuse
+the two loops into a single loop.  The optimizer can further eliminate
+the temporary array allocated for the initial operation by directly
+feeding the result of the first operation into the second, skipping
+the store and load to the intermediate array.  This elimination is
+known as shortcut deforestation.  Numba currently uses the rewrite
+pass to implement these array optimizations.  For more information,
+please consult the ":ref:`case-study-array-expressions`" subsection,
+later in this document.
+
+One can see the result of rewriting by setting the
+:envvar:`NUMBA_DUMP_IR` environment variable to a non-zero value (such
+as 1).  The following example shows the output of the rewrite pass as
+it recognizes an array expression consisting of a multiply and add,
+and outputs a fused kernel as a special operator, :func:`arrayexpr`::
+
+  ______________________________________________________________________
+  REWRITING:
+  a0 = arg(0, name=a0)                     ['a0']
+  a1 = arg(1, name=a1)                     ['a1']
+  a2 = arg(2, name=a2)                     ['a2']
+  $0.3 = a0 * a1                           ['$0.3', 'a0', 'a1']
+  del a1                                   []
+  del a0                                   []
+  $0.5 = $0.3 + a2                         ['$0.3', '$0.5', 'a2']
+  del a2                                   []
+  del $0.3                                 []
+  $0.6 = cast(value=$0.5)                  ['$0.5', '$0.6']
+  del $0.5                                 []
+  return $0.6                              ['$0.6']
+  ____________________________________________________________
+  a0 = arg(0, name=a0)                     ['a0']
+  a1 = arg(1, name=a1)                     ['a1']
+  a2 = arg(2, name=a2)                     ['a2']
+  $0.5 = arrayexpr(ty=array(float64, 1d, C), expr=('+', [('*', [Var(a0, test.py (14)), Var(a1, test.py (14))]), Var(a2, test.py (14))])) ['$0.5', 'a0', 'a1', 'a2']
+  del a0                                   []
+  del a1                                   []
+  del a2                                   []
+  $0.6 = cast(value=$0.5)                  ['$0.5', '$0.6']
+  del $0.5                                 []
+  return $0.6                              ['$0.6']
+  ______________________________________________________________________
+
+Following this rewrite, Numba lowers the array expression into a new
+ufunc-like function that is inlined into a single loop that only
+allocates a single result array.
+
+
+.. _`parallel-accelerator`:
+
+Stage 5b: Perform Automatic Parallelization
+-------------------------------------------
+
+This pass is only performed if the ``parallel`` option in the :func:`~numba.jit`
+decorator is set to ``True``.  This pass finds parallelism implicit in the
+semantics of operations in the Numba IR and replaces those operations
+with explicitly parallel representations of those operations using a
+special `parfor` operator.  Then, optimizations are performed to maximize
+the number of parfors that are adjacent to each other such that they can
+then be fused together into one parfor that takes only one pass over the
+data and will thus typically have better cache performance.  Finally,
+during lowering, these parfor operators are converted to a form similar
+to guvectorize to implement the actual parallelism.
+
+The automatic parallelization pass has a number of sub-passes, many of
+which are controllable using a dictionary of options passed via the
+``parallel`` keyword argument to :func:`~numba.jit`::
+
+   { 'comprehension': True/False,  # parallel comprehension
+     'prange':        True/False,  # parallel for-loop
+     'numpy':         True/False,  # parallel numpy calls
+     'reduction':     True/False,  # parallel reduce calls
+     'setitem':       True/False,  # parallel setitem
+     'stencil':       True/False,  # parallel stencils
+     'fusion':        True/False,  # enable fusion or not
+   }
+
+The default is set to `True` for all of them. The sub-passes are
+described in more detail in the following paragraphs.
+
+#. CFG Simplification
+    Sometimes Numba IR will contain chains of blocks containing no loops which
+    are merged in this sub-pass into single blocks.  This sub-pass simplifies
+    subsequent analysis of the IR.
+
+#. Numpy canonicalization
+    Some Numpy operations can be written as operations on Numpy objects (e.g.
+    ``arr.sum()``), or as calls to Numpy taking those objects (e.g.
+    ``numpy.sum(arr)``).  This sub-pass converts all such operations to the
+    latter form for cleaner subsequent analysis.
+
+#. Array analysis
+    A critical requirement for later parfor fusion is that parfors have
+    identical iteration spaces and these iteration spaces typically correspond
+    to the sizes of the dimensions of Numpy arrays.  In this sub-pass, the IR is
+    analyzed to determine equivalence classes for the dimensions of Numpy
+    arrays.  Consider the example, ``a = b + 1``, where ``a`` and ``b`` are both
+    Numpy arrays.  Here, we know that each dimension of ``a`` must have the same
+    equivalence class as the corresponding dimension of ``b``.  Typically,
+    routines rich in Numpy operations will enable equivalence classes to be
+    fully known for all arrays created within a function.
+
+    Array analysis will also reason about size equivalence for slice selection,
+    and boolean array masking (one dimensional only). For example, it is able to
+    infer that ``a[1 : n-1]`` is of the same size as ``b[0 : n-2]``.
+
+    Array analysis may also insert safety assumptions to ensure pre-conditions
+    related to array sizes are met before an operation can be parallelized.
+    For example, ``np.dot(X, w)`` between a 2-D matrix ``X`` and a 1-D vector ``w``
+    requires that the second dimension of ``X`` is of the same size as ``w``.
+    Usually this kind of runtime check is automatically inserted, but if array
+    analysis can infer such equivalence, it will skip them.
+
+    Users can even help array analysis by turning implicit knowledge about
+    array sizes into explicit assertions. For example, in the code below:
+
+    .. code-block:: python
+
+       @numba.njit(parallel=True)
+       def logistic_regression(Y, X, w, iterations):
+           assert(X.shape == (Y.shape[0], w.shape[0]))
+           for i in range(iterations):
+               w -= np.dot(((1.0 / (1.0 + np.exp(-Y * np.dot(X, w))) - 1.0) * Y), X)
+           return w
+
+    Making the explicit assertion helps eliminate all bounds checks in the
+    rest of the function.
+
+#. ``prange()`` to parfor
+    The use of prange (:ref:`numba-prange`) in a for loop is an explicit
+    indication from the programmer that all iterations of the for loop can
+    execute in parallel.  In this sub-pass, we analyze the CFG to locate loops
+    and to convert those loops controlled by a prange object to the explicit
+    `parfor` operator.  Each explicit parfor operator consists of:
+
+    a. A list of loop nest information that describes the iteration space of the
+       parfor.  Each entry in the loop nest list contains an indexing variable,
+       the start of the range, the end of the range, and the step value for each
+       iteration.
+    #. An initialization (init) block which contains instructions to be executed
+       one time before the parfor begins executing.
+    #. A loop body comprising a set of basic blocks that correspond to the body
+       of the loop and compute one point in the iteration space.
+    #. The index variables used for each dimension of the iteration space.
+
+    For parfor `pranges`, the loop nest is a single entry where the start,
+    stop, and step fields come from the specified `prange`.  The init block is
+    empty for `prange` parfors and the loop body is the set of blocks in the
+    loop minus the loop header.
+
+    With parallelization on, array comprehensions (:ref:`pysupported-comprehension`)
+    will also be translated to prange so as to run in parallel. This behavior
+    be disabled by setting ``parallel={'comprehension': False}``.
+
+    Likewise, the overall `prange` to `parfor` translation can be disabled by
+    setting ``parallel={'prange': False}``, in which case `prange` is treated the
+    same as `range`.
+
+#. Numpy to parfor
+    In this sub-pass, Numpy functions such as ``ones``, ``zeros``, ``dot``, most
+    of the random number generating functions, arrayexprs (from Section
+    :ref:`rewrite-typed-ir`), and Numpy reductions are converted to parfors.
+    Generally, this conversion creates the loop nest list, whose length is equal
+    to the number of dimensions of the left-hand side of the assignment
+    instruction in the IR.  The number and size of the dimensions of the
+    left-hand-side array is taken from the array analysis information generated
+    in sub-pass 3 above.  An instruction to create the result Numpy array is
+    generated and stored in the new parfor's init block.  A basic block is
+    created for the loop body and an instruction is generated and added to the
+    end of that block to store the result of the computation into the array at
+    the current point in the iteration space.  The result stored into the array
+    depends on the operation that is being converted.  For example, for ``ones``,
+    the value stored is a constant 1.  For calls to generate a random array, the
+    value comes from a call to the same random number function but with the size
+    parameter dropped and therefore returning a scalar.  For arrayexpr operators,
+    the arrayexpr tree is converted to Numba IR and the value at the root of that
+    expression tree is used to write into the output array. The translation from
+    Numpy functions and arrayexpr operators to `parfor` can be disabled by
+    setting ``parallel={'numpy': False}``.
+
+    For reductions, the loop nest list is similarly created using the array
+    analysis information for the array being reduced.  In the init block, the
+    initial value is assigned to the reduction variable.  The loop body consists
+    of a single block in which the next value in the iteration space is fetched
+    and the reduction operation is applied to that value and the current
+    reduction value and the result stored back into the reduction value.
+    The translation of reduction functions to `parfor` can be disabled by
+    setting ``parallel={'reduction': False}``.
+
+    Setting the :envvar:`NUMBA_DEBUG_ARRAY_OPT_STATS` environment variable to
+    1 will show some statistics about parfor conversions in general.
+
+#. Setitem to parfor
+    Setting a range of array elements using a slice or boolean array selection
+    can also run in parallel.  Statement such as ``A[P] = B[Q]``
+    (or a simpler case ``A[P] = c``, where ``c`` is a scalar) is translated to
+    `parfor` if one of the following conditions is met:
+
+     a. ``P`` and ``Q`` are slices or multi-dimensional selector involving
+        scalar and slices, and ``A[P]`` and ``B[Q]`` are considered size
+        equivalent by array analysis. Only 2-value slice/range is supported,
+        3-value with a step will not be translated to `parfor`.
+     #. ``P`` and ``Q`` are the same boolean array.
+
+    This translation can be disabled by setting ``parallel={'setitem': False}``.
+
+#. Simplification
+    Performs a copy propagation and dead code elimination pass.
+
+#. Fusion
+    This sub-pass first processes each basic block and does a reordering of the
+    instructions within the block with the goal of pushing parfors lower in the
+    block and lifting non-parfors towards the start of the block.  In practice,
+    this approach does a good job of getting parfors adjacent to each other in
+    the IR, which enables more parfors to then be fused.  During parfor fusion,
+    each basic block is repeatedly scanned until no further fusion is possible.
+    During this scan, each set of adjacent instructions are considered.
+    Adjacent instructions are fused together if:
+
+    a. they are both parfors
+    #. the parfors' loop nests are the same size and the array equivalence
+       classes for each dimension of the loop nests are the same, and
+    #. the first parfor does not create a reduction variable used by the
+       second parfor.
+
+    The two parfors are fused together by adding the second parfor's init block
+    to the first's, merging the two parfors' loop bodies together and replacing
+    the instances of the second parfor's loop index variables in the second
+    parfor's body with the loop index variables for the first parfor.
+    Fusion can be disabled by setting ``parallel={'fusion': False}``.
+
+    Setting the :envvar:`NUMBA_DEBUG_ARRAY_OPT_STATS` environment variable to
+    1 will show some statistics about parfor fusions.
+
+#. Push call objects and compute parfor parameters
+    In the lowering phase described in Section :ref:`lowering`, each parfor
+    becomes a separate function executed in parallel in ``guvectorize``
+    (:ref:`guvectorize`) style.  Since parfors may use variables defined
+    previously in a function, when those parfors become separate functions,
+    those variables must be passed to the parfor function as parameters.  In
+    this sub-pass, a use-def scan is made over each parfor body and liveness
+    information is used to determine which variables are used but not defined by
+    the parfor.  That list of variables is stored here in the parfor for use
+    during lowering.  Function variables are a special case in this process
+    since function variables cannot be passed to functions compiled in nopython
+    mode.  Instead, for function variables, this sub-pass pushes the assignment
+    instruction to the function variable into the parfor body so that those do
+    not need to be passed as parameters.
+
+    To see the intermediate IR between the above sub-passes and other debugging
+    information, set the :envvar:`NUMBA_DEBUG_ARRAY_OPT` environment variable to
+    1. For the example in Section :ref:`rewrite-typed-ir`, the following IR with
+    a parfor is generated during this stage::
+
+     ______________________________________________________________________
+     label 0:
+         a0 = arg(0, name=a0)                     ['a0']
+         a0_sh_attr0.0 = getattr(attr=shape, value=a0) ['a0', 'a0_sh_attr0.0']
+         $consta00.1 = const(int, 0)              ['$consta00.1']
+         a0size0.2 = static_getitem(value=a0_sh_attr0.0, index_var=$consta00.1, index=0) ['$consta00.1', 'a0_sh_attr0.0', 'a0size0.2']
+         a1 = arg(1, name=a1)                     ['a1']
+         a1_sh_attr0.3 = getattr(attr=shape, value=a1) ['a1', 'a1_sh_attr0.3']
+         $consta10.4 = const(int, 0)              ['$consta10.4']
+         a1size0.5 = static_getitem(value=a1_sh_attr0.3, index_var=$consta10.4, index=0) ['$consta10.4', 'a1_sh_attr0.3', 'a1size0.5']
+         a2 = arg(2, name=a2)                     ['a2']
+         a2_sh_attr0.6 = getattr(attr=shape, value=a2) ['a2', 'a2_sh_attr0.6']
+         $consta20.7 = const(int, 0)              ['$consta20.7']
+         a2size0.8 = static_getitem(value=a2_sh_attr0.6, index_var=$consta20.7, index=0) ['$consta20.7', 'a2_sh_attr0.6', 'a2size0.8']
+     ---begin parfor 0---
+     index_var =  parfor_index.9
+     LoopNest(index_variable=parfor_index.9, range=0,a0size0.2,1 correlation=5)
+     init block:
+         $np_g_var.10 = global(np: <module 'numpy' from '/usr/local/lib/python3.5/dist-packages/numpy/__init__.py'>) ['$np_g_var.10']
+         $empty_attr_attr.11 = getattr(attr=empty, value=$np_g_var.10) ['$empty_attr_attr.11', '$np_g_var.10']
+         $np_typ_var.12 = getattr(attr=float64, value=$np_g_var.10) ['$np_g_var.10', '$np_typ_var.12']
+         $0.5 = call $empty_attr_attr.11(a0size0.2, $np_typ_var.12, kws=(), func=$empty_attr_attr.11, vararg=None, args=[Var(a0size0.2, test2.py (7)), Var($np_typ_var.12, test2.py (7))]) ['$0.5', '$empty_attr_attr.11', '$np_typ_var.12', 'a0size0.2']
+     label 1:
+         $arg_out_var.15 = getitem(value=a0, index=parfor_index.9) ['$arg_out_var.15', 'a0', 'parfor_index.9']
+         $arg_out_var.16 = getitem(value=a1, index=parfor_index.9) ['$arg_out_var.16', 'a1', 'parfor_index.9']
+         $arg_out_var.14 = $arg_out_var.15 * $arg_out_var.16 ['$arg_out_var.14', '$arg_out_var.15', '$arg_out_var.16']
+         $arg_out_var.17 = getitem(value=a2, index=parfor_index.9) ['$arg_out_var.17', 'a2', 'parfor_index.9']
+         $expr_out_var.13 = $arg_out_var.14 + $arg_out_var.17 ['$arg_out_var.14', '$arg_out_var.17', '$expr_out_var.13']
+         $0.5[parfor_index.9] = $expr_out_var.13  ['$0.5', '$expr_out_var.13', 'parfor_index.9']
+     ----end parfor 0----
+         $0.6 = cast(value=$0.5)                  ['$0.5', '$0.6']
+         return $0.6                              ['$0.6']
+     ______________________________________________________________________
+
+  .. _`lowering`:
+
+Stage 6a: Generate nopython LLVM IR
+-----------------------------------
+
+If type inference succeeds in finding a Numba type for every intermediate
+variable, then Numba can (potentially) generate specialized native code.  This
+process is called :term:`lowering`.  The Numba IR tree is translated into
+LLVM IR by using helper classes from `llvmlite <http://llvmlite.pydata.org/>`_.
+The machine-generated LLVM IR can seem unnecessarily verbose, but the LLVM
+toolchain is able to optimize it quite easily into compact, efficient code.
+
+The basic lowering algorithm is generic, but the specifics of how particular
+Numba IR nodes are translated to LLVM instructions is handled by the
+target context selected for compilation.  The default target context is
+the "cpu" context, defined in ``numba.targets.cpu``.
+
+The LLVM IR can be displayed by setting the :envvar:`NUMBA_DUMP_LLVM` environment
+variable to 1.  For the "cpu" context, our ``add()`` example would look like:
+
+.. code-block:: llvm
+
+   define i32 @"__main__.add$1.int64.int64"(i64* %"retptr",
+                                            {i8*, i32}** %"excinfo",
+                                            i8* %"env",
+                                            i64 %"arg.a", i64 %"arg.b")
+   {
+      entry:
+        %"a" = alloca i64
+        %"b" = alloca i64
+        %"$0.3" = alloca i64
+        %"$0.4" = alloca i64
+        br label %"B0"
+      B0:
+        store i64 %"arg.a", i64* %"a"
+        store i64 %"arg.b", i64* %"b"
+        %".8" = load i64* %"a"
+        %".9" = load i64* %"b"
+        %".10" = add i64 %".8", %".9"
+        store i64 %".10", i64* %"$0.3"
+        %".12" = load i64* %"$0.3"
+        store i64 %".12", i64* %"$0.4"
+        %".14" = load i64* %"$0.4"
+        store i64 %".14", i64* %"retptr"
+        ret i32 0
+   }
+
+The post-optimization LLVM IR can be output by setting
+:envvar:`NUMBA_DUMP_OPTIMIZED` to 1.  The optimizer shortens the code
+generated above quite significantly:
+
+.. code-block:: llvm
+
+   define i32 @"__main__.add$1.int64.int64"(i64* nocapture %retptr,
+                                            { i8*, i32 }** nocapture readnone %excinfo,
+                                            i8* nocapture readnone %env,
+                                            i64 %arg.a, i64 %arg.b)
+   {
+      entry:
+        %.10 = add i64 %arg.b, %arg.a
+        store i64 %.10, i64* %retptr, align 8
+        ret i32 0
+   }
+
+If created during :ref:`parallel-accelerator`, parfor operations are
+lowered in the following manner.  First, instructions in the parfor's init
+block are lowered into the existing function using the normal lowering code.
+Second, the loop body of the parfor is turned into a separate GUFunc.
+Third, code is emitted for the current function to call the parallel GUFunc.
+
+To create a GUFunc from the parfor body, the signature of the GUFunc is
+created by taking the parfor parameters as identified in step 9 of
+Stage :ref:`parallel-accelerator` and adding to that a special `schedule`
+parameter, across which the GUFunc will be parallelized.  The schedule
+parameter is in effect a static schedule mapping portions of the parfor
+iteration space to Numba threads and so the length of the schedule
+array is the same as the number of configured Numba threads.  To make
+this process easier and somewhat less dependent on changes to Numba IR,
+this stage creates a Python function as text that contains the parameters
+to the GUFunc and iteration code that takes the current schedule entry
+and loops through the specified portion of the iteration space.  In the
+body of that loop, a special sentinel is inserted for subsequent easy
+location.  This code that handles the processing of the iteration space
+is then ``eval``'ed into existence and the Numba compiler's run_frontend
+function is called to generate IR.  That IR is scanned to locate the
+sentinel and the sentinel is replaced with the loop body of the parfor.
+Then, the process of creating the parallel GUFunc is completed by
+compiling this merged IR with the Numba compiler's ``compile_ir`` function.
+
+To call the parallel GUFunc, the static schedule must be created.
+Code is inserted to call a function named ``do_scheduling.``  This function
+is called with the size of each of the parfor's dimensions and the number
+`N` of configured Numba threads (:envvar:`NUMBA_NUM_THREADS`).
+The ``do_scheduling`` function will divide
+the iteration space into N approximately equal sized regions (linear for
+1D, rectangular for 2D, or hyperrectangles for 3+D) and the resulting
+schedule is passed to the parallel GUFunc.  The number of threads
+dedicated to a given dimension of the full iteration space is roughly
+proportional to the ratio of the size of the given dimension to the sum
+of the sizes of all the dimensions of the iteration space.
+
+Parallel reductions are not natively provided by GUFuncs but the parfor
+lowering strategy allows us to use GUFuncs in a way that reductions can
+be performed in parallel.  To accomplish this, for each reduction variable
+computed by a parfor, the parallel GUFunc and the code that calls it are
+modified to make the scalar reduction variable into an array of reduction
+variables whose length is equal to the number of Numba threads.  In addition,
+the GUFunc still contains a scalar version of the reduction variable that
+is updated by the parfor body during each iteration.  One time at the
+end of the GUFunc this local reduction variable is copied into the
+reduction array.  In this way, false sharing of the reduction array is
+prevented.  Code is also inserted into the main
+function after the parallel GUFunc has returned that does a reduction
+across this smaller reduction array and this final reduction value is
+then stored into the original scalar reduction variable.
+
+The GUFunc corresponding to the example from Section :ref:`parallel-accelerator`
+can be seen below::
+
+  ______________________________________________________________________
+  label 0:
+      sched.29 = arg(0, name=sched)            ['sched.29']
+      a0 = arg(1, name=a0)                     ['a0']
+      a1 = arg(2, name=a1)                     ['a1']
+      a2 = arg(3, name=a2)                     ['a2']
+      _0_5 = arg(4, name=_0_5)                 ['_0_5']
+      $3.1.24 = global(range: <class 'range'>) ['$3.1.24']
+      $const3.3.21 = const(int, 0)             ['$const3.3.21']
+      $3.4.23 = getitem(value=sched.29, index=$const3.3.21) ['$3.4.23', '$const3.3.21', 'sched.29']
+      $const3.6.28 = const(int, 1)             ['$const3.6.28']
+      $3.7.27 = getitem(value=sched.29, index=$const3.6.28) ['$3.7.27', '$const3.6.28', 'sched.29']
+      $const3.8.32 = const(int, 1)             ['$const3.8.32']
+      $3.9.31 = $3.7.27 + $const3.8.32         ['$3.7.27', '$3.9.31', '$const3.8.32']
+      $3.10.36 = call $3.1.24($3.4.23, $3.9.31, kws=[], func=$3.1.24, vararg=None, args=[Var($3.4.23, <string> (2)), Var($3.9.31, <string> (2))]) ['$3.1.24', '$3.10.36', '$3.4.23', '$3.9.31']
+      $3.11.30 = getiter(value=$3.10.36)       ['$3.10.36', '$3.11.30']
+      jump 1                                   []
+  label 1:
+      $28.2.35 = iternext(value=$3.11.30)      ['$28.2.35', '$3.11.30']
+      $28.3.25 = pair_first(value=$28.2.35)    ['$28.2.35', '$28.3.25']
+      $28.4.40 = pair_second(value=$28.2.35)   ['$28.2.35', '$28.4.40']
+      branch $28.4.40, 2, 3                    ['$28.4.40']
+  label 2:
+      $arg_out_var.15 = getitem(value=a0, index=$28.3.25) ['$28.3.25', '$arg_out_var.15', 'a0']
+      $arg_out_var.16 = getitem(value=a1, index=$28.3.25) ['$28.3.25', '$arg_out_var.16', 'a1']
+      $arg_out_var.14 = $arg_out_var.15 * $arg_out_var.16 ['$arg_out_var.14', '$arg_out_var.15', '$arg_out_var.16']
+      $arg_out_var.17 = getitem(value=a2, index=$28.3.25) ['$28.3.25', '$arg_out_var.17', 'a2']
+      $expr_out_var.13 = $arg_out_var.14 + $arg_out_var.17 ['$arg_out_var.14', '$arg_out_var.17', '$expr_out_var.13']
+      _0_5[$28.3.25] = $expr_out_var.13        ['$28.3.25', '$expr_out_var.13', '_0_5']
+      jump 1                                   []
+  label 3:
+      $const44.1.33 = const(NoneType, None)    ['$const44.1.33']
+      $44.2.39 = cast(value=$const44.1.33)     ['$44.2.39', '$const44.1.33']
+      return $44.2.39                          ['$44.2.39']
+  ______________________________________________________________________
+
+
+Stage 6b: Generate object mode LLVM IR
+--------------------------------------
+
+If type inference fails to find Numba types for all values inside a function,
+the function will be compiled in object mode.  The generated LLVM will be
+significantly longer, as the compiled code will need to make calls to the
+`Python C API <https://docs.python.org/3/c-api/>`_ to perform basically all
+operations.  The optimized LLVM for our example ``add()`` function is:
+
+.. code-block:: llvm
+
+   @PyExc_SystemError = external global i8
+   @".const.Numba_internal_error:_object_mode_function_called_without_an_environment" = internal constant [73 x i8] c"Numba internal error: object mode function called without an environment\00"
+   @".const.name_'a'_is_not_defined" = internal constant [24 x i8] c"name 'a' is not defined\00"
+   @PyExc_NameError = external global i8
+   @".const.name_'b'_is_not_defined" = internal constant [24 x i8] c"name 'b' is not defined\00"
+
+   define i32 @"__main__.add$1.pyobject.pyobject"(i8** nocapture %retptr, { i8*, i32 }** nocapture readnone %excinfo, i8* readnone %env, i8* %arg.a, i8* %arg.b) {
+   entry:
+     %.6 = icmp eq i8* %env, null
+     br i1 %.6, label %entry.if, label %entry.endif, !prof !0
+
+   entry.if:                                         ; preds = %entry
+     tail call void @PyErr_SetString(i8* @PyExc_SystemError, i8* getelementptr inbounds ([73 x i8]* @".const.Numba_internal_error:_object_mode_function_called_without_an_environment", i64 0, i64 0))
+     ret i32 -1
+
+   entry.endif:                                      ; preds = %entry
+     tail call void @Py_IncRef(i8* %arg.a)
+     tail call void @Py_IncRef(i8* %arg.b)
+     %.21 = icmp eq i8* %arg.a, null
+     br i1 %.21, label %B0.if, label %B0.endif, !prof !0
+
+   B0.if:                                            ; preds = %entry.endif
+     tail call void @PyErr_SetString(i8* @PyExc_NameError, i8* getelementptr inbounds ([24 x i8]* @".const.name_'a'_is_not_defined", i64 0, i64 0))
+     tail call void @Py_DecRef(i8* null)
+     tail call void @Py_DecRef(i8* %arg.b)
+     ret i32 -1
+
+   B0.endif:                                         ; preds = %entry.endif
+     %.30 = icmp eq i8* %arg.b, null
+     br i1 %.30, label %B0.endif1, label %B0.endif1.1, !prof !0
+
+   B0.endif1:                                        ; preds = %B0.endif
+     tail call void @PyErr_SetString(i8* @PyExc_NameError, i8* getelementptr inbounds ([24 x i8]* @".const.name_'b'_is_not_defined", i64 0, i64 0))
+     tail call void @Py_DecRef(i8* %arg.a)
+     tail call void @Py_DecRef(i8* null)
+     ret i32 -1
+
+   B0.endif1.1:                                      ; preds = %B0.endif
+     %.38 = tail call i8* @PyNumber_Add(i8* %arg.a, i8* %arg.b)
+     %.39 = icmp eq i8* %.38, null
+     br i1 %.39, label %B0.endif1.1.if, label %B0.endif1.1.endif, !prof !0
+
+   B0.endif1.1.if:                                   ; preds = %B0.endif1.1
+     tail call void @Py_DecRef(i8* %arg.a)
+     tail call void @Py_DecRef(i8* %arg.b)
+     ret i32 -1
+
+   B0.endif1.1.endif:                                ; preds = %B0.endif1.1
+     tail call void @Py_DecRef(i8* %arg.b)
+     tail call void @Py_DecRef(i8* %arg.a)
+     tail call void @Py_IncRef(i8* %.38)
+     tail call void @Py_DecRef(i8* %.38)
+     store i8* %.38, i8** %retptr, align 8
+     ret i32 0
+   }
+
+   declare void @PyErr_SetString(i8*, i8*)
+
+   declare void @Py_IncRef(i8*)
+
+   declare void @Py_DecRef(i8*)
+
+   declare i8* @PyNumber_Add(i8*, i8*)
+
+
+The careful reader might notice several unnecessary calls to ``Py_IncRef``
+and ``Py_DecRef`` in the generated code.  Currently Numba isn't able to
+optimize those away.
+
+Object mode compilation will also attempt to identify loops which can be
+extracted and statically-typed for "nopython" compilation.  This process is
+called *loop-lifting*, and results in the creation of a hidden nopython mode
+function just containing the loop which is then called from the original
+function.  Loop-lifting helps improve the performance of functions that
+need to access uncompilable code (such as I/O or plotting code) but still
+contain a time-intensive section of compilable code.
+
+Stage 7: Compile LLVM IR to machine code
+----------------------------------------
+
+In both :term:`object mode` and :term:`nopython mode`, the generated LLVM IR
+is compiled by the LLVM JIT compiler and the machine code is loaded into
+memory.  A Python wrapper is also created (defined in
+``numba.dispatcher.Dispatcher``) which can do the dynamic dispatch to the
+correct version of the compiled function if multiple type specializations
+were generated (for example, for both ``float32`` and ``float64`` versions
+of the same function).
+
+The machine assembly code generated by LLVM can be dumped to the screen by
+setting the :envvar:`NUMBA_DUMP_ASSEMBLY` environment variable to 1:
+
+.. code-block:: gas
+
+           .globl  __main__.add$1.int64.int64
+           .align  16, 0x90
+           .type   __main__.add$1.int64.int64,@function
+   __main__.add$1.int64.int64:
+           addq    %r8, %rcx
+           movq    %rcx, (%rdi)
+           xorl    %eax, %eax
+           retq
+
+The assembly output will also include the generated wrapper function that
+translates the Python arguments to native data types.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/caching.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/caching.rst
new file mode 100644
index 000000000..29dad8f52
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/caching.rst
@@ -0,0 +1,111 @@
+.. _developer-caching:
+
+================
+Notes on Caching
+================
+
+Numba supports caching of compiled functions into the filesystem for future
+use of the same functions.
+
+
+The Implementation
+==================
+
+Caching is done by saving the compiled *object code*, the ELF object of the
+executable code.  By using the *object code*, cached functions have minimal
+overhead because no compilation is needed. The cached data is saved under the
+cache directory (see :envvar:`NUMBA_CACHE_DIR`). The index of the cache is
+stored in a ``.nbi`` file, with one index per function, and it lists all the
+overloaded signatures compiled for the function. The *object code* is stored in
+files with an ``.nbc`` extension, one file per overload. The data in both files
+is serialized with :mod:`pickle`.
+
+.. note:: On Python <=3.7, Numba extends ``pickle`` using the pure-Python
+          pickler. To use the faster C Pickler, install ``pickle5``
+          from ``pip``. ``pickle5`` backports Python 3.8 pickler features.
+
+
+Requirements for Cacheability
+-----------------------------
+
+Developers should note the requirements of a function to permit it to be cached
+to ensure that the features they are working on are compatible with caching.
+
+Requirements for cacheable function:
+
+- The LLVM module must be *self-contained*, meaning that it cannot rely on
+  other compiled units without linking to them.
+- The only allowed external symbols are from the
+  :ref:`NRT <arch-numba-runtime>` or other common symbols from system libraries
+  (i.e. libc and libm).
+
+Debugging note:
+
+- Look for the usage of ``inttoptr`` in the LLVM IR or
+  ``target_context.add_dynamic_add()`` in the lowering code in Python.
+  They indicate potential usage of runtime address. Not all uses are
+  problematic and some are necessary. Only the conversion of constant integers
+  into pointers will affect caching.
+- Misuse of dynamic address or dynamic symbols will likely result in a
+  segfault.
+- Linking order matters because unused symbols are dropped after linking.
+  Linking should start from the leaf nodes of the dependency graph.
+
+
+Features Compatible with Caching
+--------------------------------
+
+The following features are explicitly verified to work with caching.
+
+- ufuncs and gufuncs for the ``cpu`` and ``parallel`` target
+- parallel accelerator features (i.e. ``parallel=True``)
+
+
+Caching Limitations
+-------------------
+
+This is a list of known limitation of the cache:
+
+- Cache invalidation fails to recognize changes in symbols defined in a
+  different file.
+- Global variables are treated as constants. The cache will remember the value
+  in the global variable used at compilation. On cache load, the cached
+  function will not rebind to the new value of the global variable.
+
+
+.. _cache-sharing:
+
+Cache Sharing
+-------------
+
+It is safe to share and reuse the contents in the cache directory on a
+different machine. The cache remembers the CPU model and the available
+CPU features during compilation. If the CPU model and the CPU features do
+not match exactly, the cache contents will not be considered.
+(Also see :envvar:`NUMBA_CPU_NAME`)
+
+If the cache directory is shared on a network filesystem, concurrent
+read/write of the cache is safe only if file replacement operation is atomic
+for the filesystem. Numba always writes to a unique temporary file first, it
+then replaces the target cache file path with the temporary file. Numba is
+tolerant against lost cache files and lost cache entries.
+
+.. _cache-clearing:
+
+Cache Clearing
+--------------
+
+The cache is invalidated when the corresponding source file is modified.
+However, it is necessary sometimes to clear the cache directory manually.
+For instance, changes in the compiler will not be recognized because the source
+files are not modified.
+
+To clear the cache, the cache directory can be simply removed.
+
+Removing the cache directory when a Numba application is running may cause an
+``OSError`` exception to be raised at the compilation site.
+
+Related Environment Variables
+-----------------------------
+
+See :ref:`env-vars for caching <numba-envvars-caching>`.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/compiler_pass_example.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/compiler_pass_example.py
new file mode 100644
index 000000000..15a91bd0e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/compiler_pass_example.py
@@ -0,0 +1,78 @@
+def ex_compiler_pass():
+
+    # magictoken.ex_compiler_pass.begin
+    from numba import njit
+    from numba.core import ir
+    from numba.core.compiler import CompilerBase, DefaultPassBuilder
+    from numba.core.compiler_machinery import FunctionPass, register_pass
+    from numba.core.untyped_passes import IRProcessing
+    from numbers import Number
+
+    # Register this pass with the compiler framework, declare that it will not
+    # mutate the control flow graph and that it is not an analysis_only pass (it
+    # potentially mutates the IR).
+    @register_pass(mutates_CFG=False, analysis_only=False)
+    class ConstsAddOne(FunctionPass):
+        _name = "consts_add_one" # the common name for the pass
+
+        def __init__(self):
+            FunctionPass.__init__(self)
+
+        # implement method to do the work, "state" is the internal compiler
+        # state from the CompilerBase instance.
+        def run_pass(self, state):
+            func_ir = state.func_ir # get the FunctionIR object
+            mutated = False # used to record whether this pass mutates the IR
+            # walk the blocks
+            for blk in func_ir.blocks.values():
+                # find the assignment nodes in the block and walk them
+                for assgn in blk.find_insts(ir.Assign):
+                    # if an assignment value is a ir.Consts
+                    if isinstance(assgn.value, ir.Const):
+                        const_val = assgn.value
+                        # if the value of the ir.Const is a Number
+                        if isinstance(const_val.value, Number):
+                            # then add one!
+                            const_val.value += 1
+                            mutated |= True
+            return mutated # return True if the IR was mutated, False if not.
+    # magictoken.ex_compiler_pass.end
+
+    # magictoken.ex_compiler_defn.begin
+    class MyCompiler(CompilerBase): # custom compiler extends from CompilerBase
+
+        def define_pipelines(self):
+            # define a new set of pipelines (just one in this case) and for ease
+            # base it on an existing pipeline from the DefaultPassBuilder,
+            # namely the "nopython" pipeline
+            pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+            # Add the new pass to run after IRProcessing
+            pm.add_pass_after(ConstsAddOne, IRProcessing)
+            # finalize
+            pm.finalize()
+            # return as an iterable, any number of pipelines may be defined!
+            return [pm]
+    # magictoken.ex_compiler_defn.end
+
+    # magictoken.ex_compiler_call.begin
+    @njit(pipeline_class=MyCompiler) # JIT compile using the custom compiler
+    def foo(x):
+        a = 10
+        b = 20.2
+        c = x + a + b
+        return c
+
+    print(foo(100)) # 100 + 10 + 20.2 (+ 1 + 1), extra + 1 + 1 from the rewrite!
+    # magictoken.ex_compiler_call.end
+
+    # magictoken.ex_compiler_timings.begin
+    compile_result = foo.overloads[foo.signatures[0]]
+    nopython_times = compile_result.metadata['pipeline_times']['nopython']
+    for k in nopython_times.keys():
+        if ConstsAddOne._name in k:
+            print(nopython_times[k])
+    # magictoken.ex_compiler_timings.end
+
+    assert foo(100) == 132.2
+
+ex_compiler_pass()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/contributing.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/contributing.rst
new file mode 100644
index 000000000..9ec5f9d9f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/contributing.rst
@@ -0,0 +1,491 @@
+
+Contributing to Numba
+=====================
+
+We welcome people who want to make contributions to Numba, big or small!
+Even simple documentation improvements are encouraged.  If you have
+questions, don't hesitate to ask them (see below).
+
+
+Communication
+-------------
+
+Real-time Chat
+''''''''''''''
+
+Numba uses Gitter for public real-time chat.  To help improve the
+signal-to-noise ratio, we have two channels:
+
+* `numba/numba <https://gitter.im/numba/numba>`_: General Numba discussion,
+  questions, and debugging help.
+* `numba/numba-dev <https://gitter.im/numba/numba-dev>`_: Discussion of PRs,
+  planning, release coordination, etc.
+
+Both channels are public, but we may ask that discussions on numba-dev move to
+the numba channel.  This is simply to ensure that numba-dev is easy for core
+developers to keep up with.
+
+Note that the Github issue tracker is the best place to report bugs.  Bug
+reports in chat are difficult to track and likely to be lost.
+
+Forum
+.....
+
+Numba uses Discourse as a forum for longer running threads such as design
+discussions and roadmap planning. There are various categories available and it
+can be reached at: `numba.discourse.group <https://numba.discourse.group/>`_.
+
+Weekly Meetings
+'''''''''''''''
+
+The core Numba developers have a weekly video conference to discuss roadmap,
+feature planning, and outstanding issues.  These meetings are entirely public,
+details are posted on
+`numba.discourse.group Announcements <https://numba.discourse.group/c/announcements/>`_
+and everyone is welcome to join the discussion. Minutes will be taken and will
+be posted to the
+`Numba wiki <https://github.com/numba/numba/wiki/Meeting-Minutes>`_.
+
+.. _report-numba-bugs:
+
+Bug tracker
+''''''''''''
+
+We use the `Github issue tracker <https://github.com/numba/numba/issues>`_
+to track both bug reports and feature requests.  If you report an issue,
+please include specifics:
+
+* what you are trying to do;
+* which operating system you have and which version of Numba you are running;
+* how Numba is misbehaving, e.g. the full error traceback, or the unexpected
+  results you are getting;
+* as far as possible, a code snippet that allows full reproduction of your
+  problem.
+
+Getting set up
+--------------
+
+If you want to contribute, we recommend you fork our `Github repository
+<https://github.com/numba/numba>`_, then create a branch representing
+your work.  When your work is ready, you should submit it as a pull
+request from the Github interface.
+
+If you want, you can submit a pull request even when you haven't finished
+working.  This can be useful to gather feedback, or to stress your changes
+against the :ref:`continuous integration <continuous_integration_testing>`
+platform.  In this case, please prepend ``[WIP]`` to your pull request's title.
+
+.. _buildenv:
+
+Build environment
+'''''''''''''''''
+
+Numba has a number of dependencies (mostly `NumPy <http://www.numpy.org/>`_ and
+`llvmlite <https://github.com/numba/llvmlite>`_) with non-trivial build
+instructions.  Unless you want to build those dependencies yourself, we
+recommend you use `conda <http://conda.pydata.org/miniconda.html>`_ to create a
+dedicated development environment and install precompiled versions of those
+dependencies there. Read more about the Numba dependencies here:
+`numba-source-install-check`.
+
+When working with a source checkout of Numba you will also need a development
+build of llvmlite. These are available from the ``numba/label/dev`` channel on
+`anaconda.org <https://anaconda.org/numba/llvmlite>`_.
+
+
+Then, to create an environment with a few of the most common dependencies::
+
+   $ conda create -n numbaenv python=3.10 numba/label/dev::llvmlite numpy scipy jinja2 cffi
+
+.. note::
+   This installs an environment based on Python 3.10, but you can of course
+   choose another version supported by Numba.  To test additional features,
+   you may also need to install ``tbb`` and/or ``llvm-openmp``. Check the
+   dependency list above for details.
+
+To activate the environment for the current shell session::
+
+   $ conda activate numbaenv
+
+.. note::
+   These instructions are for a standard Linux shell.  You may need to
+   adapt them for other platforms.
+
+Once the environment is activated, you have a dedicated Python with the
+required dependencies::
+
+    $ python
+    Python 3.10.3 (main, Mar 28 2022, 04:26:28) [Clang 12.0.0 ] on darwin
+    Type "help", "copyright", "credits" or "license" for more information.
+
+    >>> import llvmlite
+    >>> llvmlite.__version__
+    0.39.0dev0+61.gf27ac6f
+
+
+Building Numba
+''''''''''''''
+
+For a convenient development workflow, we recommend you build Numba inside
+its source checkout::
+
+   $ git clone git@github.com:numba/numba.git
+   $ cd numba
+   $ python setup.py build_ext --inplace
+
+This assumes you have a working C compiler and runtime on your development
+system.  You will have to run this command again whenever you modify
+C files inside the Numba source tree.
+
+The ``build_ext`` command in Numba's setup also accepts the following
+arguments:
+
+- ``--noopt``: This disables optimization when compiling Numba's CPython
+  extensions, which makes debugging them much easier. Recommended in
+  conjunction with the standard ``build_ext`` option ``--debug``.
+- ``--werror``: Compiles Numba's CPython extensions with the ``-Werror`` flag.
+- ``--wall``: Compiles Numba's CPython extensions with the ``-Wall`` flag.
+
+Note that Numba's CI and the conda recipe for Linux build with the ``--werror``
+and ``--wall`` flags, so any contributions that change the CPython extensions
+should be tested with these flags too.
+
+Running tests
+'''''''''''''
+
+Numba is validated using a test suite comprised of various kind of tests
+(unit tests, functional tests). The test suite is written using the
+standard :py:mod:`unittest` framework.
+
+The tests can be executed via ``python -m numba.runtests``.  If you are
+running Numba from a source checkout, you can type ``./runtests.py``
+as a shortcut.  Various options are supported to influence test running
+and reporting.  Pass ``-h`` or ``--help`` to get a glimpse at those options.
+Examples:
+
+* to list all available tests::
+
+    $ python -m numba.runtests -l
+
+* to list tests from a specific (sub-)suite::
+
+    $ python -m numba.runtests -l numba.tests.test_usecases
+
+* to run those tests::
+
+    $ python -m numba.runtests numba.tests.test_usecases
+
+* to run all tests in parallel, using multiple sub-processes::
+
+    $ python -m numba.runtests -m
+
+* For a detailed list of all options::
+
+    $ python -m numba.runtests -h
+
+The numba test suite can take a long time to complete.  When you want to avoid
+the long wait,  it is useful to focus on the failing tests first with the
+following test runner options:
+
+* The ``--failed-first`` option is added to capture the list of failed tests
+  and to re-execute them first::
+
+    $ python -m numba.runtests --failed-first -m -v -b
+
+* The ``--last-failed`` option is used with ``--failed-first`` to execute
+  the previously failed tests only::
+
+    $ python -m numba.runtests --last-failed -m -v -b
+
+When debugging, it is useful to turn on logging.  Numba logs using the
+standard ``logging`` module.  One can use the standard ways (i.e.
+``logging.basicConfig``) to configure the logging behavior.  To enable logging
+in the test runner, there is a ``--log`` flag for convenience::
+
+    $ python -m numba.runtests --log
+
+To enable :ref:`runtime type-checking <type_anno_check>`, set the environment
+variable ``NUMBA_USE_TYPEGUARD=1`` and use `runtests.py` from the source root
+instead. For example::
+
+    $ NUMBA_USE_TYPEGUARD=1 python runtests.py
+
+
+Running coverage
+''''''''''''''''
+
+Coverage reports can be produced using `coverage.py
+<https://coverage.readthedocs.io/en/stable/index.html>`_. To record coverage
+info for the test suite, run::
+
+    coverage run -m numba.runtests <runtests args>
+
+Next, combine coverage files (potentially for multiple runs) with::
+
+    coverage combine
+
+The combined output can be transformed into various report formats - see the
+`coverage CLI usage reference
+<https://coverage.readthedocs.io/en/stable/cmd.html#command-line-usage>`_.
+For example, to produce an HTML report, run::
+
+    coverage html
+
+Following this command, the report can be viewed by opening ``htmlcov/index.html``.
+
+
+Development rules
+-----------------
+
+Code reviews
+''''''''''''
+
+Any non-trivial change should go through a code review by one or several of
+the core developers.  The recommended process is to submit a pull request
+on github.
+
+A code review should try to assess the following criteria:
+
+* general design and correctness
+* code structure and maintainability
+* coding conventions
+* docstrings, comments
+* test coverage
+
+Coding conventions
+''''''''''''''''''
+
+All Python code should follow :pep:`8`.  Our C code doesn't have a
+well-defined coding style (would it be nice to follow :pep:`7`?).
+Code and documentation should generally fit within 80 columns, for
+maximum readability with all existing tools (such as code review UIs).
+
+Numba uses `Flake8 <http://flake8.pycqa.org/en/latest/>`_ to ensure a consistent
+Python code format throughout the project. ``flake8`` can be installed
+with ``pip`` or ``conda`` and then run from the root of the Numba repository::
+
+    flake8 numba
+
+Optionally, you may wish to setup `pre-commit hooks <https://pre-commit.com/>`_
+to automatically run ``flake8`` when you make a git commit. This can be
+done by installing ``pre-commit``::
+
+    pip install pre-commit
+
+and then running::
+
+    pre-commit install
+
+from the root of the Numba repository. Now ``flake8`` will be run each time
+you commit changes. You can skip this check with ``git commit --no-verify``.
+
+Numba has started the process of using `type hints <https://www.python.org/dev/peps/pep-0484/>`_ in its code base. This
+will be a gradual process of extending the number of files that use type hints, as well as going from voluntary to
+mandatory type hints for new features. `Mypy <http://mypy-lang.org/>`_ is used for automated static checking.
+
+At the moment, only certain files are checked by mypy. The list can be found in ``mypy.ini``. When making changes to
+those files, it is necessary to add the required type hints such that mypy tests will pass. Only in exceptional
+circumstances should ``type: ignore`` comments be used.
+
+If you are contributing a new feature, we encourage you to use type hints, even if the file is not currently in the
+checklist. If you want to contribute type hints to enable a new file to be in the checklist, please add the file to the
+``files`` variable in ``mypy.ini``, and decide what level of compliance you are targeting. Level 3 is basic static
+checks, while levels 2 and 1 represent stricter checking. The levels are described in details in ``mypy.ini``.
+
+There is potential for confusion between the Numba module ``typing`` and Python built-in module ``typing`` used for type
+hints, as well as between Numba types---such as ``Dict`` or ``Literal``---and ``typing`` types of the same name.
+To mitigate the risk of confusion we use a naming convention by which objects of the built-in ``typing`` module are
+imported with an ``pt`` prefix. For example, ``typing.Dict`` is imported as ``from typing import Dict as ptDict``.
+
+Stability
+'''''''''
+
+The repository's ``main`` branch is expected to be stable at all times.
+This translates into the fact that the test suite passes without errors
+on all supported platforms (see below).  This also means that a pull request
+also needs to pass the test suite before it is merged in.
+
+.. _platform_support:
+
+Platform support
+''''''''''''''''
+
+Every commit to the main branch is automatically tested on all of the
+platforms Numba supports. This includes ARMv8, POWER8, and NVIDIA GPUs.
+The build system however is internal to Anaconda, so we also use
+`Azure <https://dev.azure.com/numba/numba/_build>`_ to provide public continuous
+integration information for as many combinations as can be supported by the
+service.  Azure CI automatically tests all pull requests on Windows, OS X and
+Linux, as well as a sampling of different Python and NumPy versions. If you see
+problems on platforms you are unfamiliar with, feel free to ask for help in your
+pull request. The Numba core developers can help diagnose cross-platform
+compatibility issues. Also see the :ref:`continuous integration
+<continuous_integration_testing>` section on how public CI is implemented.
+
+.. _continuous_integration_testing:
+
+Continuous integration testing
+''''''''''''''''''''''''''''''
+
+The Numba test suite causes CI systems a lot of grief:
+
+#. It's huge, 9000+ tests.
+#. In part because of 1. and that compilers are pretty involved, the test suite
+   takes a long time to run.
+#. There's sections of the test suite that are deliberately designed to stress
+   systems almost to the point of failure (tests which concurrently compile and
+   execute with threads and fork processes etc).
+#. The combination of things that Numba has to test well exceeds the capacity of
+   any public CI system, (Python versions x NumPy versions x Operating systems
+   x Architectures x feature libraries (e.g. SVML) x threading backends
+   (e.g. OpenMP, TBB)) and then there's CUDA too and all its version
+   variants.
+
+As a result of the above, public CI is implemented as follows:
+
+#. The combination of OS x Python x NumPy x Various Features in the testing
+   matrix is designed to give a good indicative result for whether "this pull
+   request is probably ok".
+#. When public CI runs it:
+
+   #. Looks for files that contain tests that have been altered by the proposed
+      change and runs these on the whole testing matrix.
+   #. Runs a subset of the test suite on each part of the testing matrix. i.e.
+      slice the test suite up by the number of combinations in the testing
+      matrix and each combination runs one chunk. This is done for speed,
+      because public CI cannot cope with the load else.
+
+If a Pull Request (PR) changes CUDA code or will affect the CUDA target, it
+needs to be run on `gpuCI <https://gpuci.gpuopenanalytics.com/job/numba/>`_.
+This can be triggered by one of the Numba maintainers commenting ``run gpuCI
+tests`` on the PR discussion. This runs the CUDA testsuite with various CUDA
+toolkit versions on Linux, to provide some initial confidence in the
+correctness of the changes with respect to CUDA. Following approval, the PR
+will also be run on Numba's build farm to test other configurations with CUDA
+(including Windows, which is not tested by gpuCI).
+
+If the PR is not CUDA-related but makes changes to something that the core
+developers consider risky, then it will also be run on the Numba farm just to
+make sure. The Numba project's private build and test farm will actually
+exercise all the applicable tests on all the combinations noted above on real
+hardware!
+
+
+.. _type_anno_check:
+
+Type annotation and runtime type checking
+'''''''''''''''''''''''''''''''''''''''''
+
+Numba is slowly gaining type annotations. To facilitate the review of pull
+requests that are incrementally adding type annotations, the test suite uses
+`typeguard`_ to perform runtime type checking. This helps verify the validity
+of type annotations.
+
+To enable runtime type checking in the test suite, users can use
+`runtests.py`_ in the source root as the test runner and set environment
+variable ``NUMBA_USE_TYPEGUARD=1``. For example::
+
+    $ NUMBA_USE_TYPEGUARD=1 python runtests.py numba.tests
+
+Things that help with pull requests
+'''''''''''''''''''''''''''''''''''
+
+Even with the mitigating design above public CI can get overloaded which causes
+a backlog of builds. It's therefore really helpful when opening pull requests if
+you can limit the frequency of pushing changes. Ideally, please squash commits
+to reduce the number of patches and/or push as infrequently as possible. Also,
+once a pull request review has started, please don't rebase/force push/squash
+or do anything that rewrites history of the reviewed code as GitHub cannot track
+this and it makes it very hard for reviewers to see what has changed.
+
+The core developers thank everyone for their cooperation with the above!
+
+Why is my pull request/issue seemingly being ignored?
+'''''''''''''''''''''''''''''''''''''''''''''''''''''
+
+Numba is an open source project and like many similar projects it has limited
+resources. As a result, it is unfortunately necessary for the core developers to
+associate a priority with issues/pull requests (PR). A great way to move your
+issue/PR up the priority queue is to help out somewhere else in the project so
+as to free up core developer time. Examples of ways to help:
+
+* Perform an initial review on a PR. This often doesn't require compiler
+  engineering knowledge and just involves checking that the proposed patch is of
+  good quality, fixes the problem/implements the feature, is well tested and
+  documented.
+* Debug an issue, there are numerous issues which `"need triage" <https://github.com/numba/numba/issues?q=is%3Aissue+is%3Aopen+label%3Aneedtriage>`_
+  which essentially involves debugging the reported problem. Even if you cannot
+  get right to the bottom of a problem, leaving notes about what was discovered
+  for someone else is also helpful.
+* Answer questions/provide help for users on `discourse <https://numba.discourse.group/>`_
+  and/or `gitter.im <https://gitter.im/numba/numba>`_.
+
+The core developers thank everyone for their understanding with the above!
+
+Documentation
+-------------
+
+The Numba documentation is split over two repositories:
+
+* This documentation is in the ``docs`` directory inside the
+  `Numba repository <https://github.com/numba/numba>`_.
+
+* The `Numba homepage <https://numba.pydata.org>`_ has its sources in a
+  separate repository at https://github.com/numba/numba-webpage
+
+
+Main documentation
+''''''''''''''''''
+
+This documentation is under the ``docs`` directory of the `Numba repository`_.
+It is built with `Sphinx <http://sphinx-doc.org/>`_ and
+`numpydoc <https://numpydoc.readthedocs.io/>`_, which are available using
+conda or pip; i.e. ``conda install sphinx numpydoc``.
+
+To build the documentation, you need the bootstrap theme::
+
+   $ pip install sphinx_bootstrap_theme
+
+You can edit the source files under ``docs/source/``, after which you can
+build and check the documentation::
+
+   $ make html
+   $ open _build/html/index.html
+
+Core developers can upload this documentation to the Numba website
+at https://numba.pydata.org by using the ``gh-pages.py`` script under ``docs``::
+
+   $ python gh-pages.py version  # version can be 'dev' or '0.16' etc
+
+then verify the repository under the ``gh-pages`` directory and use
+``git push``.
+
+Web site homepage
+'''''''''''''''''
+
+The Numba homepage on https://numba.pydata.org can be fetched from here:
+https://github.com/numba/numba-webpage
+
+After pushing documentation to a new version, core developers will want to
+update the website.  Some notable files:
+
+* ``index.rst``       # Update main page
+* ``_templates/sidebar_versions.html``    # Update sidebar links
+* ``doc.rst``         # Update after adding a new version for numba docs
+* ``download.rst``    # Updata after uploading new numba version to pypi
+
+After updating run::
+
+   $ make html
+
+and check out ``_build/html/index.html``.  To push updates to the Web site::
+
+   $ python _scripts/gh-pages.py
+
+then verify the repository under the ``gh-pages`` directory.  Make sure the
+``CNAME`` file is present and contains a single line for ``numba.pydata.org``.
+Finally, use ``git push`` to update the website.
+
+
+.. _typeguard: https://typeguard.readthedocs.io/en/latest/
+.. _runtests.py: https://github.com/numba/numba/blob/main/runtests.py
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/custom_pipeline.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/custom_pipeline.rst
new file mode 100644
index 000000000..e670acf29
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/custom_pipeline.rst
@@ -0,0 +1,173 @@
+.. _arch-pipeline:
+
+========================
+Customizing the Compiler
+========================
+
+.. warning:: The custom pipeline feature is for expert use only.  Modifying
+             the compiler behavior can invalidate internal assumptions in the
+             numba source code.
+
+
+For library developers looking for a way to extend or modify the compiler
+behavior, you can do so by defining a custom compiler by inheriting from
+``numba.compiler.CompilerBase``.  The default Numba compiler is defined
+as ``numba.compiler.Compiler``, implementing the ``.define_pipelines()``
+method, which adds the *nopython-mode*, *object-mode* and *interpreted-mode*
+pipelines. For convenience these three pipelines are defined in
+``numba.compiler.DefaultPassBuilder`` by the methods:
+
+* ``.define_nopython_pipeline()``
+* ``.define_objectmode_pipeline()``
+* ``.define_interpreted_pipeline()``
+
+respectively.
+
+To use a custom subclass of ``CompilerBase``, supply it as the
+``pipeline_class`` keyword argument to the ``@jit`` and ``@generated_jit``
+decorators.  By doing so, the effect of the custom pipeline is limited to the
+function being decorated.
+
+Implementing a compiler pass
+----------------------------
+
+Numba makes it possible to implement a new compiler pass and does so through the
+use of an API similar to that of LLVM. The following demonstrates the basic
+process involved.
+
+
+Compiler pass classes
+#####################
+
+All passes must inherit from ``numba.compiler_machinery.CompilerPass``, commonly
+used subclasses are:
+
+* ``numba.compiler_machinery.FunctionPass`` for describing a pass that operates
+  on a function-at-once level and may mutate the IR state.
+* ``numba.compiler_machinery.AnalysisPass`` for describing a pass that performs
+  analysis only.
+* ``numba.compiler_machinery.LoweringPass`` for describing a pass that performs
+  lowering only.
+
+In this example a new compiler pass will be implemented that will rewrite all
+``ir.Const(x)`` nodes, where ``x`` is a subclass of ``numbers.Number``, such
+that the value of x is incremented by one. There is no use for this pass other
+than to serve as a pedagogical vehicle!
+
+The ``numba.compiler_machinery.FunctionPass`` is appropriate for the suggested
+pass behavior and so is the base class of the new pass. Further, a ``run_pass``
+method is defined to do the work (this method is abstract, all compiler passes
+must implement it).
+
+First the new class:
+
+.. literalinclude:: compiler_pass_example.py
+   :language: python
+   :dedent: 4
+   :start-after: magictoken.ex_compiler_pass.begin
+   :end-before: magictoken.ex_compiler_pass.end
+
+
+Note also that the class must be registered with Numba's compiler machinery
+using ``@register_pass``. This in part is to allow the declaration of whether
+the pass mutates the control flow graph and whether it is an analysis only pass.
+
+Next, define a new compiler based on the existing
+``numba.compiler.CompilerBase``. The compiler pipeline is defined through the
+use of an existing pipeline and the new pass declared above is added to be run
+after the ``IRProcessing`` pass.
+
+
+.. literalinclude:: compiler_pass_example.py
+   :language: python
+   :dedent: 4
+   :start-after: magictoken.ex_compiler_defn.begin
+   :end-before: magictoken.ex_compiler_defn.end
+
+Finally update the ``@njit`` decorator at the call site to make use of the newly
+defined compilation pipeline.
+
+.. literalinclude:: compiler_pass_example.py
+   :language: python
+   :dedent: 4
+   :start-after: magictoken.ex_compiler_call.begin
+   :end-before: magictoken.ex_compiler_call.end
+
+Debugging compiler passes
+-------------------------
+
+Observing IR Changes
+####################
+
+It is often useful to be able to see the changes a pass makes to the IR. Numba
+conveniently permits this through the use of the environment variable
+:envvar:`NUMBA_DEBUG_PRINT_AFTER`. In the case of the above pass, running the
+example code with ``NUMBA_DEBUG_PRINT_AFTER="ir_processing,consts_add_one"``
+gives:
+
+
+.. code-block:: none
+    :emphasize-lines: 4, 7, 24, 27
+
+    ----------------------------nopython: ir_processing-----------------------------
+    label 0:
+        x = arg(0, name=x)                       ['x']
+        $const0.1 = const(int, 10)               ['$const0.1']
+        a = $const0.1                            ['$const0.1', 'a']
+        del $const0.1                            []
+        $const0.2 = const(float, 20.2)           ['$const0.2']
+        b = $const0.2                            ['$const0.2', 'b']
+        del $const0.2                            []
+        $0.5 = x + a                             ['$0.5', 'a', 'x']
+        del x                                    []
+        del a                                    []
+        $0.7 = $0.5 + b                          ['$0.5', '$0.7', 'b']
+        del b                                    []
+        del $0.5                                 []
+        c = $0.7                                 ['$0.7', 'c']
+        del $0.7                                 []
+        $0.9 = cast(value=c)                     ['$0.9', 'c']
+        del c                                    []
+        return $0.9                              ['$0.9']
+    ----------------------------nopython: consts_add_one----------------------------
+    label 0:
+        x = arg(0, name=x)                       ['x']
+        $const0.1 = const(int, 11)               ['$const0.1']
+        a = $const0.1                            ['$const0.1', 'a']
+        del $const0.1                            []
+        $const0.2 = const(float, 21.2)           ['$const0.2']
+        b = $const0.2                            ['$const0.2', 'b']
+        del $const0.2                            []
+        $0.5 = x + a                             ['$0.5', 'a', 'x']
+        del x                                    []
+        del a                                    []
+        $0.7 = $0.5 + b                          ['$0.5', '$0.7', 'b']
+        del b                                    []
+        del $0.5                                 []
+        c = $0.7                                 ['$0.7', 'c']
+        del $0.7                                 []
+        $0.9 = cast(value=c)                     ['$0.9', 'c']
+        del c                                    []
+        return $0.9                              ['$0.9']
+
+Note the change in the values in the ``const`` nodes.
+
+Pass execution times
+####################
+
+Numba has built-in support for timing all compiler passes, the execution times
+are stored in the metadata associated with a compilation result. This
+demonstrates one way of accessing this information based on the previously
+defined function, ``foo``:
+
+.. literalinclude:: compiler_pass_example.py
+   :language: python
+   :dedent: 4
+   :start-after: magictoken.ex_compiler_timings.begin
+   :end-before: magictoken.ex_compiler_timings.end
+
+the output of which is, for example::
+
+    pass_timings(init=1.914000677061267e-06, run=4.308700044930447e-05, finalize=1.7400006981915794e-06)
+
+this displaying the pass initialization, run and finalization times in seconds.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/debugging.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/debugging.rst
new file mode 100644
index 000000000..544e9a6eb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/debugging.rst
@@ -0,0 +1,138 @@
+.. _developer-debugging:
+
+==================
+Notes on Debugging
+==================
+
+This section describes techniques that can be useful in debugging the
+compilation and execution of generated code.
+
+.. seealso::
+   :ref:`debugging-jit-compiled-code`
+
+
+Memcheck
+--------
+
+Memcheck_ is a memory error detector implemented using Valgrind_. It is useful
+for detecting memory errors in compiled code, particularly out-of-bounds
+accesses and use-after-free errors. Buggy or miscompiled native code can
+generate these kinds of errors. The `Memcheck documentation
+<https://valgrind.org/docs/manual/mc-manual.html>`_ explains its usage; here, we
+discuss only the specifics of using it with Numba.
+
+.. _Memcheck: https://valgrind.org/docs/manual/mc-manual.html
+.. _Valgrind: https://valgrind.org/
+
+The Python interpreter and some of the libraries used by Numba can generate
+false positives with Memcheck - see `this section of the manual
+<https://valgrind.org/docs/manual/mc-manual.html#mc-manual.machine>`_ for more
+information on why false positives occur. The false positives can make it
+difficult to determine when an actual error has occurred, so it is helpful to
+suppress known false positives. This can be done by supplying a suppressions
+file, which instructs Memcheck to ignore errors that match the suppressions
+defined in it.
+
+The CPython source distribution includes a suppressions file, in the file
+``Misc/valgrind-python.supp``. Using this file prevents a lot of spurious errors
+generated by Python's memory allocation implementation. Additionally, the Numba
+repository includes a suppressions file in ``contrib/valgrind-numba.supp``.
+
+.. note:: It is important to use the suppressions files from the versions of the
+   Python interpreter and Numba that you are using - these files evolve over
+   time, so non-current versions can fail to suppress some errors, or
+   erroneously suppress actual errors.
+
+To run the Python interpreter under Memcheck with both suppressions
+files, it is invoked with the following command::
+
+   valgrind --tool=memcheck \
+            --suppressions=${CPYTHON_SRC_DIR}/Misc/valgrind-python.supp \
+            --suppressions=${NUMBA_SRC_DIR}/contrib/valgrind-numba.supp \
+            python ${PYTHON_ARGS}
+
+where ``${CPYTHON_SRC_DIR}`` is set to the location of the CPython source
+distribution, ``${NUMBA_SRC_DIR}`` is the location of the Numba source dir, and
+``${PYTHON_ARGS}`` are the arguments to the Python interpreter.
+
+If there are errors, then messages describing them will be printed to standard
+error. An example of an error is::
+
+   ==77113==    at 0x24169A: PyLong_FromLong (longobject.c:251)
+   ==77113==    by 0x241881: striter_next (bytesobject.c:3084)
+   ==77113==    by 0x2D3C95: _PyEval_EvalFrameDefault (ceval.c:2809)
+   ==77113==    by 0x21B499: _PyEval_EvalCodeWithName (ceval.c:3930)
+   ==77113==    by 0x26B436: _PyFunction_FastCallKeywords (call.c:433)
+   ==77113==    by 0x2D3605: call_function (ceval.c:4616)
+   ==77113==    by 0x2D3605: _PyEval_EvalFrameDefault (ceval.c:3124)
+   ==77113==    by 0x21B977: _PyEval_EvalCodeWithName (ceval.c:3930)
+   ==77113==    by 0x21C2A4: _PyFunction_FastCallDict (call.c:376)
+   ==77113==    by 0x2D5129: do_call_core (ceval.c:4645)
+   ==77113==    by 0x2D5129: _PyEval_EvalFrameDefault (ceval.c:3191)
+   ==77113==    by 0x21B499: _PyEval_EvalCodeWithName (ceval.c:3930)
+   ==77113==    by 0x26B436: _PyFunction_FastCallKeywords (call.c:433)
+   ==77113==    by 0x2D46DA: call_function (ceval.c:4616)
+   ==77113==    by 0x2D46DA: _PyEval_EvalFrameDefault (ceval.c:3139)
+   ==77113==
+   ==77113== Use of uninitialised value of size 8
+
+The traceback provided only outlines the C call stack, which can make it
+difficult to determine what the Python interpreter was doing at the time of the
+error. One can learn more about the state of the stack by looking at the
+backtrace in the `GNU Debugger (GDB) <https://www.gnu.org/software/gdb/>`_.
+Launch ``valgrind`` with an additional argument, ``--vgdb-error=0`` and attach
+to the process using GDB as instructed by the output. Once an error is
+encountered, GDB will stop at the error and the stack can be inspected.
+
+GDB does provide support for backtracing through the Python stack, but this
+requires symbols which may not be easily available in your Python distribution.
+In this case, it is still possible to determine some information about what was
+happening in Python, but this depends on examining the backtrace closely. For
+example, in a backtrace corresponding to the above error, we see items such as:
+
+.. code-block::
+
+   #18 0x00000000002722da in slot_tp_call (
+       self=<_wrap_impl(_callable=<_wrap_missing_loc(func=<function at remote
+       0x1cf66c20>) at remote 0x1d200bd0>, _imp=<function at remote 0x1d0e7440>,
+       _context=<CUDATargetContext(address_size=64,
+       typing_context=<CUDATypingContext(_registries={<Registry(functions=[<type
+       at remote 0x65be5e0>, <type at remote 0x65be9d0>, <type at remote
+       0x65bedc0>, <type at remote 0x65bf1b0>, <type at remote 0x8b78000>, <type
+       at remote 0x8b783f0>, <type at remote 0x8b787e0>, <type at remote
+       0x8b78bd0>, <type at remote 0x8b78fc0>, <type at remote 0x8b793b0>, <type
+       at remote 0x8b797a0>, <type at remote 0x8b79b90>, <type at remote
+       0x8b79f80>, <type at remote 0x8b7a370>, <type at remote 0x8b7a760>, <type
+       at remote 0x8b7ab50>, <type at remote 0x8b7af40>, <type at remote
+       0x8b7b330>, <type at remote 0x8b7b720>, <type at remote 0x8b7bf00>, <type
+       at remote 0x8b7c2f0>, <type at remote 0x8b7c6e0>], attributes=[<type at
+       remote 0x8b7cad0>, <type at remote 0x8b7cec0>, <type at remote
+       0x8b7d2b0>, <type at remote 0x8b7d6a0>, <type at remote 0x8b7da90>,
+       <t...(truncated),
+       args=(<Builder(_block=<Block(parent=<Function(parent=<Module(context=<Context(scope=<NameScope(_useset={''},
+       _basenamemap={}) at remote 0xbb5ae10>, identified_types={}) at remote
+       0xbb5add0>, name='cuconstRecAlign$7',
+       data_layout='e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64',
+       scope=<NameScope(_useset={'',
+       '_ZN08NumbaEnv5numba4cuda5tests6cudapy13test_constmem19cuconstRecAlign$247E5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE',
+       '_ZN5numba4cuda5tests6cudapy13test_constmem19cuconstRecAlign$247E5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE'},
+       _basenamemap={}) at remote 0x1d27bf10>, triple='nvptx64-nvidia-cuda',
+       globals={'_ZN08NumbaEnv5numba4cuda5tests6cudapy13test_constmem19cuconstRecAlign$247E5ArrayIdLi1E1C7mutable7ali...(truncated),
+       kwds=0x0)
+
+We can see some of the arguments, in particular the names of the compiled functions, e.g::
+
+   _ZN5numba4cuda5tests6cudapy13test_constmem19cuconstRecAlign$247E5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE5ArrayIdLi1E1C7mutable7alignedE
+
+We can run this through ``c++filt`` to see a more human-readable representation::
+
+   numba::cuda::tests::cudapy::test_constmem::cuconstRecAlign$247(
+     Array<double, 1, C, mutable, aligned>,
+     Array<double, 1, C, mutable, aligned>,
+     Array<double, 1, C, mutable, aligned>,
+     Array<double, 1, C, mutable, aligned>,
+     Array<double, 1, C, mutable, aligned>)
+
+which is the fully qualified name of a jitted function and the types with which
+it was called.
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/dispatching.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/dispatching.rst
new file mode 100644
index 000000000..ca50e6d66
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/dispatching.rst
@@ -0,0 +1,267 @@
+
+=======================
+Polymorphic dispatching
+=======================
+
+Functions compiled using :func:`~numba.jit` or :func:`~numba.vectorize`
+are open-ended: they can be called with many different input types and
+have to select (possibly compile on-the-fly) the right low-level
+specialization.  We hereby explain how this mechanism is implemented.
+
+
+Requirements
+============
+
+JIT-compiled functions can take several arguments and each of them is
+taken into account when selecting a specialization.  Thus it is a
+form of multiple dispatch, more complex than single dispatch.
+
+Each argument weighs in the selection based on its :ref:`Numba type
+<numba-types>`.  Numba types are often more granular than Python types:
+for example, Numba types Numpy arrays differently depending on their
+dimensionality and their layout (C-contiguous, etc.).
+
+Once a Numba type is inferred for each argument, a specialization must
+be chosen amongst the available ones; or, if not suitable specialization
+is found, a new one must be compiled.  This is not a trivial decision:
+there can be multiple specializations compatible with a given concrete
+signature (for example, say a two-argument function has compiled
+specializations for ``(float64, float64)`` and ``(complex64, complex64)``,
+and it is called with ``(float32, float32)``).
+
+Therefore, there are two crucial steps in the dispatch mechanism:
+
+1. infer the Numba types of the concrete arguments
+2. select the best available specialization (or choose to compile a new one)
+   for the inferred Numba types
+
+Compile-time vs. run-time
+-------------------------
+
+This document discusses dispatching when it is done at runtime, i.e.
+when a JIT-compiled function is called from pure Python.  In that context,
+performance is important.  To stay in the realm of normal function call
+overhead in Python, the overhead of dispatching should stay under a
+microsecond.  Of course, *the faster the better*...
+
+When a JIT-compiled function is called from another JIT-compiled
+function (in :term:`nopython mode`), the polymorphism is resolved at
+compile-time, using a non-performance critical mechanism, bearing zero
+runtime performance overhead.
+
+.. note::
+   In practice, the performance-critical parts described here are coded in C.
+
+
+Type resolution
+===============
+
+The first step is therefore to infer, at call-time, a Numba type for each
+of the function's concrete arguments.  Given the finer granularity of
+Numba types compared to Python types, one cannot simply lookup an object's
+class and key a dictionary with it to obtain the corresponding Numba type.
+
+Instead, there is a machinery to inspect the object and, based on its
+Python type, query various properties to infer the appropriate Numba
+type.  This can be more or less complex: for example, a Python ``int``
+argument will always infer to a Numba ``intp`` (a pointer-sized integer),
+but a Python ``tuple`` argument can infer to multiple Numba types (depending
+on the tuple's size and the concrete type of each of its elements).
+
+The Numba type system is high-level and written in pure Python; there is
+a pure Python machinery, based on a generic function, to do said inference
+(in :mod:`numba.typing.typeof`).  That machinery is used for compile-time
+inference, e.g. on constants.  Unfortunately, it is too slow for run-time
+value-based dispatching.  It is only used as a fallback for rarely used
+(or difficult to infer) types, and exhibits multiple-microsecond overhead.
+
+Typecodes
+---------
+
+The Numba type system is really too high-level to be manipulated efficiently
+from C code.  Therefore, the C dispatching layer uses another representation
+based on integer typecodes.  Each Numba type gets a unique integer typecode
+when constructed; also, an interning system ensure no two instances of same
+type are created.  The dispatching layer is therefore able to *eschew*
+the overhead of the Numba type system by working with simple integer
+typecodes, amenable to well-known optimizations (fast hash tables, etc.).
+
+The goal of the type resolution step becomes: infer a Numba *typecode*
+for each of the function's concrete arguments.  Ideally, it doesn't deal
+with Numba types anymore...
+
+Hard-coded fast paths
+---------------------
+
+While eschewing the abstraction and object-orientation overhead of the type
+system, the integer typecodes still have the same conceptual complexity.
+Therefore, an important technique to speed up inference is to first go
+through checks for the most important types, and hard-code a fast resolution
+for each of them.
+
+Several types benefit from such an optimization, notably:
+
+* basic Python scalars (``bool``, ``int``, ``float``, ``complex``);
+* basic Numpy scalars (the various kinds of integer, floating-point,
+  complex numbers);
+* Numpy arrays of certain dimensionalities and basic element types.
+
+Each of those fast paths ideally uses a hard-coded result value or a direct
+table lookup after a few simple checks.
+
+However, we can't apply that technique to all argument types; there would
+be an explosion of ad-hoc internal caches, and it would become difficult to
+maintain.  Besides, the recursive application of hard-coded fast paths
+would not necessarily combine into a low overhead (in the nested tuple
+case, for example).
+
+Fingerprint-based typecode cache
+--------------------------------
+
+For non-so-trivial types (imagine a tuple, or a Numpy ``datetime64`` array,
+for example), the hard-coded fast paths don't match.  Another mechanism
+then kicks in, more generic.
+
+The principle here is to examine each argument value, as the pure Python
+machinery would do, and to describe its Numba type unambiguously.  The
+difference is that *we don't actually compute a Numba type*.  Instead, we
+compute a simple bytestring, a low-level possible denotation of that
+Numba type: a *fingerprint*.  The fingerprint format is designed to be
+short and extremely simple to compute from C code (in practice, it has
+a bytecode-like format).
+
+Once the fingerprint is computed, it is looked up in a cache mapping
+fingerprints to typecodes.  The cache is a hash table, and the lookup
+is fast thanks to the fingerprints being generally very short (rarely
+more than 20 bytes).
+
+If the cache lookup fails, the typecode must first be computed using the
+slow pure Python machinery.  Luckily, this would only happen once: on
+subsequent calls, the cached typecode would be returned for the given
+fingerprint.
+
+In rare cases, a fingerprint cannot be computed efficiently.  This is
+the case for some types which cannot be easily inspected from C: for
+example ``cffi`` function pointers.  Then, the slow Pure Python machinery
+is invoked at each function call with such an argument.
+
+.. note::
+   Two fingerprints may denote a single Numba type.  This does not make
+   the mechanism incorrect; it only creates more cache entries.
+
+
+Summary
+-------
+
+Type resolution of a function argument involves the following mechanisms
+in order:
+
+* Try a few hard-coded fast paths, for common simple types.
+* If the above failed, compute a fingerprint for the argument and lookup
+  its typecode in a cache.
+* If all the above failed, invoke the pure Python machinery which will
+  determine a Numba type for the argument (and look up its typecode).
+
+
+Specialization selection
+========================
+
+At the previous step, an integer typecode has been determined for each
+concrete argument to the JIT-compiled function.  Now it remains to match
+that concrete signature against each of the available specializations for
+the function.  There can be three outcomes:
+
+* There is a satisfying best match: the corresponding specialization
+  is then invoked (it will handle argument unboxing and other details).
+* There is a tie between two or more "best matches": an exception is raised,
+  refusing to solve the ambiguity.
+* There is no satisfying match: a new specialization is compiled tailored
+  for the concrete argument types that were inferred.
+
+The selection works by looping over all available specializations, and
+computing the compatibility of each concrete argument type with the
+corresponding type in the specialization's intended signature.  Specifically,
+we are interested in:
+
+1. Whether the concrete argument type is allowed to convert implicitly to
+   the specialization's argument type;
+2. If so, at what semantic (user-visible) cost the conversion comes.
+
+Implicit conversion rules
+-------------------------
+
+There are five possible kinds of implicit conversion from a source type
+to a destination type (note this is an asymmetric relationship):
+
+1. *exact match*: the two types are identical; this is the ideal case,
+   since the specialization would behave exactly as intended;
+2. *same-kind promotion*: the two types belong to the same "kind" (for
+   example ``int32`` and ``int64`` are two integer types), and the source
+   type can be converted losslessly to the destination type (e.g. from
+   ``int32`` to ``int64``, but not the reverse);
+3. *safe conversion*: the two types belong to different kinds, but the
+   source type can be reasonably converted to the destination type
+   (e.g. from ``int32`` to ``float64``, but not the reverse);
+4. *unsafe conversion*: a conversion is available from the source type
+   to the destination type, but it may lose precision, magnitude, or
+   another desirable quality.
+5. *no conversion*: there is no correct or reasonably efficient way to
+   convert between the two types (for example between an ``int64`` and a
+   ``datetime64``, or a C-contiguous array and a Fortran-contiguous array).
+
+When a specialization is examined, the latter two cases eliminate it from
+the final choice: i.e. when at least one argument has *no conversion* or
+only an *unsafe conversion* to the signature's argument type.
+
+.. note::
+   However, if the function is compiled with explicit signatures
+   in the :func:`~numba.jit` call (and therefore it is not allowed to compile
+   new specializations), *unsafe conversion* is allowed.
+
+Candidates and best match
+-------------------------
+
+If a specialization is not eliminated by the rule above, it enters the
+list of *candidates* for the final choice.  Those candidates are ranked
+by an ordered 4-uple of integers: ``(number of unsafe conversions,
+number of safe conversions, number of same-kind promotions, number of
+exact matches)`` (note the sum of the tuple's elements is equal to the
+number of arguments).  The best match is then the #1 result in sorted
+ascending order, thereby preferring exact matches over promotions,
+promotions over safe conversions, safe conversions over unsafe conversions.
+
+Implementation
+--------------
+
+The above-described mechanism works on integer typecodes, not on Numba
+types.  It uses an internal hash table storing the possible conversion
+kind for each pair of compatible types.  The internal hash table is in part
+built at startup (for built-in trivial types such as ``int32``, ``int64``
+etc.), in part filled dynamically (for arbitrarily complex types such
+as array types: for example to allow using a C-contiguous 2D array where
+a function expects a non-contiguous 2D array).
+
+Summary
+-------
+
+Selecting the right specialization involves the following steps:
+
+* Examine each available specialization and match it against the concrete
+  argument types.
+* Eliminate any specialization where at least one argument doesn't offer
+  sufficient compatibility.
+* If there are remaining candidates, choose the best one in terms of
+  preserving the types' semantics.
+
+
+Miscellaneous
+=============
+
+Some `benchmarks of dispatch performance
+<https://github.com/numba/numba-benchmark/blob/master/benchmarks/bench_dispatch.py>`_
+exist in the `Numba benchmarks <https://github.com/numba/numba-benchmark>`_
+repository.
+
+Some unit tests of specific aspects of the machinery are available
+in :mod:`numba.tests.test_typeinfer` and :mod:`numba.tests.test_typeof`.
+Higher-level dispatching tests are in :mod:`numba.tests.test_dispatcher`.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/environment.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/environment.rst
new file mode 100644
index 000000000..39f4bd234
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/environment.rst
@@ -0,0 +1,59 @@
+
+==================
+Environment Object
+==================
+
+The Environment object (Env) is used to maintain references to python objects
+that are needed to support compiled functions for both object-mode and
+nopython-mode.
+
+In nopython-mode, the Env is used for:
+
+* Storing pyobjects for reconstruction from native values,
+  such as:
+  
+  * for printing native values of NumPy arrays;
+  * for returning or yielding native values back to the interpreter.
+
+In object-mode, the Env is used for:
+
+* storing constant values referenced in the code.
+* storing a reference to the function's global dictionary to load global
+  values.
+
+
+The Implementation
+==================
+
+The Env is implemented in two parts.  In ``_dynfunc.c``, the Env is defined
+as ``EnvironmentObject`` as a Python C-extension type.  In ``lowering.py``,
+the `EnvironmentObject`` (exported as ``_dynfunc.Environment``) is extended
+to support necessary operations needed at lowering.
+
+
+Serialization
+-------------
+
+The Env supports being pickled.  Compilation cache files and ahead-of-time
+compiled modules serialize all the used Envs for recreation at the runtime.
+
+Usage
+-----
+
+At the start of the lowering for a function or a generator, an Env is created.
+Throughout the compilation, the Env is mutated to attach additional
+information.  The compiled code references an Env via a global variable in
+the emitted LLVM IR.  The global variable is zero-initialized with "common"
+linkage, which is the default linkage for C global values.  The use of this
+linkage allows multiple definitions of the global variable to be merged into
+a single definition when the modules are linked together.  The name of the
+global variable is computed from the name of the function
+(see ``FunctionDescriptor.env_name`` and ``.get_env_name()`` of the target
+context).
+
+The Env is initialized when the compiled-function is loaded. The JIT engine
+finds the address of the associated global variable for the Env and stores the
+address of the Env into it.  For cached functions, the same process applies.
+For ahead-of-time compiled functions, the module initializer in the generated
+library is responsible for initializing the global variables of all the Envs
+in the module.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/event_api.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/event_api.rst
new file mode 100644
index 000000000..31aac3313
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/event_api.rst
@@ -0,0 +1,5 @@
+Event API
+=========
+
+.. automodule:: numba.core.event
+    :members:
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/generators.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/generators.rst
new file mode 100644
index 000000000..b2936817b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/generators.rst
@@ -0,0 +1,307 @@
+
+.. _arch-generators:
+
+===================
+Notes on generators
+===================
+
+Numba recently gained support for compiling generator functions.  This
+document explains some of the implementation choices.
+
+
+Terminology
+===========
+
+For clarity, we distinguish between *generator functions* and
+*generators*.  A generator function is a function containing one or
+several ``yield`` statements.  A generator (sometimes also called "generator
+iterator") is the return value of a generator function; it resumes
+execution inside its frame each time :py:func:`next` is called.
+
+A *yield point* is the place where a ``yield`` statement is called.
+A *resumption point* is the place just after a *yield point* where execution
+is resumed when :py:func:`next` is called again.
+
+
+Function analysis
+=================
+
+Suppose we have the following simple generator function::
+
+   def gen(x, y):
+       yield x + y
+       yield x - y
+
+Here is its CPython bytecode, as printed out using :py:func:`dis.dis`::
+
+  7           0 LOAD_FAST                0 (x)
+              3 LOAD_FAST                1 (y)
+              6 BINARY_ADD
+              7 YIELD_VALUE
+              8 POP_TOP
+
+  8           9 LOAD_FAST                0 (x)
+             12 LOAD_FAST                1 (y)
+             15 BINARY_SUBTRACT
+             16 YIELD_VALUE
+             17 POP_TOP
+             18 LOAD_CONST               0 (None)
+             21 RETURN_VALUE
+
+When compiling this function with :envvar:`NUMBA_DUMP_IR` set to 1, the
+following information is printed out::
+
+   ----------------------------------IR DUMP: gen----------------------------------
+   label 0:
+       x = arg(0, name=x)                       ['x']
+       y = arg(1, name=y)                       ['y']
+       $0.3 = x + y                             ['$0.3', 'x', 'y']
+       $0.4 = yield $0.3                        ['$0.3', '$0.4']
+       del $0.4                                 []
+       del $0.3                                 []
+       $0.7 = x - y                             ['$0.7', 'x', 'y']
+       del y                                    []
+       del x                                    []
+       $0.8 = yield $0.7                        ['$0.7', '$0.8']
+       del $0.8                                 []
+       del $0.7                                 []
+       $const0.9 = const(NoneType, None)        ['$const0.9']
+       $0.10 = cast(value=$const0.9)            ['$0.10', '$const0.9']
+       del $const0.9                            []
+       return $0.10                             ['$0.10']
+   ------------------------------GENERATOR INFO: gen-------------------------------
+   generator state variables: ['$0.3', '$0.7', 'x', 'y']
+   yield point #1: live variables = ['x', 'y'], weak live variables = ['$0.3']
+   yield point #2: live variables = [], weak live variables = ['$0.7']
+
+
+What does it mean? The first part is the Numba IR, as already seen in
+:ref:`arch_generate_numba_ir`.  We can see the two yield points (``yield $0.3``
+and ``yield $0.7``).
+
+The second part shows generator-specific information.  To understand it
+we have to understand what suspending and resuming a generator means.
+
+When suspending a generator, we are not merely returning a value to the
+caller (the operand of the ``yield`` statement).  We also have to save the
+generator's *current state* in order to resume execution.  In trivial use
+cases, perhaps the CPU's register values or stack slots would be preserved
+until the next call to next().  However, any non-trivial case will hopelessly
+clobber those values, so we have to save them in a well-defined place.
+
+What are the values we need to save?  Well, in the context of the Numba
+Intermediate Representation, we must save all *live variables* at each
+yield point.  These live variables are computed thanks to the control
+flow graph.
+
+Once live variables are saved and the generator is suspended, resuming
+the generator simply involves the inverse operation: the live variables
+are restored from the saved generator state.
+
+.. note::
+   It is the same analysis which helps insert Numba ``del`` instructions
+   where appropriate.
+
+Let's go over the generator info again::
+
+   generator state variables: ['$0.3', '$0.7', 'x', 'y']
+   yield point #1: live variables = ['x', 'y'], weak live variables = ['$0.3']
+   yield point #2: live variables = [], weak live variables = ['$0.7']
+
+Numba has computed the union of all live variables (denoted as "state
+variables").  This will help define the layout of the :ref:`generator
+structure <generator-structure>`.  Also, for each yield point, we have
+computed two sets of variables:
+
+* the *live variables* are the variables which are used by code following
+  the resumption point (i.e. after the ``yield`` statement)
+
+* the *weak live variables* are variables which are del'ed immediately
+  after the resumption point; they have to be saved in :term:`object mode`,
+  to ensure proper reference cleanup
+
+
+.. _generator-structure:
+
+The generator structure
+=======================
+
+Layout
+------
+
+Function analysis helps us gather enough information to define the
+layout of the generator structure, which will store the entire execution
+state of a generator.  Here is a sketch of the generator structure's layout,
+in pseudo-code::
+
+   struct gen_struct_t {
+      int32_t resume_index;
+      struct gen_args_t {
+         arg_0_t arg0;
+         arg_1_t arg1;
+         ...
+         arg_N_t argN;
+      }
+      struct gen_state_t {
+         state_0_t state_var0;
+         state_1_t state_var1;
+         ...
+         state_N_t state_varN;
+      }
+   }
+
+Let's describe those fields in order.
+
+* The first member, the *resume index*, is an integer telling the generator
+  at which resumption point execution must resume.  By convention, it can
+  have two special values: 0 means execution must start at the beginning of
+  the generator (i.e. the first time :py:func:`next` is called); -1 means
+  the generator is exhausted and resumption must immediately raise
+  StopIteration.  Other values indicate the yield point's index starting from 1
+  (corresponding to the indices shown in the generator info above).
+
+* The second member, the *arguments structure* is read-only after it is first
+  initialized.  It stores the values of the arguments the generator function
+  was called with.  In our example, these are the values of ``x`` and ``y``.
+
+* The third member, the *state structure*, stores the live variables as
+  computed above.
+
+Concretely, our example's generator structure (assuming the generator
+function is called with floating-point numbers) is then::
+
+   struct gen_struct_t {
+      int32_t resume_index;
+      struct gen_args_t {
+         double arg0;
+         double arg1;
+      }
+      struct gen_state_t {
+         double $0.3;
+         double $0.7;
+         double x;
+         double y;
+      }
+   }
+
+Note that here, saving ``x`` and ``y`` is redundant: Numba isn't able to
+recognize that the state variables ``x`` and ``y`` have the same value
+as ``arg0`` and ``arg1``.
+
+Allocation
+----------
+
+How does Numba ensure the generator structure is preserved long enough?
+There are two cases:
+
+* When a Numba-compiled generator function is called from a Numba-compiled
+  function, the structure is allocated on the stack by the callee.  In this
+  case, generator instantiation is practically costless.
+
+* When a Numba-compiled generator function is called from regular Python
+  code, a CPython-compatible wrapper is instantiated that has the right
+  amount of allocated space to store the structure, and whose
+  :c:member:`~PyTypeObject.tp_iternext` slot is a wrapper around the
+  generator's native code.
+
+
+Compiling to native code
+========================
+
+When compiling a generator function, three native functions are actually
+generated by Numba:
+
+* An initialization function.  This is the function corresponding
+  to the generator function itself: it receives the function arguments and
+  stores them inside the generator structure (which is passed by pointer).
+  It also initialized the *resume index* to 0, indicating that the generator
+  hasn't started yet.
+
+* A next() function.  This is the function called to resume execution
+  inside the generator.  Its single argument is a pointer to the generator
+  structure and it returns the next yielded value (or a special exit code
+  is used if the generator is exhausted, for quick checking when called
+  from Numba-compiled functions).
+
+* An optional finalizer.  In object mode, this function ensures that all
+  live variables stored in the generator state are decref'ed, even if the
+  generator is destroyed without having been exhausted.
+
+The next() function
+-------------------
+
+The next() function is the least straight-forward of the three native
+functions.  It starts with a trampoline which dispatches execution to the
+right resume point depending on the *resume index* stored in the generator
+structure.  Here is how the function start may look like in our example:
+
+.. code-block:: llvm
+
+   define i32 @"__main__.gen.next"(
+      double* nocapture %retptr,
+      { i8*, i32 }** nocapture readnone %excinfo,
+      i8* nocapture readnone %env,
+      { i32, { double, double }, { double, double, double, double } }* nocapture %arg.gen)
+   {
+     entry:
+        %gen.resume_index = getelementptr { i32, { double, double }, { double, double, double, double } }* %arg.gen, i64 0, i32 0
+        %.47 = load i32* %gen.resume_index, align 4
+        switch i32 %.47, label %stop_iteration [
+          i32 0, label %B0
+          i32 1, label %generator_resume1
+          i32 2, label %generator_resume2
+        ]
+
+     ; rest of the function snipped
+
+(uninteresting stuff trimmed from the LLVM IR to make it more readable)
+
+We recognize the pointer to the generator structure in ``%arg.gen``.
+The trampoline switch has three targets (one for each *resume index* 0, 1
+and 2), and a fallback target label named ``stop_iteration``.  Label ``B0``
+represents the function's start, ``generator_resume1`` (resp.
+``generator_resume2``) is the resumption point after the first
+(resp. second) yield point.
+
+After generation by LLVM, the whole native assembly code for this function
+may look like this (on x86-64):
+
+.. code-block:: asm
+
+           .globl  __main__.gen.next
+           .align  16, 0x90
+   __main__.gen.next:
+           movl    (%rcx), %eax
+           cmpl    $2, %eax
+           je      .LBB1_5
+           cmpl    $1, %eax
+           jne     .LBB1_2
+           movsd   40(%rcx), %xmm0
+           subsd   48(%rcx), %xmm0
+           movl    $2, (%rcx)
+           movsd   %xmm0, (%rdi)
+           xorl    %eax, %eax
+           retq
+   .LBB1_5:
+           movl    $-1, (%rcx)
+           jmp     .LBB1_6
+   .LBB1_2:
+           testl   %eax, %eax
+           jne     .LBB1_6
+           movsd   8(%rcx), %xmm0
+           movsd   16(%rcx), %xmm1
+           movaps  %xmm0, %xmm2
+           addsd   %xmm1, %xmm2
+           movsd   %xmm1, 48(%rcx)
+           movsd   %xmm0, 40(%rcx)
+           movl    $1, (%rcx)
+           movsd   %xmm2, (%rdi)
+           xorl    %eax, %eax
+           retq
+   .LBB1_6:
+           movl    $-3, %eax
+           retq
+
+Note the function returns 0 to indicate a value is yielded, -3 to indicate
+StopIteration. ``%rcx`` points to the start of the generator structure,
+where the resume index is stored.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/hashing.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/hashing.rst
new file mode 100644
index 000000000..b95532441
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/hashing.rst
@@ -0,0 +1,54 @@
+
+================
+Notes on Hashing
+================
+
+Numba supports the built-in :func:`hash` and does so by simply calling the
+:func:`__hash__` member function on the supplied argument. This makes it
+trivial to add hash support for new types as all that is required is the
+application of the extension API :func:`overload_method` decorator to overload
+a function for computing the hash value for the new type registered to the
+type's :func:`__hash__` method. For example::
+
+    from numba.extending import overload_method
+
+    @overload_method(myType, '__hash__')
+    def myType_hash_overload(obj):
+        # implementation details
+
+
+The Implementation
+==================
+
+The implementation of the Numba hashing functions strictly follows that of
+Python 3. The only exception to this is that for hashing Unicode and bytes (for
+content longer than ``sys.hash_info.cutoff``) the only supported algorithm is
+``siphash24`` (default in CPython 3). As a result Numba will match Python 3
+hash values for all supported types under the default conditions described.
+
+Unicode hash cache differences
+------------------------------
+
+Both Numba and CPython Unicode string internal representations have a ``hash``
+member for the purposes of caching the string's hash value. This member is
+always checked ahead of computing a hash value with the view of simply providing
+a value from cache as it is considerably cheaper to do so. The Numba Unicode
+string hash caching implementation behaves in a similar way to that of
+CPython's. The only notable behavioral change (and its only impact is a minor
+potential change in performance) is that Numba always computes and caches the
+hash for Unicode strings created in ``nopython mode`` at the time they are boxed
+for reuse in Python, this is too eager in some cases in comparison to CPython
+which may delay hashing a new Unicode string depending on creation method. It
+should also be noted that Numba copies in the ``hash`` member of the CPython
+internal representation for Unicode strings when unboxing them to its own
+representation so as to not recompute the hash of a string that already has a
+hash value associated with it.
+
+The accommodation of ``PYTHONHASHSEED``
+---------------------------------------
+
+The ``PYTHONHASHSEED`` environment variable can be used to seed the CPython
+hashing algorithms for e.g. the purposes of reproducibility. The Numba hashing
+implementation directly reads the CPython hashing algorithms' internal state and
+as a result the influence of ``PYTHONHASHSEED`` is replicated in Numba's
+hashing implementations.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/index.rst
new file mode 100644
index 000000000..2a8cbe53e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/index.rst
@@ -0,0 +1,32 @@
+
+.. _developer-manual:
+
+Developer Manual
+================
+
+.. toctree::
+   :maxdepth: 2
+
+   contributing.rst
+   release.rst
+   repomap.rst
+   architecture.rst
+   dispatching.rst
+   generators.rst
+   numba-runtime.rst
+   rewrites.rst
+   live_variable_analysis.rst
+   listings.rst
+   stencil.rst
+   custom_pipeline.rst
+   inlining.rst
+   environment.rst
+   hashing.rst
+   caching.rst
+   threading_implementation.rst
+   literal.rst
+   llvm_timings.rst
+   debugging.rst
+   event_api.rst
+   target_extension.rst
+   mission.rst
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inline_example.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inline_example.py
new file mode 100644
index 000000000..e57ba5c58
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inline_example.py
@@ -0,0 +1,82 @@
+from numba import njit
+import numba
+from numba.core import ir
+
+
+@njit(inline='never')
+def never_inline():
+    return 100
+
+
+@njit(inline='always')
+def always_inline():
+    return 200
+
+
+def sentinel_cost_model(expr, caller_info, callee_info):
+    # this cost model will return True (i.e. do inlining) if either:
+    # a) the callee IR contains an `ir.Const(37)`
+    # b) the caller IR contains an `ir.Const(13)` logically prior to the call
+    #    site
+
+    # check the callee
+    for blk in callee_info.blocks.values():
+        for stmt in blk.body:
+            if isinstance(stmt, ir.Assign):
+                if isinstance(stmt.value, ir.Const):
+                    if stmt.value.value == 37:
+                        return True
+
+    # check the caller
+    before_expr = True
+    for blk in caller_info.blocks.values():
+        for stmt in blk.body:
+            if isinstance(stmt, ir.Assign):
+                if isinstance(stmt.value, ir.Expr):
+                    if stmt.value == expr:
+                        before_expr = False
+                if isinstance(stmt.value, ir.Const):
+                    if stmt.value.value == 13:
+                        return True & before_expr
+    return False
+
+
+@njit(inline=sentinel_cost_model)
+def maybe_inline1():
+    # Will not inline based on the callee IR with the declared cost model
+    # The following is ir.Const(300).
+    return 300
+
+
+@njit(inline=sentinel_cost_model)
+def maybe_inline2():
+    # Will inline based on the callee IR with the declared cost model
+    # The following is ir.Const(37).
+    return 37
+
+
+@njit
+def foo():
+    a = never_inline()  # will never inline
+    b = always_inline()  # will always inline
+
+    # will not inline as the function does not contain a magic constant known to
+    # the cost model, and the IR up to the call site does not contain a magic
+    # constant either
+    d = maybe_inline1()
+
+    # declare this magic constant to trigger inlining of maybe_inline1 in a
+    # subsequent call
+    magic_const = 13
+
+    # will inline due to above constant declaration
+    e = maybe_inline1()
+
+    # will inline as the maybe_inline2 function contains a magic constant known
+    # to the cost model
+    c = maybe_inline2()
+
+    return a + b + c + d + e + magic_const
+
+
+foo()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inline_overload_example.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inline_overload_example.py
new file mode 100644
index 000000000..f28f44a55
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inline_overload_example.py
@@ -0,0 +1,61 @@
+import numba
+from numba.extending import overload
+from numba import njit, types
+
+
+def bar(x):
+    """A function stub to overload"""
+    pass
+
+
+@overload(bar, inline='always')
+def ol_bar_tuple(x):
+    # An overload that will always inline, there is a type guard so that this
+    # only applies to UniTuples.
+    if isinstance(x, types.UniTuple):
+        def impl(x):
+            return x[0]
+        return impl
+
+
+def cost_model(expr, caller, callee):
+    # Only inline if the type of the argument is an Integer
+    return isinstance(caller.typemap[expr.args[0].name], types.Integer)
+
+
+@overload(bar, inline=cost_model)
+def ol_bar_scalar(x):
+    # An overload that will inline based on a cost model, it only applies to
+    # scalar values in the numerical domain as per the type guard on Number
+    if isinstance(x, types.Number):
+        def impl(x):
+            return x + 1
+        return impl
+
+
+@njit
+def foo():
+
+    # This will resolve via `ol_bar_tuple` as the argument is a types.UniTuple
+    # instance. It will always be inlined as specified in the decorator for this
+    # overload.
+    a = bar((1, 2, 3))
+
+    # This will resolve via `ol_bar_scalar` as the argument is a types.Number
+    # instance, hence the cost_model will be used to determine whether to
+    # inline.
+    # The function will be inlined as the value 100 is an IntegerLiteral which
+    # is an instance of a types.Integer as required by the cost_model function.
+    b = bar(100)
+
+    # This will also resolve via `ol_bar_scalar` as the argument is a
+    # types.Number instance, again the cost_model will be used to determine
+    # whether to inline.
+    # The function will not be inlined as the complex value is not an instance
+    # of a types.Integer as required by the cost_model function.
+    c = bar(300j)
+
+    return a + b + c
+
+
+foo()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inlining.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inlining.rst
new file mode 100644
index 000000000..d48240234
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/inlining.rst
@@ -0,0 +1,281 @@
+
+=================
+Notes on Inlining
+=================
+
+There are occasions where it is useful to be able to inline a function at its
+call site, at the Numba IR level of representation. The decorators such as
+:func:`numba.jit`, :func:`numba.extending.overload` and
+:func:`register_jitable` support the keyword argument ``inline``, to facilitate
+this behaviour.
+
+When attempting to inline at this level, it is important to understand what
+purpose this serves and what effect this will have. In contrast to the inlining
+performed by LLVM, which is aimed at improving performance, the main reason to
+inline at the Numba IR level is to allow type inference to cross function
+boundaries.
+
+As an example, consider the following snippet:
+
+.. code:: python
+
+    from numba import njit
+
+
+    @njit
+    def bar(a):
+        a.append(10)
+
+
+    @njit
+    def foo():
+        z = []
+        bar(z)
+
+
+    foo()
+
+This will fail to compile and run, because the type of ``z`` can not be inferred
+as it will only be refined within ``bar``. If we now add ``inline=True`` to the
+decorator for ``bar`` the snippet will compile and run. This is because inlining
+the call to ``a.append(10)`` will mean that ``z`` will be refined to hold integers
+and so type inference will succeed.
+
+So, to recap, inlining at the Numba IR level is unlikely to have a performance
+benefit. Whereas inlining at the LLVM level stands a better chance.
+
+The ``inline`` keyword argument can be one of three values:
+
+* The string ``'never'``, this is the default and results in the function not
+  being inlined under any circumstances.
+* The string ``'always'``, this results in the function being inlined at all
+  call sites.
+* A python function that takes three arguments. The first argument is always the
+  ``ir.Expr`` node that is the ``call`` requesting the inline, this is present
+  to allow the function to make call contextually aware decisions. The second
+  and third arguments are:
+
+  * In the case of an untyped inline, i.e. that which occurs when using the
+    :func:`numba.jit` family of decorators, both arguments are
+    ``numba.ir.FunctionIR`` instances. The second argument corresponding to the
+    IR of the caller, the third argument corresponding to the IR of the callee.
+
+  * In the case of a typed inline, i.e. that which occurs when using
+    :func:`numba.extending.overload`, both arguments are instances of a
+    ``namedtuple`` with fields (corresponding to their standard use in the
+    compiler internals):
+
+    * ``func_ir`` - the function's Numba IR.
+    * ``typemap`` - the function's type map.
+    * ``calltypes`` - the call types of any calls in the function.
+    * ``signature`` - the function's signature.
+
+    The second argument holds the information from the caller, the third holds
+    the information from the callee.
+
+  In all cases the function should return True to inline and return False to not
+  inline, this essentially permitting custom inlining rules (typical use might
+  be cost models).
+* Recursive functions with ``inline='always'`` will result in a non-terminating
+  compilation. If you wish to avoid this, supply a function to limit the
+  recursion depth (see below).
+
+.. note:: No guarantee is made about the order in which functions are assessed
+          for inlining or about the order in which they are inlined.
+
+
+Example using :func:`numba.jit`
+===============================
+
+An example of using all three options to ``inline`` in the :func:`numba.njit`
+decorator:
+
+.. literalinclude:: inline_example.py
+
+which produces the following when executed (with a print of the IR after the
+legalization pass, enabled via the environment variable
+``NUMBA_DEBUG_PRINT_AFTER="ir_legalization"``):
+
+.. code-block:: none
+    :emphasize-lines: 2, 3, 9, 16, 17, 21, 22, 26, 35
+
+    label 0:
+        $0.1 = global(never_inline: CPUDispatcher(<function never_inline at 0x7f890ccf9048>)) ['$0.1']
+        $0.2 = call $0.1(func=$0.1, args=[], kws=(), vararg=None) ['$0.1', '$0.2']
+        del $0.1                                 []
+        a = $0.2                                 ['$0.2', 'a']
+        del $0.2                                 []
+        $0.3 = global(always_inline: CPUDispatcher(<function always_inline at 0x7f890ccf9598>)) ['$0.3']
+        del $0.3                                 []
+        $const0.1.0 = const(int, 200)            ['$const0.1.0']
+        $0.2.1 = $const0.1.0                     ['$0.2.1', '$const0.1.0']
+        del $const0.1.0                          []
+        $0.4 = $0.2.1                            ['$0.2.1', '$0.4']
+        del $0.2.1                               []
+        b = $0.4                                 ['$0.4', 'b']
+        del $0.4                                 []
+        $0.5 = global(maybe_inline1: CPUDispatcher(<function maybe_inline1 at 0x7f890ccf9ae8>)) ['$0.5']
+        $0.6 = call $0.5(func=$0.5, args=[], kws=(), vararg=None) ['$0.5', '$0.6']
+        del $0.5                                 []
+        d = $0.6                                 ['$0.6', 'd']
+        del $0.6                                 []
+        $const0.7 = const(int, 13)               ['$const0.7']
+        magic_const = $const0.7                  ['$const0.7', 'magic_const']
+        del $const0.7                            []
+        $0.8 = global(maybe_inline1: CPUDispatcher(<function maybe_inline1 at 0x7f890ccf9ae8>)) ['$0.8']
+        del $0.8                                 []
+        $const0.1.2 = const(int, 300)            ['$const0.1.2']
+        $0.2.3 = $const0.1.2                     ['$0.2.3', '$const0.1.2']
+        del $const0.1.2                          []
+        $0.9 = $0.2.3                            ['$0.2.3', '$0.9']
+        del $0.2.3                               []
+        e = $0.9                                 ['$0.9', 'e']
+        del $0.9                                 []
+        $0.10 = global(maybe_inline2: CPUDispatcher(<function maybe_inline2 at 0x7f890ccf9b70>)) ['$0.10']
+        del $0.10                                []
+        $const0.1.4 = const(int, 37)             ['$const0.1.4']
+        $0.2.5 = $const0.1.4                     ['$0.2.5', '$const0.1.4']
+        del $const0.1.4                          []
+        $0.11 = $0.2.5                           ['$0.11', '$0.2.5']
+        del $0.2.5                               []
+        c = $0.11                                ['$0.11', 'c']
+        del $0.11                                []
+        $0.14 = a + b                            ['$0.14', 'a', 'b']
+        del b                                    []
+        del a                                    []
+        $0.16 = $0.14 + c                        ['$0.14', '$0.16', 'c']
+        del c                                    []
+        del $0.14                                []
+        $0.18 = $0.16 + d                        ['$0.16', '$0.18', 'd']
+        del d                                    []
+        del $0.16                                []
+        $0.20 = $0.18 + e                        ['$0.18', '$0.20', 'e']
+        del e                                    []
+        del $0.18                                []
+        $0.22 = $0.20 + magic_const              ['$0.20', '$0.22', 'magic_const']
+        del magic_const                          []
+        del $0.20                                []
+        $0.23 = cast(value=$0.22)                ['$0.22', '$0.23']
+        del $0.22                                []
+        return $0.23                             ['$0.23']
+
+
+Things to note in the above:
+
+1. The call to the function ``never_inline`` remains as a call.
+2. The ``always_inline`` function has been inlined, note its
+   ``const(int, 200)`` in the caller body.
+3. There is a call to ``maybe_inline1`` before the ``const(int, 13)``
+   declaration, the cost model prevented this from being inlined.
+4. After the ``const(int, 13)`` the subsequent call to ``maybe_inline1`` has
+   been inlined as shown by the ``const(int, 300)`` in the caller body.
+5. The function ``maybe_inline2`` has been inlined as demonstrated by
+   ``const(int, 37)`` in the caller body.
+6. That dead code elimination has not been performed and as a result there are
+   superfluous statements present in the IR.
+
+
+Example using :func:`numba.extending.overload`
+==============================================
+
+An example of using inlining with the  :func:`numba.extending.overload`
+decorator. It is most interesting to note that if a function is supplied as the
+argument to ``inline`` a lot more information is available via the supplied
+function arguments for use in decision making. Also that different
+``@overload`` s can have different inlining behaviours, with multiple ways to
+achieve this:
+
+.. literalinclude:: inline_overload_example.py
+
+which produces the following when executed (with a print of the IR after the
+legalization pass, enabled via the environment variable
+``NUMBA_DEBUG_PRINT_AFTER="ir_legalization"``):
+
+.. code-block:: none
+    :emphasize-lines: 2, 3, 4, 5, 6, 15, 16, 17, 18, 19, 20, 21, 22, 28, 29, 30
+
+    label 0:
+        $const0.2 = const(tuple, (1, 2, 3))      ['$const0.2']
+        x.0 = $const0.2                          ['$const0.2', 'x.0']
+        del $const0.2                            []
+        $const0.2.2 = const(int, 0)              ['$const0.2.2']
+        $0.3.3 = getitem(value=x.0, index=$const0.2.2) ['$0.3.3', '$const0.2.2', 'x.0']
+        del x.0                                  []
+        del $const0.2.2                          []
+        $0.4.4 = $0.3.3                          ['$0.3.3', '$0.4.4']
+        del $0.3.3                               []
+        $0.3 = $0.4.4                            ['$0.3', '$0.4.4']
+        del $0.4.4                               []
+        a = $0.3                                 ['$0.3', 'a']
+        del $0.3                                 []
+        $const0.5 = const(int, 100)              ['$const0.5']
+        x.5 = $const0.5                          ['$const0.5', 'x.5']
+        del $const0.5                            []
+        $const0.2.7 = const(int, 1)              ['$const0.2.7']
+        $0.3.8 = x.5 + $const0.2.7               ['$0.3.8', '$const0.2.7', 'x.5']
+        del x.5                                  []
+        del $const0.2.7                          []
+        $0.4.9 = $0.3.8                          ['$0.3.8', '$0.4.9']
+        del $0.3.8                               []
+        $0.6 = $0.4.9                            ['$0.4.9', '$0.6']
+        del $0.4.9                               []
+        b = $0.6                                 ['$0.6', 'b']
+        del $0.6                                 []
+        $0.7 = global(bar: <function bar at 0x7f6c3710d268>) ['$0.7']
+        $const0.8 = const(complex, 300j)         ['$const0.8']
+        $0.9 = call $0.7($const0.8, func=$0.7, args=[Var($const0.8, inline_overload_example.py (56))], kws=(), vararg=None) ['$0.7', '$0.9', '$const0.8']
+        del $const0.8                            []
+        del $0.7                                 []
+        c = $0.9                                 ['$0.9', 'c']
+        del $0.9                                 []
+        $0.12 = a + b                            ['$0.12', 'a', 'b']
+        del b                                    []
+        del a                                    []
+        $0.14 = $0.12 + c                        ['$0.12', '$0.14', 'c']
+        del c                                    []
+        del $0.12                                []
+        $0.15 = cast(value=$0.14)                ['$0.14', '$0.15']
+        del $0.14                                []
+        return $0.15                             ['$0.15']
+
+Things to note in the above:
+
+1. The first highlighted section is the always inlined overload for the
+   ``UniTuple`` argument type.
+2. The second highlighted section is the overload for the ``Number`` argument
+   type that has been inlined as the cost model function decided to do so as the
+   argument was an ``Integer`` type instance.
+3. The third highlighted section is the overload for the ``Number`` argument
+   type that has not inlined as the cost model function decided to reject it as
+   the argument was an ``Complex`` type instance.
+4. That dead code elimination has not been performed and as a result there are
+   superfluous statements present in the IR.
+
+Using a function to limit the inlining depth of a recursive function
+====================================================================
+
+When using recursive inlines, you can terminate the compilation by using
+a cost model.
+
+.. code:: python
+
+    from numba import njit
+    import numpy as np
+
+    class CostModel(object):
+        def __init__(self, max_inlines):
+            self._count = 0
+            self._max_inlines = max_inlines
+
+        def __call__(self, expr, caller, callee):
+            ret = self._count < self._max_inlines
+            self._count += 1
+            return ret
+
+    @njit(inline=CostModel(3))
+    def factorial(n):
+        if n <= 0:
+            return 1
+        return n * factorial(n - 1)
+
+    factorial(5)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/listings.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/listings.rst
new file mode 100644
index 000000000..1c4b1873d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/listings.rst
@@ -0,0 +1,29 @@
+Listings
+========
+
+This shows listings from compiler internal registries  (e.g. lowering
+definitions).  The information is provided as developer reference.
+When possible, links to source code are provided via github links.
+
+New style listings
+------------------
+
+The following listings are generated from ``numba.help.inspector.write_listings()``. Users can run ``python -m numba.help.inspector --format=rst <package>`` to recreate the the documentation.
+
+.. toctree::
+   :maxdepth: 2
+
+   autogen_builtins_listing.rst
+   autogen_math_listing.rst
+   autogen_cmath_listing.rst
+   autogen_numpy_listing.rst
+
+
+Old style listings
+------------------
+
+.. toctree::
+   :maxdepth: 2
+
+   autogen_lower_listing.rst
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/literal.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/literal.rst
new file mode 100644
index 000000000..dd6d8d187
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/literal.rst
@@ -0,0 +1,82 @@
+.. _developer-literally:
+
+======================
+Notes on Literal Types
+======================
+
+.. note:: This document describes an advanced feature designed to overcome
+          some limitations of the compilation mechanism relating to types.
+
+Some features need to specialize based on the literal value during
+compilation to produce type stable code necessary for successful compilation in
+Numba. This can be achieved by propagating the literal value through the type
+system. Numba recognizes inline literal values as :class:`numba.types.Literal`.
+For example::
+
+    def foo(x):
+        a = 123
+        return bar(x, a)
+
+Numba will infer the type of ``a`` as ``Literal[int](123)``. The definition of
+``bar()`` can subsequently specialize its implementation knowing that the
+second argument is an ``int`` with the value ``123``.
+
+``Literal`` Type
+----------------
+
+Classes and methods related to the ``Literal`` type.
+
+.. autoclass:: numba.types.Literal
+
+.. autofunction:: numba.types.literal
+
+.. autofunction:: numba.types.unliteral
+
+.. autofunction:: numba.types.maybe_literal
+
+Specifying for Literal Typing
+-----------------------------
+
+To specify a value as a ``Literal`` type in code scheduled for JIT compilation,
+use the following function:
+
+.. autofunction:: numba.literally
+
+Code Example
+~~~~~~~~~~~~
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_literally_usage.py
+   :language: python
+   :caption: from ``test_literally_usage`` of ``numba/tests/doc_examples/test_literally_usage.py``
+   :start-after: magictoken.ex_literally_usage.begin
+   :end-before: magictoken.ex_literally_usage.end
+   :dedent: 4
+   :linenos:
+
+
+Internal Details
+~~~~~~~~~~~~~~~~
+
+Internally, the compiler raises a ``ForceLiteralArgs`` exception to signal
+the dispatcher to wrap specified arguments using the ``Literal`` type.
+
+.. autoclass:: numba.errors.ForceLiteralArg
+    :members: __init__, combine, __or__
+
+
+Inside Extensions
+-----------------
+
+``@overload`` extensions can use ``literally`` inside the implementation body
+like in normal jit-code.
+
+Explicit handling of literal requirements is possible through use of the
+following:
+
+.. autoclass:: numba.extending.SentryLiteralArgs
+    :members:
+
+.. autoclass:: numba.extending.BoundLiteralArgs
+    :members:
+
+.. autofunction:: numba.extending.sentry_literal_args
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/live_variable_analysis.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/live_variable_analysis.rst
new file mode 100644
index 000000000..fdeedfa63
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/live_variable_analysis.rst
@@ -0,0 +1,86 @@
+.. _live variable analysis:
+
+======================
+Live Variable Analysis
+======================
+
+(Related issue https://github.com/numba/numba/pull/1611)
+
+Numba uses reference-counting for garbage collection, a technique that
+requires cooperation by the compiler.  The Numba IR encodes the location
+where a decref must be inserted.  These locations are determined by live
+variable analysis.  The corresponding source code is the ``_insert_var_dels()``
+method in https://github.com/numba/numba/blob/main/numba/interpreter.py.
+
+
+In Python semantic, once a variable is defined inside a function, it is alive
+until the variable is explicitly deleted or the function scope is ended.
+However, Numba analyzes the code to determine the minimum bound of the lifetime
+of each variable by its definition and usages during compilation.
+As soon as a variable is unreachable, a ``del`` instruction is inserted at the
+closest basic-block (either at the start of the next block(s) or at the
+end of the current block).  This means variables can be released earlier than in
+regular Python code.
+
+The behavior of the live variable analysis affects memory usage of the compiled
+code.  Internally, Numba does not differentiate temporary variables and user
+variables.  Since each operation generates at least one temporary variable,
+a function can accumulate a high number of temporary variables if they are not
+released as soon as possible.
+Our generator implementation can benefit from early releasing of variables,
+which reduces the size of the state to suspend at each yield point.
+
+
+Notes on behavior of the live variable analysis
+================================================
+
+
+Variable deleted before definition
+-----------------------------------
+
+(Related issue: https://github.com/numba/numba/pull/1738)
+
+When a variable lifetime is confined within the loop body (its definition and
+usage does not escape the loop body), like:
+
+.. code-block:: python
+
+    def f(arr):
+      # BB 0
+      res = 0
+      # BB 1
+      for i in (0, 1):
+          # BB 2
+          t = arr[i]
+          if t[i] > 1:
+              # BB 3
+              res += t[i]
+      # BB 4
+      return res
+
+
+Variable ``t`` is never referenced outside of the loop.
+A ``del`` instruction is emitted for ``t`` at the head of the loop (BB 1)
+before a variable is defined.  The reason is obvious once we know the control
+flow graph::
+
+             +------------------------------> BB4
+             |
+             |
+    BB 0 --> BB 1  -->  BB 2 ---> BB 3
+             ^          |          |
+             |          V          V
+             +---------------------+
+
+
+Variable ``t`` is defined in BB 1.  In BB 2, the evaluation of
+``t[i] > 1`` uses ``t``, which is the last use if execution takes the false
+branch and goto BB 1.  In BB 3, ``t`` is only used in ``res += t[i]``, which is
+the last use if execution takes the true branch.  Because BB 3, an outgoing
+branch of BB 2 uses ``t``, ``t`` must be deleted at the common predecessor.
+The closest point is BB 1, which does not have ``t`` defined from the incoming
+edge of BB 0.
+
+Alternatively, if ``t`` is deleted at BB 4, we will still have to delete the
+variable before its definition because BB4 can be executed without executing
+the loop body (BB 2 and BB 3), where the variable is defined.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/llvm_timings.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/llvm_timings.rst
new file mode 100644
index 000000000..f25a58451
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/llvm_timings.rst
@@ -0,0 +1,107 @@
+.. _developer-llvm-timings:
+
+====================
+Notes on timing LLVM
+====================
+
+
+Getting LLVM Pass Timings
+-------------------------
+
+The dispatcher stores LLVM pass timings in the dispatcher object metadata under
+the ``llvm_pass_timings`` key when :envvar:`NUMBA_LLVM_PASS_TIMINGS` is
+enabled or ``numba.config.LLVM_PASS_TIMINGS`` is set to truthy.
+The timings information contains details on how much time
+has been spent in each pass. The pass timings are also grouped by their purpose.
+For example, there will be pass timings for function-level pre-optimizations,
+module-level optimizations, and object code generation.
+
+
+Code Example
+~~~~~~~~~~~~
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_llvm_pass_timings.py
+   :language: python
+   :caption: from ``test_pass_timings`` of ``numba/tests/doc_examples/test_llvm_pass_timings.py``
+   :start-after: magictoken.ex_llvm_pass_timings.begin
+   :end-before: magictoken.ex_llvm_pass_timings.end
+   :dedent: 16
+   :linenos:
+
+Example output:
+
+.. code-block:: text
+
+  Printing pass timings for JITCodeLibrary('DocsLLVMPassTimings.test_pass_timings.<locals>.foo')
+  Total time: 0.0376
+  == #0 Function passes on '_ZN5numba5tests12doc_examples22test_llvm_pass_timings19DocsLLVMPassTimings17test_pass_timings12$3clocals$3e7foo$241Ex'
+  Percent: 4.8%
+  Total 0.0018s
+  Top timings:
+    0.0015s ( 81.6%) SROA #3
+    0.0002s (  9.3%) Early CSE #2
+    0.0001s (  4.0%) Simplify the CFG #9
+    0.0000s (  1.5%) Prune NRT refops #4
+    0.0000s (  1.1%) Post-Dominator Tree Construction #5
+  == #1 Function passes on '_ZN7cpython5numba5tests12doc_examples22test_llvm_pass_timings19DocsLLVMPassTimings17test_pass_timings12$3clocals$3e7foo$241Ex'
+  Percent: 0.8%
+  Total 0.0003s
+  Top timings:
+    0.0001s ( 30.4%) Simplify the CFG #10
+    0.0001s ( 24.1%) Early CSE #3
+    0.0001s ( 17.8%) SROA #4
+    0.0000s (  8.8%) Prune NRT refops #5
+    0.0000s (  5.6%) Post-Dominator Tree Construction #6
+  == #2 Function passes on 'cfunc._ZN5numba5tests12doc_examples22test_llvm_pass_timings19DocsLLVMPassTimings17test_pass_timings12$3clocals$3e7foo$241Ex'
+  Percent: 0.5%
+  Total 0.0002s
+  Top timings:
+    0.0001s ( 27.7%) Early CSE #4
+    0.0001s ( 26.8%) Simplify the CFG #11
+    0.0000s ( 13.8%) Prune NRT refops #6
+    0.0000s (  7.4%) Post-Dominator Tree Construction #7
+    0.0000s (  6.7%) Dominator Tree Construction #29
+  == #3 Module passes (cheap optimization for refprune)
+  Percent: 3.7%
+  Total 0.0014s
+  Top timings:
+    0.0007s ( 52.0%) Combine redundant instructions
+    0.0001s (  5.4%) Function Integration/Inlining
+    0.0001s (  4.9%) Prune NRT refops #2
+    0.0001s (  4.8%) Natural Loop Information
+    0.0001s (  4.6%) Post-Dominator Tree Construction #2
+  == #4 Module passes (full optimization)
+  Percent: 43.9%
+  Total 0.0165s
+  Top timings:
+    0.0032s ( 19.5%) Combine redundant instructions #9
+    0.0022s ( 13.5%) Combine redundant instructions #7
+    0.0010s (  6.1%) Induction Variable Simplification
+    0.0008s (  4.8%) Unroll loops #2
+    0.0007s (  4.5%) Loop Vectorization
+  == #5 Finalize object
+  Percent: 46.3%
+  Total 0.0174s
+  Top timings:
+    0.0060s ( 34.6%) X86 DAG->DAG Instruction Selection #2
+    0.0019s ( 11.0%) Greedy Register Allocator #2
+    0.0013s (  7.4%) Machine Instruction Scheduler #2
+    0.0012s (  7.1%) Loop Strength Reduction
+    0.0004s (  2.3%) Induction Variable Users
+
+
+API for custom analysis
+~~~~~~~~~~~~~~~~~~~~~~~
+
+It is possible to get more details then the summary text in the above example.
+The pass timings are stored in a
+:class:`numba.misc.llvm_pass_timings.PassTimingsCollection`, which contains
+methods for accessing individual record for each pass.
+
+.. autoclass:: numba.misc.llvm_pass_timings.PassTimingsCollection
+    :members: get_total_time, list_longest_first, summary, __getitem__, __len__
+
+.. autoclass:: numba.misc.llvm_pass_timings.ProcessedPassTimings
+    :members: get_raw_data, get_total_time, list_records, list_top, summary
+
+.. autoclass:: numba.misc.llvm_pass_timings.PassTimingRecord
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/mission.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/mission.rst
new file mode 100644
index 000000000..07fb3c2d3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/mission.rst
@@ -0,0 +1,82 @@
+Numba Mission Statement
+=======================
+
+Introduction
+------------
+
+This document is the mission statement for the Numba project. It exists to
+provide a clear description of the purposes and goals of the project.  As such,
+this document provides background on Numba's users and use-cases, and outlines
+the project's overall goals.
+
+This is a living document:
+
+=========================== =============
+The first revision date is: May 2022
+The last updated date is:   May 2022
+The next review date is:    November 2022
+=========================== =============
+
+Background
+----------
+
+The Numba project provides tools to improve the performance of Python software.
+It comprises numerous facilities including just-in-time (JIT) compilation,
+extension points for library authors, and a compiler toolkit on which new
+computational acceleration technologies can be explored and built.
+
+The range of use-cases and applications that can be targeted by Numba includes,
+but is not limited to:
+
+* Scientific Computing
+* Computationally intensive tasks
+* Numerically oriented applications
+* Data science utilities and programs
+
+The user base of Numba includes anyone needing to perform intensive
+computational work, including users from a wide range of disciplines, examples
+include:
+
+* The most common use case, a user wanting to JIT compile some numerical
+  functions.
+* Users providing JIT accelerated libraries for domain specific use cases e.g.
+  scientific researchers.
+* Users providing JIT accelerated libraries for use as part of the numerical
+  Python ecosystem.
+* Those writing more advanced JIT accelerated libraries containing their own
+  domain specific data types etc.
+* Compiler engineers who explore new compiler use-cases and/or need a custom
+  compiler.
+* Hardware vendors looking to extend Numba to provide Python support for their
+  custom silicon or new hardware.
+
+Project Goals
+-------------
+
+The primary aims of the Numba project are:
+
+* To make it easier for Python users to write high performance code.
+* To have a core package with a well defined and pragmatically selected feature
+  scope that meets the needs of the user base without being overly complex.
+* To provide a compiler toolkit for Python that is extensible and can be
+  customized to meet the needs of the user base. This comes with the expectation
+  that users potentially need to invest time and effort to extend and/or
+  customize the software themselves.
+* To support both the Python core language/standard libraries and NumPy.
+* To consistently produce high quality software:
+
+  * Feature stability across versions.
+  * Well established and tested public APIs.
+  * Clearly documented deprecation cycles.
+  * Internally stable code base.
+  * Externally tested release candidates.
+  * Regular releases with a predictable and published release cycle.
+  * Maintain suitable infrastructure for both testing and releasing. With as
+    much in public as feasible.
+
+* To make it as easy as possible for people to contribute.
+* To have a maintained public roadmap which will also include areas under active
+  development.
+* To have a governance document in place and it working in practice.
+* To ensure that Numba receives timely updates for its core dependencies: LLVM,
+  NumPy and Python.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/numba-runtime.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/numba-runtime.rst
new file mode 100644
index 000000000..2eb290062
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/numba-runtime.rst
@@ -0,0 +1,200 @@
+.. _arch-numba-runtime:
+
+======================
+Notes on Numba Runtime
+======================
+
+
+The *Numba Runtime (NRT)* provides the language runtime to the *nopython mode*
+Python subset.  NRT is a standalone C library with a Python binding.  This
+allows :term:`NPM` runtime feature to be used without the GIL.  Currently, the 
+only language feature implemented in NRT is memory management.
+
+
+Memory Management
+=================
+
+NRT implements memory management for :term:`NPM` code.  It uses *atomic 
+reference count* for threadsafe, deterministic memory management.  NRT maintains 
+a separate ``MemInfo`` structure for storing information about each allocation.
+
+Cooperating with CPython
+------------------------
+
+For NRT to cooperate with CPython, the NRT python binding provides adaptors for
+converting python objects that export a memory region.  When such an
+object is used as an argument to a :term:`NPM` function, a new ``MemInfo`` is 
+created and it acquires a reference to the Python object.  When a :term:`NPM` 
+value is returned to the Python interpreter, the associated ``MemInfo`` 
+(if any) is checked.  If the ``MemInfo`` references a Python object, the 
+underlying Python object is released and returned instead.  Otherwise, the 
+``MemInfo`` is wrapped in a Python object and returned. Additional process 
+maybe required depending on the type.
+
+The current implementation supports Numpy array and any buffer-exporting types.
+
+
+Compiler-side Cooperation
+-------------------------
+
+NRT reference counting requires the compiler to emit incref/decref operations
+according to the usage.  When the reference count drops to zero, the compiler
+must call the destructor routine in NRT.
+
+
+.. _nrt-refct-opt-pass:
+
+Optimizations
+-------------
+
+The compiler is allowed to emit incref/decref operations naively.  It relies
+on an optimization pass to remove redundant reference count operations.
+
+A new optimization pass is implemented in version 0.52.0 to remove reference
+count operations that fall into the following four categories of control-flow
+structure---per basic-block, diamond, fanout, fanout+raise. See the documentation
+for :envvar:`NUMBA_LLVM_REFPRUNE_FLAGS` for their descriptions.
+
+The old optimization pass runs at block level to avoid control flow analysis.
+It depends on LLVM function optimization pass to simplify the control flow,
+stack-to-register, and simplify instructions.  It works by matching and
+removing incref and decref pairs within each block.  The old pass can be
+enabled by setting :envvar:`NUMBA_LLVM_REFPRUNE_PASS` to `0`.
+
+Important assumptions
+---------------------
+
+Both the old (pre-0.52.0) and the new (post-0.52.0) optimization passes assume
+that the only function that can consume a reference is ``NRT_decref``.
+It is important that there are no other functions that will consume references.
+Since the passes operate on LLVM IR, the "functions" here are referring to any
+callee in a LLVM call instruction.
+
+To summarize, all functions exposed to the refcount optimization pass
+**must not** consume counted references unless done so via ``NRT_decref``.
+
+
+Quirks of the old optimization pass
+-----------------------------------
+
+Since the pre-0.52.0 `refcount optimization pass <nrt-refct-opt-pass_>`_
+requires the LLVM function optimization pass, the pass works on the LLVM IR as
+text. The optimized IR is then materialized again as a new LLVM in-memory
+bitcode object.
+
+
+Debugging Leaks
+---------------
+
+To debug reference leaks in NRT MemInfo, each MemInfo python object has a
+``.refcount`` attribute for inspection.  To get the MemInfo from a ndarray
+allocated by NRT, use the ``.base`` attribute.
+
+To debug memory leaks in NRT, the ``numba.core.runtime.rtsys`` defines
+``.get_allocation_stats()``.  It returns a namedtuple containing the
+number of allocation and deallocation since the start of the program.
+Checking that the allocation and deallocation counters are matching is the
+simplest way to know if the NRT is leaking.
+
+
+Debugging Leaks in C
+--------------------
+
+The start of `numba/core/runtime/nrt.h
+<https://github.com/numba/numba/blob/main/numba/core/runtime/nrt.h>`_
+has these lines:
+
+.. code-block:: C
+
+  /* Debugging facilities - enabled at compile-time */
+  /* #undef NDEBUG */
+  #if 0
+  #   define NRT_Debug(X) X
+  #else
+  #   define NRT_Debug(X) if (0) { X; }
+  #endif
+
+Undefining NDEBUG (uncomment the ``#undef NDEBUG`` line) enables the assertion
+check in NRT.
+
+Enabling the NRT_Debug (replace ``#if 0`` with ``#if 1``) turns on
+debug print inside NRT.
+
+
+Recursion Support
+=================
+
+During the compilation of a pair of mutually recursive functions, one of the
+functions will contain unresolved symbol references since the compiler handles
+one function at a time.  The memory for the unresolved symbols is allocated and
+initialized to the address of the *unresolved symbol abort* function
+(``nrt_unresolved_abort``) just before the machine code is
+generated by LLVM.  These symbols are tracked and resolved as new functions are
+compiled.  If a bug prevents the resolution of these symbols,
+the abort function will be called, raising a ``RuntimeError`` exception.
+
+The *unresolved symbol abort* function is defined in the NRT with a zero-argument
+signature. The caller is safe to call it with arbitrary number of
+arguments.  Therefore, it is safe to be used inplace of the intended callee.
+
+Using the NRT from C code
+=========================
+
+Externally compiled C code should use the ``NRT_api_functions`` struct as a
+function table to access the NRT API. The struct is defined in
+:ghfile:`numba/core/runtime/nrt_external.h`. Users can use the utility function
+``numba.extending.include_path()`` to determine the include directory for
+Numba provided C headers.
+
+.. literalinclude:: ../../../numba/core/runtime/nrt_external.h
+  :language: C
+  :caption: `numba/core/runtime/nrt_external.h`
+
+Inside Numba compiled code, the ``numba.core.unsafe.nrt.NRT_get_api()``
+intrinsic can be used to obtain a pointer to the ``NRT_api_functions``.
+
+Here is an example that uses the ``nrt_external.h``:
+
+.. code-block:: C
+
+  #include <stdio.h>
+  #include "numba/core/runtime/nrt_external.h"
+
+  void my_dtor(void *ptr) {
+      free(ptr);
+  }
+
+  NRT_MemInfo* my_allocate(NRT_api_functions *nrt) {
+      /* heap allocate some memory */
+      void * data = malloc(10);
+      /* wrap the allocated memory; yield a new reference */
+      NRT_MemInfo *mi = nrt->manage_memory(data, my_dtor);
+      /* acquire reference */
+      nrt->acquire(mi);
+      /* release reference */
+      nrt->release(mi);
+      return mi;
+  }
+
+It is important to ensure that the NRT is initialized prior to making calls to
+it, calling ``numba.core.runtime.nrt.rtsys.initialize(context)`` from Python
+will have the desired effect. Similarly the code snippet:
+
+.. code-block:: Python
+
+  from numba.core.registry import cpu_target # Get the CPU target singleton
+  cpu_target.target_context # Access the target_context property to initialize
+
+will achieve the same specifically for Numba's CPU target (the default). Failure
+to initialize the NRT will result in access violations as function pointers for
+various internal atomic operations will be missing in the ``NRT_MemSys`` struct.
+
+Future Plan
+===========
+
+The plan for NRT is to make a standalone shared library that can be linked to
+Numba compiled code, including use within the Python interpreter and without
+the Python interpreter.  To make that work, we will be doing some refactoring:
+
+* numba :term:`NPM` code references statically compiled code in "helperlib.c".
+  Those functions should be moved to NRT.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/release.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/release.rst
new file mode 100644
index 000000000..b547ee2bb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/release.rst
@@ -0,0 +1,49 @@
+Numba Release Process
+=====================
+
+The goal of the Numba release process -- from a high level perspective -- is to
+publish source and binary artifacts that correspond to a given version
+number. This usually involves a sequence of individual tasks that must be
+performed in the correct order and with diligence. Numba and llvmlite are
+commonly released in lockstep since there is usually a one-to-one mapping
+between a Numba version and a corresponding llvmlite version.
+
+This section contains various notes and templates that can be used to create a
+Numba release checklist on the Numba Github issue tracker. This is an aid for
+the maintainers during the release process and helps to ensure that all tasks
+are completed in the correct order and that no tasks are accidentally omitted.
+
+If new or additional items do appear during release, please do remember to add
+them to the checklist templates. Also note that the release process itself is
+always a work in progress. This means that some of the information here may be
+outdated. If you notice this please do remember to submit a pull-request to
+update this document.
+
+All release checklists are available as Gitub issue templates. To create a new
+release checklist simply open a new issue and select the correct template.
+
+
+Primary Release Candidate Checklist
+-----------------------------------
+
+This is for the first/primary release candidate for minor release i.e. the
+first release of every series. It is special, because during this release, the
+release branch will have to be created. Release candidate indexing begins at 1.
+
+.. literalinclude:: ../../../.github/ISSUE_TEMPLATE/first_rc_checklist.md
+    :language: md
+    :lines: 9-
+
+`Open a primary release checklist <https://github.com/numba/numba/issues/new?template=first_rc_checklist.md>`_.
+
+Subsequent Release Candidates, Final Releases and Patch Releases
+----------------------------------------------------------------
+
+Releases subsequent to the first release in a series usually involves a series
+of cherry-picks, the recipe is therefore slightly different.
+
+.. literalinclude:: ../../../.github/ISSUE_TEMPLATE/sub_rc_checklist.md
+    :language: md
+    :lines: 9-
+
+`Open a subsequent release checklist <https://github.com/numba/numba/issues/new?template=sub_rc_checklist.md>`_.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/repomap.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/repomap.rst
new file mode 100644
index 000000000..12bfd1b16
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/repomap.rst
@@ -0,0 +1,582 @@
+A Map of the Numba Repository
+=============================
+
+The Numba repository is quite large, and due to age has functionality spread
+around many locations.  To help orient developers, this document will try to
+summarize where different categories of functionality can be found.
+
+
+Support Files
+-------------
+
+Build and Packaging
+'''''''''''''''''''
+
+- :ghfile:`setup.py` - Standard Python distutils/setuptools script
+- :ghfile:`MANIFEST.in` - Distutils packaging instructions
+- :ghfile:`requirements.txt` - Pip package requirements, not used by conda
+- :ghfile:`versioneer.py` - Handles automatic setting of version in
+  installed package from git tags
+- :ghfile:`.flake8` - Preferences for code formatting.  Files should be
+  fixed and removed from the exception list as time allows.
+- :ghfile:`.pre-commit-config.yaml` - Configuration file for pre-commit hooks.
+- :ghfile:`.readthedocs.yml` - Configuration file for Read the Docs.
+- :ghfile:`buildscripts/condarecipe.local` - Conda build recipe
+- :ghfile:`buildscripts/condarecipe_clone_icc_rt` - Recipe to build a
+  standalone icc_rt package.
+
+
+Continuous Integration
+''''''''''''''''''''''
+- :ghfile:`azure-pipelines.yml` - Azure Pipelines CI config (active:
+  Win/Mac/Linux)
+- :ghfile:`buildscripts/azure/` - Azure Pipeline configuration for specific
+  platforms
+- :ghfile:`buildscripts/appveyor/` - Appveyor build scripts
+- :ghfile:`buildscripts/incremental/` - Generic scripts for building Numba
+  on various CI systems
+- :ghfile:`codecov.yml` - Codecov.io coverage reporting
+
+
+Documentation
+'''''''''''''
+- :ghfile:`LICENSE` - License for Numba
+- :ghfile:`LICENSES.third-party` - License for third party code vendored
+  into Numba
+- :ghfile:`README.rst` - README for repo, also uploaded to PyPI
+- :ghfile:`CONTRIBUTING.md` - Documentation on how to contribute to project
+  (out of date, should be updated to point to Sphinx docs)
+- :ghfile:`CHANGE_LOG` - History of Numba releases, also directly embedded
+  into Sphinx documentation
+- :ghfile:`docs/` - Documentation source
+- :ghfile:`docs/_templates/` - Directory for templates (to override defaults
+  with Sphinx theme)
+- :ghfile:`docs/Makefile` - Used to build Sphinx docs with ``make``
+- :ghfile:`docs/source` - ReST source for Numba documentation
+- :ghfile:`docs/_static/` - Static CSS and image assets for Numba docs
+- :ghfile:`docs/gh-pages.py` - Utility script to update Numba docs (stored
+  as gh-pages)
+- :ghfile:`docs/make.bat` - Not used (remove?)
+- :ghfile:`docs/requirements.txt` - Pip package requirements for building docs
+  with Read the Docs.
+- :ghfile:`numba/scripts/generate_lower_listing.py` - Dump all registered
+  implementations decorated with ``@lower*`` for reference
+  documentation.  Currently misses implementations from the higher
+  level extension API.
+
+
+Numba Source Code
+-----------------
+
+Numba ships with both the source code and tests in one package.
+
+- :ghfile:`numba/` - all of the source code and tests
+
+
+Public API
+''''''''''
+
+These define aspects of the public Numba interface.
+
+- :ghfile:`numba/core/decorators.py` - User-facing decorators for compiling
+  regular functions on the CPU
+- :ghfile:`numba/core/extending.py` - Public decorators for extending Numba
+  (``overload``, ``intrinsic``, etc)
+  - :ghfile:`numba/experimental/structref.py` - Public API for defining a mutable struct
+- :ghfile:`numba/core/ccallback.py` - ``@cfunc`` decorator for compiling
+  functions to a fixed C signature.  Used to make callbacks.
+- :ghfile:`numba/np/ufunc/decorators.py` - ufunc/gufunc compilation
+  decorators
+- :ghfile:`numba/core/config.py` - Numba global config options and environment
+  variable handling
+- :ghfile:`numba/core/annotations` - Gathering and printing type annotations of
+  Numba IR
+- :ghfile:`numba/core/annotations/pretty_annotate.py` - Code highlighting of
+  Numba functions and types (both ANSI terminal and HTML)
+- :ghfile:`numba/core/event.py` - A simple event system for applications to
+  listen to specific compiler events.
+
+
+Dispatching
+'''''''''''
+
+- :ghfile:`numba/core/dispatcher.py` - Dispatcher objects are compiled functions
+  produced by ``@jit``.  A dispatcher has different implementations
+  for different type signatures.
+- :ghfile:`numba/_dispatcher.cpp` - C++ dispatcher implementation (for speed on
+  common data types)
+- :ghfile:`numba/core/retarget.py` - Support for dispatcher objects to switch
+  target via a specific with-context.
+
+
+Compiler Pipeline
+'''''''''''''''''
+
+- :ghfile:`numba/core/compiler.py` - Compiler pipelines and flags
+- :ghfile:`numba/core/errors.py` - Numba exception and warning classes
+- :ghfile:`numba/core/ir.py` - Numba IR data structure objects
+- :ghfile:`numba/core/bytecode.py` - Bytecode parsing and function identity (??)
+- :ghfile:`numba/core/interpreter.py` - Translate Python interpreter bytecode to
+  Numba IR
+- :ghfile:`numba/core/analysis.py` - Utility functions to analyze Numba IR
+  (variable lifetime, prune branches, etc)
+- :ghfile:`numba/core/dataflow.py` - Dataflow analysis for Python bytecode (used
+  in analysis.py)
+- :ghfile:`numba/core/controlflow.py` - Control flow analysis of Numba IR and
+  Python bytecode
+- :ghfile:`numba/core/typeinfer.py` - Type inference algorithm
+- :ghfile:`numba/core/transforms.py` - Numba IR transformations
+- :ghfile:`numba/core/rewrites` - Rewrite passes used by compiler
+- :ghfile:`numba/core/rewrites/__init__.py` - Loads all rewrite passes so they
+  are put into the registry
+- :ghfile:`numba/core/rewrites/registry.py` - Registry object for collecting
+  rewrite passes
+- :ghfile:`numba/core/rewrites/ir_print.py` - Write print() calls into special
+  print nodes in the IR
+- :ghfile:`numba/core/rewrites/static_raise.py` - Converts exceptions with
+  static arguments into a special form that can be lowered
+- :ghfile:`numba/core/rewrites/static_getitem.py` - Rewrites getitem and setitem
+  with constant arguments to allow type inference
+- :ghfile:`numba/core/rewrites/static_binop.py` - Rewrites binary operations
+  (specifically ``**``) with constant arguments so faster code can be
+  generated
+- :ghfile:`numba/core/inline_closurecall.py` - Inlines body of closure functions
+  to call site.  Support for array comprehensions, reduction inlining,
+  and stencil inlining.
+- :ghfile:`numba/core/postproc.py` - Postprocessor for Numba IR that computes
+  variable lifetime, inserts del operations, and handles generators
+- :ghfile:`numba/core/lowering.py` - General implementation of lowering Numba IR
+  to LLVM
+  :ghfile:`numba/core/environment.py` - Runtime environment object
+- :ghfile:`numba/core/withcontexts.py` - General scaffolding for implementing
+  context managers in nopython mode, and the objectmode context
+  manager
+- :ghfile:`numba/core/pylowering.py` - Lowering of Numba IR in object mode
+- :ghfile:`numba/core/pythonapi.py` - LLVM IR code generation to interface with
+  CPython API
+- :ghfile:`numba/core/targetconfig.py` - Utils for target configurations such
+  as compiler flags.
+
+
+Type Management
+'''''''''''''''
+
+- :ghfile:`numba/core/typeconv/` - Implementation of type casting and type
+  signature matching in both C++ and Python
+- :ghfile:`numba/capsulethunk.h` - Used by typeconv
+- :ghfile:`numba/core/types/` - definition of the Numba type hierarchy, used
+  everywhere in compiler to select implementations
+- :ghfile:`numba/core/consts.py` - Constant inference (used to make constant
+  values available during codegen when possible)
+- :ghfile:`numba/core/datamodel` - LLVM IR representations of data types in
+  different contexts
+- :ghfile:`numba/core/datamodel/models.py` - Models for most standard types
+- :ghfile:`numba/core/datamodel/registry.py` - Decorator to register new data
+  models
+- :ghfile:`numba/core/datamodel/packer.py` - Pack typed values into a data
+  structure
+- :ghfile:`numba/core/datamodel/testing.py` - Data model tests (this should
+  move??)
+- :ghfile:`numba/core/datamodel/manager.py` - Map types to data models
+
+
+Compiled Extensions
+'''''''''''''''''''
+
+Numba uses a small amount of compiled C/C++ code for core
+functionality, like dispatching and type matching where performance
+matters, and it is more convenient to encapsulate direct interaction
+with CPython APIs.
+
+- :ghfile:`numba/_arraystruct.h` - Struct for holding NumPy array
+  attributes.  Used in helperlib and the Numba Runtime.
+- :ghfile:`numba/_helperlib.c` - C functions required by Numba compiled code
+  at runtime.  Linked into ahead-of-time compiled modules
+- :ghfile:`numba/_helpermod.c` - Python extension module with pointers to
+  functions from ``_helperlib.c`` and ``_npymath_exports.c``
+- :ghfile:`numba/_npymath_exports.c` - Export function pointer table to
+  NumPy C math functions
+- :ghfile:`numba/_dynfuncmod.c` - Python extension module exporting
+  _dynfunc.c functionality
+- :ghfile:`numba/_dynfunc.c` - C level Environment and Closure objects (keep
+  in sync with numba/target/base.py)
+- :ghfile:`numba/mathnames.h` - Macros for defining names of math functions
+- :ghfile:`numba/_pymodule.h` - C macros for Python 2/3 portable naming of C
+  API functions
+- :ghfile:`numba/mviewbuf.c` - Handles Python memoryviews
+- :ghfile:`numba/_typeof.{h,c}` - C implementation of type fingerprinting,
+  used by dispatcher
+- :ghfile:`numba/_numba_common.h` - Portable C macro for marking symbols
+  that can be shared between object files, but not outside the
+  library.
+
+
+
+Misc Support
+''''''''''''
+
+- :ghfile:`numba/_version.py` - Updated by versioneer
+- :ghfile:`numba/core/runtime` - Language runtime.  Currently manages
+  reference-counted memory allocated on the heap by Numba-compiled
+  functions
+- :ghfile:`numba/core/ir_utils.py` - Utility functions for working with Numba IR
+  data structures
+- :ghfile:`numba/core/cgutils.py` - Utility functions for generating common code
+  patterns in LLVM IR
+- :ghfile:`numba/core/utils.py` - Python 2 backports of Python 3 functionality
+  (also imports local copy of ``six``)
+- :ghfile:`numba/core/overload_glue.py` - Functions for wrapping split typing
+  and lowering API use cases into overloads.
+- :ghfile:`numba/misc/appdirs.py` - Vendored package for determining application
+  config directories on every platform
+- :ghfile:`numba/core/compiler_lock.py` - Global compiler lock because Numba's
+  usage of LLVM is not thread-safe
+- :ghfile:`numba/misc/special.py` - Python stub implementations of special Numba
+  functions (prange, gdb*)
+- :ghfile:`numba/core/itanium_mangler.py` - Python implementation of Itanium C++
+  name mangling
+- :ghfile:`numba/misc/findlib.py` - Helper function for locating shared
+  libraries on all platforms
+- :ghfile:`numba/core/debuginfo.py` - Helper functions to construct LLVM IR
+  debug
+  info
+- :ghfile:`numba/core/unsafe/refcount.py` - Read reference count of object
+- :ghfile:`numba/core/unsafe/eh.py` - Exception handling helpers
+- :ghfile:`numba/core/unsafe/nrt.py` - Numba runtime (NRT) helpers
+- :ghfile:`numba/cpython/unsafe/tuple.py` - Replace a value in a tuple slot
+- :ghfile:`numba/np/unsafe/ndarray.py` - NumPy array helpers
+- :ghfile:`numba/core/unsafe/bytes.py` - Copying and dereferencing data from
+  void pointers
+- :ghfile:`numba/misc/dummyarray.py` - Used by GPU backends to hold array
+  information on the host, but not the data.
+- :ghfile:`numba/core/callwrapper.py` - Handles argument unboxing and releasing
+  the GIL when moving from Python to nopython mode
+- :ghfile:`numba/np/numpy_support.py` - Helper functions for working with NumPy
+  and translating Numba types to and from NumPy dtypes.
+- :ghfile:`numba/core/tracing.py` - Decorator for tracing Python calls and
+  emitting log messages
+- :ghfile:`numba/core/funcdesc.py` - Classes for describing function metadata
+  (used in the compiler)
+- :ghfile:`numba/core/sigutils.py` - Helper functions for parsing and
+  normalizing Numba type signatures
+- :ghfile:`numba/core/serialize.py` - Support for pickling compiled functions
+- :ghfile:`numba/core/caching.py` - Disk cache for compiled functions
+- :ghfile:`numba/np/npdatetime.py` - Helper functions for implementing NumPy
+  datetime64 support
+- :ghfile:`numba/misc/llvm_pass_timings.py` - Helper to record timings of
+  LLVM passes.
+- :ghfile:`numba/cloudpickle` - Vendored cloudpickle subpackage
+
+Core Python Data Types
+''''''''''''''''''''''
+
+- :ghfile:`numba/_hashtable.{h,c}` - Adaptation of the Python 3.7 hash table
+  implementation
+- :ghfile:`numba/cext/dictobject.{h,c}` - C level implementation of typed
+  dictionary
+- :ghfile:`numba/typed/dictobject.py` - Nopython mode wrapper for typed
+  dictionary
+- :ghfile:`numba/cext/listobject.{h,c}` - C level implementation of typed list
+- :ghfile:`numba/typed/listobject.py` - Nopython mode wrapper for typed list
+- :ghfile:`numba/typed/typedobjectutils.py` - Common utilities for typed
+  dictionary and list
+- :ghfile:`numba/cpython/unicode.py` - Unicode strings (Python 3.5 and later)
+- :ghfile:`numba/typed` - Python interfaces to statically typed containers
+- :ghfile:`numba/typed/typeddict.py` - Python interface to typed dictionary
+- :ghfile:`numba/typed/typedlist.py` - Python interface to typed list
+- :ghfile:`numba/experimental/jitclass` - Implementation of experimental JIT
+  compilation of Python classes
+- :ghfile:`numba/core/generators.py` - Support for lowering Python generators
+
+
+Math
+''''
+
+- :ghfile:`numba/_random.c` - Reimplementation of NumPy / CPython random
+  number generator
+- :ghfile:`numba/_lapack.c` - Wrappers for calling BLAS and LAPACK functions
+  (requires SciPy)
+
+
+ParallelAccelerator
+'''''''''''''''''''
+
+Code transformation passes that extract parallelizable code from
+a function and convert it into multithreaded gufunc calls.
+
+- :ghfile:`numba/parfors/parfor.py` - General ParallelAccelerator
+- :ghfile:`numba/parfors/parfor_lowering.py` - gufunc lowering for
+  ParallelAccelerator
+- :ghfile:`numba/parfors/array_analysis.py` - Array analysis passes used in
+  ParallelAccelerator
+
+
+Stencil
+'''''''
+
+Implementation of ``@stencil``:
+
+- :ghfile:`numba/stencils/stencil.py` - Stencil function decorator (implemented
+  without ParallelAccelerator)
+- :ghfile:`numba/stencils/stencilparfor.py` - ParallelAccelerator implementation
+  of stencil
+
+
+Debugging Support
+'''''''''''''''''
+
+- :ghfile:`numba/misc/gdb_hook.py` - Hooks to jump into GDB from nopython
+  mode
+- :ghfile:`numba/misc/cmdlang.gdb` - Commands to setup GDB for setting
+  explicit breakpoints from Python
+
+
+Type Signatures (CPU)
+'''''''''''''''''''''
+
+Some (usually older) Numba supported functionality separates the
+declaration of allowed type signatures from the definition of
+implementations.  This package contains registries of type signatures
+that must be matched during type inference.
+
+- :ghfile:`numba/core/typing` - Type signature module
+- :ghfile:`numba/core/typing/templates.py` - Base classes for type signature
+  templates
+- :ghfile:`numba/core/typing/cmathdecl.py` - Python complex math (``cmath``)
+  module
+- :ghfile:`numba/core/typing/bufproto.py` - Interpreting objects supporting the
+  buffer protocol
+- :ghfile:`numba/core/typing/mathdecl.py` - Python ``math`` module
+- :ghfile:`numba/core/typing/listdecl.py` - Python lists
+- :ghfile:`numba/core/typing/builtins.py` - Python builtin global functions and
+  operators
+- :ghfile:`numba/core/typing/randomdecl.py` - Python and NumPy ``random``
+  modules
+- :ghfile:`numba/core/typing/setdecl.py` - Python sets
+- :ghfile:`numba/core/typing/npydecl.py` - NumPy ndarray (and operators), NumPy
+  functions
+- :ghfile:`numba/core/typing/arraydecl.py` - Python ``array`` module
+- :ghfile:`numba/core/typing/context.py` - Implementation of typing context
+  (class that collects methods used in type inference)
+- :ghfile:`numba/core/typing/collections.py` - Generic container operations and
+  namedtuples
+- :ghfile:`numba/core/typing/ctypes_utils.py` - Typing ctypes-wrapped function
+  pointers
+- :ghfile:`numba/core/typing/enumdecl.py` - Enum types
+- :ghfile:`numba/core/typing/cffi_utils.py` - Typing of CFFI objects
+- :ghfile:`numba/core/typing/typeof.py` - Implementation of typeof operations
+  (maps Python object to Numba type)
+- :ghfile:`numba/core/typing/asnumbatype.py` - Implementation of
+  ``as_numba_type`` operations (maps Python types to Numba type)
+- :ghfile:`numba/core/typing/npdatetime.py` - Datetime dtype support for NumPy
+  arrays
+
+
+Target Implementations (CPU)
+''''''''''''''''''''''''''''
+
+Implementations of Python / NumPy functions and some data models.
+These modules are responsible for generating LLVM IR during lowering.
+Note that some of these modules do not have counterparts in the typing
+package because newer Numba extension APIs (like overload) allow
+typing and implementation to be specified together.
+
+- :ghfile:`numba/core/cpu.py` - Context for code gen on CPU
+- :ghfile:`numba/core/base.py` - Base class for all target contexts
+- :ghfile:`numba/core/codegen.py` - Driver for code generation
+- :ghfile:`numba/core/boxing.py` - Boxing and unboxing for most data
+  types
+- :ghfile:`numba/core/intrinsics.py` - Utilities for converting LLVM
+  intrinsics to other math calls
+- :ghfile:`numba/core/callconv.py` - Implements different calling
+  conventions for Numba-compiled functions
+- :ghfile:`numba/core/options.py` - Container for options that control
+  lowering
+- :ghfile:`numba/core/optional.py` - Special type representing value or
+  ``None``
+- :ghfile:`numba/core/registry.py` - Registry object for collecting
+  implementations for a specific target
+- :ghfile:`numba/core/imputils.py` - Helper functions for lowering
+- :ghfile:`numba/core/externals.py` - Registers external C functions
+  needed to link generated code
+- :ghfile:`numba/core/fastmathpass.py` - Rewrite pass to add fastmath
+  attributes to function call sites and binary operations
+- :ghfile:`numba/core/removerefctpass.py` - Rewrite pass to remove
+  unnecessary incref/decref pairs
+- :ghfile:`numba/core/descriptors.py` - empty base class for all target
+  descriptors (is this needed?)
+- :ghfile:`numba/cpython/builtins.py` - Python builtin functions and
+  operators
+- :ghfile:`numba/cpython/cmathimpl.py` - Python complex math module
+- :ghfile:`numba/cpython/enumimpl.py` - Enum objects
+- :ghfile:`numba/cpython/hashing.py` - Hashing algorithms
+- :ghfile:`numba/cpython/heapq.py` - Python ``heapq`` module
+- :ghfile:`numba/cpython/iterators.py` - Iterable data types and iterators
+- :ghfile:`numba/cpython/listobj.py` - Python lists
+- :ghfile:`numba/cpython/mathimpl.py` - Python ``math`` module
+- :ghfile:`numba/cpython/numbers.py` - Numeric values (int, float, etc)
+- :ghfile:`numba/cpython/printimpl.py` - Print function
+- :ghfile:`numba/cpython/randomimpl.py` - Python and NumPy ``random``
+  modules
+- :ghfile:`numba/cpython/rangeobj.py` - Python `range` objects
+- :ghfile:`numba/cpython/slicing.py` - Slice objects, and index calculations
+  used in slicing
+- :ghfile:`numba/cpython/setobj.py` - Python set type
+- :ghfile:`numba/cpython/tupleobj.py` - Tuples (statically typed as
+  immutable struct)
+- :ghfile:`numba/misc/cffiimpl.py` - CFFI functions
+- :ghfile:`numba/misc/quicksort.py` - Quicksort implementation used with
+  list and array objects
+- :ghfile:`numba/misc/mergesort.py` - Mergesort implementation used with
+  array objects
+- :ghfile:`numba/np/arraymath.py` - Math operations on arrays (both
+  Python and NumPy)
+- :ghfile:`numba/np/arrayobj.py` - Array operations (both NumPy and
+  buffer protocol)
+- :ghfile:`numba/np/linalg.py` - NumPy linear algebra operations
+- :ghfile:`numba/np/npdatetime.py` - NumPy datetime operations
+- :ghfile:`numba/np/npyfuncs.py` - Kernels used in generating some
+  NumPy ufuncs
+- :ghfile:`numba/np/npyimpl.py` - Implementations of most NumPy ufuncs
+- :ghfile:`numba/np/polynomial.py` - ``numpy.roots`` function
+- :ghfile:`numba/np/ufunc_db.py` - Big table mapping types to ufunc
+  implementations
+
+
+Ufunc Compiler and Runtime
+''''''''''''''''''''''''''
+
+- :ghfile:`numba/np/ufunc` - ufunc compiler implementation
+- :ghfile:`numba/np/ufunc/_internal.{h,c}` - Python extension module with
+  helper functions that use CPython & NumPy C API
+- :ghfile:`numba/np/ufunc/_ufunc.c` - Used by `_internal.c`
+- :ghfile:`numba/np/ufunc/deviceufunc.py` - Custom ufunc dispatch for
+  non-CPU targets
+- :ghfile:`numba/np/ufunc/gufunc_scheduler.{h,cpp}` - Schedule work chunks
+  to threads
+- :ghfile:`numba/np/ufunc/dufunc.py` - Special ufunc that can compile new
+  implementations at call time
+- :ghfile:`numba/np/ufunc/ufuncbuilder.py` - Top-level orchestration of
+  ufunc/gufunc compiler pipeline
+- :ghfile:`numba/np/ufunc/sigparse.py` - Parser for generalized ufunc
+  indexing signatures
+- :ghfile:`numba/np/ufunc/parallel.py` - Codegen for ``parallel`` target
+- :ghfile:`numba/np/ufunc/array_exprs.py` - Rewrite pass for turning array
+  expressions in regular functions into ufuncs
+- :ghfile:`numba/np/ufunc/wrappers.py` - Wrap scalar function kernel with
+  loops
+- :ghfile:`numba/np/ufunc/workqueue.{h,c}` - Threading backend based on
+  pthreads/Windows threads and queues
+- :ghfile:`numba/np/ufunc/omppool.cpp` - Threading backend based on OpenMP
+- :ghfile:`numba/np/ufunc/tbbpool.cpp` - Threading backend based on TBB
+
+
+
+Unit Tests (CPU)
+''''''''''''''''
+
+CPU unit tests (GPU target unit tests listed in later sections
+
+- :ghfile:`runtests.py` - Convenience script that launches test runner and
+  turns on full compiler tracebacks
+- :ghfile:`.coveragerc` - Coverage.py configuration
+- :ghfile:`numba/runtests.py` - Entry point to unittest runner
+- :ghfile:`numba/testing/_runtests.py` - Implementation of custom test runner
+  command line interface
+- :ghfile:`numba/tests/test_*` - Test cases
+- :ghfile:`numba/tests/*_usecases.py` - Python functions compiled by some
+  unit tests
+- :ghfile:`numba/tests/support.py` - Helper functions for testing and
+  special TestCase implementation
+- :ghfile:`numba/tests/dummy_module.py` - Module used in
+  ``test_dispatcher.py``
+- :ghfile:`numba/tests/npyufunc` - ufunc / gufunc compiler tests
+- :ghfile:`numba/testing` - Support code for testing
+- :ghfile:`numba/testing/loader.py` - Find tests on disk
+- :ghfile:`numba/testing/notebook.py` - Support for testing notebooks
+- :ghfile:`numba/testing/main.py` - Numba test runner
+
+
+Command Line Utilities
+''''''''''''''''''''''
+- :ghfile:`bin/numba` - Command line stub, delegates to main in
+  ``numba_entry.py``
+- :ghfile:`numba/misc/numba_entry.py` - Main function for ``numba`` command line
+  tool
+- :ghfile:`numba/pycc` - Ahead of time compilation of functions to shared
+  library extension
+- :ghfile:`numba/pycc/__init__.py` - Main function for ``pycc`` command line
+  tool
+- :ghfile:`numba/pycc/cc.py` - User-facing API for tagging functions to
+  compile ahead of time
+- :ghfile:`numba/pycc/compiler.py` - Compiler pipeline for creating
+  standalone Python extension modules
+- :ghfile:`numba/pycc/llvm_types.py` - Aliases to LLVM data types used by
+  ``compiler.py``
+- :ghfile:`numba/pycc/pycc` - Stub to call main function.  Is this still
+  used?
+- :ghfile:`numba/pycc/modulemixin.c` - C file compiled into every compiled
+  extension.  Pulls in C source from Numba core that is needed to make
+  extension standalone
+- :ghfile:`numba/pycc/platform.py` - Portable interface to platform-specific
+  compiler toolchains
+- :ghfile:`numba/pycc/decorators.py` - Deprecated decorators for tagging
+  functions to compile.  Use ``cc.py`` instead.
+
+
+CUDA GPU Target
+'''''''''''''''
+
+Note that the CUDA target does reuse some parts of the CPU target.
+
+- :ghfile:`numba/cuda/` - The implementation of the CUDA (NVIDIA GPU) target
+  and associated unit tests
+- :ghfile:`numba/cuda/decorators.py` - Compiler decorators for CUDA kernels
+  and device functions
+- :ghfile:`numba/cuda/dispatcher.py` - Dispatcher for CUDA JIT functions
+- :ghfile:`numba/cuda/printimpl.py` - Special implementation of device printing
+- :ghfile:`numba/cuda/libdevice.py` - Registers libdevice functions
+- :ghfile:`numba/cuda/kernels/` - Custom kernels for reduction and transpose
+- :ghfile:`numba/cuda/device_init.py` - Initializes the CUDA target when
+  imported
+- :ghfile:`numba/cuda/compiler.py` - Compiler pipeline for CUDA target
+- :ghfile:`numba/cuda/intrinsic_wrapper.py` - CUDA device intrinsics
+  (shuffle, ballot, etc)
+- :ghfile:`numba/cuda/initialize.py` - Deferred initialization of the CUDA
+  device and subsystem.  Called only when user imports ``numba.cuda``
+- :ghfile:`numba/cuda/simulator_init.py` - Initializes the CUDA simulator
+  subsystem (only when user requests it with env var)
+- :ghfile:`numba/cuda/random.py` - Implementation of random number generator
+- :ghfile:`numba/cuda/api.py` - User facing APIs imported into ``numba.cuda.*``
+- :ghfile:`numba/cuda/stubs.py` - Python placeholders for functions that
+  only can be used in GPU device code
+- :ghfile:`numba/cuda/simulator/` - Simulate execution of CUDA kernels in
+  Python interpreter
+- :ghfile:`numba/cuda/vectorizers.py` - Subclasses of ufunc/gufunc compilers
+  for CUDA
+- :ghfile:`numba/cuda/args.py` - Management of kernel arguments, including
+  host<->device transfers
+- :ghfile:`numba/cuda/target.py` - Typing and target contexts for GPU
+- :ghfile:`numba/cuda/cudamath.py` - Type signatures for math functions in
+  CUDA Python
+- :ghfile:`numba/cuda/errors.py` - Validation of kernel launch configuration
+- :ghfile:`numba/cuda/nvvmutils.py` - Helper functions for generating
+  NVVM-specific IR
+- :ghfile:`numba/cuda/testing.py` - Support code for creating CUDA unit
+  tests and capturing standard out
+- :ghfile:`numba/cuda/cudadecl.py` - Type signatures of CUDA API (threadIdx,
+  blockIdx, atomics) in Python on GPU
+- :ghfile:`numba/cuda/cudaimpl.py` - Implementations of CUDA API functions
+  on GPU
+- :ghfile:`numba/cuda/codegen.py` - Code generator object for CUDA target
+- :ghfile:`numba/cuda/cudadrv/` - Wrapper around CUDA driver API
+- :ghfile:`numba/cuda/tests/` - CUDA unit tests, skipped when CUDA is not
+  detected
+- :ghfile:`numba/cuda/tests/cudasim/` - Tests of CUDA simulator
+- :ghfile:`numba/cuda/tests/nocuda/` - Tests for NVVM functionality when
+  CUDA not present
+- :ghfile:`numba/cuda/tests/cudapy/` - Tests of compiling Python functions
+  for GPU
+- :ghfile:`numba/cuda/tests/cudadrv/` - Tests of Python wrapper around CUDA
+  API
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/rewrites.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/rewrites.rst
new file mode 100644
index 000000000..ff162c611
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/rewrites.rst
@@ -0,0 +1,397 @@
+=====================================================
+Using the Numba Rewrite Pass for Fun and Optimization
+=====================================================
+
+Overview
+========
+
+This section introduces intermediate representation (IR) rewrites, and
+how they can be used to implement optimizations.
+
+As discussed earlier in ":ref:`rewrite-typed-ir`", rewriting the Numba
+IR allows us to perform optimizations that would be much more
+difficult to perform at the lower LLVM level.  Similar to the Numba
+type and lowering subsystems, the rewrite subsystem is user
+extensible.  This extensibility affords Numba the possibility of
+supporting a wide variety of domain-specific optimizations (DSO's).
+
+The remaining subsections detail the mechanics of implementing a
+rewrite, registering a rewrite with the rewrite registry, and provide
+examples of adding new rewrites, as well as internals of the array
+expression optimization pass.  We conclude by reviewing some use cases
+exposed in the examples, as well as reviewing any points where
+developers should take care.
+
+
+Rewriting Passes
+================
+
+Rewriting passes have a simple :func:`~Rewrite.match` and
+:func:`~Rewrite.apply` interface.  The division between matching and
+rewriting follows how one would define a term rewrite in a declarative
+domain-specific languages (DSL's).  In such DSL's, one may write a
+rewrite as follows::
+
+  <match> => <replacement>
+
+
+The ``<match>`` and ``<replacement>`` symbols represent IR term
+expressions, where the left-hand side presents a pattern to match, and
+the right-hand side an IR term constructor to build upon matching.
+Whenever the rewrite matches an IR pattern, any free variables in the
+left-hand side are bound within a custom environment.  When applied,
+the rewrite uses the pattern matching environment to bind any free
+variables in the right-hand side.
+
+As Python is not commonly used in a declarative capacity, Numba uses
+object state to handle the transfer of information between the
+matching and application steps.
+
+
+The :class:`Rewrite` Base Class
+-------------------------------
+
+.. class:: Rewrite
+
+   The :class:`Rewrite` class simply defines an abstract base class
+   for Numba rewrites.  Developers should define rewrites as
+   subclasses of this base type, overloading the
+   :func:`~Rewrite.match` and :func:`~Rewrite.apply` methods.
+
+   .. attribute:: pipeline
+
+       The pipeline attribute contains the
+       :class:`numba.compiler.Pipeline` instance that is currently
+       compiling the function under consideration for rewriting.
+
+   .. method:: __init__(self, pipeline, *args, **kws)
+
+       The base constructor for rewrites simply stashes its arguments
+       into attributes of the same name.  Unless being used in
+       debugging or testing, rewrites should only be constructed by
+       the :class:`RewriteRegistry` in the
+       :func:`RewriteRegistry.apply` method, and the construction
+       interface should remain stable (though the pipeline will
+       commonly contain just about everything there is to know).
+
+   .. method:: match(self, block, typemap, callmap)
+
+      The :func:`~Rewrite.match` method takes four arguments other
+      than *self*:
+
+      * *func_ir*: This is an instance of :class:`numba.ir.FunctionIR` for the
+        function being rewritten.
+
+      * *block*: This is an instance of :class:`numba.ir.Block`.  The
+        matching method should iterate over the instructions contained
+        in the :attr:`numba.ir.Block.body` member.
+
+      * *typemap*: This is a Python :class:`dict` instance mapping
+        from symbol names in the IR, represented as strings, to Numba
+        types.
+
+      * *callmap*: This is another :class:`dict` instance mapping from
+        calls, represented as :class:`numba.ir.Expr` instances, to
+        their corresponding call site type signatures, represented as
+        a :class:`numba.typing.templates.Signature` instance.
+
+      The :func:`~Rewrite.match` method should return a :class:`bool`
+      result.  A :obj:`True` result should indicate that one or more
+      matches were found, and the :func:`~Rewrite.apply` method will
+      return a new replacement :class:`numba.ir.Block` instance.  A
+      :obj:`False` result should indicate that no matches were found, and
+      subsequent calls to :func:`~Rewrite.apply` will return undefined
+      or invalid results.
+
+   .. method:: apply(self)
+
+      The :func:`~Rewrite.apply` method should only be invoked
+      following a successful call to :func:`~Rewrite.match`.  This
+      method takes no additional parameters other than *self*, and
+      should return a replacement :class:`numba.ir.Block` instance.
+
+      As mentioned above, the behavior of calling
+      :func:`~Rewrite.apply` is undefined unless
+      :func:`~Rewrite.match` has already been called and returned
+      :obj:`True`.
+
+
+Subclassing :class:`Rewrite`
+----------------------------
+
+Before going into the expectations for the overloaded methods any
+:class:`Rewrite` subclass must have, let's step back a minute to
+review what is taking place here.  By providing an extensible
+compiler, Numba opens itself to user-defined code generators which may
+be incomplete, or worse, incorrect.  When a code generator goes awry,
+it can cause abnormal program behavior or early termination.
+User-defined rewrites add a new level of complexity because they must
+not only generate correct code, but the code they generate should
+ensure that the compiler does not get stuck in a match/apply loop.
+Non-termination by the compiler will directly lead to non-termination
+of user function calls.
+
+There are several ways to help ensure that a rewrite terminates:
+
+* *Typing*: A rewrite should generally attempt to decompose composite
+  types, and avoid composing new types.  If the rewrite is matching a
+  specific type, changing expression types to a lower-level type will
+  ensure they will no long match after the rewrite is applied.
+
+* *Special instructions*: A rewrite may synthesize custom operators or
+  use special functions in the target IR.  This technique again
+  generates code that is no longer within the domain of the original
+  match, and the rewrite will terminate.
+
+In the ":ref:`case-study-array-expressions`" subsection, below, we'll
+see how the array expression rewriter uses both of these techniques.
+
+
+Overloading :func:`Rewrite.match`
+---------------------------------
+
+Every rewrite developer should seek to have their implementation of
+:func:`~Rewrite.match` return a :obj:`False` value as quickly as
+possible.  Numba is a just-in-time compiler, and adding compilation
+time ultimately adds to the user's run time.  When a rewrite returns
+:obj:`False` for a given block, the registry will no longer process that
+block with that rewrite, and the compiler is that much closer to
+proceeding to lowering.
+
+This need for timeliness has to be balanced against collecting the
+necessary information to make a match for a rewrite.  Rewrite
+developers should be comfortable adding dynamic attributes to their
+subclasses, and then having these new attributes guide construction of
+the replacement basic block.
+
+
+Overloading :func:`Rewrite.apply`
+-----------------------------------
+
+The :func:`~Rewrite.apply` method should return a replacement
+:class:`numba.ir.Block` instance to replace the basic block that
+contained a match for the rewrite.  As mentioned above, the IR built
+by :func:`~Rewrite.apply` methods should preserve the semantics of the
+user's code, but also seek to avoid generating another match for the
+same rewrite or set of rewrites.
+
+
+The Rewrite Registry
+====================
+
+When you want to include a rewrite in the rewrite pass, you should
+register it with the rewrite registry.  The :mod:`numba.rewrites`
+module provides both the abstract base class and a class decorator for
+hooking into the Numba rewrite subsystem.  The following illustrates a
+stub definition of a new rewrite::
+
+  from numba import rewrites
+
+  @rewrites.register_rewrite
+  class MyRewrite(rewrites.Rewrite):
+
+      def match(self, block, typemap, calltypes):
+          raise NotImplementedError("FIXME")
+
+      def apply(self):
+          raise NotImplementedError("FIXME")
+
+
+Developers should note that using the class decorator as shown above
+will register a rewrite at import time.  It is the developer's
+responsibility to ensure their extensions are loaded before
+compilation starts.
+
+
+.. _`case-study-array-expressions`:
+
+Case study: Array Expressions
+=============================
+
+This subsection looks at the array expression rewriter in more depth.
+The array expression rewriter, and most of its support functionality,
+are found in the :mod:`numba.npyufunc.array_exprs` module.  The
+rewriting pass itself is implemented in the :class:`RewriteArrayExprs`
+class.  In addition to the rewriter, the
+:mod:`~numba.npyufunc.array_exprs` module includes a function for
+lowering array expressions,
+:func:`~numba.npyufunc.array_exprs._lower_array_expr`.  The overall
+optimization process is as follows:
+
+* :func:`RewriteArrayExprs.match`: The rewrite pass looks for two or
+  more array operations that form an array expression.
+
+* :func:`RewriteArrayExprs.apply`: Once an array expression is found,
+  the rewriter replaces the individual array operations with a new
+  kind of IR expression, the ``arrayexpr``.
+
+* :func:`numba.npyufunc.array_exprs._lower_array_expr`: During
+  lowering, the code generator calls
+  :func:`~numba.npyufunc.array_exprs._lower_array_expr` whenever it
+  finds an ``arrayexpr`` IR expression.
+
+More details on each step of the optimization are given below.
+
+
+The :func:`RewriteArrayExprs.match` method
+------------------------------------------
+
+The array expression optimization pass starts by looking for array
+operations, including calls to supported :class:`~numpy.ufunc`\'s and
+user-defined :class:`~numba.DUFunc`\'s.  Numba IR follows the
+conventions of a static single assignment (SSA) language, meaning that
+the search for array operators begins with looking for assignment
+instructions.
+
+When the rewriting pass calls the :func:`RewriteArrayExprs.match`
+method, it first checks to see if it can trivially reject the basic
+block.  If the method determines the block to be a candidate for
+matching, it sets up the following state variables in the rewrite
+object:
+
+* *crnt_block*: The current basic block being matched.
+
+* *typemap*: The *typemap* for the function being matched.
+
+* *matches*: A list of variable names that reference array expressions.
+
+* *array_assigns*: A map from assignment variable names to the actual
+  assignment instructions that define the given variable.
+
+* *const_assigns*: A map from assignment variable names to the
+  constant valued expression that defines the constant variable.
+
+At this point, the match method iterates over the assignment
+instructions in the input basic block.  For each assignment
+instruction, the matcher looks for one of two things:
+
+* Array operations: If the right-hand side of the assignment
+  instruction is an expression, and the result of that expression is
+  an array type, the matcher checks to see if the expression is either
+  a known array operation, or a call to a universal function.  If an
+  array operator is found, the matcher stores the left-hand variable
+  name and the whole instruction in the *array_assigns* member.
+  Finally, the matcher tests to see if any operands of the array
+  operation have also been identified as targets of other array
+  operations.  If one or more operands are also targets of array
+  operations, then the matcher will also append the left-hand side
+  variable name to the *matches* member.
+
+* Constants: Constants (even scalars) can be operands to array
+  operations.  Without worrying about the constant being apart of an
+  array expression, the matcher stores constant names and values in
+  the *const_assigns* member.
+
+The end of the matching method simply checks for a non-empty *matches*
+list, returning :obj:`True` if there were one or more matches, and
+:obj:`False` when *matches* is empty.
+
+
+The :func:`RewriteArrayExprs.apply` method
+------------------------------------------
+
+When one or matching array expressions are found by
+:func:`RewriteArrayExprs.match`, the rewriting pass will call
+:func:`RewriteArrayExprs.apply`.  The apply method works in two
+passes.  The first pass iterates over the matches found, and builds a
+map from instructions in the old basic block to new instructions in
+the new basic block.  The second pass iterates over the instructions
+in the old basic block, copying instructions that are not changed by
+the rewrite, and replacing or deleting instructions that were
+identified by the first pass.
+
+The :func:`RewriteArrayExprs._handle_matches` implements the first
+pass of the code generation portion of the rewrite.  For each match,
+this method builds a special IR expression that contains an expression
+tree for the array expression.  To compute the leaves of the
+expression tree, the :func:`~RewriteArrayExprs._handle_matches` method
+iterates over the operands of the identified root operation.  If the
+operand is another array operation, it is translated into an
+expression sub-tree.  If the operand is a constant,
+:func:`~RewriteArrayExprs._handle_matches` copies the constant value.
+Otherwise, the operand is marked as being used by an array expression.
+As the method builds array expression nodes, it builds a map from old
+instructions to new instructions (*replace_map*), as well as sets of
+variables that may have moved (*used_vars*), and variables that should
+be removed altogether (*dead_vars*).  These three data structures are
+returned back to the calling :func:`RewriteArrayExprs.apply` method.
+
+The remaining part of the :func:`RewriteArrayExprs.apply` method
+iterates over the instructions in the old basic block.  For each
+instruction, this method either replaces, deletes, or duplicates that
+instruction based on the results of
+:func:`RewriteArrayExprs._handle_matches`.  The following list
+describes how the optimization handles individual instructions:
+
+* When an instruction is an assignment,
+  :func:`~RewriteArrayExprs.apply` checks to see if it is in the
+  replacement instruction map.  When an assignment instruction is found
+  in the instruction map, :func:`~RewriteArrayExprs.apply` must then
+  check to see if the replacement instruction is also in the replacement
+  map.  The optimizer continues this check until it either arrives at a
+  :obj:`None` value or an instruction that isn't in the replacement map.
+  Instructions that have a replacement that is :obj:`None` are deleted.
+  Instructions that have a non-:obj:`None` replacement are replaced.
+  Assignment instructions not in the replacement map are appended to the
+  new basic block with no changes made.
+
+* When the instruction is a delete instruction, the rewrite checks to
+  see if it deletes a variable that may still be used by a later array
+  expression, or if it deletes a dead variable.  Delete instructions for
+  used variables are added to a map of deferred delete instructions that
+  :func:`~RewriteArrayExprs.apply` uses to move them past any uses of
+  that variable.  The loop copies delete instructions for non-dead
+  variables, and ignores delete instructions for dead variables
+  (effectively removing them from the basic block).
+
+* All other instructions are appended to the new basic block.
+
+Finally, the :func:`~RewriteArrayExprs.apply` method returns the new
+basic block for lowering.
+
+
+The :func:`~numba.npyufunc.array_exprs._lower_array_expr` function
+------------------------------------------------------------------
+
+If we left things at just the rewrite, then the lowering stage of the
+compiler would fail, complaining it doesn't know how to lower
+``arrayexpr`` operations.  We start by hooking a lowering function
+into the target context whenever the :class:`RewriteArrayExprs` class
+is instantiated by the compiler.  This hook causes the lowering pass to
+call :func:`~numba.npyufunc.array_exprs._lower_array_expr` whenever it
+encounters an ``arrayexr`` operator.
+
+This function has two steps:
+
+* Synthesize a Python function that implements the array expression:
+  This new Python function essentially behaves like a Numpy
+  :class:`~numpy.ufunc`, returning the result of the expression on
+  scalar values in the broadcasted array arguments.  The lowering
+  function accomplishes this by translating from the array expression
+  tree into a Python AST.
+
+* Compile the synthetic Python function into a kernel:  At this point,
+  the lowering function relies on existing code for lowering ufunc and
+  DUFunc kernels, calling
+  :func:`numba.targets.numpyimpl.numpy_ufunc_kernel` after defining
+  how to lower calls to the synthetic function.
+
+The end result is similar to loop lifting in Numba's object mode.
+
+
+Conclusions and Caveats
+=======================
+
+We have seen how to implement rewrites in Numba, starting with the
+interface, and ending with an actual optimization.  The key points of
+this section are:
+
+* When writing a good plug-in, the matcher should try to get a
+  go/no-go result as soon as possible.
+
+* The rewrite application portion can be more computationally
+  expensive, but should still generate code that won't cause infinite
+  loops in the compiler.
+
+* We use object state to communicate any results of matching to the
+  rewrite application pass.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/stencil.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/stencil.rst
new file mode 100644
index 000000000..f27447a7f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/stencil.rst
@@ -0,0 +1,170 @@
+.. Copyright (c) 2017 Intel Corporation
+   SPDX-License-Identifier: BSD-2-Clause
+
+.. _arch-stencil:
+
+=================
+Notes on stencils 
+=================
+
+Numba provides the :ref:`@stencil decorator <numba-stencil>` to
+represent stencil computations.  This document explains how this
+feature is implemented in the several different modes available in
+Numba.  Currently, calls to the stencil from non-jitted code is
+supported as well as calls from jitted code, either with or without
+the :ref:`parallel=True <parallel_jit_option>` option.
+
+The stencil decorator
+=====================
+
+The stencil decorator itself just returns a ``StencilFunc`` object.
+This object encapsulates the original stencil kernel function
+as specified in the program and the options passed to the
+stencil decorator.  Also of note is that after the first compilation
+of the stencil, the computed neighborhood of the stencil is
+stored in the ``StencilFunc`` object in the ``neighborhood`` attribute.
+
+Handling the three modes
+========================
+
+As mentioned above, Numba supports the calling of stencils
+from inside or outside a ``@jit`` compiled function, with or
+without the :ref:`parallel=True <parallel_jit_option>` option.
+
+Outside jit context
+-------------------
+
+``StencilFunc`` overrides the ``__call__`` method so that calls
+to ``StencilFunc`` objects execute the stencil::
+
+    def __call__(self, *args, **kwargs):                                        
+        result = kwargs.get('out')
+                                                                                
+        new_stencil_func = self._stencil_wrapper(result, None, *args)           
+                                                                                
+        if result is None:                                                      
+            return new_stencil_func.entry_point(*args)                          
+        else:                                                                   
+            return new_stencil_func.entry_point(*args, result)                  
+
+First, the presence of the optional :ref:`out <stencil-function-out>`
+parameter is checked.  If it is present then the output array is
+stored in ``result``.  Then, the call to ``_stencil_wrapper``
+generates the stencil function given the result and argument types
+and finally the generated stencil function is executed and its result
+returned.
+
+Jit without ``parallel=True``
+-----------------------------
+
+When constructed, a ``StencilFunc`` inserts itself into the typing
+context's set of user functions and provides the ``_type_me``
+callback.  In this way, the standard Numba compiler is able to
+determine the output type and signature of a ``StencilFunc``.
+Each ``StencilFunc`` maintains a cache of previously seen combinations
+of input argument types and keyword types.  If previously seen,
+the ``StencilFunc`` returns the computed signature.  If not previously
+computed, the ``StencilFunc`` computes the return type of the stencil
+by running the Numba compiler frontend on the stencil kernel and
+then performing type inference on the :term:`Numba IR` (IR) to get the scalar
+return type of the kernel.  From that, a Numpy array type is constructed
+whose element type matches that scalar return type.
+
+After computing the signature of the stencil for a previously
+unseen combination of input and keyword types, the ``StencilFunc``
+then :ref:`creates the stencil function <arch-stencil-create-function>` itself.
+``StencilFunc`` then installs the new stencil function's definition
+in the target context so that jitted code is able to call it.
+
+Thus, in this mode, the generated stencil function is a stand-alone
+function called like a normal function from within jitted code.
+
+Jit with ``parallel=True``
+--------------------------
+
+When calling a ``StencilFunc`` from a jitted context with ``parallel=True``,
+a separate stencil function as generated by :ref:`arch-stencil-create-function`
+is not used.  Instead, `parfors` (:ref:`parallel-accelerator`) are
+created within the current function that implements the stencil.
+This code again starts with the stencil kernel and does a similar kernel
+size computation but then rather than standard Python looping syntax,
+corresponding `parfors` are created so that the execution of the stencil
+will take place in parallel.
+
+The stencil to `parfor` translations can also be selectively disabled
+by setting ``parallel={'stencil': False}``, among other sub-options
+described in :ref:`parallel-accelerator`.
+
+.. _arch-stencil-create-function:
+
+Creating the stencil function
+=============================
+
+Conceptually, a stencil function is created from the user-specified
+stencil kernel by adding looping code around the kernel, transforming
+the relative kernel indices into absolute array indices based on the
+loop indices, and replacing the kernel's ``return`` statement with
+a statement to assign the computed value into the output array.
+
+To accomplish this transformation, first, a copy of the stencil 
+kernel IR is created so that subsequent modifications of the IR
+for different stencil signatures will not effect each other.
+
+Then, an approach similar to how GUFunc's are created for `parfors`
+is employed.  In a text buffer, a Python function is created with
+a unique name.  The input array parameter is added to the function
+definition and if the ``out`` argument type is present then an
+``out`` parameter is added to the stencil function definition.
+If the ``out`` argument is not present then first an output array
+is created with ``numpy.zeros`` having the same shape as the
+input array.
+
+The kernel is then analyzed to compute the stencil size and the
+shape of the boundary (or the ``neighborhood`` stencil decorator
+argument is used for this purpose if present).
+Then, one ``for`` loop for each dimension of the input array is
+added to the stencil function definition.  The range of each
+loop is controlled by the stencil kernel size previously computed
+so that the boundary of the output image is not modified but instead
+left as is.   The body of the innermost ``for`` loop is a single
+``sentinel`` statement that is easily recognized in the IR.
+A call to ``exec`` with the text buffer is used to force the
+stencil function into existence and an ``eval`` is used to get
+access to the corresponding function on which ``run_frontend`` is
+used to get the stencil function IR.
+
+Various renaming and relabeling is performed on the stencil function
+IR and the kernel IR so that the two can be combined without conflict.
+The relative indices in the kernel IR (i.e., ``getitem`` calls) are
+replaced with expressions where the corresponding loop index variables
+are added to the relative indices.  The ``return`` statement in the
+kernel IR is replaced with a ``setitem`` for the corresponding element
+in the output array.
+The stencil function IR is then scanned for the sentinel and the
+sentinel replaced with the modified kernel IR.
+
+Next, ``compile_ir`` is used to compile the combined stencil function
+IR.  The resulting compile result is cached in the ``StencilFunc`` so that
+other calls to the same stencil do not need to undertake this process
+again.
+
+Exceptions raised
+=================
+
+Various checks are performed during stencil compilation to make sure
+that user-specified options do not conflict with each other or with
+other runtime parameters.  For example, if the user has manually
+specified a ``neighborhood`` to the stencil decorator, the length of
+that neighborhood must match the dimensionality of the input array.
+If this is not the case, a ``ValueError`` is raised.
+
+If the neighborhood has not been specified then it must be inferred
+and a requirement to infer the kernel is that all indices are constant
+integers.  If they are not, a ``ValueError`` is raised indicating that
+kernel indices may not be non-constant.
+
+Finally, the stencil implementation detects the output array type
+by running Numba type inference on the stencil kernel.  If the
+return type of this kernel does not match the type of the value
+passed to the ``cval`` stencil decorator option then a ``ValueError``
+is raised.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/target_extension.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/target_extension.rst
new file mode 100644
index 000000000..bebd574ad
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/target_extension.rst
@@ -0,0 +1,61 @@
+==========================
+Notes on Target Extensions
+==========================
+
+.. warning:: All features and APIs described in this page are in-development and
+             may change at any time without deprecation notices being issued.
+
+
+Inheriting compiler flags from the caller
+=========================================
+
+Compiler flags, i.e. options such as ``fastmath``, ``nrt`` in
+``@jit(nrt=True, fastmath=True))`` are specified per-function but their
+effects are not well-defined---some flags affect the entire callgraph, some
+flags affect only the current function. Sometimes it is necessary for callees
+to inherit flags from the caller; for example the ``fastmath`` flag should be
+infectious.
+
+To address the problem, the following are needed:
+
+1. Better definitions for the semantics of compiler flags. Preferably, all flags should
+   limit their effect to the current function. (TODO)
+2. Allow compiler flags to be inherited from the caller. (Done)
+3. Consider compiler flags in function resolution. (TODO)
+
+:class:`numba.core.targetconfig.ConfigStack` is used to propagate the compiler flags
+throughout the compiler. At the start of the compilation, the flags are pushed
+into the ``ConfigStack``, which maintains a thread-local stack for the
+compilation. Thus, callees can check the flags in the caller.
+
+.. autoclass:: numba.core.targetconfig.ConfigStack
+    :members:
+
+Compiler flags
+--------------
+
+`Compiler flags`_ are defined as a subclass of ``TargetConfig``:
+
+.. _Compiler flags: https://github.com/numba/numba/blob/7e8538140ce3f8d01a5273a39233b5481d8b20b1/numba/core/compiler.py#L39
+
+.. autoclass:: numba.core.targetconfig.TargetConfig
+    :members:
+
+
+These are internal compiler flags and they are different from the user-facing
+options used in the jit decorators.
+
+Internally, `the user-facing options are mapped to the internal compiler flags <https://github.com/numba/numba/blob/7e8538140ce3f8d01a5273a39233b5481d8b20b1/numba/core/options.py#L72>`_
+by :class:`numba.core.options.TargetOptions`. Each target can override the
+default compiler flags and control the flag inheritance in
+``TargetOptions.finalize``. `The CPU target overrides it.
+<https://github.com/numba/numba/blob/7e8538140ce3f8d01a5273a39233b5481d8b20b1/numba/core/cpu.py#L259>`_
+
+.. autoclass:: numba.core.options.TargetOptions
+    :members: finalize
+
+
+In :meth:`numba.core.options.TargetOptions.finalize`,
+use :meth:`numba.core.targetconfig.TargetConfig.inherit_if_not_set`
+to request a compiler flag from the caller if it is not set for the current
+function.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/threading_implementation.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/threading_implementation.rst
new file mode 100644
index 000000000..487bc2894
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/developer/threading_implementation.rst
@@ -0,0 +1,249 @@
+=========================================
+Notes on Numba's threading implementation
+=========================================
+
+The execution of the work presented by the Numba ``parallel`` targets is
+undertaken by the Numba threading layer. Practically, the "threading layer"
+is a Numba built-in library that can perform the required concurrent execution.
+At the time of writing there are three threading layers available, each
+implemented via a different lower level native threading library. More
+information on the threading layers and appropriate selection of a threading
+layer for a given application/system can be found in the
+:ref:`threading layer documentation <numba-threading-layer>`.
+
+The pertinent information to note for the following sections is that the
+function in the threading library that performs the parallel execution is the
+``parallel_for`` function. The job of this function is to both orchestrate and
+execute the parallel tasks.
+
+The relevant source files referenced in this document are
+
+- ``numba/np/ufunc/tbbpool.cpp``
+- ``numba/np/ufunc/omppool.cpp``
+- ``numba/np/ufunc/workqueue.c``
+
+  These files contain the TBB, OpenMP, and workqueue threadpool
+  implementations, respectively. Each includes the functions
+  ``set_num_threads()``, ``get_num_threads()``, and ``get_thread_id()``, as
+  well as the relevant logic for thread masking in their respective
+  schedulers. Note that the basic thread local variable logic is duplicated in
+  each of these files, and not shared between them.
+
+- ``numba/np/ufunc/parallel.py``
+
+  This file contains the Python and JIT compatible wrappers for
+  ``set_num_threads()``, ``get_num_threads()``, and ``get_thread_id()``, as
+  well as the code that loads the above libraries into Python and launches the
+  threadpool.
+
+- ``numba/parfors/parfor_lowering.py``
+
+  This file contains the main logic for generating code for the parallel
+  backend. The thread mask is accessed in this file in the code that generates
+  scheduler code, and passed to the relevant backend scheduler function (see
+  below).
+
+Thread masking
+--------------
+
+As part of its design, Numba never launches new threads beyond the threads
+that are launched initially with ``numba.np.ufunc.parallel._launch_threads()``
+when the first parallel execution is run. This is due to the way threads were
+already implemented in Numba prior to thread masking being implemented. This
+restriction was kept to keep the design simple, although it could be removed
+in the future. Consequently, it's possible to programmatically set the number
+of threads, but only to less than or equal to the total number that have
+already been launched. This is done by "masking" out unused threads, causing
+them to do no work. For example, on a 16 core machine, if the user were to
+call ``set_num_threads(4)``, Numba would always have 16 threads present, but
+12 of them would sit idle for parallel computations. A further call to
+``set_num_threads(16)`` would cause those same threads to do work in later
+computations.
+
+:ref:`Thread masking <numba-threading-layer-thread-masking>` was added to make
+it possible for a user to programmatically alter the number of threads
+performing work in the threading layer. Thread masking proved challenging to
+implement as it required the development of a programming model that is suitable
+for users, easy to reason about, and could be implemented safely, with
+consistent behavior across the various threading layers.
+
+Programming model
+~~~~~~~~~~~~~~~~~
+
+The programming model chosen is similar to that found in OpenMP. The reasons
+for this choice were that it is familiar to a lot of users, restricted in
+scope and also simple. The number of threads in use is specified by calling
+``set_num_threads`` and the number of threads in use can be queried by calling
+``get_num_threads``.These two functions are synonymous with their OpenMP
+counterparts (with the above restriction that the mask must be less than or
+equal to the number of launched threads). The execution semantics are also
+similar to OpenMP in that once a parallel region is launched, altering the
+thread mask has no impact on the currently executing region, but will have an
+impact on parallel regions executed subsequently.
+
+The Implementation
+~~~~~~~~~~~~~~~~~~
+
+So as to place no further restrictions on user code other than those that
+already existed in the threading layer libraries, careful consideration of the
+design of thread masking was required. The "thread mask" cannot be stored in a
+global value as concurrent use of the threading layer may result in classic
+forms of race conditions on the value itself. Numerous designs were discussed
+involving various types of mutex on such a global value, all of which were
+eventually broken through thought experiment alone. It eventually transpired
+that, following some OpenMP implementations, the "thread mask" is best
+implemented as a ``thread local``. This means each thread that executes a Numba
+parallel function will have a thread local storage (TLS) slot that contains the
+value of the thread mask to use when scheduling threads in the ``parallel_for``
+function.
+
+The above notion of TLS use for a thread mask is relatively easy to implement,
+``get_num_threads`` and ``set_num_threads`` simply need to address the TLS slot
+in a given threading layer. This also means that the execution schedule for a
+parallel region can be derived from a run time call to ``get_num_threads``. This
+is achieved via a well known and relatively easy to implement pattern of a ``C``
+library function registration and wrapping it in the internal Numba
+implementation.
+
+In addition to satisfying the original upfront thread masking requirements, a
+few more complicated scenarios needed consideration as follows.
+
+Nested parallelism
+******************
+
+In all threading layers a "main thread" will invoke the ``parallel_for``
+function and then in the parallel region, depending on the threading layer,
+some number of additional threads will assist in doing the actual work.
+If the work contains a call to another parallel function (i.e. nested
+parallelism) it is necessary for the thread making the call to know what the
+"thread mask" of the main thread is so that it can propagate it into the
+``parallel_for`` call it makes when executing the nested parallel function.
+The implementation of this behavior is threading layer specific but the general
+principle is for the "main thread" to always "send" the value of the thread mask
+from its TLS slot to all threads in the threading layer that are active in the
+parallel region. These active threads then update their TLS slots with this
+value prior to performing any work. The net result of this implementation detail
+is that:
+
+* thread masks correctly propagate into nested functions
+* it's still possible for each thread in a parallel region to safely have a
+  different mask with which to call nested functions, if it's not set explicitly
+  then the inherited mask from the "main thread" is used
+* threading layers which have dynamic scheduling with threads potentially
+  joining and leaving the active pool during a ``parallel_for`` execution are
+  successfully accommodated
+* any "main thread" thread mask is entirely decoupled from the in-flux nature
+  of the thread masks of the threads in the active thread pool
+
+Python threads independently invoking parallel functions
+********************************************************
+
+The threading layer launch sequence is heavily guarded to ensure that the
+launch is both thread and process safe and run once per process. In a system
+with numerous Python ``threading`` module threads all using Numba, the first
+thread through the launch sequence will get its thread mask set appropriately,
+but no further threads can run the launch sequence. This means that other
+threads will need their initial thread mask set some other way. This is
+achieved when ``get_num_threads`` is called and no thread mask is present, in
+this case the thread mask will be set to the default. In the implementation,
+"no thread mask is present" is represented by the value ``-1`` and the "default
+thread mask" (unset) is represented by the value ``0``. The implementation also
+immediately calls ``set_num_threads(NUMBA_NUM_THREADS)`` after doing this, so
+if either ``-1`` or ``0`` is encountered as a result from ``get_num_threads()`` it
+indicates a bug in the above processes.
+
+OS ``fork()`` calls
+*******************
+
+The use of TLS was also in part driven by the Linux (the most popular
+platform for Numba use by far) having a ``fork(2, 3P)`` call that will do TLS
+propagation into child processes, see ``clone(2)``\ 's ``CLONE_SETTLS``.
+
+Thread ID
+*********
+
+A private ``get_thread_id()`` function was added to each threading backend,
+which returns a unique ID for each thread. This can be accessed from Python by
+``numba.np.ufunc.parallel._get_thread_id()`` (it can also be used inside a
+JIT compiled function). The thread ID function is useful for testing that the
+thread masking behavior is correct, but it should not be used outside of the
+tests. For example, one can call ``set_num_threads(4)`` and then collect all
+unique ``_get_thread_id()``\ s in a parallel region to verify that only 4
+threads are run.
+
+Caveats
+~~~~~~~
+
+Some caveats to be aware of when testing thread masking:
+
+- The TBB backend may choose to schedule fewer than the given mask number of
+  threads. Thus a test such as the one described above may return fewer than 4
+  unique threads.
+
+- The workqueue backend is not threadsafe, so attempts to do multithreading
+  nested parallelism with it may result in deadlocks or other undefined
+  behavior. The workqueue backend will raise a SIGABRT signal if it detects
+  nested parallelism.
+
+- Certain backends may reuse the main thread for computation, but this
+  behavior shouldn't be relied upon (for instance, if propagating exceptions).
+
+Use in Code Generation
+~~~~~~~~~~~~~~~~~~~~~~
+
+The general pattern for using ``get_num_threads`` in code generation is
+
+.. code:: python
+
+   from llvmlite import ir as llvmir
+
+   get_num_threads = cgutils.get_or_insert_function(builder.module
+       llvmir.FunctionType(llvmir.IntType(types.intp.bitwidth), []),
+       name="get_num_threads")
+
+   num_threads = builder.call(get_num_threads, [])
+
+   with cgutils.if_unlikely(builder, builder.icmp_signed('<=', num_threads,
+                                                 num_threads.type(0))):
+       cgutils.printf(builder, "num_threads: %d\n", num_threads)
+       context.call_conv.return_user_exc(builder, RuntimeError,
+                                                 ("Invalid number of threads. "
+                                                  "This likely indicates a bug in Numba.",))
+
+   # Pass num_threads through to the appropriate backend function here
+
+See the code in ``numba/parfors/parfor_lowering.py``.
+
+The guard against ``num_threads`` being <= 0 is not strictly necessary, but it
+can protect against accidentally incorrect behavior in case the thread masking
+logic contains a bug.
+
+The ``num_threads`` variable should be passed through to the appropriate
+backend function, such as ``do_scheduling`` or ``parallel_for``. If it's used
+in some way other than passing it through to the backend function, the above
+considerations should be taken into account to ensure the use of the
+``num_threads`` variable is safe. It would probably be better to keep such
+logic in the threading backends, rather than trying to do it in code
+generation.
+
+.. _chunk-details-label:
+
+Parallel Chunksize Details
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+There are some cases in which the actual parallel work chunk sizes may differ
+from the requested
+chunk size that is requested through :func:`numba.set_parallel_chunksize`.
+First, if the number of required chunks based on the specified chunk size
+is less than the number of configured threads then Numba will use all of the configured
+threads to execute the parallel region.  In this case, the actual chunk size will be
+less than the requested chunk size.  Second, due to truncation, in cases where the
+iteration count is slightly less than a multiple of the chunk size
+(e.g., 14 iterations and a specified chunk size of 5), the actual chunk size will be
+larger than the specified chunk size.  As in the given example, the number of chunks
+would be 2 and the actual chunk size would be 7 (i.e. 14 / 2).  Lastly, since Numba
+divides an N-dimensional iteration space into N-dimensional (hyper)rectangular chunks,
+it may be the case there are not N integer factors whose product is equal to the chunk
+size.  In this case, some chunks will have an area/volume larger than the chunk size
+whereas others will be less than the specified chunk size.
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/entrypoints.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/entrypoints.rst
new file mode 100644
index 000000000..143c2e090
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/entrypoints.rst
@@ -0,0 +1,65 @@
+Registering Extensions with Entry Points
+========================================
+
+Often, third party packages will have a user-facing API as well as define
+extensions to the Numba compiler.  In those situations, the new types and
+overloads can registered with Numba when the package is imported by the user.
+However, there are situations where a Numba extension would not normally be
+imported directly by the user, but must still be registered with the Numba
+compiler.  An example of this is the `numba-scipy
+<https://github.com/numba/numba-scipy>`_ package, which adds support for some
+SciPy functions to Numba.  The end user does not need to ``import
+numba_scipy`` to enable compiler support for SciPy, the extension only needs
+to be installed in the Python environment.
+
+Numba discovers extensions using the `entry points
+<https://setuptools.readthedocs.io/en/latest/setuptools.html#dynamic-discovery-of-services-and-plugins>`_
+feature of ``setuptools``.  This allows a Python package to register an
+initializer function that will be called before ``numba`` compiles for the
+first time.  The delay ensures that the cost of importing extensions is
+deferred until it is necessary.
+
+
+Adding Support for the "Init" Entry Point
+-----------------------------------------
+
+A package can register an initialization function with Numba by adding the
+``entry_points`` argument to the ``setup()`` function call in ``setup.py``:
+
+.. code-block:: python
+
+    setup(
+        ...,
+        entry_points={
+            "numba_extensions": [
+                "init = numba_scipy:_init_extension",
+            ],
+        },
+        ...
+    )
+
+Numba currently only looks for the ``init`` entry point in the
+``numba_extensions`` group.  The entry point should be a function (any name,
+as long as it matches what is listed in ``setup.py``) that takes no arguments,
+and the return value is ignored.  This function should register types,
+overloads, or call other Numba extension APIs.  The order of initialization of
+extensions is undefined.
+
+Testing your Entry Point
+------------------------
+
+Numba loads all entry points when the first function is compiled. To test your
+entry point, it is not sufficient to just ``import numba``; you have to define
+and run a small function, like this:
+
+.. code-block:: python
+
+    import numba; numba.njit(lambda x: x + 1)(123)
+
+It is not necessary to import your module: entry points are identified by the
+``entry_points.txt`` file in your library's ``*.egg-info`` directory.
+
+The ``setup.py build`` command does not create eggs, but ``setup.py sdist``
+(for testing in a local directory) and ``setup.py install`` do. All entry points
+registered in eggs that are on the Python path are loaded. Be sure to check for
+stale ``entry_points.txt`` when debugging.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/high-level.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/high-level.rst
new file mode 100644
index 000000000..4e4877336
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/high-level.rst
@@ -0,0 +1,254 @@
+
+.. _high-level-extending:
+
+High-level extension API
+========================
+
+This extension API is exposed through the :mod:`numba.extending` module.
+
+To aid debugging extensions to Numba, it's recommended to set the following
+environment variable::
+
+    NUMBA_CAPTURED_ERRORS="new_style"
+
+this makes it easy to differentiate between errors in implementation and
+acceptable errors that can take part in e.g. type inference. For more
+information see :envvar:`NUMBA_CAPTURED_ERRORS`.
+
+Implementing functions
+----------------------
+
+The ``@overload`` decorator allows you to implement arbitrary functions
+for use in :term:`nopython mode` functions.  The function decorated with
+``@overload`` is called at compile-time with the *types* of the function's
+runtime arguments.  It should return a callable representing the
+*implementation* of the function for the given types.  The returned
+implementation is compiled by Numba as if it were a normal function
+decorated with ``@jit``.  Additional options to ``@jit`` can be passed as
+dictionary using the ``jit_options`` argument.
+
+For example, let's pretend Numba doesn't support the :func:`len` function
+on tuples yet.  Here is how to implement it using ``@overload``::
+
+   from numba import types
+   from numba.extending import overload
+
+   @overload(len)
+   def tuple_len(seq):
+      if isinstance(seq, types.BaseTuple):
+          n = len(seq)
+          def len_impl(seq):
+              return n
+          return len_impl
+
+
+You might wonder, what happens if :func:`len()` is called with something
+else than a tuple? If a function decorated with ``@overload`` doesn't
+return anything (i.e. returns None), other definitions are tried until
+one succeeds.  Therefore, multiple libraries may overload :func:`len()`
+for different types without conflicting with each other.
+
+Implementing methods
+--------------------
+
+The ``@overload_method`` decorator similarly allows implementing a
+method on a type well-known to Numba.
+
+.. autofunction:: numba.core.extending.overload_method
+
+Implementing classmethods
+-------------------------
+
+The ``@overload_classmethod`` decorator similarly allows implementing a
+classmethod on a type well-known to Numba.
+
+.. autofunction:: numba.core.extending.overload_classmethod
+
+
+Implementing attributes
+-----------------------
+
+The ``@overload_attribute`` decorator allows implementing a data
+attribute (or property) on a type.  Only reading the attribute is
+possible; writable attributes are only supported through the
+:ref:`low-level API <low-level-extending>`.
+
+The following example implements the :attr:`~numpy.ndarray.nbytes` attribute
+on Numpy arrays::
+
+   @overload_attribute(types.Array, 'nbytes')
+   def array_nbytes(arr):
+      def get(arr):
+          return arr.size * arr.itemsize
+      return get
+
+.. _cython-support:
+
+Importing Cython Functions
+--------------------------
+
+The function ``get_cython_function_address`` obtains the address of a
+C function in a Cython extension module. The address can be used to
+access the C function via a :func:`ctypes.CFUNCTYPE` callback, thus
+allowing use of the C function inside a Numba jitted function. For
+example, suppose that you have the file ``foo.pyx``::
+
+   from libc.math cimport exp
+
+   cdef api double myexp(double x):
+       return exp(x)
+
+You can access ``myexp`` from Numba in the following way::
+
+   import ctypes
+   from numba.extending import get_cython_function_address
+
+   addr = get_cython_function_address("foo", "myexp")
+   functype = ctypes.CFUNCTYPE(ctypes.c_double, ctypes.c_double)
+   myexp = functype(addr)
+
+The function ``myexp`` can now be used inside jitted functions, for
+example::
+
+   @njit
+   def double_myexp(x):
+       return 2*myexp(x)
+
+One caveat is that if your function uses Cython's fused types, then
+the function's name will be mangled. To find out the mangled name of
+your function you can check the extension module's ``__pyx_capi__``
+attribute.
+
+Implementing intrinsics
+-----------------------
+
+The ``@intrinsic`` decorator is used for marking a function *func* as typing and
+implementing the function in ``nopython`` mode using the
+`llvmlite IRBuilder API <http://llvmlite.pydata.org/en/latest/user-guide/ir/ir-builder.html>`_.
+This is an escape hatch for expert users to build custom LLVM IR that will be
+inlined into the caller, there is no safety net!
+
+The first argument to *func* is the typing context.  The rest of the arguments
+corresponds to the type of arguments of the decorated function. These arguments
+are also used as the formal argument of the decorated function.  If *func* has
+the signature ``foo(typing_context, arg0, arg1)``, the decorated function will
+have the signature ``foo(arg0, arg1)``.
+
+The return values of *func* should be a 2-tuple of expected type signature, and
+a code-generation function that will passed to
+:func:`~numba.targets.imputils.lower_builtin`. For an unsupported operation,
+return ``None``.
+
+Here is an example that cast any integer to a byte pointer::
+
+    from numba import types
+    from numba.extending import intrinsic
+
+    @intrinsic
+    def cast_int_to_byte_ptr(typingctx, src):
+        # check for accepted types
+        if isinstance(src, types.Integer):
+            # create the expected type signature
+            result_type = types.CPointer(types.uint8)
+            sig = result_type(types.uintp)
+            # defines the custom code generation
+            def codegen(context, builder, signature, args):
+                # llvm IRBuilder code here
+                [src] = args
+                rtype = signature.return_type
+                llrtype = context.get_value_type(rtype)
+                return builder.inttoptr(src, llrtype)
+            return sig, codegen
+
+it may be used as follows::
+
+    from numba import njit
+
+    @njit('void(int64)')
+    def foo(x):
+        y = cast_int_to_byte_ptr(x)
+
+    foo.inspect_types()
+
+and the output of ``.inspect_types()`` demonstrates the cast (note the
+``uint8*``)::
+
+    def foo(x):
+
+        #   x = arg(0, name=x)  :: int64
+        #   $0.1 = global(cast_int_to_byte_ptr: <intrinsic cast_int_to_byte_ptr>)  :: Function(<intrinsic cast_int_to_byte_ptr>)
+        #   $0.3 = call $0.1(x, func=$0.1, args=[Var(x, check_intrin.py (24))], kws=(), vararg=None)  :: (uint64,) -> uint8*
+        #   del x
+        #   del $0.1
+        #   y = $0.3  :: uint8*
+        #   del y
+        #   del $0.3
+        #   $const0.4 = const(NoneType, None)  :: none
+        #   $0.5 = cast(value=$const0.4)  :: none
+        #   del $const0.4
+        #   return $0.5
+
+        y = cast_int_to_byte_ptr(x)
+
+
+Implementing mutable structures
+-------------------------------
+
+.. warning:: This is an experimental feature, the API may change without warning.
+
+The ``numba.experimental.structref`` module provides utilities for defining
+mutable pass-by-reference structures, a ``StructRef``. The following example
+demonstrates how to define a basic mutable structure:
+
+Defining a StructRef
+''''''''''''''''''''
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_structref_usage.py
+   :language: python
+   :caption: from ``numba/tests/doc_examples/test_structref_usage.py``
+   :start-after: magictoken.ex_structref_type_definition.begin
+   :end-before: magictoken.ex_structref_type_definition.end
+   :dedent: 0
+   :linenos:
+
+The following demonstrates using the above mutable struct definition:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_structref_usage.py
+   :language: python
+   :caption: from ``test_type_definition`` of ``numba/tests/doc_examples/test_structref_usage.py``
+   :start-after: magictoken.ex_structref_type_definition_test.begin
+   :end-before: magictoken.ex_structref_type_definition_test.end
+   :dedent: 8
+   :linenos:
+
+
+Defining a method on StructRef
+''''''''''''''''''''''''''''''
+
+Methods and attributes can be attached using ``@overload_*`` as shown in the
+previous sections.
+
+The following demonstrates the use of ``@overload_method`` to insert a
+method for instances of ``MyStructType``:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_structref_usage.py
+   :language: python
+   :caption: from ``test_overload_method`` of ``numba/tests/doc_examples/test_structref_usage.py``
+   :start-after: magictoken.ex_structref_method.begin
+   :end-before: magictoken.ex_structref_method.end
+   :dedent: 8
+   :linenos:
+
+
+``numba.experimental.structref`` API Reference
+''''''''''''''''''''''''''''''''''''''''''''''
+
+.. automodule:: numba.experimental.structref
+    :members:
+
+Determining if a function is already wrapped by a ``jit`` family decorator
+--------------------------------------------------------------------------
+
+The following function is provided for this purpose.
+
+.. automethod:: numba.extending.is_jitted
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/index.rst
new file mode 100644
index 000000000..fb6cd5160
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/index.rst
@@ -0,0 +1,30 @@
+
+Extending Numba
+===============
+
+.. module:: numba.extending
+
+This chapter describes how to extend Numba to make it recognize and support
+additional operations, functions or types.  Numba provides two categories
+of APIs to this end:
+
+* The high-level APIs provide abstracted entry points which are sufficient
+  for simple uses.  They require little knowledge of Numba's internal
+  compilation chain.
+
+* The low-level APIs reflect Numba's internal compilation chain and allow
+  flexible interaction with its various layers, but require more effort
+  and experience with Numba internals.
+
+It may be helpful for readers of this chapter to also read some of the
+documents in the :doc:`developer manual <../developer/index>`, especially
+the :doc:`architecture document <../developer/architecture>`.
+
+
+.. toctree::
+   high-level.rst
+   low-level.rst
+   interval-example.rst
+   overloading-guide.rst
+   entrypoints.rst
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/interval-example.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/interval-example.rst
new file mode 100644
index 000000000..e561ee9bd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/interval-example.rst
@@ -0,0 +1,345 @@
+
+Example: an interval type
+=========================
+
+We will extend the Numba frontend to support a class that it does not
+currently support so as to allow:
+
+* Passing an instance of the class to a Numba function
+* Accessing attributes of the class in a Numba function
+* Constructing and returning a new instance of the class from a Numba function
+
+(all the above in :term:`nopython mode`)
+
+We will mix APIs from the :ref:`high-level extension API <high-level-extending>`
+and the :ref:`low-level extension API <low-level-extending>`, depending on what is
+available for a given task.
+
+The starting point for our example is the following pure Python class::
+
+   class Interval(object):
+       """
+       A half-open interval on the real number line.
+       """
+       def __init__(self, lo, hi):
+           self.lo = lo
+           self.hi = hi
+
+       def __repr__(self):
+           return 'Interval(%f, %f)' % (self.lo, self.hi)
+
+       @property
+       def width(self):
+           return self.hi - self.lo
+
+
+Extending the typing layer
+""""""""""""""""""""""""""
+
+Creating a new Numba type
+-------------------------
+
+As the ``Interval`` class is not known to Numba, we must create a new Numba
+type to represent instances of it.  Numba does not deal with Python types
+directly: it has its own type system that allows a different level of
+granularity as well as various meta-information not available with regular
+Python types.
+
+We first create a type class ``IntervalType`` and, since we don't need the
+type to be parametric, we instantiate a single type instance ``interval_type``::
+
+   from numba import types
+
+   class IntervalType(types.Type):
+       def __init__(self):
+           super(IntervalType, self).__init__(name='Interval')
+
+   interval_type = IntervalType()
+
+
+Type inference for Python values
+--------------------------------
+
+In itself, creating a Numba type doesn't do anything.  We must teach Numba
+how to infer some Python values as instances of that type.  In this example,
+it is trivial: any instance of the ``Interval`` class should be treated as
+belonging to the type ``interval_type``::
+
+   from numba.extending import typeof_impl
+
+   @typeof_impl.register(Interval)
+   def typeof_index(val, c):
+       return interval_type
+
+Function arguments and global values will thusly be recognized as belonging
+to ``interval_type`` whenever they are instances of ``Interval``.
+
+
+Type inference for Python annotations
+-------------------------------------
+
+While ``typeof`` is used to infer the Numba type of Python objects,
+``as_numba_type`` is used to infer the Numba type of Python types.  For simple
+cases, we can simply register that the Python type ``Interval`` corresponds with
+the Numba type ``interval_type``::
+
+   from numba.extending import as_numba_type
+
+   as_numba_type.register(Interval, interval_type)
+
+Note that ``as_numba_type`` is only used to infer types from type annotations at
+compile time.  The ``typeof`` registry above is used to infer the type of
+objects at runtime.
+
+
+Type inference for operations
+-----------------------------
+
+We want to be able to construct interval objects from Numba functions, so
+we must teach Numba to recognize the two-argument ``Interval(lo, hi)``
+constructor.  The arguments should be floating-point numbers::
+
+   from numba.extending import type_callable
+
+   @type_callable(Interval)
+   def type_interval(context):
+       def typer(lo, hi):
+           if isinstance(lo, types.Float) and isinstance(hi, types.Float):
+               return interval_type
+       return typer
+
+
+The :func:`type_callable` decorator specifies that the decorated function
+should be invoked when running type inference for the given callable object
+(here the ``Interval`` class itself).  The decorated function must simply
+return a typer function that will be called with the argument types.  The
+reason for this seemingly convoluted setup is for the typer function to have
+*exactly* the same signature as the typed callable.  This allows handling
+keyword arguments correctly.
+
+The *context* argument received by the decorated function is useful in
+more sophisticated cases where computing the callable's return type
+requires resolving other types.
+
+
+Extending the lowering layer
+""""""""""""""""""""""""""""
+
+We have finished teaching Numba about our type inference additions.
+We must now teach Numba how to actually generate code and data for
+the new operations.
+
+
+Defining the data model for native intervals
+--------------------------------------------
+
+As a general rule, :term:`nopython mode` does not work on Python objects
+as they are generated by the CPython interpreter.  The representations
+used by the interpreter are far too inefficient for fast native code.
+Each type supported in :term:`nopython mode` therefore has to define
+a tailored native representation, also called a *data model*.
+
+A common case of data model is an immutable struct-like data model, that
+is akin to a C ``struct``.  Our interval datatype conveniently falls in
+that category, and here is a possible data model for it::
+
+   from numba.extending import models, register_model
+
+   @register_model(IntervalType)
+   class IntervalModel(models.StructModel):
+       def __init__(self, dmm, fe_type):
+           members = [
+               ('lo', types.float64),
+               ('hi', types.float64),
+               ]
+           models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+This instructs Numba that values of type ``IntervalType`` (or any instance
+thereof) are represented as a structure of two fields ``lo`` and ``hi``,
+each of them a double-precision floating-point number (``types.float64``).
+
+.. note::
+   Mutable types need more sophisticated data models to be able to
+   persist their values after modification.  They typically cannot be
+   stored and passed on the stack or in registers like immutable types do.
+
+
+Exposing data model attributes
+------------------------------
+
+We want the data model attributes ``lo`` and ``hi`` to be exposed under
+the same names for use in Numba functions.  Numba provides a convenience
+function to do exactly that::
+
+   from numba.extending import make_attribute_wrapper
+
+   make_attribute_wrapper(IntervalType, 'lo', 'lo')
+   make_attribute_wrapper(IntervalType, 'hi', 'hi')
+
+This will expose the attributes in read-only mode.  As mentioned above,
+writable attributes don't fit in this model.
+
+
+Exposing a property
+-------------------
+
+As the ``width`` property is computed rather than stored in the structure,
+we cannot simply expose it like we did for ``lo`` and ``hi``.  We have to
+re-implement it explicitly::
+
+   from numba.extending import overload_attribute
+
+   @overload_attribute(IntervalType, "width")
+   def get_width(interval):
+       def getter(interval):
+           return interval.hi - interval.lo
+       return getter
+
+You might ask why we didn't need to expose a type inference hook for this
+attribute? The answer is that ``@overload_attribute`` is part of the
+high-level API: it combines type inference and code generation in a
+single API.
+
+
+Implementing the constructor
+----------------------------
+
+Now we want to implement the two-argument ``Interval`` constructor::
+
+   from numba.extending import lower_builtin
+   from numba.core import cgutils
+
+   @lower_builtin(Interval, types.Float, types.Float)
+   def impl_interval(context, builder, sig, args):
+       typ = sig.return_type
+       lo, hi = args
+       interval = cgutils.create_struct_proxy(typ)(context, builder)
+       interval.lo = lo
+       interval.hi = hi
+       return interval._getvalue()
+
+
+There is a bit more going on here.  ``@lower_builtin`` decorates the
+implementation of the given callable or operation (here the ``Interval``
+constructor) for some specific argument types.  This allows defining
+type-specific implementations of a given operation, which is important
+for heavily overloaded functions such as :func:`len`.
+
+``types.Float`` is the class of all floating-point types (``types.float64``
+is an instance of ``types.Float``).  It is generally more future-proof
+to match argument types on their class rather than on specific instances
+(however, when *returning* a type -- chiefly during the type inference
+phase --, you must usually return a type instance).
+
+``cgutils.create_struct_proxy()`` and ``interval._getvalue()`` are a bit
+of boilerplate due to how Numba passes values around.  Values are passed
+as instances of :class:`llvmlite.ir.Value`, which can be too limited:
+LLVM structure values especially are quite low-level.  A struct proxy
+is a temporary wrapper around a LLVM structure value allowing to easily
+get or set members of the structure. The ``_getvalue()`` call simply
+gets the LLVM value out of the wrapper.
+
+
+Boxing and unboxing
+-------------------
+
+If you try to use an ``Interval`` instance at this point, you'll certainly
+get the error *"cannot convert Interval to native value"*.  This is because
+Numba doesn't yet know how to make a native interval value from a Python
+``Interval`` instance.  Let's teach it how to do it::
+
+   from numba.extending import unbox, NativeValue
+
+   @unbox(IntervalType)
+   def unbox_interval(typ, obj, c):
+       """
+       Convert a Interval object to a native interval structure.
+       """
+       lo_obj = c.pyapi.object_getattr_string(obj, "lo")
+       hi_obj = c.pyapi.object_getattr_string(obj, "hi")
+       interval = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+       interval.lo = c.pyapi.float_as_double(lo_obj)
+       interval.hi = c.pyapi.float_as_double(hi_obj)
+       c.pyapi.decref(lo_obj)
+       c.pyapi.decref(hi_obj)
+       is_error = cgutils.is_not_null(c.builder, c.pyapi.err_occurred())
+       return NativeValue(interval._getvalue(), is_error=is_error)
+
+*Unbox* is the other name for "convert a Python object to a native value"
+(it fits the idea of a Python object as a sophisticated box containing
+a simple native value).  The function returns a ``NativeValue`` object
+which gives its caller access to the computed native value, the error bit
+and possibly other information.
+
+The snippet above makes abundant use of the ``c.pyapi`` object, which
+gives access to a subset of the
+`Python interpreter's C API <https://docs.python.org/3/c-api/index.html>`_.
+Note the use of ``c.pyapi.err_occurred()`` to detect any errors that
+may have happened when unboxing the object (try passing ``Interval('a', 'b')``
+for example).
+
+We also want to do the reverse operation, called *boxing*, so as to return
+interval values from Numba functions::
+
+   from numba.extending import box
+
+   @box(IntervalType)
+   def box_interval(typ, val, c):
+       """
+       Convert a native interval structure to an Interval object.
+       """
+       interval = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+       lo_obj = c.pyapi.float_from_double(interval.lo)
+       hi_obj = c.pyapi.float_from_double(interval.hi)
+       class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(Interval))
+       res = c.pyapi.call_function_objargs(class_obj, (lo_obj, hi_obj))
+       c.pyapi.decref(lo_obj)
+       c.pyapi.decref(hi_obj)
+       c.pyapi.decref(class_obj)
+       return res
+
+
+Using it
+""""""""
+
+:term:`nopython mode` functions are now able to make use of Interval objects
+and the various operations you have defined on them.  You can try for
+example the following functions::
+
+   from numba import jit
+
+   @jit(nopython=True)
+   def inside_interval(interval, x):
+       return interval.lo <= x < interval.hi
+
+   @jit(nopython=True)
+   def interval_width(interval):
+       return interval.width
+
+   @jit(nopython=True)
+   def sum_intervals(i, j):
+       return Interval(i.lo + j.lo, i.hi + j.hi)
+
+
+Conclusion
+""""""""""
+
+We have shown how to do the following tasks:
+
+* Define a new Numba type class by subclassing the ``Type`` class
+* Define a singleton Numba type instance for a non-parametric type
+* Teach Numba how to infer the Numba type of Python values of a certain class,
+  using ``typeof_impl.register``
+* Teach Numba how to infer the Numba type of the Python type itself, using
+  ``as_numba_type.register``
+* Define the data model for a Numba type using ``StructModel``
+  and ``register_model``
+* Implementing a boxing function for a Numba type using the ``@box`` decorator
+* Implementing an unboxing function for a Numba type using the ``@unbox`` decorator
+  and the ``NativeValue`` class
+* Type and implement a callable using the ``@type_callable`` and
+  ``@lower_builtin`` decorators
+* Expose a read-only structure attribute using the ``make_attribute_wrapper``
+  convenience function
+* Implement a read-only property using the ``@overload_attribute`` decorator
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/low-level.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/low-level.rst
new file mode 100644
index 000000000..8eba72b3e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/low-level.rst
@@ -0,0 +1,194 @@
+
+.. _low-level-extending:
+
+Low-level extension API
+=======================
+
+This extension API is available through the :mod:`numba.extending` module.
+It allows you to hook directly into the Numba compilation chain.  As such,
+it distinguished between several compilation phases:
+
+* The :term:`typing` phase deduces the types of variables in a compiled
+  function by looking at the operations performed.
+
+* The :term:`lowering` phase converts high-level Python operations into
+  low-level LLVM code.  This phase exploits the typing information derived
+  by the typing phase.
+
+* *Boxing* and *unboxing* convert Python objects into native values, and
+  vice-versa.  They occur at the boundaries of calling a Numba function
+  from the Python interpreter.
+
+
+Typing
+------
+
+.. XXX the API described here can be insufficient for some use cases.
+   Should we describe the whole templates menagerie?
+
+Type inference -- or simply *typing* -- is the process of assigning
+Numba types to all values involved in a function, so as to enable
+efficient code generation.  Broadly speaking, typing comes in two flavours:
+typing plain Python *values* (e.g. function arguments or global variables)
+and typing *operations* (or *functions*) on known value types.
+
+.. decorator:: typeof_impl.register(cls)
+
+   Register the decorated function as typing Python values of class *cls*.
+   The decorated function will be called with the signature ``(val, c)``
+   where *val* is the Python value being typed and *c* is a context
+   object.
+
+
+.. decorator:: type_callable(func)
+
+   Register the decorated function as typing the callable *func*.
+   *func* can be either an actual Python callable or a string denoting
+   a operation internally known to Numba (for example ``'getitem'``).
+   The decorated function is called with a single *context* argument
+   and must return a typer function.  The typer function should have
+   the same signature as the function being typed, and it is called
+   with the Numba *types* of the function arguments; it should return
+   either the Numba type of the function's return value, or ``None``
+   if inference failed.
+
+.. function:: as_numba_type.register(py_type, numba_type)
+
+   Register that the Python type *py_type* corresponds with the Numba type
+   *numba_type*.  This can be used to register a new type or overwrite the
+   existing default (e.g. to treat ``float`` as ``numba.float32`` instead of
+   ``numba.float64``).
+
+.. decorator:: as_numba_type.register
+
+   Register the decorated function as a type inference function used by
+   ``as_numba_type`` when trying to infer the Numba type of a Python type.
+   The decorated function is called with a single *py_type* argument
+   and returns either a corresponding Numba type, or None if it cannot infer
+   that *py_type*.
+
+
+Lowering
+--------
+
+The following decorators all take a type specification of some kind.
+A type specification is usually a type class (such as ``types.Float``)
+or a specific type instance (such as ``types.float64``).  Some values
+have a special meaning:
+
+* ``types.Any`` matches any type; this allows doing your own dispatching
+  inside the implementation
+
+* ``types.VarArg(<some type>)`` matches any number of arguments of the
+  given type; it can only appear as the last type specification when
+  describing a function's arguments.
+
+A *context* argument in the following APIs is a target context providing
+various utility methods for code generation (such as creating a constant,
+converting from a type to another, looking up the implementation of a
+specific function, etc.).  A *builder* argument is a
+:class:`llvmlite.ir.IRBuilder` instance for the LLVM code being generated.
+
+A *signature* is an object specifying the concrete type of an operation.
+The ``args`` attribute of the signature is a tuple of the argument types.
+The ``return_type`` attribute of the signature is the type that the
+operation should return.
+
+.. note::
+   Numba always reasons on Numba types, but the values being passed
+   around during lowering are LLVM values: they don't hold the required
+   type information, which is why Numba types are passed explicitly too.
+
+   LLVM has its own, very low-level type system: you can access the LLVM
+   type of a value by looking up its ``.type`` attribute.
+
+
+Native operations
+'''''''''''''''''
+
+.. decorator:: lower_builtin(func, typespec, ...)
+
+   Register the decorated function as implementing the callable *func*
+   for the arguments described by the given Numba *typespecs*.
+   As with :func:`type_callable`, *func* can be either an actual Python
+   callable or a string denoting a operation internally known to Numba
+   (for example ``'getitem'``).
+
+   The decorated function is called with four arguments
+   ``(context, builder, sig, args)``.  ``sig`` is the concrete signature
+   the callable is being invoked with.  ``args`` is a tuple of the values
+   of the arguments the callable is being invoked with; each value in
+   ``args`` corresponds to a type in ``sig.args``.  The function
+   must return a value compatible with the type ``sig.return_type``.
+
+.. decorator:: lower_getattr(typespec, name)
+
+   Register the decorated function as implementing the attribute *name*
+   of the given *typespec*.  The decorated function is called with four
+   arguments ``(context, builder, typ, value)``.  *typ* is the concrete
+   type the attribute is being looked up on.  *value* is the value the
+   attribute is being looked up on.
+
+.. decorator:: lower_getattr_generic(typespec)
+
+   Register the decorated function as a fallback for attribute lookup
+   on a given *typespec*.  Any attribute that does not have a corresponding
+   :func:`lower_getattr` declaration will go through
+   :func:`lower_getattr_generic`.  The decorated function is called with
+   five arguments ``(context, builder, typ, value, name)``.  *typ*
+   and *value* are as in :func:`lower_getattr`.  *name* is the name
+   of the attribute being looked up.
+
+.. decorator:: lower_cast(fromspec, tospec)
+
+   Register the decorated function as converting from types described by
+   *fromspec* to types described by *tospec*.  The decorated function
+   is called with five arguments ``(context, builder, fromty, toty, value)``.
+   *fromty* and *toty* are the concrete types being converted from and to,
+   respectively.  *value* is the value being converted.  The function
+   must return a value compatible with the type ``toty``.
+
+
+Constants
+'''''''''
+
+.. decorator:: lower_constant(typespec)
+
+   Register the decorated function as implementing the creation of
+   constants for the Numba *typespec*.  The decorated function
+   is called with four arguments ``(context, builder, ty, pyval)``.
+   *ty* is the concrete type to create a constant for.  *pyval*
+   is the Python value to convert into a LLVM constant.
+   The function must return a value compatible with the type ``ty``.
+
+
+Boxing and unboxing
+'''''''''''''''''''
+
+In these functions, *c* is a convenience object with several attributes:
+
+* its ``context`` attribute is a target context as above
+* its ``builder`` attribute is a :class:`llvmlite.ir.IRBuilder` as above
+* its ``pyapi`` attribute is an object giving access to a subset of the
+  `Python interpreter's C API <https://docs.python.org/3/c-api/index.html>`_
+
+An object, as opposed to a native value, is a ``PyObject *`` pointer.
+Such pointers can be produced or processed by the methods in the ``pyapi``
+object.
+
+.. decorator:: box(typespec)
+
+   Register the decorated function as boxing values matching the *typespec*.
+   The decorated function is called with three arguments ``(typ, val, c)``.
+   *typ* is the concrete type being boxed.  *val* is the value being
+   boxed.  The function should return a Python object, or NULL to signal
+   an error.
+
+.. decorator:: unbox(typespec)
+
+   Register the decorated function as unboxing values matching the *typespec*.
+   The decorated function is called with three arguments ``(typ, obj, c)``.
+   *typ* is the concrete type being unboxed.  *obj* is the Python object
+   (a ``PyObject *`` pointer, in C terms) being unboxed.  The function
+   should return a ``NativeValue`` object giving the unboxing result value
+   and an optional error bit.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/mynorm.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/mynorm.py
new file mode 100644
index 000000000..884634ea3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/mynorm.py
@@ -0,0 +1,72 @@
+import numpy as np
+from numba import njit, types
+from numba.extending import overload, register_jitable
+from numba.core.errors import TypingError
+
+import scipy.linalg
+
+
+@register_jitable
+def _oneD_norm_2(a):
+    # re-usable implementation of the 2-norm
+    val = np.abs(a)
+    return np.sqrt(np.sum(val * val))
+
+
+@overload(scipy.linalg.norm)
+def jit_norm(a, ord=None):
+    if isinstance(ord, types.Optional):
+        ord = ord.type
+    # Reject non integer, floating-point or None types for ord
+    if not isinstance(ord, (types.Integer, types.Float, types.NoneType)):
+        raise TypingError("'ord' must be either integer or floating-point")
+    # Reject non-ndarray types
+    if not isinstance(a, types.Array):
+        raise TypingError("Only accepts NumPy ndarray")
+    # Reject ndarrays with non integer or floating-point dtype
+    if not isinstance(a.dtype, (types.Integer, types.Float)):
+        raise TypingError("Only integer and floating point types accepted")
+    # Reject ndarrays with unsupported dimensionality
+    if not (0 <= a.ndim <= 2):
+        raise TypingError('3D and beyond are not allowed')
+    # Implementation for scalars/0d-arrays
+    elif a.ndim == 0:
+        return a.item()
+    # Implementation for vectors
+    elif a.ndim == 1:
+        def _oneD_norm_x(a, ord=None):
+            if ord == 2 or ord is None:
+                return _oneD_norm_2(a)
+            elif ord == np.inf:
+                return np.max(np.abs(a))
+            elif ord == -np.inf:
+                return np.min(np.abs(a))
+            elif ord == 0:
+                return np.sum(a != 0)
+            elif ord == 1:
+                return np.sum(np.abs(a))
+            else:
+                return np.sum(np.abs(a)**ord)**(1. / ord)
+        return _oneD_norm_x
+    # Implementation for matrices
+    elif a.ndim == 2:
+        def _two_D_norm_2(a, ord=None):
+            return _oneD_norm_2(a.ravel())
+        return _two_D_norm_2
+
+
+if __name__ == "__main__":
+    @njit
+    def use(a, ord=None):
+        # simple test function to check that the overload works
+        return scipy.linalg.norm(a, ord)
+
+    # spot check for vectors
+    a = np.arange(10)
+    print(use(a))
+    print(scipy.linalg.norm(a))
+
+    # spot check for matrices
+    b = np.arange(9).reshape((3, 3))
+    print(use(b))
+    print(scipy.linalg.norm(b))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/overloading-guide.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/overloading-guide.rst
new file mode 100644
index 000000000..a5645067e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/overloading-guide.rst
@@ -0,0 +1,192 @@
+
+.. _overloading-guide:
+
+==============================
+A guide to using ``@overload``
+==============================
+
+
+As mentioned in the :ref:`high-level extension API <high-level-extending>`, you
+can use the ``@overload`` decorator to create a Numba implementation of a
+function that can be used in :term:`nopython mode` functions. A common use case
+is to re-implement NumPy functions so that they can be called in ``@jit``
+decorated code. This section discusses how and when to use the ``@overload``
+decorator and what contributing such a function to the Numba code base might
+entail. This should help you get started when needing to use the ``@overload``
+decorator or when attempting to contribute new functions to Numba itself.
+
+The ``@overload`` decorator and it's variants are useful when you have a
+third-party library that you do not control and you wish to provide Numba
+compatible implementations for specific functions from that library.
+
+Concrete Example
+================
+
+Let's assume that you are working on a minimization algorithm that makes use of
+|scipy.linalg.norm|_ to find different vector norms and the `frobenius
+norm <https://en.wikipedia.org/wiki/Frobenius_inner_product>`_ for matrices.
+You know that only integer and real numbers will be involved. (While this may
+sound like an artificial example, especially because a Numba implementation of
+``numpy.linalg.norm`` exists, it is largely pedagogical and serves to
+illustrate how and when to use ``@overload``).
+
+.. |scipy.linalg.norm| replace:: ``scipy.linalg.norm``
+.. _scipy.linalg.norm: https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.norm.html
+
+The skeleton might look something like this::
+
+    def algorithm():
+        # setup
+        v = ...
+        while True:
+            # take a step
+            d = scipy.linalg.norm(v)
+            if d < tolerance:
+                break
+
+Now, let's further assume, that you have heard of Numba and you now wish to use
+it to accelerate your function. However, after adding the
+``jit(nopython=True)``
+decorator, Numba complains that ``scipy.linalg.norm`` isn't supported. From
+looking at the documentation, you realize that a norm is probably fairly easy
+to implement using NumPy. A good starting point is the following template.
+
+.. literalinclude:: template.py
+
+After some deliberation and tinkering, you end up with the following code:
+
+.. literalinclude:: mynorm.py
+
+As you can see, the implementation only supports what you need right now:
+
+* Only supports integer and floating-point types
+* All vector norms
+* Only the Frobenius norm for matrices
+* Code sharing between vector and matrix implementations using
+  ``@register_jitable``.
+* Norms are implemented using NumPy syntax. (This is possible because
+  Numba is very aware of NumPy and many functions are supported.)
+
+So what actually happens here? The ``overload`` decorator registers a suitable
+implementation for ``scipy.linalg.norm`` in case a call to this is encountered
+in code that is being JIT-compiled, for example when you decorate your
+``algorithm`` function with ``@jit(nopython=True)``. In that case, the function
+``jit_norm`` will be called with the currently encountered types and will then
+return either ``_oneD_norm_x`` in the vector case and ``_two_D_norm_2``.
+
+You can download the example code here: :download:`mynorm.py </extending/mynorm.py>`
+
+Implementing ``@overload`` for NumPy functions
+==============================================
+
+Numba supports NumPy through the provision of ``@jit`` compatible
+re-implementations of NumPy functions. In such cases ``@overload`` is a very
+convenient option for writing such implementations, however there are a few
+additional things to watch out for.
+
+* The Numba implementation should match the NumPy implementation as closely as
+  feasible with respect to accepted types, arguments, raised exceptions and
+  algorithmic complexity (Big-O / Landau order).
+
+* When implementing supported argument types, bear in mind that, due to
+  duck typing, NumPy does tend to accept a multitude of argument types beyond
+  NumPy arrays such as scalar, list, tuple, set, iterator, generator etc.
+  You will need to account for that during type inference and subsequently as
+  part of the tests.
+
+* A NumPy function may return a scalar, array or a data structure
+  which matches one of its inputs, you need to be aware of type
+  unification problems and dispatch to appropriate implementations. For
+  example, |np.corrcoef|_ may return an array or a scalar depending on its
+  inputs.
+
+.. |np.corrcoef| replace:: ``np.corrcoef``
+.. _np.corrcoef: https://docs.scipy.org/doc/numpy/reference/generated/numpy.corrcoef.html
+
+* If you are implementing a new function, you should always update the
+  `documentation
+  <https://numba.pydata.org/numba-doc/latest/reference/numpysupported.html>`_.
+  The sources can be found in ``docs/source/reference/numpysupported.rst``. Be
+  sure to mention any limitations that your implementation has, e.g. no support
+  for the ``axis`` keyword.
+
+* When writing tests for the functionality itself, it's useful to include
+  handling of non-finite values, arrays with different shapes and layouts,
+  complex inputs, scalar inputs, inputs with types for which support is not
+  documented (e.g. a function which the NumPy docs say requires a float or int
+  input might also 'work' if given a bool or complex input).
+
+* When writing tests for exceptions, for example if adding tests to
+  ``numba/tests/test_np_functions.py``, you may encounter the following error
+  message:
+
+  .. code::
+
+        ======================================================================
+        FAIL: test_foo (numba.tests.test_np_functions.TestNPFunctions)
+        ----------------------------------------------------------------------
+        Traceback (most recent call last):
+        File "<path>/numba/numba/tests/support.py", line 645, in tearDown
+            self.memory_leak_teardown()
+        File "<path>/numba/numba/tests/support.py", line 619, in memory_leak_teardown
+            self.assert_no_memory_leak()
+        File "<path>/numba/numba/tests/support.py", line 628, in assert_no_memory_leak
+            self.assertEqual(total_alloc, total_free)
+        AssertionError: 36 != 35
+
+  This occurs because raising exceptions from jitted code leads to reference
+  leaks. Ideally, you will place all exception testing in a separate test
+  method and then add a call in each test to ``self.disable_leak_check()`` to
+  disable the leak-check (inherit from ``numba.tests.support.TestCase`` to make
+  that available).
+
+* For many of the functions that are available in NumPy, there are
+  corresponding methods defined on the NumPy ``ndarray`` type. For example, the
+  function ``repeat`` is available as a NumPy module level function and a
+  member function on the ``ndarray`` class.
+
+  .. code:: python
+
+        import numpy as np
+        a = np.arange(10)
+        # function
+        np.repeat(a, 10)
+        # method
+        a.repeat(10)
+
+  Once you have written the function implementation, you can easily use
+  ``@overload_method`` and reuse it. Just be sure to check that NumPy doesn't
+  diverge in the implementations of its function/method.
+
+  As an example, the ``repeat`` function/method:
+
+  .. code:: python
+
+        @extending.overload_method(types.Array, 'repeat')
+        def array_repeat(a, repeats):
+            def array_repeat_impl(a, repeat):
+                # np.repeat has already been overloaded
+                return np.repeat(a, repeat)
+
+            return array_repeat_impl
+
+* If you need to create ancillary functions, for example to re-use a small
+  utility function or to split your implementation across functions for the
+  sake of readability, you can make use of the ``@register_jitable`` decorator.
+  This will make those functions available from within your ``@jit`` and
+  ``@overload`` decorated functions.
+
+* The Numba continuous integration (CI) set up tests a wide variety of NumPy
+  versions, you'll sometimes be alerted to a change in behaviour from some
+  previous NumPy version. If you can find supporting evidence in the NumPy
+  change log / repository, then you'll need to decide whether to create
+  branches and attempt to replicate the logic across versions, or use a version
+  gate (with associated wording in the documentation) to advertise that Numba
+  replicates NumPy from some particular version onwards.
+
+* You can look at the Numba source code for inspiration, many of the overloaded
+  NumPy functions and methods are in ``numba/targets/arrayobj.py``. Below, you
+  will find a list of implementations to look at that are well implemented in
+  terms of accepted types and test coverage.
+
+  * ``np.repeat``
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/template.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/template.py
new file mode 100644
index 000000000..19b98cc01
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/extending/template.py
@@ -0,0 +1,21 @@
+# Declare that function `myfunc` is going to be overloaded (have a
+# substitutable Numba implementation)
+@overload(myfunc)
+# Define the overload function with formal arguments
+# these arguments must be matched in the inner function implementation
+def jit_myfunc(arg0, arg1, arg2, ...):
+    # This scope is for typing, access is available to the *type* of all
+    # arguments. This information can be used to change the behaviour of the
+    # implementing function and check that the types are actually supported
+    # by the implementation.
+
+    print(arg0) # this will show the Numba type of arg0
+
+    # This is the definition of the function that implements the `myfunc` work.
+    # It does whatever algorithm is needed to implement myfunc.
+    def myfunc_impl(arg0, arg1, arg2, ...): # match arguments to jit_myfunc
+        # < Implementation goes here >
+        return # whatever needs to be returned by the algorithm
+
+    # return the implementation
+    return myfunc_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/glossary.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/glossary.rst
new file mode 100644
index 000000000..35b690ca3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/glossary.rst
@@ -0,0 +1,106 @@
+
+Glossary
+========
+
+.. glossary::
+
+   ahead-of-time compilation
+   AOT compilation
+   AOT
+      Compilation of a function in a separate step before running the
+      program code, producing an on-disk binary object which can be distributed
+      independently.  This is the traditional kind of compilation known
+      in languages such as C, C++ or Fortran.
+
+   bytecode
+   Python bytecode
+      The original form in which Python functions are executed.  Python
+      bytecode describes a stack-machine executing abstract (untyped)
+      operations using operands from both the function stack and the
+      execution environment (e.g. global variables).
+
+   compile-time constant
+      An expression whose value Numba can infer and freeze at compile-time.
+      Global variables and closure variables are compile-time constants.
+
+   just-in-time compilation
+   JIT compilation
+   JIT
+      Compilation of a function at execution time, as opposed to
+      :term:`ahead-of-time compilation`.
+
+   JIT function
+      Shorthand for "a function :term:`JIT-compiled <JIT>` with Numba using
+      the :ref:`@jit <jit>` decorator."
+
+   loop-lifting
+   loop-jitting
+      A feature of compilation in :term:`object mode` where a loop can be
+      automatically extracted and compiled in :term:`nopython mode`.  This
+      allows functions with operations unsupported in nopython mode to see
+      significant performance improvements if they contain loops with only
+      nopython-supported operations.
+
+   lowering
+      The act of translating :term:`Numba IR` into LLVM IR.  The term
+      "lowering" stems from the fact that LLVM IR is low-level and
+      machine-specific while Numba IR is high-level and abstract.
+
+   NPM
+   nopython mode
+      A Numba compilation mode that generates code that does not access the
+      Python C API.  This compilation mode produces the highest performance
+      code, but requires that the native types of all values in the function
+      can be :term:`inferred <type inference>`.  Unless otherwise instructed,
+      the ``@jit`` decorator will automatically fall back to :term:`object
+      mode` if nopython mode cannot be used.
+
+   Numba IR
+   Numba intermediate representation
+      A representation of a piece of Python code which is more amenable
+      to analysis and transformations than the original Python
+      :term:`bytecode`.
+
+   object mode
+      A Numba compilation mode that generates code that handles all values
+      as Python objects and uses the Python C API to perform all operations
+      on those objects.  Code compiled in object mode will often run
+      no faster than Python interpreted code, unless the Numba compiler can
+      take advantage of :term:`loop-jitting`.
+
+   ``OptionalType``
+     An ``OptionalType`` is effectively a type union of a ``type`` and ``None``.
+     They typically occur in practice due to a variable being set to ``None``
+     and then in a branch the variable being set to some other value. It's
+     often not possible at compile time to determine if the branch will execute
+     so to permit :term:`type inference` to complete, the type of the variable
+     becomes the union of a ``type`` (from the value) and ``None``,
+     i.e. ``OptionalType(type)``.
+
+   type inference
+      The process by which Numba determines the specialized types of all
+      values within a function being compiled.  Type inference can fail
+      if arguments or globals have Python types unknown to Numba, or if
+      functions are used that are not recognized by Numba.  Successful
+      type inference is a prerequisite for compilation in
+      :term:`nopython mode`.
+
+   typing
+      The act of running :term:`type inference` on a value or operation.
+
+   ufunc
+      A NumPy `universal function <http://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_.
+      Numba can create new compiled ufuncs with
+      the :ref:`@vectorize <vectorize>` decorator.
+
+   reflection
+      In numba, when a mutable container is passed as argument to a nopython
+      function from the Python interpreter, the container object and all its
+      contained elements are converted into nopython values.  To match the
+      semantics of Python, any mutation on the container inside the nopython
+      function must be visible in the Python interpreter.  To do so, Numba
+      must update the container and its elements and convert them back into
+      Python objects during the transition back into the interpreter.
+
+      Not to be confused with Python's "reflection" in the context of binary
+      operators (see https://docs.python.org/3.5/reference/datamodel.html).
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/index.rst
new file mode 100644
index 000000000..fa757b5a8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/index.rst
@@ -0,0 +1,37 @@
+.. Numba documentation master file, created by
+   sphinx-quickstart on Tue Dec 30 11:55:40 2014.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Numba documentation
+===================
+
+This is the Numba documentation.  Unless you are already acquainted
+with Numba, we suggest you start with the :doc:`User manual <user/index>`.
+
+
+.. toctree::
+   :caption: For all users
+   :maxdepth: 2
+
+   user/index.rst
+   reference/index.rst
+
+
+.. toctree::
+   :caption: For CUDA users
+   :maxdepth: 2
+
+   cuda/index.rst
+   cuda-reference/index.rst
+
+
+.. toctree::
+   :caption: For advanced users & developers
+   :maxdepth: 2
+
+   extending/index.rst
+   developer/index.rst
+   proposals/index.rst
+   glossary.rst
+   release-notes.rst
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/cfunc.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/cfunc.rst
new file mode 100644
index 000000000..7650dfb6d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/cfunc.rst
@@ -0,0 +1,147 @@
+============================
+NBEP 4: Defining C callbacks
+============================
+
+:Author: Antoine Pitrou
+:Date: April 2016
+:Status: Draft
+
+
+Interfacing with some native libraries (for example written in C
+or C++) can necessitate writing native callbacks to provide business logic
+to the library.  Some Python-facing libraries may also provide the
+alternative of passing a ctypes-wrapped native callback instead of a
+Python callback for better performance.  A simple example is the
+``scipy.integrate`` package where the user passes the function to be
+integrated as a callback.
+
+Users of those libraries may want to benefit from the performance advantage
+of running purely native code, while writing their code in Python.
+This proposal outlines a scheme to provide such a functionality in
+Numba.
+
+
+Basic usage
+===========
+
+We propose adding a new decorator, ``@cfunc``, importable from the main
+package.  This decorator allows defining a callback as in the following
+example::
+
+   from numba import cfunc
+   from numba.types import float64
+
+   # A callback with the C signature `double(double)`
+
+   @cfunc(float64(float64), nopython=True)
+   def integrand(x):
+       return 1 / x
+
+
+The ``@cfunc`` decorator returns a "C function" object holding the
+resources necessary to run the given compiled function (for example its
+LLVM module).  This object has several attributes and methods:
+
+* the ``ctypes`` attribute is a ctypes function object representing
+  the native function.
+
+* the ``address`` attribute is the address of the native function code, as
+  an integer (note this can also be computed from the ``ctypes`` attribute).
+
+* the ``native_name`` attribute is the symbol under which the function
+  can be looked up inside the current process.
+
+* the ``inspect_llvm()`` method returns the IR for the LLVM module
+  in which the function is compiled.  It is expected that the ``native_name``
+  attribute corresponds to the function's name in the LLVM IR.
+
+The general signature of the decorator is ``cfunc(signature, **options)``.
+
+The ``signature`` must specify the argument types and return type of the
+function using Numba types.  In contrary to ``@jit``, the return type cannot
+be omitted.
+
+The ``options`` are keyword-only parameters specifying compilation options.
+We are expecting that the standard ``@jit`` options (``nopython``,
+``forceobj``, ``cache``) can be made to work with ``@cfunc``.
+
+
+Calling from Numba-compiled functions
+-------------------------------------
+
+While the intended use is to pass a callback's address to foreign C
+code expecting a function pointer, it should be made possible to call
+the C callback from a Numba-compiled function.
+
+
+Passing array data
+==================
+
+Native platform ABIs as used by C or C++ don't have the notion of a shaped
+array as in Numpy.  One common solution is to pass a raw data pointer and
+one or several size arguments (depending on dimensionality).  Numba must
+provide a way to rebuild an array view of this data inside the callback.
+
+::
+
+   from numba import cfunc, carray
+   from numba.types import float64, CPointer, void, intp
+
+   # A callback with the C signature `void(double *, double *, size_t)`
+
+   @cfunc(void(CPointer(float64), CPointer(float64), intp))
+   def invert(in_ptr, out_ptr, n):
+       in_ = carray(in_ptr, (n,))
+       out = carray(out_ptr, (n,))
+       for i in range(n):
+           out[i] = 1 / in_[i]
+
+
+The ``carray`` function takes ``(pointer, shape, dtype)`` arguments
+(``dtype`` being optional) and returns a C-layout array view over the
+data *pointer*, with the given *shape* and *dtype*.  *pointer* must
+be a ctypes pointer object (not a Python integer).  The array's
+dimensionality corresponds to the *shape* tuple's length.  If *dtype*
+is not given, the array's dtype corresponds to the *pointer*'s pointee
+type.
+
+The ``farray`` function is similar except that it returns a F-layout
+array view.
+
+
+Error handling
+==============
+
+There is no standard mechanism in C for error reporting.  Unfortunately,
+Numba currently doesn't handle ``try..except`` blocks, which makes it more
+difficult for the user to implement the required error reporting scheme.
+The current stance of this proposal is to let users guard against invalid
+arguments where necessary, and do whatever is required to inform the caller
+of the error.
+
+Based on user feedback, we can later add support for some error reporting
+schemes, such as returning an integer error code depending on whether an
+exception was raised, or setting ``errno``.
+
+
+Deferred topics
+===============
+
+Ahead-of-Time compilation
+-------------------------
+
+This proposal doesn't make any provision for AOT compilation of C callbacks.
+It would probably necessitate a separate API (a new method on the
+``numba.pycc.CC`` object), and the implementation would require exposing
+a subset of the C function object's functionality from the compiled C
+extension module.
+
+Opaque data pointers
+--------------------
+
+Some libraries allow passing an opaque data pointer (``void *``) to a
+user-provided callback, to provide any required context for execution
+of the callback.  Taking advantage of this functionality would require
+adding specific support in Numba, for example the ability to do generic
+conversion from ``types.voidptr`` and to take the address of a
+Python-facing ``jitclass`` instance.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/extension-points.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/extension-points.rst
new file mode 100644
index 000000000..89197dd17
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/extension-points.rst
@@ -0,0 +1,414 @@
+========================
+NBEP 2: Extension points
+========================
+
+:Author: Antoine Pitrou
+:Date: July 2015
+:Status: Draft
+
+
+Implementing new types or functions in Numba requires hooking into
+various mechanisms along the compilation chain (and potentially
+outside of it).  This document aims, first, at examining the
+current ways of doing so and, second, at making proposals to make
+extending easier.
+
+If some of the proposals are implemented, we should first strive
+to use and exercise them internally, before exposing the APIs to the
+public.
+
+.. note::
+   This document doesn't cover CUDA or any other non-CPU backend.
+
+
+High-level API
+==============
+
+There is currently no high-level API, making some use cases more
+complicated than they should be.
+
+Proposed changes
+----------------
+
+Dedicated module
+''''''''''''''''
+
+We propose the addition of a ``numba.extending`` module exposing the main
+APIs useful for extending Numba.
+
+Implementing a function
+'''''''''''''''''''''''
+
+We propose the addition of a ``@overload`` decorator allowing the
+implementation of a given function for use in :term:`nopython mode`.
+The overloading function has the same formal signature as the implemented
+function, and receives the actual argument types.  It should return a
+Python function implementing the overloaded function for the given types.
+
+The following example implements :func:`numpy.where` with
+this approach.
+
+.. literalinclude:: np-where-override.py
+
+It is also possible to implement functions already known to Numba, to
+support additional types.  The following example implements the
+built-in function :func:`len` for tuples with this approach::
+
+   @overload(len)
+   def tuple_len(x):
+      if isinstance(x, types.BaseTuple):
+         # The tuple length is known at compile-time, so simply reify it
+         # as a constant.
+         n = len(x)
+         def len_impl(x):
+            return n
+         return len_impl
+
+
+Implementing an attribute
+'''''''''''''''''''''''''
+
+We propose the addition of a ``@overload_attribute`` decorator allowing
+the implementation of an attribute getter for use in :term:`nopython mode`.
+
+The following example implements the ``.nbytes`` attribute on Numpy arrays::
+
+   @overload_attribute(types.Array, 'nbytes')
+   def array_nbytes(arr):
+      def get(arr):
+          return arr.size * arr.itemsize
+      return get
+
+.. note::
+   The overload_attribute() signature allows for expansion to also define
+   setters and deleters, by letting the decorated function return a
+   ``getter, setter, deleter`` tuple instead of a single ``getter``.
+
+
+Implementing a method
+'''''''''''''''''''''
+
+We propose the addition of a ``@overload_method`` decorator allowing the
+implementation of an instance method for use in :term:`nopython mode`.
+
+The following example implements the ``.take()`` method on Numpy arrays::
+
+   @overload_method(types.Array, 'take')
+   def array_take(arr, indices):
+      if isinstance(indices, types.Array):
+          def take_impl(arr, indices):
+              n = indices.shape[0]
+              res = np.empty(n, arr.dtype)
+              for i in range(n):
+                  res[i] = arr[indices[i]]
+              return res
+          return take_impl
+
+
+Exposing a structure member
+'''''''''''''''''''''''''''
+
+We propose the addition of a ``make_attribute_wrapper()`` function exposing
+an internal field as a visible read-only attribute, for those types backed
+by a ``StructModel`` data model.
+
+For example, assuming ``PdIndexType`` is the Numba type of pandas indices,
+here is how to expose the underlying Numpy array as a ``._data`` attribute::
+
+   @register_model(PdIndexType)
+   class PdIndexModel(models.StructModel):
+       def __init__(self, dmm, fe_type):
+           members = [
+               ('values', fe_type.as_array),
+               ]
+           models.StructModel.__init__(self, dmm, fe_type, members)
+
+   make_attribute_wrapper(PdIndexType, 'values', '_data')
+
+
+Typing
+======
+
+Numba types
+-----------
+
+Numba's standard types are declared in :mod:`numba.types`.  To declare
+a new type, one subclasses the base :class:`Type` class or one of its
+existing abstract subclasses, and implements the required functionality.
+
+Proposed changes
+''''''''''''''''
+
+No change required.
+
+
+Type inference on values
+------------------------
+
+Values of a new type need to be type-inferred if they can appear as
+function arguments or constants.  The core machinery is in
+:mod:`numba.typing.typeof`.
+
+In the common case where some Python class or classes map exclusively
+to the new type, one can extend a generic function to dispatch on said
+classes, e.g.::
+
+   from numba.typing.typeof import typeof_impl
+
+   @typeof_impl(MyClass)
+   def _typeof_myclass(val, c):
+      if "some condition":
+         return MyType(...)
+
+The ``typeof_impl`` specialization must return a Numba type instance,
+or None if the value failed typing.
+
+(when one controls the class being type-inferred, an alternative
+to ``typeof_impl`` is to define a ``_numba_type_`` property on the class)
+
+In the rarer case where the new type can denote various Python classes
+that are impossible to enumerate, one must insert a manual check in the
+fallback implementation of the ``typeof_impl`` generic function.
+
+Proposed changes
+''''''''''''''''
+
+Allow people to define a generic hook without monkeypatching the
+fallback implementation.
+
+
+Fast path for type inference on function arguments
+--------------------------------------------------
+
+Optionally, one may want to allow a new type to participate in the
+fast type resolution (written in C code) to minimize function call
+overhead when a JIT-compiled function is called with the new type.
+One must then insert the required checks and implementation in
+the ``_typeof.c`` file, presumably inside the ``compute_fingerprint()``
+function.
+
+Proposed changes
+''''''''''''''''
+
+None.  Adding generic hooks to C code embedded in a C Python extension
+is too delicate a change.
+
+
+Type inference on operations
+----------------------------
+
+Values resulting from various operations (function calls, operators, etc.)
+are typed using a set of helpers called "templates".  One can define a
+new template by subclass one of the existing base classes and implement
+the desired inference mechanism.  The template is explicitly registered
+with the type inference machinery using a decorator.
+
+The :class:`ConcreteTemplate` base class allows one to define inference as
+a set of supported signatures for a given operation.  The following example
+types the modulo operator::
+
+   @builtin
+   class BinOpMod(ConcreteTemplate):
+       key = "%"
+       cases = [signature(op, op, op)
+                for op in sorted(types.signed_domain)]
+       cases += [signature(op, op, op)
+                 for op in sorted(types.unsigned_domain)]
+       cases += [signature(op, op, op) for op in sorted(types.real_domain)]
+
+(note that type *instances* are used in the signatures, severely
+limiting the amount of genericity that can be expressed)
+
+The :class:`AbstractTemplate` base class allows to define inference
+programmatically, giving it full flexibility.  Here is a simplistic
+example of how tuple indexing (i.e. the ``__getitem__`` operator) can
+be expressed::
+
+   @builtin
+   class GetItemUniTuple(AbstractTemplate):
+       key = "getitem"
+
+       def generic(self, args, kws):
+           tup, idx = args
+           if isinstance(tup, types.UniTuple) and isinstance(idx, types.Integer):
+               return signature(tup.dtype, tup, idx)
+
+
+The :class:`AttributeTemplate` base class allows to type the attributes
+and methods of a given type.  Here is an example, typing the ``.real``
+and ``.imag`` attributes of complex numbers::
+
+   @builtin_attr
+   class ComplexAttribute(AttributeTemplate):
+       key = types.Complex
+
+       def resolve_real(self, ty):
+           return ty.underlying_float
+
+       def resolve_imag(self, ty):
+           return ty.underlying_float
+
+.. note::
+   :class:`AttributeTemplate` only works for getting attributes.  Setting
+   an attribute's value is hardcoded in :mod:`numba.typeinfer`.
+
+The :class:`CallableTemplate` base class offers an easier way to parse
+flexible function signatures, by letting one define a callable that has
+the same definition as the function being typed.  For example, here is how
+one could hypothetically type Python's ``sorted`` function if Numba supported
+lists::
+
+   @builtin
+   class Sorted(CallableTemplate):
+       key = sorted
+
+       def generic(self):
+           def typer(iterable, key=None, reverse=None):
+               if reverse is not None and not isinstance(reverse, types.Boolean):
+                   return
+               if key is not None and not isinstance(key, types.Callable):
+                   return
+               if not isinstance(iterable, types.Iterable):
+                   return
+               return types.List(iterable.iterator_type.yield_type)
+
+           return typer
+
+(note you can return just the function's return type instead of the
+full signature)
+
+Proposed changes
+''''''''''''''''
+
+Naming of the various decorators is quite vague and confusing.  We propose
+renaming ``@builtin`` to ``@infer``, ``@builtin_attr`` to ``@infer_getattr``
+and ``builtin_global`` to ``infer_global``.
+
+The two-step declaration for global values is a bit verbose, we propose
+simplifying it by allowing the use of ``infer_global`` as a decorator::
+
+   @infer_global(len)
+   class Len(AbstractTemplate):
+       key = len
+
+       def generic(self, args, kws):
+           assert not kws
+           (val,) = args
+           if isinstance(val, (types.Buffer, types.BaseTuple)):
+               return signature(types.intp, val)
+
+The class-based API can feel clumsy, we can add a functional API for
+some of the template kinds:
+
+.. code-block:: python
+
+   @type_callable(sorted)
+   def type_sorted(context):
+       def typer(iterable, key=None, reverse=None):
+           # [same function as above]
+
+       return typer
+
+
+Code generation
+===============
+
+Concrete representation of values of a Numba type
+-------------------------------------------------
+
+Any concrete Numba type must be able to be represented in LLVM form
+(for variable storage, argument passing, etc.).  One defines that
+representation by implementing a datamodel class and registering it
+with a decorator.  Datamodel classes for standard types are defined
+in :mod:`numba.datamodel.models`.
+
+Proposed changes
+''''''''''''''''
+
+No change required.
+
+Conversion between types
+------------------------
+
+Implicit conversion between Numba types is currently implemented as a
+monolithic sequence of choices and type checks in the
+:meth:`BaseContext.cast` method.  To add a new implicit conversion, one
+appends a type-specific check in that method.
+
+Boolean evaluation is a special case of implicit conversion (the
+destination type being :class:`types.Boolean`).
+
+.. note::
+   Explicit conversion is seen as a regular operation, e.g. a constructor
+   call.
+
+Proposed changes
+''''''''''''''''
+
+Add a generic function for implicit conversion, with multiple dispatch
+based on the source and destination types.  Here is an example showing
+how to write a float-to-integer conversion::
+
+   @lower_cast(types.Float, types.Integer)
+   def float_to_integer(context, builder, fromty, toty, val):
+       lty = context.get_value_type(toty)
+       if toty.signed:
+           return builder.fptosi(val, lty)
+       else:
+           return builder.fptoui(val, lty)
+
+
+Implementation of an operation
+------------------------------
+
+Other operations are implemented and registered using a set of generic
+functions and decorators.  For example, here is how lookup for a the ``.ndim``
+attribute on Numpy arrays is implemented::
+
+   @builtin_attr
+   @impl_attribute(types.Kind(types.Array), "ndim", types.intp)
+   def array_ndim(context, builder, typ, value):
+       return context.get_constant(types.intp, typ.ndim)
+
+And here is how calling ``len()`` on a tuple value is implemented::
+
+   @builtin
+   @implement(types.len_type, types.Kind(types.BaseTuple))
+   def tuple_len(context, builder, sig, args):
+       tupty, = sig.args
+       retty = sig.return_type
+       return context.get_constant(retty, len(tupty.types))
+
+Proposed changes
+''''''''''''''''
+
+Review and streamine the API.  Drop the requirement to write
+``types.Kind(...)`` explicitly.  Remove the separate ``@implement``
+decorator and rename ``@builtin`` to ``@lower_builtin``, ``@builtin_attr``
+to ``@lower_getattr``, etc.
+
+Add decorators to implement ``setattr()`` operations, named
+``@lower_setattr`` and ``@lower_setattr_generic``.
+
+
+Conversion from / to Python objects
+-----------------------------------
+
+Some types need to be converted from or to Python objects, if they can
+be passed as function arguments or returned from a function.  The
+corresponding boxing and unboxing operations are implemented using
+a generic function.  The implementations for standard Numba types
+are in :mod:`numba.targets.boxing`.  For example, here is the boxing
+implementation for a boolean value::
+
+   @box(types.Boolean)
+   def box_bool(c, typ, val):
+       longval = c.builder.zext(val, c.pyapi.long)
+       return c.pyapi.bool_from_long(longval)
+
+Proposed changes
+''''''''''''''''
+
+Change the implementation signature from ``(c, typ, val)`` to
+``(typ, val, c)``, to match the one chosen for the ``typeof_impl``
+generic function.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/external-memory-management.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/external-memory-management.rst
new file mode 100644
index 000000000..38878d670
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/external-memory-management.rst
@@ -0,0 +1,922 @@
+.. _nbep-7:
+
+===============================================
+NBEP 7: CUDA External Memory Management Plugins
+===============================================
+
+:Author: Graham Markall, NVIDIA
+:Contributors: Thomson Comer, Peter Entschev, Leo Fang, John Kirkham, Keith Kraus
+:Date: March 2020
+:Status: Final
+
+Background and goals
+--------------------
+
+The :ref:`CUDA Array Interface <cuda-array-interface>` enables sharing of data
+between different Python libraries that access CUDA devices. However, each
+library manages its own memory distinctly from the others. For example:
+
+
+* `Numba <https://numba.pydata.org/>`_ internally manages memory for the creation
+  of device and mapped host arrays.
+* `The RAPIDS libraries <https://rapids.ai/>`_ (cuDF, cuML, etc.) use the `Rapids
+  Memory Manager <https://github.com/rapidsai/rmm>`_ for allocating device
+  memory.
+* `CuPy <https://cupy.chainer.org/>`_ includes a `memory pool
+  implementation <https://docs-cupy.chainer.org/en/stable/reference/memory.html>`_
+  for both device and pinned memory.
+
+The goal of this NBEP is to describe a plugin interface that enables Numba's
+internal memory management to be replaced with an external memory manager by the
+user. When the plugin interface is in use, Numba no longer directly allocates or
+frees any memory when creating arrays, but instead requests allocations and
+frees through the external manager.
+
+Requirements
+------------
+
+Provide an *External Memory Manager (EMM)* interface in Numba.
+
+
+* When the EMM is in use, Numba will make all memory allocation using the EMM.
+  It will never directly call functions such as ``CuMemAlloc``\ , ``cuMemFree``\ , etc.
+* When not using an *External Memory Manager (EMM)*\ , Numba's present behaviour
+  is unchanged (at the time of writing, the current version is the 0.48
+  release).
+
+If an EMM is to be used, it will entirely replace Numba's internal memory
+management for the duration of program execution. An interface for setting the
+memory manager will be provided.
+
+Device vs. Host memory
+^^^^^^^^^^^^^^^^^^^^^^^
+
+An EMM will always take responsibility for the management of device memory.
+However, not all CUDA memory management libraries also support managing host
+memory, so a facility for Numba to continue the management of host memory 
+whilst ceding control of device memory to the EMM will be provided.
+
+Deallocation strategies
+^^^^^^^^^^^^^^^^^^^^^^^
+
+Numba's internal memory management uses a :ref:`deallocation strategy
+<deallocation-behavior>` designed to increase efficiency by deferring
+deallocations until a significant quantity are pending. It also provides a
+mechanism for preventing deallocations entirely during critical sections, using
+the :func:`~numba.cuda.defer_cleanup` context manager.
+
+
+* When the EMM is not in use, the deallocation strategy and operation of
+  ``defer_cleanup`` remain unchanged.
+* When the EMM is in use, the deallocation strategy is implemented by the EMM,
+  and Numba's internal deallocation mechanism is not used. For example:
+
+  * A similar strategy to Numba's could be implemented by the EMM, or
+  * Deallocated memory might immediately be returned to a memory pool.
+
+* The ``defer_cleanup`` context manager may behave differently with an EMM - an
+  EMM should be accompanied by documentation of the behaviour of the
+  ``defer_cleanup`` context manager when it is in use.
+
+  * For example, a pool allocator could always immediately return memory to a
+    pool even when the context manager is in use, but could choose
+    not to free empty pools until ``defer_cleanup`` is not in use.
+
+Management of other objects
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In addition to memory, Numba manages the allocation and deallocation of
+:ref:`events <events>`, :ref:`streams <streams>`, and modules (a module is a
+compiled object, which is generated from ``@cuda.jit``\ -ted functions). The
+management of streams, events, and modules should be unchanged by the presence
+or absence of an EMM.
+
+Asynchronous allocation / deallocation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+An asynchronous memory manager might provide the facility for an allocation or
+free to take a CUDA stream and execute asynchronously. For freeing, this is
+unlikely to cause issues since it operates at a layer beneath Python, but for
+allocations this could be problematic if the user tries to then launch a kernel
+on the default stream from this asynchronous memory allocation.
+
+The interface described in this proposal will not be required to support
+asynchronous allocation and deallocation, and as such these use cases will not
+be considered further. However, nothing in this proposal should preclude the
+straightforward addition of asynchronous operations in future versions of the
+interface.
+
+Non-requirements
+^^^^^^^^^^^^^^^^
+
+In order to minimise complexity and constrain this proposal to a reasonable
+scope, the following will not be supported:
+
+
+* Using different memory manager implementations for different contexts. All
+  contexts will use the same memory manager implementation - either the Numba
+  internal implementation or an external implementation.
+* Changing the memory manager once execution has begun. It is not practical to
+  change the memory manager and retain all allocations. Cleaning up the entire
+  state and then changing to a different memory allocator (rather than starting
+  a new process) appears to be a rather niche use case.
+* Any changes to the ``__cuda_array_interface__`` to further define its semantics,
+  e.g. for acquiring / releasing memory as discussed in `Numba Issue
+  #4886 <https://github.com/numba/numba/issues/4886>`_ - these are independent,
+  and can be addressed as part of separate proposals.
+* Managed memory / UVM is not supported. At present Numba does not support UVM -
+  see `Numba Issue #4362 <https://github.com/numba/numba/issues/4362>`_ for
+  discussion of support.
+
+Interface for Plugin developers
+-------------------------------
+
+New classes and functions will be added to ``numba.cuda.cudadrv.driver``:
+
+* ``BaseCUDAMemoryManager`` and ``HostOnlyCUDAMemoryManager``\ : base classes for
+  EMM plugin implementations.
+* ``set_memory_manager``: a method for registering an external memory manager with
+  Numba.
+
+These will be exposed through the public API, in the ``numba.cuda`` module.
+Additionally, some classes that are already part of the `driver` module will be
+exposed as part of the public API:
+
+* ``MemoryPointer``: used to encapsulate information about a pointer to device
+  memory.
+* ``MappedMemory``: used to hold information about host memory that is mapped into
+  the device address space (a subclass of ``MemoryPointer``\ ).
+* ``PinnedMemory``: used to hold information about host memory that is pinned (a
+  subclass of ``mviewbuf.MemAlloc``\ , a class internal to Numba).
+
+As an alternative to calling the ``set_memory_manager`` function, an environment
+variable can be used to set the memory manager. The value of the environment
+variable should be the name of the module containing the memory manager in its
+global scope, named ``_numba_memory_manager``\ :
+
+.. code-block::
+
+   export NUMBA_CUDA_MEMORY_MANAGER="<module>"
+
+When this variable is set, Numba will automatically use the memory manager from
+the specified module. Calls to ``set_memory_manager`` will issue a warning, but
+otherwise be ignored.
+
+Plugin Base Classes
+^^^^^^^^^^^^^^^^^^^
+
+An EMM plugin is implemented by inheriting from the ``BaseCUDAMemoryManager``
+class, which is defined as:
+
+.. code-block:: python
+
+   class BaseCUDAMemoryManager(object, metaclass=ABCMeta):
+       @abstractmethod
+       def memalloc(self, size):
+           """
+           Allocate on-device memory in the current context. Arguments:
+
+           - `size`: Size of allocation in bytes
+
+           Returns: a `MemoryPointer` to the allocated memory.
+           """
+
+       @abstractmethod
+       def memhostalloc(self, size, mapped, portable, wc):
+           """
+           Allocate pinned host memory. Arguments:
+
+           - `size`: Size of the allocation in bytes
+           - `mapped`: Whether the allocated memory should be mapped into the CUDA
+                       address space.
+           - `portable`: Whether the memory will be considered pinned by all
+                         contexts, and not just the calling context.
+           - `wc`: Whether to allocate the memory as write-combined.
+
+           Returns a `MappedMemory` or `PinnedMemory` instance that owns the
+           allocated memory, depending on whether the region was mapped into
+           device memory.
+           """
+
+       @abstractmethod
+       def mempin(self, owner, pointer, size, mapped):
+           """
+           Pin a region of host memory that is already allocated. Arguments:
+
+           - `owner`: An object owning the memory - e.g. a `DeviceNDArray`.
+           - `pointer`: The pointer to the beginning of the region to pin.
+           - `size`: The size of the region to pin.
+           - `mapped`: Whether the region should also be mapped into device memory.
+
+           Returns a `MappedMemory` or `PinnedMemory` instance that refers to the
+           allocated memory, depending on whether the region was mapped into device
+           memory.
+           """
+
+       @abstractmethod
+       def initialize(self):
+           """
+           Perform any initialization required for the EMM plugin to be ready to
+           use.
+           """
+
+       @abstractmethod
+       def get_memory_info(self):
+           """
+           Returns (free, total) memory in bytes in the context
+           """
+
+       @abstractmethod
+       def get_ipc_handle(self, memory):
+           """
+           Return an `IpcHandle` from a GPU allocation. Arguments:
+
+           - `memory`: A `MemoryPointer` for which the IPC handle should be created.
+           """
+
+       @abstractmethod
+       def reset(self):
+           """
+           Clear up all memory allocated in this context.
+           """
+
+       @abstractmethod
+       def defer_cleanup(self):
+           """
+           Returns a context manager that ensures the implementation of deferred
+           cleanup whilst it is active.
+           """
+
+       @property
+       @abstractmethod
+       def interface_version(self):
+           """
+           Returns an integer specifying the version of the EMM Plugin interface
+           supported by the plugin implementation. Should always return 1 for
+           implementations described in this proposal.
+           """
+
+All of the methods of an EMM plugin are called from within Numba - they never
+need to be invoked directly by a Numba user.
+
+The ``initialize`` method is called by Numba prior to any memory allocations
+being requested. This gives the EMM an opportunity to initialize any data
+structures, etc., that it needs for its normal operations. The method may be
+called multiple times during the lifetime of the program - subsequent calls
+should not invalidate or reset the state of the EMM.
+
+The ``memalloc``\ , ``memhostalloc``\ , and ``mempin`` methods are called when Numba
+requires an allocation of device or host memory, or pinning of host memory.
+Device memory should always be allocated in the current context.
+
+``get_ipc_handle`` is called when an IPC handle for an array is required. Note
+that there is no method for closing an IPC handle - this is because the
+``IpcHandle`` object constructed by ``get_ipc_handle`` contains a ``close()`` method
+as part of its definition in Numba, which closes the handle by calling
+``cuIpcCloseMemHandle``. It is expected that this is sufficient for general use
+cases, so no facility for customising the closing of IPC handles is provided by
+the EMM Plugin interface.
+
+``get_memory_info`` may be called at any time after ``initialize``.
+
+``reset`` is called as part of resetting a context. Numba does not normally call
+reset spontaneously, but it may be called at the behest of the user. Calls to
+``reset`` may even occur before ``initialize`` is called, so the plugin should be
+robust against this occurrence.
+
+``defer_cleanup`` is called when the ``numba.cuda.defer_cleanup`` context manager
+is used from user code.
+
+``interface_version`` is called by Numba when the memory manager is set, to
+ensure that the version of the interface implemented by the plugin is
+compatible with the version of Numba in use.
+
+Representing pointers
+^^^^^^^^^^^^^^^^^^^^^
+
+Device Memory
+~~~~~~~~~~~~~
+
+The ``MemoryPointer`` class is used to represent a pointer to memory. Whilst there
+are various details of its implementation, the only aspect relevant to EMM
+plugin development is its initialization. The ``__init__`` method has the
+following interface:
+
+.. code-block:: python
+
+   class MemoryPointer:
+       def __init__(self, context, pointer, size, owner=None, finalizer=None):
+
+
+* ``context``\ : The context in which the pointer was allocated.
+* ``pointer``\ : A ``ctypes`` pointer (e.g. ``ctypes.c_uint64``\ ) holding the address of
+  the memory.
+* ``size``\ : The size of the allocation in bytes.
+* ``owner``\ : The owner is sometimes set by the internals of the class, or used for
+  Numba's internal memory management, but need not be provided by the writer of
+  an EMM plugin - the default of ``None`` should always suffice.
+* ``finalizer``\ : A method that is called when the last reference to the
+  ``MemoryPointer`` object is released. Usually this will make a call to the
+  external memory management library to inform it that the memory is no longer
+  required, and that it could potentially be freed (though the EMM is not
+  required to free it immediately).
+
+Host Memory
+~~~~~~~~~~~
+
+Memory mapped into the CUDA address space (which is created when the
+``memhostalloc`` or ``mempin`` methods are called with ``mapped=True``\ ) is managed
+using the ``MappedMemory`` class:
+
+.. code-block:: python
+
+   class MappedMemory(AutoFreePointer):
+       def __init__(self, context, pointer, size, owner, finalizer=None):
+
+
+* ``context``\ : The context in which the pointer was allocated.
+* ``pointer``\ : A ``ctypes`` pointer (e.g. ``ctypes.c_void_p``\ ) holding the address of
+  the allocated memory.
+* ``size``\ : The size of the allocated memory in bytes.
+* ``owner``\ : A Python object that owns the memory, e.g. a ``DeviceNDArray``
+  instance.
+* ``finalizer``\ : A method that is called when the last reference to the
+  ``MappedMemory`` object is released. For example, this method could call
+  ``cuMemFreeHost`` on the pointer to deallocate the memory immediately.
+
+Note that the inheritance from ``AutoFreePointer`` is an implementation detail and
+need not concern the developer of an EMM plugin - ``MemoryPointer`` is higher in
+the MRO of ``MappedMemory``.
+
+Memory that is only in the host address space and has been pinned is represented
+with the ``PinnedMemory`` class:
+
+.. code-block:: python
+
+   class PinnedMemory(mviewbuf.MemAlloc):
+       def __init__(self, context, pointer, size, owner, finalizer=None):
+
+
+* ``context``\ : The context in which the pointer was allocated.
+* ``pointer``\ : A ``ctypes`` pointer (e.g. ``ctypes.c_void_p``\ ) holding the address of
+  the pinned memory.
+* ``size``\ : The size of the pinned region in bytes.
+* ``owner``\ : A Python object that owns the memory, e.g. a ``DeviceNDArray``
+  instance.
+* ``finalizer``\ : A method that is called when the last reference to the
+  ``PinnedMemory`` object is released. This method could e.g. call
+  ``cuMemHostUnregister`` on the pointer to unpin the memory immediately.
+
+Providing device memory management only
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Some external memory managers will support management of on-device memory but
+not host memory. To make it easy to implement an EMM plugin using one of these
+managers, Numba will provide a memory manager class with implementations of the
+``memhostalloc`` and ``mempin`` methods. An abridged definition of this class
+follows:
+
+.. code-block:: python
+
+   class HostOnlyCUDAMemoryManager(BaseCUDAMemoryManager):
+       # Unimplemented methods:
+       #
+       # - memalloc
+       # - get_memory_info
+
+       def memhostalloc(self, size, mapped, portable, wc):
+           # Implemented.
+
+       def mempin(self, owner, pointer, size, mapped):
+           # Implemented.
+
+       def initialize(self):
+           # Implemented.
+           #
+           # Must be called by any subclass when its initialize() method is
+           # called.
+
+       def reset(self):
+           # Implemented.
+           #
+           # Must be called by any subclass when its reset() method is
+           # called.
+
+       def defer_cleanup(self):
+           # Implemented.
+           #
+           # Must be called by any subclass when its defer_cleanup() method is
+           # called.
+
+A class can subclass the ``HostOnlyCUDAMemoryManager`` and then it only needs to
+add implementations of methods for on-device memory. Any subclass must observe
+the following rules:
+
+
+* If the subclass implements ``__init__``\ , then it must also call
+  ``HostOnlyCUDAMemoryManager.__init__``\ , as this is used to initialize some of
+  its data structures (\ ``self.allocations`` and ``self.deallocations``\ ).
+* The subclass must implement ``memalloc`` and ``get_memory_info``.
+* The ``initialize`` and ``reset`` methods perform initialisation of structures
+  used by the ``HostOnlyCUDAMemoryManager``.
+
+  * If the subclass has nothing to do on initialisation (possibly) or reset
+    (unlikely) then it need not implement these methods. 
+  * However, if it does implement these methods then it must also call the
+    methods from ``HostOnlyCUDAMemoryManager`` in its own implementations.
+
+* Similarly if ``defer_cleanup`` is implemented, it should enter the context
+  provided by ``HostOnlyCUDAManager.defer_cleanup()`` prior to ``yield``\ ing (or in
+  the ``__enter__`` method) and release it prior to exiting (or in the ``__exit__``
+  method).
+
+Import order
+^^^^^^^^^^^^
+
+The order in which Numba and the library implementing an EMM Plugin should not
+matter. For example, if ``rmm`` were to implement and register an EMM Plugin,
+then:
+
+.. code-block:: python
+
+   from numba import cuda
+   import rmm
+
+and
+
+.. code-block:: python
+
+   import rmm
+   from numba import cuda
+
+are equivalent - this is because Numba does not initialize CUDA or allocate any
+memory until the first call to a CUDA function - neither instantiating and
+registering an EMM plugin, nor importing ``numba.cuda`` causes a call to a CUDA
+function.
+
+Numba as a Dependency
+^^^^^^^^^^^^^^^^^^^^^
+
+Adding the implementation of an EMM Plugin to a library naturally makes Numba a
+dependency of the library where it may not have been previously. In order to
+make the dependency optional, if this is desired, one might conditionally
+instantiate and register the EMM Plugin like:
+
+.. code-block:: python
+
+   try:
+       import numba
+       from mylib.numba_utils import MyNumbaMemoryManager
+       numba.cuda.cudadrv.driver.set_memory_manager(MyNumbaMemoryManager)
+   except:
+       print("Numba not importable - not registering EMM Plugin")
+
+so that ``mylib.numba_utils``\ , which contains the implementation of the EMM
+Plugin, is only imported if Numba is already present. If Numba is not available,
+then ``mylib.numba_utils`` (which necessarily imports ``numba``\ ), will never be
+imported.
+
+It is recommended that any library with an EMM Plugin includes at least some
+environments with Numba for testing with the EMM Plugin in use, as well as some
+environments without Numba, to avoid introducing an accidental Numba dependency.
+
+Example implementation - A RAPIDS Memory Manager (RMM) Plugin
+-------------------------------------------------------------
+
+An implementation of an EMM plugin within the `Rapids Memory Manager
+(RMM) <https://github.com/rapidsai/rmm>`_ is sketched out in this section. This is
+intended to show an overview of the implementation in order to support the
+descriptions above and to illustrate how the plugin interface can be used -
+different choices may be made for a production-ready implementation.
+
+The plugin implementation consists of additions to `python/rmm/rmm.py
+<https://github.com/rapidsai/rmm/blob/d5831ac5ebb5408ee83f63b7c7d03d8870ecb361/python/rmm/rmm.py>`_:
+
+.. code-block:: python
+
+   # New imports:
+   from contextlib import context_manager
+   # RMM already has Numba as a dependency, so these imports need not be guarded
+   # by a check for the presence of numba.
+   from numba.cuda import (HostOnlyCUDAMemoryManager, MemoryPointer, IpcHandle,
+                           set_memory_manager)
+
+
+   # New class implementing the EMM Plugin:
+   class RMMNumbaManager(HostOnlyCUDAMemoryManager):
+       def memalloc(self, size):
+           # Allocates device memory using RMM functions. The finalizer for the
+           # allocated memory calls back to RMM to free the memory.
+           addr = librmm.rmm_alloc(bytesize, 0)
+           ctx = cuda.current_context()
+           ptr = ctypes.c_uint64(int(addr))
+           finalizer = _make_finalizer(addr, stream)
+           return MemoryPointer(ctx, ptr, size, finalizer=finalizer)
+
+      def get_ipc_handle(self, memory):
+           """ 
+           Get an IPC handle for the memory with offset modified by the RMM memory
+           pool.
+           """
+           # This implementation provides a functional implementation and illustrates
+           # what get_ipc_handle needs to do, but it is not a very "clean"
+           # implementation, and it relies on borrowing bits of Numba internals to
+           # initialise ipchandle. 
+           #
+           # A more polished implementation might make use of additional functions in
+           # the RMM C++ layer for initialising IPC handles, and not use any Numba
+           # internals.
+           ipchandle = (ctypes.c_byte * 64)()  # IPC handle is 64 bytes
+           cuda.cudadrv.memory.driver_funcs.cuIpcGetMemHandle(
+               ctypes.byref(ipchandle),
+               memory.owner.handle,
+           )
+           source_info = cuda.current_context().device.get_device_identity()
+           ptr = memory.device_ctypes_pointer.value
+           offset = librmm.rmm_getallocationoffset(ptr, 0)
+           return IpcHandle(memory, ipchandle, memory.size, source_info,
+                            offset=offset)
+
+       def get_memory_info(self):
+           # Returns a tuple of (free, total) using RMM functionality.
+           return get_info() # Function defined in rmm.py
+
+       def initialize(self):
+           # Nothing required to initialize RMM here, but this method is added
+           # to illustrate that the super() method should also be called.
+           super().initialize()
+
+       @contextmanager
+       def defer_cleanup(self):
+           # Does nothing to defer cleanup - a full implementation may choose to
+           # implement a different policy.
+           with super().defer_cleanup():
+               yield
+
+       @property
+       def interface_version(self):
+           # As required by the specification
+           return 1
+
+   # The existing _make_finalizer function is used by RMMNumbaManager:
+   def _make_finalizer(handle, stream):
+       """
+       Factory to make the finalizer function.
+       We need to bind *handle* and *stream* into the actual finalizer, which
+       takes no args.
+       """
+
+       def finalizer():
+           """
+           Invoked when the MemoryPointer is freed
+           """
+           librmm.rmm_free(handle, stream)
+
+       return finalizer
+
+   # Utility function register `RMMNumbaManager` as an EMM:
+   def use_rmm_for_numba():
+       set_memory_manager(RMMNumbaManager)
+
+   # To support `NUMBA_CUDA_MEMORY_MANAGER=rmm`:
+   _numba_memory_manager = RMMNumbaManager
+
+Example usage
+^^^^^^^^^^^^^
+
+A simple example that configures Numba to use RMM for memory management and
+creates a device array is as follows:
+
+.. code-block:: python
+
+   # example.py
+   import rmm 
+   import numpy as np
+
+   from numba import cuda
+
+   rmm.use_rmm_for_numba()
+
+   a = np.zeros(10)
+   d_a = cuda.to_device(a)
+   del(d_a)
+   print(rmm.csv_log())
+
+Running this should result in output similar to the following:
+
+.. code-block::
+
+   Event Type,Device ID,Address,Stream,Size (bytes),Free Memory,Total Memory,Current Allocs,Start,End,Elapsed,Location
+   Alloc,0,0x7fae06600000,0,80,0,0,1,1.10549,1.1074,0.00191666,<path>/numba/numba/cuda/cudadrv/driver.py:683
+   Free,0,0x7fae06600000,0,0,0,0,0,1.10798,1.10921,0.00122238,<path>/numba/numba/utils.py:678
+
+Note that there is some scope for improvement in RMM for detecting the line
+number at which the allocation / free occurred, but this is outside the scope of
+the example in this proposal.
+
+Setting the memory manager through the environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Rather than calling ``rmm.use_rmm_for_numba()`` in the example above, the memory
+manager could also be set to use RMM globally with an environment variable, so
+the Python interpreter is invoked to run the example as:
+
+.. code-block::
+
+   NUMBA_CUDA_MEMORY_MANAGER="rmm.RMMNumbaManager" python example.py
+
+Numba internal changes
+----------------------
+
+This section is intended primarily for Numba developers - those with an interest
+in the external interface for implementing EMM plugins may choose to skip over
+this section.
+
+Current model / implementation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+At present, memory management is implemented in the
+:class:`~numba.cuda.cudadrv.driver.Context` class. It maintains lists of
+allocations and deallocations:
+
+* ``allocations`` is a ``numba.core.utils.UniqueDict``, created at context
+  creation time.
+* ``deallocations`` is an instance of the ``_PendingDeallocs`` class, and is created
+  when ``Context.prepare_for_use()`` is called.
+
+These are used to track allocations and deallocations of:
+
+* Device memory
+* Pinned memory
+* Mapped memory
+* Streams
+* Events
+* Modules
+
+The ``_PendingDeallocs`` class implements the deferred deallocation strategy -
+cleanup functions (such as ``cuMemFree``\ ) for the items above are added to its
+list of pending deallocations by the finalizers of objects representing
+allocations. These finalizers are run when the objects owning them are
+garbage-collected by the Python interpreter. When the addition of a new
+cleanup function to the deallocation list causes the number or size of pending
+deallocations to exceed a configured ratio, the ``_PendingDeallocs`` object runs
+deallocators for all items it knows about and then clears its internal pending
+list.
+
+See :ref:`deallocation-behavior` for more details of this implementation.
+
+Proposed changes
+^^^^^^^^^^^^^^^^
+
+This section outlines the major changes that will be made to support the EMM
+plugin interface - there will be various small changes to other parts of Numba
+that will be required in order to adapt to these changes; an exhaustive list of
+these is not provided.
+
+Context changes
+~~~~~~~~~~~~~~~
+
+The ``numba.cuda.cudadrv.driver.Context`` class will no longer directly allocate
+and free memory. Instead, the context will hold a reference to a memory manager
+instance, and its memory allocation methods will call into the memory manager,
+e.g.:
+
+.. code-block:: python
+
+       def memalloc(self, size):
+           return self.memory_manager.memalloc(size)
+
+       def memhostalloc(self, size, mapped=False, portable=False, wc=False):
+           return self.memory_manager.memhostalloc(size, mapped, portable, wc)
+
+       def mempin(self, owner, pointer, size, mapped=False):
+           if mapped and not self.device.CAN_MAP_HOST_MEMORY:
+               raise CudaDriverError("%s cannot map host memory" % self.device)
+           return self.memory_manager.mempin(owner, pointer, size, mapped)
+
+       def prepare_for_use(self):
+           self.memory_manager.initialize()
+
+       def get_memory_info(self):
+           self.memory_manager.get_memory_info()
+
+       def get_ipc_handle(self, memory):
+           return self.memory_manager.get_ipc_handle(memory)
+
+       def reset(self):
+           # ... Already-extant reset logic, plus:
+           self._memory_manager.reset()
+
+The ``memory_manager`` member is initialised when the context is created.
+
+The ``memunpin`` method (not shown above but currently exists in the ``Context``
+class) has never been implemented - it presently raises a ``NotImplementedError``.
+This method arguably un-needed - pinned memory is immediately unpinned by its
+finalizer, and unpinning before a finalizer runs would invalidate the state of
+``PinnedMemory`` objects for which references are still held. It is proposed that
+this is removed when making the other changes to the ``Context`` class.
+
+The ``Context`` class will still instantiate ``self.allocations`` and
+``self.deallocations`` as before - these will still be used by the context to
+manage the allocations and deallocations of events, streams, and modules, which
+are not handled by the EMM plugin.
+
+New components of the ``driver`` module
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+* ``BaseCUDAMemoryManager``\ : An abstract class, as defined in the plugin interface
+  above.
+* ``HostOnlyCUDAMemoryManager``\ : A subclass of ``BaseCUDAMemoryManager``\ , with the
+  logic from ``Context.memhostalloc`` and ``Context.mempin`` moved into it. This
+  class will also create its own ``allocations`` and ``deallocations`` members,
+  similarly to how the ``Context`` class creates them. These are used to manage
+  the allocations and deallocations of pinned and mapped host memory.
+* ``NumbaCUDAMemoryManager``\ : A subclass of ``HostOnlyCUDAMemoryManager``\ , which
+  also contains an implementation of ``memalloc`` based on that presently existing
+  in the ``Context`` class. This is the default memory manager, and its use
+  preserves the behaviour of Numba prior to the addition of the EMM plugin
+  interface - that is, all memory allocation and deallocation for Numba arrays
+  is handled within Numba.
+
+  * This class shares the ``allocations`` and ``deallocations`` members with its
+    parent class ``HostOnlyCUDAMemoryManager``\ , and it uses these for the
+    management of device memory that it allocates.
+
+* The ``set_memory_manager`` function, which sets a global pointing to the memory
+  manager class. This global initially holds ``NumbaCUDAMemoryManager`` (the
+  default).
+
+Staged IPC
+~~~~~~~~~~
+
+Staged IPC should not take ownership of the memory that it allocates. When the
+default internal memory manager is in use, the memory allocated for the staging
+array is already owned. When an EMM plugin is in use, it is not legitimate to
+take ownership of the memory.
+
+This change can be made by applying the following small patch, which has been
+tested to have no effect on the CUDA test suite:
+
+.. code-block:: diff
+
+   diff --git a/numba/cuda/cudadrv/driver.py b/numba/cuda/cudadrv/driver.py
+   index 7832955..f2c1352 100644
+   --- a/numba/cuda/cudadrv/driver.py
+   +++ b/numba/cuda/cudadrv/driver.py
+   @@ -922,7 +922,11 @@ class _StagedIpcImpl(object):
+            with cuda.gpus[srcdev.id]:
+                impl.close()
+
+   -        return newmem.own()
+   +        return newmem
+
+Testing
+~~~~~~~
+
+Alongside the addition of appropriate tests for new functionality, there will be
+some refactoring of existing tests required, but these changes are not
+substantial. Tests of the deallocation strategy (e.g. ``TestDeallocation``\ ,
+``TestDeferCleanup``\ ) will need to be modified to ensure that they are
+examining the correct set of deallocations. When an EMM plugin is in use, they
+will need to be skipped.
+
+Prototyping / experimental implementation
+-----------------------------------------
+
+Some prototype / experimental implementations have been produced to guide the
+designs presented in this document. The current implementations can be found in:
+
+
+* Numba branch: https://github.com/gmarkall/numba/tree/grm-numba-nbep-7.
+* RMM branch: https://github.com/gmarkall/rmm/tree/grm-numba-nbep-7.
+* CuPy implementation:
+  https://github.com/gmarkall/nbep-7/blob/master/nbep7/cupy_mempool.py - uses
+  an unmodified CuPy.
+
+  * See `CuPy memory management
+    docs <https://docs-cupy.chainer.org/en/stable/reference/memory.html>`_.
+
+Current implementation status
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+RMM Plugin
+~~~~~~~~~~
+
+For a minimal example, a simple allocation and free using RMM works as expected.
+For the example code (similar to the RMM example above):
+
+.. code-block:: python
+
+   import rmm
+   import numpy as np
+
+   from numba import cuda
+
+   rmm.use_rmm_for_numba()
+
+   a = np.zeros(10)
+   d_a = cuda.to_device(a)
+   del(d_a)
+   print(rmm.csv_log())
+
+We see the following output:
+
+.. code-block::
+
+   Event Type,Device ID,Address,Stream,Size (bytes),Free Memory,Total Memory,Current Allocs,Start,End,Elapsed,Location
+   Alloc,0,0x7f96c7400000,0,80,0,0,1,1.13396,1.13576,0.00180059,<path>/numba/numba/cuda/cudadrv/driver.py:686
+   Free,0,0x7f96c7400000,0,0,0,0,0,1.13628,1.13723,0.000956004,<path>/numba/numba/utils.py:678
+
+This output is similar to the expected output from the example usage presented
+above (though note that the pointer addresses and timestamps vary compared to
+the example), and provides some validation of the example use case.
+
+CuPy Plugin
+~~~~~~~~~~~
+
+.. code-block:: python
+
+   from nbep7.cupy_mempool import use_cupy_mm_for_numba
+   import numpy as np
+
+   from numba import cuda
+
+   use_cupy_mm_for_numba()
+
+   a = np.zeros(10)
+   d_a = cuda.to_device(a)
+   del(d_a)
+
+The prototype CuPy plugin has somewhat primitive logging, so we see the output:
+
+.. code-block::
+
+   Allocated 80 bytes at 7f004d400000
+   Freeing 80 bytes at 7f004d400000
+
+Numba CUDA Unit tests
+^^^^^^^^^^^^^^^^^^^^^
+
+As well as providing correct execution of a simple example, all relevant Numba
+CUDA unit tests also pass with the prototype branch, for both the internal memory
+manager and the RMM EMM Plugin.
+
+RMM
+~~~
+
+The unit test suite can be run with the RMM EMM Plugin with:
+
+.. code-block::
+
+   NUMBA_CUDA_MEMORY_MANAGER=rmm python -m numba.runtests numba.cuda.tests
+
+A summary of the unit test suite output is:
+
+.. code-block::
+
+   Ran 564 tests in 142.211s
+
+   OK (skipped=11)
+
+When running with the built-in Numba memory management, the output is:
+
+.. code-block::
+
+   Ran 564 tests in 133.396s
+
+   OK (skipped=5)
+
+i.e. the changes for using an external memory manager do not break the built-in
+Numba memory management. There are an additional 6 skipped tests, from:
+
+
+* ``TestDeallocation``\ : skipped as it specifically tests Numba's internal
+  deallocation strategy.
+* ``TestDeferCleanup``\ : skipped as it specifically tests Numba's implementation of
+  deferred cleanup.
+* ``TestCudaArrayInterface.test_ownership``\ : skipped as Numba does not own memory
+  when an EMM Plugin is used, but ownership is assumed by this test case.
+
+CuPy
+~~~~
+
+The test suite can be run with the CuPy plugin using:
+
+.. code-block::
+
+   NUMBA_CUDA_MEMORY_MANAGER=nbep7.cupy_mempool python -m numba.runtests numba.cuda.tests
+
+This plugin implementation is presently more primitive than the RMM
+implementation, and results in some errors with the unit test suite:
+
+.. code-block::
+
+   Ran 564 tests in 111.699s
+
+   FAILED (errors=8, skipped=11)
+
+The 8 errors are due to a lack of implementation of ``get_ipc_handle`` in the
+CuPy EMM Plugin implementation. It is expected that this implementation will be
+re-visited and completed so that CuPy can be used stably as an allocator for
+Numba in the future.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/index.rst
new file mode 100644
index 000000000..33efe8d53
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/index.rst
@@ -0,0 +1,35 @@
+===========================
+Numba Enhancement Proposals
+===========================
+
+Numba Enhancement Proposals (not really abbreviated "NEPs", since "NEP"
+is already taken by the Numpy project) describe proposed changes to Numba.
+They are modeled on Python Enhancement Proposals (PEPs) and Numpy Enhancement
+Proposals, and are typically written up when important changes
+(behavioural changes, feature additions...) to Numba are proposed.
+
+This page provides an overview of all proposals, making only a distinction
+between the ones that have been implemented and those that have not been
+implemented.
+
+Implemented proposals
+---------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   integer-typing.rst
+   external-memory-management.rst
+
+Other proposals
+---------------
+
+.. toctree::
+   :maxdepth: 1
+
+   extension-points.rst
+   jit-classes.rst
+   cfunc.rst
+   type-inference.rst
+   typing_recursion.rst
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/integer-typing.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/integer-typing.rst
new file mode 100644
index 000000000..9093d7e65
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/integer-typing.rst
@@ -0,0 +1,186 @@
+.. _nbep-1:
+
+=================================
+NBEP 1: Changes in integer typing
+=================================
+
+:Author: Antoine Pitrou
+:Date: July 2015
+:Status: Final
+
+
+Current semantics
+=================
+
+Type inference of integers in Numba currently has some subtleties
+and some corner cases.  The simple case is when some variable has an obvious
+Numba type (for example because it is the result of a constructor call to a
+Numpy scalar type such as ``np.int64``). That case suffers no ambiguity.
+
+The less simple case is when a variable doesn't bear such explicit
+information.  This can happen because it is inferred from a built-in Python
+``int`` value, or from an arithmetic operation between two integers, or
+other cases yet.  Then Numba has a number of rules to infer the resulting
+Numba type, especially its signedness and bitwidth.
+
+Currently, the generic case could be summarized as: *start small,
+grow bigger as required*.  Concretely:
+
+1. Each constant or pseudo-constant is inferred using the *smallest signed
+   integer type* that can correctly represent it (or, possibly, ``uint64``
+   for positive integers between ``2**63`` and ``2**64 - 1``).
+2. The result of an operation is typed so as to ensure safe representation
+   in the face of overflow and other magnitude increases (for example,
+   ``int32 + int32`` would be typed ``int64``).
+3. As an exception, a Python ``int`` used as function argument is always
+   typed ``intp``, a pointer-size integer.  This is to avoid the proliferation
+   of compiled specializations, as otherwise various integer bitwidths
+   in input arguments may produce multiple signatures.
+
+.. note::
+   The second rule above (the "respect magnitude increases" rule)
+   reproduces Numpy's behaviour with arithmetic on scalar values.
+   Numba, however, has different implementation and performance constraints
+   than Numpy scalars.
+
+   It is worth nothing, by the way, that Numpy arrays do not implement
+   said rule (i.e. ``array(int32) + array(int32)`` is typed ``array(int32)``,
+   not ``array(int64)``).  Probably because this makes performance more
+   controllable.
+
+This has several non-obvious side-effects:
+
+1. It is difficult to predict the precise type of a value inside a function,
+   after several operations.  The basic operands in an expression tree
+   may for example be ``int8`` but the end result may be ``int64``.  Whether
+   this is desirable or not is an open question; it is good for correctness,
+   but potentially bad for performance.
+
+2. In trying to follow the correctness over predictability rule, some values
+   can actually leave the integer realm.  For example, ``int64 + uint64``
+   is typed ``float64`` in order to avoid magnitude losses (but incidentally
+   will lose precision on large integer values...), again following Numpy's
+   semantics for scalars.  This is usually not intended by the user.
+
+3. More complicated scenarios can produce unexpected errors at the type unification
+   stage.  An example is at `Github issue 1299 <https://github.com/numba/numba/issues/1299>`_,
+   the gist of which is reproduced here::
+
+      @jit(nopython=True)
+      def f():
+          variable = 0
+          for i in range(1):
+              variable = variable + 1
+          return np.arange(variable)
+
+   At the time of this writing, this fails compiling, on a 64-bit system,
+   with the error::
+
+      numba.errors.TypingError: Failed at nopython (nopython frontend)
+      Can't unify types of variable '$48.4': $48.4 := {array(int32, 1d, C), array(int64, 1d, C)}
+
+   People expert with Numba's type unification system can understand why.
+   But the user is caught in mystery.
+
+
+Proposal: predictable width-conserving typing
+=============================================
+
+We propose to turn the current typing philosophy on its head.  Instead
+of "*start small and grow as required*", we propose "*start big and keep
+the width unchanged*".
+
+Concretely:
+
+1. The typing of Python ``int`` values used as function arguments doesn't
+   change, as it works satisfyingly and doesn't surprise the user.
+
+2. The typing of integer *constants* (and pseudo-constants) changes to match
+   the typing of integer arguments.  That is, every non-explicitly typed
+   integer constant is typed ``intp``, the pointer-sized integer; except for
+   the rare cases where ``int64`` (on 32-bit systems) or ``uint64`` is
+   required.
+
+3. Operations on integers promote bitwidth to ``intp``, if smaller, otherwise
+   they don't promote.  For example, on a 32-bit machine, ``int8 + int8``
+   is typed ``int32``, as is ``int32 + int32``.  However, ``int64 + int64``
+   is typed ``int64``.
+
+4. Furthermore, mixed operations between signed and unsigned fall back to
+   signed, while following the same bitwidth rule.  For example, on a
+   32-bit machine, ``int8 + uint16`` is typed ``int32``, as is
+   ``uint32 + int32``.
+
+
+Proposal impact
+===============
+
+Semantics
+---------
+
+With this proposal, the semantics become clearer.  Regardless of whether
+the arguments and constants of a function were explicitly typed or not,
+the results of various expressions at any point in the function have
+easily predictable types.
+
+When using built-in Python ``int``, the user gets acceptable magnitude
+(32 or 64 bits depending on the system's bitness), and the type remains
+the same across all computations.
+
+When explicitly using smaller bitwidths, intermediate results don't
+suffer from magnitude loss, since their bitwidth is promoted to ``intp``.
+
+There is also less potential for annoyances with the type unification
+system as demonstrated above.  The user would have to force several
+different types to be faced with such an error.
+
+One potential cause for concern is the discrepancy with Numpy's scalar
+semantics; but at the same time this brings Numba scalar semantics closer
+to array semantics (both Numba's and Numpy's), which seems a desirable
+outcome as well.
+
+It is worth pointing out that some sources of integer numbers, such
+as the ``range()`` built-in, always yield 32-bit integers or larger.
+This proposal could be an opportunity to standardize them on ``intp``.
+
+Performance
+-----------
+
+Except in trivial cases, it seems unlikely that the current "best fit"
+behaviour for integer constants really brings a performance benefit.  After
+all, most integers in Numba code would either be stored in arrays (with
+well-known types, chosen by the user) or be used as indices, where a ``int8``
+is highly unlikely to fare better than a ``intp`` (actually, it may be worse,
+if LLVM isn't able to optimize away the required sign-extension).
+
+As a side note, the default use of ``intp`` rather than ``int64``
+ensures that 32-bit systems won't suffer from poor arithmetic performance.
+
+Implementation
+--------------
+
+Optimistically, this proposal may simplify some Numba internals a bit.
+Or, at least, it doesn't threaten to make them significantly more complicated.
+
+Limitations
+-----------
+
+This proposal doesn't really solve the combination of signed and unsigned
+integers.  It is geared mostly at solving the bitwidth issues, which are
+a somewhat common cause of pain for users.  Unsigned integers are in
+practice very uncommon in Numba-compiled code, except when explicitly
+asked for, and therefore much less of a pain point.
+
+On the bitwidth front, 32-bit systems could still show discrepancies based
+on the values of constants: if a constant is too large to fit in 32 bits,
+it is typed ``int64``, which propagates through other computations.
+This would be a reminiscence of the current behaviour, but rarer and much
+more controlled still.
+
+Long-term horizon
+-----------------
+
+While we believe this proposal makes Numba's behaviour more regular and more
+predictable, it also pulls it further from general compatibility with pure
+Python semantics, where users can assume arbitrary-precision integers without
+any truncation issues.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/jit-classes.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/jit-classes.rst
new file mode 100644
index 000000000..2cd33d42c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/jit-classes.rst
@@ -0,0 +1,231 @@
+===================
+NBEP 3: JIT Classes
+===================
+
+:Author: Siu Kwan Lam
+:Date: Dec 2015
+:Status: Draft
+
+Introduction
+============
+
+Numba does not yet support user-defined classes.
+Classes provide useful abstraction and promote modularity when used
+right.  In the simplest sense, a class specifies the set of data and
+operations as attributes and methods, respectively.
+A class instance is an instantiation of that class.
+This proposal will focus on supporting this simple usecase of classes--with
+just attributes and methods.  Other features, such as class methods, static
+methods, and inheritance are deferred to another proposal, but we believe
+these features can be easily implemented given the foundation described here.
+
+
+Proposal: jit-classes
+=====================
+
+A JIT-classes is more restricted than a Python class.
+We will focus on the following operations on a class and its instance:
+
+* Instantiation: create an instance of a class using the class object as the
+  constructor: ``cls(*args, **kwargs)``
+* Destruction: remove resources allocated during instantiation and release
+  all references to other objects.
+* Attribute access: loading and storing attributes using ``instance.attr``
+  syntax.
+* Method access: loading methods using ``instance.method`` syntax.
+
+With these operations, a class object (not the instance) does not need to be
+materialize. Using the class object as a constructor is fully resolved (a
+runtime implementation is picked) during the typing phase in the compiler.
+This means **a class object will not be first class**.  On the other hand,
+implementing a first-class class object will require an "interface" type,
+or the type of class.
+
+The instantiation of a class will allocate resources for storing the data
+attributes.  This is described in the "Storage model" section.  Methods are
+never stored in the instance.  They are information attached to the class.
+Since a class object only exists in the type domain, the methods will also be
+fully resolved at the typing phase.  Again, numba do not have first-class
+function value and each function type maps uniquely to each function
+implementation (this needs to be changed to support function value as argument).
+
+A class instance can contain other NRT reference-counted object as attributes.
+To properly clean up an instance, a destructor is called when the reference
+count of the instance is dropped to zero.  This is described in the
+"Reference count and descructor" section.
+
+Storage model
+~~~~~~~~~~~~~
+
+For compatibility with C, attributes are stored in a simple plain-old-data
+structure.  Each attribute are stored in a user-defined order in a padded
+(for proper alignment), contiguous memory region. An instance that contains
+three fields of int32, float32, complex64 will be compatible with the following
+C structure::
+
+    struct {
+        int32     field0;
+        float32   field1;
+        complex64 field2;
+    };
+
+This will also be compatible with an aligned NumPy structured dtype.
+
+
+Methods
+~~~~~~~
+
+Methods are regular function that can be bounded to an instance.
+They can be compiled as regular function by numba.
+The operation ``getattr(instance, name)`` (getting an attribute ``name`` from
+``instance``) binds the instance to the requested method at runtime.
+
+
+The special ``__init__`` method is also handled like regular functions.
+
+
+``__del__`` is not supported at this time.
+
+
+Reference count and destructor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+An instance of jit-class is reference-counted by NRT. Since it may contain
+other NRT tracked object, it must call a destructor when its reference count
+dropped to zero.  The destructor will decrement the reference count of all
+attributes by one.
+
+At this time, there is no support for user defined ``__del__`` method.
+
+Proper cleanup for cyclic reference is not handled at this time.
+Cycles will cause memory leak.
+
+Type inference
+~~~~~~~~~~~~~~
+
+So far we have not described the type of the attributes or the methods.
+Type information is necessary to materailize the instance (e.g. allocate the
+storage).  The simplest way is to let user provide the type of each attributes
+as well as the ordering; for instance::
+
+    dct = OrderedDict()
+    dct['x'] = int32
+    dct['y'] = float32
+
+Allowing user to supply an ordered dictionary will provide the name, ordering
+and types of the attributes.  However, this statically typed semantic is not as
+flexible as the Python semantic which behaves like a generic class.
+
+Inferring the type of attributes is difficult.  In a previous attempt to
+implement JIT classes, the ``__init__`` method is specialized to capture
+the type stored into the attributes.  Since the method can contain arbitrary
+logic, the problem can become a dependent typing problem if types are assigned
+conditionally depending on the value. (Very few languages implement dependent
+typing and those that does are mostly theorem provers.)
+
+Example: typing function using an OrderedDict
+---------------------------------------------
+
+.. code-block:: python
+
+    spec = OrderedDict()
+    spec['x'] = numba.int32
+    spec['y'] = numba.float32
+
+    @jitclass(spec)
+    class Vec(object):
+        def __init__(self, x, y):
+            self.x = x
+            self.y = y
+
+        def add(self, dx, dy):
+            self.x += dx
+            self.y += dy
+
+Example: typing function using a list of 2-tuples
+-------------------------------------------------
+
+.. code-block:: python
+
+    spec = [('x', numba.int32),
+            ('y', numba.float32)]
+
+    @jitclass(spec)
+    class Vec(object):
+        ...
+
+Creating multiple jitclasses from a single class object
+-------------------------------------------------------
+
+The `jitclass(spec)` decorator creates a new jitclass type even when applied to
+the same class object and the same type specification.
+
+.. code-block:: python
+
+    class Vec(object):
+      ...
+
+    Vec1 = jitclass(spec)(Vec)
+    Vec2 = jitclass(spec)(Vec)
+    # Vec1 and Vec2 are two different jitclass types
+
+Usage from the Interpreter
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When constructing a new instance of a jitclass, a "box" is created that wraps
+the underlying jitclass instance from numba.  Attributes and methods are
+accessible from the interpreter.  The actual implementation will be in numba
+compiled code.  Any Python object is converted to its native
+representation for consumption in numba.  Similarly, the returned value is
+converted to its Python representation.  As a result, there may be overhead in
+manipulating jitclass instances in the interpreter.  This overhead is minimal
+and should be easily amortized by more efficient computation in the compiled
+methods.
+
+Support for property, staticmethod and classmethod
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The use of ``property`` is accepted for getter and setter only.  Deleter is not
+supported.
+
+The use of ``staticmethod`` is not supported.
+
+The use of ``classmethod`` is not supported.
+
+Inheritance
+~~~~~~~~~~~
+
+Class inhertance is not considered in this proposal.  The only accepted base
+class for a jitclass is `object`.
+
+Supported targets
+~~~~~~~~~~~~~~~~~~
+
+Only the CPU target (including the parallel target) is supported.
+GPUs (e.g. CUDA and HSA) targets are supported via an immutable version of the
+jitclass instance, which will be described in a separate NBEP.
+
+
+Other properties
+~~~~~~~~~~~~~~~~
+
+Given:
+
+.. code-block:: python
+
+    spec = [('x', numba.int32),
+            ('y', numba.float32)]
+
+    @jitclass(spec)
+    class Vec(object):
+        ...
+
+* ``isinstance(Vec(1, 2), Vec)`` is True.
+* ``type(Vec(1, 2))`` may not be ``Vec``.
+
+Future enhancements
+~~~~~~~~~~~~~~~~~~~
+
+This proposal has only described the basic semantic and functionality of a
+jitclass.  Additional features will be described in future enhancement
+proposals.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/np-where-override.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/np-where-override.py
new file mode 100644
index 000000000..109ba6a43
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/np-where-override.py
@@ -0,0 +1,51 @@
+import numpy as np
+
+from numba.core import types
+from numba.extending import overload
+
+@overload(np.where)
+def where(cond, x, y):
+    """
+    Implement np.where().
+    """
+    # Choose implementation based on argument types.
+    if isinstance(cond, types.Array):
+        # Array where() => return an array of the same shape
+        if all(ty.layout == 'C' for ty in (cond, x, y)):
+            def where_impl(cond, x, y):
+                """
+                Fast implementation for C-contiguous arrays
+                """
+                shape = cond.shape
+                if x.shape != shape or y.shape != shape:
+                    raise ValueError("all inputs should have the same shape")
+                res = np.empty_like(x)
+                cf = cond.flat
+                xf = x.flat
+                yf = y.flat
+                rf = res.flat
+                for i in range(cond.size):
+                    rf[i] = xf[i] if cf[i] else yf[i]
+                return res
+        else:
+            def where_impl(cond, x, y):
+                """
+                Generic implementation for other arrays
+                """
+                shape = cond.shape
+                if x.shape != shape or y.shape != shape:
+                    raise ValueError("all inputs should have the same shape")
+                res = np.empty_like(x)
+                for idx, c in np.ndenumerate(cond):
+                    res[idx] = x[idx] if c else y[idx]
+                return res
+
+    else:
+        def where_impl(cond, x, y):
+            """
+            Scalar where() => return a 0-dim array
+            """
+            scal = x if cond else y
+            return np.full_like(scal, scal)
+
+    return where_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/recursion_callstack.svg b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/recursion_callstack.svg
new file mode 100644
index 000000000..7e23f30d5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/recursion_callstack.svg
@@ -0,0 +1,4 @@
+<?xml version="1.0" standalone="yes"?>
+
+<svg version="1.1" viewBox="0.0 0.0 504.17847769028873 133.43307086614172" fill="none" stroke="none" stroke-linecap="square" stroke-miterlimit="10" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><clipPath id="p.0"><path d="m0 0l504.17847 0l0 133.43307l-504.17847 0l0 -133.43307z" clip-rule="nonzero"></path></clipPath><g clip-path="url(#p.0)"><path fill="#000000" fill-opacity="0.0" d="m0 0l504.17847 0l0 133.43307l-504.17847 0z" fill-rule="nonzero"></path><path fill="#cfe2f3" d="m135.38583 36.97638l133.79526 0l0 36.97638l-133.79526 0z" fill-rule="nonzero"></path><path stroke="#000000" stroke-width="1.0" stroke-linejoin="round" stroke-linecap="butt" d="m135.38583 36.97638l133.79526 0l0 36.97638l-133.79526 0z" fill-rule="nonzero"></path><path fill="#000000" d="m174.90775 62.384567l0 -8.546875l-1.484375 0l0 -1.3125l1.484375 0l0 -1.046875q0 -0.984375 0.171875 -1.46875q0.234375 -0.65625 0.84375 -1.046875q0.609375 -0.40625 1.703125 -0.40625q0.703125 0 1.5625 0.15625l-0.25 1.46875q-0.515625 -0.09375 -0.984375 -0.09375q-0.765625 0 -1.078125 0.328125q-0.3125 0.3125 -0.3125 1.203125l0 0.90625l1.921875 0l0 1.3125l-1.921875 0l0 8.546875l-1.65625 0zm4.152054 -4.921875q0 -2.734375 1.53125 -4.0625q1.265625 -1.09375 3.09375 -1.09375q2.03125 0 3.3125 1.34375q1.296875 1.328125 1.296875 3.671875q0 1.90625 -0.578125 3.0q-0.5625 1.078125 -1.65625 1.6875q-1.078125 0.59375 -2.375 0.59375q-2.0625 0 -3.34375 -1.328125q-1.28125 -1.328125 -1.28125 -3.8125zm1.71875 0q0 1.890625 0.828125 2.828125q0.828125 0.9375 2.078125 0.9375q1.25 0 2.0625 -0.9375q0.828125 -0.953125 0.828125 -2.890625q0 -1.828125 -0.828125 -2.765625q-0.828125 -0.9375 -2.0625 -0.9375q-1.25 0 -2.078125 0.9375q-0.828125 0.9375 -0.828125 2.828125zm8.656967 0q0 -2.734375 1.53125 -4.0625q1.265625 -1.09375 3.09375 -1.09375q2.03125 0 3.3125 1.34375q1.296875 1.328125 1.296875 3.671875q0 1.90625 -0.578125 3.0q-0.5625 1.078125 -1.65625 1.6875q-1.078125 0.59375 -2.375 0.59375q-2.0625 0 -3.34375 -1.328125q-1.28125 -1.328125 -1.28125 -3.8125zm1.71875 0q0 1.890625 0.828125 2.828125q0.828125 0.9375 2.078125 0.9375q1.25 0 2.0625 -0.9375q0.828125 -0.953125 0.828125 -2.890625q0 -1.828125 -0.828125 -2.765625q-0.828125 -0.9375 -2.0625 -0.9375q-1.25 0 -2.078125 0.9375q-0.828125 0.9375 -0.828125 2.828125zm12.469467 8.921875q-1.375 -1.75 -2.328125 -4.078125q-0.953125 -2.34375 -0.953125 -4.84375q0 -2.21875 0.703125 -4.234375q0.84375 -2.34375 2.578125 -4.671875l1.203125 0q-1.125 1.921875 -1.484375 2.75q-0.5625 1.28125 -0.890625 2.671875q-0.40625 1.734375 -0.40625 3.484375q0 4.46875 2.78125 8.921875l-1.203125 0zm3.040802 -15.6875l0 -1.90625l1.671875 0l0 1.90625l-1.671875 0zm0 11.6875l0 -9.859375l1.671875 0l0 9.859375l-1.671875 0zm4.129196 0l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm14.031967 -1.5l0.234375 1.484375q-0.703125 0.140625 -1.265625 0.140625q-0.90625 0 -1.40625 -0.28125q-0.5 -0.296875 -0.703125 -0.75q-0.203125 -0.46875 -0.203125 -1.984375l0 -5.65625l-1.234375 0l0 -1.3125l1.234375 0l0 -2.4375l1.65625 -1.0l0 3.4375l1.6875 0l0 1.3125l-1.6875 0l0 5.75q0 0.71875 0.078125 0.921875q0.09375 0.203125 0.296875 0.328125q0.203125 0.125 0.578125 0.125q0.265625 0 0.734375 -0.078125zm2.6208038 5.5l-1.1875 0q2.765625 -4.453125 2.765625 -8.921875q0 -1.734375 -0.390625 -3.453125q-0.328125 -1.390625 -0.890625 -2.671875q-0.359375 -0.84375 -1.484375 -2.78125l1.1875 0q1.75 2.328125 2.578125 4.671875q0.71875 2.015625 0.71875 4.234375q0 2.5 -0.96875 4.84375q-0.953125 2.328125 -2.328125 4.078125z" fill-rule="nonzero"></path><path fill="#cfe2f3" d="m135.38583 73.95276l133.79526 0l0 36.97637l-133.79526 0z" fill-rule="nonzero"></path><path stroke="#000000" stroke-width="1.0" stroke-linejoin="round" stroke-linecap="butt" d="m135.38583 73.95276l133.79526 0l0 36.97637l-133.79526 0z" fill-rule="nonzero"></path><path fill="#000000" d="m171.40883 99.36095l0 -13.59375l2.609375 0l0 4.890625q1.203125 -1.375 2.859375 -1.375q1.796875 0 2.96875 1.3125q1.1875 1.296875 1.1875 3.734375q0 2.53125 -1.203125 3.890625q-1.203125 1.359375 -2.921875 1.359375q-0.84375 0 -1.671875 -0.421875q-0.8125 -0.421875 -1.40625 -1.25l0 1.453125l-2.421875 0zm2.59375 -5.140625q0 1.53125 0.484375 2.265625q0.671875 1.03125 1.796875 1.03125q0.859375 0 1.46875 -0.734375q0.609375 -0.734375 0.609375 -2.328125q0 -1.6875 -0.609375 -2.421875q-0.609375 -0.75 -1.578125 -0.75q-0.9375 0 -1.5625 0.734375q-0.609375 0.71875 -0.609375 2.203125zm10.864731 -1.703125l-2.359375 -0.4375q0.390625 -1.421875 1.359375 -2.109375q0.984375 -0.6875 2.90625 -0.6875q1.734375 0 2.59375 0.421875q0.859375 0.40625 1.203125 1.046875q0.34375 0.625 0.34375 2.328125l-0.03125 3.046875q0 1.296875 0.125 1.921875q0.125 0.609375 0.46875 1.3125l-2.578125 0q-0.09375 -0.265625 -0.25 -0.765625q-0.0625 -0.234375 -0.09375 -0.3125q-0.65625 0.65625 -1.421875 0.984375q-0.765625 0.3125 -1.625 0.3125q-1.515625 0 -2.40625 -0.8125q-0.875 -0.828125 -0.875 -2.09375q0 -0.84375 0.390625 -1.484375q0.40625 -0.65625 1.125 -1.0q0.71875 -0.359375 2.078125 -0.625q1.828125 -0.328125 2.53125 -0.625l0 -0.265625q0 -0.75 -0.375 -1.0625q-0.359375 -0.328125 -1.390625 -0.328125q-0.703125 0 -1.09375 0.28125q-0.390625 0.265625 -0.625 0.953125zm3.484375 2.109375q-0.5 0.171875 -1.59375 0.40625q-1.078125 0.234375 -1.40625 0.453125q-0.515625 0.359375 -0.515625 0.921875q0 0.546875 0.40625 0.953125q0.40625 0.390625 1.046875 0.390625q0.703125 0 1.34375 -0.46875q0.46875 -0.359375 0.625 -0.859375q0.09375 -0.34375 0.09375 -1.28125l0 -0.515625zm7.438217 4.734375l-2.609375 0l0 -9.859375l2.421875 0l0 1.40625q0.625 -0.984375 1.109375 -1.296875q0.5 -0.328125 1.140625 -0.328125q0.890625 0 1.71875 0.5l-0.8125 2.265625q-0.65625 -0.421875 -1.21875 -0.421875q-0.546875 0 -0.9375 0.296875q-0.375 0.296875 -0.59375 1.09375q-0.21875 0.78125 -0.21875 3.296875l0 3.046875zm9.088394 4.0l-1.796875 0q-1.40625 -2.140625 -2.15625 -4.453125q-0.734375 -2.3125 -0.734375 -4.46875q0 -2.6875 0.90625 -5.078125q0.796875 -2.078125 2.03125 -3.828125l1.78125 0q-1.28125 2.8125 -1.765625 4.78125q-0.46875 1.96875 -0.46875 4.171875q0 1.53125 0.28125 3.125q0.28125 1.59375 0.78125 3.03125q0.328125 0.953125 1.140625 2.71875zm1.884552 -15.1875l0 -2.40625l2.609375 0l0 2.40625l-2.609375 0zm0 11.1875l0 -9.859375l2.609375 0l0 9.859375l-2.609375 0zm14.152054 0l-2.609375 0l0 -5.03125q0 -1.59375 -0.171875 -2.0625q-0.15625 -0.46875 -0.53125 -0.71875q-0.375 -0.265625 -0.90625 -0.265625q-0.6875 0 -1.234375 0.375q-0.53125 0.359375 -0.734375 0.984375q-0.1875 0.609375 -0.1875 2.25l0 4.46875l-2.609375 0l0 -9.859375l2.421875 0l0 1.453125q1.296875 -1.671875 3.25 -1.671875q0.859375 0 1.578125 0.3125q0.71875 0.3125 1.078125 0.796875q0.359375 0.484375 0.5 1.09375q0.15625 0.609375 0.15625 1.75l0 6.125zm6.942856 -9.859375l0 2.078125l-1.78125 0l0 3.984375q0 1.203125 0.046875 1.40625q0.0625 0.1875 0.234375 0.328125q0.1875 0.125 0.453125 0.125q0.359375 0 1.046875 -0.25l0.21875 2.015625q-0.90625 0.390625 -2.0625 0.390625q-0.703125 0 -1.265625 -0.234375q-0.5625 -0.234375 -0.828125 -0.609375q-0.265625 -0.375 -0.375 -1.015625q-0.078125 -0.453125 -0.078125 -1.84375l0 -4.296875l-1.203125 0l0 -2.078125l1.203125 0l0 -1.953125l2.609375 -1.515625l0 3.46875l1.78125 0zm0.978302 13.859375q0.765625 -1.65625 1.078125 -2.546875q0.328125 -0.875 0.59375 -2.015625q0.265625 -1.15625 0.390625 -2.1875q0.140625 -1.03125 0.140625 -2.125q0 -2.203125 -0.484375 -4.171875q-0.46875 -1.96875 -1.734375 -4.78125l1.765625 0q1.40625 1.984375 2.171875 4.234375q0.78125 2.234375 0.78125 4.53125q0 1.9375 -0.609375 4.15625q-0.703125 2.484375 -2.296875 4.90625l-1.796875 0z" fill-rule="nonzero"></path><path fill="#000000" fill-opacity="0.0" d="m80.89764 0l242.77164 0l0 36.97638l-242.77164 0z" fill-rule="nonzero"></path><path fill="#000000" d="m117.55966 22.154373l1.796875 0.453125q-0.5625 2.21875 -2.03125 3.390625q-1.46875 1.15625 -3.59375 1.15625q-2.203125 0 -3.578125 -0.890625q-1.375 -0.90625 -2.09375 -2.59375q-0.71875 -1.703125 -0.71875 -3.65625q0 -2.125 0.796875 -3.703125q0.8125 -1.578125 2.3125 -2.390625q1.5 -0.828125 3.296875 -0.828125q2.046875 0 3.4375 1.046875q1.390625 1.03125 1.9375 2.90625l-1.765625 0.421875q-0.46875 -1.484375 -1.375 -2.15625q-0.90625 -0.6875 -2.265625 -0.6875q-1.5625 0 -2.625 0.75q-1.046875 0.75 -1.484375 2.03125q-0.421875 1.265625 -0.421875 2.609375q0 1.734375 0.5 3.03125q0.515625 1.28125 1.578125 1.921875q1.078125 0.640625 2.3125 0.640625q1.515625 0 2.5625 -0.859375q1.046875 -0.875 1.421875 -2.59375zm2.9260712 -0.15625q0 -2.734375 1.53125 -4.0625q1.265625 -1.09375 3.09375 -1.09375q2.03125 0 3.3125 1.34375q1.296875 1.328125 1.296875 3.671875q0 1.90625 -0.578125 3.0q-0.5625 1.078125 -1.65625 1.6875q-1.078125 0.59375 -2.375 0.59375q-2.0625 0 -3.34375 -1.328125q-1.28125 -1.328125 -1.28125 -3.8125zm1.71875 0q0 1.890625 0.828125 2.828125q0.828125 0.9375 2.078125 0.9375q1.25 0 2.0625 -0.9375q0.828125 -0.953125 0.828125 -2.890625q0 -1.828125 -0.828125 -2.765625q-0.828125 -0.9375 -2.0625 -0.9375q-1.25 0 -2.078125 0.9375q-0.828125 0.9375 -0.828125 2.828125zm9.281967 4.921875l0 -9.859375l1.5 0l0 1.390625q0.453125 -0.71875 1.21875 -1.15625q0.78125 -0.453125 1.765625 -0.453125q1.09375 0 1.796875 0.453125q0.703125 0.453125 0.984375 1.28125q1.171875 -1.734375 3.046875 -1.734375q1.46875 0 2.25 0.8125q0.796875 0.8125 0.796875 2.5l0 6.765625l-1.671875 0l0 -6.203125q0 -1.0 -0.15625 -1.4375q-0.15625 -0.453125 -0.59375 -0.71875q-0.421875 -0.265625 -1.0 -0.265625q-1.03125 0 -1.71875 0.6875q-0.6875 0.6875 -0.6875 2.21875l0 5.71875l-1.671875 0l0 -6.40625q0 -1.109375 -0.40625 -1.65625q-0.40625 -0.5625 -1.34375 -0.5625q-0.703125 0 -1.3125 0.375q-0.59375 0.359375 -0.859375 1.078125q-0.265625 0.71875 -0.265625 2.0625l0 5.109375l-1.671875 0zm15.540802 3.78125l0 -13.640625l1.53125 0l0 1.28125q0.53125 -0.75 1.203125 -1.125q0.6875 -0.375 1.640625 -0.375q1.265625 0 2.234375 0.65625q0.96875 0.640625 1.453125 1.828125q0.5 1.1875 0.5 2.59375q0 1.515625 -0.546875 2.734375q-0.546875 1.203125 -1.578125 1.84375q-1.03125 0.640625 -2.171875 0.640625q-0.84375 0 -1.515625 -0.34375q-0.65625 -0.359375 -1.078125 -0.890625l0 4.796875l-1.671875 0zm1.515625 -8.65625q0 1.90625 0.765625 2.8125q0.78125 0.90625 1.875 0.90625q1.109375 0 1.890625 -0.9375q0.796875 -0.9375 0.796875 -2.921875q0 -1.875 -0.78125 -2.8125q-0.765625 -0.9375 -1.84375 -0.9375q-1.0625 0 -1.890625 1.0q-0.8125 1.0 -0.8125 2.890625zm8.875717 -6.8125l0 -1.90625l1.671875 0l0 1.90625l-1.671875 0zm0 11.6875l0 -9.859375l1.671875 0l0 9.859375l-1.671875 0zm4.097946 0l0 -13.59375l1.671875 0l0 13.59375l-1.671875 0zm10.926071 -3.171875l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm8.469467 1.796875l0 -1.6875l5.125 0l0 1.6875l-5.125 0zm10.509552 2.578125l0.234375 1.484375q-0.703125 0.140625 -1.265625 0.140625q-0.90625 0 -1.40625 -0.28125q-0.5 -0.296875 -0.703125 -0.75q-0.203125 -0.46875 -0.203125 -1.984375l0 -5.65625l-1.234375 0l0 -1.3125l1.234375 0l0 -2.4375l1.65625 -1.0l0 3.4375l1.6875 0l0 1.3125l-1.6875 0l0 5.75q0 0.71875 0.078125 0.921875q0.09375 0.203125 0.296875 0.328125q0.203125 0.125 0.578125 0.125q0.265625 0 0.734375 -0.078125zm1.5426788 -10.1875l0 -1.90625l1.671875 0l0 1.90625l-1.671875 0zm0 11.6875l0 -9.859375l1.671875 0l0 9.859375l-1.671875 0zm4.129196 0l0 -9.859375l1.5 0l0 1.390625q0.453125 -0.71875 1.21875 -1.15625q0.78125 -0.453125 1.765625 -0.453125q1.09375 0 1.796875 0.453125q0.703125 0.453125 0.984375 1.28125q1.171875 -1.734375 3.046875 -1.734375q1.46875 0 2.25 0.8125q0.796875 0.8125 0.796875 2.5l0 6.765625l-1.671875 0l0 -6.203125q0 -1.0 -0.15625 -1.4375q-0.15625 -0.453125 -0.59375 -0.71875q-0.421875 -0.265625 -1.0 -0.265625q-1.03125 0 -1.71875 0.6875q-0.6875 0.6875 -0.6875 2.21875l0 5.71875l-1.671875 0l0 -6.40625q0 -1.109375 -0.40625 -1.65625q-0.40625 -0.5625 -1.34375 -0.5625q-0.703125 0 -1.3125 0.375q-0.59375 0.359375 -0.859375 1.078125q-0.265625 0.71875 -0.265625 2.0625l0 5.109375l-1.671875 0zm22.290802 -3.171875l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm20.730896 2.265625l1.640625 0.21875q-0.265625 1.6875 -1.375 2.65625q-1.109375 0.953125 -2.734375 0.953125q-2.015625 0 -3.25 -1.3125q-1.21875 -1.328125 -1.21875 -3.796875q0 -1.59375 0.515625 -2.78125q0.53125 -1.203125 1.609375 -1.796875q1.09375 -0.609375 2.359375 -0.609375q1.609375 0 2.625 0.8125q1.015625 0.8125 1.3125 2.3125l-1.625 0.25q-0.234375 -1.0 -0.828125 -1.5q-0.59375 -0.5 -1.421875 -0.5q-1.265625 0 -2.0625 0.90625q-0.78125 0.90625 -0.78125 2.859375q0 1.984375 0.765625 2.890625q0.765625 0.890625 1.984375 0.890625q0.984375 0 1.640625 -0.59375q0.65625 -0.609375 0.84375 -1.859375zm9.328125 2.390625q-0.9375 0.796875 -1.796875 1.125q-0.859375 0.3125 -1.84375 0.3125q-1.609375 0 -2.484375 -0.78125q-0.875 -0.796875 -0.875 -2.03125q0 -0.734375 0.328125 -1.328125q0.328125 -0.59375 0.859375 -0.953125q0.53125 -0.359375 1.203125 -0.546875q0.5 -0.140625 1.484375 -0.25q2.03125 -0.25 2.984375 -0.578125q0 -0.34375 0 -0.4375q0 -1.015625 -0.46875 -1.4375q-0.640625 -0.5625 -1.90625 -0.5625q-1.171875 0 -1.734375 0.40625q-0.5625 0.40625 -0.828125 1.46875l-1.640625 -0.234375q0.234375 -1.046875 0.734375 -1.6875q0.515625 -0.640625 1.46875 -0.984375q0.96875 -0.359375 2.25 -0.359375q1.265625 0 2.046875 0.296875q0.78125 0.296875 1.15625 0.75q0.375 0.453125 0.515625 1.140625q0.09375 0.421875 0.09375 1.53125l0 2.234375q0 2.328125 0.09375 2.953125q0.109375 0.609375 0.4375 1.171875l-1.75 0q-0.265625 -0.515625 -0.328125 -1.21875zm-0.140625 -3.71875q-0.90625 0.359375 -2.734375 0.625q-1.03125 0.140625 -1.453125 0.328125q-0.421875 0.1875 -0.65625 0.546875q-0.234375 0.359375 -0.234375 0.796875q0 0.671875 0.5 1.125q0.515625 0.4375 1.484375 0.4375q0.96875 0 1.71875 -0.421875q0.75 -0.4375 1.109375 -1.15625q0.265625 -0.578125 0.265625 -1.671875l0 -0.609375zm4.0476074 4.9375l0 -13.59375l1.671875 0l0 13.59375l-1.671875 0zm4.144806 0l0 -13.59375l1.671875 0l0 13.59375l-1.671875 0zm8.6875 -2.9375l1.65625 -0.265625q0.140625 1.0 0.765625 1.53125q0.640625 0.515625 1.78125 0.515625q1.15625 0 1.703125 -0.46875q0.5625 -0.46875 0.5625 -1.09375q0 -0.5625 -0.484375 -0.890625q-0.34375 -0.21875 -1.703125 -0.5625q-1.84375 -0.46875 -2.5625 -0.796875q-0.703125 -0.34375 -1.078125 -0.9375q-0.359375 -0.609375 -0.359375 -1.328125q0 -0.65625 0.296875 -1.21875q0.3125 -0.5625 0.828125 -0.9375q0.390625 -0.28125 1.0625 -0.484375q0.671875 -0.203125 1.4375 -0.203125q1.171875 0 2.046875 0.34375q0.875 0.328125 1.28125 0.90625q0.421875 0.5625 0.578125 1.515625l-1.625 0.21875q-0.109375 -0.75 -0.65625 -1.171875q-0.53125 -0.4375 -1.5 -0.4375q-1.15625 0 -1.640625 0.390625q-0.484375 0.375 -0.484375 0.875q0 0.328125 0.203125 0.59375q0.203125 0.265625 0.640625 0.4375q0.25 0.09375 1.46875 0.4375q1.765625 0.46875 2.46875 0.765625q0.703125 0.296875 1.09375 0.875q0.40625 0.578125 0.40625 1.4375q0 0.828125 -0.484375 1.578125q-0.484375 0.734375 -1.40625 1.140625q-0.921875 0.390625 -2.078125 0.390625q-1.921875 0 -2.9375 -0.796875q-1.0 -0.796875 -1.28125 -2.359375zm13.65625 1.4375l0.234375 1.484375q-0.703125 0.140625 -1.265625 0.140625q-0.90625 0 -1.40625 -0.28125q-0.5 -0.296875 -0.703125 -0.75q-0.203125 -0.46875 -0.203125 -1.984375l0 -5.65625l-1.234375 0l0 -1.3125l1.234375 0l0 -2.4375l1.65625 -1.0l0 3.4375l1.6875 0l0 1.3125l-1.6875 0l0 5.75q0 0.71875 0.078125 0.921875q0.09375 0.203125 0.296875 0.328125q0.203125 0.125 0.578125 0.125q0.265625 0 0.734375 -0.078125zm7.964569 0.28125q-0.9375 0.796875 -1.796875 1.125q-0.859375 0.3125 -1.84375 0.3125q-1.609375 0 -2.484375 -0.78125q-0.875 -0.796875 -0.875 -2.03125q0 -0.734375 0.328125 -1.328125q0.328125 -0.59375 0.859375 -0.953125q0.53125 -0.359375 1.203125 -0.546875q0.5 -0.140625 1.484375 -0.25q2.03125 -0.25 2.984375 -0.578125q0 -0.34375 0 -0.4375q0 -1.015625 -0.46875 -1.4375q-0.640625 -0.5625 -1.90625 -0.5625q-1.171875 0 -1.734375 0.40625q-0.5625 0.40625 -0.828125 1.46875l-1.640625 -0.234375q0.234375 -1.046875 0.734375 -1.6875q0.515625 -0.640625 1.46875 -0.984375q0.96875 -0.359375 2.25 -0.359375q1.265625 0 2.046875 0.296875q0.78125 0.296875 1.15625 0.75q0.375 0.453125 0.515625 1.140625q0.09375 0.421875 0.09375 1.53125l0 2.234375q0 2.328125 0.09375 2.953125q0.109375 0.609375 0.4375 1.171875l-1.75 0q-0.265625 -0.515625 -0.328125 -1.21875zm-0.140625 -3.71875q-0.90625 0.359375 -2.734375 0.625q-1.03125 0.140625 -1.453125 0.328125q-0.421875 0.1875 -0.65625 0.546875q-0.234375 0.359375 -0.234375 0.796875q0 0.671875 0.5 1.125q0.515625 0.4375 1.484375 0.4375q0.96875 0 1.71875 -0.421875q0.75 -0.4375 1.109375 -1.15625q0.265625 -0.578125 0.265625 -1.671875l0 -0.609375zm10.516327 1.328125l1.640625 0.21875q-0.265625 1.6875 -1.375 2.65625q-1.109375 0.953125 -2.734375 0.953125q-2.015625 0 -3.25 -1.3125q-1.21875 -1.328125 -1.21875 -3.796875q0 -1.59375 0.515625 -2.78125q0.53125 -1.203125 1.609375 -1.796875q1.09375 -0.609375 2.359375 -0.609375q1.609375 0 2.625 0.8125q1.015625 0.8125 1.3125 2.3125l-1.625 0.25q-0.234375 -1.0 -0.828125 -1.5q-0.59375 -0.5 -1.421875 -0.5q-1.265625 0 -2.0625 0.90625q-0.78125 0.90625 -0.78125 2.859375q0 1.984375 0.765625 2.890625q0.765625 0.890625 1.984375 0.890625q0.984375 0 1.640625 -0.59375q0.65625 -0.609375 0.84375 -1.859375zm2.90625 3.609375l0 -13.59375l1.671875 0l0 7.75l3.953125 -4.015625l2.15625 0l-3.765625 3.65625l4.140625 6.203125l-2.0625 0l-3.25 -5.03125l-1.171875 1.125l0 3.90625l-1.671875 0z" fill-rule="nonzero"></path><path fill="#000000" fill-opacity="0.0" d="m269.1811 36.97638l263.33862 0l0 36.97638l-263.33862 0z" fill-rule="nonzero"></path><path fill="#000000" d="m283.08734 62.396378l0.234375 1.484375q-0.703125 0.140625 -1.265625 0.140625q-0.90625 0 -1.40625 -0.28125q-0.5 -0.296875 -0.703125 -0.75q-0.203125 -0.46875 -0.203125 -1.984375l0 -5.65625l-1.234375 0l0 -1.3125l1.234375 0l0 -2.4375l1.65625 -1.0l0 3.4375l1.6875 0l0 1.3125l-1.6875 0l0 5.75q0 0.71875 0.078125 0.921875q0.09375 0.203125 0.296875 0.328125q0.203125 0.125 0.578125 0.125q0.265625 0 0.734375 -0.078125zm1.4489441 5.296875l-0.171875 -1.5625q0.546875 0.140625 0.953125 0.140625q0.546875 0 0.875 -0.1875q0.34375 -0.1875 0.5625 -0.515625q0.15625 -0.25 0.5 -1.25q0.046875 -0.140625 0.15625 -0.40625l-3.734375 -9.875l1.796875 0l2.046875 5.71875q0.40625 1.078125 0.71875 2.28125q0.28125 -1.15625 0.6875 -2.25l2.09375 -5.75l1.671875 0l-3.75 10.03125q-0.59375 1.625 -0.9375 2.234375q-0.4375 0.828125 -1.015625 1.203125q-0.578125 0.390625 -1.375 0.390625q-0.484375 0 -1.078125 -0.203125zm9.40625 -0.015625l0 -13.640625l1.53125 0l0 1.28125q0.53125 -0.75 1.203125 -1.125q0.6875 -0.375 1.640625 -0.375q1.265625 0 2.234375 0.65625q0.96875 0.640625 1.453125 1.828125q0.5 1.1875 0.5 2.59375q0 1.515625 -0.546875 2.734375q-0.546875 1.203125 -1.578125 1.84375q-1.03125 0.640625 -2.171875 0.640625q-0.84375 0 -1.515625 -0.34375q-0.65625 -0.359375 -1.078125 -0.890625l0 4.796875l-1.671875 0zm1.515625 -8.65625q0 1.90625 0.765625 2.8125q0.78125 0.90625 1.875 0.90625q1.109375 0 1.890625 -0.9375q0.796875 -0.9375 0.796875 -2.921875q0 -1.875 -0.78125 -2.8125q-0.765625 -0.9375 -1.84375 -0.9375q-1.0625 0 -1.890625 1.0q-0.8125 1.0 -0.8125 2.890625zm15.610077 1.703125l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm14.309021 -5.8125l0 -1.90625l1.671875 0l0 1.90625l-1.671875 0zm0 11.6875l0 -9.859375l1.671875 0l0 9.859375l-1.671875 0zm4.1292114 0l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm10.781952 0l0 -8.546875l-1.484375 0l0 -1.3125l1.484375 0l0 -1.046875q0 -0.984375 0.171875 -1.46875q0.234375 -0.65625 0.84375 -1.046875q0.609375 -0.40625 1.703125 -0.40625q0.703125 0 1.5625 0.15625l-0.25 1.46875q-0.515625 -0.09375 -0.984375 -0.09375q-0.765625 0 -1.078125 0.328125q-0.3125 0.3125 -0.3125 1.203125l0 0.90625l1.921875 0l0 1.3125l-1.921875 0l0 8.546875l-1.65625 0zm11.527069 -3.171875l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm9.094452 5.875l0 -9.859375l1.5 0l0 1.5q0.578125 -1.046875 1.0625 -1.375q0.484375 -0.34375 1.078125 -0.34375q0.84375 0 1.71875 0.546875l-0.578125 1.546875q-0.609375 -0.359375 -1.234375 -0.359375q-0.546875 0 -0.984375 0.328125q-0.421875 0.328125 -0.609375 0.90625q-0.28125 0.890625 -0.28125 1.953125l0 5.15625l-1.671875 0zm12.978302 -3.171875l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm9.110107 5.875l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm16.813202 -3.609375l1.640625 0.21875q-0.265625 1.6875 -1.375 2.65625q-1.109375 0.953125 -2.734375 0.953125q-2.015625 0 -3.25 -1.3125q-1.21875 -1.328125 -1.21875 -3.796875q0 -1.59375 0.515625 -2.78125q0.53125 -1.203125 1.609375 -1.796875q1.09375 -0.609375 2.359375 -0.609375q1.609375 0 2.625 0.8125q1.015625 0.8125 1.3125 2.3125l-1.625 0.25q-0.234375 -1.0 -0.828125 -1.5q-0.59375 -0.5 -1.421875 -0.5q-1.265625 0 -2.0625 0.90625q-0.78125 0.90625 -0.78125 2.859375q0 1.984375 0.765625 2.890625q0.765625 0.890625 1.984375 0.890625q0.984375 0 1.640625 -0.59375q0.65625 -0.609375 0.84375 -1.859375zm9.640625 0.4375l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm13.621521 2.9375l1.65625 -0.265625q0.140625 1.0 0.765625 1.53125q0.640625 0.515625 1.78125 0.515625q1.15625 0 1.703125 -0.46875q0.5625 -0.46875 0.5625 -1.09375q0 -0.5625 -0.484375 -0.890625q-0.34375 -0.21875 -1.703125 -0.5625q-1.84375 -0.46875 -2.5625 -0.796875q-0.703125 -0.34375 -1.078125 -0.9375q-0.359375 -0.609375 -0.359375 -1.328125q0 -0.65625 0.296875 -1.21875q0.3125 -0.5625 0.828125 -0.9375q0.390625 -0.28125 1.0625 -0.484375q0.671875 -0.203125 1.4375 -0.203125q1.171875 0 2.046875 0.34375q0.875 0.328125 1.28125 0.90625q0.421875 0.5625 0.578125 1.515625l-1.625 0.21875q-0.109375 -0.75 -0.65625 -1.171875q-0.53125 -0.4375 -1.5 -0.4375q-1.15625 0 -1.640625 0.390625q-0.484375 0.375 -0.484375 0.875q0 0.328125 0.203125 0.59375q0.203125 0.265625 0.640625 0.4375q0.25 0.09375 1.46875 0.4375q1.765625 0.46875 2.46875 0.765625q0.703125 0.296875 1.09375 0.875q0.40625 0.578125 0.40625 1.4375q0 0.828125 -0.484375 1.578125q-0.484375 0.734375 -1.40625 1.140625q-0.921875 0.390625 -2.078125 0.390625q-1.921875 0 -2.9375 -0.796875q-1.0 -0.796875 -1.28125 -2.359375zm16.453125 2.9375l0 -1.453125q-1.140625 1.671875 -3.125 1.671875q-0.859375 0 -1.625 -0.328125q-0.75 -0.34375 -1.125 -0.84375q-0.359375 -0.5 -0.515625 -1.234375q-0.09375 -0.5 -0.09375 -1.5625l0 -6.109375l1.671875 0l0 5.46875q0 1.3125 0.09375 1.765625q0.15625 0.65625 0.671875 1.03125q0.515625 0.375 1.265625 0.375q0.75 0 1.40625 -0.375q0.65625 -0.390625 0.921875 -1.046875q0.28125 -0.671875 0.28125 -1.9375l0 -5.28125l1.671875 0l0 9.859375l-1.5 0zm3.2507324 -2.9375l1.65625 -0.265625q0.140625 1.0 0.765625 1.53125q0.640625 0.515625 1.78125 0.515625q1.15625 0 1.703125 -0.46875q0.5625 -0.46875 0.5625 -1.09375q0 -0.5625 -0.484375 -0.890625q-0.34375 -0.21875 -1.703125 -0.5625q-1.84375 -0.46875 -2.5625 -0.796875q-0.703125 -0.34375 -1.078125 -0.9375q-0.359375 -0.609375 -0.359375 -1.328125q0 -0.65625 0.296875 -1.21875q0.3125 -0.5625 0.828125 -0.9375q0.390625 -0.28125 1.0625 -0.484375q0.671875 -0.203125 1.4375 -0.203125q1.171875 0 2.046875 0.34375q0.875 0.328125 1.28125 0.90625q0.421875 0.5625 0.578125 1.515625l-1.625 0.21875q-0.109375 -0.75 -0.65625 -1.171875q-0.53125 -0.4375 -1.5 -0.4375q-1.15625 0 -1.640625 0.390625q-0.484375 0.375 -0.484375 0.875q0 0.328125 0.203125 0.59375q0.203125 0.265625 0.640625 0.4375q0.25 0.09375 1.46875 0.4375q1.765625 0.46875 2.46875 0.765625q0.703125 0.296875 1.09375 0.875q0.40625 0.578125 0.40625 1.4375q0 0.828125 -0.484375 1.578125q-0.484375 0.734375 -1.40625 1.140625q-0.921875 0.390625 -2.078125 0.390625q-1.921875 0 -2.9375 -0.796875q-1.0 -0.796875 -1.28125 -2.359375zm10.0 6.71875l0 -13.640625l1.53125 0l0 1.28125q0.53125 -0.75 1.203125 -1.125q0.6875 -0.375 1.640625 -0.375q1.265625 0 2.234375 0.65625q0.96875 0.640625 1.453125 1.828125q0.5 1.1875 0.5 2.59375q0 1.515625 -0.546875 2.734375q-0.546875 1.203125 -1.578125 1.84375q-1.03125 0.640625 -2.171875 0.640625q-0.84375 0 -1.515625 -0.34375q-0.65625 -0.359375 -1.078125 -0.890625l0 4.796875l-1.671875 0zm1.515625 -8.65625q0 1.90625 0.765625 2.8125q0.78125 0.90625 1.875 0.90625q1.109375 0 1.890625 -0.9375q0.796875 -0.9375 0.796875 -2.921875q0 -1.875 -0.78125 -2.8125q-0.765625 -0.9375 -1.84375 -0.9375q-1.0625 0 -1.890625 1.0q-0.8125 1.0 -0.8125 2.890625zm15.610077 1.703125l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm9.110107 5.875l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm16.766327 0l0 -1.25q-0.9375 1.46875 -2.75 1.46875q-1.171875 0 -2.171875 -0.640625q-0.984375 -0.65625 -1.53125 -1.8125q-0.53125 -1.171875 -0.53125 -2.6875q0 -1.46875 0.484375 -2.671875q0.5 -1.203125 1.46875 -1.84375q0.984375 -0.640625 2.203125 -0.640625q0.890625 0 1.578125 0.375q0.703125 0.375 1.140625 0.984375l0 -4.875l1.65625 0l0 13.59375l-1.546875 0zm-5.28125 -4.921875q0 1.890625 0.796875 2.828125q0.8125 0.9375 1.890625 0.9375q1.09375 0 1.859375 -0.890625q0.765625 -0.890625 0.765625 -2.734375q0 -2.015625 -0.78125 -2.953125q-0.78125 -0.953125 -1.921875 -0.953125q-1.109375 0 -1.859375 0.90625q-0.75 0.90625 -0.75 2.859375zm16.016357 1.75l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm15.500702 5.875l0 -1.25q-0.9375 1.46875 -2.75 1.46875q-1.171875 0 -2.171875 -0.640625q-0.984375 -0.65625 -1.53125 -1.8125q-0.53125 -1.171875 -0.53125 -2.6875q0 -1.46875 0.484375 -2.671875q0.5 -1.203125 1.46875 -1.84375q0.984375 -0.640625 2.203125 -0.640625q0.890625 0 1.578125 0.375q0.703125 0.375 1.140625 0.984375l0 -4.875l1.65625 0l0 13.59375l-1.546875 0zm-5.28125 -4.921875q0 1.890625 0.796875 2.828125q0.8125 0.9375 1.890625 0.9375q1.09375 0 1.859375 -0.890625q0.765625 -0.890625 0.765625 -2.734375q0 -2.015625 -0.78125 -2.953125q-0.78125 -0.953125 -1.921875 -0.953125q-1.109375 0 -1.859375 0.90625q-0.75 0.90625 -0.75 2.859375z" fill-rule="nonzero"></path><path fill="#000000" fill-opacity="0.0" d="m135.38583 92.44095c-12.500008 0 -25.007881 -9.244095 -25.000008 -18.48819c0.007873535 -9.244095 12.531494 -18.488194 25.062996 -18.488194" fill-rule="nonzero"></path><path stroke="#000000" stroke-width="1.0" stroke-linejoin="round" stroke-linecap="butt" d="m135.38583 92.44095c-12.500008 0 -25.007881 -9.244095 -25.000008 -18.48819c0.0039367676 -4.6220474 3.1368103 -9.244095 7.8351364 -12.710632c2.3491669 -1.7332687 5.089691 -3.177658 8.026146 -4.188732c1.4682312 -0.5055351 2.9854355 -0.9027405 4.527199 -1.1735649c0.385437 -0.06770706 0.77241516 -0.12751389 1.1605377 -0.17913818l0.09425354 -0.011520386" fill-rule="evenodd"></path><path fill="#000000" stroke="#000000" stroke-width="1.0" stroke-linecap="butt" d="m132.0291 55.68917l-1.0484619 1.1958656l3.00943 -1.3246613l-3.1568298 -0.9196701z" fill-rule="evenodd"></path><path fill="#000000" fill-opacity="0.0" d="m269.1811 73.95276l263.33862 0l0 36.97637l-263.33862 0z" fill-rule="nonzero"></path><path fill="#000000" d="m283.08734 99.37276l0.234375 1.484375q-0.703125 0.140625 -1.265625 0.140625q-0.90625 0 -1.40625 -0.28125q-0.5 -0.296875 -0.703125 -0.75q-0.203125 -0.46875 -0.203125 -1.984375l0 -5.65625l-1.234375 0l0 -1.3125l1.234375 0l0 -2.4375l1.65625 -1.0l0 3.4375l1.6875 0l0 1.3125l-1.6875 0l0 5.75q0 0.71875 0.078125 0.921875q0.09375 0.203125 0.296875 0.328125q0.203125 0.125 0.578125 0.125q0.265625 0 0.734375 -0.078125zm1.4489441 5.296875l-0.171875 -1.5625q0.546875 0.140625 0.953125 0.140625q0.546875 0 0.875 -0.1875q0.34375 -0.1875 0.5625 -0.515625q0.15625 -0.25 0.5 -1.25q0.046875 -0.140625 0.15625 -0.40625l-3.734375 -9.875l1.796875 0l2.046875 5.71875q0.40625 1.078125 0.71875 2.28125q0.28125 -1.15625 0.6875 -2.25l2.09375 -5.75l1.671875 0l-3.75 10.03125q-0.59375 1.625 -0.9375 2.234375q-0.4375 0.828125 -1.015625 1.203125q-0.578125 0.390625 -1.375 0.390625q-0.484375 0 -1.078125 -0.203125zm9.40625 -0.015625l0 -13.640625l1.53125 0l0 1.28125q0.53125 -0.75 1.203125 -1.125q0.6875 -0.375 1.640625 -0.375q1.265625 0 2.234375 0.65625q0.96875 0.640625 1.453125 1.828125q0.5 1.1875 0.5 2.59375q0 1.515625 -0.546875 2.734375q-0.546875 1.203125 -1.578125 1.84375q-1.03125 0.640625 -2.171875 0.640625q-0.84375 0 -1.515625 -0.34375q-0.65625 -0.359375 -1.078125 -0.890625l0 4.796875l-1.671875 0zm1.515625 -8.65625q0 1.90625 0.765625 2.8125q0.78125 0.90625 1.875 0.90625q1.109375 0 1.890625 -0.9375q0.796875 -0.9375 0.796875 -2.921875q0 -1.875 -0.78125 -2.8125q-0.765625 -0.9375 -1.84375 -0.9375q-1.0625 0 -1.890625 1.0q-0.8125 1.0 -0.8125 2.890625zm15.610077 1.703125l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm14.309021 -5.8125l0 -1.90625l1.671875 0l0 1.90625l-1.671875 0zm0 11.6875l0 -9.859375l1.671875 0l0 9.859375l-1.671875 0zm4.1292114 0l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm10.781952 0l0 -8.546875l-1.484375 0l0 -1.3125l1.484375 0l0 -1.046875q0 -0.984375 0.171875 -1.46875q0.234375 -0.65625 0.84375 -1.046875q0.609375 -0.40625 1.703125 -0.40625q0.703125 0 1.5625 0.15625l-0.25 1.46875q-0.515625 -0.09375 -0.984375 -0.09375q-0.765625 0 -1.078125 0.328125q-0.3125 0.3125 -0.3125 1.203125l0 0.90625l1.921875 0l0 1.3125l-1.921875 0l0 8.546875l-1.65625 0zm11.527069 -3.171875l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm9.094452 5.875l0 -9.859375l1.5 0l0 1.5q0.578125 -1.046875 1.0625 -1.375q0.484375 -0.34375 1.078125 -0.34375q0.84375 0 1.71875 0.546875l-0.578125 1.546875q-0.609375 -0.359375 -1.234375 -0.359375q-0.546875 0 -0.984375 0.328125q-0.421875 0.328125 -0.609375 0.90625q-0.28125 0.890625 -0.28125 1.953125l0 5.15625l-1.671875 0zm12.978302 -3.171875l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm9.110107 5.875l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm16.813202 -3.609375l1.640625 0.21875q-0.265625 1.6875 -1.375 2.65625q-1.109375 0.953125 -2.734375 0.953125q-2.015625 0 -3.25 -1.3125q-1.21875 -1.328125 -1.21875 -3.796875q0 -1.59375 0.515625 -2.78125q0.53125 -1.203125 1.609375 -1.796875q1.09375 -0.609375 2.359375 -0.609375q1.609375 0 2.625 0.8125q1.015625 0.8125 1.3125 2.3125l-1.625 0.25q-0.234375 -1.0 -0.828125 -1.5q-0.59375 -0.5 -1.421875 -0.5q-1.265625 0 -2.0625 0.90625q-0.78125 0.90625 -0.78125 2.859375q0 1.984375 0.765625 2.890625q0.765625 0.890625 1.984375 0.890625q0.984375 0 1.640625 -0.59375q0.65625 -0.609375 0.84375 -1.859375zm9.640625 0.4375l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm14.277771 5.875l0 -9.859375l1.5 0l0 1.5q0.578125 -1.046875 1.0625 -1.375q0.484375 -0.34375 1.078125 -0.34375q0.84375 0 1.71875 0.546875l-0.578125 1.546875q-0.609375 -0.359375 -1.234375 -0.359375q-0.546875 0 -0.984375 0.328125q-0.421875 0.328125 -0.609375 0.90625q-0.28125 0.890625 -0.28125 1.953125l0 5.15625l-1.671875 0zm12.681427 0l0 -1.453125q-1.140625 1.671875 -3.125 1.671875q-0.859375 0 -1.625 -0.328125q-0.75 -0.34375 -1.125 -0.84375q-0.359375 -0.5 -0.515625 -1.234375q-0.09375 -0.5 -0.09375 -1.5625l0 -6.109375l1.671875 0l0 5.46875q0 1.3125 0.09375 1.765625q0.15625 0.65625 0.671875 1.03125q0.515625 0.375 1.265625 0.375q0.75 0 1.40625 -0.375q0.65625 -0.390625 0.921875 -1.046875q0.28125 -0.671875 0.28125 -1.9375l0 -5.28125l1.671875 0l0 9.859375l-1.5 0zm3.9226074 0l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm10.375702 0l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm10.391357 -11.6875l0 -1.90625l1.671875 0l0 1.90625l-1.671875 0zm0 11.6875l0 -9.859375l1.671875 0l0 9.859375l-1.671875 0zm4.129181 0l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0zm10.078857 0.8125l1.609375 0.25q0.109375 0.75 0.578125 1.09375q0.609375 0.453125 1.6875 0.453125q1.171875 0 1.796875 -0.46875q0.625 -0.453125 0.859375 -1.28125q0.125 -0.515625 0.109375 -2.15625q-1.09375 1.296875 -2.71875 1.296875q-2.03125 0 -3.15625 -1.46875q-1.109375 -1.46875 -1.109375 -3.515625q0 -1.40625 0.515625 -2.59375q0.515625 -1.203125 1.484375 -1.84375q0.96875 -0.65625 2.265625 -0.65625q1.75 0 2.875 1.40625l0 -1.1875l1.546875 0l0 8.515625q0 2.3125 -0.46875 3.265625q-0.46875 0.96875 -1.484375 1.515625q-1.015625 0.5625 -2.5 0.5625q-1.765625 0 -2.859375 -0.796875q-1.078125 -0.796875 -1.03125 -2.390625zm1.375 -5.921875q0 1.953125 0.765625 2.84375q0.78125 0.890625 1.9375 0.890625q1.140625 0 1.921875 -0.890625q0.78125 -0.890625 0.78125 -2.78125q0 -1.8125 -0.8125 -2.71875q-0.796875 -0.921875 -1.921875 -0.921875q-1.109375 0 -1.890625 0.90625q-0.78125 0.890625 -0.78125 2.671875z" fill-rule="nonzero"></path><path fill="#000000" fill-opacity="0.0" d="m0 49.952755l104.56693 0l0 36.976376l-104.56693 0z" fill-rule="nonzero"></path><path fill="#000000" d="m20.072014 76.87276l0 -9.859375l1.5 0l0 1.5q0.578125 -1.046875 1.0625 -1.375q0.484375 -0.34375 1.078125 -0.34375q0.84375 0 1.71875 0.546875l-0.578125 1.546875q-0.609375 -0.359375 -1.234375 -0.359375q-0.546875 0 -0.984375 0.328125q-0.421875 0.328125 -0.609375 0.90625q-0.28125 0.890625 -0.28125 1.953125l0 5.15625l-1.671875 0zm12.978302 -3.171875l1.71875 0.21875q-0.40625 1.5 -1.515625 2.34375q-1.09375 0.828125 -2.8125 0.828125q-2.15625 0 -3.421875 -1.328125q-1.265625 -1.328125 -1.265625 -3.734375q0 -2.484375 1.265625 -3.859375q1.28125 -1.375 3.328125 -1.375q1.984375 0 3.234375 1.34375q1.25 1.34375 1.25 3.796875q0 0.140625 -0.015625 0.4375l-7.34375 0q0.09375 1.625 0.921875 2.484375q0.828125 0.859375 2.0625 0.859375q0.90625 0 1.546875 -0.46875q0.65625 -0.484375 1.046875 -1.546875zm-5.484375 -2.703125l5.5 0q-0.109375 -1.234375 -0.625 -1.859375q-0.796875 -0.96875 -2.078125 -0.96875q-1.140625 0 -1.9375 0.78125q-0.78125 0.765625 -0.859375 2.046875zm15.547592 2.265625l1.640625 0.21875q-0.265625 1.6875 -1.375 2.65625q-1.109375 0.953125 -2.734375 0.953125q-2.015625 0 -3.25 -1.3125q-1.21875 -1.328125 -1.21875 -3.796875q0 -1.59375 0.515625 -2.78125q0.53125 -1.203125 1.609375 -1.796875q1.09375 -0.609375 2.359375 -0.609375q1.609375 0 2.625 0.8125q1.015625 0.8125 1.3125 2.3125l-1.625 0.25q-0.234375 -1.0 -0.828125 -1.5q-0.59375 -0.5 -1.421875 -0.5q-1.265625 0 -2.0625 0.90625q-0.78125 0.90625 -0.78125 2.859375q0 1.984375 0.765625 2.890625q0.765625 0.890625 1.984375 0.890625q0.984375 0 1.640625 -0.59375q0.65625 -0.609375 0.84375 -1.859375zm9.34375 3.609375l0 -1.453125q-1.140625 1.671875 -3.125 1.671875q-0.859375 0 -1.625 -0.328125q-0.75 -0.34375 -1.125 -0.84375q-0.359375 -0.5 -0.515625 -1.234375q-0.09375 -0.5 -0.09375 -1.5625l0 -6.109375l1.671875 0l0 5.46875q0 1.3125 0.09375 1.765625q0.15625 0.65625 0.671875 1.03125q0.515625 0.375 1.265625 0.375q0.75 0 1.40625 -0.375q0.65625 -0.390625 0.921875 -1.046875q0.28125 -0.671875 0.28125 -1.9375l0 -5.28125l1.671875 0l0 9.859375l-1.5 0zm3.9069672 0l0 -9.859375l1.5 0l0 1.5q0.578125 -1.046875 1.0625 -1.375q0.484375 -0.34375 1.078125 -0.34375q0.84375 0 1.71875 0.546875l-0.578125 1.546875q-0.609375 -0.359375 -1.234375 -0.359375q-0.546875 0 -0.984375 0.328125q-0.421875 0.328125 -0.609375 0.90625q-0.28125 0.890625 -0.28125 1.953125l0 5.15625l-1.671875 0zm5.556427 -2.9375l1.65625 -0.265625q0.140625 1.0 0.765625 1.53125q0.640625 0.515625 1.78125 0.515625q1.15625 0 1.703125 -0.46875q0.5625 -0.46875 0.5625 -1.09375q0 -0.5625 -0.484375 -0.890625q-0.34375 -0.21875 -1.703125 -0.5625q-1.84375 -0.46875 -2.5625 -0.796875q-0.703125 -0.34375 -1.078125 -0.9375q-0.359375 -0.609375 -0.359375 -1.328125q0 -0.65625 0.296875 -1.21875q0.3125 -0.5625 0.828125 -0.9375q0.390625 -0.28125 1.0625 -0.484375q0.671875 -0.203125 1.4375 -0.203125q1.171875 0 2.046875 0.34375q0.875 0.328125 1.28125 0.90625q0.421875 0.5625 0.578125 1.515625l-1.625 0.21875q-0.109375 -0.75 -0.65625 -1.171875q-0.53125 -0.4375 -1.5 -0.4375q-1.15625 0 -1.640625 0.390625q-0.484375 0.375 -0.484375 0.875q0 0.328125 0.203125 0.59375q0.203125 0.265625 0.640625 0.4375q0.25 0.09375 1.46875 0.4375q1.765625 0.46875 2.46875 0.765625q0.703125 0.296875 1.09375 0.875q0.40625 0.578125 0.40625 1.4375q0 0.828125 -0.484375 1.578125q-0.484375 0.734375 -1.40625 1.140625q-0.921875 0.390625 -2.078125 0.390625q-1.921875 0 -2.9375 -0.796875q-1.0 -0.796875 -1.28125 -2.359375zm10.015625 -8.75l0 -1.9062538l1.671875 0l0 1.9062538l-1.671875 0zm0 11.6875l0 -9.859375l1.671875 0l0 9.859375l-1.671875 0zm3.5041962 -4.921875q0 -2.734375 1.53125 -4.0625q1.265625 -1.09375 3.09375 -1.09375q2.03125 0 3.3125 1.34375q1.296875 1.328125 1.296875 3.671875q0 1.90625 -0.578125 3.0q-0.5625 1.078125 -1.65625 1.6875q-1.078125 0.59375 -2.375 0.59375q-2.0625 0 -3.34375 -1.328125q-1.28125 -1.328125 -1.28125 -3.8125zm1.71875 0q0 1.890625 0.828125 2.828125q0.828125 0.9375 2.078125 0.9375q1.25 0 2.0625 -0.9375q0.828125 -0.953125 0.828125 -2.890625q0 -1.828125 -0.828125 -2.765625q-0.828125 -0.9375 -2.0625 -0.9375q-1.25 0 -2.078125 0.9375q-0.828125 0.9375 -0.828125 2.828125zm9.281967 4.921875l0 -9.859375l1.5 0l0 1.40625q1.09375 -1.625 3.140625 -1.625q0.890625 0 1.640625 0.328125q0.75 0.3125 1.109375 0.84375q0.375 0.515625 0.53125 1.21875q0.09375 0.46875 0.09375 1.625l0 6.0625l-1.671875 0l0 -6.0q0 -1.015625 -0.203125 -1.515625q-0.1875 -0.515625 -0.6875 -0.8125q-0.5 -0.296875 -1.171875 -0.296875q-1.0625 0 -1.84375 0.671875q-0.765625 0.671875 -0.765625 2.578125l0 5.375l-1.671875 0z" fill-rule="nonzero"></path></g></svg>
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/type-inference.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/type-inference.rst
new file mode 100644
index 000000000..24f3b262b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/type-inference.rst
@@ -0,0 +1,124 @@
+======================
+NBEP 5: Type Inference
+======================
+
+:Author: Siu Kwan Lam
+:Date: Sept 2016
+:Status: Draft
+
+
+This document describes the current type inference implementation in numba.
+
+
+Introduction
+============
+
+Numba uses type information to ensure that every variable in the user code can
+be correctly lowered (translated into a low-level representation).  The type of
+a variable describes the set of valid operations and available attributes.
+Resolving this information during compilation avoids the overhead of type
+checking and dispatching at runtime.  However, Python is dynamically typed and
+the user does not declare variable types.  Since type information is absent,
+we use type inference to reconstruct the missing information.
+
+
+Numba Type Semantic
+===================
+
+Type inference operates on :term:`Numba IR`, a mostly static-single-assignment (SSA)
+encoding of the Python bytecode.  Conceptually, all intermediate values in the
+Python code are explicitly assigned to a variable in the IR.  Numba enforces
+that each IR variable to have one type only.  A user variable (from the Python
+source code) can be mapped to multiple variables in the IR.  They are *versions*
+of a variable.  Each time a user variable is assigned to, a new version is
+created.  From that point, all subsequent references will use the new version.
+The user variable *evolves* as the function logic updates its type.  Merge
+points (e.g. subsequent block to an if-else, the loop body, etc..) in the control
+flow need extra care. At each merge point, a new version is implicitly created
+to merge the different variable versions from the incoming paths.
+The merging of the variable versions may translate into an implicit cast.
+
+Numba uses function overloading to emulate Python duck-typing.  The type of a
+function can contain multiple call signatures that accept different argument
+types and yield different return types.  The process to decide the best
+signature for an overloaded function is called *overload resolution*.
+Numba partially implements the C++ overload resolution scheme
+(`ISOCPP`_ 13.3 Overload Resolution).  The scheme uses a "best fit" algorithm by
+ranking each argument symmetrically.  The five possible rankings in increasing
+order of penalty are:
+
+* *Exact*: the expected type is the same as the actual type.
+* *Promotion*: the actual type can be upcast to the expected type by extending
+  the precision without changing the behavior.
+* *Safe conversion*: the actual type can be cast to the expected type by changing
+  the type without losing information.
+* *Unsafe conversion*: the actual type can be cast to the expected type by
+  changing the type or downcasting the type even if it is imprecise.
+* *No match*: no valid operation can convert the actual type to the expected type.
+
+It is possible to have an ambiguous resolution.  For example, a function with
+signatures ``(int16, int32)`` and ``(int32, int16)`` can become ambiguous if
+presented with the argument types ``(int32, int32)``, because demoting either
+argument to ``int16`` is equally "fit".  Fortunately, numba can usually resolve
+such ambiguity by compiling a new version with the exact signature
+``(int32, int32)``.  When compilation is disabled and there are multiple
+signatures with equal fit, an exception is raised.
+
+Type Inference
+==============
+
+The type inference in numba has three important components---type
+variable, constraint network, and typing context.
+
+* The *typing context* provides all the type information and typing related
+  operations, including the logic for type unification, and the logic for typing
+  of global and constant values.  It defines the semantic of the language that
+  can be compiled by numba.
+
+* A *type variable* holds the type of each variable (in the Numba IR).
+  Conceptually, it is initialized to the universal type and, as it is re-assigned,
+  it stores a common type by unifying the new type with the existing type.  The
+  common type must be able to represent values of the new type and the existing
+  type.  Type conversion is applied as necessary and precision loss is
+  accepted for usability reason.
+
+* The *constraint network* is a dependency graph built from the IR.  Each
+  node represents an operation in the Numba IR and updates at least one type
+  variable.  There may be cycles due to loops in user code.
+
+The type inference process starts by seeding the argument types.  These initial
+types are propagated in the constraint network, which eventually fills all the
+type variables.  Due to cycles in the network, the process repeats until all
+type variables converge or it fails with undecidable types.
+
+Type unification always returns a more "general" (quoted because unsafe conversion
+is allowed) type.  Types will converge to the least "general" type that
+can represent all possible values that the variable can hold.  Since unification
+will never move down the type hierarchy and there is a single top type, the
+universal type---``object``, the type inference is guaranteed to converge.
+
+A failure in type inference can be caused by two reasons.  The first reason is user
+error due to incorrect use of a type.  This type of error will also trigger an
+exception in regular python execution.  The second reason is due to the use of an
+unsupported feature, but the code is otherwise valid in regular python
+execution.  Upon an error, the type inference will set all types to the object
+type.  As a result, numba will fallback to *object-mode*.
+
+Since functions can be overloaded, the type inference needs to decide the
+type signature used at each call site.  The overload resolution is applied to
+all known overload versions of the callee function described in *call-templates*.
+A call-template can either be concrete or abstract.  A concrete call-template
+defines a fixed list of all possible signatures.  An abstract call-template
+defines the logic to compute the accepted signature and it is used to implement
+generic functions.
+
+Numba-compiled functions are generic functions due to their ability to compile
+new versions.  When it sees a new set of argument types, it triggers type
+inference to validate and determine the return type. When there are nested calls
+for numba-compiled functions, each call-site triggers type inference.
+This poses a problem to recursive functions because the type inference will also
+be triggered recursively.  Currently, simple single recursion is supported if
+the signature is user-annotated by the user, which avoids unbound recursion in
+type inference that will never terminate.
+
+.. _ISOCPP: http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2014/n4296.pdf
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/typing_recursion.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/typing_recursion.rst
new file mode 100644
index 000000000..a33a3a4b5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/proposals/typing_recursion.rst
@@ -0,0 +1,129 @@
+========================
+NBEP 6: Typing Recursion
+========================
+
+:Author: Siu Kwan Lam
+:Date: Sept 2016
+:Status: Draft
+
+Introduction
+============
+
+This document proposes an enhancement to the type inference algorithm to
+support recursion without explicitly annotating the function signature.
+As a result, the proposal enables numba to type-infer both self-recursive and
+mutual-recursive functions under some limitations.  In practice, these
+limitations can be easily overcome by specifying a compilation order.
+
+
+The Current State
+=================
+
+Recursion support in numba is currently limited to self-recursion with explicit
+type annotation for the function.  This limitation comes from the inability to
+determine the return type of a recursive call.  This is because the callee is
+either the current function (for self-recursion) or a parent function
+(mutual-recursion) and its type inference process has been suspended while waiting for
+the function-type of its callee.  This results in the formation of a cyclic
+dependency.  For example, given a function ``foo()`` that calls ``bar()``,
+which in turns call ``foo()``::
+
+    def foo(x):
+        if x > 0:
+            return bar(x)
+        else:
+            return 1
+
+    def bar(x):
+        return foo(x - 1)
+
+
+The type inference process of ``foo()`` depends on that of ``bar()``,
+which depends on ``foo()``.  Therefore ``foo()`` depends on itself and the type
+inference algorithm cannot terminate.
+
+
+The Solution
+============
+
+The proposed solution has two components:
+
+1. The introduction of a compile-time *callstack* that tracks the compiling functions.
+2. The allowance of a partial type inference on functions by leveraging the return type
+   on non-recursive control-flow paths.
+
+The compile-time callstack stores typing information of the functions being
+compiled.  Like an ordinary callstack, it pushes a new record every time a
+function is "called".  Since this occurs at compile-time, a "call" triggers
+a compilation of the callee.
+
+To detect recursion, the compile-time callstack is searched bottom-up
+(stack grows downward) for a record that matches the callee.
+As the record contains a reference to the type inference state,
+the type inference process can be resumed to determine the return type.
+
+Recall that the type inference process cannot be resumed normally because of the cyclic
+dependency of the return type.  In practice, we can assume that a useful
+program must have a terminating condition, a path that does not recurse.  So,
+the type inference process can make an initial guess for the return-type at the recursive
+call by using the return-type determined by the non-recursive paths.  This
+allows type information to propagate on the recursive paths to generate the
+final return type, which is used to refine the type information by the
+subsequent iteration in the type inference process.
+
+The following figure illustrates the compile-time callstack when the compiler
+reaches the recursive call to ``foo()`` from ``bar()``:
+
+.. image:: recursion_callstack.svg
+    :width: 400px
+
+At this time, the type inference process of ``foo()`` is suspended and that of ``bar()``
+is active.  The compiler can see that the callee is already compiling by
+searching the callstack.  Knowing that it is a recursive call, the compiler
+can resume the type-inference on ``foo()`` by ignoring the paths that contain
+recursive calls.  This means only the ``else`` branch is considered and we can
+easily tell that ``foo()`` returns an ``int`` in this case.  The compiler will
+then set the initial return type of ``foo()`` and ``bar()`` to ``int``.  The
+subsequent type propagation can use this information to complete the type
+inference of both functions, unifying the return-type of all returning paths.
+
+
+Limitations
+===========
+
+For the proposed type inference algorithm to terminate, it assumes that
+at least one of the control path leads to a return-statement without undertaking
+a recursive call.  Should this not be the case, the algorithm will raise an
+exception indicating a potential runaway recursion.
+
+For example::
+
+    @jit
+    def first(x):
+        # The recursing call must have a path that is non-recursing.
+        if x > 0:
+            return second(x)
+        else:
+            return 1
+
+    @jit
+    def second(x):
+        return third(x)
+
+    @jit
+    def third(x):
+        return first(x - 1)
+
+
+The ``first()`` function must be the compiled first for the type inference algorithm to
+complete successfully.  Compiling any other function first will lead to a failure
+in type inference.  The type inference algorithm will treat it as a runaway
+recursion due to the lack of a non-recursive exit in the recursive callee.
+
+For example, compiling ``second()`` first will move the recursive call to
+``first()``.  When the compiler tries to resume the type inference process of
+``second()``, it will fail to find a non-recursive path.
+
+This is a small limitation and can be overcome easily by code restructuring or
+precompiling in a specific order.
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/aot-compilation.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/aot-compilation.rst
new file mode 100644
index 000000000..a6d56ee34
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/aot-compilation.rst
@@ -0,0 +1,76 @@
+.. _aot-compilation:
+
+Ahead-of-Time compilation
+=========================
+
+.. currentmodule:: numba.pycc
+
+.. class:: CC(extension_name, source_module=None)
+
+   An object used to generate compiled extensions from Numba-compiled
+   Python functions.  *extension_name* is the name of the extension
+   to be generated.  *source_module* is the Python module
+   containing the functions; if ``None``, it is inferred by examining
+   the call stack.
+
+   :class:`CC` instances have the following attributes and methods:
+
+   .. attribute:: name
+
+      (read-only attribute) The name of the extension module to be generated.
+
+   .. attribute:: output_dir
+
+      (read-write attribute) The directory the extension module will be
+      written into.  By default it is the directory the *source_module* is
+      located in.
+
+   .. attribute:: output_file
+
+      (read-write attribute) The name of the file the extension module will
+      be written to.  By default this follows the Python naming convention
+      for the current platform.
+
+   .. attribute:: target_cpu
+
+      (read-write attribute) The name of the CPU model to generate code for.
+      This will select the appropriate instruction set extensions.  By
+      default, a generic CPU is selected in order to produce portable code.
+
+      Recognized names for this attribute depend on the current architecture
+      and LLVM version.  If you have LLVM installed, ``llc -mcpu=help``
+      will give you a list.  Examples on x86-64 are ``"ivybridge"``,
+      ``"haswell"``, ``"skylake"`` or ``"broadwell"``.  You can also give
+      the value ``"host"`` which will select the current host CPU.
+
+   .. attribute:: verbose
+
+      (read-write attribute) If true, print out information while
+      compiling the extension.  False by default.
+
+   .. decorator:: export(exported_name, sig)
+
+      Mark the decorated function for compilation with the signature *sig*.
+      The compiled function will be exposed as *exported_name* in the
+      generated extension module.
+
+      All exported names within a given :class:`CC` instance must be
+      distinct, otherwise an exception is raised.
+
+   .. method:: compile()
+
+      Compile all exported functions and generate the extension module
+      as specified by :attr:`output_dir` and :attr:`output_file`.
+
+   .. method:: distutils_extension(**kwargs)
+
+      Return a :py:class:`distutils.core.Extension` instance allowing
+      to integrate generation of the extension module in a conventional
+      ``setup.py``-driven build process.  The optional *kwargs* let you
+      pass optional parameters to the :py:class:`~distutils.core.Extension`
+      constructor.
+
+      In this mode of operation, it is not necessary to call :meth:`compile`
+      yourself.  Also, :attr:`output_dir` and :attr:`output_file` will be
+      ignored.
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/deprecation.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/deprecation.rst
new file mode 100644
index 000000000..4271f0e70
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/deprecation.rst
@@ -0,0 +1,312 @@
+.. _deprecation:
+
+===================
+Deprecation Notices
+===================
+
+This section contains information about deprecation of behaviours, features and
+APIs that have become undesirable/obsolete. Any information about the schedule
+for their deprecation and reasoning behind the changes, along with examples, is
+provided. However, first is a small section on how to suppress deprecation
+warnings that may be raised from Numba so as to prevent warnings propagating
+into code that is consuming Numba.
+
+Suppressing Deprecation warnings
+================================
+All Numba deprecations are issued via ``NumbaDeprecationWarning`` or
+``NumbaPendingDeprecationWarning`` s, to suppress the reporting of
+these the following code snippet can be used::
+
+    from numba.core.errors import NumbaDeprecationWarning, NumbaPendingDeprecationWarning
+    import warnings
+
+    warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
+    warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning)
+
+The ``action`` used above is ``'ignore'``, other actions are available, see
+`The Warnings Filter <https://docs.python.org/3/library/warnings.html#the-warnings-filter>`_
+documentation for more information.
+
+.. note:: It is **strongly recommended** that applications and libraries which
+          choose to suppress these warnings should pin their Numba dependency
+          to a suitable version because their users will no longer be aware of
+          the coming incompatibility.
+
+Deprecation of reflection for List and Set types
+================================================
+Reflection (:term:`reflection`) is the jargon used in Numba to describe the
+process of ensuring that changes made by compiled code to arguments that are
+mutable Python container data types are visible in the Python interpreter when
+the compiled function returns. Numba has for some time supported reflection of
+``list`` and ``set`` data types and it is support for this reflection that
+is scheduled for deprecation with view to replace with a better implementation.
+
+Reason for deprecation
+----------------------
+First recall that for Numba to be able to compile a function in ``nopython``
+mode all the variables must have a concrete type ascertained through type
+inference. In simple cases, it is clear how to reflect changes to containers
+inside ``nopython`` mode back to the original Python containers. However,
+reflecting changes to complex data structures with nested container types (for
+example, lists of lists of integers) quickly becomes impossible to do
+efficiently and consistently. After a number of years of experience with this
+problem, it is clear that providing this behaviour is both fraught with
+difficulty and often leads to code which does not have good performance (all
+reflected data has to go through special APIs to convert the data to native
+formats at call time and then back to CPython formats at return time). As a
+result of this, the sheer number of reported problems in the issue tracker, and
+how well a new approach that was taken with ``typed.Dict`` (typed dictionaries)
+has gone, the core developers have decided to deprecate the noted ``reflection``
+behaviour.
+
+
+Example(s) of the impact
+------------------------
+
+At present only a warning of the upcoming change is issued. In future code such
+as::
+
+  from numba import njit
+
+  @njit
+  def foo(x):
+      x.append(10)
+
+  a = [1, 2, 3]
+  foo(a)
+
+will require adjustment to use a ``typed.List`` instance, this typed container
+is synonymous to the :ref:`feature-typed-dict`. An example of translating the
+above is::
+
+    from numba import njit
+    from numba.typed import List
+
+    @njit
+    def foo(x):
+        x.append(10)
+
+    a = [1, 2, 3]
+    typed_a = List()
+    [typed_a.append(x) for x in a]
+    foo(typed_a)
+
+For more information about ``typed.List`` see :ref:`feature-typed-list`. Further
+usability enhancements for this feature were made in the 0.47.0 release
+cycle.
+
+Schedule
+--------
+This feature will be removed with respect to this schedule:
+
+* Pending-deprecation warnings will be issued in version 0.44.0
+* Prominent notice will be given for a minimum of two releases prior to full
+  removal.
+
+Recommendations
+---------------
+Projects that need/rely on the deprecated behaviour should pin their dependency
+on Numba to a version prior to removal of this behaviour, or consider following
+replacement instructions that will be issued outlining how to adjust to the
+change.
+
+Expected Replacement
+--------------------
+As noted above ``typed.List`` will be used to permit similar functionality to
+reflection in the case of ``list`` s, a ``typed.Set`` will provide the
+equivalent for ``set`` (not implemented yet!). The advantages to this approach
+are:
+
+* That the containers are typed means type inference has to work less hard.
+* Nested containers (containers of containers of ...) are more easily
+  supported.
+* Performance penalties currently incurred translating data to/from native
+  formats are largely avoided.
+* Numba's ``typed.Dict`` will be able to use these containers as values.
+
+
+Deprecation of :term:`object mode` `fall-back` behaviour when using ``@jit``
+============================================================================
+The ``numba.jit`` decorator has for a long time followed the behaviour of first
+attempting to compile the decorated function in :term:`nopython mode` and should
+this compilation fail it will `fall-back` and try again to compile but this time
+in :term:`object mode`. It it this `fall-back` behaviour which is being
+deprecated, the result of which will be that ``numba.jit`` will by default
+compile in :term:`nopython mode` and :term:`object mode` compilation will
+become `opt-in` only.
+
+
+Reason for deprecation
+----------------------
+The `fall-back` has repeatedly caused confusion for users as seemingly innocuous
+changes in user code can lead to drastic performance changes as code which may
+have once compiled in :term:`nopython mode` mode may silently switch to
+compiling in :term:`object mode` e.g::
+
+    from numba import jit
+
+    @jit
+    def foo():
+        l = []
+        for x in range(10):
+            l.append(x)
+        return l
+
+    foo()
+
+    assert foo.nopython_signatures # this was compiled in nopython mode
+
+    @jit
+    def bar():
+        l = []
+        for x in range(10):
+            l.append(x)
+        return reversed(l) # innocuous change, but no reversed support in nopython mode
+
+    bar()
+
+    assert not bar.nopython_signatures # this was not compiled in nopython mode
+
+Another reason to remove the `fall-back` is that it is confusing for the
+compiler engineers developing Numba as it causes internal state problems that
+are really hard to debug and it makes manipulating the compiler pipelines
+incredibly challenging.
+
+Further, it has long been considered best practice that the
+:term:`nopython mode` keyword argument in the ``numba.jit`` decorator is set to
+``True`` and that any user effort spent should go into making code work in this
+mode as there's very little gain if it does not. The result is that, as Numba
+has evolved, the amount of use :term:`object mode` gets in practice and its
+general utility has decreased. It can be noted that there are some minor
+improvements available through the notion of :term:`loop-lifting`, the cases of
+this being used in practice are, however, rare and often a legacy from use of
+less-recent Numba whereby such behaviour was better accommodated/the use of
+``@jit`` with `fall-back` was recommended.
+
+
+Example(s) of the impact
+------------------------
+At present a warning of the upcoming change is issued if ``@jit`` decorated code
+uses the `fall-back` compilation path. In future code such as::
+
+    @jit
+    def bar():
+        l = []
+        for x in range(10):
+            l.append(x)
+        return reversed(l)
+
+    bar()
+
+will simply not compile, a ``TypingError`` would be raised.
+
+Schedule
+--------
+This feature will be removed with respect to this schedule:
+
+* Deprecation warnings will be issued in version 0.44.0
+* Prominent notice will be given for a minimum of two releases prior to full
+  removal.
+
+Recommendations
+---------------
+Projects that need/rely on the deprecated behaviour should pin their dependency
+on Numba to a version prior to removal of this behaviour. Alternatively, to
+accommodate the scheduled deprecations, users with code compiled at present with
+``@jit`` can supply the ``nopython=True`` keyword argument, if the code
+continues to compile then the code is already ready for this change. If the code
+does not compile, continue using the ``@jit`` decorator without
+``nopython=True`` and profile the performance of the function. Then remove the
+decorator and again check the performance of the function. If there is no
+benefit to having the ``@jit`` decorator present consider removing it! If there
+is benefit to having the ``@jit`` decorator present, then to be future proof
+supply the keyword argument ``forceobj=True`` to ensure the function is always
+compiled in :term:`object mode`.
+
+
+.. _deprecation-strict-strides:
+
+
+Deprecation of eager compilation of CUDA device functions
+=========================================================
+
+In future versions of Numba, the ``device`` kwarg to the ``@cuda.jit`` decorator
+will be obviated, and whether a device function or global kernel is compiled will
+be inferred from the context. With respect to kernel / device functions and lazy
+/ eager compilation, four cases were handled:
+
+1. ``device=True``, eager compilation with a signature provided
+2. ``device=False``, eager compilation with a signature provided
+3. ``device=True``, lazy compilation with no signature
+4. ``device=False``, lazy compilation with no signature
+
+The latter two cases can be differentiated without the ``device`` kwarg, because
+it can be inferred from the calling context - if the call is from the host, then
+a global kernel should be compiled, and if the call is from a kernel or another
+device function, then a device function should be compiled.
+
+The first two cases cannot be differentiated in the absence of the ``device``
+kwarg - without it, it will not be clear from a signature alone whether a device
+function or global kernel should be compiled. In order to resolve this, device
+functions will no longer be eagerly compiled. When a signature is provided to a
+device function, it will only be used to enforce the types of arguments that
+the function accepts.
+
+.. note::
+
+   In previous releases this notice stated that support for providing
+   signatures to device functions would be removed completely - however, this
+   precludes the common use case of enforcing the types that can be passed to a
+   device function (and the automatic insertion of casts that it implies) so
+   this notice has been updated to retain support for passing signatures.
+
+
+Schedule
+--------
+
+- In Numba 0.54: Eager compilation of device functions will be deprecated.
+- In Numba 0.55: Eager compilation of device functions will be unsupported and
+  the provision of signatures for device functions will only enforce casting.
+
+
+Deprecation and removal of ``numba.core.base.BaseContext.add_user_function()``
+==============================================================================
+
+``add_user_function()``  offered the same functionality as
+``insert_user_function()``, only with a check that the function has already
+been inserted at least once.  It is now removed as it was no longer used
+internally and it was expected that it was not used externally.
+
+Recommendations
+---------------
+
+Replace any uses of ``add_user_function()`` with ``insert_user_function()``.
+
+Schedule
+--------
+
+- In Numba 0.55: ``add_user_function()`` was deprecated.
+- In Numba 0.56: ``add_user_function()`` was removed.
+
+
+Deprecation and removal of CUDA Toolkits < 10.2 and devices with CC < 5.3
+=========================================================================
+
+- Support for CUDA toolkits less than 10.2 was deprecated and removed.
+- Support for devices with Compute Capability < 5.3 is deprecated and will be
+  removed in the future.
+
+
+Recommendations
+---------------
+
+- For devices of Compute Capability 3.0 and 3.2, Numba 0.55.1 or earlier will
+  be required.
+- CUDA toolkit 10.2 or later (ideally 11.2 or later) should be installed.
+
+Schedule
+--------
+
+- In Numba 0.55.1: support for CC < 5.3 and CUDA toolkits < 10.2 was deprecated.
+- In Numba 0.56: support for CC < 3.5 and CUDA toolkits < 10.2 was removed.
+- In Numba 0.57: support for CC < 5.3 will be removed.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/envvars.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/envvars.rst
new file mode 100644
index 000000000..ec28ed816
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/envvars.rst
@@ -0,0 +1,589 @@
+.. _numba-envvars:
+
+Environment variables
+=====================
+
+.. note:: This section relates to environment variables that impact Numba's
+          runtime, for compile time environment variables see
+          :ref:`numba-source-install-env_vars`.
+
+Numba allows its behaviour to be changed through the use of environment
+variables. Unless otherwise mentioned, those variables have integer values and
+default to zero.
+
+For convenience, Numba also supports the use of a configuration file to persist
+configuration settings. Note: To use this feature ``pyyaml`` must be installed.
+
+The configuration file must be named ``.numba_config.yaml`` and be present in
+the directory from which the Python interpreter is invoked. The configuration
+file, if present, is read for configuration settings before the environment
+variables are searched. This means that the environment variable settings will
+override the settings obtained from a configuration file (the configuration file
+is for setting permanent preferences whereas the environment variables are for
+ephemeral preferences).
+
+The format of the configuration file is a dictionary in ``YAML`` format that
+maps the environment variables below (without the ``NUMBA_`` prefix) to a
+desired value. For example, to permanently switch on developer mode
+(``NUMBA_DEVELOPER_MODE`` environment variable) and control flow graph printing
+(``NUMBA_DUMP_CFG`` environment variable), create a configuration file with the
+contents::
+
+    developer_mode: 1
+    dump_cfg: 1
+
+This can be especially useful in the case of wanting to use a set color scheme
+based on terminal background color. For example, if the terminal background
+color is black, the ``dark_bg`` color scheme would be well suited and can be set
+for permanent use by adding::
+
+    color_scheme: dark_bg
+
+Jit flags
+---------
+
+These variables globally override flags to the :func:`~numba.jit` decorator.
+
+.. envvar:: NUMBA_BOUNDSCHECK
+
+   If set to 0 or 1, globally disable or enable bounds checking, respectively.
+   The default if the variable is not set or set to an empty string is to use
+   the ``boundscheck`` flag passed to the :func:`~numba.jit` decorator for a
+   given function. See the documentation of :ref:`@jit
+   <jit-decorator-boundscheck>` for more information.
+
+   Note, due to limitations in numba, the bounds checking currently produces
+   exception messages that do not match those from NumPy. If you set
+   ``NUMBA_FULL_TRACEBACKS=1``, the full exception message with the axis,
+   index, and shape information will be printed to the terminal.
+
+Debugging
+---------
+
+These variables influence what is printed out during compilation of
+:term:`JIT functions <JIT function>`.
+
+.. envvar:: NUMBA_DEVELOPER_MODE
+
+    If set to non-zero, developer mode produces full tracebacks and disables
+    help instructions. Default is zero.
+
+.. envvar:: NUMBA_FULL_TRACEBACKS
+
+    If set to non-zero, enable full tracebacks when an exception occurs.
+    Defaults to the value set by `NUMBA_DEVELOPER_MODE`.
+
+.. envvar:: NUMBA_SHOW_HELP
+
+    If set to non-zero, show resources for getting help. Default is zero.
+
+.. envvar:: NUMBA_CAPTURED_ERRORS
+
+    Alters the way in which Numba captures and handles exceptions that do not
+    inherit from ``numba.core.errors.NumbaError`` during compilation (e.g.
+    standard Python exceptions). This does not impact runtime exception
+    handling. Valid values are:
+
+    - ``"old_style"`` (default): this is the exception handling behaviour that
+      is present in Numba versions <= 0.54.x. Numba will capture and wrap all
+      errors occurring in compilation and depending on the compilation phase they
+      will likely materialize as part of the message in a ``TypingError`` or a
+      ``LoweringError``.
+    - ``"new_style"`` this will treat any exception that does not inherit from
+      ``numba.core.errors.NumbaError`` **and** is raised during compilation as a
+      "hard error", i.e. the exception will propagate and compilation will halt.
+      The purpose of this new style is to differentiate between intentionally
+      raised exceptions and those which occur due to mistakes. For example, if
+      an ``AttributeError`` occurs in the typing of an ``@overload`` function,
+      under this new behaviour it is assumed that this a mistake in the
+      implementation and compilation will halt due to this exception. This
+      behaviour will eventually become the default.
+
+.. envvar:: NUMBA_DISABLE_ERROR_MESSAGE_HIGHLIGHTING
+
+    If set to non-zero error message highlighting is disabled. This is useful
+    for running the test suite on CI systems.
+
+.. envvar:: NUMBA_COLOR_SCHEME
+
+   Alters the color scheme used in error reporting (requires the ``colorama``
+   package to be installed to work). Valid values are:
+
+   - ``no_color`` No color added, just bold font weighting.
+   - ``dark_bg`` Suitable for terminals with a dark background.
+   - ``light_bg`` Suitable for terminals with a light background.
+   - ``blue_bg`` Suitable for terminals with a blue background.
+   - ``jupyter_nb`` Suitable for use in Jupyter Notebooks.
+
+   *Default value:* ``no_color``. The type of the value is ``string``.
+
+.. envvar:: NUMBA_HIGHLIGHT_DUMPS
+
+   If set to non-zero and ``pygments`` is installed, syntax highlighting is
+   applied to Numba IR, LLVM IR and assembly dumps. Default is zero.
+
+.. envvar:: NUMBA_DISABLE_PERFORMANCE_WARNINGS
+
+   If set to non-zero the issuing of performance warnings is disabled. Default
+   is zero.
+
+.. envvar:: NUMBA_DEBUG
+
+   If set to non-zero, print out all possible debugging information during
+   function compilation.  Finer-grained control can be obtained using other
+   variables below.
+
+.. envvar:: NUMBA_DEBUG_FRONTEND
+
+   If set to non-zero, print out debugging information during operation
+   of the compiler frontend, up to and including generation of the Numba
+   Intermediate Representation.
+
+.. envvar:: NUMBA_DEBUGINFO
+
+   If set to non-zero, enable debug for the full application by setting
+   the default value of the ``debug`` option in ``jit``. Beware that
+   enabling debug info significantly increases the memory consumption
+   for each compiled function.
+   Default value equals to the value of `NUMBA_ENABLE_PROFILING`.
+
+.. envvar:: NUMBA_EXTEND_VARIABLE_LIFETIMES
+
+    If set to non-zero, extend the lifetime of variables to the end of the block
+    in which their lifetime ends. This is particularly useful in conjunction
+    with :envvar:`NUMBA_DEBUGINFO` as it helps with introspection of values.
+    Default is zero.
+
+.. envvar:: NUMBA_GDB_BINARY
+
+   Set the ``gdb`` binary for use in Numba's ``gdb`` support. This takes one of
+   two forms: 1) a path and full name of the binary to explicitly express
+   which binary to use 2) just the name of the binary and the current path will
+   be searched using the standard path resolution rules. For example:
+   ``/path/from/root/to/binary/name_of_gdb_binary`` or
+   ``custom_gdb_binary_name``. This is to permit the use of a ``gdb`` from a
+   non-default location with a non-default name. The default value is ``gdb``.
+
+.. envvar:: NUMBA_DEBUG_TYPEINFER
+
+   If set to non-zero, print out debugging information about type inference.
+
+.. envvar:: NUMBA_ENABLE_PROFILING
+
+   Enables JIT events of LLVM in order to support profiling of jitted functions.
+   This option is automatically enabled under certain profilers.
+
+.. envvar:: NUMBA_TRACE
+
+   If set to non-zero, trace certain function calls (function entry and exit
+   events, including arguments and return values).
+
+.. envvar:: NUMBA_CHROME_TRACE
+
+   If defined, chrome tracing is enabled and this variable specifies the filepath
+   of the chrome tracing json file output. The emitted file can be opened by
+   a Chromium-based browser using the profile viewer at `chrome://tracing/`.
+
+   .. warning:: This feature is not supported in multi-process applications. 
+
+.. envvar:: NUMBA_DUMP_BYTECODE
+
+   If set to non-zero, print out the Python :py:term:`bytecode` of
+   compiled functions.
+
+.. envvar:: NUMBA_DUMP_CFG
+
+   If set to non-zero, print out information about the Control Flow Graph
+   of compiled functions.
+
+.. envvar:: NUMBA_DUMP_IR
+
+   If set to non-zero, print out the Numba Intermediate Representation
+   of compiled functions.
+
+
+.. envvar:: NUMBA_DUMP_SSA
+
+   If set to non-zero, print out the Numba Intermediate Representation of
+   compiled functions after conversion to Static Single Assignment (SSA) form.
+
+.. envvar:: NUMBA_DEBUG_PRINT_AFTER
+
+   Dump the Numba IR after declared pass(es). This is useful for debugging IR
+   changes made by given passes. Accepted values are:
+
+   * Any pass name (as given by the ``.name()`` method on the class)
+   * Multiple pass names as a comma separated list, i.e. ``"foo_pass,bar_pass"``
+   * The token ``"all"``, which will print after all passes.
+
+   The default value is ``"none"`` so as to prevent output.
+
+.. envvar:: NUMBA_DUMP_ANNOTATION
+
+   If set to non-zero, print out types annotations for compiled functions.
+
+.. envvar:: NUMBA_DUMP_LLVM
+
+   Dump the unoptimized LLVM assembly source of compiled functions.
+   Unoptimized code is usually very verbose; therefore,
+   :envvar:`NUMBA_DUMP_OPTIMIZED` is recommended instead.
+
+.. envvar:: NUMBA_DUMP_FUNC_OPT
+
+   Dump the LLVM assembly source after the LLVM "function optimization"
+   pass, but before the "module optimization" pass.  This is useful mostly
+   when developing Numba itself, otherwise use :envvar:`NUMBA_DUMP_OPTIMIZED`.
+
+.. envvar:: NUMBA_DUMP_OPTIMIZED
+
+   Dump the LLVM assembly source of compiled functions after all
+   optimization passes.  The output includes the raw function as well as
+   its CPython-compatible wrapper (whose name begins with ``wrapper.``).
+   Note that the function is often inlined inside the wrapper, as well.
+
+.. envvar:: NUMBA_DEBUG_ARRAY_OPT
+
+   Dump debugging information related to the processing associated with
+   the ``parallel=True`` jit decorator option.
+
+.. envvar:: NUMBA_DEBUG_ARRAY_OPT_RUNTIME
+
+   Dump debugging information related to the runtime scheduler associated
+   with the ``parallel=True`` jit decorator option.
+
+.. envvar:: NUMBA_DEBUG_ARRAY_OPT_STATS
+
+   Dump statistics about how many operators/calls are converted to
+   parallel for-loops and how many are fused together, which are associated
+   with the ``parallel=True`` jit decorator option.
+
+.. envvar:: NUMBA_PARALLEL_DIAGNOSTICS
+
+   If set to an integer value between 1 and 4 (inclusive) diagnostic information
+   about parallel transforms undertaken by Numba will be written to STDOUT. The
+   higher the value set the more detailed the information produced.
+
+.. envvar:: NUMBA_DUMP_ASSEMBLY
+
+   Dump the native assembly code of compiled functions.
+
+.. envvar:: NUMBA_LLVM_PASS_TIMINGS
+
+    Set to ``1`` to enable recording of pass timings in LLVM;
+    e.g. ``NUMBA_LLVM_PASS_TIMINGS=1``.
+    See :ref:`developer-llvm-timings`.
+
+    *Default value*: ``0`` (Off)
+
+.. seealso::
+   :ref:`numba-troubleshooting` and :ref:`architecture`.
+
+
+Compilation options
+-------------------
+
+.. envvar:: NUMBA_OPT
+
+   The optimization level; this option is passed straight to LLVM.
+
+   *Default value:* 3
+
+.. envvar:: NUMBA_LOOP_VECTORIZE
+
+   If set to non-zero, enable LLVM loop vectorization.
+
+   *Default value:* 1 (except on 32-bit Windows)
+
+.. envvar:: NUMBA_SLP_VECTORIZE
+
+   If set to non-zero, enable LLVM superword-level parallelism vectorization.
+
+   *Default value:* 1
+
+.. envvar:: NUMBA_ENABLE_AVX
+
+   If set to non-zero, enable AVX optimizations in LLVM.  This is disabled
+   by default on Sandy Bridge and Ivy Bridge architectures as it can sometimes
+   result in slower code on those platforms.
+
+.. envvar:: NUMBA_DISABLE_INTEL_SVML
+
+    If set to non-zero and Intel SVML is available, the use of SVML will be
+    disabled.
+
+.. envvar:: NUMBA_DISABLE_JIT
+
+   Disable JIT compilation entirely.  The :func:`~numba.jit` decorator acts
+   as if it performs no operation, and the invocation of decorated functions
+   calls the original Python function instead of a compiled version.  This
+   can be useful if you want to run the Python debugger over your code.
+
+.. envvar:: NUMBA_CPU_NAME
+.. envvar:: NUMBA_CPU_FEATURES
+
+    Override CPU and CPU features detection.
+    By setting ``NUMBA_CPU_NAME=generic``, a generic CPU model is picked
+    for the CPU architecture and the feature list (``NUMBA_CPU_FEATURES``)
+    defaults to empty.  CPU features must be listed with the format
+    ``+feature1,-feature2`` where ``+`` indicates enable and ``-`` indicates
+    disable. For example, ``+sse,+sse2,-avx,-avx2`` enables SSE and SSE2, and
+    disables AVX and AVX2.
+
+    These settings are passed to LLVM for configuring the compilation target.
+    To get a list of available options, use the ``llc`` commandline tool
+    from LLVM, for example::
+
+        llc -march=x86 -mattr=help
+
+
+    .. tip:: To force all caching functions (``@jit(cache=True)``) to emit
+        portable code (portable within the same architecture and OS),
+        simply set ``NUMBA_CPU_NAME=generic``.
+
+.. envvar:: NUMBA_FUNCTION_CACHE_SIZE
+
+    Override the size of the function cache for retaining recently
+    deserialized functions in memory.  In systems like
+    `Dask <http://dask.pydata.org>`_, it is common for functions to be deserialized
+    multiple times.  Numba will cache functions as long as there is a
+    reference somewhere in the interpreter.  This cache size variable controls
+    how many functions that are no longer referenced will also be retained,
+    just in case they show up in the future.  The implementation of this is
+    not a true LRU, but the large size of the cache should be sufficient for
+    most situations.
+
+    Note: this is unrelated to the compilation cache.
+
+    *Default value:* 128
+
+.. envvar:: NUMBA_LLVM_REFPRUNE_PASS
+
+    Turns on the LLVM pass level reference-count pruning pass and disables the
+    regex based implementation in Numba.
+
+    *Default value:* 1 (On)
+
+.. envvar:: NUMBA_LLVM_REFPRUNE_FLAGS
+
+    When ``NUMBA_LLVM_REFPRUNE_PASS`` is on, this allows configuration
+    of subpasses in the reference-count pruning LLVM pass.
+
+    Valid values are any combinations of the below separated by `,`
+    (case-insensitive):
+
+    - ``all``: enable all subpasses.
+    - ``per_bb``: enable per-basic-block level pruning, which is same as the
+      old regex based implementation.
+    - ``diamond``: enable inter-basic-block pruning that is a diamond shape
+      pattern, i.e. a single-entry single-exit CFG subgraph where has an incref
+      in the entry and a corresponding decref in the exit.
+    - ``fanout``: enable inter-basic-block pruning that has a fanout pattern,
+      i.e. a single-entry multiple-exit CFG subgraph where the entry has an
+      incref and every exit has a corresponding decref.
+    - ``fanout_raise``: same as ``fanout`` but allow subgraph exit nodes to be
+      raising an exception and not have a corresponding decref.
+
+    For example, ``all`` is the same as
+    ``per_bb, diamond, fanout, fanout_raise``
+
+    *Default value:* "all"
+
+
+.. _numba-envvars-caching:
+
+Caching options
+---------------
+
+Options for the compilation cache.
+
+.. envvar:: NUMBA_DEBUG_CACHE
+
+   If set to non-zero, print out information about operation of the
+   :ref:`JIT compilation cache <jit-cache>`.
+
+.. envvar:: NUMBA_CACHE_DIR
+
+    Override the location of the cache directory. If defined, this should be
+    a valid directory path.
+
+    If not defined, Numba picks the cache directory in the following order:
+
+    1. In-tree cache. Put the cache next to the corresponding source file under
+       a ``__pycache__`` directory following how ``.pyc`` files are stored.
+    2. User-wide cache. Put the cache in the user's application directory using
+       ``appdirs.user_cache_dir`` from the
+       `Appdirs package <https://github.com/ActiveState/appdirs>`_.
+    3. IPython cache. Put the cache in an IPython specific application
+       directory.
+       Stores are made under the ``numba_cache`` in the directory returned by
+       ``IPython.paths.get_ipython_cache_dir()``.
+
+    Also see :ref:`docs on cache sharing <cache-sharing>` and
+    :ref:`docs on cache clearing <cache-clearing>`
+
+
+.. _numba-envvars-gpu-support:
+
+GPU support
+-----------
+
+.. envvar:: NUMBA_DISABLE_CUDA
+
+   If set to non-zero, disable CUDA support.
+
+.. envvar:: NUMBA_FORCE_CUDA_CC
+
+   If set, force the CUDA compute capability to the given version (a
+   string of the type ``major.minor``), regardless of attached devices.
+
+.. envvar:: NUMBA_CUDA_DEFAULT_PTX_CC
+
+   The default compute capability (a string of the type ``major.minor``) to
+   target when compiling to PTX using ``cuda.compile_ptx``. The default is
+   5.2, which is the lowest non-deprecated compute capability in the most
+   recent version of the CUDA toolkit supported (10.2 at present).
+
+.. envvar:: NUMBA_ENABLE_CUDASIM
+
+   If set, don't compile and execute code for the GPU, but use the CUDA
+   Simulator instead. For debugging purposes.
+
+
+.. envvar:: NUMBA_CUDA_ARRAY_INTERFACE_SYNC
+
+   Whether to synchronize on streams provided by objects imported using the CUDA
+   Array Interface. This defaults to 1. If set to 0, then no synchronization
+   takes place, and the user of Numba (and other CUDA libraries) is responsible
+   for ensuring correctness with respect to synchronization on streams.
+
+.. envvar:: NUMBA_CUDA_LOG_LEVEL
+
+   For debugging purposes. If no other logging is configured, the value of this
+   variable is the logging level for CUDA API calls. The default value is
+   ``CRITICAL`` - to trace all API calls on standard error, set this to
+   ``DEBUG``.
+
+.. envvar:: NUMBA_CUDA_LOG_API_ARGS
+
+   By default the CUDA API call logs only give the names of functions called.
+   Setting this variable to 1 also includes the values of arguments to Driver
+   API calls in the logs.
+
+.. envvar:: NUMBA_CUDA_DRIVER
+
+   Path of the directory in which the CUDA driver libraries are to be found.
+   Normally this should not need to be set as Numba can locate the driver in
+   standard locations. However, this variable can be used if the driver is in a
+   non-standard location.
+
+.. envvar:: NUMBA_CUDA_LOG_SIZE
+
+   Buffer size for logs produced by CUDA driver API operations. This defaults
+   to 1024 and should not normally need to be modified - however, if an error
+   in an API call produces a large amount of output that appears to be
+   truncated (perhaps due to multiple long function names, for example) then
+   this variable can be used to increase the buffer size and view the full
+   error message.
+
+.. envvar:: NUMBA_CUDA_VERBOSE_JIT_LOG
+
+   Whether the CUDA driver should produce verbose log messages. Defaults to 1,
+   indicating that verbose messaging is enabled. This should not need to be
+   modified under normal circumstances.
+
+.. envvar:: NUMBA_CUDA_PER_THREAD_DEFAULT_STREAM
+
+   When set to 1, the default stream is the per-thread default stream. When set
+   to 0, the default stream is the legacy default stream. This defaults to 0,
+   for the legacy default stream. See `Stream Synchronization Behavior
+   <https://docs.nvidia.com/cuda/cuda-runtime-api/stream-sync-behavior.html>`_
+   for an explanation of the legacy and per-thread default streams.
+
+   This variable only takes effect when using Numba's internal CUDA bindings;
+   when using the NVIDIA bindings, use the environment variable
+   ``CUDA_PYTHON_CUDA_PER_THREAD_DEFAULT_STREAM`` instead.
+
+   .. seealso::
+
+      The `Default Stream section
+      <https://nvidia.github.io/cuda-python/release/11.6.0-notes.html#default-stream>`_
+      in the NVIDIA Bindings documentation.
+
+.. envvar:: NUMBA_CUDA_LOW_OCCUPANCY_WARNINGS
+
+   Enable warnings if the grid size is too small relative to the number of
+   streaming multiprocessors (SM). This option is on by default (default value is 1).
+
+   The heuristic checked is whether ``gridsize < 2 * (number of SMs)``. NOTE: The absence of
+   a warning does not imply a good gridsize relative to the number of SMs. Disabling
+   this warning will reduce the number of CUDA API calls (during JIT compilation), as the
+   heuristic needs to check the number of SMs available on the device in the
+   current context.
+
+.. envvar:: NUMBA_CUDA_WARN_ON_IMPLICIT_COPY
+
+   Enable warnings if a kernel is launched with host memory which forces a copy to and
+   from the device. This option is on by default (default value is 1).
+
+.. envvar:: NUMBA_CUDA_USE_NVIDIA_BINDING
+
+   When set to 1, Numba will attempt to use the `NVIDIA CUDA Python binding
+   <https://nvidia.github.io/cuda-python/>`_ to make calls to the driver API
+   instead of using its own ctypes binding. This defaults to 0 (off), as the
+   NVIDIA binding is currently missing support for Per-Thread Default
+   Streams and the profiler APIs.
+
+.. envvar:: NUMBA_CUDA_INCLUDE_PATH
+
+   The location of the CUDA include files. This is used when linking CUDA C/C++
+   sources to Python kernels, and needs to be correctly set for CUDA includes to
+   be available to linked C/C++ sources. On Linux, it defaults to
+   ``/usr/local/cuda/include``. On Windows, the default is
+   ``$env:CUDA_PATH\include``.
+
+
+Threading Control
+-----------------
+
+.. envvar:: NUMBA_NUM_THREADS
+
+   If set, the number of threads in the thread pool for the parallel CPU target
+   will take this value. Must be greater than zero. This value is independent
+   of ``OMP_NUM_THREADS`` and ``MKL_NUM_THREADS``.
+
+   *Default value:* The number of CPU cores on the system as determined at run
+   time. This can be accessed via :obj:`numba.config.NUMBA_DEFAULT_NUM_THREADS`.
+
+   See also the section on :ref:`setting_the_number_of_threads` for
+   information on how to set the number of threads at runtime.
+
+.. envvar:: NUMBA_THREADING_LAYER
+
+   This environment variable controls the library used for concurrent execution
+   for the CPU parallel targets (``@vectorize(target='parallel')``,
+   ``@guvectorize(target='parallel')``  and ``@njit(parallel=True)``). The
+   variable type is string and by default is ``default`` which will select a
+   threading layer based on what is available in the runtime. The valid values
+   are (for more information about these see
+   :ref:`the threading layer documentation <numba-threading-layer>`):
+
+   * ``default`` - select a threading layer based on what is available in the
+     current runtime.
+   * ``safe`` - select a threading layer that is both fork and thread safe
+     (requires the TBB package).
+   * ``forksafe`` - select a threading layer that is fork safe.
+   * ``threadsafe`` - select a threading layer that is thread safe.
+   * ``tbb`` - A threading layer backed by Intel TBB.
+   * ``omp`` - A threading layer backed by OpenMP.
+   * ``workqueue`` - A simple built-in work-sharing task scheduler.
+
+.. envvar:: NUMBA_THREADING_LAYER_PRIORITY
+
+   This environment variable controls the order in which the libraries used for
+   concurrent execution, for the CPU parallel targets
+   (``@vectorize(target='parallel')``, ``@guvectorize(target='parallel')``
+   and ``@njit(parallel=True)``), are prioritized for use. The variable type is
+   string and by default is ``tbb omp workqueue``, with the priority taken based
+   on position from the left of the string, left most being the highest. Valid
+   values are any permutation of the three choices (for more information about
+   these see :ref:`the threading layer documentation <numba-threading-layer>`.)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/fpsemantics.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/fpsemantics.rst
new file mode 100644
index 000000000..7973d4e50
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/fpsemantics.rst
@@ -0,0 +1,81 @@
+
+Floating-point pitfalls
+=======================
+
+Precision and accuracy
+----------------------
+
+For some operations, Numba may use a different algorithm than Python or
+Numpy.  The results may not be bit-by-bit compatible.  The difference
+should generally be small and within reasonable expectations.  However,
+small accumulated differences might produce large differences at the end,
+especially if a divergent function is involved.
+
+Math library implementations
+''''''''''''''''''''''''''''
+
+Numba supports a variety of platforms and operating systems, each of which
+has its own math library implementation (referred to as ``libm`` from here
+in).  The majority of math functions included in ``libm`` have specific
+requirements as set out by the IEEE 754 standard (like ``sin()``, ``exp()``
+etc.), but each implementation may have bugs.  Thus, on some platforms
+Numba has to exercise special care in order to workaround known ``libm``
+issues.
+
+Another typical problem is when an operating system's ``libm`` function
+set is incomplete and needs to be supplemented by additional functions.
+These are provided with reference to the IEEE 754 and C99 standards
+and are often implemented in Numba in a manner similar to equivalent
+CPython functions.
+
+Linear algebra
+''''''''''''''
+
+Numpy forces some linear algebra operations to run in double-precision mode
+even when a ``float32`` input is given.  Numba will always observe
+the input's precision, and invoke single-precision linear algebra routines
+when all inputs are ``float32`` or ``complex64``.
+
+The implementations of the ``numpy.linalg`` routines in Numba only support the
+floating point types that are used in the LAPACK functions that provide
+the underlying core functionality. As a result  only ``float32``, ``float64``,
+``complex64`` and ``complex128`` types are supported. If a user has e.g. an
+``int32`` type, an appropriate type conversion must be performed to a
+floating point type prior to its use in these routines. The reason for this
+decision is to essentially avoid having to replicate type conversion choices
+made in Numpy and to also encourage the user to choose the optimal floating
+point type for the operation they are undertaking.
+
+
+Mixed-types operations
+''''''''''''''''''''''
+
+Numpy will most often return a ``float64`` as a result of a computation
+with mixed integer and floating-point operands (a typical example is the
+power operator ``**``).  Numba by contrast will select the highest precision
+amongst the floating-point operands, so for example ``float32 ** int32``
+will return a ``float32``, regardless of the input values.  This makes
+performance characteristics easier to predict, but you should explicitly
+cast the input to ``float64`` if you need the extra precision.
+
+
+.. _ufunc-fpu-errors:
+
+Warnings and errors
+-------------------
+
+When calling a :term:`ufunc` created with :func:`~numba.vectorize`,
+Numpy will determine whether an error occurred by examining the FPU
+error word.  It may then print out a warning or raise an exception
+(such as ``RuntimeWarning: divide by zero encountered``),
+depending on the current error handling settings.
+
+Depending on how LLVM optimized the ufunc's code, however, some spurious
+warnings or errors may appear.  If you get caught by this issue, we
+recommend you call :func:`numpy.seterr` to change Numpy's error handling
+settings, or the :class:`numpy.errstate` context manager to switch them
+temporarily::
+
+   with np.errstate(all='ignore'):
+       x = my_ufunc(y)
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/index.rst
new file mode 100644
index 000000000..e099d2a31
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/index.rst
@@ -0,0 +1,16 @@
+
+Reference Manual
+================
+
+.. toctree::
+
+   types.rst
+   jit-compilation.rst
+   aot-compilation.rst
+   utils.rst
+   envvars.rst
+   pysupported.rst
+   numpysupported.rst
+   pysemantics.rst
+   fpsemantics.rst
+   deprecation.rst
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/jit-compilation.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/jit-compilation.rst
new file mode 100644
index 000000000..ac67a593b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/jit-compilation.rst
@@ -0,0 +1,572 @@
+Just-in-Time compilation
+========================
+
+
+JIT functions
+-------------
+
+.. _jit-decorator:
+
+.. decorator:: numba.jit(signature=None, nopython=False, nogil=False, cache=False, forceobj=False, parallel=False, error_model='python', fastmath=False, locals={}, boundscheck=False)
+
+   Compile the decorated function on-the-fly to produce efficient machine
+   code.  All parameters are optional.
+
+   If present, the *signature* is either a single signature or a list of
+   signatures representing the expected :ref:`numba-types` of function
+   arguments and return values.  Each signature can be given in several
+   forms:
+
+   * A tuple of :ref:`numba-types` arguments (for example
+     ``(numba.int32, numba.double)``) representing the types of the
+     function's arguments; Numba will then infer an appropriate return
+     type from the arguments.
+   * A call signature using :ref:`numba-types`, specifying both return
+     type and argument types. This can be given in intuitive form
+     (for example ``numba.void(numba.int32, numba.double)``).
+   * A string representation of one of the above, for example
+     ``"void(int32, double)"``.  All type names used in the string are assumed
+     to be defined in the ``numba.types`` module.
+
+   *nopython* and *nogil* are boolean flags.  *locals* is a mapping of
+   local variable names to :ref:`numba-types`.
+
+   This decorator has several modes of operation:
+
+   * If one or more signatures are given in *signature*, a specialization is
+     compiled for each of them.  Calling the decorated function will then try
+     to choose the best matching signature, and raise a :class:`TypeError` if
+     no appropriate conversion is available for the function arguments.  If
+     converting succeeds, the compiled machine code is executed with the
+     converted arguments and the return value is converted back according to
+     the signature.
+
+   * If no *signature* is given, the decorated function implements
+     lazy compilation.  Each call to the decorated function will try to
+     re-use an existing specialization if it exists (for example, a call
+     with two integer arguments may re-use a specialization for argument
+     types ``(numba.int64, numba.int64)``).  If no suitable specialization
+     exists, a new specialization is compiled on-the-fly, stored for later
+     use, and executed with the converted arguments.
+
+   If true, *nopython* forces the function to be compiled in :term:`nopython
+   mode`. If not possible, compilation will raise an error.
+
+   If true, *forceobj* forces the function to be compiled in :term:`object
+   mode`.  Since object mode is slower than nopython mode, this is mostly
+   useful for testing purposes.
+
+   If true, *nogil* tries to release the :py:term:`global interpreter lock`
+   inside the compiled function.  The GIL will only be released if Numba can
+   compile the function in :term:`nopython mode`, otherwise a compilation
+   warning will be printed.
+
+   .. _jit-decorator-cache:
+
+   If true, *cache* enables a file-based cache to shorten compilation times
+   when the function was already compiled in a previous invocation.
+   The cache is maintained in the ``__pycache__`` subdirectory of
+   the directory containing the source file; if the current user is not
+   allowed to write to it, though, it falls back to a platform-specific
+   user-wide cache directory (such as ``$HOME/.cache/numba`` on Unix
+   platforms).
+
+   .. _jit-decorator-parallel:
+
+   If true, *parallel* enables the automatic parallelization of a number of
+   common NumPy constructs as well as the fusion of adjacent parallel
+   operations to maximize cache locality.
+
+   The *error_model* option controls the divide-by-zero behavior.
+   Setting it to 'python' causes divide-by-zero to raise exception like CPython.
+   Setting it to 'numpy' causes divide-by-zero to set the result to *+/-inf* or
+   *nan*.
+
+   Not all functions can be cached, since some functionality cannot be
+   always persisted to disk.  When a function cannot be cached, a
+   warning is emitted.
+
+   .. _jit-decorator-fastmath:
+
+   If true, *fastmath* enables the use of otherwise unsafe floating point
+   transforms as described in the
+   `LLVM documentation <https://llvm.org/docs/LangRef.html#fast-math-flags>`_.
+   Further, if :ref:`Intel SVML <intel-svml>` is installed faster but less
+   accurate versions of some math intrinsics are used (answers to within
+   ``4 ULP``).
+
+   .. _jit-decorator-boundscheck:
+
+   If true, *boundscheck* enables bounds checking for array indices. Out of
+   bounds accesses will raise IndexError. The default is to not do bounds
+   checking. If bounds checking is disabled, out of bounds accesses can
+   produce garbage results or segfaults. However, enabling bounds checking
+   will slow down typical functions, so it is recommended to only use this
+   flag for debugging. You can also set the `NUMBA_BOUNDSCHECK` environment
+   variable to 0 or 1 to globally override this flag.
+
+   The *locals* dictionary may be used to force the :ref:`numba-types`
+   of particular local variables, for example if you want to force the
+   use of single precision floats at some point.  In general, we recommend
+   you let Numba's compiler infer the types of local variables by itself.
+
+   Here is an example with two signatures::
+
+      @jit(["int32(int32)", "float32(float32)"], nopython=True)
+      def f(x): ...
+
+   Not putting any parentheses after the decorator is equivalent to calling
+   the decorator without any arguments, i.e.::
+
+      @jit
+      def f(x): ...
+
+   is equivalent to::
+
+      @jit()
+      def f(x): ...
+
+   The decorator returns a :class:`Dispatcher` object.
+
+   .. note::
+      If no *signature* is given, compilation errors will be raised when
+      the actual compilation occurs, i.e. when the function is first called
+      with some given argument types.
+
+   .. note::
+      Compilation can be influenced by some dedicated :ref:`numba-envvars`.
+
+
+Generated JIT functions
+-----------------------
+
+.. decorator:: numba.generated_jit(nopython=False, nogil=False, cache=False, forceobj=False, locals={})
+
+   Like the :func:`~numba.jit` decorator, but calls the decorated function at
+   compile-time, passing the *types* of the function's arguments.
+   The decorated function must return a callable which will be compiled as
+   the function's implementation for those types, allowing flexible kinds of
+   specialization.
+
+   The :func:`~numba.generated_jit` decorator returns a :class:`Dispatcher` object.
+
+
+Dispatcher objects
+------------------
+
+.. class:: Dispatcher
+
+   The class of objects created by calling :func:`~numba.jit` or
+   :func:`~numba.generated_jit`.  You shouldn't try to create such an object
+   in any other way.  Calling a Dispatcher object calls the compiled
+   specialization for the arguments with which it is called, letting it
+   act as an accelerated replacement for the Python function which was compiled.
+
+   In addition, Dispatcher objects have the following methods and attributes:
+
+   .. attribute:: py_func
+
+      The pure Python function which was compiled.
+
+   .. method:: inspect_types(file=None, pretty=False)
+
+      Print out a listing of the function source code annotated line-by-line
+      with the corresponding Numba IR, and the inferred types of the various
+      variables.  If *file* is specified, printing is done to that file
+      object, otherwise to sys.stdout. If *pretty* is set to True then colored
+      ANSI will be produced in a terminal and HTML in a notebook.
+
+      .. seealso:: :ref:`architecture`
+
+   .. method:: inspect_llvm(signature=None)
+
+      Return a dictionary keying compiled function signatures to the human
+      readable LLVM IR generated for the function.  If the signature
+      keyword is specified a string corresponding to that individual
+      signature is returned.
+
+   .. method:: inspect_asm(signature=None)
+
+      Return a dictionary keying compiled function signatures to the
+      human-readable native assembly code for the function.  If the
+      signature keyword is specified a string corresponding to that
+      individual signature is returned.
+
+   .. method:: inspect_cfg(signature=None, show_wrapped)
+
+      Return a dictionary keying compiled function signatures to the
+      control-flow graph objects for the function.  If the signature keyword is
+      specified a string corresponding to that individual signature is returned.
+
+      The control-flow graph objects can be stringified (``str`` or ``repr``)
+      to get the textual representation of the graph in DOT format.  Or, use
+      its ``.display(filename=None, view=False)`` method to plot the graph.
+      The *filename* option can be set to a specific path for the rendered
+      output to write to.  If *view* option is True, the plot is opened by
+      the system default application for the image format (PDF). In IPython
+      notebook, the returned object can be plot inlined.
+
+      Usage::
+
+        @jit
+        def foo():
+          ...
+
+        # opens the CFG in system default application
+        foo.inspect_cfg(foo.signatures[0]).display(view=True)
+
+
+   .. method:: inspect_disasm_cfg(signature=None)
+
+      Return a dictionary keying compiled function signatures to the
+      control-flow graph of the disassembly of the underlying compiled ``ELF``
+      object.  If the signature keyword is specified a control-flow graph
+      corresponding to that individual signature is returned. This function is
+      execution environment aware and will produce SVG output in Jupyter
+      notebooks and ASCII in terminals.
+
+      Example::
+
+        @njit
+        def foo(x):
+            if x < 3:
+                return x + 1
+            return x + 2
+
+        foo(10)
+
+        print(foo.inspect_disasm_cfg(signature=foo.signatures[0]))
+
+      Gives::
+
+        [0x08000040]>  # method.__main__.foo_241_long_long (int64_t arg1, int64_t arg3);
+         ─────────────────────────────────────────────────────────────────────┐
+        │  0x8000040                                                          │
+        │ ; arg3 ; [02] -r-x section size 279 named .text                     │
+        │   ;-- section..text:                                                │
+        │   ;-- .text:                                                        │
+        │   ;-- __main__::foo$241(long long):                                 │
+        │   ;-- rip:                                                          │
+        │ 25: method.__main__.foo_241_long_long (int64_t arg1, int64_t arg3); │
+        │ ; arg int64_t arg1 @ rdi                                            │
+        │ ; arg int64_t arg3 @ rdx                                            │
+        │ ; 2                                                                 │
+        │ cmp rdx, 2                                                          │
+        │ jg 0x800004f                                                        │
+        └─────────────────────────────────────────────────────────────────────┘
+                f t
+                │ │
+                │ └──────────────────────────────┐
+                └──┐                             │
+                   │                             │
+            ┌─────────────────────────┐   ┌─────────────────────────┐
+            │  0x8000046              │   │  0x800004f              │
+            │ ; arg3                  │   │ ; arg3                  │
+            │ inc rdx                 │   │ add rdx, 2              │
+            │ ; arg3                  │   │ ; arg3                  │
+            │ mov qword [rdi], rdx    │   │ mov qword [rdi], rdx    │
+            │ xor eax, eax            │   │ xor eax, eax            │
+            │ ret                     │   │ ret                     │
+            └─────────────────────────┘   └─────────────────────────┘
+
+   .. method:: recompile()
+
+      Recompile all existing signatures.  This can be useful for example if
+      a global or closure variable was frozen by your function and its value
+      in Python has changed.  Since compiling isn't cheap, this is mainly
+      for testing and interactive use.
+
+   .. method:: parallel_diagnostics(signature=None, level=1)
+
+      Print parallel diagnostic information for the given signature. If no
+      signature is present it is printed for all known signatures. ``level`` is
+      used to adjust the verbosity, ``level=1`` (default) is minimum verbosity,
+      levels 2, 3, and 4 provide increasing levels of verbosity.
+
+   .. method:: get_metadata(signature=None)
+
+      Obtain the compilation metadata for a given signature. This is useful for
+      developers of Numba and Numba extensions.
+
+
+Vectorized functions (ufuncs and DUFuncs)
+-----------------------------------------
+
+.. decorator:: numba.vectorize(*, signatures=[], identity=None, nopython=True, target='cpu', forceobj=False, cache=False, locals={})
+
+   Compile the decorated function and wrap it either as a `NumPy
+   ufunc`_ or a Numba :class:`~numba.DUFunc`.  The optional
+   *nopython*, *forceobj* and *locals* arguments have the same meaning
+   as in :func:`numba.jit`.
+
+   *signatures* is an optional list of signatures expressed in the
+   same form as in the :func:`numba.jit` *signature* argument.  If
+   *signatures* is non-empty, then the decorator will compile the user
+   Python function into a NumPy ufunc.  If no *signatures* are given,
+   then the decorator will wrap the user Python function in a
+   :class:`~numba.DUFunc` instance, which will compile the user
+   function at call time whenever NumPy can not find a matching loop
+   for the input arguments.  *signatures* is required if *target* is
+   ``"parallel"``.
+
+   *identity* is the identity (or unit) value of the function being
+   implemented.  Possible values are 0, 1, None, and the string
+   ``"reorderable"``.  The default is None.  Both None and
+   ``"reorderable"`` mean the function has no identity value;
+   ``"reorderable"`` additionally specifies that reductions along multiple
+   axes can be reordered.
+
+   If there are several *signatures*, they must be ordered from the more
+   specific to the least specific.  Otherwise, NumPy's type-based
+   dispatching may not work as expected.  For example, the following is
+   wrong::
+
+      @vectorize(["float64(float64)", "float32(float32)"])
+      def f(x): ...
+
+   as running it over a single-precision array will choose the ``float64``
+   version of the compiled function, leading to much less efficient
+   execution.  The correct invocation is::
+
+      @vectorize(["float32(float32)", "float64(float64)"])
+      def f(x): ...
+
+   *target* is a string for backend target; Available values are "cpu",
+   "parallel", and "cuda".  To use a multithreaded version, change the
+   target to "parallel" (which requires signatures to be specified)::
+
+      @vectorize(["float64(float64)", "float32(float32)"], target='parallel')
+      def f(x): ...
+
+   For the CUDA target, use "cuda"::
+
+      @vectorize(["float64(float64)", "float32(float32)"], target='cuda')
+      def f(x): ...
+
+   The compiled function can be cached to reduce future compilation time.
+   It is enabled by setting *cache* to True. Only the "cpu" and "parallel"
+   targets support caching.
+
+
+.. decorator:: numba.guvectorize(signatures, layout, *, identity=None, nopython=True, target='cpu', forceobj=False, cache=False, locals={})
+
+   Generalized version of :func:`numba.vectorize`.  While
+   :func:`numba.vectorize` will produce a simple ufunc whose core
+   functionality (the function you are decorating) operates on scalar
+   operands and returns a scalar value, :func:`numba.guvectorize`
+   allows you to create a `NumPy ufunc`_ whose core function takes array
+   arguments of various dimensions.
+
+   The additional argument *layout* is a string specifying, in symbolic
+   form, the dimensionality and size relationship of the argument types
+   and return types.  For example, a matrix multiplication will have
+   a layout string of ``"(m,n),(n,p)->(m,p)"``.  Its definition might
+   be (function body omitted)::
+
+      @guvectorize(["void(float64[:,:], float64[:,:], float64[:,:])"],
+                   "(m,n),(n,p)->(m,p)")
+      def f(a, b, result):
+          """Fill-in *result* matrix such as result := a * b"""
+          ...
+
+   If one of the arguments should be a scalar, the corresponding layout
+   specification is ``()`` and the argument will really be given to
+   you as a zero-dimension array (you have to dereference it to get the
+   scalar value).  For example, a :ref:`one-dimension moving average <example-movemean>`
+   with a parameterable window width may have a layout string of ``"(n),()->(n)"``.
+
+   Note that any output will be given to you preallocated as an additional
+   function argument: your code has to fill it with the appropriate values
+   for the function you are implementing.
+
+   If your function doesn't take an output array, you should omit the "arrow"
+   in the layout string (e.g. ``"(n),(n)"``). When doing this, it is important
+   to be aware that changes to the input arrays cannot always be relied on to be
+   visible outside the execution of the ufunc, as NumPy may pass in temporary
+   arrays as inputs (for example, if a cast is required).
+
+   .. seealso::
+      Specification of the `layout string <https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html#details-of-signature>`_
+      as supported by NumPy.  Note that NumPy uses the term "signature",
+      which we unfortunately use for something else.
+
+   The compiled function can be cached to reduce future compilation time.
+   It is enabled by setting *cache* to True. Only the "cpu" and "parallel"
+   targets support caching.
+
+.. _NumPy ufunc: http://docs.scipy.org/doc/numpy/reference/ufuncs.html
+
+.. class:: numba.DUFunc
+
+   The class of objects created by calling :func:`numba.vectorize`
+   with no signatures.
+
+   DUFunc instances should behave similarly to NumPy
+   :class:`~numpy.ufunc` objects with one important difference:
+   call-time loop generation.  When calling a ufunc, NumPy looks at
+   the existing loops registered for that ufunc, and will raise a
+   :class:`~python.TypeError` if it cannot find a loop that it cannot
+   safely cast the inputs to suit.  When calling a DUFunc, Numba
+   delegates the call to NumPy.  If the NumPy ufunc call fails, then
+   Numba attempts to build a new loop for the given input types, and
+   calls the ufunc again.  If this second call attempt fails or a
+   compilation error occurs, then DUFunc passes along the exception to
+   the caller.
+
+   .. seealso::
+
+      The ":ref:`dynamic-universal-functions`" section in the user's
+      guide demonstrates the call-time behavior of
+      :class:`~numba.DUFunc`, and discusses the impact of call order
+      on how Numba generates the underlying :class:`~numpy.ufunc`.
+
+   .. attribute:: ufunc
+
+      The actual NumPy :class:`~numpy.ufunc` object being built by the
+      :class:`~numba.DUFunc` instance.  Note that the
+      :class:`~numba.DUFunc` object maintains several important data
+      structures required for proper ufunc functionality (specifically
+      the dynamically compiled loops).  Users should not pass the
+      :class:`~numpy.ufunc` value around without ensuring the
+      underlying :class:`~numba.DUFunc` will not be garbage collected.
+
+   .. attribute:: nin
+
+      The number of DUFunc (ufunc) inputs.  See `ufunc.nin`_.
+
+   .. attribute:: nout
+
+      The number of DUFunc outputs.  See `ufunc.nout`_.
+
+   .. attribute:: nargs
+
+      The total number of possible DUFunc arguments (should be
+      :attr:`~numba.DUFunc.nin` + :attr:`~numba.DUFunc.nout`).
+      See `ufunc.nargs`_.
+
+   .. attribute:: ntypes
+
+      The number of input types supported by the DUFunc.  See
+      `ufunc.ntypes`_.
+
+   .. attribute:: types
+
+      A list of the supported types given as strings.  See
+      `ufunc.types`_.
+
+   .. attribute:: identity
+
+      The identity value when using the ufunc as a reduction.  See
+      `ufunc.identity`_.
+
+   .. method:: reduce(A, *, axis, dtype, out, keepdims)
+
+      Reduces *A*\'s dimension by one by applying the DUFunc along one
+      axis.  See `ufunc.reduce`_.
+
+   .. method:: accumulate(A, *, axis, dtype, out)
+
+      Accumulate the result of applying the operator to all elements.
+      See `ufunc.accumulate`_.
+
+   .. method:: reduceat(A, indices, *, axis, dtype, out)
+
+      Performs a (local) reduce with specified slices over a single
+      axis.  See `ufunc.reduceat`_.
+
+   .. method:: outer(A, B)
+
+      Apply the ufunc to all pairs (*a*, *b*) with *a* in *A*, and *b*
+      in *B*.  See `ufunc.outer`_.
+
+   .. method:: at(A, indices, *, B)
+
+      Performs unbuffered in place operation on operand *A* for
+      elements specified by *indices*.  If you are using NumPy 1.7 or
+      earlier, this method will not be present.  See `ufunc.at`_.
+
+
+.. note::
+   Vectorized functions can, in rare circumstances, show
+   :ref:`unexpected warnings or errors <ufunc-fpu-errors>`.
+
+
+.. _`ufunc.nin`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.nin.html#numpy.ufunc.nin
+
+.. _`ufunc.nout`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.nout.html#numpy.ufunc.nout
+
+.. _`ufunc.nargs`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.nargs.html#numpy.ufunc.nargs
+
+.. _`ufunc.ntypes`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.ntypes.html#numpy.ufunc.ntypes
+
+.. _`ufunc.types`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.types.html#numpy.ufunc.types
+
+.. _`ufunc.identity`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.identity.html#numpy.ufunc.identity
+
+.. _`ufunc.reduce`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.reduce.html#numpy.ufunc.reduce
+
+.. _`ufunc.accumulate`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.accumulate.html#numpy.ufunc.accumulate
+
+.. _`ufunc.reduceat`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.reduceat.html#numpy.ufunc.reduceat
+
+.. _`ufunc.outer`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.outer.html#numpy.ufunc.outer
+
+.. _`ufunc.at`: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.at.html#numpy.ufunc.at
+
+
+C callbacks
+-----------
+
+.. decorator:: numba.cfunc(signature, nopython=False, cache=False, locals={})
+
+   Compile the decorated function on-the-fly to produce efficient machine
+   code.  The compiled code is wrapped in a thin C callback that makes it
+   callable using the natural C ABI.
+
+   The *signature* is a single signature representing the signature of the
+   C callback.  It must have the same form as in :func:`~numba.jit`.
+   The decorator does not check that the types in the signature have
+   a well-defined representation in C.
+
+   *nopython* and *cache* are boolean flags.  *locals* is a mapping of
+   local variable names to :ref:`numba-types`.  They all have the same
+   meaning as in :func:`~numba.jit`.
+
+   The decorator returns a :class:`CFunc` object.
+
+   .. note::
+      C callbacks currently do not support :term:`object mode`.
+
+
+.. class:: CFunc
+
+   The class of objects created by :func:`~numba.cfunc`.  :class:`CFunc`
+   objects expose the following attributes and methods:
+
+   .. attribute:: address
+
+      The address of the compiled C callback, as an integer.
+
+   .. attribute:: cffi
+
+      A `cffi`_ function pointer instance, to be passed as an argument to
+      `cffi`_-wrapped functions.  The pointer's type is ``void *``, so
+      only minimal type checking will happen when passing it to `cffi`_.
+
+   .. attribute:: ctypes
+
+      A :mod:`ctypes` callback instance, as if it were created using
+      :func:`ctypes.CFUNCTYPE`.
+
+   .. attribute:: native_name
+
+      The name of the compiled C callback.
+
+   .. method:: inspect_llvm()
+
+      Return the human-readable LLVM IR generated for the C callback.
+      :attr:`native_name` is the name under which this callback is defined
+      in the IR.
+
+
+.. _cffi: https://cffi.readthedocs.org/
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/numpysupported.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/numpysupported.rst
new file mode 100644
index 000000000..54060fd90
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/numpysupported.rst
@@ -0,0 +1,925 @@
+
+.. _numpy-support:
+
+========================
+Supported NumPy features
+========================
+
+One objective of Numba is having a seamless integration with `NumPy`_.
+NumPy arrays provide an efficient storage method for homogeneous sets of
+data.  NumPy dtypes provide type information useful when compiling, and
+the regular, structured storage of potentially large amounts of data
+in memory provides an ideal memory layout for code generation.  Numba
+excels at generating code that executes on top of NumPy arrays.
+
+NumPy support in Numba comes in many forms:
+
+* Numba understands calls to NumPy `ufuncs`_ and is able to generate
+  equivalent native code for many of them.
+
+* NumPy arrays are directly supported in Numba.  Access to NumPy arrays
+  is very efficient, as indexing is lowered to direct memory accesses
+  when possible.
+
+* Numba is able to generate `ufuncs`_ and `gufuncs`_. This means that it
+  is possible to implement ufuncs and gufuncs within Python, getting
+  speeds comparable to that of ufuncs/gufuncs implemented in C extension
+  modules using the NumPy C API.
+
+.. _NumPy: http://www.numpy.org/
+.. _ufuncs: http://docs.scipy.org/doc/numpy/reference/ufuncs.html
+.. _gufuncs: http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html
+
+The following sections focus on the NumPy features supported in
+:term:`nopython mode`, unless otherwise stated.
+
+
+Scalar types
+============
+
+Numba supports the following NumPy scalar types:
+
+* **Integers**: all integers of either signedness, and any width up to 64 bits
+* **Booleans**
+* **Real numbers:** single-precision (32-bit) and double-precision (64-bit) reals
+* **Complex numbers:** single-precision (2x32-bit) and double-precision (2x64-bit) complex numbers
+* **Datetimes and timestamps:** of any unit
+* **Character sequences** (but no operations are available on them)
+* **Structured scalars:** structured scalars made of any of the types above and arrays of the types above
+
+The following scalar types and features are not supported:
+
+* **Arbitrary Python objects**
+* **Half-precision and extended-precision** real and complex numbers
+* **Nested structured scalars** the fields of structured scalars may not contain other structured scalars
+
+The operations supported on NumPy scalars are almost the same as on the
+equivalent built-in types such as ``int`` or ``float``.  You can use a type's
+constructor to convert from a different type or width. In addition you can use
+the ``view(np.<dtype>)`` method to bitcast all ``int`` and ``float`` types
+within the same width. However, you must define the scalar using a NumPy
+constructor within a jitted function. For example, the following will work:
+
+.. code:: pycon
+
+    >>> import numpy as np
+    >>> from numba import njit
+    >>> @njit
+    ... def bitcast():
+    ...     i = np.int64(-1)
+    ...     print(i.view(np.uint64))
+    ...
+    >>> bitcast()
+    18446744073709551615
+
+
+Whereas the following will not work:
+
+
+.. code:: pycon
+
+    >>> import numpy as np
+    >>> from numba import njit
+    >>> @njit
+    ... def bitcast(i):
+    ...     print(i.view(np.uint64))
+    ...
+    >>> bitcast(np.int64(-1))
+    ---------------------------------------------------------------------------
+    TypingError                               Traceback (most recent call last)
+        ...
+    TypingError: Failed in nopython mode pipeline (step: ensure IR is legal prior to lowering)
+    'view' can only be called on NumPy dtypes, try wrapping the variable with 'np.<dtype>()'
+
+    File "<ipython-input-3-fc40aaab84c4>", line 3:
+    def bitcast(i):
+        print(i.view(np.uint64))
+
+Structured scalars support attribute getting and setting, as well as
+member lookup using constant strings. Strings stored in a local or global tuple
+are considered constant strings and can be used for member lookup.
+
+
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_rec_array.py
+   :language: python
+   :start-after: magictoken.ex_rec_arr_const_index.begin
+   :end-before: magictoken.ex_rec_arr_const_index.end
+   :dedent: 8
+
+It is also possible to use local or global tuples together with ``literal_unroll``:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_rec_array.py
+   :language: python
+   :start-after: magictoken.ex_rec_arr_lit_unroll_index.begin
+   :end-before: magictoken.ex_rec_arr_lit_unroll_index.end
+   :dedent: 8
+
+
+Record subtyping
+----------------
+.. warning::
+   This is an experimental feature.
+
+Numba allows `width subtyping <https://en.wikipedia.org/wiki/Subtyping#Record_types>`_ of structured scalars.
+For example, ``dtype([('a', 'f8'), ('b', 'i8')])`` will be considered a subtype of ``dtype([('a', 'f8')]``, because
+the second is a strict subset of the first, i.e. field ``a`` is of the same type and is in the same position in both
+types. The subtyping relationship will matter in cases where compilation for a certain input is not allowed, but the
+input is a subtype of another, allowed type.
+
+.. code-block:: python
+
+    import numpy as np
+    from numba import njit, typeof
+    from numba.core import types
+    record1 = np.array([1], dtype=[('a', 'f8')])[0]
+    record2 = np.array([(2,3)], dtype=[('a', 'f8'), ('b', 'f8')])[0]
+
+    @njit(types.float64(typeof(record1)))
+    def foo(rec):
+        return rec['a']
+
+    foo(record1)
+    foo(record2)
+
+Without subtyping the last line would fail. With subtyping, no new compilation will be triggered, but the
+compiled function for ``record1`` will be used for ``record2``.
+
+.. seealso::
+   `NumPy scalars <http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`_
+   reference.
+
+
+Array types
+===========
+
+`NumPy arrays <http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html>`_
+of any of the scalar types above are supported, regardless of the shape
+or layout.
+
+Array access
+------------
+
+Arrays support normal iteration.  Full basic indexing and slicing is
+supported.  A subset of advanced indexing is also supported: only one
+advanced index is allowed, and it has to be a one-dimensional array
+(it can be combined with an arbitrary number of basic indices as well).
+
+.. seealso::
+   `NumPy indexing <http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_
+   reference.
+
+
+.. _structured-array-access:
+
+Structured array access
+-----------------------
+
+Numba presently supports accessing fields of individual elements in structured
+arrays by attribute as well as by getting and setting. This goes slightly
+beyond the NumPy API, which only allows accessing fields by getting and
+setting. For example:
+
+.. code:: python
+
+   from numba import njit
+   import numpy as np
+
+   record_type = np.dtype([("ival", np.int32), ("fval", np.float64)], align=True)
+
+   def f(rec):
+       value = 2.5
+       rec[0].ival = int(value)
+       rec[0].fval = value
+       return rec
+
+   arr = np.ones(1, dtype=record_type)
+
+   cfunc = njit(f)
+
+   # Works
+   print(cfunc(arr))
+
+   # Does not work
+   print(f(arr))
+
+The above code results in the output:
+
+.. code:: none
+
+   [(2, 2.5)]
+   Traceback (most recent call last):
+     File "repro.py", line 22, in <module>
+       print(f(arr))
+     File "repro.py", line 9, in f
+       rec[0].ival = int(value)
+   AttributeError: 'numpy.void' object has no attribute 'ival'
+
+The Numba-compiled version of the function executes, but the pure Python
+version raises an error because of the unsupported use of attribute access.
+
+.. note::
+   This behavior will eventually be deprecated and removed.
+
+Attributes
+----------
+
+The following attributes of NumPy arrays are supported:
+
+* :attr:`~numpy.ndarray.dtype`
+* :attr:`~numpy.ndarray.flags`
+* :attr:`~numpy.ndarray.flat`
+* :attr:`~numpy.ndarray.itemsize`
+* :attr:`~numpy.ndarray.ndim`
+* :attr:`~numpy.ndarray.shape`
+* :attr:`~numpy.ndarray.size`
+* :attr:`~numpy.ndarray.strides`
+* :attr:`~numpy.ndarray.T`
+* :attr:`~numpy.ndarray.real`
+* :attr:`~numpy.ndarray.imag`
+
+The ``flags`` object
+''''''''''''''''''''
+
+The object returned by the :attr:`~numpy.ndarray.flags` attribute supports
+the ``contiguous``, ``c_contiguous`` and ``f_contiguous`` attributes.
+
+The ``flat`` object
+'''''''''''''''''''
+
+The object returned by the :attr:`~numpy.ndarray.flat` attribute supports
+iteration and indexing, but be careful: indexing is very slow on
+non-C-contiguous arrays.
+
+The ``real`` and ``imag`` attributes
+''''''''''''''''''''''''''''''''''''
+
+NumPy supports these attributes regardless of the dtype but Numba chooses to
+limit their support to avoid potential user error.  For numeric dtypes,
+Numba follows NumPy's behavior.  The :attr:`~numpy.ndarray.real` attribute
+returns a view of the real part of the complex array and it behaves as an identity
+function for other numeric dtypes.  The :attr:`~numpy.ndarray.imag` attribute
+returns a view of the imaginary part of the complex array and it returns a zero
+array with the same shape and dtype for other numeric dtypes.  For non-numeric
+dtypes, including all structured/record dtypes, using these attributes will
+result in a compile-time (`TypingError`) error.  This behavior differs from
+NumPy's but it is chosen to avoid the potential confusion with field names that
+overlap these attributes.
+
+Calculation
+-----------
+
+The following methods of NumPy arrays are supported in their basic form
+(without any optional arguments):
+
+* :meth:`~numpy.ndarray.all`
+* :meth:`~numpy.ndarray.any`
+* :meth:`~numpy.ndarray.clip`
+* :meth:`~numpy.ndarray.conj`
+* :meth:`~numpy.ndarray.conjugate`
+* :meth:`~numpy.ndarray.cumprod`
+* :meth:`~numpy.ndarray.cumsum`
+* :meth:`~numpy.ndarray.max`
+* :meth:`~numpy.ndarray.mean`
+* :meth:`~numpy.ndarray.min`
+* :meth:`~numpy.ndarray.nonzero`
+* :meth:`~numpy.ndarray.prod`
+* :meth:`~numpy.ndarray.std`
+* :meth:`~numpy.ndarray.take`
+* :meth:`~numpy.ndarray.var`
+
+The corresponding top-level NumPy functions (such as :func:`numpy.prod`)
+are similarly supported.
+
+Other methods
+-------------
+
+The following methods of NumPy arrays are supported:
+
+* :meth:`~numpy.ndarray.argmax` (``axis`` keyword argument supported).
+* :meth:`~numpy.ndarray.argmin` (``axis`` keyword argument supported).
+* :meth:`~numpy.ndarray.argsort` (``kind`` key word argument supported for
+  values ``'quicksort'`` and ``'mergesort'``)
+* :meth:`~numpy.ndarray.astype` (only the 1-argument form)
+* :meth:`~numpy.ndarray.copy` (without arguments)
+* :meth:`~numpy.ndarray.dot` (only the 1-argument form)
+* :meth:`~numpy.ndarray.flatten` (no order argument; 'C' order only)
+* :meth:`~numpy.ndarray.item` (without arguments)
+* :meth:`~numpy.ndarray.itemset` (only the 1-argument form)
+* :meth:`~numpy.ndarray.ptp` (without arguments)
+* :meth:`~numpy.ndarray.ravel` (no order argument; 'C' order only)
+* :meth:`~numpy.ndarray.repeat` (no axis argument)
+* :meth:`~numpy.ndarray.reshape` (only the 1-argument form)
+* :meth:`~numpy.ndarray.sort` (without arguments)
+* :meth:`~numpy.ndarray.sum` (with or without the ``axis`` and/or ``dtype``
+  arguments.)
+
+  * ``axis`` only supports ``integer`` values.
+  * If the ``axis`` argument is a compile-time constant, all valid values
+    are supported.
+    An out-of-range value will result in a ``LoweringError`` at compile-time.
+  * If the ``axis`` argument is not a compile-time constant, only values
+    from 0 to 3 are supported.
+    An out-of-range value will result in a runtime exception.
+  * All numeric ``dtypes`` are supported in the ``dtype`` parameter.
+    ``timedelta`` arrays can be used as input arrays but ``timedelta`` is not
+    supported as ``dtype`` parameter.
+  * When a ``dtype`` is given, it determines the type of the internal
+    accumulator. When it is not, the selection is made automatically based on
+    the input array's ``dtype``, mostly following the same rules as NumPy.
+    However, on 64-bit Windows, Numba uses a 64-bit accumulator for integer
+    inputs (``int64`` for ``int32`` inputs and ``uint64`` for ``uint32``
+    inputs), while NumPy would use a 32-bit accumulator in those cases.
+
+
+* :meth:`~numpy.ndarray.transpose`
+* :meth:`~numpy.ndarray.view` (only the 1-argument form)
+* :meth:`~numpy.ndarray.__contains__`
+
+Where applicable, the corresponding top-level NumPy functions (such as
+:func:`numpy.argmax`) are similarly supported.
+
+.. warning::
+   Sorting may be slightly slower than NumPy's implementation.
+
+
+Functions
+=========
+
+Linear algebra
+--------------
+
+Basic linear algebra is supported on 1-D and 2-D contiguous arrays of
+floating-point and complex numbers:
+
+* :func:`numpy.dot`
+* :func:`numpy.kron` ('C' and 'F' order only)
+* :func:`numpy.outer`
+* :func:`numpy.trace` (only the first argument).
+* :func:`numpy.vdot`
+* On Python 3.5 and above, the matrix multiplication operator from
+  :pep:`465` (i.e. ``a @ b`` where ``a`` and ``b`` are 1-D or 2-D arrays).
+* :func:`numpy.linalg.cholesky`
+* :func:`numpy.linalg.cond` (only non string values in ``p``).
+* :func:`numpy.linalg.det`
+* :func:`numpy.linalg.eig` (only running with data that does not cause a domain
+  change is supported e.g. real input -> real
+  output, complex input -> complex output).
+* :func:`numpy.linalg.eigh` (only the first argument).
+* :func:`numpy.linalg.eigvals` (only running with data that does not cause a
+  domain change is supported e.g. real input -> real output,
+  complex input -> complex output).
+* :func:`numpy.linalg.eigvalsh` (only the first argument).
+* :func:`numpy.linalg.inv`
+* :func:`numpy.linalg.lstsq`
+* :func:`numpy.linalg.matrix_power`
+* :func:`numpy.linalg.matrix_rank`
+* :func:`numpy.linalg.norm` (only the 2 first arguments and only non string
+  values in ``ord``).
+* :func:`numpy.linalg.pinv`
+* :func:`numpy.linalg.qr` (only the first argument).
+* :func:`numpy.linalg.slogdet`
+* :func:`numpy.linalg.solve`
+* :func:`numpy.linalg.svd` (only the 2 first arguments).
+
+.. note::
+   The implementation of these functions needs SciPy to be installed.
+
+Reductions
+----------
+
+The following reduction functions are supported:
+
+* :func:`numpy.diff` (only the 2 first arguments)
+* :func:`numpy.median` (only the first argument)
+* :func:`numpy.nancumprod` (only the first argument)
+* :func:`numpy.nancumsum` (only the first argument)
+* :func:`numpy.nanmax` (only the first argument)
+* :func:`numpy.nanmean` (only the first argument)
+* :func:`numpy.nanmedian` (only the first argument)
+* :func:`numpy.nanmin` (only the first argument)
+* :func:`numpy.nanpercentile` (only the 2 first arguments, complex dtypes
+  unsupported)
+* :func:`numpy.nanquantile` (only the 2 first arguments, complex dtypes
+  unsupported)
+* :func:`numpy.nanprod` (only the first argument)
+* :func:`numpy.nanstd` (only the first argument)
+* :func:`numpy.nansum` (only the first argument)
+* :func:`numpy.nanvar` (only the first argument)
+* :func:`numpy.percentile` (only the 2 first arguments, complex dtypes
+  unsupported)
+* :func:`numpy.quantile` (only the 2 first arguments, complex dtypes
+  unsupported)
+
+Other functions
+---------------
+
+The following top-level functions are supported:
+
+* :func:`numpy.allclose`
+* :func:`numpy.append`
+* :func:`numpy.arange`
+* :func:`numpy.argsort` (``kind`` key word argument supported for values
+  ``'quicksort'`` and ``'mergesort'``)
+* :func:`numpy.argwhere`
+* :func:`numpy.array` (only the 2 first arguments)
+* :func:`numpy.array_equal`
+* :func:`numpy.array_split`
+* :func:`numpy.asarray` (only the 2 first arguments)
+* :func:`numpy.asarray_chkfinite` (only the 2 first arguments)
+* :func:`numpy.asfarray`
+* :func:`numpy.asfortranarray` (only the first argument)
+* :func:`numpy.atleast_1d`
+* :func:`numpy.atleast_2d`
+* :func:`numpy.atleast_3d`
+* :func:`numpy.bartlett`
+* :func:`numpy.bincount`
+* :func:`numpy.blackman`
+* :func:`numpy.broadcast_to` (only the 2 first arguments)
+* :func:`numpy.broadcast_arrays` (only the first argument)
+* :func:`numpy.broadcast_shapes`
+* :func:`numpy.column_stack`
+* :func:`numpy.concatenate`
+* :func:`numpy.convolve` (only the 2 first arguments)
+* :func:`numpy.copy` (only the first argument)
+* :func:`numpy.corrcoef` (only the 3 first arguments, requires SciPy)
+* :func:`numpy.correlate` (only the 2 first arguments)
+* :func:`numpy.count_nonzero` (axis only supports scalar values)
+* :func:`numpy.cov` (only the 5 first arguments)
+* :func:`numpy.cross` (only the 2 first arguments; at least one of the input
+  arrays should have ``shape[-1] == 3``)
+
+  * If ``shape[-1] == 2`` for both inputs, please replace your
+    :func:`numpy.cross` call with :func:`numba.np.extensions.cross2d`.
+
+* :func:`numpy.delete` (only the 2 first arguments)
+* :func:`numpy.diag`
+* :func:`numpy.digitize`
+* :func:`numpy.dstack`
+* :func:`numpy.dtype` (only the first argument)
+* :func:`numpy.ediff1d`
+* :func:`numpy.empty` (only the 2 first arguments)
+* :func:`numpy.empty_like` (only the 2 first arguments)
+* :func:`numpy.expand_dims`
+* :func:`numpy.extract`
+* :func:`numpy.eye`
+* :func:`numpy.fill_diagonal`
+* :func:`numpy.flatten` (no order argument; 'C' order only)
+* :func:`numpy.flatnonzero`
+* :func:`numpy.flip` (no axis argument)
+* :func:`numpy.fliplr`
+* :func:`numpy.flipud`
+* :func:`numpy.frombuffer` (only the 2 first arguments)
+* :func:`numpy.full` (only the 3 first arguments)
+* :func:`numpy.full_like` (only the 3 first arguments)
+* :func:`numpy.hamming`
+* :func:`numpy.hanning`
+* :func:`numpy.histogram` (only the 3 first arguments)
+* :func:`numpy.hstack`
+* :func:`numpy.identity`
+* :func:`numpy.kaiser`
+* :func:`numpy.iscomplex`
+* :func:`numpy.iscomplexobj`
+* :func:`numpy.isneginf`
+* :func:`numpy.isposinf`
+* :func:`numpy.isreal`
+* :func:`numpy.isrealobj`
+* :func:`numpy.isscalar`
+* :func:`numpy.interp` (only the 3 first arguments)
+* :func:`numpy.intersect1d` (only first 2 arguments, ar1 and ar2)
+* :func:`numpy.linspace` (only the 3-argument form)
+* :func:`numpy.logspace` (only the 3 first arguments)
+* :class:`numpy.ndenumerate`
+* :class:`numpy.ndindex`
+* :class:`numpy.nditer` (only the first argument)
+* :func:`numpy.ones` (only the 2 first arguments)
+* :func:`numpy.ones_like` (only the 2 first arguments)
+* :func:`numpy.partition` (only the 2 first arguments)
+* :func:`numpy.ptp` (only the first argument)
+* :func:`numpy.ravel` (no order argument; 'C' order only)
+* :func:`numpy.repeat` (no axis argument)
+* :func:`numpy.reshape` (no order argument; 'C' order only)
+* :func:`numpy.roll` (only the 2 first arguments; second argument ``shift``
+  must be an integer)
+* :func:`numpy.roots`
+* :func:`numpy.rot90` (only the 2 first arguments)
+* :func:`numpy.round_`
+* :func:`numpy.searchsorted` (only the 3 first arguments)
+* :func:`numpy.select` (only using homogeneous lists or tuples for the first
+  two arguments, condlist and choicelist). Additionally, these two arguments
+  can only contain arrays (unlike NumPy that also accepts tuples).
+* :func:`numpy.shape`
+* :func:`numpy.sinc`
+* :func:`numpy.sort` (no optional arguments, quicksort accepts
+  multi-dimensional array and sorts its last axis).
+* :func:`numpy.split`
+* :func:`numpy.stack`
+* :func:`numpy.swapaxes`
+* :func:`numpy.take` (only the 2 first arguments)
+* :func:`numpy.take_along_axis` (the axis argument must be a literal value)
+* :func:`numpy.transpose`
+* :func:`numpy.trapz` (only the 3 first arguments)
+* :func:`numpy.tri` (only the 3 first arguments; third argument ``k`` must be an integer)
+* :func:`numpy.tril` (second argument ``k`` must be an integer)
+* :func:`numpy.tril_indices` (all arguments must be integer)
+* :func:`numpy.tril_indices_from` (second argument ``k`` must be an integer)
+* :func:`numpy.triu` (second argument ``k`` must be an integer)
+* :func:`numpy.triu_indices` (all arguments must be integer)
+* :func:`numpy.triu_indices_from` (second argument ``k`` must be an integer)
+* :func:`numpy.unique` (only the first argument)
+* :func:`numpy.vander`
+* :func:`numpy.vstack`
+* :func:`numpy.where`
+* :func:`numpy.zeros` (only the 2 first arguments)
+* :func:`numpy.zeros_like` (only the 2 first arguments)
+
+The following constructors are supported, both with a numeric input (to
+construct a scalar) or a sequence (to construct an array):
+
+* :class:`numpy.bool_`
+* :class:`numpy.complex64`
+* :class:`numpy.complex128`
+* :class:`numpy.float32`
+* :class:`numpy.float64`
+* :class:`numpy.int8`
+* :class:`numpy.int16`
+* :class:`numpy.int32`
+* :class:`numpy.int64`
+* :class:`numpy.intc`
+* :class:`numpy.intp`
+* :class:`numpy.uint8`
+* :class:`numpy.uint16`
+* :class:`numpy.uint32`
+* :class:`numpy.uint64`
+* :class:`numpy.uintc`
+* :class:`numpy.uintp`
+
+The following machine parameter classes are supported, with all purely numerical
+attributes:
+
+* :class:`numpy.iinfo`
+* :class:`numpy.finfo` (``machar`` attribute not supported)
+* :class:`numpy.MachAr` (with no arguments to the constructor)
+
+
+Literal arrays
+--------------
+
+.. XXX should this part of the user's guide?
+
+Neither Python nor Numba has actual array literals, but you can construct
+arbitrary arrays by calling :func:`numpy.array` on a nested tuple::
+
+   a = numpy.array(((a, b, c), (d, e, f)))
+
+(nested lists are not yet supported by Numba)
+
+
+Modules
+=======
+
+.. _numpy-random:
+
+``random``
+----------
+
+Generator Objects
+'''''''''''''''''
+Numba supports :py:class:`numpy.random.Generator()` objects. As of version 0.56, users can pass
+individual NumPy :py:class:`Generator` objects into Numba functions and use their
+methods inside the functions. The same algorithms are used as NumPy for
+random number generation hence maintaining parity between the random
+number generated using NumPy and Numba under identical arguments 
+(also the same documentation notes as NumPy :py:class:`Generator` methods apply).
+The current Numba support for :py:class:`Generator` is not thread-safe, hence we
+do not recommend using :py:class:`Generator` methods in methods with parallel
+execution logic.
+
+.. note::
+  NumPy's :py:class:`Generator` objects rely on :py:class:`BitGenerator` to manage state
+  and generate the random bits, which are then transformed into random
+  values from useful distributions. Numba will ``unbox`` the :py:class:`Generator` objects
+  and will maintain a reference to the underlying :py:class:`BitGenerator` objects using NumPy's
+  ``ctypes`` interface bindings. Hence :py:class:`Generator` objects can cross the JIT boundary
+  and their functions be used within Numba-Jit code. Note that since only references
+  to :py:class:`BitGenerator` objects are maintained, any change to the state of a particular
+  :py:class:`Generator` object outside Numba code would affect the state of :py:class:`Generator`
+  inside the Numba code.
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_numpy_generators.py
+   :language: python
+   :start-after: magictoken.npgen_usage.begin
+   :end-before: magictoken.npgen_usage.end
+   :dedent: 8
+
+The following :py:class:`Generator` methods are supported:
+
+* :func:`numpy.random.Generator().random()`
+
+RandomState and legacy Random number generation
+'''''''''''''''''''''''''''''''''''''''''''''''
+
+Numba supports top-level functions from the
+`numpy.random <http://docs.scipy.org/doc/numpy/reference/routines.random.html>`_
+module, but does not allow you to create individual RandomState instances.
+The same algorithms are used as for :ref:`the standard
+random module <pysupported-random>` (and therefore the same notes apply),
+but with an independent internal state: seeding or drawing numbers from
+one generator won't affect the other.
+
+The following functions are supported.
+
+Initialization
+''''''''''''''
+
+* :func:`numpy.random.seed`: with an integer argument only
+
+.. warning::
+   Calling :func:`numpy.random.seed` from interpreted code (including from :term:`object mode`
+   code) will seed the NumPy random generator, not the Numba random generator.
+   To seed the Numba random generator, see the example below.
+
+.. code-block:: python
+
+  from numba import njit
+  import numpy as np
+
+  @njit
+  def seed(a):
+      np.random.seed(a)
+
+  @njit
+  def rand():
+      return np.random.rand()
+
+
+  # Incorrect seeding
+  np.random.seed(1234)
+  print(rand())
+
+  np.random.seed(1234)
+  print(rand())
+
+  # Correct seeding
+  seed(1234)
+  print(rand())
+
+  seed(1234)
+  print(rand())
+
+
+
+
+Simple random data
+''''''''''''''''''
+
+* :func:`numpy.random.rand`
+* :func:`numpy.random.randint` (only the first two arguments)
+* :func:`numpy.random.randn`
+* :func:`numpy.random.random`
+* :func:`numpy.random.random_sample`
+* :func:`numpy.random.ranf`
+* :func:`numpy.random.sample`
+
+Permutations
+''''''''''''
+
+* :func:`numpy.random.choice`: the optional *p* argument (probabilities
+  array) is not supported
+* :func:`numpy.random.permutation`
+* :func:`numpy.random.shuffle`: the sequence argument must be a one-dimension
+  NumPy array or buffer-providing object (such as a :class:`bytearray`
+  or :class:`array.array`)
+
+Distributions
+'''''''''''''
+
+The following functions support all arguments.
+
+* :func:`numpy.random.beta`
+* :func:`numpy.random.binomial`
+* :func:`numpy.random.chisquare`
+* :func:`numpy.random.dirichlet`
+* :func:`numpy.random.exponential`
+* :func:`numpy.random.f`
+* :func:`numpy.random.gamma`
+* :func:`numpy.random.geometric`
+* :func:`numpy.random.gumbel`
+* :func:`numpy.random.hypergeometric`
+* :func:`numpy.random.laplace`
+* :func:`numpy.random.logistic`
+* :func:`numpy.random.lognormal`
+* :func:`numpy.random.logseries`
+* :func:`numpy.random.multinomial`
+* :func:`numpy.random.negative_binomial`
+* :func:`numpy.random.noncentral_chisquare`
+* :func:`numpy.random.normal`
+* :func:`numpy.random.pareto`
+* :func:`numpy.random.poisson`
+* :func:`numpy.random.power`
+* :func:`numpy.random.rayleigh`
+* :func:`numpy.random.standard_cauchy`
+* :func:`numpy.random.standard_exponential`
+* :func:`numpy.random.standard_gamma`
+* :func:`numpy.random.standard_normal`
+* :func:`numpy.random.standard_t`
+* :func:`numpy.random.triangular`
+* :func:`numpy.random.uniform`
+* :func:`numpy.random.vonmises`
+* :func:`numpy.random.wald`
+* :func:`numpy.random.weibull`
+* :func:`numpy.random.zipf`
+
+.. note::
+   Calling :func:`numpy.random.seed` from non-Numba code (or from
+   :term:`object mode` code) will seed the NumPy random generator, not the
+   Numba random generator.
+
+.. note::
+   Since version 0.28.0, the generator is thread-safe and fork-safe.  Each
+   thread and each process will produce independent streams of random numbers.
+
+
+``stride_tricks``
+-----------------
+
+The following function from the :mod:`numpy.lib.stride_tricks` module
+is supported:
+
+* :func:`~numpy.lib.stride_tricks.as_strided` (the *strides* argument
+  is mandatory, the *subok* argument is not supported)
+
+.. _supported_ufuncs:
+
+Standard ufuncs
+===============
+
+One objective of Numba is having all the
+`standard ufuncs in NumPy <http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs>`_
+understood by Numba.  When a supported ufunc is found when compiling a
+function, Numba maps the ufunc to equivalent native code.  This allows the
+use of those ufuncs in Numba code that gets compiled in :term:`nopython mode`.
+
+Limitations
+-----------
+
+Right now, only a selection of the standard ufuncs work in :term:`nopython mode`.
+Following is a list of the different standard ufuncs that Numba is aware of,
+sorted in the same way as in the NumPy documentation.
+
+
+Math operations
+---------------
+
+==============  =============  ===============
+    UFUNC                  MODE
+--------------  ------------------------------
+    name         object mode    nopython mode
+==============  =============  ===============
+ add                 Yes          Yes
+ subtract            Yes          Yes
+ multiply            Yes          Yes
+ divide              Yes          Yes
+ logaddexp           Yes          Yes
+ logaddexp2          Yes          Yes
+ true_divide         Yes          Yes
+ floor_divide        Yes          Yes
+ negative            Yes          Yes
+ power               Yes          Yes
+ float_power         Yes          Yes
+ remainder           Yes          Yes
+ mod                 Yes          Yes
+ fmod                Yes          Yes
+ divmod (*)          Yes          Yes
+ abs                 Yes          Yes
+ absolute            Yes          Yes
+ fabs                Yes          Yes
+ rint                Yes          Yes
+ sign                Yes          Yes
+ conj                Yes          Yes
+ exp                 Yes          Yes
+ exp2                Yes          Yes
+ log                 Yes          Yes
+ log2                Yes          Yes
+ log10               Yes          Yes
+ expm1               Yes          Yes
+ log1p               Yes          Yes
+ sqrt                Yes          Yes
+ square              Yes          Yes
+ cbrt                Yes          Yes
+ reciprocal          Yes          Yes
+ conjugate           Yes          Yes
+ gcd                 Yes          Yes
+ lcm                 Yes          Yes
+==============  =============  ===============
+
+(\*) not supported on timedelta types
+
+Trigonometric functions
+-----------------------
+
+==============  =============  ===============
+    UFUNC                  MODE
+--------------  ------------------------------
+    name         object mode    nopython mode
+==============  =============  ===============
+ sin                 Yes          Yes
+ cos                 Yes          Yes
+ tan                 Yes          Yes
+ arcsin              Yes          Yes
+ arccos              Yes          Yes
+ arctan              Yes          Yes
+ arctan2             Yes          Yes
+ hypot               Yes          Yes
+ sinh                Yes          Yes
+ cosh                Yes          Yes
+ tanh                Yes          Yes
+ arcsinh             Yes          Yes
+ arccosh             Yes          Yes
+ arctanh             Yes          Yes
+ deg2rad             Yes          Yes
+ rad2deg             Yes          Yes
+ degrees             Yes          Yes
+ radians             Yes          Yes
+==============  =============  ===============
+
+
+Bit-twiddling functions
+-----------------------
+
+==============  =============  ===============
+    UFUNC                  MODE
+--------------  ------------------------------
+    name         object mode    nopython mode
+==============  =============  ===============
+ bitwise_and         Yes          Yes
+ bitwise_or          Yes          Yes
+ bitwise_xor         Yes          Yes
+ bitwise_not         Yes          Yes
+ invert              Yes          Yes
+ left_shift          Yes          Yes
+ right_shift         Yes          Yes
+==============  =============  ===============
+
+
+Comparison functions
+--------------------
+
+==============  =============  ===============
+    UFUNC                  MODE
+--------------  ------------------------------
+    name         object mode    nopython mode
+==============  =============  ===============
+ greater             Yes          Yes
+ greater_equal       Yes          Yes
+ less                Yes          Yes
+ less_equal          Yes          Yes
+ not_equal           Yes          Yes
+ equal               Yes          Yes
+ logical_and         Yes          Yes
+ logical_or          Yes          Yes
+ logical_xor         Yes          Yes
+ logical_not         Yes          Yes
+ maximum             Yes          Yes
+ minimum             Yes          Yes
+ fmax                Yes          Yes
+ fmin                Yes          Yes
+==============  =============  ===============
+
+
+Floating functions
+------------------
+
+==============  =============  ===============
+    UFUNC                  MODE
+--------------  ------------------------------
+    name         object mode    nopython mode
+==============  =============  ===============
+ isfinite            Yes          Yes
+ isinf               Yes          Yes
+ isnan               Yes          Yes
+ signbit             Yes          Yes
+ copysign            Yes          Yes
+ nextafter           Yes          Yes
+ modf                Yes          No
+ ldexp               Yes (*)      Yes
+ frexp               Yes          No
+ floor               Yes          Yes
+ ceil                Yes          Yes
+ trunc               Yes          Yes
+ spacing             Yes          Yes
+==============  =============  ===============
+
+(\*) not supported on windows 32 bit
+
+
+Datetime functions
+------------------
+
+==============  =============  ===============
+    UFUNC                  MODE
+--------------  ------------------------------
+    name         object mode    nopython mode
+==============  =============  ===============
+ isnat            Yes          Yes
+==============  =============  ===============
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/pysemantics.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/pysemantics.rst
new file mode 100644
index 000000000..296d76b08
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/pysemantics.rst
@@ -0,0 +1,88 @@
+.. _pysemantics:
+
+Deviations from Python Semantics
+================================
+
+Bounds Checking
+---------------
+
+By default, instead of causing an :class:`IndexError`, accessing an
+out-of-bound index of an array in a Numba-compiled function will return
+invalid values or lead to an access violation error (it's reading from
+invalid memory locations). Bounds checking can be enabled on a specific
+function via the :ref:`boundscheck <jit-decorator-boundscheck>`
+option of the jit decorator. Additionally, the :envvar:`NUMBA_BOUNDSCHECK`
+can be set to 0 or 1 to globally override this flag.
+
+.. note::
+  Bounds checking will slow down typical functions so it is recommended to only
+  use this flag for debugging purposes.
+
+Exceptions and Memory Allocation
+--------------------------------
+
+Due to limitations in the current compiler when handling exceptions, memory
+allocated (almost always NumPy arrays) within a function that raises an
+exception will **leak**.  This is a known issue that will be fixed, but in the
+meantime, it is best to do memory allocation outside of functions that can
+also raise exceptions.
+
+Integer width
+-------------
+
+While Python has arbitrary-sized integers, integers in Numba-compiled
+functions get a fixed size through :term:`type inference` (usually,
+the size of a machine integer).  This means that arithmetic
+operations can wrapround or produce undefined results or overflow.
+
+Type inference can be overridden by an explicit type specification,
+if fine-grained control of integer width is desired.
+
+.. seealso::
+   :ref:`Enhancement proposal 1: Changes in integer typing <nbep-1>`
+
+
+Boolean inversion
+-----------------
+
+Calling the bitwise complement operator (the ``~`` operator) on a Python
+boolean returns an integer, while the same operator on a Numpy boolean
+returns another boolean::
+
+   >>> ~True
+   -2
+   >>> ~np.bool_(True)
+   False
+
+Numba follows the Numpy semantics.
+
+
+Global and closure variables
+----------------------------
+
+In :term:`nopython mode`, global and closure variables are *frozen* by
+Numba: a Numba-compiled function sees the value of those variables at the
+time the function was compiled.  Also, it is not possible to change their
+values from the function.
+
+Numba **may or may not** copy global variables referenced inside a compiled
+function.  Small global arrays are copied for potential compiler optimization
+with immutability assumption.  However, large global arrays are not copied to
+conserve memory.  The definition of "small" and "large" may change.
+
+
+Zero initialization of variables
+--------------------------------
+
+Numba does not track variable liveness at runtime. For simplicity of
+implementation, all variables are zero-initialized. Example::
+
+    from numba import njit
+
+    @njit
+    def foo():
+        for i in range(0):
+            pass
+        print(i) # will print 0 and not raise UnboundLocalError
+
+    foo()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/pysupported.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/pysupported.rst
new file mode 100644
index 000000000..f0a6e45d0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/pysupported.rst
@@ -0,0 +1,1284 @@
+.. _pysupported:
+
+=========================
+Supported Python features
+=========================
+
+Apart from the :ref:`pysupported-language` part below, which applies to both
+:term:`object mode` and :term:`nopython mode`, this page only lists the
+features supported in :term:`nopython mode`.
+
+.. warning::
+    Numba behavior differs from Python semantics in some situations.  We
+    strongly advise reviewing :ref:`pysemantics` to become familiar with these
+    differences.
+
+
+.. _pysupported-language:
+
+Language
+========
+
+Constructs
+----------
+
+Numba strives to support as much of the Python language as possible, but
+some language features are not available inside Numba-compiled functions.
+Below is a quick reference for the support level of Python constructs.
+
+
+**Supported** constructs:
+
+- conditional branch: ``if .. elif .. else``
+- loops: ``while``, ``for .. in``, ``break``, ``continue``
+- basic generator: ``yield``
+- assertion: ``assert``
+
+**Partially supported** constructs:
+
+- exceptions: ``try .. except``, ``raise``, ``else`` and ``finally``
+  (See details in this :ref:`section <pysupported-exception-handling>`)
+
+- context manager:
+  ``with`` (only support :ref:`numba.objmode() <with_objmode>`)
+
+- list comprehension (see details in this
+  :ref:`section <pysupported-comprehension>`)
+
+**Unsupported** constructs:
+
+- async features: ``async with``, ``async for`` and ``async def``
+- class definition: ``class`` (except for :ref:`@jitclass <jitclass>`)
+- set, dict and generator comprehensions
+- generator delegation: ``yield from``
+
+Functions
+---------
+
+Function calls
+''''''''''''''
+
+Numba supports function calls using positional and named arguments, as well
+as arguments with default values and ``*args`` (note the argument for
+``*args`` can only be a tuple, not a list).  Explicit ``**kwargs`` are
+not supported.
+
+Function calls to locally defined inner functions are supported as long as
+they can be fully inlined.
+
+Functions as arguments
+''''''''''''''''''''''
+
+Functions can be passed as argument into another function.  But, they cannot
+be returned. For example:
+
+.. code-block:: python
+
+  from numba import jit
+
+  @jit
+  def add1(x):
+      return x + 1
+
+  @jit
+  def bar(fn, x):
+      return fn(x)
+
+  @jit
+  def foo(x):
+      return bar(add1, x)
+
+  # Passing add1 within numba compiled code.
+  print(foo(1))
+  # Passing add1 into bar from interpreted code
+  print(bar(add1, 1))
+
+.. note:: Numba does not handle function objects as real objects.  Once a
+          function is assigned to a variable, the variable cannot be
+          re-assigned to a different function.
+
+
+Inner function and closure
+'''''''''''''''''''''''''''
+
+Numba now supports inner functions as long as they are non-recursive
+and only called locally, but not passed as argument or returned as
+result. The use of closure variables (variables defined in outer scopes)
+within an inner function is also supported.
+
+Recursive calls
+'''''''''''''''
+
+Most recursive call patterns are supported.  The only restriction is that the
+recursive callee must have a control-flow path that returns without recursing.
+Numba is able to type-infer recursive functions without specifying the function
+type signature (which is required in numba 0.28 and earlier).
+Recursive calls can even call into a different overload of the function.
+
+.. XXX add reference to NBEP
+
+Generators
+----------
+
+Numba supports generator functions and is able to compile them in
+:term:`object mode` and :term:`nopython mode`.  The returned generator
+can be used both from Numba-compiled code and from regular Python code.
+
+Coroutine features of generators are not supported (i.e. the
+:meth:`generator.send`, :meth:`generator.throw`, :meth:`generator.close`
+methods).
+
+.. _pysupported-exception-handling:
+
+Exception handling
+------------------
+
+``raise`` statement
+'''''''''''''''''''
+
+The ``raise`` statement is only supported in the following forms:
+
+* ``raise SomeException``
+* ``raise SomeException(<arguments>)``: in :term:`nopython mode`, constructor
+  arguments must be :term:`compile-time constants <compile-time constant>`
+
+It is currently unsupported to re-raise an exception created in compiled code.
+
+``try .. except``
+'''''''''''''''''
+
+The ``try .. except`` construct is partially supported. The following forms
+of are supported:
+
+* the *bare* except that captures all exceptions:
+
+  .. code-block:: python
+
+    try:
+        ...
+    except:
+        ...
+
+* using exactly the ``Exception`` class in the ``except`` clause:
+
+  .. code-block:: python
+
+    try:
+      ...
+    except Exception:
+      ...
+
+  This will match any exception that is a subclass of ``Exception`` as
+  expected. Currently, instances of ``Exception`` and it's subclasses are the
+  only kind of exception that can be raised in compiled code.
+
+.. warning:: Numba currently masks signals like ``KeyboardInterrupt`` and
+  ``SystemExit``. These signaling exceptions are ignored during the execution of
+  Numba compiled code. The Python interpreter will handle them as soon as
+  the control is returned to it.
+
+Currently, exception objects are not materialized inside compiled functions.
+As a result, it is not possible to store an exception object into a user
+variable or to re-raise an exception. With this limitation, the only realistic
+use-case would look like:
+
+.. code-block:: python
+
+  try:
+     do_work()
+  except Exception:
+     handle_error_case()
+     return error_code
+
+``try .. except .. else .. finally``
+''''''''''''''''''''''''''''''''''''
+
+The ``else`` block and the ``finally`` block of a ``try .. except`` are
+supported:
+
+  .. code-block:: python
+
+    >>> @jit(nopython=True)
+    ... def foo():
+    ...     try:
+    ...         print('main block')
+    ...     except Exception:
+    ...         print('handler block')
+    ...     else:
+    ...         print('else block')
+    ...     finally:
+    ...         print('final block')
+    ...
+    >>> foo()
+    main block
+    else block
+    final block
+
+The ``try .. finally`` construct without the ``except`` clause is also
+supported.
+
+.. _pysupported-builtin-types:
+
+Built-in types
+==============
+
+int, bool
+---------
+
+Arithmetic operations as well as truth values are supported.
+
+The following attributes and methods are supported:
+
+* ``.conjugate()``
+* ``.real``
+* ``.imag``
+
+float, complex
+--------------
+
+Arithmetic operations as well as truth values are supported.
+
+The following attributes and methods are supported:
+
+* ``.conjugate()``
+* ``.real``
+* ``.imag``
+
+str
+---
+
+Numba supports (Unicode) strings in Python 3.  Strings can be passed into
+:term:`nopython mode` as arguments, as well as constructed and returned from
+:term:`nopython mode`. As in Python, slices (even of length 1) return a new,
+reference counted string.  Optimized code paths for efficiently accessing
+single characters may be introduced in the future.
+
+The in-memory representation is the same as was introduced in Python 3.4, with
+each string having a tag to indicate whether the string is using a 1, 2, or 4
+byte character width in memory.  When strings of different encodings are
+combined (as in concatenation), the resulting string automatically uses the
+larger character width of the two input strings.  String slices also use the
+same character width as the original string, even if the slice could be
+represented with a narrower character width.  (These details are invisible to
+the user, of course.)
+
+The following constructors, functions, attributes and methods are currently
+supported:
+
+* ``str(int)``
+* ``len()``
+* ``+`` (concatenation of strings)
+* ``*`` (repetition of strings)
+* ``in``, ``.contains()``
+* ``==``, ``<``, ``<=``, ``>``, ``>=`` (comparison)
+* ``.capitalize()``
+* ``.casefold()``
+* ``.center()``
+* ``.count()``
+* ``.endswith()``
+* ``.endswith()``
+* ``.expandtabs()``
+* ``.find()``
+* ``.index()``
+* ``.isalnum()``
+* ``.isalpha()``
+* ``.isdecimal()``
+* ``.isdigit()``
+* ``.isidentifier()``
+* ``.islower()``
+* ``.isnumeric()``
+* ``.isprintable()``
+* ``.isspace()``
+* ``.istitle()``
+* ``.isupper()``
+* ``.join()``
+* ``.ljust()``
+* ``.lower()``
+* ``.lstrip()``
+* ``.partition()``
+* ``.replace()``
+* ``.rfind()``
+* ``.rindex()``
+* ``.rjust()``
+* ``.rpartition()``
+* ``.rsplit()``
+* ``.rstrip()``
+* ``.split()``
+* ``.splitlines()``
+* ``.startswith()``
+* ``.strip()``
+* ``.swapcase()``
+* ``.title()``
+* ``.upper()``
+* ``.zfill()``
+
+Regular string literals (e.g. ``"ABC"``) as well as f-strings without format specs
+(e.g. ``"ABC_{a+1}"``)
+that only use string and integer variables (types with ``str()`` overload)
+are supported in :term:`nopython mode`.
+
+Additional operations as well as support for Python 2 strings / Python 3 bytes
+will be added in a future version of Numba.  Python 2 Unicode objects will
+likely never be supported.
+
+.. warning::
+    The performance of some operations is known to be slower than the CPython
+    implementation. These include substring search (``in``, ``.contains()``
+    and ``find()``) and string creation (like ``.split()``).  Improving the
+    string performance is an ongoing task, but the speed of CPython is
+    unlikely to be surpassed for basic string operation in isolation.
+    Numba is most successfully used for larger algorithms that happen to
+    involve strings, where basic string operations are not the bottleneck.
+
+
+tuple
+-----
+
+Tuple support is categorised into two categories based on the contents of a
+tuple. The first category is homogeneous tuples, these are tuples where the type
+of all the values in the tuple are the same, the second is heterogeneous tuples,
+these are tuples where the types of the values are different.
+
+.. note::
+
+    The ``tuple()`` constructor itself is NOT supported.
+
+homogeneous tuples
+------------------
+
+An example of a homogeneous tuple:
+
+.. code-block:: python
+
+    homogeneous_tuple = (1, 2, 3, 4)
+
+The following operations are supported on homogeneous tuples:
+
+* Tuple construction.
+* Tuple unpacking.
+* Comparison between tuples.
+* Iteration and indexing.
+* Addition (concatenation) between tuples.
+* Slicing tuples with a constant slice.
+* The index method on tuples.
+
+heterogeneous tuples
+--------------------
+
+An example of a heterogeneous tuple:
+
+.. code-block:: python
+
+    heterogeneous_tuple = (1, 2j, 3.0, "a")
+
+The following operations are supported on heterogeneous tuples:
+
+* Comparison between tuples.
+* Indexing using an index value that is a compile time constant
+  e.g. ``mytuple[7]``, where ``7`` is evidently a constant.
+* Iteration over a tuple (requires experimental :func:`literal_unroll` feature,
+  see below).
+
+.. warning::
+   The following feature (:func:`literal_unroll`) is experimental and was added
+   in version 0.47.
+
+To permit iteration over a heterogeneous tuple the special function
+:func:`numba.literal_unroll` must be used. This function has no effect other
+than to act as a token to permit the use of this feature. Example use:
+
+.. code-block:: python
+
+    from numba import njit, literal_unroll
+
+    @njit
+    def foo():
+        heterogeneous_tuple = (1, 2j, 3.0, "a")
+        for i in literal_unroll(heterogeneous_tuple):
+            print(i)
+
+.. warning::
+    The following restrictions apply to the use of :func:`literal_unroll`:
+
+    * :func:`literal_unroll` can only be used on tuples and constant lists of
+      compile time constants, e.g. ``[1, 2j, 3, "a"]`` and the list not being
+      mutated.
+    * The only supported use pattern for :func:`literal_unroll` is loop
+      iteration.
+    * Only one :func:`literal_unroll` call is permitted per loop nest (i.e.
+      nested heterogeneous tuple iteration loops are forbidden).
+    * The usual type inference/stability rules still apply.
+
+A more involved use of :func:`literal_unroll` might be type specific dispatch,
+recall that string and integer literal values are considered their own type,
+for example:
+
+.. code-block:: python
+
+    from numba import njit, types, literal_unroll
+    from numba.extending import overload
+
+    def dt(x):
+        # dummy function to overload
+        pass
+
+    @overload(dt, inline='always')
+    def ol_dt(li):
+        if isinstance(li, types.StringLiteral):
+            value = li.literal_value
+            if value == "apple":
+                def impl(li):
+                    return 1
+            elif value == "orange":
+                def impl(li):
+                    return 2
+            elif value == "banana":
+                def impl(li):
+                    return 3
+            return impl
+        elif isinstance(li, types.IntegerLiteral):
+            value = li.literal_value
+            if value == 0xca11ab1e:
+                def impl(li):
+                    # capture the dispatcher literal value
+                    return 0x5ca1ab1e + value
+                return impl
+
+    @njit
+    def foo():
+        acc = 0
+        for t in literal_unroll(('apple', 'orange', 'banana', 3390155550)):
+            acc += dt(t)
+        return acc
+
+    print(foo())
+
+
+list
+----
+
+
+.. warning::
+    As of version 0.45.x the internal implementation for the list datatype in
+    Numba is changing. Until recently, only a single implementation of the list
+    datatype was available, the so-called *reflected-list* (see below).
+    However, it was scheduled for deprecation from version 0.44.0 onwards due
+    to its limitations. As of version 0.45.0 a new implementation, the
+    so-called *typed-list* (see below), is available as an experimental
+    feature. For more information, please see: :ref:`deprecation`.
+
+Creating and returning lists from JIT-compiled functions is supported,
+as well as all methods and operations.  Lists must be strictly homogeneous:
+Numba will reject any list containing objects of different types, even if
+the types are compatible (for example, ``[1, 2.5]`` is rejected as it
+contains a :class:`int` and a :class:`float`).
+
+For example, to create a list of arrays::
+
+  In [1]: from numba import njit
+
+  In [2]: import numpy as np
+
+  In [3]: @njit
+    ...: def foo(x):
+    ...:     lst = []
+    ...:     for i in range(x):
+    ...:         lst.append(np.arange(i))
+    ...:     return lst
+    ...:
+
+  In [4]: foo(4)
+  Out[4]: [array([], dtype=int64), array([0]), array([0, 1]), array([0, 1, 2])]
+
+
+.. _feature-reflected-list:
+
+List Reflection
+'''''''''''''''
+
+In nopython mode, Numba does not operate on Python objects.  ``list`` are
+compiled into an internal representation.  Any ``list`` arguments must be
+converted into this representation on the way in to nopython mode and their
+contained elements must be restored in the original Python objects via a
+process called :term:`reflection`.  Reflection is required to maintain the same
+semantics as found in regular Python code.  However, the reflection process
+can be expensive for large lists and it is not supported for lists that contain
+reflected data types.  Users cannot use list-of-list as an argument because
+of this limitation.
+
+.. note::
+   When passing a list into a JIT-compiled function, any modifications
+   made to the list will not be visible to the Python interpreter until
+   the function returns.  (A limitation of the reflection process.)
+
+.. warning::
+   List sorting currently uses a quicksort algorithm, which has different
+   performance characterics than the algorithm used by Python.
+
+.. _feature-list-initial-value:
+
+Initial Values
+''''''''''''''
+.. warning::
+  This is an experimental feature!
+
+Lists that:
+
+* Are constructed using the square braces syntax
+* Have values of a literal type
+
+will have their initial value stored in the ``.initial_value`` property on the
+type so as to permit inspection of these values at compile time. If required,
+to force value based dispatch the :ref:`literally <developer-literally>`
+function will accept such a list.
+
+Example:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_literal_container_usage.py
+   :language: python
+   :caption: from ``test_ex_initial_value_list_compile_time_consts`` of ``numba/tests/doc_examples/test_literal_container_usage.py``
+   :start-after: magictoken.test_ex_initial_value_list_compile_time_consts.begin
+   :end-before: magictoken.test_ex_initial_value_list_compile_time_consts.end
+   :dedent: 12
+   :linenos:
+
+.. _feature-typed-list:
+
+Typed List
+''''''''''
+
+.. note::
+  ``numba.typed.List`` is an experimental feature, if you encounter any bugs in
+  functionality or suffer from unexpectedly bad performance, please report
+  this, ideally by opening an issue on the Numba issue tracker.
+
+As of version 0.45.0 a new implementation of the list data type is available,
+the so-called *typed-list*. This is compiled library backed, type-homogeneous
+list data type that is an improvement over the *reflected-list* mentioned
+above.  Additionally, lists can now be arbitrarily nested. Since the
+implementation is considered experimental, you will need to import it
+explicitly from the `numba.typed` module::
+
+    In [1]: from numba.typed import List
+
+    In [2]: from numba import njit
+
+    In [3]: @njit
+    ...: def foo(l):
+    ...:     l.append(23)
+    ...:     return l
+    ...:
+
+    In [4]: mylist = List()
+
+    In [5]: mylist.append(1)
+
+    In [6]: foo(mylist)
+    Out[6]: ListType[int64]([1, 23])
+
+
+.. note::
+    As the typed-list stabilizes it will fully replace the reflected-list and the
+    constructors `[]` and `list()` will create a typed-list instead of a
+    reflected one.
+
+
+Here's an example using ``List()`` to create ``numba.typed.List`` inside a
+jit-compiled function and letting the compiler infer the item type:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_list_usage.py
+   :language: python
+   :caption: from ``ex_inferred_list_jit`` of ``numba/tests/doc_examples/test_typed_list_usage.py``
+   :start-after: magictoken.ex_inferred_list_jit.begin
+   :end-before: magictoken.ex_inferred_list_jit.end
+   :dedent: 12
+   :linenos:
+
+Here's an example of using ``List()`` to create a ``numba.typed.List`` outside of
+a jit-compiled function and then using it as an argument to a jit-compiled
+function:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_list_usage.py
+   :language: python
+   :caption: from ``ex_inferred_list`` of ``numba/tests/doc_examples/test_typed_list_usage.py``
+   :start-after: magictoken.ex_inferred_list.begin
+   :end-before: magictoken.ex_inferred_list.end
+   :dedent: 12
+   :linenos:
+
+Finally, here's an example of using a nested `List()`:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_list_usage.py
+   :language: python
+   :caption: from ``ex_nested_list`` of ``numba/tests/doc_examples/test_typed_list_usage.py``
+   :start-after: magictoken.ex_nested_list.begin
+   :end-before: magictoken.ex_nested_list.end
+   :dedent: 12
+   :linenos:
+
+.. _feature-literal-list:
+
+Literal List
+''''''''''''
+
+.. warning::
+  This is an experimental feature!
+
+Numba supports the use of literal lists containing any values, for example::
+
+  l = ['a', 1, 2j, np.zeros(5,)]
+
+the predominant use of these lists is for use as a configuration object.
+The lists appear as a ``LiteralList`` type which inherits from ``Literal``, as a
+result the literal values of the list items are available at compile time.
+For example:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_literal_container_usage.py
+   :language: python
+   :caption: from ``test_ex_literal_list`` of ``numba/tests/doc_examples/test_literal_container_usage.py``
+   :start-after: magictoken.test_ex_literal_list.begin
+   :end-before: magictoken.test_ex_literal_list.end
+   :dedent: 12
+   :linenos:
+
+Important things to note about these kinds of lists:
+
+#. They are immutable, use of mutating methods e.g. ``.pop()`` will result in
+   compilation failure. Read-only static access and read only methods are
+   supported e.g. ``len()``.
+#. Dynamic access of items is not possible, e.g. ``some_list[x]``, for a
+   value ``x`` which is not a compile time constant. This is because it's
+   impossible to statically determine the type of the item being accessed.
+#. Inside the compiler, these lists are actually just tuples with some extra
+   things added to make them look like they are lists.
+#. They cannot be returned to the interpreter from a compiled function.
+
+.. _pysupported-comprehension:
+
+List comprehension
+''''''''''''''''''
+
+Numba supports list comprehension.  For example::
+
+
+  In [1]: from numba import njit
+
+  In [2]: @njit
+    ...: def foo(x):
+    ...:     return [[i for i in range(n)] for n in range(x)]
+    ...:
+
+  In [3]: foo(3)
+  Out[3]: [[], [0], [0, 1]]
+
+
+.. note::
+  Prior to version 0.39.0, Numba did not support the creation of nested lists.
+
+
+Numba also supports "array comprehension" that is a list comprehension
+followed immediately by a call to :func:`numpy.array`. The following
+is an example that produces a 2D Numpy array::
+
+    from numba import jit
+    import numpy as np
+
+    @jit(nopython=True)
+    def f(n):
+      return np.array([ [ x * y for x in range(n) ] for y in range(n) ])
+
+In this case, Numba is able to optimize the program to allocate and
+initialize the result array directly without allocating intermediate
+list objects.  Therefore, the nesting of list comprehension here is
+not a problem since a multi-dimensional array is being created here
+instead of a nested list.
+
+Additionally, Numba supports parallel array comprehension when combined
+with the :ref:`parallel_jit_option` option on CPUs.
+
+set
+---
+
+All methods and operations on sets are supported in JIT-compiled functions.
+
+Sets must be strictly homogeneous: Numba will reject any set containing
+objects of different types, even if the types are compatible (for example,
+``{1, 2.5}`` is rejected as it contains a :class:`int` and a :class:`float`).
+The use of reference counted types, e.g. strings, in sets is unsupported.
+
+.. note::
+   When passing a set into a JIT-compiled function, any modifications
+   made to the set will not be visible to the Python interpreter until
+   the function returns.
+
+.. _feature-typed-dict:
+
+Typed Dict
+----------
+
+.. warning::
+  ``numba.typed.Dict`` is an experimental feature.  The API may change
+  in the future releases.
+
+.. note::
+  ``dict()`` was not supported in versions prior to 0.44.  Currently, calling
+  ``dict()`` translates to calling ``numba.typed.Dict()``.
+
+Numba only supports the use of ``dict()`` without any arguments.  Such use is
+semantically equivalent to ``{}`` and ``numba.typed.Dict()``.  It will create
+an instance of ``numba.typed.Dict`` where the key-value types will be later
+inferred by usage.
+
+Numba does not fully support the Python ``dict`` because it is an untyped
+container that can have any Python types as members. To generate efficient
+machine code, Numba needs the keys and the values of the dictionary to have
+fixed types, declared in advance. To achieve this, Numba has a typed dictionary,
+``numba.typed.Dict``, for which the type-inference mechanism must be able to
+infer the key-value types by use, or the user must explicitly declare the
+key-value type using the ``Dict.empty()`` constructor method.
+This typed dictionary has the same API as the Python ``dict``,  it implements
+the ``collections.MutableMapping`` interface and is usable in both interpreted
+Python code and JIT-compiled Numba functions.
+Because the typed dictionary stores keys and values in Numba's native,
+unboxed data layout, passing a Numba dictionary into nopython mode has very low
+overhead. However, this means that using a typed dictionary from the Python
+interpreter is slower than a regular dictionary because Numba has to box and
+unbox key and value objects when getting or setting items.
+
+An important difference of the typed dictionary in comparison to Python's
+``dict`` is that **implicit casting** occurs when a key or value is stored.
+As a result the *setitem* operation may fail should the type-casting fail.
+
+It should be noted that the Numba typed dictionary is implemented using the same
+algorithm as the CPython 3.7 dictionary. As a consequence, the typed dictionary
+is ordered and has the same collision resolution as the CPython implementation.
+
+Further to the above in relation to type specification, there are limitations
+placed on the types that can be used as keys and/or values in the typed
+dictionary, most notably the Numba ``Set`` and ``List`` types are currently
+unsupported. Acceptable key/value types include but are not limited to: unicode
+strings, arrays (value only), scalars, tuples. It is expected that these
+limitations will be relaxed as Numba continues to improve.
+
+Here's an example of using ``dict()`` and ``{}`` to create ``numba.typed.Dict``
+instances and letting the compiler infer the key-value types:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_dict_usage.py
+   :language: python
+   :caption: from ``test_ex_inferred_dict_njit`` of ``numba/tests/doc_examples/test_typed_dict_usage.py``
+   :start-after: magictoken.ex_inferred_dict_njit.begin
+   :end-before: magictoken.ex_inferred_dict_njit.end
+   :dedent: 12
+   :linenos:
+
+Here's an example of creating a ``numba.typed.Dict`` instance from interpreted
+code and using the dictionary in jit code:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_dict_usage.py
+   :language: python
+   :caption: from ``test_ex_typed_dict_from_cpython`` of ``numba/tests/doc_examples/test_typed_dict_usage.py``
+   :start-after: magictoken.ex_typed_dict_from_cpython.begin
+   :end-before: magictoken.ex_typed_dict_from_cpython.end
+   :dedent: 12
+   :linenos:
+
+Here's an example of creating a ``numba.typed.Dict`` instance from jit code and
+using the dictionary in interpreted code:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_dict_usage.py
+   :language: python
+   :caption: from ``test_ex_typed_dict_njit`` of ``numba/tests/doc_examples/test_typed_dict_usage.py``
+   :start-after: magictoken.ex_typed_dict_njit.begin
+   :end-before: magictoken.ex_typed_dict_njit.end
+   :dedent: 12
+   :linenos:
+
+It should be noted that ``numba.typed.Dict`` is not thread-safe.
+Specifically, functions which modify a dictionary from multiple
+threads will potentially corrupt memory, causing a
+range of possible failures. However, the dictionary can be safely read from
+multiple threads as long as the contents of the dictionary do not
+change during the parallel access.
+
+Dictionary comprehension
+''''''''''''''''''''''''
+
+Numba supports dictionary comprehension under the assumption that a
+``numba.typed.Dict`` instance can be created from the comprehension.  For
+example::
+
+  In [1]: from numba import njit
+
+  In [2]: @njit
+     ...: def foo(n):
+     ...:     return {i: i**2 for i in range(n)}
+     ...:
+
+  In [3]: foo(3)
+  Out[3]: DictType[int64,int64]<iv=None>({0: 0, 1: 1, 2: 4})
+
+.. _feature-dict-initial-value:
+
+Initial Values
+''''''''''''''
+.. warning::
+  This is an experimental feature!
+
+Typed dictionaries that:
+
+* Are constructed using the curly braces syntax
+* Have literal string keys
+* Have values of a literal type
+
+will have their initial value stored in the ``.initial_value`` property on the
+type so as to permit inspection of these values at compile time. If required,
+to force value based dispatch the :ref:`literally <developer-literally>`
+function will accept a typed dictionary.
+
+Example:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_literal_container_usage.py
+   :language: python
+   :caption: from ``test_ex_initial_value_dict_compile_time_consts`` of ``numba/tests/doc_examples/test_literal_container_usage.py``
+   :start-after: magictoken.test_ex_initial_value_dict_compile_time_consts.begin
+   :end-before: magictoken.test_ex_initial_value_dict_compile_time_consts.end
+   :dedent: 12
+   :linenos:
+
+.. _feature-literal-str-key-dict:
+
+Heterogeneous Literal String Key Dictionary
+-------------------------------------------
+
+.. warning::
+  This is an experimental feature!
+
+Numba supports the use of statically declared string key to any value
+dictionaries, for example::
+
+  d = {'a': 1, 'b': 'data', 'c': 2j}
+
+the predominant use of these dictionaries is to orchestrate advanced compilation
+dispatch or as a container for use as a configuration object. The dictionaries
+appear as a ``LiteralStrKeyDict`` type which inherits from ``Literal``, as a
+result the literal values of the keys and the types of the items are available
+at compile time. For example:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_literal_container_usage.py
+   :language: python
+   :caption: from ``test_ex_literal_dict_compile_time_consts`` of ``numba/tests/doc_examples/test_literal_container_usage.py``
+   :start-after: magictoken.test_ex_literal_dict_compile_time_consts.begin
+   :end-before: magictoken.test_ex_literal_dict_compile_time_consts.end
+   :dedent: 12
+   :linenos:
+
+Important things to note about these kinds of dictionaries:
+
+#. They are immutable, use of mutating methods e.g. ``.pop()`` will result in
+   compilation failure. Read-only static access and read only methods are
+   supported e.g. ``len()``.
+#. Dynamic access of items is not possible, e.g. ``some_dictionary[x]``, for a
+   value ``x`` which is not a compile time constant. This is because it's
+   impossible statically determine the type of the item being accessed.
+#. Inside the compiler, these dictionaries are actually just named tuples with
+   some extra things added to make them look like they are dictionaries.
+#. They cannot be returned to the interpreter from a compiled function.
+#. The ``.keys()``, ``.values()`` and ``.items()`` methods all functionally
+   operate but return tuples opposed to iterables.
+
+None
+----
+
+The None value is supported for identity testing (when using an
+:class:`~numba.optional` type).
+
+
+bytes, bytearray, memoryview
+----------------------------
+
+The :class:`bytearray` type and, on Python 3, the :class:`bytes` type
+support indexing, iteration and retrieving the len().
+
+The :class:`memoryview` type supports indexing, slicing, iteration,
+retrieving the len(), and also the following attributes:
+
+* :attr:`~memoryview.contiguous`
+* :attr:`~memoryview.c_contiguous`
+* :attr:`~memoryview.f_contiguous`
+* :attr:`~memoryview.itemsize`
+* :attr:`~memoryview.nbytes`
+* :attr:`~memoryview.ndim`
+* :attr:`~memoryview.readonly`
+* :attr:`~memoryview.shape`
+* :attr:`~memoryview.strides`
+
+
+Built-in functions
+==================
+
+The following built-in functions are supported:
+
+.. warning::
+  Support for ``isinstance`` is an experimental feature. This feature is
+  automatically enabled by simply using ``isinstance`` in JIT compiled code.
+
+* :func:`abs`
+* :class:`bool`
+* :func:`chr`
+* :class:`complex`
+* :func:`divmod`
+* :func:`enumerate`
+* :func:`filter`
+* :class:`float`
+* :func:`hash` (see :ref:`pysupported-hashing` below)
+* :class:`int`: only the one-argument form
+* :func:`iter`: only the one-argument form
+* :func:`isinstance` (experimental support only)
+* :func:`len`
+* :func:`min`
+* :func:`map`
+* :func:`max`
+* :func:`next`: only the one-argument form
+* :func:`ord`
+* :func:`print`: only numbers and strings; no ``file`` or ``sep`` argument
+* :class:`range`: The only permitted use of range is as a callable function
+  (cannot pass range as an argument to a jitted function or return a range from
+  a jitted function).
+* :func:`round`
+* :func:`sorted`: the ``key`` argument is not supported
+* :func:`sum`
+* :func:`type`: only the one-argument form, and only on some types
+  (e.g. numbers and named tuples)
+* :func:`zip`
+
+.. _pysupported-hashing:
+
+Hashing
+-------
+
+The :func:`hash` built-in is supported and produces hash values for all
+supported hashable types with the following Python version specific behavior:
+
+Under Python 3, hash values computed by Numba will exactly match those computed
+in CPython under the condition that the :attr:`sys.hash_info.algorithm` is
+``siphash24`` (default).
+
+The ``PYTHONHASHSEED`` environment variable influences the hashing behavior in
+precisely the manner described in the CPython documentation.
+
+
+Standard library modules
+========================
+
+``array``
+---------
+
+Limited support for the :class:`array.array` type is provided through
+the buffer protocol.  Indexing, iteration and taking the len() is supported.
+All type codes are supported except for ``"u"``.
+
+``cmath``
+---------
+
+The following functions from the :mod:`cmath` module are supported:
+
+* :func:`cmath.acos`
+* :func:`cmath.acosh`
+* :func:`cmath.asin`
+* :func:`cmath.asinh`
+* :func:`cmath.atan`
+* :func:`cmath.atanh`
+* :func:`cmath.cos`
+* :func:`cmath.cosh`
+* :func:`cmath.exp`
+* :func:`cmath.isfinite`
+* :func:`cmath.isinf`
+* :func:`cmath.isnan`
+* :func:`cmath.log`
+* :func:`cmath.log10`
+* :func:`cmath.phase`
+* :func:`cmath.polar`
+* :func:`cmath.rect`
+* :func:`cmath.sin`
+* :func:`cmath.sinh`
+* :func:`cmath.sqrt`
+* :func:`cmath.tan`
+* :func:`cmath.tanh`
+
+``collections``
+---------------
+
+Named tuple classes, as returned by :func:`collections.namedtuple`, are
+supported in the same way regular tuples are supported.  Attribute access
+and named parameters in the constructor are also supported.
+
+Creating a named tuple class inside Numba code is *not* supported; the class
+must be created at the global level.
+
+.. _ctypes-support:
+
+``ctypes``
+----------
+
+Numba is able to call ctypes-declared functions with the following argument
+and return types:
+
+* :class:`ctypes.c_int8`
+* :class:`ctypes.c_int16`
+* :class:`ctypes.c_int32`
+* :class:`ctypes.c_int64`
+* :class:`ctypes.c_uint8`
+* :class:`ctypes.c_uint16`
+* :class:`ctypes.c_uint32`
+* :class:`ctypes.c_uint64`
+* :class:`ctypes.c_float`
+* :class:`ctypes.c_double`
+* :class:`ctypes.c_void_p`
+
+``enum``
+--------
+
+Both :class:`enum.Enum` and :class:`enum.IntEnum` subclasses are supported.
+
+``math``
+--------
+
+The following functions from the :mod:`math` module are supported:
+
+* :func:`math.acos`
+* :func:`math.acosh`
+* :func:`math.asin`
+* :func:`math.asinh`
+* :func:`math.atan`
+* :func:`math.atan2`
+* :func:`math.atanh`
+* :func:`math.ceil`
+* :func:`math.copysign`
+* :func:`math.cos`
+* :func:`math.cosh`
+* :func:`math.degrees`
+* :func:`math.erf`
+* :func:`math.erfc`
+* :func:`math.exp`
+* :func:`math.expm1`
+* :func:`math.fabs`
+* :func:`math.floor`
+* :func:`math.frexp`
+* :func:`math.gamma`
+* :func:`math.gcd`
+* :func:`math.hypot`
+* :func:`math.isfinite`
+* :func:`math.isinf`
+* :func:`math.isnan`
+* :func:`math.ldexp`
+* :func:`math.lgamma`
+* :func:`math.log`
+* :func:`math.log10`
+* :func:`math.log1p`
+* :func:`math.pow`
+* :func:`math.radians`
+* :func:`math.sin`
+* :func:`math.sinh`
+* :func:`math.sqrt`
+* :func:`math.tan`
+* :func:`math.tanh`
+* :func:`math.trunc`
+
+``operator``
+------------
+
+The following functions from the :mod:`operator` module are supported:
+
+* :func:`operator.add`
+* :func:`operator.and_`
+* :func:`operator.eq`
+* :func:`operator.floordiv`
+* :func:`operator.ge`
+* :func:`operator.gt`
+* :func:`operator.iadd`
+* :func:`operator.iand`
+* :func:`operator.ifloordiv`
+* :func:`operator.ilshift`
+* :func:`operator.imatmul` (Python 3.5 and above)
+* :func:`operator.imod`
+* :func:`operator.imul`
+* :func:`operator.invert`
+* :func:`operator.ior`
+* :func:`operator.ipow`
+* :func:`operator.irshift`
+* :func:`operator.isub`
+* :func:`operator.itruediv`
+* :func:`operator.ixor`
+* :func:`operator.le`
+* :func:`operator.lshift`
+* :func:`operator.lt`
+* :func:`operator.matmul` (Python 3.5 and above)
+* :func:`operator.mod`
+* :func:`operator.mul`
+* :func:`operator.ne`
+* :func:`operator.neg`
+* :func:`operator.not_`
+* :func:`operator.or_`
+* :func:`operator.pos`
+* :func:`operator.pow`
+* :func:`operator.rshift`
+* :func:`operator.sub`
+* :func:`operator.truediv`
+* :func:`operator.xor`
+
+``functools``
+-------------
+
+The :func:`functools.reduce` function is supported but the `initializer`
+argument is required.
+
+.. _pysupported-random:
+
+``random``
+----------
+
+Numba supports top-level functions from the :mod:`random` module, but does
+not allow you to create individual Random instances.  A Mersenne-Twister
+generator is used, with a dedicated internal state.  It is initialized at
+startup with entropy drawn from the operating system.
+
+* :func:`random.betavariate`
+* :func:`random.expovariate`
+* :func:`random.gammavariate`
+* :func:`random.gauss`
+* :func:`random.getrandbits`: number of bits must not be greater than 64
+* :func:`random.lognormvariate`
+* :func:`random.normalvariate`
+* :func:`random.paretovariate`
+* :func:`random.randint`
+* :func:`random.random`
+* :func:`random.randrange`
+* :func:`random.seed`: with an integer argument only
+* :func:`random.shuffle`: the sequence argument must be a one-dimension
+  Numpy array or buffer-providing object (such as a :class:`bytearray`
+  or :class:`array.array`); the second (optional) argument is not supported
+* :func:`random.uniform`
+* :func:`random.triangular`
+* :func:`random.vonmisesvariate`
+* :func:`random.weibullvariate`
+
+.. warning::
+   Calling :func:`random.seed` from non-Numba code (or from :term:`object mode`
+   code) will seed the Python random generator, not the Numba random generator.
+   To seed the Numba random generator, see the example below.
+
+.. code-block:: python
+
+  from numba import njit
+  import random
+
+  @njit
+  def seed(a):
+      random.seed(a)
+
+  @njit
+  def rand():
+      return random.random()
+
+
+  # Incorrect seeding
+  random.seed(1234)
+  print(rand())
+
+  random.seed(1234)
+  print(rand())
+
+  # Correct seeding
+  seed(1234)
+  print(rand())
+
+  seed(1234)
+  print(rand())
+
+
+.. note::
+   Since version 0.28.0, the generator is thread-safe and fork-safe.  Each
+   thread and each process will produce independent streams of random numbers.
+
+.. seealso::
+   Numba also supports most additional distributions from the :ref:`Numpy
+   random module <numpy-random>`.
+
+``heapq``
+---------
+
+The following functions from the :mod:`heapq` module are supported:
+
+* :func:`heapq.heapify`
+* :func:`heapq.heappop`
+* :func:`heapq.heappush`
+* :func:`heapq.heappushpop`
+* :func:`heapq.heapreplace`
+* :func:`heapq.nlargest` : first two arguments only
+* :func:`heapq.nsmallest` : first two arguments only
+
+Note: the heap must be seeded with at least one value to allow its type to be
+inferred; heap items are assumed to be homogeneous in type.
+
+
+Third-party modules
+===================
+
+.. I put this here as there's only one module (apart from Numpy), otherwise
+   it should be a separate page.
+
+.. _cffi-support:
+
+``cffi``
+--------
+
+Similarly to ctypes, Numba is able to call into `cffi`_-declared external
+functions, using the following C types and any derived pointer types:
+
+* :c:type:`char`
+* :c:type:`short`
+* :c:type:`int`
+* :c:type:`long`
+* :c:type:`long long`
+* :c:type:`unsigned char`
+* :c:type:`unsigned short`
+* :c:type:`unsigned int`
+* :c:type:`unsigned long`
+* :c:type:`unsigned long long`
+* :c:type:`int8_t`
+* :c:type:`uint8_t`
+* :c:type:`int16_t`
+* :c:type:`uint16_t`
+* :c:type:`int32_t`
+* :c:type:`uint32_t`
+* :c:type:`int64_t`
+* :c:type:`uint64_t`
+* :c:type:`float`
+* :c:type:`double`
+* :c:type:`ssize_t`
+* :c:type:`size_t`
+* :c:type:`void`
+
+The ``from_buffer()`` method of ``cffi.FFI`` and ``CompiledFFI`` objects is
+supported for passing Numpy arrays and other buffer-like objects.  Only
+*contiguous* arguments are accepted.  The argument to ``from_buffer()``
+is converted to a raw pointer of the appropriate C type (for example a
+``double *`` for a ``float64`` array).
+
+Additional type mappings for the conversion from a buffer to the appropriate C
+type may be registered with Numba. This may include struct types, though it is
+only permitted to call functions that accept pointers to structs - passing a
+struct by value is unsupported. For registering a mapping, use:
+
+.. function:: numba.core.typing.cffi_utils.register_type(cffi_type, numba_type)
+
+Out-of-line cffi modules must be registered with Numba prior to the use of any
+of their functions from within Numba-compiled functions:
+
+.. function:: numba.core.typing.cffi_utils.register_module(mod)
+
+   Register the cffi out-of-line module ``mod`` with Numba.
+
+Inline cffi modules require no registration.
+
+.. _cffi: https://cffi.readthedocs.org/
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/types.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/types.rst
new file mode 100644
index 000000000..75ff34320
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/types.rst
@@ -0,0 +1,375 @@
+.. _numba-types:
+
+====================
+Types and signatures
+====================
+
+Rationale
+=========
+
+As an optimizing compiler, Numba needs to decide on the type of each
+variable to generate efficient machine code.  Python's standard types
+are not precise enough for that, so we had to develop our own fine-grained
+type system.
+
+You will encounter Numba types mainly when trying to inspect the results
+of Numba's type inference, for :ref:`debugging <numba-envvars>` or
+:ref:`educational <architecture>` purposes.  However, you need to use
+types explicitly if compiling code :ref:`ahead-of-time <pycc>`.
+
+
+Signatures
+==========
+
+A signature specifies the type of a function.  Exactly which kind
+of signature is allowed depends on the context (:term:`AOT` or :term:`JIT`
+compilation), but signatures always involve some representation of Numba
+types to specify the concrete types for the function's arguments and,
+if required, the function's return type.
+
+An example function signature would be the string ``"f8(i4, i4)"``
+(or the equivalent ``"float64(int32, int32)"``) which specifies a
+function taking two 32-bit integers and returning a double-precision float.
+
+
+Basic types
+===========
+
+The most basic types can be expressed through simple expressions.  The
+symbols below refer to attributes of the main ``numba`` module (so if
+you read "boolean", it means that symbol can be accessed as ``numba.boolean``).
+Many types are available both as a canonical name and a shorthand alias,
+following NumPy's conventions.
+
+Numbers
+-------
+
+The following table contains the elementary numeric types currently defined
+by Numba and their aliases.
+
+===================     =========        ===================================
+Type name(s)            Shorthand        Comments
+===================     =========        ===================================
+boolean                 b1               represented as a byte
+uint8, byte             u1               8-bit unsigned byte
+uint16                  u2               16-bit unsigned integer
+uint32                  u4               32-bit unsigned integer
+uint64                  u8               64-bit unsigned integer
+
+int8, char              i1               8-bit signed byte
+int16                   i2               16-bit signed integer
+int32                   i4               32-bit signed integer
+int64                   i8               64-bit signed integer
+
+intc                    --               C int-sized integer
+uintc                   --               C int-sized unsigned integer
+intp                    --               pointer-sized integer
+uintp                   --               pointer-sized unsigned integer
+ssize_t                 --               C ssize_t
+size_t                  --               C size_t
+
+float32                 f4               single-precision floating-point number
+float64, double         f8               double-precision floating-point number
+
+complex64               c8               single-precision complex number
+complex128              c16              double-precision complex number
+===================     =========        ===================================
+
+Arrays
+------
+
+The easy way to declare :class:`~numba.types.Array` types is to subscript an
+elementary type according to the number of dimensions. For example a 
+1-dimension single-precision array::
+
+   >>> numba.float32[:]
+   array(float32, 1d, A)
+
+or a 3-dimension array of the same underlying type::
+
+   >>> numba.float32[:, :, :]
+   array(float32, 3d, A)
+
+This syntax defines array types with no particular layout (producing code
+that accepts both non-contiguous and contiguous arrays), but you can
+specify a particular contiguity by using the ``::1`` index either at
+the beginning or the end of the index specification::
+
+   >>> numba.float32[::1]
+   array(float32, 1d, C)
+   >>> numba.float32[:, :, ::1]
+   array(float32, 3d, C)
+   >>> numba.float32[::1, :, :]
+   array(float32, 3d, F)
+
+Functions
+---------
+
+.. warning::
+   The feature of considering functions as first-class type objects is
+   under development.
+
+Functions are often considered as certain transformations of
+input arguments to output values. Within Numba :term:`JIT` compiled
+functions, the functions can also be considered as objects, that is,
+functions can be passed around as arguments or return values, or used
+as items in sequences, in addition to being callable.
+
+First-class function support is enabled for all Numba :term:`JIT`
+compiled functions and Numba ``cfunc`` compiled functions except when:
+
+- using a non-CPU compiler,
+- the compiled function is a Python generator,
+- the compiled function has Omitted arguments,
+- or the compiled function returns Optional value.
+
+To disable first-class function support, use ``no_cfunc_wrapper=True``
+decorator option.
+
+For instance, consider an example where the Numba :term:`JIT` compiled
+function applies user-specified functions as a composition to an input
+argument::
+
+    >>> @numba.njit
+    ... def composition(funcs, x):
+    ...     r = x
+    ...     for f in funcs[::-1]:
+    ...         r = f(r)
+    ...     return r
+    ...
+    >>> @numba.cfunc("double(double)")
+    ... def a(x):
+    ...     return x + 1.0
+    ...
+    >>> @numba.njit
+    ... def b(x):
+    ...     return x * x
+    ...
+    >>> composition((a, b), 0.5), 0.5 ** 2 + 1
+    (1.25, 1.25)
+    >>> composition((b, a, b, b, a), 0.5), b(a(b(b(a(0.5)))))
+    (36.75390625, 36.75390625)
+
+Here, ``cfunc`` compiled functions ``a`` and ``b`` are considered as
+first-class function objects because these are passed in to the Numba
+:term:`JIT` compiled function ``composition`` as arguments, that is, the
+``composition`` is :term:`JIT` compiled independently from its argument function
+objects (that are collected in the input argument ``funcs``).
+
+Currently, first-class function objects can be Numba ``cfunc`` compiled
+functions, :term:`JIT` compiled functions, and objects that implement the
+Wrapper Address Protocol (WAP, see below) with the following restrictions:
+
+========================   ============   ==============   ===========
+Context                    JIT compiled   cfunc compiled   WAP objects
+========================   ============   ==============   ===========
+Can be used as arguments   yes            yes              yes
+Can be called              yes            yes              yes
+Can be used as items       yes\*          yes              yes
+Can be returned            yes            yes              yes
+Namespace scoping          yes            yes              yes
+Automatic overload         yes            no               no
+========================   ============   ==============   ===========
+
+\* at least one of the items in a sequence of first-class function objects must
+have a precise type.
+
+
+Wrapper Address Protocol - WAP
+++++++++++++++++++++++++++++++
+
+Wrapper Address Protocol provides an API for making any Python object
+a first-class function for Numba :term:`JIT` compiled functions. This assumes
+that the Python object represents a compiled function that can be
+called via its memory address (function pointer value) from Numba :term:`JIT`
+compiled functions. The so-called WAP objects must define the
+following two methods:
+
+.. method:: __wrapper_address__(self) -> int
+
+            Return the memory address of a first-class function. This
+            method is used when a Numba :term:`JIT` compiled function tries to
+            call the given WAP instance.
+
+.. method:: signature(self) -> numba.typing.Signature
+
+            Return the signature of the given first-class
+            function. This method is used when passing in the given
+            WAP instance to a Numba :term:`JIT` compiled function.
+
+In addition, the WAP object may implement the ``__call__``
+method. This is necessary when calling WAP objects from Numba
+:term:`JIT` compiled functions in :term:`object mode`.
+
+As an example, let us call the standard math library function ``cos``
+within a Numba :term:`JIT` compiled function. The memory address of ``cos`` can
+be established after loading the math library and using the ``ctypes``
+package::
+
+    >>> import numba, ctypes, ctypes.util, math
+    >>> libm = ctypes.cdll.LoadLibrary(ctypes.util.find_library('m'))
+    >>> class LibMCos(numba.types.WrapperAddressProtocol):
+    ...     def __wrapper_address__(self):
+    ...         return ctypes.cast(libm.cos, ctypes.c_voidp).value
+    ...     def signature(self):
+    ...         return numba.float64(numba.float64)
+    ...
+    >>> @numba.njit
+    ... def foo(f, x):
+    ...     return f(x)
+    ...
+    >>> foo(LibMCos(), 0.0)
+    1.0
+    >>> foo(LibMCos(), 0.5), math.cos(0.5)
+    (0.8775825618903728, 0.8775825618903728)
+
+Miscellaneous Types
+-------------------
+
+There are some non-numerical types that do not fit into the other categories.
+
+===================   =================================================
+Type name(s)          Comments
+===================   =================================================
+pyobject              generic Python object
+voidptr               raw pointer, no operations can be performed on it
+===================   =================================================
+
+Advanced types
+==============
+
+For more advanced declarations, you have to explicitly call helper
+functions or classes provided by Numba.
+
+.. warning::
+   The APIs documented here are not guaranteed to be stable.  Unless
+   necessary, it is recommended to let Numba infer argument types by using
+   the :ref:`signature-less variant of @jit <jit-lazy>`.
+
+.. A word of note: I only documented those types that can be genuinely
+   useful to users, i.e. types that can be passed as parameters to a JIT
+   function.  Other types such as tuple are only usable in type inference.
+
+
+Inference
+---------
+
+.. function:: numba.typeof(value)
+
+   Create a Numba type accurately describing the given Python *value*.
+   ``ValueError`` is raised if the value isn't supported in
+   :term:`nopython mode`.
+
+   ::
+
+      >>> numba.typeof(np.empty(3))
+      array(float64, 1d, C)
+      >>> numba.typeof((1, 2.0))
+      (int64, float64)
+      >>> numba.typeof([0])
+      reflected list(int64)
+
+
+NumPy scalars
+-------------
+
+Instead of using :func:`~numba.typeof`, non-trivial scalars such as
+structured types can also be constructed programmatically.
+
+.. function:: numba.from_dtype(dtype)
+
+   Create a Numba type corresponding to the given NumPy *dtype*::
+
+      >>> struct_dtype = np.dtype([('row', np.float64), ('col', np.float64)])
+      >>> ty = numba.from_dtype(struct_dtype)
+      >>> ty
+      Record([('row', '<f8'), ('col', '<f8')])
+      >>> ty[:, :]
+      unaligned array(Record([('row', '<f8'), ('col', '<f8')]), 2d, A)
+
+.. class:: numba.types.NPDatetime(unit)
+
+   Create a Numba type for NumPy datetimes of the given *unit*.  *unit*
+   should be a string amongst the codes recognized by NumPy (e.g.
+   ``Y``, ``M``, ``D``, etc.).
+
+.. class:: numba.types.NPTimedelta(unit)
+
+   Create a Numba type for NumPy timedeltas of the given *unit*.  *unit*
+   should be a string amongst the codes recognized by NumPy (e.g.
+   ``Y``, ``M``, ``D``, etc.).
+
+   .. seealso::
+      NumPy `datetime units <http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html#datetime-units>`_.
+
+
+Arrays
+------
+
+.. class:: numba.types.Array(dtype, ndim, layout)
+
+   Create an array type.  *dtype* should be a Numba type.  *ndim* is the
+   number of dimensions of the array (a positive integer).  *layout*
+   is a string giving the layout of the array: ``A`` means any layout, ``C``
+   means C-contiguous and ``F`` means Fortran-contiguous.
+
+
+Optional types
+--------------
+
+.. class:: numba.optional(typ)
+
+   Create an optional type based on the underlying Numba type *typ*.
+   The optional type will allow any value of either *typ* or :const:`None`.
+
+   ::
+
+      >>> @jit((optional(intp),))
+      ... def f(x):
+      ...     return x is not None
+      ...
+      >>> f(0)
+      True
+      >>> f(None)
+      False
+
+
+Type annotations
+-----------------
+
+.. function:: numba.extending.as_numba_type(py_type)
+
+   Create a Numba type corresponding to the given Python *type annotation*.
+   ``TypingError`` is raised if the type annotation can't be mapped to a Numba
+   type.  This function is meant to be used at statically compile time to
+   evaluate Python type annotations.  For runtime checking of Python objects
+   see ``typeof`` above.
+
+   For any numba type, ``as_numba_type(nb_type) == nb_type``.
+
+      >>> numba.extending.as_numba_type(int)
+      int64
+      >>> import typing  # the Python library, not the Numba one
+      >>> numba.extending.as_numba_type(typing.List[float])
+      ListType[float64]
+      >>> numba.extending.as_numba_type(numba.int32)
+      int32
+
+   ``as_numba_type`` is automatically updated to include any ``@jitclass``.
+
+      >>> @jitclass
+      ... class Counter:
+      ...     x: int
+      ...
+      ...     def __init__(self):
+      ...         self.x = 0
+      ...
+      ...     def inc(self):
+      ...         old_val = self.x
+      ...         self.x += 1
+      ...         return old_val
+      ...
+      >>> numba.extending.as_numba_type(Counter)
+      instance.jitclass.Counter#11bad4278<x:int64>
+
+   Currently ``as_numba_type`` is only used to infer fields for ``@jitclass``.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/utils.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/utils.rst
new file mode 100644
index 000000000..3fc866e5c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/reference/utils.rst
@@ -0,0 +1,34 @@
+
+=========
+Utilities
+=========
+
+Dealing with pointers
+=====================
+
+These functions can be called from pure Python as well as in
+:term:`nopython mode`.
+
+
+.. function:: numba.carray(ptr, shape, dtype=None)
+
+   Return a Numpy array view over the data pointed to by *ptr* with the
+   given *shape*, in C order.  If *dtype* is given, it is used as the array's
+   dtype, otherwise the array's dtype is inferred from *ptr*'s type.
+   As the returned array is a view, not a copy, writing to it will modify
+   the original data.
+
+   *ptr* should be a ctypes pointer object (either a typed pointer
+   as created using :func:`~ctypes.POINTER`, or a :class:`~ctypes.c_void_p`).
+
+   *shape* should be an integer or a tuple of integers.
+
+   *dtype* should be a Numpy dtype or scalar class (i.e. both
+   ``np.dtype('int8')`` and ``np.int8`` are accepted).
+
+
+.. function:: numba.farray(ptr, shape, dtype=None)
+
+   Same as :func:`~numba.carray`, but the data is assumed to be laid out
+   in Fortran order, and the array view is constructed accordingly.
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/release-notes.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/release-notes.rst
new file mode 100644
index 000000000..103366e15
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/release-notes.rst
@@ -0,0 +1,5 @@
+======================
+Release Notes
+======================
+
+.. include:: ../../CHANGE_LOG
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/5minguide.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/5minguide.rst
new file mode 100644
index 000000000..4ab382a6b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/5minguide.rst
@@ -0,0 +1,223 @@
+.. _numba-5_mins:
+
+A ~5 minute guide to Numba
+==========================
+
+Numba is a just-in-time compiler for Python that works best on code that uses
+NumPy arrays and functions, and loops. The most common way to use Numba is
+through its collection of decorators that can be applied to your functions to
+instruct Numba to compile them. When a call is made to a Numba-decorated
+function it is compiled to machine code "just-in-time" for execution and all or
+part of your code can subsequently run at native machine code speed!
+
+Out of the box Numba works with the following:
+
+* OS: Windows (32 and 64 bit), OSX, Linux (32 and 64 bit). Unofficial support on
+  \*BSD.
+* Architecture: x86, x86_64, ppc64le, armv7l, armv8l (aarch64). Unofficial
+  support on M1/Arm64.
+* GPUs: Nvidia CUDA.
+* CPython
+* NumPy 1.18 - latest
+
+How do I get it?
+----------------
+Numba is available as a `conda <https://conda.io/docs/>`_ package for the
+`Anaconda Python distribution <https://www.anaconda.com/>`_::
+
+  $ conda install numba
+
+Numba also has wheels available::
+
+  $ pip install numba
+
+Numba can also be
+:ref:`compiled from source <numba-source-install-instructions>`, although we do
+not recommend it for first-time Numba users.
+
+Numba is often used as a core package so its dependencies are kept to an
+absolute minimum, however, extra packages can be installed as follows to provide
+additional functionality:
+
+* ``scipy`` - enables support for compiling ``numpy.linalg`` functions.
+* ``colorama`` - enables support for color highlighting in backtraces/error
+  messages.
+* ``pyyaml`` - enables configuration of Numba via a YAML config file.
+* ``icc_rt`` - allows the use of the Intel SVML (high performance short vector
+  math library, x86_64 only). Installation instructions are in the
+  :ref:`performance tips <intel-svml>`.
+
+Will Numba work for my code?
+----------------------------
+This depends on what your code looks like, if your code is numerically
+orientated (does a lot of math), uses NumPy a lot and/or has a lot of loops,
+then Numba is often a good choice. In these examples we'll apply the most
+fundamental of Numba's JIT decorators, ``@jit``, to try and speed up some
+functions to demonstrate what works well and what does not.
+
+Numba works well on code that looks like this::
+
+    from numba import jit
+    import numpy as np
+
+    x = np.arange(100).reshape(10, 10)
+
+    @jit(nopython=True) # Set "nopython" mode for best performance, equivalent to @njit
+    def go_fast(a): # Function is compiled to machine code when called the first time
+        trace = 0.0
+        for i in range(a.shape[0]):   # Numba likes loops
+            trace += np.tanh(a[i, i]) # Numba likes NumPy functions
+        return a + trace              # Numba likes NumPy broadcasting
+
+    print(go_fast(x))
+
+
+It won't work very well, if at all, on code that looks like this::
+
+    from numba import jit
+    import pandas as pd
+
+    x = {'a': [1, 2, 3], 'b': [20, 30, 40]}
+
+    @jit
+    def use_pandas(a): # Function will not benefit from Numba jit
+        df = pd.DataFrame.from_dict(a) # Numba doesn't know about pd.DataFrame
+        df += 1                        # Numba doesn't understand what this is
+        return df.cov()                # or this!
+
+    print(use_pandas(x))
+
+Note that Pandas is not understood by Numba and as a result Numba would simply
+run this code via the interpreter but with the added cost of the Numba internal
+overheads!
+
+What is ``nopython`` mode?
+--------------------------
+The Numba ``@jit`` decorator fundamentally operates in two compilation modes,
+``nopython`` mode and ``object`` mode. In the ``go_fast`` example above,
+``nopython=True`` is set in the ``@jit`` decorator; this is instructing Numba to
+operate in ``nopython`` mode. The behaviour of the ``nopython`` compilation mode
+is to essentially compile the decorated function so that it will run entirely
+without the involvement of the Python interpreter. This is the recommended and
+best-practice way to use the Numba ``jit`` decorator as it leads to the best
+performance.
+
+Should the compilation in ``nopython`` mode fail, Numba can compile using
+``object mode``. This is a fall back mode for the ``@jit`` decorator if
+``nopython=True`` is not set (as seen in the ``use_pandas`` example above). In
+this mode Numba will identify loops that it can compile and compile those into
+functions that run in machine code, and it will run the rest of the code in the
+interpreter. For best performance avoid using this mode!
+
+How to measure the performance of Numba?
+----------------------------------------
+First, recall that Numba has to compile your function for the argument types
+given before it executes the machine code version of your function. This takes
+time. However, once the compilation has taken place Numba caches the machine
+code version of your function for the particular types of arguments presented.
+If it is called again with the same types, it can reuse the cached version
+instead of having to compile again.
+
+A really common mistake when measuring performance is to not account for the
+above behaviour and to time code once with a simple timer that includes the
+time taken to compile your function in the execution time.
+
+For example::
+
+    from numba import jit
+    import numpy as np
+    import time
+
+    x = np.arange(100).reshape(10, 10)
+
+    @jit(nopython=True)
+    def go_fast(a): # Function is compiled and runs in machine code
+        trace = 0.0
+        for i in range(a.shape[0]):
+            trace += np.tanh(a[i, i])
+        return a + trace
+
+    # DO NOT REPORT THIS... COMPILATION TIME IS INCLUDED IN THE EXECUTION TIME!
+    start = time.perf_counter()
+    go_fast(x)
+    end = time.perf_counter()
+    print("Elapsed (with compilation) = {}s".format((end - start)))
+
+    # NOW THE FUNCTION IS COMPILED, RE-TIME IT EXECUTING FROM CACHE
+    start = time.perf_counter()
+    go_fast(x)
+    end = time.perf_counter()
+    print("Elapsed (after compilation) = {}s".format((end - start)))
+
+This, for example prints::
+
+    Elapsed (with compilation) = 0.33030009269714355s
+    Elapsed (after compilation) = 6.67572021484375e-06s
+
+A good way to measure the impact Numba JIT has on your code is to time execution
+using the `timeit <https://docs.python.org/3/library/timeit.html>`_ module
+functions; these measure multiple iterations of execution and, as a result,
+can be made to accommodate for the compilation time in the first execution.
+
+As a side note, if compilation time is an issue, Numba JIT supports
+:ref:`on-disk caching <jit-decorator-cache>` of compiled functions and also has
+an :ref:`Ahead-Of-Time <aot-compilation>` compilation mode.
+
+How fast is it?
+---------------
+Assuming Numba can operate in ``nopython`` mode, or at least compile some loops,
+it will target compilation to your specific CPU. Speed up varies depending on
+application but can be one to two orders of magnitude. Numba has a
+:ref:`performance guide <performance-tips>` that covers common options for
+gaining extra performance.
+
+How does Numba work?
+--------------------
+Numba reads the Python bytecode for a decorated function and combines this with
+information about the types of the input arguments to the function. It analyzes
+and optimizes your code, and finally uses the LLVM compiler library to generate
+a machine code version of your function, tailored to your CPU capabilities. This
+compiled version is then used every time your function is called.
+
+Other things of interest:
+-------------------------
+Numba has quite a few decorators, we've seen ``@jit``, but there's
+also:
+
+* ``@njit`` - this is an alias for ``@jit(nopython=True)`` as it is so commonly
+  used!
+* ``@vectorize`` - produces NumPy ``ufunc`` s (with all the ``ufunc`` methods
+  supported). :ref:`Docs are here <vectorize>`.
+* ``@guvectorize`` - produces NumPy generalized ``ufunc`` s.
+  :ref:`Docs are here <guvectorize>`.
+* ``@stencil`` - declare a function as a kernel for a stencil like operation.
+  :ref:`Docs are here <numba-stencil>`.
+* ``@jitclass`` - for jit aware classes. :ref:`Docs are here <jitclass>`.
+* ``@cfunc`` - declare a function for use as a native call back (to be called
+  from C/C++ etc). :ref:`Docs are here <cfunc>`.
+* ``@overload`` - register your own implementation of a function for use in
+  nopython mode, e.g. ``@overload(scipy.special.j0)``.
+  :ref:`Docs are here <high-level-extending>`.
+
+Extra options available in some decorators:
+
+* ``parallel = True`` - :ref:`enable <jit-decorator-parallel>` the
+  :ref:`automatic parallelization <numba-parallel>` of the function.
+* ``fastmath = True`` - enable :ref:`fast-math <jit-decorator-fastmath>`
+  behaviour for the function.
+
+ctypes/cffi/cython interoperability:
+
+* ``cffi`` - The calling of :ref:`CFFI  <cffi-support>` functions is supported
+  in ``nopython`` mode.
+* ``ctypes`` - The calling of :ref:`ctypes  <ctypes-support>` wrapped
+  functions is supported in ``nopython`` mode.
+* Cython exported functions :ref:`are callable <cython-support>`.
+
+GPU targets:
+~~~~~~~~~~~~
+
+Numba can target `Nvidia CUDA <https://developer.nvidia.com/cuda-zone>`_ GPUs.
+You can write a kernel in pure Python and have Numba handle the computation and
+data movement (or do this explicitly). Click for Numba documentation on
+:ref:`CUDA <cuda-index>`.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/cfunc.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/cfunc.rst
new file mode 100644
index 000000000..845dc9634
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/cfunc.rst
@@ -0,0 +1,218 @@
+.. _cfunc:
+
+====================================
+Creating C callbacks with ``@cfunc``
+====================================
+
+Interfacing with some native libraries (for example written in C or C++)
+can necessitate writing native callbacks to provide business logic to the
+library.  The :func:`numba.cfunc` decorator creates a compiled function
+callable from foreign C code, using the signature of your choice.
+
+
+Basic usage
+===========
+
+The ``@cfunc`` decorator has a similar usage to ``@jit``, but with an
+important difference: passing a single signature is mandatory.
+It determines the visible signature of the C callback::
+
+   from numba import cfunc
+
+   @cfunc("float64(float64, float64)")
+   def add(x, y):
+       return x + y
+
+
+The C function object exposes the address of the compiled C callback as
+the :attr:`~CFunc.address` attribute, so that you can pass it to any
+foreign C or C++ library.  It also exposes a :mod:`ctypes` callback
+object pointing to that callback; that object is also callable from
+Python, making it easy to check the compiled code::
+
+   @cfunc("float64(float64, float64)")
+   def add(x, y):
+       return x + y
+
+   print(add.ctypes(4.0, 5.0))  # prints "9.0"
+
+
+Example
+=======
+
+In this example, we are going to be using the ``scipy.integrate.quad``
+function.  That function accepts either a regular Python callback or
+a C callback wrapped in a :mod:`ctypes` callback object.
+
+Let's define a pure Python integrand and compile it as a
+C callback::
+
+   >>> import numpy as np
+   >>> from numba import cfunc
+   >>> def integrand(t):
+           return np.exp(-t) / t**2
+      ...:
+   >>> nb_integrand = cfunc("float64(float64)")(integrand)
+
+We can pass the ``nb_integrand`` object's :mod:`ctypes` callback to
+``scipy.integrate.quad`` and check that the results are the same as with
+the pure Python function::
+
+   >>> import scipy.integrate as si
+   >>> def do_integrate(func):
+           """
+           Integrate the given function from 1.0 to +inf.
+           """
+           return si.quad(func, 1, np.inf)
+      ...:
+   >>> do_integrate(integrand)
+   (0.14849550677592208, 3.8736750296130505e-10)
+   >>> do_integrate(nb_integrand.ctypes)
+   (0.14849550677592208, 3.8736750296130505e-10)
+
+
+Using the compiled callback, the integration function does not invoke the
+Python interpreter each time it evaluates the integrand.  In our case, the
+integration is made 18 times faster::
+
+   >>> %timeit do_integrate(integrand)
+   1000 loops, best of 3: 242 µs per loop
+   >>> %timeit do_integrate(nb_integrand.ctypes)
+   100000 loops, best of 3: 13.5 µs per loop
+
+
+Dealing with pointers and array memory
+======================================
+
+A less trivial use case of C callbacks involves doing operation on some
+array of data passed by the caller.  As C doesn't have a high-level
+abstraction similar to Numpy arrays, the C callback's signature will pass
+low-level pointer and size arguments.  Nevertheless, the Python code for
+the callback will expect to exploit the power and expressiveness of Numpy
+arrays.
+
+In the following example, the C callback is expected to operate on 2-d arrays,
+with the signature ``void(double *input, double *output, int m, int n)``.
+You can implement such a callback thusly::
+
+   from numba import cfunc, types, carray
+
+   c_sig = types.void(types.CPointer(types.double),
+                      types.CPointer(types.double),
+                      types.intc, types.intc)
+
+   @cfunc(c_sig)
+   def my_callback(in_, out, m, n):
+       in_array = carray(in_, (m, n))
+       out_array = carray(out, (m, n))
+       for i in range(m):
+           for j in range(n):
+               out_array[i, j] = 2 * in_array[i, j]
+
+
+The :func:`numba.carray` function takes as input a data pointer and a shape
+and returns an array view of the given shape over that data.  The data is
+assumed to be laid out in C order.  If the data is laid out in Fortran order,
+:func:`numba.farray` should be used instead.
+
+
+Handling C structures
+=====================
+
+
+With CFFI
+---------
+
+For applications that have a lot of state, it is useful to pass data in C
+structures.  To simplify the interoperability with C code, numba can convert
+a ``cffi`` type into a numba ``Record`` type using
+``numba.core.typing.cffi_utils.map_type``::
+
+   from numba.core.typing import cffi_utils
+
+   nbtype = cffi_utils.map_type(cffi_type, use_record_dtype=True)
+
+.. note:: **use_record_dtype=True** is needed otherwise pointers to C
+    structures are returned as void pointers.
+
+.. note:: From v0.49 the ``numba.cffi_support`` module has been phased out
+    in favour of ``numba.core.typing.cffi_utils``
+
+
+For example::
+
+   from cffi import FFI
+
+   src = """
+
+   /* Define the C struct */
+   typedef struct my_struct {
+      int    i1;
+      float  f2;
+      double d3;
+      float  af4[7]; // arrays are supported
+   } my_struct;
+
+   /* Define a callback function */
+   typedef double (*my_func)(my_struct*, size_t);
+   """
+
+   ffi = FFI()
+   ffi.cdef(src)
+
+   # Get the function signature from *my_func*
+   sig = cffi_utils.map_type(ffi.typeof('my_func'), use_record_dtype=True)
+
+   # Make the cfunc
+   from numba import cfunc, carray
+
+   @cfunc(sig)
+   def foo(ptr, n):
+      base = carray(ptr, n)  # view pointer as an array of my_struct
+      tmp = 0
+      for i in range(n):
+         tmp += base[i].i1 * base[i].f2 / base[i].d3
+         tmp += base[i].af4.sum()  # nested arrays are like normal NumPy arrays
+      return tmp
+
+
+With ``numba.types.Record.make_c_struct``
+-----------------------------------------
+
+The ``numba.types.Record`` type can be created manually to follow a
+C-structure's layout.  To do that, use ``Record.make_c_struct``, for example::
+
+   my_struct = types.Record.make_c_struct([
+      # Provides a sequence of 2-tuples i.e. (name:str, type:Type)
+      ('i1', types.int32),
+      ('f2', types.float32),
+      ('d3', types.float64),
+      ('af4', types.NestedArray(dtype=types.float32, shape=(7,))),
+   ])
+
+Due to ABI limitations, structures should be passed as pointers
+using ``types.CPointer(my_struct)`` as the argument type.  Inside the ``cfunc``
+body, the ``my_struct*`` can be accessed with ``carray``.
+
+Full example
+------------
+
+See full example in ``examples/notebooks/Accessing C Struct Data.ipynb``.
+
+
+Signature specification
+=======================
+
+The explicit ``@cfunc`` signature can use any :ref:`Numba types <numba-types>`,
+but only a subset of them make sense for a C callback.  You should
+generally limit yourself to scalar types (such as ``int8`` or ``float64``)
+,pointers to them (for example ``types.CPointer(types.int8)``), or pointers
+to ``Record`` type.
+
+
+Compilation options
+===================
+
+A number of keyword-only arguments can be passed to the ``@cfunc``
+decorator: ``nopython`` and ``cache``.  Their meaning is similar to those
+in the ``@jit`` decorator.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/cli.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/cli.rst
new file mode 100644
index 000000000..3764c687f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/cli.rst
@@ -0,0 +1,165 @@
+.. _cli:
+
+Command line interface
+======================
+
+Numba is a Python package, usually you ``import numba`` from Python and use the
+Python application programming interface (API). However, Numba also ships with a
+command line interface (CLI), i.e. a tool ``numba`` that is installed when you
+install Numba.
+
+Currently, the only purpose of the CLI is to allow you to quickly show some
+information about your system and installation, or to quickly get some debugging
+information for a Python script using Numba.
+
+.. _cli_usage:
+
+Usage
+-----
+
+To use the Numba CLI from the terminal, use ``numba`` followed by the options
+and arguments like ``--help`` or ``-s``, as explained below.
+
+Sometimes it can happen that you get a "command not found" error when you type
+``numba``, because your ``PATH`` isn't configured properly. In that case you can
+use the equivalent command ``python -m numba``. If that still gives "command
+not found", try to ``import numba`` as suggested here:
+:ref:`numba-source-install-check`.
+
+The two versions ``numba`` and ``python -m numba`` are the same. The first is
+shorter to type, but if you get a "command not found" error because your
+``PATH`` doesn't contain the location where ``numba`` is installed, having the
+``python -m numba`` variant is useful.
+
+To use the Numba CLI from IPython or Jupyter, use ``!numba``, i.e. prefix the
+command with an exclamation mark. This is a general IPython/Jupyter feature to
+execute shell commands, it is not available in the regular ``python`` terminal.
+
+.. _cli_help:
+
+Help
+----
+
+To see all available options, use ``numba --help``::
+
+    $ numba --help
+    usage: numba [-h] [--annotate] [--dump-llvm] [--dump-optimized]
+                 [--dump-assembly] [--annotate-html ANNOTATE_HTML] [-s]
+                 [--sys-json SYS_JSON]
+                 [filename]
+
+    positional arguments:
+    filename              Python source filename
+
+    optional arguments:
+    -h, --help            show this help message and exit
+    --annotate            Annotate source
+    --dump-llvm           Print generated llvm assembly
+    --dump-optimized      Dump the optimized llvm assembly
+    --dump-assembly       Dump the LLVM generated assembly
+    --annotate-html ANNOTATE_HTML
+                            Output source annotation as html
+    -s, --sysinfo         Output system information for bug reporting
+    --sys-json SYS_JSON   Saves the system info dict as a json file
+
+
+.. _cli_sysinfo:
+
+System information
+------------------
+
+The ``numba -s`` (or the equivalent ``numba --sysinfo``) command prints a lot of
+information about your system and your Numba installation and relevant
+dependencies.
+
+Remember: you can use ``!numba -s`` with an exclamation mark to see this
+information from IPython or Jupyter.
+
+Example output::
+
+    $ numba -s
+
+    System info:
+    --------------------------------------------------------------------------------
+    __Time Stamp__
+    2019-05-07 14:15:39.733994
+
+    __Hardware Information__
+    Machine                                       : x86_64
+    CPU Name                                      : haswell
+    CPU count                                     : 8
+    CPU Features                                  : 
+    aes avx avx2 bmi bmi2 cmov cx16 f16c fma fsgsbase invpcid lzcnt mmx movbe pclmul
+    popcnt rdrnd sahf sse sse2 sse3 sse4.1 sse4.2 ssse3 xsave xsaveopt
+
+    __OS Information__
+    Platform                                      : Darwin-18.5.0-x86_64-i386-64bit
+    Release                                       : 18.5.0
+    System Name                                   : Darwin
+    Version                                       : Darwin Kernel Version 18.5.0: Mon Mar 11 20:40:32 PDT 2019; root:xnu-4903.251.3~3/RELEASE_X86_64
+    OS specific info                              : 10.14.4   x86_64
+
+    __Python Information__
+    Python Compiler                               : Clang 4.0.1 (tags/RELEASE_401/final)
+    Python Implementation                         : CPython
+    Python Version                                : 3.7.3
+    Python Locale                                 : en_US UTF-8
+
+    __LLVM information__
+    LLVM version                                  : 7.0.0
+
+    __CUDA Information__
+    CUDA driver library cannot be found or no CUDA enabled devices are present.
+    Error class: <class 'numba.cuda.cudadrv.error.CudaSupportError'>
+
+    __SVML Information__
+    SVML state, config.USING_SVML                 : False
+    SVML library found and loaded                 : False
+    llvmlite using SVML patched LLVM              : True
+    SVML operational                              : False
+
+    __Threading Layer Information__
+    TBB Threading layer available                 : False
+    +--> Disabled due to                          : Unknown import problem.
+    OpenMP Threading layer available              : False
+    +--> Disabled due to                          : Unknown import problem.
+    Workqueue Threading layer available           : True
+
+    __Numba Environment Variable Information__
+    None set.
+
+    __Conda Information__
+    conda_build_version                           : 3.17.8
+    conda_env_version                             : 4.6.14
+    platform                                      : osx-64
+    python_version                                : 3.7.3.final.0
+    root_writable                                 : True
+
+    __Current Conda Env__
+    (output truncated due to length)
+
+.. _cli_debug:
+
+Debugging
+---------
+
+As shown in the help output above, the ``numba`` command includes options that
+can help you to debug Numba compiled code.
+
+To try it out, create an example script called ``myscript.py``::
+
+    import numba
+
+    @numba.jit
+    def f(x):
+        return 2 * x
+    
+    f(42)
+
+and then execute one of the following commands::
+
+    $ numba myscript.py --annotate
+    $ numba myscript.py --annotate-html myscript.html
+    $ numba myscript.py --dump-llvm
+    $ numba myscript.py --dump-optimized
+    $ numba myscript.py --dump-assembly
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/examples.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/examples.rst
new file mode 100644
index 000000000..2651c12ef
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/examples.rst
@@ -0,0 +1,52 @@
+========
+Examples
+========
+
+
+Mandelbrot
+----------
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_examples.py
+   :language: python
+   :caption: from ``test_mandelbrot`` of ``numba/tests/doc_examples/test_examples.py``
+   :start-after: magictoken.ex_mandelbrot.begin
+   :end-before: magictoken.ex_mandelbrot.end
+   :dedent: 12
+   :linenos:
+
+.. _example-movemean:
+
+Moving average
+--------------
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_examples.py
+   :language: python
+   :caption: from ``test_moving_average`` of ``numba/tests/doc_examples/test_examples.py``
+   :start-after: magictoken.ex_moving_average.begin
+   :end-before: magictoken.ex_moving_average.end
+   :dedent: 12
+   :linenos:
+
+Multi-threading
+---------------
+
+The code below showcases the potential performance improvement when
+using the :ref:`nogil <jit-nogil>` feature.  For example, on a 4-core machine,
+the following results were printed::
+
+   numpy (1 thread)       145 ms
+   numba (1 thread)       128 ms
+   numba (4 threads)       35 ms
+
+.. note::
+   If preferred it's possible to use the standard `concurrent.futures
+   <https://docs.python.org/3/library/concurrent.futures.html>`_ module
+   rather than spawn threads and dispatch tasks by hand.
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_examples.py
+   :language: python
+   :caption: from ``test_no_gil`` of ``numba/tests/doc_examples/test_examples.py``
+   :start-after: magictoken.ex_no_gil.begin
+   :end-before: magictoken.ex_no_gil.end
+   :dedent: 12
+   :linenos:
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/faq.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/faq.rst
new file mode 100644
index 000000000..b8fc588b0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/faq.rst
@@ -0,0 +1,413 @@
+
+==========================
+Frequently Asked Questions
+==========================
+
+Installation
+============
+
+Numba could not be imported
+---------------------------
+
+If you are seeing an exception on importing Numba with an error message
+that starts with::
+
+    ImportError: Numba could not be imported.
+
+here are some common issues and things to try to fix it.
+
+#. Your installation has more than one version of Numba a given environment.
+
+   Common ways this occurs include:
+
+   * Installing Numba with conda and then installing again with pip.
+   * Installing Numba with pip and then updating to a new version with pip (pip
+     re-installations don't seem to always clean up very well).
+
+   To fix this the best approach is to create an entirely new environment and
+   install a single version of Numba in that environment using a package manager
+   of your choice.
+
+#. Your installation has Numba for Python version X but you are running with
+   Python version Y.
+
+   This occurs due to a variety of Python environment mix-up/mismatch problems.
+   The most common mismatch comes from installing Numba into the
+   site-packages/environment of one version of Python by using a base or
+   system installation of Python that is a different version, this typically
+   happens through the use of the "wrong" ``pip`` binary. This will obviously
+   cause problems as the C-Extensions on which Numba relies are bound to
+   specific Python versions. A way to check if this likely the problem is to
+   see if the path to the ``python`` binary at::
+
+       python -c 'import sys; print(sys.executable)'
+
+   matches the path to your installation tool and/or matches the reported
+   installation location and if the Python versions match up across all of
+   these. Note that Python version ``X.Y.A`` is compatible with ``X.Y.B``.
+
+   To fix this the best approach is to create an entirely new environment and
+   ensure that the installation tool used to install Numba is the one from that
+   environment/the Python versions at install and run time match.
+
+#. Your core system libraries are too old.
+
+   This is a somewhat rare occurrence, but there are occasions when a very old
+   (typically out of support) version of Linux is in use it doesn't have a
+   ``glibc`` library with sufficiently new versioned symbols for Numba's shared
+   libraries to resolve against. The fix for this is to update your OS system
+   libraries/update your OS.
+
+#. You are using an IDE e.g. Spyder.
+
+   There are some unknown issues in relation to installing Numba via IDEs, but
+   it would appear that these are likely variations of 1. or 2. with the same
+   suggested fixes. Also, try installation from outside of the IDE with the
+   command line.
+
+
+If you have an installation problem which is not one of the above problems,
+please do ask on `numba.discourse.group <https://numba.discourse.group/>`_ and
+if possible include the path where Numba is installed and also the output of::
+
+    python -c 'import sys; print(sys.executable)'
+
+
+Programming
+===========
+
+Can I pass a function as an argument to a jitted function?
+----------------------------------------------------------
+
+As of Numba 0.39, you can, so long as the function argument has also been
+JIT-compiled::
+
+   @jit(nopython=True)
+   def f(g, x):
+       return g(x) + g(-x)
+
+   result = f(jitted_g_function, 1)
+
+However, dispatching with arguments that are functions has extra overhead.
+If this matters for your application, you can also use a factory function to 
+capture the function argument in a closure::
+
+   def make_f(g):
+       # Note: a new f() is created each time make_f() is called!
+       @jit(nopython=True)
+       def f(x):
+           return g(x) + g(-x)
+       return f
+
+   f = make_f(jitted_g_function)
+   result = f(1)
+
+Improving the dispatch performance of functions in Numba is an ongoing task.
+
+Numba doesn't seem to care when I modify a global variable
+----------------------------------------------------------
+
+Numba considers global variables as compile-time constants.  If you want
+your jitted function to update itself when you have modified a global
+variable's value, one solution is to recompile it using the
+:meth:`~Dispatcher.recompile` method.  This is a relatively slow operation,
+though, so you may instead decide to rearchitect your code and turn the
+global variable into a function argument.
+
+Can I debug a jitted function?
+------------------------------
+
+Calling into :mod:`pdb` or other such high-level facilities is currently not
+supported from Numba-compiled code.  However, you can temporarily disable
+compilation by setting the :envvar:`NUMBA_DISABLE_JIT` environment
+variable.
+
+How can I create a Fortran-ordered array?
+-----------------------------------------
+
+Numba currently doesn't support the ``order`` argument to most Numpy
+functions such as :func:`numpy.empty` (because of limitations in the
+:term:`type inference` algorithm).  You can work around this issue by
+creating a C-ordered array and then transposing it.  For example::
+
+   a = np.empty((3, 5), order='F')
+   b = np.zeros(some_shape, order='F')
+
+can be rewritten as::
+
+   a = np.empty((5, 3)).T
+   b = np.zeros(some_shape[::-1]).T
+
+How can I increase integer width?
+---------------------------------
+
+By default, Numba will generally use machine integer width for integer
+variables.  On a 32-bit machine, you may sometimes need the magnitude of
+64-bit integers instead.  You can simply initialize relevant variables as
+``np.int64`` (for example ``np.int64(0)`` instead of ``0``).  It will
+propagate to all computations involving those variables.
+
+.. _parallel_faqs:
+
+How can I tell if ``parallel=True`` worked?
+-------------------------------------------
+
+If the ``parallel=True`` transformations failed for a function
+decorated as such, a warning will be displayed. See also
+:ref:`numba-parallel-diagnostics` for information about parallel diagnostics.
+
+Performance
+===========
+
+Does Numba inline functions?
+----------------------------
+
+Numba gives enough information to LLVM so that functions short enough
+can be inlined.  This only works in :term:`nopython mode`.
+
+Does Numba vectorize array computations (SIMD)?
+-----------------------------------------------
+
+Numba doesn't implement such optimizations by itself, but it lets LLVM
+apply them.
+
+Why has my loop not vectorized?
+-------------------------------
+
+Numba enables the loop-vectorize optimization in LLVM by default.
+While it is a powerful optimization, not all loops are applicable.
+Sometimes, loop-vectorization may fail due to subtle details like memory access
+pattern. To see additional diagnostic information from LLVM,
+add the following lines:
+
+.. code-block:: python
+
+    import llvmlite.binding as llvm
+    llvm.set_option('', '--debug-only=loop-vectorize')
+
+This tells LLVM to print debug information from the **loop-vectorize**
+pass to stderr.  Each function entry looks like:
+
+
+.. note::
+   Using ``--debug-only`` requires LLVM to be build with assertions enabled to
+   work. Use the build of llvmlite in the `Numba channel <https://anaconda.org/numba/llvmlite>`_
+   which is linked against LLVM with assertions enabled.
+
+.. code-block:: text
+
+    LV: Checking a loop in "<low-level symbol name>" from <function name>
+    LV: Loop hints: force=? width=0 unroll=0
+    ...
+    LV: Vectorization is possible but not beneficial.
+    LV: Interleaving is not beneficial.
+
+Each function entry is separated by an empty line.  The reason for rejecting
+the vectorization is usually at the end of the entry.  In the example above,
+LLVM rejected the vectorization because doing so will not speedup the loop.
+In this case, it can be due to memory access pattern.  For instance, the
+array being looped over may not be in contiguous layout.
+
+When memory access pattern is non-trivial such that it cannot determine the
+access memory region, LLVM may reject with the following message:
+
+.. code-block:: text
+
+    LV: Can't vectorize due to memory conflicts
+
+Another common reason is:
+
+.. code-block:: text
+
+    LV: Not vectorizing: loop did not meet vectorization requirements.
+
+In this case, vectorization is rejected because the vectorized code may behave
+differently.  This is a case to try turning on ``fastmath=True`` to allow
+fastmath instructions.
+
+Why are the ``typed`` containers slower when used from the interpreter?
+-----------------------------------------------------------------------
+
+The Numba ``typed`` containers found in ``numba.typed`` e.g.
+``numba.typed.List`` store their data in an efficient form for access from JIT
+compiled code. When these containers are used from the CPython interpreter, the
+data involved has to be converted from/to the container format. This process is
+relatively costly and as a result impacts performance. In JIT compiled code no
+such penalty exists and so operations on the containers are much quicker and
+often faster than the pure Python equivalent.
+
+Does Numba automatically parallelize code?
+------------------------------------------
+
+It can, in some cases:
+
+* Ufuncs and gufuncs with the ``target="parallel"`` option will run on multiple threads.
+* The ``parallel=True`` option to ``@jit`` will attempt to optimize array
+  operations and run them in parallel.  It also adds support for ``prange()`` to
+  explicitly parallelize a loop.
+
+You can also manually run computations on multiple threads yourself and use
+the ``nogil=True`` option (see :ref:`releasing the GIL <jit-nogil>`).  Numba
+can also target parallel execution on GPU architectures using its CUDA and HSA
+backends.
+
+
+Can Numba speed up short-running functions?
+-------------------------------------------
+
+Not significantly.  New users sometimes expect to JIT-compile such
+functions::
+
+   def f(x, y):
+       return x + y
+
+and get a significant speedup over the Python interpreter.  But there isn't
+much Numba can improve here: most of the time is probably spent in CPython's
+function call mechanism, rather than the function itself.  As a rule of
+thumb, if a function takes less than 10 µs to execute: leave it.
+
+The exception is that you *should* JIT-compile that function if it is called
+from another jitted function.
+
+There is a delay when JIT-compiling a complicated function, how can I improve it?
+---------------------------------------------------------------------------------
+
+Try to pass ``cache=True`` to the ``@jit`` decorator.  It will keep the
+compiled version on disk for later use.
+
+A more radical alternative is :ref:`ahead-of-time compilation <pycc>`.
+
+
+GPU Programming
+===============
+
+How do I work around the ``CUDA initialized before forking`` error?
+-------------------------------------------------------------------
+
+On Linux, the ``multiprocessing`` module in the Python standard library
+defaults to using the ``fork`` method for creating new processes.  Because of
+the way process forking duplicates state between the parent and child
+processes, CUDA will not work correctly in the child process if the CUDA
+runtime was initialized *prior* to the fork.  Numba detects this and raises a
+``CudaDriverError`` with the message ``CUDA initialized before forking``.
+
+One approach to avoid this error is to make all calls to ``numba.cuda``
+functions inside the child processes or after the process pool is created.
+However, this is not always possible, as you might want to query the number of
+available GPUs before starting the process pool.  In Python 3, you can change
+the process start method, as described in the `multiprocessing documentation
+<https://docs.python.org/3.9/library/multiprocessing.html#contexts-and-start-methods>`_.
+Switching from ``fork`` to ``spawn`` or ``forkserver`` will avoid the CUDA
+initialization issue, although the child processes will not inherit any global
+variables from their parent.
+
+
+Integration with other utilities
+================================
+
+Can I "freeze" an application which uses Numba?
+-----------------------------------------------
+
+If you're using PyInstaller or a similar utility to freeze an application,
+you may encounter issues with llvmlite.  llvmlite needs a non-Python DLL
+for its working, but it won't be automatically detected by freezing utilities.
+You have to inform the freezing utility of the DLL's location: it will
+usually be named ``llvmlite/binding/libllvmlite.so`` or
+``llvmlite/binding/llvmlite.dll``, depending on your system.
+
+I get errors when running a script twice under Spyder
+-----------------------------------------------------
+
+When you run a script in a console under Spyder, Spyder first tries to
+reload existing modules.  This doesn't work well with Numba, and can
+produce errors like ``TypeError: No matching definition for argument type(s)``.
+
+There is a fix in the Spyder preferences. Open the "Preferences" window,
+select "Console", then "Advanced Settings", click the "Set UMR excluded
+modules" button, and add ``numba`` inside the text box that pops up.
+
+To see the setting take effect, be sure to restart the IPython console or
+kernel.
+
+.. _llvm-locale-bug:
+
+Why does Numba complain about the current locale?
+-------------------------------------------------
+
+If you get an error message such as the following::
+
+   RuntimeError: Failed at nopython (nopython mode backend)
+   LLVM will produce incorrect floating-point code in the current locale
+
+it means you have hit a LLVM bug which causes incorrect handling of
+floating-point constants.  This is known to happen with certain third-party
+libraries such as the Qt backend to matplotlib.
+
+To work around the bug, you need to force back the locale to its default
+value, for example::
+
+   import locale
+   locale.setlocale(locale.LC_NUMERIC, 'C')
+
+How do I get Numba development builds?
+--------------------------------------
+
+Pre-release versions of Numba can be installed with conda::
+
+    $ conda install -c numba/label/dev numba
+
+
+Miscellaneous
+=============
+
+Where does the project name "Numba" come from?
+----------------------------------------------
+
+"Numba" is a combination of "NumPy" and "Mamba". Mambas are some of the fastest
+snakes in the world, and Numba makes your Python code fast.
+
+How do I reference/cite/acknowledge Numba in other work?
+--------------------------------------------------------
+For academic use, the best option is to cite our ACM Proceedings: `Numba: a
+LLVM-based Python JIT compiler.
+<http://dl.acm.org/citation.cfm?id=2833162&dl=ACM&coll=DL>`_ You can also find
+`the sources on github <https://github.com/numba/Numba-SC15-Paper>`_, including
+`a pre-print pdf
+<https://github.com/numba/Numba-SC15-Paper/raw/master/numba_sc15.pdf>`_, in case
+you don't have access to the ACM site but would like to read the paper.
+
+Other related papers
+~~~~~~~~~~~~~~~~~~~~
+A paper describing ParallelAccelerator technology, that is activated when the
+``parallel=True`` jit option is used, can be found `here
+<http://drops.dagstuhl.de/opus/volltexte/2017/7269/pdf/LIPIcs-ECOOP-2017-4.pdf>`_.
+
+How do I write a minimal working reproducer for a problem with Numba?
+---------------------------------------------------------------------
+
+A minimal working reproducer for Numba should include:
+
+1. The source code of the function(s) that reproduce the problem.
+2. Some example data and a demonstration of calling the reproducing code with
+   that data. As Numba compiles based on type information, unless your problem
+   is numerical, it's fine to just provide dummy data of the right type, e.g.
+   use ``numpy.ones`` of the correct ``dtype``/size/shape for arrays.
+3. Ideally put 1. and 2. into a script with all the correct imports. Make sure
+   your script actually executes and reproduces the problem before submitting
+   it! The target is to make it so that the script can just be copied directly
+   from the `issue tracker <https://github.com/numba/numba/issues>`_ and run by
+   someone else such that they can see the same problem as you are having.
+
+Having made a reproducer, now remove every part of the code that does not
+contribute directly to reproducing the problem to create a "minimal" reproducer.
+This means removing imports that aren't used, removing variables that aren't
+used or have no effect, removing lines of code which have no effect, reducing
+the complexity of expressions, and shrinking input data to the minimal amount
+required to trigger the problem.
+
+Doing the above really helps out the Numba issue triage process and will enable
+a faster response to your problem!
+
+`Suggested further reading
+<http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports>`_ on
+writing minimal working reproducers.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/generated-jit.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/generated-jit.rst
new file mode 100644
index 000000000..975df08c5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/generated-jit.rst
@@ -0,0 +1,71 @@
+.. _generated-jit:
+
+================================================
+Flexible specializations with ``@generated_jit``
+================================================
+
+
+While the :func:`~numba.jit` decorator is useful for many situations,
+sometimes you want to write a function that has different implementations
+depending on its input types.  The :func:`~numba.generated_jit` decorator
+allows the user to control the selection of a specialization at compile-time,
+while fully retaining runtime execution speed of a JIT function.
+
+
+Example
+=======
+
+Suppose you want to write a function which returns whether a given value
+is a "missing" value according to certain conventions.  For the sake of
+the example, let's adopt the following definition:
+
+- for floating-point arguments, a missing value is a ``NaN``
+- for Numpy datetime64 and timedelta64 arguments, a missing value is a ``NaT``
+- other types don't have the concept of a missing value.
+
+That compile-time logic is easily implemented using the
+:func:`~numba.generated_jit` decorator::
+
+   import numpy as np
+
+   from numba import generated_jit, types
+
+   @generated_jit(nopython=True)
+   def is_missing(x):
+       """
+       Return True if the value is missing, False otherwise.
+       """
+       if isinstance(x, types.Float):
+           return lambda x: np.isnan(x)
+       elif isinstance(x, (types.NPDatetime, types.NPTimedelta)):
+           # The corresponding Not-a-Time value
+           missing = x('NaT')
+           return lambda x: x == missing
+       else:
+           return lambda x: False
+
+
+There are several things to note here:
+
+* The decorated function is called with the :ref:`Numba types <numba-types>`
+  of the arguments, not their values.
+
+* The decorated function doesn't actually compute a result, it returns
+  a callable implementing the actual definition of the function for the
+  given types.
+
+* It is possible to pre-compute some data at compile-time (the ``missing``
+  variable above) to have them reused inside the compiled implementation.
+
+* The function definitions use the same names for arguments as in the
+  decorated function, this is required to ensure passing arguments by
+  name works as expected.
+
+
+Compilation options
+===================
+
+The :func:`~numba.generated_jit` decorator supports the same keyword-only
+arguments as the :func:`~numba.jit` decorator, for example the ``nopython``
+and ``cache`` options.
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/index.rst
new file mode 100644
index 000000000..74d2575fb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/index.rst
@@ -0,0 +1,26 @@
+
+User Manual
+===========
+
+.. toctree::
+
+   5minguide.rst
+   overview.rst
+   installing.rst
+   jit.rst
+   generated-jit.rst
+   vectorize.rst
+   jitclass.rst
+   cfunc.rst
+   pycc.rst
+   parallel.rst
+   stencil.rst
+   withobjmode.rst
+   jit-module.rst
+   performance-tips.rst
+   threading-layer.rst
+   cli.rst
+   troubleshoot.rst
+   faq.rst
+   examples.rst
+   talks.rst
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/installing.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/installing.rst
new file mode 100644
index 000000000..d06a91ee1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/installing.rst
@@ -0,0 +1,373 @@
+
+Installation
+============
+
+Compatibility
+-------------
+
+Numba is compatible with Python 3.7--3.10, and Numpy versions 1.18 up to 1.23.
+
+Our supported platforms are:
+
+* Linux x86 (32-bit and 64-bit)
+* Linux ppcle64 (POWER8, POWER9)
+* Windows 7 and later (32-bit and 64-bit)
+* OS X 10.9 and later (64-bit and unofficial support on M1/Arm64)
+* \*BSD (unofficial support only)
+* NVIDIA GPUs of compute capability 5.3 and later
+
+  * Compute capabilities 3.5 - 5.2 are supported, but deprecated.
+* ARMv7 (32-bit little-endian, such as Raspberry Pi 2 and 3)
+* ARMv8 (64-bit little-endian, such as the NVIDIA Jetson)
+
+:ref:`numba-parallel` is only available on 64-bit platforms.
+
+Installing using conda on x86/x86_64/POWER Platforms
+----------------------------------------------------
+
+The easiest way to install Numba and get updates is by using ``conda``,
+a cross-platform package manager and software distribution maintained
+by Anaconda, Inc.  You can either use `Anaconda
+<https://www.anaconda.com/download>`_ to get the full stack in one download,
+or `Miniconda <https://conda.io/miniconda.html>`_ which will install
+the minimum packages required for a conda environment.
+
+Once you have conda installed, just type::
+
+    $ conda install numba
+
+or::
+
+    $ conda update numba
+
+Note that Numba, like Anaconda, only supports PPC in 64-bit little-endian mode.
+
+To enable CUDA GPU support for Numba, install the latest `graphics drivers from
+NVIDIA <https://www.nvidia.com/Download/index.aspx>`_ for your platform.
+(Note that the open source Nouveau drivers shipped by default with many Linux
+distributions do not support CUDA.)  Then install the ``cudatoolkit`` package::
+
+    $ conda install cudatoolkit
+
+You do not need to install the CUDA SDK from NVIDIA.
+
+
+Installing using pip on x86/x86_64 Platforms
+--------------------------------------------
+
+Binary wheels for Windows, Mac, and Linux are also available from `PyPI
+<https://pypi.org/project/numba/>`_.  You can install Numba using ``pip``::
+
+    $ pip install numba
+
+This will download all of the needed dependencies as well.  You do not need to
+have LLVM installed to use Numba (in fact, Numba will ignore all LLVM
+versions installed on the system) as the required components are bundled into
+the llvmlite wheel.
+
+To use CUDA with Numba installed by `pip`, you need to install the `CUDA SDK
+<https://developer.nvidia.com/cuda-downloads>`_ from NVIDIA.  Please refer to
+:ref:`cudatoolkit-lookup` for details. Numba can also detect CUDA libraries
+installed system-wide on Linux.
+
+
+.. _numba-install-armv7:
+
+Installing on Linux ARMv7 Platforms
+-----------------------------------
+
+`Berryconda <https://github.com/jjhelmus/berryconda>`_ is a
+conda-based Python distribution for the Raspberry Pi.  We are now uploading
+packages to the ``numba`` channel on Anaconda Cloud for 32-bit little-endian,
+ARMv7-based boards, which currently includes the Raspberry Pi 2 and 3,
+but not the Pi 1 or Zero.  These can be installed using conda from the
+``numba`` channel::
+
+    $ conda install -c numba numba
+
+Berryconda and Numba may work on other Linux-based ARMv7 systems, but this has
+not been tested.
+
+
+Installing on Linux ARMv8 (AArch64) Platforms
+---------------------------------------------
+
+We build and test conda packages on the `NVIDIA Jetson TX2
+<https://www.nvidia.com/en-us/autonomous-machines/embedded-systems-dev-kits-modules/>`_,
+but they are likely to work for other AArch64 platforms.  (Note that while the
+Raspberry Pi CPU is 64-bit, Raspbian runs it in 32-bit mode, so look at
+:ref:`numba-install-armv7` instead.)
+
+Conda-forge support for AArch64 is still quite experimental and packages are limited,
+but it does work enough for Numba to build and pass tests.  To set up the environment:
+
+* Install `miniforge <https://github.com/conda-forge/miniforge>`_.
+  This will create a minimal conda environment.
+
+* Then you can install Numba from the ``numba`` channel::
+
+    $ conda install -c numba numba
+
+On CUDA-enabled systems, like the Jetson, the CUDA toolkit should be
+automatically detected in the environment.
+
+.. _numba-source-install-instructions:
+
+Installing from source
+----------------------
+
+Installing Numba from source is fairly straightforward (similar to other
+Python packages), but installing `llvmlite
+<https://github.com/numba/llvmlite>`_ can be quite challenging due to the need
+for a special LLVM build.  If you are building from source for the purposes of
+Numba development, see :ref:`buildenv` for details on how to create a Numba
+development environment with conda.
+
+If you are building Numba from source for other reasons, first follow the
+`llvmlite installation guide <https://llvmlite.readthedocs.io/en/latest/admin-guide/install.html>`_.
+Once that is completed, you can download the latest Numba source code from
+`Github <https://github.com/numba/numba>`_::
+
+    $ git clone git://github.com/numba/numba.git
+
+Source archives of the latest release can also be found on
+`PyPI <https://pypi.org/project/numba/>`_.  In addition to ``llvmlite``, you will also need:
+
+* A C compiler compatible with your Python installation.  If you are using
+  Anaconda, you can use the following conda packages:
+
+  * Linux ``x86``: ``gcc_linux-32`` and ``gxx_linux-32``
+  * Linux ``x86_64``: ``gcc_linux-64`` and ``gxx_linux-64``
+  * Linux ``POWER``: ``gcc_linux-ppc64le`` and ``gxx_linux-ppc64le``
+  * Linux ``ARM``: no conda packages, use the system compiler
+  * Mac OSX: ``clang_osx-64`` and ``clangxx_osx-64`` or the system compiler at
+    ``/usr/bin/clang`` (Mojave onwards)
+  * Mac OSX (M1): ``clang_osx-arm64`` and ``clangxx_osx-arm64``
+  * Windows: a version of Visual Studio appropriate for the Python version in
+    use
+
+* `NumPy <http://www.numpy.org/>`_
+
+Then you can build and install Numba from the top level of the source tree::
+
+    $ python setup.py install
+
+.. _numba-source-install-env_vars:
+
+Build time environment variables and configuration of optional components
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Below are environment variables that are applicable to altering how Numba would
+otherwise build by default along with information on configuration options.
+
+.. envvar:: NUMBA_DISABLE_OPENMP (default: not set)
+
+  To disable compilation of the OpenMP threading backend set this environment
+  variable to a non-empty string when building. If not set (default):
+
+  * For Linux and Windows it is necessary to provide OpenMP C headers and
+    runtime  libraries compatible with the compiler tool chain mentioned above,
+    and for these to be accessible to the compiler via standard flags.
+  * For OSX the conda package ``llvm-openmp`` provides suitable C headers and
+    libraries. If the compilation requirements are not met the OpenMP threading
+    backend will not be compiled.
+
+.. envvar:: NUMBA_DISABLE_TBB (default: not set)
+
+  To disable the compilation of the TBB threading backend set this environment
+  variable to a non-empty string when building. If not set (default) the TBB C
+  headers and libraries must be available at compile time. If building with
+  ``conda build`` this requirement can be met by installing the ``tbb-devel``
+  package. If not building with ``conda build`` the requirement can be met via a
+  system installation of TBB or through the use of the ``TBBROOT`` environment
+  variable to provide the location of the TBB installation. For more
+  information about setting ``TBBROOT`` see the `Intel documentation <https://software.intel.com/content/www/us/en/develop/documentation/advisor-user-guide/top/appendix/adding-parallelism-to-your-program/adding-the-parallel-framework-to-your-build-environment/defining-the-tbbroot-environment-variable.html>`_.
+
+.. _numba-source-install-check:
+
+Dependency List
+---------------
+
+Numba has numerous required and optional dependencies which additionally may
+vary with target operating system and hardware. The following lists them all
+(as of July 2020).
+
+* Required build time:
+
+  * ``setuptools``
+  * ``numpy``
+  * ``llvmlite``
+  * Compiler toolchain mentioned above
+
+* Required run time:
+
+  * ``setuptools``
+  * ``numpy``
+  * ``llvmlite``
+
+* Optional build time:
+
+  See :ref:`numba-source-install-env_vars` for more details about additional
+  options for the configuration and specification of these optional components.
+
+  * ``llvm-openmp`` (OSX) - provides headers for compiling OpenMP support into
+    Numba's threading backend
+  * ``tbb-devel`` - provides TBB headers/libraries for compiling TBB support
+    into Numba's threading backend (2021 <= version < 2021.6 required).
+  * ``importlib_metadata`` (for Python versions < 3.9)
+
+* Optional runtime are:
+
+  * ``scipy`` - provides cython bindings used in Numba's ``np.linalg.*``
+    support
+  * ``tbb`` - provides the TBB runtime libraries used by Numba's TBB threading
+    backend (version >= 2021 required).
+  * ``jinja2`` - for "pretty" type annotation output (HTML) via the ``numba``
+    CLI
+  * ``cffi`` - permits use of CFFI bindings in Numba compiled functions
+  * ``llvm-openmp`` - (OSX) provides OpenMP library support for Numba's OpenMP
+    threading backend.
+  * ``intel-openmp`` - (OSX) provides an alternative OpenMP library for use with
+    Numba's OpenMP threading backend.
+  * ``ipython`` - if in use, caching will use IPython's cache
+    directories/caching still works
+  * ``pyyaml`` - permits the use of a ``.numba_config.yaml``
+    file for storing per project configuration options
+  * ``colorama`` - makes error message highlighting work
+  * ``icc_rt`` - (numba channel) allows Numba to use Intel SVML for extra
+    performance
+  * ``pygments`` - for "pretty" type annotation
+  * ``gdb`` as an executable on the ``$PATH`` - if you would like to use the gdb
+    support
+  * Compiler toolchain mentioned above, if you would like to use ``pycc`` for
+    Ahead-of-Time (AOT) compilation
+  * ``r2pipe`` - required for assembly CFG inspection.
+  * ``radare2`` as an executable on the ``$PATH`` - required for assembly CFG
+    inspection. `See here <https://github.com/radareorg/radare2>`_ for
+    information on obtaining and installing.
+  * ``graphviz`` - for some CFG inspection functionality.
+  * ``pickle5`` - provides Python 3.8 pickling features for faster pickling in
+    Python 3.7.
+  * ``typeguard`` - used by ``runtests.py`` for
+    :ref:`runtime type-checking <type_anno_check>`.
+  * ``cuda-python`` - The NVIDIA CUDA Python bindings. See :ref:`cuda-bindings`.
+    Numba requires Version 11.6 or greater.
+
+* To build the documentation:
+
+  * ``sphinx``
+  * ``pygments``
+  * ``sphinx_rtd_theme``
+  * ``numpydoc``
+  * ``make`` as an executable on the ``$PATH``
+
+.. _numba_support_info:
+
+Version support information
+---------------------------
+
+This is the canonical reference for information concerning which versions of
+Numba's dependencies were tested and known to work against a given version of
+Numba. Other versions of the dependencies (especially NumPy) may work reasonably
+well but were not tested. The use of ``x`` in a version number indicates all
+patch levels supported. The use of ``?`` as a version is due to missing
+information.
+
++----------++--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| Numba     | Release date | Python                    | NumPy                      | llvmlite                     | LLVM              | TBB                         |
++===========+==============+===========================+============================+==============================+===================+=============================+
+| 0.57.x    | TBC          | 3.8.x <= version < 3.12   | 1.19 <= version < 1.24     | 0.40.x                       | 11.x              | 2021.x                      |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.56.4    | 2022-11-03   | 3.7.x <= version < 3.11   | 1.18 <= version < 1.24     | 0.39.x                       | 11.x              | 2021.x                      |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.56.3    | 2022-10-13   | 3.7.x <= version < 3.11   | 1.18 <= version < 1.24     | 0.39.x                       | 11.x              | 2021.x                      |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.56.2    | 2022-09-01   | 3.7.x <= version < 3.11   | 1.18 <= version < 1.24     | 0.39.x                       | 11.x              | 2021.x                      |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.56.0    | 2022-07-25   | 3.7.x <= version < 3.11   | 1.18 <= version < 1.23     | 0.39.x                       | 11.x              | 2021.x                      |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.55.2    | 2022-05-25   | 3.7.x <= version < 3.11   | 1.18 <= version < 1.23     | 0.38.x                       | 11.x              | 2021.x                      |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.55.{0,1}| 2022-01-13   | 3.7.x <= version < 3.11   | 1.18 <= version < 1.22     | 0.38.x                       | 11.x              | 2021.x                      |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.54.x    | 2021-08-19   | 3.6.x <= version < 3.10   | 1.17 <= version < 1.21     | 0.37.x                       | 11.x              | 2021.x                      |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.53.x    | 2021-03-11   | 3.6.x <= version < 3.10   | 1.15 <= version < 1.21     | 0.36.x                       | 11.x              | 2019.5 <= version < 2021.4  |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.52.x    | 2020-11-30   | 3.6.x <= version < 3.9    | 1.15 <= version < 1.20     | 0.35.x                       | 10.x              | 2019.5 <= version < 2020.3  |
+|           |              |                           |                            |                              | (9.x for aarch64) |                             |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.51.x    | 2020-08-12   | 3.6.x <= version < 3.9    | 1.15 <= version < 1.19     | 0.34.x                       | 10.x              | 2019.5 <= version < 2020.0  |
+|           |              |                           |                            |                              | (9.x for aarch64) |                             |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.50.x    | 2020-06-10   | 3.6.x <= version < 3.9    | 1.15 <= version < 1.19     | 0.33.x                       | 9.x               | 2019.5 <= version < 2020.0  |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.49.x    | 2020-04-16   | 3.6.x <= version < 3.9    | 1.15 <= version < 1.18     | 0.31.x <= version < 0.33.x   | 9.x               | 2019.5 <= version < 2020.0  |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.48.x    | 2020-01-27   | 3.6.x <= version < 3.9    | 1.15 <= version < 1.18     | 0.31.x                       | 8.x               | 2018.0.5 <= version < ?     |
+|           |              |                           |                            |                              | (7.x for ppc64le) |                             |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+| 0.47.x    | 2020-01-02   | 3.5.x <= version < 3.9;   | 1.15 <= version < 1.18     | 0.30.x                       | 8.x               | 2018.0.5 <= version < ?     |
+|           |              | version == 2.7.x          |                            |                              | (7.x for ppc64le) |                             |
++-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
+
+Checking your installation
+--------------------------
+
+You should be able to import Numba from the Python prompt::
+
+    $ python
+    Python 3.10.2 | packaged by conda-forge | (main, Jan 14 2022, 08:02:09) [GCC 9.4.0] on linux
+    Type "help", "copyright", "credits" or "license" for more information.
+    >>> import numba
+    >>> numba.__version__
+    '0.55.1'
+
+You can also try executing the ``numba --sysinfo`` (or ``numba -s`` for short)
+command to report information about your system capabilities. See :ref:`cli` for
+further information.
+
+::
+
+    $ numba -s
+    System info:
+    --------------------------------------------------------------------------------
+    __Time Stamp__
+    Report started (local time)                   : 2022-01-18 10:35:08.981319
+
+    __Hardware Information__
+    Machine                                       : x86_64
+    CPU Name                                      : skylake-avx512
+    CPU Count                                     : 12
+    CPU Features                                  :
+    64bit adx aes avx avx2 avx512bw avx512cd avx512dq avx512f avx512vl bmi bmi2
+    clflushopt clwb cmov cx16 cx8 f16c fma fsgsbase fxsr invpcid lzcnt mmx
+    movbe pclmul pku popcnt prfchw rdrnd rdseed rtm sahf sse sse2 sse3 sse4.1
+    sse4.2 ssse3 xsave xsavec xsaveopt xsaves
+
+    __OS Information__
+    Platform Name                                 : Linux-5.4.0-94-generic-x86_64-with-glibc2.31
+    Platform Release                              : 5.4.0-94-generic
+    OS Name                                       : Linux
+    OS Version                                    : #106-Ubuntu SMP Thu Jan 6 23:58:14 UTC 2022
+
+    __Python Information__
+    Python Compiler                               : GCC 9.4.0
+    Python Implementation                         : CPython
+    Python Version                                : 3.10.2
+    Python Locale                                 : en_GB.UTF-8
+
+    __LLVM information__
+    LLVM Version                                  : 11.1.0
+
+    __CUDA Information__
+    Found 1 CUDA devices
+    id 0      b'Quadro RTX 8000'                              [SUPPORTED]
+                          Compute Capability: 7.5
+                               PCI Device ID: 0
+                                  PCI Bus ID: 21
+                                        UUID: GPU-e6489c45-5b68-3b03-bab7-0e7c8e809643
+                                    Watchdog: Enabled
+                 FP32/FP64 Performance Ratio: 32
+
+(output truncated due to length)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jit-module.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jit-module.rst
new file mode 100644
index 000000000..cf2315b6c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jit-module.rst
@@ -0,0 +1,103 @@
+.. _jit-module:
+
+============================================
+Automatic module jitting with ``jit_module``
+============================================
+
+A common usage pattern is to have an entire module containing user-defined
+functions that all need to be jitted. One option to accomplish this is to
+manually apply the ``@jit`` decorator to each function definition. This approach
+works and is great in many cases. However, for large modules with many functions,
+manually ``jit``-wrapping each function definition can be tedious. For these
+situations, Numba provides another option, the ``jit_module`` function, to
+automatically replace functions declared in a module with their ``jit``-wrapped
+equivalents.
+
+It's important to note the conditions under which ``jit_module`` will *not*
+impact a function:
+
+1. Functions which have already been wrapped with a Numba decorator (e.g.
+   ``jit``, ``vectorize``, ``cfunc``, etc.) are not impacted by ``jit_module``.
+
+2. Functions which are declared outside the module from which ``jit_module``
+   is called are not automatically ``jit``-wrapped.
+
+3. Function declarations which occur logically after calling ``jit_module``
+   are not impacted.
+
+All other functions in a module will have the ``@jit`` decorator automatically
+applied to them. See the following section for an example use case.
+
+.. note:: This feature is for use by module authors. ``jit_module`` should not
+    be called outside the context of a module containing functions to be jitted.
+
+
+Example usage
+=============
+
+Let's assume we have a Python module we've created, ``mymodule.py`` (shown
+below), which contains several functions. Some of these functions are defined
+in ``mymodule.py`` while others are imported from other modules. We wish to have
+all the functions which are defined in ``mymodule.py`` jitted using
+``jit_module``.
+
+.. _jit-module-usage:
+
+.. code-block:: python
+
+   # mymodule.py
+
+   from numba import jit, jit_module
+
+   def inc(x):
+      return x + 1
+   
+   def add(x, y):
+      return x + y
+   
+   import numpy as np
+   # Use NumPy's mean function
+   mean = np.mean
+   
+   @jit(nogil=True)
+   def mul(a, b):
+      return a * b
+   
+   jit_module(nopython=True, error_model="numpy")
+
+   def div(a, b):
+       return a / b
+
+There are several things to note in the above example:
+
+- Both the ``inc`` and ``add`` functions will be replaced with their
+  ``jit``-wrapped equivalents with :ref:`compilation options <jit-options>`
+  ``nopython=True`` and ``error_model="numpy"``.
+
+- The ``mean`` function, because it's defined *outside* of ``mymodule.py`` in
+  NumPy, will not be modified.
+
+- ``mul`` will not be modified because it has been manually decorated with
+  ``jit``.
+
+- ``div`` will not be automatically ``jit``-wrapped because it is declared
+  after ``jit_module`` is called.
+
+When the above module is imported, we have:
+
+.. code-block:: python
+
+   >>> import mymodule
+   >>> mymodule.inc
+   CPUDispatcher(<function inc at 0x1032f86a8>)
+   >>> mymodule.mean
+   <function mean at 0x1096b8950>
+
+
+API
+===
+.. warning:: This feature is experimental. The supported features may change
+    with or without notice.
+
+.. autofunction:: numba.jit_module
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jit.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jit.rst
new file mode 100644
index 000000000..36b4ba985
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jit.rst
@@ -0,0 +1,201 @@
+.. _jit:
+
+===================================
+Compiling Python code with ``@jit``
+===================================
+
+Numba provides several utilities for code generation, but its central
+feature is the :func:`numba.jit` decorator.  Using this decorator, you can mark
+a function for optimization by Numba's JIT compiler.  Various invocation
+modes trigger differing compilation options and behaviours.
+
+
+Basic usage
+===========
+
+.. _jit-lazy:
+
+Lazy compilation
+----------------
+
+The recommended way to use the ``@jit`` decorator is to let Numba decide
+when and how to optimize::
+
+   from numba import jit
+
+   @jit
+   def f(x, y):
+       # A somewhat trivial example
+       return x + y
+
+In this mode, compilation will be deferred until the first function
+execution.  Numba will infer the argument types at call time, and generate
+optimized code based on this information.  Numba will also be able to
+compile separate specializations depending on the input types.  For example,
+calling the ``f()`` function above with integer or complex numbers will
+generate different code paths::
+
+   >>> f(1, 2)
+   3
+   >>> f(1j, 2)
+   (2+1j)
+
+Eager compilation
+-----------------
+
+You can also tell Numba the function signature you are expecting.  The
+function ``f()`` would now look like::
+
+   from numba import jit, int32
+
+   @jit(int32(int32, int32))
+   def f(x, y):
+       # A somewhat trivial example
+       return x + y
+
+``int32(int32, int32)`` is the function's signature.  In this case, the
+corresponding specialization will be compiled by the ``@jit`` decorator,
+and no other specialization will be allowed. This is useful if you want
+fine-grained control over types chosen by the compiler (for example,
+to use single-precision floats).
+
+If you omit the return type, e.g. by writing ``(int32, int32)`` instead of
+``int32(int32, int32)``, Numba will try to infer it for you.  Function
+signatures can also be strings, and you can pass several of them as a list;
+see the :func:`numba.jit` documentation for more details.
+
+Of course, the compiled function gives the expected results::
+
+   >>> f(1,2)
+   3
+
+and if we specified ``int32`` as return type, the higher-order bits get
+discarded::
+
+   >>> f(2**31, 2**31 + 1)
+   1
+
+
+Calling and inlining other functions
+====================================
+
+Numba-compiled functions can call other compiled functions.  The function
+calls may even be inlined in the native code, depending on optimizer
+heuristics.  For example::
+
+   @jit
+   def square(x):
+       return x ** 2
+
+   @jit
+   def hypot(x, y):
+       return math.sqrt(square(x) + square(y))
+
+The ``@jit`` decorator *must* be added to any such library function,
+otherwise Numba may generate much slower code.
+
+
+Signature specifications
+========================
+
+Explicit ``@jit`` signatures can use a number of types.  Here are some
+common ones:
+
+* ``void`` is the return type of functions returning nothing (which
+  actually return :const:`None` when called from Python)
+* ``intp`` and ``uintp`` are pointer-sized integers (signed and unsigned,
+  respectively)
+* ``intc`` and ``uintc`` are equivalent to C ``int`` and ``unsigned int``
+  integer types
+* ``int8``, ``uint8``, ``int16``, ``uint16``, ``int32``, ``uint32``,
+  ``int64``, ``uint64`` are fixed-width integers of the corresponding bit
+  width (signed and unsigned)
+* ``float32`` and ``float64`` are single- and double-precision floating-point
+  numbers, respectively
+* ``complex64`` and ``complex128`` are single- and double-precision complex
+  numbers, respectively
+* array types can be specified by indexing any numeric type, e.g. ``float32[:]``
+  for a one-dimensional single-precision array or ``int8[:,:]`` for a
+  two-dimensional array of 8-bit integers.
+
+
+.. _jit-options:
+
+Compilation options
+===================
+
+A number of keyword-only arguments can be passed to the ``@jit`` decorator.
+
+.. _jit-nopython:
+
+``nopython``
+------------
+
+Numba has two compilation modes: :term:`nopython mode` and
+:term:`object mode`.  The former produces much faster code, but has
+limitations that can force Numba to fall back to the latter.  To prevent
+Numba from falling back, and instead raise an error, pass ``nopython=True``.
+
+::
+
+   @jit(nopython=True)
+   def f(x, y):
+       return x + y
+
+.. seealso:: :ref:`numba-troubleshooting`
+
+.. _jit-nogil:
+
+``nogil``
+---------
+
+Whenever Numba optimizes Python code to native code that only works on
+native types and variables (rather than Python objects), it is not necessary
+anymore to hold Python's :py:term:`global interpreter lock` (GIL).
+Numba will release the GIL when entering such a compiled function if you
+passed ``nogil=True``.
+
+::
+
+   @jit(nogil=True)
+   def f(x, y):
+       return x + y
+
+Code running with the GIL released runs concurrently with other
+threads executing Python or Numba code (either the same compiled function,
+or another one), allowing you to take advantage of multi-core systems.
+This will not be possible if the function is compiled in :term:`object mode`.
+
+When using ``nogil=True``, you'll have to be wary of the usual pitfalls
+of multi-threaded programming (consistency, synchronization, race conditions,
+etc.).
+
+.. _jit-cache:
+
+``cache``
+---------
+
+To avoid compilation times each time you invoke a Python program,
+you can instruct Numba to write the result of function compilation into
+a file-based cache.  This is done by passing ``cache=True``::
+
+   @jit(cache=True)
+   def f(x, y):
+       return x + y
+
+.. _parallel_jit_option:
+
+``parallel``
+------------
+
+Enables automatic parallelization (and related optimizations) for those
+operations in the function known to have parallel semantics.  For a list of
+supported operations, see :ref:`numba-parallel`.  This feature is enabled by
+passing ``parallel=True`` and must be used in conjunction with
+``nopython=True``::
+
+   @jit(nopython=True, parallel=True)
+   def f(x, y):
+       return x + y
+
+.. seealso:: :ref:`numba-parallel`
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jitclass.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jitclass.rst
new file mode 100644
index 000000000..9000bf436
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/jitclass.rst
@@ -0,0 +1,270 @@
+.. _jitclass:
+
+===========================================
+Compiling Python classes with ``@jitclass``
+===========================================
+
+.. note::
+
+  This is a early version of jitclass support. Not all compiling features are
+  exposed or implemented, yet.
+
+
+Numba supports code generation for classes via the
+:func:`numba.experimental.jitclass` decorator.  A class can be marked for
+optimization using this decorator along with a specification of the types of
+each field.  We call the resulting class object a *jitclass*.  All methods of a
+jitclass are compiled into nopython functions.  The data of a jitclass instance
+is allocated on the heap as a C-compatible structure so that any compiled
+functions can have direct access to the underlying data, bypassing the
+interpreter.
+
+
+Basic usage
+===========
+
+Here's an example of a jitclass:
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_jitclass.py
+   :language: python
+   :start-after: magictoken.ex_jitclass.begin
+   :end-before: magictoken.ex_jitclass.end
+   :dedent: 8
+
+In the above example, a ``spec`` is provided as a list of 2-tuples.  The tuples
+contain the name of the field and the Numba type of the field.  Alternatively,
+user can use a dictionary (an ``OrderedDict`` preferably for stable field
+ordering), which maps field names to types.
+
+The definition of the class requires at least a ``__init__`` method for
+initializing each defined fields.  Uninitialized fields contains garbage data.
+Methods and properties (getters and setters only) can be defined.  They will be
+automatically compiled.
+
+
+Inferred class member types from type annotations with ``as_numba_type``
+========================================================================
+
+Fields of a ``jitclass`` can also be inferred from Python type annotations.
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_jitclass.py
+   :language: python
+   :start-after: magictoken.ex_jitclass_type_hints.begin
+   :end-before: magictoken.ex_jitclass_type_hints.end
+   :dedent: 8
+
+Any type annotations on the class will be used to extend the spec if that field
+is not already present.  The Numba type corresponding to the given Python type
+is inferred using ``as_numba_type``.  For example, if we have the class
+
+.. code-block:: python
+
+    @jitclass([("w", int32), ("y", float64[:])])
+    class Foo:
+        w: int
+        x: float
+        y: np.ndarray
+        z: SomeOtherType
+
+        def __init__(self, w: int, x: float, y: np.ndarray, z: SomeOtherType):
+            ...
+
+then the full spec used for ``Foo`` will be:
+
+* ``"w": int32`` (specified in the ``spec``)
+* ``"x": float64`` (added from type annotation)
+* ``"y": array(float64, 1d, A)`` (specified in the ``spec``)
+* ``"z": numba.as_numba_type(SomeOtherType)`` (added from type annotation)
+
+Here ``SomeOtherType`` could be any supported Python type (e.g.
+``bool``, ``typing.Dict[int, typing.Tuple[float, float]]``, or another
+``jitclass``).
+
+Note that only type annotations on the class will be used to infer spec
+elements.  Method type annotations (e.g. those of ``__init__`` above) are
+ignored.
+
+Numba requires knowing the dtype and rank of NumPy arrays, which cannot
+currently be expressed with type annotations. Because of this, NumPy arrays need
+to be included in the ``spec`` explicitly.
+
+
+Specifying ``numba.typed`` containers as class members explicitly
+=================================================================
+
+The following patterns demonstrate how to specify a ``numba.typed.Dict`` or
+``numba.typed.List`` explicitly as part of the ``spec`` passed to ``jitclass``.
+
+First, using explicit Numba types and explicit construction.
+
+.. code-block:: python
+
+    from numba import jitclass, types, typed
+
+    # key and value types
+    kv_ty = (types.int64, types.unicode_type)
+
+    # A container class with:
+    # * member 'd' holding a typed dictionary of int64 -> unicode string (kv_ty)
+    # * member 'l' holding a typed list of float64
+    @jitclass([('d', types.DictType(*kv_ty)),
+               ('l', types.ListType(types.float64))])
+    class ContainerHolder(object):
+        def __init__(self):
+            # initialize the containers
+            self.d = typed.Dict.empty(*kv_ty)
+            self.l = typed.List.empty_list(types.float64)
+
+    container = ContainerHolder()
+    container.d[1] = "apple"
+    container.d[2] = "orange"
+    container.l.append(123.)
+    container.l.append(456.)
+    print(container.d) # {1: apple, 2: orange}
+    print(container.l) # [123.0, 456.0]
+
+Another useful pattern is to use the ``numba.typed`` container attribute
+``_numba_type_`` to find the type of a container, this can be accessed directly
+from an instance of the container in the Python interpreter. The same
+information can be obtained by calling :func:`numba.typeof` on the instance. For
+example:
+
+.. code-block:: python
+
+    from numba import jitclass, typed, typeof
+
+    d = typed.Dict()
+    d[1] = "apple"
+    d[2] = "orange"
+    l = typed.List()
+    l.append(123.)
+    l.append(456.)
+
+
+    @jitclass([('d', typeof(d)), ('l', typeof(l))])
+    class ContainerInstHolder(object):
+        def __init__(self, dict_inst, list_inst):
+            self.d = dict_inst
+            self.l = list_inst
+
+    container = ContainerInstHolder(d, l)
+    print(container.d) # {1: apple, 2: orange}
+    print(container.l) # [123.0, 456.0]
+
+It is worth noting that the instance of the container in a ``jitclass`` must be
+initialized before use, for example, this will cause an invalid memory access
+as ``self.d`` is written to without ``d`` being initialized as a ``type.Dict``
+instance of the type specified.
+
+.. code-block:: python
+
+    from numba import jitclass, types
+
+    dict_ty = types.DictType(types.int64, types.unicode_type)
+
+    @jitclass([('d', dict_ty)])
+    class NotInitialisingContainer(object):
+        def __init__(self):
+            self.d[10] = "apple" # this is invalid, `d` is not initialized
+
+    NotInitialisingContainer() # segmentation fault/memory access violation
+
+
+Support operations
+==================
+
+The following operations of jitclasses work in both the interpreter and Numba
+compiled functions:
+
+* calling the jitclass class object to construct a new instance
+  (e.g. ``mybag = Bag(123)``);
+* read/write access to attributes and properties (e.g. ``mybag.value``);
+* calling methods (e.g. ``mybag.increment(3)``);
+* calling static methods as instance attributes (e.g. ``mybag.add(1, 1)``);
+* calling static methods as class attributes (e.g. ``Bag.add(1, 2)``);
+* using select dunder methods (e.g. ``__add__`` with ``mybag + otherbag``);
+
+Using jitclasses in Numba compiled function is more efficient.
+Short methods can be inlined (at the discretion of LLVM inliner).
+Attributes access are simply reading from a C structure.
+Using jitclasses from the interpreter has the same overhead of calling any
+Numba compiled function from the interpreter.  Arguments and return values
+must be unboxed or boxed between Python objects and native representation.
+Values encapsulated by a jitclass does not get boxed into Python object when
+the jitclass instance is handed to the interpreter.  It is during attribute
+access to the field values that they are boxed.
+Calling static methods as class attributes is only supported outside of the
+class definition (i.e. code cannot call ``Bag.add()`` from within another method
+of ``Bag``).
+
+
+Supported dunder methods
+------------------------
+
+The following dunder methods may be defined for jitclasses:
+
+* ``__abs__``
+* ``__bool__``
+* ``__complex__``
+* ``__contains__``
+* ``__float__``
+* ``__getitem__``
+* ``__hash__``
+* ``__index__``
+* ``__int__``
+* ``__len__``
+* ``__setitem__``
+* ``__str__``
+* ``__eq__``
+* ``__ne__``
+* ``__ge__``
+* ``__gt__``
+* ``__le__``
+* ``__lt__``
+* ``__add__``
+* ``__floordiv__``
+* ``__lshift__``
+* ``__mod__``
+* ``__mul__``
+* ``__neg__``
+* ``__pos__``
+* ``__pow__``
+* ``__rshift__``
+* ``__sub__``
+* ``__truediv__``
+* ``__and__``
+* ``__or__``
+* ``__xor__``
+* ``__iadd__``
+* ``__ifloordiv__``
+* ``__ilshift__``
+* ``__imod__``
+* ``__imul__``
+* ``__ipow__``
+* ``__irshift__``
+* ``__isub__``
+* ``__itruediv__``
+* ``__iand__``
+* ``__ior__``
+* ``__ixor__``
+
+Refer to the `Python Data Model documentation
+<https://docs.python.org/3/reference/datamodel.html>`_ for descriptions of
+these methods.
+
+
+Limitations
+===========
+
+* A jitclass class object is treated as a function (the constructor) inside
+  a Numba compiled function.
+* ``isinstance()`` only works in the interpreter.
+* Manipulating jitclass instances in the interpreter is not optimized, yet.
+* Support for jitclasses are available on CPU only.
+  (Note: Support for GPU devices is planned for a future release.)
+
+
+The decorator: ``@jitclass``
+============================
+
+.. autofunction:: numba.experimental.jitclass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/overview.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/overview.rst
new file mode 100644
index 000000000..9b11b5a49
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/overview.rst
@@ -0,0 +1,34 @@
+
+Overview
+========
+
+Numba is a compiler for Python array and numerical functions that gives 
+you the power to speed up your applications with high performance
+functions written directly in Python.
+
+Numba generates optimized machine code from pure Python code using
+the `LLVM compiler infrastructure <http://llvm.org/>`_.  With a few simple
+annotations, array-oriented and math-heavy Python code can be
+just-in-time optimized to performance similar as C, C++ and Fortran, without
+having to switch languages or Python interpreters.
+
+Numba's main features are:
+
+* :ref:`on-the-fly code generation <jit>` (at import time or runtime, at the
+  user's preference)
+* native code generation for the CPU (default) and
+  :doc:`GPU hardware <../cuda/index>`
+* integration with the Python scientific software stack (thanks to Numpy)
+
+Here is how a Numba-optimized function, taking a Numpy array as argument,
+might look like::
+
+   @numba.jit
+   def sum2d(arr):
+       M, N = arr.shape
+       result = 0.0
+       for i in range(M):
+           for j in range(N):
+               result += arr[i,j]
+       return result
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/parallel.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/parallel.rst
new file mode 100644
index 000000000..3f09a0bc9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/parallel.rst
@@ -0,0 +1,719 @@
+.. Copyright (c) 2017 Intel Corporation
+   SPDX-License-Identifier: BSD-2-Clause
+
+.. _numba-parallel:
+
+=======================================
+Automatic parallelization with ``@jit``
+=======================================
+
+Setting the :ref:`parallel_jit_option` option for :func:`~numba.jit` enables
+a Numba transformation pass that attempts to automatically parallelize and
+perform other optimizations on (part of) a function. At the moment, this
+feature only works on CPUs.
+
+Some operations inside a user defined function, e.g. adding a scalar value to
+an array, are known to have parallel semantics.  A user program may contain
+many such operations and while each operation could be parallelized
+individually, such an approach often has lackluster performance due to poor
+cache behavior.  Instead, with auto-parallelization, Numba attempts to
+identify such operations in a user program, and fuse adjacent ones together,
+to form one or more kernels that are automatically run in parallel.
+The process is fully automated without modifications to the user program,
+which is in contrast to Numba's :func:`~numba.vectorize` or
+:func:`~numba.guvectorize` mechanism, where manual effort is required
+to create parallel kernels.
+
+.. _numba-parallel-supported:
+
+Supported Operations
+====================
+
+In this section, we give a list of all the array operations that have
+parallel semantics and for which we attempt to parallelize.
+
+#. All numba array operations that are supported by :ref:`case-study-array-expressions`,
+   which include common arithmetic functions between Numpy arrays, and between
+   arrays and scalars, as well as Numpy ufuncs. They are often called
+   `element-wise` or `point-wise` array operations:
+
+    * unary operators: ``+`` ``-`` ``~``
+    * binary operators: ``+`` ``-`` ``*`` ``/`` ``/?`` ``%`` ``|`` ``>>`` ``^`` ``<<`` ``&`` ``**`` ``//``
+    * comparison operators: ``==`` ``!=`` ``<`` ``<=`` ``>`` ``>=``
+    * :ref:`Numpy ufuncs <supported_ufuncs>` that are supported in :term:`nopython mode`.
+    * User defined :class:`~numba.DUFunc` through :func:`~numba.vectorize`.
+
+#. Numpy reduction functions ``sum``, ``prod``, ``min``, ``max``, ``argmin``,
+   and ``argmax``. Also, array math functions ``mean``, ``var``, and ``std``.
+
+#. Numpy array creation functions ``zeros``, ``ones``, ``arange``, ``linspace``,
+   and several random functions (rand, randn, ranf, random_sample, sample,
+   random, standard_normal, chisquare, weibull, power, geometric, exponential,
+   poisson, rayleigh, normal, uniform, beta, binomial, f, gamma, lognormal,
+   laplace, randint, triangular).
+
+#. Numpy ``dot`` function between a matrix and a vector, or two vectors.
+   In all other cases, Numba's default implementation is used.
+
+#. Multi-dimensional arrays are also supported for the above operations
+   when operands have matching dimension and size. The full semantics of
+   Numpy broadcast between arrays with mixed dimensionality or size is
+   not supported, nor is the reduction across a selected dimension.
+
+#. Array assignment in which the target is an array selection using a slice
+   or a boolean array, and the value being assigned is either a scalar or
+   another selection where the slice range or bitarray are inferred to be
+   compatible.
+
+#. The ``reduce`` operator of ``functools`` is supported for specifying parallel
+   reductions on 1D Numpy arrays but the initial value argument is mandatory.
+
+.. _numba-prange:
+
+Explicit Parallel Loops
+========================
+
+Another feature of the code transformation pass (when ``parallel=True``) is
+support for explicit parallel loops. One can use Numba's ``prange`` instead of
+``range`` to specify that a loop can be parallelized. The user is required to
+make sure that the loop does not have cross iteration dependencies except for
+supported reductions.
+
+A reduction is inferred automatically if a variable is updated by a supported binary
+function/operator using its previous value in the loop body.  The following
+functions/operators are supported: ``+=``, ``+``, ``-=``, ``-``, ``*=``,
+``*``, ``/=``, ``/``, ``max()``, ``min()``.
+The initial value of the reduction is inferred automatically for the
+supported operators (i.e., not the ``max`` and ``min`` functions).
+Note that the ``//=`` operator is not supported because
+in the general case the result depends on the order in which the divisors are
+applied.  However, if all divisors are integers then the programmer may be
+able to rewrite the ``//=`` reduction as a ``*=`` reduction followed by
+a single floor division after the parallel region where the divisor is the
+accumulated product.
+For the ``max`` and ``min`` functions, the reduction variable should hold the identity
+value right before entering the ``prange`` loop.  Reductions in this manner
+are supported for scalars and for arrays of arbitrary dimensions.
+
+The example below demonstrates a parallel loop with a
+reduction (``A`` is a one-dimensional Numpy array)::
+
+    from numba import njit, prange
+
+    @njit(parallel=True)
+    def prange_test(A):
+        s = 0
+        # Without "parallel=True" in the jit-decorator
+        # the prange statement is equivalent to range
+        for i in prange(A.shape[0]):
+            s += A[i]
+        return s
+
+The following example demonstrates a product reduction on a two-dimensional array::
+
+    from numba import njit, prange
+    import numpy as np
+
+    @njit(parallel=True)
+    def two_d_array_reduction_prod(n):
+        shp = (13, 17)
+        result1 = 2 * np.ones(shp, np.int_)
+        tmp = 2 * np.ones_like(result1)
+
+        for i in prange(n):
+            result1 *= tmp
+
+        return result1
+
+.. note:: When using Python's ``range`` to induce a loop, Numba types the
+          induction variable as a signed integer. This is also the case for
+          Numba's ``prange`` when ``parallel=False``. However, for
+          ``parallel=True``, if the range is identifiable as strictly positive,
+          the type of the induction variable  will be ``uint64``. The impact of
+          a ``uint64`` induction variable is often most noticable when
+          undertaking operations involving it and a signed integer. Under
+          Numba's type coercion rules, such a case will commonly result in the
+          operation producing a floating point result type.
+
+
+Care should be taken, however, when reducing into slices or elements of an array
+if the elements specified by the slice or index are written to simultaneously by
+multiple parallel threads. The compiler may not detect such cases and then a race condition
+would occur.
+
+The following example demonstrates such a case where a race condition in the execution of the
+parallel for-loop results in an incorrect return value::
+
+    from numba import njit, prange
+    import numpy as np
+
+    @njit(parallel=True)
+    def prange_wrong_result(x):
+        n = x.shape[0]
+        y = np.zeros(4)
+        for i in prange(n):
+            # accumulating into the same element of `y` from different
+            # parallel iterations of the loop results in a race condition
+            y[:] += x[i]
+
+        return y
+
+as does the following example where the accumulating element is explicitly specified::
+
+    from numba import njit, prange
+    import numpy as np
+
+    @njit(parallel=True)
+    def prange_wrong_result(x):
+        n = x.shape[0]
+        y = np.zeros(4)
+        for i in prange(n):
+            # accumulating into the same element of `y` from different
+            # parallel iterations of the loop results in a race condition
+            y[i % 4] += x[i]
+
+        return y
+
+whereas performing a whole array reduction is fine::
+
+   from numba import njit, prange
+   import numpy as np
+
+   @njit(parallel=True)
+   def prange_ok_result_whole_arr(x):
+       n = x.shape[0]
+       y = np.zeros(4)
+       for i in prange(n):
+           y += x[i]
+       return y
+
+as is creating a slice reference outside of the parallel reduction loop::
+
+   from numba import njit, prange
+   import numpy as np
+
+   @njit(parallel=True)
+   def prange_ok_result_outer_slice(x):
+       n = x.shape[0]
+       y = np.zeros(4)
+       z = y[:]
+       for i in prange(n):
+           z += x[i]
+       return y
+
+Examples
+========
+
+In this section, we give an example of how this feature helps
+parallelize Logistic Regression::
+
+    @numba.jit(nopython=True, parallel=True)
+    def logistic_regression(Y, X, w, iterations):
+        for i in range(iterations):
+            w -= np.dot(((1.0 / (1.0 + np.exp(-Y * np.dot(X, w))) - 1.0) * Y), X)
+        return w
+
+We will not discuss details of the algorithm, but instead focus on how
+this program behaves with auto-parallelization:
+
+1. Input ``Y`` is a vector of size ``N``, ``X`` is an ``N x D`` matrix,
+   and ``w`` is a vector of size ``D``.
+
+2. The function body is an iterative loop that updates variable ``w``.
+   The loop body consists of a sequence of vector and matrix operations.
+
+3. The inner ``dot`` operation produces a vector of size ``N``, followed by a
+   sequence of arithmetic operations either between a scalar and vector of
+   size ``N``, or two vectors both of size ``N``.
+
+4. The outer ``dot`` produces a vector of size ``D``, followed by an inplace
+   array subtraction on variable ``w``.
+
+5. With auto-parallelization, all operations that produce array of size
+   ``N`` are fused together to become a single parallel kernel. This includes
+   the inner ``dot`` operation and all point-wise array operations following it.
+
+6. The outer ``dot`` operation produces a result array of different dimension,
+   and is not fused with the above kernel.
+
+Here, the only thing required to take advantage of parallel hardware is to set
+the :ref:`parallel_jit_option` option for :func:`~numba.jit`, with no
+modifications to the ``logistic_regression`` function itself.  If we were to
+give an equivalence parallel implementation using :func:`~numba.guvectorize`,
+it would require a pervasive change that rewrites the code to extract kernel
+computation that can be parallelized, which was both tedious and challenging.
+
+Unsupported Operations
+======================
+
+This section contains a non-exhaustive list of commonly encountered but
+currently unsupported features:
+
+#. **Mutating a list is not threadsafe**
+
+   Concurrent write operations on container types (i.e. lists, sets and
+   dictionaries) in a ``prange`` parallel region are not threadsafe e.g.::
+
+    @njit(parallel=True)
+    def invalid():
+        z = []
+        for i in prange(10000):
+            z.append(i)
+        return z
+
+   It is highly likely that the above will result in corruption or an access
+   violation as containers require thread-safety under mutation but this feature
+   is not implemented.
+
+#. **Induction variables are not associated with thread ID**
+
+   The use of the induction variable induced by a ``prange`` based loop in
+   conjunction with ``get_num_threads`` as a method of ensuring safe writes into
+   a pre-sized container is not valid e.g.::
+
+    @njit(parallel=True)
+    def invalid():
+        n = get_num_threads()
+        z = [0 for _ in range(n)]
+        for i in prange(100):
+            z[i % n] += i
+        return z
+
+   The above can on occasion appear to work, but it does so by luck. There's no
+   guarantee about which indexes are assigned to which executing threads or the
+   order in which the loop iterations execute.
+
+.. _numba-parallel-diagnostics:
+
+Diagnostics
+===========
+
+.. note:: At present not all parallel transforms and functions can be tracked
+          through the code generation process. Occasionally diagnostics about
+          some loops or transforms may be missing.
+
+The :ref:`parallel_jit_option` option for :func:`~numba.jit` can produce
+diagnostic information about the transforms undertaken in automatically
+parallelizing the decorated code. This information can be accessed in two ways,
+the first is by setting the environment variable
+:envvar:`NUMBA_PARALLEL_DIAGNOSTICS`, the second is by calling
+:meth:`~Dispatcher.parallel_diagnostics`, both methods give the same information
+and print to ``STDOUT``. The level of verbosity in the diagnostic information is
+controlled by an integer argument of value between 1 and 4 inclusive, 1 being
+the least verbose and 4 the most. For example::
+
+    @njit(parallel=True)
+    def test(x):
+        n = x.shape[0]
+        a = np.sin(x)
+        b = np.cos(a * a)
+        acc = 0
+        for i in prange(n - 2):
+            for j in prange(n - 1):
+                acc += b[i] + b[j + 1]
+        return acc
+
+    test(np.arange(10))
+
+    test.parallel_diagnostics(level=4)
+
+produces::
+
+    ================================================================================
+    ======= Parallel Accelerator Optimizing:  Function test, example.py (4)  =======
+    ================================================================================
+
+
+    Parallel loop listing for  Function test, example.py (4)
+    --------------------------------------|loop #ID
+    @njit(parallel=True)                  |
+    def test(x):                          |
+        n = x.shape[0]                    |
+        a = np.sin(x)---------------------| #0
+        b = np.cos(a * a)-----------------| #1
+        acc = 0                           |
+        for i in prange(n - 2):-----------| #3
+            for j in prange(n - 1):-------| #2
+                acc += b[i] + b[j + 1]    |
+        return acc                        |
+    --------------------------------- Fusing loops ---------------------------------
+    Attempting fusion of parallel loops (combines loops with similar properties)...
+    Trying to fuse loops #0 and #1:
+        - fusion succeeded: parallel for-loop #1 is fused into for-loop #0.
+    Trying to fuse loops #0 and #3:
+        - fusion failed: loop dimension mismatched in axis 0. slice(0, x_size0.1, 1)
+    != slice(0, $40.4, 1)
+    ----------------------------- Before Optimization ------------------------------
+    Parallel region 0:
+    +--0 (parallel)
+    +--1 (parallel)
+
+
+    Parallel region 1:
+    +--3 (parallel)
+    +--2 (parallel)
+
+
+    --------------------------------------------------------------------------------
+    ------------------------------ After Optimization ------------------------------
+    Parallel region 0:
+    +--0 (parallel, fused with loop(s): 1)
+
+
+    Parallel region 1:
+    +--3 (parallel)
+    +--2 (serial)
+
+
+
+    Parallel region 0 (loop #0) had 1 loop(s) fused.
+
+    Parallel region 1 (loop #3) had 0 loop(s) fused and 1 loop(s) serialized as part
+    of the larger parallel loop (#3).
+    --------------------------------------------------------------------------------
+    --------------------------------------------------------------------------------
+
+    ---------------------------Loop invariant code motion---------------------------
+
+    Instruction hoisting:
+    loop #0:
+    Failed to hoist the following:
+        dependency: $arg_out_var.10 = getitem(value=x, index=$parfor__index_5.99)
+        dependency: $0.6.11 = getattr(value=$0.5, attr=sin)
+        dependency: $expr_out_var.9 = call $0.6.11($arg_out_var.10, func=$0.6.11, args=[Var($arg_out_var.10, example.py (7))], kws=(), vararg=None)
+        dependency: $arg_out_var.17 = $expr_out_var.9 * $expr_out_var.9
+        dependency: $0.10.20 = getattr(value=$0.9, attr=cos)
+        dependency: $expr_out_var.16 = call $0.10.20($arg_out_var.17, func=$0.10.20, args=[Var($arg_out_var.17, example.py (8))], kws=(), vararg=None)
+    loop #3:
+    Has the following hoisted:
+        $const58.3 = const(int, 1)
+        $58.4 = _n_23 - $const58.3
+    --------------------------------------------------------------------------------
+
+
+
+To aid users unfamiliar with the transforms undertaken when the
+:ref:`parallel_jit_option` option is used, and to assist in the understanding of
+the subsequent sections, the following definitions are provided:
+
+* Loop fusion
+    `Loop fusion <https://en.wikipedia.org/wiki/Loop_fission_and_fusion>`_ is a
+    technique whereby loops with equivalent bounds may be combined under certain
+    conditions to produce a loop with a larger body (aiming to improve data
+    locality).
+
+* Loop serialization
+    Loop serialization occurs when any number of ``prange`` driven loops are
+    present inside another ``prange`` driven loop. In this case the outermost
+    of all the ``prange`` loops executes in parallel and any inner ``prange``
+    loops (nested or otherwise) are treated as standard ``range`` based loops.
+    Essentially, nested parallelism does not occur.
+
+* Loop invariant code motion
+    `Loop invariant code motion
+    <https://en.wikipedia.org/wiki/Loop-invariant_code_motion>`_ is an
+    optimization technique that analyses a loop to look for statements that can
+    be moved outside the loop body without changing the result of executing the
+    loop, these statements are then "hoisted" out of the loop to save repeated
+    computation.
+
+* Allocation hoisting
+    Allocation hoisting is a specialized case of loop invariant code motion that
+    is possible due to the design of some common NumPy allocation methods.
+    Explanation of this technique is best driven by an example:
+
+    .. code-block:: python
+
+        @njit(parallel=True)
+        def test(n):
+            for i in prange(n):
+                temp = np.zeros((50, 50)) # <--- Allocate a temporary array with np.zeros()
+                for j in range(50):
+                    temp[j, j] = i
+
+            # ...do something with temp
+
+    internally, this is transformed to approximately the following:
+
+    .. code-block:: python
+
+        @njit(parallel=True)
+        def test(n):
+            for i in prange(n):
+                temp = np.empty((50, 50)) # <--- np.zeros() is rewritten as np.empty()
+                temp[:] = 0               # <--- and then a zero initialisation
+                for j in range(50):
+                    temp[j, j] = i
+
+            # ...do something with temp
+
+    then after hoisting:
+
+    .. code-block:: python
+
+        @njit(parallel=True)
+        def test(n):
+            temp = np.empty((50, 50)) # <--- allocation is hoisted as a loop invariant as `np.empty` is considered pure
+            for i in prange(n):
+                temp[:] = 0           # <--- this remains as assignment is a side effect
+                for j in range(50):
+                    temp[j, j] = i
+
+            # ...do something with temp
+
+    it can be seen that the ``np.zeros`` allocation is split into an allocation
+    and an assignment, and then the allocation is hoisted out of the loop in
+    ``i``, this producing more efficient code as the allocation only occurs
+    once.
+
+The parallel diagnostics report sections
+----------------------------------------
+
+The report is split into the following sections:
+
+#. Code annotation
+    This is the first section and contains the source code of the decorated
+    function with loops that have parallel semantics identified and enumerated.
+    The ``loop #ID`` column on the right of the source code lines up with
+    identified parallel loops. From the example, ``#0`` is ``np.sin``, ``#1``
+    is ``np.cos`` and ``#2`` and ``#3`` are ``prange()``:
+
+    .. code-block:: python
+
+        Parallel loop listing for  Function test, example.py (4)
+        --------------------------------------|loop #ID
+        @njit(parallel=True)                  |
+        def test(x):                          |
+            n = x.shape[0]                    |
+            a = np.sin(x)---------------------| #0
+            b = np.cos(a * a)-----------------| #1
+            acc = 0                           |
+            for i in prange(n - 2):-----------| #3
+                for j in prange(n - 1):-------| #2
+                    acc += b[i] + b[j + 1]    |
+            return acc                        |
+
+    It is worth noting that the loop IDs are enumerated in the order they are
+    discovered which is not necessarily the same order as present in the source.
+    Further, it should also be noted that the parallel transforms use a static
+    counter for loop ID indexing. As a consequence it is possible for the loop
+    ID index to not start at 0 due to use of the same counter for internal
+    optimizations/transforms taking place that are invisible to the user.
+
+#. Fusing loops
+    This section describes the attempts made at fusing discovered
+    loops noting which succeeded and which failed. In the case of failure to
+    fuse a reason is given (e.g. dependency on other data). From the example:
+
+    .. code-block:: text
+
+        --------------------------------- Fusing loops ---------------------------------
+        Attempting fusion of parallel loops (combines loops with similar properties)...
+        Trying to fuse loops #0 and #1:
+            - fusion succeeded: parallel for-loop #1 is fused into for-loop #0.
+        Trying to fuse loops #0 and #3:
+            - fusion failed: loop dimension mismatched in axis 0. slice(0, x_size0.1, 1)
+        != slice(0, $40.4, 1)
+
+    It can be seen that fusion of loops ``#0`` and ``#1`` was attempted and this
+    succeeded (both are based on the same dimensions of ``x``). Following the
+    successful fusion of ``#0`` and ``#1``, fusion was attempted between ``#0``
+    (now including the fused ``#1`` loop) and ``#3``. This fusion failed because
+    there is a loop dimension mismatch, ``#0`` is size ``x.shape`` whereas
+    ``#3`` is size ``x.shape[0] - 2``.
+
+#. Before Optimization
+    This section shows the structure of the parallel regions in the code before
+    any optimization has taken place, but with loops associated with their final
+    parallel region (this is to make before/after optimization output directly
+    comparable). Multiple parallel regions may exist if there are loops which
+    cannot be fused, in this case code within each region will execute in
+    parallel, but each parallel region will run sequentially. From the example:
+
+    .. code-block:: text
+
+        Parallel region 0:
+        +--0 (parallel)
+        +--1 (parallel)
+
+
+        Parallel region 1:
+        +--3 (parallel)
+        +--2 (parallel)
+
+    As alluded to by the `Fusing loops` section, there are necessarily two
+    parallel regions in the code. The first contains loops ``#0`` and ``#1``,
+    the second contains ``#3`` and ``#2``, all loops are marked ``parallel`` as
+    no optimization has taken place yet.
+
+#. After Optimization
+    This section shows the structure of the parallel regions in the code after
+    optimization has taken place. Again, parallel regions are enumerated with
+    their corresponding loops but this time loops which are fused or serialized
+    are noted and a summary is presented. From the example:
+
+    .. code-block:: text
+
+        Parallel region 0:
+        +--0 (parallel, fused with loop(s): 1)
+
+
+        Parallel region 1:
+        +--3 (parallel)
+           +--2 (serial)
+
+        Parallel region 0 (loop #0) had 1 loop(s) fused.
+
+        Parallel region 1 (loop #3) had 0 loop(s) fused and 1 loop(s) serialized as part
+        of the larger parallel loop (#3).
+
+
+    It can be noted that parallel region 0 contains loop ``#0`` and, as seen in
+    the `fusing loops` section, loop ``#1`` is fused into loop ``#0``. It can
+    also be noted that parallel region 1 contains loop ``#3`` and that loop
+    ``#2`` (the inner ``prange()``) has been serialized for execution in the
+    body of loop ``#3``.
+
+#. Loop invariant code motion
+    This section shows for each loop, after optimization has occurred:
+
+    * the instructions that failed to be hoisted and the reason for failure
+      (dependency/impure).
+    * the instructions that were hoisted.
+    * any allocation hoisting that may have occurred.
+
+    From the example:
+
+    .. code-block:: text
+
+        Instruction hoisting:
+        loop #0:
+        Failed to hoist the following:
+            dependency: $arg_out_var.10 = getitem(value=x, index=$parfor__index_5.99)
+            dependency: $0.6.11 = getattr(value=$0.5, attr=sin)
+            dependency: $expr_out_var.9 = call $0.6.11($arg_out_var.10, func=$0.6.11, args=[Var($arg_out_var.10, example.py (7))], kws=(), vararg=None)
+            dependency: $arg_out_var.17 = $expr_out_var.9 * $expr_out_var.9
+            dependency: $0.10.20 = getattr(value=$0.9, attr=cos)
+            dependency: $expr_out_var.16 = call $0.10.20($arg_out_var.17, func=$0.10.20, args=[Var($arg_out_var.17, example.py (8))], kws=(), vararg=None)
+        loop #3:
+        Has the following hoisted:
+            $const58.3 = const(int, 1)
+            $58.4 = _n_23 - $const58.3
+
+    The first thing to note is that this information is for advanced users as it
+    refers to the :term:`Numba IR` of the function being transformed. As an
+    example, the expression ``a * a`` in the example source partly translates to
+    the expression ``$arg_out_var.17 = $expr_out_var.9 * $expr_out_var.9`` in
+    the IR, this clearly cannot be hoisted out of ``loop #0`` because it is not
+    loop invariant! Whereas in ``loop #3``, the expression
+    ``$const58.3 = const(int, 1)`` comes from the source ``b[j + 1]``, the
+    number ``1`` is clearly a constant and so can be hoisted out of the loop.
+
+.. _numba-parallel-scheduling:
+
+Scheduling
+==========
+
+By default, Numba divides the iterations of a parallel region into approximately equal
+sized chunks and gives one such chunk to each configured thread.
+(See :ref:`setting_the_number_of_threads`).
+This scheduling approach is equivalent to OpenMP's static schedule with no specified
+chunk size and is appropriate when the work required for each iteration is nearly constant.
+Conversely, if the work required per iteration, as shown in the ``prange`` loop below,
+varies significantly then this static
+scheduling approach can lead to load imbalances and longer execution times.
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_parallel_chunksize.py
+   :language: python
+   :caption: from ``test_unbalanced_example`` of ``numba/tests/doc_examples/test_parallel_chunksize.py``
+   :start-after: magictoken.ex_unbalanced.begin
+   :end-before: magictoken.ex_unbalanced.end
+   :dedent: 12
+   :linenos:
+
+In such cases,
+Numba provides a mechanism to control how many iterations of a parallel region
+(i.e., the chunk size) go into each chunk.
+Numba then computes the number of required chunks which is
+equal to the number of iterations divided by the chunk size, truncated to the nearest
+integer.  All of these chunks are then approximately, equally sized.
+Numba then gives one such chunk to each configured
+thread as above and when a thread finishes a chunk, Numba gives that thread the next
+available chunk.
+This scheduling approach is similar to OpenMP's dynamic scheduling
+option with the specified chunk size.
+(Note that Numba is only capable of supporting this dynamic scheduling
+of parallel regions if the underlying Numba threading backend,
+:ref:`numba-threading-layer`, is also capable of dynamic scheduling.
+At the moment, only the ``tbb`` backend is capable of dynamic
+scheduling and so is required if any performance
+benefit is to be achieved from this chunk size selection mechanism.)
+To minimize execution time, the programmer must
+pick a chunk size that strikes a balance between greater load balancing with smaller
+chunk sizes and less scheduling overhead with larger chunk sizes.
+See :ref:`chunk-details-label` for additional details on the internal implementation
+of chunk sizes.
+
+The number of iterations of a parallel region in a chunk is stored as a thread-local
+variable and can be set using
+:func:`numba.set_parallel_chunksize`.  This function takes one integer parameter
+whose value must be greater than
+or equal to 0.  A value of 0 is the default value and instructs Numba to use the
+static scheduling approach above.  Values greater than 0 instruct Numba to use that value
+as the chunk size in the dynamic scheduling approach described above.
+:func:`numba.set_parallel_chunksize` returns the previous value of the chunk size.
+The current value of this thread local variable is used as the chunk size for all
+subsequent parallel regions invoked by this thread.
+However, upon entering a parallel region, Numba sets the chunk size thread local variable
+for each of the threads executing that parallel region back to the default of 0,
+since it is unlikely
+that any nested parallel regions would require the same chunk size.  If the same thread is
+used to execute a sequential and parallel region then that thread's chunk size
+variable is set to 0 at the beginning of the parallel region and restored to
+its original value upon exiting the parallel region.
+This behavior is demonstrated in ``func1`` in the example below in that the
+reported chunk size inside the ``prange`` parallel region is 0 but is 4 outside
+the parallel region.  Note that if the ``prange`` is not executed in parallel for
+any reason (e.g., setting ``parallel=False``) then the chunk size reported inside
+the non-parallel prange would be reported as 4.
+This behavior may initially be counterintuitive to programmers as it differs from
+how thread local variables typically behave in other languages.
+A programmer may use
+the chunk size API described in this section within the threads executing a parallel
+region if the programmer wishes to specify a chunk size for any nested parallel regions
+that may be launched.
+The current value of the parallel chunk size can be obtained by calling
+:func:`numba.get_parallel_chunksize`.
+Both of these functions can be used from standard Python and from within Numba JIT compiled functions
+as shown below.  Both invocations of ``func1`` would be executed with a chunk size of 4 whereas
+``func2`` would use a chunk size of 8.
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_parallel_chunksize.py
+   :language: python
+   :caption: from ``test_chunksize_manual`` of ``numba/tests/doc_examples/test_parallel_chunksize.py``
+   :start-after: magictoken.ex_chunksize_manual.begin
+   :end-before: magictoken.ex_chunksize_manual.end
+   :dedent: 12
+   :linenos:
+
+Since this idiom of saving and restoring is so common, Numba provides the
+:func:`parallel_chunksize` with clause context-manager to simplify the idiom.
+As shown below, this with clause can be invoked from both standard Python and
+within Numba JIT compiled functions.  As with other Numba context-managers, be
+aware that the raising of exceptions is not supported from within a context managed
+block that is part of a Numba JIT compiled function.
+
+.. literalinclude:: ../../../numba/tests/doc_examples/test_parallel_chunksize.py
+   :language: python
+   :caption: from ``test_chunksize_with`` of ``numba/tests/doc_examples/test_parallel_chunksize.py``
+   :start-after: magictoken.ex_chunksize_with.begin
+   :end-before: magictoken.ex_chunksize_with.end
+   :dedent: 12
+   :linenos:
+
+Note that these functions to set the chunk size only have an effect on
+Numba automatic parallelization with the :ref:`parallel_jit_option` option.
+Chunk size specification has no effect on the :func:`~numba.vectorize` decorator
+or the :func:`~numba.guvectorize` decorator.
+
+.. seealso:: :ref:`parallel_jit_option`, :ref:`Parallel FAQs <parallel_FAQs>`
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/performance-tips.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/performance-tips.rst
new file mode 100644
index 000000000..688071dec
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/performance-tips.rst
@@ -0,0 +1,248 @@
+.. _performance-tips:
+
+Performance Tips
+================
+
+This is a short guide to features present in Numba that can help with obtaining
+the best performance from code. Two examples are used, both are entirely
+contrived and exist purely for pedagogical reasons to motivate discussion.
+The first is the computation of the trigonometric identity
+``cos(x)^2 + sin(x)^2``, the second is a simple element wise square root of a
+vector with reduction over summation. All performance numbers are indicative
+only and unless otherwise stated were taken from running on an Intel ``i7-4790``
+CPU (4 hardware threads) with an input of ``np.arange(1.e7)``.
+
+.. note::
+   A reasonably effective approach to achieving high performance code is to
+   profile the code running with real data and use that to guide performance
+   tuning. The information presented here is to demonstrate features, not to act
+   as canonical guidance!
+
+No Python mode vs Object mode
+-----------------------------
+
+A common pattern is to decorate functions with ``@jit`` as this is the most
+flexible decorator offered by Numba. ``@jit`` essentially encompasses two modes
+of compilation, first it will try and compile the decorated function in no
+Python mode, if this fails it will try again to compile the function using
+object mode. Whilst the use of looplifting in object mode can enable some
+performance increase, getting functions to compile under no python mode is
+really the key to good performance. To make it such that only no python mode is
+used and if compilation fails an exception is raised the decorators ``@njit``
+and ``@jit(nopython=True)`` can be used (the first is an alias of the
+second for convenience).
+
+Loops
+-----
+Whilst NumPy has developed a strong idiom around the use of vector operations,
+Numba is perfectly happy with loops too. For users familiar with C or Fortran,
+writing Python in this style will work fine in Numba (after all, LLVM gets a
+lot of use in compiling C lineage languages). For example::
+
+    @njit
+    def ident_np(x):
+        return np.cos(x) ** 2 + np.sin(x) ** 2
+
+    @njit
+    def ident_loops(x):
+        r = np.empty_like(x)
+        n = len(x)
+        for i in range(n):
+            r[i] = np.cos(x[i]) ** 2 + np.sin(x[i]) ** 2
+        return r
+
+The above run at almost identical speeds when decorated with ``@njit``, without
+the decorator the vectorized function is a couple of orders of magnitude faster.
+
++-----------------+-------+----------------+
+| Function Name   | @njit | Execution time |
++=================+=======+================+
+| ``ident_np``    | No    |     0.581s     |
++-----------------+-------+----------------+
+| ``ident_np``    | Yes   |     0.659s     |
++-----------------+-------+----------------+
+| ``ident_loops`` | No    |     25.2s      |
++-----------------+-------+----------------+
+| ``ident_loops`` | Yes   |     0.670s     |
++-----------------+-------+----------------+
+
+.. _fast-math:
+
+Fastmath
+--------
+In certain classes of applications strict IEEE 754 compliance is less
+important. As a result it is possible to relax some numerical rigour with
+view of gaining additional performance. The way to achieve this behaviour in
+Numba is through the use of the ``fastmath`` keyword argument::
+
+    @njit(fastmath=False)
+    def do_sum(A):
+        acc = 0.
+        # without fastmath, this loop must accumulate in strict order
+        for x in A:
+            acc += np.sqrt(x)
+        return acc
+
+    @njit(fastmath=True)
+    def do_sum_fast(A):
+        acc = 0.
+        # with fastmath, the reduction can be vectorized as floating point
+        # reassociation is permitted.
+        for x in A:
+            acc += np.sqrt(x)
+        return acc
+
+
++-----------------+-----------------+
+| Function Name   | Execution time  |
++=================+=================+
+| ``do_sum``      |      35.2 ms    |
++-----------------+-----------------+
+| ``do_sum_fast`` |      17.8 ms    |
++-----------------+-----------------+
+
+In some cases you may wish to opt-in to only a subset of possible fast-math
+optimizations. This can be done by supplying a set of `LLVM fast-math flags
+<https://llvm.org/docs/LangRef.html#fast-math-flags>`_ to ``fastmath``.::
+
+    def add_assoc(x, y):
+        return (x - y) + y
+
+    print(njit(fastmath=False)(add_assoc)(0, np.inf)) # nan
+    print(njit(fastmath=True) (add_assoc)(0, np.inf)) # 0.0
+    print(njit(fastmath={'reassoc', 'nsz'})(add_assoc)(0, np.inf)) # 0.0
+    print(njit(fastmath={'reassoc'})       (add_assoc)(0, np.inf)) # nan
+    print(njit(fastmath={'nsz'})           (add_assoc)(0, np.inf)) # nan
+
+
+Parallel=True
+-------------
+If code contains operations that are parallelisable (:ref:`and supported
+<numba-parallel-supported>`) Numba can compile a version that will run in
+parallel on multiple native threads (no GIL!). This parallelisation is performed
+automatically and is enabled by simply adding the ``parallel`` keyword
+argument::
+
+    @njit(parallel=True)
+    def ident_parallel(x):
+        return np.cos(x) ** 2 + np.sin(x) ** 2
+
+
+Executions times are as follows:
+
++--------------------+-----------------+
+| Function Name      | Execution time  |
++====================+=================+
+| ``ident_parallel`` | 112 ms          |
++--------------------+-----------------+
+
+
+The execution speed of this function with ``parallel=True`` present is
+approximately 5x that of the NumPy equivalent and 6x that of standard
+``@njit``.
+
+
+Numba parallel execution also has support for explicit parallel loop
+declaration similar to that in OpenMP. To indicate that a loop should be
+executed in parallel the ``numba.prange`` function should be used, this function
+behaves like Python ``range`` and if ``parallel=True`` is not set it acts
+simply as an alias of ``range``. Loops induced with ``prange`` can be used for
+embarrassingly parallel computation and also reductions.
+
+Revisiting the reduce over sum example, assuming it is safe for the sum to be
+accumulated out of order, the loop in ``n`` can be parallelised through the use
+of ``prange``. Further, the ``fastmath=True`` keyword argument can be added
+without concern in this case as the assumption that out of order execution is
+valid has already been made through the use of ``parallel=True`` (as each thread
+computes a partial sum).
+::
+
+    @njit(parallel=True)
+    def do_sum_parallel(A):
+        # each thread can accumulate its own partial sum, and then a cross
+        # thread reduction is performed to obtain the result to return
+        n = len(A)
+        acc = 0.
+        for i in prange(n):
+            acc += np.sqrt(A[i])
+        return acc
+
+    @njit(parallel=True, fastmath=True)
+    def do_sum_parallel_fast(A):
+        n = len(A)
+        acc = 0.
+        for i in prange(n):
+            acc += np.sqrt(A[i])
+        return acc
+
+
+Execution times are as follows, ``fastmath`` again improves performance.
+
++-------------------------+-----------------+
+| Function Name           | Execution time  |
++=========================+=================+
+| ``do_sum_parallel``     |      9.81 ms    |
++-------------------------+-----------------+
+| ``do_sum_parallel_fast``|      5.37 ms    |
++-------------------------+-----------------+
+
+.. _intel-svml:
+
+Intel SVML
+----------
+
+Intel provides a short vector math library (SVML) that contains a large number
+of optimised transcendental functions available for use as compiler
+intrinsics. If the ``icc_rt`` package is present in the environment (or the SVML
+libraries are simply locatable!) then Numba automatically configures the LLVM
+back end to use the SVML intrinsic functions where ever possible. SVML provides
+both high and low accuracy versions of each intrinsic and the version that is
+used is determined through the use of the ``fastmath`` keyword. The default is
+to use high accuracy which is accurate to within ``1 ULP``, however if 
+``fastmath`` is set to ``True`` then the lower accuracy versions of the
+intrinsics are used (answers to within ``4 ULP``).
+
+
+First obtain SVML, using conda for example::
+
+    conda install -c numba icc_rt
+
+Rerunning the identity function example ``ident_np`` from above with various
+combinations of options to ``@njit`` and with/without SVML yields the following
+performance results (input size ``np.arange(1.e8)``). For reference, with just
+NumPy the function executed in ``5.84s``:
+
++-----------------------------------+--------+-------------------+
+| ``@njit`` kwargs                  |  SVML  | Execution time    |
++===================================+========+===================+
+| ``None``                          | No     | 5.95s             |
++-----------------------------------+--------+-------------------+
+| ``None``                          | Yes    | 2.26s             |
++-----------------------------------+--------+-------------------+
+| ``fastmath=True``                 | No     | 5.97s             |
++-----------------------------------+--------+-------------------+
+| ``fastmath=True``                 | Yes    | 1.8s              |
++-----------------------------------+--------+-------------------+
+| ``parallel=True``                 | No     | 1.36s             |
++-----------------------------------+--------+-------------------+
+| ``parallel=True``                 | Yes    | 0.624s            |
++-----------------------------------+--------+-------------------+
+| ``parallel=True, fastmath=True``  | No     | 1.32s             |
++-----------------------------------+--------+-------------------+
+| ``parallel=True, fastmath=True``  | Yes    | 0.576s            |
++-----------------------------------+--------+-------------------+
+
+It is evident that SVML significantly increases the performance of this
+function. The impact of ``fastmath`` in the case of SVML not being present is
+zero, this is expected as there is nothing in the original function that would
+benefit from relaxing numerical strictness.
+
+Linear algebra
+--------------
+Numba supports most of ``numpy.linalg`` in no Python mode. The internal
+implementation relies on a LAPACK and BLAS library to do the numerical work
+and it obtains the bindings for the necessary functions from SciPy. Therefore,
+to achieve good performance in ``numpy.linalg`` functions with Numba it is
+necessary to use a SciPy built against a well optimised LAPACK/BLAS library.
+In the case of the Anaconda distribution SciPy is built against Intel's MKL
+which is highly optimised and as a result Numba makes use of this performance.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/pycc.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/pycc.rst
new file mode 100644
index 000000000..b0f1275a5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/pycc.rst
@@ -0,0 +1,140 @@
+
+============================
+Compiling code ahead of time
+============================
+
+.. _pycc:
+
+While Numba's main use case is :term:`Just-in-Time compilation`, it also
+provides a facility for :term:`Ahead-of-Time compilation` (AOT).
+
+
+Overview
+========
+
+Benefits
+--------
+
+#. AOT compilation produces a compiled extension module which does not depend
+   on Numba: you can distribute the module on machines which do not have
+   Numba installed (but Numpy is required).
+
+#. There is no compilation overhead at runtime (but see the
+   ``@jit`` :ref:`cache <jit-cache>` option), nor any overhead of importing
+   Numba.
+
+.. seealso::
+   Compiled extension modules are discussed in the
+   `Python packaging user guide <https://packaging.python.org/en/latest/extensions/>`_.
+
+
+Limitations
+-----------
+
+#. AOT compilation only allows for regular functions, not :term:`ufuncs <ufunc>`.
+
+#. You have to specify function signatures explicitly.
+
+#. Each exported function can have only one signature (but you can export
+   several different signatures under different names).
+
+#. Exported functions do not check the types of the arguments that are passed
+   to them; the caller is expected to provide arguments of the correct type.
+
+#. AOT compilation produces generic code for your CPU's architectural family
+   (for example "x86-64"), while JIT compilation produces code optimized
+   for your particular CPU model.
+
+
+Usage
+=====
+
+Standalone example
+------------------
+
+::
+
+   from numba.pycc import CC
+
+   cc = CC('my_module')
+   # Uncomment the following line to print out the compilation steps
+   #cc.verbose = True
+
+   @cc.export('multf', 'f8(f8, f8)')
+   @cc.export('multi', 'i4(i4, i4)')
+   def mult(a, b):
+       return a * b
+
+   @cc.export('square', 'f8(f8)')
+   def square(a):
+       return a ** 2
+
+   if __name__ == "__main__":
+       cc.compile()
+
+
+If you run this Python script, it will generate an extension module named
+``my_module``.  Depending on your platform, the actual filename may be
+``my_module.so``, ``my_module.pyd``, ``my_module.cpython-34m.so``, etc.
+
+The generated module has three functions: ``multf``, ``multi`` and ``square``.
+``multi`` operates on 32-bit integers (``i4``), while ``multf`` and ``square``
+operate on double-precision floats (``f8``)::
+
+   >>> import my_module
+   >>> my_module.multi(3, 4)
+   12
+   >>> my_module.square(1.414)
+   1.9993959999999997
+
+
+Distutils integration
+---------------------
+
+You can also integrate the compilation step for your extension modules
+in your ``setup.py`` script, using distutils or setuptools::
+
+   from distutils.core import setup
+
+   from source_module import cc
+
+   setup(...,
+         ext_modules=[cc.distutils_extension()])
+
+
+The ``source_module`` above is the module defining the ``cc`` object.
+Extensions compiled like this will be automatically included in the
+build files for your Python project, so you can distribute them inside
+binary packages such as wheels or Conda packages. Note that in the case of
+using conda, the compilers used for AOT need to be those that are available
+in the Anaconda distribution.
+
+
+Signature syntax
+----------------
+
+The syntax for exported signatures is the same as in the ``@jit``
+decorator.  You can read more about it in the :ref:`types <numba-types>`
+reference.
+
+Here is an example of exporting an implementation of the second-order
+centered difference on a 1d array::
+
+   @cc.export('centdiff_1d', 'f8[:](f8[:], f8)')
+   def centdiff_1d(u, dx):
+       D = np.empty_like(u)
+       D[0] = 0
+       D[-1] = 0
+       for i in range(1, len(D) - 1):
+           D[i] = (u[i+1] - 2 * u[i] + u[i-1]) / dx**2
+       return D
+
+.. (example from http://nbviewer.ipython.org/gist/ketch/ae87a94f4ef0793d5d52)
+
+You can also omit the return type, which will then be inferred by Numba::
+
+   @cc.export('centdiff_1d', '(f8[:], f8)')
+   def centdiff_1d(u, dx):
+       # Same code as above
+       ...
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/stencil.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/stencil.rst
new file mode 100644
index 000000000..6888a556f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/stencil.rst
@@ -0,0 +1,255 @@
+.. Copyright (c) 2017 Intel Corporation
+   SPDX-License-Identifier: BSD-2-Clause
+
+.. _numba-stencil:
+
+================================
+Using the ``@stencil`` decorator
+================================
+
+Stencils are a common computational pattern in which array elements
+are updated according to some fixed pattern called the stencil kernel.
+Numba provides the ``@stencil`` decorator so that users may
+easily specify a stencil kernel and Numba then generates the looping
+code necessary to apply that kernel to some input array.  Thus, the
+stencil decorator allows clearer, more concise code and in conjunction
+with :ref:`the parallel jit option <parallel_jit_option>` enables higher
+performance through parallelization of the stencil execution.
+
+
+Basic usage
+===========
+
+An example use of the ``@stencil`` decorator::
+
+   from numba import stencil
+
+   @stencil
+   def kernel1(a):
+       return 0.25 * (a[0, 1] + a[1, 0] + a[0, -1] + a[-1, 0])
+
+The stencil kernel is specified by what looks like a standard Python
+function definition but there are different semantics with
+respect to array indexing.
+Stencils produce an output array of the same size and shape as the
+input array although depending on the kernel definition may have a
+different type.
+Conceptually, the stencil kernel is run once for each element in the
+output array.  The return value from the stencil kernel is the value
+written into the output array for that particular element.
+
+The parameter ``a`` represents the input array over which the
+kernel is applied.
+Indexing into this array takes place with respect to the current element
+of the output array being processed.  For example, if element ``(x, y)``
+is being processed then ``a[0, 0]`` in the stencil kernel corresponds to
+``a[x + 0, y + 0]`` in the input array.  Similarly, ``a[-1, 1]`` in the stencil
+kernel corresponds to ``a[x - 1, y + 1]`` in the input array.
+
+Depending on the specified kernel, the kernel may not be applicable to the
+borders of the output array as this may cause the input array to be
+accessed out-of-bounds.  The way in which the stencil decorator handles
+this situation is dependent upon which :ref:`stencil-mode` is selected.
+The default mode is for the stencil decorator to set the border elements
+of the output array to zero.
+
+To invoke a stencil on an input array, call the stencil as if it were
+a regular function and pass the input array as the argument. For example, using
+the kernel defined above::
+
+   >>> import numpy as np
+   >>> input_arr = np.arange(100).reshape((10, 10))
+   array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
+          [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
+          [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
+          [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
+          [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
+          [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
+          [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
+          [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
+          [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
+          [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])
+   >>> output_arr = kernel1(input_arr)
+   array([[  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],
+          [  0.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,   0.],
+          [  0.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,   0.],
+          [  0.,  31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,   0.],
+          [  0.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,   0.],
+          [  0.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,   0.],
+          [  0.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,   0.],
+          [  0.,  71.,  72.,  73.,  74.,  75.,  76.,  77.,  78.,   0.],
+          [  0.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,  88.,   0.],
+          [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.]])
+   >>> input_arr.dtype
+   dtype('int64')
+   >>> output_arr.dtype
+   dtype('float64')
+
+Note that the stencil decorator has determined that the output type
+of the specified stencil kernel is ``float64`` and has thus created the
+output array as ``float64`` while the input array is of type ``int64``.
+
+Stencil Parameters
+==================
+
+Stencil kernel definitions may take any number of arguments with
+the following provisions.  The first argument must be an array.
+The size and shape of the output array will be the same as that of the
+first argument.  Additional arguments may either be scalars or
+arrays.  For array arguments, those arrays must be at least as large
+as the first argument (array) in each dimension.  Array indexing is relative for
+all such input array arguments.
+
+.. _stencil-kernel-shape-inference:
+
+Kernel shape inference and border handling
+==========================================
+
+In the above example and in most cases, the array indexing in the
+stencil kernel will exclusively use ``Integer`` literals.
+In such cases, the stencil decorator is able to analyze the stencil
+kernel to determine its size.  In the above example, the stencil
+decorator determines that the kernel is ``3 x 3`` in shape since indices
+``-1`` to ``1`` are used for both the first and second dimensions.  Note that
+the stencil decorator also correctly handles non-symmetric and
+non-square stencil kernels.
+
+Based on the size of the stencil kernel, the stencil decorator is
+able to compute the size of the border in the output array.  If
+applying the kernel to some element of input array would cause
+an index to be out-of-bounds then that element belongs to the border
+of the output array.  In the above example, points ``-1`` and ``+1`` are
+accessed in each dimension and thus the output array has a border
+of size one in all dimensions.
+
+The parallel mode is able to infer kernel indices as constants from
+simple expressions if possible. For example::
+
+    @njit(parallel=True)
+    def stencil_test(A):
+        c = 2
+        B = stencil(
+            lambda a, c: 0.3 * (a[-c+1] + a[0] + a[c-1]))(A, c)
+        return B
+
+
+Stencil decorator options
+=========================
+
+.. note::
+   The stencil decorator may be augmented in the future to provide additional
+   mechanisms for border handling. At present, only one behaviour is
+   implemented, ``"constant"`` (see ``func_or_mode`` below for details).
+
+.. _stencil-neighborhood:
+
+``neighborhood``
+----------------
+
+Sometimes it may be inconvenient to write the stencil kernel
+exclusively with ``Integer`` literals.  For example, let us say we
+would like to compute the trailing 30-day moving average of a
+time series of data.  One could write
+``(a[-29] + a[-28] + ... + a[-1] + a[0]) / 30`` but the stencil
+decorator offers a more concise form using the ``neighborhood``
+option::
+
+   @stencil(neighborhood = ((-29, 0),))
+   def kernel2(a):
+       cumul = 0
+       for i in range(-29, 1):
+           cumul += a[i]
+       return cumul / 30
+
+The neighborhood option is a tuple of tuples.  The outer tuple's
+length is equal to the number of dimensions of the input array.
+The inner tuple's lengths are always two because
+each element of the inner tuple corresponds to minimum and
+maximum index offsets used in the corresponding dimension.
+
+If a user specifies a neighborhood but the kernel accesses elements outside the
+specified neighborhood, **the behavior is undefined.**
+
+.. _stencil-mode:
+
+``func_or_mode``
+----------------
+
+The optional ``func_or_mode`` parameter controls how the border of the output array
+is handled.  Currently, there is only one supported value, ``"constant"``.
+In ``constant`` mode, the stencil kernel is not applied in cases where
+the kernel would access elements outside the valid range of the input
+array.  In such cases, those elements in the output array are assigned
+to a constant value, as specified by the ``cval`` parameter.
+
+``cval``
+--------
+
+The optional cval parameter defaults to zero but can be set to any
+desired value, which is then used for the border of the output array
+if the ``func_or_mode`` parameter is set to ``constant``.  The cval parameter is
+ignored in all other modes.  The type of the cval parameter must match
+the return type of the stencil kernel.  If the user wishes the output
+array to be constructed from a particular type then they should ensure
+that the stencil kernel returns that type.
+
+``standard_indexing``
+---------------------
+
+By default, all array accesses in a stencil kernel are processed as
+relative indices as described above.  However, sometimes it may be
+advantageous to pass an auxiliary array (e.g. an array of weights)
+to a stencil kernel and have that array use standard Python indexing
+rather than relative indexing.  For this purpose, there is the
+stencil decorator option ``standard_indexing`` whose value is a
+collection of strings whose names match those parameters to the
+stencil function that are to be accessed with standard Python indexing
+rather than relative indexing::
+
+    @stencil(standard_indexing=("b",))
+    def kernel3(a, b):
+        return a[-1] * b[0] + a[0] + b[1]
+
+``StencilFunc``
+===============
+
+The stencil decorator returns a callable object of type ``StencilFunc``.
+``StencilFunc`` objects contains a number of attributes but the only one of
+potential interest to users is the ``neighborhood`` attribute.
+If the ``neighborhood`` option was passed to the stencil decorator then
+the provided neighborhood is stored in this attribute.  Else, upon
+first execution or compilation, the system calculates the neighborhood
+as described above and then stores the computed neighborhood into this
+attribute.  A user may then inspect the attribute if they wish to verify
+that the calculated neighborhood is correct.
+
+Stencil invocation options
+==========================
+
+Internally, the stencil decorator transforms the specified stencil
+kernel into a regular Python function.  This function will have the
+same parameters as specified in the stencil kernel definition but will
+also include the following optional parameter.
+
+.. _stencil-function-out:
+
+``out``
+-------
+
+The optional ``out`` parameter is added to every stencil function
+generated by Numba.  If specified, the ``out`` parameter tells
+Numba that the user is providing their own pre-allocated array
+to be used for the output of the stencil.  In this case, the
+stencil function will not allocate its own output array.
+Users should assure that the return type of the stencil kernel can
+be safely cast to the element-type of the user-specified output array
+following the `NumPy ufunc casting rules`_.
+
+.. _`NumPy ufunc casting rules`: http://docs.scipy.org/doc/numpy/reference/ufuncs.html#casting-rules
+
+An example usage is shown below::
+
+   >>> import numpy as np
+   >>> input_arr = np.arange(100).reshape((10, 10))
+   >>> output_arr = np.full(input_arr.shape, 0.0)
+   >>> kernel1(input_arr, out=output_arr)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/talks.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/talks.rst
new file mode 100644
index 000000000..ef35ae944
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/talks.rst
@@ -0,0 +1,36 @@
+
+Talks and Tutorials
+===================
+
+.. note:: This is a selection of talks and tutorials that have been given by members of
+    the Numba team as well as Numba users.  If you know of a Numba-related talk
+    that should be included on this list, please `open an issue <https://github.com/numba/numba/issues>`_.
+
+Talks on Numba
+--------------
+
+* AnacondaCON 2018 - Accelerating Scientific Workloads with Numba - Siu Kwan Lam (`Video <https://www.youtube.com/watch?v=6oXedk2tGfk>`__)
+* `DIANA-HEP Meeting, 23 April 2018 <https://indico.cern.ch/event/709711/>`__ - Overview of Numba - Stan Seibert
+
+Talks on Applications of Numba
+------------------------------
+
+* GPU Technology Conference 2016 - Accelerating a Spectral Algorithm for Plasma Physics with Python/Numba on GPU - Manuel Kirchen & Rémi Lehe (`Slides <http://on-demand.gputechconf.com/gtc/2016/presentation/s6353-manuel-kirchen-spectral-algorithm-plasma-physics.pdf>`__)
+* `DIANA-HEP Meeting, 23 April 2018 <https://indico.cern.ch/event/709711/>`_ - Use of Numba in XENONnT - Chris Tunnell
+* `DIANA-HEP Meeting, 23 April 2018 <https://indico.cern.ch/event/709711/>`_ - Extending Numba for HEP data types - Jim Pivarski
+* STAC Summit, Nov 1 2017 - Scaling High-Performance Python with Minimal Effort - Ehsan Totoni (`Video <https://stacresearch.com/STAC-Summit-1-Nov-2017-Intel-Totoni>`__, `Slides <https://stacresearch.com/system/files/resource/files/STAC-Summit-1-Nov-2017-Intel-Totoni.pdf>`__)
+* SciPy 2018 - UMAP: Uniform Manifold Approximation and Projection for Dimensional Reduction - Leland McInnes (`Video <https://www.youtube.com/watch?v=nq6iPZVUxZU>`__, `Github <https://github.com/lmcinnes/umap>`__)
+* PyData Berlin 2018 - Extending Pandas using Apache Arrow and Numba - Uwe L. Korn (`Video <https://www.youtube.com/watch?v=tvmX8YAFK80>`__, `Blog <https://uwekorn.com/2018/08/03/use-numba-to-work-with-apache-arrow-in-pure-python.html>`__)
+* FOSDEM 2019 - Extending Numba - Joris Geessels  (`Video, Slides & Examples <https://fosdem.org/2019/schedule/event/python_extending_numba/>`__)
+* PyCon India 2019 - Real World Numba: Taking the Path of Least Resistance - Ankit Mahato (`Video <https://www.youtube.com/watch?v=rhbegsr8stc>`__)
+* SciPy 2019 - How to Accelerate an Existing Codebase with Numba - Siu Kwan Lam & Stanley Seibert (`Video <https://www.youtube.com/watch?v=-4tD8kNHdXs>`__)
+* SciPy 2019 - Real World Numba: Creating a Skeleton Analysis Library - Juan Nunez-Iglesias (`Video <https://www.youtube.com/watch?v=0pUPNMglnaE>`__)
+* SciPy 2019 - Fast Gradient Boosting Decision Trees with PyGBM and Numba - Nicholas Hug (`Video <https://www.youtube.com/watch?v=cLpIh8Aiy2w>`__)
+* PyCon Sweden 2020 - Accelerating Scientific Computing using Numba - Ankit Mahato (`Video <https://www.youtube.com/watch?v=d_21Q0UoWrQ>`__)
+
+Tutorials
+---------
+
+* SciPy 2017 - Numba: Tell those C++ Bullies to Get Lost - Gil Forsyth & Lorena Barba (`Video <https://www.youtube.com/watch?v=1AwG0T4gaO0>`__, `Notebooks <https://github.com/gforsyth/numba_tutorial_scipy2017>`__)
+* GPU Technology Conference 2018 - GPU Computing in Python with Numba - Stan Seibert (`Notebooks <https://github.com/ContinuumIO/gtc2018-numba>`__)
+* PyData Amsterdam 2019 - Create CUDA kernels from Python using Numba and CuPy - Valentin Haenel (`Video <https://www.youtube.com/watch?v=CQDsT81GyS8>`__)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/threading-layer.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/threading-layer.rst
new file mode 100644
index 000000000..6aa5fef43
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/threading-layer.rst
@@ -0,0 +1,313 @@
+.. _numba-threading-layer:
+
+The Threading Layers
+====================
+
+This section is about the Numba threading layer, this is the library that is
+used internally to perform the parallel execution that occurs through the use of
+the ``parallel`` targets for CPUs, namely:
+
+* The use of the ``parallel=True`` kwarg in ``@jit`` and ``@njit``.
+* The use of the ``target='parallel'`` kwarg in ``@vectorize`` and
+  ``@guvectorize``.
+
+.. note::
+    If a code base does not use the ``threading`` or ``multiprocessing``
+    modules (or any other sort of parallelism) the defaults for the threading
+    layer that ship with Numba will work well, no further action is required!
+
+
+Which threading layers are available?
+-------------------------------------
+There are three threading layers available and they are named as follows:
+
+* ``tbb`` - A threading layer backed by Intel TBB.
+* ``omp`` - A threading layer backed by OpenMP.
+* ``workqueue`` -A simple built-in work-sharing task scheduler.
+
+In practice, the only threading layer guaranteed to be present is ``workqueue``.
+The ``omp`` layer requires the presence of a suitable OpenMP runtime library.
+The ``tbb`` layer requires the presence of Intel's TBB libraries, these can be
+obtained via the conda command::
+
+    $ conda install tbb
+
+If you installed Numba with ``pip``, TBB can be enabled by running::
+
+    $ pip install tbb
+
+Due to compatibility issues with manylinux1 and other portability concerns,
+the OpenMP threading layer is disabled in the Numba binary wheels on PyPI.
+
+.. note::
+    The default manner in which Numba searches for and loads a threading layer
+    is tolerant of missing libraries, incompatible runtimes etc.
+
+
+.. _numba-threading-layer-setting-mech:
+
+Setting the threading layer
+---------------------------
+
+
+The threading layer is set via the environment variable
+``NUMBA_THREADING_LAYER`` or through assignment to
+``numba.config.THREADING_LAYER``. If the programmatic approach to setting the
+threading layer is used it must occur logically before any Numba based
+compilation for a parallel target has occurred. There are two approaches to
+choosing a threading layer, the first is by selecting a threading layer that is
+safe under various forms of parallel execution, the second is through explicit
+selection via the threading layer name (e.g. ``tbb``).
+
+Setting the threading layer selection priority
+----------------------------------------------
+
+By default the threading layers are searched in the order of ``'tbb'``,
+``'omp'``, then ``'workqueue'``. To change this search order whilst
+maintaining the selection of a threading layer based on availability, the
+environment variable :envvar:`NUMBA_THREADING_LAYER_PRIORITY` can be used.
+
+Note that it can also be set via
+:py:data:`numba.config.THREADING_LAYER_PRIORITY`.
+Similar to :py:data:`numba.config.THREADING_LAYER`,
+it must occur logically before any Numba based
+compilation for a parallel target has occurred.
+
+For example, to instruct Numba to choose ``omp`` first if available,
+then ``tbb`` and so on, set the environment variable as
+``NUMBA_THREADING_LAYER_PRIORITY="omp tbb workqueue"``.
+Or programmatically,
+``numba.config.THREADING_LAYER_PRIORITY = ["omp", "tbb", "workqueue"]``.
+
+Selecting a threading layer for safe parallel execution
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Parallel execution is fundamentally derived from core Python libraries in four
+forms (the first three also apply to code using parallel execution via other
+means!):
+
+* ``threads`` from the ``threading`` module.
+* ``spawn`` ing processes from the ``multiprocessing`` module via ``spawn``
+  (default on Windows, only available in Python 3.4+ on Unix)
+* ``fork`` ing processes from the ``multiprocessing`` module via ``fork``
+  (default on Unix).
+* ``fork`` ing processes from the ``multiprocessing`` module through the use of
+  a ``forkserver`` (only available in Python 3 on Unix). Essentially a new
+  process is spawned and then forks are made from this new process on request.
+
+Any library in use with these forms of parallelism must exhibit safe behaviour
+under the given paradigm. As a result, the threading layer selection methods
+are designed to provide a way to choose a threading layer library that is safe
+for a given paradigm in an easy, cross platform and environment tolerant manner.
+The options that can be supplied to the
+:ref:`setting mechanisms <numba-threading-layer-setting-mech>` are as
+follows:
+
+* ``default`` provides no specific safety guarantee and is the default.
+* ``safe`` is both fork and thread safe, this requires the ``tbb`` package
+  (Intel TBB libraries) to be installed.
+* ``forksafe`` provides a fork safe library.
+* ``threadsafe`` provides a thread safe library.
+
+To discover the threading layer that was selected, the function
+``numba.threading_layer()`` may be called after parallel execution. For example,
+on a Linux machine with no TBB installed::
+
+    from numba import config, njit, threading_layer
+    import numpy as np
+
+    # set the threading layer before any parallel target compilation
+    config.THREADING_LAYER = 'threadsafe'
+
+    @njit(parallel=True)
+    def foo(a, b):
+        return a + b
+
+    x = np.arange(10.)
+    y = x.copy()
+
+    # this will force the compilation of the function, select a threading layer
+    # and then execute in parallel
+    foo(x, y)
+
+    # demonstrate the threading layer chosen
+    print("Threading layer chosen: %s" % threading_layer())
+
+which produces::
+
+    Threading layer chosen: omp
+
+and this makes sense as GNU OpenMP, as present on Linux, is thread safe.
+
+Selecting a named threading layer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Advanced users may wish to select a specific threading layer for their use case,
+this is done by directly supplying the threading layer name to the
+:ref:`setting mechanisms <numba-threading-layer-setting-mech>`. The options
+and requirements are as follows:
+
++----------------------+-----------+-------------------------------------------+
+| Threading Layer Name | Platform  | Requirements                              |
++======================+===========+===========================================+
+| ``tbb``              | All       | The ``tbb`` package (``$ conda install    |
+|                      |           | tbb``)                                    |
++----------------------+-----------+-------------------------------------------+
+| ``omp``              | Linux     | GNU OpenMP libraries (very likely this    |
+|                      |           | will already exist)                       |
+|                      |           |                                           |
+|                      | Windows   | MS OpenMP libraries (very likely this will|
+|                      |           | already exist)                            |
+|                      |           |                                           |
+|                      | OSX       | Either the ``intel-openmp`` package or the|
+|                      |           | ``llvm-openmp`` package                   |
+|                      |           | (``conda install`` the package as named). |
++----------------------+-----------+-------------------------------------------+
+| ``workqueue``        | All       | None                                      |
++----------------------+-----------+-------------------------------------------+
+
+Should the threading layer not load correctly Numba will detect this and provide
+a hint about how to resolve the problem. It should also be noted that the Numba
+diagnostic command ``numba -s`` has a section
+``__Threading Layer Information__`` that reports on the availability of
+threading layers in the current environment.
+
+
+Extra notes
+-----------
+The threading layers have fairly complex interactions with CPython internals and
+system level libraries, some additional things to note:
+
+* The installation of Intel's TBB libraries vastly widens the options available
+  in the threading layer selection process.
+* On Linux, the ``omp`` threading layer is not fork safe due to the GNU OpenMP
+  runtime library (``libgomp``) not being fork safe. If a fork occurs in a
+  program that is using the ``omp`` threading layer, a detection mechanism is
+  present that will try and gracefully terminate the forked child and print an
+  error message to ``STDERR``.
+* On systems with the ``fork(2)`` system call available, if the TBB backed
+  threading layer is in use and a ``fork`` call is made from a thread other than
+  the thread that launched TBB (typically the main thread) then this results in
+  undefined behaviour and a warning will be displayed on ``STDERR``. As
+  ``spawn`` is essentially ``fork`` followed by ``exec`` it is safe to ``spawn``
+  from a non-main thread, but as this cannot be differentiated from just a
+  ``fork`` call the warning message will still be displayed.
+* On OSX, the ``intel-openmp`` package is required to enable the OpenMP based
+  threading layer.
+
+.. _setting_the_number_of_threads:
+
+Setting the Number of Threads
+-----------------------------
+
+The number of threads used by numba is based on the number of CPU cores
+available (see :obj:`numba.config.NUMBA_DEFAULT_NUM_THREADS`), but it can be
+overridden with the :envvar:`NUMBA_NUM_THREADS` environment variable.
+
+The total number of threads that numba launches is in the variable
+:obj:`numba.config.NUMBA_NUM_THREADS`.
+
+For some use cases, it may be desirable to set the number of threads to a
+lower value, so that numba can be used with higher level parallelism.
+
+The number of threads can be set dynamically at runtime using
+:func:`numba.set_num_threads`. Note that :func:`~.set_num_threads` only allows
+setting the number of threads to a smaller value than
+:obj:`~.NUMBA_NUM_THREADS`. Numba always launches
+:obj:`numba.config.NUMBA_NUM_THREADS` threads, but :func:`~.set_num_threads`
+causes it to mask out unused threads so they aren't used in computations.
+
+The current number of threads used by numba can be accessed with
+:func:`numba.get_num_threads`. Both functions work inside of a jitted
+function.
+
+.. _numba-threading-layer-thread-masking:
+
+Example of Limiting the Number of Threads
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In this example, suppose the machine we are running on has 8 cores (so
+:obj:`numba.config.NUMBA_NUM_THREADS` would be ``8``). Suppose we want to run
+some code with ``@njit(parallel=True)``, but we also want to run our code
+concurrently in 4 different processes. With the default number of threads,
+each Python process would run 8 threads, for a total in 4*8 = 32 threads,
+which is oversubscription for our 8 cores. We should rather limit each process
+to 2 threads, so that the total will be 4*2 = 8, which matches our number of
+physical cores.
+
+There are two ways to do this. One is to set the :envvar:`NUMBA_NUM_THREADS`
+environment variable to ``2``.
+
+.. code:: bash
+
+   $ NUMBA_NUM_THREADS=2 python ourcode.py
+
+However, there are two downsides to this approach:
+
+1. :envvar:`NUMBA_NUM_THREADS` must be set before Numba is imported, and
+   ideally before Python is launched. As soon as Numba is imported the
+   environment variable is read and that number of threads is locked in as the
+   number of threads Numba launches.
+
+2. If we want to later increase the number of threads used by the process, we
+   cannot. :envvar:`NUMBA_NUM_THREADS` sets the *maximum* number of threads
+   that are launched for a process. Calling :func:`~.set_num_threads()` with a
+   value greater than :obj:`numba.config.NUMBA_NUM_THREADS` results in an
+   error.
+
+The advantage of this approach is that we can do it from outside of the
+process without changing the code.
+
+Another approach is to use the :func:`numba.set_num_threads` function in our code
+
+.. code:: python
+
+   from numba import njit, set_num_threads
+
+   @njit(parallel=True)
+   def func():
+       ...
+
+   set_num_threads(2)
+   func()
+
+If we call ``set_num_threads(2)`` before executing our parallel code, it has
+the same effect as calling the process with ``NUMBA_NUM_THREADS=2``, in that
+the parallel code will only execute on 2 threads. However, we can later call
+``set_num_threads(8)`` to increase the number of threads back to the default
+size. And we do not have to worry about setting it before Numba gets imported.
+It only needs to be called before the parallel function is run.
+
+.. _numba-threading-layer-thread-id:
+
+Getting a Thread ID
+-------------------
+
+In some cases it may be beneficial to have access to a unique identifier for the
+current thread that is executing a parallel region in Numba. For that purpose,
+Numba provides the :func:`numba.get_thread_id` function. This function is the
+corollary of OpenMP's function ``omp_get_thread_num`` and returns an integer
+between 0 (inclusive) and the number of configured threads as described above
+(exclusive).
+
+API Reference
+~~~~~~~~~~~~~
+
+.. py:data:: numba.config.NUMBA_NUM_THREADS
+
+   The total (maximum) number of threads launched by numba.
+
+   Defaults to :obj:`numba.config.NUMBA_DEFAULT_NUM_THREADS`, but can be
+   overridden with the :envvar:`NUMBA_NUM_THREADS` environment variable.
+
+.. py:data:: numba.config.NUMBA_DEFAULT_NUM_THREADS
+
+   The number of usable CPU cores on the system (as determined by
+   ``len(os.sched_getaffinity(0))``, if supported by the OS, or
+   ``multiprocessing.cpu_count()`` if not).
+   This is the default value for :obj:`numba.config.NUMBA_NUM_THREADS` unless
+   the :envvar:`NUMBA_NUM_THREADS` environment variable is set.
+
+.. autofunction:: numba.set_num_threads
+
+.. autofunction:: numba.get_num_threads
+
+.. autofunction:: numba.get_thread_id
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/troubleshoot.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/troubleshoot.rst
new file mode 100644
index 000000000..b92144346
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/troubleshoot.rst
@@ -0,0 +1,1179 @@
+
+.. _numba-troubleshooting:
+
+========================
+Troubleshooting and tips
+========================
+
+.. _what-to-compile:
+
+What to compile
+===============
+
+The general recommendation is that you should only try to compile the
+critical paths in your code.  If you have a piece of performance-critical
+computational code amongst some higher-level code, you may factor out
+the performance-critical code in a separate function and compile the
+separate function with Numba.  Letting Numba focus on that small piece
+of performance-critical code has several advantages:
+
+* it reduces the risk of hitting unsupported features;
+* it reduces the compilation times;
+* it allows you to evolve the higher-level code which is outside of the
+  compiled function much easier.
+
+.. _code-doesnt-compile:
+
+My code doesn't compile
+=======================
+
+There can be various reasons why Numba cannot compile your code, and raises
+an error instead.  One common reason is that your code relies on an
+unsupported Python feature, especially in :term:`nopython mode`.
+Please see the list of :ref:`pysupported`.  If you find something that
+is listed there and still fails compiling, please
+:ref:`report a bug <report-numba-bugs>`.
+
+When Numba tries to compile your code it first tries to work out the types of
+all the variables in use, this is so it can generate a type specific
+implementation of your code that can be compiled down to machine code. A common
+reason for Numba failing to compile (especially in :term:`nopython mode`) is a
+type inference failure, essentially Numba cannot work out what the type of all
+the variables in your code should be.
+
+For example, let's consider this trivial function::
+
+    @jit(nopython=True)
+    def f(x, y):
+        return x + y
+
+If you call it with two numbers, Numba is able to infer the types properly::
+
+    >>> f(1, 2)
+        3
+
+If however you call it with a tuple and a number, Numba is unable to say
+what the result of adding a tuple and number is, and therefore compilation
+errors out::
+
+    >>> f(1, (2,))
+    Traceback (most recent call last):
+    File "<stdin>", line 1, in <module>
+    File "<path>/numba/numba/dispatcher.py", line 339, in _compile_for_args
+        reraise(type(e), e, None)
+    File "<path>/numba/numba/six.py", line 658, in reraise
+        raise value.with_traceback(tb)
+    numba.errors.TypingError: Failed at nopython (nopython frontend)
+    Invalid use of + with parameters (int64, tuple(int64 x 1))
+    Known signatures:
+    * (int64, int64) -> int64
+    * (int64, uint64) -> int64
+    * (uint64, int64) -> int64
+    * (uint64, uint64) -> uint64
+    * (float32, float32) -> float32
+    * (float64, float64) -> float64
+    * (complex64, complex64) -> complex64
+    * (complex128, complex128) -> complex128
+    * (uint16,) -> uint64
+    * (uint8,) -> uint64
+    * (uint64,) -> uint64
+    * (uint32,) -> uint64
+    * (int16,) -> int64
+    * (int64,) -> int64
+    * (int8,) -> int64
+    * (int32,) -> int64
+    * (float32,) -> float32
+    * (float64,) -> float64
+    * (complex64,) -> complex64
+    * (complex128,) -> complex128
+    * parameterized
+    [1] During: typing of intrinsic-call at <stdin> (3)
+
+    File "<stdin>", line 3:
+
+The error message helps you find out what went wrong:
+"Invalid use of + with parameters (int64, tuple(int64 x 1))" is to be
+interpreted as "Numba encountered an addition of variables typed as integer
+and 1-tuple of integer, respectively, and doesn't know about any such
+operation".
+
+Note that if you allow object mode::
+
+    @jit
+    def g(x, y):
+        return x + y
+
+compilation will succeed and the compiled function will raise at runtime as
+Python would do::
+
+   >>> g(1, (2,))
+   Traceback (most recent call last):
+     File "<stdin>", line 1, in <module>
+   TypeError: unsupported operand type(s) for +: 'int' and 'tuple'
+
+
+My code has a type unification problem
+======================================
+
+Another common reason for Numba not being able to compile your code is that it
+cannot statically determine the return type of a function. The most likely
+cause of this is the return type depending on a value that is available only at
+runtime. Again, this is most often problematic when using
+:term:`nopython mode`. The concept of type unification is simply trying to find
+a type in which two variables could safely be represented. For example a 64 bit
+float and a 64 bit complex number could both be represented in a 128 bit complex
+number.
+
+As an example of type unification failure, this function has a return type that
+is determined at runtime based on the value of `x`::
+
+    In [1]: from numba import jit
+
+    In [2]: @jit(nopython=True)
+    ...: def f(x):
+    ...:     if x > 10:
+    ...:         return (1,)
+    ...:     else:
+    ...:         return 1
+    ...:
+
+    In [3]: f(10)
+
+Trying to execute this function, errors out as follows::
+
+    TypingError: Failed at nopython (nopython frontend)
+    Can't unify return type from the following types: tuple(int64 x 1), int64
+    Return of: IR name '$8.2', type '(int64 x 1)', location:
+    File "<ipython-input-2-51ef1cc64bea>", line 4:
+    def f(x):
+        <source elided>
+        if x > 10:
+            return (1,)
+            ^
+    Return of: IR name '$12.2', type 'int64', location:
+    File "<ipython-input-2-51ef1cc64bea>", line 6:
+    def f(x):
+        <source elided>
+        else:
+            return 1
+
+The error message "Can't unify return type from the following types:
+tuple(int64 x 1), int64" should be read as "Numba cannot find a type that
+can safely represent a 1-tuple of integer and an integer".
+
+.. _code-has-untyped-list:
+
+My code has an untyped list problem
+===================================
+
+As :ref:`noted previously <code-doesnt-compile>` the first part of Numba
+compiling your code involves working out what the types of all the variables
+are. In the case of lists, a list must contain items that are of the same type
+or can be empty if the type can be inferred from some later operation. What is
+not possible is to have a list which is defined as empty and has no inferable
+type (i.e. an untyped list).
+
+For example, this is using a list of a known type::
+
+    from numba import jit
+    @jit(nopython=True)
+    def f():
+        return [1, 2, 3] # this list is defined on construction with `int` type
+
+This is using an empty list, but the type can be inferred::
+
+    from numba import jit
+    @jit(nopython=True)
+    def f(x):
+        tmp = [] # defined empty
+        for i in range(x):
+            tmp.append(i) # list type can be inferred from the type of `i`
+        return tmp
+
+This is using an empty list and the type cannot be inferred::
+
+    from numba import jit
+    @jit(nopython=True)
+    def f(x):
+        tmp = [] # defined empty
+        return (tmp, x) # ERROR: the type of `tmp` is unknown
+
+Whilst slightly contrived, if you need an empty list and the type cannot be
+inferred but you know what type you want the list to be, this "trick" can be
+used to instruct the typing mechanism::
+
+    from numba import jit
+    import numpy as np
+    @jit(nopython=True)
+    def f(x):
+        # define empty list, but instruct that the type is np.complex64
+        tmp = [np.complex64(x) for x in range(0)]
+        return (tmp, x) # the type of `tmp` is known, but it is still empty
+
+The compiled code is too slow
+=============================
+
+The most common reason for slowness of a compiled JIT function is that
+compiling in :term:`nopython mode` has failed and the Numba compiler has
+fallen back to :term:`object mode`.  :term:`object mode` currently provides
+little to no speedup compared to regular Python interpretation, and its
+main point is to allow an internal optimization known as
+:term:`loop-lifting`: this optimization will allow to compile inner
+loops in :term:`nopython mode` regardless of what code surrounds those
+inner loops.
+
+To find out if type inference succeeded on your function, you can use
+the :meth:`~Dispatcher.inspect_types` method on the compiled function.
+
+For example, let's take the following function::
+
+   @jit
+   def f(a, b):
+       s = a + float(b)
+       return s
+
+When called with numbers, this function should be fast as Numba is able
+to convert number types to floating-point numbers.  Let's see::
+
+   >>> f(1, 2)
+   3.0
+   >>> f.inspect_types()
+   f (int64, int64)
+   --------------------------------------------------------------------------------
+   # --- LINE 7 ---
+
+   @jit
+
+   # --- LINE 8 ---
+
+   def f(a, b):
+
+       # --- LINE 9 ---
+       # label 0
+       #   a.1 = a  :: int64
+       #   del a
+       #   b.1 = b  :: int64
+       #   del b
+       #   $0.2 = global(float: <class 'float'>)  :: Function(<class 'float'>)
+       #   $0.4 = call $0.2(b.1, )  :: (int64,) -> float64
+       #   del b.1
+       #   del $0.2
+       #   $0.5 = a.1 + $0.4  :: float64
+       #   del a.1
+       #   del $0.4
+       #   s = $0.5  :: float64
+       #   del $0.5
+
+       s = a + float(b)
+
+       # --- LINE 10 ---
+       #   $0.7 = cast(value=s)  :: float64
+       #   del s
+       #   return $0.7
+
+       return s
+
+Without trying to understand too much of the Numba intermediate representation,
+it is still visible that all variables and temporary values have had their
+types inferred properly: for example *a* has the type ``int64``, *$0.5* has
+the type ``float64``, etc.
+
+However, if *b* is passed as a string, compilation will fall back on object
+mode as the float() constructor with a string is currently not supported
+by Numba::
+
+   >>> f(1, "2")
+   3.0
+   >>> f.inspect_types()
+   [... snip annotations for other signatures, see above ...]
+   ================================================================================
+   f (int64, str)
+   --------------------------------------------------------------------------------
+   # --- LINE 7 ---
+
+   @jit
+
+   # --- LINE 8 ---
+
+   def f(a, b):
+
+       # --- LINE 9 ---
+       # label 0
+       #   a.1 = a  :: pyobject
+       #   del a
+       #   b.1 = b  :: pyobject
+       #   del b
+       #   $0.2 = global(float: <class 'float'>)  :: pyobject
+       #   $0.4 = call $0.2(b.1, )  :: pyobject
+       #   del b.1
+       #   del $0.2
+       #   $0.5 = a.1 + $0.4  :: pyobject
+       #   del a.1
+       #   del $0.4
+       #   s = $0.5  :: pyobject
+       #   del $0.5
+
+       s = a + float(b)
+
+       # --- LINE 10 ---
+       #   $0.7 = cast(value=s)  :: pyobject
+       #   del s
+       #   return $0.7
+
+       return s
+
+Here we see that all variables end up typed as ``pyobject``.  This means
+that the function was compiled in object mode and values are passed
+around as generic Python objects, without Numba trying to look into them
+to reason about their raw values.  This is a situation you want to avoid
+when caring about the speed of your code.
+
+If a function fails to compile in ``nopython`` mode warnings will be emitted
+with explanation as to why compilation failed. For example with the ``f()``
+function above (slightly edited for documentation purposes)::
+
+    >>> f(1, 2)
+    3.0
+    >>> f(1, "2")
+    example.py:7: NumbaWarning:
+    Compilation is falling back to object mode WITH looplifting enabled because Function "f" failed type inference due to: Invalid use of Function(<class 'float'>) with argument(s) of type(s): (unicode_type)
+    * parameterized
+    In definition 0:
+        TypeError: float() only support for numbers
+        raised from <path>/numba/typing/builtins.py:880
+    In definition 1:
+        TypeError: float() only support for numbers
+        raised from <path>/numba/typing/builtins.py:880
+    This error is usually caused by passing an argument of a type that is unsupported by the named function.
+    [1] During: resolving callee type: Function(<class 'float'>)
+    [2] During: typing of call at example.py (9)
+
+
+    File "example.py", line 9:
+    def f(a, b):
+        s = a + float(b)
+        ^
+
+    <path>/numba/compiler.py:722: NumbaWarning: Function "f" was compiled in object mode without forceobj=True.
+
+    File "example.py", line 8:
+    @jit
+    def f(a, b):
+    ^
+
+    3.0
+
+
+Disabling JIT compilation
+=========================
+
+In order to debug code, it is possible to disable JIT compilation, which makes
+the ``jit`` decorator (and the ``njit`` decorator) act as if
+they perform no operation, and the invocation of decorated functions calls the
+original Python function instead of a compiled version. This can be toggled by
+setting the :envvar:`NUMBA_DISABLE_JIT` environment variable to ``1``.
+
+When this mode is enabled, the ``vectorize`` and ``guvectorize`` decorators will
+still result in compilation of a ufunc, as there is no straightforward pure
+Python implementation of these functions.
+
+
+.. _debugging-jit-compiled-code:
+
+Debugging JIT compiled code with GDB
+====================================
+
+Setting the ``debug`` keyword argument in the ``jit`` decorator
+(e.g. ``@jit(debug=True)``) enables the emission of debug info in the jitted
+code.  To debug, GDB version 7.0 or above is required.  Currently, the following
+debug info is available:
+
+* Function name will be shown in the backtrace along with type information and
+  values (if available).
+* Source location (filename and line number) is available.  For example,
+  users can set a break point by the absolute filename and line number;
+  e.g. ``break /path/to/myfile.py:6``.
+* Arguments to the current function can be show with ``info args``
+* Local variables in the current function can be shown with ``info locals``.
+* The type of variables can be shown with ``whatis myvar``.
+* The value of variables can be shown with ``print myvar`` or ``display myvar``.
+
+  * Simple numeric types, i.e. int, float and double, are shown in their
+    native representation.
+  * Other types are shown as a structure based on Numba's memory model
+    representation of the type.
+
+Further, the Numba ``gdb`` printing extension can be loaded into ``gdb`` (if the
+``gdb`` has Python support) to permit the printing of variables as they would be
+in native Python. The extension does this by reinterpreting Numba's memory model
+representations as Python types. Information about the ``gdb`` installation that
+Numba is using, including the path to load the ``gdb`` printing extension, can
+be displayed by using the ``numba -g`` command. For best results ensure that the
+Python that ``gdb`` is using has a NumPy module accessible. An example output
+of the ``gdb`` information follows:
+
+.. code-block:: none
+  :emphasize-lines: 1
+
+    $ numba -g
+    GDB info:
+    --------------------------------------------------------------------------------
+    Binary location                               : <some path>/gdb
+    Print extension location                      : <some python path>/numba/misc/gdb_print_extension.py
+    Python version                                : 3.8
+    NumPy version                                 : 1.20.0
+    Numba printing extension supported            : True
+
+    To load the Numba gdb printing extension, execute the following from the gdb prompt:
+
+    source <some python path>/numba/misc/gdb_print_extension.py
+
+    --------------------------------------------------------------------------------
+
+Known issues:
+
+* Stepping depends heavily on optimization level. At full optimization
+  (equivalent to O3), most of the variables are optimized out. It is often
+  beneficial to use the jit option ``_dbg_optnone=True`` 
+  or the environment variable :envvar:`NUMBA_OPT` to adjust the 
+  optimization level and the jit option ``_dbg_extend_lifetimes=True`` 
+  (which is on by default if ``debug=True``) or
+  :envvar:`NUMBA_EXTEND_VARIABLE_LIFETIMES` to extend
+  the lifetime of variables to the end of their scope so as to get a debugging
+  experience closer to the semantics of Python execution.
+
+* Memory consumption increases significantly with debug info enabled.
+  The compiler emits extra information (`DWARF <http://www.dwarfstd.org/>`_)
+  along with the instructions.  The emitted object code can be 2x bigger with
+  debug info.
+
+Internal details:
+
+* Since Python semantics allow variables to bind to value of different types,
+  Numba internally creates multiple versions of the variable for each type.
+  So for code like::
+
+    x = 1         # type int
+    x = 2.3       # type float
+    x = (1, 2, 3) # type 3-tuple of int
+
+  Each assignments will store to a different variable name.  In the debugger,
+  the variables will be ``x``, ``x$1`` and ``x$2``.  (In the Numba IR, they are
+  ``x``, ``x.1`` and ``x.2``.)
+
+* When debug is enabled, inlining of functions at LLVM IR level is disabled.
+
+JIT options for debug
+---------------------
+
+* ``debug`` (bool). Set to ``True`` to enable debug info. Defaults to ``False``.
+* ``_dbg_optnone`` (bool). Set to ``True`` to disable all LLVM optimization passes 
+  on the function. Defaults to ``False``. See :envvar:`NUMBA_OPT` for a global setting
+  to disable optimization.
+* ``_dbg_extend_lifetimes`` (bool). Set to ``True`` to extend the lifetime of
+  objects such that they more closely follow the semantics of Python.
+  Automatically set to ``True`` when 
+  ``debug=True``; otherwise, defaults to ``False``. Users can explicitly set this option 
+  to ``False`` to retain the normal execution semantics of compiled code.
+  See :envvar:`NUMBA_EXTEND_VARIABLE_LIFETIMES` for a global option to extend object 
+  lifetimes.
+
+Example debug usage
+-------------------
+
+The python source:
+
+.. code-block:: python
+  :linenos:
+
+  from numba import njit
+
+  @njit(debug=True)
+  def foo(a):
+      b = a + 1
+      c = a * 2.34
+      d = (a, b, c)
+      print(a, b, c, d)
+
+  r = foo(123)
+  print(r)
+
+In the terminal:
+
+.. code-block:: none
+  :emphasize-lines: 1, 3, 7, 12, 14, 16, 20, 22, 26, 28, 30, 32, 34, 36
+
+    $ NUMBA_OPT=0 NUMBA_EXTEND_VARIABLE_LIFETIMES=1 gdb -q python
+    Reading symbols from python...
+    (gdb) break test1.py:5
+    No source file named test1.py.
+    Make breakpoint pending on future shared library load? (y or [n]) y
+    Breakpoint 1 (test1.py:5) pending.
+    (gdb) run test1.py
+    Starting program: <path>/bin/python test1.py
+    ...
+    Breakpoint 1, __main__::foo_241[abi:c8tJTC_2fWgEeGLSgydRTQUgiqKEZ6gEoDvQJmaQIA](long long) (a=123) at test1.py:5
+    5           b = a + 1
+    (gdb) info args
+    a = 123
+    (gdb) n
+    6           c = a * 2.34
+    (gdb) info locals
+    b = 124
+    c = 0
+    d = {f0 = 0, f1 = 0, f2 = 0}
+    (gdb) n
+    7           d = (a, b, c)
+    (gdb) info locals
+    b = 124
+    c = 287.81999999999999
+    d = {f0 = 0, f1 = 0, f2 = 0}
+    (gdb) whatis b
+    type = int64
+    (gdb) whatis d
+    type = Tuple(int64, int64, float64) ({i64, i64, double})
+    (gdb) n
+    8           print(a, b, c, d)
+    (gdb) print b
+    $1 = 124
+    (gdb) print d
+    $2 = {f0 = 123, f1 = 124, f2 = 287.81999999999999}
+    (gdb) bt
+    #0  __main__::foo_241[abi:c8tJTC_2fWgEeGLSgydRTQUgiqKEZ6gEoDvQJmaQIA](long long) (a=123) at test1.py:8
+    #1  0x00007ffff06439fa in cpython::__main__::foo_241[abi:c8tJTC_2fWgEeGLSgydRTQUgiqKEZ6gEoDvQJmaQIA](long long) ()
+
+
+Another example follows that makes use of the Numba ``gdb`` printing extension
+mentioned above, note the change in the print format once the extension is
+loaded with ``source`` :
+
+The Python source:
+
+.. code-block:: python
+  :linenos:
+
+    from numba import njit
+    import numpy as np
+
+    @njit(debug=True)
+    def foo(n):
+        x = np.arange(n)
+        y = (x[0], x[-1])
+        return x, y
+
+    foo(4)
+
+In the terminal:
+
+.. code-block:: none
+  :emphasize-lines: 1, 3, 4, 7, 12, 14, 16, 17, 20
+
+    $ NUMBA_OPT=0 NUMBA_EXTEND_VARIABLE_LIFETIMES=1 gdb -q python
+    Reading symbols from python...
+    (gdb) set breakpoint pending on
+    (gdb) break test2.py:8
+    No source file named test2.py.
+    Breakpoint 1 (test2.py:8) pending.
+    (gdb) run test2.py
+    Starting program: <path>/bin/python test2.py
+    ...
+    Breakpoint 1, __main__::foo_241[abi:c8tJTC_2fWgEeGLSgydRTQUgiqKEZ6gEoDvQJmaQIA](long long) (n=4) at test2.py:8
+    8           return x, y
+    (gdb) print x
+    $1 = {meminfo = 0x55555688f470 "\001", parent = 0x0, nitems = 4, itemsize = 8, data = 0x55555688f4a0, shape = {4}, strides = {8}}
+    (gdb) print y
+    $2 = {0, 3}
+    (gdb) source numba/misc/gdb_print_extension.py
+    (gdb) print x
+    $3 =
+    [0 1 2 3]
+    (gdb) print y
+    $4 = (0, 3)
+
+
+
+Globally override debug setting
+-------------------------------
+
+It is possible to enable debug for the full application by setting environment
+variable ``NUMBA_DEBUGINFO=1``.  This sets the default value of the ``debug``
+option in ``jit``.  Debug can be turned off on individual functions by setting
+``debug=False``.
+
+Beware that enabling debug info significantly increases the memory consumption
+for each compiled function.  For large application, this may cause out-of-memory
+error.
+
+Using Numba's direct ``gdb`` bindings in ``nopython``  mode
+===========================================================
+
+Numba (version 0.42.0 and later) has some additional functions relating to
+``gdb`` support for CPUs that make it easier to debug programs. All the ``gdb``
+related functions described in the following work in the same manner
+irrespective of whether they are called from the standard CPython interpreter or
+code compiled in either :term:`nopython mode` or :term:`object mode`.
+
+.. note:: This feature is experimental!
+
+.. warning:: This feature does unexpected things if used from Jupyter or
+             alongside the ``pdb`` module. It's behaviour is harmless, just hard
+             to predict!
+
+Set up
+------
+Numba's ``gdb`` related functions make use of a ``gdb`` binary, the location and
+name of this binary can be configured via the :envvar:`NUMBA_GDB_BINARY`
+environment variable if desired.
+
+.. note:: Numba's ``gdb`` support requires the ability for ``gdb`` to attach to
+          another process. On some systems (notably Ubuntu Linux) default
+          security restrictions placed on ``ptrace`` prevent this from being
+          possible. This restriction is enforced at the system level by the
+          Linux security module `Yama`. Documentation for this module and the
+          security implications of making changes to its behaviour can be found
+          in the `Linux Kernel documentation <https://www.kernel.org/doc/Documentation/admin-guide/LSM/Yama.rst>`_.
+          The `Ubuntu Linux security documentation <https://wiki.ubuntu.com/Security/Features#ptrace>`_
+          discusses how to adjust the behaviour of `Yama` on with regards to
+          ``ptrace_scope`` so as to permit the required behaviour.
+
+Basic ``gdb`` support
+---------------------
+
+.. warning:: Calling :func:`numba.gdb` and/or :func:`numba.gdb_init` more than
+             once in the same program is not advisable, unexpected things may
+             happen. If multiple breakpoints are desired within a program,
+             launch ``gdb`` once via :func:`numba.gdb` or :func:`numba.gdb_init`
+             and then use :func:`numba.gdb_breakpoint` to register additional
+             breakpoint locations.
+
+The most simple function for adding ``gdb`` support is :func:`numba.gdb`, which,
+at the call location, will:
+
+* launch ``gdb`` and attach it to the running process.
+* create a breakpoint at the site of the :func:`numba.gdb()` function call, the
+  attached ``gdb`` will pause execution here awaiting user input.
+
+use of this functionality is best motivated by example, continuing with the
+example used above:
+
+.. code-block:: python
+  :linenos:
+
+  from numba import njit, gdb
+
+  @njit(debug=True)
+  def foo(a):
+      b = a + 1
+      gdb() # instruct Numba to attach gdb at this location and pause execution
+      c = a * 2.34
+      d = (a, b, c)
+      print(a, b, c, d)
+
+  r= foo(123)
+  print(r)
+
+In the terminal (``...`` on a line by itself indicates output that is not
+presented for brevity):
+
+.. code-block:: none
+    :emphasize-lines: 1, 4, 8, 13, 24, 26, 28, 30, 32, 37
+
+    $ NUMBA_OPT=0 NUMBA_EXTEND_VARIABLE_LIFETIMES=1 python demo_gdb.py
+    ...
+    Breakpoint 1, 0x00007fb75238d830 in numba_gdb_breakpoint () from numba/_helperlib.cpython-39-x86_64-linux-gnu.so
+    (gdb) s
+    Single stepping until exit from function numba_gdb_breakpoint,
+    which has no line number information.
+    0x00007fb75233e1cf in numba::misc::gdb_hook::hook_gdb::_3clocals_3e::impl_242[abi:c8tJTIeFCjyCbUFRqqOAK_2f6h0phxApMogijRBAA_3d](StarArgTuple) ()
+    (gdb) s
+    Single stepping until exit from function _ZN5numba4misc8gdb_hook8hook_gdb12_3clocals_3e8impl_242B44c8tJTIeFCjyCbUFRqqOAK_2f6h0phxApMogijRBAA_3dE12StarArgTuple,
+    which has no line number information.
+    __main__::foo_241[abi:c8tJTC_2fWgEeGLSgydRTQUgiqKEZ6gEoDvQJmaQIA](long long) (a=123) at demo_gdb.py:7
+    7           c = a * 2.34
+    (gdb) l
+    2
+    3       @njit(debug=True)
+    4       def foo(a):
+    5           b = a + 1
+    6           gdb() # instruct Numba to attach gdb at this location and pause execution
+    7           c = a * 2.34
+    8           d = (a, b, c)
+    9           print(a, b, c, d)
+    10
+    11      r= foo(123)
+    (gdb) p a
+    $1 = 123
+    (gdb) p b
+    $2 = 124
+    (gdb) p c
+    $3 = 0
+    (gdb) b 9
+    Breakpoint 2 at 0x7fb73d1f7287: file demo_gdb.py, line 9.
+    (gdb) c
+    Continuing.
+
+    Breakpoint 2, __main__::foo_241[abi:c8tJTC_2fWgEeGLSgydRTQUgiqKEZ6gEoDvQJmaQIA](long long) (a=123) at demo_gdb.py:9
+    9           print(a, b, c, d)
+    (gdb) info locals
+    b = 124
+    c = 287.81999999999999
+    d = {f0 = 123, f1 = 124, f2 = 287.81999999999999}
+
+
+It can be seen in the above example that execution of the code is paused at the
+location of the ``gdb()`` function call at end of the ``numba_gdb_breakpoint``
+function (this is the Numba internal symbol registered as breakpoint with
+``gdb``). Issuing a ``step`` twice at this point moves to the stack frame of the
+compiled Python source. From there, it can be seen that the variables ``a`` and
+``b`` have been evaluated but ``c`` has not, as demonstrated by printing their
+values, this is precisely as expected given the location of the ``gdb()`` call.
+Issuing a ``break`` on line 9 and then continuing execution leads to the
+evaluation of line ``7``. The variable ``c`` is assigned a value as a result of
+the execution and this can be seen in output of ``info locals`` when the
+breakpoint is hit.
+
+Running with ``gdb`` enabled
+----------------------------
+
+The functionality provided by :func:`numba.gdb` (launch and attach ``gdb`` to
+the executing process and pause on a breakpoint) is also available as two
+separate functions:
+
+* :func:`numba.gdb_init` this function injects code at the call site to launch
+  and attach ``gdb`` to the executing process but does not pause execution.
+* :func:`numba.gdb_breakpoint` this function injects code at the call site that
+  will call the special ``numba_gdb_breakpoint`` function that is registered as
+  a breakpoint in Numba's ``gdb`` support. This is demonstrated in the next
+  section.
+
+This functionality enables more complex debugging capabilities. Again, motivated
+by example, debugging a 'segfault' (memory access violation signalling
+``SIGSEGV``):
+
+.. code-block:: python
+  :linenos:
+
+    from numba import njit, gdb_init
+    import numpy as np
+
+    # NOTE debug=True switches bounds-checking on, but for the purposes of this
+    # example it is explicitly turned off so that the out of bounds index is
+    # not caught!
+    @njit(debug=True, boundscheck=False)
+    def foo(a, index):
+        gdb_init() # instruct Numba to attach gdb at this location, but not to pause execution
+        b = a + 1
+        c = a * 2.34
+        d = c[index] # access an address that is a) invalid b) out of the page
+        print(a, b, c, d)
+
+    bad_index = int(1e9) # this index is invalid
+    z = np.arange(10)
+    r = foo(z, bad_index)
+    print(r)
+
+In the terminal (``...`` on a line by itself indicates output that is not
+presented for brevity):
+
+.. code-block:: none
+    :emphasize-lines: 1, 6, 8, 10, 12
+
+    $ NUMBA_OPT=0 python demo_gdb_segfault.py
+    ...
+    Program received signal SIGSEGV, Segmentation fault.
+    0x00007f5a4ca655eb in __main__::foo_241[abi:c8tJTC_2fWgEeGLSgydRTQUgiqKEZ6gEoDvQJmaQIA](Array<long long, 1, C, mutable, aligned>, long long) (a=..., index=1000000000) at demo_gdb_segfault.py:12
+    12          d = c[index] # access an address that is a) invalid b) out of the page
+    (gdb) p index
+    $1 = 1000000000
+    (gdb) p c
+    $2 = {meminfo = 0x5586cfb95830 "\001", parent = 0x0, nitems = 10, itemsize = 8, data = 0x5586cfb95860, shape = {10}, strides = {8}}
+    (gdb) whatis c
+    type = array(float64, 1d, C) ({i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64]})
+    (gdb) p c.nitems
+    $3 = 10
+
+In the ``gdb`` output it can be noted that a ``SIGSEGV`` signal was caught, and
+the line in which the access violation occurred is printed.
+
+Continuing the example as a debugging session demonstration, first ``index``
+can be printed, and it is evidently 1e9. Printing ``c`` shows that it is a
+structure, so the type needs looking up and it can be seen that is it an
+``array(float64, 1d, C)`` type. Given the segfault came from an invalid access
+it would be informative to check the number of items in the array and compare
+that to the index requested. Inspecting the ``nitems`` member of the structure
+``c`` shows 10 items. It's therefore clear that the segfault comes from an
+invalid access of index ``1000000000`` in an array containing ``10`` items.
+
+Adding breakpoints to code
+--------------------------
+
+The next example demonstrates using multiple breakpoints that are defined
+through the invocation of the :func:`numba.gdb_breakpoint` function:
+
+.. code-block:: python
+  :linenos:
+
+  from numba import njit, gdb_init, gdb_breakpoint
+
+  @njit(debug=True)
+  def foo(a):
+      gdb_init() # instruct Numba to attach gdb at this location
+      b = a + 1
+      gdb_breakpoint() # instruct gdb to break at this location
+      c = a * 2.34
+      d = (a, b, c)
+      gdb_breakpoint() # and to break again at this location
+      print(a, b, c, d)
+
+  r= foo(123)
+  print(r)
+
+In the terminal (``...`` on a line by itself indicates output that is not
+presented for brevity):
+
+.. code-block:: none
+    :emphasize-lines: 1, 4, 9, 20, 22, 24, 29, 31
+
+    $ NUMBA_OPT=0 python demo_gdb_breakpoints.py
+    ...
+    Breakpoint 1, 0x00007fb65bb4c830 in numba_gdb_breakpoint () from numba/_helperlib.cpython-39-x86_64-linux-gnu.so
+    (gdb) step
+    Single stepping until exit from function numba_gdb_breakpoint,
+    which has no line number information.
+    __main__::foo_241[abi:c8tJTC_2fWgEeGLSgydRTQUgiqKEZ6gEoDvQJmaQIA](long long) (a=123) at demo_gdb_breakpoints.py:8
+    8           c = a * 2.34
+    (gdb) l
+    3       @njit(debug=True)
+    4       def foo(a):
+    5           gdb_init() # instruct Numba to attach gdb at this location
+    6           b = a + 1
+    7           gdb_breakpoint() # instruct gdb to break at this location
+    8           c = a * 2.34
+    9           d = (a, b, c)
+    10          gdb_breakpoint() # and to break again at this location
+    11          print(a, b, c, d)
+    12
+    (gdb) p b
+    $1 = 124
+    (gdb) p c
+    $2 = 0
+    (gdb) c
+    Continuing.
+
+    Breakpoint 1, 0x00007fb65bb4c830 in numba_gdb_breakpoint ()
+    from numba/_helperlib.cpython-39-x86_64-linux-gnu.so
+    (gdb) step
+    11          print(a, b, c, d)
+    (gdb) p c
+    $3 = 287.81999999999999
+
+From the ``gdb`` output it can be seen that execution paused at line 8 as a
+breakpoint was hit, and after a ``continue`` was issued, it broke again at line
+11 where the next breakpoint was hit.
+
+Debugging in parallel regions
+-----------------------------
+
+The follow example is quite involved, it executes with ``gdb`` instrumentation
+from the outset as per the example above, but it also uses threads and makes use
+of the breakpoint functionality. Further, the last iteration of the parallel
+section calls the function ``work``, which is actually just a binding to
+``glibc``'s ``free(3)`` in this case, but could equally be some involved
+function that is presenting a segfault for unknown reasons.
+
+.. code-block:: python
+  :linenos:
+
+    from numba import njit, prange, gdb_init, gdb_breakpoint
+    import ctypes
+
+    def get_free():
+        lib = ctypes.cdll.LoadLibrary('libc.so.6')
+        free_binding = lib.free
+        free_binding.argtypes = [ctypes.c_void_p,]
+        free_binding.restype = None
+        return free_binding
+
+    work = get_free()
+
+    @njit(debug=True, parallel=True)
+    def foo():
+        gdb_init() # instruct Numba to attach gdb at this location, but not to pause execution
+        counter = 0
+        n = 9
+        for i in prange(n):
+            if i > 3 and i < 8: # iterations 4, 5, 6, 7 will break here
+                gdb_breakpoint()
+
+            if i == 8: # last iteration segfaults
+                work(0xBADADD)
+
+            counter += 1
+        return counter
+
+    r = foo()
+    print(r)
+
+In the terminal (``...`` on a line by itself indicates output that is not
+presented for brevity), note the setting of ``NUMBA_NUM_THREADS`` to 4 to ensure
+that there are 4 threads running in the parallel section:
+
+.. code-block:: none
+    :emphasize-lines: 1, 19, 29, 44, 50, 56, 62, 69
+
+    $ NUMBA_NUM_THREADS=4 NUMBA_OPT=0 python demo_gdb_threads.py
+    Attaching to PID: 21462
+    ...
+    Attaching to process 21462
+    [New LWP 21467]
+    [New LWP 21468]
+    [New LWP 21469]
+    [New LWP 21470]
+    [Thread debugging using libthread_db enabled]
+    Using host libthread_db library "/lib64/libthread_db.so.1".
+    0x00007f59ec31756d in nanosleep () at ../sysdeps/unix/syscall-template.S:81
+    81      T_PSEUDO (SYSCALL_SYMBOL, SYSCALL_NAME, SYSCALL_NARGS)
+    Breakpoint 1 at 0x7f59d631e8f0: file numba/_helperlib.c, line 1090.
+    Continuing.
+    [Switching to Thread 0x7f59d1fd1700 (LWP 21470)]
+
+    Thread 5 "python" hit Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
+    1090    }
+    (gdb) info threads
+    Id   Target Id         Frame
+    1    Thread 0x7f59eca2f740 (LWP 21462) "python" pthread_cond_wait@@GLIBC_2.3.2 ()
+        at ../nptl/sysdeps/unix/sysv/linux/x86_64/pthread_cond_wait.S:185
+    2    Thread 0x7f59d37d4700 (LWP 21467) "python" pthread_cond_wait@@GLIBC_2.3.2 ()
+        at ../nptl/sysdeps/unix/sysv/linux/x86_64/pthread_cond_wait.S:185
+    3    Thread 0x7f59d2fd3700 (LWP 21468) "python" pthread_cond_wait@@GLIBC_2.3.2 ()
+        at ../nptl/sysdeps/unix/sysv/linux/x86_64/pthread_cond_wait.S:185
+    4    Thread 0x7f59d27d2700 (LWP 21469) "python" numba_gdb_breakpoint () at numba/_helperlib.c:1090
+    * 5    Thread 0x7f59d1fd1700 (LWP 21470) "python" numba_gdb_breakpoint () at numba/_helperlib.c:1090
+    (gdb) thread apply 2-5 info locals
+
+    Thread 2 (Thread 0x7f59d37d4700 (LWP 21467)):
+    No locals.
+
+    Thread 3 (Thread 0x7f59d2fd3700 (LWP 21468)):
+    No locals.
+
+    Thread 4 (Thread 0x7f59d27d2700 (LWP 21469)):
+    No locals.
+
+    Thread 5 (Thread 0x7f59d1fd1700 (LWP 21470)):
+    sched$35 = '\000' <repeats 55 times>
+    counter__arr = '\000' <repeats 16 times>, "\001\000\000\000\000\000\000\000\b\000\000\000\000\000\000\000\370B]\"hU\000\000\001", '\000' <repeats 14 times>
+    counter = 0
+    (gdb) continue
+    Continuing.
+    [Switching to Thread 0x7f59d27d2700 (LWP 21469)]
+
+    Thread 4 "python" hit Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
+    1090    }
+    (gdb) continue
+    Continuing.
+    [Switching to Thread 0x7f59d1fd1700 (LWP 21470)]
+
+    Thread 5 "python" hit Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
+    1090    }
+    (gdb) continue
+    Continuing.
+    [Switching to Thread 0x7f59d27d2700 (LWP 21469)]
+
+    Thread 4 "python" hit Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
+    1090    }
+    (gdb) continue
+    Continuing.
+
+    Thread 5 "python" received signal SIGSEGV, Segmentation fault.
+    [Switching to Thread 0x7f59d1fd1700 (LWP 21470)]
+    __GI___libc_free (mem=0xbadadd) at malloc.c:2935
+    2935      if (chunk_is_mmapped(p))                       /* release mmapped memory. */
+    (gdb) bt
+    #0  __GI___libc_free (mem=0xbadadd) at malloc.c:2935
+    #1  0x00007f59d37ded84 in $3cdynamic$3e::__numba_parfor_gufunc__0x7ffff80a61ae3e31$244(Array<unsigned long long, 1, C, mutable, aligned>, Array<long long, 1, C, mutable, aligned>) () at <string>:24
+    #2  0x00007f59d17ce326 in __gufunc__._ZN13$3cdynamic$3e45__numba_parfor_gufunc__0x7ffff80a61ae3e31$244E5ArrayIyLi1E1C7mutable7alignedE5ArrayIxLi1E1C7mutable7alignedE ()
+    #3  0x00007f59d37d7320 in thread_worker ()
+    from <path>/numba/numba/npyufunc/workqueue.cpython-37m-x86_64-linux-gnu.so
+    #4  0x00007f59ec626e25 in start_thread (arg=0x7f59d1fd1700) at pthread_create.c:308
+    #5  0x00007f59ec350bad in clone () at ../sysdeps/unix/sysv/linux/x86_64/clone.S:113
+
+In the output it can be seen that there are 4 threads launched and that they all
+break at the breakpoint, further that ``Thread 5`` receives a signal ``SIGSEGV``
+and that back tracing shows that it came from ``__GI___libc_free`` with the
+invalid address in ``mem``, as expected.
+
+Using the ``gdb`` command language
+----------------------------------
+Both the :func:`numba.gdb` and :func:`numba.gdb_init` functions accept unlimited
+string arguments which will be passed directly to ``gdb`` as command line
+arguments when it initializes, this makes it easy to set breakpoints on other
+functions and perform repeated debugging tasks without having to manually type
+them every time. For example, this code runs with ``gdb`` attached and sets a
+breakpoint on ``_dgesdd`` (say for example the arguments passed to the LAPACK's
+double precision divide and conqueror SVD function need debugging).
+
+.. code-block:: python
+  :linenos:
+
+    from numba import njit, gdb
+    import numpy as np
+
+    @njit(debug=True)
+    def foo(a):
+        # instruct Numba to attach gdb at this location and on launch, switch
+        # breakpoint pending on , and then set a breakpoint on the function
+        # _dgesdd, continue execution, and once the breakpoint is hit, backtrace
+        gdb('-ex', 'set breakpoint pending on',
+            '-ex', 'b dgesdd_',
+            '-ex','c',
+            '-ex','bt')
+        b = a + 10
+        u, s, vh = np.linalg.svd(b)
+        return s # just return singular values
+
+    z = np.arange(70.).reshape(10, 7)
+    r = foo(z)
+    print(r)
+
+In the terminal (``...`` on a line by itself indicates output that is not
+presented for brevity), note that no interaction is required to break and
+backtrace:
+
+.. code-block:: none
+    :emphasize-lines: 1
+
+    $ NUMBA_OPT=0 python demo_gdb_args.py
+    Attaching to PID: 22300
+    GNU gdb (GDB) Red Hat Enterprise Linux 8.0.1-36.el7
+    ...
+    Attaching to process 22300
+    Reading symbols from <py_env>/bin/python3.7...done.
+    0x00007f652305a550 in __nanosleep_nocancel () at ../sysdeps/unix/syscall-template.S:81
+    81      T_PSEUDO (SYSCALL_SYMBOL, SYSCALL_NAME, SYSCALL_NARGS)
+    Breakpoint 1 at 0x7f650d0618f0: file numba/_helperlib.c, line 1090.
+    Continuing.
+
+    Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
+    1090    }
+    Breakpoint 2 at 0x7f65102322e0 (2 locations)
+    Continuing.
+
+    Breakpoint 2, 0x00007f65182be5f0 in mkl_lapack.dgesdd_ ()
+    from <py_env>/lib/python3.7/site-packages/numpy/core/../../../../libmkl_rt.so
+    #0  0x00007f65182be5f0 in mkl_lapack.dgesdd_ ()
+    from <py_env>/lib/python3.7/site-packages/numpy/core/../../../../libmkl_rt.so
+    #1  0x00007f650d065b71 in numba_raw_rgesdd (kind=kind@entry=100 'd', jobz=<optimized out>, jobz@entry=65 'A', m=m@entry=10,
+        n=n@entry=7, a=a@entry=0x561c6fbb20c0, lda=lda@entry=10, s=0x561c6facf3a0, u=0x561c6fb680e0, ldu=10, vt=0x561c6fd375c0,
+        ldvt=7, work=0x7fff4c926c30, lwork=-1, iwork=0x7fff4c926c40, info=0x7fff4c926c20) at numba/_lapack.c:1277
+    #2  0x00007f650d06768f in numba_ez_rgesdd (ldvt=7, vt=0x561c6fd375c0, ldu=10, u=0x561c6fb680e0, s=0x561c6facf3a0, lda=10,
+        a=0x561c6fbb20c0, n=7, m=10, jobz=65 'A', kind=<optimized out>) at numba/_lapack.c:1307
+    #3  numba_ez_gesdd (kind=<optimized out>, jobz=<optimized out>, m=10, n=7, a=0x561c6fbb20c0, lda=10, s=0x561c6facf3a0,
+        u=0x561c6fb680e0, ldu=10, vt=0x561c6fd375c0, ldvt=7) at numba/_lapack.c:1477
+    #4  0x00007f650a3147a3 in numba::targets::linalg::svd_impl::$3clocals$3e::svd_impl$243(Array<double, 2, C, mutable, aligned>, omitted$28default$3d1$29) ()
+    #5  0x00007f650a1c0489 in __main__::foo$241(Array<double, 2, C, mutable, aligned>) () at demo_gdb_args.py:15
+    #6  0x00007f650a1c2110 in cpython::__main__::foo$241(Array<double, 2, C, mutable, aligned>) ()
+    #7  0x00007f650cd096a4 in call_cfunc ()
+    from <path>/numba/numba/_dispatcher.cpython-37m-x86_64-linux-gnu.so
+    ...
+
+
+How does the ``gdb`` binding work?
+----------------------------------
+For advanced users and debuggers of Numba applications it's important to know
+some of the internal implementation details of the outlined ``gdb`` bindings.
+The :func:`numba.gdb` and :func:`numba.gdb_init` functions work by injecting the
+following into the function's LLVM IR:
+
+* At the call site of the function first inject a call to ``getpid(3)`` to get
+  the PID of the executing process and store this for use later, then inject a
+  ``fork(3)`` call:
+
+  * In the parent:
+
+    * Inject a call ``sleep(3)`` (hence the pause whilst ``gdb`` loads).
+    * Inject a call to the ``numba_gdb_breakpoint`` function (only
+      :func:`numba.gdb` does this).
+
+  * In the child:
+
+    * Inject a call to ``execl(3)`` with the arguments
+      ``numba.config.GDB_BINARY``, the ``attach`` command and the PID recorded
+      earlier. Numba has a special ``gdb`` command file that contains
+      instructions to break on the symbol ``numba_gdb_breakpoint`` and then
+      ``finish``, this is to make sure that the program stops on the
+      breakpoint but the frame it stops in is the compiled Python frame (or
+      one ``step`` away from, depending on optimisation). This command file is
+      also added to the arguments and finally and any user specified arguments
+      are added.
+
+At the call site of a :func:`numba.gdb_breakpoint` a call is injected to the
+special ``numba_gdb_breakpoint`` symbol, which is already registered and
+instrumented as a place to break and ``finish`` immediately.
+
+As a result of this, a e.g. :func:`numba.gdb` call will cause a fork in the
+program, the parent will sleep whilst the child launches ``gdb`` and attaches it
+to the parent and tells the parent to continue. The launched ``gdb`` has the
+``numba_gdb_breakpoint`` symbol registered as a breakpoint and when the parent
+continues and stops sleeping it will immediately call ``numba_gdb_breakpoint``
+on which the child will break. Additional :func:`numba.gdb_breakpoint` calls
+create calls to the registered breakpoint hence the program will also break at
+these locations.
+
+.. _debugging-cuda-python-code:
+
+Debugging CUDA Python code
+==========================
+
+Using the simulator
+-------------------
+
+CUDA Python code can be run in the Python interpreter using the CUDA Simulator,
+allowing it to be debugged with the Python debugger or with print statements. To
+enable the CUDA simulator, set the environment variable
+:envvar:`NUMBA_ENABLE_CUDASIM` to 1. For more information on the CUDA Simulator,
+see :ref:`the CUDA Simulator documentation <simulator>`.
+
+
+Debug Info
+----------
+
+By setting the ``debug`` argument to ``cuda.jit`` to ``True``
+(``@cuda.jit(debug=True)``), Numba will emit source location in the compiled
+CUDA code.  Unlike the CPU target, only filename and line information are
+available, but no variable type information is emitted.  The information
+is sufficient to debug memory error with
+`cuda-memcheck <http://docs.nvidia.com/cuda/cuda-memcheck/index.html>`_.
+
+For example, given the following cuda python code:
+
+.. code-block:: python
+  :linenos:
+
+  import numpy as np
+  from numba import cuda
+
+  @cuda.jit(debug=True)
+  def foo(arr):
+      arr[cuda.threadIdx.x] = 1
+
+  arr = np.arange(30)
+  foo[1, 32](arr)   # more threads than array elements
+
+We can use ``cuda-memcheck`` to find the memory error:
+
+.. code-block:: none
+
+  $ cuda-memcheck python chk_cuda_debug.py
+  ========= CUDA-MEMCHECK
+  ========= Invalid __global__ write of size 8
+  =========     at 0x00000148 in /home/user/chk_cuda_debug.py:6:cudapy::__main__::foo$241(Array<__int64, int=1, C, mutable, aligned>)
+  =========     by thread (31,0,0) in block (0,0,0)
+  =========     Address 0x500a600f8 is out of bounds
+  ...
+  =========
+  ========= Invalid __global__ write of size 8
+  =========     at 0x00000148 in /home/user/chk_cuda_debug.py:6:cudapy::__main__::foo$241(Array<__int64, int=1, C, mutable, aligned>)
+  =========     by thread (30,0,0) in block (0,0,0)
+  =========     Address 0x500a600f0 is out of bounds
+  ...
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/vectorize.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/vectorize.rst
new file mode 100644
index 000000000..dc15cda1b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/vectorize.rst
@@ -0,0 +1,423 @@
+==================================
+Creating NumPy universal functions
+==================================
+
+There are two types of universal functions:
+
+* Those which operate on scalars, these are "universal functions" or *ufuncs*
+  (see ``@vectorize`` below).
+* Those which operate on higher dimensional arrays and scalars, these are
+  "generalized universal functions" or *gufuncs* (``@guvectorize`` below).
+
+.. _vectorize:
+
+The ``@vectorize`` decorator
+============================
+
+Numba's vectorize allows Python functions taking scalar input arguments to
+be used as NumPy `ufuncs`_.  Creating a traditional NumPy ufunc is
+not the most straightforward process and involves writing some C code.
+Numba makes this easy.  Using the :func:`~numba.vectorize` decorator, Numba
+can compile a pure Python function into a ufunc that operates over NumPy
+arrays as fast as traditional ufuncs written in C.
+
+.. _ufuncs: http://docs.scipy.org/doc/numpy/reference/ufuncs.html
+
+Using :func:`~numba.vectorize`, you write your function as operating over
+input scalars, rather than arrays.  Numba will generate the surrounding
+loop (or *kernel*) allowing efficient iteration over the actual inputs.
+
+The :func:`~numba.vectorize` decorator has two modes of operation:
+
+* Eager, or decoration-time, compilation: If you pass one or more type
+  signatures to the decorator, you will be building a NumPy universal
+  function (ufunc).  The rest of this subsection describes building
+  ufuncs using decoration-time compilation.
+
+* Lazy, or call-time, compilation: When not given any signatures, the
+  decorator will give you a Numba dynamic universal function
+  (:class:`~numba.DUFunc`) that dynamically compiles a new kernel when
+  called with a previously unsupported input type.  A later
+  subsection, ":ref:`dynamic-universal-functions`", describes this mode in
+  more depth.
+
+As described above, if you pass a list of signatures to the
+:func:`~numba.vectorize` decorator, your function will be compiled
+into a NumPy ufunc.  In the basic case, only one signature will be
+passed::
+
+   from numba import vectorize, float64
+
+   @vectorize([float64(float64, float64)])
+   def f(x, y):
+       return x + y
+
+If you pass several signatures, beware that you have to pass most specific
+signatures before least specific ones (e.g., single-precision floats
+before double-precision floats), otherwise type-based dispatching will not work
+as expected::
+
+   @vectorize([int32(int32, int32),
+               int64(int64, int64),
+               float32(float32, float32),
+               float64(float64, float64)])
+   def f(x, y):
+       return x + y
+
+The function will work as expected over the specified array types::
+
+   >>> a = np.arange(6)
+   >>> f(a, a)
+   array([ 0,  2,  4,  6,  8, 10])
+   >>> a = np.linspace(0, 1, 6)
+   >>> f(a, a)
+   array([ 0. ,  0.4,  0.8,  1.2,  1.6,  2. ])
+
+but it will fail working on other types::
+
+   >>> a = np.linspace(0, 1+1j, 6)
+   >>> f(a, a)
+   Traceback (most recent call last):
+     File "<stdin>", line 1, in <module>
+   TypeError: ufunc 'ufunc' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
+
+
+You might ask yourself, "why would I go through this instead of compiling
+a simple iteration loop using the :ref:`@jit <jit>` decorator?".  The
+answer is that NumPy ufuncs automatically get other features such as
+reduction, accumulation or broadcasting.  Using the example above::
+
+   >>> a = np.arange(12).reshape(3, 4)
+   >>> a
+   array([[ 0,  1,  2,  3],
+          [ 4,  5,  6,  7],
+          [ 8,  9, 10, 11]])
+   >>> f.reduce(a, axis=0)
+   array([12, 15, 18, 21])
+   >>> f.reduce(a, axis=1)
+   array([ 6, 22, 38])
+   >>> f.accumulate(a)
+   array([[ 0,  1,  2,  3],
+          [ 4,  6,  8, 10],
+          [12, 15, 18, 21]])
+   >>> f.accumulate(a, axis=1)
+   array([[ 0,  1,  3,  6],
+          [ 4,  9, 15, 22],
+          [ 8, 17, 27, 38]])
+
+.. seealso::
+   `Standard features of ufuncs <http://docs.scipy.org/doc/numpy/reference/ufuncs.html#ufunc>`_ (NumPy documentation).
+
+.. note::
+   Only the broadcasting features of ufuncs are supported in compiled code.
+
+The :func:`~numba.vectorize` decorator supports multiple ufunc targets:
+
+=================       ===============================================================
+Target                    Description
+=================       ===============================================================
+cpu                     Single-threaded CPU
+
+
+parallel                Multi-core CPU
+
+
+cuda                    CUDA GPU
+
+                        .. NOTE:: This creates an *ufunc-like* object.
+			  See `documentation for CUDA ufunc <../cuda/ufunc.html>`_ for detail.
+=================       ===============================================================
+
+A general guideline is to choose different targets for different data sizes
+and algorithms.
+The "cpu" target works well for small data sizes (approx. less than 1KB) and low
+compute intensity algorithms. It has the least amount of overhead.
+The "parallel" target works well for medium data sizes (approx. less than 1MB).
+Threading adds a small delay.
+The "cuda" target works well for big data sizes (approx. greater than 1MB) and
+high compute intensity algorithms.  Transferring memory to and from the GPU adds
+significant overhead.
+
+
+.. _guvectorize:
+
+The ``@guvectorize`` decorator
+==============================
+
+While :func:`~numba.vectorize` allows you to write ufuncs that work on one
+element at a time, the :func:`~numba.guvectorize` decorator takes the concept
+one step further and allows you to write ufuncs that will work on an
+arbitrary number of elements of input arrays, and take and return arrays of
+differing dimensions.  The typical example is a running median or a
+convolution filter.
+
+Contrary to :func:`~numba.vectorize` functions, :func:`~numba.guvectorize`
+functions don't return their result value: they take it as an array
+argument, which must be filled in by the function.  This is because the
+array is actually allocated by NumPy's dispatch mechanism, which calls into
+the Numba-generated code.
+
+Similar to :func:`~numba.vectorize` decorator, :func:`~numba.guvectorize`
+also has two modes of operation: Eager, or decoration-time compilation and
+lazy, or call-time compilation.
+
+
+Here is a very simple example::
+
+   @guvectorize([(int64[:], int64, int64[:])], '(n),()->(n)')
+   def g(x, y, res):
+       for i in range(x.shape[0]):
+           res[i] = x[i] + y
+
+The underlying Python function simply adds a given scalar (``y``) to all
+elements of a 1-dimension array.  What's more interesting is the declaration.
+There are two things there:
+
+* the declaration of input and output *layouts*, in symbolic form:
+  ``(n),()->(n)`` tells NumPy that the function takes a *n*-element one-dimension
+  array, a scalar (symbolically denoted by the empty tuple ``()``) and
+  returns a *n*-element one-dimension array;
+
+* the list of supported concrete *signatures* as per ``@vectorize``; here,
+  as in the above example, we demonstrate ``int64`` arrays.
+
+.. note::
+   1D array type can also receive scalar arguments (those with shape ``()``).
+   In the above example, the second argument also could be declared as
+   ``int64[:]``.  In that case, the value must be read by ``y[0]``.
+
+We can now check what the compiled ufunc does, over a simple example::
+
+   >>> a = np.arange(5)
+   >>> a
+   array([0, 1, 2, 3, 4])
+   >>> g(a, 2)
+   array([2, 3, 4, 5, 6])
+
+The nice thing is that NumPy will automatically dispatch over more
+complicated inputs, depending on their shapes::
+
+   >>> a = np.arange(6).reshape(2, 3)
+   >>> a
+   array([[0, 1, 2],
+          [3, 4, 5]])
+   >>> g(a, 10)
+   array([[10, 11, 12],
+          [13, 14, 15]])
+   >>> g(a, np.array([10, 20]))
+   array([[10, 11, 12],
+          [23, 24, 25]])
+
+
+.. note::
+   Both :func:`~numba.vectorize` and :func:`~numba.guvectorize` support
+   passing ``nopython=True`` :ref:`as in the @jit decorator <jit-nopython>`.
+   Use it to ensure the generated code does not fallback to
+   :term:`object mode`.
+
+.. _overwriting-input-values:
+
+Overwriting input values
+------------------------
+
+In most cases, writing to inputs may also appear to work - however, this
+behaviour cannot be relied on. Consider the following example function::
+
+   @guvectorize([(float64[:], float64[:])], '()->()')
+   def init_values(invals, outvals):
+       invals[0] = 6.5
+       outvals[0] = 4.2
+
+Calling the `init_values` function with an array of `float64` type results in
+visible changes to the input::
+
+   >>> invals = np.zeros(shape=(3, 3), dtype=np.float64)
+   >>> outvals = init_values(invals)
+   >>> invals
+   array([[6.5, 6.5, 6.5],
+          [6.5, 6.5, 6.5],
+          [6.5, 6.5, 6.5]])
+   >>> outvals
+   array([[4.2, 4.2, 4.2],
+       [4.2, 4.2, 4.2],
+       [4.2, 4.2, 4.2]])
+
+This works because NumPy can pass the input data directly into the `init_values`
+function as the data `dtype` matches that of the declared argument.  However, it
+may also create and pass in a temporary array, in which case changes to the
+input are lost. For example, this can occur when casting is required. To
+demonstrate, we can  use an array of `float32` with the `init_values` function::
+
+   >>> invals = np.zeros(shape=(3, 3), dtype=np.float32)
+   >>> outvals = init_values(invals)
+   >>> invals
+   array([[0., 0., 0.],
+          [0., 0., 0.],
+          [0., 0., 0.]], dtype=float32)
+
+In this case, there is no change to the `invals` array because the temporary
+casted array was mutated instead.
+
+.. _dynamic-universal-functions:
+
+Dynamic universal functions
+===========================
+
+As described above, if you do not pass any signatures to the
+:func:`~numba.vectorize` decorator, your Python function will be used
+to build a dynamic universal function, or :class:`~numba.DUFunc`.  For
+example::
+
+   from numba import vectorize
+
+   @vectorize
+   def f(x, y):
+       return x * y
+
+The resulting :func:`f` is a :class:`~numba.DUFunc` instance that
+starts with no supported input types.  As you make calls to :func:`f`,
+Numba generates new kernels whenever you pass a previously unsupported
+input type.  Given the example above, the following set of interpreter
+interactions illustrate how dynamic compilation works::
+
+   >>> f
+   <numba._DUFunc 'f'>
+   >>> f.ufunc
+   <ufunc 'f'>
+   >>> f.ufunc.types
+   []
+
+The example above shows that :class:`~numba.DUFunc` instances are not
+ufuncs.  Rather than subclass ufunc's, :class:`~numba.DUFunc`
+instances work by keeping a :attr:`~numba.DUFunc.ufunc` member, and
+then delegating ufunc property reads and method calls to this member
+(also known as type aggregation).  When we look at the initial types
+supported by the ufunc, we can verify there are none.
+
+Let's try to make a call to :func:`f`::
+
+   >>> f(3,4)
+   12
+   >>> f.types   # shorthand for f.ufunc.types
+   ['ll->l']
+
+If this was a normal NumPy ufunc, we would have seen an exception
+complaining that the ufunc couldn't handle the input types.  When we
+call :func:`f` with integer arguments, not only do we receive an
+answer, but we can verify that Numba created a loop supporting C
+:code:`long` integers.
+
+We can add additional loops by calling :func:`f` with different inputs::
+
+   >>> f(1.,2.)
+   2.0
+   >>> f.types
+   ['ll->l', 'dd->d']
+
+We can now verify that Numba added a second loop for dealing with
+floating-point inputs, :code:`"dd->d"`.
+
+If we mix input types to :func:`f`, we can verify that `NumPy ufunc
+casting rules`_ are still in effect::
+
+   >>> f(1,2.)
+   2.0
+   >>> f.types
+   ['ll->l', 'dd->d']
+
+.. _`NumPy ufunc casting rules`: http://docs.scipy.org/doc/numpy/reference/ufuncs.html#casting-rules
+
+This example demonstrates that calling :func:`f` with mixed types
+caused NumPy to select the floating-point loop, and cast the integer
+argument to a floating-point value.  Thus, Numba did not create a
+special :code:`"dl->d"` kernel.
+
+This :class:`~numba.DUFunc` behavior leads us to a point similar to
+the warning given above in "`The @vectorize decorator`_" subsection,
+but instead of signature declaration order in the decorator, call
+order matters.  If we had passed in floating-point arguments first,
+any calls with integer arguments would be cast to double-precision
+floating-point values.  For example::
+
+   >>> @vectorize
+   ... def g(a, b): return a / b
+   ...
+   >>> g(2.,3.)
+   0.66666666666666663
+   >>> g(2,3)
+   0.66666666666666663
+   >>> g.types
+   ['dd->d']
+
+If you require precise support for various type signatures, you should
+specify them in the :func:`~numba.vectorize` decorator, and not rely
+on dynamic compilation.
+
+Dynamic generalized universal functions
+=======================================
+
+Similar to a dynamic universal function, if you do not specify any types to
+the :func:`~numba.guvectorize` decorator, your Python function will be used
+to build a dynamic generalized universal function, or :class:`~numba.GUFunc`.
+For example::
+
+   from numba import guvectorize
+
+   @guvectorize('(n),()->(n)')
+   def g(x, y, res):
+       for i in range(x.shape[0]):
+           res[i] = x[i] + y
+
+We can verify the resulting function :func:`g` is a :class:`~numba.GUFunc`
+instance that starts with no supported input types. For instance::
+
+   >>> g
+   <numba._GUFunc 'g'>
+   >>> g.ufunc
+   <ufunc 'g'>
+   >>> g.ufunc.types
+   []
+
+Similar to a :class:`~numba.DUFunc`, as one make calls to :func:`g()`,
+numba generates new kernels for previously unsupported input types. The
+following set of interpreter interactions will illustrate how dynamic
+compilation works for a :class:`~numba.GUFunc`::
+
+   >>> x = np.arange(5, dtype=np.int64)
+   >>> y = 10
+   >>> res = np.zeros_like(x)
+   >>> g(x, y, res)
+   >>> res
+   array([5, 6, 7, 8, 9])
+   >>> g.types
+   ['ll->l']
+
+If this was a normal :func:`guvectorize` function, we would have seen an
+exception complaining that the ufunc could not handle the given input types.
+When we call :func:`g()` with the input arguments, numba creates a new loop
+for the input types.
+
+We can add additional loops by calling :func:`g` with new arguments::
+
+   >>> x = np.arange(5, dtype=np.double)
+   >>> y = 2.2
+   >>> res = np.zeros_like(x)
+   >>> g(x, y, res)
+
+We can now verify that Numba added a second loop for dealing with
+floating-point inputs, :code:`"dd->d"`.
+
+   >>> g.types  # shorthand for g.ufunc.types
+   ['ll->l', 'dd->d']
+
+One can also verify that NumPy ufunc casting rules are working as expected::
+
+   >>> x = np.arange(5, dtype=np.int64)
+   >>> y = 2.2
+   >>> res = np.zeros_like(x)
+   >>> g(x, y, res)
+   >>> res
+
+If you need precise support for various type signatures, you should not rely on dynamic
+compilation and instead, specify the types them as first
+argument in the :func:`~numba.guvectorize` decorator.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/withobjmode.rst b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/withobjmode.rst
new file mode 100644
index 000000000..e94237e8b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/docs/source/user/withobjmode.rst
@@ -0,0 +1,34 @@
+============================================================
+Callback into the Python Interpreter from within JIT'ed code
+============================================================
+
+There are rare but real cases when a nopython-mode function needs to callback
+into the Python interpreter to invoke code that cannot be compiled by Numba.
+Such cases include:
+
+- logging progress for long running JIT'ed functions;
+- use data structures that are not currently supported by Numba;
+- debugging inside JIT'ed code using the Python debugger.
+
+When Numba callbacks into the Python interpreter, the following has to happen:
+
+- acquire the GIL;
+- convert values in native representation back into Python objects;
+- call-back into the Python interpreter;
+- convert returned values from the Python-code into native representation;
+- release the GIL.
+
+These steps can be expensive.  Users **should not** rely on the feature
+described here on performance-critical paths.
+
+
+.. _with_objmode:
+
+The ``objmode`` context-manager
+===============================
+
+.. warning:: This feature can be easily mis-used.  Users should first consider
+    alternative approaches to achieve their intended goal before using
+    this feature.
+
+.. autofunction:: numba.objmode
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/install_numba.sh b/cv/3d_detection/pointrcnn-iou/pytorch/numba/install_numba.sh
new file mode 100644
index 000000000..207cbc9c3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/install_numba.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+
+clang_version=`clang --version | grep "clang version 16."`
+if [[ "${clang_version}" != "" ]]; then
+  echo "Not support LLVM16 now!"
+  exit 0
+fi
+
+TARGET_DIR=${TARGET_DIR:-}
+PYTHON_PATH=$(which python3)
+PYTHON_DIST_PATH=${TARGET_DIR}/lib/python3/dist-packages
+
+PKG_DIR="build_pip"
+PKG_NAME="numba"
+
+if [[ ! -d ${PKG_DIR} ]]; then
+  echo "ERROR: Package directory ${PKG_DIR} doesn't exist"
+  exit 1
+fi
+
+latest_pkg="$(ls -t ${PKG_DIR} | grep ${PKG_NAME} | head -1)"
+if [[ "${latest_pkg}" == "" ]]; then
+  echo "ERROR: Cannot find latest ${PKG_NAME} package"
+  exit 1
+else
+  echo "INFO: Found latest package ${latest_pkg} in directory ${PKG_DIR}"
+fi
+
+if [[ "${TARGET_DIR}" != ""  ]]; then
+  mkdir tmp
+  cp -R ${PYTHON_DIST_PATH}/bin ./tmp/
+  ${PYTHON_PATH} -m pip install --upgrade -t ${PYTHON_DIST_PATH} ${PKG_DIR}/${latest_pkg} || exit
+  cp -n ./tmp/bin/* ${PYTHON_DIST_PATH}/bin
+  rm -rf ./tmp
+  echo "${PKG_NAME} installed in ${PYTHON_DIST_PATH}; please add it to your PYTHONPATH."
+else
+  ${PYTHON_PATH} -m pip uninstall ${PKG_NAME} -y
+  ${PYTHON_PATH} -m pip install ${PKG_DIR}/${latest_pkg} || exit
+fi
+
+# finish installing successfully
+exit 0
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/mypy.ini b/cv/3d_detection/pointrcnn-iou/pytorch/numba/mypy.ini
new file mode 100644
index 000000000..0b790befd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/mypy.ini
@@ -0,0 +1,52 @@
+# Global options:
+
+[mypy]
+warn_unused_configs = True
+follow_imports = silent
+show_error_context = True
+files = **/numba/core/types/*.py, **/numba/core/datamodel/*.py, **/numba/core/rewrites/*.py, **/numba/core/unsafe/*.py
+
+# Per-module options:
+# To classify a given module as Level 1, 2 or 3 it must be added both in files (variable above) and in the lists below.
+# Level 1 - modules checked on the strictest settings.
+;[mypy-]
+;warn_return_any = True
+;disallow_any_expr = True
+;disallow_any_explicit = True
+;disallow_any_generics = True
+;disallow_subclassing_any = True
+;disallow_untyped_calls = True
+;disallow_untyped_defs = True
+;disallow_incomplete_defs = True
+;check_untyped_defs = True
+;disallow_untyped_decorators = True
+;warn_unused_ignores = True
+;follow_imports = normal
+;warn_unreachable = True
+;strict_equality = True
+
+# Level 2 - module that pass reasonably strict settings.
+#           No untyped functions allowed. Imports must be typed or explicitly ignored.
+;[mypy-]
+;warn_return_any = True
+;disallow_untyped_defs = True
+;disallow_incomplete_defs = True
+;follow_imports = normal
+
+# Level 3 - modules that pass mypy default settings (only those in `files` global setting and not in previous levels)
+#           Function/variables are annotated to avoid mypy errors, but annotations are not complete.
+[mypy-numba.core.*]
+warn_return_any = True
+
+# Level 4 - modules that do not pass mypy check: they are excluded from "files" setting in global section
+
+# External packages that lack annotations
+[mypy-llvmlite.*]
+ignore_missing_imports = True
+
+[mypy-numpy.*]
+ignore_missing_imports = True
+
+[mypy-winreg.*]
+# this can be removed after Mypy 0.78 is out with the latest typeshed
+ignore_missing_imports = True
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/__init__.py
new file mode 100644
index 000000000..0e437a600
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/__init__.py
@@ -0,0 +1,224 @@
+"""
+Expose top-level symbols that are safe for import *
+"""
+
+import platform
+import re
+import sys
+import warnings
+
+from ._version import get_versions
+from numba.misc.init_utils import generate_version_info
+
+__version__ = get_versions()['version']
+version_info = generate_version_info(__version__)
+del get_versions
+del generate_version_info
+
+
+from numba.core import config
+from numba.core import types, errors
+
+# Re-export typeof
+from numba.misc.special import (
+    typeof, prange, pndindex, gdb, gdb_breakpoint, gdb_init,
+    literally, literal_unroll,
+)
+
+# Re-export error classes
+from numba.core.errors import *
+
+# Re-export types itself
+import numba.core.types as types
+
+# Re-export all type names
+from numba.core.types import *
+
+# Re-export decorators
+from numba.core.decorators import (cfunc, generated_jit, jit, njit, stencil,
+                                   jit_module)
+
+# Re-export vectorize decorators and the thread layer querying function
+from numba.np.ufunc import (vectorize, guvectorize, threading_layer,
+                            get_num_threads, set_num_threads,
+                            set_parallel_chunksize, get_parallel_chunksize,
+                            get_thread_id)
+
+# Re-export Numpy helpers
+from numba.np.numpy_support import carray, farray, from_dtype
+
+# Re-export experimental
+from numba import experimental
+
+# Initialize withcontexts
+import numba.core.withcontexts
+from numba.core.withcontexts import objmode_context as objmode
+from numba.core.withcontexts import parallel_chunksize
+
+# Initialize target extensions
+import numba.core.target_extension
+
+# Initialize typed containers
+import numba.typed
+
+# Keep this for backward compatibility.
+def test(argv, **kwds):
+    # To speed up the import time, avoid importing `unittest` and other test
+    # dependencies unless the user is actually trying to run tests.
+    from numba.testing import _runtests as runtests
+    return runtests.main(argv, **kwds)
+
+__all__ = """
+    cfunc
+    from_dtype
+    guvectorize
+    jit
+    experimental
+    njit
+    stencil
+    jit_module
+    typeof
+    prange
+    gdb
+    gdb_breakpoint
+    gdb_init
+    vectorize
+    objmode
+    literal_unroll
+    get_num_threads
+    set_num_threads
+    set_parallel_chunksize
+    get_parallel_chunksize
+    parallel_chunksize
+    """.split() + types.__all__ + errors.__all__
+
+
+_min_llvmlite_version = (0, 39, 0)
+_min_llvm_version = (11, 0, 0)
+
+def _ensure_llvm():
+    """
+    Make sure llvmlite is operational.
+    """
+    import warnings
+    import llvmlite
+
+    # Only look at the the major, minor and bugfix version numbers.
+    # Ignore other stuffs
+    regex = re.compile(r'(\d+)\.(\d+).(\d+)')
+    m = regex.match(llvmlite.__version__)
+    if m:
+        ver = tuple(map(int, m.groups()))
+        if ver < _min_llvmlite_version:
+            msg = ("Numba requires at least version %d.%d.%d of llvmlite.\n"
+                   "Installed version is %s.\n"
+                   "Please update llvmlite." %
+                   (_min_llvmlite_version + (llvmlite.__version__,)))
+            raise ImportError(msg)
+    else:
+        # Not matching?
+        warnings.warn("llvmlite version format not recognized!")
+
+    from llvmlite.binding import llvm_version_info, check_jit_execution
+
+    if llvm_version_info < _min_llvm_version:
+        msg = ("Numba requires at least version %d.%d.%d of LLVM.\n"
+               "Installed llvmlite is built against version %d.%d.%d.\n"
+               "Please update llvmlite." %
+               (_min_llvm_version + llvm_version_info))
+        raise ImportError(msg)
+
+    check_jit_execution()
+
+def _ensure_critical_deps():
+    """
+    Make sure Python, NumPy and SciPy have supported versions.
+    """
+    from numba.np.numpy_support import numpy_version
+    from numba.core.utils import PYVERSION
+
+    if PYVERSION < (3, 7):
+        raise ImportError("Numba needs Python 3.7 or greater")
+
+    if numpy_version < (1, 18):
+        raise ImportError("Numba needs NumPy 1.18 or greater")
+    elif numpy_version > (1, 23):
+        raise ImportError("Numba needs NumPy 1.23 or less")
+
+    try:
+        import scipy
+    except ImportError:
+        pass
+    else:
+        sp_version = tuple(map(int, scipy.__version__.split('.')[:2]))
+        if sp_version < (1, 0):
+            raise ImportError("Numba requires SciPy version 1.0 or greater")
+
+
+def _try_enable_svml():
+    """
+    Tries to enable SVML if configuration permits use and the library is found.
+    """
+    if not config.DISABLE_INTEL_SVML:
+        try:
+            if sys.platform.startswith('linux'):
+                llvmlite.binding.load_library_permanently("libsvml.so")
+            elif sys.platform.startswith('darwin'):
+                llvmlite.binding.load_library_permanently("libsvml.dylib")
+            elif sys.platform.startswith('win'):
+                llvmlite.binding.load_library_permanently("svml_dispmd")
+            else:
+                return False
+            # The SVML library is loaded, therefore SVML *could* be supported.
+            # Now see if LLVM has been compiled with the SVML support patch.
+            # If llvmlite has the checking function `has_svml` and it returns
+            # True, then LLVM was compiled with SVML support and the the setup
+            # for SVML can proceed. We err on the side of caution and if the
+            # checking function is missing, regardless of that being fine for
+            # most 0.23.{0,1} llvmlite instances (i.e. conda or pip installed),
+            # we assume that SVML was not compiled in. llvmlite 0.23.2 is a
+            # bugfix release with the checking function present that will always
+            # produce correct behaviour. For context see: #3006.
+            try:
+                if not getattr(llvmlite.binding.targets, "has_svml")():
+                    # has detection function, but no svml compiled in, therefore
+                    # disable SVML
+                    return False
+            except AttributeError:
+                if platform.machine() == 'x86_64' and config.DEBUG:
+                    msg = ("SVML was found but llvmlite >= 0.23.2 is "
+                           "needed to support it.")
+                    warnings.warn(msg)
+                # does not have detection function, cannot detect reliably,
+                # disable SVML.
+                return False
+
+            # All is well, detection function present and reports SVML is
+            # compiled in, set the vector library to SVML.
+            llvmlite.binding.set_option('SVML', '-vector-library=SVML')
+            return True
+        except:
+            if platform.machine() == 'x86_64' and config.DEBUG:
+                warnings.warn("SVML was not found/could not be loaded.")
+    return False
+
+_ensure_llvm()
+_ensure_critical_deps()
+
+# we know llvmlite is working as the above tests passed, import it now as SVML
+# needs to mutate runtime options (sets the `-vector-library`).
+import llvmlite
+
+"""
+Is set to True if Intel SVML is in use.
+"""
+config.USING_SVML = _try_enable_svml()
+
+
+# ---------------------- WARNING WARNING WARNING ----------------------------
+# The following imports occur below here (SVML init) because somewhere in their
+# import sequence they have a `@njit` wrapped function. This triggers too early
+# a bind to the underlying LLVM libraries which then irretrievably sets the LLVM
+# SVML state to "no SVML". See https://github.com/numba/numba/issues/4689 for
+# context.
+# ---------------------- WARNING WARNING WARNING ----------------------------
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/__main__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/__main__.py
new file mode 100644
index 000000000..4e85bf372
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/__main__.py
@@ -0,0 +1,6 @@
+"""Expose Numba command via ``python -m numba``."""
+import sys
+from numba.misc.numba_entry import main
+
+if __name__ == '__main__':
+    sys.exit(main())
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_arraystruct.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_arraystruct.h
new file mode 100644
index 000000000..dcb866e2b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_arraystruct.h
@@ -0,0 +1,21 @@
+#ifndef NUMBA_ARYSTRUCT_H_
+#define NUMBA_ARYSTRUCT_H_
+/*
+ * Fill in the *arystruct* with information from the Numpy array *obj*.
+ * *arystruct*'s layout is defined in numba.targets.arrayobj (look
+ * for the ArrayTemplate class).
+ */
+
+typedef struct {
+    void     *meminfo;  /* see _nrt_python.c and nrt.h in numba/core/runtime */
+    PyObject *parent;
+    npy_intp nitems;
+    npy_intp itemsize;
+    void *data;
+
+    npy_intp shape_and_strides[];
+} arystruct_t;
+
+
+#endif  /* NUMBA_ARYSTRUCT_H_ */
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_devicearray.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_devicearray.cpp
new file mode 100644
index 000000000..3d40bee8b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_devicearray.cpp
@@ -0,0 +1,142 @@
+/* This file contains the base class implementation for all device arrays. The
+ * base class is implemented in C so that computing typecodes for device arrays
+ * can be implemented efficiently. */
+
+#include "_pymodule.h"
+
+
+/* Include _devicearray., but make sure we don't get the definitions intended
+ * for consumers of the Device Array API.
+ */
+#define NUMBA_IN_DEVICEARRAY_CPP_
+#include "_devicearray.h"
+
+/* DeviceArray PyObject implementation. Note that adding more members here is
+ * presently prohibited because mapped and managed arrays derive from both
+ * DeviceArray and NumPy's ndarray, which is also a C extension class - the
+ * layout of the object cannot be resolved if this class also has members beyond
+ * PyObject_HEAD. */
+class DeviceArray {
+    PyObject_HEAD
+};
+
+/* Trivial traversal - DeviceArray instances own nothing. */
+static int
+DeviceArray_traverse(DeviceArray *self, visitproc visit, void *arg)
+{
+    return 0;
+}
+
+/* Trivial clear of all references - DeviceArray instances own nothing. */
+static int
+DeviceArray_clear(DeviceArray *self)
+{
+    return 0;
+}
+
+/* The _devicearray.DeviceArray type */
+PyTypeObject DeviceArrayType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "_devicearray.DeviceArray",                  /* tp_name */
+    sizeof(DeviceArray),                         /* tp_basicsize */
+    0,                                           /* tp_itemsize */
+    0,                                           /* tp_dealloc */
+    0,                                           /* tp_print */
+    0,                                           /* tp_getattr */
+    0,                                           /* tp_setattr */
+    0,                                           /* tp_compare */
+    0,                                           /* tp_repr */
+    0,                                           /* tp_as_number */
+    0,                                           /* tp_as_sequence */
+    0,                                           /* tp_as_mapping */
+    0,                                           /* tp_hash */
+    0,                                           /* tp_call*/
+    0,                                           /* tp_str*/
+    0,                                           /* tp_getattro*/
+    0,                                           /* tp_setattro*/
+    0,                                           /* tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HAVE_GC,
+                                                 /* tp_flags*/
+    "DeviceArray object",                        /* tp_doc */
+    (traverseproc) DeviceArray_traverse,         /* tp_traverse */
+    (inquiry) DeviceArray_clear,                 /* tp_clear */
+    0,                                           /* tp_richcompare */
+    0,                                           /* tp_weaklistoffset */
+    0,                                           /* tp_iter */
+    0,                                           /* tp_iternext */
+    0,                                           /* tp_methods */
+    0,                                           /* tp_members */
+    0,                                           /* tp_getset */
+    0,                                           /* tp_base */
+    0,                                           /* tp_dict */
+    0,                                           /* tp_descr_get */
+    0,                                           /* tp_descr_set */
+    0,                                           /* tp_dictoffset */
+    0,                                           /* tp_init */
+    0,                                           /* tp_alloc */
+    0,                                           /* tp_new */
+    0,                                           /* tp_free */
+    0,                                           /* tp_is_gc */
+    0,                                           /* tp_bases */
+    0,                                           /* tp_mro */
+    0,                                           /* tp_cache */
+    0,                                           /* tp_subclasses */
+    0,                                           /* tp_weaklist */
+    0,                                           /* tp_del */
+    0,                                           /* tp_version_tag */
+    0,                                           /* tp_finalize */
+#if PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 8
+    0,                                           /* tp_vectorcall */
+    0,                                           /* tp_print */
+#endif
+};
+
+/* CUDA device array C API */
+static void *_DeviceArray_API[1] = {
+    (void*)&DeviceArrayType
+};
+
+MOD_INIT(_devicearray) {
+    PyObject *m = nullptr;
+    PyObject *d = nullptr;
+    PyObject *c_api = nullptr;
+    int error = 0;
+
+    MOD_DEF(m, "_devicearray", "No docs", NULL)
+    if (m == NULL)
+        goto error_occurred;
+
+    c_api = PyCapsule_New((void *)_DeviceArray_API, "numba._devicearray._DEVICEARRAY_API", NULL);
+    if (c_api == NULL)
+        goto error_occurred;
+
+    DeviceArrayType.tp_new = PyType_GenericNew;
+    if (PyType_Ready(&DeviceArrayType) < 0)
+        goto error_occurred;
+
+    Py_INCREF(&DeviceArrayType);
+    error = PyModule_AddObject(m, "DeviceArray", (PyObject*)(&DeviceArrayType));
+    if (error)
+        goto error_occurred;
+
+    d = PyModule_GetDict(m);
+    if (d == NULL)
+        goto error_occurred;
+
+    error = PyDict_SetItemString(d, "_DEVICEARRAY_API", c_api);
+    /* Decref and set c_api to NULL, Py_XDECREF in error_occurred will have no
+     * effect. */
+    Py_CLEAR(c_api);
+
+    if (error)
+        goto error_occurred;
+
+    return MOD_SUCCESS_VAL(m);
+
+error_occurred:
+    Py_XDECREF(m);
+    Py_XDECREF(c_api);
+    Py_XDECREF((PyObject*)&DeviceArrayType);
+
+    return MOD_ERROR_VAL;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_devicearray.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_devicearray.h
new file mode 100644
index 000000000..5b276eacf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_devicearray.h
@@ -0,0 +1,25 @@
+#ifndef NUMBA_DEVICEARRAY_H_
+#define NUMBA_DEVICEARRAY_H_
+
+#ifdef __cplusplus
+    extern "C" {
+#endif
+
+/* These definitions should only be used by consumers of the Device Array API.
+ * Consumers access the API through the opaque pointer stored in
+ * _devicearray._DEVICEARRAY_API.  We don't want these definitions in
+ * _devicearray.cpp itself because they would conflict with the actual
+ * implementations there.
+ */
+#ifndef NUMBA_IN_DEVICEARRAY_CPP_
+
+    extern void **DeviceArray_API;
+    #define DeviceArrayType (*(PyTypeObject*)DeviceArray_API[0])
+
+#endif /* ndef NUMBA_IN_DEVICEARRAY_CPP */
+
+#ifdef __cplusplus
+    }
+#endif
+
+#endif  /* NUMBA_DEVICEARRAY_H_ */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dispatcher.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dispatcher.cpp
new file mode 100644
index 000000000..5ffefb245
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dispatcher.cpp
@@ -0,0 +1,1223 @@
+#include "_pymodule.h"
+
+#include <cstring>
+#include <ctime>
+#include <cassert>
+#include <vector>
+
+#include "_typeof.h"
+#include "frameobject.h"
+#include "core/typeconv/typeconv.hpp"
+#include "_devicearray.h"
+
+/*
+ * Notes on the C_TRACE macro:
+ *
+ * The original C_TRACE macro (from ceval.c) would call
+ * PyTrace_C_CALL et al., for which the frame argument wouldn't
+ * be usable. Since we explicitly synthesize a frame using the
+ * original Python code object, we call PyTrace_CALL instead so
+ * the profiler can report the correct source location.
+ *
+ * Likewise, while ceval.c would call PyTrace_C_EXCEPTION in case
+ * of error, the profiler would simply expect a RETURN in case of
+ * a Python function, so we generate that here (making sure the
+ * exception state is preserved correctly).
+ *
+ */
+
+/*
+ * NOTE: There is a version split for tracing code. Python 3.10 introduced a
+ * trace_info structure to help make tracing more robust. See:
+ * https://github.com/python/cpython/pull/24726
+ */
+#if (PY_MAJOR_VERSION >= 3) && (PY_MINOR_VERSION >= 10)
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Python/ceval.c#L36-L40
+ */
+typedef struct {
+    PyCodeObject *code; // The code object for the bounds. May be NULL.
+    PyCodeAddressRange bounds; // Only valid if code != NULL.
+    CFrame cframe;
+} PyTraceInfo;
+
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Objects/codeobject.c#L1257-L1266
+ * NOTE: The function is renamed.
+ */
+static void
+_nb_PyLineTable_InitAddressRange(const char *linetable, Py_ssize_t length, int firstlineno, PyCodeAddressRange *range)
+{
+    range->opaque.lo_next = linetable;
+    range->opaque.limit = range->opaque.lo_next + length;
+    range->ar_start = -1;
+    range->ar_end = 0;
+    range->opaque.computed_line = firstlineno;
+    range->ar_line = -1;
+}
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Objects/codeobject.c#L1269-L1275
+ * NOTE: The function is renamed.
+ */
+static int
+_nb_PyCode_InitAddressRange(PyCodeObject* co, PyCodeAddressRange *bounds)
+{
+    const char *linetable = PyBytes_AS_STRING(co->co_linetable);
+    Py_ssize_t length = PyBytes_GET_SIZE(co->co_linetable);
+    _nb_PyLineTable_InitAddressRange(linetable, length, co->co_firstlineno, bounds);
+    return bounds->ar_line;
+}
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Python/ceval.c#L5468-L5475
+ * NOTE: The call to _PyCode_InitAddressRange is renamed.
+ */
+static void
+initialize_trace_info(PyTraceInfo *trace_info, PyFrameObject *frame)
+{
+    if (trace_info->code != frame->f_code) {
+        trace_info->code = frame->f_code;
+        _nb_PyCode_InitAddressRange(frame->f_code, &trace_info->bounds);
+    }
+}
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Python/ceval.c#L5477-L5501
+ */
+static int
+call_trace(Py_tracefunc func, PyObject *obj,
+           PyThreadState *tstate, PyFrameObject *frame,
+           PyTraceInfo *trace_info,
+           int what, PyObject *arg)
+{
+    int result;
+    if (tstate->tracing)
+        return 0;
+    tstate->tracing++;
+    tstate->cframe->use_tracing = 0;
+    if (frame->f_lasti < 0) {
+        frame->f_lineno = frame->f_code->co_firstlineno;
+    }
+    else {
+        initialize_trace_info(trace_info, frame);
+        frame->f_lineno = _PyCode_CheckLineNumber(frame->f_lasti*sizeof(_Py_CODEUNIT), &trace_info->bounds);
+    }
+    result = func(obj, frame, what, arg);
+    frame->f_lineno = 0;
+    tstate->cframe->use_tracing = ((tstate->c_tracefunc != NULL)
+                           || (tstate->c_profilefunc != NULL));
+    tstate->tracing--;
+    return result;
+}
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Python/ceval.c#L5445-L5466
+ */
+static int
+call_trace_protected(Py_tracefunc func, PyObject *obj,
+                     PyThreadState *tstate, PyFrameObject *frame,
+                     PyTraceInfo *trace_info,
+                     int what, PyObject *arg)
+{
+    PyObject *type, *value, *traceback;
+    int err;
+    PyErr_Fetch(&type, &value, &traceback);
+    err = call_trace(func, obj, tstate, frame, trace_info, what, arg);
+    if (err == 0)
+    {
+        PyErr_Restore(type, value, traceback);
+        return 0;
+    }
+    else
+    {
+        Py_XDECREF(type);
+        Py_XDECREF(value);
+        Py_XDECREF(traceback);
+        return -1;
+    }
+}
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Python/ceval.c#L5810-L5839
+ * NOTE: The state test https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Python/ceval.c#L5811
+ * has been removed, it's dealt with in call_cfunc.
+ */
+#define C_TRACE(x, call)                                        \
+if (call_trace(tstate->c_profilefunc, tstate->c_profileobj,     \
+               tstate, tstate->frame, &trace_info, PyTrace_CALL,\
+               cfunc))	                                        \
+    x = NULL;                                                   \
+else                                                            \
+{                                                               \
+    x = call;                                                   \
+    if (tstate->c_profilefunc != NULL)                          \
+    {                                                           \
+        if (x == NULL)                                          \
+        {                                                       \
+            call_trace_protected(tstate->c_profilefunc,         \
+                                 tstate->c_profileobj,          \
+                                 tstate, tstate->frame,         \
+                                 &trace_info,                   \
+                                 PyTrace_RETURN, cfunc);	\
+            /* XXX should pass (type, value, tb) */             \
+        }                                                       \
+        else                                                    \
+        {                                                       \
+            if (call_trace(tstate->c_profilefunc,               \
+                           tstate->c_profileobj,                \
+                           tstate, tstate->frame,               \
+                           &trace_info,                         \
+                           PyTrace_RETURN, cfunc))		\
+            {                                                   \
+                Py_DECREF(x);                                   \
+                x = NULL;                                       \
+            }                                                   \
+        }                                                       \
+    }                                                           \
+}
+
+#else
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/d5650a1738fe34f6e1db4af5f4c4edb7cae90a36/Python/ceval.c#L4242-L4257
+ */
+static int
+call_trace(Py_tracefunc func, PyObject *obj,
+           PyThreadState *tstate, PyFrameObject *frame,
+           int what, PyObject *arg)
+{
+    int result;
+    if (tstate->tracing)
+        return 0;
+    tstate->tracing++;
+    tstate->use_tracing = 0;
+    result = func(obj, frame, what, arg);
+    tstate->use_tracing = ((tstate->c_tracefunc != NULL)
+                           || (tstate->c_profilefunc != NULL));
+    tstate->tracing--;
+    return result;
+}
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/d5650a1738fe34f6e1db4af5f4c4edb7cae90a36/Python/ceval.c#L4220-L4240
+ */
+static int
+call_trace_protected(Py_tracefunc func, PyObject *obj,
+                     PyThreadState *tstate, PyFrameObject *frame,
+                     int what, PyObject *arg)
+{
+    PyObject *type, *value, *traceback;
+    int err;
+    PyErr_Fetch(&type, &value, &traceback);
+    err = call_trace(func, obj, tstate, frame, what, arg);
+    if (err == 0)
+    {
+        PyErr_Restore(type, value, traceback);
+        return 0;
+    }
+    else
+    {
+        Py_XDECREF(type);
+        Py_XDECREF(value);
+        Py_XDECREF(traceback);
+        return -1;
+    }
+}
+
+/*
+ * Code originally from:
+ * https://github.com/python/cpython/blob/d5650a1738fe34f6e1db4af5f4c4edb7cae90a36/Python/ceval.c#L4520-L4549
+ * NOTE: The state test https://github.com/python/cpython/blob/d5650a1738fe34f6e1db4af5f4c4edb7cae90a36/Python/ceval.c#L4521
+ * has been removed, it's dealt with in call_cfunc.
+ */
+#define C_TRACE(x, call)                                        \
+if (call_trace(tstate->c_profilefunc, tstate->c_profileobj,     \
+               tstate, tstate->frame, PyTrace_CALL, cfunc))     \
+    x = NULL;                                                   \
+else                                                            \
+{                                                               \
+    x = call;                                                   \
+    if (tstate->c_profilefunc != NULL)                          \
+    {                                                           \
+        if (x == NULL)                                          \
+        {                                                       \
+            call_trace_protected(tstate->c_profilefunc,         \
+                                 tstate->c_profileobj,          \
+                                 tstate, tstate->frame,         \
+                                 PyTrace_RETURN, cfunc);        \
+            /* XXX should pass (type, value, tb) */             \
+        }                                                       \
+        else                                                    \
+        {                                                       \
+            if (call_trace(tstate->c_profilefunc,               \
+                           tstate->c_profileobj,                \
+                           tstate, tstate->frame,               \
+                           PyTrace_RETURN, cfunc))              \
+            {                                                   \
+                Py_DECREF(x);                                   \
+                x = NULL;                                       \
+            }                                                   \
+        }                                                       \
+    }                                                           \
+}
+
+
+#endif
+
+typedef std::vector<Type> TypeTable;
+typedef std::vector<PyObject*> Functions;
+
+/* The Dispatcher class is the base class of all dispatchers in the CPU and
+   CUDA targets. Its main responsibilities are:
+
+   - Resolving the best overload to call for a given set of arguments, and
+   - Calling the resolved overload.
+
+   This logic is implemented within this class for efficiency (lookup of the
+   appropriate overload needs to be fast) and ease of implementation (calling
+   directly into a compiled function using a function pointer is easier within
+   the C++ code where the overload has been resolved). */
+class Dispatcher {
+public:
+    PyObject_HEAD
+    /* Whether compilation of new overloads is permitted */
+    char can_compile;
+    /* Whether fallback to object mode is permitted */
+    char can_fallback;
+    /* Whether types must match exactly when resolving overloads.
+       If not, conversions (e.g. float32 -> float64) are permitted when
+       searching for a match. */
+    char exact_match_required;
+    /* Borrowed reference */
+    PyObject *fallbackdef;
+    /* Whether to fold named arguments and default values
+      (false for lifted loops) */
+    int fold_args;
+    /* Whether the last positional argument is a stararg */
+    int has_stararg;
+    /* Tuple of argument names */
+    PyObject *argnames;
+    /* Tuple of default values */
+    PyObject *defargs;
+    /* Number of arguments to function */
+    int argct;
+    /* Used for selecting overloaded function implementations */
+    TypeManager *tm;
+    /* An array of overloads */
+    Functions functions;
+    /* A flattened array of argument types to all overloads
+     * (invariant: sizeof(overloads) == argct * sizeof(functions)) */
+    TypeTable overloads;
+
+    /* Add a new overload. Parameters:
+
+       - args: An array of Type objects, one for each parameter
+       - callable: The callable implementing this overload. */
+    void addDefinition(Type args[], PyObject *callable) {
+        overloads.reserve(argct + overloads.size());
+        for (int i=0; i<argct; ++i) {
+            overloads.push_back(args[i]);
+        }
+        functions.push_back(callable);
+    }
+
+    /* Given a list of types, find the overloads that have a matching signature.
+       Returns the best match, as well as the number of matches found.
+
+       Parameters:
+
+       - sig: an array of Type objects, one for each parameter.
+       - matches: the number of matches found (mutated by this function).
+       - allow_unsafe: whether to match overloads that would require an unsafe
+                       cast.
+       - exact_match_required: Whether all arguments types must match the
+                               overload's types exactly. When false,
+                               overloads that would require a type conversion
+                               can also be matched. */
+    PyObject* resolve(Type sig[], int &matches, bool allow_unsafe,
+                      bool exact_match_required) const {
+        const int ovct = functions.size();
+        int selected;
+        matches = 0;
+        if (0 == ovct) {
+            // No overloads registered
+            return NULL;
+        }
+        if (argct == 0) {
+            // Nullary function: trivial match on first overload
+            matches = 1;
+            selected = 0;
+        }
+        else {
+            matches = tm->selectOverload(sig, &overloads[0], selected, argct,
+                                         ovct, allow_unsafe,
+                                         exact_match_required);
+        }
+        if (matches == 1) {
+            return functions[selected];
+        }
+        return NULL;
+    }
+
+    /* Remove all overloads */
+    void clear() {
+        functions.clear();
+        overloads.clear();
+    }
+
+};
+
+
+static int
+Dispatcher_traverse(Dispatcher *self, visitproc visit, void *arg)
+{
+    Py_VISIT(self->defargs);
+    return 0;
+}
+
+static void
+Dispatcher_dealloc(Dispatcher *self)
+{
+    Py_XDECREF(self->argnames);
+    Py_XDECREF(self->defargs);
+    self->clear();
+    Py_TYPE(self)->tp_free((PyObject*)self);
+}
+
+
+static int
+Dispatcher_init(Dispatcher *self, PyObject *args, PyObject *kwds)
+{
+    PyObject *tmaddrobj;
+    void *tmaddr;
+    int argct;
+    int can_fallback;
+    int has_stararg = 0;
+    int exact_match_required = 0;
+
+    if (!PyArg_ParseTuple(args, "OiiO!O!i|ii", &tmaddrobj, &argct,
+                          &self->fold_args,
+                          &PyTuple_Type, &self->argnames,
+                          &PyTuple_Type, &self->defargs,
+                          &can_fallback,
+                          &has_stararg,
+                          &exact_match_required
+                         )) {
+        return -1;
+    }
+    Py_INCREF(self->argnames);
+    Py_INCREF(self->defargs);
+    tmaddr = PyLong_AsVoidPtr(tmaddrobj);
+    self->tm = static_cast<TypeManager*>(tmaddr);
+    self->argct = argct;
+    self->can_compile = 1;
+    self->can_fallback = can_fallback;
+    self->fallbackdef = NULL;
+    self->has_stararg = has_stararg;
+    self->exact_match_required = exact_match_required;
+    return 0;
+}
+
+static PyObject *
+Dispatcher_clear(Dispatcher *self, PyObject *args)
+{
+    self->clear();
+    Py_RETURN_NONE;
+}
+
+static
+PyObject*
+Dispatcher_Insert(Dispatcher *self, PyObject *args, PyObject *kwds)
+{
+    /* The cuda kwarg is a temporary addition until CUDA overloads are compiled
+     * functions. Once they are compiled functions, kwargs can be removed from
+     * this function. */
+    static char *keywords[] = {
+        (char*)"sig",
+        (char*)"func",
+        (char*)"objectmode",
+        (char*)"cuda",
+        NULL
+    };
+
+    PyObject *sigtup, *cfunc;
+    int i, sigsz;
+    int *sig;
+    int objectmode = 0;
+    int cuda = 0;
+
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "OO|ip", keywords, &sigtup,
+                                     &cfunc, &objectmode, &cuda)) {
+        return NULL;
+    }
+
+    if (!cuda && !PyObject_TypeCheck(cfunc, &PyCFunction_Type) ) {
+        PyErr_SetString(PyExc_TypeError, "must be builtin_function_or_method");
+        return NULL;
+    }
+
+    sigsz = PySequence_Fast_GET_SIZE(sigtup);
+    sig = new int[sigsz];
+
+    for (i = 0; i < sigsz; ++i) {
+        sig[i] = PyLong_AsLong(PySequence_Fast_GET_ITEM(sigtup, i));
+    }
+
+    /* The reference to cfunc is borrowed; this only works because the
+       derived Python class also stores an (owned) reference to cfunc. */
+    self->addDefinition(sig, cfunc);
+
+    /* Add pure python fallback */
+    if (!self->fallbackdef && objectmode){
+        self->fallbackdef = cfunc;
+    }
+
+    delete[] sig;
+
+    Py_RETURN_NONE;
+}
+
+static
+void explain_issue(PyObject *dispatcher, PyObject *args, PyObject *kws,
+                   const char *method_name, const char *default_msg)
+{
+    PyObject *callback, *result;
+    callback = PyObject_GetAttrString(dispatcher, method_name);
+    if (!callback) {
+        PyErr_SetString(PyExc_TypeError, default_msg);
+        return;
+    }
+    result = PyObject_Call(callback, args, kws);
+    Py_DECREF(callback);
+    if (result != NULL) {
+        PyErr_Format(PyExc_RuntimeError, "%s must raise an exception",
+                     method_name);
+        Py_DECREF(result);
+    }
+}
+
+static
+void explain_ambiguous(PyObject *dispatcher, PyObject *args, PyObject *kws)
+{
+    explain_issue(dispatcher, args, kws, "_explain_ambiguous",
+                  "Ambiguous overloading");
+}
+
+static
+void explain_matching_error(PyObject *dispatcher, PyObject *args, PyObject *kws)
+{
+    explain_issue(dispatcher, args, kws, "_explain_matching_error",
+                  "No matching definition");
+}
+
+static
+int search_new_conversions(PyObject *dispatcher, PyObject *args, PyObject *kws)
+{
+    PyObject *callback, *result;
+    int res;
+
+    callback = PyObject_GetAttrString(dispatcher,
+                                      "_search_new_conversions");
+    if (!callback) {
+        return -1;
+    }
+    result = PyObject_Call(callback, args, kws);
+    Py_DECREF(callback);
+    if (result == NULL) {
+        return -1;
+    }
+    if (!PyBool_Check(result)) {
+        Py_DECREF(result);
+        PyErr_SetString(PyExc_TypeError,
+                        "_search_new_conversions() should return a boolean");
+        return -1;
+    }
+    res = (result == Py_True) ? 1 : 0;
+    Py_DECREF(result);
+    return res;
+}
+
+
+/* A custom, fast, inlinable version of PyCFunction_Call() */
+static PyObject *
+call_cfunc(Dispatcher *self, PyObject *cfunc, PyObject *args, PyObject *kws, PyObject *locals)
+{
+    PyCFunctionWithKeywords fn;
+    PyThreadState *tstate;
+
+    assert(PyCFunction_Check(cfunc));
+    assert(PyCFunction_GET_FLAGS(cfunc) == (METH_VARARGS | METH_KEYWORDS));
+    fn = (PyCFunctionWithKeywords) PyCFunction_GET_FUNCTION(cfunc);
+    tstate = PyThreadState_GET();
+
+#if (PY_MAJOR_VERSION >= 3) && (PY_MINOR_VERSION >= 10)
+    /*
+     * On Python 3.10+ trace_info comes from somewhere up in PyFrameEval et al,
+     * Numba doesn't have access to that so creates an equivalent struct and
+     * wires it up against the cframes. This is passed into the tracing
+     * functions.
+     *
+     * Code originally from:
+     * https://github.com/python/cpython/blob/c5bfb88eb6f82111bb1603ae9d78d0476b552d66/Python/ceval.c#L1611-L1622
+     */
+    PyTraceInfo trace_info;
+    trace_info.code = NULL; // not initialized
+    CFrame *prev_cframe = tstate->cframe;
+    trace_info.cframe.use_tracing = prev_cframe->use_tracing;
+    trace_info.cframe.previous = prev_cframe;
+
+    if (trace_info.cframe.use_tracing && tstate->c_profilefunc)
+#else
+    /*
+     * On Python prior to 3.10, tracing state is a member of the threadstate
+     */
+    if (tstate->use_tracing && tstate->c_profilefunc)
+#endif
+    {
+        /*
+         * The following code requires some explaining:
+         *
+         * We want the jit-compiled function to be visible to the profiler, so we
+         * need to synthesize a frame for it.
+         * The PyFrame_New() constructor doesn't do anything with the 'locals' value if the 'code's
+         * 'CO_NEWLOCALS' flag is set (which is always the case nowadays).
+         * So, to get local variables into the frame, we have to manually set the 'f_locals'
+         * member, then call `PyFrame_LocalsToFast`, where a subsequent call to the `frame.f_locals`
+         * property (by virtue of the `frame_getlocals` function in frameobject.c) will find them.
+         */
+        PyCodeObject *code = (PyCodeObject*)PyObject_GetAttrString((PyObject*)self, "__code__");
+        PyObject *globals = PyDict_New();
+        PyObject *builtins = PyEval_GetBuiltins();
+        PyFrameObject *frame = NULL;
+        PyObject *result = NULL;
+
+        if (!code) {
+            PyErr_Format(PyExc_RuntimeError, "No __code__ attribute found.");
+            goto error;
+        }
+        /* Populate builtins, which is required by some JITted functions */
+        if (PyDict_SetItemString(globals, "__builtins__", builtins)) {
+            goto error;
+        }
+
+        /* unset the CO_OPTIMIZED flag, make the frame get a new locals dict */
+        code->co_flags &= 0xFFFE;
+
+        frame = PyFrame_New(tstate, code, globals, locals);
+        if (frame == NULL) {
+            goto error;
+        }
+        /* Populate the 'fast locals' in `frame` */
+        PyFrame_LocalsToFast(frame, 0);
+        tstate->frame = frame;
+        C_TRACE(result, fn(PyCFunction_GET_SELF(cfunc), args, kws));
+        /* write changes back to locals? */
+        PyFrame_FastToLocals(frame);
+        tstate->frame = frame->f_back;
+
+    error:
+        Py_XDECREF(frame);
+        Py_XDECREF(globals);
+        Py_XDECREF(code);
+        return result;
+    }
+    else
+    {
+        return fn(PyCFunction_GET_SELF(cfunc), args, kws);
+    }
+}
+
+static
+PyObject*
+compile_and_invoke(Dispatcher *self, PyObject *args, PyObject *kws, PyObject *locals)
+{
+    /* Compile a new one */
+    PyObject *cfa, *cfunc, *retval;
+    cfa = PyObject_GetAttrString((PyObject*)self, "_compile_for_args");
+    if (cfa == NULL)
+        return NULL;
+
+    /* NOTE: we call the compiled function ourselves instead of
+       letting the Python derived class do it.  This is for proper
+       behaviour of globals() in jitted functions (issue #476). */
+    cfunc = PyObject_Call(cfa, args, kws);
+    Py_DECREF(cfa);
+
+    if (cfunc == NULL)
+        return NULL;
+
+    if (PyObject_TypeCheck(cfunc, &PyCFunction_Type)) {
+        retval = call_cfunc(self, cfunc, args, kws, locals);
+    } else {
+        /* Re-enter interpreter */
+        retval = PyObject_Call(cfunc, args, kws);
+    }
+    Py_DECREF(cfunc);
+
+    return retval;
+}
+
+/* A copy of compile_and_invoke, that only compiles. This is needed for CUDA
+ * kernels, because its overloads are Python instances of the _Kernel class,
+ * rather than compiled functions. Once CUDA overloads are compiled functions,
+ * cuda_compile_only can be removed. */
+static
+PyObject*
+cuda_compile_only(Dispatcher *self, PyObject *args, PyObject *kws, PyObject *locals)
+{
+    /* Compile a new one */
+    PyObject *cfa, *cfunc;
+    cfa = PyObject_GetAttrString((PyObject*)self, "_compile_for_args");
+    if (cfa == NULL)
+        return NULL;
+
+    cfunc = PyObject_Call(cfa, args, kws);
+    Py_DECREF(cfa);
+
+    return cfunc;
+}
+
+static int
+find_named_args(Dispatcher *self, PyObject **pargs, PyObject **pkws)
+{
+    PyObject *oldargs = *pargs, *newargs;
+    PyObject *kws = *pkws;
+    Py_ssize_t pos_args = PyTuple_GET_SIZE(oldargs);
+    Py_ssize_t named_args, total_args, i;
+    Py_ssize_t func_args = PyTuple_GET_SIZE(self->argnames);
+    Py_ssize_t defaults = PyTuple_GET_SIZE(self->defargs);
+    /* Last parameter with a default value */
+    Py_ssize_t last_def = (self->has_stararg)
+                          ? func_args - 2
+                          : func_args - 1;
+    /* First parameter with a default value */
+    Py_ssize_t first_def = last_def - defaults + 1;
+    /* Minimum number of required arguments */
+    Py_ssize_t minargs = first_def;
+
+    if (kws != NULL)
+        named_args = PyDict_Size(kws);
+    else
+        named_args = 0;
+    total_args = pos_args + named_args;
+    if (!self->has_stararg && total_args > func_args) {
+        PyErr_Format(PyExc_TypeError,
+                     "too many arguments: expected %d, got %d",
+                     (int) func_args, (int) total_args);
+        return -1;
+    }
+    else if (total_args < minargs) {
+        if (minargs == func_args)
+            PyErr_Format(PyExc_TypeError,
+                         "not enough arguments: expected %d, got %d",
+                         (int) minargs, (int) total_args);
+        else
+            PyErr_Format(PyExc_TypeError,
+                         "not enough arguments: expected at least %d, got %d",
+                         (int) minargs, (int) total_args);
+        return -1;
+    }
+    newargs = PyTuple_New(func_args);
+    if (!newargs)
+        return -1;
+    /* First pack the stararg */
+    if (self->has_stararg) {
+        Py_ssize_t stararg_size = Py_MAX(0, pos_args - func_args + 1);
+        PyObject *stararg = PyTuple_New(stararg_size);
+        if (!stararg) {
+            Py_DECREF(newargs);
+            return -1;
+        }
+        for (i = 0; i < stararg_size; i++) {
+            PyObject *value = PyTuple_GET_ITEM(oldargs, func_args - 1 + i);
+            Py_INCREF(value);
+            PyTuple_SET_ITEM(stararg, i, value);
+        }
+        /* Put it in last position */
+        PyTuple_SET_ITEM(newargs, func_args - 1, stararg);
+
+    }
+    for (i = 0; i < pos_args; i++) {
+        PyObject *value = PyTuple_GET_ITEM(oldargs, i);
+        if (self->has_stararg && i >= func_args - 1) {
+            /* Skip stararg */
+            break;
+        }
+        Py_INCREF(value);
+        PyTuple_SET_ITEM(newargs, i, value);
+    }
+
+    /* Iterate over missing positional arguments, try to find them in
+       named arguments or default values. */
+    for (i = pos_args; i < func_args; i++) {
+        PyObject *name = PyTuple_GET_ITEM(self->argnames, i);
+        if (self->has_stararg && i >= func_args - 1) {
+            /* Skip stararg */
+            break;
+        }
+        if (kws != NULL) {
+            /* Named argument? */
+            PyObject *value = PyDict_GetItem(kws, name);
+            if (value != NULL) {
+                Py_INCREF(value);
+                PyTuple_SET_ITEM(newargs, i, value);
+                named_args--;
+                continue;
+            }
+        }
+        if (i >= first_def && i <= last_def) {
+            /* Argument has a default value? */
+            PyObject *value = PyTuple_GET_ITEM(self->defargs, i - first_def);
+            Py_INCREF(value);
+            PyTuple_SET_ITEM(newargs, i, value);
+            continue;
+        }
+        else if (i < func_args - 1 || !self->has_stararg) {
+            PyErr_Format(PyExc_TypeError,
+                         "missing argument '%s'",
+                         PyString_AsString(name));
+            Py_DECREF(newargs);
+            return -1;
+        }
+    }
+    if (named_args) {
+        PyErr_Format(PyExc_TypeError,
+                     "some keyword arguments unexpected");
+        Py_DECREF(newargs);
+        return -1;
+    }
+    *pargs = newargs;
+    *pkws = NULL;
+    return 0;
+}
+
+
+/*
+ * Management of thread-local
+ */
+
+#ifdef _MSC_VER
+#define THREAD_LOCAL(ty) __declspec(thread) ty
+#else
+/* Non-standard C99 extension that's understood by gcc and clang */
+#define THREAD_LOCAL(ty) __thread ty
+#endif
+
+static THREAD_LOCAL(bool) use_tls_target_stack;
+
+
+struct raii_use_tls_target_stack {
+    bool old_setting;
+
+    raii_use_tls_target_stack(bool new_setting)
+        : old_setting(use_tls_target_stack)
+    {
+        use_tls_target_stack = new_setting;
+    }
+
+    ~raii_use_tls_target_stack() {
+        use_tls_target_stack = old_setting;
+    }
+};
+
+static PyObject*
+Dispatcher_call(Dispatcher *self, PyObject *args, PyObject *kws)
+{
+    PyObject *tmptype, *retval = NULL;
+    int *tys = NULL;
+    int argct;
+    int i;
+    int prealloc[24];
+    int matches;
+    PyObject *cfunc;
+    PyThreadState *ts = PyThreadState_Get();
+    PyObject *locals = NULL;
+
+    // Check TLS target stack
+    if (use_tls_target_stack) {
+        raii_use_tls_target_stack turn_off(false);
+        PyObject * meth_call_tls_target;
+        meth_call_tls_target = PyObject_GetAttrString((PyObject*)self,
+                                                      "_call_tls_target");
+        if (!meth_call_tls_target) return NULL;
+        // Transfer control to self._call_tls_target
+        retval = PyObject_Call(meth_call_tls_target, args, kws);
+        Py_DECREF(meth_call_tls_target);
+        return retval;
+    }
+
+    /* If compilation is enabled, ensure that an exact match is found and if
+     * not compile one */
+    int exact_match_required = self->can_compile ? 1 : self->exact_match_required;
+
+#if (PY_MAJOR_VERSION >= 3) && (PY_MINOR_VERSION >= 10)
+    if (ts->tracing && ts->c_profilefunc) {
+#else
+    if (ts->use_tracing && ts->c_profilefunc) {
+#endif
+        locals = PyEval_GetLocals();
+        if (locals == NULL) {
+            goto CLEANUP;
+        }
+    }
+    if (self->fold_args) {
+        if (find_named_args(self, &args, &kws))
+            return NULL;
+    }
+    else
+        Py_INCREF(args);
+    /* Now we own a reference to args */
+
+    argct = PySequence_Fast_GET_SIZE(args);
+
+    if (argct < (Py_ssize_t) (sizeof(prealloc) / sizeof(int)))
+        tys = prealloc;
+    else
+        tys = new int[argct];
+
+    for (i = 0; i < argct; ++i) {
+        tmptype = PySequence_Fast_GET_ITEM(args, i);
+        tys[i] = typeof_typecode((PyObject *) self, tmptype);
+        if (tys[i] == -1) {
+            if (self->can_fallback){
+                /* We will clear the exception if fallback is allowed. */
+                PyErr_Clear();
+            } else {
+                goto CLEANUP;
+            }
+        }
+    }
+
+    /* We only allow unsafe conversions if compilation of new specializations
+       has been disabled.
+
+       Note that the number of matches is returned in matches by resolve, which
+       accepts it as a reference. */
+    cfunc = self->resolve(tys, matches, !self->can_compile,
+                          exact_match_required);
+
+    if (matches == 0 && !self->can_compile) {
+        /*
+         * If we can't compile a new specialization, look for
+         * matching signatures for which conversions haven't been
+         * registered on the C++ TypeManager.
+         */
+        int res = search_new_conversions((PyObject *) self, args, kws);
+        if (res < 0) {
+            retval = NULL;
+            goto CLEANUP;
+        }
+        if (res > 0) {
+            /* Retry with the newly registered conversions */
+            cfunc = self->resolve(tys, matches, !self->can_compile,
+                                  exact_match_required);
+        }
+    }
+    if (matches == 1) {
+        /* Definition is found */
+        retval = call_cfunc(self, cfunc, args, kws, locals);
+    } else if (matches == 0) {
+        /* No matching definition */
+        if (self->can_compile) {
+            retval = compile_and_invoke(self, args, kws, locals);
+        } else if (self->fallbackdef) {
+            /* Have object fallback */
+            retval = call_cfunc(self, self->fallbackdef, args, kws, locals);
+        } else {
+            /* Raise TypeError */
+            explain_matching_error((PyObject *) self, args, kws);
+            retval = NULL;
+        }
+    } else if (self->can_compile) {
+        /* Ambiguous, but are allowed to compile */
+        retval = compile_and_invoke(self, args, kws, locals);
+    } else {
+        /* Ambiguous */
+        explain_ambiguous((PyObject *) self, args, kws);
+        retval = NULL;
+    }
+
+CLEANUP:
+    if (tys != prealloc)
+        delete[] tys;
+    Py_DECREF(args);
+
+    return retval;
+}
+
+/* Based on Dispatcher_call above, with the following differences:
+   1. It does not invoke the definition of the function.
+   2. It returns the definition, instead of a value returned by the function.
+
+   This is because CUDA functions are, at present, _Kernel objects rather than
+   compiled functions. */
+static PyObject*
+Dispatcher_cuda_call(Dispatcher *self, PyObject *args, PyObject *kws)
+{
+    PyObject *tmptype, *retval = NULL;
+    int *tys = NULL;
+    int argct;
+    int i;
+    int prealloc[24];
+    int matches;
+    PyObject *cfunc;
+    PyThreadState *ts = PyThreadState_Get();
+    PyObject *locals = NULL;
+
+    /* If compilation is enabled, ensure that an exact match is found and if
+     * not compile one */
+    int exact_match_required = self->can_compile ? 1 : self->exact_match_required;
+
+#if (PY_MAJOR_VERSION >= 3) && (PY_MINOR_VERSION >= 10)
+    if (ts->tracing && ts->c_profilefunc) {
+#else
+    if (ts->use_tracing && ts->c_profilefunc) {
+#endif
+        locals = PyEval_GetLocals();
+        if (locals == NULL) {
+            goto CLEANUP;
+        }
+    }
+    if (self->fold_args) {
+        if (find_named_args(self, &args, &kws))
+            return NULL;
+    }
+    else
+        Py_INCREF(args);
+    /* Now we own a reference to args */
+
+    argct = PySequence_Fast_GET_SIZE(args);
+
+    if (argct < (Py_ssize_t) (sizeof(prealloc) / sizeof(int)))
+        tys = prealloc;
+    else
+        tys = new int[argct];
+
+    for (i = 0; i < argct; ++i) {
+        tmptype = PySequence_Fast_GET_ITEM(args, i);
+        tys[i] = typeof_typecode((PyObject *) self, tmptype);
+        if (tys[i] == -1) {
+            if (self->can_fallback){
+                /* We will clear the exception if fallback is allowed. */
+                PyErr_Clear();
+            } else {
+                goto CLEANUP;
+            }
+        }
+    }
+
+    /* We only allow unsafe conversions if compilation of new specializations
+       has been disabled. */
+    cfunc = self->resolve(tys, matches, !self->can_compile,
+                          exact_match_required);
+
+    if (matches == 0 && !self->can_compile) {
+        /*
+         * If we can't compile a new specialization, look for
+         * matching signatures for which conversions haven't been
+         * registered on the C++ TypeManager.
+         */
+        int res = search_new_conversions((PyObject *) self, args, kws);
+        if (res < 0) {
+            retval = NULL;
+            goto CLEANUP;
+        }
+        if (res > 0) {
+            /* Retry with the newly registered conversions */
+            cfunc = self->resolve(tys, matches, !self->can_compile,
+                                  exact_match_required);
+        }
+    }
+
+    if (matches == 1) {
+        /* Definition is found */
+        retval = cfunc;
+        Py_INCREF(retval);
+    } else if (matches == 0) {
+        /* No matching definition */
+        if (self->can_compile) {
+            retval = cuda_compile_only(self, args, kws, locals);
+        } else if (self->fallbackdef) {
+            /* Have object fallback */
+            retval = call_cfunc(self, self->fallbackdef, args, kws, locals);
+        } else {
+            /* Raise TypeError */
+            explain_matching_error((PyObject *) self, args, kws);
+            retval = NULL;
+        }
+    } else if (self->can_compile) {
+        /* Ambiguous, but are allowed to compile */
+        retval = cuda_compile_only(self, args, kws, locals);
+    } else {
+        /* Ambiguous */
+        explain_ambiguous((PyObject *) self, args, kws);
+        retval = NULL;
+    }
+
+CLEANUP:
+    if (tys != prealloc)
+        delete[] tys;
+    Py_DECREF(args);
+
+    return retval;
+}
+
+static int
+import_devicearray(void)
+{
+    PyObject *devicearray = PyImport_ImportModule("numba._devicearray");
+    if (devicearray == NULL) {
+        return -1;
+    }
+    Py_DECREF(devicearray);
+
+    DeviceArray_API = (void**)PyCapsule_Import("numba._devicearray._DEVICEARRAY_API", 0);
+    if (DeviceArray_API == NULL) {
+        return -1;
+    }
+
+    return 0;
+}
+
+static PyMethodDef Dispatcher_methods[] = {
+    { "_clear", (PyCFunction)Dispatcher_clear, METH_NOARGS, NULL },
+    { "_insert", (PyCFunction)Dispatcher_Insert, METH_VARARGS | METH_KEYWORDS,
+      "insert new definition"},
+    { "_cuda_call", (PyCFunction)Dispatcher_cuda_call,
+      METH_VARARGS | METH_KEYWORDS, "CUDA call resolution" },
+    { NULL },
+};
+
+static PyMemberDef Dispatcher_members[] = {
+    {(char*)"_can_compile", T_BOOL, offsetof(Dispatcher, can_compile), 0, NULL },
+    {NULL}  /* Sentinel */
+};
+
+
+static PyTypeObject DispatcherType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "_dispatcher.Dispatcher",                    /* tp_name */
+    sizeof(Dispatcher),                          /* tp_basicsize */
+    0,                                           /* tp_itemsize */
+    (destructor)Dispatcher_dealloc,              /* tp_dealloc */
+    0,                                           /* tp_print */
+    0,                                           /* tp_getattr */
+    0,                                           /* tp_setattr */
+    0,                                           /* tp_compare */
+    0,                                           /* tp_repr */
+    0,                                           /* tp_as_number */
+    0,                                           /* tp_as_sequence */
+    0,                                           /* tp_as_mapping */
+    0,                                           /* tp_hash */
+    (PyCFunctionWithKeywords)Dispatcher_call,    /* tp_call*/
+    0,                                           /* tp_str*/
+    0,                                           /* tp_getattro*/
+    0,                                           /* tp_setattro*/
+    0,                                           /* tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HAVE_GC, /* tp_flags*/
+    "Dispatcher object",                         /* tp_doc */
+    (traverseproc) Dispatcher_traverse,          /* tp_traverse */
+    0,                                           /* tp_clear */
+    0,                                           /* tp_richcompare */
+    0,                                           /* tp_weaklistoffset */
+    0,                                           /* tp_iter */
+    0,                                           /* tp_iternext */
+    Dispatcher_methods,                          /* tp_methods */
+    Dispatcher_members,                          /* tp_members */
+    0,                                           /* tp_getset */
+    0,                                           /* tp_base */
+    0,                                           /* tp_dict */
+    0,                                           /* tp_descr_get */
+    0,                                           /* tp_descr_set */
+    0,                                           /* tp_dictoffset */
+    (initproc)Dispatcher_init,                   /* tp_init */
+    0,                                           /* tp_alloc */
+    0,                                           /* tp_new */
+    0,                                           /* tp_free */
+    0,                                           /* tp_is_gc */
+    0,                                           /* tp_bases */
+    0,                                           /* tp_mro */
+    0,                                           /* tp_cache */
+    0,                                           /* tp_subclasses */
+    0,                                           /* tp_weaklist */
+    0,                                           /* tp_del */
+    0,                                           /* tp_version_tag */
+    0,                                           /* tp_finalize */
+#if PY_MAJOR_VERSION == 3
+/* Python 3.8 has two slots, 3.9 has one. */
+#if PY_MINOR_VERSION > 7
+    0,                                           /* tp_vectorcall */
+#if PY_MINOR_VERSION == 8
+    0,                                           /* tp_print */
+#endif
+#endif
+#endif
+};
+
+
+static PyObject *compute_fingerprint(PyObject *self, PyObject *args)
+{
+    PyObject *val;
+    if (!PyArg_ParseTuple(args, "O:compute_fingerprint", &val))
+        return NULL;
+    return typeof_compute_fingerprint(val);
+}
+
+static PyObject *set_use_tls_target_stack(PyObject *self, PyObject *args)
+{
+    int val;
+    if (!PyArg_ParseTuple(args, "p", &val))
+        return NULL;
+    bool old = use_tls_target_stack;
+    use_tls_target_stack = val;
+    // return the old value
+    if (old) {
+        Py_RETURN_TRUE;
+    } else {
+        Py_RETURN_FALSE;
+    }
+}
+
+static PyMethodDef ext_methods[] = {
+#define declmethod(func) { #func , ( PyCFunction )func , METH_VARARGS , NULL }
+    declmethod(typeof_init),
+    declmethod(compute_fingerprint),
+    declmethod(set_use_tls_target_stack),
+    { NULL },
+#undef declmethod
+};
+
+
+MOD_INIT(_dispatcher) {
+    if (import_devicearray() < 0) {
+      PyErr_Print();
+      PyErr_SetString(PyExc_ImportError, "numba._devicearray failed to import");
+      return MOD_ERROR_VAL;
+    }
+
+    PyObject *m;
+    MOD_DEF(m, "_dispatcher", "No docs", ext_methods)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    DispatcherType.tp_new = PyType_GenericNew;
+    if (PyType_Ready(&DispatcherType) < 0) {
+        return MOD_ERROR_VAL;
+    }
+    Py_INCREF(&DispatcherType);
+    PyModule_AddObject(m, "Dispatcher", (PyObject*)(&DispatcherType));
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dynfunc.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dynfunc.c
new file mode 100644
index 000000000..7c228a830
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dynfunc.c
@@ -0,0 +1,507 @@
+/*
+ * Definition of Environment and Closure objects.
+ * This module is included by _dynfuncmod.c and by pycc-compiled modules.
+ */
+
+#include "_pymodule.h"
+
+#include <string.h>
+
+/* NOTE: EnvironmentObject and ClosureObject must be kept in sync with
+ * the definitions in numba/targets/base.py (EnvBody and ClosureBody).
+ */
+
+/*
+ * EnvironmentObject hosts data needed for execution of compiled functions.
+ */
+typedef struct {
+    PyObject_HEAD
+    PyObject *globals;
+    /* Assorted "constants" that are needed at runtime to execute
+       the compiled function.  This can include frozen closure variables,
+       lifted loops, etc. */
+    PyObject *consts;
+} EnvironmentObject;
+
+
+static PyMemberDef env_members[] = {
+    {"globals", T_OBJECT, offsetof(EnvironmentObject, globals), READONLY, NULL},
+    {"consts", T_OBJECT, offsetof(EnvironmentObject, consts), READONLY, NULL},
+    {NULL}  /* Sentinel */
+};
+
+static int
+env_traverse(EnvironmentObject *env, visitproc visit, void *arg)
+{
+    Py_VISIT(env->globals);
+    Py_VISIT(env->consts);
+    return 0;
+}
+
+static int
+env_clear(EnvironmentObject *env)
+{
+    Py_CLEAR(env->globals);
+    Py_CLEAR(env->consts);
+    return 0;
+}
+
+static void
+env_dealloc(EnvironmentObject *env)
+{
+    PyObject_GC_UnTrack((PyObject *) env);
+    env_clear(env);
+    Py_TYPE(env)->tp_free((PyObject *) env);
+}
+
+static EnvironmentObject *
+env_new_empty(PyTypeObject* type)
+{
+    return (EnvironmentObject *) PyType_GenericNew(type, NULL, NULL);
+}
+
+static PyObject *
+env_new(PyTypeObject* type, PyObject* args, PyObject* kwds)
+{
+    PyObject *globals;
+    EnvironmentObject *env;
+    static char *kwlist[] = {"globals", 0};
+
+    if (!PyArg_ParseTupleAndKeywords(
+            args, kwds, "O!:function", kwlist,
+            &PyDict_Type, &globals))
+        return NULL;
+
+    env = env_new_empty(type);
+    if (env == NULL)
+        return NULL;
+    Py_INCREF(globals);
+    env->globals = globals;
+    env->consts = PyList_New(0);
+    if (!env->consts) {
+        Py_DECREF(env);
+        return NULL;
+    }
+    return (PyObject *) env;
+}
+
+
+static PyTypeObject EnvironmentType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "_dynfunc.Environment",   /*tp_name*/
+    sizeof(EnvironmentObject), /*tp_basicsize*/
+    0,                         /*tp_itemsize*/
+    (destructor) env_dealloc,  /*tp_dealloc*/
+    0,                         /*tp_print*/
+    0,                         /*tp_getattr*/
+    0,                         /*tp_setattr*/
+    0,                         /*tp_compare*/
+    0,                         /*tp_repr*/
+    0,                         /*tp_as_number*/
+    0,                         /*tp_as_sequence*/
+    0,                         /*tp_as_mapping*/
+    0,                         /*tp_hash */
+    0,                         /*tp_call*/
+    0,                         /*tp_str*/
+    0,                         /*tp_getattro*/
+    0,                         /*tp_setattro*/
+    0,                         /*tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+    0,                         /* tp_doc */
+    (traverseproc) env_traverse, /* tp_traverse */
+    (inquiry) env_clear,       /* tp_clear */
+    0,                         /* tp_richcompare */
+    0,                         /* tp_weaklistoffset */
+    0,                         /* tp_iter */
+    0,                         /* tp_iternext */
+    0,                         /* tp_methods */
+    env_members,               /* tp_members */
+    0,                         /* tp_getset */
+    0,                         /* tp_base */
+    0,                         /* tp_dict */
+    0,                         /* tp_descr_get */
+    0,                         /* tp_descr_set */
+    0,                         /* tp_dictoffset */
+    0,                         /* tp_init */
+    0,                         /* tp_alloc */
+    env_new,                   /* tp_new */
+    0,                         /* tp_free */
+    0,                         /* tp_is_gc */
+    0,                         /* tp_bases */
+    0,                         /* tp_mro */
+    0,                         /* tp_cache */
+    0,                         /* tp_subclasses */
+    0,                         /* tp_weaklist */
+    0,                         /* tp_del */
+    0,                         /* tp_version_tag */
+    0,                         /* tp_finalize */
+#if PY_MAJOR_VERSION == 3
+/* Python 3.8 has two slots, 3.9 has one. */
+#if PY_MINOR_VERSION > 7
+    0,                         /* tp_vectorcall */
+#if PY_MINOR_VERSION == 8
+    0,                         /* tp_print */
+#endif
+#endif
+#endif
+};
+
+/* A closure object is created for each call to make_function(), and stored
+   as the resulting PyCFunction object's "self" pointer.  It points to an
+   EnvironmentObject which is constructed during compilation.  This allows
+   for two things:
+       - lifetime management of dependent data (e.g. lifted loop dispatchers)
+       - access to the execution environment by the compiled function
+         (for example the globals module)
+   */
+
+/* Closure is a variable-sized object for binary compatibility with
+   Generator (see below). */
+#define CLOSURE_HEAD          \
+    PyObject_VAR_HEAD         \
+    EnvironmentObject *env;
+
+typedef struct {
+    CLOSURE_HEAD
+    /* The dynamically-filled method definition for the PyCFunction object
+       using this closure. */
+    PyMethodDef def;
+    /* Arbitrary object to keep alive during the closure's lifetime.
+       (put a tuple to put several objects alive).
+       In practice, this helps keep the LLVM module and its generated
+       code alive. */
+    PyObject *keepalive;
+    PyObject *weakreflist;
+} ClosureObject;
+
+
+static int
+closure_traverse(ClosureObject *clo, visitproc visit, void *arg)
+{
+    Py_VISIT(clo->env);
+    Py_VISIT(clo->keepalive);
+    return 0;
+}
+
+static void
+closure_dealloc(ClosureObject *clo)
+{
+    PyObject_GC_UnTrack((PyObject *) clo);
+    if (clo->weakreflist != NULL)
+        PyObject_ClearWeakRefs((PyObject *) clo);
+    PyObject_Free((void *) clo->def.ml_name);
+    PyObject_Free((void *) clo->def.ml_doc);
+    Py_XDECREF(clo->env);
+    Py_XDECREF(clo->keepalive);
+    Py_TYPE(clo)->tp_free((PyObject *) clo);
+}
+
+static PyTypeObject ClosureType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "_dynfunc._Closure",    /*tp_name*/
+    sizeof(ClosureObject),     /*tp_basicsize*/
+    0,                         /*tp_itemsize*/
+    (destructor) closure_dealloc, /*tp_dealloc*/
+    0,                         /*tp_print*/
+    0,                         /*tp_getattr*/
+    0,                         /*tp_setattr*/
+    0,                         /*tp_compare*/
+    0,                         /*tp_repr*/
+    0,                         /*tp_as_number*/
+    0,                         /*tp_as_sequence*/
+    0,                         /*tp_as_mapping*/
+    0,                         /*tp_hash */
+    0,                         /*tp_call*/
+    0,                         /*tp_str*/
+    0,                         /*tp_getattro*/
+    0,                         /*tp_setattro*/
+    0,                         /*tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+    0,                         /* tp_doc */
+    (traverseproc) closure_traverse, /* tp_traverse */
+    0,                         /* tp_clear */
+    0,                         /* tp_richcompare */
+    offsetof(ClosureObject, weakreflist), /* tp_weaklistoffset */
+    0,                         /* tp_iter */
+    0,                         /* tp_iternext */
+    0,                         /* tp_methods */
+    0,                         /* tp_members */
+    0,                         /* tp_getset */
+    0,                         /* tp_base */
+    0,                         /* tp_dict */
+    0,                         /* tp_descr_get */
+    0,                         /* tp_descr_set */
+    0,                         /* tp_dictoffset */
+    0,                         /* tp_init */
+    0,                         /* tp_alloc */
+    0,                         /* tp_new */
+    0,                         /* tp_free */
+    0,                         /* tp_is_gc */
+    0,                         /* tp_bases */
+    0,                         /* tp_mro */
+    0,                         /* tp_cache */
+    0,                         /* tp_subclasses */
+    0,                         /* tp_weaklist */
+    0,                         /* tp_del */
+    0,                         /* tp_version_tag */
+    0,                         /* tp_finalize */
+#if PY_MAJOR_VERSION == 3
+/* Python 3.8 has two slots, 3.9 has one. */
+#if PY_MINOR_VERSION > 7
+    0,                         /* tp_vectorcall */
+#if PY_MINOR_VERSION == 8
+    0,                         /* tp_print */
+#endif
+#endif
+#endif
+};
+
+
+/* Return an owned piece of character data duplicating a Python string
+   object's value. */
+static char *
+dup_string(PyObject *strobj)
+{
+    const char *tmp = NULL;
+    char *str;
+    tmp = PyString_AsString(strobj);
+    if (tmp == NULL)
+        return NULL;
+    /* Using PyObject_Malloc allows this memory to be tracked for
+       leaks. */
+    str = PyObject_Malloc(strlen(tmp) + 1);
+    if (str == NULL) {
+        PyErr_NoMemory();
+        return NULL;
+    }
+    strcpy(str, tmp);
+    return str;
+}
+
+/* Create and initialize a new Closure object */
+static ClosureObject *
+closure_new(PyObject *name, PyObject *doc, PyCFunction fnaddr,
+            EnvironmentObject *env, PyObject *keepalive)
+{
+    ClosureObject *clo = (ClosureObject *) PyType_GenericAlloc(&ClosureType, 0);
+    if (clo == NULL)
+        return NULL;
+
+    clo->def.ml_name = dup_string(name);
+    if (!clo->def.ml_name) {
+        Py_DECREF(clo);
+        return NULL;
+    }
+    clo->def.ml_meth = fnaddr;
+    clo->def.ml_flags = METH_VARARGS | METH_KEYWORDS;
+    clo->def.ml_doc = dup_string(doc);
+    if (!clo->def.ml_doc) {
+        Py_DECREF(clo);
+        return NULL;
+    }
+    Py_INCREF(env);
+    clo->env = env;
+    Py_XINCREF(keepalive);
+    clo->keepalive = keepalive;
+    return clo;
+}
+
+/* Create a new PyCFunction object wrapping a closure defined by
+   the given arguments. */
+static PyObject *
+pycfunction_new(PyObject *module, PyObject *name, PyObject *doc,
+                PyCFunction fnaddr, EnvironmentObject *env, PyObject *keepalive)
+{
+    PyObject *funcobj;
+    PyObject *modname = NULL;
+    ClosureObject *closure = NULL;
+
+    closure = closure_new(name, doc, fnaddr, env, keepalive);
+    if (closure == NULL) goto FAIL;
+
+    modname = PyObject_GetAttrString(module, "__name__");
+    if (modname == NULL) goto FAIL;
+
+    funcobj = PyCFunction_NewEx(&closure->def, (PyObject *) closure, modname);
+    Py_DECREF(closure);
+    Py_DECREF(modname);
+
+    return funcobj;
+
+FAIL:
+    Py_XDECREF(closure);
+    Py_XDECREF(modname);
+    return NULL;
+}
+
+/*
+ * Python-facing wrapper for Numba-compiled generator.
+ * Note the Environment's offset inside the struct is the same as in the
+ * Closure object.  This is required to simplify generation of Python wrappers.
+ */
+
+typedef void (*gen_finalizer_t)(void *);
+
+typedef struct {
+    CLOSURE_HEAD
+    PyCFunctionWithKeywords nextfunc;
+    gen_finalizer_t finalizer;
+    PyObject *weakreflist;
+    union {
+        double dummy;   /* Force alignment */
+        char state[0];
+    };
+} GeneratorObject;
+
+static int
+generator_traverse(GeneratorObject *gen, visitproc visit, void *arg)
+{
+    /* XXX this doesn't traverse the state, which can own references to
+       PyObjects */
+    Py_VISIT(gen->env);
+    return 0;
+}
+
+static int
+generator_clear(GeneratorObject *gen)
+{
+    if (gen->finalizer != NULL) {
+        gen->finalizer(gen->state);
+        gen->finalizer = NULL;
+    }
+    Py_CLEAR(gen->env);
+    gen->nextfunc = NULL;
+    return 0;
+}
+
+static void
+generator_dealloc(GeneratorObject *gen)
+{
+    PyObject_GC_UnTrack((PyObject *) gen);
+    if (gen->weakreflist != NULL)
+        PyObject_ClearWeakRefs((PyObject *) gen);
+    /* XXX The finalizer may be called after the LLVM module has been
+       destroyed (typically at interpreter shutdown) */
+#if PY_MAJOR_VERSION >= 3
+#if PY_MINOR_VERSION >= 7
+    if (!_Py_IsFinalizing())
+#else
+    if (!_Py_Finalizing)
+#endif
+#endif
+        if (gen->finalizer != NULL)
+            gen->finalizer(gen->state);
+    Py_XDECREF(gen->env);
+    Py_TYPE(gen)->tp_free((PyObject *) gen);
+}
+
+static PyObject *
+generator_iternext(GeneratorObject *gen)
+{
+    PyObject *res, *args;
+    if (gen->nextfunc == NULL) {
+        PyErr_SetString(PyExc_RuntimeError,
+                        "cannot call next() on finalized generator");
+        return NULL;
+    }
+    args = PyTuple_Pack(1, (PyObject *) gen);
+    if (args == NULL)
+        return NULL;
+    res = (*gen->nextfunc)((PyObject *) gen, args, NULL);
+    Py_DECREF(args);
+    return res;
+}
+
+static PyTypeObject GeneratorType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "_dynfunc._Generator",                    /* tp_name*/
+    offsetof(GeneratorObject, state),         /* tp_basicsize*/
+    1,                                        /* tp_itemsize*/
+    (destructor) generator_dealloc,           /* tp_dealloc*/
+    0,                                        /* tp_print*/
+    0,                                        /* tp_getattr*/
+    0,                                        /* tp_setattr*/
+    0,                                        /* tp_compare*/
+    0,                                        /* tp_repr*/
+    0,                                        /* tp_as_number*/
+    0,                                        /* tp_as_sequence*/
+    0,                                        /* tp_as_mapping*/
+    0,                                        /* tp_hash */
+    0,                                        /* tp_call*/
+    0,                                        /* tp_str*/
+    0,                                        /* tp_getattro*/
+    0,                                        /* tp_setattro*/
+    0,                                        /* tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC
+                       | Py_TPFLAGS_BASETYPE,  /* tp_flags*/
+    0,                                        /* tp_doc */
+    (traverseproc) generator_traverse,        /* tp_traverse */
+    (inquiry) generator_clear,                /* tp_clear */
+    0,                                        /* tp_richcompare */
+    offsetof(GeneratorObject, weakreflist),   /* tp_weaklistoffset */
+    PyObject_SelfIter,                        /* tp_iter */
+    (iternextfunc) generator_iternext,        /* tp_iternext */
+    0,                                        /* tp_methods */
+    0,                                        /* tp_members */
+    0,                                        /* tp_getset */
+    0,                                        /* tp_base */
+    0,                                        /* tp_dict */
+    0,                                        /* tp_descr_get */
+    0,                                        /* tp_descr_set */
+    0,                                        /* tp_dictoffset */
+    0,                                        /* tp_init */
+    0,                                        /* tp_alloc */
+    0,                                        /* tp_new */
+    0,                                        /* tp_free */
+    0,                                        /* tp_is_gc */
+    0,                                        /* tp_bases */
+    0,                                        /* tp_mro */
+    0,                                        /* tp_cache */
+    0,                                        /* tp_subclasses */
+    0,                                        /* tp_weaklist */
+    0,                                        /* tp_del */
+    0,                                        /* tp_version_tag */
+    0,                                        /* tp_finalize */
+#if PY_MAJOR_VERSION == 3
+/* Python 3.8 has two slots, 3.9 has one. */
+#if PY_MINOR_VERSION > 7
+    0,                         /* tp_vectorcall */
+#if PY_MINOR_VERSION == 8
+    0,                         /* tp_print */
+#endif
+#endif
+#endif
+};
+
+/* Dynamically create a new generator object */
+static PyObject *
+Numba_make_generator(Py_ssize_t gen_state_size,
+                     void *initial_state,
+                     PyCFunctionWithKeywords nextfunc,
+                     gen_finalizer_t finalizer,
+                     EnvironmentObject *env)
+{
+    GeneratorObject *gen;
+    gen = (GeneratorObject *) PyType_GenericAlloc(&GeneratorType, gen_state_size);
+    if (gen == NULL)
+        return NULL;
+    memcpy(gen->state, initial_state, gen_state_size);
+    gen->nextfunc = nextfunc;
+    Py_XINCREF(env);
+    gen->env = env;
+    gen->finalizer = finalizer;
+    return (PyObject *) gen;
+}
+
+/* Initialization subroutine for use by modules including this */
+static int
+init_dynfunc_module(PyObject *module)
+{
+    if (PyType_Ready(&ClosureType))
+        return -1;
+    if (PyType_Ready(&EnvironmentType))
+        return -1;
+    if (PyType_Ready(&GeneratorType))
+        return -1;
+    return 0;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dynfuncmod.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dynfuncmod.c
new file mode 100644
index 000000000..5d80529c0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_dynfuncmod.c
@@ -0,0 +1,93 @@
+#include "_dynfunc.c"
+
+/* Python-facing function to dynamically create a new C function object */
+static PyObject*
+make_function(PyObject *self, PyObject *args)
+{
+    PyObject *module, *fname, *fdoc, *fnaddrobj;
+    void *fnaddr;
+    EnvironmentObject *env;
+    PyObject *keepalive;
+
+    if (!PyArg_ParseTuple(args, "OOOOO!|O",
+            &module, &fname, &fdoc, &fnaddrobj, &EnvironmentType, &env,
+            &keepalive)) {
+        return NULL;
+    }
+
+    fnaddr = PyLong_AsVoidPtr(fnaddrobj);
+    if (fnaddr == NULL && PyErr_Occurred())
+        return NULL;
+
+    return pycfunction_new(module, fname, fdoc, fnaddr, env, keepalive);
+}
+
+static PyMethodDef ext_methods[] = {
+#define declmethod(func) { #func , ( PyCFunction )func , METH_VARARGS , NULL }
+    declmethod(make_function),
+    { NULL },
+#undef declmethod
+};
+
+
+static PyObject *
+build_c_helpers_dict(void)
+{
+    PyObject *dct = PyDict_New();
+    if (dct == NULL)
+        goto error;
+
+#define _declpointer(name, value) do {                 \
+    PyObject *o = PyLong_FromVoidPtr(value);           \
+    if (o == NULL) goto error;                         \
+    if (PyDict_SetItemString(dct, name, o)) {          \
+        Py_DECREF(o);                                  \
+        goto error;                                    \
+    }                                                  \
+    Py_DECREF(o);                                      \
+} while (0)
+
+#define declmethod(func) _declpointer(#func, &Numba_##func)
+
+#define declpointer(ptr) _declpointer(#ptr, &ptr)
+
+    declmethod(make_generator);
+
+#undef declmethod
+    return dct;
+error:
+    Py_XDECREF(dct);
+    return NULL;
+}
+
+MOD_INIT(_dynfunc) {
+    PyObject *m, *impl_info;
+
+    MOD_DEF(m, "_dynfunc", "No docs", ext_methods)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    if (init_dynfunc_module(m))
+        return MOD_ERROR_VAL;
+
+    impl_info = Py_BuildValue(
+        "{snsnsn}",
+        "offsetof_closure_body", offsetof(ClosureObject, env),
+        "offsetof_env_body", offsetof(EnvironmentObject, globals),
+        "offsetof_generator_state", offsetof(GeneratorObject, state)
+        );
+    if (impl_info == NULL)
+        return MOD_ERROR_VAL;
+    PyModule_AddObject(m, "_impl_info", impl_info);
+
+    Py_INCREF(&ClosureType);
+    PyModule_AddObject(m, "_Closure", (PyObject *) (&ClosureType));
+    Py_INCREF(&EnvironmentType);
+    PyModule_AddObject(m, "Environment", (PyObject *) (&EnvironmentType));
+    Py_INCREF(&GeneratorType);
+    PyModule_AddObject(m, "_Generator", (PyObject *) (&GeneratorType));
+
+    PyModule_AddObject(m, "c_helpers", build_c_helpers_dict());
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_hashtable.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_hashtable.c
new file mode 100644
index 000000000..76392f79e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_hashtable.c
@@ -0,0 +1,530 @@
+/*
+ * This file and _hashtable.h are from CPython 3.5.  The symbols have been
+ * renamed from _Py_hashxxx to _Numba_hashxxx to avoid name clashes with
+ * the CPython definitions (including at runtime through dynamic linking).
+ * Those CPython APIs are private and can change in incompatible ways at
+ * any time.
+ *
+ * Command line used for renaming:
+ * $ sed -i -r 's/\b_Py_(has[h]table)/_Numba_\1/ig' numba/_hashtable.h numba/_hashtable.c
+ */
+
+/* The implementation of the hash table (_Numba_hashtable_t) is based on the cfuhash
+   project:
+   http://sourceforge.net/projects/libcfu/
+
+   Copyright of cfuhash:
+   ----------------------------------
+   Creation date: 2005-06-24 21:22:40
+   Authors: Don
+   Change log:
+
+   Copyright (c) 2005 Don Owens
+   All rights reserved.
+
+   This code is released under the BSD license:
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+     * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+     * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+     * Neither the name of the author nor the names of its
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+   FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+   COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+   INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+   (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+   SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+   HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+   STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+   ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
+   OF THE POSSIBILITY OF SUCH DAMAGE.
+   ----------------------------------
+*/
+
+#include "_pymodule.h"
+#include "_hashtable.h"
+
+#define HASHTABLE_MIN_SIZE 16
+#define HASHTABLE_HIGH 0.50
+#define HASHTABLE_LOW 0.10
+#define HASHTABLE_REHASH_FACTOR 2.0 / (HASHTABLE_LOW + HASHTABLE_HIGH)
+
+#define BUCKETS_HEAD(SLIST) \
+        ((_Numba_hashtable_entry_t *)_Py_SLIST_HEAD(&(SLIST)))
+#define TABLE_HEAD(HT, BUCKET) \
+        ((_Numba_hashtable_entry_t *)_Py_SLIST_HEAD(&(HT)->buckets[BUCKET]))
+#define ENTRY_NEXT(ENTRY) \
+        ((_Numba_hashtable_entry_t *)_Py_SLIST_ITEM_NEXT(ENTRY))
+#define HASHTABLE_ITEM_SIZE(HT) \
+        (sizeof(_Numba_hashtable_entry_t) + (HT)->data_size)
+
+/* Forward declaration */
+static void hashtable_rehash(_Numba_hashtable_t *ht);
+
+static void
+_Py_slist_init(_Py_slist_t *list)
+{
+    list->head = NULL;
+}
+
+static void
+_Py_slist_prepend(_Py_slist_t *list, _Py_slist_item_t *item)
+{
+    item->next = list->head;
+    list->head = item;
+}
+
+static void
+_Py_slist_remove(_Py_slist_t *list, _Py_slist_item_t *previous,
+                 _Py_slist_item_t *item)
+{
+    if (previous != NULL)
+        previous->next = item->next;
+    else
+        list->head = item->next;
+}
+
+Py_uhash_t
+_Numba_hashtable_hash_int(const void *key)
+{
+    return (Py_uhash_t)key;
+}
+
+Py_uhash_t
+_Numba_hashtable_hash_ptr(const void *key)
+{
+    return (Py_uhash_t)_Py_HashPointer((void *)key);
+}
+
+int
+_Numba_hashtable_compare_direct(const void *key, const _Numba_hashtable_entry_t *entry)
+{
+    return entry->key == key;
+}
+
+/* makes sure the real size of the buckets array is a power of 2 */
+static size_t
+round_size(size_t s)
+{
+    size_t i;
+    if (s < HASHTABLE_MIN_SIZE)
+        return HASHTABLE_MIN_SIZE;
+    i = 1;
+    while (i < s)
+        i <<= 1;
+    return i;
+}
+
+_Numba_hashtable_t *
+_Numba_hashtable_new_full(size_t data_size, size_t init_size,
+                       _Numba_hashtable_hash_func hash_func,
+                       _Numba_hashtable_compare_func compare_func,
+                       _Numba_hashtable_copy_data_func copy_data_func,
+                       _Numba_hashtable_free_data_func free_data_func,
+                       _Numba_hashtable_get_data_size_func get_data_size_func,
+                       _Numba_hashtable_allocator_t *allocator)
+{
+    _Numba_hashtable_t *ht;
+    size_t buckets_size;
+    _Numba_hashtable_allocator_t alloc;
+
+    if (allocator == NULL) {
+        alloc.malloc = PyMem_RawMalloc;
+        alloc.free = PyMem_RawFree;
+    }
+    else
+        alloc = *allocator;
+
+    ht = (_Numba_hashtable_t *)alloc.malloc(sizeof(_Numba_hashtable_t));
+    if (ht == NULL)
+        return ht;
+
+    ht->num_buckets = round_size(init_size);
+    ht->entries = 0;
+    ht->data_size = data_size;
+
+    buckets_size = ht->num_buckets * sizeof(ht->buckets[0]);
+    ht->buckets = alloc.malloc(buckets_size);
+    if (ht->buckets == NULL) {
+        alloc.free(ht);
+        return NULL;
+    }
+    memset(ht->buckets, 0, buckets_size);
+
+    ht->hash_func = hash_func;
+    ht->compare_func = compare_func;
+    ht->copy_data_func = copy_data_func;
+    ht->free_data_func = free_data_func;
+    ht->get_data_size_func = get_data_size_func;
+    ht->alloc = alloc;
+    return ht;
+}
+
+_Numba_hashtable_t *
+_Numba_hashtable_new(size_t data_size,
+                  _Numba_hashtable_hash_func hash_func,
+                  _Numba_hashtable_compare_func compare_func)
+{
+    return _Numba_hashtable_new_full(data_size, HASHTABLE_MIN_SIZE,
+                                  hash_func, compare_func,
+                                  NULL, NULL, NULL, NULL);
+}
+
+size_t
+_Numba_hashtable_size(_Numba_hashtable_t *ht)
+{
+    size_t size;
+    size_t hv;
+
+    size = sizeof(_Numba_hashtable_t);
+
+    /* buckets */
+    size += ht->num_buckets * sizeof(_Numba_hashtable_entry_t *);
+
+    /* entries */
+    size += ht->entries * HASHTABLE_ITEM_SIZE(ht);
+
+    /* data linked from entries */
+    if (ht->get_data_size_func) {
+        for (hv = 0; hv < ht->num_buckets; hv++) {
+            _Numba_hashtable_entry_t *entry;
+
+            for (entry = TABLE_HEAD(ht, hv); entry; entry = ENTRY_NEXT(entry)) {
+                void *data;
+
+                data = _Numba_HASHTABLE_ENTRY_DATA_AS_VOID_P(entry);
+                size += ht->get_data_size_func(data);
+            }
+        }
+    }
+    return size;
+}
+
+#ifdef Py_DEBUG
+void
+_Numba_hashtable_print_stats(_Numba_hashtable_t *ht)
+{
+    size_t size;
+    size_t chain_len, max_chain_len, total_chain_len, nchains;
+    _Numba_hashtable_entry_t *entry;
+    size_t hv;
+    double load;
+
+    size = _Numba_hashtable_size(ht);
+
+    load = (double)ht->entries / ht->num_buckets;
+
+    max_chain_len = 0;
+    total_chain_len = 0;
+    nchains = 0;
+    for (hv = 0; hv < ht->num_buckets; hv++) {
+        entry = TABLE_HEAD(ht, hv);
+        if (entry != NULL) {
+            chain_len = 0;
+            for (; entry; entry = ENTRY_NEXT(entry)) {
+                chain_len++;
+            }
+            if (chain_len > max_chain_len)
+                max_chain_len = chain_len;
+            total_chain_len += chain_len;
+            nchains++;
+        }
+    }
+    printf("hash table %p: entries=%"
+           PY_FORMAT_SIZE_T "u/%" PY_FORMAT_SIZE_T "u (%.0f%%), ",
+           ht, ht->entries, ht->num_buckets, load * 100.0);
+    if (nchains)
+        printf("avg_chain_len=%.1f, ", (double)total_chain_len / nchains);
+    printf("max_chain_len=%" PY_FORMAT_SIZE_T "u, %" PY_FORMAT_SIZE_T "u kB\n",
+           max_chain_len, size / 1024);
+}
+#endif
+
+/* Get an entry. Return NULL if the key does not exist. */
+_Numba_hashtable_entry_t *
+_Numba_hashtable_get_entry(_Numba_hashtable_t *ht, const void *key)
+{
+    Py_uhash_t key_hash;
+    size_t index;
+    _Numba_hashtable_entry_t *entry;
+
+    key_hash = ht->hash_func(key);
+    index = key_hash & (ht->num_buckets - 1);
+
+    for (entry = TABLE_HEAD(ht, index); entry != NULL; entry = ENTRY_NEXT(entry)) {
+        if (entry->key_hash == key_hash && ht->compare_func(key, entry))
+            break;
+    }
+
+    return entry;
+}
+
+static int
+_hashtable_pop_entry(_Numba_hashtable_t *ht, const void *key, void *data, size_t data_size)
+{
+    Py_uhash_t key_hash;
+    size_t index;
+    _Numba_hashtable_entry_t *entry, *previous;
+
+    key_hash = ht->hash_func(key);
+    index = key_hash & (ht->num_buckets - 1);
+
+    previous = NULL;
+    for (entry = TABLE_HEAD(ht, index); entry != NULL; entry = ENTRY_NEXT(entry)) {
+        if (entry->key_hash == key_hash && ht->compare_func(key, entry))
+            break;
+        previous = entry;
+    }
+
+    if (entry == NULL)
+        return 0;
+
+    _Py_slist_remove(&ht->buckets[index], (_Py_slist_item_t *)previous,
+                     (_Py_slist_item_t *)entry);
+    ht->entries--;
+
+    if (data != NULL)
+        _Numba_HASHTABLE_ENTRY_READ_DATA(ht, data, data_size, entry);
+    ht->alloc.free(entry);
+
+    if ((float)ht->entries / (float)ht->num_buckets < HASHTABLE_LOW)
+        hashtable_rehash(ht);
+    return 1;
+}
+
+/* Add a new entry to the hash. The key must not be present in the hash table.
+   Return 0 on success, -1 on memory error. */
+int
+_Numba_hashtable_set(_Numba_hashtable_t *ht, const void *key,
+                  void *data, size_t data_size)
+{
+    Py_uhash_t key_hash;
+    size_t index;
+    _Numba_hashtable_entry_t *entry;
+
+    assert(data != NULL || data_size == 0);
+#ifndef NDEBUG
+    /* Don't write the assertion on a single line because it is interesting
+       to know the duplicated entry if the assertion failed. The entry can
+       be read using a debugger. */
+    entry = _Numba_hashtable_get_entry(ht, key);
+    assert(entry == NULL);
+#endif
+
+    key_hash = ht->hash_func(key);
+    index = key_hash & (ht->num_buckets - 1);
+
+    entry = ht->alloc.malloc(HASHTABLE_ITEM_SIZE(ht));
+    if (entry == NULL) {
+        /* memory allocation failed */
+        return -1;
+    }
+
+    entry->key = (void *)key;
+    entry->key_hash = key_hash;
+
+    assert(data_size == ht->data_size);
+    memcpy(_Numba_HASHTABLE_ENTRY_DATA(entry), data, data_size);
+
+    _Py_slist_prepend(&ht->buckets[index], (_Py_slist_item_t*)entry);
+    ht->entries++;
+
+    if ((float)ht->entries / (float)ht->num_buckets > HASHTABLE_HIGH)
+        hashtable_rehash(ht);
+    return 0;
+}
+
+/* Get data from an entry. Copy entry data into data and return 1 if the entry
+   exists, return 0 if the entry does not exist. */
+int
+_Numba_hashtable_get(_Numba_hashtable_t *ht, const void *key, void *data, size_t data_size)
+{
+    _Numba_hashtable_entry_t *entry;
+
+    assert(data != NULL);
+
+    entry = _Numba_hashtable_get_entry(ht, key);
+    if (entry == NULL)
+        return 0;
+    _Numba_HASHTABLE_ENTRY_READ_DATA(ht, data, data_size, entry);
+    return 1;
+}
+
+int
+_Numba_hashtable_pop(_Numba_hashtable_t *ht, const void *key, void *data, size_t data_size)
+{
+    assert(data != NULL);
+    assert(ht->free_data_func == NULL);
+    return _hashtable_pop_entry(ht, key, data, data_size);
+}
+
+/* Delete an entry. The entry must exist. */
+void
+_Numba_hashtable_delete(_Numba_hashtable_t *ht, const void *key)
+{
+#ifndef NDEBUG
+    int found = _hashtable_pop_entry(ht, key, NULL, 0);
+    assert(found);
+#else
+    (void)_hashtable_pop_entry(ht, key, NULL, 0);
+#endif
+}
+
+/* Prototype for a pointer to a function to be called foreach
+   key/value pair in the hash by hashtable_foreach().  Iteration
+   stops if a non-zero value is returned. */
+int
+_Numba_hashtable_foreach(_Numba_hashtable_t *ht,
+                      int (*func) (_Numba_hashtable_entry_t *entry, void *arg),
+                      void *arg)
+{
+    _Numba_hashtable_entry_t *entry;
+    size_t hv;
+
+    for (hv = 0; hv < ht->num_buckets; hv++) {
+        for (entry = TABLE_HEAD(ht, hv); entry; entry = ENTRY_NEXT(entry)) {
+            int res = func(entry, arg);
+            if (res)
+                return res;
+        }
+    }
+    return 0;
+}
+
+static void
+hashtable_rehash(_Numba_hashtable_t *ht)
+{
+    size_t buckets_size, new_size, bucket;
+    _Py_slist_t *old_buckets = NULL;
+    size_t old_num_buckets;
+
+    new_size = round_size((size_t)(ht->entries * HASHTABLE_REHASH_FACTOR));
+    if (new_size == ht->num_buckets)
+        return;
+
+    old_num_buckets = ht->num_buckets;
+
+    buckets_size = new_size * sizeof(ht->buckets[0]);
+    old_buckets = ht->buckets;
+    ht->buckets = ht->alloc.malloc(buckets_size);
+    if (ht->buckets == NULL) {
+        /* cancel rehash on memory allocation failure */
+        ht->buckets = old_buckets ;
+        /* memory allocation failed */
+        return;
+    }
+    memset(ht->buckets, 0, buckets_size);
+
+    ht->num_buckets = new_size;
+
+    for (bucket = 0; bucket < old_num_buckets; bucket++) {
+        _Numba_hashtable_entry_t *entry, *next;
+        for (entry = BUCKETS_HEAD(old_buckets[bucket]); entry != NULL; entry = next) {
+            size_t entry_index;
+
+            assert(ht->hash_func(entry->key) == entry->key_hash);
+            next = ENTRY_NEXT(entry);
+            entry_index = entry->key_hash & (new_size - 1);
+
+            _Py_slist_prepend(&ht->buckets[entry_index], (_Py_slist_item_t*)entry);
+        }
+    }
+
+    ht->alloc.free(old_buckets);
+}
+
+void
+_Numba_hashtable_clear(_Numba_hashtable_t *ht)
+{
+    _Numba_hashtable_entry_t *entry, *next;
+    size_t i;
+
+    for (i=0; i < ht->num_buckets; i++) {
+        for (entry = TABLE_HEAD(ht, i); entry != NULL; entry = next) {
+            next = ENTRY_NEXT(entry);
+            if (ht->free_data_func)
+                ht->free_data_func(_Numba_HASHTABLE_ENTRY_DATA_AS_VOID_P(entry));
+            ht->alloc.free(entry);
+        }
+        _Py_slist_init(&ht->buckets[i]);
+    }
+    ht->entries = 0;
+    hashtable_rehash(ht);
+}
+
+void
+_Numba_hashtable_destroy(_Numba_hashtable_t *ht)
+{
+    size_t i;
+
+    for (i = 0; i < ht->num_buckets; i++) {
+        _Py_slist_item_t *entry = ht->buckets[i].head;
+        while (entry) {
+            _Py_slist_item_t *entry_next = entry->next;
+            if (ht->free_data_func)
+                ht->free_data_func(_Numba_HASHTABLE_ENTRY_DATA_AS_VOID_P(entry));
+            ht->alloc.free(entry);
+            entry = entry_next;
+        }
+    }
+
+    ht->alloc.free(ht->buckets);
+    ht->alloc.free(ht);
+}
+
+/* Return a copy of the hash table */
+_Numba_hashtable_t *
+_Numba_hashtable_copy(_Numba_hashtable_t *src)
+{
+    _Numba_hashtable_t *dst;
+    _Numba_hashtable_entry_t *entry;
+    size_t bucket;
+    int err;
+    void *data, *new_data;
+
+    dst = _Numba_hashtable_new_full(src->data_size, src->num_buckets,
+                            src->hash_func, src->compare_func,
+                            src->copy_data_func, src->free_data_func,
+                            src->get_data_size_func, &src->alloc);
+    if (dst == NULL)
+        return NULL;
+
+    for (bucket=0; bucket < src->num_buckets; bucket++) {
+        entry = TABLE_HEAD(src, bucket);
+        for (; entry; entry = ENTRY_NEXT(entry)) {
+            if (src->copy_data_func) {
+                data = _Numba_HASHTABLE_ENTRY_DATA_AS_VOID_P(entry);
+                new_data = src->copy_data_func(data);
+                if (new_data != NULL)
+                    err = _Numba_hashtable_set(dst, entry->key,
+                                        &new_data, src->data_size);
+                else
+                    err = 1;
+            }
+            else {
+                data = _Numba_HASHTABLE_ENTRY_DATA(entry);
+                err = _Numba_hashtable_set(dst, entry->key, data, src->data_size);
+            }
+            if (err) {
+                _Numba_hashtable_destroy(dst);
+                return NULL;
+            }
+        }
+    }
+    return dst;
+}
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_hashtable.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_hashtable.h
new file mode 100644
index 000000000..37430429d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_hashtable.h
@@ -0,0 +1,132 @@
+/*
+ * See _hashtable.c for more information about this file.
+ */
+
+#ifndef Py_HASHTABLE_H
+#define Py_HASHTABLE_H
+
+/* The whole API is private */
+#ifndef Py_LIMITED_API
+
+typedef struct _Py_slist_item_s {
+    struct _Py_slist_item_s *next;
+} _Py_slist_item_t;
+
+typedef struct {
+    _Py_slist_item_t *head;
+} _Py_slist_t;
+
+#define _Py_SLIST_ITEM_NEXT(ITEM) (((_Py_slist_item_t *)ITEM)->next)
+
+#define _Py_SLIST_HEAD(SLIST) (((_Py_slist_t *)SLIST)->head)
+
+typedef struct {
+    /* used by _Numba_hashtable_t.buckets to link entries */
+    _Py_slist_item_t _Py_slist_item;
+
+    const void *key;
+    Py_uhash_t key_hash;
+
+    /* data follows */
+} _Numba_hashtable_entry_t;
+
+#define _Numba_HASHTABLE_ENTRY_DATA(ENTRY) \
+        ((char *)(ENTRY) + sizeof(_Numba_hashtable_entry_t))
+
+#define _Numba_HASHTABLE_ENTRY_DATA_AS_VOID_P(ENTRY) \
+        (*(void **)_Numba_HASHTABLE_ENTRY_DATA(ENTRY))
+
+#define _Numba_HASHTABLE_ENTRY_READ_DATA(TABLE, DATA, DATA_SIZE, ENTRY) \
+    do { \
+        assert((DATA_SIZE) == (TABLE)->data_size); \
+        memcpy(DATA, _Numba_HASHTABLE_ENTRY_DATA(ENTRY), DATA_SIZE); \
+    } while (0)
+
+typedef Py_uhash_t (*_Numba_hashtable_hash_func) (const void *key);
+typedef int (*_Numba_hashtable_compare_func) (const void *key, const _Numba_hashtable_entry_t *he);
+typedef void* (*_Numba_hashtable_copy_data_func)(void *data);
+typedef void (*_Numba_hashtable_free_data_func)(void *data);
+typedef size_t (*_Numba_hashtable_get_data_size_func)(void *data);
+
+typedef struct {
+    /* allocate a memory block */
+    void* (*malloc) (size_t size);
+
+    /* release a memory block */
+    void (*free) (void *ptr);
+} _Numba_hashtable_allocator_t;
+
+typedef struct {
+    size_t num_buckets;
+    size_t entries; /* Total number of entries in the table. */
+    _Py_slist_t *buckets;
+    size_t data_size;
+
+    _Numba_hashtable_hash_func hash_func;
+    _Numba_hashtable_compare_func compare_func;
+    _Numba_hashtable_copy_data_func copy_data_func;
+    _Numba_hashtable_free_data_func free_data_func;
+    _Numba_hashtable_get_data_size_func get_data_size_func;
+    _Numba_hashtable_allocator_t alloc;
+} _Numba_hashtable_t;
+
+/* hash and compare functions for integers and pointers */
+PyAPI_FUNC(Py_uhash_t) _Numba_hashtable_hash_ptr(const void *key);
+PyAPI_FUNC(Py_uhash_t) _Numba_hashtable_hash_int(const void *key);
+PyAPI_FUNC(int) _Numba_hashtable_compare_direct(const void *key, const _Numba_hashtable_entry_t *entry);
+
+PyAPI_FUNC(_Numba_hashtable_t *) _Numba_hashtable_new(
+    size_t data_size,
+    _Numba_hashtable_hash_func hash_func,
+    _Numba_hashtable_compare_func compare_func);
+PyAPI_FUNC(_Numba_hashtable_t *) _Numba_hashtable_new_full(
+    size_t data_size,
+    size_t init_size,
+    _Numba_hashtable_hash_func hash_func,
+    _Numba_hashtable_compare_func compare_func,
+    _Numba_hashtable_copy_data_func copy_data_func,
+    _Numba_hashtable_free_data_func free_data_func,
+    _Numba_hashtable_get_data_size_func get_data_size_func,
+    _Numba_hashtable_allocator_t *allocator);
+PyAPI_FUNC(_Numba_hashtable_t *) _Numba_hashtable_copy(_Numba_hashtable_t *src);
+PyAPI_FUNC(void) _Numba_hashtable_clear(_Numba_hashtable_t *ht);
+PyAPI_FUNC(void) _Numba_hashtable_destroy(_Numba_hashtable_t *ht);
+
+typedef int (*_Numba_hashtable_foreach_func) (_Numba_hashtable_entry_t *entry, void *arg);
+
+PyAPI_FUNC(int) _Numba_hashtable_foreach(
+    _Numba_hashtable_t *ht,
+    _Numba_hashtable_foreach_func func, void *arg);
+PyAPI_FUNC(size_t) _Numba_hashtable_size(_Numba_hashtable_t *ht);
+
+PyAPI_FUNC(_Numba_hashtable_entry_t*) _Numba_hashtable_get_entry(
+    _Numba_hashtable_t *ht,
+    const void *key);
+PyAPI_FUNC(int) _Numba_hashtable_set(
+    _Numba_hashtable_t *ht,
+    const void *key,
+    void *data,
+    size_t data_size);
+PyAPI_FUNC(int) _Numba_hashtable_get(
+    _Numba_hashtable_t *ht,
+    const void *key,
+    void *data,
+    size_t data_size);
+PyAPI_FUNC(int) _Numba_hashtable_pop(
+    _Numba_hashtable_t *ht,
+    const void *key,
+    void *data,
+    size_t data_size);
+PyAPI_FUNC(void) _Numba_hashtable_delete(
+    _Numba_hashtable_t *ht,
+    const void *key);
+
+#define _Numba_HASHTABLE_SET(TABLE, KEY, DATA) \
+    _Numba_hashtable_set(TABLE, KEY, &(DATA), sizeof(DATA))
+
+#define _Numba_HASHTABLE_GET(TABLE, KEY, DATA) \
+    _Numba_hashtable_get(TABLE, KEY, &(DATA), sizeof(DATA))
+
+#endif   /* Py_LIMITED_API */
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_helperlib.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_helperlib.c
new file mode 100644
index 000000000..c1da22477
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_helperlib.c
@@ -0,0 +1,1186 @@
+/*
+ * Helper functions used by Numba at runtime.
+ * This C file is meant to be included after defining the
+ * NUMBA_EXPORT_FUNC() and NUMBA_EXPORT_DATA() macros.
+ */
+
+#include "_pymodule.h"
+#include <stddef.h>
+#include <stdio.h>
+#include <math.h>
+#ifdef _MSC_VER
+    #define int64_t signed __int64
+    #define uint64_t unsigned __int64
+    #define uint32_t unsigned __int32
+#else
+    #include <stdint.h>
+#endif
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+#include <numpy/ndarrayobject.h>
+#include <numpy/arrayscalars.h>
+#include <numpy/npy_math.h>
+
+#include "_arraystruct.h"
+
+/*
+ * Other helpers.
+ */
+
+
+/* Fix fmod() and fmodf() for windows x64 VC 9.0 (VS 2008)
+https://support.microsoft.com/en-us/kb/982107
+*/
+static  void (*fnclex)(void) = NULL;
+
+NUMBA_EXPORT_FUNC(double)
+numba_fixed_fmod(double x, double y){
+    fnclex();  /* no inline asm in x64 =( */
+    return fmod(x, y);
+}
+
+NUMBA_EXPORT_FUNC(float)
+numba_fixed_fmodf(float x, float y) {
+    fnclex();  /* no inline asm in x64 =( */
+    return fmodf(x, y);
+}
+
+NUMBA_EXPORT_FUNC(void)
+numba_set_fnclex(void *fn){
+    fnclex = fn;
+}
+
+/* provide 64-bit division function to 32-bit platforms */
+NUMBA_EXPORT_FUNC(int64_t)
+numba_sdiv(int64_t a, int64_t b) {
+    return a / b;
+}
+
+NUMBA_EXPORT_FUNC(uint64_t)
+numba_udiv(uint64_t a, uint64_t b) {
+    return a / b;
+}
+
+/* provide 64-bit remainder function to 32-bit platforms */
+NUMBA_EXPORT_FUNC(int64_t)
+numba_srem(int64_t a, int64_t b) {
+    return a % b;
+}
+
+NUMBA_EXPORT_FUNC(uint64_t)
+numba_urem(uint64_t a, uint64_t b) {
+    return a % b;
+}
+
+/* provide frexp and ldexp; these wrappers deal with special cases
+ * (zero, nan, infinity) directly, to sidestep platform differences.
+ */
+NUMBA_EXPORT_FUNC(double)
+numba_frexp(double x, int *exp)
+{
+    if (!Py_IS_FINITE(x) || !x)
+        *exp = 0;
+    else
+        x = frexp(x, exp);
+    return x;
+}
+
+NUMBA_EXPORT_FUNC(float)
+numba_frexpf(float x, int *exp)
+{
+    if (Py_IS_NAN(x) || Py_IS_INFINITY(x) || !x)
+        *exp = 0;
+    else
+        x = frexpf(x, exp);
+    return x;
+}
+
+NUMBA_EXPORT_FUNC(double)
+numba_ldexp(double x, int exp)
+{
+    if (Py_IS_FINITE(x) && x && exp)
+        x = ldexp(x, exp);
+    return x;
+}
+
+NUMBA_EXPORT_FUNC(float)
+numba_ldexpf(float x, int exp)
+{
+    if (Py_IS_FINITE(x) && x && exp)
+        x = ldexpf(x, exp);
+    return x;
+}
+
+/* provide complex power */
+NUMBA_EXPORT_FUNC(void)
+numba_cpow(Py_complex *a, Py_complex *b, Py_complex *out) {
+    errno = 0;
+    *out = _Py_c_pow(*a, *b);
+    if (errno == EDOM) {
+        /* _Py_c_pow() doesn't bother returning the right value
+           in this case, as Python raises ZeroDivisionError */
+        out->real = out->imag = Py_NAN;
+    }
+}
+
+NUMBA_EXPORT_FUNC(void)
+numba_cpowf(npy_cfloat *a, npy_cfloat *b, npy_cfloat *out) {
+    Py_complex _a, _b, _out;
+    _a.real = npy_crealf(*a);
+    _a.imag = npy_cimagf(*a);
+    _b.real = npy_crealf(*b);
+    _b.imag = npy_cimagf(*b);
+    numba_cpow(&_a, &_b, &_out);
+    *out = npy_cpackf((float) _out.real, (float) _out.imag);
+}
+
+/* C99 math functions: redirect to system implementations */
+
+NUMBA_EXPORT_FUNC(double)
+numba_gamma(double x)
+{
+    return tgamma(x);
+}
+
+NUMBA_EXPORT_FUNC(float)
+numba_gammaf(float x)
+{
+    return tgammaf(x);
+}
+
+NUMBA_EXPORT_FUNC(double)
+numba_lgamma(double x)
+{
+    return lgamma(x);
+}
+
+NUMBA_EXPORT_FUNC(float)
+numba_lgammaf(float x)
+{
+    return lgammaf(x);
+}
+
+NUMBA_EXPORT_FUNC(double)
+numba_erf(double x)
+{
+    return erf(x);
+}
+
+NUMBA_EXPORT_FUNC(float)
+numba_erff(float x)
+{
+    return erff(x);
+}
+
+NUMBA_EXPORT_FUNC(double)
+numba_erfc(double x)
+{
+    return erfc(x);
+}
+
+NUMBA_EXPORT_FUNC(float)
+numba_erfcf(float x)
+{
+    return erfcf(x);
+}
+
+/* Note npy_signbit() is actually a polymorphic macro */
+NUMBA_EXPORT_FUNC(int)
+numba_signbitf(float a)
+{
+    return npy_signbit(a);
+}
+
+NUMBA_EXPORT_FUNC(int)
+numba_signbit(npy_double a)
+{
+    return npy_signbit(a);
+}
+
+/* Unpack any Python complex-like object into a Py_complex structure */
+NUMBA_EXPORT_FUNC(int)
+numba_complex_adaptor(PyObject* obj, Py_complex *out) {
+    PyObject* fobj;
+    PyArray_Descr *dtype;
+    double val[2];
+
+    // Convert from python complex or numpy complex128
+    if (PyComplex_Check(obj)) {
+        out->real = PyComplex_RealAsDouble(obj);
+        out->imag = PyComplex_ImagAsDouble(obj);
+    }
+    // Convert from numpy complex64
+    else if (PyArray_IsScalar(obj, ComplexFloating)) {
+        dtype = PyArray_DescrFromScalar(obj);
+        if (dtype == NULL) {
+            return 0;
+        }
+        if (PyArray_CastScalarDirect(obj, dtype, &val[0], NPY_CDOUBLE) < 0) {
+            Py_DECREF(dtype);
+            return 0;
+        }
+        out->real = val[0];
+        out->imag = val[1];
+        Py_DECREF(dtype);
+    } else {
+        fobj = PyNumber_Float(obj);
+        if (!fobj) return 0;
+        out->real = PyFloat_AsDouble(fobj);
+        out->imag = 0.;
+        Py_DECREF(fobj);
+    }
+    return 1;
+}
+
+/* Minimum PyBufferObject structure to hack inside it */
+typedef struct {
+    PyObject_HEAD
+    PyObject *b_base;
+    void *b_ptr;
+    Py_ssize_t b_size;
+    Py_ssize_t b_offset;
+}  PyBufferObject_Hack;
+
+/*
+Get data address of record data buffer
+*/
+NUMBA_EXPORT_FUNC(void *)
+numba_extract_record_data(PyObject *recordobj, Py_buffer *pbuf) {
+    PyObject *attrdata;
+    void *ptr;
+
+    attrdata = PyObject_GetAttrString(recordobj, "data");
+    if (!attrdata) return NULL;
+
+    if (-1 == PyObject_GetBuffer(attrdata, pbuf, 0)){
+        Py_DECREF(attrdata);
+        return NULL;
+    } else {
+        ptr = pbuf->buf;
+    }
+    Py_DECREF(attrdata);
+    return ptr;
+}
+
+/*
+ * Return a record instance with dtype as the record type, and backed
+ * by a copy of the memory area pointed to by (pdata, size).
+ */
+NUMBA_EXPORT_FUNC(PyObject *)
+numba_recreate_record(void *pdata, int size, PyObject *dtype) {
+    PyObject *numpy = NULL;
+    PyObject *numpy_record = NULL;
+    PyObject *aryobj = NULL;
+    PyObject *dtypearg = NULL;
+    PyObject *record = NULL;
+    PyArray_Descr *descr = NULL;
+
+    if (dtype == NULL) {
+        PyErr_Format(PyExc_RuntimeError,
+            "In 'numba_recreate_record', 'dtype' is NULL");
+        return NULL;
+    }
+
+    numpy = PyImport_ImportModuleNoBlock("numpy");
+    if (!numpy) goto CLEANUP;
+
+    numpy_record = PyObject_GetAttrString(numpy, "record");
+    if (!numpy_record) goto CLEANUP;
+
+    dtypearg = PyTuple_Pack(2, numpy_record, dtype);
+    if (!dtypearg || !PyArray_DescrConverter(dtypearg, &descr))
+        goto CLEANUP;
+
+    /* This steals a reference to descr, so we don't have to DECREF it */
+    aryobj = PyArray_FromString(pdata, size, descr, 1, NULL);
+    if (!aryobj) goto CLEANUP;
+
+    record = PySequence_GetItem(aryobj, 0);
+
+CLEANUP:
+    Py_XDECREF(numpy);
+    Py_XDECREF(numpy_record);
+    Py_XDECREF(aryobj);
+    Py_XDECREF(dtypearg);
+
+    return record;
+}
+
+NUMBA_EXPORT_FUNC(int)
+numba_adapt_ndarray(PyObject *obj, arystruct_t* arystruct) {
+    PyArrayObject *ndary;
+    int i, ndim;
+    npy_intp *p;
+
+    if (!PyArray_Check(obj)) {
+        return -1;
+    }
+
+    ndary = (PyArrayObject*)obj;
+    ndim = PyArray_NDIM(ndary);
+
+    arystruct->data = PyArray_DATA(ndary);
+    arystruct->nitems = PyArray_SIZE(ndary);
+    arystruct->itemsize = PyArray_ITEMSIZE(ndary);
+    arystruct->parent = obj;
+    p = arystruct->shape_and_strides;
+    for (i = 0; i < ndim; i++, p++) {
+        *p = PyArray_DIM(ndary, i);
+    }
+    for (i = 0; i < ndim; i++, p++) {
+        *p = PyArray_STRIDE(ndary, i);
+    }
+    arystruct->meminfo = NULL;
+    return 0;
+}
+
+NUMBA_EXPORT_FUNC(int)
+numba_get_buffer(PyObject *obj, Py_buffer *buf)
+{
+    /* Ask for shape and strides, but no suboffsets */
+    return PyObject_GetBuffer(obj, buf, PyBUF_RECORDS_RO);
+}
+
+NUMBA_EXPORT_FUNC(void)
+numba_adapt_buffer(Py_buffer *buf, arystruct_t *arystruct)
+{
+    int i;
+    npy_intp *p;
+
+    arystruct->data = buf->buf;
+    arystruct->itemsize = buf->itemsize;
+    arystruct->parent = buf->obj;
+    arystruct->nitems = 1;
+    p = arystruct->shape_and_strides;
+    for (i = 0; i < buf->ndim; i++, p++) {
+        *p = buf->shape[i];
+        arystruct->nitems *= buf->shape[i];
+    }
+    for (i = 0; i < buf->ndim; i++, p++) {
+        *p = buf->strides[i];
+    }
+    arystruct->meminfo = NULL;
+}
+
+NUMBA_EXPORT_FUNC(void)
+numba_release_buffer(Py_buffer *buf)
+{
+    PyBuffer_Release(buf);
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+numba_ndarray_new(int nd,
+                  npy_intp *dims,   /* shape */
+                  npy_intp *strides,
+                  void* data,
+                  int type_num,
+                  int itemsize)
+{
+    PyObject *ndary;
+    int flags = NPY_ARRAY_BEHAVED;
+    ndary = PyArray_New((PyTypeObject*)&PyArray_Type, nd, dims, type_num,
+                       strides, data, 0, flags, NULL);
+    return ndary;
+}
+
+
+/*
+ * Handle reshaping of zero-sized array.
+ * See numba_attempt_nocopy_reshape() below.
+ */
+static int
+nocopy_empty_reshape(npy_intp nd, const npy_intp *dims, const npy_intp *strides,
+                     npy_intp newnd, const npy_intp *newdims,
+                     npy_intp *newstrides, npy_intp itemsize,
+                     int is_f_order)
+{
+    int i;
+    /* Just make the strides vaguely reasonable
+     * (they can have any value in theory).
+     */
+    for (i = 0; i < newnd; i++)
+        newstrides[i] = itemsize;
+    return 1;  /* reshape successful */
+}
+
+/*
+ * Straight from Numpy's _attempt_nocopy_reshape()
+ * (np/core/src/multiarray/shape.c).
+ * Attempt to reshape an array without copying data
+ *
+ * This function should correctly handle all reshapes, including
+ * axes of length 1. Zero strides should work but are untested.
+ *
+ * If a copy is needed, returns 0
+ * If no copy is needed, returns 1 and fills `npy_intp *newstrides`
+ *     with appropriate strides
+ */
+
+NUMBA_EXPORT_FUNC(int)
+numba_attempt_nocopy_reshape(npy_intp nd, const npy_intp *dims, const npy_intp *strides,
+                             npy_intp newnd, const npy_intp *newdims,
+                             npy_intp *newstrides, npy_intp itemsize,
+                             int is_f_order)
+{
+    int oldnd;
+    npy_intp olddims[NPY_MAXDIMS];
+    npy_intp oldstrides[NPY_MAXDIMS];
+    npy_intp np, op, last_stride;
+    int oi, oj, ok, ni, nj, nk;
+
+    oldnd = 0;
+    /*
+     * Remove axes with dimension 1 from the old array. They have no effect
+     * but would need special cases since their strides do not matter.
+     */
+    for (oi = 0; oi < nd; oi++) {
+        if (dims[oi]!= 1) {
+            olddims[oldnd] = dims[oi];
+            oldstrides[oldnd] = strides[oi];
+            oldnd++;
+        }
+    }
+
+    np = 1;
+    for (ni = 0; ni < newnd; ni++) {
+        np *= newdims[ni];
+    }
+    op = 1;
+    for (oi = 0; oi < oldnd; oi++) {
+        op *= olddims[oi];
+    }
+    if (np != op) {
+        /* different total sizes; no hope */
+        return 0;
+    }
+
+    if (np == 0) {
+        /* the Numpy code does not handle 0-sized arrays */
+        return nocopy_empty_reshape(nd, dims, strides,
+                                    newnd, newdims, newstrides,
+                                    itemsize, is_f_order);
+    }
+
+    /* oi to oj and ni to nj give the axis ranges currently worked with */
+    oi = 0;
+    oj = 1;
+    ni = 0;
+    nj = 1;
+    while (ni < newnd && oi < oldnd) {
+        np = newdims[ni];
+        op = olddims[oi];
+
+        while (np != op) {
+            if (np < op) {
+                /* Misses trailing 1s, these are handled later */
+                np *= newdims[nj++];
+            } else {
+                op *= olddims[oj++];
+            }
+        }
+
+        /* Check whether the original axes can be combined */
+        for (ok = oi; ok < oj - 1; ok++) {
+            if (is_f_order) {
+                if (oldstrides[ok+1] != olddims[ok]*oldstrides[ok]) {
+                     /* not contiguous enough */
+                    return 0;
+                }
+            }
+            else {
+                /* C order */
+                if (oldstrides[ok] != olddims[ok+1]*oldstrides[ok+1]) {
+                    /* not contiguous enough */
+                    return 0;
+                }
+            }
+        }
+
+        /* Calculate new strides for all axes currently worked with */
+        if (is_f_order) {
+            newstrides[ni] = oldstrides[oi];
+            for (nk = ni + 1; nk < nj; nk++) {
+                newstrides[nk] = newstrides[nk - 1]*newdims[nk - 1];
+            }
+        }
+        else {
+            /* C order */
+            newstrides[nj - 1] = oldstrides[oj - 1];
+            for (nk = nj - 1; nk > ni; nk--) {
+                newstrides[nk - 1] = newstrides[nk]*newdims[nk];
+            }
+        }
+        ni = nj++;
+        oi = oj++;
+    }
+
+    /*
+     * Set strides corresponding to trailing 1s of the new shape.
+     */
+    if (ni >= 1) {
+        last_stride = newstrides[ni - 1];
+    }
+    else {
+        last_stride = itemsize;
+    }
+    if (is_f_order) {
+        last_stride *= newdims[ni - 1];
+    }
+    for (nk = ni; nk < newnd; nk++) {
+        newstrides[nk] = last_stride;
+    }
+
+    return 1;
+}
+
+/*
+ * Cython utilities.
+ */
+
+/* Fetch the address of the given function, as exposed by
+   a cython module */
+static void *
+import_cython_function(const char *module_name, const char *function_name)
+{
+    PyObject *module, *capi, *cobj;
+    void *res = NULL;
+    const char *capsule_name;
+
+    module = PyImport_ImportModule(module_name);
+    if (module == NULL)
+        return NULL;
+    capi = PyObject_GetAttrString(module, "__pyx_capi__");
+    Py_DECREF(module);
+    if (capi == NULL)
+        return NULL;
+    cobj = PyMapping_GetItemString(capi, (char *)function_name);
+    Py_DECREF(capi);
+    if (cobj == NULL) {
+        PyErr_Clear();
+        PyErr_Format(PyExc_ValueError,
+                     "No function '%s' found in __pyx_capi__ of '%s'",
+                     function_name, module_name);
+        return NULL;
+    }
+    /* 2.7+ => Cython exports a PyCapsule */
+    capsule_name = PyCapsule_GetName(cobj);
+    if (capsule_name != NULL) {
+        res = PyCapsule_GetPointer(cobj, capsule_name);
+    }
+    Py_DECREF(cobj);
+    return res;
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+_numba_import_cython_function(PyObject *self, PyObject *args)
+{
+    const char *module_name;
+    const char *function_name;
+    void *p = NULL;
+    PyObject *res;
+
+    if (!PyArg_ParseTuple(args, "ss", &module_name, &function_name)) {
+        return NULL;
+    }
+    p = import_cython_function(module_name, function_name);
+    if (p == NULL) {
+        return NULL;
+    }
+    res = PyLong_FromVoidPtr(p);
+    if (res == NULL) {
+      PyErr_SetString(PyExc_RuntimeError,
+                      "Could not convert function address to int");
+      return NULL;
+    }
+    return res;
+}
+
+/* We use separate functions for datetime64 and timedelta64, to ensure
+ * proper type checking.
+ */
+NUMBA_EXPORT_FUNC(npy_int64)
+numba_extract_np_datetime(PyObject *td)
+{
+    if (!PyArray_IsScalar(td, Datetime)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "expected a numpy.datetime64 object");
+        return -1;
+    }
+    return PyArrayScalar_VAL(td, Timedelta);
+}
+
+NUMBA_EXPORT_FUNC(npy_int64)
+numba_extract_np_timedelta(PyObject *td)
+{
+    if (!PyArray_IsScalar(td, Timedelta)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "expected a numpy.timedelta64 object");
+        return -1;
+    }
+    return PyArrayScalar_VAL(td, Timedelta);
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+numba_create_np_datetime(npy_int64 value, int unit_code)
+{
+    PyDatetimeScalarObject *obj = (PyDatetimeScalarObject *)
+        PyArrayScalar_New(Datetime);
+    if (obj != NULL) {
+        obj->obval = value;
+        obj->obmeta.base = unit_code;
+        obj->obmeta.num = 1;
+    }
+    return (PyObject *) obj;
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+numba_create_np_timedelta(npy_int64 value, int unit_code)
+{
+    PyTimedeltaScalarObject *obj = (PyTimedeltaScalarObject *)
+        PyArrayScalar_New(Timedelta);
+    if (obj != NULL) {
+        obj->obval = value;
+        obj->obmeta.base = unit_code;
+        obj->obmeta.num = 1;
+    }
+    return (PyObject *) obj;
+}
+
+NUMBA_EXPORT_FUNC(uint64_t)
+numba_fptoui(double x) {
+    /* First cast to signed int of the full width to make sure sign extension
+       happens (this can make a difference on some platforms...). */
+    return (uint64_t) (int64_t) x;
+}
+
+NUMBA_EXPORT_FUNC(uint64_t)
+numba_fptouif(float x) {
+    return (uint64_t) (int64_t) x;
+}
+
+NUMBA_EXPORT_FUNC(void)
+numba_gil_ensure(PyGILState_STATE *state) {
+    *state = PyGILState_Ensure();
+}
+
+NUMBA_EXPORT_FUNC(void)
+numba_gil_release(PyGILState_STATE *state) {
+    PyGILState_Release(*state);
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+numba_py_type(PyObject *obj) {
+    return (PyObject *) Py_TYPE(obj);
+}
+
+
+/*
+ * Functions for tagging an arbitrary Python object with an arbitrary pointer.
+ * These functions make strong lifetime assumptions, see below.
+ */
+
+static PyObject *private_data_dict = NULL;
+
+static PyObject *
+_get_private_data_dict(void)
+{
+    if (private_data_dict == NULL)
+        private_data_dict = PyDict_New();
+    return private_data_dict;
+}
+
+NUMBA_EXPORT_FUNC(void)
+numba_set_pyobject_private_data(PyObject *obj, void *ptr)
+{
+    PyObject *dct = _get_private_data_dict();
+    /* This assumes the reference to setobj is kept alive until the
+       call to numba_reset_set_private_data()! */
+    PyObject *key = PyLong_FromVoidPtr((void *) obj);
+    PyObject *value = PyLong_FromVoidPtr(ptr);
+
+    if (!dct || !value || !key)
+        goto error;
+    if (PyDict_SetItem(dct, key, value))
+        goto error;
+    Py_DECREF(key);
+    Py_DECREF(value);
+    return;
+
+error:
+    Py_FatalError("unable to set private data");
+}
+
+NUMBA_EXPORT_FUNC(void *)
+numba_get_pyobject_private_data(PyObject *obj)
+{
+    PyObject *dct = _get_private_data_dict();
+    PyObject *value, *key = PyLong_FromVoidPtr((void *) obj);
+    void *ptr;
+    if (!dct || !key)
+        goto error;
+
+    value = PyDict_GetItem(dct, key);
+    Py_DECREF(key);
+    if (!value)
+        return NULL;
+    else {
+        ptr = PyLong_AsVoidPtr(value);
+        if (ptr == NULL && PyErr_Occurred())
+            goto error;
+        return ptr;
+    }
+
+error:
+    Py_FatalError("unable to get private data");
+    return NULL;
+}
+
+NUMBA_EXPORT_FUNC(void)
+numba_reset_pyobject_private_data(PyObject *obj)
+{
+    PyObject *dct = _get_private_data_dict();
+    PyObject *key = PyLong_FromVoidPtr((void *) obj);
+
+    if (!key)
+        goto error;
+    if (PyDict_DelItem(dct, key))
+        PyErr_Clear();
+    Py_DECREF(key);
+    return;
+
+error:
+    Py_FatalError("unable to reset private data");
+}
+
+NUMBA_EXPORT_FUNC(int)
+numba_unpack_slice(PyObject *obj,
+                   Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t *step)
+{
+    PySliceObject *slice = (PySliceObject *) obj;
+    if (!PySlice_Check(obj)) {
+        PyErr_Format(PyExc_TypeError,
+                     "Expected a slice object, got '%s'",
+                     Py_TYPE(slice)->tp_name);
+        return -1;
+    }
+#define FETCH_MEMBER(NAME, DEFAULT)                             \
+    if (slice->NAME != Py_None) {                               \
+        Py_ssize_t v = PyNumber_AsSsize_t(slice->NAME,          \
+                                          PyExc_OverflowError); \
+        if (v == -1 && PyErr_Occurred())                        \
+            return -1;                                          \
+        *NAME = v;                                              \
+    }                                                           \
+    else {                                                      \
+        *NAME = DEFAULT;                                        \
+    }
+    FETCH_MEMBER(step, 1)
+    FETCH_MEMBER(stop, (*step > 0) ? PY_SSIZE_T_MAX : PY_SSIZE_T_MIN)
+    FETCH_MEMBER(start, (*step > 0) ? 0 : PY_SSIZE_T_MAX)
+    return 0;
+
+#undef FETCH_MEMBER
+}
+
+NUMBA_EXPORT_FUNC(int)
+numba_fatal_error(void)
+{
+    PyGILState_Ensure();
+    Py_FatalError("in Numba-compiled function");
+    return 0; /* unreachable */
+}
+
+/* Insert a frame into the traceback for (funcname, filename, lineno). */
+/* This function is CPython's _PyTraceback_Add, renamed, see:
+ * https://github.com/python/cpython/blob/d545869d084e70d4838310e79b52a25a72a1ca56/Python/traceback.c#L246
+ * and modified for Python 2.x based on
+ * https://github.com/python/cpython/blob/2e1a34025cde19bddf12a2eac8fedb6afcca8339/Modules/_ctypes/callbacks.c#L151-L174
+ */
+static void traceback_add(const char *funcname, const char *filename, int lineno)
+{
+    PyObject *globals = NULL;
+    PyCodeObject *code = NULL;
+    PyFrameObject *frame = NULL;
+    PyObject *exc, *val, *tb;
+
+    /* Save and clear the current exception. Python functions must not be
+       called with an exception set. Calling Python functions happens when
+       the codec of the filesystem encoding is implemented in pure Python. */
+    PyErr_Fetch(&exc, &val, &tb);
+
+    globals = PyDict_New();
+    if (!globals)
+        goto error;
+    code = PyCode_NewEmpty(filename, funcname, lineno);
+    if (!code) {
+        goto error;
+    }
+    frame = PyFrame_New(PyThreadState_Get(), code, globals, NULL);
+    Py_DECREF(globals);
+    Py_DECREF(code);
+    if (!frame)
+        goto error;
+    frame->f_lineno = lineno;
+
+    PyErr_Restore(exc, val, tb);
+    PyTraceBack_Here(frame);
+    Py_DECREF(frame);
+    return;
+
+error:
+    _PyErr_ChainExceptions(exc, val, tb);
+}
+
+
+/*
+ * Add traceback information to *loc* to the active exception.
+ * loc can be NULL, which causes this function to become a no-op.
+ */
+static
+void traceback_add_loc(PyObject *loc) {
+    const char *function_name_str = NULL, *filename_str = NULL;
+    PyObject *function_name = NULL, *filename = NULL, *lineno = NULL;
+    Py_ssize_t pos;
+
+    /* instance is instantiated/internal exception is raised, if loc is present
+     * add a frame for it into the traceback */
+    if(loc && loc != Py_None && PyTuple_Check(loc))
+    {
+        pos = 0;
+        function_name = PyTuple_GET_ITEM(loc, pos);
+        function_name_str = PyString_AsString(function_name);
+        pos = 1;
+        filename = PyTuple_GET_ITEM(loc, pos);
+        filename_str = PyString_AsString(filename);
+        pos = 2;
+        lineno = PyTuple_GET_ITEM(loc, pos);
+        traceback_add(function_name_str, filename_str, \
+                      (int)PyLong_AsLong(lineno));
+    }
+}
+
+/**
+ * Re-raise the current active exception.
+ * Called internal by process_raise() when *exc* is None.
+ */
+static
+int reraise_exc_is_none(void) {
+    /* Reraise */
+    PyThreadState *tstate = PyThreadState_GET();
+    PyObject *tb, *type, *value;
+#if (PY_MAJOR_VERSION >= 3) && (PY_MINOR_VERSION >= 7)
+    _PyErr_StackItem *tstate_exc = tstate->exc_info;
+#else
+    PyThreadState *tstate_exc = tstate;
+#endif
+    type = tstate_exc->exc_type;
+    value = tstate_exc->exc_value;
+    tb = tstate_exc->exc_traceback;
+    if (type == Py_None) {
+        PyErr_SetString(PyExc_RuntimeError,
+                        "No active exception to reraise");
+        return 0;
+    }
+    /* incref needed because PyErr_Restore DOES NOT */
+    Py_XINCREF(type);
+    Py_XINCREF(value);
+    Py_XINCREF(tb);
+    PyErr_Restore(type, value, tb);
+    return 1;
+}
+
+/*
+ * Set exception given the Exception type and the constructor argument.
+ * Equivalent to ``raise exc(value)``.
+ * PyExceptionClass_Check(exc) must be True.
+ * value can be NULL.
+ */
+static
+int process_exception_class(PyObject *exc, PyObject *value) {
+    PyObject *type;
+    /* It is a class, type used here just as a tmp var */
+    type = PyObject_CallObject(exc, value);
+    if (type == NULL){
+        return 0;
+    }
+    if (!PyExceptionInstance_Check(type)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "exceptions must derive from BaseException");
+        Py_DECREF(type);
+        return 0;
+    }
+    /* all ok, set type to the exc */
+    Py_DECREF(type);
+    type = exc;
+    PyErr_SetObject(type, value);
+    return 1;
+}
+
+/*
+ * Internal routine to process exceptions.
+ * exc cannot be NULL. It can be a None, Exception type, or Exception instance.
+ * value can be NULL for absent, or any PyObject valid for the exception.
+ */
+static
+int process_raise(PyObject *exc, PyObject *value) {
+    /* exc is None */
+    if (exc == Py_None) {
+        return reraise_exc_is_none();
+    }
+    /* exc should be an exception class */
+    else if (PyExceptionClass_Check(exc)) {
+        return process_exception_class(exc, value);
+    }
+    /* exc is an instance of an Exception */
+    else if (PyExceptionInstance_Check(exc)) {
+        PyObject *type = PyExceptionInstance_Class(exc);
+        PyErr_SetObject(type, exc);
+        return 0;
+    }
+    else {
+        /* Not something you can raise.  You get an exception
+        anyway, just not what you specified :-) */
+        PyErr_SetString(PyExc_TypeError,
+                        "exceptions must derive from BaseException");
+        return 0;
+    }
+}
+
+/* Logic for raising an arbitrary object.  Adapted from CPython's ceval.c.
+   This *consumes* a reference count to its argument. */
+NUMBA_EXPORT_FUNC(int)
+numba_do_raise(PyObject *exc_packed)
+{
+    int status;
+    PyObject *exc = NULL, *value = NULL, *loc = NULL;
+
+    /* We support the following forms of raise:
+       raise
+       raise <instance>
+       raise <type> */
+
+    /* could be a tuple from npm (some exc like thing, args, location) */
+    if (PyTuple_CheckExact(exc_packed)) {
+        /* Unpack a (class/inst/tuple, arguments, location) tuple. */
+        if (!PyArg_ParseTuple(exc_packed, "OOO", &exc, &value, &loc)) {
+            traceback_add_loc(loc);
+            return 0;
+        }
+    } else {
+        /* could be a reraise or an exception from objmode */
+        exc = exc_packed;
+        /* branch exit with value = NULL and loc = NULL */
+    }
+    /* value is either NULL or borrowed */
+    status = process_raise(exc, value);
+    traceback_add_loc(loc);
+    Py_DECREF(exc_packed);
+    return status;
+}
+
+#ifdef PYCC_COMPILING
+/* AOT avoid the use of `numba.core.serialize` */
+NUMBA_EXPORT_FUNC(PyObject *)
+numba_unpickle(const char *data, int n, const char *hashed)
+{
+    PyObject *buf, *obj;
+    static PyObject *loads;
+
+    /* Caching the pickle.loads function shaves a couple µs here. */
+    if (loads == NULL) {
+        PyObject *picklemod;
+        picklemod = PyImport_ImportModule("pickle");
+        if (picklemod == NULL)
+            return NULL;
+        loads = PyObject_GetAttrString(picklemod, "loads");
+        Py_DECREF(picklemod);
+        if (loads == NULL)
+            return NULL;
+    }
+
+    buf = PyBytes_FromStringAndSize(data, n);
+    if (buf == NULL)
+        return NULL;
+    obj = PyObject_CallFunctionObjArgs(loads, buf, NULL);
+    Py_DECREF(buf);
+    return obj;
+}
+
+#else
+
+NUMBA_EXPORT_FUNC(PyObject *)
+numba_unpickle(const char *data, int n, const char *hashed)
+{
+    PyObject *buf=NULL, *obj=NULL, *addr=NULL, *hashedbuf=NULL;
+    static PyObject *loads=NULL;
+
+    /* Caching the pickle.loads function shaves a couple µs here. */
+    if (loads == NULL) {
+        PyObject *picklemod;
+        picklemod = PyImport_ImportModule("numba.core.serialize");
+        if (picklemod == NULL)
+            return NULL;
+        loads = PyObject_GetAttrString(picklemod, "_numba_unpickle");
+        Py_DECREF(picklemod);
+        if (loads == NULL)
+            return NULL;
+    }
+
+    buf = PyBytes_FromStringAndSize(data, n);
+    if (buf == NULL)
+        return NULL;
+    /* SHA1 produces 160 bit or 20 bytes */
+    hashedbuf = PyBytes_FromStringAndSize(hashed, 20);
+    if (hashedbuf == NULL)
+        goto error;
+    addr = PyLong_FromVoidPtr((void*)data);
+    if (addr == NULL)
+        goto error;
+    obj = PyObject_CallFunctionObjArgs(loads, addr, buf, hashedbuf, NULL);
+error:
+    Py_XDECREF(addr);
+    Py_XDECREF(hashedbuf);
+    Py_DECREF(buf);
+    return obj;
+}
+#endif
+
+/*
+ * Unicode helpers
+ */
+
+/* Developer note:
+ *
+ * The hash value of unicode objects is obtained via:
+ * ((PyASCIIObject *)(obj))->hash;
+ * The use comes from this definition:
+ * https://github.com/python/cpython/blob/6d43f6f081023b680d9db4542d19b9e382149f0a/Objects/unicodeobject.c#L119-L120
+ * and it's used extensively throughout the `cpython/Object/unicodeobject.c`
+ * source, not least in `unicode_hash` itself:
+ * https://github.com/python/cpython/blob/6d43f6f081023b680d9db4542d19b9e382149f0a/Objects/unicodeobject.c#L11662-L11679
+ *
+ * The Unicode string struct layouts are described here:
+ * https://github.com/python/cpython/blob/6d43f6f081023b680d9db4542d19b9e382149f0a/Include/cpython/unicodeobject.h#L82-L161
+ * essentially, all the unicode string layouts start with a `PyASCIIObject` at
+ * offset 0 (as of commit 6d43f6f081023b680d9db4542d19b9e382149f0a, somewhere
+ * in the 3.8 development cycle).
+ *
+ * For safety against future CPython internal changes, the code checks that the
+ * _base members of the unicode structs are what is expected in 3.7, and that
+ * their offset is 0. It then walks the struct to the hash location to make sure
+ * the offset is indeed the same as PyASCIIObject->hash.
+ * Note: The large condition in the if should evaluate to a compile time
+ * constant.
+ */
+
+#define MEMBER_SIZE(structure, member) sizeof(((structure *)0)->member)
+
+NUMBA_EXPORT_FUNC(void *)
+numba_extract_unicode(PyObject *obj, Py_ssize_t *length, int *kind,
+                      unsigned int *ascii, Py_ssize_t *hash) {
+    if (!PyUnicode_READY(obj)) {
+        *length = PyUnicode_GET_LENGTH(obj);
+        *kind = PyUnicode_KIND(obj);
+        /* could also use PyUnicode_IS_ASCII but it is not publicly advertised in https://docs.python.org/3/c-api/unicode.html */
+        *ascii = (unsigned int)(PyUnicode_MAX_CHAR_VALUE(obj) == (0x7f));
+        /* this is here as a crude check for safe casting of all unicode string
+         * structs to a PyASCIIObject */
+        if (MEMBER_SIZE(PyCompactUnicodeObject, _base) == sizeof(PyASCIIObject)             &&
+            MEMBER_SIZE(PyUnicodeObject, _base) == sizeof(PyCompactUnicodeObject)           &&
+            offsetof(PyCompactUnicodeObject, _base) == 0                                    &&
+            offsetof(PyUnicodeObject, _base) == 0                                           &&
+            offsetof(PyCompactUnicodeObject, _base.hash) == offsetof(PyASCIIObject, hash)   &&
+            offsetof(PyUnicodeObject, _base._base.hash) == offsetof(PyASCIIObject, hash)
+           ) {
+            /* Grab the hash from the type object cache, do not compute it. */
+            *hash = ((PyASCIIObject *)(obj))->hash;
+        }
+        else {
+            /* cast is not safe, fail */
+            return NULL;
+        }
+        return PyUnicode_DATA(obj);
+    } else {
+        return NULL;
+    }
+}
+
+/* this is late included as it #defines e.g. SHIFT that should not impact
+ * the above */
+#include "_unicodetype_db.h"
+
+/* This function is a modified copy of the private function gettyperecord from
+ * CPython's Objects/unicodectype.c
+ *
+ * See:https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L45-L59
+ */
+NUMBA_EXPORT_FUNC(void)
+numba_gettyperecord(Py_UCS4 code, int *upper, int *lower, int *title,
+                    unsigned char *decimal, unsigned char *digit,
+                    unsigned short *flags)
+{
+    int index;
+    const numba_PyUnicode_TypeRecord *rec;
+
+    if (code >= 0x110000)
+        index = 0;
+    else
+    {
+        index = index1[(code>>SHIFT)];
+        index = index2[(index<<SHIFT)+(code&((1<<SHIFT)-1))];
+    }
+
+    rec = &numba_PyUnicode_TypeRecords[index];
+    *upper = rec->upper;
+    *lower = rec->lower;
+    *title = rec->title;
+    *decimal = rec->decimal;
+    *digit = rec->digit;
+    *flags = rec->flags;
+}
+
+/* This function provides a consistent access point for the
+ * _PyUnicode_ExtendedCase array defined in CPython's Objects/unicodectype.c
+ * and now also as numba_PyUnicode_ExtendedCase in Numba's _unicodetype_db.h
+ */
+NUMBA_EXPORT_FUNC(Py_UCS4)
+numba_get_PyUnicode_ExtendedCase(int code)
+{
+    return numba_PyUnicode_ExtendedCase[code];
+}
+
+/* from _unicodetype_db.h */
+#undef SHIFT
+
+/*
+ * defined break point for gdb
+ */
+NUMBA_EXPORT_FUNC(void)
+numba_gdb_breakpoint(void) {
+  /* does nothing */
+}
+
+/*
+ * Define bridge for all math functions
+ */
+
+#define MATH_UNARY(F, R, A) \
+    NUMBA_EXPORT_FUNC(R) numba_##F(A a) { return F(a); }
+#define MATH_BINARY(F, R, A, B) \
+    NUMBA_EXPORT_FUNC(R) numba_##F(A a, B b) { return F(a, b); }
+
+#include "mathnames.h"
+
+#undef MATH_UNARY
+#undef MATH_BINARY
+
+/*
+ * BLAS and LAPACK wrappers
+ */
+
+#include "_lapack.c"
+
+/*
+ * PRNG support
+ */
+
+#include "_random.c"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_helpermod.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_helpermod.c
new file mode 100644
index 000000000..bbbac20bd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_helpermod.c
@@ -0,0 +1,307 @@
+/*
+Expose all functions as pointers in a dedicated C extension.
+*/
+#include "cext/cext.h"
+/* Import _pymodule.h first, for a recent _POSIX_C_SOURCE */
+#include "_pymodule.h"
+
+#include <math.h>
+#ifdef _MSC_VER
+    #define false 0
+    #define true 1
+    #define bool int
+#else
+    #include <stdbool.h>
+#endif
+
+/*
+Include C-extension here
+*/
+#include "cext/cext.h"
+
+/* Numba C helpers */
+#include "_helperlib.c"
+
+/* Numpy C math function exports */
+#include "_npymath_exports.c"
+
+static PyObject *
+build_c_helpers_dict(void)
+{
+    PyObject *dct = PyDict_New();
+    if (dct == NULL)
+        goto error;
+
+#define _declpointer(name, value) do {                 \
+    PyObject *o = PyLong_FromVoidPtr(value);           \
+    if (o == NULL) goto error;                         \
+    if (PyDict_SetItemString(dct, name, o)) {          \
+        Py_DECREF(o);                                  \
+        goto error;                                    \
+    }                                                  \
+    Py_DECREF(o);                                      \
+} while (0)
+
+#define declmethod(func) _declpointer(#func, &numba_##func)
+
+#define declpointer(ptr) _declpointer(#ptr, &numba_##ptr)
+
+    declmethod(fixed_fmod);
+    declmethod(fixed_fmodf);
+    declmethod(set_fnclex);
+
+    declmethod(sdiv);
+    declmethod(srem);
+    declmethod(udiv);
+    declmethod(urem);
+    declmethod(frexp);
+    declmethod(frexpf);
+    declmethod(ldexp);
+    declmethod(ldexpf);
+    declmethod(cpow);
+    declmethod(cpowf);
+    declmethod(erf);
+    declmethod(erff);
+    declmethod(erfc);
+    declmethod(erfcf);
+    declmethod(gamma);
+    declmethod(gammaf);
+    declmethod(lgamma);
+    declmethod(lgammaf);
+    declmethod(signbit);
+    declmethod(signbitf);
+    declmethod(complex_adaptor);
+    declmethod(adapt_ndarray);
+    declmethod(ndarray_new);
+    declmethod(extract_record_data);
+    declmethod(get_buffer);
+    declmethod(adapt_buffer);
+    declmethod(release_buffer);
+    declmethod(extract_np_datetime);
+    declmethod(create_np_datetime);
+    declmethod(extract_np_timedelta);
+    declmethod(create_np_timedelta);
+    declmethod(recreate_record);
+    declmethod(fptoui);
+    declmethod(fptouif);
+    declmethod(gil_ensure);
+    declmethod(gil_release);
+    declmethod(fatal_error);
+    declmethod(py_type);
+    declmethod(unpack_slice);
+    declmethod(do_raise);
+    declmethod(unpickle);
+    declmethod(attempt_nocopy_reshape);
+    declmethod(get_pyobject_private_data);
+    declmethod(set_pyobject_private_data);
+    declmethod(reset_pyobject_private_data);
+
+    /* BLAS / LAPACK */
+    declmethod(xxgemm);
+    declmethod(xxgemv);
+    declmethod(xxdot);
+    declmethod(xxgetrf);
+    declmethod(ez_xxgetri);
+    declmethod(xxpotrf);
+    declmethod(ez_rgeev);
+    declmethod(ez_cgeev);
+    declmethod(ez_xxxevd);
+    declmethod(ez_gesdd);
+    declmethod(ez_geqrf);
+    declmethod(ez_xxgqr);
+    declmethod(ez_gelsd);
+    declmethod(xgesv);
+    declmethod(xxnrm2);
+
+    /* PRNG support */
+    declmethod(get_py_random_state);
+    declmethod(get_np_random_state);
+    declmethod(get_internal_random_state);
+    declmethod(rnd_shuffle);
+    declmethod(rnd_init);
+    declmethod(poisson_ptrs);
+
+    /* Unicode string support */
+    declmethod(extract_unicode);
+    declmethod(gettyperecord);
+    declmethod(get_PyUnicode_ExtendedCase);
+
+    /* for gdb breakpoint */
+    declmethod(gdb_breakpoint);
+
+    /* for dictionary support */
+    declmethod(test_dict);
+    declmethod(dict_new_minsize);
+    declmethod(dict_set_method_table);
+    declmethod(dict_free);
+    declmethod(dict_length);
+    declmethod(dict_lookup);
+    declmethod(dict_insert);
+    declmethod(dict_insert_ez);
+    declmethod(dict_delitem);
+    declmethod(dict_popitem);
+    declmethod(dict_iter_sizeof);
+    declmethod(dict_iter);
+    declmethod(dict_iter_next);
+    declmethod(dict_dump);
+
+    /* for list support */
+    declmethod(test_list);
+    declmethod(list_new);
+    declmethod(list_set_method_table);
+    declmethod(list_free);
+    declmethod(list_base_ptr);
+    declmethod(list_size_address);
+    declmethod(list_length);
+    declmethod(list_allocated);
+    declmethod(list_is_mutable);
+    declmethod(list_set_is_mutable);
+    declmethod(list_setitem);
+    declmethod(list_getitem);
+    declmethod(list_append);
+    declmethod(list_delitem);
+    declmethod(list_delete_slice);
+    declmethod(list_iter_sizeof);
+    declmethod(list_iter);
+    declmethod(list_iter_next);
+
+#define MATH_UNARY(F, R, A) declmethod(F);
+#define MATH_BINARY(F, R, A, B) declmethod(F);
+    #include "mathnames.h"
+#undef MATH_UNARY
+#undef MATH_BINARY
+
+#undef declmethod
+    return dct;
+error:
+    Py_XDECREF(dct);
+    return NULL;
+}
+
+static int
+register_npymath_exports(PyObject *dct)
+{
+    size_t count = sizeof(npymath_exports) / sizeof(npymath_exports[0]);
+    size_t i;
+
+    for (i = 0; i < count; ++i) {
+        PyObject *ptr = PyLong_FromVoidPtr(npymath_exports[i].func);
+        if (ptr == NULL)
+            return -1;
+        if (PyDict_SetItemString(dct, npymath_exports[i].name, ptr) < 0) {
+            Py_DECREF(ptr);
+            return -1;
+        }
+        Py_DECREF(ptr);
+    }
+
+    return 0;
+}
+
+static PyObject *
+build_npymath_exports_dict(void)
+{
+    PyObject *dct = PyDict_New();
+    if (dct != NULL) {
+        if (register_npymath_exports(dct) < 0)
+            Py_CLEAR(dct);
+    }
+    return dct;
+}
+
+
+/*
+ * Helper to deal with flushing stdout
+ */
+PyAPI_FUNC(void) _numba_flush_stdout(void) ;
+
+void
+_numba_flush_stdout(void) {
+  fflush(stdout);
+}
+
+
+static PyMethodDef ext_methods[] = {
+    { "rnd_get_state", (PyCFunction) _numba_rnd_get_state, METH_O, NULL },
+    { "rnd_get_py_state_ptr", (PyCFunction) _numba_rnd_get_py_state_ptr, METH_NOARGS, NULL },
+    { "rnd_get_np_state_ptr", (PyCFunction) _numba_rnd_get_np_state_ptr, METH_NOARGS, NULL },
+    { "rnd_seed", (PyCFunction) _numba_rnd_seed, METH_VARARGS, NULL },
+    { "rnd_set_state", (PyCFunction) _numba_rnd_set_state, METH_VARARGS, NULL },
+    { "rnd_shuffle", (PyCFunction) _numba_rnd_shuffle, METH_O, NULL },
+    { "_import_cython_function", (PyCFunction) _numba_import_cython_function, METH_VARARGS, NULL },
+    { NULL },
+};
+
+/*
+ * These functions are exported by the module's DLL, to exercise ctypes / cffi
+ * without relying on libc availability (see https://bugs.python.org/issue23606)
+ */
+
+PyAPI_FUNC(double) _numba_test_sin(double x);
+PyAPI_FUNC(double) _numba_test_cos(double x);
+PyAPI_FUNC(double) _numba_test_exp(double x);
+PyAPI_FUNC(void) _numba_test_vsquare(int n, double *x, double *out);
+PyAPI_FUNC(double) _numba_test_funcptr(double (*func)(double));
+PyAPI_FUNC(bool) _numba_test_boolean(void);
+
+double _numba_test_sin(double x)
+{
+    return sin(x);
+}
+
+double _numba_test_cos(double x)
+{
+    return cos(x);
+}
+
+double _numba_test_exp(double x)
+{
+    return exp(x);
+}
+
+void _numba_test_vsquare(int n, double *x, double *out)
+{
+    int i;
+    for (i = 0; i < n; i++)
+        out[i] = pow(x[i], 2.0);
+}
+
+void _numba_test_vcube(int n, double *x, double *out)
+{
+    int i;
+    for (i = 0; i < n; i++)
+        out[i] = pow(x[i], 3.0);
+}
+
+double _numba_test_funcptr(double (*func)(double))
+{
+    return func(1.5);
+}
+
+bool _numba_test_boolean()
+{
+    return true;
+}
+
+MOD_INIT(_helperlib) {
+    PyObject *m;
+    MOD_DEF(m, "_helperlib", "No docs", ext_methods)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    import_array();
+
+    PyModule_AddObject(m, "c_helpers", build_c_helpers_dict());
+    PyModule_AddObject(m, "npymath_exports", build_npymath_exports_dict());
+    PyModule_AddIntConstant(m, "long_min", LONG_MIN);
+    PyModule_AddIntConstant(m, "long_max", LONG_MAX);
+    PyModule_AddIntConstant(m, "py_buffer_size", sizeof(Py_buffer));
+    PyModule_AddIntConstant(m, "py_gil_state_size", sizeof(PyGILState_STATE));
+    PyModule_AddIntConstant(m, "py_unicode_1byte_kind", PyUnicode_1BYTE_KIND);
+    PyModule_AddIntConstant(m, "py_unicode_2byte_kind", PyUnicode_2BYTE_KIND);
+    PyModule_AddIntConstant(m, "py_unicode_4byte_kind", PyUnicode_4BYTE_KIND);
+    PyModule_AddIntConstant(m, "py_unicode_wchar_kind", PyUnicode_WCHAR_KIND);
+    numba_rnd_ensure_global_init();
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_lapack.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_lapack.c
new file mode 100644
index 000000000..6d5d183ec
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_lapack.c
@@ -0,0 +1,1946 @@
+/*
+ * This file contains wrappers of BLAS and LAPACK functions
+ */
+/*
+ * BLAS calling helpers.  The helpers can be called without the GIL held.
+ * The caller is responsible for checking arguments (especially dimensions).
+ */
+
+/* Fast getters caching the value of a function's address after
+   the first call to import_cblas_function(). */
+
+#define EMIT_GET_CBLAS_FUNC(name)                                 \
+    static void *cblas_ ## name = NULL;                           \
+    static void *get_cblas_ ## name(void) {                       \
+        if (cblas_ ## name == NULL) {                             \
+            PyGILState_STATE st = PyGILState_Ensure();            \
+            const char *mod = "scipy.linalg.cython_blas";         \
+            cblas_ ## name = import_cython_function(mod, # name); \
+            PyGILState_Release(st);                               \
+        }                                                         \
+        return cblas_ ## name;                                    \
+    }
+
+EMIT_GET_CBLAS_FUNC(dgemm)
+EMIT_GET_CBLAS_FUNC(sgemm)
+EMIT_GET_CBLAS_FUNC(cgemm)
+EMIT_GET_CBLAS_FUNC(zgemm)
+EMIT_GET_CBLAS_FUNC(dgemv)
+EMIT_GET_CBLAS_FUNC(sgemv)
+EMIT_GET_CBLAS_FUNC(cgemv)
+EMIT_GET_CBLAS_FUNC(zgemv)
+EMIT_GET_CBLAS_FUNC(ddot)
+EMIT_GET_CBLAS_FUNC(sdot)
+EMIT_GET_CBLAS_FUNC(cdotu)
+EMIT_GET_CBLAS_FUNC(zdotu)
+EMIT_GET_CBLAS_FUNC(cdotc)
+EMIT_GET_CBLAS_FUNC(zdotc)
+EMIT_GET_CBLAS_FUNC(snrm2)
+EMIT_GET_CBLAS_FUNC(dnrm2)
+EMIT_GET_CBLAS_FUNC(scnrm2)
+EMIT_GET_CBLAS_FUNC(dznrm2)
+
+
+#undef EMIT_GET_CBLAS_FUNC
+
+/*
+ * NOTE: On return value convention.
+ * For LAPACK wrapper development the following conventions are followed:
+ * Publicly exposed wrapper functions must return:-
+ * STATUS_ERROR  : For an unrecoverable error e.g. caught by xerbla, this is so
+ *                 a Py_FatalError can be raised.
+ * STATUS_SUCCESS: For successful execution
+ * +n            : Where n is an integer for a routine specific error
+ *                 (typically derived from an `info` argument).
+ *
+ * The caller is responsible for checking and handling the error status.
+ */
+
+/* return STATUS_SUCCESS if everything went ok */
+#define STATUS_SUCCESS  (0)
+
+/* return STATUS_ERROR if an unrecoverable error is encountered */
+#define STATUS_ERROR  (-1)
+
+/*
+ * A union of all the types accepted by BLAS/LAPACK for use in cases where
+ * stack based allocation is needed (typically for work space query args length
+ * 1).
+ */
+typedef union all_dtypes_
+{
+    float  s;
+    double d;
+    npy_complex64 c;
+    npy_complex128 z;
+} all_dtypes;
+
+/*
+ * A checked PyMem_RawMalloc, ensures that the var is either NULL
+ * and an exception is raised, or that the allocation was successful.
+ * Returns zero on success for status checking.
+ */
+static int checked_PyMem_RawMalloc(void** var, size_t bytes)
+{
+    *var = NULL;
+    *var = PyMem_RawMalloc(bytes);
+    if (!(*var))
+    {
+        {
+            PyGILState_STATE st = PyGILState_Ensure();
+
+            PyErr_SetString(PyExc_MemoryError,
+                            "Insufficient memory for buffer allocation\
+                             required by LAPACK.");
+            PyGILState_Release(st);
+        }
+        return 1;
+    }
+    return 0;
+}
+
+/*
+ * Checks that the char kind is valid (one of [s,d,c,z]) for use in blas/lapack.
+ * Returns zero on success for status checking.
+ */
+static int check_kind(char kind)
+{
+    switch (kind)
+    {
+        case 's':
+        case 'd':
+        case 'c':
+        case 'z':
+            break;
+        default:
+        {
+            PyGILState_STATE st = PyGILState_Ensure();
+            PyErr_SetString(PyExc_ValueError,
+                            "invalid data type (kind) found");
+            PyGILState_Release(st);
+        }
+        return 1;
+    }
+    return 0;
+}
+
+/*
+ * Guard macro for ensuring a valid data "kind" is being used.
+ * Place at the top of all routines with switches on "kind" that accept
+ * one of [s,d,c,z].
+ */
+#define ENSURE_VALID_KIND(__KIND) \
+if (check_kind( __KIND ))         \
+{                                 \
+    return STATUS_ERROR;          \
+}                                 \
+
+/*
+ * Checks that the char kind is valid for the real domain (one of [s,d])
+ * for use in blas/lapack.
+ * Returns zero on success for status checking.
+ */
+static int check_real_kind(char kind)
+{
+    switch (kind)
+    {
+        case 's':
+        case 'd':
+            break;
+        default:
+        {
+            PyGILState_STATE st = PyGILState_Ensure();
+            PyErr_SetString(PyExc_ValueError,
+                            "invalid data type (kind) found");
+            PyGILState_Release(st);
+        }
+        return 1;
+    }
+    return 0;
+}
+
+/*
+ * Guard macro for ensuring a valid data "kind" is being used for the
+ * real domain routines.
+ * Place at the top of all routines with switches on "kind" that accept
+ * one of [s,d].
+ */
+#define ENSURE_VALID_REAL_KIND(__KIND) \
+if (check_real_kind( __KIND ))         \
+{                                      \
+    return STATUS_ERROR;               \
+}                                      \
+
+
+/*
+ * Checks that the char kind is valid for the complex domain (one of [c,z])
+ * for use in blas/lapack.
+ * Returns zero on success for status checking.
+ */
+static int check_complex_kind(char kind)
+{
+    switch (kind)
+    {
+        case 'c':
+        case 'z':
+            break;
+        default:
+        {
+            PyGILState_STATE st = PyGILState_Ensure();
+            PyErr_SetString(PyExc_ValueError,
+                            "invalid data type (kind) found");
+            PyGILState_Release(st);
+        }
+        return 1;
+    }
+    return 0;
+}
+
+/*
+ * Guard macro for ensuring a valid data "kind" is being used for the
+ * real domain routines.
+ * Place at the top of all routines with switches on "kind" that accept
+ * one of [c,z].
+ */
+#define ENSURE_VALID_COMPLEX_KIND(__KIND) \
+if (check_complex_kind( __KIND ))         \
+{                                         \
+    return STATUS_ERROR;                  \
+}                                         \
+
+
+/*
+ * Checks that a function is found (i.e. not null)
+ * Returns zero on success for status checking.
+ */
+static int check_func(void *func)
+{
+    if (func == NULL)
+    {
+        PyGILState_STATE st = PyGILState_Ensure();
+        PyErr_SetString(PyExc_RuntimeError,
+                        "Specified LAPACK function could not be found.");
+        PyGILState_Release(st);
+        return STATUS_ERROR;
+    }
+    return STATUS_SUCCESS;
+}
+
+
+/*
+ * Guard macro for ensuring a valid function is found.
+ */
+#define ENSURE_VALID_FUNC(__FUNC)         \
+if (check_func(__FUNC))                   \
+{                                         \
+    return STATUS_ERROR;                  \
+}                                         \
+
+
+/*
+ * Define what a Fortran "int" is, some LAPACKs have 64 bit integer support
+ * numba presently opts for a 32 bit C int.
+ * This definition allows scope for later configuration time magic to adjust
+ * the size of int at all the call sites.
+ */
+#define F_INT int
+
+
+typedef float (*sdot_t)(F_INT *n, void *dx, F_INT *incx, void *dy, F_INT *incy);
+typedef double (*ddot_t)(F_INT *n, void *dx, F_INT *incx, void *dy, F_INT
+                         *incy);
+typedef npy_complex64 (*cdot_t)(F_INT *n, void *dx, F_INT *incx, void *dy,
+                                F_INT *incy);
+typedef npy_complex128 (*zdot_t)(F_INT *n, void *dx, F_INT *incx, void *dy,
+                                 F_INT *incy);
+
+typedef void (*xxgemv_t)(char *trans, F_INT *m, F_INT *n,
+                         void *alpha, void *a, F_INT *lda,
+                         void *x, F_INT *incx, void *beta,
+                         void *y, F_INT *incy);
+
+typedef void (*xxgemm_t)(char *transa, char *transb,
+                         F_INT *m, F_INT *n, F_INT *k,
+                         void *alpha, void *a, F_INT *lda,
+                         void *b, F_INT *ldb, void *beta,
+                         void *c, F_INT *ldc);
+
+typedef float (*sxnrm2_t) (F_INT *n, void *x, F_INT *incx);
+typedef double (*dxnrm2_t) (F_INT *n, void *x, F_INT *incx);
+
+/* Vector * vector: result = dx * dy */
+NUMBA_EXPORT_FUNC(int)
+numba_xxdot(char kind, char conjugate, Py_ssize_t n, void *dx, void *dy,
+            void *result)
+{
+    void *raw_func = NULL;
+    F_INT _n;
+    F_INT inc = 1;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_cblas_sdot();
+            break;
+        case 'd':
+            raw_func = get_cblas_ddot();
+            break;
+        case 'c':
+            raw_func = conjugate ? get_cblas_cdotc() : get_cblas_cdotu();
+            break;
+        case 'z':
+            raw_func = conjugate ? get_cblas_zdotc() : get_cblas_zdotu();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _n = (F_INT) n;
+
+    switch (kind)
+    {
+        case 's':
+            *(float *) result = (*(sdot_t) raw_func)(&_n, dx, &inc, dy, &inc);;
+            break;
+        case 'd':
+            *(double *) result = (*(ddot_t) raw_func)(&_n, dx, &inc, dy, &inc);;
+            break;
+        case 'c':
+            *(npy_complex64 *) result = (*(cdot_t) raw_func)(&_n, dx, &inc, dy,\
+                                        &inc);;
+            break;
+        case 'z':
+            *(npy_complex128 *) result = (*(zdot_t) raw_func)(&_n, dx, &inc,\
+                                         dy, &inc);;
+            break;
+    }
+
+    return 0;
+}
+
+/* Matrix * vector: y = alpha * a * x + beta * y */
+NUMBA_EXPORT_FUNC(int)
+numba_xxgemv(char kind, char trans, Py_ssize_t m, Py_ssize_t n,
+             void *alpha, void *a, Py_ssize_t lda,
+             void *x, void *beta, void *y)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n;
+    F_INT _lda;
+    F_INT inc = 1;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_cblas_sgemv();
+            break;
+        case 'd':
+            raw_func = get_cblas_dgemv();
+            break;
+        case 'c':
+            raw_func = get_cblas_cgemv();
+            break;
+        case 'z':
+            raw_func = get_cblas_zgemv();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+
+    (*(xxgemv_t) raw_func)(&trans, &_m, &_n, alpha, a, &_lda,
+                           x, &inc, beta, y, &inc);
+    return 0;
+}
+
+/* Matrix * matrix: c = alpha * a * b + beta * c */
+NUMBA_EXPORT_FUNC(int)
+numba_xxgemm(char kind, char transa, char transb,
+             Py_ssize_t m, Py_ssize_t n, Py_ssize_t k,
+             void *alpha, void *a, Py_ssize_t lda,
+             void *b, Py_ssize_t ldb, void *beta,
+             void *c, Py_ssize_t ldc)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n, _k;
+    F_INT _lda, _ldb, _ldc;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_cblas_sgemm();
+            break;
+        case 'd':
+            raw_func = get_cblas_dgemm();
+            break;
+        case 'c':
+            raw_func = get_cblas_cgemm();
+            break;
+        case 'z':
+            raw_func = get_cblas_zgemm();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _k = (F_INT) k;
+    _lda = (F_INT) lda;
+    _ldb = (F_INT) ldb;
+    _ldc = (F_INT) ldc;
+
+    (*(xxgemm_t) raw_func)(&transa, &transb, &_m, &_n, &_k, alpha, a, &_lda,
+                           b, &_ldb, beta, c, &_ldc);
+    return 0;
+}
+
+
+/* L2-norms */
+NUMBA_EXPORT_FUNC(F_INT)
+numba_xxnrm2(char kind, Py_ssize_t n, void * x, Py_ssize_t incx, void * result)
+{
+    void *raw_func = NULL;
+    F_INT _incx;
+    F_INT _n;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_cblas_snrm2();
+            break;
+        case 'd':
+            raw_func = get_cblas_dnrm2();
+            break;
+        case 'c':
+            raw_func = get_cblas_scnrm2();
+            break;
+        case 'z':
+            raw_func = get_cblas_dznrm2();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _n = (F_INT) n;
+    _incx = (F_INT) incx;
+
+    switch (kind)
+    {
+        case 's':
+            *(float *) result = (*(sxnrm2_t) raw_func)(&_n, x, &_incx);;
+            break;
+        case 'd':
+            *(double *) result = (*(dxnrm2_t) raw_func)(&_n, x, &_incx);;
+            break;
+        case 'c':
+            *(float *) result = (*(sxnrm2_t) raw_func)(&_n, x, &_incx);;
+            break;
+        case 'z':
+            *(double *) result = (*(dxnrm2_t) raw_func)(&_n, x, &_incx);;
+            break;
+    }
+
+    return 0;
+}
+
+
+/*
+ * LAPACK calling helpers.  The helpers can be called without the GIL held.
+ * The caller is responsible for checking arguments (especially dimensions).
+ */
+
+/* Fast getters caching the value of a function's address after
+   the first call to import_clapack_function(). */
+
+#define EMIT_GET_CLAPACK_FUNC(name)                                 \
+    static void *clapack_ ## name = NULL;                           \
+    static void *get_clapack_ ## name(void) {                       \
+        if (clapack_ ## name == NULL) {                             \
+            PyGILState_STATE st = PyGILState_Ensure();              \
+            const char *mod = "scipy.linalg.cython_lapack";         \
+            clapack_ ## name = import_cython_function(mod, # name); \
+            PyGILState_Release(st);                                 \
+        }                                                           \
+        return clapack_ ## name;                                    \
+    }
+
+/* Computes an LU factorization of a general M-by-N matrix A
+ * using partial pivoting with row interchanges.
+ */
+EMIT_GET_CLAPACK_FUNC(sgetrf)
+EMIT_GET_CLAPACK_FUNC(dgetrf)
+EMIT_GET_CLAPACK_FUNC(cgetrf)
+EMIT_GET_CLAPACK_FUNC(zgetrf)
+
+/* Computes the inverse of a matrix using the LU factorization
+ * computed by xGETRF.
+ */
+EMIT_GET_CLAPACK_FUNC(sgetri)
+EMIT_GET_CLAPACK_FUNC(dgetri)
+EMIT_GET_CLAPACK_FUNC(cgetri)
+EMIT_GET_CLAPACK_FUNC(zgetri)
+
+/* Compute Cholesky factorizations */
+EMIT_GET_CLAPACK_FUNC(spotrf)
+EMIT_GET_CLAPACK_FUNC(dpotrf)
+EMIT_GET_CLAPACK_FUNC(cpotrf)
+EMIT_GET_CLAPACK_FUNC(zpotrf)
+
+/* Computes for an N-by-N real nonsymmetric matrix A, the
+ * eigenvalues and, optionally, the left and/or right eigenvectors.
+ */
+EMIT_GET_CLAPACK_FUNC(sgeev)
+EMIT_GET_CLAPACK_FUNC(dgeev)
+EMIT_GET_CLAPACK_FUNC(cgeev)
+EMIT_GET_CLAPACK_FUNC(zgeev)
+
+/* Computes for an N-by-N Hermitian matrix A, the
+ * eigenvalues and, optionally, the left and/or right eigenvectors.
+ */
+EMIT_GET_CLAPACK_FUNC(ssyevd)
+EMIT_GET_CLAPACK_FUNC(dsyevd)
+EMIT_GET_CLAPACK_FUNC(cheevd)
+EMIT_GET_CLAPACK_FUNC(zheevd)
+
+/* Computes generalised SVD */
+EMIT_GET_CLAPACK_FUNC(sgesdd)
+EMIT_GET_CLAPACK_FUNC(dgesdd)
+EMIT_GET_CLAPACK_FUNC(cgesdd)
+EMIT_GET_CLAPACK_FUNC(zgesdd)
+
+/* Computes QR decompositions */
+EMIT_GET_CLAPACK_FUNC(sgeqrf)
+EMIT_GET_CLAPACK_FUNC(dgeqrf)
+EMIT_GET_CLAPACK_FUNC(cgeqrf)
+EMIT_GET_CLAPACK_FUNC(zgeqrf)
+
+/* Computes columns of Q from elementary reflectors produced by xgeqrf() (QR).
+ */
+EMIT_GET_CLAPACK_FUNC(sorgqr)
+EMIT_GET_CLAPACK_FUNC(dorgqr)
+EMIT_GET_CLAPACK_FUNC(cungqr)
+EMIT_GET_CLAPACK_FUNC(zungqr)
+
+/* Computes the minimum norm solution to linear least squares problems */
+EMIT_GET_CLAPACK_FUNC(sgelsd)
+EMIT_GET_CLAPACK_FUNC(dgelsd)
+EMIT_GET_CLAPACK_FUNC(cgelsd)
+EMIT_GET_CLAPACK_FUNC(zgelsd)
+
+// Computes the solution to a system of linear equations
+EMIT_GET_CLAPACK_FUNC(sgesv)
+EMIT_GET_CLAPACK_FUNC(dgesv)
+EMIT_GET_CLAPACK_FUNC(cgesv)
+EMIT_GET_CLAPACK_FUNC(zgesv)
+
+
+#undef EMIT_GET_CLAPACK_FUNC
+
+typedef void (*xxgetrf_t)(F_INT *m, F_INT *n, void *a, F_INT *lda, F_INT *ipiv,
+                          F_INT *info);
+
+typedef void (*xxgetri_t)(F_INT *n, void *a, F_INT *lda, F_INT *ipiv, void
+                          *work, F_INT *lwork, F_INT *info);
+
+typedef void (*xxpotrf_t)(char *uplo, F_INT *n, void *a, F_INT *lda, F_INT
+                          *info);
+
+typedef void (*rgeev_t)(char *jobvl, char *jobvr, F_INT *n, void *a, F_INT *lda,
+                        void *wr, void *wi, void *vl, F_INT *ldvl, void *vr,
+                        F_INT *ldvr, void *work, F_INT *lwork, F_INT *info);
+
+typedef void (*cgeev_t)(char *jobvl, char *jobvr, F_INT *n, void *a, F_INT
+                        *lda, void *w, void *vl, F_INT *ldvl, void *vr,
+                        F_INT *ldvr, void *work, F_INT *lwork, void *rwork,
+                        F_INT *info);
+
+typedef void (*rgesdd_t)(char *jobz, F_INT *m, F_INT *n, void *a, F_INT *lda,
+                         void *s, void *u, F_INT *ldu, void *vt, F_INT *ldvt,
+                         void *work, F_INT *lwork, F_INT *iwork, F_INT *info);
+
+typedef void (*cgesdd_t)(char *jobz, F_INT *m, F_INT *n, void *a, F_INT *lda,
+                         void *s, void * u, F_INT *ldu, void * vt, F_INT *ldvt,
+                         void *work, F_INT *lwork, void *rwork, F_INT *iwork,
+                         F_INT *info);
+
+typedef void (*xsyevd_t)(char *jobz, char *uplo, F_INT *n, void *a, F_INT *lda,
+                         void *w, void *work, F_INT *lwork, F_INT *iwork,
+                         F_INT *liwork, F_INT *info);
+
+typedef void (*xheevd_t)(char *jobz, char *uplo, F_INT *n, void *a, F_INT *lda,
+                         void *w, void *work, F_INT *lwork, void *rwork,
+                         F_INT *lrwork, F_INT *iwork, F_INT *liwork,
+                         F_INT *info);
+
+typedef void (*xgeqrf_t)(F_INT *m, F_INT *n, void *a, F_INT *lda, void *tau,
+                         void *work, F_INT *lwork, F_INT *info);
+
+typedef void (*xxxgqr_t)(F_INT *m, F_INT *n, F_INT *k, void *a, F_INT *lda,
+                         void *tau, void *work, F_INT *lwork, F_INT *info);
+
+typedef void (*rgelsd_t)(F_INT *m, F_INT *n, F_INT *nrhs, void *a, F_INT *lda,
+                         void *b, F_INT *ldb, void *s, void *rcond, F_INT *rank,
+                         void *work, F_INT *lwork, F_INT *iwork, F_INT *info);
+
+typedef void (*cgelsd_t)(F_INT *m, F_INT *n, F_INT *nrhs, void *a, F_INT *lda,
+                         void *b, F_INT *ldb, void *s, void *rcond, F_INT *rank,
+                         void *work, F_INT *lwork, void *rwork, F_INT *iwork,
+                         F_INT *info);
+
+typedef void (*xgesv_t)(F_INT *n, F_INT *nrhs, void *a, F_INT *lda, F_INT *ipiv,
+                        void *b, F_INT *ldb, F_INT *info);
+
+
+
+/*
+ * kind_size()
+ * gets the data size appropriate for a specified kind.
+ *
+ * Input:
+ * kind - the kind, one of:
+ *         (s, d, c, z) = (float, double, complex, double complex).
+ *
+ * Returns:
+ * data_size - the appropriate data size.
+ *
+ */
+static size_t kind_size(char kind)
+{
+    size_t data_size = 0;
+    switch (kind)
+    {
+        case 's':
+            data_size  = sizeof(float);
+            break;
+        case 'd':
+            data_size  = sizeof(double);
+            break;
+        case 'c':
+            data_size  = sizeof(npy_complex64);
+            break;
+        case 'z':
+            data_size  = sizeof(npy_complex128);
+            break;
+    }
+    return data_size;
+
+}
+
+/*
+ * underlying_float_kind()
+ * gets the underlying float kind for a given kind.
+ *
+ * Input:
+ * kind - the kind, one of:
+ *         (s, d, c, z) = (float, double, complex, double complex).
+ *
+ * Returns:
+ * underlying_float_kind - the underlying float kind, one of:
+ *         (s, d) = (float, double).
+ *
+ * This function essentially provides a map between the char kind
+ * of a type and the char kind of the underlying float used in the
+ * type. Essentially:
+ * ---------------
+ * Input -> Output
+ * ---------------
+ *     s -> s
+ *     d -> d
+ *     c -> s
+ *     z -> d
+ * ---------------
+ *
+ */
+static char underlying_float_kind(char kind)
+{
+    switch(kind)
+    {
+        case 's':
+        case 'c':
+            return 's';
+        case 'd':
+        case 'z':
+            return 'd';
+        default:
+        {
+            PyGILState_STATE st = PyGILState_Ensure();
+            PyErr_SetString(PyExc_ValueError,
+                            "invalid kind in underlying_float_kind()");
+            PyGILState_Release(st);
+        }
+    }
+    return -1;
+}
+
+/*
+ * cast_from_X()
+ * cast from a kind (s, d, c, z) = (float, double, complex, double complex)
+ * to a Fortran integer.
+ *
+ * Parameters:
+ * kind the kind of val
+ * val  a pointer to the value to cast
+ *
+ * Returns:
+ * A Fortran int from a cast of val (in complex case, takes the real part).
+ *
+ * Struct access via non c99 (python only) cmplx types, used for compatibility.
+ */
+static F_INT
+cast_from_X(char kind, void *val)
+{
+    switch(kind)
+    {
+        case 's':
+            return (F_INT)(*((float *) val));
+        case 'd':
+            return (F_INT)(*((double *) val));
+        case 'c':
+            return (F_INT)(*((npy_complex64 *)val)).real;
+        case 'z':
+            return (F_INT)(*((npy_complex128 *)val)).real;
+        default:
+        {
+            PyGILState_STATE st = PyGILState_Ensure();
+            PyErr_SetString(PyExc_ValueError,
+                            "invalid kind in cast");
+            PyGILState_Release(st);
+        }
+    }
+    return -1;
+}
+
+
+#define CATCH_LAPACK_INVALID_ARG(__routine, info)                      \
+    do {                                                               \
+        if (info < 0) {                                                \
+            PyGILState_STATE st = PyGILState_Ensure();                 \
+            PyErr_Format(PyExc_RuntimeError,                           \
+                 "LAPACK Error: Routine " #__routine ". On input %d\n",\
+                  -(int) info);                                        \
+            PyGILState_Release(st);                                    \
+            return STATUS_ERROR;                                       \
+        }                                                              \
+    } while(0)
+
+/* Compute LU decomposition of A
+ * NOTE: ipiv is an array of Fortran integers allocated by the caller,
+ * which is therefore expected to use the right dtype.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_xxgetrf(char kind, Py_ssize_t m, Py_ssize_t n, void *a, Py_ssize_t lda,
+              F_INT *ipiv)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n, _lda, info;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_sgetrf();
+            break;
+        case 'd':
+            raw_func = get_clapack_dgetrf();
+            break;
+        case 'c':
+            raw_func = get_clapack_cgetrf();
+            break;
+        case 'z':
+            raw_func = get_clapack_zgetrf();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+
+    (*(xxgetrf_t) raw_func)(&_m, &_n, a, &_lda, ipiv, &info);
+    CATCH_LAPACK_INVALID_ARG("xxgetrf", info);
+
+    return (int)info;
+}
+
+/* Compute the inverse of a matrix given its LU decomposition
+ * Args are as per LAPACK.
+ */
+static int
+numba_raw_xxgetri(char kind, F_INT n, void *a, F_INT lda,
+                  F_INT *ipiv, void *work, F_INT *lwork, F_INT *info)
+{
+    void *raw_func = NULL;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_sgetri();
+            break;
+        case 'd':
+            raw_func = get_clapack_dgetri();
+            break;
+        case 'c':
+            raw_func = get_clapack_cgetri();
+            break;
+        case 'z':
+            raw_func = get_clapack_zgetri();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    (*(xxgetri_t) raw_func)(&n, a, &lda, ipiv, work, lwork, info);
+
+    return 0;
+}
+
+/* Compute the inverse of a matrix from the factorization provided by
+ * xxgetrf. (see numba_xxgetrf() about ipiv)
+ * Args are as per LAPACK.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_ez_xxgetri(char kind, Py_ssize_t n, void *a, Py_ssize_t lda,
+                 F_INT *ipiv)
+{
+    F_INT _n, _lda;
+    F_INT lwork = -1;
+    F_INT info = 0;
+    size_t base_size = -1;
+    void * work = NULL;
+    all_dtypes stack_slot;
+
+    ENSURE_VALID_KIND(kind)
+
+    _n = (F_INT)n;
+    _lda = (F_INT)lda;
+
+    base_size = kind_size(kind);
+
+    work = &stack_slot;
+
+    numba_raw_xxgetri(kind, _n, a, _lda, ipiv, work, &lwork, &info);
+    CATCH_LAPACK_INVALID_ARG("xxgetri", info);
+
+    lwork = cast_from_X(kind, work);
+
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+    {
+        return STATUS_ERROR;
+    }
+
+    numba_raw_xxgetri(kind, _n, a, _lda, ipiv, work, &lwork, &info);
+    PyMem_RawFree(work);
+    CATCH_LAPACK_INVALID_ARG("xxgetri", info);
+
+    return (int)info;
+}
+
+/* Compute the Cholesky factorization of a matrix. */
+NUMBA_EXPORT_FUNC(int)
+numba_xxpotrf(char kind, char uplo, Py_ssize_t n, void *a, Py_ssize_t lda)
+{
+    void *raw_func = NULL;
+    F_INT _n, _lda, info;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_spotrf();
+            break;
+        case 'd':
+            raw_func = get_clapack_dpotrf();
+            break;
+        case 'c':
+            raw_func = get_clapack_cpotrf();
+            break;
+        case 'z':
+            raw_func = get_clapack_zpotrf();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+
+    (*(xxpotrf_t) raw_func)(&uplo, &_n, a, &_lda, &info);
+    CATCH_LAPACK_INVALID_ARG("xxpotrf", info);
+    return (int)info;
+}
+
+
+/* real space eigen systems info from dgeev/sgeev */
+static int
+numba_raw_rgeev(char kind, char jobvl, char jobvr,
+                Py_ssize_t n, void *a, Py_ssize_t lda, void *wr, void *wi,
+                void *vl, Py_ssize_t ldvl, void *vr, Py_ssize_t ldvr,
+                void *work, Py_ssize_t lwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _n, _lda, _ldvl, _ldvr, _lwork;
+
+    ENSURE_VALID_REAL_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_sgeev();
+            break;
+        case 'd':
+            raw_func = get_clapack_dgeev();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _ldvl = (F_INT) ldvl;
+    _ldvr = (F_INT) ldvr;
+    _lwork = (F_INT) lwork;
+
+    (*(rgeev_t) raw_func)(&jobvl, &jobvr, &_n, a, &_lda, wr, wi, vl, &_ldvl, vr,
+                          &_ldvr, work, &_lwork, info);
+    return 0;
+}
+
+/* Real space eigen systems info from dgeev/sgeev
+ * as numba_raw_rgeev but the allocation and error handling is done for the user.
+ * Args are as per LAPACK.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_ez_rgeev(char kind, char jobvl, char jobvr, Py_ssize_t n, void *a,
+               Py_ssize_t lda, void *wr, void *wi, void *vl, Py_ssize_t ldvl,
+               void *vr, Py_ssize_t ldvr)
+{
+    F_INT info = 0;
+    F_INT lwork = -1;
+    F_INT _n, _lda, _ldvl, _ldvr;
+    size_t base_size = -1;
+    void * work = NULL;
+    all_dtypes stack_slot;
+
+    ENSURE_VALID_REAL_KIND(kind)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _ldvl = (F_INT) ldvl;
+    _ldvr = (F_INT) ldvr;
+
+    base_size = kind_size(kind);
+
+    work = &stack_slot;
+    numba_raw_rgeev(kind, jobvl, jobvr, _n, a, _lda, wr, wi, vl, _ldvl,
+                    vr, _ldvr, work, lwork, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_rgeev", info);
+
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+    {
+        return STATUS_ERROR;
+    }
+    numba_raw_rgeev(kind, jobvl, jobvr, _n, a, _lda, wr, wi, vl, _ldvl,
+                    vr, _ldvr, work, lwork, &info);
+    PyMem_RawFree(work);
+
+    CATCH_LAPACK_INVALID_ARG("numba_raw_rgeev", info);
+
+    return (int)info;
+}
+
+/* Complex space eigen systems info from cgeev/zgeev
+ * Args are as per LAPACK.
+ */
+static int
+numba_raw_cgeev(char kind, char jobvl, char jobvr,
+                Py_ssize_t n, void *a, Py_ssize_t lda, void *w, void *vl,
+                Py_ssize_t ldvl, void *vr, Py_ssize_t ldvr, void *work,
+                Py_ssize_t lwork, void *rwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _n, _lda, _ldvl, _ldvr, _lwork;
+
+    ENSURE_VALID_COMPLEX_KIND(kind)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _ldvl = (F_INT) ldvl;
+    _ldvr = (F_INT) ldvr;
+    _lwork = (F_INT) lwork;
+
+    switch (kind)
+    {
+        case 'c':
+            raw_func = get_clapack_cgeev();
+            break;
+        case 'z':
+            raw_func = get_clapack_zgeev();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    (*(cgeev_t) raw_func)(&jobvl, &jobvr, &_n, a, &_lda, w, vl, &_ldvl, vr,
+                          &_ldvr, work, &_lwork, rwork, info);
+    return 0;
+}
+
+
+/* Complex space eigen systems info from cgeev/zgeev
+ * as numba_raw_cgeev but the allocation and error handling is done for the user.
+ * Args are as per LAPACK.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_ez_cgeev(char kind, char jobvl, char jobvr,  Py_ssize_t n, void *a,
+               Py_ssize_t lda, void *w, void *vl, Py_ssize_t ldvl, void *vr,
+               Py_ssize_t ldvr)
+{
+    F_INT info = 0;
+    F_INT lwork = -1;
+    F_INT _n, _lda, _ldvl, _ldvr;
+    size_t base_size = -1;
+    all_dtypes stack_slot, wk;
+    void * work = NULL;
+    void * rwork = (void *)&wk;
+
+    ENSURE_VALID_COMPLEX_KIND(kind)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _ldvl = (F_INT) ldvl;
+    _ldvr = (F_INT) ldvr;
+
+    base_size = kind_size(kind);
+
+    work = &stack_slot;
+    numba_raw_cgeev(kind, jobvl, jobvr, n, a, lda, w, vl, ldvl,
+                    vr, ldvr, work, lwork, rwork, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_cgeev", info);
+
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc((void**)&rwork, 2*n*base_size))
+    {
+        return STATUS_ERROR;
+    }
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+    {
+        PyMem_RawFree(rwork);
+        return STATUS_ERROR;
+    }
+    numba_raw_cgeev(kind, jobvl, jobvr, _n, a, _lda, w, vl, _ldvl,
+                    vr, _ldvr, work, lwork, rwork, &info);
+    PyMem_RawFree(work);
+    PyMem_RawFree(rwork);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_cgeev", info);
+
+    return (int)info;
+}
+
+/* real space symmetric eigen systems info from ssyevd/dsyevd */
+static int
+numba_raw_rsyevd(char kind, char jobz, char uplo, Py_ssize_t n, void *a,
+                 Py_ssize_t lda, void *w, void *work, Py_ssize_t lwork,
+                 F_INT *iwork, Py_ssize_t liwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _n, _lda, _lwork, _liwork;
+
+    ENSURE_VALID_REAL_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_ssyevd();
+            break;
+        case 'd':
+            raw_func = get_clapack_dsyevd();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _lwork = (F_INT) lwork;
+    _liwork = (F_INT) liwork;
+
+    (*(xsyevd_t) raw_func)(&jobz, &uplo, &_n, a, &_lda, w, work, &_lwork, iwork, &_liwork, info);
+    return 0;
+}
+
+/* Real space eigen systems info from dsyevd/ssyevd
+ * as numba_raw_rsyevd but the allocation and error handling is done for the user.
+ * Args are as per LAPACK.
+ */
+static int
+numba_ez_rsyevd(char kind, char jobz, char uplo, Py_ssize_t n, void *a, Py_ssize_t lda, void *w)
+{
+    F_INT info = 0;
+    F_INT lwork = -1, liwork=-1;
+    F_INT _n, _lda;
+    size_t base_size = -1;
+    void *work = NULL;
+    F_INT *iwork = NULL;
+    all_dtypes stack_slot;
+    int stack_int = -1;
+
+    ENSURE_VALID_REAL_KIND(kind)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+
+    base_size = kind_size(kind);
+
+    work = &stack_slot;
+    iwork = &stack_int;
+    numba_raw_rsyevd(kind, jobz, uplo, _n, a, _lda, w, work, lwork, iwork, liwork, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_rsyevd", info);
+
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+    {
+        return STATUS_ERROR;
+    }
+    liwork = *iwork;
+    if (checked_PyMem_RawMalloc((void**)&iwork, base_size * liwork))
+    {
+        PyMem_RawFree(work);
+        return STATUS_ERROR;
+    }
+    numba_raw_rsyevd(kind, jobz, uplo, _n, a, _lda, w, work, lwork, iwork, liwork, &info);
+    PyMem_RawFree(work);
+    PyMem_RawFree(iwork);
+
+    CATCH_LAPACK_INVALID_ARG("numba_raw_rsyevd", info);
+
+    return (int)info;
+}
+
+
+/* complex space symmetric eigen systems info from cheevd/zheevd*/
+static int
+numba_raw_cheevd(char kind, char jobz, char uplo, Py_ssize_t n, void *a,
+                 Py_ssize_t lda, void *w, void *work, Py_ssize_t lwork,
+                 void *rwork, Py_ssize_t lrwork, F_INT *iwork,
+                 Py_ssize_t liwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _n, _lda, _lwork, _lrwork, _liwork;
+
+    ENSURE_VALID_COMPLEX_KIND(kind)
+
+    switch (kind)
+    {
+        case 'c':
+            raw_func = get_clapack_cheevd();
+            break;
+        case 'z':
+            raw_func = get_clapack_zheevd();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _lwork = (F_INT) lwork;
+    _lrwork = (F_INT) lrwork;
+    _liwork = (F_INT) liwork;
+
+    (*(xheevd_t) raw_func)(&jobz, &uplo, &_n, a, &_lda, w, work, &_lwork, rwork, &_lrwork, iwork, &_liwork, info);
+    return 0;
+}
+
+/* complex space eigen systems info from cheevd/zheevd
+ * as numba_raw_cheevd but the allocation and error handling is done for the user.
+ * Args are as per LAPACK.
+ */
+static int
+numba_ez_cheevd(char kind, char jobz, char uplo, Py_ssize_t n, void *a, Py_ssize_t lda, void *w)
+{
+    F_INT info = 0;
+    F_INT lwork = -1, lrwork = -1, liwork=-1;
+    F_INT _n, _lda;
+    size_t base_size = -1, underlying_float_size = -1;
+    void *work = NULL, *rwork = NULL;
+    F_INT *iwork = NULL;
+    all_dtypes stack_slot1, stack_slot2;
+    char uf_kind;
+    int stack_int = -1;
+
+    ENSURE_VALID_COMPLEX_KIND(kind)
+
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+
+    base_size = kind_size(kind);
+    uf_kind = underlying_float_kind(kind);
+    underlying_float_size = kind_size(uf_kind);
+
+    work = &stack_slot1;
+    rwork = &stack_slot2;
+    iwork = &stack_int;
+    numba_raw_cheevd(kind, jobz, uplo, _n, a, _lda, w, work, lwork, rwork, lrwork, iwork, liwork, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_cheevd", info);
+
+    lwork = cast_from_X(uf_kind, work);
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+    {
+        return STATUS_ERROR;
+    }
+
+    lrwork = cast_from_X(uf_kind, rwork);
+    if (checked_PyMem_RawMalloc(&rwork, underlying_float_size * lrwork))
+    {
+        PyMem_RawFree(work);
+        return STATUS_ERROR;
+    }
+
+    liwork = *iwork;
+    if (checked_PyMem_RawMalloc((void**)&iwork, base_size * liwork))
+    {
+        PyMem_RawFree(work);
+        PyMem_RawFree(rwork);
+        return STATUS_ERROR;
+    }
+    numba_raw_cheevd(kind, jobz, uplo, _n, a, _lda, w, work, lwork, rwork, lrwork, iwork, liwork, &info);
+    PyMem_RawFree(work);
+    PyMem_RawFree(rwork);
+    PyMem_RawFree(iwork);
+
+    CATCH_LAPACK_INVALID_ARG("numba_raw_cheevd", info);
+
+    return (int)info;
+}
+
+/* Hermitian eigenvalue systems info from *syevd and *heevd.
+ * This routine hides the type and general complexity involved with making the
+ * calls. The work space computation and error handling etc is hidden.
+ * Args are as per LAPACK.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_ez_xxxevd(char kind, char jobz, char uplo, Py_ssize_t n, void *a, Py_ssize_t lda, void *w)
+{
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+        case 'd':
+            return numba_ez_rsyevd(kind, jobz, uplo, n, a, lda, w);
+        case 'c':
+        case 'z':
+            return numba_ez_cheevd(kind, jobz, uplo, n, a, lda, w);
+    }
+    return STATUS_ERROR; /* unreachable */
+}
+
+/* Real space svd systems info from dgesdd/sgesdd
+ * Args are as per LAPACK.
+ */
+static int
+numba_raw_rgesdd(char kind, char jobz, Py_ssize_t m, Py_ssize_t n, void *a,
+                 Py_ssize_t lda, void *s, void *u, Py_ssize_t ldu, void *vt,
+                 Py_ssize_t ldvt, void *work, Py_ssize_t lwork,
+                 F_INT *iwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n, _lda, _ldu, _ldvt, _lwork;
+
+    ENSURE_VALID_REAL_KIND(kind)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _ldu = (F_INT) ldu;
+    _ldvt = (F_INT) ldvt;
+    _lwork = (F_INT) lwork;
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_sgesdd();
+            break;
+        case 'd':
+            raw_func = get_clapack_dgesdd();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    (*(rgesdd_t) raw_func)(&jobz, &_m, &_n, a, &_lda, s, u, &_ldu, vt, &_ldvt,
+                           work, &_lwork, iwork, info);
+    return 0;
+}
+
+/* Real space svd info from dgesdd/sgesdd.
+ * As numba_raw_rgesdd but the allocation and error handling is done for the
+ * user.
+ * Args are as per LAPACK.
+ */
+static int
+numba_ez_rgesdd(char kind, char jobz, Py_ssize_t m, Py_ssize_t n, void *a,
+                Py_ssize_t lda, void *s, void *u, Py_ssize_t ldu, void *vt,
+                Py_ssize_t ldvt)
+{
+    F_INT info = 0;
+    Py_ssize_t minmn = -1;
+    Py_ssize_t lwork = -1;
+    all_dtypes stack_slot, wk;
+    size_t base_size = -1;
+    F_INT *iwork = (F_INT *)&wk;
+    void *work = NULL;
+
+    ENSURE_VALID_REAL_KIND(kind)
+
+    base_size = kind_size(kind);
+
+    work = &stack_slot;
+
+    /* Compute optimal work size (lwork) */
+    numba_raw_rgesdd(kind, jobz, m, n, a, lda, s, u, ldu, vt, ldvt, work,
+                     lwork, iwork, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_rgesdd", info);
+
+    /* Allocate work array */
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+        return -1;
+    minmn = m > n ? n : m;
+    if (checked_PyMem_RawMalloc((void**) &iwork, 8 * minmn * sizeof(F_INT)))
+    {
+        PyMem_RawFree(work);
+        return STATUS_ERROR;
+    }
+    numba_raw_rgesdd(kind, jobz, m, n, a, lda, s, u ,ldu, vt, ldvt, work, lwork,
+                     iwork, &info);
+    PyMem_RawFree(work);
+    PyMem_RawFree(iwork);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_rgesdd", info);
+
+    return (int)info;
+}
+
+/* Complex space svd systems info from cgesdd/zgesdd
+ * Args are as per LAPACK.
+ */
+static int
+numba_raw_cgesdd(char kind, char jobz, Py_ssize_t m, Py_ssize_t n, void *a,
+                 Py_ssize_t lda, void *s, void *u, Py_ssize_t ldu, void *vt,
+                 Py_ssize_t ldvt, void *work, Py_ssize_t lwork, void *rwork,
+                 F_INT *iwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n, _lda, _ldu, _ldvt, _lwork;
+
+    ENSURE_VALID_COMPLEX_KIND(kind)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _ldu = (F_INT) ldu;
+    _ldvt = (F_INT) ldvt;
+    _lwork = (F_INT) lwork;
+
+    switch (kind)
+    {
+        case 'c':
+            raw_func = get_clapack_cgesdd();
+            break;
+        case 'z':
+            raw_func = get_clapack_zgesdd();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    (*(cgesdd_t) raw_func)(&jobz, &_m, &_n, a, &_lda, s, u, &_ldu, vt, &_ldvt,
+                           work, &_lwork, rwork, iwork, info);
+    return 0;
+}
+
+/* complex space svd info from cgesdd/zgesdd.
+ * As numba_raw_cgesdd but the allocation and error handling is done for the
+ * user.
+ * Args are as per LAPACK.
+ */
+static int
+numba_ez_cgesdd(char kind, char jobz, Py_ssize_t m, Py_ssize_t n, void *a,
+                Py_ssize_t lda, void *s, void *u, Py_ssize_t ldu, void *vt,
+                Py_ssize_t ldvt)
+{
+    F_INT info = 0;
+    Py_ssize_t lwork = -1;
+    Py_ssize_t lrwork = -1;
+    Py_ssize_t minmn = -1;
+    Py_ssize_t tmp1, tmp2;
+    Py_ssize_t maxmn = -1;
+    size_t real_base_size = -1;
+    size_t complex_base_size = -1;
+    all_dtypes stack_slot, wk1, wk2;
+    void *work = NULL;
+    void *rwork = (void *)&wk1;
+    F_INT *iwork = (F_INT *)&wk2;
+
+    ENSURE_VALID_COMPLEX_KIND(kind)
+
+    switch (kind)
+    {
+        case 'c':
+            real_base_size = sizeof(float);
+            complex_base_size = sizeof(npy_complex64);
+            break;
+        case 'z':
+            real_base_size = sizeof(double);
+            complex_base_size = sizeof(npy_complex128);
+            break;
+        default:
+        {
+            PyGILState_STATE st = PyGILState_Ensure();
+            PyErr_SetString(PyExc_ValueError,\
+                            "Invalid kind in numba_ez_rgesdd");
+            PyGILState_Release(st);
+        }
+        return STATUS_ERROR;
+    }
+
+    work = &stack_slot;
+
+    /* Compute optimal work size (lwork) */
+    numba_raw_cgesdd(kind, jobz, m, n, a, lda, s, u ,ldu, vt, ldvt, work, lwork,
+                     rwork, iwork, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_cgesdd", info);
+
+    /* Allocate work array */
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc(&work, complex_base_size * lwork))
+        return STATUS_ERROR;
+
+    minmn = m > n ? n : m;
+    if (jobz == 'n')
+    {
+        lrwork = 7 * minmn;
+    }
+    else
+    {
+        maxmn = m > n ? m : n;
+        tmp1 = 5 * minmn + 7;
+        tmp2 = 2 * maxmn + 2 * minmn + 1;
+        lrwork = minmn * (tmp1 > tmp2 ? tmp1: tmp2);
+    }
+
+    if (checked_PyMem_RawMalloc(&rwork,
+                                real_base_size * (lrwork > 1 ? lrwork : 1)))
+    {
+        PyMem_RawFree(work);
+        return STATUS_ERROR;
+    }
+    if (checked_PyMem_RawMalloc((void **) &iwork,
+                                8 * minmn * sizeof(F_INT)))
+    {
+        PyMem_RawFree(work);
+        PyMem_RawFree(rwork);
+        return STATUS_ERROR;
+    }
+    numba_raw_cgesdd(kind, jobz, m, n, a, lda, s, u ,ldu, vt, ldvt, work, lwork,
+                     rwork, iwork, &info);
+    PyMem_RawFree(work);
+    PyMem_RawFree(rwork);
+    PyMem_RawFree(iwork);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_cgesdd", info);
+
+    return (int)info;
+}
+
+
+/* SVD systems info from *gesdd.
+ * This routine hides the type and general complexity involved with making the
+ * calls to *gesdd. The work space computation and error handling etc is hidden.
+ * Args are as per LAPACK.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_ez_gesdd(char kind, char jobz, Py_ssize_t m, Py_ssize_t n, void *a,
+               Py_ssize_t lda, void *s, void *u, Py_ssize_t ldu, void *vt,
+               Py_ssize_t ldvt)
+{
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+        case 'd':
+            return numba_ez_rgesdd(kind, jobz, m, n, a, lda, s, u, ldu, vt,
+                                   ldvt);
+        case 'c':
+        case 'z':
+            return numba_ez_cgesdd(kind, jobz, m, n, a, lda, s, u, ldu, vt,
+                                   ldvt);
+    }
+    return STATUS_ERROR; /* unreachable */
+}
+
+
+/*
+ * Compute the QR factorization of a matrix.
+ * Return -1 on internal error, 0 on success, > 0 on failure.
+ */
+static int
+numba_raw_xgeqrf(char kind, Py_ssize_t m, Py_ssize_t n, void *a, Py_ssize_t
+                 lda, void *tau, void *work, Py_ssize_t lwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n, _lda, _lwork;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_sgeqrf();
+            break;
+        case 'd':
+            raw_func = get_clapack_dgeqrf();
+            break;
+        case 'c':
+            raw_func = get_clapack_cgeqrf();
+            break;
+        case 'z':
+            raw_func = get_clapack_zgeqrf();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _lda = (F_INT) lda;
+    _lwork = (F_INT) lwork;
+
+    (*(xgeqrf_t) raw_func)(&_m, &_n, a, &_lda, tau, work, &_lwork, info);
+    return 0;
+}
+
+/*
+ * Compute the QR factorization of a matrix.
+ * This routine hides the type and general complexity involved with making the
+ * xgeqrf calls. The work space computation and error handling etc is hidden.
+ * Args are as per LAPACK.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_ez_geqrf(char kind, Py_ssize_t m, Py_ssize_t n, void *a, Py_ssize_t
+               lda, void *tau)
+{
+    F_INT info = 0;
+    Py_ssize_t lwork = -1;
+    size_t base_size = -1;
+    all_dtypes stack_slot;
+    void *work = NULL;
+
+    base_size = kind_size(kind);
+
+    work = &stack_slot;
+
+    /* Compute optimal work size (lwork) */
+    numba_raw_xgeqrf(kind, m, n, a, lda, tau, work, lwork, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_xgeqrf", info);
+
+    /* Allocate work array */
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+        return STATUS_ERROR;
+
+    numba_raw_xgeqrf(kind, m, n, a, lda, tau, work, lwork, &info);
+    PyMem_RawFree(work);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_xgeqrf", info);
+
+    return 0; /* info cannot be >0 */
+
+}
+
+
+/*
+ * Compute the orthogonal Q matrix (in QR) from elementary relectors.
+ */
+static int
+numba_raw_xxxgqr(char kind, Py_ssize_t m, Py_ssize_t n, Py_ssize_t k, void *a,
+                 Py_ssize_t lda, void *tau, void * work, Py_ssize_t lwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n, _k, _lda, _lwork;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_sorgqr();
+            break;
+        case 'd':
+            raw_func = get_clapack_dorgqr();
+            break;
+        case 'c':
+            raw_func = get_clapack_cungqr();
+            break;
+        case 'z':
+            raw_func = get_clapack_zungqr();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _k = (F_INT) k;
+    _lda = (F_INT) lda;
+    _lwork = (F_INT) lwork;
+
+    (*(xxxgqr_t) raw_func)(&_m, &_n, &_k, a, &_lda, tau, work, &_lwork, info);
+    return 0;
+}
+
+
+/*
+ * Compute the orthogonal Q matrix (in QR) from elementary reflectors.
+ * This routine hides the type and general complexity involved with making the
+ * x{or,un}qrf calls. The work space computation and error handling etc is
+ * hidden. Args are as per LAPACK.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_ez_xxgqr(char kind, Py_ssize_t m, Py_ssize_t n, Py_ssize_t k, void *a,
+               Py_ssize_t lda, void *tau)
+{
+    F_INT info = 0;
+    Py_ssize_t lwork = -1;
+    size_t base_size = -1;
+    all_dtypes stack_slot;
+    void *work = NULL;
+
+    work = &stack_slot;
+
+    /* Compute optimal work size (lwork) */
+    numba_raw_xxxgqr(kind, m, n, k, a, lda, tau, work, lwork, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_xxxgqr", info);
+
+    base_size = kind_size(kind);
+
+    /* Allocate work array */
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+        return STATUS_ERROR;
+
+    numba_raw_xxxgqr(kind, m, n, k, a, lda, tau, work, lwork, &info);
+    PyMem_RawFree(work);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_xxxgqr", info);
+
+    return 0;  /* info cannot be >0 */
+
+}
+
+
+/*
+ * Compute the minimum-norm solution to a real linear least squares problem.
+ */
+static int
+numba_raw_rgelsd(char kind, Py_ssize_t m, Py_ssize_t n, Py_ssize_t nrhs,
+                 void *a, Py_ssize_t lda, void *b, Py_ssize_t ldb, void *S,
+                 void * rcond, Py_ssize_t * rank, void * work,
+                 Py_ssize_t lwork, F_INT *iwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n, _nrhs, _lda, _ldb, _rank, _lwork;
+
+    ENSURE_VALID_REAL_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_sgelsd();
+            break;
+        case 'd':
+            raw_func = get_clapack_dgelsd();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _nrhs = (F_INT) nrhs;
+    _lda = (F_INT) lda;
+    _ldb = (F_INT) ldb;
+    _lwork = (F_INT) lwork;
+
+    (*(rgelsd_t) raw_func)(&_m, &_n, &_nrhs, a, &_lda, b, &_ldb, S, rcond,
+                           &_rank, work, &_lwork, iwork, info);
+    *rank = (Py_ssize_t) _rank;
+    return 0;
+}
+
+/*
+ * Compute the minimum-norm solution to a real linear least squares problem.
+ * This routine hides the type and general complexity involved with making the
+ * {s,d}gelsd calls. The work space computation and error handling etc is
+ * hidden. Args are as per LAPACK.
+ */
+static int
+numba_ez_rgelsd(char kind, Py_ssize_t m, Py_ssize_t n, Py_ssize_t nrhs,
+                void *a, Py_ssize_t lda, void *b, Py_ssize_t ldb, void *S,
+                double rcond, Py_ssize_t * rank)
+{
+    F_INT info = 0;
+    Py_ssize_t lwork = -1;
+    size_t base_size = -1;
+    all_dtypes stack_slot;
+    void *work = NULL, *rcond_cast = NULL;
+    F_INT *iwork = NULL;
+    F_INT iwork_tmp;
+    float tmpf;
+
+    ENSURE_VALID_REAL_KIND(kind)
+
+    base_size = kind_size(kind);
+
+    work = &stack_slot;
+    rcond_cast = work; /* stop checks on null ptr complaining */
+
+    /* Compute optimal work size (lwork) */
+    numba_raw_rgelsd(kind, m, n, nrhs, a, lda, b, ldb, S, rcond_cast, rank,
+                     work, lwork, &iwork_tmp, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_rgelsd", info);
+
+    /* Allocate work array */
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+        return STATUS_ERROR;
+
+    /* Allocate iwork array */
+    if (checked_PyMem_RawMalloc((void **)&iwork, sizeof(F_INT) * iwork_tmp))
+    {
+        PyMem_RawFree(work);
+        return STATUS_ERROR;
+    }
+
+    /* cast rcond to the right type */
+    switch (kind)
+    {
+        case 's':
+            tmpf = (float)rcond;
+            rcond_cast = (void * )&tmpf;
+            break;
+        case 'd':
+            rcond_cast = (void * )&rcond;
+            break;
+    }
+
+    numba_raw_rgelsd(kind, m, n, nrhs, a, lda, b, ldb, S, rcond_cast, rank,
+                     work, lwork, iwork, &info);
+    PyMem_RawFree(work);
+    PyMem_RawFree(iwork);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_rgelsd", info);
+
+    return (int)info;
+}
+
+
+/*
+ * Compute the minimum-norm solution to a complex linear least squares problem.
+ */
+static int
+numba_raw_cgelsd(char kind, Py_ssize_t m, Py_ssize_t n, Py_ssize_t nrhs,
+                 void *a, Py_ssize_t lda, void *b, Py_ssize_t ldb, void *S,
+                 void *rcond, Py_ssize_t * rank, void * work,
+                 Py_ssize_t lwork, void * rwork, F_INT *iwork, F_INT *info)
+{
+    void *raw_func = NULL;
+    F_INT _m, _n, _nrhs, _lda, _ldb, _rank, _lwork;
+
+    ENSURE_VALID_COMPLEX_KIND(kind)
+
+    switch (kind)
+    {
+        case 'c':
+            raw_func = get_clapack_cgelsd();
+            break;
+        case 'z':
+            raw_func = get_clapack_zgelsd();
+            break;
+    }
+    ENSURE_VALID_FUNC(raw_func)
+
+    _m = (F_INT) m;
+    _n = (F_INT) n;
+    _nrhs = (F_INT) nrhs;
+    _lda = (F_INT) lda;
+    _ldb = (F_INT) ldb;
+    _lwork = (F_INT) lwork;
+
+    (*(cgelsd_t) raw_func)(&_m, &_n, &_nrhs, a, &_lda, b, &_ldb, S, rcond,
+                           &_rank, work, &_lwork, rwork, iwork, info);
+    *rank = (Py_ssize_t) _rank;
+    return 0;
+}
+
+
+/*
+ * Compute the minimum-norm solution to a complex linear least squares problem.
+ * This routine hides the type and general complexity involved with making the
+ * {c,z}gelsd calls. The work space computation and error handling etc is
+ * hidden. Args are as per LAPACK.
+ */
+static int
+numba_ez_cgelsd(char kind, Py_ssize_t m, Py_ssize_t n, Py_ssize_t nrhs,
+                void *a, Py_ssize_t lda, void *b, Py_ssize_t ldb, void *S,
+                double rcond, Py_ssize_t * rank)
+{
+    F_INT info = 0;
+    Py_ssize_t lwork = -1;
+    size_t base_size = -1;
+    all_dtypes stack_slot1, stack_slot2;
+    size_t real_base_size = 0;
+    void *work = NULL, *rwork = NULL, *rcond_cast = NULL;
+    Py_ssize_t lrwork;
+    F_INT *iwork = NULL;
+    F_INT iwork_tmp;
+    char real_kind = '-';
+    float tmpf;
+
+    ENSURE_VALID_COMPLEX_KIND(kind)
+
+    base_size = kind_size(kind);
+
+    work = &stack_slot1;
+    rwork = &stack_slot2;
+    rcond_cast = work; /* stop checks on null ptr complaining */
+
+    /* Compute optimal work size */
+    numba_raw_cgelsd(kind, m, n, nrhs, a, lda, b, ldb, S, rcond_cast, rank,
+                     work, lwork, rwork, &iwork_tmp, &info);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_cgelsd", info);
+
+    /* Allocate work array */
+    lwork = cast_from_X(kind, work);
+    if (checked_PyMem_RawMalloc(&work, base_size * lwork))
+        return STATUS_ERROR;
+
+    /* Allocate iwork array */
+    if (checked_PyMem_RawMalloc((void **)&iwork, sizeof(F_INT) * iwork_tmp))
+    {
+        PyMem_RawFree(work);
+        return STATUS_ERROR;
+    }
+
+    switch (kind)
+    {
+        case 'c':
+            real_kind = 's';
+            tmpf = (float)rcond;
+            rcond_cast = (void * )&tmpf;
+            break;
+        case 'z':
+            real_kind = 'd';
+            rcond_cast = (void * )&rcond;
+            break;
+    }
+
+    real_base_size = kind_size(real_kind);
+
+    lrwork = cast_from_X(real_kind, rwork);
+    if (checked_PyMem_RawMalloc((void **)&rwork, real_base_size * lrwork))
+    {
+        PyMem_RawFree(work);
+        PyMem_RawFree(iwork);
+        return STATUS_ERROR;
+    }
+
+    numba_raw_cgelsd(kind, m, n, nrhs, a, lda, b, ldb, S, rcond_cast, rank,
+                     work, lwork, rwork, iwork, &info);
+    PyMem_RawFree(work);
+    PyMem_RawFree(rwork);
+    PyMem_RawFree(iwork);
+    CATCH_LAPACK_INVALID_ARG("numba_raw_cgelsd", info);
+
+    return (int)info;
+}
+
+
+/*
+ * Compute the minimum-norm solution to a linear least squares problems.
+ * This routine hides the type and general complexity involved with making the
+ * calls to *gelsd. The work space computation and error handling etc is hidden.
+ * Args are as per LAPACK.
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_ez_gelsd(char kind, Py_ssize_t m, Py_ssize_t n, Py_ssize_t nrhs,
+               void *a, Py_ssize_t lda, void *b, Py_ssize_t ldb, void *S,
+               double rcond, Py_ssize_t * rank)
+{
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+        case 'd':
+            return numba_ez_rgelsd(kind, m, n, nrhs, a, lda, b, ldb, S, rcond,
+                                   rank);
+        case 'c':
+        case 'z':
+            return numba_ez_cgelsd(kind, m, n, nrhs, a, lda, b, ldb, S, rcond,
+                                   rank);
+    }
+    return STATUS_ERROR; /* unreachable */
+}
+
+
+/*
+ * Compute the solution to a system of linear equations
+ */
+NUMBA_EXPORT_FUNC(int)
+numba_xgesv(char kind, Py_ssize_t n, Py_ssize_t nrhs, void *a, Py_ssize_t lda,
+            F_INT *ipiv, void *b, Py_ssize_t ldb)
+{
+    void *raw_func = NULL;
+    F_INT _n, _nrhs, _lda, _ldb, info;
+
+    ENSURE_VALID_KIND(kind)
+
+    switch (kind)
+    {
+        case 's':
+            raw_func = get_clapack_sgesv();
+            break;
+        case 'd':
+            raw_func = get_clapack_dgesv();
+            break;
+        case 'c':
+            raw_func = get_clapack_cgesv();
+            break;
+        case 'z':
+            raw_func = get_clapack_zgesv();
+            break;
+    }
+
+    ENSURE_VALID_FUNC(raw_func)
+
+    _n = (F_INT) n;
+    _nrhs = (F_INT) nrhs;
+    _lda = (F_INT) lda;
+    _ldb = (F_INT) ldb;
+
+    (*(xgesv_t) raw_func)(&_n, &_nrhs, a, &_lda, ipiv, b, &_ldb, &info);
+    CATCH_LAPACK_INVALID_ARG("xgesv", info);
+
+    return (int)info;
+}
+
+/* undef defines and macros */
+#undef STATUS_SUCCESS
+#undef STATUS_ERROR
+#undef ENSURE_VALID_KIND
+#undef ENSURE_VALID_REAL_KIND
+#undef ENSURE_VALID_COMPLEX_KIND
+#undef ENSURE_VALID_FUNC
+#undef F_INT
+#undef EMIT_GET_CLAPACK_FUNC
+#undef CATCH_LAPACK_INVALID_ARG
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_npymath_exports.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_npymath_exports.c
new file mode 100644
index 000000000..881b56c91
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_npymath_exports.c
@@ -0,0 +1,46 @@
+/*
+ * This file contains exports of Numpy math functions needed by numba.
+ */
+
+#include "_pymodule.h"
+#include <numpy/npy_math.h>
+#include <math.h>
+
+
+/*
+ * Map Numpy C function symbols to their addresses.
+ */
+
+struct npymath_entry {
+    const char *name;
+    void *func;
+};
+
+#define NPYMATH_SYMBOL(name) \
+    { "npy_" #name, (void*) npy_##name }
+
+static struct npymath_entry npymath_exports[] = {
+    /* double functions */
+    NPYMATH_SYMBOL(exp2),
+    NPYMATH_SYMBOL(log2),
+
+    NPYMATH_SYMBOL(logaddexp),
+    NPYMATH_SYMBOL(logaddexp2),
+    NPYMATH_SYMBOL(nextafter),
+    NPYMATH_SYMBOL(spacing),
+
+    NPYMATH_SYMBOL(modf),
+
+    /* float functions */
+    NPYMATH_SYMBOL(exp2f),
+    NPYMATH_SYMBOL(log2f),
+
+    NPYMATH_SYMBOL(logaddexpf),
+    NPYMATH_SYMBOL(logaddexp2f),
+    NPYMATH_SYMBOL(nextafterf),
+    NPYMATH_SYMBOL(spacingf),
+
+    NPYMATH_SYMBOL(modff),
+};
+
+#undef NPYMATH_SYMBOL
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_numba_common.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_numba_common.h
new file mode 100644
index 000000000..c5e67d9c6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_numba_common.h
@@ -0,0 +1,39 @@
+#ifndef NUMBA_COMMON_H_
+#define NUMBA_COMMON_H_
+
+/* __has_attribute() is a clang / gcc-5 macro */
+#ifndef __has_attribute
+#   define __has_attribute(x) 0
+#endif
+
+/* This attribute marks symbols that can be shared across C objects
+ * but are not exposed outside of a shared library or executable.
+ * Note this is default behaviour for global symbols under Windows.
+ */
+#if (__has_attribute(visibility) || \
+     (defined(__GNUC__) && __GNUC__ >= 4))
+#define VISIBILITY_HIDDEN __attribute__ ((visibility("hidden")))
+#else
+#define VISIBILITY_HIDDEN
+#endif
+
+/*
+ * Numba's version of the PyArray_DescrCheck macro from NumPy, use it as a
+ * direct replacement of NumPy's PyArray_DescrCheck to ensure binary
+ * compatibility.
+ *
+ * Details of why this is needed:
+ * NumPy 1.18 changed the definition of the PyArray_DescrCheck macro here:
+ * https://github.com/numpy/numpy/commit/6108b5d1e138d07e3c9f2a4e3b1933749ad0e698
+ * the result of this being that building against NumPy <1.18 would prevent
+ * Numba running against NumPy >= 1.20 as noted here:
+ * https://github.com/numba/numba/issues/6041#issuecomment-665132199
+ *
+ * This macro definition is copied from:
+ * https://github.com/numpy/numpy/commit/6108b5d1e138d07e3c9f2a4e3b1933749ad0e698#diff-ad2213da23136c5fc5883d9eb2d88666R26
+ *
+ * NOTE: This is the NumPy 1.18 and above version of the macro.
+ */
+#define NUMBA_PyArray_DescrCheck(op) PyObject_TypeCheck(op, &PyArrayDescr_Type)
+
+#endif /* NUMBA_COMMON_H_ */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_pymodule.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_pymodule.h
new file mode 100644
index 000000000..8622598a7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_pymodule.h
@@ -0,0 +1,32 @@
+#ifndef NUMBA_PY_MODULE_H_
+#define NUMBA_PY_MODULE_H_
+
+#define PY_SSIZE_T_CLEAN
+
+#include <Python.h>
+#include <structmember.h>
+#include <frameobject.h>
+
+#define MOD_ERROR_VAL NULL
+#define MOD_SUCCESS_VAL(val) val
+#define MOD_INIT(name) PyMODINIT_FUNC PyInit_##name(void)
+#define MOD_DEF(ob, name, doc, methods) { \
+        static struct PyModuleDef moduledef = { \
+          PyModuleDef_HEAD_INIT, name, doc, -1, methods, NULL, NULL, NULL, NULL }; \
+        ob = PyModule_Create(&moduledef); }
+#define MOD_INIT_EXEC(name) PyInit_##name();
+
+#define PyString_AsString PyUnicode_AsUTF8
+#define PyString_Check PyUnicode_Check
+#define PyString_FromFormat PyUnicode_FromFormat
+#define PyString_FromString PyUnicode_FromString
+#define PyString_InternFromString PyUnicode_InternFromString
+#define PyInt_Type PyLong_Type
+#define PyInt_Check PyLong_Check
+#define PyInt_CheckExact PyLong_CheckExact
+#define SetAttrStringFromVoidPointer(m, name) do { \
+        PyObject *tmp = PyLong_FromVoidPtr((void *) &name); \
+        PyObject_SetAttrString(m, #name, tmp); \
+        Py_DECREF(tmp); } while (0)
+
+#endif /* NUMBA_PY_MODULE_H_ */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_random.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_random.c
new file mode 100644
index 000000000..bf95a3639
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_random.c
@@ -0,0 +1,492 @@
+/*
+ * PRNG support.
+ */
+
+#ifdef _MSC_VER
+#define HAVE_PTHREAD_ATFORK 0
+#else
+#define HAVE_PTHREAD_ATFORK 1
+#include <pthread.h>
+#endif
+
+
+/* Magic Mersenne Twister constants */
+#define MT_N 624
+#define MT_M 397
+#define MT_MATRIX_A 0x9908b0dfU
+#define MT_UPPER_MASK 0x80000000U
+#define MT_LOWER_MASK 0x7fffffffU
+
+/*
+ * Note this structure is accessed in numba.targets.randomimpl,
+ * any changes here should be reflected there too.
+ */
+typedef struct {
+    int index;
+    /* unsigned int is sufficient on modern machines as we only need 32 bits */
+    unsigned int mt[MT_N];
+    int has_gauss;
+    double gauss;
+    int is_initialized;
+} rnd_state_t;
+
+/* Some code portions below from CPython's _randommodule.c, some others
+   from Numpy's and Jean-Sebastien Roy's randomkit.c. */
+
+NUMBA_EXPORT_FUNC(void)
+numba_rnd_shuffle(rnd_state_t *state)
+{
+    int i;
+    unsigned int y;
+
+    for (i = 0; i < MT_N - MT_M; i++) {
+        y = (state->mt[i] & MT_UPPER_MASK) | (state->mt[i+1] & MT_LOWER_MASK);
+        state->mt[i] = state->mt[i+MT_M] ^ (y >> 1) ^
+                       (-(int) (y & 1) & MT_MATRIX_A);
+    }
+    for (; i < MT_N - 1; i++) {
+        y = (state->mt[i] & MT_UPPER_MASK) | (state->mt[i+1] & MT_LOWER_MASK);
+        state->mt[i] = state->mt[i+(MT_M-MT_N)] ^ (y >> 1) ^
+                       (-(int) (y & 1) & MT_MATRIX_A);
+    }
+    y = (state->mt[MT_N - 1] & MT_UPPER_MASK) | (state->mt[0] & MT_LOWER_MASK);
+    state->mt[MT_N - 1] = state->mt[MT_M - 1] ^ (y >> 1) ^
+                          (-(int) (y & 1) & MT_MATRIX_A);
+}
+
+/* Initialize mt[] with an integer seed */
+NUMBA_EXPORT_FUNC(void)
+numba_rnd_init(rnd_state_t *state, unsigned int seed)
+{
+    unsigned int pos;
+    seed &= 0xffffffffU;
+
+    /* Knuth's PRNG as used in the Mersenne Twister reference implementation */
+    for (pos = 0; pos < MT_N; pos++) {
+        state->mt[pos] = seed;
+        seed = (1812433253U * (seed ^ (seed >> 30)) + pos + 1) & 0xffffffffU;
+    }
+    state->index = MT_N;
+    state->has_gauss = 0;
+    state->gauss = 0.0;
+    state->is_initialized = 1;
+}
+
+/* Perturb mt[] with a key array */
+static void
+rnd_init_by_array(rnd_state_t *state, unsigned int init_key[], size_t key_length)
+{
+    size_t i, j, k;
+    unsigned int *mt = state->mt;
+
+    numba_rnd_init(state, 19650218U);
+    i = 1; j = 0;
+    k = (MT_N > key_length ? MT_N : key_length);
+    for (; k; k--) {
+        mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525U))
+                 + init_key[j] + (unsigned int) j; /* non linear */
+        mt[i] &= 0xffffffffU;
+        i++; j++;
+        if (i >= MT_N) { mt[0] = mt[MT_N - 1]; i = 1; }
+        if (j >= key_length) j = 0;
+    }
+    for (k = MT_N - 1; k; k--) {
+        mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941U))
+                 - (unsigned int) i; /* non linear */
+        mt[i] &= 0xffffffffU;
+        i++;
+        if (i >= MT_N) { mt[0] = mt[MT_N - 1]; i=1; }
+    }
+
+    mt[0] = 0x80000000U; /* MSB is 1; ensuring non-zero initial array */
+    state->index = MT_N;
+    state->has_gauss = 0;
+    state->gauss = 0.0;
+    state->is_initialized = 1;
+}
+
+/*
+ * Management of thread-local random state.
+ */
+
+static int rnd_globally_initialized;
+
+#ifdef _MSC_VER
+#define THREAD_LOCAL(ty) __declspec(thread) ty
+#else
+/* Non-standard C99 extension that's understood by gcc and clang */
+#define THREAD_LOCAL(ty) __thread ty
+#endif
+
+static THREAD_LOCAL(rnd_state_t) numba_py_random_state;
+static THREAD_LOCAL(rnd_state_t) numba_np_random_state;
+static THREAD_LOCAL(rnd_state_t) numba_internal_random_state;
+
+/* Seed the state with random bytes */
+static int
+rnd_seed_with_bytes(rnd_state_t *state, Py_buffer *buf)
+{
+    unsigned int *keys;
+    unsigned char *bytes;
+    size_t i, nkeys;
+
+    nkeys = buf->len / sizeof(unsigned int);
+    keys = (unsigned int *) PyMem_Malloc(nkeys * sizeof(unsigned int));
+    if (keys == NULL) {
+        PyBuffer_Release(buf);
+        return -1;
+    }
+    bytes = (unsigned char *) buf->buf;
+    /* Convert input bytes to int32 keys, without violating alignment
+     * constraints.
+     */
+    for (i = 0; i < nkeys; i++, bytes += 4) {
+        keys[i] =
+            ((unsigned int)bytes[3] << 24) +
+            ((unsigned int)bytes[2] << 16) +
+            ((unsigned int)bytes[1] << 8) +
+            ((unsigned int)bytes[0] << 0);
+    }
+    PyBuffer_Release(buf);
+    rnd_init_by_array(state, keys, nkeys);
+    PyMem_Free(keys);
+    return 0;
+}
+
+#if HAVE_PTHREAD_ATFORK
+/* After a fork(), the child should reseed its random states.
+ * Since only the main thread survives in the child, it's enough to mark
+ * the current thread-local states as uninitialized.
+ */
+static void
+rnd_atfork_child(void)
+{
+    numba_py_random_state.is_initialized = 0;
+    numba_np_random_state.is_initialized = 0;
+    numba_internal_random_state.is_initialized = 0;
+}
+#endif
+
+/* Global initialization routine.  It must be called as early as possible.
+ */
+NUMBA_EXPORT_FUNC(void)
+numba_rnd_ensure_global_init(void)
+{
+    if (!rnd_globally_initialized) {
+#if HAVE_PTHREAD_ATFORK
+        pthread_atfork(NULL, NULL, rnd_atfork_child);
+#endif
+        numba_py_random_state.is_initialized = 0;
+        numba_np_random_state.is_initialized = 0;
+        numba_internal_random_state.is_initialized = 0;
+        rnd_globally_initialized = 1;
+    }
+}
+
+/* First-time init a random state */
+static void
+rnd_implicit_init(rnd_state_t *state)
+{
+    /* Initialize with random bytes.  The easiest way to get good-quality
+     * cross-platform random bytes is still to call os.urandom()
+     * using the Python interpreter...
+     */
+    PyObject *module, *bufobj;
+    Py_buffer buf;
+    PyGILState_STATE gilstate = PyGILState_Ensure();
+
+    module = PyImport_ImportModuleNoBlock("os");
+    if (module == NULL)
+        goto error;
+    /* Read as many bytes as necessary to get the full entropy
+     * exploitable by the MT generator.
+     */
+    bufobj = PyObject_CallMethod(module, "urandom", "i",
+                                 (int) (MT_N * sizeof(unsigned int)));
+    Py_DECREF(module);
+    if (bufobj == NULL)
+        goto error;
+    if (PyObject_GetBuffer(bufobj, &buf, PyBUF_SIMPLE))
+        goto error;
+    Py_DECREF(bufobj);
+    if (rnd_seed_with_bytes(state, &buf))
+        goto error;
+    /* state->is_initialized is set now */
+
+    PyGILState_Release(gilstate);
+    return;
+
+error:
+    /* In normal conditions, os.urandom() and PyMem_Malloc() shouldn't fail,
+     * and we don't want the caller to deal with errors, so just bail out.
+     */
+    if (PyErr_Occurred())
+        PyErr_Print();
+    Py_FatalError(NULL);
+}
+
+/* Functions returning the thread-local random state pointer.
+ * The LLVM JIT doesn't support thread-local variables so we rely
+ * on the C compiler instead.
+ */
+
+NUMBA_EXPORT_FUNC(rnd_state_t *)
+numba_get_py_random_state(void)
+{
+    rnd_state_t *state = &numba_py_random_state;
+    if (!state->is_initialized)
+        rnd_implicit_init(state);
+    return state;
+}
+
+NUMBA_EXPORT_FUNC(rnd_state_t *)
+numba_get_np_random_state(void)
+{
+    rnd_state_t *state = &numba_np_random_state;
+    if (!state->is_initialized)
+        rnd_implicit_init(state);
+    return state;
+}
+
+NUMBA_EXPORT_FUNC(rnd_state_t *)
+numba_get_internal_random_state(void)
+{
+    rnd_state_t *state = &numba_internal_random_state;
+    if (!state->is_initialized)
+        rnd_implicit_init(state);
+    return state;
+}
+
+/*
+ * Python-exposed helpers for state management and testing.
+ */
+static int
+rnd_state_converter(PyObject *obj, rnd_state_t **state)
+{
+    *state = (rnd_state_t *) PyLong_AsVoidPtr(obj);
+    return (*state != NULL || !PyErr_Occurred());
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+_numba_rnd_get_py_state_ptr(PyObject *self)
+{
+    return PyLong_FromVoidPtr(numba_get_py_random_state());
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+_numba_rnd_get_np_state_ptr(PyObject *self)
+{
+    return PyLong_FromVoidPtr(numba_get_np_random_state());
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+_numba_rnd_shuffle(PyObject *self, PyObject *arg)
+{
+    rnd_state_t *state;
+    if (!rnd_state_converter(arg, &state))
+        return NULL;
+    numba_rnd_shuffle(state);
+    Py_RETURN_NONE;
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+_numba_rnd_set_state(PyObject *self, PyObject *args)
+{
+    int i, index;
+    rnd_state_t *state;
+    PyObject *tuplearg, *intlist;
+
+    if (!PyArg_ParseTuple(args, "O&O!:rnd_set_state",
+                          rnd_state_converter, &state,
+                          &PyTuple_Type, &tuplearg))
+        return NULL;
+    if (!PyArg_ParseTuple(tuplearg, "iO!", &index, &PyList_Type, &intlist))
+        return NULL;
+    if (PyList_GET_SIZE(intlist) != MT_N) {
+        PyErr_SetString(PyExc_ValueError, "list object has wrong size");
+        return NULL;
+    }
+    state->index = index;
+    for (i = 0; i < MT_N; i++) {
+        PyObject *v = PyList_GET_ITEM(intlist, i);
+        unsigned long x = PyLong_AsUnsignedLong(v);
+        if (x == (unsigned long) -1 && PyErr_Occurred())
+            return NULL;
+        state->mt[i] = (unsigned int) x;
+    }
+    state->has_gauss = 0;
+    state->gauss = 0.0;
+    state->is_initialized = 1;
+    Py_RETURN_NONE;
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+_numba_rnd_get_state(PyObject *self, PyObject *arg)
+{
+    PyObject *intlist;
+    int i;
+    rnd_state_t *state;
+    if (!rnd_state_converter(arg, &state))
+        return NULL;
+
+    intlist = PyList_New(MT_N);
+    if (intlist == NULL)
+        return NULL;
+    for (i = 0; i < MT_N; i++) {
+        PyObject *v = PyLong_FromUnsignedLong(state->mt[i]);
+        if (v == NULL) {
+            Py_DECREF(intlist);
+            return NULL;
+        }
+        PyList_SET_ITEM(intlist, i, v);
+    }
+    return Py_BuildValue("iN", state->index, intlist);
+}
+
+NUMBA_EXPORT_FUNC(PyObject *)
+_numba_rnd_seed(PyObject *self, PyObject *args)
+{
+    unsigned int seed;
+    rnd_state_t *state;
+
+    if (!PyArg_ParseTuple(args, "O&I:rnd_seed",
+                          rnd_state_converter, &state, &seed)) {
+        /* rnd_seed_*(bytes-like object) */
+        Py_buffer buf;
+
+        PyErr_Clear();
+        if (!PyArg_ParseTuple(args, "O&s*:rnd_seed",
+                              rnd_state_converter, &state, &buf))
+            return NULL;
+
+        if (rnd_seed_with_bytes(state, &buf))
+            return NULL;
+        else
+            Py_RETURN_NONE;
+    }
+    else {
+        /* rnd_seed_*(int32) */
+        numba_rnd_init(state, seed);
+        Py_RETURN_NONE;
+    }
+}
+
+/*
+ * Random distribution helpers.
+ * Most code straight from Numpy's distributions.c.
+ */
+
+#ifndef M_PI
+#define M_PI 3.14159265358979323846264338328
+#endif
+
+NUMBA_EXPORT_FUNC(unsigned int)
+get_next_int32(rnd_state_t *state)
+{
+    unsigned int y;
+
+    if (state->index == MT_N) {
+        numba_rnd_shuffle(state);
+        state->index = 0;
+    }
+    y = state->mt[state->index++];
+    /* Tempering */
+    y ^= (y >> 11);
+    y ^= (y << 7) & 0x9d2c5680U;
+    y ^= (y << 15) & 0xefc60000U;
+    y ^= (y >> 18);
+    return y;
+}
+
+NUMBA_EXPORT_FUNC(double)
+get_next_double(rnd_state_t *state)
+{
+    double a = get_next_int32(state) >> 5;
+    double b = get_next_int32(state) >> 6;
+    return (a * 67108864.0 + b) / 9007199254740992.0;
+}
+
+NUMBA_EXPORT_FUNC(double)
+loggam(double x)
+{
+    double x0, x2, xp, gl, gl0;
+    long k, n;
+
+    static double a[10] = {8.333333333333333e-02,-2.777777777777778e-03,
+         7.936507936507937e-04,-5.952380952380952e-04,
+         8.417508417508418e-04,-1.917526917526918e-03,
+         6.410256410256410e-03,-2.955065359477124e-02,
+         1.796443723688307e-01,-1.39243221690590e+00};
+    x0 = x;
+    n = 0;
+    if ((x == 1.0) || (x == 2.0))
+    {
+        return 0.0;
+    }
+    else if (x <= 7.0)
+    {
+        n = (long)(7 - x);
+        x0 = x + n;
+    }
+    x2 = 1.0/(x0*x0);
+    xp = 2*M_PI;
+    gl0 = a[9];
+    for (k=8; k>=0; k--)
+    {
+        gl0 *= x2;
+        gl0 += a[k];
+    }
+    gl = gl0/x0 + 0.5*log(xp) + (x0-0.5)*log(x0) - x0;
+    if (x <= 7.0)
+    {
+        for (k=1; k<=n; k++)
+        {
+            gl -= log(x0-1.0);
+            x0 -= 1.0;
+        }
+    }
+    return gl;
+}
+
+
+NUMBA_EXPORT_FUNC(int64_t)
+numba_poisson_ptrs(rnd_state_t *state, double lam)
+{
+    /* This method is invoked only if the parameter lambda of this
+     * distribution is big enough ( >= 10 ). The algorithm used is
+     * described in "Hörmann, W. 1992. 'The Transformed Rejection
+     * Method for Generating Poisson Random Variables'.
+     * The implementation comes straight from Numpy.
+     */
+    int64_t k;
+    double U, V, slam, loglam, a, b, invalpha, vr, us;
+
+    slam = sqrt(lam);
+    loglam = log(lam);
+    b = 0.931 + 2.53*slam;
+    a = -0.059 + 0.02483*b;
+    invalpha = 1.1239 + 1.1328/(b-3.4);
+    vr = 0.9277 - 3.6224/(b-2);
+
+    while (1)
+    {
+        U = get_next_double(state) - 0.5;
+        V = get_next_double(state);
+        us = 0.5 - fabs(U);
+        k = (int64_t) floor((2*a/us + b)*U + lam + 0.43);
+        if ((us >= 0.07) && (V <= vr))
+        {
+            return k;
+        }
+        if ((k < 0) ||
+            ((us < 0.013) && (V > us)))
+        {
+            continue;
+        }
+        if ((log(V) + log(invalpha) - log(a/(us*us)+b)) <=
+            (-lam + (double) k*loglam - loggam((double) k+1)))
+        {
+            return k;
+        }
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_typeof.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_typeof.c
new file mode 100644
index 000000000..334ff2b99
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_typeof.c
@@ -0,0 +1,1133 @@
+#include "_pymodule.h"
+
+#include <string.h>
+#include <time.h>
+#include <assert.h>
+
+#include "_numba_common.h"
+#include "_typeof.h"
+#include "_hashtable.h"
+#include "_devicearray.h"
+#include "pyerrors.h"
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+#include <numpy/ndarrayobject.h>
+
+
+/* Cached typecodes for basic scalar types */
+static int tc_int8;
+static int tc_int16;
+static int tc_int32;
+static int tc_int64;
+static int tc_uint8;
+static int tc_uint16;
+static int tc_uint32;
+static int tc_uint64;
+static int tc_float32;
+static int tc_float64;
+static int tc_complex64;
+static int tc_complex128;
+static int BASIC_TYPECODES[12];
+
+static int tc_intp;
+
+/* The type object for the numba .dispatcher.OmittedArg class
+ * that wraps omitted arguments.
+ */
+static PyObject *omittedarg_type;
+
+static PyObject *typecache;
+static PyObject *ndarray_typecache;
+static PyObject *structured_dtypes;
+
+static PyObject *str_typeof_pyval = NULL;
+static PyObject *str_value = NULL;
+static PyObject *str_numba_type = NULL;
+
+/* CUDA device array API */
+void **DeviceArray_API;
+
+/*
+ * Type fingerprint computation.
+ */
+
+typedef struct {
+    /* A buffer the fingerprint will be written to */
+    char *buf;
+    size_t n;
+    size_t allocated;
+    /* A preallocated buffer, sufficient to fit the fingerprint for most types */
+    char static_buf[40];
+} string_writer_t;
+
+static void
+string_writer_init(string_writer_t *w)
+{
+    w->buf = w->static_buf;
+    w->n = 0;
+    w->allocated = sizeof(w->static_buf) / sizeof(unsigned char);
+}
+
+static void
+string_writer_clear(string_writer_t *w)
+{
+    if (w->buf != w->static_buf)
+        free(w->buf);
+}
+
+static void
+string_writer_move(string_writer_t *dest, const string_writer_t *src)
+{
+    dest->n = src->n;
+    dest->allocated = src->allocated;
+    if (src->buf == src->static_buf) {
+        dest->buf = dest->static_buf;
+        memcpy(dest->buf, src->buf, src->n);
+    }
+    else {
+        dest->buf = src->buf;
+    }
+}
+
+/* Ensure at least *bytes* can be appended to the string writer's buffer. */
+static int
+string_writer_ensure(string_writer_t *w, size_t bytes)
+{
+    size_t newsize;
+    bytes += w->n;
+    if (bytes <= w->allocated)
+        return 0;
+    newsize = (w->allocated << 2) + 1;
+    if (newsize < bytes)
+        newsize = bytes;
+    if (w->buf == w->static_buf)
+        w->buf = malloc(newsize);
+    else
+        w->buf = realloc(w->buf, newsize);
+    if (w->buf) {
+        w->allocated = newsize;
+        return 0;
+    }
+    else {
+        PyErr_NoMemory();
+        return -1;
+    }
+}
+
+static int
+string_writer_put_char(string_writer_t *w, unsigned char c)
+{
+    if (string_writer_ensure(w, 1))
+        return -1;
+    w->buf[w->n++] = c;
+    return 0;
+}
+
+static int
+string_writer_put_int32(string_writer_t *w, unsigned int v)
+{
+    if (string_writer_ensure(w, 4))
+        return -1;
+    w->buf[w->n] = v & 0xff;
+    w->buf[w->n + 1] = (v >> 8) & 0xff;
+    w->buf[w->n + 2] = (v >> 16) & 0xff;
+    w->buf[w->n + 3] = (v >> 24) & 0xff;
+    w->n += 4;
+    return 0;
+}
+
+static int
+string_writer_put_intp(string_writer_t *w, npy_intp v)
+{
+    if (string_writer_ensure(w, NPY_SIZEOF_PY_INTPTR_T))
+        return -1;
+    w->buf[w->n] = v & 0xff;
+    w->buf[w->n + 1] = (v >> 8) & 0xff;
+    w->buf[w->n + 2] = (v >> 16) & 0xff;
+    w->buf[w->n + 3] = (v >> 24) & 0xff;
+#if NPY_SIZEOF_PY_INTPTR_T == 8
+    w->buf[w->n + 4] = (v >> 32) & 0xff;
+    w->buf[w->n + 5] = (v >> 40) & 0xff;
+    w->buf[w->n + 6] = (v >> 48) & 0xff;
+    w->buf[w->n + 7] = (v >> 56) & 0xff;
+#endif
+    w->n += NPY_SIZEOF_PY_INTPTR_T;
+    return 0;
+}
+
+static int
+string_writer_put_string(string_writer_t *w, const char *s)
+{
+    if (s == NULL) {
+        return string_writer_put_char(w, 0);
+    }
+    else {
+        size_t N = strlen(s) + 1;
+        if (string_writer_ensure(w, N))
+            return -1;
+        memcpy(w->buf + w->n, s, N);
+        w->n += N;
+        return 0;
+    }
+}
+
+enum opcode {
+    OP_START_TUPLE = '(',
+    OP_END_TUPLE = ')',
+    OP_INT = 'i',
+    OP_FLOAT = 'f',
+    OP_COMPLEX = 'c',
+    OP_BOOL = '?',
+    OP_OMITTED = '!',
+
+    OP_BYTEARRAY = 'a',
+    OP_BYTES = 'b',
+    OP_NONE = 'n',
+    OP_LIST = '[',
+    OP_SET = '{',
+
+    OP_BUFFER = 'B',
+    OP_NP_SCALAR = 'S',
+    OP_NP_ARRAY = 'A',
+    OP_NP_DTYPE = 'D'
+};
+
+#define TRY(func, w, arg) \
+    do { \
+        if (func(w, arg)) return -1; \
+    } while (0)
+
+
+static int
+fingerprint_unrecognized(void)
+{
+    PyErr_SetString(PyExc_NotImplementedError,
+                    "cannot compute type fingerprint for value");
+    return -1;
+}
+
+static int
+compute_dtype_fingerprint(string_writer_t *w, PyArray_Descr *descr)
+{
+    int typenum = descr->type_num;
+    if (typenum < NPY_OBJECT)
+        return string_writer_put_char(w, (char) typenum);
+    if (typenum == NPY_VOID) {
+        /* Structured dtype: serialize the dtype pointer.  Unfortunately,
+         * some structured dtypes can be ephemeral, so we have to
+         * intern them to avoid pointer reuse and fingerprint collisions.
+         * (e.g. np.recarray(dtype=some_dtype) creates a new dtype
+         *  equal to some_dtype)
+         */
+        PyObject *interned = PyDict_GetItem(structured_dtypes,
+                                            (PyObject *) descr);
+        if (interned == NULL) {
+            interned = (PyObject *) descr;
+            if (PyDict_SetItem(structured_dtypes, interned, interned))
+                return -1;
+        }
+        TRY(string_writer_put_char, w, (char) typenum);
+        return string_writer_put_intp(w, (npy_intp) interned);
+    }
+#if NPY_API_VERSION >= 0x00000007
+    if (PyTypeNum_ISDATETIME(typenum)) {
+        PyArray_DatetimeMetaData *md;
+        md = &(((PyArray_DatetimeDTypeMetaData *)descr->c_metadata)->meta);
+        TRY(string_writer_put_char, w, (char) typenum);
+        TRY(string_writer_put_char, w, (char) md->base);
+        return string_writer_put_int32(w, (char) md->num);
+    }
+#endif
+
+    return fingerprint_unrecognized();
+}
+
+static int
+compute_fingerprint(string_writer_t *w, PyObject *val)
+{
+    /*
+     * Implementation note: for performance, we start with common
+     * types that can be tested with fast checks.
+     */
+    if (val == Py_None)
+        return string_writer_put_char(w, OP_NONE);
+    if (PyBool_Check(val))
+        return string_writer_put_char(w, OP_BOOL);
+    /* Note we avoid matching int subclasses such as IntEnum */
+    if (PyInt_CheckExact(val) || PyLong_CheckExact(val))
+        return string_writer_put_char(w, OP_INT);
+    if (PyFloat_Check(val))
+        return string_writer_put_char(w, OP_FLOAT);
+    if (PyComplex_CheckExact(val))
+        return string_writer_put_char(w, OP_COMPLEX);
+    if (PyTuple_Check(val)) {
+        if(PyTuple_CheckExact(val)) {
+            Py_ssize_t i, n;
+            n = PyTuple_GET_SIZE(val);
+            TRY(string_writer_put_char, w, OP_START_TUPLE);
+            for (i = 0; i < n; i++)
+                TRY(compute_fingerprint, w, PyTuple_GET_ITEM(val, i));
+            TRY(string_writer_put_char, w, OP_END_TUPLE);
+            return 0;
+        }
+        /* as per typeof.py, check "_asdict" for namedtuple. */
+        else if(PyObject_HasAttrString(val, "_asdict"))
+        {
+            /*
+             * This encodes the class name and field names of a namedtuple into
+             * the fingerprint on the condition that the number of fields is
+             * small (<10) and that the class name and field names are encodable
+             * as ASCII.
+             */
+            PyObject * clazz = NULL;
+            PyObject * name = NULL;
+            PyObject * _fields =  PyObject_GetAttrString(val, "_fields");
+            PyObject * field = NULL;
+            PyObject * ascii_str = NULL;
+            Py_ssize_t i, n, j, flen;
+            char * buf = NULL;
+            int ret;
+
+            clazz = PyObject_GetAttrString(val, "__class__");
+            if (clazz == NULL)
+                return -1;
+
+            name = PyObject_GetAttrString(clazz, "__name__");
+            Py_DECREF(clazz);
+            if (name == NULL)
+                return -1;
+
+            ascii_str = PyUnicode_AsEncodedString(name, "ascii", "ignore");
+            Py_DECREF(name);
+            if (ascii_str == NULL)
+                return -1;
+            ret = PyBytes_AsStringAndSize(ascii_str, &buf, &flen);
+
+            if (ret == -1)
+                return -1;
+            for(j = 0; j < flen; j++) {
+                TRY(string_writer_put_char, w, buf[j]);
+            }
+            Py_DECREF(ascii_str);
+
+            if (_fields == NULL)
+                return -1;
+
+            n = PyTuple_GET_SIZE(val);
+
+            TRY(string_writer_put_char, w, OP_START_TUPLE);
+            for (i = 0; i < n; i++) {
+                field = PyTuple_GET_ITEM(_fields, i);
+                if (field == NULL)
+                    return -1;
+                ascii_str = PyUnicode_AsEncodedString(field, "ascii", "ignore");
+                if (ascii_str == NULL)
+                    return -1;
+                ret = PyBytes_AsStringAndSize(ascii_str, &buf, &flen);
+                if (ret == -1)
+                    return -1;
+                for(j = 0; j < flen; j++) {
+                    TRY(string_writer_put_char, w, buf[j]);
+                }
+                Py_DECREF(ascii_str);
+                TRY(compute_fingerprint, w, PyTuple_GET_ITEM(val, i));
+            }
+            TRY(string_writer_put_char, w, OP_END_TUPLE);
+            Py_DECREF(_fields);
+            return 0;
+        }
+    }
+    if (PyBytes_Check(val))
+        return string_writer_put_char(w, OP_BYTES);
+    if (PyByteArray_Check(val))
+        return string_writer_put_char(w, OP_BYTEARRAY);
+    if ((PyObject *) Py_TYPE(val) == omittedarg_type) {
+        PyObject *default_val = PyObject_GetAttr(val, str_value);
+        if (default_val == NULL)
+            return -1;
+        TRY(string_writer_put_char, w, OP_OMITTED);
+        TRY(compute_fingerprint, w, default_val);
+        Py_DECREF(default_val);
+        return 0;
+    }
+    if (PyArray_IsScalar(val, Generic)) {
+        /* Note: PyArray_DescrFromScalar() may be a bit slow on
+           non-trivial types. */
+        PyArray_Descr *descr = PyArray_DescrFromScalar(val);
+        if (descr == NULL)
+            return -1;
+        TRY(string_writer_put_char, w, OP_NP_SCALAR);
+        TRY(compute_dtype_fingerprint, w, descr);
+        Py_DECREF(descr);
+        return 0;
+    }
+    if (PyArray_Check(val)) {
+        PyArrayObject *ary = (PyArrayObject *) val;
+        int ndim = PyArray_NDIM(ary);
+
+        TRY(string_writer_put_char, w, OP_NP_ARRAY);
+        TRY(string_writer_put_int32, w, ndim);
+        if (PyArray_IS_C_CONTIGUOUS(ary))
+            TRY(string_writer_put_char, w, 'C');
+        else if (PyArray_IS_F_CONTIGUOUS(ary))
+            TRY(string_writer_put_char, w, 'F');
+        else
+            TRY(string_writer_put_char, w, 'A');
+        if (PyArray_ISWRITEABLE(ary))
+            TRY(string_writer_put_char, w, 'W');
+        else
+            TRY(string_writer_put_char, w, 'R');
+        return compute_dtype_fingerprint(w, PyArray_DESCR(ary));
+    }
+    if (PyList_Check(val)) {
+        Py_ssize_t n = PyList_GET_SIZE(val);
+        if (n == 0) {
+            PyErr_SetString(PyExc_ValueError,
+                            "cannot compute fingerprint of empty list");
+            return -1;
+        }
+        /* Only the first item is considered, as in typeof.py */
+        TRY(string_writer_put_char, w, OP_LIST);
+        TRY(compute_fingerprint, w, PyList_GET_ITEM(val, 0));
+        return 0;
+    }
+    /* Note we only accept sets, not frozensets */
+    if (Py_TYPE(val) == &PySet_Type) {
+        Py_hash_t h;
+        PyObject *item;
+        Py_ssize_t pos = 0;
+        /* Only one item is considered, as in typeof.py */
+        if (!_PySet_NextEntry(val, &pos, &item, &h)) {
+            /* Empty set */
+            PyErr_SetString(PyExc_ValueError,
+                            "cannot compute fingerprint of empty set");
+            return -1;
+        }
+        TRY(string_writer_put_char, w, OP_SET);
+        TRY(compute_fingerprint, w, item);
+        return 0;
+    }
+    if (PyObject_CheckBuffer(val)) {
+        Py_buffer buf;
+        int flags = PyBUF_ND | PyBUF_STRIDES | PyBUF_FORMAT;
+        char contig;
+        int ndim;
+        char readonly;
+
+        /* Attempt to get a writable buffer, then fallback on read-only */
+        if (PyObject_GetBuffer(val, &buf, flags | PyBUF_WRITABLE)) {
+            PyErr_Clear();
+            if (PyObject_GetBuffer(val, &buf, flags))
+                goto _unrecognized;
+        }
+        if (PyBuffer_IsContiguous(&buf, 'C'))
+            contig = 'C';
+        else if (PyBuffer_IsContiguous(&buf, 'F'))
+            contig = 'F';
+        else
+            contig = 'A';
+        ndim = buf.ndim;
+        readonly = buf.readonly ? 'R' : 'W';
+        if (string_writer_put_char(w, OP_BUFFER) ||
+            string_writer_put_int32(w, ndim) ||
+            string_writer_put_char(w, contig) ||
+            string_writer_put_char(w, readonly) ||
+            string_writer_put_string(w, buf.format) ||
+            /* We serialize the object's Python type as well, to
+               distinguish between types which have Numba specializations
+               (e.g. array.array() vs. memoryview)
+            */
+            string_writer_put_intp(w, (npy_intp) Py_TYPE(val))) {
+            PyBuffer_Release(&buf);
+            return -1;
+        }
+        PyBuffer_Release(&buf);
+        return 0;
+    }
+    if (NUMBA_PyArray_DescrCheck(val)) {
+        TRY(string_writer_put_char, w, OP_NP_DTYPE);
+        return compute_dtype_fingerprint(w, (PyArray_Descr *) val);
+    }
+
+_unrecognized:
+    /* Type not recognized */
+    return fingerprint_unrecognized();
+}
+
+PyObject *
+typeof_compute_fingerprint(PyObject *val)
+{
+    PyObject *res;
+    string_writer_t w;
+
+    string_writer_init(&w);
+
+    if (compute_fingerprint(&w, val))
+        goto error;
+    res = PyBytes_FromStringAndSize(w.buf, w.n);
+
+    string_writer_clear(&w);
+    return res;
+
+error:
+    string_writer_clear(&w);
+    return NULL;
+}
+
+/*
+ * Getting the typecode from a Type object.
+ */
+static int
+_typecode_from_type_object(PyObject *tyobj) {
+    int typecode;
+    PyObject *tmpcode = PyObject_GetAttrString(tyobj, "_code");
+    if (tmpcode == NULL) {
+        return -1;
+    }
+    typecode = PyLong_AsLong(tmpcode);
+    Py_DECREF(tmpcode);
+    return typecode;
+}
+
+/* When we want to cache the type's typecode for later lookup, we need to
+   keep a reference to the returned type object so that it cannot be
+   deleted. This is because of the following events occurring when first
+   using a @jit function for a given set of types:
+
+    1. typecode_fallback requests a new typecode for an arbitrary Python value;
+       this implies creating a Numba type object (on the first dispatcher call);
+       the typecode cache is then populated.
+    2. matching of the typecode list in _dispatcherimpl.cpp fails, since the
+       typecode is new.
+    3. we have to compile: compile_and_invoke() is called, it will invoke
+       Dispatcher_Insert to register the new signature.
+
+   The reference to the Numba type object returned in step 1 is deleted as
+   soon as we call Py_DECREF() on it, since we are holding the only
+   reference. If this happens and we use the typecode we got to populate the
+   cache, then the cache won't ever return the correct typecode, and the
+   dispatcher will never successfully match the typecodes with those of
+   some already-compiled instance. So we need to make sure that we don't
+   call Py_DECREF() on objects whose typecode will be used to populate the
+   cache. This is ensured by calling _typecode_fallback with
+   retain_reference == 0.
+
+   Note that technically we are leaking the reference, since we do not continue
+   to hold a pointer to the type object that we get back from typeof_pyval.
+   However, we don't need to refer to it again, we just need to make sure that
+   it is never deleted.
+*/
+static int
+_typecode_fallback(PyObject *dispatcher, PyObject *val,
+                   int retain_reference) {
+    PyObject *numba_type;
+    int typecode;
+
+    /*
+     * For values that define "_numba_type_", which holds a numba Type
+     * instance that should be used as the type of the value.
+     * Note this is done here, not in typeof_typecode(), so that
+     * some values can still benefit from fingerprint caching.
+     */
+    if (PyObject_HasAttr(val, str_numba_type)) {
+        numba_type = PyObject_GetAttrString(val, "_numba_type_");
+        if (!numba_type)
+            return -1;
+    }
+    else {
+        // Go back to the interpreter
+        numba_type = PyObject_CallMethodObjArgs((PyObject *) dispatcher,
+                                                str_typeof_pyval, val, NULL);
+    }
+    if (!numba_type)
+        return -1;
+    typecode = _typecode_from_type_object(numba_type);
+    if (!retain_reference)
+        Py_DECREF(numba_type);
+    return typecode;
+}
+
+/* Variations on _typecode_fallback for convenience */
+
+static
+int typecode_fallback(PyObject *dispatcher, PyObject *val) {
+    return _typecode_fallback(dispatcher, val, 0);
+}
+
+static
+int typecode_fallback_keep_ref(PyObject *dispatcher, PyObject *val) {
+    return _typecode_fallback(dispatcher, val, 1);
+}
+
+
+/* A cache mapping fingerprints (string_writer_t *) to typecodes (int). */
+static _Numba_hashtable_t *fingerprint_hashtable = NULL;
+
+static Py_uhash_t
+hash_writer(const void *key)
+{
+    string_writer_t *writer = (string_writer_t *) key;
+    Py_uhash_t x = 0;
+
+    /* The old FNV algorithm used by Python 2 */
+    if (writer->n > 0) {
+        unsigned char *p = (unsigned char *) writer->buf;
+        Py_ssize_t len = writer->n;
+        x ^= *p << 7;
+        while (--len >= 0)
+            x = (1000003*x) ^ *p++;
+        x ^= writer->n;
+        if (x == (Py_uhash_t) -1)
+            x = -2;
+    }
+    return x;
+}
+
+static int
+compare_writer(const void *key, const _Numba_hashtable_entry_t *entry)
+{
+    string_writer_t *v = (string_writer_t *) key;
+    string_writer_t *w = (string_writer_t *) entry->key;
+    if (v->n != w->n)
+        return 0;
+    return memcmp(v->buf, w->buf, v->n) == 0;
+}
+
+/* Try to compute *val*'s typecode using its fingerprint and the
+ * fingerprint->typecode cache.
+ */
+static int
+typecode_using_fingerprint(PyObject *dispatcher, PyObject *val)
+{
+    int typecode;
+    string_writer_t w;
+
+    string_writer_init(&w);
+
+    if (compute_fingerprint(&w, val)) {
+        string_writer_clear(&w);
+        if (PyErr_ExceptionMatches(PyExc_NotImplementedError)) {
+            /* Can't compute a type fingerprint for the given value,
+               fall back on typeof() without caching. */
+            PyErr_Clear();
+            return typecode_fallback(dispatcher, val);
+        }
+        return -1;
+    }
+    if (_Numba_HASHTABLE_GET(fingerprint_hashtable, &w, typecode) > 0) {
+        /* Cache hit */
+        string_writer_clear(&w);
+        return typecode;
+    }
+
+    /* Not found in cache: invoke pure Python typeof() and cache result.
+     * Note we have to keep the type alive forever as explained
+     * above in _typecode_fallback().
+     */
+    typecode = typecode_fallback_keep_ref(dispatcher, val);
+    if (typecode >= 0) {
+        string_writer_t *key = (string_writer_t *) malloc(sizeof(string_writer_t));
+        if (key == NULL) {
+            string_writer_clear(&w);
+            PyErr_NoMemory();
+            return -1;
+        }
+        /* Ownership of the string writer's buffer will be transferred
+         * to the hash table.
+         */
+        string_writer_move(key, &w);
+        if (_Numba_HASHTABLE_SET(fingerprint_hashtable, key, typecode)) {
+            string_writer_clear(&w);
+            PyErr_NoMemory();
+            return -1;
+        }
+    }
+    return typecode;
+}
+
+
+/*
+ * Direct lookup table for extra-fast typecode resolution of simple array types.
+ */
+
+#define N_DTYPES 12
+#define N_NDIM 5    /* Fast path for up to 5D array */
+#define N_LAYOUT 3
+static int cached_arycode[N_NDIM][N_LAYOUT][N_DTYPES];
+
+/* Convert a Numpy dtype number to an internal index into cached_arycode.
+   The returned value must also be a valid index into BASIC_TYPECODES. */
+static int dtype_num_to_typecode(int type_num) {
+    int dtype;
+    switch(type_num) {
+    case NPY_INT8:
+        dtype = 0;
+        break;
+    case NPY_INT16:
+        dtype = 1;
+        break;
+    case NPY_INT32:
+        dtype = 2;
+        break;
+    case NPY_INT64:
+        dtype = 3;
+        break;
+    case NPY_UINT8:
+        dtype = 4;
+        break;
+    case NPY_UINT16:
+        dtype = 5;
+        break;
+    case NPY_UINT32:
+        dtype = 6;
+        break;
+    case NPY_UINT64:
+        dtype = 7;
+        break;
+    case NPY_FLOAT32:
+        dtype = 8;
+        break;
+    case NPY_FLOAT64:
+        dtype = 9;
+        break;
+    case NPY_COMPLEX64:
+        dtype = 10;
+        break;
+    case NPY_COMPLEX128:
+        dtype = 11;
+        break;
+    default:
+        /* Type not included in the global lookup table */
+        dtype = -1;
+    }
+    return dtype;
+}
+
+static
+int get_cached_typecode(PyArray_Descr* descr) {
+    PyObject* tmpobject = PyDict_GetItem(typecache, (PyObject*)descr);
+    if (tmpobject == NULL)
+        return -1;
+
+    return PyLong_AsLong(tmpobject);
+}
+
+static
+void cache_typecode(PyArray_Descr* descr, int typecode) {
+    PyObject* value = PyLong_FromLong(typecode);
+    PyDict_SetItem(typecache, (PyObject*)descr, value);
+    Py_DECREF(value);
+}
+
+static
+PyObject* ndarray_key(int ndim, int layout, PyArray_Descr* descr) {
+    PyObject* tmpndim = PyLong_FromLong(ndim);
+    PyObject* tmplayout = PyLong_FromLong(layout);
+    PyObject* key = PyTuple_Pack(3, tmpndim, tmplayout, descr);
+    Py_DECREF(tmpndim);
+    Py_DECREF(tmplayout);
+    return key;
+}
+
+static
+int get_cached_ndarray_typecode(int ndim, int layout, PyArray_Descr* descr) {
+    PyObject* key = ndarray_key(ndim, layout, descr);
+    PyObject *tmpobject = PyDict_GetItem(ndarray_typecache, key);
+    if (tmpobject == NULL)
+        return -1;
+
+    Py_DECREF(key);
+    return PyLong_AsLong(tmpobject);
+}
+
+static
+void cache_ndarray_typecode(int ndim, int layout, PyArray_Descr* descr,
+                            int typecode) {
+    PyObject* key = ndarray_key(ndim, layout, descr);
+    PyObject* value = PyLong_FromLong(typecode);
+    PyDict_SetItem(ndarray_typecache, key, value);
+    Py_DECREF(key);
+    Py_DECREF(value);
+}
+
+static
+int typecode_ndarray(PyObject *dispatcher, PyArrayObject *ary) {
+    int typecode;
+    int dtype;
+    int ndim = PyArray_NDIM(ary);
+    int layout = 0;
+
+    /* The order in which we check for the right contiguous-ness is important.
+       The order must match the order by numba.numpy_support.map_layout.
+       Further, only *contiguous-ness* is checked, not alignment, byte order or
+       write permissions.
+    */
+    if (PyArray_IS_C_CONTIGUOUS(ary)){
+        layout = 1;
+    } else if (PyArray_IS_F_CONTIGUOUS(ary)) {
+        layout = 2;
+    }
+
+    /* the typecode cache by convention is for "behaved" arrays (aligned and
+     * writeable), all others must be forced to the fall back */
+    if (!PyArray_ISBEHAVED(ary)) goto FALLBACK;
+
+    if (ndim <= 0 || ndim > N_NDIM) goto FALLBACK;
+
+    dtype = dtype_num_to_typecode(PyArray_TYPE(ary));
+    if (dtype == -1) goto FALLBACK;
+
+    /* Fast path, using direct table lookup */
+    assert(layout < N_LAYOUT);
+    assert(ndim <= N_NDIM);
+    assert(dtype < N_DTYPES);
+
+    typecode = cached_arycode[ndim - 1][layout][dtype];
+    if (typecode == -1) {
+        /* First use of this table entry, so it requires populating */
+        typecode = typecode_fallback_keep_ref(dispatcher, (PyObject*)ary);
+        cached_arycode[ndim - 1][layout][dtype] = typecode;
+    }
+    return typecode;
+
+FALLBACK:
+    /* Slower path, for non-trivial array types */
+
+    /* If this isn't a structured array then we can't use the cache */
+    if (PyArray_TYPE(ary) != NPY_VOID)
+        return typecode_using_fingerprint(dispatcher, (PyObject *) ary);
+
+    /* Check type cache */
+    typecode = get_cached_ndarray_typecode(ndim, layout, PyArray_DESCR(ary));
+    if (typecode == -1) {
+        /* First use of this type, use fallback and populate the cache */
+        typecode = typecode_fallback_keep_ref(dispatcher, (PyObject*)ary);
+        cache_ndarray_typecode(ndim, layout, PyArray_DESCR(ary), typecode);
+    }
+    return typecode;
+}
+
+static
+int typecode_arrayscalar(PyObject *dispatcher, PyObject* aryscalar) {
+    int typecode;
+    PyArray_Descr *descr;
+    descr = PyArray_DescrFromScalar(aryscalar);
+    if (!descr)
+        return typecode_using_fingerprint(dispatcher, aryscalar);
+
+    /* Is it a structured scalar? */
+    if (descr->type_num == NPY_VOID) {
+        typecode = get_cached_typecode(descr);
+        if (typecode == -1) {
+            /* Resolve through fallback then populate cache */
+            typecode = typecode_fallback_keep_ref(dispatcher, aryscalar);
+            cache_typecode(descr, typecode);
+        }
+        Py_DECREF(descr);
+        return typecode;
+    }
+
+    /* Is it one of the well-known basic types? */
+    typecode = dtype_num_to_typecode(descr->type_num);
+    Py_DECREF(descr);
+    if (typecode == -1)
+        return typecode_using_fingerprint(dispatcher, aryscalar);
+    return BASIC_TYPECODES[typecode];
+}
+
+static
+int typecode_devicendarray(PyObject *dispatcher, PyObject *ary)
+{
+    int typecode;
+    int dtype;
+    int ndim;
+    int layout = 0;
+
+    PyObject* flags = PyObject_GetAttrString(ary, "flags");
+    if (flags == NULL)
+    {
+        PyErr_Clear();
+        goto FALLBACK;
+    }
+
+    if (PyDict_GetItemString(flags, "C_CONTIGUOUS") == Py_True) {
+        layout = 1;
+    } else if (PyDict_GetItemString(flags, "F_CONTIGUOUS") == Py_True) {
+        layout = 2;
+    }
+
+    Py_DECREF(flags);
+
+    PyObject *ndim_obj = PyObject_GetAttrString(ary, "ndim");
+    if (ndim_obj == NULL) {
+        /* If there's no ndim, try to proceed by clearing the error and using the
+         * fallback. */
+        PyErr_Clear();
+        goto FALLBACK;
+    }
+
+    ndim = PyLong_AsLong(ndim_obj);
+    Py_DECREF(ndim_obj);
+
+    if (PyErr_Occurred()) {
+        /* ndim wasn't an integer for some reason - unlikely to happen, but try
+         * the fallback. */
+        PyErr_Clear();
+        goto FALLBACK;
+    }
+
+    if (ndim <= 0 || ndim > N_NDIM)
+        goto FALLBACK;
+
+    PyObject* dtype_obj = PyObject_GetAttrString(ary, "dtype");
+    if (dtype_obj == NULL) {
+        /* No dtype: try the fallback. */
+        PyErr_Clear();
+        goto FALLBACK;
+    }
+
+    PyObject* num_obj = PyObject_GetAttrString(dtype_obj, "num");
+    Py_DECREF(dtype_obj);
+
+    if (num_obj == NULL) {
+        /* This strange dtype has no num - try the fallback. */
+        PyErr_Clear();
+        goto FALLBACK;
+    }
+
+    int dtype_num = PyLong_AsLong(num_obj);
+    Py_DECREF(num_obj);
+
+    if (PyErr_Occurred()) {
+        /* num wasn't an integer for some reason - unlikely to happen, but try
+         * the fallback. */
+        PyErr_Clear();
+        goto FALLBACK;
+    }
+
+    dtype = dtype_num_to_typecode(dtype_num);
+    if (dtype == -1) {
+        /* Not a dtype we have in the global lookup table. */
+        goto FALLBACK;
+    }
+
+    /* Fast path, using direct table lookup */
+    assert(layout < N_LAYOUT);
+    assert(ndim <= N_NDIM);
+    assert(dtype < N_DTYPES);
+    typecode = cached_arycode[ndim - 1][layout][dtype];
+
+    if (typecode == -1) {
+        /* First use of this table entry, so it requires populating */
+        typecode = typecode_fallback_keep_ref(dispatcher, (PyObject*)ary);
+        cached_arycode[ndim - 1][layout][dtype] = typecode;
+    }
+
+    return typecode;
+
+FALLBACK:
+    /* Slower path, for non-trivial array types. At present this always uses
+       the fingerprinting to get the typecode. Future optimization might
+       implement a cache, but this would require some fast equivalent of
+       PyArray_DESCR for a device array. */
+
+    return typecode_using_fingerprint(dispatcher, (PyObject *) ary);
+}
+
+int
+typeof_typecode(PyObject *dispatcher, PyObject *val)
+{
+    PyTypeObject *tyobj = Py_TYPE(val);
+    int subtype_attr;
+    /* This needs to be kept in sync with Dispatcher.typeof_pyval(),
+     * otherwise funny things may happen.
+     */
+    if (tyobj == &PyInt_Type || tyobj == &PyLong_Type) {
+#if SIZEOF_VOID_P < 8
+        /* On 32-bit platforms, choose between tc_intp (32-bit) and tc_int64 */
+        PY_LONG_LONG ll = PyLong_AsLongLong(val);
+        if (ll == -1 && PyErr_Occurred()) {
+            /* The integer is too large, let us truncate it */
+            PyErr_Clear();
+            return tc_int64;
+        }
+        if ((ll & 0xffffffff) != ll)
+            return tc_int64;
+#endif
+        return tc_intp;
+    }
+    else if (tyobj == &PyFloat_Type)
+        return tc_float64;
+    else if (tyobj == &PyComplex_Type)
+        return tc_complex128;
+    /* Array scalar handling */
+    else if (PyArray_CheckScalar(val)) {
+        return typecode_arrayscalar(dispatcher, val);
+    }
+    /* Array handling */
+    else if (tyobj == &PyArray_Type) {
+        return typecode_ndarray(dispatcher, (PyArrayObject*)val);
+    }
+    /* Subtype of CUDA device array */
+    else if (PyType_IsSubtype(tyobj, &DeviceArrayType)) {
+        return typecode_devicendarray(dispatcher, val);
+    }
+    /* Subtypes of Array handling */
+    else if (PyType_IsSubtype(tyobj, &PyArray_Type)) {
+        /* By default, Numba will treat all numpy.ndarray subtypes as if they
+           were the base numpy.ndarray type.  In this way, ndarray subtypes
+           can easily use all of the support that Numba has for ndarray
+           methods.
+           EXPERIMENTAL: There may be cases where a programmer would NOT want
+           ndarray subtypes to be treated exactly like the base numpy.ndarray.
+           For this purpose, a currently experimental feature allows a
+           programmer to add an attribute named
+           __numba_array_subtype_dispatch__ to their ndarray subtype.  This
+           attribute can have any value as Numba only checks for the presence
+           of the attribute and not its value.  When present, a ndarray subtype
+           will NOT be typed by Numba as a regular ndarray but this code will
+           fallthrough to the typecode_using_fingerprint call, which will
+           create a new unique Numba typecode for this ndarray subtype.  This
+           behavior has several significant effects.  First, since this
+           ndarray subtype will be treated as a different type by Numba,
+           the Numba dispatcher would then specialize on this type.  So, if
+           there was a function that had several parameters that were
+           expected to be either numpy.ndarray or a subtype of ndarray, then
+           Numba would compile a custom version of this function for each
+           combination of base and subtypes that were actually passed to the
+           function.  Second, because this subtype would now be treated as
+           a totally separate type, it will cease to function in Numba unless
+           an implementation of that type is provided to Numba through the
+           Numba type extension mechanisms (e.g., overload).  This would
+           typically start with defining a Numba type corresponding to the
+           ndarray subtype. This is the same concept as how Numba has a
+           corollary of numpy.ndarray in its type system as types.Array.
+           Next, one would typically defining boxing and unboxing routines
+           and the associated memory model.  Then, overloads for NumPy
+           functions on that type would be created.  However,
+           if the same default array memory model is used then there are tricks
+           one can do to look at Numba's internal types.Array registries and
+           to quickly apply those to the subtype as well.  In this manner,
+           only those cases where the base ndarray and the ndarray subtype
+           behavior differ would new custom functions need to be written for
+           the subtype. Finally,
+           after adding support for the new type, you would have a separate
+           ndarray subtype that could operate with other objects of the same
+           subtype but would not support interoperation with regular NumPy
+           ndarrays.  In standard Python, this interoperation is provided
+           through the __array_ufunc__ magic method in the ndarray subtype
+           class and in that case the function operates on ndarrays or their
+           subtypes.  This idea is extended into Numba such that
+           __array_ufunc__ can be present in a Numba array type object.
+           In this case, this function is consulted during Numba typing and
+           so the arguments to __array_ufunc__ are Numba types instead of
+           ndarray subtypes.  The array type __array_ufunc__ returns the
+           type of the output of the given ufunc.
+         */
+        subtype_attr = PyObject_HasAttrString(val, "__numba_array_subtype_dispatch__");
+        if (!subtype_attr) {
+            return typecode_ndarray(dispatcher, (PyArrayObject*)val);
+        }
+    }
+
+    return typecode_using_fingerprint(dispatcher, val);
+}
+
+
+static
+void* wrap_import_array(void) {
+    import_array(); /* import array returns NULL on failure */
+    return (void*)1;
+}
+
+
+static
+int init_numpy(void) {
+    return wrap_import_array() != NULL;
+}
+
+
+/*
+ * typeof_init(omittedarg_type, typecode_dict)
+ * (called from dispatcher.py to fill in missing information)
+ */
+PyObject *
+typeof_init(PyObject *self, PyObject *args)
+{
+    PyObject *tmpobj;
+    PyObject *dict;
+    int index = 0;
+
+    if (!PyArg_ParseTuple(args, "O!O!:typeof_init",
+                          &PyType_Type, &omittedarg_type,
+                          &PyDict_Type, &dict))
+        return NULL;
+
+    /* Initialize Numpy API */
+    if ( ! init_numpy() ) {
+        return NULL;
+    }
+
+    #define UNWRAP_TYPE(S)                                              \
+        if(!(tmpobj = PyDict_GetItemString(dict, #S))) return NULL;     \
+        else {  tc_##S = PyLong_AsLong(tmpobj);                         \
+                BASIC_TYPECODES[index++] = tc_##S;  }
+
+    UNWRAP_TYPE(int8)
+    UNWRAP_TYPE(int16)
+    UNWRAP_TYPE(int32)
+    UNWRAP_TYPE(int64)
+
+    UNWRAP_TYPE(uint8)
+    UNWRAP_TYPE(uint16)
+    UNWRAP_TYPE(uint32)
+    UNWRAP_TYPE(uint64)
+
+    UNWRAP_TYPE(float32)
+    UNWRAP_TYPE(float64)
+
+    UNWRAP_TYPE(complex64)
+    UNWRAP_TYPE(complex128)
+
+    switch(sizeof(void*)) {
+    case 4:
+        tc_intp = tc_int32;
+        break;
+    case 8:
+        tc_intp = tc_int64;
+        break;
+    default:
+        PyErr_SetString(PyExc_AssertionError, "sizeof(void*) != {4, 8}");
+        return NULL;
+    }
+
+    #undef UNWRAP_TYPE
+
+    typecache = PyDict_New();
+    ndarray_typecache = PyDict_New();
+    structured_dtypes = PyDict_New();
+    if (typecache == NULL || ndarray_typecache == NULL ||
+        structured_dtypes == NULL) {
+        PyErr_SetString(PyExc_RuntimeError, "failed to create type cache");
+        return NULL;
+    }
+
+    fingerprint_hashtable = _Numba_hashtable_new(sizeof(int),
+                                              hash_writer,
+                                              compare_writer);
+    if (fingerprint_hashtable == NULL) {
+        PyErr_NoMemory();
+        return NULL;
+    }
+
+    /* initialize cached_arycode to all ones (in bits) */
+    memset(cached_arycode, 0xFF, sizeof(cached_arycode));
+
+    str_typeof_pyval = PyString_InternFromString("typeof_pyval");
+    str_value = PyString_InternFromString("value");
+    str_numba_type = PyString_InternFromString("_numba_type_");
+    if (!str_value || !str_typeof_pyval || !str_numba_type)
+        return NULL;
+
+    Py_RETURN_NONE;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_typeof.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_typeof.h
new file mode 100644
index 000000000..6e0039b5f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_typeof.h
@@ -0,0 +1,16 @@
+#ifndef NUMBA_TYPEOF_H_
+#define NUMBA_TYPEOF_H_
+
+#ifdef __cplusplus
+    extern "C" {
+#endif
+
+extern PyObject *typeof_init(PyObject *self, PyObject *args);
+extern int typeof_typecode(PyObject *dispatcher, PyObject *val);
+extern PyObject *typeof_compute_fingerprint(PyObject *val);
+
+#ifdef __cplusplus
+    }
+#endif
+
+#endif  /* NUMBA_TYPEOF_H_ */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_unicodetype_db.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_unicodetype_db.h
new file mode 100644
index 000000000..d4dca060d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_unicodetype_db.h
@@ -0,0 +1,6091 @@
+/* This file is from CPython:
+ * https://github.com/python/cpython/blob/3.7/Objects/unicodetype_db.h
+ * As of Commit SHA: 1d4b6ba19466aba0eb91c4ba01ba509acf18c723
+ *
+ * Changes made include:
+ *  - Renaming all functions and structures with a `numba` prefix to prevent
+ *    collisions.
+ *
+ * NOTE: Numba devs, this may need updating from time to time as the unicode
+ * standard is updated.
+ */
+
+#ifndef _UNICODETYPE_DB_H
+#define _UNICODETYPE_DB_H
+
+/*Py_UCS4 definition from Include/unicodeobject.h */
+#define Py_UCS4 uint32_t
+
+typedef struct {
+    /*
+       These are either deltas to the character or offsets in
+       _PyUnicode_ExtendedCase.
+    */
+    const int upper;
+    const int lower;
+    const int title;
+    /* Note if more flag space is needed, decimal and digit could be unified. */
+    const unsigned char decimal;
+    const unsigned char digit;
+    const unsigned short flags;
+} numba_PyUnicode_TypeRecord;
+
+/* -------------------------------------------------------------------------- */
+/* CPython unicodetype_db.h definitions start here                            */
+/* -------------------------------------------------------------------------- */
+
+/* this file was generated by Tools/unicode/makeunicodedata.py 3.2 */
+
+/* a list of unique character type descriptors */
+const numba_PyUnicode_TypeRecord numba_PyUnicode_TypeRecords[] = {
+    {0, 0, 0, 0, 0, 0},
+    {0, 0, 0, 0, 0, 0},
+    {0, 0, 0, 0, 0, 32},
+    {0, 0, 0, 0, 0, 48},
+    {0, 0, 0, 0, 0, 1056},
+    {0, 0, 0, 0, 0, 1024},
+    {0, 0, 0, 0, 0, 5120},
+    {0, 0, 0, 0, 0, 3590},
+    {0, 0, 0, 1, 1, 3590},
+    {0, 0, 0, 2, 2, 3590},
+    {0, 0, 0, 3, 3, 3590},
+    {0, 0, 0, 4, 4, 3590},
+    {0, 0, 0, 5, 5, 3590},
+    {0, 0, 0, 6, 6, 3590},
+    {0, 0, 0, 7, 7, 3590},
+    {0, 0, 0, 8, 8, 3590},
+    {0, 0, 0, 9, 9, 3590},
+    {0, 32, 0, 0, 0, 10113},
+    {0, 0, 0, 0, 0, 1536},
+    {-32, 0, -32, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 4096},
+    {0, 0, 0, 0, 2, 3076},
+    {0, 0, 0, 0, 3, 3076},
+    {16777218, 17825792, 16777218, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 5632},
+    {0, 0, 0, 0, 1, 3076},
+    {0, 0, 0, 0, 0, 3072},
+    {33554438, 18874371, 33554440, 0, 0, 26377},
+    {121, 0, 121, 0, 0, 9993},
+    {0, 1, 0, 0, 0, 10113},
+    {-1, 0, -1, 0, 0, 9993},
+    {16777228, 33554442, 16777228, 0, 0, 26497},
+    {-232, 0, -232, 0, 0, 9993},
+    {33554448, 18874381, 33554448, 0, 0, 26377},
+    {0, -121, 0, 0, 0, 10113},
+    {16777236, 17825810, 16777236, 0, 0, 26377},
+    {195, 0, 195, 0, 0, 9993},
+    {0, 210, 0, 0, 0, 10113},
+    {0, 206, 0, 0, 0, 10113},
+    {0, 205, 0, 0, 0, 10113},
+    {0, 79, 0, 0, 0, 10113},
+    {0, 202, 0, 0, 0, 10113},
+    {0, 203, 0, 0, 0, 10113},
+    {0, 207, 0, 0, 0, 10113},
+    {97, 0, 97, 0, 0, 9993},
+    {0, 211, 0, 0, 0, 10113},
+    {0, 209, 0, 0, 0, 10113},
+    {163, 0, 163, 0, 0, 9993},
+    {0, 213, 0, 0, 0, 10113},
+    {130, 0, 130, 0, 0, 9993},
+    {0, 214, 0, 0, 0, 10113},
+    {0, 218, 0, 0, 0, 10113},
+    {0, 217, 0, 0, 0, 10113},
+    {0, 219, 0, 0, 0, 10113},
+    {0, 0, 0, 0, 0, 1793},
+    {56, 0, 56, 0, 0, 9993},
+    {0, 2, 1, 0, 0, 10113},
+    {-1, 1, 0, 0, 0, 10049},
+    {-2, 0, -1, 0, 0, 9993},
+    {-79, 0, -79, 0, 0, 9993},
+    {33554456, 18874389, 33554456, 0, 0, 26377},
+    {0, -97, 0, 0, 0, 10113},
+    {0, -56, 0, 0, 0, 10113},
+    {0, -130, 0, 0, 0, 10113},
+    {0, 10795, 0, 0, 0, 10113},
+    {0, -163, 0, 0, 0, 10113},
+    {0, 10792, 0, 0, 0, 10113},
+    {10815, 0, 10815, 0, 0, 9993},
+    {0, -195, 0, 0, 0, 10113},
+    {0, 69, 0, 0, 0, 10113},
+    {0, 71, 0, 0, 0, 10113},
+    {10783, 0, 10783, 0, 0, 9993},
+    {10780, 0, 10780, 0, 0, 9993},
+    {10782, 0, 10782, 0, 0, 9993},
+    {-210, 0, -210, 0, 0, 9993},
+    {-206, 0, -206, 0, 0, 9993},
+    {-205, 0, -205, 0, 0, 9993},
+    {-202, 0, -202, 0, 0, 9993},
+    {-203, 0, -203, 0, 0, 9993},
+    {42319, 0, 42319, 0, 0, 9993},
+    {42315, 0, 42315, 0, 0, 9993},
+    {-207, 0, -207, 0, 0, 9993},
+    {42280, 0, 42280, 0, 0, 9993},
+    {42308, 0, 42308, 0, 0, 9993},
+    {-209, 0, -209, 0, 0, 9993},
+    {-211, 0, -211, 0, 0, 9993},
+    {10743, 0, 10743, 0, 0, 9993},
+    {42305, 0, 42305, 0, 0, 9993},
+    {10749, 0, 10749, 0, 0, 9993},
+    {-213, 0, -213, 0, 0, 9993},
+    {-214, 0, -214, 0, 0, 9993},
+    {10727, 0, 10727, 0, 0, 9993},
+    {-218, 0, -218, 0, 0, 9993},
+    {42282, 0, 42282, 0, 0, 9993},
+    {-69, 0, -69, 0, 0, 9993},
+    {-217, 0, -217, 0, 0, 9993},
+    {-71, 0, -71, 0, 0, 9993},
+    {-219, 0, -219, 0, 0, 9993},
+    {42261, 0, 42261, 0, 0, 9993},
+    {42258, 0, 42258, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 14089},
+    {0, 0, 0, 0, 0, 5889},
+    {16777244, 17825818, 16777244, 0, 0, 30216},
+    {0, 0, 0, 0, 0, 13321},
+    {0, 116, 0, 0, 0, 10113},
+    {0, 38, 0, 0, 0, 10113},
+    {0, 37, 0, 0, 0, 10113},
+    {0, 64, 0, 0, 0, 10113},
+    {0, 63, 0, 0, 0, 10113},
+    {50331681, 19922973, 50331681, 0, 0, 26377},
+    {-38, 0, -38, 0, 0, 9993},
+    {-37, 0, -37, 0, 0, 9993},
+    {50331688, 19922980, 50331688, 0, 0, 26377},
+    {16777261, 17825835, 16777261, 0, 0, 26377},
+    {-64, 0, -64, 0, 0, 9993},
+    {-63, 0, -63, 0, 0, 9993},
+    {0, 8, 0, 0, 0, 10113},
+    {16777264, 17825838, 16777264, 0, 0, 26377},
+    {16777267, 17825841, 16777267, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 10113},
+    {16777270, 17825844, 16777270, 0, 0, 26377},
+    {16777273, 17825847, 16777273, 0, 0, 26377},
+    {-8, 0, -8, 0, 0, 9993},
+    {16777276, 17825850, 16777276, 0, 0, 26377},
+    {16777279, 17825853, 16777279, 0, 0, 26377},
+    {7, 0, 7, 0, 0, 9993},
+    {-116, 0, -116, 0, 0, 9993},
+    {0, -60, 0, 0, 0, 10113},
+    {16777282, 17825856, 16777282, 0, 0, 26377},
+    {0, -7, 0, 0, 0, 10113},
+    {0, 80, 0, 0, 0, 10113},
+    {-80, 0, -80, 0, 0, 9993},
+    {0, 15, 0, 0, 0, 10113},
+    {-15, 0, -15, 0, 0, 9993},
+    {0, 48, 0, 0, 0, 10113},
+    {-48, 0, -48, 0, 0, 9993},
+    {33554502, 18874435, 33554504, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 1537},
+    {0, 7264, 0, 0, 0, 10113},
+    {3008, 0, 0, 0, 0, 9993},
+    {0, 0, 0, 0, 1, 3588},
+    {0, 0, 0, 0, 2, 3588},
+    {0, 0, 0, 0, 3, 3588},
+    {0, 0, 0, 0, 4, 3588},
+    {0, 0, 0, 0, 5, 3588},
+    {0, 0, 0, 0, 6, 3588},
+    {0, 0, 0, 0, 7, 3588},
+    {0, 0, 0, 0, 8, 3588},
+    {0, 0, 0, 0, 9, 3588},
+    {16777292, 17825866, 16777292, 0, 0, 26497},
+    {16777295, 17825869, 16777295, 0, 0, 26497},
+    {16777298, 17825872, 16777298, 0, 0, 26497},
+    {16777301, 17825875, 16777301, 0, 0, 26497},
+    {16777304, 17825878, 16777304, 0, 0, 26497},
+    {16777307, 17825881, 16777307, 0, 0, 26497},
+    {16777310, 17825884, 16777310, 0, 0, 26497},
+    {16777313, 17825887, 16777313, 0, 0, 26497},
+    {16777316, 17825890, 16777316, 0, 0, 26497},
+    {16777319, 17825893, 16777319, 0, 0, 26497},
+    {16777322, 17825896, 16777322, 0, 0, 26497},
+    {16777325, 17825899, 16777325, 0, 0, 26497},
+    {16777328, 17825902, 16777328, 0, 0, 26497},
+    {16777331, 17825905, 16777331, 0, 0, 26497},
+    {16777334, 17825908, 16777334, 0, 0, 26497},
+    {16777337, 17825911, 16777337, 0, 0, 26497},
+    {16777340, 17825914, 16777340, 0, 0, 26497},
+    {16777343, 17825917, 16777343, 0, 0, 26497},
+    {16777346, 17825920, 16777346, 0, 0, 26497},
+    {16777349, 17825923, 16777349, 0, 0, 26497},
+    {16777352, 17825926, 16777352, 0, 0, 26497},
+    {16777355, 17825929, 16777355, 0, 0, 26497},
+    {16777358, 17825932, 16777358, 0, 0, 26497},
+    {16777361, 17825935, 16777361, 0, 0, 26497},
+    {16777364, 17825938, 16777364, 0, 0, 26497},
+    {16777367, 17825941, 16777367, 0, 0, 26497},
+    {16777370, 17825944, 16777370, 0, 0, 26497},
+    {16777373, 17825947, 16777373, 0, 0, 26497},
+    {16777376, 17825950, 16777376, 0, 0, 26497},
+    {16777379, 17825953, 16777379, 0, 0, 26497},
+    {16777382, 17825956, 16777382, 0, 0, 26497},
+    {16777385, 17825959, 16777385, 0, 0, 26497},
+    {16777388, 17825962, 16777388, 0, 0, 26497},
+    {16777391, 17825965, 16777391, 0, 0, 26497},
+    {16777394, 17825968, 16777394, 0, 0, 26497},
+    {16777397, 17825971, 16777397, 0, 0, 26497},
+    {16777400, 17825974, 16777400, 0, 0, 26497},
+    {16777403, 17825977, 16777403, 0, 0, 26497},
+    {16777406, 17825980, 16777406, 0, 0, 26497},
+    {16777409, 17825983, 16777409, 0, 0, 26497},
+    {16777412, 17825986, 16777412, 0, 0, 26497},
+    {16777415, 17825989, 16777415, 0, 0, 26497},
+    {16777418, 17825992, 16777418, 0, 0, 26497},
+    {16777421, 17825995, 16777421, 0, 0, 26497},
+    {16777424, 17825998, 16777424, 0, 0, 26497},
+    {16777427, 17826001, 16777427, 0, 0, 26497},
+    {16777430, 17826004, 16777430, 0, 0, 26497},
+    {16777433, 17826007, 16777433, 0, 0, 26497},
+    {16777436, 17826010, 16777436, 0, 0, 26497},
+    {16777439, 17826013, 16777439, 0, 0, 26497},
+    {16777442, 17826016, 16777442, 0, 0, 26497},
+    {16777445, 17826019, 16777445, 0, 0, 26497},
+    {16777448, 17826022, 16777448, 0, 0, 26497},
+    {16777451, 17826025, 16777451, 0, 0, 26497},
+    {16777454, 17826028, 16777454, 0, 0, 26497},
+    {16777457, 17826031, 16777457, 0, 0, 26497},
+    {16777460, 17826034, 16777460, 0, 0, 26497},
+    {16777463, 17826037, 16777463, 0, 0, 26497},
+    {16777466, 17826040, 16777466, 0, 0, 26497},
+    {16777469, 17826043, 16777469, 0, 0, 26497},
+    {16777472, 17826046, 16777472, 0, 0, 26497},
+    {16777475, 17826049, 16777475, 0, 0, 26497},
+    {16777478, 17826052, 16777478, 0, 0, 26497},
+    {16777481, 17826055, 16777481, 0, 0, 26497},
+    {16777484, 17826058, 16777484, 0, 0, 26497},
+    {16777487, 17826061, 16777487, 0, 0, 26497},
+    {16777490, 17826064, 16777490, 0, 0, 26497},
+    {16777493, 17826067, 16777493, 0, 0, 26497},
+    {16777496, 17826070, 16777496, 0, 0, 26497},
+    {16777499, 17826073, 16777499, 0, 0, 26497},
+    {16777502, 17826076, 16777502, 0, 0, 26497},
+    {16777505, 17826079, 16777505, 0, 0, 26497},
+    {16777508, 17826082, 16777508, 0, 0, 26497},
+    {16777511, 17826085, 16777511, 0, 0, 26497},
+    {16777514, 17826088, 16777514, 0, 0, 26497},
+    {16777517, 17826091, 16777517, 0, 0, 26497},
+    {16777520, 17826094, 16777520, 0, 0, 26497},
+    {16777523, 17826097, 16777523, 0, 0, 26497},
+    {16777526, 17826100, 16777526, 0, 0, 26497},
+    {16777529, 17826103, 16777529, 0, 0, 26497},
+    {16777532, 17826106, 16777532, 0, 0, 26497},
+    {16777535, 17826109, 16777535, 0, 0, 26497},
+    {16777538, 17826112, 16777538, 0, 0, 26497},
+    {16777541, 17826115, 16777541, 0, 0, 26497},
+    {16777544, 17826118, 16777544, 0, 0, 26497},
+    {16777547, 17826121, 16777547, 0, 0, 26497},
+    {16777550, 17826124, 16777550, 0, 0, 26377},
+    {16777553, 17826127, 16777553, 0, 0, 26377},
+    {16777556, 17826130, 16777556, 0, 0, 26377},
+    {16777559, 17826133, 16777559, 0, 0, 26377},
+    {16777562, 17826136, 16777562, 0, 0, 26377},
+    {16777565, 17826139, 16777565, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 3840},
+    {0, 0, 0, 0, 0, 5888},
+    {16777568, 17826142, 16777568, 0, 0, 26377},
+    {16777571, 17826145, 16777571, 0, 0, 26377},
+    {16777574, 17826148, 16777574, 0, 0, 26377},
+    {16777577, 17826151, 16777577, 0, 0, 26377},
+    {16777580, 17826154, 16777580, 0, 0, 26377},
+    {16777583, 17826157, 16777583, 0, 0, 26377},
+    {16777586, 17826160, 16777586, 0, 0, 26377},
+    {16777589, 17826163, 16777589, 0, 0, 26377},
+    {16777592, 17826166, 16777592, 0, 0, 26377},
+    {0, -3008, 0, 0, 0, 10113},
+    {35332, 0, 35332, 0, 0, 9993},
+    {3814, 0, 3814, 0, 0, 9993},
+    {33554812, 18874745, 33554812, 0, 0, 26377},
+    {33554817, 18874750, 33554817, 0, 0, 26377},
+    {33554822, 18874755, 33554822, 0, 0, 26377},
+    {33554827, 18874760, 33554827, 0, 0, 26377},
+    {33554832, 18874765, 33554832, 0, 0, 26377},
+    {16777620, 17826194, 16777620, 0, 0, 26377},
+    {16777624, 18874773, 16777624, 0, 0, 26497},
+    {8, 0, 8, 0, 0, 9993},
+    {0, -8, 0, 0, 0, 10113},
+    {33554844, 18874777, 33554844, 0, 0, 26377},
+    {50332066, 19923358, 50332066, 0, 0, 26377},
+    {50332073, 19923365, 50332073, 0, 0, 26377},
+    {50332080, 19923372, 50332080, 0, 0, 26377},
+    {74, 0, 74, 0, 0, 9993},
+    {86, 0, 86, 0, 0, 9993},
+    {100, 0, 100, 0, 0, 9993},
+    {128, 0, 128, 0, 0, 9993},
+    {112, 0, 112, 0, 0, 9993},
+    {126, 0, 126, 0, 0, 9993},
+    {33554870, 18874803, 16777656, 0, 0, 26377},
+    {33554876, 18874809, 16777662, 0, 0, 26377},
+    {33554882, 18874815, 16777668, 0, 0, 26377},
+    {33554888, 18874821, 16777674, 0, 0, 26377},
+    {33554894, 18874827, 16777680, 0, 0, 26377},
+    {33554900, 18874833, 16777686, 0, 0, 26377},
+    {33554906, 18874839, 16777692, 0, 0, 26377},
+    {33554912, 18874845, 16777698, 0, 0, 26377},
+    {33554918, 18874851, 16777704, 0, 0, 26433},
+    {33554924, 18874857, 16777710, 0, 0, 26433},
+    {33554930, 18874863, 16777716, 0, 0, 26433},
+    {33554936, 18874869, 16777722, 0, 0, 26433},
+    {33554942, 18874875, 16777728, 0, 0, 26433},
+    {33554948, 18874881, 16777734, 0, 0, 26433},
+    {33554954, 18874887, 16777740, 0, 0, 26433},
+    {33554960, 18874893, 16777746, 0, 0, 26433},
+    {33554966, 18874899, 16777752, 0, 0, 26377},
+    {33554972, 18874905, 16777758, 0, 0, 26377},
+    {33554978, 18874911, 16777764, 0, 0, 26377},
+    {33554984, 18874917, 16777770, 0, 0, 26377},
+    {33554990, 18874923, 16777776, 0, 0, 26377},
+    {33554996, 18874929, 16777782, 0, 0, 26377},
+    {33555002, 18874935, 16777788, 0, 0, 26377},
+    {33555008, 18874941, 16777794, 0, 0, 26377},
+    {33555014, 18874947, 16777800, 0, 0, 26433},
+    {33555020, 18874953, 16777806, 0, 0, 26433},
+    {33555026, 18874959, 16777812, 0, 0, 26433},
+    {33555032, 18874965, 16777818, 0, 0, 26433},
+    {33555038, 18874971, 16777824, 0, 0, 26433},
+    {33555044, 18874977, 16777830, 0, 0, 26433},
+    {33555050, 18874983, 16777836, 0, 0, 26433},
+    {33555056, 18874989, 16777842, 0, 0, 26433},
+    {33555062, 18874995, 16777848, 0, 0, 26377},
+    {33555068, 18875001, 16777854, 0, 0, 26377},
+    {33555074, 18875007, 16777860, 0, 0, 26377},
+    {33555080, 18875013, 16777866, 0, 0, 26377},
+    {33555086, 18875019, 16777872, 0, 0, 26377},
+    {33555092, 18875025, 16777878, 0, 0, 26377},
+    {33555098, 18875031, 16777884, 0, 0, 26377},
+    {33555104, 18875037, 16777890, 0, 0, 26377},
+    {33555110, 18875043, 16777896, 0, 0, 26433},
+    {33555116, 18875049, 16777902, 0, 0, 26433},
+    {33555122, 18875055, 16777908, 0, 0, 26433},
+    {33555128, 18875061, 16777914, 0, 0, 26433},
+    {33555134, 18875067, 16777920, 0, 0, 26433},
+    {33555140, 18875073, 16777926, 0, 0, 26433},
+    {33555146, 18875079, 16777932, 0, 0, 26433},
+    {33555152, 18875085, 16777938, 0, 0, 26433},
+    {33555158, 18875091, 33555160, 0, 0, 26377},
+    {33555165, 18875098, 16777951, 0, 0, 26377},
+    {33555171, 18875104, 33555173, 0, 0, 26377},
+    {33555178, 18875111, 33555178, 0, 0, 26377},
+    {50332400, 19923692, 50332403, 0, 0, 26377},
+    {0, -74, 0, 0, 0, 10113},
+    {33555193, 18875126, 16777979, 0, 0, 26433},
+    {16777982, 17826556, 16777982, 0, 0, 26377},
+    {33555202, 18875135, 33555204, 0, 0, 26377},
+    {33555209, 18875142, 16777995, 0, 0, 26377},
+    {33555215, 18875148, 33555217, 0, 0, 26377},
+    {33555222, 18875155, 33555222, 0, 0, 26377},
+    {50332444, 19923736, 50332447, 0, 0, 26377},
+    {0, -86, 0, 0, 0, 10113},
+    {33555237, 18875170, 16778023, 0, 0, 26433},
+    {50332460, 19923752, 50332460, 0, 0, 26377},
+    {50332467, 19923759, 50332467, 0, 0, 26377},
+    {33555257, 18875190, 33555257, 0, 0, 26377},
+    {50332479, 19923771, 50332479, 0, 0, 26377},
+    {0, -100, 0, 0, 0, 10113},
+    {50332486, 19923778, 50332486, 0, 0, 26377},
+    {50332493, 19923785, 50332493, 0, 0, 26377},
+    {33555283, 18875216, 33555283, 0, 0, 26377},
+    {33555288, 18875221, 33555288, 0, 0, 26377},
+    {50332510, 19923802, 50332510, 0, 0, 26377},
+    {0, -112, 0, 0, 0, 10113},
+    {33555300, 18875233, 33555302, 0, 0, 26377},
+    {33555307, 18875240, 16778093, 0, 0, 26377},
+    {33555313, 18875246, 33555315, 0, 0, 26377},
+    {33555320, 18875253, 33555320, 0, 0, 26377},
+    {50332542, 19923834, 50332545, 0, 0, 26377},
+    {0, -128, 0, 0, 0, 10113},
+    {0, -126, 0, 0, 0, 10113},
+    {33555335, 18875268, 16778121, 0, 0, 26433},
+    {0, 0, 0, 0, 0, 3076},
+    {0, 0, 0, 0, 4, 3076},
+    {0, 0, 0, 0, 5, 3076},
+    {0, 0, 0, 0, 6, 3076},
+    {0, 0, 0, 0, 7, 3076},
+    {0, 0, 0, 0, 8, 3076},
+    {0, 0, 0, 0, 9, 3076},
+    {0, 0, 0, 0, 0, 1792},
+    {0, -7517, 0, 0, 0, 10113},
+    {0, -8383, 0, 0, 0, 10113},
+    {0, -8262, 0, 0, 0, 10113},
+    {0, 28, 0, 0, 0, 10113},
+    {-28, 0, -28, 0, 0, 9993},
+    {0, 16, 0, 0, 0, 12160},
+    {-16, 0, -16, 0, 0, 12040},
+    {0, 26, 0, 0, 0, 9344},
+    {-26, 0, -26, 0, 0, 9224},
+    {0, -10743, 0, 0, 0, 10113},
+    {0, -3814, 0, 0, 0, 10113},
+    {0, -10727, 0, 0, 0, 10113},
+    {-10795, 0, -10795, 0, 0, 9993},
+    {-10792, 0, -10792, 0, 0, 9993},
+    {0, -10780, 0, 0, 0, 10113},
+    {0, -10749, 0, 0, 0, 10113},
+    {0, -10783, 0, 0, 0, 10113},
+    {0, -10782, 0, 0, 0, 10113},
+    {0, -10815, 0, 0, 0, 10113},
+    {-7264, 0, -7264, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 5121},
+    {0, 0, 0, 0, 0, 3841},
+    {0, -35332, 0, 0, 0, 10113},
+    {0, -42280, 0, 0, 0, 10113},
+    {0, -42308, 0, 0, 0, 10113},
+    {0, -42319, 0, 0, 0, 10113},
+    {0, -42315, 0, 0, 0, 10113},
+    {0, -42305, 0, 0, 0, 10113},
+    {0, -42258, 0, 0, 0, 10113},
+    {0, -42282, 0, 0, 0, 10113},
+    {0, -42261, 0, 0, 0, 10113},
+    {0, 928, 0, 0, 0, 10113},
+    {-928, 0, -928, 0, 0, 9993},
+    {16778124, 17826698, 16778124, 0, 0, 26377},
+    {16778127, 17826701, 16778127, 0, 0, 26377},
+    {16778130, 17826704, 16778130, 0, 0, 26377},
+    {16778133, 17826707, 16778133, 0, 0, 26377},
+    {16778136, 17826710, 16778136, 0, 0, 26377},
+    {16778139, 17826713, 16778139, 0, 0, 26377},
+    {16778142, 17826716, 16778142, 0, 0, 26377},
+    {16778145, 17826719, 16778145, 0, 0, 26377},
+    {16778148, 17826722, 16778148, 0, 0, 26377},
+    {16778151, 17826725, 16778151, 0, 0, 26377},
+    {16778154, 17826728, 16778154, 0, 0, 26377},
+    {16778157, 17826731, 16778157, 0, 0, 26377},
+    {16778160, 17826734, 16778160, 0, 0, 26377},
+    {16778163, 17826737, 16778163, 0, 0, 26377},
+    {16778166, 17826740, 16778166, 0, 0, 26377},
+    {16778169, 17826743, 16778169, 0, 0, 26377},
+    {16778172, 17826746, 16778172, 0, 0, 26377},
+    {16778175, 17826749, 16778175, 0, 0, 26377},
+    {16778178, 17826752, 16778178, 0, 0, 26377},
+    {16778181, 17826755, 16778181, 0, 0, 26377},
+    {16778184, 17826758, 16778184, 0, 0, 26377},
+    {16778187, 17826761, 16778187, 0, 0, 26377},
+    {16778190, 17826764, 16778190, 0, 0, 26377},
+    {16778193, 17826767, 16778193, 0, 0, 26377},
+    {16778196, 17826770, 16778196, 0, 0, 26377},
+    {16778199, 17826773, 16778199, 0, 0, 26377},
+    {16778202, 17826776, 16778202, 0, 0, 26377},
+    {16778205, 17826779, 16778205, 0, 0, 26377},
+    {16778208, 17826782, 16778208, 0, 0, 26377},
+    {16778211, 17826785, 16778211, 0, 0, 26377},
+    {16778214, 17826788, 16778214, 0, 0, 26377},
+    {16778217, 17826791, 16778217, 0, 0, 26377},
+    {16778220, 17826794, 16778220, 0, 0, 26377},
+    {16778223, 17826797, 16778223, 0, 0, 26377},
+    {16778226, 17826800, 16778226, 0, 0, 26377},
+    {16778229, 17826803, 16778229, 0, 0, 26377},
+    {16778232, 17826806, 16778232, 0, 0, 26377},
+    {16778235, 17826809, 16778235, 0, 0, 26377},
+    {16778238, 17826812, 16778238, 0, 0, 26377},
+    {16778241, 17826815, 16778241, 0, 0, 26377},
+    {16778244, 17826818, 16778244, 0, 0, 26377},
+    {16778247, 17826821, 16778247, 0, 0, 26377},
+    {16778250, 17826824, 16778250, 0, 0, 26377},
+    {16778253, 17826827, 16778253, 0, 0, 26377},
+    {16778256, 17826830, 16778256, 0, 0, 26377},
+    {16778259, 17826833, 16778259, 0, 0, 26377},
+    {16778262, 17826836, 16778262, 0, 0, 26377},
+    {16778265, 17826839, 16778265, 0, 0, 26377},
+    {16778268, 17826842, 16778268, 0, 0, 26377},
+    {16778271, 17826845, 16778271, 0, 0, 26377},
+    {16778274, 17826848, 16778274, 0, 0, 26377},
+    {16778277, 17826851, 16778277, 0, 0, 26377},
+    {16778280, 17826854, 16778280, 0, 0, 26377},
+    {16778283, 17826857, 16778283, 0, 0, 26377},
+    {16778286, 17826860, 16778286, 0, 0, 26377},
+    {16778289, 17826863, 16778289, 0, 0, 26377},
+    {16778292, 17826866, 16778292, 0, 0, 26377},
+    {16778295, 17826869, 16778295, 0, 0, 26377},
+    {16778298, 17826872, 16778298, 0, 0, 26377},
+    {16778301, 17826875, 16778301, 0, 0, 26377},
+    {16778304, 17826878, 16778304, 0, 0, 26377},
+    {16778307, 17826881, 16778307, 0, 0, 26377},
+    {16778310, 17826884, 16778310, 0, 0, 26377},
+    {16778313, 17826887, 16778313, 0, 0, 26377},
+    {16778316, 17826890, 16778316, 0, 0, 26377},
+    {16778319, 17826893, 16778319, 0, 0, 26377},
+    {16778322, 17826896, 16778322, 0, 0, 26377},
+    {16778325, 17826899, 16778325, 0, 0, 26377},
+    {16778328, 17826902, 16778328, 0, 0, 26377},
+    {16778331, 17826905, 16778331, 0, 0, 26377},
+    {16778334, 17826908, 16778334, 0, 0, 26377},
+    {16778337, 17826911, 16778337, 0, 0, 26377},
+    {16778340, 17826914, 16778340, 0, 0, 26377},
+    {16778343, 17826917, 16778343, 0, 0, 26377},
+    {16778346, 17826920, 16778346, 0, 0, 26377},
+    {16778349, 17826923, 16778349, 0, 0, 26377},
+    {16778352, 17826926, 16778352, 0, 0, 26377},
+    {16778355, 17826929, 16778355, 0, 0, 26377},
+    {16778358, 17826932, 16778358, 0, 0, 26377},
+    {16778361, 17826935, 16778361, 0, 0, 26377},
+    {33555581, 18875514, 33555583, 0, 0, 26377},
+    {33555588, 18875521, 33555590, 0, 0, 26377},
+    {33555595, 18875528, 33555597, 0, 0, 26377},
+    {50332819, 19924111, 50332822, 0, 0, 26377},
+    {50332829, 19924121, 50332832, 0, 0, 26377},
+    {33555622, 18875555, 33555624, 0, 0, 26377},
+    {33555629, 18875562, 33555631, 0, 0, 26377},
+    {33555636, 18875569, 33555638, 0, 0, 26377},
+    {33555643, 18875576, 33555645, 0, 0, 26377},
+    {33555650, 18875583, 33555652, 0, 0, 26377},
+    {33555657, 18875590, 33555659, 0, 0, 26377},
+    {33555664, 18875597, 33555666, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 1025},
+    {0, 0, 0, 0, 0, 5633},
+    {0, 40, 0, 0, 0, 10113},
+    {-40, 0, -40, 0, 0, 9993},
+    {0, 34, 0, 0, 0, 10113},
+    {-34, 0, -34, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 9344},
+};
+
+/* extended case mappings */
+
+const Py_UCS4 numba_PyUnicode_ExtendedCase[] = {
+    181,
+    956,
+    924,
+    223,
+    115,
+    115,
+    83,
+    83,
+    83,
+    115,
+    105,
+    775,
+    304,
+    329,
+    700,
+    110,
+    700,
+    78,
+    383,
+    115,
+    83,
+    496,
+    106,
+    780,
+    74,
+    780,
+    837,
+    953,
+    921,
+    912,
+    953,
+    776,
+    769,
+    921,
+    776,
+    769,
+    944,
+    965,
+    776,
+    769,
+    933,
+    776,
+    769,
+    962,
+    963,
+    931,
+    976,
+    946,
+    914,
+    977,
+    952,
+    920,
+    981,
+    966,
+    934,
+    982,
+    960,
+    928,
+    1008,
+    954,
+    922,
+    1009,
+    961,
+    929,
+    1013,
+    949,
+    917,
+    1415,
+    1381,
+    1410,
+    1333,
+    1362,
+    1333,
+    1410,
+    43888,
+    5024,
+    5024,
+    43889,
+    5025,
+    5025,
+    43890,
+    5026,
+    5026,
+    43891,
+    5027,
+    5027,
+    43892,
+    5028,
+    5028,
+    43893,
+    5029,
+    5029,
+    43894,
+    5030,
+    5030,
+    43895,
+    5031,
+    5031,
+    43896,
+    5032,
+    5032,
+    43897,
+    5033,
+    5033,
+    43898,
+    5034,
+    5034,
+    43899,
+    5035,
+    5035,
+    43900,
+    5036,
+    5036,
+    43901,
+    5037,
+    5037,
+    43902,
+    5038,
+    5038,
+    43903,
+    5039,
+    5039,
+    43904,
+    5040,
+    5040,
+    43905,
+    5041,
+    5041,
+    43906,
+    5042,
+    5042,
+    43907,
+    5043,
+    5043,
+    43908,
+    5044,
+    5044,
+    43909,
+    5045,
+    5045,
+    43910,
+    5046,
+    5046,
+    43911,
+    5047,
+    5047,
+    43912,
+    5048,
+    5048,
+    43913,
+    5049,
+    5049,
+    43914,
+    5050,
+    5050,
+    43915,
+    5051,
+    5051,
+    43916,
+    5052,
+    5052,
+    43917,
+    5053,
+    5053,
+    43918,
+    5054,
+    5054,
+    43919,
+    5055,
+    5055,
+    43920,
+    5056,
+    5056,
+    43921,
+    5057,
+    5057,
+    43922,
+    5058,
+    5058,
+    43923,
+    5059,
+    5059,
+    43924,
+    5060,
+    5060,
+    43925,
+    5061,
+    5061,
+    43926,
+    5062,
+    5062,
+    43927,
+    5063,
+    5063,
+    43928,
+    5064,
+    5064,
+    43929,
+    5065,
+    5065,
+    43930,
+    5066,
+    5066,
+    43931,
+    5067,
+    5067,
+    43932,
+    5068,
+    5068,
+    43933,
+    5069,
+    5069,
+    43934,
+    5070,
+    5070,
+    43935,
+    5071,
+    5071,
+    43936,
+    5072,
+    5072,
+    43937,
+    5073,
+    5073,
+    43938,
+    5074,
+    5074,
+    43939,
+    5075,
+    5075,
+    43940,
+    5076,
+    5076,
+    43941,
+    5077,
+    5077,
+    43942,
+    5078,
+    5078,
+    43943,
+    5079,
+    5079,
+    43944,
+    5080,
+    5080,
+    43945,
+    5081,
+    5081,
+    43946,
+    5082,
+    5082,
+    43947,
+    5083,
+    5083,
+    43948,
+    5084,
+    5084,
+    43949,
+    5085,
+    5085,
+    43950,
+    5086,
+    5086,
+    43951,
+    5087,
+    5087,
+    43952,
+    5088,
+    5088,
+    43953,
+    5089,
+    5089,
+    43954,
+    5090,
+    5090,
+    43955,
+    5091,
+    5091,
+    43956,
+    5092,
+    5092,
+    43957,
+    5093,
+    5093,
+    43958,
+    5094,
+    5094,
+    43959,
+    5095,
+    5095,
+    43960,
+    5096,
+    5096,
+    43961,
+    5097,
+    5097,
+    43962,
+    5098,
+    5098,
+    43963,
+    5099,
+    5099,
+    43964,
+    5100,
+    5100,
+    43965,
+    5101,
+    5101,
+    43966,
+    5102,
+    5102,
+    43967,
+    5103,
+    5103,
+    5112,
+    5104,
+    5104,
+    5113,
+    5105,
+    5105,
+    5114,
+    5106,
+    5106,
+    5115,
+    5107,
+    5107,
+    5116,
+    5108,
+    5108,
+    5117,
+    5109,
+    5109,
+    5112,
+    5104,
+    5104,
+    5113,
+    5105,
+    5105,
+    5114,
+    5106,
+    5106,
+    5115,
+    5107,
+    5107,
+    5116,
+    5108,
+    5108,
+    5117,
+    5109,
+    5109,
+    7296,
+    1074,
+    1042,
+    7297,
+    1076,
+    1044,
+    7298,
+    1086,
+    1054,
+    7299,
+    1089,
+    1057,
+    7300,
+    1090,
+    1058,
+    7301,
+    1090,
+    1058,
+    7302,
+    1098,
+    1066,
+    7303,
+    1123,
+    1122,
+    7304,
+    42571,
+    42570,
+    7830,
+    104,
+    817,
+    72,
+    817,
+    7831,
+    116,
+    776,
+    84,
+    776,
+    7832,
+    119,
+    778,
+    87,
+    778,
+    7833,
+    121,
+    778,
+    89,
+    778,
+    7834,
+    97,
+    702,
+    65,
+    702,
+    7835,
+    7777,
+    7776,
+    223,
+    115,
+    115,
+    7838,
+    8016,
+    965,
+    787,
+    933,
+    787,
+    8018,
+    965,
+    787,
+    768,
+    933,
+    787,
+    768,
+    8020,
+    965,
+    787,
+    769,
+    933,
+    787,
+    769,
+    8022,
+    965,
+    787,
+    834,
+    933,
+    787,
+    834,
+    8064,
+    7936,
+    953,
+    7944,
+    921,
+    8072,
+    8065,
+    7937,
+    953,
+    7945,
+    921,
+    8073,
+    8066,
+    7938,
+    953,
+    7946,
+    921,
+    8074,
+    8067,
+    7939,
+    953,
+    7947,
+    921,
+    8075,
+    8068,
+    7940,
+    953,
+    7948,
+    921,
+    8076,
+    8069,
+    7941,
+    953,
+    7949,
+    921,
+    8077,
+    8070,
+    7942,
+    953,
+    7950,
+    921,
+    8078,
+    8071,
+    7943,
+    953,
+    7951,
+    921,
+    8079,
+    8064,
+    7936,
+    953,
+    7944,
+    921,
+    8072,
+    8065,
+    7937,
+    953,
+    7945,
+    921,
+    8073,
+    8066,
+    7938,
+    953,
+    7946,
+    921,
+    8074,
+    8067,
+    7939,
+    953,
+    7947,
+    921,
+    8075,
+    8068,
+    7940,
+    953,
+    7948,
+    921,
+    8076,
+    8069,
+    7941,
+    953,
+    7949,
+    921,
+    8077,
+    8070,
+    7942,
+    953,
+    7950,
+    921,
+    8078,
+    8071,
+    7943,
+    953,
+    7951,
+    921,
+    8079,
+    8080,
+    7968,
+    953,
+    7976,
+    921,
+    8088,
+    8081,
+    7969,
+    953,
+    7977,
+    921,
+    8089,
+    8082,
+    7970,
+    953,
+    7978,
+    921,
+    8090,
+    8083,
+    7971,
+    953,
+    7979,
+    921,
+    8091,
+    8084,
+    7972,
+    953,
+    7980,
+    921,
+    8092,
+    8085,
+    7973,
+    953,
+    7981,
+    921,
+    8093,
+    8086,
+    7974,
+    953,
+    7982,
+    921,
+    8094,
+    8087,
+    7975,
+    953,
+    7983,
+    921,
+    8095,
+    8080,
+    7968,
+    953,
+    7976,
+    921,
+    8088,
+    8081,
+    7969,
+    953,
+    7977,
+    921,
+    8089,
+    8082,
+    7970,
+    953,
+    7978,
+    921,
+    8090,
+    8083,
+    7971,
+    953,
+    7979,
+    921,
+    8091,
+    8084,
+    7972,
+    953,
+    7980,
+    921,
+    8092,
+    8085,
+    7973,
+    953,
+    7981,
+    921,
+    8093,
+    8086,
+    7974,
+    953,
+    7982,
+    921,
+    8094,
+    8087,
+    7975,
+    953,
+    7983,
+    921,
+    8095,
+    8096,
+    8032,
+    953,
+    8040,
+    921,
+    8104,
+    8097,
+    8033,
+    953,
+    8041,
+    921,
+    8105,
+    8098,
+    8034,
+    953,
+    8042,
+    921,
+    8106,
+    8099,
+    8035,
+    953,
+    8043,
+    921,
+    8107,
+    8100,
+    8036,
+    953,
+    8044,
+    921,
+    8108,
+    8101,
+    8037,
+    953,
+    8045,
+    921,
+    8109,
+    8102,
+    8038,
+    953,
+    8046,
+    921,
+    8110,
+    8103,
+    8039,
+    953,
+    8047,
+    921,
+    8111,
+    8096,
+    8032,
+    953,
+    8040,
+    921,
+    8104,
+    8097,
+    8033,
+    953,
+    8041,
+    921,
+    8105,
+    8098,
+    8034,
+    953,
+    8042,
+    921,
+    8106,
+    8099,
+    8035,
+    953,
+    8043,
+    921,
+    8107,
+    8100,
+    8036,
+    953,
+    8044,
+    921,
+    8108,
+    8101,
+    8037,
+    953,
+    8045,
+    921,
+    8109,
+    8102,
+    8038,
+    953,
+    8046,
+    921,
+    8110,
+    8103,
+    8039,
+    953,
+    8047,
+    921,
+    8111,
+    8114,
+    8048,
+    953,
+    8122,
+    921,
+    8122,
+    837,
+    8115,
+    945,
+    953,
+    913,
+    921,
+    8124,
+    8116,
+    940,
+    953,
+    902,
+    921,
+    902,
+    837,
+    8118,
+    945,
+    834,
+    913,
+    834,
+    8119,
+    945,
+    834,
+    953,
+    913,
+    834,
+    921,
+    913,
+    834,
+    837,
+    8115,
+    945,
+    953,
+    913,
+    921,
+    8124,
+    8126,
+    953,
+    921,
+    8130,
+    8052,
+    953,
+    8138,
+    921,
+    8138,
+    837,
+    8131,
+    951,
+    953,
+    919,
+    921,
+    8140,
+    8132,
+    942,
+    953,
+    905,
+    921,
+    905,
+    837,
+    8134,
+    951,
+    834,
+    919,
+    834,
+    8135,
+    951,
+    834,
+    953,
+    919,
+    834,
+    921,
+    919,
+    834,
+    837,
+    8131,
+    951,
+    953,
+    919,
+    921,
+    8140,
+    8146,
+    953,
+    776,
+    768,
+    921,
+    776,
+    768,
+    8147,
+    953,
+    776,
+    769,
+    921,
+    776,
+    769,
+    8150,
+    953,
+    834,
+    921,
+    834,
+    8151,
+    953,
+    776,
+    834,
+    921,
+    776,
+    834,
+    8162,
+    965,
+    776,
+    768,
+    933,
+    776,
+    768,
+    8163,
+    965,
+    776,
+    769,
+    933,
+    776,
+    769,
+    8164,
+    961,
+    787,
+    929,
+    787,
+    8166,
+    965,
+    834,
+    933,
+    834,
+    8167,
+    965,
+    776,
+    834,
+    933,
+    776,
+    834,
+    8178,
+    8060,
+    953,
+    8186,
+    921,
+    8186,
+    837,
+    8179,
+    969,
+    953,
+    937,
+    921,
+    8188,
+    8180,
+    974,
+    953,
+    911,
+    921,
+    911,
+    837,
+    8182,
+    969,
+    834,
+    937,
+    834,
+    8183,
+    969,
+    834,
+    953,
+    937,
+    834,
+    921,
+    937,
+    834,
+    837,
+    8179,
+    969,
+    953,
+    937,
+    921,
+    8188,
+    43888,
+    5024,
+    5024,
+    43889,
+    5025,
+    5025,
+    43890,
+    5026,
+    5026,
+    43891,
+    5027,
+    5027,
+    43892,
+    5028,
+    5028,
+    43893,
+    5029,
+    5029,
+    43894,
+    5030,
+    5030,
+    43895,
+    5031,
+    5031,
+    43896,
+    5032,
+    5032,
+    43897,
+    5033,
+    5033,
+    43898,
+    5034,
+    5034,
+    43899,
+    5035,
+    5035,
+    43900,
+    5036,
+    5036,
+    43901,
+    5037,
+    5037,
+    43902,
+    5038,
+    5038,
+    43903,
+    5039,
+    5039,
+    43904,
+    5040,
+    5040,
+    43905,
+    5041,
+    5041,
+    43906,
+    5042,
+    5042,
+    43907,
+    5043,
+    5043,
+    43908,
+    5044,
+    5044,
+    43909,
+    5045,
+    5045,
+    43910,
+    5046,
+    5046,
+    43911,
+    5047,
+    5047,
+    43912,
+    5048,
+    5048,
+    43913,
+    5049,
+    5049,
+    43914,
+    5050,
+    5050,
+    43915,
+    5051,
+    5051,
+    43916,
+    5052,
+    5052,
+    43917,
+    5053,
+    5053,
+    43918,
+    5054,
+    5054,
+    43919,
+    5055,
+    5055,
+    43920,
+    5056,
+    5056,
+    43921,
+    5057,
+    5057,
+    43922,
+    5058,
+    5058,
+    43923,
+    5059,
+    5059,
+    43924,
+    5060,
+    5060,
+    43925,
+    5061,
+    5061,
+    43926,
+    5062,
+    5062,
+    43927,
+    5063,
+    5063,
+    43928,
+    5064,
+    5064,
+    43929,
+    5065,
+    5065,
+    43930,
+    5066,
+    5066,
+    43931,
+    5067,
+    5067,
+    43932,
+    5068,
+    5068,
+    43933,
+    5069,
+    5069,
+    43934,
+    5070,
+    5070,
+    43935,
+    5071,
+    5071,
+    43936,
+    5072,
+    5072,
+    43937,
+    5073,
+    5073,
+    43938,
+    5074,
+    5074,
+    43939,
+    5075,
+    5075,
+    43940,
+    5076,
+    5076,
+    43941,
+    5077,
+    5077,
+    43942,
+    5078,
+    5078,
+    43943,
+    5079,
+    5079,
+    43944,
+    5080,
+    5080,
+    43945,
+    5081,
+    5081,
+    43946,
+    5082,
+    5082,
+    43947,
+    5083,
+    5083,
+    43948,
+    5084,
+    5084,
+    43949,
+    5085,
+    5085,
+    43950,
+    5086,
+    5086,
+    43951,
+    5087,
+    5087,
+    43952,
+    5088,
+    5088,
+    43953,
+    5089,
+    5089,
+    43954,
+    5090,
+    5090,
+    43955,
+    5091,
+    5091,
+    43956,
+    5092,
+    5092,
+    43957,
+    5093,
+    5093,
+    43958,
+    5094,
+    5094,
+    43959,
+    5095,
+    5095,
+    43960,
+    5096,
+    5096,
+    43961,
+    5097,
+    5097,
+    43962,
+    5098,
+    5098,
+    43963,
+    5099,
+    5099,
+    43964,
+    5100,
+    5100,
+    43965,
+    5101,
+    5101,
+    43966,
+    5102,
+    5102,
+    43967,
+    5103,
+    5103,
+    64256,
+    102,
+    102,
+    70,
+    70,
+    70,
+    102,
+    64257,
+    102,
+    105,
+    70,
+    73,
+    70,
+    105,
+    64258,
+    102,
+    108,
+    70,
+    76,
+    70,
+    108,
+    64259,
+    102,
+    102,
+    105,
+    70,
+    70,
+    73,
+    70,
+    102,
+    105,
+    64260,
+    102,
+    102,
+    108,
+    70,
+    70,
+    76,
+    70,
+    102,
+    108,
+    64261,
+    115,
+    116,
+    83,
+    84,
+    83,
+    116,
+    64262,
+    115,
+    116,
+    83,
+    84,
+    83,
+    116,
+    64275,
+    1396,
+    1398,
+    1348,
+    1350,
+    1348,
+    1398,
+    64276,
+    1396,
+    1381,
+    1348,
+    1333,
+    1348,
+    1381,
+    64277,
+    1396,
+    1387,
+    1348,
+    1339,
+    1348,
+    1387,
+    64278,
+    1406,
+    1398,
+    1358,
+    1350,
+    1358,
+    1398,
+    64279,
+    1396,
+    1389,
+    1348,
+    1341,
+    1348,
+    1389,
+};
+
+/* type indexes */
+#define SHIFT 7
+static unsigned short index1[] = {
+    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
+    21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 34, 35, 36, 37,
+    38, 39, 34, 34, 34, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
+    53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 64, 64, 64, 65, 66, 64,
+    64, 64, 64, 67, 68, 64, 64, 64, 64, 64, 64, 69, 70, 71, 72, 73, 74, 75,
+    76, 64, 77, 78, 79, 80, 81, 82, 83, 64, 64, 84, 85, 34, 34, 34, 34, 34,
+    34, 86, 34, 34, 34, 34, 34, 87, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 88, 89, 90, 91, 34, 34, 34, 92, 34, 34,
+    34, 93, 94, 34, 34, 34, 34, 34, 95, 34, 34, 34, 96, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 97, 98, 99, 34, 34, 34, 34, 34, 34, 100, 101, 34, 34,
+    34, 34, 34, 34, 34, 34, 102, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 103, 34, 34, 34, 34, 34, 34, 34, 34, 104, 34, 34, 34, 34,
+    100, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 103, 34, 34, 34, 34, 34, 34, 105, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 106, 107, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 108, 109, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 110, 111, 34, 34, 34, 34, 34,
+    34, 34, 34, 112, 34, 34, 113, 114, 115, 116, 117, 118, 119, 120, 121,
+    122, 123, 124, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 125, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 127, 128, 129,
+    130, 131, 132, 133, 34, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,
+    144, 145, 146, 147, 148, 149, 150, 144, 34, 34, 151, 144, 152, 153, 154,
+    155, 156, 157, 158, 159, 160, 161, 162, 144, 163, 144, 164, 144, 165,
+    166, 167, 168, 169, 170, 171, 144, 172, 173, 144, 174, 175, 176, 177,
+    144, 178, 179, 144, 144, 180, 181, 144, 144, 182, 183, 184, 185, 144,
+    186, 144, 144, 34, 34, 34, 34, 34, 34, 34, 187, 188, 34, 189, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 34, 34, 34, 34, 34, 34, 34, 34, 190, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 34, 34, 34, 34, 191, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 34, 34, 34, 34, 192, 193, 194, 195, 144, 144, 144, 144, 196,
+    197, 198, 199, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 200, 34, 34,
+    34, 34, 34, 201, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 34, 34, 202, 34, 34, 203, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 204, 205, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 64,
+    206, 207, 208, 209, 210, 211, 144, 212, 213, 214, 215, 216, 217, 218,
+    219, 64, 64, 64, 64, 220, 221, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 222, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 34, 223, 224, 144, 144, 144, 144, 144, 225, 226, 144,
+    144, 227, 228, 144, 144, 229, 230, 231, 232, 233, 144, 64, 234, 64, 64,
+    64, 64, 64, 235, 236, 237, 238, 239, 240, 241, 242, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 243, 244, 245, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 86, 246, 34, 247, 248, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 249, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 250, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 251, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 252, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 253, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 254, 34,
+    255, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 256, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 257, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 34, 249, 34, 34, 258, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 259, 144,
+    260, 261, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 262,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 262,
+};
+
+static unsigned short index2[] = {
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 3, 3, 3, 2, 4, 5, 5, 5, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 6, 5,
+    7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 6, 5, 5, 5, 5, 5, 5, 17, 17, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 5, 5, 5, 6, 18, 6, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 5, 5,
+    5, 5, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 5, 5, 5, 5, 5, 5, 5, 6, 5, 20, 5, 5,
+    21, 5, 6, 5, 5, 22, 23, 6, 24, 5, 25, 6, 26, 20, 5, 27, 27, 27, 5, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 5, 17, 17, 17, 17, 17, 17, 17, 28, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    5, 19, 19, 19, 19, 19, 19, 19, 29, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 32, 33, 30, 31, 30, 31, 30, 31, 20, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 34, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 35, 30, 31, 30, 31, 30, 31, 36, 37, 38, 30, 31, 30, 31, 39,
+    30, 31, 40, 40, 30, 31, 20, 41, 42, 43, 30, 31, 40, 44, 45, 46, 47, 30,
+    31, 48, 20, 46, 49, 50, 51, 30, 31, 30, 31, 30, 31, 52, 30, 31, 52, 20,
+    20, 30, 31, 52, 30, 31, 53, 53, 30, 31, 30, 31, 54, 30, 31, 20, 55, 30,
+    31, 20, 56, 55, 55, 55, 55, 57, 58, 59, 57, 58, 59, 57, 58, 59, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 60, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 61, 57, 58,
+    59, 30, 31, 62, 63, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 64, 20, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 20, 20, 20, 20, 20, 20, 65,
+    30, 31, 66, 67, 68, 68, 30, 31, 69, 70, 71, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 72, 73, 74, 75, 76, 20, 77, 77, 20, 78, 20, 79, 80, 20, 20,
+    20, 77, 81, 20, 82, 20, 83, 84, 20, 85, 86, 84, 87, 88, 20, 20, 86, 20,
+    89, 90, 20, 20, 91, 20, 20, 20, 20, 20, 20, 20, 92, 20, 20, 93, 20, 20,
+    93, 20, 20, 20, 94, 93, 95, 96, 96, 97, 20, 20, 20, 20, 20, 98, 20, 55,
+    20, 20, 20, 20, 20, 20, 20, 20, 99, 100, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 101, 101, 101, 101, 101, 101, 101,
+    101, 101, 102, 102, 102, 102, 102, 102, 102, 101, 101, 6, 6, 6, 6, 102,
+    102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 6, 6, 6, 6, 6, 6,
+    6, 6, 6, 6, 6, 6, 6, 6, 101, 101, 101, 101, 101, 6, 6, 6, 6, 6, 6, 6,
+    102, 6, 102, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 103, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 30, 31, 30, 31, 102, 6, 30, 31, 0, 0, 104, 50, 50, 50, 5, 105, 0,
+    0, 0, 0, 6, 6, 106, 25, 107, 107, 107, 0, 108, 0, 109, 109, 110, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 0, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 111, 112, 112, 112, 113, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 114, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 115, 116, 116, 117, 118, 119, 120, 120, 120, 121, 122,
+    123, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 124, 125, 126, 127, 128, 129, 5, 30, 31, 130,
+    30, 31, 20, 64, 64, 64, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131,
+    131, 131, 131, 131, 131, 131, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 132,
+    132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132,
+    132, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 5,
+    25, 25, 25, 25, 25, 6, 6, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 133, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 134, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 0, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 0, 0, 102, 5, 5, 5, 5, 5, 5, 20, 136, 136, 136, 136,
+    136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
+    136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
+    136, 136, 136, 136, 136, 136, 137, 20, 5, 5, 0, 0, 5, 5, 5, 0, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 5, 25, 5, 25, 25, 5, 25, 25, 5, 25, 0, 0, 0,
+    0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 55, 55,
+    55, 55, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 21, 21, 21, 21, 21, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 5,
+    21, 0, 5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 102,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 7, 8, 9, 10, 11, 12,
+    13, 14, 15, 16, 5, 5, 5, 5, 55, 55, 25, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 5, 55, 25, 25, 25, 25, 25, 25, 25, 21, 5, 25, 25, 25, 25, 25, 25,
+    102, 102, 25, 25, 5, 25, 25, 25, 25, 55, 55, 7, 8, 9, 10, 11, 12, 13, 14,
+    15, 16, 55, 55, 55, 5, 5, 55, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    0, 21, 55, 25, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13,
+    14, 15, 16, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 102, 102, 5, 5, 5, 5, 102, 0, 0, 25,
+    5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 25, 25, 25, 25, 102, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 102, 25, 25, 25, 102, 25, 25, 25, 25, 25, 0, 0, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 0, 0,
+    5, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 21, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 18, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 25, 18, 25, 55, 18, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25, 18,
+    18, 18, 18, 25, 18, 18, 55, 25, 25, 25, 25, 25, 25, 25, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 25, 25, 5, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 5, 102, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    25, 18, 18, 0, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 0, 0, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 0, 0, 0, 55, 55, 55,
+    55, 0, 0, 25, 55, 18, 18, 18, 25, 25, 25, 25, 0, 0, 18, 18, 0, 0, 18, 18,
+    25, 55, 0, 0, 0, 0, 0, 0, 0, 0, 18, 0, 0, 0, 0, 55, 55, 0, 55, 55, 55,
+    25, 25, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 55, 55, 5, 5, 27, 27,
+    27, 27, 27, 27, 5, 5, 55, 5, 25, 0, 0, 25, 25, 18, 0, 55, 55, 55, 55, 55,
+    55, 0, 0, 0, 0, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55,
+    55, 0, 55, 55, 0, 55, 55, 0, 55, 55, 0, 0, 25, 0, 18, 18, 18, 25, 25, 0,
+    0, 0, 0, 25, 25, 0, 0, 25, 25, 25, 0, 0, 0, 25, 0, 0, 0, 0, 0, 0, 0, 55,
+    55, 55, 55, 0, 55, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 25, 25, 55, 55, 55, 25, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25, 18,
+    0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 0, 55, 55, 55, 55, 55, 0, 0,
+    25, 55, 18, 18, 18, 25, 25, 25, 25, 25, 0, 25, 25, 18, 0, 18, 18, 25, 0,
+    0, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 25, 25, 0, 0,
+    7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 5, 5, 0, 0, 0, 0, 0, 0, 0, 55, 25,
+    25, 25, 25, 25, 25, 0, 25, 18, 18, 0, 55, 55, 55, 55, 55, 55, 55, 55, 0,
+    0, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55,
+    0, 55, 55, 55, 55, 55, 0, 0, 25, 55, 18, 25, 18, 25, 25, 25, 25, 0, 0,
+    18, 18, 0, 0, 18, 18, 25, 0, 0, 0, 0, 0, 0, 0, 0, 25, 18, 0, 0, 0, 0, 55,
+    55, 0, 55, 55, 55, 25, 25, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 5,
+    55, 27, 27, 27, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 55, 0, 55,
+    55, 55, 55, 55, 55, 0, 0, 0, 55, 55, 55, 0, 55, 55, 55, 55, 0, 0, 0, 55,
+    55, 0, 55, 0, 55, 55, 0, 0, 0, 55, 55, 0, 0, 0, 55, 55, 55, 0, 0, 0, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 18, 18, 25, 18,
+    18, 0, 0, 0, 18, 18, 18, 0, 18, 18, 18, 25, 0, 0, 55, 0, 0, 0, 0, 0, 0,
+    18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13,
+    14, 15, 16, 27, 27, 27, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 25, 18,
+    18, 18, 25, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 0, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 0, 0, 55, 25, 25, 25, 18, 18, 18, 18, 0, 25, 25, 25, 0, 25, 25,
+    25, 25, 0, 0, 0, 0, 0, 0, 0, 25, 25, 0, 55, 55, 55, 0, 0, 0, 0, 0, 55,
+    55, 25, 25, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0, 0, 0, 0, 0, 0,
+    0, 0, 27, 27, 27, 27, 27, 27, 27, 5, 55, 25, 18, 18, 5, 55, 55, 55, 55,
+    55, 55, 55, 55, 0, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 0, 0, 25, 55, 18, 25, 18,
+    18, 18, 18, 18, 0, 25, 18, 18, 0, 18, 18, 25, 25, 0, 0, 0, 0, 0, 0, 0,
+    18, 18, 0, 0, 0, 0, 0, 0, 0, 55, 0, 55, 55, 25, 25, 0, 0, 7, 8, 9, 10,
+    11, 12, 13, 14, 15, 16, 0, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    25, 25, 18, 18, 0, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 0, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 25, 25, 55, 18, 18, 18, 25, 25, 25, 25, 0, 18, 18, 18, 0,
+    18, 18, 18, 25, 55, 5, 0, 0, 0, 0, 55, 55, 55, 18, 27, 27, 27, 27, 27,
+    27, 27, 55, 55, 55, 25, 25, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 5, 55, 55, 55, 55, 55, 55, 0, 0, 18,
+    18, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 0, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 25, 0, 0, 0, 0,
+    18, 18, 18, 25, 25, 25, 0, 25, 0, 18, 18, 18, 18, 18, 18, 18, 18, 0, 0,
+    0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0, 0, 18, 18, 5, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 25, 55, 138, 25, 25, 25, 25, 25, 25, 25, 0, 0, 0, 0, 5, 55, 55, 55,
+    55, 55, 55, 102, 25, 25, 25, 25, 25, 25, 25, 25, 5, 7, 8, 9, 10, 11, 12,
+    13, 14, 15, 16, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 0,
+    55, 0, 0, 55, 55, 0, 55, 0, 0, 55, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 0,
+    55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 0, 55, 0, 55, 0, 0, 55, 55, 0,
+    55, 55, 55, 55, 25, 55, 138, 25, 25, 25, 25, 25, 25, 0, 25, 25, 55, 0, 0,
+    55, 55, 55, 55, 55, 0, 102, 0, 25, 25, 25, 25, 25, 25, 0, 0, 7, 8, 9, 10,
+    11, 12, 13, 14, 15, 16, 0, 0, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 25,
+    25, 5, 5, 5, 5, 5, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 5, 25, 5, 25, 5, 25, 5, 5, 5, 5, 18, 18, 55,
+    55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 18, 25, 25, 25, 25, 25, 5, 25, 25, 55, 55,
+    55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 0, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 0, 5, 5, 5,
+    5, 5, 5, 5, 5, 25, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    18, 18, 25, 25, 25, 25, 18, 25, 25, 25, 25, 25, 25, 18, 25, 25, 18, 18,
+    25, 25, 55, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 5, 5, 5, 5, 5, 5, 55,
+    55, 55, 55, 55, 55, 18, 18, 25, 25, 55, 55, 55, 55, 25, 25, 25, 55, 18,
+    18, 18, 55, 55, 18, 18, 18, 18, 18, 18, 18, 55, 55, 55, 25, 25, 25, 25,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 18, 18, 25, 25,
+    18, 18, 18, 18, 18, 18, 25, 55, 18, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+    18, 18, 18, 25, 5, 5, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139,
+    139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139,
+    139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 0,
+    139, 0, 0, 0, 0, 0, 139, 0, 0, 140, 140, 140, 140, 140, 140, 140, 140,
+    140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140,
+    140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140,
+    140, 140, 140, 140, 140, 140, 140, 5, 102, 140, 140, 140, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0,
+    55, 55, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 0, 55, 55, 55,
+    55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 0, 0, 55, 55,
+    55, 55, 55, 55, 55, 0, 55, 0, 55, 55, 55, 55, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55,
+    55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 25, 25,
+    25, 5, 5, 5, 5, 5, 5, 5, 5, 5, 141, 142, 143, 144, 145, 146, 147, 148,
+    149, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 0, 0, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 0, 0, 0, 0, 0, 0, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
+    160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,
+    174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187,
+    188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201,
+    202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215,
+    216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
+    230, 231, 232, 233, 234, 235, 0, 0, 236, 237, 238, 239, 240, 241, 0, 0,
+    5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 2, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5, 5, 0, 0, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 5, 5, 5, 242, 242, 242, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0,
+    0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55,
+    55, 55, 25, 25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 5, 5, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 0, 25, 25,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 25, 25, 18, 25, 25, 25, 25, 25, 25, 25, 18,
+    18, 18, 18, 18, 18, 18, 18, 25, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 5, 5, 5, 102, 5, 5, 5, 5, 55, 25, 0, 0, 7, 8, 9, 10, 11, 12,
+    13, 14, 15, 16, 0, 0, 0, 0, 0, 0, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 25, 25, 25, 21, 0, 7,
+    8, 9, 10, 11, 12, 13, 14, 15, 16, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 102, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 55,
+    55, 55, 55, 55, 243, 243, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 25, 55, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 25, 25, 25, 18, 18, 18, 18,
+    25, 25, 18, 18, 18, 0, 0, 0, 0, 18, 18, 25, 18, 18, 18, 18, 18, 18, 25,
+    25, 25, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 55, 55,
+    55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0,
+    0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11,
+    12, 13, 14, 15, 16, 141, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 25, 25, 18, 18, 25, 0, 0, 5, 5, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 18, 25, 18, 25, 25, 25, 25, 25,
+    25, 25, 0, 25, 18, 25, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25, 18, 18,
+    18, 18, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 0, 0, 25, 7, 8,
+    9, 10, 11, 12, 13, 14, 15, 16, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13,
+    14, 15, 16, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 102, 5, 5, 5, 5, 5, 5,
+    0, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 6, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25, 25, 25, 18, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 25, 18, 25, 25, 25, 25, 25, 18, 25, 18,
+    18, 18, 18, 18, 25, 18, 18, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 7, 8,
+    9, 10, 11, 12, 13, 14, 15, 16, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 25, 25, 25, 25, 25, 25, 25, 25, 25, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    0, 0, 0, 25, 25, 18, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 18,
+    25, 25, 25, 25, 18, 18, 25, 25, 18, 25, 25, 25, 55, 55, 7, 8, 9, 10, 11,
+    12, 13, 14, 15, 16, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 18, 25, 25, 18,
+    18, 18, 25, 18, 25, 25, 25, 18, 18, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    18, 18, 18, 18, 18, 18, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25, 18, 18,
+    25, 25, 0, 0, 0, 5, 5, 5, 5, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0,
+    0, 0, 55, 55, 55, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 102, 102, 102, 102, 102, 102, 5, 5, 244,
+    245, 246, 247, 248, 249, 250, 251, 252, 0, 0, 0, 0, 0, 0, 0, 253, 253,
+    253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253,
+    253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253,
+    253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 253, 0, 0,
+    253, 253, 253, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25,
+    25, 5, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 18, 25, 25,
+    25, 25, 25, 25, 25, 55, 55, 55, 55, 25, 55, 55, 55, 55, 18, 18, 25, 55,
+    55, 18, 25, 25, 0, 0, 0, 0, 0, 0, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 101, 101,
+    101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101,
+    101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101,
+    101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101,
+    101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101,
+    101, 101, 101, 101, 101, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 101, 254, 20, 20, 20, 255, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101,
+    101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101,
+    101, 101, 101, 101, 101, 101, 101, 101, 101, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 0, 25, 25,
+    25, 25, 25, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 256, 257, 258, 259, 260, 261, 20, 20,
+    262, 20, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 263, 263, 263, 263, 263, 263, 263, 263,
+    264, 264, 264, 264, 264, 264, 264, 264, 263, 263, 263, 263, 263, 263, 0,
+    0, 264, 264, 264, 264, 264, 264, 0, 0, 263, 263, 263, 263, 263, 263, 263,
+    263, 264, 264, 264, 264, 264, 264, 264, 264, 263, 263, 263, 263, 263,
+    263, 263, 263, 264, 264, 264, 264, 264, 264, 264, 264, 263, 263, 263,
+    263, 263, 263, 0, 0, 264, 264, 264, 264, 264, 264, 0, 0, 265, 263, 266,
+    263, 267, 263, 268, 263, 0, 264, 0, 264, 0, 264, 0, 264, 263, 263, 263,
+    263, 263, 263, 263, 263, 264, 264, 264, 264, 264, 264, 264, 264, 269,
+    269, 270, 270, 270, 270, 271, 271, 272, 272, 273, 273, 274, 274, 0, 0,
+    275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288,
+    289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302,
+    303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316,
+    317, 318, 319, 320, 321, 322, 263, 263, 323, 324, 325, 0, 326, 327, 264,
+    264, 328, 328, 329, 6, 330, 6, 6, 6, 331, 332, 333, 0, 334, 335, 336,
+    336, 336, 336, 337, 6, 6, 6, 263, 263, 338, 339, 0, 0, 340, 341, 264,
+    264, 342, 342, 0, 6, 6, 6, 263, 263, 343, 344, 345, 126, 346, 347, 264,
+    264, 348, 348, 130, 6, 6, 6, 0, 0, 349, 350, 351, 0, 352, 353, 354, 354,
+    355, 355, 356, 6, 6, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 21, 21, 21, 21,
+    21, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 5, 5,
+    6, 3, 3, 21, 21, 21, 21, 21, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 18, 18, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 18,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 21, 21, 21, 21, 21, 0, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 357, 101, 0, 0, 358, 359, 360, 361, 362, 363, 5,
+    5, 5, 5, 5, 101, 357, 26, 22, 23, 358, 359, 360, 361, 362, 363, 5, 5, 5,
+    5, 5, 0, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101,
+    0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 6, 6, 6, 6,
+    25, 6, 6, 6, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 120, 5, 5, 5, 5, 120, 5, 5, 20,
+    120, 120, 120, 20, 20, 120, 120, 120, 20, 5, 120, 5, 5, 364, 120, 120,
+    120, 120, 120, 5, 5, 5, 5, 5, 5, 120, 5, 365, 5, 120, 5, 366, 367, 120,
+    120, 364, 20, 120, 120, 368, 120, 20, 55, 55, 55, 55, 20, 5, 5, 20, 20,
+    120, 120, 5, 5, 5, 5, 5, 120, 20, 20, 20, 20, 5, 5, 5, 5, 369, 5, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 370, 370, 370,
+    370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 371,
+    371, 371, 371, 371, 371, 371, 371, 371, 371, 371, 371, 371, 371, 371,
+    371, 242, 242, 242, 30, 31, 242, 242, 242, 242, 27, 5, 5, 0, 0, 0, 0, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26,
+    22, 23, 358, 359, 360, 361, 362, 363, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 26, 22, 23, 358, 359, 360, 361, 362, 363, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 26, 22, 23, 358, 359, 360, 361, 362, 363, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 372, 372, 372, 372, 372, 372, 372,
+    372, 372, 372, 372, 372, 372, 372, 372, 372, 372, 372, 372, 372, 372,
+    372, 372, 372, 372, 372, 373, 373, 373, 373, 373, 373, 373, 373, 373,
+    373, 373, 373, 373, 373, 373, 373, 373, 373, 373, 373, 373, 373, 373,
+    373, 373, 373, 357, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 26, 22, 23,
+    358, 359, 360, 361, 362, 363, 27, 357, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 26, 22, 23, 358, 359, 360, 361, 362,
+    363, 27, 26, 22, 23, 358, 359, 360, 361, 362, 363, 27, 26, 22, 23, 358,
+    359, 360, 361, 362, 363, 27, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 135, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 0, 136, 136,
+    136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
+    136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
+    136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
+    136, 136, 136, 0, 30, 31, 374, 375, 376, 377, 378, 30, 31, 30, 31, 30,
+    31, 379, 380, 381, 382, 20, 30, 31, 20, 30, 31, 20, 20, 20, 20, 20, 101,
+    101, 383, 383, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 20, 5, 5, 5, 5,
+    5, 5, 30, 31, 30, 31, 25, 25, 25, 30, 31, 0, 0, 0, 0, 0, 5, 5, 5, 5, 27,
+    5, 5, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384,
+    384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384,
+    384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 0, 384, 0, 0, 0,
+    0, 0, 384, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 102, 5, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 25, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55,
+    55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55,
+    55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55,
+    55, 55, 55, 55, 55, 55, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 385, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    0, 0, 0, 0, 2, 5, 5, 5, 5, 102, 55, 242, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 25, 25, 25, 25, 18, 18, 5, 102, 102, 102, 102, 102, 5, 5,
+    242, 242, 242, 102, 55, 5, 5, 5, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 0, 0, 25, 25, 6, 6, 102, 102, 55, 5, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5, 102, 102, 102,
+    55, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 5,
+    5, 27, 27, 27, 27, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 27, 27, 27, 27, 27, 27, 27, 27, 5, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 55, 55, 55, 55, 55, 386,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 386, 55, 55, 386, 55, 55, 55, 386, 55, 386, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    386, 55, 55, 55, 55, 55, 55, 55, 386, 55, 386, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 386,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55,
+    55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 386, 55, 386, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 386, 55, 386, 386, 386, 55, 55, 55, 55, 55,
+    55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 386, 386, 386, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 386,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 386, 386, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 386, 386, 386, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386,
+    55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 386, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 102, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 102, 102, 102, 102, 102, 102, 5,
+    5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 102, 5, 5, 5, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 7, 8, 9, 10, 11,
+    12, 13, 14, 15, 16, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 55, 25, 6, 6, 6,
+    5, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 5, 102, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 101, 101, 25, 25, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 25, 25, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6,
+    6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 102, 102, 102, 102,
+    102, 102, 102, 102, 102, 6, 6, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 20, 20, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 101, 20, 20, 20,
+    20, 20, 20, 20, 20, 30, 31, 30, 31, 387, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 102, 6, 6, 30, 31, 388, 20, 55, 30, 31, 30, 31, 20, 20, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    389, 390, 391, 392, 389, 20, 393, 394, 395, 396, 30, 31, 30, 31, 30, 31,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 101, 101, 20, 55, 55, 55, 55,
+    55, 55, 55, 25, 55, 55, 55, 25, 55, 55, 55, 55, 25, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    18, 18, 25, 25, 18, 5, 5, 5, 5, 0, 0, 0, 0, 27, 27, 27, 27, 27, 27, 5, 5,
+    5, 5, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 18, 18, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 18, 18, 18, 18, 18, 18, 18,
+    18, 18, 18, 18, 18, 18, 18, 18, 18, 25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5,
+    7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0, 0, 0, 0, 0, 0, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 55, 55, 55, 55,
+    55, 55, 5, 5, 5, 55, 5, 55, 55, 25, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25,
+    5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 18,
+    18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 0, 0, 0, 25, 25, 25, 18, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 25, 18, 18, 25, 25, 25, 25, 18, 18, 25, 18, 18, 18, 18, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 102, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 0, 0, 0, 0, 5, 5, 55, 55, 55, 55, 55, 25, 102, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 55, 55, 55, 55, 55,
+    0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 18, 18, 25, 25, 18, 18,
+    25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 25, 55, 55, 55, 55, 55,
+    55, 55, 55, 25, 18, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0, 0, 5,
+    5, 5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    102, 55, 55, 55, 55, 55, 55, 5, 5, 5, 55, 18, 25, 18, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 55, 25, 25, 25, 55, 55, 25, 25,
+    55, 55, 55, 55, 55, 25, 25, 55, 25, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 102, 5, 5, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 18, 25, 25, 18, 18, 5, 5, 55, 102, 102, 18,
+    25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 0, 0, 55, 55,
+    55, 55, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 397, 20, 20, 20,
+    20, 20, 20, 20, 6, 101, 101, 101, 101, 20, 20, 20, 20, 20, 20, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,
+    408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421,
+    422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435,
+    436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449,
+    450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463,
+    464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 18, 18,
+    25, 18, 18, 25, 18, 18, 5, 18, 25, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0,
+    0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55,
+    55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    386, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 478, 479, 480, 481, 482, 483, 484, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 485, 486, 487, 488, 489, 0, 0, 0, 0, 0, 55, 25, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 0, 55, 0, 55, 55, 0, 55, 55, 0,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 490, 490, 490, 490, 490, 490, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 490, 490, 5, 5, 0, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 5, 5, 5, 18, 18,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    18, 18, 18, 5, 5, 6, 0, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 0, 0, 490, 55, 490, 55, 490, 0, 490, 55,
+    490, 55, 490, 55, 490, 55, 490, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 21, 0, 5, 5, 5, 5, 5, 5, 6, 5, 5,
+    5, 5, 5, 5, 6, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 6, 5, 5, 5, 5, 5,
+    5, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 5, 5, 5, 6, 18, 6, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 102, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 491, 491, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 0, 0, 55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 0, 0, 55,
+    55, 55, 0, 0, 0, 5, 5, 5, 6, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 21, 21, 21, 5, 5, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 0,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0,
+    0, 0, 5, 5, 5, 0, 0, 0, 0, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 0, 0, 0,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 27, 27, 27, 27, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 27, 27, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 25, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 25, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 0, 0, 0,
+    0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 27, 27, 27,
+    27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 242, 55, 55, 55, 55, 55, 55, 55,
+    55, 242, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 0, 0, 0, 0, 0, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 5, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 55, 55, 55,
+    55, 55, 55, 55, 55, 5, 242, 242, 242, 242, 242, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 492, 492, 492, 492, 492, 492, 492, 492,
+    492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492,
+    492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492,
+    492, 492, 492, 492, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493,
+    493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493,
+    493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493,
+    493, 493, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 0, 0, 0, 0, 0, 0, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492,
+    492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492,
+    492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 492, 0, 0, 0, 0,
+    493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493,
+    493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493, 493,
+    493, 493, 493, 493, 493, 493, 493, 493, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0,
+    0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 0, 0, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 0, 0, 0, 55,
+    0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 0, 5, 27, 27, 27, 27, 27, 27, 27, 27, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 5, 5, 27, 27, 27, 27, 27, 27, 27, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 0, 55, 55, 0, 0, 0, 0, 0, 27, 27, 27, 27, 27, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 27,
+    27, 27, 27, 27, 27, 0, 0, 0, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0,
+    0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0,
+    0, 27, 27, 55, 55, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 0, 0, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 55, 25, 25, 25,
+    0, 25, 25, 0, 0, 0, 0, 0, 25, 25, 25, 25, 55, 55, 55, 55, 0, 55, 55, 55,
+    0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 25, 25, 25, 0, 0,
+    0, 0, 25, 26, 22, 23, 358, 27, 27, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 27, 27, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 27,
+    27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 5, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 0, 0, 0, 0, 27, 27, 27, 27, 27,
+    5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 5, 5, 5, 5, 5,
+    5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 0, 0, 27, 27, 27, 27, 27, 27, 27, 27, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0,
+    0, 27, 27, 27, 27, 27, 27, 27, 27, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 27, 27, 27, 27, 27, 27, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 108, 108, 108, 108, 108, 108, 108,
+    108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
+    108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
+    108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108,
+    108, 108, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 115, 115, 115, 115, 115,
+    115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115,
+    115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115,
+    115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115,
+    115, 115, 115, 115, 0, 0, 0, 0, 0, 0, 0, 27, 27, 27, 27, 27, 27, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25,
+    25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 22,
+    23, 358, 359, 360, 361, 362, 363, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 55, 0, 0,
+    0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 27, 27, 27, 27, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 18, 25, 18, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 26, 22, 23, 358, 359, 360, 361, 362,
+    363, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 7, 8, 9, 10, 11, 12, 13,
+    14, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25, 25, 18,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 18, 18, 18, 25, 25, 25, 25, 18, 18,
+    25, 25, 5, 5, 21, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 0, 0,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13,
+    14, 15, 16, 0, 0, 0, 0, 0, 0, 25, 25, 25, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 18, 25, 25,
+    25, 25, 25, 25, 25, 25, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 5, 5, 5,
+    5, 55, 18, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 5, 5, 55, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 25, 25, 18, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 18, 18,
+    18, 25, 25, 25, 25, 25, 25, 25, 25, 25, 18, 18, 55, 55, 55, 55, 5, 5, 5,
+    5, 25, 25, 25, 25, 5, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 55, 5,
+    55, 5, 5, 5, 0, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 18, 18, 18, 25, 25, 25, 18, 18, 25, 18, 25, 25, 5, 5, 5,
+    5, 5, 5, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55,
+    55, 55, 55, 55, 55, 0, 55, 0, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 5, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    25, 18, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25, 0, 0, 0, 0, 0, 7, 8, 9,
+    10, 11, 12, 13, 14, 15, 16, 0, 0, 0, 0, 0, 0, 25, 25, 18, 18, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55,
+    55, 55, 55, 55, 55, 0, 55, 55, 0, 55, 55, 55, 55, 55, 0, 25, 25, 55, 18,
+    18, 25, 18, 18, 18, 18, 0, 0, 18, 18, 0, 0, 18, 18, 18, 0, 0, 55, 0, 0,
+    0, 0, 0, 0, 18, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 18, 18, 0, 0, 25, 25,
+    25, 25, 25, 25, 25, 0, 0, 0, 25, 25, 25, 25, 25, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 18, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25, 18, 18, 25, 25, 25, 18,
+    25, 55, 55, 55, 55, 5, 5, 5, 5, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+    0, 5, 0, 5, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 18, 18, 18, 25, 25, 25, 25, 25, 25, 18, 25, 18, 18, 18,
+    18, 25, 25, 18, 25, 25, 55, 55, 5, 55, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9,
+    10, 11, 12, 13, 14, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 18, 18, 18, 25, 25, 25, 25, 0, 0,
+    18, 18, 18, 18, 25, 25, 18, 25, 25, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 55, 55, 55, 55, 25, 25, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 18, 18,
+    18, 25, 25, 25, 25, 25, 25, 25, 25, 18, 18, 25, 18, 25, 25, 5, 5, 5, 55,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0,
+    0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25,
+    18, 25, 18, 18, 25, 25, 25, 25, 25, 25, 18, 25, 0, 0, 0, 0, 0, 0, 0, 0,
+    7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 0, 0, 25, 25, 25, 18, 18, 25, 25, 25, 25, 18, 25, 25, 25,
+    25, 25, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 27, 27, 5, 5, 5,
+    5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 18, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25, 25, 18, 25, 25,
+    5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 7, 8, 9, 10, 11,
+    12, 13, 14, 15, 16, 27, 27, 27, 27, 27, 27, 27, 27, 27, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 25, 25, 25, 25, 25, 25, 18, 55, 25, 25, 25, 25, 5, 5, 5, 5, 5, 5,
+    5, 5, 25, 0, 0, 0, 0, 0, 0, 0, 0, 55, 25, 25, 25, 25, 25, 25, 18, 18, 25,
+    25, 25, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 18, 25, 25, 5, 5, 5, 55, 5, 5, 5, 5, 5, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 18, 25, 25, 25, 25, 25, 25, 25, 0, 25, 25, 25, 25, 25, 25, 18,
+    25, 55, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12,
+    13, 14, 15, 16, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 0, 0, 0, 5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 0, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 0, 18, 25, 25, 25, 25, 25, 25, 25, 18,
+    25, 25, 18, 25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    25, 25, 25, 25, 25, 25, 0, 0, 0, 25, 0, 25, 25, 0, 25, 25, 25, 25, 25,
+    25, 25, 55, 25, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 0, 55, 55, 0, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 18, 18, 18, 18, 18, 0, 25,
+    25, 0, 18, 18, 25, 18, 25, 55, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12,
+    13, 14, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 18,
+    18, 5, 5, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242,
+    242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 242, 0,
+    5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0, 0,
+    0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 25, 25,
+    25, 25, 25, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 102, 102, 102, 102, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10,
+    11, 12, 13, 14, 15, 16, 0, 27, 27, 27, 27, 27, 27, 27, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0,
+    0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    19, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    55, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
+    18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
+    18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 25, 25, 25, 25, 102, 102, 102, 102, 102, 102, 102, 102,
+    102, 102, 102, 102, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    102, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 5, 25, 25, 5, 21, 21, 21,
+    21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 18, 18, 25, 25, 25, 5, 5, 5, 18, 18, 18,
+    18, 18, 18, 21, 21, 21, 21, 21, 21, 21, 21, 25, 25, 25, 25, 25, 25, 25,
+    25, 5, 5, 25, 25, 25, 25, 25, 25, 25, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 25, 25, 25, 25, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 25, 25, 25, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 20, 20, 20, 20, 20, 20, 20, 0, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 120, 0, 120,
+    120, 0, 0, 120, 0, 0, 120, 120, 0, 0, 120, 120, 120, 120, 0, 120, 120,
+    120, 120, 120, 120, 120, 120, 20, 20, 20, 20, 0, 20, 0, 20, 20, 20, 20,
+    20, 20, 20, 0, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 120, 120, 0, 120, 120, 120, 120, 0, 0, 120, 120, 120, 120, 120, 120,
+    120, 120, 0, 120, 120, 120, 120, 120, 120, 120, 0, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 120, 120, 0, 120, 120, 120, 120, 0, 120, 120, 120, 120, 120,
+    0, 120, 0, 0, 0, 120, 120, 120, 120, 120, 120, 120, 0, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 0, 0,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 5, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 5, 20, 20, 20, 20, 20, 20, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 5, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 5, 20, 20, 20, 20,
+    20, 20, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 5, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 5, 20, 20, 20, 20, 20, 20, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 5, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 5, 20, 20,
+    20, 20, 20, 20, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120,
+    120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 5,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 5, 20, 20, 20, 20, 20, 20, 120, 20, 0, 0, 7,
+    8, 9, 10, 11, 12, 13, 14, 15, 16, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 7,
+    8, 9, 10, 11, 12, 13, 14, 15, 16, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 7,
+    8, 9, 10, 11, 12, 13, 14, 15, 16, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 5, 5, 5, 5, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 5, 5, 5, 5, 5, 5, 5, 5, 25, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 25, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25, 25, 25, 25, 0, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25, 25,
+    25, 25, 25, 25, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 0, 0, 25, 25, 25, 25, 25, 25, 25, 0, 25, 25, 0, 25, 25,
+    25, 25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 25, 25, 25, 25, 25, 25, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 494, 494, 494, 494, 494, 494, 494, 494, 494, 494, 494,
+    494, 494, 494, 494, 494, 494, 494, 494, 494, 494, 494, 494, 494, 494,
+    494, 494, 494, 494, 494, 494, 494, 494, 494, 495, 495, 495, 495, 495,
+    495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495,
+    495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495, 495,
+    495, 25, 25, 25, 25, 25, 25, 25, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13,
+    14, 15, 16, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,
+    27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 5, 27, 27, 27, 5, 27,
+    27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 55, 55, 0, 55, 0, 0, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 55, 55, 55, 55, 0, 55, 0, 55, 0, 0, 0, 0, 0, 0, 55, 0, 0, 0, 0,
+    55, 0, 55, 0, 55, 0, 55, 55, 55, 0, 55, 55, 0, 55, 0, 0, 55, 0, 55, 0,
+    55, 0, 55, 0, 55, 0, 55, 55, 0, 55, 0, 0, 55, 55, 55, 55, 0, 55, 55, 55,
+    55, 55, 55, 55, 0, 55, 55, 55, 55, 0, 55, 55, 55, 55, 0, 55, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 55, 55, 55, 0, 55, 55, 55,
+    55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 357, 357, 26, 22, 23, 358, 359,
+    360, 361, 362, 363, 27, 27, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 496, 496,
+    496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496,
+    496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 5, 5, 5, 5, 5, 5, 496,
+    496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496,
+    496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 5, 5, 0, 0, 0, 0,
+    496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496,
+    496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 496, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0,
+    0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0,
+    0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0,
+    0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5,
+    5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0,
+    0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55,
+    55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
+};
+
+/* Returns the numeric value as double for Unicode characters
+ * having this property, -1.0 otherwise.
+ */
+double numba_PyUnicode_ToNumeric(Py_UCS4 ch)
+{
+    switch (ch) {
+    case 0x0F33:
+        return (double) -1.0/2.0;
+    case 0x0030:
+    case 0x0660:
+    case 0x06F0:
+    case 0x07C0:
+    case 0x0966:
+    case 0x09E6:
+    case 0x0A66:
+    case 0x0AE6:
+    case 0x0B66:
+    case 0x0BE6:
+    case 0x0C66:
+    case 0x0C78:
+    case 0x0CE6:
+    case 0x0D66:
+    case 0x0DE6:
+    case 0x0E50:
+    case 0x0ED0:
+    case 0x0F20:
+    case 0x1040:
+    case 0x1090:
+    case 0x17E0:
+    case 0x17F0:
+    case 0x1810:
+    case 0x1946:
+    case 0x19D0:
+    case 0x1A80:
+    case 0x1A90:
+    case 0x1B50:
+    case 0x1BB0:
+    case 0x1C40:
+    case 0x1C50:
+    case 0x2070:
+    case 0x2080:
+    case 0x2189:
+    case 0x24EA:
+    case 0x24FF:
+    case 0x3007:
+    case 0x96F6:
+    case 0xA620:
+    case 0xA6EF:
+    case 0xA8D0:
+    case 0xA900:
+    case 0xA9D0:
+    case 0xA9F0:
+    case 0xAA50:
+    case 0xABF0:
+    case 0xF9B2:
+    case 0xFF10:
+    case 0x1018A:
+    case 0x104A0:
+    case 0x10D30:
+    case 0x11066:
+    case 0x110F0:
+    case 0x11136:
+    case 0x111D0:
+    case 0x112F0:
+    case 0x11450:
+    case 0x114D0:
+    case 0x11650:
+    case 0x116C0:
+    case 0x11730:
+    case 0x118E0:
+    case 0x11C50:
+    case 0x11D50:
+    case 0x11DA0:
+    case 0x16A60:
+    case 0x16B50:
+    case 0x16E80:
+    case 0x1D2E0:
+    case 0x1D7CE:
+    case 0x1D7D8:
+    case 0x1D7E2:
+    case 0x1D7EC:
+    case 0x1D7F6:
+    case 0x1E950:
+    case 0x1F100:
+    case 0x1F101:
+    case 0x1F10B:
+    case 0x1F10C:
+        return (double) 0.0;
+    case 0x0031:
+    case 0x00B9:
+    case 0x0661:
+    case 0x06F1:
+    case 0x07C1:
+    case 0x0967:
+    case 0x09E7:
+    case 0x0A67:
+    case 0x0AE7:
+    case 0x0B67:
+    case 0x0BE7:
+    case 0x0C67:
+    case 0x0C79:
+    case 0x0C7C:
+    case 0x0CE7:
+    case 0x0D67:
+    case 0x0DE7:
+    case 0x0E51:
+    case 0x0ED1:
+    case 0x0F21:
+    case 0x1041:
+    case 0x1091:
+    case 0x1369:
+    case 0x17E1:
+    case 0x17F1:
+    case 0x1811:
+    case 0x1947:
+    case 0x19D1:
+    case 0x19DA:
+    case 0x1A81:
+    case 0x1A91:
+    case 0x1B51:
+    case 0x1BB1:
+    case 0x1C41:
+    case 0x1C51:
+    case 0x2081:
+    case 0x215F:
+    case 0x2160:
+    case 0x2170:
+    case 0x2460:
+    case 0x2474:
+    case 0x2488:
+    case 0x24F5:
+    case 0x2776:
+    case 0x2780:
+    case 0x278A:
+    case 0x3021:
+    case 0x3192:
+    case 0x3220:
+    case 0x3280:
+    case 0x4E00:
+    case 0x58F1:
+    case 0x58F9:
+    case 0x5E7A:
+    case 0x5F0C:
+    case 0xA621:
+    case 0xA6E6:
+    case 0xA8D1:
+    case 0xA901:
+    case 0xA9D1:
+    case 0xA9F1:
+    case 0xAA51:
+    case 0xABF1:
+    case 0xFF11:
+    case 0x10107:
+    case 0x10142:
+    case 0x10158:
+    case 0x10159:
+    case 0x1015A:
+    case 0x102E1:
+    case 0x10320:
+    case 0x103D1:
+    case 0x104A1:
+    case 0x10858:
+    case 0x10879:
+    case 0x108A7:
+    case 0x108FB:
+    case 0x10916:
+    case 0x109C0:
+    case 0x10A40:
+    case 0x10A7D:
+    case 0x10A9D:
+    case 0x10AEB:
+    case 0x10B58:
+    case 0x10B78:
+    case 0x10BA9:
+    case 0x10CFA:
+    case 0x10D31:
+    case 0x10E60:
+    case 0x10F1D:
+    case 0x10F51:
+    case 0x11052:
+    case 0x11067:
+    case 0x110F1:
+    case 0x11137:
+    case 0x111D1:
+    case 0x111E1:
+    case 0x112F1:
+    case 0x11451:
+    case 0x114D1:
+    case 0x11651:
+    case 0x116C1:
+    case 0x11731:
+    case 0x118E1:
+    case 0x11C51:
+    case 0x11C5A:
+    case 0x11D51:
+    case 0x11DA1:
+    case 0x12415:
+    case 0x1241E:
+    case 0x1242C:
+    case 0x12434:
+    case 0x1244F:
+    case 0x12458:
+    case 0x16A61:
+    case 0x16B51:
+    case 0x16E81:
+    case 0x16E94:
+    case 0x1D2E1:
+    case 0x1D360:
+    case 0x1D372:
+    case 0x1D377:
+    case 0x1D7CF:
+    case 0x1D7D9:
+    case 0x1D7E3:
+    case 0x1D7ED:
+    case 0x1D7F7:
+    case 0x1E8C7:
+    case 0x1E951:
+    case 0x1EC71:
+    case 0x1ECA3:
+    case 0x1ECB1:
+    case 0x1F102:
+    case 0x2092A:
+        return (double) 1.0;
+    case 0x0D5C:
+    case 0x2152:
+        return (double) 1.0/10.0;
+    case 0x109F6:
+        return (double) 1.0/12.0;
+    case 0x09F4:
+    case 0x0B75:
+    case 0x0D76:
+    case 0xA833:
+        return (double) 1.0/16.0;
+    case 0x0D58:
+        return (double) 1.0/160.0;
+    case 0x00BD:
+    case 0x0B73:
+    case 0x0D74:
+    case 0x0F2A:
+    case 0x2CFD:
+    case 0xA831:
+    case 0x10141:
+    case 0x10175:
+    case 0x10176:
+    case 0x109BD:
+    case 0x10A48:
+    case 0x10E7B:
+    case 0x10F26:
+    case 0x12464:
+    case 0x1ECAE:
+        return (double) 1.0/2.0;
+    case 0x0D5B:
+        return (double) 1.0/20.0;
+    case 0x2153:
+    case 0x10E7D:
+    case 0x1245A:
+    case 0x1245D:
+    case 0x12465:
+        return (double) 1.0/3.0;
+    case 0x00BC:
+    case 0x09F7:
+    case 0x0B72:
+    case 0x0D73:
+    case 0xA830:
+    case 0x10140:
+    case 0x1018B:
+    case 0x10E7C:
+    case 0x12460:
+    case 0x12462:
+    case 0x12463:
+    case 0x1ECAD:
+        return (double) 1.0/4.0;
+    case 0x0D59:
+        return (double) 1.0/40.0;
+    case 0x0D5E:
+    case 0x2155:
+        return (double) 1.0/5.0;
+    case 0x2159:
+    case 0x12461:
+        return (double) 1.0/6.0;
+    case 0x2150:
+        return (double) 1.0/7.0;
+    case 0x09F5:
+    case 0x0B76:
+    case 0x0D77:
+    case 0x215B:
+    case 0xA834:
+    case 0x1245F:
+        return (double) 1.0/8.0;
+    case 0x2151:
+        return (double) 1.0/9.0;
+    case 0x0BF0:
+    case 0x0D70:
+    case 0x1372:
+    case 0x2169:
+    case 0x2179:
+    case 0x2469:
+    case 0x247D:
+    case 0x2491:
+    case 0x24FE:
+    case 0x277F:
+    case 0x2789:
+    case 0x2793:
+    case 0x3038:
+    case 0x3229:
+    case 0x3248:
+    case 0x3289:
+    case 0x4EC0:
+    case 0x5341:
+    case 0x62FE:
+    case 0xF973:
+    case 0xF9FD:
+    case 0x10110:
+    case 0x10149:
+    case 0x10150:
+    case 0x10157:
+    case 0x10160:
+    case 0x10161:
+    case 0x10162:
+    case 0x10163:
+    case 0x10164:
+    case 0x102EA:
+    case 0x10322:
+    case 0x103D3:
+    case 0x1085B:
+    case 0x1087E:
+    case 0x108AD:
+    case 0x108FD:
+    case 0x10917:
+    case 0x109C9:
+    case 0x10A44:
+    case 0x10A9E:
+    case 0x10AED:
+    case 0x10B5C:
+    case 0x10B7C:
+    case 0x10BAD:
+    case 0x10CFC:
+    case 0x10E69:
+    case 0x10F22:
+    case 0x10F52:
+    case 0x1105B:
+    case 0x111EA:
+    case 0x1173A:
+    case 0x118EA:
+    case 0x11C63:
+    case 0x16B5B:
+    case 0x16E8A:
+    case 0x1D2EA:
+    case 0x1D369:
+    case 0x1EC7A:
+        return (double) 10.0;
+    case 0x109FF:
+        return (double) 10.0/12.0;
+    case 0x0BF1:
+    case 0x0D71:
+    case 0x137B:
+    case 0x216D:
+    case 0x217D:
+    case 0x4F70:
+    case 0x767E:
+    case 0x964C:
+    case 0x10119:
+    case 0x1014B:
+    case 0x10152:
+    case 0x1016A:
+    case 0x102F3:
+    case 0x103D5:
+    case 0x1085D:
+    case 0x108AF:
+    case 0x108FF:
+    case 0x10919:
+    case 0x109D2:
+    case 0x10A46:
+    case 0x10AEF:
+    case 0x10B5E:
+    case 0x10B7E:
+    case 0x10BAF:
+    case 0x10CFE:
+    case 0x10E72:
+    case 0x10F25:
+    case 0x10F54:
+    case 0x11064:
+    case 0x111F3:
+    case 0x11C6C:
+    case 0x16B5C:
+    case 0x1EC83:
+        return (double) 100.0;
+    case 0x0BF2:
+    case 0x0D72:
+    case 0x216F:
+    case 0x217F:
+    case 0x2180:
+    case 0x4EDF:
+    case 0x5343:
+    case 0x9621:
+    case 0x10122:
+    case 0x1014D:
+    case 0x10154:
+    case 0x10171:
+    case 0x1085E:
+    case 0x109DB:
+    case 0x10A47:
+    case 0x10B5F:
+    case 0x10B7F:
+    case 0x10CFF:
+    case 0x11065:
+    case 0x111F4:
+    case 0x1EC8C:
+        return (double) 1000.0;
+    case 0x137C:
+    case 0x2182:
+    case 0x4E07:
+    case 0x842C:
+    case 0x1012B:
+    case 0x10155:
+    case 0x1085F:
+    case 0x109E4:
+    case 0x16B5D:
+    case 0x1EC95:
+    case 0x1ECB3:
+        return (double) 10000.0;
+    case 0x2188:
+    case 0x109ED:
+    case 0x1EC9E:
+    case 0x1ECA0:
+    case 0x1ECB4:
+        return (double) 100000.0;
+    case 0x16B5E:
+        return (double) 1000000.0;
+    case 0x1ECA1:
+        return (double) 10000000.0;
+    case 0x4EBF:
+    case 0x5104:
+    case 0x16B5F:
+        return (double) 100000000.0;
+    case 0x16B60:
+        return (double) 10000000000.0;
+    case 0x5146:
+    case 0x16B61:
+        return (double) 1000000000000.0;
+    case 0x216A:
+    case 0x217A:
+    case 0x246A:
+    case 0x247E:
+    case 0x2492:
+    case 0x24EB:
+    case 0x16E8B:
+    case 0x1D2EB:
+        return (double) 11.0;
+    case 0x109BC:
+        return (double) 11.0/12.0;
+    case 0x0F2F:
+        return (double) 11.0/2.0;
+    case 0x216B:
+    case 0x217B:
+    case 0x246B:
+    case 0x247F:
+    case 0x2493:
+    case 0x24EC:
+    case 0x16E8C:
+    case 0x1D2EC:
+        return (double) 12.0;
+    case 0x246C:
+    case 0x2480:
+    case 0x2494:
+    case 0x24ED:
+    case 0x16E8D:
+    case 0x1D2ED:
+        return (double) 13.0;
+    case 0x0F30:
+        return (double) 13.0/2.0;
+    case 0x246D:
+    case 0x2481:
+    case 0x2495:
+    case 0x24EE:
+    case 0x16E8E:
+    case 0x1D2EE:
+        return (double) 14.0;
+    case 0x246E:
+    case 0x2482:
+    case 0x2496:
+    case 0x24EF:
+    case 0x16E8F:
+    case 0x1D2EF:
+        return (double) 15.0;
+    case 0x0F31:
+        return (double) 15.0/2.0;
+    case 0x09F9:
+    case 0x246F:
+    case 0x2483:
+    case 0x2497:
+    case 0x24F0:
+    case 0x16E90:
+    case 0x1D2F0:
+        return (double) 16.0;
+    case 0x16EE:
+    case 0x2470:
+    case 0x2484:
+    case 0x2498:
+    case 0x24F1:
+    case 0x16E91:
+    case 0x1D2F1:
+        return (double) 17.0;
+    case 0x0F32:
+        return (double) 17.0/2.0;
+    case 0x16EF:
+    case 0x2471:
+    case 0x2485:
+    case 0x2499:
+    case 0x24F2:
+    case 0x16E92:
+    case 0x1D2F2:
+        return (double) 18.0;
+    case 0x16F0:
+    case 0x2472:
+    case 0x2486:
+    case 0x249A:
+    case 0x24F3:
+    case 0x16E93:
+    case 0x1D2F3:
+        return (double) 19.0;
+    case 0x0032:
+    case 0x00B2:
+    case 0x0662:
+    case 0x06F2:
+    case 0x07C2:
+    case 0x0968:
+    case 0x09E8:
+    case 0x0A68:
+    case 0x0AE8:
+    case 0x0B68:
+    case 0x0BE8:
+    case 0x0C68:
+    case 0x0C7A:
+    case 0x0C7D:
+    case 0x0CE8:
+    case 0x0D68:
+    case 0x0DE8:
+    case 0x0E52:
+    case 0x0ED2:
+    case 0x0F22:
+    case 0x1042:
+    case 0x1092:
+    case 0x136A:
+    case 0x17E2:
+    case 0x17F2:
+    case 0x1812:
+    case 0x1948:
+    case 0x19D2:
+    case 0x1A82:
+    case 0x1A92:
+    case 0x1B52:
+    case 0x1BB2:
+    case 0x1C42:
+    case 0x1C52:
+    case 0x2082:
+    case 0x2161:
+    case 0x2171:
+    case 0x2461:
+    case 0x2475:
+    case 0x2489:
+    case 0x24F6:
+    case 0x2777:
+    case 0x2781:
+    case 0x278B:
+    case 0x3022:
+    case 0x3193:
+    case 0x3221:
+    case 0x3281:
+    case 0x3483:
+    case 0x4E8C:
+    case 0x5169:
+    case 0x5F0D:
+    case 0x5F10:
+    case 0x8CAE:
+    case 0x8CB3:
+    case 0x8D30:
+    case 0xA622:
+    case 0xA6E7:
+    case 0xA8D2:
+    case 0xA902:
+    case 0xA9D2:
+    case 0xA9F2:
+    case 0xAA52:
+    case 0xABF2:
+    case 0xF978:
+    case 0xFF12:
+    case 0x10108:
+    case 0x1015B:
+    case 0x1015C:
+    case 0x1015D:
+    case 0x1015E:
+    case 0x102E2:
+    case 0x103D2:
+    case 0x104A2:
+    case 0x10859:
+    case 0x1087A:
+    case 0x108A8:
+    case 0x1091A:
+    case 0x109C1:
+    case 0x10A41:
+    case 0x10B59:
+    case 0x10B79:
+    case 0x10BAA:
+    case 0x10D32:
+    case 0x10E61:
+    case 0x10F1E:
+    case 0x11053:
+    case 0x11068:
+    case 0x110F2:
+    case 0x11138:
+    case 0x111D2:
+    case 0x111E2:
+    case 0x112F2:
+    case 0x11452:
+    case 0x114D2:
+    case 0x11652:
+    case 0x116C2:
+    case 0x11732:
+    case 0x118E2:
+    case 0x11C52:
+    case 0x11C5B:
+    case 0x11D52:
+    case 0x11DA2:
+    case 0x12400:
+    case 0x12416:
+    case 0x1241F:
+    case 0x12423:
+    case 0x1242D:
+    case 0x12435:
+    case 0x1244A:
+    case 0x12450:
+    case 0x12456:
+    case 0x12459:
+    case 0x16A62:
+    case 0x16B52:
+    case 0x16E82:
+    case 0x16E95:
+    case 0x1D2E2:
+    case 0x1D361:
+    case 0x1D373:
+    case 0x1D7D0:
+    case 0x1D7DA:
+    case 0x1D7E4:
+    case 0x1D7EE:
+    case 0x1D7F8:
+    case 0x1E8C8:
+    case 0x1E952:
+    case 0x1EC72:
+    case 0x1ECA4:
+    case 0x1ECB2:
+    case 0x1F103:
+    case 0x22390:
+        return (double) 2.0;
+    case 0x109F7:
+        return (double) 2.0/12.0;
+    case 0x2154:
+    case 0x10177:
+    case 0x10E7E:
+    case 0x1245B:
+    case 0x1245E:
+    case 0x12466:
+        return (double) 2.0/3.0;
+    case 0x2156:
+        return (double) 2.0/5.0;
+    case 0x1373:
+    case 0x2473:
+    case 0x2487:
+    case 0x249B:
+    case 0x24F4:
+    case 0x3039:
+    case 0x3249:
+    case 0x5344:
+    case 0x5EFF:
+    case 0x10111:
+    case 0x102EB:
+    case 0x103D4:
+    case 0x1085C:
+    case 0x1087F:
+    case 0x108AE:
+    case 0x108FE:
+    case 0x10918:
+    case 0x109CA:
+    case 0x10A45:
+    case 0x10A9F:
+    case 0x10AEE:
+    case 0x10B5D:
+    case 0x10B7D:
+    case 0x10BAE:
+    case 0x10E6A:
+    case 0x10F23:
+    case 0x10F53:
+    case 0x1105C:
+    case 0x111EB:
+    case 0x1173B:
+    case 0x118EB:
+    case 0x11C64:
+    case 0x1D36A:
+    case 0x1EC7B:
+        return (double) 20.0;
+    case 0x1011A:
+    case 0x102F4:
+    case 0x109D3:
+    case 0x10E73:
+    case 0x1EC84:
+        return (double) 200.0;
+    case 0x10123:
+    case 0x109DC:
+    case 0x1EC8D:
+        return (double) 2000.0;
+    case 0x1012C:
+    case 0x109E5:
+    case 0x1EC96:
+        return (double) 20000.0;
+    case 0x109EE:
+    case 0x1EC9F:
+        return (double) 200000.0;
+    case 0x1ECA2:
+        return (double) 20000000.0;
+    case 0x3251:
+        return (double) 21.0;
+    case 0x12432:
+        return (double) 216000.0;
+    case 0x3252:
+        return (double) 22.0;
+    case 0x3253:
+        return (double) 23.0;
+    case 0x3254:
+        return (double) 24.0;
+    case 0x3255:
+        return (double) 25.0;
+    case 0x3256:
+        return (double) 26.0;
+    case 0x3257:
+        return (double) 27.0;
+    case 0x3258:
+        return (double) 28.0;
+    case 0x3259:
+        return (double) 29.0;
+    case 0x0033:
+    case 0x00B3:
+    case 0x0663:
+    case 0x06F3:
+    case 0x07C3:
+    case 0x0969:
+    case 0x09E9:
+    case 0x0A69:
+    case 0x0AE9:
+    case 0x0B69:
+    case 0x0BE9:
+    case 0x0C69:
+    case 0x0C7B:
+    case 0x0C7E:
+    case 0x0CE9:
+    case 0x0D69:
+    case 0x0DE9:
+    case 0x0E53:
+    case 0x0ED3:
+    case 0x0F23:
+    case 0x1043:
+    case 0x1093:
+    case 0x136B:
+    case 0x17E3:
+    case 0x17F3:
+    case 0x1813:
+    case 0x1949:
+    case 0x19D3:
+    case 0x1A83:
+    case 0x1A93:
+    case 0x1B53:
+    case 0x1BB3:
+    case 0x1C43:
+    case 0x1C53:
+    case 0x2083:
+    case 0x2162:
+    case 0x2172:
+    case 0x2462:
+    case 0x2476:
+    case 0x248A:
+    case 0x24F7:
+    case 0x2778:
+    case 0x2782:
+    case 0x278C:
+    case 0x3023:
+    case 0x3194:
+    case 0x3222:
+    case 0x3282:
+    case 0x4E09:
+    case 0x4EE8:
+    case 0x53C1:
+    case 0x53C2:
+    case 0x53C3:
+    case 0x53C4:
+    case 0x5F0E:
+    case 0xA623:
+    case 0xA6E8:
+    case 0xA8D3:
+    case 0xA903:
+    case 0xA9D3:
+    case 0xA9F3:
+    case 0xAA53:
+    case 0xABF3:
+    case 0xF96B:
+    case 0xFF13:
+    case 0x10109:
+    case 0x102E3:
+    case 0x104A3:
+    case 0x1085A:
+    case 0x1087B:
+    case 0x108A9:
+    case 0x1091B:
+    case 0x109C2:
+    case 0x10A42:
+    case 0x10B5A:
+    case 0x10B7A:
+    case 0x10BAB:
+    case 0x10D33:
+    case 0x10E62:
+    case 0x10F1F:
+    case 0x11054:
+    case 0x11069:
+    case 0x110F3:
+    case 0x11139:
+    case 0x111D3:
+    case 0x111E3:
+    case 0x112F3:
+    case 0x11453:
+    case 0x114D3:
+    case 0x11653:
+    case 0x116C3:
+    case 0x11733:
+    case 0x118E3:
+    case 0x11C53:
+    case 0x11C5C:
+    case 0x11D53:
+    case 0x11DA3:
+    case 0x12401:
+    case 0x12408:
+    case 0x12417:
+    case 0x12420:
+    case 0x12424:
+    case 0x12425:
+    case 0x1242E:
+    case 0x1242F:
+    case 0x12436:
+    case 0x12437:
+    case 0x1243A:
+    case 0x1243B:
+    case 0x1244B:
+    case 0x12451:
+    case 0x12457:
+    case 0x16A63:
+    case 0x16B53:
+    case 0x16E83:
+    case 0x16E96:
+    case 0x1D2E3:
+    case 0x1D362:
+    case 0x1D374:
+    case 0x1D7D1:
+    case 0x1D7DB:
+    case 0x1D7E5:
+    case 0x1D7EF:
+    case 0x1D7F9:
+    case 0x1E8C9:
+    case 0x1E953:
+    case 0x1EC73:
+    case 0x1ECA5:
+    case 0x1F104:
+    case 0x20AFD:
+    case 0x20B19:
+    case 0x22998:
+    case 0x23B1B:
+        return (double) 3.0;
+    case 0x109F8:
+        return (double) 3.0/12.0;
+    case 0x09F6:
+    case 0x0B77:
+    case 0x0D78:
+    case 0xA835:
+        return (double) 3.0/16.0;
+    case 0x0F2B:
+        return (double) 3.0/2.0;
+    case 0x0D5D:
+        return (double) 3.0/20.0;
+    case 0x00BE:
+    case 0x09F8:
+    case 0x0B74:
+    case 0x0D75:
+    case 0xA832:
+    case 0x10178:
+    case 0x1ECAF:
+        return (double) 3.0/4.0;
+    case 0x2157:
+        return (double) 3.0/5.0;
+    case 0x215C:
+        return (double) 3.0/8.0;
+    case 0x0D5A:
+        return (double) 3.0/80.0;
+    case 0x1374:
+    case 0x303A:
+    case 0x324A:
+    case 0x325A:
+    case 0x5345:
+    case 0x10112:
+    case 0x10165:
+    case 0x102EC:
+    case 0x109CB:
+    case 0x10E6B:
+    case 0x10F24:
+    case 0x1105D:
+    case 0x111EC:
+    case 0x118EC:
+    case 0x11C65:
+    case 0x1D36B:
+    case 0x1EC7C:
+    case 0x20983:
+        return (double) 30.0;
+    case 0x1011B:
+    case 0x1016B:
+    case 0x102F5:
+    case 0x109D4:
+    case 0x10E74:
+    case 0x1EC85:
+        return (double) 300.0;
+    case 0x10124:
+    case 0x109DD:
+    case 0x1EC8E:
+        return (double) 3000.0;
+    case 0x1012D:
+    case 0x109E6:
+    case 0x1EC97:
+        return (double) 30000.0;
+    case 0x109EF:
+        return (double) 300000.0;
+    case 0x325B:
+        return (double) 31.0;
+    case 0x325C:
+        return (double) 32.0;
+    case 0x325D:
+        return (double) 33.0;
+    case 0x325E:
+        return (double) 34.0;
+    case 0x325F:
+        return (double) 35.0;
+    case 0x32B1:
+        return (double) 36.0;
+    case 0x32B2:
+        return (double) 37.0;
+    case 0x32B3:
+        return (double) 38.0;
+    case 0x32B4:
+        return (double) 39.0;
+    case 0x0034:
+    case 0x0664:
+    case 0x06F4:
+    case 0x07C4:
+    case 0x096A:
+    case 0x09EA:
+    case 0x0A6A:
+    case 0x0AEA:
+    case 0x0B6A:
+    case 0x0BEA:
+    case 0x0C6A:
+    case 0x0CEA:
+    case 0x0D6A:
+    case 0x0DEA:
+    case 0x0E54:
+    case 0x0ED4:
+    case 0x0F24:
+    case 0x1044:
+    case 0x1094:
+    case 0x136C:
+    case 0x17E4:
+    case 0x17F4:
+    case 0x1814:
+    case 0x194A:
+    case 0x19D4:
+    case 0x1A84:
+    case 0x1A94:
+    case 0x1B54:
+    case 0x1BB4:
+    case 0x1C44:
+    case 0x1C54:
+    case 0x2074:
+    case 0x2084:
+    case 0x2163:
+    case 0x2173:
+    case 0x2463:
+    case 0x2477:
+    case 0x248B:
+    case 0x24F8:
+    case 0x2779:
+    case 0x2783:
+    case 0x278D:
+    case 0x3024:
+    case 0x3195:
+    case 0x3223:
+    case 0x3283:
+    case 0x4E96:
+    case 0x56DB:
+    case 0x8086:
+    case 0xA624:
+    case 0xA6E9:
+    case 0xA8D4:
+    case 0xA904:
+    case 0xA9D4:
+    case 0xA9F4:
+    case 0xAA54:
+    case 0xABF4:
+    case 0xFF14:
+    case 0x1010A:
+    case 0x102E4:
+    case 0x104A4:
+    case 0x1087C:
+    case 0x108AA:
+    case 0x108AB:
+    case 0x109C3:
+    case 0x10A43:
+    case 0x10B5B:
+    case 0x10B7B:
+    case 0x10BAC:
+    case 0x10D34:
+    case 0x10E63:
+    case 0x10F20:
+    case 0x11055:
+    case 0x1106A:
+    case 0x110F4:
+    case 0x1113A:
+    case 0x111D4:
+    case 0x111E4:
+    case 0x112F4:
+    case 0x11454:
+    case 0x114D4:
+    case 0x11654:
+    case 0x116C4:
+    case 0x11734:
+    case 0x118E4:
+    case 0x11C54:
+    case 0x11C5D:
+    case 0x11D54:
+    case 0x11DA4:
+    case 0x12402:
+    case 0x12409:
+    case 0x1240F:
+    case 0x12418:
+    case 0x12421:
+    case 0x12426:
+    case 0x12430:
+    case 0x12438:
+    case 0x1243C:
+    case 0x1243D:
+    case 0x1243E:
+    case 0x1243F:
+    case 0x1244C:
+    case 0x12452:
+    case 0x12453:
+    case 0x12469:
+    case 0x16A64:
+    case 0x16B54:
+    case 0x16E84:
+    case 0x1D2E4:
+    case 0x1D363:
+    case 0x1D375:
+    case 0x1D7D2:
+    case 0x1D7DC:
+    case 0x1D7E6:
+    case 0x1D7F0:
+    case 0x1D7FA:
+    case 0x1E8CA:
+    case 0x1E954:
+    case 0x1EC74:
+    case 0x1ECA6:
+    case 0x1F105:
+    case 0x20064:
+    case 0x200E2:
+    case 0x2626D:
+        return (double) 4.0;
+    case 0x109F9:
+        return (double) 4.0/12.0;
+    case 0x2158:
+        return (double) 4.0/5.0;
+    case 0x1375:
+    case 0x324B:
+    case 0x32B5:
+    case 0x534C:
+    case 0x10113:
+    case 0x102ED:
+    case 0x109CC:
+    case 0x10E6C:
+    case 0x1105E:
+    case 0x111ED:
+    case 0x118ED:
+    case 0x11C66:
+    case 0x12467:
+    case 0x1D36C:
+    case 0x1EC7D:
+    case 0x2098C:
+    case 0x2099C:
+        return (double) 40.0;
+    case 0x1011C:
+    case 0x102F6:
+    case 0x109D5:
+    case 0x10E75:
+    case 0x1EC86:
+        return (double) 400.0;
+    case 0x10125:
+    case 0x109DE:
+    case 0x1EC8F:
+        return (double) 4000.0;
+    case 0x1012E:
+    case 0x109E7:
+    case 0x1EC98:
+        return (double) 40000.0;
+    case 0x109F0:
+        return (double) 400000.0;
+    case 0x32B6:
+        return (double) 41.0;
+    case 0x32B7:
+        return (double) 42.0;
+    case 0x32B8:
+        return (double) 43.0;
+    case 0x12433:
+        return (double) 432000.0;
+    case 0x32B9:
+        return (double) 44.0;
+    case 0x32BA:
+        return (double) 45.0;
+    case 0x32BB:
+        return (double) 46.0;
+    case 0x32BC:
+        return (double) 47.0;
+    case 0x32BD:
+        return (double) 48.0;
+    case 0x32BE:
+        return (double) 49.0;
+    case 0x0035:
+    case 0x0665:
+    case 0x06F5:
+    case 0x07C5:
+    case 0x096B:
+    case 0x09EB:
+    case 0x0A6B:
+    case 0x0AEB:
+    case 0x0B6B:
+    case 0x0BEB:
+    case 0x0C6B:
+    case 0x0CEB:
+    case 0x0D6B:
+    case 0x0DEB:
+    case 0x0E55:
+    case 0x0ED5:
+    case 0x0F25:
+    case 0x1045:
+    case 0x1095:
+    case 0x136D:
+    case 0x17E5:
+    case 0x17F5:
+    case 0x1815:
+    case 0x194B:
+    case 0x19D5:
+    case 0x1A85:
+    case 0x1A95:
+    case 0x1B55:
+    case 0x1BB5:
+    case 0x1C45:
+    case 0x1C55:
+    case 0x2075:
+    case 0x2085:
+    case 0x2164:
+    case 0x2174:
+    case 0x2464:
+    case 0x2478:
+    case 0x248C:
+    case 0x24F9:
+    case 0x277A:
+    case 0x2784:
+    case 0x278E:
+    case 0x3025:
+    case 0x3224:
+    case 0x3284:
+    case 0x3405:
+    case 0x382A:
+    case 0x4E94:
+    case 0x4F0D:
+    case 0xA625:
+    case 0xA6EA:
+    case 0xA8D5:
+    case 0xA905:
+    case 0xA9D5:
+    case 0xA9F5:
+    case 0xAA55:
+    case 0xABF5:
+    case 0xFF15:
+    case 0x1010B:
+    case 0x10143:
+    case 0x10148:
+    case 0x1014F:
+    case 0x1015F:
+    case 0x10173:
+    case 0x102E5:
+    case 0x10321:
+    case 0x104A5:
+    case 0x1087D:
+    case 0x108AC:
+    case 0x108FC:
+    case 0x109C4:
+    case 0x10AEC:
+    case 0x10CFB:
+    case 0x10D35:
+    case 0x10E64:
+    case 0x10F21:
+    case 0x11056:
+    case 0x1106B:
+    case 0x110F5:
+    case 0x1113B:
+    case 0x111D5:
+    case 0x111E5:
+    case 0x112F5:
+    case 0x11455:
+    case 0x114D5:
+    case 0x11655:
+    case 0x116C5:
+    case 0x11735:
+    case 0x118E5:
+    case 0x11C55:
+    case 0x11C5E:
+    case 0x11D55:
+    case 0x11DA5:
+    case 0x12403:
+    case 0x1240A:
+    case 0x12410:
+    case 0x12419:
+    case 0x12422:
+    case 0x12427:
+    case 0x12431:
+    case 0x12439:
+    case 0x1244D:
+    case 0x12454:
+    case 0x12455:
+    case 0x1246A:
+    case 0x16A65:
+    case 0x16B55:
+    case 0x16E85:
+    case 0x1D2E5:
+    case 0x1D364:
+    case 0x1D376:
+    case 0x1D378:
+    case 0x1D7D3:
+    case 0x1D7DD:
+    case 0x1D7E7:
+    case 0x1D7F1:
+    case 0x1D7FB:
+    case 0x1E8CB:
+    case 0x1E955:
+    case 0x1EC75:
+    case 0x1ECA7:
+    case 0x1F106:
+    case 0x20121:
+        return (double) 5.0;
+    case 0x109FA:
+        return (double) 5.0/12.0;
+    case 0x0F2C:
+        return (double) 5.0/2.0;
+    case 0x215A:
+    case 0x1245C:
+        return (double) 5.0/6.0;
+    case 0x215D:
+        return (double) 5.0/8.0;
+    case 0x1376:
+    case 0x216C:
+    case 0x217C:
+    case 0x2186:
+    case 0x324C:
+    case 0x32BF:
+    case 0x10114:
+    case 0x10144:
+    case 0x1014A:
+    case 0x10151:
+    case 0x10166:
+    case 0x10167:
+    case 0x10168:
+    case 0x10169:
+    case 0x10174:
+    case 0x102EE:
+    case 0x10323:
+    case 0x109CD:
+    case 0x10A7E:
+    case 0x10CFD:
+    case 0x10E6D:
+    case 0x1105F:
+    case 0x111EE:
+    case 0x118EE:
+    case 0x11C67:
+    case 0x12468:
+    case 0x1D36D:
+    case 0x1EC7E:
+        return (double) 50.0;
+    case 0x216E:
+    case 0x217E:
+    case 0x1011D:
+    case 0x10145:
+    case 0x1014C:
+    case 0x10153:
+    case 0x1016C:
+    case 0x1016D:
+    case 0x1016E:
+    case 0x1016F:
+    case 0x10170:
+    case 0x102F7:
+    case 0x109D6:
+    case 0x10E76:
+    case 0x1EC87:
+        return (double) 500.0;
+    case 0x2181:
+    case 0x10126:
+    case 0x10146:
+    case 0x1014E:
+    case 0x10172:
+    case 0x109DF:
+    case 0x1EC90:
+        return (double) 5000.0;
+    case 0x2187:
+    case 0x1012F:
+    case 0x10147:
+    case 0x10156:
+    case 0x109E8:
+    case 0x1EC99:
+        return (double) 50000.0;
+    case 0x109F1:
+        return (double) 500000.0;
+    case 0x0036:
+    case 0x0666:
+    case 0x06F6:
+    case 0x07C6:
+    case 0x096C:
+    case 0x09EC:
+    case 0x0A6C:
+    case 0x0AEC:
+    case 0x0B6C:
+    case 0x0BEC:
+    case 0x0C6C:
+    case 0x0CEC:
+    case 0x0D6C:
+    case 0x0DEC:
+    case 0x0E56:
+    case 0x0ED6:
+    case 0x0F26:
+    case 0x1046:
+    case 0x1096:
+    case 0x136E:
+    case 0x17E6:
+    case 0x17F6:
+    case 0x1816:
+    case 0x194C:
+    case 0x19D6:
+    case 0x1A86:
+    case 0x1A96:
+    case 0x1B56:
+    case 0x1BB6:
+    case 0x1C46:
+    case 0x1C56:
+    case 0x2076:
+    case 0x2086:
+    case 0x2165:
+    case 0x2175:
+    case 0x2185:
+    case 0x2465:
+    case 0x2479:
+    case 0x248D:
+    case 0x24FA:
+    case 0x277B:
+    case 0x2785:
+    case 0x278F:
+    case 0x3026:
+    case 0x3225:
+    case 0x3285:
+    case 0x516D:
+    case 0x9646:
+    case 0x9678:
+    case 0xA626:
+    case 0xA6EB:
+    case 0xA8D6:
+    case 0xA906:
+    case 0xA9D6:
+    case 0xA9F6:
+    case 0xAA56:
+    case 0xABF6:
+    case 0xF9D1:
+    case 0xF9D3:
+    case 0xFF16:
+    case 0x1010C:
+    case 0x102E6:
+    case 0x104A6:
+    case 0x109C5:
+    case 0x10D36:
+    case 0x10E65:
+    case 0x11057:
+    case 0x1106C:
+    case 0x110F6:
+    case 0x1113C:
+    case 0x111D6:
+    case 0x111E6:
+    case 0x112F6:
+    case 0x11456:
+    case 0x114D6:
+    case 0x11656:
+    case 0x116C6:
+    case 0x11736:
+    case 0x118E6:
+    case 0x11C56:
+    case 0x11C5F:
+    case 0x11D56:
+    case 0x11DA6:
+    case 0x12404:
+    case 0x1240B:
+    case 0x12411:
+    case 0x1241A:
+    case 0x12428:
+    case 0x12440:
+    case 0x1244E:
+    case 0x1246B:
+    case 0x16A66:
+    case 0x16B56:
+    case 0x16E86:
+    case 0x1D2E6:
+    case 0x1D365:
+    case 0x1D7D4:
+    case 0x1D7DE:
+    case 0x1D7E8:
+    case 0x1D7F2:
+    case 0x1D7FC:
+    case 0x1E8CC:
+    case 0x1E956:
+    case 0x1EC76:
+    case 0x1ECA8:
+    case 0x1F107:
+    case 0x20AEA:
+        return (double) 6.0;
+    case 0x109FB:
+        return (double) 6.0/12.0;
+    case 0x1377:
+    case 0x324D:
+    case 0x10115:
+    case 0x102EF:
+    case 0x109CE:
+    case 0x10E6E:
+    case 0x11060:
+    case 0x111EF:
+    case 0x118EF:
+    case 0x11C68:
+    case 0x1D36E:
+    case 0x1EC7F:
+        return (double) 60.0;
+    case 0x1011E:
+    case 0x102F8:
+    case 0x109D7:
+    case 0x10E77:
+    case 0x1EC88:
+        return (double) 600.0;
+    case 0x10127:
+    case 0x109E0:
+    case 0x1EC91:
+        return (double) 6000.0;
+    case 0x10130:
+    case 0x109E9:
+    case 0x1EC9A:
+        return (double) 60000.0;
+    case 0x109F2:
+        return (double) 600000.0;
+    case 0x0037:
+    case 0x0667:
+    case 0x06F7:
+    case 0x07C7:
+    case 0x096D:
+    case 0x09ED:
+    case 0x0A6D:
+    case 0x0AED:
+    case 0x0B6D:
+    case 0x0BED:
+    case 0x0C6D:
+    case 0x0CED:
+    case 0x0D6D:
+    case 0x0DED:
+    case 0x0E57:
+    case 0x0ED7:
+    case 0x0F27:
+    case 0x1047:
+    case 0x1097:
+    case 0x136F:
+    case 0x17E7:
+    case 0x17F7:
+    case 0x1817:
+    case 0x194D:
+    case 0x19D7:
+    case 0x1A87:
+    case 0x1A97:
+    case 0x1B57:
+    case 0x1BB7:
+    case 0x1C47:
+    case 0x1C57:
+    case 0x2077:
+    case 0x2087:
+    case 0x2166:
+    case 0x2176:
+    case 0x2466:
+    case 0x247A:
+    case 0x248E:
+    case 0x24FB:
+    case 0x277C:
+    case 0x2786:
+    case 0x2790:
+    case 0x3027:
+    case 0x3226:
+    case 0x3286:
+    case 0x3B4D:
+    case 0x4E03:
+    case 0x67D2:
+    case 0x6F06:
+    case 0xA627:
+    case 0xA6EC:
+    case 0xA8D7:
+    case 0xA907:
+    case 0xA9D7:
+    case 0xA9F7:
+    case 0xAA57:
+    case 0xABF7:
+    case 0xFF17:
+    case 0x1010D:
+    case 0x102E7:
+    case 0x104A7:
+    case 0x109C6:
+    case 0x10D37:
+    case 0x10E66:
+    case 0x11058:
+    case 0x1106D:
+    case 0x110F7:
+    case 0x1113D:
+    case 0x111D7:
+    case 0x111E7:
+    case 0x112F7:
+    case 0x11457:
+    case 0x114D7:
+    case 0x11657:
+    case 0x116C7:
+    case 0x11737:
+    case 0x118E7:
+    case 0x11C57:
+    case 0x11C60:
+    case 0x11D57:
+    case 0x11DA7:
+    case 0x12405:
+    case 0x1240C:
+    case 0x12412:
+    case 0x1241B:
+    case 0x12429:
+    case 0x12441:
+    case 0x12442:
+    case 0x12443:
+    case 0x1246C:
+    case 0x16A67:
+    case 0x16B57:
+    case 0x16E87:
+    case 0x1D2E7:
+    case 0x1D366:
+    case 0x1D7D5:
+    case 0x1D7DF:
+    case 0x1D7E9:
+    case 0x1D7F3:
+    case 0x1D7FD:
+    case 0x1E8CD:
+    case 0x1E957:
+    case 0x1EC77:
+    case 0x1ECA9:
+    case 0x1F108:
+    case 0x20001:
+        return (double) 7.0;
+    case 0x109FC:
+        return (double) 7.0/12.0;
+    case 0x0F2D:
+        return (double) 7.0/2.0;
+    case 0x215E:
+        return (double) 7.0/8.0;
+    case 0x1378:
+    case 0x324E:
+    case 0x10116:
+    case 0x102F0:
+    case 0x109CF:
+    case 0x10E6F:
+    case 0x11061:
+    case 0x111F0:
+    case 0x118F0:
+    case 0x11C69:
+    case 0x1D36F:
+    case 0x1EC80:
+        return (double) 70.0;
+    case 0x1011F:
+    case 0x102F9:
+    case 0x109D8:
+    case 0x10E78:
+    case 0x1EC89:
+        return (double) 700.0;
+    case 0x10128:
+    case 0x109E1:
+    case 0x1EC92:
+        return (double) 7000.0;
+    case 0x10131:
+    case 0x109EA:
+    case 0x1EC9B:
+        return (double) 70000.0;
+    case 0x109F3:
+        return (double) 700000.0;
+    case 0x0038:
+    case 0x0668:
+    case 0x06F8:
+    case 0x07C8:
+    case 0x096E:
+    case 0x09EE:
+    case 0x0A6E:
+    case 0x0AEE:
+    case 0x0B6E:
+    case 0x0BEE:
+    case 0x0C6E:
+    case 0x0CEE:
+    case 0x0D6E:
+    case 0x0DEE:
+    case 0x0E58:
+    case 0x0ED8:
+    case 0x0F28:
+    case 0x1048:
+    case 0x1098:
+    case 0x1370:
+    case 0x17E8:
+    case 0x17F8:
+    case 0x1818:
+    case 0x194E:
+    case 0x19D8:
+    case 0x1A88:
+    case 0x1A98:
+    case 0x1B58:
+    case 0x1BB8:
+    case 0x1C48:
+    case 0x1C58:
+    case 0x2078:
+    case 0x2088:
+    case 0x2167:
+    case 0x2177:
+    case 0x2467:
+    case 0x247B:
+    case 0x248F:
+    case 0x24FC:
+    case 0x277D:
+    case 0x2787:
+    case 0x2791:
+    case 0x3028:
+    case 0x3227:
+    case 0x3287:
+    case 0x516B:
+    case 0x634C:
+    case 0xA628:
+    case 0xA6ED:
+    case 0xA8D8:
+    case 0xA908:
+    case 0xA9D8:
+    case 0xA9F8:
+    case 0xAA58:
+    case 0xABF8:
+    case 0xFF18:
+    case 0x1010E:
+    case 0x102E8:
+    case 0x104A8:
+    case 0x109C7:
+    case 0x10D38:
+    case 0x10E67:
+    case 0x11059:
+    case 0x1106E:
+    case 0x110F8:
+    case 0x1113E:
+    case 0x111D8:
+    case 0x111E8:
+    case 0x112F8:
+    case 0x11458:
+    case 0x114D8:
+    case 0x11658:
+    case 0x116C8:
+    case 0x11738:
+    case 0x118E8:
+    case 0x11C58:
+    case 0x11C61:
+    case 0x11D58:
+    case 0x11DA8:
+    case 0x12406:
+    case 0x1240D:
+    case 0x12413:
+    case 0x1241C:
+    case 0x1242A:
+    case 0x12444:
+    case 0x12445:
+    case 0x1246D:
+    case 0x16A68:
+    case 0x16B58:
+    case 0x16E88:
+    case 0x1D2E8:
+    case 0x1D367:
+    case 0x1D7D6:
+    case 0x1D7E0:
+    case 0x1D7EA:
+    case 0x1D7F4:
+    case 0x1D7FE:
+    case 0x1E8CE:
+    case 0x1E958:
+    case 0x1EC78:
+    case 0x1ECAA:
+    case 0x1F109:
+        return (double) 8.0;
+    case 0x109FD:
+        return (double) 8.0/12.0;
+    case 0x1379:
+    case 0x324F:
+    case 0x10117:
+    case 0x102F1:
+    case 0x10E70:
+    case 0x11062:
+    case 0x111F1:
+    case 0x118F1:
+    case 0x11C6A:
+    case 0x1D370:
+    case 0x1EC81:
+        return (double) 80.0;
+    case 0x10120:
+    case 0x102FA:
+    case 0x109D9:
+    case 0x10E79:
+    case 0x1EC8A:
+        return (double) 800.0;
+    case 0x10129:
+    case 0x109E2:
+    case 0x1EC93:
+        return (double) 8000.0;
+    case 0x10132:
+    case 0x109EB:
+    case 0x1EC9C:
+        return (double) 80000.0;
+    case 0x109F4:
+        return (double) 800000.0;
+    case 0x0039:
+    case 0x0669:
+    case 0x06F9:
+    case 0x07C9:
+    case 0x096F:
+    case 0x09EF:
+    case 0x0A6F:
+    case 0x0AEF:
+    case 0x0B6F:
+    case 0x0BEF:
+    case 0x0C6F:
+    case 0x0CEF:
+    case 0x0D6F:
+    case 0x0DEF:
+    case 0x0E59:
+    case 0x0ED9:
+    case 0x0F29:
+    case 0x1049:
+    case 0x1099:
+    case 0x1371:
+    case 0x17E9:
+    case 0x17F9:
+    case 0x1819:
+    case 0x194F:
+    case 0x19D9:
+    case 0x1A89:
+    case 0x1A99:
+    case 0x1B59:
+    case 0x1BB9:
+    case 0x1C49:
+    case 0x1C59:
+    case 0x2079:
+    case 0x2089:
+    case 0x2168:
+    case 0x2178:
+    case 0x2468:
+    case 0x247C:
+    case 0x2490:
+    case 0x24FD:
+    case 0x277E:
+    case 0x2788:
+    case 0x2792:
+    case 0x3029:
+    case 0x3228:
+    case 0x3288:
+    case 0x4E5D:
+    case 0x5EFE:
+    case 0x7396:
+    case 0xA629:
+    case 0xA6EE:
+    case 0xA8D9:
+    case 0xA909:
+    case 0xA9D9:
+    case 0xA9F9:
+    case 0xAA59:
+    case 0xABF9:
+    case 0xFF19:
+    case 0x1010F:
+    case 0x102E9:
+    case 0x104A9:
+    case 0x109C8:
+    case 0x10D39:
+    case 0x10E68:
+    case 0x1105A:
+    case 0x1106F:
+    case 0x110F9:
+    case 0x1113F:
+    case 0x111D9:
+    case 0x111E9:
+    case 0x112F9:
+    case 0x11459:
+    case 0x114D9:
+    case 0x11659:
+    case 0x116C9:
+    case 0x11739:
+    case 0x118E9:
+    case 0x11C59:
+    case 0x11C62:
+    case 0x11D59:
+    case 0x11DA9:
+    case 0x12407:
+    case 0x1240E:
+    case 0x12414:
+    case 0x1241D:
+    case 0x1242B:
+    case 0x12446:
+    case 0x12447:
+    case 0x12448:
+    case 0x12449:
+    case 0x1246E:
+    case 0x16A69:
+    case 0x16B59:
+    case 0x16E89:
+    case 0x1D2E9:
+    case 0x1D368:
+    case 0x1D7D7:
+    case 0x1D7E1:
+    case 0x1D7EB:
+    case 0x1D7F5:
+    case 0x1D7FF:
+    case 0x1E8CF:
+    case 0x1E959:
+    case 0x1EC79:
+    case 0x1ECAB:
+    case 0x1F10A:
+    case 0x2F890:
+        return (double) 9.0;
+    case 0x109FE:
+        return (double) 9.0/12.0;
+    case 0x0F2E:
+        return (double) 9.0/2.0;
+    case 0x137A:
+    case 0x10118:
+    case 0x102F2:
+    case 0x10341:
+    case 0x10E71:
+    case 0x11063:
+    case 0x111F2:
+    case 0x118F2:
+    case 0x11C6B:
+    case 0x1D371:
+    case 0x1EC82:
+        return (double) 90.0;
+    case 0x10121:
+    case 0x102FB:
+    case 0x1034A:
+    case 0x109DA:
+    case 0x10E7A:
+    case 0x1EC8B:
+        return (double) 900.0;
+    case 0x1012A:
+    case 0x109E3:
+    case 0x1EC94:
+        return (double) 9000.0;
+    case 0x10133:
+    case 0x109EC:
+    case 0x1EC9D:
+        return (double) 90000.0;
+    case 0x109F5:
+        return (double) 900000.0;
+    }
+    return -1.0;
+}
+
+/* Returns 1 for Unicode characters having the bidirectional
+ * type 'WS', 'B' or 'S' or the category 'Zs', 0 otherwise.
+ */
+int numba_PyUnicode_IsWhitespace(const Py_UCS4 ch)
+{
+    switch (ch) {
+    case 0x0009:
+    case 0x000A:
+    case 0x000B:
+    case 0x000C:
+    case 0x000D:
+    case 0x001C:
+    case 0x001D:
+    case 0x001E:
+    case 0x001F:
+    case 0x0020:
+    case 0x0085:
+    case 0x00A0:
+    case 0x1680:
+    case 0x2000:
+    case 0x2001:
+    case 0x2002:
+    case 0x2003:
+    case 0x2004:
+    case 0x2005:
+    case 0x2006:
+    case 0x2007:
+    case 0x2008:
+    case 0x2009:
+    case 0x200A:
+    case 0x2028:
+    case 0x2029:
+    case 0x202F:
+    case 0x205F:
+    case 0x3000:
+        return 1;
+    }
+    return 0;
+}
+
+/* Returns 1 for Unicode characters having the line break
+ * property 'BK', 'CR', 'LF' or 'NL' or having bidirectional
+ * type 'B', 0 otherwise.
+ */
+int numba_PyUnicode_IsLinebreak(const Py_UCS4 ch)
+{
+    switch (ch) {
+    case 0x000A:
+    case 0x000B:
+    case 0x000C:
+    case 0x000D:
+    case 0x001C:
+    case 0x001D:
+    case 0x001E:
+    case 0x0085:
+    case 0x2028:
+    case 0x2029:
+        return 1;
+    }
+    return 0;
+}
+
+#endif /* _UNICODETYPE_DB_H */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_version.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_version.py
new file mode 100644
index 000000000..06d328843
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/_version.py
@@ -0,0 +1,238 @@
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain. Generated by
+# versioneer-0.14 (https://github.com/warner/python-versioneer)
+
+import errno
+import os
+import re
+import subprocess
+import sys
+
+# these strings will be replaced by git during git-archive
+git_refnames = "$Format:%d$"
+git_full = "$Format:%H$"
+
+# these strings are filled in when 'setup.py versioneer' creates _version.py
+tag_prefix = ""
+parentdir_prefix = "numba-"
+versionfile_source = "numba/_version.py"
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
+    assert isinstance(commands, list)
+    p = None
+    for c in commands:
+        try:
+            # remember shell=False, so use git.cmd on windows, not just git
+            p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,
+                                 stderr=(subprocess.PIPE if hide_stderr
+                                         else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % args[0])
+                print(e)
+            return None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None
+    stdout = p.communicate()[0].strip()
+    if sys.version_info[0] >= 3:
+        stdout = stdout.decode()
+    if p.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % args[0])
+        return None
+    return stdout
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose=False):
+    # Source tarballs conventionally unpack into a directory that includes
+    # both the project name and a version string.
+    dirname = os.path.basename(root)
+    if not dirname.startswith(parentdir_prefix):
+        if verbose:
+            print("guessing rootdir is '%s', but '%s' doesn't start with "
+                  "prefix '%s'" % (root, dirname, parentdir_prefix))
+        return None
+    return {"version": dirname[len(parentdir_prefix):], "full": ""}
+
+
+def git_get_keywords(versionfile_abs):
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        f = open(versionfile_abs, "r")
+        for line in f.readlines():
+            if line.strip().startswith("git_refnames ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["refnames"] = mo.group(1)
+            if line.strip().startswith("git_full ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["full"] = mo.group(1)
+        f.close()
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+def git_versions_from_keywords(keywords, tag_prefix, verbose=False):
+    if not keywords:
+        return {}  # keyword-finding function failed to find keywords
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        return {}  # unexpanded, so not in an unpacked git-archive tarball
+    refs = set([r.strip() for r in refnames.strip("()").split(",")])
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = set([r for r in refs if re.search(r'\d', r)])
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs-tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full": keywords["full"].strip()}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full": keywords["full"].strip()}
+
+
+def git_parse_vcs_describe(git_describe, tag_prefix, verbose=False):
+    # TAG-NUM-gHEX[-dirty] or HEX[-dirty] . TAG might have hyphens.
+
+    # dirty
+    dirty = git_describe.endswith("-dirty")
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+    dirty_suffix = ".dirty" if dirty else ""
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" not in git_describe:  # just HEX
+        return "0+untagged.g"+git_describe+dirty_suffix, dirty
+
+    # just TAG-NUM-gHEX
+    mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+    if not mo:
+        # unparseable. Maybe git-describe is misbehaving?
+        return "0+unparseable"+dirty_suffix, dirty
+
+    # tag
+    full_tag = mo.group(1)
+    if not full_tag.startswith(tag_prefix):
+        if verbose:
+            fmt = "tag '%s' doesn't start with prefix '%s'"
+            print(fmt % (full_tag, tag_prefix))
+        return None, dirty
+    tag = full_tag[len(tag_prefix):]
+
+    # distance: number of commits since tag
+    distance = int(mo.group(2))
+
+    # commit: short hex revision ID
+    commit = mo.group(3)
+
+    # now build up version string, with post-release "local version
+    # identifier". Our goal: TAG[+NUM.gHEX[.dirty]] . Note that if you get a
+    # tagged build and then dirty it, you'll get TAG+0.gHEX.dirty . So you
+    # can always test version.endswith(".dirty").
+    version = tag
+    if distance or dirty:
+        version += "+%d.g%s" % (distance, commit) + dirty_suffix
+
+    return version, dirty
+
+
+def git_versions_from_vcs(tag_prefix, root, verbose=False):
+    # this runs 'git' from the root of the source tree. This only gets called
+    # if the git-archive 'subst' keywords were *not* expanded, and
+    # _version.py hasn't already been rewritten with a short version string,
+    # meaning we're inside a checked out source tree.
+
+    if not os.path.exists(os.path.join(root, ".git")):
+        if verbose:
+            print("no .git in %s" % root)
+        return {}  # get_versions() will try next method
+
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+    # if there is a tag, this yields TAG-NUM-gHEX[-dirty]
+    # if there are no tags, this yields HEX[-dirty] (no NUM)
+    stdout = run_command(GITS, ["describe", "--tags", "--dirty",
+                                "--always", "--long"],
+                         cwd=root)
+    # --long was added in git-1.5.5
+    if stdout is None:
+        return {}  # try next method
+    version, dirty = git_parse_vcs_describe(stdout, tag_prefix, verbose)
+
+    # build "full", which is FULLHEX[.dirty]
+    stdout = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if stdout is None:
+        return {}
+    full = stdout.strip()
+    if dirty:
+        full += ".dirty"
+
+    return {"version": version, "full": full}
+
+
+def get_versions(default={"version": "0+unknown", "full": ""}, verbose=False):
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    keywords = {"refnames": git_refnames, "full": git_full}
+    ver = git_versions_from_keywords(keywords, tag_prefix, verbose)
+    if ver:
+        return ver
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for i in versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return default
+
+    return (git_versions_from_vcs(tag_prefix, root, verbose)
+            or versions_from_parentdir(parentdir_prefix, root, verbose)
+            or default)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/capsulethunk.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/capsulethunk.h
new file mode 100644
index 000000000..4bdf5b41f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/capsulethunk.h
@@ -0,0 +1,108 @@
+/**
+ 
+ This is a modified version of capsulethunk.h for use in llvmpy 
+ 
+**/
+
+#ifndef __CAPSULETHUNK_H
+#define __CAPSULETHUNK_H
+
+#if (    (PY_VERSION_HEX <  0x02070000) \
+     || ((PY_VERSION_HEX >= 0x03000000) \
+      && (PY_VERSION_HEX <  0x03010000)) )
+
+//#define Assert(X) do_assert(!!(X), #X, __FILE__, __LINE__)
+#define Assert(X)
+
+static
+void do_assert(int cond, const char * msg, const char *file, unsigned line){
+    if (!cond) {
+        fprintf(stderr, "Assertion failed %s:%d\n%s\n", file, line, msg);
+        exit(1);
+    }
+}
+
+typedef void (*PyCapsule_Destructor)(PyObject *);
+
+struct FakePyCapsule_Desc {
+    const char *name;
+    void *context;
+    PyCapsule_Destructor dtor;
+    PyObject *parent;
+
+    FakePyCapsule_Desc() : name(0), context(0), dtor(0) {}
+};
+
+static
+FakePyCapsule_Desc* get_pycobj_desc(PyObject *p){
+    void *desc = ((PyCObject*)p)->desc;
+    Assert(desc && "No desc in PyCObject");
+    return static_cast<FakePyCapsule_Desc*>(desc);
+}
+
+static
+void pycobject_pycapsule_dtor(void *p, void *desc){
+    Assert(desc);
+    Assert(p);
+    FakePyCapsule_Desc *fpc_desc = static_cast<FakePyCapsule_Desc*>(desc);
+    Assert(fpc_desc->parent);
+    Assert(PyCObject_Check(fpc_desc->parent));
+    fpc_desc->dtor(static_cast<PyObject*>(fpc_desc->parent));
+    delete fpc_desc;
+}
+
+static
+PyObject* PyCapsule_New(void* ptr, const char *name, PyCapsule_Destructor dtor)
+{
+    FakePyCapsule_Desc *desc = new FakePyCapsule_Desc;
+    desc->name = name;
+    desc->context = NULL;
+    desc->dtor = dtor;
+    PyObject *p = PyCObject_FromVoidPtrAndDesc(ptr, desc,
+                                               pycobject_pycapsule_dtor);
+    desc->parent = p;
+    return p;
+}
+
+static
+int PyCapsule_CheckExact(PyObject *p)
+{
+    return PyCObject_Check(p);
+}
+
+static
+void* PyCapsule_GetPointer(PyObject *p, const char *name)
+{
+    Assert(PyCapsule_CheckExact(p));
+    if (strcmp(get_pycobj_desc(p)->name, name) != 0) {
+        PyErr_SetString(PyExc_ValueError, "Invalid PyCapsule object");
+    }
+    return PyCObject_AsVoidPtr(p);
+}
+
+static
+void* PyCapsule_GetContext(PyObject *p)
+{
+    Assert(p);
+    Assert(PyCapsule_CheckExact(p));
+    return get_pycobj_desc(p)->context;
+}
+
+static
+int PyCapsule_SetContext(PyObject *p, void *context)
+{
+    Assert(PyCapsule_CheckExact(p));
+    get_pycobj_desc(p)->context = context;
+    return 0;
+}
+
+static
+const char * PyCapsule_GetName(PyObject *p)
+{
+//    Assert(PyCapsule_CheckExact(p));
+    return get_pycobj_desc(p)->name;
+}
+
+#endif /* #if PY_VERSION_HEX < 0x02070000 */
+
+#endif /* __CAPSULETHUNK_H */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/__init__.py
new file mode 100644
index 000000000..0ff54a4d6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/__init__.py
@@ -0,0 +1,23 @@
+"""
+Utilities for getting information about Numba C extensions
+"""
+
+import os
+
+
+def get_extension_libs():
+    """Return the .c files in the `numba.cext` directory.
+    """
+    libs = []
+    base = get_path()
+    for fn in os.listdir(base):
+        if fn.endswith('.c'):
+            fn = os.path.join(base, fn)
+            libs.append(fn)
+    return libs
+
+
+def get_path():
+    """Returns the path to the directory for `numba.cext`.
+    """
+    return os.path.abspath(os.path.join(os.path.dirname(__file__)))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/cext.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/cext.h
new file mode 100644
index 000000000..88188dc85
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/cext.h
@@ -0,0 +1,18 @@
+#ifndef NUMBA_EXTENSION_HELPER_H_
+#define NUMBA_EXTENSION_HELPER_H_
+
+#include "Python.h"
+#include "../_numba_common.h"
+
+/* Define all runtime-required symbols in this C module, but do not
+   export them outside the shared library if possible. */
+#define NUMBA_EXPORT_FUNC(_rettype) VISIBILITY_HIDDEN _rettype
+#define NUMBA_EXPORT_DATA(_vartype) VISIBILITY_HIDDEN _vartype
+
+NUMBA_EXPORT_FUNC(Py_ssize_t)
+aligned_size(Py_ssize_t sz);
+
+#include "dictobject.h"
+#include "listobject.h"
+
+#endif // end NUMBA_EXTENSION_HELPER_H_
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/dictobject.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/dictobject.c
new file mode 100644
index 000000000..37db7593b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/dictobject.c
@@ -0,0 +1,1191 @@
+/* The following is adapted from CPython3.7.
+The exact commit is:
+
+- https://github.com/python/cpython/blob/44467e8ea4cea390b0718702291b4cfe8ddd67ed/Objects/dictobject.c
+
+
+*/
+
+/* Dictionary object implementation using a hash table */
+
+/* The distribution includes a separate file, Objects/dictnotes.txt,
+   describing explorations into dictionary design and optimization.
+   It covers typical dictionary use patterns, the parameters for
+   tuning dictionaries, and several ideas for possible optimizations.
+*/
+
+/* PyDictKeysObject
+
+This implements the dictionary's hashtable.
+
+As of Python 3.6, this is compact and ordered. Basic idea is described here:
+* https://mail.python.org/pipermail/python-dev/2012-December/123028.html
+* https://morepypy.blogspot.com/2015/01/faster-more-memory-efficient-and-more.html
+
+layout:
+
++---------------+
+| dk_refcnt     |
+| dk_size       |
+| dk_lookup     |
+| dk_usable     |
+| dk_nentries   |
++---------------+
+| dk_indices    |
+|               |
++---------------+
+| dk_entries    |
+|               |
++---------------+
+
+dk_indices is actual hashtable.  It holds index in entries, or DKIX_EMPTY(-1)
+or DKIX_DUMMY(-2).
+Size of indices is dk_size.  Type of each index in indices is vary on dk_size:
+
+* int8  for          dk_size <= 128
+* int16 for 256   <= dk_size <= 2**15
+* int32 for 2**16 <= dk_size <= 2**31
+* int64 for 2**32 <= dk_size
+
+dk_entries is array of PyDictKeyEntry.  It's size is USABLE_FRACTION(dk_size).
+DK_ENTRIES(dk) can be used to get pointer to entries.
+
+NOTE: Since negative value is used for DKIX_EMPTY and DKIX_DUMMY, type of
+dk_indices entry is signed integer and int16 is used for table which
+dk_size == 256.
+*/
+
+
+/*
+The DictObject can be in one of two forms.
+
+Either:
+  A combined table:
+    ma_values == NULL, dk_refcnt == 1.
+    Values are stored in the me_value field of the PyDictKeysObject.
+Or:
+
+  (Numba dev notes: split table logic is removed)
+
+  A split table:
+    ma_values != NULL, dk_refcnt >= 1
+    Values are stored in the ma_values array.
+    Only string (unicode) keys are allowed.
+    All dicts sharing same key must have same insertion order.
+
+There are four kinds of slots in the table (slot is index, and
+DK_ENTRIES(keys)[index] if index >= 0):
+
+1. Unused.  index == DKIX_EMPTY
+   Does not hold an active (key, value) pair now and never did.  Unused can
+   transition to Active upon key insertion.  This is each slot's initial state.
+
+2. Active.  index >= 0, me_key != NULL and me_value != NULL
+   Holds an active (key, value) pair.  Active can transition to Dummy or
+   Pending upon key deletion (for combined and split tables respectively).
+   This is the only case in which me_value != NULL.
+
+3. Dummy.  index == DKIX_DUMMY  (combined only)
+   Previously held an active (key, value) pair, but that was deleted and an
+   active pair has not yet overwritten the slot.  Dummy can transition to
+   Active upon key insertion.  Dummy slots cannot be made Unused again
+   else the probe sequence in case of collision would have no way to know
+   they were once active.
+
+4. Pending. index >= 0, key != NULL, and value == NULL  (split only)
+   Not yet inserted in split-table.
+*/
+
+/*
+Preserving insertion order
+
+It's simple for combined table.  Since dk_entries is mostly append only, we can
+get insertion order by just iterating dk_entries.
+
+One exception is .popitem().  It removes last item in dk_entries and decrement
+dk_nentries to achieve amortized O(1).  Since there are DKIX_DUMMY remains in
+dk_indices, we can't increment dk_usable even though dk_nentries is
+decremented.
+
+In split table, inserting into pending entry is allowed only for dk_entries[ix]
+where ix == mp->ma_used. Inserting into other index and deleting item cause
+converting the dict to the combined table.
+*/
+
+
+/* D_MINSIZE (adapted from PyDict_MINSIZE)
+ * is the starting size for any new dict.
+ * 8 allows dicts with no more than 5 active entries; experiments suggested
+ * this suffices for the majority of dicts (consisting mostly of usually-small
+ * dicts created to pass keyword arguments).
+ * Making this 8, rather than 4 reduces the number of resizes for most
+ * dictionaries, without any significant extra memory use.
+ */
+#define D_MINSIZE 8
+
+#include "dictobject.h"
+
+
+#if defined(_MSC_VER)
+#   if _MSC_VER <= 1900  /* Visual Studio 2014 */
+        typedef __int8 int8_t;
+        typedef __int16 int16_t;
+        typedef __int32 int32_t;
+        typedef __int64 int64_t;
+#   endif
+    /* Use _alloca() to dynamically allocate on the stack on MSVC */
+    #define STACK_ALLOC(Type, Name, Size) Type * const Name = _alloca(Size);
+#else
+    #define STACK_ALLOC(Type, Name, Size) Type Name[Size];
+#endif
+
+
+/*[clinic input]
+class dict "PyDictObject *" "&PyDict_Type"
+[clinic start generated code]*/
+/*[clinic end generated code: output=da39a3ee5e6b4b0d input=f157a5a0ce9589d6]*/
+
+
+/*
+To ensure the lookup algorithm terminates, there must be at least one Unused
+slot (NULL key) in the table.
+To avoid slowing down lookups on a near-full table, we resize the table when
+it's USABLE_FRACTION (currently two-thirds) full.
+*/
+
+#define PERTURB_SHIFT 5
+
+/*
+Major subtleties ahead:  Most hash schemes depend on having a "good" hash
+function, in the sense of simulating randomness.  Python doesn't:  its most
+important hash functions (for ints) are very regular in common
+cases:
+
+  >>>[hash(i) for i in range(4)]
+  [0, 1, 2, 3]
+
+This isn't necessarily bad!  To the contrary, in a table of size 2**i, taking
+the low-order i bits as the initial table index is extremely fast, and there
+are no collisions at all for dicts indexed by a contiguous range of ints. So
+this gives better-than-random behavior in common cases, and that's very
+desirable.
+
+OTOH, when collisions occur, the tendency to fill contiguous slices of the
+hash table makes a good collision resolution strategy crucial.  Taking only
+the last i bits of the hash code is also vulnerable:  for example, consider
+the list [i << 16 for i in range(20000)] as a set of keys.  Since ints are
+their own hash codes, and this fits in a dict of size 2**15, the last 15 bits
+ of every hash code are all 0:  they *all* map to the same table index.
+
+But catering to unusual cases should not slow the usual ones, so we just take
+the last i bits anyway.  It's up to collision resolution to do the rest.  If
+we *usually* find the key we're looking for on the first try (and, it turns
+out, we usually do -- the table load factor is kept under 2/3, so the odds
+are solidly in our favor), then it makes best sense to keep the initial index
+computation dirt cheap.
+
+The first half of collision resolution is to visit table indices via this
+recurrence:
+
+    j = ((5*j) + 1) mod 2**i
+
+For any initial j in range(2**i), repeating that 2**i times generates each
+int in range(2**i) exactly once (see any text on random-number generation for
+proof).  By itself, this doesn't help much:  like linear probing (setting
+j += 1, or j -= 1, on each loop trip), it scans the table entries in a fixed
+order.  This would be bad, except that's not the only thing we do, and it's
+actually *good* in the common cases where hash keys are consecutive.  In an
+example that's really too small to make this entirely clear, for a table of
+size 2**3 the order of indices is:
+
+    0 -> 1 -> 6 -> 7 -> 4 -> 5 -> 2 -> 3 -> 0 [and here it's repeating]
+
+If two things come in at index 5, the first place we look after is index 2,
+not 6, so if another comes in at index 6 the collision at 5 didn't hurt it.
+Linear probing is deadly in this case because there the fixed probe order
+is the *same* as the order consecutive keys are likely to arrive.  But it's
+extremely unlikely hash codes will follow a 5*j+1 recurrence by accident,
+and certain that consecutive hash codes do not.
+
+The other half of the strategy is to get the other bits of the hash code
+into play.  This is done by initializing a (unsigned) vrbl "perturb" to the
+full hash code, and changing the recurrence to:
+
+    perturb >>= PERTURB_SHIFT;
+    j = (5*j) + 1 + perturb;
+    use j % 2**i as the next table index;
+
+Now the probe sequence depends (eventually) on every bit in the hash code,
+and the pseudo-scrambling property of recurring on 5*j+1 is more valuable,
+because it quickly magnifies small differences in the bits that didn't affect
+the initial index.  Note that because perturb is unsigned, if the recurrence
+is executed often enough perturb eventually becomes and remains 0.  At that
+point (very rarely reached) the recurrence is on (just) 5*j+1 again, and
+that's certain to find an empty slot eventually (since it generates every int
+in range(2**i), and we make sure there's always at least one empty slot).
+
+Selecting a good value for PERTURB_SHIFT is a balancing act.  You want it
+small so that the high bits of the hash code continue to affect the probe
+sequence across iterations; but you want it large so that in really bad cases
+the high-order hash bits have an effect on early iterations.  5 was "the
+best" in minimizing total collisions across experiments Tim Peters ran (on
+both normal and pathological cases), but 4 and 6 weren't significantly worse.
+
+Historical: Reimer Behrends contributed the idea of using a polynomial-based
+approach, using repeated multiplication by x in GF(2**n) where an irreducible
+polynomial for each table size was chosen such that x was a primitive root.
+Christian Tismer later extended that to use division by x instead, as an
+efficient way to get the high bits of the hash code into play.  This scheme
+also gave excellent collision statistics, but was more expensive:  two
+if-tests were required inside the loop; computing "the next" index took about
+the same number of operations but without as much potential parallelism
+(e.g., computing 5*j can go on at the same time as computing 1+perturb in the
+above, and then shifting perturb can be done while the table index is being
+masked); and the PyDictObject struct required a member to hold the table's
+polynomial.  In Tim's experiments the current scheme ran faster, produced
+equally good collision statistics, needed less code & used less memory.
+
+*/
+
+#define DKIX_EMPTY (-1)
+#define DKIX_DUMMY (-2)  /* Used internally */
+#define DKIX_ERROR (-3)
+
+typedef enum {
+    OK = 0,
+    OK_REPLACED = 1,
+    ERR_NO_MEMORY = -1,
+    ERR_DICT_MUTATED = -2,
+    ERR_ITER_EXHAUSTED = -3,
+    ERR_DICT_EMPTY = -4,
+    ERR_CMP_FAILED = -5,
+} Status;
+
+
+#ifndef NDEBUG
+static
+int mem_cmp_zeros(void *obj, size_t n){
+    int diff = 0;
+    char *mem = obj;
+    char *it;
+    for (it = mem; it < mem + n; ++it) {
+        if (*it != 0) diff += 1;
+    }
+    return diff;
+}
+#endif
+
+#define D_MASK(dk) ((dk)->size-1)
+#define D_GROWTH_RATE(d) ((d)->used*3)
+
+static int
+ix_size(Py_ssize_t size) {
+    if ( size < 0xff ) return 1;
+    if ( size < 0xffff ) return 2;
+    if ( size < 0xffffffff ) return 4;
+    return sizeof(int64_t);
+}
+
+#ifndef NDEBUG
+/* NOTE: This function is only used in assert()s */
+/* Align pointer *ptr* to pointer size */
+static void*
+aligned_pointer(void *ptr) {
+    return (void*)aligned_size((size_t)ptr);
+}
+#endif
+
+/* lookup indices.  returns DKIX_EMPTY, DKIX_DUMMY, or ix >=0 */
+static Py_ssize_t
+get_index(NB_DictKeys *dk, Py_ssize_t i)
+{
+    Py_ssize_t s = dk->size;
+    Py_ssize_t ix;
+
+    if (s <= 0xff) {
+        int8_t *indices = (int8_t*)(dk->indices);
+        assert (i < dk->size);
+        ix = indices[i];
+    }
+    else if (s <= 0xffff) {
+        int16_t *indices = (int16_t*)(dk->indices);
+        ix = indices[i];
+    }
+#if SIZEOF_VOID_P > 4
+    else if (s > 0xffffffff) {
+        int64_t *indices = (int64_t*)(dk->indices);
+        ix = indices[i];
+    }
+#endif
+    else {
+        int32_t *indices = (int32_t*)(dk->indices);
+        ix = indices[i];
+    }
+    assert(ix >= DKIX_DUMMY);
+    return ix;
+}
+
+/* write to indices. */
+static void
+set_index(NB_DictKeys *dk, Py_ssize_t i, Py_ssize_t ix)
+{
+    Py_ssize_t s = dk->size;
+
+    assert(ix >= DKIX_DUMMY);
+
+    if (s <= 0xff) {
+        int8_t *indices = (int8_t*)(dk->indices);
+        assert(ix <= 0x7f);
+        indices[i] = (char)ix;
+    }
+    else if (s <= 0xffff) {
+        int16_t *indices = (int16_t*)(dk->indices);
+        assert(ix <= 0x7fff);
+        indices[i] = (int16_t)ix;
+    }
+#if SIZEOF_VOID_P > 4
+    else if (s > 0xffffffff) {
+        int64_t *indices = (int64_t*)(dk->indices);
+        indices[i] = ix;
+    }
+#endif
+    else {
+        int32_t *indices = (int32_t*)(dk->indices);
+        assert(ix <= 0x7fffffff);
+        indices[i] = (int32_t)ix;
+    }
+}
+
+
+/* USABLE_FRACTION is the maximum dictionary load.
+ * Increasing this ratio makes dictionaries more dense resulting in more
+ * collisions.  Decreasing it improves sparseness at the expense of spreading
+ * indices over more cache lines and at the cost of total memory consumed.
+ *
+ * USABLE_FRACTION must obey the following:
+ *     (0 < USABLE_FRACTION(n) < n) for all n >= 2
+ *
+ * USABLE_FRACTION should be quick to calculate.
+ * Fractions around 1/2 to 2/3 seem to work well in practice.
+ */
+#define USABLE_FRACTION(n) (((n) << 1)/3)
+
+/* Alternative fraction that is otherwise close enough to 2n/3 to make
+ * little difference. 8 * 2/3 == 8 * 5/8 == 5. 16 * 2/3 == 16 * 5/8 == 10.
+ * 32 * 2/3 = 21, 32 * 5/8 = 20.
+ * Its advantage is that it is faster to compute on machines with slow division.
+ * #define USABLE_FRACTION(n) (((n) >> 1) + ((n) >> 2) - ((n) >> 3))
+ */
+
+/* GROWTH_RATE. Growth rate upon hitting maximum load.
+ * Currently set to used*3.
+ * This means that dicts double in size when growing without deletions,
+ * but have more head room when the number of deletions is on a par with the
+ * number of insertions.  See also bpo-17563 and bpo-33205.
+ *
+ * GROWTH_RATE was set to used*4 up to version 3.2.
+ * GROWTH_RATE was set to used*2 in version 3.3.0
+ * GROWTH_RATE was set to used*2 + capacity/2 in 3.4.0-3.6.0.
+ */
+#define GROWTH_RATE(d) ((d)->ma_used*3)
+
+
+static NB_DictEntry*
+get_entry(NB_DictKeys *dk, Py_ssize_t idx) {
+    Py_ssize_t offset;
+    char *ptr;
+
+    assert (idx < dk->size);
+    offset = idx * dk->entry_size;
+    ptr = dk->indices + dk->entry_offset + offset;
+    return (NB_DictEntry*)ptr;
+}
+
+static void
+zero_key(NB_DictKeys *dk, char *data){
+    memset(data, 0, dk->key_size);
+}
+
+static void
+zero_val(NB_DictKeys *dk, char *data){
+    memset(data, 0, dk->val_size);
+}
+
+static void
+copy_key(NB_DictKeys *dk, char *dst, const char *src){
+    memcpy(dst, src, dk->key_size);
+}
+
+static void
+copy_val(NB_DictKeys *dk, char *dst, const char *src){
+    memcpy(dst, src, dk->val_size);
+}
+
+/* Returns -1 for error; 0 for not equal; 1 for equal */
+static int
+key_equal(NB_DictKeys *dk, const char *lhs, const char *rhs) {
+    if ( dk->methods.key_equal ) {
+        return dk->methods.key_equal(lhs, rhs);
+    } else {
+        return memcmp(lhs, rhs, dk->key_size) == 0;
+    }
+}
+
+static char *
+entry_get_key(NB_DictKeys *dk, NB_DictEntry* entry) {
+    char * out = entry->keyvalue;
+    assert (out == aligned_pointer(out));
+    return out;
+}
+
+static char *
+entry_get_val(NB_DictKeys *dk, NB_DictEntry* entry) {
+    char * out = entry_get_key(dk, entry) + aligned_size(dk->key_size);
+    assert (out == aligned_pointer(out));
+    return out;
+}
+
+static void
+dk_incref_key(NB_DictKeys *dk, const char *key) {
+    if ( dk->methods.key_incref ) {
+        dk->methods.key_incref(key);
+    }
+}
+
+static void
+dk_decref_key(NB_DictKeys *dk, const char *key) {
+    if ( dk->methods.key_decref ) {
+        dk->methods.key_decref(key);
+    }
+}
+
+static void
+dk_incref_val(NB_DictKeys *dk, const char *val) {
+    if ( dk->methods.value_incref ) {
+        dk->methods.value_incref(val);
+    }
+}
+
+static void
+dk_decref_val(NB_DictKeys *dk, const char *val) {
+    if ( dk->methods.value_decref ) {
+        dk->methods.value_decref(val);
+    }
+}
+
+
+void
+numba_dictkeys_free(NB_DictKeys *dk) {
+    /* Clear all references from the entries */
+    Py_ssize_t i;
+    NB_DictEntry *ep;
+
+    for (i = 0; i < dk->nentries; i++) {
+        ep = get_entry(dk, i);
+        if (ep->hash != DKIX_EMPTY) {
+            dk_decref_key(dk, entry_get_key(dk, ep));
+            dk_decref_val(dk, entry_get_val(dk, ep));
+        }
+    }
+    /* Deallocate */
+    free(dk);
+}
+
+void
+numba_dict_free(NB_Dict *d) {
+    numba_dictkeys_free(d->keys);
+    free(d);
+}
+
+Py_ssize_t
+numba_dict_length(NB_Dict *d) {
+    return d->used;
+}
+
+/* Allocate new dictionary keys
+
+Adapted from CPython's new_keys_object().
+*/
+int
+numba_dictkeys_new(NB_DictKeys **out, Py_ssize_t size, Py_ssize_t key_size, Py_ssize_t val_size) {
+    Py_ssize_t usable = USABLE_FRACTION(size);
+    Py_ssize_t index_size = ix_size(size);
+    Py_ssize_t entry_size = aligned_size(sizeof(NB_DictEntry) + aligned_size(key_size) + aligned_size(val_size));
+    Py_ssize_t entry_offset = aligned_size(index_size * size);
+    Py_ssize_t alloc_size = sizeof(NB_DictKeys) + entry_offset + entry_size * usable;
+
+    NB_DictKeys *dk = malloc(aligned_size(alloc_size));
+    if (!dk) return ERR_NO_MEMORY;
+
+    assert ( size >= D_MINSIZE );
+
+    dk->size = size;
+    dk->usable = usable;
+    dk->nentries = 0;
+    dk->key_size = key_size;
+    dk->val_size = val_size;
+    dk->entry_offset = entry_offset;
+    dk->entry_size = entry_size;
+
+    assert (aligned_pointer(dk->indices) == dk->indices );
+    /* Ensure that the method table is all nulls */
+    memset(&dk->methods, 0x00, sizeof(type_based_methods_table));
+    /* Ensure hash is (-1) for empty entry */
+    memset(dk->indices, 0xff, entry_offset + entry_size * usable);
+
+    *out = dk;
+    return OK;
+}
+
+
+/* Allocate new dictionary */
+int
+numba_dict_new(NB_Dict **out, Py_ssize_t size, Py_ssize_t key_size, Py_ssize_t val_size) {
+    NB_DictKeys* dk;
+    NB_Dict *d;
+    int status = numba_dictkeys_new(&dk, size, key_size, val_size);
+    if (status != OK) return status;
+
+    d = malloc(sizeof(NB_Dict));
+    if (!d) {
+        numba_dictkeys_free(dk);
+        return ERR_NO_MEMORY;
+    }
+
+    d->used = 0;
+    d->keys = dk;
+    *out = d;
+    return OK;
+}
+
+/*
+Adapted from CPython lookdict_index().
+
+Search index of hash table from offset of entry table
+*/
+static Py_ssize_t
+lookdict_index(NB_DictKeys *dk, Py_hash_t hash, Py_ssize_t index)
+{
+    size_t mask = D_MASK(dk);
+    size_t perturb = (size_t)hash;
+    size_t i = (size_t)hash & mask;
+
+    for (;;) {
+        Py_ssize_t ix = get_index(dk, i);
+        if (ix == index) {
+            return i;
+        }
+        if (ix == DKIX_EMPTY) {
+            return DKIX_EMPTY;
+        }
+        perturb >>= PERTURB_SHIFT;
+        i = mask & (i*5 + perturb + 1);
+    }
+    assert(0 && "unreachable");
+}
+
+/*
+
+Adapted from the CPython3.7 lookdict().
+
+The basic lookup function used by all operations.
+This is based on Algorithm D from Knuth Vol. 3, Sec. 6.4.
+Open addressing is preferred over chaining since the link overhead for
+chaining would be substantial (100% with typical malloc overhead).
+
+The initial probe index is computed as hash mod the table size. Subsequent
+probe indices are computed as explained earlier.
+
+All arithmetic on hash should ignore overflow.
+
+The details in this version are due to Tim Peters, building on many past
+contributions by Reimer Behrends, Jyrki Alakuijala, Vladimir Marangozov and
+Christian Tismer.
+
+lookdict() is general-purpose, and may return DKIX_ERROR if (and only if) a
+comparison raises an exception.
+lookdict_unicode() below is specialized to string keys, comparison of which can
+never raise an exception; that function can never return DKIX_ERROR when key
+is string.  Otherwise, it falls back to lookdict().
+lookdict_unicode_nodummy is further specialized for string keys that cannot be
+the <dummy> value.
+For both, when the key isn't found a DKIX_EMPTY is returned.
+*/
+Py_ssize_t
+numba_dict_lookup(NB_Dict *d, const char *key_bytes, Py_hash_t hash, char *oldval_bytes)
+{
+    NB_DictKeys *dk = d->keys;
+    size_t mask = D_MASK(dk);
+    size_t perturb = hash;
+    size_t i = (size_t)hash & mask;
+
+    for (;;) {
+        Py_ssize_t ix = get_index(dk, i);
+        if (ix == DKIX_EMPTY) {
+            zero_val(dk, oldval_bytes);
+            return ix;
+        }
+        if (ix >= 0) {
+            NB_DictEntry *ep = get_entry(dk, ix);
+            const char *startkey = NULL;
+            if (ep->hash == hash) {
+                int cmp;
+
+                startkey = entry_get_key(dk, ep);
+                cmp = key_equal(dk, startkey, key_bytes);
+                if (cmp < 0) {
+                    // error'ed in comparison
+                    memset(oldval_bytes, 0, dk->val_size);
+                    return DKIX_ERROR;
+                }
+                if (cmp > 0) {
+                    // key is equal; retrieve the value.
+                    copy_val(dk, oldval_bytes, entry_get_val(dk, ep));
+                    return ix;
+                }
+            }
+        }
+        perturb >>= PERTURB_SHIFT;
+        i = (i*5 + perturb + 1) & mask;
+    }
+    assert(0 && "unreachable");
+}
+
+
+/* Internal function to find slot for an item from its hash
+   when it is known that the key is not present in the dict.
+
+   The dict must be combined. */
+static Py_ssize_t
+find_empty_slot(NB_DictKeys *dk, Py_hash_t hash){
+    size_t mask;
+    size_t i;
+    Py_ssize_t ix;
+    size_t perturb;
+
+    assert(dk != NULL);
+
+    mask = D_MASK(dk);
+    i = hash & mask;
+    ix = get_index(dk, i);
+    for (perturb = hash; ix >= 0;) {
+        perturb >>= PERTURB_SHIFT;
+        i = (i*5 + perturb + 1) & mask;
+        ix = get_index(dk, i);
+    }
+    return i;
+}
+
+static int
+insertion_resize(NB_Dict *d)
+{
+    return numba_dict_resize(d, D_GROWTH_RATE(d));
+}
+
+int
+numba_dict_insert(
+    NB_Dict    *d,
+    const char *key_bytes,
+    Py_hash_t   hash,
+    const char *val_bytes,
+    char       *oldval_bytes
+    )
+{
+
+    NB_DictKeys *dk = d->keys;
+
+    Py_ssize_t ix = numba_dict_lookup(d, key_bytes, hash, oldval_bytes);
+    if (ix == DKIX_ERROR) {
+        // exception in key comparison in lookup.
+        return ERR_CMP_FAILED;
+    }
+
+    if (ix == DKIX_EMPTY) {
+        /* Insert into new slot */
+        Py_ssize_t hashpos;
+        NB_DictEntry *ep;
+
+        if (dk->usable <= 0) {
+            /* Need to resize */
+            if (insertion_resize(d) != OK)
+                return ERR_NO_MEMORY;
+            else
+                dk = d->keys;     // reload
+        }
+        hashpos = find_empty_slot(dk, hash);
+        ep = get_entry(dk, dk->nentries);
+        set_index(dk, hashpos, dk->nentries);
+        copy_key(dk, entry_get_key(dk, ep), key_bytes);
+        assert ( hash != -1 );
+        ep->hash = hash;
+        copy_val(dk, entry_get_val(dk, ep), val_bytes);
+
+        /* incref */
+        dk_incref_key(dk, key_bytes);
+        dk_incref_val(dk, val_bytes);
+
+        d->used += 1;
+        dk->usable -= 1;
+        dk->nentries += 1;
+        assert (dk->usable >= 0);
+        return OK;
+    } else {
+        /* Replace existing value in the slot at ix */
+        /* decref old value */
+        dk_decref_val(dk, oldval_bytes);
+        // Replace the previous value
+        copy_val(dk, entry_get_val(dk, get_entry(dk, ix)), val_bytes);
+
+        /* incref */
+        dk_incref_val(dk, val_bytes);
+        return OK_REPLACED;
+    }
+}
+
+/*
+Adapted from build_indices().
+Internal routine used by dictresize() to build a hashtable of entries.
+*/
+void
+build_indices(NB_DictKeys *keys, Py_ssize_t n) {
+    size_t mask = (size_t)D_MASK(keys);
+    Py_ssize_t ix;
+    for (ix = 0; ix != n; ix++) {
+        size_t perturb;
+        Py_hash_t hash = get_entry(keys, ix)->hash;
+        size_t i = hash & mask;
+        for (perturb = hash; get_index(keys, i) != DKIX_EMPTY;) {
+            perturb >>= PERTURB_SHIFT;
+            i = mask & (i*5 + perturb + 1);
+        }
+        set_index(keys, i, ix);
+    }
+}
+
+/*
+
+Adapted from CPython dictresize().
+
+Restructure the table by allocating a new table and reinserting all
+items again.  When entries have been deleted, the new table may
+actually be smaller than the old one.
+If a table is split (its keys and hashes are shared, its values are not),
+then the values are temporarily copied into the table, it is resized as
+a combined table, then the me_value slots in the old table are NULLed out.
+After resizing a table is always combined,
+but can be resplit by make_keys_shared().
+*/
+int
+numba_dict_resize(NB_Dict *d, Py_ssize_t minsize) {
+    Py_ssize_t newsize, numentries;
+    NB_DictKeys *oldkeys;
+    int status;
+
+    /* Find the smallest table size > minused. */
+    for (newsize = D_MINSIZE;
+         newsize < minsize && newsize > 0;
+         newsize <<= 1)
+        ;
+    if (newsize <= 0) {
+        return ERR_NO_MEMORY;
+    }
+    oldkeys = d->keys;
+
+    /* NOTE: Current odict checks mp->ma_keys to detect resize happen.
+     * So we can't reuse oldkeys even if oldkeys->dk_size == newsize.
+     * TODO: Try reusing oldkeys when reimplement odict.
+     */
+
+    /* Allocate a new table. */
+    status = numba_dictkeys_new(
+        &d->keys, newsize, oldkeys->key_size, oldkeys->val_size
+    );
+    if (status != OK) {
+        d->keys = oldkeys;
+        return status;
+    }
+    // New table must be large enough.
+    assert(d->keys->usable >= d->used);
+    // Copy method table
+    memcpy(&d->keys->methods, &oldkeys->methods, sizeof(type_based_methods_table));
+
+    numentries = d->used;
+
+    if (oldkeys->nentries == numentries) {
+        NB_DictEntry *oldentries, *newentries;
+
+        oldentries = get_entry(oldkeys, 0);
+        newentries = get_entry(d->keys, 0);
+        memcpy(newentries, oldentries, numentries * oldkeys->entry_size);
+        // to avoid decref
+        memset(oldentries, 0xff, numentries * oldkeys->entry_size);
+    }
+    else {
+        Py_ssize_t i;
+        size_t epi = 0;
+        for (i=0; i<numentries; ++i) {
+            /*
+                ep->hash == (-1) hash means it is empty
+
+                Here, we skip until a non empty entry is encountered.
+            */
+            while( get_entry(oldkeys, epi)->hash == DKIX_EMPTY ) {
+                assert( mem_cmp_zeros(entry_get_val(oldkeys, get_entry(oldkeys, epi)), oldkeys->val_size) == 0 );
+                epi += 1;
+            }
+            memcpy(
+                get_entry(d->keys, i),
+                get_entry(oldkeys, epi),
+                oldkeys->entry_size
+            );
+            get_entry(oldkeys, epi)->hash = DKIX_EMPTY;  // to avoid decref
+            epi += 1;
+
+        }
+
+    }
+    numba_dictkeys_free(oldkeys);
+
+    build_indices(d->keys, numentries);
+    d->keys->usable -= numentries;
+    d->keys->nentries = numentries;
+    return OK;
+}
+
+/*
+    Adapted from CPython delitem_common
+ */
+int
+numba_dict_delitem(NB_Dict *d, Py_hash_t hash, Py_ssize_t ix)
+{
+    Py_ssize_t hashpos;
+    NB_DictEntry *ep;
+    NB_DictKeys *dk = d->keys;
+
+    hashpos = lookdict_index(dk, hash, ix);
+    assert(hashpos >= 0);
+
+    d->used -= 1;
+    ep = get_entry(dk, ix);
+    set_index(dk, hashpos, DKIX_DUMMY);
+
+    /* decref */
+    dk_decref_key(dk, entry_get_key(dk, ep));
+    dk_decref_val(dk, entry_get_val(dk, ep));
+
+    /* zero the entries */
+    zero_key(dk, entry_get_key(dk, ep));
+    zero_val(dk, entry_get_val(dk, ep));
+    ep->hash = DKIX_EMPTY; // to mark it as empty;
+
+    return OK;
+}
+
+
+/**
+ * Adapted from dict_popitem
+ *
+ */
+int
+numba_dict_popitem(NB_Dict *d, char *key_bytes, char *val_bytes)
+{
+    Py_ssize_t i, j;
+    char *key_ptr, *val_ptr;
+    NB_DictEntry *ep = NULL;
+
+    if (d->used == 0) {
+        return ERR_DICT_EMPTY;
+    }
+
+    /* Pop last item */
+    i = d->keys->nentries - 1;
+    while (i >= 0 && (ep = get_entry(d->keys, i))->hash == DKIX_EMPTY ) {
+        i--;
+    }
+    assert(i >= 0);
+
+    j = lookdict_index(d->keys, ep->hash, i);
+    assert(j >= 0);
+    assert(get_index(d->keys, j) == i);
+    set_index(d->keys, j, DKIX_DUMMY);
+
+    key_ptr = entry_get_key(d->keys, ep);
+    val_ptr = entry_get_val(d->keys, ep);
+
+    copy_key(d->keys, key_bytes, key_ptr);
+    copy_val(d->keys, val_bytes, val_ptr);
+
+    zero_key(d->keys, key_ptr);
+    zero_val(d->keys, val_ptr);
+
+    /* We can't dk_usable++ since there is DKIX_DUMMY in indices */
+    d->keys->nentries = i;
+    d->used--;
+
+    return OK;
+}
+
+void
+numba_dict_dump(NB_Dict *d) {
+    long long i, j, k;
+    long long size, n;
+    char *cp;
+    NB_DictEntry *ep;
+    NB_DictKeys *dk = d->keys;
+
+    n = d->used;
+    size = dk->nentries;
+
+    printf("Dict dump\n");
+    printf("   key_size = %lld\n", (long long)d->keys->key_size);
+    printf("   val_size = %lld\n", (long long)d->keys->val_size);
+
+    for (i = 0, j = 0; i < size; i++) {
+        ep = get_entry(dk, i);
+        if (ep->hash != DKIX_EMPTY) {
+            long long hash = ep->hash;
+            printf("  key=");
+            for (cp=entry_get_key(dk, ep), k=0; k < d->keys->key_size; ++k, ++cp){
+                printf("%02x ", ((int)*cp) & 0xff);
+            }
+            printf(" hash=%llu value=", hash);
+            for (cp=entry_get_val(dk, ep), k=0; k < d->keys->val_size; ++k, ++cp){
+                printf("%02x ", ((int)*cp) & 0xff);
+            }
+            printf("\n");
+            j++;
+        }
+    }
+    printf("j = %lld; n = %lld\n", j, n);
+    assert(j == n);
+}
+
+size_t
+numba_dict_iter_sizeof() {
+    return sizeof(NB_DictIter);
+}
+
+void
+numba_dict_iter(NB_DictIter *it, NB_Dict *d) {
+    it->parent = d;
+    it->parent_keys = d->keys;
+    it->size = d->used;
+    it->pos = 0;
+}
+
+int
+numba_dict_iter_next(NB_DictIter *it, const char **key_ptr, const char **val_ptr) {
+    /* Detect dictionary mutation during iteration */
+    NB_DictKeys *dk;
+    if (it->parent->keys != it->parent_keys ||
+        it->parent->used != it->size) {
+        return ERR_DICT_MUTATED;
+    }
+    dk = it->parent_keys;
+    while ( it->pos < dk->nentries ) {
+        NB_DictEntry *ep = get_entry(dk, it->pos++);
+        if ( ep->hash != DKIX_EMPTY ) {
+            *key_ptr = entry_get_key(dk, ep);
+            *val_ptr = entry_get_val(dk, ep);
+            return OK;
+        }
+    }
+    return ERR_ITER_EXHAUSTED;
+}
+
+int
+numba_dict_insert_ez(
+    NB_Dict    *d,
+    const char *key_bytes,
+    Py_hash_t   hash,
+    const char *val_bytes
+    )
+{
+    STACK_ALLOC(char, old, d->keys->val_size);
+    return numba_dict_insert(d, key_bytes, hash, val_bytes, old);
+}
+
+int
+numba_dict_new_minsize(NB_Dict **out, Py_ssize_t key_size, Py_ssize_t val_size)
+{
+    return numba_dict_new(out, D_MINSIZE, key_size, val_size);
+}
+
+void
+numba_dict_set_method_table(NB_Dict *d, type_based_methods_table *methods)
+{
+    memcpy(&d->keys->methods, methods, sizeof(type_based_methods_table));
+}
+
+
+#define CHECK(CASE) {                                                   \
+    if ( !(CASE) ) {                                                    \
+        printf("'%s' failed file %s:%d\n", #CASE, __FILE__, __LINE__);   \
+        return 1;                                                       \
+    }                                                                   \
+}
+
+int
+numba_test_dict(void) {
+    NB_Dict *d;
+    int status;
+    Py_ssize_t ix;
+    Py_ssize_t usable;
+    Py_ssize_t it_count;
+    const char *it_key, *it_val;
+    NB_DictIter iter;
+
+#if defined(_MSC_VER)
+    /* So that VS2008 compiler is happy */
+    char *got_key, *got_value;
+    got_key = _alloca(4);
+    got_value = _alloca(8);
+#else
+    char got_key[4];
+    char got_value[8];
+#endif
+    puts("test_dict");
+
+    status = numba_dict_new(&d, D_MINSIZE, 4, 8);
+    CHECK(status == OK);
+    CHECK(d->keys->size == D_MINSIZE);
+    CHECK(d->keys->key_size == 4);
+    CHECK(d->keys->val_size == 8);
+    CHECK(ix_size(d->keys->size) == 1);
+    printf("aligned_size(index_size * size) = %d\n", (int)(aligned_size(ix_size(d->keys->size) * d->keys->size)));
+
+    printf("d %p\n", d);
+    printf("d->usable = %u\n", (int)d->keys->usable);
+    usable = d->keys->usable;
+    printf("d[0] %d\n", (int)((char*)get_entry(d->keys, 0) - (char*)d->keys));
+    CHECK ((char*)get_entry(d->keys, 0) - (char*)d->keys->indices == d->keys->entry_offset);
+    printf("d[1] %d\n", (int)((char*)get_entry(d->keys, 1) - (char*)d->keys));
+    CHECK ((char*)get_entry(d->keys, 1) - (char*)d->keys->indices == d->keys->entry_offset + d->keys->entry_size);
+
+    ix = numba_dict_lookup(d, "bef", 0xbeef, got_value);
+    printf("ix = %d\n", (int)ix);
+    CHECK (ix == DKIX_EMPTY);
+
+    // insert 1st key
+    status = numba_dict_insert(d, "bef", 0xbeef, "1234567", got_value);
+    CHECK (status == OK);
+    CHECK (d->used == 1);
+    CHECK (d->keys->usable == usable - d->used);
+
+    // insert same key
+    status = numba_dict_insert(d, "bef", 0xbeef, "1234567", got_value);
+    CHECK (status == OK_REPLACED);
+    printf("got_value %s\n", got_value);
+    CHECK (d->used == 1);
+    CHECK (d->keys->usable == usable - d->used);
+
+    // insert 2nd key
+    status = numba_dict_insert(d, "beg", 0xbeef, "1234568", got_value);
+    CHECK (status == OK);
+    CHECK (d->used == 2);
+    CHECK (d->keys->usable == usable - d->used);
+
+    // insert 3rd key
+    status = numba_dict_insert(d, "beh", 0xcafe, "1234569", got_value);
+    CHECK (status == OK);
+    CHECK (d->used == 3);
+    CHECK (d->keys->usable == usable - d->used);
+
+    // replace key "bef"'s value
+    status = numba_dict_insert(d, "bef", 0xbeef, "7654321", got_value);
+    CHECK (status == OK_REPLACED);
+    CHECK (d->used == 3);
+    CHECK (d->keys->usable == usable - d->used);
+
+    // insert 4th key
+    status = numba_dict_insert(d, "bei", 0xcafe, "0_0_0_1", got_value);
+    CHECK (status == OK);
+    CHECK (d->used == 4);
+    CHECK (d->keys->usable == usable - d->used);
+
+    // insert 5th key
+    status = numba_dict_insert(d, "bej", 0xcafe, "0_0_0_2", got_value);
+    CHECK (status == OK);
+    CHECK (d->used == 5);
+    CHECK (d->keys->usable == usable - d->used);
+
+    // insert 6th key & triggers resize
+    status = numba_dict_insert(d, "bek", 0xcafe, "0_0_0_3", got_value);
+    CHECK (status == OK);
+    CHECK (d->used == 6);
+    CHECK (d->keys->usable == USABLE_FRACTION(d->keys->size) - d->used);
+
+    // Dump
+    numba_dict_dump(d);
+
+    // Make sure everything are still in there
+    ix = numba_dict_lookup(d, "bef", 0xbeef, got_value);
+    CHECK (ix >= 0);
+    CHECK (memcpy(got_value, "7654321", d->keys->val_size));
+
+    ix = numba_dict_lookup(d, "beg", 0xbeef, got_value);
+    CHECK (ix >= 0);
+    CHECK (memcpy(got_value, "1234567", d->keys->val_size));
+
+    ix = numba_dict_lookup(d, "beh", 0xcafe, got_value);
+    printf("ix = %d\n", (int)ix);
+    CHECK (ix >= 0);
+    CHECK (memcpy(got_value, "1234569", d->keys->val_size));
+
+    ix = numba_dict_lookup(d, "bei", 0xcafe, got_value);
+    CHECK (ix >= 0);
+    CHECK (memcpy(got_value, "0_0_0_1", d->keys->val_size));
+
+    ix = numba_dict_lookup(d, "bej", 0xcafe, got_value);
+    CHECK (ix >= 0);
+    CHECK (memcpy(got_value, "0_0_0_2", d->keys->val_size));
+
+    ix = numba_dict_lookup(d, "bek", 0xcafe, got_value);
+    CHECK (ix >= 0);
+    CHECK (memcpy(got_value, "0_0_0_3", d->keys->val_size));
+
+    // Test delete
+    ix = numba_dict_lookup(d, "beg", 0xbeef, got_value);
+    status = numba_dict_delitem(d, 0xbeef, ix);
+    CHECK (status == OK);
+
+    ix = numba_dict_lookup(d, "beg", 0xbeef, got_value);
+    CHECK (ix == DKIX_EMPTY); // not found
+
+    ix = numba_dict_lookup(d, "bef", 0xbeef, got_value);
+    CHECK (ix >= 0);
+    ix = numba_dict_lookup(d, "beh", 0xcafe, got_value);
+    CHECK (ix >= 0);
+
+
+    // Test popitem
+    // They are always the last item
+    status = numba_dict_popitem(d, got_key, got_value);
+    CHECK(status == OK);
+    CHECK(memcmp("bek", got_key, d->keys->key_size) == 0);
+    CHECK(memcmp("0_0_0_3", got_value, d->keys->val_size) == 0);
+
+    status = numba_dict_popitem(d, got_key, got_value);
+    CHECK(status == OK);
+    CHECK(memcmp("bej", got_key, d->keys->key_size) == 0);
+    CHECK(memcmp("0_0_0_2", got_value, d->keys->val_size) == 0);
+
+    // Test iterator
+    CHECK( d->used > 0 );
+    numba_dict_iter(&iter, d);
+    it_count = 0;
+    while ( (status = numba_dict_iter_next(&iter, &it_key, &it_val)) == OK) {
+        it_count += 1;  // valid items
+        CHECK(it_key != NULL);
+        CHECK(it_val != NULL);
+    }
+
+    CHECK(status == ERR_ITER_EXHAUSTED);
+    CHECK(d->used == it_count);
+
+    numba_dict_free(d);
+    return 0;
+
+}
+
+#undef CHECK
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/dictobject.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/dictobject.h
new file mode 100644
index 000000000..74b555a02
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/dictobject.h
@@ -0,0 +1,222 @@
+/* Adapted from CPython3.7 Objects/dict-common.h */
+#include "Python.h"
+#include "../_pymodule.h"
+#include "cext.h"
+
+#ifndef NUMBA_DICT_COMMON_H
+#define NUMBA_DICT_COMMON_H
+
+typedef struct {
+    /* Uses Py_ssize_t instead of Py_hash_t to guarantee word size alignment */
+    Py_ssize_t  hash;
+    char        keyvalue[];
+} NB_DictEntry;
+
+
+typedef int (*dict_key_comparator_t)(const char *lhs, const char *rhs);
+typedef void (*dict_refcount_op_t)(const void*);
+
+
+typedef struct {
+    dict_key_comparator_t    key_equal;
+    dict_refcount_op_t       key_incref;
+    dict_refcount_op_t       key_decref;
+    dict_refcount_op_t       value_incref;
+    dict_refcount_op_t       value_decref;
+} type_based_methods_table;
+
+
+typedef struct {
+   /* hash table size */
+    Py_ssize_t      size;
+    /* Usable size of the hash table.
+       Also, size of the entries */
+    Py_ssize_t      usable;
+    /* hash table used entries */
+    Py_ssize_t      nentries;
+    /* Entry info
+        - key_size is the sizeof key type
+        - val_size is the sizeof value type
+        - entry_size is key_size + val_size + alignment
+    */
+    Py_ssize_t      key_size, val_size, entry_size;
+    /* Byte offset from indices to the first entry. */
+    Py_ssize_t      entry_offset;
+
+    /* Method table for type-dependent operations. */
+    type_based_methods_table methods;
+
+    /* hash table */
+    char            indices[];
+} NB_DictKeys;
+
+
+typedef struct {
+    /* num of elements in the hashtable */
+    Py_ssize_t        used;
+    NB_DictKeys      *keys;
+} NB_Dict;
+
+
+typedef struct {
+    /* parent dictionary */
+    NB_Dict         *parent;
+    /* parent keys object */
+    NB_DictKeys     *parent_keys;
+    /* dict size */
+    Py_ssize_t       size;
+    /* iterator position; indicates the next position to read */
+    Py_ssize_t       pos;
+} NB_DictIter;
+
+
+
+/* A test function for the dict
+Returns 0 for OK; 1 for failure.
+*/
+NUMBA_EXPORT_FUNC(int)
+numba_test_dict(void);
+
+/* Allocate a new dict
+Parameters
+- NB_Dict **out
+    Output for the new dictionary.
+- Py_ssize_t size
+    Hashtable size. Must be power of two.
+- Py_ssize_t key_size
+    Size of a key entry.
+- Py_ssize_t val_size
+    Size of a value entry.
+*/
+NUMBA_EXPORT_FUNC(int)
+numba_dict_new(NB_Dict **out, Py_ssize_t size, Py_ssize_t key_size, Py_ssize_t val_size);
+
+/* Free a dict */
+NUMBA_EXPORT_FUNC(void)
+numba_dict_free(NB_Dict *d);
+
+/* Returns length of a dict */
+NUMBA_EXPORT_FUNC(Py_ssize_t)
+numba_dict_length(NB_Dict *d);
+
+/* Allocates a new dict at the minimal size
+See numba_dict_new().
+*/
+NUMBA_EXPORT_FUNC(int)
+numba_dict_new_minsize(NB_Dict **out, Py_ssize_t key_size, Py_ssize_t val_size);
+
+/* Set the method table for type specific operations
+*/
+NUMBA_EXPORT_FUNC(void)
+numba_dict_set_method_table(NB_Dict *d, type_based_methods_table *methods);
+
+/* Lookup a key
+
+Parameters
+- NB_Dict *d
+    The dictionary object.
+- const char *key_bytes
+    The key as a byte buffer.
+- Py_hash_t hash
+    The precomputed hash of the key.
+- char *oldval_bytes
+    An output parameter to store the associated value if the key is found.
+    Must point to memory of sufficient size to store the value.
+*/
+NUMBA_EXPORT_FUNC(Py_ssize_t)
+numba_dict_lookup(NB_Dict *d, const char *key_bytes, Py_hash_t hash, char *oldval_bytes);
+
+/* Resize the dict to at least *minsize*.
+*/
+NUMBA_EXPORT_FUNC(int)
+numba_dict_resize(NB_Dict *d, Py_ssize_t minsize);
+
+/* Insert to the dict
+
+Parameters
+- NB_Dict *d
+    The dictionary object.
+- const char *key_bytes
+    The key as a byte buffer.
+- Py_hash_t hash
+    The precomputed hash of key.
+- const char *val_bytes
+    The value as a byte buffer.
+- char *oldval_bytes
+    An output buffer to store the replaced value.
+    Must point to memory of sufficient size to store the value.
+
+Returns
+- < 0 for error
+- 0 for ok
+- 1 for ok and oldval_bytes has a copy of the replaced value.
+*/
+NUMBA_EXPORT_FUNC(int)
+numba_dict_insert(NB_Dict *d, const char *key_bytes, Py_hash_t hash, const char *val_bytes, char *oldval_bytes);
+
+/* Same as numba_dict_insert() but oldval_bytes is not needed */
+NUMBA_EXPORT_FUNC(int)
+numba_dict_insert_ez(NB_Dict *d, const char *key_bytes, Py_hash_t hash, const char *val_bytes);
+
+/* Delete an entry from the dict
+Parameters
+- NB_Dict *d
+    The dictionary
+- Py_hash_t hash
+    Precomputed hash of the key to be deleted
+- Py_ssize_t ix
+    Precomputed entry index of the key to be deleted.
+    Usually results of numba_dict_lookup().
+*/
+NUMBA_EXPORT_FUNC(int)
+numba_dict_delitem(NB_Dict *d, Py_hash_t hash, Py_ssize_t ix);
+
+/* Remove an item from the dict
+Parameters
+- NB_Dict *d
+    The dictionary
+- char *key_bytes
+    Output. The key as a byte buffer
+- char *val_bytes
+    Output. The value as a byte buffer
+*/
+NUMBA_EXPORT_FUNC(int)
+numba_dict_popitem(NB_Dict *d, char *key_bytes, char *val_bytes);
+
+/* Returns the sizeof a dictionary iterator
+*/
+NUMBA_EXPORT_FUNC(size_t)
+numba_dict_iter_sizeof(void);
+
+/* Fill a NB_DictIter for a dictionary to begin iteration
+Parameters
+- NB_DictIter *it
+    Output.  Must points to memory of size at least `numba_dict_iter_sizeof()`.
+- NB_Dict *d
+    The dictionary to be iterated.
+*/
+NUMBA_EXPORT_FUNC(void)
+numba_dict_iter(NB_DictIter *it, NB_Dict *d);
+
+/* Advance the iterator
+Parameters
+- NB_DictIter *it
+    The iterator
+- const char **key_ptr
+    Output pointer for the key.  Points to data in the dictionary.
+- const char **val_ptr
+    Output pointer for the key.  Points to data in the dictionary.
+
+Returns
+- 0 for success; valid key_ptr and val_ptr
+- ERR_ITER_EXHAUSTED for end of iterator.
+- ERR_DICT_MUTATED for detected dictionary mutation.
+*/
+NUMBA_EXPORT_FUNC(int)
+numba_dict_iter_next(NB_DictIter *it, const char **key_ptr, const char **val_ptr);
+
+
+NUMBA_EXPORT_FUNC(void)
+numba_dict_dump(NB_Dict *);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/listobject.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/listobject.c
new file mode 100644
index 000000000..9de03f5ff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/listobject.c
@@ -0,0 +1,977 @@
+#include "listobject.h"
+
+/* This implements the C component of the Numba typed list. It is loosely
+ * inspired by the list implementation of the cpython list with some parts
+ * taken from the cpython slice implementation. The exact commit-id of the
+ * relevant files are:
+ *
+ * https://github.com/python/cpython/blob/51ddab8dae056867f3595ab3400bffc93f67c8d4/Objects/listobject.c
+ * https://github.com/python/cpython/blob/51ddab8dae056867f3595ab3400bffc93f67c8d4/Objects/sliceobject.c
+ *
+ * Algorithmically, this list is very similar to the cpython implementation so
+ * it should have the same performance (Big-O) characteristics for accessing,
+ * adding and removing elements/items. Specifically, it implements the same
+ * algorithms for list overallocation and growth. However, it never deals with
+ * PyObject types and instead must be typed with a type-size.  As a result, the
+ * typed-list is type homogeneous and in contrast to the cpython version can
+ * not store a mixture of arbitrarily typed objects. Reference counting via the
+ * Numba Runtime (NRT) is supported and incrementing and decrementing functions
+ * are store as part of the struct and can be setup from the compiler level.
+ *
+ * Importantly, only a very limited subset of the cpython c functions have been
+ * ported over and the rest have been implemented (in Python) at the compiler
+ * level using the c functions provided. Additionally, initialization of, and
+ * iteration over, a ListIter is provided
+ *
+ * The following functions are implemented for the list:
+ *
+ * - Check valid index        valid_index
+ * - Creation                 numba_list_new
+ * - Deletion                 numba_list_free
+ * - Accessing the length     numba_list_length
+ * - Appending to the list    numba_list_append
+ * - Getting an item          numba_list_setitem
+ * - Setting an item          numba_list_getitem
+ * - Resizing the list        numba_list_resize
+ * - Deleting an item         numba_list_delitem
+ * - Deleting a slice         numba_list_delete_slice
+ *
+ * As you can see, only a single function for slices is implemented. The rest
+ * is all done entirely at the compiler level which then calls the c functions
+ * to mutate the list accordingly. Since slicing allows for replace, insert and
+ * delete operations over multiple items, we can simply implement those using
+ * the basic functions above.
+ *
+ * The following additional functions are implemented for the list, these are
+ * needed to make the list work within Numba.
+ *
+ * - Accessing the allocation numba_list_allocated
+ * - Copying an item          copy_item
+ * - Calling incref on item   list_incref_item
+ * - Calling decref on item   list_decref_item
+ * - Set method table         numba_list_set_method_table
+ *
+ * The following functions are implemented for the iterator:
+ *
+ * - Size of the iterator     numba_list_iter_size
+ * - Initialization of iter   numba_list_iter
+ * - Get next item from iter  numba_list_iter_next
+ *
+ * Two methods are provided to query and set the 'is_mutable':
+ *
+ * - Query                    numba_list_is_mutable
+ * - Set                      numba_list_set_is_mutable
+ *
+ * Lastly a set of pure C level tests are provided which come in handy when
+ * needing to use valgrind and friends.
+ *
+ */
+
+
+/* Return status for the list functions.
+ */
+typedef enum {
+    LIST_OK = 0,
+    LIST_ERR_INDEX = -1,
+    LIST_ERR_NO_MEMORY = -2,
+    LIST_ERR_MUTATED = -3,
+    LIST_ERR_ITER_EXHAUSTED = -4,
+    LIST_ERR_IMMUTABLE = -5,
+} ListStatus;
+
+/* Copy an item from a list.
+ *
+ * lp: a list
+ * dst: destination pointer
+ * src: source pointer
+ */
+static void
+copy_item(NB_List *lp, char *dst, const char *src){
+    memcpy(dst, src, lp->item_size);
+}
+
+/* Increment a reference to an item in a list.
+ *
+ * lp: a list
+ * item: the item to increment the reference for
+ */
+static void
+list_incref_item(NB_List *lp, const char *item){
+    if (lp->methods.item_incref) {
+        lp->methods.item_incref(item);
+    }
+}
+
+/* Decrement a reference to an item in a list.
+ *
+ * lp: a list
+ * item: the item to decrement the reference for
+ */
+static void
+list_decref_item(NB_List *lp, const char *item){
+    if (lp->methods.item_decref) {
+        lp->methods.item_decref(item);
+    }
+}
+
+/* Setup the method table for a list.
+ *
+ * This function is used from the compiler level to initialize the internal
+ * method table.
+ *
+ * lp: a list
+ * methods: the methods table to set up
+ */
+void
+numba_list_set_method_table(NB_List *lp, list_type_based_methods_table *methods)
+{
+    memcpy(&lp->methods, methods, sizeof(list_type_based_methods_table));
+}
+
+/* Check if a list index is valid.
+ *
+ * i: the index to check
+ * limit: the size of a list
+ *
+ * Adapted from CPython's valid_index().
+ *
+ * FIXME: need to find a way to inline this, even for Python 2.7 on Windows
+ */
+static int
+valid_index(Py_ssize_t i, Py_ssize_t limit){
+    /* The cast to size_t lets us use just a single comparison
+       to check whether i is in the range: 0 <= i < limit.
+
+       See:  Section 14.2 "Bounds Checking" in the Agner Fog
+       optimization manual found at:
+       https://www.agner.org/optimize/optimizing_cpp.pdf
+    */
+    return (size_t) i < (size_t) limit;
+}
+
+/* Initialize a new list.
+ *
+ * out: pointer to hold an initialized list
+ * item_size: the size in bytes of the items in the list
+ * allocated: preallocation of the list in items
+ *
+ * This will allocate sufficient memory to hold the list structure and any
+ * items if requested (allocated != 0). See _listobject.h for more information
+ * on the NB_List struct.
+ */
+int
+numba_list_new(NB_List **out, Py_ssize_t item_size, Py_ssize_t allocated){
+    NB_List *lp;
+    char *items;
+    // allocate memory to hold the struct
+    lp = malloc(aligned_size(sizeof(NB_List)));
+    if (lp == NULL) {
+        return LIST_ERR_NO_MEMORY;
+    }
+    // set up members
+    lp->size = 0;
+    lp->item_size = item_size;
+    lp->allocated = allocated;
+    lp->is_mutable = 1;
+    // set method table to zero */
+    memset(&lp->methods, 0x00, sizeof(list_type_based_methods_table));
+    // allocate memory to hold items, if requested
+    if (allocated != 0) {
+        items = malloc(aligned_size(lp->item_size * allocated));
+        // allocated was definitely not zero, if malloc returns NULL
+        // this is definitely an error
+        if (items == NULL) {
+            // free previously allocated struct to avoid leaking memory
+            free(lp);
+            return LIST_ERR_NO_MEMORY;
+        }
+        lp->items = items;
+    }
+    else {
+        // be explicit
+        lp->items = NULL;
+    }
+    *out = lp;
+    return LIST_OK;
+}
+
+/* Free the memory associated with a list.
+ *
+ * lp: a list
+ */
+void
+numba_list_free(NB_List *lp) {
+    // decref all items, if needed
+    Py_ssize_t i;
+    if (lp->methods.item_decref) {
+        for (i = 0; i < lp->size; i++) {
+            char *item = lp->items + lp->item_size * i;
+            list_decref_item(lp, item);
+        }
+    }
+    // free items and list
+    if (lp->items != NULL) {
+        free(lp->items);
+    }
+    free(lp);
+}
+
+/* Return the base pointer of the list items.
+ */
+char *
+numba_list_base_ptr(NB_List *lp)
+{
+    return lp->items;
+}
+
+/* Return the address of the list size.
+ */
+Py_ssize_t
+numba_list_size_address(NB_List *lp)
+{
+    return (Py_ssize_t)&lp->size;
+}
+
+
+/* Return the length of a list.
+ *
+ * lp: a list
+ */
+Py_ssize_t
+numba_list_length(NB_List *lp) {
+    return lp->size;
+}
+
+/* Return the current allocation of a list.
+ *
+ * lp: a list
+ */
+Py_ssize_t
+numba_list_allocated(NB_List *lp) {
+    return lp->allocated;
+}
+
+/* Return the mutability status of the list
+ *
+ * lp: a list
+ *
+ */
+int
+numba_list_is_mutable(NB_List *lp){
+    return lp->is_mutable;
+}
+
+/* Set the is_mutable attribute
+ *
+ * lp: a list
+ * is_mutable: an int, 0(False) or 1(True)
+ *
+ */
+void
+numba_list_set_is_mutable(NB_List *lp, int is_mutable){
+    lp->is_mutable = is_mutable;
+}
+
+/* Set an item in a list.
+ *
+ * lp: a list
+ * index: the index of the item to set (must be in range 0 <= index < len(list))
+ * item: the item to set
+ *
+ * This assume there is already an element at the given index that will be
+ * overwritten and thereby have its reference decremented.  DO NOT use this to
+ * write to an unassigned location.
+ */
+int
+numba_list_setitem(NB_List *lp, Py_ssize_t index, const char *item) {
+    char *loc;
+    // check for mutability
+    if (!lp->is_mutable) {
+        return LIST_ERR_IMMUTABLE;
+    }
+    // check index is valid
+    // FIXME: this can be (and probably is) checked at the compiler level
+    if (!valid_index(index, lp->size)) {
+        return LIST_ERR_INDEX;
+    }
+    // set item at desired location
+    loc = lp->items + lp-> item_size * index;
+    list_decref_item(lp, loc);
+    copy_item(lp, loc, item);
+    list_incref_item(lp, loc);
+    return LIST_OK;
+}
+
+/* Get an item from a list.
+ *
+ * lp: a list
+ * index: the index of the item to get (must be in range 0 <= index < len(list))
+ * out: a pointer to hold the item
+ */
+int
+numba_list_getitem(NB_List *lp, Py_ssize_t index, char *out) {
+    char *loc;
+    // check index is valid
+    // FIXME: this can be (and probably is) checked at the compiler level
+    if (!valid_index(index, lp->size)) {
+        return LIST_ERR_INDEX;
+    }
+    // get item at desired location
+    loc = lp->items + lp->item_size * index;
+    copy_item(lp, out, loc);
+    return LIST_OK;
+}
+
+/* Append an item to the end of a list.
+ *
+ * lp: a list
+ * item: the item to append.
+ */
+int
+numba_list_append(NB_List *lp, const char *item) {
+    char *loc;
+    // check for mutability
+    if (!lp->is_mutable) {
+        return LIST_ERR_IMMUTABLE;
+    }
+    // resize by one, will change list size
+    int result = numba_list_resize(lp, lp->size + 1);
+    if(result < LIST_OK) {
+        return result;
+    }
+    // insert item at index: original size before resize
+    loc = lp->items + lp->item_size * (lp->size - 1);
+    copy_item(lp, loc, item);
+    list_incref_item(lp, loc);
+    return LIST_OK;
+}
+
+/* Resize a list.
+ *
+ * lp: a list
+ * newsize: the desired new size of the list.
+ *
+ * This will increase or decrease the size of the list, including reallocating
+ * the required memory and increasing the total allocation (additional free
+ * space to hold new items).
+ *
+ *
+ * Adapted from CPython's list_resize().
+ *
+ * Ensure lp->items has room for at least newsize elements, and set
+ * lp->size to newsize.  If newsize > lp->size on entry, the content
+ * of the new slots at exit is undefined heap trash; it's the caller's
+ * responsibility to overwrite them with sane values.
+ * The number of allocated elements may grow, shrink, or stay the same.
+ * Failure is impossible if newsize <= lp->allocated on entry, although
+ * that partly relies on an assumption that the system realloc() never
+ * fails when passed a number of bytes <= the number of bytes last
+ * allocated (the C standard doesn't guarantee this, but it's hard to
+ * imagine a realloc implementation where it wouldn't be true).
+ * Note that lp->items may change, and even if newsize is less
+ * than lp->size on entry.
+ */
+int
+numba_list_resize(NB_List *lp, Py_ssize_t newsize) {
+    char * items;
+    // check for mutability
+    if (!lp->is_mutable) {
+        return LIST_ERR_IMMUTABLE;
+    }
+    size_t new_allocated, num_allocated_bytes;
+    /* Bypass realloc() when a previous overallocation is large enough
+       to accommodate the newsize.  If the newsize falls lower than half
+       the allocated size, then proceed with the realloc() to shrink the list.
+    */
+    if (lp->allocated >= newsize && newsize >= (lp->allocated >> 1)) {
+        assert(lp->items != NULL || newsize == 0);
+        lp->size = newsize;
+        return LIST_OK;
+    }
+    /* This over-allocates proportional to the list size, making room
+     * for additional growth.  The over-allocation is mild, but is
+     * enough to give linear-time amortized behavior over a long
+     * sequence of appends() in the presence of a poorly-performing
+     * system realloc().
+     * The growth pattern is:  0, 4, 8, 16, 25, 35, 46, 58, 72, 88, ...
+     * Note: new_allocated won't overflow because the largest possible value
+     *       is PY_SSIZE_T_MAX * (9 / 8) + 6 which always fits in a size_t.
+     */
+    new_allocated = (size_t)newsize + (newsize >> 3) + (newsize < 9 ? 3 : 6);
+    if (new_allocated > (size_t)PY_SSIZE_T_MAX / lp->item_size) {
+        return LIST_ERR_NO_MEMORY;
+    }
+
+    if (newsize == 0)
+        new_allocated = 0;
+    num_allocated_bytes = new_allocated * lp->item_size;
+    items = realloc(lp->items, aligned_size(num_allocated_bytes));
+    // realloc may return NULL if requested size is 0
+    if (num_allocated_bytes != 0 && items == NULL) {
+        return LIST_ERR_NO_MEMORY;
+    }
+    lp->items = items;
+    lp->size = newsize;
+    lp->allocated = (Py_ssize_t)new_allocated;
+    return LIST_OK;
+}
+
+/* Delete a single item.
+ *
+ * lp: a list
+ * index: the index of the item to delete
+ *        (must be in range 0 <= index < len(list))
+ *
+ * */
+int
+numba_list_delitem(NB_List *lp, Py_ssize_t index) {
+    int result;
+    char *loc, *new_loc;
+    Py_ssize_t leftover_bytes;
+    // check for mutability
+    if (!lp->is_mutable) {
+        return LIST_ERR_IMMUTABLE;
+    }
+    // check index is valid
+    // FIXME: this can be (and probably is) checked at the compiler level
+    if (!valid_index(index, lp->size)) {
+        return LIST_ERR_INDEX;
+    }
+    // obtain item and decref if needed
+    loc = lp->items + lp->item_size * index;
+    list_decref_item(lp, loc);
+    if (index != lp->size - 1) {
+        // delitem from somewhere other than the end, incur the memory copy
+        leftover_bytes = (lp->size - 1 - index) * lp->item_size;
+        new_loc = lp->items + (lp->item_size * (index + 1));
+        // use memmove instead of memcpy since we may be dealing with
+        // overlapping regions of memory and the behaviour of memcpy is
+        // undefined in such situation (C99).
+        memmove(loc, new_loc, leftover_bytes);
+    }
+    // finally, shrink list by one
+    result = numba_list_resize(lp, lp->size - 1);
+    if(result < LIST_OK) {
+         // Since we are decreasing the size, this should never happen
+        return result;
+    }
+    return LIST_OK;
+
+}
+
+/* Delete a slice
+ *
+ * start: the start index of ths slice
+ * stop: the stop index of the slice (not included)
+ * step: the step to take
+ *
+ * This function assumes that the start and stop were clipped appropriately.
+ * I.e. if step > 0 start >= 0 and stop <= len(l) and
+ *      if step < 0 start <= length and stop >= -1
+ *      step != 0 and no Python negative indexing allowed.
+ *
+ * This code was copied and edited from the relevant section in
+ * list_ass_subscript from the cpython implementation, see the top of this file
+ * for the exact source
+ */
+int
+numba_list_delete_slice(NB_List *lp,
+                        Py_ssize_t start, Py_ssize_t stop, Py_ssize_t step) {
+    int result, i, slicelength, new_length;
+    char *loc, *new_loc;
+    Py_ssize_t leftover_bytes, cur, lim;
+    // check for mutability
+    if (!lp->is_mutable) {
+        return LIST_ERR_IMMUTABLE;
+    }
+    // calculate the slicelength, taken from PySlice_AdjustIndices, see the top
+    // of this file for the exact source
+    if (step > 0) {
+        slicelength = start < stop ? (stop - start - 1) / step + 1 : 0;
+    } else {
+        slicelength = stop < start ? (start - stop - 1) / -step + 1 : 0;
+    }
+    if (slicelength <= 0){
+        return LIST_OK;
+    }
+    new_length = lp->size - slicelength;
+    // reverse step and indices
+    if (step < 0) {
+        stop = start + 1;
+        start = stop + step * (slicelength - 1) - 1;
+        step = -step;
+    }
+    if (step == 1) {
+        // decref if needed
+        if (lp->methods.item_decref) {
+            for (i = start ; i < stop ; i++){
+                loc = lp->items + lp->item_size * i;
+                lp->methods.item_decref(loc);
+            }
+        }
+        // memmove items into place
+        leftover_bytes = (lp->size - stop) * lp->item_size;
+        loc = lp->items + lp->item_size * start;
+        new_loc = lp->items + lp->item_size * stop;
+        memmove(loc, new_loc, leftover_bytes);
+    }
+    else { // step != 1
+        /* drawing pictures might help understand these for
+         * loops. Basically, we memmove the parts of the
+         * list that are *not* part of the slice: step-1
+         * items for each item that is part of the slice,
+         * and then tail end of the list that was not
+         * covered by the slice
+         *
+         * */
+        for (cur = start,   // index of item to be deleted
+             i = 0;         // counter of total items deleted so far
+             cur < stop;
+             cur += step,
+             i++) {
+            lim = step - 1; // number of leftover items after deletion of item
+            // clip limit, in case we are at the end of the slice, and there
+            // are now less than step-1 items to be moved
+            if (cur + step >= lp->size) {
+                lim = lp->size - cur - 1;
+            }
+            // decref item being removed
+            loc = lp->items + lp->item_size * cur;
+            list_decref_item(lp, loc);
+            /* memmove the aforementioned step-1 (or less) items
+             * dst : index of deleted item minus total deleted sofar
+             * src : index of deleted item plus one (next item)
+             */
+            memmove(lp->items + lp->item_size * (cur - i),
+                    lp->items + lp->item_size * (cur + 1),
+                    lim * lp->item_size);
+        }
+        // memmove tail of the list
+        cur = start + slicelength * step;
+        if (cur < lp->size) {
+            memmove(lp->items + lp->item_size * (cur - slicelength),
+                    lp->items + lp->item_size * cur,
+                    (lp->size - cur) * lp->item_size);
+        }
+    }
+    // resize to correct size
+    result = numba_list_resize(lp, new_length);
+    if(result < LIST_OK) {
+        // Since we are decreasing the size, this should never happen
+        return result;
+    }
+    return LIST_OK;
+}
+
+
+/* Return the size of the list iterator (NB_ListIter) struct.
+ */
+size_t
+numba_list_iter_sizeof() {
+    return sizeof(NB_ListIter);
+}
+
+/* Initialize a list iterator (NB_ListIter).
+ *
+ * it: an iterator
+ * lp: a list to iterate over
+ */
+void
+numba_list_iter(NB_ListIter *it, NB_List *lp) {
+    // set members of iterator
+    it->parent = lp;
+    it->size = lp->size;
+    it->pos = 0;
+}
+
+/* Obtain the next item from a list iterator.
+ *
+ * it: an iterator
+ * item_ptr: pointer to hold the next item
+ */
+int
+numba_list_iter_next(NB_ListIter *it, const char **item_ptr) {
+    NB_List *lp;
+    lp = it->parent;
+    /* FIXME: Detect list mutation during iteration */
+    if (lp->size != it->size) {
+        return LIST_ERR_MUTATED;
+    }
+    // get next element
+    if (it->pos < lp->size) {
+        *item_ptr = lp->items + lp->item_size * it->pos++;
+        return LIST_OK;
+    }else{
+        return LIST_ERR_ITER_EXHAUSTED;
+    }
+}
+
+
+#define CHECK(CASE) {                                                   \
+    if ( !(CASE) ) {                                                    \
+        printf("'%s' failed file %s:%d\n", #CASE, __FILE__, __LINE__);   \
+        return -1;                                                       \
+    }                                                                   \
+}
+
+/* Basic C based tests for the list.
+ */
+int
+numba_test_list(void) {
+    NB_List *lp = NULL;
+    int status, i;
+    Py_ssize_t it_count;
+    const char *it_item = NULL;
+    NB_ListIter iter;
+    char got_item[4] = "\x00\x00\x00\x00";
+    const char *test_items_1 = NULL, *test_items_2 = NULL;
+    char *test_items_3 = NULL;
+    puts("test_list");
+
+
+    status = numba_list_new(&lp, 4, 0);
+    CHECK(status == LIST_OK);
+    CHECK(lp->item_size == 4);
+    CHECK(lp->size == 0);
+    CHECK(lp->allocated == 0);
+    CHECK(lp->is_mutable == 1);
+
+    // flip and check the is_mutable bit
+    CHECK(numba_list_is_mutable(lp) == 1);
+    numba_list_set_is_mutable(lp, 0);
+    CHECK(numba_list_is_mutable(lp) == 0);
+    numba_list_set_is_mutable(lp, 1);
+    CHECK(numba_list_is_mutable(lp) == 1);
+
+    // append 1st item, this will cause a realloc
+    status = numba_list_append(lp, "abc");
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 1);
+    CHECK(lp->allocated == 4);
+    status = numba_list_getitem(lp, 0, got_item);
+    CHECK(status == LIST_OK);
+    CHECK(memcmp(got_item, "abc", 4) == 0);
+
+    // append 2nd item
+    status = numba_list_append(lp, "def");
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 2);
+    CHECK(lp->allocated == 4);
+    status = numba_list_getitem(lp, 1, got_item);
+    CHECK(status == LIST_OK);
+    CHECK(memcmp(got_item, "def", 4) == 0);
+
+    // append 3rd item
+    status = numba_list_append(lp, "ghi");
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 3);
+    CHECK(lp->allocated == 4);
+    status = numba_list_getitem(lp, 2, got_item);
+    CHECK(status == LIST_OK);
+    CHECK(memcmp(got_item, "ghi", 4) == 0);
+
+    // append 4th item
+    status = numba_list_append(lp, "jkl");
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 4);
+    CHECK(lp->allocated == 4);
+    status = numba_list_getitem(lp, 3, got_item);
+    CHECK(status == LIST_OK);
+    CHECK(memcmp(got_item, "jkl", 4) == 0);
+
+    // append 5th item, this will cause another realloc
+    status = numba_list_append(lp, "mno");
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 5);
+    CHECK(lp->allocated == 8);
+    status = numba_list_getitem(lp, 4, got_item);
+    CHECK(status == LIST_OK);
+    CHECK(memcmp(got_item, "mno", 4) == 0);
+
+    // overwrite 1st item
+    status = numba_list_setitem(lp, 0, "pqr");
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 5);
+    CHECK(lp->allocated == 8);
+    status = numba_list_getitem(lp, 0, got_item);
+    CHECK(status == LIST_OK);
+    CHECK(memcmp(got_item, "pqr", 4) == 0);
+
+    // get and del 1st item, check item shift
+    status = numba_list_getitem(lp, 0, got_item);
+    status = numba_list_delitem(lp, 0);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 4);
+    CHECK(lp->allocated == 8);
+    CHECK(memcmp(got_item, "pqr", 4) == 0);
+    CHECK(memcmp(lp->items, "def\x00ghi\x00jkl\x00mno\x00", 16) == 0);
+
+    // get and del last (4th) item, no shift since only last item affected
+    status = numba_list_getitem(lp, 3, got_item);
+    status = numba_list_delitem(lp, 3);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 3);
+    CHECK(lp->allocated == 6);  // this also shrinks the allocation
+    CHECK(memcmp(got_item, "mno", 4) == 0);
+    CHECK(memcmp(lp->items, "def\x00ghi\x00jkl\x00", 12) == 0);
+
+    // flip and check the is_mutable member
+    CHECK(numba_list_is_mutable(lp) == 1);
+    numba_list_set_is_mutable(lp, 0);
+    CHECK(numba_list_is_mutable(lp) == 0);
+
+    // ensure that any attempts to mutate an immutable list fail
+    CHECK(numba_list_setitem(lp, 0, "zzz") == LIST_ERR_IMMUTABLE);
+    CHECK(numba_list_append(lp, "zzz") == LIST_ERR_IMMUTABLE);
+    CHECK(numba_list_delitem(lp, 0) == LIST_ERR_IMMUTABLE);
+    CHECK(numba_list_resize(lp, 23) == LIST_ERR_IMMUTABLE);
+    CHECK(numba_list_delete_slice(lp, 0, 3, 1) == LIST_ERR_IMMUTABLE);
+
+    // ensure that all attempts to query/read from and immutable list succeed
+    CHECK(numba_list_length(lp) == 3);
+    status = numba_list_getitem(lp, 0, got_item);
+    CHECK(status == LIST_OK);
+    CHECK(memcmp(got_item, "def", 4) == 0);
+
+    // flip the is_mutable member back  and check
+    numba_list_set_is_mutable(lp, 1);
+    CHECK(numba_list_is_mutable(lp) == 1);
+
+    // test iterator
+    CHECK(lp->size > 0);
+    numba_list_iter(&iter, lp);
+    it_count = 0;
+    CHECK(iter.parent == lp);
+    CHECK(iter.pos == it_count);
+
+    // current contents of list
+    test_items_1 = "def\x00ghi\x00jkl\x00";
+    while ( (status = numba_list_iter_next(&iter, &it_item)) == LIST_OK) {
+        it_count += 1;
+        CHECK(iter.pos == it_count); // check iterator position
+        CHECK(it_item != NULL); // quick check item is non-null
+        // go fishing in test_items_1
+        CHECK(memcmp((const char *)test_items_1 + ((it_count - 1) * 4), it_item, 4) == 0);
+    }
+
+    CHECK(status == LIST_ERR_ITER_EXHAUSTED);
+    CHECK(lp->size == it_count);
+
+    // free existing list
+    numba_list_free(lp);
+
+    // test growth upon append and shrink during delitem
+    status = numba_list_new(&lp, 1, 0);
+    CHECK(status == LIST_OK);
+    CHECK(lp->item_size == 1);
+    CHECK(lp->size == 0);
+    CHECK(lp->allocated == 0);
+
+    // first, grow the list
+    // Use exactly 17 elements, should go through the allocation pattern:
+    // 0, 4, 8, 16, 25
+    for (i = 0; i < 17 ; i++) {
+        switch(i) {
+            // Check the allocation before
+            case 0:  CHECK(lp->allocated == 0); break;
+            case 4:  CHECK(lp->allocated == 4); break;
+            case 8:  CHECK(lp->allocated == 8); break;
+            case 16: CHECK(lp->allocated == 16); break;
+        }
+        status = numba_list_append(lp, (const char*)&i);
+        CHECK(status == LIST_OK);
+        switch(i) {
+            // Check that the growth happened accordingly
+            case 0:  CHECK(lp->allocated == 4); break;
+            case 4:  CHECK(lp->allocated == 8); break;
+            case 8:  CHECK(lp->allocated == 16); break;
+            case 16: CHECK(lp->allocated == 25); break;
+        }
+    }
+    CHECK(lp->size == 17);
+
+    // Check current contents of list
+    test_items_2  = "\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f\x10";
+    CHECK(memcmp(lp->items, test_items_2, 17) == 0);
+
+    // Now, delete them again and check that list shrinks
+    for (i = 17; i > 0 ; i--) {
+        switch(i) {
+             // Check the allocation before delitem
+             case 17:  CHECK(lp->allocated == 25); break;
+             case 12:  CHECK(lp->allocated == 25); break;
+             case 9:   CHECK(lp->allocated == 18); break;
+             case 6:   CHECK(lp->allocated == 12); break;
+             case 4:   CHECK(lp->allocated == 8); break;
+             case 3:   CHECK(lp->allocated == 6); break;
+             case 2:   CHECK(lp->allocated == 5); break;
+             case 1:   CHECK(lp->allocated == 4); break;
+        }
+        status = numba_list_getitem(lp, i-1, got_item);
+        status = numba_list_delitem(lp, i-1);
+        CHECK(status == LIST_OK);
+        switch(i) {
+             // Check that the shrink happened accordingly
+             case 17:  CHECK(lp->allocated == 25); break;
+             case 12:  CHECK(lp->allocated == 18); break;
+             case 9:   CHECK(lp->allocated == 12); break;
+             case 6:   CHECK(lp->allocated == 8); break;
+             case 4:   CHECK(lp->allocated == 6); break;
+             case 3:   CHECK(lp->allocated == 5); break;
+             case 2:   CHECK(lp->allocated == 4); break;
+             case 1:   CHECK(lp->allocated == 0); break;
+        }
+    }
+    // free existing list
+    numba_list_free(lp);
+
+
+    // Setup list for testing delete_slice
+    status = numba_list_new(&lp, 1, 0);
+    CHECK(status == LIST_OK);
+    CHECK(lp->item_size == 1);
+    CHECK(lp->size == 0);
+    CHECK(lp->allocated == 0);
+    for (i = 0; i < 17 ; i++) {
+        status = numba_list_append(lp, (const char*)&i);
+        CHECK(status == LIST_OK);
+    }
+    CHECK(lp->size == 17);
+    test_items_3 = "\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f\x10";
+    CHECK(memcmp(lp->items, test_items_3, 17) == 0);
+
+    // delete multiple elements from the middle
+    status = numba_list_delete_slice(lp, 2, 5, 1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 14);
+    test_items_3  = "\x00\x01\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f\x10";
+    CHECK(memcmp(lp->items, test_items_3, 14) == 0);
+
+    // delete single element from start
+    status = numba_list_delete_slice(lp, 0, 1, 1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 13);
+    test_items_3  = "\x01\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f\x10";
+    CHECK(memcmp(lp->items, test_items_3, 13) == 0);
+
+    // delete single element from end
+    status = numba_list_delete_slice(lp, 12, 13, 1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 12);
+    test_items_3  = "\x01\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 12) == 0);
+
+    // delete single element from middle
+    status = numba_list_delete_slice(lp, 4, 5, 1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 11);
+    test_items_3  = "\x01\x05\x06\x07\x09\x0a\x0b\x0c\x0d\x0e\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 11) == 0);
+
+    // delete all elements except first and last
+    status = numba_list_delete_slice(lp, 1, 10, 1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 2);
+    test_items_3  = "\x01\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 2) == 0);
+
+    // delete all remaining elements
+    status = numba_list_delete_slice(lp, 0, lp->size, 1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 0);
+    test_items_3  = "";
+    CHECK(memcmp(lp->items, test_items_3, 0) == 0);
+
+    // free existing list
+    numba_list_free(lp);
+
+    // Setup list for testing delete_slice with non unary step
+    status = numba_list_new(&lp, 1, 0);
+    CHECK(status == LIST_OK);
+    CHECK(lp->item_size == 1);
+    CHECK(lp->size == 0);
+    CHECK(lp->allocated == 0);
+    for (i = 0; i < 17 ; i++) {
+        status = numba_list_append(lp, (const char*)&i);
+        CHECK(status == LIST_OK);
+    }
+    CHECK(lp->size == 17);
+
+    // delete all items with odd index
+    status = numba_list_delete_slice(lp, 0, 17, 2);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 8);
+    test_items_3 = "\x01\x03\x05\x07\x09\x0b\x0d\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 8) == 0);
+
+    // delete with a step of 4, starting at index 1
+    status = numba_list_delete_slice(lp, 1, 8, 4);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 6);
+    test_items_3 = "\x01\x05\x07\x09\x0d\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 6) == 0);
+
+    // delete with a step of 2, but finish before end of list
+    status = numba_list_delete_slice(lp, 0, 4, 2);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 4);
+    test_items_3 = "\x05\x09\x0d\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 4) == 0);
+
+    // no-op on empty slice
+    status = numba_list_delete_slice(lp, 0, 0, 1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 4);
+    test_items_3 = "\x05\x09\x0d\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 4) == 0);
+
+    // no-op on empty slice, non-zero index
+    status = numba_list_delete_slice(lp, 2, 2, 1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 4);
+    test_items_3 = "\x05\x09\x0d\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 4) == 0);
+
+    // free list and return 0
+    numba_list_free(lp);
+
+    // Setup list for testing delete_slice with negative step
+    status = numba_list_new(&lp, 1, 0);
+    CHECK(status == LIST_OK);
+    CHECK(lp->item_size == 1);
+    CHECK(lp->size == 0);
+    CHECK(lp->allocated == 0);
+    for (i = 0; i < 17 ; i++) {
+        status = numba_list_append(lp, (const char*)&i);
+        CHECK(status == LIST_OK);
+    }
+    CHECK(lp->size == 17);
+
+    // delete all items using unary negative slice
+    status = numba_list_delete_slice(lp, 16, -1, -1);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 0);
+
+    // refill list
+    for (i = 0; i < 17 ; i++) {
+        status = numba_list_append(lp, (const char*)&i);
+        CHECK(status == LIST_OK);
+    }
+
+    // delete all items using unary negative slice
+    // need to start at index of last item (16) and
+    // go beyond first item, i.e. -1 in Cd
+    status = numba_list_delete_slice(lp, 16, -1, -2);
+    CHECK(status == LIST_OK);
+    CHECK(lp->size == 8);
+    test_items_3 = "\x01\x03\x05\x07\x09\x0b\x0d\x0f";
+    CHECK(memcmp(lp->items, test_items_3, 8) == 0);
+
+    // free list and return 0
+    numba_list_free(lp);
+    return 0;
+
+
+}
+
+#undef CHECK
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/listobject.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/listobject.h
new file mode 100644
index 000000000..848ffe640
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/listobject.h
@@ -0,0 +1,135 @@
+/* Adapted from CPython3.7 Include/listobject.h
+ *
+ * The exact commit-id of the relevant file is:
+ *
+ * https://github.com/python/cpython/blob/51ddab8dae056867f3595ab3400bffc93f67c8d4/Include/listobject.h
+ *
+ *
+ * */
+
+#ifndef NUMBA_LIST_H
+#define NUMBA_LIST_H
+
+#include "Python.h"
+#include "cext.h"
+
+typedef void (*list_refcount_op_t)(const void*);
+
+typedef struct {
+    list_refcount_op_t       item_incref;
+    list_refcount_op_t       item_decref;
+} list_type_based_methods_table;
+
+/* This is the struct for the Numba typed list. It is largely inspired by the
+ * CPython list struct in listobject.h. In essence the list is a homogeneously
+ * typed container that can grow and shrink upon insertion and deletion. This
+ * means that appending an item to, or removing an item from, the end of the
+ * list, this will have a O(1) amortized runtime. This matches the
+ * behaviour of the CPython list type and it will grow with the same
+ * increments.
+ *
+ * 'items' contains space for 'allocated' elements.  The number
+ * currently in use is 'size'. The size in bytes of the items stored in the
+ * list is given by 'item_size'.
+ *
+ * Invariants:
+ *     0 <= size <= allocated
+ *     len(list) == size
+ *     item == NULL implies size == allocated == 0
+ *
+ * FIXME: list.sort() temporarily sets allocated to -1 to detect mutations.
+ *
+ * Items must normally not be NULL, except during construction when
+ * the list is not yet visible outside the function that builds it.
+ *
+ * Additionally, this list has boolean member 'is_mutable' that can be used to
+ * set a list as immutable. Two functions to query and set this member are
+ * provided. Any attempt to mutate an immutable list will result in a status
+ * of LIST_ERR_IMMUTABLE.
+ *
+ */
+typedef struct {
+    /* size of the list in items  */
+    Py_ssize_t      size;
+    /* size of the list items in bytes */
+    Py_ssize_t      item_size;
+    /* total allocated slots in items */
+    Py_ssize_t allocated;
+    /* is the list mutable */
+    int is_mutable;
+    /* method table for type-dependent operations */
+    list_type_based_methods_table methods;
+    /* array/pointer for items. Interpretation is governed by item_size */
+    char  * items;
+} NB_List;
+
+
+typedef struct {
+    /* parent list */
+    NB_List         *parent;
+    /* list size */
+    Py_ssize_t       size;
+    /* iterator position; indicates the next position to read */
+    Py_ssize_t       pos;
+} NB_ListIter;
+
+NUMBA_EXPORT_FUNC(void)
+numba_list_set_method_table(NB_List *lp, list_type_based_methods_table *methods);
+
+NUMBA_EXPORT_FUNC(int)
+numba_list_new(NB_List **out, Py_ssize_t item_size, Py_ssize_t allocated);
+
+NUMBA_EXPORT_FUNC(void)
+numba_list_free(NB_List *lp);
+
+NUMBA_EXPORT_FUNC(char *)
+numba_list_base_ptr(NB_List *lp);
+
+NUMBA_EXPORT_FUNC(Py_ssize_t)
+numba_list_size_address(NB_List *lp);
+
+NUMBA_EXPORT_FUNC(Py_ssize_t)
+numba_list_length(NB_List *lp);
+
+NUMBA_EXPORT_FUNC(Py_ssize_t)
+numba_list_allocated(NB_List *lp);
+
+NUMBA_EXPORT_FUNC(int)
+numba_list_is_mutable(NB_List *lp);
+
+NUMBA_EXPORT_FUNC(void)
+numba_list_set_is_mutable(NB_List *lp, int is_mutable);
+
+NUMBA_EXPORT_FUNC(int)
+numba_list_setitem(NB_List *lp, Py_ssize_t index, const char *item);
+
+NUMBA_EXPORT_FUNC(int)
+numba_list_getitem(NB_List *lp, Py_ssize_t index, char *out);
+
+NUMBA_EXPORT_FUNC(int)
+numba_list_append(NB_List *lp, const char *item);
+
+// FIXME: should this be public?
+NUMBA_EXPORT_FUNC(int)
+numba_list_resize(NB_List *lp, Py_ssize_t newsize);
+
+NUMBA_EXPORT_FUNC(int)
+numba_list_delitem(NB_List *lp, Py_ssize_t index);
+
+NUMBA_EXPORT_FUNC(int)
+numba_list_delete_slice(NB_List *lp,
+                        Py_ssize_t start, Py_ssize_t stop, Py_ssize_t step);
+
+NUMBA_EXPORT_FUNC(size_t)
+numba_list_iter_sizeof(void);
+
+NUMBA_EXPORT_FUNC(void)
+numba_list_iter(NB_ListIter *it, NB_List *l);
+
+NUMBA_EXPORT_FUNC(int)
+numba_list_iter_next(NB_ListIter *it, const char **item_ptr);
+
+NUMBA_EXPORT_FUNC(int)
+numba_test_list(void);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/utils.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/utils.c
new file mode 100644
index 000000000..17f4992f4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cext/utils.c
@@ -0,0 +1,8 @@
+#include "cext.h"
+
+/* Align size *sz* to pointer width */
+Py_ssize_t
+aligned_size(Py_ssize_t sz) {
+    Py_ssize_t alignment = sizeof(void*);
+    return sz + (alignment - sz % alignment) % alignment;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/__init__.py
new file mode 100644
index 000000000..231a09795
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/__init__.py
@@ -0,0 +1,12 @@
+from __future__ import absolute_import
+
+# NOTE: The following imports are adapted to use as a vendored subpackage.
+# from https://github.com/cloudpipe/cloudpickle/blob/d3279a0689b769d5315fc6ff00cd0f5897844526/cloudpickle/init.py
+from .cloudpickle import *  # noqa
+from .cloudpickle_fast import CloudPickler, dumps, dump  # noqa
+
+# Conform to the convention used by python serialization libraries, which
+# expose their Pickler subclass at top-level under the  "Pickler" name.
+Pickler = CloudPickler
+
+__version__ = '1.6.0'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/cloudpickle.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/cloudpickle.py
new file mode 100644
index 000000000..f7fd4dcbf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/cloudpickle.py
@@ -0,0 +1,848 @@
+"""
+This class is defined to override standard pickle functionality
+
+The goals of it follow:
+-Serialize lambdas and nested functions to compiled byte code
+-Deal with main module correctly
+-Deal with other non-serializable objects
+
+It does not include an unpickler, as standard python unpickling suffices.
+
+This module was extracted from the `cloud` package, developed by `PiCloud, Inc.
+<https://web.archive.org/web/20140626004012/http://www.picloud.com/>`_.
+
+Copyright (c) 2012, Regents of the University of California.
+Copyright (c) 2009 `PiCloud, Inc. <https://web.archive.org/web/20140626004012/http://www.picloud.com/>`_.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the University of California, Berkeley nor the
+      names of its contributors may be used to endorse or promote
+      products derived from this software without specific prior written
+      permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+from __future__ import print_function
+
+import builtins
+import dis
+import opcode
+import platform
+import sys
+import types
+import weakref
+import uuid
+import threading
+import typing
+import warnings
+
+from .compat import pickle
+from typing import Generic, Union, Tuple, Callable
+from pickle import _getattribute
+from importlib._bootstrap import _find_spec
+
+try:  # pragma: no branch
+    import typing_extensions as _typing_extensions
+    from typing_extensions import Literal, Final
+except ImportError:
+    _typing_extensions = Literal = Final = None
+
+if sys.version_info >= (3, 5, 3):
+    from typing import ClassVar
+else:  # pragma: no cover
+    ClassVar = None
+
+if sys.version_info >= (3, 8):
+    from types import CellType
+else:
+    def f():
+        a = 1
+
+        def g():
+            return a
+        return g
+    CellType = type(f().__closure__[0])
+
+
+# cloudpickle is meant for inter process communication: we expect all
+# communicating processes to run the same Python version hence we favor
+# communication speed over compatibility:
+DEFAULT_PROTOCOL = pickle.HIGHEST_PROTOCOL
+
+# Track the provenance of reconstructed dynamic classes to make it possible to
+# reconstruct instances from the matching singleton class definition when
+# appropriate and preserve the usual "isinstance" semantics of Python objects.
+_DYNAMIC_CLASS_TRACKER_BY_CLASS = weakref.WeakKeyDictionary()
+_DYNAMIC_CLASS_TRACKER_BY_ID = weakref.WeakValueDictionary()
+_DYNAMIC_CLASS_TRACKER_LOCK = threading.Lock()
+_DYNAMIC_CLASS_TRACKER_REUSING = weakref.WeakSet()
+
+PYPY = platform.python_implementation() == "PyPy"
+
+builtin_code_type = None
+if PYPY:
+    # builtin-code objects only exist in pypy
+    builtin_code_type = type(float.__new__.__code__)
+
+_extract_code_globals_cache = weakref.WeakKeyDictionary()
+
+
+def _get_or_create_tracker_id(class_def):
+    with _DYNAMIC_CLASS_TRACKER_LOCK:
+        class_tracker_id = _DYNAMIC_CLASS_TRACKER_BY_CLASS.get(class_def)
+        if class_tracker_id is None:
+            class_tracker_id = uuid.uuid4().hex
+            _DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
+            _DYNAMIC_CLASS_TRACKER_BY_ID[class_tracker_id] = class_def
+    return class_tracker_id
+
+
+def _lookup_class_or_track(class_tracker_id, class_def):
+    if class_tracker_id is not None:
+        with _DYNAMIC_CLASS_TRACKER_LOCK:
+            orig_class_def = class_def
+            class_def = _DYNAMIC_CLASS_TRACKER_BY_ID.setdefault(
+                class_tracker_id, class_def)
+            _DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
+            # Check if we are reusing a previous class_def
+            if orig_class_def is not class_def:
+                # Remember the class_def is being reused
+                _DYNAMIC_CLASS_TRACKER_REUSING.add(class_def)
+    return class_def
+
+
+def _whichmodule(obj, name):
+    """Find the module an object belongs to.
+
+    This function differs from ``pickle.whichmodule`` in two ways:
+    - it does not mangle the cases where obj's module is __main__ and obj was
+      not found in any module.
+    - Errors arising during module introspection are ignored, as those errors
+      are considered unwanted side effects.
+    """
+    if sys.version_info[:2] < (3, 7) and isinstance(obj, typing.TypeVar):  # pragma: no branch  # noqa
+        # Workaround bug in old Python versions: prior to Python 3.7,
+        # T.__module__ would always be set to "typing" even when the TypeVar T
+        # would be defined in a different module.
+        #
+        # For such older Python versions, we ignore the __module__ attribute of
+        # TypeVar instances and instead exhaustively lookup those instances in
+        # all currently imported modules.
+        module_name = None
+    else:
+        module_name = getattr(obj, '__module__', None)
+
+    if module_name is not None:
+        return module_name
+    # Protect the iteration by using a copy of sys.modules against dynamic
+    # modules that trigger imports of other modules upon calls to getattr or
+    # other threads importing at the same time.
+    for module_name, module in sys.modules.copy().items():
+        # Some modules such as coverage can inject non-module objects inside
+        # sys.modules
+        if (
+                module_name == '__main__' or
+                module is None or
+                not isinstance(module, types.ModuleType)
+        ):
+            continue
+        try:
+            if _getattribute(module, name)[0] is obj:
+                return module_name
+        except Exception:
+            pass
+    return None
+
+
+def _is_importable(obj, name=None):
+    """Dispatcher utility to test the importability of various constructs."""
+    if isinstance(obj, types.FunctionType):
+        return _lookup_module_and_qualname(obj, name=name) is not None
+    elif issubclass(type(obj), type):
+        return _lookup_module_and_qualname(obj, name=name) is not None
+    elif isinstance(obj, types.ModuleType):
+        # We assume that sys.modules is primarily used as a cache mechanism for
+        # the Python import machinery. Checking if a module has been added in
+        # is sys.modules therefore a cheap and simple heuristic to tell us whether
+        # we can assume  that a given module could be imported by name in
+        # another Python process.
+        return obj.__name__ in sys.modules
+    else:
+        raise TypeError(
+            "cannot check importability of {} instances".format(
+                type(obj).__name__)
+        )
+
+
+def _lookup_module_and_qualname(obj, name=None):
+    if name is None:
+        name = getattr(obj, '__qualname__', None)
+    if name is None:  # pragma: no cover
+        # This used to be needed for Python 2.7 support but is probably not
+        # needed anymore. However we keep the __name__ introspection in case
+        # users of cloudpickle rely on this old behavior for unknown reasons.
+        name = getattr(obj, '__name__', None)
+
+    module_name = _whichmodule(obj, name)
+
+    if module_name is None:
+        # In this case, obj.__module__ is None AND obj was not found in any
+        # imported module. obj is thus treated as dynamic.
+        return None
+
+    if module_name == "__main__":
+        return None
+
+    # Note: if module_name is in sys.modules, the corresponding module is
+    # assumed importable at unpickling time. See #357
+    module = sys.modules.get(module_name, None)
+    if module is None:
+        # The main reason why obj's module would not be imported is that this
+        # module has been dynamically created, using for example
+        # types.ModuleType. The other possibility is that module was removed
+        # from sys.modules after obj was created/imported. But this case is not
+        # supported, as the standard pickle does not support it either.
+        return None
+
+    try:
+        obj2, parent = _getattribute(module, name)
+    except AttributeError:
+        # obj was not found inside the module it points to
+        return None
+    if obj2 is not obj:
+        return None
+    return module, name
+
+
+def _extract_code_globals(co):
+    """
+    Find all globals names read or written to by codeblock co
+    """
+    out_names = _extract_code_globals_cache.get(co)
+    if out_names is None:
+        names = co.co_names
+        out_names = {names[oparg] for _, oparg in _walk_global_ops(co)}
+
+        # Declaring a function inside another one using the "def ..."
+        # syntax generates a constant code object corresponding to one
+        # of the nested function's As the nested function may itself need
+        # global variables, we need to introspect its code, extract its
+        # globals, (look for code object in it's co_consts attribute..) and
+        # add the result to code_globals
+        if co.co_consts:
+            for const in co.co_consts:
+                if isinstance(const, types.CodeType):
+                    out_names |= _extract_code_globals(const)
+
+        _extract_code_globals_cache[co] = out_names
+
+    return out_names
+
+
+def _find_imported_submodules(code, top_level_dependencies):
+    """
+    Find currently imported submodules used by a function.
+
+    Submodules used by a function need to be detected and referenced for the
+    function to work correctly at depickling time. Because submodules can be
+    referenced as attribute of their parent package (``package.submodule``), we
+    need a special introspection technique that does not rely on GLOBAL-related
+    opcodes to find references of them in a code object.
+
+    Example:
+    ```
+    import concurrent.futures
+    import cloudpickle
+    def func():
+        x = concurrent.futures.ThreadPoolExecutor
+    if __name__ == '__main__':
+        cloudpickle.dumps(func)
+    ```
+    The globals extracted by cloudpickle in the function's state include the
+    concurrent package, but not its submodule (here, concurrent.futures), which
+    is the module used by func. Find_imported_submodules will detect the usage
+    of concurrent.futures. Saving this module alongside with func will ensure
+    that calling func once depickled does not fail due to concurrent.futures
+    not being imported
+    """
+
+    subimports = []
+    # check if any known dependency is an imported package
+    for x in top_level_dependencies:
+        if (isinstance(x, types.ModuleType) and
+                hasattr(x, '__package__') and x.__package__):
+            # check if the package has any currently loaded sub-imports
+            prefix = x.__name__ + '.'
+            # A concurrent thread could mutate sys.modules,
+            # make sure we iterate over a copy to avoid exceptions
+            for name in list(sys.modules):
+                # Older versions of pytest will add a "None" module to
+                # sys.modules.
+                if name is not None and name.startswith(prefix):
+                    # check whether the function can address the sub-module
+                    tokens = set(name[len(prefix):].split('.'))
+                    if not tokens - set(code.co_names):
+                        subimports.append(sys.modules[name])
+    return subimports
+
+
+def cell_set(cell, value):
+    """Set the value of a closure cell.
+
+    The point of this function is to set the cell_contents attribute of a cell
+    after its creation. This operation is necessary in case the cell contains a
+    reference to the function the cell belongs to, as when calling the
+    function's constructor
+    ``f = types.FunctionType(code, globals, name, argdefs, closure)``,
+    closure will not be able to contain the yet-to-be-created f.
+
+    In Python3.7, cell_contents is writeable, so setting the contents of a cell
+    can be done simply using
+    >>> cell.cell_contents = value
+
+    In earlier Python3 versions, the cell_contents attribute of a cell is read
+    only, but this limitation can be worked around by leveraging the Python 3
+    ``nonlocal`` keyword.
+
+    In Python2 however, this attribute is read only, and there is no
+    ``nonlocal`` keyword. For this reason, we need to come up with more
+    complicated hacks to set this attribute.
+
+    The chosen approach is to create a function with a STORE_DEREF opcode,
+    which sets the content of a closure variable. Typically:
+
+    >>> def inner(value):
+    ...     lambda: cell  # the lambda makes cell a closure
+    ...     cell = value  # cell is a closure, so this triggers a STORE_DEREF
+
+    (Note that in Python2, A STORE_DEREF can never be triggered from an inner
+    function. The function g for example here
+    >>> def f(var):
+    ...     def g():
+    ...         var += 1
+    ...     return g
+
+    will not modify the closure variable ``var```inplace, but instead try to
+    load a local variable var and increment it. As g does not assign the local
+    variable ``var`` any initial value, calling f(1)() will fail at runtime.)
+
+    Our objective is to set the value of a given cell ``cell``. So we need to
+    somewhat reference our ``cell`` object into the ``inner`` function so that
+    this object (and not the smoke cell of the lambda function) gets affected
+    by the STORE_DEREF operation.
+
+    In inner, ``cell`` is referenced as a cell variable (an enclosing variable
+    that is referenced by the inner function). If we create a new function
+    cell_set with the exact same code as ``inner``, but with ``cell`` marked as
+    a free variable instead, the STORE_DEREF will be applied on its closure -
+    ``cell``, which we can specify explicitly during construction! The new
+    cell_set variable thus actually sets the contents of a specified cell!
+
+    Note: we do not make use of the ``nonlocal`` keyword to set the contents of
+    a cell in early python3 versions to limit possible syntax errors in case
+    test and checker libraries decide to parse the whole file.
+    """
+
+    if sys.version_info[:2] >= (3, 7):  # pragma: no branch
+        cell.cell_contents = value
+    else:
+        _cell_set = types.FunctionType(
+            _cell_set_template_code, {}, '_cell_set', (), (cell,),)
+        _cell_set(value)
+
+
+def _make_cell_set_template_code():
+    def _cell_set_factory(value):
+        lambda: cell
+        cell = value
+
+    co = _cell_set_factory.__code__
+
+    _cell_set_template_code = types.CodeType(
+        co.co_argcount,
+        co.co_kwonlyargcount,   # Python 3 only argument
+        co.co_nlocals,
+        co.co_stacksize,
+        co.co_flags,
+        co.co_code,
+        co.co_consts,
+        co.co_names,
+        co.co_varnames,
+        co.co_filename,
+        co.co_name,
+        co.co_firstlineno,
+        co.co_lnotab,
+        co.co_cellvars,  # co_freevars is initialized with co_cellvars
+        (),  # co_cellvars is made empty
+    )
+    return _cell_set_template_code
+
+
+if sys.version_info[:2] < (3, 7):
+    _cell_set_template_code = _make_cell_set_template_code()
+
+# relevant opcodes
+STORE_GLOBAL = opcode.opmap['STORE_GLOBAL']
+DELETE_GLOBAL = opcode.opmap['DELETE_GLOBAL']
+LOAD_GLOBAL = opcode.opmap['LOAD_GLOBAL']
+GLOBAL_OPS = (STORE_GLOBAL, DELETE_GLOBAL, LOAD_GLOBAL)
+HAVE_ARGUMENT = dis.HAVE_ARGUMENT
+EXTENDED_ARG = dis.EXTENDED_ARG
+
+
+_BUILTIN_TYPE_NAMES = {}
+for k, v in types.__dict__.items():
+    if type(v) is type:
+        _BUILTIN_TYPE_NAMES[v] = k
+
+
+def _builtin_type(name):
+    if name == "ClassType":  # pragma: no cover
+        # Backward compat to load pickle files generated with cloudpickle
+        # < 1.3 even if loading pickle files from older versions is not
+        # officially supported.
+        return type
+    return getattr(types, name)
+
+
+def _walk_global_ops(code):
+    """
+    Yield (opcode, argument number) tuples for all
+    global-referencing instructions in *code*.
+    """
+    for instr in dis.get_instructions(code):
+        op = instr.opcode
+        if op in GLOBAL_OPS:
+            yield op, instr.arg
+
+
+def _extract_class_dict(cls):
+    """Retrieve a copy of the dict of a class without the inherited methods"""
+    clsdict = dict(cls.__dict__)  # copy dict proxy to a dict
+    if len(cls.__bases__) == 1:
+        inherited_dict = cls.__bases__[0].__dict__
+    else:
+        inherited_dict = {}
+        for base in reversed(cls.__bases__):
+            inherited_dict.update(base.__dict__)
+    to_remove = []
+    for name, value in clsdict.items():
+        try:
+            base_value = inherited_dict[name]
+            if value is base_value:
+                to_remove.append(name)
+        except KeyError:
+            pass
+    for name in to_remove:
+        clsdict.pop(name)
+    return clsdict
+
+
+if sys.version_info[:2] < (3, 7):  # pragma: no branch
+    def _is_parametrized_type_hint(obj):
+        # This is very cheap but might generate false positives.
+        # general typing Constructs
+        is_typing = getattr(obj, '__origin__', None) is not None
+
+        # typing_extensions.Literal
+        is_litteral = getattr(obj, '__values__', None) is not None
+
+        # typing_extensions.Final
+        is_final = getattr(obj, '__type__', None) is not None
+
+        # typing.Union/Tuple for old Python 3.5
+        is_union = getattr(obj, '__union_params__', None) is not None
+        is_tuple = getattr(obj, '__tuple_params__', None) is not None
+        is_callable = (
+            getattr(obj, '__result__', None) is not None and
+            getattr(obj, '__args__', None) is not None
+        )
+        return any((is_typing, is_litteral, is_final, is_union, is_tuple,
+                    is_callable))
+
+    def _create_parametrized_type_hint(origin, args):
+        return origin[args]
+else:
+    _is_parametrized_type_hint = None
+    _create_parametrized_type_hint = None
+
+
+def parametrized_type_hint_getinitargs(obj):
+    # The distorted type check semantic for typing construct becomes:
+    # ``type(obj) is type(TypeHint)``, which means "obj is a
+    # parametrized TypeHint"
+    if type(obj) is type(Literal):  # pragma: no branch
+        initargs = (Literal, obj.__values__)
+    elif type(obj) is type(Final):  # pragma: no branch
+        initargs = (Final, obj.__type__)
+    elif type(obj) is type(ClassVar):
+        initargs = (ClassVar, obj.__type__)
+    elif type(obj) is type(Generic):
+        parameters = obj.__parameters__
+        if len(obj.__parameters__) > 0:
+            # in early Python 3.5, __parameters__ was sometimes
+            # preferred to __args__
+            initargs = (obj.__origin__, parameters)
+
+        else:
+            initargs = (obj.__origin__, obj.__args__)
+    elif type(obj) is type(Union):
+        if sys.version_info < (3, 5, 3):  # pragma: no cover
+            initargs = (Union, obj.__union_params__)
+        else:
+            initargs = (Union, obj.__args__)
+    elif type(obj) is type(Tuple):
+        if sys.version_info < (3, 5, 3):  # pragma: no cover
+            initargs = (Tuple, obj.__tuple_params__)
+        else:
+            initargs = (Tuple, obj.__args__)
+    elif type(obj) is type(Callable):
+        if sys.version_info < (3, 5, 3):  # pragma: no cover
+            args = obj.__args__
+            result = obj.__result__
+            if args != Ellipsis:
+                if isinstance(args, tuple):
+                    args = list(args)
+                else:
+                    args = [args]
+        else:
+            (*args, result) = obj.__args__
+            if len(args) == 1 and args[0] is Ellipsis:
+                args = Ellipsis
+            else:
+                args = list(args)
+        initargs = (Callable, (args, result))
+    else:  # pragma: no cover
+        raise pickle.PicklingError(
+            "Cloudpickle Error: Unknown type {}".format(type(obj))
+        )
+    return initargs
+
+
+# Tornado support
+
+def is_tornado_coroutine(func):
+    """
+    Return whether *func* is a Tornado coroutine function.
+    Running coroutines are not supported.
+    """
+    if 'tornado.gen' not in sys.modules:
+        return False
+    gen = sys.modules['tornado.gen']
+    if not hasattr(gen, "is_coroutine_function"):
+        # Tornado version is too old
+        return False
+    return gen.is_coroutine_function(func)
+
+
+def _rebuild_tornado_coroutine(func):
+    from tornado import gen
+    return gen.coroutine(func)
+
+
+# including pickles unloading functions in this namespace
+load = pickle.load
+loads = pickle.loads
+
+
+# hack for __import__ not working as desired
+def subimport(name):
+    __import__(name)
+    return sys.modules[name]
+
+
+def dynamic_subimport(name, vars):
+    mod = types.ModuleType(name)
+    mod.__dict__.update(vars)
+    mod.__dict__['__builtins__'] = builtins.__dict__
+    return mod
+
+
+def _gen_ellipsis():
+    return Ellipsis
+
+
+def _gen_not_implemented():
+    return NotImplemented
+
+
+def _get_cell_contents(cell):
+    try:
+        return cell.cell_contents
+    except ValueError:
+        # sentinel used by ``_fill_function`` which will leave the cell empty
+        return _empty_cell_value
+
+
+def instance(cls):
+    """Create a new instance of a class.
+
+    Parameters
+    ----------
+    cls : type
+        The class to create an instance of.
+
+    Returns
+    -------
+    instance : cls
+        A new instance of ``cls``.
+    """
+    return cls()
+
+
+@instance
+class _empty_cell_value(object):
+    """sentinel for empty closures
+    """
+    @classmethod
+    def __reduce__(cls):
+        return cls.__name__
+
+
+def _fill_function(*args):
+    """Fills in the rest of function data into the skeleton function object
+
+    The skeleton itself is create by _make_skel_func().
+    """
+    if len(args) == 2:
+        func = args[0]
+        state = args[1]
+    elif len(args) == 5:
+        # Backwards compat for cloudpickle v0.4.0, after which the `module`
+        # argument was introduced
+        func = args[0]
+        keys = ['globals', 'defaults', 'dict', 'closure_values']
+        state = dict(zip(keys, args[1:]))
+    elif len(args) == 6:
+        # Backwards compat for cloudpickle v0.4.1, after which the function
+        # state was passed as a dict to the _fill_function it-self.
+        func = args[0]
+        keys = ['globals', 'defaults', 'dict', 'module', 'closure_values']
+        state = dict(zip(keys, args[1:]))
+    else:
+        raise ValueError('Unexpected _fill_value arguments: %r' % (args,))
+
+    # - At pickling time, any dynamic global variable used by func is
+    #   serialized by value (in state['globals']).
+    # - At unpickling time, func's __globals__ attribute is initialized by
+    #   first retrieving an empty isolated namespace that will be shared
+    #   with other functions pickled from the same original module
+    #   by the same CloudPickler instance and then updated with the
+    #   content of state['globals'] to populate the shared isolated
+    #   namespace with all the global variables that are specifically
+    #   referenced for this function.
+    func.__globals__.update(state['globals'])
+
+    func.__defaults__ = state['defaults']
+    func.__dict__ = state['dict']
+    if 'annotations' in state:
+        func.__annotations__ = state['annotations']
+    if 'doc' in state:
+        func.__doc__ = state['doc']
+    if 'name' in state:
+        func.__name__ = state['name']
+    if 'module' in state:
+        func.__module__ = state['module']
+    if 'qualname' in state:
+        func.__qualname__ = state['qualname']
+    if 'kwdefaults' in state:
+        func.__kwdefaults__ = state['kwdefaults']
+    # _cloudpickle_subimports is a set of submodules that must be loaded for
+    # the pickled function to work correctly at unpickling time. Now that these
+    # submodules are depickled (hence imported), they can be removed from the
+    # object's state (the object state only served as a reference holder to
+    # these submodules)
+    if '_cloudpickle_submodules' in state:
+        state.pop('_cloudpickle_submodules')
+
+    cells = func.__closure__
+    if cells is not None:
+        for cell, value in zip(cells, state['closure_values']):
+            if value is not _empty_cell_value:
+                cell_set(cell, value)
+
+    return func
+
+
+def _make_empty_cell():
+    if False:
+        # trick the compiler into creating an empty cell in our lambda
+        cell = None
+        raise AssertionError('this route should not be executed')
+
+    return (lambda: cell).__closure__[0]
+
+
+def _make_cell(value=_empty_cell_value):
+    cell = _make_empty_cell()
+    if value is not _empty_cell_value:
+        cell_set(cell, value)
+    return cell
+
+
+def _make_skel_func(code, cell_count, base_globals=None):
+    """ Creates a skeleton function object that contains just the provided
+        code and the correct number of cells in func_closure.  All other
+        func attributes (e.g. func_globals) are empty.
+    """
+    # This function is deprecated and should be removed in cloudpickle 1.7
+    warnings.warn(
+        "A pickle file created using an old (<=1.4.1) version of cloudpicke "
+        "is currently being loaded. This is not supported by cloudpickle and "
+        "will break in cloudpickle 1.7", category=UserWarning
+    )
+    # This is backward-compatibility code: for cloudpickle versions between
+    # 0.5.4 and 0.7, base_globals could be a string or None. base_globals
+    # should now always be a dictionary.
+    if base_globals is None or isinstance(base_globals, str):
+        base_globals = {}
+
+    base_globals['__builtins__'] = __builtins__
+
+    closure = (
+        tuple(_make_empty_cell() for _ in range(cell_count))
+        if cell_count >= 0 else
+        None
+    )
+    return types.FunctionType(code, base_globals, None, None, closure)
+
+
+def _make_skeleton_class(type_constructor, name, bases, type_kwargs,
+                         class_tracker_id, extra):
+    """Build dynamic class with an empty __dict__ to be filled once memoized
+
+    If class_tracker_id is not None, try to lookup an existing class definition
+    matching that id. If none is found, track a newly reconstructed class
+    definition under that id so that other instances stemming from the same
+    class id will also reuse this class definition.
+
+    The "extra" variable is meant to be a dict (or None) that can be used for
+    forward compatibility shall the need arise.
+    """
+    skeleton_class = types.new_class(
+        name, bases, {'metaclass': type_constructor},
+        lambda ns: ns.update(type_kwargs)
+    )
+    return _lookup_class_or_track(class_tracker_id, skeleton_class)
+
+
+def _rehydrate_skeleton_class(skeleton_class, class_dict):
+    """Put attributes from `class_dict` back on `skeleton_class`.
+
+    See CloudPickler.save_dynamic_class for more info.
+    """
+    registry = None
+    for attrname, attr in class_dict.items():
+        if attrname == "_abc_impl":
+            registry = attr
+        else:
+            setattr(skeleton_class, attrname, attr)
+    if registry is not None:
+        for subclass in registry:
+            skeleton_class.register(subclass)
+
+    return skeleton_class
+
+
+def _make_skeleton_enum(bases, name, qualname, members, module,
+                        class_tracker_id, extra):
+    """Build dynamic enum with an empty __dict__ to be filled once memoized
+
+    The creation of the enum class is inspired by the code of
+    EnumMeta._create_.
+
+    If class_tracker_id is not None, try to lookup an existing enum definition
+    matching that id. If none is found, track a newly reconstructed enum
+    definition under that id so that other instances stemming from the same
+    class id will also reuse this enum definition.
+
+    The "extra" variable is meant to be a dict (or None) that can be used for
+    forward compatibility shall the need arise.
+    """
+    # enums always inherit from their base Enum class at the last position in
+    # the list of base classes:
+    enum_base = bases[-1]
+    metacls = enum_base.__class__
+    classdict = metacls.__prepare__(name, bases)
+
+    for member_name, member_value in members.items():
+        classdict[member_name] = member_value
+    enum_class = metacls.__new__(metacls, name, bases, classdict)
+    enum_class.__module__ = module
+    enum_class.__qualname__ = qualname
+
+    return _lookup_class_or_track(class_tracker_id, enum_class)
+
+
+def _make_typevar(name, bound, constraints, covariant, contravariant,
+                  class_tracker_id):
+    tv = typing.TypeVar(
+        name, *constraints, bound=bound,
+        covariant=covariant, contravariant=contravariant
+    )
+    if class_tracker_id is not None:
+        return _lookup_class_or_track(class_tracker_id, tv)
+    else:  # pragma: nocover
+        # Only for Python 3.5.3 compat.
+        return tv
+
+
+def _decompose_typevar(obj):
+    try:
+        class_tracker_id = _get_or_create_tracker_id(obj)
+    except TypeError:  # pragma: nocover
+        # TypeVar instances are not weakref-able in Python 3.5.3
+        class_tracker_id = None
+    return (
+        obj.__name__, obj.__bound__, obj.__constraints__,
+        obj.__covariant__, obj.__contravariant__,
+        class_tracker_id,
+    )
+
+
+def _typevar_reduce(obj):
+    # TypeVar instances have no __qualname__ hence we pass the name explicitly.
+    module_and_name = _lookup_module_and_qualname(obj, name=obj.__name__)
+    if module_and_name is None:
+        return (_make_typevar, _decompose_typevar(obj))
+    return (getattr, module_and_name)
+
+
+def _get_bases(typ):
+    if hasattr(typ, '__orig_bases__'):
+        # For generic types (see PEP 560)
+        bases_attr = '__orig_bases__'
+    else:
+        # For regular class objects
+        bases_attr = '__bases__'
+    return getattr(typ, bases_attr)
+
+
+def _make_dict_keys(obj):
+    return dict.fromkeys(obj).keys()
+
+
+def _make_dict_values(obj):
+    return {i: _ for i, _ in enumerate(obj)}.values()
+
+
+def _make_dict_items(obj):
+    return obj.items()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/cloudpickle_fast.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/cloudpickle_fast.py
new file mode 100644
index 000000000..216450495
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/cloudpickle_fast.py
@@ -0,0 +1,775 @@
+"""
+New, fast version of the CloudPickler.
+
+This new CloudPickler class can now extend the fast C Pickler instead of the
+previous Python implementation of the Pickler class. Because this functionality
+is only available for Python versions 3.8+, a lot of backward-compatibility
+code is also removed.
+
+Note that the C Pickler sublassing API is CPython-specific. Therefore, some
+guards present in cloudpickle.py that were written to handle PyPy specificities
+are not present in cloudpickle_fast.py
+"""
+import _collections_abc
+import abc
+import copyreg
+import io
+import itertools
+import logging
+import sys
+import struct
+import types
+import weakref
+import typing
+
+from enum import Enum
+from collections import ChainMap
+
+from .compat import pickle, Pickler
+from .cloudpickle import (
+    _extract_code_globals, _BUILTIN_TYPE_NAMES, DEFAULT_PROTOCOL,
+    _find_imported_submodules, _get_cell_contents, _is_importable,
+    _builtin_type, _get_or_create_tracker_id,  _make_skeleton_class,
+    _make_skeleton_enum, _extract_class_dict, dynamic_subimport, subimport,
+    _typevar_reduce, _get_bases, _make_cell, _make_empty_cell, CellType,
+    _is_parametrized_type_hint, PYPY, cell_set,
+    parametrized_type_hint_getinitargs, _create_parametrized_type_hint,
+    builtin_code_type,
+    _make_dict_keys, _make_dict_values, _make_dict_items,
+    _DYNAMIC_CLASS_TRACKER_REUSING,
+)
+
+
+if pickle.HIGHEST_PROTOCOL >= 5 and not PYPY:
+    # Shorthands similar to pickle.dump/pickle.dumps
+
+    def dump(obj, file, protocol=None, buffer_callback=None):
+        """Serialize obj as bytes streamed into file
+
+        protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
+        pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
+        speed between processes running the same Python version.
+
+        Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
+        compatibility with older versions of Python.
+        """
+        CloudPickler(
+            file, protocol=protocol, buffer_callback=buffer_callback
+        ).dump(obj)
+
+    def dumps(obj, protocol=None, buffer_callback=None):
+        """Serialize obj as a string of bytes allocated in memory
+
+        protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
+        pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
+        speed between processes running the same Python version.
+
+        Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
+        compatibility with older versions of Python.
+        """
+        with io.BytesIO() as file:
+            cp = CloudPickler(
+                file, protocol=protocol, buffer_callback=buffer_callback
+            )
+            cp.dump(obj)
+            return file.getvalue()
+
+else:
+    # Shorthands similar to pickle.dump/pickle.dumps
+    def dump(obj, file, protocol=None):
+        """Serialize obj as bytes streamed into file
+
+        protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
+        pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
+        speed between processes running the same Python version.
+
+        Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
+        compatibility with older versions of Python.
+        """
+        CloudPickler(file, protocol=protocol).dump(obj)
+
+    def dumps(obj, protocol=None):
+        """Serialize obj as a string of bytes allocated in memory
+
+        protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
+        pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
+        speed between processes running the same Python version.
+
+        Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
+        compatibility with older versions of Python.
+        """
+        with io.BytesIO() as file:
+            cp = CloudPickler(file, protocol=protocol)
+            cp.dump(obj)
+            return file.getvalue()
+
+
+load, loads = pickle.load, pickle.loads
+
+
+# COLLECTION OF OBJECTS __getnewargs__-LIKE METHODS
+# -------------------------------------------------
+
+def _class_getnewargs(obj):
+    type_kwargs = {}
+    if "__slots__" in obj.__dict__:
+        type_kwargs["__slots__"] = obj.__slots__
+
+    __dict__ = obj.__dict__.get('__dict__', None)
+    if isinstance(__dict__, property):
+        type_kwargs['__dict__'] = __dict__
+
+    return (type(obj), obj.__name__, _get_bases(obj), type_kwargs,
+            _get_or_create_tracker_id(obj), None)
+
+
+def _enum_getnewargs(obj):
+    members = dict((e.name, e.value) for e in obj)
+    return (obj.__bases__, obj.__name__, obj.__qualname__, members,
+            obj.__module__, _get_or_create_tracker_id(obj), None)
+
+
+# COLLECTION OF OBJECTS RECONSTRUCTORS
+# ------------------------------------
+def _file_reconstructor(retval):
+    return retval
+
+
+# COLLECTION OF OBJECTS STATE GETTERS
+# -----------------------------------
+def _function_getstate(func):
+    # - Put func's dynamic attributes (stored in func.__dict__) in state. These
+    #   attributes will be restored at unpickling time using
+    #   f.__dict__.update(state)
+    # - Put func's members into slotstate. Such attributes will be restored at
+    #   unpickling time by iterating over slotstate and calling setattr(func,
+    #   slotname, slotvalue)
+    slotstate = {
+        "__name__": func.__name__,
+        "__qualname__": func.__qualname__,
+        "__annotations__": func.__annotations__,
+        "__kwdefaults__": func.__kwdefaults__,
+        "__defaults__": func.__defaults__,
+        "__module__": func.__module__,
+        "__doc__": func.__doc__,
+        "__closure__": func.__closure__,
+    }
+
+    f_globals_ref = _extract_code_globals(func.__code__)
+    f_globals = {k: func.__globals__[k] for k in f_globals_ref if k in
+                 func.__globals__}
+
+    closure_values = (
+        list(map(_get_cell_contents, func.__closure__))
+        if func.__closure__ is not None else ()
+    )
+
+    # Extract currently-imported submodules used by func. Storing these modules
+    # in a smoke _cloudpickle_subimports attribute of the object's state will
+    # trigger the side effect of importing these modules at unpickling time
+    # (which is necessary for func to work correctly once depickled)
+    slotstate["_cloudpickle_submodules"] = _find_imported_submodules(
+        func.__code__, itertools.chain(f_globals.values(), closure_values))
+    slotstate["__globals__"] = f_globals
+
+    state = func.__dict__
+    return state, slotstate
+
+
+def _class_getstate(obj):
+    clsdict = _extract_class_dict(obj)
+    clsdict.pop('__weakref__', None)
+
+    if issubclass(type(obj), abc.ABCMeta):
+        # If obj is an instance of an ABCMeta subclass, dont pickle the
+        # cache/negative caches populated during isinstance/issubclass
+        # checks, but pickle the list of registered subclasses of obj.
+        clsdict.pop('_abc_cache', None)
+        clsdict.pop('_abc_negative_cache', None)
+        clsdict.pop('_abc_negative_cache_version', None)
+        registry = clsdict.pop('_abc_registry', None)
+        if registry is None:
+            # in Python3.7+, the abc caches and registered subclasses of a
+            # class are bundled into the single _abc_impl attribute
+            clsdict.pop('_abc_impl', None)
+            (registry, _, _, _) = abc._get_dump(obj)
+
+            clsdict["_abc_impl"] = [subclass_weakref()
+                                    for subclass_weakref in registry]
+        else:
+            # In the above if clause, registry is a set of weakrefs -- in
+            # this case, registry is a WeakSet
+            clsdict["_abc_impl"] = [type_ for type_ in registry]
+
+    if "__slots__" in clsdict:
+        # pickle string length optimization: member descriptors of obj are
+        # created automatically from obj's __slots__ attribute, no need to
+        # save them in obj's state
+        if isinstance(obj.__slots__, str):
+            clsdict.pop(obj.__slots__)
+        else:
+            for k in obj.__slots__:
+                clsdict.pop(k, None)
+
+    clsdict.pop('__dict__', None)  # unpicklable property object
+
+    return (clsdict, {})
+
+
+def _enum_getstate(obj):
+    clsdict, slotstate = _class_getstate(obj)
+
+    members = dict((e.name, e.value) for e in obj)
+    # Cleanup the clsdict that will be passed to _rehydrate_skeleton_class:
+    # Those attributes are already handled by the metaclass.
+    for attrname in ["_generate_next_value_", "_member_names_",
+                     "_member_map_", "_member_type_",
+                     "_value2member_map_"]:
+        clsdict.pop(attrname, None)
+    for member in members:
+        clsdict.pop(member)
+        # Special handling of Enum subclasses
+    return clsdict, slotstate
+
+
+# COLLECTIONS OF OBJECTS REDUCERS
+# -------------------------------
+# A reducer is a function taking a single argument (obj), and that returns a
+# tuple with all the necessary data to re-construct obj. Apart from a few
+# exceptions (list, dict, bytes, int, etc.), a reducer is necessary to
+# correctly pickle an object.
+# While many built-in objects (Exceptions objects, instances of the "object"
+# class, etc), are shipped with their own built-in reducer (invoked using
+# obj.__reduce__), some do not. The following methods were created to "fill
+# these holes".
+
+def _code_reduce(obj):
+    """codeobject reducer"""
+    if hasattr(obj, "co_posonlyargcount"):  # pragma: no branch
+        args = (
+            obj.co_argcount, obj.co_posonlyargcount,
+            obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize,
+            obj.co_flags, obj.co_code, obj.co_consts, obj.co_names,
+            obj.co_varnames, obj.co_filename, obj.co_name,
+            obj.co_firstlineno, obj.co_lnotab, obj.co_freevars,
+            obj.co_cellvars
+        )
+    else:
+        args = (
+            obj.co_argcount, obj.co_kwonlyargcount, obj.co_nlocals,
+            obj.co_stacksize, obj.co_flags, obj.co_code, obj.co_consts,
+            obj.co_names, obj.co_varnames, obj.co_filename,
+            obj.co_name, obj.co_firstlineno, obj.co_lnotab,
+            obj.co_freevars, obj.co_cellvars
+        )
+    return types.CodeType, args
+
+
+def _cell_reduce(obj):
+    """Cell (containing values of a function's free variables) reducer"""
+    try:
+        obj.cell_contents
+    except ValueError:  # cell is empty
+        return _make_empty_cell, ()
+    else:
+        return _make_cell, (obj.cell_contents, )
+
+
+def _classmethod_reduce(obj):
+    orig_func = obj.__func__
+    return type(obj), (orig_func,)
+
+
+def _file_reduce(obj):
+    """Save a file"""
+    import io
+
+    if not hasattr(obj, "name") or not hasattr(obj, "mode"):
+        raise pickle.PicklingError(
+            "Cannot pickle files that do not map to an actual file"
+        )
+    if obj is sys.stdout:
+        return getattr, (sys, "stdout")
+    if obj is sys.stderr:
+        return getattr, (sys, "stderr")
+    if obj is sys.stdin:
+        raise pickle.PicklingError("Cannot pickle standard input")
+    if obj.closed:
+        raise pickle.PicklingError("Cannot pickle closed files")
+    if hasattr(obj, "isatty") and obj.isatty():
+        raise pickle.PicklingError(
+            "Cannot pickle files that map to tty objects"
+        )
+    if "r" not in obj.mode and "+" not in obj.mode:
+        raise pickle.PicklingError(
+            "Cannot pickle files that are not opened for reading: %s"
+            % obj.mode
+        )
+
+    name = obj.name
+
+    retval = io.StringIO()
+
+    try:
+        # Read the whole file
+        curloc = obj.tell()
+        obj.seek(0)
+        contents = obj.read()
+        obj.seek(curloc)
+    except IOError as e:
+        raise pickle.PicklingError(
+            "Cannot pickle file %s as it cannot be read" % name
+        ) from e
+    retval.write(contents)
+    retval.seek(curloc)
+
+    retval.name = name
+    return _file_reconstructor, (retval,)
+
+
+def _getset_descriptor_reduce(obj):
+    return getattr, (obj.__objclass__, obj.__name__)
+
+
+def _mappingproxy_reduce(obj):
+    return types.MappingProxyType, (dict(obj),)
+
+
+def _memoryview_reduce(obj):
+    return bytes, (obj.tobytes(),)
+
+
+def _module_reduce(obj):
+    if _is_importable(obj):
+        return subimport, (obj.__name__,)
+    else:
+        obj.__dict__.pop('__builtins__', None)
+        return dynamic_subimport, (obj.__name__, vars(obj))
+
+
+def _method_reduce(obj):
+    return (types.MethodType, (obj.__func__, obj.__self__))
+
+
+def _logger_reduce(obj):
+    return logging.getLogger, (obj.name,)
+
+
+def _root_logger_reduce(obj):
+    return logging.getLogger, ()
+
+
+def _property_reduce(obj):
+    return property, (obj.fget, obj.fset, obj.fdel, obj.__doc__)
+
+
+def _weakset_reduce(obj):
+    return weakref.WeakSet, (list(obj),)
+
+
+def _dynamic_class_reduce(obj):
+    """
+    Save a class that can't be stored as module global.
+
+    This method is used to serialize classes that are defined inside
+    functions, or that otherwise can't be serialized as attribute lookups
+    from global modules.
+    """
+    if Enum is not None and issubclass(obj, Enum):
+        return (
+            _make_skeleton_enum, _enum_getnewargs(obj), _enum_getstate(obj),
+            None, None, _class_setstate
+        )
+    else:
+        return (
+            _make_skeleton_class, _class_getnewargs(obj), _class_getstate(obj),
+            None, None, _class_setstate
+        )
+
+
+def _class_reduce(obj):
+    """Select the reducer depending on the dynamic nature of the class obj"""
+    if obj is type(None):  # noqa
+        return type, (None,)
+    elif obj is type(Ellipsis):
+        return type, (Ellipsis,)
+    elif obj is type(NotImplemented):
+        return type, (NotImplemented,)
+    elif obj in _BUILTIN_TYPE_NAMES:
+        return _builtin_type, (_BUILTIN_TYPE_NAMES[obj],)
+    elif not _is_importable(obj):
+        return _dynamic_class_reduce(obj)
+    return NotImplemented
+
+
+def _dict_keys_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_keys, (list(obj), )
+
+
+def _dict_values_reduce(obj):
+    # Safer not to ship the full dict as sending the rest might
+    # be unintended and could potentially cause leaking of
+    # sensitive information
+    return _make_dict_values, (list(obj), )
+
+
+def _dict_items_reduce(obj):
+    return _make_dict_items, (dict(obj), )
+
+
+# COLLECTIONS OF OBJECTS STATE SETTERS
+# ------------------------------------
+# state setters are called at unpickling time, once the object is created and
+# it has to be updated to how it was at unpickling time.
+
+
+def _function_setstate(obj, state):
+    """Update the state of a dynaamic function.
+
+    As __closure__ and __globals__ are readonly attributes of a function, we
+    cannot rely on the native setstate routine of pickle.load_build, that calls
+    setattr on items of the slotstate. Instead, we have to modify them inplace.
+    """
+    state, slotstate = state
+    obj.__dict__.update(state)
+
+    obj_globals = slotstate.pop("__globals__")
+    obj_closure = slotstate.pop("__closure__")
+    # _cloudpickle_subimports is a set of submodules that must be loaded for
+    # the pickled function to work correctly at unpickling time. Now that these
+    # submodules are depickled (hence imported), they can be removed from the
+    # object's state (the object state only served as a reference holder to
+    # these submodules)
+    slotstate.pop("_cloudpickle_submodules")
+
+    obj.__globals__.update(obj_globals)
+    obj.__globals__["__builtins__"] = __builtins__
+
+    if obj_closure is not None:
+        for i, cell in enumerate(obj_closure):
+            try:
+                value = cell.cell_contents
+            except ValueError:  # cell is empty
+                continue
+            cell_set(obj.__closure__[i], value)
+
+    for k, v in slotstate.items():
+        setattr(obj, k, v)
+
+
+def _class_setstate(obj, state):
+    # Check if class is being reused and needs bypass setstate logic.
+    if obj in _DYNAMIC_CLASS_TRACKER_REUSING:
+        return obj
+
+    state, slotstate = state
+    registry = None
+    for attrname, attr in state.items():
+        if attrname == "_abc_impl":
+            registry = attr
+        else:
+            setattr(obj, attrname, attr)
+    if registry is not None:
+        for subclass in registry:
+            obj.register(subclass)
+
+    return obj
+
+
+class CloudPickler(Pickler):
+    # set of reducers defined and used by cloudpickle (private)
+    _dispatch_table = {}
+    _dispatch_table[classmethod] = _classmethod_reduce
+    _dispatch_table[io.TextIOWrapper] = _file_reduce
+    _dispatch_table[logging.Logger] = _logger_reduce
+    _dispatch_table[logging.RootLogger] = _root_logger_reduce
+    _dispatch_table[memoryview] = _memoryview_reduce
+    _dispatch_table[property] = _property_reduce
+    _dispatch_table[staticmethod] = _classmethod_reduce
+    _dispatch_table[CellType] = _cell_reduce
+    _dispatch_table[types.CodeType] = _code_reduce
+    _dispatch_table[types.GetSetDescriptorType] = _getset_descriptor_reduce
+    _dispatch_table[types.ModuleType] = _module_reduce
+    _dispatch_table[types.MethodType] = _method_reduce
+    _dispatch_table[types.MappingProxyType] = _mappingproxy_reduce
+    _dispatch_table[weakref.WeakSet] = _weakset_reduce
+    _dispatch_table[typing.TypeVar] = _typevar_reduce
+    _dispatch_table[_collections_abc.dict_keys] = _dict_keys_reduce
+    _dispatch_table[_collections_abc.dict_values] = _dict_values_reduce
+    _dispatch_table[_collections_abc.dict_items] = _dict_items_reduce
+
+
+    dispatch_table = ChainMap(_dispatch_table, copyreg.dispatch_table)
+
+    # function reducers are defined as instance methods of CloudPickler
+    # objects, as they rely on a CloudPickler attribute (globals_ref)
+    def _dynamic_function_reduce(self, func):
+        """Reduce a function that is not pickleable via attribute lookup."""
+        newargs = self._function_getnewargs(func)
+        state = _function_getstate(func)
+        return (types.FunctionType, newargs, state, None, None,
+                _function_setstate)
+
+    def _function_reduce(self, obj):
+        """Reducer for function objects.
+
+        If obj is a top-level attribute of a file-backed module, this
+        reducer returns NotImplemented, making the CloudPickler fallback to
+        traditional _pickle.Pickler routines to save obj. Otherwise, it reduces
+        obj using a custom cloudpickle reducer designed specifically to handle
+        dynamic functions.
+
+        As opposed to cloudpickle.py, There no special handling for builtin
+        pypy functions because cloudpickle_fast is CPython-specific.
+        """
+        if _is_importable(obj):
+            return NotImplemented
+        else:
+            return self._dynamic_function_reduce(obj)
+
+    def _function_getnewargs(self, func):
+        code = func.__code__
+
+        # base_globals represents the future global namespace of func at
+        # unpickling time. Looking it up and storing it in
+        # CloudpiPickler.globals_ref allow functions sharing the same globals
+        # at pickling time to also share them once unpickled, at one condition:
+        # since globals_ref is an attribute of a CloudPickler instance, and
+        # that a new CloudPickler is created each time pickle.dump or
+        # pickle.dumps is called, functions also need to be saved within the
+        # same invocation of cloudpickle.dump/cloudpickle.dumps (for example:
+        # cloudpickle.dumps([f1, f2])). There is no such limitation when using
+        # CloudPickler.dump, as long as the multiple invocations are bound to
+        # the same CloudPickler.
+        base_globals = self.globals_ref.setdefault(id(func.__globals__), {})
+
+        if base_globals == {}:
+            # Add module attributes used to resolve relative imports
+            # instructions inside func.
+            for k in ["__package__", "__name__", "__path__", "__file__"]:
+                if k in func.__globals__:
+                    base_globals[k] = func.__globals__[k]
+
+        # Do not bind the free variables before the function is created to
+        # avoid infinite recursion.
+        if func.__closure__ is None:
+            closure = None
+        else:
+            closure = tuple(
+                _make_empty_cell() for _ in range(len(code.co_freevars)))
+
+        return code, base_globals, None, None, closure
+
+    def dump(self, obj):
+        try:
+            return Pickler.dump(self, obj)
+        except RuntimeError as e:
+            if "recursion" in e.args[0]:
+                msg = (
+                    "Could not pickle object as excessively deep recursion "
+                    "required."
+                )
+                raise pickle.PicklingError(msg) from e
+            else:
+                raise
+
+    if pickle.HIGHEST_PROTOCOL >= 5:
+        # `CloudPickler.dispatch` is only left for backward compatibility - note
+        # that when using protocol 5, `CloudPickler.dispatch` is not an
+        # extension of `Pickler.dispatch` dictionary, because CloudPickler
+        # subclasses the C-implemented Pickler, which does not expose a
+        # `dispatch` attribute.  Earlier versions of the protocol 5 CloudPickler
+        # used `CloudPickler.dispatch` as a class-level attribute storing all
+        # reducers implemented by cloudpickle, but the attribute name was not a
+        # great choice given the meaning of `Cloudpickler.dispatch` when
+        # `CloudPickler` extends the pure-python pickler.
+        dispatch = dispatch_table
+
+        # Implementation of the reducer_override callback, in order to
+        # efficiently serialize dynamic functions and classes by subclassing
+        # the C-implemented Pickler.
+        # TODO: decorrelate reducer_override (which is tied to CPython's
+        # implementation - would it make sense to backport it to pypy? - and
+        # pickle's protocol 5 which is implementation agnostic. Currently, the
+        # availability of both notions coincide on CPython's pickle and the
+        # pickle5 backport, but it may not be the case anymore when pypy
+        # implements protocol 5
+        def __init__(self, file, protocol=None, buffer_callback=None):
+            if protocol is None:
+                protocol = DEFAULT_PROTOCOL
+            Pickler.__init__(
+                self, file, protocol=protocol, buffer_callback=buffer_callback
+            )
+            # map functions __globals__ attribute ids, to ensure that functions
+            # sharing the same global namespace at pickling time also share
+            # their global namespace at unpickling time.
+            self.globals_ref = {}
+            self.proto = int(protocol)
+
+        def reducer_override(self, obj):
+            """Type-agnostic reducing callback for function and classes.
+
+            For performance reasons, subclasses of the C _pickle.Pickler class
+            cannot register custom reducers for functions and classes in the
+            dispatch_table. Reducer for such types must instead implemented in
+            the special reducer_override method.
+
+            Note that method will be called for any object except a few
+            builtin-types (int, lists, dicts etc.), which differs from reducers
+            in the Pickler's dispatch_table, each of them being invoked for
+            objects of a specific type only.
+
+            This property comes in handy for classes: although most classes are
+            instances of the ``type`` metaclass, some of them can be instances
+            of other custom metaclasses (such as enum.EnumMeta for example). In
+            particular, the metaclass will likely not be known in advance, and
+            thus cannot be special-cased using an entry in the dispatch_table.
+            reducer_override, among other things, allows us to register a
+            reducer that will be called for any class, independently of its
+            type.
+
+
+            Notes:
+
+            * reducer_override has the priority over dispatch_table-registered
+            reducers.
+            * reducer_override can be used to fix other limitations of
+              cloudpickle for other types that suffered from type-specific
+              reducers, such as Exceptions. See
+              https://github.com/cloudpipe/cloudpickle/issues/248
+            """
+            if sys.version_info[:2] < (3, 7) and _is_parametrized_type_hint(obj):  # noqa  # pragma: no branch
+                return (
+                    _create_parametrized_type_hint,
+                    parametrized_type_hint_getinitargs(obj)
+                )
+            t = type(obj)
+            try:
+                is_anyclass = issubclass(t, type)
+            except TypeError:  # t is not a class (old Boost; see SF #502085)
+                is_anyclass = False
+
+            if is_anyclass:
+                return _class_reduce(obj)
+            elif isinstance(obj, types.FunctionType):
+                return self._function_reduce(obj)
+            else:
+                # fallback to save_global, including the Pickler's
+                # distpatch_table
+                return NotImplemented
+
+    else:
+        # When reducer_override is not available, hack the pure-Python
+        # Pickler's types.FunctionType and type savers. Note: the type saver
+        # must override Pickler.save_global, because pickle.py contains a
+        # hard-coded call to save_global when pickling meta-classes.
+        dispatch = Pickler.dispatch.copy()
+
+        def __init__(self, file, protocol=None):
+            if protocol is None:
+                protocol = DEFAULT_PROTOCOL
+            Pickler.__init__(self, file, protocol=protocol)
+            # map functions __globals__ attribute ids, to ensure that functions
+            # sharing the same global namespace at pickling time also share
+            # their global namespace at unpickling time.
+            self.globals_ref = {}
+            assert hasattr(self, 'proto')
+
+        def _save_reduce_pickle5(self, func, args, state=None, listitems=None,
+                                 dictitems=None, state_setter=None, obj=None):
+            save = self.save
+            write = self.write
+            self.save_reduce(
+                func, args, state=None, listitems=listitems,
+                dictitems=dictitems, obj=obj
+            )
+            # backport of the Python 3.8 state_setter pickle operations
+            save(state_setter)
+            save(obj)  # simple BINGET opcode as obj is already memoized.
+            save(state)
+            write(pickle.TUPLE2)
+            # Trigger a state_setter(obj, state) function call.
+            write(pickle.REDUCE)
+            # The purpose of state_setter is to carry-out an
+            # inplace modification of obj. We do not care about what the
+            # method might return, so its output is eventually removed from
+            # the stack.
+            write(pickle.POP)
+
+        def save_global(self, obj, name=None, pack=struct.pack):
+            """
+            Save a "global".
+
+            The name of this method is somewhat misleading: all types get
+            dispatched here.
+            """
+            if obj is type(None):  # noqa
+                return self.save_reduce(type, (None,), obj=obj)
+            elif obj is type(Ellipsis):
+                return self.save_reduce(type, (Ellipsis,), obj=obj)
+            elif obj is type(NotImplemented):
+                return self.save_reduce(type, (NotImplemented,), obj=obj)
+            elif obj in _BUILTIN_TYPE_NAMES:
+                return self.save_reduce(
+                    _builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj)
+
+            if sys.version_info[:2] < (3, 7) and _is_parametrized_type_hint(obj):  # noqa  # pragma: no branch
+                # Parametrized typing constructs in Python < 3.7 are not
+                # compatible with type checks and ``isinstance`` semantics. For
+                # this reason, it is easier to detect them using a
+                # duck-typing-based check (``_is_parametrized_type_hint``) than
+                # to populate the Pickler's dispatch with type-specific savers.
+                self.save_reduce(
+                    _create_parametrized_type_hint,
+                    parametrized_type_hint_getinitargs(obj),
+                    obj=obj
+                )
+            elif name is not None:
+                Pickler.save_global(self, obj, name=name)
+            elif not _is_importable(obj, name=name):
+                self._save_reduce_pickle5(*_dynamic_class_reduce(obj), obj=obj)
+            else:
+                Pickler.save_global(self, obj, name=name)
+        dispatch[type] = save_global
+
+        def save_function(self, obj, name=None):
+            """ Registered with the dispatch to handle all function types.
+
+            Determines what kind of function obj is (e.g. lambda, defined at
+            interactive prompt, etc) and handles the pickling appropriately.
+            """
+            if _is_importable(obj, name=name):
+                return Pickler.save_global(self, obj, name=name)
+            elif PYPY and isinstance(obj.__code__, builtin_code_type):
+                return self.save_pypy_builtin_func(obj)
+            else:
+                return self._save_reduce_pickle5(
+                    *self._dynamic_function_reduce(obj), obj=obj
+                )
+
+        def save_pypy_builtin_func(self, obj):
+            """Save pypy equivalent of builtin functions.
+            PyPy does not have the concept of builtin-functions. Instead,
+            builtin-functions are simple function instances, but with a
+            builtin-code attribute.
+            Most of the time, builtin functions should be pickled by attribute.
+            But PyPy has flaky support for __qualname__, so some builtin
+            functions such as float.__new__ will be classified as dynamic. For
+            this reason only, we created this special routine. Because
+            builtin-functions are not expected to have closure or globals,
+            there is no additional hack (compared the one already implemented
+            in pickle) to protect ourselves from reference cycles. A simple
+            (reconstructor, newargs, obj.__dict__) tuple is save_reduced.  Note
+            also that PyPy improved their support for __qualname__ in v3.6, so
+            this routing should be removed when cloudpickle supports only PyPy
+            3.6 and later.
+            """
+            rv = (types.FunctionType, (obj.__code__, {}, obj.__name__,
+                                       obj.__defaults__, obj.__closure__),
+                  obj.__dict__)
+            self.save_reduce(*rv, obj=obj)
+
+        dispatch[types.FunctionType] = save_function
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/compat.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/compat.py
new file mode 100644
index 000000000..afa285f62
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cloudpickle/compat.py
@@ -0,0 +1,13 @@
+import sys
+
+
+if sys.version_info < (3, 8):
+    try:
+        import pickle5 as pickle  # noqa: F401
+        from pickle5 import Pickler  # noqa: F401
+    except ImportError:
+        import pickle  # noqa: F401
+        from pickle import _Pickler as Pickler  # noqa: F401
+else:
+    import pickle  # noqa: F401
+    from _pickle import Pickler  # noqa: F401
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/analysis.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/analysis.py
new file mode 100644
index 000000000..5b65599c5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/analysis.py
@@ -0,0 +1,722 @@
+"""
+Utils for IR analysis
+"""
+import operator
+from functools import reduce
+from collections import namedtuple, defaultdict
+
+from .controlflow import CFGraph
+from numba.core import types, errors, ir, consts
+from numba.misc import special
+
+#
+# Analysis related to variable lifetime
+#
+
+_use_defs_result = namedtuple('use_defs_result', 'usemap,defmap')
+
+# other packages that define new nodes add calls for finding defs
+# format: {type:function}
+ir_extension_usedefs = {}
+
+
+def compute_use_defs(blocks):
+    """
+    Find variable use/def per block.
+    """
+
+    var_use_map = {}   # { block offset -> set of vars }
+    var_def_map = {}   # { block offset -> set of vars }
+    for offset, ir_block in blocks.items():
+        var_use_map[offset] = use_set = set()
+        var_def_map[offset] = def_set = set()
+        for stmt in ir_block.body:
+            if type(stmt) in ir_extension_usedefs:
+                func = ir_extension_usedefs[type(stmt)]
+                func(stmt, use_set, def_set)
+                continue
+            if isinstance(stmt, ir.Assign):
+                if isinstance(stmt.value, ir.Inst):
+                    rhs_set = set(var.name for var in stmt.value.list_vars())
+                elif isinstance(stmt.value, ir.Var):
+                    rhs_set = set([stmt.value.name])
+                elif isinstance(stmt.value, (ir.Arg, ir.Const, ir.Global,
+                                             ir.FreeVar)):
+                    rhs_set = ()
+                else:
+                    raise AssertionError('unreachable', type(stmt.value))
+                # If lhs not in rhs of the assignment
+                if stmt.target.name not in rhs_set:
+                    def_set.add(stmt.target.name)
+
+            for var in stmt.list_vars():
+                # do not include locally defined vars to use-map
+                if var.name not in def_set:
+                    use_set.add(var.name)
+
+    return _use_defs_result(usemap=var_use_map, defmap=var_def_map)
+
+
+def compute_live_map(cfg, blocks, var_use_map, var_def_map):
+    """
+    Find variables that must be alive at the ENTRY of each block.
+    We use a simple fix-point algorithm that iterates until the set of
+    live variables is unchanged for each block.
+    """
+    def fix_point_progress(dct):
+        """Helper function to determine if a fix-point has been reached.
+        """
+        return tuple(len(v) for v in dct.values())
+
+    def fix_point(fn, dct):
+        """Helper function to run fix-point algorithm.
+        """
+        old_point = None
+        new_point = fix_point_progress(dct)
+        while old_point != new_point:
+            fn(dct)
+            old_point = new_point
+            new_point = fix_point_progress(dct)
+
+    def def_reach(dct):
+        """Find all variable definition reachable at the entry of a block
+        """
+        for offset in var_def_map:
+            used_or_defined = var_def_map[offset] | var_use_map[offset]
+            dct[offset] |= used_or_defined
+            # Propagate to outgoing nodes
+            for out_blk, _ in cfg.successors(offset):
+                dct[out_blk] |= dct[offset]
+
+    def liveness(dct):
+        """Find live variables.
+
+        Push var usage backward.
+        """
+        for offset in dct:
+            # Live vars here
+            live_vars = dct[offset]
+            for inc_blk, _data in cfg.predecessors(offset):
+                # Reachable at the predecessor
+                reachable = live_vars & def_reach_map[inc_blk]
+                # But not defined in the predecessor
+                dct[inc_blk] |= reachable - var_def_map[inc_blk]
+
+    live_map = {}
+    for offset in blocks.keys():
+        live_map[offset] = set(var_use_map[offset])
+
+    def_reach_map = defaultdict(set)
+    fix_point(def_reach, def_reach_map)
+    fix_point(liveness, live_map)
+    return live_map
+
+
+_dead_maps_result = namedtuple('dead_maps_result', 'internal,escaping,combined')
+
+
+def compute_dead_maps(cfg, blocks, live_map, var_def_map):
+    """
+    Compute the end-of-live information for variables.
+    `live_map` contains a mapping of block offset to all the living
+    variables at the ENTRY of the block.
+    """
+    # The following three dictionaries will be
+    # { block offset -> set of variables to delete }
+    # all vars that should be deleted at the start of the successors
+    escaping_dead_map = defaultdict(set)
+    # all vars that should be deleted within this block
+    internal_dead_map = defaultdict(set)
+    # all vars that should be deleted after the function exit
+    exit_dead_map = defaultdict(set)
+
+    for offset, ir_block in blocks.items():
+        # live vars WITHIN the block will include all the locally
+        # defined variables
+        cur_live_set = live_map[offset] | var_def_map[offset]
+        # vars alive in the outgoing blocks
+        outgoing_live_map = dict((out_blk, live_map[out_blk])
+                                 for out_blk, _data in cfg.successors(offset))
+        # vars to keep alive for the terminator
+        terminator_liveset = set(v.name
+                                 for v in ir_block.terminator.list_vars())
+        # vars to keep alive in the successors
+        combined_liveset = reduce(operator.or_, outgoing_live_map.values(),
+                                  set())
+        # include variables used in terminator
+        combined_liveset |= terminator_liveset
+        # vars that are dead within the block because they are not
+        # propagated to any outgoing blocks
+        internal_set = cur_live_set - combined_liveset
+        internal_dead_map[offset] = internal_set
+        # vars that escape this block
+        escaping_live_set = cur_live_set - internal_set
+        for out_blk, new_live_set in outgoing_live_map.items():
+            # successor should delete the unused escaped vars
+            new_live_set = new_live_set | var_def_map[out_blk]
+            escaping_dead_map[out_blk] |= escaping_live_set - new_live_set
+
+        # if no outgoing blocks
+        if not outgoing_live_map:
+            # insert var used by terminator
+            exit_dead_map[offset] = terminator_liveset
+
+    # Verify that the dead maps cover all live variables
+    all_vars = reduce(operator.or_, live_map.values(), set())
+    internal_dead_vars = reduce(operator.or_, internal_dead_map.values(),
+                                set())
+    escaping_dead_vars = reduce(operator.or_, escaping_dead_map.values(),
+                                set())
+    exit_dead_vars = reduce(operator.or_, exit_dead_map.values(), set())
+    dead_vars = (internal_dead_vars | escaping_dead_vars | exit_dead_vars)
+    missing_vars = all_vars - dead_vars
+    if missing_vars:
+        # There are no exit points
+        if not cfg.exit_points():
+            # We won't be able to verify this
+            pass
+        else:
+            msg = 'liveness info missing for vars: {0}'.format(missing_vars)
+            raise RuntimeError(msg)
+
+    combined = dict((k, internal_dead_map[k] | escaping_dead_map[k])
+                    for k in blocks)
+
+    return _dead_maps_result(internal=internal_dead_map,
+                             escaping=escaping_dead_map,
+                             combined=combined)
+
+
+def compute_live_variables(cfg, blocks, var_def_map, var_dead_map):
+    """
+    Compute the live variables at the beginning of each block
+    and at each yield point.
+    The ``var_def_map`` and ``var_dead_map`` indicates the variable defined
+    and deleted at each block, respectively.
+    """
+    # live var at the entry per block
+    block_entry_vars = defaultdict(set)
+
+    def fix_point_progress():
+        return tuple(map(len, block_entry_vars.values()))
+
+    old_point = None
+    new_point = fix_point_progress()
+
+    # Propagate defined variables and still live the successors.
+    # (note the entry block automatically gets an empty set)
+
+    # Note: This is finding the actual available variables at the entry
+    #       of each block. The algorithm in compute_live_map() is finding
+    #       the variable that must be available at the entry of each block.
+    #       This is top-down in the dataflow.  The other one is bottom-up.
+    while old_point != new_point:
+        # We iterate until the result stabilizes.  This is necessary
+        # because of loops in the graphself.
+        for offset in blocks:
+            # vars available + variable defined
+            avail = block_entry_vars[offset] | var_def_map[offset]
+            # subtract variables deleted
+            avail -= var_dead_map[offset]
+            # add ``avail`` to each successors
+            for succ, _data in cfg.successors(offset):
+                block_entry_vars[succ] |= avail
+
+        old_point = new_point
+        new_point = fix_point_progress()
+
+    return block_entry_vars
+
+
+#
+# Analysis related to controlflow
+#
+
+def compute_cfg_from_blocks(blocks):
+    cfg = CFGraph()
+    for k in blocks:
+        cfg.add_node(k)
+
+    for k, b in blocks.items():
+        term = b.terminator
+        for target in term.get_targets():
+            cfg.add_edge(k, target)
+
+    cfg.set_entry_point(min(blocks))
+    cfg.process()
+    return cfg
+
+
+def find_top_level_loops(cfg):
+    """
+    A generator that yields toplevel loops given a control-flow-graph
+    """
+    blocks_in_loop = set()
+    # get loop bodies
+    for loop in cfg.loops().values():
+        insiders = set(loop.body) | set(loop.entries) | set(loop.exits)
+        insiders.discard(loop.header)
+        blocks_in_loop |= insiders
+    # find loop that is not part of other loops
+    for loop in cfg.loops().values():
+        if loop.header not in blocks_in_loop:
+            yield _fix_loop_exit(cfg, loop)
+
+
+def _fix_loop_exit(cfg, loop):
+    """
+    Fixes loop.exits for Py3.8 bytecode CFG changes.
+    This is to handle `break` inside loops.
+    """
+    # Computes the common postdoms of exit nodes
+    postdoms = cfg.post_dominators()
+    exits = reduce(
+        operator.and_,
+        [postdoms[b] for b in loop.exits],
+        loop.exits,
+    )
+    if exits:
+        # Put the non-common-exits as body nodes
+        body = loop.body | loop.exits - exits
+        return loop._replace(exits=exits, body=body)
+    else:
+        return loop
+
+
+# Used to describe a nullified condition in dead branch pruning
+nullified = namedtuple('nullified', 'condition, taken_br, rewrite_stmt')
+
+
+# Functions to manipulate IR
+def dead_branch_prune(func_ir, called_args):
+    """
+    Removes dead branches based on constant inference from function args.
+    This directly mutates the IR.
+
+    func_ir is the IR
+    called_args are the actual arguments with which the function is called
+    """
+    from numba.core.ir_utils import (get_definition, guard, find_const,
+                                     GuardException)
+
+    DEBUG = 0
+
+    def find_branches(func_ir):
+        # find *all* branches
+        branches = []
+        for blk in func_ir.blocks.values():
+            branch_or_jump = blk.body[-1]
+            if isinstance(branch_or_jump, ir.Branch):
+                branch = branch_or_jump
+                pred = guard(get_definition, func_ir, branch.cond.name)
+                if pred is not None and pred.op == "call":
+                    function = guard(get_definition, func_ir, pred.func)
+                    if (function is not None and
+                        isinstance(function, ir.Global) and
+                            function.value is bool):
+                        condition = guard(get_definition, func_ir, pred.args[0])
+                        if condition is not None:
+                            branches.append((branch, condition, blk))
+        return branches
+
+    def do_prune(take_truebr, blk):
+        keep = branch.truebr if take_truebr else branch.falsebr
+        # replace the branch with a direct jump
+        jmp = ir.Jump(keep, loc=branch.loc)
+        blk.body[-1] = jmp
+        return 1 if keep == branch.truebr else 0
+
+    def prune_by_type(branch, condition, blk, *conds):
+        # this prunes a given branch and fixes up the IR
+        # at least one needs to be a NoneType
+        lhs_cond, rhs_cond = conds
+        lhs_none = isinstance(lhs_cond, types.NoneType)
+        rhs_none = isinstance(rhs_cond, types.NoneType)
+        if lhs_none or rhs_none:
+            try:
+                take_truebr = condition.fn(lhs_cond, rhs_cond)
+            except Exception:
+                return False, None
+            if DEBUG > 0:
+                kill = branch.falsebr if take_truebr else branch.truebr
+                print("Pruning %s" % kill, branch, lhs_cond, rhs_cond,
+                      condition.fn)
+            taken = do_prune(take_truebr, blk)
+            return True, taken
+        return False, None
+
+    def prune_by_value(branch, condition, blk, *conds):
+        lhs_cond, rhs_cond = conds
+        try:
+            take_truebr = condition.fn(lhs_cond, rhs_cond)
+        except Exception:
+            return False, None
+        if DEBUG > 0:
+            kill = branch.falsebr if take_truebr else branch.truebr
+            print("Pruning %s" % kill, branch, lhs_cond, rhs_cond, condition.fn)
+        taken = do_prune(take_truebr, blk)
+        return True, taken
+
+    def prune_by_predicate(branch, pred, blk):
+        try:
+            # Just to prevent accidents, whilst already guarded, ensure this
+            # is an ir.Const
+            if not isinstance(pred, (ir.Const, ir.FreeVar, ir.Global)):
+                raise TypeError('Expected constant Numba IR node')
+            take_truebr = bool(pred.value)
+        except TypeError:
+            return False, None
+        if DEBUG > 0:
+            kill = branch.falsebr if take_truebr else branch.truebr
+            print("Pruning %s" % kill, branch, pred)
+        taken = do_prune(take_truebr, blk)
+        return True, taken
+
+    class Unknown(object):
+        pass
+
+    def resolve_input_arg_const(input_arg_idx):
+        """
+        Resolves an input arg to a constant (if possible)
+        """
+        input_arg_ty = called_args[input_arg_idx]
+
+        # comparing to None?
+        if isinstance(input_arg_ty, types.NoneType):
+            return input_arg_ty
+
+        # is it a kwarg default
+        if isinstance(input_arg_ty, types.Omitted):
+            val = input_arg_ty.value
+            if isinstance(val, types.NoneType):
+                return val
+            elif val is None:
+                return types.NoneType('none')
+
+        # literal type, return the type itself so comparisons like `x == None`
+        # still work as e.g. x = types.int64 will never be None/NoneType so
+        # the branch can still be pruned
+        return getattr(input_arg_ty, 'literal_type', Unknown())
+
+    if DEBUG > 1:
+        print("before".center(80, '-'))
+        print(func_ir.dump())
+
+    phi2lbl = dict()
+    phi2asgn = dict()
+    for lbl, blk in func_ir.blocks.items():
+        for stmt in blk.body:
+            if isinstance(stmt, ir.Assign):
+                if isinstance(stmt.value, ir.Expr) and stmt.value.op == 'phi':
+                    phi2lbl[stmt.value] = lbl
+                    phi2asgn[stmt.value] = stmt
+
+    # This looks for branches where:
+    # at least one arg of the condition is in input args and const
+    # at least one an arg of the condition is a const
+    # if the condition is met it will replace the branch with a jump
+    branch_info = find_branches(func_ir)
+    # stores conditions that have no impact post prune
+    nullified_conditions = []
+
+    for branch, condition, blk in branch_info:
+        const_conds = []
+        if isinstance(condition, ir.Expr) and condition.op == 'binop':
+            prune = prune_by_value
+            for arg in [condition.lhs, condition.rhs]:
+                resolved_const = Unknown()
+                arg_def = guard(get_definition, func_ir, arg)
+                if isinstance(arg_def, ir.Arg):
+                    # it's an e.g. literal argument to the function
+                    resolved_const = resolve_input_arg_const(arg_def.index)
+                    prune = prune_by_type
+                else:
+                    # it's some const argument to the function, cannot use guard
+                    # here as the const itself may be None
+                    try:
+                        resolved_const = find_const(func_ir, arg)
+                        if resolved_const is None:
+                            resolved_const = types.NoneType('none')
+                    except GuardException:
+                        pass
+
+                if not isinstance(resolved_const, Unknown):
+                    const_conds.append(resolved_const)
+
+            # lhs/rhs are consts
+            if len(const_conds) == 2:
+                # prune the branch, switch the branch for an unconditional jump
+                prune_stat, taken = prune(branch, condition, blk, *const_conds)
+                if(prune_stat):
+                    # add the condition to the list of nullified conditions
+                    nullified_conditions.append(nullified(condition, taken,
+                                                          True))
+        else:
+            # see if this is a branch on a constant value predicate
+            resolved_const = Unknown()
+            try:
+                pred_call = get_definition(func_ir, branch.cond)
+                resolved_const = find_const(func_ir, pred_call.args[0])
+                if resolved_const is None:
+                    resolved_const = types.NoneType('none')
+            except GuardException:
+                pass
+
+            if not isinstance(resolved_const, Unknown):
+                prune_stat, taken = prune_by_predicate(branch, condition, blk)
+                if(prune_stat):
+                    # add the condition to the list of nullified conditions
+                    nullified_conditions.append(nullified(condition, taken,
+                                                          False))
+
+    # 'ERE BE DRAGONS...
+    # It is the evaluation of the condition expression that often trips up type
+    # inference, so ideally it would be removed as it is effectively rendered
+    # dead by the unconditional jump if a branch was pruned. However, there may
+    # be references to the condition that exist in multiple places (e.g. dels)
+    # and we cannot run DCE here as typing has not taken place to give enough
+    # information to run DCE safely. Upshot of all this is the condition gets
+    # rewritten below into a benign const that typing will be happy with and DCE
+    # can remove it and its reference post typing when it is safe to do so
+    # (if desired). It is required that the const is assigned a value that
+    # indicates the branch taken as its mutated value would be read in the case
+    # of object mode fall back in place of the condition itself. For
+    # completeness the func_ir._definitions and ._consts are also updated to
+    # make the IR state self consistent.
+
+    deadcond = [x.condition for x in nullified_conditions]
+    for _, cond, blk in branch_info:
+        if cond in deadcond:
+            for x in blk.body:
+                if isinstance(x, ir.Assign) and x.value is cond:
+                    # rewrite the condition as a true/false bit
+                    nullified_info = nullified_conditions[deadcond.index(cond)]
+                    # only do a rewrite of conditions, predicates need to retain
+                    # their value as they may be used later.
+                    if nullified_info.rewrite_stmt:
+                        branch_bit = nullified_info.taken_br
+                        x.value = ir.Const(branch_bit, loc=x.loc)
+                        # update the specific definition to the new const
+                        defns = func_ir._definitions[x.target.name]
+                        repl_idx = defns.index(cond)
+                        defns[repl_idx] = x.value
+
+    # Check post dominators of dead nodes from in the original CFG for use of
+    # vars that are being removed in the dead blocks which might be referred to
+    # by phi nodes.
+    #
+    # Multiple things to fix up:
+    #
+    # 1. Cases like:
+    #
+    # A        A
+    # |\       |
+    # | B  --> B
+    # |/       |
+    # C        C
+    #
+    # i.e. the branch is dead but the block is still alive. In this case CFG
+    # simplification will fuse A-B-C and any phi in C can be updated as an
+    # direct assignment from the last assigned version in the dominators of the
+    # fused block.
+    #
+    # 2. Cases like:
+    #
+    #   A        A
+    #  / \       |
+    # B   C  --> B
+    #  \ /       |
+    #   D        D
+    #
+    # i.e. the block C is dead. In this case the phis in D need updating to
+    # reflect the collapse of the phi condition. This should result in a direct
+    # assignment of the surviving version in B to the LHS of the phi in D.
+
+    new_cfg = compute_cfg_from_blocks(func_ir.blocks)
+    dead_blocks = new_cfg.dead_nodes()
+
+    # for all phis that are still in live blocks.
+    for phi, lbl in phi2lbl.items():
+        if lbl in dead_blocks:
+            continue
+        new_incoming = [x[0] for x in new_cfg.predecessors(lbl)]
+        if set(new_incoming) != set(phi.incoming_blocks):
+            # Something has changed in the CFG...
+            if len(new_incoming) == 1:
+                # There's now just one incoming. Replace the PHI node by a
+                # direct assignment
+                idx = phi.incoming_blocks.index(new_incoming[0])
+                phi2asgn[phi].value = phi.incoming_values[idx]
+            else:
+                # There's more than one incoming still, then look through the
+                # incoming and remove dead
+                ic_val_tmp = []
+                ic_blk_tmp = []
+                for ic_val, ic_blk in zip(phi.incoming_values,
+                                          phi.incoming_blocks):
+                    if ic_blk in dead_blocks:
+                        continue
+                    else:
+                        ic_val_tmp.append(ic_val)
+                        ic_blk_tmp.append(ic_blk)
+                phi.incoming_values.clear()
+                phi.incoming_values.extend(ic_val_tmp)
+                phi.incoming_blocks.clear()
+                phi.incoming_blocks.extend(ic_blk_tmp)
+
+    # Remove dead blocks, this is safe as it relies on the CFG only.
+    for dead in dead_blocks:
+        del func_ir.blocks[dead]
+
+    # if conditions were nullified then consts were rewritten, update
+    if nullified_conditions:
+        func_ir._consts = consts.ConstantInference(func_ir)
+
+    if DEBUG > 1:
+        print("after".center(80, '-'))
+        print(func_ir.dump())
+
+
+def rewrite_semantic_constants(func_ir, called_args):
+    """
+    This rewrites values known to be constant by their semantics as ir.Const
+    nodes, this is to give branch pruning the best chance possible of killing
+    branches. An example might be rewriting len(tuple) as the literal length.
+
+    func_ir is the IR
+    called_args are the actual arguments with which the function is called
+    """
+    DEBUG = 0
+
+    if DEBUG > 1:
+        print(("rewrite_semantic_constants: " +
+               func_ir.func_id.func_name).center(80, '-'))
+        print("before".center(80, '*'))
+        func_ir.dump()
+
+    def rewrite_statement(func_ir, stmt, new_val):
+        """
+        Rewrites the stmt as a ir.Const new_val and fixes up the entries in
+        func_ir._definitions
+        """
+        stmt.value = ir.Const(new_val, stmt.loc)
+        defns = func_ir._definitions[stmt.target.name]
+        repl_idx = defns.index(val)
+        defns[repl_idx] = stmt.value
+
+    def rewrite_array_ndim(val, func_ir, called_args):
+        # rewrite Array.ndim as const(ndim)
+        if getattr(val, 'op', None) == 'getattr':
+            if val.attr == 'ndim':
+                arg_def = guard(get_definition, func_ir, val.value)
+                if isinstance(arg_def, ir.Arg):
+                    argty = called_args[arg_def.index]
+                    if isinstance(argty, types.Array):
+                        rewrite_statement(func_ir, stmt, argty.ndim)
+
+    def rewrite_tuple_len(val, func_ir, called_args):
+        # rewrite len(tuple) as const(len(tuple))
+        if getattr(val, 'op', None) == 'call':
+            func = guard(get_definition, func_ir, val.func)
+            if (func is not None and isinstance(func, ir.Global) and
+                    getattr(func, 'value', None) is len):
+
+                (arg,) = val.args
+                arg_def = guard(get_definition, func_ir, arg)
+                if isinstance(arg_def, ir.Arg):
+                    argty = called_args[arg_def.index]
+                    if isinstance(argty, types.BaseTuple):
+                        rewrite_statement(func_ir, stmt, argty.count)
+                elif (isinstance(arg_def, ir.Expr) and
+                      arg_def.op == 'typed_getitem'):
+                    argty = arg_def.dtype
+                    if isinstance(argty, types.BaseTuple):
+                        rewrite_statement(func_ir, stmt, argty.count)
+
+    from numba.core.ir_utils import get_definition, guard
+    for blk in func_ir.blocks.values():
+        for stmt in blk.body:
+            if isinstance(stmt, ir.Assign):
+                val = stmt.value
+                if isinstance(val, ir.Expr):
+                    rewrite_array_ndim(val, func_ir, called_args)
+                    rewrite_tuple_len(val, func_ir, called_args)
+
+    if DEBUG > 1:
+        print("after".center(80, '*'))
+        func_ir.dump()
+        print('-' * 80)
+
+
+def find_literally_calls(func_ir, argtypes):
+    """An analysis to find `numba.literally` call inside the given IR.
+    When an unsatisfied literal typing request is found, a `ForceLiteralArg`
+    exception is raised.
+
+    Parameters
+    ----------
+
+    func_ir : numba.ir.FunctionIR
+
+    argtypes : Sequence[numba.types.Type]
+        The argument types.
+    """
+    from numba.core import ir_utils
+
+    marked_args = set()
+    first_loc = {}
+    # Scan for literally calls
+    for blk in func_ir.blocks.values():
+        for assign in blk.find_exprs(op='call'):
+            var = ir_utils.guard(ir_utils.get_definition, func_ir, assign.func)
+            if isinstance(var, (ir.Global, ir.FreeVar)):
+                fnobj = var.value
+            else:
+                fnobj = ir_utils.guard(ir_utils.resolve_func_from_module,
+                                       func_ir, var)
+            if fnobj is special.literally:
+                # Found
+                [arg] = assign.args
+                defarg = func_ir.get_definition(arg)
+                if isinstance(defarg, ir.Arg):
+                    argindex = defarg.index
+                    marked_args.add(argindex)
+                    first_loc.setdefault(argindex, assign.loc)
+    # Signal the dispatcher to force literal typing
+    for pos in marked_args:
+        query_arg = argtypes[pos]
+        do_raise = (isinstance(query_arg, types.InitialValue) and
+                    query_arg.initial_value is None)
+        if do_raise:
+            loc = first_loc[pos]
+            raise errors.ForceLiteralArg(marked_args, loc=loc)
+
+        if not isinstance(query_arg, (types.Literal, types.InitialValue)):
+            loc = first_loc[pos]
+            raise errors.ForceLiteralArg(marked_args, loc=loc)
+
+
+ir_extension_use_alloca = {}
+
+
+def must_use_alloca(blocks):
+    """
+    Analyzes a dictionary of blocks to find variables that must be
+    stack allocated with alloca.  For each statement in the blocks,
+    determine if that statement requires certain variables to be
+    stack allocated.  This function uses the extension point
+    ir_extension_use_alloca to allow other IR node types like parfors
+    to register to be processed by this analysis function.  At the
+    moment, parfors are the only IR node types that may require
+    something to be stack allocated.
+    """
+    use_alloca_vars = set()
+
+    for ir_block in blocks.values():
+        for stmt in ir_block.body:
+            if type(stmt) in ir_extension_use_alloca:
+                func = ir_extension_use_alloca[type(stmt)]
+                func(stmt, use_alloca_vars)
+                continue
+
+    return use_alloca_vars
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/pretty_annotate.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/pretty_annotate.py
new file mode 100644
index 000000000..6e4f43b91
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/pretty_annotate.py
@@ -0,0 +1,283 @@
+"""
+This module implements code highlighting of numba function annotations.
+"""
+
+from warnings import warn
+
+warn("The pretty_annotate functionality is experimental and might change API",
+         FutureWarning)
+
+def hllines(code, style):
+    try:
+        from pygments import highlight
+        from pygments.lexers import PythonLexer
+        from pygments.formatters import HtmlFormatter
+    except ImportError:
+        raise ImportError("please install the 'pygments' package")
+    pylex = PythonLexer()
+    "Given a code string, return a list of html-highlighted lines"
+    hf = HtmlFormatter(noclasses=True, style=style, nowrap=True)
+    res = highlight(code, pylex, hf)
+    return res.splitlines()
+
+
+def htlines(code, style):
+    try:
+        from pygments import highlight
+        from pygments.lexers import PythonLexer
+        # TerminalFormatter does not support themes, Terminal256 should,
+        # but seem to not work.
+        from pygments.formatters import TerminalFormatter
+    except ImportError:
+        raise ImportError("please install the 'pygments' package")
+    pylex = PythonLexer()
+    "Given a code string, return a list of ANSI-highlighted lines"
+    hf = TerminalFormatter(style=style)
+    res = highlight(code, pylex, hf)
+    return res.splitlines()
+
+def get_ansi_template():
+    try:
+        from jinja2 import Template
+    except ImportError:
+        raise ImportError("please install the 'jinja2' package")
+    return Template("""
+    {%- for func_key in func_data.keys() -%}
+        Function name: \x1b[34m{{func_data[func_key]['funcname']}}\x1b[39;49;00m
+        {%- if func_data[func_key]['filename'] -%}
+        {{'\n'}}In file: \x1b[34m{{func_data[func_key]['filename'] -}}\x1b[39;49;00m
+        {%- endif -%}
+        {{'\n'}}With signature: \x1b[34m{{func_key[1]}}\x1b[39;49;00m
+        {{- "\n" -}}
+        {%- for num, line, hl, hc in func_data[func_key]['pygments_lines'] -%}
+                {{-'\n'}}{{ num}}: {{hc-}}
+                {%- if func_data[func_key]['ir_lines'][num] -%}
+                    {%- for ir_line, ir_line_type in func_data[func_key]['ir_lines'][num] %}
+                        {{-'\n'}}--{{- ' '*func_data[func_key]['python_indent'][num]}}
+                        {{- ' '*(func_data[func_key]['ir_indent'][num][loop.index0]+4)
+                        }}{{ir_line }}\x1b[41m{{ir_line_type-}}\x1b[39;49;00m
+                    {%- endfor -%}
+                {%- endif -%}
+            {%- endfor -%}
+    {%- endfor -%}
+    """)
+    return ansi_template
+
+def get_html_template():
+    try:
+        from jinja2 import Template
+    except ImportError:
+        raise ImportError("please install the 'jinja2' package")
+    return Template("""
+    <html>
+    <head>
+        <style>
+
+            .annotation_table {
+                color: #000000;
+                font-family: monospace;
+                margin: 5px;
+                width: 100%;
+            }
+
+            /* override JupyterLab style */
+            .annotation_table td {
+                text-align: left;
+                background-color: transparent; 
+                padding: 1px;
+            }
+
+            .annotation_table tbody tr:nth-child(even) {
+                background: white;
+            }
+
+            .annotation_table code
+            {
+                background-color: transparent; 
+                white-space: normal;
+            }
+
+            /* End override JupyterLab style */
+
+            tr:hover {
+                background-color: rgba(92, 200, 249, 0.25);
+            }
+
+            td.object_tag summary ,
+            td.lifted_tag summary{
+                font-weight: bold;
+                display: list-item;
+            }
+
+            span.lifted_tag {
+                color: #00cc33;
+            }
+
+            span.object_tag {
+                color: #cc3300;
+            }
+
+
+            td.lifted_tag {
+                background-color: #cdf7d8;
+            }
+
+            td.object_tag {
+                background-color: #fef5c8;
+            }
+
+            code.ir_code {
+                color: grey;
+                font-style: italic;
+            }
+
+            .metadata {
+                border-bottom: medium solid black;
+                display: inline-block;
+                padding: 5px;
+                width: 100%;
+            }
+
+            .annotations {
+                padding: 5px;
+            }
+
+            .hidden {
+                display: none;
+            }
+
+            .buttons {
+                padding: 10px;
+                cursor: pointer;
+            }
+        </style>
+    </head>
+
+    <body>
+        {% for func_key in func_data.keys() %}
+            <div class="metadata">
+            Function name: {{func_data[func_key]['funcname']}}<br />
+            {% if func_data[func_key]['filename'] %}
+                in file: {{func_data[func_key]['filename']|escape}}<br />
+            {% endif %}
+            with signature: {{func_key[1]|e}}
+            </div>
+            <div class="annotations">
+            <table class="annotation_table tex2jax_ignore">
+                {%- for num, line, hl, hc in func_data[func_key]['pygments_lines'] -%}
+                    {%- if func_data[func_key]['ir_lines'][num] %}
+                        <tr><td style="text-align:left;" class="{{func_data[func_key]['python_tags'][num]}}">
+                            <details>
+                                <summary>
+                                    <code>
+                                    {{num}}:
+                                    {{'&nbsp;'*func_data[func_key]['python_indent'][num]}}{{hl}}
+                                    </code>
+                                </summary>
+                                <table class="annotation_table">
+                                    <tbody>
+                                        {%- for ir_line, ir_line_type in func_data[func_key]['ir_lines'][num] %}
+                                            <tr class="ir_code">
+                                                <td style="text-align: left;"><code>
+                                                &nbsp;
+                                                {{- '&nbsp;'*func_data[func_key]['python_indent'][num]}}
+                                                {{ '&nbsp;'*func_data[func_key]['ir_indent'][num][loop.index0]}}{{ir_line|e -}}
+                                                <span class="object_tag">{{ir_line_type}}</span>
+                                                </code>
+                                                </td>
+                                            </tr>
+                                        {%- endfor -%}
+                                    </tbody>
+                                </table>
+                                </details>
+                        </td></tr>
+                    {% else -%}
+                        <tr><td style="text-align:left; padding-left: 22px;" class="{{func_data[func_key]['python_tags'][num]}}">
+                            <code>
+                                {{num}}:
+                                {{'&nbsp;'*func_data[func_key]['python_indent'][num]}}{{hl}}
+                            </code>
+                        </td></tr>
+                    {%- endif -%}
+                {%- endfor -%}
+            </table>
+            </div>
+        {% endfor %}
+    </body>
+    </html>
+    """)
+
+
+def reform_code(annotation):
+    """
+    Extract the code from the Numba annotation datastructure. 
+
+    Pygments can only highlight full multi-line strings, the Numba
+    annotation is list of single lines, with indentation removed.
+    """
+    ident_dict = annotation['python_indent']
+    s= ''
+    for n,l in annotation['python_lines']:
+        s = s+' '*ident_dict[n]+l+'\n'
+    return s
+
+
+class Annotate:
+    """
+    Construct syntax highlighted annotation for a given jitted function:
+
+    Example:
+
+    >>> import numba
+    >>> from numba.pretty_annotate import Annotate
+    >>> @numba.jit
+    ... def test(q):
+    ...     res = 0
+    ...     for i in range(q):
+    ...         res += i
+    ...     return res
+    ...
+    >>> test(10)
+    45
+    >>> Annotate(test)
+
+    The last line will return an HTML and/or ANSI representation that will be
+    displayed accordingly in Jupyter/IPython.
+
+    Function annotations persist across compilation for newly encountered
+    type signatures and as a result annotations are shown for all signatures
+    by default.
+
+    Annotations for a specific signature can be shown by using the
+    ``signature`` parameter.
+
+    >>> @numba.jit
+    ... def add(x, y):
+    ...     return x + y
+    ...
+    >>> add(1, 2)
+    3
+    >>> add(1.3, 5.7)
+    7.0
+    >>> add.signatures
+    [(int64, int64), (float64, float64)]
+    >>> Annotate(add, signature=add.signatures[1])  # annotation for (float64, float64)
+    """
+    def __init__(self, function, signature=None, **kwargs):
+
+        style = kwargs.get('style', 'default')
+        if not function.signatures:
+            raise ValueError('function need to be jitted for at least one signature')
+        ann = function.get_annotation_info(signature=signature)
+        self.ann = ann
+
+        for k,v in ann.items():
+            res = hllines(reform_code(v), style)
+            rest = htlines(reform_code(v), style)
+            v['pygments_lines'] = [(a,b,c, d) for (a,b),c, d in zip(v['python_lines'], res, rest)]
+
+    def _repr_html_(self):
+        return get_html_template().render(func_data=self.ann)
+
+    def __repr__(self):
+        return get_ansi_template().render(func_data=self.ann)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/template.html b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/template.html
new file mode 100644
index 000000000..73e2f6f85
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/template.html
@@ -0,0 +1,144 @@
+<html>
+
+<head>
+
+<style>
+
+.annotation_table {
+    color: #000000;
+    font-family: monospace;
+    margin: 5px;
+    width: 100%;
+}
+
+/* override JupyterLab style */
+.annotation_table td {
+    text-align: left;
+    background-color: transparent; 
+    padding: 1px;
+}
+
+.annotation_table code
+{
+    background-color: transparent; 
+    white-space: normal;
+}
+
+/* End override JupyterLab style */
+
+tr:hover {
+    background-color: rgba(92, 200, 249, 0.25);
+}
+
+td.object_tag summary ,
+td.lifted_tag summary{
+    font-weight: bold;
+    display: list-item;
+}
+
+span.lifted_tag {
+    color: #00cc33;
+}
+
+span.object_tag {
+    color: #cc3300;
+}
+
+
+td.lifted_tag {
+    background-color: #cdf7d8;
+}
+
+td.object_tag {
+    background-color: #ffd3d3;
+}
+
+code.ir_code {
+    color: grey;
+    font-style: italic;
+}
+
+.metadata {
+    border-bottom: medium solid black;
+    display: inline-block;
+    padding: 5px;
+    width: 100%;
+}
+
+.annotations {
+padding: 5px;
+}
+
+.hidden {
+display: none;
+}
+
+.buttons {
+padding: 10px;
+cursor: pointer;
+}
+
+</style>
+
+</head>
+
+<body>
+
+    {% for func_key in func_data.keys() %}
+
+        {% set loop1 = loop %}
+
+        <div class="metadata">
+        Function name: {{func_data[func_key]['funcname']}}<br />
+        in file: {{func_data[func_key]['filename']}}<br />
+        with signature: {{func_key[1]|e}}
+        </div>
+
+        <div class="annotations">
+
+        <table class="annotation_table tex2jax_ignore">
+            {%- for num, line in func_data[func_key]['python_lines'] -%}
+                {%- if func_data[func_key]['ir_lines'][num] %}
+                    <tr><td class="{{func_data[func_key]['python_tags'][num]}}">
+                        <details>
+                            <summary>
+                                <code>
+                                {{num}}:
+                                {{func_data[func_key]['python_indent'][num]}}{{line|e}}
+                                </code>
+                            </summary>
+                            <table class="annotation_table">
+                                <tbody>
+                                    {%- for ir_line, ir_line_type in func_data[func_key]['ir_lines'][num] %}
+                                        <tr class="ir_code func{{loop1.index0}}_ir">
+                                            <td><code>&nbsp;
+                                            {{- func_data[func_key]['python_indent'][num]}}
+                                            {{func_data[func_key]['ir_indent'][num][loop.index0]}}{{ir_line|e -}}
+                                            <span class="object_tag">{{ir_line_type}}</span>
+                                            </code>
+                                            </td>
+                                        </tr>
+                                    {%- endfor -%}
+                                </tbody>
+                            </table>
+                            </details>
+                    </td></tr>
+                {% else -%}
+                    <tr><td style=" padding-left: 22px;" class="{{func_data[func_key]['python_tags'][num]}}">
+                        <code>
+                            {{num}}:
+                            {{func_data[func_key]['python_indent'][num]}}{{line|e}}
+                        </code>
+                    </td></tr>
+                {%- endif -%}
+            {%- endfor -%}
+        </table>
+        </div>
+
+        <br /><br /><br />
+
+    {% endfor %}
+
+</body>
+
+</html>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/type_annotations.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/type_annotations.py
new file mode 100644
index 000000000..47bd01250
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/annotations/type_annotations.py
@@ -0,0 +1,283 @@
+from collections import defaultdict, OrderedDict
+from collections.abc import Mapping
+from contextlib import closing
+import copy
+import inspect
+import os
+import re
+import sys
+import textwrap
+from io import StringIO
+
+import numba.core.dispatcher
+from numba.core import ir
+
+
+class SourceLines(Mapping):
+    def __init__(self, func):
+
+        try:
+            lines, startno = inspect.getsourcelines(func)
+        except OSError:
+            self.lines = ()
+            self.startno = 0
+        else:
+            self.lines = textwrap.dedent(''.join(lines)).splitlines()
+            self.startno = startno
+
+    def __getitem__(self, lineno):
+        try:
+            return self.lines[lineno - self.startno].rstrip()
+        except IndexError:
+            return ''
+
+    def __iter__(self):
+        return iter((self.startno + i) for i in range(len(self.lines)))
+
+    def __len__(self):
+        return len(self.lines)
+
+    @property
+    def avail(self):
+        return bool(self.lines)
+
+
+class TypeAnnotation(object):
+
+    # func_data dict stores annotation data for all functions that are
+    # compiled. We store the data in the TypeAnnotation class since a new
+    # TypeAnnotation instance is created for each function that is compiled.
+    # For every function that is compiled, we add the type annotation data to
+    # this dict and write the html annotation file to disk (rewrite the html
+    # file for every function since we don't know if this is the last function
+    # to be compiled).
+    func_data = OrderedDict()
+
+    def __init__(self, func_ir, typemap, calltypes, lifted, lifted_from,
+                 args, return_type, html_output=None):
+        self.func_id = func_ir.func_id
+        self.blocks = func_ir.blocks
+        self.typemap = typemap
+        self.calltypes = calltypes
+        self.filename = func_ir.loc.filename
+        self.linenum = str(func_ir.loc.line)
+        self.signature = str(args) + ' -> ' + str(return_type)
+
+        # lifted loop information
+        self.lifted = lifted
+        self.num_lifted_loops = len(lifted)
+
+        # If this is a lifted loop function that is being compiled, lifted_from
+        # points to annotation data from function that this loop lifted function
+        # was lifted from. This is used to stick lifted loop annotations back
+        # into original function.
+        self.lifted_from = lifted_from
+
+    def prepare_annotations(self):
+        # Prepare annotations
+        groupedinst = defaultdict(list)
+        found_lifted_loop = False
+        #for blkid, blk in self.blocks.items():
+        for blkid in sorted(self.blocks.keys()):
+            blk = self.blocks[blkid]
+            groupedinst[blk.loc.line].append("label %s" % blkid)
+            for inst in blk.body:
+                lineno = inst.loc.line
+
+                if isinstance(inst, ir.Assign):
+                    if found_lifted_loop:
+                        atype = 'XXX Lifted Loop XXX'
+                        found_lifted_loop = False
+                    elif (isinstance(inst.value, ir.Expr) and
+                            inst.value.op ==  'call'):
+                        atype = self.calltypes[inst.value]
+                    elif (isinstance(inst.value, ir.Const) and
+                            isinstance(inst.value.value, numba.core.dispatcher.LiftedLoop)):
+                        atype = 'XXX Lifted Loop XXX'
+                        found_lifted_loop = True
+                    else:
+                        # TODO: fix parfor lowering so that typemap is valid.
+                        atype = self.typemap.get(inst.target.name, "<missing>")
+
+                    aline = "%s = %s  :: %s" % (inst.target, inst.value, atype)
+                elif isinstance(inst, ir.SetItem):
+                    atype = self.calltypes[inst]
+                    aline = "%s  :: %s" % (inst, atype)
+                else:
+                    aline = "%s" % inst
+                groupedinst[lineno].append("  %s" % aline)
+        return groupedinst
+
+    def annotate(self):
+        source = SourceLines(self.func_id.func)
+        # if not source.avail:
+        #     return "Source code unavailable"
+
+        groupedinst = self.prepare_annotations()
+
+        # Format annotations
+        io = StringIO()
+        with closing(io):
+            if source.avail:
+                print("# File: %s" % self.filename, file=io)
+                for num in source:
+                    srcline = source[num]
+                    ind = _getindent(srcline)
+                    print("%s# --- LINE %d --- " % (ind, num), file=io)
+                    for inst in groupedinst[num]:
+                        print('%s# %s' % (ind, inst), file=io)
+                    print(file=io)
+                    print(srcline, file=io)
+                    print(file=io)
+                if self.lifted:
+                    print("# The function contains lifted loops", file=io)
+                    for loop in self.lifted:
+                        print("# Loop at line %d" % loop.get_source_location(),
+                              file=io)
+                        print("# Has %d overloads" % len(loop.overloads),
+                              file=io)
+                        for cres in loop.overloads.values():
+                            print(cres.type_annotation, file=io)
+            else:
+                print("# Source code unavailable", file=io)
+                for num in groupedinst:
+                    for inst in groupedinst[num]:
+                        print('%s' % (inst,), file=io)
+                    print(file=io)
+
+            return io.getvalue()
+
+    def html_annotate(self, outfile):
+        # ensure that annotation information is assembled
+        self.annotate_raw()
+        # make a deep copy ahead of the pending mutations
+        func_data = copy.deepcopy(self.func_data)
+
+        key = 'python_indent'
+        for this_func in func_data.values():
+            if key in this_func:
+                idents = {}
+                for line, amount in this_func[key].items():
+                    idents[line] = '&nbsp;' * amount
+                this_func[key] = idents
+
+        key = 'ir_indent'
+        for this_func in func_data.values():
+            if key in this_func:
+                idents = {}
+                for line, ir_id in this_func[key].items():
+                    idents[line] = ['&nbsp;' * amount for amount in ir_id]
+                this_func[key] = idents
+
+
+
+        try:
+            from jinja2 import Template
+        except ImportError:
+            raise ImportError("please install the 'jinja2' package")
+
+        root = os.path.join(os.path.dirname(__file__))
+        template_filename = os.path.join(root, 'template.html')
+        with open(template_filename, 'r') as template:
+            html = template.read()
+
+        template = Template(html)
+        rendered = template.render(func_data=func_data)
+        outfile.write(rendered)
+
+    def annotate_raw(self):
+        """
+        This returns "raw" annotation information i.e. it has no output format
+        specific markup included.
+        """
+        python_source = SourceLines(self.func_id.func)
+        ir_lines = self.prepare_annotations()
+        line_nums = [num for num in python_source]
+        lifted_lines = [l.get_source_location() for l in self.lifted]
+
+        def add_ir_line(func_data, line):
+            line_str = line.strip()
+            line_type = ''
+            if line_str.endswith('pyobject'):
+                line_str = line_str.replace('pyobject', '')
+                line_type = 'pyobject'
+            func_data['ir_lines'][num].append((line_str, line_type))
+            indent_len = len(_getindent(line))
+            func_data['ir_indent'][num].append(indent_len)
+
+        func_key = (self.func_id.filename + ':' + str(self.func_id.firstlineno + 1),
+                    self.signature)
+        if self.lifted_from is not None and self.lifted_from[1]['num_lifted_loops'] > 0:
+            # This is a lifted loop function that is being compiled. Get the
+            # numba ir for lines in loop function to use for annotating
+            # original python function that the loop was lifted from.
+            func_data = self.lifted_from[1]
+            for num in line_nums:
+                if num not in ir_lines.keys():
+                    continue
+                func_data['ir_lines'][num] = []
+                func_data['ir_indent'][num] = []
+                for line in ir_lines[num]:
+                    add_ir_line(func_data, line)
+                    if line.strip().endswith('pyobject'):
+                        func_data['python_tags'][num] = 'object_tag'
+                        # If any pyobject line is found, make sure original python
+                        # line that was marked as a lifted loop start line is tagged
+                        # as an object line instead. Lifted loop start lines should
+                        # only be marked as lifted loop lines if the lifted loop
+                        # was successfully compiled in nopython mode.
+                        func_data['python_tags'][self.lifted_from[0]] = 'object_tag'
+
+            # We're done with this lifted loop, so decrement lifted loop counter.
+            # When lifted loop counter hits zero, that means we're ready to write
+            # out annotations to html file.
+            self.lifted_from[1]['num_lifted_loops'] -= 1
+
+        elif func_key not in TypeAnnotation.func_data.keys():
+            TypeAnnotation.func_data[func_key] = {}
+            func_data = TypeAnnotation.func_data[func_key]
+
+            for i, loop in enumerate(self.lifted):
+                # Make sure that when we process each lifted loop function later,
+                # we'll know where it originally came from.
+                loop.lifted_from = (lifted_lines[i], func_data)
+            func_data['num_lifted_loops'] = self.num_lifted_loops
+
+            func_data['filename'] = self.filename
+            func_data['funcname'] = self.func_id.func_name
+            func_data['python_lines'] = []
+            func_data['python_indent'] = {}
+            func_data['python_tags'] = {}
+            func_data['ir_lines'] = {}
+            func_data['ir_indent'] = {}
+
+            for num in line_nums:
+                func_data['python_lines'].append((num, python_source[num].strip()))
+                indent_len = len(_getindent(python_source[num]))
+                func_data['python_indent'][num] = indent_len
+                func_data['python_tags'][num] = ''
+                func_data['ir_lines'][num] = []
+                func_data['ir_indent'][num] = []
+
+                for line in ir_lines[num]:
+                    add_ir_line(func_data, line)
+                    if num in lifted_lines:
+                        func_data['python_tags'][num] = 'lifted_tag'
+                    elif line.strip().endswith('pyobject'):
+                        func_data['python_tags'][num] = 'object_tag'
+        return self.func_data
+
+
+    def __str__(self):
+        return self.annotate()
+
+
+re_longest_white_prefix = re.compile(r'^\s*')
+
+
+def _getindent(text):
+    m = re_longest_white_prefix.match(text)
+    if not m:
+        return ''
+    else:
+        return ' ' * len(m.group(0))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/base.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/base.py
new file mode 100644
index 000000000..9622a3f09
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/base.py
@@ -0,0 +1,1255 @@
+from collections import defaultdict
+import copy
+import sys
+from itertools import permutations, takewhile
+from contextlib import contextmanager
+
+from llvmlite import ir as llvmir
+from llvmlite.ir import Constant
+import llvmlite.binding as ll
+
+from numba.core import types, utils, datamodel, debuginfo, funcdesc, config, cgutils, imputils
+from numba.core import event, errors, targetconfig
+from numba import _dynfunc, _helperlib
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core.pythonapi import PythonAPI
+from numba.core.imputils import (user_function, user_generator,
+                       builtin_registry, impl_ret_borrowed,
+                       RegistryLoader)
+from numba.cpython import builtins
+
+GENERIC_POINTER = llvmir.PointerType(llvmir.IntType(8))
+PYOBJECT = GENERIC_POINTER
+void_ptr = GENERIC_POINTER
+
+
+class OverloadSelector(object):
+    """
+    An object matching an actual signature against a registry of formal
+    signatures and choosing the best candidate, if any.
+
+    In the current implementation:
+    - a "signature" is a tuple of type classes or type instances
+    - the "best candidate" is the most specific match
+    """
+
+    def __init__(self):
+        # A list of (formal args tuple, value)
+        self.versions = []
+        self._cache = {}
+
+    def find(self, sig):
+        out = self._cache.get(sig)
+        if out is None:
+            out = self._find(sig)
+            self._cache[sig] = out
+        return out
+
+    def _find(self, sig):
+        candidates = self._select_compatible(sig)
+        if candidates:
+            return candidates[self._best_signature(candidates)]
+        else:
+            raise errors.NumbaNotImplementedError(f'{self}, {sig}')
+
+    def _select_compatible(self, sig):
+        """
+        Select all compatible signatures and their implementation.
+        """
+        out = {}
+        for ver_sig, impl in self.versions:
+            if self._match_arglist(ver_sig, sig):
+                out[ver_sig] = impl
+        return out
+
+    def _best_signature(self, candidates):
+        """
+        Returns the best signature out of the candidates
+        """
+        ordered, genericity = self._sort_signatures(candidates)
+        # check for ambiguous signatures
+        if len(ordered) > 1:
+            firstscore = genericity[ordered[0]]
+            same = list(takewhile(lambda x: genericity[x] == firstscore,
+                                  ordered))
+            if len(same) > 1:
+                msg = ["{n} ambiguous signatures".format(n=len(same))]
+                for sig in same:
+                    msg += ["{0} => {1}".format(sig, candidates[sig])]
+                raise errors.NumbaTypeError('\n'.join(msg))
+        return ordered[0]
+
+    def _sort_signatures(self, candidates):
+        """
+        Sort signatures in ascending level of genericity.
+
+        Returns a 2-tuple:
+
+            * ordered list of signatures
+            * dictionary containing genericity scores
+        """
+        # score by genericity
+        genericity = defaultdict(int)
+        for this, other in permutations(candidates.keys(), r=2):
+            matched = self._match_arglist(formal_args=this, actual_args=other)
+            if matched:
+                # genericity score +1 for every another compatible signature
+                genericity[this] += 1
+        # order candidates in ascending level of genericity
+        ordered = sorted(candidates.keys(), key=lambda x: genericity[x])
+        return ordered, genericity
+
+    def _match_arglist(self, formal_args, actual_args):
+        """
+        Returns True if the signature is "matching".
+        A formal signature is "matching" if the actual signature matches exactly
+        or if the formal signature is a compatible generic signature.
+        """
+        # normalize VarArg
+        if formal_args and isinstance(formal_args[-1], types.VarArg):
+            ndiff = len(actual_args) - len(formal_args) + 1
+            formal_args = formal_args[:-1] + (formal_args[-1].dtype,) * ndiff
+
+        if len(formal_args) != len(actual_args):
+            return False
+
+        for formal, actual in zip(formal_args, actual_args):
+            if not self._match(formal, actual):
+                return False
+
+        return True
+
+    def _match(self, formal, actual):
+        if formal == actual:
+            # formal argument matches actual arguments
+            return True
+        elif types.Any == formal:
+            # formal argument is any
+            return True
+        elif isinstance(formal, type) and issubclass(formal, types.Type):
+            if isinstance(actual, type) and issubclass(actual, formal):
+                # formal arg is a type class and actual arg is a subclass
+                return True
+            elif isinstance(actual, formal):
+                # formal arg is a type class of which actual arg is an instance
+                return True
+
+    def append(self, value, sig):
+        """
+        Add a formal signature and its associated value.
+        """
+        assert isinstance(sig, tuple), (value, sig)
+        self.versions.append((sig, value))
+        self._cache.clear()
+
+
+@utils.runonce
+def _load_global_helpers():
+    """
+    Execute once to install special symbols into the LLVM symbol table.
+    """
+    # This is Py_None's real C name
+    ll.add_symbol("_Py_NoneStruct", id(None))
+
+    # Add Numba C helper functions
+    for c_helpers in (_helperlib.c_helpers, _dynfunc.c_helpers):
+        for py_name, c_address in c_helpers.items():
+            c_name = "numba_" + py_name
+            ll.add_symbol(c_name, c_address)
+
+    # Add Numpy C helpers (npy_XXX)
+    for c_name, c_address in _helperlib.npymath_exports.items():
+        ll.add_symbol(c_name, c_address)
+
+    # Add all built-in exception classes
+    for obj in utils.builtins.__dict__.values():
+        if isinstance(obj, type) and issubclass(obj, BaseException):
+            ll.add_symbol("PyExc_%s" % (obj.__name__), id(obj))
+
+
+class BaseContext(object):
+    """
+
+    Notes on Structure
+    ------------------
+
+    Most objects are lowered as plain-old-data structure in the generated
+    llvm.  They are passed around by reference (a pointer to the structure).
+    Only POD structure can live across function boundaries by copying the
+    data.
+    """
+    # True if the target requires strict alignment
+    # Causes exception to be raised if the record members are not aligned.
+    strict_alignment = False
+
+    # Force powi implementation as math.pow call
+    implement_powi_as_math_call = False
+    implement_pow_as_math_call = False
+
+    # Emit Debug info
+    enable_debuginfo = False
+    DIBuilder = debuginfo.DIBuilder
+
+    # Bound checking
+    @property
+    def enable_boundscheck(self):
+        if config.BOUNDSCHECK is not None:
+            return config.BOUNDSCHECK
+        return self._boundscheck
+
+    @enable_boundscheck.setter
+    def enable_boundscheck(self, value):
+        self._boundscheck = value
+
+    # NRT
+    enable_nrt = False
+
+    # Auto parallelization
+    auto_parallel = False
+
+    # PYCC
+    aot_mode = False
+
+    # Error model for various operations (only FP exceptions currently)
+    error_model = None
+
+    # Whether dynamic globals (CPU runtime addresses) is allowed
+    allow_dynamic_globals = False
+
+    # Fast math flags
+    fastmath = False
+
+    # python execution environment
+    environment = None
+
+    # the function descriptor
+    fndesc = None
+
+    def __init__(self, typing_context, target):
+        _load_global_helpers()
+
+        self.address_size = utils.MACHINE_BITS
+        self.typing_context = typing_context
+        from numba.core.target_extension import target_registry
+        self.target_name = target
+        self.target = target_registry[target]
+
+        # A mapping of installed registries to their loaders
+        self._registries = {}
+        # Declarations loaded from registries and other sources
+        self._defns = defaultdict(OverloadSelector)
+        self._getattrs = defaultdict(OverloadSelector)
+        self._setattrs = defaultdict(OverloadSelector)
+        self._casts = OverloadSelector()
+        self._get_constants = OverloadSelector()
+        # Other declarations
+        self._generators = {}
+        self.special_ops = {}
+        self.cached_internal_func = {}
+        self._pid = None
+        self._codelib_stack = []
+
+        self._boundscheck = False
+
+        self.data_model_manager = datamodel.default_manager
+
+        # Initialize
+        self.init()
+
+    def init(self):
+        """
+        For subclasses to add initializer
+        """
+
+    def refresh(self):
+        """
+        Refresh context with new declarations from known registries.
+        Useful for third-party extensions.
+        """
+        # load target specific registries
+        self.load_additional_registries()
+
+        # Populate the builtin registry, this has to happen after loading
+        # additional registries as some of the "additional" registries write
+        # their implementations into the builtin_registry and would be missed if
+        # this ran first.
+        self.install_registry(builtin_registry)
+
+        # Also refresh typing context, since @overload declarations can
+        # affect it.
+        self.typing_context.refresh()
+
+    def load_additional_registries(self):
+        """
+        Load target-specific registries.  Can be overridden by subclasses.
+        """
+
+    def mangler(self, name, types, *, abi_tags=(), uid=None):
+        """
+        Perform name mangling.
+        """
+        return funcdesc.default_mangler(name, types, abi_tags=abi_tags, uid=uid)
+
+    def get_env_name(self, fndesc):
+        """Get the environment name given a FunctionDescriptor.
+
+        Use this instead of the ``fndesc.env_name`` so that the target-context
+        can provide necessary mangling of the symbol to meet ABI requirements.
+        """
+        return fndesc.env_name
+
+    def declare_env_global(self, module, envname):
+        """Declare the Environment pointer as a global of the module.
+
+        The pointer is initialized to NULL.  It must be filled by the runtime
+        with the actual address of the Env before the associated function
+        can be executed.
+
+        Parameters
+        ----------
+        module :
+            The LLVM Module
+        envname : str
+            The name of the global variable.
+        """
+        if envname not in module.globals:
+            gv = llvmir.GlobalVariable(module, cgutils.voidptr_t, name=envname)
+            gv.linkage = 'common'
+            gv.initializer = cgutils.get_null_value(gv.type.pointee)
+
+        return module.globals[envname]
+
+    def get_arg_packer(self, fe_args):
+        return datamodel.ArgPacker(self.data_model_manager, fe_args)
+
+    def get_data_packer(self, fe_types):
+        return datamodel.DataPacker(self.data_model_manager, fe_types)
+
+    @property
+    def target_data(self):
+        raise NotImplementedError
+
+    @utils.cached_property
+    def nonconst_module_attrs(self):
+        """
+        All module attrs are constant for targets using BaseContext.
+        """
+        return tuple()
+
+    @utils.cached_property
+    def nrt(self):
+        from numba.core.runtime.context import NRTContext
+        return NRTContext(self, self.enable_nrt)
+
+    def subtarget(self, **kws):
+        obj = copy.copy(self)  # shallow copy
+        for k, v in kws.items():
+            if not hasattr(obj, k):
+                raise NameError("unknown option {0!r}".format(k))
+            setattr(obj, k, v)
+        if obj.codegen() is not self.codegen():
+            # We can't share functions across different codegens
+            obj.cached_internal_func = {}
+        return obj
+
+    def install_registry(self, registry):
+        """
+        Install a *registry* (a imputils.Registry instance) of function
+        and attribute implementations.
+        """
+        try:
+            loader = self._registries[registry]
+        except KeyError:
+            loader = RegistryLoader(registry)
+            self._registries[registry] = loader
+        self.insert_func_defn(loader.new_registrations('functions'))
+        self._insert_getattr_defn(loader.new_registrations('getattrs'))
+        self._insert_setattr_defn(loader.new_registrations('setattrs'))
+        self._insert_cast_defn(loader.new_registrations('casts'))
+        self._insert_get_constant_defn(loader.new_registrations('constants'))
+
+    def insert_func_defn(self, defns):
+        for impl, func, sig in defns:
+            self._defns[func].append(impl, sig)
+
+    def _insert_getattr_defn(self, defns):
+        for impl, attr, sig in defns:
+            self._getattrs[attr].append(impl, sig)
+
+    def _insert_setattr_defn(self, defns):
+        for impl, attr, sig in defns:
+            self._setattrs[attr].append(impl, sig)
+
+    def _insert_cast_defn(self, defns):
+        for impl, sig in defns:
+            self._casts.append(impl, sig)
+
+    def _insert_get_constant_defn(self, defns):
+        for impl, sig in defns:
+            self._get_constants.append(impl, sig)
+
+    def insert_user_function(self, func, fndesc, libs=()):
+        impl = user_function(fndesc, libs)
+        self._defns[func].append(impl, impl.signature)
+
+    def insert_generator(self, genty, gendesc, libs=()):
+        assert isinstance(genty, types.Generator)
+        impl = user_generator(gendesc, libs)
+        self._generators[genty] = gendesc, impl
+
+    def remove_user_function(self, func):
+        """
+        Remove user function *func*.
+        KeyError is raised if the function isn't known to us.
+        """
+        del self._defns[func]
+
+    def get_external_function_type(self, fndesc):
+        argtypes = [self.get_argument_type(aty)
+                    for aty in fndesc.argtypes]
+        # don't wrap in pointer
+        restype = self.get_argument_type(fndesc.restype)
+        fnty = llvmir.FunctionType(restype, argtypes)
+        return fnty
+
+    def declare_function(self, module, fndesc):
+        fnty = self.call_conv.get_function_type(fndesc.restype, fndesc.argtypes)
+        fn = cgutils.get_or_insert_function(module, fnty, fndesc.mangled_name)
+        self.call_conv.decorate_function(fn, fndesc.args, fndesc.argtypes, noalias=fndesc.noalias)
+        if fndesc.inline:
+            fn.attributes.add('alwaysinline')
+            # alwaysinline overrides optnone
+            fn.attributes.discard('noinline')
+            fn.attributes.discard('optnone')
+        return fn
+
+    def declare_external_function(self, module, fndesc):
+        fnty = self.get_external_function_type(fndesc)
+        fn = cgutils.get_or_insert_function(module, fnty, fndesc.mangled_name)
+        assert fn.is_declaration
+        for ak, av in zip(fndesc.args, fn.args):
+            av.name = "arg.%s" % ak
+        return fn
+
+    def insert_const_string(self, mod, string):
+        """
+        Insert constant *string* (a str object) into module *mod*.
+        """
+        stringtype = GENERIC_POINTER
+        name = ".const.%s" % string
+        text = cgutils.make_bytearray(string.encode("utf-8") + b"\x00")
+        gv = self.insert_unique_const(mod, name, text)
+        return Constant.bitcast(gv, stringtype)
+
+    def insert_const_bytes(self, mod, bytes, name=None):
+        """
+        Insert constant *byte* (a `bytes` object) into module *mod*.
+        """
+        stringtype = GENERIC_POINTER
+        name = ".bytes.%s" % (name or hash(bytes))
+        text = cgutils.make_bytearray(bytes)
+        gv = self.insert_unique_const(mod, name, text)
+        return Constant.bitcast(gv, stringtype)
+
+    def insert_unique_const(self, mod, name, val):
+        """
+        Insert a unique internal constant named *name*, with LLVM value
+        *val*, into module *mod*.
+        """
+        try:
+            gv = mod.get_global(name)
+        except KeyError:
+            return cgutils.global_constant(mod, name, val)
+        else:
+            return gv
+
+    def get_argument_type(self, ty):
+        return self.data_model_manager[ty].get_argument_type()
+
+    def get_return_type(self, ty):
+        return self.data_model_manager[ty].get_return_type()
+
+    def get_data_type(self, ty):
+        """
+        Get a LLVM data representation of the Numba type *ty* that is safe
+        for storage.  Record data are stored as byte array.
+
+        The return value is a llvmlite.ir.Type object, or None if the type
+        is an opaque pointer (???).
+        """
+        return self.data_model_manager[ty].get_data_type()
+
+    def get_value_type(self, ty):
+        return self.data_model_manager[ty].get_value_type()
+
+    def pack_value(self, builder, ty, value, ptr, align=None):
+        """
+        Pack value into the array storage at *ptr*.
+        If *align* is given, it is the guaranteed alignment for *ptr*
+        (by default, the standard ABI alignment).
+        """
+        dataval = self.data_model_manager[ty].as_data(builder, value)
+        builder.store(dataval, ptr, align=align)
+
+    def unpack_value(self, builder, ty, ptr, align=None):
+        """
+        Unpack value from the array storage at *ptr*.
+        If *align* is given, it is the guaranteed alignment for *ptr*
+        (by default, the standard ABI alignment).
+        """
+        dm = self.data_model_manager[ty]
+        return dm.load_from_data_pointer(builder, ptr, align)
+
+    def get_constant_generic(self, builder, ty, val):
+        """
+        Return a LLVM constant representing value *val* of Numba type *ty*.
+        """
+        try:
+            impl = self._get_constants.find((ty,))
+            return impl(self, builder, ty, val)
+        except NotImplementedError:
+            raise NotImplementedError("Cannot lower constant of type '%s'" % (ty,))
+
+    def get_constant(self, ty, val):
+        """
+        Same as get_constant_generic(), but without specifying *builder*.
+        Works only for simple types.
+        """
+        # HACK: pass builder=None to preserve get_constant() API
+        return self.get_constant_generic(None, ty, val)
+
+    def get_constant_undef(self, ty):
+        lty = self.get_value_type(ty)
+        return Constant(lty, llvmir.Undefined)
+
+    def get_constant_null(self, ty):
+        lty = self.get_value_type(ty)
+        return Constant(lty, None)
+
+    def get_function(self, fn, sig, _firstcall=True):
+        """
+        Return the implementation of function *fn* for signature *sig*.
+        The return value is a callable with the signature (builder, args).
+        """
+        assert sig is not None
+        sig = sig.as_function()
+        if isinstance(fn, types.Callable):
+            key = fn.get_impl_key(sig)
+            overloads = self._defns[key]
+        else:
+            key = fn
+            overloads = self._defns[key]
+
+        try:
+            return _wrap_impl(overloads.find(sig.args), self, sig)
+        except errors.NumbaNotImplementedError:
+            pass
+        if isinstance(fn, types.Type):
+            # It's a type instance => try to find a definition for the type class
+            try:
+                return self.get_function(type(fn), sig)
+            except NotImplementedError:
+                # Raise exception for the type instance, for a better error message
+                pass
+
+        # Automatically refresh the context to load new registries if we are
+        # calling the first time.
+        if _firstcall:
+            self.refresh()
+            return self.get_function(fn, sig, _firstcall=False)
+
+        raise NotImplementedError("No definition for lowering %s%s" % (key, sig))
+
+    def get_generator_desc(self, genty):
+        """
+        """
+        return self._generators[genty][0]
+
+    def get_generator_impl(self, genty):
+        """
+        """
+        res = self._generators[genty][1]
+        self.add_linking_libs(getattr(res, 'libs', ()))
+        return res
+
+    def get_bound_function(self, builder, obj, ty):
+        assert self.get_value_type(ty) == obj.type
+        return obj
+
+    def get_getattr(self, typ, attr):
+        """
+        Get the getattr() implementation for the given type and attribute name.
+        The return value is a callable with the signature
+        (context, builder, typ, val, attr).
+        """
+        const_attr = (typ, attr) not in self.nonconst_module_attrs
+        is_module = isinstance(typ, types.Module)
+        if is_module and const_attr:
+            # Implement getattr for module-level globals that we treat as
+            # constants.
+            # XXX We shouldn't have to retype this
+            attrty = self.typing_context.resolve_module_constants(typ, attr)
+            if attrty is None or isinstance(attrty, types.Dummy):
+                # No implementation required for dummies (functions, modules...),
+                # which are dealt with later
+                return None
+            else:
+                pyval = getattr(typ.pymod, attr)
+                def imp(context, builder, typ, val, attr):
+                    llval = self.get_constant_generic(builder, attrty, pyval)
+                    return impl_ret_borrowed(context, builder, attrty, llval)
+                return imp
+
+        # Lookup specific getattr implementation for this type and attribute
+        overloads = self._getattrs[attr]
+        try:
+            return overloads.find((typ,))
+        except errors.NumbaNotImplementedError:
+            pass
+        # Lookup generic getattr implementation for this type
+        overloads = self._getattrs[None]
+        try:
+            return overloads.find((typ,))
+        except errors.NumbaNotImplementedError:
+            pass
+
+        raise NotImplementedError("No definition for lowering %s.%s" % (typ, attr))
+
+    def get_setattr(self, attr, sig):
+        """
+        Get the setattr() implementation for the given attribute name
+        and signature.
+        The return value is a callable with the signature (builder, args).
+        """
+        assert len(sig.args) == 2
+        typ = sig.args[0]
+        valty = sig.args[1]
+
+        def wrap_setattr(impl):
+            def wrapped(builder, args):
+                return impl(self, builder, sig, args, attr)
+            return wrapped
+
+        # Lookup specific setattr implementation for this type and attribute
+        overloads = self._setattrs[attr]
+        try:
+            return wrap_setattr(overloads.find((typ, valty)))
+        except errors.NumbaNotImplementedError:
+            pass
+        # Lookup generic setattr implementation for this type
+        overloads = self._setattrs[None]
+        try:
+            return wrap_setattr(overloads.find((typ, valty)))
+        except errors.NumbaNotImplementedError:
+            pass
+
+        raise NotImplementedError("No definition for lowering %s.%s = %s"
+                                  % (typ, attr, valty))
+
+    def get_argument_value(self, builder, ty, val):
+        """
+        Argument representation to local value representation
+        """
+        return self.data_model_manager[ty].from_argument(builder, val)
+
+    def get_returned_value(self, builder, ty, val):
+        """
+        Return value representation to local value representation
+        """
+        return self.data_model_manager[ty].from_return(builder, val)
+
+    def get_return_value(self, builder, ty, val):
+        """
+        Local value representation to return type representation
+        """
+        return self.data_model_manager[ty].as_return(builder, val)
+
+    def get_value_as_argument(self, builder, ty, val):
+        """Prepare local value representation as argument type representation
+        """
+        return self.data_model_manager[ty].as_argument(builder, val)
+
+    def get_value_as_data(self, builder, ty, val):
+        return self.data_model_manager[ty].as_data(builder, val)
+
+    def get_data_as_value(self, builder, ty, val):
+        return self.data_model_manager[ty].from_data(builder, val)
+
+    def pair_first(self, builder, val, ty):
+        """
+        Extract the first element of a heterogeneous pair.
+        """
+        pair = self.make_helper(builder, ty, val)
+        return pair.first
+
+    def pair_second(self, builder, val, ty):
+        """
+        Extract the second element of a heterogeneous pair.
+        """
+        pair = self.make_helper(builder, ty, val)
+        return pair.second
+
+    def cast(self, builder, val, fromty, toty):
+        """
+        Cast a value of type *fromty* to type *toty*.
+        This implements implicit conversions as can happen due to the
+        granularity of the Numba type system, or lax Python semantics.
+        """
+        if fromty == toty or toty == types.Any:
+            return val
+        try:
+            impl = self._casts.find((fromty, toty))
+            return impl(self, builder, fromty, toty, val)
+        except errors.NumbaNotImplementedError:
+            raise errors.NumbaNotImplementedError(
+                "Cannot cast %s to %s: %s" % (fromty, toty, val))
+
+    def generic_compare(self, builder, key, argtypes, args):
+        """
+        Compare the given LLVM values of the given Numba types using
+        the comparison *key* (e.g. '==').  The values are first cast to
+        a common safe conversion type.
+        """
+        at, bt = argtypes
+        av, bv = args
+        ty = self.typing_context.unify_types(at, bt)
+        assert ty is not None
+        cav = self.cast(builder, av, at, ty)
+        cbv = self.cast(builder, bv, bt, ty)
+        fnty = self.typing_context.resolve_value_type(key)
+        # the sig is homogeneous in the unified casted type
+        cmpsig = fnty.get_call_type(self.typing_context, (ty, ty), {})
+        cmpfunc = self.get_function(fnty, cmpsig)
+        self.add_linking_libs(getattr(cmpfunc, 'libs', ()))
+        return cmpfunc(builder, (cav, cbv))
+
+    def make_optional_none(self, builder, valtype):
+        optval = self.make_helper(builder, types.Optional(valtype))
+        optval.valid = cgutils.false_bit
+        return optval._getvalue()
+
+    def make_optional_value(self, builder, valtype, value):
+        optval = self.make_helper(builder, types.Optional(valtype))
+        optval.valid = cgutils.true_bit
+        optval.data = value
+        return optval._getvalue()
+
+    def is_true(self, builder, typ, val):
+        """
+        Return the truth value of a value of the given Numba type.
+        """
+        fnty = self.typing_context.resolve_value_type(bool)
+        sig = fnty.get_call_type(self.typing_context, (typ,), {})
+        impl = self.get_function(fnty, sig)
+        return impl(builder, (val,))
+
+    def get_c_value(self, builder, typ, name, dllimport=False):
+        """
+        Get a global value through its C-accessible *name*, with the given
+        LLVM type.
+        If *dllimport* is true, the symbol will be marked as imported
+        from a DLL (necessary for AOT compilation under Windows).
+        """
+        module = builder.function.module
+        try:
+            gv = module.globals[name]
+        except KeyError:
+            gv = cgutils.add_global_variable(module, typ, name)
+            if dllimport and self.aot_mode and sys.platform == 'win32':
+                gv.storage_class = "dllimport"
+        return gv
+
+    def call_external_function(self, builder, callee, argtys, args):
+        args = [self.get_value_as_argument(builder, ty, arg)
+                for ty, arg in zip(argtys, args)]
+        retval = builder.call(callee, args)
+        return retval
+
+    def get_function_pointer_type(self, typ):
+        return self.data_model_manager[typ].get_data_type()
+
+    def call_function_pointer(self, builder, funcptr, args, cconv=None):
+        return builder.call(funcptr, args, cconv=cconv)
+
+    def print_string(self, builder, text):
+        mod = builder.module
+        cstring = GENERIC_POINTER
+        fnty = llvmir.FunctionType(llvmir.IntType(32), [cstring])
+        puts = cgutils.get_or_insert_function(mod, fnty, "puts")
+        return builder.call(puts, [text])
+
+    def debug_print(self, builder, text):
+        mod = builder.module
+        cstr = self.insert_const_string(mod, str(text))
+        self.print_string(builder, cstr)
+
+    def printf(self, builder, format_string, *args):
+        mod = builder.module
+        if isinstance(format_string, str):
+            cstr = self.insert_const_string(mod, format_string)
+        else:
+            cstr = format_string
+        fnty = llvmir.FunctionType(llvmir.IntType(32), (GENERIC_POINTER,), var_arg=True)
+        fn = cgutils.get_or_insert_function(mod, fnty, "printf")
+        return builder.call(fn, (cstr,) + tuple(args))
+
+    def get_struct_type(self, struct):
+        """
+        Get the LLVM struct type for the given Structure class *struct*.
+        """
+        fields = [self.get_value_type(v) for _, v in struct._fields]
+        return llvmir.LiteralStructType(fields)
+
+    def get_dummy_value(self):
+        return Constant(self.get_dummy_type(), None)
+
+    def get_dummy_type(self):
+        return GENERIC_POINTER
+
+    def _compile_subroutine_no_cache(self, builder, impl, sig, locals={},
+                                     flags=None):
+        """
+        Invoke the compiler to compile a function to be used inside a
+        nopython function, but without generating code to call that
+        function.
+
+        Note this context's flags are not inherited.
+        """
+        # Compile
+        from numba.core import compiler
+
+        with global_compiler_lock:
+            codegen = self.codegen()
+            library = codegen.create_library(impl.__name__)
+            if flags is None:
+
+                cstk = targetconfig.ConfigStack()
+                flags = compiler.Flags()
+                if cstk:
+                    tls_flags = cstk.top()
+                    if tls_flags.is_set("nrt") and tls_flags.nrt:
+                        flags.nrt = True
+
+            flags.no_compile = True
+            flags.no_cpython_wrapper = True
+            flags.no_cfunc_wrapper = True
+
+            cres = compiler.compile_internal(self.typing_context, self,
+                                             library,
+                                             impl, sig.args,
+                                             sig.return_type, flags,
+                                             locals=locals)
+
+            # Allow inlining the function inside callers.
+            self.active_code_library.add_linking_library(cres.library)
+            return cres
+
+    def compile_subroutine(self, builder, impl, sig, locals={}, flags=None,
+                           caching=True):
+        """
+        Compile the function *impl* for the given *sig* (in nopython mode).
+        Return an instance of CompileResult.
+
+        If *caching* evaluates True, the function keeps the compiled function
+        for reuse in *.cached_internal_func*.
+        """
+        cache_key = (impl.__code__, sig, type(self.error_model))
+        if not caching:
+            cached = None
+        else:
+            if impl.__closure__:
+                # XXX This obviously won't work if a cell's value is
+                # unhashable.
+                cache_key += tuple(c.cell_contents for c in impl.__closure__)
+            cached = self.cached_internal_func.get(cache_key)
+        if cached is None:
+            cres = self._compile_subroutine_no_cache(builder, impl, sig,
+                                                     locals=locals,
+                                                     flags=flags)
+            self.cached_internal_func[cache_key] = cres
+
+        cres = self.cached_internal_func[cache_key]
+        # Allow inlining the function inside callers.
+        self.active_code_library.add_linking_library(cres.library)
+        return cres
+
+    def compile_internal(self, builder, impl, sig, args, locals={}):
+        """
+        Like compile_subroutine(), but also call the function with the given
+        *args*.
+        """
+        cres = self.compile_subroutine(builder, impl, sig, locals)
+        return self.call_internal(builder, cres.fndesc, sig, args)
+
+    def call_internal(self, builder, fndesc, sig, args):
+        """
+        Given the function descriptor of an internally compiled function,
+        emit a call to that function with the given arguments.
+        """
+        status, res = self.call_internal_no_propagate(builder, fndesc, sig, args)
+        with cgutils.if_unlikely(builder, status.is_error):
+            self.call_conv.return_status_propagate(builder, status)
+
+        res = imputils.fix_returning_optional(self, builder, sig, status, res)
+        return res
+
+    def call_internal_no_propagate(self, builder, fndesc, sig, args):
+        """Similar to `.call_internal()` but does not handle or propagate
+        the return status automatically.
+        """
+        # Add call to the generated function
+        llvm_mod = builder.module
+        fn = self.declare_function(llvm_mod, fndesc)
+        status, res = self.call_conv.call_function(builder, fn, sig.return_type,
+                                                   sig.args, args)
+        return status, res
+
+    def call_unresolved(self, builder, name, sig, args):
+        """
+        Insert a function call to an unresolved symbol with the given *name*.
+
+        Note: this is used for recursive call.
+
+        In the mutual recursion case::
+
+            @njit
+            def foo():
+                ...  # calls bar()
+
+            @njit
+            def bar():
+                ... # calls foo()
+
+            foo()
+
+        When foo() is called, the compilation of bar() is fully completed
+        (codegen'ed and loaded) before foo() is. Since MCJIT's eager compilation
+        doesn't allow loading modules with declare-only functions (which is
+        needed for foo() in bar()), the call_unresolved injects a global
+        variable that the "linker" can update even after the module is loaded by
+        MCJIT. The linker would allocate space for the global variable before
+        the bar() module is loaded. When later foo() module is defined, it will
+        update bar()'s reference to foo().
+
+        The legacy lazy JIT and the new ORC JIT would allow a declare-only
+        function be used in a module as long as it is defined by the time of its
+        first use.
+        """
+        # Insert an unresolved reference to the function being called.
+        codegen = self.codegen()
+        fnty = self.call_conv.get_function_type(sig.return_type, sig.args)
+        fn = codegen.insert_unresolved_ref(builder, fnty, name)
+        # Normal call sequence
+        status, res = self.call_conv.call_function(builder, fn, sig.return_type,
+                                                   sig.args, args)
+        with cgutils.if_unlikely(builder, status.is_error):
+            self.call_conv.return_status_propagate(builder, status)
+
+        res = imputils.fix_returning_optional(self, builder, sig, status, res)
+        return res
+
+    def get_executable(self, func, fndesc, env):
+        raise NotImplementedError
+
+    def get_python_api(self, builder):
+        return PythonAPI(self, builder)
+
+    def sentry_record_alignment(self, rectyp, attr):
+        """
+        Assumes offset starts from a properly aligned location
+        """
+        if self.strict_alignment:
+            offset = rectyp.offset(attr)
+            elemty = rectyp.typeof(attr)
+            if isinstance(elemty, types.NestedArray):
+                # For a NestedArray we need to consider the data type of
+                # elements of the array for alignment, not the array structure
+                # itself
+                elemty = elemty.dtype
+            align = self.get_abi_alignment(self.get_data_type(elemty))
+            if offset % align:
+                msg = "{rec}.{attr} of type {type} is not aligned".format(
+                    rec=rectyp, attr=attr, type=elemty)
+                raise TypeError(msg)
+
+    def get_helper_class(self, typ, kind='value'):
+        """
+        Get a helper class for the given *typ*.
+        """
+        # XXX handle all types: complex, array, etc.
+        # XXX should it be a method on the model instead? this would allow a default kind...
+        return cgutils.create_struct_proxy(typ, kind)
+
+    def _make_helper(self, builder, typ, value=None, ref=None, kind='value'):
+        cls = self.get_helper_class(typ, kind)
+        return cls(self, builder, value=value, ref=ref)
+
+    def make_helper(self, builder, typ, value=None, ref=None):
+        """
+        Get a helper object to access the *typ*'s members,
+        for the given value or reference.
+        """
+        return self._make_helper(builder, typ, value, ref, kind='value')
+
+    def make_data_helper(self, builder, typ, ref=None):
+        """
+        As make_helper(), but considers the value as stored in memory,
+        rather than a live value.
+        """
+        return self._make_helper(builder, typ, ref=ref, kind='data')
+
+    def make_array(self, typ):
+        from numba.np import arrayobj
+        return arrayobj.make_array(typ)
+
+    def populate_array(self, arr, **kwargs):
+        """
+        Populate array structure.
+        """
+        from numba.np import arrayobj
+        return arrayobj.populate_array(arr, **kwargs)
+
+    def make_complex(self, builder, typ, value=None):
+        """
+        Get a helper object to access the given complex numbers' members.
+        """
+        assert isinstance(typ, types.Complex), typ
+        return self.make_helper(builder, typ, value)
+
+    def make_tuple(self, builder, typ, values):
+        """
+        Create a tuple of the given *typ* containing the *values*.
+        """
+        tup = self.get_constant_undef(typ)
+        for i, val in enumerate(values):
+            tup = builder.insert_value(tup, val, i)
+        return tup
+
+    def make_constant_array(self, builder, typ, ary):
+        """
+        Create an array structure reifying the given constant array.
+        A low-level contiguous array constant is created in the LLVM IR.
+        """
+        datatype = self.get_data_type(typ.dtype)
+        # don't freeze ary of non-contig or bigger than 1MB
+        size_limit = 10**6
+
+        if (self.allow_dynamic_globals and
+                (typ.layout not in 'FC' or ary.nbytes > size_limit)):
+            # get pointer from the ary
+            dataptr = ary.ctypes.data
+            data = self.add_dynamic_addr(builder, dataptr, info=str(type(dataptr)))
+            rt_addr = self.add_dynamic_addr(builder, id(ary), info=str(type(ary)))
+        else:
+            # Handle data: reify the flattened array in "C" or "F" order as a
+            # global array of bytes.
+            flat = ary.flatten(order=typ.layout)
+            # Note: we use `bytearray(flat.data)` instead of `bytearray(flat)` to
+            #       workaround issue #1850 which is due to numpy issue #3147
+            consts = cgutils.create_constant_array(llvmir.IntType(8), bytearray(flat.data))
+            data = cgutils.global_constant(builder, ".const.array.data", consts)
+            # Ensure correct data alignment (issue #1933)
+            data.align = self.get_abi_alignment(datatype)
+            # No reference to parent ndarray
+            rt_addr = None
+
+        # Handle shape
+        llintp = self.get_value_type(types.intp)
+        shapevals = [self.get_constant(types.intp, s) for s in ary.shape]
+        cshape = cgutils.create_constant_array(llintp, shapevals)
+
+        # Handle strides
+        stridevals = [self.get_constant(types.intp, s) for s in ary.strides]
+        cstrides = cgutils.create_constant_array(llintp, stridevals)
+
+        # Create array structure
+        cary = self.make_array(typ)(self, builder)
+
+        intp_itemsize = self.get_constant(types.intp, ary.dtype.itemsize)
+        self.populate_array(cary,
+                            data=builder.bitcast(data, cary.data.type),
+                            shape=cshape,
+                            strides=cstrides,
+                            itemsize=intp_itemsize,
+                            parent=rt_addr,
+                            meminfo=None)
+
+        return cary._getvalue()
+
+    def add_dynamic_addr(self, builder, intaddr, info):
+        """
+        Returns dynamic address as a void pointer `i8*`.
+
+        Internally, a global variable is added to inform the lowerer about
+        the usage of dynamic addresses.  Caching will be disabled.
+        """
+        assert self.allow_dynamic_globals, "dyn globals disabled in this target"
+        assert isinstance(intaddr, int), 'dyn addr not of int type'
+        mod = builder.module
+        llvoidptr = self.get_value_type(types.voidptr)
+        addr = self.get_constant(types.uintp, intaddr).inttoptr(llvoidptr)
+        # Use a unique name by embedding the address value
+        symname = 'numba.dynamic.globals.{:x}'.format(intaddr)
+        gv = cgutils.add_global_variable(mod, llvoidptr, symname)
+        # Use linkonce linkage to allow merging with other GV of the same name.
+        # And, avoid optimization from assuming its value.
+        gv.linkage = 'linkonce'
+        gv.initializer = addr
+        return builder.load(gv)
+
+    def get_abi_sizeof(self, ty):
+        """
+        Get the ABI size of LLVM type *ty*.
+        """
+        assert isinstance(ty, llvmir.Type), "Expected LLVM type"
+        return ty.get_abi_size(self.target_data)
+
+    def get_abi_alignment(self, ty):
+        """
+        Get the ABI alignment of LLVM type *ty*.
+        """
+        assert isinstance(ty, llvmir.Type), "Expected LLVM type"
+        return ty.get_abi_alignment(self.target_data)
+
+    def get_preferred_array_alignment(context, ty):
+        """
+        Get preferred array alignment for Numba type *ty*.
+        """
+        # AVX prefers 32-byte alignment
+        return 32
+
+    def post_lowering(self, mod, library):
+        """Run target specific post-lowering transformation here.
+        """
+
+    def create_module(self, name):
+        """Create a LLVM module
+
+        The default implementation in BaseContext always raises a
+        ``NotImplementedError`` exception. Subclasses should implement
+        this method.
+        """
+        raise NotImplementedError
+
+    @property
+    def active_code_library(self):
+        """Get the active code library
+        """
+        return self._codelib_stack[-1]
+
+    @contextmanager
+    def push_code_library(self, lib):
+        """Push the active code library for the context
+        """
+        self._codelib_stack.append(lib)
+        try:
+            yield
+        finally:
+            self._codelib_stack.pop()
+
+    def add_linking_libs(self, libs):
+        """Add iterable of linking libraries to the *active_code_library*.
+        """
+        colib = self.active_code_library
+        for lib in libs:
+            colib.add_linking_library(lib)
+
+    def get_ufunc_info(self, ufunc_key):
+        """Get the ufunc implementation for a given ufunc object.
+
+        The default implementation in BaseContext always raises a
+        ``NotImplementedError`` exception. Subclasses may raise ``KeyError``
+        to signal that the given ``ufunc_key`` is not available.
+
+        Parameters
+        ----------
+        ufunc_key : NumPy ufunc
+
+        Returns
+        -------
+        res : dict[str, callable]
+            A mapping of a NumPy ufunc type signature to a lower-level
+            implementation.
+        """
+        raise NotImplementedError(f"{self} does not support ufunc")
+
+class _wrap_impl(object):
+    """
+    A wrapper object to call an implementation function with some predefined
+    (context, signature) arguments.
+    The wrapper also forwards attribute queries, which is important.
+    """
+
+    def __init__(self, imp, context, sig):
+        self._callable = _wrap_missing_loc(imp)
+        self._imp = self._callable()
+        self._context = context
+        self._sig = sig
+
+    def __call__(self, builder, args, loc=None):
+        res = self._imp(self._context, builder, self._sig, args, loc=loc)
+        self._context.add_linking_libs(getattr(self, 'libs', ()))
+        return res
+
+    def __getattr__(self, item):
+        return getattr(self._imp, item)
+
+    def __repr__(self):
+        return "<wrapped %s>" % repr(self._callable)
+
+def _has_loc(fn):
+    """Does function *fn* take ``loc`` argument?
+    """
+    sig = utils.pysignature(fn)
+    return 'loc' in sig.parameters
+
+
+class _wrap_missing_loc(object):
+
+    def __init__(self, fn):
+        self.func = fn # store this to help with debug
+
+    def __call__(self):
+        """Wrap function for missing ``loc`` keyword argument.
+        Otherwise, return the original *fn*.
+        """
+        fn = self.func
+        if not _has_loc(fn):
+            def wrapper(*args, **kwargs):
+                kwargs.pop('loc')     # drop unused loc
+                return fn(*args, **kwargs)
+
+            # Copy the following attributes from the wrapped.
+            # Following similar implementation as functools.wraps but
+            # ignore attributes if not available (i.e fix py2.7)
+            attrs = '__name__', 'libs'
+            for attr in attrs:
+                try:
+                    val = getattr(fn, attr)
+                except AttributeError:
+                    pass
+                else:
+                    setattr(wrapper, attr, val)
+
+            return wrapper
+        else:
+            return fn
+
+    def __repr__(self):
+        return "<wrapped %s>" % self.func
+
+
+@utils.runonce
+def _initialize_llvm_lock_event():
+    """Initial event triggers for LLVM lock
+    """
+    def enter_fn():
+        event.start_event("numba:llvm_lock")
+
+    def exit_fn():
+        event.end_event("numba:llvm_lock")
+
+    ll.ffi.register_lock_callback(enter_fn, exit_fn)
+
+
+_initialize_llvm_lock_event()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/boxing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/boxing.py
new file mode 100644
index 000000000..011d3a87b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/boxing.py
@@ -0,0 +1,1317 @@
+"""
+Boxing and unboxing of native Numba values to / from CPython objects.
+"""
+
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core.pythonapi import box, unbox, reflect, NativeValue
+from numba.core.errors import NumbaNotImplementedError
+from numba.core.typing.typeof import typeof, Purpose
+
+from numba.cpython import setobj, listobj
+from numba.np import numpy_support
+from contextlib import contextmanager, ExitStack
+
+
+#
+# Scalar types
+#
+
+@box(types.Boolean)
+def box_bool(typ, val, c):
+    return c.pyapi.bool_from_bool(val)
+
+@unbox(types.Boolean)
+def unbox_boolean(typ, obj, c):
+    istrue = c.pyapi.object_istrue(obj)
+    zero = ir.Constant(istrue.type, 0)
+    val = c.builder.icmp_signed('!=', istrue, zero)
+    return NativeValue(val, is_error=c.pyapi.c_api_error())
+
+
+@box(types.IntegerLiteral)
+@box(types.BooleanLiteral)
+def box_literal_integer(typ, val, c):
+    val = c.context.cast(c.builder, val, typ, typ.literal_type)
+    return c.box(typ.literal_type, val)
+
+
+@box(types.Integer)
+def box_integer(typ, val, c):
+    if typ.signed:
+        ival = c.builder.sext(val, c.pyapi.longlong)
+        return c.pyapi.long_from_longlong(ival)
+    else:
+        ullval = c.builder.zext(val, c.pyapi.ulonglong)
+        return c.pyapi.long_from_ulonglong(ullval)
+
+@unbox(types.Integer)
+def unbox_integer(typ, obj, c):
+    ll_type = c.context.get_argument_type(typ)
+    val = cgutils.alloca_once(c.builder, ll_type)
+    longobj = c.pyapi.number_long(obj)
+    with c.pyapi.if_object_ok(longobj):
+        if typ.signed:
+            llval = c.pyapi.long_as_longlong(longobj)
+        else:
+            llval = c.pyapi.long_as_ulonglong(longobj)
+        c.pyapi.decref(longobj)
+        c.builder.store(c.builder.trunc(llval, ll_type), val)
+    return NativeValue(c.builder.load(val),
+                       is_error=c.pyapi.c_api_error())
+
+
+@box(types.Float)
+def box_float(typ, val, c):
+    if typ == types.float32:
+        dbval = c.builder.fpext(val, c.pyapi.double)
+    else:
+        assert typ == types.float64
+        dbval = val
+    return c.pyapi.float_from_double(dbval)
+
+@unbox(types.Float)
+def unbox_float(typ, obj, c):
+    fobj = c.pyapi.number_float(obj)
+    dbval = c.pyapi.float_as_double(fobj)
+    c.pyapi.decref(fobj)
+    if typ == types.float32:
+        val = c.builder.fptrunc(dbval,
+                                c.context.get_argument_type(typ))
+    else:
+        assert typ == types.float64
+        val = dbval
+    return NativeValue(val, is_error=c.pyapi.c_api_error())
+
+
+@box(types.Complex)
+def box_complex(typ, val, c):
+    cval = c.context.make_complex(c.builder, typ, value=val)
+
+    if typ == types.complex64:
+        freal = c.builder.fpext(cval.real, c.pyapi.double)
+        fimag = c.builder.fpext(cval.imag, c.pyapi.double)
+    else:
+        assert typ == types.complex128
+        freal, fimag = cval.real, cval.imag
+    return c.pyapi.complex_from_doubles(freal, fimag)
+
+@unbox(types.Complex)
+def unbox_complex(typ, obj, c):
+    # First unbox to complex128, since that's what CPython gives us
+    c128 = c.context.make_complex(c.builder, types.complex128)
+    ok = c.pyapi.complex_adaptor(obj, c128._getpointer())
+    failed = cgutils.is_false(c.builder, ok)
+
+    with cgutils.if_unlikely(c.builder, failed):
+        c.pyapi.err_set_string("PyExc_TypeError",
+                               "conversion to %s failed" % (typ,))
+
+    if typ == types.complex64:
+        # Downcast to complex64 if necessary
+        cplx = c.context.make_complex(c.builder, typ)
+        cplx.real = c.context.cast(c.builder, c128.real,
+                                   types.float64, types.float32)
+        cplx.imag = c.context.cast(c.builder, c128.imag,
+                                   types.float64, types.float32)
+    else:
+        assert typ == types.complex128
+        cplx = c128
+    return NativeValue(cplx._getvalue(), is_error=failed)
+
+
+@box(types.NoneType)
+def box_none(typ, val, c):
+    return c.pyapi.make_none()
+
+@unbox(types.NoneType)
+@unbox(types.EllipsisType)
+def unbox_none(typ, val, c):
+    return NativeValue(c.context.get_dummy_value())
+
+
+@box(types.NPDatetime)
+def box_npdatetime(typ, val, c):
+    return c.pyapi.create_np_datetime(val, typ.unit_code)
+
+@unbox(types.NPDatetime)
+def unbox_npdatetime(typ, obj, c):
+    val = c.pyapi.extract_np_datetime(obj)
+    return NativeValue(val, is_error=c.pyapi.c_api_error())
+
+
+@box(types.NPTimedelta)
+def box_nptimedelta(typ, val, c):
+    return c.pyapi.create_np_timedelta(val, typ.unit_code)
+
+@unbox(types.NPTimedelta)
+def unbox_nptimedelta(typ, obj, c):
+    val = c.pyapi.extract_np_timedelta(obj)
+    return NativeValue(val, is_error=c.pyapi.c_api_error())
+
+
+@box(types.RawPointer)
+def box_raw_pointer(typ, val, c):
+    """
+    Convert a raw pointer to a Python int.
+    """
+    ll_intp = c.context.get_value_type(types.uintp)
+    addr = c.builder.ptrtoint(val, ll_intp)
+    return c.box(types.uintp, addr)
+
+
+@box(types.EnumMember)
+def box_enum(typ, val, c):
+    """
+    Fetch an enum member given its native value.
+    """
+    valobj = c.box(typ.dtype, val)
+    # Call the enum class with the value object
+    cls_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.instance_class))
+    return c.pyapi.call_function_objargs(cls_obj, (valobj,))
+
+
+@unbox(types.EnumMember)
+def unbox_enum(typ, obj, c):
+    """
+    Convert an enum member's value to its native value.
+    """
+    valobj = c.pyapi.object_getattr_string(obj, "value")
+    return c.unbox(typ.dtype, valobj)
+
+#
+# Composite types
+#
+
+@box(types.Record)
+def box_record(typ, val, c):
+    # Note we will create a copy of the record
+    # This is the only safe way.
+    size = ir.Constant(ir.IntType(32), val.type.pointee.count)
+    ptr = c.builder.bitcast(val, ir.PointerType(ir.IntType(8)))
+    return c.pyapi.recreate_record(ptr, size, typ.dtype, c.env_manager)
+
+
+@unbox(types.Record)
+def unbox_record(typ, obj, c):
+    buf = c.pyapi.alloca_buffer()
+    ptr = c.pyapi.extract_record_data(obj, buf)
+    is_error = cgutils.is_null(c.builder, ptr)
+
+    ltyp = c.context.get_value_type(typ)
+    val = c.builder.bitcast(ptr, ltyp)
+
+    def cleanup():
+        c.pyapi.release_buffer(buf)
+    return NativeValue(val, cleanup=cleanup, is_error=is_error)
+
+
+@box(types.UnicodeCharSeq)
+def box_unicodecharseq(typ, val, c):
+    # XXX could kind be determined from strptr?
+    unicode_kind = {
+        1: c.pyapi.py_unicode_1byte_kind,
+        2: c.pyapi.py_unicode_2byte_kind,
+        4: c.pyapi.py_unicode_4byte_kind}[numpy_support.sizeof_unicode_char]
+    kind = c.context.get_constant(types.int32, unicode_kind)
+    rawptr = cgutils.alloca_once_value(c.builder, value=val)
+    strptr = c.builder.bitcast(rawptr, c.pyapi.cstring)
+
+    fullsize = c.context.get_constant(types.intp, typ.count)
+    zero = fullsize.type(0)
+    one = fullsize.type(1)
+    step = fullsize.type(numpy_support.sizeof_unicode_char)
+    count = cgutils.alloca_once_value(c.builder, zero)
+    with cgutils.loop_nest(c.builder, [fullsize], fullsize.type) as [idx]:
+        # Get char at idx
+        ch = c.builder.load(c.builder.gep(strptr, [c.builder.mul(idx, step)]))
+        # If the char is a non-null-byte, store the next index as count
+        with c.builder.if_then(cgutils.is_not_null(c.builder, ch)):
+            c.builder.store(c.builder.add(idx, one), count)
+    strlen = c.builder.load(count)
+    return c.pyapi.string_from_kind_and_data(kind, strptr, strlen)
+
+
+@unbox(types.UnicodeCharSeq)
+def unbox_unicodecharseq(typ, obj, c):
+    lty = c.context.get_value_type(typ)
+
+    ok, buffer, size, kind, is_ascii, hashv = \
+        c.pyapi.string_as_string_size_and_kind(obj)
+
+    # If conversion is ok, copy the buffer to the output storage.
+    with cgutils.if_likely(c.builder, ok):
+        # Check if the returned string size fits in the charseq
+        storage_size = ir.Constant(size.type, typ.count)
+        size_fits = c.builder.icmp_unsigned("<=", size, storage_size)
+
+        # Allow truncation of string
+        size = c.builder.select(size_fits, size, storage_size)
+
+        # Initialize output to zero bytes
+        null_string = ir.Constant(lty, None)
+        outspace  = cgutils.alloca_once_value(c.builder, null_string)
+
+        # We don't need to set the NULL-terminator because the storage
+        # is already zero-filled.
+        cgutils.memcpy(c.builder,
+                        c.builder.bitcast(outspace, buffer.type),
+                        buffer, size)
+
+    ret = c.builder.load(outspace)
+    return NativeValue(ret, is_error=c.builder.not_(ok))
+
+
+@box(types.Bytes)
+def box_bytes(typ, val, c):
+    obj = c.context.make_helper(c.builder, typ, val)
+    ret = c.pyapi.bytes_from_string_and_size(obj.data, obj.nitems)
+    c.context.nrt.decref(c.builder, typ, val)
+    return ret
+
+
+@box(types.CharSeq)
+def box_charseq(typ, val, c):
+    rawptr = cgutils.alloca_once_value(c.builder, value=val)
+    strptr = c.builder.bitcast(rawptr, c.pyapi.cstring)
+    fullsize = c.context.get_constant(types.intp, typ.count)
+    zero = fullsize.type(0)
+    one = fullsize.type(1)
+    count = cgutils.alloca_once_value(c.builder, zero)
+
+    # Find the length of the string, mimicking Numpy's behaviour:
+    # search for the last non-null byte in the underlying storage
+    # (e.g. b'A\0\0B\0\0\0' will return the logical string b'A\0\0B')
+    with cgutils.loop_nest(c.builder, [fullsize], fullsize.type) as [idx]:
+        # Get char at idx
+        ch = c.builder.load(c.builder.gep(strptr, [idx]))
+        # If the char is a non-null-byte, store the next index as count
+        with c.builder.if_then(cgutils.is_not_null(c.builder, ch)):
+            c.builder.store(c.builder.add(idx, one), count)
+
+    strlen = c.builder.load(count)
+    return c.pyapi.bytes_from_string_and_size(strptr, strlen)
+
+
+@unbox(types.CharSeq)
+def unbox_charseq(typ, obj, c):
+    lty = c.context.get_value_type(typ)
+    ok, buffer, size = c.pyapi.string_as_string_and_size(obj)
+
+    # If conversion is ok, copy the buffer to the output storage.
+    with cgutils.if_likely(c.builder, ok):
+        # Check if the returned string size fits in the charseq
+        storage_size = ir.Constant(size.type, typ.count)
+        size_fits = c.builder.icmp_unsigned("<=", size, storage_size)
+
+        # Allow truncation of string
+        size = c.builder.select(size_fits, size, storage_size)
+
+        # Initialize output to zero bytes
+        null_string = ir.Constant(lty, None)
+        outspace  = cgutils.alloca_once_value(c.builder, null_string)
+
+        # We don't need to set the NULL-terminator because the storage
+        # is already zero-filled.
+        cgutils.memcpy(c.builder,
+                       c.builder.bitcast(outspace, buffer.type),
+                       buffer, size)
+
+    ret = c.builder.load(outspace)
+    return NativeValue(ret, is_error=c.builder.not_(ok))
+
+
+@box(types.Optional)
+def box_optional(typ, val, c):
+    optval = c.context.make_helper(c.builder, typ, val)
+    ret = cgutils.alloca_once_value(c.builder, c.pyapi.borrow_none())
+    with c.builder.if_else(optval.valid) as (then, otherwise):
+        with then:
+            validres = c.box(typ.type, optval.data)
+            c.builder.store(validres, ret)
+        with otherwise:
+            c.builder.store(c.pyapi.make_none(), ret)
+    return c.builder.load(ret)
+
+
+@unbox(types.Optional)
+def unbox_optional(typ, obj, c):
+    """
+    Convert object *obj* to a native optional structure.
+    """
+    noneval = c.context.make_optional_none(c.builder, typ.type)
+    is_not_none = c.builder.icmp_signed('!=', obj, c.pyapi.borrow_none())
+
+    retptr = cgutils.alloca_once(c.builder, noneval.type)
+    errptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
+
+    with c.builder.if_else(is_not_none) as (then, orelse):
+        with then:
+            native = c.unbox(typ.type, obj)
+            just = c.context.make_optional_value(c.builder,
+                                                 typ.type, native.value)
+            c.builder.store(just, retptr)
+            c.builder.store(native.is_error, errptr)
+
+        with orelse:
+            c.builder.store(noneval, retptr)
+
+    if native.cleanup is not None:
+        def cleanup():
+            with c.builder.if_then(is_not_none):
+                native.cleanup()
+    else:
+        cleanup = None
+
+    ret = c.builder.load(retptr)
+    return NativeValue(ret, is_error=c.builder.load(errptr),
+                       cleanup=cleanup)
+
+
+@unbox(types.SliceType)
+def unbox_slice(typ, obj, c):
+    """
+    Convert object *obj* to a native slice structure.
+    """
+    from numba.cpython import slicing
+    ok, start, stop, step = c.pyapi.slice_as_ints(obj)
+    sli = c.context.make_helper(c.builder, typ)
+    sli.start = start
+    sli.stop = stop
+    sli.step = step
+    return NativeValue(sli._getvalue(), is_error=c.builder.not_(ok))
+
+@box(types.SliceLiteral)
+def box_slice_literal(typ, val, c):
+    # Check for integer overflows at compile time.
+    slice_lit = typ.literal_value
+    for field_name in ("start", "stop", "step"):
+        field_obj = getattr(slice_lit, field_name)
+        if isinstance(field_obj, int):
+            try:
+                typeof(field_obj, Purpose)
+            except ValueError as e:
+                raise ValueError((
+                    f"Unable to create literal slice. "
+                    f"Error encountered with {field_name} "
+                    f"attribute. {str(e)}")
+                )
+
+    py_ctor, py_args = typ.literal_value.__reduce__()
+    serialized_ctor = c.pyapi.serialize_object(py_ctor)
+    serialized_args = c.pyapi.serialize_object(py_args)
+    ctor = c.pyapi.unserialize(serialized_ctor)
+    args = c.pyapi.unserialize(serialized_args)
+    obj = c.pyapi.call(ctor, args)
+    c.pyapi.decref(ctor)
+    c.pyapi.decref(args)
+    return obj
+
+@unbox(types.StringLiteral)
+def unbox_string_literal(typ, obj, c):
+    # A string literal is a dummy value
+    return NativeValue(c.context.get_dummy_value())
+
+#
+# Collections
+#
+
+# NOTE: boxing functions are supposed to steal any NRT references in
+# the given native value.
+
+@box(types.Array)
+def box_array(typ, val, c):
+    nativearycls = c.context.make_array(typ)
+    nativeary = nativearycls(c.context, c.builder, value=val)
+    if c.context.enable_nrt:
+        np_dtype = numpy_support.as_dtype(typ.dtype)
+        dtypeptr = c.env_manager.read_const(c.env_manager.add_const(np_dtype))
+        newary = c.pyapi.nrt_adapt_ndarray_to_python(typ, val, dtypeptr)
+        # Steals NRT ref
+        c.context.nrt.decref(c.builder, typ, val)
+        return newary
+    else:
+        parent = nativeary.parent
+        c.pyapi.incref(parent)
+        return parent
+
+
+@unbox(types.Buffer)
+def unbox_buffer(typ, obj, c):
+    """
+    Convert a Py_buffer-providing object to a native array structure.
+    """
+    buf = c.pyapi.alloca_buffer()
+    res = c.pyapi.get_buffer(obj, buf)
+    is_error = cgutils.is_not_null(c.builder, res)
+
+    nativearycls = c.context.make_array(typ)
+    nativeary = nativearycls(c.context, c.builder)
+    aryptr = nativeary._getpointer()
+
+    with cgutils.if_likely(c.builder, c.builder.not_(is_error)):
+        ptr = c.builder.bitcast(aryptr, c.pyapi.voidptr)
+        if c.context.enable_nrt:
+            c.pyapi.nrt_adapt_buffer_from_python(buf, ptr)
+        else:
+            c.pyapi.numba_buffer_adaptor(buf, ptr)
+
+    def cleanup():
+        c.pyapi.release_buffer(buf)
+
+    return NativeValue(c.builder.load(aryptr), is_error=is_error,
+                       cleanup=cleanup)
+
+@unbox(types.Array)
+def unbox_array(typ, obj, c):
+    """
+    Convert a Numpy array object to a native array structure.
+    """
+    # This is necessary because unbox_buffer() does not work on some
+    # dtypes, e.g. datetime64 and timedelta64.
+    # TODO check matching dtype.
+    #      currently, mismatching dtype will still work and causes
+    #      potential memory corruption
+    nativearycls = c.context.make_array(typ)
+    nativeary = nativearycls(c.context, c.builder)
+    aryptr = nativeary._getpointer()
+
+    ptr = c.builder.bitcast(aryptr, c.pyapi.voidptr)
+    if c.context.enable_nrt:
+        errcode = c.pyapi.nrt_adapt_ndarray_from_python(obj, ptr)
+    else:
+        errcode = c.pyapi.numba_array_adaptor(obj, ptr)
+
+    # TODO: here we have minimal typechecking by the itemsize.
+    #       need to do better
+    try:
+        expected_itemsize = numpy_support.as_dtype(typ.dtype).itemsize
+    except NumbaNotImplementedError:
+        # Don't check types that can't be `as_dtype()`-ed
+        itemsize_mismatch = cgutils.false_bit
+    else:
+        expected_itemsize = nativeary.itemsize.type(expected_itemsize)
+        itemsize_mismatch = c.builder.icmp_unsigned(
+            '!=',
+            nativeary.itemsize,
+            expected_itemsize,
+            )
+
+    failed = c.builder.or_(
+        cgutils.is_not_null(c.builder, errcode),
+        itemsize_mismatch,
+    )
+    # Handle error
+    with c.builder.if_then(failed, likely=False):
+        c.pyapi.err_set_string("PyExc_TypeError",
+                               "can't unbox array from PyObject into "
+                               "native value.  The object maybe of a "
+                               "different type")
+    return NativeValue(c.builder.load(aryptr), is_error=failed)
+
+
+@box(types.Tuple)
+@box(types.UniTuple)
+def box_tuple(typ, val, c):
+    """
+    Convert native array or structure *val* to a tuple object.
+    """
+    tuple_val = c.pyapi.tuple_new(typ.count)
+
+    for i, dtype in enumerate(typ):
+        item = c.builder.extract_value(val, i)
+        obj = c.box(dtype, item)
+        c.pyapi.tuple_setitem(tuple_val, i, obj)
+
+    return tuple_val
+
+@box(types.NamedTuple)
+@box(types.NamedUniTuple)
+def box_namedtuple(typ, val, c):
+    """
+    Convert native array or structure *val* to a namedtuple object.
+    """
+    cls_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.instance_class))
+    tuple_obj = box_tuple(typ, val, c)
+    obj = c.pyapi.call(cls_obj, tuple_obj)
+    c.pyapi.decref(cls_obj)
+    c.pyapi.decref(tuple_obj)
+    return obj
+
+
+@unbox(types.BaseTuple)
+def unbox_tuple(typ, obj, c):
+    """
+    Convert tuple *obj* to a native array (if homogeneous) or structure.
+    """
+    n = len(typ)
+    values = []
+    cleanups = []
+    lty = c.context.get_value_type(typ)
+
+    is_error_ptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
+    value_ptr = cgutils.alloca_once(c.builder, lty)
+
+    # Issue #1638: need to check the tuple size
+    actual_size = c.pyapi.tuple_size(obj)
+    size_matches = c.builder.icmp_unsigned('==', actual_size,
+                                            ir.Constant(actual_size.type, n))
+    with c.builder.if_then(c.builder.not_(size_matches), likely=False):
+        c.pyapi.err_format(
+            "PyExc_ValueError",
+            "size mismatch for tuple, expected %d element(s) but got %%zd" % (n,),
+            actual_size)
+        c.builder.store(cgutils.true_bit, is_error_ptr)
+
+    # We unbox the items even if not `size_matches`, to avoid issues with
+    # the generated IR (instruction doesn't dominate all uses)
+    for i, eltype in enumerate(typ):
+        elem = c.pyapi.tuple_getitem(obj, i)
+        native = c.unbox(eltype, elem)
+        values.append(native.value)
+        with c.builder.if_then(native.is_error, likely=False):
+            c.builder.store(cgutils.true_bit, is_error_ptr)
+        if native.cleanup is not None:
+            cleanups.append(native.cleanup)
+
+    value = c.context.make_tuple(c.builder, typ, values)
+    c.builder.store(value, value_ptr)
+
+    if cleanups:
+        with c.builder.if_then(size_matches, likely=True):
+            def cleanup():
+                for func in reversed(cleanups):
+                    func()
+    else:
+        cleanup = None
+
+    return NativeValue(c.builder.load(value_ptr), cleanup=cleanup,
+                       is_error=c.builder.load(is_error_ptr))
+
+
+@box(types.List)
+def box_list(typ, val, c):
+    """
+    Convert native list *val* to a list object.
+    """
+    list = listobj.ListInstance(c.context, c.builder, typ, val)
+    obj = list.parent
+    res = cgutils.alloca_once_value(c.builder, obj)
+    with c.builder.if_else(cgutils.is_not_null(c.builder, obj)) as (has_parent, otherwise):
+        with has_parent:
+            # List is actually reflected => return the original object
+            # (note not all list instances whose *type* is reflected are
+            #  actually reflected; see numba.tests.test_lists for an example)
+            c.pyapi.incref(obj)
+
+        with otherwise:
+            # Build a new Python list
+            nitems = list.size
+            obj = c.pyapi.list_new(nitems)
+            with c.builder.if_then(cgutils.is_not_null(c.builder, obj),
+                                   likely=True):
+                with cgutils.for_range(c.builder, nitems) as loop:
+                    item = list.getitem(loop.index)
+                    list.incref_value(item)
+                    itemobj = c.box(typ.dtype, item)
+                    c.pyapi.list_setitem(obj, loop.index, itemobj)
+
+            c.builder.store(obj, res)
+
+    # Steal NRT ref
+    c.context.nrt.decref(c.builder, typ, val)
+    return c.builder.load(res)
+
+
+class _NumbaTypeHelper(object):
+    """A helper for acquiring `numba.typeof` for type checking.
+
+    Usage
+    -----
+
+        # `c` is the boxing context.
+        with _NumbaTypeHelper(c) as nth:
+            # This contextmanager maintains the lifetime of the `numba.typeof`
+            # function.
+            the_numba_type = nth.typeof(some_object)
+            # Do work on the type object
+            do_checks(the_numba_type)
+            # Cleanup
+            c.pyapi.decref(the_numba_type)
+        # At this point *nth* should not be used.
+    """
+    def __init__(self, c):
+        self.c = c
+
+    def __enter__(self):
+        c = self.c
+        numba_name = c.context.insert_const_string(c.builder.module, 'numba')
+        numba_mod = c.pyapi.import_module_noblock(numba_name)
+        typeof_fn = c.pyapi.object_getattr_string(numba_mod, 'typeof')
+        self.typeof_fn = typeof_fn
+        c.pyapi.decref(numba_mod)
+        return self
+
+    def __exit__(self, *args, **kwargs):
+        c = self.c
+        c.pyapi.decref(self.typeof_fn)
+
+    def typeof(self, obj):
+        res = self.c.pyapi.call_function_objargs(self.typeof_fn, [obj])
+        return res
+
+
+def _python_list_to_native(typ, obj, c, size, listptr, errorptr):
+    """
+    Construct a new native list from a Python list.
+    """
+    def check_element_type(nth, itemobj, expected_typobj):
+        typobj = nth.typeof(itemobj)
+        # Check if *typobj* is NULL
+        with c.builder.if_then(
+                cgutils.is_null(c.builder, typobj),
+                likely=False,
+                ):
+            c.builder.store(cgutils.true_bit, errorptr)
+            loop.do_break()
+        # Mandate that objects all have the same exact type
+        type_mismatch = c.builder.icmp_signed('!=', typobj, expected_typobj)
+
+        with c.builder.if_then(type_mismatch, likely=False):
+            c.builder.store(cgutils.true_bit, errorptr)
+            c.pyapi.err_format(
+                "PyExc_TypeError",
+                "can't unbox heterogeneous list: %S != %S",
+                expected_typobj, typobj,
+                )
+            c.pyapi.decref(typobj)
+            loop.do_break()
+        c.pyapi.decref(typobj)
+
+    # Allocate a new native list
+    ok, list = listobj.ListInstance.allocate_ex(c.context, c.builder, typ, size)
+    with c.builder.if_else(ok, likely=True) as (if_ok, if_not_ok):
+        with if_ok:
+            list.size = size
+            zero = ir.Constant(size.type, 0)
+            with c.builder.if_then(c.builder.icmp_signed('>', size, zero),
+                                   likely=True):
+                # Traverse Python list and unbox objects into native list
+                with _NumbaTypeHelper(c) as nth:
+                    # Note: *expected_typobj* can't be NULL
+                    expected_typobj = nth.typeof(c.pyapi.list_getitem(obj, zero))
+                    with cgutils.for_range(c.builder, size) as loop:
+                        itemobj = c.pyapi.list_getitem(obj, loop.index)
+                        check_element_type(nth, itemobj, expected_typobj)
+                        # XXX we don't call native cleanup for each
+                        # list element, since that would require keeping
+                        # of which unboxings have been successful.
+                        native = c.unbox(typ.dtype, itemobj)
+                        with c.builder.if_then(native.is_error, likely=False):
+                            c.builder.store(cgutils.true_bit, errorptr)
+                            loop.do_break()
+                        # The reference is borrowed so incref=False
+                        list.setitem(loop.index, native.value, incref=False)
+                    c.pyapi.decref(expected_typobj)
+            if typ.reflected:
+                list.parent = obj
+            # Stuff meminfo pointer into the Python object for
+            # later reuse.
+            with c.builder.if_then(c.builder.not_(c.builder.load(errorptr)),
+                                                  likely=False):
+                c.pyapi.object_set_private_data(obj, list.meminfo)
+            list.set_dirty(False)
+            c.builder.store(list.value, listptr)
+
+        with if_not_ok:
+            c.builder.store(cgutils.true_bit, errorptr)
+
+    # If an error occurred, drop the whole native list
+    with c.builder.if_then(c.builder.load(errorptr)):
+        c.context.nrt.decref(c.builder, typ, list.value)
+
+
+@unbox(types.List)
+def unbox_list(typ, obj, c):
+    """
+    Convert list *obj* to a native list.
+
+    If list was previously unboxed, we reuse the existing native list
+    to ensure consistency.
+    """
+    size = c.pyapi.list_size(obj)
+
+    errorptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
+    listptr = cgutils.alloca_once(c.builder, c.context.get_value_type(typ))
+
+    # See if the list was previously unboxed, if so, re-use the meminfo.
+    ptr = c.pyapi.object_get_private_data(obj)
+
+    with c.builder.if_else(cgutils.is_not_null(c.builder, ptr)) \
+        as (has_meminfo, otherwise):
+
+        with has_meminfo:
+            # List was previously unboxed => reuse meminfo
+            list = listobj.ListInstance.from_meminfo(c.context, c.builder, typ, ptr)
+            list.size = size
+            if typ.reflected:
+                list.parent = obj
+            c.builder.store(list.value, listptr)
+
+        with otherwise:
+            _python_list_to_native(typ, obj, c, size, listptr, errorptr)
+
+    def cleanup():
+        # Clean up the associated pointer, as the meminfo is now invalid.
+        c.pyapi.object_reset_private_data(obj)
+
+    return NativeValue(c.builder.load(listptr),
+                       is_error=c.builder.load(errorptr),
+                       cleanup=cleanup)
+
+
+@reflect(types.List)
+def reflect_list(typ, val, c):
+    """
+    Reflect the native list's contents into the Python object.
+    """
+    if not typ.reflected:
+        return
+    if typ.dtype.reflected:
+        msg = "cannot reflect element of reflected container: {}\n".format(typ)
+        raise TypeError(msg)
+
+    list = listobj.ListInstance(c.context, c.builder, typ, val)
+    with c.builder.if_then(list.dirty, likely=False):
+        obj = list.parent
+        size = c.pyapi.list_size(obj)
+        new_size = list.size
+        diff = c.builder.sub(new_size, size)
+        diff_gt_0 = c.builder.icmp_signed('>=', diff,
+                                          ir.Constant(diff.type, 0))
+        with c.builder.if_else(diff_gt_0) as (if_grow, if_shrink):
+            # XXX no error checking below
+            with if_grow:
+                # First overwrite existing items
+                with cgutils.for_range(c.builder, size) as loop:
+                    item = list.getitem(loop.index)
+                    list.incref_value(item)
+                    itemobj = c.box(typ.dtype, item)
+                    c.pyapi.list_setitem(obj, loop.index, itemobj)
+                # Then add missing items
+                with cgutils.for_range(c.builder, diff) as loop:
+                    idx = c.builder.add(size, loop.index)
+                    item = list.getitem(idx)
+                    list.incref_value(item)
+                    itemobj = c.box(typ.dtype, item)
+                    c.pyapi.list_append(obj, itemobj)
+                    c.pyapi.decref(itemobj)
+
+            with if_shrink:
+                # First delete list tail
+                c.pyapi.list_setslice(obj, new_size, size, None)
+                # Then overwrite remaining items
+                with cgutils.for_range(c.builder, new_size) as loop:
+                    item = list.getitem(loop.index)
+                    list.incref_value(item)
+                    itemobj = c.box(typ.dtype, item)
+                    c.pyapi.list_setitem(obj, loop.index, itemobj)
+
+        # Mark the list clean, in case it is reflected twice
+        list.set_dirty(False)
+
+
+def _python_set_to_native(typ, obj, c, size, setptr, errorptr):
+    """
+    Construct a new native set from a Python set.
+    """
+    # Allocate a new native set
+    ok, inst = setobj.SetInstance.allocate_ex(c.context, c.builder, typ, size)
+    with c.builder.if_else(ok, likely=True) as (if_ok, if_not_ok):
+        with if_ok:
+            # Traverse Python set and unbox objects into native set
+            typobjptr = cgutils.alloca_once_value(c.builder,
+                                                  ir.Constant(c.pyapi.pyobj, None))
+
+            with c.pyapi.set_iterate(obj) as loop:
+                itemobj = loop.value
+                # Mandate that objects all have the same exact type
+                typobj = c.pyapi.get_type(itemobj)
+                expected_typobj = c.builder.load(typobjptr)
+
+                with c.builder.if_else(
+                    cgutils.is_null(c.builder, expected_typobj),
+                    likely=False) as (if_first, if_not_first):
+                    with if_first:
+                        # First iteration => store item type
+                        c.builder.store(typobj, typobjptr)
+                    with if_not_first:
+                        # Otherwise, check item type
+                        type_mismatch = c.builder.icmp_signed('!=', typobj,
+                                                              expected_typobj)
+                        with c.builder.if_then(type_mismatch, likely=False):
+                            c.builder.store(cgutils.true_bit, errorptr)
+                            c.pyapi.err_set_string("PyExc_TypeError",
+                                                   "can't unbox heterogeneous set")
+                            loop.do_break()
+
+                # XXX we don't call native cleanup for each set element,
+                # since that would require keeping track
+                # of which unboxings have been successful.
+                native = c.unbox(typ.dtype, itemobj)
+                with c.builder.if_then(native.is_error, likely=False):
+                    c.builder.store(cgutils.true_bit, errorptr)
+                inst.add_pyapi(c.pyapi, native.value, do_resize=False)
+
+            if typ.reflected:
+                inst.parent = obj
+            # Associate meminfo pointer with the Python object for later reuse.
+            with c.builder.if_then(c.builder.not_(c.builder.load(errorptr)),
+                                   likely=False):
+                c.pyapi.object_set_private_data(obj, inst.meminfo)
+            inst.set_dirty(False)
+            c.builder.store(inst.value, setptr)
+
+        with if_not_ok:
+            c.builder.store(cgutils.true_bit, errorptr)
+
+    # If an error occurred, drop the whole native set
+    with c.builder.if_then(c.builder.load(errorptr)):
+        c.context.nrt.decref(c.builder, typ, inst.value)
+
+
+@unbox(types.Set)
+def unbox_set(typ, obj, c):
+    """
+    Convert set *obj* to a native set.
+
+    If set was previously unboxed, we reuse the existing native set
+    to ensure consistency.
+    """
+    size = c.pyapi.set_size(obj)
+
+    errorptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
+    setptr = cgutils.alloca_once(c.builder, c.context.get_value_type(typ))
+
+    # See if the set was previously unboxed, if so, re-use the meminfo.
+    ptr = c.pyapi.object_get_private_data(obj)
+
+    with c.builder.if_else(cgutils.is_not_null(c.builder, ptr)) \
+        as (has_meminfo, otherwise):
+
+        with has_meminfo:
+            # Set was previously unboxed => reuse meminfo
+            inst = setobj.SetInstance.from_meminfo(c.context, c.builder, typ, ptr)
+            if typ.reflected:
+                inst.parent = obj
+            c.builder.store(inst.value, setptr)
+
+        with otherwise:
+            _python_set_to_native(typ, obj, c, size, setptr, errorptr)
+
+    def cleanup():
+        # Clean up the associated pointer, as the meminfo is now invalid.
+        c.pyapi.object_reset_private_data(obj)
+
+    return NativeValue(c.builder.load(setptr),
+                       is_error=c.builder.load(errorptr),
+                       cleanup=cleanup)
+
+
+def _native_set_to_python_list(typ, payload, c):
+    """
+    Create a Python list from a native set's items.
+    """
+    nitems = payload.used
+    listobj = c.pyapi.list_new(nitems)
+    ok = cgutils.is_not_null(c.builder, listobj)
+    with c.builder.if_then(ok, likely=True):
+        index = cgutils.alloca_once_value(c.builder,
+                                          ir.Constant(nitems.type, 0))
+        with payload._iterate() as loop:
+            i = c.builder.load(index)
+            item = loop.entry.key
+            c.context.nrt.incref(c.builder, typ.dtype, item)
+            itemobj = c.box(typ.dtype, item)
+            c.pyapi.list_setitem(listobj, i, itemobj)
+            i = c.builder.add(i, ir.Constant(i.type, 1))
+            c.builder.store(i, index)
+
+    return ok, listobj
+
+
+@box(types.Set)
+def box_set(typ, val, c):
+    """
+    Convert native set *val* to a set object.
+    """
+    inst = setobj.SetInstance(c.context, c.builder, typ, val)
+    obj = inst.parent
+    res = cgutils.alloca_once_value(c.builder, obj)
+
+    with c.builder.if_else(cgutils.is_not_null(c.builder, obj)) as (has_parent, otherwise):
+        with has_parent:
+            # Set is actually reflected => return the original object
+            # (note not all set instances whose *type* is reflected are
+            #  actually reflected; see numba.tests.test_sets for an example)
+            c.pyapi.incref(obj)
+
+        with otherwise:
+            # Build a new Python list and then create a set from that
+            payload = inst.payload
+            ok, listobj = _native_set_to_python_list(typ, payload, c)
+            with c.builder.if_then(ok, likely=True):
+                obj = c.pyapi.set_new(listobj)
+                c.pyapi.decref(listobj)
+                c.builder.store(obj, res)
+
+    # Steal NRT ref
+    c.context.nrt.decref(c.builder, typ, val)
+    return c.builder.load(res)
+
+@reflect(types.Set)
+def reflect_set(typ, val, c):
+    """
+    Reflect the native set's contents into the Python object.
+    """
+    if not typ.reflected:
+        return
+    inst = setobj.SetInstance(c.context, c.builder, typ, val)
+    payload = inst.payload
+
+    with c.builder.if_then(payload.dirty, likely=False):
+        obj = inst.parent
+        # XXX errors are not dealt with below
+        c.pyapi.set_clear(obj)
+
+        # Build a new Python list and then update the set with that
+        ok, listobj = _native_set_to_python_list(typ, payload, c)
+        with c.builder.if_then(ok, likely=True):
+            c.pyapi.set_update(obj, listobj)
+            c.pyapi.decref(listobj)
+
+        # Mark the set clean, in case it is reflected twice
+        inst.set_dirty(False)
+
+
+#
+# Other types
+#
+
+@box(types.Generator)
+def box_generator(typ, val, c):
+    return c.pyapi.from_native_generator(val, typ, c.env_manager.env_ptr)
+
+@unbox(types.Generator)
+def unbox_generator(typ, obj, c):
+    return c.pyapi.to_native_generator(obj, typ)
+
+
+@box(types.DType)
+def box_dtype(typ, val, c):
+    np_dtype = numpy_support.as_dtype(typ.dtype)
+    return c.pyapi.unserialize(c.pyapi.serialize_object(np_dtype))
+
+@unbox(types.DType)
+def unbox_dtype(typ, val, c):
+    return NativeValue(c.context.get_dummy_value())
+
+
+@box(types.NumberClass)
+def box_number_class(typ, val, c):
+    np_dtype = numpy_support.as_dtype(typ.dtype)
+    return c.pyapi.unserialize(c.pyapi.serialize_object(np_dtype))
+
+@unbox(types.NumberClass)
+def unbox_number_class(typ, val, c):
+    return NativeValue(c.context.get_dummy_value())
+
+
+@box(types.PyObject)
+@box(types.Object)
+def box_pyobject(typ, val, c):
+    return val
+
+@unbox(types.PyObject)
+@unbox(types.Object)
+def unbox_pyobject(typ, obj, c):
+    return NativeValue(obj)
+
+
+@unbox(types.ExternalFunctionPointer)
+def unbox_funcptr(typ, obj, c):
+    if typ.get_pointer is None:
+        raise NotImplementedError(typ)
+
+    # Call get_pointer() on the object to get the raw pointer value
+    ptrty = c.context.get_function_pointer_type(typ)
+    ret = cgutils.alloca_once_value(c.builder,
+                                    ir.Constant(ptrty, None),
+                                    name='fnptr')
+    ser = c.pyapi.serialize_object(typ.get_pointer)
+    get_pointer = c.pyapi.unserialize(ser)
+    with cgutils.if_likely(c.builder,
+                           cgutils.is_not_null(c.builder, get_pointer)):
+        intobj = c.pyapi.call_function_objargs(get_pointer, (obj,))
+        c.pyapi.decref(get_pointer)
+        with cgutils.if_likely(c.builder,
+                               cgutils.is_not_null(c.builder, intobj)):
+            ptr = c.pyapi.long_as_voidptr(intobj)
+            c.pyapi.decref(intobj)
+            c.builder.store(c.builder.bitcast(ptr, ptrty), ret)
+    return NativeValue(c.builder.load(ret), is_error=c.pyapi.c_api_error())
+
+@box(types.DeferredType)
+def box_deferred(typ, val, c):
+    out = c.pyapi.from_native_value(typ.get(),
+                                    c.builder.extract_value(val, [0]),
+                                    env_manager=c.env_manager)
+    return out
+
+
+@unbox(types.DeferredType)
+def unbox_deferred(typ, obj, c):
+    native_value = c.pyapi.to_native_value(typ.get(), obj)
+    model = c.context.data_model_manager[typ]
+    res = model.set(c.builder, model.make_uninitialized(), native_value.value)
+    return NativeValue(res, is_error=native_value.is_error,
+                       cleanup=native_value.cleanup)
+
+
+@unbox(types.Dispatcher)
+def unbox_dispatcher(typ, obj, c):
+    # In native code, Dispatcher types can be casted to FunctionType.
+    return NativeValue(obj)
+
+
+@box(types.Dispatcher)
+def box_pyobject(typ, val, c):
+    c.pyapi.incref(val)
+    return val
+
+
+def unbox_unsupported(typ, obj, c):
+    c.pyapi.err_set_string("PyExc_TypeError",
+                           "can't unbox {!r} type".format(typ))
+    res = c.context.get_constant_null(typ)
+    return NativeValue(res, is_error=cgutils.true_bit)
+
+
+def box_unsupported(typ, val, c):
+    msg = "cannot convert native %s to Python object" % (typ,)
+    c.pyapi.err_set_string("PyExc_TypeError", msg)
+    res = c.pyapi.get_null_object()
+    return res
+
+
+@box(types.Literal)
+def box_literal(typ, val, c):
+    # Const type contains the python object of the constant value,
+    # which we can directly return.
+    retval = typ.literal_value
+    # Serialize the value into the IR
+    return c.pyapi.unserialize(c.pyapi.serialize_object(retval))
+
+
+@box(types.MemInfoPointer)
+def box_meminfo_pointer(typ, val, c):
+    return c.pyapi.nrt_meminfo_as_pyobject(val)
+
+
+@unbox(types.MemInfoPointer)
+def unbox_meminfo_pointer(typ, obj, c):
+    res = c.pyapi.nrt_meminfo_from_pyobject(obj)
+    errored = cgutils.is_null(c.builder, res)
+    return NativeValue(res, is_error=errored)
+
+@unbox(types.TypeRef)
+def unbox_typeref(typ, val, c):
+    return NativeValue(c.context.get_dummy_value(), is_error=cgutils.false_bit)
+
+
+@box(types.LiteralStrKeyDict)
+def box_LiteralStrKeyDict(typ, val, c):
+    return box_unsupported(typ, val, c)
+
+
+@contextmanager
+def early_exit_if(builder, stack: ExitStack, cond):
+    then, otherwise = stack.enter_context(builder.if_else(cond, likely=False))
+    with then:
+        yield
+    stack.enter_context(otherwise)
+
+
+def early_exit_if_null(builder, stack, obj):
+    return early_exit_if(builder, stack, cgutils.is_null(builder, obj))
+
+
+# Original implementation at: https://github.com/numba/numba/issues/4499#issuecomment-1063138477
+@unbox(types.NumPyRandomBitGeneratorType)
+def unbox_numpy_random_bitgenerator(typ, obj, c):
+    """
+    The bit_generator instance has a `.ctypes` attr which is a namedtuple
+    with the following members (types):
+    * state_address (Python int)
+    * state (ctypes.c_void_p)
+    * next_uint64 (ctypes.CFunctionType instance)
+    * next_uint32 (ctypes.CFunctionType instance)
+    * next_double (ctypes.CFunctionType instance)
+    * bit_generator (ctypes.c_void_p)
+    """
+
+    is_error_ptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
+    extra_refs = []
+
+    def clear_extra_refs():
+        for _ref in extra_refs:
+            c.pyapi.decref(_ref)
+
+    def handle_failure():
+        c.builder.store(cgutils.true_bit, is_error_ptr)
+        clear_extra_refs()
+
+    with ExitStack() as stack:
+
+        def object_getattr_safely(obj, attr):
+            attr_obj = c.pyapi.object_getattr_string(obj, attr)
+            extra_refs.append(attr_obj)
+            return attr_obj
+
+        struct_ptr = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+        struct_ptr.parent = obj
+
+        # Get the .ctypes attr
+        ctypes_binding = object_getattr_safely(obj, 'ctypes')
+        with early_exit_if_null(c.builder, stack, ctypes_binding):
+            handle_failure()
+
+        # Look up the "state_address" member and wire it into the struct
+        interface_state_address = object_getattr_safely(
+            ctypes_binding, 'state_address')
+        with early_exit_if_null(c.builder, stack, interface_state_address):
+            handle_failure()
+
+        setattr(struct_ptr, 'state_address',
+                c.unbox(types.uintp, interface_state_address).value)
+
+        # Look up the "state" member and wire it into the struct
+        interface_state = object_getattr_safely(ctypes_binding, 'state')
+        with early_exit_if_null(c.builder, stack, interface_state):
+            handle_failure()
+    
+        interface_state_value = object_getattr_safely(
+            interface_state, 'value')
+        with early_exit_if_null(c.builder, stack, interface_state_value):
+            handle_failure()
+        setattr(
+            struct_ptr,
+            'state',
+            c.unbox(
+                types.uintp,
+                interface_state_value).value)
+
+        # Want to store callable function pointers to these CFunctionTypes, so
+        # import ctypes and use it to cast the CFunctionTypes to c_void_p and
+        # store the results.
+        # First find ctypes.cast, and ctypes.c_void_p
+        ctypes_name = c.context.insert_const_string(c.builder.module, 'ctypes')
+        ctypes_module = c.pyapi.import_module_noblock(ctypes_name)
+        extra_refs.append(ctypes_module)
+        with early_exit_if_null(c.builder, stack, ctypes_module):
+            handle_failure()
+
+        ct_cast = object_getattr_safely(ctypes_module, 'cast')
+        with early_exit_if_null(c.builder, stack, ct_cast):
+            handle_failure()
+
+        ct_voidptr_ty = object_getattr_safely(ctypes_module, 'c_void_p')
+        with early_exit_if_null(c.builder, stack, ct_voidptr_ty):
+            handle_failure()
+
+        # This wires in the fnptrs referred to by name
+        def wire_in_fnptrs(name):
+            # Find the CFunctionType function
+            interface_next_fn = c.pyapi.object_getattr_string(
+                ctypes_binding, name)
+
+            extra_refs.append(interface_next_fn)
+            with early_exit_if_null(c.builder, stack, interface_next_fn):
+                handle_failure()
+
+            # Want to do ctypes.cast(CFunctionType, ctypes.c_void_p), create an
+            # args tuple for that.
+            extra_refs.append(ct_voidptr_ty)
+            args = c.pyapi.tuple_pack([interface_next_fn, ct_voidptr_ty])
+            with early_exit_if_null(c.builder, stack, args):
+                handle_failure()
+            extra_refs.append(ct_voidptr_ty)
+
+            # Call ctypes.cast()
+            interface_next_fn_casted = c.pyapi.call(ct_cast, args)
+
+            # Fetch the .value attr on the resulting ctypes.c_void_p for storage
+            # in the function pointer slot.
+            interface_next_fn_casted_value = object_getattr_safely(
+                interface_next_fn_casted, 'value')
+            with early_exit_if_null(c.builder, stack, interface_next_fn_casted_value):
+                handle_failure()
+
+            # Wire up
+            setattr(struct_ptr, f'fnptr_{name}',
+                    c.unbox(types.uintp, interface_next_fn_casted_value).value)
+
+
+        wire_in_fnptrs('next_double')
+        wire_in_fnptrs('next_uint64')
+        wire_in_fnptrs('next_uint32')
+
+        clear_extra_refs()
+
+    return NativeValue(struct_ptr._getvalue(), is_error=c.builder.load(is_error_ptr))
+
+_bit_gen_type = types.NumPyRandomBitGeneratorType('bit_generator')
+
+@unbox(types.NumPyRandomGeneratorType)
+def unbox_numpy_random_generator(typ, obj, c):
+    """
+    Here we're creating a NumPyRandomGeneratorType StructModel with following fields:
+    * ('bit_generator', _bit_gen_type): The unboxed BitGenerator associated with
+                                        this Generator object instance.
+    * ('parent', types.pyobject): Pointer to the original Generator PyObject.
+    * ('meminfo', types.MemInfoPointer(types.voidptr)): The information about the memory
+        stored at the pointer (to the original Generator PyObject). This is useful for
+        keeping track of reference counts within the Python runtime. Helps prevent cases
+        where deletion happens in Python runtime without NRT being awareness of it. 
+    """
+    is_error_ptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
+
+    with ExitStack() as stack:
+        struct_ptr = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+        bit_gen_inst = c.pyapi.object_getattr_string(obj, 'bit_generator')
+        with early_exit_if_null(c.builder, stack, bit_gen_inst):
+            c.builder.store(cgutils.true_bit, is_error_ptr)
+        unboxed = c.unbox(_bit_gen_type, bit_gen_inst).value
+        struct_ptr.bit_generator = unboxed
+        struct_ptr.parent = obj
+        NULL = cgutils.voidptr_t(None)
+        struct_ptr.meminfo = c.pyapi.nrt_meminfo_new_from_pyobject(
+            NULL,  # there's no data
+            obj,   # the python object, the call to nrt_meminfo_new_from_pyobject
+                # will py_incref
+        )
+        c.pyapi.decref(bit_gen_inst)
+
+    return NativeValue(struct_ptr._getvalue(), is_error=c.builder.load(is_error_ptr))
+
+
+@box(types.NumPyRandomGeneratorType)
+def box_numpy_random_generator(typ, val, c):
+    inst = c.context.make_helper(c.builder, typ, val)
+    obj = inst.parent
+    res = cgutils.alloca_once_value(c.builder, obj)
+    c.pyapi.incref(obj)
+    # Steal NRT ref
+    c.context.nrt.decref(c.builder, typ, val)
+    return c.builder.load(res)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/bytecode.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/bytecode.py
new file mode 100644
index 000000000..13516cd15
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/bytecode.py
@@ -0,0 +1,369 @@
+from collections import namedtuple, OrderedDict
+import dis
+import inspect
+import itertools
+from types import CodeType, ModuleType
+
+from numba.core import errors, utils, serialize
+from numba.core.utils import PYVERSION
+
+opcode_info = namedtuple('opcode_info', ['argsize'])
+
+# The following offset is used as a hack to inject a NOP at the start of the
+# bytecode. So that function starting with `while True` will not have block-0
+# as a jump target. The Lowerer puts argument initialization at block-0.
+_FIXED_OFFSET = 2
+
+
+def get_function_object(obj):
+    """
+    Objects that wraps function should provide a "__numba__" magic attribute
+    that contains a name of an attribute that contains the actual python
+    function object.
+    """
+    attr = getattr(obj, "__numba__", None)
+    if attr:
+        return getattr(obj, attr)
+    return obj
+
+
+def get_code_object(obj):
+    "Shamelessly borrowed from llpython"
+    return getattr(obj, '__code__', getattr(obj, 'func_code', None))
+
+
+def _as_opcodes(seq):
+    lst = []
+    for s in seq:
+        c = dis.opmap.get(s)
+        if c is not None:
+            lst.append(c)
+    return lst
+
+
+JREL_OPS = frozenset(dis.hasjrel)
+JABS_OPS = frozenset(dis.hasjabs)
+JUMP_OPS = JREL_OPS | JABS_OPS
+TERM_OPS = frozenset(_as_opcodes(['RETURN_VALUE', 'RAISE_VARARGS']))
+EXTENDED_ARG = dis.EXTENDED_ARG
+HAVE_ARGUMENT = dis.HAVE_ARGUMENT
+
+
+class ByteCodeInst(object):
+    '''
+    Attributes
+    ----------
+    - offset:
+        byte offset of opcode
+    - opcode:
+        opcode integer value
+    - arg:
+        instruction arg
+    - lineno:
+        -1 means unknown
+    '''
+    __slots__ = 'offset', 'next', 'opcode', 'opname', 'arg', 'lineno'
+
+    def __init__(self, offset, opcode, arg, nextoffset):
+        self.offset = offset
+        self.next = nextoffset
+        self.opcode = opcode
+        self.opname = dis.opname[opcode]
+        self.arg = arg
+        self.lineno = -1  # unknown line number
+
+    @property
+    def is_jump(self):
+        return self.opcode in JUMP_OPS
+
+    @property
+    def is_terminator(self):
+        return self.opcode in TERM_OPS
+
+    def get_jump_target(self):
+        # With Python 3.10 the addressing of "bytecode" instructions has
+        # changed from using bytes to using 16-bit words instead. As a
+        # consequence the code to determine where a jump will lead had to be
+        # adapted.
+        # See also:
+        # https://bugs.python.org/issue26647
+        # https://bugs.python.org/issue27129
+        # https://github.com/python/cpython/pull/25069
+        assert self.is_jump
+        if PYVERSION >= (3, 10):
+            if self.opcode in JREL_OPS:
+                return self.next + self.arg * 2
+            else:
+                assert self.opcode in JABS_OPS
+                return self.arg * 2 - 2
+        else:
+            if self.opcode in JREL_OPS:
+                return self.next + self.arg
+            else:
+                assert self.opcode in JABS_OPS
+                return self.arg
+
+    def __repr__(self):
+        return '%s(arg=%s, lineno=%d)' % (self.opname, self.arg, self.lineno)
+
+    @property
+    def block_effect(self):
+        """Effect of the block stack
+        Returns +1 (push), 0 (none) or -1 (pop)
+        """
+        if self.opname.startswith('SETUP_'):
+            return 1
+        elif self.opname == 'POP_BLOCK':
+            return -1
+        else:
+            return 0
+
+
+CODE_LEN = 1
+ARG_LEN = 1
+NO_ARG_LEN = 1
+
+OPCODE_NOP = dis.opname.index('NOP')
+
+
+# Adapted from Lib/dis.py
+def _unpack_opargs(code):
+    """
+    Returns a 4-int-tuple of
+    (bytecode offset, opcode, argument, offset of next bytecode).
+    """
+    extended_arg = 0
+    n = len(code)
+    offset = i = 0
+    while i < n:
+        op = code[i]
+        i += CODE_LEN
+        if op >= HAVE_ARGUMENT:
+            arg = code[i] | extended_arg
+            for j in range(ARG_LEN):
+                arg |= code[i + j] << (8 * j)
+            i += ARG_LEN
+            if op == EXTENDED_ARG:
+                extended_arg = arg << 8 * ARG_LEN
+                continue
+        else:
+            arg = None
+            i += NO_ARG_LEN
+
+        extended_arg = 0
+        yield (offset, op, arg, i)
+        offset = i  # Mark inst offset at first extended
+
+
+def _patched_opargs(bc_stream):
+    """Patch the bytecode stream.
+
+    - Adds a NOP bytecode at the start to avoid jump target being at the entry.
+    """
+    # Injected NOP
+    yield (0, OPCODE_NOP, None, _FIXED_OFFSET)
+    # Adjust bytecode offset for the rest of the stream
+    for offset, opcode, arg, nextoffset in bc_stream:
+        # If the opcode has an absolute jump target, adjust it.
+        if opcode in JABS_OPS:
+            arg += _FIXED_OFFSET
+        yield offset + _FIXED_OFFSET, opcode, arg, nextoffset + _FIXED_OFFSET
+
+
+class ByteCodeIter(object):
+    def __init__(self, code):
+        self.code = code
+        self.iter = iter(_patched_opargs(_unpack_opargs(self.code.co_code)))
+
+    def __iter__(self):
+        return self
+
+    def _fetch_opcode(self):
+        return next(self.iter)
+
+    def next(self):
+        offset, opcode, arg, nextoffset = self._fetch_opcode()
+        return offset, ByteCodeInst(offset=offset, opcode=opcode, arg=arg,
+                                    nextoffset=nextoffset)
+
+    __next__ = next
+
+    def read_arg(self, size):
+        buf = 0
+        for i in range(size):
+            _offset, byte = next(self.iter)
+            buf |= byte << (8 * i)
+        return buf
+
+
+class ByteCode(object):
+    """
+    The decoded bytecode of a function, and related information.
+    """
+    __slots__ = ('func_id', 'co_names', 'co_varnames', 'co_consts',
+                 'co_cellvars', 'co_freevars', 'table', 'labels')
+
+    def __init__(self, func_id):
+        code = func_id.code
+
+        labels = set(x + _FIXED_OFFSET for x in dis.findlabels(code.co_code))
+        labels.add(0)
+
+        # A map of {offset: ByteCodeInst}
+        table = OrderedDict(ByteCodeIter(code))
+        self._compute_lineno(table, code)
+
+        self.func_id = func_id
+        self.co_names = code.co_names
+        self.co_varnames = code.co_varnames
+        self.co_consts = code.co_consts
+        self.co_cellvars = code.co_cellvars
+        self.co_freevars = code.co_freevars
+        self.table = table
+        self.labels = sorted(labels)
+
+    @classmethod
+    def _compute_lineno(cls, table, code):
+        """
+        Compute the line numbers for all bytecode instructions.
+        """
+        for offset, lineno in dis.findlinestarts(code):
+            adj_offset = offset + _FIXED_OFFSET
+            if adj_offset in table:
+                table[adj_offset].lineno = lineno
+        # Assign unfilled lineno
+        # Start with first bytecode's lineno
+        known = table[_FIXED_OFFSET].lineno
+        for inst in table.values():
+            if inst.lineno >= 0:
+                known = inst.lineno
+            else:
+                inst.lineno = known
+        return table
+
+    def __iter__(self):
+        return iter(self.table.values())
+
+    def __getitem__(self, offset):
+        return self.table[offset]
+
+    def __contains__(self, offset):
+        return offset in self.table
+
+    def dump(self):
+        def label_marker(i):
+            if i[1].offset in self.labels:
+                return '>'
+            else:
+                return ' '
+
+        return '\n'.join('%s %10s\t%s' % ((label_marker(i),) + i)
+                         for i in self.table.items())
+
+    @classmethod
+    def _compute_used_globals(cls, func, table, co_consts, co_names):
+        """
+        Compute the globals used by the function with the given
+        bytecode table.
+        """
+        d = {}
+        globs = func.__globals__
+        builtins = globs.get('__builtins__', utils.builtins)
+        if isinstance(builtins, ModuleType):
+            builtins = builtins.__dict__
+        # Look for LOAD_GLOBALs in the bytecode
+        for inst in table.values():
+            if inst.opname == 'LOAD_GLOBAL':
+                name = co_names[inst.arg]
+                if name not in d:
+                    try:
+                        value = globs[name]
+                    except KeyError:
+                        value = builtins[name]
+                    d[name] = value
+        # Add globals used by any nested code object
+        for co in co_consts:
+            if isinstance(co, CodeType):
+                subtable = OrderedDict(ByteCodeIter(co))
+                d.update(cls._compute_used_globals(func, subtable,
+                                                   co.co_consts, co.co_names))
+        return d
+
+    def get_used_globals(self):
+        """
+        Get a {name: value} map of the globals used by this code
+        object and any nested code objects.
+        """
+        return self._compute_used_globals(self.func_id.func, self.table,
+                                          self.co_consts, self.co_names)
+
+
+class FunctionIdentity(serialize.ReduceMixin):
+    """
+    A function's identity and metadata.
+
+    Note this typically represents a function whose bytecode is
+    being compiled, not necessarily the top-level user function
+    (the two might be distinct, e.g. in the `@generated_jit` case).
+    """
+    _unique_ids = itertools.count(1)
+
+    @classmethod
+    def from_function(cls, pyfunc):
+        """
+        Create the FunctionIdentity of the given function.
+        """
+        func = get_function_object(pyfunc)
+        code = get_code_object(func)
+        pysig = utils.pysignature(func)
+        if not code:
+            raise errors.ByteCodeSupportError(
+                "%s does not provide its bytecode" % func)
+
+        try:
+            func_qualname = func.__qualname__
+        except AttributeError:
+            func_qualname = func.__name__
+
+        self = cls()
+        self.func = func
+        self.func_qualname = func_qualname
+        self.func_name = func_qualname.split('.')[-1]
+        self.code = code
+        self.module = inspect.getmodule(func)
+        self.modname = (utils._dynamic_modname
+                        if self.module is None
+                        else self.module.__name__)
+        self.is_generator = inspect.isgeneratorfunction(func)
+        self.pysig = pysig
+        self.filename = code.co_filename
+        self.firstlineno = code.co_firstlineno
+        self.arg_count = len(pysig.parameters)
+        self.arg_names = list(pysig.parameters)
+
+        # Even the same function definition can be compiled into
+        # several different function objects with distinct closure
+        # variables, so we make sure to disambiguate using an unique id.
+        uid = next(cls._unique_ids)
+        self.unique_name = '{}${}'.format(self.func_qualname, uid)
+        self.unique_id = uid
+
+        return self
+
+    def derive(self):
+        """Copy the object and increment the unique counter.
+        """
+        return self.from_function(self.func)
+
+    def _reduce_states(self):
+        """
+        NOTE: part of ReduceMixin protocol
+        """
+        return dict(pyfunc=self.func)
+
+    @classmethod
+    def _rebuild(cls, pyfunc):
+        """
+        NOTE: part of ReduceMixin protocol
+        """
+        return cls.from_function(pyfunc)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/byteflow.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/byteflow.py
new file mode 100644
index 000000000..19b595967
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/byteflow.py
@@ -0,0 +1,1555 @@
+"""
+Implement python 3.8+ bytecode analysis
+"""
+
+from pprint import pformat
+import logging
+from collections import namedtuple, defaultdict, deque
+from functools import total_ordering
+
+from numba.core.utils import UniqueDict, PYVERSION
+from numba.core.controlflow import NEW_BLOCKERS, CFGraph
+from numba.core.ir import Loc
+from numba.core.errors import UnsupportedError
+
+
+_logger = logging.getLogger(__name__)
+
+
+_EXCEPT_STACK_OFFSET = 6
+_FINALLY_POP = _EXCEPT_STACK_OFFSET if PYVERSION >= (3, 8) else 1
+_NO_RAISE_OPS = frozenset({
+    'LOAD_CONST',
+})
+
+
+@total_ordering
+class BlockKind(object):
+    """Kinds of block to make related code safer than just `str`.
+    """
+    _members = frozenset({
+        'LOOP',
+        'TRY', 'EXCEPT', 'FINALLY',
+        'WITH', 'WITH_FINALLY',
+    })
+
+    def __init__(self, value):
+        assert value in self._members
+        self._value = value
+
+    def __hash__(self):
+        return hash((type(self), self._value))
+
+    def __lt__(self, other):
+        if isinstance(other, BlockKind):
+            return self._value < other._value
+        else:
+            raise TypeError('cannot compare to {!r}'.format(type(other)))
+
+    def __eq__(self, other):
+        if isinstance(other, BlockKind):
+            return self._value == other._value
+        else:
+            raise TypeError('cannot compare to {!r}'.format(type(other)))
+
+    def __repr__(self):
+        return "BlockKind({})".format(self._value)
+
+
+class _lazy_pformat(object):
+    def __init__(self, *args, **kwargs):
+        self.args = args
+        self.kwargs = kwargs
+
+    def __str__(self):
+        return pformat(*self.args, **self.kwargs)
+
+
+class Flow(object):
+    """Data+Control Flow analysis.
+
+    Simulate execution to recover dataflow and controlflow information.
+    """
+    def __init__(self, bytecode):
+        _logger.debug("bytecode dump:\n%s", bytecode.dump())
+        self._bytecode = bytecode
+        self.block_infos = UniqueDict()
+
+    def run(self):
+        """Run a trace over the bytecode over all reachable path.
+
+        The trace starts at bytecode offset 0 and gathers stack and control-
+        flow information by partially interpreting each bytecode.
+        Each ``State`` instance in the trace corresponds to a basic-block.
+        The State instances forks when a jump instruction is encountered.
+        A newly forked state is then added to the list of pending states.
+        The trace ends when there are no more pending states.
+        """
+        firststate = State(bytecode=self._bytecode, pc=0, nstack=0,
+                           blockstack=())
+        runner = TraceRunner(debug_filename=self._bytecode.func_id.filename)
+        runner.pending.append(firststate)
+
+        # Enforce unique-ness on initial PC to avoid re-entering the PC with
+        # a different stack-depth. We don't know if such a case is ever
+        # possible, but no such case has been encountered in our tests.
+        first_encounter = UniqueDict()
+        # Loop over each pending state at a initial PC.
+        # Each state is tracing a basic block
+        while runner.pending:
+            _logger.debug("pending: %s", runner.pending)
+            state = runner.pending.popleft()
+            if state not in runner.finished:
+                _logger.debug("stack: %s", state._stack)
+                first_encounter[state.pc_initial] = state
+                # Loop over the state until it is terminated.
+                while True:
+                    runner.dispatch(state)
+                    # Terminated?
+                    if state.has_terminated():
+                        break
+                    elif (state.has_active_try() and
+                            state.get_inst().opname not in _NO_RAISE_OPS):
+                        # Is in a *try* block
+                        state.fork(pc=state.get_inst().next)
+                        tryblk = state.get_top_block('TRY')
+                        state.pop_block_and_above(tryblk)
+                        nstack = state.stack_depth
+                        kwargs = {}
+                        if nstack > tryblk['entry_stack']:
+                            kwargs['npop'] = nstack - tryblk['entry_stack']
+                        handler = tryblk['handler']
+                        kwargs['npush'] = {
+                            BlockKind('EXCEPT'): _EXCEPT_STACK_OFFSET,
+                            BlockKind('FINALLY'): _FINALLY_POP
+                        }[handler['kind']]
+                        kwargs['extra_block'] = handler
+                        state.fork(pc=tryblk['end'], **kwargs)
+                        break
+                    else:
+                        state.advance_pc()
+                        # Must the new PC be a new block?
+                        if self._is_implicit_new_block(state):
+                            # check if this is a with...as, abort if so
+                            self._guard_with_as(state)
+                            # else split
+                            state.split_new_block()
+                            break
+                _logger.debug("end state. edges=%s", state.outgoing_edges)
+                runner.finished.add(state)
+                out_states = state.get_outgoing_states()
+                runner.pending.extend(out_states)
+
+        # Complete controlflow
+        self._build_cfg(runner.finished)
+        # Prune redundant PHI-nodes
+        self._prune_phis(runner)
+        # Post process
+        for state in sorted(runner.finished, key=lambda x: x.pc_initial):
+            self.block_infos[state.pc_initial] = si = adapt_state_infos(state)
+            _logger.debug("block_infos %s:\n%s", state, si)
+
+    def _build_cfg(self, all_states):
+        graph = CFGraph()
+        for state in all_states:
+            b = state.pc_initial
+            graph.add_node(b)
+        for state in all_states:
+            for edge in state.outgoing_edges:
+                graph.add_edge(state.pc_initial, edge.pc, 0)
+        graph.set_entry_point(0)
+        graph.process()
+        self.cfgraph = graph
+
+    def _prune_phis(self, runner):
+        # Find phis that are unused in the local block
+        _logger.debug("Prune PHIs".center(60, '-'))
+
+        # Compute dataflow for used phis and propagate
+
+        # 1. Get used-phis for each block
+        # Map block to used_phis
+        def get_used_phis_per_state():
+            used_phis = defaultdict(set)
+            phi_set = set()
+            for state in runner.finished:
+                used = set(state._used_regs)
+                phis = set(state._phis)
+                used_phis[state] |= phis & used
+                phi_set |= phis
+            return used_phis, phi_set
+
+        # Find use-defs
+        def find_use_defs():
+            defmap = {}
+            phismap = defaultdict(set)
+            for state in runner.finished:
+                for phi, rhs in state._outgoing_phis.items():
+                    if rhs not in phi_set:
+                        # Is a definition
+                        defmap[phi] = state
+                    phismap[phi].add((rhs, state))
+            _logger.debug("defmap: %s", _lazy_pformat(defmap))
+            _logger.debug("phismap: %s", _lazy_pformat(phismap))
+            return defmap, phismap
+
+        def propagate_phi_map(phismap):
+            """An iterative dataflow algorithm to find the definition
+            (the source) of each PHI node.
+            """
+            blacklist = defaultdict(set)
+
+            while True:
+                changing = False
+                for phi, defsites in sorted(list(phismap.items())):
+                    for rhs, state in sorted(list(defsites)):
+                        if rhs in phi_set:
+                            defsites |= phismap[rhs]
+                            blacklist[phi].add((rhs, state))
+                    to_remove = blacklist[phi]
+                    if to_remove & defsites:
+                        defsites -= to_remove
+                        changing = True
+
+                _logger.debug("changing phismap: %s", _lazy_pformat(phismap))
+                if not changing:
+                    break
+
+        def apply_changes(used_phis, phismap):
+            keep = {}
+            for state, used_set in used_phis.items():
+                for phi in used_set:
+                    keep[phi] = phismap[phi]
+            _logger.debug("keep phismap: %s", _lazy_pformat(keep))
+            new_out = defaultdict(dict)
+            for phi in keep:
+                for rhs, state in keep[phi]:
+                    new_out[state][phi] = rhs
+
+            _logger.debug("new_out: %s", _lazy_pformat(new_out))
+            for state in runner.finished:
+                state._outgoing_phis.clear()
+                state._outgoing_phis.update(new_out[state])
+
+        used_phis, phi_set = get_used_phis_per_state()
+        _logger.debug("Used_phis: %s", _lazy_pformat(used_phis))
+        defmap, phismap = find_use_defs()
+        propagate_phi_map(phismap)
+        apply_changes(used_phis, phismap)
+        _logger.debug("DONE Prune PHIs".center(60, '-'))
+
+    def _is_implicit_new_block(self, state):
+        inst = state.get_inst()
+
+        if inst.offset in self._bytecode.labels:
+            return True
+        elif inst.opname in NEW_BLOCKERS:
+            return True
+        else:
+            return False
+
+    def _guard_with_as(self, state):
+        """Checks if the next instruction after a SETUP_WITH is something other
+        than a POP_TOP, if it is something else it'll be some sort of store
+        which is not supported (this corresponds to `with CTXMGR as VAR(S)`)."""
+        current_inst = state.get_inst()
+        if current_inst.opname == "SETUP_WITH":
+            next_op = self._bytecode[current_inst.next].opname
+            if next_op != "POP_TOP":
+                msg = ("The 'with (context manager) as "
+                       "(variable):' construct is not "
+                       "supported.")
+                raise UnsupportedError(msg)
+
+
+class TraceRunner(object):
+    """Trace runner contains the states for the trace and the opcode dispatch.
+    """
+    def __init__(self, debug_filename):
+        self.debug_filename = debug_filename
+        self.pending = deque()
+        self.finished = set()
+
+    def get_debug_loc(self, lineno):
+        return Loc(self.debug_filename, lineno)
+
+    def dispatch(self, state):
+        inst = state.get_inst()
+        _logger.debug("dispatch pc=%s, inst=%s", state._pc, inst)
+        _logger.debug("stack %s", state._stack)
+        fn = getattr(self, "op_{}".format(inst.opname), None)
+        if fn is not None:
+            fn(state, inst)
+        else:
+            msg = "Use of unsupported opcode (%s) found" % inst.opname
+            raise UnsupportedError(msg, loc=self.get_debug_loc(inst.lineno))
+
+    def op_NOP(self, state, inst):
+        state.append(inst)
+
+    def op_FORMAT_VALUE(self, state, inst):
+        """
+        FORMAT_VALUE(flags): flags argument specifies format spec which is
+        not supported yet. Currently, we just call str() on the value.
+        Pops a value from stack and pushes results back.
+        Required for supporting f-strings.
+        https://docs.python.org/3/library/dis.html#opcode-FORMAT_VALUE
+        """
+        if inst.arg != 0:
+            msg = "format spec in f-strings not supported yet"
+            raise UnsupportedError(msg, loc=self.get_debug_loc(inst.lineno))
+        value = state.pop()
+        strvar = state.make_temp()
+        res = state.make_temp()
+        state.append(inst, value=value, res=res, strvar=strvar)
+        state.push(res)
+
+    def op_BUILD_STRING(self, state, inst):
+        """
+        BUILD_STRING(count): Concatenates count strings from the stack and
+        pushes the resulting string onto the stack.
+        Required for supporting f-strings.
+        https://docs.python.org/3/library/dis.html#opcode-BUILD_STRING
+        """
+        count = inst.arg
+        strings = list(reversed([state.pop() for _ in range(count)]))
+        # corner case: f""
+        if count == 0:
+            tmps = [state.make_temp()]
+        else:
+            tmps = [state.make_temp() for _ in range(count - 1)]
+        state.append(inst, strings=strings, tmps=tmps)
+        state.push(tmps[-1])
+
+    def op_POP_TOP(self, state, inst):
+        state.pop()
+
+    def op_LOAD_GLOBAL(self, state, inst):
+        res = state.make_temp()
+        state.append(inst, res=res)
+        state.push(res)
+
+    def op_LOAD_DEREF(self, state, inst):
+        res = state.make_temp()
+        state.append(inst, res=res)
+        state.push(res)
+
+    def op_LOAD_CONST(self, state, inst):
+        res = state.make_temp("const")
+        state.push(res)
+        state.append(inst, res=res)
+
+    def op_LOAD_ATTR(self, state, inst):
+        item = state.pop()
+        res = state.make_temp()
+        state.append(inst, item=item, res=res)
+        state.push(res)
+
+    def op_LOAD_FAST(self, state, inst):
+        name = state.get_varname(inst)
+        res = state.make_temp(name)
+        state.append(inst, res=res)
+        state.push(res)
+
+    def op_DELETE_FAST(self, state, inst):
+        state.append(inst)
+
+    def op_DELETE_ATTR(self, state, inst):
+        target = state.pop()
+        state.append(inst, target=target)
+
+    def op_STORE_ATTR(self, state, inst):
+        target = state.pop()
+        value = state.pop()
+        state.append(inst, target=target, value=value)
+
+    def op_STORE_DEREF(self, state, inst):
+        value = state.pop()
+        state.append(inst, value=value)
+
+    def op_STORE_FAST(self, state, inst):
+        value = state.pop()
+        state.append(inst, value=value)
+
+    def op_SLICE_1(self, state, inst):
+        """
+        TOS = TOS1[TOS:]
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        res = state.make_temp()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        nonevar = state.make_temp()
+        state.append(
+            inst,
+            base=tos1,
+            start=tos,
+            res=res,
+            slicevar=slicevar,
+            indexvar=indexvar,
+            nonevar=nonevar,
+        )
+        state.push(res)
+
+    def op_SLICE_2(self, state, inst):
+        """
+        TOS = TOS1[:TOS]
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        res = state.make_temp()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        nonevar = state.make_temp()
+        state.append(
+            inst,
+            base=tos1,
+            stop=tos,
+            res=res,
+            slicevar=slicevar,
+            indexvar=indexvar,
+            nonevar=nonevar,
+        )
+        state.push(res)
+
+    def op_SLICE_3(self, state, inst):
+        """
+        TOS = TOS2[TOS1:TOS]
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        tos2 = state.pop()
+        res = state.make_temp()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        state.append(
+            inst,
+            base=tos2,
+            start=tos1,
+            stop=tos,
+            res=res,
+            slicevar=slicevar,
+            indexvar=indexvar,
+        )
+        state.push(res)
+
+    def op_STORE_SLICE_0(self, state, inst):
+        """
+        TOS[:] = TOS1
+        """
+        tos = state.pop()
+        value = state.pop()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        nonevar = state.make_temp()
+        state.append(
+            inst,
+            base=tos,
+            value=value,
+            slicevar=slicevar,
+            indexvar=indexvar,
+            nonevar=nonevar,
+        )
+
+    def op_STORE_SLICE_1(self, state, inst):
+        """
+        TOS1[TOS:] = TOS2
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        value = state.pop()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        nonevar = state.make_temp()
+        state.append(
+            inst,
+            base=tos1,
+            start=tos,
+            slicevar=slicevar,
+            value=value,
+            indexvar=indexvar,
+            nonevar=nonevar,
+        )
+
+    def op_STORE_SLICE_2(self, state, inst):
+        """
+        TOS1[:TOS] = TOS2
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        value = state.pop()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        nonevar = state.make_temp()
+        state.append(
+            inst,
+            base=tos1,
+            stop=tos,
+            value=value,
+            slicevar=slicevar,
+            indexvar=indexvar,
+            nonevar=nonevar,
+        )
+
+    def op_STORE_SLICE_3(self, state, inst):
+        """
+        TOS2[TOS1:TOS] = TOS3
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        tos2 = state.pop()
+        value = state.pop()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        state.append(
+            inst,
+            base=tos2,
+            start=tos1,
+            stop=tos,
+            value=value,
+            slicevar=slicevar,
+            indexvar=indexvar,
+        )
+
+    def op_DELETE_SLICE_0(self, state, inst):
+        """
+        del TOS[:]
+        """
+        tos = state.pop()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        nonevar = state.make_temp()
+        state.append(
+            inst, base=tos, slicevar=slicevar, indexvar=indexvar,
+            nonevar=nonevar,
+        )
+
+    def op_DELETE_SLICE_1(self, state, inst):
+        """
+        del TOS1[TOS:]
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        nonevar = state.make_temp()
+        state.append(
+            inst,
+            base=tos1,
+            start=tos,
+            slicevar=slicevar,
+            indexvar=indexvar,
+            nonevar=nonevar,
+        )
+
+    def op_DELETE_SLICE_2(self, state, inst):
+        """
+        del TOS1[:TOS]
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        nonevar = state.make_temp()
+        state.append(
+            inst,
+            base=tos1,
+            stop=tos,
+            slicevar=slicevar,
+            indexvar=indexvar,
+            nonevar=nonevar,
+        )
+
+    def op_DELETE_SLICE_3(self, state, inst):
+        """
+        del TOS2[TOS1:TOS]
+        """
+        tos = state.pop()
+        tos1 = state.pop()
+        tos2 = state.pop()
+        slicevar = state.make_temp()
+        indexvar = state.make_temp()
+        state.append(
+            inst, base=tos2, start=tos1, stop=tos, slicevar=slicevar,
+            indexvar=indexvar
+        )
+
+    def op_BUILD_SLICE(self, state, inst):
+        """
+        slice(TOS1, TOS) or slice(TOS2, TOS1, TOS)
+        """
+        argc = inst.arg
+        if argc == 2:
+            tos = state.pop()
+            tos1 = state.pop()
+            start = tos1
+            stop = tos
+            step = None
+        elif argc == 3:
+            tos = state.pop()
+            tos1 = state.pop()
+            tos2 = state.pop()
+            start = tos2
+            stop = tos1
+            step = tos
+        else:
+            raise Exception("unreachable")
+        slicevar = state.make_temp()
+        res = state.make_temp()
+        state.append(
+            inst, start=start, stop=stop, step=step, res=res, slicevar=slicevar
+        )
+        state.push(res)
+
+    def _op_POP_JUMP_IF(self, state, inst):
+        pred = state.pop()
+        state.append(inst, pred=pred)
+
+        target_inst = inst.get_jump_target()
+        next_inst = inst.next
+        # if the next inst and the jump target are the same location, issue one
+        # fork else issue a fork for the next and the target.
+        state.fork(pc=next_inst)
+        if target_inst != next_inst:
+            state.fork(pc=target_inst)
+
+    op_POP_JUMP_IF_TRUE = _op_POP_JUMP_IF
+    op_POP_JUMP_IF_FALSE = _op_POP_JUMP_IF
+
+    def _op_JUMP_IF_OR_POP(self, state, inst):
+        pred = state.get_tos()
+        state.append(inst, pred=pred)
+        state.fork(pc=inst.next, npop=1)
+        state.fork(pc=inst.get_jump_target())
+
+    op_JUMP_IF_FALSE_OR_POP = _op_JUMP_IF_OR_POP
+    op_JUMP_IF_TRUE_OR_POP = _op_JUMP_IF_OR_POP
+
+    def op_JUMP_FORWARD(self, state, inst):
+        state.append(inst)
+        state.fork(pc=inst.get_jump_target())
+
+    def op_JUMP_ABSOLUTE(self, state, inst):
+        state.append(inst)
+        state.fork(pc=inst.get_jump_target())
+
+    def op_BREAK_LOOP(self, state, inst):
+        # NOTE: bytecode removed since py3.8
+        end = state.get_top_block('LOOP')['end']
+        state.append(inst, end=end)
+        state.pop_block()
+        state.fork(pc=end)
+
+    def op_RETURN_VALUE(self, state, inst):
+        state.append(inst, retval=state.pop(), castval=state.make_temp())
+        state.terminate()
+
+    def op_YIELD_VALUE(self, state, inst):
+        val = state.pop()
+        res = state.make_temp()
+        state.append(inst, value=val, res=res)
+        state.push(res)
+
+    def op_RAISE_VARARGS(self, state, inst):
+        in_exc_block = any([
+            state.get_top_block("EXCEPT") is not None,
+            state.get_top_block("FINALLY") is not None
+        ])
+        if inst.arg == 0:
+            exc = None
+            if in_exc_block:
+                raise UnsupportedError(
+                    "The re-raising of an exception is not yet supported.",
+                    loc=self.get_debug_loc(inst.lineno),
+                )
+        elif inst.arg == 1:
+            exc = state.pop()
+        else:
+            raise ValueError("Multiple argument raise is not supported.")
+        state.append(inst, exc=exc)
+        state.terminate()
+
+    def op_BEGIN_FINALLY(self, state, inst):
+        temps = []
+        for i in range(_EXCEPT_STACK_OFFSET):
+            tmp = state.make_temp()
+            temps.append(tmp)
+            state.push(tmp)
+        state.append(inst, temps=temps)
+
+    def op_END_FINALLY(self, state, inst):
+        blk = state.pop_block()
+        state.reset_stack(blk['entry_stack'])
+
+    def op_POP_FINALLY(self, state, inst):
+        # we don't emulate the exact stack behavior
+        if inst.arg != 0:
+            msg = ('Unsupported use of a bytecode related to try..finally'
+                   ' or a with-context')
+            raise UnsupportedError(msg, loc=self.get_debug_loc(inst.lineno))
+
+    def op_CALL_FINALLY(self, state, inst):
+        pass
+
+    def op_WITH_CLEANUP_START(self, state, inst):
+        # we don't emulate the exact stack behavior
+        state.append(inst)
+
+    def op_WITH_CLEANUP_FINISH(self, state, inst):
+        # we don't emulate the exact stack behavior
+        state.append(inst)
+
+    def op_SETUP_LOOP(self, state, inst):
+        # NOTE: bytecode removed since py3.8
+        state.push_block(
+            state.make_block(
+                kind='LOOP',
+                end=inst.get_jump_target(),
+            )
+        )
+
+    def op_SETUP_WITH(self, state, inst):
+        cm = state.pop()    # the context-manager
+
+        yielded = state.make_temp()
+        exitfn = state.make_temp(prefix='setup_with_exitfn')
+        state.append(inst, contextmanager=cm, exitfn=exitfn)
+
+        # py39 doesn't have with-finally
+        if PYVERSION < (3, 9):
+            state.push_block(
+                state.make_block(
+                    kind='WITH_FINALLY',
+                    end=inst.get_jump_target(),
+                )
+            )
+
+        state.push(exitfn)
+        state.push(yielded)
+
+        state.push_block(
+            state.make_block(
+                kind='WITH',
+                end=inst.get_jump_target(),
+            )
+        )
+        # Forces a new block
+        state.fork(pc=inst.next)
+
+    def _setup_try(self, kind, state, next, end):
+        handler_block = state.make_block(
+            kind=kind,
+            end=None,
+            reset_stack=False,
+        )
+        # Forces a new block
+        # Fork to the body of the finally
+        state.fork(
+            pc=next,
+            extra_block=state.make_block(
+                kind='TRY',
+                end=end,
+                reset_stack=False,
+                handler=handler_block,
+            )
+        )
+
+    def op_SETUP_EXCEPT(self, state, inst):
+        # Opcode removed since py3.8
+        state.append(inst)
+        self._setup_try(
+            'EXCEPT', state, next=inst.next, end=inst.get_jump_target(),
+        )
+
+    def op_SETUP_FINALLY(self, state, inst):
+        state.append(inst)
+        self._setup_try(
+            'FINALLY', state, next=inst.next, end=inst.get_jump_target(),
+        )
+
+    def op_POP_EXCEPT(self, state, inst):
+        blk = state.pop_block()
+        if blk['kind'] not in {BlockKind('EXCEPT'), BlockKind('FINALLY')}:
+            raise UnsupportedError(
+                "POP_EXCEPT got an unexpected block: {}".format(blk['kind']),
+                loc=self.get_debug_loc(inst.lineno),
+            )
+        state.pop()
+        state.pop()
+        state.pop()
+        # Forces a new block
+        state.fork(pc=inst.next)
+
+    def op_POP_BLOCK(self, state, inst):
+        blk = state.pop_block()
+        if blk['kind'] == BlockKind('TRY'):
+            state.append(inst, kind='try')
+        elif blk['kind'] == BlockKind('WITH'):
+            state.append(inst, kind='with')
+        state.fork(pc=inst.next)
+
+    def op_BINARY_SUBSCR(self, state, inst):
+        index = state.pop()
+        target = state.pop()
+        res = state.make_temp()
+        state.append(inst, index=index, target=target, res=res)
+        state.push(res)
+
+    def op_STORE_SUBSCR(self, state, inst):
+        index = state.pop()
+        target = state.pop()
+        value = state.pop()
+        state.append(inst, target=target, index=index, value=value)
+
+    def op_DELETE_SUBSCR(self, state, inst):
+        index = state.pop()
+        target = state.pop()
+        state.append(inst, target=target, index=index)
+
+    def op_CALL_FUNCTION(self, state, inst):
+        narg = inst.arg
+        args = list(reversed([state.pop() for _ in range(narg)]))
+        func = state.pop()
+
+        res = state.make_temp()
+        state.append(inst, func=func, args=args, res=res)
+        state.push(res)
+
+    def op_CALL_FUNCTION_KW(self, state, inst):
+        narg = inst.arg
+        names = state.pop()  # tuple of names
+        args = list(reversed([state.pop() for _ in range(narg)]))
+        func = state.pop()
+
+        res = state.make_temp()
+        state.append(inst, func=func, args=args, names=names, res=res)
+        state.push(res)
+
+    def op_CALL_FUNCTION_EX(self, state, inst):
+        if inst.arg & 1 and PYVERSION != (3, 10):
+            errmsg = "CALL_FUNCTION_EX with **kwargs not supported"
+            raise UnsupportedError(errmsg)
+        if inst.arg & 1:
+            varkwarg = state.pop()
+        else:
+            varkwarg = None
+        vararg = state.pop()
+        func = state.pop()
+        res = state.make_temp()
+        state.append(inst, func=func, vararg=vararg, varkwarg=varkwarg, res=res)
+        state.push(res)
+
+    def _dup_topx(self, state, inst, count):
+        orig = [state.pop() for _ in range(count)]
+        orig.reverse()
+        # We need to actually create new temporaries if we want the
+        # IR optimization pass to work correctly (see issue #580)
+        duped = [state.make_temp() for _ in range(count)]
+        state.append(inst, orig=orig, duped=duped)
+        for val in orig:
+            state.push(val)
+        for val in duped:
+            state.push(val)
+
+    def op_DUP_TOPX(self, state, inst):
+        count = inst.arg
+        assert 1 <= count <= 5, "Invalid DUP_TOPX count"
+        self._dup_topx(state, inst, count)
+
+    def op_DUP_TOP(self, state, inst):
+        self._dup_topx(state, inst, count=1)
+
+    def op_DUP_TOP_TWO(self, state, inst):
+        self._dup_topx(state, inst, count=2)
+
+    def op_ROT_TWO(self, state, inst):
+        first = state.pop()
+        second = state.pop()
+        state.push(first)
+        state.push(second)
+
+    def op_ROT_THREE(self, state, inst):
+        first = state.pop()
+        second = state.pop()
+        third = state.pop()
+        state.push(first)
+        state.push(third)
+        state.push(second)
+
+    def op_ROT_FOUR(self, state, inst):
+        first = state.pop()
+        second = state.pop()
+        third = state.pop()
+        forth = state.pop()
+        state.push(first)
+        state.push(forth)
+        state.push(third)
+        state.push(second)
+
+    def op_UNPACK_SEQUENCE(self, state, inst):
+        count = inst.arg
+        iterable = state.pop()
+        stores = [state.make_temp() for _ in range(count)]
+        tupleobj = state.make_temp()
+        state.append(inst, iterable=iterable, stores=stores, tupleobj=tupleobj)
+        for st in reversed(stores):
+            state.push(st)
+
+    def op_BUILD_TUPLE(self, state, inst):
+        count = inst.arg
+        items = list(reversed([state.pop() for _ in range(count)]))
+        tup = state.make_temp()
+        state.append(inst, items=items, res=tup)
+        state.push(tup)
+
+    def _build_tuple_unpack(self, state, inst):
+        # Builds tuple from other tuples on the stack
+        tuples = list(reversed([state.pop() for _ in range(inst.arg)]))
+        temps = [state.make_temp() for _ in range(len(tuples) - 1)]
+
+        # if the unpack is assign-like, e.g. x = (*y,), it needs handling
+        # differently.
+        is_assign = len(tuples) == 1
+        if is_assign:
+            temps = [state.make_temp(),]
+
+        state.append(inst, tuples=tuples, temps=temps, is_assign=is_assign)
+        # The result is in the last temp var
+        state.push(temps[-1])
+
+    def op_BUILD_TUPLE_UNPACK_WITH_CALL(self, state, inst):
+        # just unpack the input tuple, call inst will be handled afterwards
+        self._build_tuple_unpack(state, inst)
+
+    def op_BUILD_TUPLE_UNPACK(self, state, inst):
+        self._build_tuple_unpack(state, inst)
+
+    def op_LIST_TO_TUPLE(self, state, inst):
+        # "Pops a list from the stack and pushes a tuple containing the same
+        #  values."
+        tos = state.pop()
+        res = state.make_temp() # new tuple var
+        state.append(inst, const_list=tos, res=res)
+        state.push(res)
+
+    def op_BUILD_CONST_KEY_MAP(self, state, inst):
+        keys = state.pop()
+        vals = list(reversed([state.pop() for _ in range(inst.arg)]))
+        keytmps = [state.make_temp() for _ in range(inst.arg)]
+        res = state.make_temp()
+        state.append(inst, keys=keys, keytmps=keytmps, values=vals, res=res)
+        state.push(res)
+
+    def op_BUILD_LIST(self, state, inst):
+        count = inst.arg
+        items = list(reversed([state.pop() for _ in range(count)]))
+        lst = state.make_temp()
+        state.append(inst, items=items, res=lst)
+        state.push(lst)
+
+    def op_LIST_APPEND(self, state, inst):
+        value = state.pop()
+        index = inst.arg
+        target = state.peek(index)
+        appendvar = state.make_temp()
+        res = state.make_temp()
+        state.append(inst, target=target, value=value, appendvar=appendvar,
+                     res=res)
+
+    def op_LIST_EXTEND(self, state, inst):
+        value = state.pop()
+        index = inst.arg
+        target = state.peek(index)
+        extendvar = state.make_temp()
+        res = state.make_temp()
+        state.append(inst, target=target, value=value, extendvar=extendvar,
+                     res=res)
+
+    def op_BUILD_MAP(self, state, inst):
+        dct = state.make_temp()
+        count = inst.arg
+        items = []
+        # In 3.5+, BUILD_MAP takes <count> pairs from the stack
+        for i in range(count):
+            v, k = state.pop(), state.pop()
+            items.append((k, v))
+        state.append(inst, items=items[::-1], size=count, res=dct)
+        state.push(dct)
+
+    def op_MAP_ADD(self, state, inst):
+        # NOTE: https://docs.python.org/3/library/dis.html#opcode-MAP_ADD
+        # Python >= 3.8: TOS and TOS1 are value and key respectively
+        # Python < 3.8: TOS and TOS1 are key and value respectively
+        TOS = state.pop()
+        TOS1 = state.pop()
+        key, value = (TOS, TOS1) if PYVERSION < (3, 8) else (TOS1, TOS)
+        index = inst.arg
+        target = state.peek(index)
+        setitemvar = state.make_temp()
+        res = state.make_temp()
+        state.append(inst, target=target, key=key, value=value,
+                     setitemvar=setitemvar, res=res)
+
+    def op_BUILD_SET(self, state, inst):
+        count = inst.arg
+        # Note: related python bug http://bugs.python.org/issue26020
+        items = list(reversed([state.pop() for _ in range(count)]))
+        res = state.make_temp()
+        state.append(inst, items=items, res=res)
+        state.push(res)
+
+    def op_SET_UPDATE(self, state, inst):
+        value = state.pop()
+        index = inst.arg
+        target = state.peek(index)
+        updatevar = state.make_temp()
+        res = state.make_temp()
+        state.append(inst, target=target, value=value, updatevar=updatevar,
+                     res=res)
+
+    def op_DICT_UPDATE(self, state, inst):
+        value = state.pop()
+        index = inst.arg
+        target = state.peek(index)
+        updatevar = state.make_temp()
+        res = state.make_temp()
+        state.append(inst, target=target, value=value, updatevar=updatevar,
+                     res=res)
+
+    def op_GET_ITER(self, state, inst):
+        value = state.pop()
+        res = state.make_temp()
+        state.append(inst, value=value, res=res)
+        state.push(res)
+
+    def op_FOR_ITER(self, state, inst):
+        iterator = state.get_tos()
+        pair = state.make_temp()
+        indval = state.make_temp()
+        pred = state.make_temp()
+        state.append(inst, iterator=iterator, pair=pair, indval=indval,
+                     pred=pred)
+        state.push(indval)
+        end = inst.get_jump_target()
+        state.fork(pc=end, npop=2)
+        state.fork(pc=inst.next)
+
+    def op_GEN_START(self, state, inst):
+        """Pops TOS. If TOS was not None, raises an exception. The kind
+        operand corresponds to the type of generator or coroutine and
+        determines the error message. The legal kinds are 0 for generator,
+        1 for coroutine, and 2 for async generator.
+
+        New in version 3.10.
+        """
+        # no-op in Numba
+        pass
+
+    def _unaryop(self, state, inst):
+        val = state.pop()
+        res = state.make_temp()
+        state.append(inst, value=val, res=res)
+        state.push(res)
+
+    op_UNARY_NEGATIVE = _unaryop
+    op_UNARY_POSITIVE = _unaryop
+    op_UNARY_NOT = _unaryop
+    op_UNARY_INVERT = _unaryop
+
+    def _binaryop(self, state, inst):
+        rhs = state.pop()
+        lhs = state.pop()
+        res = state.make_temp()
+        state.append(inst, lhs=lhs, rhs=rhs, res=res)
+        state.push(res)
+
+    op_COMPARE_OP = _binaryop
+    op_IS_OP = _binaryop
+    op_CONTAINS_OP = _binaryop
+
+    op_INPLACE_ADD = _binaryop
+    op_INPLACE_SUBTRACT = _binaryop
+    op_INPLACE_MULTIPLY = _binaryop
+    op_INPLACE_DIVIDE = _binaryop
+    op_INPLACE_TRUE_DIVIDE = _binaryop
+    op_INPLACE_FLOOR_DIVIDE = _binaryop
+    op_INPLACE_MODULO = _binaryop
+    op_INPLACE_POWER = _binaryop
+    op_INPLACE_MATRIX_MULTIPLY = _binaryop
+
+    op_INPLACE_LSHIFT = _binaryop
+    op_INPLACE_RSHIFT = _binaryop
+    op_INPLACE_AND = _binaryop
+    op_INPLACE_OR = _binaryop
+    op_INPLACE_XOR = _binaryop
+
+    op_BINARY_ADD = _binaryop
+    op_BINARY_SUBTRACT = _binaryop
+    op_BINARY_MULTIPLY = _binaryop
+    op_BINARY_DIVIDE = _binaryop
+    op_BINARY_TRUE_DIVIDE = _binaryop
+    op_BINARY_FLOOR_DIVIDE = _binaryop
+    op_BINARY_MODULO = _binaryop
+    op_BINARY_POWER = _binaryop
+    op_BINARY_MATRIX_MULTIPLY = _binaryop
+
+    op_BINARY_LSHIFT = _binaryop
+    op_BINARY_RSHIFT = _binaryop
+    op_BINARY_AND = _binaryop
+    op_BINARY_OR = _binaryop
+    op_BINARY_XOR = _binaryop
+
+    def op_MAKE_FUNCTION(self, state, inst, MAKE_CLOSURE=False):
+        name = state.pop()
+        code = state.pop()
+        closure = annotations = kwdefaults = defaults = None
+        if PYVERSION < (3, 6):
+            num_posdefaults = inst.arg & 0xFF
+            num_kwdefaults = (inst.arg >> 8) & 0xFF
+            num_annotations = (inst.arg >> 16) & 0x7FFF
+            if MAKE_CLOSURE:
+                closure = state.pop()
+            if num_annotations > 0:
+                annotations = state.pop()
+            if num_kwdefaults > 0:
+                kwdefaults = []
+                for i in range(num_kwdefaults):
+                    v = state.pop()
+                    k = state.pop()
+                    kwdefaults.append((k, v))
+                kwdefaults = tuple(kwdefaults)
+            if num_posdefaults:
+                defaults = []
+                for i in range(num_posdefaults):
+                    defaults.append(state.pop())
+                defaults = tuple(defaults)
+        else:
+            if inst.arg & 0x8:
+                closure = state.pop()
+            if inst.arg & 0x4:
+                annotations = state.pop()
+            if inst.arg & 0x2:
+                kwdefaults = state.pop()
+            if inst.arg & 0x1:
+                defaults = state.pop()
+        res = state.make_temp()
+        state.append(
+            inst,
+            name=name,
+            code=code,
+            closure=closure,
+            annotations=annotations,
+            kwdefaults=kwdefaults,
+            defaults=defaults,
+            res=res,
+        )
+        state.push(res)
+
+    def op_MAKE_CLOSURE(self, state, inst):
+        self.op_MAKE_FUNCTION(state, inst, MAKE_CLOSURE=True)
+
+    def op_LOAD_CLOSURE(self, state, inst):
+        res = state.make_temp()
+        state.append(inst, res=res)
+        state.push(res)
+
+    def op_LOAD_ASSERTION_ERROR(self, state, inst):
+        res = state.make_temp("assertion_error")
+        state.append(inst, res=res)
+        state.push(res)
+
+    def op_JUMP_IF_NOT_EXC_MATCH(self, state, inst):
+        # Tests whether the second value on the stack is an exception matching
+        # TOS, and jumps if it is not. Pops two values from the stack.
+        pred = state.make_temp("predicate")
+        tos = state.pop()
+        tos1 = state.pop()
+        state.append(inst, pred=pred, tos=tos, tos1=tos1)
+        state.fork(pc=inst.next)
+        state.fork(pc=inst.get_jump_target())
+
+    def op_RERAISE(self, state, inst):
+        # This isn't handled, but the state is set up anyway
+        exc = state.pop()
+        state.append(inst, exc=exc)
+        state.terminate()
+
+    # NOTE: Please see notes in `interpreter.py` surrounding the implementation
+    # of LOAD_METHOD and CALL_METHOD.
+
+    def op_LOAD_METHOD(self, state, inst):
+        self.op_LOAD_ATTR(state, inst)
+
+    def op_CALL_METHOD(self, state, inst):
+        self.op_CALL_FUNCTION(state, inst)
+
+
+@total_ordering
+class State(object):
+    """State of the trace
+    """
+    def __init__(self, bytecode, pc, nstack, blockstack):
+        """
+        Parameters
+        ----------
+        bytecode : numba.bytecode.ByteCode
+            function bytecode
+        pc : int
+            program counter
+        nstack : int
+            stackdepth at entry
+        blockstack : Sequence[Dict]
+            A sequence of dictionary denoting entries on the blockstack.
+        """
+        self._bytecode = bytecode
+        self._pc_initial = pc
+        self._pc = pc
+        self._nstack_initial = nstack
+        self._stack = []
+        self._blockstack_initial = tuple(blockstack)
+        self._blockstack = list(blockstack)
+        self._temp_registers = []
+        self._insts = []
+        self._outedges = []
+        self._terminated = False
+        self._phis = {}
+        self._outgoing_phis = UniqueDict()
+        self._used_regs = set()
+        for i in range(nstack):
+            phi = self.make_temp("phi")
+            self._phis[phi] = i
+            self.push(phi)
+
+    def __repr__(self):
+        return "State(pc_initial={} nstack_initial={})".format(
+            self._pc_initial, self._nstack_initial
+        )
+
+    def get_identity(self):
+        return (self._pc_initial, self._nstack_initial)
+
+    def __hash__(self):
+        return hash(self.get_identity())
+
+    def __lt__(self, other):
+        return self.get_identity() < other.get_identity()
+
+    def __eq__(self, other):
+        return self.get_identity() == other.get_identity()
+
+    @property
+    def pc_initial(self):
+        """The starting bytecode offset of this State.
+        The PC given to the constructor.
+        """
+        return self._pc_initial
+
+    @property
+    def instructions(self):
+        """The list of instructions information as a 2-tuple of
+        ``(pc : int, register_map : Dict)``
+        """
+        return self._insts
+
+    @property
+    def outgoing_edges(self):
+        """The list of outgoing edges.
+
+        Returns
+        -------
+        edges : List[State]
+        """
+        return self._outedges
+
+    @property
+    def outgoing_phis(self):
+        """The dictionary of outgoing phi nodes.
+
+        The keys are the name of the PHI nodes.
+        The values are the outgoing states.
+        """
+        return self._outgoing_phis
+
+    @property
+    def blockstack_initial(self):
+        """A copy of the initial state of the blockstack
+        """
+        return self._blockstack_initial
+
+    @property
+    def stack_depth(self):
+        """The current size of the stack
+
+        Returns
+        -------
+        res : int
+        """
+        return len(self._stack)
+
+    def find_initial_try_block(self):
+        """Find the initial *try* block.
+        """
+        for blk in reversed(self._blockstack_initial):
+            if blk['kind'] == BlockKind('TRY'):
+                return blk
+
+    def has_terminated(self):
+        return self._terminated
+
+    def get_inst(self):
+        return self._bytecode[self._pc]
+
+    def advance_pc(self):
+        inst = self.get_inst()
+        self._pc = inst.next
+
+    def make_temp(self, prefix=""):
+        if not prefix:
+            name = "${prefix}{offset}{opname}.{tempct}".format(
+                prefix=prefix,
+                offset=self._pc,
+                opname=self.get_inst().opname.lower(),
+                tempct=len(self._temp_registers),
+            )
+        else:
+            name = "${prefix}{offset}.{tempct}".format(
+                prefix=prefix,
+                offset=self._pc,
+                tempct=len(self._temp_registers),
+            )
+
+        self._temp_registers.append(name)
+        return name
+
+    def append(self, inst, **kwargs):
+        """Append new inst"""
+        self._insts.append((inst.offset, kwargs))
+        self._used_regs |= set(_flatten_inst_regs(kwargs.values()))
+
+    def get_tos(self):
+        return self.peek(1)
+
+    def peek(self, k):
+        """Return the k'th element on the stack
+        """
+        return self._stack[-k]
+
+    def push(self, item):
+        """Push to stack"""
+        self._stack.append(item)
+
+    def pop(self):
+        """Pop the stack"""
+        return self._stack.pop()
+
+    def push_block(self, synblk):
+        """Push a block to blockstack
+        """
+        assert 'stack_depth' in synblk
+        self._blockstack.append(synblk)
+
+    def reset_stack(self, depth):
+        """Reset the stack to the given stack depth.
+        Returning the popped items.
+        """
+        self._stack, popped = self._stack[:depth], self._stack[depth:]
+        return popped
+
+    def make_block(self, kind, end, reset_stack=True, handler=None):
+        """Make a new block
+        """
+        d = {
+            'kind': BlockKind(kind),
+            'end': end,
+            'entry_stack': len(self._stack),
+        }
+        if reset_stack:
+            d['stack_depth'] = len(self._stack)
+        else:
+            d['stack_depth'] = None
+        d['handler'] = handler
+        return d
+
+    def pop_block(self):
+        """Pop a block and unwind the stack
+        """
+        b = self._blockstack.pop()
+        self.reset_stack(b['stack_depth'])
+        return b
+
+    def pop_block_and_above(self, blk):
+        """Find *blk* in the blockstack and remove it and all blocks above it
+        from the stack.
+        """
+        idx = self._blockstack.index(blk)
+        assert 0 <= idx < len(self._blockstack)
+        self._blockstack = self._blockstack[:idx]
+
+    def get_top_block(self, kind):
+        """Find the first block that matches *kind*
+        """
+        kind = BlockKind(kind)
+        for bs in reversed(self._blockstack):
+            if bs['kind'] == kind:
+                return bs
+
+    def has_active_try(self):
+        """Returns a boolean indicating if the top-block is a *try* block
+        """
+        return self.get_top_block('TRY') is not None
+
+    def get_varname(self, inst):
+        """Get referenced variable name from the oparg
+        """
+        return self._bytecode.co_varnames[inst.arg]
+
+    def terminate(self):
+        """Mark block as terminated
+        """
+        self._terminated = True
+
+    def fork(self, pc, npop=0, npush=0, extra_block=None):
+        """Fork the state
+        """
+        # Handle changes on the stack
+        stack = list(self._stack)
+        if npop:
+            assert 0 <= npop <= len(self._stack)
+            nstack = len(self._stack) - npop
+            stack = stack[:nstack]
+        if npush:
+            assert 0 <= npush
+            for i in range(npush):
+                stack.append(self.make_temp())
+        # Handle changes on the blockstack
+        blockstack = list(self._blockstack)
+        if extra_block:
+            blockstack.append(extra_block)
+        self._outedges.append(Edge(
+            pc=pc, stack=tuple(stack), npush=npush,
+            blockstack=tuple(blockstack),
+        ))
+        self.terminate()
+
+    def split_new_block(self):
+        """Split the state
+        """
+        self.fork(pc=self._pc)
+
+    def get_outgoing_states(self):
+        """Get states for each outgoing edges
+        """
+        # Should only call once
+        assert not self._outgoing_phis
+        ret = []
+        for edge in self._outedges:
+            state = State(bytecode=self._bytecode, pc=edge.pc,
+                          nstack=len(edge.stack), blockstack=edge.blockstack)
+            ret.append(state)
+            # Map outgoing_phis
+            for phi, i in state._phis.items():
+                self._outgoing_phis[phi] = edge.stack[i]
+        return ret
+
+    def get_outgoing_edgepushed(self):
+        """
+        Returns
+        -------
+        Dict[int, int]
+            where keys are the PC
+            values are the edge-pushed stack values
+        """
+
+        return {edge.pc: tuple(edge.stack[-edge.npush:])
+                for edge in self._outedges}
+
+
+Edge = namedtuple("Edge", ["pc", "stack", "blockstack", "npush"])
+
+
+class AdaptDFA(object):
+    """Adapt Flow to the old DFA class expected by Interpreter
+    """
+    def __init__(self, flow):
+        self._flow = flow
+
+    @property
+    def infos(self):
+        return self._flow.block_infos
+
+
+AdaptBlockInfo = namedtuple(
+    "AdaptBlockInfo",
+    ["insts", "outgoing_phis", "blockstack", "active_try_block",
+     "outgoing_edgepushed"],
+)
+
+
+def adapt_state_infos(state):
+    return AdaptBlockInfo(
+        insts=tuple(state.instructions),
+        outgoing_phis=state.outgoing_phis,
+        blockstack=state.blockstack_initial,
+        active_try_block=state.find_initial_try_block(),
+        outgoing_edgepushed=state.get_outgoing_edgepushed(),
+    )
+
+
+def _flatten_inst_regs(iterable):
+    """Flatten an iterable of registers used in an instruction
+    """
+    for item in iterable:
+        if isinstance(item, str):
+            yield item
+        elif isinstance(item, (tuple, list)):
+            for x in _flatten_inst_regs(item):
+                yield x
+
+
+class AdaptCFA(object):
+    """Adapt Flow to the old CFA class expected by Interpreter
+    """
+    def __init__(self, flow):
+        self._flow = flow
+        self._blocks = {}
+        for offset, blockinfo in flow.block_infos.items():
+            self._blocks[offset] = AdaptCFBlock(blockinfo, offset)
+        backbone = self._flow.cfgraph.backbone()
+
+        graph = flow.cfgraph
+        # Find backbone
+        backbone = graph.backbone()
+        # Filter out in loop blocks (Assuming no other cyclic control blocks)
+        # This is to unavoid variables defined in loops being considered as
+        # function scope.
+        inloopblocks = set()
+        for b in self.blocks.keys():
+            if graph.in_loops(b):
+                inloopblocks.add(b)
+        self._backbone = backbone - inloopblocks
+
+    @property
+    def graph(self):
+        return self._flow.cfgraph
+
+    @property
+    def backbone(self):
+        return self._backbone
+
+    @property
+    def blocks(self):
+        return self._blocks
+
+    def iterliveblocks(self):
+        for b in sorted(self.blocks):
+            yield self.blocks[b]
+
+    def dump(self):
+        self._flow.cfgraph.dump()
+
+
+class AdaptCFBlock(object):
+    def __init__(self, blockinfo, offset):
+        self.offset = offset
+        self.body = tuple(i for i, _ in blockinfo.insts)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/caching.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/caching.py
new file mode 100644
index 000000000..4339f5e30
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/caching.py
@@ -0,0 +1,731 @@
+"""
+Caching mechanism for compiled functions.
+"""
+
+
+from abc import ABCMeta, abstractmethod, abstractproperty
+import contextlib
+import errno
+import hashlib
+import inspect
+import itertools
+import os
+import pickle
+import sys
+import tempfile
+import uuid
+import warnings
+
+from numba.misc.appdirs import AppDirs
+
+import numba
+from numba.core.errors import NumbaWarning
+from numba.core.base import BaseContext
+from numba.core.codegen import CodeLibrary
+from numba.core.compiler import CompileResult
+from numba.core import config, compiler
+from numba.core.serialize import dumps
+
+
+def _cache_log(msg, *args):
+    if config.DEBUG_CACHE:
+        msg = msg % args
+        print(msg)
+
+
+class _Cache(metaclass=ABCMeta):
+
+    @abstractproperty
+    def cache_path(self):
+        """
+        The base filesystem path of this cache (for example its root folder).
+        """
+
+    @abstractmethod
+    def load_overload(self, sig, target_context):
+        """
+        Load an overload for the given signature using the target context.
+        The saved object must be returned if successful, None if not found
+        in the cache.
+        """
+
+    @abstractmethod
+    def save_overload(self, sig, data):
+        """
+        Save the overload for the given signature.
+        """
+
+    @abstractmethod
+    def enable(self):
+        """
+        Enable the cache.
+        """
+
+    @abstractmethod
+    def disable(self):
+        """
+        Disable the cache.
+        """
+
+    @abstractmethod
+    def flush(self):
+        """
+        Flush the cache.
+        """
+
+
+class NullCache(_Cache):
+    @property
+    def cache_path(self):
+        return None
+
+    def load_overload(self, sig, target_context):
+        pass
+
+    def save_overload(self, sig, cres):
+        pass
+
+    def enable(self):
+        pass
+
+    def disable(self):
+        pass
+
+    def flush(self):
+        pass
+
+
+class _CacheLocator(metaclass=ABCMeta):
+    """
+    A filesystem locator for caching a given function.
+    """
+
+    def ensure_cache_path(self):
+        path = self.get_cache_path()
+        os.makedirs(path, exist_ok=True)
+        # Ensure the directory is writable by trying to write a temporary file
+        tempfile.TemporaryFile(dir=path).close()
+
+    @abstractmethod
+    def get_cache_path(self):
+        """
+        Return the directory the function is cached in.
+        """
+
+    @abstractmethod
+    def get_source_stamp(self):
+        """
+        Get a timestamp representing the source code's freshness.
+        Can return any picklable Python object.
+        """
+
+    @abstractmethod
+    def get_disambiguator(self):
+        """
+        Get a string disambiguator for this locator's function.
+        It should allow disambiguating different but similarly-named functions.
+        """
+
+    @classmethod
+    def from_function(cls, py_func, py_file):
+        """
+        Create a locator instance for the given function located in the
+        given file.
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def get_suitable_cache_subpath(cls, py_file):
+        """Given the Python file path, compute a suitable path inside the
+        cache directory.
+
+        This will reduce a file path that is too long, which can be a problem
+        on some operating system (i.e. Windows 7).
+        """
+        path = os.path.abspath(py_file)
+        subpath = os.path.dirname(path)
+        parentdir = os.path.split(subpath)[-1]
+        # Use SHA1 to reduce path length.
+        # Note: windows doesn't like long path.
+        hashed = hashlib.sha1(subpath.encode()).hexdigest()
+        # Retain parent directory name for easier debugging
+        return '_'.join([parentdir, hashed])
+
+
+class _SourceFileBackedLocatorMixin(object):
+    """
+    A cache locator mixin for functions which are backed by a well-known
+    Python source file.
+    """
+
+    def get_source_stamp(self):
+        if getattr(sys, 'frozen', False):
+            st = os.stat(sys.executable)
+        else:
+            st = os.stat(self._py_file)
+        # We use both timestamp and size as some filesystems only have second
+        # granularity.
+        return st.st_mtime, st.st_size
+
+    def get_disambiguator(self):
+        return str(self._lineno)
+
+    @classmethod
+    def from_function(cls, py_func, py_file):
+        if not os.path.exists(py_file):
+            # Perhaps a placeholder (e.g. "<ipython-XXX>")
+            return
+        self = cls(py_func, py_file)
+        try:
+            self.ensure_cache_path()
+        except OSError:
+            # Cannot ensure the cache directory exists or is writable
+            return
+        return self
+
+
+class _UserProvidedCacheLocator(_SourceFileBackedLocatorMixin, _CacheLocator):
+    """
+    A locator that always point to the user provided directory in
+    `numba.config.CACHE_DIR`
+    """
+    def __init__(self, py_func, py_file):
+        self._py_file = py_file
+        self._lineno = py_func.__code__.co_firstlineno
+        cache_subpath = self.get_suitable_cache_subpath(py_file)
+        self._cache_path = os.path.join(config.CACHE_DIR, cache_subpath)
+
+    def get_cache_path(self):
+        return self._cache_path
+
+    @classmethod
+    def from_function(cls, py_func, py_file):
+        if not config.CACHE_DIR:
+            return
+        parent = super(_UserProvidedCacheLocator, cls)
+        return parent.from_function(py_func, py_file)
+
+
+class _InTreeCacheLocator(_SourceFileBackedLocatorMixin, _CacheLocator):
+    """
+    A locator for functions backed by a regular Python module with a
+    writable __pycache__ directory.
+    """
+
+    def __init__(self, py_func, py_file):
+        self._py_file = py_file
+        self._lineno = py_func.__code__.co_firstlineno
+        self._cache_path = os.path.join(os.path.dirname(self._py_file), '__pycache__')
+
+    def get_cache_path(self):
+        return self._cache_path
+
+
+class _UserWideCacheLocator(_SourceFileBackedLocatorMixin, _CacheLocator):
+    """
+    A locator for functions backed by a regular Python module or a
+    frozen executable, cached into a user-wide cache directory.
+    """
+
+    def __init__(self, py_func, py_file):
+        self._py_file = py_file
+        self._lineno = py_func.__code__.co_firstlineno
+        appdirs = AppDirs(appname="numba", appauthor=False)
+        cache_dir = appdirs.user_cache_dir
+        cache_subpath = self.get_suitable_cache_subpath(py_file)
+        self._cache_path = os.path.join(cache_dir, cache_subpath)
+
+    def get_cache_path(self):
+        return self._cache_path
+
+    @classmethod
+    def from_function(cls, py_func, py_file):
+        if not (os.path.exists(py_file) or getattr(sys, 'frozen', False)):
+            # Perhaps a placeholder (e.g. "<ipython-XXX>")
+            # stop function exit if frozen, since it uses a temp placeholder
+            return
+        self = cls(py_func, py_file)
+        try:
+            self.ensure_cache_path()
+        except OSError:
+            # Cannot ensure the cache directory exists or is writable
+            return
+        return self
+
+
+class _IPythonCacheLocator(_CacheLocator):
+    """
+    A locator for functions entered at the IPython prompt (notebook or other).
+    """
+
+    def __init__(self, py_func, py_file):
+        self._py_file = py_file
+        # Note IPython enhances the linecache module to be able to
+        # inspect source code of functions defined on the interactive prompt.
+        source = inspect.getsource(py_func)
+        if isinstance(source, bytes):
+            self._bytes_source = source
+        else:
+            self._bytes_source = source.encode('utf-8')
+
+    def get_cache_path(self):
+        # We could also use jupyter_core.paths.jupyter_runtime_dir()
+        # In both cases this is a user-wide directory, so we need to
+        # be careful when disambiguating if we don't want too many
+        # conflicts (see below).
+        try:
+            from IPython.paths import get_ipython_cache_dir
+        except ImportError:
+            # older IPython version
+            from IPython.utils.path import get_ipython_cache_dir
+        return os.path.join(get_ipython_cache_dir(), 'numba_cache')
+
+    def get_source_stamp(self):
+        return hashlib.sha256(self._bytes_source).hexdigest()
+
+    def get_disambiguator(self):
+        # Heuristic: we don't want too many variants being saved, but
+        # we don't want similar named functions (e.g. "f") to compete
+        # for the cache, so we hash the first two lines of the function
+        # source (usually this will be the @jit decorator + the function
+        # signature).
+        firstlines = b''.join(self._bytes_source.splitlines(True)[:2])
+        return hashlib.sha256(firstlines).hexdigest()[:10]
+
+    @classmethod
+    def from_function(cls, py_func, py_file):
+        if not (
+            py_file.startswith("<ipython-")
+            or os.path.basename(os.path.dirname(py_file)).startswith("ipykernel_")
+        ):
+            return
+        self = cls(py_func, py_file)
+        try:
+            self.ensure_cache_path()
+        except OSError:
+            # Cannot ensure the cache directory exists
+            return
+        return self
+
+
+class CacheImpl(metaclass=ABCMeta):
+    """
+    Provides the core machinery for caching.
+    - implement how to serialize and deserialize the data in the cache.
+    - control the filename of the cache.
+    - provide the cache locator
+    """
+    _locator_classes = [_UserProvidedCacheLocator,
+                        _InTreeCacheLocator,
+                        _UserWideCacheLocator,
+                        _IPythonCacheLocator]
+
+    def __init__(self, py_func):
+        self._lineno = py_func.__code__.co_firstlineno
+        # Get qualname
+        try:
+            qualname = py_func.__qualname__
+        except AttributeError:
+            qualname = py_func.__name__
+        # Find a locator
+        source_path = inspect.getfile(py_func)
+        for cls in self._locator_classes:
+            locator = cls.from_function(py_func, source_path)
+            if locator is not None:
+                break
+        else:
+            raise RuntimeError("cannot cache function %r: no locator available "
+                               "for file %r" % (qualname, source_path))
+        self._locator = locator
+        # Use filename base name as module name to avoid conflict between
+        # foo/__init__.py and foo/foo.py
+        filename = inspect.getfile(py_func)
+        modname = os.path.splitext(os.path.basename(filename))[0]
+        fullname = "%s.%s" % (modname, qualname)
+        abiflags = getattr(sys, 'abiflags', '')
+        self._filename_base = self.get_filename_base(fullname, abiflags)
+
+    def get_filename_base(self, fullname, abiflags):
+        # '<' and '>' can appear in the qualname (e.g. '<locals>') but
+        # are forbidden in Windows filenames
+        fixed_fullname = fullname.replace('<', '').replace('>', '')
+        fmt = '%s-%s.py%d%d%s'
+        return fmt % (fixed_fullname, self.locator.get_disambiguator(),
+                      sys.version_info[0], sys.version_info[1], abiflags)
+
+    @property
+    def filename_base(self):
+        return self._filename_base
+
+    @property
+    def locator(self):
+        return self._locator
+
+    @abstractmethod
+    def reduce(self, data):
+        "Returns the serialized form the data"
+        pass
+
+    @abstractmethod
+    def rebuild(self, target_context, reduced_data):
+        "Returns the de-serialized form of the *reduced_data*"
+        pass
+
+    @abstractmethod
+    def check_cachable(self, data):
+        "Returns True if the given data is cachable; otherwise, returns False."
+        pass
+
+
+class CompileResultCacheImpl(CacheImpl):
+    """
+    Implements the logic to cache CompileResult objects.
+    """
+
+    def reduce(self, cres):
+        """
+        Returns a serialized CompileResult
+        """
+        return cres._reduce()
+
+    def rebuild(self, target_context, payload):
+        """
+        Returns the unserialized CompileResult
+        """
+        return compiler.CompileResult._rebuild(target_context, *payload)
+
+    def check_cachable(self, cres):
+        """
+        Check cachability of the given compile result.
+        """
+        cannot_cache = None
+        if any(not x.can_cache for x in cres.lifted):
+            cannot_cache = "as it uses lifted code"
+        elif cres.library.has_dynamic_globals:
+            cannot_cache = ("as it uses dynamic globals "
+                            "(such as ctypes pointers and large global arrays)")
+        if cannot_cache:
+            msg = ('Cannot cache compiled function "%s" %s'
+                   % (cres.fndesc.qualname.split('.')[-1], cannot_cache))
+            warnings.warn_explicit(msg, NumbaWarning,
+                                   self._locator._py_file, self._lineno)
+            return False
+        return True
+
+
+class CodeLibraryCacheImpl(CacheImpl):
+    """
+    Implements the logic to cache CodeLibrary objects.
+    """
+
+    _filename_prefix = None  # must be overridden
+
+    def reduce(self, codelib):
+        """
+        Returns a serialized CodeLibrary
+        """
+        return codelib.serialize_using_object_code()
+
+    def rebuild(self, target_context, payload):
+        """
+        Returns the unserialized CodeLibrary
+        """
+        return target_context.codegen().unserialize_library(payload)
+
+    def check_cachable(self, codelib):
+        """
+        Check cachability of the given CodeLibrary.
+        """
+        return not codelib.has_dynamic_globals
+
+    def get_filename_base(self, fullname, abiflags):
+        parent = super(CodeLibraryCacheImpl, self)
+        res = parent.get_filename_base(fullname, abiflags)
+        return '-'.join([self._filename_prefix, res])
+
+
+class IndexDataCacheFile(object):
+    """
+    Implements the logic for the index file and data file used by a cache.
+    """
+    def __init__(self, cache_path, filename_base, source_stamp):
+        self._cache_path = cache_path
+        self._index_name = '%s.nbi' % (filename_base,)
+        self._index_path = os.path.join(self._cache_path, self._index_name)
+        self._data_name_pattern = '%s.{number:d}.nbc' % (filename_base,)
+        self._source_stamp = source_stamp
+        self._version = numba.__version__
+
+    def flush(self):
+        self._save_index({})
+
+    def save(self, key, data):
+        """
+        Save a new cache entry with *key* and *data*.
+        """
+        overloads = self._load_index()
+        try:
+            # If key already exists, we will overwrite the file
+            data_name = overloads[key]
+        except KeyError:
+            # Find an available name for the data file
+            existing = set(overloads.values())
+            for i in itertools.count(1):
+                data_name = self._data_name(i)
+                if data_name not in existing:
+                    break
+            overloads[key] = data_name
+            self._save_index(overloads)
+        self._save_data(data_name, data)
+
+    def load(self, key):
+        """
+        Load a cache entry with *key*.
+        """
+        overloads = self._load_index()
+        data_name = overloads.get(key)
+        if data_name is None:
+            return
+        try:
+            return self._load_data(data_name)
+        except OSError:
+            # File could have been removed while the index still refers it.
+            return
+
+    def _load_index(self):
+        """
+        Load the cache index and return it as a dictionary (possibly
+        empty if cache is empty or obsolete).
+        """
+        try:
+            with open(self._index_path, "rb") as f:
+                version = pickle.load(f)
+                data = f.read()
+        except FileNotFoundError:
+            # Index doesn't exist yet?
+            return {}
+        if version != self._version:
+            # This is another version.  Avoid trying to unpickling the
+            # rest of the stream, as that may fail.
+            return {}
+        stamp, overloads = pickle.loads(data)
+        _cache_log("[cache] index loaded from %r", self._index_path)
+        if stamp != self._source_stamp:
+            # Cache is not fresh.  Stale data files will be eventually
+            # overwritten, since they are numbered in incrementing order.
+            return {}
+        else:
+            return overloads
+
+    def _save_index(self, overloads):
+        data = self._source_stamp, overloads
+        data = self._dump(data)
+        with self._open_for_write(self._index_path) as f:
+            pickle.dump(self._version, f, protocol=-1)
+            f.write(data)
+        _cache_log("[cache] index saved to %r", self._index_path)
+
+    def _load_data(self, name):
+        path = self._data_path(name)
+        with open(path, "rb") as f:
+            data = f.read()
+        tup = pickle.loads(data)
+        _cache_log("[cache] data loaded from %r", path)
+        return tup
+
+    def _save_data(self, name, data):
+        data = self._dump(data)
+        path = self._data_path(name)
+        with self._open_for_write(path) as f:
+            f.write(data)
+        _cache_log("[cache] data saved to %r", path)
+
+    def _data_name(self, number):
+        return self._data_name_pattern.format(number=number)
+
+    def _data_path(self, name):
+        return os.path.join(self._cache_path, name)
+
+    def _dump(self, obj):
+        return dumps(obj)
+
+    @contextlib.contextmanager
+    def _open_for_write(self, filepath):
+        """
+        Open *filepath* for writing in a race condition-free way (hopefully).
+        uuid4 is used to try and avoid name collisions on a shared filesystem.
+        """
+        uid = uuid.uuid4().hex[:16]  # avoid long paths
+        tmpname = '%s.tmp.%s' % (filepath, uid)
+        try:
+            with open(tmpname, "wb") as f:
+                yield f
+            os.replace(tmpname, filepath)
+        except Exception:
+            # In case of error, remove dangling tmp file
+            try:
+                os.unlink(tmpname)
+            except OSError:
+                pass
+            raise
+
+
+class Cache(_Cache):
+    """
+    A per-function compilation cache.  The cache saves data in separate
+    data files and maintains information in an index file.
+
+    There is one index file per function and Python version
+    ("function_name-<lineno>.pyXY.nbi") which contains a mapping of
+    signatures and architectures to data files.
+    It is prefixed by a versioning key and a timestamp of the Python source
+    file containing the function.
+
+    There is one data file ("function_name-<lineno>.pyXY.<number>.nbc")
+    per function, function signature, target architecture and Python version.
+
+    Separate index and data files per Python version avoid pickle
+    compatibility problems.
+
+    Note:
+    This contains the driver logic only.  The core logic is provided
+    by a subclass of ``CacheImpl`` specified as *_impl_class* in the subclass.
+    """
+
+    # The following class variables must be overridden by subclass.
+    _impl_class = None
+
+    def __init__(self, py_func):
+        self._name = repr(py_func)
+        self._py_func = py_func
+        self._impl = self._impl_class(py_func)
+        self._cache_path = self._impl.locator.get_cache_path()
+        # This may be a bit strict but avoids us maintaining a magic number
+        source_stamp = self._impl.locator.get_source_stamp()
+        filename_base = self._impl.filename_base
+        self._cache_file = IndexDataCacheFile(cache_path=self._cache_path,
+                                              filename_base=filename_base,
+                                              source_stamp=source_stamp)
+        self.enable()
+
+    def __repr__(self):
+        return "<%s py_func=%r>" % (self.__class__.__name__, self._name)
+
+    @property
+    def cache_path(self):
+        return self._cache_path
+
+    def enable(self):
+        self._enabled = True
+
+    def disable(self):
+        self._enabled = False
+
+    def flush(self):
+        self._cache_file.flush()
+
+    def load_overload(self, sig, target_context):
+        """
+        Load and recreate the cached object for the given signature,
+        using the *target_context*.
+        """
+        # Refresh the context to ensure it is initialized
+        target_context.refresh()
+        with self._guard_against_spurious_io_errors():
+            return self._load_overload(sig, target_context)
+        # None returned if the `with` block swallows an exception
+
+    def _load_overload(self, sig, target_context):
+        if not self._enabled:
+            return
+        key = self._index_key(sig, target_context.codegen())
+        data = self._cache_file.load(key)
+        if data is not None:
+            data = self._impl.rebuild(target_context, data)
+        return data
+
+    def save_overload(self, sig, data):
+        """
+        Save the data for the given signature in the cache.
+        """
+        with self._guard_against_spurious_io_errors():
+            self._save_overload(sig, data)
+
+    def _save_overload(self, sig, data):
+        if not self._enabled:
+            return
+        if not self._impl.check_cachable(data):
+            return
+        self._impl.locator.ensure_cache_path()
+        key = self._index_key(sig, data.codegen)
+        data = self._impl.reduce(data)
+        self._cache_file.save(key, data)
+
+    @contextlib.contextmanager
+    def _guard_against_spurious_io_errors(self):
+        if os.name == 'nt':
+            # Guard against permission errors due to accessing the file
+            # from several processes (see #2028)
+            try:
+                yield
+            except OSError as e:
+                if e.errno != errno.EACCES:
+                    raise
+        else:
+            # No such conditions under non-Windows OSes
+            yield
+
+    def _index_key(self, sig, codegen):
+        """
+        Compute index key for the given signature and codegen.
+        It includes a description of the OS, target architecture and hashes of
+        the bytecode for the function and, if the function has a __closure__,
+        a hash of the cell_contents.
+        """
+        codebytes = self._py_func.__code__.co_code
+        if self._py_func.__closure__ is not None:
+            cvars = tuple([x.cell_contents for x in self._py_func.__closure__])
+            # Note: cloudpickle serializes a function differently depending
+            #       on how the process is launched; e.g. multiprocessing.Process
+            cvarbytes = dumps(cvars)
+        else:
+            cvarbytes = b''
+
+        hasher = lambda x: hashlib.sha256(x).hexdigest()
+        return (sig, codegen.magic_tuple(), (hasher(codebytes),
+                                             hasher(cvarbytes),))
+
+
+class FunctionCache(Cache):
+    """
+    Implements Cache that saves and loads CompileResult objects.
+    """
+    _impl_class = CompileResultCacheImpl
+
+
+# Remember used cache filename prefixes.
+_lib_cache_prefixes = set([''])
+
+
+def make_library_cache(prefix):
+    """
+    Create a Cache class for additional compilation features to cache their
+    result for reuse.  The cache is saved in filename pattern like
+    in ``FunctionCache`` but with additional *prefix* as specified.
+    """
+    # avoid cache prefix reuse
+    assert prefix not in _lib_cache_prefixes
+    _lib_cache_prefixes.add(prefix)
+
+    class CustomCodeLibraryCacheImpl(CodeLibraryCacheImpl):
+        _filename_prefix = prefix
+
+    class LibraryCache(Cache):
+        """
+        Implements Cache that saves and loads CodeLibrary objects for additional
+        feature for the specified python function.
+        """
+        _impl_class = CustomCodeLibraryCacheImpl
+
+    return LibraryCache
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/callconv.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/callconv.py
new file mode 100644
index 000000000..9347b92b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/callconv.py
@@ -0,0 +1,650 @@
+"""
+Calling conventions for Numba-compiled functions.
+"""
+
+from collections import namedtuple
+from collections.abc import Iterable
+import itertools
+
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core.base import PYOBJECT, GENERIC_POINTER
+
+
+TryStatus = namedtuple('TryStatus', ['in_try', 'excinfo'])
+
+
+Status = namedtuple("Status",
+                    ("code",
+                     # If the function returned ok (a value or None)
+                     "is_ok",
+                     # If the function returned None
+                     "is_none",
+                     # If the function errored out (== not is_ok)
+                     "is_error",
+                     # If the generator exited with StopIteration
+                     "is_stop_iteration",
+                     # If the function errored with an already set exception
+                     "is_python_exc",
+                     # If the function errored with a user exception
+                     "is_user_exc",
+                     # The pointer to the exception info structure (for user exceptions)
+                     "excinfoptr",
+                     ))
+
+int32_t = ir.IntType(32)
+errcode_t = int32_t
+
+def _const_int(code):
+    return ir.Constant(errcode_t, code)
+
+RETCODE_OK = _const_int(0)
+RETCODE_EXC = _const_int(-1)
+RETCODE_NONE = _const_int(-2)
+# StopIteration
+RETCODE_STOPIT = _const_int(-3)
+
+FIRST_USEREXC = 1
+
+RETCODE_USEREXC = _const_int(FIRST_USEREXC)
+
+
+
+
+class BaseCallConv(object):
+
+    def __init__(self, context):
+        self.context = context
+
+    def return_optional_value(self, builder, retty, valty, value):
+        if valty == types.none:
+            # Value is none
+            self.return_native_none(builder)
+
+        elif retty == valty:
+            # Value is an optional, need a runtime switch
+            optval = self.context.make_helper(builder, retty, value=value)
+
+            validbit = cgutils.as_bool_bit(builder, optval.valid)
+            with builder.if_then(validbit):
+                retval = self.context.get_return_value(builder, retty.type,
+                                                       optval.data)
+                self.return_value(builder, retval)
+
+            self.return_native_none(builder)
+
+        elif not isinstance(valty, types.Optional):
+            # Value is not an optional, need a cast
+            if valty != retty.type:
+                value = self.context.cast(builder, value, fromty=valty,
+                                          toty=retty.type)
+            retval = self.context.get_return_value(builder, retty.type, value)
+            self.return_value(builder, retval)
+
+        else:
+            raise NotImplementedError("returning {0} for {1}".format(valty,
+                                                                     retty))
+
+    def return_native_none(self, builder):
+        self._return_errcode_raw(builder, RETCODE_NONE)
+
+    def return_exc(self, builder):
+        self._return_errcode_raw(builder, RETCODE_EXC, mark_exc=True)
+
+    def return_stop_iteration(self, builder):
+        self._return_errcode_raw(builder, RETCODE_STOPIT)
+
+    def get_return_type(self, ty):
+        """
+        Get the actual type of the return argument for Numba type *ty*.
+        """
+        restype = self.context.data_model_manager[ty].get_return_type()
+        return restype.as_pointer()
+
+    def init_call_helper(self, builder):
+        """
+        Initialize and return a call helper object for the given builder.
+        """
+        ch = self._make_call_helper(builder)
+        builder.__call_helper = ch
+        return ch
+
+    def _get_call_helper(self, builder):
+        return builder.__call_helper
+
+    def raise_error(self, builder, api, status):
+        """
+        Given a non-ok *status*, raise the corresponding Python exception.
+        """
+        bbend = builder.function.append_basic_block()
+
+        with builder.if_then(status.is_user_exc):
+            # Unserialize user exception.
+            # Make sure another error may not interfere.
+            api.err_clear()
+            exc = api.unserialize(status.excinfoptr)
+            with cgutils.if_likely(builder,
+                                   cgutils.is_not_null(builder, exc)):
+                api.raise_object(exc)  # steals ref
+            builder.branch(bbend)
+
+        with builder.if_then(status.is_stop_iteration):
+            api.err_set_none("PyExc_StopIteration")
+            builder.branch(bbend)
+
+        with builder.if_then(status.is_python_exc):
+            # Error already raised => nothing to do
+            builder.branch(bbend)
+
+        api.err_set_string("PyExc_SystemError",
+                           "unknown error when calling native function")
+        builder.branch(bbend)
+
+        builder.position_at_end(bbend)
+
+    def decode_arguments(self, builder, argtypes, func):
+        """
+        Get the decoded (unpacked) Python arguments with *argtypes*
+        from LLVM function *func*.  A tuple of LLVM values is returned.
+        """
+        raw_args = self.get_arguments(func)
+        arginfo = self._get_arg_packer(argtypes)
+        return arginfo.from_arguments(builder, raw_args)
+
+    def _get_arg_packer(self, argtypes):
+        """
+        Get an argument packer for the given argument types.
+        """
+        return self.context.get_arg_packer(argtypes)
+
+
+class MinimalCallConv(BaseCallConv):
+    """
+    A minimal calling convention, suitable for e.g. GPU targets.
+    The implemented function signature is:
+
+        retcode_t (<Python return type>*, ... <Python arguments>)
+
+    The return code will be one of the RETCODE_* constants or a
+    function-specific user exception id (>= RETCODE_USEREXC).
+
+    Caller is responsible for allocating a slot for the return value
+    (passed as a pointer in the first argument).
+    """
+
+    def _make_call_helper(self, builder):
+        return _MinimalCallHelper()
+
+    def return_value(self, builder, retval):
+        retptr = builder.function.args[0]
+        assert retval.type == retptr.type.pointee, \
+            (str(retval.type), str(retptr.type.pointee))
+        builder.store(retval, retptr)
+        self._return_errcode_raw(builder, RETCODE_OK)
+
+    def return_user_exc(self, builder, exc, exc_args=None, loc=None,
+                        func_name=None):
+        if exc is not None and not issubclass(exc, BaseException):
+            raise TypeError("exc should be None or exception class, got %r"
+                            % (exc,))
+        if exc_args is not None and not isinstance(exc_args, tuple):
+            raise TypeError("exc_args should be None or tuple, got %r"
+                            % (exc_args,))
+
+        # Build excinfo struct
+        if loc is not None:
+            fname = loc._raw_function_name()
+            if fname is None:
+                # could be exec(<string>) or REPL, try func_name
+                fname = func_name
+
+            locinfo = (fname, loc.filename, loc.line)
+            if None in locinfo:
+                locinfo = None
+        else:
+            locinfo = None
+
+        call_helper = self._get_call_helper(builder)
+        exc_id = call_helper._add_exception(exc, exc_args, locinfo)
+        self._return_errcode_raw(builder, _const_int(exc_id), mark_exc=True)
+
+    def return_status_propagate(self, builder, status):
+        self._return_errcode_raw(builder, status.code)
+
+    def _return_errcode_raw(self, builder, code, mark_exc=False):
+        if isinstance(code, int):
+            code = _const_int(code)
+        builder.ret(code)
+
+    def _get_return_status(self, builder, code):
+        """
+        Given a return *code*, get a Status instance.
+        """
+        norm = builder.icmp_signed('==', code, RETCODE_OK)
+        none = builder.icmp_signed('==', code, RETCODE_NONE)
+        ok = builder.or_(norm, none)
+        err = builder.not_(ok)
+        exc = builder.icmp_signed('==', code, RETCODE_EXC)
+        is_stop_iteration = builder.icmp_signed('==', code, RETCODE_STOPIT)
+        is_user_exc = builder.icmp_signed('>=', code, RETCODE_USEREXC)
+
+        status = Status(code=code,
+                        is_ok=ok,
+                        is_error=err,
+                        is_python_exc=exc,
+                        is_none=none,
+                        is_user_exc=is_user_exc,
+                        is_stop_iteration=is_stop_iteration,
+                        excinfoptr=None)
+        return status
+
+    def get_function_type(self, restype, argtypes):
+        """
+        Get the implemented Function type for *restype* and *argtypes*.
+        """
+        arginfo = self._get_arg_packer(argtypes)
+        argtypes = list(arginfo.argument_types)
+        resptr = self.get_return_type(restype)
+        fnty = ir.FunctionType(errcode_t, [resptr] + argtypes)
+        return fnty
+
+    def decorate_function(self, fn, args, fe_argtypes, noalias=False):
+        """
+        Set names and attributes of function arguments.
+        """
+        assert not noalias
+        arginfo = self._get_arg_packer(fe_argtypes)
+        arginfo.assign_names(self.get_arguments(fn),
+                             ['arg.' + a for a in args])
+        fn.args[0].name = ".ret"
+        return fn
+
+    def get_arguments(self, func):
+        """
+        Get the Python-level arguments of LLVM *func*.
+        """
+        return func.args[1:]
+
+    def call_function(self, builder, callee, resty, argtys, args):
+        """
+        Call the Numba-compiled *callee*.
+        """
+        retty = callee.args[0].type.pointee
+        retvaltmp = cgutils.alloca_once(builder, retty)
+        # initialize return value
+        builder.store(cgutils.get_null_value(retty), retvaltmp)
+
+        arginfo = self._get_arg_packer(argtys)
+        args = arginfo.as_arguments(builder, args)
+        realargs = [retvaltmp] + list(args)
+        code = builder.call(callee, realargs)
+        status = self._get_return_status(builder, code)
+        retval = builder.load(retvaltmp)
+        out = self.context.get_returned_value(builder, resty, retval)
+        return status, out
+
+
+class _MinimalCallHelper(object):
+    """
+    A call helper object for the "minimal" calling convention.
+    User exceptions are represented as integer codes and stored in
+    a mapping for retrieval from the caller.
+    """
+
+    def __init__(self):
+        self.exceptions = {}
+
+    def _add_exception(self, exc, exc_args, locinfo):
+        """
+        Add a new user exception to this helper. Returns an integer that can be
+        used to refer to the added exception in future.
+
+        Parameters
+        ----------
+        exc :
+            exception type
+        exc_args : None or tuple
+            exception args
+        locinfo : tuple
+            location information
+        """
+        exc_id = len(self.exceptions) + FIRST_USEREXC
+        self.exceptions[exc_id] = exc, exc_args, locinfo
+        return exc_id
+
+    def get_exception(self, exc_id):
+        """
+        Get information about a user exception. Returns a tuple of
+        (exception type, exception args, location information).
+
+        Parameters
+        ----------
+        id : integer
+            The ID of the exception to look up
+        """
+        try:
+            return self.exceptions[exc_id]
+        except KeyError:
+            msg = "unknown error %d in native function" % exc_id
+            exc = SystemError
+            exc_args = (msg,)
+            locinfo = None
+            return exc, exc_args, locinfo
+
+# The structure type constructed by PythonAPI.serialize_uncached()
+# i.e a {i8* pickle_buf, i32 pickle_bufsz, i8* hash_buf}
+excinfo_t = ir.LiteralStructType([GENERIC_POINTER, int32_t, GENERIC_POINTER])
+excinfo_ptr_t = ir.PointerType(excinfo_t)
+
+
+class CPUCallConv(BaseCallConv):
+    """
+    The calling convention for CPU targets.
+    The implemented function signature is:
+
+        retcode_t (<Python return type>*, excinfo **, ... <Python arguments>)
+
+    The return code will be one of the RETCODE_* constants.
+    If RETCODE_USEREXC, the exception info pointer will be filled with
+    a pointer to a constant struct describing the raised exception.
+
+    Caller is responsible for allocating slots for the return value
+    and the exception info pointer (passed as first and second arguments,
+    respectively).
+    """
+    _status_ids = itertools.count(1)
+
+    def _make_call_helper(self, builder):
+        return None
+
+    def return_value(self, builder, retval):
+        retptr = self._get_return_argument(builder.function)
+        assert retval.type == retptr.type.pointee, \
+            (str(retval.type), str(retptr.type.pointee))
+        builder.store(retval, retptr)
+        self._return_errcode_raw(builder, RETCODE_OK)
+
+    def set_static_user_exc(self, builder, exc, exc_args=None, loc=None,
+                            func_name=None):
+        if exc is not None and not issubclass(exc, BaseException):
+            raise TypeError("exc should be None or exception class, got %r"
+                            % (exc,))
+        if exc_args is not None and not isinstance(exc_args, tuple):
+            raise TypeError("exc_args should be None or tuple, got %r"
+                            % (exc_args,))
+        # None is indicative of no args, set the exc_args to an empty tuple
+        # as PyObject_CallObject(exc, exc_args) requires the second argument to
+        # be a tuple (or nullptr, but doing this makes it consistent)
+        if exc_args is None:
+            exc_args = tuple()
+
+        pyapi = self.context.get_python_api(builder)
+        # Build excinfo struct
+        if loc is not None:
+            fname = loc._raw_function_name()
+            if fname is None:
+                # could be exec(<string>) or REPL, try func_name
+                fname = func_name
+
+            locinfo = (fname, loc.filename, loc.line)
+            if None in locinfo:
+                locinfo = None
+        else:
+            locinfo = None
+        exc = (exc, exc_args, locinfo)
+        struct_gv = pyapi.serialize_object(exc)
+        excptr = self._get_excinfo_argument(builder.function)
+        builder.store(struct_gv, excptr)
+
+    def return_user_exc(self, builder, exc, exc_args=None, loc=None,
+                        func_name=None):
+        try_info = getattr(builder, '_in_try_block', False)
+        self.set_static_user_exc(builder, exc, exc_args=exc_args,
+                                   loc=loc, func_name=func_name)
+        trystatus = self.check_try_status(builder)
+        if try_info:
+            # This is a hack for old-style impl.
+            # We will branch directly to the exception handler.
+            builder.branch(try_info['target'])
+        else:
+            # Return from the current function
+            self._return_errcode_raw(builder, RETCODE_USEREXC, mark_exc=True)
+
+    def _get_try_state(self, builder):
+        try:
+            return builder.__eh_try_state
+        except AttributeError:
+            ptr = cgutils.alloca_once(
+                builder, cgutils.intp_t, name='try_state', zfill=True,
+            )
+            builder.__eh_try_state = ptr
+            return ptr
+
+    def check_try_status(self, builder):
+        try_state_ptr = self._get_try_state(builder)
+        try_depth = builder.load(try_state_ptr)
+        # try_depth > 0
+        in_try = builder.icmp_unsigned('>', try_depth, try_depth.type(0))
+
+        excinfoptr = self._get_excinfo_argument(builder.function)
+        excinfo = builder.load(excinfoptr)
+
+        return TryStatus(in_try=in_try, excinfo=excinfo)
+
+    def set_try_status(self, builder):
+        try_state_ptr = self._get_try_state(builder)
+        # Increment try depth
+        old = builder.load(try_state_ptr)
+        new = builder.add(old, old.type(1))
+        builder.store(new, try_state_ptr)
+
+    def unset_try_status(self, builder):
+        try_state_ptr = self._get_try_state(builder)
+        # Decrement try depth
+        old = builder.load(try_state_ptr)
+        new = builder.sub(old, old.type(1))
+        builder.store(new, try_state_ptr)
+
+        # Needs to reset the exception state so that the exception handler
+        # will run normally.
+        excinfoptr = self._get_excinfo_argument(builder.function)
+        null = cgutils.get_null_value(excinfoptr.type.pointee)
+        builder.store(null, excinfoptr)
+
+    def return_status_propagate(self, builder, status):
+        trystatus = self.check_try_status(builder)
+        excptr = self._get_excinfo_argument(builder.function)
+        builder.store(status.excinfoptr, excptr)
+        with builder.if_then(builder.not_(trystatus.in_try)):
+            self._return_errcode_raw(builder, status.code, mark_exc=True)
+
+    def _return_errcode_raw(self, builder, code, mark_exc=False):
+        ret = builder.ret(code)
+
+        if mark_exc:
+            md = builder.module.add_metadata([ir.IntType(1)(1)])
+            ret.set_metadata("ret_is_raise", md)
+
+    def _get_return_status(self, builder, code, excinfoptr):
+        """
+        Given a return *code* and *excinfoptr*, get a Status instance.
+        """
+        norm = builder.icmp_signed('==', code, RETCODE_OK)
+        none = builder.icmp_signed('==', code, RETCODE_NONE)
+        exc = builder.icmp_signed('==', code, RETCODE_EXC)
+        is_stop_iteration = builder.icmp_signed('==', code, RETCODE_STOPIT)
+        ok = builder.or_(norm, none)
+        err = builder.not_(ok)
+        is_user_exc = builder.icmp_signed('>=', code, RETCODE_USEREXC)
+        excinfoptr = builder.select(is_user_exc, excinfoptr,
+                                    ir.Constant(excinfo_ptr_t, ir.Undefined))
+
+        status = Status(code=code,
+                        is_ok=ok,
+                        is_error=err,
+                        is_python_exc=exc,
+                        is_none=none,
+                        is_user_exc=is_user_exc,
+                        is_stop_iteration=is_stop_iteration,
+                        excinfoptr=excinfoptr)
+        return status
+
+    def get_function_type(self, restype, argtypes):
+        """
+        Get the implemented Function type for *restype* and *argtypes*.
+        """
+        arginfo = self._get_arg_packer(argtypes)
+        argtypes = list(arginfo.argument_types)
+        resptr = self.get_return_type(restype)
+        fnty = ir.FunctionType(errcode_t,
+                               [resptr, ir.PointerType(excinfo_ptr_t)]
+                               + argtypes)
+        return fnty
+
+    def decorate_function(self, fn, args, fe_argtypes, noalias=False):
+        """
+        Set names of function arguments, and add useful attributes to them.
+        """
+        arginfo = self._get_arg_packer(fe_argtypes)
+        arginfo.assign_names(self.get_arguments(fn),
+                             ['arg.' + a for a in args])
+        retarg = self._get_return_argument(fn)
+        retarg.name = "retptr"
+        retarg.add_attribute("nocapture")
+        retarg.add_attribute("noalias")
+        excarg = self._get_excinfo_argument(fn)
+        excarg.name = "excinfo"
+        excarg.add_attribute("nocapture")
+        excarg.add_attribute("noalias")
+
+        if noalias:
+            args = self.get_arguments(fn)
+            for a in args:
+                if isinstance(a.type, ir.PointerType):
+                    a.add_attribute("nocapture")
+                    a.add_attribute("noalias")
+
+        # Add metadata to mark functions that may need NRT
+        # thus disabling aggressive refct pruning in removerefctpass.py
+        def type_may_always_need_nrt(ty):
+            # Returns True if it's a non-Array type that is contains MemInfo
+            if not isinstance(ty, types.Array):
+                dmm = self.context.data_model_manager
+                if dmm[ty].contains_nrt_meminfo():
+                    return True
+            return False
+
+        args_may_always_need_nrt = any(
+            map(type_may_always_need_nrt, fe_argtypes)
+        )
+
+        if args_may_always_need_nrt:
+            nmd = fn.module.add_named_metadata(
+                'numba_args_may_always_need_nrt',
+            )
+            nmd.add(fn.module.add_metadata([fn]))
+
+        return fn
+
+    def get_arguments(self, func):
+        """
+        Get the Python-level arguments of LLVM *func*.
+        """
+        return func.args[2:]
+
+    def _get_return_argument(self, func):
+        return func.args[0]
+
+    def _get_excinfo_argument(self, func):
+        return func.args[1]
+
+    def call_function(self, builder, callee, resty, argtys, args,
+                      attrs=None):
+        """
+        Call the Numba-compiled *callee*.
+        Parameters:
+        -----------
+        attrs: LLVM style string or iterable of individual attributes, default
+               is None which specifies no attributes. Examples:
+               LLVM style string: "noinline fast"
+               Equivalent iterable: ("noinline", "fast")
+        """
+        # XXX better fix for callees that are not function values
+        #     (pointers to function; thus have no `.args` attribute)
+        retty = self._get_return_argument(callee.function_type).pointee
+
+        retvaltmp = cgutils.alloca_once(builder, retty)
+        # initialize return value to zeros
+        builder.store(cgutils.get_null_value(retty), retvaltmp)
+
+        excinfoptr = cgutils.alloca_once(builder, ir.PointerType(excinfo_t),
+                                         name="excinfo")
+
+        arginfo = self._get_arg_packer(argtys)
+        args = list(arginfo.as_arguments(builder, args))
+        realargs = [retvaltmp, excinfoptr] + args
+        # deal with attrs, it's fine to specify a load in a string like
+        # "noinline fast" as per LLVM or equally as an iterable of individual
+        # attributes.
+        if attrs is None:
+            _attrs = ()
+        elif isinstance(attrs, Iterable) and not isinstance(attrs, str):
+            _attrs = tuple(attrs)
+        else:
+            raise TypeError("attrs must be an iterable of strings or None")
+        code = builder.call(callee, realargs, attrs=_attrs)
+        status = self._get_return_status(builder, code,
+                                         builder.load(excinfoptr))
+        retval = builder.load(retvaltmp)
+        out = self.context.get_returned_value(builder, resty, retval)
+        return status, out
+
+
+class ErrorModel(object):
+
+    def __init__(self, call_conv):
+        self.call_conv = call_conv
+
+    def fp_zero_division(self, builder, exc_args=None, loc=None):
+        if self.raise_on_fp_zero_division:
+            self.call_conv.return_user_exc(builder, ZeroDivisionError, exc_args,
+                                           loc)
+            return True
+        else:
+            return False
+
+
+class PythonErrorModel(ErrorModel):
+    """
+    The Python error model.  Any invalid FP input raises an exception.
+    """
+    raise_on_fp_zero_division = True
+
+
+class NumpyErrorModel(ErrorModel):
+    """
+    In the Numpy error model, floating-point errors don't raise an
+    exception.  The FPU exception state is inspected by Numpy at the
+    end of a ufunc's execution and a warning is raised if appropriate.
+
+    Note there's no easy way to set the FPU exception state from LLVM.
+    Instructions known to set an FP exception can be optimized away:
+        https://llvm.org/bugs/show_bug.cgi?id=6050
+        http://lists.llvm.org/pipermail/llvm-dev/2014-September/076918.html
+        http://lists.llvm.org/pipermail/llvm-commits/Week-of-Mon-20140929/237997.html
+    """
+    raise_on_fp_zero_division = False
+
+
+error_models = {
+    'python': PythonErrorModel,
+    'numpy': NumpyErrorModel,
+    }
+
+
+def create_error_model(model_name, context):
+    """
+    Create an error model instance for the given target context.
+    """
+    return error_models[model_name](context.call_conv)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/callwrapper.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/callwrapper.py
new file mode 100644
index 000000000..5508aa38e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/callwrapper.py
@@ -0,0 +1,226 @@
+from llvmlite.ir import Constant, IRBuilder
+import llvmlite.ir
+
+from numba.core import types, config, cgutils
+
+
+class _ArgManager(object):
+    """
+    A utility class to handle argument unboxing and cleanup
+    """
+    def __init__(self, context, builder, api, env_manager, endblk, nargs):
+        self.context = context
+        self.builder = builder
+        self.api = api
+        self.env_manager = env_manager
+        self.arg_count = 0  # how many function arguments have been processed
+        self.cleanups = []
+        self.nextblk = endblk
+
+    def add_arg(self, obj, ty):
+        """
+        Unbox argument and emit code that handles any error during unboxing.
+        Args are cleaned up in reverse order of the parameter list, and
+        cleanup begins as soon as unboxing of any argument fails. E.g. failure
+        on arg2 will result in control flow going through:
+
+            arg2.err -> arg1.err -> arg0.err -> arg.end (returns)
+        """
+        # Unbox argument
+        native = self.api.to_native_value(ty, obj)
+
+        # If an error occurred, go to the cleanup block for
+        # the previous argument
+        with cgutils.if_unlikely(self.builder, native.is_error):
+            self.builder.branch(self.nextblk)
+
+        # Define the cleanup function for the argument
+        def cleanup_arg():
+            # Native value reflection
+            self.api.reflect_native_value(ty, native.value, self.env_manager)
+
+            # Native value cleanup
+            if native.cleanup is not None:
+                native.cleanup()
+
+            # NRT cleanup
+            # (happens after the native value cleanup as the latter
+            #  may need the native value)
+            if self.context.enable_nrt:
+                self.context.nrt.decref(self.builder, ty, native.value)
+
+        self.cleanups.append(cleanup_arg)
+
+        # Write the on-error cleanup block for this argument
+        cleanupblk = self.builder.append_basic_block(
+            "arg%d.err" % self.arg_count)
+        with self.builder.goto_block(cleanupblk):
+            cleanup_arg()
+            # Go to next cleanup block
+            self.builder.branch(self.nextblk)
+
+        self.nextblk = cleanupblk
+        self.arg_count += 1
+        return native.value
+
+    def emit_cleanup(self):
+        """
+        Emit the cleanup code after returning from the wrapped function.
+        """
+        for dtor in self.cleanups:
+            dtor()
+
+
+class _GilManager(object):
+    """
+    A utility class to handle releasing the GIL and then re-acquiring it
+    again.
+    """
+
+    def __init__(self, builder, api, argman):
+        self.builder = builder
+        self.api = api
+        self.argman = argman
+        self.thread_state = api.save_thread()
+
+    def emit_cleanup(self):
+        self.api.restore_thread(self.thread_state)
+        self.argman.emit_cleanup()
+
+
+class PyCallWrapper(object):
+    def __init__(self, context, module, func, fndesc, env, call_helper,
+                 release_gil):
+        self.context = context
+        self.module = module
+        self.func = func
+        self.fndesc = fndesc
+        self.env = env
+        self.release_gil = release_gil
+
+    def build(self):
+        wrapname = self.fndesc.llvm_cpython_wrapper_name
+
+        # This is the signature of PyCFunctionWithKeywords
+        # (see CPython's methodobject.h)
+        pyobj = self.context.get_argument_type(types.pyobject)
+        wrapty = llvmlite.ir.FunctionType(pyobj, [pyobj, pyobj, pyobj])
+        wrapper = llvmlite.ir.Function(self.module, wrapty, name=wrapname)
+
+        builder = IRBuilder(wrapper.append_basic_block('entry'))
+
+        # - `closure` will receive the `self` pointer stored in the
+        #   PyCFunction object (see _dynfunc.c)
+        # - `args` and `kws` will receive the tuple and dict objects
+        #   of positional and keyword arguments, respectively.
+        closure, args, kws = wrapper.args
+        closure.name = 'py_closure'
+        args.name = 'py_args'
+        kws.name = 'py_kws'
+
+        api = self.context.get_python_api(builder)
+        self.build_wrapper(api, builder, closure, args, kws)
+
+        return wrapper, api
+
+    def build_wrapper(self, api, builder, closure, args, kws):
+        nargs = len(self.fndesc.argtypes)
+
+        objs = [api.alloca_obj() for _ in range(nargs)]
+        parseok = api.unpack_tuple(args, self.fndesc.qualname,
+                                   nargs, nargs, *objs)
+
+        pred = builder.icmp_unsigned(
+            '==',
+            parseok,
+            Constant(parseok.type, None))
+        with cgutils.if_unlikely(builder, pred):
+            builder.ret(api.get_null_object())
+
+        # Block that returns after erroneous argument unboxing/cleanup
+        endblk = builder.append_basic_block("arg.end")
+        with builder.goto_block(endblk):
+            builder.ret(api.get_null_object())
+
+        # Get the Environment object
+        env_manager = self.get_env(api, builder)
+
+        cleanup_manager = _ArgManager(self.context, builder, api,
+                                      env_manager, endblk, nargs)
+
+        # Compute the arguments to the compiled Numba function.
+        innerargs = []
+        for obj, ty in zip(objs, self.fndesc.argtypes):
+            if isinstance(ty, types.Omitted):
+                # It's an omitted value => ignore dummy Python object
+                innerargs.append(None)
+            else:
+                val = cleanup_manager.add_arg(builder.load(obj), ty)
+                innerargs.append(val)
+
+        if self.release_gil:
+            cleanup_manager = _GilManager(builder, api, cleanup_manager)
+
+        # We elect to not inline the top level user function into the call
+        # wrapper, this incurs an overhead of a function call, however, it
+        # increases optimisation stability in that the optimised user function
+        # is what will actually be run and it is this function that all the
+        # inspection tools "see". Further, this makes optimisation "stable" in
+        # that calling the user function from e.g. C or from this wrapper will
+        # result in the same code executing, were inlining permitted this may
+        # not be the case as the inline could trigger additional optimisation
+        # as the function goes into the wrapper, this resulting in the executing
+        # instruction stream being different from that of the instruction stream
+        # present in the user function.
+        status, retval = self.context.call_conv.call_function(
+            builder, self.func, self.fndesc.restype, self.fndesc.argtypes,
+            innerargs, attrs=('noinline',))
+        # Do clean up
+        self.debug_print(builder, "# callwrapper: emit_cleanup")
+        cleanup_manager.emit_cleanup()
+        self.debug_print(builder, "# callwrapper: emit_cleanup end")
+
+        # Determine return status
+        with builder.if_then(status.is_ok, likely=True):
+            # Ok => return boxed Python value
+            with builder.if_then(status.is_none):
+                api.return_none()
+
+            retty = self._simplified_return_type()
+            obj = api.from_native_return(retty, retval, env_manager)
+            builder.ret(obj)
+
+        # Error out
+        self.context.call_conv.raise_error(builder, api, status)
+        builder.ret(api.get_null_object())
+
+    def get_env(self, api, builder):
+        """Get the Environment object which is declared as a global
+        in the module of the wrapped function.
+        """
+        envname = self.context.get_env_name(self.fndesc)
+        gvptr = self.context.declare_env_global(builder.module, envname)
+        envptr = builder.load(gvptr)
+
+        env_body = self.context.get_env_body(builder, envptr)
+
+        api.emit_environment_sentry(envptr, return_pyobject=True,
+                                    debug_msg=self.fndesc.env_name)
+        env_manager = api.get_env_manager(self.env, env_body, envptr)
+        return env_manager
+
+    def _simplified_return_type(self):
+        """
+        The NPM callconv has already converted simplified optional types.
+        We can simply use the value type from it.
+        """
+        restype = self.fndesc.restype
+        # Optional type
+        if isinstance(restype, types.Optional):
+            return restype.type
+        else:
+            return restype
+
+    def debug_print(self, builder, msg):
+        if config.DEBUG_JIT:
+            self.context.debug_print(builder, "DEBUGJIT: {0}".format(msg))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ccallback.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ccallback.py
new file mode 100644
index 000000000..2fd222db9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ccallback.py
@@ -0,0 +1,134 @@
+"""
+Implementation of compiled C callbacks (@cfunc).
+"""
+
+
+import ctypes
+
+from numba.core import utils, compiler, registry
+from numba.core.caching import NullCache, FunctionCache
+from numba.core.dispatcher import _FunctionCompiler
+from numba.core.typing import signature
+from numba.core.typing.ctypes_utils import to_ctypes
+from numba.core.compiler_lock import global_compiler_lock
+
+
+class _CFuncCompiler(_FunctionCompiler):
+
+    def _customize_flags(self, flags):
+        flags.no_cpython_wrapper = True
+        flags.no_cfunc_wrapper = False
+        # Disable compilation of the IR module, because we first want to
+        # add the cfunc wrapper.
+        flags.no_compile = True
+        # Object mode is not currently supported in C callbacks
+        # (no reliable way to get the environment)
+        flags.enable_pyobject = False
+        if flags.force_pyobject:
+            raise NotImplementedError("object mode not allowed in C callbacks")
+        return flags
+
+
+class CFunc(object):
+    """
+    A compiled C callback, as created by the @cfunc decorator.
+    """
+    _targetdescr = registry.cpu_target
+
+    def __init__(self, pyfunc, sig, locals, options,
+                 pipeline_class=compiler.Compiler):
+        args, return_type = sig
+        if return_type is None:
+            raise TypeError("C callback needs an explicit return type")
+        self.__name__ = pyfunc.__name__
+        self.__qualname__ = getattr(pyfunc, '__qualname__', self.__name__)
+        self.__wrapped__ = pyfunc
+
+        self._pyfunc = pyfunc
+        self._sig = signature(return_type, *args)
+        self._compiler = _CFuncCompiler(pyfunc, self._targetdescr,
+                                        options, locals,
+                                        pipeline_class=pipeline_class)
+
+        self._wrapper_name = None
+        self._wrapper_address = None
+        self._cache = NullCache()
+        self._cache_hits = 0
+
+    def enable_caching(self):
+        self._cache = FunctionCache(self._pyfunc)
+
+    @global_compiler_lock
+    def compile(self):
+        # Try to load from cache
+        cres = self._cache.load_overload(self._sig,
+                                         self._targetdescr.target_context)
+        if cres is None:
+            cres = self._compile_uncached()
+            self._cache.save_overload(self._sig, cres)
+        else:
+            self._cache_hits += 1
+
+        self._library = cres.library
+        self._wrapper_name = cres.fndesc.llvm_cfunc_wrapper_name
+        self._wrapper_address = self._library.get_pointer_to_function(
+            self._wrapper_name)
+
+    def _compile_uncached(self):
+        sig = self._sig
+
+        # Compile native function as well as cfunc wrapper
+        return self._compiler.compile(sig.args, sig.return_type)
+
+    @property
+    def native_name(self):
+        """
+        The process-wide symbol the C callback is exposed as.
+        """
+        # Note from our point of view, the C callback is the wrapper around
+        # the native function.
+        return self._wrapper_name
+
+    @property
+    def address(self):
+        """
+        The address of the C callback.
+        """
+        return self._wrapper_address
+
+    @utils.cached_property
+    def cffi(self):
+        """
+        A cffi function pointer representing the C callback.
+        """
+        import cffi
+        ffi = cffi.FFI()
+        # cffi compares types by name, so using precise types would risk
+        # spurious mismatches (such as "int32_t" vs. "int").
+        return ffi.cast("void *", self.address)
+
+    @utils.cached_property
+    def ctypes(self):
+        """
+        A ctypes function object representing the C callback.
+        """
+        ctypes_args = [to_ctypes(ty) for ty in self._sig.args]
+        ctypes_restype = to_ctypes(self._sig.return_type)
+        functype = ctypes.CFUNCTYPE(ctypes_restype, *ctypes_args)
+        return functype(self.address)
+
+    def inspect_llvm(self):
+        """
+        Return the LLVM IR of the C callback definition.
+        """
+        return self._library.get_llvm_str()
+
+    @property
+    def cache_hits(self):
+        return self._cache_hits
+
+    def __repr__(self):
+        return "<Numba C callback %r>" % (self.__qualname__,)
+
+    def __call__(self, *args, **kwargs):
+        return self._pyfunc(*args, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cgutils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cgutils.py
new file mode 100644
index 000000000..3efe47f84
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cgutils.py
@@ -0,0 +1,1194 @@
+"""
+Generic helpers for LLVM code generation.
+"""
+
+
+import collections
+from contextlib import contextmanager
+import functools
+
+from llvmlite import ir
+
+from numba.core import utils, types, config, debuginfo
+import numba.core.datamodel
+
+
+bool_t = ir.IntType(1)
+int8_t = ir.IntType(8)
+int32_t = ir.IntType(32)
+intp_t = ir.IntType(utils.MACHINE_BITS)
+voidptr_t = int8_t.as_pointer()
+
+true_bit = bool_t(1)
+false_bit = bool_t(0)
+true_byte = int8_t(1)
+false_byte = int8_t(0)
+
+
+def as_bool_bit(builder, value):
+    return builder.icmp_unsigned('!=', value, value.type(0))
+
+
+def make_anonymous_struct(builder, values, struct_type=None):
+    """
+    Create an anonymous struct containing the given LLVM *values*.
+    """
+    if struct_type is None:
+        struct_type = ir.LiteralStructType([v.type for v in values])
+    struct_val = struct_type(ir.Undefined)
+    for i, v in enumerate(values):
+        struct_val = builder.insert_value(struct_val, v, i)
+    return struct_val
+
+
+def make_bytearray(buf):
+    """
+    Make a byte array constant from *buf*.
+    """
+    b = bytearray(buf)
+    n = len(b)
+    return ir.Constant(ir.ArrayType(ir.IntType(8), n), b)
+
+
+_struct_proxy_cache = {}
+
+
+def create_struct_proxy(fe_type, kind='value'):
+    """
+    Returns a specialized StructProxy subclass for the given fe_type.
+    """
+    cache_key = (fe_type, kind)
+    res = _struct_proxy_cache.get(cache_key)
+    if res is None:
+        base = {'value': ValueStructProxy,
+                'data': DataStructProxy,
+                }[kind]
+        clsname = base.__name__ + '_' + str(fe_type)
+        bases = (base,)
+        clsmembers = dict(_fe_type=fe_type)
+        res = type(clsname, bases, clsmembers)
+
+        _struct_proxy_cache[cache_key] = res
+    return res
+
+
+def copy_struct(dst, src, repl={}):
+    """
+    Copy structure from *src* to *dst* with replacement from *repl*.
+    """
+    repl = repl.copy()
+    # copy data from src or use those in repl
+    for k in src._datamodel._fields:
+        v = repl.pop(k, getattr(src, k))
+        setattr(dst, k, v)
+    # use remaining key-values in repl
+    for k, v in repl.items():
+        setattr(dst, k, v)
+    return dst
+
+
+class _StructProxy(object):
+    """
+    Creates a `Structure` like interface that is constructed with information
+    from DataModel instance.  FE type must have a data model that is a
+    subclass of StructModel.
+    """
+    # The following class members must be overridden by subclass
+    _fe_type = None
+
+    def __init__(self, context, builder, value=None, ref=None):
+        self._context = context
+        self._datamodel = self._context.data_model_manager[self._fe_type]
+        if not isinstance(self._datamodel, numba.core.datamodel.StructModel):
+            raise TypeError(
+                "Not a structure model: {0}".format(self._datamodel))
+        self._builder = builder
+
+        self._be_type = self._get_be_type(self._datamodel)
+        assert not is_pointer(self._be_type)
+
+        outer_ref, ref = self._make_refs(ref)
+        if ref.type.pointee != self._be_type:
+            raise AssertionError("bad ref type: expected %s, got %s"
+                                 % (self._be_type.as_pointer(), ref.type))
+
+        if value is not None:
+            if value.type != outer_ref.type.pointee:
+                raise AssertionError("bad value type: expected %s, got %s"
+                                     % (outer_ref.type.pointee, value.type))
+            self._builder.store(value, outer_ref)
+
+        self._value = ref
+        self._outer_ref = outer_ref
+
+    def _make_refs(self, ref):
+        """
+        Return an (outer ref, value ref) pair.  By default, these are
+        the same pointers, but a derived class may override this.
+        """
+        if ref is None:
+            ref = alloca_once(self._builder, self._be_type, zfill=True)
+        return ref, ref
+
+    def _get_be_type(self, datamodel):
+        raise NotImplementedError
+
+    def _cast_member_to_value(self, index, val):
+        raise NotImplementedError
+
+    def _cast_member_from_value(self, index, val):
+        raise NotImplementedError
+
+    def _get_ptr_by_index(self, index):
+        return gep_inbounds(self._builder, self._value, 0, index)
+
+    def _get_ptr_by_name(self, attrname):
+        index = self._datamodel.get_field_position(attrname)
+        return self._get_ptr_by_index(index)
+
+    def __getattr__(self, field):
+        """
+        Load the LLVM value of the named *field*.
+        """
+        if not field.startswith('_'):
+            return self[self._datamodel.get_field_position(field)]
+        else:
+            raise AttributeError(field)
+
+    def __setattr__(self, field, value):
+        """
+        Store the LLVM *value* into the named *field*.
+        """
+        if field.startswith('_'):
+            return super(_StructProxy, self).__setattr__(field, value)
+        self[self._datamodel.get_field_position(field)] = value
+
+    def __getitem__(self, index):
+        """
+        Load the LLVM value of the field at *index*.
+        """
+        member_val = self._builder.load(self._get_ptr_by_index(index))
+        return self._cast_member_to_value(index, member_val)
+
+    def __setitem__(self, index, value):
+        """
+        Store the LLVM *value* into the field at *index*.
+        """
+        ptr = self._get_ptr_by_index(index)
+        value = self._cast_member_from_value(index, value)
+        if value.type != ptr.type.pointee:
+            if (is_pointer(value.type) and is_pointer(ptr.type.pointee)
+                    and value.type.pointee == ptr.type.pointee.pointee):
+                # Differ by address-space only
+                # Auto coerce it
+                value = self._context.addrspacecast(self._builder,
+                                                    value,
+                                                    ptr.type.pointee.addrspace)
+            else:
+                raise TypeError("Invalid store of {value.type} to "
+                                "{ptr.type.pointee} in "
+                                "{self._datamodel} "
+                                "(trying to write member #{index})"
+                                .format(value=value, ptr=ptr, self=self,
+                                        index=index))
+        self._builder.store(value, ptr)
+
+    def __len__(self):
+        """
+        Return the number of fields.
+        """
+        return self._datamodel.field_count
+
+    def _getpointer(self):
+        """
+        Return the LLVM pointer to the underlying structure.
+        """
+        return self._outer_ref
+
+    def _getvalue(self):
+        """
+        Load and return the value of the underlying LLVM structure.
+        """
+        return self._builder.load(self._outer_ref)
+
+    def _setvalue(self, value):
+        """
+        Store the value in this structure.
+        """
+        assert not is_pointer(value.type)
+        assert value.type == self._be_type, (value.type, self._be_type)
+        self._builder.store(value, self._value)
+
+
+class ValueStructProxy(_StructProxy):
+    """
+    Create a StructProxy suitable for accessing regular values
+    (e.g. LLVM values or alloca slots).
+    """
+    def _get_be_type(self, datamodel):
+        return datamodel.get_value_type()
+
+    def _cast_member_to_value(self, index, val):
+        return val
+
+    def _cast_member_from_value(self, index, val):
+        return val
+
+
+class DataStructProxy(_StructProxy):
+    """
+    Create a StructProxy suitable for accessing data persisted in memory.
+    """
+    def _get_be_type(self, datamodel):
+        return datamodel.get_data_type()
+
+    def _cast_member_to_value(self, index, val):
+        model = self._datamodel.get_model(index)
+        return model.from_data(self._builder, val)
+
+    def _cast_member_from_value(self, index, val):
+        model = self._datamodel.get_model(index)
+        return model.as_data(self._builder, val)
+
+
+class Structure(object):
+    """
+    A high-level object wrapping a alloca'ed LLVM structure, including
+    named fields and attribute access.
+    """
+
+    # XXX Should this warrant several separate constructors?
+    def __init__(self, context, builder, value=None, ref=None, cast_ref=False):
+        self._type = context.get_struct_type(self)
+        self._context = context
+        self._builder = builder
+        if ref is None:
+            self._value = alloca_once(builder, self._type, zfill=True)
+            if value is not None:
+                assert not is_pointer(value.type)
+                assert value.type == self._type, (value.type, self._type)
+                builder.store(value, self._value)
+        else:
+            assert value is None
+            assert is_pointer(ref.type)
+            if self._type != ref.type.pointee:
+                if cast_ref:
+                    ref = builder.bitcast(ref, self._type.as_pointer())
+                else:
+                    raise TypeError(
+                        "mismatching pointer type: got %s, expected %s"
+                        % (ref.type.pointee, self._type))
+            self._value = ref
+
+        self._namemap = {}
+        self._fdmap = []
+        self._typemap = []
+        base = int32_t(0)
+        for i, (k, tp) in enumerate(self._fields):
+            self._namemap[k] = i
+            self._fdmap.append((base, int32_t(i)))
+            self._typemap.append(tp)
+
+    def _get_ptr_by_index(self, index):
+        ptr = self._builder.gep(self._value, self._fdmap[index], inbounds=True)
+        return ptr
+
+    def _get_ptr_by_name(self, attrname):
+        return self._get_ptr_by_index(self._namemap[attrname])
+
+    def __getattr__(self, field):
+        """
+        Load the LLVM value of the named *field*.
+        """
+        if not field.startswith('_'):
+            return self[self._namemap[field]]
+        else:
+            raise AttributeError(field)
+
+    def __setattr__(self, field, value):
+        """
+        Store the LLVM *value* into the named *field*.
+        """
+        if field.startswith('_'):
+            return super(Structure, self).__setattr__(field, value)
+        self[self._namemap[field]] = value
+
+    def __getitem__(self, index):
+        """
+        Load the LLVM value of the field at *index*.
+        """
+
+        return self._builder.load(self._get_ptr_by_index(index))
+
+    def __setitem__(self, index, value):
+        """
+        Store the LLVM *value* into the field at *index*.
+        """
+        ptr = self._get_ptr_by_index(index)
+        if ptr.type.pointee != value.type:
+            fmt = "Type mismatch: __setitem__(%d, ...) expected %r but got %r"
+            raise AssertionError(fmt % (index,
+                                        str(ptr.type.pointee),
+                                        str(value.type)))
+        self._builder.store(value, ptr)
+
+    def __len__(self):
+        """
+        Return the number of fields.
+        """
+        return len(self._namemap)
+
+    def _getpointer(self):
+        """
+        Return the LLVM pointer to the underlying structure.
+        """
+        return self._value
+
+    def _getvalue(self):
+        """
+        Load and return the value of the underlying LLVM structure.
+        """
+        return self._builder.load(self._value)
+
+    def _setvalue(self, value):
+        """Store the value in this structure"""
+        assert not is_pointer(value.type)
+        assert value.type == self._type, (value.type, self._type)
+        self._builder.store(value, self._value)
+
+    # __iter__ is derived by Python from __len__ and __getitem__
+
+
+def alloca_once(builder, ty, size=None, name='', zfill=False):
+    """Allocate stack memory at the entry block of the current function
+    pointed by ``builder`` with llvm type ``ty``.  The optional ``size`` arg
+    set the number of element to allocate.  The default is 1.  The optional
+    ``name`` arg set the symbol name inside the llvm IR for debugging.
+    If ``zfill`` is set, fill the memory with zeros at the current
+    use-site location.  Note that the memory is always zero-filled after the
+    ``alloca`` at init-site (the entry block).
+    """
+    if isinstance(size, int):
+        size = ir.Constant(intp_t, size)
+    # suspend debug metadata emission else it links up python source lines with
+    # alloca in the entry block as well as their actual location and it makes
+    # the debug info "jump about".
+    with debuginfo.suspend_emission(builder):
+        with builder.goto_entry_block():
+            ptr = builder.alloca(ty, size=size, name=name)
+            # Always zero-fill at init-site.  This is safe.
+            builder.store(ty(None), ptr)
+        # Also zero-fill at the use-site
+        if zfill:
+            builder.store(ptr.type.pointee(None), ptr)
+        return ptr
+
+
+def sizeof(builder, ptr_type):
+    """Compute sizeof using GEP
+    """
+    null = ptr_type(None)
+    offset = null.gep([int32_t(1)])
+    return builder.ptrtoint(offset, intp_t)
+
+
+def alloca_once_value(builder, value, name='', zfill=False):
+    """
+    Like alloca_once(), but passing a *value* instead of a type.  The
+    type is inferred and the allocated slot is also initialized with the
+    given value.
+    """
+    storage = alloca_once(builder, value.type, zfill=zfill)
+    builder.store(value, storage)
+    return storage
+
+
+def insert_pure_function(module, fnty, name):
+    """
+    Insert a pure function (in the functional programming sense) in the
+    given module.
+    """
+    fn = get_or_insert_function(module, fnty, name)
+    fn.attributes.add("readonly")
+    fn.attributes.add("nounwind")
+    return fn
+
+
+def get_or_insert_function(module, fnty, name):
+    """
+    Get the function named *name* with type *fnty* from *module*, or insert it
+    if it doesn't exist.
+    """
+    fn = module.globals.get(name, None)
+    if fn is None:
+        fn = ir.Function(module, fnty, name)
+    return fn
+
+
+def get_or_insert_named_metadata(module, name):
+    try:
+        return module.get_named_metadata(name)
+    except KeyError:
+        return module.add_named_metadata(name)
+
+
+def add_global_variable(module, ty, name, addrspace=0):
+    unique_name = module.get_unique_name(name)
+    return ir.GlobalVariable(module, ty, unique_name, addrspace)
+
+
+def terminate(builder, bbend):
+    bb = builder.basic_block
+    if bb.terminator is None:
+        builder.branch(bbend)
+
+
+def get_null_value(ltype):
+    return ltype(None)
+
+
+def is_null(builder, val):
+    null = get_null_value(val.type)
+    return builder.icmp_unsigned('==', null, val)
+
+
+def is_not_null(builder, val):
+    null = get_null_value(val.type)
+    return builder.icmp_unsigned('!=', null, val)
+
+
+def if_unlikely(builder, pred):
+    return builder.if_then(pred, likely=False)
+
+
+def if_likely(builder, pred):
+    return builder.if_then(pred, likely=True)
+
+
+def ifnot(builder, pred):
+    return builder.if_then(builder.not_(pred))
+
+
+def increment_index(builder, val):
+    """
+    Increment an index *val*.
+    """
+    one = val.type(1)
+    # We pass the "nsw" flag in the hope that LLVM understands the index
+    # never changes sign.  Unfortunately this doesn't always work
+    # (e.g. ndindex()).
+    return builder.add(val, one, flags=['nsw'])
+
+
+Loop = collections.namedtuple('Loop', ('index', 'do_break'))
+
+
+@contextmanager
+def for_range(builder, count, start=None, intp=None):
+    """
+    Generate LLVM IR for a for-loop in [start, count).
+    *start* is equal to 0 by default.
+
+    Yields a Loop namedtuple with the following members:
+    - `index` is the loop index's value
+    - `do_break` is a no-argument callable to break out of the loop
+    """
+    if intp is None:
+        intp = count.type
+    if start is None:
+        start = intp(0)
+    stop = count
+
+    bbcond = builder.append_basic_block("for.cond")
+    bbbody = builder.append_basic_block("for.body")
+    bbend = builder.append_basic_block("for.end")
+
+    def do_break():
+        builder.branch(bbend)
+
+    bbstart = builder.basic_block
+    builder.branch(bbcond)
+
+    with builder.goto_block(bbcond):
+        index = builder.phi(intp, name="loop.index")
+        pred = builder.icmp_signed('<', index, stop)
+        builder.cbranch(pred, bbbody, bbend)
+
+    with builder.goto_block(bbbody):
+        yield Loop(index, do_break)
+        # Update bbbody as a new basic block may have been activated
+        bbbody = builder.basic_block
+        incr = increment_index(builder, index)
+        terminate(builder, bbcond)
+
+    index.add_incoming(start, bbstart)
+    index.add_incoming(incr, bbbody)
+
+    builder.position_at_end(bbend)
+
+
+@contextmanager
+def for_range_slice(builder, start, stop, step, intp=None, inc=True):
+    """
+    Generate LLVM IR for a for-loop based on a slice.  Yields a
+    (index, count) tuple where `index` is the slice index's value
+    inside the loop, and `count` the iteration count.
+
+    Parameters
+    -------------
+    builder : object
+        IRBuilder object
+    start : int
+        The beginning value of the slice
+    stop : int
+        The end value of the slice
+    step : int
+        The step value of the slice
+    intp :
+        The data type
+    inc : boolean, optional
+        Signals whether the step is positive (True) or negative (False).
+
+    Returns
+    -----------
+        None
+    """
+    if intp is None:
+        intp = start.type
+
+    bbcond = builder.append_basic_block("for.cond")
+    bbbody = builder.append_basic_block("for.body")
+    bbend = builder.append_basic_block("for.end")
+    bbstart = builder.basic_block
+    builder.branch(bbcond)
+
+    with builder.goto_block(bbcond):
+        index = builder.phi(intp, name="loop.index")
+        count = builder.phi(intp, name="loop.count")
+        if (inc):
+            pred = builder.icmp_signed('<', index, stop)
+        else:
+            pred = builder.icmp_signed('>', index, stop)
+        builder.cbranch(pred, bbbody, bbend)
+
+    with builder.goto_block(bbbody):
+        yield index, count
+        bbbody = builder.basic_block
+        incr = builder.add(index, step)
+        next_count = increment_index(builder, count)
+        terminate(builder, bbcond)
+
+    index.add_incoming(start, bbstart)
+    index.add_incoming(incr, bbbody)
+    count.add_incoming(ir.Constant(intp, 0), bbstart)
+    count.add_incoming(next_count, bbbody)
+    builder.position_at_end(bbend)
+
+
+@contextmanager
+def for_range_slice_generic(builder, start, stop, step):
+    """
+    A helper wrapper for for_range_slice().  This is a context manager which
+    yields two for_range_slice()-alike context managers, the first for
+    the positive step case, the second for the negative step case.
+
+    Use:
+        with for_range_slice_generic(...) as (pos_range, neg_range):
+            with pos_range as (idx, count):
+                ...
+            with neg_range as (idx, count):
+                ...
+    """
+    intp = start.type
+    is_pos_step = builder.icmp_signed('>=', step, ir.Constant(intp, 0))
+
+    pos_for_range = for_range_slice(builder, start, stop, step, intp, inc=True)
+    neg_for_range = for_range_slice(builder, start, stop, step, intp, inc=False)
+
+    @contextmanager
+    def cm_cond(cond, inner_cm):
+        with cond:
+            with inner_cm as value:
+                yield value
+
+    with builder.if_else(is_pos_step, likely=True) as (then, otherwise):
+        yield cm_cond(then, pos_for_range), cm_cond(otherwise, neg_for_range)
+
+
+@contextmanager
+def loop_nest(builder, shape, intp, order='C'):
+    """
+    Generate a loop nest walking a N-dimensional array.
+    Yields a tuple of N indices for use in the inner loop body,
+    iterating over the *shape* space.
+
+    If *order* is 'C' (the default), indices are incremented inside-out
+    (i.e. (0,0), (0,1), (0,2), (1,0) etc.).
+    If *order* is 'F', they are incremented outside-in
+    (i.e. (0,0), (1,0), (2,0), (0,1) etc.).
+    This has performance implications when walking an array as it impacts
+    the spatial locality of memory accesses.
+    """
+    assert order in 'CF'
+    if not shape:
+        # 0-d array
+        yield ()
+    else:
+        if order == 'F':
+            _swap = lambda x: x[::-1]
+        else:
+            _swap = lambda x: x
+        with _loop_nest(builder, _swap(shape), intp) as indices:
+            assert len(indices) == len(shape)
+            yield _swap(indices)
+
+
+@contextmanager
+def _loop_nest(builder, shape, intp):
+    with for_range(builder, shape[0], intp=intp) as loop:
+        if len(shape) > 1:
+            with _loop_nest(builder, shape[1:], intp) as indices:
+                yield (loop.index,) + indices
+        else:
+            yield (loop.index,)
+
+
+def pack_array(builder, values, ty=None):
+    """
+    Pack a sequence of values in a LLVM array.  *ty* should be given
+    if the array may be empty, in which case the type can't be inferred
+    from the values.
+    """
+    n = len(values)
+    if ty is None:
+        ty = values[0].type
+    ary = ir.ArrayType(ty, n)(ir.Undefined)
+    for i, v in enumerate(values):
+        ary = builder.insert_value(ary, v, i)
+    return ary
+
+
+def pack_struct(builder, values):
+    """
+    Pack a sequence of values into a LLVM struct.
+    """
+    structty = ir.LiteralStructType([v.type for v in values])
+    st = structty(ir.Undefined)
+    for i, v in enumerate(values):
+        st = builder.insert_value(st, v, i)
+    return st
+
+
+def unpack_tuple(builder, tup, count=None):
+    """
+    Unpack an array or structure of values, return a Python tuple.
+    """
+    if count is None:
+        # Assuming *tup* is an aggregate
+        count = len(tup.type.elements)
+    vals = [builder.extract_value(tup, i)
+            for i in range(count)]
+    return vals
+
+
+def get_item_pointer(context, builder, aryty, ary, inds, wraparound=False,
+                     boundscheck=False):
+    # Set boundscheck=True for any pointer access that should be
+    # boundschecked. do_boundscheck() will handle enabling or disabling the
+    # actual boundschecking based on the user config.
+    shapes = unpack_tuple(builder, ary.shape, count=aryty.ndim)
+    strides = unpack_tuple(builder, ary.strides, count=aryty.ndim)
+    return get_item_pointer2(context, builder, data=ary.data, shape=shapes,
+                             strides=strides, layout=aryty.layout, inds=inds,
+                             wraparound=wraparound, boundscheck=boundscheck)
+
+
+def do_boundscheck(context, builder, ind, dimlen, axis=None):
+    def _dbg():
+        # Remove this when we figure out how to include this information
+        # in the error message.
+        if axis is not None:
+            if isinstance(axis, int):
+                printf(builder, "debug: IndexError: index %d is out of bounds "
+                       "for axis {} with size %d\n".format(axis), ind, dimlen)
+            else:
+                printf(builder, "debug: IndexError: index %d is out of bounds "
+                       "for axis %d with size %d\n", ind, axis,
+                       dimlen)
+        else:
+            printf(builder,
+                   "debug: IndexError: index %d is out of bounds for size %d\n",
+                   ind, dimlen)
+
+    msg = "index is out of bounds"
+    out_of_bounds_upper = builder.icmp_signed('>=', ind, dimlen)
+    with if_unlikely(builder, out_of_bounds_upper):
+        if config.FULL_TRACEBACKS:
+            _dbg()
+        context.call_conv.return_user_exc(builder, IndexError, (msg,))
+    out_of_bounds_lower = builder.icmp_signed('<', ind, ind.type(0))
+    with if_unlikely(builder, out_of_bounds_lower):
+        if config.FULL_TRACEBACKS:
+            _dbg()
+        context.call_conv.return_user_exc(builder, IndexError, (msg,))
+
+
+def get_item_pointer2(context, builder, data, shape, strides, layout, inds,
+                      wraparound=False, boundscheck=False):
+    # Set boundscheck=True for any pointer access that should be
+    # boundschecked. do_boundscheck() will handle enabling or disabling the
+    # actual boundschecking based on the user config.
+    if wraparound:
+        # Wraparound
+        indices = []
+        for ind, dimlen in zip(inds, shape):
+            negative = builder.icmp_signed('<', ind, ind.type(0))
+            wrapped = builder.add(dimlen, ind)
+            selected = builder.select(negative, wrapped, ind)
+            indices.append(selected)
+    else:
+        indices = inds
+    if boundscheck:
+        for axis, (ind, dimlen) in enumerate(zip(indices, shape)):
+            do_boundscheck(context, builder, ind, dimlen, axis)
+
+    if not indices:
+        # Indexing with empty tuple
+        return builder.gep(data, [int32_t(0)])
+    intp = indices[0].type
+    # Indexing code
+    if layout in 'CF':
+        steps = []
+        # Compute steps for each dimension
+        if layout == 'C':
+            # C contiguous
+            for i in range(len(shape)):
+                last = intp(1)
+                for j in shape[i + 1:]:
+                    last = builder.mul(last, j)
+                steps.append(last)
+        elif layout == 'F':
+            # F contiguous
+            for i in range(len(shape)):
+                last = intp(1)
+                for j in shape[:i]:
+                    last = builder.mul(last, j)
+                steps.append(last)
+        else:
+            raise Exception("unreachable")
+
+        # Compute index
+        loc = intp(0)
+        for i, s in zip(indices, steps):
+            tmp = builder.mul(i, s)
+            loc = builder.add(loc, tmp)
+        ptr = builder.gep(data, [loc])
+        return ptr
+    else:
+        # Any layout
+        dimoffs = [builder.mul(s, i) for s, i in zip(strides, indices)]
+        offset = functools.reduce(builder.add, dimoffs)
+        return pointer_add(builder, data, offset)
+
+
+def _scalar_pred_against_zero(builder, value, fpred, icond):
+    nullval = value.type(0)
+    if isinstance(value.type, (ir.FloatType, ir.DoubleType)):
+        isnull = fpred(value, nullval)
+    elif isinstance(value.type, ir.IntType):
+        isnull = builder.icmp_signed(icond, value, nullval)
+    else:
+        raise TypeError("unexpected value type %s" % (value.type,))
+    return isnull
+
+
+def is_scalar_zero(builder, value):
+    """
+    Return a predicate representing whether *value* is equal to zero.
+    """
+    return _scalar_pred_against_zero(
+        builder, value, functools.partial(builder.fcmp_ordered, '=='), '==')
+
+
+def is_not_scalar_zero(builder, value):
+    """
+    Return a predicate representing whether a *value* is not equal to zero.
+    (not exactly "not is_scalar_zero" because of nans)
+    """
+    return _scalar_pred_against_zero(
+        builder, value, functools.partial(builder.fcmp_unordered, '!='), '!=')
+
+
+def is_scalar_zero_or_nan(builder, value):
+    """
+    Return a predicate representing whether *value* is equal to either zero
+    or NaN.
+    """
+    return _scalar_pred_against_zero(
+        builder, value, functools.partial(builder.fcmp_unordered, '=='), '==')
+
+
+is_true = is_not_scalar_zero
+is_false = is_scalar_zero
+
+
+def is_scalar_neg(builder, value):
+    """
+    Is *value* negative?  Assumes *value* is signed.
+    """
+    return _scalar_pred_against_zero(
+        builder, value, functools.partial(builder.fcmp_ordered, '<'), '<')
+
+
+def guard_null(context, builder, value, exc_tuple):
+    """
+    Guard against *value* being null or zero.
+    *exc_tuple* should be a (exception type, arguments...) tuple.
+    """
+    with builder.if_then(is_scalar_zero(builder, value), likely=False):
+        exc = exc_tuple[0]
+        exc_args = exc_tuple[1:] or None
+        context.call_conv.return_user_exc(builder, exc, exc_args)
+
+
+def guard_memory_error(context, builder, pointer, msg=None):
+    """
+    Guard against *pointer* being NULL (and raise a MemoryError).
+    """
+    assert isinstance(pointer.type, ir.PointerType), pointer.type
+    exc_args = (msg,) if msg else ()
+    with builder.if_then(is_null(builder, pointer), likely=False):
+        context.call_conv.return_user_exc(builder, MemoryError, exc_args)
+
+
+@contextmanager
+def if_zero(builder, value, likely=False):
+    """
+    Execute the given block if the scalar value is zero.
+    """
+    with builder.if_then(is_scalar_zero(builder, value), likely=likely):
+        yield
+
+
+guard_zero = guard_null
+
+
+def is_pointer(ltyp):
+    """
+    Whether the LLVM type *typ* is a struct type.
+    """
+    return isinstance(ltyp, ir.PointerType)
+
+
+def get_record_member(builder, record, offset, typ):
+    pval = gep_inbounds(builder, record, 0, offset)
+    assert not is_pointer(pval.type.pointee)
+    return builder.bitcast(pval, typ.as_pointer())
+
+
+def is_neg_int(builder, val):
+    return builder.icmp_signed('<', val, val.type(0))
+
+
+def gep_inbounds(builder, ptr, *inds, **kws):
+    """
+    Same as *gep*, but add the `inbounds` keyword.
+    """
+    return gep(builder, ptr, *inds, inbounds=True, **kws)
+
+
+def gep(builder, ptr, *inds, **kws):
+    """
+    Emit a getelementptr instruction for the given pointer and indices.
+    The indices can be LLVM values or Python int constants.
+    """
+    name = kws.pop('name', '')
+    inbounds = kws.pop('inbounds', False)
+    assert not kws
+    idx = []
+    for i in inds:
+        if isinstance(i, int):
+            # NOTE: llvm only accepts int32 inside structs, not int64
+            ind = int32_t(i)
+        else:
+            ind = i
+        idx.append(ind)
+    return builder.gep(ptr, idx, name=name, inbounds=inbounds)
+
+
+def pointer_add(builder, ptr, offset, return_type=None):
+    """
+    Add an integral *offset* to pointer *ptr*, and return a pointer
+    of *return_type* (or, if omitted, the same type as *ptr*).
+
+    Note the computation is done in bytes, and ignores the width of
+    the pointed item type.
+    """
+    intptr = builder.ptrtoint(ptr, intp_t)
+    if isinstance(offset, int):
+        offset = intp_t(offset)
+    intptr = builder.add(intptr, offset)
+    return builder.inttoptr(intptr, return_type or ptr.type)
+
+
+def memset(builder, ptr, size, value):
+    """
+    Fill *size* bytes starting from *ptr* with *value*.
+    """
+    fn = builder.module.declare_intrinsic('llvm.memset', (voidptr_t, size.type))
+    ptr = builder.bitcast(ptr, voidptr_t)
+    if isinstance(value, int):
+        value = int8_t(value)
+    builder.call(fn, [ptr, value, size, bool_t(0)])
+
+
+def memset_padding(builder, ptr):
+    """
+    Fill padding bytes of the pointee with zeros.
+    """
+    # Load existing value
+    val = builder.load(ptr)
+    # Fill pointee with zeros
+    memset(builder, ptr, sizeof(builder, ptr.type), 0)
+    # Store value back
+    builder.store(val, ptr)
+
+
+def global_constant(builder_or_module, name, value, linkage='internal'):
+    """
+    Get or create a (LLVM module-)global constant with *name* or *value*.
+    """
+    if isinstance(builder_or_module, ir.Module):
+        module = builder_or_module
+    else:
+        module = builder_or_module.module
+    data = add_global_variable(module, value.type, name)
+    data.linkage = linkage
+    data.global_constant = True
+    data.initializer = value
+    return data
+
+
+def divmod_by_constant(builder, val, divisor):
+    """
+    Compute the (quotient, remainder) of *val* divided by the constant
+    positive *divisor*.  The semantics reflects those of Python integer
+    floor division, rather than C's / LLVM's signed division and modulo.
+    The difference lies with a negative *val*.
+    """
+    assert divisor > 0
+    divisor = val.type(divisor)
+    one = val.type(1)
+
+    quot = alloca_once(builder, val.type)
+
+    with builder.if_else(is_neg_int(builder, val)) as (if_neg, if_pos):
+        with if_pos:
+            # quot = val / divisor
+            quot_val = builder.sdiv(val, divisor)
+            builder.store(quot_val, quot)
+        with if_neg:
+            # quot = -1 + (val + 1) / divisor
+            val_plus_one = builder.add(val, one)
+            quot_val = builder.sdiv(val_plus_one, divisor)
+            builder.store(builder.sub(quot_val, one), quot)
+
+    # rem = val - quot * divisor
+    # (should be slightly faster than a separate modulo operation)
+    quot_val = builder.load(quot)
+    rem_val = builder.sub(val, builder.mul(quot_val, divisor))
+    return quot_val, rem_val
+
+
+def cbranch_or_continue(builder, cond, bbtrue):
+    """
+    Branch conditionally or continue.
+
+    Note: a new block is created and builder is moved to the end of the new
+          block.
+    """
+    bbcont = builder.append_basic_block('.continue')
+    builder.cbranch(cond, bbtrue, bbcont)
+    builder.position_at_end(bbcont)
+    return bbcont
+
+
+def memcpy(builder, dst, src, count):
+    """
+    Emit a memcpy to the builder.
+
+    Copies each element of dst to src. Unlike the C equivalent, each element
+    can be any LLVM type.
+
+    Assumes
+    -------
+    * dst.type == src.type
+    * count is positive
+    """
+    # Note this does seem to be optimized as a raw memcpy() by LLVM
+    # whenever possible...
+    assert dst.type == src.type
+    with for_range(builder, count, intp=count.type) as loop:
+        out_ptr = builder.gep(dst, [loop.index])
+        in_ptr = builder.gep(src, [loop.index])
+        builder.store(builder.load(in_ptr), out_ptr)
+
+
+def _raw_memcpy(builder, func_name, dst, src, count, itemsize, align):
+    size_t = count.type
+    if isinstance(itemsize, int):
+        itemsize = ir.Constant(size_t, itemsize)
+
+    memcpy = builder.module.declare_intrinsic(func_name,
+                                              [voidptr_t, voidptr_t, size_t])
+    is_volatile = false_bit
+    builder.call(memcpy, [builder.bitcast(dst, voidptr_t),
+                          builder.bitcast(src, voidptr_t),
+                          builder.mul(count, itemsize),
+                          is_volatile])
+
+
+def raw_memcpy(builder, dst, src, count, itemsize, align=1):
+    """
+    Emit a raw memcpy() call for `count` items of size `itemsize`
+    from `src` to `dest`.
+    """
+    return _raw_memcpy(builder, 'llvm.memcpy', dst, src, count, itemsize, align)
+
+
+def raw_memmove(builder, dst, src, count, itemsize, align=1):
+    """
+    Emit a raw memmove() call for `count` items of size `itemsize`
+    from `src` to `dest`.
+    """
+    return _raw_memcpy(builder, 'llvm.memmove', dst, src, count,
+                       itemsize, align)
+
+
+def muladd_with_overflow(builder, a, b, c):
+    """
+    Compute (a * b + c) and return a (result, overflow bit) pair.
+    The operands must be signed integers.
+    """
+    p = builder.smul_with_overflow(a, b)
+    prod = builder.extract_value(p, 0)
+    prod_ovf = builder.extract_value(p, 1)
+    s = builder.sadd_with_overflow(prod, c)
+    res = builder.extract_value(s, 0)
+    ovf = builder.or_(prod_ovf, builder.extract_value(s, 1))
+    return res, ovf
+
+
+def printf(builder, format, *args):
+    """
+    Calls printf().
+    Argument `format` is expected to be a Python string.
+    Values to be printed are listed in `args`.
+
+    Note: There is no checking to ensure there is correct number of values
+    in `args` and there type matches the declaration in the format string.
+    """
+    assert isinstance(format, str)
+    mod = builder.module
+    # Make global constant for format string
+    cstring = voidptr_t
+    fmt_bytes = make_bytearray((format + '\00').encode('ascii'))
+    global_fmt = global_constant(mod, "printf_format", fmt_bytes)
+    fnty = ir.FunctionType(int32_t, [cstring], var_arg=True)
+    # Insert printf()
+    try:
+        fn = mod.get_global('printf')
+    except KeyError:
+        fn = ir.Function(mod, fnty, name="printf")
+    # Call
+    ptr_fmt = builder.bitcast(global_fmt, cstring)
+    return builder.call(fn, [ptr_fmt] + list(args))
+
+
+def snprintf(builder, buffer, bufsz, format, *args):
+    """Calls libc snprintf(buffer, bufsz, format, ...args)
+    """
+    assert isinstance(format, str)
+    mod = builder.module
+    # Make global constant for format string
+    cstring = voidptr_t
+    fmt_bytes = make_bytearray((format + '\00').encode('ascii'))
+    global_fmt = global_constant(mod, "snprintf_format", fmt_bytes)
+    fnty = ir.FunctionType(
+        int32_t, [cstring, intp_t, cstring], var_arg=True,
+    )
+    # Actual symbol name of snprintf is different on win32.
+    symbol = 'snprintf'
+    if config.IS_WIN32:
+        symbol = '_' + symbol
+    # Insert snprintf()
+    try:
+        fn = mod.get_global(symbol)
+    except KeyError:
+        fn = ir.Function(mod, fnty, name=symbol)
+    # Call
+    ptr_fmt = builder.bitcast(global_fmt, cstring)
+    return builder.call(fn, [buffer, bufsz, ptr_fmt] + list(args))
+
+
+def snprintf_stackbuffer(builder, bufsz, format, *args):
+    """Similar to `snprintf()` but the buffer is stack allocated to size *bufsz*.
+
+    Returns the buffer pointer as i8*.
+    """
+    assert isinstance(bufsz, int)
+    spacety = ir.ArrayType(ir.IntType(8), bufsz)
+    space = alloca_once(builder, spacety, zfill=True)
+    buffer = builder.bitcast(space, voidptr_t)
+    snprintf(builder, buffer, intp_t(bufsz), format, *args)
+    return buffer
+
+
+def normalize_ir_text(text):
+    """
+    Normalize the given string to latin1 compatible encoding that is
+    suitable for use in LLVM IR.
+    """
+    # Just re-encoding to latin1 is enough
+    return text.encode('utf8').decode('latin1')
+
+
+def hexdump(builder, ptr, nbytes):
+    """Debug print the memory region in *ptr* to *ptr + nbytes*
+    as hex.
+    """
+    bytes_per_line = 16
+    nbytes = builder.zext(nbytes, intp_t)
+    printf(builder, "hexdump p=%p n=%zu",
+           ptr, nbytes)
+    byte_t = ir.IntType(8)
+    ptr = builder.bitcast(ptr, byte_t.as_pointer())
+    # Loop to print the bytes in *ptr* as hex
+    with for_range(builder, nbytes) as idx:
+        div_by = builder.urem(idx.index, intp_t(bytes_per_line))
+        do_new_line = builder.icmp_unsigned("==", div_by, intp_t(0))
+        with builder.if_then(do_new_line):
+            printf(builder, "\n")
+
+        offset = builder.gep(ptr, [idx.index])
+        val = builder.load(offset)
+        printf(builder, " %02x", val)
+    printf(builder, "\n")
+
+
+def is_nonelike(ty):
+    """ returns if 'ty' is none """
+    return (
+        ty is None or
+        isinstance(ty, types.NoneType) or
+        isinstance(ty, types.Omitted)
+    )
+
+
+def create_constant_array(ty, val):
+    """
+    Create an LLVM-constant of a fixed-length array from Python values.
+
+    The type provided is the type of the elements.
+    """
+    return ir.Constant(ir.ArrayType(ty, len(val)), val)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/codegen.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/codegen.py
new file mode 100644
index 000000000..e988fab12
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/codegen.py
@@ -0,0 +1,1437 @@
+import warnings
+import functools
+import locale
+import weakref
+import ctypes
+import html
+import textwrap
+
+import llvmlite.binding as ll
+import llvmlite.ir as llvmir
+
+from abc import abstractmethod, ABCMeta
+from numba.core import utils, config, cgutils
+from numba.core.llvm_bindings import create_pass_manager_builder
+from numba.core.runtime.nrtopt import remove_redundant_nrt_refct
+from numba.core.runtime import rtsys
+from numba.core.compiler_lock import require_global_compiler_lock
+from numba.core.errors import NumbaInvalidConfigWarning
+from numba.misc.inspection import disassemble_elf_to_cfg
+from numba.misc.llvm_pass_timings import PassTimingsCollection
+
+
+_x86arch = frozenset(['x86', 'i386', 'i486', 'i586', 'i686', 'i786',
+                      'i886', 'i986'])
+
+
+def _is_x86(triple):
+    arch = triple.split('-')[0]
+    return arch in _x86arch
+
+
+def _parse_refprune_flags():
+    """Parse refprune flags from the `config`.
+
+    Invalid values are ignored an warn via a `NumbaInvalidConfigWarning`
+    category.
+
+    Returns
+    -------
+    flags : llvmlite.binding.RefPruneSubpasses
+    """
+    flags = config.LLVM_REFPRUNE_FLAGS.split(',')
+    if not flags:
+        return 0
+    val = 0
+    for item in flags:
+        item = item.strip()
+        try:
+            val |= getattr(ll.RefPruneSubpasses, item.upper())
+        except AttributeError:
+            warnings.warn(f"invalid refprune flags {item!r}",
+                          NumbaInvalidConfigWarning)
+    return val
+
+
+def dump(header, body, lang):
+    if config.HIGHLIGHT_DUMPS:
+        try:
+            import pygments
+        except ImportError:
+            msg = "Please install pygments to see highlighted dumps"
+            raise ValueError(msg)
+        else:
+            from pygments import highlight
+            from pygments.lexers import GasLexer as gas_lexer
+            from pygments.lexers import LlvmLexer as llvm_lexer
+            from pygments.formatters import Terminal256Formatter
+            from numba.misc.dump_style import by_colorscheme
+
+            lexer_map = {'llvm': llvm_lexer, 'asm': gas_lexer}
+            lexer = lexer_map[lang]
+            def printer(arg):
+                print(highlight(arg, lexer(),
+                      Terminal256Formatter(style=by_colorscheme())))
+    else:
+        printer = print
+    print('=' * 80)
+    print(header.center(80, '-'))
+    printer(body)
+    print('=' * 80)
+
+
+class _CFG(object):
+    """
+    Wraps the CFG graph for different display method.
+
+    Instance of the class can be stringified (``__repr__`` is defined) to get
+    the graph in DOT format.  The ``.display()`` method plots the graph in
+    PDF.  If in IPython notebook, the returned image can be inlined.
+    """
+    def __init__(self, cres, name, py_func, **kwargs):
+        self.cres = cres
+        self.name = name
+        self.py_func = py_func
+        fn = cres.get_function(name)
+        self.dot = ll.get_function_cfg(fn)
+        self.kwargs = kwargs
+
+    def pretty_printer(self, filename=None, view=None, render_format=None,
+                       highlight=True,
+                       interleave=False, strip_ir=False, show_key=True,
+                       fontsize=10):
+        """
+        "Pretty" prints the DOT graph of the CFG.
+        For explanation of the parameters see the docstring for
+        numba.core.dispatcher::inspect_cfg.
+        """
+        import graphviz as gv
+        import re
+        import json
+        import inspect
+        from llvmlite import binding as ll
+        from numba.typed import List
+        from types import SimpleNamespace
+        from collections import defaultdict
+
+        _default = False
+        _highlight = SimpleNamespace(incref=_default,
+                                    decref=_default,
+                                    returns=_default,
+                                    raises=_default,
+                                    meminfo=_default,
+                                    branches=_default,
+                                    llvm_intrin_calls=_default,
+                                    function_calls=_default,)
+        _interleave = SimpleNamespace(python=_default, lineinfo=_default)
+
+        def parse_config(_config, kwarg):
+            """ Parses the kwarg into a consistent format for use in configuring
+            the Digraph rendering. _config is the configuration instance to
+            update, kwarg is the kwarg on which to base the updates.
+            """
+            if isinstance(kwarg, bool):
+                for attr in _config.__dict__:
+                    setattr(_config, attr, kwarg)
+            elif isinstance(kwarg, dict):
+                for k, v in kwarg.items():
+                    if k not in _config.__dict__:
+                        raise ValueError("Unexpected key in kwarg: %s" % k)
+                    if isinstance(v, bool):
+                        setattr(_config, k, v)
+                    else:
+                        msg = "Unexpected value for key: %s, got:%s"
+                        raise ValueError(msg % (k, v))
+            elif isinstance(kwarg, set):
+                for item in kwarg:
+                    if item not in _config.__dict__:
+                        raise ValueError("Unexpected key in kwarg: %s" % item)
+                    else:
+                        setattr(_config, item, True)
+            else:
+                msg = "Unhandled configuration type for kwarg %s"
+                raise ValueError(msg % type(kwarg))
+
+        parse_config(_highlight, highlight)
+        parse_config(_interleave, interleave)
+
+        # This is the colour scheme. The graphviz HTML label renderer only takes
+        # names for colours: https://www.graphviz.org/doc/info/shapes.html#html
+        cs = defaultdict(lambda: 'white') # default bg colour is white
+        cs['marker'] = 'orange'
+        cs['python'] = 'yellow'
+        cs['truebr'] = 'green'
+        cs['falsebr'] = 'red'
+        cs['incref'] = 'cyan'
+        cs['decref'] = 'turquoise'
+        cs['raise'] = 'lightpink'
+        cs['meminfo'] = 'lightseagreen'
+        cs['return'] = 'purple'
+        cs['llvm_intrin_calls'] = 'rosybrown'
+        cs['function_calls'] = 'tomato'
+
+        # Get the raw dot format information from LLVM and the LLVM IR
+        fn = self.cres.get_function(self.name)
+        #raw_dot = ll.get_function_cfg(fn).replace('\\l...', '')
+        llvm_str = self.cres.get_llvm_str()
+
+        def get_metadata(llvm_str):
+            """ Gets the metadata entries from the LLVM IR, these look something
+            like '!123 = INFORMATION'. Returns a map of metadata key to metadata
+            value, i.e. from the example {'!123': INFORMATION}"""
+            md = {}
+            metadata_entry = re.compile(r'(^[!][0-9]+)(\s+=\s+.*)')
+            for x in llvm_str.splitlines():
+                match = metadata_entry.match(x)
+                if match is not None:
+                    g = match.groups()
+                    if g is not None:
+                        assert len(g) == 2
+                        md[g[0]] = g[1]
+            return md
+
+        md = get_metadata(llvm_str)
+
+        # setup digraph with initial properties
+        def init_digraph(name, fname, fontsize):
+            # name and fname are arbitrary graph and file names, they appear in
+            # some rendering formats, the fontsize determines the output
+            # fontsize.
+
+            # truncate massive mangled names as file names as it causes OSError
+            # when trying to render to pdf
+            cmax = 200
+            if len(fname) > cmax:
+                wstr = (f'CFG output filename "{fname}" exceeds maximum '
+                        f'supported length, it will be truncated.')
+                warnings.warn(wstr, NumbaInvalidConfigWarning)
+                fname = fname[:cmax]
+            f = gv.Digraph(name, filename=fname)
+            f.attr(rankdir='TB')
+            f.attr('node', shape='none', fontsize='%s' % str(fontsize))
+            return f
+
+        f = init_digraph(self.name, self.name, fontsize)
+
+        # A lot of regex is needed to parse the raw dot output. This output
+        # contains a mix of LLVM IR in the labels, and also DOT markup.
+
+        # DOT syntax, matches a "port" (where the tail of an edge starts)
+        port_match = re.compile('.*{(.*)}.*')
+        # DOT syntax, matches the "port" value from a found "port_match"
+        port_jmp_match = re.compile('.*<(.*)>(.*)')
+        # LLVM syntax, matches a LLVM debug marker
+        metadata_marker = re.compile(r'.*!dbg\s+(![0-9]+).*')
+        # LLVM syntax, matches a location entry
+        location_expr = (r'.*!DILocation\(line:\s+([0-9]+),'
+                         r'\s+column:\s+([0-9]),.*')
+        location_entry = re.compile(location_expr)
+        # LLVM syntax, matches LLVMs internal debug value calls
+        dbg_value = re.compile(r'.*call void @llvm.dbg.value.*')
+        # LLVM syntax, matches tokens for highlighting
+        nrt_incref = re.compile(r"@NRT_incref\b")
+        nrt_decref = re.compile(r"@NRT_decref\b")
+        nrt_meminfo = re.compile("@NRT_MemInfo")
+        ll_intrin_calls = re.compile(r".*call.*@llvm\..*")
+        ll_function_call = re.compile(r".*call.*@.*")
+        ll_raise = re.compile(r"ret i32.*\!ret_is_raise.*")
+        ll_return = re.compile("ret i32 [^1],?.*")
+
+        # wrapper function for line wrapping LLVM lines
+        def wrap(s):
+            return textwrap.wrap(s, width=120, subsequent_indent='... ')
+
+        # function to fix (sometimes escaped for DOT!) LLVM IR etc that needs to
+        # be HTML escaped
+        def clean(s):
+            # Grab first 300 chars only, 1. this should be enough to identify
+            # the token and it keeps names short. 2. graphviz/dot has a maximum
+            # buffer size near 585?!, with additional transforms it's hard to
+            # know if this would be exceeded. 3. hash of the token string is
+            # written into the rendering to permit exact identification against
+            # e.g. LLVM IR dump if necessary.
+            n = 300
+            if len(s) > n:
+                hs = str(hash(s))
+                s = '{}...<hash={}>'.format(s[:n], hs)
+            s = html.escape(s) # deals with  &, < and >
+            s = s.replace('\\{', "&#123;")
+            s = s.replace('\\}', "&#125;")
+            s = s.replace('\\', "&#92;")
+            s = s.replace('%', "&#37;")
+            s = s.replace('!', "&#33;")
+            return s
+
+        # These hold the node and edge ids from the raw dot information. They
+        # are used later to wire up a new DiGraph that has the same structure
+        # as the raw dot but with new nodes.
+        node_ids = {}
+        edge_ids = {}
+
+        # Python source lines, used if python source interleave is requested
+        if _interleave.python:
+            src_code, firstlineno = inspect.getsourcelines(self.py_func)
+
+        # This is the dot info from LLVM, it's in DOT form and has continuation
+        # lines, strip them and then re-parse into `dot_json` form for use in
+        # producing a formatted output.
+        raw_dot = ll.get_function_cfg(fn).replace('\\l...', '')
+        json_bytes = gv.Source(raw_dot).pipe(format='dot_json')
+        jzon = json.loads(json_bytes.decode('utf-8'))
+
+        idc = 0
+        # Walk the "objects" (nodes) in the DOT output
+        for obj in jzon['objects']:
+            # These are used to keep tabs on the current line and column numbers
+            # as per the markers. They are tracked so as to make sure a marker
+            # is only emitted if there's a change in the marker.
+            cur_line, cur_col = -1, -1
+            label = obj['label']
+            name = obj['name']
+            gvid = obj['_gvid']
+            node_ids[gvid] = name
+            # Label is DOT format, it needs the head and tail removing and then
+            # splitting for walking.
+            label = label[1:-1]
+            lines = label.split('\\l')
+
+            # Holds the new lines
+            new_lines = []
+
+            # Aim is to produce an HTML table a bit like this:
+            #
+            # |------------|
+            # | HEADER     | <-- this is the block header
+            # |------------|
+            # | LLVM SRC   | <--
+            # | Marker?    | < this is the label/block body
+            # | Python src?| <--
+            # |------------|
+            # | T   |  F   |  <-- this is the "ports", also determines col_span
+            # --------------
+            #
+
+            # This is HTML syntax, its the column span. If there's a switch or a
+            # branch at the bottom of the node this is rendered as multiple
+            # columns in a table. First job is to go and render that and work
+            # out how many columns are needed as that dictates how many columns
+            # the rest of the source lines must span. In DOT syntax the places
+            # that edges join nodes are referred to as "ports". Syntax in DOT
+            # is like `node:port`.
+            col_span = 1
+
+            # First see if there is a port entry for this node
+            port_line = ''
+            matched = port_match.match(lines[-1])
+            sliced_lines = lines
+            if matched is not None:
+                # There is a port
+                ports = matched.groups()[0]
+                ports_tokens = ports.split('|')
+                col_span = len(ports_tokens)
+                # Generate HTML table data cells, one for each port. If the
+                # ports correspond to a branch then they can optionally
+                # highlighted based on T/F.
+                tdfmt = ('<td BGCOLOR="{}" BORDER="1" ALIGN="center" '
+                         'PORT="{}">{}</td>')
+                tbl_data = []
+                if _highlight.branches:
+                    colors = {'T': cs['truebr'], 'F': cs['falsebr']}
+                else:
+                    colors = {}
+                for tok in ports_tokens:
+                    target, value = port_jmp_match.match(tok).groups()
+                    color = colors.get(value, 'white')
+                    tbl_data.append(tdfmt.format(color, target, value))
+                port_line = ''.join(tbl_data)
+                # Drop the last line from the rest of the parse as it's the port
+                # and just been dealt with.
+                sliced_lines = lines[:-1]
+
+            # loop peel the block header, it needs a HTML border
+            fmtheader = ('<tr><td BGCOLOR="{}" BORDER="1" ALIGN="left" '
+                         'COLSPAN="{}">{}</td></tr>')
+            new_lines.append(fmtheader.format(cs['default'], col_span,
+                                              clean(sliced_lines[0].strip())))
+
+            # process rest of block creating the table row at a time.
+            fmt = ('<tr><td BGCOLOR="{}" BORDER="0" ALIGN="left" '
+                   'COLSPAN="{}">{}</td></tr>')
+
+            def metadata_interleave(l, new_lines):
+                """
+                Search line `l` for metadata associated with python or line info
+                and inject it into `new_lines` if requested.
+                """
+                matched = metadata_marker.match(l)
+                if matched is not None:
+                    # there's a metadata marker
+                    g = matched.groups()
+                    if g is not None:
+                        assert len(g) == 1, g
+                        marker = g[0]
+                        debug_data = md.get(marker, None)
+                        if debug_data is not None:
+                            # and the metadata marker has a corresponding piece
+                            # of metadata
+                            ld = location_entry.match(debug_data)
+                            if ld is not None:
+                                # and the metadata is line info... proceed
+                                assert len(ld.groups()) == 2, ld
+                                line, col = ld.groups()
+                                # only emit a new marker if the line number in
+                                # the metadata is "new".
+                                if line != cur_line or col != cur_col:
+                                    if _interleave.lineinfo:
+                                        mfmt = 'Marker %s, Line %s, column %s'
+                                        mark_line = mfmt % (marker, line, col)
+                                        ln = fmt.format(cs['marker'], col_span,
+                                                        clean(mark_line))
+                                        new_lines.append(ln)
+                                    if _interleave.python:
+                                        # TODO:
+                                        # +1 for decorator, this probably needs
+                                        # the same thing doing as for the
+                                        # error messages where the decorator
+                                        # is scanned for, its not always +1!
+                                        lidx = int(line) - (firstlineno + 1)
+                                        source_line = src_code[lidx + 1]
+                                        ln = fmt.format(cs['python'], col_span,
+                                                        clean(source_line))
+                                        new_lines.append(ln)
+                                    return line, col
+
+            for l in sliced_lines[1:]:
+
+                # Drop LLVM debug call entries
+                if dbg_value.match(l):
+                    continue
+
+                # if requested generate interleaving of markers or python from
+                # metadata
+                if _interleave.lineinfo or _interleave.python:
+                    updated_lineinfo = metadata_interleave(l, new_lines)
+                    if updated_lineinfo is not None:
+                        cur_line, cur_col = updated_lineinfo
+
+                # Highlight other LLVM features if requested, HTML BGCOLOR
+                # property is set by this.
+                if _highlight.incref and nrt_incref.search(l):
+                    colour = cs['incref']
+                elif _highlight.decref and nrt_decref.search(l):
+                    colour = cs['decref']
+                elif _highlight.meminfo and nrt_meminfo.search(l):
+                    colour = cs['meminfo']
+                elif _highlight.raises and ll_raise.search(l):
+                    # search for raise as its more specific than exit
+                    colour = cs['raise']
+                elif _highlight.returns and ll_return.search(l):
+                    colour = cs['return']
+                elif _highlight.llvm_intrin_calls and ll_intrin_calls.search(l):
+                    colour = cs['llvm_intrin_calls']
+                elif _highlight.function_calls and ll_function_call.search(l):
+                    colour = cs['function_calls']
+                else:
+                    colour = cs['default']
+
+                # Use the default coloring as a flag to force printing if a
+                # special token print was requested AND LLVM ir stripping is
+                # required
+                if colour is not cs['default'] or not strip_ir:
+                    for x in wrap(clean(l)):
+                        new_lines.append(fmt.format(colour, col_span, x))
+
+            # add in the port line at the end of the block if it was present
+            # (this was built right at the top of the parse)
+            if port_line:
+                new_lines.append('<tr>{}</tr>'.format(port_line))
+
+            # If there was data, create a table, else don't!
+            dat = ''.join(new_lines)
+            if dat:
+                tab = (('<table id="%s" BORDER="1" CELLBORDER="0" '
+                       'CELLPADDING="0" CELLSPACING="0">%s</table>') % (idc,
+                                                                        dat))
+                label = '<{}>'.format(tab)
+            else:
+                label = ''
+
+            # finally, add a replacement node for the original with a new marked
+            # up label.
+            f.node(name, label=label)
+
+        # Parse the edge data
+        if 'edges' in jzon: # might be a single block, no edges
+            for edge in jzon['edges']:
+                gvid = edge['_gvid']
+                tp = edge.get('tailport', None)
+                edge_ids[gvid] = (edge['head'], edge['tail'], tp)
+
+        # Write in the edge wiring with respect to the new nodes:ports.
+        for gvid, edge in edge_ids.items():
+            tail = node_ids[edge[1]]
+            head = node_ids[edge[0]]
+            port = edge[2]
+            if port is not None:
+                tail += ':%s' % port
+            f.edge(tail, head)
+
+        # Add a key to the graph if requested.
+        if show_key:
+            key_tab = []
+            for k, v in cs.items():
+                key_tab.append(('<tr><td BGCOLOR="{}" BORDER="0" ALIGN="center"'
+                                '>{}</td></tr>').format(v, k))
+            # The first < and last > are DOT syntax, rest is DOT HTML.
+            f.node("Key", label=('<<table BORDER="1" CELLBORDER="1" '
+                    'CELLPADDING="2" CELLSPACING="1"><tr><td BORDER="0">'
+                    'Key:</td></tr>{}</table>>').format(''.join(key_tab)))
+
+        # Render if required
+        if filename is not None or view is not None:
+            f.render(filename=filename, view=view, format=render_format)
+
+        # Else pipe out a SVG
+        return f.pipe(format='svg')
+
+    def display(self, filename=None, format='pdf', view=False):
+        """
+        Plot the CFG.  In IPython notebook, the return image object can be
+        inlined.
+
+        The *filename* option can be set to a specific path for the rendered
+        output to write to.  If *view* option is True, the plot is opened by
+        the system default application for the image format (PDF). *format* can
+        be any valid format string accepted by graphviz, default is 'pdf'.
+        """
+        rawbyt = self.pretty_printer(filename=filename, view=view,
+                                     render_format=format, **self.kwargs)
+        return rawbyt.decode('utf-8')
+
+    def _repr_svg_(self):
+        return self.pretty_printer(**self.kwargs).decode('utf-8')
+
+    def __repr__(self):
+        return self.dot
+
+
+class CodeLibrary(metaclass=ABCMeta):
+    """
+    An interface for bundling LLVM code together and compiling it.
+    It is tied to a *codegen* instance (e.g. JITCPUCodegen) that will
+    determine how the LLVM code is transformed and linked together.
+    """
+
+    _finalized = False
+    _object_caching_enabled = False
+    _disable_inspection = False
+
+    def __init__(self, codegen: "CPUCodegen", name: str):
+        self._codegen = codegen
+        self._name = name
+        ptc_name = f"{self.__class__.__name__}({self._name!r})"
+        self._recorded_timings = PassTimingsCollection(ptc_name)
+        # Track names of the dynamic globals
+        self._dynamic_globals = []
+
+    @property
+    def has_dynamic_globals(self):
+        self._ensure_finalized()
+        return len(self._dynamic_globals) > 0
+
+    @property
+    def recorded_timings(self):
+        return self._recorded_timings
+
+    @property
+    def codegen(self):
+        """
+        The codegen object owning this library.
+        """
+        return self._codegen
+
+    @property
+    def name(self):
+        return self._name
+
+    def __repr__(self):
+        return "<Library %r at 0x%x>" % (self.name, id(self))
+
+    def _raise_if_finalized(self):
+        if self._finalized:
+            raise RuntimeError("operation impossible on finalized object %r"
+                               % (self,))
+
+    def _ensure_finalized(self):
+        if not self._finalized:
+            self.finalize()
+
+    def create_ir_module(self, name):
+        """
+        Create an LLVM IR module for use by this library.
+        """
+        self._raise_if_finalized()
+        ir_module = self._codegen._create_empty_module(name)
+        return ir_module
+
+    @abstractmethod
+    def add_linking_library(self, library):
+        """
+        Add a library for linking into this library, without losing
+        the original library.
+        """
+
+    @abstractmethod
+    def add_ir_module(self, ir_module):
+        """
+        Add an LLVM IR module's contents to this library.
+        """
+
+    @abstractmethod
+    def finalize(self):
+        """
+        Finalize the library.  After this call, nothing can be added anymore.
+        Finalization involves various stages of code optimization and
+        linking.
+        """
+
+    @abstractmethod
+    def get_function(self, name):
+        """
+        Return the function named ``name``.
+        """
+
+    @abstractmethod
+    def get_llvm_str(self):
+        """
+        Get the human-readable form of the LLVM module.
+        """
+
+    @abstractmethod
+    def get_asm_str(self):
+        """
+        Get the human-readable assembly.
+        """
+
+    #
+    # Object cache hooks and serialization
+    #
+
+    def enable_object_caching(self):
+        self._object_caching_enabled = True
+        self._compiled_object = None
+        self._compiled = False
+
+    def _get_compiled_object(self):
+        if not self._object_caching_enabled:
+            raise ValueError("object caching not enabled in %s" % (self,))
+        if self._compiled_object is None:
+            raise RuntimeError("no compiled object yet for %s" % (self,))
+        return self._compiled_object
+
+    def _set_compiled_object(self, value):
+        if not self._object_caching_enabled:
+            raise ValueError("object caching not enabled in %s" % (self,))
+        if self._compiled:
+            raise ValueError("library already compiled: %s" % (self,))
+        self._compiled_object = value
+        self._disable_inspection = True
+
+
+class CPUCodeLibrary(CodeLibrary):
+
+    def __init__(self, codegen, name):
+        super().__init__(codegen, name)
+        self._linking_libraries = []   # maintain insertion order
+        self._final_module = ll.parse_assembly(
+            str(self._codegen._create_empty_module(self.name)))
+        self._final_module.name = cgutils.normalize_ir_text(self.name)
+        self._shared_module = None
+
+    def _optimize_functions(self, ll_module):
+        """
+        Internal: run function-level optimizations inside *ll_module*.
+        """
+        # Enforce data layout to enable layout-specific optimizations
+        ll_module.data_layout = self._codegen._data_layout
+        with self._codegen._function_pass_manager(ll_module) as fpm:
+            # Run function-level optimizations to reduce memory usage and improve
+            # module-level optimization.
+            for func in ll_module.functions:
+                k = f"Function passes on {func.name!r}"
+                with self._recorded_timings.record(k):
+                    fpm.initialize()
+                    fpm.run(func)
+                    fpm.finalize()
+
+    def _optimize_final_module(self):
+        """
+        Internal: optimize this library's final module.
+        """
+        cheap_name = "Module passes (cheap optimization for refprune)"
+        with self._recorded_timings.record(cheap_name):
+            # A cheaper optimisation pass is run first to try and get as many
+            # refops into the same function as possible via inlining
+            self._codegen._mpm_cheap.run(self._final_module)
+        # Refop pruning is then run on the heavily inlined function
+        if not config.LLVM_REFPRUNE_PASS:
+            self._final_module = remove_redundant_nrt_refct(self._final_module)
+        full_name = "Module passes (full optimization)"
+        with self._recorded_timings.record(full_name):
+            # The full optimisation suite is then run on the refop pruned IR
+            self._codegen._mpm_full.run(self._final_module)
+
+    def _get_module_for_linking(self):
+        """
+        Internal: get a LLVM module suitable for linking multiple times
+        into another library.  Exported functions are made "linkonce_odr"
+        to allow for multiple definitions, inlining, and removal of
+        unused exports.
+
+        See discussion in https://github.com/numba/numba/pull/890
+        """
+        self._ensure_finalized()
+        if self._shared_module is not None:
+            return self._shared_module
+        mod = self._final_module
+        to_fix = []
+        nfuncs = 0
+        for fn in mod.functions:
+            nfuncs += 1
+            if not fn.is_declaration and fn.linkage == ll.Linkage.external:
+                to_fix.append(fn.name)
+        if nfuncs == 0:
+            # This is an issue which can occur if loading a module
+            # from an object file and trying to link with it, so detect it
+            # here to make debugging easier.
+            raise RuntimeError("library unfit for linking: "
+                               "no available functions in %s"
+                               % (self,))
+        if to_fix:
+            mod = mod.clone()
+            for name in to_fix:
+                # NOTE: this will mark the symbol WEAK if serialized
+                # to an ELF file
+                mod.get_function(name).linkage = 'linkonce_odr'
+        self._shared_module = mod
+        return mod
+
+    def add_linking_library(self, library):
+        library._ensure_finalized()
+        self._linking_libraries.append(library)
+
+    def add_ir_module(self, ir_module):
+        self._raise_if_finalized()
+        assert isinstance(ir_module, llvmir.Module)
+        ir = cgutils.normalize_ir_text(str(ir_module))
+        ll_module = ll.parse_assembly(ir)
+        ll_module.name = ir_module.name
+        ll_module.verify()
+        self.add_llvm_module(ll_module)
+
+    def add_llvm_module(self, ll_module):
+        self._optimize_functions(ll_module)
+        # TODO: we shouldn't need to recreate the LLVM module object
+        if not config.LLVM_REFPRUNE_PASS:
+            ll_module = remove_redundant_nrt_refct(ll_module)
+        self._final_module.link_in(ll_module)
+
+    def finalize(self):
+        require_global_compiler_lock()
+
+        # Report any LLVM-related problems to the user
+        self._codegen._check_llvm_bugs()
+
+        self._raise_if_finalized()
+
+        if config.DUMP_FUNC_OPT:
+            dump("FUNCTION OPTIMIZED DUMP %s" % self.name,
+                 self.get_llvm_str(), 'llvm')
+
+        # Link libraries for shared code
+        seen = set()
+        for library in self._linking_libraries:
+            if library not in seen:
+                seen.add(library)
+                self._final_module.link_in(
+                    library._get_module_for_linking(), preserve=True,
+                )
+
+        # Optimize the module after all dependences are linked in above,
+        # to allow for inlining.
+        self._optimize_final_module()
+
+        self._final_module.verify()
+        self._finalize_final_module()
+
+    def _finalize_dynamic_globals(self):
+        # Scan for dynamic globals
+        for gv in self._final_module.global_variables:
+            if gv.name.startswith('numba.dynamic.globals'):
+                self._dynamic_globals.append(gv.name)
+
+    def _verify_declare_only_symbols(self):
+        # Verify that no declare-only function compiled by numba.
+        for fn in self._final_module.functions:
+            # We will only check for symbol name starting with '_ZN5numba'
+            if fn.is_declaration and fn.name.startswith('_ZN5numba'):
+                msg = 'Symbol {} not linked properly'
+                raise AssertionError(msg.format(fn.name))
+
+    def _finalize_final_module(self):
+        """
+        Make the underlying LLVM module ready to use.
+        """
+        self._finalize_dynamic_globals()
+        self._verify_declare_only_symbols()
+
+        # Remember this on the module, for the object cache hooks
+        self._final_module.__library = weakref.proxy(self)
+
+        # It seems add_module() must be done only here and not before
+        # linking in other modules, otherwise get_pointer_to_function()
+        # could fail.
+        cleanup = self._codegen._add_module(self._final_module)
+        if cleanup:
+            weakref.finalize(self, cleanup)
+        self._finalize_specific()
+
+        self._finalized = True
+
+        if config.DUMP_OPTIMIZED:
+            dump("OPTIMIZED DUMP %s" % self.name, self.get_llvm_str(), 'llvm')
+
+        if config.DUMP_ASSEMBLY:
+            dump("ASSEMBLY %s" % self.name, self.get_asm_str(), 'asm')
+
+    def get_defined_functions(self):
+        """
+        Get all functions defined in the library.  The library must have
+        been finalized.
+        """
+        mod = self._final_module
+        for fn in mod.functions:
+            if not fn.is_declaration:
+                yield fn
+
+    def get_function(self, name):
+        return self._final_module.get_function(name)
+
+    def _sentry_cache_disable_inspection(self):
+        if self._disable_inspection:
+            warnings.warn('Inspection disabled for cached code. '
+                          'Invalid result is returned.')
+
+    def get_llvm_str(self):
+        self._sentry_cache_disable_inspection()
+        return str(self._final_module)
+
+    def get_asm_str(self):
+        self._sentry_cache_disable_inspection()
+        return str(self._codegen._tm.emit_assembly(self._final_module))
+
+    def get_function_cfg(self, name, py_func=None, **kwargs):
+        """
+        Get control-flow graph of the LLVM function
+        """
+        self._sentry_cache_disable_inspection()
+        return _CFG(self, name, py_func, **kwargs)
+
+    def get_disasm_cfg(self, mangled_name):
+        """
+        Get the CFG of the disassembly of the ELF object at symbol mangled_name.
+
+        Requires python package: r2pipe
+        Requires radare2 binary on $PATH.
+        Notebook rendering requires python package: graphviz
+        Optionally requires a compiler toolchain (via pycc) to link the ELF to
+        get better disassembly results.
+        """
+        elf = self._get_compiled_object()
+        return disassemble_elf_to_cfg(elf, mangled_name)
+
+    @classmethod
+    def _dump_elf(cls, buf):
+        """
+        Dump the symbol table of an ELF file.
+        Needs pyelftools (https://github.com/eliben/pyelftools)
+        """
+        from elftools.elf.elffile import ELFFile
+        from elftools.elf import descriptions
+        from io import BytesIO
+        f = ELFFile(BytesIO(buf))
+        print("ELF file:")
+        for sec in f.iter_sections():
+            if sec['sh_type'] == 'SHT_SYMTAB':
+                symbols = sorted(sec.iter_symbols(), key=lambda sym: sym.name)
+                print("    symbols:")
+                for sym in symbols:
+                    if not sym.name:
+                        continue
+                    print("    - %r: size=%d, value=0x%x, type=%s, bind=%s"
+                          % (sym.name.decode(),
+                             sym['st_size'],
+                             sym['st_value'],
+                             descriptions.describe_symbol_type(sym['st_info']['type']),
+                             descriptions.describe_symbol_bind(sym['st_info']['bind']),
+                             ))
+        print()
+
+    @classmethod
+    def _object_compiled_hook(cls, ll_module, buf):
+        """
+        `ll_module` was compiled into object code `buf`.
+        """
+        try:
+            self = ll_module.__library
+        except AttributeError:
+            return
+        if self._object_caching_enabled:
+            self._compiled = True
+            self._compiled_object = buf
+
+    @classmethod
+    def _object_getbuffer_hook(cls, ll_module):
+        """
+        Return a cached object code for `ll_module`.
+        """
+        try:
+            self = ll_module.__library
+        except AttributeError:
+            return
+        if self._object_caching_enabled and self._compiled_object:
+            buf = self._compiled_object
+            self._compiled_object = None
+            return buf
+
+    def serialize_using_bitcode(self):
+        """
+        Serialize this library using its bitcode as the cached representation.
+        """
+        self._ensure_finalized()
+        return (self.name, 'bitcode', self._final_module.as_bitcode())
+
+    def serialize_using_object_code(self):
+        """
+        Serialize this library using its object code as the cached
+        representation.  We also include its bitcode for further inlining
+        with other libraries.
+        """
+        self._ensure_finalized()
+        data = (self._get_compiled_object(),
+                self._get_module_for_linking().as_bitcode())
+        return (self.name, 'object', data)
+
+    @classmethod
+    def _unserialize(cls, codegen, state):
+        name, kind, data = state
+        self = codegen.create_library(name)
+        assert isinstance(self, cls)
+        if kind == 'bitcode':
+            # No need to re-run optimizations, just make the module ready
+            self._final_module = ll.parse_bitcode(data)
+            self._finalize_final_module()
+            return self
+        elif kind == 'object':
+            object_code, shared_bitcode = data
+            self.enable_object_caching()
+            self._set_compiled_object(object_code)
+            self._shared_module = ll.parse_bitcode(shared_bitcode)
+            self._finalize_final_module()
+            # Load symbols from cache
+            self._codegen._engine._load_defined_symbols(self._shared_module)
+            return self
+        else:
+            raise ValueError("unsupported serialization kind %r" % (kind,))
+
+
+class AOTCodeLibrary(CPUCodeLibrary):
+
+    def emit_native_object(self):
+        """
+        Return this library as a native object (a bytestring) -- for example
+        ELF under Linux.
+
+        This function implicitly calls .finalize().
+        """
+        self._ensure_finalized()
+        return self._codegen._tm.emit_object(self._final_module)
+
+    def emit_bitcode(self):
+        """
+        Return this library as LLVM bitcode (a bytestring).
+
+        This function implicitly calls .finalize().
+        """
+        self._ensure_finalized()
+        return self._final_module.as_bitcode()
+
+    def _finalize_specific(self):
+        pass
+
+
+class JITCodeLibrary(CPUCodeLibrary):
+
+    def get_pointer_to_function(self, name):
+        """
+        Generate native code for function named *name* and return a pointer
+        to the start of the function (as an integer).
+
+        This function implicitly calls .finalize().
+
+        Returns
+        -------
+        pointer : int
+            - zero (null) if no symbol of *name* is defined by this code
+              library.
+            - non-zero if the symbol is defined.
+        """
+        self._ensure_finalized()
+        ee = self._codegen._engine
+        if not ee.is_symbol_defined(name):
+            return 0
+        else:
+            return self._codegen._engine.get_function_address(name)
+
+    def _finalize_specific(self):
+        self._codegen._scan_and_fix_unresolved_refs(self._final_module)
+        with self._recorded_timings.record("Finalize object"):
+            self._codegen._engine.finalize_object()
+
+
+class RuntimeLinker(object):
+    """
+    For tracking unresolved symbols generated at runtime due to recursion.
+    """
+    PREFIX = '.numba.unresolved$'
+
+    def __init__(self):
+        self._unresolved = utils.UniqueDict()
+        self._defined = set()
+        self._resolved = []
+
+    def scan_unresolved_symbols(self, module, engine):
+        """
+        Scan and track all unresolved external symbols in the module and
+        allocate memory for it.
+        """
+        prefix = self.PREFIX
+
+        for gv in module.global_variables:
+            if gv.name.startswith(prefix):
+                sym = gv.name[len(prefix):]
+                # Avoid remapping to existing GV
+                if engine.is_symbol_defined(gv.name):
+                    continue
+                # Allocate a memory space for the pointer
+                abortfn = rtsys.library.get_pointer_to_function("nrt_unresolved_abort")
+                ptr = ctypes.c_void_p(abortfn)
+                engine.add_global_mapping(gv, ctypes.addressof(ptr))
+                self._unresolved[sym] = ptr
+
+    def scan_defined_symbols(self, module):
+        """
+        Scan and track all defined symbols.
+        """
+        for fn in module.functions:
+            if not fn.is_declaration:
+                self._defined.add(fn.name)
+
+    def resolve(self, engine):
+        """
+        Fix unresolved symbols if they are defined.
+        """
+        # An iterator to get all unresolved but available symbols
+        pending = [name for name in self._unresolved if name in self._defined]
+        # Resolve pending symbols
+        for name in pending:
+            # Get runtime address
+            fnptr = engine.get_function_address(name)
+            # Fix all usage
+            ptr = self._unresolved[name]
+            ptr.value = fnptr
+            self._resolved.append((name, ptr))   # keep ptr alive
+            # Delete resolved
+            del self._unresolved[name]
+
+def _proxy(old):
+    @functools.wraps(old)
+    def wrapper(self, *args, **kwargs):
+        return old(self._ee, *args, **kwargs)
+    return wrapper
+
+
+class JitEngine(object):
+    """Wraps an ExecutionEngine to provide custom symbol tracking.
+    Since the symbol tracking is incomplete  (doesn't consider
+    loaded code object), we are not putting it in llvmlite.
+    """
+    def __init__(self, ee):
+        self._ee = ee
+        # Track symbol defined via codegen'd Module
+        # but not any cached object.
+        # NOTE: `llvm::ExecutionEngine` will catch duplicated symbols and
+        # we are not going to protect against that.  A proper duplicated
+        # symbol detection will need a more logic to check for the linkage
+        # (e.g. like `weak` linkage symbol can override).   This
+        # `_defined_symbols` set will be just enough to tell if a symbol
+        # exists and will not cause the `EE` symbol lookup to `exit(1)`
+        # when symbol-not-found.
+        self._defined_symbols = set()
+
+    def is_symbol_defined(self, name):
+        """Is the symbol defined in this session?
+        """
+        return name in self._defined_symbols
+
+    def _load_defined_symbols(self, mod):
+        """Extract symbols from the module
+        """
+        for gsets in (mod.functions, mod.global_variables):
+            self._defined_symbols |= {gv.name for gv in gsets
+                                      if not gv.is_declaration}
+
+    def add_module(self, module):
+        """Override ExecutionEngine.add_module
+        to keep info about defined symbols.
+        """
+        self._load_defined_symbols(module)
+        return self._ee.add_module(module)
+
+    def add_global_mapping(self, gv, addr):
+        """Override ExecutionEngine.add_global_mapping
+        to keep info about defined symbols.
+        """
+        self._defined_symbols.add(gv.name)
+        return self._ee.add_global_mapping(gv, addr)
+
+    #
+    # The remaining methods are re-export of the ExecutionEngine APIs
+    #
+    set_object_cache = _proxy(ll.ExecutionEngine.set_object_cache)
+    finalize_object = _proxy(ll.ExecutionEngine.finalize_object)
+    get_function_address = _proxy(ll.ExecutionEngine.get_function_address)
+    get_global_value_address = _proxy(
+        ll.ExecutionEngine.get_global_value_address
+        )
+
+
+class Codegen(metaclass=ABCMeta):
+    """
+    Base Codegen class. It is expected that subclasses set the class attribute
+    ``_library_class``, indicating the CodeLibrary class for the target.
+
+    Subclasses should also initialize:
+
+    ``self._data_layout``: the data layout for the target.
+    ``self._target_data``: the binding layer ``TargetData`` for the target.
+    """
+
+    @abstractmethod
+    def _create_empty_module(self, name):
+        """
+        Create a new empty module suitable for the target.
+        """
+
+    @abstractmethod
+    def _add_module(self, module):
+        """
+        Add a module to the execution engine. Ownership of the module is
+        transferred to the engine.
+        """
+
+    @property
+    def target_data(self):
+        """
+        The LLVM "target data" object for this codegen instance.
+        """
+        return self._target_data
+
+    def create_library(self, name, **kwargs):
+        """
+        Create a :class:`CodeLibrary` object for use with this codegen
+        instance.
+        """
+        return self._library_class(self, name, **kwargs)
+
+    def unserialize_library(self, serialized):
+        return self._library_class._unserialize(self, serialized)
+
+
+class CPUCodegen(Codegen):
+
+    def __init__(self, module_name):
+        initialize_llvm()
+
+        self._data_layout = None
+        self._llvm_module = ll.parse_assembly(
+            str(self._create_empty_module(module_name)))
+        self._llvm_module.name = "global_codegen_module"
+        self._rtlinker = RuntimeLinker()
+        self._init(self._llvm_module)
+
+    def _init(self, llvm_module):
+        assert list(llvm_module.global_variables) == [], "Module isn't empty"
+
+        target = ll.Target.from_triple(ll.get_process_triple())
+        tm_options = dict(opt=config.OPT)
+        self._tm_features = self._customize_tm_features()
+        self._customize_tm_options(tm_options)
+        tm = target.create_target_machine(**tm_options)
+        engine = ll.create_mcjit_compiler(llvm_module, tm)
+
+        if config.ENABLE_PROFILING:
+            engine.enable_jit_events()
+
+        self._tm = tm
+        self._engine = JitEngine(engine)
+        self._target_data = engine.target_data
+        self._data_layout = str(self._target_data)
+        self._mpm_cheap = self._module_pass_manager(loop_vectorize=False,
+                                                    slp_vectorize=False,
+                                                    opt=0,
+                                                    cost="cheap")
+        self._mpm_full = self._module_pass_manager()
+
+        self._engine.set_object_cache(self._library_class._object_compiled_hook,
+                                      self._library_class._object_getbuffer_hook)
+
+    def _create_empty_module(self, name):
+        ir_module = llvmir.Module(cgutils.normalize_ir_text(name))
+        ir_module.triple = ll.get_process_triple()
+        if self._data_layout:
+            ir_module.data_layout = self._data_layout
+        return ir_module
+
+    def _module_pass_manager(self, **kwargs):
+        pm = ll.create_module_pass_manager()
+        self._tm.add_analysis_passes(pm)
+        cost = kwargs.pop("cost", None)
+        with self._pass_manager_builder(**kwargs) as pmb:
+            pmb.populate(pm)
+        # If config.OPT==0 do not include these extra passes to help with
+        # vectorization.
+        if cost is not None and cost == "cheap" and config.OPT != 0:
+            # This knocks loops into rotated form early to reduce the likelihood
+            # of vectorization failing due to unknown PHI nodes.
+            pm.add_loop_rotate_pass()
+            # LLVM 11 added LFTR to the IV Simplification pass, this interacted
+            # badly with the existing use of the InstructionCombiner here and
+            # ended up with PHI nodes that prevented vectorization from
+            # working. The desired vectorization effects can be achieved
+            # with this in LLVM 11 (and also < 11) but at a potentially
+            # slightly higher cost:
+            pm.add_licm_pass()
+            pm.add_cfg_simplification_pass()
+        if config.LLVM_REFPRUNE_PASS:
+            pm.add_refprune_pass(_parse_refprune_flags())
+        return pm
+
+    def _function_pass_manager(self, llvm_module, **kwargs):
+        pm = ll.create_function_pass_manager(llvm_module)
+        self._tm.add_analysis_passes(pm)
+        with self._pass_manager_builder(**kwargs) as pmb:
+            pmb.populate(pm)
+        if config.LLVM_REFPRUNE_PASS:
+            pm.add_refprune_pass(_parse_refprune_flags())
+        return pm
+
+    def _pass_manager_builder(self, **kwargs):
+        """
+        Create a PassManagerBuilder.
+
+        Note: a PassManagerBuilder seems good only for one use, so you
+        should call this method each time you want to populate a module
+        or function pass manager.  Otherwise some optimizations will be
+        missed...
+        """
+        opt_level = kwargs.pop('opt', config.OPT)
+        loop_vectorize = kwargs.pop('loop_vectorize', config.LOOP_VECTORIZE)
+        slp_vectorize = kwargs.pop('slp_vectorize', config.SLP_VECTORIZE)
+
+        pmb = create_pass_manager_builder(opt=opt_level,
+                                          loop_vectorize=loop_vectorize,
+                                          slp_vectorize=slp_vectorize,
+                                          **kwargs)
+
+        return pmb
+
+    def _check_llvm_bugs(self):
+        """
+        Guard against some well-known LLVM bug(s).
+        """
+        # Check the locale bug at https://github.com/numba/numba/issues/1569
+        # Note we can't cache the result as locale settings can change
+        # across a process's lifetime.  Also, for this same reason,
+        # the check here is a mere heuristic (there may be a race condition
+        # between now and actually compiling IR).
+        ir = """
+            define double @func()
+            {
+                ret double 1.23e+01
+            }
+            """
+        mod = ll.parse_assembly(ir)
+        ir_out = str(mod)
+        if "12.3" in ir_out or "1.23" in ir_out:
+            # Everything ok
+            return
+        if "1.0" in ir_out:
+            loc = locale.getlocale()
+            raise RuntimeError(
+                "LLVM will produce incorrect floating-point code "
+                "in the current locale %s.\nPlease read "
+                "https://numba.readthedocs.io/en/stable/user/faq.html#llvm-locale-bug "
+                "for more information."
+                % (loc,))
+        raise AssertionError("Unexpected IR:\n%s\n" % (ir_out,))
+
+    def magic_tuple(self):
+        """
+        Return a tuple unambiguously describing the codegen behaviour.
+        """
+        return (self._llvm_module.triple, self._get_host_cpu_name(),
+                self._tm_features)
+
+    def _scan_and_fix_unresolved_refs(self, module):
+        self._rtlinker.scan_unresolved_symbols(module, self._engine)
+        self._rtlinker.scan_defined_symbols(module)
+        self._rtlinker.resolve(self._engine)
+
+    def insert_unresolved_ref(self, builder, fnty, name):
+        voidptr = llvmir.IntType(8).as_pointer()
+        ptrname = self._rtlinker.PREFIX + name
+        llvm_mod = builder.module
+        try:
+            fnptr = llvm_mod.get_global(ptrname)
+        except KeyError:
+            # Not defined?
+            fnptr = llvmir.GlobalVariable(llvm_mod, voidptr, name=ptrname)
+            fnptr.linkage = 'external'
+        return builder.bitcast(builder.load(fnptr), fnty.as_pointer())
+
+    def _get_host_cpu_name(self):
+        return (ll.get_host_cpu_name()
+                if config.CPU_NAME is None
+                else config.CPU_NAME)
+
+    def _get_host_cpu_features(self):
+        if config.CPU_FEATURES is not None:
+            return config.CPU_FEATURES
+        return get_host_cpu_features()
+
+
+class AOTCPUCodegen(CPUCodegen):
+    """
+    A codegen implementation suitable for Ahead-Of-Time compilation
+    (e.g. generation of object files).
+    """
+
+    _library_class = AOTCodeLibrary
+
+    def __init__(self, module_name, cpu_name=None):
+        # By default, use generic cpu model for the arch
+        self._cpu_name = cpu_name or ''
+        CPUCodegen.__init__(self, module_name)
+
+    def _customize_tm_options(self, options):
+        cpu_name = self._cpu_name
+        if cpu_name == 'host':
+            cpu_name = self._get_host_cpu_name()
+        options['cpu'] = cpu_name
+        options['reloc'] = 'pic'
+        options['codemodel'] = 'default'
+        options['features'] = self._tm_features
+
+    def _customize_tm_features(self):
+        # ISA features are selected according to the requested CPU model
+        # in _customize_tm_options()
+        return ''
+
+    def _add_module(self, module):
+        pass
+
+
+class JITCPUCodegen(CPUCodegen):
+    """
+    A codegen implementation suitable for Just-In-Time compilation.
+    """
+
+    _library_class = JITCodeLibrary
+
+    def _customize_tm_options(self, options):
+        # As long as we don't want to ship the code to another machine,
+        # we can specialize for this CPU.
+        options['cpu'] = self._get_host_cpu_name()
+        # LLVM 7 change: # https://reviews.llvm.org/D47211#inline-425406
+        # JIT needs static relocation on x86*
+        # native target is already initialized from base class __init__
+        arch = ll.Target.from_default_triple().name
+        if arch.startswith('x86'): # one of x86 or x86_64
+            reloc_model = 'static'
+        elif arch.startswith('ppc'):
+            reloc_model = 'pic'
+        else:
+            reloc_model = 'default'
+        options['reloc'] = reloc_model
+        options['codemodel'] = 'jitdefault'
+
+        # Set feature attributes (such as ISA extensions)
+        # This overrides default feature selection by CPU model above
+        options['features'] = self._tm_features
+
+        # Deal with optional argument to ll.Target.create_target_machine
+        sig = utils.pysignature(ll.Target.create_target_machine)
+        if 'jit' in sig.parameters:
+            # Mark that this is making a JIT engine
+            options['jit'] = True
+
+    def _customize_tm_features(self):
+        # For JIT target, we will use LLVM to get the feature map
+        return self._get_host_cpu_features()
+
+    def _add_module(self, module):
+        self._engine.add_module(module)
+        # XXX: disabling remove module due to MCJIT engine leakage in
+        #      removeModule.  The removeModule causes consistent access
+        #      violation with certain test combinations.
+        # # Early bind the engine method to avoid keeping a reference to self.
+        # return functools.partial(self._engine.remove_module, module)
+
+    def set_env(self, env_name, env):
+        """Set the environment address.
+
+        Update the GlobalVariable named *env_name* to the address of *env*.
+        """
+        gvaddr = self._engine.get_global_value_address(env_name)
+        envptr = (ctypes.c_void_p * 1).from_address(gvaddr)
+        envptr[0] = ctypes.c_void_p(id(env))
+
+
+def initialize_llvm():
+    """Safe to use multiple times.
+    """
+    ll.initialize()
+    ll.initialize_native_target()
+    ll.initialize_native_asmprinter()
+
+
+def get_host_cpu_features():
+    """Get host CPU features using LLVM.
+
+    The features may be modified due to user setting.
+    See numba.config.ENABLE_AVX.
+    """
+    try:
+        features = ll.get_host_cpu_features()
+    except RuntimeError:
+        return ''
+    else:
+        if not config.ENABLE_AVX:
+            # Disable all features with name starting with 'avx'
+            for k in features:
+                if k.startswith('avx'):
+                    features[k] = False
+
+        # Set feature attributes
+        return features.flatten()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler.py
new file mode 100644
index 000000000..1ff376280
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler.py
@@ -0,0 +1,790 @@
+from collections import namedtuple
+import copy
+import warnings
+from numba.core.tracing import event
+
+from numba.core import (utils, errors, typing, interpreter, bytecode, postproc,
+                        config, callconv, cpu)
+from numba.parfors.parfor import ParforDiagnostics
+from numba.core.errors import CompilerError
+from numba.core.environment import lookup_environment
+
+from numba.core.compiler_machinery import PassManager
+
+from numba.core.untyped_passes import (ExtractByteCode, TranslateByteCode,
+                                       FixupArgs, IRProcessing, DeadBranchPrune,
+                                       RewriteSemanticConstants,
+                                       InlineClosureLikes, GenericRewrites,
+                                       WithLifting, InlineInlinables,
+                                       FindLiterallyCalls,
+                                       MakeFunctionToJitFunction,
+                                       CanonicalizeLoopExit,
+                                       CanonicalizeLoopEntry, LiteralUnroll,
+                                       ReconstructSSA,
+                                       LiteralPropagationSubPipelinePass,
+                                       )
+
+from numba.core.typed_passes import (NopythonTypeInference, AnnotateTypes,
+                                     NopythonRewrites, PreParforPass,
+                                     ParforPass, DumpParforDiagnostics,
+                                     IRLegalization, NoPythonBackend,
+                                     InlineOverloads, PreLowerStripPhis,
+                                     NativeLowering,
+                                     NoPythonSupportedFeatureValidation,
+                                     )
+
+from numba.core.object_mode_passes import (ObjectModeFrontEnd,
+                                           ObjectModeBackEnd)
+from numba.core.targetconfig import TargetConfig, Option, ConfigStack
+
+
+class Flags(TargetConfig):
+    enable_looplift = Option(
+        type=bool,
+        default=False,
+        doc="Enable loop-lifting",
+    )
+    enable_pyobject = Option(
+        type=bool,
+        default=False,
+        doc="Enable pyobject mode (in general)",
+    )
+    enable_pyobject_looplift = Option(
+        type=bool,
+        default=False,
+        doc="Enable pyobject mode inside lifted loops",
+    )
+    enable_ssa = Option(
+        type=bool,
+        default=True,
+        doc="Enable SSA",
+    )
+    force_pyobject = Option(
+        type=bool,
+        default=False,
+        doc="Force pyobject mode inside the whole function",
+    )
+    release_gil = Option(
+        type=bool,
+        default=False,
+        doc="Release GIL inside the native function",
+    )
+    no_compile = Option(
+        type=bool,
+        default=False,
+        doc="TODO",
+    )
+    debuginfo = Option(
+        type=bool,
+        default=False,
+        doc="TODO",
+    )
+    boundscheck = Option(
+        type=bool,
+        default=False,
+        doc="TODO",
+    )
+    forceinline = Option(
+        type=bool,
+        default=False,
+        doc="Force inlining of the function. Overrides _dbg_optnone.",
+    )
+    no_cpython_wrapper = Option(
+        type=bool,
+        default=False,
+        doc="TODO",
+    )
+    no_cfunc_wrapper = Option(
+        type=bool,
+        default=False,
+        doc="TODO",
+    )
+    auto_parallel = Option(
+        type=cpu.ParallelOptions,
+        default=cpu.ParallelOptions(False),
+        doc="""Enable automatic parallel optimization, can be fine-tuned by
+taking a dictionary of sub-options instead of a boolean, see parfor.py for
+detail""",
+    )
+    nrt = Option(
+        type=bool,
+        default=False,
+        doc="TODO",
+    )
+    no_rewrites = Option(
+        type=bool,
+        default=False,
+        doc="TODO",
+    )
+    error_model = Option(
+        type=str,
+        default="python",
+        doc="TODO",
+    )
+    fastmath = Option(
+        type=cpu.FastMathOptions,
+        default=cpu.FastMathOptions(False),
+        doc="TODO",
+    )
+    noalias = Option(
+        type=bool,
+        default=False,
+        doc="TODO",
+    )
+    inline = Option(
+        type=cpu.InlineOptions,
+        default=cpu.InlineOptions("never"),
+        doc="TODO",
+    )
+    # Defines a new target option for tracking the "target backend".
+    # This will be the XYZ in @jit(_target=XYZ).
+    target_backend = Option(
+        type=str,
+        default="cpu", # if not set, default to CPU
+        doc="backend"
+    )
+
+    dbg_extend_lifetimes = Option(
+        type=bool,
+        default=False,
+        doc=("Extend variable lifetime for debugging. "
+             "This automatically turns on with debug=True."),
+    )
+
+    dbg_optnone = Option(
+        type=bool,
+        default=False,
+        doc=("Disable optimization for debug. "
+             "Equivalent to adding optnone attribute in the LLVM Function.")
+    )
+
+
+DEFAULT_FLAGS = Flags()
+DEFAULT_FLAGS.nrt = True
+
+
+CR_FIELDS = ["typing_context",
+             "target_context",
+             "entry_point",
+             "typing_error",
+             "type_annotation",
+             "signature",
+             "objectmode",
+             "lifted",
+             "fndesc",
+             "library",
+             "call_helper",
+             "environment",
+             "metadata",
+             # List of functions to call to initialize on unserialization
+             # (i.e cache load).
+             "reload_init",
+             "referenced_envs",
+             ]
+
+
+class CompileResult(namedtuple("_CompileResult", CR_FIELDS)):
+    """
+    A structure holding results from the compilation of a function.
+    """
+
+    __slots__ = ()
+
+    def _reduce(self):
+        """
+        Reduce a CompileResult to picklable components.
+        """
+        libdata = self.library.serialize_using_object_code()
+        # Make it (un)picklable efficiently
+        typeann = str(self.type_annotation)
+        fndesc = self.fndesc
+        # Those don't need to be pickled and may fail
+        fndesc.typemap = fndesc.calltypes = None
+        # Include all referenced environments
+        referenced_envs = self._find_referenced_environments()
+        return (libdata, self.fndesc, self.environment, self.signature,
+                self.objectmode, self.lifted, typeann, self.reload_init,
+                tuple(referenced_envs))
+
+    def _find_referenced_environments(self):
+        """Returns a list of referenced environments
+        """
+        mod = self.library._final_module
+        # Find environments
+        referenced_envs = []
+        for gv in mod.global_variables:
+            gvn = gv.name
+            if gvn.startswith("_ZN08NumbaEnv"):
+                env = lookup_environment(gvn)
+                if env is not None:
+                    if env.can_cache():
+                        referenced_envs.append(env)
+        return referenced_envs
+
+    @classmethod
+    def _rebuild(cls, target_context, libdata, fndesc, env,
+                 signature, objectmode, lifted, typeann,
+                 reload_init, referenced_envs):
+        if reload_init:
+            # Re-run all
+            for fn in reload_init:
+                fn()
+
+        library = target_context.codegen().unserialize_library(libdata)
+        cfunc = target_context.get_executable(library, fndesc, env)
+        cr = cls(target_context=target_context,
+                 typing_context=target_context.typing_context,
+                 library=library,
+                 environment=env,
+                 entry_point=cfunc,
+                 fndesc=fndesc,
+                 type_annotation=typeann,
+                 signature=signature,
+                 objectmode=objectmode,
+                 lifted=lifted,
+                 typing_error=None,
+                 call_helper=None,
+                 metadata=None,  # Do not store, arbitrary & potentially large!
+                 reload_init=reload_init,
+                 referenced_envs=referenced_envs,
+                 )
+
+        # Load Environments
+        for env in referenced_envs:
+            library.codegen.set_env(env.env_name, env)
+
+        return cr
+
+    @property
+    def codegen(self):
+        return self.target_context.codegen()
+
+    def dump(self, tab=''):
+        print(f'{tab}DUMP {type(self).__name__} {self.entry_point}')
+        self.signature.dump(tab=tab + '  ')
+        print(f'{tab}END DUMP')
+
+
+_LowerResult = namedtuple("_LowerResult", [
+    "fndesc",
+    "call_helper",
+    "cfunc",
+    "env",
+])
+
+
+def sanitize_compile_result_entries(entries):
+    keys = set(entries.keys())
+    fieldset = set(CR_FIELDS)
+    badnames = keys - fieldset
+    if badnames:
+        raise NameError(*badnames)
+    missing = fieldset - keys
+    for k in missing:
+        entries[k] = None
+    # Avoid keeping alive traceback variables
+    err = entries['typing_error']
+    if err is not None:
+        entries['typing_error'] = err.with_traceback(None)
+    return entries
+
+
+def compile_result(**entries):
+    entries = sanitize_compile_result_entries(entries)
+    return CompileResult(**entries)
+
+
+def compile_isolated(func, args, return_type=None, flags=DEFAULT_FLAGS,
+                     locals={}):
+    """
+    Compile the function in an isolated environment (typing and target
+    context).
+    Good for testing.
+    """
+    from numba.core.registry import cpu_target
+    typingctx = typing.Context()
+    targetctx = cpu.CPUContext(typingctx, target='cpu')
+    # Register the contexts in case for nested @jit or @overload calls
+    with cpu_target.nested_context(typingctx, targetctx):
+        return compile_extra(typingctx, targetctx, func, args, return_type,
+                             flags, locals)
+
+
+def run_frontend(func, inline_closures=False, emit_dels=False):
+    """
+    Run the compiler frontend over the given Python function, and return
+    the function's canonical Numba IR.
+
+    If inline_closures is Truthy then closure inlining will be run
+    If emit_dels is Truthy the ir.Del nodes will be emitted appropriately
+    """
+    # XXX make this a dedicated Pipeline?
+    func_id = bytecode.FunctionIdentity.from_function(func)
+    interp = interpreter.Interpreter(func_id)
+    bc = bytecode.ByteCode(func_id=func_id)
+    func_ir = interp.interpret(bc)
+    if inline_closures:
+        from numba.core.inline_closurecall import InlineClosureCallPass
+        inline_pass = InlineClosureCallPass(func_ir, cpu.ParallelOptions(False),
+                                            {}, False)
+        inline_pass.run()
+    post_proc = postproc.PostProcessor(func_ir)
+    post_proc.run(emit_dels)
+    return func_ir
+
+
+class _CompileStatus(object):
+    """
+    Describes the state of compilation. Used like a C record.
+    """
+    __slots__ = ['fail_reason', 'can_fallback']
+
+    def __init__(self, can_fallback):
+        self.fail_reason = None
+        self.can_fallback = can_fallback
+
+    def __repr__(self):
+        vals = []
+        for k in self.__slots__:
+            vals.append("{k}={v}".format(k=k, v=getattr(self, k)))
+        return ', '.join(vals)
+
+
+class _EarlyPipelineCompletion(Exception):
+    """
+    Raised to indicate that a pipeline has completed early
+    """
+
+    def __init__(self, result):
+        self.result = result
+
+
+class StateDict(dict):
+    """
+    A dictionary that has an overloaded getattr and setattr to permit getting
+    and setting key/values through the use of attributes.
+    """
+
+    def __getattr__(self, attr):
+        try:
+            return self[attr]
+        except KeyError:
+            raise AttributeError(attr)
+
+    def __setattr__(self, attr, value):
+        self[attr] = value
+
+
+def _make_subtarget(targetctx, flags):
+    """
+    Make a new target context from the given target context and flags.
+    """
+    subtargetoptions = {}
+    if flags.debuginfo:
+        subtargetoptions['enable_debuginfo'] = True
+    if flags.boundscheck:
+        subtargetoptions['enable_boundscheck'] = True
+    if flags.nrt:
+        subtargetoptions['enable_nrt'] = True
+    if flags.auto_parallel:
+        subtargetoptions['auto_parallel'] = flags.auto_parallel
+    if flags.fastmath:
+        subtargetoptions['fastmath'] = flags.fastmath
+    error_model = callconv.create_error_model(flags.error_model, targetctx)
+    subtargetoptions['error_model'] = error_model
+
+    return targetctx.subtarget(**subtargetoptions)
+
+
+class CompilerBase(object):
+    """
+    Stores and manages states for the compiler
+    """
+
+    def __init__(self, typingctx, targetctx, library, args, return_type, flags,
+                 locals):
+        # Make sure the environment is reloaded
+        config.reload_config()
+        typingctx.refresh()
+        targetctx.refresh()
+
+        self.state = StateDict()
+
+        self.state.typingctx = typingctx
+        self.state.targetctx = _make_subtarget(targetctx, flags)
+        self.state.library = library
+        self.state.args = args
+        self.state.return_type = return_type
+        self.state.flags = flags
+        self.state.locals = locals
+
+        # Results of various steps of the compilation pipeline
+        self.state.bc = None
+        self.state.func_id = None
+        self.state.func_ir = None
+        self.state.lifted = None
+        self.state.lifted_from = None
+        self.state.typemap = None
+        self.state.calltypes = None
+        self.state.type_annotation = None
+        # holds arbitrary inter-pipeline stage meta data
+        self.state.metadata = {}
+        self.state.reload_init = []
+        # hold this for e.g. with_lifting, null out on exit
+        self.state.pipeline = self
+
+        # parfor diagnostics info, add to metadata
+        self.state.parfor_diagnostics = ParforDiagnostics()
+        self.state.metadata['parfor_diagnostics'] = \
+            self.state.parfor_diagnostics
+        self.state.metadata['parfors'] = {}
+
+        self.state.status = _CompileStatus(
+            can_fallback=self.state.flags.enable_pyobject
+        )
+
+    def compile_extra(self, func):
+        self.state.func_id = bytecode.FunctionIdentity.from_function(func)
+        ExtractByteCode().run_pass(self.state)
+
+        self.state.lifted = ()
+        self.state.lifted_from = None
+        return self._compile_bytecode()
+
+    def compile_ir(self, func_ir, lifted=(), lifted_from=None):
+        self.state.func_id = func_ir.func_id
+        self.state.lifted = lifted
+        self.state.lifted_from = lifted_from
+        self.state.func_ir = func_ir
+        self.state.nargs = self.state.func_ir.arg_count
+
+        FixupArgs().run_pass(self.state)
+        return self._compile_ir()
+
+    def define_pipelines(self):
+        """Child classes override this to customize the pipelines in use.
+        """
+        raise NotImplementedError()
+
+    def _compile_core(self):
+        """
+        Populate and run compiler pipeline
+        """
+        with ConfigStack().enter(self.state.flags.copy()):
+            pms = self.define_pipelines()
+            for pm in pms:
+                pipeline_name = pm.pipeline_name
+                func_name = "%s.%s" % (self.state.func_id.modname,
+                                       self.state.func_id.func_qualname)
+
+                event("Pipeline: %s for %s" % (pipeline_name, func_name))
+                self.state.metadata['pipeline_times'] = {pipeline_name:
+                                                         pm.exec_times}
+                is_final_pipeline = pm == pms[-1]
+                res = None
+                try:
+                    pm.run(self.state)
+                    if self.state.cr is not None:
+                        break
+                except _EarlyPipelineCompletion as e:
+                    res = e.result
+                    break
+                except Exception as e:
+                    if (utils.use_new_style_errors() and not
+                            isinstance(e, errors.NumbaError)):
+                        raise e
+
+                    self.state.status.fail_reason = e
+                    if is_final_pipeline:
+                        raise e
+            else:
+                raise CompilerError("All available pipelines exhausted")
+
+            # Pipeline is done, remove self reference to release refs to user
+            # code
+            self.state.pipeline = None
+
+            # organise a return
+            if res is not None:
+                # Early pipeline completion
+                return res
+            else:
+                assert self.state.cr is not None
+                return self.state.cr
+
+    def _compile_bytecode(self):
+        """
+        Populate and run pipeline for bytecode input
+        """
+        assert self.state.func_ir is None
+        return self._compile_core()
+
+    def _compile_ir(self):
+        """
+        Populate and run pipeline for IR input
+        """
+        assert self.state.func_ir is not None
+        return self._compile_core()
+
+
+class Compiler(CompilerBase):
+    """The default compiler
+    """
+
+    def define_pipelines(self):
+        # this maintains the objmode fallback behaviour
+        pms = []
+        if not self.state.flags.force_pyobject:
+            pms.append(DefaultPassBuilder.define_nopython_pipeline(self.state))
+        if self.state.status.can_fallback or self.state.flags.force_pyobject:
+            pms.append(
+                DefaultPassBuilder.define_objectmode_pipeline(self.state)
+            )
+        return pms
+
+
+class DefaultPassBuilder(object):
+    """
+    This is the default pass builder, it contains the "classic" default
+    pipelines as pre-canned PassManager instances:
+      - nopython
+      - objectmode
+      - interpreted
+      - typed
+      - untyped
+      - nopython lowering
+    """
+    @staticmethod
+    def define_nopython_pipeline(state, name='nopython'):
+        """Returns an nopython mode pipeline based PassManager
+        """
+        # compose pipeline from untyped, typed and lowering parts
+        dpb = DefaultPassBuilder
+        pm = PassManager(name)
+        untyped_passes = dpb.define_untyped_pipeline(state)
+        pm.passes.extend(untyped_passes.passes)
+
+        typed_passes = dpb.define_typed_pipeline(state)
+        pm.passes.extend(typed_passes.passes)
+
+        lowering_passes = dpb.define_nopython_lowering_pipeline(state)
+        pm.passes.extend(lowering_passes.passes)
+
+        pm.finalize()
+        return pm
+
+    @staticmethod
+    def define_nopython_lowering_pipeline(state, name='nopython_lowering'):
+        pm = PassManager(name)
+        # legalise
+        pm.add_pass(NoPythonSupportedFeatureValidation,
+                    "ensure features that are in use are in a valid form")
+        pm.add_pass(IRLegalization,
+                    "ensure IR is legal prior to lowering")
+        # Annotate only once legalized
+        pm.add_pass(AnnotateTypes, "annotate types")
+        # lower
+        pm.add_pass(NativeLowering, "native lowering")
+        pm.add_pass(NoPythonBackend, "nopython mode backend")
+        pm.add_pass(DumpParforDiagnostics, "dump parfor diagnostics")
+        pm.finalize()
+        return pm
+
+    @staticmethod
+    def define_typed_pipeline(state, name="typed"):
+        """Returns the typed part of the nopython pipeline"""
+        pm = PassManager(name)
+        # typing
+        pm.add_pass(NopythonTypeInference, "nopython frontend")
+
+        # strip phis
+        pm.add_pass(PreLowerStripPhis, "remove phis nodes")
+
+        # optimisation
+        pm.add_pass(InlineOverloads, "inline overloaded functions")
+        if state.flags.auto_parallel.enabled:
+            pm.add_pass(PreParforPass, "Preprocessing for parfors")
+        if not state.flags.no_rewrites:
+            pm.add_pass(NopythonRewrites, "nopython rewrites")
+        if state.flags.auto_parallel.enabled:
+            pm.add_pass(ParforPass, "convert to parfors")
+
+        pm.finalize()
+        return pm
+
+    @staticmethod
+    def define_untyped_pipeline(state, name='untyped'):
+        """Returns an untyped part of the nopython pipeline"""
+        pm = PassManager(name)
+        if state.func_ir is None:
+            pm.add_pass(TranslateByteCode, "analyzing bytecode")
+            pm.add_pass(FixupArgs, "fix up args")
+        pm.add_pass(IRProcessing, "processing IR")
+        pm.add_pass(WithLifting, "Handle with contexts")
+
+        # inline closures early in case they are using nonlocal's
+        # see issue #6585.
+        pm.add_pass(InlineClosureLikes,
+                    "inline calls to locally defined closures")
+
+        # pre typing
+        if not state.flags.no_rewrites:
+            pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
+            pm.add_pass(DeadBranchPrune, "dead branch pruning")
+            pm.add_pass(GenericRewrites, "nopython rewrites")
+
+        # convert any remaining closures into functions
+        pm.add_pass(MakeFunctionToJitFunction,
+                    "convert make_function into JIT functions")
+        # inline functions that have been determined as inlinable and rerun
+        # branch pruning, this needs to be run after closures are inlined as
+        # the IR repr of a closure masks call sites if an inlinable is called
+        # inside a closure
+        pm.add_pass(InlineInlinables, "inline inlinable functions")
+        if not state.flags.no_rewrites:
+            pm.add_pass(DeadBranchPrune, "dead branch pruning")
+
+        pm.add_pass(FindLiterallyCalls, "find literally calls")
+        pm.add_pass(LiteralUnroll, "handles literal_unroll")
+
+        if state.flags.enable_ssa:
+            pm.add_pass(ReconstructSSA, "ssa")
+
+        pm.add_pass(LiteralPropagationSubPipelinePass, "Literal propagation")
+
+        pm.finalize()
+        return pm
+
+    @staticmethod
+    def define_objectmode_pipeline(state, name='object'):
+        """Returns an object-mode pipeline based PassManager
+        """
+        pm = PassManager(name)
+        if state.func_ir is None:
+            pm.add_pass(TranslateByteCode, "analyzing bytecode")
+            pm.add_pass(FixupArgs, "fix up args")
+        else:
+            # Reaches here if it's a fallback from nopython mode.
+            # Strip the phi nodes.
+            pm.add_pass(PreLowerStripPhis, "remove phis nodes")
+        pm.add_pass(IRProcessing, "processing IR")
+
+        if utils.PYVERSION >= (3, 7):
+            # The following passes are needed to adjust for looplifting
+            pm.add_pass(CanonicalizeLoopEntry, "canonicalize loop entry")
+            pm.add_pass(CanonicalizeLoopExit, "canonicalize loop exit")
+
+        pm.add_pass(ObjectModeFrontEnd, "object mode frontend")
+        pm.add_pass(InlineClosureLikes,
+                    "inline calls to locally defined closures")
+        # convert any remaining closures into functions
+        pm.add_pass(MakeFunctionToJitFunction,
+                    "convert make_function into JIT functions")
+        pm.add_pass(IRLegalization, "ensure IR is legal prior to lowering")
+        pm.add_pass(AnnotateTypes, "annotate types")
+        pm.add_pass(ObjectModeBackEnd, "object mode backend")
+        pm.finalize()
+        return pm
+
+
+def compile_extra(typingctx, targetctx, func, args, return_type, flags,
+                  locals, library=None, pipeline_class=Compiler):
+    """Compiler entry point
+
+    Parameter
+    ---------
+    typingctx :
+        typing context
+    targetctx :
+        target context
+    func : function
+        the python function to be compiled
+    args : tuple, list
+        argument types
+    return_type :
+        Use ``None`` to indicate void return
+    flags : numba.compiler.Flags
+        compiler flags
+    library : numba.codegen.CodeLibrary
+        Used to store the compiled code.
+        If it is ``None``, a new CodeLibrary is used.
+    pipeline_class : type like numba.compiler.CompilerBase
+        compiler pipeline
+    """
+    pipeline = pipeline_class(typingctx, targetctx, library,
+                              args, return_type, flags, locals)
+    return pipeline.compile_extra(func)
+
+
+def compile_ir(typingctx, targetctx, func_ir, args, return_type, flags,
+               locals, lifted=(), lifted_from=None, is_lifted_loop=False,
+               library=None, pipeline_class=Compiler):
+    """
+    Compile a function with the given IR.
+
+    For internal use only.
+    """
+
+    # This is a special branch that should only run on IR from a lifted loop
+    if is_lifted_loop:
+        # This code is pessimistic and costly, but it is a not often trodden
+        # path and it will go away once IR is made immutable. The problem is
+        # that the rewrite passes can mutate the IR into a state that makes
+        # it possible for invalid tokens to be transmitted to lowering which
+        # then trickle through into LLVM IR and causes RuntimeErrors as LLVM
+        # cannot compile it. As a result the following approach is taken:
+        # 1. Create some new flags that copy the original ones but switch
+        #    off rewrites.
+        # 2. Compile with 1. to get a compile result
+        # 3. Try and compile another compile result but this time with the
+        #    original flags (and IR being rewritten).
+        # 4. If 3 was successful, use the result, else use 2.
+
+        # create flags with no rewrites
+        norw_flags = copy.deepcopy(flags)
+        norw_flags.no_rewrites = True
+
+        def compile_local(the_ir, the_flags):
+            pipeline = pipeline_class(typingctx, targetctx, library,
+                                      args, return_type, the_flags, locals)
+            return pipeline.compile_ir(func_ir=the_ir, lifted=lifted,
+                                       lifted_from=lifted_from)
+
+        # compile with rewrites off, IR shouldn't be mutated irreparably
+        norw_cres = compile_local(func_ir.copy(), norw_flags)
+
+        # try and compile with rewrites on if no_rewrites was not set in the
+        # original flags, IR might get broken but we've got a CompileResult
+        # that's usable from above.
+        rw_cres = None
+        if not flags.no_rewrites:
+            # Suppress warnings in compilation retry
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", errors.NumbaWarning)
+                try:
+                    rw_cres = compile_local(func_ir.copy(), flags)
+                except Exception:
+                    pass
+        # if the rewrite variant of compilation worked, use it, else use
+        # the norewrites backup
+        if rw_cres is not None:
+            cres = rw_cres
+        else:
+            cres = norw_cres
+        return cres
+
+    else:
+        pipeline = pipeline_class(typingctx, targetctx, library,
+                                  args, return_type, flags, locals)
+        return pipeline.compile_ir(func_ir=func_ir, lifted=lifted,
+                                   lifted_from=lifted_from)
+
+
+def compile_internal(typingctx, targetctx, library,
+                     func, args, return_type, flags, locals):
+    """
+    For internal use only.
+    """
+    pipeline = Compiler(typingctx, targetctx, library,
+                        args, return_type, flags, locals)
+    return pipeline.compile_extra(func)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler_lock.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler_lock.py
new file mode 100644
index 000000000..874fced9f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler_lock.py
@@ -0,0 +1,56 @@
+import threading
+import functools
+import numba.core.event as ev
+
+
+# Lock for the preventing multiple compiler execution
+class _CompilerLock(object):
+    def __init__(self):
+        self._lock = threading.RLock()
+
+    def acquire(self):
+        ev.start_event("numba:compiler_lock")
+        self._lock.acquire()
+
+    def release(self):
+        self._lock.release()
+        ev.end_event("numba:compiler_lock")
+
+    def __enter__(self):
+        self.acquire()
+
+    def __exit__(self, exc_val, exc_type, traceback):
+        self.release()
+
+    def is_locked(self):
+        is_owned = getattr(self._lock, '_is_owned')
+        if not callable(is_owned):
+            is_owned = self._is_owned
+        return is_owned()
+
+    def __call__(self, func):
+        @functools.wraps(func)
+        def _acquire_compile_lock(*args, **kwargs):
+            with self:
+                return func(*args, **kwargs)
+        return _acquire_compile_lock
+
+    def _is_owned(self):
+        # This method is borrowed from threading.Condition.
+        # Return True if lock is owned by current_thread.
+        # This method is called only if _lock doesn't have _is_owned().
+        if self._lock.acquire(0):
+            self._lock.release()
+            return False
+        else:
+            return True
+
+
+global_compiler_lock = _CompilerLock()
+
+
+def require_global_compiler_lock():
+    """Sentry that checks the global_compiler_lock is acquired.
+    """
+    # Use assert to allow turning off this check
+    assert global_compiler_lock.is_locked()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler_machinery.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler_machinery.py
new file mode 100644
index 000000000..2573888cd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/compiler_machinery.py
@@ -0,0 +1,463 @@
+import timeit
+from abc import abstractmethod, ABCMeta
+from collections import namedtuple, OrderedDict
+import inspect
+from pprint import pformat
+
+
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core import errors, config, transforms, utils
+from numba.core.tracing import event
+from numba.core.postproc import PostProcessor
+from numba.core.ir_utils import enforce_no_dels, legalize_single_scope
+import numba.core.event as ev
+
+# terminal color markup
+_termcolor = errors.termcolor()
+
+
+class SimpleTimer(object):
+    """
+    A simple context managed timer
+    """
+
+    def __enter__(self):
+        self.ts = timeit.default_timer()
+        return self
+
+    def __exit__(self, *exc):
+        self.elapsed = timeit.default_timer() - self.ts
+
+
+class CompilerPass(metaclass=ABCMeta):
+    """ The base class for all compiler passes.
+    """
+
+    @abstractmethod
+    def __init__(self, *args, **kwargs):
+        self._analysis = None
+        self._pass_id = None
+
+    @classmethod
+    def name(cls):
+        """
+        Returns the name of the pass
+        """
+        return cls._name
+
+    @property
+    def pass_id(self):
+        """
+        The ID of the pass
+        """
+        return self._pass_id
+
+    @pass_id.setter
+    def pass_id(self, val):
+        """
+        Sets the ID of the pass
+        """
+        self._pass_id = val
+
+    @property
+    def analysis(self):
+        """
+        Analysis data for the pass
+        """
+        return self._analysis
+
+    @analysis.setter
+    def analysis(self, val):
+        """
+        Set the analysis data for the pass
+        """
+        self._analysis = val
+
+    def run_initialization(self, *args, **kwargs):
+        """
+        Runs the initialization sequence for the pass, will run before
+        `run_pass`.
+        """
+        return False
+
+    @abstractmethod
+    def run_pass(self, *args, **kwargs):
+        """
+        Runs the pass itself. Must return True/False depending on whether
+        statement level modification took place.
+        """
+        pass
+
+    def run_finalizer(self, *args, **kwargs):
+        """
+        Runs the initialization sequence for the pass, will run before
+        `run_pass`.
+        """
+        return False
+
+    def get_analysis_usage(self, AU):
+        """ Override to set analysis usage
+        """
+        pass
+
+    def get_analysis(self, pass_name):
+        """
+        Gets the analysis from a given pass
+        """
+        return self._analysis[pass_name]
+
+
+class SSACompliantMixin(object):
+    """ Mixin to indicate a pass is SSA form compliant. Nothing is asserted
+    about this condition at present.
+    """
+    pass
+
+
+class FunctionPass(CompilerPass):
+    """ Base class for function passes
+    """
+    pass
+
+
+class AnalysisPass(CompilerPass):
+    """ Base class for analysis passes (no modification made to state)
+    """
+    pass
+
+
+class LoweringPass(CompilerPass):
+    """ Base class for lowering passes
+    """
+    pass
+
+
+class AnalysisUsage(object):
+    """This looks and behaves like LLVM's AnalysisUsage because its like that.
+    """
+
+    def __init__(self):
+        self._required = set()
+        self._preserved = set()
+
+    def get_required_set(self):
+        return self._required
+
+    def get_preserved_set(self):
+        return self._preserved
+
+    def add_required(self, pss):
+        self._required.add(pss)
+
+    def add_preserved(self, pss):
+        self._preserved.add(pss)
+
+    def __str__(self):
+        return "required: %s\n" % self._required
+
+
+_DEBUG = False
+
+
+def debug_print(*args, **kwargs):
+    if _DEBUG:
+        print(*args, **kwargs)
+
+
+pass_timings = namedtuple('pass_timings', 'init run finalize')
+
+
+class PassManager(object):
+    """
+    The PassManager is a named instance of a particular compilation pipeline
+    """
+    # TODO: Eventually enable this, it enforces self consistency after each pass
+    _ENFORCING = False
+
+    def __init__(self, pipeline_name):
+        """
+        Create a new pipeline with name "pipeline_name"
+        """
+        self.passes = []
+        self.exec_times = OrderedDict()
+        self._finalized = False
+        self._analysis = None
+        self._print_after = None
+        self.pipeline_name = pipeline_name
+
+    def _validate_pass(self, pass_cls):
+        if (not (isinstance(pass_cls, str) or
+                 (inspect.isclass(pass_cls) and
+                  issubclass(pass_cls, CompilerPass)))):
+            msg = ("Pass must be referenced by name or be a subclass of a "
+                   "CompilerPass. Have %s" % pass_cls)
+            raise TypeError(msg)
+        if isinstance(pass_cls, str):
+            pass_cls = _pass_registry.find_by_name(pass_cls)
+        else:
+            if not _pass_registry.is_registered(pass_cls):
+                raise ValueError("Pass %s is not registered" % pass_cls)
+
+    def add_pass(self, pss, description=""):
+        """
+        Append a pass to the PassManager's compilation pipeline
+        """
+        self._validate_pass(pss)
+        func_desc_tuple = (pss, description)
+        self.passes.append(func_desc_tuple)
+        self._finalized = False
+
+    def add_pass_after(self, pass_cls, location):
+        """
+        Add a pass `pass_cls` to the PassManager's compilation pipeline after
+        the pass `location`.
+        """
+        assert self.passes
+        self._validate_pass(pass_cls)
+        self._validate_pass(location)
+        for idx, (x, _) in enumerate(self.passes):
+            if x == location:
+                break
+        else:
+            raise ValueError("Could not find pass %s" % location)
+        self.passes.insert(idx + 1, (pass_cls, str(pass_cls)))
+        # if a pass has been added, it's not finalized
+        self._finalized = False
+
+    def _debug_init(self):
+        # determine after which passes IR dumps should take place
+        def parse(conf_item):
+            print_passes = []
+            if conf_item != "none":
+                if conf_item == "all":
+                    print_passes = [x.name() for (x, _) in self.passes]
+                else:
+                    # we don't validate whether the named passes exist in this
+                    # pipeline the compiler may be used reentrantly and
+                    # different pipelines may contain different passes
+                    splitted = conf_item.split(',')
+                    print_passes = [x.strip() for x in splitted]
+            return print_passes
+        ret = (parse(config.DEBUG_PRINT_AFTER),
+               parse(config.DEBUG_PRINT_BEFORE),
+               parse(config.DEBUG_PRINT_WRAP),)
+        return ret
+
+    def finalize(self):
+        """
+        Finalize the PassManager, after which no more passes may be added
+        without re-finalization.
+        """
+        self._analysis = self.dependency_analysis()
+        self._print_after, self._print_before, self._print_wrap = \
+            self._debug_init()
+        self._finalized = True
+
+    @property
+    def finalized(self):
+        return self._finalized
+
+    def _patch_error(self, desc, exc):
+        """
+        Patches the error to show the stage that it arose in.
+        """
+        newmsg = "{desc}\n{exc}".format(desc=desc, exc=exc)
+        exc.args = (newmsg,)
+        return exc
+
+    @global_compiler_lock  # this need a lock, likely calls LLVM
+    def _runPass(self, index, pss, internal_state):
+        mutated = False
+
+        def check(func, compiler_state):
+            mangled = func(compiler_state)
+            if mangled not in (True, False):
+                msg = ("CompilerPass implementations should return True/False. "
+                       "CompilerPass with name '%s' did not.")
+                raise ValueError(msg % pss.name())
+            return mangled
+
+        def debug_print(pass_name, print_condition, printable_condition):
+            if pass_name in print_condition:
+                fid = internal_state.func_id
+                args = (fid.modname, fid.func_qualname, self.pipeline_name,
+                        printable_condition, pass_name)
+                print(("%s.%s: %s: %s %s" % args).center(120, '-'))
+                if internal_state.func_ir is not None:
+                    internal_state.func_ir.dump()
+                else:
+                    print("func_ir is None")
+
+        # debug print before this pass?
+        debug_print(pss.name(), self._print_before + self._print_wrap, "BEFORE")
+
+        # wire in the analysis info so it's accessible
+        pss.analysis = self._analysis
+
+        qualname = internal_state.func_id.func_qualname
+
+        ev_details = dict(
+            name=f"{pss.name()} [{qualname}]",
+            qualname=qualname,
+            module=internal_state.func_id.modname,
+            flags=pformat(internal_state.flags.values()),
+            args=str(internal_state.args),
+            return_type=str(internal_state.return_type),
+        )
+        with ev.trigger_event("numba:run_pass", data=ev_details):
+            with SimpleTimer() as init_time:
+                mutated |= check(pss.run_initialization, internal_state)
+            with SimpleTimer() as pass_time:
+                mutated |= check(pss.run_pass, internal_state)
+            with SimpleTimer() as finalize_time:
+                mutated |= check(pss.run_finalizer, internal_state)
+
+        # Check that if the pass is an instance of a FunctionPass that it hasn't
+        # emitted ir.Dels.
+        if isinstance(pss, FunctionPass):
+            enforce_no_dels(internal_state.func_ir)
+
+        if self._ENFORCING:
+            # TODO: Add in self consistency enforcement for
+            # `func_ir._definitions` etc
+            if _pass_registry.get(pss.__class__).mutates_CFG:
+                if mutated:  # block level changes, rebuild all
+                    PostProcessor(internal_state.func_ir).run()
+                else:  # CFG level changes rebuild CFG
+                    internal_state.func_ir.blocks = transforms.canonicalize_cfg(
+                        internal_state.func_ir.blocks)
+            # Check the func_ir has exactly one Scope instance
+            if not legalize_single_scope(internal_state.func_ir.blocks):
+                raise errors.CompilerError(
+                    f"multiple scope in func_ir detected in {pss}",
+                )
+        # inject runtimes
+        pt = pass_timings(init_time.elapsed, pass_time.elapsed,
+                          finalize_time.elapsed)
+        self.exec_times["%s_%s" % (index, pss.name())] = pt
+
+        # debug print after this pass?
+        debug_print(pss.name(), self._print_after + self._print_wrap, "AFTER")
+
+    def run(self, state):
+        """
+        Run the defined pipelines on the state.
+        """
+        from numba.core.compiler import _EarlyPipelineCompletion
+        if not self.finalized:
+            raise RuntimeError("Cannot run non-finalised pipeline")
+
+        # walk the passes and run them
+        for idx, (pss, pass_desc) in enumerate(self.passes):
+            try:
+                event("-- %s" % pass_desc)
+                pass_inst = _pass_registry.get(pss).pass_inst
+                if isinstance(pass_inst, CompilerPass):
+                    self._runPass(idx, pass_inst, state)
+                else:
+                    raise BaseException("Legacy pass in use")
+            except _EarlyPipelineCompletion as e:
+                raise e
+            except Exception as e:
+                if (utils.use_new_style_errors() and not
+                        isinstance(e, errors.NumbaError)):
+                    raise e
+                msg = "Failed in %s mode pipeline (step: %s)" % \
+                    (self.pipeline_name, pass_desc)
+                patched_exception = self._patch_error(msg, e)
+                raise patched_exception
+
+    def dependency_analysis(self):
+        """
+        Computes dependency analysis
+        """
+        deps = dict()
+        for (pss, _) in self.passes:
+            x = _pass_registry.get(pss).pass_inst
+            au = AnalysisUsage()
+            x.get_analysis_usage(au)
+            deps[type(x)] = au
+
+        requires_map = dict()
+        for k, v in deps.items():
+            requires_map[k] = v.get_required_set()
+
+        def resolve_requires(key, rmap):
+            def walk(lkey, rmap):
+                dep_set = rmap[lkey] if lkey in rmap else set()
+                if dep_set:
+                    for x in dep_set:
+                        dep_set |= (walk(x, rmap))
+                    return dep_set
+                else:
+                    return set()
+            ret = set()
+            for k in key:
+                ret |= walk(k, rmap)
+            return ret
+
+        dep_chain = dict()
+        for k, v in requires_map.items():
+            dep_chain[k] = set(v) | (resolve_requires(v, requires_map))
+
+        return dep_chain
+
+
+pass_info = namedtuple('pass_info', 'pass_inst mutates_CFG analysis_only')
+
+
+class PassRegistry(object):
+    """
+    Pass registry singleton class.
+    """
+
+    _id = 0
+
+    _registry = dict()
+
+    def register(self, mutates_CFG, analysis_only):
+        def make_festive(pass_class):
+            assert not self.is_registered(pass_class)
+            assert not self._does_pass_name_alias(pass_class.name())
+            pass_class.pass_id = self._id
+            self._id += 1
+            self._registry[pass_class] = pass_info(pass_class(), mutates_CFG,
+                                                   analysis_only)
+            return pass_class
+        return make_festive
+
+    def is_registered(self, clazz):
+        return clazz in self._registry.keys()
+
+    def get(self, clazz):
+        assert self.is_registered(clazz)
+        return self._registry[clazz]
+
+    def _does_pass_name_alias(self, check):
+        for k, v in self._registry.items():
+            if v.pass_inst.name == check:
+                return True
+        return False
+
+    def find_by_name(self, class_name):
+        assert isinstance(class_name, str)
+        for k, v in self._registry.items():
+            if v.pass_inst.name == class_name:
+                return v
+        else:
+            raise ValueError("No pass with name %s is registered" % class_name)
+
+    def dump(self):
+        for k, v in self._registry.items():
+            print("%s: %s" % (k, v))
+
+
+_pass_registry = PassRegistry()
+del PassRegistry
+
+
+"""
+register_pass is used to register a compiler pass class for use with PassManager
+instances.
+"""
+register_pass = _pass_registry.register
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/config.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/config.py
new file mode 100644
index 000000000..da5bed21b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/config.py
@@ -0,0 +1,527 @@
+import platform
+import sys
+import os
+import re
+import shutil
+import warnings
+
+# YAML needed to use file based Numba config
+try:
+    import yaml
+    _HAVE_YAML = True
+except ImportError:
+    _HAVE_YAML = False
+
+
+import llvmlite.binding as ll
+
+
+IS_WIN32 = sys.platform.startswith('win32')
+IS_OSX = sys.platform.startswith('darwin')
+MACHINE_BITS = tuple.__itemsize__ * 8
+IS_32BITS = MACHINE_BITS == 32
+# Python version in (major, minor) tuple
+PYVERSION = sys.version_info[:2]
+
+# this is the name of the user supplied configuration file
+_config_fname = '.numba_config.yaml'
+
+
+def _parse_cc(text):
+    """
+    Parse CUDA compute capability version string.
+    """
+    if not text:
+        return None
+    else:
+        m = re.match(r'(\d+)\.(\d+)', text)
+        if not m:
+            raise ValueError("Compute capability must be specified as a "
+                             "string of \"major.minor\" where major "
+                             "and minor are decimals")
+        grp = m.groups()
+        return int(grp[0]), int(grp[1])
+
+
+def _os_supports_avx():
+    """
+    Whether the current OS supports AVX, regardless of the CPU.
+
+    This is necessary because the user may be running a very old Linux
+    kernel (e.g. CentOS 5) on a recent CPU.
+    """
+    if (not sys.platform.startswith('linux')
+            or platform.machine() not in ('i386', 'i586', 'i686', 'x86_64')):
+        return True
+    # Executing the CPUID instruction may report AVX available even though
+    # the kernel doesn't support it, so parse /proc/cpuinfo instead.
+    try:
+        f = open('/proc/cpuinfo', 'r')
+    except OSError:
+        # If /proc isn't available, assume yes
+        return True
+    with f:
+        for line in f:
+            head, _, body = line.partition(':')
+            if head.strip() == 'flags' and 'avx' in body.split():
+                return True
+        else:
+            return False
+
+
+# Choose how to handle captured errors
+def _validate_captured_errors_style(style_str):
+    rendered_style = str(style_str)
+    if rendered_style not in ('new_style', 'old_style'):
+        msg = ("Invalid style in NUMBA_CAPTURED_ERRORS: "
+               f"{rendered_style}")
+        raise ValueError(msg)
+    else:
+        return rendered_style
+
+
+class _EnvReloader(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.old_environ = {}
+        self.update(force=True)
+
+    def update(self, force=False):
+        new_environ = {}
+
+        # first check if there's a .numba_config.yaml and use values from that
+        if os.path.exists(_config_fname) and os.path.isfile(_config_fname):
+            if not _HAVE_YAML:
+                msg = ("A Numba config file is found but YAML parsing "
+                       "capabilities appear to be missing. "
+                       "To use this feature please install `pyyaml`. e.g. "
+                       "`conda install pyyaml`.")
+                warnings.warn(msg)
+            else:
+                with open(_config_fname, 'rt') as f:
+                    y_conf = yaml.safe_load(f)
+                if y_conf is not None:
+                    for k, v in y_conf.items():
+                        new_environ['NUMBA_' + k.upper()] = v
+
+        # clobber file based config with any locally defined env vars
+        for name, value in os.environ.items():
+            if name.startswith('NUMBA_'):
+                new_environ[name] = value
+        # We update the config variables if at least one NUMBA environment
+        # variable was modified.  This lets the user modify values
+        # directly in the config module without having them when
+        # reload_config() is called by the compiler.
+        if force or self.old_environ != new_environ:
+            self.process_environ(new_environ)
+            # Store a copy
+            self.old_environ = dict(new_environ)
+
+        self.validate()
+
+    def validate(self):
+        global CUDA_USE_NVIDIA_BINDING
+
+        if CUDA_USE_NVIDIA_BINDING:  # noqa: F821
+            try:
+                import cuda  # noqa: F401
+            except ImportError as ie:
+                msg = ("CUDA Python bindings requested (the environment "
+                       "variable NUMBA_CUDA_USE_NVIDIA_BINDING is set), "
+                       f"but they are not importable: {ie.msg}.")
+                warnings.warn(msg)
+
+                CUDA_USE_NVIDIA_BINDING = False
+
+            if CUDA_PER_THREAD_DEFAULT_STREAM:  # noqa: F821
+                warnings.warn("PTDS support is handled by CUDA Python when "
+                              "using the NVIDIA binding. Please set the "
+                              "environment variable "
+                              "CUDA_PYTHON_CUDA_PER_THREAD_DEFAULT_STREAM to 1 "
+                              "instead.")
+
+    def process_environ(self, environ):
+        def _readenv(name, ctor, default):
+            value = environ.get(name)
+            if value is None:
+                return default() if callable(default) else default
+            try:
+                return ctor(value)
+            except Exception:
+                warnings.warn("environ %s defined but failed to parse '%s'" %
+                              (name, value), RuntimeWarning)
+                return default
+
+        def optional_str(x):
+            return str(x) if x is not None else None
+
+        # developer mode produces full tracebacks, disables help instructions
+        DEVELOPER_MODE = _readenv("NUMBA_DEVELOPER_MODE", int, 0)
+
+        # disable performance warnings, will switch of the generation of
+        # warnings of the class NumbaPerformanceWarning
+        DISABLE_PERFORMANCE_WARNINGS = _readenv(
+            "NUMBA_DISABLE_PERFORMANCE_WARNINGS", int, 0)
+
+        # Flag to enable full exception reporting
+        FULL_TRACEBACKS = _readenv(
+            "NUMBA_FULL_TRACEBACKS", int, DEVELOPER_MODE)
+
+        # Show help text when an error occurs
+        SHOW_HELP = _readenv("NUMBA_SHOW_HELP", int, 0)
+
+        # The color scheme to use for error messages, default is no color
+        # just bold fonts in use.
+        COLOR_SCHEME = _readenv("NUMBA_COLOR_SCHEME", str, "no_color")
+
+        # Whether to globally enable bounds checking. The default None means
+        # to use the value of the flag to @njit. 0 or 1 overrides the flag
+        # globally.
+        BOUNDSCHECK = _readenv("NUMBA_BOUNDSCHECK", int, None)
+
+        # Whether to always warn about potential uninitialized variables
+        # because static controlflow analysis cannot find a definition
+        # in one or more of the incoming paths.
+        ALWAYS_WARN_UNINIT_VAR = _readenv(
+            "NUMBA_ALWAYS_WARN_UNINIT_VAR", int, 0,
+        )
+
+        # Whether to warn about kernel launches where the grid size will
+        # under utilize the GPU due to low occupancy. On by default.
+        CUDA_LOW_OCCUPANCY_WARNINGS = _readenv(
+            "NUMBA_CUDA_LOW_OCCUPANCY_WARNINGS", int, 1)
+
+        # Whether to use the official CUDA Python API Bindings
+        CUDA_USE_NVIDIA_BINDING = _readenv(
+            "NUMBA_CUDA_USE_NVIDIA_BINDING", int, 0)
+
+        # Debug flag to control compiler debug print
+        DEBUG = _readenv("NUMBA_DEBUG", int, 0)
+
+        # DEBUG print IR after pass names
+        DEBUG_PRINT_AFTER = _readenv("NUMBA_DEBUG_PRINT_AFTER", str, "none")
+
+        # DEBUG print IR before pass names
+        DEBUG_PRINT_BEFORE = _readenv("NUMBA_DEBUG_PRINT_BEFORE", str, "none")
+
+        # DEBUG print IR before and after pass names
+        DEBUG_PRINT_WRAP = _readenv("NUMBA_DEBUG_PRINT_WRAP", str, "none")
+
+        # Highlighting in intermediate dumps
+        HIGHLIGHT_DUMPS = _readenv("NUMBA_HIGHLIGHT_DUMPS", int, 0)
+
+        # JIT Debug flag to trigger IR instruction print
+        DEBUG_JIT = _readenv("NUMBA_DEBUG_JIT", int, 0)
+
+        # Enable debugging of front-end operation
+        # (up to and including IR generation)
+        DEBUG_FRONTEND = _readenv("NUMBA_DEBUG_FRONTEND", int, 0)
+
+        # Enable debug prints in nrtdynmod
+        DEBUG_NRT = _readenv("NUMBA_DEBUG_NRT", int, 0)
+
+        # How many recently deserialized functions to retain regardless
+        # of external references
+        FUNCTION_CACHE_SIZE = _readenv("NUMBA_FUNCTION_CACHE_SIZE", int, 128)
+
+        # Maximum tuple size that parfors will unpack and pass to
+        # internal gufunc.
+        PARFOR_MAX_TUPLE_SIZE = _readenv("NUMBA_PARFOR_MAX_TUPLE_SIZE",
+                                         int, 100)
+
+        # Enable logging of cache operation
+        DEBUG_CACHE = _readenv("NUMBA_DEBUG_CACHE", int, DEBUG)
+
+        # Redirect cache directory
+        # Contains path to the directory
+        CACHE_DIR = _readenv("NUMBA_CACHE_DIR", str, "")
+
+        # Enable tracing support
+        TRACE = _readenv("NUMBA_TRACE", int, 0)
+
+        # Enable chrome tracing support
+        CHROME_TRACE = _readenv("NUMBA_CHROME_TRACE", str, "")
+
+        # Enable debugging of type inference
+        DEBUG_TYPEINFER = _readenv("NUMBA_DEBUG_TYPEINFER", int, 0)
+
+        # Configure compilation target to use the specified CPU name
+        # and CPU feature as the host information.
+        # Note: this overrides "host" option for AOT compilation.
+        CPU_NAME = _readenv("NUMBA_CPU_NAME", optional_str, None)
+        CPU_FEATURES = _readenv("NUMBA_CPU_FEATURES", optional_str,
+                                ("" if str(CPU_NAME).lower() == 'generic'
+                                 else None))
+        # Optimization level
+        OPT = _readenv("NUMBA_OPT", int, 3)
+
+        # Force dump of Python bytecode
+        DUMP_BYTECODE = _readenv("NUMBA_DUMP_BYTECODE", int, DEBUG_FRONTEND)
+
+        # Force dump of control flow graph
+        DUMP_CFG = _readenv("NUMBA_DUMP_CFG", int, DEBUG_FRONTEND)
+
+        # Force dump of Numba IR
+        DUMP_IR = _readenv("NUMBA_DUMP_IR", int,
+                           DEBUG_FRONTEND)
+
+        # Force dump of Numba IR in SSA form
+        DUMP_SSA = _readenv("NUMBA_DUMP_SSA", int,
+                            DEBUG_FRONTEND or DEBUG_TYPEINFER)
+
+        # print debug info of analysis and optimization on array operations
+        DEBUG_ARRAY_OPT = _readenv("NUMBA_DEBUG_ARRAY_OPT", int, 0)
+
+        # insert debug stmts to print information at runtime
+        DEBUG_ARRAY_OPT_RUNTIME = _readenv(
+            "NUMBA_DEBUG_ARRAY_OPT_RUNTIME", int, 0)
+
+        # print stats about parallel for-loops
+        DEBUG_ARRAY_OPT_STATS = _readenv("NUMBA_DEBUG_ARRAY_OPT_STATS", int, 0)
+
+        # prints user friendly information about parallel
+        PARALLEL_DIAGNOSTICS = _readenv("NUMBA_PARALLEL_DIAGNOSTICS", int, 0)
+
+        # print debug info of inline closure pass
+        DEBUG_INLINE_CLOSURE = _readenv("NUMBA_DEBUG_INLINE_CLOSURE", int, 0)
+
+        # Force dump of LLVM IR
+        DUMP_LLVM = _readenv("NUMBA_DUMP_LLVM", int, DEBUG)
+
+        # Force dump of Function optimized LLVM IR
+        DUMP_FUNC_OPT = _readenv("NUMBA_DUMP_FUNC_OPT", int, DEBUG)
+
+        # Force dump of Optimized LLVM IR
+        DUMP_OPTIMIZED = _readenv("NUMBA_DUMP_OPTIMIZED", int, DEBUG)
+
+        # Force disable loop vectorize
+        # Loop vectorizer is disabled on 32-bit win32 due to a bug (#649)
+        LOOP_VECTORIZE = _readenv("NUMBA_LOOP_VECTORIZE", int,
+                                  not (IS_WIN32 and IS_32BITS))
+
+        # Switch on  superword-level parallelism vectorization, default is on.
+        SLP_VECTORIZE = _readenv("NUMBA_SLP_VECTORIZE", int, 1)
+
+        # Force dump of generated assembly
+        DUMP_ASSEMBLY = _readenv("NUMBA_DUMP_ASSEMBLY", int, DEBUG)
+
+        # Force dump of type annotation
+        ANNOTATE = _readenv("NUMBA_DUMP_ANNOTATION", int, 0)
+
+        # Dump IR in such as way as to aid in "diff"ing.
+        DIFF_IR = _readenv("NUMBA_DIFF_IR", int, 0)
+
+        # Dump type annotation in html format
+        def fmt_html_path(path):
+            if path is None:
+                return path
+            else:
+                return os.path.abspath(path)
+
+        HTML = _readenv("NUMBA_DUMP_HTML", fmt_html_path, None)
+
+        # x86-64 specific
+        # Enable AVX on supported platforms where it won't degrade performance.
+        def avx_default():
+            if not _os_supports_avx():
+                return False
+            else:
+                # There are various performance issues with AVX and LLVM
+                # on some CPUs (list at
+                # http://llvm.org/bugs/buglist.cgi?quicksearch=avx).
+                # For now we'd rather disable it, since it can pessimize code
+                cpu_name = ll.get_host_cpu_name()
+                return cpu_name not in ('corei7-avx', 'core-avx-i',
+                                        'sandybridge', 'ivybridge')
+
+        ENABLE_AVX = _readenv("NUMBA_ENABLE_AVX", int, avx_default)
+
+        # if set and SVML is available, it will be disabled
+        # By default, it's disabled on 32-bit platforms.
+        DISABLE_INTEL_SVML = _readenv(
+            "NUMBA_DISABLE_INTEL_SVML", int, IS_32BITS)
+
+        # Disable jit for debugging
+        DISABLE_JIT = _readenv("NUMBA_DISABLE_JIT", int, 0)
+
+        # choose parallel backend to use
+        THREADING_LAYER_PRIORITY = _readenv(
+            "NUMBA_THREADING_LAYER_PRIORITY",
+            lambda string: string.split(),
+            ['tbb', 'omp', 'workqueue'],
+        )
+        THREADING_LAYER = _readenv("NUMBA_THREADING_LAYER", str, 'default')
+
+        CAPTURED_ERRORS = _readenv("NUMBA_CAPTURED_ERRORS",
+                                   _validate_captured_errors_style,
+                                   'old_style')
+
+        # CUDA Configs
+
+        # Whether to warn about kernel launches where a host array
+        # is used as a parameter, forcing a copy to and from the device.
+        # On by default.
+        CUDA_WARN_ON_IMPLICIT_COPY = _readenv(
+            "NUMBA_CUDA_WARN_ON_IMPLICIT_COPY", int, 1)
+
+        # Force CUDA compute capability to a specific version
+        FORCE_CUDA_CC = _readenv("NUMBA_FORCE_CUDA_CC", _parse_cc, None)
+
+        # The default compute capability to target when compiling to PTX.
+        CUDA_DEFAULT_PTX_CC = _readenv("NUMBA_CUDA_DEFAULT_PTX_CC", _parse_cc,
+                                       (5, 3))
+
+        # Disable CUDA support
+        DISABLE_CUDA = _readenv("NUMBA_DISABLE_CUDA",
+                                int, int(MACHINE_BITS == 32))
+
+        # Enable CUDA simulator
+        ENABLE_CUDASIM = _readenv("NUMBA_ENABLE_CUDASIM", int, 0)
+
+        # CUDA logging level
+        # Any level name from the *logging* module.  Case insensitive.
+        # Defaults to CRITICAL if not set or invalid.
+        # Note: This setting only applies when logging is not configured.
+        #       Any existing logging configuration is preserved.
+        CUDA_LOG_LEVEL = _readenv("NUMBA_CUDA_LOG_LEVEL", str, '')
+
+        # Include argument values in the CUDA Driver API logs
+        CUDA_LOG_API_ARGS = _readenv("NUMBA_CUDA_LOG_API_ARGS", int, 0)
+
+        # Maximum number of pending CUDA deallocations (default: 10)
+        CUDA_DEALLOCS_COUNT = _readenv("NUMBA_CUDA_MAX_PENDING_DEALLOCS_COUNT",
+                                       int, 10)
+
+        # Maximum ratio of pending CUDA deallocations to capacity (default: 0.2)
+        CUDA_DEALLOCS_RATIO = _readenv("NUMBA_CUDA_MAX_PENDING_DEALLOCS_RATIO",
+                                       float, 0.2)
+
+        CUDA_ARRAY_INTERFACE_SYNC = _readenv("NUMBA_CUDA_ARRAY_INTERFACE_SYNC",
+                                             int, 1)
+
+        # Path of the directory that the CUDA driver libraries are located
+        CUDA_DRIVER = _readenv("NUMBA_CUDA_DRIVER", str, '')
+
+        # Buffer size for logs produced by CUDA driver operations (e.g.
+        # linking)
+        CUDA_LOG_SIZE = _readenv("NUMBA_CUDA_LOG_SIZE", int, 1024)
+
+        # Whether to generate verbose log messages when JIT linking
+        CUDA_VERBOSE_JIT_LOG = _readenv("NUMBA_CUDA_VERBOSE_JIT_LOG", int, 1)
+
+        # Whether the default stream is the per-thread default stream
+        CUDA_PER_THREAD_DEFAULT_STREAM = _readenv(
+            "NUMBA_CUDA_PER_THREAD_DEFAULT_STREAM", int, 0)
+
+        # Location of the CUDA include files
+        if IS_WIN32:
+            cuda_path = os.environ.get('CUDA_PATH')
+            if cuda_path:
+                default_cuda_include_path = os.path.join(cuda_path, "include")
+            else:
+                default_cuda_include_path = "cuda_include_not_found"
+        else:
+            default_cuda_include_path = os.path.join(os.sep, 'usr', 'local',
+                                                     'cuda', 'include')
+        CUDA_INCLUDE_PATH = _readenv("NUMBA_CUDA_INCLUDE_PATH", str,
+                                     default_cuda_include_path)
+
+        # Threading settings
+
+        # The default number of threads to use.
+        def num_threads_default():
+            try:
+                sched_getaffinity = os.sched_getaffinity
+            except AttributeError:
+                pass
+            else:
+                return max(1, len(sched_getaffinity(0)))
+
+            cpu_count = os.cpu_count()
+            if cpu_count is not None:
+                return max(1, cpu_count)
+
+            return 1
+
+        NUMBA_DEFAULT_NUM_THREADS = num_threads_default()
+
+        # Numba thread pool size (defaults to number of CPUs on the system).
+        _NUMBA_NUM_THREADS = _readenv("NUMBA_NUM_THREADS", int,
+                                      NUMBA_DEFAULT_NUM_THREADS)
+        if ('NUMBA_NUM_THREADS' in globals()
+                and globals()['NUMBA_NUM_THREADS'] != _NUMBA_NUM_THREADS):
+
+            from numba.np.ufunc import parallel
+            if parallel._is_initialized:
+                raise RuntimeError("Cannot set NUMBA_NUM_THREADS to a "
+                                   "different value once the threads have been "
+                                   "launched (currently have %s, "
+                                   "trying to set %s)" %
+                                   (_NUMBA_NUM_THREADS,
+                                    globals()['NUMBA_NUM_THREADS']))
+
+        NUMBA_NUM_THREADS = _NUMBA_NUM_THREADS
+        del _NUMBA_NUM_THREADS
+
+        # Profiling support
+
+        # Indicates if a profiler detected. Only VTune can be detected for now
+        RUNNING_UNDER_PROFILER = 'VS_PROFILER' in os.environ
+
+        # Enables jit events in LLVM to support profiling of dynamic code
+        ENABLE_PROFILING = _readenv(
+            "NUMBA_ENABLE_PROFILING", int, int(RUNNING_UNDER_PROFILER))
+
+        # Debug Info
+
+        # The default value for the `debug` flag
+        DEBUGINFO_DEFAULT = _readenv("NUMBA_DEBUGINFO", int, ENABLE_PROFILING)
+        CUDA_DEBUGINFO_DEFAULT = _readenv("NUMBA_CUDA_DEBUGINFO", int, 0)
+
+        EXTEND_VARIABLE_LIFETIMES = _readenv("NUMBA_EXTEND_VARIABLE_LIFETIMES",
+                                             int, 0)
+
+        # gdb binary location
+        def which_gdb(path_or_bin):
+            gdb = shutil.which(path_or_bin)
+            return gdb if gdb is not None else path_or_bin
+
+        GDB_BINARY = _readenv("NUMBA_GDB_BINARY", which_gdb, 'gdb')
+
+        # CUDA Memory management
+        CUDA_MEMORY_MANAGER = _readenv("NUMBA_CUDA_MEMORY_MANAGER", str,
+                                       'default')
+
+        # Experimental refprune pass
+        LLVM_REFPRUNE_PASS = _readenv(
+            "NUMBA_LLVM_REFPRUNE_PASS", int, 1,
+        )
+        LLVM_REFPRUNE_FLAGS = _readenv(
+            "NUMBA_LLVM_REFPRUNE_FLAGS", str,
+            "all" if LLVM_REFPRUNE_PASS else "",
+        )
+
+        # Timing support.
+
+        # LLVM_PASS_TIMINGS enables LLVM recording of pass timings.
+        LLVM_PASS_TIMINGS = _readenv(
+            "NUMBA_LLVM_PASS_TIMINGS", int, 0,
+        )
+
+        # Inject the configuration values into the module globals
+        for name, value in locals().copy().items():
+            if name.isupper():
+                globals()[name] = value
+
+
+_env_reloader = _EnvReloader()
+
+
+def reload_config():
+    """
+    Reload the configuration from environment variables, if necessary.
+    """
+    _env_reloader.update()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/consts.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/consts.py
new file mode 100644
index 000000000..d062b3208
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/consts.py
@@ -0,0 +1,118 @@
+from types import ModuleType
+
+import weakref
+
+from numba.core.errors import ConstantInferenceError, NumbaError
+from numba.core import ir
+
+
+class ConstantInference(object):
+    """
+    A constant inference engine for a given interpreter.
+    Inference inspects the IR to try and compute a compile-time constant for
+    a variable.
+
+    This shouldn't be used directly, instead call Interpreter.infer_constant().
+    """
+
+    def __init__(self, func_ir):
+        # Avoid cyclic references as some user-visible objects may be
+        # held alive in the cache
+        self._func_ir = weakref.proxy(func_ir)
+        self._cache = {}
+
+    def infer_constant(self, name, loc=None):
+        """
+        Infer a constant value for the given variable *name*.
+        If no value can be inferred, numba.errors.ConstantInferenceError
+        is raised.
+        """
+        if name not in self._cache:
+            try:
+                self._cache[name] = (True, self._do_infer(name))
+            except ConstantInferenceError as exc:
+                # Store the exception args only, to avoid keeping
+                # a whole traceback alive.
+                self._cache[name] = (False, (exc.__class__, exc.args))
+        success, val = self._cache[name]
+        if success:
+            return val
+        else:
+            exc, args = val
+            if issubclass(exc, NumbaError):
+                raise exc(*args, loc=loc)
+            else:
+                raise exc(*args)
+
+    def _fail(self, val):
+        # The location here is set to None because `val` is the ir.Var name
+        # and not the actual offending use of the var. When this is raised it is
+        # caught in the flow control of `infer_constant` and the class and args
+        # (the message) are captured and then raised again but with the location
+        # set to the expression that caused the constant inference error.
+        raise ConstantInferenceError(
+            "Constant inference not possible for: %s" % (val,), loc=None)
+
+    def _do_infer(self, name):
+        if not isinstance(name, str):
+            raise TypeError("infer_constant() called with non-str %r"
+                            % (name,))
+        try:
+            defn = self._func_ir.get_definition(name)
+        except KeyError:
+            raise ConstantInferenceError(
+                "no single definition for %r" % (name,))
+        try:
+            const = defn.infer_constant()
+        except ConstantInferenceError:
+            if isinstance(defn, ir.Expr):
+                return self._infer_expr(defn)
+            self._fail(defn)
+        return const
+
+    def _infer_expr(self, expr):
+        # Infer an expression: handle supported cases
+        if expr.op == 'call':
+            func = self.infer_constant(expr.func.name, loc=expr.loc)
+            return self._infer_call(func, expr)
+        elif expr.op == 'getattr':
+            value = self.infer_constant(expr.value.name, loc=expr.loc)
+            return self._infer_getattr(value, expr)
+        elif expr.op == 'build_list':
+            return [self.infer_constant(i.name, loc=expr.loc) for i in
+                    expr.items]
+        elif expr.op == 'build_tuple':
+            return tuple(self.infer_constant(i.name, loc=expr.loc) for i in
+                         expr.items)
+        self._fail(expr)
+
+    def _infer_call(self, func, expr):
+        if expr.kws or expr.vararg:
+            self._fail(expr)
+        # Check supported callables
+        _slice = func in (slice,)
+        _exc = isinstance(func, type) and issubclass(func, BaseException)
+        if _slice or _exc:
+            args = [self.infer_constant(a.name, loc=expr.loc) for a in
+                    expr.args]
+            if _slice:
+                return func(*args)
+            elif _exc:
+                # If the exception class is user defined it may implement a ctor
+                # that does not pass the args to the super. Therefore return the
+                # raw class and the args so this can be instantiated at the call
+                # site in the way the user source expects it to be.
+                return func, args
+            else:
+                assert 0, 'Unreachable'
+
+        self._fail(expr)
+
+    def _infer_getattr(self, value, expr):
+        if isinstance(value, (ModuleType, type)):
+            # Allow looking up a constant on a class or module
+            try:
+                return getattr(value, expr.attr)
+            except AttributeError:
+                pass
+        self._fail(expr)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/controlflow.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/controlflow.py
new file mode 100644
index 000000000..344f6e2ce
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/controlflow.py
@@ -0,0 +1,954 @@
+import collections
+import functools
+import sys
+
+from numba.core import utils
+from numba.core.ir import Loc
+from numba.core.errors import UnsupportedError
+
+# List of bytecodes creating a new block in the control flow graph
+# (in addition to explicit jump labels).
+NEW_BLOCKERS = frozenset(['SETUP_LOOP', 'FOR_ITER', 'SETUP_WITH'])
+
+
+class CFBlock(object):
+
+    def __init__(self, offset):
+        self.offset = offset
+        self.body = []
+        # A map of jumps to outgoing blocks (successors):
+        #   { offset of outgoing block -> number of stack pops }
+        self.outgoing_jumps = {}
+        # A map of jumps to incoming blocks (predecessors):
+        #   { offset of incoming block -> number of stack pops }
+        self.incoming_jumps = {}
+        self.terminating = False
+
+    def __repr__(self):
+        args = (self.offset,
+                sorted(self.outgoing_jumps),
+                sorted(self.incoming_jumps))
+        return "block(offset:%d, outgoing: %s, incoming: %s)" % args
+
+    def __iter__(self):
+        return iter(self.body)
+
+
+class Loop(collections.namedtuple("Loop",
+                                  ("entries", "exits", "header", "body"))):
+    """
+    A control flow loop, as detected by a CFGraph object.
+    """
+
+    __slots__ = ()
+
+    # The loop header is enough to detect that two loops are really
+    # the same, assuming they belong to the same graph.
+    # (note: in practice, only one loop instance is created per graph
+    #  loop, so identity would be fine)
+
+    def __eq__(self, other):
+        return isinstance(other, Loop) and other.header == self.header
+
+    def __hash__(self):
+        return hash(self.header)
+
+
+class _DictOfContainers(collections.defaultdict):
+    """A defaultdict with customized equality checks that ignore empty values.
+
+    Non-empty value is checked by: `bool(value_item) == True`.
+    """
+
+    def __eq__(self, other):
+        if isinstance(other, _DictOfContainers):
+            mine = self._non_empty_items()
+            theirs = other._non_empty_items()
+            return mine == theirs
+
+        return NotImplemented
+
+    def __ne__(self, other):
+        ret = self.__eq__(other)
+        if ret is NotImplemented:
+            return ret
+        else:
+            return not ret
+
+    def _non_empty_items(self):
+        return [(k, vs) for k, vs in sorted(self.items()) if vs]
+
+
+class CFGraph(object):
+    """
+    Generic (almost) implementation of a Control Flow Graph.
+    """
+
+    def __init__(self):
+        self._nodes = set()
+        self._preds = _DictOfContainers(set)
+        self._succs = _DictOfContainers(set)
+        self._edge_data = {}
+        self._entry_point = None
+
+    def add_node(self, node):
+        """
+        Add *node* to the graph.  This is necessary before adding any
+        edges from/to the node.  *node* can be any hashable object.
+        """
+        self._nodes.add(node)
+
+    def add_edge(self, src, dest, data=None):
+        """
+        Add an edge from node *src* to node *dest*, with optional
+        per-edge *data*.
+        If such an edge already exists, it is replaced (duplicate edges
+        are not possible).
+        """
+        if src not in self._nodes:
+            raise ValueError("Cannot add edge as src node %s not in nodes %s" %
+                             (src, self._nodes))
+        if dest not in self._nodes:
+            raise ValueError("Cannot add edge as dest node %s not in nodes %s" %
+                             (dest, self._nodes))
+        self._add_edge(src, dest, data)
+
+    def successors(self, src):
+        """
+        Yield (node, data) pairs representing the successors of node *src*.
+        (*data* will be None if no data was specified when adding the edge)
+        """
+        for dest in self._succs[src]:
+            yield dest, self._edge_data[src, dest]
+
+    def predecessors(self, dest):
+        """
+        Yield (node, data) pairs representing the predecessors of node *dest*.
+        (*data* will be None if no data was specified when adding the edge)
+        """
+        for src in self._preds[dest]:
+            yield src, self._edge_data[src, dest]
+
+    def set_entry_point(self, node):
+        """
+        Set the entry point of the graph to *node*.
+        """
+        assert node in self._nodes
+        self._entry_point = node
+
+    def process(self):
+        """
+        Compute essential properties of the control flow graph.  The graph
+        must have been fully populated, and its entry point specified. Other
+        graph properties are computed on-demand.
+        """
+        if self._entry_point is None:
+            raise RuntimeError("no entry point defined!")
+        self._eliminate_dead_blocks()
+
+    def dominators(self):
+        """
+        Return a dictionary of {node -> set(nodes)} mapping each node to
+        the nodes dominating it.
+
+        A node D dominates a node N when any path leading to N must go through D
+        """
+        return self._doms
+
+    def post_dominators(self):
+        """
+        Return a dictionary of {node -> set(nodes)} mapping each node to
+        the nodes post-dominating it.
+
+        A node P post-dominates a node N when any path starting from N must go
+        through P.
+        """
+        return self._post_doms
+
+    def immediate_dominators(self):
+        """
+        Return a dictionary of {node -> node} mapping each node to its
+        immediate dominator (idom).
+
+        The idom(B) is the closest strict dominator of V
+        """
+        return self._idom
+
+    def dominance_frontier(self):
+        """
+        Return a dictionary of {node -> set(nodes)} mapping each node to
+        the nodes in its dominance frontier.
+
+        The dominance frontier _df(N) is the set of all nodes that are
+        immediate successors to blocks dominated by N but which aren't
+        strictly dominated by N
+        """
+        return self._df
+
+    def dominator_tree(self):
+        """
+        return a dictionary of {node -> set(nodes)} mapping each node to
+        the set of nodes it immediately dominates
+
+        The domtree(B) is the closest strict set of nodes that B dominates
+        """
+        return self._domtree
+
+    @utils.cached_property
+    def _exit_points(self):
+        return self._find_exit_points()
+
+    @utils.cached_property
+    def _doms(self):
+        return self._find_dominators()
+
+    @utils.cached_property
+    def _back_edges(self):
+        return self._find_back_edges()
+
+    @utils.cached_property
+    def _topo_order(self):
+        return self._find_topo_order()
+
+    @utils.cached_property
+    def _descs(self):
+        return self._find_descendents()
+
+    @utils.cached_property
+    def _loops(self):
+        return self._find_loops()
+
+    @utils.cached_property
+    def _in_loops(self):
+        return self._find_in_loops()
+
+    @utils.cached_property
+    def _post_doms(self):
+        return self._find_post_dominators()
+
+    @utils.cached_property
+    def _idom(self):
+        return self._find_immediate_dominators()
+
+    @utils.cached_property
+    def _df(self):
+        return self._find_dominance_frontier()
+
+    @utils.cached_property
+    def _domtree(self):
+        return self._find_dominator_tree()
+
+    def descendents(self, node):
+        """
+        Return the set of descendents of the given *node*, in topological
+        order (ignoring back edges).
+        """
+        return self._descs[node]
+
+    def entry_point(self):
+        """
+        Return the entry point node.
+        """
+        assert self._entry_point is not None
+        return self._entry_point
+
+    def exit_points(self):
+        """
+        Return the computed set of exit nodes (may be empty).
+        """
+        return self._exit_points
+
+    def backbone(self):
+        """
+        Return the set of nodes constituting the graph's backbone.
+        (i.e. the nodes that every path starting from the entry point
+         must go through).  By construction, it is non-empty: it contains
+         at least the entry point.
+        """
+        return self._post_doms[self._entry_point]
+
+    def loops(self):
+        """
+        Return a dictionary of {node -> loop} mapping each loop header
+        to the loop (a Loop instance) starting with it.
+        """
+        return self._loops
+
+    def in_loops(self, node):
+        """
+        Return the list of Loop objects the *node* belongs to,
+        from innermost to outermost.
+        """
+        return [self._loops[x] for x in self._in_loops.get(node, ())]
+
+    def dead_nodes(self):
+        """
+        Return the set of dead nodes (eliminated from the graph).
+        """
+        return self._dead_nodes
+
+    def nodes(self):
+        """
+        Return the set of live nodes.
+        """
+        return self._nodes
+
+    def topo_order(self):
+        """
+        Return the sequence of nodes in topological order (ignoring back
+        edges).
+        """
+        return self._topo_order
+
+    def topo_sort(self, nodes, reverse=False):
+        """
+        Iterate over the *nodes* in topological order (ignoring back edges).
+        The sort isn't guaranteed to be stable.
+        """
+        nodes = set(nodes)
+        it = self._topo_order
+        if reverse:
+            it = reversed(it)
+        for n in it:
+            if n in nodes:
+                yield n
+
+    def dump(self, file=None):
+        """
+        Dump extensive debug information.
+        """
+        import pprint
+        file = file or sys.stdout
+        if 1:
+            print("CFG adjacency lists:", file=file)
+            self._dump_adj_lists(file)
+        print("CFG dominators:", file=file)
+        pprint.pprint(self._doms, stream=file)
+        print("CFG post-dominators:", file=file)
+        pprint.pprint(self._post_doms, stream=file)
+        print("CFG back edges:", sorted(self._back_edges), file=file)
+        print("CFG loops:", file=file)
+        pprint.pprint(self._loops, stream=file)
+        print("CFG node-to-loops:", file=file)
+        pprint.pprint(self._in_loops, stream=file)
+        print("CFG backbone:", file=file)
+        pprint.pprint(self.backbone(), stream=file)
+
+    def render_dot(self, filename="numba_cfg.dot"):
+        """Render the controlflow graph with GraphViz DOT via the
+        ``graphviz`` python binding.
+
+        Returns
+        -------
+        g : graphviz.Digraph
+            Use `g.view()` to open the graph in the default PDF application.
+        """
+
+        try:
+            import graphviz as gv
+        except ImportError:
+            raise ImportError(
+                "The feature requires `graphviz` but it is not available. "
+                "Please install with `pip install graphviz`"
+            )
+        g = gv.Digraph(filename=filename)
+        # Populate the nodes
+        for n in self._nodes:
+            g.node(str(n))
+        # Populate the edges
+        for n in self._nodes:
+            for edge in self._succs[n]:
+                g.edge(str(n), str(edge))
+        return g
+
+    # Internal APIs
+
+    def _add_edge(self, from_, to, data=None):
+        # This internal version allows adding edges to/from unregistered
+        # (ghost) nodes.
+        self._preds[to].add(from_)
+        self._succs[from_].add(to)
+        self._edge_data[from_, to] = data
+
+    def _remove_node_edges(self, node):
+        for succ in self._succs.pop(node, ()):
+            self._preds[succ].remove(node)
+            del self._edge_data[node, succ]
+        for pred in self._preds.pop(node, ()):
+            self._succs[pred].remove(node)
+            del self._edge_data[pred, node]
+
+    def _dfs(self, entries=None):
+        if entries is None:
+            entries = (self._entry_point,)
+        seen = set()
+        stack = list(entries)
+        while stack:
+            node = stack.pop()
+            if node not in seen:
+                yield node
+                seen.add(node)
+                for succ in self._succs[node]:
+                    stack.append(succ)
+
+    def _eliminate_dead_blocks(self):
+        """
+        Eliminate all blocks not reachable from the entry point, and
+        stash them into self._dead_nodes.
+        """
+        live = set()
+        for node in self._dfs():
+            live.add(node)
+        self._dead_nodes = self._nodes - live
+        self._nodes = live
+        # Remove all edges leading from dead nodes
+        for dead in self._dead_nodes:
+            self._remove_node_edges(dead)
+
+    def _find_exit_points(self):
+        """
+        Compute the graph's exit points.
+        """
+        exit_points = set()
+        for n in self._nodes:
+            if not self._succs.get(n):
+                exit_points.add(n)
+        return exit_points
+
+    def _find_postorder(self):
+        succs = self._succs
+        back_edges = self._back_edges
+        post_order = []
+        seen = set()
+
+        post_order = []
+
+        # DFS
+        def dfs_rec(node):
+            if node not in seen:
+                seen.add(node)
+                stack.append((post_order.append, node))
+                for dest in succs[node]:
+                    if (node, dest) not in back_edges:
+                        stack.append((dfs_rec, dest))
+
+        stack = [(dfs_rec, self._entry_point)]
+        while stack:
+            cb, data = stack.pop()
+            cb(data)
+
+        return post_order
+
+    def _find_immediate_dominators(self):
+        # The algorithm implemented computes the immediate dominator
+        # for each node in the CFG which is equivalent to build a dominator tree
+        # Based on the implementation from NetworkX
+        # library - nx.immediate_dominators
+        # https://github.com/networkx/networkx/blob/858e7cb183541a78969fed0cbcd02346f5866c02/networkx/algorithms/dominance.py    # noqa: E501
+        # References:
+        #   Keith D. Cooper, Timothy J. Harvey, and Ken Kennedy
+        #   A Simple, Fast Dominance Algorithm
+        #   https://www.cs.rice.edu/~keith/EMBED/dom.pdf
+        def intersect(u, v):
+            while u != v:
+                while idx[u] < idx[v]:
+                    u = idom[u]
+                while idx[u] > idx[v]:
+                    v = idom[v]
+            return u
+
+        entry = self._entry_point
+        preds_table = self._preds
+
+        order = self._find_postorder()
+        idx = {e: i for i, e in enumerate(order)} # index of each node
+        idom = {entry : entry}
+        order.pop()
+        order.reverse()
+
+        changed = True
+        while changed:
+            changed = False
+            for u in order:
+                new_idom = functools.reduce(intersect,
+                                            (v for v in preds_table[u]
+                                             if v in idom))
+                if u not in idom or idom[u] != new_idom:
+                    idom[u] = new_idom
+                    changed = True
+
+        return idom
+
+    def _find_dominator_tree(self):
+        idom = self._idom
+        domtree = _DictOfContainers(set)
+
+        for u, v in idom.items():
+            # v dominates u
+            if u not in domtree:
+                domtree[u] = set()
+            if u != v:
+                domtree[v].add(u)
+
+        return domtree
+
+    def _find_dominance_frontier(self):
+        idom = self._idom
+        preds_table = self._preds
+        df = {u: set() for u in idom}
+
+        for u in idom:
+            if len(preds_table[u]) < 2:
+                continue
+            for v in preds_table[u]:
+                while v != idom[u]:
+                    df[v].add(u)
+                    v = idom[v]
+
+        return df
+
+    def _find_dominators_internal(self, post=False):
+        # See theoretical description in
+        # http://en.wikipedia.org/wiki/Dominator_%28graph_theory%29
+        # The algorithm implemented here uses a todo-list as described
+        # in http://pages.cs.wisc.edu/~fischer/cs701.f08/finding.loops.html
+        if post:
+            entries = set(self._exit_points)
+            preds_table = self._succs
+            succs_table = self._preds
+        else:
+            entries = set([self._entry_point])
+            preds_table = self._preds
+            succs_table = self._succs
+
+        if not entries:
+            raise RuntimeError("no entry points: dominator algorithm "
+                               "cannot be seeded")
+
+        doms = {}
+        for e in entries:
+            doms[e] = set([e])
+
+        todo = []
+        for n in self._nodes:
+            if n not in entries:
+                doms[n] = set(self._nodes)
+                todo.append(n)
+
+        while todo:
+            n = todo.pop()
+            if n in entries:
+                continue
+            new_doms = set([n])
+            preds = preds_table[n]
+            if preds:
+                new_doms |= functools.reduce(set.intersection,
+                                             [doms[p] for p in preds])
+            if new_doms != doms[n]:
+                assert len(new_doms) < len(doms[n])
+                doms[n] = new_doms
+                todo.extend(succs_table[n])
+        return doms
+
+    def _find_dominators(self):
+        return self._find_dominators_internal(post=False)
+
+    def _find_post_dominators(self):
+        # To handle infinite loops correctly, we need to add a dummy
+        # exit point, and link members of infinite loops to it.
+        dummy_exit = object()
+        self._exit_points.add(dummy_exit)
+        for loop in self._loops.values():
+            if not loop.exits:
+                for b in loop.body:
+                    self._add_edge(b, dummy_exit)
+        pdoms = self._find_dominators_internal(post=True)
+        # Fix the _post_doms table to make no reference to the dummy exit
+        del pdoms[dummy_exit]
+        for doms in pdoms.values():
+            doms.discard(dummy_exit)
+        self._remove_node_edges(dummy_exit)
+        self._exit_points.remove(dummy_exit)
+        return pdoms
+
+    # Finding loops and back edges: see
+    # http://pages.cs.wisc.edu/~fischer/cs701.f08/finding.loops.html
+
+    def _find_back_edges(self, stats=None):
+        """
+        Find back edges.  An edge (src, dest) is a back edge if and
+        only if *dest* dominates *src*.
+        """
+        # Prepare stats to capture execution information
+        if stats is not None:
+            if not isinstance(stats, dict):
+                raise TypeError(f"*stats* must be a dict; got {type(stats)}")
+            stats.setdefault('iteration_count', 0)
+
+        # Uses a simple DFS to find back-edges.
+        # The new algorithm is faster than the the previous dominator based
+        # algorithm.
+        back_edges = set()
+        # stack: keeps track of the traversal path
+        stack = []
+        # succs_state: keep track of unvisited successors of a node
+        succs_state = {}
+        entry_point = self.entry_point()
+
+        checked = set()
+
+        def push_state(node):
+            stack.append(node)
+            succs_state[node] = [dest for dest in self._succs[node]]
+
+        push_state(entry_point)
+
+        # Keep track for iteration count for debugging
+        iter_ct = 0
+        while stack:
+            iter_ct += 1
+            tos = stack[-1]
+            tos_succs = succs_state[tos]
+            # Are there successors not checked?
+            if tos_succs:
+                # Check the next successor
+                cur_node = tos_succs.pop()
+                # Is it in our traversal path?
+                if cur_node in stack:
+                    # Yes, it's a backedge
+                    back_edges.add((tos, cur_node))
+                elif cur_node not in checked:
+                    # Push
+                    push_state(cur_node)
+            else:
+                # Checked all successors. Pop
+                stack.pop()
+                checked.add(tos)
+
+        if stats is not None:
+            stats['iteration_count'] += iter_ct
+        return back_edges
+
+    def _find_topo_order(self):
+        succs = self._succs
+        back_edges = self._back_edges
+        post_order = []
+        seen = set()
+
+        def _dfs_rec(node):
+            if node not in seen:
+                seen.add(node)
+                for dest in succs[node]:
+                    if (node, dest) not in back_edges:
+                        _dfs_rec(dest)
+                post_order.append(node)
+
+        _dfs_rec(self._entry_point)
+        post_order.reverse()
+        return post_order
+
+    def _find_descendents(self):
+        descs = {}
+        for node in reversed(self._topo_order):
+            descs[node] = node_descs = set()
+            for succ in self._succs[node]:
+                if (node, succ) not in self._back_edges:
+                    node_descs.add(succ)
+                    node_descs.update(descs[succ])
+        return descs
+
+    def _find_loops(self):
+        """
+        Find the loops defined by the graph's back edges.
+        """
+        bodies = {}
+        for src, dest in self._back_edges:
+            # The destination of the back edge is the loop header
+            header = dest
+            # Build up the loop body from the back edge's source node,
+            # up to the source header.
+            body = set([header])
+            queue = [src]
+            while queue:
+                n = queue.pop()
+                if n not in body:
+                    body.add(n)
+                    queue.extend(self._preds[n])
+            # There can be several back edges to a given loop header;
+            # if so, merge the resulting body fragments.
+            if header in bodies:
+                bodies[header].update(body)
+            else:
+                bodies[header] = body
+
+        # Create a Loop object for each header.
+        loops = {}
+        for header, body in bodies.items():
+            entries = set()
+            exits = set()
+            for n in body:
+                entries.update(self._preds[n] - body)
+                exits.update(self._succs[n] - body)
+            loop = Loop(header=header, body=body, entries=entries, exits=exits)
+            loops[header] = loop
+        return loops
+
+    def _find_in_loops(self):
+        loops = self._loops
+        # Compute the loops to which each node belongs.
+        in_loops = dict((n, []) for n in self._nodes)
+        # Sort loops from longest to shortest
+        # This ensures that outer loops will come before inner loops
+        for loop in sorted(loops.values(), key=lambda loop: len(loop.body)):
+            for n in loop.body:
+                in_loops[n].append(loop.header)
+        return in_loops
+
+    def _dump_adj_lists(self, file):
+        adj_lists = dict((src, sorted(list(dests)))
+                         for src, dests in self._succs.items())
+        import pprint
+        pprint.pprint(adj_lists, stream=file)
+
+    def __eq__(self, other):
+        if not isinstance(other, CFGraph):
+            raise NotImplementedError
+
+        for x in ['_nodes', '_edge_data', '_entry_point', '_preds', '_succs']:
+            this = getattr(self, x, None)
+            that = getattr(other, x, None)
+            if this != that:
+                return False
+        return True
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+class ControlFlowAnalysis(object):
+    """
+    Attributes
+    ----------
+    - bytecode
+
+    - blocks
+
+    - blockseq
+
+    - doms: dict of set
+        Dominators
+
+    - backbone: set of block offsets
+        The set of block that is common to all possible code path.
+
+    """
+    def __init__(self, bytecode):
+        self.bytecode = bytecode
+        self.blocks = {}
+        self.liveblocks = {}
+        self.blockseq = []
+        self.doms = None
+        self.backbone = None
+        # Internal temp states
+        self._force_new_block = True
+        self._curblock = None
+        self._blockstack = []
+        self._loops = []
+        self._withs = []
+
+    def iterblocks(self):
+        """
+        Return all blocks in sequence of occurrence
+        """
+        for i in self.blockseq:
+            yield self.blocks[i]
+
+    def iterliveblocks(self):
+        """
+        Return all live blocks in sequence of occurrence
+        """
+        for i in self.blockseq:
+            if i in self.liveblocks:
+                yield self.blocks[i]
+
+    def incoming_blocks(self, block):
+        """
+        Yield (incoming block, number of stack pops) pairs for *block*.
+        """
+        for i, pops in block.incoming_jumps.items():
+            if i in self.liveblocks:
+                yield self.blocks[i], pops
+
+    def dump(self, file=None):
+        self.graph.dump(file=None)
+
+    def run(self):
+        for inst in self._iter_inst():
+            fname = "op_%s" % inst.opname
+            fn = getattr(self, fname, None)
+            if fn is not None:
+                fn(inst)
+            elif inst.is_jump:
+                # this catches e.g. try... except
+                l = Loc(self.bytecode.func_id.filename, inst.lineno)
+                if inst.opname in {"SETUP_EXCEPT", "SETUP_FINALLY"}:
+                    msg = "'try' block not supported until python3.7 or later"
+                else:
+                    msg = "Use of unsupported opcode (%s) found" % inst.opname
+                raise UnsupportedError(msg, loc=l)
+            else:
+                # Non-jump instructions are ignored
+                pass  # intentionally
+
+        # Close all blocks
+        for cur, nxt in zip(self.blockseq, self.blockseq[1:]):
+            blk = self.blocks[cur]
+            if not blk.outgoing_jumps and not blk.terminating:
+                blk.outgoing_jumps[nxt] = 0
+
+        graph = CFGraph()
+        for b in self.blocks:
+            graph.add_node(b)
+        for b in self.blocks.values():
+            for out, pops in b.outgoing_jumps.items():
+                graph.add_edge(b.offset, out, pops)
+        graph.set_entry_point(min(self.blocks))
+        graph.process()
+        self.graph = graph
+
+        # Fill incoming
+        for b in self.blocks.values():
+            for out, pops in b.outgoing_jumps.items():
+                self.blocks[out].incoming_jumps[b.offset] = pops
+
+        # Find liveblocks
+        self.liveblocks = dict((i, self.blocks[i])
+                               for i in self.graph.nodes())
+
+        for lastblk in reversed(self.blockseq):
+            if lastblk in self.liveblocks:
+                break
+        else:
+            raise AssertionError("No live block that exits!?")
+
+        # Find backbone
+        backbone = self.graph.backbone()
+        # Filter out in loop blocks (Assuming no other cyclic control blocks)
+        # This is to unavoid variable defined in loops to be considered as
+        # function scope.
+        inloopblocks = set()
+
+        for b in self.blocks.keys():
+            if self.graph.in_loops(b):
+                inloopblocks.add(b)
+
+        self.backbone = backbone - inloopblocks
+
+    def jump(self, target, pops=0):
+        """
+        Register a jump (conditional or not) to *target* offset.
+        *pops* is the number of stack pops implied by the jump (default 0).
+        """
+        self._curblock.outgoing_jumps[target] = pops
+
+    def _iter_inst(self):
+        for inst in self.bytecode:
+            if self._use_new_block(inst):
+                self._guard_with_as(inst)
+                self._start_new_block(inst)
+            self._curblock.body.append(inst.offset)
+            yield inst
+
+    def _use_new_block(self, inst):
+        if inst.offset in self.bytecode.labels:
+            res = True
+        elif inst.opname in NEW_BLOCKERS:
+            res = True
+        else:
+            res = self._force_new_block
+
+        self._force_new_block = False
+        return res
+
+    def _start_new_block(self, inst):
+        self._curblock = CFBlock(inst.offset)
+        self.blocks[inst.offset] = self._curblock
+        self.blockseq.append(inst.offset)
+
+    def _guard_with_as(self, current_inst):
+        """Checks if the next instruction after a SETUP_WITH is something other
+        than a POP_TOP, if it is something else it'll be some sort of store
+        which is not supported (this corresponds to `with CTXMGR as VAR(S)`)."""
+        if current_inst.opname == "SETUP_WITH":
+            next_op = self.bytecode[current_inst.next].opname
+            if next_op != "POP_TOP":
+                msg = ("The 'with (context manager) as "
+                       "(variable):' construct is not "
+                       "supported.")
+                raise UnsupportedError(msg)
+
+    def op_SETUP_LOOP(self, inst):
+        end = inst.get_jump_target()
+        self._blockstack.append(end)
+        self._loops.append((inst.offset, end))
+        # TODO: Looplifting requires the loop entry be its own block.
+        #       Forcing a new block here is the simplest solution for now.
+        #       But, we should consider other less ad-hoc ways.
+        self.jump(inst.next)
+        self._force_new_block = True
+
+    def op_SETUP_WITH(self, inst):
+        end = inst.get_jump_target()
+        self._blockstack.append(end)
+        self._withs.append((inst.offset, end))
+        # TODO: WithLifting requires the loop entry be its own block.
+        #       Forcing a new block here is the simplest solution for now.
+        #       But, we should consider other less ad-hoc ways.
+        self.jump(inst.next)
+        self._force_new_block = True
+
+    def op_POP_BLOCK(self, inst):
+        self._blockstack.pop()
+
+    def op_FOR_ITER(self, inst):
+        self.jump(inst.get_jump_target())
+        self.jump(inst.next)
+        self._force_new_block = True
+
+    def _op_ABSOLUTE_JUMP_IF(self, inst):
+        self.jump(inst.get_jump_target())
+        self.jump(inst.next)
+        self._force_new_block = True
+
+    op_POP_JUMP_IF_FALSE = _op_ABSOLUTE_JUMP_IF
+    op_POP_JUMP_IF_TRUE = _op_ABSOLUTE_JUMP_IF
+    op_JUMP_IF_FALSE = _op_ABSOLUTE_JUMP_IF
+    op_JUMP_IF_TRUE = _op_ABSOLUTE_JUMP_IF
+
+    def _op_ABSOLUTE_JUMP_OR_POP(self, inst):
+        self.jump(inst.get_jump_target())
+        self.jump(inst.next, pops=1)
+        self._force_new_block = True
+
+    op_JUMP_IF_FALSE_OR_POP = _op_ABSOLUTE_JUMP_OR_POP
+    op_JUMP_IF_TRUE_OR_POP = _op_ABSOLUTE_JUMP_OR_POP
+
+    def op_JUMP_ABSOLUTE(self, inst):
+        self.jump(inst.get_jump_target())
+        self._force_new_block = True
+
+    def op_JUMP_FORWARD(self, inst):
+        self.jump(inst.get_jump_target())
+        self._force_new_block = True
+
+    def op_RETURN_VALUE(self, inst):
+        self._curblock.terminating = True
+        self._force_new_block = True
+
+    def op_RAISE_VARARGS(self, inst):
+        self._curblock.terminating = True
+        self._force_new_block = True
+
+    def op_BREAK_LOOP(self, inst):
+        self.jump(self._blockstack[-1])
+        self._force_new_block = True
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cpu.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cpu.py
new file mode 100644
index 000000000..a27deb136
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cpu.py
@@ -0,0 +1,379 @@
+import sys
+import platform
+
+import llvmlite.binding as ll
+from llvmlite import ir
+
+from numba import _dynfunc
+from numba.core.callwrapper import PyCallWrapper
+from numba.core.base import BaseContext, PYOBJECT
+from numba.core import utils, types, config, cgutils, callconv, codegen, externals, fastmathpass, intrinsics
+from numba.core.utils import cached_property
+from numba.core.options import TargetOptions, include_default_options
+from numba.core.runtime import rtsys
+from numba.core.compiler_lock import global_compiler_lock
+import numba.core.entrypoints
+from numba.core.cpu_options import (ParallelOptions, FastMathOptions,
+                                    InlineOptions)
+from numba.np import ufunc_db
+
+# Keep those structures in sync with _dynfunc.c.
+
+class ClosureBody(cgutils.Structure):
+    _fields = [('env', types.pyobject)]
+
+
+class EnvBody(cgutils.Structure):
+    _fields = [
+        ('globals', types.pyobject),
+        ('consts', types.pyobject),
+    ]
+
+
+class CPUContext(BaseContext):
+    """
+    Changes BaseContext calling convention
+    """
+    allow_dynamic_globals = True
+
+    def __init__(self, typingctx, target='cpu'):
+        super().__init__(typingctx, target)
+
+    # Overrides
+    def create_module(self, name):
+        return self._internal_codegen._create_empty_module(name)
+
+    @global_compiler_lock
+    def init(self):
+        self.is32bit = (utils.MACHINE_BITS == 32)
+        self._internal_codegen = codegen.JITCPUCodegen("numba.exec")
+
+        # Add ARM ABI functions from libgcc_s
+        if platform.machine() == 'armv7l':
+            ll.load_library_permanently('libgcc_s.so.1')
+
+        # Map external C functions.
+        externals.c_math_functions.install(self)
+
+        # Initialize NRT runtime
+        rtsys.initialize(self)
+
+        # Add lower_extension attribute
+        self.lower_extensions = {}
+        from numba.parfors.parfor_lowering import _lower_parfor_parallel
+        from numba.parfors.parfor import Parfor
+        # Specify how to lower Parfor nodes using the lower_extensions
+        self.lower_extensions[Parfor] = _lower_parfor_parallel
+
+    def load_additional_registries(self):
+        # Add implementations that work via import
+        from numba.cpython import (builtins, charseq, enumimpl, hashing, heapq,
+                                   iterators, listobj, numbers, rangeobj,
+                                   setobj, slicing, tupleobj, unicode,)
+        from numba.core import optional
+        from numba.misc import gdb_hook, literal
+        from numba.np import linalg, polynomial, arraymath, arrayobj
+        from numba.np.random import generator_core, generator_methods
+        from numba.typed import typeddict, dictimpl
+        from numba.typed import typedlist, listobject
+        from numba.experimental import jitclass, function_type
+        from numba.np import npdatetime
+
+        # Add target specific implementations
+        from numba.np import npyimpl
+        from numba.cpython import cmathimpl, mathimpl, printimpl, randomimpl
+        from numba.misc import cffiimpl
+        from numba.experimental.jitclass.base import ClassBuilder as \
+            jitclassimpl
+        self.install_registry(cmathimpl.registry)
+        self.install_registry(cffiimpl.registry)
+        self.install_registry(mathimpl.registry)
+        self.install_registry(npyimpl.registry)
+        self.install_registry(printimpl.registry)
+        self.install_registry(randomimpl.registry)
+        self.install_registry(jitclassimpl.class_impl_registry)
+
+        # load 3rd party extensions
+        numba.core.entrypoints.init_all()
+
+    @property
+    def target_data(self):
+        return self._internal_codegen.target_data
+
+    def with_aot_codegen(self, name, **aot_options):
+        aot_codegen = codegen.AOTCPUCodegen(name, **aot_options)
+        return self.subtarget(_internal_codegen=aot_codegen,
+                              aot_mode=True)
+
+    def codegen(self):
+        return self._internal_codegen
+
+    @cached_property
+    def call_conv(self):
+        return callconv.CPUCallConv(self)
+
+    def get_env_body(self, builder, envptr):
+        """
+        From the given *envptr* (a pointer to a _dynfunc.Environment object),
+        get a EnvBody allowing structured access to environment fields.
+        """
+        body_ptr = cgutils.pointer_add(
+            builder, envptr, _dynfunc._impl_info['offsetof_env_body'])
+        return EnvBody(self, builder, ref=body_ptr, cast_ref=True)
+
+    def get_env_manager(self, builder):
+        envgv = self.declare_env_global(builder.module,
+                                        self.get_env_name(self.fndesc))
+        envarg = builder.load(envgv)
+        pyapi = self.get_python_api(builder)
+        pyapi.emit_environment_sentry(
+            envarg, debug_msg=self.fndesc.env_name,
+        )
+        env_body = self.get_env_body(builder, envarg)
+        return pyapi.get_env_manager(self.environment, env_body, envarg)
+
+    def get_generator_state(self, builder, genptr, return_type):
+        """
+        From the given *genptr* (a pointer to a _dynfunc.Generator object),
+        get a pointer to its state area.
+        """
+        return cgutils.pointer_add(
+            builder, genptr, _dynfunc._impl_info['offsetof_generator_state'],
+            return_type=return_type)
+
+    def build_list(self, builder, list_type, items):
+        """
+        Build a list from the Numba *list_type* and its initial *items*.
+        """
+        from numba.cpython import listobj
+        return listobj.build_list(self, builder, list_type, items)
+
+    def build_set(self, builder, set_type, items):
+        """
+        Build a set from the Numba *set_type* and its initial *items*.
+        """
+        from numba.cpython import setobj
+        return setobj.build_set(self, builder, set_type, items)
+
+    def build_map(self, builder, dict_type, item_types, items):
+        from numba.typed import dictobject
+
+        return dictobject.build_map(self, builder, dict_type, item_types, items)
+
+
+    def post_lowering(self, mod, library):
+        if self.fastmath:
+            fastmathpass.rewrite_module(mod, self.fastmath)
+
+        if self.is32bit:
+            # 32-bit machine needs to replace all 64-bit div/rem to avoid
+            # calls to compiler-rt
+            intrinsics.fix_divmod(mod)
+
+        library.add_linking_library(rtsys.library)
+
+    def create_cpython_wrapper(self, library, fndesc, env, call_helper,
+                               release_gil=False):
+        wrapper_module = self.create_module("wrapper")
+        fnty = self.call_conv.get_function_type(fndesc.restype, fndesc.argtypes)
+        wrapper_callee = ir.Function(wrapper_module, fnty, fndesc.llvm_func_name)
+        builder = PyCallWrapper(self, wrapper_module, wrapper_callee,
+                                fndesc, env, call_helper=call_helper,
+                                release_gil=release_gil)
+        builder.build()
+        library.add_ir_module(wrapper_module)
+
+    def create_cfunc_wrapper(self, library, fndesc, env, call_helper):
+        wrapper_module = self.create_module("cfunc_wrapper")
+        fnty = self.call_conv.get_function_type(fndesc.restype, fndesc.argtypes)
+        wrapper_callee = ir.Function(wrapper_module, fnty, fndesc.llvm_func_name)
+
+        ll_argtypes = [self.get_value_type(ty) for ty in fndesc.argtypes]
+        ll_return_type = self.get_value_type(fndesc.restype)
+        wrapty = ir.FunctionType(ll_return_type, ll_argtypes)
+        wrapfn = ir.Function(wrapper_module, wrapty, fndesc.llvm_cfunc_wrapper_name)
+        builder = ir.IRBuilder(wrapfn.append_basic_block('entry'))
+
+        status, out = self.call_conv.call_function(
+            builder, wrapper_callee, fndesc.restype, fndesc.argtypes,
+            wrapfn.args, attrs=('noinline',))
+
+        with builder.if_then(status.is_error, likely=False):
+            # If (and only if) an error occurred, acquire the GIL
+            # and use the interpreter to write out the exception.
+            pyapi = self.get_python_api(builder)
+            gil_state = pyapi.gil_ensure()
+            self.call_conv.raise_error(builder, pyapi, status)
+            cstr = self.insert_const_string(builder.module, repr(self))
+            strobj = pyapi.string_from_string(cstr)
+            pyapi.err_write_unraisable(strobj)
+            pyapi.decref(strobj)
+            pyapi.gil_release(gil_state)
+
+        builder.ret(out)
+        library.add_ir_module(wrapper_module)
+
+    def get_executable(self, library, fndesc, env):
+        """
+        Returns
+        -------
+        (cfunc, fnptr)
+
+        - cfunc
+            callable function (Can be None)
+        - fnptr
+            callable function address
+        - env
+            an execution environment (from _dynfunc)
+        """
+        # Code generation
+        baseptr = library.get_pointer_to_function(fndesc.llvm_func_name)
+        fnptr = library.get_pointer_to_function(fndesc.llvm_cpython_wrapper_name)
+
+        # Note: we avoid reusing the original docstring to avoid encoding
+        # issues on Python 2, see issue #1908
+        doc = "compiled wrapper for %r" % (fndesc.qualname,)
+        cfunc = _dynfunc.make_function(fndesc.lookup_module(),
+                                       fndesc.qualname.split('.')[-1],
+                                       doc, fnptr, env,
+                                       # objects to keepalive with the function
+                                       (library,)
+                                       )
+        library.codegen.set_env(self.get_env_name(fndesc), env)
+        return cfunc
+
+    def calc_array_sizeof(self, ndim):
+        '''
+        Calculate the size of an array struct on the CPU target
+        '''
+        aryty = types.Array(types.int32, ndim, 'A')
+        return self.get_abi_sizeof(self.get_value_type(aryty))
+
+    # Overrides
+    def get_ufunc_info(self, ufunc_key):
+        return ufunc_db.get_ufunc_info(ufunc_key)
+
+
+# ----------------------------------------------------------------------------
+# TargetOptions
+
+_options_mixin = include_default_options(
+    "nopython",
+    "forceobj",
+    "looplift",
+    "_nrt",
+    "debug",
+    "boundscheck",
+    "nogil",
+    "no_rewrites",
+    "no_cpython_wrapper",
+    "no_cfunc_wrapper",
+    "parallel",
+    "fastmath",
+    "error_model",
+    "inline",
+    "forceinline",
+    # Add "target_backend" as a accepted option for the CPU in @jit(...)
+    "target_backend",
+    "_dbg_extend_lifetimes",
+    "_dbg_optnone",
+)
+
+class CPUTargetOptions(_options_mixin, TargetOptions):
+    def finalize(self, flags, options):
+        if not flags.is_set("enable_pyobject"):
+            flags.enable_pyobject = True
+
+        if not flags.is_set("enable_looplift"):
+            flags.enable_looplift = True
+
+        flags.inherit_if_not_set("nrt", default=True)
+
+        if not flags.is_set("debuginfo"):
+            flags.debuginfo = config.DEBUGINFO_DEFAULT
+
+        if not flags.is_set("dbg_extend_lifetimes"):
+            if flags.debuginfo:
+                # auto turn on extend-lifetimes if debuginfo is on and
+                # dbg_extend_lifetimes is not set
+                flags.dbg_extend_lifetimes = True
+            else:
+                # set flag using env-var config
+                flags.dbg_extend_lifetimes = config.EXTEND_VARIABLE_LIFETIMES
+
+        if not flags.is_set("boundscheck"):
+            flags.boundscheck = flags.debuginfo
+
+        flags.enable_pyobject_looplift = True
+
+        flags.inherit_if_not_set("fastmath")
+
+        flags.inherit_if_not_set("error_model", default="python")
+
+        # Add "target_backend" as a option that inherits from the caller
+        flags.inherit_if_not_set("target_backend")
+
+        flags.inherit_if_not_set("forceinline")
+
+        if flags.forceinline:
+            # forceinline turns off optnone, just like clang.
+            flags.optnone = False
+
+# ----------------------------------------------------------------------------
+# Internal
+
+def remove_refct_calls(func):
+    """
+    Remove redundant incref/decref within on a per block basis
+    """
+    for bb in func.basic_blocks:
+        remove_null_refct_call(bb)
+        remove_refct_pairs(bb)
+
+
+def remove_null_refct_call(bb):
+    """
+    Remove refct api calls to NULL pointer
+    """
+    pass
+    ## Skipped for now
+    # for inst in bb.instructions:
+    #     if isinstance(inst, ir.CallInstr):
+    #         fname = inst.called_function.name
+    #         if fname == "Py_IncRef" or fname == "Py_DecRef":
+    #             arg = inst.args[0]
+    #             print(type(arg))
+    #             if isinstance(arg, lc.ConstantPointerNull):
+    #                 inst.erase_from_parent()
+
+
+def remove_refct_pairs(bb):
+    """
+    Remove incref decref pairs on the same variable
+    """
+
+    didsomething = True
+
+    while didsomething:
+        didsomething = False
+
+        increfs = {}
+        decrefs = {}
+
+        # Mark
+        for inst in bb.instructions:
+            if isinstance(inst, ir.CallInstr):
+                fname = inst.called_function.name
+                if fname == "Py_IncRef":
+                    arg = inst.operands[0]
+                    increfs[arg] = inst
+                elif fname == "Py_DecRef":
+                    arg = inst.operands[0]
+                    decrefs[arg] = inst
+
+        # Sweep
+        for val in increfs.keys():
+            if val in decrefs:
+                increfs[val].erase_from_parent()
+                decrefs[val].erase_from_parent()
+                didsomething = True
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cpu_options.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cpu_options.py
new file mode 100644
index 000000000..e2136c319
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/cpu_options.py
@@ -0,0 +1,181 @@
+"""
+Defines CPU Options for use in the CPU target
+"""
+from abc import ABCMeta, abstractmethod
+
+
+class AbstractOptionValue(metaclass=ABCMeta):
+    """Abstract base class for custom option values.
+    """
+    @abstractmethod
+    def encode(self) -> str:
+        """Returns an encoding of the values
+        """
+        ...
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({self.encode()})"
+
+
+class FastMathOptions(AbstractOptionValue):
+    """
+    Options for controlling fast math optimization.
+    """
+
+    def __init__(self, value):
+        # https://releases.llvm.org/7.0.0/docs/LangRef.html#fast-math-flags
+        valid_flags = {
+            'fast',
+            'nnan', 'ninf', 'nsz', 'arcp',
+            'contract', 'afn', 'reassoc',
+        }
+
+        if isinstance(value, FastMathOptions):
+            self.flags = value.flags.copy()
+        elif value is True:
+            self.flags = {'fast'}
+        elif value is False:
+            self.flags = set()
+        elif isinstance(value, set):
+            invalid = value - valid_flags
+            if invalid:
+                raise ValueError("Unrecognized fastmath flags: %s" % invalid)
+            self.flags = value
+        elif isinstance(value, dict):
+            invalid = set(value.keys()) - valid_flags
+            if invalid:
+                raise ValueError("Unrecognized fastmath flags: %s" % invalid)
+            self.flags = {v for v, enable in value.items() if enable}
+        else:
+            msg = "Expected fastmath option(s) to be either a bool, dict or set"
+            raise ValueError(msg)
+
+    def __bool__(self):
+        return bool(self.flags)
+
+    __nonzero__ = __bool__
+
+    def encode(self) -> str:
+        return str(self.flags)
+
+    def __eq__(self, other):
+        if type(other) is type(self):
+            return self.flags == other.flags
+        return NotImplemented
+
+
+class ParallelOptions(AbstractOptionValue):
+    """
+    Options for controlling auto parallelization.
+    """
+    __slots__ = ("enabled", "comprehension", "reduction", "inplace_binop",
+                 "setitem", "numpy", "stencil", "fusion", "prange")
+
+    def __init__(self, value):
+        if isinstance(value, bool):
+            self.enabled = value
+            self.comprehension = value
+            self.reduction = value
+            self.inplace_binop = value
+            self.setitem = value
+            self.numpy = value
+            self.stencil = value
+            self.fusion = value
+            self.prange = value
+        elif isinstance(value, dict):
+            self.enabled = True
+            self.comprehension = value.pop('comprehension', True)
+            self.reduction = value.pop('reduction', True)
+            self.inplace_binop = value.pop('inplace_binop', True)
+            self.setitem = value.pop('setitem', True)
+            self.numpy = value.pop('numpy', True)
+            self.stencil = value.pop('stencil', True)
+            self.fusion = value.pop('fusion', True)
+            self.prange = value.pop('prange', True)
+            if value:
+                msg = "Unrecognized parallel options: %s" % value.keys()
+                raise NameError(msg)
+        elif isinstance(value, ParallelOptions):
+            self.enabled = value.enabled
+            self.comprehension = value.comprehension
+            self.reduction = value.reduction
+            self.inplace_binop = value.inplace_binop
+            self.setitem = value.setitem
+            self.numpy = value.numpy
+            self.stencil = value.stencil
+            self.fusion = value.fusion
+            self.prange = value.prange
+        else:
+            msg = "Expect parallel option to be either a bool or a dict"
+            raise ValueError(msg)
+
+    def _get_values(self):
+        """Get values as dictionary.
+        """
+        return {k: getattr(self, k) for k in self.__slots__}
+
+    def __eq__(self, other):
+        if type(other) is type(self):
+            return self._get_values() == other._get_values()
+        return NotImplemented
+
+    def encode(self) -> str:
+        return ", ".join(f"{k}={v}" for k, v in self._get_values().items())
+
+
+class InlineOptions(AbstractOptionValue):
+    """
+    Options for controlling inlining
+    """
+
+    def __init__(self, value):
+        ok = False
+        if isinstance(value, str):
+            if value in ('always', 'never'):
+                ok = True
+        else:
+            ok = hasattr(value, '__call__')
+
+        if ok:
+            self._inline = value
+        else:
+            msg = ("kwarg 'inline' must be one of the strings 'always' or "
+                   "'never', or it can be a callable that returns True/False. "
+                   "Found value %s" % value)
+            raise ValueError(msg)
+
+    @property
+    def is_never_inline(self):
+        """
+        True if never inline
+        """
+        return self._inline == 'never'
+
+    @property
+    def is_always_inline(self):
+        """
+        True if always inline
+        """
+        return self._inline == 'always'
+
+    @property
+    def has_cost_model(self):
+        """
+        True if a cost model is provided
+        """
+        return not (self.is_always_inline or self.is_never_inline)
+
+    @property
+    def value(self):
+        """
+        The raw value
+        """
+        return self._inline
+
+    def __eq__(self, other):
+        if type(other) is type(self):
+            return self.value == other.value
+        return NotImplemented
+
+    def encode(self) -> str:
+        return repr(self._inline)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/dataflow.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/dataflow.py
new file mode 100644
index 000000000..5415bd5a1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/dataflow.py
@@ -0,0 +1,914 @@
+import collections
+from pprint import pprint
+import sys
+import warnings
+
+from numba.core.errors import UnsupportedError
+from numba.core.ir import Loc
+
+
+class DataFlowAnalysis(object):
+    """
+    Perform stack2reg
+
+    This is necessary to resolve blocks that propagates stack value.
+    This would allow the use of `and` and `or` and python2.6 jumps.
+    """
+
+    def __init__(self, cfa):
+        self.cfa = cfa
+        self.bytecode = cfa.bytecode
+        # { block offset -> BlockInfo }
+        self.infos = {}
+        self.edge_process = {}
+
+    def run(self):
+        for blk in self.cfa.iterliveblocks():
+            self.infos[blk.offset] = self.run_on_block(blk)
+
+    def run_on_block(self, blk):
+        incoming_blocks = []
+        info = BlockInfo(blk, blk.offset, incoming_blocks)
+        edge_callbacks = []
+
+        for ib, pops in self.cfa.incoming_blocks(blk):
+            # By nature of Python bytecode, there will be no incoming
+            # variables from subsequent blocks.  This is an easy way
+            # of breaking the potential circularity of the problem.
+            if ib.offset >= blk.offset:
+                continue
+            ib = self.infos[ib.offset]
+            incoming_blocks.append(ib)
+            if (ib.offset, blk.offset) in self.edge_process:
+                edge_callbacks.append(self.edge_process[(ib.offset, blk.offset)])
+
+            # Compute stack offset at block entry
+            # The stack effect of our predecessors should be known
+            assert ib.stack_offset is not None, ib
+            new_offset = ib.stack_offset + ib.stack_effect - pops
+            if new_offset < 0:
+                raise RuntimeError("computed negative stack offset for %s"
+                                   % blk)
+            if info.stack_offset is None:
+                info.stack_offset = new_offset
+            elif info.stack_offset != new_offset:
+                warnings.warn("inconsistent stack offset for %s" % blk,
+                              RuntimeWarning)
+
+            # Compute syntax blocks at block entry
+            assert ib.syntax_blocks is not None, ib
+            if info.syntax_blocks is None:
+                info.syntax_blocks = ib.syntax_blocks[:]
+            elif info.syntax_blocks != ib.syntax_blocks:
+                warnings.warn("inconsistent entry syntax blocks for %s" % blk,
+                              RuntimeWarning)
+
+        if info.stack_offset is None:
+            # No incoming blocks => assume it's the entry block
+            info.stack_offset = 0
+            info.syntax_blocks = []
+        info.stack_effect = 0
+
+        for callback in edge_callbacks:
+            callback(info)
+
+        for offset in blk:
+            inst = self.bytecode[offset]
+            self.dispatch(info, inst)
+        return info
+
+    def dump(self):
+        for blk in self.infos.values():
+            blk.dump()
+
+    def dispatch(self, info, inst):
+        fname = "op_%s" % inst.opname.replace('+', '_')
+        fn = getattr(self, fname, self.handle_unknown_opcode)
+        fn(info, inst)
+
+    def handle_unknown_opcode(self, info, inst):
+        raise UnsupportedError(
+            "Use of unknown opcode '{}'".format(inst.opname),
+            loc=Loc(filename=self.bytecode.func_id.filename,
+                    line=inst.lineno)
+        )
+
+    def dup_topx(self, info, inst, count):
+        orig = [info.pop() for _ in range(count)]
+        orig.reverse()
+        # We need to actually create new temporaries if we want the
+        # IR optimization pass to work correctly (see issue #580)
+        duped = [info.make_temp() for _ in range(count)]
+        info.append(inst, orig=orig, duped=duped)
+        for val in orig:
+            info.push(val)
+        for val in duped:
+            info.push(val)
+
+    def add_syntax_block(self, info, block):
+        """
+        Add an inner syntax block.
+        """
+        block.stack_offset = info.stack_offset
+        info.syntax_blocks.append(block)
+
+    def pop_syntax_block(self, info):
+        """
+        Pop the innermost syntax block and revert its stack effect.
+        """
+        block = info.syntax_blocks.pop()
+        assert info.stack_offset >= block.stack_offset
+        while info.stack_offset + info.stack_effect > block.stack_offset:
+            info.pop(discard=True)
+        return block
+
+    def op_NOP(self, info, inst):
+        pass
+
+    def op_DUP_TOPX(self, info, inst):
+        count = inst.arg
+        assert 1 <= count <= 5, "Invalid DUP_TOPX count"
+        self.dup_topx(info, inst, count)
+
+    def op_DUP_TOP(self, info, inst):
+        self.dup_topx(info, inst, count=1)
+
+    def op_DUP_TOP_TWO(self, info, inst):
+        self.dup_topx(info, inst, count=2)
+
+    def op_ROT_TWO(self, info, inst):
+        first = info.pop()
+        second = info.pop()
+        info.push(first)
+        info.push(second)
+
+    def op_ROT_THREE(self, info, inst):
+        first = info.pop()
+        second = info.pop()
+        third = info.pop()
+        info.push(first)
+        info.push(third)
+        info.push(second)
+
+    def op_ROT_FOUR(self, info, inst):
+        first = info.pop()
+        second = info.pop()
+        third = info.pop()
+        forth = info.pop()
+        info.push(first)
+        info.push(forth)
+        info.push(third)
+        info.push(second)
+
+    def op_UNPACK_SEQUENCE(self, info, inst):
+        count = inst.arg
+        iterable = info.pop()
+        stores = [info.make_temp() for _ in range(count)]
+        tupleobj = info.make_temp()
+        info.append(inst, iterable=iterable, stores=stores, tupleobj=tupleobj)
+        for st in reversed(stores):
+            info.push(st)
+
+    def op_FORMAT_VALUE(self, info, inst):
+        """
+        FORMAT_VALUE(flags): flags argument specifies format spec which is
+        not supported yet. Currently, str() is simply called on the value.
+        Pops a value from stack and pushes results back.
+        Required for supporting f-strings.
+        https://docs.python.org/3/library/dis.html#opcode-FORMAT_VALUE
+        """
+        if inst.arg != 0:
+            msg = "format spec in f-strings not supported yet"
+            raise UnsupportedError(
+                msg,
+                loc=Loc(filename=self.bytecode.func_id.filename,
+                line=inst.lineno)
+            )
+        value = info.pop()
+        strvar = info.make_temp()
+        res = info.make_temp()
+        info.append(inst, value=value, res=res, strvar=strvar)
+        info.push(res)
+
+    def op_BUILD_STRING(self, info, inst):
+        """
+        BUILD_STRING(count): Concatenates count strings from the stack and
+        pushes the resulting string onto the stack.
+        Required for supporting f-strings.
+        https://docs.python.org/3/library/dis.html#opcode-BUILD_STRING
+        """
+        count = inst.arg
+        strings = list(reversed([info.pop() for _ in range(count)]))
+        # corner case: f""
+        if count == 0:
+            tmps = [info.make_temp()]
+        else:
+            tmps = [info.make_temp() for _ in range(count - 1)]
+        info.append(inst, strings=strings, tmps=tmps)
+        info.push(tmps[-1])
+
+    def op_BUILD_TUPLE(self, info, inst):
+        count = inst.arg
+        items = list(reversed([info.pop() for _ in range(count)]))
+        tup = info.make_temp()
+        info.append(inst, items=items, res=tup)
+        info.push(tup)
+
+    def op_BUILD_LIST(self, info, inst):
+        count = inst.arg
+        items = list(reversed([info.pop() for _ in range(count)]))
+        lst = info.make_temp()
+        info.append(inst, items=items, res=lst)
+        info.push(lst)
+
+    def op_LIST_APPEND(self, info, inst):
+        value = info.pop()
+        index = inst.arg
+        target = info.peek(index)
+        appendvar = info.make_temp()
+        res = info.make_temp()
+        info.append(inst, target=target, value=value, appendvar=appendvar, res=res)
+
+    def op_BUILD_MAP(self, info, inst):
+        dct = info.make_temp()
+        count = inst.arg
+        items = []
+        # BUILD_MAP takes <count> pairs from the stack
+        for i in range(count):
+            v, k = info.pop(), info.pop()
+            items.append((k, v))
+        info.append(inst, items=items[::-1], size=count, res=dct)
+        info.push(dct)
+
+    def op_MAP_ADD(self, info, inst):
+        key = info.pop()
+        value = info.pop()
+        index = inst.arg
+        target = info.peek(index)
+        setitemvar = info.make_temp()
+        res = info.make_temp()
+        info.append(inst, target=target, key=key, value=value,
+                     setitemvar=setitemvar, res=res)
+
+    def op_BUILD_SET(self, info, inst):
+        count = inst.arg
+        # Note: related python bug http://bugs.python.org/issue26020
+        items = list(reversed([info.pop() for _ in range(count)]))
+        res = info.make_temp()
+        info.append(inst, items=items, res=res)
+        info.push(res)
+
+    def op_POP_TOP(self, info, inst):
+        info.pop(discard=True)
+
+    def op_STORE_ATTR(self, info, inst):
+        target = info.pop()
+        value = info.pop()
+        info.append(inst, target=target, value=value)
+
+    def op_DELETE_ATTR(self, info, inst):
+        target = info.pop()
+        info.append(inst, target=target)
+
+    def op_STORE_FAST(self, info, inst):
+        value = info.pop()
+        info.append(inst, value=value)
+
+    def op_STORE_MAP(self, info, inst):
+        key = info.pop()
+        value = info.pop()
+        dct = info.tos
+        info.append(inst, dct=dct, key=key, value=value)
+
+    def op_STORE_DEREF(self, info, inst):
+        value = info.pop()
+        info.append(inst, value=value)
+
+    def op_LOAD_FAST(self, info, inst):
+        name = self.bytecode.co_varnames[inst.arg]
+        res = info.make_temp(name)
+        info.append(inst, res=res)
+        info.push(res)
+
+    def op_LOAD_CONST(self, info, inst):
+        res = info.make_temp('const')
+        info.append(inst, res=res)
+        info.push(res)
+
+    def op_LOAD_GLOBAL(self, info, inst):
+        res = info.make_temp()
+        info.append(inst, res=res)
+        info.push(res)
+
+    def op_LOAD_DEREF(self, info, inst):
+        res = info.make_temp()
+        info.append(inst, res=res)
+        info.push(res)
+
+    def op_LOAD_ATTR(self, info, inst):
+        item = info.pop()
+        res = info.make_temp()
+        info.append(inst, item=item, res=res)
+        info.push(res)
+
+    def op_BINARY_SUBSCR(self, info, inst):
+        index = info.pop()
+        target = info.pop()
+        res = info.make_temp()
+        info.append(inst, index=index, target=target, res=res)
+        info.push(res)
+
+    def op_STORE_SUBSCR(self, info, inst):
+        index = info.pop()
+        target = info.pop()
+        value = info.pop()
+        info.append(inst, target=target, index=index, value=value)
+
+    def op_DELETE_SUBSCR(self, info, inst):
+        index = info.pop()
+        target = info.pop()
+        info.append(inst, target=target, index=index)
+
+    def op_GET_ITER(self, info, inst):
+        value = info.pop()
+        res = info.make_temp()
+        info.append(inst, value=value, res=res)
+        info.push(res)
+
+    def op_FOR_ITER(self, info, inst):
+        iterator = info.tos
+        pair = info.make_temp()
+        indval = info.make_temp()
+        pred = info.make_temp()
+        info.append(inst, iterator=iterator, pair=pair, indval=indval, pred=pred)
+        info.push(indval)
+        # Setup for stack POP (twice) at loop exit (before processing instruction at jump target)
+        def pop_info(info):
+            info.pop()
+            info.pop()
+        self.edge_process[(info.block.offset, inst.get_jump_target())] = pop_info
+
+    def op_CALL_FUNCTION(self, info, inst):
+        narg = inst.arg
+        args = list(reversed([info.pop() for _ in range(narg)]))
+        func = info.pop()
+
+        res = info.make_temp()
+        info.append(inst, func=func, args=args, res=res)
+        info.push(res)
+
+    def op_CALL_FUNCTION_KW(self, info, inst):
+        narg = inst.arg
+        names = info.pop()  # tuple of names
+        args = list(reversed([info.pop() for _ in range(narg)]))
+        func = info.pop()
+
+        res = info.make_temp()
+        info.append(inst, func=func, args=args, names=names, res=res)
+        info.push(res)
+
+    def op_CALL_FUNCTION_EX(self, info, inst):
+        if inst.arg & 1:
+            errmsg = 'CALL_FUNCTION_EX with **kwargs not supported'
+            raise NotImplementedError(errmsg)
+        vararg = info.pop()
+        func = info.pop()
+        res = info.make_temp()
+        info.append(inst, func=func, vararg=vararg, res=res)
+        info.push(res)
+
+    def _build_tuple_unpack(self, info, inst):
+        # Builds tuple from other tuples on the stack
+        tuples = list(reversed([info.pop() for _ in range(inst.arg)]))
+        temps = [info.make_temp() for _ in range(len(tuples) - 1)]
+        # if the unpack is assign-like, e.g. x = (*y,), it needs handling
+        # differently.
+        is_assign = len(tuples) == 1
+        if is_assign:
+            temps = [info.make_temp(),]
+
+        info.append(inst, tuples=tuples, temps=temps, is_assign=is_assign)
+        # The result is in the last temp var
+        info.push(temps[-1])
+
+    def op_BUILD_TUPLE_UNPACK_WITH_CALL(self, info, inst):
+        # just unpack the input tuple, call inst will be handled afterwards
+        self._build_tuple_unpack(info, inst)
+
+    def op_BUILD_TUPLE_UNPACK(self, info, inst):
+        self._build_tuple_unpack(info, inst)
+
+    def op_BUILD_CONST_KEY_MAP(self, info, inst):
+        keys = info.pop()
+        vals = list(reversed([info.pop() for _ in range(inst.arg)]))
+        keytmps = [info.make_temp() for _ in range(inst.arg)]
+        res = info.make_temp()
+        info.append(inst, keys=keys, keytmps=keytmps, values=vals, res=res)
+        info.push(res)
+
+    def op_PRINT_ITEM(self, info, inst):
+        warnings.warn("Python2 style print partially supported.  Please use "
+                      "Python3 style print.", RuntimeWarning)
+        item = info.pop()
+        printvar = info.make_temp()
+        res = info.make_temp()
+        info.append(inst, item=item, printvar=printvar, res=res)
+
+    def op_PRINT_NEWLINE(self, info, inst):
+        printvar = info.make_temp()
+        res = info.make_temp()
+        info.append(inst, printvar=printvar, res=res)
+
+    def _unaryop(self, info, inst):
+        val = info.pop()
+        res = info.make_temp()
+        info.append(inst, value=val, res=res)
+        info.push(res)
+
+    op_UNARY_NEGATIVE = _unaryop
+    op_UNARY_POSITIVE = _unaryop
+    op_UNARY_NOT = _unaryop
+    op_UNARY_INVERT = _unaryop
+
+    def _binaryop(self, info, inst):
+        rhs = info.pop()
+        lhs = info.pop()
+        res = info.make_temp()
+        info.append(inst, lhs=lhs, rhs=rhs, res=res)
+        info.push(res)
+
+    op_COMPARE_OP = _binaryop
+    op_IS_OP = _binaryop
+    op_CONTAINS_OP = _binaryop
+
+    op_INPLACE_ADD = _binaryop
+    op_INPLACE_SUBTRACT = _binaryop
+    op_INPLACE_MULTIPLY = _binaryop
+    op_INPLACE_DIVIDE = _binaryop
+    op_INPLACE_TRUE_DIVIDE = _binaryop
+    op_INPLACE_FLOOR_DIVIDE = _binaryop
+    op_INPLACE_MODULO = _binaryop
+    op_INPLACE_POWER = _binaryop
+    op_INPLACE_MATRIX_MULTIPLY = _binaryop
+
+    op_INPLACE_LSHIFT = _binaryop
+    op_INPLACE_RSHIFT = _binaryop
+    op_INPLACE_AND = _binaryop
+    op_INPLACE_OR = _binaryop
+    op_INPLACE_XOR = _binaryop
+
+    op_BINARY_ADD = _binaryop
+    op_BINARY_SUBTRACT = _binaryop
+    op_BINARY_MULTIPLY = _binaryop
+    op_BINARY_DIVIDE = _binaryop
+    op_BINARY_TRUE_DIVIDE = _binaryop
+    op_BINARY_FLOOR_DIVIDE = _binaryop
+    op_BINARY_MODULO = _binaryop
+    op_BINARY_POWER = _binaryop
+    op_BINARY_MATRIX_MULTIPLY = _binaryop
+
+    op_BINARY_LSHIFT = _binaryop
+    op_BINARY_RSHIFT = _binaryop
+    op_BINARY_AND = _binaryop
+    op_BINARY_OR = _binaryop
+    op_BINARY_XOR = _binaryop
+
+    def op_SLICE_0(self, info, inst):
+        """
+        TOS = TOS[:]
+        """
+        tos = info.pop()
+        res = info.make_temp()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos, res=res, slicevar=slicevar,
+                    indexvar=indexvar, nonevar=nonevar)
+        info.push(res)
+
+    def op_SLICE_1(self, info, inst):
+        """
+        TOS = TOS1[TOS:]
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        res = info.make_temp()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos1, start=tos, res=res, slicevar=slicevar,
+                    indexvar=indexvar, nonevar=nonevar)
+        info.push(res)
+
+    def op_SLICE_2(self, info, inst):
+        """
+        TOS = TOS1[:TOS]
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        res = info.make_temp()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos1, stop=tos, res=res, slicevar=slicevar,
+                    indexvar=indexvar, nonevar=nonevar)
+        info.push(res)
+
+    def op_SLICE_3(self, info, inst):
+        """
+        TOS = TOS2[TOS1:TOS]
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        tos2 = info.pop()
+        res = info.make_temp()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        info.append(inst, base=tos2, start=tos1, stop=tos, res=res,
+                    slicevar=slicevar, indexvar=indexvar)
+        info.push(res)
+
+    def op_STORE_SLICE_0(self, info, inst):
+        """
+        TOS[:] = TOS1
+        """
+        tos = info.pop()
+        value = info.pop()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos, value=value, slicevar=slicevar,
+                    indexvar=indexvar, nonevar=nonevar)
+
+    def op_STORE_SLICE_1(self, info, inst):
+        """
+        TOS1[TOS:] = TOS2
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        value = info.pop()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos1, start=tos, slicevar=slicevar,
+                    value=value, indexvar=indexvar, nonevar=nonevar)
+
+    def op_STORE_SLICE_2(self, info, inst):
+        """
+        TOS1[:TOS] = TOS2
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        value = info.pop()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos1, stop=tos, value=value, slicevar=slicevar,
+                    indexvar=indexvar, nonevar=nonevar)
+
+    def op_STORE_SLICE_3(self, info, inst):
+        """
+        TOS2[TOS1:TOS] = TOS3
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        tos2 = info.pop()
+        value = info.pop()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        info.append(inst, base=tos2, start=tos1, stop=tos, value=value,
+                    slicevar=slicevar, indexvar=indexvar)
+
+    def op_DELETE_SLICE_0(self, info, inst):
+        """
+        del TOS[:]
+        """
+        tos = info.pop()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos, slicevar=slicevar,
+                    indexvar=indexvar, nonevar=nonevar)
+
+    def op_DELETE_SLICE_1(self, info, inst):
+        """
+        del TOS1[TOS:]
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos1, start=tos, slicevar=slicevar,
+                    indexvar=indexvar, nonevar=nonevar)
+
+    def op_DELETE_SLICE_2(self, info, inst):
+        """
+        del TOS1[:TOS]
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        nonevar = info.make_temp()
+        info.append(inst, base=tos1, stop=tos, slicevar=slicevar,
+                    indexvar=indexvar, nonevar=nonevar)
+
+    def op_DELETE_SLICE_3(self, info, inst):
+        """
+        del TOS2[TOS1:TOS]
+        """
+        tos = info.pop()
+        tos1 = info.pop()
+        tos2 = info.pop()
+        slicevar = info.make_temp()
+        indexvar = info.make_temp()
+        info.append(inst, base=tos2, start=tos1, stop=tos,
+                    slicevar=slicevar, indexvar=indexvar)
+
+    def op_BUILD_SLICE(self, info, inst):
+        """
+        slice(TOS1, TOS) or slice(TOS2, TOS1, TOS)
+        """
+        argc = inst.arg
+        if argc == 2:
+            tos = info.pop()
+            tos1 = info.pop()
+            start = tos1
+            stop = tos
+            step = None
+        elif argc == 3:
+            tos = info.pop()
+            tos1 = info.pop()
+            tos2 = info.pop()
+            start = tos2
+            stop = tos1
+            step = tos
+        else:
+            raise Exception("unreachable")
+        slicevar = info.make_temp()
+        res = info.make_temp()
+        info.append(inst, start=start, stop=stop, step=step, res=res,
+                    slicevar=slicevar)
+        info.push(res)
+
+    def op_POP_JUMP_IF_TRUE(self, info, inst):
+        pred = info.pop()
+        info.append(inst, pred=pred)
+        info.terminator = inst
+
+    def op_POP_JUMP_IF_FALSE(self, info, inst):
+        pred = info.pop()
+        info.append(inst, pred=pred)
+        info.terminator = inst
+
+    def op_JUMP_IF_TRUE(self, info, inst):
+        pred = info.tos
+        info.append(inst, pred=pred)
+        info.terminator = inst
+
+    def op_JUMP_IF_FALSE(self, info, inst):
+        pred = info.tos
+        info.append(inst, pred=pred)
+        info.terminator = inst
+
+    op_JUMP_IF_FALSE_OR_POP = op_JUMP_IF_FALSE
+    op_JUMP_IF_TRUE_OR_POP = op_JUMP_IF_TRUE
+
+    def op_JUMP_ABSOLUTE(self, info, inst):
+        info.append(inst)
+        info.terminator = inst
+
+    def op_JUMP_FORWARD(self, info, inst):
+        info.append(inst)
+        info.terminator = inst
+
+    def op_BREAK_LOOP(self, info, inst):
+        self.pop_syntax_block(info)
+        info.append(inst)
+        info.terminator = inst
+
+    def op_RETURN_VALUE(self, info, inst):
+        info.append(inst, retval=info.pop(), castval=info.make_temp())
+        info.terminator = inst
+
+    def op_YIELD_VALUE(self, info, inst):
+        val = info.pop()
+        res = info.make_temp()
+        info.append(inst, value=val, res=res)
+        info.push(res)
+
+    def op_SETUP_LOOP(self, info, inst):
+        self.add_syntax_block(info, LoopBlock())
+        info.append(inst)
+
+    def op_SETUP_WITH(self, info, inst):
+        cm = info.pop()    # the context-manager
+        self.add_syntax_block(info, WithBlock())
+        yielded = info.make_temp()
+        info.push(yielded)
+        info.append(inst, contextmanager=cm)
+
+    def op_WITH_CLEANUP(self, info, inst):
+        """
+        Note: py2 only opcode
+        """
+        # TOS is the return value of __exit__()
+        info.pop()
+        info.append(inst)
+
+    def op_WITH_CLEANUP_START(self, info, inst):
+        # TOS is the return value of __exit__()
+        info.pop()
+        info.append(inst)
+
+    def op_WITH_CLEANUP_FINISH(self, info, inst):
+        info.append(inst)
+
+    def op_END_FINALLY(self, info, inst):
+        info.append(inst)
+
+    def op_POP_BLOCK(self, info, inst):
+        block = self.pop_syntax_block(info)
+        info.append(inst)
+
+    def op_RAISE_VARARGS(self, info, inst):
+        if inst.arg == 0:
+            exc = None
+        elif inst.arg == 1:
+            exc = info.pop()
+        else:
+            raise ValueError("Multiple argument raise is not supported.")
+        info.append(inst, exc=exc)
+
+    def op_MAKE_FUNCTION(self, info, inst, MAKE_CLOSURE=False):
+        name = info.pop()
+        code = info.pop()
+        closure = annotations = kwdefaults = defaults = None
+        if inst.arg & 0x8:
+            closure = info.pop()
+        if inst.arg & 0x4:
+            annotations = info.pop()
+        if inst.arg & 0x2:
+            kwdefaults = info.pop()
+        if inst.arg & 0x1:
+            defaults = info.pop()
+        res = info.make_temp()
+        info.append(inst, name=name, code=code, closure=closure, annotations=annotations,
+                    kwdefaults=kwdefaults, defaults=defaults, res=res)
+        info.push(res)
+
+    def op_MAKE_CLOSURE(self, info, inst):
+        self.op_MAKE_FUNCTION(info, inst, MAKE_CLOSURE=True)
+
+    def op_LOAD_CLOSURE(self, info, inst):
+        res = info.make_temp()
+        info.append(inst, res=res)
+        info.push(res)
+
+    #NOTE: Please see notes in `interpreter.py` surrounding the implementation
+    # of LOAD_METHOD and CALL_METHOD.
+
+    def op_LOAD_METHOD(self, *args, **kws):
+        self.op_LOAD_ATTR(*args, **kws)
+
+    def op_CALL_METHOD(self, *args, **kws):
+        self.op_CALL_FUNCTION(*args, **kws)
+
+    def _ignored(self, info, inst):
+        pass
+
+
+class LoopBlock(object):
+    __slots__ = ('stack_offset',)
+
+    def __init__(self):
+        self.stack_offset = None
+
+
+class WithBlock(object):
+    __slots__ = ('stack_offset',)
+
+    def __init__(self):
+        self.stack_offset = None
+
+
+class BlockInfo(object):
+    def __init__(self, block, offset, incoming_blocks):
+        self.block = block
+        self.offset = offset
+        # The list of incoming BlockInfo objects (obtained by control
+        # flow analysis).
+        self.incoming_blocks = incoming_blocks
+        self.stack = []
+        # Outgoing variables from this block:
+        #   { outgoing phi name -> var name }
+        self.outgoing_phis = {}
+        self.insts = []
+        self.tempct = 0
+        self._term = None
+        self.stack_offset = None
+        self.stack_effect = 0
+        self.syntax_blocks = None
+
+    def __repr__(self):
+        return "<%s at offset %d>" % (self.__class__.__name__, self.offset)
+
+    def dump(self):
+        print("offset", self.offset, "{")
+        print("  stack: ", end='')
+        pprint(self.stack)
+        pprint(self.insts)
+        print("}")
+
+    def make_temp(self, prefix=''):
+        self.tempct += 1
+        name = '$%s%s.%s' % (prefix, self.offset, self.tempct)
+        return name
+
+    def push(self, val):
+        self.stack_effect += 1
+        self.stack.append(val)
+
+    def pop(self, discard=False):
+        """
+        Pop a variable from the stack, or request it from incoming blocks if
+        the stack is empty.
+        If *discard* is true, the variable isn't meant to be used anymore,
+        which allows reducing the number of temporaries created.
+        """
+        if not self.stack:
+            self.stack_offset -= 1
+            if not discard:
+                return self.make_incoming()
+        else:
+            self.stack_effect -= 1
+            return self.stack.pop()
+
+    def peek(self, k):
+        """
+        Return the k'th element back from the top of the stack.
+        peek(1) is the top of the stack.
+        """
+        num_pops = k
+        top_k = [self.pop() for _ in range(num_pops)]
+        r = top_k[-1]
+        for i in range(num_pops - 1, -1, -1):
+            self.push(top_k[i])
+        return r
+
+    def make_incoming(self):
+        """
+        Create an incoming variable (due to not enough values being
+        available on our stack) and request its assignment from our
+        incoming blocks' own stacks.
+        """
+        assert self.incoming_blocks
+        ret = self.make_temp('phi')
+        for ib in self.incoming_blocks:
+            stack_index = self.stack_offset + self.stack_effect
+            ib.request_outgoing(self, ret, stack_index)
+        return ret
+
+    def request_outgoing(self, outgoing_block, phiname, stack_index):
+        """
+        Request the assignment of the next available stack variable
+        for block *outgoing_block* with target name *phiname*.
+        """
+        if phiname in self.outgoing_phis:
+            # If phiname was already requested, ignore this new request
+            # (can happen with a diamond-shaped block flow structure).
+            return
+        if stack_index < self.stack_offset:
+            assert self.incoming_blocks
+            for ib in self.incoming_blocks:
+                ib.request_outgoing(self, phiname, stack_index)
+        else:
+            varname = self.stack[stack_index - self.stack_offset]
+            self.outgoing_phis[phiname] = varname
+
+    @property
+    def tos(self):
+        r = self.pop()
+        self.push(r)
+        return r
+
+    def append(self, inst, **kws):
+        self.insts.append((inst.offset, kws))
+
+    @property
+    def terminator(self):
+        assert self._term is None
+        return self._term
+
+    @terminator.setter
+    def terminator(self, inst):
+        self._term = inst
+
+    @property
+    def active_try_block(self):
+        """Try except not supported.
+
+        See byteflow.py
+        """
+        return None
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/__init__.py
new file mode 100644
index 000000000..2cb8b104e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/__init__.py
@@ -0,0 +1,4 @@
+from .manager import DataModelManager
+from .packer import ArgPacker, DataPacker
+from .registry import register_default, default_manager, register
+from .models import PrimitiveModel, CompositeModel, StructModel
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/manager.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/manager.py
new file mode 100644
index 000000000..819f33511
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/manager.py
@@ -0,0 +1,47 @@
+import weakref
+
+from numba.core import types
+
+
+class DataModelManager(object):
+    """Manages mapping of FE types to their corresponding data model
+    """
+
+    def __init__(self):
+        # { numba type class -> model factory }
+        self._handlers = {}
+        # { numba type instance -> model instance }
+        self._cache = weakref.WeakKeyDictionary()
+
+    def register(self, fetypecls, handler):
+        """Register the datamodel factory corresponding to a frontend-type class
+        """
+        assert issubclass(fetypecls, types.Type)
+        self._handlers[fetypecls] = handler
+
+    def lookup(self, fetype):
+        """Returns the corresponding datamodel given the frontend-type instance
+        """
+        try:
+            return self._cache[fetype]
+        except KeyError:
+            pass
+        handler = self._handlers[type(fetype)]
+        model = self._cache[fetype] = handler(self, fetype)
+        return model
+
+    def __getitem__(self, fetype):
+        """Shorthand for lookup()
+        """
+        return self.lookup(fetype)
+
+    def copy(self):
+        """
+        Make a copy of the manager.
+        Use this to inherit from the default data model and specialize it
+        for custom target.
+        """
+        dmm = DataModelManager()
+        dmm._handlers = self._handlers.copy()
+        return dmm
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/models.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/models.py
new file mode 100644
index 000000000..cc62f6035
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/models.py
@@ -0,0 +1,1384 @@
+from functools import partial
+from collections import deque
+
+from llvmlite import ir
+
+from numba.core.datamodel.registry import register_default
+from numba.core import types, cgutils
+from numba.np import numpy_support
+
+
+class DataModel(object):
+    """
+    DataModel describe how a FE type is represented in the LLVM IR at
+    different contexts.
+
+    Contexts are:
+
+    - value: representation inside function body.  Maybe stored in stack.
+    The representation here are flexible.
+
+    - data: representation used when storing into containers (e.g. arrays).
+
+    - argument: representation used for function argument.  All composite
+    types are unflattened into multiple primitive types.
+
+    - return: representation used for return argument.
+
+    Throughput the compiler pipeline, a LLVM value is usually passed around
+    in the "value" representation.  All "as_" prefix function converts from
+    "value" representation.  All "from_" prefix function converts to the
+    "value"  representation.
+
+    """
+    def __init__(self, dmm, fe_type):
+        self._dmm = dmm
+        self._fe_type = fe_type
+
+    @property
+    def fe_type(self):
+        return self._fe_type
+
+    def get_value_type(self):
+        raise NotImplementedError(self)
+
+    def get_data_type(self):
+        return self.get_value_type()
+
+    def get_argument_type(self):
+        """Return a LLVM type or nested tuple of LLVM type
+        """
+        return self.get_value_type()
+
+    def get_return_type(self):
+        return self.get_value_type()
+
+    def as_data(self, builder, value):
+        raise NotImplementedError(self)
+
+    def as_argument(self, builder, value):
+        """
+        Takes one LLVM value
+        Return a LLVM value or nested tuple of LLVM value
+        """
+        raise NotImplementedError(self)
+
+    def as_return(self, builder, value):
+        raise NotImplementedError(self)
+
+    def from_data(self, builder, value):
+        raise NotImplementedError(self)
+
+    def from_argument(self, builder, value):
+        """
+        Takes a LLVM value or nested tuple of LLVM value
+        Returns one LLVM value
+        """
+        raise NotImplementedError(self)
+
+    def from_return(self, builder, value):
+        raise NotImplementedError(self)
+
+    def load_from_data_pointer(self, builder, ptr, align=None):
+        """
+        Load value from a pointer to data.
+        This is the default implementation, sufficient for most purposes.
+        """
+        return self.from_data(builder, builder.load(ptr, align=align))
+
+    def traverse(self, builder):
+        """
+        Traverse contained members.
+        Returns a iterable of contained (types, getters).
+        Each getter is a one-argument function accepting a LLVM value.
+        """
+        return []
+
+    def traverse_models(self):
+        """
+        Recursively list all models involved in this model.
+        """
+        return [self._dmm[t] for t in self.traverse_types()]
+
+    def traverse_types(self):
+        """
+        Recursively list all frontend types involved in this model.
+        """
+        types = [self._fe_type]
+        queue = deque([self])
+        while len(queue) > 0:
+            dm = queue.popleft()
+
+            for i_dm in dm.inner_models():
+                if i_dm._fe_type not in types:
+                    queue.append(i_dm)
+                    types.append(i_dm._fe_type)
+
+        return types
+
+    def inner_models(self):
+        """
+        List all *inner* models.
+        """
+        return []
+
+    def get_nrt_meminfo(self, builder, value):
+        """
+        Returns the MemInfo object or None if it is not tracked.
+        It is only defined for types.meminfo_pointer
+        """
+        return None
+
+    def has_nrt_meminfo(self):
+        return False
+
+    def contains_nrt_meminfo(self):
+        """
+        Recursively check all contained types for need for NRT meminfo.
+        """
+        return any(model.has_nrt_meminfo() for model in self.traverse_models())
+
+    def _compared_fields(self):
+        return (type(self), self._fe_type)
+
+    def __hash__(self):
+        return hash(tuple(self._compared_fields()))
+
+    def __eq__(self, other):
+        if type(self) is type(other):
+            return self._compared_fields() == other._compared_fields()
+        else:
+            return False
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+@register_default(types.Omitted)
+class OmittedArgDataModel(DataModel):
+    """
+    A data model for omitted arguments.  Only the "argument" representation
+    is defined, other representations raise a NotImplementedError.
+    """
+    # Omitted arguments are using a dummy value type
+    def get_value_type(self):
+        return ir.LiteralStructType([])
+
+    # Omitted arguments don't produce any LLVM function argument.
+    def get_argument_type(self):
+        return ()
+
+    def as_argument(self, builder, val):
+        return ()
+
+    def from_argument(self, builder, val):
+        assert val == (), val
+        return None
+
+
+@register_default(types.Boolean)
+@register_default(types.BooleanLiteral)
+class BooleanModel(DataModel):
+    _bit_type = ir.IntType(1)
+    _byte_type = ir.IntType(8)
+
+    def get_value_type(self):
+        return self._bit_type
+
+    def get_data_type(self):
+        return self._byte_type
+
+    def get_return_type(self):
+        return self.get_data_type()
+
+    def get_argument_type(self):
+        return self.get_data_type()
+
+    def as_data(self, builder, value):
+        return builder.zext(value, self.get_data_type())
+
+    def as_argument(self, builder, value):
+        return self.as_data(builder, value)
+
+    def as_return(self, builder, value):
+        return self.as_data(builder, value)
+
+    def from_data(self, builder, value):
+        ty = self.get_value_type()
+        resalloca = cgutils.alloca_once(builder, ty)
+        cond = builder.icmp_unsigned('==', value, value.type(0))
+        with builder.if_else(cond) as (then, otherwise):
+            with then:
+                builder.store(ty(0), resalloca)
+            with otherwise:
+                builder.store(ty(1), resalloca)
+        return builder.load(resalloca)
+
+    def from_argument(self, builder, value):
+        return self.from_data(builder, value)
+
+    def from_return(self, builder, value):
+        return self.from_data(builder, value)
+
+
+class PrimitiveModel(DataModel):
+    """A primitive type can be represented natively in the target in all
+    usage contexts.
+    """
+
+    def __init__(self, dmm, fe_type, be_type):
+        super(PrimitiveModel, self).__init__(dmm, fe_type)
+        self.be_type = be_type
+
+    def get_value_type(self):
+        return self.be_type
+
+    def as_data(self, builder, value):
+        return value
+
+    def as_argument(self, builder, value):
+        return value
+
+    def as_return(self, builder, value):
+        return value
+
+    def from_data(self, builder, value):
+        return value
+
+    def from_argument(self, builder, value):
+        return value
+
+    def from_return(self, builder, value):
+        return value
+
+
+class ProxyModel(DataModel):
+    """
+    Helper class for models which delegate to another model.
+    """
+
+    def get_value_type(self):
+        return self._proxied_model.get_value_type()
+
+    def get_data_type(self):
+        return self._proxied_model.get_data_type()
+
+    def get_return_type(self):
+        return self._proxied_model.get_return_type()
+
+    def get_argument_type(self):
+        return self._proxied_model.get_argument_type()
+
+    def as_data(self, builder, value):
+        return self._proxied_model.as_data(builder, value)
+
+    def as_argument(self, builder, value):
+        return self._proxied_model.as_argument(builder, value)
+
+    def as_return(self, builder, value):
+        return self._proxied_model.as_return(builder, value)
+
+    def from_data(self, builder, value):
+        return self._proxied_model.from_data(builder, value)
+
+    def from_argument(self, builder, value):
+        return self._proxied_model.from_argument(builder, value)
+
+    def from_return(self, builder, value):
+        return self._proxied_model.from_return(builder, value)
+
+
+@register_default(types.EnumMember)
+@register_default(types.IntEnumMember)
+class EnumModel(ProxyModel):
+    """
+    Enum members are represented exactly like their values.
+    """
+    def __init__(self, dmm, fe_type):
+        super(EnumModel, self).__init__(dmm, fe_type)
+        self._proxied_model = dmm.lookup(fe_type.dtype)
+
+
+@register_default(types.Opaque)
+@register_default(types.PyObject)
+@register_default(types.RawPointer)
+@register_default(types.NoneType)
+@register_default(types.StringLiteral)
+@register_default(types.EllipsisType)
+@register_default(types.Function)
+@register_default(types.Type)
+@register_default(types.Object)
+@register_default(types.Module)
+@register_default(types.Phantom)
+@register_default(types.ContextManager)
+@register_default(types.Dispatcher)
+@register_default(types.ObjModeDispatcher)
+@register_default(types.ExceptionClass)
+@register_default(types.Dummy)
+@register_default(types.ExceptionInstance)
+@register_default(types.ExternalFunction)
+@register_default(types.EnumClass)
+@register_default(types.IntEnumClass)
+@register_default(types.NumberClass)
+@register_default(types.TypeRef)
+@register_default(types.NamedTupleClass)
+@register_default(types.DType)
+@register_default(types.RecursiveCall)
+@register_default(types.MakeFunctionLiteral)
+@register_default(types.Poison)
+class OpaqueModel(PrimitiveModel):
+    """
+    Passed as opaque pointers
+    """
+    _ptr_type = ir.IntType(8).as_pointer()
+
+    def __init__(self, dmm, fe_type):
+        be_type = self._ptr_type
+        super(OpaqueModel, self).__init__(dmm, fe_type, be_type)
+
+
+@register_default(types.MemInfoPointer)
+class MemInfoModel(OpaqueModel):
+
+    def inner_models(self):
+        return [self._dmm.lookup(self._fe_type.dtype)]
+
+    def has_nrt_meminfo(self):
+        return True
+
+    def get_nrt_meminfo(self, builder, value):
+        return value
+
+
+@register_default(types.Integer)
+@register_default(types.IntegerLiteral)
+class IntegerModel(PrimitiveModel):
+    def __init__(self, dmm, fe_type):
+        be_type = ir.IntType(fe_type.bitwidth)
+        super(IntegerModel, self).__init__(dmm, fe_type, be_type)
+
+
+@register_default(types.Float)
+class FloatModel(PrimitiveModel):
+    def __init__(self, dmm, fe_type):
+        if fe_type == types.float32:
+            be_type = ir.FloatType()
+        elif fe_type == types.float64:
+            be_type = ir.DoubleType()
+        else:
+            raise NotImplementedError(fe_type)
+        super(FloatModel, self).__init__(dmm, fe_type, be_type)
+
+
+@register_default(types.CPointer)
+class PointerModel(PrimitiveModel):
+    def __init__(self, dmm, fe_type):
+        self._pointee_model = dmm.lookup(fe_type.dtype)
+        self._pointee_be_type = self._pointee_model.get_data_type()
+        be_type = self._pointee_be_type.as_pointer()
+        super(PointerModel, self).__init__(dmm, fe_type, be_type)
+
+
+@register_default(types.EphemeralPointer)
+class EphemeralPointerModel(PointerModel):
+
+    def get_data_type(self):
+        return self._pointee_be_type
+
+    def as_data(self, builder, value):
+        value = builder.load(value)
+        return self._pointee_model.as_data(builder, value)
+
+    def from_data(self, builder, value):
+        raise NotImplementedError("use load_from_data_pointer() instead")
+
+    def load_from_data_pointer(self, builder, ptr, align=None):
+        return builder.bitcast(ptr, self.get_value_type())
+
+
+@register_default(types.EphemeralArray)
+class EphemeralArrayModel(PointerModel):
+
+    def __init__(self, dmm, fe_type):
+        super(EphemeralArrayModel, self).__init__(dmm, fe_type)
+        self._data_type = ir.ArrayType(self._pointee_be_type,
+                                       self._fe_type.count)
+
+    def get_data_type(self):
+        return self._data_type
+
+    def as_data(self, builder, value):
+        values = [builder.load(cgutils.gep_inbounds(builder, value, i))
+                  for i in range(self._fe_type.count)]
+        return cgutils.pack_array(builder, values)
+
+    def from_data(self, builder, value):
+        raise NotImplementedError("use load_from_data_pointer() instead")
+
+    def load_from_data_pointer(self, builder, ptr, align=None):
+        return builder.bitcast(ptr, self.get_value_type())
+
+
+@register_default(types.ExternalFunctionPointer)
+class ExternalFuncPointerModel(PrimitiveModel):
+    def __init__(self, dmm, fe_type):
+        sig = fe_type.sig
+        # Since the function is non-Numba, there is no adaptation
+        # of arguments and return value, hence get_value_type().
+        retty = dmm.lookup(sig.return_type).get_value_type()
+        args = [dmm.lookup(t).get_value_type() for t in sig.args]
+        be_type = ir.PointerType(ir.FunctionType(retty, args))
+        super(ExternalFuncPointerModel, self).__init__(dmm, fe_type, be_type)
+
+
+@register_default(types.UniTuple)
+@register_default(types.NamedUniTuple)
+@register_default(types.StarArgUniTuple)
+class UniTupleModel(DataModel):
+    def __init__(self, dmm, fe_type):
+        super(UniTupleModel, self).__init__(dmm, fe_type)
+        self._elem_model = dmm.lookup(fe_type.dtype)
+        self._count = len(fe_type)
+        self._value_type = ir.ArrayType(self._elem_model.get_value_type(),
+                                        self._count)
+        self._data_type = ir.ArrayType(self._elem_model.get_data_type(),
+                                       self._count)
+
+    def get_value_type(self):
+        return self._value_type
+
+    def get_data_type(self):
+        return self._data_type
+
+    def get_return_type(self):
+        return self.get_value_type()
+
+    def get_argument_type(self):
+        return (self._elem_model.get_argument_type(),) * self._count
+
+    def as_argument(self, builder, value):
+        out = []
+        for i in range(self._count):
+            v = builder.extract_value(value, [i])
+            v = self._elem_model.as_argument(builder, v)
+            out.append(v)
+        return out
+
+    def from_argument(self, builder, value):
+        out = ir.Constant(self.get_value_type(), ir.Undefined)
+        for i, v in enumerate(value):
+            v = self._elem_model.from_argument(builder, v)
+            out = builder.insert_value(out, v, [i])
+        return out
+
+    def as_data(self, builder, value):
+        out = ir.Constant(self.get_data_type(), ir.Undefined)
+        for i in range(self._count):
+            val = builder.extract_value(value, [i])
+            dval = self._elem_model.as_data(builder, val)
+            out = builder.insert_value(out, dval, [i])
+        return out
+
+    def from_data(self, builder, value):
+        out = ir.Constant(self.get_value_type(), ir.Undefined)
+        for i in range(self._count):
+            val = builder.extract_value(value, [i])
+            dval = self._elem_model.from_data(builder, val)
+            out = builder.insert_value(out, dval, [i])
+        return out
+
+    def as_return(self, builder, value):
+        return value
+
+    def from_return(self, builder, value):
+        return value
+
+    def traverse(self, builder):
+        def getter(i, value):
+            return builder.extract_value(value, i)
+        return [(self._fe_type.dtype, partial(getter, i))
+                for i in range(self._count)]
+
+    def inner_models(self):
+        return [self._elem_model]
+
+
+class CompositeModel(DataModel):
+    """Any model that is composed of multiple other models should subclass from
+    this.
+    """
+    pass
+
+
+class StructModel(CompositeModel):
+    _value_type = None
+    _data_type = None
+
+    def __init__(self, dmm, fe_type, members):
+        super(StructModel, self).__init__(dmm, fe_type)
+        if members:
+            self._fields, self._members = zip(*members)
+        else:
+            self._fields = self._members = ()
+        self._models = tuple([self._dmm.lookup(t) for t in self._members])
+
+    def get_member_fe_type(self, name):
+        """
+        StructModel-specific: get the Numba type of the field named *name*.
+        """
+        pos = self.get_field_position(name)
+        return self._members[pos]
+
+    def get_value_type(self):
+        if self._value_type is None:
+            self._value_type = ir.LiteralStructType([t.get_value_type()
+                                                    for t in self._models])
+        return self._value_type
+
+    def get_data_type(self):
+        if self._data_type is None:
+            self._data_type = ir.LiteralStructType([t.get_data_type()
+                                                    for t in self._models])
+        return self._data_type
+
+    def get_argument_type(self):
+        return tuple([t.get_argument_type() for t in self._models])
+
+    def get_return_type(self):
+        return self.get_data_type()
+
+    def _as(self, methname, builder, value):
+        extracted = []
+        for i, dm in enumerate(self._models):
+            extracted.append(getattr(dm, methname)(builder,
+                                                   self.get(builder, value, i)))
+        return tuple(extracted)
+
+    def _from(self, methname, builder, value):
+        struct = ir.Constant(self.get_value_type(), ir.Undefined)
+
+        for i, (dm, val) in enumerate(zip(self._models, value)):
+            v = getattr(dm, methname)(builder, val)
+            struct = self.set(builder, struct, v, i)
+
+        return struct
+
+    def as_data(self, builder, value):
+        """
+        Converts the LLVM struct in `value` into a representation suited for
+        storing into arrays.
+
+        Note
+        ----
+        Current implementation rarely changes how types are represented for
+        "value" and "data".  This is usually a pointless rebuild of the
+        immutable LLVM struct value.  Luckily, LLVM optimization removes all
+        redundancy.
+
+        Sample usecase: Structures nested with pointers to other structures
+        that can be serialized into  a flat representation when storing into
+        array.
+        """
+        elems = self._as("as_data", builder, value)
+        struct = ir.Constant(self.get_data_type(), ir.Undefined)
+        for i, el in enumerate(elems):
+            struct = builder.insert_value(struct, el, [i])
+        return struct
+
+    def from_data(self, builder, value):
+        """
+        Convert from "data" representation back into "value" representation.
+        Usually invoked when loading from array.
+
+        See notes in `as_data()`
+        """
+        vals = [builder.extract_value(value, [i])
+                for i in range(len(self._members))]
+        return self._from("from_data", builder, vals)
+
+    def load_from_data_pointer(self, builder, ptr, align=None):
+        values = []
+        for i, model in enumerate(self._models):
+            elem_ptr = cgutils.gep_inbounds(builder, ptr, 0, i)
+            val = model.load_from_data_pointer(builder, elem_ptr, align)
+            values.append(val)
+
+        struct = ir.Constant(self.get_value_type(), ir.Undefined)
+        for i, val in enumerate(values):
+            struct = self.set(builder, struct, val, i)
+        return struct
+
+    def as_argument(self, builder, value):
+        return self._as("as_argument", builder, value)
+
+    def from_argument(self, builder, value):
+        return self._from("from_argument", builder, value)
+
+    def as_return(self, builder, value):
+        elems = self._as("as_data", builder, value)
+        struct = ir.Constant(self.get_data_type(), ir.Undefined)
+        for i, el in enumerate(elems):
+            struct = builder.insert_value(struct, el, [i])
+        return struct
+
+    def from_return(self, builder, value):
+        vals = [builder.extract_value(value, [i])
+                for i in range(len(self._members))]
+        return self._from("from_data", builder, vals)
+
+    def get(self, builder, val, pos):
+        """Get a field at the given position or the fieldname
+
+        Args
+        ----
+        builder:
+            LLVM IRBuilder
+        val:
+            value to be inserted
+        pos: int or str
+            field index or field name
+
+        Returns
+        -------
+        Extracted value
+        """
+        if isinstance(pos, str):
+            pos = self.get_field_position(pos)
+        return builder.extract_value(val, [pos],
+                                     name="extracted." + self._fields[pos])
+
+    def set(self, builder, stval, val, pos):
+        """Set a field at the given position or the fieldname
+
+        Args
+        ----
+        builder:
+            LLVM IRBuilder
+        stval:
+            LLVM struct value
+        val:
+            value to be inserted
+        pos: int or str
+            field index or field name
+
+        Returns
+        -------
+        A new LLVM struct with the value inserted
+        """
+        if isinstance(pos, str):
+            pos = self.get_field_position(pos)
+        return builder.insert_value(stval, val, [pos],
+                                    name="inserted." + self._fields[pos])
+
+    def get_field_position(self, field):
+        try:
+            return self._fields.index(field)
+        except ValueError:
+            raise KeyError("%s does not have a field named %r"
+                           % (self.__class__.__name__, field))
+
+    @property
+    def field_count(self):
+        return len(self._fields)
+
+    def get_type(self, pos):
+        """Get the frontend type (numba type) of a field given the position
+         or the fieldname
+
+        Args
+        ----
+        pos: int or str
+            field index or field name
+        """
+        if isinstance(pos, str):
+            pos = self.get_field_position(pos)
+        return self._members[pos]
+
+    def get_model(self, pos):
+        """
+        Get the datamodel of a field given the position or the fieldname.
+
+        Args
+        ----
+        pos: int or str
+            field index or field name
+        """
+        return self._models[pos]
+
+    def traverse(self, builder):
+        def getter(k, value):
+            if value.type != self.get_value_type():
+                args = self.get_value_type(), value.type
+                raise TypeError("expecting {0} but got {1}".format(*args))
+            return self.get(builder, value, k)
+
+        return [(self.get_type(k), partial(getter, k)) for k in self._fields]
+
+    def inner_models(self):
+        return self._models
+
+
+@register_default(types.Complex)
+class ComplexModel(StructModel):
+    _element_type = NotImplemented
+
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('real', fe_type.underlying_float),
+            ('imag', fe_type.underlying_float),
+        ]
+        super(ComplexModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.LiteralList)
+@register_default(types.LiteralStrKeyDict)
+@register_default(types.Tuple)
+@register_default(types.NamedTuple)
+@register_default(types.StarArgTuple)
+class TupleModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [('f' + str(i), t) for i, t in enumerate(fe_type)]
+        super(TupleModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.UnionType)
+class UnionModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('tag', types.uintp),
+            # XXX: it should really be a MemInfoPointer(types.voidptr)
+            ('payload', types.Tuple.from_types(fe_type.types)),
+        ]
+        super(UnionModel, self).__init__(dmm, fe_type, members)
+
+
+
+@register_default(types.Pair)
+class PairModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [('first', fe_type.first_type),
+                   ('second', fe_type.second_type)]
+        super(PairModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.ListPayload)
+class ListPayloadModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        # The fields are mutable but the payload is always manipulated
+        # by reference.  This scheme allows mutations of an array to
+        # be seen by its iterators.
+        members = [
+            ('size', types.intp),
+            ('allocated', types.intp),
+            # This member is only used only for reflected lists
+            ('dirty', types.boolean),
+            # Actually an inlined var-sized array
+            ('data', fe_type.container.dtype),
+        ]
+        super(ListPayloadModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.List)
+class ListModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        payload_type = types.ListPayload(fe_type)
+        members = [
+            # The meminfo data points to a ListPayload
+            ('meminfo', types.MemInfoPointer(payload_type)),
+            # This member is only used only for reflected lists
+            ('parent', types.pyobject),
+        ]
+        super(ListModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.ListIter)
+class ListIterModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        payload_type = types.ListPayload(fe_type.container)
+        members = [
+            # The meminfo data points to a ListPayload (shared with the
+            # original list object)
+            ('meminfo', types.MemInfoPointer(payload_type)),
+            ('index', types.EphemeralPointer(types.intp)),
+            ]
+        super(ListIterModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.SetEntry)
+class SetEntryModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        dtype = fe_type.set_type.dtype
+        members = [
+            # -1 = empty, -2 = deleted
+            ('hash', types.intp),
+            ('key', dtype),
+        ]
+        super(SetEntryModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.SetPayload)
+class SetPayloadModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        entry_type = types.SetEntry(fe_type.container)
+        members = [
+            # Number of active + deleted entries
+            ('fill', types.intp),
+            # Number of active entries
+            ('used', types.intp),
+            # Allocated size - 1 (size being a power of 2)
+            ('mask', types.intp),
+            # Search finger
+            ('finger', types.intp),
+            # This member is only used only for reflected sets
+            ('dirty', types.boolean),
+            # Actually an inlined var-sized array
+            ('entries', entry_type),
+        ]
+        super(SetPayloadModel, self).__init__(dmm, fe_type, members)
+
+@register_default(types.Set)
+class SetModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        payload_type = types.SetPayload(fe_type)
+        members = [
+            # The meminfo data points to a SetPayload
+            ('meminfo', types.MemInfoPointer(payload_type)),
+            # This member is only used only for reflected sets
+            ('parent', types.pyobject),
+        ]
+        super(SetModel, self).__init__(dmm, fe_type, members)
+
+@register_default(types.SetIter)
+class SetIterModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        payload_type = types.SetPayload(fe_type.container)
+        members = [
+            # The meminfo data points to a SetPayload (shared with the
+            # original set object)
+            ('meminfo', types.MemInfoPointer(payload_type)),
+            # The index into the entries table
+            ('index', types.EphemeralPointer(types.intp)),
+            ]
+        super(SetIterModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.Array)
+@register_default(types.Buffer)
+@register_default(types.ByteArray)
+@register_default(types.Bytes)
+@register_default(types.MemoryView)
+@register_default(types.PyArray)
+class ArrayModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        ndim = fe_type.ndim
+        members = [
+            ('meminfo', types.MemInfoPointer(fe_type.dtype)),
+            ('parent', types.pyobject),
+            ('nitems', types.intp),
+            ('itemsize', types.intp),
+            ('data', types.CPointer(fe_type.dtype)),
+            ('shape', types.UniTuple(types.intp, ndim)),
+            ('strides', types.UniTuple(types.intp, ndim)),
+
+        ]
+        super(ArrayModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.ArrayFlags)
+class ArrayFlagsModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('parent', fe_type.array_type),
+        ]
+        super(ArrayFlagsModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.NestedArray)
+class NestedArrayModel(ArrayModel):
+    def __init__(self, dmm, fe_type):
+        self._be_type = dmm.lookup(fe_type.dtype).get_data_type()
+        super(NestedArrayModel, self).__init__(dmm, fe_type)
+
+    def as_storage_type(self):
+        """Return the LLVM type representation for the storage of
+        the nestedarray.
+        """
+        ret = ir.ArrayType(self._be_type, self._fe_type.nitems)
+        return ret
+
+
+@register_default(types.Optional)
+class OptionalModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('data', fe_type.type),
+            ('valid', types.boolean),
+        ]
+        self._value_model = dmm.lookup(fe_type.type)
+        super(OptionalModel, self).__init__(dmm, fe_type, members)
+
+    def get_return_type(self):
+        return self._value_model.get_return_type()
+
+    def as_return(self, builder, value):
+        raise NotImplementedError
+
+    def from_return(self, builder, value):
+        return self._value_model.from_return(builder, value)
+
+    def traverse(self, builder):
+        def get_data(value):
+            valid = get_valid(value)
+            data = self.get(builder, value, "data")
+            return builder.select(valid, data, ir.Constant(data.type, None))
+        def get_valid(value):
+            return self.get(builder, value, "valid")
+
+        return [(self.get_type("data"), get_data),
+                (self.get_type("valid"), get_valid)]
+
+
+@register_default(types.Record)
+class RecordModel(CompositeModel):
+    def __init__(self, dmm, fe_type):
+        super(RecordModel, self).__init__(dmm, fe_type)
+        self._models = [self._dmm.lookup(t) for _, t in fe_type.members]
+        self._be_type = ir.ArrayType(ir.IntType(8), fe_type.size)
+        self._be_ptr_type = self._be_type.as_pointer()
+
+    def get_value_type(self):
+        """Passed around as reference to underlying data
+        """
+        return self._be_ptr_type
+
+    def get_argument_type(self):
+        return self._be_ptr_type
+
+    def get_return_type(self):
+        return self._be_ptr_type
+
+    def get_data_type(self):
+        return self._be_type
+
+    def as_data(self, builder, value):
+        return builder.load(value)
+
+    def from_data(self, builder, value):
+        raise NotImplementedError("use load_from_data_pointer() instead")
+
+    def as_argument(self, builder, value):
+        return value
+
+    def from_argument(self, builder, value):
+        return value
+
+    def as_return(self, builder, value):
+        return value
+
+    def from_return(self, builder, value):
+        return value
+
+    def load_from_data_pointer(self, builder, ptr, align=None):
+        return builder.bitcast(ptr, self.get_value_type())
+
+
+@register_default(types.UnicodeCharSeq)
+class UnicodeCharSeq(DataModel):
+    def __init__(self, dmm, fe_type):
+        super(UnicodeCharSeq, self).__init__(dmm, fe_type)
+        charty = ir.IntType(numpy_support.sizeof_unicode_char * 8)
+        self._be_type = ir.ArrayType(charty, fe_type.count)
+
+    def get_value_type(self):
+        return self._be_type
+
+    def get_data_type(self):
+        return self._be_type
+
+    def as_data(self, builder, value):
+        return value
+
+    def from_data(self, builder, value):
+        return value
+
+    def as_return(self, builder, value):
+        return value
+
+    def from_return(self, builder, value):
+        return value
+
+    def as_argument(self, builder, value):
+        return value
+
+    def from_argument(self, builder, value):
+        return value
+
+
+@register_default(types.CharSeq)
+class CharSeq(DataModel):
+    def __init__(self, dmm, fe_type):
+        super(CharSeq, self).__init__(dmm, fe_type)
+        charty = ir.IntType(8)
+        self._be_type = ir.ArrayType(charty, fe_type.count)
+
+    def get_value_type(self):
+        return self._be_type
+
+    def get_data_type(self):
+        return self._be_type
+
+    def as_data(self, builder, value):
+        return value
+
+    def from_data(self, builder, value):
+        return value
+
+    def as_return(self, builder, value):
+        return value
+
+    def from_return(self, builder, value):
+        return value
+
+    def as_argument(self, builder, value):
+        return value
+
+    def from_argument(self, builder, value):
+        return value
+
+
+class CContiguousFlatIter(StructModel):
+    def __init__(self, dmm, fe_type, need_indices):
+        assert fe_type.array_type.layout == 'C'
+        array_type = fe_type.array_type
+        dtype = array_type.dtype
+        ndim = array_type.ndim
+        members = [('array', array_type),
+                   ('stride', types.intp),
+                   ('index', types.EphemeralPointer(types.intp)),
+                   ]
+        if need_indices:
+            # For ndenumerate()
+            members.append(('indices', types.EphemeralArray(types.intp, ndim)))
+        super(CContiguousFlatIter, self).__init__(dmm, fe_type, members)
+
+
+class FlatIter(StructModel):
+    def __init__(self, dmm, fe_type):
+        array_type = fe_type.array_type
+        dtype = array_type.dtype
+        ndim = array_type.ndim
+        members = [('array', array_type),
+                   ('pointers', types.EphemeralArray(types.CPointer(dtype), ndim)),
+                   ('indices', types.EphemeralArray(types.intp, ndim)),
+                   ('exhausted', types.EphemeralPointer(types.boolean)),
+        ]
+        super(FlatIter, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.UniTupleIter)
+class UniTupleIter(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [('index', types.EphemeralPointer(types.intp)),
+                   ('tuple', fe_type.container,)]
+        super(UniTupleIter, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.misc.SliceLiteral)
+@register_default(types.SliceType)
+class SliceModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [('start', types.intp),
+                   ('stop', types.intp),
+                   ('step', types.intp),
+                   ]
+        super(SliceModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.NPDatetime)
+@register_default(types.NPTimedelta)
+class NPDatetimeModel(PrimitiveModel):
+    def __init__(self, dmm, fe_type):
+        be_type = ir.IntType(64)
+        super(NPDatetimeModel, self).__init__(dmm, fe_type, be_type)
+
+
+@register_default(types.ArrayIterator)
+class ArrayIterator(StructModel):
+    def __init__(self, dmm, fe_type):
+        # We use an unsigned index to avoid the cost of negative index tests.
+        members = [('index', types.EphemeralPointer(types.uintp)),
+                   ('array', fe_type.array_type)]
+        super(ArrayIterator, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.EnumerateType)
+class EnumerateType(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [('count', types.EphemeralPointer(types.intp)),
+                   ('iter', fe_type.source_type)]
+
+        super(EnumerateType, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.ZipType)
+class ZipType(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [('iter%d' % i, source_type.iterator_type)
+                   for i, source_type in enumerate(fe_type.source_types)]
+        super(ZipType, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.RangeIteratorType)
+class RangeIteratorType(StructModel):
+    def __init__(self, dmm, fe_type):
+        int_type = fe_type.yield_type
+        members = [('iter', types.EphemeralPointer(int_type)),
+                   ('stop', int_type),
+                   ('step', int_type),
+                   ('count', types.EphemeralPointer(int_type))]
+        super(RangeIteratorType, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.Generator)
+class GeneratorModel(CompositeModel):
+    def __init__(self, dmm, fe_type):
+        super(GeneratorModel, self).__init__(dmm, fe_type)
+        # XXX Fold this in DataPacker?
+        self._arg_models = [self._dmm.lookup(t) for t in fe_type.arg_types
+                            if not isinstance(t, types.Omitted)]
+        self._state_models = [self._dmm.lookup(t) for t in fe_type.state_types]
+
+        self._args_be_type = ir.LiteralStructType(
+            [t.get_data_type() for t in self._arg_models])
+        self._state_be_type = ir.LiteralStructType(
+            [t.get_data_type() for t in self._state_models])
+        # The whole generator closure
+        self._be_type = ir.LiteralStructType(
+            [self._dmm.lookup(types.int32).get_value_type(),
+             self._args_be_type, self._state_be_type])
+        self._be_ptr_type = self._be_type.as_pointer()
+
+    def get_value_type(self):
+        """
+        The generator closure is passed around as a reference.
+        """
+        return self._be_ptr_type
+
+    def get_argument_type(self):
+        return self._be_ptr_type
+
+    def get_return_type(self):
+        return self._be_type
+
+    def get_data_type(self):
+        return self._be_type
+
+    def as_argument(self, builder, value):
+        return value
+
+    def from_argument(self, builder, value):
+        return value
+
+    def as_return(self, builder, value):
+        return self.as_data(builder, value)
+
+    def from_return(self, builder, value):
+        return self.from_data(builder, value)
+
+    def as_data(self, builder, value):
+        return builder.load(value)
+
+    def from_data(self, builder, value):
+        stack = cgutils.alloca_once(builder, value.type)
+        builder.store(value, stack)
+        return stack
+
+
+@register_default(types.ArrayCTypes)
+class ArrayCTypesModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        # ndim = fe_type.ndim
+        members = [('data', types.CPointer(fe_type.dtype)),
+                   ('meminfo', types.MemInfoPointer(fe_type.dtype))]
+        super(ArrayCTypesModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.RangeType)
+class RangeModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        int_type = fe_type.iterator_type.yield_type
+        members = [('start', int_type),
+                   ('stop', int_type),
+                   ('step', int_type)]
+        super(RangeModel, self).__init__(dmm, fe_type, members)
+
+
+# =============================================================================
+
+@register_default(types.NumpyNdIndexType)
+class NdIndexModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        ndim = fe_type.ndim
+        members = [('shape', types.UniTuple(types.intp, ndim)),
+                   ('indices', types.EphemeralArray(types.intp, ndim)),
+                   ('exhausted', types.EphemeralPointer(types.boolean)),
+                   ]
+        super(NdIndexModel, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.NumpyFlatType)
+def handle_numpy_flat_type(dmm, ty):
+    if ty.array_type.layout == 'C':
+        return CContiguousFlatIter(dmm, ty, need_indices=False)
+    else:
+        return FlatIter(dmm, ty)
+
+@register_default(types.NumpyNdEnumerateType)
+def handle_numpy_ndenumerate_type(dmm, ty):
+    if ty.array_type.layout == 'C':
+        return CContiguousFlatIter(dmm, ty, need_indices=True)
+    else:
+        return FlatIter(dmm, ty)
+
+@register_default(types.BoundFunction)
+def handle_bound_function(dmm, ty):
+    # The same as the underlying type
+    return dmm[ty.this]
+
+
+@register_default(types.NumpyNdIterType)
+class NdIter(StructModel):
+    def __init__(self, dmm, fe_type):
+        array_types = fe_type.arrays
+        ndim = fe_type.ndim
+        shape_len = ndim if fe_type.need_shaped_indexing else 1
+        members = [('exhausted', types.EphemeralPointer(types.boolean)),
+                   ('arrays', types.Tuple(array_types)),
+                   # The iterator's main shape and indices
+                   ('shape', types.UniTuple(types.intp, shape_len)),
+                   ('indices', types.EphemeralArray(types.intp, shape_len)),
+                   ]
+        # Indexing state for the various sub-iterators
+        # XXX use a tuple instead?
+        for i, sub in enumerate(fe_type.indexers):
+            kind, start_dim, end_dim, _ = sub
+            member_name = 'index%d' % i
+            if kind == 'flat':
+                # A single index into the flattened array
+                members.append((member_name, types.EphemeralPointer(types.intp)))
+            elif kind in ('scalar', 'indexed', '0d'):
+                # Nothing required
+                pass
+            else:
+                assert 0
+        # Slots holding values of the scalar args
+        # XXX use a tuple instead?
+        for i, ty in enumerate(fe_type.arrays):
+            if not isinstance(ty, types.Array):
+                member_name = 'scalar%d' % i
+                members.append((member_name, types.EphemeralPointer(ty)))
+
+        super(NdIter, self).__init__(dmm, fe_type, members)
+
+
+@register_default(types.DeferredType)
+class DeferredStructModel(CompositeModel):
+    def __init__(self, dmm, fe_type):
+        super(DeferredStructModel, self).__init__(dmm, fe_type)
+        self.typename = "deferred.{0}".format(id(fe_type))
+        self.actual_fe_type = fe_type.get()
+
+    def get_value_type(self):
+        return ir.global_context.get_identified_type(self.typename + '.value')
+
+    def get_data_type(self):
+        return ir.global_context.get_identified_type(self.typename + '.data')
+
+    def get_argument_type(self):
+        return self._actual_model.get_argument_type()
+
+    def as_argument(self, builder, value):
+        inner = self.get(builder, value)
+        return self._actual_model.as_argument(builder, inner)
+
+    def from_argument(self, builder, value):
+        res = self._actual_model.from_argument(builder, value)
+        return self.set(builder, self.make_uninitialized(), res)
+
+    def from_data(self, builder, value):
+        self._define()
+        elem = self.get(builder, value)
+        value = self._actual_model.from_data(builder, elem)
+        out = self.make_uninitialized()
+        return self.set(builder, out, value)
+
+    def as_data(self, builder, value):
+        self._define()
+        elem = self.get(builder, value)
+        value = self._actual_model.as_data(builder, elem)
+        out = self.make_uninitialized(kind='data')
+        return self.set(builder, out, value)
+
+    def from_return(self, builder, value):
+        return value
+
+    def as_return(self, builder, value):
+        return value
+
+    def get(self, builder, value):
+        return builder.extract_value(value, [0])
+
+    def set(self, builder, value, content):
+        return builder.insert_value(value, content, [0])
+
+    def make_uninitialized(self, kind='value'):
+        self._define()
+        if kind == 'value':
+            ty = self.get_value_type()
+        else:
+            ty = self.get_data_type()
+        return ir.Constant(ty, ir.Undefined)
+
+    def _define(self):
+        valty = self.get_value_type()
+        self._define_value_type(valty)
+        datty = self.get_data_type()
+        self._define_data_type(datty)
+
+    def _define_value_type(self, value_type):
+        if value_type.is_opaque:
+            value_type.set_body(self._actual_model.get_value_type())
+
+    def _define_data_type(self, data_type):
+        if data_type.is_opaque:
+            data_type.set_body(self._actual_model.get_data_type())
+
+    @property
+    def _actual_model(self):
+        return self._dmm.lookup(self.actual_fe_type)
+
+    def traverse(self, builder):
+        return [(self.actual_fe_type,
+                 lambda value: builder.extract_value(value, [0]))]
+
+
+@register_default(types.StructRefPayload)
+class StructPayloadModel(StructModel):
+    """Model for the payload of a mutable struct
+    """
+    def __init__(self, dmm, fe_typ):
+        members = tuple(fe_typ.field_dict.items())
+        super().__init__(dmm, fe_typ, members)
+
+
+class StructRefModel(StructModel):
+    """Model for a mutable struct.
+    A reference to the payload
+    """
+    def __init__(self, dmm, fe_typ):
+        dtype = fe_typ.get_data_type()
+        members = [
+            ("meminfo", types.MemInfoPointer(dtype)),
+        ]
+        super().__init__(dmm, fe_typ, members)
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/packer.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/packer.py
new file mode 100644
index 000000000..9efc51449
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/packer.py
@@ -0,0 +1,213 @@
+from collections import deque
+
+from numba.core import types, cgutils
+
+
+
+class DataPacker(object):
+    """
+    A helper to pack a number of typed arguments into a data structure.
+    Omitted arguments (i.e. values with the type `Omitted`) are automatically
+    skipped.
+    """
+    # XXX should DataPacker be a model for a dedicated type?
+
+    def __init__(self, dmm, fe_types):
+        self._dmm = dmm
+        self._fe_types = fe_types
+        self._models = [dmm.lookup(ty) for ty in fe_types]
+
+        self._pack_map = []
+        self._be_types = []
+        for i, ty in enumerate(fe_types):
+            if not isinstance(ty, types.Omitted):
+                self._pack_map.append(i)
+                self._be_types.append(self._models[i].get_data_type())
+
+    def as_data(self, builder, values):
+        """
+        Return the given values packed as a data structure.
+        """
+        elems = [self._models[i].as_data(builder, values[i])
+                 for i in self._pack_map]
+        return cgutils.make_anonymous_struct(builder, elems)
+
+    def _do_load(self, builder, ptr, formal_list=None):
+        res = []
+        for i, i_formal in enumerate(self._pack_map):
+            elem_ptr = cgutils.gep_inbounds(builder, ptr, 0, i)
+            val = self._models[i_formal].load_from_data_pointer(builder, elem_ptr)
+            if formal_list is None:
+                res.append((self._fe_types[i_formal], val))
+            else:
+                formal_list[i_formal] = val
+        return res
+
+    def load(self, builder, ptr):
+        """
+        Load the packed values and return a (type, value) tuples.
+        """
+        return self._do_load(builder, ptr)
+
+    def load_into(self, builder, ptr, formal_list):
+        """
+        Load the packed values into a sequence indexed by formal
+        argument number (skipping any Omitted position).
+        """
+        self._do_load(builder, ptr, formal_list)
+
+
+class ArgPacker(object):
+    """
+    Compute the position for each high-level typed argument.
+    It flattens every composite argument into primitive types.
+    It maintains a position map for unflattening the arguments.
+
+    Since struct (esp. nested struct) have specific ABI requirements (e.g.
+    alignment, pointer address-space, ...) in different architecture (e.g.
+    OpenCL, CUDA), flattening composite argument types simplifes the call
+    setup from the Python side.  Functions are receiving simple primitive
+    types and there are only a handful of these.
+    """
+
+    def __init__(self, dmm, fe_args):
+        self._dmm = dmm
+        self._fe_args = fe_args
+        self._nargs = len(fe_args)
+
+        self._dm_args = []
+        argtys = []
+        for ty in fe_args:
+            dm = self._dmm.lookup(ty)
+            self._dm_args.append(dm)
+            argtys.append(dm.get_argument_type())
+        self._unflattener = _Unflattener(argtys)
+        self._be_args = list(_flatten(argtys))
+
+    def as_arguments(self, builder, values):
+        """Flatten all argument values
+        """
+        if len(values) != self._nargs:
+            raise TypeError("invalid number of args: expected %d, got %d"
+                            % (self._nargs, len(values)))
+
+        if not values:
+            return ()
+
+        args = [dm.as_argument(builder, val)
+                for dm, val in zip(self._dm_args, values)
+                ]
+
+        args = tuple(_flatten(args))
+        return args
+
+    def from_arguments(self, builder, args):
+        """Unflatten all argument values
+        """
+
+        valtree = self._unflattener.unflatten(args)
+        values = [dm.from_argument(builder, val)
+                  for dm, val in zip(self._dm_args, valtree)
+                  ]
+
+        return values
+
+    def assign_names(self, args, names):
+        """Assign names for each flattened argument values.
+        """
+
+        valtree = self._unflattener.unflatten(args)
+        for aval, aname in zip(valtree, names):
+            self._assign_names(aval, aname)
+
+    def _assign_names(self, val_or_nested, name, depth=()):
+        if isinstance(val_or_nested, (tuple, list)):
+            for pos, aval in enumerate(val_or_nested):
+                self._assign_names(aval, name, depth=depth + (pos,))
+        else:
+            postfix = '.'.join(map(str, depth))
+            parts = [name, postfix]
+            val_or_nested.name = '.'.join(filter(bool, parts))
+
+    @property
+    def argument_types(self):
+        """Return a list of LLVM types that are results of flattening
+        composite types.
+        """
+        return tuple(ty for ty in self._be_args if ty != ())
+
+
+def _flatten(iterable):
+    """
+    Flatten nested iterable of (tuple, list).
+    """
+    def rec(iterable):
+        for i in iterable:
+            if isinstance(i, (tuple, list)):
+                for j in rec(i):
+                    yield j
+            else:
+                yield i
+    return rec(iterable)
+
+
+_PUSH_LIST = 1
+_APPEND_NEXT_VALUE = 2
+_APPEND_EMPTY_TUPLE = 3
+_POP = 4
+
+class _Unflattener(object):
+    """
+    An object used to unflatten nested sequences after a given pattern
+    (an arbitrarily nested sequence).
+    The pattern shows the nested sequence shape desired when unflattening;
+    the values it contains are irrelevant.
+    """
+
+    def __init__(self, pattern):
+        self._code = self._build_unflatten_code(pattern)
+
+    def _build_unflatten_code(self, iterable):
+        """Build the unflatten opcode sequence for the given *iterable* structure
+        (an iterable of nested sequences).
+        """
+        code = []
+        def rec(iterable):
+            for i in iterable:
+                if isinstance(i, (tuple, list)):
+                    if len(i) > 0:
+                        code.append(_PUSH_LIST)
+                        rec(i)
+                        code.append(_POP)
+                    else:
+                        code.append(_APPEND_EMPTY_TUPLE)
+                else:
+                    code.append(_APPEND_NEXT_VALUE)
+
+        rec(iterable)
+        return code
+
+    def unflatten(self, flatiter):
+        """Rebuild a nested tuple structure.
+        """
+        vals = deque(flatiter)
+
+        res = []
+        cur = res
+        stack = []
+        for op in self._code:
+            if op is _PUSH_LIST:
+                stack.append(cur)
+                cur.append([])
+                cur = cur[-1]
+            elif op is _APPEND_NEXT_VALUE:
+                cur.append(vals.popleft())
+            elif op is _APPEND_EMPTY_TUPLE:
+                cur.append(())
+            elif op is _POP:
+                cur = stack.pop()
+
+        assert not stack, stack
+        assert not vals, vals
+
+        return res
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/registry.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/registry.py
new file mode 100644
index 000000000..18bdc475e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/registry.py
@@ -0,0 +1,18 @@
+import functools
+from .manager import DataModelManager
+
+
+def register(dmm, typecls):
+    """Used as decorator to simplify datamodel registration.
+    Returns the object being decorated so that chaining is possible.
+    """
+    def wraps(fn):
+        dmm.register(typecls, fn)
+        return fn
+
+    return wraps
+
+
+default_manager = DataModelManager()
+
+register_default = functools.partial(register, default_manager)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/testing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/testing.py
new file mode 100644
index 000000000..e2e8a2818
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/datamodel/testing.py
@@ -0,0 +1,150 @@
+from llvmlite import ir
+from llvmlite import binding as ll
+
+from numba.core import datamodel
+import unittest
+
+
+class DataModelTester(unittest.TestCase):
+    """
+    Test the implementation of a DataModel for a frontend type.
+    """
+    fe_type = NotImplemented
+
+    def setUp(self):
+        self.module = ir.Module()
+        self.datamodel = datamodel.default_manager[self.fe_type]
+
+    def test_as_arg(self):
+        """
+        - Is as_arg() and from_arg() implemented?
+        - Are they the inverse of each other?
+        """
+        fnty = ir.FunctionType(ir.VoidType(), [])
+        function = ir.Function(self.module, fnty, name="test_as_arg")
+        builder = ir.IRBuilder()
+        builder.position_at_end(function.append_basic_block())
+
+        undef_value = ir.Constant(self.datamodel.get_value_type(), None)
+        args = self.datamodel.as_argument(builder, undef_value)
+        self.assertIsNot(args, NotImplemented, "as_argument returned "
+                                               "NotImplementedError")
+
+        if isinstance(args, (tuple, list)):
+            def recur_tuplize(args, func=None):
+                for arg in args:
+                    if isinstance(arg, (tuple, list)):
+                        yield tuple(recur_tuplize(arg, func=func))
+                    else:
+                        if func is None:
+                            yield arg
+                        else:
+                            yield func(arg)
+
+            argtypes = tuple(recur_tuplize(args, func=lambda x: x.type))
+            exptypes = tuple(recur_tuplize(
+                self.datamodel.get_argument_type()))
+            self.assertEqual(exptypes, argtypes)
+        else:
+            self.assertEqual(args.type,
+                             self.datamodel.get_argument_type())
+
+        rev_value = self.datamodel.from_argument(builder, args)
+        self.assertEqual(rev_value.type, self.datamodel.get_value_type())
+
+        builder.ret_void()  # end function
+
+        # Ensure valid LLVM generation
+        materialized = ll.parse_assembly(str(self.module))
+        str(materialized)
+
+    def test_as_return(self):
+        """
+        - Is as_return() and from_return() implemented?
+        - Are they the inverse of each other?
+        """
+        fnty = ir.FunctionType(ir.VoidType(), [])
+        function = ir.Function(self.module, fnty, name="test_as_return")
+        builder = ir.IRBuilder()
+        builder.position_at_end(function.append_basic_block())
+
+        undef_value = ir.Constant(self.datamodel.get_value_type(), None)
+        ret = self.datamodel.as_return(builder, undef_value)
+        self.assertIsNot(ret, NotImplemented, "as_return returned "
+                                              "NotImplementedError")
+
+        self.assertEqual(ret.type, self.datamodel.get_return_type())
+
+        rev_value = self.datamodel.from_return(builder, ret)
+        self.assertEqual(rev_value.type, self.datamodel.get_value_type())
+
+        builder.ret_void()  # end function
+
+        # Ensure valid LLVM generation
+        materialized = ll.parse_assembly(str(self.module))
+        str(materialized)
+
+
+class SupportAsDataMixin(object):
+    """Test as_data() and from_data()
+    """
+    # XXX test load_from_data_pointer() as well
+
+    def test_as_data(self):
+        fnty = ir.FunctionType(ir.VoidType(), [])
+        function = ir.Function(self.module, fnty, name="test_as_data")
+        builder = ir.IRBuilder()
+        builder.position_at_end(function.append_basic_block())
+
+        undef_value = ir.Constant(self.datamodel.get_value_type(), None)
+        data = self.datamodel.as_data(builder, undef_value)
+        self.assertIsNot(data, NotImplemented,
+                         "as_data returned NotImplemented")
+
+        self.assertEqual(data.type, self.datamodel.get_data_type())
+
+        rev_value = self.datamodel.from_data(builder, data)
+        self.assertEqual(rev_value.type,
+                         self.datamodel.get_value_type())
+
+        builder.ret_void()  # end function
+
+        # Ensure valid LLVM generation
+        materialized = ll.parse_assembly(str(self.module))
+        str(materialized)
+
+
+class NotSupportAsDataMixin(object):
+    """Ensure as_data() and from_data() raise NotImplementedError.
+    """
+
+    def test_as_data_not_supported(self):
+        fnty = ir.FunctionType(ir.VoidType(), [])
+        function = ir.Function(self.module, fnty, name="test_as_data")
+        builder = ir.IRBuilder()
+        builder.position_at_end(function.append_basic_block())
+
+        undef_value = ir.Constant(self.datamodel.get_value_type(), None)
+        with self.assertRaises(NotImplementedError):
+            data = self.datamodel.as_data(builder, undef_value)
+        with self.assertRaises(NotImplementedError):
+            rev_data = self.datamodel.from_data(builder, undef_value)
+
+
+class DataModelTester_SupportAsDataMixin(DataModelTester,
+                                         SupportAsDataMixin):
+    pass
+
+
+class DataModelTester_NotSupportAsDataMixin(DataModelTester,
+                                            NotSupportAsDataMixin):
+    pass
+
+
+def test_factory(support_as_data=True):
+    """A helper for returning a unittest TestCase for testing
+    """
+    if support_as_data:
+        return DataModelTester_SupportAsDataMixin
+    else:
+        return DataModelTester_NotSupportAsDataMixin
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/debuginfo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/debuginfo.py
new file mode 100644
index 000000000..692241a35
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/debuginfo.py
@@ -0,0 +1,609 @@
+"""
+Implements helpers to build LLVM debuginfo.
+"""
+
+
+import abc
+import os.path
+from contextlib import contextmanager
+
+from llvmlite import ir
+from numba.core import cgutils, types
+from numba.core.datamodel.models import ComplexModel, UniTupleModel
+from numba.core import config
+
+
+@contextmanager
+def suspend_emission(builder):
+    """Suspends the emission of debug_metadata for the duration of the context
+    managed block."""
+    ref = builder.debug_metadata
+    builder.debug_metadata = None
+    try:
+        yield
+    finally:
+        builder.debug_metadata = ref
+
+
+class AbstractDIBuilder(metaclass=abc.ABCMeta):
+    @abc.abstractmethod
+    def mark_variable(self, builder, allocavalue, name, lltype, size, line,
+                      datamodel=None, argidx=None):
+        """Emit debug info for the variable.
+        """
+        pass
+
+    @abc.abstractmethod
+    def mark_location(self, builder, line):
+        """Emit source location information to the given IRBuilder.
+        """
+        pass
+
+    @abc.abstractmethod
+    def mark_subprogram(self, function, qualname, argnames, argtypes, line):
+        """Emit source location information for the given function.
+        """
+        pass
+
+    @abc.abstractmethod
+    def initialize(self):
+        """Initialize the debug info. An opportunity for the debuginfo to
+        prepare any necessary data structures.
+        """
+
+    @abc.abstractmethod
+    def finalize(self):
+        """Finalize the debuginfo by emitting all necessary metadata.
+        """
+        pass
+
+
+class DummyDIBuilder(AbstractDIBuilder):
+
+    def __init__(self, module, filepath, cgctx):
+        pass
+
+    def mark_variable(self, builder, allocavalue, name, lltype, size, line,
+                      datamodel=None, argidx=None):
+        pass
+
+    def mark_location(self, builder, line):
+        pass
+
+    def mark_subprogram(self, function, qualname, argnames, argtypes, line):
+        pass
+
+    def initialize(self):
+        pass
+
+    def finalize(self):
+        pass
+
+
+_BYTE_SIZE = 8
+
+
+class DIBuilder(AbstractDIBuilder):
+    DWARF_VERSION = 4
+    DEBUG_INFO_VERSION = 3
+    DBG_CU_NAME = 'llvm.dbg.cu'
+    _DEBUG = False
+
+    def __init__(self, module, filepath, cgctx):
+        self.module = module
+        self.filepath = os.path.abspath(filepath)
+        self.difile = self._di_file()
+        self.subprograms = []
+        self.cgctx = cgctx
+        self.initialize()
+
+    def initialize(self):
+        # Create the compile unit now because it is referenced when
+        # constructing subprograms
+        self.dicompileunit = self._di_compile_unit()
+
+    def _var_type(self, lltype, size, datamodel=None):
+        if self._DEBUG:
+            print("-->", lltype, size, datamodel,
+                  getattr(datamodel, 'fe_type', 'NO FE TYPE'))
+        m = self.module
+        bitsize = _BYTE_SIZE * size
+
+        int_type = ir.IntType,
+        real_type = ir.FloatType, ir.DoubleType
+        # For simple numeric types, choose the closest encoding.
+        # We treat all integers as unsigned when there's no known datamodel.
+        if isinstance(lltype, int_type + real_type):
+            if datamodel is None:
+                # This is probably something like an `i8*` member of a struct
+                name = str(lltype)
+                if isinstance(lltype, int_type):
+                    ditok = 'DW_ATE_unsigned'
+                else:
+                    ditok = 'DW_ATE_float'
+            else:
+                # This is probably a known int/float scalar type
+                name = str(datamodel.fe_type)
+                if isinstance(datamodel.fe_type, types.Integer):
+                    if datamodel.fe_type.signed:
+                        ditok = 'DW_ATE_signed'
+                    else:
+                        ditok = 'DW_ATE_unsigned'
+                else:
+                    ditok = 'DW_ATE_float'
+            mdtype = m.add_debug_info('DIBasicType', {
+                'name': name,
+                'size': bitsize,
+                'encoding': ir.DIToken(ditok),
+            })
+        elif isinstance(datamodel, ComplexModel):
+            # TODO: Is there a better way of determining "this is a complex
+            # number"?
+            #
+            # NOTE: Commented below is the way to generate the metadata for a
+            # C99 complex type that's directly supported by DWARF. Numba however
+            # generates a struct with real/imag cf. CPython to give a more
+            # pythonic feel to inspection.
+            #
+            # mdtype = m.add_debug_info('DIBasicType', {
+            #  'name': f"{datamodel.fe_type} ({str(lltype)})",
+            #  'size': bitsize,
+            # 'encoding': ir.DIToken('DW_ATE_complex_float'),
+            #})
+            meta = []
+            offset = 0
+            for ix, name in enumerate(('real', 'imag')):
+                component = lltype.elements[ix]
+                component_size = self.cgctx.get_abi_sizeof(component)
+                component_basetype = m.add_debug_info('DIBasicType', {
+                    'name': str(component),
+                    'size': _BYTE_SIZE * component_size, # bits
+                    'encoding': ir.DIToken('DW_ATE_float'),
+                })
+                derived_type = m.add_debug_info('DIDerivedType', {
+                    'tag': ir.DIToken('DW_TAG_member'),
+                    'name': name,
+                    'baseType': component_basetype,
+                    'size': _BYTE_SIZE * component_size, # DW_TAG_member size is in bits
+                    'offset': offset,
+                })
+                meta.append(derived_type)
+                offset += (_BYTE_SIZE * component_size) # offset is in bits
+            mdtype = m.add_debug_info('DICompositeType', {
+                'tag': ir.DIToken('DW_TAG_structure_type'),
+                'name': f"{datamodel.fe_type} ({str(lltype)})",
+                'identifier': str(lltype),
+                'elements': m.add_metadata(meta),
+                'size': offset,
+            }, is_distinct=True)
+        elif isinstance(datamodel, UniTupleModel):
+            element = lltype.element
+            el_size = self.cgctx.get_abi_sizeof(element)
+            basetype = self._var_type(element, el_size)
+            name = f"{datamodel.fe_type} ({str(lltype)})"
+            count = size // el_size
+            mdrange = m.add_debug_info('DISubrange', {
+                'count': count,
+            })
+            mdtype = m.add_debug_info('DICompositeType', {
+                'tag': ir.DIToken('DW_TAG_array_type'),
+                'baseType': basetype,
+                'name': name,
+                'size': bitsize,
+                'identifier': str(lltype),
+                'elements': m.add_metadata([mdrange]),
+            })
+        elif isinstance(lltype, ir.PointerType):
+            model = getattr(datamodel, '_pointee_model', None)
+            basetype = self._var_type(lltype.pointee,
+                                      self.cgctx.get_abi_sizeof(lltype.pointee),
+                                      model)
+            mdtype = m.add_debug_info('DIDerivedType', {
+                'tag': ir.DIToken('DW_TAG_pointer_type'),
+                'baseType': basetype,
+                'size': _BYTE_SIZE * self.cgctx.get_abi_sizeof(lltype)
+            })
+        elif isinstance(lltype, ir.LiteralStructType):
+            # Struct type
+            meta = []
+            offset = 0
+            if datamodel is None or not datamodel.inner_models():
+                name = f"Anonymous struct ({str(lltype)})"
+                for field_id, element in enumerate(lltype.elements):
+                    size = self.cgctx.get_abi_sizeof(element)
+                    basetype = self._var_type(element, size)
+                    derived_type = m.add_debug_info('DIDerivedType', {
+                        'tag': ir.DIToken('DW_TAG_member'),
+                        'name': f'<field {field_id}>',
+                        'baseType': basetype,
+                        'size': _BYTE_SIZE * size, # DW_TAG_member size is in bits
+                        'offset': offset,
+                    })
+                    meta.append(derived_type)
+                    offset += (_BYTE_SIZE * size) # offset is in bits
+            else:
+                name = f"{datamodel.fe_type} ({str(lltype)})"
+                for element, field, model in zip(lltype.elements,
+                                                 datamodel._fields,
+                                                 datamodel.inner_models()):
+                    size = self.cgctx.get_abi_sizeof(element)
+                    basetype = self._var_type(element, size, datamodel=model)
+                    derived_type = m.add_debug_info('DIDerivedType', {
+                        'tag': ir.DIToken('DW_TAG_member'),
+                        'name': field,
+                        'baseType': basetype,
+                        'size': _BYTE_SIZE * size, # DW_TAG_member size is in bits
+                        'offset': offset,
+                    })
+                    meta.append(derived_type)
+                    offset += (_BYTE_SIZE * size) # offset is in bits
+
+            mdtype = m.add_debug_info('DICompositeType', {
+                'tag': ir.DIToken('DW_TAG_structure_type'),
+                'name': name,
+                'identifier': str(lltype),
+                'elements': m.add_metadata(meta),
+                'size': offset,
+            }, is_distinct=True)
+        elif isinstance(lltype, ir.ArrayType):
+            element = lltype.element
+            el_size = self.cgctx.get_abi_sizeof(element)
+            basetype = self._var_type(element, el_size)
+            count = size // el_size
+            mdrange = m.add_debug_info('DISubrange', {
+                'count': count,
+            })
+            mdtype = m.add_debug_info('DICompositeType', {
+                'tag': ir.DIToken('DW_TAG_array_type'),
+                'baseType': basetype,
+                'name': str(lltype),
+                'size': bitsize,
+                'identifier': str(lltype),
+                'elements': m.add_metadata([mdrange]),
+            })
+        else:
+            # For all other types, describe it as sequence of bytes
+            count = size
+            mdrange = m.add_debug_info('DISubrange', {
+                'count': count,
+            })
+            mdbase = m.add_debug_info('DIBasicType', {
+                'name': 'byte',
+                'size': _BYTE_SIZE,
+                'encoding': ir.DIToken('DW_ATE_unsigned_char'),
+            })
+            mdtype = m.add_debug_info('DICompositeType', {
+                'tag': ir.DIToken('DW_TAG_array_type'),
+                'baseType': mdbase,
+                'name': str(lltype),
+                'size': bitsize,
+                'identifier': str(lltype),
+                'elements': m.add_metadata([mdrange]),
+            })
+
+        return mdtype
+
+    def mark_variable(self, builder, allocavalue, name, lltype, size, line,
+                      datamodel=None, argidx=None):
+
+        arg_index = 0 if argidx is None else argidx
+        m = self.module
+        fnty = ir.FunctionType(ir.VoidType(), [ir.MetaDataType()] * 3)
+        decl = cgutils.get_or_insert_function(m, fnty, 'llvm.dbg.declare')
+
+        mdtype = self._var_type(lltype, size, datamodel=datamodel)
+        name = name.replace('.', '$')    # for gdb to work correctly
+        mdlocalvar = m.add_debug_info('DILocalVariable', {
+            'name': name,
+            'arg': arg_index,
+            'scope': self.subprograms[-1],
+            'file': self.difile,
+            'line': line,
+            'type': mdtype,
+        })
+        mdexpr = m.add_debug_info('DIExpression', {})
+
+        return builder.call(decl, [allocavalue, mdlocalvar, mdexpr])
+
+    def mark_location(self, builder, line):
+        builder.debug_metadata = self._add_location(line)
+
+    def mark_subprogram(self, function, qualname, argnames, argtypes, line):
+        name = qualname
+        argmap = dict(zip(argnames, argtypes))
+        di_subp = self._add_subprogram(name=name, linkagename=function.name,
+                                       line=line, function=function,
+                                       argmap=argmap)
+        function.set_metadata("dbg", di_subp)
+
+        # Don't marked alwaysinline functions as noinline.
+        if 'alwaysinline' not in function.attributes:
+            # disable inlining for this function for easier debugging
+            function.attributes.add('noinline')
+
+    def finalize(self):
+        dbgcu = cgutils.get_or_insert_named_metadata(self.module, self.DBG_CU_NAME)
+        dbgcu.add(self.dicompileunit)
+        self._set_module_flags()
+
+    #
+    # Internal APIs
+    #
+
+    def _set_module_flags(self):
+        """Set the module flags metadata
+        """
+        module = self.module
+        mflags = cgutils.get_or_insert_named_metadata(module, 'llvm.module.flags')
+        # Set *require* behavior to warning
+        # See http://llvm.org/docs/LangRef.html#module-flags-metadata
+        require_warning_behavior = self._const_int(2)
+        if self.DWARF_VERSION is not None:
+            dwarf_version = module.add_metadata([
+                require_warning_behavior,
+                "Dwarf Version",
+                self._const_int(self.DWARF_VERSION)
+            ])
+            if dwarf_version not in mflags.operands:
+                mflags.add(dwarf_version)
+        debuginfo_version = module.add_metadata([
+            require_warning_behavior,
+            "Debug Info Version",
+            self._const_int(self.DEBUG_INFO_VERSION)
+        ])
+        if debuginfo_version not in mflags.operands:
+            mflags.add(debuginfo_version)
+
+    def _add_subprogram(self, name, linkagename, line, function, argmap):
+        """Emit subprogram metadata
+        """
+        subp = self._di_subprogram(name, linkagename, line, function, argmap)
+        self.subprograms.append(subp)
+        return subp
+
+    def _add_location(self, line):
+        """Emit location metatdaa
+        """
+        loc = self._di_location(line)
+        return loc
+
+    @classmethod
+    def _const_int(cls, num, bits=32):
+        """Util to create constant int in metadata
+        """
+        return ir.IntType(bits)(num)
+
+    @classmethod
+    def _const_bool(cls, boolean):
+        """Util to create constant boolean in metadata
+        """
+        return ir.IntType(1)(boolean)
+
+    #
+    # Helpers to emit the metadata nodes
+    #
+
+    def _di_file(self):
+        return self.module.add_debug_info('DIFile', {
+            'directory': os.path.dirname(self.filepath),
+            'filename': os.path.basename(self.filepath),
+        })
+
+    def _di_compile_unit(self):
+        return self.module.add_debug_info('DICompileUnit', {
+            'language': ir.DIToken('DW_LANG_C_plus_plus'),
+            'file': self.difile,
+            # Numba has to pretend to be clang to ensure the prologue is skipped
+            # correctly in gdb. See:
+            # https://sourceware.org/git/?p=binutils-gdb.git;a=blob;f=gdb/amd64-tdep.c;h=e563d369d8cb3eb3c2f732c2fa850ec70ba8d63b;hb=a4b0231e179607e47b1cdf1fe15c5dc25e482fad#l2521
+            # Note the "producer_is_llvm" call to specialise the prologue
+            # handling, this is defined here:
+            # https://sourceware.org/git/?p=binutils-gdb.git;a=blob;f=gdb/producer.c;h=cdfd80d904c09394febd18749bb90359b2d128cc;hb=a4b0231e179607e47b1cdf1fe15c5dc25e482fad#l124
+            # and to get a match for this condition the 'producer' must start
+            # with "clang ", hence the following...
+            'producer': 'clang (Numba)',
+            'runtimeVersion': 0,
+            'isOptimized': config.OPT != 0,
+            'emissionKind': 1,  # 0-NoDebug, 1-FullDebug
+        }, is_distinct=True)
+
+    def _di_subroutine_type(self, line, function, argmap):
+        # The function call conv needs encoding.
+        llfunc = function
+        md = []
+
+        for idx, llarg in enumerate(llfunc.args):
+            if not llarg.name.startswith('arg.'):
+                name = llarg.name.replace('.', '$')    # for gdb to work correctly
+                lltype = llarg.type
+                size = self.cgctx.get_abi_sizeof(lltype)
+                mdtype = self._var_type(lltype, size, datamodel=None)
+                md.append(mdtype)
+
+        for idx, (name, nbtype) in enumerate(argmap.items()):
+            name = name.replace('.', '$')    # for gdb to work correctly
+            datamodel = self.cgctx.data_model_manager[nbtype]
+            lltype = self.cgctx.get_value_type(nbtype)
+            size = self.cgctx.get_abi_sizeof(lltype)
+            mdtype = self._var_type(lltype, size, datamodel=datamodel)
+            md.append(mdtype)
+
+        return self.module.add_debug_info('DISubroutineType', {
+            'types': self.module.add_metadata(md),
+        })
+
+    def _di_subprogram(self, name, linkagename, line, function, argmap):
+        return self.module.add_debug_info('DISubprogram', {
+            'name': name,
+            'linkageName': linkagename,
+            'scope': self.difile,
+            'file': self.difile,
+            'line': line,
+            'type': self._di_subroutine_type(line, function, argmap),
+            'isLocal': False,
+            'isDefinition': True,
+            'scopeLine': line,
+            'isOptimized': config.OPT != 0,
+            'unit': self.dicompileunit,
+        }, is_distinct=True)
+
+    def _di_location(self, line):
+        return self.module.add_debug_info('DILocation', {
+            'line': line,
+            'column': 1,
+            'scope': self.subprograms[-1],
+        })
+
+
+class NvvmDIBuilder(DIBuilder):
+    """
+    Only implemented the minimal metadata to get line number information.
+    See http://llvm.org/releases/3.4/docs/LangRef.html
+    """
+    # These constants are copied from llvm3.4
+    DW_LANG_Python = 0x0014
+    DI_Compile_unit = 786449
+    DI_Subroutine_type = 786453
+    DI_Subprogram = 786478
+    DI_File = 786473
+
+    DWARF_VERSION = None  # don't emit DWARF version
+    DEBUG_INFO_VERSION = 1  # as required by NVVM IR Spec
+    # Rename DIComputeUnit MD to hide it from llvm.parse_assembly()
+    # which strips invalid/outdated debug metadata
+    DBG_CU_NAME = 'numba.llvm.dbg.cu'
+
+    # Default member
+    # Used in mark_location to remember last lineno to avoid duplication
+    _last_lineno = None
+
+    def mark_variable(self, builder, allocavalue, name, lltype, size, line,
+                      datamodel=None, argidx=None):
+        # unsupported
+        pass
+
+    def mark_location(self, builder, line):
+        # Avoid duplication
+        if self._last_lineno == line:
+            return
+        self._last_lineno = line
+        # Add call to an inline asm to mark line location
+        asmty = ir.FunctionType(ir.VoidType(), [])
+        asm = ir.InlineAsm(asmty, "// dbg {}".format(line), "",
+                           side_effect=True)
+        call = builder.call(asm, [])
+        md = self._di_location(line)
+        call.set_metadata('numba.dbg', md)
+
+    def mark_subprogram(self, function, qualname, argnames, argtypes, line):
+        argmap = dict(zip(argnames, argtypes))
+        self._add_subprogram(name=qualname, linkagename=function.name,
+                             line=line)
+
+    def _add_subprogram(self, name, linkagename, line):
+        """Emit subprogram metadata
+        """
+        subp = self._di_subprogram(name, linkagename, line)
+        self.subprograms.append(subp)
+        return subp
+
+    #
+    # Helper methods to create the metadata nodes.
+    #
+
+    def _filepair(self):
+        return self.module.add_metadata([
+            os.path.basename(self.filepath),
+            os.path.dirname(self.filepath),
+        ])
+
+    def _di_file(self):
+        return self.module.add_metadata([
+            self._const_int(self.DI_File),
+            self._filepair(),
+        ])
+
+    def _di_compile_unit(self):
+        filepair = self._filepair()
+        empty = self.module.add_metadata([self._const_int(0)])
+        sp_metadata = self.module.add_metadata(self.subprograms)
+        return self.module.add_metadata([
+            self._const_int(self.DI_Compile_unit),         # tag
+            filepair,                   # source directory and file pair
+            self._const_int(self.DW_LANG_Python),  # language
+            'Numba',                     # producer
+            self._const_bool(True),      # optimized
+            "",                          # flags??
+            self._const_int(0),          # runtime version
+            empty,                       # enums types
+            empty,                       # retained types
+            self.module.add_metadata(self.subprograms),  # subprograms
+            empty,                       # global variables
+            empty,                       # imported entities
+            "",                          # split debug filename
+        ])
+
+    def _di_subroutine_type(self):
+        types = self.module.add_metadata([None])
+        return self.module.add_metadata([
+            self._const_int(self.DI_Subroutine_type),                # tag
+            self._const_int(0),
+            None,
+            "",
+            self._const_int(0),                 # line of definition
+            self._const_int(0, 64),             # size in bits
+            self._const_int(0, 64),             # offset in bits
+            self._const_int(0, 64),             # align in bits
+            self._const_int(0),                 # flags
+            None,
+            types,
+            self._const_int(0),
+            None,
+            None,
+            None,
+        ])
+
+    def _di_subprogram(self, name, linkagename, line):
+        function_ptr = self.module.get_global(linkagename)
+        subroutine_type = self._di_subroutine_type()
+        funcvars = self.module.add_metadata([self._const_int(0)])
+        context = self._di_file()
+        return self.module.add_metadata([
+            self._const_int(self.DI_Subprogram),   # tag
+            self._filepair(),          # source dir & file
+            context,                   # context descriptor
+            name,                      # name
+            name,                      # display name
+            linkagename,               # linkage name
+            self._const_int(line),     # line
+            subroutine_type,           # type descriptor
+            self._const_bool(False),   # is local
+            self._const_bool(True),    # is definition
+            self._const_int(0),        # virtuality
+            self._const_int(0),        # virtual function index
+            None,                     # vtable base type
+            self._const_int(0),        # flags
+            self._const_bool(True),    # is optimized
+            function_ptr,              # pointer to function
+            None,                      # function template parameters
+            None,                      # function declaration descriptor
+            funcvars,                     # function variables
+            self._const_int(line)      # scope line
+        ])
+
+    def _di_location(self, line):
+        return self.module.add_metadata([
+            self._const_int(line),   # line
+            self._const_int(0),      # column
+            self.subprograms[-1],    # scope
+            None,                    # original scope
+        ])
+
+    def initialize(self):
+        pass
+
+    def finalize(self):
+        # We create the compile unit at this point because subprograms is
+        # populated and can be referred to by the compile unit.
+        self.dicompileunit = self._di_compile_unit()
+        super().finalize()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/decorators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/decorators.py
new file mode 100644
index 000000000..2dfc4633f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/decorators.py
@@ -0,0 +1,309 @@
+"""
+Define @jit and related decorators.
+"""
+
+
+import sys
+import warnings
+import inspect
+import logging
+
+from numba.core.errors import DeprecationError, NumbaDeprecationWarning
+from numba.stencils.stencil import stencil
+from numba.core import config, extending, sigutils, registry
+
+_logger = logging.getLogger(__name__)
+
+
+# -----------------------------------------------------------------------------
+# Decorators
+
+_msg_deprecated_signature_arg = ("Deprecated keyword argument `{0}`. "
+                                 "Signatures should be passed as the first "
+                                 "positional argument.")
+
+
+def jit(signature_or_function=None, locals={}, cache=False,
+        pipeline_class=None, boundscheck=None, **options):
+    """
+    This decorator is used to compile a Python function into native code.
+
+    Args
+    -----
+    signature_or_function:
+        The (optional) signature or list of signatures to be compiled.
+        If not passed, required signatures will be compiled when the
+        decorated function is called, depending on the argument values.
+        As a convenience, you can directly pass the function to be compiled
+        instead.
+
+    locals: dict
+        Mapping of local variable names to Numba types. Used to override the
+        types deduced by Numba's type inference engine.
+
+    pipeline_class: type numba.compiler.CompilerBase
+            The compiler pipeline type for customizing the compilation stages.
+
+    options:
+        For a cpu target, valid options are:
+            nopython: bool
+                Set to True to disable the use of PyObjects and Python API
+                calls. The default behavior is to allow the use of PyObjects
+                and Python API. Default value is False.
+
+            forceobj: bool
+                Set to True to force the use of PyObjects for every value.
+                Default value is False.
+
+            looplift: bool
+                Set to True to enable jitting loops in nopython mode while
+                leaving surrounding code in object mode. This allows functions
+                to allocate NumPy arrays and use Python objects, while the
+                tight loops in the function can still be compiled in nopython
+                mode. Any arrays that the tight loop uses should be created
+                before the loop is entered. Default value is True.
+
+            error_model: str
+                The error-model affects divide-by-zero behavior.
+                Valid values are 'python' and 'numpy'. The 'python' model
+                raises exception.  The 'numpy' model sets the result to
+                *+/-inf* or *nan*. Default value is 'python'.
+
+            inline: str or callable
+                The inline option will determine whether a function is inlined
+                at into its caller if called. String options are 'never'
+                (default) which will never inline, and 'always', which will
+                always inline. If a callable is provided it will be called with
+                the call expression node that is requesting inlining, the
+                caller's IR and callee's IR as arguments, it is expected to
+                return Truthy as to whether to inline.
+                NOTE: This inlining is performed at the Numba IR level and is in
+                no way related to LLVM inlining.
+
+            boundscheck: bool or None
+                Set to True to enable bounds checking for array indices. Out
+                of bounds accesses will raise IndexError. The default is to
+                not do bounds checking. If False, bounds checking is disabled,
+                out of bounds accesses can produce garbage results or segfaults.
+                However, enabling bounds checking will slow down typical
+                functions, so it is recommended to only use this flag for
+                debugging. You can also set the NUMBA_BOUNDSCHECK environment
+                variable to 0 or 1 to globally override this flag. The default
+                value is None, which under normal execution equates to False,
+                but if debug is set to True then bounds checking will be
+                enabled.
+
+    Returns
+    --------
+    A callable usable as a compiled function.  Actual compiling will be
+    done lazily if no explicit signatures are passed.
+
+    Examples
+    --------
+    The function can be used in the following ways:
+
+    1) jit(signatures, **targetoptions) -> jit(function)
+
+        Equivalent to:
+
+            d = dispatcher(function, targetoptions)
+            for signature in signatures:
+                d.compile(signature)
+
+        Create a dispatcher object for a python function.  Then, compile
+        the function with the given signature(s).
+
+        Example:
+
+            @jit("int32(int32, int32)")
+            def foo(x, y):
+                return x + y
+
+            @jit(["int32(int32, int32)", "float32(float32, float32)"])
+            def bar(x, y):
+                return x + y
+
+    2) jit(function, **targetoptions) -> dispatcher
+
+        Create a dispatcher function object that specializes at call site.
+
+        Examples:
+
+            @jit
+            def foo(x, y):
+                return x + y
+
+            @jit(nopython=True)
+            def bar(x, y):
+                return x + y
+
+    """
+    if 'argtypes' in options:
+        raise DeprecationError(_msg_deprecated_signature_arg.format('argtypes'))
+    if 'restype' in options:
+        raise DeprecationError(_msg_deprecated_signature_arg.format('restype'))
+    if options.get('nopython', False) and options.get('forceobj', False):
+        raise ValueError("Only one of 'nopython' or 'forceobj' can be True.")
+
+    if "_target" in options:
+        # Set the "target_backend" option if "_target" is defined.
+        options['target_backend'] = options['_target']
+    target = options.pop('_target', 'cpu')
+
+    options['boundscheck'] = boundscheck
+
+    # Handle signature
+    if signature_or_function is None:
+        # No signature, no function
+        pyfunc = None
+        sigs = None
+    elif isinstance(signature_or_function, list):
+        # A list of signatures is passed
+        pyfunc = None
+        sigs = signature_or_function
+    elif sigutils.is_signature(signature_or_function):
+        # A single signature is passed
+        pyfunc = None
+        sigs = [signature_or_function]
+    else:
+        # A function is passed
+        pyfunc = signature_or_function
+        sigs = None
+
+    dispatcher_args = {}
+    if pipeline_class is not None:
+        dispatcher_args['pipeline_class'] = pipeline_class
+    wrapper = _jit(sigs, locals=locals, target=target, cache=cache,
+                   targetoptions=options, **dispatcher_args)
+    if pyfunc is not None:
+        return wrapper(pyfunc)
+    else:
+        return wrapper
+
+
+def _jit(sigs, locals, target, cache, targetoptions, **dispatcher_args):
+
+    from numba.core.target_extension import resolve_dispatcher_from_str
+    dispatcher = resolve_dispatcher_from_str(target)
+
+    def wrapper(func):
+        if extending.is_jitted(func):
+            raise TypeError(
+                "A jit decorator was called on an already jitted function "
+                f"{func}.  If trying to access the original python "
+                f"function, use the {func}.py_func attribute."
+            )
+
+        if not inspect.isfunction(func):
+            raise TypeError(
+                "The decorated object is not a function (got type "
+                f"{type(func)})."
+            )
+
+        if config.ENABLE_CUDASIM and target == 'cuda':
+            from numba import cuda
+            return cuda.jit(func)
+        if config.DISABLE_JIT and not target == 'npyufunc':
+            return func
+        disp = dispatcher(py_func=func, locals=locals,
+                          targetoptions=targetoptions,
+                          **dispatcher_args)
+        if cache:
+            disp.enable_caching()
+        if sigs is not None:
+            # Register the Dispatcher to the type inference mechanism,
+            # even though the decorator hasn't returned yet.
+            from numba.core import typeinfer
+            with typeinfer.register_dispatcher(disp):
+                for sig in sigs:
+                    disp.compile(sig)
+                disp.disable_compile()
+        return disp
+
+    return wrapper
+
+
+def generated_jit(function=None, cache=False,
+                  pipeline_class=None, **options):
+    """
+    This decorator allows flexible type-based compilation
+    of a jitted function.  It works as `@jit`, except that the decorated
+    function is called at compile-time with the *types* of the arguments
+    and should return an implementation function for those types.
+    """
+    dispatcher_args = {}
+    if pipeline_class is not None:
+        dispatcher_args['pipeline_class'] = pipeline_class
+    wrapper = _jit(sigs=None, locals={}, target='cpu', cache=cache,
+                   targetoptions=options, impl_kind='generated',
+                   **dispatcher_args)
+    if function is not None:
+        return wrapper(function)
+    else:
+        return wrapper
+
+
+def njit(*args, **kws):
+    """
+    Equivalent to jit(nopython=True)
+
+    See documentation for jit function/decorator for full description.
+    """
+    if 'nopython' in kws:
+        warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
+    if 'forceobj' in kws:
+        warnings.warn('forceobj is set for njit and is ignored', RuntimeWarning)
+        del kws['forceobj']
+    kws.update({'nopython': True})
+    return jit(*args, **kws)
+
+
+def cfunc(sig, locals={}, cache=False, pipeline_class=None, **options):
+    """
+    This decorator is used to compile a Python function into a C callback
+    usable with foreign C libraries.
+
+    Usage::
+        @cfunc("float64(float64, float64)", nopython=True, cache=True)
+        def add(a, b):
+            return a + b
+
+    """
+    sig = sigutils.normalize_signature(sig)
+
+    def wrapper(func):
+        from numba.core.ccallback import CFunc
+        additional_args = {}
+        if pipeline_class is not None:
+            additional_args['pipeline_class'] = pipeline_class
+        res = CFunc(func, sig, locals=locals, options=options, **additional_args)
+        if cache:
+            res.enable_caching()
+        res.compile()
+        return res
+
+    return wrapper
+
+
+def jit_module(**kwargs):
+    """ Automatically ``jit``-wraps functions defined in a Python module
+
+    Note that ``jit_module`` should only be called at the end of the module to
+    be jitted. In addition, only functions which are defined in the module
+    ``jit_module`` is called from are considered for automatic jit-wrapping.
+    See the Numba documentation for more information about what can/cannot be
+    jitted.
+
+    :param kwargs: Keyword arguments to pass to ``jit`` such as ``nopython``
+                   or ``error_model``.
+
+    """
+    # Get the module jit_module is being called from
+    frame = inspect.stack()[1]
+    module = inspect.getmodule(frame[0])
+    # Replace functions in module with jit-wrapped versions
+    for name, obj in module.__dict__.items():
+        if inspect.isfunction(obj) and inspect.getmodule(obj) == module:
+            _logger.debug("Auto decorating function {} from module {} with jit "
+                          "and options: {}".format(obj, module.__name__, kwargs))
+            module.__dict__[name] = jit(obj, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/descriptors.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/descriptors.py
new file mode 100644
index 000000000..9c0c367e9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/descriptors.py
@@ -0,0 +1,21 @@
+"""
+Target Descriptors
+"""
+
+from abc import ABCMeta, abstractmethod
+
+
+class TargetDescriptor(metaclass=ABCMeta):
+
+    def __init__(self, target_name):
+        self._target_name = target_name
+
+    @property
+    @abstractmethod
+    def typing_context(self):
+        ...
+
+    @property
+    @abstractmethod
+    def target_context(self):
+        ...
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/dispatcher.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/dispatcher.py
new file mode 100644
index 000000000..69414a7c5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/dispatcher.py
@@ -0,0 +1,1322 @@
+# -*- coding: utf-8 -*-
+
+
+import collections
+import functools
+import sys
+import types as pytypes
+import uuid
+import weakref
+from contextlib import ExitStack
+
+from numba import _dispatcher
+from numba.core import (
+    utils, types, errors, typing, serialize, config, compiler, sigutils
+)
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core.typeconv.rules import default_type_manager
+from numba.core.typing.templates import fold_arguments
+from numba.core.typing.typeof import Purpose, typeof
+from numba.core.bytecode import get_code_object
+from numba.core.caching import NullCache, FunctionCache
+from numba.core import entrypoints
+from numba.core.retarget import BaseRetarget
+import numba.core.event as ev
+
+
+class _RetargetStack(utils.ThreadLocalStack, stack_name="retarget"):
+    def push(self, state):
+        super().push(state)
+        _dispatcher.set_use_tls_target_stack(len(self) > 0)
+
+    def pop(self):
+        super().pop()
+        _dispatcher.set_use_tls_target_stack(len(self) > 0)
+
+
+class TargetConfigurationStack:
+    """The target configuration stack.
+
+    Uses the BORG pattern and stores states in threadlocal storage.
+
+    WARNING: features associated with this class are experimental. The API
+    may change without notice.
+    """
+
+    def __init__(self):
+        self._stack = _RetargetStack()
+
+    def get(self):
+        """Get the current target from the top of the stack.
+
+        May raise IndexError if the stack is empty. Users should check the size
+        of the stack beforehand.
+        """
+        return self._stack.top()
+
+    def __len__(self):
+        """Size of the stack
+        """
+        return len(self._stack)
+
+    @classmethod
+    def switch_target(cls, retarget: BaseRetarget):
+        """Returns a contextmanager that pushes a new retarget handler,
+        an instance of `numba.core.retarget.BaseRetarget`, onto the
+        target-config stack for the duration of the context-manager.
+        """
+        return cls()._stack.enter(retarget)
+
+
+class OmittedArg(object):
+    """
+    A placeholder for omitted arguments with a default value.
+    """
+
+    def __init__(self, value):
+        self.value = value
+
+    def __repr__(self):
+        return "omitted arg(%r)" % (self.value,)
+
+    @property
+    def _numba_type_(self):
+        return types.Omitted(self.value)
+
+
+class _FunctionCompiler(object):
+    def __init__(self, py_func, targetdescr, targetoptions, locals,
+                 pipeline_class):
+        self.py_func = py_func
+        self.targetdescr = targetdescr
+        self.targetoptions = targetoptions
+        self.locals = locals
+        self.pysig = utils.pysignature(self.py_func)
+        self.pipeline_class = pipeline_class
+        # Remember key=(args, return_type) combinations that will fail
+        # compilation to avoid compilation attempt on them.  The values are
+        # the exceptions.
+        self._failed_cache = {}
+
+    def fold_argument_types(self, args, kws):
+        """
+        Given positional and named argument types, fold keyword arguments
+        and resolve defaults by inserting types.Omitted() instances.
+
+        A (pysig, argument types) tuple is returned.
+        """
+        def normal_handler(index, param, value):
+            return value
+
+        def default_handler(index, param, default):
+            return types.Omitted(default)
+
+        def stararg_handler(index, param, values):
+            return types.StarArgTuple(values)
+        # For now, we take argument values from the @jit function, even
+        # in the case of generated jit.
+        args = fold_arguments(self.pysig, args, kws,
+                              normal_handler,
+                              default_handler,
+                              stararg_handler)
+        return self.pysig, args
+
+    def compile(self, args, return_type):
+        status, retval = self._compile_cached(args, return_type)
+        if status:
+            return retval
+        else:
+            raise retval
+
+    def _compile_cached(self, args, return_type):
+        key = tuple(args), return_type
+        try:
+            return False, self._failed_cache[key]
+        except KeyError:
+            pass
+
+        try:
+            retval = self._compile_core(args, return_type)
+        except errors.TypingError as e:
+            self._failed_cache[key] = e
+            return False, e
+        else:
+            return True, retval
+
+    def _compile_core(self, args, return_type):
+        flags = compiler.Flags()
+        self.targetdescr.options.parse_as_flags(flags, self.targetoptions)
+        flags = self._customize_flags(flags)
+
+        impl = self._get_implementation(args, {})
+        cres = compiler.compile_extra(self.targetdescr.typing_context,
+                                      self.targetdescr.target_context,
+                                      impl,
+                                      args=args, return_type=return_type,
+                                      flags=flags, locals=self.locals,
+                                      pipeline_class=self.pipeline_class)
+        # Check typing error if object mode is used
+        if cres.typing_error is not None and not flags.enable_pyobject:
+            raise cres.typing_error
+        return cres
+
+    def get_globals_for_reduction(self):
+        return serialize._get_function_globals_for_reduction(self.py_func)
+
+    def _get_implementation(self, args, kws):
+        return self.py_func
+
+    def _customize_flags(self, flags):
+        return flags
+
+
+class _GeneratedFunctionCompiler(_FunctionCompiler):
+
+    def __init__(self, py_func, targetdescr, targetoptions, locals,
+                 pipeline_class):
+        super(_GeneratedFunctionCompiler, self).__init__(
+            py_func, targetdescr, targetoptions, locals, pipeline_class)
+        self.impls = set()
+
+    def get_globals_for_reduction(self):
+        # This will recursively get the globals used by any nested
+        # implementation function.
+        return serialize._get_function_globals_for_reduction(self.py_func)
+
+    def _get_implementation(self, args, kws):
+        impl = self.py_func(*args, **kws)
+        # Check the generating function and implementation signatures are
+        # compatible, otherwise compiling would fail later.
+        pysig = utils.pysignature(self.py_func)
+        implsig = utils.pysignature(impl)
+        ok = len(pysig.parameters) == len(implsig.parameters)
+        if ok:
+            for pyparam, implparam in zip(pysig.parameters.values(),
+                                          implsig.parameters.values()):
+                # We allow the implementation to omit default values, but
+                # if it mentions them, they should have the same value...
+                if (pyparam.name != implparam.name or
+                    pyparam.kind != implparam.kind or
+                    (implparam.default is not implparam.empty and
+                     implparam.default != pyparam.default)):
+                    ok = False
+        if not ok:
+            raise TypeError("generated implementation %s should be compatible "
+                            "with signature '%s', but has signature '%s'"
+                            % (impl, pysig, implsig))
+        self.impls.add(impl)
+        return impl
+
+
+_CompileStats = collections.namedtuple(
+    '_CompileStats', ('cache_path', 'cache_hits', 'cache_misses'))
+
+
+class CompilingCounter(object):
+    """
+    A simple counter that increment in __enter__ and decrement in __exit__.
+    """
+
+    def __init__(self):
+        self.counter = 0
+
+    def __enter__(self):
+        assert self.counter >= 0
+        self.counter += 1
+
+    def __exit__(self, *args, **kwargs):
+        self.counter -= 1
+        assert self.counter >= 0
+
+    def __bool__(self):
+        return self.counter > 0
+
+    __nonzero__ = __bool__
+
+
+class _DispatcherBase(_dispatcher.Dispatcher):
+    """
+    Common base class for dispatcher Implementations.
+    """
+
+    __numba__ = "py_func"
+
+    def __init__(self, arg_count, py_func, pysig, can_fallback,
+                 exact_match_required):
+        self._tm = default_type_manager
+
+        # A mapping of signatures to compile results
+        self.overloads = collections.OrderedDict()
+
+        self.py_func = py_func
+        # other parts of Numba assume the old Python 2 name for code object
+        self.func_code = get_code_object(py_func)
+        # but newer python uses a different name
+        self.__code__ = self.func_code
+        # a place to keep an active reference to the types of the active call
+        self._types_active_call = []
+        # Default argument values match the py_func
+        self.__defaults__ = py_func.__defaults__
+
+        argnames = tuple(pysig.parameters)
+        default_values = self.py_func.__defaults__ or ()
+        defargs = tuple(OmittedArg(val) for val in default_values)
+        try:
+            lastarg = list(pysig.parameters.values())[-1]
+        except IndexError:
+            has_stararg = False
+        else:
+            has_stararg = lastarg.kind == lastarg.VAR_POSITIONAL
+        _dispatcher.Dispatcher.__init__(self, self._tm.get_pointer(),
+                                        arg_count, self._fold_args,
+                                        argnames, defargs,
+                                        can_fallback,
+                                        has_stararg,
+                                        exact_match_required)
+
+        self.doc = py_func.__doc__
+        self._compiling_counter = CompilingCounter()
+        weakref.finalize(self, self._make_finalizer())
+
+    def _compilation_chain_init_hook(self):
+        """
+        This will be called ahead of any part of compilation taking place (this
+        even includes being ahead of working out the types of the arguments).
+        This permits activities such as initialising extension entry points so
+        that the compiler knows about additional externally defined types etc
+        before it does anything.
+        """
+        entrypoints.init_all()
+
+    def _reset_overloads(self):
+        self._clear()
+        self.overloads.clear()
+
+    def _make_finalizer(self):
+        """
+        Return a finalizer function that will release references to
+        related compiled functions.
+        """
+        overloads = self.overloads
+        targetctx = self.targetctx
+
+        # Early-bind utils.shutting_down() into the function's local namespace
+        # (see issue #689)
+        def finalizer(shutting_down=utils.shutting_down):
+            # The finalizer may crash at shutdown, skip it (resources
+            # will be cleared by the process exiting, anyway).
+            if shutting_down():
+                return
+            # This function must *not* hold any reference to self:
+            # we take care to bind the necessary objects in the closure.
+            for cres in overloads.values():
+                try:
+                    targetctx.remove_user_function(cres.entry_point)
+                except KeyError:
+                    pass
+
+        return finalizer
+
+    @property
+    def signatures(self):
+        """
+        Returns a list of compiled function signatures.
+        """
+        return list(self.overloads)
+
+    @property
+    def nopython_signatures(self):
+        return [cres.signature for cres in self.overloads.values()
+                if not cres.objectmode]
+
+    def disable_compile(self, val=True):
+        """Disable the compilation of new signatures at call time.
+        """
+        # If disabling compilation then there must be at least one signature
+        assert (not val) or len(self.signatures) > 0
+        self._can_compile = not val
+
+    def add_overload(self, cres):
+        args = tuple(cres.signature.args)
+        sig = [a._code for a in args]
+        self._insert(sig, cres.entry_point, cres.objectmode)
+        self.overloads[args] = cres
+
+    def fold_argument_types(self, args, kws):
+        return self._compiler.fold_argument_types(args, kws)
+
+    def get_call_template(self, args, kws):
+        """
+        Get a typing.ConcreteTemplate for this dispatcher and the given
+        *args* and *kws* types.  This allows to resolve the return type.
+
+        A (template, pysig, args, kws) tuple is returned.
+        """
+        # XXX how about a dispatcher template class automating the
+        # following?
+
+        # Fold keyword arguments and resolve default values
+        pysig, args = self._compiler.fold_argument_types(args, kws)
+        kws = {}
+        # Ensure an overload is available
+        if self._can_compile:
+            self.compile(tuple(args))
+
+        # Create function type for typing
+        func_name = self.py_func.__name__
+        name = "CallTemplate({0})".format(func_name)
+        # The `key` isn't really used except for diagnosis here,
+        # so avoid keeping a reference to `cfunc`.
+        call_template = typing.make_concrete_template(
+            name, key=func_name, signatures=self.nopython_signatures)
+        return call_template, pysig, args, kws
+
+    def get_overload(self, sig):
+        """
+        Return the compiled function for the given signature.
+        """
+        args, return_type = sigutils.normalize_signature(sig)
+        return self.overloads[tuple(args)].entry_point
+
+    @property
+    def is_compiling(self):
+        """
+        Whether a specialization is currently being compiled.
+        """
+        return self._compiling_counter
+
+    def _compile_for_args(self, *args, **kws):
+        """
+        For internal use.  Compile a specialized version of the function
+        for the given *args* and *kws*, and return the resulting callable.
+        """
+        assert not kws
+        # call any initialisation required for the compilation chain (e.g.
+        # extension point registration).
+        self._compilation_chain_init_hook()
+
+        def error_rewrite(e, issue_type):
+            """
+            Rewrite and raise Exception `e` with help supplied based on the
+            specified issue_type.
+            """
+            if config.SHOW_HELP:
+                help_msg = errors.error_extras[issue_type]
+                e.patch_message('\n'.join((str(e).rstrip(), help_msg)))
+            if config.FULL_TRACEBACKS:
+                raise e
+            else:
+                raise e.with_traceback(None)
+
+        argtypes = []
+        for a in args:
+            if isinstance(a, OmittedArg):
+                argtypes.append(types.Omitted(a.value))
+            else:
+                argtypes.append(self.typeof_pyval(a))
+
+        return_val = None
+        try:
+            return_val = self.compile(tuple(argtypes))
+        except errors.ForceLiteralArg as e:
+            # Received request for compiler re-entry with the list of arguments
+            # indicated by e.requested_args.
+            # First, check if any of these args are already Literal-ized
+            already_lit_pos = [i for i in e.requested_args
+                               if isinstance(args[i], types.Literal)]
+            if already_lit_pos:
+                # Abort compilation if any argument is already a Literal.
+                # Letting this continue will cause infinite compilation loop.
+                m = ("Repeated literal typing request.\n"
+                     "{}.\n"
+                     "This is likely caused by an error in typing. "
+                     "Please see nested and suppressed exceptions.")
+                info = ', '.join('Arg #{} is {}'.format(i, args[i])
+                                 for i in sorted(already_lit_pos))
+                raise errors.CompilerError(m.format(info))
+            # Convert requested arguments into a Literal.
+            args = [(types.literal
+                     if i in e.requested_args
+                     else lambda x: x)(args[i])
+                    for i, v in enumerate(args)]
+            # Re-enter compilation with the Literal-ized arguments
+            return_val = self._compile_for_args(*args)
+
+        except errors.TypingError as e:
+            # Intercept typing error that may be due to an argument
+            # that failed inferencing as a Numba type
+            failed_args = []
+            for i, arg in enumerate(args):
+                val = arg.value if isinstance(arg, OmittedArg) else arg
+                try:
+                    tp = typeof(val, Purpose.argument)
+                except ValueError as typeof_exc:
+                    failed_args.append((i, str(typeof_exc)))
+                else:
+                    if tp is None:
+                        failed_args.append(
+                            (i, f"cannot determine Numba type of value {val}"))
+            if failed_args:
+                # Patch error message to ease debugging
+                args_str = "\n".join(
+                    f"- argument {i}: {err}" for i, err in failed_args
+                )
+                msg = (f"{str(e).rstrip()} \n\nThis error may have been caused "
+                       f"by the following argument(s):\n{args_str}\n")
+                e.patch_message(msg)
+
+            error_rewrite(e, 'typing')
+        except errors.UnsupportedError as e:
+            # Something unsupported is present in the user code, add help info
+            error_rewrite(e, 'unsupported_error')
+        except (errors.NotDefinedError, errors.RedefinedError,
+                errors.VerificationError) as e:
+            # These errors are probably from an issue with either the code
+            # supplied being syntactically or otherwise invalid
+            error_rewrite(e, 'interpreter')
+        except errors.ConstantInferenceError as e:
+            # this is from trying to infer something as constant when it isn't
+            # or isn't supported as a constant
+            error_rewrite(e, 'constant_inference')
+        except Exception as e:
+            if config.SHOW_HELP:
+                if hasattr(e, 'patch_message'):
+                    help_msg = errors.error_extras['reportable']
+                    e.patch_message('\n'.join((str(e).rstrip(), help_msg)))
+            # ignore the FULL_TRACEBACKS config, this needs reporting!
+            raise e
+        finally:
+            self._types_active_call = []
+        return return_val
+
+    def inspect_llvm(self, signature=None):
+        """Get the LLVM intermediate representation generated by compilation.
+
+        Parameters
+        ----------
+        signature : tuple of numba types, optional
+            Specify a signature for which to obtain the LLVM IR. If None, the
+            IR is returned for all available signatures.
+
+        Returns
+        -------
+        llvm : dict[signature, str] or str
+            Either the LLVM IR string for the specified signature, or, if no
+            signature was given, a dictionary mapping signatures to LLVM IR
+            strings.
+        """
+        if signature is not None:
+            lib = self.overloads[signature].library
+            return lib.get_llvm_str()
+
+        return dict((sig, self.inspect_llvm(sig)) for sig in self.signatures)
+
+    def inspect_asm(self, signature=None):
+        """Get the generated assembly code.
+
+        Parameters
+        ----------
+        signature : tuple of numba types, optional
+            Specify a signature for which to obtain the assembly code. If
+            None, the assembly code is returned for all available signatures.
+
+        Returns
+        -------
+        asm : dict[signature, str] or str
+            Either the assembly code for the specified signature, or, if no
+            signature was given, a dictionary mapping signatures to assembly
+            code.
+        """
+        if signature is not None:
+            lib = self.overloads[signature].library
+            return lib.get_asm_str()
+
+        return dict((sig, self.inspect_asm(sig)) for sig in self.signatures)
+
+    def inspect_types(self, file=None, signature=None,
+                      pretty=False, style='default', **kwargs):
+        """Print/return Numba intermediate representation (IR)-annotated code.
+
+        Parameters
+        ----------
+        file : file-like object, optional
+            File to which to print. Defaults to sys.stdout if None. Must be
+            None if ``pretty=True``.
+        signature : tuple of numba types, optional
+            Print/return the intermediate representation for only the given
+            signature. If None, the IR is printed for all available signatures.
+        pretty : bool, optional
+            If True, an Annotate object will be returned that can render the
+            IR with color highlighting in Jupyter and IPython. ``file`` must
+            be None if ``pretty`` is True. Additionally, the ``pygments``
+            library must be installed for ``pretty=True``.
+        style : str, optional
+            Choose a style for rendering. Ignored if ``pretty`` is ``False``.
+            This is directly consumed by ``pygments`` formatters. To see a
+            list of available styles, import ``pygments`` and run
+            ``list(pygments.styles.get_all_styles())``.
+
+        Returns
+        -------
+        annotated : Annotate object, optional
+            Only returned if ``pretty=True``, otherwise this function is only
+            used for its printing side effect. If ``pretty=True``, an Annotate
+            object is returned that can render itself in Jupyter and IPython.
+        """
+        overloads = self.overloads
+        if signature is not None:
+            overloads = {signature: self.overloads[signature]}
+
+        if not pretty:
+            if file is None:
+                file = sys.stdout
+
+            for ver, res in overloads.items():
+                print("%s %s" % (self.py_func.__name__, ver), file=file)
+                print('-' * 80, file=file)
+                print(res.type_annotation, file=file)
+                print('=' * 80, file=file)
+        else:
+            if file is not None:
+                raise ValueError("`file` must be None if `pretty=True`")
+            from numba.core.annotations.pretty_annotate import Annotate
+            return Annotate(self, signature=signature, style=style)
+
+    def inspect_cfg(self, signature=None, show_wrapper=None, **kwargs):
+        """
+        For inspecting the CFG of the function.
+
+        By default the CFG of the user function is shown.  The *show_wrapper*
+        option can be set to "python" or "cfunc" to show the python wrapper
+        function or the *cfunc* wrapper function, respectively.
+
+        Parameters accepted in kwargs
+        -----------------------------
+        filename : string, optional
+            the name of the output file, if given this will write the output to
+            filename
+        view : bool, optional
+            whether to immediately view the optional output file
+        highlight : bool, set, dict, optional
+            what, if anything, to highlight, options are:
+            { incref : bool, # highlight NRT_incref calls
+              decref : bool, # highlight NRT_decref calls
+              returns : bool, # highlight exits which are normal returns
+              raises : bool, # highlight exits which are from raise
+              meminfo : bool, # highlight calls to NRT*meminfo
+              branches : bool, # highlight true/false branches
+             }
+            Default is True which sets all of the above to True. Supplying a set
+            of strings is also accepted, these are interpreted as key:True with
+            respect to the above dictionary. e.g. {'incref', 'decref'} would
+            switch on highlighting on increfs and decrefs.
+        interleave: bool, set, dict, optional
+            what, if anything, to interleave in the LLVM IR, options are:
+            { python: bool # interleave python source code with the LLVM IR
+              lineinfo: bool # interleave line information markers with the LLVM
+                             # IR
+            }
+            Default is True which sets all of the above to True. Supplying a set
+            of strings is also accepted, these are interpreted as key:True with
+            respect to the above dictionary. e.g. {'python',} would
+            switch on interleaving of python source code in the LLVM IR.
+        strip_ir : bool, optional
+            Default is False. If set to True all LLVM IR that is superfluous to
+            that requested in kwarg `highlight` will be removed.
+        show_key : bool, optional
+            Default is True. Create a "key" for the highlighting in the rendered
+            CFG.
+        fontsize : int, optional
+            Default is 8. Set the fontsize in the output to this value.
+        """
+        if signature is not None:
+            cres = self.overloads[signature]
+            lib = cres.library
+            if show_wrapper == 'python':
+                fname = cres.fndesc.llvm_cpython_wrapper_name
+            elif show_wrapper == 'cfunc':
+                fname = cres.fndesc.llvm_cfunc_wrapper_name
+            else:
+                fname = cres.fndesc.mangled_name
+            return lib.get_function_cfg(fname, py_func=self.py_func, **kwargs)
+
+        return dict((sig, self.inspect_cfg(sig, show_wrapper=show_wrapper))
+                    for sig in self.signatures)
+
+    def inspect_disasm_cfg(self, signature=None):
+        """
+        For inspecting the CFG of the disassembly of the function.
+
+        Requires python package: r2pipe
+        Requires radare2 binary on $PATH.
+        Notebook rendering requires python package: graphviz
+
+        signature : tuple of Numba types, optional
+            Print/return the disassembly CFG for only the given signatures.
+            If None, the IR is printed for all available signatures.
+        """
+        if signature is not None:
+            cres = self.overloads[signature]
+            lib = cres.library
+            return lib.get_disasm_cfg(cres.fndesc.mangled_name)
+
+        return dict((sig, self.inspect_disasm_cfg(sig))
+                    for sig in self.signatures)
+
+    def get_annotation_info(self, signature=None):
+        """
+        Gets the annotation information for the function specified by
+        signature. If no signature is supplied a dictionary of signature to
+        annotation information is returned.
+        """
+        signatures = self.signatures if signature is None else [signature]
+        out = collections.OrderedDict()
+        for sig in signatures:
+            cres = self.overloads[sig]
+            ta = cres.type_annotation
+            key = (ta.func_id.filename + ':' + str(ta.func_id.firstlineno + 1),
+                   ta.signature)
+            out[key] = ta.annotate_raw()[key]
+        return out
+
+    def _explain_ambiguous(self, *args, **kws):
+        """
+        Callback for the C _Dispatcher object.
+        """
+        assert not kws, "kwargs not handled"
+        args = tuple([self.typeof_pyval(a) for a in args])
+        # The order here must be deterministic for testing purposes, which
+        # is ensured by the OrderedDict.
+        sigs = self.nopython_signatures
+        # This will raise
+        self.typingctx.resolve_overload(self.py_func, sigs, args, kws,
+                                        allow_ambiguous=False)
+
+    def _explain_matching_error(self, *args, **kws):
+        """
+        Callback for the C _Dispatcher object.
+        """
+        assert not kws, "kwargs not handled"
+        args = [self.typeof_pyval(a) for a in args]
+        msg = ("No matching definition for argument type(s) %s"
+               % ', '.join(map(str, args)))
+        raise TypeError(msg)
+
+    def _search_new_conversions(self, *args, **kws):
+        """
+        Callback for the C _Dispatcher object.
+        Search for approximately matching signatures for the given arguments,
+        and ensure the corresponding conversions are registered in the C++
+        type manager.
+        """
+        assert not kws, "kwargs not handled"
+        args = [self.typeof_pyval(a) for a in args]
+        found = False
+        for sig in self.nopython_signatures:
+            conv = self.typingctx.install_possible_conversions(args, sig.args)
+            if conv:
+                found = True
+        return found
+
+    def __repr__(self):
+        return "%s(%s)" % (type(self).__name__, self.py_func)
+
+    def typeof_pyval(self, val):
+        """
+        Resolve the Numba type of Python value *val*.
+        This is called from numba._dispatcher as a fallback if the native code
+        cannot decide the type.
+        """
+        # Not going through the resolve_argument_type() indirection
+        # can save a couple µs.
+        try:
+            tp = typeof(val, Purpose.argument)
+        except ValueError:
+            tp = types.pyobject
+        else:
+            if tp is None:
+                tp = types.pyobject
+        self._types_active_call.append(tp)
+        return tp
+
+    def _callback_add_timer(self, duration, cres, lock_name):
+        md = cres.metadata
+        # md can be None when code is loaded from cache
+        if md is not None:
+            timers = md.setdefault("timers", {})
+            if lock_name not in timers:
+                # Only write if the metadata does not exist
+                timers[lock_name] = duration
+            else:
+                msg = f"'{lock_name} metadata is already defined."
+                raise AssertionError(msg)
+
+    def _callback_add_compiler_timer(self, duration, cres):
+        return self._callback_add_timer(duration, cres,
+                                        lock_name="compiler_lock")
+
+    def _callback_add_llvm_timer(self, duration, cres):
+        return self._callback_add_timer(duration, cres,
+                                        lock_name="llvm_lock")
+
+
+class _MemoMixin:
+    __uuid = None
+    # A {uuid -> instance} mapping, for deserialization
+    _memo = weakref.WeakValueDictionary()
+    # hold refs to last N functions deserialized, retaining them in _memo
+    # regardless of whether there is another reference
+    _recent = collections.deque(maxlen=config.FUNCTION_CACHE_SIZE)
+
+    @property
+    def _uuid(self):
+        """
+        An instance-specific UUID, to avoid multiple deserializations of
+        a given instance.
+
+        Note: this is lazily-generated, for performance reasons.
+        """
+        u = self.__uuid
+        if u is None:
+            u = str(uuid.uuid4())
+            self._set_uuid(u)
+        return u
+
+    def _set_uuid(self, u):
+        assert self.__uuid is None
+        self.__uuid = u
+        self._memo[u] = self
+        self._recent.append(self)
+
+
+class Dispatcher(serialize.ReduceMixin, _MemoMixin, _DispatcherBase):
+    """
+    Implementation of user-facing dispatcher objects (i.e. created using
+    the @jit decorator).
+    This is an abstract base class. Subclasses should define the targetdescr
+    class attribute.
+    """
+    _fold_args = True
+    _impl_kinds = {
+        'direct': _FunctionCompiler,
+        'generated': _GeneratedFunctionCompiler,
+    }
+
+    __numba__ = 'py_func'
+
+    def __init__(self, py_func, locals={}, targetoptions={},
+                 impl_kind='direct', pipeline_class=compiler.Compiler):
+        """
+        Parameters
+        ----------
+        py_func: function object to be compiled
+        locals: dict, optional
+            Mapping of local variable names to Numba types.  Used to override
+            the types deduced by the type inference engine.
+        targetoptions: dict, optional
+            Target-specific config options.
+        impl_kind: str
+            Select the compiler mode for `@jit` and `@generated_jit`
+        pipeline_class: type numba.compiler.CompilerBase
+            The compiler pipeline type.
+        """
+        self.typingctx = self.targetdescr.typing_context
+        self.targetctx = self.targetdescr.target_context
+
+        pysig = utils.pysignature(py_func)
+        arg_count = len(pysig.parameters)
+        can_fallback = not targetoptions.get('nopython', False)
+
+        _DispatcherBase.__init__(self, arg_count, py_func, pysig, can_fallback,
+                                 exact_match_required=False)
+
+        functools.update_wrapper(self, py_func)
+
+        self.targetoptions = targetoptions
+        self.locals = locals
+        self._cache = NullCache()
+        compiler_class = self._impl_kinds[impl_kind]
+        self._impl_kind = impl_kind
+        self._compiler = compiler_class(py_func, self.targetdescr,
+                                        targetoptions, locals, pipeline_class)
+        self._cache_hits = collections.Counter()
+        self._cache_misses = collections.Counter()
+
+        self._type = types.Dispatcher(self)
+        self.typingctx.insert_global(self, self._type)
+
+        # Remember target restriction
+        self._required_target_backend = targetoptions.get('target_backend')
+
+    def dump(self, tab=''):
+        print(f'{tab}DUMP {type(self).__name__}[{self.py_func.__name__}'
+              f', type code={self._type._code}]')
+        for cres in self.overloads.values():
+            cres.dump(tab=tab + '  ')
+        print(f'{tab}END DUMP {type(self).__name__}[{self.py_func.__name__}]')
+
+    @property
+    def _numba_type_(self):
+        return types.Dispatcher(self)
+
+    def enable_caching(self):
+        self._cache = FunctionCache(self.py_func)
+
+    def __get__(self, obj, objtype=None):
+        '''Allow a JIT function to be bound as a method to an object'''
+        if obj is None:  # Unbound method
+            return self
+        else:  # Bound method
+            return pytypes.MethodType(self, obj)
+
+    def _reduce_states(self):
+        """
+        Reduce the instance for pickling.  This will serialize
+        the original function as well the compilation options and
+        compiled signatures, but not the compiled code itself.
+
+        NOTE: part of ReduceMixin protocol
+        """
+        if self._can_compile:
+            sigs = []
+        else:
+            sigs = [cr.signature for cr in self.overloads.values()]
+
+        return dict(
+            uuid=str(self._uuid),
+            py_func=self.py_func,
+            locals=self.locals,
+            targetoptions=self.targetoptions,
+            impl_kind=self._impl_kind,
+            can_compile=self._can_compile,
+            sigs=sigs,
+        )
+
+    @classmethod
+    def _rebuild(cls, uuid, py_func, locals, targetoptions, impl_kind,
+                 can_compile, sigs):
+        """
+        Rebuild an Dispatcher instance after it was __reduce__'d.
+
+        NOTE: part of ReduceMixin protocol
+        """
+        try:
+            return cls._memo[uuid]
+        except KeyError:
+            pass
+        self = cls(py_func, locals, targetoptions, impl_kind)
+        # Make sure this deserialization will be merged with subsequent ones
+        self._set_uuid(uuid)
+        for sig in sigs:
+            self.compile(sig)
+        self._can_compile = can_compile
+        return self
+
+    def compile(self, sig):
+        disp = self._get_dispatcher_for_current_target()
+        if disp is not self:
+            return disp.compile(sig)
+
+        with ExitStack() as scope:
+            cres = None
+
+            def cb_compiler(dur):
+                if cres is not None:
+                    self._callback_add_compiler_timer(dur, cres)
+
+            def cb_llvm(dur):
+                if cres is not None:
+                    self._callback_add_llvm_timer(dur, cres)
+
+            scope.enter_context(ev.install_timer("numba:compiler_lock",
+                                                 cb_compiler))
+            scope.enter_context(ev.install_timer("numba:llvm_lock", cb_llvm))
+            scope.enter_context(global_compiler_lock)
+
+            if not self._can_compile:
+                raise RuntimeError("compilation disabled")
+            # Use counter to track recursion compilation depth
+            with self._compiling_counter:
+                args, return_type = sigutils.normalize_signature(sig)
+                # Don't recompile if signature already exists
+                existing = self.overloads.get(tuple(args))
+                if existing is not None:
+                    return existing.entry_point
+                # Try to load from disk cache
+                cres = self._cache.load_overload(sig, self.targetctx)
+                if cres is not None:
+                    self._cache_hits[sig] += 1
+                    # XXX fold this in add_overload()? (also see compiler.py)
+                    if not cres.objectmode:
+                        self.targetctx.insert_user_function(cres.entry_point,
+                                                            cres.fndesc,
+                                                            [cres.library])
+                    self.add_overload(cres)
+                    return cres.entry_point
+
+                self._cache_misses[sig] += 1
+                ev_details = dict(
+                    dispatcher=self,
+                    args=args,
+                    return_type=return_type,
+                )
+                with ev.trigger_event("numba:compile", data=ev_details):
+                    try:
+                        cres = self._compiler.compile(args, return_type)
+                    except errors.ForceLiteralArg as e:
+                        def folded(args, kws):
+                            return self._compiler.fold_argument_types(args,
+                                                                      kws)[1]
+                        raise e.bind_fold_arguments(folded)
+                    self.add_overload(cres)
+                self._cache.save_overload(sig, cres)
+                return cres.entry_point
+
+    def get_compile_result(self, sig):
+        """Compile (if needed) and return the compilation result with the
+        given signature.
+        """
+        atypes = tuple(sig.args)
+        if atypes not in self.overloads:
+            self.compile(atypes)
+        return self.overloads[atypes]
+
+    def recompile(self):
+        """
+        Recompile all signatures afresh.
+        """
+        sigs = list(self.overloads)
+        old_can_compile = self._can_compile
+        # Ensure the old overloads are disposed of,
+        # including compiled functions.
+        self._make_finalizer()()
+        self._reset_overloads()
+        self._cache.flush()
+        self._can_compile = True
+        try:
+            for sig in sigs:
+                self.compile(sig)
+        finally:
+            self._can_compile = old_can_compile
+
+    @property
+    def stats(self):
+        return _CompileStats(
+            cache_path=self._cache.cache_path,
+            cache_hits=self._cache_hits,
+            cache_misses=self._cache_misses,
+        )
+
+    def parallel_diagnostics(self, signature=None, level=1):
+        """
+        Print parallel diagnostic information for the given signature. If no
+        signature is present it is printed for all known signatures. level is
+        used to adjust the verbosity, level=1 (default) is minimal verbosity,
+        and 2, 3, and 4 provide increasing levels of verbosity.
+        """
+        def dump(sig):
+            ol = self.overloads[sig]
+            pfdiag = ol.metadata.get('parfor_diagnostics', None)
+            if pfdiag is None:
+                msg = "No parfors diagnostic available, is 'parallel=True' set?"
+                raise ValueError(msg)
+            pfdiag.dump(level)
+        if signature is not None:
+            dump(signature)
+        else:
+            [dump(sig) for sig in self.signatures]
+
+    def get_metadata(self, signature=None):
+        """
+        Obtain the compilation metadata for a given signature.
+        """
+        if signature is not None:
+            return self.overloads[signature].metadata
+        else:
+            return dict(
+                (sig,self.overloads[sig].metadata) for sig in self.signatures
+            )
+
+    def get_function_type(self):
+        """Return unique function type of dispatcher when possible, otherwise
+        return None.
+
+        A Dispatcher instance has unique function type when it
+        contains exactly one compilation result and its compilation
+        has been disabled (via its disable_compile method).
+        """
+        if not self._can_compile and len(self.overloads) == 1:
+            cres = tuple(self.overloads.values())[0]
+            return types.FunctionType(cres.signature)
+
+    def _get_retarget_dispatcher(self):
+        """Returns a dispatcher for the retarget request.
+        """
+        # Check TLS target configuration
+        tc = TargetConfigurationStack()
+        retarget = tc.get()
+        retarget.check_compatible(self)
+        disp = retarget.retarget(self)
+        return disp
+
+    def _get_dispatcher_for_current_target(self):
+        """Returns a dispatcher for the current target registered in
+        `TargetConfigurationStack`. `self` is returned if no target is
+        specified.
+        """
+        tc = TargetConfigurationStack()
+        if tc:
+            return self._get_retarget_dispatcher()
+        else:
+            return self
+
+    def _call_tls_target(self, *args, **kwargs):
+        """This is called when the C dispatcher logic sees a retarget request.
+        """
+        disp = self._get_retarget_dispatcher()
+        # Call the new dispatcher
+        return disp(*args, **kwargs)
+
+
+class LiftedCode(serialize.ReduceMixin, _MemoMixin, _DispatcherBase):
+    """
+    Implementation of the hidden dispatcher objects used for lifted code
+    (a lifted loop is really compiled as a separate function).
+    """
+    _fold_args = False
+    can_cache = False
+
+    def __init__(self, func_ir, typingctx, targetctx, flags, locals):
+        self.func_ir = func_ir
+        self.lifted_from = None
+
+        self.typingctx = typingctx
+        self.targetctx = targetctx
+        self.flags = flags
+        self.locals = locals
+
+        _DispatcherBase.__init__(self, self.func_ir.arg_count,
+                                 self.func_ir.func_id.func,
+                                 self.func_ir.func_id.pysig,
+                                 can_fallback=True,
+                                 exact_match_required=False)
+
+    def _reduce_states(self):
+        """
+        Reduce the instance for pickling.  This will serialize
+        the original function as well the compilation options and
+        compiled signatures, but not the compiled code itself.
+
+        NOTE: part of ReduceMixin protocol
+        """
+        return dict(
+            uuid=self._uuid, func_ir=self.func_ir, flags=self.flags,
+            locals=self.locals, extras=self._reduce_extras(),
+        )
+
+    def _reduce_extras(self):
+        """
+        NOTE: sub-class can override to add extra states
+        """
+        return {}
+
+    @classmethod
+    def _rebuild(cls, uuid, func_ir, flags, locals, extras):
+        """
+        Rebuild an Dispatcher instance after it was __reduce__'d.
+
+        NOTE: part of ReduceMixin protocol
+        """
+        try:
+            return cls._memo[uuid]
+        except KeyError:
+            pass
+
+        # NOTE: We are assuming that this is must be cpu_target, which is true
+        #       for now.
+        # TODO: refactor this to not assume on `cpu_target`
+
+        from numba.core import registry
+        typingctx = registry.cpu_target.typing_context
+        targetctx = registry.cpu_target.target_context
+
+        self = cls(func_ir, typingctx, targetctx, flags, locals, **extras)
+        self._set_uuid(uuid)
+        return self
+
+    def get_source_location(self):
+        """Return the starting line number of the loop.
+        """
+        return self.func_ir.loc.line
+
+    def _pre_compile(self, args, return_type, flags):
+        """Pre-compile actions
+        """
+        pass
+
+    def compile(self, sig):
+        with ExitStack() as scope:
+            cres = None
+
+            def cb_compiler(dur):
+                if cres is not None:
+                    self._callback_add_compiler_timer(dur, cres)
+
+            def cb_llvm(dur):
+                if cres is not None:
+                    self._callback_add_llvm_timer(dur, cres)
+
+            scope.enter_context(ev.install_timer("numba:compiler_lock",
+                                                 cb_compiler))
+            scope.enter_context(ev.install_timer("numba:llvm_lock", cb_llvm))
+            scope.enter_context(global_compiler_lock)
+
+            # Use counter to track recursion compilation depth
+            with self._compiling_counter:
+                # XXX this is mostly duplicated from Dispatcher.
+                flags = self.flags
+                args, return_type = sigutils.normalize_signature(sig)
+
+                # Don't recompile if signature already exists
+                # (e.g. if another thread compiled it before we got the lock)
+                existing = self.overloads.get(tuple(args))
+                if existing is not None:
+                    return existing.entry_point
+
+                self._pre_compile(args, return_type, flags)
+
+                # Clone IR to avoid (some of the) mutation in the rewrite pass
+                cloned_func_ir = self.func_ir.copy()
+
+                ev_details = dict(
+                    dispatcher=self,
+                    args=args,
+                    return_type=return_type,
+                )
+                with ev.trigger_event("numba:compile", data=ev_details):
+                    cres = compiler.compile_ir(typingctx=self.typingctx,
+                                               targetctx=self.targetctx,
+                                               func_ir=cloned_func_ir,
+                                               args=args,
+                                               return_type=return_type,
+                                               flags=flags, locals=self.locals,
+                                               lifted=(),
+                                               lifted_from=self.lifted_from,
+                                               is_lifted_loop=True,)
+
+                    # Check typing error if object mode is used
+                    if (cres.typing_error is not None and
+                            not flags.enable_pyobject):
+                        raise cres.typing_error
+                    self.add_overload(cres)
+                return cres.entry_point
+
+    def _get_dispatcher_for_current_target(self):
+        # Lifted code does not honor the target switch currently.
+        # No work has been done to check if this can be allowed.
+        return self
+
+
+class LiftedLoop(LiftedCode):
+    def _pre_compile(self, args, return_type, flags):
+        assert not flags.enable_looplift, "Enable looplift flags is on"
+
+
+class LiftedWith(LiftedCode):
+
+    can_cache = True
+
+    def _reduce_extras(self):
+        return dict(output_types=self.output_types)
+
+    @property
+    def _numba_type_(self):
+        return types.Dispatcher(self)
+
+    def get_call_template(self, args, kws):
+        """
+        Get a typing.ConcreteTemplate for this dispatcher and the given
+        *args* and *kws* types.  This enables the resolving of the return type.
+
+        A (template, pysig, args, kws) tuple is returned.
+        """
+        # Ensure an overload is available
+        if self._can_compile:
+            self.compile(tuple(args))
+
+        pysig = None
+        # Create function type for typing
+        func_name = self.py_func.__name__
+        name = "CallTemplate({0})".format(func_name)
+        # The `key` isn't really used except for diagnosis here,
+        # so avoid keeping a reference to `cfunc`.
+        call_template = typing.make_concrete_template(
+            name, key=func_name, signatures=self.nopython_signatures)
+        return call_template, pysig, args, kws
+
+
+class ObjModeLiftedWith(LiftedWith):
+    def __init__(self, *args, **kwargs):
+        self.output_types = kwargs.pop('output_types', None)
+        super(LiftedWith, self).__init__(*args, **kwargs)
+        if not self.flags.force_pyobject:
+            raise ValueError("expecting `flags.force_pyobject`")
+        if self.output_types is None:
+            raise TypeError('`output_types` must be provided')
+        # switch off rewrites, they have no effect
+        self.flags.no_rewrites = True
+
+    @property
+    def _numba_type_(self):
+        return types.ObjModeDispatcher(self)
+
+    def get_call_template(self, args, kws):
+        """
+        Get a typing.ConcreteTemplate for this dispatcher and the given
+        *args* and *kws* types.  This enables the resolving of the return type.
+
+        A (template, pysig, args, kws) tuple is returned.
+        """
+        assert not kws
+        self._legalize_arg_types(args)
+        # Coerce to object mode
+        args = [types.ffi_forced_object] * len(args)
+
+        if self._can_compile:
+            self.compile(tuple(args))
+
+        signatures = [typing.signature(self.output_types, *args)]
+        pysig = None
+        func_name = self.py_func.__name__
+        name = "CallTemplate({0})".format(func_name)
+        call_template = typing.make_concrete_template(
+            name, key=func_name, signatures=signatures)
+
+        return call_template, pysig, args, kws
+
+    def _legalize_arg_types(self, args):
+        for i, a in enumerate(args, start=1):
+            if isinstance(a, types.List):
+                msg = (
+                    'Does not support list type inputs into '
+                    'with-context for arg {}'
+                )
+                raise errors.TypingError(msg.format(i))
+            elif isinstance(a, types.Dispatcher):
+                msg = (
+                    'Does not support function type inputs into '
+                    'with-context for arg {}'
+                )
+                raise errors.TypingError(msg.format(i))
+
+    @global_compiler_lock
+    def compile(self, sig):
+        args, _ = sigutils.normalize_signature(sig)
+        sig = (types.ffi_forced_object,) * len(args)
+        return super().compile(sig)
+
+
+# Initialize typeof machinery
+_dispatcher.typeof_init(
+    OmittedArg,
+    dict((str(t), t._code) for t in types.number_domain))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/entrypoints.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/entrypoints.py
new file mode 100644
index 000000000..ad42782df
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/entrypoints.py
@@ -0,0 +1,58 @@
+import logging
+import warnings
+
+from numba.core.config import PYVERSION
+
+if PYVERSION < (3, 9):
+    try:
+        import importlib_metadata
+    except ImportError as ex:
+        raise ImportError(
+            "importlib_metadata backport is required for Python version < 3.9, "
+            "try:\n"
+            "$ conda/pip install importlib_metadata"
+        ) from ex
+else:
+    from importlib import metadata as importlib_metadata
+
+
+_already_initialized = False
+logger = logging.getLogger(__name__)
+
+
+def init_all():
+    """Execute all `numba_extensions` entry points with the name `init`
+
+    If extensions have already been initialized, this function does nothing.
+    """
+    global _already_initialized
+    if _already_initialized:
+        return
+
+    # Must put this here to avoid extensions re-triggering initialization
+    _already_initialized = True
+
+    def load_ep(entry_point):
+        """Loads a given entry point. Warns and logs on failure.
+        """
+        logger.debug('Loading extension: %s', entry_point)
+        try:
+            func = entry_point.load()
+            func()
+        except Exception as e:
+            msg = (f"Numba extension module '{entry_point.module}' "
+                   f"failed to load due to '{type(e).__name__}({str(e)})'.")
+            warnings.warn(msg, stacklevel=3)
+            logger.debug('Extension loading failed for: %s', entry_point)
+
+    eps = importlib_metadata.entry_points()
+    # Split, Python 3.10+ and importlib_metadata 3.6+ have the "selectable"
+    # interface, versions prior to that do not. See "compatibility note" in:
+    # https://docs.python.org/3.10/library/importlib.metadata.html#entry-points
+    if hasattr(eps, 'select'):
+        for entry_point in eps.select(group="numba_extensions", name="init"):
+            load_ep(entry_point)
+    else:
+        for entry_point in eps.get("numba_extensions", ()):
+            if entry_point.name == "init":
+                load_ep(entry_point)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/environment.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/environment.py
new file mode 100644
index 000000000..ffc5feb59
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/environment.py
@@ -0,0 +1,64 @@
+import weakref
+import importlib
+
+from numba import _dynfunc
+
+
+class Environment(_dynfunc.Environment):
+    """Stores globals and constant pyobjects for runtime.
+
+    It is often needed to convert b/w nopython objects and pyobjects.
+    """
+    __slots__ = ('env_name', '__weakref__')
+    # A weak-value dictionary to store live environment with env_name as the
+    # key.
+    _memo = weakref.WeakValueDictionary()
+
+    @classmethod
+    def from_fndesc(cls, fndesc):
+        try:
+            # Avoid creating new Env
+            return cls._memo[fndesc.env_name]
+        except KeyError:
+            inst = cls(fndesc.lookup_globals())
+            inst.env_name = fndesc.env_name
+            cls._memo[fndesc.env_name] = inst
+            return inst
+
+    def can_cache(self):
+        is_dyn = '__name__' not in self.globals
+        return not is_dyn
+
+    def __reduce__(self):
+        return _rebuild_env, (
+            self.globals.get('__name__'),
+            self.consts,
+            self.env_name,
+        )
+
+    def __del__(self):
+        return
+
+    def __repr__(self):
+        return f"<Environment {self.env_name!r} >"
+
+
+def _rebuild_env(modname, consts, env_name):
+    env = lookup_environment(env_name)
+    if env is not None:
+        return env
+
+    mod = importlib.import_module(modname)
+    env = Environment(mod.__dict__)
+    env.consts[:] = consts
+    env.env_name = env_name
+    # Cache loaded object
+    Environment._memo[env_name] = env
+    return env
+
+
+def lookup_environment(env_name):
+    """Returns the Environment object for the given name;
+    or None if not found
+    """
+    return Environment._memo.get(env_name)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/errors.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/errors.py
new file mode 100644
index 000000000..7c3a1ac96
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/errors.py
@@ -0,0 +1,848 @@
+"""
+Numba-specific errors and warnings.
+"""
+
+
+import abc
+import contextlib
+import os
+import sys
+import warnings
+import numba.core.config
+import numpy as np
+from collections import defaultdict
+from numba.core.utils import (chain_exception, use_old_style_errors,
+                              use_new_style_errors)
+from functools import wraps
+from abc import abstractmethod
+
+# Filled at the end
+__all__ = []
+
+
+class NumbaWarning(Warning):
+    """
+    Base category for all Numba compiler warnings.
+    """
+
+    def __init__(self, msg, loc=None, highlighting=True, ):
+        self.msg = msg
+        self.loc = loc
+        if highlighting:
+            highlight = termcolor().errmsg
+        else:
+            def highlight(x):
+                return x
+        if loc:
+            super(NumbaWarning, self).__init__(
+                highlight("%s\n%s\n" % (msg, loc.strformat())))
+        else:
+            super(NumbaWarning, self).__init__(highlight("%s" % (msg,)))
+
+
+class NumbaPerformanceWarning(NumbaWarning):
+    """
+    Warning category for when an operation might not be
+    as fast as expected.
+    """
+
+
+class NumbaDeprecationWarning(NumbaWarning):
+    """
+    Warning category for use of a deprecated feature.
+    """
+
+
+class NumbaPendingDeprecationWarning(NumbaWarning):
+    """
+    Warning category for use of a feature that is pending deprecation.
+    """
+
+
+class NumbaParallelSafetyWarning(NumbaWarning):
+    """
+    Warning category for when an operation in a prange
+    might not have parallel semantics.
+    """
+
+
+class NumbaTypeSafetyWarning(NumbaWarning):
+    """
+    Warning category for unsafe casting operations.
+    """
+
+
+class NumbaExperimentalFeatureWarning(NumbaWarning):
+    """
+    Warning category for using an experimental feature.
+    """
+
+
+class NumbaInvalidConfigWarning(NumbaWarning):
+    """
+    Warning category for using an invalid configuration.
+    """
+
+
+class NumbaPedanticWarning(NumbaWarning):
+    """
+    Warning category for reporting pedantic messages.
+    """
+    def __init__(self, msg, **kwargs):
+        super().__init__(f"{msg}\n{pedantic_warning_info}")
+
+
+class NumbaIRAssumptionWarning(NumbaPedanticWarning):
+    """
+    Warning category for reporting an IR assumption violation.
+    """
+
+
+class NumbaDebugInfoWarning(NumbaWarning):
+    """
+    Warning category for an issue with the emission of debug information.
+    """
+
+# These are needed in the color formatting of errors setup
+
+
+class _ColorScheme(metaclass=abc.ABCMeta):
+
+    @abstractmethod
+    def code(self, msg):
+        pass
+
+    @abstractmethod
+    def errmsg(self, msg):
+        pass
+
+    @abstractmethod
+    def filename(self, msg):
+        pass
+
+    @abstractmethod
+    def indicate(self, msg):
+        pass
+
+    @abstractmethod
+    def highlight(self, msg):
+        pass
+
+    @abstractmethod
+    def reset(self, msg):
+        pass
+
+
+class _DummyColorScheme(_ColorScheme):
+
+    def __init__(self, theme=None):
+        pass
+
+    def code(self, msg):
+        pass
+
+    def errmsg(self, msg):
+        pass
+
+    def filename(self, msg):
+        pass
+
+    def indicate(self, msg):
+        pass
+
+    def highlight(self, msg):
+        pass
+
+    def reset(self, msg):
+        pass
+
+
+# holds reference to the instance of the terminal color scheme in use
+_termcolor_inst = None
+
+try:
+    import colorama
+
+    # If the colorama version is < 0.3.9 it can break stdout/stderr in some
+    # situations, as a result if this condition is met colorama is disabled and
+    # the user is warned. Note that early versions did not have a __version__.
+    colorama_version = getattr(colorama, '__version__', '0.0.0')
+
+    if tuple([int(x) for x in colorama_version.split('.')]) < (0, 3, 9):
+        msg = ("Insufficiently recent colorama version found. "
+               "Numba requires colorama >= 0.3.9")
+        # warn the user
+        warnings.warn(msg)
+        # trip the exception to disable color errors
+        raise ImportError
+
+    # If Numba is running in testsuite mode then do not use error message
+    # coloring so CI system output is consistently readable without having
+    # to read between shell escape characters.
+    if os.environ.get('NUMBA_DISABLE_ERROR_MESSAGE_HIGHLIGHTING', None):
+        raise ImportError  # just to trigger the exception handler below
+
+except ImportError:
+
+    class NOPColorScheme(_DummyColorScheme):
+        def __init__(self, theme=None):
+            if theme is not None:
+                raise ValueError("specifying a theme has no effect")
+            _DummyColorScheme.__init__(self, theme=theme)
+
+        def code(self, msg):
+            return msg
+
+        def errmsg(self, msg):
+            return msg
+
+        def filename(self, msg):
+            return msg
+
+        def indicate(self, msg):
+            return msg
+
+        def highlight(self, msg):
+            return msg
+
+        def reset(self, msg):
+            return msg
+
+    def termcolor():
+        global _termcolor_inst
+        if _termcolor_inst is None:
+            _termcolor_inst = NOPColorScheme()
+        return _termcolor_inst
+
+else:
+
+    from colorama import init, reinit, deinit, Fore, Style
+
+    class ColorShell(object):
+        _has_initialized = False
+
+        def __init__(self):
+            init()
+            self._has_initialized = True
+
+        def __enter__(self):
+            if self._has_initialized:
+                reinit()
+
+        def __exit__(self, *exc_detail):
+            Style.RESET_ALL
+            deinit()
+
+    class reset_terminal(object):
+        def __init__(self):
+            self._buf = bytearray(b'')
+
+        def __enter__(self):
+            return self._buf
+
+        def __exit__(self, *exc_detail):
+            self._buf += bytearray(Style.RESET_ALL.encode('utf-8'))
+
+    # define some default themes, if more are added, update the envvars docs!
+    themes = {}
+
+    # No color added, just bold weighting
+    themes['no_color'] = {'code': None,
+                          'errmsg': None,
+                          'filename': None,
+                          'indicate': None,
+                          'highlight': None,
+                          'reset': None, }
+
+    # suitable for terminals with a dark background
+    themes['dark_bg'] = {'code': Fore.BLUE,
+                         'errmsg': Fore.YELLOW,
+                         'filename': Fore.WHITE,
+                         'indicate': Fore.GREEN,
+                         'highlight': Fore.RED,
+                         'reset': Style.RESET_ALL, }
+
+    # suitable for terminals with a light background
+    themes['light_bg'] = {'code': Fore.BLUE,
+                          'errmsg': Fore.BLACK,
+                          'filename': Fore.MAGENTA,
+                          'indicate': Fore.BLACK,
+                          'highlight': Fore.RED,
+                          'reset': Style.RESET_ALL, }
+
+    # suitable for terminals with a blue background
+    themes['blue_bg'] = {'code': Fore.WHITE,
+                         'errmsg': Fore.YELLOW,
+                         'filename': Fore.MAGENTA,
+                         'indicate': Fore.CYAN,
+                         'highlight': Fore.RED,
+                         'reset': Style.RESET_ALL, }
+
+    # suitable for use in jupyter notebooks
+    themes['jupyter_nb'] = {'code': Fore.BLACK,
+                            'errmsg': Fore.BLACK,
+                            'filename': Fore.GREEN,
+                            'indicate': Fore.CYAN,
+                            'highlight': Fore.RED,
+                            'reset': Style.RESET_ALL, }
+
+    default_theme = themes['no_color']
+
+    class HighlightColorScheme(_DummyColorScheme):
+        def __init__(self, theme=default_theme):
+            self._code = theme['code']
+            self._errmsg = theme['errmsg']
+            self._filename = theme['filename']
+            self._indicate = theme['indicate']
+            self._highlight = theme['highlight']
+            self._reset = theme['reset']
+            _DummyColorScheme.__init__(self, theme=theme)
+
+        def _markup(self, msg, color=None, style=Style.BRIGHT):
+            features = ''
+            if color:
+                features += color
+            if style:
+                features += style
+            with ColorShell():
+                with reset_terminal() as mu:
+                    mu += features.encode('utf-8')
+                    mu += (msg).encode('utf-8')
+                return mu.decode('utf-8')
+
+        def code(self, msg):
+            return self._markup(msg, self._code)
+
+        def errmsg(self, msg):
+            return self._markup(msg, self._errmsg)
+
+        def filename(self, msg):
+            return self._markup(msg, self._filename)
+
+        def indicate(self, msg):
+            return self._markup(msg, self._indicate)
+
+        def highlight(self, msg):
+            return self._markup(msg, self._highlight)
+
+        def reset(self, msg):
+            return self._markup(msg, self._reset)
+
+    def termcolor():
+        global _termcolor_inst
+        if _termcolor_inst is None:
+            scheme = themes[numba.core.config.COLOR_SCHEME]
+            _termcolor_inst = HighlightColorScheme(scheme)
+        return _termcolor_inst
+
+
+pedantic_warning_info = """
+This warning came from an internal pedantic check. Please report the warning
+message and traceback, along with a minimal reproducer at:
+https://github.com/numba/numba/issues/new?template=bug_report.md
+"""
+
+feedback_details = """
+Please report the error message and traceback, along with a minimal reproducer
+at: https://github.com/numba/numba/issues/new?template=bug_report.md
+
+If more help is needed please feel free to speak to the Numba core developers
+directly at: https://gitter.im/numba/numba
+
+Thanks in advance for your help in improving Numba!
+"""
+
+unsupported_error_info = """
+Unsupported functionality was found in the code Numba was trying to compile.
+
+If this functionality is important to you please file a feature request at:
+https://github.com/numba/numba/issues/new?template=feature_request.md
+"""
+
+interpreter_error_info = """
+Unsupported Python functionality was found in the code Numba was trying to
+compile. This error could be due to invalid code, does the code work
+without Numba? (To temporarily disable Numba JIT, set the `NUMBA_DISABLE_JIT`
+environment variable to non-zero, and then rerun the code).
+
+If the code is valid and the unsupported functionality is important to you
+please file a feature request at:
+https://github.com/numba/numba/issues/new?template=feature_request.md
+
+To see Python/NumPy features supported by the latest release of Numba visit:
+https://numba.readthedocs.io/en/stable/reference/pysupported.html
+and
+https://numba.readthedocs.io/en/stable/reference/numpysupported.html
+"""
+
+constant_inference_info = """
+Numba could not make a constant out of something that it decided should be
+a constant. This could well be a current limitation in Numba's internals,
+however please first check that your code is valid for compilation,
+particularly with respect to string interpolation (not supported!) and
+the requirement of compile time constants as arguments to exceptions:
+https://numba.readthedocs.io/en/stable/reference/pysupported.html?highlight=exceptions#constructs
+
+If the code is valid and the unsupported functionality is important to you
+please file a feature request at:
+https://github.com/numba/numba/issues/new?template=feature_request.md
+
+If you think your code should work with Numba. %s
+""" % feedback_details
+
+typing_error_info = """
+This is not usually a problem with Numba itself but instead often caused by
+the use of unsupported features or an issue in resolving types.
+
+To see Python/NumPy features supported by the latest release of Numba visit:
+https://numba.readthedocs.io/en/stable/reference/pysupported.html
+and
+https://numba.readthedocs.io/en/stable/reference/numpysupported.html
+
+For more information about typing errors and how to debug them visit:
+https://numba.readthedocs.io/en/stable/user/troubleshoot.html#my-code-doesn-t-compile
+
+If you think your code should work with Numba, please report the error message
+and traceback, along with a minimal reproducer at:
+https://github.com/numba/numba/issues/new?template=bug_report.md
+"""
+
+reportable_issue_info = """
+-------------------------------------------------------------------------------
+This should not have happened, a problem has occurred in Numba's internals.
+You are currently using Numba version %s.
+%s
+""" % (numba.__version__, feedback_details)
+
+error_extras = dict()
+error_extras['unsupported_error'] = unsupported_error_info
+error_extras['typing'] = typing_error_info
+error_extras['reportable'] = reportable_issue_info
+error_extras['interpreter'] = interpreter_error_info
+error_extras['constant_inference'] = constant_inference_info
+
+
+def deprecated(arg):
+    """Define a deprecation decorator.
+    An optional string should refer to the new API to be used instead.
+
+    Example:
+      @deprecated
+      def old_func(): ...
+
+      @deprecated('new_func')
+      def old_func(): ..."""
+
+    subst = arg if isinstance(arg, str) else None
+
+    def decorator(func):
+        def wrapper(*args, **kwargs):
+            msg = "Call to deprecated function \"{}\"."
+            if subst:
+                msg += "\n Use \"{}\" instead."
+            warnings.warn(msg.format(func.__name__, subst),
+                          category=DeprecationWarning, stacklevel=2)
+            return func(*args, **kwargs)
+
+        return wraps(func)(wrapper)
+
+    if not subst:
+        return decorator(arg)
+    else:
+        return decorator
+
+
+class WarningsFixer(object):
+    """
+    An object "fixing" warnings of a given category caught during
+    certain phases.  The warnings can have their filename and lineno fixed,
+    and they are deduplicated as well.
+    """
+
+    def __init__(self, category):
+        self._category = category
+        # {(filename, lineno, category) -> messages}
+        self._warnings = defaultdict(set)
+
+    @contextlib.contextmanager
+    def catch_warnings(self, filename=None, lineno=None):
+        """
+        Store warnings and optionally fix their filename and lineno.
+        """
+        with warnings.catch_warnings(record=True) as wlist:
+            warnings.simplefilter('always', self._category)
+            yield
+
+        for w in wlist:
+            msg = str(w.message)
+            if issubclass(w.category, self._category):
+                # Store warnings of this category for deduplication
+                filename = filename or w.filename
+                lineno = lineno or w.lineno
+                self._warnings[filename, lineno, w.category].add(msg)
+            else:
+                # Simply emit other warnings again
+                warnings.warn_explicit(msg, w.category,
+                                       w.filename, w.lineno)
+
+    def flush(self):
+        """
+        Emit all stored warnings.
+        """
+        def key(arg):
+            # It is possible through codegen to create entirely identical
+            # warnings, this leads to comparing types when sorting which breaks
+            # on Python 3. Key as str() and if the worse happens then `id`
+            # creates some uniqueness
+            return str(arg) + str(id(arg))
+
+        for (filename, lineno, category), messages in sorted(
+                self._warnings.items(), key=key):
+            for msg in sorted(messages):
+                warnings.warn_explicit(msg, category, filename, lineno)
+        self._warnings.clear()
+
+
+class NumbaError(Exception):
+
+    def __init__(self, msg, loc=None, highlighting=True):
+        self.msg = msg
+        self.loc = loc
+        if highlighting:
+            highlight = termcolor().errmsg
+        else:
+            def highlight(x):
+                return x
+
+        if loc:
+            new_msg = "%s\n%s\n" % (msg, loc.strformat())
+        else:
+            new_msg = "%s" % (msg,)
+        super(NumbaError, self).__init__(highlight(new_msg))
+
+    @property
+    def contexts(self):
+        try:
+            return self._contexts
+        except AttributeError:
+            self._contexts = lst = []
+            return lst
+
+    def add_context(self, msg):
+        """
+        Add contextual info.  The exception message is expanded with the new
+        contextual information.
+        """
+        self.contexts.append(msg)
+        f = termcolor().errmsg('{0}\n') + termcolor().filename('During: {1}')
+        newmsg = f.format(self, msg)
+        self.args = (newmsg,)
+        return self
+
+    def patch_message(self, new_message):
+        """
+        Change the error message to the given new message.
+        """
+        self.args = (new_message,) + self.args[1:]
+
+
+class UnsupportedError(NumbaError):
+    """
+    Numba does not have an implementation for this functionality.
+    """
+    pass
+
+
+class UnsupportedRewriteError(UnsupportedError):
+    """UnsupportedError from rewrite passes
+    """
+    pass
+
+
+class IRError(NumbaError):
+    """
+    An error occurred during Numba IR generation.
+    """
+    pass
+
+
+class RedefinedError(IRError):
+    """
+    An error occurred during interpretation of IR due to variable redefinition.
+    """
+    pass
+
+
+class NotDefinedError(IRError):
+    """
+    An undefined variable is encountered during interpretation of IR.
+    """
+
+    def __init__(self, name, loc=None):
+        self.name = name
+        msg = ("The compiler failed to analyze the bytecode. "
+               "Variable '%s' is not defined." % name)
+        super(NotDefinedError, self).__init__(msg, loc=loc)
+
+
+class VerificationError(IRError):
+    """
+    An error occurred during IR verification. Once Numba's internal
+    representation (IR) is constructed it is then verified to ensure that
+    terminators are both present and in the correct places within the IR. If
+    it is the case that this condition is not met, a VerificationError is
+    raised.
+    """
+    pass
+
+
+class DeprecationError(NumbaError):
+    """
+    Functionality is deprecated.
+    """
+    pass
+
+
+class LoweringError(NumbaError):
+    """
+    An error occurred during lowering.
+    """
+
+    def __init__(self, msg, loc=None):
+        super(LoweringError, self).__init__(msg, loc=loc)
+
+
+class UnsupportedParforsError(NumbaError):
+    """
+    An error occurred because parfors is not supported on the platform.
+    """
+    pass
+
+
+class ForbiddenConstruct(LoweringError):
+    """
+    A forbidden Python construct was encountered (e.g. use of locals()).
+    """
+    pass
+
+
+class TypingError(NumbaError):
+    """
+    A type inference failure.
+    """
+    pass
+
+
+class UntypedAttributeError(TypingError):
+    def __init__(self, value, attr, loc=None):
+        module = getattr(value, 'pymod', None)
+        if module is not None and module == np:
+            # unsupported numpy feature.
+            msg = ("Use of unsupported NumPy function 'numpy.%s' "
+                   "or unsupported use of the function.") % attr
+        else:
+            msg = "Unknown attribute '{attr}' of type {type}"
+            msg = msg.format(type=value, attr=attr)
+        super(UntypedAttributeError, self).__init__(msg, loc=loc)
+
+
+class ByteCodeSupportError(NumbaError):
+    """
+    Failure to extract the bytecode of the user's function.
+    """
+
+    def __init__(self, msg, loc=None):
+        super(ByteCodeSupportError, self).__init__(msg, loc=loc)
+
+
+class CompilerError(NumbaError):
+    """
+    Some high-level error in the compiler.
+    """
+    pass
+
+
+class ConstantInferenceError(NumbaError):
+    """
+    Failure during constant inference.
+    """
+
+    def __init__(self, value, loc=None):
+        super(ConstantInferenceError, self).__init__(value, loc=loc)
+
+
+class InternalError(NumbaError):
+    """
+    For wrapping internal error occurred within the compiler
+    """
+
+    def __init__(self, exception):
+        super(InternalError, self).__init__(str(exception))
+        self.old_exception = exception
+
+
+class InternalTargetMismatchError(InternalError):
+    """For signalling a target mismatch error occurred internally within the
+    compiler.
+    """
+    def __init__(self, kind, target_hw, hw_clazz):
+        msg = (f"{kind.title()} being resolved on a target from which it does "
+               f"not inherit. Local target is {target_hw}, declared "
+               f"target class is {hw_clazz}.")
+        super().__init__(msg)
+
+
+class RequireLiteralValue(TypingError):
+    """
+    For signalling that a function's typing requires a constant value for
+    some of its arguments.
+    """
+    pass
+
+
+class ForceLiteralArg(NumbaError):
+    """A Pseudo-exception to signal the dispatcher to type an argument literally
+
+    Attributes
+    ----------
+    requested_args : frozenset[int]
+        requested positions of the arguments.
+    """
+    def __init__(self, arg_indices, fold_arguments=None, loc=None):
+        """
+        Parameters
+        ----------
+        arg_indices : Sequence[int]
+            requested positions of the arguments.
+        fold_arguments: callable
+            A function ``(tuple, dict) -> tuple`` that binds and flattens
+            the ``args`` and ``kwargs``.
+        loc : numba.ir.Loc or None
+        """
+        super(ForceLiteralArg, self).__init__(
+            "Pseudo-exception to force literal arguments in the dispatcher",
+            loc=loc,
+        )
+        self.requested_args = frozenset(arg_indices)
+        self.fold_arguments = fold_arguments
+
+    def bind_fold_arguments(self, fold_arguments):
+        """Bind the fold_arguments function
+        """
+        e = ForceLiteralArg(self.requested_args, fold_arguments,
+                            loc=self.loc)
+        return chain_exception(e, self)
+
+    def combine(self, other):
+        """Returns a new instance by or'ing the requested_args.
+        """
+        if not isinstance(other, ForceLiteralArg):
+            m = '*other* must be a {} but got a {} instead'
+            raise TypeError(m.format(ForceLiteralArg, type(other)))
+        return ForceLiteralArg(self.requested_args | other.requested_args)
+
+    def __or__(self, other):
+        """Same as self.combine(other)
+        """
+        return self.combine(other)
+
+
+class LiteralTypingError(TypingError):
+    """
+    Failure in typing a Literal type
+    """
+    pass
+
+
+# These Exception classes are just Numba copies of their Python equivalents for
+# use internally in cases where we want e.g. type inference to keep on trying.
+# Exceptions extending from NumbaError are considered "special" by Numba's
+# internals and are treated differently to standard Python exceptions which are
+# permitted to just propagate up the stack.
+
+class NumbaValueError(TypingError):
+    pass
+
+
+class NumbaTypeError(TypingError):
+    pass
+
+
+class NumbaAttributeError(TypingError):
+    pass
+
+
+class NumbaAssertionError(TypingError):
+    pass
+
+
+class NumbaNotImplementedError(TypingError):
+    pass
+
+
+class NumbaKeyError(TypingError):
+    pass
+
+
+class NumbaIndexError(TypingError):
+    pass
+
+
+class NumbaRuntimeError(NumbaError):
+    pass
+
+
+def _format_msg(fmt, args, kwargs):
+    return fmt.format(*args, **kwargs)
+
+
+_numba_path = os.path.dirname(__file__)
+loc_info = {}
+
+
+@contextlib.contextmanager
+def new_error_context(fmt_, *args, **kwargs):
+    """
+    A contextmanager that prepend contextual information to any exception
+    raised within.  If the exception type is not an instance of NumbaError,
+    it will be wrapped into a InternalError.   The exception class can be
+    changed by providing a "errcls_" keyword argument with the exception
+    constructor.
+
+    The first argument is a message that describes the context.  It can be a
+    format string.  If there are additional arguments, it will be used as
+    ``fmt_.format(*args, **kwargs)`` to produce the final message string.
+    """
+    errcls = kwargs.pop('errcls_', InternalError)
+
+    loc = kwargs.get('loc', None)
+    if loc is not None and not loc.filename.startswith(_numba_path):
+        loc_info.update(kwargs)
+
+    try:
+        yield
+    except NumbaError as e:
+        e.add_context(_format_msg(fmt_, args, kwargs))
+        raise
+    except AssertionError:
+        # Let assertion error pass through for shorter traceback in debugging
+        raise
+    except Exception as e:
+        if use_old_style_errors():
+            newerr = errcls(e).add_context(_format_msg(fmt_, args, kwargs))
+            if numba.core.config.FULL_TRACEBACKS:
+                tb = sys.exc_info()[2]
+            else:
+                tb = None
+            raise newerr.with_traceback(tb)
+        elif use_new_style_errors():
+            raise e
+        else:
+            msg = ("Unknown CAPTURED_ERRORS style: "
+                   f"'{numba.core.config.CAPTURED_ERRORS}'.")
+            assert 0, msg
+
+
+__all__ += [name for (name, value) in globals().items()
+            if not name.startswith('_') and isinstance(value, type)
+            and issubclass(value, (Exception, Warning))]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/event.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/event.py
new file mode 100644
index 000000000..e2a1b0bde
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/event.py
@@ -0,0 +1,491 @@
+"""
+The ``numba.core.event`` module provides a simple event system for applications
+to register callbacks to listen to specific compiler events.
+
+The following events are built in:
+
+- ``"numba:compile"`` is broadcast when a dispatcher is compiling. Events of
+  this kind have ``data`` defined to be a ``dict`` with the following
+  key-values:
+
+  - ``"dispatcher"``: the dispatcher object that is compiling.
+  - ``"args"``: the argument types.
+  - ``"return_type"``: the return type.
+
+- ``"numba:compiler_lock"`` is broadcast when the internal compiler-lock is
+  acquired. This is mostly used internally to measure time spent with the lock
+  acquired.
+
+- ``"numba:llvm_lock"`` is broadcast when the internal LLVM-lock is acquired.
+  This is used internally to measure time spent with the lock acquired.
+
+- ``"numba:run_pass"`` is broadcast when a compiler pass is running.
+
+    - ``"name"``: pass name.
+    - ``"qualname"``: qualified name of the function being compiled.
+    - ``"module"``: module name of the function being compiled.
+    - ``"flags"``: compilation flags.
+    - ``"args"``: argument types.
+    - ``"return_type"`` return type.
+
+Applications can register callbacks that are listening for specific events using
+``register(kind: str, listener: Listener)``, where ``listener`` is an instance
+of ``Listener`` that defines custom actions on occurrence of the specific event.
+"""
+
+import os
+import json
+import atexit
+import abc
+import enum
+import time
+import threading
+from timeit import default_timer as timer
+from contextlib import contextmanager, ExitStack
+from collections import defaultdict
+
+from numba.core import config
+
+
+class EventStatus(enum.Enum):
+    """Status of an event.
+    """
+    START = enum.auto()
+    END = enum.auto()
+
+
+# Builtin event kinds.
+_builtin_kinds = frozenset([
+    "numba:compiler_lock",
+    "numba:compile",
+    "numba:llvm_lock",
+    "numba:run_pass",
+])
+
+
+def _guard_kind(kind):
+    """Guard to ensure that an event kind is valid.
+
+    All event kinds with a "numba:" prefix must be defined in the pre-defined
+    ``numba.core.event._builtin_kinds``.
+    Custom event kinds are allowed by not using the above prefix.
+
+    Parameters
+    ----------
+    kind : str
+
+    Return
+    ------
+    res : str
+    """
+    if kind.startswith("numba:") and kind not in _builtin_kinds:
+        msg = (f"{kind} is not a valid event kind, "
+               "it starts with the reserved prefix 'numba:'")
+        raise ValueError(msg)
+    return kind
+
+
+class Event:
+    """An event.
+
+    Parameters
+    ----------
+    kind : str
+    status : EventStatus
+    data : any; optional
+        Additional data for the event.
+    exc_details : 3-tuple; optional
+        Same 3-tuple for ``__exit__``.
+    """
+    def __init__(self, kind, status, data=None, exc_details=None):
+        self._kind = _guard_kind(kind)
+        self._status = status
+        self._data = data
+        self._exc_details = (None
+                             if exc_details is None or exc_details[0] is None
+                             else exc_details)
+
+    @property
+    def kind(self):
+        """Event kind
+
+        Returns
+        -------
+        res : str
+        """
+        return self._kind
+
+    @property
+    def status(self):
+        """Event status
+
+        Returns
+        -------
+        res : EventStatus
+        """
+        return self._status
+
+    @property
+    def data(self):
+        """Event data
+
+        Returns
+        -------
+        res : object
+        """
+        return self._data
+
+    @property
+    def is_start(self):
+        """Is it a *START* event?
+
+        Returns
+        -------
+        res : bool
+        """
+        return self._status == EventStatus.START
+
+    @property
+    def is_end(self):
+        """Is it an *END* event?
+
+        Returns
+        -------
+        res : bool
+        """
+        return self._status == EventStatus.END
+
+    @property
+    def is_failed(self):
+        """Is the event carrying an exception?
+
+        This is used for *END* event. This method will never return ``True``
+        in a *START* event.
+
+        Returns
+        -------
+        res : bool
+        """
+        return self._exc_details is None
+
+    def __str__(self):
+        data = (f"{type(self.data).__qualname__}"
+                if self.data is not None else "None")
+        return f"Event({self._kind}, {self._status}, data: {data})"
+
+    __repr__ = __str__
+
+
+_registered = defaultdict(list)
+
+
+def register(kind, listener):
+    """Register a listener for a given event kind.
+
+    Parameters
+    ----------
+    kind : str
+    listener : Listener
+    """
+    assert isinstance(listener, Listener)
+    kind = _guard_kind(kind)
+    _registered[kind].append(listener)
+
+
+def unregister(kind, listener):
+    """Unregister a listener for a given event kind.
+
+    Parameters
+    ----------
+    kind : str
+    listener : Listener
+    """
+    assert isinstance(listener, Listener)
+    kind = _guard_kind(kind)
+    lst = _registered[kind]
+    lst.remove(listener)
+
+
+def broadcast(event):
+    """Broadcast an event to all registered listeners.
+
+    Parameters
+    ----------
+    event : Event
+    """
+    for listener in _registered[event.kind]:
+        listener.notify(event)
+
+
+class Listener(abc.ABC):
+    """Base class for all event listeners.
+    """
+    @abc.abstractmethod
+    def on_start(self, event):
+        """Called when there is a *START* event.
+
+        Parameters
+        ----------
+        event : Event
+        """
+        pass
+
+    @abc.abstractmethod
+    def on_end(self, event):
+        """Called when there is a *END* event.
+
+        Parameters
+        ----------
+        event : Event
+        """
+        pass
+
+    def notify(self, event):
+        """Notify this Listener with the given Event.
+
+        Parameters
+        ----------
+        event : Event
+        """
+        if event.is_start:
+            self.on_start(event)
+        elif event.is_end:
+            self.on_end(event)
+        else:
+            raise AssertionError("unreachable")
+
+
+class TimingListener(Listener):
+    """A listener that measures the total time spent between *START* and
+    *END* events during the time this listener is active.
+    """
+    def __init__(self):
+        self._depth = 0
+
+    def on_start(self, event):
+        if self._depth == 0:
+            self._ts = timer()
+        self._depth += 1
+
+    def on_end(self, event):
+        self._depth -= 1
+        if self._depth == 0:
+            last = getattr(self, "_duration", 0)
+            self._duration = (timer() - self._ts) + last
+
+    @property
+    def done(self):
+        """Returns a ``bool`` indicating whether a measurement has been made.
+
+        When this returns ``False``, the matching event has never fired.
+        If and only if this returns ``True``, ``.duration`` can be read without
+        error.
+        """
+        return hasattr(self, "_duration")
+
+    @property
+    def duration(self):
+        """Returns the measured duration.
+
+        This may raise ``AttributeError``. Users can use ``.done`` to check
+        that a measurement has been made.
+        """
+        return self._duration
+
+
+class RecordingListener(Listener):
+    """A listener that records all events and stores them in the ``.buffer``
+    attribute as a list of 2-tuple ``(float, Event)``, where the first element
+    is the time the event occurred as returned by ``time.time()`` and the second
+    element is the event.
+    """
+    def __init__(self):
+        self.buffer = []
+
+    def on_start(self, event):
+        self.buffer.append((time.time(), event))
+
+    def on_end(self, event):
+        self.buffer.append((time.time(), event))
+
+
+@contextmanager
+def install_listener(kind, listener):
+    """Install a listener for event "kind" temporarily within the duration of
+    the context.
+
+    Returns
+    -------
+    res : Listener
+        The *listener* provided.
+
+    Examples
+    --------
+
+    >>> with install_listener("numba:compile", listener):
+    >>>     some_code()  # listener will be active here.
+    >>> other_code()     # listener will be unregistered by this point.
+
+    """
+    register(kind, listener)
+    try:
+        yield listener
+    finally:
+        unregister(kind, listener)
+
+
+@contextmanager
+def install_timer(kind, callback):
+    """Install a TimingListener temporarily to measure the duration of
+    an event.
+
+    If the context completes successfully, the *callback* function is executed.
+    The *callback* function is expected to take a float argument for the
+    duration in seconds.
+
+    Returns
+    -------
+    res : TimingListener
+
+    Examples
+    --------
+
+    This is equivalent to:
+
+    >>> with install_listener(kind, TimingListener()) as res:
+    >>>    ...
+    """
+    tl = TimingListener()
+    with install_listener(kind, tl):
+        yield tl
+
+    if tl.done:
+        callback(tl.duration)
+
+
+@contextmanager
+def install_recorder(kind):
+    """Install a RecordingListener temporarily to record all events.
+
+    Once the context is closed, users can use ``RecordingListener.buffer``
+    to access the recorded events.
+
+    Returns
+    -------
+    res : RecordingListener
+
+    Examples
+    --------
+
+    This is equivalent to:
+
+    >>> with install_listener(kind, RecordingListener()) as res:
+    >>>    ...
+    """
+    rl = RecordingListener()
+    with install_listener(kind, rl):
+        yield rl
+
+
+def start_event(kind, data=None):
+    """Trigger the start of an event of *kind* with *data*.
+
+    Parameters
+    ----------
+    kind : str
+        Event kind.
+    data : any; optional
+        Extra event data.
+    """
+    evt = Event(kind=kind, status=EventStatus.START, data=data)
+    broadcast(evt)
+
+
+def end_event(kind, data=None, exc_details=None):
+    """Trigger the end of an event of *kind*, *exc_details*.
+
+    Parameters
+    ----------
+    kind : str
+        Event kind.
+    data : any; optional
+        Extra event data.
+    exc_details : 3-tuple; optional
+        Same 3-tuple for ``__exit__``. Or, ``None`` if no error.
+    """
+    evt = Event(
+        kind=kind, status=EventStatus.END, data=data, exc_details=exc_details,
+    )
+    broadcast(evt)
+
+
+@contextmanager
+def trigger_event(kind, data=None):
+    """A context manager to trigger the start and end events of *kind* with
+    *data*. The start event is triggered when entering the context.
+    The end event is triggered when exiting the context.
+
+    Parameters
+    ----------
+    kind : str
+        Event kind.
+    data : any; optional
+        Extra event data.
+    """
+    with ExitStack() as scope:
+        @scope.push
+        def on_exit(*exc_details):
+            end_event(kind, data=data, exc_details=exc_details)
+
+        start_event(kind, data=data)
+        yield
+
+
+def _get_native_ident():
+    try:
+        return threading.get_native_ident()
+    except AttributeError:
+        # Fallback for python <3.8
+        return threading.get_ident()
+
+
+def _prepare_chrome_trace_data(listener: RecordingListener):
+    """Prepare events in `listener` for serializing as chrome trace data.
+    """
+    # The spec for the trace event format can be found at:
+    # https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/edit   # noqa
+    # This code only uses the JSON Array Format for simplicity.
+    pid = os.getpid()
+    tid = _get_native_ident()
+    evs = []
+    for ts, rec in listener.buffer:
+        data = rec.data
+        cat = str(rec.kind)
+        ph = 'B' if rec.is_start else 'E'
+        name = data['name']
+        args = data
+        ev = dict(
+            cat=cat, pid=pid, tid=tid, ts=ts, ph=ph, name=name, args=args,
+        )
+        evs.append(ev)
+    return evs
+
+
+def _setup_chrome_trace_exit_handler():
+    """Setup a RecordingListener and an exit handler to write the captured events
+    to file.
+    """
+    listener = RecordingListener()
+    register("numba:run_pass", listener)
+    filename = config.CHROME_TRACE
+
+    @atexit.register
+    def _write_chrome_trace():
+        # The following output file is not multi-process safe.
+        evs = _prepare_chrome_trace_data(listener)
+        with open(filename, "w") as out:
+            json.dump(evs, out)
+
+
+if config.CHROME_TRACE:
+    _setup_chrome_trace_exit_handler()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/extending.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/extending.py
new file mode 100644
index 000000000..9d005fe74
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/extending.py
@@ -0,0 +1,583 @@
+import os
+import uuid
+import weakref
+import collections
+import functools
+
+import numba
+from numba.core import types, errors, utils, config
+
+# Exported symbols
+from numba.core.typing.typeof import typeof_impl  # noqa: F401
+from numba.core.typing.asnumbatype import as_numba_type  # noqa: F401
+from numba.core.typing.templates import infer, infer_getattr  # noqa: F401
+from numba.core.imputils import (  # noqa: F401
+    lower_builtin, lower_getattr, lower_getattr_generic,  # noqa: F401
+    lower_setattr, lower_setattr_generic, lower_cast)  # noqa: F401
+from numba.core.datamodel import models   # noqa: F401
+from numba.core.datamodel import register_default as register_model  # noqa: F401, E501
+from numba.core.pythonapi import box, unbox, reflect, NativeValue  # noqa: F401
+from numba._helperlib import _import_cython_function  # noqa: F401
+from numba.core.serialize import ReduceMixin
+
+
+def type_callable(func):
+    """
+    Decorate a function as implementing typing for the callable *func*.
+    *func* can be a callable object (probably a global) or a string
+    denoting a built-in operation (such 'getitem' or '__array_wrap__')
+    """
+    from numba.core.typing.templates import (CallableTemplate, infer,
+                                             infer_global)
+    if not callable(func) and not isinstance(func, str):
+        raise TypeError("`func` should be a function or string")
+    try:
+        func_name = func.__name__
+    except AttributeError:
+        func_name = str(func)
+
+    def decorate(typing_func):
+        def generic(self):
+            return typing_func(self.context)
+
+        name = "%s_CallableTemplate" % (func_name,)
+        bases = (CallableTemplate,)
+        class_dict = dict(key=func, generic=generic)
+        template = type(name, bases, class_dict)
+        infer(template)
+        if callable(func):
+            infer_global(func, types.Function(template))
+        return typing_func
+
+    return decorate
+
+
+# By default, an *overload* does not have a cpython wrapper because it is not
+# callable from python.
+_overload_default_jit_options = {'no_cpython_wrapper': True}
+
+
+def overload(func, jit_options={}, strict=True, inline='never',
+             prefer_literal=False, **kwargs):
+    """
+    A decorator marking the decorated function as typing and implementing
+    *func* in nopython mode.
+
+    The decorated function will have the same formal parameters as *func*
+    and be passed the Numba types of those parameters.  It should return
+    a function implementing *func* for the given types.
+
+    Here is an example implementing len() for tuple types::
+
+        @overload(len)
+        def tuple_len(seq):
+            if isinstance(seq, types.BaseTuple):
+                n = len(seq)
+                def len_impl(seq):
+                    return n
+                return len_impl
+
+    Compiler options can be passed as an dictionary using the **jit_options**
+    argument.
+
+    Overloading strictness (that the typing and implementing signatures match)
+    is enforced by the **strict** keyword argument, it is recommended that this
+    is set to True (default).
+
+    To handle a function that accepts imprecise types, an overload
+    definition can return 2-tuple of ``(signature, impl_function)``, where
+    the ``signature`` is a ``typing.Signature`` specifying the precise
+    signature to be used; and ``impl_function`` is the same implementation
+    function as in the simple case.
+
+    If the kwarg inline determines whether the overload is inlined in the
+    calling function and can be one of three values:
+    * 'never' (default) - the overload is never inlined.
+    * 'always' - the overload is always inlined.
+    * a function that takes two arguments, both of which are instances of a
+      namedtuple with fields:
+        * func_ir
+        * typemap
+        * calltypes
+        * signature
+      The first argument holds the information from the caller, the second
+      holds the information from the callee. The function should return Truthy
+      to determine whether to inline, this essentially permitting custom
+      inlining rules (typical use might be cost models).
+
+    The *prefer_literal* option allows users to control if literal types should
+    be tried first or last. The default (`False`) is to use non-literal types.
+    Implementations that can specialize based on literal values should set the
+    option to `True`. Note, this option maybe expanded in the near future to
+    allow for more control (e.g. disabling non-literal types).
+
+    **kwargs prescribes additional arguments passed through to the overload
+    template. The only accepted key at present is 'target' which is a string
+    corresponding to the target that this overload should be bound against.
+    """
+    from numba.core.typing.templates import make_overload_template, infer_global
+
+    # set default options
+    opts = _overload_default_jit_options.copy()
+    opts.update(jit_options)  # let user options override
+
+    # TODO: abort now if the kwarg 'target' relates to an unregistered target,
+    # this requires sorting out the circular imports first.
+
+    def decorate(overload_func):
+        template = make_overload_template(func, overload_func, opts, strict,
+                                          inline, prefer_literal, **kwargs)
+        infer(template)
+        if callable(func):
+            infer_global(func, types.Function(template))
+        return overload_func
+
+    return decorate
+
+
+def register_jitable(*args, **kwargs):
+    """
+    Register a regular python function that can be executed by the python
+    interpreter and can be compiled into a nopython function when referenced
+    by other jit'ed functions.  Can be used as::
+
+        @register_jitable
+        def foo(x, y):
+            return x + y
+
+    Or, with compiler options::
+
+        @register_jitable(_nrt=False) # disable runtime allocation
+        def foo(x, y):
+            return x + y
+
+    """
+    def wrap(fn):
+        # It is just a wrapper for @overload
+        inline = kwargs.pop('inline', 'never')
+
+        @overload(fn, jit_options=kwargs, inline=inline, strict=False)
+        def ov_wrap(*args, **kwargs):
+            return fn
+        return fn
+
+    if kwargs:
+        return wrap
+    else:
+        return wrap(*args)
+
+
+def overload_attribute(typ, attr, **kwargs):
+    """
+    A decorator marking the decorated function as typing and implementing
+    attribute *attr* for the given Numba type in nopython mode.
+
+    *kwargs* are passed to the underlying `@overload` call.
+
+    Here is an example implementing .nbytes for array types::
+
+        @overload_attribute(types.Array, 'nbytes')
+        def array_nbytes(arr):
+            def get(arr):
+                return arr.size * arr.itemsize
+            return get
+    """
+    # TODO implement setters
+    from numba.core.typing.templates import make_overload_attribute_template
+
+    def decorate(overload_func):
+        template = make_overload_attribute_template(
+            typ, attr, overload_func,
+            inline=kwargs.get('inline', 'never'),
+        )
+        infer_getattr(template)
+        overload(overload_func, **kwargs)(overload_func)
+        return overload_func
+
+    return decorate
+
+
+def _overload_method_common(typ, attr, **kwargs):
+    """Common code for overload_method and overload_classmethod
+    """
+    from numba.core.typing.templates import make_overload_method_template
+
+    def decorate(overload_func):
+        copied_kwargs = kwargs.copy() # avoid mutating parent dict
+        template = make_overload_method_template(
+            typ, attr, overload_func,
+            inline=copied_kwargs.pop('inline', 'never'),
+            prefer_literal=copied_kwargs.pop('prefer_literal', False),
+            **copied_kwargs,
+        )
+        infer_getattr(template)
+        overload(overload_func, **kwargs)(overload_func)
+        return overload_func
+
+    return decorate
+
+
+def overload_method(typ, attr, **kwargs):
+    """
+    A decorator marking the decorated function as typing and implementing
+    method *attr* for the given Numba type in nopython mode.
+
+    *kwargs* are passed to the underlying `@overload` call.
+
+    Here is an example implementing .take() for array types::
+
+        @overload_method(types.Array, 'take')
+        def array_take(arr, indices):
+            if isinstance(indices, types.Array):
+                def take_impl(arr, indices):
+                    n = indices.shape[0]
+                    res = np.empty(n, arr.dtype)
+                    for i in range(n):
+                        res[i] = arr[indices[i]]
+                    return res
+                return take_impl
+    """
+    return _overload_method_common(typ, attr, **kwargs)
+
+
+def overload_classmethod(typ, attr, **kwargs):
+    """
+    A decorator marking the decorated function as typing and implementing
+    classmethod *attr* for the given Numba type in nopython mode.
+
+
+    Similar to ``overload_method``.
+
+
+    Here is an example implementing a classmethod on the Array type to call
+    ``np.arange()``::
+
+        @overload_classmethod(types.Array, "make")
+        def ov_make(cls, nitems):
+            def impl(cls, nitems):
+                return np.arange(nitems)
+            return impl
+
+    The above code will allow the following to work in jit-compiled code::
+
+        @njit
+        def foo(n):
+            return types.Array.make(n)
+    """
+    return _overload_method_common(types.TypeRef(typ), attr, **kwargs)
+
+
+def make_attribute_wrapper(typeclass, struct_attr, python_attr):
+    """
+    Make an automatic attribute wrapper exposing member named *struct_attr*
+    as a read-only attribute named *python_attr*.
+    The given *typeclass*'s model must be a StructModel subclass.
+    """
+    from numba.core.typing.templates import AttributeTemplate
+    from numba.core.datamodel import default_manager
+    from numba.core.datamodel.models import StructModel
+    from numba.core.imputils import impl_ret_borrowed
+    from numba.core import cgutils
+
+    if not isinstance(typeclass, type) or not issubclass(typeclass, types.Type):
+        raise TypeError("typeclass should be a Type subclass, got %s"
+                        % (typeclass,))
+
+    def get_attr_fe_type(typ):
+        """
+        Get the Numba type of member *struct_attr* in *typ*.
+        """
+        model = default_manager.lookup(typ)
+        if not isinstance(model, StructModel):
+            raise TypeError("make_struct_attribute_wrapper() needs a type "
+                            "with a StructModel, but got %s" % (model,))
+        return model.get_member_fe_type(struct_attr)
+
+    @infer_getattr
+    class StructAttribute(AttributeTemplate):
+        key = typeclass
+
+        def generic_resolve(self, typ, attr):
+            if attr == python_attr:
+                return get_attr_fe_type(typ)
+
+    @lower_getattr(typeclass, python_attr)
+    def struct_getattr_impl(context, builder, typ, val):
+        val = cgutils.create_struct_proxy(typ)(context, builder, value=val)
+        attrty = get_attr_fe_type(typ)
+        attrval = getattr(val, struct_attr)
+        return impl_ret_borrowed(context, builder, attrty, attrval)
+
+
+class _Intrinsic(ReduceMixin):
+    """
+    Dummy callable for intrinsic
+    """
+    _memo = weakref.WeakValueDictionary()
+    # hold refs to last N functions deserialized, retaining them in _memo
+    # regardless of whether there is another reference
+    _recent = collections.deque(maxlen=config.FUNCTION_CACHE_SIZE)
+
+    __uuid = None
+
+    def __init__(self, name, defn, **kwargs):
+        self._ctor_kwargs = kwargs
+        self._name = name
+        self._defn = defn
+        functools.update_wrapper(self, defn)
+
+    @property
+    def _uuid(self):
+        """
+        An instance-specific UUID, to avoid multiple deserializations of
+        a given instance.
+
+        Note this is lazily-generated, for performance reasons.
+        """
+        u = self.__uuid
+        if u is None:
+            u = str(uuid.uuid1())
+            self._set_uuid(u)
+        return u
+
+    def _set_uuid(self, u):
+        assert self.__uuid is None
+        self.__uuid = u
+        self._memo[u] = self
+        self._recent.append(self)
+
+    def _register(self):
+        # _ctor_kwargs
+        from numba.core.typing.templates import (make_intrinsic_template,
+                                                 infer_global)
+
+        template = make_intrinsic_template(self, self._defn, self._name,
+                                           self._ctor_kwargs)
+        infer(template)
+        infer_global(self, types.Function(template))
+
+    def __call__(self, *args, **kwargs):
+        """
+        This is only defined to pretend to be a callable from CPython.
+        """
+        msg = '{0} is not usable in pure-python'.format(self)
+        raise NotImplementedError(msg)
+
+    def __repr__(self):
+        return "<intrinsic {0}>".format(self._name)
+
+    def __deepcopy__(self, memo):
+        # NOTE: Intrinsic are immutable and we don't need to copy.
+        #       This is triggered from deepcopy of statements.
+        return self
+
+    def _reduce_states(self):
+        """
+        NOTE: part of ReduceMixin protocol
+        """
+        return dict(uuid=self._uuid, name=self._name, defn=self._defn)
+
+    @classmethod
+    def _rebuild(cls, uuid, name, defn):
+        """
+        NOTE: part of ReduceMixin protocol
+        """
+        try:
+            return cls._memo[uuid]
+        except KeyError:
+            llc = cls(name=name, defn=defn)
+            llc._register()
+            llc._set_uuid(uuid)
+            return llc
+
+
+def intrinsic(*args, **kwargs):
+    """
+    A decorator marking the decorated function as typing and implementing
+    *func* in nopython mode using the llvmlite IRBuilder API.  This is an escape
+    hatch for expert users to build custom LLVM IR that will be inlined to
+    the caller.
+
+    The first argument to *func* is the typing context.  The rest of the
+    arguments corresponds to the type of arguments of the decorated function.
+    These arguments are also used as the formal argument of the decorated
+    function.  If *func* has the signature ``foo(typing_context, arg0, arg1)``,
+    the decorated function will have the signature ``foo(arg0, arg1)``.
+
+    The return values of *func* should be a 2-tuple of expected type signature,
+    and a code-generation function that will passed to ``lower_builtin``.
+    For unsupported operation, return None.
+
+    Here is an example implementing a ``cast_int_to_byte_ptr`` that cast
+    any integer to a byte pointer::
+
+        @intrinsic
+        def cast_int_to_byte_ptr(typingctx, src):
+            # check for accepted types
+            if isinstance(src, types.Integer):
+                # create the expected type signature
+                result_type = types.CPointer(types.uint8)
+                sig = result_type(types.uintp)
+                # defines the custom code generation
+                def codegen(context, builder, signature, args):
+                    # llvm IRBuilder code here
+                    [src] = args
+                    rtype = signature.return_type
+                    llrtype = context.get_value_type(rtype)
+                    return builder.inttoptr(src, llrtype)
+                return sig, codegen
+    """
+    # Make inner function for the actual work
+    def _intrinsic(func):
+        name = getattr(func, '__name__', str(func))
+        llc = _Intrinsic(name, func, **kwargs)
+        llc._register()
+        return llc
+
+    if not kwargs:
+        # No option is given
+        return _intrinsic(*args)
+    else:
+        # options are given, create a new callable to recv the
+        # definition function
+        def wrapper(func):
+            return _intrinsic(func)
+        return wrapper
+
+
+def get_cython_function_address(module_name, function_name):
+    """
+    Get the address of a Cython function.
+
+    Args
+    ----
+    module_name:
+        Name of the Cython module
+    function_name:
+        Name of the Cython function
+
+    Returns
+    -------
+    A Python int containing the address of the function
+
+    """
+    return _import_cython_function(module_name, function_name)
+
+
+def include_path():
+    """Returns the C include directory path.
+    """
+    include_dir = os.path.dirname(os.path.dirname(numba.__file__))
+    path = os.path.abspath(include_dir)
+    return path
+
+
+def sentry_literal_args(pysig, literal_args, args, kwargs):
+    """Ensures that the given argument types (in *args* and *kwargs*) are
+    literally typed for a function with the python signature *pysig* and the
+    list of literal argument names in *literal_args*.
+
+    Alternatively, this is the same as::
+
+        SentryLiteralArgs(literal_args).for_pysig(pysig).bind(*args, **kwargs)
+    """
+    boundargs = pysig.bind(*args, **kwargs)
+
+    # Find literal argument positions and whether it is satisfied.
+    request_pos = set()
+    missing = False
+    for i, (k, v) in enumerate(boundargs.arguments.items()):
+        if k in literal_args:
+            request_pos.add(i)
+            if not isinstance(v, types.Literal):
+                missing = True
+    if missing:
+        # Yes, there are missing required literal arguments
+        e = errors.ForceLiteralArg(request_pos)
+
+        # A helper function to fold arguments
+        def folded(args, kwargs):
+            out = pysig.bind(*args, **kwargs).arguments.values()
+            return tuple(out)
+
+        raise e.bind_fold_arguments(folded)
+
+
+class SentryLiteralArgs(collections.namedtuple(
+        '_SentryLiteralArgs', ['literal_args'])):
+    """
+    Parameters
+    ----------
+    literal_args : Sequence[str]
+        A sequence of names for literal arguments
+
+    Examples
+    --------
+
+    The following line:
+
+    >>> SentryLiteralArgs(literal_args).for_pysig(pysig).bind(*args, **kwargs)
+
+    is equivalent to:
+
+    >>> sentry_literal_args(pysig, literal_args, args, kwargs)
+    """
+    def for_function(self, func):
+        """Bind the sentry to the signature of *func*.
+
+        Parameters
+        ----------
+        func : Function
+            A python function.
+
+        Returns
+        -------
+        obj : BoundLiteralArgs
+        """
+        return self.for_pysig(utils.pysignature(func))
+
+    def for_pysig(self, pysig):
+        """Bind the sentry to the given signature *pysig*.
+
+        Parameters
+        ----------
+        pysig : inspect.Signature
+
+
+        Returns
+        -------
+        obj : BoundLiteralArgs
+        """
+        return BoundLiteralArgs(
+            pysig=pysig,
+            literal_args=self.literal_args,
+        )
+
+
+class BoundLiteralArgs(collections.namedtuple(
+        'BoundLiteralArgs', ['pysig', 'literal_args'])):
+    """
+    This class is usually created by SentryLiteralArgs.
+    """
+    def bind(self, *args, **kwargs):
+        """Bind to argument types.
+        """
+        return sentry_literal_args(
+            self.pysig,
+            self.literal_args,
+            args,
+            kwargs,
+        )
+
+
+def is_jitted(function):
+    """Returns True if a function is wrapped by one of the Numba @jit
+    decorators, for example: numba.jit, numba.njit
+
+    The purpose of this function is to provide a means to check if a function is
+    already JIT decorated.
+    """
+
+    # don't want to export this so import locally
+    from numba.core.dispatcher import Dispatcher
+    return isinstance(function, Dispatcher)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/externals.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/externals.py
new file mode 100644
index 000000000..e181b5f43
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/externals.py
@@ -0,0 +1,155 @@
+"""
+Register external C functions necessary for Numba code generation.
+"""
+
+import sys
+
+from llvmlite import ir
+import llvmlite.binding as ll
+
+from numba.core import utils, intrinsics
+from numba import _helperlib
+
+
+def _add_missing_symbol(symbol, addr):
+    """Add missing symbol into LLVM internal symtab
+    """
+    if not ll.address_of_symbol(symbol):
+        ll.add_symbol(symbol, addr)
+
+
+def _get_msvcrt_symbol(symbol):
+    """
+    Under Windows, look up a symbol inside the C runtime
+    and return the raw pointer value as an integer.
+    """
+    from ctypes import cdll, cast, c_void_p
+    f = getattr(cdll.msvcrt, symbol)
+    return cast(f, c_void_p).value
+
+
+def compile_multi3(context):
+    """
+    Compile the multi3() helper function used by LLVM
+    for 128-bit multiplication on 32-bit platforms.
+    """
+    codegen = context.codegen()
+    library = codegen.create_library("multi3")
+
+    ir_mod = library.create_ir_module("multi3")
+
+    i64 = ir.IntType(64)
+    i128 = ir.IntType(128)
+    lower_mask = ir.Constant(i64, 0xffffffff)
+    _32 = ir.Constant(i64, 32)
+    _64 = ir.Constant(i128, 64)
+
+    fn_type = ir.FunctionType(i128, [i128, i128])
+    fn = ir.Function(ir_mod, fn_type, name="multi3")
+
+    a, b = fn.args
+    bb = fn.append_basic_block()
+    builder = ir.IRBuilder(bb)
+
+    # This implementation mimics compiler-rt's.
+    al = builder.trunc(a, i64)
+    bl = builder.trunc(b, i64)
+    ah = builder.trunc(builder.ashr(a, _64), i64)
+    bh = builder.trunc(builder.ashr(b, _64), i64)
+
+    # Compute {rh, rl} = al * bl   (unsigned 64-bit multiplication)
+    # rl = (al & 0xffffffff) * (bl & 0xffffffff)
+    rl = builder.mul(builder.and_(al, lower_mask), builder.and_(bl, lower_mask))
+    # t = rl >> 32
+    t = builder.lshr(rl, _32)
+    # rl &= 0xffffffff
+    rl = builder.and_(rl, lower_mask)
+    # t += (al >> 32) * (bl & 0xffffffff)
+    t = builder.add(t, builder.mul(builder.lshr(al, _32),
+                                   builder.and_(bl, lower_mask)))
+    # rl += t << 32
+    rl = builder.add(rl, builder.shl(t, _32))
+    # rh = t >> 32
+    rh = builder.lshr(t, _32)
+    # t = rl >> 32
+    t = builder.lshr(rl, _32)
+    # rl &= 0xffffffff
+    rl = builder.and_(rl, lower_mask)
+    # t += (bl >> 32) * (al & 0xffffffff)
+    t = builder.add(t, builder.mul(builder.lshr(bl, _32),
+                                   builder.and_(al, lower_mask)))
+    # rl += t << 32
+    rl = builder.add(rl, builder.shl(t, _32))
+    # rh += t >> 32
+    rh = builder.add(rh, builder.lshr(t, _32))
+    # rh += (al >> 32) * (bl >> 32)
+    rh = builder.add(rh, builder.mul(builder.lshr(al, _32),
+                                     builder.lshr(bl, _32)))
+
+    # rh += (bh * al) + (bl * ah)
+    rh = builder.add(rh, builder.mul(bh, al))
+    rh = builder.add(rh, builder.mul(bl, ah))
+
+    # r = rl + (rh << 64)
+    r = builder.zext(rl, i128)
+    r = builder.add(r, builder.shl(builder.zext(rh, i128), _64))
+    builder.ret(r)
+
+    library.add_ir_module(ir_mod)
+    library.finalize()
+
+    return library
+
+
+class _Installer(object):
+
+    _installed = False
+
+    def install(self, context):
+        """
+        Install the functions into LLVM.  This only needs to be done once,
+        as the mappings are persistent during the process lifetime.
+        """
+        if not self._installed:
+            self._do_install(context)
+            self._installed = True
+
+
+class _ExternalMathFunctions(_Installer):
+    """
+    Map the math functions from the C runtime library into the LLVM
+    execution environment.
+    """
+
+    def _do_install(self, context):
+        is32bit = utils.MACHINE_BITS == 32
+        c_helpers = _helperlib.c_helpers
+
+        if sys.platform.startswith('win32') and is32bit:
+            # For Windows XP _ftol2 is not defined, we will just use
+            # _ftol as a replacement.
+            # On Windows 7, this is not necessary but will work anyway.
+            ftol = _get_msvcrt_symbol("_ftol")
+            _add_missing_symbol("_ftol2", ftol)
+
+        elif sys.platform.startswith('linux') and is32bit:
+            _add_missing_symbol("__fixunsdfdi", c_helpers["fptoui"])
+            _add_missing_symbol("__fixunssfdi", c_helpers["fptouif"])
+
+        if is32bit:
+            # Make the library immortal
+            self._multi3_lib = compile_multi3(context)
+            ptr = self._multi3_lib.get_pointer_to_function("multi3")
+            assert ptr
+            _add_missing_symbol("__multi3", ptr)
+
+        # List available C-math
+        for fname in intrinsics.INTR_MATH:
+            # Force binding from CPython's C runtime library.
+            # (under Windows, different versions of the C runtime can
+            #  be loaded at the same time, for example msvcrt100 by
+            #  CPython and msvcrt120 by LLVM)
+            ll.add_symbol(fname, c_helpers[fname])
+
+
+c_math_functions = _ExternalMathFunctions()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/fastmathpass.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/fastmathpass.py
new file mode 100644
index 000000000..d6dd1b89c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/fastmathpass.py
@@ -0,0 +1,44 @@
+from llvmlite import ir
+from llvmlite.ir.transforms import Visitor, CallVisitor
+
+
+class FastFloatBinOpVisitor(Visitor):
+    """
+    A pass to add fastmath flag to float-binop instruction if they don't have
+    any flags.
+    """
+    float_binops = frozenset(['fadd', 'fsub', 'fmul', 'fdiv', 'frem', 'fcmp'])
+
+    def __init__(self, flags):
+        self.flags = flags
+
+    def visit_Instruction(self, instr):
+        if instr.opname in self.float_binops:
+            if not instr.flags:
+                for flag in self.flags:
+                    instr.flags.append(flag)
+
+
+class FastFloatCallVisitor(CallVisitor):
+    """
+    A pass to change all float function calls to use fastmath.
+    """
+
+    def __init__(self, flags):
+        self.flags = flags
+
+    def visit_Call(self, instr):
+        # Add to any call that has float/double return type
+        if instr.type in (ir.FloatType(), ir.DoubleType()):
+            for flag in self.flags:
+                instr.fastmath.add(flag)
+
+
+def rewrite_module(mod, options):
+    """
+    Rewrite the given LLVM module to use fastmath everywhere.
+    """
+    flags = options.flags
+    FastFloatBinOpVisitor(flags).visit(mod)
+    FastFloatCallVisitor(flags).visit(mod)
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/funcdesc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/funcdesc.py
new file mode 100644
index 000000000..2a1a4fe17
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/funcdesc.py
@@ -0,0 +1,230 @@
+"""
+Function descriptors.
+"""
+
+from collections import defaultdict
+import importlib
+
+from numba.core import types, itanium_mangler
+from numba.core.utils import _dynamic_modname, _dynamic_module
+
+
+def default_mangler(name, argtypes, *, abi_tags=(), uid=None):
+    return itanium_mangler.mangle(name, argtypes, abi_tags=abi_tags, uid=uid)
+
+
+def qualifying_prefix(modname, qualname):
+    """
+    Returns a new string that is used for the first half of the mangled name.
+    """
+    # XXX choose a different convention for object mode
+    return '{}.{}'.format(modname, qualname) if modname else qualname
+
+
+class FunctionDescriptor(object):
+    """
+    Base class for function descriptors: an object used to carry
+    useful metadata about a natively callable function.
+
+    Note that while `FunctionIdentity` denotes a Python function
+    which is being concretely compiled by Numba, `FunctionDescriptor`
+    may be more "abstract": e.g. a function decorated with `@generated_jit`.
+    """
+    __slots__ = ('native', 'modname', 'qualname', 'doc', 'typemap',
+                 'calltypes', 'args', 'kws', 'restype', 'argtypes',
+                 'mangled_name', 'unique_name', 'env_name', 'global_dict',
+                 'inline', 'noalias', 'abi_tags', 'uid')
+
+    def __init__(self, native, modname, qualname, unique_name, doc,
+                 typemap, restype, calltypes, args, kws, mangler=None,
+                 argtypes=None, inline=False, noalias=False, env_name=None,
+                 global_dict=None, abi_tags=(), uid=None):
+        self.native = native
+        self.modname = modname
+        self.global_dict = global_dict
+        self.qualname = qualname
+        self.unique_name = unique_name
+        self.doc = doc
+        # XXX typemap and calltypes should be on the compile result,
+        # not the FunctionDescriptor
+        self.typemap = typemap
+        self.calltypes = calltypes
+        self.args = args
+        self.kws = kws
+        self.restype = restype
+        # Argument types
+        if argtypes is not None:
+            assert isinstance(argtypes, tuple), argtypes
+            self.argtypes = argtypes
+        else:
+            # Get argument types from the type inference result
+            # (note the "arg.FOO" convention as used in typeinfer
+            self.argtypes = tuple(self.typemap['arg.' + a] for a in args)
+        mangler = default_mangler if mangler is None else mangler
+        # The mangled name *must* be unique, else the wrong function can
+        # be chosen at link time.
+        qualprefix = qualifying_prefix(self.modname, self.qualname)
+        self.uid = uid
+        self.mangled_name = mangler(
+            qualprefix, self.argtypes, abi_tags=abi_tags, uid=uid,
+        )
+        if env_name is None:
+            env_name = mangler(".NumbaEnv.{}".format(qualprefix),
+                               self.argtypes, abi_tags=abi_tags, uid=uid)
+        self.env_name = env_name
+        self.inline = inline
+        self.noalias = noalias
+        self.abi_tags = abi_tags
+
+    def lookup_globals(self):
+        """
+        Return the global dictionary of the function.
+        It may not match the Module's globals if the function is created
+        dynamically (i.e. exec)
+        """
+        return self.global_dict or self.lookup_module().__dict__
+
+    def lookup_module(self):
+        """
+        Return the module in which this function is supposed to exist.
+        This may be a dummy module if the function was dynamically
+        generated or the module can't be found.
+        """
+        if self.modname == _dynamic_modname:
+            return _dynamic_module
+        else:
+            try:
+                # ensure module exist
+                return importlib.import_module(self.modname)
+            except ImportError:
+                return _dynamic_module
+
+    def lookup_function(self):
+        """
+        Return the original function object described by this object.
+        """
+        return getattr(self.lookup_module(), self.qualname)
+
+    @property
+    def llvm_func_name(self):
+        """
+        The LLVM-registered name for the raw function.
+        """
+        return self.mangled_name
+
+    # XXX refactor this
+
+    @property
+    def llvm_cpython_wrapper_name(self):
+        """
+        The LLVM-registered name for a CPython-compatible wrapper of the
+        raw function (i.e. a PyCFunctionWithKeywords).
+        """
+        return itanium_mangler.prepend_namespace(self.mangled_name,
+                                                 ns='cpython')
+
+    @property
+    def llvm_cfunc_wrapper_name(self):
+        """
+        The LLVM-registered name for a C-compatible wrapper of the
+        raw function.
+        """
+        return 'cfunc.' + self.mangled_name
+
+    def __repr__(self):
+        return "<function descriptor %r>" % (self.unique_name)
+
+    @classmethod
+    def _get_function_info(cls, func_ir):
+        """
+        Returns
+        -------
+        qualname, unique_name, modname, doc, args, kws, globals
+
+        ``unique_name`` must be a unique name.
+        """
+        func = func_ir.func_id.func
+        qualname = func_ir.func_id.func_qualname
+        # XXX to func_id
+        modname = func.__module__
+        doc = func.__doc__ or ''
+        args = tuple(func_ir.arg_names)
+        kws = ()        # TODO
+        global_dict = None
+
+        if modname is None:
+            # Dynamically generated function.
+            modname = _dynamic_modname
+            # Retain a reference to the dictionary of the function.
+            # This disables caching, serialization and pickling.
+            global_dict = func_ir.func_id.func.__globals__
+
+        unique_name = func_ir.func_id.unique_name
+
+        return qualname, unique_name, modname, doc, args, kws, global_dict
+
+    @classmethod
+    def _from_python_function(cls, func_ir, typemap, restype,
+                              calltypes, native, mangler=None,
+                              inline=False, noalias=False, abi_tags=()):
+        (qualname, unique_name, modname, doc, args, kws, global_dict,
+         ) = cls._get_function_info(func_ir)
+
+        self = cls(native, modname, qualname, unique_name, doc,
+                   typemap, restype, calltypes,
+                   args, kws, mangler=mangler, inline=inline, noalias=noalias,
+                   global_dict=global_dict, abi_tags=abi_tags,
+                   uid=func_ir.func_id.unique_id)
+        return self
+
+
+class PythonFunctionDescriptor(FunctionDescriptor):
+    """
+    A FunctionDescriptor subclass for Numba-compiled functions.
+    """
+    __slots__ = ()
+
+    @classmethod
+    def from_specialized_function(cls, func_ir, typemap, restype, calltypes,
+                                  mangler, inline, noalias, abi_tags):
+        """
+        Build a FunctionDescriptor for a given specialization of a Python
+        function (in nopython mode).
+        """
+        return cls._from_python_function(func_ir, typemap, restype, calltypes,
+                                         native=True, mangler=mangler,
+                                         inline=inline, noalias=noalias,
+                                         abi_tags=abi_tags)
+
+    @classmethod
+    def from_object_mode_function(cls, func_ir):
+        """
+        Build a FunctionDescriptor for an object mode variant of a Python
+        function.
+        """
+        typemap = defaultdict(lambda: types.pyobject)
+        calltypes = typemap.copy()
+        restype = types.pyobject
+        return cls._from_python_function(func_ir, typemap, restype, calltypes,
+                                         native=False)
+
+
+class ExternalFunctionDescriptor(FunctionDescriptor):
+    """
+    A FunctionDescriptor subclass for opaque external functions
+    (e.g. raw C functions).
+    """
+    __slots__ = ()
+
+    def __init__(self, name, restype, argtypes):
+        args = ["arg%d" % i for i in range(len(argtypes))]
+
+        def mangler(a, x, abi_tags, uid=None):
+            return a
+        super(ExternalFunctionDescriptor, self
+              ).__init__(native=True, modname=None, qualname=name,
+                         unique_name=name, doc='', typemap=None,
+                         restype=restype, calltypes=None, args=args,
+                         kws=None,
+                         mangler=mangler,
+                         argtypes=argtypes)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/generators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/generators.py
new file mode 100644
index 000000000..6060d160c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/generators.py
@@ -0,0 +1,356 @@
+"""
+Support for lowering generators.
+"""
+
+import llvmlite.ir
+from llvmlite.ir import Constant, IRBuilder
+
+from numba.core import types, config, cgutils
+from numba.core.funcdesc import FunctionDescriptor
+
+
+class GeneratorDescriptor(FunctionDescriptor):
+    """
+    The descriptor for a generator's next function.
+    """
+    __slots__ = ()
+
+    @classmethod
+    def from_generator_fndesc(cls, func_ir, fndesc, gentype, mangler):
+        """
+        Build a GeneratorDescriptor for the generator returned by the
+        function described by *fndesc*, with type *gentype*.
+
+        The generator inherits the env_name from the *fndesc*.
+        All emitted functions for the generator shares the same Env.
+        """
+        assert isinstance(gentype, types.Generator)
+        restype = gentype.yield_type
+        args = ['gen']
+        argtypes = (gentype,)
+        qualname = fndesc.qualname + '.next'
+        unique_name = fndesc.unique_name + '.next'
+        self = cls(fndesc.native, fndesc.modname, qualname, unique_name,
+                   fndesc.doc, fndesc.typemap, restype, fndesc.calltypes,
+                   args, fndesc.kws, argtypes=argtypes, mangler=mangler,
+                   inline=False, env_name=fndesc.env_name)
+        return self
+
+    @property
+    def llvm_finalizer_name(self):
+        """
+        The LLVM name of the generator's finalizer function
+        (if <generator type>.has_finalizer is true).
+        """
+        return 'finalize_' + self.mangled_name
+
+
+class BaseGeneratorLower(object):
+    """
+    Base support class for lowering generators.
+    """
+
+    def __init__(self, lower):
+        self.context = lower.context
+        self.fndesc = lower.fndesc
+        self.library = lower.library
+        self.call_conv = lower.call_conv
+        self.func_ir = lower.func_ir
+
+        self.geninfo = lower.generator_info
+        self.gentype = self.get_generator_type()
+        self.gendesc = GeneratorDescriptor.from_generator_fndesc(
+            lower.func_ir, self.fndesc, self.gentype, self.context.mangler)
+        # Helps packing non-omitted arguments into a structure
+        self.arg_packer = self.context.get_data_packer(self.fndesc.argtypes)
+
+        self.resume_blocks = {}
+
+    def get_args_ptr(self, builder, genptr):
+        return cgutils.gep_inbounds(builder, genptr, 0, 1)
+
+    def get_resume_index_ptr(self, builder, genptr):
+        return cgutils.gep_inbounds(builder, genptr, 0, 0,
+                                    name='gen.resume_index')
+
+    def get_state_ptr(self, builder, genptr):
+        return cgutils.gep_inbounds(builder, genptr, 0, 2,
+                                    name='gen.state')
+
+    def lower_init_func(self, lower):
+        """
+        Lower the generator's initialization function (which will fill up
+        the passed-by-reference generator structure).
+        """
+        lower.setup_function(self.fndesc)
+
+        builder = lower.builder
+
+        # Insert the generator into the target context in order to allow
+        # calling from other Numba-compiled functions.
+        lower.context.insert_generator(self.gentype, self.gendesc,
+                                       [self.library])
+
+        # Init argument values
+        lower.extract_function_arguments()
+
+        lower.pre_lower()
+
+        # Initialize the return structure (i.e. the generator structure).
+        retty = self.context.get_return_type(self.gentype)
+        # Structure index #0: the initial resume index (0 == start of generator)
+        resume_index = self.context.get_constant(types.int32, 0)
+        # Structure index #1: the function arguments
+        argsty = retty.elements[1]
+        statesty = retty.elements[2]
+
+        lower.debug_print("# low_init_func incref")
+        # Incref all NRT arguments before storing into generator states
+        if self.context.enable_nrt:
+            for argty, argval in zip(self.fndesc.argtypes, lower.fnargs):
+                self.context.nrt.incref(builder, argty, argval)
+
+        # Filter out omitted arguments
+        argsval = self.arg_packer.as_data(builder, lower.fnargs)
+
+        # Zero initialize states
+        statesval = Constant(statesty, None)
+        gen_struct = cgutils.make_anonymous_struct(builder,
+                                                   [resume_index, argsval,
+                                                    statesval],
+                                                   retty)
+
+        retval = self.box_generator_struct(lower, gen_struct)
+
+        lower.debug_print("# low_init_func before return")
+        self.call_conv.return_value(builder, retval)
+        lower.post_lower()
+
+    def lower_next_func(self, lower):
+        """
+        Lower the generator's next() function (which takes the
+        passed-by-reference generator structure and returns the next
+        yielded value).
+        """
+        lower.setup_function(self.gendesc)
+        lower.debug_print("# lower_next_func: {0}".format(self.gendesc.unique_name))
+        assert self.gendesc.argtypes[0] == self.gentype
+        builder = lower.builder
+        function = lower.function
+
+        # Extract argument values and other information from generator struct
+        genptr, = self.call_conv.get_arguments(function)
+        self.arg_packer.load_into(builder,
+                                  self.get_args_ptr(builder, genptr),
+                                  lower.fnargs)
+
+        self.resume_index_ptr = self.get_resume_index_ptr(builder, genptr)
+        self.gen_state_ptr = self.get_state_ptr(builder, genptr)
+
+        prologue = function.append_basic_block("generator_prologue")
+
+        # Lower the generator's Python code
+        entry_block_tail = lower.lower_function_body()
+
+        # Add block for StopIteration on entry
+        stop_block = function.append_basic_block("stop_iteration")
+        builder.position_at_end(stop_block)
+        self.call_conv.return_stop_iteration(builder)
+
+        # Add prologue switch to resume blocks
+        builder.position_at_end(prologue)
+        # First Python block is also the resume point on first next() call
+        first_block = self.resume_blocks[0] = lower.blkmap[lower.firstblk]
+
+        # Create front switch to resume points
+        switch = builder.switch(builder.load(self.resume_index_ptr),
+                                stop_block)
+        for index, block in self.resume_blocks.items():
+            switch.add_case(index, block)
+
+        # Close tail of entry block
+        builder.position_at_end(entry_block_tail)
+        builder.branch(prologue)
+
+    def lower_finalize_func(self, lower):
+        """
+        Lower the generator's finalizer.
+        """
+        fnty = llvmlite.ir.FunctionType(llvmlite.ir.VoidType(),
+                                        [self.context.get_value_type(self.gentype)])
+        function = cgutils.get_or_insert_function(
+            lower.module, fnty, self.gendesc.llvm_finalizer_name)
+        entry_block = function.append_basic_block('entry')
+        builder = IRBuilder(entry_block)
+
+        genptrty = self.context.get_value_type(self.gentype)
+        genptr = builder.bitcast(function.args[0], genptrty)
+        self.lower_finalize_func_body(builder, genptr)
+
+    def return_from_generator(self, lower):
+        """
+        Emit a StopIteration at generator end and mark the generator exhausted.
+        """
+        indexval = Constant(self.resume_index_ptr.type.pointee, -1)
+        lower.builder.store(indexval, self.resume_index_ptr)
+        self.call_conv.return_stop_iteration(lower.builder)
+
+    def create_resumption_block(self, lower, index):
+        block_name = "generator_resume%d" % (index,)
+        block = lower.function.append_basic_block(block_name)
+        lower.builder.position_at_end(block)
+        self.resume_blocks[index] = block
+
+    def debug_print(self, builder, msg):
+        if config.DEBUG_JIT:
+            self.context.debug_print(builder, "DEBUGJIT: {0}".format(msg))
+
+class GeneratorLower(BaseGeneratorLower):
+    """
+    Support class for lowering nopython generators.
+    """
+
+    def get_generator_type(self):
+        return self.fndesc.restype
+
+    def box_generator_struct(self, lower, gen_struct):
+        return gen_struct
+
+    def lower_finalize_func_body(self, builder, genptr):
+        """
+        Lower the body of the generator's finalizer: decref all live
+        state variables.
+        """
+        self.debug_print(builder, "# generator: finalize")
+        if self.context.enable_nrt:
+
+            # Always dereference all arguments
+            # self.debug_print(builder, "# generator: clear args")
+            args_ptr = self.get_args_ptr(builder, genptr)
+            for ty, val in self.arg_packer.load(builder, args_ptr):
+                self.context.nrt.decref(builder, ty, val)
+
+        self.debug_print(builder, "# generator: finalize end")
+        builder.ret_void()
+
+class PyGeneratorLower(BaseGeneratorLower):
+    """
+    Support class for lowering object mode generators.
+    """
+
+    def get_generator_type(self):
+        """
+        Compute the actual generator type (the generator function's return
+        type is simply "pyobject").
+        """
+        return types.Generator(
+            gen_func=self.func_ir.func_id.func,
+            yield_type=types.pyobject,
+            arg_types=(types.pyobject,) * self.func_ir.arg_count,
+            state_types=(types.pyobject,) * len(self.geninfo.state_vars),
+            has_finalizer=True,
+            )
+
+    def box_generator_struct(self, lower, gen_struct):
+        """
+        Box the raw *gen_struct* as a Python object.
+        """
+        gen_ptr = cgutils.alloca_once_value(lower.builder, gen_struct)
+        return lower.pyapi.from_native_generator(gen_ptr, self.gentype, lower.envarg)
+
+    def init_generator_state(self, lower):
+        """
+        NULL-initialize all generator state variables, to avoid spurious
+        decref's on cleanup.
+        """
+        lower.builder.store(Constant(self.gen_state_ptr.type.pointee, None),
+                            self.gen_state_ptr)
+
+    def lower_finalize_func_body(self, builder, genptr):
+        """
+        Lower the body of the generator's finalizer: decref all live
+        state variables.
+        """
+        pyapi = self.context.get_python_api(builder)
+        resume_index_ptr = self.get_resume_index_ptr(builder, genptr)
+        resume_index = builder.load(resume_index_ptr)
+        # If resume_index is 0, next() was never called
+        # If resume_index is -1, generator terminated cleanly
+        # (note function arguments are saved in state variables,
+        #  so they don't need a separate cleanup step)
+        need_cleanup = builder.icmp_signed(
+            '>', resume_index, Constant(resume_index.type, 0))
+
+        with cgutils.if_unlikely(builder, need_cleanup):
+            # Decref all live vars (some may be NULL)
+            gen_state_ptr = self.get_state_ptr(builder, genptr)
+            for state_index in range(len(self.gentype.state_types)):
+                state_slot = cgutils.gep_inbounds(builder, gen_state_ptr,
+                                                  0, state_index)
+                ty = self.gentype.state_types[state_index]
+                val = self.context.unpack_value(builder, ty, state_slot)
+                pyapi.decref(val)
+
+        builder.ret_void()
+
+
+class LowerYield(object):
+    """
+    Support class for lowering a particular yield point.
+    """
+
+    def __init__(self, lower, yield_point, live_vars):
+        self.lower = lower
+        self.context = lower.context
+        self.builder = lower.builder
+        self.genlower = lower.genlower
+        self.gentype = self.genlower.gentype
+
+        self.gen_state_ptr = self.genlower.gen_state_ptr
+        self.resume_index_ptr = self.genlower.resume_index_ptr
+        self.yp = yield_point
+        self.inst = self.yp.inst
+        self.live_vars = live_vars
+        self.live_var_indices = [lower.generator_info.state_vars.index(v)
+                                 for v in live_vars]
+
+    def lower_yield_suspend(self):
+        self.lower.debug_print("# generator suspend")
+        # Save live vars in state
+        for state_index, name in zip(self.live_var_indices, self.live_vars):
+            state_slot = cgutils.gep_inbounds(self.builder, self.gen_state_ptr,
+                                              0, state_index)
+            ty = self.gentype.state_types[state_index]
+            # The yield might be in a loop, in which case the state might
+            # contain a predicate var that branches back to the loop head, in
+            # this case the var is live but in sequential lowering won't have
+            # been alloca'd yet, so do this here.
+            fetype = self.lower.typeof(name)
+            self.lower._alloca_var(name, fetype)
+            val = self.lower.loadvar(name)
+            # IncRef newly stored value
+            if self.context.enable_nrt:
+                self.context.nrt.incref(self.builder, ty, val)
+
+            self.context.pack_value(self.builder, ty, val, state_slot)
+        # Save resume index
+        indexval = Constant(self.resume_index_ptr.type.pointee,
+                            self.inst.index)
+        self.builder.store(indexval, self.resume_index_ptr)
+        self.lower.debug_print("# generator suspend end")
+
+    def lower_yield_resume(self):
+        # Emit resumption point
+        self.genlower.create_resumption_block(self.lower, self.inst.index)
+        self.lower.debug_print("# generator resume")
+        # Reload live vars from state
+        for state_index, name in zip(self.live_var_indices, self.live_vars):
+            state_slot = cgutils.gep_inbounds(self.builder, self.gen_state_ptr,
+                                              0, state_index)
+            ty = self.gentype.state_types[state_index]
+            val = self.context.unpack_value(self.builder, ty, state_slot)
+            self.lower.storevar(val, name)
+            # Previous storevar is making an extra incref
+            if self.context.enable_nrt:
+                self.context.nrt.decref(self.builder, ty, val)
+        self.lower.debug_print("# generator resume end")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/imputils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/imputils.py
new file mode 100644
index 000000000..06c35abb9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/imputils.py
@@ -0,0 +1,469 @@
+"""
+Utilities to simplify the boilerplate for native lowering.
+"""
+
+
+import collections
+import contextlib
+import inspect
+import functools
+from enum import Enum
+
+from numba.core import typing, types, utils, cgutils
+from numba.core.typing.templates import BaseRegistryLoader
+
+
+class Registry(object):
+    """
+    A registry of function and attribute implementations.
+    """
+    def __init__(self, name='unspecified'):
+        self.name = name
+        self.functions = []
+        self.getattrs = []
+        self.setattrs = []
+        self.casts = []
+        self.constants = []
+
+    def lower(self, func, *argtys):
+        """
+        Decorate an implementation of *func* for the given argument types.
+        *func* may be an actual global function object, or any
+        pseudo-function supported by Numba, such as "getitem".
+
+        The decorated implementation has the signature
+        (context, builder, sig, args).
+        """
+        def decorate(impl):
+            self.functions.append((impl, func, argtys))
+            return impl
+        return decorate
+
+    def _decorate_attr(self, impl, ty, attr, impl_list, decorator):
+        real_impl = decorator(impl, ty, attr)
+        impl_list.append((real_impl, attr, real_impl.signature))
+        return impl
+
+    def lower_getattr(self, ty, attr):
+        """
+        Decorate an implementation of __getattr__ for type *ty* and
+        the attribute *attr*.
+
+        The decorated implementation will have the signature
+        (context, builder, typ, val).
+        """
+        def decorate(impl):
+            return self._decorate_attr(impl, ty, attr, self.getattrs,
+                                       _decorate_getattr)
+        return decorate
+
+    def lower_getattr_generic(self, ty):
+        """
+        Decorate the fallback implementation of __getattr__ for type *ty*.
+
+        The decorated implementation will have the signature
+        (context, builder, typ, val, attr).  The implementation is
+        called for attributes which haven't been explicitly registered
+        with lower_getattr().
+        """
+        return self.lower_getattr(ty, None)
+
+    def lower_setattr(self, ty, attr):
+        """
+        Decorate an implementation of __setattr__ for type *ty* and
+        the attribute *attr*.
+
+        The decorated implementation will have the signature
+        (context, builder, sig, args).
+        """
+        def decorate(impl):
+            return self._decorate_attr(impl, ty, attr, self.setattrs,
+                                       _decorate_setattr)
+        return decorate
+
+    def lower_setattr_generic(self, ty):
+        """
+        Decorate the fallback implementation of __setattr__ for type *ty*.
+
+        The decorated implementation will have the signature
+        (context, builder, sig, args, attr).  The implementation is
+        called for attributes which haven't been explicitly registered
+        with lower_setattr().
+        """
+        return self.lower_setattr(ty, None)
+
+    def lower_cast(self, fromty, toty):
+        """
+        Decorate the implementation of implicit conversion between
+        *fromty* and *toty*.
+
+        The decorated implementation will have the signature
+        (context, builder, fromty, toty, val).
+        """
+        def decorate(impl):
+            self.casts.append((impl, (fromty, toty)))
+            return impl
+        return decorate
+
+    def lower_constant(self, ty):
+        """
+        Decorate the implementation for creating a constant of type *ty*.
+
+        The decorated implementation will have the signature
+        (context, builder, ty, pyval).
+        """
+        def decorate(impl):
+            self.constants.append((impl, (ty,)))
+            return impl
+        return decorate
+
+    def __repr__(self):
+        return f"Lowering Registry<{self.name}>"
+
+
+class RegistryLoader(BaseRegistryLoader):
+    """
+    An incremental loader for a target registry.
+    """
+    registry_items = ('functions', 'getattrs', 'setattrs', 'casts', 'constants')
+
+
+# Global registry for implementations of builtin operations
+# (functions, attributes, type casts)
+builtin_registry = Registry('builtin_registry')
+
+lower_builtin = builtin_registry.lower
+lower_getattr = builtin_registry.lower_getattr
+lower_getattr_generic = builtin_registry.lower_getattr_generic
+lower_setattr = builtin_registry.lower_setattr
+lower_setattr_generic = builtin_registry.lower_setattr_generic
+lower_cast = builtin_registry.lower_cast
+lower_constant = builtin_registry.lower_constant
+
+
+def _decorate_getattr(impl, ty, attr):
+    real_impl = impl
+
+    if attr is not None:
+        def res(context, builder, typ, value, attr):
+            return real_impl(context, builder, typ, value)
+    else:
+        def res(context, builder, typ, value, attr):
+            return real_impl(context, builder, typ, value, attr)
+
+    res.signature = (ty,)
+    res.attr = attr
+    return res
+
+def _decorate_setattr(impl, ty, attr):
+    real_impl = impl
+
+    if attr is not None:
+        def res(context, builder, sig, args, attr):
+            return real_impl(context, builder, sig, args)
+    else:
+        def res(context, builder, sig, args, attr):
+            return real_impl(context, builder, sig, args, attr)
+
+    res.signature = (ty, types.Any)
+    res.attr = attr
+    return res
+
+
+def fix_returning_optional(context, builder, sig, status, retval):
+    # Reconstruct optional return type
+    if isinstance(sig.return_type, types.Optional):
+        value_type = sig.return_type.type
+        optional_none = context.make_optional_none(builder, value_type)
+        retvalptr = cgutils.alloca_once_value(builder, optional_none)
+        with builder.if_then(builder.not_(status.is_none)):
+            optional_value = context.make_optional_value(
+                builder, value_type, retval,
+                )
+            builder.store(optional_value, retvalptr)
+        retval = builder.load(retvalptr)
+    return retval
+
+def user_function(fndesc, libs):
+    """
+    A wrapper inserting code calling Numba-compiled *fndesc*.
+    """
+
+    def imp(context, builder, sig, args):
+        func = context.declare_function(builder.module, fndesc)
+        # env=None assumes this is a nopython function
+        status, retval = context.call_conv.call_function(
+            builder, func, fndesc.restype, fndesc.argtypes, args)
+        with cgutils.if_unlikely(builder, status.is_error):
+            context.call_conv.return_status_propagate(builder, status)
+        assert sig.return_type == fndesc.restype
+        # Reconstruct optional return type
+        retval = fix_returning_optional(context, builder, sig, status, retval)
+        # If the data representations don't match up
+        if retval.type != context.get_value_type(sig.return_type):
+            msg = "function returned {0} but expect {1}"
+            raise TypeError(msg.format(retval.type, sig.return_type))
+
+        return impl_ret_new_ref(context, builder, fndesc.restype, retval)
+
+    imp.signature = fndesc.argtypes
+    imp.libs = tuple(libs)
+    return imp
+
+
+def user_generator(gendesc, libs):
+    """
+    A wrapper inserting code calling Numba-compiled *gendesc*.
+    """
+
+    def imp(context, builder, sig, args):
+        func = context.declare_function(builder.module, gendesc)
+        # env=None assumes this is a nopython function
+        status, retval = context.call_conv.call_function(
+            builder, func, gendesc.restype, gendesc.argtypes, args)
+        # Return raw status for caller to process StopIteration
+        return status, retval
+
+    imp.libs = tuple(libs)
+    return imp
+
+
+def iterator_impl(iterable_type, iterator_type):
+    """
+    Decorator a given class as implementing *iterator_type*
+    (by providing an `iternext()` method).
+    """
+
+    def wrapper(cls):
+        # These are unbound methods
+        iternext = cls.iternext
+
+        @iternext_impl(RefType.BORROWED)
+        def iternext_wrapper(context, builder, sig, args, result):
+            (value,) = args
+            iterobj = cls(context, builder, value)
+            return iternext(iterobj, context, builder, result)
+
+        lower_builtin('iternext', iterator_type)(iternext_wrapper)
+        return cls
+
+    return wrapper
+
+
+class _IternextResult(object):
+    """
+    A result wrapper for iteration, passed by iternext_impl() into the
+    wrapped function.
+    """
+    __slots__ = ('_context', '_builder', '_pairobj')
+
+    def __init__(self, context, builder, pairobj):
+        self._context = context
+        self._builder = builder
+        self._pairobj = pairobj
+
+    def set_exhausted(self):
+        """
+        Mark the iterator as exhausted.
+        """
+        self._pairobj.second = self._context.get_constant(types.boolean, False)
+
+    def set_valid(self, is_valid=True):
+        """
+        Mark the iterator as valid according to *is_valid* (which must
+        be either a Python boolean or a LLVM inst).
+        """
+        if is_valid in (False, True):
+            is_valid = self._context.get_constant(types.boolean, is_valid)
+        self._pairobj.second = is_valid
+
+    def yield_(self, value):
+        """
+        Mark the iterator as yielding the given *value* (a LLVM inst).
+        """
+        self._pairobj.first = value
+
+    def is_valid(self):
+        """
+        Return whether the iterator is marked valid.
+        """
+        return self._context.get_argument_value(self._builder,
+                                                types.boolean,
+                                                self._pairobj.second)
+
+    def yielded_value(self):
+        """
+        Return the iterator's yielded value, if any.
+        """
+        return self._pairobj.first
+
+class RefType(Enum):
+    """
+    Enumerate the reference type
+    """
+    """
+    A new reference
+    """
+    NEW = 1
+    """
+    A borrowed reference
+    """
+    BORROWED = 2
+    """
+    An untracked reference
+    """
+    UNTRACKED = 3
+
+def iternext_impl(ref_type=None):
+    """
+    Wrap the given iternext() implementation so that it gets passed
+    an _IternextResult() object easing the returning of the iternext()
+    result pair.
+
+    ref_type: a numba.targets.imputils.RefType value, the reference type used is
+    that specified through the RefType enum.
+
+    The wrapped function will be called with the following signature:
+        (context, builder, sig, args, iternext_result)
+    """
+    if ref_type not in [x for x in RefType]:
+        raise ValueError("ref_type must be an enum member of imputils.RefType")
+
+    def outer(func):
+        def wrapper(context, builder, sig, args):
+            pair_type = sig.return_type
+            pairobj = context.make_helper(builder, pair_type)
+            func(context, builder, sig, args,
+                _IternextResult(context, builder, pairobj))
+            if ref_type == RefType.NEW:
+                impl_ret = impl_ret_new_ref
+            elif ref_type == RefType.BORROWED:
+                impl_ret = impl_ret_borrowed
+            elif ref_type == RefType.UNTRACKED:
+                impl_ret = impl_ret_untracked
+            else:
+                raise ValueError("Unknown ref_type encountered")
+            return impl_ret(context, builder,
+                                    pair_type, pairobj._getvalue())
+        return wrapper
+    return outer
+
+
+def call_getiter(context, builder, iterable_type, val):
+    """
+    Call the `getiter()` implementation for the given *iterable_type*
+    of value *val*, and return the corresponding LLVM inst.
+    """
+    getiter_sig = typing.signature(iterable_type.iterator_type, iterable_type)
+    getiter_impl = context.get_function('getiter', getiter_sig)
+    return getiter_impl(builder, (val,))
+
+
+def call_iternext(context, builder, iterator_type, val):
+    """
+    Call the `iternext()` implementation for the given *iterator_type*
+    of value *val*, and return a convenience _IternextResult() object
+    reflecting the results.
+    """
+    itemty = iterator_type.yield_type
+    pair_type = types.Pair(itemty, types.boolean)
+    iternext_sig = typing.signature(pair_type, iterator_type)
+    iternext_impl = context.get_function('iternext', iternext_sig)
+    val = iternext_impl(builder, (val,))
+    pairobj = context.make_helper(builder, pair_type, val)
+    return _IternextResult(context, builder, pairobj)
+
+
+def call_len(context, builder, ty, val):
+    """
+    Call len() on the given value.  Return None if len() isn't defined on
+    this type.
+    """
+    try:
+        len_impl = context.get_function(len, typing.signature(types.intp, ty,))
+    except NotImplementedError:
+        return None
+    else:
+        return len_impl(builder, (val,))
+
+
+_ForIterLoop = collections.namedtuple('_ForIterLoop',
+                                      ('value', 'do_break'))
+
+
+@contextlib.contextmanager
+def for_iter(context, builder, iterable_type, val):
+    """
+    Simulate a for loop on the given iterable.  Yields a namedtuple with
+    the given members:
+    - `value` is the value being yielded
+    - `do_break` is a callable to early out of the loop
+    """
+    iterator_type = iterable_type.iterator_type
+    iterval = call_getiter(context, builder, iterable_type, val)
+
+    bb_body = builder.append_basic_block('for_iter.body')
+    bb_end = builder.append_basic_block('for_iter.end')
+
+    def do_break():
+        builder.branch(bb_end)
+
+    builder.branch(bb_body)
+
+    with builder.goto_block(bb_body):
+        res = call_iternext(context, builder, iterator_type, iterval)
+        with builder.if_then(builder.not_(res.is_valid()), likely=False):
+            builder.branch(bb_end)
+        yield _ForIterLoop(res.yielded_value(), do_break)
+        builder.branch(bb_body)
+
+    builder.position_at_end(bb_end)
+    if context.enable_nrt:
+        context.nrt.decref(builder, iterator_type, iterval)
+
+
+def impl_ret_new_ref(ctx, builder, retty, ret):
+    """
+    The implementation returns a new reference.
+    """
+    return ret
+
+
+def impl_ret_borrowed(ctx, builder, retty, ret):
+    """
+    The implementation returns a borrowed reference.
+    This function automatically incref so that the implementation is
+    returning a new reference.
+    """
+    if ctx.enable_nrt:
+        ctx.nrt.incref(builder, retty, ret)
+    return ret
+
+
+def impl_ret_untracked(ctx, builder, retty, ret):
+    """
+    The return type is not a NRT object.
+    """
+    return ret
+
+
+@contextlib.contextmanager
+def force_error_model(context, model_name='numpy'):
+    """
+    Temporarily change the context's error model.
+    """
+    from numba.core import callconv
+
+    old_error_model = context.error_model
+    context.error_model = callconv.create_error_model(model_name, context)
+    try:
+        yield
+    finally:
+        context.error_model = old_error_model
+
+
+def numba_typeref_ctor(*args, **kwargs):
+    """A stub for use internally by Numba when a call is emitted
+    on a TypeRef.
+    """
+    raise NotImplementedError("This function should not be executed.")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/inline_closurecall.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/inline_closurecall.py
new file mode 100644
index 000000000..badb6b49d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/inline_closurecall.py
@@ -0,0 +1,1557 @@
+import types as pytypes  # avoid confusion with numba.types
+import copy
+import ctypes
+import numba.core.analysis
+from numba.core import utils, types, typing, errors, ir, rewrites, config, ir_utils
+from numba import prange
+from numba.parfors.parfor import internal_prange
+from numba.core.ir_utils import (
+    mk_unique_var,
+    next_label,
+    add_offset_to_labels,
+    replace_vars,
+    remove_dels,
+    rename_labels,
+    find_topo_order,
+    merge_adjacent_blocks,
+    GuardException,
+    require,
+    guard,
+    get_definition,
+    find_callname,
+    find_build_sequence,
+    get_np_ufunc_typ,
+    get_ir_of_code,
+    simplify_CFG,
+    canonicalize_array_math,
+    dead_code_elimination,
+    )
+
+from numba.core.analysis import (
+    compute_cfg_from_blocks,
+    compute_use_defs,
+    compute_live_variables)
+from numba.core import postproc
+from numba.np.unsafe.ndarray import empty_inferred as unsafe_empty_inferred
+import numpy as np
+import operator
+import numba.misc.special
+
+"""
+Variable enable_inline_arraycall is only used for testing purpose.
+"""
+enable_inline_arraycall = True
+
+
+def callee_ir_validator(func_ir):
+    """Checks the IR of a callee is supported for inlining
+    """
+    for blk in func_ir.blocks.values():
+        for stmt in blk.find_insts(ir.Assign):
+            if isinstance(stmt.value, ir.Yield):
+                msg = "The use of yield in a closure is unsupported."
+                raise errors.UnsupportedError(msg, loc=stmt.loc)
+
+
+def _created_inlined_var_name(function_name, var_name):
+    """Creates a name for an inlined variable based on the function name and the
+    variable name. It does this "safely" to avoid the use of characters that are
+    illegal in python variable names as there are occasions when function
+    generation needs valid python name tokens."""
+    inlined_name = f'{function_name}.{var_name}'
+    # Replace angle brackets, e.g. "<locals>" is replaced with "_locals_"
+    new_name = inlined_name.replace('<', '_').replace('>', '_')
+    # The version "version" of the closure function e.g. foo$2 (id 2) is
+    # rewritten as "foo_v2". Further "." is also replaced with "_".
+    new_name = new_name.replace('.', '_').replace('$', '_v')
+    return new_name
+
+
+class InlineClosureCallPass(object):
+    """InlineClosureCallPass class looks for direct calls to locally defined
+    closures, and inlines the body of the closure function to the call site.
+    """
+
+    def __init__(self, func_ir, parallel_options, swapped={}, typed=False):
+        self.func_ir = func_ir
+        self.parallel_options = parallel_options
+        self.swapped = swapped
+        self._processed_stencils = []
+        self.typed = typed
+
+    def run(self):
+        """Run inline closure call pass.
+        """
+        # Analysis relies on ir.Del presence, strip out later
+        pp = postproc.PostProcessor(self.func_ir)
+        pp.run(True)
+
+        modified = False
+        work_list = list(self.func_ir.blocks.items())
+        debug_print = _make_debug_print("InlineClosureCallPass")
+        debug_print("START")
+        while work_list:
+            label, block = work_list.pop()
+            for i, instr in enumerate(block.body):
+                if isinstance(instr, ir.Assign):
+                    lhs = instr.target
+                    expr = instr.value
+                    if isinstance(expr, ir.Expr) and expr.op == 'call':
+                        call_name = guard(find_callname, self.func_ir, expr)
+                        func_def = guard(get_definition, self.func_ir, expr.func)
+
+                        if guard(self._inline_reduction,
+                                 work_list, block, i, expr, call_name):
+                            modified = True
+                            break # because block structure changed
+
+                        if guard(self._inline_closure,
+                                work_list, block, i, func_def):
+                            modified = True
+                            break # because block structure changed
+
+                        if guard(self._inline_stencil,
+                                instr, call_name, func_def):
+                            modified = True
+
+        if enable_inline_arraycall:
+            # Identify loop structure
+            if modified:
+                # Need to do some cleanups if closure inlining kicked in
+                merge_adjacent_blocks(self.func_ir.blocks)
+            cfg = compute_cfg_from_blocks(self.func_ir.blocks)
+            debug_print("start inline arraycall")
+            _debug_dump(cfg)
+            loops = cfg.loops()
+            sized_loops = [(k, len(loops[k].body)) for k in loops.keys()]
+            visited = []
+            # We go over all loops, bigger loops first (outer first)
+            for k, s in sorted(sized_loops, key=lambda tup: tup[1], reverse=True):
+                visited.append(k)
+                if guard(_inline_arraycall, self.func_ir, cfg, visited, loops[k],
+                         self.swapped, self.parallel_options.comprehension,
+                         self.typed):
+                    modified = True
+            if modified:
+                _fix_nested_array(self.func_ir)
+
+        if modified:
+            # clean up now dead/unreachable blocks, e.g. unconditionally raising
+            # an exception in an inlined function would render some parts of the
+            # inliner unreachable
+            cfg = compute_cfg_from_blocks(self.func_ir.blocks)
+            for dead in cfg.dead_nodes():
+                del self.func_ir.blocks[dead]
+
+            # run dead code elimination
+            dead_code_elimination(self.func_ir)
+            # do label renaming
+            self.func_ir.blocks = rename_labels(self.func_ir.blocks)
+
+        # inlining done, strip dels
+        remove_dels(self.func_ir.blocks)
+
+        debug_print("END")
+
+    def _inline_reduction(self, work_list, block, i, expr, call_name):
+        # only inline reduction in sequential execution, parallel handling
+        # is done in ParforPass.
+        require(not self.parallel_options.reduction)
+        require(call_name == ('reduce', 'builtins') or
+                call_name == ('reduce', '_functools'))
+        if len(expr.args) not in (2, 3):
+            raise TypeError("invalid reduce call, "
+                            "two arguments are required (optional initial "
+                            "value can also be specified)")
+        check_reduce_func(self.func_ir, expr.args[0])
+        def reduce_func(f, A, v=None):
+            it = iter(A)
+            if v is not None:
+                s = v
+            else:
+                s = next(it)
+            for a in it:
+               s = f(s, a)
+            return s
+        inline_closure_call(self.func_ir,
+                        self.func_ir.func_id.func.__globals__,
+                        block, i, reduce_func, work_list=work_list,
+                        callee_validator=callee_ir_validator)
+        return True
+
+    def _inline_stencil(self, instr, call_name, func_def):
+        from numba.stencils.stencil import StencilFunc
+        lhs = instr.target
+        expr = instr.value
+        # We keep the escaping variables of the stencil kernel
+        # alive by adding them to the actual kernel call as extra
+        # keyword arguments, which is ignored anyway.
+        if (isinstance(func_def, ir.Global) and
+            func_def.name == 'stencil' and
+            isinstance(func_def.value, StencilFunc)):
+            if expr.kws:
+                expr.kws += func_def.value.kws
+            else:
+                expr.kws = func_def.value.kws
+            return True
+        # Otherwise we proceed to check if it is a call to numba.stencil
+        require(call_name == ('stencil', 'numba.stencils.stencil') or
+                call_name == ('stencil', 'numba'))
+        require(expr not in self._processed_stencils)
+        self._processed_stencils.append(expr)
+        if not len(expr.args) == 1:
+            raise ValueError("As a minimum Stencil requires"
+                " a kernel as an argument")
+        stencil_def = guard(get_definition, self.func_ir, expr.args[0])
+        require(isinstance(stencil_def, ir.Expr) and
+                stencil_def.op == "make_function")
+        kernel_ir = get_ir_of_code(self.func_ir.func_id.func.__globals__,
+                stencil_def.code)
+        options = dict(expr.kws)
+        if 'neighborhood' in options:
+            fixed = guard(self._fix_stencil_neighborhood, options)
+            if not fixed:
+               raise ValueError("stencil neighborhood option should be a tuple"
+                        " with constant structure such as ((-w, w),)")
+        if 'index_offsets' in options:
+            fixed = guard(self._fix_stencil_index_offsets, options)
+            if not fixed:
+               raise ValueError("stencil index_offsets option should be a tuple"
+                        " with constant structure such as (offset, )")
+        sf = StencilFunc(kernel_ir, 'constant', options)
+        sf.kws = expr.kws # hack to keep variables live
+        sf_global = ir.Global('stencil', sf, expr.loc)
+        self.func_ir._definitions[lhs.name] = [sf_global]
+        instr.value = sf_global
+        return True
+
+    def _fix_stencil_neighborhood(self, options):
+        """
+        Extract the two-level tuple representing the stencil neighborhood
+        from the program IR to provide a tuple to StencilFunc.
+        """
+        # build_tuple node with neighborhood for each dimension
+        dims_build_tuple = get_definition(self.func_ir, options['neighborhood'])
+        require(hasattr(dims_build_tuple, 'items'))
+        res = []
+        for window_var in dims_build_tuple.items:
+            win_build_tuple = get_definition(self.func_ir, window_var)
+            require(hasattr(win_build_tuple, 'items'))
+            res.append(tuple(win_build_tuple.items))
+        options['neighborhood'] = tuple(res)
+        return True
+
+    def _fix_stencil_index_offsets(self, options):
+        """
+        Extract the tuple representing the stencil index offsets
+        from the program IR to provide to StencilFunc.
+        """
+        offset_tuple = get_definition(self.func_ir, options['index_offsets'])
+        require(hasattr(offset_tuple, 'items'))
+        options['index_offsets'] = tuple(offset_tuple.items)
+        return True
+
+    def _inline_closure(self, work_list, block, i, func_def):
+        require(isinstance(func_def, ir.Expr) and
+                func_def.op == "make_function")
+        inline_closure_call(self.func_ir,
+                            self.func_ir.func_id.func.__globals__,
+                            block, i, func_def, work_list=work_list,
+                            callee_validator=callee_ir_validator)
+        return True
+
+def check_reduce_func(func_ir, func_var):
+    """Checks the function at func_var in func_ir to make sure it's amenable
+    for inlining. Returns the function itself"""
+    reduce_func = guard(get_definition, func_ir, func_var)
+    if reduce_func is None:
+        raise ValueError("Reduce function cannot be found for njit \
+                            analysis")
+    if isinstance(reduce_func, (ir.FreeVar, ir.Global)):
+        if not isinstance(reduce_func.value,
+                          numba.core.registry.CPUDispatcher):
+            raise ValueError("Invalid reduction function")
+        # pull out the python function for inlining
+        reduce_func = reduce_func.value.py_func
+    elif not (hasattr(reduce_func, 'code')
+            or hasattr(reduce_func, '__code__')):
+        raise ValueError("Invalid reduction function")
+    f_code = (reduce_func.code if hasattr(reduce_func, 'code')
+                                    else reduce_func.__code__)
+    if not f_code.co_argcount == 2:
+        raise TypeError("Reduction function should take 2 arguments")
+    return reduce_func
+
+
+class InlineWorker(object):
+    """ A worker class for inlining, this is a more advanced version of
+    `inline_closure_call` in that it permits inlining from function type, Numba
+    IR and code object. It also, runs the entire untyped compiler pipeline on
+    the inlinee to ensure that it is transformed as though it were compiled
+    directly.
+    """
+
+    def __init__(self,
+                 typingctx=None,
+                 targetctx=None,
+                 locals=None,
+                 pipeline=None,
+                 flags=None,
+                 validator=callee_ir_validator,
+                 typemap=None,
+                 calltypes=None):
+        """
+        Instantiate a new InlineWorker, all arguments are optional though some
+        must be supplied together for certain use cases. The methods will refuse
+        to run if the object isn't configured in the manner needed. Args are the
+        same as those in a numba.core.Compiler.state, except the validator which
+        is a function taking Numba IR and validating it for use when inlining
+        (this is optional and really to just provide better error messages about
+        things which the inliner cannot handle like yield in closure).
+        """
+        def check(arg, name):
+            if arg is None:
+                raise TypeError("{} must not be None".format(name))
+
+        from numba.core.compiler import DefaultPassBuilder
+
+        # check the stuff needed to run the more advanced compilation pipeline
+        # is valid if any of it is provided
+        compiler_args = (targetctx, locals, pipeline, flags)
+        compiler_group = [x is not None for x in compiler_args]
+        if any(compiler_group) and not all(compiler_group):
+            check(targetctx, 'targetctx')
+            check(locals, 'locals')
+            check(pipeline, 'pipeline')
+            check(flags, 'flags')
+        elif all(compiler_group):
+            check(typingctx, 'typingctx')
+
+        self._compiler_pipeline = DefaultPassBuilder.define_untyped_pipeline
+
+        self.typingctx = typingctx
+        self.targetctx = targetctx
+        self.locals = locals
+        self.pipeline = pipeline
+        self.flags = flags
+        self.validator = validator
+        self.debug_print = _make_debug_print("InlineWorker")
+
+        # check whether this inliner can also support typemap and calltypes
+        # update and if what's provided is valid
+        pair = (typemap, calltypes)
+        pair_is_none = [x is None for x in pair]
+        if any(pair_is_none) and not all(pair_is_none):
+            msg = ("typemap and calltypes must both be either None or have a "
+                   "value, got: %s, %s")
+            raise TypeError(msg % pair)
+        self._permit_update_type_and_call_maps = not all(pair_is_none)
+        self.typemap = typemap
+        self.calltypes = calltypes
+
+
+    def inline_ir(self, caller_ir, block, i, callee_ir, callee_freevars,
+                  arg_typs=None):
+        """ Inlines the callee_ir in the caller_ir at statement index i of block
+        `block`, callee_freevars are the free variables for the callee_ir. If
+        the callee_ir is derived from a function `func` then this is
+        `func.__code__.co_freevars`. If `arg_typs` is given and the InlineWorker
+        instance was initialized with a typemap and calltypes then they will be
+        appropriately updated based on the arg_typs.
+        """
+
+        # Always copy the callee IR, it gets mutated
+        def copy_ir(the_ir):
+            kernel_copy = the_ir.copy()
+            kernel_copy.blocks = {}
+            for block_label, block in the_ir.blocks.items():
+                new_block = copy.deepcopy(the_ir.blocks[block_label])
+                new_block.body = []
+                for stmt in the_ir.blocks[block_label].body:
+                    scopy = copy.deepcopy(stmt)
+                    new_block.body.append(scopy)
+                kernel_copy.blocks[block_label] = new_block
+            return kernel_copy
+
+        callee_ir = copy_ir(callee_ir)
+
+        # check that the contents of the callee IR is something that can be
+        # inlined if a validator is present
+        if self.validator is not None:
+            self.validator(callee_ir)
+
+        # save an unmutated copy of the callee_ir to return
+        callee_ir_original = copy_ir(callee_ir)
+        scope = block.scope
+        instr = block.body[i]
+        call_expr = instr.value
+        callee_blocks = callee_ir.blocks
+
+        # 1. relabel callee_ir by adding an offset
+        max_label = max(ir_utils._the_max_label.next(), max(caller_ir.blocks.keys()))
+        callee_blocks = add_offset_to_labels(callee_blocks, max_label + 1)
+        callee_blocks = simplify_CFG(callee_blocks)
+        callee_ir.blocks = callee_blocks
+        min_label = min(callee_blocks.keys())
+        max_label = max(callee_blocks.keys())
+        #    reset globals in ir_utils before we use it
+        ir_utils._the_max_label.update(max_label)
+        self.debug_print("After relabel")
+        _debug_dump(callee_ir)
+
+        # 2. rename all local variables in callee_ir with new locals created in
+        # caller_ir
+        callee_scopes = _get_all_scopes(callee_blocks)
+        self.debug_print("callee_scopes = ", callee_scopes)
+        #    one function should only have one local scope
+        assert(len(callee_scopes) == 1)
+        callee_scope = callee_scopes[0]
+        var_dict = {}
+        for var in tuple(callee_scope.localvars._con.values()):
+            if not (var.name in callee_freevars):
+                inlined_name = _created_inlined_var_name(
+                    callee_ir.func_id.unique_name, var.name)
+                # Update the caller scope with the new names
+                new_var = scope.redefine(inlined_name, loc=var.loc)
+                # Also update the callee scope with the new names. Should the
+                # type and call maps need updating (which requires SSA form) the
+                # transformation to SSA is valid as the IR object is internally
+                # consistent.
+                callee_scope.redefine(inlined_name, loc=var.loc)
+                var_dict[var.name] = new_var
+        self.debug_print("var_dict = ", var_dict)
+        replace_vars(callee_blocks, var_dict)
+        self.debug_print("After local var rename")
+        _debug_dump(callee_ir)
+
+        # 3. replace formal parameters with actual arguments
+        callee_func = callee_ir.func_id.func
+        args = _get_callee_args(call_expr, callee_func, block.body[i].loc,
+                                caller_ir)
+
+        # 4. Update typemap
+        if self._permit_update_type_and_call_maps:
+            if arg_typs is None:
+                raise TypeError('arg_typs should have a value not None')
+            self.update_type_and_call_maps(callee_ir, arg_typs)
+            # update_type_and_call_maps replaces blocks
+            callee_blocks = callee_ir.blocks
+
+        self.debug_print("After arguments rename: ")
+        _debug_dump(callee_ir)
+
+        _replace_args_with(callee_blocks, args)
+        # 5. split caller blocks into two
+        new_blocks = []
+        new_block = ir.Block(scope, block.loc)
+        new_block.body = block.body[i + 1:]
+        new_label = next_label()
+        caller_ir.blocks[new_label] = new_block
+        new_blocks.append((new_label, new_block))
+        block.body = block.body[:i]
+        block.body.append(ir.Jump(min_label, instr.loc))
+
+        # 6. replace Return with assignment to LHS
+        topo_order = find_topo_order(callee_blocks)
+        _replace_returns(callee_blocks, instr.target, new_label)
+
+        # remove the old definition of instr.target too
+        if (instr.target.name in caller_ir._definitions
+                and call_expr in caller_ir._definitions[instr.target.name]):
+            # NOTE: target can have multiple definitions due to control flow
+            caller_ir._definitions[instr.target.name].remove(call_expr)
+
+        # 7. insert all new blocks, and add back definitions
+        for label in topo_order:
+            # block scope must point to parent's
+            block = callee_blocks[label]
+            block.scope = scope
+            _add_definitions(caller_ir, block)
+            caller_ir.blocks[label] = block
+            new_blocks.append((label, block))
+        self.debug_print("After merge in")
+        _debug_dump(caller_ir)
+
+        return callee_ir_original, callee_blocks, var_dict, new_blocks
+
+    def inline_function(self, caller_ir, block, i, function, arg_typs=None):
+        """ Inlines the function in the caller_ir at statement index i of block
+        `block`. If `arg_typs` is given and the InlineWorker instance was
+        initialized with a typemap and calltypes then they will be appropriately
+        updated based on the arg_typs.
+        """
+        callee_ir = self.run_untyped_passes(function)
+        freevars = function.__code__.co_freevars
+        return self.inline_ir(caller_ir, block, i, callee_ir, freevars,
+                              arg_typs=arg_typs)
+
+    def run_untyped_passes(self, func, enable_ssa=False):
+        """
+        Run the compiler frontend's untyped passes over the given Python
+        function, and return the function's canonical Numba IR.
+
+        Disable SSA transformation by default, since the call site won't be in SSA
+        form and self.inline_ir depends on this being the case.
+        """
+        from numba.core.compiler import StateDict, _CompileStatus
+        from numba.core.untyped_passes import ExtractByteCode, WithLifting
+        from numba.core import bytecode
+        from numba.parfors.parfor import ParforDiagnostics
+        state = StateDict()
+        state.func_ir = None
+        state.typingctx = self.typingctx
+        state.targetctx = self.targetctx
+        state.locals = self.locals
+        state.pipeline = self.pipeline
+        state.flags = self.flags
+        state.flags.enable_ssa = enable_ssa
+
+        state.func_id = bytecode.FunctionIdentity.from_function(func)
+
+        state.typemap = None
+        state.calltypes = None
+        state.type_annotation = None
+        state.status = _CompileStatus(False)
+        state.return_type = None
+        state.parfor_diagnostics = ParforDiagnostics()
+        state.metadata = {}
+
+        ExtractByteCode().run_pass(state)
+        # This is a lie, just need *some* args for the case where an obj mode
+        # with lift is needed
+        state.args = len(state.bc.func_id.pysig.parameters) * (types.pyobject,)
+
+        pm = self._compiler_pipeline(state)
+
+        pm.finalize()
+        pm.run(state)
+        return state.func_ir
+
+    def update_type_and_call_maps(self, callee_ir, arg_typs):
+        """ Updates the type and call maps based on calling callee_ir with arguments
+        from arg_typs"""
+        from numba.core.ssa import reconstruct_ssa
+        from numba.core.typed_passes import PreLowerStripPhis
+
+        if not self._permit_update_type_and_call_maps:
+            msg = ("InlineWorker instance not configured correctly, typemap or "
+                   "calltypes missing in initialization.")
+            raise ValueError(msg)
+        from numba.core import typed_passes
+        # call branch pruning to simplify IR and avoid inference errors
+        callee_ir._definitions = ir_utils.build_definitions(callee_ir.blocks)
+        numba.core.analysis.dead_branch_prune(callee_ir, arg_typs)
+        # callee's typing may require SSA
+        callee_ir = reconstruct_ssa(callee_ir)
+        callee_ir._definitions = ir_utils.build_definitions(callee_ir.blocks)
+        f_typemap, f_return_type, f_calltypes, _ = typed_passes.type_inference_stage(
+                self.typingctx, self.targetctx, callee_ir, arg_typs, None)
+        callee_ir = PreLowerStripPhis()._strip_phi_nodes(callee_ir)
+        callee_ir._definitions = ir_utils.build_definitions(callee_ir.blocks)
+        canonicalize_array_math(callee_ir, f_typemap,
+                                f_calltypes, self.typingctx)
+        # remove argument entries like arg.a from typemap
+        arg_names = [vname for vname in f_typemap if vname.startswith("arg.")]
+        for a in arg_names:
+            f_typemap.pop(a)
+        self.typemap.update(f_typemap)
+        self.calltypes.update(f_calltypes)
+
+
+def inline_closure_call(func_ir, glbls, block, i, callee, typingctx=None,
+                        targetctx=None, arg_typs=None, typemap=None,
+                        calltypes=None, work_list=None, callee_validator=None,
+                        replace_freevars=True):
+    """Inline the body of `callee` at its callsite (`i`-th instruction of `block`)
+
+    `func_ir` is the func_ir object of the caller function and `glbls` is its
+    global variable environment (func_ir.func_id.func.__globals__).
+    `block` is the IR block of the callsite and `i` is the index of the
+    callsite's node. `callee` is either the called function or a
+    make_function node. `typingctx`, `typemap` and `calltypes` are typing
+    data structures of the caller, available if we are in a typed pass.
+    `arg_typs` includes the types of the arguments at the callsite.
+    `callee_validator` is an optional callable which can be used to validate the
+    IR of the callee to ensure that it contains IR supported for inlining, it
+    takes one argument, the func_ir of the callee
+
+    Returns IR blocks of the callee and the variable renaming dictionary used
+    for them to facilitate further processing of new blocks.
+    """
+    scope = block.scope
+    instr = block.body[i]
+    call_expr = instr.value
+    debug_print = _make_debug_print("inline_closure_call")
+    debug_print("Found closure call: ", instr, " with callee = ", callee)
+    # support both function object and make_function Expr
+    callee_code = callee.code if hasattr(callee, 'code') else callee.__code__
+    callee_closure = callee.closure if hasattr(callee, 'closure') else callee.__closure__
+    # first, get the IR of the callee
+    if isinstance(callee, pytypes.FunctionType):
+        from numba.core import compiler
+        callee_ir = compiler.run_frontend(callee, inline_closures=True)
+    else:
+        callee_ir = get_ir_of_code(glbls, callee_code)
+
+    # check that the contents of the callee IR is something that can be inlined
+    # if a validator is supplied
+    if callee_validator is not None:
+        callee_validator(callee_ir)
+
+    callee_blocks = callee_ir.blocks
+
+    # 1. relabel callee_ir by adding an offset
+    max_label = max(ir_utils._the_max_label.next(), max(func_ir.blocks.keys()))
+    callee_blocks = add_offset_to_labels(callee_blocks, max_label + 1)
+    callee_blocks = simplify_CFG(callee_blocks)
+    callee_ir.blocks = callee_blocks
+    min_label = min(callee_blocks.keys())
+    max_label = max(callee_blocks.keys())
+    #    reset globals in ir_utils before we use it
+    ir_utils._the_max_label.update(max_label)
+    debug_print("After relabel")
+    _debug_dump(callee_ir)
+
+    # 2. rename all local variables in callee_ir with new locals created in func_ir
+    callee_scopes = _get_all_scopes(callee_blocks)
+    debug_print("callee_scopes = ", callee_scopes)
+    #    one function should only have one local scope
+    assert(len(callee_scopes) == 1)
+    callee_scope = callee_scopes[0]
+    var_dict = {}
+    for var in callee_scope.localvars._con.values():
+        if not (var.name in callee_code.co_freevars):
+            inlined_name = _created_inlined_var_name(
+                callee_ir.func_id.unique_name, var.name)
+            new_var = scope.redefine(inlined_name, loc=var.loc)
+            var_dict[var.name] = new_var
+    debug_print("var_dict = ", var_dict)
+    replace_vars(callee_blocks, var_dict)
+    debug_print("After local var rename")
+    _debug_dump(callee_ir)
+
+    # 3. replace formal parameters with actual arguments
+    args = _get_callee_args(call_expr, callee, block.body[i].loc, func_ir)
+
+    debug_print("After arguments rename: ")
+    _debug_dump(callee_ir)
+
+    # 4. replace freevar with actual closure var
+    if callee_closure and replace_freevars:
+        closure = func_ir.get_definition(callee_closure)
+        debug_print("callee's closure = ", closure)
+        if isinstance(closure, tuple):
+            cellget = ctypes.pythonapi.PyCell_Get
+            cellget.restype = ctypes.py_object
+            cellget.argtypes = (ctypes.py_object,)
+            items = tuple(cellget(x) for x in closure)
+        else:
+            assert(isinstance(closure, ir.Expr)
+                   and closure.op == 'build_tuple')
+            items = closure.items
+        assert(len(callee_code.co_freevars) == len(items))
+        _replace_freevars(callee_blocks, items)
+        debug_print("After closure rename")
+        _debug_dump(callee_ir)
+
+    if typingctx:
+        from numba.core import typed_passes
+        # call branch pruning to simplify IR and avoid inference errors
+        callee_ir._definitions = ir_utils.build_definitions(callee_ir.blocks)
+        numba.core.analysis.dead_branch_prune(callee_ir, arg_typs)
+        try:
+            f_typemap, f_return_type, f_calltypes, _ = typed_passes.type_inference_stage(
+                    typingctx, targetctx, callee_ir, arg_typs, None)
+        except Exception as e:
+            f_typemap, f_return_type, f_calltypes, _ = typed_passes.type_inference_stage(
+                    typingctx, targetctx, callee_ir, arg_typs, None)
+            pass
+        canonicalize_array_math(callee_ir, f_typemap,
+                                f_calltypes, typingctx)
+        # remove argument entries like arg.a from typemap
+        arg_names = [vname for vname in f_typemap if vname.startswith("arg.")]
+        for a in arg_names:
+            f_typemap.pop(a)
+        typemap.update(f_typemap)
+        calltypes.update(f_calltypes)
+
+    _replace_args_with(callee_blocks, args)
+    # 5. split caller blocks into two
+    new_blocks = []
+    new_block = ir.Block(scope, block.loc)
+    new_block.body = block.body[i + 1:]
+    new_label = next_label()
+    func_ir.blocks[new_label] = new_block
+    new_blocks.append((new_label, new_block))
+    block.body = block.body[:i]
+    block.body.append(ir.Jump(min_label, instr.loc))
+
+    # 6. replace Return with assignment to LHS
+    topo_order = find_topo_order(callee_blocks)
+    _replace_returns(callee_blocks, instr.target, new_label)
+
+    # remove the old definition of instr.target too
+    if (instr.target.name in func_ir._definitions
+            and call_expr in func_ir._definitions[instr.target.name]):
+        # NOTE: target can have multiple definitions due to control flow
+        func_ir._definitions[instr.target.name].remove(call_expr)
+
+    # 7. insert all new blocks, and add back definitions
+    for label in topo_order:
+        # block scope must point to parent's
+        block = callee_blocks[label]
+        block.scope = scope
+        _add_definitions(func_ir, block)
+        func_ir.blocks[label] = block
+        new_blocks.append((label, block))
+    debug_print("After merge in")
+    _debug_dump(func_ir)
+
+    if work_list is not None:
+        for block in new_blocks:
+            work_list.append(block)
+    return callee_blocks, var_dict
+
+
+def _get_callee_args(call_expr, callee, loc, func_ir):
+    """Get arguments for calling 'callee', including the default arguments.
+    keyword arguments are currently only handled when 'callee' is a function.
+    """
+    if call_expr.op == 'call':
+        args = list(call_expr.args)
+        if call_expr.vararg:
+            msg = "Calling a closure with *args is unsupported."
+            raise errors.UnsupportedError(msg, call_expr.loc)
+    elif call_expr.op == 'getattr':
+        args = [call_expr.value]
+    elif ir_utils.is_operator_or_getitem(call_expr):
+        args = call_expr.list_vars()
+    else:
+        raise TypeError("Unsupported ir.Expr.{}".format(call_expr.op))
+
+    debug_print = _make_debug_print("inline_closure_call default handling")
+
+    # handle defaults and kw arguments using pysignature if callee is function
+    if isinstance(callee, pytypes.FunctionType):
+        pysig = numba.core.utils.pysignature(callee)
+        normal_handler = lambda index, param, default: default
+        default_handler = lambda index, param, default: ir.Const(default, loc)
+        # Throw error for stararg
+        # TODO: handle stararg
+        def stararg_handler(index, param, default):
+            raise NotImplementedError(
+                "Stararg not supported in inliner for arg {} {}".format(
+                    index, param))
+        if call_expr.op == 'call':
+            kws = dict(call_expr.kws)
+        else:
+            kws = {}
+        return numba.core.typing.fold_arguments(
+            pysig, args, kws, normal_handler, default_handler,
+            stararg_handler)
+    else:
+        # TODO: handle arguments for make_function case similar to function
+        # case above
+        callee_defaults = (callee.defaults if hasattr(callee, 'defaults')
+                           else callee.__defaults__)
+        if callee_defaults:
+            debug_print("defaults = ", callee_defaults)
+            if isinstance(callee_defaults, tuple):  # Python 3.5
+                defaults_list = []
+                for x in callee_defaults:
+                    if isinstance(x, ir.Var):
+                        defaults_list.append(x)
+                    else:
+                        # this branch is predominantly for kwargs from
+                        # inlinable functions
+                        defaults_list.append(ir.Const(value=x, loc=loc))
+                args = args + defaults_list
+            elif (isinstance(callee_defaults, ir.Var)
+                    or isinstance(callee_defaults, str)):
+                default_tuple = func_ir.get_definition(callee_defaults)
+                assert(isinstance(default_tuple, ir.Expr))
+                assert(default_tuple.op == "build_tuple")
+                const_vals = [func_ir.get_definition(x) for
+                              x in default_tuple.items]
+                args = args + const_vals
+            else:
+                raise NotImplementedError(
+                    "Unsupported defaults to make_function: {}".format(
+                        defaults))
+        return args
+
+
+def _make_debug_print(prefix):
+    def debug_print(*args):
+        if config.DEBUG_INLINE_CLOSURE:
+            print(prefix + ": " + "".join(str(x) for x in args))
+    return debug_print
+
+
+def _debug_dump(func_ir):
+    if config.DEBUG_INLINE_CLOSURE:
+        func_ir.dump()
+
+
+def _get_all_scopes(blocks):
+    """Get all block-local scopes from an IR.
+    """
+    all_scopes = []
+    for label, block in blocks.items():
+        if not (block.scope in all_scopes):
+            all_scopes.append(block.scope)
+    return all_scopes
+
+
+def _replace_args_with(blocks, args):
+    """
+    Replace ir.Arg(...) with real arguments from call site
+    """
+    for label, block in blocks.items():
+        assigns = block.find_insts(ir.Assign)
+        for stmt in assigns:
+            if isinstance(stmt.value, ir.Arg):
+                idx = stmt.value.index
+                assert(idx < len(args))
+                stmt.value = args[idx]
+
+
+def _replace_freevars(blocks, args):
+    """
+    Replace ir.FreeVar(...) with real variables from parent function
+    """
+    for label, block in blocks.items():
+        assigns = block.find_insts(ir.Assign)
+        for stmt in assigns:
+            if isinstance(stmt.value, ir.FreeVar):
+                idx = stmt.value.index
+                assert(idx < len(args))
+                if isinstance(args[idx], ir.Var):
+                    stmt.value = args[idx]
+                else:
+                    stmt.value = ir.Const(args[idx], stmt.loc)
+
+
+def _replace_returns(blocks, target, return_label):
+    """
+    Return return statement by assigning directly to target, and a jump.
+    """
+    for label, block in blocks.items():
+        casts = []
+        for i in range(len(block.body)):
+            stmt = block.body[i]
+            if isinstance(stmt, ir.Return):
+                assert(i + 1 == len(block.body))
+                block.body[i] = ir.Assign(stmt.value, target, stmt.loc)
+                block.body.append(ir.Jump(return_label, stmt.loc))
+                # remove cast of the returned value
+                for cast in casts:
+                    if cast.target.name == stmt.value.name:
+                        cast.value = cast.value.value
+            elif isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Expr) and stmt.value.op == 'cast':
+                casts.append(stmt)
+
+def _add_definitions(func_ir, block):
+    """
+    Add variable definitions found in a block to parent func_ir.
+    """
+    definitions = func_ir._definitions
+    assigns = block.find_insts(ir.Assign)
+    for stmt in assigns:
+        definitions[stmt.target.name].append(stmt.value)
+
+def _find_arraycall(func_ir, block):
+    """Look for statement like "x = numpy.array(y)" or "x[..] = y"
+    immediately after the closure call that creates list y (the i-th
+    statement in block).  Return the statement index if found, or
+    raise GuardException.
+    """
+    array_var = None
+    array_call_index = None
+    list_var_dead_after_array_call = False
+    list_var = None
+
+    i = 0
+    while i < len(block.body):
+        instr = block.body[i]
+        if isinstance(instr, ir.Del):
+            # Stop the process if list_var becomes dead
+            if list_var and array_var and instr.value == list_var.name:
+                list_var_dead_after_array_call = True
+                break
+            pass
+        elif isinstance(instr, ir.Assign):
+            # Found array_var = array(list_var)
+            lhs  = instr.target
+            expr = instr.value
+            if (guard(find_callname, func_ir, expr) == ('array', 'numpy') and
+                isinstance(expr.args[0], ir.Var)):
+                list_var = expr.args[0]
+                array_var = lhs
+                array_stmt_index = i
+                array_kws = dict(expr.kws)
+        elif (isinstance(instr, ir.SetItem) and
+              isinstance(instr.value, ir.Var) and
+              not list_var):
+            list_var = instr.value
+            # Found array_var[..] = list_var, the case for nested array
+            array_var = instr.target
+            array_def = get_definition(func_ir, array_var)
+            require(guard(_find_unsafe_empty_inferred, func_ir, array_def))
+            array_stmt_index = i
+            array_kws = {}
+        else:
+            # Bail out otherwise
+            break
+        i = i + 1
+    # require array_var is found, and list_var is dead after array_call.
+    require(array_var and list_var_dead_after_array_call)
+    _make_debug_print("find_array_call")(block.body[array_stmt_index])
+    return list_var, array_stmt_index, array_kws
+
+
+def _find_iter_range(func_ir, range_iter_var, swapped):
+    """Find the iterator's actual range if it is either range(n), or range(m, n),
+    otherwise return raise GuardException.
+    """
+    debug_print = _make_debug_print("find_iter_range")
+    range_iter_def = get_definition(func_ir, range_iter_var)
+    debug_print("range_iter_var = ", range_iter_var, " def = ", range_iter_def)
+    require(isinstance(range_iter_def, ir.Expr) and range_iter_def.op == 'getiter')
+    range_var = range_iter_def.value
+    range_def = get_definition(func_ir, range_var)
+    debug_print("range_var = ", range_var, " range_def = ", range_def)
+    require(isinstance(range_def, ir.Expr) and range_def.op == 'call')
+    func_var = range_def.func
+    func_def = get_definition(func_ir, func_var)
+    debug_print("func_var = ", func_var, " func_def = ", func_def)
+    require(isinstance(func_def, ir.Global) and
+            (func_def.value == range or func_def.value == numba.misc.special.prange))
+    nargs = len(range_def.args)
+    swapping = [('"array comprehension"', 'closure of'), range_def.func.loc]
+    if nargs == 1:
+        swapped[range_def.func.name] = swapping
+        stop = get_definition(func_ir, range_def.args[0], lhs_only=True)
+        return (0, range_def.args[0], func_def)
+    elif nargs == 2:
+        swapped[range_def.func.name] = swapping
+        start = get_definition(func_ir, range_def.args[0], lhs_only=True)
+        stop = get_definition(func_ir, range_def.args[1], lhs_only=True)
+        return (start, stop, func_def)
+    else:
+        raise GuardException
+
+def _inline_arraycall(func_ir, cfg, visited, loop, swapped, enable_prange=False,
+                      typed=False):
+    """Look for array(list) call in the exit block of a given loop, and turn list operations into
+    array operations in the loop if the following conditions are met:
+      1. The exit block contains an array call on the list;
+      2. The list variable is no longer live after array call;
+      3. The list is created in the loop entry block;
+      4. The loop is created from an range iterator whose length is known prior to the loop;
+      5. There is only one list_append operation on the list variable in the loop body;
+      6. The block that contains list_append dominates the loop head, which ensures list
+         length is the same as loop length;
+    If any condition check fails, no modification will be made to the incoming IR.
+    """
+    debug_print = _make_debug_print("inline_arraycall")
+    # There should only be one loop exit
+    require(len(loop.exits) == 1)
+    exit_block = next(iter(loop.exits))
+    list_var, array_call_index, array_kws = _find_arraycall(func_ir, func_ir.blocks[exit_block])
+
+    # check if dtype is present in array call
+    dtype_def = None
+    dtype_mod_def = None
+    if 'dtype' in array_kws:
+        require(isinstance(array_kws['dtype'], ir.Var))
+        # We require that dtype argument to be a constant of getattr Expr, and we'll
+        # remember its definition for later use.
+        dtype_def = get_definition(func_ir, array_kws['dtype'])
+        require(isinstance(dtype_def, ir.Expr) and dtype_def.op == 'getattr')
+        dtype_mod_def = get_definition(func_ir, dtype_def.value)
+
+    list_var_def = get_definition(func_ir, list_var)
+    debug_print("list_var = ", list_var, " def = ", list_var_def)
+    if isinstance(list_var_def, ir.Expr) and list_var_def.op == 'cast':
+        list_var_def = get_definition(func_ir, list_var_def.value)
+    # Check if the definition is a build_list
+    require(isinstance(list_var_def, ir.Expr) and list_var_def.op ==  'build_list')
+    # The build_list must be empty
+    require(len(list_var_def.items) == 0)
+
+    # Look for list_append in "last" block in loop body, which should be a block that is
+    # a post-dominator of the loop header.
+    list_append_stmts = []
+    for label in loop.body:
+        # We have to consider blocks of this loop, but not sub-loops.
+        # To achieve this, we require the set of "in_loops" of "label" to be visited loops.
+        in_visited_loops = [l.header in visited for l in cfg.in_loops(label)]
+        if not all(in_visited_loops):
+            continue
+        block = func_ir.blocks[label]
+        debug_print("check loop body block ", label)
+        for stmt in block.find_insts(ir.Assign):
+            lhs = stmt.target
+            expr = stmt.value
+            if isinstance(expr, ir.Expr) and expr.op == 'call':
+                func_def = get_definition(func_ir, expr.func)
+                if isinstance(func_def, ir.Expr) and func_def.op == 'getattr' \
+                  and func_def.attr == 'append':
+                    list_def = get_definition(func_ir, func_def.value)
+                    debug_print("list_def = ", list_def, list_def is list_var_def)
+                    if list_def is list_var_def:
+                        # found matching append call
+                        list_append_stmts.append((label, block, stmt))
+
+    # Require only one list_append, otherwise we won't know the indices
+    require(len(list_append_stmts) == 1)
+    append_block_label, append_block, append_stmt = list_append_stmts[0]
+
+    # Check if append_block (besides loop entry) dominates loop header.
+    # Since CFG doesn't give us this info without loop entry, we approximate
+    # by checking if the predecessor set of the header block is the same
+    # as loop_entries plus append_block, which is certainly more restrictive
+    # than necessary, and can be relaxed if needed.
+    preds = set(l for l, b in cfg.predecessors(loop.header))
+    debug_print("preds = ", preds, (loop.entries | set([append_block_label])))
+    require(preds == (loop.entries | set([append_block_label])))
+
+    # Find iterator in loop header
+    iter_vars = []
+    iter_first_vars = []
+    loop_header = func_ir.blocks[loop.header]
+    for stmt in loop_header.find_insts(ir.Assign):
+        expr = stmt.value
+        if isinstance(expr, ir.Expr):
+            if expr.op == 'iternext':
+                iter_def = get_definition(func_ir, expr.value)
+                debug_print("iter_def = ", iter_def)
+                iter_vars.append(expr.value)
+            elif expr.op == 'pair_first':
+                iter_first_vars.append(stmt.target)
+
+    # Require only one iterator in loop header
+    require(len(iter_vars) == 1 and len(iter_first_vars) == 1)
+    iter_var = iter_vars[0] # variable that holds the iterator object
+    iter_first_var = iter_first_vars[0] # variable that holds the value out of iterator
+
+    # Final requirement: only one loop entry, and we're going to modify it by:
+    # 1. replacing the list definition with an array definition;
+    # 2. adding a counter for the array iteration.
+    require(len(loop.entries) == 1)
+    loop_entry = func_ir.blocks[next(iter(loop.entries))]
+    terminator = loop_entry.terminator
+    scope = loop_entry.scope
+    loc = loop_entry.loc
+    stmts = []
+    removed = []
+    def is_removed(val, removed):
+        if isinstance(val, ir.Var):
+            for x in removed:
+                if x.name == val.name:
+                    return True
+        return False
+    # Skip list construction and skip terminator, add the rest to stmts
+    for i in range(len(loop_entry.body) - 1):
+        stmt = loop_entry.body[i]
+        if isinstance(stmt, ir.Assign) and (stmt.value is list_def or is_removed(stmt.value, removed)):
+            removed.append(stmt.target)
+        else:
+            stmts.append(stmt)
+    debug_print("removed variables: ", removed)
+
+    # Define an index_var to index the array.
+    # If the range happens to be single step ranges like range(n), or range(m, n),
+    # then the index_var correlates to iterator index; otherwise we'll have to
+    # define a new counter.
+    range_def = guard(_find_iter_range, func_ir, iter_var, swapped)
+    index_var = ir.Var(scope, mk_unique_var("index"), loc)
+    if range_def and range_def[0] == 0:
+        # iterator starts with 0, index_var can just be iter_first_var
+        index_var = iter_first_var
+    else:
+        # index_var = -1 # starting the index with -1 since it will incremented in loop header
+        stmts.append(_new_definition(func_ir, index_var, ir.Const(value=-1, loc=loc), loc))
+
+    # Insert statement to get the size of the loop iterator
+    size_var = ir.Var(scope, mk_unique_var("size"), loc)
+    if range_def:
+        start, stop, range_func_def = range_def
+        if start == 0:
+            size_val = stop
+        else:
+            size_val = ir.Expr.binop(fn=operator.sub, lhs=stop, rhs=start, loc=loc)
+        # we can parallelize this loop if enable_prange = True, by changing
+        # range function from range, to prange.
+        if enable_prange and isinstance(range_func_def, ir.Global):
+            range_func_def.name = 'internal_prange'
+            range_func_def.value = internal_prange
+
+    else:
+        # this doesn't work in objmode as it's effectively untyped
+        if typed:
+            len_func_var = ir.Var(scope, mk_unique_var("len_func"), loc)
+            from numba.cpython.rangeobj import length_of_iterator
+            stmts.append(_new_definition(func_ir, len_func_var,
+                                         ir.Global('length_of_iterator',
+                                                   length_of_iterator,
+                                                   loc=loc),
+                                         loc))
+            size_val = ir.Expr.call(len_func_var, (iter_var,), (), loc=loc)
+        else:
+            raise GuardException
+
+
+    stmts.append(_new_definition(func_ir, size_var, size_val, loc))
+
+    size_tuple_var = ir.Var(scope, mk_unique_var("size_tuple"), loc)
+    stmts.append(_new_definition(func_ir, size_tuple_var,
+                 ir.Expr.build_tuple(items=[size_var], loc=loc), loc))
+
+    # Insert array allocation
+    array_var = ir.Var(scope, mk_unique_var("array"), loc)
+    empty_func = ir.Var(scope, mk_unique_var("empty_func"), loc)
+    if dtype_def and dtype_mod_def:
+        # when dtype is present, we'll call empty with dtype
+        dtype_mod_var = ir.Var(scope, mk_unique_var("dtype_mod"), loc)
+        dtype_var = ir.Var(scope, mk_unique_var("dtype"), loc)
+        stmts.append(_new_definition(func_ir, dtype_mod_var, dtype_mod_def, loc))
+        stmts.append(_new_definition(func_ir, dtype_var,
+                         ir.Expr.getattr(dtype_mod_var, dtype_def.attr, loc), loc))
+        stmts.append(_new_definition(func_ir, empty_func,
+                         ir.Global('empty', np.empty, loc=loc), loc))
+        array_kws = [('dtype', dtype_var)]
+    else:
+        # this doesn't work in objmode as it's effectively untyped
+        if typed:
+            # otherwise we'll call unsafe_empty_inferred
+            stmts.append(_new_definition(func_ir, empty_func,
+                            ir.Global('unsafe_empty_inferred',
+                                unsafe_empty_inferred, loc=loc), loc))
+            array_kws = []
+        else:
+            raise GuardException
+
+    # array_var = empty_func(size_tuple_var)
+    stmts.append(_new_definition(func_ir, array_var,
+                 ir.Expr.call(empty_func, (size_tuple_var,), list(array_kws), loc=loc), loc))
+
+    # Add back removed just in case they are used by something else
+    for var in removed:
+        stmts.append(_new_definition(func_ir, var, array_var, loc))
+
+    # Add back terminator
+    stmts.append(terminator)
+    # Modify loop_entry
+    loop_entry.body = stmts
+
+    if range_def:
+        if range_def[0] != 0:
+            # when range doesn't start from 0, index_var becomes loop index
+            # (iter_first_var) minus an offset (range_def[0])
+            terminator = loop_header.terminator
+            assert(isinstance(terminator, ir.Branch))
+            # find the block in the loop body that header jumps to
+            block_id = terminator.truebr
+            blk = func_ir.blocks[block_id]
+            loc = blk.loc
+            blk.body.insert(0, _new_definition(func_ir, index_var,
+                ir.Expr.binop(fn=operator.sub, lhs=iter_first_var,
+                                      rhs=range_def[0], loc=loc),
+                loc))
+    else:
+        # Insert index_var increment to the end of loop header
+        loc = loop_header.loc
+        terminator = loop_header.terminator
+        stmts = loop_header.body[0:-1]
+        next_index_var = ir.Var(scope, mk_unique_var("next_index"), loc)
+        one = ir.Var(scope, mk_unique_var("one"), loc)
+        # one = 1
+        stmts.append(_new_definition(func_ir, one,
+                     ir.Const(value=1,loc=loc), loc))
+        # next_index_var = index_var + 1
+        stmts.append(_new_definition(func_ir, next_index_var,
+                     ir.Expr.binop(fn=operator.add, lhs=index_var, rhs=one, loc=loc), loc))
+        # index_var = next_index_var
+        stmts.append(_new_definition(func_ir, index_var, next_index_var, loc))
+        stmts.append(terminator)
+        loop_header.body = stmts
+
+    # In append_block, change list_append into array assign
+    for i in range(len(append_block.body)):
+        if append_block.body[i] is append_stmt:
+            debug_print("Replace append with SetItem")
+            append_block.body[i] = ir.SetItem(target=array_var, index=index_var,
+                                              value=append_stmt.value.args[0], loc=append_stmt.loc)
+
+    # replace array call, by changing "a = array(b)" to "a = b"
+    stmt = func_ir.blocks[exit_block].body[array_call_index]
+    # stmt can be either array call or SetItem, we only replace array call
+    if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Expr):
+        stmt.value = array_var
+        func_ir._definitions[stmt.target.name] = [stmt.value]
+
+    return True
+
+
+def _find_unsafe_empty_inferred(func_ir, expr):
+    unsafe_empty_inferred
+    require(isinstance(expr, ir.Expr) and expr.op == 'call')
+    callee = expr.func
+    callee_def = get_definition(func_ir, callee)
+    require(isinstance(callee_def, ir.Global))
+    _make_debug_print("_find_unsafe_empty_inferred")(callee_def.value)
+    return callee_def.value == unsafe_empty_inferred
+
+
+def _fix_nested_array(func_ir):
+    """Look for assignment like: a[..] = b, where both a and b are numpy arrays, and
+    try to eliminate array b by expanding a with an extra dimension.
+    """
+    blocks = func_ir.blocks
+    cfg = compute_cfg_from_blocks(blocks)
+    usedefs = compute_use_defs(blocks)
+    empty_deadmap = dict([(label, set()) for label in blocks.keys()])
+    livemap = compute_live_variables(cfg, blocks, usedefs.defmap, empty_deadmap)
+
+    def find_array_def(arr):
+        """Find numpy array definition such as
+            arr = numba.unsafe.ndarray.empty_inferred(...).
+        If it is arr = b[...], find array definition of b recursively.
+        """
+        arr_def = get_definition(func_ir, arr)
+        _make_debug_print("find_array_def")(arr, arr_def)
+        if isinstance(arr_def, ir.Expr):
+            if guard(_find_unsafe_empty_inferred, func_ir, arr_def):
+                return arr_def
+            elif arr_def.op == 'getitem':
+                return find_array_def(arr_def.value)
+        raise GuardException
+
+    def fix_dependencies(expr, varlist):
+        """Double check if all variables in varlist are defined before
+        expr is used. Try to move constant definition when the check fails.
+        Bails out by raising GuardException if it can't be moved.
+        """
+        debug_print = _make_debug_print("fix_dependencies")
+        for label, block in blocks.items():
+            scope = block.scope
+            body = block.body
+            defined = set()
+            for i in range(len(body)):
+                inst = body[i]
+                if isinstance(inst, ir.Assign):
+                    defined.add(inst.target.name)
+                    if inst.value is expr:
+                        new_varlist = []
+                        for var in varlist:
+                            # var must be defined before this inst, or live
+                            # and not later defined.
+                            if (var.name in defined or
+                                (var.name in livemap[label] and
+                                 not (var.name in usedefs.defmap[label]))):
+                                debug_print(var.name, " already defined")
+                                new_varlist.append(var)
+                            else:
+                                debug_print(var.name, " not yet defined")
+                                var_def = get_definition(func_ir, var.name)
+                                if isinstance(var_def, ir.Const):
+                                    loc = var.loc
+                                    new_var = ir.Var(scope, mk_unique_var("new_var"), loc)
+                                    new_const = ir.Const(var_def.value, loc)
+                                    new_vardef = _new_definition(func_ir,
+                                                    new_var, new_const, loc)
+                                    new_body = []
+                                    new_body.extend(body[:i])
+                                    new_body.append(new_vardef)
+                                    new_body.extend(body[i:])
+                                    block.body = new_body
+                                    new_varlist.append(new_var)
+                                else:
+                                    raise GuardException
+                        return new_varlist
+        # when expr is not found in block
+        raise GuardException
+
+    def fix_array_assign(stmt):
+        """For assignment like lhs[idx] = rhs, where both lhs and rhs are arrays, do the
+        following:
+        1. find the definition of rhs, which has to be a call to numba.unsafe.ndarray.empty_inferred
+        2. find the source array creation for lhs, insert an extra dimension of size of b.
+        3. replace the definition of rhs = numba.unsafe.ndarray.empty_inferred(...) with rhs = lhs[idx]
+        """
+        require(isinstance(stmt, ir.SetItem))
+        require(isinstance(stmt.value, ir.Var))
+        debug_print = _make_debug_print("fix_array_assign")
+        debug_print("found SetItem: ", stmt)
+        lhs = stmt.target
+        # Find the source array creation of lhs
+        lhs_def = find_array_def(lhs)
+        debug_print("found lhs_def: ", lhs_def)
+        rhs_def = get_definition(func_ir, stmt.value)
+        debug_print("found rhs_def: ", rhs_def)
+        require(isinstance(rhs_def, ir.Expr))
+        if rhs_def.op == 'cast':
+            rhs_def = get_definition(func_ir, rhs_def.value)
+            require(isinstance(rhs_def, ir.Expr))
+        require(_find_unsafe_empty_inferred(func_ir, rhs_def))
+        # Find the array dimension of rhs
+        dim_def = get_definition(func_ir, rhs_def.args[0])
+        require(isinstance(dim_def, ir.Expr) and dim_def.op == 'build_tuple')
+        debug_print("dim_def = ", dim_def)
+        extra_dims = [ get_definition(func_ir, x, lhs_only=True) for x in dim_def.items ]
+        debug_print("extra_dims = ", extra_dims)
+        # Expand size tuple when creating lhs_def with extra_dims
+        size_tuple_def = get_definition(func_ir, lhs_def.args[0])
+        require(isinstance(size_tuple_def, ir.Expr) and size_tuple_def.op == 'build_tuple')
+        debug_print("size_tuple_def = ", size_tuple_def)
+        extra_dims = fix_dependencies(size_tuple_def, extra_dims)
+        size_tuple_def.items += extra_dims
+        # In-place modify rhs_def to be getitem
+        rhs_def.op = 'getitem'
+        rhs_def.fn = operator.getitem
+        rhs_def.value = get_definition(func_ir, lhs, lhs_only=True)
+        rhs_def.index = stmt.index
+        del rhs_def._kws['func']
+        del rhs_def._kws['args']
+        del rhs_def._kws['vararg']
+        del rhs_def._kws['kws']
+        # success
+        return True
+
+    for label in find_topo_order(func_ir.blocks):
+        block = func_ir.blocks[label]
+        for stmt in block.body:
+            if guard(fix_array_assign, stmt):
+                block.body.remove(stmt)
+
+def _new_definition(func_ir, var, value, loc):
+    func_ir._definitions[var.name] = [value]
+    return ir.Assign(value=value, target=var, loc=loc)
+
+@rewrites.register_rewrite('after-inference')
+class RewriteArrayOfConsts(rewrites.Rewrite):
+    '''The RewriteArrayOfConsts class is responsible for finding
+    1D array creations from a constant list, and rewriting it into
+    direct initialization of array elements without creating the list.
+    '''
+    def __init__(self, state, *args, **kws):
+        self.typingctx = state.typingctx
+        super(RewriteArrayOfConsts, self).__init__(*args, **kws)
+
+    def match(self, func_ir, block, typemap, calltypes):
+        if len(calltypes) == 0:
+            return False
+        self.crnt_block = block
+        self.new_body = guard(_inline_const_arraycall, block, func_ir,
+                              self.typingctx, typemap, calltypes)
+        return self.new_body is not None
+
+    def apply(self):
+        self.crnt_block.body = self.new_body
+        return self.crnt_block
+
+
+def _inline_const_arraycall(block, func_ir, context, typemap, calltypes):
+    """Look for array(list) call where list is a constant list created by build_list,
+    and turn them into direct array creation and initialization, if the following
+    conditions are met:
+      1. The build_list call immediate precedes the array call;
+      2. The list variable is no longer live after array call;
+    If any condition check fails, no modification will be made.
+    """
+    debug_print = _make_debug_print("inline_const_arraycall")
+    scope = block.scope
+
+    def inline_array(array_var, expr, stmts, list_vars, dels):
+        """Check to see if the given "array_var" is created from a list
+        of constants, and try to inline the list definition as array
+        initialization.
+
+        Extra statements produced with be appended to "stmts".
+        """
+        callname = guard(find_callname, func_ir, expr)
+        require(callname and callname[1] == 'numpy' and callname[0] == 'array')
+        require(expr.args[0].name in list_vars)
+        ret_type = calltypes[expr].return_type
+        require(isinstance(ret_type, types.ArrayCompatible) and
+                           ret_type.ndim == 1)
+        loc = expr.loc
+        list_var = expr.args[0]
+        # Get the type of the array to be created.
+        array_typ = typemap[array_var.name]
+        debug_print("inline array_var = ", array_var, " list_var = ", list_var)
+        # Get the element type of the array to be created.
+        dtype = array_typ.dtype
+        # Get the sequence of operations to provide values to the new array.
+        seq, _ = find_build_sequence(func_ir, list_var)
+        size = len(seq)
+        # Create a tuple to pass to empty below to specify the new array size.
+        size_var = ir.Var(scope, mk_unique_var("size"), loc)
+        size_tuple_var = ir.Var(scope, mk_unique_var("size_tuple"), loc)
+        size_typ = types.intp
+        size_tuple_typ = types.UniTuple(size_typ, 1)
+        typemap[size_var.name] = size_typ
+        typemap[size_tuple_var.name] = size_tuple_typ
+        stmts.append(_new_definition(func_ir, size_var,
+                 ir.Const(size, loc=loc), loc))
+        stmts.append(_new_definition(func_ir, size_tuple_var,
+                 ir.Expr.build_tuple(items=[size_var], loc=loc), loc))
+
+        # The general approach is to create an empty array and then fill
+        # the elements in one-by-one from their specification.
+
+        # Get the numpy type to pass to empty.
+        nptype = types.DType(dtype)
+
+        # Create a variable to hold the numpy empty function.
+        empty_func = ir.Var(scope, mk_unique_var("empty_func"), loc)
+        fnty = get_np_ufunc_typ(np.empty)
+        sig = context.resolve_function_type(fnty, (size_typ,), {'dtype':nptype})
+
+        typemap[empty_func.name] = fnty
+
+        stmts.append(_new_definition(func_ir, empty_func,
+                         ir.Global('empty', np.empty, loc=loc), loc))
+
+        # We pass two arguments to empty, first the size tuple and second
+        # the dtype of the new array.  Here, we created typ_var which is
+        # the dtype argument of the new array.  typ_var in turn is created
+        # by getattr of the dtype string on the numpy module.
+
+        # Create var for numpy module.
+        g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc)
+        typemap[g_np_var.name] = types.misc.Module(np)
+        g_np = ir.Global('np', np, loc)
+        stmts.append(_new_definition(func_ir, g_np_var, g_np, loc))
+
+        # Create var for result of numpy.<dtype>.
+        typ_var = ir.Var(scope, mk_unique_var("$np_typ_var"), loc)
+        typemap[typ_var.name] = nptype
+        dtype_str = str(dtype)
+        if dtype_str == 'bool':
+            dtype_str = 'bool_'
+        # Get dtype attribute of numpy module.
+        np_typ_getattr = ir.Expr.getattr(g_np_var, dtype_str, loc)
+        stmts.append(_new_definition(func_ir, typ_var, np_typ_getattr, loc))
+
+        # Create the call to numpy.empty passing the size tuple and dtype var.
+        empty_call = ir.Expr.call(empty_func, [size_var, typ_var], {}, loc=loc)
+        calltypes[empty_call] = typing.signature(array_typ, size_typ, nptype)
+        stmts.append(_new_definition(func_ir, array_var, empty_call, loc))
+
+        # Fill in the new empty array one-by-one.
+        for i in range(size):
+            index_var = ir.Var(scope, mk_unique_var("index"), loc)
+            index_typ = types.intp
+            typemap[index_var.name] = index_typ
+            stmts.append(_new_definition(func_ir, index_var,
+                    ir.Const(i, loc), loc))
+            setitem = ir.SetItem(array_var, index_var, seq[i], loc)
+            calltypes[setitem] = typing.signature(types.none, array_typ,
+                                                  index_typ, dtype)
+            stmts.append(setitem)
+
+        stmts.extend(dels)
+        return True
+
+    class State(object):
+        """
+        This class is used to hold the state in the following loop so as to make
+        it easy to reset the state of the variables tracking the various
+        statement kinds
+        """
+
+        def __init__(self):
+            # list_vars keep track of the variable created from the latest
+            # build_list instruction, as well as its synonyms.
+            self.list_vars = []
+            # dead_vars keep track of those in list_vars that are considered dead.
+            self.dead_vars = []
+            # list_items keep track of the elements used in build_list.
+            self.list_items = []
+            self.stmts = []
+            # dels keep track of the deletion of list_items, which will need to be
+            # moved after array initialization.
+            self.dels = []
+            # tracks if a modification has taken place
+            self.modified = False
+
+        def reset(self):
+            """
+            Resets the internal state of the variables used for tracking
+            """
+            self.list_vars = []
+            self.dead_vars = []
+            self.list_items = []
+            self.dels = []
+
+        def list_var_used(self, inst):
+            """
+            Returns True if the list being analysed is used between the
+            build_list and the array call.
+            """
+            return any([x.name in self.list_vars for x in inst.list_vars()])
+
+    state = State()
+
+    for inst in block.body:
+        if isinstance(inst, ir.Assign):
+            if isinstance(inst.value, ir.Var):
+                if inst.value.name in state.list_vars:
+                    state.list_vars.append(inst.target.name)
+                    state.stmts.append(inst)
+                    continue
+            elif isinstance(inst.value, ir.Expr):
+                expr = inst.value
+                if expr.op == 'build_list':
+                    # new build_list encountered, reset state
+                    state.reset()
+                    state.list_items = [x.name for x in expr.items]
+                    state.list_vars = [inst.target.name]
+                    state.stmts.append(inst)
+                    continue
+                elif expr.op == 'call' and expr in calltypes:
+                    arr_var = inst.target
+                    if guard(inline_array, inst.target, expr,
+                             state.stmts, state.list_vars, state.dels):
+                        state.modified = True
+                        continue
+        elif isinstance(inst, ir.Del):
+            removed_var = inst.value
+            if removed_var in state.list_items:
+                state.dels.append(inst)
+                continue
+            elif removed_var in state.list_vars:
+                # one of the list_vars is considered dead.
+                state.dead_vars.append(removed_var)
+                state.list_vars.remove(removed_var)
+                state.stmts.append(inst)
+                if state.list_vars == []:
+                    # if all list_vars are considered dead, we need to filter
+                    # them out from existing stmts to completely remove
+                    # build_list.
+                    # Note that if a translation didn't take place, dead_vars
+                    # will also be empty when we reach this point.
+                    body = []
+                    for inst in state.stmts:
+                        if ((isinstance(inst, ir.Assign) and
+                             inst.target.name in state.dead_vars) or
+                             (isinstance(inst, ir.Del) and
+                             inst.value in state.dead_vars)):
+                            continue
+                        body.append(inst)
+                    state.stmts = body
+                    state.dead_vars = []
+                    state.modified = True
+                    continue
+        state.stmts.append(inst)
+
+        # If the list is used in any capacity between build_list and array
+        # call, then we must call off the translation for this list because
+        # it could be mutated and list_items would no longer be applicable.
+        if state.list_var_used(inst):
+            state.reset()
+
+    return state.stmts if state.modified else None
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/interpreter.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/interpreter.py
new file mode 100644
index 000000000..71ea49831
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/interpreter.py
@@ -0,0 +1,2876 @@
+import builtins
+import collections
+import dis
+import operator
+import logging
+import textwrap
+
+from numba.core import errors, dataflow, controlflow, ir, config
+from numba.core.errors import NotDefinedError, UnsupportedError, error_extras
+from numba.core.ir_utils import get_definition, guard
+from numba.core.utils import (PYVERSION, BINOPS_TO_OPERATORS,
+                              INPLACE_BINOPS_TO_OPERATORS,)
+from numba.core.byteflow import Flow, AdaptDFA, AdaptCFA
+from numba.core.unsafe import eh
+from numba.cpython.unsafe.tuple import unpack_single_tuple
+
+
+class _UNKNOWN_VALUE(object):
+    """Represents an unknown value, this is for ease of debugging purposes only.
+    """
+
+    def __init__(self, varname):
+        self._varname = varname
+
+    def __repr__(self):
+        return "_UNKNOWN_VALUE({})".format(self._varname)
+
+
+_logger = logging.getLogger(__name__)
+
+
+class Assigner(object):
+    """
+    This object keeps track of potential assignment simplifications
+    inside a code block.
+    For example `$O.1 = x` followed by `y = $0.1` can be simplified
+    into `y = x`, but it's not possible anymore if we have `x = z`
+    in-between those two instructions.
+
+    NOTE: this is not only an optimization, but is actually necessary
+    due to certain limitations of Numba - such as only accepting the
+    returning of an array passed as function argument.
+    """
+
+    def __init__(self):
+        # { destination variable name -> source Var object }
+        self.dest_to_src = {}
+        # Basically a reverse mapping of dest_to_src:
+        # { source variable name -> all destination names in dest_to_src }
+        self.src_invalidate = collections.defaultdict(list)
+        self.unused_dests = set()
+
+    def assign(self, srcvar, destvar):
+        """
+        Assign *srcvar* to *destvar*. Return either *srcvar* or a possible
+        simplified assignment source (earlier assigned to *srcvar*).
+        """
+        srcname = srcvar.name
+        destname = destvar.name
+        if destname in self.src_invalidate:
+            # destvar will change, invalidate all previously known
+            # simplifications
+            for d in self.src_invalidate.pop(destname):
+                self.dest_to_src.pop(d)
+        if srcname in self.dest_to_src:
+            srcvar = self.dest_to_src[srcname]
+        if destvar.is_temp:
+            self.dest_to_src[destname] = srcvar
+            self.src_invalidate[srcname].append(destname)
+            self.unused_dests.add(destname)
+        return srcvar
+
+    def get_assignment_source(self, destname):
+        """
+        Get a possible assignment source (a ir.Var instance) to replace
+        *destname*, otherwise None.
+        """
+        if destname in self.dest_to_src:
+            return self.dest_to_src[destname]
+        self.unused_dests.discard(destname)
+        return None
+
+
+def _remove_assignment_definition(old_body, idx, func_ir, already_deleted_defs):
+    """
+    Deletes the definition defined for old_body at index idx
+    from func_ir. We assume this stmt will be deleted from
+    new_body.
+
+    In some optimizations we may update the same variable multiple times.
+    In this situation, we only need to delete a particular definition once,
+    this is tracked in already_deleted_def, which is a map from
+    assignment name to the set of values that have already been
+    deleted.
+    """
+    lhs = old_body[idx].target.name
+    rhs = old_body[idx].value
+    if rhs in func_ir._definitions[lhs]:
+        func_ir._definitions[lhs].remove(rhs)
+        already_deleted_defs[lhs].add(rhs)
+    elif rhs not in already_deleted_defs[lhs]:
+        raise UnsupportedError(
+            "Inconsistency found in the definitions while executing"
+            " a peephole optimization. This suggests an internal"
+            " error or inconsistency elsewhere in the compiler."
+        )
+
+
+def _call_function_ex_replace_kws_small(
+    old_body,
+    keyword_expr,
+    new_body,
+    buildmap_idx,
+    func_ir,
+    already_deleted_defs
+):
+    """
+    Extracts the kws args passed as varkwarg
+    for CALL_FUNCTION_EX. This pass is taken when
+    n_kws <= 15 and the bytecode looks like:
+
+        # Start for each argument
+        LOAD_FAST  # Load each argument.
+        # End for each argument
+        ...
+        BUILD_CONST_KEY_MAP # Build a map
+
+    In the generated IR, the varkwarg refers
+    to a single build_map that contains all of the
+    kws. In addition to returning the kws, this
+    function updates new_body to remove all usage
+    of the map.
+    """
+    kws = keyword_expr.items.copy()
+    # kws are required to have constant keys.
+    # We update these with the value_indexes
+    value_indexes = keyword_expr.value_indexes
+    for key, index in value_indexes.items():
+        kws[index] = (key, kws[index][1])
+    # Remove the build_map by setting the list
+    # index to None. Nones will be removed later.
+    new_body[buildmap_idx] = None
+    # Remove the definition.
+    _remove_assignment_definition(
+        old_body, buildmap_idx, func_ir, already_deleted_defs
+    )
+    return kws
+
+
+def _call_function_ex_replace_kws_large(
+    old_body,
+    buildmap_name,
+    buildmap_idx,
+    search_end,
+    new_body,
+    func_ir,
+    errmsg,
+    already_deleted_defs
+):
+    """
+    Extracts the kws args passed as varkwarg
+    for CALL_FUNCTION_EX. This pass is taken when
+    n_kws > 15 and the bytecode looks like:
+
+        BUILD_MAP # Construct the map
+        # Start for each argument
+        LOAD_CONST # Load a constant for the name of the argument
+        LOAD_FAST  # Load each argument.
+        MAP_ADD # Append the (key, value) pair to the map
+        # End for each argument
+
+    In the IR generated, the initial build map is empty and a series
+    of setitems are applied afterwards. THE IR looks like:
+
+        $build_map_var = build_map(items=[])
+        $constvar = const(str, ...) # create the const key
+        # CREATE THE ARGUMENT, This may take multiple lines.
+        $created_arg = ...
+        $var = getattr(
+            value=$build_map_var,
+            attr=__setitem__,
+        )
+        $unused_var = call $var($constvar, $created_arg)
+
+    We iterate through the IR, deleting all usages of the buildmap
+    from the new_body, and adds the kws to a new kws list.
+    """
+    # Remove the build_map from the body.
+    new_body[buildmap_idx] = None
+    # Remove the definition.
+    _remove_assignment_definition(
+        old_body, buildmap_idx, func_ir, already_deleted_defs
+    )
+    kws = []
+    search_start = buildmap_idx + 1
+    while search_start <= search_end:
+        # The first value must be a constant.
+        const_stmt = old_body[search_start]
+        if not (
+            isinstance(const_stmt, ir.Assign)
+            and isinstance(const_stmt.value, ir.Const)
+        ):
+            # We cannot handle this format so raise the
+            # original error message.
+            raise UnsupportedError(errmsg)
+        key_var_name = const_stmt.target.name
+        key_val = const_stmt.value.value
+        search_start += 1
+        # Now we need to search for a getattr with setitem
+        found_getattr = False
+        while (
+            search_start <= search_end
+            and not found_getattr
+        ):
+            getattr_stmt = old_body[search_start]
+            if (
+                isinstance(getattr_stmt, ir.Assign)
+                and isinstance(getattr_stmt.value, ir.Expr)
+                and getattr_stmt.value.op == "getattr"
+                and (
+                    getattr_stmt.value.value.name
+                    == buildmap_name
+                )
+                and getattr_stmt.value.attr == "__setitem__"
+            ):
+                found_getattr = True
+            else:
+                # If the argument is "created" in JIT, then there
+                # will be intermediate operations in between setitems.
+                # For example we have arg5=pow(arg5, 2),
+                # then the IR would look like:
+                #
+                #   # Creation of the constant key.
+                #   $const44.26 = const(str, arg5)
+                #
+                #   # Argument creation. This is the section we are skipping
+                #   $46load_global.27 = global(pow: <built-in function pow>)
+                #   $const50.29 = const(int, 2)
+                #   $call.30 = call $46load_global.27(arg5, $const50.29)
+                #
+                #   # Setitem with arg5
+                #   $54map_add.31 = getattr(value=$map.2, attr=__setitem__)
+                #   $54map_add.32 = call $54map_add.31($const44.26, $call.30)
+                search_start += 1
+        if (
+            not found_getattr
+            or search_start == search_end
+        ):
+            # We cannot handle this format so raise the
+            # original error message.
+            raise UnsupportedError(errmsg)
+        setitem_stmt = old_body[search_start + 1]
+        if not (
+            isinstance(setitem_stmt, ir.Assign)
+            and isinstance(setitem_stmt.value, ir.Expr)
+            and setitem_stmt.value.op == "call"
+            and (
+                setitem_stmt.value.func.name
+                == getattr_stmt.target.name
+            )
+            and len(setitem_stmt.value.args) == 2
+            and (
+                setitem_stmt.value.args[0].name
+                == key_var_name
+            )
+        ):
+            # A call statement should always immediately follow the
+            # getattr. If for some reason this doesn't match the code
+            # format, we raise the original error message. This check
+            # is meant as a precaution.
+            raise UnsupportedError(errmsg)
+        arg_var = setitem_stmt.value.args[1]
+        # Append the (key, value) pair.
+        kws.append((key_val, arg_var))
+        # Remove the __setitem__ getattr and call
+        new_body[search_start] = None
+        new_body[search_start + 1] = None
+        # Remove the definitions.
+        _remove_assignment_definition(
+            old_body, search_start, func_ir, already_deleted_defs
+        )
+        _remove_assignment_definition(
+            old_body, search_start + 1, func_ir, already_deleted_defs
+        )
+        search_start += 2
+    return kws
+
+
+def _call_function_ex_replace_args_small(
+    old_body,
+    tuple_expr,
+    new_body,
+    buildtuple_idx,
+    func_ir,
+    already_deleted_defs
+):
+    """
+    Extracts the args passed as vararg
+    for CALL_FUNCTION_EX. This pass is taken when
+    n_args <= 30 and the bytecode looks like:
+
+        # Start for each argument
+        LOAD_FAST  # Load each argument.
+        # End for each argument
+        ...
+        BUILD_TUPLE # Create a tuple of the arguments
+
+    In the IR generated, the vararg refer
+    to a single build_tuple that contains all of the
+    args. In addition to returning the args, this
+    function updates new_body to remove all usage
+    of the tuple.
+    """
+    # Delete the build tuple
+    new_body[buildtuple_idx] = None
+    # Remove the definition.
+    _remove_assignment_definition(
+        old_body, buildtuple_idx, func_ir, already_deleted_defs
+    )
+    # Return the args.
+    return tuple_expr.items
+
+
+def _call_function_ex_replace_args_large(
+    old_body,
+    vararg_stmt,
+    new_body,
+    search_end,
+    func_ir,
+    errmsg,
+    already_deleted_defs
+):
+    """
+    Extracts the args passed as vararg
+    for CALL_FUNCTION_EX. This pass is taken when
+    n_args > 30 and the bytecode looks like:
+
+        BUILD_TUPLE # Create a list to append to
+        # Start for each argument
+        LOAD_FAST  # Load each argument.
+        LIST_APPEND # Add the argument to the list
+        # End for each argument
+        ...
+        LIST_TO_TUPLE # Convert the args to a tuple.
+
+    In the IR generated, the tuple is created by concatenating
+    together several 1 element tuples to an initial empty tuple.
+    We traverse backwards in the IR, collecting args, until we
+    find the original empty tuple. For example, the IR might
+    look like:
+
+        $orig_tuple = build_tuple(items=[])
+        $first_var = build_tuple(items=[Var(arg0, test.py:6)])
+        $next_tuple = $orig_tuple + $first_var
+        ...
+        $final_var = build_tuple(items=[Var(argn, test.py:6)])
+        $final_tuple = $prev_tuple + $final_var
+        $varargs_var = $final_tuple
+    """
+    # We traverse to the front of the block to look for the original
+    # tuple.
+    search_start = 0
+    total_args = []
+    if (
+        isinstance(vararg_stmt, ir.Assign)
+        and isinstance(vararg_stmt.value, ir.Var)
+    ):
+        target_name = vararg_stmt.value.name
+        # If there is an initial assignment, delete it
+        new_body[search_end] = None
+        # Remove the definition.
+        _remove_assignment_definition(
+            old_body, search_end, func_ir, already_deleted_defs
+        )
+        search_end -= 1
+    else:
+        # There must always be an initial assignment
+        # https://github.com/numba/numba/blob/59fa2e335be68148b3bd72a29de3ff011430038d/numba/core/interpreter.py#L259-L260
+        # If this changes we may need to support this branch.
+        raise AssertionError("unreachable")
+    # Traverse backwards to find all concatenations
+    # until eventually reaching the original empty tuple.
+    while search_end >= search_start:
+        concat_stmt = old_body[search_end]
+        if (
+            isinstance(concat_stmt, ir.Assign)
+            and concat_stmt.target.name == target_name
+            and isinstance(concat_stmt.value, ir.Expr)
+            and concat_stmt.value.op == "build_tuple"
+            and not concat_stmt.value.items
+        ):
+            new_body[search_end] = None
+            # Remove the definition.
+            _remove_assignment_definition(
+                old_body, search_end, func_ir, already_deleted_defs
+            )
+            # If we have reached the build_tuple we exit.
+            break
+        else:
+            # We expect to find another arg to append.
+            # The first stmt must be a binop "add"
+            if (search_end == search_start) or not (
+                isinstance(concat_stmt, ir.Assign)
+                and (
+                    concat_stmt.target.name
+                    == target_name
+                )
+                and isinstance(
+                    concat_stmt.value, ir.Expr
+                )
+                and concat_stmt.value.op == "binop"
+                and concat_stmt.value.fn == operator.add
+            ):
+                # We cannot handle this format.
+                raise UnsupportedError(errmsg)
+            lhs_name = concat_stmt.value.lhs.name
+            rhs_name = concat_stmt.value.rhs.name
+            # The previous statement should be a
+            # build_tuple containing the arg.
+            arg_tuple_stmt = old_body[search_end - 1]
+            if not (
+                isinstance(arg_tuple_stmt, ir.Assign)
+                and isinstance(
+                    arg_tuple_stmt.value, ir.Expr
+                )
+                and (
+                    arg_tuple_stmt.value.op
+                    == "build_tuple"
+                )
+                and len(arg_tuple_stmt.value.items) == 1
+            ):
+                # We cannot handle this format.
+                raise UnsupportedError(errmsg)
+            if arg_tuple_stmt.target.name == lhs_name:
+                # The tuple should always be generated on the RHS.
+                raise AssertionError("unreachable")
+            elif arg_tuple_stmt.target.name == rhs_name:
+                target_name = lhs_name
+            else:
+                # We cannot handle this format.
+                raise UnsupportedError(errmsg)
+            total_args.append(
+                arg_tuple_stmt.value.items[0]
+            )
+            new_body[search_end] = None
+            new_body[search_end - 1] = None
+            # Remove the definitions.
+            _remove_assignment_definition(
+                old_body, search_end, func_ir, already_deleted_defs
+            )
+            _remove_assignment_definition(
+                old_body, search_end - 1, func_ir, already_deleted_defs
+            )
+            search_end -= 2
+            # Avoid any space between appends
+            keep_looking = True
+            while search_end >= search_start and keep_looking:
+                next_stmt = old_body[search_end]
+                if (
+                    isinstance(next_stmt, ir.Assign)
+                    and (
+                        next_stmt.target.name
+                        == target_name
+                    )
+                ):
+                    keep_looking = False
+                else:
+                    # If the argument is "created" in JIT, then there
+                    # will be intermediate operations in between appends.
+                    # For example if the next arg after arg4 is pow(arg5, 2),
+                    # then the IR would look like:
+                    #
+                    #   # Appending arg4
+                    #   $arg4_tup = build_tuple(items=[arg4])
+                    #   $append_var.5 = $append_var.4 + $arg4_tup
+                    #
+                    #   # Creation of arg5.
+                    #   # This is the section that we are skipping.
+                    #   $32load_global.20 = global(pow: <built-in function pow>)
+                    #   $const36.22 = const(int, 2)
+                    #   $call.23 = call $32load_global.20(arg5, $const36.22)
+                    #
+                    #   # Appending arg5
+                    #   $arg5_tup = build_tuple(items=[$call.23])
+                    #   $append_var.6 = $append_var.5 + $arg5_tup
+                    search_end -= 1
+    if search_end == search_start:
+        # If we reached the start we never found the build_tuple.
+        # We cannot handle this format so raise the
+        # original error message.
+        raise UnsupportedError(errmsg)
+    # Reverse the arguments so we get the correct order.
+    return total_args[::-1]
+
+
+def peep_hole_call_function_ex_to_call_function_kw(func_ir):
+    """
+    This peephole rewrites a bytecode sequence unique to Python 3.10
+    where CALL_FUNCTION_EX is used instead of CALL_FUNCTION_KW because of
+    stack limitations set by CPython. This limitation is imposed whenever
+    a function call has too many arguments or keyword arguments.
+
+    https://github.com/python/cpython/blob/a58ebcc701dd6c43630df941481475ff0f615a81/Python/compile.c#L55
+    https://github.com/python/cpython/blob/a58ebcc701dd6c43630df941481475ff0f615a81/Python/compile.c#L4442
+
+    In particular, this change is imposed whenever (n_args / 2) + n_kws > 15.
+
+    Different bytecode is generated for args depending on if n_args > 30
+    or n_args <= 30 and similarly if n_kws > 15 or n_kws <= 15.
+
+    This function unwraps the *args and **kwargs in the function call
+    and places these values directly into the args and kwargs of the call.
+    """
+    # All changes are local to the a single block
+    # so it can be traversed in any order.
+    errmsg = textwrap.dedent("""
+        CALL_FUNCTION_EX with **kwargs not supported.
+        If you are not using **kwargs this may indicate that
+        you have a large number of kwargs and are using inlined control
+        flow. You can resolve this issue by moving the control flow out of
+        the function call. For example, if you have
+
+            f(a=1 if flag else 0, ...)
+
+        Replace that with:
+
+            a_val = 1 if flag else 0
+            f(a=a_val, ...)""")
+
+    # Track which definitions have already been deleted
+    already_deleted_defs = collections.defaultdict(set)
+    for blk in func_ir.blocks.values():
+        blk_changed = False
+        new_body = []
+        for i, stmt in enumerate(blk.body):
+            if (
+                isinstance(stmt, ir.Assign)
+                and isinstance(stmt.value, ir.Expr)
+                and stmt.value.op == "call"
+                and stmt.value.varkwarg is not None
+            ):
+                blk_changed = True
+                call = stmt.value
+                args = call.args
+                kws = call.kws
+                # We need to check the call expression contents if
+                # it contains either vararg or varkwarg. If it contains
+                # varkwarg we need to update the IR. If it just contains
+                # vararg we don't need to update the IR, but we need to
+                # check if peep_hole_list_to_tuple failed to replace the
+                # vararg list with a tuple. If so, we output an error
+                # message with suggested code changes.
+                vararg = call.vararg
+                varkwarg = call.varkwarg
+                start_search = i - 1
+                # varkwarg should be defined second so we start there.
+                varkwarg_loc = start_search
+                keyword_def = None
+                found = False
+                while varkwarg_loc >= 0 and not found:
+                    keyword_def = blk.body[varkwarg_loc]
+                    if (
+                        isinstance(keyword_def, ir.Assign)
+                        and keyword_def.target.name == varkwarg.name
+                    ):
+                        found = True
+                    else:
+                        varkwarg_loc -= 1
+                if (
+                    kws
+                    or not found
+                    or not (
+                        isinstance(keyword_def.value, ir.Expr)
+                        and keyword_def.value.op == "build_map"
+                    )
+                ):
+                    # If we couldn't find where the kwargs are created
+                    # then it should be a normal **kwargs call
+                    # so we produce an unsupported message.
+                    raise UnsupportedError(errmsg)
+                # Determine the kws
+                if keyword_def.value.items:
+                    # n_kws <= 15 case.
+                    # Here the IR looks like a series of
+                    # constants, then the arguments and finally
+                    # a build_map that contains all of the pairs.
+                    # For Example:
+                    #
+                    #   $const_n = const("arg_name")
+                    #   $arg_n = ...
+                    #   $kwargs_var = build_map(items=[
+                    #              ($const_0, $arg_0),
+                    #              ...,
+                    #              ($const_n, $arg_n),])
+                    kws = _call_function_ex_replace_kws_small(
+                        blk.body,
+                        keyword_def.value,
+                        new_body,
+                        varkwarg_loc,
+                        func_ir,
+                        already_deleted_defs,
+                    )
+                else:
+                    # n_kws > 15 case.
+                    # Here the IR is an initial empty build_map
+                    # followed by a series of setitems with a constant
+                    # key and then the argument.
+                    # For example:
+                    #
+                    #   $kwargs_var = build_map(items=[])
+                    #   $const_0 = const("arg_name")
+                    #   $arg_0 = ...
+                    #   $my_attr = getattr(const_0, attr=__setitem__)
+                    #   $unused_var = call $my_attr($const_0, $arg_0)
+                    #   ...
+                    kws = _call_function_ex_replace_kws_large(
+                        blk.body,
+                        varkwarg.name,
+                        varkwarg_loc,
+                        i - 1,
+                        new_body,
+                        func_ir,
+                        errmsg,
+                        already_deleted_defs,
+                    )
+                start_search = varkwarg_loc
+                # Vararg isn't required to be provided.
+                if vararg is not None:
+                    if args:
+                        # If we have vararg then args is expected to
+                        # be an empty list.
+                        raise UnsupportedError(errmsg)
+                    vararg_loc = start_search
+                    args_def = None
+                    found = False
+                    while vararg_loc >= 0 and not found:
+                        args_def = blk.body[vararg_loc]
+                        if (
+                            isinstance(args_def, ir.Assign)
+                            and args_def.target.name == vararg.name
+                        ):
+                            found = True
+                        else:
+                            vararg_loc -= 1
+                    if not found:
+                        # If we couldn't find where the args are created
+                        # then we can't handle this format.
+                        raise UnsupportedError(errmsg)
+                    if (
+                        isinstance(args_def.value, ir.Expr)
+                        and args_def.value.op == "build_tuple"
+                    ):
+                        # n_args <= 30 case.
+                        # Here the IR is a simple build_tuple containing
+                        # all of the args.
+                        # For example:
+                        #
+                        #  $arg_n = ...
+                        #  $varargs = build_tuple(
+                        #   items=[$arg_0, ..., $arg_n]
+                        #  )
+                        args = _call_function_ex_replace_args_small(
+                            blk.body,
+                            args_def.value,
+                            new_body,
+                            vararg_loc,
+                            func_ir,
+                            already_deleted_defs,
+                        )
+                    elif (
+                        isinstance(args_def.value, ir.Expr)
+                        and args_def.value.op == "list_to_tuple"
+                    ):
+                        # If there is a call with vararg we need to check
+                        # if the list -> tuple conversion failed and if so
+                        # throw an error.
+                        raise UnsupportedError(errmsg)
+                    else:
+                        # Here the IR is an initial empty build_tuple.
+                        # Then for each arg, a new tuple with a single
+                        # element is created and one by one these are
+                        # added to a growing tuple.
+                        # For example:
+                        #
+                        #  $combo_tup_0 = build_tuple(items=[])
+                        #  $arg0 = ...
+                        #  $arg0_tup = build_tuple(items=[$arg0])
+                        #  $combo_tup_1 = $combo_tup_0 + $arg0_tup
+                        #  $arg1 = ...
+                        #  $arg1_tup = build_tuple(items=[$arg1])
+                        #  $combo_tup_2 = $combo_tup_1 + $arg1_tup
+                        #  ...
+                        #  $combo_tup_n = $combo_tup_{n-1} + $argn_tup
+                        #
+                        # In addition, the IR contains a final
+                        # assignment for the varargs that looks like:
+                        #
+                        #  $varargs_var = $combo_tup_n
+                        #
+                        # Here args_def is expected to be a simple assignment.
+                        args = _call_function_ex_replace_args_large(
+                            blk.body,
+                            args_def,
+                            new_body,
+                            vararg_loc,
+                            func_ir,
+                            errmsg,
+                            already_deleted_defs,
+                        )
+                # Create a new call updating the args and kws
+                new_call = ir.Expr.call(
+                    call.func, args, kws, call.loc, target=call.target
+                )
+                # Drop the existing definition for this stmt.
+                _remove_assignment_definition(
+                    blk.body, i, func_ir, already_deleted_defs
+                )
+                # Update the statement
+                stmt = ir.Assign(new_call, stmt.target, stmt.loc)
+                # Update the definition
+                func_ir._definitions[stmt.target.name].append(new_call)
+            elif (
+                isinstance(stmt, ir.Assign)
+                and isinstance(stmt.value, ir.Expr)
+                and stmt.value.op == "call"
+                and stmt.value.vararg is not None
+            ):
+                # If there is a call with vararg we need to check
+                # if the list -> tuple conversion failed and if so
+                # throw an error.
+                call = stmt.value
+                vararg_name = call.vararg.name
+                if (
+                    vararg_name in func_ir._definitions
+                    and len(func_ir._definitions[vararg_name]) == 1
+                ):
+                    # If this value is still a list to tuple raise the
+                    # exception.
+                    expr = func_ir._definitions[vararg_name][0]
+                    if isinstance(expr, ir.Expr) and expr.op == "list_to_tuple":
+                        raise UnsupportedError(errmsg)
+
+            new_body.append(stmt)
+        # Replace the block body if we changed the IR
+        if blk_changed:
+            blk.body.clear()
+            blk.body.extend([x for x in new_body if x is not None])
+    return func_ir
+
+
+def peep_hole_list_to_tuple(func_ir):
+    """
+    This peephole rewrites a bytecode sequence new to Python 3.9 that looks
+    like e.g.:
+
+    def foo(a):
+        return (*a,)
+
+    41          0 BUILD_LIST               0
+                2 LOAD_FAST                0 (a)
+                4 LIST_EXTEND              1
+                6 LIST_TO_TUPLE
+                8 RETURN_VAL
+
+    essentially, the unpacking of tuples is written as a list which is appended
+    to/extended and then "magicked" into a tuple by the new LIST_TO_TUPLE
+    opcode.
+
+    This peephole repeatedly analyses the bytecode in a block looking for a
+    window between a `LIST_TO_TUPLE` and `BUILD_LIST` and...
+
+    1. Turns the BUILD_LIST into a BUILD_TUPLE
+    2. Sets an accumulator's initial value as the target of the BUILD_TUPLE
+    3. Searches for 'extend' on the original list and turns these into binary
+       additions on the accumulator.
+    4. Searches for 'append' on the original list and turns these into a
+       `BUILD_TUPLE` which is then appended via binary addition to the
+       accumulator.
+    5. Assigns the accumulator to the variable that exits the peephole and the
+       rest of the block/code refers to as the result of the unpack operation.
+    6. Patches up
+    """
+    _DEBUG = False
+
+    # For all blocks
+    for offset, blk in func_ir.blocks.items():
+        # keep doing the peephole rewrite until nothing is left that matches
+        while True:
+            # first try and find a matching region
+            # i.e. BUILD_LIST...<stuff>...LIST_TO_TUPLE
+            def find_postive_region():
+                found = False
+                for idx in reversed(range(len(blk.body))):
+                    stmt = blk.body[idx]
+                    if isinstance(stmt, ir.Assign):
+                        value = stmt.value
+                        if (isinstance(value, ir.Expr) and
+                                value.op == 'list_to_tuple'):
+                            target_list = value.info[0]
+                            found = True
+                            bt = (idx, stmt)
+                    if found:
+                        if isinstance(stmt, ir.Assign):
+                            if stmt.target.name == target_list:
+                                region = (bt, (idx, stmt))
+                                return region
+
+            region = find_postive_region()
+            # if there's a peep hole region then do something with it
+            if region is not None:
+                peep_hole = blk.body[region[1][0] : region[0][0]]
+                if _DEBUG:
+                    print("\nWINDOW:")
+                    for x in peep_hole:
+                        print(x)
+                    print("")
+
+                appends = []
+                extends = []
+                init = region[1][1]
+                const_list = init.target.name
+                # Walk through the peep_hole and find things that are being
+                # "extend"ed and "append"ed to the BUILD_LIST
+                for x in peep_hole:
+                    if isinstance(x, ir.Assign):
+                        if isinstance(x.value, ir.Expr):
+                            expr = x.value
+                            if (expr.op == 'getattr' and
+                                    expr.value.name == const_list):
+                                # it's not strictly necessary to split out
+                                # extends and appends, but it helps with
+                                # debugging to do so!
+                                if expr.attr == 'extend':
+                                    extends.append(x.target.name)
+                                elif expr.attr == 'append':
+                                    appends.append(x.target.name)
+                                else:
+                                    assert 0
+                # go back through the peep hole build new IR based on it.
+                new_hole = []
+
+                def append_and_fix(x):
+                    """ Adds to the new_hole and fixes up definitions"""
+                    new_hole.append(x)
+                    if x.target.name in func_ir._definitions:
+                        # if there's already a definition, drop it, should only
+                        # be 1 as the way cpython emits the sequence for
+                        # `list_to_tuple` should ensure this.
+                        assert len(func_ir._definitions[x.target.name]) == 1
+                        func_ir._definitions[x.target.name].clear()
+                    func_ir._definitions[x.target.name].append(x.value)
+
+                the_build_list = init.target
+
+                # Do the transform on the peep hole
+                if _DEBUG:
+                    print("\nBLOCK:")
+                    blk.dump()
+
+                # This section basically accumulates list appends and extends
+                # as binop(+) on tuples, it drops all the getattr() for extend
+                # and append as they are now dead and replaced with binop(+).
+                # It also switches out the build_list for a build_tuple and then
+                # ensures everything is wired up and defined ok.
+                t2l_agn = region[0][1]
+                acc = the_build_list
+                for x in peep_hole:
+                    if isinstance(x, ir.Assign):
+                        if isinstance(x.value, ir.Expr):
+                            expr = x.value
+                            if expr.op == 'getattr':
+                                if (x.target.name in extends or
+                                        x.target.name in appends):
+                                    # drop definition, it's being wholesale
+                                    # replaced.
+                                    func_ir._definitions.pop(x.target.name)
+                                    continue
+                                else:
+                                    # a getattr on something we're not
+                                    # interested in
+                                    new_hole.append(x)
+                            elif expr.op == 'call':
+                                fname = expr.func.name
+                                if fname in extends or fname in appends:
+                                    arg = expr.args[0]
+                                    if isinstance(arg, ir.Var):
+                                        tmp_name = "%s_var_%s" % (fname,
+                                                                  arg.name)
+                                        if fname in appends:
+                                            bt = ir.Expr.build_tuple([arg,],
+                                                                     expr.loc)
+                                        else:
+                                            # Extend as tuple
+                                            gv_tuple = ir.Global(
+                                                name="tuple", value=tuple,
+                                                loc=expr.loc,
+                                            )
+                                            tuple_var = arg.scope.redefine(
+                                                "$_list_extend_gv_tuple",
+                                                loc=expr.loc,
+                                            )
+                                            new_hole.append(
+                                                ir.Assign(
+                                                    target=tuple_var,
+                                                    value=gv_tuple,
+                                                    loc=expr.loc,
+                                                ),
+                                            )
+                                            bt = ir.Expr.call(
+                                                tuple_var, (arg,), (),
+                                                loc=expr.loc,
+                                            )
+                                        var = ir.Var(arg.scope, tmp_name,
+                                                     expr.loc)
+                                        asgn = ir.Assign(bt, var, expr.loc)
+                                        append_and_fix(asgn)
+                                        arg = var
+
+                                    # this needs to be a binary add
+                                    new = ir.Expr.binop(fn=operator.add,
+                                                        lhs=acc,
+                                                        rhs=arg,
+                                                        loc=x.loc)
+                                    asgn = ir.Assign(new, x.target, expr.loc)
+                                    append_and_fix(asgn)
+                                    acc = asgn.target
+                                else:
+                                    # there could be a call in the unpack, like
+                                    # *(a, x.append(y))
+                                    new_hole.append(x)
+                            elif (expr.op == 'build_list' and
+                                    x.target.name == const_list):
+                                new = ir.Expr.build_tuple(expr.items, expr.loc)
+                                asgn = ir.Assign(new, x.target, expr.loc)
+                                # Not a temporary any more
+                                append_and_fix(asgn)
+                            else:
+                                new_hole.append(x)
+                        else:
+                            new_hole.append(x)
+
+                    else:
+                        # stick everything else in as-is
+                        new_hole.append(x)
+                # Finally write the result back into the original build list as
+                # everything refers to it.
+                append_and_fix(ir.Assign(acc, t2l_agn.target,
+                                         the_build_list.loc))
+                if _DEBUG:
+                    print("\nNEW HOLE:")
+                    for x in new_hole:
+                        print(x)
+
+                # and then update the block body with the modified region
+                cpy = blk.body[:]
+                head = cpy[:region[1][0]]
+                tail = blk.body[region[0][0] + 1:]
+                tmp = head + new_hole + tail
+                blk.body.clear()
+                blk.body.extend(tmp)
+
+                if _DEBUG:
+                    print("\nDUMP post hole:")
+                    blk.dump()
+
+            else:
+                # else escape
+                break
+
+    return func_ir
+
+
+def peep_hole_delete_with_exit(func_ir):
+    """
+    This rewrite removes variables used to store the `__exit__` function
+    loaded by SETUP_WITH.
+    """
+    dead_vars = set()
+
+    for blk in func_ir.blocks.values():
+        for stmt in blk.body:
+            # Any statement that uses a variable with the '$setup_with_exitfn'
+            # prefix is considered dead.
+            used = set(stmt.list_vars())
+            for v in used:
+                if v.name.startswith('$setup_with_exitfn'):
+                    dead_vars.add(v)
+            # Any assignment that uses any of the dead variable is considered
+            # dead.
+            if used & dead_vars:
+                if isinstance(stmt, ir.Assign):
+                    dead_vars.add(stmt.target)
+
+        new_body = []
+        for stmt in blk.body:
+            # Skip any statements that uses anyone of the dead variable.
+            if not (set(stmt.list_vars()) & dead_vars):
+                new_body.append(stmt)
+        blk.body.clear()
+        blk.body.extend(new_body)
+
+    return func_ir
+
+
+def peep_hole_fuse_dict_add_updates(func_ir):
+    """
+    This rewrite removes d1._update_from_bytecode(d2)
+    calls that are between two dictionaries, d1 and d2,
+    in the same basic block. This pattern can appear as a
+    result of Python 3.10 bytecode emission changes, which
+    prevent large constant literal dictionaries
+    (> 15 elements) from being constant. If both dictionaries
+    are constant dictionaries defined in the same block and
+    neither is used between the update call, then we replace d1
+    with a new definition that combines the two dictionaries. At
+    the bytecode translation stage we convert DICT_UPDATE into
+    _update_from_bytecode, so we know that _update_from_bytecode
+    always comes from the bytecode change and not user code.
+
+    Python 3.10 may also rewrite the individual dictionaries
+    as an empty build_map + many map_add. Here we again look
+    for an _update_from_bytecode, and if so we replace these
+    with a single constant dictionary.
+
+    When running this algorithm we can always safely remove d2.
+
+    This is the relevant section of the CPython 3.10 that causes
+    this bytecode change:
+    https://github.com/python/cpython/blob/3.10/Python/compile.c#L4048
+    """
+
+    # This algorithm fuses build_map expressions into the largest
+    # possible build map before use. For example, if we have an
+    # IR that looks like this:
+    #
+    #   $d1 = build_map([])
+    #   $key = const("a")
+    #   $value = const(2)
+    #   $setitem_func = getattr($d1, "__setitem__")
+    #   $unused1 = call (setitem_func, ($key, $value))
+    #   $key2 = const("b")
+    #   $value2 = const(3)
+    #   $d2 = build_map([($key2, $value2)])
+    #   $update_func = getattr($d1, "_update_from_bytecode")
+    #   $unused2 = call ($update_func, ($d2,))
+    #   $othervar = None
+    #   $retvar = cast($othervar)
+    #   return $retvar
+    #
+    # Then the IR is rewritten such that any __setitem__ and
+    # _update_from_bytecode operations are fused into the original buildmap.
+    # The new buildmap is then added to the
+    # last location where it had previously had encountered a __setitem__,
+    # _update_from_bytecode, or build_map before any other uses.
+    # The new IR would look like:
+    #
+    #   $key = const("a")
+    #   $value = const(2)
+    #   $key2 = const("b")
+    #   $value2 = const(3)
+    #   $d1 = build_map([($key, $value), ($key2, $value2)])
+    #   $othervar = None
+    #   $retvar = cast($othervar)
+    #   return $retvar
+    #
+    # Note that we don't push $d1 to the bottom of the block. This is because
+    # some values may be found below this block (e.g pop_block) that are pattern
+    # matched in other locations, such as objmode handling. It should be safe to
+    # move a map to the last location at which there was _update_from_bytecode.
+
+    errmsg = textwrap.dedent("""
+        A DICT_UPDATE op-code was encountered that could not be replaced.
+        If you have created a large constant dictionary, this may
+        be an an indication that you are using inlined control
+        flow. You can resolve this issue by moving the control flow out of
+        the dicitonary constructor. For example, if you have
+
+            d = {a: 1 if flag else 0, ...)
+
+        Replace that with:
+
+            a_val = 1 if flag else 0
+            d = {a: a_val, ...)""")
+
+    already_deleted_defs = collections.defaultdict(set)
+    for blk in func_ir.blocks.values():
+        new_body = []
+        # literal map var name -> block idx of the original build_map
+        lit_map_def_idx = {}
+        # literal map var name -> list(map_uses)
+        # This is the index of every build_map or __setitem__
+        # in the IR that will need to be removed if the map
+        # is updated.
+        lit_map_use_idx = collections.defaultdict(list)
+        # literal map var name -> list of key/value items for build map
+        map_updates = {}
+        blk_changed = False
+
+        for i, stmt in enumerate(blk.body):
+            # What instruction should we append
+            new_inst = stmt
+            # Name that should be skipped when tracking used
+            # vars in statement. This is always the lhs with
+            # a build_map.
+            stmt_build_map_out = None
+            if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Expr):
+                if stmt.value.op == "build_map":
+                    # Skip the output build_map when looking for used vars.
+                    stmt_build_map_out = stmt.target.name
+                    # If we encounter a build map add it to the
+                    # tracked maps.
+                    lit_map_def_idx[stmt.target.name] = i
+                    lit_map_use_idx[stmt.target.name].append(i)
+                    map_updates[stmt.target.name] = stmt.value.items.copy()
+                elif stmt.value.op == "call" and i > 0:
+                    # If we encounter a call we may need to replace
+                    # the body
+                    func_name = stmt.value.func.name
+                    # If we have an update or a setitem
+                    # it will be the previous expression.
+                    getattr_stmt = blk.body[i - 1]
+                    args = stmt.value.args
+                    if (
+                        isinstance(getattr_stmt, ir.Assign)
+                        and getattr_stmt.target.name == func_name
+                        and isinstance(getattr_stmt.value, ir.Expr)
+                        and getattr_stmt.value.op == "getattr"
+                        and getattr_stmt.value.attr in (
+                            "__setitem__", "_update_from_bytecode"
+                        )
+                    ):
+                        update_map_name = getattr_stmt.value.value.name
+                        attr = getattr_stmt.value.attr
+                        if (attr == "__setitem__"
+                           and update_map_name in lit_map_use_idx):
+                            # If we have a setitem, update the lists
+                            map_updates[update_map_name].append(args)
+                            # Update the list of instructions that would
+                            # need to be removed to include the setitem
+                            # and the the getattr
+                            lit_map_use_idx[update_map_name].extend([i - 1, i])
+                        elif attr == "_update_from_bytecode":
+                            d2_map_name = args[0].name
+                            if (update_map_name in lit_map_use_idx
+                               and d2_map_name in lit_map_use_idx):
+                                # If we have an update and the arg is also
+                                # a literal dictionary, fuse the lists.
+                                map_updates[update_map_name].extend(
+                                    map_updates[d2_map_name]
+                                )
+                                # Delete the old IR for d1 and d2
+                                lit_map_use_idx[update_map_name].extend(
+                                    lit_map_use_idx[d2_map_name]
+                                )
+                                lit_map_use_idx[update_map_name].append(i - 1)
+                                for linenum in lit_map_use_idx[update_map_name]:
+                                    # Drop the existing definition.
+                                    _remove_assignment_definition(
+                                        blk.body,
+                                        linenum,
+                                        func_ir,
+                                        already_deleted_defs,
+                                    )
+                                    # Delete it from the new block
+                                    new_body[linenum] = None
+                                # Delete the maps from dicts
+                                del lit_map_def_idx[d2_map_name]
+                                del lit_map_use_idx[d2_map_name]
+                                del map_updates[d2_map_name]
+                                # Add d1 as the new instruction, removing the
+                                # old definition.
+                                _remove_assignment_definition(
+                                    blk.body, i, func_ir, already_deleted_defs
+                                )
+                                new_inst = _build_new_build_map(
+                                    func_ir,
+                                    update_map_name,
+                                    blk.body,
+                                    lit_map_def_idx[update_map_name],
+                                    map_updates[update_map_name],
+                                )
+                                # Update d1 in lit_map_use_idx to just the new
+                                # definition and clear the previous list.
+                                lit_map_use_idx[update_map_name].clear()
+                                lit_map_use_idx[update_map_name].append(i)
+                                # Mark that this block has been modified
+                                blk_changed = True
+                            else:
+                                # If we cannot remove _update_from_bytecode
+                                # Then raise an error for the user.
+                                raise UnsupportedError(errmsg)
+
+            # Check if we need to drop any maps from being tracked.
+            # Skip the setitem/_update_from_bytecode getattr that
+            # will be removed when handling their call in the next
+            # iteration.
+            if not (
+                isinstance(stmt, ir.Assign)
+                and isinstance(stmt.value, ir.Expr)
+                and stmt.value.op == "getattr"
+                and stmt.value.value.name in lit_map_use_idx
+                and stmt.value.attr in ("__setitem__", "_update_from_bytecode")
+            ):
+                for var in stmt.list_vars():
+                    # If a map is used it cannot be fused later in
+                    # the block. As a result we delete it from
+                    # the dicitonaries
+                    if (
+                        var.name in lit_map_use_idx
+                        and var.name != stmt_build_map_out
+                    ):
+                        del lit_map_def_idx[var.name]
+                        del lit_map_use_idx[var.name]
+                        del map_updates[var.name]
+
+            # Append the instruction to the new block
+            new_body.append(new_inst)
+
+        if blk_changed:
+            # If the block is changed replace the block body.
+            blk.body.clear()
+            blk.body.extend([x for x in new_body if x is not None])
+
+    return func_ir
+
+
+def _build_new_build_map(func_ir, name, old_body, old_lineno, new_items):
+    """
+    Create a new build_map with a new set of key/value items
+    but all the other info the same.
+    """
+    old_assign = old_body[old_lineno]
+    old_target = old_assign.target
+    old_bm = old_assign.value
+    # Build the literals
+    literal_keys = []
+    # Track the constant key/values to set the literal_value
+    # field of build_map properly
+    values = []
+    for pair in new_items:
+        k, v = pair
+        key_def = guard(get_definition, func_ir, k)
+        if isinstance(key_def, (ir.Const, ir.Global, ir.FreeVar)):
+            literal_keys.append(key_def.value)
+        value_def = guard(get_definition, func_ir, v)
+        if isinstance(value_def, (ir.Const, ir.Global, ir.FreeVar)):
+            values.append(value_def.value)
+        else:
+            # Append unknown value if not a literal.
+            values.append(_UNKNOWN_VALUE(v.name))
+
+    value_indexes = {}
+    if len(literal_keys) == len(new_items):
+        # All keys must be literals to have any literal values.
+        literal_value = {x: y for x, y in zip(literal_keys, values)}
+        for i, k in enumerate(literal_keys):
+            value_indexes[k] = i
+    else:
+        literal_value = None
+
+    # Construct a new build map.
+    new_bm = ir.Expr.build_map(
+        items=new_items,
+        size=len(new_items),
+        literal_value=literal_value,
+        value_indexes=value_indexes,
+        loc=old_bm.loc,
+    )
+
+    # The previous definition has already been removed
+    # when updating the IR in peep_hole_fuse_dict_add_updates
+    func_ir._definitions[name].append(new_bm)
+
+    # Return a new assign.
+    return ir.Assign(
+        new_bm, ir.Var(old_target.scope, name, old_target.loc), new_bm.loc
+    )
+
+
+class Interpreter(object):
+    """A bytecode interpreter that builds up the IR.
+    """
+
+    def __init__(self, func_id):
+        self.func_id = func_id
+        self.arg_count = func_id.arg_count
+        self.arg_names = func_id.arg_names
+        self.loc = self.first_loc = ir.Loc.from_function_id(func_id)
+        self.is_generator = func_id.is_generator
+
+        # { inst offset : ir.Block }
+        self.blocks = {}
+        # { name: [definitions] } of local variables
+        self.definitions = collections.defaultdict(list)
+        # A set to keep track of all exception variables.
+        # To be used in _legalize_exception_vars()
+        self._exception_vars = set()
+
+    def interpret(self, bytecode):
+        """
+        Generate IR for this bytecode.
+        """
+        self.bytecode = bytecode
+
+        self.scopes = []
+        global_scope = ir.Scope(parent=None, loc=self.loc)
+        self.scopes.append(global_scope)
+
+        if PYVERSION < (3, 7):
+            # Control flow analysis
+            self.cfa = controlflow.ControlFlowAnalysis(bytecode)
+            self.cfa.run()
+            if config.DUMP_CFG:
+                self.cfa.dump()
+
+            # Data flow analysis
+            self.dfa = dataflow.DataFlowAnalysis(self.cfa)
+            self.dfa.run()
+        else:
+            flow = Flow(bytecode)
+            flow.run()
+            self.dfa = AdaptDFA(flow)
+            self.cfa = AdaptCFA(flow)
+            if config.DUMP_CFG:
+                self.cfa.dump()
+
+        # Temp states during interpretation
+        self.current_block = None
+        self.current_block_offset = None
+        self.syntax_blocks = []
+        self.dfainfo = None
+
+        self.scopes.append(ir.Scope(parent=self.current_scope, loc=self.loc))
+        # Interpret loop
+        for inst, kws in self._iter_inst():
+            self._dispatch(inst, kws)
+        self._legalize_exception_vars()
+        # Prepare FunctionIR
+        func_ir = ir.FunctionIR(self.blocks, self.is_generator, self.func_id,
+                                self.first_loc, self.definitions,
+                                self.arg_count, self.arg_names)
+        _logger.debug(func_ir.dump_to_string())
+
+        # post process the IR to rewrite opcodes/byte sequences that are too
+        # involved to risk handling as part of direct interpretation
+        peepholes = []
+        if PYVERSION in [(3, 9), (3, 10)]:
+            peepholes.append(peep_hole_list_to_tuple)
+        peepholes.append(peep_hole_delete_with_exit)
+        if PYVERSION == (3, 10):
+            # peep_hole_call_function_ex_to_call_function_kw
+            # depends on peep_hole_list_to_tuple converting
+            # any large number of arguments from a list to a
+            # tuple.
+            peepholes.append(peep_hole_call_function_ex_to_call_function_kw)
+            peepholes.append(peep_hole_fuse_dict_add_updates)
+
+        post_processed_ir = self.post_process(peepholes, func_ir)
+        return post_processed_ir
+
+    def post_process(self, peepholes, func_ir):
+        for peep in peepholes:
+            func_ir = peep(func_ir)
+        return func_ir
+
+    def _legalize_exception_vars(self):
+        """Search for unsupported use of exception variables.
+        Note, they cannot be stored into user variable.
+        """
+        # Build a set of exception variables
+        excvars = self._exception_vars.copy()
+        # Propagate the exception variables to LHS of assignment
+        for varname, defnvars in self.definitions.items():
+            for v in defnvars:
+                if isinstance(v, ir.Var):
+                    k = v.name
+                    if k in excvars:
+                        excvars.add(varname)
+        # Filter out the user variables.
+        uservar = list(filter(lambda x: not x.startswith('$'), excvars))
+        if uservar:
+            # Complain about the first user-variable storing an exception
+            first = uservar[0]
+            loc = self.current_scope.get(first).loc
+            msg = "Exception object cannot be stored into variable ({})."
+            raise errors.UnsupportedError(msg.format(first), loc=loc)
+
+    def init_first_block(self):
+        # Define variables receiving the function arguments
+        for index, name in enumerate(self.arg_names):
+            val = ir.Arg(index=index, name=name, loc=self.loc)
+            self.store(val, name)
+
+    def _iter_inst(self):
+        for blkct, block in enumerate(self.cfa.iterliveblocks()):
+            firstinst = self.bytecode[block.offset]
+            self.loc = self.loc.with_lineno(firstinst.lineno)
+            self._start_new_block(block.offset)
+            if blkct == 0:
+                # Is first block
+                self.init_first_block()
+            for offset, kws in self.dfainfo.insts:
+                inst = self.bytecode[offset]
+                self.loc = self.loc.with_lineno(inst.lineno)
+                yield inst, kws
+            self._end_current_block()
+
+    def _start_new_block(self, offset):
+        oldblock = self.current_block
+        self.insert_block(offset)
+        # Ensure the last block is terminated
+        if oldblock is not None and not oldblock.is_terminated:
+            # Handle ending try block.
+            tryblk = self.dfainfo.active_try_block
+            # If there's an active try-block and the handler block is live.
+            if tryblk is not None and tryblk['end'] in self.cfa.graph.nodes():
+                # We are in a try-block, insert a branch to except-block.
+                # This logic cannot be in self._end_current_block()
+                # because we the non-raising next block-offset.
+                branch = ir.Branch(
+                    cond=self.get('$exception_check'),
+                    truebr=tryblk['end'],
+                    falsebr=offset,
+                    loc=self.loc,
+                )
+                oldblock.append(branch)
+            # Handle normal case
+            else:
+                jmp = ir.Jump(offset, loc=self.loc)
+                oldblock.append(jmp)
+        # Get DFA block info
+        self.dfainfo = self.dfa.infos[self.current_block_offset]
+        self.assigner = Assigner()
+        # Check out-of-scope syntactic-block
+        while self.syntax_blocks:
+            if offset >= self.syntax_blocks[-1].exit:
+                self.syntax_blocks.pop()
+            else:
+                break
+
+    def _end_current_block(self):
+        # Handle try block
+        if not self.current_block.is_terminated:
+            tryblk = self.dfainfo.active_try_block
+            if tryblk is not None:
+                self._insert_exception_check()
+        # Handle normal block cleanup
+        self._remove_unused_temporaries()
+        self._insert_outgoing_phis()
+
+    def _inject_call(self, func, gv_name, res_name=None):
+        """A helper function to inject a call to *func* which is a python
+        function.
+        Parameters
+        ----------
+        func : callable
+            The function object to be called.
+        gv_name : str
+            The variable name to be used to store the function object.
+        res_name : str; optional
+            The variable name to be used to store the call result.
+            If ``None``, a name is created automatically.
+        """
+        gv_fn = ir.Global(gv_name, func, loc=self.loc)
+        self.store(value=gv_fn, name=gv_name, redefine=True)
+        callres = ir.Expr.call(self.get(gv_name), (), (), loc=self.loc)
+        res_name = res_name or '$callres_{}'.format(gv_name)
+        self.store(value=callres, name=res_name, redefine=True)
+
+    def _insert_try_block_begin(self):
+        """Insert IR-nodes to mark the start of a `try` block.
+        """
+        self._inject_call(eh.mark_try_block, 'mark_try_block')
+
+    def _insert_try_block_end(self):
+        """Insert IR-nodes to mark the end of a `try` block.
+        """
+        self._inject_call(eh.end_try_block, 'end_try_block')
+
+    def _insert_exception_variables(self):
+        """Insert IR-nodes to initialize the exception variables.
+        """
+        tryblk = self.dfainfo.active_try_block
+        # Get exception variables
+        endblk = tryblk['end']
+        edgepushed = self.dfainfo.outgoing_edgepushed.get(endblk)
+        # Note: the last value on the stack is the exception value
+        # Note: due to the current limitation, all exception variables are None
+        if edgepushed:
+            const_none = ir.Const(value=None, loc=self.loc)
+            # For each variable going to the handler block.
+            for var in edgepushed:
+                if var in self.definitions:
+                    raise AssertionError(
+                        "exception variable CANNOT be defined by other code",
+                    )
+                self.store(value=const_none, name=var)
+                self._exception_vars.add(var)
+
+    def _insert_exception_check(self):
+        """Called before the end of a block to inject checks if raised.
+        """
+        self._insert_exception_variables()
+        # Do exception check
+        self._inject_call(eh.exception_check, 'exception_check',
+                          '$exception_check')
+
+    def _remove_unused_temporaries(self):
+        """
+        Remove assignments to unused temporary variables from the
+        current block.
+        """
+        new_body = []
+        replaced_var = {}
+        for inst in self.current_block.body:
+            # the same temporary is assigned to multiple variables in cases
+            # like a = b[i] = 1, so need to handle replaced temporaries in
+            # later setitem/setattr nodes
+            if (isinstance(inst, (ir.SetItem, ir.SetAttr))
+                    and inst.value.name in replaced_var):
+                inst.value = replaced_var[inst.value.name]
+            elif isinstance(inst, ir.Assign):
+                if (inst.target.is_temp
+                        and inst.target.name in self.assigner.unused_dests):
+                    continue
+                # the same temporary is assigned to multiple variables in cases
+                # like a = b = 1, so need to handle replaced temporaries in
+                # later assignments
+                if (isinstance(inst.value, ir.Var)
+                        and inst.value.name in replaced_var):
+                    inst.value = replaced_var[inst.value.name]
+                    new_body.append(inst)
+                    continue
+                # chained unpack cases may reuse temporary
+                # e.g. a = (b, c) = (x, y)
+                if (isinstance(inst.value, ir.Expr)
+                        and inst.value.op == "exhaust_iter"
+                        and inst.value.value.name in replaced_var):
+                    inst.value.value = replaced_var[inst.value.value.name]
+                    new_body.append(inst)
+                    continue
+                # eliminate temporary variables that are assigned to user
+                # variables right after creation. E.g.:
+                # $1 = f(); a = $1 -> a = f()
+                # the temporary variable is not reused elsewhere since CPython
+                # bytecode is stack-based and this pattern corresponds to a pop
+                if (isinstance(inst.value, ir.Var) and inst.value.is_temp
+                        and new_body and isinstance(new_body[-1], ir.Assign)):
+                    prev_assign = new_body[-1]
+                    # _var_used_in_binop check makes sure we don't create a new
+                    # inplace binop operation which can fail
+                    # (see TestFunctionType.test_in_iter_func_call)
+                    if (prev_assign.target.name == inst.value.name
+                            and not self._var_used_in_binop(
+                                inst.target.name, prev_assign.value)):
+                        replaced_var[inst.value.name] = inst.target
+                        prev_assign.target = inst.target
+                        # replace temp var definition in target with proper defs
+                        self.definitions[inst.target.name].remove(inst.value)
+                        self.definitions[inst.target.name].extend(
+                            self.definitions.pop(inst.value.name)
+                        )
+                        continue
+
+            new_body.append(inst)
+
+        self.current_block.body = new_body
+
+    def _var_used_in_binop(self, varname, expr):
+        """return True if 'expr' is a binary expression and 'varname' is used
+        in it as an argument
+        """
+        return (isinstance(expr, ir.Expr)
+                and expr.op in ("binop", "inplace_binop")
+                and (varname == expr.lhs.name or varname == expr.rhs.name))
+
+    def _insert_outgoing_phis(self):
+        """
+        Add assignments to forward requested outgoing values
+        to subsequent blocks.
+        """
+        for phiname, varname in self.dfainfo.outgoing_phis.items():
+            target = self.current_scope.get_or_define(phiname,
+                                                      loc=self.loc)
+            stmt = ir.Assign(value=self.get(varname), target=target,
+                             loc=self.loc)
+            self.definitions[target.name].append(stmt.value)
+            if not self.current_block.is_terminated:
+                self.current_block.append(stmt)
+            else:
+                self.current_block.insert_before_terminator(stmt)
+
+    def get_global_value(self, name):
+        """
+        Get a global value from the func_global (first) or
+        as a builtins (second).  If both failed, return a ir.UNDEFINED.
+        """
+        try:
+            return self.func_id.func.__globals__[name]
+        except KeyError:
+            return getattr(builtins, name, ir.UNDEFINED)
+
+    def get_closure_value(self, index):
+        """
+        Get a value from the cell contained in this function's closure.
+        If not set, return a ir.UNDEFINED.
+        """
+        cell = self.func_id.func.__closure__[index]
+        try:
+            return cell.cell_contents
+        except ValueError:
+            return ir.UNDEFINED
+
+    @property
+    def current_scope(self):
+        return self.scopes[-1]
+
+    @property
+    def code_consts(self):
+        return self.bytecode.co_consts
+
+    @property
+    def code_locals(self):
+        return self.bytecode.co_varnames
+
+    @property
+    def code_names(self):
+        return self.bytecode.co_names
+
+    @property
+    def code_cellvars(self):
+        return self.bytecode.co_cellvars
+
+    @property
+    def code_freevars(self):
+        return self.bytecode.co_freevars
+
+    def _dispatch(self, inst, kws):
+        assert self.current_block is not None
+        fname = "op_%s" % inst.opname.replace('+', '_')
+        try:
+            fn = getattr(self, fname)
+        except AttributeError:
+            raise NotImplementedError(inst)
+        else:
+            try:
+                return fn(inst, **kws)
+            except errors.NotDefinedError as e:
+                if e.loc is None:
+                    loc = self.loc
+                else:
+                    loc = e.loc
+
+                err = errors.NotDefinedError(e.name, loc=loc)
+                if not config.FULL_TRACEBACKS:
+                    raise err from None
+                else:
+                    raise err
+
+    # --- Scope operations ---
+
+    def store(self, value, name, redefine=False):
+        """
+        Store *value* (a Expr or Var instance) into the variable named *name*
+        (a str object). Returns the target variable.
+        """
+        if redefine or self.current_block_offset in self.cfa.backbone:
+            rename = not (name in self.code_cellvars)
+            target = self.current_scope.redefine(name, loc=self.loc,
+                                                 rename=rename)
+        else:
+            target = self.current_scope.get_or_define(name, loc=self.loc)
+        if isinstance(value, ir.Var):
+            value = self.assigner.assign(value, target)
+        stmt = ir.Assign(value=value, target=target, loc=self.loc)
+        self.current_block.append(stmt)
+        self.definitions[target.name].append(value)
+        return target
+
+    def get(self, name):
+        """
+        Get the variable (a Var instance) with the given *name*.
+        """
+        # Implicit argument for comprehension starts with '.'
+        # See Parameter class in inspect.py (from Python source)
+        if name[0] == '.' and name[1:].isdigit():
+            name = 'implicit{}'.format(name[1:])
+
+        # Try to simplify the variable lookup by returning an earlier
+        # variable assigned to *name*.
+        var = self.assigner.get_assignment_source(name)
+        if var is None:
+            var = self.current_scope.get(name)
+        return var
+
+    # --- Block operations ---
+
+    def insert_block(self, offset, scope=None, loc=None):
+        scope = scope or self.current_scope
+        loc = loc or self.loc
+        blk = ir.Block(scope=scope, loc=loc)
+        self.blocks[offset] = blk
+        self.current_block = blk
+        self.current_block_offset = offset
+        return blk
+
+    # --- Bytecode handlers ---
+
+    def op_NOP(self, inst):
+        pass
+
+    def op_PRINT_ITEM(self, inst, item, printvar, res):
+        item = self.get(item)
+        printgv = ir.Global("print", print, loc=self.loc)
+        self.store(value=printgv, name=printvar)
+        call = ir.Expr.call(self.get(printvar), (item,), (), loc=self.loc)
+        self.store(value=call, name=res)
+
+    def op_PRINT_NEWLINE(self, inst, printvar, res):
+        printgv = ir.Global("print", print, loc=self.loc)
+        self.store(value=printgv, name=printvar)
+        call = ir.Expr.call(self.get(printvar), (), (), loc=self.loc)
+        self.store(value=call, name=res)
+
+    def op_UNPACK_SEQUENCE(self, inst, iterable, stores, tupleobj):
+        count = len(stores)
+        # Exhaust the iterable into a tuple-like object
+        tup = ir.Expr.exhaust_iter(value=self.get(iterable), loc=self.loc,
+                                   count=count)
+        self.store(name=tupleobj, value=tup)
+
+        # then index the tuple-like object to extract the values
+        for i, st in enumerate(stores):
+            expr = ir.Expr.static_getitem(self.get(tupleobj),
+                                          index=i, index_var=None,
+                                          loc=self.loc)
+            self.store(expr, st)
+
+    def op_FORMAT_VALUE(self, inst, value, res, strvar):
+        """
+        FORMAT_VALUE(flags): flags argument specifies format spec which is not
+        supported yet. Currently, str() is simply called on the value.
+        https://docs.python.org/3/library/dis.html#opcode-FORMAT_VALUE
+        """
+        value = self.get(value)
+        strgv = ir.Global("str", str, loc=self.loc)
+        self.store(value=strgv, name=strvar)
+        call = ir.Expr.call(self.get(strvar), (value,), (), loc=self.loc)
+        self.store(value=call, name=res)
+
+    def op_BUILD_STRING(self, inst, strings, tmps):
+        """
+        BUILD_STRING(count): Concatenates count strings.
+        Required for supporting f-strings.
+        https://docs.python.org/3/library/dis.html#opcode-BUILD_STRING
+        """
+        count = inst.arg
+        # corner case: f""
+        if count == 0:
+            const = ir.Const("", loc=self.loc)
+            self.store(const, tmps[-1])
+            return
+
+        prev = self.get(strings[0])
+        for other, tmp in zip(strings[1:], tmps):
+            other = self.get(other)
+            expr = ir.Expr.binop(
+                operator.add, lhs=prev, rhs=other, loc=self.loc
+            )
+            self.store(expr, tmp)
+            prev = self.get(tmp)
+
+    def op_BUILD_SLICE(self, inst, start, stop, step, res, slicevar):
+        start = self.get(start)
+        stop = self.get(stop)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        if step is None:
+            sliceinst = ir.Expr.call(self.get(slicevar), (start, stop), (),
+                                     loc=self.loc)
+        else:
+            step = self.get(step)
+            sliceinst = ir.Expr.call(self.get(slicevar), (start, stop, step),
+                                     (), loc=self.loc)
+        self.store(value=sliceinst, name=res)
+
+    def op_SLICE_0(self, inst, base, res, slicevar, indexvar, nonevar):
+        base = self.get(base)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        index = ir.Expr.call(self.get(slicevar), (none, none), (), loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        expr = ir.Expr.getitem(base, self.get(indexvar), loc=self.loc)
+        self.store(value=expr, name=res)
+
+    def op_SLICE_1(self, inst, base, start, nonevar, res, slicevar, indexvar):
+        base = self.get(base)
+        start = self.get(start)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (start, none), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        expr = ir.Expr.getitem(base, self.get(indexvar), loc=self.loc)
+        self.store(value=expr, name=res)
+
+    def op_SLICE_2(self, inst, base, nonevar, stop, res, slicevar, indexvar):
+        base = self.get(base)
+        stop = self.get(stop)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (none, stop,), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        expr = ir.Expr.getitem(base, self.get(indexvar), loc=self.loc)
+        self.store(value=expr, name=res)
+
+    def op_SLICE_3(self, inst, base, start, stop, res, slicevar, indexvar):
+        base = self.get(base)
+        start = self.get(start)
+        stop = self.get(stop)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (start, stop), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        expr = ir.Expr.getitem(base, self.get(indexvar), loc=self.loc)
+        self.store(value=expr, name=res)
+
+    def op_STORE_SLICE_0(self, inst, base, value, slicevar, indexvar, nonevar):
+        base = self.get(base)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        index = ir.Expr.call(self.get(slicevar), (none, none), (), loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        stmt = ir.SetItem(base, self.get(indexvar), self.get(value),
+                          loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_STORE_SLICE_1(self, inst, base, start, nonevar, value, slicevar,
+                         indexvar):
+        base = self.get(base)
+        start = self.get(start)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (start, none), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        stmt = ir.SetItem(base, self.get(indexvar), self.get(value),
+                          loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_STORE_SLICE_2(self, inst, base, nonevar, stop, value, slicevar,
+                         indexvar):
+        base = self.get(base)
+        stop = self.get(stop)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (none, stop,), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        stmt = ir.SetItem(base, self.get(indexvar), self.get(value),
+                          loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_STORE_SLICE_3(self, inst, base, start, stop, value, slicevar,
+                         indexvar):
+        base = self.get(base)
+        start = self.get(start)
+        stop = self.get(stop)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (start, stop), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+        stmt = ir.SetItem(base, self.get(indexvar), self.get(value),
+                          loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_DELETE_SLICE_0(self, inst, base, slicevar, indexvar, nonevar):
+        base = self.get(base)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        index = ir.Expr.call(self.get(slicevar), (none, none), (), loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        stmt = ir.DelItem(base, self.get(indexvar), loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_DELETE_SLICE_1(self, inst, base, start, nonevar, slicevar, indexvar):
+        base = self.get(base)
+        start = self.get(start)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (start, none), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        stmt = ir.DelItem(base, self.get(indexvar), loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_DELETE_SLICE_2(self, inst, base, nonevar, stop, slicevar, indexvar):
+        base = self.get(base)
+        stop = self.get(stop)
+
+        nonegv = ir.Const(None, loc=self.loc)
+        self.store(value=nonegv, name=nonevar)
+        none = self.get(nonevar)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (none, stop,), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+
+        stmt = ir.DelItem(base, self.get(indexvar), loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_DELETE_SLICE_3(self, inst, base, start, stop, slicevar, indexvar):
+        base = self.get(base)
+        start = self.get(start)
+        stop = self.get(stop)
+
+        slicegv = ir.Global("slice", slice, loc=self.loc)
+        self.store(value=slicegv, name=slicevar)
+
+        index = ir.Expr.call(self.get(slicevar), (start, stop), (),
+                             loc=self.loc)
+        self.store(value=index, name=indexvar)
+        stmt = ir.DelItem(base, self.get(indexvar), loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_LOAD_FAST(self, inst, res):
+        srcname = self.code_locals[inst.arg]
+        self.store(value=self.get(srcname), name=res)
+
+    def op_STORE_FAST(self, inst, value):
+        dstname = self.code_locals[inst.arg]
+        value = self.get(value)
+        self.store(value=value, name=dstname)
+
+    def op_DELETE_FAST(self, inst):
+        dstname = self.code_locals[inst.arg]
+        self.current_block.append(ir.Del(dstname, loc=self.loc))
+
+    def op_DUP_TOPX(self, inst, orig, duped):
+        for src, dst in zip(orig, duped):
+            self.store(value=self.get(src), name=dst)
+
+    op_DUP_TOP = op_DUP_TOPX
+    op_DUP_TOP_TWO = op_DUP_TOPX
+
+    def op_STORE_ATTR(self, inst, target, value):
+        attr = self.code_names[inst.arg]
+        sa = ir.SetAttr(target=self.get(target), value=self.get(value),
+                        attr=attr, loc=self.loc)
+        self.current_block.append(sa)
+
+    def op_DELETE_ATTR(self, inst, target):
+        attr = self.code_names[inst.arg]
+        sa = ir.DelAttr(target=self.get(target), attr=attr, loc=self.loc)
+        self.current_block.append(sa)
+
+    def op_LOAD_ATTR(self, inst, item, res):
+        item = self.get(item)
+        attr = self.code_names[inst.arg]
+        getattr = ir.Expr.getattr(item, attr, loc=self.loc)
+        self.store(getattr, res)
+
+    def op_LOAD_CONST(self, inst, res):
+        value = self.code_consts[inst.arg]
+        if isinstance(value, tuple):
+            st = []
+            for x in value:
+                nm = '$const_%s' % str(x)
+                val_const = ir.Const(x, loc=self.loc)
+                target = self.store(val_const, name=nm, redefine=True)
+                st.append(target)
+            const = ir.Expr.build_tuple(st, loc=self.loc)
+        elif isinstance(value, frozenset):
+            st = []
+            for x in value:
+                nm = '$const_%s' % str(x)
+                val_const = ir.Const(x, loc=self.loc)
+                target = self.store(val_const, name=nm, redefine=True)
+                st.append(target)
+            const = ir.Expr.build_set(st, loc=self.loc)
+        else:
+            const = ir.Const(value, loc=self.loc)
+        self.store(const, res)
+
+    def op_LOAD_GLOBAL(self, inst, res):
+        name = self.code_names[inst.arg]
+        value = self.get_global_value(name)
+        gl = ir.Global(name, value, loc=self.loc)
+        self.store(gl, res)
+
+    def op_LOAD_DEREF(self, inst, res):
+        n_cellvars = len(self.code_cellvars)
+        if inst.arg < n_cellvars:
+            name = self.code_cellvars[inst.arg]
+            gl = self.get(name)
+        else:
+            idx = inst.arg - n_cellvars
+            name = self.code_freevars[idx]
+            value = self.get_closure_value(idx)
+            gl = ir.FreeVar(idx, name, value, loc=self.loc)
+        self.store(gl, res)
+
+    def op_STORE_DEREF(self, inst, value):
+        n_cellvars = len(self.code_cellvars)
+        if inst.arg < n_cellvars:
+            dstname = self.code_cellvars[inst.arg]
+        else:
+            dstname = self.code_freevars[inst.arg - n_cellvars]
+        value = self.get(value)
+        self.store(value=value, name=dstname)
+
+    def op_SETUP_LOOP(self, inst):
+        assert self.blocks[inst.offset] is self.current_block
+        loop = ir.Loop(inst.offset, exit=(inst.next + inst.arg))
+        self.syntax_blocks.append(loop)
+
+    def op_SETUP_WITH(self, inst, contextmanager, exitfn=None):
+        assert self.blocks[inst.offset] is self.current_block
+        # Handle with
+        exitpt = inst.next + inst.arg
+        wth = ir.With(inst.offset, exit=exitpt)
+        self.syntax_blocks.append(wth)
+        ctxmgr = self.get(contextmanager)
+        self.current_block.append(ir.EnterWith(contextmanager=ctxmgr,
+                                               begin=inst.offset,
+                                               end=exitpt, loc=self.loc,))
+
+        # Store exit fn
+        exit_fn_obj = ir.Const(None, loc=self.loc)
+        self.store(value=exit_fn_obj, name=exitfn)
+
+    def op_SETUP_EXCEPT(self, inst):
+        # Removed since python3.8
+        self._insert_try_block_begin()
+
+    def op_SETUP_FINALLY(self, inst):
+        self._insert_try_block_begin()
+
+    def op_WITH_CLEANUP(self, inst):
+        "no-op"
+
+    def op_WITH_CLEANUP_START(self, inst):
+        "no-op"
+
+    def op_WITH_CLEANUP_FINISH(self, inst):
+        "no-op"
+
+    def op_END_FINALLY(self, inst):
+        "no-op"
+
+    def op_BEGIN_FINALLY(self, inst, temps):
+        # The *temps* are the exception variables
+        const_none = ir.Const(None, loc=self.loc)
+        for tmp in temps:
+            # Set to None for now
+            self.store(const_none, name=tmp)
+            self._exception_vars.add(tmp)
+
+    if PYVERSION < (3, 6):
+
+        def op_CALL_FUNCTION(self, inst, func, args, kws, res, vararg):
+            func = self.get(func)
+            args = [self.get(x) for x in args]
+            if vararg is not None:
+                vararg = self.get(vararg)
+
+            # Process keywords
+            keyvalues = []
+            removethese = []
+            for k, v in kws:
+                k, v = self.get(k), self.get(v)
+                for inst in self.current_block.body:
+                    if isinstance(inst, ir.Assign) and inst.target is k:
+                        removethese.append(inst)
+                        keyvalues.append((inst.value.value, v))
+
+            # Remove keyword constant statements
+            for inst in removethese:
+                self.current_block.remove(inst)
+
+            expr = ir.Expr.call(func, args, keyvalues, loc=self.loc,
+                                vararg=vararg)
+            self.store(expr, res)
+
+        op_CALL_FUNCTION_VAR = op_CALL_FUNCTION
+    else:
+        def op_CALL_FUNCTION(self, inst, func, args, res):
+            func = self.get(func)
+            args = [self.get(x) for x in args]
+            expr = ir.Expr.call(func, args, (), loc=self.loc)
+            self.store(expr, res)
+
+        def op_CALL_FUNCTION_KW(self, inst, func, args, names, res):
+            func = self.get(func)
+            args = [self.get(x) for x in args]
+            # Find names const
+            names = self.get(names)
+            for inst in self.current_block.body:
+                if isinstance(inst, ir.Assign) and inst.target is names:
+                    self.current_block.remove(inst)
+                    # scan up the block looking for the values, remove them
+                    # and find their name strings
+                    named_items = []
+                    for x in inst.value.items:
+                        for y in self.current_block.body[::-1]:
+                            if x == y.target:
+                                self.current_block.remove(y)
+                                named_items.append(y.value.value)
+                                break
+                    keys = named_items
+                    break
+
+            nkeys = len(keys)
+            posvals = args[:-nkeys]
+            kwvals = args[-nkeys:]
+            keyvalues = list(zip(keys, kwvals))
+
+            expr = ir.Expr.call(func, posvals, keyvalues, loc=self.loc)
+            self.store(expr, res)
+
+        def op_CALL_FUNCTION_EX(self, inst, func, vararg, varkwarg, res):
+            func = self.get(func)
+            vararg = self.get(vararg)
+            if varkwarg is not None:
+                varkwarg = self.get(varkwarg)
+            expr = ir.Expr.call(
+                func, [], [], loc=self.loc, vararg=vararg, varkwarg=varkwarg
+            )
+            self.store(expr, res)
+
+    def _build_tuple_unpack(self, inst, tuples, temps, is_assign):
+        first = self.get(tuples[0])
+        if is_assign:
+            # it's assign-like, defer handling to an intrinsic that will have
+            # type information.
+            # Can deal with tuples only, i.e. y = (*x,). where x = <tuple>
+            gv_name = "unpack_single_tuple"
+            gv_fn = ir.Global(gv_name, unpack_single_tuple, loc=self.loc,)
+            self.store(value=gv_fn, name=gv_name, redefine=True)
+            exc = ir.Expr.call(self.get(gv_name), args=(first,), kws=(),
+                               loc=self.loc,)
+            self.store(exc, temps[0])
+        else:
+            loc = self.loc
+            for other, tmp in zip(map(self.get, tuples[1:]), temps):
+                # Emit as `first + tuple(other)`
+                gv_tuple = ir.Global(
+                    name="tuple", value=tuple,
+                    loc=loc,
+                )
+                tuple_var = self.store(
+                    gv_tuple, "$_list_extend_gv_tuple", redefine=True,
+                )
+                tuplify_val = ir.Expr.call(
+                    tuple_var, (other,), (),
+                    loc=loc,
+                )
+                tuplify_var = self.store(tuplify_val, "$_tuplify",
+                                         redefine=True)
+                out = ir.Expr.binop(
+                    fn=operator.add, lhs=first, rhs=self.get(tuplify_var.name),
+                    loc=self.loc,
+                )
+                self.store(out, tmp)
+                first = self.get(tmp)
+
+    def op_BUILD_TUPLE_UNPACK_WITH_CALL(self, inst, tuples, temps, is_assign):
+        # just unpack the input tuple, call inst will be handled afterwards
+        self._build_tuple_unpack(inst, tuples, temps, is_assign)
+
+    def op_BUILD_TUPLE_UNPACK(self, inst, tuples, temps, is_assign):
+        self._build_tuple_unpack(inst, tuples, temps, is_assign)
+
+    def op_LIST_TO_TUPLE(self, inst, const_list, res):
+        expr = ir.Expr.dummy('list_to_tuple', (const_list,), loc=self.loc)
+        self.store(expr, res)
+
+    def op_BUILD_CONST_KEY_MAP(self, inst, keys, keytmps, values, res):
+        # Unpack the constant key-tuple and reused build_map which takes
+        # a sequence of (key, value) pair.
+        keyvar = self.get(keys)
+        # TODO: refactor this pattern. occurred several times.
+        for inst in self.current_block.body:
+            if isinstance(inst, ir.Assign) and inst.target is keyvar:
+                self.current_block.remove(inst)
+                # scan up the block looking for the values, remove them
+                # and find their name strings
+                named_items = []
+                for x in inst.value.items:
+                    for y in self.current_block.body[::-1]:
+                        if x == y.target:
+                            self.current_block.remove(y)
+                            named_items.append(y.value.value)
+                            break
+                keytup = named_items
+                break
+        assert len(keytup) == len(values)
+        keyconsts = [ir.Const(value=x, loc=self.loc) for x in keytup]
+        for kval, tmp in zip(keyconsts, keytmps):
+            self.store(kval, tmp)
+        items = list(zip(map(self.get, keytmps), map(self.get, values)))
+
+        # sort out literal values
+        literal_items = []
+        for v in values:
+            defns = self.definitions[v]
+            if len(defns) != 1:
+                break
+            defn = defns[0]
+            if not isinstance(defn, ir.Const):
+                break
+            literal_items.append(defn.value)
+
+        def resolve_const(v):
+            defns = self.definitions[v]
+            if len(defns) != 1:
+                return _UNKNOWN_VALUE(self.get(v).name)
+            defn = defns[0]
+            if not isinstance(defn, ir.Const):
+                return _UNKNOWN_VALUE(self.get(v).name)
+            return defn.value
+
+        if len(literal_items) != len(values):
+            literal_dict = {x: resolve_const(y) for x, y in
+                            zip(keytup, values)}
+        else:
+            literal_dict = {x:y for x, y in zip(keytup, literal_items)}
+
+        # to deal with things like {'a': 1, 'a': 'cat', 'b': 2, 'a': 2j}
+        # store the index of the actual used value for a given key, this is
+        # used when lowering to pull the right value out into the tuple repr
+        # of a mixed value type dictionary.
+        value_indexes = {}
+        for i, k in enumerate(keytup):
+            value_indexes[k] = i
+
+        expr = ir.Expr.build_map(items=items,
+                                 size=2,
+                                 literal_value=literal_dict,
+                                 value_indexes=value_indexes,
+                                 loc=self.loc)
+
+        self.store(expr, res)
+
+    def op_GET_ITER(self, inst, value, res):
+        expr = ir.Expr.getiter(value=self.get(value), loc=self.loc)
+        self.store(expr, res)
+
+    def op_FOR_ITER(self, inst, iterator, pair, indval, pred):
+        """
+        Assign new block other this instruction.
+        """
+        assert inst.offset in self.blocks, "FOR_ITER must be block head"
+
+        # Emit code
+        val = self.get(iterator)
+
+        pairval = ir.Expr.iternext(value=val, loc=self.loc)
+        self.store(pairval, pair)
+
+        iternext = ir.Expr.pair_first(value=self.get(pair), loc=self.loc)
+        self.store(iternext, indval)
+
+        isvalid = ir.Expr.pair_second(value=self.get(pair), loc=self.loc)
+        self.store(isvalid, pred)
+
+        # Conditional jump
+        br = ir.Branch(cond=self.get(pred), truebr=inst.next,
+                       falsebr=inst.get_jump_target(),
+                       loc=self.loc)
+        self.current_block.append(br)
+
+    def op_BINARY_SUBSCR(self, inst, target, index, res):
+        index = self.get(index)
+        target = self.get(target)
+        expr = ir.Expr.getitem(target, index=index, loc=self.loc)
+        self.store(expr, res)
+
+    def op_STORE_SUBSCR(self, inst, target, index, value):
+        index = self.get(index)
+        target = self.get(target)
+        value = self.get(value)
+        stmt = ir.SetItem(target=target, index=index, value=value,
+                          loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_DELETE_SUBSCR(self, inst, target, index):
+        index = self.get(index)
+        target = self.get(target)
+        stmt = ir.DelItem(target=target, index=index, loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_BUILD_TUPLE(self, inst, items, res):
+        expr = ir.Expr.build_tuple(items=[self.get(x) for x in items],
+                                   loc=self.loc)
+        self.store(expr, res)
+
+    def op_BUILD_LIST(self, inst, items, res):
+        expr = ir.Expr.build_list(items=[self.get(x) for x in items],
+                                  loc=self.loc)
+        self.store(expr, res)
+
+    def op_BUILD_SET(self, inst, items, res):
+        expr = ir.Expr.build_set(items=[self.get(x) for x in items],
+                                 loc=self.loc)
+        self.store(expr, res)
+
+    def op_SET_UPDATE(self, inst, target, value, updatevar, res):
+        target = self.get(target)
+        value = self.get(value)
+        updateattr = ir.Expr.getattr(target, 'update', loc=self.loc)
+        self.store(value=updateattr, name=updatevar)
+        updateinst = ir.Expr.call(self.get(updatevar), (value,), (),
+                                  loc=self.loc)
+        self.store(value=updateinst, name=res)
+
+    def op_DICT_UPDATE(self, inst, target, value, updatevar, res):
+        target = self.get(target)
+        value = self.get(value)
+        # We generate _update_from_bytecode instead of update so we can
+        # differentiate between user .update() calls and those from the
+        # bytecode. This is then used to recombine dictionaries in peephole
+        # optimizations. See the dicussion in this PR about why:
+        # https://github.com/numba/numba/pull/7964/files#r868229306
+        updateattr = ir.Expr.getattr(
+            target, '_update_from_bytecode', loc=self.loc
+        )
+        self.store(value=updateattr, name=updatevar)
+        updateinst = ir.Expr.call(self.get(updatevar), (value,), (),
+                                  loc=self.loc)
+        self.store(value=updateinst, name=res)
+
+    def op_BUILD_MAP(self, inst, items, size, res):
+        got_items = [(self.get(k), self.get(v)) for k, v in items]
+
+        # sort out literal values, this is a bit contrived but is to handle
+        # situations like `{1: 10, 1: 10}` where the size of the literal dict
+        # is smaller than the definition
+        def get_literals(target):
+            literal_items = []
+            values = [self.get(v.name) for v in target]
+            for v in values:
+                defns = self.definitions[v.name]
+                if len(defns) != 1:
+                    break
+                defn = defns[0]
+                if not isinstance(defn, ir.Const):
+                    break
+                literal_items.append(defn.value)
+            return literal_items
+
+        literal_keys = get_literals(x[0] for x in got_items)
+        literal_values = get_literals(x[1] for x in got_items)
+
+        has_literal_keys = len(literal_keys) == len(got_items)
+        has_literal_values = len(literal_values) == len(got_items)
+
+        value_indexes = {}
+        if not has_literal_keys and not has_literal_values:
+            literal_dict = None
+        elif has_literal_keys and not has_literal_values:
+            literal_dict = {x: _UNKNOWN_VALUE(y[1]) for x, y in
+                            zip(literal_keys, got_items)}
+            for i, k in enumerate(literal_keys):
+                value_indexes[k] = i
+        else:
+            literal_dict = {x: y for x, y in zip(literal_keys, literal_values)}
+            for i, k in enumerate(literal_keys):
+                value_indexes[k] = i
+
+        expr = ir.Expr.build_map(items=got_items, size=size,
+                                 literal_value=literal_dict,
+                                 value_indexes=value_indexes,
+                                 loc=self.loc)
+        self.store(expr, res)
+
+    def op_STORE_MAP(self, inst, dct, key, value):
+        stmt = ir.StoreMap(dct=self.get(dct), key=self.get(key),
+                           value=self.get(value), loc=self.loc)
+        self.current_block.append(stmt)
+
+    def op_UNARY_NEGATIVE(self, inst, value, res):
+        value = self.get(value)
+        expr = ir.Expr.unary('-', value=value, loc=self.loc)
+        return self.store(expr, res)
+
+    def op_UNARY_POSITIVE(self, inst, value, res):
+        value = self.get(value)
+        expr = ir.Expr.unary('+', value=value, loc=self.loc)
+        return self.store(expr, res)
+
+    def op_UNARY_INVERT(self, inst, value, res):
+        value = self.get(value)
+        expr = ir.Expr.unary('~', value=value, loc=self.loc)
+        return self.store(expr, res)
+
+    def op_UNARY_NOT(self, inst, value, res):
+        value = self.get(value)
+        expr = ir.Expr.unary('not', value=value, loc=self.loc)
+        return self.store(expr, res)
+
+    def _binop(self, op, lhs, rhs, res):
+        op = BINOPS_TO_OPERATORS[op]
+        lhs = self.get(lhs)
+        rhs = self.get(rhs)
+        expr = ir.Expr.binop(op, lhs=lhs, rhs=rhs, loc=self.loc)
+        self.store(expr, res)
+
+    def _inplace_binop(self, op, lhs, rhs, res):
+        immuop = BINOPS_TO_OPERATORS[op]
+        op = INPLACE_BINOPS_TO_OPERATORS[op + '=']
+        lhs = self.get(lhs)
+        rhs = self.get(rhs)
+        expr = ir.Expr.inplace_binop(op, immuop, lhs=lhs, rhs=rhs,
+                                     loc=self.loc)
+        self.store(expr, res)
+
+    def op_BINARY_ADD(self, inst, lhs, rhs, res):
+        self._binop('+', lhs, rhs, res)
+
+    def op_BINARY_SUBTRACT(self, inst, lhs, rhs, res):
+        self._binop('-', lhs, rhs, res)
+
+    def op_BINARY_MULTIPLY(self, inst, lhs, rhs, res):
+        self._binop('*', lhs, rhs, res)
+
+    def op_BINARY_DIVIDE(self, inst, lhs, rhs, res):
+        self._binop('/?', lhs, rhs, res)
+
+    def op_BINARY_TRUE_DIVIDE(self, inst, lhs, rhs, res):
+        self._binop('/', lhs, rhs, res)
+
+    def op_BINARY_FLOOR_DIVIDE(self, inst, lhs, rhs, res):
+        self._binop('//', lhs, rhs, res)
+
+    def op_BINARY_MODULO(self, inst, lhs, rhs, res):
+        self._binop('%', lhs, rhs, res)
+
+    def op_BINARY_POWER(self, inst, lhs, rhs, res):
+        self._binop('**', lhs, rhs, res)
+
+    def op_BINARY_MATRIX_MULTIPLY(self, inst, lhs, rhs, res):
+        self._binop('@', lhs, rhs, res)
+
+    def op_BINARY_LSHIFT(self, inst, lhs, rhs, res):
+        self._binop('<<', lhs, rhs, res)
+
+    def op_BINARY_RSHIFT(self, inst, lhs, rhs, res):
+        self._binop('>>', lhs, rhs, res)
+
+    def op_BINARY_AND(self, inst, lhs, rhs, res):
+        self._binop('&', lhs, rhs, res)
+
+    def op_BINARY_OR(self, inst, lhs, rhs, res):
+        self._binop('|', lhs, rhs, res)
+
+    def op_BINARY_XOR(self, inst, lhs, rhs, res):
+        self._binop('^', lhs, rhs, res)
+
+    def op_INPLACE_ADD(self, inst, lhs, rhs, res):
+        self._inplace_binop('+', lhs, rhs, res)
+
+    def op_INPLACE_SUBTRACT(self, inst, lhs, rhs, res):
+        self._inplace_binop('-', lhs, rhs, res)
+
+    def op_INPLACE_MULTIPLY(self, inst, lhs, rhs, res):
+        self._inplace_binop('*', lhs, rhs, res)
+
+    def op_INPLACE_DIVIDE(self, inst, lhs, rhs, res):
+        self._inplace_binop('/?', lhs, rhs, res)
+
+    def op_INPLACE_TRUE_DIVIDE(self, inst, lhs, rhs, res):
+        self._inplace_binop('/', lhs, rhs, res)
+
+    def op_INPLACE_FLOOR_DIVIDE(self, inst, lhs, rhs, res):
+        self._inplace_binop('//', lhs, rhs, res)
+
+    def op_INPLACE_MODULO(self, inst, lhs, rhs, res):
+        self._inplace_binop('%', lhs, rhs, res)
+
+    def op_INPLACE_POWER(self, inst, lhs, rhs, res):
+        self._inplace_binop('**', lhs, rhs, res)
+
+    def op_INPLACE_MATRIX_MULTIPLY(self, inst, lhs, rhs, res):
+        self._inplace_binop('@', lhs, rhs, res)
+
+    def op_INPLACE_LSHIFT(self, inst, lhs, rhs, res):
+        self._inplace_binop('<<', lhs, rhs, res)
+
+    def op_INPLACE_RSHIFT(self, inst, lhs, rhs, res):
+        self._inplace_binop('>>', lhs, rhs, res)
+
+    def op_INPLACE_AND(self, inst, lhs, rhs, res):
+        self._inplace_binop('&', lhs, rhs, res)
+
+    def op_INPLACE_OR(self, inst, lhs, rhs, res):
+        self._inplace_binop('|', lhs, rhs, res)
+
+    def op_INPLACE_XOR(self, inst, lhs, rhs, res):
+        self._inplace_binop('^', lhs, rhs, res)
+
+    def op_JUMP_ABSOLUTE(self, inst):
+        jmp = ir.Jump(inst.get_jump_target(), loc=self.loc)
+        self.current_block.append(jmp)
+
+    def op_JUMP_FORWARD(self, inst):
+        jmp = ir.Jump(inst.get_jump_target(), loc=self.loc)
+        self.current_block.append(jmp)
+
+    def op_POP_BLOCK(self, inst, kind=None):
+        if kind is None:
+            self.syntax_blocks.pop()
+        elif kind == 'with':
+            d = ir.PopBlock(loc=self.loc)
+            self.current_block.append(d)
+        elif kind == 'try':
+            self._insert_try_block_end()
+
+    def op_RETURN_VALUE(self, inst, retval, castval):
+        self.store(ir.Expr.cast(self.get(retval), loc=self.loc), castval)
+        ret = ir.Return(self.get(castval), loc=self.loc)
+        self.current_block.append(ret)
+
+    def op_COMPARE_OP(self, inst, lhs, rhs, res):
+        op = dis.cmp_op[inst.arg]
+        if op == 'in' or op == 'not in':
+            lhs, rhs = rhs, lhs
+
+        if op == 'not in':
+            self._binop('in', lhs, rhs, res)
+            tmp = self.get(res)
+            out = ir.Expr.unary('not', value=tmp, loc=self.loc)
+            self.store(out, res)
+        elif op == 'exception match':
+            gv_fn = ir.Global(
+                "exception_match", eh.exception_match, loc=self.loc,
+            )
+            exc_match_name = '$exc_match'
+            self.store(value=gv_fn, name=exc_match_name, redefine=True)
+            lhs = self.get(lhs)
+            rhs = self.get(rhs)
+            exc = ir.Expr.call(
+                self.get(exc_match_name), args=(lhs, rhs), kws=(), loc=self.loc,
+            )
+            self.store(exc, res)
+        else:
+            self._binop(op, lhs, rhs, res)
+
+    def op_IS_OP(self, inst, lhs, rhs, res):
+        # invert if op case is 1
+        op = 'is not' if inst.arg == 1 else 'is'
+        self._binop(op, lhs, rhs, res)
+
+    def op_CONTAINS_OP(self, inst, lhs, rhs, res):
+        lhs, rhs = rhs, lhs
+        self._binop('in', lhs, rhs, res)
+        # invert if op case is 1
+        if inst.arg == 1:
+            tmp = self.get(res)
+            out = ir.Expr.unary('not', value=tmp, loc=self.loc)
+            self.store(out, res)
+
+    def op_BREAK_LOOP(self, inst, end=None):
+        if end is None:
+            loop = self.syntax_blocks[-1]
+            assert isinstance(loop, ir.Loop)
+            end = loop.exit
+        jmp = ir.Jump(target=end, loc=self.loc)
+        self.current_block.append(jmp)
+
+    def _op_JUMP_IF(self, inst, pred, iftrue):
+        brs = {
+            True: inst.get_jump_target(),
+            False: inst.next,
+        }
+        truebr = brs[iftrue]
+        falsebr = brs[not iftrue]
+
+        name = "bool%s" % (inst.offset)
+        gv_fn = ir.Global("bool", bool, loc=self.loc)
+        self.store(value=gv_fn, name=name)
+
+        callres = ir.Expr.call(self.get(name), (self.get(pred),), (),
+                               loc=self.loc)
+
+        pname = "$%spred" % (inst.offset)
+        predicate = self.store(value=callres, name=pname)
+        bra = ir.Branch(cond=predicate, truebr=truebr, falsebr=falsebr,
+                        loc=self.loc)
+        self.current_block.append(bra)
+
+    def op_JUMP_IF_FALSE(self, inst, pred):
+        self._op_JUMP_IF(inst, pred=pred, iftrue=False)
+
+    def op_JUMP_IF_TRUE(self, inst, pred):
+        self._op_JUMP_IF(inst, pred=pred, iftrue=True)
+
+    def op_POP_JUMP_IF_FALSE(self, inst, pred):
+        self._op_JUMP_IF(inst, pred=pred, iftrue=False)
+
+    def op_POP_JUMP_IF_TRUE(self, inst, pred):
+        self._op_JUMP_IF(inst, pred=pred, iftrue=True)
+
+    def op_JUMP_IF_FALSE_OR_POP(self, inst, pred):
+        self._op_JUMP_IF(inst, pred=pred, iftrue=False)
+
+    def op_JUMP_IF_TRUE_OR_POP(self, inst, pred):
+        self._op_JUMP_IF(inst, pred=pred, iftrue=True)
+
+    def op_JUMP_IF_NOT_EXC_MATCH(self, inst, pred, tos, tos1):
+        truebr = inst.next
+        falsebr = inst.get_jump_target()
+        gv_fn = ir.Global(
+            "exception_match", eh.exception_match, loc=self.loc,
+        )
+        exc_match_name = '$exc_match'
+        self.store(value=gv_fn, name=exc_match_name, redefine=True)
+        lhs = self.get(tos1)
+        rhs = self.get(tos)
+        exc = ir.Expr.call(
+            self.get(exc_match_name), args=(lhs, rhs), kws=(), loc=self.loc,
+        )
+        predicate = self.store(exc, pred)
+        bra = ir.Branch(cond=predicate, truebr=truebr, falsebr=falsebr,
+                        loc=self.loc)
+        self.current_block.append(bra)
+
+    def op_RERAISE(self, inst, exc):
+        # Numba can't handle this case and it's caught else where, this is a
+        # runtime guard in case this is reached by unknown means.
+        msg = (f"Unreachable condition reached (op code RERAISE executed)"
+               f"{error_extras['reportable']}")
+        stmt = ir.StaticRaise(AssertionError, (msg,), self.loc)
+        self.current_block.append(stmt)
+
+    def op_RAISE_VARARGS(self, inst, exc):
+        if exc is not None:
+            exc = self.get(exc)
+        tryblk = self.dfainfo.active_try_block
+        if tryblk is not None:
+            # In a try block
+            stmt = ir.TryRaise(exception=exc, loc=self.loc)
+            self.current_block.append(stmt)
+            self._insert_try_block_end()
+            self.current_block.append(ir.Jump(tryblk['end'], loc=self.loc))
+        else:
+            # Not in a try block
+            stmt = ir.Raise(exception=exc, loc=self.loc)
+            self.current_block.append(stmt)
+
+    def op_YIELD_VALUE(self, inst, value, res):
+        # initialize index to None.  it's being set later in post-processing
+        index = None
+        inst = ir.Yield(value=self.get(value), index=index, loc=self.loc)
+        return self.store(inst, res)
+
+    def op_MAKE_FUNCTION(self, inst, name, code, closure, annotations,
+                         kwdefaults, defaults, res):
+        # annotations are ignored by numba but useful for static analysis
+        # re. https://github.com/numba/numba/issues/7269
+        if kwdefaults is not None:
+            msg = "op_MAKE_FUNCTION with kwdefaults is not implemented"
+            raise NotImplementedError(msg)
+        if defaults:
+            if isinstance(defaults, tuple):
+                defaults = tuple([self.get(name) for name in defaults])
+            else:
+                defaults = self.get(defaults)
+
+        assume_code_const = self.definitions[code][0]
+        if not isinstance(assume_code_const, ir.Const):
+            msg = (
+                "Unsupported use of closure. "
+                "Probably caused by complex control-flow constructs; "
+                "e.g. try-except"
+            )
+            raise errors.UnsupportedError(msg, loc=self.loc)
+        fcode = assume_code_const.value
+        if name:
+            name = self.get(name)
+        if closure:
+            closure = self.get(closure)
+        expr = ir.Expr.make_function(name, fcode, closure, defaults, self.loc)
+        self.store(expr, res)
+
+    def op_MAKE_CLOSURE(self, inst, name, code, closure, annotations,
+                        kwdefaults, defaults, res):
+        self.op_MAKE_FUNCTION(inst, name, code, closure, annotations,
+                              kwdefaults, defaults, res)
+
+    def op_LOAD_CLOSURE(self, inst, res):
+        n_cellvars = len(self.code_cellvars)
+        if inst.arg < n_cellvars:
+            name = self.code_cellvars[inst.arg]
+            try:
+                gl = self.get(name)
+            except NotDefinedError:
+                msg = "Unsupported use of op_LOAD_CLOSURE encountered"
+                raise NotImplementedError(msg)
+        else:
+            idx = inst.arg - n_cellvars
+            name = self.code_freevars[idx]
+            value = self.get_closure_value(idx)
+            gl = ir.FreeVar(idx, name, value, loc=self.loc)
+        self.store(gl, res)
+
+    def op_LIST_APPEND(self, inst, target, value, appendvar, res):
+        target = self.get(target)
+        value = self.get(value)
+        appendattr = ir.Expr.getattr(target, 'append', loc=self.loc)
+        self.store(value=appendattr, name=appendvar)
+        appendinst = ir.Expr.call(self.get(appendvar), (value,), (),
+                                  loc=self.loc)
+        self.store(value=appendinst, name=res)
+
+    def op_LIST_EXTEND(self, inst, target, value, extendvar, res):
+        target = self.get(target)
+        value = self.get(value)
+        # If the statements between the current instruction and the target
+        # are N * consts followed by build_tuple AND the target has no items,
+        # it's a situation where a list is being statically initialised, rewrite
+        # the build_tuple as a build_list, drop the extend, and wire up the
+        # target as the result from the build_tuple that's been rewritten.
+
+        # See if this is the first statement in a block, if so its probably from
+        # control flow in a tuple unpack like:
+        # `(*(1, (2,) if predicate else (3,)))`
+        # this cannot be handled as present so raise
+        msg = ("An unsupported bytecode sequence has been encountered: "
+               "op_LIST_EXTEND at the start of a block.\n\nThis could be "
+               "due to the use of a branch in a tuple unpacking statement.")
+        if not self.current_block.body:
+            raise errors.UnsupportedError(msg)
+
+        # is last emitted statement a build_tuple?
+        stmt = self.current_block.body[-1]
+        ok = isinstance(stmt.value, ir.Expr) and stmt.value.op == "build_tuple"
+        # check statements from self.current_block.body[-1] through to target,
+        # make sure they are consts
+        build_empty_list = None
+        if ok:
+            for stmt in reversed(self.current_block.body[:-1]):
+                if not isinstance(stmt, ir.Assign):
+                    ok = False
+                    break
+                # if its not a const, it needs to be the `build_list` for the
+                # target, else it's something else we don't know about so just
+                # bail
+                if isinstance(stmt.value, ir.Const):
+                    continue
+
+                # it's not a const, check for target
+                elif isinstance(stmt.value, ir.Expr) and stmt.target == target:
+                    build_empty_list = stmt
+                    # it's only ok to do this if the target has no initializer
+                    # already
+                    ok = not stmt.value.items
+                    break
+                else:
+                    ok = False
+                    break
+        if ok and build_empty_list is None:
+            raise errors.UnsupportedError(msg)
+        if ok:
+            stmts = self.current_block.body
+            build_tuple_asgn = self.current_block.body[-1]
+            # move build list to last issued statement
+            stmts.append(stmts.pop(stmts.index(build_empty_list)))
+            # fix the build list
+            build_tuple = build_tuple_asgn.value
+            build_list = build_empty_list.value
+            build_list.items = build_tuple.items
+        else:
+            # it's just a list extend with no static init, let it be
+            extendattr = ir.Expr.getattr(target, 'extend', loc=self.loc)
+            self.store(value=extendattr, name=extendvar)
+            extendinst = ir.Expr.call(self.get(extendvar), (value,), (),
+                                      loc=self.loc)
+            self.store(value=extendinst, name=res)
+
+    def op_MAP_ADD(self, inst, target, key, value, setitemvar, res):
+        target = self.get(target)
+        key = self.get(key)
+        value = self.get(value)
+        setitemattr = ir.Expr.getattr(target, '__setitem__', loc=self.loc)
+        self.store(value=setitemattr, name=setitemvar)
+        appendinst = ir.Expr.call(self.get(setitemvar), (key, value,), (),
+                                  loc=self.loc)
+        self.store(value=appendinst, name=res)
+
+    def op_LOAD_ASSERTION_ERROR(self, inst, res):
+        gv_fn = ir.Global("AssertionError", AssertionError, loc=self.loc)
+        self.store(value=gv_fn, name=res)
+
+    # NOTE: The LOAD_METHOD opcode is implemented as a LOAD_ATTR for ease,
+    # however this means a new object (the bound-method instance) could be
+    # created. Conversely, using a pure LOAD_METHOD no intermediary is present
+    # and it is essentially like a pointer grab and forward to CALL_METHOD. The
+    # net outcome is that the implementation in Numba produces the same result,
+    # but in object mode it may be that it runs more slowly than it would if
+    # run in CPython.
+
+    def op_LOAD_METHOD(self, *args, **kws):
+        self.op_LOAD_ATTR(*args, **kws)
+
+    def op_CALL_METHOD(self, *args, **kws):
+        self.op_CALL_FUNCTION(*args, **kws)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/intrinsics.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/intrinsics.py
new file mode 100644
index 000000000..8e85bb354
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/intrinsics.py
@@ -0,0 +1,101 @@
+"""
+LLVM pass that converts intrinsic into other math calls
+"""
+
+from llvmlite import ir
+
+
+class _DivmodFixer(ir.Visitor):
+    def visit_Instruction(self, instr):
+        if instr.type == ir.IntType(64):
+            if instr.opname in ['srem', 'urem', 'sdiv', 'udiv']:
+                name = 'numba_{op}'.format(op=instr.opname)
+                fn = self.module.globals.get(name)
+                # Declare the function if it doesn't already exist
+                if fn is None:
+                    opty = instr.type
+                    sdivfnty = ir.FunctionType(opty, [opty, opty])
+                    fn = ir.Function(self.module, sdivfnty, name=name)
+                # Replace the operation with a call to the builtin
+                repl = ir.CallInstr(parent=instr.parent, func=fn,
+                                    args=instr.operands, name=instr.name)
+                instr.parent.replace(instr, repl)
+
+
+def fix_divmod(mod):
+    """Replace division and reminder instructions to builtins calls
+    """
+    _DivmodFixer().visit(mod)
+
+
+INTR_TO_CMATH = {
+    "llvm.pow.f32": "powf",
+    "llvm.pow.f64": "pow",
+
+    "llvm.sin.f32": "sinf",
+    "llvm.sin.f64": "sin",
+
+    "llvm.cos.f32": "cosf",
+    "llvm.cos.f64": "cos",
+
+    "llvm.sqrt.f32": "sqrtf",
+    "llvm.sqrt.f64": "sqrt",
+
+    "llvm.exp.f32": "expf",
+    "llvm.exp.f64": "exp",
+
+    "llvm.log.f32": "logf",
+    "llvm.log.f64": "log",
+
+    "llvm.log10.f32": "log10f",
+    "llvm.log10.f64": "log10",
+
+    "llvm.fabs.f32": "fabsf",
+    "llvm.fabs.f64": "fabs",
+
+    "llvm.floor.f32": "floorf",
+    "llvm.floor.f64": "floor",
+
+    "llvm.ceil.f32": "ceilf",
+    "llvm.ceil.f64": "ceil",
+
+    "llvm.trunc.f32": "truncf",
+    "llvm.trunc.f64": "trunc",
+}
+
+OTHER_CMATHS = '''
+tan
+tanf
+sinh
+sinhf
+cosh
+coshf
+tanh
+tanhf
+asin
+asinf
+acos
+acosf
+atan
+atanf
+atan2
+atan2f
+asinh
+asinhf
+acosh
+acoshf
+atanh
+atanhf
+expm1
+expm1f
+log1p
+log1pf
+log10
+log10f
+fmod
+fmodf
+round
+roundf
+'''.split()
+
+INTR_MATH = frozenset(INTR_TO_CMATH.values()) | frozenset(OTHER_CMATHS)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ir.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ir.py
new file mode 100644
index 000000000..d2b546996
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ir.py
@@ -0,0 +1,1630 @@
+from collections import defaultdict
+import copy
+import itertools
+import os
+import linecache
+import pprint
+import re
+import sys
+import operator
+from types import FunctionType, BuiltinFunctionType
+from functools import total_ordering
+from io import StringIO
+
+from numba.core import errors, config
+from numba.core.utils import (BINOPS_TO_OPERATORS, INPLACE_BINOPS_TO_OPERATORS,
+                              UNARY_BUITINS_TO_OPERATORS, OPERATORS_TO_BUILTINS)
+from numba.core.errors import (NotDefinedError, RedefinedError,
+                               VerificationError, ConstantInferenceError)
+from numba.core import consts
+
+# terminal color markup
+_termcolor = errors.termcolor()
+
+
+class Loc(object):
+    """Source location
+
+    """
+    _defmatcher = re.compile(r'def\s+(\w+)\(.*')
+
+    def __init__(self, filename, line, col=None, maybe_decorator=False):
+        """ Arguments:
+        filename - name of the file
+        line - line in file
+        col - column
+        maybe_decorator - Set to True if location is likely a jit decorator
+        """
+        self.filename = filename
+        self.line = line
+        self.col = col
+        self.lines = None # the source lines from the linecache
+        self.maybe_decorator = maybe_decorator
+
+    def __eq__(self, other):
+        # equivalence is solely based on filename, line and col
+        if type(self) is not type(other): return False
+        if self.filename != other.filename: return False
+        if self.line != other.line: return False
+        if self.col != other.col: return False
+        return True
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    @classmethod
+    def from_function_id(cls, func_id):
+        return cls(func_id.filename, func_id.firstlineno, maybe_decorator=True)
+
+    def __repr__(self):
+        return "Loc(filename=%s, line=%s, col=%s)" % (self.filename,
+                                                      self.line, self.col)
+
+    def __str__(self):
+        if self.col is not None:
+            return "%s (%s:%s)" % (self.filename, self.line, self.col)
+        else:
+            return "%s (%s)" % (self.filename, self.line)
+
+    def _find_definition(self):
+        # try and find a def, go backwards from error line
+        fn_name = None
+        lines = self.get_lines()
+        for x in reversed(lines[:self.line - 1]):
+            # the strip and startswith is to handle user code with commented out
+            # 'def' or use of 'def' in a docstring.
+            if x.strip().startswith('def '):
+                fn_name = x
+                break
+
+        return fn_name
+
+    def _raw_function_name(self):
+        defn = self._find_definition()
+        if defn:
+            return self._defmatcher.match(defn.strip()).groups()[0]
+        else:
+            # Probably exec(<string>) or REPL.
+            return None
+
+    def get_lines(self):
+        if self.lines is None:
+
+            self.lines = linecache.getlines(self._get_path())
+
+        return self.lines
+
+    def _get_path(self):
+        path = None
+        try:
+            # Try to get a relative path
+            # ipython/jupyter input just returns as self.filename
+            path = os.path.relpath(self.filename)
+        except ValueError:
+            # Fallback to absolute path if error occurred in getting the
+            # relative path.
+            # This may happen on windows if the drive is different
+            path = os.path.abspath(self.filename)
+        return path
+
+
+    def strformat(self, nlines_up=2):
+
+        lines = self.get_lines()
+
+        use_line = self.line
+
+        if self.maybe_decorator:
+            # try and sort out a better `loc`, if it's suspected that this loc
+            # points at a jit decorator by virtue of
+            # `__code__.co_firstlineno`
+
+            # get lines, add a dummy entry at the start as lines count from
+            # 1 but list index counts from 0
+            tmplines = [''] + lines
+
+            if lines and use_line and 'def ' not in tmplines[use_line]:
+                # look forward 10 lines, unlikely anyone managed to stretch
+                # a jit call declaration over >10 lines?!
+                min_line = max(0, use_line)
+                max_line = use_line + 10
+                selected = tmplines[min_line : max_line]
+                index = 0
+                for idx, x in enumerate(selected):
+                    if 'def ' in x:
+                        index = idx
+                        break
+                use_line = use_line + index
+
+
+        ret = [] # accumulates output
+        if lines and use_line:
+
+            def count_spaces(string):
+                spaces = 0
+                for x in itertools.takewhile(str.isspace, str(string)):
+                    spaces += 1
+                return spaces
+
+            # A few places in the code still use no `loc` or default to line 1
+            # this is often in places where exceptions are used for the purposes
+            # of flow control. As a result max is in use to prevent slice from
+            # `[negative: positive]`
+            selected = lines[max(0, use_line - nlines_up):use_line]
+
+            # see if selected contains a definition
+            def_found = False
+            for x in selected:
+                if 'def ' in x:
+                    def_found = True
+
+            # no definition found, try and find one
+            if not def_found:
+                # try and find a def, go backwards from error line
+                fn_name = None
+                for x in reversed(lines[:use_line - 1]):
+                    if 'def ' in x:
+                        fn_name = x
+                        break
+                if fn_name:
+                    ret.append(fn_name)
+                    spaces = count_spaces(x)
+                    ret.append(' '*(4 + spaces) + '<source elided>\n')
+
+            if selected:
+                ret.extend(selected[:-1])
+                ret.append(_termcolor.highlight(selected[-1]))
+
+                # point at the problem with a caret
+                spaces = count_spaces(selected[-1])
+                ret.append(' '*(spaces) + _termcolor.indicate("^"))
+
+        # if in the REPL source may not be available
+        if not ret:
+            ret = "<source missing, REPL/exec in use?>"
+
+        err = _termcolor.filename('\nFile "%s", line %d:')+'\n%s'
+        tmp = err % (self._get_path(), use_line, _termcolor.code(''.join(ret)))
+        return tmp
+
+    def with_lineno(self, line, col=None):
+        """
+        Return a new Loc with this line number.
+        """
+        return type(self)(self.filename, line, col)
+
+    def short(self):
+        """
+        Returns a short string
+        """
+        shortfilename = os.path.basename(self.filename)
+        return "%s:%s" % (shortfilename, self.line)
+
+
+# Used for annotating errors when source location is unknown.
+unknown_loc = Loc("unknown location", 0, 0)
+
+
+@total_ordering
+class SlotEqualityCheckMixin(object):
+    # some ir nodes are __dict__ free using __slots__ instead, this mixin
+    # should not trigger the unintended creation of __dict__.
+    __slots__ = tuple()
+
+    def __eq__(self, other):
+        if type(self) is type(other):
+            for name in self.__slots__:
+                if getattr(self, name) != getattr(other, name):
+                    return False
+            else:
+                return True
+        return False
+
+    def __le__(self, other):
+        return str(self) <= str(other)
+
+    def __hash__(self):
+        return id(self)
+
+
+@total_ordering
+class EqualityCheckMixin(object):
+    """ Mixin for basic equality checking """
+
+    def __eq__(self, other):
+        if type(self) is type(other):
+            def fixup(adict):
+                bad = ('loc', 'scope')
+                d = dict(adict)
+                for x in bad:
+                    d.pop(x, None)
+                return d
+            d1 = fixup(self.__dict__)
+            d2 = fixup(other.__dict__)
+            if d1 == d2:
+                return True
+        return False
+
+    def __le__(self, other):
+        return str(self) < str(other)
+
+    def __hash__(self):
+        return id(self)
+
+
+class VarMap(object):
+    def __init__(self):
+        self._con = {}
+
+    def define(self, name, var):
+        if name in self._con:
+            raise RedefinedError(name)
+        else:
+            self._con[name] = var
+
+    def get(self, name):
+        try:
+            return self._con[name]
+        except KeyError:
+            raise NotDefinedError(name)
+
+    def __contains__(self, name):
+        return name in self._con
+
+    def __len__(self):
+        return len(self._con)
+
+    def __repr__(self):
+        return pprint.pformat(self._con)
+
+    def __hash__(self):
+        return hash(self.name)
+
+    def __iter__(self):
+        return self._con.iterkeys()
+
+    def __eq__(self, other):
+        if type(self) is type(other):
+            # check keys only, else __eq__ ref cycles, scope -> varmap -> var
+            return self._con.keys() == other._con.keys()
+        return False
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+class AbstractRHS(object):
+    """Abstract base class for anything that can be the RHS of an assignment.
+    This class **does not** define any methods.
+    """
+
+
+class Inst(EqualityCheckMixin, AbstractRHS):
+    """
+    Base class for all IR instructions.
+    """
+
+    def list_vars(self):
+        """
+        List the variables used (read or written) by the instruction.
+        """
+        raise NotImplementedError
+
+    def _rec_list_vars(self, val):
+        """
+        A recursive helper used to implement list_vars() in subclasses.
+        """
+        if isinstance(val, Var):
+            return [val]
+        elif isinstance(val, Inst):
+            return val.list_vars()
+        elif isinstance(val, (list, tuple)):
+            lst = []
+            for v in val:
+                lst.extend(self._rec_list_vars(v))
+            return lst
+        elif isinstance(val, dict):
+            lst = []
+            for v in val.values():
+                lst.extend(self._rec_list_vars(v))
+            return lst
+        else:
+            return []
+
+
+class Stmt(Inst):
+    """
+    Base class for IR statements (instructions which can appear on their
+    own in a Block).
+    """
+    # Whether this statement ends its basic block (i.e. it will either jump
+    # to another block or exit the function).
+    is_terminator = False
+    # Whether this statement exits the function.
+    is_exit = False
+
+    def list_vars(self):
+        return self._rec_list_vars(self.__dict__)
+
+
+class Terminator(Stmt):
+    """
+    IR statements that are terminators: the last statement in a block.
+    A terminator must either:
+    - exit the function
+    - jump to a block
+
+    All subclass of Terminator must override `.get_targets()` to return a list
+    of jump targets.
+    """
+    is_terminator = True
+
+    def get_targets(self):
+        raise NotImplementedError(type(self))
+
+
+class Expr(Inst):
+    """
+    An IR expression (an instruction which can only be part of a larger
+    statement).
+    """
+
+    def __init__(self, op, loc, **kws):
+        assert isinstance(op, str)
+        assert isinstance(loc, Loc)
+        self.op = op
+        self.loc = loc
+        self._kws = kws
+
+    def __getattr__(self, name):
+        if name.startswith('_'):
+            return Inst.__getattr__(self, name)
+        return self._kws[name]
+
+    def __setattr__(self, name, value):
+        if name in ('op', 'loc', '_kws'):
+            self.__dict__[name] = value
+        else:
+            self._kws[name] = value
+
+    @classmethod
+    def binop(cls, fn, lhs, rhs, loc):
+        assert isinstance(fn, BuiltinFunctionType)
+        assert isinstance(lhs, Var)
+        assert isinstance(rhs, Var)
+        assert isinstance(loc, Loc)
+        op = 'binop'
+        return cls(op=op, loc=loc, fn=fn, lhs=lhs, rhs=rhs,
+                   static_lhs=UNDEFINED, static_rhs=UNDEFINED)
+
+    @classmethod
+    def inplace_binop(cls, fn, immutable_fn, lhs, rhs, loc):
+        assert isinstance(fn, BuiltinFunctionType)
+        assert isinstance(immutable_fn, BuiltinFunctionType)
+        assert isinstance(lhs, Var)
+        assert isinstance(rhs, Var)
+        assert isinstance(loc, Loc)
+        op = 'inplace_binop'
+        return cls(op=op, loc=loc, fn=fn, immutable_fn=immutable_fn,
+                   lhs=lhs, rhs=rhs,
+                   static_lhs=UNDEFINED, static_rhs=UNDEFINED)
+
+    @classmethod
+    def unary(cls, fn, value, loc):
+        assert isinstance(value, (str, Var, FunctionType))
+        assert isinstance(loc, Loc)
+        op = 'unary'
+        fn = UNARY_BUITINS_TO_OPERATORS.get(fn, fn)
+        return cls(op=op, loc=loc, fn=fn, value=value)
+
+    @classmethod
+    def call(cls, func, args, kws, loc, vararg=None, varkwarg=None, target=None):
+        assert isinstance(func, Var)
+        assert isinstance(loc, Loc)
+        op = 'call'
+        return cls(op=op, loc=loc, func=func, args=args, kws=kws,
+                   vararg=vararg, varkwarg=varkwarg, target=target)
+
+    @classmethod
+    def build_tuple(cls, items, loc):
+        assert isinstance(loc, Loc)
+        op = 'build_tuple'
+        return cls(op=op, loc=loc, items=items)
+
+    @classmethod
+    def build_list(cls, items, loc):
+        assert isinstance(loc, Loc)
+        op = 'build_list'
+        return cls(op=op, loc=loc, items=items)
+
+    @classmethod
+    def build_set(cls, items, loc):
+        assert isinstance(loc, Loc)
+        op = 'build_set'
+        return cls(op=op, loc=loc, items=items)
+
+    @classmethod
+    def build_map(cls, items, size, literal_value, value_indexes, loc):
+        assert isinstance(loc, Loc)
+        op = 'build_map'
+        return cls(op=op, loc=loc, items=items, size=size,
+                   literal_value=literal_value, value_indexes=value_indexes)
+
+    @classmethod
+    def pair_first(cls, value, loc):
+        assert isinstance(value, Var)
+        op = 'pair_first'
+        return cls(op=op, loc=loc, value=value)
+
+    @classmethod
+    def pair_second(cls, value, loc):
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        op = 'pair_second'
+        return cls(op=op, loc=loc, value=value)
+
+    @classmethod
+    def getiter(cls, value, loc):
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        op = 'getiter'
+        return cls(op=op, loc=loc, value=value)
+
+    @classmethod
+    def iternext(cls, value, loc):
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        op = 'iternext'
+        return cls(op=op, loc=loc, value=value)
+
+    @classmethod
+    def exhaust_iter(cls, value, count, loc):
+        assert isinstance(value, Var)
+        assert isinstance(count, int)
+        assert isinstance(loc, Loc)
+        op = 'exhaust_iter'
+        return cls(op=op, loc=loc, value=value, count=count)
+
+    @classmethod
+    def getattr(cls, value, attr, loc):
+        assert isinstance(value, Var)
+        assert isinstance(attr, str)
+        assert isinstance(loc, Loc)
+        op = 'getattr'
+        return cls(op=op, loc=loc, value=value, attr=attr)
+
+    @classmethod
+    def getitem(cls, value, index, loc):
+        assert isinstance(value, Var)
+        assert isinstance(index, Var)
+        assert isinstance(loc, Loc)
+        op = 'getitem'
+        fn = operator.getitem
+        return cls(op=op, loc=loc, value=value, index=index, fn=fn)
+
+    @classmethod
+    def typed_getitem(cls, value, dtype, index, loc):
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        op = 'typed_getitem'
+        return cls(op=op, loc=loc, value=value, dtype=dtype,
+                   index=index)
+
+    @classmethod
+    def static_getitem(cls, value, index, index_var, loc):
+        assert isinstance(value, Var)
+        assert index_var is None or isinstance(index_var, Var)
+        assert isinstance(loc, Loc)
+        op = 'static_getitem'
+        fn = operator.getitem
+        return cls(op=op, loc=loc, value=value, index=index,
+                   index_var=index_var, fn=fn)
+
+    @classmethod
+    def cast(cls, value, loc):
+        """
+        A node for implicit casting at the return statement
+        """
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        op = 'cast'
+        return cls(op=op, value=value, loc=loc)
+
+    @classmethod
+    def phi(cls, loc):
+        """Phi node
+        """
+        assert isinstance(loc, Loc)
+        return cls(op='phi', incoming_values=[], incoming_blocks=[], loc=loc)
+
+    @classmethod
+    def make_function(cls, name, code, closure, defaults, loc):
+        """
+        A node for making a function object.
+        """
+        assert isinstance(loc, Loc)
+        op = 'make_function'
+        return cls(op=op, name=name, code=code, closure=closure, defaults=defaults, loc=loc)
+
+    @classmethod
+    def null(cls, loc):
+        """
+        A node for null value.
+
+        This node is not handled by type inference. It is only added by
+        post-typing passes.
+        """
+        assert isinstance(loc, Loc)
+        op = 'null'
+        return cls(op=op, loc=loc)
+
+    @classmethod
+    def dummy(cls, op, info, loc):
+        """
+        A node for a dummy value.
+
+        This node is a place holder for carrying information through to a point
+        where it is rewritten into something valid. This node is not handled
+        by type inference or lowering. It's presence outside of the interpreter
+        renders IR as illegal.
+        """
+        assert isinstance(loc, Loc)
+        assert isinstance(op, str)
+        return cls(op=op, info=info, loc=loc)
+
+    def __repr__(self):
+        if self.op == 'call':
+            args = ', '.join(str(a) for a in self.args)
+            pres_order = self._kws.items() if config.DIFF_IR == 0 else sorted(self._kws.items())
+            kws = ', '.join('%s=%s' % (k, v) for k, v in pres_order)
+            vararg = '*%s' % (self.vararg,) if self.vararg is not None else ''
+            arglist = ', '.join(filter(None, [args, vararg, kws]))
+            return 'call %s(%s)' % (self.func, arglist)
+        elif self.op == 'binop':
+            lhs, rhs = self.lhs, self.rhs
+            if self.fn == operator.contains:
+                lhs, rhs = rhs, lhs
+            fn = OPERATORS_TO_BUILTINS.get(self.fn, self.fn)
+            return '%s %s %s' % (lhs, fn, rhs)
+        else:
+            pres_order = self._kws.items() if config.DIFF_IR == 0 else sorted(self._kws.items())
+            args = ('%s=%s' % (k, v) for k, v in pres_order)
+            return '%s(%s)' % (self.op, ', '.join(args))
+
+    def list_vars(self):
+        return self._rec_list_vars(self._kws)
+
+    def infer_constant(self):
+        raise ConstantInferenceError('%s' % self, loc=self.loc)
+
+
+class SetItem(Stmt):
+    """
+    target[index] = value
+    """
+
+    def __init__(self, target, index, value, loc):
+        assert isinstance(target, Var)
+        assert isinstance(index, Var)
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        self.target = target
+        self.index = index
+        self.value = value
+        self.loc = loc
+
+    def __repr__(self):
+        return '%s[%s] = %s' % (self.target, self.index, self.value)
+
+
+class StaticSetItem(Stmt):
+    """
+    target[constant index] = value
+    """
+
+    def __init__(self, target, index, index_var, value, loc):
+        assert isinstance(target, Var)
+        assert not isinstance(index, Var)
+        assert isinstance(index_var, Var)
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        self.target = target
+        self.index = index
+        self.index_var = index_var
+        self.value = value
+        self.loc = loc
+
+    def __repr__(self):
+        return '%s[%r] = %s' % (self.target, self.index, self.value)
+
+
+class DelItem(Stmt):
+    """
+    del target[index]
+    """
+
+    def __init__(self, target, index, loc):
+        assert isinstance(target, Var)
+        assert isinstance(index, Var)
+        assert isinstance(loc, Loc)
+        self.target = target
+        self.index = index
+        self.loc = loc
+
+    def __repr__(self):
+        return 'del %s[%s]' % (self.target, self.index)
+
+
+class SetAttr(Stmt):
+    def __init__(self, target, attr, value, loc):
+        assert isinstance(target, Var)
+        assert isinstance(attr, str)
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        self.target = target
+        self.attr = attr
+        self.value = value
+        self.loc = loc
+
+    def __repr__(self):
+        return '(%s).%s = %s' % (self.target, self.attr, self.value)
+
+
+class DelAttr(Stmt):
+    def __init__(self, target, attr, loc):
+        assert isinstance(target, Var)
+        assert isinstance(attr, str)
+        assert isinstance(loc, Loc)
+        self.target = target
+        self.attr = attr
+        self.loc = loc
+
+    def __repr__(self):
+        return 'del (%s).%s' % (self.target, self.attr)
+
+
+class StoreMap(Stmt):
+    def __init__(self, dct, key, value, loc):
+        assert isinstance(dct, Var)
+        assert isinstance(key, Var)
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        self.dct = dct
+        self.key = key
+        self.value = value
+        self.loc = loc
+
+    def __repr__(self):
+        return '%s[%s] = %s' % (self.dct, self.key, self.value)
+
+
+class Del(Stmt):
+    def __init__(self, value, loc):
+        assert isinstance(value, str)
+        assert isinstance(loc, Loc)
+        self.value = value
+        self.loc = loc
+
+    def __str__(self):
+        return "del %s" % self.value
+
+
+class Raise(Terminator):
+    is_exit = True
+
+    def __init__(self, exception, loc):
+        assert exception is None or isinstance(exception, Var)
+        assert isinstance(loc, Loc)
+        self.exception = exception
+        self.loc = loc
+
+    def __str__(self):
+        return "raise %s" % self.exception
+
+    def get_targets(self):
+        return []
+
+
+class StaticRaise(Terminator):
+    """
+    Raise an exception class and arguments known at compile-time.
+    Note that if *exc_class* is None, a bare "raise" statement is implied
+    (i.e. re-raise the current exception).
+    """
+    is_exit = True
+
+    def __init__(self, exc_class, exc_args, loc):
+        assert exc_class is None or isinstance(exc_class, type)
+        assert isinstance(loc, Loc)
+        assert exc_args is None or isinstance(exc_args, tuple)
+        self.exc_class = exc_class
+        self.exc_args = exc_args
+        self.loc = loc
+
+    def __str__(self):
+        if self.exc_class is None:
+            return "<static> raise"
+        elif self.exc_args is None:
+            return "<static> raise %s" % (self.exc_class,)
+        else:
+            return "<static> raise %s(%s)" % (self.exc_class,
+                                     ", ".join(map(repr, self.exc_args)))
+
+    def get_targets(self):
+        return []
+
+
+class TryRaise(Stmt):
+    """A raise statement inside a try-block
+    Similar to ``Raise`` but does not terminate.
+    """
+    def __init__(self, exception, loc):
+        assert exception is None or isinstance(exception, Var)
+        assert isinstance(loc, Loc)
+        self.exception = exception
+        self.loc = loc
+
+    def __str__(self):
+        return "try_raise %s" % self.exception
+
+
+class StaticTryRaise(Stmt):
+    """A raise statement inside a try-block.
+    Similar to ``StaticRaise`` but does not terminate.
+    """
+
+    def __init__(self, exc_class, exc_args, loc):
+        assert exc_class is None or isinstance(exc_class, type)
+        assert isinstance(loc, Loc)
+        assert exc_args is None or isinstance(exc_args, tuple)
+        self.exc_class = exc_class
+        self.exc_args = exc_args
+        self.loc = loc
+
+    def __str__(self):
+        if self.exc_class is None:
+            return "static_try_raise"
+        elif self.exc_args is None:
+            return "static_try_raise %s" % (self.exc_class,)
+        else:
+            return "static_try_raise %s(%s)" % (self.exc_class,
+                                     ", ".join(map(repr, self.exc_args)))
+
+
+class Return(Terminator):
+    """
+    Return to caller.
+    """
+    is_exit = True
+
+    def __init__(self, value, loc):
+        assert isinstance(value, Var), type(value)
+        assert isinstance(loc, Loc)
+        self.value = value
+        self.loc = loc
+
+    def __str__(self):
+        return 'return %s' % self.value
+
+    def get_targets(self):
+        return []
+
+
+class Jump(Terminator):
+    """
+    Unconditional branch.
+    """
+
+    def __init__(self, target, loc):
+        assert isinstance(loc, Loc)
+        self.target = target
+        self.loc = loc
+
+    def __str__(self):
+        return 'jump %s' % self.target
+
+    def get_targets(self):
+        return [self.target]
+
+
+class Branch(Terminator):
+    """
+    Conditional branch.
+    """
+
+    def __init__(self, cond, truebr, falsebr, loc):
+        assert isinstance(cond, Var)
+        assert isinstance(loc, Loc)
+        self.cond = cond
+        self.truebr = truebr
+        self.falsebr = falsebr
+        self.loc = loc
+
+    def __str__(self):
+        return 'branch %s, %s, %s' % (self.cond, self.truebr, self.falsebr)
+
+    def get_targets(self):
+        return [self.truebr, self.falsebr]
+
+
+class Assign(Stmt):
+    """
+    Assign to a variable.
+    """
+    def __init__(self, value, target, loc):
+        assert isinstance(value, AbstractRHS)
+        assert isinstance(target, Var)
+        assert isinstance(loc, Loc)
+        self.value = value
+        self.target = target
+        self.loc = loc
+
+    def __str__(self):
+        return '%s = %s' % (self.target, self.value)
+
+
+class Print(Stmt):
+    """
+    Print some values.
+    """
+    def __init__(self, args, vararg, loc):
+        assert all(isinstance(x, Var) for x in args)
+        assert vararg is None or isinstance(vararg, Var)
+        assert isinstance(loc, Loc)
+        self.args = tuple(args)
+        self.vararg = vararg
+        # Constant-inferred arguments
+        self.consts = {}
+        self.loc = loc
+
+    def __str__(self):
+        return 'print(%s)' % ', '.join(str(v) for v in self.args)
+
+
+class Yield(Inst):
+    def __init__(self, value, loc, index):
+        assert isinstance(value, Var)
+        assert isinstance(loc, Loc)
+        self.value = value
+        self.loc = loc
+        self.index = index
+
+    def __str__(self):
+        return 'yield %s' % (self.value,)
+
+    def list_vars(self):
+        return [self.value]
+
+
+class EnterWith(Stmt):
+    """Enter a "with" context
+    """
+    def __init__(self, contextmanager, begin, end, loc):
+        """
+        Parameters
+        ----------
+        contextmanager : IR value
+        begin, end : int
+            The beginning and the ending offset of the with-body.
+        loc : ir.Loc instance
+            Source location
+        """
+        assert isinstance(contextmanager, Var)
+        assert isinstance(loc, Loc)
+        self.contextmanager = contextmanager
+        self.begin = begin
+        self.end = end
+        self.loc = loc
+
+    def __str__(self):
+        return 'enter_with {}'.format(self.contextmanager)
+
+    def list_vars(self):
+        return [self.contextmanager]
+
+
+class PopBlock(Stmt):
+    """Marker statement for a pop block op code"""
+    def __init__(self, loc):
+        assert isinstance(loc, Loc)
+        self.loc = loc
+
+    def __str__(self):
+        return 'pop_block'
+
+
+class Arg(EqualityCheckMixin, AbstractRHS):
+    def __init__(self, name, index, loc):
+        assert isinstance(name, str)
+        assert isinstance(index, int)
+        assert isinstance(loc, Loc)
+        self.name = name
+        self.index = index
+        self.loc = loc
+
+    def __repr__(self):
+        return 'arg(%d, name=%s)' % (self.index, self.name)
+
+    def infer_constant(self):
+        raise ConstantInferenceError('%s' % self, loc=self.loc)
+
+
+class Const(EqualityCheckMixin, AbstractRHS):
+    def __init__(self, value, loc, use_literal_type=True):
+        assert isinstance(loc, Loc)
+        self.value = value
+        self.loc = loc
+        # Note: need better way to tell if this is a literal or not.
+        self.use_literal_type = use_literal_type
+
+    def __repr__(self):
+        return 'const(%s, %s)' % (type(self.value).__name__, self.value)
+
+    def infer_constant(self):
+        return self.value
+
+    def __deepcopy__(self, memo):
+        # Override to not copy constant values in code
+        return Const(
+            value=self.value, loc=self.loc,
+            use_literal_type=self.use_literal_type,
+        )
+
+
+class Global(EqualityCheckMixin, AbstractRHS):
+    def __init__(self, name, value, loc):
+        assert isinstance(loc, Loc)
+        self.name = name
+        self.value = value
+        self.loc = loc
+
+    def __str__(self):
+        return 'global(%s: %s)' % (self.name, self.value)
+
+    def infer_constant(self):
+        return self.value
+
+    def __deepcopy__(self, memo):
+        # don't copy value since it can fail (e.g. modules)
+        # value is readonly and doesn't need copying
+        return Global(self.name, self.value, copy.deepcopy(self.loc))
+
+
+class FreeVar(EqualityCheckMixin, AbstractRHS):
+    """
+    A freevar, as loaded by LOAD_DECREF.
+    (i.e. a variable defined in an enclosing non-global scope)
+    """
+
+    def __init__(self, index, name, value, loc):
+        assert isinstance(index, int)
+        assert isinstance(name, str)
+        assert isinstance(loc, Loc)
+        # index inside __code__.co_freevars
+        self.index = index
+        # variable name
+        self.name = name
+        # frozen value
+        self.value = value
+        self.loc = loc
+
+    def __str__(self):
+        return 'freevar(%s: %s)' % (self.name, self.value)
+
+    def infer_constant(self):
+        return self.value
+
+    def __deepcopy__(self, memo):
+        # Override to not copy constant values in code
+        return FreeVar(index=self.index, name=self.name, value=self.value,
+                       loc=self.loc)
+
+
+
+class Var(EqualityCheckMixin, AbstractRHS):
+    """
+    Attributes
+    -----------
+    - scope: Scope
+
+    - name: str
+
+    - loc: Loc
+        Definition location
+    """
+
+    def __init__(self, scope, name, loc):
+        # NOTE: Use of scope=None should be removed.
+        assert scope is None or isinstance(scope, Scope)
+        assert isinstance(name, str)
+        assert isinstance(loc, Loc)
+        self.scope = scope
+        self.name = name
+        self.loc = loc
+
+    def __repr__(self):
+        return 'Var(%s, %s)' % (self.name, self.loc.short())
+
+    def __str__(self):
+        return self.name
+
+    @property
+    def is_temp(self):
+        return self.name.startswith("$")
+
+    @property
+    def unversioned_name(self):
+        """The unversioned name of this variable, i.e. SSA renaming removed
+        """
+        for k, redef_set in self.scope.var_redefinitions.items():
+            if self.name in redef_set:
+                return k
+        return self.name
+
+    @property
+    def versioned_names(self):
+        """Known versioned names for this variable, i.e. known variable names in
+        the scope that have been formed from applying SSA to this variable
+        """
+        return self.scope.get_versions_of(self.unversioned_name)
+
+    @property
+    def all_names(self):
+        """All known versioned and unversioned names for this variable
+        """
+        return self.versioned_names | {self.unversioned_name,}
+
+
+class Scope(EqualityCheckMixin):
+    """
+    Attributes
+    -----------
+    - parent: Scope
+        Parent scope
+
+    - localvars: VarMap
+        Scope-local variable map
+
+    - loc: Loc
+        Start of scope location
+
+    """
+
+    def __init__(self, parent, loc):
+        assert parent is None or isinstance(parent, Scope)
+        assert isinstance(loc, Loc)
+        self.parent = parent
+        self.localvars = VarMap()
+        self.loc = loc
+        self.redefined = defaultdict(int)
+        self.var_redefinitions = defaultdict(set)
+
+    def define(self, name, loc):
+        """
+        Define a variable
+        """
+        v = Var(scope=self, name=name, loc=loc)
+        self.localvars.define(v.name, v)
+        return v
+
+    def get(self, name):
+        """
+        Refer to a variable.  Returns the latest version.
+        """
+        if name in self.redefined:
+            name = "%s.%d" % (name, self.redefined[name])
+        return self.get_exact(name)
+
+    def get_exact(self, name):
+        """
+        Refer to a variable.  The returned variable has the exact
+        name (exact variable version).
+        """
+        try:
+            return self.localvars.get(name)
+        except NotDefinedError:
+            if self.has_parent:
+                return self.parent.get(name)
+            else:
+                raise
+
+    def get_or_define(self, name, loc):
+        if name in self.redefined:
+            name = "%s.%d" % (name, self.redefined[name])
+
+        if name not in self.localvars:
+            return self.define(name, loc)
+        else:
+            return self.localvars.get(name)
+
+    def redefine(self, name, loc, rename=True):
+        """
+        Redefine if the name is already defined
+        """
+        if name not in self.localvars:
+            return self.define(name, loc)
+        elif not rename:
+            # Must use the same name if the variable is a cellvar, which
+            # means it could be captured in a closure.
+            return self.localvars.get(name)
+        else:
+            while True:
+                ct = self.redefined[name]
+                self.redefined[name] = ct + 1
+                newname = "%s.%d" % (name, ct + 1)
+                try:
+                    res = self.define(newname, loc)
+                except RedefinedError:
+                    continue
+                else:
+                    self.var_redefinitions[name].add(newname)
+                return res
+
+    def get_versions_of(self, name):
+        """
+        Gets all known versions of a given name
+        """
+        vers = set()
+        def walk(thename):
+            redefs = self.var_redefinitions.get(thename, None)
+            if redefs:
+                for v in redefs:
+                    vers.add(v)
+                    walk(v)
+        walk(name)
+        return vers
+
+    def make_temp(self, loc):
+        n = len(self.localvars)
+        v = Var(scope=self, name='$%d' % n, loc=loc)
+        self.localvars.define(v.name, v)
+        return v
+
+    @property
+    def has_parent(self):
+        return self.parent is not None
+
+    def __repr__(self):
+        return "Scope(has_parent=%r, num_vars=%d, %s)" % (self.has_parent,
+                                                          len(self.localvars),
+                                                          self.loc)
+
+
+class Block(EqualityCheckMixin):
+    """A code block
+
+    """
+
+    def __init__(self, scope, loc):
+        assert isinstance(scope, Scope)
+        assert isinstance(loc, Loc)
+        self.scope = scope
+        self.body = []
+        self.loc = loc
+
+    def copy(self):
+        block = Block(self.scope, self.loc)
+        block.body = self.body[:]
+        return block
+
+    def find_exprs(self, op=None):
+        """
+        Iterate over exprs of the given *op* in this block.
+        """
+        for inst in self.body:
+            if isinstance(inst, Assign):
+                expr = inst.value
+                if isinstance(expr, Expr):
+                    if op is None or expr.op == op:
+                        yield expr
+
+    def find_insts(self, cls=None):
+        """
+        Iterate over insts of the given class in this block.
+        """
+        for inst in self.body:
+            if isinstance(inst, cls):
+                yield inst
+
+    def find_variable_assignment(self, name):
+        """
+        Returns the assignment inst associated with variable "name", None if
+        it cannot be found.
+        """
+        for x in self.find_insts(cls=Assign):
+            if x.target.name == name:
+                return x
+        return None
+
+    def prepend(self, inst):
+        assert isinstance(inst, Stmt)
+        self.body.insert(0, inst)
+
+    def append(self, inst):
+        assert isinstance(inst, Stmt)
+        self.body.append(inst)
+
+    def remove(self, inst):
+        assert isinstance(inst, Stmt)
+        del self.body[self.body.index(inst)]
+
+    def clear(self):
+        del self.body[:]
+
+    def dump(self, file=None):
+        # Avoid early bind of sys.stdout as default value
+        file = file or sys.stdout
+        for inst in self.body:
+            if hasattr(inst, 'dump'):
+                inst.dump(file)
+            else:
+                inst_vars = sorted(str(v) for v in inst.list_vars())
+                print('    %-40s %s' % (inst, inst_vars), file=file)
+
+    @property
+    def terminator(self):
+        return self.body[-1]
+
+    @property
+    def is_terminated(self):
+        return self.body and self.body[-1].is_terminator
+
+    def verify(self):
+        if not self.is_terminated:
+            raise VerificationError("Missing block terminator")
+            # Only the last instruction can be a terminator
+        for inst in self.body[:-1]:
+            if inst.is_terminator:
+                raise VerificationError("Terminator before the last "
+                                        "instruction")
+
+    def insert_after(self, stmt, other):
+        """
+        Insert *stmt* after *other*.
+        """
+        index = self.body.index(other)
+        self.body.insert(index + 1, stmt)
+
+    def insert_before_terminator(self, stmt):
+        assert isinstance(stmt, Stmt)
+        assert self.is_terminated
+        self.body.insert(-1, stmt)
+
+    def __repr__(self):
+        return "<ir.Block at %s>" % (self.loc,)
+
+
+class Loop(SlotEqualityCheckMixin):
+    """Describes a loop-block
+    """
+    __slots__ = "entry", "exit"
+
+    def __init__(self, entry, exit):
+        self.entry = entry
+        self.exit = exit
+
+    def __repr__(self):
+        args = self.entry, self.exit
+        return "Loop(entry=%s, exit=%s)" % args
+
+
+class With(SlotEqualityCheckMixin):
+    """Describes a with-block
+    """
+    __slots__ = "entry", "exit"
+
+    def __init__(self, entry, exit):
+        self.entry = entry
+        self.exit = exit
+
+    def __repr__(self):
+        args = self.entry, self.exit
+        return "With(entry=%s, exit=%s)" % args
+
+
+class FunctionIR(object):
+
+    def __init__(self, blocks, is_generator, func_id, loc,
+                 definitions, arg_count, arg_names):
+        self.blocks = blocks
+        self.is_generator = is_generator
+        self.func_id = func_id
+        self.loc = loc
+        self.arg_count = arg_count
+        self.arg_names = arg_names
+
+        self._definitions = definitions
+
+        self._reset_analysis_variables()
+
+    def equal_ir(self, other):
+        """ Checks that the IR contained within is equal to the IR in other.
+        Equality is defined by being equal in fundamental structure (blocks,
+        labels, IR node type and the order in which they are defined) and the
+        IR nodes being equal. IR node equality essentially comes down to
+        ensuring a node's `.__dict__` or `.__slots__` is equal, with the
+        exception of ignoring 'loc' and 'scope' entries. The upshot is that the
+        comparison is essentially location and scope invariant, but otherwise
+        behaves as unsurprisingly as possible.
+        """
+        if type(self) is type(other):
+            return self.blocks == other.blocks
+        return False
+
+    def diff_str(self, other):
+        """
+        Compute a human readable difference in the IR, returns a formatted
+        string ready for printing.
+        """
+        msg = []
+        for label, block in self.blocks.items():
+            other_blk = other.blocks.get(label, None)
+            if other_blk is not None:
+                if block != other_blk:
+                    msg.append(("Block %s differs" % label).center(80, '-'))
+                    # see if the instructions are just a permutation
+                    block_del = [x for x in block.body if isinstance(x, Del)]
+                    oth_del = [x for x in other_blk.body if isinstance(x, Del)]
+                    if block_del != oth_del:
+                        # this is a common issue, dels are all present, but
+                        # order shuffled.
+                        if sorted(block_del) == sorted(oth_del):
+                            msg.append(("Block %s contains the same dels but "
+                                        "their order is different") % label)
+                    if len(block.body) > len(other_blk.body):
+                        msg.append("This block contains more statements")
+                    elif len(block.body) < len(other_blk.body):
+                        msg.append("Other block contains more statements")
+
+                    # find the indexes where they don't match
+                    tmp = []
+                    for idx, stmts in enumerate(zip(block.body,
+                                                    other_blk.body)):
+                        b_s, o_s = stmts
+                        if b_s != o_s:
+                            tmp.append(idx)
+
+                    def get_pad(ablock, l):
+                        pointer = '-> '
+                        sp = len(pointer) * ' '
+                        pad = []
+                        nstmt = len(ablock)
+                        for i in range(nstmt):
+                            if i in tmp:
+                                item = pointer
+                            elif i >= l:
+                                item = pointer
+                            else:
+                                item = sp
+                            pad.append(item)
+                        return pad
+
+                    min_stmt_len = min(len(block.body), len(other_blk.body))
+
+                    with StringIO() as buf:
+                        it = [("self", block), ("other", other_blk)]
+                        for name, _block in it:
+                            buf.truncate(0)
+                            _block.dump(file=buf)
+                            stmts = buf.getvalue().splitlines()
+                            pad = get_pad(_block.body, min_stmt_len)
+                            title = ("%s: block %s" % (name, label))
+                            msg.append(title.center(80, '-'))
+                            msg.extend(["{0}{1}".format(a, b) for a, b in
+                                        zip(pad, stmts)])
+        if msg == []:
+            msg.append("IR is considered equivalent.")
+        return '\n'.join(msg)
+
+    def _reset_analysis_variables(self):
+
+        self._consts = consts.ConstantInference(self)
+
+        # Will be computed by PostProcessor
+        self.generator_info = None
+        self.variable_lifetime = None
+        # { ir.Block: { variable names (potentially) alive at start of block } }
+        self.block_entry_vars = {}
+
+    def derive(self, blocks, arg_count=None, arg_names=None,
+               force_non_generator=False):
+        """
+        Derive a new function IR from this one, using the given blocks,
+        and possibly modifying the argument count and generator flag.
+
+        Post-processing will have to be run again on the new IR.
+        """
+        firstblock = blocks[min(blocks)]
+
+        new_ir = copy.copy(self)
+        new_ir.blocks = blocks
+        new_ir.loc = firstblock.loc
+        if force_non_generator:
+            new_ir.is_generator = False
+        if arg_count is not None:
+            new_ir.arg_count = arg_count
+        if arg_names is not None:
+            new_ir.arg_names = arg_names
+        new_ir._reset_analysis_variables()
+        # Make fresh func_id
+        new_ir.func_id = new_ir.func_id.derive()
+        return new_ir
+
+    def copy(self):
+        new_ir = copy.copy(self)
+        blocks = {}
+        block_entry_vars = {}
+        for label, block in self.blocks.items():
+            new_block = block.copy()
+            blocks[label] = new_block
+            if block in self.block_entry_vars:
+                block_entry_vars[new_block] = self.block_entry_vars[block]
+        new_ir.blocks = blocks
+        new_ir.block_entry_vars = block_entry_vars
+        return new_ir
+
+    def get_block_entry_vars(self, block):
+        """
+        Return a set of variable names possibly alive at the beginning of
+        the block.
+        """
+        return self.block_entry_vars[block]
+
+    def infer_constant(self, name):
+        """
+        Try to infer the constant value of a given variable.
+        """
+        if isinstance(name, Var):
+            name = name.name
+        return self._consts.infer_constant(name)
+
+    def get_definition(self, value, lhs_only=False):
+        """
+        Get the definition site for the given variable name or instance.
+        A Expr instance is returned by default, but if lhs_only is set
+        to True, the left-hand-side variable is returned instead.
+        """
+        lhs = value
+        while True:
+            if isinstance(value, Var):
+                lhs = value
+                name = value.name
+            elif isinstance(value, str):
+                lhs = value
+                name = value
+            else:
+                return lhs if lhs_only else value
+            defs = self._definitions[name]
+            if len(defs) == 0:
+                raise KeyError("no definition for %r"
+                               % (name,))
+            if len(defs) > 1:
+                raise KeyError("more than one definition for %r"
+                               % (name,))
+            value = defs[0]
+
+    def get_assignee(self, rhs_value, in_blocks=None):
+        """
+        Finds the assignee for a given RHS value. If in_blocks is given the
+        search will be limited to the specified blocks.
+        """
+        if in_blocks is None:
+            blocks = self.blocks.values()
+        elif isinstance(in_blocks, int):
+            blocks = [self.blocks[in_blocks]]
+        else:
+            blocks = [self.blocks[blk] for blk in list(in_blocks)]
+
+        assert isinstance(rhs_value, AbstractRHS)
+
+        for blk in blocks:
+            for assign in blk.find_insts(Assign):
+                if assign.value == rhs_value:
+                    return assign.target
+
+        raise ValueError("Could not find an assignee for %s" % rhs_value)
+
+
+    def dump(self, file=None):
+        nofile = file is None
+        # Avoid early bind of sys.stdout as default value
+        file = file or StringIO()
+        for offset, block in sorted(self.blocks.items()):
+            print('label %s:' % (offset,), file=file)
+            block.dump(file=file)
+        if nofile:
+            text = file.getvalue()
+            if config.HIGHLIGHT_DUMPS:
+                try:
+                    import pygments
+                except ImportError:
+                    msg = "Please install pygments to see highlighted dumps"
+                    raise ValueError(msg)
+                else:
+                    from pygments import highlight
+                    from numba.misc.dump_style import NumbaIRLexer as lexer
+                    from numba.misc.dump_style import by_colorscheme
+                    from pygments.formatters import Terminal256Formatter
+                    print(highlight(text, lexer(), Terminal256Formatter(
+                        style=by_colorscheme())))
+            else:
+                print(text)
+
+
+    def dump_to_string(self):
+        with StringIO() as sb:
+            self.dump(file=sb)
+            return sb.getvalue()
+
+    def dump_generator_info(self, file=None):
+        file = file or sys.stdout
+        gi = self.generator_info
+        print("generator state variables:", sorted(gi.state_vars), file=file)
+        for index, yp in sorted(gi.yield_points.items()):
+            print("yield point #%d: live variables = %s, weak live variables = %s"
+                  % (index, sorted(yp.live_vars), sorted(yp.weak_live_vars)),
+                  file=file)
+
+    def render_dot(self, filename_prefix="numba_ir", include_ir=True):
+        """Render the CFG of the IR with GraphViz DOT via the
+        ``graphviz`` python binding.
+
+        Returns
+        -------
+        g : graphviz.Digraph
+            Use `g.view()` to open the graph in the default PDF application.
+        """
+
+        try:
+            import graphviz as gv
+        except ImportError:
+            raise ImportError(
+                "The feature requires `graphviz` but it is not available. "
+                "Please install with `pip install graphviz`"
+            )
+        g = gv.Digraph(
+            filename="{}{}.dot".format(
+                filename_prefix,
+                self.func_id.unique_name,
+            )
+        )
+        # Populate the nodes
+        for k, blk in self.blocks.items():
+            with StringIO() as sb:
+                blk.dump(sb)
+                label = sb.getvalue()
+            if include_ir:
+                label = ''.join(
+                    [r'  {}\l'.format(x) for x in label.splitlines()],
+                )
+                label = r"block {}\l".format(k) + label
+                g.node(str(k), label=label, shape='rect')
+            else:
+                label = r"{}\l".format(k)
+                g.node(str(k), label=label, shape='circle')
+        # Populate the edges
+        for src, blk in self.blocks.items():
+            for dst in blk.terminator.get_targets():
+                g.edge(str(src), str(dst))
+        return g
+
+
+# A stub for undefined global reference
+class UndefinedType(EqualityCheckMixin):
+
+    _singleton = None
+
+    def __new__(cls):
+        obj = cls._singleton
+        if obj is not None:
+            return obj
+        else:
+            obj = object.__new__(cls)
+            cls._singleton = obj
+        return obj
+
+    def __repr__(self):
+        return "Undefined"
+
+
+UNDEFINED = UndefinedType()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ir_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ir_utils.py
new file mode 100644
index 000000000..9d58bc5da
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ir_utils.py
@@ -0,0 +1,2350 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+import numpy
+
+import types as pytypes
+import collections
+import warnings
+
+import numba
+from numba.core.extending import _Intrinsic
+from numba.core import types, typing, ir, analysis, postproc, rewrites, config
+from numba.core.typing.templates import signature
+from numba.core.analysis import (compute_live_map, compute_use_defs,
+                            compute_cfg_from_blocks)
+from numba.core.errors import (TypingError, UnsupportedError,
+                               NumbaPendingDeprecationWarning,
+                               CompilerError)
+
+import copy
+
+_unique_var_count = 0
+
+
+def mk_unique_var(prefix):
+    global _unique_var_count
+    var = prefix + "." + str(_unique_var_count)
+    _unique_var_count = _unique_var_count + 1
+    return var
+
+
+class _MaxLabel:
+    def __init__(self, value=0):
+        self._value = value
+
+    def next(self):
+        self._value += 1
+        return self._value
+
+    def update(self, newval):
+        self._value = max(newval, self._value)
+
+
+_the_max_label = _MaxLabel()
+del _MaxLabel
+
+
+def get_unused_var_name(prefix, var_table):
+    """ Get a new var name with a given prefix and
+        make sure it is unused in the given variable table.
+    """
+    cur = 0
+    while True:
+        var = prefix + str(cur)
+        if var not in var_table:
+            return var
+        cur += 1
+
+
+def next_label():
+    return _the_max_label.next()
+
+
+def mk_alloc(typingctx, typemap, calltypes, lhs, size_var, dtype, scope, loc,
+             lhs_typ):
+    """generate an array allocation with np.empty() and return list of nodes.
+    size_var can be an int variable or tuple of int variables.
+    lhs_typ is the type of the array being allocated.
+    """
+    out = []
+    ndims = 1
+    size_typ = types.intp
+    if isinstance(size_var, tuple):
+        if len(size_var) == 1:
+            size_var = size_var[0]
+            size_var = convert_size_to_var(size_var, typemap, scope, loc, out)
+        else:
+            # tuple_var = build_tuple([size_var...])
+            ndims = len(size_var)
+            tuple_var = ir.Var(scope, mk_unique_var("$tuple_var"), loc)
+            if typemap:
+                typemap[tuple_var.name] = types.containers.UniTuple(
+                    types.intp, ndims)
+            # constant sizes need to be assigned to vars
+            new_sizes = [convert_size_to_var(s, typemap, scope, loc, out)
+                         for s in size_var]
+            tuple_call = ir.Expr.build_tuple(new_sizes, loc)
+            tuple_assign = ir.Assign(tuple_call, tuple_var, loc)
+            out.append(tuple_assign)
+            size_var = tuple_var
+            size_typ = types.containers.UniTuple(types.intp, ndims)
+    # g_np_var = Global(numpy)
+    g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc)
+    if typemap:
+        typemap[g_np_var.name] = types.misc.Module(numpy)
+    g_np = ir.Global('np', numpy, loc)
+    g_np_assign = ir.Assign(g_np, g_np_var, loc)
+    # attr call: empty_attr = getattr(g_np_var, empty)
+    empty_attr_call = ir.Expr.getattr(g_np_var, "empty", loc)
+    attr_var = ir.Var(scope, mk_unique_var("$empty_attr_attr"), loc)
+    if typemap:
+        typemap[attr_var.name] = get_np_ufunc_typ(numpy.empty)
+    attr_assign = ir.Assign(empty_attr_call, attr_var, loc)
+     # Assume str(dtype) returns a valid type
+    dtype_str = str(dtype)
+    # alloc call: lhs = empty_attr(size_var, typ_var)
+    typ_var = ir.Var(scope, mk_unique_var("$np_typ_var"), loc)
+    if typemap:
+        typemap[typ_var.name] = types.functions.NumberClass(dtype)
+    # If dtype is a datetime/timedelta with a unit,
+    # then it won't return a valid type and instead can be created
+    # with a string. i.e. "datetime64[ns]")
+    if (isinstance(dtype, (types.NPDatetime, types.NPTimedelta)) and
+        dtype.unit != ''):
+            typename_const = ir.Const(dtype_str, loc)
+            typ_var_assign = ir.Assign(typename_const, typ_var, loc)
+    else:
+        if dtype_str=='bool':
+            # empty doesn't like 'bool' sometimes (e.g. kmeans example)
+            dtype_str = 'bool_'
+        np_typ_getattr = ir.Expr.getattr(g_np_var, dtype_str, loc)
+        typ_var_assign = ir.Assign(np_typ_getattr, typ_var, loc)
+    alloc_call = ir.Expr.call(attr_var, [size_var, typ_var], (), loc)
+
+    if calltypes:
+        cac = typemap[attr_var.name].get_call_type(
+            typingctx, [size_typ, types.functions.NumberClass(dtype)], {})
+        # By default, all calls to "empty" are typed as returning a standard
+        # NumPy ndarray.  If we are allocating a ndarray subclass here then
+        # just change the return type to be that of the subclass.
+        cac._return_type = (lhs_typ.copy(layout='C')
+                            if lhs_typ.layout == 'F'
+                            else lhs_typ)
+        calltypes[alloc_call] = cac
+    if lhs_typ.layout == 'F':
+        empty_c_typ = lhs_typ.copy(layout='C')
+        empty_c_var = ir.Var(scope, mk_unique_var("$empty_c_var"), loc)
+        if typemap:
+            typemap[empty_c_var.name] = lhs_typ.copy(layout='C')
+        empty_c_assign = ir.Assign(alloc_call, empty_c_var, loc)
+
+        # attr call: asfortranarray = getattr(g_np_var, asfortranarray)
+        asfortranarray_attr_call = ir.Expr.getattr(g_np_var, "asfortranarray", loc)
+        afa_attr_var = ir.Var(scope, mk_unique_var("$asfortran_array_attr"), loc)
+        if typemap:
+            typemap[afa_attr_var.name] = get_np_ufunc_typ(numpy.asfortranarray)
+        afa_attr_assign = ir.Assign(asfortranarray_attr_call, afa_attr_var, loc)
+        # call asfortranarray
+        asfortranarray_call = ir.Expr.call(afa_attr_var, [empty_c_var], (), loc)
+        if calltypes:
+            calltypes[asfortranarray_call] = typemap[afa_attr_var.name].get_call_type(
+                typingctx, [empty_c_typ], {})
+
+        asfortranarray_assign = ir.Assign(asfortranarray_call, lhs, loc)
+
+        out.extend([g_np_assign, attr_assign, typ_var_assign, empty_c_assign,
+                    afa_attr_assign, asfortranarray_assign])
+    else:
+        alloc_assign = ir.Assign(alloc_call, lhs, loc)
+        out.extend([g_np_assign, attr_assign, typ_var_assign, alloc_assign])
+
+    return out
+
+
+def convert_size_to_var(size_var, typemap, scope, loc, nodes):
+    if isinstance(size_var, int):
+        new_size = ir.Var(scope, mk_unique_var("$alloc_size"), loc)
+        if typemap:
+            typemap[new_size.name] = types.intp
+        size_assign = ir.Assign(ir.Const(size_var, loc), new_size, loc)
+        nodes.append(size_assign)
+        return new_size
+    assert isinstance(size_var, ir.Var)
+    return size_var
+
+
+def get_np_ufunc_typ(func):
+    """get type of the incoming function from builtin registry"""
+    for (k, v) in typing.npydecl.registry.globals:
+        if k == func:
+            return v
+    for (k, v) in typing.templates.builtin_registry.globals:
+        if k == func:
+            return v
+    raise RuntimeError("type for func ", func, " not found")
+
+
+def mk_range_block(typemap, start, stop, step, calltypes, scope, loc):
+    """make a block that initializes loop range and iteration variables.
+    target label in jump needs to be set.
+    """
+    # g_range_var = Global(range)
+    g_range_var = ir.Var(scope, mk_unique_var("$range_g_var"), loc)
+    typemap[g_range_var.name] = get_global_func_typ(range)
+    g_range = ir.Global('range', range, loc)
+    g_range_assign = ir.Assign(g_range, g_range_var, loc)
+    arg_nodes, args = _mk_range_args(typemap, start, stop, step, scope, loc)
+    # range_call_var = call g_range_var(start, stop, step)
+    range_call = ir.Expr.call(g_range_var, args, (), loc)
+    calltypes[range_call] = typemap[g_range_var.name].get_call_type(
+        typing.Context(), [types.intp] * len(args), {})
+    #signature(types.range_state64_type, types.intp)
+    range_call_var = ir.Var(scope, mk_unique_var("$range_c_var"), loc)
+    typemap[range_call_var.name] = types.iterators.RangeType(types.intp)
+    range_call_assign = ir.Assign(range_call, range_call_var, loc)
+    # iter_var = getiter(range_call_var)
+    iter_call = ir.Expr.getiter(range_call_var, loc)
+    calltypes[iter_call] = signature(types.range_iter64_type,
+                                     types.range_state64_type)
+    iter_var = ir.Var(scope, mk_unique_var("$iter_var"), loc)
+    typemap[iter_var.name] = types.iterators.RangeIteratorType(types.intp)
+    iter_call_assign = ir.Assign(iter_call, iter_var, loc)
+    # $phi = iter_var
+    phi_var = ir.Var(scope, mk_unique_var("$phi"), loc)
+    typemap[phi_var.name] = types.iterators.RangeIteratorType(types.intp)
+    phi_assign = ir.Assign(iter_var, phi_var, loc)
+    # jump to header
+    jump_header = ir.Jump(-1, loc)
+    range_block = ir.Block(scope, loc)
+    range_block.body = arg_nodes + [g_range_assign, range_call_assign,
+                                    iter_call_assign, phi_assign, jump_header]
+    return range_block
+
+
+def _mk_range_args(typemap, start, stop, step, scope, loc):
+    nodes = []
+    if isinstance(stop, ir.Var):
+        g_stop_var = stop
+    else:
+        assert isinstance(stop, int)
+        g_stop_var = ir.Var(scope, mk_unique_var("$range_stop"), loc)
+        if typemap:
+            typemap[g_stop_var.name] = types.intp
+        stop_assign = ir.Assign(ir.Const(stop, loc), g_stop_var, loc)
+        nodes.append(stop_assign)
+    if start == 0 and step == 1:
+        return nodes, [g_stop_var]
+
+    if isinstance(start, ir.Var):
+        g_start_var = start
+    else:
+        assert isinstance(start, int)
+        g_start_var = ir.Var(scope, mk_unique_var("$range_start"), loc)
+        if typemap:
+            typemap[g_start_var.name] = types.intp
+        start_assign = ir.Assign(ir.Const(start, loc), g_start_var, loc)
+        nodes.append(start_assign)
+    if step == 1:
+        return nodes, [g_start_var, g_stop_var]
+
+    if isinstance(step, ir.Var):
+        g_step_var = step
+    else:
+        assert isinstance(step, int)
+        g_step_var = ir.Var(scope, mk_unique_var("$range_step"), loc)
+        if typemap:
+            typemap[g_step_var.name] = types.intp
+        step_assign = ir.Assign(ir.Const(step, loc), g_step_var, loc)
+        nodes.append(step_assign)
+
+    return nodes, [g_start_var, g_stop_var, g_step_var]
+
+
+def get_global_func_typ(func):
+    """get type variable for func() from builtin registry"""
+    for (k, v) in typing.templates.builtin_registry.globals:
+        if k == func:
+            return v
+    raise RuntimeError("func type not found {}".format(func))
+
+
+def mk_loop_header(typemap, phi_var, calltypes, scope, loc):
+    """make a block that is a loop header updating iteration variables.
+    target labels in branch need to be set.
+    """
+    # iternext_var = iternext(phi_var)
+    iternext_var = ir.Var(scope, mk_unique_var("$iternext_var"), loc)
+    typemap[iternext_var.name] = types.containers.Pair(
+        types.intp, types.boolean)
+    iternext_call = ir.Expr.iternext(phi_var, loc)
+    calltypes[iternext_call] = signature(
+        types.containers.Pair(
+            types.intp,
+            types.boolean),
+        types.range_iter64_type)
+    iternext_assign = ir.Assign(iternext_call, iternext_var, loc)
+    # pair_first_var = pair_first(iternext_var)
+    pair_first_var = ir.Var(scope, mk_unique_var("$pair_first_var"), loc)
+    typemap[pair_first_var.name] = types.intp
+    pair_first_call = ir.Expr.pair_first(iternext_var, loc)
+    pair_first_assign = ir.Assign(pair_first_call, pair_first_var, loc)
+    # pair_second_var = pair_second(iternext_var)
+    pair_second_var = ir.Var(scope, mk_unique_var("$pair_second_var"), loc)
+    typemap[pair_second_var.name] = types.boolean
+    pair_second_call = ir.Expr.pair_second(iternext_var, loc)
+    pair_second_assign = ir.Assign(pair_second_call, pair_second_var, loc)
+    # phi_b_var = pair_first_var
+    phi_b_var = ir.Var(scope, mk_unique_var("$phi"), loc)
+    typemap[phi_b_var.name] = types.intp
+    phi_b_assign = ir.Assign(pair_first_var, phi_b_var, loc)
+    # branch pair_second_var body_block out_block
+    branch = ir.Branch(pair_second_var, -1, -1, loc)
+    header_block = ir.Block(scope, loc)
+    header_block.body = [iternext_assign, pair_first_assign,
+                         pair_second_assign, phi_b_assign, branch]
+    return header_block
+
+
+def legalize_names(varnames):
+    """returns a dictionary for conversion of variable names to legal
+    parameter names.
+    """
+    var_map = {}
+    for var in varnames:
+        new_name = var.replace("_", "__").replace("$", "_").replace(".", "_")
+        assert new_name not in var_map
+        var_map[var] = new_name
+    return var_map
+
+
+def get_name_var_table(blocks):
+    """create a mapping from variable names to their ir.Var objects"""
+    def get_name_var_visit(var, namevar):
+        namevar[var.name] = var
+        return var
+    namevar = {}
+    visit_vars(blocks, get_name_var_visit, namevar)
+    return namevar
+
+
+def replace_var_names(blocks, namedict):
+    """replace variables (ir.Var to ir.Var) from dictionary (name -> name)"""
+    # remove identity values to avoid infinite loop
+    new_namedict = {}
+    for l, r in namedict.items():
+        if l != r:
+            new_namedict[l] = r
+
+    def replace_name(var, namedict):
+        assert isinstance(var, ir.Var)
+        while var.name in namedict:
+            var = ir.Var(var.scope, namedict[var.name], var.loc)
+        return var
+    visit_vars(blocks, replace_name, new_namedict)
+
+
+def replace_var_callback(var, vardict):
+    assert isinstance(var, ir.Var)
+    while var.name in vardict.keys():
+        assert(vardict[var.name].name != var.name)
+        new_var = vardict[var.name]
+        var = ir.Var(new_var.scope, new_var.name, new_var.loc)
+    return var
+
+
+def replace_vars(blocks, vardict):
+    """replace variables (ir.Var to ir.Var) from dictionary (name -> ir.Var)"""
+    # remove identity values to avoid infinite loop
+    new_vardict = {}
+    for l, r in vardict.items():
+        if l != r.name:
+            new_vardict[l] = r
+    visit_vars(blocks, replace_var_callback, new_vardict)
+
+
+def replace_vars_stmt(stmt, vardict):
+    visit_vars_stmt(stmt, replace_var_callback, vardict)
+
+
+def replace_vars_inner(node, vardict):
+    return visit_vars_inner(node, replace_var_callback, vardict)
+
+
+# other packages that define new nodes add calls to visit variables in them
+# format: {type:function}
+visit_vars_extensions = {}
+
+
+def visit_vars(blocks, callback, cbdata):
+    """go over statements of block bodies and replace variable names with
+    dictionary.
+    """
+    for block in blocks.values():
+        for stmt in block.body:
+            visit_vars_stmt(stmt, callback, cbdata)
+    return
+
+
+def visit_vars_stmt(stmt, callback, cbdata):
+    # let external calls handle stmt if type matches
+    for t, f in visit_vars_extensions.items():
+        if isinstance(stmt, t):
+            f(stmt, callback, cbdata)
+            return
+    if isinstance(stmt, ir.Assign):
+        stmt.target = visit_vars_inner(stmt.target, callback, cbdata)
+        stmt.value = visit_vars_inner(stmt.value, callback, cbdata)
+    elif isinstance(stmt, ir.Arg):
+        stmt.name = visit_vars_inner(stmt.name, callback, cbdata)
+    elif isinstance(stmt, ir.Return):
+        stmt.value = visit_vars_inner(stmt.value, callback, cbdata)
+    elif isinstance(stmt, ir.Raise):
+        stmt.exception = visit_vars_inner(stmt.exception, callback, cbdata)
+    elif isinstance(stmt, ir.Branch):
+        stmt.cond = visit_vars_inner(stmt.cond, callback, cbdata)
+    elif isinstance(stmt, ir.Jump):
+        stmt.target = visit_vars_inner(stmt.target, callback, cbdata)
+    elif isinstance(stmt, ir.Del):
+        # Because Del takes only a var name, we make up by
+        # constructing a temporary variable.
+        var = ir.Var(None, stmt.value, stmt.loc)
+        var = visit_vars_inner(var, callback, cbdata)
+        stmt.value = var.name
+    elif isinstance(stmt, ir.DelAttr):
+        stmt.target = visit_vars_inner(stmt.target, callback, cbdata)
+        stmt.attr = visit_vars_inner(stmt.attr, callback, cbdata)
+    elif isinstance(stmt, ir.SetAttr):
+        stmt.target = visit_vars_inner(stmt.target, callback, cbdata)
+        stmt.attr = visit_vars_inner(stmt.attr, callback, cbdata)
+        stmt.value = visit_vars_inner(stmt.value, callback, cbdata)
+    elif isinstance(stmt, ir.DelItem):
+        stmt.target = visit_vars_inner(stmt.target, callback, cbdata)
+        stmt.index = visit_vars_inner(stmt.index, callback, cbdata)
+    elif isinstance(stmt, ir.StaticSetItem):
+        stmt.target = visit_vars_inner(stmt.target, callback, cbdata)
+        stmt.index_var = visit_vars_inner(stmt.index_var, callback, cbdata)
+        stmt.value = visit_vars_inner(stmt.value, callback, cbdata)
+    elif isinstance(stmt, ir.SetItem):
+        stmt.target = visit_vars_inner(stmt.target, callback, cbdata)
+        stmt.index = visit_vars_inner(stmt.index, callback, cbdata)
+        stmt.value = visit_vars_inner(stmt.value, callback, cbdata)
+    elif isinstance(stmt, ir.Print):
+        stmt.args = [visit_vars_inner(x, callback, cbdata) for x in stmt.args]
+    else:
+        # TODO: raise NotImplementedError("no replacement for IR node: ", stmt)
+        pass
+    return
+
+
+def visit_vars_inner(node, callback, cbdata):
+    if isinstance(node, ir.Var):
+        return callback(node, cbdata)
+    elif isinstance(node, list):
+        return [visit_vars_inner(n, callback, cbdata) for n in node]
+    elif isinstance(node, tuple):
+        return tuple([visit_vars_inner(n, callback, cbdata) for n in node])
+    elif isinstance(node, ir.Expr):
+        # if node.op in ['binop', 'inplace_binop']:
+        #     lhs = node.lhs.name
+        #     rhs = node.rhs.name
+        #     node.lhs.name = callback, cbdata.get(lhs, lhs)
+        #     node.rhs.name = callback, cbdata.get(rhs, rhs)
+        for arg in node._kws.keys():
+            node._kws[arg] = visit_vars_inner(node._kws[arg], callback, cbdata)
+    elif isinstance(node, ir.Yield):
+        node.value = visit_vars_inner(node.value, callback, cbdata)
+    return node
+
+
+add_offset_to_labels_extensions = {}
+
+
+def add_offset_to_labels(blocks, offset):
+    """add an offset to all block labels and jump/branch targets
+    """
+    new_blocks = {}
+    for l, b in blocks.items():
+        # some parfor last blocks might be empty
+        term = None
+        if b.body:
+            term = b.body[-1]
+            for inst in b.body:
+                for T, f in add_offset_to_labels_extensions.items():
+                    if isinstance(inst, T):
+                        f_max = f(inst, offset)
+        if isinstance(term, ir.Jump):
+            b.body[-1] = ir.Jump(term.target + offset, term.loc)
+        if isinstance(term, ir.Branch):
+            b.body[-1] = ir.Branch(term.cond, term.truebr + offset,
+                                   term.falsebr + offset, term.loc)
+        new_blocks[l + offset] = b
+    return new_blocks
+
+
+find_max_label_extensions = {}
+
+
+def find_max_label(blocks):
+    max_label = 0
+    for l, b in blocks.items():
+        term = None
+        if b.body:
+            term = b.body[-1]
+            for inst in b.body:
+                for T, f in find_max_label_extensions.items():
+                    if isinstance(inst, T):
+                        f_max = f(inst)
+                        if f_max > max_label:
+                            max_label = f_max
+        if l > max_label:
+            max_label = l
+    return max_label
+
+
+def flatten_labels(blocks):
+    """makes the labels in range(0, len(blocks)), useful to compare CFGs
+    """
+    # first bulk move the labels out of the rewrite range
+    blocks = add_offset_to_labels(blocks, find_max_label(blocks) + 1)
+    # order them in topo order because it's easier to read
+    new_blocks = {}
+    topo_order = find_topo_order(blocks)
+    l_map = dict()
+    idx = 0
+    for x in topo_order:
+        l_map[x] = idx
+        idx += 1
+
+    for t_node in topo_order:
+        b = blocks[t_node]
+        # some parfor last blocks might be empty
+        term = None
+        if b.body:
+            term = b.body[-1]
+        if isinstance(term, ir.Jump):
+            b.body[-1] = ir.Jump(l_map[term.target], term.loc)
+        if isinstance(term, ir.Branch):
+            b.body[-1] = ir.Branch(term.cond, l_map[term.truebr],
+                                   l_map[term.falsebr], term.loc)
+        new_blocks[l_map[t_node]] = b
+    return new_blocks
+
+
+def remove_dels(blocks):
+    """remove ir.Del nodes"""
+    for block in blocks.values():
+        new_body = []
+        for stmt in block.body:
+            if not isinstance(stmt, ir.Del):
+                new_body.append(stmt)
+        block.body = new_body
+    return
+
+
+def remove_args(blocks):
+    """remove ir.Arg nodes"""
+    for block in blocks.values():
+        new_body = []
+        for stmt in block.body:
+            if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Arg):
+                continue
+            new_body.append(stmt)
+        block.body = new_body
+    return
+
+
+def dead_code_elimination(func_ir, typemap=None, alias_map=None,
+                          arg_aliases=None):
+    """ Performs dead code elimination and leaves the IR in a valid state on
+    exit
+    """
+    do_post_proc = False
+    while (remove_dead(func_ir.blocks, func_ir.arg_names, func_ir, typemap,
+                       alias_map, arg_aliases)):
+        do_post_proc = True
+
+    if do_post_proc:
+        post_proc = postproc.PostProcessor(func_ir)
+        post_proc.run()
+
+
+def remove_dead(blocks, args, func_ir, typemap=None, alias_map=None, arg_aliases=None):
+    """dead code elimination using liveness and CFG info.
+    Returns True if something has been removed, or False if nothing is removed.
+    """
+    cfg = compute_cfg_from_blocks(blocks)
+    usedefs = compute_use_defs(blocks)
+    live_map = compute_live_map(cfg, blocks, usedefs.usemap, usedefs.defmap)
+    call_table, _ = get_call_table(blocks)
+    if alias_map is None or arg_aliases is None:
+        alias_map, arg_aliases = find_potential_aliases(blocks, args, typemap,
+                                                        func_ir)
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("args:", args)
+        print("alias map:", alias_map)
+        print("arg_aliases:", arg_aliases)
+        print("live_map:", live_map)
+        print("usemap:", usedefs.usemap)
+        print("defmap:", usedefs.defmap)
+    # keep set for easier search
+    alias_set = set(alias_map.keys())
+
+    removed = False
+    for label, block in blocks.items():
+        # find live variables at each statement to delete dead assignment
+        lives = {v.name for v in block.terminator.list_vars()}
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print("remove_dead processing block", label, lives)
+        # find live variables at the end of block
+        for out_blk, _data in cfg.successors(label):
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print("succ live_map", out_blk, live_map[out_blk])
+            lives |= live_map[out_blk]
+        removed |= remove_dead_block(block, lives, call_table, arg_aliases,
+                                     alias_map, alias_set, func_ir, typemap)
+
+    return removed
+
+
+# other packages that define new nodes add calls to remove dead code in them
+# format: {type:function}
+remove_dead_extensions = {}
+
+
+def remove_dead_block(block, lives, call_table, arg_aliases, alias_map,
+                                                  alias_set, func_ir, typemap):
+    """remove dead code using liveness info.
+    Mutable arguments (e.g. arrays) that are not definitely assigned are live
+    after return of function.
+    """
+    # TODO: find mutable args that are not definitely assigned instead of
+    # assuming all args are live after return
+    removed = False
+
+    # add statements in reverse order
+    new_body = [block.terminator]
+    # for each statement in reverse order, excluding terminator
+    for stmt in reversed(block.body[:-1]):
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print("remove_dead_block", stmt)
+        # aliases of lives are also live
+        alias_lives = set()
+        init_alias_lives = lives & alias_set
+        for v in init_alias_lives:
+            alias_lives |= alias_map[v]
+        lives_n_aliases = lives | alias_lives | arg_aliases
+
+        # let external calls handle stmt if type matches
+        if type(stmt) in remove_dead_extensions:
+            f = remove_dead_extensions[type(stmt)]
+            stmt = f(stmt, lives, lives_n_aliases, arg_aliases, alias_map, func_ir,
+                     typemap)
+            if stmt is None:
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("Statement was removed.")
+                removed = True
+                continue
+
+        # ignore assignments that their lhs is not live or lhs==rhs
+        if isinstance(stmt, ir.Assign):
+            lhs = stmt.target
+            rhs = stmt.value
+            if lhs.name not in lives and has_no_side_effect(
+                    rhs, lives_n_aliases, call_table):
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("Statement was removed.")
+                removed = True
+                continue
+            if isinstance(rhs, ir.Var) and lhs.name == rhs.name:
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("Statement was removed.")
+                removed = True
+                continue
+            # TODO: remove other nodes like SetItem etc.
+
+        if isinstance(stmt, ir.Del):
+            if stmt.value not in lives:
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("Statement was removed.")
+                removed = True
+                continue
+
+        if isinstance(stmt, ir.SetItem):
+            name = stmt.target.name
+            if name not in lives_n_aliases:
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("Statement was removed.")
+                continue
+
+        if type(stmt) in analysis.ir_extension_usedefs:
+            def_func = analysis.ir_extension_usedefs[type(stmt)]
+            uses, defs = def_func(stmt)
+            lives -= defs
+            lives |= uses
+        else:
+            lives |= {v.name for v in stmt.list_vars()}
+            if isinstance(stmt, ir.Assign):
+                # make sure lhs is not used in rhs, e.g. a = g(a)
+                if isinstance(stmt.value, ir.Expr):
+                    rhs_vars = {v.name for v in stmt.value.list_vars()}
+                    if lhs.name not in rhs_vars:
+                        lives.remove(lhs.name)
+                else:
+                    lives.remove(lhs.name)
+
+        new_body.append(stmt)
+    new_body.reverse()
+    block.body = new_body
+    return removed
+
+# list of functions
+remove_call_handlers = []
+
+def remove_dead_random_call(rhs, lives, call_list):
+    if len(call_list) == 3 and call_list[1:] == ['random', numpy]:
+        return call_list[0] not in {'seed', 'shuffle'}
+    return False
+
+remove_call_handlers.append(remove_dead_random_call)
+
+def has_no_side_effect(rhs, lives, call_table):
+    """ Returns True if this expression has no side effects that
+        would prevent re-ordering.
+    """
+    from numba.parfors import array_analysis, parfor
+    from numba.misc.special import prange
+    if isinstance(rhs, ir.Expr) and rhs.op == 'call':
+        func_name = rhs.func.name
+        if func_name not in call_table or call_table[func_name] == []:
+            return False
+        call_list = call_table[func_name]
+        if (call_list == ['empty', numpy] or
+            call_list == [slice] or
+            call_list == ['stencil', numba] or
+            call_list == ['log', numpy] or
+            call_list == ['dtype', numpy] or
+            call_list == [array_analysis.wrap_index] or
+            call_list == [prange] or
+            call_list == ['prange', numba] or
+            call_list == [parfor.internal_prange]):
+            return True
+        elif (isinstance(call_list[0], _Intrinsic) and
+              (call_list[0]._name == 'empty_inferred' or
+               call_list[0]._name == 'unsafe_empty_inferred')):
+            return True
+        from numba.core.registry import CPUDispatcher
+        from numba.np.linalg import dot_3_mv_check_args
+        if isinstance(call_list[0], CPUDispatcher):
+            py_func = call_list[0].py_func
+            if py_func == dot_3_mv_check_args:
+                return True
+        for f in remove_call_handlers:
+            if f(rhs, lives, call_list):
+                return True
+        return False
+    if isinstance(rhs, ir.Expr) and rhs.op == 'inplace_binop':
+        return rhs.lhs.name not in lives
+    if isinstance(rhs, ir.Yield):
+        return False
+    if isinstance(rhs, ir.Expr) and rhs.op == 'pair_first':
+        # don't remove pair_first since prange looks for it
+        return False
+    return True
+
+is_pure_extensions = []
+
+def is_pure(rhs, lives, call_table):
+    """ Returns True if every time this expression is evaluated it
+        returns the same result.  This is not the case for things
+        like calls to numpy.random.
+    """
+    if isinstance(rhs, ir.Expr):
+        if rhs.op == 'call':
+            func_name = rhs.func.name
+            if func_name not in call_table or call_table[func_name] == []:
+                return False
+            call_list = call_table[func_name]
+            if (call_list == [slice] or
+                call_list == ['log', numpy] or
+                call_list == ['empty', numpy]):
+                return True
+            for f in is_pure_extensions:
+                if f(rhs, lives, call_list):
+                    return True
+            return False
+        elif rhs.op == 'getiter' or rhs.op == 'iternext':
+            return False
+    if isinstance(rhs, ir.Yield):
+        return False
+    return True
+
+def is_const_call(module_name, func_name):
+    # Returns True if there is no state in the given module changed by the given function.
+    if module_name == 'numpy':
+        if func_name in ['empty']:
+            return True
+    return False
+
+alias_analysis_extensions = {}
+alias_func_extensions = {}
+
+def get_canonical_alias(v, alias_map):
+    if v not in alias_map:
+        return v
+
+    v_aliases = sorted(list(alias_map[v]))
+    return v_aliases[0]
+
+def find_potential_aliases(blocks, args, typemap, func_ir, alias_map=None,
+                                                           arg_aliases=None):
+    "find all array aliases and argument aliases to avoid remove as dead"
+    if alias_map is None:
+        alias_map = {}
+    if arg_aliases is None:
+        arg_aliases = set(a for a in args if not is_immutable_type(a, typemap))
+
+    # update definitions since they are not guaranteed to be up-to-date
+    # FIXME keep definitions up-to-date to avoid the need for rebuilding
+    func_ir._definitions = build_definitions(func_ir.blocks)
+    np_alias_funcs = ['ravel', 'transpose', 'reshape']
+
+    for bl in blocks.values():
+        for instr in bl.body:
+            if type(instr) in alias_analysis_extensions:
+                f = alias_analysis_extensions[type(instr)]
+                f(instr, args, typemap, func_ir, alias_map, arg_aliases)
+            if isinstance(instr, ir.Assign):
+                expr = instr.value
+                lhs = instr.target.name
+                # only mutable types can alias
+                if is_immutable_type(lhs, typemap):
+                    continue
+                if isinstance(expr, ir.Var) and lhs!=expr.name:
+                    _add_alias(lhs, expr.name, alias_map, arg_aliases)
+                # subarrays like A = B[0] for 2D B
+                if (isinstance(expr, ir.Expr) and (expr.op == 'cast' or
+                    expr.op in ['getitem', 'static_getitem'])):
+                    _add_alias(lhs, expr.value.name, alias_map, arg_aliases)
+                if isinstance(expr, ir.Expr) and expr.op == 'inplace_binop':
+                    _add_alias(lhs, expr.lhs.name, alias_map, arg_aliases)
+                # array attributes like A.T
+                if (isinstance(expr, ir.Expr) and expr.op == 'getattr'
+                        and expr.attr in ['T', 'ctypes', 'flat']):
+                    _add_alias(lhs, expr.value.name, alias_map, arg_aliases)
+                # a = b.c.  a should alias b
+                if (isinstance(expr, ir.Expr) and expr.op == 'getattr'
+                        and expr.attr not in ['shape']
+                        and expr.value.name in arg_aliases):
+                    _add_alias(lhs, expr.value.name, alias_map, arg_aliases)
+                # calls that can create aliases such as B = A.ravel()
+                if isinstance(expr, ir.Expr) and expr.op == 'call':
+                    fdef = guard(find_callname, func_ir, expr, typemap)
+                    # TODO: sometimes gufunc backend creates duplicate code
+                    # causing find_callname to fail. Example: test_argmax
+                    # ignored here since those cases don't create aliases
+                    # but should be fixed in general
+                    if fdef is None:
+                        continue
+                    fname, fmod = fdef
+                    if fdef in alias_func_extensions:
+                        alias_func = alias_func_extensions[fdef]
+                        alias_func(lhs, expr.args, alias_map, arg_aliases)
+                    if fmod == 'numpy' and fname in np_alias_funcs:
+                        _add_alias(lhs, expr.args[0].name, alias_map, arg_aliases)
+                    if isinstance(fmod, ir.Var) and fname in np_alias_funcs:
+                        _add_alias(lhs, fmod.name, alias_map, arg_aliases)
+
+    # copy to avoid changing size during iteration
+    old_alias_map = copy.deepcopy(alias_map)
+    # combine all aliases transitively
+    for v in old_alias_map:
+        for w in old_alias_map[v]:
+            alias_map[v] |= alias_map[w]
+        for w in old_alias_map[v]:
+            alias_map[w] = alias_map[v]
+
+    return alias_map, arg_aliases
+
+def _add_alias(lhs, rhs, alias_map, arg_aliases):
+    if rhs in arg_aliases:
+        arg_aliases.add(lhs)
+    else:
+        if rhs not in alias_map:
+            alias_map[rhs] = set()
+        if lhs not in alias_map:
+            alias_map[lhs] = set()
+        alias_map[rhs].add(lhs)
+        alias_map[lhs].add(rhs)
+    return
+
+def is_immutable_type(var, typemap):
+    # Conservatively, assume mutable if type not available
+    if typemap is None or var not in typemap:
+        return False
+    typ = typemap[var]
+    # TODO: add more immutable types
+    if isinstance(typ, (types.Number, types.scalars._NPDatetimeBase,
+                        types.iterators.RangeType)):
+        return True
+    if typ==types.string:
+        return True
+    # conservatively, assume mutable
+    return False
+
+def copy_propagate(blocks, typemap):
+    """compute copy propagation information for each block using fixed-point
+     iteration on data flow equations:
+     in_b = intersect(predec(B))
+     out_b = gen_b | (in_b - kill_b)
+    """
+    cfg = compute_cfg_from_blocks(blocks)
+    entry = cfg.entry_point()
+
+    # format: dict of block labels to copies as tuples
+    # label -> (l,r)
+    c_data = init_copy_propagate_data(blocks, entry, typemap)
+    (gen_copies, all_copies, kill_copies, in_copies, out_copies) = c_data
+
+    old_point = None
+    new_point = copy.deepcopy(out_copies)
+    # comparison works since dictionary of built-in types
+    while old_point != new_point:
+        for label in blocks.keys():
+            if label == entry:
+                continue
+            predecs = [i for i, _d in cfg.predecessors(label)]
+            # in_b =  intersect(predec(B))
+            in_copies[label] = out_copies[predecs[0]].copy()
+            for p in predecs:
+                in_copies[label] &= out_copies[p]
+
+            # out_b = gen_b | (in_b - kill_b)
+            out_copies[label] = (gen_copies[label]
+                                 | (in_copies[label] - kill_copies[label]))
+        old_point = new_point
+        new_point = copy.deepcopy(out_copies)
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("copy propagate out_copies:", out_copies)
+    return in_copies, out_copies
+
+
+def init_copy_propagate_data(blocks, entry, typemap):
+    """get initial condition of copy propagation data flow for each block.
+    """
+    # gen is all definite copies, extra_kill is additional ones that may hit
+    # for example, parfors can have control flow so they may hit extra copies
+    gen_copies, extra_kill = get_block_copies(blocks, typemap)
+    # set of all program copies
+    all_copies = set()
+    for l, s in gen_copies.items():
+        all_copies |= gen_copies[l]
+    kill_copies = {}
+    for label, gen_set in gen_copies.items():
+        kill_copies[label] = set()
+        for lhs, rhs in all_copies:
+            if lhs in extra_kill[label] or rhs in extra_kill[label]:
+                kill_copies[label].add((lhs, rhs))
+            # a copy is killed if it is not in this block and lhs or rhs are
+            # assigned in this block
+            assigned = {lhs for lhs, rhs in gen_set}
+            if ((lhs, rhs) not in gen_set
+                    and (lhs in assigned or rhs in assigned)):
+                kill_copies[label].add((lhs, rhs))
+    # set initial values
+    # all copies are in for all blocks except entry
+    in_copies = {l: all_copies.copy() for l in blocks.keys()}
+    in_copies[entry] = set()
+    out_copies = {}
+    for label in blocks.keys():
+        # out_b = gen_b | (in_b - kill_b)
+        out_copies[label] = (gen_copies[label]
+                             | (in_copies[label] - kill_copies[label]))
+    out_copies[entry] = gen_copies[entry]
+    return (gen_copies, all_copies, kill_copies, in_copies, out_copies)
+
+
+# other packages that define new nodes add calls to get copies in them
+# format: {type:function}
+copy_propagate_extensions = {}
+
+
+def get_block_copies(blocks, typemap):
+    """get copies generated and killed by each block
+    """
+    block_copies = {}
+    extra_kill = {}
+    for label, block in blocks.items():
+        assign_dict = {}
+        extra_kill[label] = set()
+        # assignments as dict to replace with latest value
+        for stmt in block.body:
+            for T, f in copy_propagate_extensions.items():
+                if isinstance(stmt, T):
+                    gen_set, kill_set = f(stmt, typemap)
+                    for lhs, rhs in gen_set:
+                        assign_dict[lhs] = rhs
+                    # if a=b is in dict and b is killed, a is also killed
+                    new_assign_dict = {}
+                    for l, r in assign_dict.items():
+                        if l not in kill_set and r not in kill_set:
+                            new_assign_dict[l] = r
+                        if r in kill_set:
+                            extra_kill[label].add(l)
+                    assign_dict = new_assign_dict
+                    extra_kill[label] |= kill_set
+            if isinstance(stmt, ir.Assign):
+                lhs = stmt.target.name
+                if isinstance(stmt.value, ir.Var):
+                    rhs = stmt.value.name
+                    # copy is valid only if same type (see
+                    # TestCFunc.test_locals)
+                    # Some transformations can produce assignments of the
+                    # form A = A.  We don't put these mapping in the
+                    # copy propagation set because then you get cycles and
+                    # infinite loops in the replacement phase.
+                    if typemap[lhs] == typemap[rhs] and lhs != rhs:
+                        assign_dict[lhs] = rhs
+                        continue
+                if isinstance(stmt.value,
+                              ir.Expr) and stmt.value.op == 'inplace_binop':
+                    in1_var = stmt.value.lhs.name
+                    in1_typ = typemap[in1_var]
+                    # inplace_binop assigns first operand if mutable
+                    if not (isinstance(in1_typ, types.Number)
+                            or in1_typ == types.string):
+                        extra_kill[label].add(in1_var)
+                        # if a=b is in dict and b is killed, a is also killed
+                        new_assign_dict = {}
+                        for l, r in assign_dict.items():
+                            if l != in1_var and r != in1_var:
+                                new_assign_dict[l] = r
+                            if r == in1_var:
+                                extra_kill[label].add(l)
+                        assign_dict = new_assign_dict
+                extra_kill[label].add(lhs)
+        block_cps = set(assign_dict.items())
+        block_copies[label] = block_cps
+    return block_copies, extra_kill
+
+
+# other packages that define new nodes add calls to apply copy propagate in them
+# format: {type:function}
+apply_copy_propagate_extensions = {}
+
+
+def apply_copy_propagate(blocks, in_copies, name_var_table, typemap, calltypes,
+                         save_copies=None):
+    """apply copy propagation to IR: replace variables when copies available"""
+    # save_copies keeps an approximation of the copies that were applied, so
+    # that the variable names of removed user variables can be recovered to some
+    # extent.
+    if save_copies is None:
+        save_copies = []
+
+    for label, block in blocks.items():
+        var_dict = {l: name_var_table[r] for l, r in in_copies[label]}
+        # assignments as dict to replace with latest value
+        for stmt in block.body:
+            if type(stmt) in apply_copy_propagate_extensions:
+                f = apply_copy_propagate_extensions[type(stmt)]
+                f(stmt, var_dict, name_var_table,
+                    typemap, calltypes, save_copies)
+            # only rhs of assignments should be replaced
+            # e.g. if x=y is available, x in x=z shouldn't be replaced
+            elif isinstance(stmt, ir.Assign):
+                stmt.value = replace_vars_inner(stmt.value, var_dict)
+            else:
+                replace_vars_stmt(stmt, var_dict)
+            fix_setitem_type(stmt, typemap, calltypes)
+            for T, f in copy_propagate_extensions.items():
+                if isinstance(stmt, T):
+                    gen_set, kill_set = f(stmt, typemap)
+                    for lhs, rhs in gen_set:
+                        if rhs in name_var_table:
+                            var_dict[lhs] = name_var_table[rhs]
+                    for l, r in var_dict.copy().items():
+                        if l in kill_set or r.name in kill_set:
+                            var_dict.pop(l)
+            if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Var):
+                lhs = stmt.target.name
+                rhs = stmt.value.name
+                # rhs could be replaced with lhs from previous copies
+                if lhs != rhs:
+                    # copy is valid only if same type (see
+                    # TestCFunc.test_locals)
+                    if typemap[lhs] == typemap[rhs] and rhs in name_var_table:
+                        var_dict[lhs] = name_var_table[rhs]
+                    else:
+                        var_dict.pop(lhs, None)
+                    # a=b kills previous t=a
+                    lhs_kill = []
+                    for k, v in var_dict.items():
+                        if v.name == lhs:
+                            lhs_kill.append(k)
+                    for k in lhs_kill:
+                        var_dict.pop(k, None)
+            if (isinstance(stmt, ir.Assign)
+                                        and not isinstance(stmt.value, ir.Var)):
+                lhs = stmt.target.name
+                var_dict.pop(lhs, None)
+                # previous t=a is killed if a is killed
+                lhs_kill = []
+                for k, v in var_dict.items():
+                    if v.name == lhs:
+                        lhs_kill.append(k)
+                for k in lhs_kill:
+                    var_dict.pop(k, None)
+        save_copies.extend(var_dict.items())
+
+    return save_copies
+
+def fix_setitem_type(stmt, typemap, calltypes):
+    """Copy propagation can replace setitem target variable, which can be array
+    with 'A' layout. The replaced variable can be 'C' or 'F', so we update
+    setitem call type reflect this (from matrix power test)
+    """
+    if not isinstance(stmt, (ir.SetItem, ir.StaticSetItem)):
+        return
+    t_typ = typemap[stmt.target.name]
+    s_typ = calltypes[stmt].args[0]
+    # test_optional t_typ can be Optional with array
+    if not isinstance(
+            s_typ,
+            types.npytypes.Array) or not isinstance(
+            t_typ,
+            types.npytypes.Array):
+        return
+    if s_typ.layout == 'A' and t_typ.layout != 'A':
+        new_s_typ = s_typ.copy(layout=t_typ.layout)
+        calltypes[stmt].args = (
+            new_s_typ,
+            calltypes[stmt].args[1],
+            calltypes[stmt].args[2])
+    return
+
+
+def dprint_func_ir(func_ir, title, blocks=None):
+    """Debug print function IR, with an optional blocks argument
+    that may differ from the IR's original blocks.
+    """
+    if config.DEBUG_ARRAY_OPT >= 1:
+        ir_blocks = func_ir.blocks
+        func_ir.blocks = ir_blocks if blocks == None else blocks
+        name = func_ir.func_id.func_qualname
+        print(("IR %s: %s" % (title, name)).center(80, "-"))
+        func_ir.dump()
+        print("-" * 40)
+        func_ir.blocks = ir_blocks
+
+
+def find_topo_order(blocks, cfg = None):
+    """find topological order of blocks such that true branches are visited
+    first (e.g. for_break test in test_dataflow).
+    """
+    if cfg is None:
+        cfg = compute_cfg_from_blocks(blocks)
+    post_order = []
+    seen = set()
+
+    def _dfs_rec(node):
+        if node not in seen:
+            seen.add(node)
+            succs = cfg._succs[node]
+            last_inst = blocks[node].body[-1]
+            if isinstance(last_inst, ir.Branch):
+                succs = [last_inst.falsebr, last_inst.truebr]
+            for dest in succs:
+                if (node, dest) not in cfg._back_edges:
+                    _dfs_rec(dest)
+            post_order.append(node)
+
+    _dfs_rec(cfg.entry_point())
+    post_order.reverse()
+    return post_order
+
+
+# other packages that define new nodes add calls to get call table
+# format: {type:function}
+call_table_extensions = {}
+
+
+def get_call_table(blocks, call_table=None, reverse_call_table=None, topological_ordering=True):
+    """returns a dictionary of call variables and their references.
+    """
+    # call_table example: c = np.zeros becomes c:["zeroes", np]
+    # reverse_call_table example: c = np.zeros becomes np_var:c
+    if call_table is None:
+        call_table = {}
+    if reverse_call_table is None:
+        reverse_call_table = {}
+
+    if topological_ordering:
+        order = find_topo_order(blocks)
+    else:
+        order = list(blocks.keys())
+
+    for label in reversed(order):
+        for inst in reversed(blocks[label].body):
+            if isinstance(inst, ir.Assign):
+                lhs = inst.target.name
+                rhs = inst.value
+                if isinstance(rhs, ir.Expr) and rhs.op == 'call':
+                    call_table[rhs.func.name] = []
+                if isinstance(rhs, ir.Expr) and rhs.op == 'getattr':
+                    if lhs in call_table:
+                        call_table[lhs].append(rhs.attr)
+                        reverse_call_table[rhs.value.name] = lhs
+                    if lhs in reverse_call_table:
+                        call_var = reverse_call_table[lhs]
+                        call_table[call_var].append(rhs.attr)
+                        reverse_call_table[rhs.value.name] = call_var
+                if isinstance(rhs, ir.Global):
+                    if lhs in call_table:
+                        call_table[lhs].append(rhs.value)
+                    if lhs in reverse_call_table:
+                        call_var = reverse_call_table[lhs]
+                        call_table[call_var].append(rhs.value)
+                if isinstance(rhs, ir.FreeVar):
+                    if lhs in call_table:
+                        call_table[lhs].append(rhs.value)
+                    if lhs in reverse_call_table:
+                        call_var = reverse_call_table[lhs]
+                        call_table[call_var].append(rhs.value)
+                if isinstance(rhs, ir.Var):
+                    if lhs in call_table:
+                        call_table[lhs].append(rhs.name)
+                        reverse_call_table[rhs.name] = lhs
+                    if lhs in reverse_call_table:
+                        call_var = reverse_call_table[lhs]
+                        call_table[call_var].append(rhs.name)
+            for T, f in call_table_extensions.items():
+                if isinstance(inst, T):
+                    f(inst, call_table, reverse_call_table)
+    return call_table, reverse_call_table
+
+
+# other packages that define new nodes add calls to get tuple table
+# format: {type:function}
+tuple_table_extensions = {}
+
+
+def get_tuple_table(blocks, tuple_table=None):
+    """returns a dictionary of tuple variables and their values.
+    """
+    if tuple_table is None:
+        tuple_table = {}
+
+    for block in blocks.values():
+        for inst in block.body:
+            if isinstance(inst, ir.Assign):
+                lhs = inst.target.name
+                rhs = inst.value
+                if isinstance(rhs, ir.Expr) and rhs.op == 'build_tuple':
+                    tuple_table[lhs] = rhs.items
+                if isinstance(rhs, ir.Const) and isinstance(rhs.value, tuple):
+                    tuple_table[lhs] = rhs.value
+            for T, f in tuple_table_extensions.items():
+                if isinstance(inst, T):
+                    f(inst, tuple_table)
+    return tuple_table
+
+
+def get_stmt_writes(stmt):
+    writes = set()
+    if isinstance(stmt, (ir.Assign, ir.SetItem, ir.StaticSetItem)):
+        writes.add(stmt.target.name)
+    return writes
+
+
+def rename_labels(blocks):
+    """rename labels of function body blocks according to topological sort.
+    The set of labels of these blocks will remain unchanged.
+    """
+    topo_order = find_topo_order(blocks)
+
+    # make a block with return last if available (just for readability)
+    return_label = -1
+    for l, b in blocks.items():
+        if isinstance(b.body[-1], ir.Return):
+            return_label = l
+    # some cases like generators can have no return blocks
+    if return_label != -1:
+        topo_order.remove(return_label)
+        topo_order.append(return_label)
+
+    label_map = {}
+    all_labels = sorted(topo_order, reverse=True)
+    for label in topo_order:
+        label_map[label] = all_labels.pop()
+    # update target labels in jumps/branches
+    for b in blocks.values():
+        term = b.terminator
+        if isinstance(term, ir.Jump):
+            term.target = label_map[term.target]
+        if isinstance(term, ir.Branch):
+            term.truebr = label_map[term.truebr]
+            term.falsebr = label_map[term.falsebr]
+    # update blocks dictionary keys
+    new_blocks = {}
+    for k, b in blocks.items():
+        new_label = label_map[k]
+        new_blocks[new_label] = b
+
+    return new_blocks
+
+
+def simplify_CFG(blocks):
+    """transform chains of blocks that have no loop into a single block"""
+    # first, inline single-branch-block to its predecessors
+    cfg = compute_cfg_from_blocks(blocks)
+    def find_single_branch(label):
+        block = blocks[label]
+        return len(block.body) == 1 and isinstance(block.body[0], ir.Branch)
+    single_branch_blocks = list(filter(find_single_branch, blocks.keys()))
+    marked_for_del = set()
+    for label in single_branch_blocks:
+        inst = blocks[label].body[0]
+        predecessors = cfg.predecessors(label)
+        delete_block = True
+        for (p, q) in predecessors:
+            block = blocks[p]
+            if isinstance(block.body[-1], ir.Jump):
+                block.body[-1] = copy.copy(inst)
+            else:
+                delete_block = False
+        if delete_block:
+            marked_for_del.add(label)
+    # Delete marked labels
+    for label in marked_for_del:
+        del blocks[label]
+    merge_adjacent_blocks(blocks)
+    return rename_labels(blocks)
+
+
+arr_math = ['min', 'max', 'sum', 'prod', 'mean', 'var', 'std',
+            'cumsum', 'cumprod', 'argmax', 'argmin', 'argsort',
+            'nonzero', 'ravel']
+
+
+def canonicalize_array_math(func_ir, typemap, calltypes, typingctx):
+    # save array arg to call
+    # call_varname -> array
+    blocks = func_ir.blocks
+    saved_arr_arg = {}
+    topo_order = find_topo_order(blocks)
+    for label in topo_order:
+        block = blocks[label]
+        new_body = []
+        for stmt in block.body:
+            if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Expr):
+                lhs = stmt.target.name
+                rhs = stmt.value
+                # replace A.func with np.func, and save A in saved_arr_arg
+                if (rhs.op == 'getattr' and rhs.attr in arr_math
+                        and isinstance(
+                            typemap[rhs.value.name], types.npytypes.Array)):
+                    rhs = stmt.value
+                    arr = rhs.value
+                    saved_arr_arg[lhs] = arr
+                    scope = arr.scope
+                    loc = arr.loc
+                    # g_np_var = Global(numpy)
+                    g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc)
+                    typemap[g_np_var.name] = types.misc.Module(numpy)
+                    g_np = ir.Global('np', numpy, loc)
+                    g_np_assign = ir.Assign(g_np, g_np_var, loc)
+                    rhs.value = g_np_var
+                    new_body.append(g_np_assign)
+                    func_ir._definitions[g_np_var.name] = [g_np]
+                    # update func var type
+                    func = getattr(numpy, rhs.attr)
+                    func_typ = get_np_ufunc_typ(func)
+                    typemap.pop(lhs)
+                    typemap[lhs] = func_typ
+                if rhs.op == 'call' and rhs.func.name in saved_arr_arg:
+                    # add array as first arg
+                    arr = saved_arr_arg[rhs.func.name]
+                    # update call type signature to include array arg
+                    old_sig = calltypes.pop(rhs)
+                    # argsort requires kws for typing so sig.args can't be used
+                    # reusing sig.args since some types become Const in sig
+                    argtyps = old_sig.args[:len(rhs.args)]
+                    kwtyps = {name: typemap[v.name] for name, v in rhs.kws}
+                    calltypes[rhs] = typemap[rhs.func.name].get_call_type(
+                        typingctx, [typemap[arr.name]] + list(argtyps), kwtyps)
+                    rhs.args = [arr] + rhs.args
+
+            new_body.append(stmt)
+        block.body = new_body
+    return
+
+
+# format: {type:function}
+array_accesses_extensions = {}
+
+
+def get_array_accesses(blocks, accesses=None):
+    """returns a set of arrays accessed and their indices.
+    """
+    if accesses is None:
+        accesses = set()
+
+    for block in blocks.values():
+        for inst in block.body:
+            if isinstance(inst, ir.SetItem):
+                accesses.add((inst.target.name, inst.index.name))
+            if isinstance(inst, ir.StaticSetItem):
+                accesses.add((inst.target.name, inst.index_var.name))
+            if isinstance(inst, ir.Assign):
+                lhs = inst.target.name
+                rhs = inst.value
+                if isinstance(rhs, ir.Expr) and rhs.op == 'getitem':
+                    accesses.add((rhs.value.name, rhs.index.name))
+                if isinstance(rhs, ir.Expr) and rhs.op == 'static_getitem':
+                    index = rhs.index
+                    # slice is unhashable, so just keep the variable
+                    if index is None or is_slice_index(index):
+                        index = rhs.index_var.name
+                    accesses.add((rhs.value.name, index))
+            for T, f in array_accesses_extensions.items():
+                if isinstance(inst, T):
+                    f(inst, accesses)
+    return accesses
+
+def is_slice_index(index):
+    """see if index is a slice index or has slice in it"""
+    if isinstance(index, slice):
+        return True
+    if isinstance(index, tuple):
+        for i in index:
+            if isinstance(i, slice):
+                return True
+    return False
+
+def merge_adjacent_blocks(blocks):
+    cfg = compute_cfg_from_blocks(blocks)
+    # merge adjacent blocks
+    removed = set()
+    for label in list(blocks.keys()):
+        if label in removed:
+            continue
+        block = blocks[label]
+        succs = list(cfg.successors(label))
+        while True:
+            if len(succs) != 1:
+                break
+            next_label = succs[0][0]
+            if next_label in removed:
+                break
+            preds = list(cfg.predecessors(next_label))
+            succs = list(cfg.successors(next_label))
+            if len(preds) != 1 or preds[0][0] != label:
+                break
+            next_block = blocks[next_label]
+            # XXX: commented out since scope objects are not consistent
+            # throughout the compiler. for example, pieces of code are compiled
+            # and inlined on the fly without proper scope merge.
+            # if block.scope != next_block.scope:
+            #     break
+            # merge
+            block.body.pop()  # remove Jump
+            block.body += next_block.body
+            del blocks[next_label]
+            removed.add(next_label)
+            label = next_label
+
+
+def restore_copy_var_names(blocks, save_copies, typemap):
+    """
+    restores variable names of user variables after applying copy propagation
+    """
+    if not save_copies:
+        return {}
+
+    rename_dict = {}
+    var_rename_map = {}
+    for (a, b) in save_copies:
+        # a is string name, b is variable
+        # if a is user variable and b is generated temporary and b is not
+        # already renamed
+        if (not a.startswith('$') and b.name.startswith('$')
+                                                and b.name not in rename_dict):
+            new_name = mk_unique_var('${}'.format(a));
+            rename_dict[b.name] = new_name
+            var_rename_map[new_name] = a
+            typ = typemap.pop(b.name)
+            typemap[new_name] = typ
+
+    replace_var_names(blocks, rename_dict)
+    return var_rename_map
+
+
+def simplify(func_ir, typemap, calltypes, metadata):
+    # get copies in to blocks and out from blocks
+    in_cps, _ = copy_propagate(func_ir.blocks, typemap)
+    # table mapping variable names to ir.Var objects to help replacement
+    name_var_table = get_name_var_table(func_ir.blocks)
+    save_copies = apply_copy_propagate(
+        func_ir.blocks,
+        in_cps,
+        name_var_table,
+        typemap,
+        calltypes)
+    var_rename_map = restore_copy_var_names(func_ir.blocks, save_copies, typemap)
+    if "var_rename_map" not in metadata:
+            metadata["var_rename_map"] = {}
+    metadata["var_rename_map"].update(var_rename_map)
+    # remove dead code to enable fusion
+    if config.DEBUG_ARRAY_OPT >= 1:
+        dprint_func_ir(func_ir, "after copy prop")
+    remove_dead(func_ir.blocks, func_ir.arg_names, func_ir, typemap)
+    func_ir.blocks = simplify_CFG(func_ir.blocks)
+    if config.DEBUG_ARRAY_OPT >= 1:
+        dprint_func_ir(func_ir, "after simplify")
+
+
+class GuardException(Exception):
+    pass
+
+
+def require(cond):
+    """
+    Raise GuardException if the given condition is False.
+    """
+    if not cond:
+       raise GuardException
+
+def guard(func, *args, **kwargs):
+    """
+    Run a function with given set of arguments, and guard against
+    any GuardException raised by the function by returning None,
+    or the expected return results if no such exception was raised.
+    """
+    try:
+        return func(*args, **kwargs)
+    except GuardException:
+        return None
+
+def get_definition(func_ir, name, **kwargs):
+    """
+    Same as func_ir.get_definition(name), but raise GuardException if
+    exception KeyError is caught.
+    """
+    try:
+        return func_ir.get_definition(name, **kwargs)
+    except KeyError:
+        raise GuardException
+
+def build_definitions(blocks, definitions=None):
+    """Build the definitions table of the given blocks by scanning
+    through all blocks and instructions, useful when the definitions
+    table is out-of-sync.
+    Will return a new definition table if one is not passed.
+    """
+    if definitions is None:
+        definitions = collections.defaultdict(list)
+
+    for block in blocks.values():
+        for inst in block.body:
+            if isinstance(inst, ir.Assign):
+                name = inst.target.name
+                definition = definitions.get(name, [])
+                if definition == []:
+                    definitions[name] = definition
+                definition.append(inst.value)
+            if type(inst) in build_defs_extensions:
+                f = build_defs_extensions[type(inst)]
+                f(inst, definitions)
+
+    return definitions
+
+build_defs_extensions = {}
+
+def find_callname(func_ir, expr, typemap=None, definition_finder=get_definition):
+    """Try to find a call expression's function and module names and return
+    them as strings for unbounded calls. If the call is a bounded call, return
+    the self object instead of module name. Raise GuardException if failed.
+
+    Providing typemap can make the call matching more accurate in corner cases
+    such as bounded call on an object which is inside another object.
+    """
+    require(isinstance(expr, ir.Expr) and expr.op == 'call')
+    callee = expr.func
+    callee_def = definition_finder(func_ir, callee)
+    attrs = []
+    obj = None
+    while True:
+        if isinstance(callee_def, (ir.Global, ir.FreeVar)):
+            # require(callee_def.value == numpy)
+            # these checks support modules like numpy, numpy.random as well as
+            # calls like len() and intrinsics like assertEquiv
+            keys = ['name', '_name', '__name__']
+            value = None
+            for key in keys:
+                if hasattr(callee_def.value, key):
+                    value = getattr(callee_def.value, key)
+                    break
+            if not value or not isinstance(value, str):
+                raise GuardException
+            attrs.append(value)
+            def_val = callee_def.value
+            # get the underlying definition of Intrinsic object to be able to
+            # find the module effectively.
+            # Otherwise, it will return numba.extending
+            if isinstance(def_val, _Intrinsic):
+                def_val = def_val._defn
+            if hasattr(def_val, '__module__'):
+                mod_name = def_val.__module__
+                # The reason for first checking if the function is in NumPy's
+                # top level name space by module is that some functions are
+                # deprecated in NumPy but the functions' names are aliased with
+                # other common names. This prevents deprecation warnings on
+                # e.g. getattr(numpy, 'bool') were a bool the target.
+                # For context see #6175, impacts NumPy>=1.20.
+                mod_not_none = mod_name is not None
+                numpy_toplevel = (mod_not_none and
+                                  (mod_name == 'numpy'
+                                   or mod_name.startswith('numpy.')))
+                # it might be a numpy function imported directly
+                if (numpy_toplevel and hasattr(numpy, value)
+                        and def_val == getattr(numpy, value)):
+                    attrs += ['numpy']
+                # it might be a np.random function imported directly
+                elif (hasattr(numpy.random, value)
+                        and def_val == getattr(numpy.random, value)):
+                    attrs += ['random', 'numpy']
+                elif mod_not_none:
+                    attrs.append(mod_name)
+            else:
+                class_name = def_val.__class__.__name__
+                if class_name == 'builtin_function_or_method':
+                    class_name = 'builtin'
+                if class_name != 'module':
+                    attrs.append(class_name)
+            break
+        elif isinstance(callee_def, ir.Expr) and callee_def.op == 'getattr':
+            obj = callee_def.value
+            attrs.append(callee_def.attr)
+            if typemap and obj.name in typemap:
+                typ = typemap[obj.name]
+                if not isinstance(typ, types.Module):
+                    return attrs[0], obj
+            callee_def = definition_finder(func_ir, obj)
+        else:
+            # obj.func calls where obj is not np array
+            if obj is not None:
+                return '.'.join(reversed(attrs)), obj
+            raise GuardException
+    return attrs[0], '.'.join(reversed(attrs[1:]))
+
+def find_build_sequence(func_ir, var):
+    """Check if a variable is constructed via build_tuple or
+    build_list or build_set, and return the sequence and the
+    operator, or raise GuardException otherwise.
+    Note: only build_tuple is immutable, so use with care.
+    """
+    require(isinstance(var, ir.Var))
+    var_def = get_definition(func_ir, var)
+    require(isinstance(var_def, ir.Expr))
+    build_ops = ['build_tuple', 'build_list', 'build_set']
+    require(var_def.op in build_ops)
+    return var_def.items, var_def.op
+
+def find_const(func_ir, var):
+    """Check if a variable is defined as constant, and return
+    the constant value, or raise GuardException otherwise.
+    """
+    require(isinstance(var, ir.Var))
+    var_def = get_definition(func_ir, var)
+    require(isinstance(var_def, (ir.Const, ir.Global, ir.FreeVar)))
+    return var_def.value
+
+def compile_to_numba_ir(mk_func, glbls, typingctx=None, targetctx=None,
+                        arg_typs=None, typemap=None, calltypes=None):
+    """
+    Compile a function or a make_function node to Numba IR.
+
+    Rename variables and
+    labels to avoid conflict if inlined somewhere else. Perform type inference
+    if typingctx and other typing inputs are available and update typemap and
+    calltypes.
+    """
+    from numba.core import typed_passes
+    # mk_func can be actual function or make_function node, or a njit function
+    if hasattr(mk_func, 'code'):
+        code = mk_func.code
+    elif hasattr(mk_func, '__code__'):
+        code = mk_func.__code__
+    else:
+        raise NotImplementedError("function type not recognized {}".format(mk_func))
+    f_ir = get_ir_of_code(glbls, code)
+    remove_dels(f_ir.blocks)
+
+    # relabel by adding an offset
+    f_ir.blocks = add_offset_to_labels(f_ir.blocks, _the_max_label.next())
+    max_label = max(f_ir.blocks.keys())
+    _the_max_label.update(max_label)
+
+    # rename all variables to avoid conflict
+    var_table = get_name_var_table(f_ir.blocks)
+    new_var_dict = {}
+    for name, var in var_table.items():
+        new_var_dict[name] = mk_unique_var(name)
+    replace_var_names(f_ir.blocks, new_var_dict)
+
+    # perform type inference if typingctx is available and update type
+    # data structures typemap and calltypes
+    if typingctx:
+        f_typemap, f_return_type, f_calltypes, _ = typed_passes.type_inference_stage(
+                typingctx, targetctx, f_ir, arg_typs, None)
+        # remove argument entries like arg.a from typemap
+        arg_names = [vname for vname in f_typemap if vname.startswith("arg.")]
+        for a in arg_names:
+            f_typemap.pop(a)
+        typemap.update(f_typemap)
+        calltypes.update(f_calltypes)
+    return f_ir
+
+def _create_function_from_code_obj(fcode, func_env, func_arg, func_clo, glbls):
+    """
+    Creates a function from a code object. Args:
+    * fcode - the code object
+    * func_env - string for the freevar placeholders
+    * func_arg - string for the function args (e.g. "a, b, c, d=None")
+    * func_clo - string for the closure args
+    * glbls - the function globals
+    """
+    sanitized_co_name = fcode.co_name.replace('<', '_').replace('>', '_')
+    func_text = (f"def closure():\n{func_env}\n"
+                 f"\tdef {sanitized_co_name}({func_arg}):\n"
+                 f"\t\treturn ({func_clo})\n"
+                 f"\treturn {sanitized_co_name}")
+    loc = {}
+    exec(func_text, glbls, loc)
+
+    f = loc['closure']()
+    # replace the code body
+    f.__code__ = fcode
+    f.__name__ = fcode.co_name
+    return f
+
+def get_ir_of_code(glbls, fcode):
+    """
+    Compile a code object to get its IR, ir.Del nodes are emitted
+    """
+    nfree = len(fcode.co_freevars)
+    func_env = "\n".join(["\tc_%d = None" % i for i in range(nfree)])
+    func_clo = ",".join(["c_%d" % i for i in range(nfree)])
+    func_arg = ",".join(["x_%d" % i for i in range(fcode.co_argcount)])
+
+    f = _create_function_from_code_obj(fcode, func_env, func_arg, func_clo,
+                                       glbls)
+
+    from numba.core import compiler
+    ir = compiler.run_frontend(f)
+    # we need to run the before inference rewrite pass to normalize the IR
+    # XXX: check rewrite pass flag?
+    # for example, Raise nodes need to become StaticRaise before type inference
+    class DummyPipeline(object):
+        def __init__(self, f_ir):
+            self.state = compiler.StateDict()
+            self.state.typingctx = None
+            self.state.targetctx = None
+            self.state.args = None
+            self.state.func_ir = f_ir
+            self.state.typemap = None
+            self.state.return_type = None
+            self.state.calltypes = None
+    state = DummyPipeline(ir).state
+    rewrites.rewrite_registry.apply('before-inference', state)
+    # call inline pass to handle cases like stencils and comprehensions
+    swapped = {} # TODO: get this from diagnostics store
+    import numba.core.inline_closurecall
+    inline_pass = numba.core.inline_closurecall.InlineClosureCallPass(
+        ir, numba.core.cpu.ParallelOptions(False), swapped)
+    inline_pass.run()
+
+    # TODO: DO NOT ADD MORE THINGS HERE!
+    # If adding more things here is being contemplated, it really is time to
+    # retire this function and work on getting the InlineWorker class from
+    # numba.core.inline_closurecall into sufficient shape as a replacement.
+    # The issue with `get_ir_of_code` is that it doesn't run a full compilation
+    # pipeline and as a result various additional things keep needing to be
+    # added to create valid IR.
+
+    # rebuild IR in SSA form
+    from numba.core.untyped_passes import ReconstructSSA
+    from numba.core.typed_passes import PreLowerStripPhis
+    reconstruct_ssa = ReconstructSSA()
+    phistrip = PreLowerStripPhis()
+    reconstruct_ssa.run_pass(state)
+    phistrip.run_pass(state)
+
+    post_proc = postproc.PostProcessor(ir)
+    post_proc.run(True)
+    return ir
+
+def replace_arg_nodes(block, args):
+    """
+    Replace ir.Arg(...) with variables
+    """
+    for stmt in block.body:
+        if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Arg):
+            idx = stmt.value.index
+            assert(idx < len(args))
+            stmt.value = args[idx]
+    return
+
+
+def replace_returns(blocks, target, return_label):
+    """
+    Return return statement by assigning directly to target, and a jump.
+    """
+    for block in blocks.values():
+        # some blocks may be empty during transformations
+        if not block.body:
+            continue
+        stmt = block.terminator
+        if isinstance(stmt, ir.Return):
+            block.body.pop()  # remove return
+            cast_stmt = block.body.pop()
+            assert (isinstance(cast_stmt, ir.Assign)
+                and isinstance(cast_stmt.value, ir.Expr)
+                and cast_stmt.value.op == 'cast'), "invalid return cast"
+            block.body.append(ir.Assign(cast_stmt.value.value, target, stmt.loc))
+            block.body.append(ir.Jump(return_label, stmt.loc))
+
+
+def gen_np_call(func_as_str, func, lhs, args, typingctx, typemap, calltypes):
+    scope = args[0].scope
+    loc = args[0].loc
+
+    # g_np_var = Global(numpy)
+    g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc)
+    typemap[g_np_var.name] = types.misc.Module(numpy)
+    g_np = ir.Global('np', numpy, loc)
+    g_np_assign = ir.Assign(g_np, g_np_var, loc)
+    # attr call: <something>_attr = getattr(g_np_var, func_as_str)
+    np_attr_call = ir.Expr.getattr(g_np_var, func_as_str, loc)
+    attr_var = ir.Var(scope, mk_unique_var("$np_attr_attr"), loc)
+    func_var_typ = get_np_ufunc_typ(func)
+    typemap[attr_var.name] = func_var_typ
+    attr_assign = ir.Assign(np_attr_call, attr_var, loc)
+    # np call: lhs = np_attr(*args)
+    np_call = ir.Expr.call(attr_var, args, (), loc)
+    arg_types = [typemap[x.name] for x in args]
+    func_typ = func_var_typ.get_call_type(typingctx, arg_types, {})
+    calltypes[np_call] = func_typ
+    np_assign = ir.Assign(np_call, lhs, loc)
+    return [g_np_assign, attr_assign, np_assign]
+
+def dump_blocks(blocks):
+    for label, block in blocks.items():
+        print(label, ":")
+        for stmt in block.body:
+            print("    ", stmt)
+
+def is_operator_or_getitem(expr):
+    """true if expr is unary or binary operator or getitem"""
+    return (isinstance(expr, ir.Expr)
+            and getattr(expr, 'op', False)
+            and expr.op in ['unary', 'binop', 'inplace_binop', 'getitem', 'static_getitem'])
+
+def is_get_setitem(stmt):
+    """stmt is getitem assignment or setitem (and static cases)"""
+    return is_getitem(stmt) or is_setitem(stmt)
+
+
+def is_getitem(stmt):
+    """true if stmt is a getitem or static_getitem assignment"""
+    return (isinstance(stmt, ir.Assign)
+            and isinstance(stmt.value, ir.Expr)
+            and stmt.value.op in ['getitem', 'static_getitem'])
+
+def is_setitem(stmt):
+    """true if stmt is a SetItem or StaticSetItem node"""
+    return isinstance(stmt, (ir.SetItem, ir.StaticSetItem))
+
+def index_var_of_get_setitem(stmt):
+    """get index variable for getitem/setitem nodes (and static cases)"""
+    if is_getitem(stmt):
+        if stmt.value.op == 'getitem':
+            return stmt.value.index
+        else:
+            return stmt.value.index_var
+
+    if is_setitem(stmt):
+        if isinstance(stmt, ir.SetItem):
+            return stmt.index
+        else:
+            return stmt.index_var
+
+    return None
+
+def set_index_var_of_get_setitem(stmt, new_index):
+    if is_getitem(stmt):
+        if stmt.value.op == 'getitem':
+            stmt.value.index = new_index
+        else:
+            stmt.value.index_var = new_index
+    elif is_setitem(stmt):
+        if isinstance(stmt, ir.SetItem):
+            stmt.index = new_index
+        else:
+            stmt.index_var = new_index
+    else:
+        raise ValueError("getitem or setitem node expected but received {}".format(
+                     stmt))
+
+
+def is_namedtuple_class(c):
+    """check if c is a namedtuple class"""
+    if not isinstance(c, type):
+        return False
+    # should have only tuple as superclass
+    bases = c.__bases__
+    if len(bases) != 1 or bases[0] != tuple:
+        return False
+    # should have _make method
+    if not hasattr(c, '_make'):
+        return False
+    # should have _fields that is all string
+    fields = getattr(c, '_fields', None)
+    if not isinstance(fields, tuple):
+        return False
+    return all(isinstance(f, str) for f in fields)
+
+
+def fill_block_with_call(newblock, callee, label_next, inputs, outputs):
+    """Fill *newblock* to call *callee* with arguments listed in *inputs*.
+    The returned values are unwrapped into variables in *outputs*.
+    The block would then jump to *label_next*.
+    """
+    scope = newblock.scope
+    loc = newblock.loc
+
+    fn = ir.Const(value=callee, loc=loc)
+    fnvar = scope.make_temp(loc=loc)
+    newblock.append(ir.Assign(target=fnvar, value=fn, loc=loc))
+    # call
+    args = [scope.get_exact(name) for name in inputs]
+    callexpr = ir.Expr.call(func=fnvar, args=args, kws=(), loc=loc)
+    callres = scope.make_temp(loc=loc)
+    newblock.append(ir.Assign(target=callres, value=callexpr, loc=loc))
+    # unpack return value
+    for i, out in enumerate(outputs):
+        target = scope.get_exact(out)
+        getitem = ir.Expr.static_getitem(value=callres, index=i,
+                                         index_var=None, loc=loc)
+        newblock.append(ir.Assign(target=target, value=getitem, loc=loc))
+    # jump to next block
+    newblock.append(ir.Jump(target=label_next, loc=loc))
+    return newblock
+
+
+def fill_callee_prologue(block, inputs, label_next):
+    """
+    Fill a new block *block* that unwraps arguments using names in *inputs* and
+    then jumps to *label_next*.
+
+    Expected to use with *fill_block_with_call()*
+    """
+    scope = block.scope
+    loc = block.loc
+    # load args
+    args = [ir.Arg(name=k, index=i, loc=loc)
+            for i, k in enumerate(inputs)]
+    for aname, aval in zip(inputs, args):
+        tmp = ir.Var(scope=scope, name=aname, loc=loc)
+        block.append(ir.Assign(target=tmp, value=aval, loc=loc))
+    # jump to loop entry
+    block.append(ir.Jump(target=label_next, loc=loc))
+    return block
+
+
+def fill_callee_epilogue(block, outputs):
+    """
+    Fill a new block *block* to prepare the return values.
+    This block is the last block of the function.
+
+    Expected to use with *fill_block_with_call()*
+    """
+    scope = block.scope
+    loc = block.loc
+    # prepare tuples to return
+    vals = [scope.get_exact(name=name) for name in outputs]
+    tupexpr = ir.Expr.build_tuple(items=vals, loc=loc)
+    tup = scope.make_temp(loc=loc)
+    block.append(ir.Assign(target=tup, value=tupexpr, loc=loc))
+    # return
+    block.append(ir.Return(value=tup, loc=loc))
+    return block
+
+
+def find_outer_value(func_ir, var):
+    """Check if a variable is a global value, and return the value,
+    or raise GuardException otherwise.
+    """
+    dfn = get_definition(func_ir, var)
+    if isinstance(dfn, (ir.Global, ir.FreeVar)):
+        return dfn.value
+
+    if isinstance(dfn, ir.Expr) and dfn.op == 'getattr':
+        prev_val = find_outer_value(func_ir, dfn.value)
+        try:
+            val = getattr(prev_val, dfn.attr)
+            return val
+        except AttributeError:
+            raise GuardException
+
+    raise GuardException
+
+
+def raise_on_unsupported_feature(func_ir, typemap):
+    """
+    Helper function to walk IR and raise if it finds op codes
+    that are unsupported. Could be extended to cover IR sequences
+    as well as op codes. Intended use is to call it as a pipeline
+    stage just prior to lowering to prevent LoweringErrors for known
+    unsupported features.
+    """
+    gdb_calls = [] # accumulate calls to gdb/gdb_init
+
+    # issue 2195: check for excessively large tuples
+    for arg_name in func_ir.arg_names:
+        if arg_name in typemap and \
+           isinstance(typemap[arg_name], types.containers.UniTuple) and \
+           typemap[arg_name].count > 1000:
+            # Raise an exception when len(tuple) > 1000. The choice of this number (1000)
+            # was entirely arbitrary
+            msg = ("Tuple '{}' length must be smaller than 1000.\n"
+                   "Large tuples lead to the generation of a prohibitively large "
+                   "LLVM IR which causes excessive memory pressure "
+                   "and large compile times.\n"
+                   "As an alternative, the use of a 'list' is recommended in "
+                   "place of a 'tuple' as lists do not suffer from this problem.".format(arg_name))
+            raise UnsupportedError(msg, func_ir.loc)
+
+    for blk in func_ir.blocks.values():
+        for stmt in blk.find_insts(ir.Assign):
+            # This raises on finding `make_function`
+            if isinstance(stmt.value, ir.Expr):
+                if stmt.value.op == 'make_function':
+                    val = stmt.value
+
+                    # See if the construct name can be refined
+                    code = getattr(val, 'code', None)
+                    if code is not None:
+                        # check if this is a closure, the co_name will
+                        # be the captured function name which is not
+                        # useful so be explicit
+                        if getattr(val, 'closure', None) is not None:
+                            use = '<creating a function from a closure>'
+                            expr = ''
+                        else:
+                            use = code.co_name
+                            expr = '(%s) ' % use
+                    else:
+                        use = '<could not ascertain use case>'
+                        expr = ''
+
+                    msg = ("Numba encountered the use of a language "
+                            "feature it does not support in this context: "
+                            "%s (op code: make_function not supported). If "
+                            "the feature is explicitly supported it is "
+                            "likely that the result of the expression %s"
+                            "is being used in an unsupported manner.") % \
+                            (use, expr)
+                    raise UnsupportedError(msg, stmt.value.loc)
+
+            # this checks for gdb initialization calls, only one is permitted
+            if isinstance(stmt.value, (ir.Global, ir.FreeVar)):
+                val = stmt.value
+                val = getattr(val, 'value', None)
+                if val is None:
+                    continue
+
+                # check global function
+                found = False
+                if isinstance(val, pytypes.FunctionType):
+                    found = val in {numba.gdb, numba.gdb_init}
+                if not found: # freevar bind to intrinsic
+                    found = getattr(val, '_name', "") == "gdb_internal"
+                if found:
+                    gdb_calls.append(stmt.loc) # report last seen location
+
+            # this checks that np.<type> was called if view is called
+            if isinstance(stmt.value, ir.Expr):
+                if stmt.value.op == 'getattr' and stmt.value.attr == 'view':
+                    var = stmt.value.value.name
+                    if isinstance(typemap[var], types.Array):
+                        continue
+                    df = func_ir.get_definition(var)
+                    cn = guard(find_callname, func_ir, df)
+                    if cn and cn[1] == 'numpy':
+                        ty = getattr(numpy, cn[0])
+                        if (numpy.issubdtype(ty, numpy.integer) or
+                                numpy.issubdtype(ty, numpy.floating)):
+                            continue
+
+                    vardescr = '' if var.startswith('$') else "'{}' ".format(var)
+                    raise TypingError(
+                        "'view' can only be called on NumPy dtypes, "
+                        "try wrapping the variable {}with 'np.<dtype>()'".
+                        format(vardescr), loc=stmt.loc)
+
+            # checks for globals that are also reflected
+            if isinstance(stmt.value, ir.Global):
+                ty = typemap[stmt.target.name]
+                msg = ("The use of a %s type, assigned to variable '%s' in "
+                       "globals, is not supported as globals are considered "
+                       "compile-time constants and there is no known way to "
+                       "compile a %s type as a constant.")
+                if (getattr(ty, 'reflected', False) or
+                    isinstance(ty, (types.DictType, types.ListType))):
+                    raise TypingError(msg % (ty, stmt.value.name, ty), loc=stmt.loc)
+
+            # checks for generator expressions (yield in use when func_ir has
+            # not been identified as a generator).
+            if isinstance(stmt.value, ir.Yield) and not func_ir.is_generator:
+                msg = "The use of generator expressions is unsupported."
+                raise UnsupportedError(msg, loc=stmt.loc)
+
+    # There is more than one call to function gdb/gdb_init
+    if len(gdb_calls) > 1:
+        msg = ("Calling either numba.gdb() or numba.gdb_init() more than once "
+               "in a function is unsupported (strange things happen!), use "
+               "numba.gdb_breakpoint() to create additional breakpoints "
+               "instead.\n\nRelevant documentation is available here:\n"
+               "https://numba.readthedocs.io/en/stable/user/troubleshoot.html"
+               "#using-numba-s-direct-gdb-bindings-in-nopython-mode\n\n"
+               "Conflicting calls found at:\n %s")
+        buf = '\n'.join([x.strformat() for x in gdb_calls])
+        raise UnsupportedError(msg % buf)
+
+
+def warn_deprecated(func_ir, typemap):
+    # first pass, just walk the type map
+    for name, ty in typemap.items():
+        # the Type Metaclass has a reflected member
+        if ty.reflected:
+            # if its an arg, report function call
+            if name.startswith('arg.'):
+                loc = func_ir.loc
+                arg = name.split('.')[1]
+                fname = func_ir.func_id.func_qualname
+                tyname = 'list' if isinstance(ty, types.List) else 'set'
+                url = ("https://numba.readthedocs.io/en/stable/reference/"
+                       "deprecation.html#deprecation-of-reflection-for-list-and"
+                       "-set-types")
+                msg = ("\nEncountered the use of a type that is scheduled for "
+                        "deprecation: type 'reflected %s' found for argument "
+                        "'%s' of function '%s'.\n\nFor more information visit "
+                        "%s" % (tyname, arg, fname, url))
+                warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))
+
+
+def resolve_func_from_module(func_ir, node):
+    """
+    This returns the python function that is being getattr'd from a module in
+    some IR, it resolves import chains/submodules recursively. Should it not be
+    possible to find the python function being called None will be returned.
+
+    func_ir - the FunctionIR object
+    node - the IR node from which to start resolving (should be a `getattr`).
+    """
+    getattr_chain = []
+    def resolve_mod(mod):
+        if getattr(mod, 'op', False) == 'getattr':
+            getattr_chain.insert(0, mod.attr)
+            try:
+                mod = func_ir.get_definition(mod.value)
+            except KeyError: # multiple definitions
+                return None
+            return resolve_mod(mod)
+        elif isinstance(mod, (ir.Global, ir.FreeVar)):
+            if isinstance(mod.value, pytypes.ModuleType):
+                return mod
+        return None
+
+    mod = resolve_mod(node)
+    if mod is not None:
+        defn = mod.value
+        for x in getattr_chain:
+            defn = getattr(defn, x, False)
+            if not defn:
+                break
+        else:
+            return defn
+    else:
+        return None
+
+
+def enforce_no_dels(func_ir):
+    """
+    Enforce there being no ir.Del nodes in the IR.
+    """
+    for blk in func_ir.blocks.values():
+        dels = [x for x in blk.find_insts(ir.Del)]
+        if dels:
+            msg = "Illegal IR, del found at: %s" % dels[0]
+            raise CompilerError(msg, loc=dels[0].loc)
+
+def enforce_no_phis(func_ir):
+    """
+    Enforce there being no ir.Expr.phi nodes in the IR.
+    """
+    for blk in func_ir.blocks.values():
+        phis = [x for x in blk.find_exprs(op='phi')]
+        if phis:
+            msg = "Illegal IR, phi found at: %s" % phis[0]
+            raise CompilerError(msg, loc=phis[0].loc)
+
+
+def legalize_single_scope(blocks):
+    """Check the given mapping of ir.Block for containing a single scope.
+    """
+    return len({blk.scope for blk in blocks.values()}) == 1
+
+
+def check_and_legalize_ir(func_ir, flags: "numba.core.compiler.Flags"):
+    """
+    This checks that the IR presented is legal
+    """
+    enforce_no_phis(func_ir)
+    enforce_no_dels(func_ir)
+    # postprocess and emit ir.Dels
+    post_proc = postproc.PostProcessor(func_ir)
+    post_proc.run(True, extend_lifetimes=flags.dbg_extend_lifetimes)
+
+def convert_code_obj_to_function(code_obj, caller_ir):
+    """
+    Converts a code object from a `make_function.code` attr in the IR into a
+    python function, caller_ir is the FunctionIR of the caller and is used for
+    the resolution of freevars.
+    """
+    fcode = code_obj.code
+    nfree = len(fcode.co_freevars)
+
+    # try and resolve freevars if they are consts in the caller's IR
+    # these can be baked into the new function
+    freevars = []
+    for x in fcode.co_freevars:
+        # not using guard here to differentiate between multiple definition and
+        # non-const variable
+        try:
+            freevar_def = caller_ir.get_definition(x)
+        except KeyError:
+            msg = ("Cannot capture a constant value for variable '%s' as there "
+                   "are multiple definitions present." % x)
+            raise TypingError(msg, loc=code_obj.loc)
+        if isinstance(freevar_def, ir.Const):
+            freevars.append(freevar_def.value)
+        else:
+            msg = ("Cannot capture the non-constant value associated with "
+                   "variable '%s' in a function that will escape." % x)
+            raise TypingError(msg, loc=code_obj.loc)
+
+    func_env = "\n".join(["\tc_%d = %s" % (i, x) for i, x in enumerate(freevars)])
+    func_clo = ",".join(["c_%d" % i for i in range(nfree)])
+    co_varnames = list(fcode.co_varnames)
+
+    # This is horrible. The code object knows about the number of args present
+    # it also knows the name of the args but these are bundled in with other
+    # vars in `co_varnames`. The make_function IR node knows what the defaults
+    # are, they are defined in the IR as consts. The following finds the total
+    # number of args (args + kwargs with defaults), finds the default values
+    # and infers the number of "kwargs with defaults" from this and then infers
+    # the number of actual arguments from that.
+    n_kwargs = 0
+    n_allargs = fcode.co_argcount
+    kwarg_defaults = caller_ir.get_definition(code_obj.defaults)
+    if kwarg_defaults is not None:
+        if isinstance(kwarg_defaults, tuple):
+            d = [caller_ir.get_definition(x).value for x in kwarg_defaults]
+            kwarg_defaults_tup = tuple(d)
+        else:
+            d = [caller_ir.get_definition(x).value
+                 for x in kwarg_defaults.items]
+            kwarg_defaults_tup = tuple(d)
+        n_kwargs = len(kwarg_defaults_tup)
+    nargs = n_allargs - n_kwargs
+
+    func_arg = ",".join(["%s" % (co_varnames[i]) for i in range(nargs)])
+    if n_kwargs:
+        kw_const = ["%s = %s" % (co_varnames[i + nargs], kwarg_defaults_tup[i])
+                    for i in range(n_kwargs)]
+        func_arg += ", "
+        func_arg += ", ".join(kw_const)
+
+    # globals are the same as those in the caller
+    glbls = caller_ir.func_id.func.__globals__
+
+    # create the function and return it
+    return _create_function_from_code_obj(fcode, func_env, func_arg, func_clo,
+                                          glbls)
+
+
+def fixup_var_define_in_scope(blocks):
+    """Fixes the mapping of ir.Block to ensure all referenced ir.Var are
+    defined in every scope used by the function. Such that looking up a variable
+    from any scope in this function will not fail.
+
+    Note: This is a workaround. Ideally, all the blocks should refer to the
+    same ir.Scope, but that property is not maintained by all the passes.
+    """
+    # Scan for all used variables
+    used_var = {}
+    for blk in blocks.values():
+        scope = blk.scope
+        for inst in blk.body:
+            for var in inst.list_vars():
+                used_var[var] = inst
+    # Note: not all blocks share a single scope even though they should.
+    # Ensure the scope of each block defines all used variables.
+    for blk in blocks.values():
+        scope = blk.scope
+        for var, inst in used_var.items():
+            # add this variable if it's not in scope
+            if var.name not in scope.localvars:
+                # Note: using a internal method to reuse the same
+                scope.localvars.define(var.name, var)
+
+
+def transfer_scope(block, scope):
+    """Transfer the ir.Block to use the given ir.Scope.
+    """
+    old_scope = block.scope
+    if old_scope is scope:
+        # bypass if the block is already using the given scope
+        return block
+    # Ensure variables are defined in the new scope
+    for var in old_scope.localvars._con.values():
+        if var.name not in scope.localvars:
+            scope.localvars.define(var.name, var)
+    # replace scope
+    block.scope = scope
+    return block
+
+
+def is_setup_with(stmt):
+    return isinstance(stmt, ir.EnterWith)
+
+
+def is_terminator(stmt):
+    return isinstance(stmt, ir.Terminator)
+
+
+def is_raise(stmt):
+    return isinstance(stmt, ir.Raise)
+
+
+def is_return(stmt):
+    return isinstance(stmt, ir.Return)
+
+
+def is_pop_block(stmt):
+    return isinstance(stmt, ir.PopBlock)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/itanium_mangler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/itanium_mangler.py
new file mode 100644
index 000000000..963ee2318
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/itanium_mangler.py
@@ -0,0 +1,205 @@
+"""
+Itanium CXX ABI Mangler
+
+Reference: http://mentorembedded.github.io/cxx-abi/abi.html
+
+The basics of the mangling scheme.
+
+We are hijacking the CXX mangling scheme for our use.  We map Python modules
+into CXX namespace.  A `module1.submodule2.foo` is mapped to
+`module1::submodule2::foo`.   For parameterized numba types, we treat them as
+templated types; for example, `array(int64, 1d, C)` becomes an
+`array<int64, 1, C>`.
+
+All mangled names are prefixed with "_Z".  It is followed by the name of the
+entity.  A name contains one or more identifiers.  Each identifier is encoded
+as "<num of char><name>".   If the name is namespaced and, therefore,
+has multiple identifiers, the entire name is encoded as "N<name>E".
+
+For functions, arguments types follow.  There are condensed encodings for basic
+built-in types; e.g. "i" for int, "f" for float.  For other types, the
+previously mentioned name encoding should be used.
+
+For templated types, the template parameters are encoded immediately after the
+name.  If it is namespaced, it should be within the 'N' 'E' marker.  Template
+parameters are encoded in "I<params>E", where each parameter is encoded using
+the mentioned name encoding scheme.  Template parameters can contain literal
+values like the '1' in the array type shown earlier.  There is special encoding
+scheme for them to avoid leading digits.
+"""
+
+
+import re
+
+from numba.core import types
+
+
+# According the scheme, valid characters for mangled names are [a-zA-Z0-9_].
+# We borrow the '_' as the escape character to encode invalid char into
+# '_xx' where 'xx' is the hex codepoint.
+_re_invalid_char = re.compile(r'[^a-z0-9_]', re.I)
+
+PREFIX = "_Z"
+
+# Numba types to mangled type code. These correspond with the codes listed in
+# https://itanium-cxx-abi.github.io/cxx-abi/abi.html#mangling-builtin
+N2CODE = {
+    types.void: 'v',
+    types.boolean: 'b',
+    types.uint8: 'h',
+    types.int8: 'a',
+    types.uint16: 't',
+    types.int16: 's',
+    types.uint32: 'j',
+    types.int32: 'i',
+    types.uint64: 'y',
+    types.int64: 'x',
+    types.float16: 'Dh',
+    types.float32: 'f',
+    types.float64: 'd'
+}
+
+
+def _escape_string(text):
+    """Escape the given string so that it only contains ASCII characters
+    of [a-zA-Z0-9_$].
+
+    The dollar symbol ($) and other invalid characters are escaped into
+    the string sequence of "$xx" where "xx" is the hex codepoint of the char.
+
+    Multibyte characters are encoded into utf8 and converted into the above
+    hex format.
+    """
+
+    def repl(m):
+        return ''.join(('_%02x' % ch)
+                       for ch in m.group(0).encode('utf8'))
+    ret = re.sub(_re_invalid_char, repl, text)
+    # Return str if we got a unicode (for py2)
+    if not isinstance(ret, str):
+        return ret.encode('ascii')
+    return ret
+
+
+def _fix_lead_digit(text):
+    """
+    Fix text with leading digit
+    """
+    if text and text[0].isdigit():
+        return '_' + text
+    else:
+        return text
+
+
+def _len_encoded(string):
+    """
+    Prefix string with digit indicating the length.
+    Add underscore if string is prefixed with digits.
+    """
+    string = _fix_lead_digit(string)
+    return '%u%s' % (len(string), string)
+
+
+def mangle_abi_tag(abi_tag: str) -> str:
+    return "B" + _len_encoded(_escape_string(abi_tag))
+
+
+def mangle_identifier(ident, template_params='', *, abi_tags=(), uid=None):
+    """
+    Mangle the identifier with optional template parameters and abi_tags.
+
+    Note:
+
+    This treats '.' as '::' in C++.
+    """
+    if uid is not None:
+        # Add uid to abi-tags
+        abi_tags = (f"v{uid}", *abi_tags)
+    parts = [_len_encoded(_escape_string(x)) for x in ident.split('.')]
+    enc_abi_tags = list(map(mangle_abi_tag, abi_tags))
+    extras = template_params + ''.join(enc_abi_tags)
+    if len(parts) > 1:
+        return 'N%s%sE' % (''.join(parts), extras)
+    else:
+        return '%s%s' % (parts[0], extras)
+
+
+def mangle_type_or_value(typ):
+    """
+    Mangle type parameter and arbitrary value.
+    """
+    # Handle numba types
+    if isinstance(typ, types.Type):
+        if typ in N2CODE:
+            return N2CODE[typ]
+        else:
+            return mangle_templated_ident(*typ.mangling_args)
+    # Handle integer literal
+    elif isinstance(typ, int):
+        return 'Li%dE' % typ
+    # Handle str as identifier
+    elif isinstance(typ, str):
+        return mangle_identifier(typ)
+    # Otherwise
+    else:
+        enc = _escape_string(str(typ))
+        return _len_encoded(enc)
+
+
+# Alias
+mangle_type = mangle_type_or_value
+mangle_value = mangle_type_or_value
+
+
+def mangle_templated_ident(identifier, parameters):
+    """
+    Mangle templated identifier.
+    """
+    template_params = ('I%sE' % ''.join(map(mangle_type_or_value, parameters))
+                       if parameters else '')
+    return mangle_identifier(identifier, template_params)
+
+
+def mangle_args(argtys):
+    """
+    Mangle sequence of Numba type objects and arbitrary values.
+    """
+    return ''.join([mangle_type_or_value(t) for t in argtys])
+
+
+def mangle(ident, argtys, *, abi_tags=(), uid=None):
+    """
+    Mangle identifier with Numba type objects and abi-tags.
+    """
+    return ''.join([PREFIX,
+                    mangle_identifier(ident, abi_tags=abi_tags, uid=uid),
+                    mangle_args(argtys)])
+
+
+def prepend_namespace(mangled, ns):
+    """
+    Prepend namespace to mangled name.
+    """
+    if not mangled.startswith(PREFIX):
+        raise ValueError('input is not a mangled name')
+    elif mangled.startswith(PREFIX + 'N'):
+        # nested
+        remaining = mangled[3:]
+        ret = PREFIX + 'N' + mangle_identifier(ns) + remaining
+    else:
+        # non-nested
+        remaining = mangled[2:]
+        head, tail = _split_mangled_ident(remaining)
+        ret = PREFIX + 'N' + mangle_identifier(ns) + head + 'E' + tail
+    return ret
+
+
+def _split_mangled_ident(mangled):
+    """
+    Returns `(head, tail)` where `head` is the `<len> + <name>` encoded
+    identifier and `tail` is the remaining.
+    """
+    ct = int(mangled)
+    ctlen = len(str(ct))
+    at = ctlen + ct
+    return mangled[:at], mangled[at:]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/llvm_bindings.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/llvm_bindings.py
new file mode 100644
index 000000000..fa94ad428
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/llvm_bindings.py
@@ -0,0 +1,46 @@
+"""
+Useful options to debug LLVM passes
+
+llvm.set_option("test", "-debug-pass=Details")
+llvm.set_option("test", "-debug-pass=Executions")
+llvm.set_option("test", "-debug-pass=Arguments")
+llvm.set_option("test", "-debug-pass=Structure")
+llvm.set_option("test", "-debug-only=loop-vectorize")
+llvm.set_option("test", "-help-hidden")
+
+"""
+
+from llvmlite import binding as llvm
+
+
+def _inlining_threshold(optlevel, sizelevel=0):
+    """
+    Compute the inlining threshold for the desired optimisation level
+
+    Refer to http://llvm.org/docs/doxygen/html/InlineSimple_8cpp_source.html
+    """
+    if optlevel > 2:
+        return 275
+
+    # -Os
+    if sizelevel == 1:
+        return 75
+
+    # -Oz
+    if sizelevel == 2:
+        return 25
+
+    return 225
+
+
+def create_pass_manager_builder(opt=2, loop_vectorize=False,
+                                slp_vectorize=False):
+    """
+    Create an LLVM pass manager with the desired optimisation level and options.
+    """
+    pmb = llvm.create_pass_manager_builder()
+    pmb.opt_level = opt
+    pmb.loop_vectorize = loop_vectorize
+    pmb.slp_vectorize = slp_vectorize
+    pmb.inlining_threshold = _inlining_threshold(opt)
+    return pmb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/lowering.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/lowering.py
new file mode 100644
index 000000000..84ce3514c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/lowering.py
@@ -0,0 +1,1536 @@
+from collections import namedtuple, defaultdict
+import operator
+import warnings
+from functools import partial
+
+import llvmlite.ir
+from llvmlite.ir import Constant, IRBuilder
+
+from numba.core import (typing, utils, types, ir, debuginfo, funcdesc,
+                        generators, config, ir_utils, cgutils, removerefctpass,
+                        targetconfig)
+from numba.core.errors import (LoweringError, new_error_context, TypingError,
+                               LiteralTypingError, UnsupportedError,
+                               NumbaDebugInfoWarning)
+from numba.core.funcdesc import default_mangler
+from numba.core.environment import Environment
+from numba.core.analysis import compute_use_defs, must_use_alloca
+from numba.misc.firstlinefinder import get_func_body_first_lineno
+
+
+_VarArgItem = namedtuple("_VarArgItem", ("vararg", "index"))
+
+
+class BaseLower(object):
+    """
+    Lower IR to LLVM
+    """
+
+    def __init__(self, context, library, fndesc, func_ir, metadata=None):
+        self.library = library
+        self.fndesc = fndesc
+        self.blocks = utils.SortedMap(func_ir.blocks.items())
+        self.func_ir = func_ir
+        self.call_conv = context.call_conv
+        self.generator_info = func_ir.generator_info
+        self.metadata = metadata
+        self.flags = targetconfig.ConfigStack.top_or_none()
+
+        # Initialize LLVM
+        self.module = self.library.create_ir_module(self.fndesc.unique_name)
+
+        # Python execution environment (will be available to the compiled
+        # function).
+        self.env = Environment.from_fndesc(self.fndesc)
+
+        # Internal states
+        self.blkmap = {}
+        self.pending_phis = {}
+        self.varmap = {}
+        self.firstblk = min(self.blocks.keys())
+        self.loc = -1
+
+        # Specializes the target context as seen inside the Lowerer
+        # This adds:
+        #  - environment: the python execution environment
+        self.context = context.subtarget(environment=self.env,
+                                         fndesc=self.fndesc)
+
+        # Debuginfo
+        dibuildercls = (self.context.DIBuilder
+                        if self.context.enable_debuginfo
+                        else debuginfo.DummyDIBuilder)
+
+        # debuginfo def location
+        self.defn_loc = self._compute_def_location()
+
+        self.debuginfo = dibuildercls(module=self.module,
+                                      filepath=func_ir.loc.filename,
+                                      cgctx=context)
+
+        # Subclass initialization
+        self.init()
+
+    def init(self):
+        pass
+
+    def init_pyapi(self):
+        """
+        Init the Python API and Environment Manager for the function being
+        lowered.
+        """
+        if self.pyapi is not None:
+            return
+        self.pyapi = self.context.get_python_api(self.builder)
+
+        # Store environment argument for later use
+        self.env_manager = self.context.get_env_manager(self.builder)
+        self.env_body = self.env_manager.env_body
+        self.envarg = self.env_manager.env_ptr
+
+    def _compute_def_location(self):
+        # Debuginfo requires source to be accurate. Find it and warn if not
+        # found. If it's not found, use the func_ir line + 1, this assumes that
+        # the function definition is decorated with a 1 line jit decorator.
+        defn_loc = self.func_ir.loc.with_lineno(self.func_ir.loc.line + 1)
+        if self.context.enable_debuginfo:
+            fn = self.func_ir.func_id.func
+            optional_lno = get_func_body_first_lineno(fn)
+            if optional_lno is not None:
+                # -1 as lines start at 1 and this is an offset.
+                offset = optional_lno - 1
+                defn_loc = self.func_ir.loc.with_lineno(offset)
+            else:
+                msg = ("Could not find source for function: "
+                       f"{self.func_ir.func_id.func}. Debug line information "
+                       "may be inaccurate.")
+                warnings.warn(NumbaDebugInfoWarning(msg))
+        return defn_loc
+
+    def pre_lower(self):
+        """
+        Called before lowering all blocks.
+        """
+        # A given Lower object can be used for several LL functions
+        # (for generators) and it's important to use a new API and
+        # EnvironmentManager.
+        self.pyapi = None
+        self.debuginfo.mark_subprogram(function=self.builder.function,
+                                       qualname=self.fndesc.qualname,
+                                       argnames=self.fndesc.args,
+                                       argtypes=self.fndesc.argtypes,
+                                       line=self.defn_loc.line)
+
+    def post_lower(self):
+        """
+        Called after all blocks are lowered
+        """
+        self.debuginfo.finalize()
+
+    def pre_block(self, block):
+        """
+        Called before lowering a block.
+        """
+
+    def post_block(self, block):
+        """
+        Called after lowering a block.
+        """
+
+    def return_exception(self, exc_class, exc_args=None, loc=None):
+        """Propagate exception to the caller.
+        """
+        self.call_conv.return_user_exc(
+            self.builder, exc_class, exc_args,
+            loc=loc, func_name=self.func_ir.func_id.func_name,
+        )
+
+    def set_exception(self, exc_class, exc_args=None, loc=None):
+        """Set exception state in the current function.
+        """
+        self.call_conv.set_static_user_exc(
+            self.builder, exc_class, exc_args,
+            loc=loc, func_name=self.func_ir.func_id.func_name,
+        )
+
+    def emit_environment_object(self):
+        """Emit a pointer to hold the Environment object.
+        """
+        # Define global for the environment and initialize it to NULL
+        envname = self.context.get_env_name(self.fndesc)
+        self.context.declare_env_global(self.module, envname)
+
+    def lower(self):
+        # Emit the Env into the module
+        self.emit_environment_object()
+        if self.generator_info is None:
+            self.genlower = None
+            self.lower_normal_function(self.fndesc)
+        else:
+            self.genlower = self.GeneratorLower(self)
+            self.gentype = self.genlower.gentype
+
+            self.genlower.lower_init_func(self)
+            self.genlower.lower_next_func(self)
+            if self.gentype.has_finalizer:
+                self.genlower.lower_finalize_func(self)
+
+        if config.DUMP_LLVM:
+            print(("LLVM DUMP %s" % self.fndesc).center(80, '-'))
+            if config.HIGHLIGHT_DUMPS:
+                try:
+                    from pygments import highlight
+                    from pygments.lexers import LlvmLexer as lexer
+                    from pygments.formatters import Terminal256Formatter
+                    from numba.misc.dump_style import by_colorscheme
+                    print(highlight(self.module.__repr__(), lexer(),
+                                    Terminal256Formatter(
+                                        style=by_colorscheme())))
+                except ImportError:
+                    msg = "Please install pygments to see highlighted dumps"
+                    raise ValueError(msg)
+            else:
+                print(self.module)
+            print('=' * 80)
+
+        # Special optimization to remove NRT on functions that do not need it.
+        if self.context.enable_nrt and self.generator_info is None:
+            removerefctpass.remove_unnecessary_nrt_usage(self.function,
+                                                         context=self.context,
+                                                         fndesc=self.fndesc)
+
+        # Run target specific post lowering transformation
+        self.context.post_lowering(self.module, self.library)
+
+        # Materialize LLVM Module
+        self.library.add_ir_module(self.module)
+
+    def extract_function_arguments(self):
+        self.fnargs = self.call_conv.decode_arguments(self.builder,
+                                                      self.fndesc.argtypes,
+                                                      self.function)
+        return self.fnargs
+
+    def lower_normal_function(self, fndesc):
+        """
+        Lower non-generator *fndesc*.
+        """
+        self.setup_function(fndesc)
+
+        # Init argument values
+        self.extract_function_arguments()
+        entry_block_tail = self.lower_function_body()
+
+        # Close tail of entry block, do not emit debug metadata else the
+        # unconditional jump gets associated with the metadata from the function
+        # body end.
+        with debuginfo.suspend_emission(self.builder):
+            self.builder.position_at_end(entry_block_tail)
+            self.builder.branch(self.blkmap[self.firstblk])
+
+    def lower_function_body(self):
+        """
+        Lower the current function's body, and return the entry block.
+        """
+        # Init Python blocks
+        for offset in self.blocks:
+            bname = "B%s" % offset
+            self.blkmap[offset] = self.function.append_basic_block(bname)
+
+        self.pre_lower()
+        # pre_lower() may have changed the current basic block
+        entry_block_tail = self.builder.basic_block
+
+        self.debug_print("# function begin: {0}".format(
+            self.fndesc.unique_name))
+
+        # Lower all blocks
+        for offset, block in sorted(self.blocks.items()):
+            bb = self.blkmap[offset]
+            self.builder.position_at_end(bb)
+            self.lower_block(block)
+        self.post_lower()
+        return entry_block_tail
+
+    def lower_block(self, block):
+        """
+        Lower the given block.
+        """
+        self.pre_block(block)
+        for inst in block.body:
+            self.loc = inst.loc
+            defaulterrcls = partial(LoweringError, loc=self.loc)
+            with new_error_context('lowering "{inst}" at {loc}', inst=inst,
+                                   loc=self.loc, errcls_=defaulterrcls):
+                self.lower_inst(inst)
+        self.post_block(block)
+
+    def create_cpython_wrapper(self, release_gil=False):
+        """
+        Create CPython wrapper(s) around this function (or generator).
+        """
+        if self.genlower:
+            self.context.create_cpython_wrapper(self.library,
+                                                self.genlower.gendesc,
+                                                self.env, self.call_helper,
+                                                release_gil=release_gil)
+        self.context.create_cpython_wrapper(self.library, self.fndesc,
+                                            self.env, self.call_helper,
+                                            release_gil=release_gil)
+
+    def create_cfunc_wrapper(self):
+        """
+        Create C wrapper around this function.
+        """
+        if self.genlower:
+            raise UnsupportedError('generator as a first-class function type')
+        self.context.create_cfunc_wrapper(self.library, self.fndesc,
+                                          self.env, self.call_helper)
+
+    def setup_function(self, fndesc):
+        # Setup function
+        self.function = self.context.declare_function(self.module, fndesc)
+        if self.flags.dbg_optnone:
+            attrset = self.function.attributes
+            if "alwaysinline" not in attrset:
+                attrset.add("optnone")
+                attrset.add("noinline")
+        self.entry_block = self.function.append_basic_block('entry')
+        self.builder = IRBuilder(self.entry_block)
+        self.call_helper = self.call_conv.init_call_helper(self.builder)
+
+    def typeof(self, varname):
+        return self.fndesc.typemap[varname]
+
+    def debug_print(self, msg):
+        if config.DEBUG_JIT:
+            self.context.debug_print(self.builder, "DEBUGJIT: {0}".format(msg))
+
+    def print_variable(self, msg, varname):
+        """Helper to emit ``print(msg, varname)`` for debugging.
+
+        Parameters
+        ----------
+        msg : str
+            Literal string to be printed.
+        varname : str
+            A variable name whose value will be printed.
+        """
+        argtys = (
+            types.literal(msg),
+            self.fndesc.typemap[varname]
+        )
+        args = (
+            self.context.get_dummy_value(),
+            self.loadvar(varname),
+        )
+        sig = typing.signature(types.none, *argtys)
+
+        impl = self.context.get_function(print, sig)
+        impl(self.builder, args)
+
+
+class Lower(BaseLower):
+    GeneratorLower = generators.GeneratorLower
+
+    def init(self):
+        super().init()
+        # find all singly assigned variables
+        self._find_singly_assigned_variable()
+
+    @property
+    def _disable_sroa_like_opt(self):
+        """Flags that the SROA like optimisation that Numba performs (which
+        prevent alloca and subsequent load/store for locals) should be disabled.
+        Currently, this is conditional solely on the presence of a request for
+        the emission of debug information."""
+        return False if self.flags is None else self.flags.debuginfo
+
+    def _find_singly_assigned_variable(self):
+        func_ir = self.func_ir
+        blocks = func_ir.blocks
+
+        sav = set()
+
+        if not self.func_ir.func_id.is_generator:
+            use_defs = compute_use_defs(blocks)
+            alloca_vars = must_use_alloca(blocks)
+
+            # Compute where variables are defined
+            var_assign_map = defaultdict(set)
+            for blk, vl in use_defs.defmap.items():
+                for var in vl:
+                    var_assign_map[var].add(blk)
+
+            # Compute where variables are used
+            var_use_map = defaultdict(set)
+            for blk, vl in use_defs.usemap.items():
+                for var in vl:
+                    var_use_map[var].add(blk)
+
+            # Keep only variables that are defined locally and used locally
+            for var in var_assign_map:
+                if var not in alloca_vars and len(var_assign_map[var]) == 1:
+                    # Usemap does not keep locally defined variables.
+                    if len(var_use_map[var]) == 0:
+                        # Ensure that the variable is not defined multiple times
+                        # in the block
+                        [defblk] = var_assign_map[var]
+                        assign_stmts = self.blocks[defblk].find_insts(ir.Assign)
+                        assigns = [stmt for stmt in assign_stmts
+                                   if stmt.target.name == var]
+                        if len(assigns) == 1:
+                            sav.add(var)
+
+        self._singly_assigned_vars = sav
+        self._blk_local_varmap = {}
+
+    def pre_block(self, block):
+        from numba.core.unsafe import eh
+
+        super(Lower, self).pre_block(block)
+        self._cur_ir_block = block
+
+        if block == self.firstblk:
+            # create slots for all the vars, irrespective of whether they are
+            # initialized, SSA will pick this up and warn users about using
+            # uninitialized variables. Slots are added as alloca in the first
+            # block
+            bb = self.blkmap[self.firstblk]
+            self.builder.position_at_end(bb)
+            all_names = set()
+            for block in self.blocks.values():
+                for x in block.find_insts(ir.Del):
+                    if x.value not in all_names:
+                        all_names.add(x.value)
+            for name in all_names:
+                fetype = self.typeof(name)
+                self._alloca_var(name, fetype)
+
+        # Detect if we are in a TRY block by looking for a call to
+        # `eh.exception_check`.
+        for call in block.find_exprs(op='call'):
+            defn = ir_utils.guard(
+                ir_utils.get_definition, self.func_ir, call.func,
+            )
+            if defn is not None and isinstance(defn, ir.Global):
+                if defn.value is eh.exception_check:
+                    if isinstance(block.terminator, ir.Branch):
+                        targetblk = self.blkmap[block.terminator.truebr]
+                        # NOTE: This hacks in an attribute for call_conv to
+                        #       pick up. This hack is no longer needed when
+                        #       all old-style implementations are gone.
+                        self.builder._in_try_block = {'target': targetblk}
+                        break
+
+    def post_block(self, block):
+        # Clean-up
+        try:
+            del self.builder._in_try_block
+        except AttributeError:
+            pass
+
+    def lower_inst(self, inst):
+        # Set debug location for all subsequent LL instructions
+        self.debuginfo.mark_location(self.builder, self.loc.line)
+        self.debug_print(str(inst))
+        if isinstance(inst, ir.Assign):
+            ty = self.typeof(inst.target.name)
+            val = self.lower_assign(ty, inst)
+            argidx = None
+            # If this is a store from an arg, like x = arg.x then tell debuginfo
+            # that this is the arg
+            if isinstance(inst.value, ir.Arg):
+                # NOTE: debug location is the `def <func>` line
+                self.debuginfo.mark_location(self.builder, self.defn_loc.line)
+                argidx = inst.value.index + 1 # args start at 1
+            self.storevar(val, inst.target.name, argidx=argidx)
+
+        elif isinstance(inst, ir.Branch):
+            cond = self.loadvar(inst.cond.name)
+            tr = self.blkmap[inst.truebr]
+            fl = self.blkmap[inst.falsebr]
+
+            condty = self.typeof(inst.cond.name)
+            pred = self.context.cast(self.builder, cond, condty, types.boolean)
+            assert pred.type == llvmlite.ir.IntType(1),\
+                ("cond is not i1: %s" % pred.type)
+            self.builder.cbranch(pred, tr, fl)
+
+        elif isinstance(inst, ir.Jump):
+            target = self.blkmap[inst.target]
+            self.builder.branch(target)
+
+        elif isinstance(inst, ir.Return):
+            if self.generator_info:
+                # StopIteration
+                self.genlower.return_from_generator(self)
+                return
+            val = self.loadvar(inst.value.name)
+            oty = self.typeof(inst.value.name)
+            ty = self.fndesc.restype
+            if isinstance(ty, types.Optional):
+                # If returning an optional type
+                self.call_conv.return_optional_value(self.builder, ty, oty, val)
+                return
+            assert ty == oty, (
+                "type '{}' does not match return type '{}'".format(oty, ty))
+            retval = self.context.get_return_value(self.builder, ty, val)
+            self.call_conv.return_value(self.builder, retval)
+
+        elif isinstance(inst, ir.PopBlock):
+            pass # this is just a marker
+
+        elif isinstance(inst, ir.StaticSetItem):
+            signature = self.fndesc.calltypes[inst]
+            assert signature is not None
+            try:
+                impl = self.context.get_function('static_setitem', signature)
+            except NotImplementedError:
+                return self.lower_setitem(inst.target, inst.index_var,
+                                          inst.value, signature)
+            else:
+                target = self.loadvar(inst.target.name)
+                value = self.loadvar(inst.value.name)
+                valuety = self.typeof(inst.value.name)
+                value = self.context.cast(self.builder, value, valuety,
+                                          signature.args[2])
+                return impl(self.builder, (target, inst.index, value))
+
+        elif isinstance(inst, ir.Print):
+            self.lower_print(inst)
+
+        elif isinstance(inst, ir.SetItem):
+            signature = self.fndesc.calltypes[inst]
+            assert signature is not None
+            return self.lower_setitem(inst.target, inst.index, inst.value,
+                                      signature)
+
+        elif isinstance(inst, ir.StoreMap):
+            signature = self.fndesc.calltypes[inst]
+            assert signature is not None
+            return self.lower_setitem(inst.dct, inst.key, inst.value, signature)
+
+        elif isinstance(inst, ir.DelItem):
+            target = self.loadvar(inst.target.name)
+            index = self.loadvar(inst.index.name)
+
+            targetty = self.typeof(inst.target.name)
+            indexty = self.typeof(inst.index.name)
+
+            signature = self.fndesc.calltypes[inst]
+            assert signature is not None
+
+            op = operator.delitem
+            fnop = self.context.typing_context.resolve_value_type(op)
+            callsig = fnop.get_call_type(
+                self.context.typing_context, signature.args, {},
+            )
+            impl = self.context.get_function(fnop, callsig)
+
+            assert targetty == signature.args[0]
+            index = self.context.cast(self.builder, index, indexty,
+                                      signature.args[1])
+
+            return impl(self.builder, (target, index))
+
+        elif isinstance(inst, ir.Del):
+            self.delvar(inst.value)
+
+        elif isinstance(inst, ir.SetAttr):
+            target = self.loadvar(inst.target.name)
+            value = self.loadvar(inst.value.name)
+            signature = self.fndesc.calltypes[inst]
+
+            targetty = self.typeof(inst.target.name)
+            valuety = self.typeof(inst.value.name)
+            assert signature is not None
+            assert signature.args[0] == targetty
+            impl = self.context.get_setattr(inst.attr, signature)
+
+            # Convert argument to match
+            value = self.context.cast(self.builder, value, valuety,
+                                      signature.args[1])
+
+            return impl(self.builder, (target, value))
+
+        elif isinstance(inst, ir.StaticRaise):
+            self.lower_static_raise(inst)
+
+        elif isinstance(inst, ir.StaticTryRaise):
+            self.lower_static_try_raise(inst)
+
+        else:
+            if hasattr(self.context, "lower_extensions"):
+                for _class, func in self.context.lower_extensions.items():
+                    if isinstance(inst, _class):
+                        func(self, inst)
+                        return
+            raise NotImplementedError(type(inst))
+
+    def lower_setitem(self, target_var, index_var, value_var, signature):
+        target = self.loadvar(target_var.name)
+        value = self.loadvar(value_var.name)
+        index = self.loadvar(index_var.name)
+
+        targetty = self.typeof(target_var.name)
+        valuety = self.typeof(value_var.name)
+        indexty = self.typeof(index_var.name)
+
+        op = operator.setitem
+        fnop = self.context.typing_context.resolve_value_type(op)
+        callsig = fnop.get_call_type(
+            self.context.typing_context, signature.args, {},
+        )
+        impl = self.context.get_function(fnop, callsig)
+
+        # Convert argument to match
+        if isinstance(targetty, types.Optional):
+            target = self.context.cast(self.builder, target, targetty,
+                                       targetty.type)
+        else:
+            ul = types.unliteral
+            assert ul(targetty) == ul(signature.args[0])
+
+        index = self.context.cast(self.builder, index, indexty,
+                                  signature.args[1])
+        value = self.context.cast(self.builder, value, valuety,
+                                  signature.args[2])
+
+        return impl(self.builder, (target, index, value))
+
+    def lower_static_raise(self, inst):
+        if inst.exc_class is None:
+            # Reraise
+            self.return_exception(None, loc=self.loc)
+        else:
+            self.return_exception(inst.exc_class, inst.exc_args, loc=self.loc)
+
+    def lower_static_try_raise(self, inst):
+        if inst.exc_class is None:
+            # Reraise
+            self.set_exception(None, loc=self.loc)
+        else:
+            self.set_exception(inst.exc_class, inst.exc_args, loc=self.loc)
+
+    def lower_assign(self, ty, inst):
+        value = inst.value
+        # In nopython mode, closure vars are frozen like globals
+        if isinstance(value, (ir.Const, ir.Global, ir.FreeVar)):
+            res = self.context.get_constant_generic(self.builder, ty,
+                                                    value.value)
+            self.incref(ty, res)
+            return res
+
+        elif isinstance(value, ir.Expr):
+            return self.lower_expr(ty, value)
+
+        elif isinstance(value, ir.Var):
+            val = self.loadvar(value.name)
+            oty = self.typeof(value.name)
+            res = self.context.cast(self.builder, val, oty, ty)
+            self.incref(ty, res)
+            return res
+
+        elif isinstance(value, ir.Arg):
+            # Suspend debug info else all the arg repacking ends up being
+            # associated with some line or other and it's actually just a detail
+            # of Numba's CC.
+            with debuginfo.suspend_emission(self.builder):
+                # Cast from the argument type to the local variable type
+                # (note the "arg.FOO" convention as used in typeinfer)
+                argty = self.typeof("arg." + value.name)
+                if isinstance(argty, types.Omitted):
+                    pyval = argty.value
+                    tyctx = self.context.typing_context
+                    valty = tyctx.resolve_value_type_prefer_literal(pyval)
+                    # use the type of the constant value
+                    const = self.context.get_constant_generic(
+                        self.builder, valty, pyval,
+                    )
+                    # cast it to the variable type
+                    res = self.context.cast(self.builder, const, valty, ty)
+                else:
+                    val = self.fnargs[value.index]
+                    res = self.context.cast(self.builder, val, argty, ty)
+                self.incref(ty, res)
+                return res
+
+        elif isinstance(value, ir.Yield):
+            res = self.lower_yield(ty, value)
+            self.incref(ty, res)
+            return res
+
+        raise NotImplementedError(type(value), value)
+
+    def lower_yield(self, retty, inst):
+        yp = self.generator_info.yield_points[inst.index]
+        assert yp.inst is inst
+        y = generators.LowerYield(self, yp, yp.live_vars)
+        y.lower_yield_suspend()
+        # Yield to caller
+        val = self.loadvar(inst.value.name)
+        typ = self.typeof(inst.value.name)
+        actual_rettyp = self.gentype.yield_type
+
+        # cast the local val to the type yielded
+        yret = self.context.cast(self.builder, val, typ, actual_rettyp)
+
+        # get the return repr of yielded value
+        retval = self.context.get_return_value(
+            self.builder, actual_rettyp, yret,
+        )
+
+        # return
+        self.call_conv.return_value(self.builder, retval)
+
+        # Resumption point
+        y.lower_yield_resume()
+        # None is returned by the yield expression
+        return self.context.get_constant_generic(self.builder, retty, None)
+
+    def lower_binop(self, resty, expr, op):
+        # if op in utils.OPERATORS_TO_BUILTINS:
+        # map operator.the_op => the corresponding types.Function()
+        # TODO: is this looks dodgy ...
+        op = self.context.typing_context.resolve_value_type(op)
+
+        lhs = expr.lhs
+        rhs = expr.rhs
+        static_lhs = expr.static_lhs
+        static_rhs = expr.static_rhs
+        lty = self.typeof(lhs.name)
+        rty = self.typeof(rhs.name)
+        lhs = self.loadvar(lhs.name)
+        rhs = self.loadvar(rhs.name)
+
+        # Convert argument to match
+        signature = self.fndesc.calltypes[expr]
+        lhs = self.context.cast(self.builder, lhs, lty, signature.args[0])
+        rhs = self.context.cast(self.builder, rhs, rty, signature.args[1])
+
+        def cast_result(res):
+            return self.context.cast(self.builder, res,
+                                     signature.return_type, resty)
+
+        # First try with static operands, if known
+        def try_static_impl(tys, args):
+            if any(a is ir.UNDEFINED for a in args):
+                return None
+            try:
+                if isinstance(op, types.Function):
+                    static_sig = op.get_call_type(self.context.typing_context,
+                                                  tys, {})
+                else:
+                    static_sig = typing.signature(signature.return_type, *tys)
+            except TypingError:
+                return None
+            try:
+                static_impl = self.context.get_function(op, static_sig)
+                return static_impl(self.builder, args)
+            except NotImplementedError:
+                return None
+
+        res = try_static_impl(
+            (_lit_or_omitted(static_lhs), _lit_or_omitted(static_rhs)),
+            (static_lhs, static_rhs),
+        )
+        if res is not None:
+            return cast_result(res)
+
+        res = try_static_impl(
+            (_lit_or_omitted(static_lhs), rty),
+            (static_lhs, rhs),
+        )
+        if res is not None:
+            return cast_result(res)
+
+        res = try_static_impl(
+            (lty, _lit_or_omitted(static_rhs)),
+            (lhs, static_rhs),
+        )
+        if res is not None:
+            return cast_result(res)
+
+        # Normal implementation for generic arguments
+
+        sig = op.get_call_type(self.context.typing_context, signature.args, {})
+        impl = self.context.get_function(op, sig)
+        res = impl(self.builder, (lhs, rhs))
+        return cast_result(res)
+
+    def lower_getitem(self, resty, expr, value, index, signature):
+        baseval = self.loadvar(value.name)
+        indexval = self.loadvar(index.name)
+        # Get implementation of getitem
+        op = operator.getitem
+        fnop = self.context.typing_context.resolve_value_type(op)
+        callsig = fnop.get_call_type(
+            self.context.typing_context, signature.args, {},
+        )
+        impl = self.context.get_function(fnop, callsig)
+
+        argvals = (baseval, indexval)
+        argtyps = (self.typeof(value.name),
+                   self.typeof(index.name))
+        castvals = [self.context.cast(self.builder, av, at, ft)
+                    for av, at, ft in zip(argvals, argtyps,
+                                          signature.args)]
+        res = impl(self.builder, castvals)
+        return self.context.cast(self.builder, res,
+                                 signature.return_type,
+                                 resty)
+
+    def _cast_var(self, var, ty):
+        """
+        Cast a Numba IR variable to the given Numba type, returning a
+        low-level value.
+        """
+        if isinstance(var, _VarArgItem):
+            varty = self.typeof(var.vararg.name)[var.index]
+            val = self.builder.extract_value(self.loadvar(var.vararg.name),
+                                             var.index)
+        else:
+            varty = self.typeof(var.name)
+            val = self.loadvar(var.name)
+        return self.context.cast(self.builder, val, varty, ty)
+
+    def fold_call_args(self, fnty, signature, pos_args, vararg, kw_args):
+        if vararg:
+            # Inject *args from function call
+            # The lowering will be done in _cast_var() above.
+            tp_vararg = self.typeof(vararg.name)
+            assert isinstance(tp_vararg, types.BaseTuple)
+            pos_args = pos_args + [_VarArgItem(vararg, i)
+                                   for i in range(len(tp_vararg))]
+
+        # Fold keyword arguments and resolve default argument values
+        pysig = signature.pysig
+        if pysig is None:
+            if kw_args:
+                raise NotImplementedError("unsupported keyword arguments "
+                                          "when calling %s" % (fnty,))
+            argvals = [self._cast_var(var, sigty)
+                       for var, sigty in zip(pos_args, signature.args)]
+        else:
+            def normal_handler(index, param, var):
+                return self._cast_var(var, signature.args[index])
+
+            def default_handler(index, param, default):
+                return self.context.get_constant_generic(
+                    self.builder, signature.args[index], default)
+
+            def stararg_handler(index, param, vars):
+                stararg_ty = signature.args[index]
+                assert isinstance(stararg_ty, types.BaseTuple), stararg_ty
+                values = [self._cast_var(var, sigty)
+                          for var, sigty in zip(vars, stararg_ty)]
+                return cgutils.make_anonymous_struct(self.builder, values)
+
+            argvals = typing.fold_arguments(pysig,
+                                            pos_args, dict(kw_args),
+                                            normal_handler,
+                                            default_handler,
+                                            stararg_handler)
+        return argvals
+
+    def lower_print(self, inst):
+        """
+        Lower a ir.Print()
+        """
+        # We handle this, as far as possible, as a normal call to built-in
+        # print().  This will make it easy to undo the special ir.Print
+        # rewrite when it becomes unnecessary (e.g. when we have native
+        # strings).
+        sig = self.fndesc.calltypes[inst]
+        assert sig.return_type == types.none
+        fnty = self.context.typing_context.resolve_value_type(print)
+
+        # Fix the call signature to inject any constant-inferred
+        # string argument
+        pos_tys = list(sig.args)
+        pos_args = list(inst.args)
+        for i in range(len(pos_args)):
+            if i in inst.consts:
+                pyval = inst.consts[i]
+                if isinstance(pyval, str):
+                    pos_tys[i] = types.literal(pyval)
+
+        fixed_sig = typing.signature(sig.return_type, *pos_tys)
+        fixed_sig = fixed_sig.replace(pysig=sig.pysig)
+
+        argvals = self.fold_call_args(fnty, sig, pos_args, inst.vararg, {})
+        impl = self.context.get_function(print, fixed_sig)
+        impl(self.builder, argvals)
+
+    def lower_call(self, resty, expr):
+        signature = self.fndesc.calltypes[expr]
+        self.debug_print("# lower_call: expr = {0}".format(expr))
+        if isinstance(signature.return_type, types.Phantom):
+            return self.context.get_dummy_value()
+
+        fnty = self.typeof(expr.func.name)
+
+        if isinstance(fnty, types.ObjModeDispatcher):
+            res = self._lower_call_ObjModeDispatcher(fnty, expr, signature)
+
+        elif isinstance(fnty, types.ExternalFunction):
+            res = self._lower_call_ExternalFunction(fnty, expr, signature)
+
+        elif isinstance(fnty, types.ExternalFunctionPointer):
+            res = self._lower_call_ExternalFunctionPointer(
+                fnty, expr, signature)
+
+        elif isinstance(fnty, types.RecursiveCall):
+            res = self._lower_call_RecursiveCall(fnty, expr, signature)
+
+        elif isinstance(fnty, types.FunctionType):
+            res = self._lower_call_FunctionType(fnty, expr, signature)
+
+        else:
+            res = self._lower_call_normal(fnty, expr, signature)
+
+        # If lowering the call returned None, interpret that as returning dummy
+        # value if the return type of the function is void, otherwise there is
+        # a problem
+        if res is None:
+            if signature.return_type == types.void:
+                res = self.context.get_dummy_value()
+            else:
+                raise LoweringError(
+                    msg="non-void function returns None from implementation",
+                    loc=self.loc
+                )
+
+        return self.context.cast(self.builder, res, signature.return_type,
+                                 resty)
+
+    def _lower_call_ObjModeDispatcher(self, fnty, expr, signature):
+        from numba.core.pythonapi import ObjModeUtils
+
+        self.init_pyapi()
+        # Acquire the GIL
+        gil_state = self.pyapi.gil_ensure()
+        # Fix types
+        argnames = [a.name for a in expr.args]
+        argtypes = [self.typeof(a) for a in argnames]
+        argvalues = [self.loadvar(a) for a in argnames]
+        for v, ty in zip(argvalues, argtypes):
+            # Because .from_native_value steal the reference
+            self.incref(ty, v)
+
+        argobjs = [self.pyapi.from_native_value(atyp, aval,
+                                                self.env_manager)
+                   for atyp, aval in zip(argtypes, argvalues)]
+
+        # Load objmode dispatcher
+        callee = ObjModeUtils(self.pyapi).load_dispatcher(fnty, argtypes)
+        # Make Call
+        ret_obj = self.pyapi.call_function_objargs(callee, argobjs)
+        has_exception = cgutils.is_null(self.builder, ret_obj)
+        with self. builder.if_else(has_exception) as (then, orelse):
+            # Handles exception
+            # This branch must exit the function
+            with then:
+                # Clean arg
+                for obj in argobjs:
+                    self.pyapi.decref(obj)
+
+                # Release the GIL
+                self.pyapi.gil_release(gil_state)
+
+                # Return and signal exception
+                self.call_conv.return_exc(self.builder)
+
+            # Handles normal return
+            with orelse:
+                # Fix output value
+                native = self.pyapi.to_native_value(
+                    fnty.dispatcher.output_types,
+                    ret_obj,
+                )
+                output = native.value
+
+                # Release objs
+                self.pyapi.decref(ret_obj)
+                for obj in argobjs:
+                    self.pyapi.decref(obj)
+
+                # cleanup output
+                if callable(native.cleanup):
+                    native.cleanup()
+
+                # Release the GIL
+                self.pyapi.gil_release(gil_state)
+
+                # Error during unboxing
+                with self.builder.if_then(native.is_error):
+                    self.call_conv.return_exc(self.builder)
+
+                return output
+
+    def _lower_call_ExternalFunction(self, fnty, expr, signature):
+        # Handle a named external function
+        self.debug_print("# external function")
+        argvals = self.fold_call_args(
+            fnty, signature, expr.args, expr.vararg, expr.kws,
+        )
+        fndesc = funcdesc.ExternalFunctionDescriptor(
+            fnty.symbol, fnty.sig.return_type, fnty.sig.args)
+        func = self.context.declare_external_function(
+            self.builder.module, fndesc)
+        return self.context.call_external_function(
+            self.builder, func, fndesc.argtypes, argvals,
+        )
+
+    def _lower_call_ExternalFunctionPointer(self, fnty, expr, signature):
+        # Handle a C function pointer
+        self.debug_print("# calling external function pointer")
+        argvals = self.fold_call_args(
+            fnty, signature, expr.args, expr.vararg, expr.kws,
+        )
+        pointer = self.loadvar(expr.func.name)
+        # If the external function pointer uses libpython
+        if fnty.requires_gil:
+            self.init_pyapi()
+            # Acquire the GIL
+            gil_state = self.pyapi.gil_ensure()
+            # Make PyObjects
+            newargvals = []
+            pyvals = []
+            for exptyp, gottyp, aval in zip(fnty.sig.args, signature.args,
+                                            argvals):
+                # Adjust argument values to pyobjects
+                if exptyp == types.ffi_forced_object:
+                    self.incref(gottyp, aval)
+                    obj = self.pyapi.from_native_value(
+                        gottyp, aval, self.env_manager,
+                    )
+                    newargvals.append(obj)
+                    pyvals.append(obj)
+                else:
+                    newargvals.append(aval)
+
+            # Call external function
+            res = self.context.call_function_pointer(
+                self.builder, pointer, newargvals, fnty.cconv,
+            )
+            # Release PyObjects
+            for obj in pyvals:
+                self.pyapi.decref(obj)
+
+            # Release the GIL
+            self.pyapi.gil_release(gil_state)
+        # If the external function pointer does NOT use libpython
+        else:
+            res = self.context.call_function_pointer(
+                self.builder, pointer, argvals, fnty.cconv,
+            )
+        return res
+
+    def _lower_call_RecursiveCall(self, fnty, expr, signature):
+        # Recursive call
+        argvals = self.fold_call_args(
+            fnty, signature, expr.args, expr.vararg, expr.kws,
+        )
+        rec_ov = fnty.get_overloads(signature.args)
+        mangler = self.context.mangler or default_mangler
+        abi_tags = self.fndesc.abi_tags
+        mangled_name = mangler(rec_ov.qualname, signature.args,
+                               abi_tags=abi_tags, uid=rec_ov.uid)
+        # special case self recursion
+        if self.builder.function.name.startswith(mangled_name):
+            res = self.context.call_internal(
+                self.builder, self.fndesc, signature, argvals,
+            )
+        else:
+            res = self.context.call_unresolved(
+                self.builder, mangled_name, signature, argvals,
+            )
+        return res
+
+    def _lower_call_FunctionType(self, fnty, expr, signature):
+        self.debug_print("# calling first-class function type")
+        sig = types.unliteral(signature)
+        if not fnty.check_signature(signature):
+            # value dependent polymorphism?
+            raise UnsupportedError(
+                f'mismatch of function types:'
+                f' expected {fnty} but got {types.FunctionType(sig)}')
+        ftype = fnty.ftype
+        argvals = self.fold_call_args(
+            fnty, sig, expr.args, expr.vararg, expr.kws,
+        )
+        func_ptr = self.__get_function_pointer(ftype, expr.func.name, sig=sig)
+        res = self.builder.call(func_ptr, argvals, cconv=fnty.cconv)
+        return res
+
+    def __get_function_pointer(self, ftype, fname, sig=None):
+        from numba.experimental.function_type import lower_get_wrapper_address
+
+        llty = self.context.get_value_type(ftype)
+        fstruct = self.loadvar(fname)
+        addr = self.builder.extract_value(fstruct, 0,
+                                          name='addr_of_%s' % (fname))
+
+        fptr = cgutils.alloca_once(self.builder, llty,
+                                   name="fptr_of_%s" % (fname))
+        with self.builder.if_else(
+                cgutils.is_null(self.builder, addr),
+                likely=False) as (then, orelse):
+            with then:
+                self.init_pyapi()
+                # Acquire the GIL
+                gil_state = self.pyapi.gil_ensure()
+                pyaddr = self.builder.extract_value(
+                    fstruct, 1,
+                    name='pyaddr_of_%s' % (fname))
+                # try to recover the function address, see
+                # test_zero_address BadToGood example in
+                # test_function_type.py
+                addr1 = lower_get_wrapper_address(
+                    self.context, self.builder, pyaddr, sig,
+                    failure_mode='ignore')
+                with self.builder.if_then(
+                        cgutils.is_null(self.builder, addr1), likely=False):
+                    self.return_exception(
+                        RuntimeError,
+                        exc_args=(f"{ftype} function address is null",),
+                        loc=self.loc)
+                addr2 = self.pyapi.long_as_voidptr(addr1)
+                self.builder.store(self.builder.bitcast(addr2, llty), fptr)
+                self.pyapi.decref(addr1)
+                self.pyapi.gil_release(gil_state)
+            with orelse:
+                self.builder.store(self.builder.bitcast(addr, llty), fptr)
+        return self.builder.load(fptr)
+
+    def _lower_call_normal(self, fnty, expr, signature):
+        # Normal function resolution
+        self.debug_print("# calling normal function: {0}".format(fnty))
+        self.debug_print("# signature: {0}".format(signature))
+        if isinstance(fnty, types.ObjModeDispatcher):
+            argvals = expr.func.args
+        else:
+            argvals = self.fold_call_args(
+                fnty, signature, expr.args, expr.vararg, expr.kws,
+            )
+        tname = expr.target
+        if tname is not None:
+            from numba.core.target_extension import resolve_dispatcher_from_str
+            disp = resolve_dispatcher_from_str(tname)
+            hw_ctx = disp.targetdescr.target_context
+            impl = hw_ctx.get_function(fnty, signature)
+        else:
+            impl = self.context.get_function(fnty, signature)
+        if signature.recvr:
+            # The "self" object is passed as the function object
+            # for bounded function
+            the_self = self.loadvar(expr.func.name)
+            # Prepend the self reference
+            argvals = [the_self] + list(argvals)
+
+        res = impl(self.builder, argvals, self.loc)
+        return res
+
+    def lower_expr(self, resty, expr):
+        if expr.op == 'binop':
+            return self.lower_binop(resty, expr, expr.fn)
+        elif expr.op == 'inplace_binop':
+            lty = self.typeof(expr.lhs.name)
+            if lty.mutable:
+                return self.lower_binop(resty, expr, expr.fn)
+            else:
+                # inplace operators on non-mutable types reuse the same
+                # definition as the corresponding copying operators.)
+                return self.lower_binop(resty, expr, expr.immutable_fn)
+        elif expr.op == 'unary':
+            val = self.loadvar(expr.value.name)
+            typ = self.typeof(expr.value.name)
+            func_ty = self.context.typing_context.resolve_value_type(expr.fn)
+            # Get function
+            signature = self.fndesc.calltypes[expr]
+            impl = self.context.get_function(func_ty, signature)
+            # Convert argument to match
+            val = self.context.cast(self.builder, val, typ, signature.args[0])
+            res = impl(self.builder, [val])
+            res = self.context.cast(self.builder, res,
+                                    signature.return_type, resty)
+            return res
+
+        elif expr.op == 'call':
+            res = self.lower_call(resty, expr)
+            return res
+
+        elif expr.op == 'pair_first':
+            val = self.loadvar(expr.value.name)
+            ty = self.typeof(expr.value.name)
+            res = self.context.pair_first(self.builder, val, ty)
+            self.incref(resty, res)
+            return res
+
+        elif expr.op == 'pair_second':
+            val = self.loadvar(expr.value.name)
+            ty = self.typeof(expr.value.name)
+            res = self.context.pair_second(self.builder, val, ty)
+            self.incref(resty, res)
+            return res
+
+        elif expr.op in ('getiter', 'iternext'):
+            val = self.loadvar(expr.value.name)
+            ty = self.typeof(expr.value.name)
+            signature = self.fndesc.calltypes[expr]
+            impl = self.context.get_function(expr.op, signature)
+            [fty] = signature.args
+            castval = self.context.cast(self.builder, val, ty, fty)
+            res = impl(self.builder, (castval,))
+            res = self.context.cast(self.builder, res, signature.return_type,
+                                    resty)
+            return res
+
+        elif expr.op == 'exhaust_iter':
+            val = self.loadvar(expr.value.name)
+            ty = self.typeof(expr.value.name)
+            # Unpack optional
+            if isinstance(ty, types.Optional):
+                val = self.context.cast(self.builder, val, ty, ty.type)
+                ty = ty.type
+
+            # If we have a tuple, we needn't do anything
+            # (and we can't iterate over the heterogeneous ones).
+            if isinstance(ty, types.BaseTuple):
+                assert ty == resty
+                self.incref(ty, val)
+                return val
+
+            itemty = ty.iterator_type.yield_type
+            tup = self.context.get_constant_undef(resty)
+            pairty = types.Pair(itemty, types.boolean)
+            getiter_sig = typing.signature(ty.iterator_type, ty)
+            getiter_impl = self.context.get_function('getiter',
+                                                     getiter_sig)
+            iternext_sig = typing.signature(pairty, ty.iterator_type)
+            iternext_impl = self.context.get_function('iternext',
+                                                      iternext_sig)
+            iterobj = getiter_impl(self.builder, (val,))
+            # We call iternext() as many times as desired (`expr.count`).
+            for i in range(expr.count):
+                pair = iternext_impl(self.builder, (iterobj,))
+                is_valid = self.context.pair_second(self.builder,
+                                                    pair, pairty)
+                with cgutils.if_unlikely(self.builder,
+                                         self.builder.not_(is_valid)):
+                    self.return_exception(ValueError, loc=self.loc)
+                item = self.context.pair_first(self.builder,
+                                               pair, pairty)
+                tup = self.builder.insert_value(tup, item, i)
+
+            # Call iternext() once more to check that the iterator
+            # is exhausted.
+            pair = iternext_impl(self.builder, (iterobj,))
+            is_valid = self.context.pair_second(self.builder,
+                                                pair, pairty)
+            with cgutils.if_unlikely(self.builder, is_valid):
+                self.return_exception(ValueError, loc=self.loc)
+
+            self.decref(ty.iterator_type, iterobj)
+            return tup
+
+        elif expr.op == "getattr":
+            val = self.loadvar(expr.value.name)
+            ty = self.typeof(expr.value.name)
+
+            if isinstance(resty, types.BoundFunction):
+                # if we are getting out a method, assume we have typed this
+                # properly and just build a bound function object
+                casted = self.context.cast(self.builder, val, ty, resty.this)
+                res = self.context.get_bound_function(self.builder, casted,
+                                                      resty.this)
+                self.incref(resty, res)
+                return res
+            else:
+                impl = self.context.get_getattr(ty, expr.attr)
+                attrty = self.context.typing_context.resolve_getattr(ty,
+                                                                     expr.attr)
+
+                if impl is None:
+                    # ignore the attribute
+                    return self.context.get_dummy_value()
+                else:
+                    res = impl(self.context, self.builder, ty, val, expr.attr)
+
+                # Cast the attribute type to the expected output type
+                res = self.context.cast(self.builder, res, attrty, resty)
+                return res
+
+        elif expr.op == "static_getitem":
+            signature = typing.signature(
+                resty,
+                self.typeof(expr.value.name),
+                _lit_or_omitted(expr.index),
+            )
+            try:
+                # Both get_function() and the returned implementation can
+                # raise NotImplementedError if the types aren't supported
+                impl = self.context.get_function("static_getitem", signature)
+                return impl(self.builder,
+                            (self.loadvar(expr.value.name), expr.index))
+            except NotImplementedError:
+                if expr.index_var is None:
+                    raise
+                # Fall back on the generic getitem() implementation
+                # for this type.
+                signature = self.fndesc.calltypes[expr]
+                return self.lower_getitem(resty, expr, expr.value,
+                                          expr.index_var, signature)
+        elif expr.op == "typed_getitem":
+            signature = typing.signature(
+                resty,
+                self.typeof(expr.value.name),
+                self.typeof(expr.index.name),
+            )
+            impl = self.context.get_function("typed_getitem", signature)
+            return impl(self.builder, (self.loadvar(expr.value.name),
+                        self.loadvar(expr.index.name)))
+        elif expr.op == "getitem":
+            signature = self.fndesc.calltypes[expr]
+            return self.lower_getitem(resty, expr, expr.value, expr.index,
+                                      signature)
+
+        elif expr.op == "build_tuple":
+            itemvals = [self.loadvar(i.name) for i in expr.items]
+            itemtys = [self.typeof(i.name) for i in expr.items]
+            castvals = [self.context.cast(self.builder, val, fromty, toty)
+                        for val, toty, fromty in zip(itemvals, resty, itemtys)]
+            tup = self.context.make_tuple(self.builder, resty, castvals)
+            self.incref(resty, tup)
+            return tup
+
+        elif expr.op == "build_list":
+            itemvals = [self.loadvar(i.name) for i in expr.items]
+            itemtys = [self.typeof(i.name) for i in expr.items]
+            if isinstance(resty, types.LiteralList):
+                castvals = [self.context.cast(self.builder, val, fromty, toty)
+                            for val, toty, fromty in zip(itemvals, resty.types,
+                                                         itemtys)]
+                tup = self.context.make_tuple(self.builder,
+                                              types.Tuple(resty.types),
+                                              castvals)
+                self.incref(resty, tup)
+                return tup
+            else:
+                castvals = [self.context.cast(self.builder, val, fromty,
+                                              resty.dtype)
+                            for val, fromty in zip(itemvals, itemtys)]
+                return self.context.build_list(self.builder, resty, castvals)
+
+        elif expr.op == "build_set":
+            # Insert in reverse order, as Python does
+            items = expr.items[::-1]
+            itemvals = [self.loadvar(i.name) for i in items]
+            itemtys = [self.typeof(i.name) for i in items]
+            castvals = [self.context.cast(self.builder, val, fromty,
+                                          resty.dtype)
+                        for val, fromty in zip(itemvals, itemtys)]
+            return self.context.build_set(self.builder, resty, castvals)
+
+        elif expr.op == "build_map":
+            items = expr.items
+            keys, values = [], []
+            key_types, value_types = [], []
+            for k, v in items:
+                key = self.loadvar(k.name)
+                keytype = self.typeof(k.name)
+                val = self.loadvar(v.name)
+                valtype = self.typeof(v.name)
+                keys.append(key)
+                values.append(val)
+                key_types.append(keytype)
+                value_types.append(valtype)
+            return self.context.build_map(self.builder, resty,
+                                          list(zip(key_types, value_types)),
+                                          list(zip(keys, values)))
+
+        elif expr.op == "cast":
+            val = self.loadvar(expr.value.name)
+            ty = self.typeof(expr.value.name)
+            castval = self.context.cast(self.builder, val, ty, resty)
+            self.incref(resty, castval)
+            return castval
+
+        elif expr.op == "phi":
+            raise LoweringError("PHI not stripped")
+
+        elif expr.op == 'null':
+            return self.context.get_constant_null(resty)
+
+        elif expr.op in self.context.special_ops:
+            res = self.context.special_ops[expr.op](self, expr)
+            return res
+
+        raise NotImplementedError(expr)
+
+    def _alloca_var(self, name, fetype):
+        """
+        Ensure the given variable has an allocated stack slot (if needed).
+        """
+        if name in self.varmap:
+            # quit early
+            return
+
+        # If the name is used in multiple blocks or lowering with debuginfo...
+        if ((name not in self._singly_assigned_vars) or
+                self._disable_sroa_like_opt):
+            # If not already defined, allocate it
+            ptr = self.alloca(name, fetype)
+            # Remember the pointer
+            self.varmap[name] = ptr
+
+    def getvar(self, name):
+        """
+        Get a pointer to the given variable's slot.
+        """
+        if not self._disable_sroa_like_opt:
+            assert name not in self._blk_local_varmap
+            assert name not in self._singly_assigned_vars
+        return self.varmap[name]
+
+    def loadvar(self, name):
+        """
+        Load the given variable's value.
+        """
+        if name in self._blk_local_varmap and not self._disable_sroa_like_opt:
+            return self._blk_local_varmap[name]
+        ptr = self.getvar(name)
+
+        # Don't associate debuginfo with the load for a function arg else it
+        # creates instructions ahead of the first source line of the
+        # function which then causes problems with breaking on the function
+        # symbol (it hits the symbol, not the first line).
+        if name in self.func_ir.arg_names:
+            with debuginfo.suspend_emission(self.builder):
+                return self.builder.load(ptr)
+        else:
+            return self.builder.load(ptr)
+
+    def storevar(self, value, name, argidx=None):
+        """
+        Store the value into the given variable.
+        """
+        fetype = self.typeof(name)
+        # Define if not already
+        self._alloca_var(name, fetype)
+
+        # Store variable
+        if (name in self._singly_assigned_vars and
+                not self._disable_sroa_like_opt):
+            self._blk_local_varmap[name] = value
+        else:
+            if argidx is None:
+                # Clean up existing value stored in the variable, not needed
+                # if it's an arg
+                old = self.loadvar(name)
+                self.decref(fetype, old)
+
+            # stack stored variable
+            ptr = self.getvar(name)
+            if value.type != ptr.type.pointee:
+                msg = ("Storing {value.type} to ptr of {ptr.type.pointee} "
+                       "('{name}'). FE type {fetype}").format(value=value,
+                                                              ptr=ptr,
+                                                              fetype=fetype,
+                                                              name=name)
+                raise AssertionError(msg)
+
+            # If this store is associated with an argument to the function (i.e.
+            # store following reassemble from CC splatting structs as many args
+            # to the function) then mark this variable as such.
+            if argidx is not None:
+                with debuginfo.suspend_emission(self.builder):
+                    self.builder.store(value, ptr)
+                loc = self.defn_loc # the line with `def <func>`
+                lltype = self.context.get_value_type(fetype)
+                sizeof = self.context.get_abi_sizeof(lltype)
+                datamodel = self.context.data_model_manager[fetype]
+                self.debuginfo.mark_variable(self.builder, ptr, name=name,
+                                             lltype=lltype, size=sizeof,
+                                             line=loc.line, datamodel=datamodel,
+                                             argidx=argidx)
+            else:
+                self.builder.store(value, ptr)
+
+    def delvar(self, name):
+        """
+        Delete the given variable.
+        """
+        fetype = self.typeof(name)
+
+        # Out-of-order
+        if (name not in self._blk_local_varmap and
+                not self._disable_sroa_like_opt):
+            if name in self._singly_assigned_vars:
+                self._singly_assigned_vars.discard(name)
+
+        # Define if not already (may happen if the variable is deleted
+        # at the beginning of a loop, but only set later in the loop)
+        self._alloca_var(name, fetype)
+
+        if name in self._blk_local_varmap and not self._disable_sroa_like_opt:
+            llval = self._blk_local_varmap[name]
+            self.decref(fetype, llval)
+        else:
+            ptr = self.getvar(name)
+            self.decref(fetype, self.builder.load(ptr))
+            # Zero-fill variable to avoid double frees on subsequent dels
+            self.builder.store(Constant(ptr.type.pointee, None), ptr)
+
+    def alloca(self, name, type):
+        lltype = self.context.get_value_type(type)
+        datamodel = self.context.data_model_manager[type]
+        return self.alloca_lltype(name, lltype, datamodel=datamodel)
+
+    def alloca_lltype(self, name, lltype, datamodel=None):
+        # Is user variable?
+        is_uservar = not name.startswith('$')
+        # Allocate space for variable
+        aptr = cgutils.alloca_once(self.builder, lltype,
+                                   name=name, zfill=False)
+
+        # Emit debug info for user variable
+        if is_uservar:
+            # Don't associate debuginfo with the alloca for a function arg, this
+            # is handled by the first store to the alloca so that repacking the
+            # splatted args from the CC is dealt with.
+            if name not in self.func_ir.arg_names:
+                sizeof = self.context.get_abi_sizeof(lltype)
+                self.debuginfo.mark_variable(self.builder, aptr, name=name,
+                                             lltype=lltype, size=sizeof,
+                                             line=self.loc.line,
+                                             datamodel=datamodel,)
+        return aptr
+
+    def incref(self, typ, val):
+        if not self.context.enable_nrt:
+            return
+
+        self.context.nrt.incref(self.builder, typ, val)
+
+    def decref(self, typ, val):
+        if not self.context.enable_nrt:
+            return
+
+        # do not associate decref with "use", it creates "jumpy" line info as
+        # the decrefs are usually where the ir.Del nodes are, which is at the
+        # end of the block.
+        with debuginfo.suspend_emission(self.builder):
+            self.context.nrt.decref(self.builder, typ, val)
+
+
+def _lit_or_omitted(value):
+    """Returns a Literal instance if the type of value is supported;
+    otherwise, return `Omitted(value)`.
+    """
+    try:
+        return types.literal(value)
+    except LiteralTypingError:
+        return types.Omitted(value)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/object_mode_passes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/object_mode_passes.py
new file mode 100644
index 000000000..f5cd52383
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/object_mode_passes.py
@@ -0,0 +1,169 @@
+import warnings
+from numba.core import (errors, types, typing, funcdesc, config, pylowering,
+                        transforms)
+from numba.core.compiler_machinery import (FunctionPass, LoweringPass,
+                                           register_pass)
+from collections import defaultdict
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class ObjectModeFrontEnd(FunctionPass):
+    _name = "object_mode_front_end"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def _frontend_looplift(self, state):
+        """
+        Loop lifting analysis and transformation
+        """
+        loop_flags = state.flags.copy()
+        outer_flags = state.flags.copy()
+        # Do not recursively loop lift
+        outer_flags.enable_looplift = False
+        loop_flags.enable_looplift = False
+        if not state.flags.enable_pyobject_looplift:
+            loop_flags.enable_pyobject = False
+        loop_flags.enable_ssa = False
+
+        main, loops = transforms.loop_lifting(state.func_ir,
+                                              typingctx=state.typingctx,
+                                              targetctx=state.targetctx,
+                                              locals=state.locals,
+                                              flags=loop_flags)
+        if loops:
+            # Some loops were extracted
+            if config.DEBUG_FRONTEND or config.DEBUG:
+                for loop in loops:
+                    print("Lifting loop", loop.get_source_location())
+            from numba.core.compiler import compile_ir
+            cres = compile_ir(state.typingctx, state.targetctx, main,
+                              state.args, state.return_type,
+                              outer_flags, state.locals,
+                              lifted=tuple(loops), lifted_from=None,
+                              is_lifted_loop=True)
+            return cres
+
+    def run_pass(self, state):
+        from numba.core.compiler import _EarlyPipelineCompletion
+        # NOTE: That so much stuff, including going back into the compiler, is
+        # captured in a single pass is not ideal.
+        if state.flags.enable_looplift:
+            assert not state.lifted
+            cres = self._frontend_looplift(state)
+            if cres is not None:
+                raise _EarlyPipelineCompletion(cres)
+
+        # Fallback typing: everything is a python object
+        state.typemap = defaultdict(lambda: types.pyobject)
+        state.calltypes = defaultdict(lambda: types.pyobject)
+        state.return_type = types.pyobject
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class ObjectModeBackEnd(LoweringPass):
+
+    _name = "object_mode_back_end"
+
+    def __init__(self):
+        LoweringPass.__init__(self)
+
+    def _py_lowering_stage(self, targetctx, library, interp, flags):
+        fndesc = funcdesc.PythonFunctionDescriptor.from_object_mode_function(
+            interp
+        )
+        with targetctx.push_code_library(library):
+            lower = pylowering.PyLower(targetctx, library, fndesc, interp)
+            lower.lower()
+            if not flags.no_cpython_wrapper:
+                lower.create_cpython_wrapper()
+            env = lower.env
+            call_helper = lower.call_helper
+            del lower
+        from numba.core.compiler import _LowerResult  # TODO: move this
+        if flags.no_compile:
+            return _LowerResult(fndesc, call_helper, cfunc=None, env=env)
+        else:
+            # Prepare for execution
+            cfunc = targetctx.get_executable(library, fndesc, env)
+            return _LowerResult(fndesc, call_helper, cfunc=cfunc, env=env)
+
+    def run_pass(self, state):
+        """
+        Lowering for object mode
+        """
+
+        if state.library is None:
+            codegen = state.targetctx.codegen()
+            state.library = codegen.create_library(state.func_id.func_qualname)
+            # Enable object caching upfront, so that the library can
+            # be later serialized.
+            state.library.enable_object_caching()
+
+        def backend_object_mode():
+            """
+            Object mode compilation
+            """
+            if len(state.args) != state.nargs:
+                # append missing
+                # BUG?: What's going on with nargs here?
+                # check state.nargs vs self.nargs on original code
+                state.args = (tuple(state.args) + (types.pyobject,) *
+                              (state.nargs - len(state.args)))
+
+            return self._py_lowering_stage(state.targetctx,
+                                           state.library,
+                                           state.func_ir,
+                                           state.flags)
+
+        lowered = backend_object_mode()
+        signature = typing.signature(state.return_type, *state.args)
+        from numba.core.compiler import compile_result
+        state.cr = compile_result(
+            typing_context=state.typingctx,
+            target_context=state.targetctx,
+            entry_point=lowered.cfunc,
+            typing_error=state.status.fail_reason,
+            type_annotation=state.type_annotation,
+            library=state.library,
+            call_helper=lowered.call_helper,
+            signature=signature,
+            objectmode=True,
+            lifted=state.lifted,
+            fndesc=lowered.fndesc,
+            environment=lowered.env,
+            metadata=state.metadata,
+            reload_init=state.reload_init,
+        )
+
+        # Warn, deprecated behaviour, code compiled in objmode without
+        # force_pyobject indicates fallback from nopython mode
+        if not state.flags.force_pyobject:
+            # first warn about object mode and yes/no to lifted loops
+            if len(state.lifted) > 0:
+                warn_msg = ('Function "%s" was compiled in object mode without'
+                            ' forceobj=True, but has lifted loops.' %
+                            (state.func_id.func_name,))
+            else:
+                warn_msg = ('Function "%s" was compiled in object mode without'
+                            ' forceobj=True.' % (state.func_id.func_name,))
+            warnings.warn(errors.NumbaWarning(warn_msg,
+                                              state.func_ir.loc))
+
+            url = ("https://numba.readthedocs.io/en/stable/reference/"
+                   "deprecation.html#deprecation-of-object-mode-fall-"
+                   "back-behaviour-when-using-jit")
+            msg = ("\nFall-back from the nopython compilation path to the "
+                   "object mode compilation path has been detected, this is "
+                   "deprecated behaviour.\n\nFor more information visit %s" %
+                   url)
+            warnings.warn(errors.NumbaDeprecationWarning(msg,
+                                                         state.func_ir.loc))
+            if state.flags.release_gil:
+                warn_msg = ("Code running in object mode won't allow parallel"
+                            " execution despite nogil=True.")
+                warnings.warn_explicit(warn_msg, errors.NumbaWarning,
+                                       state.func_id.filename,
+                                       state.func_id.firstlineno)
+        return True
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/optional.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/optional.py
new file mode 100644
index 000000000..16dcb5236
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/optional.py
@@ -0,0 +1,121 @@
+import operator
+
+from numba.core import types, typing, cgutils
+
+from numba.core.imputils import (lower_cast, lower_builtin,
+                                 lower_getattr_generic, impl_ret_untracked,
+                                 lower_setattr_generic)
+
+
+def always_return_true_impl(context, builder, sig, args):
+    return cgutils.true_bit
+
+
+def always_return_false_impl(context, builder, sig, args):
+    return cgutils.false_bit
+
+
+def optional_is_none(context, builder, sig, args):
+    """
+    Check if an Optional value is invalid
+    """
+    [lty, rty] = sig.args
+    [lval, rval] = args
+
+    # Make sure None is on the right
+    if lty == types.none:
+        lty, rty = rty, lty
+        lval, rval = rval, lval
+
+    opt_type = lty
+    opt_val = lval
+
+    opt = context.make_helper(builder, opt_type, opt_val)
+    res = builder.not_(cgutils.as_bool_bit(builder, opt.valid))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+# None is/not None
+lower_builtin(operator.is_, types.none, types.none)(always_return_true_impl)
+
+# Optional is None
+lower_builtin(operator.is_, types.Optional, types.none)(optional_is_none)
+lower_builtin(operator.is_, types.none, types.Optional)(optional_is_none)
+
+
+@lower_getattr_generic(types.Optional)
+def optional_getattr(context, builder, typ, value, attr):
+    """
+    Optional.__getattr__ => redirect to the wrapped type.
+    """
+    inner_type = typ.type
+    val = context.cast(builder, value, typ, inner_type)
+    imp = context.get_getattr(inner_type, attr)
+    return imp(context, builder, inner_type, val, attr)
+
+
+@lower_setattr_generic(types.Optional)
+def optional_setattr(context, builder, sig, args, attr):
+    """
+    Optional.__setattr__ => redirect to the wrapped type.
+    """
+    basety, valty = sig.args
+    target, val = args
+    target_type = basety.type
+    target = context.cast(builder, target, basety, target_type)
+
+    newsig = typing.signature(sig.return_type, target_type, valty)
+    imp = context.get_setattr(attr, newsig)
+    return imp(builder, (target, val))
+
+
+@lower_cast(types.Optional, types.Optional)
+def optional_to_optional(context, builder, fromty, toty, val):
+    """
+    The handling of optional->optional cast must be special cased for
+    correct propagation of None value.  Given type T and U. casting of
+    T? to U? (? denotes optional) should always succeed.   If the from-value
+    is None, the None value the casted value (U?) should be None; otherwise,
+    the from-value is casted to U. This is different from casting T? to U,
+    which requires the from-value must not be None.
+    """
+    optval = context.make_helper(builder, fromty, value=val)
+    validbit = cgutils.as_bool_bit(builder, optval.valid)
+    # Create uninitialized optional value
+    outoptval = context.make_helper(builder, toty)
+
+    with builder.if_else(validbit) as (is_valid, is_not_valid):
+        with is_valid:
+            # Cast internal value
+            outoptval.valid = cgutils.true_bit
+            outoptval.data = context.cast(builder, optval.data,
+                                          fromty.type, toty.type)
+
+        with is_not_valid:
+            # Store None to result
+            outoptval.valid = cgutils.false_bit
+            outoptval.data = cgutils.get_null_value(
+                outoptval.data.type)
+
+    return outoptval._getvalue()
+
+
+@lower_cast(types.Any, types.Optional)
+def any_to_optional(context, builder, fromty, toty, val):
+    if fromty == types.none:
+        return context.make_optional_none(builder, toty.type)
+    else:
+        val = context.cast(builder, val, fromty, toty.type)
+        return context.make_optional_value(builder, toty.type, val)
+
+
+@lower_cast(types.Optional, types.Any)
+@lower_cast(types.Optional, types.Boolean)
+def optional_to_any(context, builder, fromty, toty, val):
+    optval = context.make_helper(builder, fromty, value=val)
+    validbit = cgutils.as_bool_bit(builder, optval.valid)
+    with builder.if_then(builder.not_(validbit), likely=False):
+        msg = "expected %s, got None" % (fromty.type,)
+        context.call_conv.return_user_exc(builder, TypeError, (msg,))
+
+    return context.cast(builder, optval.data, fromty.type, toty)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/options.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/options.py
new file mode 100644
index 000000000..3ca89a8e1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/options.py
@@ -0,0 +1,109 @@
+"""
+Target Options
+"""
+import operator
+
+from numba.core import config, utils
+from numba.core.targetconfig import TargetConfig, Option
+
+
+class TargetOptions:
+    """Target options maps user options from decorators to the
+    ``numba.core.compiler.Flags`` used by lowering and target context.
+    """
+    class Mapping:
+        def __init__(self, flag_name, apply=lambda x: x):
+            self.flag_name = flag_name
+            self.apply = apply
+
+    def finalize(self, flags, options):
+        """Subclasses can override this method to make target specific
+        customizations of default flags.
+
+        Parameters
+        ----------
+        flags : Flags
+        options : dict
+        """
+        pass
+
+    @classmethod
+    def parse_as_flags(cls, flags, options):
+        """Parse target options defined in ``options`` and set ``flags``
+        accordingly.
+
+        Parameters
+        ----------
+        flags : Flags
+        options : dict
+        """
+        opt = cls()
+        opt._apply(flags, options)
+        opt.finalize(flags, options)
+        return flags
+
+    def _apply(self, flags, options):
+        # Find all Mapping instances in the class
+        mappings = {}
+        cls = type(self)
+        for k in dir(cls):
+            v = getattr(cls, k)
+            if isinstance(v, cls.Mapping):
+                mappings[k] = v
+
+        used = set()
+        for k, mapping in mappings.items():
+            if k in options:
+                v = mapping.apply(options[k])
+                setattr(flags, mapping.flag_name, v)
+                used.add(k)
+
+        unused = set(options) - used
+        if unused:
+            # Unread options?
+            m = (f"Unrecognized options: {unused}. "
+                 f"Known options are {mappings.keys()}")
+            raise KeyError(m)
+
+
+_mapping = TargetOptions.Mapping
+
+
+class DefaultOptions:
+    """Defines how user-level target options are mapped to the target flags.
+    """
+    nopython = _mapping("enable_pyobject", operator.not_)
+    forceobj = _mapping("force_pyobject")
+    looplift = _mapping("enable_looplift")
+    _nrt = _mapping("nrt")
+    debug = _mapping("debuginfo")
+    boundscheck = _mapping("boundscheck")
+    nogil = _mapping("release_gil")
+
+    no_rewrites = _mapping("no_rewrites")
+    no_cpython_wrapper = _mapping("no_cpython_wrapper")
+    no_cfunc_wrapper = _mapping("no_cfunc_wrapper")
+
+    parallel = _mapping("auto_parallel")
+    fastmath = _mapping("fastmath")
+    error_model = _mapping("error_model")
+    inline = _mapping("inline")
+    forceinline = _mapping("forceinline")
+
+    target_backend = _mapping("target_backend")
+
+    _dbg_extend_lifetimes = _mapping("dbg_extend_lifetimes")
+    _dbg_optnone = _mapping("dbg_optnone")
+
+
+def include_default_options(*args):
+    """Returns a mixin class with a subset of the options
+
+    Parameters
+    ----------
+    *args : str
+        Option names to include.
+    """
+    glbs = {k: getattr(DefaultOptions, k) for k in args}
+
+    return type("OptionMixins", (), glbs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/overload_glue.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/overload_glue.py
new file mode 100644
index 000000000..03eb97bfb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/overload_glue.py
@@ -0,0 +1,308 @@
+"""
+Provides wrapper functions for "glueing" together Numba implementations that are
+written in the "old" style of a separate typing and lowering implementation.
+"""
+import types as pytypes
+import textwrap
+from threading import RLock
+from collections import defaultdict
+
+from numba.core import errors
+
+
+class _OverloadWrapper(object):
+    """This class does all the work of assembling and registering wrapped split
+    implementations.
+    """
+
+    def __init__(self, function, typing_key=None):
+        assert function is not None
+        self._function = function
+        self._typing_key = typing_key
+        self._BIND_TYPES = dict()
+        self._selector = None
+        self._TYPER = None
+        # run to register overload, the intrinsic sorts out the binding to the
+        # registered impls at the point the overload is evaluated, i.e. this
+        # is all lazy.
+        self._build()
+
+    def _stub_generator(self, body_func, varnames):
+        """This generates a function based on the argnames provided in
+        "varnames", the "body_func" is the function that'll type the overloaded
+        function and then work out which lowering to return"""
+        def stub(tyctx):
+            # body is supplied when the function is magic'd into life via glbls
+            return body(tyctx)  # noqa: F821
+
+        stub_code = stub.__code__
+        new_varnames = [*stub_code.co_varnames]
+        new_varnames.extend(varnames)
+        co_argcount = len(new_varnames)
+        co_args = [co_argcount]
+        additional_co_nlocals = len(varnames)
+
+        from numba.core import utils
+        if utils.PYVERSION >= (3, 8):
+            co_args.append(stub_code.co_posonlyargcount)
+        co_args.append(stub_code.co_kwonlyargcount)
+        co_args.extend([stub_code.co_nlocals + additional_co_nlocals,
+                        stub_code.co_stacksize,
+                        stub_code.co_flags,
+                        stub_code.co_code,
+                        stub_code.co_consts,
+                        stub_code.co_names,
+                        tuple(new_varnames),
+                        stub_code.co_filename,
+                        stub_code.co_name,
+                        stub_code.co_firstlineno,
+                        stub_code.co_lnotab,
+                        stub_code.co_freevars,
+                        stub_code.co_cellvars
+                        ])
+
+        new_code = pytypes.CodeType(*co_args)
+
+        # get function
+        new_func = pytypes.FunctionType(new_code, {'body': body_func})
+        return new_func
+
+    def wrap_typing(self):
+        """
+        Use this to replace @infer_global, it records the decorated function
+        as a typer for the argument `concrete_function`.
+        """
+        if self._typing_key is None:
+            key = self._function
+        else:
+            key = self._typing_key
+
+        def inner(typing_class):
+            # Note that two templates could be used for the same function, to
+            # avoid @infer_global etc the typing template is copied. This is to
+            # ensure there's a 1:1 relationship between the typing templates and
+            # their keys.
+            clazz_dict = dict(typing_class.__dict__)
+            clazz_dict['key'] = key
+            cloned = type(f"cloned_template_for_{key}", typing_class.__bases__,
+                          clazz_dict)
+            self._TYPER = cloned
+            _overload_glue.add_no_defer(key)
+            self._build()
+            return typing_class
+        return inner
+
+    def wrap_impl(self, *args):
+        """
+        Use this to replace @lower*, it records the decorated function as the
+        lowering implementation
+        """
+        assert self._TYPER is not None
+
+        def inner(lowerer):
+            self._BIND_TYPES[args] = lowerer
+            return lowerer
+        return inner
+
+    def _assemble(self):
+        """Assembles the OverloadSelector definitions from the registered
+        typing to lowering map.
+        """
+        from numba.core.base import OverloadSelector
+
+        if self._typing_key is None:
+            key = self._function
+        else:
+            key = self._typing_key
+
+        _overload_glue.flush_deferred_lowering(key)
+
+        self._selector = OverloadSelector()
+        msg = f"No entries in the typing->lowering map for {self._function}"
+        assert self._BIND_TYPES, msg
+        for sig, impl in self._BIND_TYPES.items():
+            self._selector.append(impl, sig)
+
+    def _build(self):
+        from numba.core.extending import overload, intrinsic
+
+        @overload(self._function, strict=False,
+                  jit_options={'forceinline': True})
+        def ol_generated(*ol_args, **ol_kwargs):
+            def body(tyctx):
+                msg = f"No typer registered for {self._function}"
+                if self._TYPER is None:
+                    raise errors.InternalError(msg)
+                typing = self._TYPER(tyctx)
+                sig = typing.apply(ol_args, ol_kwargs)
+                if sig is None:
+                    # this follows convention of something not typeable
+                    # returning None
+                    return None
+                if self._selector is None:
+                    self._assemble()
+                lowering = self._selector.find(sig.args)
+                msg = (f"Could not find implementation to lower {sig} for ",
+                       f"{self._function}")
+                if lowering is None:
+                    raise errors.InternalError(msg)
+                return sig, lowering
+
+            # Need a typing context now so as to get a signature and a binding
+            # for the kwarg order.
+            from numba.core.target_extension import (dispatcher_registry,
+                                                     resolve_target_str,
+                                                     current_target)
+            disp = dispatcher_registry[resolve_target_str(current_target())]
+            typing_context = disp.targetdescr.typing_context
+            typing = self._TYPER(typing_context)
+            sig = typing.apply(ol_args, ol_kwargs)
+            if not sig:
+                # No signature is a typing error, there's no match, so report it
+                raise errors.TypingError("No match")
+
+            # The following code branches based on whether the signature has a
+            # "pysig", if it does, it's from a CallableTemplate and
+            # specialisation is required based on precise arg/kwarg names and
+            # default values, if it does not, then it just requires
+            # specialisation based on the arg count.
+            #
+            # The "gen_var_names" function is defined to generate the variable
+            # names at the call site of the intrinsic.
+            #
+            # The "call_str_specific" is the list of args to the function
+            # returned by the @overload, it has to have matching arg names and
+            # kwargs names/defaults if the underlying typing template supports
+            # it (CallableTemplate), else it has to have a matching number of
+            # arguments (AbstractTemplate). The "call_str" is the list of args
+            # that will be passed to the intrinsic that deals with typing and
+            # selection of the lowering etc, so it just needs to be a list of
+            # the argument names.
+
+            if sig.pysig: # CallableTemplate, has pysig
+                pysig_params = sig.pysig.parameters
+
+                # Define the var names
+                gen_var_names = [x for x in pysig_params.keys()]
+                # CallableTemplate, pysig is present so generate the exact thing
+                # this is to permit calling with positional args specified by
+                # name.
+                buf = []
+                for k, v in pysig_params.items():
+                    if v.default is v.empty: # no default ~= positional arg
+                        buf.append(k)
+                    else: # is kwarg, wire in default
+                        buf.append(f'{k} = {v.default}')
+                call_str_specific = ', '.join(buf)
+                call_str = ', '.join(pysig_params.keys())
+            else: # AbstractTemplate, need to bind 1:1 vars to the arg count
+                # Define the var names
+                gen_var_names = [f'tmp{x}' for x in range(len(ol_args))]
+                # Everything is just passed by position, there should be no
+                # kwargs.
+                assert not ol_kwargs
+                call_str_specific = ', '.join(gen_var_names)
+                call_str = call_str_specific
+
+            stub = self._stub_generator(body, gen_var_names)
+            intrin = intrinsic(stub)
+
+            # NOTE: The jit_wrapper functions cannot take `*args`
+            # albeit this an obvious choice for accepting an unknown number
+            # of arguments. If this is done, `*args` ends up as a cascade of
+            # Tuple assembling in the IR which ends up with literal
+            # information being lost. As a result the _exact_ argument list
+            # is generated to match the number of arguments and kwargs.
+            name = str(self._function)
+            # This is to name the function with something vaguely identifiable
+            name = ''.join([x if x not in {'>','<',' ','-','.'} else '_'
+                            for x in name])
+            gen = textwrap.dedent(("""
+            def jit_wrapper_{}({}):
+                return intrin({})
+            """)).format(name, call_str_specific, call_str)
+            l = {}
+            g = {'intrin': intrin}
+            exec(gen, g, l)
+            return l['jit_wrapper_{}'.format(name)]
+
+
+class _Gluer:
+    """This is a helper class to make sure that each concrete overload has only
+    one wrapper as the code relies on the wrapper being a singleton."""
+    def __init__(self):
+        self._registered = dict()
+        self._lock = RLock()
+        # `_no_defer` stores keys that should not defer lowering because typing
+        # is already provided.
+        self._no_defer = set()
+        # `_deferred` stores lowering that must be deferred because the typing
+        # has not been provided.
+        self._deferred = defaultdict(list)
+
+    def __call__(self, func, typing_key=None):
+        with self._lock:
+            if typing_key is None:
+                key = func
+            else:
+                key = typing_key
+            if key in self._registered:
+                return self._registered[key]
+            else:
+                wrapper = _OverloadWrapper(func, typing_key=typing_key)
+                self._registered[key] = wrapper
+                return wrapper
+
+    def defer_lowering(self, key, lower_fn):
+        """Defer lowering of the given key and lowering function.
+        """
+        with self._lock:
+            if key in self._no_defer:
+                # Key is marked as no defer, register lowering now
+                lower_fn()
+            else:
+                # Defer
+                self._deferred[key].append(lower_fn)
+
+    def add_no_defer(self, key):
+        """Stop lowering to be deferred for the given key.
+        """
+        with self._lock:
+            self._no_defer.add(key)
+
+    def flush_deferred_lowering(self, key):
+        """Flush the deferred lowering for the given key.
+        """
+        with self._lock:
+            deferred = self._deferred.pop(key, [])
+            for cb in deferred:
+                cb()
+
+
+_overload_glue = _Gluer()
+del _Gluer
+
+
+def glue_typing(concrete_function, typing_key=None):
+    """This is a decorator for wrapping the typing part for a concrete function
+    'concrete_function', it's a text-only replacement for '@infer_global'"""
+    return _overload_glue(concrete_function,
+                          typing_key=typing_key).wrap_typing()
+
+
+def glue_lowering(*args):
+    """This is a decorator for wrapping the implementation (lowering) part for
+    a concrete function. 'args[0]' is the concrete_function, 'args[1:]' are the
+    types the lowering will accept. This acts as a text-only replacement for
+    '@lower/@lower_builtin'"""
+
+    def wrap(fn):
+        key = args[0]
+
+        def real_call():
+            glue = _overload_glue(args[0], typing_key=key)
+            return glue.wrap_impl(*args[1:])(fn)
+
+        _overload_glue.defer_lowering(key, real_call)
+        return fn
+    return wrap
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/postproc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/postproc.py
new file mode 100644
index 000000000..a43c601df
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/postproc.py
@@ -0,0 +1,238 @@
+from numba.core import utils, ir, analysis, transforms, ir_utils
+
+
+class YieldPoint(object):
+
+    def __init__(self, block, inst):
+        assert isinstance(block, ir.Block)
+        assert isinstance(inst, ir.Yield)
+        self.block = block
+        self.inst = inst
+        self.live_vars = None
+        self.weak_live_vars = None
+
+
+class GeneratorInfo(object):
+
+    def __init__(self):
+        # { index: YieldPoint }
+        self.yield_points = {}
+        # Ordered list of variable names
+        self.state_vars = []
+
+    def get_yield_points(self):
+        """
+        Return an iterable of YieldPoint instances.
+        """
+        return self.yield_points.values()
+
+
+class VariableLifetime(object):
+    """
+    For lazily building information of variable lifetime
+    """
+    def __init__(self, blocks):
+        self._blocks = blocks
+
+    @utils.cached_property
+    def cfg(self):
+        return analysis.compute_cfg_from_blocks(self._blocks)
+
+    @utils.cached_property
+    def usedefs(self):
+        return analysis.compute_use_defs(self._blocks)
+
+    @utils.cached_property
+    def livemap(self):
+        return analysis.compute_live_map(self.cfg, self._blocks,
+                                         self.usedefs.usemap,
+                                         self.usedefs.defmap)
+
+    @utils.cached_property
+    def deadmaps(self):
+        return analysis.compute_dead_maps(self.cfg, self._blocks, self.livemap,
+                                          self.usedefs.defmap)
+
+
+# other packages that define new nodes add calls for inserting dels
+# format: {type:function}
+ir_extension_insert_dels = {}
+
+
+class PostProcessor(object):
+    """
+    A post-processor for Numba IR.
+    """
+
+    def __init__(self, func_ir):
+        self.func_ir = func_ir
+
+    def run(self, emit_dels: bool = False, extend_lifetimes: bool = False):
+        """
+        Run the following passes over Numba IR:
+        - canonicalize the CFG
+        - emit explicit `del` instructions for variables
+        - compute lifetime of variables
+        - compute generator info (if function is a generator function)
+        """
+        self.func_ir.blocks = transforms.canonicalize_cfg(self.func_ir.blocks)
+        vlt = VariableLifetime(self.func_ir.blocks)
+        self.func_ir.variable_lifetime = vlt
+
+        bev = analysis.compute_live_variables(vlt.cfg, self.func_ir.blocks,
+                                              vlt.usedefs.defmap,
+                                              vlt.deadmaps.combined)
+        for offset, ir_block in self.func_ir.blocks.items():
+            self.func_ir.block_entry_vars[ir_block] = bev[offset]
+
+        if self.func_ir.is_generator:
+            self.func_ir.generator_info = GeneratorInfo()
+            self._compute_generator_info()
+        else:
+            self.func_ir.generator_info = None
+
+        # Emit del nodes, do this last as the generator info parsing generates
+        # and then strips dels as part of its analysis.
+        if emit_dels:
+            self._insert_var_dels(extend_lifetimes=extend_lifetimes)
+
+    def _populate_generator_info(self):
+        """
+        Fill `index` for the Yield instruction and create YieldPoints.
+        """
+        dct = self.func_ir.generator_info.yield_points
+        assert not dct, 'rerunning _populate_generator_info'
+        for block in self.func_ir.blocks.values():
+            for inst in block.body:
+                if isinstance(inst, ir.Assign):
+                    yieldinst = inst.value
+                    if isinstance(yieldinst, ir.Yield):
+                        index = len(dct) + 1
+                        yieldinst.index = index
+                        yp = YieldPoint(block, yieldinst)
+                        dct[yieldinst.index] = yp
+
+    def _compute_generator_info(self):
+        """
+        Compute the generator's state variables as the union of live variables
+        at all yield points.
+        """
+        # generate del info, it's used in analysis here, strip it out at the end
+        self._insert_var_dels()
+        self._populate_generator_info()
+        gi = self.func_ir.generator_info
+        for yp in gi.get_yield_points():
+            live_vars = set(self.func_ir.get_block_entry_vars(yp.block))
+            weak_live_vars = set()
+            stmts = iter(yp.block.body)
+            for stmt in stmts:
+                if isinstance(stmt, ir.Assign):
+                    if stmt.value is yp.inst:
+                        break
+                    live_vars.add(stmt.target.name)
+                elif isinstance(stmt, ir.Del):
+                    live_vars.remove(stmt.value)
+            else:
+                assert 0, "couldn't find yield point"
+            # Try to optimize out any live vars that are deleted immediately
+            # after the yield point.
+            for stmt in stmts:
+                if isinstance(stmt, ir.Del):
+                    name = stmt.value
+                    if name in live_vars:
+                        live_vars.remove(name)
+                        weak_live_vars.add(name)
+                else:
+                    break
+            yp.live_vars = live_vars
+            yp.weak_live_vars = weak_live_vars
+
+        st = set()
+        for yp in gi.get_yield_points():
+            st |= yp.live_vars
+            st |= yp.weak_live_vars
+        gi.state_vars = sorted(st)
+        self.remove_dels()
+
+    def _insert_var_dels(self, extend_lifetimes=False):
+        """
+        Insert del statements for each variable.
+        Returns a 2-tuple of (variable definition map, variable deletion map)
+        which indicates variables defined and deleted in each block.
+
+        The algorithm avoids relying on explicit knowledge on loops and
+        distinguish between variables that are defined locally vs variables that
+        come from incoming blocks.
+        We start with simple usage (variable reference) and definition (variable
+        creation) maps on each block. Propagate the liveness info to predecessor
+        blocks until it stabilize, at which point we know which variables must
+        exist before entering each block. Then, we compute the end of variable
+        lives and insert del statements accordingly. Variables are deleted after
+        the last use. Variable referenced by terminators (e.g. conditional
+        branch and return) are deleted by the successors or the caller.
+        """
+        vlt = self.func_ir.variable_lifetime
+        self._patch_var_dels(vlt.deadmaps.internal, vlt.deadmaps.escaping,
+                             extend_lifetimes=extend_lifetimes)
+
+    def _patch_var_dels(self, internal_dead_map, escaping_dead_map,
+                        extend_lifetimes=False):
+        """
+        Insert delete in each block
+        """
+        for offset, ir_block in self.func_ir.blocks.items():
+            # for each internal var, insert delete after the last use
+            internal_dead_set = internal_dead_map[offset].copy()
+            delete_pts = []
+            # for each statement in reverse order
+            for stmt in reversed(ir_block.body[:-1]):
+                # internal vars that are used here
+                live_set = set(v.name for v in stmt.list_vars())
+                dead_set = live_set & internal_dead_set
+                for T, def_func in ir_extension_insert_dels.items():
+                    if isinstance(stmt, T):
+                        done_dels = def_func(stmt, dead_set)
+                        dead_set -= done_dels
+                        internal_dead_set -= done_dels
+                # used here but not afterwards
+                delete_pts.append((stmt, dead_set))
+                internal_dead_set -= dead_set
+
+            # rewrite body and insert dels
+            body = []
+            lastloc = ir_block.loc
+            del_store = []
+            for stmt, delete_set in reversed(delete_pts):
+                # If using extended lifetimes then the Dels are all put at the
+                # block end just ahead of the terminator, so associate their
+                # location with the terminator.
+                if extend_lifetimes:
+                    lastloc = ir_block.body[-1].loc
+                else:
+                    lastloc = stmt.loc
+                # Ignore dels (assuming no user inserted deletes)
+                if not isinstance(stmt, ir.Del):
+                    body.append(stmt)
+                # note: the reverse sort is not necessary for correctness
+                #       it is just to minimize changes to test for now
+                for var_name in sorted(delete_set, reverse=True):
+                    delnode = ir.Del(var_name, loc=lastloc)
+                    if extend_lifetimes:
+                        del_store.append(delnode)
+                    else:
+                        body.append(delnode)
+            if extend_lifetimes:
+                body.extend(del_store)
+            body.append(ir_block.body[-1])  # terminator
+            ir_block.body = body
+
+            # vars to delete at the start
+            escape_dead_set = escaping_dead_map[offset]
+            for var_name in sorted(escape_dead_set):
+                ir_block.prepend(ir.Del(var_name, loc=ir_block.body[0].loc))
+
+    def remove_dels(self):
+        """
+        Strips the IR of Del nodes
+        """
+        ir_utils.remove_dels(self.func_ir.blocks)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/pylowering.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/pylowering.py
new file mode 100644
index 000000000..017124c63
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/pylowering.py
@@ -0,0 +1,655 @@
+"""
+Lowering implementation for object mode.
+"""
+
+
+import builtins
+import operator
+import inspect
+
+import llvmlite.ir
+
+from numba.core import types, utils, ir, generators, cgutils
+from numba.core.errors import (ForbiddenConstruct, LoweringError,
+                               NumbaNotImplementedError)
+from numba.core.lowering import BaseLower
+
+
+# Issue #475: locals() is unsupported as calling it naively would give
+# out wrong results.
+_unsupported_builtins = set([locals])
+
+# Map operators to methods on the PythonAPI class
+PYTHON_BINOPMAP = {
+    operator.add: ("number_add", False),
+    operator.sub: ("number_subtract", False),
+    operator.mul: ("number_multiply", False),
+    operator.truediv: ("number_truedivide", False),
+    operator.floordiv: ("number_floordivide", False),
+    operator.mod: ("number_remainder", False),
+    operator.pow: ("number_power", False),
+    operator.lshift: ("number_lshift", False),
+    operator.rshift: ("number_rshift", False),
+    operator.and_: ("number_and", False),
+    operator.or_: ("number_or", False),
+    operator.xor: ("number_xor", False),
+    # inplace operators
+    operator.iadd: ("number_add", True),
+    operator.isub: ("number_subtract", True),
+    operator.imul: ("number_multiply", True),
+    operator.itruediv: ("number_truedivide", True),
+    operator.ifloordiv: ("number_floordivide", True),
+    operator.imod: ("number_remainder", True),
+    operator.ipow: ("number_power", True),
+    operator.ilshift: ("number_lshift", True),
+    operator.irshift: ("number_rshift", True),
+    operator.iand: ("number_and", True),
+    operator.ior: ("number_or", True),
+    operator.ixor: ("number_xor", True),
+}
+
+PYTHON_BINOPMAP[operator.matmul] = ("number_matrix_multiply", False)
+PYTHON_BINOPMAP[operator.imatmul] = ("number_matrix_multiply", True)
+
+PYTHON_COMPAREOPMAP = {
+    operator.eq: '==',
+    operator.ne: '!=',
+    operator.lt: '<',
+    operator.le: '<=',
+    operator.gt: '>',
+    operator.ge: '>=',
+    operator.is_: 'is',
+    operator.is_not: 'is not',
+    operator.contains: 'in'
+}
+
+class PyLower(BaseLower):
+
+    GeneratorLower = generators.PyGeneratorLower
+
+    def init(self):
+        # Strings to be frozen into the Environment object
+        self._frozen_strings = set()
+
+        self._live_vars = set()
+
+    def pre_lower(self):
+        super(PyLower, self).pre_lower()
+        self.init_pyapi()
+
+    def post_lower(self):
+        pass
+
+    def pre_block(self, block):
+        self.init_vars(block)
+
+    def lower_inst(self, inst):
+        if isinstance(inst, ir.Assign):
+            value = self.lower_assign(inst)
+            self.storevar(value, inst.target.name)
+
+        elif isinstance(inst, ir.SetItem):
+            target = self.loadvar(inst.target.name)
+            index = self.loadvar(inst.index.name)
+            value = self.loadvar(inst.value.name)
+            ok = self.pyapi.object_setitem(target, index, value)
+            self.check_int_status(ok)
+
+        elif isinstance(inst, ir.DelItem):
+            target = self.loadvar(inst.target.name)
+            index = self.loadvar(inst.index.name)
+            ok = self.pyapi.object_delitem(target, index)
+            self.check_int_status(ok)
+
+        elif isinstance(inst, ir.SetAttr):
+            target = self.loadvar(inst.target.name)
+            value = self.loadvar(inst.value.name)
+            ok = self.pyapi.object_setattr(target,
+                                           self._freeze_string(inst.attr),
+                                           value)
+            self.check_int_status(ok)
+
+        elif isinstance(inst, ir.DelAttr):
+            target = self.loadvar(inst.target.name)
+            ok = self.pyapi.object_delattr(target,
+                                           self._freeze_string(inst.attr))
+            self.check_int_status(ok)
+
+        elif isinstance(inst, ir.StoreMap):
+            dct = self.loadvar(inst.dct.name)
+            key = self.loadvar(inst.key.name)
+            value = self.loadvar(inst.value.name)
+            ok = self.pyapi.dict_setitem(dct, key, value)
+            self.check_int_status(ok)
+
+        elif isinstance(inst, ir.Return):
+            retval = self.loadvar(inst.value.name)
+            if self.generator_info:
+                # StopIteration
+                # We own a reference to the "return value", but we
+                # don't return it.
+                self.pyapi.decref(retval)
+                self.genlower.return_from_generator(self)
+                return
+            # No need to incref() as the reference is already owned.
+            self.call_conv.return_value(self.builder, retval)
+
+        elif isinstance(inst, ir.Branch):
+            cond = self.loadvar(inst.cond.name)
+            if cond.type == llvmlite.ir.IntType(1):
+                istrue = cond
+            else:
+                istrue = self.pyapi.object_istrue(cond)
+            zero = llvmlite.ir.Constant(istrue.type, None)
+            pred = self.builder.icmp_unsigned('!=', istrue, zero)
+            tr = self.blkmap[inst.truebr]
+            fl = self.blkmap[inst.falsebr]
+            self.builder.cbranch(pred, tr, fl)
+
+        elif isinstance(inst, ir.Jump):
+            target = self.blkmap[inst.target]
+            self.builder.branch(target)
+
+        elif isinstance(inst, ir.Del):
+            self.delvar(inst.value)
+
+        elif isinstance(inst, ir.PopBlock):
+            pass # this is just a marker
+
+        elif isinstance(inst, ir.Raise):
+            if inst.exception is not None:
+                exc = self.loadvar(inst.exception.name)
+                # A reference will be stolen by raise_object() and another
+                # by return_exception_raised().
+                self.incref(exc)
+            else:
+                exc = None
+            self.pyapi.raise_object(exc)
+            self.return_exception_raised()
+
+        else:
+            msg = f"{type(inst)}, {inst}"
+            raise NumbaNotImplementedError(msg)
+
+    @utils.cached_property
+    def _omitted_typobj(self):
+        """Return a `OmittedArg` type instance as a LLVM value suitable for
+        testing at runtime.
+        """
+        from numba.core.dispatcher import OmittedArg
+        return self.pyapi.unserialize(
+            self.pyapi.serialize_object(OmittedArg))
+
+    def lower_assign(self, inst):
+        """
+        The returned object must have a new reference
+        """
+        value = inst.value
+        if isinstance(value, (ir.Const, ir.FreeVar)):
+            return self.lower_const(value.value)
+        elif isinstance(value, ir.Var):
+            val = self.loadvar(value.name)
+            self.incref(val)
+            return val
+        elif isinstance(value, ir.Expr):
+            return self.lower_expr(value)
+        elif isinstance(value, ir.Global):
+            return self.lower_global(value.name, value.value)
+        elif isinstance(value, ir.Yield):
+            return self.lower_yield(value)
+        elif isinstance(value, ir.Arg):
+            param = self.func_ir.func_id.pysig.parameters.get(value.name)
+
+            obj = self.fnargs[value.index]
+            slot = cgutils.alloca_once_value(self.builder, obj)
+            # Don't check for OmittedArg unless the argument has a default
+            if param is not None and param.default is inspect.Parameter.empty:
+                self.incref(obj)
+                self.builder.store(obj, slot)
+            else:
+                # When an argument is omitted, the dispatcher hands it as
+                # _OmittedArg(<default value>)
+                typobj = self.pyapi.get_type(obj)
+                is_omitted = self.builder.icmp_unsigned('==', typobj,
+                                                        self._omitted_typobj)
+                with self.builder.if_else(is_omitted, likely=False) as (omitted, present):
+                    with present:
+                        self.incref(obj)
+                        self.builder.store(obj, slot)
+                    with omitted:
+                        # The argument is omitted => get the default value
+                        obj = self.pyapi.object_getattr_string(obj, 'value')
+                        self.builder.store(obj, slot)
+
+            return self.builder.load(slot)
+        else:
+            raise NotImplementedError(type(value), value)
+
+    def lower_yield(self, inst):
+        yp = self.generator_info.yield_points[inst.index]
+        assert yp.inst is inst
+        self.genlower.init_generator_state(self)
+
+        # Save live vars in state
+        # We also need to save live vars that are del'ed afterwards.
+        y = generators.LowerYield(self, yp, yp.live_vars | yp.weak_live_vars)
+        y.lower_yield_suspend()
+        # Yield to caller
+        val = self.loadvar(inst.value.name)
+        # Let caller own the reference
+        self.pyapi.incref(val)
+        self.call_conv.return_value(self.builder, val)
+
+        # Resumption point
+        y.lower_yield_resume()
+        # None is returned by the yield expression
+        return self.pyapi.make_none()
+
+    def lower_binop(self, expr, op, inplace=False):
+        lhs = self.loadvar(expr.lhs.name)
+        rhs = self.loadvar(expr.rhs.name)
+        assert not isinstance(op, str)
+        if op in PYTHON_BINOPMAP:
+            fname, inplace = PYTHON_BINOPMAP[op]
+            fn = getattr(self.pyapi, fname)
+            res = fn(lhs, rhs, inplace=inplace)
+        else:
+            # Assumed to be rich comparison
+            fn = PYTHON_COMPAREOPMAP.get(expr.fn, expr.fn)
+            if fn == 'in':      # 'in' and operator.contains have args reversed
+                lhs, rhs = rhs, lhs
+            res = self.pyapi.object_richcompare(lhs, rhs, fn)
+        self.check_error(res)
+        return res
+
+    def lower_expr(self, expr):
+        if expr.op == 'binop':
+            return self.lower_binop(expr, expr.fn, inplace=False)
+        elif expr.op == 'inplace_binop':
+            return self.lower_binop(expr, expr.fn, inplace=True)
+        elif expr.op == 'unary':
+            value = self.loadvar(expr.value.name)
+            if expr.fn == operator.neg:
+                res = self.pyapi.number_negative(value)
+            elif expr.fn == operator.pos:
+                res = self.pyapi.number_positive(value)
+            elif expr.fn == operator.not_:
+                res = self.pyapi.object_not(value)
+                self.check_int_status(res)
+                res = self.pyapi.bool_from_bool(res)
+            elif expr.fn == operator.invert:
+                res = self.pyapi.number_invert(value)
+            else:
+                raise NotImplementedError(expr)
+            self.check_error(res)
+            return res
+        elif expr.op == 'call':
+            argvals = [self.loadvar(a.name) for a in expr.args]
+            fn = self.loadvar(expr.func.name)
+            args = self.pyapi.tuple_pack(argvals)
+            if expr.vararg:
+                # Expand *args
+                varargs = self.pyapi.sequence_tuple(
+                                self.loadvar(expr.vararg.name))
+                new_args = self.pyapi.sequence_concat(args, varargs)
+                self.decref(varargs)
+                self.decref(args)
+                args = new_args
+            if not expr.kws:
+                # No named arguments
+                ret = self.pyapi.call(fn, args, None)
+            else:
+                # Named arguments
+                keyvalues = [(k, self.loadvar(v.name)) for k, v in expr.kws]
+                kws = self.pyapi.dict_pack(keyvalues)
+                ret = self.pyapi.call(fn, args, kws)
+                self.decref(kws)
+            self.decref(args)
+            self.check_error(ret)
+            return ret
+        elif expr.op == 'getattr':
+            obj = self.loadvar(expr.value.name)
+            res = self.pyapi.object_getattr(obj, self._freeze_string(expr.attr))
+            self.check_error(res)
+            return res
+        elif expr.op == 'build_tuple':
+            items = [self.loadvar(it.name) for it in expr.items]
+            res = self.pyapi.tuple_pack(items)
+            self.check_error(res)
+            return res
+        elif expr.op == 'build_list':
+            items = [self.loadvar(it.name) for it in expr.items]
+            res = self.pyapi.list_pack(items)
+            self.check_error(res)
+            return res
+        elif expr.op == 'build_map':
+            res = self.pyapi.dict_new(expr.size)
+            self.check_error(res)
+            for k, v in expr.items:
+                key = self.loadvar(k.name)
+                value = self.loadvar(v.name)
+                ok = self.pyapi.dict_setitem(res, key, value)
+                self.check_int_status(ok)
+            return res
+        elif expr.op == 'build_set':
+            items = [self.loadvar(it.name) for it in expr.items]
+            res = self.pyapi.set_new()
+            self.check_error(res)
+            for it in items:
+                ok = self.pyapi.set_add(res, it)
+                self.check_int_status(ok)
+            return res
+        elif expr.op == 'getiter':
+            obj = self.loadvar(expr.value.name)
+            res = self.pyapi.object_getiter(obj)
+            self.check_error(res)
+            return res
+        elif expr.op == 'iternext':
+            iterobj = self.loadvar(expr.value.name)
+            item = self.pyapi.iter_next(iterobj)
+            is_valid = cgutils.is_not_null(self.builder, item)
+            pair = self.pyapi.tuple_new(2)
+            with self.builder.if_else(is_valid) as (then, otherwise):
+                with then:
+                    self.pyapi.tuple_setitem(pair, 0, item)
+                with otherwise:
+                    self.check_occurred()
+                    # Make the tuple valid by inserting None as dummy
+                    # iteration "result" (it will be ignored).
+                    self.pyapi.tuple_setitem(pair, 0, self.pyapi.make_none())
+            self.pyapi.tuple_setitem(pair, 1, self.pyapi.bool_from_bool(is_valid))
+            return pair
+        elif expr.op == 'pair_first':
+            pair = self.loadvar(expr.value.name)
+            first = self.pyapi.tuple_getitem(pair, 0)
+            self.incref(first)
+            return first
+        elif expr.op == 'pair_second':
+            pair = self.loadvar(expr.value.name)
+            second = self.pyapi.tuple_getitem(pair, 1)
+            self.incref(second)
+            return second
+        elif expr.op == 'exhaust_iter':
+            iterobj = self.loadvar(expr.value.name)
+            tup = self.pyapi.sequence_tuple(iterobj)
+            self.check_error(tup)
+            # Check tuple size is as expected
+            tup_size = self.pyapi.tuple_size(tup)
+            expected_size = self.context.get_constant(types.intp, expr.count)
+            has_wrong_size = self.builder.icmp_unsigned('!=',
+                                               tup_size, expected_size)
+            with cgutils.if_unlikely(self.builder, has_wrong_size):
+                self.return_exception(ValueError)
+            return tup
+        elif expr.op == 'getitem':
+            value = self.loadvar(expr.value.name)
+            index = self.loadvar(expr.index.name)
+            res = self.pyapi.object_getitem(value, index)
+            self.check_error(res)
+            return res
+        elif expr.op == 'static_getitem':
+            value = self.loadvar(expr.value.name)
+            index = self.context.get_constant(types.intp, expr.index)
+            indexobj = self.pyapi.long_from_ssize_t(index)
+            self.check_error(indexobj)
+            res = self.pyapi.object_getitem(value, indexobj)
+            self.decref(indexobj)
+            self.check_error(res)
+            return res
+        elif expr.op == 'getslice':
+            target = self.loadvar(expr.target.name)
+            start = self.loadvar(expr.start.name)
+            stop = self.loadvar(expr.stop.name)
+
+            slicefn = self.get_builtin_obj("slice")
+            sliceobj = self.pyapi.call_function_objargs(slicefn, (start, stop))
+            self.decref(slicefn)
+            self.check_error(sliceobj)
+
+            res = self.pyapi.object_getitem(target, sliceobj)
+            self.check_error(res)
+
+            return res
+
+        elif expr.op == 'cast':
+            val = self.loadvar(expr.value.name)
+            self.incref(val)
+            return val
+        elif expr.op == 'phi':
+            raise LoweringError("PHI not stripped")
+
+        elif expr.op == 'null':
+            # Make null value
+            return cgutils.get_null_value(self.pyapi.pyobj)
+
+        else:
+            raise NotImplementedError(expr)
+
+    def lower_const(self, const):
+        # All constants are frozen inside the environment
+        index = self.env_manager.add_const(const)
+        ret = self.env_manager.read_const(index)
+        self.check_error(ret)
+        self.incref(ret)
+        return ret
+
+    def lower_global(self, name, value):
+        """
+        1) Check global scope dictionary.
+        2) Check __builtins__.
+            2a) is it a dictionary (for non __main__ module)
+            2b) is it a module (for __main__ module)
+        """
+        moddict = self.get_module_dict()
+        obj = self.pyapi.dict_getitem(moddict, self._freeze_string(name))
+        self.incref(obj)  # obj is borrowed
+
+        try:
+            if value in _unsupported_builtins:
+                raise ForbiddenConstruct("builtins %s() is not supported"
+                                         % name, loc=self.loc)
+        except TypeError:
+            # `value` is unhashable, ignore
+            pass
+
+        if hasattr(builtins, name):
+            obj_is_null = self.is_null(obj)
+            bbelse = self.builder.basic_block
+
+            with self.builder.if_then(obj_is_null):
+                mod = self.pyapi.dict_getitem(moddict,
+                                          self._freeze_string("__builtins__"))
+                builtin = self.builtin_lookup(mod, name)
+                bbif = self.builder.basic_block
+
+            retval = self.builder.phi(self.pyapi.pyobj)
+            retval.add_incoming(obj, bbelse)
+            retval.add_incoming(builtin, bbif)
+
+        else:
+            retval = obj
+            with cgutils.if_unlikely(self.builder, self.is_null(retval)):
+                self.pyapi.raise_missing_global_error(name)
+                self.return_exception_raised()
+
+        return retval
+
+    # -------------------------------------------------------------------------
+
+    def get_module_dict(self):
+        return self.env_body.globals
+
+    def get_builtin_obj(self, name):
+        # XXX The builtins dict could be bound into the environment
+        moddict = self.get_module_dict()
+        mod = self.pyapi.dict_getitem(moddict,
+                                      self._freeze_string("__builtins__"))
+        return self.builtin_lookup(mod, name)
+
+    def builtin_lookup(self, mod, name):
+        """
+        Args
+        ----
+        mod:
+            The __builtins__ dictionary or module, as looked up in
+            a module's globals.
+        name: str
+            The object to lookup
+        """
+        fromdict = self.pyapi.dict_getitem(mod, self._freeze_string(name))
+        self.incref(fromdict)       # fromdict is borrowed
+        bbifdict = self.builder.basic_block
+
+        with cgutils.if_unlikely(self.builder, self.is_null(fromdict)):
+            # This happen if we are using the __main__ module
+            frommod = self.pyapi.object_getattr(mod, self._freeze_string(name))
+
+            with cgutils.if_unlikely(self.builder, self.is_null(frommod)):
+                self.pyapi.raise_missing_global_error(name)
+                self.return_exception_raised()
+
+            bbifmod = self.builder.basic_block
+
+        builtin = self.builder.phi(self.pyapi.pyobj)
+        builtin.add_incoming(fromdict, bbifdict)
+        builtin.add_incoming(frommod, bbifmod)
+
+        return builtin
+
+    def check_occurred(self):
+        """
+        Return if an exception occurred.
+        """
+        err_occurred = cgutils.is_not_null(self.builder,
+                                           self.pyapi.err_occurred())
+
+        with cgutils.if_unlikely(self.builder, err_occurred):
+            self.return_exception_raised()
+
+    def check_error(self, obj):
+        """
+        Return if *obj* is NULL.
+        """
+        with cgutils.if_unlikely(self.builder, self.is_null(obj)):
+            self.return_exception_raised()
+
+        return obj
+
+    def check_int_status(self, num, ok_value=0):
+        """
+        Raise an exception if *num* is smaller than *ok_value*.
+        """
+        ok = llvmlite.ir.Constant(num.type, ok_value)
+        pred = self.builder.icmp_signed('<', num, ok)
+        with cgutils.if_unlikely(self.builder, pred):
+            self.return_exception_raised()
+
+    def is_null(self, obj):
+        return cgutils.is_null(self.builder, obj)
+
+    def return_exception_raised(self):
+        """
+        Return with the currently raised exception.
+        """
+        self.cleanup_vars()
+        self.call_conv.return_exc(self.builder)
+
+    def init_vars(self, block):
+        """
+        Initialize live variables for *block*.
+        """
+        self._live_vars = set(self.func_ir.get_block_entry_vars(block))
+
+    def _getvar(self, name, ltype=None):
+        if name not in self.varmap:
+            self.varmap[name] = self.alloca(name, ltype=ltype)
+        return self.varmap[name]
+
+    def loadvar(self, name):
+        """
+        Load the llvm value of the variable named *name*.
+        """
+        # If this raises then the live variables analysis is wrong
+        assert name in self._live_vars, name
+        ptr = self.varmap[name]
+        val = self.builder.load(ptr)
+        with cgutils.if_unlikely(self.builder, self.is_null(val)):
+            self.pyapi.raise_missing_name_error(name)
+            self.return_exception_raised()
+        return val
+
+    def delvar(self, name):
+        """
+        Delete the variable slot with the given name. This will decref
+        the corresponding Python object.
+        """
+        # If this raises then the live variables analysis is wrong
+        self._live_vars.remove(name)
+        ptr = self._getvar(name)  # initializes `name` if not already
+        self.decref(self.builder.load(ptr))
+        # This is a safety guard against double decref's, but really
+        # the IR should be correct and have only one Del per variable
+        # and code path.
+        self.builder.store(cgutils.get_null_value(ptr.type.pointee), ptr)
+
+    def storevar(self, value, name, clobber=False):
+        """
+        Stores a llvm value and allocate stack slot if necessary.
+        The llvm value can be of arbitrary type.
+        """
+        is_redefine = name in self._live_vars and not clobber
+        ptr = self._getvar(name, ltype=value.type)
+        if is_redefine:
+            old = self.builder.load(ptr)
+        else:
+            self._live_vars.add(name)
+        assert value.type == ptr.type.pointee, (str(value.type),
+                                                str(ptr.type.pointee))
+        self.builder.store(value, ptr)
+        # Safe to call decref even on non python object
+        if is_redefine:
+            self.decref(old)
+
+    def cleanup_vars(self):
+        """
+        Cleanup live variables.
+        """
+        for name in self._live_vars:
+            ptr = self._getvar(name)
+            self.decref(self.builder.load(ptr))
+
+    def alloca(self, name, ltype=None):
+        """
+        Allocate a stack slot and initialize it to NULL.
+        The default is to allocate a pyobject pointer.
+        Use ``ltype`` to override.
+        """
+        if ltype is None:
+            ltype = self.context.get_value_type(types.pyobject)
+        with self.builder.goto_block(self.entry_block):
+            ptr = self.builder.alloca(ltype, name=name)
+            self.builder.store(cgutils.get_null_value(ltype), ptr)
+        return ptr
+
+    def _alloca_var(self, name, fetype):
+        # This is here for API compatibility with lowering.py::Lower.
+        # NOTE: fetype is unused
+        return self.alloca(name)
+
+    def incref(self, value):
+        self.pyapi.incref(value)
+
+    def decref(self, value):
+        """
+        This is allow to be called on non pyobject pointer, in which case
+        no code is inserted.
+        """
+        lpyobj = self.context.get_value_type(types.pyobject)
+        if value.type == lpyobj:
+            self.pyapi.decref(value)
+
+    def _freeze_string(self, string):
+        """
+        Freeze a Python string object into the code.
+        """
+        return self.lower_const(string)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/pythonapi.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/pythonapi.py
new file mode 100644
index 000000000..5ea6dc222
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/pythonapi.py
@@ -0,0 +1,1686 @@
+from collections import namedtuple
+import contextlib
+import pickle
+import hashlib
+import sys
+
+from llvmlite import ir
+from llvmlite.ir import Constant
+
+import ctypes
+from numba import _helperlib
+from numba.core import (
+    types, utils, config, lowering, cgutils, imputils, serialize,
+)
+
+PY_UNICODE_1BYTE_KIND = _helperlib.py_unicode_1byte_kind
+PY_UNICODE_2BYTE_KIND = _helperlib.py_unicode_2byte_kind
+PY_UNICODE_4BYTE_KIND = _helperlib.py_unicode_4byte_kind
+PY_UNICODE_WCHAR_KIND = _helperlib.py_unicode_wchar_kind
+
+
+class _Registry(object):
+
+    def __init__(self):
+        self.functions = {}
+
+    def register(self, typeclass):
+        assert issubclass(typeclass, types.Type)
+        def decorator(func):
+            if typeclass in self.functions:
+                raise KeyError("duplicate registration for %s" % (typeclass,))
+            self.functions[typeclass] = func
+            return func
+        return decorator
+
+    def lookup(self, typeclass, default=None):
+        assert issubclass(typeclass, types.Type)
+        for cls in typeclass.__mro__:
+            func = self.functions.get(cls)
+            if func is not None:
+                return func
+        return default
+
+# Registries of boxing / unboxing implementations
+_boxers = _Registry()
+_unboxers = _Registry()
+_reflectors = _Registry()
+
+box = _boxers.register
+unbox = _unboxers.register
+reflect = _reflectors.register
+
+class _BoxContext(namedtuple("_BoxContext",
+                  ("context", "builder", "pyapi", "env_manager"))):
+    """
+    The facilities required by boxing implementations.
+    """
+    __slots__ = ()
+
+    def box(self, typ, val):
+        return self.pyapi.from_native_value(typ, val, self.env_manager)
+
+
+class _UnboxContext(namedtuple("_UnboxContext",
+                    ("context", "builder", "pyapi"))):
+    """
+    The facilities required by unboxing implementations.
+    """
+    __slots__ = ()
+
+    def unbox(self, typ, obj):
+        return self.pyapi.to_native_value(typ, obj)
+
+
+class _ReflectContext(namedtuple("_ReflectContext",
+                      ("context", "builder", "pyapi", "env_manager",
+                       "is_error"))):
+    """
+    The facilities required by reflection implementations.
+    """
+    __slots__ = ()
+
+    # XXX the error bit is currently unused by consumers (e.g. PyCallWrapper)
+    def set_error(self):
+        self.builder.store(self.is_error, cgutils.true_bit)
+
+    def box(self, typ, val):
+        return self.pyapi.from_native_value(typ, val, self.env_manager)
+
+    def reflect(self, typ, val):
+        return self.pyapi.reflect_native_value(typ, val, self.env_manager)
+
+
+class NativeValue(object):
+    """
+    Encapsulate the result of converting a Python object to a native value,
+    recording whether the conversion was successful and how to cleanup.
+    """
+
+    def __init__(self, value, is_error=None, cleanup=None):
+        self.value = value
+        self.is_error = is_error if is_error is not None else cgutils.false_bit
+        self.cleanup = cleanup
+
+
+class EnvironmentManager(object):
+
+    def __init__(self, pyapi, env, env_body, env_ptr):
+        assert isinstance(env, lowering.Environment)
+        self.pyapi = pyapi
+        self.env = env
+        self.env_body = env_body
+        self.env_ptr = env_ptr
+
+    def add_const(self, const):
+        """
+        Add a constant to the environment, return its index.
+        """
+        # All constants are frozen inside the environment
+        if isinstance(const, str):
+            const = sys.intern(const)
+        for index, val in enumerate(self.env.consts):
+            if val is const:
+                break
+        else:
+            index = len(self.env.consts)
+            self.env.consts.append(const)
+        return index
+
+    def read_const(self, index):
+        """
+        Look up constant number *index* inside the environment body.
+        A borrowed reference is returned.
+
+        The returned LLVM value may have NULL value at runtime which indicates
+        an error at runtime.
+        """
+        assert index < len(self.env.consts)
+
+        builder = self.pyapi.builder
+        consts = self.env_body.consts
+        ret = cgutils.alloca_once(builder, self.pyapi.pyobj, zfill=True)
+        with builder.if_else(cgutils.is_not_null(builder, consts)) as \
+                (br_not_null, br_null):
+            with br_not_null:
+                getitem = self.pyapi.list_getitem(consts, index)
+                builder.store(getitem, ret)
+            with br_null:
+                # This can happen when the Environment is accidentally released
+                # and has subsequently been garbage collected.
+                self.pyapi.err_set_string(
+                    "PyExc_RuntimeError",
+                    "`env.consts` is NULL in `read_const`",
+                )
+        return builder.load(ret)
+
+
+_IteratorLoop = namedtuple('_IteratorLoop', ('value', 'do_break'))
+
+
+class PythonAPI(object):
+    """
+    Code generation facilities to call into the CPython C API (and related
+    helpers).
+    """
+
+    def __init__(self, context, builder):
+        """
+        Note: Maybe called multiple times when lowering a function
+        """
+        self.context = context
+        self.builder = builder
+
+        self.module = builder.basic_block.function.module
+        # A unique mapping of serialized objects in this module
+        try:
+            self.module.__serialized
+        except AttributeError:
+            self.module.__serialized = {}
+
+        # Initialize types
+        self.pyobj = self.context.get_argument_type(types.pyobject)
+        self.pyobjptr = self.pyobj.as_pointer()
+        self.voidptr = ir.PointerType(ir.IntType(8))
+        self.long = ir.IntType(ctypes.sizeof(ctypes.c_long) * 8)
+        self.ulong = self.long
+        self.longlong = ir.IntType(ctypes.sizeof(ctypes.c_ulonglong) * 8)
+        self.ulonglong = self.longlong
+        self.double = ir.DoubleType()
+        self.py_ssize_t = self.context.get_value_type(types.intp)
+        self.cstring = ir.PointerType(ir.IntType(8))
+        self.gil_state = ir.IntType(_helperlib.py_gil_state_size * 8)
+        self.py_buffer_t = ir.ArrayType(ir.IntType(8), _helperlib.py_buffer_size)
+        self.py_hash_t = self.py_ssize_t
+        self.py_unicode_1byte_kind = _helperlib.py_unicode_1byte_kind
+        self.py_unicode_2byte_kind = _helperlib.py_unicode_2byte_kind
+        self.py_unicode_4byte_kind = _helperlib.py_unicode_4byte_kind
+        self.py_unicode_wchar_kind = _helperlib.py_unicode_wchar_kind
+
+    def get_env_manager(self, env, env_body, env_ptr):
+        return EnvironmentManager(self, env, env_body, env_ptr)
+
+    def emit_environment_sentry(self, envptr, return_pyobject=False,
+                                debug_msg=''):
+        """Emits LLVM code to ensure the `envptr` is not NULL
+        """
+        is_null = cgutils.is_null(self.builder, envptr)
+        with cgutils.if_unlikely(self.builder, is_null):
+            if return_pyobject:
+                fnty = self.builder.function.type.pointee
+                assert fnty.return_type == self.pyobj
+                self.err_set_string(
+                    "PyExc_RuntimeError", f"missing Environment: {debug_msg}",
+                )
+                self.builder.ret(self.get_null_object())
+            else:
+                self.context.call_conv.return_user_exc(
+                    self.builder, RuntimeError,
+                    (f"missing Environment: {debug_msg}",),
+                )
+
+    # ------ Python API -----
+
+    #
+    # Basic object API
+    #
+
+    def incref(self, obj):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj])
+        fn = self._get_function(fnty, name="Py_IncRef")
+        self.builder.call(fn, [obj])
+
+    def decref(self, obj):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj])
+        fn = self._get_function(fnty, name="Py_DecRef")
+        self.builder.call(fn, [obj])
+
+    def get_type(self, obj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="numba_py_type")
+        return self.builder.call(fn, [obj])
+
+    #
+    # Argument unpacking
+    #
+
+    def parse_tuple_and_keywords(self, args, kws, fmt, keywords, *objs):
+        charptr = ir.PointerType(ir.IntType(8))
+        charptrary = ir.PointerType(charptr)
+        argtypes = [self.pyobj, self.pyobj, charptr, charptrary]
+        fnty = ir.FunctionType(ir.IntType(32), argtypes, var_arg=True)
+        fn = self._get_function(fnty, name="PyArg_ParseTupleAndKeywords")
+        return self.builder.call(fn, [args, kws, fmt, keywords] + list(objs))
+
+    def parse_tuple(self, args, fmt, *objs):
+        charptr = ir.PointerType(ir.IntType(8))
+        argtypes = [self.pyobj, charptr]
+        fnty = ir.FunctionType(ir.IntType(32), argtypes, var_arg=True)
+        fn = self._get_function(fnty, name="PyArg_ParseTuple")
+        return self.builder.call(fn, [args, fmt] + list(objs))
+
+    def unpack_tuple(self, args, name, n_min, n_max, *objs):
+        charptr = ir.PointerType(ir.IntType(8))
+        argtypes = [self.pyobj, charptr, self.py_ssize_t, self.py_ssize_t]
+        fnty = ir.FunctionType(ir.IntType(32), argtypes, var_arg=True)
+        fn = self._get_function(fnty, name="PyArg_UnpackTuple")
+        n_min = Constant(self.py_ssize_t, int(n_min))
+        n_max = Constant(self.py_ssize_t, int(n_max))
+        if isinstance(name, str):
+            name = self.context.insert_const_string(self.builder.module, name)
+        return self.builder.call(fn, [args, name, n_min, n_max] + list(objs))
+
+    #
+    # Exception and errors
+    #
+
+    def err_occurred(self):
+        fnty = ir.FunctionType(self.pyobj, ())
+        fn = self._get_function(fnty, name="PyErr_Occurred")
+        return self.builder.call(fn, ())
+
+    def err_clear(self):
+        fnty = ir.FunctionType(ir.VoidType(), ())
+        fn = self._get_function(fnty, name="PyErr_Clear")
+        return self.builder.call(fn, ())
+
+    def err_set_string(self, exctype, msg):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj, self.cstring])
+        fn = self._get_function(fnty, name="PyErr_SetString")
+        if isinstance(exctype, str):
+            exctype = self.get_c_object(exctype)
+        if isinstance(msg, str):
+            msg = self.context.insert_const_string(self.module, msg)
+        return self.builder.call(fn, (exctype, msg))
+
+    def err_format(self, exctype, msg, *format_args):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj, self.cstring], var_arg=True)
+        fn = self._get_function(fnty, name="PyErr_Format")
+        if isinstance(exctype, str):
+            exctype = self.get_c_object(exctype)
+        if isinstance(msg, str):
+            msg = self.context.insert_const_string(self.module, msg)
+        return self.builder.call(fn, (exctype, msg) + tuple(format_args))
+
+    def raise_object(self, exc=None):
+        """
+        Raise an arbitrary exception (type or value or (type, args)
+        or None - if reraising).  A reference to the argument is consumed.
+        """
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj])
+        fn = self._get_function(fnty, name="numba_do_raise")
+        if exc is None:
+            exc = self.make_none()
+        return self.builder.call(fn, (exc,))
+
+    def err_set_object(self, exctype, excval):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyErr_SetObject")
+        if isinstance(exctype, str):
+            exctype = self.get_c_object(exctype)
+        return self.builder.call(fn, (exctype, excval))
+
+    def err_set_none(self, exctype):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj])
+        fn = self._get_function(fnty, name="PyErr_SetNone")
+        if isinstance(exctype, str):
+            exctype = self.get_c_object(exctype)
+        return self.builder.call(fn, (exctype,))
+
+    def err_write_unraisable(self, obj):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj])
+        fn = self._get_function(fnty, name="PyErr_WriteUnraisable")
+        return self.builder.call(fn, (obj,))
+
+    def err_fetch(self, pty, pval, ptb):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobjptr] * 3)
+        fn = self._get_function(fnty, name="PyErr_Fetch")
+        return self.builder.call(fn, (pty, pval, ptb))
+
+    def err_restore(self, ty, val, tb):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj] * 3)
+        fn = self._get_function(fnty, name="PyErr_Restore")
+        return self.builder.call(fn, (ty, val, tb))
+
+    @contextlib.contextmanager
+    def err_push(self, keep_new=False):
+        """
+        Temporarily push the current error indicator while the code
+        block is executed.  If *keep_new* is True and the code block
+        raises a new error, the new error is kept, otherwise the old
+        error indicator is restored at the end of the block.
+        """
+        pty, pval, ptb = [cgutils.alloca_once(self.builder, self.pyobj)
+                          for i in range(3)]
+        self.err_fetch(pty, pval, ptb)
+        yield
+        ty = self.builder.load(pty)
+        val = self.builder.load(pval)
+        tb = self.builder.load(ptb)
+        if keep_new:
+            new_error = cgutils.is_not_null(self.builder, self.err_occurred())
+            with self.builder.if_else(new_error, likely=False) as (if_error, if_ok):
+                with if_error:
+                    # Code block raised an error, keep it
+                    self.decref(ty)
+                    self.decref(val)
+                    self.decref(tb)
+                with if_ok:
+                    # Restore previous error
+                    self.err_restore(ty, val, tb)
+        else:
+            self.err_restore(ty, val, tb)
+
+    def get_c_object(self, name):
+        """
+        Get a Python object through its C-accessible *name*
+        (e.g. "PyExc_ValueError").  The underlying variable must be
+        a `PyObject *`, and the value of that pointer is returned.
+        """
+        # A LLVM global variable is implicitly a pointer to the declared
+        # type, so fix up by using pyobj.pointee.
+        return self.context.get_c_value(self.builder, self.pyobj.pointee, name,
+                                        dllimport=True)
+
+    def raise_missing_global_error(self, name):
+        msg = "global name '%s' is not defined" % name
+        cstr = self.context.insert_const_string(self.module, msg)
+        self.err_set_string("PyExc_NameError", cstr)
+
+    def raise_missing_name_error(self, name):
+        msg = "name '%s' is not defined" % name
+        cstr = self.context.insert_const_string(self.module, msg)
+        self.err_set_string("PyExc_NameError", cstr)
+
+    def fatal_error(self, msg):
+        fnty = ir.FunctionType(ir.VoidType(), [self.cstring])
+        fn = self._get_function(fnty, name="Py_FatalError")
+        fn.attributes.add("noreturn")
+        cstr = self.context.insert_const_string(self.module, msg)
+        self.builder.call(fn, (cstr,))
+
+    #
+    # Concrete dict API
+    #
+
+    def dict_getitem_string(self, dic, name):
+        """Lookup name inside dict
+
+        Returns a borrowed reference
+        """
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.cstring])
+        fn = self._get_function(fnty, name="PyDict_GetItemString")
+        cstr = self.context.insert_const_string(self.module, name)
+        return self.builder.call(fn, [dic, cstr])
+
+    def dict_getitem(self, dic, name):
+        """Lookup name inside dict
+
+        Returns a borrowed reference
+        """
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyDict_GetItem")
+        return self.builder.call(fn, [dic, name])
+
+    def dict_new(self, presize=0):
+        if presize == 0:
+            fnty = ir.FunctionType(self.pyobj, ())
+            fn = self._get_function(fnty, name="PyDict_New")
+            return self.builder.call(fn, ())
+        else:
+            fnty = ir.FunctionType(self.pyobj, [self.py_ssize_t])
+            fn = self._get_function(fnty, name="_PyDict_NewPresized")
+            return self.builder.call(fn,
+                                     [Constant(self.py_ssize_t, int(presize))])
+
+    def dict_setitem(self, dictobj, nameobj, valobj):
+        fnty = ir.FunctionType(ir.IntType(32), (self.pyobj, self.pyobj,
+                                                self.pyobj))
+        fn = self._get_function(fnty, name="PyDict_SetItem")
+        return self.builder.call(fn, (dictobj, nameobj, valobj))
+
+    def dict_setitem_string(self, dictobj, name, valobj):
+        fnty = ir.FunctionType(ir.IntType(32), (self.pyobj, self.cstring,
+                                                self.pyobj))
+        fn = self._get_function(fnty, name="PyDict_SetItemString")
+        cstr = self.context.insert_const_string(self.module, name)
+        return self.builder.call(fn, (dictobj, cstr, valobj))
+
+    def dict_pack(self, keyvalues):
+        """
+        Args
+        -----
+        keyvalues: iterable of (str, llvm.Value of PyObject*)
+        """
+        dictobj = self.dict_new()
+        with self.if_object_ok(dictobj):
+            for k, v in keyvalues:
+                self.dict_setitem_string(dictobj, k, v)
+        return dictobj
+
+    #
+    # Concrete number APIs
+    #
+
+    def float_from_double(self, fval):
+        fnty = ir.FunctionType(self.pyobj, [self.double])
+        fn = self._get_function(fnty, name="PyFloat_FromDouble")
+        return self.builder.call(fn, [fval])
+
+    def number_as_ssize_t(self, numobj):
+        fnty = ir.FunctionType(self.py_ssize_t, [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyNumber_AsSsize_t")
+        # We don't want any clipping, so pass OverflowError as the 2nd arg
+        exc_class = self.get_c_object("PyExc_OverflowError")
+        return self.builder.call(fn, [numobj, exc_class])
+
+    def number_long(self, numobj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyNumber_Long")
+        return self.builder.call(fn, [numobj])
+
+    def long_as_ulonglong(self, numobj):
+        fnty = ir.FunctionType(self.ulonglong, [self.pyobj])
+        fn = self._get_function(fnty, name="PyLong_AsUnsignedLongLong")
+        return self.builder.call(fn, [numobj])
+
+    def long_as_longlong(self, numobj):
+        fnty = ir.FunctionType(self.ulonglong, [self.pyobj])
+        fn = self._get_function(fnty, name="PyLong_AsLongLong")
+        return self.builder.call(fn, [numobj])
+
+    def long_as_voidptr(self, numobj):
+        """
+        Convert the given Python integer to a void*.  This is recommended
+        over number_as_ssize_t as it isn't affected by signedness.
+        """
+        fnty = ir.FunctionType(self.voidptr, [self.pyobj])
+        fn = self._get_function(fnty, name="PyLong_AsVoidPtr")
+        return self.builder.call(fn, [numobj])
+
+    def _long_from_native_int(self, ival, func_name, native_int_type,
+                              signed):
+        fnty = ir.FunctionType(self.pyobj, [native_int_type])
+        fn = self._get_function(fnty, name=func_name)
+        resptr = cgutils.alloca_once(self.builder, self.pyobj)
+        fn = self._get_function(fnty, name=func_name)
+        self.builder.store(self.builder.call(fn, [ival]), resptr)
+
+        return self.builder.load(resptr)
+
+    def long_from_long(self, ival):
+        func_name = "PyLong_FromLong"
+        fnty = ir.FunctionType(self.pyobj, [self.long])
+        fn = self._get_function(fnty, name=func_name)
+        return self.builder.call(fn, [ival])
+
+    def long_from_ulong(self, ival):
+        return self._long_from_native_int(ival, "PyLong_FromUnsignedLong",
+                                          self.long, signed=False)
+
+    def long_from_ssize_t(self, ival):
+        return self._long_from_native_int(ival, "PyLong_FromSsize_t",
+                                          self.py_ssize_t, signed=True)
+
+    def long_from_longlong(self, ival):
+        return self._long_from_native_int(ival, "PyLong_FromLongLong",
+                                          self.longlong, signed=True)
+
+    def long_from_ulonglong(self, ival):
+        return self._long_from_native_int(ival, "PyLong_FromUnsignedLongLong",
+                                          self.ulonglong, signed=False)
+
+    def long_from_signed_int(self, ival):
+        """
+        Return a Python integer from any native integer value.
+        """
+        bits = ival.type.width
+        if bits <= self.long.width:
+            return self.long_from_long(self.builder.sext(ival, self.long))
+        elif bits <= self.longlong.width:
+            return self.long_from_longlong(self.builder.sext(ival, self.longlong))
+        else:
+            raise OverflowError("integer too big (%d bits)" % (bits))
+
+    def long_from_unsigned_int(self, ival):
+        """
+        Same as long_from_signed_int, but for unsigned values.
+        """
+        bits = ival.type.width
+        if bits <= self.ulong.width:
+            return self.long_from_ulong(self.builder.zext(ival, self.ulong))
+        elif bits <= self.ulonglong.width:
+            return self.long_from_ulonglong(self.builder.zext(ival, self.ulonglong))
+        else:
+            raise OverflowError("integer too big (%d bits)" % (bits))
+
+    def _get_number_operator(self, name):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyNumber_%s" % name)
+        return fn
+
+    def _call_number_operator(self, name, lhs, rhs, inplace=False):
+        if inplace:
+            name = "InPlace" + name
+        fn = self._get_number_operator(name)
+        return self.builder.call(fn, [lhs, rhs])
+
+    def number_add(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("Add", lhs, rhs, inplace=inplace)
+
+    def number_subtract(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("Subtract", lhs, rhs, inplace=inplace)
+
+    def number_multiply(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("Multiply", lhs, rhs, inplace=inplace)
+
+    def number_truedivide(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("TrueDivide", lhs, rhs, inplace=inplace)
+
+    def number_floordivide(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("FloorDivide", lhs, rhs, inplace=inplace)
+
+    def number_remainder(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("Remainder", lhs, rhs, inplace=inplace)
+
+    def number_matrix_multiply(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("MatrixMultiply", lhs, rhs, inplace=inplace)
+
+    def number_lshift(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("Lshift", lhs, rhs, inplace=inplace)
+
+    def number_rshift(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("Rshift", lhs, rhs, inplace=inplace)
+
+    def number_and(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("And", lhs, rhs, inplace=inplace)
+
+    def number_or(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("Or", lhs, rhs, inplace=inplace)
+
+    def number_xor(self, lhs, rhs, inplace=False):
+        return self._call_number_operator("Xor", lhs, rhs, inplace=inplace)
+
+    def number_power(self, lhs, rhs, inplace=False):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj] * 3)
+        fname = "PyNumber_InPlacePower" if inplace else "PyNumber_Power"
+        fn = self._get_function(fnty, fname)
+        return self.builder.call(fn, [lhs, rhs, self.borrow_none()])
+
+    def number_negative(self, obj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyNumber_Negative")
+        return self.builder.call(fn, (obj,))
+
+    def number_positive(self, obj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyNumber_Positive")
+        return self.builder.call(fn, (obj,))
+
+    def number_float(self, val):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyNumber_Float")
+        return self.builder.call(fn, [val])
+
+    def number_invert(self, obj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyNumber_Invert")
+        return self.builder.call(fn, (obj,))
+
+    def float_as_double(self, fobj):
+        fnty = ir.FunctionType(self.double, [self.pyobj])
+        fn = self._get_function(fnty, name="PyFloat_AsDouble")
+        return self.builder.call(fn, [fobj])
+
+    def bool_from_bool(self, bval):
+        """
+        Get a Python bool from a LLVM boolean.
+        """
+        longval = self.builder.zext(bval, self.long)
+        return self.bool_from_long(longval)
+
+    def bool_from_long(self, ival):
+        fnty = ir.FunctionType(self.pyobj, [self.long])
+        fn = self._get_function(fnty, name="PyBool_FromLong")
+        return self.builder.call(fn, [ival])
+
+    def complex_from_doubles(self, realval, imagval):
+        fnty = ir.FunctionType(self.pyobj, [ir.DoubleType(), ir.DoubleType()])
+        fn = self._get_function(fnty, name="PyComplex_FromDoubles")
+        return self.builder.call(fn, [realval, imagval])
+
+    def complex_real_as_double(self, cobj):
+        fnty = ir.FunctionType(ir.DoubleType(), [self.pyobj])
+        fn = self._get_function(fnty, name="PyComplex_RealAsDouble")
+        return self.builder.call(fn, [cobj])
+
+    def complex_imag_as_double(self, cobj):
+        fnty = ir.FunctionType(ir.DoubleType(), [self.pyobj])
+        fn = self._get_function(fnty, name="PyComplex_ImagAsDouble")
+        return self.builder.call(fn, [cobj])
+
+    #
+    # Concrete slice API
+    #
+    def slice_as_ints(self, obj):
+        """
+        Read the members of a slice of integers.
+
+        Returns a (ok, start, stop, step) tuple where ok is a boolean and
+        the following members are pointer-sized ints.
+        """
+        pstart = cgutils.alloca_once(self.builder, self.py_ssize_t)
+        pstop = cgutils.alloca_once(self.builder, self.py_ssize_t)
+        pstep = cgutils.alloca_once(self.builder, self.py_ssize_t)
+        fnty = ir.FunctionType(ir.IntType(32),
+                               [self.pyobj] + [self.py_ssize_t.as_pointer()] * 3)
+        fn = self._get_function(fnty, name="numba_unpack_slice")
+        res = self.builder.call(fn, (obj, pstart, pstop, pstep))
+        start = self.builder.load(pstart)
+        stop = self.builder.load(pstop)
+        step = self.builder.load(pstep)
+        return cgutils.is_null(self.builder, res), start, stop, step
+
+    #
+    # List and sequence APIs
+    #
+
+    def sequence_getslice(self, obj, start, stop):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.py_ssize_t,
+                                            self.py_ssize_t])
+        fn = self._get_function(fnty, name="PySequence_GetSlice")
+        return self.builder.call(fn, (obj, start, stop))
+
+    def sequence_tuple(self, obj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PySequence_Tuple")
+        return self.builder.call(fn, [obj])
+
+    def sequence_concat(self, obj1, obj2):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PySequence_Concat")
+        return self.builder.call(fn, [obj1, obj2])
+
+    def list_new(self, szval):
+        fnty = ir.FunctionType(self.pyobj, [self.py_ssize_t])
+        fn = self._get_function(fnty, name="PyList_New")
+        return self.builder.call(fn, [szval])
+
+    def list_size(self, lst):
+        fnty = ir.FunctionType(self.py_ssize_t, [self.pyobj])
+        fn = self._get_function(fnty, name="PyList_Size")
+        return self.builder.call(fn, [lst])
+
+    def list_append(self, lst, val):
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyList_Append")
+        return self.builder.call(fn, [lst, val])
+
+    def list_setitem(self, lst, idx, val):
+        """
+        Warning: Steals reference to ``val``
+        """
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.py_ssize_t,
+                                                self.pyobj])
+        fn = self._get_function(fnty, name="PyList_SetItem")
+        return self.builder.call(fn, [lst, idx, val])
+
+    def list_getitem(self, lst, idx):
+        """
+        Returns a borrowed reference.
+        """
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.py_ssize_t])
+        fn = self._get_function(fnty, name="PyList_GetItem")
+        if isinstance(idx, int):
+            idx = self.context.get_constant(types.intp, idx)
+        return self.builder.call(fn, [lst, idx])
+
+    def list_setslice(self, lst, start, stop, obj):
+        if obj is None:
+            obj = self.get_null_object()
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.py_ssize_t,
+                                                self.py_ssize_t, self.pyobj])
+        fn = self._get_function(fnty, name="PyList_SetSlice")
+        return self.builder.call(fn, (lst, start, stop, obj))
+
+
+    #
+    # Concrete tuple API
+    #
+
+    def tuple_getitem(self, tup, idx):
+        """
+        Borrow reference
+        """
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.py_ssize_t])
+        fn = self._get_function(fnty, name="PyTuple_GetItem")
+        idx = self.context.get_constant(types.intp, idx)
+        return self.builder.call(fn, [tup, idx])
+
+    def tuple_pack(self, items):
+        fnty = ir.FunctionType(self.pyobj, [self.py_ssize_t], var_arg=True)
+        fn = self._get_function(fnty, name="PyTuple_Pack")
+        n = self.context.get_constant(types.intp, len(items))
+        args = [n]
+        args.extend(items)
+        return self.builder.call(fn, args)
+
+    def tuple_size(self, tup):
+        fnty = ir.FunctionType(self.py_ssize_t, [self.pyobj])
+        fn = self._get_function(fnty, name="PyTuple_Size")
+        return self.builder.call(fn, [tup])
+
+    def tuple_new(self, count):
+        fnty = ir.FunctionType(self.pyobj, [ir.IntType(32)])
+        fn = self._get_function(fnty, name='PyTuple_New')
+        return self.builder.call(fn, [self.context.get_constant(types.int32,
+                                                                count)])
+
+    def tuple_setitem(self, tuple_val, index, item):
+        """
+        Steals a reference to `item`.
+        """
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, ir.IntType(32), self.pyobj])
+        setitem_fn = self._get_function(fnty, name='PyTuple_SetItem')
+        index = self.context.get_constant(types.int32, index)
+        self.builder.call(setitem_fn, [tuple_val, index, item])
+
+    #
+    # Concrete set API
+    #
+
+    def set_new(self, iterable=None):
+        if iterable is None:
+            iterable = self.get_null_object()
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PySet_New")
+        return self.builder.call(fn, [iterable])
+
+    def set_add(self, set, value):
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PySet_Add")
+        return self.builder.call(fn, [set, value])
+
+    def set_clear(self, set):
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj])
+        fn = self._get_function(fnty, name="PySet_Clear")
+        return self.builder.call(fn, [set])
+
+    def set_size(self, set):
+        fnty = ir.FunctionType(self.py_ssize_t, [self.pyobj])
+        fn = self._get_function(fnty, name="PySet_Size")
+        return self.builder.call(fn, [set])
+
+    def set_update(self, set, iterable):
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="_PySet_Update")
+        return self.builder.call(fn, [set, iterable])
+
+    def set_next_entry(self, set, posptr, keyptr, hashptr):
+        fnty = ir.FunctionType(ir.IntType(32),
+                               [self.pyobj, self.py_ssize_t.as_pointer(),
+                                self.pyobj.as_pointer(), self.py_hash_t.as_pointer()])
+        fn = self._get_function(fnty, name="_PySet_NextEntry")
+        return self.builder.call(fn, (set, posptr, keyptr, hashptr))
+
+    @contextlib.contextmanager
+    def set_iterate(self, set):
+        builder = self.builder
+
+        hashptr = cgutils.alloca_once(builder, self.py_hash_t, name="hashptr")
+        keyptr = cgutils.alloca_once(builder, self.pyobj, name="keyptr")
+        posptr = cgutils.alloca_once_value(builder,
+                                           Constant(self.py_ssize_t, 0),
+                                           name="posptr")
+
+        bb_body = builder.append_basic_block("bb_body")
+        bb_end = builder.append_basic_block("bb_end")
+
+        builder.branch(bb_body)
+        def do_break():
+            builder.branch(bb_end)
+
+        with builder.goto_block(bb_body):
+            r = self.set_next_entry(set, posptr, keyptr, hashptr)
+            finished = cgutils.is_null(builder, r)
+            with builder.if_then(finished, likely=False):
+                builder.branch(bb_end)
+            yield _IteratorLoop(builder.load(keyptr), do_break)
+            builder.branch(bb_body)
+
+        builder.position_at_end(bb_end)
+
+    #
+    # GIL APIs
+    #
+
+    def gil_ensure(self):
+        """
+        Ensure the GIL is acquired.
+        The returned value must be consumed by gil_release().
+        """
+        gilptrty = ir.PointerType(self.gil_state)
+        fnty = ir.FunctionType(ir.VoidType(), [gilptrty])
+        fn = self._get_function(fnty, "numba_gil_ensure")
+        gilptr = cgutils.alloca_once(self.builder, self.gil_state)
+        self.builder.call(fn, [gilptr])
+        return gilptr
+
+    def gil_release(self, gil):
+        """
+        Release the acquired GIL by gil_ensure().
+        Must be paired with a gil_ensure().
+        """
+        gilptrty = ir.PointerType(self.gil_state)
+        fnty = ir.FunctionType(ir.VoidType(), [gilptrty])
+        fn = self._get_function(fnty, "numba_gil_release")
+        return self.builder.call(fn, [gil])
+
+    def save_thread(self):
+        """
+        Release the GIL and return the former thread state
+        (an opaque non-NULL pointer).
+        """
+        fnty = ir.FunctionType(self.voidptr, [])
+        fn = self._get_function(fnty, name="PyEval_SaveThread")
+        return self.builder.call(fn, [])
+
+    def restore_thread(self, thread_state):
+        """
+        Restore the given thread state by reacquiring the GIL.
+        """
+        fnty = ir.FunctionType(ir.VoidType(), [self.voidptr])
+        fn = self._get_function(fnty, name="PyEval_RestoreThread")
+        self.builder.call(fn, [thread_state])
+
+    #
+    # Generic object private data (a way of associating an arbitrary void *
+    # pointer to an arbitrary Python object).
+    #
+
+    def object_get_private_data(self, obj):
+        fnty = ir.FunctionType(self.voidptr, [self.pyobj])
+        fn = self._get_function(fnty, name="numba_get_pyobject_private_data")
+        return self.builder.call(fn, (obj,))
+
+    def object_set_private_data(self, obj, ptr):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj, self.voidptr])
+        fn = self._get_function(fnty, name="numba_set_pyobject_private_data")
+        return self.builder.call(fn, (obj, ptr))
+
+    def object_reset_private_data(self, obj):
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj])
+        fn = self._get_function(fnty, name="numba_reset_pyobject_private_data")
+        return self.builder.call(fn, (obj,))
+
+
+    #
+    # Other APIs (organize them better!)
+    #
+
+    def import_module_noblock(self, modname):
+        fnty = ir.FunctionType(self.pyobj, [self.cstring])
+        fn = self._get_function(fnty, name="PyImport_ImportModuleNoBlock")
+        return self.builder.call(fn, [modname])
+
+    def call_function_objargs(self, callee, objargs):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj], var_arg=True)
+        fn = self._get_function(fnty, name="PyObject_CallFunctionObjArgs")
+        args = [callee] + list(objargs)
+        args.append(self.context.get_constant_null(types.pyobject))
+        return self.builder.call(fn, args)
+
+    def call_method(self, callee, method, objargs=()):
+        cname = self.context.insert_const_string(self.module, method)
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.cstring, self.cstring],
+                             var_arg=True)
+        fn = self._get_function(fnty, name="PyObject_CallMethod")
+        fmt = 'O' * len(objargs)
+        cfmt = self.context.insert_const_string(self.module, fmt)
+        args = [callee, cname, cfmt]
+        if objargs:
+            args.extend(objargs)
+        args.append(self.context.get_constant_null(types.pyobject))
+        return self.builder.call(fn, args)
+
+    def call(self, callee, args=None, kws=None):
+        if args is None:
+            args = self.get_null_object()
+        if kws is None:
+            kws = self.get_null_object()
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj] * 3)
+        fn = self._get_function(fnty, name="PyObject_Call")
+        return self.builder.call(fn, (callee, args, kws))
+
+    def object_type(self, obj):
+        """Emit a call to ``PyObject_Type(obj)`` to get the type of ``obj``.
+        """
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_Type")
+        return self.builder.call(fn, (obj,))
+
+    def object_istrue(self, obj):
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_IsTrue")
+        return self.builder.call(fn, [obj])
+
+    def object_not(self, obj):
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_Not")
+        return self.builder.call(fn, [obj])
+
+    def object_richcompare(self, lhs, rhs, opstr):
+        """
+        Refer to Python source Include/object.h for macros definition
+        of the opid.
+        """
+        ops = ['<', '<=', '==', '!=', '>', '>=']
+        if opstr in ops:
+            opid = ops.index(opstr)
+            fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.pyobj, ir.IntType(32)])
+            fn = self._get_function(fnty, name="PyObject_RichCompare")
+            lopid = self.context.get_constant(types.int32, opid)
+            return self.builder.call(fn, (lhs, rhs, lopid))
+        elif opstr == 'is':
+            bitflag = self.builder.icmp_unsigned('==', lhs, rhs)
+            return self.bool_from_bool(bitflag)
+        elif opstr == 'is not':
+            bitflag = self.builder.icmp_unsigned('!=', lhs, rhs)
+            return self.bool_from_bool(bitflag)
+        elif opstr in ('in', 'not in'):
+            fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.pyobj])
+            fn = self._get_function(fnty, name="PySequence_Contains")
+            status = self.builder.call(fn, (rhs, lhs))
+            negone = self.context.get_constant(types.int32, -1)
+            is_good = self.builder.icmp_unsigned('!=', status, negone)
+            # Stack allocate output and initialize to Null
+            outptr = cgutils.alloca_once_value(self.builder,
+                                               Constant(self.pyobj, None))
+            # If PySequence_Contains returns non-error value
+            with cgutils.if_likely(self.builder, is_good):
+                if opstr == 'not in':
+                    status = self.builder.not_(status)
+                # Store the status as a boolean object
+                truncated = self.builder.trunc(status, ir.IntType(1))
+                self.builder.store(self.bool_from_bool(truncated),
+                                   outptr)
+
+            return self.builder.load(outptr)
+        else:
+            raise NotImplementedError("Unknown operator {op!r}".format(
+                op=opstr))
+
+    def iter_next(self, iterobj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyIter_Next")
+        return self.builder.call(fn, [iterobj])
+
+    def object_getiter(self, obj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_GetIter")
+        return self.builder.call(fn, [obj])
+
+    def object_getattr_string(self, obj, attr):
+        cstr = self.context.insert_const_string(self.module, attr)
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.cstring])
+        fn = self._get_function(fnty, name="PyObject_GetAttrString")
+        return self.builder.call(fn, [obj, cstr])
+
+    def object_getattr(self, obj, attr):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_GetAttr")
+        return self.builder.call(fn, [obj, attr])
+
+    def object_setattr_string(self, obj, attr, val):
+        cstr = self.context.insert_const_string(self.module, attr)
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.cstring, self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_SetAttrString")
+        return self.builder.call(fn, [obj, cstr, val])
+
+    def object_setattr(self, obj, attr, val):
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_SetAttr")
+        return self.builder.call(fn, [obj, attr, val])
+
+    def object_delattr_string(self, obj, attr):
+        # PyObject_DelAttrString() is actually a C macro calling
+        # PyObject_SetAttrString() with value == NULL.
+        return self.object_setattr_string(obj, attr, self.get_null_object())
+
+    def object_delattr(self, obj, attr):
+        # PyObject_DelAttr() is actually a C macro calling
+        # PyObject_SetAttr() with value == NULL.
+        return self.object_setattr(obj, attr, self.get_null_object())
+
+    def object_getitem(self, obj, key):
+        """
+        Return obj[key]
+        """
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_GetItem")
+        return self.builder.call(fn, (obj, key))
+
+    def object_setitem(self, obj, key, val):
+        """
+        obj[key] = val
+        """
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_SetItem")
+        return self.builder.call(fn, (obj, key, val))
+
+    def object_delitem(self, obj, key):
+        """
+        del obj[key]
+        """
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_DelItem")
+        return self.builder.call(fn, (obj, key))
+
+    def string_as_string(self, strobj):
+        fnty = ir.FunctionType(self.cstring, [self.pyobj])
+        fname = "PyUnicode_AsUTF8"
+        fn = self._get_function(fnty, name=fname)
+        return self.builder.call(fn, [strobj])
+
+    def string_as_string_and_size(self, strobj):
+        """
+        Returns a tuple of ``(ok, buffer, length)``.
+        The ``ok`` is i1 value that is set if ok.
+        The ``buffer`` is a i8* of the output buffer.
+        The ``length`` is a i32/i64 (py_ssize_t) of the length of the buffer.
+        """
+
+        p_length = cgutils.alloca_once(self.builder, self.py_ssize_t)
+        fnty = ir.FunctionType(self.cstring, [self.pyobj,
+                                              self.py_ssize_t.as_pointer()])
+        fname = "PyUnicode_AsUTF8AndSize"
+        fn = self._get_function(fnty, name=fname)
+
+        buffer = self.builder.call(fn, [strobj, p_length])
+        ok = self.builder.icmp_unsigned('!=',
+                                        Constant(buffer.type, None),
+                                        buffer)
+        return (ok, buffer, self.builder.load(p_length))
+
+    def string_as_string_size_and_kind(self, strobj):
+        """
+        Returns a tuple of ``(ok, buffer, length, kind)``.
+        The ``ok`` is i1 value that is set if ok.
+        The ``buffer`` is a i8* of the output buffer.
+        The ``length`` is a i32/i64 (py_ssize_t) of the length of the buffer.
+        The ``kind`` is a i32 (int32) of the Unicode kind constant
+        The ``hash`` is a long/uint64_t (py_hash_t) of the Unicode constant hash
+        """
+        p_length = cgutils.alloca_once(self.builder, self.py_ssize_t)
+        p_kind = cgutils.alloca_once(self.builder, ir.IntType(32))
+        p_ascii = cgutils.alloca_once(self.builder, ir.IntType(32))
+        p_hash = cgutils.alloca_once(self.builder, self.py_hash_t)
+        fnty = ir.FunctionType(self.cstring, [self.pyobj,
+                                              self.py_ssize_t.as_pointer(),
+                                              ir.IntType(32).as_pointer(),
+                                              ir.IntType(32).as_pointer(),
+                                              self.py_hash_t.as_pointer()])
+        fname = "numba_extract_unicode"
+        fn = self._get_function(fnty, name=fname)
+
+        buffer = self.builder.call(
+            fn, [strobj, p_length, p_kind, p_ascii, p_hash])
+        ok = self.builder.icmp_unsigned('!=',
+                                        Constant(buffer.type, None),
+                                        buffer)
+        return (ok, buffer, self.builder.load(p_length),
+                self.builder.load(p_kind), self.builder.load(p_ascii),
+                self.builder.load(p_hash))
+
+    def string_from_string_and_size(self, string, size):
+        fnty = ir.FunctionType(self.pyobj, [self.cstring, self.py_ssize_t])
+        fname = "PyString_FromStringAndSize"
+        fn = self._get_function(fnty, name=fname)
+        return self.builder.call(fn, [string, size])
+
+    def string_from_string(self, string):
+        fnty = ir.FunctionType(self.pyobj, [self.cstring])
+        fname = "PyUnicode_FromString"
+        fn = self._get_function(fnty, name=fname)
+        return self.builder.call(fn, [string])
+
+    def string_from_kind_and_data(self, kind, string, size):
+        fnty = ir.FunctionType(self.pyobj, [ir.IntType(32), self.cstring, self.py_ssize_t])
+        fname = "PyUnicode_FromKindAndData"
+        fn = self._get_function(fnty, name=fname)
+        return self.builder.call(fn, [kind, string, size])
+
+    def bytes_from_string_and_size(self, string, size):
+        fnty = ir.FunctionType(self.pyobj, [self.cstring, self.py_ssize_t])
+        fname = "PyBytes_FromStringAndSize"
+        fn = self._get_function(fnty, name=fname)
+        return self.builder.call(fn, [string, size])
+
+    def object_hash(self, obj):
+        fnty = ir.FunctionType(self.py_hash_t, [self.pyobj, ])
+        fname = "PyObject_Hash"
+        fn = self._get_function(fnty, name=fname)
+        return self.builder.call(fn, [obj,])
+
+    def object_str(self, obj):
+        fnty = ir.FunctionType(self.pyobj, [self.pyobj])
+        fn = self._get_function(fnty, name="PyObject_Str")
+        return self.builder.call(fn, [obj])
+
+    def make_none(self):
+        obj = self.borrow_none()
+        self.incref(obj)
+        return obj
+
+    def borrow_none(self):
+        return self.get_c_object("_Py_NoneStruct")
+
+    def sys_write_stdout(self, fmt, *args):
+        fnty = ir.FunctionType(ir.VoidType(), [self.cstring], var_arg=True)
+        fn = self._get_function(fnty, name="PySys_FormatStdout")
+        return self.builder.call(fn, (fmt,) + args)
+
+    def object_dump(self, obj):
+        """
+        Dump a Python object on C stderr.  For debugging purposes.
+        """
+        fnty = ir.FunctionType(ir.VoidType(), [self.pyobj])
+        fn = self._get_function(fnty, name="_PyObject_Dump")
+        return self.builder.call(fn, (obj,))
+
+    #
+    # NRT (Numba runtime) APIs
+    #
+
+    def nrt_adapt_ndarray_to_python(self, aryty, ary, dtypeptr):
+        assert self.context.enable_nrt, "NRT required"
+
+        intty = ir.IntType(32)
+        # Embed the Python type of the array (maybe subclass) in the LLVM IR.
+        serial_aryty_pytype = self.unserialize(self.serialize_object(aryty.box_type))
+
+        fnty = ir.FunctionType(self.pyobj,
+                               [self.voidptr, self.pyobj, intty, intty, self.pyobj])
+        fn = self._get_function(fnty, name="NRT_adapt_ndarray_to_python_acqref")
+        fn.args[0].add_attribute('nocapture')
+
+        ndim = self.context.get_constant(types.int32, aryty.ndim)
+        writable = self.context.get_constant(types.int32, int(aryty.mutable))
+
+        aryptr = cgutils.alloca_once_value(self.builder, ary)
+        return self.builder.call(fn, [self.builder.bitcast(aryptr,
+                                                           self.voidptr),
+                                      serial_aryty_pytype,
+                                      ndim, writable, dtypeptr])
+
+    def nrt_meminfo_new_from_pyobject(self, data, pyobj):
+        """
+        Allocate a new MemInfo with data payload borrowed from a python
+        object.
+        """
+        mod = self.builder.module
+        fnty = ir.FunctionType(
+            cgutils.voidptr_t,
+            [cgutils.voidptr_t, cgutils.voidptr_t],
+            )
+        fn = cgutils.get_or_insert_function(
+            mod,
+            fnty,
+            "NRT_meminfo_new_from_pyobject",
+            )
+        fn.args[0].add_attribute('nocapture')
+        fn.args[1].add_attribute('nocapture')
+        fn.return_value.add_attribute("noalias")
+        return self.builder.call(fn, [data, pyobj])
+
+    def nrt_meminfo_as_pyobject(self, miptr):
+        mod = self.builder.module
+        fnty = ir.FunctionType(
+            self.pyobj,
+            [cgutils.voidptr_t]
+        )
+        fn = cgutils.get_or_insert_function(
+            mod,
+            fnty,
+            'NRT_meminfo_as_pyobject',
+        )
+        fn.return_value.add_attribute("noalias")
+        return self.builder.call(fn, [miptr])
+
+    def nrt_meminfo_from_pyobject(self, miobj):
+        mod = self.builder.module
+        fnty = ir.FunctionType(
+            cgutils.voidptr_t,
+            [self.pyobj]
+        )
+        fn = cgutils.get_or_insert_function(
+            mod,
+            fnty,
+            'NRT_meminfo_from_pyobject',
+        )
+        fn.return_value.add_attribute("noalias")
+        return self.builder.call(fn, [miobj])
+
+    def nrt_adapt_ndarray_from_python(self, ary, ptr):
+        assert self.context.enable_nrt
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.voidptr])
+        fn = self._get_function(fnty, name="NRT_adapt_ndarray_from_python")
+        fn.args[0].add_attribute('nocapture')
+        fn.args[1].add_attribute('nocapture')
+        return self.builder.call(fn, (ary, ptr))
+
+    def nrt_adapt_buffer_from_python(self, buf, ptr):
+        assert self.context.enable_nrt
+        fnty = ir.FunctionType(ir.VoidType(), [ir.PointerType(self.py_buffer_t),
+                                               self.voidptr])
+        fn = self._get_function(fnty, name="NRT_adapt_buffer_from_python")
+        fn.args[0].add_attribute('nocapture')
+        fn.args[1].add_attribute('nocapture')
+        return self.builder.call(fn, (buf, ptr))
+
+    # ------ utils -----
+
+    def _get_function(self, fnty, name):
+        return cgutils.get_or_insert_function(self.module, fnty, name)
+
+    def alloca_obj(self):
+        return self.builder.alloca(self.pyobj)
+
+    def alloca_buffer(self):
+        """
+        Return a pointer to a stack-allocated, zero-initialized Py_buffer.
+        """
+        # Treat the buffer as an opaque array of bytes
+        ptr = cgutils.alloca_once_value(self.builder,
+                                        Constant(self.py_buffer_t, None))
+        return ptr
+
+    @contextlib.contextmanager
+    def if_object_ok(self, obj):
+        with cgutils.if_likely(self.builder,
+                               cgutils.is_not_null(self.builder, obj)):
+            yield
+
+    def print_object(self, obj):
+        strobj = self.object_str(obj)
+        cstr = self.string_as_string(strobj)
+        fmt = self.context.insert_const_string(self.module, "%s")
+        self.sys_write_stdout(fmt, cstr)
+        self.decref(strobj)
+
+    def print_string(self, text):
+        fmt = self.context.insert_const_string(self.module, text)
+        self.sys_write_stdout(fmt)
+
+    def get_null_object(self):
+        return Constant(self.pyobj, None)
+
+    def return_none(self):
+        none = self.make_none()
+        self.builder.ret(none)
+
+    def list_pack(self, items):
+        n = len(items)
+        seq = self.list_new(self.context.get_constant(types.intp, n))
+        with self.if_object_ok(seq):
+            for i in range(n):
+                idx = self.context.get_constant(types.intp, i)
+                self.incref(items[i])
+                self.list_setitem(seq, idx, items[i])
+        return seq
+
+    def unserialize(self, structptr):
+        """
+        Unserialize some data.  *structptr* should be a pointer to
+        a {i8* data, i32 length} structure.
+        """
+        fnty = ir.FunctionType(self.pyobj,
+                             (self.voidptr, ir.IntType(32), self.voidptr))
+        fn = self._get_function(fnty, name="numba_unpickle")
+        ptr = self.builder.extract_value(self.builder.load(structptr), 0)
+        n = self.builder.extract_value(self.builder.load(structptr), 1)
+        hashed = self.builder.extract_value(self.builder.load(structptr), 2)
+        return self.builder.call(fn, (ptr, n, hashed))
+
+    def serialize_uncached(self, obj):
+        """
+        Same as serialize_object(), but don't create a global variable,
+        simply return a literal {i8* data, i32 length, i8* hashbuf} structure.
+        """
+        # First make the array constant
+        data = serialize.dumps(obj)
+        assert len(data) < 2**31
+        name = ".const.pickledata.%s" % (id(obj) if config.DIFF_IR == 0 else "DIFF_IR")
+        bdata = cgutils.make_bytearray(data)
+        # Make SHA1 hash on the pickled content
+        # NOTE: update buffer size in numba_unpickle() when changing the
+        #       hash algorithm.
+        hashed = cgutils.make_bytearray(hashlib.sha1(data).digest())
+        arr = self.context.insert_unique_const(self.module, name, bdata)
+        hasharr = self.context.insert_unique_const(
+            self.module, f"{name}.sha1", hashed,
+        )
+        # Then populate the structure constant
+        struct = Constant.literal_struct([
+            arr.bitcast(self.voidptr),
+            Constant(ir.IntType(32), arr.type.pointee.count),
+            hasharr.bitcast(self.voidptr),
+            ])
+        return struct
+
+    def serialize_object(self, obj):
+        """
+        Serialize the given object in the bitcode, and return it
+        as a pointer to a {i8* data, i32 length}, structure constant
+        (suitable for passing to unserialize()).
+        """
+        try:
+            gv = self.module.__serialized[obj]
+        except KeyError:
+            struct = self.serialize_uncached(obj)
+            name = ".const.picklebuf.%s" % (id(obj) if config.DIFF_IR == 0 else "DIFF_IR")
+            gv = self.context.insert_unique_const(self.module, name, struct)
+            # Make the id() (and hence the name) unique while populating the module.
+            self.module.__serialized[obj] = gv
+        return gv
+
+    def c_api_error(self):
+        return cgutils.is_not_null(self.builder, self.err_occurred())
+
+    def to_native_value(self, typ, obj):
+        """
+        Unbox the Python object as the given Numba type.
+        A NativeValue instance is returned.
+        """
+        from numba.core.boxing import unbox_unsupported
+
+        impl = _unboxers.lookup(typ.__class__, unbox_unsupported)
+        c = _UnboxContext(self.context, self.builder, self)
+        return impl(typ, obj, c)
+
+    def from_native_return(self, typ, val, env_manager):
+        assert not isinstance(typ, types.Optional), "callconv should have " \
+                                                    "prevented the return of " \
+                                                    "optional value"
+        out = self.from_native_value(typ, val, env_manager)
+        return out
+
+    def from_native_value(self, typ, val, env_manager=None):
+        """
+        Box the native value of the given Numba type.  A Python object
+        pointer is returned (NULL if an error occurred).
+        This method steals any native (NRT) reference embedded in *val*.
+        """
+        from numba.core.boxing import box_unsupported
+
+        impl = _boxers.lookup(typ.__class__, box_unsupported)
+
+        c = _BoxContext(self.context, self.builder, self, env_manager)
+        return impl(typ, val, c)
+
+    def reflect_native_value(self, typ, val, env_manager=None):
+        """
+        Reflect the native value onto its Python original, if any.
+        An error bit (as an LLVM value) is returned.
+        """
+        impl = _reflectors.lookup(typ.__class__)
+        if impl is None:
+            # Reflection isn't needed for most types
+            return cgutils.false_bit
+
+        is_error = cgutils.alloca_once_value(self.builder, cgutils.false_bit)
+        c = _ReflectContext(self.context, self.builder, self, env_manager,
+                            is_error)
+        impl(typ, val, c)
+        return self.builder.load(c.is_error)
+
+    def to_native_generator(self, obj, typ):
+        """
+        Extract the generator structure pointer from a generator *obj*
+        (a _dynfunc.Generator instance).
+        """
+        gen_ptr_ty = ir.PointerType(self.context.get_data_type(typ))
+        value = self.context.get_generator_state(self.builder, obj, gen_ptr_ty)
+        return NativeValue(value)
+
+    def from_native_generator(self, val, typ, env=None):
+        """
+        Make a Numba generator (a _dynfunc.Generator instance) from a
+        generator structure pointer *val*.
+        *env* is an optional _dynfunc.Environment instance to be wrapped
+        in the generator.
+        """
+        llty = self.context.get_data_type(typ)
+        assert not llty.is_pointer
+        gen_struct_size = self.context.get_abi_sizeof(llty)
+
+        gendesc = self.context.get_generator_desc(typ)
+
+        # This is the PyCFunctionWithKeywords generated by PyCallWrapper
+        genfnty = ir.FunctionType(self.pyobj, [self.pyobj, self.pyobj, self.pyobj])
+        genfn = self._get_function(genfnty, name=gendesc.llvm_cpython_wrapper_name)
+
+        # This is the raw finalizer generated by _lower_generator_finalize_func()
+        finalizerty = ir.FunctionType(ir.VoidType(), [self.voidptr])
+        if typ.has_finalizer:
+            finalizer = self._get_function(finalizerty, name=gendesc.llvm_finalizer_name)
+        else:
+            finalizer = Constant(ir.PointerType(finalizerty), None)
+
+        # PyObject *numba_make_generator(state_size, initial_state, nextfunc, finalizer, env)
+        fnty = ir.FunctionType(self.pyobj, [self.py_ssize_t,
+                                            self.voidptr,
+                                            ir.PointerType(genfnty),
+                                            ir.PointerType(finalizerty),
+                                            self.voidptr])
+        fn = self._get_function(fnty, name="numba_make_generator")
+
+        state_size = Constant(self.py_ssize_t, gen_struct_size)
+        initial_state = self.builder.bitcast(val, self.voidptr)
+        if env is None:
+            env = self.get_null_object()
+        env = self.builder.bitcast(env, self.voidptr)
+
+        return self.builder.call(fn,
+                                 (state_size, initial_state, genfn, finalizer, env))
+
+    def numba_array_adaptor(self, ary, ptr):
+        assert not self.context.enable_nrt
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, self.voidptr])
+        fn = self._get_function(fnty, name="numba_adapt_ndarray")
+        fn.args[0].add_attribute('nocapture')
+        fn.args[1].add_attribute('nocapture')
+        return self.builder.call(fn, (ary, ptr))
+
+    def numba_buffer_adaptor(self, buf, ptr):
+        fnty = ir.FunctionType(ir.VoidType(),
+                             [ir.PointerType(self.py_buffer_t), self.voidptr])
+        fn = self._get_function(fnty, name="numba_adapt_buffer")
+        fn.args[0].add_attribute('nocapture')
+        fn.args[1].add_attribute('nocapture')
+        return self.builder.call(fn, (buf, ptr))
+
+    def complex_adaptor(self, cobj, cmplx):
+        fnty = ir.FunctionType(ir.IntType(32), [self.pyobj, cmplx.type])
+        fn = self._get_function(fnty, name="numba_complex_adaptor")
+        return self.builder.call(fn, [cobj, cmplx])
+
+    def extract_record_data(self, obj, pbuf):
+        fnty = ir.FunctionType(self.voidptr,
+                               [self.pyobj, ir.PointerType(self.py_buffer_t)])
+        fn = self._get_function(fnty, name="numba_extract_record_data")
+        return self.builder.call(fn, [obj, pbuf])
+
+    def get_buffer(self, obj, pbuf):
+        fnty = ir.FunctionType(ir.IntType(32),
+                               [self.pyobj, ir.PointerType(self.py_buffer_t)])
+        fn = self._get_function(fnty, name="numba_get_buffer")
+        return self.builder.call(fn, [obj, pbuf])
+
+    def release_buffer(self, pbuf):
+        fnty = ir.FunctionType(ir.VoidType(), [ir.PointerType(self.py_buffer_t)])
+        fn = self._get_function(fnty, name="numba_release_buffer")
+        return self.builder.call(fn, [pbuf])
+
+    def extract_np_datetime(self, obj):
+        fnty = ir.FunctionType(ir.IntType(64), [self.pyobj])
+        fn = self._get_function(fnty, name="numba_extract_np_datetime")
+        return self.builder.call(fn, [obj])
+
+    def extract_np_timedelta(self, obj):
+        fnty = ir.FunctionType(ir.IntType(64), [self.pyobj])
+        fn = self._get_function(fnty, name="numba_extract_np_timedelta")
+        return self.builder.call(fn, [obj])
+
+    def create_np_datetime(self, val, unit_code):
+        unit_code = Constant(ir.IntType(32), int(unit_code))
+        fnty = ir.FunctionType(self.pyobj, [ir.IntType(64), ir.IntType(32)])
+        fn = self._get_function(fnty, name="numba_create_np_datetime")
+        return self.builder.call(fn, [val, unit_code])
+
+    def create_np_timedelta(self, val, unit_code):
+        unit_code = Constant(ir.IntType(32), int(unit_code))
+        fnty = ir.FunctionType(self.pyobj, [ir.IntType(64), ir.IntType(32)])
+        fn = self._get_function(fnty, name="numba_create_np_timedelta")
+        return self.builder.call(fn, [val, unit_code])
+
+    def recreate_record(self, pdata, size, dtype, env_manager):
+        fnty = ir.FunctionType(self.pyobj, [ir.PointerType(ir.IntType(8)),
+                                            ir.IntType(32), self.pyobj])
+        fn = self._get_function(fnty, name="numba_recreate_record")
+        dtypeaddr = env_manager.read_const(env_manager.add_const(dtype))
+        return self.builder.call(fn, [pdata, size, dtypeaddr])
+
+    def string_from_constant_string(self, string):
+        cstr = self.context.insert_const_string(self.module, string)
+        sz = self.context.get_constant(types.intp, len(string))
+        return self.string_from_string_and_size(cstr, sz)
+
+    def call_jit_code(self, func, sig, args):
+        """Calls into Numba jitted code and propagate error using the Python
+        calling convention.
+
+        Parameters
+        ----------
+        func : function
+            The Python function to be compiled. This function is compiled
+            in nopython-mode.
+        sig : numba.typing.Signature
+            The function signature for *func*.
+        args : Sequence[llvmlite.binding.Value]
+            LLVM values to use as arguments.
+
+        Returns
+        -------
+        (is_error, res) :  2-tuple of llvmlite.binding.Value.
+            is_error : true iff *func* raised an exception.
+            res : Returned value from *func* iff *is_error* is false.
+
+        If *is_error* is true, this method will adapt the nopython exception
+        into a Python exception. Caller should return NULL to Python to
+        indicate an error.
+        """
+        # Compile *func*
+        builder = self.builder
+        cres = self.context.compile_subroutine(builder, func, sig)
+        got_retty = cres.signature.return_type
+        retty = sig.return_type
+        if got_retty != retty:
+            # This error indicates an error in *func* or the caller of this
+            # method.
+            raise errors.LoweringError(
+                f'mismatching signature {got_retty} != {retty}.\n'
+            )
+        # Call into *func*
+        status, res = self.context.call_internal_no_propagate(
+            builder, cres.fndesc, sig, args,
+        )
+        # Post-call handling for *func*
+        is_error_ptr = cgutils.alloca_once(builder, cgutils.bool_t, zfill=True)
+        res_type = self.context.get_value_type(sig.return_type)
+        res_ptr = cgutils.alloca_once(builder, res_type, zfill=True)
+
+        # Handle error and adapt the nopython exception into cpython exception
+        with builder.if_else(status.is_error) as (has_err, no_err):
+            with has_err:
+                builder.store(status.is_error, is_error_ptr)
+                # Set error state in the Python interpreter
+                self.context.call_conv.raise_error(builder, self, status)
+            with no_err:
+                # Handle returned value
+                res = imputils.fix_returning_optional(
+                    self.context, builder, sig, status, res,
+                )
+                builder.store(res, res_ptr)
+
+        is_error = builder.load(is_error_ptr)
+        res = builder.load(res_ptr)
+        return is_error, res
+
+
+class ObjModeUtils:
+    """Internal utils for calling objmode dispatcher from within NPM code.
+    """
+    def __init__(self, pyapi):
+        self.pyapi = pyapi
+
+    def load_dispatcher(self, fnty, argtypes):
+        builder = self.pyapi.builder
+        tyctx = self.pyapi.context
+        m = builder.module
+
+        # Add a global variable to cache the objmode dispatcher
+        gv = ir.GlobalVariable(
+            m, self.pyapi.pyobj,
+            name=m.get_unique_name("cached_objmode_dispatcher"),
+        )
+        gv.initializer = gv.type.pointee(None)
+        gv.linkage = 'internal'
+
+        # Make a basic-block to common exit
+        bb_end = builder.append_basic_block("bb_end")
+
+        if serialize.is_serialiable(fnty.dispatcher):
+            serialized_dispatcher = self.pyapi.serialize_object(
+                (fnty.dispatcher, tuple(argtypes)),
+            )
+            compile_args = self.pyapi.unserialize(serialized_dispatcher)
+            # unserialize (unpickling) can fail
+            failed_unser = cgutils.is_null(builder, compile_args)
+            with builder.if_then(failed_unser):
+                # early exit. `gv` is still null.
+                builder.branch(bb_end)
+
+        cached = builder.load(gv)
+        with builder.if_then(cgutils.is_null(builder, cached)):
+            if serialize.is_serialiable(fnty.dispatcher):
+                cls = type(self)
+                compiler = self.pyapi.unserialize(
+                    self.pyapi.serialize_object(cls._call_objmode_dispatcher)
+                )
+                callee = self.pyapi.call_function_objargs(
+                    compiler, [compile_args],
+                )
+                # Clean up
+                self.pyapi.decref(compiler)
+                self.pyapi.decref(compile_args)
+            else:
+                entry_pt = fnty.dispatcher.compile(tuple(argtypes))
+                callee = tyctx.add_dynamic_addr(
+                    builder, id(entry_pt), info="with_objectmode",
+                )
+            # Incref the dispatcher and cache it
+            self.pyapi.incref(callee)
+            builder.store(callee, gv)
+        # Jump to the exit block
+        builder.branch(bb_end)
+        # Define the exit block
+        builder.position_at_end(bb_end)
+        callee = builder.load(gv)
+        return callee
+
+    @staticmethod
+    def _call_objmode_dispatcher(compile_args):
+        dispatcher, argtypes = compile_args
+        entrypt = dispatcher.compile(argtypes)
+        return entrypt
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/registry.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/registry.py
new file mode 100644
index 000000000..eef492c40
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/registry.py
@@ -0,0 +1,112 @@
+import contextlib
+
+from numba.core.descriptors import TargetDescriptor
+from numba.core import utils, typing, dispatcher, cpu
+
+# -----------------------------------------------------------------------------
+# Default CPU target descriptors
+
+class _NestedContext(object):
+    _typing_context = None
+    _target_context = None
+
+    @contextlib.contextmanager
+    def nested(self, typing_context, target_context):
+        old_nested = self._typing_context, self._target_context
+        try:
+            self._typing_context = typing_context
+            self._target_context = target_context
+            yield
+        finally:
+            self._typing_context, self._target_context = old_nested
+
+
+class CPUTarget(TargetDescriptor):
+    options = cpu.CPUTargetOptions
+    _nested = _NestedContext()
+
+    @utils.cached_property
+    def _toplevel_target_context(self):
+        # Lazily-initialized top-level target context, for all threads
+        return cpu.CPUContext(self.typing_context, self._target_name)
+
+    @utils.cached_property
+    def _toplevel_typing_context(self):
+        # Lazily-initialized top-level typing context, for all threads
+        return typing.Context()
+
+    @property
+    def target_context(self):
+        """
+        The target context for CPU targets.
+        """
+        nested = self._nested._target_context
+        if nested is not None:
+            return nested
+        else:
+            return self._toplevel_target_context
+
+    @property
+    def typing_context(self):
+        """
+        The typing context for CPU targets.
+        """
+        nested = self._nested._typing_context
+        if nested is not None:
+            return nested
+        else:
+            return self._toplevel_typing_context
+
+    def nested_context(self, typing_context, target_context):
+        """
+        A context manager temporarily replacing the contexts with the
+        given ones, for the current thread of execution.
+        """
+        return self._nested.nested(typing_context, target_context)
+
+
+# The global CPU target
+cpu_target = CPUTarget('cpu')
+
+
+class CPUDispatcher(dispatcher.Dispatcher):
+    targetdescr = cpu_target
+
+
+class DelayedRegistry(utils.UniqueDict):
+    """
+    A unique dictionary but with deferred initialisation of the values.
+
+    Attributes
+    ----------
+    ondemand:
+
+        A dictionary of key -> value, where value is executed
+        the first time it is is used.  It is used for part of a deferred
+        initialization strategy.
+    """
+    def __init__(self, *args, **kws):
+        self.ondemand = utils.UniqueDict()
+        self.key_type = kws.pop('key_type', None)
+        self.value_type = kws.pop('value_type', None)
+        self._type_check = self.key_type or self.value_type
+        super(DelayedRegistry, self).__init__(*args, **kws)
+
+    def __getitem__(self, item):
+        if item in self.ondemand:
+            self[item] = self.ondemand[item]()
+            del self.ondemand[item]
+        return super(DelayedRegistry, self).__getitem__(item)
+
+    def __setitem__(self, key, value):
+        if self._type_check:
+            def check(x, ty_x):
+                if isinstance(ty_x, type):
+                    assert ty_x in x.__mro__, (x, ty_x)
+                else:
+                    assert isinstance(x, ty_x), (x, ty_x)
+            if self.key_type is not None:
+                check(key, self.key_type)
+            if self.value_type is not None:
+                check(value, self.value_type)
+        return super(DelayedRegistry, self).__setitem__(key, value)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/removerefctpass.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/removerefctpass.py
new file mode 100644
index 000000000..98c04869a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/removerefctpass.py
@@ -0,0 +1,120 @@
+"""
+Implement a rewrite pass on a LLVM module to remove unnecessary 
+refcount operations.
+"""
+
+from llvmlite.ir.transforms import CallVisitor
+
+from numba.core import types
+
+
+class _MarkNrtCallVisitor(CallVisitor):
+    """
+    A pass to mark all NRT_incref and NRT_decref.
+    """
+    def __init__(self):
+        self.marked = set()
+
+    def visit_Call(self, instr):
+        if getattr(instr.callee, 'name', '') in _accepted_nrtfns:
+            self.marked.add(instr)
+
+
+def _rewrite_function(function):
+    # Mark NRT usage
+    markpass = _MarkNrtCallVisitor()
+    markpass.visit_Function(function)
+    # Remove NRT usage
+    for bb in function.basic_blocks:
+        for inst in list(bb.instructions):
+            if inst in markpass.marked:
+                bb.instructions.remove(inst)
+
+
+_accepted_nrtfns = 'NRT_incref', 'NRT_decref'
+
+
+def _legalize(module, dmm, fndesc):
+    """
+    Legalize the code in the module.
+    Returns True if the module is legal for the rewrite pass that removes
+    unnecessary refcounts.
+    """
+
+    def valid_output(ty):
+        """
+        Valid output are any type that does not need refcount
+        """
+        model = dmm[ty]
+        return not model.contains_nrt_meminfo()
+
+    def valid_input(ty):
+        """
+        Valid input are any type that does not need refcount except Array.
+        """
+        return valid_output(ty) or isinstance(ty, types.Array)
+
+
+    # Ensure no reference to function marked as
+    # "numba_args_may_always_need_nrt"
+    try:
+        nmd = module.get_named_metadata("numba_args_may_always_need_nrt")
+    except KeyError:
+        # Nothing marked
+        pass
+    else:
+        # Has functions marked as "numba_args_may_always_need_nrt"
+        if len(nmd.operands) > 0:
+            # The pass is illegal for this compilation unit.
+            return False
+
+    # More legalization base on function type
+    argtypes = fndesc.argtypes
+    restype = fndesc.restype
+    calltypes = fndesc.calltypes
+
+    # Legalize function arguments
+    for argty in argtypes:
+        if not valid_input(argty):
+            return False
+
+    # Legalize function return
+    if not valid_output(restype):
+        return False
+
+    # Legalize all called functions
+    for callty in calltypes.values():
+        if callty is not None and not valid_output(callty.return_type):
+            return False
+
+    # Ensure no allocation
+    for fn in module.functions:
+        if fn.name.startswith("NRT_"):
+            if fn.name not in _accepted_nrtfns:
+                return False
+
+    return True
+
+
+def remove_unnecessary_nrt_usage(function, context, fndesc):
+    """
+    Remove unnecessary NRT incref/decref in the given LLVM function.
+    It uses highlevel type info to determine if the function does not need NRT.
+    Such a function does not:
+
+    - return array object(s);
+    - take arguments that need refcounting except array;
+    - call function(s) that return refcounted object.
+
+    In effect, the function will not capture or create references that extend
+    the lifetime of any refcounted objects beyond the lifetime of the function.
+
+    The rewrite is performed in place.
+    If rewrite has happened, this function returns True, otherwise, it returns False.
+    """
+    dmm = context.data_model_manager
+    if _legalize(function.module, dmm, fndesc):
+        _rewrite_function(function)
+        return True
+    else:
+        return False
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/retarget.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/retarget.py
new file mode 100644
index 000000000..8fa98c78b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/retarget.py
@@ -0,0 +1,135 @@
+"""
+Implement utils for supporting retargeting of dispatchers.
+
+WARNING: Features defined in this file are experimental. The API may change
+         without notice.
+"""
+import abc
+import weakref
+
+from numba.core import errors
+
+
+class RetargetCache:
+    """Cache for retargeted dispatchers.
+
+    The cache uses the original dispatcher as the key.
+    """
+    container_type = weakref.WeakKeyDictionary
+
+    def __init__(self):
+        self._cache = self.container_type()
+        self._stat_hit = 0
+        self._stat_miss = 0
+
+    def save_cache(self, orig_disp, new_disp):
+        """Save a dispatcher associated with the given key.
+        """
+        self._cache[orig_disp] = new_disp
+
+    def load_cache(self, orig_disp):
+        """Load a dispatcher associated with the given key.
+        """
+        out = self._cache.get(orig_disp)
+        if out is None:
+            self._stat_miss += 1
+        else:
+            self._stat_hit += 1
+        return out
+
+    def items(self):
+        """Returns the contents of the cache.
+        """
+        return self._cache.items()
+
+    def stats(self):
+        """Returns stats regarding cache hit/miss.
+        """
+        return {'hit': self._stat_hit, 'miss': self._stat_miss}
+
+
+class BaseRetarget(abc.ABC):
+    """Abstract base class for retargeting logic.
+    """
+    @abc.abstractmethod
+    def check_compatible(self, orig_disp):
+        """Check that the retarget is compatible.
+
+        This method does not return anything meaningful (e.g. None)
+        Incompatibility is signalled via raising an exception.
+        """
+        pass
+
+    @abc.abstractmethod
+    def retarget(self, orig_disp):
+        """Retargets the given dispatcher and returns a new dispatcher-like
+        callable. Or, returns the original dispatcher if the the target_backend
+        will not change.
+        """
+        pass
+
+
+class BasicRetarget(BaseRetarget):
+    """A basic retargeting implementation for a single output target.
+
+    This class has two abstract methods/properties that subclasses must define.
+
+    - `output_target` must return output target name.
+    - `compile_retarget` must define the logic to retarget the given dispatcher.
+
+    By default, this class uses `RetargetCache` as the internal cache. This
+    can be modified by overriding the `.cache_type` class attribute.
+
+    """
+    cache_type = RetargetCache
+
+    def __init__(self):
+        self.cache = self.cache_type()
+
+    @abc.abstractproperty
+    def output_target(self) -> str:
+        """Returns the output target name.
+
+        See numba/tests/test_retargeting.py for example usage.
+        """
+        pass
+
+    @abc.abstractmethod
+    def compile_retarget(self, orig_disp):
+        """Returns the retargeted dispatcher.
+
+        See numba/tests/test_retargeting.py for example usage.
+        """
+        pass
+
+    def check_compatible(self, orig_disp):
+        """
+        This implementation checks that
+        `self.output_target == orig_disp._required_target_backend`
+        """
+        required_target = orig_disp._required_target_backend
+        output_target = self.output_target
+        if required_target is not None:
+            if output_target != required_target:
+                m = ("The output target does match the required target: "
+                     f"{output_target} != {required_target}.")
+                raise errors.CompilerError(m)
+
+    def retarget(self, orig_disp):
+        """Apply retargeting to orig_disp.
+
+        The retargeted dispatchers are cached for future use.
+        """
+        cache = self.cache
+        opts = orig_disp.targetoptions
+        # Skip if the original dispatcher is targeting the same output target
+        if opts.get('target_backend') == self.output_target:
+            return orig_disp
+        cached = cache.load_cache(orig_disp)
+        # No cache?
+        if cached is None:
+            out = self.compile_retarget(orig_disp)
+            cache.save_cache(orig_disp, out)
+        else:
+            out = cached
+        return out
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/__init__.py
new file mode 100644
index 000000000..61f55a8ec
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/__init__.py
@@ -0,0 +1,8 @@
+"""
+A subpackage hosting Numba IR rewrite passes.
+"""
+
+from .registry import register_rewrite, rewrite_registry, Rewrite
+# Register various built-in rewrite passes
+from numba.core.rewrites import (static_getitem, static_raise, static_binop,
+                                 ir_print)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/ir_print.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/ir_print.py
new file mode 100644
index 000000000..6d678381b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/ir_print.py
@@ -0,0 +1,82 @@
+from numba.core import errors, ir
+from numba.core.rewrites import register_rewrite, Rewrite
+
+
+@register_rewrite('before-inference')
+class RewritePrintCalls(Rewrite):
+    """
+    Rewrite calls to the print() global function to dedicated IR print() nodes.
+    """
+
+    def match(self, func_ir, block, typemap, calltypes):
+        self.prints = prints = {}
+        self.block = block
+        # Find all assignments with a right-hand print() call
+        for inst in block.find_insts(ir.Assign):
+            if isinstance(inst.value, ir.Expr) and inst.value.op == 'call':
+                expr = inst.value
+                try:
+                    callee = func_ir.infer_constant(expr.func)
+                except errors.ConstantInferenceError:
+                    continue
+                if callee is print:
+                    if expr.kws:
+                        # Only positional args are supported
+                        msg = ("Numba's print() function implementation does not "
+                            "support keyword arguments.")
+                        raise errors.UnsupportedError(msg, inst.loc)
+                    prints[inst] = expr
+        return len(prints) > 0
+
+    def apply(self):
+        """
+        Rewrite `var = call <print function>(...)` as a sequence of
+        `print(...)` and `var = const(None)`.
+        """
+        new_block = self.block.copy()
+        new_block.clear()
+        for inst in self.block.body:
+            if inst in self.prints:
+                expr = self.prints[inst]
+                print_node = ir.Print(args=expr.args, vararg=expr.vararg,
+                                      loc=expr.loc)
+                new_block.append(print_node)
+                assign_node = ir.Assign(value=ir.Const(None, loc=expr.loc),
+                                        target=inst.target,
+                                        loc=inst.loc)
+                new_block.append(assign_node)
+            else:
+                new_block.append(inst)
+        return new_block
+
+
+@register_rewrite('before-inference')
+class DetectConstPrintArguments(Rewrite):
+    """
+    Detect and store constant arguments to print() nodes.
+    """
+
+    def match(self, func_ir, block, typemap, calltypes):
+        self.consts = consts = {}
+        self.block = block
+        for inst in block.find_insts(ir.Print):
+            if inst.consts:
+                # Already rewritten
+                continue
+            for idx, var in enumerate(inst.args):
+                try:
+                    const = func_ir.infer_constant(var)
+                except errors.ConstantInferenceError:
+                    continue
+                consts.setdefault(inst, {})[idx] = const
+
+        return len(consts) > 0
+
+    def apply(self):
+        """
+        Store detected constant arguments on their nodes.
+        """
+        for inst in self.block.body:
+            if inst in self.consts:
+                inst.consts = self.consts[inst]
+        return self.block
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/registry.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/registry.py
new file mode 100644
index 000000000..ea22fc8e3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/registry.py
@@ -0,0 +1,98 @@
+from collections import defaultdict
+
+from numba.core import config
+
+
+class Rewrite(object):
+    '''Defines the abstract base class for Numba rewrites.
+    '''
+
+    def __init__(self, state=None):
+        '''Constructor for the Rewrite class.
+        '''
+        pass
+
+    def match(self, func_ir, block, typemap, calltypes):
+        '''Overload this method to check an IR block for matching terms in the
+        rewrite.
+        '''
+        return False
+
+    def apply(self):
+        '''Overload this method to return a rewritten IR basic block when a
+        match has been found.
+        '''
+        raise NotImplementedError("Abstract Rewrite.apply() called!")
+
+
+class RewriteRegistry(object):
+    '''Defines a registry for Numba rewrites.
+    '''
+    _kinds = frozenset(['before-inference', 'after-inference'])
+
+    def __init__(self):
+        '''Constructor for the rewrite registry.  Initializes the rewrites
+        member to an empty list.
+        '''
+        self.rewrites = defaultdict(list)
+
+    def register(self, kind):
+        """
+        Decorator adding a subclass of Rewrite to the registry for
+        the given *kind*.
+        """
+        if kind not in self._kinds:
+            raise KeyError("invalid kind %r" % (kind,))
+        def do_register(rewrite_cls):
+            if not issubclass(rewrite_cls, Rewrite):
+                raise TypeError('{0} is not a subclass of Rewrite'.format(
+                    rewrite_cls))
+            self.rewrites[kind].append(rewrite_cls)
+            return rewrite_cls
+        return do_register
+
+    def apply(self, kind, state):
+        '''Given a pipeline and a dictionary of basic blocks, exhaustively
+        attempt to apply all registered rewrites to all basic blocks.
+        '''
+        assert kind in self._kinds
+        blocks = state.func_ir.blocks
+        old_blocks = blocks.copy()
+        for rewrite_cls in self.rewrites[kind]:
+            # Exhaustively apply a rewrite until it stops matching.
+            rewrite = rewrite_cls(state)
+            work_list = list(blocks.items())
+            while work_list:
+                key, block = work_list.pop()
+                matches = rewrite.match(state.func_ir, block, state.typemap,
+                                        state.calltypes)
+                if matches:
+                    if config.DEBUG or config.DUMP_IR:
+                        print("_" * 70)
+                        print("REWRITING (%s):" % rewrite_cls.__name__)
+                        block.dump()
+                        print("_" * 60)
+                    new_block = rewrite.apply()
+                    blocks[key] = new_block
+                    work_list.append((key, new_block))
+                    if config.DEBUG or config.DUMP_IR:
+                        new_block.dump()
+                        print("_" * 70)
+        # If any blocks were changed, perform a sanity check.
+        for key, block in blocks.items():
+            if block != old_blocks[key]:
+                block.verify()
+
+        # Some passes, e.g. _inline_const_arraycall are known to occasionally
+        # do invalid things WRT ir.Del, others, e.g. RewriteArrayExprs do valid
+        # things with ir.Del, but the placement is not optimal. The lines below
+        # fix-up the IR so that ref counts are valid and optimally placed,
+        # see #4093 for context. This has to be run here opposed to in
+        # apply() as the CFG needs computing so full IR is needed.
+        from numba.core import postproc
+        post_proc = postproc.PostProcessor(state.func_ir)
+        post_proc.run()
+
+
+rewrite_registry = RewriteRegistry()
+register_rewrite = rewrite_registry.register
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_binop.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_binop.py
new file mode 100644
index 000000000..33487a675
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_binop.py
@@ -0,0 +1,35 @@
+from numba.core import errors, ir
+from numba.core.rewrites import register_rewrite, Rewrite
+
+
+@register_rewrite('before-inference')
+class DetectStaticBinops(Rewrite):
+    """
+    Detect constant arguments to select binops.
+    """
+
+    # Those operators can benefit from a constant-inferred argument
+    rhs_operators = {'**'}
+
+    def match(self, func_ir, block, typemap, calltypes):
+        self.static_lhs = {}
+        self.static_rhs = {}
+        self.block = block
+        # Find binop expressions with a constant lhs or rhs
+        for expr in block.find_exprs(op='binop'):
+            try:
+                if (expr.fn in self.rhs_operators
+                    and expr.static_rhs is ir.UNDEFINED):
+                    self.static_rhs[expr] = func_ir.infer_constant(expr.rhs)
+            except errors.ConstantInferenceError:
+                continue
+
+        return len(self.static_lhs) > 0 or len(self.static_rhs) > 0
+
+    def apply(self):
+        """
+        Store constant arguments that were detected in match().
+        """
+        for expr, rhs in self.static_rhs.items():
+            expr.static_rhs = rhs
+        return self.block
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_getitem.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_getitem.py
new file mode 100644
index 000000000..56343d0ea
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_getitem.py
@@ -0,0 +1,175 @@
+from numba.core import errors, ir, types
+from numba.core.rewrites import register_rewrite, Rewrite
+
+
+@register_rewrite('before-inference')
+class RewriteConstGetitems(Rewrite):
+    """
+    Rewrite IR expressions of the kind `getitem(value=arr, index=$constXX)`
+    where `$constXX` is a known constant as
+    `static_getitem(value=arr, index=<constant value>)`.
+    """
+
+    def match(self, func_ir, block, typemap, calltypes):
+        self.getitems = getitems = {}
+        self.block = block
+        # Detect all getitem expressions and find which ones can be
+        # rewritten
+        for expr in block.find_exprs(op='getitem'):
+            if expr.op == 'getitem':
+                try:
+                    const = func_ir.infer_constant(expr.index)
+                except errors.ConstantInferenceError:
+                    continue
+                getitems[expr] = const
+
+        return len(getitems) > 0
+
+    def apply(self):
+        """
+        Rewrite all matching getitems as static_getitems.
+        """
+        new_block = self.block.copy()
+        new_block.clear()
+        for inst in self.block.body:
+            if isinstance(inst, ir.Assign):
+                expr = inst.value
+                if expr in self.getitems:
+                    const = self.getitems[expr]
+                    new_expr = ir.Expr.static_getitem(value=expr.value,
+                                                      index=const,
+                                                      index_var=expr.index,
+                                                      loc=expr.loc)
+                    inst = ir.Assign(value=new_expr, target=inst.target,
+                                     loc=inst.loc)
+            new_block.append(inst)
+        return new_block
+
+
+@register_rewrite('after-inference')
+class RewriteStringLiteralGetitems(Rewrite):
+    """
+    Rewrite IR expressions of the kind `getitem(value=arr, index=$XX)`
+    where `$XX` is a StringLiteral value as
+    `static_getitem(value=arr, index=<literal value>)`.
+    """
+
+    def match(self, func_ir, block, typemap, calltypes):
+        """
+        Detect all getitem expressions and find which ones have
+        string literal indexes
+        """
+        self.getitems = getitems = {}
+        self.block = block
+        self.calltypes = calltypes
+        for expr in block.find_exprs(op='getitem'):
+            if expr.op == 'getitem':
+                index_ty = typemap[expr.index.name]
+                if isinstance(index_ty, types.StringLiteral):
+                    getitems[expr] = (expr.index, index_ty.literal_value)
+
+        return len(getitems) > 0
+
+    def apply(self):
+        """
+        Rewrite all matching getitems as static_getitems where the index
+        is the literal value of the string.
+        """
+        new_block = ir.Block(self.block.scope, self.block.loc)
+        for inst in self.block.body:
+            if isinstance(inst, ir.Assign):
+                expr = inst.value
+                if expr in self.getitems:
+                    const, lit_val = self.getitems[expr]
+                    new_expr = ir.Expr.static_getitem(value=expr.value,
+                                                      index=lit_val,
+                                                      index_var=expr.index,
+                                                      loc=expr.loc)
+                    self.calltypes[new_expr] = self.calltypes[expr]
+                    inst = ir.Assign(value=new_expr, target=inst.target,
+                                     loc=inst.loc)
+            new_block.append(inst)
+        return new_block
+
+
+@register_rewrite('after-inference')
+class RewriteStringLiteralSetitems(Rewrite):
+    """
+    Rewrite IR expressions of the kind `setitem(value=arr, index=$XX, value=)`
+    where `$XX` is a StringLiteral value as
+    `static_setitem(value=arr, index=<literal value>, value=)`.
+    """
+
+    def match(self, func_ir, block, typemap, calltypes):
+        """
+        Detect all setitem expressions and find which ones have
+        string literal indexes
+        """
+        self.setitems = setitems = {}
+        self.block = block
+        self.calltypes = calltypes
+        for inst in block.find_insts(ir.SetItem):
+            index_ty = typemap[inst.index.name]
+            if isinstance(index_ty, types.StringLiteral):
+                setitems[inst] = (inst.index, index_ty.literal_value)
+
+        return len(setitems) > 0
+
+    def apply(self):
+        """
+        Rewrite all matching setitems as static_setitems where the index
+        is the literal value of the string.
+        """
+        new_block = ir.Block(self.block.scope, self.block.loc)
+        for inst in self.block.body:
+            if isinstance(inst, ir.SetItem):
+                if inst in self.setitems:
+                    const, lit_val = self.setitems[inst]
+                    new_inst = ir.StaticSetItem(target=inst.target,
+                                                index=lit_val,
+                                                index_var=inst.index,
+                                                value=inst.value,
+                                                loc=inst.loc)
+                    self.calltypes[new_inst] = self.calltypes[inst]
+                    inst = new_inst
+            new_block.append(inst)
+        return new_block
+
+
+@register_rewrite('before-inference')
+class RewriteConstSetitems(Rewrite):
+    """
+    Rewrite IR statements of the kind `setitem(target=arr, index=$constXX, ...)`
+    where `$constXX` is a known constant as
+    `static_setitem(target=arr, index=<constant value>, ...)`.
+    """
+
+    def match(self, func_ir, block, typemap, calltypes):
+        self.setitems = setitems = {}
+        self.block = block
+        # Detect all setitem statements and find which ones can be
+        # rewritten
+        for inst in block.find_insts(ir.SetItem):
+            try:
+                const = func_ir.infer_constant(inst.index)
+            except errors.ConstantInferenceError:
+                continue
+            setitems[inst] = const
+
+        return len(setitems) > 0
+
+    def apply(self):
+        """
+        Rewrite all matching setitems as static_setitems.
+        """
+        new_block = self.block.copy()
+        new_block.clear()
+        for inst in self.block.body:
+            if inst in self.setitems:
+                const = self.setitems[inst]
+                new_inst = ir.StaticSetItem(inst.target, const,
+                                            inst.index, inst.value, inst.loc)
+                new_block.append(new_inst)
+            else:
+                new_block.append(inst)
+        return new_block
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_raise.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_raise.py
new file mode 100644
index 000000000..61f7b5742
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/rewrites/static_raise.py
@@ -0,0 +1,79 @@
+from numba.core import errors, ir
+from numba.core.rewrites import register_rewrite, Rewrite
+
+
+@register_rewrite('before-inference')
+class RewriteConstRaises(Rewrite):
+    """
+    Rewrite IR statements of the kind `raise(value)`
+    where `value` is the result of instantiating an exception with
+    constant arguments
+    into `static_raise(exception_type, constant args)`.
+
+    This allows lowering in nopython mode, where one can't instantiate
+    exception instances from runtime data.
+    """
+
+    def _is_exception_type(self, const):
+        return isinstance(const, type) and issubclass(const, Exception)
+
+    def _break_constant(self, const, loc):
+        """
+        Break down constant exception.
+        """
+        if isinstance(const, tuple): # it's a tuple(exception class, args)
+            if not self._is_exception_type(const[0]):
+                msg = "Encountered unsupported exception constant %r"
+                raise errors.UnsupportedError(msg % (const[0],), loc)
+            return const[0], tuple(const[1])
+        elif self._is_exception_type(const):
+            return const, None
+        else:
+            if isinstance(const, str):
+                msg = ("Directly raising a string constant as an exception is "
+                       "not supported.")
+            else:
+                msg = "Encountered unsupported constant type used for exception"
+            raise errors.UnsupportedError(msg, loc)
+
+    def match(self, func_ir, block, typemap, calltypes):
+        self.raises = raises = {}
+        self.tryraises = tryraises = {}
+        self.block = block
+        # Detect all raise statements and find which ones can be
+        # rewritten
+        for inst in block.find_insts((ir.Raise, ir.TryRaise)):
+            if inst.exception is None:
+                # re-reraise
+                exc_type, exc_args = None, None
+            else:
+                # raise <something> => find the definition site for <something>
+                const = func_ir.infer_constant(inst.exception)
+                loc = inst.exception.loc
+                exc_type, exc_args = self._break_constant(const, loc)
+            if isinstance(inst, ir.Raise):
+                raises[inst] = exc_type, exc_args
+            elif isinstance(inst, ir.TryRaise):
+                tryraises[inst] = exc_type, exc_args
+            else:
+                raise ValueError('unexpected: {}'.format(type(inst)))
+        return (len(raises) + len(tryraises)) > 0
+
+    def apply(self):
+        """
+        Rewrite all matching setitems as static_setitems.
+        """
+        new_block = self.block.copy()
+        new_block.clear()
+        for inst in self.block.body:
+            if inst in self.raises:
+                exc_type, exc_args = self.raises[inst]
+                new_inst = ir.StaticRaise(exc_type, exc_args, inst.loc)
+                new_block.append(new_inst)
+            elif inst in self.tryraises:
+                exc_type, exc_args = self.tryraises[inst]
+                new_inst = ir.StaticTryRaise(exc_type, exc_args, inst.loc)
+                new_block.append(new_inst)
+            else:
+                new_block.append(inst)
+        return new_block
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/__init__.py
new file mode 100644
index 000000000..590470031
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/__init__.py
@@ -0,0 +1 @@
+from .nrt import rtsys
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/_nrt_python.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/_nrt_python.c
new file mode 100644
index 000000000..b4c847e26
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/_nrt_python.c
@@ -0,0 +1,459 @@
+/*
+ * Definition of NRT functions for marshalling from / to Python objects.
+ * This module is included by _nrt_pythonmod.c and by pycc-compiled modules.
+ */
+
+#include "../../_pymodule.h"
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+#include <numpy/ndarrayobject.h>
+#include <numpy/arrayscalars.h>
+
+#include "../../_arraystruct.h"
+#include "../../_numba_common.h"
+#include "nrt.h"
+
+
+/*
+ * Create a NRT MemInfo for data owned by a PyObject.
+ */
+
+static void
+pyobject_dtor(void *ptr, size_t size, void* info) {
+    PyGILState_STATE gstate;
+    PyObject *ownerobj = info;
+
+    gstate = PyGILState_Ensure();   /* ensure the GIL */
+    Py_DECREF(ownerobj);            /* release the python object */
+    PyGILState_Release(gstate);     /* release the GIL */
+}
+
+NUMBA_EXPORT_FUNC(NRT_MemInfo *)
+NRT_meminfo_new_from_pyobject(void *data, PyObject *ownerobj) {
+    size_t dummy_size = 0;
+    Py_INCREF(ownerobj);
+    return NRT_MemInfo_new(data, dummy_size, pyobject_dtor, ownerobj);
+}
+
+
+/*
+ * A Python object wrapping a NRT meminfo.
+ */
+
+typedef struct {
+    PyObject_HEAD
+    NRT_MemInfo *meminfo;
+} MemInfoObject;
+
+
+static
+int MemInfo_init(MemInfoObject *self, PyObject *args, PyObject *kwds) {
+    static char *keywords[] = {"ptr", NULL};
+    PyObject *raw_ptr_obj;
+    void *raw_ptr;
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "O", keywords, &raw_ptr_obj)) {
+        return -1;
+    }
+    raw_ptr = PyLong_AsVoidPtr(raw_ptr_obj);
+    NRT_Debug(nrt_debug_print("MemInfo_init self=%p raw_ptr=%p\n", self, raw_ptr));
+
+    if(PyErr_Occurred()) return -1;
+    self->meminfo = (NRT_MemInfo *)raw_ptr;
+    assert (NRT_MemInfo_refcount(self->meminfo) > 0 && "0 refcount");
+    return 0;
+}
+
+
+static int
+MemInfo_getbuffer(PyObject *exporter, Py_buffer *view, int flags) {
+    Py_ssize_t len;
+    void *buf;
+    int readonly = 0;
+
+    MemInfoObject *miobj = (MemInfoObject*)exporter;
+    NRT_MemInfo *mi = miobj->meminfo;
+
+    buf = NRT_MemInfo_data(mi);
+    len = NRT_MemInfo_size(mi);
+    return PyBuffer_FillInfo(view, exporter, buf, len, readonly, flags);
+}
+
+static PyBufferProcs MemInfo_bufferProcs = {MemInfo_getbuffer, NULL};
+
+static
+PyObject*
+MemInfo_acquire(MemInfoObject *self) {
+    NRT_MemInfo_acquire(self->meminfo);
+    Py_RETURN_NONE;
+}
+
+static
+PyObject*
+MemInfo_release(MemInfoObject *self) {
+    NRT_MemInfo_release(self->meminfo);
+    Py_RETURN_NONE;
+}
+
+static
+PyObject*
+MemInfo_get_data(MemInfoObject *self, void *closure) {
+    return PyLong_FromVoidPtr(NRT_MemInfo_data(self->meminfo));
+}
+
+static
+PyObject*
+MemInfo_get_refcount(MemInfoObject *self, void *closure) {
+    size_t refct = NRT_MemInfo_refcount(self->meminfo);
+    if ( refct == (size_t)-1 ) {
+        PyErr_SetString(PyExc_ValueError, "invalid MemInfo");
+        return NULL;
+    }
+    return PyLong_FromSize_t(refct);
+}
+
+static
+PyObject*
+MemInfo_get_external_allocator(MemInfoObject *self, void *closure) {
+    void *p = NRT_MemInfo_external_allocator(self->meminfo);
+    return PyLong_FromVoidPtr(p);
+}
+
+static
+PyObject*
+MemInfo_get_parent(MemInfoObject *self, void *closure) {
+    void *p = NRT_MemInfo_parent(self->meminfo);
+    if (p) {
+        Py_INCREF(p);
+        return (PyObject*)p;
+    } else {
+        Py_INCREF(Py_None);
+        return Py_None;
+    }
+}
+
+static void
+MemInfo_dealloc(MemInfoObject *self)
+{
+    NRT_MemInfo_release(self->meminfo);
+    Py_TYPE(self)->tp_free((PyObject*)self);
+}
+
+static PyMethodDef MemInfo_methods[] = {
+    {"acquire", (PyCFunction)MemInfo_acquire, METH_NOARGS,
+     "Increment the reference count"
+    },
+    {"release", (PyCFunction)MemInfo_release, METH_NOARGS,
+     "Decrement the reference count"
+    },
+    {NULL}  /* Sentinel */
+};
+
+
+static PyGetSetDef MemInfo_getsets[] = {
+    {"data",
+     (getter)MemInfo_get_data, NULL,
+     "Get the data pointer as an integer",
+     NULL},
+    {"refcount",
+     (getter)MemInfo_get_refcount, NULL,
+     "Get the refcount",
+     NULL},
+    {"external_allocator",
+     (getter)MemInfo_get_external_allocator, NULL,
+     "Get the external allocator",
+     NULL},
+    {"parent",
+     (getter)MemInfo_get_parent, NULL,
+     NULL},
+    {NULL}  /* Sentinel */
+};
+
+
+static PyTypeObject MemInfoType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "_nrt_python._MemInfo",                   /* tp_name*/
+    sizeof(MemInfoObject),                    /* tp_basicsize*/
+    0,                                        /* tp_itemsize*/
+    (destructor)MemInfo_dealloc,              /* tp_dealloc*/
+    0,                                        /* tp_print*/
+    0,                                        /* tp_getattr*/
+    0,                                        /* tp_setattr*/
+    0,                                        /* tp_compare*/
+    0,                                        /* tp_repr*/
+    0,                                        /* tp_as_number*/
+    0,                                        /* tp_as_sequence*/
+    0,                                        /* tp_as_mapping*/
+    0,                                        /* tp_hash */
+    0,                                        /* tp_call*/
+    0,                                        /* tp_str*/
+    0,                                        /* tp_getattro*/
+    0,                                        /* tp_setattro*/
+    &MemInfo_bufferProcs,                      /* tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE,  /* tp_flags*/
+    0,                                        /* tp_doc */
+    0,                                        /* tp_traverse */
+    0,                                        /* tp_clear */
+    0,                                        /* tp_richcompare */
+    0,                                        /* tp_weaklistoffset */
+    0,                                        /* tp_iter */
+    0,                                        /* tp_iternext */
+    MemInfo_methods,                          /* tp_methods */
+    0,                                        /* tp_members */
+    MemInfo_getsets,                          /* tp_getset */
+    0,                                        /* tp_base */
+    0,                                        /* tp_dict */
+    0,                                        /* tp_descr_get */
+    0,                                        /* tp_descr_set */
+    0,                                        /* tp_dictoffset */
+    (initproc)MemInfo_init,                   /* tp_init */
+    0,                                        /* tp_alloc */
+    0,                                        /* tp_new */
+};
+
+
+/*
+Return a MemInfo* as a MemInfoObject*
+The NRT reference to the MemInfo is borrowed.
+*/
+NUMBA_EXPORT_FUNC(MemInfoObject*)
+NRT_meminfo_as_pyobject(NRT_MemInfo *meminfo) {
+    MemInfoObject *mi;
+    PyObject *addr;
+
+    addr = PyLong_FromVoidPtr(meminfo);
+    if (!addr) return NULL;
+    mi = (MemInfoObject*)PyObject_CallFunctionObjArgs((PyObject *)&MemInfoType, addr, NULL);
+    Py_DECREF(addr);
+    if (!mi) return NULL;
+    return mi;
+}
+
+
+/*
+Return a MemInfo* from a MemInfoObject*
+A new reference is returned.
+*/
+NUMBA_EXPORT_FUNC(NRT_MemInfo*)
+NRT_meminfo_from_pyobject(MemInfoObject *miobj) {
+    NRT_MemInfo_acquire(miobj->meminfo);
+    return miobj->meminfo;
+}
+
+
+/*
+ * Array adaptor code
+ */
+
+NUMBA_EXPORT_FUNC(int)
+NRT_adapt_ndarray_from_python(PyObject *obj, arystruct_t* arystruct) {
+    PyArrayObject *ndary;
+    int i, ndim;
+    npy_intp *p;
+    void *data;
+
+    if (!PyArray_Check(obj)) {
+        return -1;
+    }
+
+    ndary = (PyArrayObject*)obj;
+    ndim = PyArray_NDIM(ndary);
+    data = PyArray_DATA(ndary);
+
+    arystruct->meminfo = NRT_meminfo_new_from_pyobject((void*)data, obj);
+    arystruct->data = data;
+    arystruct->nitems = PyArray_SIZE(ndary);
+    arystruct->itemsize = PyArray_ITEMSIZE(ndary);
+    arystruct->parent = obj;
+    p = arystruct->shape_and_strides;
+    for (i = 0; i < ndim; i++, p++) {
+        *p = PyArray_DIM(ndary, i);
+    }
+    for (i = 0; i < ndim; i++, p++) {
+        *p = PyArray_STRIDE(ndary, i);
+    }
+
+    NRT_Debug(nrt_debug_print("NRT_adapt_ndarray_from_python %p\n",
+                              arystruct->meminfo));
+    return 0;
+}
+
+static
+PyObject* try_to_return_parent(arystruct_t *arystruct, int ndim,
+                               PyArray_Descr *descr)
+{
+    int i;
+    PyArrayObject *array = (PyArrayObject *)arystruct->parent;
+
+    if (!PyArray_Check(arystruct->parent))
+        /* Parent is a generic buffer-providing object */
+        goto RETURN_ARRAY_COPY;
+
+    if (PyArray_DATA(array) != arystruct->data)
+        goto RETURN_ARRAY_COPY;
+
+    if (PyArray_NDIM(array) != ndim)
+        goto RETURN_ARRAY_COPY;
+
+    if (PyObject_RichCompareBool((PyObject *) PyArray_DESCR(array),
+                                 (PyObject *) descr, Py_EQ) <= 0)
+        goto RETURN_ARRAY_COPY;
+
+    for(i = 0; i < ndim; ++i) {
+        if (PyArray_DIMS(array)[i] != arystruct->shape_and_strides[i])
+            goto RETURN_ARRAY_COPY;
+        if (PyArray_STRIDES(array)[i] != arystruct->shape_and_strides[ndim + i])
+            goto RETURN_ARRAY_COPY;
+    }
+
+    /* Yes, it is the same array
+       Return new reference */
+    Py_INCREF((PyObject *)array);
+    return (PyObject *)array;
+
+RETURN_ARRAY_COPY:
+    return NULL;
+}
+
+/**
+ * This function is used during the boxing of ndarray type.
+ * `arystruct` is a structure containing essential information from the
+ *             unboxed array.
+ * `retty` is the subtype of the NumPy PyArray_Type this function should return.
+ *         This is related to `numba.core.types.Array.box_type`.
+ * `ndim` is the number of dimension of the array.
+ * `writeable` corresponds to the "writable" flag in NumPy ndarray.
+ * `descr` is the NumPy data type description.
+ *
+ * This function was renamed in 0.52.0 to specify that it acquires references.
+ * It used to steal the reference of the arystruct.
+ * Refer to https://github.com/numba/numba/pull/6446
+ */
+NUMBA_EXPORT_FUNC(PyObject *)
+NRT_adapt_ndarray_to_python_acqref(arystruct_t* arystruct, PyTypeObject *retty,
+                            int ndim, int writeable, PyArray_Descr *descr)
+{
+    PyArrayObject *array;
+    MemInfoObject *miobj = NULL;
+    PyObject *args;
+    npy_intp *shape, *strides;
+    int flags = 0;
+
+    if (descr == NULL) {
+        PyErr_Format(PyExc_RuntimeError,
+                     "In 'NRT_adapt_ndarray_to_python', 'descr' is NULL");
+        return NULL;
+    }
+
+    if (!NUMBA_PyArray_DescrCheck(descr)) {
+        PyErr_Format(PyExc_TypeError,
+                     "expected dtype object, got '%.200s'",
+                     Py_TYPE(descr)->tp_name);
+        return NULL;
+    }
+
+    if (arystruct->parent) {
+        PyObject *obj = try_to_return_parent(arystruct, ndim, descr);
+        if (obj) {
+            return obj;
+        }
+    }
+
+    if (arystruct->meminfo) {
+        /* wrap into MemInfoObject */
+        miobj = PyObject_New(MemInfoObject, &MemInfoType);
+        args = PyTuple_New(1);
+        /* SETITEM steals reference */
+        PyTuple_SET_ITEM(args, 0, PyLong_FromVoidPtr(arystruct->meminfo));
+        NRT_Debug(nrt_debug_print("NRT_adapt_ndarray_to_python arystruct->meminfo=%p\n", arystruct->meminfo));
+        /*  Note: MemInfo_init() does not incref.  This function steals the
+         *        NRT reference, which we need to acquire.
+         */
+        NRT_Debug(nrt_debug_print("NRT_adapt_ndarray_to_python_acqref created MemInfo=%p\n", miobj));
+        NRT_MemInfo_acquire(arystruct->meminfo);
+        if (MemInfo_init(miobj, args, NULL)) {
+            NRT_Debug(nrt_debug_print("MemInfo_init failed.\n"));
+            return NULL;
+        }
+        Py_DECREF(args);
+    }
+
+    shape = arystruct->shape_and_strides;
+    strides = shape + ndim;
+    Py_INCREF((PyObject *) descr);
+    array = (PyArrayObject *) PyArray_NewFromDescr(retty, descr, ndim,
+                                                   shape, strides, arystruct->data,
+                                                   flags, (PyObject *) miobj);
+
+    if (array == NULL)
+        return NULL;
+
+    /* Set writable */
+#if NPY_API_VERSION >= 0x00000007
+    if (writeable) {
+        PyArray_ENABLEFLAGS(array, NPY_ARRAY_WRITEABLE);
+    }
+    else {
+        PyArray_CLEARFLAGS(array, NPY_ARRAY_WRITEABLE);
+    }
+#else
+    if (writeable) {
+        array->flags |= NPY_WRITEABLE;
+    }
+    else {
+        array->flags &= ~NPY_WRITEABLE;
+    }
+#endif
+
+    if (miobj) {
+        /* Set the MemInfoObject as the base object */
+#if NPY_API_VERSION >= 0x00000007
+        if (-1 == PyArray_SetBaseObject(array,
+                                        (PyObject *) miobj))
+        {
+            Py_DECREF(array);
+            Py_DECREF(miobj);
+            return NULL;
+        }
+#else
+        PyArray_BASE(array) = (PyObject *) miobj;
+#endif
+
+    }
+    return (PyObject *) array;
+}
+
+NUMBA_EXPORT_FUNC(void)
+NRT_adapt_buffer_from_python(Py_buffer *buf, arystruct_t *arystruct)
+{
+    int i;
+    npy_intp *p;
+
+    if (buf->obj) {
+        /* Allocate new MemInfo only if the buffer has a parent */
+        arystruct->meminfo = NRT_meminfo_new_from_pyobject((void*)buf->buf, buf->obj);
+    }
+    arystruct->data = buf->buf;
+    arystruct->itemsize = buf->itemsize;
+    arystruct->parent = buf->obj;
+    arystruct->nitems = 1;
+    p = arystruct->shape_and_strides;
+    for (i = 0; i < buf->ndim; i++, p++) {
+        *p = buf->shape[i];
+        arystruct->nitems *= buf->shape[i];
+    }
+    for (i = 0; i < buf->ndim; i++, p++) {
+        *p = buf->strides[i];
+    }
+}
+
+
+/* Initialization subroutines for modules including this source file */
+
+static int
+init_nrt_python_module(PyObject *module)
+{
+    MemInfoType.tp_new = PyType_GenericNew;
+    if (PyType_Ready(&MemInfoType))
+        return -1;
+    return 0;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/_nrt_pythonmod.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/_nrt_pythonmod.c
new file mode 100644
index 000000000..3552c7095
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/_nrt_pythonmod.c
@@ -0,0 +1,207 @@
+#define NUMBA_EXPORT_FUNC(_rettype) static _rettype
+#define NUMBA_EXPORT_DATA(_vartype) static _vartype
+
+#include "_nrt_python.c"
+
+static PyObject *
+memsys_shutdown(PyObject *self, PyObject *args) {
+    NRT_MemSys_shutdown();
+    Py_RETURN_NONE;
+}
+
+static PyObject *
+memsys_use_cpython_allocator(PyObject *self, PyObject *args) {
+    NRT_MemSys_set_allocator(PyMem_RawMalloc,
+                             PyMem_RawRealloc,
+                             PyMem_RawFree);
+    Py_RETURN_NONE;
+}
+
+static PyObject *
+memsys_set_atomic_inc_dec(PyObject *self, PyObject *args) {
+    PyObject *addr_inc_obj, *addr_dec_obj;
+    void *addr_inc, *addr_dec;
+    if (!PyArg_ParseTuple(args, "OO", &addr_inc_obj, &addr_dec_obj)) {
+        return NULL;
+    }
+    addr_inc = PyLong_AsVoidPtr(addr_inc_obj);
+    if(PyErr_Occurred()) return NULL;
+    addr_dec = PyLong_AsVoidPtr(addr_dec_obj);
+    if(PyErr_Occurred()) return NULL;
+    NRT_MemSys_set_atomic_inc_dec(addr_inc, addr_dec);
+    Py_RETURN_NONE;
+}
+
+static PyObject *
+memsys_set_atomic_cas(PyObject *self, PyObject *args) {
+    PyObject *addr_cas_obj;
+    void *addr_cas;
+    if (!PyArg_ParseTuple(args, "O", &addr_cas_obj)) {
+        return NULL;
+    }
+    addr_cas = PyLong_AsVoidPtr(addr_cas_obj);
+    if(PyErr_Occurred()) return NULL;
+    NRT_MemSys_set_atomic_cas(addr_cas);
+    Py_RETURN_NONE;
+}
+
+static PyObject *
+memsys_get_stats_alloc(PyObject *self, PyObject *args) {
+    return PyLong_FromSize_t(NRT_MemSys_get_stats_alloc());
+}
+
+static PyObject *
+memsys_get_stats_free(PyObject *self, PyObject *args) {
+    return PyLong_FromSize_t(NRT_MemSys_get_stats_free());
+}
+
+static PyObject *
+memsys_get_stats_mi_alloc(PyObject *self, PyObject *args) {
+    return PyLong_FromSize_t(NRT_MemSys_get_stats_mi_alloc());
+}
+
+static PyObject *
+memsys_get_stats_mi_free(PyObject *self, PyObject *args) {
+    return PyLong_FromSize_t(NRT_MemSys_get_stats_mi_free());
+}
+
+
+/*
+ * Create a new MemInfo with a owner PyObject
+ */
+static PyObject *
+meminfo_new(PyObject *self, PyObject *args) {
+    PyObject *addr_data_obj;
+    void *addr_data;
+    PyObject *ownerobj;
+    NRT_MemInfo *mi;
+    if (!PyArg_ParseTuple(args, "OO", &addr_data_obj, &ownerobj)) {
+        return NULL;
+    }
+    addr_data = PyLong_AsVoidPtr(addr_data_obj);
+    if (PyErr_Occurred())
+        return NULL;
+    mi = NRT_meminfo_new_from_pyobject(addr_data, ownerobj);
+    return PyLong_FromVoidPtr(mi);
+}
+
+/*
+ * Create a new MemInfo with a new NRT allocation
+ */
+static PyObject *
+meminfo_alloc(PyObject *self, PyObject *args) {
+    NRT_MemInfo *mi;
+    Py_ssize_t size;
+    if (!PyArg_ParseTuple(args, "n", &size)) {
+        return NULL;
+    }
+    mi = NRT_MemInfo_alloc(size);
+    return PyLong_FromVoidPtr(mi);
+}
+
+/*
+ * Like meminfo_alloc but set memory to zero after allocation and before
+ * deallocation.
+ */
+static PyObject *
+meminfo_alloc_safe(PyObject *self, PyObject *args) {
+    NRT_MemInfo *mi;
+    Py_ssize_t size;
+    if (!PyArg_ParseTuple(args, "n", &size)) {
+        return NULL;
+    }
+    mi = NRT_MemInfo_alloc_safe(size);
+    return PyLong_FromVoidPtr(mi);
+}
+
+static PyMethodDef ext_methods[] = {
+#define declmethod(func) { #func , ( PyCFunction )func , METH_VARARGS , NULL }
+#define declmethod_noargs(func) { #func , ( PyCFunction )func , METH_NOARGS, NULL }
+    declmethod_noargs(memsys_use_cpython_allocator),
+    declmethod_noargs(memsys_shutdown),
+    declmethod(memsys_set_atomic_inc_dec),
+    declmethod(memsys_set_atomic_cas),
+    declmethod_noargs(memsys_get_stats_alloc),
+    declmethod_noargs(memsys_get_stats_free),
+    declmethod_noargs(memsys_get_stats_mi_alloc),
+    declmethod_noargs(memsys_get_stats_mi_free),
+    declmethod(meminfo_new),
+    declmethod(meminfo_alloc),
+    declmethod(meminfo_alloc_safe),
+    { NULL },
+#undef declmethod
+};
+
+
+
+static PyObject *
+build_c_helpers_dict(void)
+{
+    PyObject *dct = PyDict_New();
+    if (dct == NULL)
+        goto error;
+
+#define _declpointer(name, value) do {                 \
+    PyObject *o = PyLong_FromVoidPtr(value);           \
+    if (o == NULL) goto error;                         \
+    if (PyDict_SetItemString(dct, name, o)) {          \
+        Py_DECREF(o);                                  \
+        goto error;                                    \
+    }                                                  \
+    Py_DECREF(o);                                      \
+} while (0)
+
+#define declmethod(func) _declpointer(#func, &NRT_##func)
+#define declmethod_internal(func) _declpointer(#func, &func)
+
+declmethod(adapt_ndarray_from_python);
+declmethod(adapt_ndarray_to_python_acqref);
+declmethod(adapt_buffer_from_python);
+declmethod(meminfo_new_from_pyobject);
+declmethod(meminfo_as_pyobject);
+declmethod(meminfo_from_pyobject);
+declmethod(MemInfo_alloc);
+declmethod(MemInfo_alloc_safe);
+declmethod(MemInfo_alloc_aligned);
+declmethod(MemInfo_alloc_safe_aligned);
+declmethod(MemInfo_alloc_safe_aligned_external);
+declmethod_internal(_nrt_get_sample_external_allocator);
+declmethod(MemInfo_alloc_dtor_safe);
+declmethod(MemInfo_call_dtor);
+declmethod(MemInfo_new_varsize);
+declmethod(MemInfo_new_varsize_dtor);
+declmethod(MemInfo_varsize_alloc);
+declmethod(MemInfo_data);
+declmethod(MemInfo_varsize_free);
+declmethod(MemInfo_varsize_realloc);
+declmethod(MemInfo_release);
+declmethod(Allocate);
+declmethod(Free);
+declmethod(get_api);
+
+
+#undef declmethod
+#undef declmethod_internal
+    return dct;
+error:
+    Py_XDECREF(dct);
+    return NULL;
+}
+
+MOD_INIT(_nrt_python) {
+    PyObject *m;
+    MOD_DEF(m, "_nrt_python", "No docs", ext_methods)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+    import_array();
+    NRT_MemSys_init();
+    if (init_nrt_python_module(m))
+        return MOD_ERROR_VAL;
+
+    Py_INCREF(&MemInfoType);
+    PyModule_AddObject(m, "_MemInfo", (PyObject *) (&MemInfoType));
+
+    PyModule_AddObject(m, "c_helpers", build_c_helpers_dict());
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/context.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/context.py
new file mode 100644
index 000000000..9b73bbcf6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/context.py
@@ -0,0 +1,401 @@
+import functools
+
+from llvmlite import ir
+
+from numba.core import types, cgutils, errors
+
+
+class NRTContext(object):
+    """
+    An object providing access to NRT APIs in the lowering pass.
+    """
+
+    def __init__(self, context, enabled):
+        self._context = context
+        self._enabled = enabled
+
+    def _require_nrt(self):
+        if not self._enabled:
+            raise errors.NumbaRuntimeError("NRT required but not enabled")
+
+    def _check_null_result(func):
+        @functools.wraps(func)
+        def wrap(self, builder, *args, **kwargs):
+            memptr = func(self, builder, *args, **kwargs)
+            msg = "Allocation failed (probably too large)."
+            cgutils.guard_memory_error(self._context, builder, memptr, msg=msg)
+            return memptr
+        return wrap
+
+    @_check_null_result
+    def allocate(self, builder, size):
+        """
+        Low-level allocate a new memory area of `size` bytes. The result of the
+        call is checked and if it is NULL, i.e. allocation failed, then a
+        MemoryError is raised.
+        """
+        return self.allocate_unchecked(builder, size)
+
+    def allocate_unchecked(self, builder, size):
+        """
+        Low-level allocate a new memory area of `size` bytes. Returns NULL to
+        indicate error/failure to allocate.
+        """
+        self._require_nrt()
+
+        mod = builder.module
+        fnty = ir.FunctionType(cgutils.voidptr_t, [cgutils.intp_t])
+        fn = cgutils.get_or_insert_function(mod, fnty, "NRT_Allocate")
+        fn.return_value.add_attribute("noalias")
+        return builder.call(fn, [size])
+
+    def free(self, builder, ptr):
+        """
+        Low-level free a memory area allocated with allocate().
+        """
+        self._require_nrt()
+
+        mod = builder.module
+        fnty = ir.FunctionType(ir.VoidType(), [cgutils.voidptr_t])
+        fn = cgutils.get_or_insert_function(mod, fnty, "NRT_Free")
+        return builder.call(fn, [ptr])
+
+    @_check_null_result
+    def meminfo_alloc(self, builder, size):
+        """
+        Allocate a new MemInfo with a data payload of `size` bytes.
+
+        A pointer to the MemInfo is returned.
+
+        The result of the call is checked and if it is NULL, i.e. allocation
+        failed, then a MemoryError is raised.
+        """
+        return self.meminfo_alloc_unchecked(builder, size)
+
+    def meminfo_alloc_unchecked(self, builder, size):
+        """
+        Allocate a new MemInfo with a data payload of `size` bytes.
+
+        A pointer to the MemInfo is returned.
+
+        Returns NULL to indicate error/failure to allocate.
+        """
+        self._require_nrt()
+
+        mod = builder.module
+        fnty = ir.FunctionType(cgutils.voidptr_t, [cgutils.intp_t])
+        fn = cgutils.get_or_insert_function(mod, fnty, "NRT_MemInfo_alloc_safe")
+        fn.return_value.add_attribute("noalias")
+        return builder.call(fn, [size])
+
+    @_check_null_result
+    def meminfo_alloc_dtor(self, builder, size, dtor):
+        """
+        Allocate a new MemInfo with a data payload of `size` bytes and a
+        destructor `dtor`.
+
+        A pointer to the MemInfo is returned.
+
+        The result of the call is checked and if it is NULL, i.e. allocation
+        failed, then a MemoryError is raised.
+        """
+        return self.meminfo_alloc_dtor_unchecked(builder, size, dtor)
+
+    def meminfo_alloc_dtor_unchecked(self, builder, size, dtor):
+        """
+        Allocate a new MemInfo with a data payload of `size` bytes and a
+        destructor `dtor`.
+
+        A pointer to the MemInfo is returned.
+
+        Returns NULL to indicate error/failure to allocate.
+        """
+        self._require_nrt()
+
+        mod = builder.module
+        fnty = ir.FunctionType(cgutils.voidptr_t,
+                               [cgutils.intp_t, cgutils.voidptr_t])
+        fn = cgutils.get_or_insert_function(mod, fnty,
+                                            "NRT_MemInfo_alloc_dtor_safe")
+        fn.return_value.add_attribute("noalias")
+        return builder.call(fn, [size,
+                                 builder.bitcast(dtor, cgutils.voidptr_t)])
+
+    @_check_null_result
+    def meminfo_alloc_aligned(self, builder, size, align):
+        """
+        Allocate a new MemInfo with an aligned data payload of `size` bytes.
+        The data pointer is aligned to `align` bytes.  `align` can be either
+        a Python int or a LLVM uint32 value.
+
+        A pointer to the MemInfo is returned.
+
+        The result of the call is checked and if it is NULL, i.e. allocation
+        failed, then a MemoryError is raised.
+        """
+        return self.meminfo_alloc_aligned_unchecked(builder, size, align)
+
+    def meminfo_alloc_aligned_unchecked(self, builder, size, align):
+        """
+        Allocate a new MemInfo with an aligned data payload of `size` bytes.
+        The data pointer is aligned to `align` bytes.  `align` can be either
+        a Python int or a LLVM uint32 value.
+
+        A pointer to the MemInfo is returned.
+
+        Returns NULL to indicate error/failure to allocate.
+        """
+        self._require_nrt()
+
+        mod = builder.module
+        u32 = ir.IntType(32)
+        fnty = ir.FunctionType(cgutils.voidptr_t, [cgutils.intp_t, u32])
+        fn = cgutils.get_or_insert_function(mod, fnty,
+                                            "NRT_MemInfo_alloc_safe_aligned")
+        fn.return_value.add_attribute("noalias")
+        if isinstance(align, int):
+            align = self._context.get_constant(types.uint32, align)
+        else:
+            assert align.type == u32, "align must be a uint32"
+        return builder.call(fn, [size, align])
+
+    @_check_null_result
+    def meminfo_new_varsize(self, builder, size):
+        """
+        Allocate a MemInfo pointing to a variable-sized data area.  The area
+        is separately allocated (i.e. two allocations are made) so that
+        re-allocating it doesn't change the MemInfo's address.
+
+        A pointer to the MemInfo is returned.
+
+        The result of the call is checked and if it is NULL, i.e. allocation
+        failed, then a MemoryError is raised.
+        """
+        return self.meminfo_new_varsize_unchecked(builder, size)
+
+    def meminfo_new_varsize_unchecked(self, builder, size):
+        """
+        Allocate a MemInfo pointing to a variable-sized data area.  The area
+        is separately allocated (i.e. two allocations are made) so that
+        re-allocating it doesn't change the MemInfo's address.
+
+        A pointer to the MemInfo is returned.
+
+        Returns NULL to indicate error/failure to allocate.
+        """
+        self._require_nrt()
+
+        mod = builder.module
+        fnty = ir.FunctionType(cgutils.voidptr_t, [cgutils.intp_t])
+        fn = cgutils.get_or_insert_function(mod, fnty,
+                                            "NRT_MemInfo_new_varsize")
+        fn.return_value.add_attribute("noalias")
+        return builder.call(fn, [size])
+
+    @_check_null_result
+    def meminfo_new_varsize_dtor(self, builder, size, dtor):
+        """
+        Like meminfo_new_varsize() but also set the destructor for
+        cleaning up references to objects inside the allocation.
+
+        A pointer to the MemInfo is returned.
+
+        The result of the call is checked and if it is NULL, i.e. allocation
+        failed, then a MemoryError is raised.
+        """
+        return self.meminfo_new_varsize_dtor_unchecked(builder, size, dtor)
+
+    def meminfo_new_varsize_dtor_unchecked(self, builder, size, dtor):
+        """
+        Like meminfo_new_varsize() but also set the destructor for
+        cleaning up references to objects inside the allocation.
+
+        A pointer to the MemInfo is returned.
+
+        Returns NULL to indicate error/failure to allocate.
+        """
+        self._require_nrt()
+
+        mod = builder.module
+        fnty = ir.FunctionType(cgutils.voidptr_t,
+                               [cgutils.intp_t, cgutils.voidptr_t])
+        fn = cgutils.get_or_insert_function(
+            mod, fnty, "NRT_MemInfo_new_varsize_dtor")
+        return builder.call(fn, [size, dtor])
+
+    @_check_null_result
+    def meminfo_varsize_alloc(self, builder, meminfo, size):
+        """
+        Allocate a new data area for a MemInfo created by meminfo_new_varsize().
+        The new data pointer is returned, for convenience.
+
+        Contrary to realloc(), this always allocates a new area and doesn't
+        copy the old data.  This is useful if resizing a container needs
+        more than simply copying the data area (e.g. for hash tables).
+
+        The old pointer will have to be freed with meminfo_varsize_free().
+
+        The result of the call is checked and if it is NULL, i.e. allocation
+        failed, then a MemoryError is raised.
+        """
+        return self.meminfo_varsize_alloc_unchecked(builder, meminfo, size)
+
+    def meminfo_varsize_alloc_unchecked(self, builder, meminfo, size):
+        """
+        Allocate a new data area for a MemInfo created by meminfo_new_varsize().
+        The new data pointer is returned, for convenience.
+
+        Contrary to realloc(), this always allocates a new area and doesn't
+        copy the old data.  This is useful if resizing a container needs
+        more than simply copying the data area (e.g. for hash tables).
+
+        The old pointer will have to be freed with meminfo_varsize_free().
+
+        Returns NULL to indicate error/failure to allocate.
+        """
+        return self._call_varsize_alloc(builder, meminfo, size,
+                                        "NRT_MemInfo_varsize_alloc")
+
+    @_check_null_result
+    def meminfo_varsize_realloc(self, builder, meminfo, size):
+        """
+        Reallocate a data area allocated by meminfo_new_varsize().
+        The new data pointer is returned, for convenience.
+
+        The result of the call is checked and if it is NULL, i.e. allocation
+        failed, then a MemoryError is raised.
+        """
+        return self.meminfo_varsize_realloc_unchecked(builder, meminfo, size)
+
+    def meminfo_varsize_realloc_unchecked(self, builder, meminfo, size):
+        """
+        Reallocate a data area allocated by meminfo_new_varsize().
+        The new data pointer is returned, for convenience.
+
+        Returns NULL to indicate error/failure to allocate.
+        """
+        return self._call_varsize_alloc(builder, meminfo, size,
+                                        "NRT_MemInfo_varsize_realloc")
+
+    def meminfo_varsize_free(self, builder, meminfo, ptr):
+        """
+        Free a memory area allocated for a NRT varsize object.
+        Note this does *not* free the NRT object itself!
+        """
+        self._require_nrt()
+
+        mod = builder.module
+        fnty = ir.FunctionType(ir.VoidType(),
+                               [cgutils.voidptr_t, cgutils.voidptr_t])
+        fn = cgutils.get_or_insert_function(mod, fnty,
+                                            "NRT_MemInfo_varsize_free")
+        return builder.call(fn, (meminfo, ptr))
+
+    def _call_varsize_alloc(self, builder, meminfo, size, funcname):
+        self._require_nrt()
+
+        mod = builder.module
+        fnty = ir.FunctionType(cgutils.voidptr_t,
+                               [cgutils.voidptr_t, cgutils.intp_t])
+        fn = cgutils.get_or_insert_function(mod, fnty, funcname)
+        fn.return_value.add_attribute("noalias")
+        return builder.call(fn, [meminfo, size])
+
+    def meminfo_data(self, builder, meminfo):
+        """
+        Given a MemInfo pointer, return a pointer to the allocated data
+        managed by it.  This works for MemInfos allocated with all the
+        above methods.
+        """
+        self._require_nrt()
+
+        from numba.core.runtime.nrtdynmod import meminfo_data_ty
+
+        mod = builder.module
+        fn = cgutils.get_or_insert_function(mod, meminfo_data_ty,
+                                            "NRT_MemInfo_data_fast")
+        return builder.call(fn, [meminfo])
+
+    def get_meminfos(self, builder, ty, val):
+        """Return a list of *(type, meminfo)* inside the given value.
+        """
+        datamodel = self._context.data_model_manager[ty]
+        members = datamodel.traverse(builder)
+
+        meminfos = []
+        if datamodel.has_nrt_meminfo():
+            mi = datamodel.get_nrt_meminfo(builder, val)
+            meminfos.append((ty, mi))
+
+        for mtyp, getter in members:
+            field = getter(val)
+            inner_meminfos = self.get_meminfos(builder, mtyp, field)
+            meminfos.extend(inner_meminfos)
+        return meminfos
+
+    def _call_incref_decref(self, builder, typ, value, funcname):
+        """Call function of *funcname* on every meminfo found in *value*.
+        """
+        self._require_nrt()
+
+        from numba.core.runtime.nrtdynmod import incref_decref_ty
+
+        meminfos = self.get_meminfos(builder, typ, value)
+        for _, mi in meminfos:
+            mod = builder.module
+            fn = cgutils.get_or_insert_function(mod, incref_decref_ty,
+                                                funcname)
+            # XXX "nonnull" causes a crash in test_dyn_array: can this
+            # function be called with a NULL pointer?
+            fn.args[0].add_attribute("noalias")
+            fn.args[0].add_attribute("nocapture")
+            builder.call(fn, [mi])
+
+    def incref(self, builder, typ, value):
+        """
+        Recursively incref the given *value* and its members.
+        """
+        self._call_incref_decref(builder, typ, value, "NRT_incref")
+
+    def decref(self, builder, typ, value):
+        """
+        Recursively decref the given *value* and its members.
+        """
+        self._call_incref_decref(builder, typ, value, "NRT_decref")
+
+    def get_nrt_api(self, builder):
+        """Calls NRT_get_api(), which returns the NRT API function table.
+        """
+        self._require_nrt()
+
+        fnty = ir.FunctionType(cgutils.voidptr_t, ())
+        mod = builder.module
+        fn = cgutils.get_or_insert_function(mod, fnty, "NRT_get_api")
+        return builder.call(fn, ())
+
+    def eh_check(self, builder):
+        """Check if an exception is raised
+        """
+        ctx = self._context
+        cc = ctx.call_conv
+        # Inspect the excinfo argument on the function
+        trystatus = cc.check_try_status(builder)
+        excinfo = trystatus.excinfo
+        has_raised = builder.not_(cgutils.is_null(builder, excinfo))
+        with builder.if_then(has_raised):
+            self.eh_end_try(builder)
+        return has_raised
+
+    def eh_try(self, builder):
+        """Begin a try-block.
+        """
+        ctx = self._context
+        cc = ctx.call_conv
+        cc.set_try_status(builder)
+
+    def eh_end_try(self, builder):
+        """End a try-block
+        """
+        ctx = self._context
+        cc = ctx.call_conv
+        cc.unset_try_status(builder)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.c
new file mode 100644
index 000000000..3f67182e4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.c
@@ -0,0 +1,595 @@
+#include <stdarg.h>
+#include <string.h> /* for memset */
+#include "nrt.h"
+#include "assert.h"
+
+#if !defined MIN
+#define MIN(a, b) ((a) < (b)) ? (a) : (b)
+#endif
+
+
+typedef int (*atomic_meminfo_cas_func)(void **ptr, void *cmp,
+                                       void *repl, void **oldptr);
+
+
+/* NOTE: if changing the layout, please update numba.core.runtime.atomicops */
+struct MemInfo {
+    size_t            refct;
+    NRT_dtor_function dtor;
+    void              *dtor_info;
+    void              *data;
+    size_t            size;    /* only used for NRT allocated memory */
+    NRT_ExternalAllocator *external_allocator;
+};
+
+
+/*
+ * Misc helpers.
+ */
+
+static void nrt_fatal_error(const char *msg)
+{
+    fprintf(stderr, "Fatal Numba error: %s\n", msg);
+    fflush(stderr); /* it helps in Windows debug build */
+
+#if defined(MS_WINDOWS) && defined(_DEBUG)
+    DebugBreak();
+#endif
+    abort();
+}
+
+/*
+ * Global resources.
+ */
+
+struct MemSys {
+    /* Atomic increment and decrement function */
+    NRT_atomic_inc_dec_func atomic_inc, atomic_dec;
+    /* Atomic CAS */
+    atomic_meminfo_cas_func atomic_cas;
+    /* Shutdown flag */
+    int shutting;
+    /* Stats */
+    size_t stats_alloc, stats_free, stats_mi_alloc, stats_mi_free;
+    /* System allocation functions */
+    struct {
+        NRT_malloc_func malloc;
+        NRT_realloc_func realloc;
+        NRT_free_func free;
+    } allocator;
+};
+
+/* The Memory System object */
+static NRT_MemSys TheMSys;
+
+
+void NRT_MemSys_init(void) {
+    memset(&TheMSys, 0, sizeof(NRT_MemSys));
+    /* Bind to libc allocator */
+    TheMSys.allocator.malloc = malloc;
+    TheMSys.allocator.realloc = realloc;
+    TheMSys.allocator.free = free;
+}
+
+void NRT_MemSys_shutdown(void) {
+    TheMSys.shutting = 1;
+    /* Revert to use our non-atomic stub for all atomic operations
+       because the JIT-ed version will be removed.
+       Since we are at interpreter shutdown,
+       it cannot be running multiple threads anymore. */
+    NRT_MemSys_set_atomic_inc_dec_stub();
+    NRT_MemSys_set_atomic_cas_stub();
+}
+
+void NRT_MemSys_set_allocator(NRT_malloc_func malloc_func,
+                              NRT_realloc_func realloc_func,
+                              NRT_free_func free_func)
+{
+    if ((malloc_func != TheMSys.allocator.malloc ||
+         realloc_func != TheMSys.allocator.realloc ||
+         free_func != TheMSys.allocator.free) &&
+         (TheMSys.stats_alloc != TheMSys.stats_free ||
+          TheMSys.stats_mi_alloc != TheMSys.stats_mi_free)) {
+        nrt_fatal_error("cannot change allocator while blocks are allocated");
+    }
+    TheMSys.allocator.malloc = malloc_func;
+    TheMSys.allocator.realloc = realloc_func;
+    TheMSys.allocator.free = free_func;
+}
+
+void NRT_MemSys_set_atomic_inc_dec(NRT_atomic_inc_dec_func inc,
+                                   NRT_atomic_inc_dec_func dec)
+{
+    TheMSys.atomic_inc = inc;
+    TheMSys.atomic_dec = dec;
+}
+
+void NRT_MemSys_set_atomic_cas(NRT_atomic_cas_func cas) {
+    TheMSys.atomic_cas = (atomic_meminfo_cas_func) cas;
+}
+
+size_t NRT_MemSys_get_stats_alloc() {
+    return TheMSys.stats_alloc;
+}
+
+size_t NRT_MemSys_get_stats_free() {
+    return TheMSys.stats_free;
+}
+
+size_t NRT_MemSys_get_stats_mi_alloc() {
+    return TheMSys.stats_mi_alloc;
+}
+
+size_t NRT_MemSys_get_stats_mi_free() {
+    return TheMSys.stats_mi_free;
+}
+
+static
+size_t nrt_testing_atomic_inc(size_t *ptr){
+    /* non atomic */
+    size_t out = *ptr;
+    out += 1;
+    *ptr = out;
+    return out;
+}
+
+static
+size_t nrt_testing_atomic_dec(size_t *ptr){
+    /* non atomic */
+    size_t out = *ptr;
+    out -= 1;
+    *ptr = out;
+    return out;
+}
+
+static
+int nrt_testing_atomic_cas(void* volatile *ptr, void *cmp, void *val,
+                           void * *oldptr){
+    /* non atomic */
+    void *old = *ptr;
+    *oldptr = old;
+    if (old == cmp) {
+        *ptr = val;
+         return 1;
+    }
+    return 0;
+
+}
+
+void NRT_MemSys_set_atomic_inc_dec_stub(void){
+    NRT_MemSys_set_atomic_inc_dec(nrt_testing_atomic_inc,
+                                  nrt_testing_atomic_dec);
+}
+
+void NRT_MemSys_set_atomic_cas_stub(void) {
+    NRT_MemSys_set_atomic_cas(nrt_testing_atomic_cas);
+}
+
+
+/*
+ * The MemInfo structure.
+ */
+
+void NRT_MemInfo_init(NRT_MemInfo *mi,void *data, size_t size,
+                      NRT_dtor_function dtor, void *dtor_info,
+                      NRT_ExternalAllocator *external_allocator)
+{
+    mi->refct = 1;  /* starts with 1 refct */
+    mi->dtor = dtor;
+    mi->dtor_info = dtor_info;
+    mi->data = data;
+    mi->size = size;
+    mi->external_allocator = external_allocator;
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_init mi=%p external_allocator=%p\n", mi, external_allocator));
+    /* Update stats */
+    TheMSys.atomic_inc(&TheMSys.stats_mi_alloc);
+}
+
+NRT_MemInfo *NRT_MemInfo_new(void *data, size_t size,
+                             NRT_dtor_function dtor, void *dtor_info)
+{
+    NRT_MemInfo *mi = NRT_Allocate(sizeof(NRT_MemInfo));
+    if (mi != NULL) {
+        NRT_Debug(nrt_debug_print("NRT_MemInfo_new mi=%p\n", mi));
+        NRT_MemInfo_init(mi, data, size, dtor, dtor_info, NULL);
+    }
+    return mi;
+}
+
+size_t NRT_MemInfo_refcount(NRT_MemInfo *mi) {
+    /* Should never returns 0 for a valid MemInfo */
+    if (mi && mi->data)
+        return mi->refct;
+    else{
+        return (size_t)-1;
+    }
+}
+
+static
+void nrt_internal_dtor_safe(void *ptr, size_t size, void *info) {
+    NRT_Debug(nrt_debug_print("nrt_internal_dtor_safe %p, %p\n", ptr, info));
+    /* See NRT_MemInfo_alloc_safe() */
+    memset(ptr, 0xDE, MIN(size, 256));
+}
+
+static
+void *nrt_allocate_meminfo_and_data(size_t size, NRT_MemInfo **mi_out, NRT_ExternalAllocator *allocator) {
+    NRT_MemInfo *mi = NULL;
+    NRT_Debug(nrt_debug_print("nrt_allocate_meminfo_and_data %p\n", allocator));
+    char *base = NRT_Allocate_External(sizeof(NRT_MemInfo) + size, allocator);
+    if (base == NULL) {
+        *mi_out = NULL; /* set meminfo to NULL as allocation failed */
+        return NULL; /* return early as allocation failed */
+    }
+    mi = (NRT_MemInfo *) base;
+    *mi_out = mi;
+    return base + sizeof(NRT_MemInfo);
+}
+
+
+static
+void nrt_internal_custom_dtor_safe(void *ptr, size_t size, void *info) {
+    NRT_dtor_function dtor = info;
+    NRT_Debug(nrt_debug_print("nrt_internal_custom_dtor_safe %p, %p\n",
+                              ptr, info));
+    if (dtor) {
+        dtor(ptr, size, NULL);
+    }
+
+    nrt_internal_dtor_safe(ptr, size, NULL);
+}
+
+
+NRT_MemInfo *NRT_MemInfo_alloc(size_t size) {
+    NRT_MemInfo *mi = NULL;
+    void *data = nrt_allocate_meminfo_and_data(size, &mi, NULL);
+    if (data == NULL) {
+        return NULL; /* return early as allocation failed */
+    }
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_alloc %p\n", data));
+    NRT_MemInfo_init(mi, data, size, NULL, NULL, NULL);
+    return mi;
+}
+
+NRT_MemInfo *NRT_MemInfo_alloc_external(size_t size, NRT_ExternalAllocator *allocator) {
+    NRT_MemInfo *mi = NULL;
+    void *data = nrt_allocate_meminfo_and_data(size, &mi, allocator);
+    if (data == NULL) {
+        return NULL; /* return early as allocation failed */
+    }
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_alloc %p\n", data));
+    NRT_MemInfo_init(mi, data, size, NULL, NULL, allocator);
+    return mi;
+}
+
+NRT_MemInfo *NRT_MemInfo_alloc_safe(size_t size) {
+    return NRT_MemInfo_alloc_dtor_safe(size, NULL);
+}
+
+NRT_MemInfo* NRT_MemInfo_alloc_dtor_safe(size_t size, NRT_dtor_function dtor) {
+    NRT_MemInfo *mi = NULL;
+    void *data = nrt_allocate_meminfo_and_data(size, &mi, NULL);
+    if (data == NULL) {
+        return NULL; /* return early as allocation failed */
+    }
+    /* Only fill up a couple cachelines with debug markers, to minimize
+       overhead. */
+    memset(data, 0xCB, MIN(size, 256));
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_alloc_dtor_safe %p %zu\n", data, size));
+    NRT_MemInfo_init(mi, data, size, nrt_internal_custom_dtor_safe, dtor, NULL);
+    return mi;
+}
+
+
+static
+void *nrt_allocate_meminfo_and_data_align(size_t size, unsigned align,
+                                          NRT_MemInfo **mi, NRT_ExternalAllocator *allocator)
+{
+    size_t offset = 0, intptr = 0, remainder = 0;
+    NRT_Debug(nrt_debug_print("nrt_allocate_meminfo_and_data_align %p\n", allocator));
+    char *base = nrt_allocate_meminfo_and_data(size + 2 * align, mi, allocator);
+    if (base == NULL) {
+        return NULL; /* return early as allocation failed */
+    }
+    intptr = (size_t) base;
+    /* See if we are aligned */
+    remainder = intptr % align;
+    if (remainder == 0){ /* Yes */
+        offset = 0;
+    } else { /* No, move forward `offset` bytes */
+        offset = align - remainder;
+    }
+    return base + offset;
+}
+
+NRT_MemInfo *NRT_MemInfo_alloc_aligned(size_t size, unsigned align) {
+    NRT_MemInfo *mi = NULL;
+    void *data = nrt_allocate_meminfo_and_data_align(size, align, &mi, NULL);
+    if (data == NULL) {
+        return NULL; /* return early as allocation failed */
+    }
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_alloc_aligned %p\n", data));
+    NRT_MemInfo_init(mi, data, size, NULL, NULL, NULL);
+    return mi;
+}
+
+NRT_MemInfo *NRT_MemInfo_alloc_safe_aligned(size_t size, unsigned align) {
+    NRT_MemInfo *mi = NULL;
+    void *data = nrt_allocate_meminfo_and_data_align(size, align, &mi, NULL);
+    if (data == NULL) {
+        return NULL; /* return early as allocation failed */
+    }
+    /* Only fill up a couple cachelines with debug markers, to minimize
+       overhead. */
+    memset(data, 0xCB, MIN(size, 256));
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_alloc_safe_aligned %p %zu\n",
+                              data, size));
+    NRT_MemInfo_init(mi, data, size, nrt_internal_dtor_safe, (void*)size, NULL);
+    return mi;
+}
+
+NRT_MemInfo *NRT_MemInfo_alloc_safe_aligned_external(size_t size, unsigned align, NRT_ExternalAllocator *allocator) {
+    NRT_MemInfo *mi = NULL;
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_alloc_safe_aligned_external %p\n", allocator));
+    void *data = nrt_allocate_meminfo_and_data_align(size, align, &mi, allocator);
+    if (data == NULL) {
+        return NULL; /* return early as allocation failed */
+    }
+    /* Only fill up a couple cachelines with debug markers, to minimize
+       overhead. */
+    memset(data, 0xCB, MIN(size, 256));
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_alloc_safe_aligned %p %zu\n",
+                              data, size));
+    NRT_MemInfo_init(mi, data, size, nrt_internal_dtor_safe, (void*)size, allocator);
+    return mi;
+}
+
+void NRT_dealloc(NRT_MemInfo *mi) {
+    NRT_Debug(nrt_debug_print("NRT_dealloc meminfo: %p external_allocator: %p\n", mi, mi->external_allocator));
+    if (mi->external_allocator) {
+        mi->external_allocator->free(mi, mi->external_allocator->opaque_data);
+        TheMSys.atomic_inc(&TheMSys.stats_free);
+    } else {
+        NRT_Free(mi);
+    }
+}
+
+void NRT_MemInfo_destroy(NRT_MemInfo *mi) {
+    NRT_dealloc(mi);
+    TheMSys.atomic_inc(&TheMSys.stats_mi_free);
+}
+
+void NRT_MemInfo_acquire(NRT_MemInfo *mi) {
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_acquire %p refct=%zu\n", mi,
+                                                            mi->refct));
+    assert(mi->refct > 0 && "RefCt cannot be zero");
+    TheMSys.atomic_inc(&mi->refct);
+}
+
+void NRT_MemInfo_call_dtor(NRT_MemInfo *mi) {
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_call_dtor %p\n", mi));
+    if (mi->dtor && !TheMSys.shutting)
+        /* We have a destructor and the system is not shutting down */
+        mi->dtor(mi->data, mi->size, mi->dtor_info);
+    /* Clear and release MemInfo */
+    NRT_MemInfo_destroy(mi);
+}
+
+void NRT_MemInfo_release(NRT_MemInfo *mi) {
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_release %p refct=%zu\n", mi,
+                                                            mi->refct));
+    assert (mi->refct > 0 && "RefCt cannot be 0");
+    /* RefCt drop to zero */
+    if (TheMSys.atomic_dec(&mi->refct) == 0) {
+        NRT_MemInfo_call_dtor(mi);
+    }
+}
+
+void* NRT_MemInfo_data(NRT_MemInfo* mi) {
+    return mi->data;
+}
+
+size_t NRT_MemInfo_size(NRT_MemInfo* mi) {
+    return mi->size;
+}
+
+void * NRT_MemInfo_external_allocator(NRT_MemInfo *mi) {
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_external_allocator meminfo: %p external_allocator: %p\n", mi, mi->external_allocator));
+    return mi->external_allocator;
+}
+
+void *NRT_MemInfo_parent(NRT_MemInfo *mi) {
+    return mi->dtor_info;
+}
+
+void NRT_MemInfo_dump(NRT_MemInfo *mi, FILE *out) {
+    fprintf(out, "MemInfo %p refcount %zu\n", mi, mi->refct);
+}
+
+/*
+ * Resizable buffer API.
+ */
+
+static void
+nrt_varsize_dtor(void *ptr, size_t size, void *info) {
+    NRT_Debug(nrt_debug_print("nrt_varsize_dtor %p\n", ptr));
+    if (info) {
+        /* call element dtor */
+        typedef void dtor_fn_t(void *ptr);
+        dtor_fn_t *dtor = info;
+        dtor(ptr);
+    }
+    NRT_Free(ptr);
+}
+
+NRT_MemInfo *NRT_MemInfo_new_varsize(size_t size)
+{
+    NRT_MemInfo *mi = NULL;
+    void *data = NRT_Allocate(size);
+    if (data == NULL) {
+        return NULL; /* return early as allocation failed */
+    }
+
+    mi = NRT_MemInfo_new(data, size, nrt_varsize_dtor, NULL);
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_new_varsize size=%zu "
+                              "-> meminfo=%p, data=%p\n", size, mi, data));
+    return mi;
+}
+
+NRT_MemInfo *NRT_MemInfo_new_varsize_dtor(size_t size, NRT_dtor_function dtor) {
+    NRT_MemInfo *mi = NRT_MemInfo_new_varsize(size);
+    if (mi) {
+        mi->dtor_info = dtor;
+    }
+    return mi;
+}
+
+void *NRT_MemInfo_varsize_alloc(NRT_MemInfo *mi, size_t size)
+{
+    if (mi->dtor != nrt_varsize_dtor) {
+        nrt_fatal_error("ERROR: NRT_MemInfo_varsize_alloc called "
+                        "with a non varsize-allocated meminfo");
+        return NULL;  /* unreachable */
+    }
+    mi->data = NRT_Allocate(size);
+    if (mi->data == NULL)
+        return NULL;
+    mi->size = size;
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_varsize_alloc %p size=%zu "
+                              "-> data=%p\n", mi, size, mi->data));
+    return mi->data;
+}
+
+void *NRT_MemInfo_varsize_realloc(NRT_MemInfo *mi, size_t size)
+{
+    if (mi->dtor != nrt_varsize_dtor) {
+        nrt_fatal_error("ERROR: NRT_MemInfo_varsize_realloc called "
+                        "with a non varsize-allocated meminfo");
+        return NULL;  /* unreachable */
+    }
+    mi->data = NRT_Reallocate(mi->data, size);
+    if (mi->data == NULL)
+        return NULL;
+    mi->size = size;
+    NRT_Debug(nrt_debug_print("NRT_MemInfo_varsize_realloc %p size=%zu "
+                              "-> data=%p\n", mi, size, mi->data));
+    return mi->data;
+}
+
+void NRT_MemInfo_varsize_free(NRT_MemInfo *mi, void *ptr)
+{
+    NRT_Free(ptr);
+    if (ptr == mi->data)
+        mi->data = NULL;
+}
+
+/*
+ * Low-level allocation wrappers.
+ */
+
+void* NRT_Allocate(size_t size) {
+    return NRT_Allocate_External(size, NULL);
+}
+
+void* NRT_Allocate_External(size_t size, NRT_ExternalAllocator *allocator) {
+    void *ptr = NULL;
+    if (allocator) {
+        ptr = allocator->malloc(size, allocator->opaque_data);
+        NRT_Debug(nrt_debug_print("NRT_Allocate_External custom bytes=%zu ptr=%p\n", size, ptr));
+    } else {
+        ptr = TheMSys.allocator.malloc(size);
+        NRT_Debug(nrt_debug_print("NRT_Allocate_External bytes=%zu ptr=%p\n", size, ptr));
+    }
+    TheMSys.atomic_inc(&TheMSys.stats_alloc);
+    return ptr;
+}
+
+void *NRT_Reallocate(void *ptr, size_t size) {
+    void *new_ptr = TheMSys.allocator.realloc(ptr, size);
+    NRT_Debug(nrt_debug_print("NRT_Reallocate bytes=%zu ptr=%p -> %p\n",
+                              size, ptr, new_ptr));
+    return new_ptr;
+}
+
+void NRT_Free(void *ptr) {
+    NRT_Debug(nrt_debug_print("NRT_Free %p\n", ptr));
+    TheMSys.allocator.free(ptr);
+    TheMSys.atomic_inc(&TheMSys.stats_free);
+}
+
+/*
+ * Sample external allocator implementation for internal testing.
+ */
+
+static int sample_external_opaque_data = 0xabacad;
+
+static
+void* sample_external_malloc(size_t size, void* opaque_data) {
+    if (opaque_data != &sample_external_opaque_data) return NULL;
+    return TheMSys.allocator.malloc(size);
+}
+
+static
+void* sample_external_realloc(void *ptr, size_t new_size, void *opaque_data) {
+    if (opaque_data != &sample_external_opaque_data) return NULL;
+    return TheMSys.allocator.realloc(ptr, new_size);
+}
+
+static
+void sample_external_free(void *ptr, void* opaque_data) {
+    TheMSys.allocator.free(ptr);
+}
+
+static NRT_ExternalAllocator sample_external_allocator = {
+    // malloc
+    sample_external_malloc,
+    // realloc
+    sample_external_realloc,
+    // free
+    sample_external_free,
+    // opaque_data
+    &sample_external_opaque_data
+};
+
+NRT_ExternalAllocator* _nrt_get_sample_external_allocator() {
+    return &sample_external_allocator;
+}
+
+/*
+ * Debugging printf function used internally
+ */
+void nrt_debug_print(char *fmt, ...) {
+   va_list args;
+
+   va_start(args, fmt);
+   vfprintf(stderr, fmt, args);
+   va_end(args);
+}
+
+
+static
+void nrt_manage_memory_dtor(void *data, size_t size, void *info) {
+    NRT_managed_dtor* dtor = (NRT_managed_dtor*)info;
+    dtor(data);
+}
+
+static
+NRT_MemInfo* nrt_manage_memory(void *data, NRT_managed_dtor dtor) {
+    return NRT_MemInfo_new(data, 0, nrt_manage_memory_dtor, dtor);
+}
+
+
+static const
+NRT_api_functions nrt_functions_table = {
+    NRT_MemInfo_alloc,
+    NRT_MemInfo_alloc_external,
+    nrt_manage_memory,
+    NRT_MemInfo_acquire,
+    NRT_MemInfo_release,
+    NRT_MemInfo_data
+};
+
+
+const NRT_api_functions* NRT_get_api(void) {
+    return &nrt_functions_table;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.h
new file mode 100644
index 000000000..2bfc9033e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.h
@@ -0,0 +1,272 @@
+/*
+All functions described here are threadsafe.
+*/
+
+#ifndef NUMBA_NRT_H_
+#define NUMBA_NRT_H_
+
+
+#include <stdlib.h>
+#include <stdio.h>
+#include "../../_numba_common.h"
+
+#include "nrt_external.h"
+
+/* Debugging facilities - enabled at compile-time */
+/* #undef NDEBUG */
+#if 0
+#   define NRT_Debug(X) {X; fflush(stdout); }
+#else
+#   define NRT_Debug(X) if (0) { X; }
+#endif
+
+/* TypeDefs */
+typedef void (*NRT_dtor_function)(void *ptr, size_t size, void *info);
+typedef void (*NRT_dealloc_func)(void *ptr, void *dealloc_info);
+typedef size_t (*NRT_atomic_inc_dec_func)(size_t *ptr);
+typedef int (*NRT_atomic_cas_func)(void * volatile *ptr, void *cmp, void *repl,
+                                   void **oldptr);
+
+typedef struct MemSys NRT_MemSys;
+
+typedef void *(*NRT_malloc_func)(size_t size);
+typedef void *(*NRT_realloc_func)(void *ptr, size_t new_size);
+typedef void (*NRT_free_func)(void *ptr);
+
+/* Memory System API */
+
+/* Initialize the memory system */
+VISIBILITY_HIDDEN
+void NRT_MemSys_init(void);
+
+/* Shutdown the memory system */
+VISIBILITY_HIDDEN
+void NRT_MemSys_shutdown(void);
+
+/*
+ * Register the system allocation functions
+ */
+VISIBILITY_HIDDEN
+void NRT_MemSys_set_allocator(NRT_malloc_func, NRT_realloc_func, NRT_free_func);
+
+/*
+ * Register the atomic increment and decrement functions
+ */
+VISIBILITY_HIDDEN
+void NRT_MemSys_set_atomic_inc_dec(NRT_atomic_inc_dec_func inc,
+                                   NRT_atomic_inc_dec_func dec);
+
+
+/*
+ * Register the atomic compare and swap function
+ */
+VISIBILITY_HIDDEN
+void NRT_MemSys_set_atomic_cas(NRT_atomic_cas_func cas);
+
+/*
+ * Register a non-atomic STUB for increment and decrement
+ */
+VISIBILITY_HIDDEN
+void NRT_MemSys_set_atomic_inc_dec_stub(void);
+
+/*
+ * Register a non-atomic STUB for compare and swap
+ */
+VISIBILITY_HIDDEN
+void NRT_MemSys_set_atomic_cas_stub(void);
+
+/*
+ * The following functions get internal statistics of the memory subsystem.
+ */
+VISIBILITY_HIDDEN
+size_t NRT_MemSys_get_stats_alloc(void);
+VISIBILITY_HIDDEN
+size_t NRT_MemSys_get_stats_free(void);
+VISIBILITY_HIDDEN
+size_t NRT_MemSys_get_stats_mi_alloc(void);
+VISIBILITY_HIDDEN
+size_t NRT_MemSys_get_stats_mi_free(void);
+
+/* Memory Info API */
+
+/* Create a new MemInfo for external memory
+ *
+ * data: data pointer being tracked
+ * dtor: destructor to execute
+ * dtor_info: additional information to pass to the destructor
+ */
+VISIBILITY_HIDDEN
+NRT_MemInfo* NRT_MemInfo_new(void *data, size_t size,
+                             NRT_dtor_function dtor, void *dtor_info);
+
+/*
+ * The `external_allocator` is for experimental API to customize the allocator.
+ * Set to NULL to use the default builtin allocator.
+ */
+VISIBILITY_HIDDEN
+void NRT_MemInfo_init(NRT_MemInfo *mi, void *data, size_t size,
+                      NRT_dtor_function dtor, void *dtor_info,
+                      NRT_ExternalAllocator *external_allocator);
+
+/*
+ * Returns the refcount of a MemInfo or (size_t)-1 if error.
+ */
+VISIBILITY_HIDDEN
+size_t NRT_MemInfo_refcount(NRT_MemInfo *mi);
+
+/*
+ * Allocate memory of `size` bytes and return a pointer to a MemInfo structure
+ * that describes the allocation
+ */
+VISIBILITY_HIDDEN
+NRT_MemInfo *NRT_MemInfo_alloc(size_t size);
+
+NRT_MemInfo *NRT_MemInfo_alloc_external(size_t size, NRT_ExternalAllocator *allocator);
+
+/*
+ * The "safe" NRT_MemInfo_alloc performs additional steps to help debug
+ * memory errors.
+ * It is guaranteed to:
+ *   - zero-fill to the memory region after allocation and before deallocation.
+ *   - may do more in the future
+ */
+VISIBILITY_HIDDEN
+NRT_MemInfo *NRT_MemInfo_alloc_safe(size_t size);
+
+/*
+ * Similar to NRT_MemInfo_alloc_safe but with a custom dtor.
+ */
+VISIBILITY_HIDDEN
+NRT_MemInfo* NRT_MemInfo_alloc_dtor_safe(size_t size, NRT_dtor_function dtor);
+
+/*
+ * Aligned versions of the NRT_MemInfo_alloc and NRT_MemInfo_alloc_safe.
+ * These take an additional argument `align` for number of bytes to align to.
+ */
+VISIBILITY_HIDDEN
+NRT_MemInfo *NRT_MemInfo_alloc_aligned(size_t size, unsigned align);
+VISIBILITY_HIDDEN
+NRT_MemInfo *NRT_MemInfo_alloc_safe_aligned(size_t size, unsigned align);
+
+/*
+ * Experimental.
+ * A variation to use an external allocator.
+ */
+NRT_MemInfo *NRT_MemInfo_alloc_safe_aligned_external(size_t size, unsigned align, NRT_ExternalAllocator *allocator);
+
+/*
+ * Internal API.
+ * Release a MemInfo. Calls NRT_MemSys_insert_meminfo.
+ */
+VISIBILITY_HIDDEN
+void NRT_MemInfo_destroy(NRT_MemInfo *mi);
+
+/*
+ * Acquire a reference to a MemInfo
+ */
+VISIBILITY_HIDDEN
+void NRT_MemInfo_acquire(NRT_MemInfo* mi);
+
+/*
+ * Release a reference to a MemInfo
+ */
+VISIBILITY_HIDDEN
+void NRT_MemInfo_release(NRT_MemInfo* mi);
+
+/*
+ * Internal/Compiler API.
+ * Invoke the registered destructor of a MemInfo.
+ */
+VISIBILITY_HIDDEN
+void NRT_MemInfo_call_dtor(NRT_MemInfo *mi);
+
+/*
+ * Returns the data pointer
+ */
+VISIBILITY_HIDDEN
+void* NRT_MemInfo_data(NRT_MemInfo* mi);
+
+/*
+ * Returns the allocated size
+ */
+VISIBILITY_HIDDEN
+size_t NRT_MemInfo_size(NRT_MemInfo* mi);
+
+
+/*
+ * Experimental.
+ * Returns the external allocator
+ */
+VISIBILITY_HIDDEN
+void* NRT_MemInfo_external_allocator(NRT_MemInfo* mi);
+
+/*
+ * Returns the parent MemInfo
+ */
+VISIBILITY_HIDDEN
+void* NRT_MemInfo_parent(NRT_MemInfo* mi);
+
+
+/*
+ * NRT API for resizable buffers.
+ */
+VISIBILITY_HIDDEN
+NRT_MemInfo *NRT_MemInfo_new_varsize(size_t size);
+VISIBILITY_HIDDEN
+NRT_MemInfo *NRT_MemInfo_new_varsize_dtor(size_t size, NRT_dtor_function dtor);
+VISIBILITY_HIDDEN
+void *NRT_MemInfo_varsize_alloc(NRT_MemInfo *mi, size_t size);
+VISIBILITY_HIDDEN
+void *NRT_MemInfo_varsize_realloc(NRT_MemInfo *mi, size_t size);
+VISIBILITY_HIDDEN
+void NRT_MemInfo_varsize_free(NRT_MemInfo *mi, void *ptr);
+
+/*
+ * Print debug info to FILE
+ */
+VISIBILITY_HIDDEN
+void NRT_MemInfo_dump(NRT_MemInfo *mi, FILE *out);
+
+
+/* Low-level allocation wrappers. */
+
+/*
+ * Allocate memory of `size` bytes.
+ */
+VISIBILITY_HIDDEN void* NRT_Allocate(size_t size);
+
+/*
+ * Experimental
+ *
+ * An alternative allocator that allows using an external allocator.
+ */
+VISIBILITY_HIDDEN void* NRT_Allocate_External(size_t size, NRT_ExternalAllocator *allocator);
+
+/*
+ * Deallocate memory pointed by `ptr`.
+ */
+VISIBILITY_HIDDEN void NRT_Free(void *ptr);
+
+/*
+ * Reallocate memory at `ptr`.
+ */
+VISIBILITY_HIDDEN void *NRT_Reallocate(void *ptr, size_t size);
+
+/*
+ * Debugging printf function used internally
+ */
+VISIBILITY_HIDDEN void nrt_debug_print(char *fmt, ...);
+
+/*
+ * Get API function table.
+ */
+VISIBILITY_HIDDEN const NRT_api_functions* NRT_get_api(void);
+
+
+/*
+ * FOR INTERNAL USE ONLY.
+ * Get a sample external allocator for testing
+ */
+VISIBILITY_HIDDEN NRT_ExternalAllocator* _nrt_get_sample_external_allocator(void);
+
+#endif /* NUMBA_NRT_H_ */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.py
new file mode 100644
index 000000000..fef29e3c8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt.py
@@ -0,0 +1,136 @@
+from collections import namedtuple
+from weakref import finalize as _finalize
+
+from numba.core.runtime import nrtdynmod
+from llvmlite import binding as ll
+
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core.typing.typeof import typeof_impl
+from numba.core import types
+from numba.core.runtime import _nrt_python as _nrt
+
+_nrt_mstats = namedtuple("nrt_mstats", ["alloc", "free", "mi_alloc", "mi_free"])
+
+
+class _Runtime(object):
+    def __init__(self):
+        self._init = False
+
+    @global_compiler_lock
+    def initialize(self, ctx):
+        """Initializes the NRT
+
+        Must be called before any actual call to the NRT API.
+        Safe to be called multiple times.
+        """
+        if self._init:
+            # Already initialized
+            return
+
+        # Register globals into the system
+        for py_name in _nrt.c_helpers:
+            if py_name.startswith("_"):
+                # internal API
+                c_name = py_name
+            else:
+                c_name = "NRT_" + py_name
+            c_address = _nrt.c_helpers[py_name]
+            ll.add_symbol(c_name, c_address)
+
+        # Compile atomic operations
+        self._library = nrtdynmod.compile_nrt_functions(ctx)
+
+        self._ptr_inc = self._library.get_pointer_to_function("nrt_atomic_add")
+        self._ptr_dec = self._library.get_pointer_to_function("nrt_atomic_sub")
+        self._ptr_cas = self._library.get_pointer_to_function("nrt_atomic_cas")
+
+        # Install atomic ops to NRT
+        _nrt.memsys_set_atomic_inc_dec(self._ptr_inc, self._ptr_dec)
+        _nrt.memsys_set_atomic_cas(self._ptr_cas)
+
+        self._init = True
+
+    def _init_guard(self):
+        if not self._init:
+            msg = "Runtime must be initialized before use."
+            raise RuntimeError(msg)
+
+    @staticmethod
+    def shutdown():
+        """
+        Shutdown the NRT
+        Safe to be called without calling Runtime.initialize first
+        """
+        _nrt.memsys_shutdown()
+
+    @property
+    def library(self):
+        """
+        Return the Library object containing the various NRT functions.
+        """
+        self._init_guard()
+        return self._library
+
+    def meminfo_new(self, data, pyobj):
+        """
+        Returns a MemInfo object that tracks memory at `data` owned by `pyobj`.
+        MemInfo will acquire a reference on `pyobj`.
+        The release of MemInfo will release a reference on `pyobj`.
+        """
+        self._init_guard()
+        mi = _nrt.meminfo_new(data, pyobj)
+        return MemInfo(mi)
+
+    def meminfo_alloc(self, size, safe=False):
+        """
+        Allocate a new memory of `size` bytes and returns a MemInfo object
+        that tracks the allocation.  When there is no more reference to the
+        MemInfo object, the underlying memory will be deallocated.
+
+        If `safe` flag is True, the memory is allocated using the `safe` scheme.
+        This is used for debugging and testing purposes.
+        See `NRT_MemInfo_alloc_safe()` in "nrt.h" for details.
+        """
+        self._init_guard()
+        if size < 0:
+            msg = f"Cannot allocate a negative number of bytes: {size}."
+            raise ValueError(msg)
+        if safe:
+            mi = _nrt.meminfo_alloc_safe(size)
+        else:
+            mi = _nrt.meminfo_alloc(size)
+        if mi == 0: # alloc failed or size was 0 and alloc returned NULL.
+            msg = f"Requested allocation of {size} bytes failed."
+            raise MemoryError(msg)
+        return MemInfo(mi)
+
+    def get_allocation_stats(self):
+        """
+        Returns a namedtuple of (alloc, free, mi_alloc, mi_free) for count of
+        each memory operations.
+        """
+        # No init guard needed to access stats members
+        return _nrt_mstats(alloc=_nrt.memsys_get_stats_alloc(),
+                           free=_nrt.memsys_get_stats_free(),
+                           mi_alloc=_nrt.memsys_get_stats_mi_alloc(),
+                           mi_free=_nrt.memsys_get_stats_mi_free())
+
+
+# Alias to _nrt_python._MemInfo
+MemInfo = _nrt._MemInfo
+
+
+@typeof_impl.register(MemInfo)
+def typeof_meminfo(val, c):
+    return types.MemInfoPointer(types.voidptr)
+
+
+# Create runtime
+_nrt.memsys_use_cpython_allocator()
+rtsys = _Runtime()
+
+# Install finalizer
+_finalize(rtsys, _Runtime.shutdown)
+
+# Avoid future use of the class
+del _Runtime
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt_external.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt_external.h
new file mode 100644
index 000000000..868955015
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrt_external.h
@@ -0,0 +1,65 @@
+#ifndef NUMBA_NRT_EXTERNAL_H_
+#define NUMBA_NRT_EXTERNAL_H_
+
+#include <stdlib.h>
+
+typedef struct MemInfo NRT_MemInfo;
+
+typedef void NRT_managed_dtor(void *data);
+
+typedef void *(*NRT_external_malloc_func)(size_t size, void *opaque_data);
+typedef void *(*NRT_external_realloc_func)(void *ptr, size_t new_size, void *opaque_data);
+typedef void (*NRT_external_free_func)(void *ptr, void *opaque_data);
+
+struct ExternalMemAllocator {
+    NRT_external_malloc_func malloc;
+    NRT_external_realloc_func realloc;
+    NRT_external_free_func free;
+    void *opaque_data;
+};
+
+typedef struct ExternalMemAllocator NRT_ExternalAllocator;
+
+typedef struct {
+    /* Methods to create MemInfos.
+
+    MemInfos are like smart pointers for objects that are managed by the Numba.
+    */
+
+    /* Allocate memory
+
+    *nbytes* is the number of bytes to be allocated
+
+    Returning a new reference.
+    */
+    NRT_MemInfo* (*allocate)(size_t nbytes);
+    /* Allocates memory using an external allocator but still using Numba's MemInfo.
+     *
+     * NOTE: An externally provided allocator must behave the same way as C99
+     *       stdlib.h's "malloc" function with respect to return value
+     *       (including the behaviour that occurs when requesting an allocation
+     *        of size 0 bytes).
+     */
+    NRT_MemInfo* (*allocate_external)(size_t nbytes, NRT_ExternalAllocator *allocator);
+
+    /* Convert externally allocated memory into a MemInfo.
+
+    *data* is the memory pointer
+    *dtor* is the deallocator of the memory
+    */
+    NRT_MemInfo* (*manage_memory)(void *data, NRT_managed_dtor dtor);
+
+    /* Acquire a reference */
+    void (*acquire)(NRT_MemInfo* mi);
+
+    /* Release a reference */
+    void (*release)(NRT_MemInfo* mi);
+
+    /* Get MemInfo data pointer */
+    void* (*get_data)(NRT_MemInfo* mi);
+
+} NRT_api_functions;
+
+
+
+#endif /* NUMBA_NRT_EXTERNAL_H_ */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrtdynmod.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrtdynmod.py
new file mode 100644
index 000000000..c8cc1973d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrtdynmod.py
@@ -0,0 +1,215 @@
+"""
+Dynamically generate the NRT module
+"""
+
+
+from numba.core import config
+from numba.core import types, cgutils
+from llvmlite import ir, binding
+
+
+_word_type = ir.IntType(config.MACHINE_BITS)
+_pointer_type = ir.PointerType(ir.IntType(8))
+
+_meminfo_struct_type = ir.LiteralStructType([
+    _word_type,     # size_t refct
+    _pointer_type,  # dtor_function dtor
+    _pointer_type,  # void *dtor_info
+    _pointer_type,  # void *data
+    _word_type,     # size_t size
+    ])
+
+
+incref_decref_ty = ir.FunctionType(ir.VoidType(), [_pointer_type])
+meminfo_data_ty = ir.FunctionType(_pointer_type, [_pointer_type])
+
+
+def _define_nrt_meminfo_data(module):
+    """
+    Implement NRT_MemInfo_data_fast in the module.  This allows LLVM
+    to inline lookup of the data pointer.
+    """
+    fn = cgutils.get_or_insert_function(module, meminfo_data_ty,
+                                        "NRT_MemInfo_data_fast")
+    builder = ir.IRBuilder(fn.append_basic_block())
+    [ptr] = fn.args
+    struct_ptr = builder.bitcast(ptr, _meminfo_struct_type.as_pointer())
+    data_ptr = builder.load(cgutils.gep(builder, struct_ptr, 0, 3))
+    builder.ret(data_ptr)
+
+
+def _define_nrt_incref(module, atomic_incr):
+    """
+    Implement NRT_incref in the module
+    """
+    fn_incref = cgutils.get_or_insert_function(module, incref_decref_ty,
+                                              "NRT_incref")
+    # Cannot inline this for refcount pruning to work
+    fn_incref.attributes.add('noinline')
+    builder = ir.IRBuilder(fn_incref.append_basic_block())
+    [ptr] = fn_incref.args
+    is_null = builder.icmp_unsigned("==", ptr, cgutils.get_null_value(ptr.type))
+    with cgutils.if_unlikely(builder, is_null):
+        builder.ret_void()
+
+    word_ptr = builder.bitcast(ptr, atomic_incr.args[0].type)
+    if config.DEBUG_NRT:
+        cgutils.printf(builder, "*** NRT_Incref %zu [%p]\n", builder.load(word_ptr),
+                       ptr)
+    builder.call(atomic_incr, [word_ptr])
+    builder.ret_void()
+
+
+def _define_nrt_decref(module, atomic_decr):
+    """
+    Implement NRT_decref in the module
+    """
+    fn_decref = cgutils.get_or_insert_function(module, incref_decref_ty,
+                                               "NRT_decref")
+    # Cannot inline this for refcount pruning to work
+    fn_decref.attributes.add('noinline')
+    calldtor = ir.Function(module,
+                           ir.FunctionType(ir.VoidType(), [_pointer_type]),
+                           name="NRT_MemInfo_call_dtor")
+
+    builder = ir.IRBuilder(fn_decref.append_basic_block())
+    [ptr] = fn_decref.args
+    is_null = builder.icmp_unsigned("==", ptr, cgutils.get_null_value(ptr.type))
+    with cgutils.if_unlikely(builder, is_null):
+        builder.ret_void()
+
+
+    # For memory fence usage, see https://llvm.org/docs/Atomics.html
+
+    # A release fence is used before the relevant write operation.
+    # No-op on x86.  On POWER, it lowers to lwsync.
+    builder.fence("release")
+
+    word_ptr = builder.bitcast(ptr, atomic_decr.args[0].type)
+
+    if config.DEBUG_NRT:
+        cgutils.printf(builder, "*** NRT_Decref %zu [%p]\n", builder.load(word_ptr),
+                       ptr)
+    newrefct = builder.call(atomic_decr,
+                            [word_ptr])
+
+    refct_eq_0 = builder.icmp_unsigned("==", newrefct,
+                                       ir.Constant(newrefct.type, 0))
+    with cgutils.if_unlikely(builder, refct_eq_0):
+        # An acquire fence is used after the relevant read operation.
+        # No-op on x86.  On POWER, it lowers to lwsync.
+        builder.fence("acquire")
+        builder.call(calldtor, [ptr])
+    builder.ret_void()
+
+
+# Set this to True to measure the overhead of atomic refcounts compared
+# to non-atomic.
+_disable_atomicity = 0
+
+
+def _define_atomic_inc_dec(module, op, ordering):
+    """Define a llvm function for atomic increment/decrement to the given module
+    Argument ``op`` is the operation "add"/"sub".  Argument ``ordering`` is
+    the memory ordering.  The generated function returns the new value.
+    """
+    ftype = ir.FunctionType(_word_type, [_word_type.as_pointer()])
+    fn_atomic = ir.Function(module, ftype, name="nrt_atomic_{0}".format(op))
+
+    [ptr] = fn_atomic.args
+    bb = fn_atomic.append_basic_block()
+    builder = ir.IRBuilder(bb)
+    ONE = ir.Constant(_word_type, 1)
+    if not _disable_atomicity:
+        oldval = builder.atomic_rmw(op, ptr, ONE, ordering=ordering)
+        # Perform the operation on the old value so that we can pretend returning
+        # the "new" value.
+        res = getattr(builder, op)(oldval, ONE)
+        builder.ret(res)
+    else:
+        oldval = builder.load(ptr)
+        newval = getattr(builder, op)(oldval, ONE)
+        builder.store(newval, ptr)
+        builder.ret(oldval)
+
+    return fn_atomic
+
+
+def _define_atomic_cas(module, ordering):
+    """Define a llvm function for atomic compare-and-swap.
+    The generated function is a direct wrapper of the LLVM cmpxchg with the
+    difference that the a int indicate success (1) or failure (0) is returned
+    and the last argument is a output pointer for storing the old value.
+
+    Note
+    ----
+    On failure, the generated function behaves like an atomic load.  The loaded
+    value is stored to the last argument.
+    """
+    ftype = ir.FunctionType(ir.IntType(32), [_word_type.as_pointer(),
+                                             _word_type, _word_type,
+                                             _word_type.as_pointer()])
+    fn_cas = ir.Function(module, ftype, name="nrt_atomic_cas")
+
+    [ptr, cmp, repl, oldptr] = fn_cas.args
+    bb = fn_cas.append_basic_block()
+    builder = ir.IRBuilder(bb)
+    outtup = builder.cmpxchg(ptr, cmp, repl, ordering=ordering)
+    old, ok = cgutils.unpack_tuple(builder, outtup, 2)
+    builder.store(old, oldptr)
+    builder.ret(builder.zext(ok, ftype.return_type))
+
+    return fn_cas
+
+
+def _define_nrt_unresolved_abort(ctx, module):
+    """
+    Defines an abort function due to unresolved symbol.
+
+    The function takes no args and will always raise an exception.
+    It should be safe to call this function with incorrect number of arguments.
+    """
+    fnty = ctx.call_conv.get_function_type(types.none, ())
+    fn = ir.Function(module, fnty, name="nrt_unresolved_abort")
+    bb = fn.append_basic_block()
+    builder = ir.IRBuilder(bb)
+    msg = "numba jitted function aborted due to unresolved symbol"
+    ctx.call_conv.return_user_exc(builder, RuntimeError, (msg,))
+    return fn
+
+
+def create_nrt_module(ctx):
+    """
+    Create an IR module defining the LLVM NRT functions.
+    A (IR module, library) tuple is returned.
+    """
+    codegen = ctx.codegen()
+    library = codegen.create_library("nrt")
+
+    # Implement LLVM module with atomic ops
+    ir_mod = library.create_ir_module("nrt_module")
+
+    atomic_inc = _define_atomic_inc_dec(ir_mod, "add", ordering='monotonic')
+    atomic_dec = _define_atomic_inc_dec(ir_mod, "sub", ordering='monotonic')
+    _define_atomic_cas(ir_mod, ordering='monotonic')
+
+    _define_nrt_meminfo_data(ir_mod)
+    _define_nrt_incref(ir_mod, atomic_inc)
+    _define_nrt_decref(ir_mod, atomic_dec)
+
+    _define_nrt_unresolved_abort(ctx, ir_mod)
+
+    return ir_mod, library
+
+
+def compile_nrt_functions(ctx):
+    """
+    Compile all LLVM NRT functions and return a library containing them.
+    The library is created using the given target context.
+    """
+    ir_mod, library = create_nrt_module(ctx)
+
+    library.add_ir_module(ir_mod)
+    library.finalize()
+
+    return library
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrtopt.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrtopt.py
new file mode 100644
index 000000000..2a6f56b09
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/runtime/nrtopt.py
@@ -0,0 +1,182 @@
+"""
+NRT specific optimizations
+"""
+import re
+from collections import defaultdict, deque
+from llvmlite import binding as ll
+from numba.core import cgutils
+
+_regex_incref = re.compile(r'\s*(?:tail)?\s*call void @NRT_incref\((.*)\)')
+_regex_decref = re.compile(r'\s*(?:tail)?\s*call void @NRT_decref\((.*)\)')
+_regex_bb = re.compile(
+    r'|'.join([
+        # unnamed BB is just a plain number
+        r'[0-9]+:',
+        # with a proper identifier (see llvm langref)
+        r'[\'"]?[-a-zA-Z$._0-9][-a-zA-Z$._0-9]*[\'"]?:',
+        # is a start of a function definition
+        r'^define',
+        # no name
+        r'^;\s*<label>',
+    ])
+)
+
+
+def _remove_redundant_nrt_refct(llvmir):
+    # Note: As soon as we have better utility in analyzing materialized LLVM
+    #       module in llvmlite, we can redo this without so much string
+    #       processing.
+    def _extract_functions(module):
+        cur = []
+        for line in str(module).splitlines():
+            if line.startswith('define'):
+                # start of function
+                assert not cur
+                cur.append(line)
+            elif line.startswith('}'):
+                # end of function
+                assert cur
+                cur.append(line)
+                yield True, cur
+                cur = []
+            elif cur:
+                cur.append(line)
+            else:
+                yield False, [line]
+
+    def _process_function(func_lines):
+        out = []
+        for is_bb, bb_lines in _extract_basic_blocks(func_lines):
+            if is_bb and bb_lines:
+                bb_lines = _process_basic_block(bb_lines)
+            out += bb_lines
+        return out
+
+    def _extract_basic_blocks(func_lines):
+        assert func_lines[0].startswith('define')
+        assert func_lines[-1].startswith('}')
+        yield False, [func_lines[0]]
+
+        cur = []
+        for ln in func_lines[1:-1]:
+            m = _regex_bb.match(ln)
+            if m is not None:
+                # line is a basic block separator
+                yield True, cur
+                cur = []
+                yield False, [ln]
+            elif ln:
+                cur.append(ln)
+
+        yield True, cur
+        yield False, [func_lines[-1]]
+
+    def _process_basic_block(bb_lines):
+        bb_lines = _move_and_group_decref_after_all_increfs(bb_lines)
+        bb_lines = _prune_redundant_refct_ops(bb_lines)
+        return bb_lines
+
+    def _examine_refct_op(bb_lines):
+        for num, ln in enumerate(bb_lines):
+            m = _regex_incref.match(ln)
+            if m is not None:
+                yield num, m.group(1), None
+                continue
+
+            m = _regex_decref.match(ln)
+            if m is not None:
+                yield num, None, m.group(1)
+                continue
+
+            yield ln, None, None
+
+    def _prune_redundant_refct_ops(bb_lines):
+        incref_map = defaultdict(deque)
+        decref_map = defaultdict(deque)
+        to_remove = set()
+        for num, incref_var, decref_var in _examine_refct_op(bb_lines):
+            assert not (incref_var and decref_var)
+            if incref_var:
+                if incref_var == 'i8* null':
+                    to_remove.add(num)
+                else:
+                    incref_map[incref_var].append(num)
+            elif decref_var:
+                if decref_var == 'i8* null':
+                    to_remove.add(num)
+                else:
+                    decref_map[decref_var].append(num)
+
+        for var, decops in decref_map.items():
+            incops = incref_map[var]
+            ct = min(len(incops), len(decops))
+            for _ in range(ct):
+                to_remove.add(incops.pop())
+                to_remove.add(decops.popleft())
+
+        return [ln for num, ln in enumerate(bb_lines)
+                if num not in to_remove]
+
+    def _move_and_group_decref_after_all_increfs(bb_lines):
+        # find last incref
+        last_incref_pos = 0
+        for pos, ln in enumerate(bb_lines):
+            if _regex_incref.match(ln) is not None:
+                last_incref_pos = pos + 1
+
+        # find last decref
+        last_decref_pos = 0
+        for pos, ln in enumerate(bb_lines):
+            if _regex_decref.match(ln) is not None:
+                last_decref_pos = pos + 1
+
+        last_pos = max(last_incref_pos, last_decref_pos)
+
+        # find decrefs before last_pos
+        decrefs = []
+        head = []
+        for ln in bb_lines[:last_pos]:
+            if _regex_decref.match(ln) is not None:
+                decrefs.append(ln)
+            else:
+                head.append(ln)
+
+        # insert decrefs at last_pos
+        return head + decrefs + bb_lines[last_pos:]
+
+    # Driver
+    processed = []
+
+    for is_func, lines in _extract_functions(llvmir):
+        if is_func:
+            lines = _process_function(lines)
+
+        processed += lines
+
+    return '\n'.join(processed)
+
+
+def remove_redundant_nrt_refct(ll_module):
+    """
+    Remove redundant reference count operations from the
+    `llvmlite.binding.ModuleRef`. This parses the ll_module as a string and
+    line by line to remove the unnecessary nrt refct pairs within each block.
+    Decref calls are moved after the last incref call in the block to avoid
+    temporarily decref'ing to zero (which can happen due to hidden decref from
+    alias).
+
+    Note: non-threadsafe due to usage of global LLVMcontext
+    """
+    # Early escape if NRT_incref is not used
+    try:
+        ll_module.get_function('NRT_incref')
+    except NameError:
+        return ll_module
+
+    # the optimisation pass loses the name of module as it operates on
+    # strings, so back it up and reset it on completion
+    name = ll_module.name
+    newll = _remove_redundant_nrt_refct(str(ll_module))
+    new_mod = ll.parse_assembly(newll)
+    new_mod.name = cgutils.normalize_ir_text(name)
+    return new_mod
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/serialize.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/serialize.py
new file mode 100644
index 000000000..2a55bade3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/serialize.py
@@ -0,0 +1,248 @@
+"""
+Serialization support for compiled functions.
+"""
+import sys
+import abc
+import io
+import copyreg
+
+
+import pickle
+from numba import cloudpickle
+
+
+#
+# Pickle support
+#
+
+def _rebuild_reduction(cls, *args):
+    """
+    Global hook to rebuild a given class from its __reduce__ arguments.
+    """
+    return cls._rebuild(*args)
+
+
+# Keep unpickled object via `numba_unpickle` alive.
+_unpickled_memo = {}
+
+
+def _numba_unpickle(address, bytedata, hashed):
+    """Used by `numba_unpickle` from _helperlib.c
+
+    Parameters
+    ----------
+    address : int
+    bytedata : bytes
+    hashed : bytes
+
+    Returns
+    -------
+    obj : object
+        unpickled object
+    """
+    key = (address, hashed)
+    try:
+        obj = _unpickled_memo[key]
+    except KeyError:
+        _unpickled_memo[key] = obj = cloudpickle.loads(bytedata)
+    return obj
+
+
+def dumps(obj):
+    """Similar to `pickle.dumps()`. Returns the serialized object in bytes.
+    """
+    pickler = NumbaPickler
+    with io.BytesIO() as buf:
+        p = pickler(buf, protocol=4)
+        p.dump(obj)
+        pickled = buf.getvalue()
+
+    return pickled
+
+
+# Alias to pickle.loads to allow `serialize.loads()`
+loads = cloudpickle.loads
+
+
+class _CustomPickled:
+    """A wrapper for objects that must be pickled with `NumbaPickler`.
+
+    Standard `pickle` will pick up the implementation registered via `copyreg`.
+    This will spawn a `NumbaPickler` instance to serialize the data.
+
+    `NumbaPickler` overrides the handling of this type so as not to spawn a
+    new pickler for the object when it is already being pickled by a
+    `NumbaPickler`.
+    """
+
+    __slots__ = 'ctor', 'states'
+
+    def __init__(self, ctor, states):
+        self.ctor = ctor
+        self.states = states
+
+    def _reduce(self):
+        return _CustomPickled._rebuild, (self.ctor, self.states)
+
+    @classmethod
+    def _rebuild(cls, ctor, states):
+        return cls(ctor, states)
+
+
+def _unpickle__CustomPickled(serialized):
+    """standard unpickling for `_CustomPickled`.
+
+    Uses `NumbaPickler` to load.
+    """
+    ctor, states = loads(serialized)
+    return _CustomPickled(ctor, states)
+
+
+def _pickle__CustomPickled(cp):
+    """standard pickling for `_CustomPickled`.
+
+    Uses `NumbaPickler` to dump.
+    """
+    serialized = dumps((cp.ctor, cp.states))
+    return _unpickle__CustomPickled, (serialized,)
+
+
+# Register custom pickling for the standard pickler.
+copyreg.pickle(_CustomPickled, _pickle__CustomPickled)
+
+
+def custom_reduce(cls, states):
+    """For customizing object serialization in `__reduce__`.
+
+    Object states provided here are used as keyword arguments to the
+    `._rebuild()` class method.
+
+    Parameters
+    ----------
+    states : dict
+        Dictionary of object states to be serialized.
+
+    Returns
+    -------
+    result : tuple
+        This tuple conforms to the return type requirement for `__reduce__`.
+    """
+    return custom_rebuild, (_CustomPickled(cls, states),)
+
+
+def custom_rebuild(custom_pickled):
+    """Customized object deserialization.
+
+    This function is referenced internally by `custom_reduce()`.
+    """
+    cls, states = custom_pickled.ctor, custom_pickled.states
+    return cls._rebuild(**states)
+
+
+def is_serialiable(obj):
+    """Check if *obj* can be serialized.
+
+    Parameters
+    ----------
+    obj : object
+
+    Returns
+    --------
+    can_serialize : bool
+    """
+    with io.BytesIO() as fout:
+        pickler = NumbaPickler(fout)
+        try:
+            pickler.dump(obj)
+        except pickle.PicklingError:
+            return False
+        else:
+            return True
+
+
+def _no_pickle(obj):
+    raise pickle.PicklingError(f"Pickling of {type(obj)} is unsupported")
+
+
+def disable_pickling(typ):
+    """This is called on a type to disable pickling
+    """
+    NumbaPickler.disabled_types.add(typ)
+    # The following is needed for Py3.7
+    NumbaPickler.dispatch_table[typ] = _no_pickle
+    # Return `typ` to allow use as a decorator
+    return typ
+
+
+class NumbaPickler(cloudpickle.CloudPickler):
+    disabled_types = set()
+    """A set of types that pickling cannot is disabled.
+    """
+
+    def reducer_override(self, obj):
+        # Overridden to disable pickling of certain types
+        if type(obj) in self.disabled_types:
+            _no_pickle(obj)  # noreturn
+        return super().reducer_override(obj)
+
+
+def _custom_reduce__custompickled(cp):
+    return cp._reduce()
+
+
+NumbaPickler.dispatch_table[_CustomPickled] = _custom_reduce__custompickled
+
+
+class ReduceMixin(abc.ABC):
+    """A mixin class for objects that should be reduced by the NumbaPickler instead
+    of the standard pickler.
+    """
+    # Subclass MUST override the below methods
+
+    @abc.abstractmethod
+    def _reduce_states(self):
+        raise NotImplementedError
+
+    @abc.abstractclassmethod
+    def _rebuild(cls, **kwargs):
+        raise NotImplementedError
+
+    # Subclass can override the below methods
+
+    def _reduce_class(self):
+        return self.__class__
+
+    # Private methods
+
+    def __reduce__(self):
+        return custom_reduce(self._reduce_class(), self._reduce_states())
+
+
+class PickleCallableByPath:
+    """Wrap a callable object to be pickled by path to workaround limitation
+    in pickling due to non-pickleable objects in function non-locals.
+
+    Note:
+    - Do not use this as a decorator.
+    - Wrapped object must be a global that exist in its parent module and it
+      can be imported by `from the_module import the_object`.
+
+    Usage:
+
+    >>> def my_fn(x):
+    >>>     ...
+    >>> wrapped_fn = PickleCallableByPath(my_fn)
+    >>> # refer to `wrapped_fn` instead of `my_fn`
+    """
+    def __init__(self, fn):
+        self._fn = fn
+
+    def __call__(self, *args, **kwargs):
+        return self._fn(*args, **kwargs)
+
+    def __reduce__(self):
+        return type(self)._rebuild, (self._fn.__module__, self._fn.__name__,)
+
+    @classmethod
+    def _rebuild(cls, modname, fn_path):
+        return cls(getattr(sys.modules[modname], fn_path))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/sigutils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/sigutils.py
new file mode 100644
index 000000000..aecdb8f65
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/sigutils.py
@@ -0,0 +1,53 @@
+from numba.core import types, typing
+
+
+def is_signature(sig):
+    """
+    Return whether *sig* is a potentially valid signature
+    specification (for user-facing APIs).
+    """
+    return isinstance(sig, (str, tuple, typing.Signature))
+
+
+def _parse_signature_string(signature_str):
+    """
+    Parameters
+    ----------
+    signature_str : str
+    """
+    # Just eval signature_str using the types submodules as globals
+    return eval(signature_str, {}, types.__dict__)
+
+
+def normalize_signature(sig):
+    """
+    From *sig* (a signature specification), return a ``(args, return_type)``
+    tuple, where ``args`` itself is a tuple of types, and ``return_type``
+    can be None if not specified.
+    """
+    if isinstance(sig, str):
+        parsed = _parse_signature_string(sig)
+    else:
+        parsed = sig
+    if isinstance(parsed, tuple):
+        args, return_type = parsed, None
+    elif isinstance(parsed, typing.Signature):
+        args, return_type = parsed.args, parsed.return_type
+    else:
+        raise TypeError("invalid signature: %r (type: %r) evaluates to %r "
+                        "instead of tuple or Signature" % (
+                            sig, sig.__class__.__name__,
+                            parsed.__class__.__name__
+                        ))
+
+    def check_type(ty):
+        if not isinstance(ty, types.Type):
+            raise TypeError("invalid type in signature: expected a type "
+                            "instance, got %r" % (ty,))
+
+    if return_type is not None:
+        check_type(return_type)
+    for ty in args:
+        check_type(ty)
+
+    return args, return_type
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ssa.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ssa.py
new file mode 100644
index 000000000..8a368229d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/ssa.py
@@ -0,0 +1,457 @@
+"""
+Implement Dominance-Fronter-based SSA by Choi et al described in Inria SSA book
+
+References:
+
+- Static Single Assignment Book by Inria
+  http://ssabook.gforge.inria.fr/latest/book.pdf
+- Choi et al. Incremental computation of static single assignment form.
+"""
+import logging
+import operator
+import warnings
+from functools import reduce
+from copy import copy
+from pprint import pformat
+from collections import defaultdict
+
+from numba import config
+from numba.core import ir, ir_utils, errors
+from numba.core.analysis import compute_cfg_from_blocks
+
+
+_logger = logging.getLogger(__name__)
+
+
+def reconstruct_ssa(func_ir):
+    """Apply SSA reconstruction algorithm on the given IR.
+
+    Produces minimal SSA using Choi et al algorithm.
+    """
+    func_ir.blocks = _run_ssa(func_ir.blocks)
+
+    return func_ir
+
+
+class _CacheListVars:
+    def __init__(self):
+        self._saved = {}
+
+    def get(self, inst):
+        got = self._saved.get(inst)
+        if got is None:
+            self._saved[inst] = got = inst.list_vars()
+        return got
+
+
+def _run_ssa(blocks):
+    """Run SSA reconstruction on IR blocks of a function.
+    """
+    if not blocks:
+        # Empty blocks?
+        return {}
+    # Run CFG on the blocks
+    cfg = compute_cfg_from_blocks(blocks)
+    df_plus = _iterated_domfronts(cfg)
+    # Find SSA violators
+    violators = _find_defs_violators(blocks)
+    # Make cache for .list_vars()
+    cache_list_vars = _CacheListVars()
+
+    # Process one SSA-violating variable at a time
+    for varname in violators:
+        _logger.debug(
+            "Fix SSA violator on var %s", varname,
+        )
+        # Fix up the LHS
+        # Put fresh variables for all assignments to the variable
+        blocks, defmap = _fresh_vars(blocks, varname)
+        _logger.debug("Replaced assignments: %s", pformat(defmap))
+        # Fix up the RHS
+        # Re-associate the variable uses with the reaching definition
+        blocks = _fix_ssa_vars(blocks, varname, defmap, cfg, df_plus,
+                               cache_list_vars)
+
+    # Post-condition checks.
+    # CFG invariant
+    cfg_post = compute_cfg_from_blocks(blocks)
+    if cfg_post != cfg:
+        raise errors.CompilerError("CFG mutated in SSA pass")
+    return blocks
+
+
+def _fix_ssa_vars(blocks, varname, defmap, cfg, df_plus, cache_list_vars):
+    """Rewrite all uses to ``varname`` given the definition map
+    """
+    states = _make_states(blocks)
+    states['varname'] = varname
+    states['defmap'] = defmap
+    states['phimap'] = phimap = defaultdict(list)
+    states['cfg'] = cfg
+    states['phi_locations'] = _compute_phi_locations(df_plus, defmap)
+    newblocks = _run_block_rewrite(blocks, states, _FixSSAVars(cache_list_vars))
+    # insert phi nodes
+    for label, philist in phimap.items():
+        curblk = newblocks[label]
+        # Prepend PHI nodes to the block
+        curblk.body = philist + curblk.body
+    return newblocks
+
+
+def _iterated_domfronts(cfg):
+    """Compute the iterated dominance frontiers (DF+ in literatures).
+
+    Returns a dictionary which maps block label to the set of labels of its
+    iterated dominance frontiers.
+    """
+    domfronts = {k: set(vs) for k, vs in cfg.dominance_frontier().items()}
+    keep_going = True
+    while keep_going:
+        keep_going = False
+        for k, vs in domfronts.items():
+            inner = reduce(operator.or_, [domfronts[v] for v in vs], set())
+            if inner.difference(vs):
+                vs |= inner
+                keep_going = True
+    return domfronts
+
+
+def _compute_phi_locations(iterated_df, defmap):
+    # See basic algorithm in Ch 4.1 in Inria SSA Book
+    # Compute DF+(defs)
+    # DF of all DFs is the union of all DFs
+    phi_locations = set()
+    for deflabel, defstmts in defmap.items():
+        if defstmts:
+            phi_locations |= iterated_df[deflabel]
+    return phi_locations
+
+
+def _fresh_vars(blocks, varname):
+    """Rewrite to put fresh variable names
+    """
+    states = _make_states(blocks)
+    states['varname'] = varname
+    states['defmap'] = defmap = defaultdict(list)
+    newblocks = _run_block_rewrite(blocks, states, _FreshVarHandler())
+    return newblocks, defmap
+
+
+def _get_scope(blocks):
+    first, *_ = blocks.values()
+    return first.scope
+
+
+def _find_defs_violators(blocks):
+    """
+    Returns
+    -------
+    res : Set[str]
+        The SSA violators in a dictionary of variable names.
+    """
+    defs = defaultdict(list)
+    _run_block_analysis(blocks, defs, _GatherDefsHandler())
+    _logger.debug("defs %s", pformat(defs))
+    violators = {k for k, vs in defs.items() if len(vs) > 1}
+    _logger.debug("SSA violators %s", pformat(violators))
+    return violators
+
+
+def _run_block_analysis(blocks, states, handler):
+    for label, blk in blocks.items():
+        _logger.debug("==== SSA block analysis pass on %s", label)
+        for _ in _run_ssa_block_pass(states, blk, handler):
+            pass
+
+
+def _run_block_rewrite(blocks, states, handler):
+    newblocks = {}
+    for label, blk in blocks.items():
+        _logger.debug("==== SSA block rewrite pass on %s", label)
+        newblk = ir.Block(scope=blk.scope, loc=blk.loc)
+
+        newbody = []
+        states['label'] = label
+        states['block'] = blk
+        for stmt in _run_ssa_block_pass(states, blk, handler):
+            assert stmt is not None
+            newbody.append(stmt)
+        newblk.body = newbody
+        newblocks[label] = newblk
+    return newblocks
+
+
+def _make_states(blocks):
+    return dict(
+        scope=_get_scope(blocks),
+    )
+
+
+def _run_ssa_block_pass(states, blk, handler):
+    _logger.debug("Running %s", handler)
+    for stmt in blk.body:
+        _logger.debug("on stmt: %s", stmt)
+        if isinstance(stmt, ir.Assign):
+            ret = handler.on_assign(states, stmt)
+        else:
+            ret = handler.on_other(states, stmt)
+        if ret is not stmt and ret is not None:
+            _logger.debug("replaced with: %s", ret)
+        yield ret
+
+
+class _BaseHandler:
+    """A base handler for all the passes used here for the SSA algorithm.
+    """
+    def on_assign(self, states, assign):
+        """
+        Called when the pass sees an ``ir.Assign``.
+
+        Subclasses should override this for custom behavior
+
+        Parameters
+        -----------
+        states : dict
+        assign : numba.ir.Assign
+
+        Returns
+        -------
+        stmt : numba.ir.Assign or None
+            For rewrite passes, the return value is used as the replacement
+            for the given statement.
+        """
+
+    def on_other(self, states, stmt):
+        """
+        Called when the pass sees an ``ir.Stmt`` that's not an assignment.
+
+        Subclasses should override this for custom behavior
+
+        Parameters
+        -----------
+        states : dict
+        assign : numba.ir.Stmt
+
+        Returns
+        -------
+        stmt : numba.ir.Stmt or None
+            For rewrite passes, the return value is used as the replacement
+            for the given statement.
+        """
+
+
+class _GatherDefsHandler(_BaseHandler):
+    """Find all defs
+
+    ``states`` is a Mapping[str, List[ir.Assign]]
+    """
+    def on_assign(self, states, assign):
+        states[assign.target.name].append(assign)
+
+
+class UndefinedVariable:
+    def __init__(self):
+        raise NotImplementedError("Not intended for instantiation")
+
+    target = ir.UNDEFINED
+
+
+class _FreshVarHandler(_BaseHandler):
+    """Replaces assignment target with new fresh variables.
+    """
+    def on_assign(self, states, assign):
+        if assign.target.name == states['varname']:
+            scope = states['scope']
+            defmap = states['defmap']
+            # Allow first assignment to retain the name
+            if len(defmap) == 0:
+                newtarget = assign.target
+                _logger.debug("first assign: %s", newtarget)
+                if newtarget.name not in scope.localvars:
+                    wmsg = f"variable {newtarget.name!r} is not in scope."
+                    warnings.warn(errors.NumbaIRAssumptionWarning(wmsg,
+                                  loc=assign.loc))
+            else:
+                newtarget = scope.redefine(assign.target.name, loc=assign.loc)
+            assign = ir.Assign(
+                target=newtarget,
+                value=assign.value,
+                loc=assign.loc
+            )
+            defmap[states['label']].append(assign)
+        return assign
+
+    def on_other(self, states, stmt):
+        return stmt
+
+
+class _FixSSAVars(_BaseHandler):
+    """Replace variable uses in IR nodes to the correct reaching variable
+    and introduce Phi nodes if necessary. This class contains the core of
+    the SSA reconstruction algorithm.
+
+    See Ch 5 of the Inria SSA book for reference. The method names used here
+    are similar to the names used in the pseudocode in the book.
+    """
+
+    def __init__(self, cache_list_vars):
+        self._cache_list_vars = cache_list_vars
+
+    def on_assign(self, states, assign):
+        rhs = assign.value
+        if isinstance(rhs, ir.Inst):
+            newdef = self._fix_var(
+                states, assign, self._cache_list_vars.get(assign.value),
+            )
+            # Has a replacement that is not the current variable
+            if newdef is not None and newdef.target is not ir.UNDEFINED:
+                if states['varname'] != newdef.target.name:
+                    replmap = {states['varname']: newdef.target}
+                    rhs = copy(rhs)
+
+                    ir_utils.replace_vars_inner(rhs, replmap)
+                    return ir.Assign(
+                        target=assign.target,
+                        value=rhs,
+                        loc=assign.loc,
+                    )
+        elif isinstance(rhs, ir.Var):
+            newdef = self._fix_var(states, assign, [rhs])
+            # Has a replacement that is not the current variable
+            if newdef is not None and newdef.target is not ir.UNDEFINED:
+                if states['varname'] != newdef.target.name:
+                    return ir.Assign(
+                        target=assign.target,
+                        value=newdef.target,
+                        loc=assign.loc,
+                    )
+
+        return assign
+
+    def on_other(self, states, stmt):
+        newdef = self._fix_var(
+            states, stmt, self._cache_list_vars.get(stmt),
+        )
+        if newdef is not None and newdef.target is not ir.UNDEFINED:
+            if states['varname'] != newdef.target.name:
+                replmap = {states['varname']: newdef.target}
+                stmt = copy(stmt)
+                ir_utils.replace_vars_stmt(stmt, replmap)
+        return stmt
+
+    def _fix_var(self, states, stmt, used_vars):
+        """Fix all variable uses in ``used_vars``.
+        """
+        varnames = [k.name for k in used_vars]
+        phivar = states['varname']
+        if phivar in varnames:
+            return self._find_def(states, stmt)
+
+    def _find_def(self, states, stmt):
+        """Find definition of ``stmt`` for the statement ``stmt``
+        """
+        _logger.debug("find_def var=%r stmt=%s", states['varname'], stmt)
+        selected_def = None
+        label = states['label']
+        local_defs = states['defmap'][label]
+        local_phis = states['phimap'][label]
+        block = states['block']
+
+        cur_pos = self._stmt_index(stmt, block)
+        for defstmt in reversed(local_defs):
+            # Phi nodes have no index
+            def_pos = self._stmt_index(defstmt, block, stop=cur_pos)
+            if def_pos < cur_pos:
+                selected_def = defstmt
+                break
+            # Maybe it's a PHI
+            elif defstmt in local_phis:
+                selected_def = local_phis[-1]
+                break
+
+        if selected_def is None:
+            selected_def = self._find_def_from_top(
+                states, label, loc=stmt.loc,
+            )
+        return selected_def
+
+    def _find_def_from_top(self, states, label, loc):
+        """Find definition reaching block of ``label``.
+
+        This method would look at all dominance frontiers.
+        Insert phi node if necessary.
+        """
+        _logger.debug("find_def_from_top label %r", label)
+        cfg = states['cfg']
+        defmap = states['defmap']
+        phimap = states['phimap']
+        phi_locations = states['phi_locations']
+
+        if label in phi_locations:
+            scope = states['scope']
+            loc = states['block'].loc
+            # fresh variable
+            freshvar = scope.redefine(states['varname'], loc=loc)
+            # insert phi
+            phinode = ir.Assign(
+                target=freshvar,
+                value=ir.Expr.phi(loc=loc),
+                loc=loc,
+            )
+            _logger.debug("insert phi node %s at %s", phinode, label)
+            defmap[label].insert(0, phinode)
+            phimap[label].append(phinode)
+            # Find incoming values for the Phi node
+            for pred, _ in cfg.predecessors(label):
+                incoming_def = self._find_def_from_bottom(
+                    states, pred, loc=loc,
+                )
+                _logger.debug("incoming_def %s", incoming_def)
+                phinode.value.incoming_values.append(incoming_def.target)
+                phinode.value.incoming_blocks.append(pred)
+            return phinode
+        else:
+            idom = cfg.immediate_dominators()[label]
+            if idom == label:
+                # We have searched to the top of the idom tree.
+                # Since we still cannot find a definition,
+                # we will warn.
+                _warn_about_uninitialized_variable(states['varname'], loc)
+                return UndefinedVariable
+            _logger.debug("idom %s from label %s", idom, label)
+            return self._find_def_from_bottom(states, idom, loc=loc)
+
+    def _find_def_from_bottom(self, states, label, loc):
+        """Find definition from within the block at ``label``.
+        """
+        _logger.debug("find_def_from_bottom label %r", label)
+        defmap = states['defmap']
+        defs = defmap[label]
+        if defs:
+            lastdef = defs[-1]
+            return lastdef
+        else:
+            return self._find_def_from_top(states, label, loc=loc)
+
+    def _stmt_index(self, defstmt, block, stop=-1):
+        """Find the positional index of the statement at ``block``.
+
+        Assumptions:
+        - no two statements can point to the same object.
+        """
+        # Compare using id() as IR node equality is for semantic equivalence
+        # opposed to direct equality (the location and scope are not considered
+        # as part of the equality measure, this is important here).
+        for i in range(len(block.body))[:stop]:
+            if block.body[i] is defstmt:
+                return i
+        return len(block.body)
+
+
+def _warn_about_uninitialized_variable(varname, loc):
+    if config.ALWAYS_WARN_UNINIT_VAR:
+        warnings.warn(
+            errors.NumbaWarning(
+                f"Detected uninitialized variable {varname}",
+                loc=loc),
+        )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/target_extension.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/target_extension.py
new file mode 100644
index 000000000..d0546671c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/target_extension.py
@@ -0,0 +1,168 @@
+from abc import ABC, abstractmethod
+from numba.core.registry import DelayedRegistry, CPUDispatcher
+from numba.core.decorators import jit
+from numba.core.errors import InternalTargetMismatchError, NumbaValueError
+from threading import local as tls
+
+
+_active_context = tls()
+_active_context_default = 'cpu'
+
+
+class _TargetRegistry(DelayedRegistry):
+
+    def __getitem__(self, item):
+        try:
+            return super().__getitem__(item)
+        except KeyError:
+            msg = "No target is registered against '{}', known targets:\n{}"
+            known = '\n'.join([f"{k: <{10}} -> {v}"
+                               for k, v in target_registry.items()])
+            raise NumbaValueError(msg.format(item, known)) from None
+
+
+# Registry mapping target name strings to Target classes
+target_registry = _TargetRegistry()
+
+# Registry mapping Target classes the @jit decorator for that target
+jit_registry = DelayedRegistry()
+
+
+class target_override(object):
+    """Context manager to temporarily override the current target with that
+       prescribed."""
+    def __init__(self, name):
+        self._orig_target = getattr(_active_context, 'target',
+                                    _active_context_default)
+        self.target = name
+
+    def __enter__(self):
+        _active_context.target = self.target
+
+    def __exit__(self, ty, val, tb):
+        _active_context.target = self._orig_target
+
+
+def current_target():
+    """Returns the current target
+    """
+    return getattr(_active_context, 'target', _active_context_default)
+
+
+def get_local_target(context):
+    """
+    Gets the local target from the call stack if available and the TLS
+    override if not.
+    """
+    # TODO: Should this logic be reversed to prefer TLS override?
+    if len(context.callstack._stack) > 0:
+        target = context.callstack[0].target
+    else:
+        target = target_registry.get(current_target(), None)
+    if target is None:
+        msg = ("The target found is not registered."
+               "Given target was {}.")
+        raise ValueError(msg.format(target))
+    else:
+        return target
+
+
+def resolve_target_str(target_str):
+    """Resolves a target specified as a string to its Target class."""
+    return target_registry[target_str]
+
+
+def resolve_dispatcher_from_str(target_str):
+    """Returns the dispatcher associated with a target string"""
+    target_hw = resolve_target_str(target_str)
+    return dispatcher_registry[target_hw]
+
+
+def _get_local_target_checked(tyctx, hwstr, reason):
+    """Returns the local target if it is compatible with the given target
+    name during a type resolution; otherwise, raises an exception.
+
+    Parameters
+    ----------
+    tyctx: typing context
+    hwstr: str
+        target name to check against
+    reason: str
+        Reason for the resolution. Expects a noun.
+    Returns
+    -------
+    target_hw : Target
+
+    Raises
+    ------
+    InternalTargetMismatchError
+    """
+    # Get the class for the target declared by the function
+    hw_clazz = resolve_target_str(hwstr)
+    # get the local target
+    target_hw = get_local_target(tyctx)
+    # make sure the target_hw is in the MRO for hw_clazz else bail
+    if not target_hw.inherits_from(hw_clazz):
+        raise InternalTargetMismatchError(reason, target_hw, hw_clazz)
+    return target_hw
+
+
+class JitDecorator(ABC):
+
+    @abstractmethod
+    def __call__(self):
+        return NotImplemented
+
+
+class Target(ABC):
+    """ Implements a target """
+
+    @classmethod
+    def inherits_from(cls, other):
+        """Returns True if this target inherits from 'other' False otherwise"""
+        return issubclass(cls, other)
+
+
+class Generic(Target):
+    """Mark the target as generic, i.e. suitable for compilation on
+    any target. All must inherit from this.
+    """
+
+
+class CPU(Generic):
+    """Mark the target as CPU.
+    """
+
+
+class GPU(Generic):
+    """Mark the target as GPU, i.e. suitable for compilation on a GPU
+    target.
+    """
+
+
+class CUDA(GPU):
+    """Mark the target as CUDA.
+    """
+
+
+class NPyUfunc(Target):
+    """Mark the target as a ufunc
+    """
+
+
+target_registry['generic'] = Generic
+target_registry['CPU'] = CPU
+target_registry['cpu'] = CPU
+target_registry['GPU'] = GPU
+target_registry['gpu'] = GPU
+target_registry['CUDA'] = CUDA
+target_registry['cuda'] = CUDA
+target_registry['npyufunc'] = NPyUfunc
+
+dispatcher_registry = DelayedRegistry(key_type=Target)
+
+
+# Register the cpu target token with its dispatcher and jit
+cpu_target = target_registry['cpu']
+dispatcher_registry[cpu_target] = CPUDispatcher
+jit_registry[cpu_target] = jit
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/targetconfig.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/targetconfig.py
new file mode 100644
index 000000000..1ef986fed
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/targetconfig.py
@@ -0,0 +1,321 @@
+"""
+This module contains utils for manipulating target configurations such as
+compiler flags.
+"""
+import re
+import zlib
+import base64
+
+from types import MappingProxyType
+from numba.core import utils
+
+
+class Option:
+    """An option to be used in ``TargetConfig``.
+    """
+    __slots__ = "_type", "_default", "_doc"
+
+    def __init__(self, type, *, default, doc):
+        """
+        Parameters
+        ----------
+        type :
+            Type of the option value. It can be a callable.
+            The setter always calls ``self._type(value)``.
+        default :
+            The default value for the option.
+        doc : str
+            Docstring for the option.
+        """
+        self._type = type
+        self._default = default
+        self._doc = doc
+
+    @property
+    def type(self):
+        return self._type
+
+    @property
+    def default(self):
+        return self._default
+
+    @property
+    def doc(self):
+        return self._doc
+
+
+class _FlagsStack(utils.ThreadLocalStack, stack_name="flags"):
+    pass
+
+
+class ConfigStack:
+    """A stack for tracking target configurations in the compiler.
+
+    It stores the stack in a thread-local class attribute. All instances in the
+    same thread will see the same stack.
+    """
+    @classmethod
+    def top_or_none(cls):
+        """Get the TOS or return None if no config is set.
+        """
+        self = cls()
+        if self:
+            flags = self.top()
+        else:
+            # Note: should this be the default flag for the target instead?
+            flags = None
+        return flags
+
+    def __init__(self):
+        self._stk = _FlagsStack()
+
+    def top(self):
+        return self._stk.top()
+
+    def __len__(self):
+        return len(self._stk)
+
+    def enter(self, flags):
+        """Returns a contextmanager that performs ``push(flags)`` on enter and
+        ``pop()`` on exit.
+        """
+        return self._stk.enter(flags)
+
+
+class _MetaTargetConfig(type):
+    """Metaclass for ``TargetConfig``.
+
+    When a subclass of ``TargetConfig`` is created, all ``Option`` defined
+    as class members will be parsed and corresponding getters, setters, and
+    delters will be inserted.
+    """
+    def __init__(cls, name, bases, dct):
+        """Invoked when subclass is created.
+
+        Insert properties for each ``Option`` that are class members.
+        All the options will be grouped inside the ``.options`` class
+        attribute.
+        """
+        # Gather options from base classes and class dict
+        opts = {}
+        # Reversed scan into the base classes to follow MRO ordering such that
+        # the closest base class is overriding
+        for base_cls in reversed(bases):
+            opts.update(base_cls.options)
+        opts.update(cls.find_options(dct))
+        # Store the options into class attribute as a ready-only mapping.
+        cls.options = MappingProxyType(opts)
+
+        # Make properties for each of the options
+        def make_prop(name, option):
+            def getter(self):
+                return self._values.get(name, option.default)
+
+            def setter(self, val):
+                self._values[name] = option.type(val)
+
+            def delter(self):
+                del self._values[name]
+
+            return property(getter, setter, delter, option.doc)
+
+        for name, option in cls.options.items():
+            setattr(cls, name, make_prop(name, option))
+
+    def find_options(cls, dct):
+        """Returns a new dict with all the items that are a mapping to an
+        ``Option``.
+        """
+        return {k: v for k, v in dct.items() if isinstance(v, Option)}
+
+
+class _NotSetType:
+    def __repr__(self):
+        return "<NotSet>"
+
+
+_NotSet = _NotSetType()
+
+
+class TargetConfig(metaclass=_MetaTargetConfig):
+    """Base class for ``TargetConfig``.
+
+    Subclass should fill class members with ``Option``. For example:
+
+    >>> class MyTargetConfig(TargetConfig):
+    >>>     a_bool_option = Option(type=bool, default=False, doc="a bool")
+    >>>     an_int_option = Option(type=int, default=0, doc="an int")
+
+    The metaclass will insert properties for each ``Option``. For example:
+
+    >>> tc = MyTargetConfig()
+    >>> tc.a_bool_option = True  # invokes the setter
+    >>> print(tc.an_int_option)  # print the default
+    """
+
+    # Used for compression in mangling.
+    # Set to -15 to disable the header and checksum for smallest output.
+    _ZLIB_CONFIG = {"wbits": -15}
+
+    def __init__(self, copy_from=None):
+        """
+        Parameters
+        ----------
+        copy_from : TargetConfig or None
+            if None, creates an empty ``TargetConfig``.
+            Otherwise, creates a copy.
+        """
+        self._values = {}
+        if copy_from is not None:
+            assert isinstance(copy_from, TargetConfig)
+            self._values.update(copy_from._values)
+
+    def __repr__(self):
+        # NOTE: default options will be placed at the end and grouped inside
+        #       a square bracket; i.e. [optname=optval, ...]
+        args = []
+        defs = []
+        for k in self.options:
+            msg = f"{k}={getattr(self, k)}"
+            if not self.is_set(k):
+                defs.append(msg)
+            else:
+                args.append(msg)
+        clsname = self.__class__.__name__
+        return f"{clsname}({', '.join(args)}, [{', '.join(defs)}])"
+
+    def __hash__(self):
+        return hash(tuple(sorted(self.values())))
+
+    def __eq__(self, other):
+        if isinstance(other, TargetConfig):
+            return self.values() == other.values()
+        else:
+            return NotImplemented
+
+    def values(self):
+        """Returns a dict of all the values
+        """
+        return {k: getattr(self, k) for k in self.options}
+
+    def is_set(self, name):
+        """Is the option set?
+        """
+        self._guard_option(name)
+        return name in self._values
+
+    def discard(self, name):
+        """Remove the option by name if it is defined.
+
+        After this, the value for the option will be set to its default value.
+        """
+        self._guard_option(name)
+        self._values.pop(name, None)
+
+    def inherit_if_not_set(self, name, default=_NotSet):
+        """Inherit flag from ``ConfigStack``.
+
+        Parameters
+        ----------
+        name : str
+            Option name.
+        default : optional
+            When given, it overrides the default value.
+            It is only used when the flag is not defined locally and there is
+            no entry in the ``ConfigStack``.
+        """
+        self._guard_option(name)
+        if not self.is_set(name):
+            cstk = ConfigStack()
+            if cstk:
+                # inherit
+                top = cstk.top()
+                setattr(self, name, getattr(top, name))
+            elif default is not _NotSet:
+                setattr(self, name, default)
+
+    def copy(self):
+        """Clone this instance.
+        """
+        return type(self)(self)
+
+    def summary(self) -> str:
+        """Returns a ``str`` that summarizes this instance.
+
+        In contrast to ``__repr__``, only options that are explicitly set will
+        be shown.
+        """
+        args = [f"{k}={v}" for k, v in self._summary_args()]
+        clsname = self.__class__.__name__
+        return f"{clsname}({', '.join(args)})"
+
+    def _guard_option(self, name):
+        if name not in self.options:
+            msg = f"{name!r} is not a valid option for {type(self)}"
+            raise ValueError(msg)
+
+    def _summary_args(self):
+        """returns a sorted sequence of 2-tuple containing the
+        ``(flag_name, flag_value)`` for flag that are set with a non-default
+        value.
+        """
+        args = []
+        for k in sorted(self.options):
+            opt = self.options[k]
+            if self.is_set(k):
+                flagval = getattr(self, k)
+                if opt.default != flagval:
+                    v = (k, flagval)
+                    args.append(v)
+        return args
+
+    @classmethod
+    def _make_compression_dictionary(cls) -> bytes:
+        """Returns a ``bytes`` object suitable for use as a dictionary for
+        compression.
+        """
+        buf = []
+        # include package name
+        buf.append("numba")
+        # include class name
+        buf.append(cls.__class__.__name__)
+        # include common values
+        buf.extend(["True", "False"])
+        # include all options name and their default value
+        for k, opt in cls.options.items():
+            buf.append(k)
+            buf.append(str(opt.default))
+        return ''.join(buf).encode()
+
+    def get_mangle_string(self) -> str:
+        """Return a string suitable for symbol mangling.
+        """
+        zdict = self._make_compression_dictionary()
+
+        comp = zlib.compressobj(zdict=zdict, level=zlib.Z_BEST_COMPRESSION,
+                                **self._ZLIB_CONFIG)
+        # The mangled string is a compressed and base64 encoded version of the
+        # summary
+        buf = [comp.compress(self.summary().encode())]
+        buf.append(comp.flush())
+        return base64.b64encode(b''.join(buf)).decode()
+
+    @classmethod
+    def demangle(cls, mangled: str) -> str:
+        """Returns the demangled result from ``.get_mangle_string()``
+        """
+        # unescape _XX sequence
+        def repl(x):
+            return chr(int('0x' + x.group(0)[1:], 16))
+        unescaped = re.sub(r"_[a-zA-Z0-9][a-zA-Z0-9]", repl, mangled)
+        # decode base64
+        raw = base64.b64decode(unescaped)
+        # decompress
+        zdict = cls._make_compression_dictionary()
+        dc = zlib.decompressobj(zdict=zdict, **cls._ZLIB_CONFIG)
+        buf = []
+        while raw:
+            buf.append(dc.decompress(raw))
+            raw = dc.unconsumed_tail
+        buf.append(dc.flush())
+        return b''.join(buf).decode()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/tracing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/tracing.py
new file mode 100644
index 000000000..282e84420
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/tracing.py
@@ -0,0 +1,216 @@
+import logging
+import sys
+import types
+import threading
+import inspect
+from functools import wraps
+from itertools import chain
+from numba.core import config
+
+class TLS(threading.local):
+    """Use a subclass to properly initialize the TLS variables in all threads."""
+    def __init__(self):
+        self.tracing = False
+        self.indent = 0
+
+tls = TLS()
+
+def find_function_info(func, spec, args):
+    """Return function meta-data in a tuple.
+
+    (name, type)"""
+
+    module = getattr(func, '__module__', None)
+    name = getattr(func, '__name__', None)
+    self = getattr(func, '__self__', None)
+    cname = None
+    if self:
+        cname = self.__name__
+        #cname = self.__class__.__name__
+    # Try to deduce the class' name even for unbound methods from their
+    # first argument, which we assume to be a class instance if named 'self'...
+    elif len(spec.args) and spec.args[0] == 'self':
+        cname = args[0].__class__.__name__
+    # ...or a class object if named 'cls'
+    elif len(spec.args) and spec.args[0] == 'cls':
+        cname = args[0].__name__
+    if name:
+        qname = []
+        if module and module != '__main__':
+            qname.append(module)
+            qname.append('.')
+        if cname:
+            qname.append(cname)
+            qname.append('.')
+        qname.append(name)
+        name = ''.join(qname)
+    return name, None
+
+def chop(value):
+    MAX_SIZE = 320
+    s = repr(value)
+    if len(s) > MAX_SIZE:
+        return s[:MAX_SIZE] + '...' + s[-1]
+    else:
+        return s
+
+def create_events(fname, spec, args, kwds):
+
+    values = dict()
+    if spec.defaults:
+        values = dict(zip(spec.args[-len(spec.defaults):],spec.defaults))
+    values.update(kwds)
+    values.update(list(zip(spec.args[:len(args)], args)))
+    positional = ['%s=%r'%(a, values.pop(a)) for a in spec.args]
+    anonymous = [str(a) for a in args[len(positional):]]
+    keywords = ['%s=%r'%(k, values[k]) for k in sorted(values.keys())]
+    params = ', '.join([f for f in chain(positional, anonymous, keywords) if f])
+
+    enter = ['>> ', tls.indent * ' ', fname, '(', params, ')']
+    leave = ['<< ', tls.indent * ' ', fname]
+    return enter, leave
+
+
+def dotrace(*args, **kwds):
+    """Function decorator to trace a function's entry and exit.
+
+    *args: categories in which to trace this function. Example usage:
+
+    @trace
+    def function(...):...
+
+    @trace('mycategory')
+    def function(...):...
+
+
+    """
+
+    recursive = kwds.get('recursive', False)
+    def decorator(func):
+
+        spec = None
+        logger = logging.getLogger('trace')
+        def wrapper(*args, **kwds):
+            if not logger.isEnabledFor(logging.INFO) or tls.tracing:
+                return func(*args, **kwds)
+
+            fname, ftype = find_function_info(func, spec, args)
+
+            try:
+                tls.tracing = True
+                enter, leave = create_events(fname, spec, args, kwds)
+
+                try:
+                    logger.info(''.join(enter))
+                    tls.indent += 1
+                    try:
+                        try:
+                            tls.tracing = False
+                            result = func(*args, **kwds)
+                        finally:
+                            tls.tracing = True
+                    except:
+                        type, value, traceback = sys.exc_info()
+                        leave.append(' => exception thrown\n\traise ')
+                        mname = type.__module__
+                        if mname != '__main__':
+                            leave.append(mname)
+                            leave.append('.')
+                        leave.append(type.__name__)
+                        if value.args:
+                            leave.append('(')
+                            leave.append(', '.join(chop(v) for v in value.args))
+                            leave.append(')')
+                        else:
+                            leave.append('()')
+                        raise
+                    else:
+                        if result is not None:
+                            leave.append(' -> ')
+                            leave.append(chop(result))
+                finally:
+                    tls.indent -= 1
+                    logger.info(''.join(leave))
+            finally:
+                tls.tracing = False
+            return result
+        # wrapper end
+
+        result = None
+        rewrap = lambda x: x
+        # Unwrap already wrapped functions
+        # (to be rewrapped again later)
+        if type(func) == classmethod:
+            rewrap = type(func)
+            # Note: 'func.__func__' only works in Python 3
+            func = func.__get__(True).__func__
+        elif type(func) == staticmethod:
+            rewrap = type(func)
+            # Note: 'func.__func__' only works in Python 3
+            func = func.__get__(True)
+        elif type(func) == property:
+            raise NotImplementedError
+
+        spec = inspect.getfullargspec(func)
+        return rewrap(wraps(func)(wrapper))
+
+    arg0 = len(args) and args[0] or None
+    # not supported yet...
+    if recursive:
+        raise NotImplementedError
+        if inspect.ismodule(arg0):
+            for n, f in inspect.getmembers(arg0, inspect.isfunction):
+                setattr(arg0, n, decorator(f))
+            for n, c in inspect.getmembers(arg0, inspect.isclass):
+                dotrace(c, *args, recursive=recursive)
+        elif inspect.isclass(arg0):
+            for n, f in inspect.getmembers(arg0, lambda x: (inspect.isfunction(x) or
+                                                            inspect.ismethod(x))):
+                setattr(arg0, n, decorator(f))
+
+
+    if callable(arg0) or type(arg0) in (classmethod, staticmethod):
+        return decorator(arg0)
+    elif type(arg0) == property:
+        # properties combine up to three functions: 'get', 'set', 'del',
+        # so let's wrap them all.
+        pget, pset, pdel = None, None, None
+        if arg0.fget:
+            pget = decorator(arg0.fget)
+        if arg0.fset:
+            pset = decorator(arg0.fset)
+        if arg0.fdel:
+            pdel = decorator(arg0.fdel)
+        return property(pget, pset, pdel)
+
+    else:
+        return decorator
+
+def notrace(*args, **kwds):
+    """Just a no-op in case tracing is disabled."""
+    def decorator(func):
+        return func
+    arg0 = len(args) and args[0] or None
+
+    if callable(arg0) or type(arg0) in (classmethod, staticmethod):
+        return decorator(arg0)
+    else:
+        return decorator
+
+def doevent(msg):
+    msg = ['== ', tls.indent * ' ', msg]
+    logger = logging.getLogger('trace')
+    logger.info(''.join(msg))
+
+def noevent(msg):
+    pass
+
+if config.TRACE:
+    logger = logging.getLogger('trace')
+    logger.setLevel(logging.INFO)
+    logger.handlers = [logging.StreamHandler()]
+    trace = dotrace
+    event = doevent
+else:
+    trace = notrace
+    event = noevent
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/transforms.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/transforms.py
new file mode 100644
index 000000000..1169c7dfc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/transforms.py
@@ -0,0 +1,918 @@
+"""
+Implement transformation on Numba IR
+"""
+
+
+from collections import namedtuple, defaultdict
+import logging
+import operator
+
+from numba.core.analysis import compute_cfg_from_blocks, find_top_level_loops
+from numba.core import errors, ir, ir_utils
+from numba.core.analysis import compute_use_defs, compute_cfg_from_blocks
+from numba.core.utils import PYVERSION
+
+
+_logger = logging.getLogger(__name__)
+
+
+def _extract_loop_lifting_candidates(cfg, blocks):
+    """
+    Returns a list of loops that are candidate for loop lifting
+    """
+    # check well-formed-ness of the loop
+    def same_exit_point(loop):
+        "all exits must point to the same location"
+        outedges = set()
+        for k in loop.exits:
+            succs = set(x for x, _ in cfg.successors(k))
+            if not succs:
+                # If the exit point has no successor, it contains an return
+                # statement, which is not handled by the looplifting code.
+                # Thus, this loop is not a candidate.
+                _logger.debug("return-statement in loop.")
+                return False
+            outedges |= succs
+        ok = len(outedges) == 1
+        _logger.debug("same_exit_point=%s (%s)", ok, outedges)
+        return ok
+
+    def one_entry(loop):
+        "there is one entry"
+        ok = len(loop.entries) == 1
+        _logger.debug("one_entry=%s", ok)
+        return ok
+
+    def cannot_yield(loop):
+        "cannot have yield inside the loop"
+        insiders = set(loop.body) | set(loop.entries) | set(loop.exits)
+        for blk in map(blocks.__getitem__, insiders):
+            for inst in blk.body:
+                if isinstance(inst, ir.Assign):
+                    if isinstance(inst.value, ir.Yield):
+                        _logger.debug("has yield")
+                        return False
+        _logger.debug("no yield")
+        return True
+
+    _logger.info('finding looplift candidates')
+    # the check for cfg.entry_point in the loop.entries is to prevent a bad
+    # rewrite where a prelude for a lifted loop would get written into block -1
+    # if a loop entry were in block 0
+    candidates = []
+    for loop in find_top_level_loops(cfg):
+        _logger.debug("top-level loop: %s", loop)
+        if (same_exit_point(loop) and one_entry(loop) and cannot_yield(loop) and
+            cfg.entry_point() not in loop.entries):
+            candidates.append(loop)
+            _logger.debug("add candidate: %s", loop)
+    return candidates
+
+
+def find_region_inout_vars(blocks, livemap, callfrom, returnto, body_block_ids):
+    """Find input and output variables to a block region.
+    """
+    inputs = livemap[callfrom]
+    outputs = livemap[returnto]
+
+    # ensure live variables are actually used in the blocks, else remove,
+    # saves having to create something valid to run through postproc
+    # to achieve similar
+    loopblocks = {}
+    for k in body_block_ids:
+        loopblocks[k] = blocks[k]
+
+    used_vars = set()
+    def_vars = set()
+    defs = compute_use_defs(loopblocks)
+    for vs in defs.usemap.values():
+        used_vars |= vs
+    for vs in defs.defmap.values():
+        def_vars |= vs
+    used_or_defined = used_vars | def_vars
+
+    # note: sorted for stable ordering
+    inputs = sorted(set(inputs) & used_or_defined)
+    outputs = sorted(set(outputs) & used_or_defined & def_vars)
+    return inputs, outputs
+
+
+_loop_lift_info = namedtuple('loop_lift_info',
+                             'loop,inputs,outputs,callfrom,returnto')
+
+
+def _loop_lift_get_candidate_infos(cfg, blocks, livemap):
+    """
+    Returns information on looplifting candidates.
+    """
+    loops = _extract_loop_lifting_candidates(cfg, blocks)
+    loopinfos = []
+    for loop in loops:
+
+        [callfrom] = loop.entries   # requirement checked earlier
+        an_exit = next(iter(loop.exits))  # anyone of the exit block
+        if len(loop.exits) > 1:
+            # Pre-Py3.8 may have multiple exits
+            [(returnto, _)] = cfg.successors(an_exit)  # requirement checked earlier
+        else:
+            # Post-Py3.8 DO NOT have multiple exits
+            returnto = an_exit
+
+        local_block_ids = set(loop.body) | set(loop.entries) | set(loop.exits)
+        inputs, outputs = find_region_inout_vars(
+            blocks=blocks,
+            livemap=livemap,
+            callfrom=callfrom,
+            returnto=returnto,
+            body_block_ids=local_block_ids,
+        )
+
+        lli = _loop_lift_info(loop=loop, inputs=inputs, outputs=outputs,
+                              callfrom=callfrom, returnto=returnto)
+        loopinfos.append(lli)
+
+    return loopinfos
+
+
+def _loop_lift_modify_call_block(liftedloop, block, inputs, outputs, returnto):
+    """
+    Transform calling block from top-level function to call the lifted loop.
+    """
+    scope = block.scope
+    loc = block.loc
+    blk = ir.Block(scope=scope, loc=loc)
+
+    ir_utils.fill_block_with_call(
+        newblock=blk,
+        callee=liftedloop,
+        label_next=returnto,
+        inputs=inputs,
+        outputs=outputs,
+    )
+    return blk
+
+
+def _loop_lift_prepare_loop_func(loopinfo, blocks):
+    """
+    Inplace transform loop blocks for use as lifted loop.
+    """
+    entry_block = blocks[loopinfo.callfrom]
+    scope = entry_block.scope
+    loc = entry_block.loc
+
+    # Lowering assumes the first block to be the one with the smallest offset
+    firstblk = min(blocks) - 1
+    blocks[firstblk] = ir_utils.fill_callee_prologue(
+        block=ir.Block(scope=scope, loc=loc),
+        inputs=loopinfo.inputs,
+        label_next=loopinfo.callfrom,
+    )
+    blocks[loopinfo.returnto] = ir_utils.fill_callee_epilogue(
+        block=ir.Block(scope=scope, loc=loc),
+        outputs=loopinfo.outputs,
+    )
+
+
+def _loop_lift_modify_blocks(func_ir, loopinfo, blocks,
+                             typingctx, targetctx, flags, locals):
+    """
+    Modify the block inplace to call to the lifted-loop.
+    Returns a dictionary of blocks of the lifted-loop.
+    """
+    from numba.core.dispatcher import LiftedLoop
+
+    # Copy loop blocks
+    loop = loopinfo.loop
+
+    loopblockkeys = set(loop.body) | set(loop.entries)
+    if len(loop.exits) > 1:
+        # Pre-Py3.8 may have multiple exits
+        loopblockkeys |= loop.exits
+    loopblocks = dict((k, blocks[k].copy()) for k in loopblockkeys)
+    # Modify the loop blocks
+    _loop_lift_prepare_loop_func(loopinfo, loopblocks)
+
+    # Create a new IR for the lifted loop
+    lifted_ir = func_ir.derive(blocks=loopblocks,
+                               arg_names=tuple(loopinfo.inputs),
+                               arg_count=len(loopinfo.inputs),
+                               force_non_generator=True)
+    liftedloop = LiftedLoop(lifted_ir,
+                            typingctx, targetctx, flags, locals)
+
+    # modify for calling into liftedloop
+    callblock = _loop_lift_modify_call_block(liftedloop, blocks[loopinfo.callfrom],
+                                             loopinfo.inputs, loopinfo.outputs,
+                                             loopinfo.returnto)
+    # remove blocks
+    for k in loopblockkeys:
+        del blocks[k]
+    # update main interpreter callsite into the liftedloop
+    blocks[loopinfo.callfrom] = callblock
+    return liftedloop
+
+
+def _has_multiple_loop_exits(cfg, lpinfo):
+    """Returns True if there is more than one exit in the loop.
+
+    NOTE: "common exits" refers to the situation where a loop exit has another
+    loop exit as its successor. In that case, we do not need to alter it.
+    """
+    if len(lpinfo.exits) <= 1:
+        return False
+    exits = set(lpinfo.exits)
+    pdom = cfg.post_dominators()
+
+    # Eliminate blocks that have other blocks as post-dominators.
+    processed = set()
+    remain = set(exits) # create a copy to work on
+    while remain:
+        node = remain.pop()
+        processed.add(node)
+        exits -= pdom[node] - {node}
+        remain = exits - processed
+
+    return len(exits) > 1
+
+
+def _pre_looplift_transform(func_ir):
+    """Canonicalize loops for looplifting.
+    """
+    from numba.core.postproc import PostProcessor
+
+    cfg = compute_cfg_from_blocks(func_ir.blocks)
+    # For every loop that has multiple exits, combine the exits into one.
+    for loop_info in cfg.loops().values():
+        if _has_multiple_loop_exits(cfg, loop_info):
+            func_ir, _common_key = _fix_multi_exit_blocks(
+                func_ir, loop_info.exits
+            )
+    # Reset and reprocess the func_ir
+    func_ir._reset_analysis_variables()
+    PostProcessor(func_ir).run()
+    return func_ir
+
+
+def loop_lifting(func_ir, typingctx, targetctx, flags, locals):
+    """
+    Loop lifting transformation.
+
+    Given a interpreter `func_ir` returns a 2 tuple of
+    `(toplevel_interp, [loop0_interp, loop1_interp, ....])`
+    """
+    func_ir = _pre_looplift_transform(func_ir)
+    blocks = func_ir.blocks.copy()
+    cfg = compute_cfg_from_blocks(blocks)
+    loopinfos = _loop_lift_get_candidate_infos(cfg, blocks,
+                                               func_ir.variable_lifetime.livemap)
+    loops = []
+    if loopinfos:
+        _logger.debug('loop lifting this IR with %d candidates:\n%s',
+                      len(loopinfos), func_ir.dump_to_string())
+    for loopinfo in loopinfos:
+        lifted = _loop_lift_modify_blocks(func_ir, loopinfo, blocks,
+                                          typingctx, targetctx, flags, locals)
+        loops.append(lifted)
+
+    # Make main IR
+    main = func_ir.derive(blocks=blocks)
+
+    return main, loops
+
+
+def canonicalize_cfg_single_backedge(blocks):
+    """
+    Rewrite loops that have multiple backedges.
+    """
+    cfg = compute_cfg_from_blocks(blocks)
+    newblocks = blocks.copy()
+
+    def new_block_id():
+        return max(newblocks.keys()) + 1
+
+    def has_multiple_backedges(loop):
+        count = 0
+        for k in loop.body:
+            blk = blocks[k]
+            edges = blk.terminator.get_targets()
+            # is a backedge?
+            if loop.header in edges:
+                count += 1
+                if count > 1:
+                    # early exit
+                    return True
+        return False
+
+    def yield_loops_with_multiple_backedges():
+        for lp in cfg.loops().values():
+            if has_multiple_backedges(lp):
+                yield lp
+
+    def replace_target(term, src, dst):
+        def replace(target):
+            return (dst if target == src else target)
+
+        if isinstance(term, ir.Branch):
+            return ir.Branch(cond=term.cond,
+                             truebr=replace(term.truebr),
+                             falsebr=replace(term.falsebr),
+                             loc=term.loc)
+        elif isinstance(term, ir.Jump):
+            return ir.Jump(target=replace(term.target), loc=term.loc)
+        else:
+            assert not term.get_targets()
+            return term
+
+    def rewrite_single_backedge(loop):
+        """
+        Add new tail block that gathers all the backedges
+        """
+        header = loop.header
+        tailkey = new_block_id()
+        for blkkey in loop.body:
+            blk = newblocks[blkkey]
+            if header in blk.terminator.get_targets():
+                newblk = blk.copy()
+                # rewrite backedge into jumps to new tail block
+                newblk.body[-1] = replace_target(blk.terminator, header,
+                                                 tailkey)
+                newblocks[blkkey] = newblk
+        # create new tail block
+        entryblk = newblocks[header]
+        tailblk = ir.Block(scope=entryblk.scope, loc=entryblk.loc)
+        # add backedge
+        tailblk.append(ir.Jump(target=header, loc=tailblk.loc))
+        newblocks[tailkey] = tailblk
+
+    for loop in yield_loops_with_multiple_backedges():
+        rewrite_single_backedge(loop)
+
+    return newblocks
+
+
+def canonicalize_cfg(blocks):
+    """
+    Rewrite the given blocks to canonicalize the CFG.
+    Returns a new dictionary of blocks.
+    """
+    return canonicalize_cfg_single_backedge(blocks)
+
+
+def with_lifting(func_ir, typingctx, targetctx, flags, locals):
+    """With-lifting transformation
+
+    Rewrite the IR to extract all withs.
+    Only the top-level withs are extracted.
+    Returns the (the_new_ir, the_lifted_with_ir)
+    """
+    from numba.core import postproc
+
+    def dispatcher_factory(func_ir, objectmode=False, **kwargs):
+        from numba.core.dispatcher import LiftedWith, ObjModeLiftedWith
+
+        myflags = flags.copy()
+        if objectmode:
+            # Lifted with-block cannot looplift
+            myflags.enable_looplift = False
+            # Lifted with-block uses object mode
+            myflags.enable_pyobject = True
+            myflags.force_pyobject = True
+            myflags.no_cpython_wrapper = False
+            cls = ObjModeLiftedWith
+        else:
+            cls = LiftedWith
+        return cls(func_ir, typingctx, targetctx, myflags, locals, **kwargs)
+
+    # find where with-contexts regions are
+    withs, func_ir = find_setupwiths(func_ir)
+
+    if not withs:
+        return func_ir, []
+
+    postproc.PostProcessor(func_ir).run()  # ensure we have variable lifetime
+    assert func_ir.variable_lifetime
+    vlt = func_ir.variable_lifetime
+    blocks = func_ir.blocks.copy()
+    cfg = vlt.cfg
+    # For each with-regions, mutate them according to
+    # the kind of contextmanager
+    sub_irs = []
+    for (blk_start, blk_end) in withs:
+        body_blocks = []
+        for node in _cfg_nodes_in_region(cfg, blk_start, blk_end):
+            body_blocks.append(node)
+        _legalize_with_head(blocks[blk_start])
+        # Find the contextmanager
+        cmkind, extra = _get_with_contextmanager(func_ir, blocks, blk_start)
+        # Mutate the body and get new IR
+        sub = cmkind.mutate_with_body(func_ir, blocks, blk_start, blk_end,
+                                      body_blocks, dispatcher_factory,
+                                      extra)
+        sub_irs.append(sub)
+    if not sub_irs:
+        # Unchanged
+        new_ir = func_ir
+    else:
+        new_ir = func_ir.derive(blocks)
+    return new_ir, sub_irs
+
+
+def _get_with_contextmanager(func_ir, blocks, blk_start):
+    """Get the global object used for the context manager
+    """
+    _illegal_cm_msg = "Illegal use of context-manager."
+
+    def get_var_dfn(var):
+        """Get the definition given a variable"""
+        return func_ir.get_definition(var)
+
+    def get_ctxmgr_obj(var_ref):
+        """Return the context-manager object and extra info.
+
+        The extra contains the arguments if the context-manager is used
+        as a call.
+        """
+        # If the contextmanager used as a Call
+        dfn = func_ir.get_definition(var_ref)
+        if isinstance(dfn, ir.Expr) and dfn.op == 'call':
+            args = [get_var_dfn(x) for x in dfn.args]
+            kws = {k: get_var_dfn(v) for k, v in dfn.kws}
+            extra = {'args': args, 'kwargs': kws}
+            var_ref = dfn.func
+        else:
+            extra = None
+
+        ctxobj = ir_utils.guard(ir_utils.find_outer_value, func_ir, var_ref)
+
+        # check the contextmanager object
+        if ctxobj is ir.UNDEFINED:
+            raise errors.CompilerError(
+                "Undefined variable used as context manager",
+                loc=blocks[blk_start].loc,
+                )
+
+        if ctxobj is None:
+            raise errors.CompilerError(_illegal_cm_msg, loc=dfn.loc)
+
+        return ctxobj, extra
+
+    # Scan the start of the with-region for the contextmanager
+    for stmt in blocks[blk_start].body:
+        if isinstance(stmt, ir.EnterWith):
+            var_ref = stmt.contextmanager
+            ctxobj, extra = get_ctxmgr_obj(var_ref)
+            if not hasattr(ctxobj, 'mutate_with_body'):
+                raise errors.CompilerError(
+                    "Unsupported context manager in use",
+                    loc=blocks[blk_start].loc,
+                    )
+            return ctxobj, extra
+    # No contextmanager found?
+    raise errors.CompilerError(
+        "malformed with-context usage",
+        loc=blocks[blk_start].loc,
+        )
+
+
+def _legalize_with_head(blk):
+    """Given *blk*, the head block of the with-context, check that it doesn't
+    do anything else.
+    """
+    counters = defaultdict(int)
+    for stmt in blk.body:
+        counters[type(stmt)] += 1
+
+    if counters.pop(ir.EnterWith) != 1:
+        raise errors.CompilerError(
+            "with's head-block must have exactly 1 ENTER_WITH",
+            loc=blk.loc,
+            )
+    if counters.pop(ir.Jump) != 1:
+        raise errors.CompilerError(
+            "with's head-block must have exactly 1 JUMP",
+            loc=blk.loc,
+            )
+    # Can have any number of del
+    counters.pop(ir.Del, None)
+    # There MUST NOT be any other statements
+    if counters:
+        raise errors.CompilerError(
+            "illegal statements in with's head-block",
+            loc=blk.loc,
+            )
+
+
+def _cfg_nodes_in_region(cfg, region_begin, region_end):
+    """Find the set of CFG nodes that are in the given region
+    """
+    region_nodes = set()
+    stack = [region_begin]
+    while stack:
+        tos = stack.pop()
+        succs, _ = zip(*cfg.successors(tos))
+        nodes = set([node for node in succs
+                     if node not in region_nodes and
+                     node != region_end])
+        stack.extend(nodes)
+        region_nodes |= nodes
+
+    return region_nodes
+
+
+def find_setupwiths(func_ir):
+    """Find all top-level with.
+
+    Returns a list of ranges for the with-regions.
+    """
+    def find_ranges(blocks):
+
+        cfg = compute_cfg_from_blocks(blocks)
+        sus_setups, sus_pops = set(), set()
+        # traverse the cfg and collect all suspected SETUP_WITH and POP_BLOCK
+        # statements so that we can iterate over them
+        for label, block in blocks.items():
+            for stmt in block.body:
+                if ir_utils.is_setup_with(stmt):
+                    sus_setups.add(label)
+                if ir_utils.is_pop_block(stmt):
+                    sus_pops.add(label)
+
+        # now that we do have the statements, iterate through them in reverse
+        # topo order and from each start looking for pop_blocks
+        setup_with_to_pop_blocks_map = defaultdict(set)
+        for setup_block in cfg.topo_sort(sus_setups, reverse=True):
+            # begin pop_block, search
+            to_visit, seen = [], []
+            to_visit.append(setup_block)
+            while to_visit:
+                # get whatever is next and record that we have seen it
+                block = to_visit.pop()
+                seen.append(block)
+                # go through the body of the block, looking for statements
+                for stmt in blocks[block].body:
+                    # raise detected before pop_block
+                    if ir_utils.is_raise(stmt):
+                            raise errors.CompilerError(
+                                'unsupported control flow due to raise '
+                                'statements inside with block'
+                                )
+                    # special case 3.7, return before POP_BLOCK
+                    if PYVERSION < (3, 8) and ir_utils.is_return(stmt):
+                            raise errors.CompilerError(
+                                'unsupported control flow: due to return '
+                                'statements inside with block'
+                                )
+                    # if a pop_block, process it
+                    if ir_utils.is_pop_block(stmt) and block in sus_pops:
+                        # record the jump target of this block belonging to this setup
+                        setup_with_to_pop_blocks_map[setup_block].add(block)
+                        # remove the block from blocks to be matched
+                        sus_pops.remove(block)
+                        # stop looking, we have reached the frontier
+                        break
+                    # if we are still here, by the block terminator,
+                    # add all its targets to the to_visit stack, unless we
+                    # have seen them already
+                    if ir_utils.is_terminator(stmt):
+                        for t in stmt.get_targets():
+                            if t not in seen:
+                                to_visit.append(t)
+
+        return setup_with_to_pop_blocks_map
+
+    blocks = func_ir.blocks
+    # initial find, will return a dictionary, mapping indices of blocks
+    # containing SETUP_WITH statements to a set of indices of blocks containing
+    # POP_BLOCK statements
+    with_ranges_dict = find_ranges(blocks)
+    # rewrite the CFG in case there are multiple POP_BLOCK statements for one
+    # with
+    func_ir = consolidate_multi_exit_withs(with_ranges_dict, blocks, func_ir)
+
+    # here we need to turn the withs back into a list of tuples so that the
+    # rest of the code can cope
+    with_ranges_tuple = [(s, list(p)[0])
+             for (s, p) in with_ranges_dict.items()]
+
+    # check for POP_BLOCKS with multiple outgoing edges and reject
+    for (_, p) in with_ranges_tuple:
+        targets = blocks[p].terminator.get_targets()
+        if len(targets) != 1:
+            raise errors.CompilerError(
+                "unsupported control flow: with-context contains branches "
+                "(i.e. break/return/raise) that can leave the block "
+            )
+    # now we check for returns inside with and reject them
+    for (_, p) in with_ranges_tuple:
+        target_block = blocks[p]
+        if ir_utils.is_return(func_ir.blocks[
+                target_block.terminator.get_targets()[0]].terminator):
+            if PYVERSION == (3, 8):
+                # 3.8 needs to bail here, if this is the case, because the
+                # later code can't handle it.
+                raise errors.CompilerError(
+                    "unsupported control flow: due to return statements "
+                    "inside with block"
+                )
+            _rewrite_return(func_ir, p)
+
+    # now we need to rewrite the tuple such that we have SETUP_WITH matching the
+    # successor of the block that contains the POP_BLOCK.
+    with_ranges_tuple = [(s, func_ir.blocks[p].terminator.get_targets()[0])
+             for (s, p) in with_ranges_tuple]
+
+    # finally we check for nested with statements and reject them
+    with_ranges_tuple = _eliminate_nested_withs(with_ranges_tuple)
+
+    return with_ranges_tuple, func_ir
+
+
+def _rewrite_return(func_ir, target_block_label):
+    """Rewrite a return block inside a with statement.
+
+    Arguments
+    ---------
+
+    func_ir: Function IR
+      the CFG to transform
+    target_block_label: int
+      the block index/label of the block containing the POP_BLOCK statement
+
+
+    This implements a CFG transformation to insert a block between two other
+    blocks.
+
+    The input situation is:
+
+    ┌───────────────┐
+    │   top         │
+    │   POP_BLOCK   │
+    │   bottom      │
+    └───────┬───────┘
+            │
+    ┌───────▼───────┐
+    │               │
+    │    RETURN     │
+    │               │
+    └───────────────┘
+
+    If such a pattern is detected in IR, it means there is a `return` statement
+    within a `with` context. The basic idea is to rewrite the CFG as follows:
+
+    ┌───────────────┐
+    │   top         │
+    │   POP_BLOCK   │
+    │               │
+    └───────┬───────┘
+            │
+    ┌───────▼───────┐
+    │               │
+    │     bottom    │
+    │               │
+    └───────┬───────┘
+            │
+    ┌───────▼───────┐
+    │               │
+    │    RETURN     │
+    │               │
+    └───────────────┘
+
+    We split the block that contains the `POP_BLOCK` statement into two blocks.
+    Everything from the beginning of the block up to and including the
+    `POP_BLOCK` statement is considered the 'top' and everything below is
+    considered 'bottom'. Finally the jump statements are re-wired to make sure
+    the CFG remains valid.
+
+    """
+    # the block itself from the index
+    target_block = func_ir.blocks[target_block_label]
+    # get the index of the block containing the return
+    target_block_successor_label = target_block.terminator.get_targets()[0]
+    # the return block
+    target_block_successor = func_ir.blocks[target_block_successor_label]
+
+    # create the new return block with an appropriate label
+    max_label = ir_utils.find_max_label(func_ir.blocks)
+    new_label = max_label + 1
+    # create the new return block
+    new_block_loc = target_block_successor.loc
+    new_block_scope = ir.Scope(None, loc=new_block_loc)
+    new_block = ir.Block(new_block_scope, loc=new_block_loc)
+
+    # Split the block containing the POP_BLOCK into top and bottom
+    # Block must be of the form:
+    # -----------------
+    # <some stmts>
+    # POP_BLOCK
+    # <some more stmts>
+    # JUMP
+    # -----------------
+    top_body, bottom_body = [], []
+    pop_blocks = [*target_block.find_insts(ir.PopBlock)]
+    assert len(pop_blocks) == 1
+    assert len([*target_block.find_insts(ir.Jump)]) == 1
+    assert isinstance(target_block.body[-1], ir.Jump)
+    pb_marker = pop_blocks[0]
+    pb_is = target_block.body.index(pb_marker)
+    top_body.extend(target_block.body[:pb_is])
+    top_body.append(ir.Jump(target_block_successor_label, target_block.loc))
+    bottom_body.extend(target_block.body[pb_is:-1])
+    bottom_body.append(ir.Jump(new_label, target_block.loc))
+
+    # get the contents of the return block
+    return_body = func_ir.blocks[target_block_successor_label].body
+    # finally, re-assign all blocks
+    new_block.body.extend(return_body)
+    target_block_successor.body.clear()
+    target_block_successor.body.extend(bottom_body)
+    target_block.body.clear()
+    target_block.body.extend(top_body)
+
+    # finally, append the new return block and rebuild the IR properties
+    func_ir.blocks[new_label] = new_block
+    func_ir._definitions = ir_utils.build_definitions(func_ir.blocks)
+    return func_ir
+
+
+def _eliminate_nested_withs(with_ranges):
+    known_ranges = []
+    def within_known_range(start, end, known_ranges):
+        for a, b in known_ranges:
+            # FIXME: this should be a comparison in topological order, right
+            # now we are comparing the integers of the blocks, stuff probably
+            # works by accident.
+            if start > a and end < b:
+                return True
+        return False
+
+    for s, e in sorted(with_ranges):
+        if not within_known_range(s, e, known_ranges):
+            known_ranges.append((s, e))
+
+    return known_ranges
+
+def consolidate_multi_exit_withs(withs: dict, blocks, func_ir):
+    """Modify the FunctionIR to merge the exit blocks of with constructs.
+    """
+    out = []
+    for k in withs:
+        vs : set = withs[k]
+        if len(vs) > 1:
+            func_ir, common = _fix_multi_exit_blocks(
+                func_ir, vs, split_condition=ir_utils.is_pop_block,
+            )
+            withs[k] = {common}
+    return func_ir
+
+
+def _fix_multi_exit_blocks(func_ir, exit_nodes, *, split_condition=None):
+    """Modify the FunctionIR to create a single common exit node given the
+    original exit nodes.
+
+    Parameters
+    ----------
+    func_ir :
+        The FunctionIR. Mutated inplace.
+    exit_nodes :
+        The original exit nodes. A sequence of block keys.
+    split_condition : callable or None
+        If not None, it is a callable with the signature
+        `split_condition(statement)` that determines if the `statement` is the
+        splitting point (e.g. `POP_BLOCK`) in an exit node.
+        If it's None, the exit node is not split.
+    """
+
+    # Convert the following:
+    #
+    #     |           |
+    # +-------+   +-------+
+    # | exit0 |   | exit1 |
+    # +-------+   +-------+
+    #     |           |
+    # +-------+   +-------+
+    # | after0|   | after1|
+    # +-------+   +-------+
+    #     |           |
+    #
+    # To roughly:
+    #
+    #     |           |
+    # +-------+   +-------+
+    # | exit0 |   | exit1 |
+    # +-------+   +-------+
+    #     |           |
+    #     +-----+-----+
+    #           |
+    #      +---------+
+    #      | common  |
+    #      +---------+
+    #           |
+    #       +-------+
+    #       | post  |
+    #       +-------+
+    #           |
+    #     +-----+-----+
+    #     |           |
+    # +-------+   +-------+
+    # | after0|   | after1|
+    # +-------+   +-------+
+
+    blocks = func_ir.blocks
+    # Getting the scope
+    any_blk = min(func_ir.blocks.values())
+    scope = any_blk.scope
+    # Getting the maximum block label
+    max_label = max(func_ir.blocks) + 1
+    # Define the new common block for the new exit.
+    common_block = ir.Block(any_blk.scope, loc=ir.unknown_loc)
+    common_label = max_label
+    max_label += 1
+    blocks[common_label] = common_block
+    # Define the new block after the exit.
+    post_block = ir.Block(any_blk.scope, loc=ir.unknown_loc)
+    post_label = max_label
+    max_label += 1
+    blocks[post_label] = post_block
+
+    # Adjust each exit node
+    remainings = []
+    for i, k in enumerate(exit_nodes):
+        blk = blocks[k]
+
+        # split the block if needed
+        if split_condition is not None:
+            for pt, stmt in enumerate(blk.body):
+                if split_condition(stmt):
+                    break
+        else:
+            # no splitting
+            pt = -1
+
+        before = blk.body[:pt]
+        after = blk.body[pt:]
+        remainings.append(after)
+
+        # Add control-point variable to mark which exit block this is.
+        blk.body = before
+        loc = blk.loc
+        blk.body.append(
+            ir.Assign(value=ir.Const(i, loc=loc),
+                      target=scope.get_or_define("$cp", loc=loc),
+                      loc=loc)
+        )
+        # Replace terminator with a jump to the common block
+        assert not blk.is_terminated
+        blk.body.append(ir.Jump(common_label, loc=ir.unknown_loc))
+
+    if split_condition is not None:
+        # Move the splitting statement to the common block
+        common_block.body.append(remainings[0][0])
+    assert not common_block.is_terminated
+    # Append jump from common block to post block
+    common_block.body.append(ir.Jump(post_label, loc=loc))
+
+    # Make if-else tree to jump to target
+    remain_blocks = []
+    for remain in remainings:
+        remain_blocks.append(max_label)
+        max_label += 1
+
+    switch_block = post_block
+    loc = ir.unknown_loc
+    for i, remain in enumerate(remainings):
+        match_expr = scope.redefine("$cp_check", loc=loc)
+        match_rhs = scope.redefine("$cp_rhs", loc=loc)
+
+        # Do comparison to match control-point variable to the exit block
+        switch_block.body.append(
+            ir.Assign(
+                value=ir.Const(i, loc=loc),
+                target=match_rhs,
+                loc=loc
+            ),
+        )
+
+        # Add assignment for the comparison
+        switch_block.body.append(
+            ir.Assign(
+                value=ir.Expr.binop(
+                    fn=operator.eq, lhs=scope.get("$cp"), rhs=match_rhs,
+                    loc=loc,
+                ),
+                target=match_expr,
+                loc=loc
+            ),
+        )
+
+        # Insert jump to the next case
+        [jump_target] = remain[-1].get_targets()
+        switch_block.body.append(
+            ir.Branch(match_expr, jump_target, remain_blocks[i], loc=loc),
+        )
+        switch_block = ir.Block(scope=scope, loc=loc)
+        blocks[remain_blocks[i]] = switch_block
+
+    # Add the final jump
+    switch_block.body.append(ir.Jump(jump_target, loc=loc))
+
+    return func_ir, common_label
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/__init__.py
new file mode 100644
index 000000000..4ae3acabf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/__init__.py
@@ -0,0 +1 @@
+from .castgraph import Conversion
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/_typeconv.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/_typeconv.cpp
new file mode 100644
index 000000000..305e651dc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/_typeconv.cpp
@@ -0,0 +1,205 @@
+#include "../../_pymodule.h"
+#include "../../capsulethunk.h"
+#include "typeconv.hpp"
+
+extern "C" {
+
+
+static PyObject*
+new_type_manager(PyObject* self, PyObject* args);
+
+static void
+del_type_manager(PyObject *);
+
+static PyObject*
+select_overload(PyObject* self, PyObject* args);
+
+static PyObject*
+check_compatible(PyObject* self, PyObject* args);
+
+static PyObject*
+set_compatible(PyObject* self, PyObject* args);
+
+static PyObject*
+get_pointer(PyObject* self, PyObject* args);
+
+
+static PyMethodDef ext_methods[] = {
+#define declmethod(func) { #func , ( PyCFunction )func , METH_VARARGS , NULL }
+    declmethod(new_type_manager),
+    declmethod(select_overload),
+    declmethod(check_compatible),
+    declmethod(set_compatible),
+    declmethod(get_pointer),
+    { NULL },
+#undef declmethod
+};
+
+
+MOD_INIT(_typeconv) {
+    PyObject *m;
+    MOD_DEF(m, "_typeconv", "No docs", ext_methods)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    return MOD_SUCCESS_VAL(m);
+}
+
+} // end extern C
+
+///////////////////////////////////////////////////////////////////////////////
+
+const char PY_CAPSULE_TM_NAME[] = "*tm";
+#define BAD_TM_ARGUMENT PyErr_SetString(PyExc_TypeError,                    \
+                                        "1st argument not TypeManager")
+
+static
+TypeManager* unwrap_TypeManager(PyObject *tm) {
+    void* p = PyCapsule_GetPointer(tm, PY_CAPSULE_TM_NAME);
+    return reinterpret_cast<TypeManager*>(p);
+}
+
+PyObject*
+new_type_manager(PyObject* self, PyObject* args)
+{
+    TypeManager* tm = new TypeManager();
+    return PyCapsule_New(tm, PY_CAPSULE_TM_NAME, &del_type_manager);
+}
+
+void
+del_type_manager(PyObject *tm)
+{
+    delete unwrap_TypeManager(tm);
+}
+
+PyObject*
+select_overload(PyObject* self, PyObject* args)
+{
+    PyObject *tmcap, *sigtup, *ovsigstup;
+    int allow_unsafe;
+    int exact_match_required;
+
+    if (!PyArg_ParseTuple(args, "OOOii", &tmcap, &sigtup, &ovsigstup,
+                          &allow_unsafe, &exact_match_required)) {
+        return NULL;
+    }
+
+    TypeManager *tm = unwrap_TypeManager(tmcap);
+    if (!tm) {
+        BAD_TM_ARGUMENT;
+    }
+
+    Py_ssize_t sigsz = PySequence_Size(sigtup);
+    Py_ssize_t ovsz = PySequence_Size(ovsigstup);
+
+    Type *sig = new Type[sigsz];
+    Type *ovsigs = new Type[ovsz * sigsz];
+
+    for (int i = 0; i < sigsz; ++i) {
+        sig[i] = Type(PyNumber_AsSsize_t(PySequence_Fast_GET_ITEM(sigtup,
+                                                                  i), NULL));
+    }
+
+    for (int i = 0; i < ovsz; ++i) {
+        PyObject *cursig = PySequence_Fast_GET_ITEM(ovsigstup, i);
+        for (int j = 0; j < sigsz; ++j) {
+            long tid = PyNumber_AsSsize_t(PySequence_Fast_GET_ITEM(cursig,
+                                                                   j), NULL);
+            ovsigs[i * sigsz + j] = Type(tid);
+        }
+    }
+
+    int selected = -42;
+    int matches = tm->selectOverload(sig, ovsigs, selected, sigsz, ovsz,
+                                     (bool) allow_unsafe,
+                                     (bool) exact_match_required);
+
+    delete [] sig;
+    delete [] ovsigs;
+
+    if (matches > 1) {
+        PyErr_SetString(PyExc_TypeError, "Ambiguous overloading");
+        return NULL;
+    } else if (matches == 0) {
+        PyErr_SetString(PyExc_TypeError, "No compatible overload");
+        return NULL;
+    }
+
+    return PyLong_FromLong(selected);
+}
+
+PyObject*
+check_compatible(PyObject* self, PyObject* args)
+{
+    PyObject *tmcap;
+    int from, to;
+    if (!PyArg_ParseTuple(args, "Oii", &tmcap, &from, &to)) {
+        return NULL;
+    }
+
+    TypeManager *tm = unwrap_TypeManager(tmcap);
+    if(!tm) {
+        BAD_TM_ARGUMENT;
+        return NULL;
+    }
+
+    switch(tm->isCompatible(Type(from), Type(to))){
+    case TCC_EXACT:
+        return PyString_FromString("exact");
+    case TCC_PROMOTE:
+        return PyString_FromString("promote");
+    case TCC_CONVERT_SAFE:
+        return PyString_FromString("safe");
+        case TCC_CONVERT_UNSAFE:
+        return PyString_FromString("unsafe");
+    default:
+        Py_RETURN_NONE;
+    }
+}
+
+PyObject*
+set_compatible(PyObject* self, PyObject* args)
+{
+    PyObject *tmcap;
+    int from, to, by;
+    if (!PyArg_ParseTuple(args, "Oiii", &tmcap, &from, &to, &by)) {
+        return NULL;
+    }
+
+    TypeManager *tm = unwrap_TypeManager(tmcap);
+    if (!tm) {
+        BAD_TM_ARGUMENT;
+        return NULL;
+    }
+    TypeCompatibleCode tcc;
+    switch (by) {
+    case 'p': // promote
+        tcc = TCC_PROMOTE;
+        break;
+    case 's': // safe convert
+        tcc = TCC_CONVERT_SAFE;
+        break;
+    case 'u': // unsafe convert
+        tcc = TCC_CONVERT_UNSAFE;
+        break;
+    default:
+        PyErr_SetString(PyExc_ValueError, "Unknown TCC");
+        return NULL;
+    }
+
+    tm->addCompatibility(Type(from), Type(to), tcc);
+    Py_RETURN_NONE;
+}
+
+
+PyObject*
+get_pointer(PyObject* self, PyObject* args)
+{
+    PyObject *tmcap;
+    if (!PyArg_ParseTuple(args, "O", &tmcap)) {
+        return NULL;
+    }
+    return PyLong_FromVoidPtr(unwrap_TypeManager(tmcap));
+}
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/castgraph.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/castgraph.py
new file mode 100644
index 000000000..2591c0cc5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/castgraph.py
@@ -0,0 +1,133 @@
+from collections import defaultdict
+from functools import total_ordering
+import enum
+
+
+class Conversion(enum.IntEnum):
+    """
+    A conversion kind from one type to the other.  The enum members
+    are ordered from stricter to looser.
+    """
+    # The two types are identical
+    exact = 1
+    # The two types are of the same kind, the destination type has more
+    # extension or precision than the source type (e.g. float32 -> float64,
+    # or int32 -> int64)
+    promote = 2
+    # The source type can be converted to the destination type without loss
+    # of information (e.g. int32 -> int64).  Note that the conversion may
+    # still fail explicitly at runtime (e.g. Optional(int32) -> int32)
+    safe = 3
+    # The conversion may appear to succeed at runtime while losing information
+    # or precision (e.g. int32 -> uint32, float64 -> float32, int64 -> int32,
+    # etc.)
+    unsafe = 4
+
+    # This value is only used internally
+    nil = 99
+
+
+class CastSet(object):
+    """A set of casting rules.
+
+    There is at most one rule per target type.
+    """
+
+    def __init__(self):
+        self._rels = {}
+
+    def insert(self, to, rel):
+        old = self.get(to)
+        setrel = min(rel, old)
+        self._rels[to] = setrel
+        return old != setrel
+
+    def items(self):
+        return self._rels.items()
+
+    def get(self, item):
+        return self._rels.get(item, Conversion.nil)
+
+    def __len__(self):
+        return len(self._rels)
+
+    def __repr__(self):
+        body = ["{rel}({ty})".format(rel=rel, ty=ty)
+                for ty, rel in self._rels.items()]
+        return "{" + ', '.join(body) + "}"
+
+    def __contains__(self, item):
+        return item in self._rels
+
+    def __iter__(self):
+        return iter(self._rels.keys())
+
+    def __getitem__(self, item):
+        return self._rels[item]
+
+
+class TypeGraph(object):
+    """A graph that maintains the casting relationship of all types.
+
+    This simplifies the definition of casting rules by automatically
+    propagating the rules.
+    """
+
+    def __init__(self, callback=None):
+        """
+        Args
+        ----
+        - callback: callable or None
+            It is called for each new casting rule with
+            (from_type, to_type, castrel).
+        """
+        assert callback is None or callable(callback)
+        self._forwards = defaultdict(CastSet)
+        self._backwards = defaultdict(set)
+        self._callback = callback
+
+    def get(self, ty):
+        return self._forwards[ty]
+
+    def propagate(self, a, b, baserel):
+        backset = self._backwards[a]
+
+        # Forward propagate the relationship to all nodes that b leads to
+        for child in self._forwards[b]:
+            rel = max(baserel, self._forwards[b][child])
+            if a != child:
+                if self._forwards[a].insert(child, rel):
+                    self._callback(a, child, rel)
+                self._backwards[child].add(a)
+
+            # Propagate the relationship from nodes that connects to a
+            for backnode in backset:
+                if backnode != child:
+                    backrel = max(rel, self._forwards[backnode][a])
+                    if self._forwards[backnode].insert(child, backrel):
+                        self._callback(backnode, child, backrel)
+                    self._backwards[child].add(backnode)
+
+        # Every node that leads to a connects to b
+        for child in self._backwards[a]:
+            rel = max(baserel, self._forwards[child][a])
+            if b != child:
+                if self._forwards[child].insert(b, rel):
+                    self._callback(child, b, rel)
+                self._backwards[b].add(child)
+
+    def insert_rule(self, a, b, rel):
+        self._forwards[a].insert(b, rel)
+        self._callback(a, b, rel)
+        self._backwards[b].add(a)
+        self.propagate(a, b, rel)
+
+    def promote(self, a, b):
+        self.insert_rule(a, b, Conversion.promote)
+
+    def safe(self, a, b):
+        self.insert_rule(a, b, Conversion.safe)
+
+    def unsafe(self, a, b):
+        self.insert_rule(a, b, Conversion.unsafe)
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/rules.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/rules.py
new file mode 100644
index 000000000..f8d090dcc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/rules.py
@@ -0,0 +1,60 @@
+import itertools
+from .typeconv import TypeManager, TypeCastingRules
+from numba.core import types
+
+
+default_type_manager = TypeManager()
+
+
+def dump_number_rules():
+    tm = default_type_manager
+    for a, b in itertools.product(types.number_domain, types.number_domain):
+        print(a, '->', b, tm.check_compatible(a, b))
+
+
+def _init_casting_rules(tm):
+    tcr = TypeCastingRules(tm)
+    tcr.safe_unsafe(types.boolean, types.int8)
+    tcr.safe_unsafe(types.boolean, types.uint8)
+
+    tcr.promote_unsafe(types.int8, types.int16)
+    tcr.promote_unsafe(types.uint8, types.uint16)
+
+    tcr.promote_unsafe(types.int16, types.int32)
+    tcr.promote_unsafe(types.uint16, types.uint32)
+
+    tcr.promote_unsafe(types.int32, types.int64)
+    tcr.promote_unsafe(types.uint32, types.uint64)
+
+    tcr.safe_unsafe(types.uint8, types.int16)
+    tcr.safe_unsafe(types.uint16, types.int32)
+    tcr.safe_unsafe(types.uint32, types.int64)
+
+    tcr.safe_unsafe(types.int16, types.float32)
+    tcr.safe_unsafe(types.int32, types.float64)
+
+    tcr.unsafe_unsafe(types.int16, types.float16)
+    tcr.unsafe_unsafe(types.int32, types.float32)
+    # XXX this is inconsistent with the above; but we want to prefer
+    # float64 over int64 when typing a heterogeneous operation,
+    # e.g. `float64 + int64`.  Perhaps we need more granularity in the
+    # conversion kinds.
+    tcr.safe_unsafe(types.int64, types.float64)
+    tcr.safe_unsafe(types.uint64, types.float64)
+
+    tcr.promote_unsafe(types.float16, types.float32)
+    tcr.promote_unsafe(types.float32, types.float64)
+
+    tcr.safe(types.float32, types.complex64)
+    tcr.safe(types.float64, types.complex128)
+
+    tcr.promote_unsafe(types.complex64, types.complex128)
+
+    # Allow integers to cast ot void*
+    tcr.unsafe_unsafe(types.uintp, types.voidptr)
+
+    return tcr
+
+
+default_casting_rules = _init_casting_rules(default_type_manager)
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/test.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/test.cpp
new file mode 100644
index 000000000..6ffe4e5b4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/test.cpp
@@ -0,0 +1,39 @@
+#include <iostream>
+#include "typeconv.hpp"
+
+using std::cout;
+const char EOL = '\n';
+
+int main() {
+	TypeManager tm;
+	Type t_int32 = tm.get("int32");
+	Type t_float = tm.get("float");
+	Type t_int64 = tm.get("int64");
+
+	tm.addConversion(t_int32, t_float);
+	tm.addConversion(t_float, t_int32);
+	tm.addConversion(t_float, t_int64);
+	tm.addPromotion(t_int32, t_int64);
+
+	cout << "int32 -> float " 
+		 << TCCString(tm.isCompatible(tm.get("int32"), tm.get("float"))) 
+		 << EOL;
+	cout << "int32 -> int64 " 
+	     << TCCString(tm.isCompatible(tm.get("int32"), tm.get("int64")))
+	     << EOL;
+
+	Type sig[] = {t_int32, t_float};
+	Type ovsigs[] = {
+		t_float, t_float,
+		t_int64, t_int64,
+		t_int32, t_float,
+	};
+
+	int sel = tm.selectOverload(sig, ovsigs, 2, 3);
+
+	cout << "Selected " << sel << '\n';
+
+
+	
+	return 0;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.cpp
new file mode 100644
index 000000000..fea9a17f5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.cpp
@@ -0,0 +1,210 @@
+#include <cstring>
+#include <cstdio>
+#include <algorithm>
+#include <limits.h>
+
+#include "typeconv.hpp"
+
+
+// ------ TypeManager ------
+
+TCCMap::TCCMap()
+    : nb_records(0)
+{
+}
+
+size_t TCCMap::hash(const TypePair &key) const {
+    return std::hash<size_t>()(std::hash<Type>()(key.first)) ^
+           std::hash<Type>()(key.second);
+}
+
+void TCCMap::insert(const TypePair &key, TypeCompatibleCode val) {
+    size_t i = hash(key) & (TCCMAP_SIZE - 1);
+    TCCMapBin &bin = records[i];
+    for (unsigned int j = 0; j < bin.size(); ++j) {
+        if (bin[j].key == key) {
+            bin[j].val = val;
+            return;
+        }
+    }
+    bin.push_back({key, val});
+    nb_records++;
+}
+
+TypeCompatibleCode TCCMap::find(const TypePair &key) const {
+    size_t i = hash(key) & (TCCMAP_SIZE - 1);
+    const TCCMapBin &bin = records[i];
+    for (unsigned int j = 0; j < bin.size(); ++j) {
+        if (bin[j].key == key) {
+            return bin[j].val;
+        }
+    }
+    return TCC_FALSE;
+}
+
+// ----- Ratings -----
+Rating::Rating() : promote(0), safe_convert(0), unsafe_convert(0) { }
+
+inline bool Rating::operator < (const Rating &other) const {
+    if (unsafe_convert < other.unsafe_convert)
+        return true;
+    else if (unsafe_convert > other.unsafe_convert)
+        return false;
+    if (safe_convert < other.safe_convert)
+        return true;
+    else if (safe_convert > other.safe_convert)
+        return false;
+    return (promote < other.promote);
+}
+
+inline bool Rating::operator == (const Rating &other) const {
+    return promote == other.promote && safe_convert == other.safe_convert &&
+           unsafe_convert == other.unsafe_convert;
+}
+
+// ------ TypeManager ------
+
+bool TypeManager::canPromote(Type from, Type to) const {
+    return isCompatible(from, to) == TCC_PROMOTE;
+}
+
+bool TypeManager::canSafeConvert(Type from, Type to) const {
+    return isCompatible(from, to) == TCC_CONVERT_SAFE;
+}
+
+bool TypeManager::canUnsafeConvert(Type from, Type to) const {
+    return isCompatible(from, to) == TCC_CONVERT_UNSAFE;
+}
+
+void TypeManager::addPromotion(Type from, Type to) {
+    return addCompatibility(from, to, TCC_PROMOTE);
+}
+
+void TypeManager::addUnsafeConversion(Type from, Type to) {
+    return addCompatibility(from, to, TCC_CONVERT_UNSAFE);
+}
+
+void TypeManager::addSafeConversion(Type from, Type to) {
+    return addCompatibility(from, to, TCC_CONVERT_SAFE);
+}
+
+void TypeManager::addCompatibility(Type from, Type to, TypeCompatibleCode tcc) {
+    TypePair pair(from, to);
+    tccmap.insert(pair, tcc);
+}
+
+TypeCompatibleCode TypeManager::isCompatible(Type from, Type to) const {
+    if (from == to)
+        return TCC_EXACT;
+    TypePair pair(from, to);
+    return tccmap.find(pair);
+}
+
+
+int TypeManager::selectOverload(const Type sig[], const Type ovsigs[],
+                                int &selected,
+                                int sigsz, int ovct, bool allow_unsafe,
+                                bool exact_match_required
+                               ) const {
+    int count;
+    if (ovct <= 16) {
+        Rating ratings[16];
+        int candidates[16];
+        count = _selectOverload(sig, ovsigs, selected, sigsz, ovct,
+                                allow_unsafe, exact_match_required, ratings,
+                                candidates);
+    }
+    else {
+        Rating *ratings = new Rating[ovct];
+        int *candidates = new int[ovct];
+        count = _selectOverload(sig, ovsigs, selected, sigsz, ovct,
+                                allow_unsafe, exact_match_required, ratings,
+                                candidates);
+        delete [] ratings;
+        delete [] candidates;
+    }
+    return count;
+}
+
+int TypeManager::_selectOverload(const Type sig[], const Type ovsigs[],
+                                 int &selected, int sigsz, int ovct,
+                                 bool allow_unsafe, bool exact_match_required,
+                                 Rating ratings[], int candidates[]) const {
+    // Generate rating table
+    // Use a penalize scheme.
+    int nb_candidates = 0;
+
+    for (int i = 0; i < ovct; ++i) {
+        const Type *entry = &ovsigs[i * sigsz];
+        Rating rate;
+
+        for (int j = 0; j < sigsz; ++j) {
+            TypeCompatibleCode tcc = isCompatible(sig[j], entry[j]);
+            if (tcc == TCC_FALSE ||
+                (tcc == TCC_CONVERT_UNSAFE && !allow_unsafe) ||
+                (tcc != TCC_EXACT && exact_match_required)) {
+                // stop the loop early
+                goto _incompatible;
+            }
+            switch(tcc) {
+            case TCC_PROMOTE:
+                rate.promote += 1;
+                break;
+            case TCC_CONVERT_SAFE:
+                rate.safe_convert += 1;
+                break;
+            case TCC_CONVERT_UNSAFE:
+                rate.unsafe_convert += 1;
+                break;
+            default:
+                break;
+            }
+        }
+        ratings[nb_candidates] = rate;
+        candidates[nb_candidates] = i;
+        nb_candidates++;
+    _incompatible:
+        ;
+    }
+
+    // Bail if no match
+    if (nb_candidates == 0)
+        return 0;
+
+    // Find lowest rating
+    Rating best = ratings[0];
+    selected = candidates[0];
+
+    int matchcount = 1;
+    for (int i = 1; i < nb_candidates; ++i) {
+        if (ratings[i] < best) {
+            best = ratings[i];
+            selected = candidates[i];
+            matchcount = 1;
+        }
+        else if (ratings[i] == best) {
+            matchcount += 1;
+        }
+    }
+    return matchcount;
+}
+
+// ----- utils -----
+
+const char* TCCString(TypeCompatibleCode tcc) {
+    switch(tcc) {
+    case TCC_EXACT:
+        return "exact";
+    case TCC_SUBTYPE:
+        return "subtype";
+    case TCC_PROMOTE:
+        return "promote";
+    case TCC_CONVERT_SAFE:
+        return "safe_convert";
+    case TCC_CONVERT_UNSAFE:
+        return "unsafe_convert";
+    default:
+        return "false";
+    }
+}
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.hpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.hpp
new file mode 100644
index 000000000..1f3cb9359
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.hpp
@@ -0,0 +1,98 @@
+#ifndef NUMBA_TYPECONV_HPP_
+#define NUMBA_TYPECONV_HPP_
+#include <string>
+#include <vector>
+
+
+typedef int Type;
+
+enum TypeCompatibleCode{
+    // No match
+    TCC_FALSE = 0,
+    // Exact match
+    TCC_EXACT,
+    // Subtype is UNUSED
+    TCC_SUBTYPE,
+    // Promotion with no precision loss
+    TCC_PROMOTE,
+    // Conversion with no precision loss
+    // e.g. int32 to double
+    TCC_CONVERT_SAFE,
+    // Conversion with precision loss
+    // e.g. int64 to double (53 bits precision)
+    TCC_CONVERT_UNSAFE,
+};
+
+typedef std::pair<Type, Type> TypePair;
+
+struct TCCRecord {
+    TypePair key;
+    TypeCompatibleCode val;
+};
+
+typedef std::vector<TCCRecord> TCCMapBin;
+
+class TCCMap {
+public:
+    TCCMap();
+
+    void insert(const TypePair &key, TypeCompatibleCode val);
+    TypeCompatibleCode find(const TypePair &key) const;
+private:
+    size_t hash(const TypePair &key) const;
+
+    /* Must be a power of two */
+    static const size_t TCCMAP_SIZE = 512;
+    TCCMapBin records[TCCMAP_SIZE];
+    int nb_records;
+};
+
+struct Rating {
+    unsigned int promote;
+    unsigned int safe_convert;
+    unsigned int unsafe_convert;
+
+    Rating();
+
+    bool operator < (const Rating &other) const;
+    bool operator == (const Rating &other) const;
+};
+
+
+class TypeManager{
+public:
+    bool canPromote(Type from, Type to) const;
+    bool canUnsafeConvert(Type from, Type to) const;
+    bool canSafeConvert(Type from, Type to) const;
+
+    void addPromotion(Type from, Type to);
+    void addUnsafeConversion(Type from, Type to);
+    void addSafeConversion(Type from, Type to);
+    void addCompatibility(Type from, Type to, TypeCompatibleCode by);
+
+    TypeCompatibleCode isCompatible(Type from, Type to) const;
+
+    /**
+    Output stored in selected.
+    Returns
+        Number of matches
+    */
+    int selectOverload(const Type sig[], const Type ovsigs[], int &selected,
+                       int sigsz, int ovct, bool allow_unsafe,
+                       bool exact_match_required
+                      ) const;
+
+private:
+    int _selectOverload(const Type sig[], const Type ovsigs[], int &selected,
+                        int sigsz, int ovct, bool allow_unsafe,
+                        bool exact_match_required,
+                        Rating ratings[], int candidates[]) const;
+
+    TCCMap tccmap;
+};
+
+
+const char* TCCString(TypeCompatibleCode tcc);
+
+
+#endif // NUMBA_TYPECONV_HPP_
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.py
new file mode 100644
index 000000000..1d380bc01
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeconv/typeconv.py
@@ -0,0 +1,128 @@
+try:
+    # This is usually the the first C extension import performed when importing
+    # Numba, if it fails to import, provide some feedback
+    from numba.core.typeconv import _typeconv
+except ImportError as e:
+    base_url = "https://numba.readthedocs.io/en/stable"
+    dev_url = f"{base_url}/developer/contributing.html"
+    user_url = f"{base_url}/user/faq.html#numba-could-not-be-imported"
+    dashes = '-' * 80
+    msg = (f"Numba could not be imported.\n{dashes}\nIf you are seeing this "
+           "message and are undertaking Numba development work, you may need "
+           "to rebuild Numba.\nPlease see the development set up guide:\n\n"
+           f"{dev_url}.\n\n{dashes}\nIf you are not working on Numba "
+           f"development, the original error was: '{str(e)}'.\nFor help, "
+           f"please visit:\n\n{user_url}\n")
+    raise ImportError(msg)
+
+from numba.core.typeconv import castgraph, Conversion
+from numba.core import types
+
+
+class TypeManager(object):
+
+    # The character codes used by the C/C++ API (_typeconv.cpp)
+    _conversion_codes = {Conversion.safe: ord("s"),
+                         Conversion.unsafe: ord("u"),
+                         Conversion.promote: ord("p"),}
+
+    def __init__(self):
+        self._ptr = _typeconv.new_type_manager()
+        self._types = set()
+
+    def select_overload(self, sig, overloads, allow_unsafe,
+                        exact_match_required):
+        sig = [t._code for t in sig]
+        overloads = [[t._code for t in s] for s in overloads]
+        return _typeconv.select_overload(self._ptr, sig, overloads,
+                                         allow_unsafe, exact_match_required)
+
+    def check_compatible(self, fromty, toty):
+        if not isinstance(toty, types.Type):
+            raise ValueError("Specified type '%s' (%s) is not a Numba type" %
+                             (toty, type(toty)))
+        name = _typeconv.check_compatible(self._ptr, fromty._code, toty._code)
+        conv = Conversion[name] if name is not None else None
+        assert conv is not Conversion.nil
+        return conv
+
+    def set_compatible(self, fromty, toty, by):
+        code = self._conversion_codes[by]
+        _typeconv.set_compatible(self._ptr, fromty._code, toty._code, code)
+        # Ensure the types don't die, otherwise they may be recreated with
+        # other type codes and pollute the hash table.
+        self._types.add(fromty)
+        self._types.add(toty)
+
+    def set_promote(self, fromty, toty):
+        self.set_compatible(fromty, toty, Conversion.promote)
+
+    def set_unsafe_convert(self, fromty, toty):
+        self.set_compatible(fromty, toty, Conversion.unsafe)
+
+    def set_safe_convert(self, fromty, toty):
+        self.set_compatible(fromty, toty, Conversion.safe)
+
+    def get_pointer(self):
+        return _typeconv.get_pointer(self._ptr)
+
+
+class TypeCastingRules(object):
+    """
+    A helper for establishing type casting rules.
+    """
+    def __init__(self, tm):
+        self._tm = tm
+        self._tg = castgraph.TypeGraph(self._cb_update)
+
+    def promote(self, a, b):
+        """
+        Set `a` can promote to `b`
+        """
+        self._tg.promote(a, b)
+
+    def unsafe(self, a, b):
+        """
+        Set `a` can unsafe convert to `b`
+        """
+        self._tg.unsafe(a, b)
+
+    def safe(self, a, b):
+        """
+        Set `a` can safe convert to `b`
+        """
+        self._tg.safe(a, b)
+
+    def promote_unsafe(self, a, b):
+        """
+        Set `a` can promote to `b` and `b` can unsafe convert to `a`
+        """
+        self.promote(a, b)
+        self.unsafe(b, a)
+
+    def safe_unsafe(self, a, b):
+        """
+        Set `a` can safe convert to `b` and `b` can unsafe convert to `a`
+        """
+        self._tg.safe(a, b)
+        self._tg.unsafe(b, a)
+
+    def unsafe_unsafe(self, a, b):
+        """
+        Set `a` can unsafe convert to `b` and `b` can unsafe convert to `a`
+        """
+        self._tg.unsafe(a, b)
+        self._tg.unsafe(b, a)
+
+    def _cb_update(self, a, b, rel):
+        """
+        Callback for updating.
+        """
+        if rel == Conversion.promote:
+            self._tm.set_promote(a, b)
+        elif rel == Conversion.safe:
+            self._tm.set_safe_convert(a, b)
+        elif rel == Conversion.unsafe:
+            self._tm.set_unsafe_convert(a, b)
+        else:
+            raise AssertionError(rel)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typed_passes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typed_passes.py
new file mode 100644
index 000000000..94f680023
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typed_passes.py
@@ -0,0 +1,864 @@
+from contextlib import contextmanager
+from collections import defaultdict, namedtuple
+from copy import copy
+import warnings
+
+from numba.core import (errors, types, typing, ir, funcdesc, rewrites,
+                        typeinfer, config, lowering)
+
+from numba.parfors.parfor import PreParforPass as _parfor_PreParforPass
+from numba.parfors.parfor import ParforPass as _parfor_ParforPass
+from numba.parfors.parfor import Parfor
+
+from numba.core.compiler_machinery import (FunctionPass, LoweringPass,
+                                           AnalysisPass, register_pass)
+from numba.core.annotations import type_annotations
+from numba.core.ir_utils import (raise_on_unsupported_feature, warn_deprecated,
+                                 check_and_legalize_ir, guard,
+                                 dead_code_elimination, simplify_CFG,
+                                 get_definition,
+                                 build_definitions, compute_cfg_from_blocks,
+                                 is_operator_or_getitem)
+from numba.core import postproc
+from llvmlite import binding as llvm
+
+
+# Outputs of type inference pass
+_TypingResults = namedtuple("_TypingResults", [
+    "typemap",
+    "return_type",
+    "calltypes",
+    "typing_errors",
+])
+
+
+@contextmanager
+def fallback_context(state, msg):
+    """
+    Wraps code that would signal a fallback to object mode
+    """
+    try:
+        yield
+    except Exception as e:
+        if not state.status.can_fallback:
+            raise
+        else:
+            # Clear all references attached to the traceback
+            e = e.with_traceback(None)
+            # this emits a warning containing the error message body in the
+            # case of fallback from npm to objmode
+            loop_lift = '' if state.flags.enable_looplift else 'OUT'
+            msg_rewrite = ("\nCompilation is falling back to object mode "
+                           "WITH%s looplifting enabled because %s"
+                           % (loop_lift, msg))
+            warnings.warn_explicit('%s due to: %s' % (msg_rewrite, e),
+                                   errors.NumbaWarning,
+                                   state.func_id.filename,
+                                   state.func_id.firstlineno)
+            raise
+
+
+def type_inference_stage(typingctx, targetctx, interp, args, return_type,
+                         locals={}, raise_errors=True):
+    if len(args) != interp.arg_count:
+        raise TypeError("Mismatch number of argument types")
+    warnings = errors.WarningsFixer(errors.NumbaWarning)
+    infer = typeinfer.TypeInferer(typingctx, interp, warnings)
+    with typingctx.callstack.register(targetctx.target, infer, interp.func_id,
+                                      args):
+        # Seed argument types
+        for index, (name, ty) in enumerate(zip(interp.arg_names, args)):
+            infer.seed_argument(name, index, ty)
+
+        # Seed return type
+        if return_type is not None:
+            infer.seed_return(return_type)
+
+        # Seed local types
+        for k, v in locals.items():
+            infer.seed_type(k, v)
+
+        infer.build_constraint()
+        # return errors in case of partial typing
+        errs = infer.propagate(raise_errors=raise_errors)
+        typemap, restype, calltypes = infer.unify(raise_errors=raise_errors)
+
+    # Output all Numba warnings
+    warnings.flush()
+
+    return _TypingResults(typemap, restype, calltypes, errs)
+
+
+class BaseTypeInference(FunctionPass):
+    _raise_errors = True
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Type inference and legalization
+        """
+        with fallback_context(state, 'Function "%s" failed type inference'
+                              % (state.func_id.func_name,)):
+            # Type inference
+            typemap, return_type, calltypes, errs = type_inference_stage(
+                state.typingctx,
+                state.targetctx,
+                state.func_ir,
+                state.args,
+                state.return_type,
+                state.locals,
+                raise_errors=self._raise_errors)
+            state.typemap = typemap
+            # save errors in case of partial typing
+            state.typing_errors = errs
+            if self._raise_errors:
+                state.return_type = return_type
+            state.calltypes = calltypes
+
+        def legalize_return_type(return_type, interp, targetctx):
+            """
+            Only accept array return type iff it is passed into the function.
+            Reject function object return types if in nopython mode.
+            """
+            if (not targetctx.enable_nrt and
+                    isinstance(return_type, types.Array)):
+                # Walk IR to discover all arguments and all return statements
+                retstmts = []
+                caststmts = {}
+                argvars = set()
+                for bid, blk in interp.blocks.items():
+                    for inst in blk.body:
+                        if isinstance(inst, ir.Return):
+                            retstmts.append(inst.value.name)
+                        elif isinstance(inst, ir.Assign):
+                            if (isinstance(inst.value, ir.Expr)
+                                    and inst.value.op == 'cast'):
+                                caststmts[inst.target.name] = inst.value
+                            elif isinstance(inst.value, ir.Arg):
+                                argvars.add(inst.target.name)
+
+                assert retstmts, "No return statements?"
+
+                for var in retstmts:
+                    cast = caststmts.get(var)
+                    if cast is None or cast.value.name not in argvars:
+                        if self._raise_errors:
+                            msg = ("Only accept returning of array passed into "
+                                   "the function as argument")
+                            raise errors.NumbaTypeError(msg)
+
+            elif (isinstance(return_type, types.Function) or
+                    isinstance(return_type, types.Phantom)):
+                if self._raise_errors:
+                    msg = "Can't return function object ({}) in nopython mode"
+                    raise errors.NumbaTypeError(msg.format(return_type))
+
+        with fallback_context(state, 'Function "%s" has invalid return type'
+                              % (state.func_id.func_name,)):
+            legalize_return_type(state.return_type, state.func_ir,
+                                 state.targetctx)
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class NopythonTypeInference(BaseTypeInference):
+    _name = "nopython_type_inference"
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class PartialTypeInference(BaseTypeInference):
+    _name = "partial_type_inference"
+    _raise_errors = False
+
+
+@register_pass(mutates_CFG=False, analysis_only=False)
+class AnnotateTypes(AnalysisPass):
+    _name = "annotate_types"
+
+    def __init__(self):
+        AnalysisPass.__init__(self)
+
+    def get_analysis_usage(self, AU):
+        AU.add_required(IRLegalization)
+
+    def run_pass(self, state):
+        """
+        Create type annotation after type inference
+        """
+        func_ir = state.func_ir.copy()
+        state.type_annotation = type_annotations.TypeAnnotation(
+            func_ir=func_ir,
+            typemap=state.typemap,
+            calltypes=state.calltypes,
+            lifted=state.lifted,
+            lifted_from=state.lifted_from,
+            args=state.args,
+            return_type=state.return_type,
+            html_output=config.HTML)
+
+        if config.ANNOTATE:
+            print("ANNOTATION".center(80, '-'))
+            print(state.type_annotation)
+            print('=' * 80)
+        if config.HTML:
+            with open(config.HTML, 'w') as fout:
+                state.type_annotation.html_annotate(fout)
+
+        return False
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class NopythonRewrites(FunctionPass):
+    _name = "nopython_rewrites"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Perform any intermediate representation rewrites after type
+        inference.
+        """
+        # a bunch of these passes are either making assumptions or rely on some
+        # very picky and slightly bizarre state particularly in relation to
+        # ir.Del presence. To accommodate, ir.Dels are added ahead of running
+        # this pass and stripped at the end.
+
+        # Ensure we have an IR and type information.
+        assert state.func_ir
+        assert isinstance(getattr(state, 'typemap', None), dict)
+        assert isinstance(getattr(state, 'calltypes', None), dict)
+        msg = ('Internal error in post-inference rewriting '
+               'pass encountered during compilation of '
+               'function "%s"' % (state.func_id.func_name,))
+
+        pp = postproc.PostProcessor(state.func_ir)
+        pp.run(True)
+        with fallback_context(state, msg):
+            rewrites.rewrite_registry.apply('after-inference', state)
+        pp.remove_dels()
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class PreParforPass(FunctionPass):
+
+    _name = "pre_parfor_pass"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Preprocessing for data-parallel computations.
+        """
+        # Ensure we have an IR and type information.
+        assert state.func_ir
+        preparfor_pass = _parfor_PreParforPass(
+            state.func_ir,
+            state.typemap,
+            state.calltypes,
+            state.typingctx,
+            state.targetctx,
+            state.flags.auto_parallel,
+            state.parfor_diagnostics.replaced_fns
+        )
+
+        preparfor_pass.run()
+        return True
+
+
+# this is here so it pickles and for no other reason
+def _reload_parfors():
+    """Reloader for cached parfors
+    """
+    # Re-initialize the parallel backend when load from cache.
+    from numba.np.ufunc.parallel import _launch_threads
+    _launch_threads()
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class ParforPass(FunctionPass):
+
+    _name = "parfor_pass"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Convert data-parallel computations into Parfor nodes
+        """
+        # Ensure we have an IR and type information.
+        assert state.func_ir
+        parfor_pass = _parfor_ParforPass(state.func_ir,
+                                         state.typemap,
+                                         state.calltypes,
+                                         state.return_type,
+                                         state.typingctx,
+                                         state.targetctx,
+                                         state.flags.auto_parallel,
+                                         state.flags,
+                                         state.metadata,
+                                         state.parfor_diagnostics)
+        parfor_pass.run()
+
+        # check the parfor pass worked and warn if it didn't
+        has_parfor = False
+        for blk in state.func_ir.blocks.values():
+            for stmnt in blk.body:
+                if isinstance(stmnt, Parfor):
+                    has_parfor = True
+                    break
+            else:
+                continue
+            break
+
+        if not has_parfor:
+            # parfor calls the compiler chain again with a string
+            if not (config.DISABLE_PERFORMANCE_WARNINGS or
+                    state.func_ir.loc.filename == '<string>'):
+                url = ("https://numba.readthedocs.io/en/stable/user/"
+                       "parallel.html#diagnostics")
+                msg = ("\nThe keyword argument 'parallel=True' was specified "
+                       "but no transformation for parallel execution was "
+                       "possible.\n\nTo find out why, try turning on parallel "
+                       "diagnostics, see %s for help." % url)
+                warnings.warn(errors.NumbaPerformanceWarning(msg,
+                                                             state.func_ir.loc))
+
+        # Add reload function to initialize the parallel backend.
+        state.reload_init.append(_reload_parfors)
+        return True
+
+
+@register_pass(mutates_CFG=False, analysis_only=True)
+class DumpParforDiagnostics(AnalysisPass):
+
+    _name = "dump_parfor_diagnostics"
+
+    def __init__(self):
+        AnalysisPass.__init__(self)
+
+    def run_pass(self, state):
+        if state.flags.auto_parallel.enabled:
+            if config.PARALLEL_DIAGNOSTICS:
+                if state.parfor_diagnostics is not None:
+                    state.parfor_diagnostics.dump(config.PARALLEL_DIAGNOSTICS)
+                else:
+                    raise RuntimeError("Diagnostics failed.")
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class NativeLowering(LoweringPass):
+
+    _name = "native_lowering"
+
+    def __init__(self):
+        LoweringPass.__init__(self)
+
+    def run_pass(self, state):
+        if state.library is None:
+            codegen = state.targetctx.codegen()
+            state.library = codegen.create_library(state.func_id.func_qualname)
+            # Enable object caching upfront, so that the library can
+            # be later serialized.
+            state.library.enable_object_caching()
+
+        library = state.library
+        targetctx = state.targetctx
+        interp = state.func_ir  # why is it called this?!
+        typemap = state.typemap
+        restype = state.return_type
+        calltypes = state.calltypes
+        flags = state.flags
+        metadata = state.metadata
+        pre_stats = llvm.passmanagers.dump_refprune_stats()
+
+        msg = ("Function %s failed at nopython "
+               "mode lowering" % (state.func_id.func_name,))
+        with fallback_context(state, msg):
+            # Lowering
+            fndesc = \
+                funcdesc.PythonFunctionDescriptor.from_specialized_function(
+                    interp, typemap, restype, calltypes,
+                    mangler=targetctx.mangler, inline=flags.forceinline,
+                    noalias=flags.noalias, abi_tags=[flags.get_mangle_string()])
+
+            with targetctx.push_code_library(library):
+                lower = lowering.Lower(targetctx, library, fndesc, interp,
+                                       metadata=metadata)
+                lower.lower()
+                if not flags.no_cpython_wrapper:
+                    lower.create_cpython_wrapper(flags.release_gil)
+
+                if not flags.no_cfunc_wrapper:
+                    # skip cfunc wrapper generation if unsupported
+                    # argument or return types are used
+                    for t in state.args:
+                        if isinstance(t, (types.Omitted, types.Generator)):
+                            break
+                    else:
+                        if isinstance(restype,
+                                      (types.Optional, types.Generator)):
+                            pass
+                        else:
+                            lower.create_cfunc_wrapper()
+
+                env = lower.env
+                call_helper = lower.call_helper
+                del lower
+
+            from numba.core.compiler import _LowerResult  # TODO: move this
+            if flags.no_compile:
+                state['cr'] = _LowerResult(fndesc, call_helper,
+                                           cfunc=None, env=env)
+            else:
+                # Prepare for execution
+                # Insert native function for use by other jitted-functions.
+                # We also register its library to allow for inlining.
+                cfunc = targetctx.get_executable(library, fndesc, env)
+                targetctx.insert_user_function(cfunc, fndesc, [library])
+                state['cr'] = _LowerResult(fndesc, call_helper,
+                                           cfunc=cfunc, env=env)
+
+            # capture pruning stats
+            post_stats = llvm.passmanagers.dump_refprune_stats()
+            metadata['prune_stats'] = post_stats - pre_stats
+
+            # Save the LLVM pass timings
+            metadata['llvm_pass_timings'] = library.recorded_timings
+        return True
+
+
+@register_pass(mutates_CFG=False, analysis_only=True)
+class NoPythonSupportedFeatureValidation(AnalysisPass):
+    """NoPython Mode check: Validates the IR to ensure that features in use are
+    in a form that is supported"""
+
+    _name = "nopython_supported_feature_validation"
+
+    def __init__(self):
+        AnalysisPass.__init__(self)
+
+    def run_pass(self, state):
+        raise_on_unsupported_feature(state.func_ir, state.typemap)
+        warn_deprecated(state.func_ir, state.typemap)
+        return False
+
+
+@register_pass(mutates_CFG=False, analysis_only=True)
+class IRLegalization(AnalysisPass):
+
+    _name = "ir_legalization"
+
+    def __init__(self):
+        AnalysisPass.__init__(self)
+
+    def run_pass(self, state):
+        # NOTE: this function call must go last, it checks and fixes invalid IR!
+        check_and_legalize_ir(state.func_ir, flags=state.flags)
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class NoPythonBackend(LoweringPass):
+
+    _name = "nopython_backend"
+
+    def __init__(self):
+        LoweringPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Back-end: Generate LLVM IR from Numba IR, compile to machine code
+        """
+        lowered = state['cr']
+        signature = typing.signature(state.return_type, *state.args)
+
+        from numba.core.compiler import compile_result
+        state.cr = compile_result(
+            typing_context=state.typingctx,
+            target_context=state.targetctx,
+            entry_point=lowered.cfunc,
+            typing_error=state.status.fail_reason,
+            type_annotation=state.type_annotation,
+            library=state.library,
+            call_helper=lowered.call_helper,
+            signature=signature,
+            objectmode=False,
+            lifted=state.lifted,
+            fndesc=lowered.fndesc,
+            environment=lowered.env,
+            metadata=state.metadata,
+            reload_init=state.reload_init,
+        )
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class InlineOverloads(FunctionPass):
+    """
+    This pass will inline a function wrapped by the numba.extending.overload
+    decorator directly into the site of its call depending on the value set in
+    the 'inline' kwarg to the decorator.
+
+    This is a typed pass. CFG simplification and DCE are performed on
+    completion.
+    """
+
+    _name = "inline_overloads"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    _DEBUG = False
+
+    def run_pass(self, state):
+        """Run inlining of overloads
+        """
+        if self._DEBUG:
+            print('before overload inline'.center(80, '-'))
+            print(state.func_id.unique_name)
+            print(state.func_ir.dump())
+            print(''.center(80, '-'))
+        from numba.core.inline_closurecall import (InlineWorker,
+                                                   callee_ir_validator)
+        inline_worker = InlineWorker(state.typingctx,
+                                     state.targetctx,
+                                     state.locals,
+                                     state.pipeline,
+                                     state.flags,
+                                     callee_ir_validator,
+                                     state.typemap,
+                                     state.calltypes,
+                                     )
+        modified = False
+        work_list = list(state.func_ir.blocks.items())
+        # use a work list, look for call sites via `ir.Expr.op == call` and
+        # then pass these to `self._do_work` to make decisions about inlining.
+        while work_list:
+            label, block = work_list.pop()
+            for i, instr in enumerate(block.body):
+                # TO-DO: other statements (setitem)
+                if isinstance(instr, ir.Assign):
+                    expr = instr.value
+                    if isinstance(expr, ir.Expr):
+                        workfn = self._do_work_expr
+
+                        if guard(workfn, state, work_list, block, i, expr,
+                                 inline_worker):
+                            modified = True
+                            break  # because block structure changed
+
+        if self._DEBUG:
+            print('after overload inline'.center(80, '-'))
+            print(state.func_id.unique_name)
+            print(state.func_ir.dump())
+            print(''.center(80, '-'))
+
+        if modified:
+            # Remove dead blocks, this is safe as it relies on the CFG only.
+            cfg = compute_cfg_from_blocks(state.func_ir.blocks)
+            for dead in cfg.dead_nodes():
+                del state.func_ir.blocks[dead]
+            # clean up blocks
+            dead_code_elimination(state.func_ir,
+                                  typemap=state.typemap)
+            # clean up unconditional branches that appear due to inlined
+            # functions introducing blocks
+            state.func_ir.blocks = simplify_CFG(state.func_ir.blocks)
+
+        if self._DEBUG:
+            print('after overload inline DCE'.center(80, '-'))
+            print(state.func_id.unique_name)
+            print(state.func_ir.dump())
+            print(''.center(80, '-'))
+        return True
+
+    def _get_attr_info(self, state, expr):
+        recv_type = state.typemap[expr.value.name]
+        recv_type = types.unliteral(recv_type)
+        matched = state.typingctx.find_matching_getattr_template(
+            recv_type, expr.attr,
+        )
+        if not matched:
+            return None
+
+        template = matched['template']
+        if getattr(template, 'is_method', False):
+            # The attribute template is representing a method.
+            # Don't inline the getattr.
+            return None
+
+        templates = [template]
+        sig = typing.signature(matched['return_type'], recv_type)
+        arg_typs = sig.args
+        is_method = False
+
+        return templates, sig, arg_typs, is_method
+
+    def _get_callable_info(self, state, expr):
+
+        def get_func_type(state, expr):
+            func_ty = None
+            if expr.op == 'call':
+                # check this is a known and typed function
+                try:
+                    func_ty = state.typemap[expr.func.name]
+                except KeyError:
+                    # e.g. Calls to CUDA Intrinsic have no mapped type
+                    # so KeyError
+                    return None
+                if not hasattr(func_ty, 'get_call_type'):
+                    return None
+
+            elif is_operator_or_getitem(expr):
+                func_ty = state.typingctx.resolve_value_type(expr.fn)
+            else:
+                return None
+
+            return func_ty
+
+        if expr.op == 'call':
+            # try and get a definition for the call, this isn't always
+            # possible as it might be a eval(str)/part generated
+            # awaiting update etc. (parfors)
+            to_inline = None
+            try:
+                to_inline = state.func_ir.get_definition(expr.func)
+            except Exception:
+                return None
+
+            # do not handle closure inlining here, another pass deals with that
+            if getattr(to_inline, 'op', False) == 'make_function':
+                return None
+
+        func_ty = get_func_type(state, expr)
+        if func_ty is None:
+            return None
+
+        sig = state.calltypes[expr]
+        if not sig:
+            return None
+
+        templates, arg_typs, is_method = None, None, False
+        if getattr(func_ty, 'template', None) is not None:
+            # @overload_method
+            is_method = True
+            templates = [func_ty.template]
+            arg_typs = (func_ty.template.this,) + sig.args
+        else:
+            # @overload case
+            templates = getattr(func_ty, 'templates', None)
+            arg_typs = sig.args
+
+        return templates, sig, arg_typs, is_method
+
+    def _do_work_expr(self, state, work_list, block, i, expr, inline_worker):
+
+        def select_template(templates, args):
+            if templates is None:
+                return None
+
+            impl = None
+            for template in templates:
+                inline_type = getattr(template, '_inline', None)
+                if inline_type is None:
+                    # inline not defined
+                    continue
+                if args not in template._inline_overloads:
+                    # skip overloads not matching signature
+                    continue
+                if not inline_type.is_never_inline:
+                    try:
+                        impl = template._overload_func(*args)
+                        if impl is None:
+                            raise Exception  # abort for this template
+                        break
+                    except Exception:
+                        continue
+            else:
+                return None
+
+            return template, inline_type, impl
+
+        inlinee_info = None
+        if expr.op == 'getattr':
+            inlinee_info = self._get_attr_info(state, expr)
+        else:
+            inlinee_info = self._get_callable_info(state, expr)
+
+        if not inlinee_info:
+            return False
+
+        templates, sig, arg_typs, is_method = inlinee_info
+        inlinee = select_template(templates, arg_typs)
+        if inlinee is None:
+            return False
+        template, inlinee_type, impl = inlinee
+
+        return self._run_inliner(
+            state, inlinee_type, sig, template, arg_typs, expr, i, impl, block,
+            work_list, is_method, inline_worker,
+        )
+
+    def _run_inliner(
+        self, state, inline_type, sig, template, arg_typs, expr, i, impl, block,
+        work_list, is_method, inline_worker,
+    ):
+
+        do_inline = True
+        if not inline_type.is_always_inline:
+            from numba.core.typing.templates import _inline_info
+            caller_inline_info = _inline_info(state.func_ir,
+                                              state.typemap,
+                                              state.calltypes,
+                                              sig)
+
+            # must be a cost-model function, run the function
+            iinfo = template._inline_overloads[arg_typs]['iinfo']
+            if inline_type.has_cost_model:
+                do_inline = inline_type.value(expr, caller_inline_info, iinfo)
+            else:
+                assert 'unreachable'
+
+        if do_inline:
+            if is_method:
+                if not self._add_method_self_arg(state, expr):
+                    return False
+            arg_typs = template._inline_overloads[arg_typs]['folded_args']
+            iinfo = template._inline_overloads[arg_typs]['iinfo']
+            freevars = iinfo.func_ir.func_id.func.__code__.co_freevars
+            _, _, _, new_blocks = inline_worker.inline_ir(state.func_ir,
+                                                          block,
+                                                          i,
+                                                          iinfo.func_ir,
+                                                          freevars,
+                                                          arg_typs=arg_typs)
+            if work_list is not None:
+                for blk in new_blocks:
+                    work_list.append(blk)
+            return True
+        else:
+            return False
+
+    def _add_method_self_arg(self, state, expr):
+        func_def = guard(get_definition, state.func_ir, expr.func)
+        if func_def is None:
+            return False
+        expr.args.insert(0, func_def.value)
+        return True
+
+
+@register_pass(mutates_CFG=False, analysis_only=False)
+class DeadCodeElimination(FunctionPass):
+    """
+    Does dead code elimination
+    """
+
+    _name = "dead_code_elimination"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        dead_code_elimination(state.func_ir, state.typemap)
+        return True
+
+
+@register_pass(mutates_CFG=False, analysis_only=False)
+class PreLowerStripPhis(FunctionPass):
+    """Remove phi nodes (ir.Expr.phi) introduced by SSA.
+
+    This is needed before Lowering because the phi nodes in Numba IR do not
+    match the semantics of phi nodes in LLVM IR. In Numba IR, phi nodes may
+    expand into multiple LLVM instructions.
+    """
+
+    _name = "strip_phis"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        state.func_ir = self._strip_phi_nodes(state.func_ir)
+        state.func_ir._definitions = build_definitions(state.func_ir.blocks)
+        # Rerun postprocessor to update metadata
+        post_proc = postproc.PostProcessor(state.func_ir)
+        post_proc.run(emit_dels=False)
+
+        # Ensure we are not in objectmode generator
+        if (state.func_ir.generator_info is not None
+                and state.typemap is not None):
+            # Rebuild generator type
+            # TODO: move this into PostProcessor
+            gentype = state.return_type
+            state_vars = state.func_ir.generator_info.state_vars
+            state_types = [state.typemap[k] for k in state_vars]
+            state.return_type = types.Generator(
+                gen_func=gentype.gen_func,
+                yield_type=gentype.yield_type,
+                arg_types=gentype.arg_types,
+                state_types=state_types,
+                has_finalizer=gentype.has_finalizer,
+            )
+        return True
+
+    def _strip_phi_nodes(self, func_ir):
+        """Strip Phi nodes from ``func_ir``
+
+        For each phi node, put incoming value to their respective incoming
+        basic-block at possibly the latest position (i.e. after the latest
+        assignment to the corresponding variable).
+        """
+        exporters = defaultdict(list)
+        phis = set()
+        # Find all variables that needs to be exported
+        for label, block in func_ir.blocks.items():
+            for assign in block.find_insts(ir.Assign):
+                if isinstance(assign.value, ir.Expr):
+                    if assign.value.op == 'phi':
+                        phis.add(assign)
+                        phi = assign.value
+                        for ib, iv in zip(phi.incoming_blocks,
+                                          phi.incoming_values):
+                            exporters[ib].append((assign.target, iv))
+
+        # Rewrite the blocks with the new exporting assignments
+        newblocks = {}
+        for label, block in func_ir.blocks.items():
+            newblk = copy(block)
+            newblocks[label] = newblk
+
+            # strip phis
+            newblk.body = [stmt for stmt in block.body if stmt not in phis]
+
+            # insert exporters
+            for target, rhs in exporters[label]:
+                # If RHS is undefined
+                if rhs is ir.UNDEFINED:
+                    # Put in a NULL initializer, set the location to be in what
+                    # will eventually materialize as the prologue.
+                    rhs = ir.Expr.null(loc=func_ir.loc)
+
+                assign = ir.Assign(
+                    target=target,
+                    value=rhs,
+                    loc=rhs.loc
+                )
+                # Insert at the earliest possible location; i.e. after the
+                # last assignment to rhs
+                assignments = [stmt for stmt in newblk.find_insts(ir.Assign)
+                               if stmt.target == rhs]
+                if assignments:
+                    last_assignment = assignments[-1]
+                    newblk.insert_after(assign, last_assignment)
+                else:
+                    newblk.prepend(assign)
+
+        func_ir.blocks = newblocks
+        return func_ir
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeinfer.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeinfer.py
new file mode 100644
index 000000000..98295d899
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typeinfer.py
@@ -0,0 +1,1780 @@
+"""
+Type inference base on CPA.
+The algorithm guarantees monotonic growth of type-sets for each variable.
+
+Steps:
+    1. seed initial types
+    2. build constraints
+    3. propagate constraints
+    4. unify types
+
+Constraint propagation is precise and does not regret (no backtracing).
+Constraints push types forward following the dataflow.
+"""
+
+
+import logging
+import operator
+import contextlib
+import itertools
+from pprint import pprint
+from collections import OrderedDict, defaultdict
+from functools import reduce
+
+from numba.core import types, utils, typing, ir, config
+from numba.core.typing.templates import Signature
+from numba.core.errors import (TypingError, UntypedAttributeError,
+                               new_error_context, termcolor, UnsupportedError,
+                               ForceLiteralArg, CompilerError, NumbaValueError)
+from numba.core.funcdesc import qualifying_prefix
+from numba.core.typeconv import Conversion
+
+_logger = logging.getLogger(__name__)
+
+
+class NOTSET:
+    pass
+
+
+# terminal color markup
+_termcolor = termcolor()
+
+
+class TypeVar(object):
+    def __init__(self, context, var):
+        self.context = context
+        self.var = var
+        self.type = None
+        self.locked = False
+        # Stores source location of first definition
+        self.define_loc = None
+        # Qualifiers
+        self.literal_value = NOTSET
+
+    def add_type(self, tp, loc):
+        assert isinstance(tp, types.Type), type(tp)
+
+        if self.locked:
+            if tp != self.type:
+                if self.context.can_convert(tp, self.type) is None:
+                    msg = ("No conversion from %s to %s for '%s', "
+                           "defined at %s")
+                    raise TypingError(msg % (tp, self.type, self.var,
+                                             self.define_loc),
+                                      loc=loc)
+        else:
+            if self.type is not None:
+                unified = self.context.unify_pairs(self.type, tp)
+                if unified is None:
+                    msg = "Cannot unify %s and %s for '%s', defined at %s"
+                    raise TypingError(msg % (self.type, tp, self.var,
+                                             self.define_loc),
+                                      loc=self.define_loc)
+            else:
+                # First time definition
+                unified = tp
+                self.define_loc = loc
+
+            self.type = unified
+
+        return self.type
+
+    def lock(self, tp, loc, literal_value=NOTSET):
+        assert isinstance(tp, types.Type), type(tp)
+
+        if self.locked:
+            msg = ("Invalid reassignment of a type-variable detected, type "
+                   "variables are locked according to the user provided "
+                   "function signature or from an ir.Const node. This is a "
+                   "bug! Type={}. {}").format(tp, self.type)
+            raise CompilerError(msg, loc)
+
+        # If there is already a type, ensure we can convert it to the
+        # locked type.
+        if (self.type is not None and
+                self.context.can_convert(self.type, tp) is None):
+            raise TypingError("No conversion from %s to %s for "
+                              "'%s'" % (tp, self.type, self.var), loc=loc)
+
+        self.type = tp
+        self.locked = True
+        if self.define_loc is None:
+            self.define_loc = loc
+        self.literal_value = literal_value
+
+    def union(self, other, loc):
+        if other.type is not None:
+            self.add_type(other.type, loc=loc)
+
+        return self.type
+
+    def __repr__(self):
+        return '%s := %s' % (self.var, self.type or "<undecided>")
+
+    @property
+    def defined(self):
+        return self.type is not None
+
+    def get(self):
+        return (self.type,) if self.type is not None else ()
+
+    def getone(self):
+        if self.type is None:
+            raise TypingError("Undecided type {}".format(self))
+        return self.type
+
+    def __len__(self):
+        return 1 if self.type is not None else 0
+
+
+class ConstraintNetwork(object):
+    """
+    TODO: It is possible to optimize constraint propagation to consider only
+          dirty type variables.
+    """
+
+    def __init__(self):
+        self.constraints = []
+
+    def append(self, constraint):
+        self.constraints.append(constraint)
+
+    def propagate(self, typeinfer):
+        """
+        Execute all constraints.  Errors are caught and returned as a list.
+        This allows progressing even though some constraints may fail
+        due to lack of information
+        (e.g. imprecise types such as List(undefined)).
+        """
+        errors = []
+        for constraint in self.constraints:
+            loc = constraint.loc
+            with typeinfer.warnings.catch_warnings(filename=loc.filename,
+                                                   lineno=loc.line):
+                try:
+                    constraint(typeinfer)
+                except ForceLiteralArg as e:
+                    errors.append(e)
+                except TypingError as e:
+                    _logger.debug("captured error", exc_info=e)
+                    new_exc = TypingError(
+                        str(e), loc=constraint.loc,
+                        highlighting=False,
+                    )
+                    errors.append(utils.chain_exception(new_exc, e))
+                except Exception as e:
+                    if utils.use_old_style_errors():
+                        _logger.debug("captured error", exc_info=e)
+                        msg = ("Internal error at {con}.\n{err}\n"
+                               "Enable logging at debug level for details.")
+                        new_exc = TypingError(
+                            msg.format(con=constraint, err=str(e)),
+                            loc=constraint.loc,
+                            highlighting=False,
+                        )
+                        errors.append(utils.chain_exception(new_exc, e))
+                    elif utils.use_new_style_errors():
+                        raise e
+                    else:
+                        msg = ("Unknown CAPTURED_ERRORS style: "
+                               f"'{config.CAPTURED_ERRORS}'.")
+                        assert 0, msg
+
+        return errors
+
+
+class Propagate(object):
+    """
+    A simple constraint for direct propagation of types for assignments.
+    """
+
+    def __init__(self, dst, src, loc):
+        self.dst = dst
+        self.src = src
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of assignment at {0}", self.loc,
+                               loc=self.loc):
+            typeinfer.copy_type(self.src, self.dst, loc=self.loc)
+            # If `dst` is refined, notify us
+            typeinfer.refine_map[self.dst] = self
+
+    def refine(self, typeinfer, target_type):
+        # Do not back-propagate to locked variables (e.g. constants)
+        assert target_type.is_precise()
+        typeinfer.add_type(self.src, target_type, unless_locked=True,
+                           loc=self.loc)
+
+
+class ArgConstraint(object):
+
+    def __init__(self, dst, src, loc):
+        self.dst = dst
+        self.src = src
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of argument at {0}", self.loc):
+            typevars = typeinfer.typevars
+            src = typevars[self.src]
+            if not src.defined:
+                return
+            ty = src.getone()
+            if isinstance(ty, types.Omitted):
+                ty = typeinfer.context.resolve_value_type_prefer_literal(
+                    ty.value,
+                )
+            if not ty.is_precise():
+                raise TypingError('non-precise type {}'.format(ty))
+            typeinfer.add_type(self.dst, ty, loc=self.loc)
+
+
+class BuildTupleConstraint(object):
+    def __init__(self, target, items, loc):
+        self.target = target
+        self.items = items
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of tuple at {0}", self.loc):
+            typevars = typeinfer.typevars
+            tsets = [typevars[i.name].get() for i in self.items]
+            for vals in itertools.product(*tsets):
+                if vals and all(vals[0] == v for v in vals):
+                    tup = types.UniTuple(dtype=vals[0], count=len(vals))
+                else:
+                    # empty tuples fall here as well
+                    tup = types.Tuple(vals)
+                assert tup.is_precise()
+                typeinfer.add_type(self.target, tup, loc=self.loc)
+
+
+class _BuildContainerConstraint(object):
+
+    def __init__(self, target, items, loc):
+        self.target = target
+        self.items = items
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of {0} at {1}",
+                               self.container_type, self.loc):
+            typevars = typeinfer.typevars
+            tsets = [typevars[i.name].get() for i in self.items]
+            if not tsets:
+                typeinfer.add_type(self.target,
+                                   self.container_type(types.undefined),
+                                   loc=self.loc)
+            else:
+                for typs in itertools.product(*tsets):
+                    unified = typeinfer.context.unify_types(*typs)
+                    if unified is not None:
+                        typeinfer.add_type(self.target,
+                                           self.container_type(unified),
+                                           loc=self.loc)
+
+
+class BuildListConstraint(_BuildContainerConstraint):
+
+    def __init__(self, target, items, loc):
+        self.target = target
+        self.items = items
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of {0} at {1}",
+                               types.List, self.loc):
+            typevars = typeinfer.typevars
+            tsets = [typevars[i.name].get() for i in self.items]
+            if not tsets:
+                typeinfer.add_type(self.target,
+                                   types.List(types.undefined),
+                                   loc=self.loc)
+            else:
+                for typs in itertools.product(*tsets):
+                    unified = typeinfer.context.unify_types(*typs)
+                    if unified is not None:
+                        # pull out literals if available
+                        islit = [isinstance(x, types.Literal) for x in typs]
+                        iv = None
+                        if all(islit):
+                            iv = [x.literal_value for x in typs]
+                        typeinfer.add_type(self.target,
+                                           types.List(unified,
+                                                      initial_value=iv),
+                                           loc=self.loc)
+                    else:
+                        typeinfer.add_type(self.target,
+                                           types.LiteralList(typs),
+                                           loc=self.loc)
+
+
+class BuildSetConstraint(_BuildContainerConstraint):
+    container_type = types.Set
+
+
+class BuildMapConstraint(object):
+
+    def __init__(self, target, items, special_value, value_indexes, loc):
+        self.target = target
+        self.items = items
+        self.special_value = special_value
+        self.value_indexes = value_indexes
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+
+        with new_error_context("typing of dict at {0}", self.loc):
+            typevars = typeinfer.typevars
+
+            # figure out what sort of dict is being dealt with
+            tsets = [(typevars[k.name].getone(), typevars[v.name].getone())
+                     for k, v in self.items]
+
+            if not tsets:
+                typeinfer.add_type(self.target,
+                                   types.DictType(types.undefined,
+                                                  types.undefined,
+                                                  self.special_value),
+                                   loc=self.loc)
+            else:
+                # all the info is known about the dict, if its
+                # str keys -> random heterogeneous values treat as literalstrkey
+                ktys = [x[0] for x in tsets]
+                vtys = [x[1] for x in tsets]
+                strkey = all([isinstance(x, types.StringLiteral) for x in ktys])
+                literalvty = all([isinstance(x, types.Literal) for x in vtys])
+                vt0 = types.unliteral(vtys[0])
+
+                # homogeneous values comes in the form of being able to cast
+                # all the other values in the ctor to the type of the first.
+                # The order is important as `typed.Dict` takes it's type from
+                # the first element.
+                def check(other):
+                    conv = typeinfer.context.can_convert(other, vt0)
+                    return conv is not None and conv < Conversion.unsafe
+                homogeneous = all([check(types.unliteral(x)) for x in vtys])
+
+                # Special cases:
+                # Single key:value in ctor, key is str, value is an otherwise
+                # illegal container type, e.g. LiteralStrKeyDict or
+                # List, there's no way to put this into a typed.Dict, so make it
+                # a LiteralStrKeyDict, same goes for LiteralList.
+                if len(vtys) == 1:
+                    valty = vtys[0]
+                    if isinstance(valty, (types.LiteralStrKeyDict,
+                                          types.List,
+                                          types.LiteralList)):
+                        homogeneous = False
+
+                if strkey and not homogeneous:
+                    resolved_dict = {x: y for x, y in zip(ktys, vtys)}
+                    ty = types.LiteralStrKeyDict(resolved_dict,
+                                                 self.value_indexes)
+                    typeinfer.add_type(self.target, ty, loc=self.loc)
+                else:
+                    init_value = self.special_value if literalvty else None
+                    key_type, value_type = tsets[0]
+                    typeinfer.add_type(self.target,
+                                       types.DictType(key_type,
+                                                      value_type,
+                                                      init_value),
+                                       loc=self.loc)
+
+
+class ExhaustIterConstraint(object):
+    def __init__(self, target, count, iterator, loc):
+        self.target = target
+        self.count = count
+        self.iterator = iterator
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of exhaust iter at {0}", self.loc):
+            typevars = typeinfer.typevars
+            for tp in typevars[self.iterator.name].get():
+                # unpack optional
+                tp = tp.type if isinstance(tp, types.Optional) else tp
+                if isinstance(tp, types.BaseTuple):
+                    if len(tp) == self.count:
+                        assert tp.is_precise()
+                        typeinfer.add_type(self.target, tp, loc=self.loc)
+                        break
+                    else:
+                        msg = (f"wrong tuple length for {self.iterator.name}: ",
+                               f"expected {self.count}, got {len(tp)}")
+                        raise NumbaValueError(msg)
+                elif isinstance(tp, types.IterableType):
+                    tup = types.UniTuple(dtype=tp.iterator_type.yield_type,
+                                         count=self.count)
+                    assert tup.is_precise()
+                    typeinfer.add_type(self.target, tup, loc=self.loc)
+                    break
+                else:
+                    raise TypingError("failed to unpack {}".format(tp),
+                                      loc=self.loc)
+
+
+class PairFirstConstraint(object):
+    def __init__(self, target, pair, loc):
+        self.target = target
+        self.pair = pair
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of pair-first at {0}", self.loc):
+            typevars = typeinfer.typevars
+            for tp in typevars[self.pair.name].get():
+                if not isinstance(tp, types.Pair):
+                    # XXX is this an error?
+                    continue
+                assert (isinstance(tp.first_type, types.UndefinedFunctionType)
+                        or tp.first_type.is_precise())
+                typeinfer.add_type(self.target, tp.first_type, loc=self.loc)
+
+
+class PairSecondConstraint(object):
+    def __init__(self, target, pair, loc):
+        self.target = target
+        self.pair = pair
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of pair-second at {0}", self.loc):
+            typevars = typeinfer.typevars
+            for tp in typevars[self.pair.name].get():
+                if not isinstance(tp, types.Pair):
+                    # XXX is this an error?
+                    continue
+                assert tp.second_type.is_precise()
+                typeinfer.add_type(self.target, tp.second_type, loc=self.loc)
+
+
+class StaticGetItemConstraint(object):
+    def __init__(self, target, value, index, index_var, loc):
+        self.target = target
+        self.value = value
+        self.index = index
+        if index_var is not None:
+            self.fallback = IntrinsicCallConstraint(target, operator.getitem,
+                                                    (value, index_var), {},
+                                                    None, loc)
+        else:
+            self.fallback = None
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of static-get-item at {0}", self.loc):
+            typevars = typeinfer.typevars
+            for ty in typevars[self.value.name].get():
+                sig = typeinfer.context.resolve_static_getitem(
+                    value=ty, index=self.index,
+                )
+
+                if sig is not None:
+                    itemty = sig.return_type
+                    # if the itemty is not precise, let it through, unification
+                    # will catch it and produce a better error message
+                    typeinfer.add_type(self.target, itemty, loc=self.loc)
+                elif self.fallback is not None:
+                    self.fallback(typeinfer)
+
+    def get_call_signature(self):
+        # The signature is only needed for the fallback case in lowering
+        return self.fallback and self.fallback.get_call_signature()
+
+
+class TypedGetItemConstraint(object):
+    def __init__(self, target, value, dtype, index, loc):
+        self.target = target
+        self.value = value
+        self.dtype = dtype
+        self.index = index
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of typed-get-item at {0}", self.loc):
+            typevars = typeinfer.typevars
+            idx_ty = typevars[self.index.name].get()
+            ty = typevars[self.value.name].get()
+            self.signature = Signature(self.dtype, ty + idx_ty, None)
+            typeinfer.add_type(self.target, self.dtype, loc=self.loc)
+
+    def get_call_signature(self):
+        return self.signature
+
+
+def fold_arg_vars(typevars, args, vararg, kws):
+    """
+    Fold and resolve the argument variables of a function call.
+    """
+    # Fetch all argument types, bail if any is unknown
+    n_pos_args = len(args)
+    kwds = [kw for (kw, var) in kws]
+    argtypes = [typevars[a.name] for a in args]
+    argtypes += [typevars[var.name] for (kw, var) in kws]
+    if vararg is not None:
+        argtypes.append(typevars[vararg.name])
+
+    if not all(a.defined for a in argtypes):
+        return
+
+    args = tuple(a.getone() for a in argtypes)
+
+    pos_args = args[:n_pos_args]
+    if vararg is not None:
+        errmsg = "*args in function call should be a tuple, got %s"
+        # Handle constant literal used for `*args`
+        if isinstance(args[-1], types.Literal):
+            const_val = args[-1].literal_value
+            # Is the constant value a tuple?
+            if not isinstance(const_val, tuple):
+                raise TypeError(errmsg % (args[-1],))
+            # Append the elements in the const tuple to the positional args
+            pos_args += const_val
+        # Handle non-constant
+        elif not isinstance(args[-1], types.BaseTuple):
+            # Unsuitable for *args
+            # (Python is more lenient and accepts all iterables)
+            raise TypeError(errmsg % (args[-1],))
+        else:
+            # Append the elements in the tuple to the positional args
+            pos_args += args[-1].types
+        # Drop the last arg
+        args = args[:-1]
+    kw_args = dict(zip(kwds, args[n_pos_args:]))
+    return pos_args, kw_args
+
+
+def _is_array_not_precise(arrty):
+    """Check type is array and it is not precise
+    """
+    return isinstance(arrty, types.Array) and not arrty.is_precise()
+
+
+class CallConstraint(object):
+    """Constraint for calling functions.
+    Perform case analysis foreach combinations of argument types.
+    """
+    signature = None
+
+    def __init__(self, target, func, args, kws, vararg, loc):
+        self.target = target
+        self.func = func
+        self.args = args
+        self.kws = kws or {}
+        self.vararg = vararg
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        msg = "typing of call at {0}\n".format(self.loc)
+        with new_error_context(msg):
+            typevars = typeinfer.typevars
+            with new_error_context(
+                    "resolving caller type: {}".format(self.func)):
+                fnty = typevars[self.func].getone()
+            with new_error_context("resolving callee type: {0}", fnty):
+                self.resolve(typeinfer, typevars, fnty)
+
+    def resolve(self, typeinfer, typevars, fnty):
+        assert fnty
+        context = typeinfer.context
+
+        r = fold_arg_vars(typevars, self.args, self.vararg, self.kws)
+        if r is None:
+            # Cannot resolve call type until all argument types are known
+            return
+        pos_args, kw_args = r
+
+        # Check argument to be precise
+        for a in itertools.chain(pos_args, kw_args.values()):
+            # Forbids imprecise type except array of undefined dtype
+            if not a.is_precise() and not isinstance(a, types.Array):
+                return
+
+        # Resolve call type
+        if isinstance(fnty, types.TypeRef):
+            # Unwrap TypeRef
+            fnty = fnty.instance_type
+        try:
+            sig = typeinfer.resolve_call(fnty, pos_args, kw_args)
+        except ForceLiteralArg as e:
+            # Adjust for bound methods
+            folding_args = ((fnty.this,) + tuple(self.args)
+                            if isinstance(fnty, types.BoundFunction)
+                            else self.args)
+            folded = e.fold_arguments(folding_args, self.kws)
+            requested = set()
+            unsatisfied = set()
+            for idx in e.requested_args:
+                maybe_arg = typeinfer.func_ir.get_definition(folded[idx])
+                if isinstance(maybe_arg, ir.Arg):
+                    requested.add(maybe_arg.index)
+                else:
+                    unsatisfied.add(idx)
+            if unsatisfied:
+                raise TypingError("Cannot request literal type.", loc=self.loc)
+            elif requested:
+                raise ForceLiteralArg(requested, loc=self.loc)
+        if sig is None:
+            # Note: duplicated error checking.
+            #       See types.BaseFunction.get_call_type
+            # Arguments are invalid => explain why
+            headtemp = "Invalid use of {0} with parameters ({1})"
+            args = [str(a) for a in pos_args]
+            args += ["%s=%s" % (k, v) for k, v in sorted(kw_args.items())]
+            head = headtemp.format(fnty, ', '.join(map(str, args)))
+            desc = context.explain_function_type(fnty)
+            msg = '\n'.join([head, desc])
+            raise TypingError(msg)
+
+        typeinfer.add_type(self.target, sig.return_type, loc=self.loc)
+
+        # If the function is a bound function and its receiver type
+        # was refined, propagate it.
+        if (isinstance(fnty, types.BoundFunction)
+                and sig.recvr is not None
+                and sig.recvr != fnty.this):
+            refined_this = context.unify_pairs(sig.recvr, fnty.this)
+            if (refined_this is None and
+                    fnty.this.is_precise() and
+                    sig.recvr.is_precise()):
+                msg = "Cannot refine type {} to {}".format(
+                    sig.recvr, fnty.this,
+                )
+                raise TypingError(msg, loc=self.loc)
+            if refined_this is not None and refined_this.is_precise():
+                refined_fnty = fnty.copy(this=refined_this)
+                typeinfer.propagate_refined_type(self.func, refined_fnty)
+
+        # If the return type is imprecise but can be unified with the
+        # target variable's inferred type, use the latter.
+        # Useful for code such as::
+        #    s = set()
+        #    s.add(1)
+        # (the set() call must be typed as int64(), not undefined())
+        if not sig.return_type.is_precise():
+            target = typevars[self.target]
+            if target.defined:
+                targetty = target.getone()
+                if context.unify_pairs(targetty, sig.return_type) == targetty:
+                    sig = sig.replace(return_type=targetty)
+
+        self.signature = sig
+        self._add_refine_map(typeinfer, typevars, sig)
+
+    def _add_refine_map(self, typeinfer, typevars, sig):
+        """Add this expression to the refine_map base on the type of target_type
+        """
+        target_type = typevars[self.target].getone()
+        # Array
+        if (isinstance(target_type, types.Array)
+                and isinstance(sig.return_type.dtype, types.Undefined)):
+            typeinfer.refine_map[self.target] = self
+        # DictType
+        if (isinstance(target_type, types.DictType) and
+                not target_type.is_precise()):
+            typeinfer.refine_map[self.target] = self
+
+    def refine(self, typeinfer, updated_type):
+        # Is getitem?
+        if self.func == operator.getitem:
+            aryty = typeinfer.typevars[self.args[0].name].getone()
+            # is array not precise?
+            if _is_array_not_precise(aryty):
+                # allow refinement of dtype
+                assert updated_type.is_precise()
+                newtype = aryty.copy(dtype=updated_type.dtype)
+                typeinfer.add_type(self.args[0].name, newtype, loc=self.loc)
+        else:
+            m = 'no type refinement implemented for function {} updating to {}'
+            raise TypingError(m.format(self.func, updated_type))
+
+    def get_call_signature(self):
+        return self.signature
+
+
+class IntrinsicCallConstraint(CallConstraint):
+    def __call__(self, typeinfer):
+        with new_error_context("typing of intrinsic-call at {0}", self.loc):
+            fnty = self.func
+            if fnty in utils.OPERATORS_TO_BUILTINS:
+                fnty = typeinfer.resolve_value_type(None, fnty)
+            self.resolve(typeinfer, typeinfer.typevars, fnty=fnty)
+
+
+class GetAttrConstraint(object):
+    def __init__(self, target, attr, value, loc, inst):
+        self.target = target
+        self.attr = attr
+        self.value = value
+        self.loc = loc
+        self.inst = inst
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of get attribute at {0}", self.loc):
+            typevars = typeinfer.typevars
+            valtys = typevars[self.value.name].get()
+            for ty in valtys:
+                attrty = typeinfer.context.resolve_getattr(ty, self.attr)
+                if attrty is None:
+                    raise UntypedAttributeError(ty, self.attr,
+                                                loc=self.inst.loc)
+                else:
+                    assert attrty.is_precise()
+                    typeinfer.add_type(self.target, attrty, loc=self.loc)
+            typeinfer.refine_map[self.target] = self
+
+    def refine(self, typeinfer, target_type):
+        if isinstance(target_type, types.BoundFunction):
+            recvr = target_type.this
+            assert recvr.is_precise()
+            typeinfer.add_type(self.value.name, recvr, loc=self.loc)
+            source_constraint = typeinfer.refine_map.get(self.value.name)
+            if source_constraint is not None:
+                source_constraint.refine(typeinfer, recvr)
+
+    def __repr__(self):
+        return 'resolving type of attribute "{attr}" of "{value}"'.format(
+            value=self.value, attr=self.attr)
+
+
+class SetItemRefinement(object):
+    """A mixin class to provide the common refinement logic in setitem
+    and static setitem.
+    """
+
+    def _refine_target_type(self, typeinfer, targetty, idxty, valty, sig):
+        """Refine the target-type given the known index type and value type.
+        """
+        # For array setitem, refine imprecise array dtype
+        if _is_array_not_precise(targetty):
+            typeinfer.add_type(self.target.name, sig.args[0], loc=self.loc)
+        # For Dict setitem
+        if isinstance(targetty, types.DictType):
+            if not targetty.is_precise():
+                refined = targetty.refine(idxty, valty)
+                typeinfer.add_type(
+                    self.target.name, refined,
+                    loc=self.loc,
+                )
+            elif isinstance(targetty, types.LiteralStrKeyDict):
+                typeinfer.add_type(
+                    self.target.name, types.DictType(idxty, valty),
+                    loc=self.loc,
+                )
+
+
+class SetItemConstraint(SetItemRefinement):
+    def __init__(self, target, index, value, loc):
+        self.target = target
+        self.index = index
+        self.value = value
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of setitem at {0}", self.loc):
+            typevars = typeinfer.typevars
+            if not all(typevars[var.name].defined
+                       for var in (self.target, self.index, self.value)):
+                return
+            targetty = typevars[self.target.name].getone()
+            idxty = typevars[self.index.name].getone()
+            valty = typevars[self.value.name].getone()
+
+            sig = typeinfer.context.resolve_setitem(targetty, idxty, valty)
+            if sig is None:
+                raise TypingError("Cannot resolve setitem: %s[%s] = %s" %
+                                  (targetty, idxty, valty), loc=self.loc)
+
+            self.signature = sig
+            self._refine_target_type(typeinfer, targetty, idxty, valty, sig)
+
+    def get_call_signature(self):
+        return self.signature
+
+
+class StaticSetItemConstraint(SetItemRefinement):
+    def __init__(self, target, index, index_var, value, loc):
+        self.target = target
+        self.index = index
+        self.index_var = index_var
+        self.value = value
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of staticsetitem at {0}", self.loc):
+            typevars = typeinfer.typevars
+            if not all(typevars[var.name].defined
+                       for var in (self.target, self.index_var, self.value)):
+                return
+            targetty = typevars[self.target.name].getone()
+            idxty = typevars[self.index_var.name].getone()
+            valty = typevars[self.value.name].getone()
+
+            sig = typeinfer.context.resolve_static_setitem(targetty,
+                                                           self.index, valty)
+            if sig is None:
+                sig = typeinfer.context.resolve_setitem(targetty, idxty, valty)
+            if sig is None:
+                raise TypingError("Cannot resolve setitem: %s[%r] = %s" %
+                                  (targetty, self.index, valty), loc=self.loc)
+            self.signature = sig
+            self._refine_target_type(typeinfer, targetty, idxty, valty, sig)
+
+    def get_call_signature(self):
+        return self.signature
+
+
+class DelItemConstraint(object):
+    def __init__(self, target, index, loc):
+        self.target = target
+        self.index = index
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of delitem at {0}", self.loc):
+            typevars = typeinfer.typevars
+            if not all(typevars[var.name].defined
+                       for var in (self.target, self.index)):
+                return
+            targetty = typevars[self.target.name].getone()
+            idxty = typevars[self.index.name].getone()
+
+            sig = typeinfer.context.resolve_delitem(targetty, idxty)
+            if sig is None:
+                raise TypingError("Cannot resolve delitem: %s[%s]" %
+                                  (targetty, idxty), loc=self.loc)
+            self.signature = sig
+
+    def get_call_signature(self):
+        return self.signature
+
+
+class SetAttrConstraint(object):
+    def __init__(self, target, attr, value, loc):
+        self.target = target
+        self.attr = attr
+        self.value = value
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        with new_error_context("typing of set attribute {0!r} at {1}",
+                               self.attr, self.loc):
+            typevars = typeinfer.typevars
+            if not all(typevars[var.name].defined
+                       for var in (self.target, self.value)):
+                return
+            targetty = typevars[self.target.name].getone()
+            valty = typevars[self.value.name].getone()
+            sig = typeinfer.context.resolve_setattr(targetty, self.attr,
+                                                    valty)
+            if sig is None:
+                raise TypingError("Cannot resolve setattr: (%s).%s = %s" %
+                                  (targetty, self.attr, valty),
+                                  loc=self.loc)
+            self.signature = sig
+
+    def get_call_signature(self):
+        return self.signature
+
+
+class PrintConstraint(object):
+    def __init__(self, args, vararg, loc):
+        self.args = args
+        self.vararg = vararg
+        self.loc = loc
+
+    def __call__(self, typeinfer):
+        typevars = typeinfer.typevars
+
+        r = fold_arg_vars(typevars, self.args, self.vararg, {})
+        if r is None:
+            # Cannot resolve call type until all argument types are known
+            return
+        pos_args, kw_args = r
+
+        fnty = typeinfer.context.resolve_value_type(print)
+        assert fnty is not None
+        sig = typeinfer.resolve_call(fnty, pos_args, kw_args)
+        self.signature = sig
+
+    def get_call_signature(self):
+        return self.signature
+
+
+class TypeVarMap(dict):
+    def set_context(self, context):
+        self.context = context
+
+    def __getitem__(self, name):
+        if name not in self:
+            self[name] = TypeVar(self.context, name)
+        return super(TypeVarMap, self).__getitem__(name)
+
+    def __setitem__(self, name, value):
+        assert isinstance(name, str)
+        if name in self:
+            raise KeyError("Cannot redefine typevar %s" % name)
+        else:
+            super(TypeVarMap, self).__setitem__(name, value)
+
+
+# A temporary mapping of {function name: dispatcher object}
+_temporary_dispatcher_map = {}
+# A temporary mapping of {function name: dispatcher object reference count}
+# Reference: https://github.com/numba/numba/issues/3658
+_temporary_dispatcher_map_ref_count = defaultdict(int)
+
+
+@contextlib.contextmanager
+def register_dispatcher(disp):
+    """
+    Register a Dispatcher for inference while it is not yet stored
+    as global or closure variable (e.g. during execution of the @jit()
+    call).  This allows resolution of recursive calls with eager
+    compilation.
+    """
+    assert callable(disp)
+    assert callable(disp.py_func)
+    name = disp.py_func.__name__
+    _temporary_dispatcher_map[name] = disp
+    _temporary_dispatcher_map_ref_count[name] += 1
+    try:
+        yield
+    finally:
+        _temporary_dispatcher_map_ref_count[name] -= 1
+        if not _temporary_dispatcher_map_ref_count[name]:
+            del _temporary_dispatcher_map[name]
+
+
+typeinfer_extensions = {}
+
+
+class TypeInferer(object):
+    """
+    Operates on block that shares the same ir.Scope.
+    """
+
+    def __init__(self, context, func_ir, warnings):
+        self.context = context
+        # sort based on label, ensure iteration order!
+        self.blocks = OrderedDict()
+        for k in sorted(func_ir.blocks.keys()):
+            self.blocks[k] = func_ir.blocks[k]
+        self.generator_info = func_ir.generator_info
+        self.func_id = func_ir.func_id
+        self.func_ir = func_ir
+
+        self.typevars = TypeVarMap()
+        self.typevars.set_context(context)
+        self.constraints = ConstraintNetwork()
+        self.warnings = warnings
+
+        # { index: mangled name }
+        self.arg_names = {}
+        # self.return_type = None
+        # Set of assumed immutable globals
+        self.assumed_immutables = set()
+        # Track all calls and associated constraints
+        self.calls = []
+        # The inference result of the above calls
+        self.calltypes = utils.UniqueDict()
+        # Target var -> constraint with refine hook
+        self.refine_map = {}
+
+        if config.DEBUG or config.DEBUG_TYPEINFER:
+            self.debug = TypeInferDebug(self)
+        else:
+            self.debug = NullDebug()
+
+        self._skip_recursion = False
+
+    def copy(self, skip_recursion=False):
+        clone = TypeInferer(self.context, self.func_ir, self.warnings)
+        clone.arg_names = self.arg_names.copy()
+        clone._skip_recursion = skip_recursion
+
+        for k, v in self.typevars.items():
+            if not v.locked and v.defined:
+                clone.typevars[k].add_type(v.getone(), loc=v.define_loc)
+
+        return clone
+
+    def _mangle_arg_name(self, name):
+        # Disambiguise argument name
+        return "arg.%s" % (name,)
+
+    def _get_return_vars(self):
+        rets = []
+        for blk in self.blocks.values():
+            inst = blk.terminator
+            if isinstance(inst, ir.Return):
+                rets.append(inst.value)
+        return rets
+
+    def get_argument_types(self):
+        return [self.typevars[k].getone() for k in self.arg_names.values()]
+
+    def seed_argument(self, name, index, typ):
+        name = self._mangle_arg_name(name)
+        self.seed_type(name, typ)
+        self.arg_names[index] = name
+
+    def seed_type(self, name, typ):
+        """All arguments should be seeded.
+        """
+        self.lock_type(name, typ, loc=None)
+
+    def seed_return(self, typ):
+        """Seeding of return value is optional.
+        """
+        for var in self._get_return_vars():
+            self.lock_type(var.name, typ, loc=None)
+
+    def build_constraint(self):
+        for blk in self.blocks.values():
+            for inst in blk.body:
+                self.constrain_statement(inst)
+
+    def return_types_from_partial(self):
+        """
+        Resume type inference partially to deduce the return type.
+        Note: No side-effect to `self`.
+
+        Returns the inferred return type or None if it cannot deduce the return
+        type.
+        """
+        # Clone the typeinferer and disable typing recursive calls
+        cloned = self.copy(skip_recursion=True)
+        # rebuild constraint network
+        cloned.build_constraint()
+        # propagate without raising
+        cloned.propagate(raise_errors=False)
+        # get return types
+        rettypes = set()
+        for retvar in cloned._get_return_vars():
+            if retvar.name in cloned.typevars:
+                typevar = cloned.typevars[retvar.name]
+                if typevar and typevar.defined:
+                    rettypes.add(types.unliteral(typevar.getone()))
+        if not rettypes:
+            return
+        # unify return types
+        return cloned._unify_return_types(rettypes)
+
+    def propagate(self, raise_errors=True):
+        newtoken = self.get_state_token()
+        oldtoken = None
+        # Since the number of types are finite, the typesets will eventually
+        # stop growing.
+
+        while newtoken != oldtoken:
+            self.debug.propagate_started()
+            oldtoken = newtoken
+            # Errors can appear when the type set is incomplete; only
+            # raise them when there is no progress anymore.
+            errors = self.constraints.propagate(self)
+            newtoken = self.get_state_token()
+            self.debug.propagate_finished()
+        if errors:
+            if raise_errors:
+                force_lit_args = [e for e in errors
+                                  if isinstance(e, ForceLiteralArg)]
+                if not force_lit_args:
+                    raise errors[0]
+                else:
+                    raise reduce(operator.or_, force_lit_args)
+            else:
+                return errors
+
+    def add_type(self, var, tp, loc, unless_locked=False):
+        assert isinstance(var, str), type(var)
+        tv = self.typevars[var]
+        if unless_locked and tv.locked:
+            return
+        oldty = tv.type
+        unified = tv.add_type(tp, loc=loc)
+        if unified != oldty:
+            self.propagate_refined_type(var, unified)
+
+    def add_calltype(self, inst, signature):
+        assert signature is not None
+        self.calltypes[inst] = signature
+
+    def copy_type(self, src_var, dest_var, loc):
+        self.typevars[dest_var].union(self.typevars[src_var], loc=loc)
+
+    def lock_type(self, var, tp, loc, literal_value=NOTSET):
+        tv = self.typevars[var]
+        tv.lock(tp, loc=loc, literal_value=literal_value)
+
+    def propagate_refined_type(self, updated_var, updated_type):
+        source_constraint = self.refine_map.get(updated_var)
+        if source_constraint is not None:
+            source_constraint.refine(self, updated_type)
+
+    def unify(self, raise_errors=True):
+        """
+        Run the final unification pass over all inferred types, and
+        catch imprecise types.
+        """
+        typdict = utils.UniqueDict()
+
+        def find_offender(name, exhaustive=False):
+            # finds the offending variable definition by name
+            # if exhaustive is set it will try and trace through temporary
+            # variables to find a concrete offending definition.
+            offender = None
+            for block in self.func_ir.blocks.values():
+                offender = block.find_variable_assignment(name)
+                if offender is not None:
+                    if not exhaustive:
+                        break
+                    try:  # simple assignment
+                        hasattr(offender.value, 'name')
+                        offender_value = offender.value.name
+                    except (AttributeError, KeyError):
+                        break
+                    orig_offender = offender
+                    if offender_value.startswith('$'):
+                        offender = find_offender(offender_value,
+                                                 exhaustive=exhaustive)
+                        if offender is None:
+                            offender = orig_offender
+                    break
+            return offender
+
+        def diagnose_imprecision(offender):
+            # helper for diagnosing imprecise types
+
+            list_msg = """\n
+For Numba to be able to compile a list, the list must have a known and
+precise type that can be inferred from the other variables. Whilst sometimes
+the type of empty lists can be inferred, this is not always the case, see this
+documentation for help:
+
+https://numba.readthedocs.io/en/stable/user/troubleshoot.html#my-code-has-an-untyped-list-problem
+"""
+            if offender is not None:
+                # This block deals with imprecise lists
+                if hasattr(offender, 'value'):
+                    if hasattr(offender.value, 'op'):
+                        # might be `foo = []`
+                        if offender.value.op == 'build_list':
+                            return list_msg
+                        # or might be `foo = list()`
+                        elif offender.value.op == 'call':
+                            try:  # assignment involving a call
+                                call_name = offender.value.func.name
+                                # find the offender based on the call name
+                                offender = find_offender(call_name)
+                                if isinstance(offender.value, ir.Global):
+                                    if offender.value.name == 'list':
+                                        return list_msg
+                            except (AttributeError, KeyError):
+                                pass
+            return ""  # no help possible
+
+        def check_var(name):
+            tv = self.typevars[name]
+            if not tv.defined:
+                if raise_errors:
+                    offender = find_offender(name)
+                    val = getattr(offender, 'value', 'unknown operation')
+                    loc = getattr(offender, 'loc', ir.unknown_loc)
+                    msg = ("Type of variable '%s' cannot be determined, "
+                           "operation: %s, location: %s")
+                    raise TypingError(msg % (var, val, loc), loc)
+                else:
+                    typdict[var] = types.unknown
+                    return
+            tp = tv.getone()
+
+            if isinstance(tp, types.UndefinedFunctionType):
+                tp = tp.get_precise()
+
+            if not tp.is_precise():
+                offender = find_offender(name, exhaustive=True)
+                msg = ("Cannot infer the type of variable '%s'%s, "
+                       "have imprecise type: %s. %s")
+                istmp = " (temporary variable)" if var.startswith('$') else ""
+                loc = getattr(offender, 'loc', ir.unknown_loc)
+                # is this an untyped list? try and provide help
+                extra_msg = diagnose_imprecision(offender)
+                if raise_errors:
+                    raise TypingError(msg % (var, istmp, tp, extra_msg), loc)
+                else:
+                    typdict[var] = types.unknown
+                    return
+            else:  # type is precise, hold it
+                typdict[var] = tp
+
+        # For better error display, check first user-visible vars, then
+        # temporaries
+        temps = set(k for k in self.typevars if not k[0].isalpha())
+        others = set(self.typevars) - temps
+        for var in sorted(others):
+            check_var(var)
+        for var in sorted(temps):
+            check_var(var)
+
+        try:
+            retty = self.get_return_type(typdict)
+        except Exception as e:
+            # partial type inference may raise e.g. attribute error if a
+            # constraint has no computable signature, ignore this as needed
+            if raise_errors:
+                raise e
+            else:
+                retty = None
+        else:
+            typdict = utils.UniqueDict(
+                typdict, **{v.name: retty for v in self._get_return_vars()})
+
+        try:
+            fntys = self.get_function_types(typdict)
+        except Exception as e:
+            # partial type inference may raise e.g. attribute error if a
+            # constraint has no computable signature, ignore this as needed
+            if raise_errors:
+                raise e
+            else:
+                fntys = None
+
+        if self.generator_info:
+            retty = self.get_generator_type(typdict, retty,
+                                            raise_errors=raise_errors)
+
+        self.debug.unify_finished(typdict, retty, fntys)
+
+        return typdict, retty, fntys
+
+    def get_generator_type(self, typdict, retty, raise_errors=True):
+        gi = self.generator_info
+        arg_types = [None] * len(self.arg_names)
+        for index, name in self.arg_names.items():
+            arg_types[index] = typdict[name]
+
+        state_types = None
+        try:
+            state_types = [typdict[var_name] for var_name in gi.state_vars]
+        except KeyError:
+            msg = "Cannot type generator: state variable types cannot be found"
+            if raise_errors:
+                raise TypingError(msg)
+            state_types = [types.unknown for _ in gi.state_vars]
+
+        yield_types = None
+        try:
+            yield_types = [typdict[y.inst.value.name]
+                           for y in gi.get_yield_points()]
+        except KeyError:
+            msg = "Cannot type generator: yield type cannot be found"
+            if raise_errors:
+                raise TypingError(msg)
+        if not yield_types:
+            msg = "Cannot type generator: it does not yield any value"
+            if raise_errors:
+                raise TypingError(msg)
+            yield_types = [types.unknown for _ in gi.get_yield_points()]
+
+        if not yield_types or all(yield_types) == types.unknown:
+            # unknown yield, probably partial type inference, escape
+            return types.Generator(self.func_id.func, types.unknown, arg_types,
+                                   state_types, has_finalizer=True)
+
+        yield_type = self.context.unify_types(*yield_types)
+        if yield_type is None or isinstance(yield_type, types.Optional):
+            msg = "Cannot type generator: cannot unify yielded types %s"
+            yp_highlights = []
+            for y in gi.get_yield_points():
+                msg = (_termcolor.errmsg("Yield of: IR '%s', type '%s', "
+                                         "location: %s"))
+                yp_highlights.append(msg % (str(y.inst),
+                                            typdict[y.inst.value.name],
+                                            y.inst.loc.strformat()))
+
+            explain_ty = set()
+            for ty in yield_types:
+                if isinstance(ty, types.Optional):
+                    explain_ty.add(ty.type)
+                    explain_ty.add(types.NoneType('none'))
+                else:
+                    explain_ty.add(ty)
+            if raise_errors:
+                raise TypingError("Can't unify yield type from the "
+                                  "following types: %s"
+                                  % ", ".join(sorted(map(str, explain_ty))) +
+                                  "\n\n" + "\n".join(yp_highlights))
+
+        return types.Generator(self.func_id.func, yield_type, arg_types,
+                               state_types, has_finalizer=True)
+
+    def get_function_types(self, typemap):
+        """
+        Fill and return the calltypes map.
+        """
+        # XXX why can't this be done on the fly?
+        calltypes = self.calltypes
+        for call, constraint in self.calls:
+            calltypes[call] = constraint.get_call_signature()
+        return calltypes
+
+    def _unify_return_types(self, rettypes):
+        if rettypes:
+            unified = self.context.unify_types(*rettypes)
+            if isinstance(unified, types.FunctionType):
+                # unified is allowed to be UndefinedFunctionType
+                # instance (that is imprecise).
+                return unified
+            if unified is None or not unified.is_precise():
+                def check_type(atype):
+                    lst = []
+                    for k, v in self.typevars.items():
+                        if atype == v.type:
+                            lst.append(k)
+                    returns = {}
+                    for x in reversed(lst):
+                        for block in self.func_ir.blocks.values():
+                            for instr in block.find_insts(ir.Return):
+                                value = instr.value
+                                if isinstance(value, ir.Var):
+                                    name = value.name
+                                else:
+                                    pass
+                                if x == name:
+                                    returns[x] = instr
+                                    break
+
+                    interped = ""
+                    for name, offender in returns.items():
+                        loc = getattr(offender, 'loc', ir.unknown_loc)
+                        msg = ("Return of: IR name '%s', type '%s', "
+                               "location: %s")
+                        interped = msg % (name, atype, loc.strformat())
+                    return interped
+
+                problem_str = []
+                for xtype in rettypes:
+                    problem_str.append(_termcolor.errmsg(check_type(xtype)))
+
+                raise TypingError("Can't unify return type from the "
+                                  "following types: %s"
+                                  % ", ".join(sorted(map(str, rettypes))) +
+                                  "\n" + "\n".join(problem_str))
+            return unified
+        else:
+            # Function without a successful return path
+            return types.none
+
+    def get_return_type(self, typemap):
+        rettypes = set()
+        for var in self._get_return_vars():
+            rettypes.add(typemap[var.name])
+        return self._unify_return_types(rettypes)
+
+    def get_state_token(self):
+        """The algorithm is monotonic.  It can only grow or "refine" the
+        typevar map.
+        """
+        return [tv.type for name, tv in sorted(self.typevars.items())]
+
+    def constrain_statement(self, inst):
+        if isinstance(inst, ir.Assign):
+            self.typeof_assign(inst)
+        elif isinstance(inst, ir.SetItem):
+            self.typeof_setitem(inst)
+        elif isinstance(inst, ir.StaticSetItem):
+            self.typeof_static_setitem(inst)
+        elif isinstance(inst, ir.DelItem):
+            self.typeof_delitem(inst)
+        elif isinstance(inst, ir.SetAttr):
+            self.typeof_setattr(inst)
+        elif isinstance(inst, ir.Print):
+            self.typeof_print(inst)
+        elif isinstance(inst, ir.StoreMap):
+            self.typeof_storemap(inst)
+        elif isinstance(inst, (ir.Jump, ir.Branch, ir.Return, ir.Del)):
+            pass
+        elif isinstance(inst, (ir.StaticRaise, ir.StaticTryRaise)):
+            pass
+        elif isinstance(inst, ir.PopBlock):
+            pass # It's a marker statement
+        elif type(inst) in typeinfer_extensions:
+            # let external calls handle stmt if type matches
+            f = typeinfer_extensions[type(inst)]
+            f(inst, self)
+        else:
+            msg = "Unsupported constraint encountered: %s" % inst
+            raise UnsupportedError(msg, loc=inst.loc)
+
+    def typeof_setitem(self, inst):
+        constraint = SetItemConstraint(target=inst.target, index=inst.index,
+                                       value=inst.value, loc=inst.loc)
+        self.constraints.append(constraint)
+        self.calls.append((inst, constraint))
+
+    def typeof_storemap(self, inst):
+        constraint = SetItemConstraint(target=inst.dct, index=inst.key,
+                                       value=inst.value, loc=inst.loc)
+        self.constraints.append(constraint)
+        self.calls.append((inst, constraint))
+
+    def typeof_static_setitem(self, inst):
+        constraint = StaticSetItemConstraint(target=inst.target,
+                                             index=inst.index,
+                                             index_var=inst.index_var,
+                                             value=inst.value, loc=inst.loc)
+        self.constraints.append(constraint)
+        self.calls.append((inst, constraint))
+
+    def typeof_delitem(self, inst):
+        constraint = DelItemConstraint(target=inst.target, index=inst.index,
+                                       loc=inst.loc)
+        self.constraints.append(constraint)
+        self.calls.append((inst, constraint))
+
+    def typeof_setattr(self, inst):
+        constraint = SetAttrConstraint(target=inst.target, attr=inst.attr,
+                                       value=inst.value, loc=inst.loc)
+        self.constraints.append(constraint)
+        self.calls.append((inst, constraint))
+
+    def typeof_print(self, inst):
+        constraint = PrintConstraint(args=inst.args, vararg=inst.vararg,
+                                     loc=inst.loc)
+        self.constraints.append(constraint)
+        self.calls.append((inst, constraint))
+
+    def typeof_assign(self, inst):
+        value = inst.value
+        if isinstance(value, ir.Const):
+            self.typeof_const(inst, inst.target, value.value)
+        elif isinstance(value, ir.Var):
+            self.constraints.append(Propagate(dst=inst.target.name,
+                                              src=value.name, loc=inst.loc))
+        elif isinstance(value, (ir.Global, ir.FreeVar)):
+            self.typeof_global(inst, inst.target, value)
+        elif isinstance(value, ir.Arg):
+            self.typeof_arg(inst, inst.target, value)
+        elif isinstance(value, ir.Expr):
+            self.typeof_expr(inst, inst.target, value)
+        elif isinstance(value, ir.Yield):
+            self.typeof_yield(inst, inst.target, value)
+        else:
+            msg = ("Unsupported assignment encountered: %s %s" %
+                   (type(value), str(value)))
+            raise UnsupportedError(msg, loc=inst.loc)
+
+    def resolve_value_type(self, inst, val):
+        """
+        Resolve the type of a simple Python value, such as can be
+        represented by literals.
+        """
+        try:
+            return self.context.resolve_value_type(val)
+        except ValueError as e:
+            msg = str(e)
+        raise TypingError(msg, loc=inst.loc)
+
+    def typeof_arg(self, inst, target, arg):
+        src_name = self._mangle_arg_name(arg.name)
+        self.constraints.append(ArgConstraint(dst=target.name,
+                                              src=src_name,
+                                              loc=inst.loc))
+
+    def typeof_const(self, inst, target, const):
+        ty = self.resolve_value_type(inst, const)
+        if inst.value.use_literal_type:
+            lit = types.maybe_literal(value=const)
+        else:
+            lit = None
+        self.add_type(target.name, lit or ty, loc=inst.loc)
+
+    def typeof_yield(self, inst, target, yield_):
+        # Sending values into generators isn't supported.
+        self.add_type(target.name, types.none, loc=inst.loc)
+
+    def sentry_modified_builtin(self, inst, gvar):
+        """
+        Ensure that builtins are not modified.
+        """
+        if gvar.name == 'range' and gvar.value is not range:
+            bad = True
+        elif gvar.name == 'slice' and gvar.value is not slice:
+            bad = True
+        elif gvar.name == 'len' and gvar.value is not len:
+            bad = True
+        else:
+            bad = False
+
+        if bad:
+            raise TypingError("Modified builtin '%s'" % gvar.name,
+                              loc=inst.loc)
+
+    def resolve_call(self, fnty, pos_args, kw_args):
+        """
+        Resolve a call to a given function type.  A signature is returned.
+        """
+        if isinstance(fnty, types.FunctionType):
+            return fnty.get_call_type(self, pos_args, kw_args)
+        if isinstance(fnty, types.RecursiveCall) and not self._skip_recursion:
+            # Recursive call
+            disp = fnty.dispatcher_type.dispatcher
+            pysig, args = disp.fold_argument_types(pos_args, kw_args)
+
+            frame = self.context.callstack.match(disp.py_func, args)
+
+            # If the signature is not being compiled
+            if frame is None:
+                sig = self.context.resolve_function_type(fnty.dispatcher_type,
+                                                         pos_args, kw_args)
+                fndesc = disp.overloads[args].fndesc
+                qual = qualifying_prefix(fndesc.modname, fndesc.qualname)
+                fnty.add_overloads(args, qual, fndesc.uid)
+                return sig
+
+            fnid = frame.func_id
+            qual = qualifying_prefix(fnid.modname, fnid.func_qualname)
+            fnty.add_overloads(args, qual, fnid.unique_id)
+            # Resume propagation in parent frame
+            return_type = frame.typeinfer.return_types_from_partial()
+            # No known return type
+            if return_type is None:
+                raise TypingError("cannot type infer runaway recursion")
+
+            sig = typing.signature(return_type, *args)
+            sig = sig.replace(pysig=pysig)
+            # Keep track of unique return_type
+            frame.add_return_type(return_type)
+            return sig
+        else:
+            # Normal non-recursive call
+            return self.context.resolve_function_type(fnty, pos_args, kw_args)
+
+    def typeof_global(self, inst, target, gvar):
+        try:
+            typ = self.resolve_value_type(inst, gvar.value)
+        except TypingError as e:
+            if (gvar.name == self.func_id.func_name
+                    and gvar.name in _temporary_dispatcher_map):
+                # Self-recursion case where the dispatcher is not (yet?) known
+                # as a global variable
+                typ = types.Dispatcher(_temporary_dispatcher_map[gvar.name])
+            else:
+                from numba.misc import special
+
+                nm = gvar.name
+                # check if the problem is actually a name error
+                func_glbls = self.func_id.func.__globals__
+                if (nm not in func_glbls.keys() and
+                        nm not in special.__all__ and
+                        nm not in __builtins__.keys() and
+                        nm not in self.func_id.code.co_freevars):
+                    errstr = "NameError: name '%s' is not defined"
+                    msg = _termcolor.errmsg(errstr % nm)
+                    e.patch_message(msg)
+                    raise
+                else:
+                    msg = _termcolor.errmsg("Untyped global name '%s':" % nm)
+                msg += " %s"  # interps the actual error
+
+                # if the untyped global is a numba internal function then add
+                # to the error message asking if it's been imported.
+
+                if nm in special.__all__:
+                    tmp = ("\n'%s' looks like a Numba internal function, has "
+                           "it been imported (i.e. 'from numba import %s')?\n" %
+                           (nm, nm))
+                    msg += _termcolor.errmsg(tmp)
+                e.patch_message(msg % e)
+                raise
+
+        if isinstance(typ, types.Dispatcher) and typ.dispatcher.is_compiling:
+            # Recursive call
+            callstack = self.context.callstack
+            callframe = callstack.findfirst(typ.dispatcher.py_func)
+            if callframe is not None:
+                typ = types.RecursiveCall(typ)
+            else:
+                raise NotImplementedError(
+                    "call to %s: unsupported recursion"
+                    % typ.dispatcher)
+
+        if isinstance(typ, types.Array):
+            # Global array in nopython mode is constant
+            typ = typ.copy(readonly=True)
+
+        if isinstance(typ, types.BaseAnonymousTuple):
+            # if it's a tuple of literal types, swap the type for the more
+            # specific literal version
+            literaled = [types.maybe_literal(x) for x in gvar.value]
+            if all(literaled):
+                typ = types.Tuple(literaled)
+
+            # if any of the items in the tuple are arrays, they need to be
+            # typed as readonly, mutating an array in a global container
+            # is not supported (should be compile time constant etc).
+            def mark_array_ro(tup):
+                newtup = []
+                for item in tup.types:
+                    if isinstance(item, types.Array):
+                        item = item.copy(readonly=True)
+                    elif isinstance(item, types.BaseAnonymousTuple):
+                        item = mark_array_ro(item)
+                    newtup.append(item)
+                return types.BaseTuple.from_types(newtup)
+            typ = mark_array_ro(typ)
+
+        self.sentry_modified_builtin(inst, gvar)
+        # Setting literal_value for globals because they are handled
+        # like const value in numba
+        lit = types.maybe_literal(gvar.value)
+        # The user may have provided the type for this variable already.
+        # In this case, call add_type() to make sure the value type is
+        # consistent. See numba.tests.test_array_reductions
+        # TestArrayReductions.test_array_cumsum for examples.
+        # Variable type locked by using the locals dict.
+        tv = self.typevars[target.name]
+        if tv.locked:
+            tv.add_type(lit or typ, loc=inst.loc)
+        else:
+            self.lock_type(target.name, lit or typ, loc=inst.loc)
+        self.assumed_immutables.add(inst)
+
+    def typeof_expr(self, inst, target, expr):
+        if expr.op == 'call':
+            self.typeof_call(inst, target, expr)
+        elif expr.op in ('getiter', 'iternext'):
+            self.typeof_intrinsic_call(inst, target, expr.op, expr.value)
+        elif expr.op == 'exhaust_iter':
+            constraint = ExhaustIterConstraint(target.name, count=expr.count,
+                                               iterator=expr.value,
+                                               loc=expr.loc)
+            self.constraints.append(constraint)
+        elif expr.op == 'pair_first':
+            constraint = PairFirstConstraint(target.name, pair=expr.value,
+                                             loc=expr.loc)
+            self.constraints.append(constraint)
+        elif expr.op == 'pair_second':
+            constraint = PairSecondConstraint(target.name, pair=expr.value,
+                                              loc=expr.loc)
+            self.constraints.append(constraint)
+        elif expr.op == 'binop':
+            self.typeof_intrinsic_call(inst, target, expr.fn, expr.lhs,
+                                       expr.rhs)
+        elif expr.op == 'inplace_binop':
+            self.typeof_intrinsic_call(inst, target, expr.fn,
+                                       expr.lhs, expr.rhs)
+        elif expr.op == 'unary':
+            self.typeof_intrinsic_call(inst, target, expr.fn, expr.value)
+        elif expr.op == 'static_getitem':
+            constraint = StaticGetItemConstraint(target.name, value=expr.value,
+                                                 index=expr.index,
+                                                 index_var=expr.index_var,
+                                                 loc=expr.loc)
+            self.constraints.append(constraint)
+            self.calls.append((inst.value, constraint))
+        elif expr.op == 'getitem':
+            self.typeof_intrinsic_call(inst, target, operator.getitem,
+                                       expr.value, expr.index,)
+        elif expr.op == 'typed_getitem':
+            constraint = TypedGetItemConstraint(target.name, value=expr.value,
+                                                dtype=expr.dtype,
+                                                index=expr.index,
+                                                loc=expr.loc)
+            self.constraints.append(constraint)
+            self.calls.append((inst.value, constraint))
+
+        elif expr.op == 'getattr':
+            constraint = GetAttrConstraint(target.name, attr=expr.attr,
+                                           value=expr.value, loc=inst.loc,
+                                           inst=inst)
+            self.constraints.append(constraint)
+        elif expr.op == 'build_tuple':
+            constraint = BuildTupleConstraint(target.name, items=expr.items,
+                                              loc=inst.loc)
+            self.constraints.append(constraint)
+        elif expr.op == 'build_list':
+            constraint = BuildListConstraint(target.name, items=expr.items,
+                                             loc=inst.loc)
+            self.constraints.append(constraint)
+        elif expr.op == 'build_set':
+            constraint = BuildSetConstraint(target.name, items=expr.items,
+                                            loc=inst.loc)
+            self.constraints.append(constraint)
+        elif expr.op == 'build_map':
+            constraint = BuildMapConstraint(
+                target.name,
+                items=expr.items,
+                special_value=expr.literal_value,
+                value_indexes=expr.value_indexes,
+                loc=inst.loc)
+            self.constraints.append(constraint)
+        elif expr.op == 'cast':
+            self.constraints.append(Propagate(dst=target.name,
+                                              src=expr.value.name,
+                                              loc=inst.loc))
+        elif expr.op == 'phi':
+            for iv in expr.incoming_values:
+                if iv is not ir.UNDEFINED:
+                    self.constraints.append(Propagate(dst=target.name,
+                                                      src=iv.name,
+                                                      loc=inst.loc))
+        elif expr.op == 'make_function':
+            self.lock_type(target.name, types.MakeFunctionLiteral(expr),
+                           loc=inst.loc, literal_value=expr)
+
+        else:
+            msg = "Unsupported op-code encountered: %s" % expr
+            raise UnsupportedError(msg, loc=inst.loc)
+
+    def typeof_call(self, inst, target, call):
+        constraint = CallConstraint(target.name, call.func.name, call.args,
+                                    call.kws, call.vararg, loc=inst.loc)
+        self.constraints.append(constraint)
+        self.calls.append((inst.value, constraint))
+
+    def typeof_intrinsic_call(self, inst, target, func, *args):
+        constraint = IntrinsicCallConstraint(target.name, func, args,
+                                             kws=(), vararg=None, loc=inst.loc)
+        self.constraints.append(constraint)
+        self.calls.append((inst.value, constraint))
+
+
+class NullDebug(object):
+
+    def propagate_started(self):
+        pass
+
+    def propagate_finished(self):
+        pass
+
+    def unify_finished(self, typdict, retty, fntys):
+        pass
+
+
+class TypeInferDebug(object):
+
+    def __init__(self, typeinfer):
+        self.typeinfer = typeinfer
+
+    def _dump_state(self):
+        print('---- type variables ----')
+        pprint([v for k, v in sorted(self.typeinfer.typevars.items())])
+
+    def propagate_started(self):
+        print("propagate".center(80, '-'))
+
+    def propagate_finished(self):
+        self._dump_state()
+
+    def unify_finished(self, typdict, retty, fntys):
+        print("Variable types".center(80, "-"))
+        pprint(typdict)
+        print("Return type".center(80, "-"))
+        pprint(retty)
+        print("Call types".center(80, "-"))
+        pprint(fntys)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/__init__.py
new file mode 100644
index 000000000..5755e109c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/__init__.py
@@ -0,0 +1,178 @@
+import struct
+
+import numpy as np
+from numba.core import utils
+
+from .abstract import *
+from .containers import *
+from .functions import *
+from .iterators import *
+from .misc import *
+from .npytypes import *
+from .scalars import *
+from .function_type import *
+
+# Short names
+
+pyobject = PyObject('pyobject')
+ffi_forced_object = Opaque('ffi_forced_object')
+ffi = Opaque('ffi')
+none = NoneType('none')
+ellipsis = EllipsisType('...')
+Any = Phantom('any')
+undefined = Undefined('undefined')
+py2_string_type = Opaque('str')
+unicode_type = UnicodeType('unicode_type')
+string = unicode_type
+unknown = Dummy('unknown')
+npy_rng = NumPyRandomGeneratorType('rng')
+npy_bitgen = NumPyRandomBitGeneratorType('bitgen')
+
+code_type = Opaque('code')
+pyfunc_type = Opaque('pyfunc')
+
+# No operation is defined on voidptr
+# Can only pass it around
+voidptr = RawPointer('void*')
+
+boolean = bool_ = Boolean('bool')
+
+byte = uint8 = Integer('uint8')
+uint16 = Integer('uint16')
+uint32 = Integer('uint32')
+uint64 = Integer('uint64')
+
+int8 = Integer('int8')
+int16 = Integer('int16')
+int32 = Integer('int32')
+int64 = Integer('int64')
+intp = int32 if utils.MACHINE_BITS == 32 else int64
+uintp = uint32 if utils.MACHINE_BITS == 32 else uint64
+intc = int32 if struct.calcsize('i') == 4 else int64
+uintc = uint32 if struct.calcsize('I') == 4 else uint64
+ssize_t = int32 if struct.calcsize('n') == 4 else int64
+size_t = uint32 if struct.calcsize('N') == 4 else uint64
+
+float32 = Float('float32')
+float64 = Float('float64')
+float16 = Float('float16')
+
+complex64 = Complex('complex64', float32)
+complex128 = Complex('complex128', float64)
+
+range_iter32_type = RangeIteratorType(int32)
+range_iter64_type = RangeIteratorType(int64)
+unsigned_range_iter64_type = RangeIteratorType(uint64)
+range_state32_type = RangeType(int32)
+range_state64_type = RangeType(int64)
+unsigned_range_state64_type = RangeType(uint64)
+
+slice2_type = SliceType('slice<a:b>', 2)
+slice3_type = SliceType('slice<a:b:c>', 3)
+
+signed_domain = frozenset([int8, int16, int32, int64])
+unsigned_domain = frozenset([uint8, uint16, uint32, uint64])
+integer_domain = signed_domain | unsigned_domain
+real_domain = frozenset([float32, float64])
+complex_domain = frozenset([complex64, complex128])
+number_domain = real_domain | integer_domain | complex_domain
+
+# Aliases to NumPy type names
+
+b1 = bool_
+i1 = int8
+i2 = int16
+i4 = int32
+i8 = int64
+u1 = uint8
+u2 = uint16
+u4 = uint32
+u8 = uint64
+
+f2 = float16
+f4 = float32
+f8 = float64
+
+c8 = complex64
+c16 = complex128
+
+float_ = float32
+double = float64
+void = none
+
+_make_signed = lambda x: globals()["int%d" % (np.dtype(x).itemsize * 8)]
+_make_unsigned = lambda x: globals()["uint%d" % (np.dtype(x).itemsize * 8)]
+
+char = _make_signed(np.byte)
+uchar = byte = _make_unsigned(np.byte)
+short = _make_signed(np.short)
+ushort = _make_unsigned(np.short)
+int_ = _make_signed(np.int_)
+uint = _make_unsigned(np.int_)
+intc = _make_signed(np.intc) # C-compat int
+uintc = _make_unsigned(np.uintc) # C-compat uint
+long_ = _make_signed(np.int_)  # C-compat long
+ulong = _make_unsigned(np.int_)  # C-compat ulong
+longlong = _make_signed(np.longlong)
+ulonglong = _make_unsigned(np.longlong)
+
+# optional types
+optional = Optional
+
+
+deferred_type = DeferredType
+
+__all__ = '''
+int8
+int16
+int32
+int64
+uint8
+uint16
+uint32
+uint64
+intp
+uintp
+intc
+uintc
+ssize_t
+size_t
+boolean
+float32
+float64
+complex64
+complex128
+bool_
+byte
+char
+uchar
+short
+ushort
+int_
+uint
+long_
+ulong
+longlong
+ulonglong
+float_
+double
+void
+none
+b1
+i1
+i2
+i4
+i8
+u1
+u2
+u4
+u8
+f4
+f8
+c8
+c16
+optional
+ffi_forced_object
+ffi
+deferred_type
+'''.split()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/abstract.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/abstract.py
new file mode 100644
index 000000000..863d74554
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/abstract.py
@@ -0,0 +1,507 @@
+from abc import ABCMeta, abstractmethod, abstractproperty
+from typing import Dict as ptDict, Type as ptType
+import itertools
+import weakref
+
+import numpy as np
+
+from numba.core.utils import cached_property, get_hashable_key
+
+# Types are added to a global registry (_typecache) in order to assign
+# them unique integer codes for fast matching in _dispatcher.c.
+# However, we also want types to be disposable, therefore we ensure
+# each type is interned as a weak reference, so that it lives only as
+# long as necessary to keep a stable type code.
+# NOTE: some types can still be made immortal elsewhere (for example
+# in _dispatcher.c's internal caches).
+_typecodes = itertools.count()
+
+def _autoincr():
+    n = next(_typecodes)
+    # 4 billion types should be enough, right?
+    assert n < 2 ** 32, "Limited to 4 billion types"
+    return n
+
+_typecache: ptDict[weakref.ref, weakref.ref] = {}
+
+def _on_type_disposal(wr, _pop=_typecache.pop):
+    _pop(wr, None)
+
+
+class _TypeMetaclass(ABCMeta):
+    """
+    A metaclass that will intern instances after they are created.
+    This is done by first creating a new instance (including calling
+    __init__, which sets up the required attributes for equality
+    and hashing), then looking it up in the _typecache registry.
+    """
+
+    def __init__(cls, name, bases, orig_vars):
+        # __init__ is hooked to mark whether a Type class being defined is a
+        # Numba internal type (one which is defined somewhere under the `numba`
+        # module) or an external type (one which is defined elsewhere, for
+        # example a user defined type).
+        super(_TypeMetaclass, cls).__init__(name, bases, orig_vars)
+        root = (cls.__module__.split('.'))[0]
+        cls._is_internal = root == "numba"
+
+    def _intern(cls, inst):
+        # Try to intern the created instance
+        wr = weakref.ref(inst, _on_type_disposal)
+        orig = _typecache.get(wr)
+        orig = orig and orig()
+        if orig is not None:
+            return orig
+        else:
+            inst._code = _autoincr()
+            _typecache[wr] = wr
+            return inst
+
+    def __call__(cls, *args, **kwargs):
+        """
+        Instantiate *cls* (a Type subclass, presumably) and intern it.
+        If an interned instance already exists, it is returned, otherwise
+        the new instance is returned.
+        """
+        inst = type.__call__(cls, *args, **kwargs)
+        return cls._intern(inst)
+
+
+def _type_reconstructor(reconstructor, reconstructor_args, state):
+    """
+    Rebuild function for unpickling types.
+    """
+    obj = reconstructor(*reconstructor_args)
+    if state:
+        obj.__dict__.update(state)
+    return type(obj)._intern(obj)
+
+
+class Type(metaclass=_TypeMetaclass):
+    """
+    The base class for all Numba types.
+    It is essential that proper equality comparison is implemented.  The
+    default implementation uses the "key" property (overridable in subclasses)
+    for both comparison and hashing, to ensure sane behaviour.
+    """
+
+    mutable = False
+    # Rather the type is reflected at the python<->nopython boundary
+    reflected = False
+
+    def __init__(self, name):
+        self.name = name
+
+    @property
+    def key(self):
+        """
+        A property used for __eq__, __ne__ and __hash__.  Can be overridden
+        in subclasses.
+        """
+        return self.name
+
+    @property
+    def mangling_args(self):
+        """
+        Returns `(basename, args)` where `basename` is the name of the type
+        and `args` is a sequence of parameters of the type.
+
+        Subclass should override to specialize the behavior.
+        By default, this returns `(self.name, ())`.
+        """
+        return self.name, ()
+
+    def __repr__(self):
+        return self.name
+
+    def __hash__(self):
+        return hash(self.key)
+
+    def __eq__(self, other):
+        return self.__class__ is other.__class__ and self.key == other.key
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __reduce__(self):
+        reconstructor, args, state = super(Type, self).__reduce__()
+        return (_type_reconstructor, (reconstructor, args, state))
+
+    def unify(self, typingctx, other):
+        """
+        Try to unify this type with the *other*.  A third type must
+        be returned, or None if unification is not possible.
+        Only override this if the coercion logic cannot be expressed
+        as simple casting rules.
+        """
+        return None
+
+    def can_convert_to(self, typingctx, other):
+        """
+        Check whether this type can be converted to the *other*.
+        If successful, must return a string describing the conversion, e.g.
+        "exact", "promote", "unsafe", "safe"; otherwise None is returned.
+        """
+        return None
+
+    def can_convert_from(self, typingctx, other):
+        """
+        Similar to *can_convert_to*, but in reverse.  Only needed if
+        the type provides conversion from other types.
+        """
+        return None
+
+    def is_precise(self):
+        """
+        Whether this type is precise, i.e. can be part of a successful
+        type inference.  Default implementation returns True.
+        """
+        return True
+
+    def augment(self, other):
+        """
+        Augment this type with the *other*.  Return the augmented type,
+        or None if not supported.
+        """
+        return None
+
+    # User-facing helpers.  These are not part of the core Type API but
+    # are provided so that users can write e.g. `numba.boolean(1.5)`
+    # (returns True) or `types.int32(types.int32[:])` (returns something
+    # usable as a function signature).
+
+    def __call__(self, *args):
+        from numba.core.typing import signature
+        if len(args) == 1 and not isinstance(args[0], Type):
+            return self.cast_python_value(args[0])
+        return signature(self, # return_type
+                         *args)
+
+    def __getitem__(self, args):
+        """
+        Return an array of this type.
+        """
+        from numba.core.types import Array
+        ndim, layout = self._determine_array_spec(args)
+        return Array(dtype=self, ndim=ndim, layout=layout)
+
+    def _determine_array_spec(self, args):
+        # XXX non-contiguous by default, even for 1d arrays,
+        # doesn't sound very intuitive
+        def validate_slice(s):
+            return isinstance(s, slice) and s.start is None and s.stop is None
+
+        if isinstance(args, (tuple, list)) and all(map(validate_slice, args)):
+            ndim = len(args)
+            if args[0].step == 1:
+                layout = 'F'
+            elif args[-1].step == 1:
+                layout = 'C'
+            else:
+                layout = 'A'
+        elif validate_slice(args):
+            ndim = 1
+            if args.step == 1:
+                layout = 'C'
+            else:
+                layout = 'A'
+        else:
+            # Raise a KeyError to not be handled by collection constructors (e.g. list).
+            raise KeyError(f"Can only index numba types with slices with no start or stop, got {args}.")
+
+        return ndim, layout
+
+    def cast_python_value(self, args):
+        raise NotImplementedError
+
+
+    @property
+    def is_internal(self):
+        """ Returns True if this class is an internally defined Numba type by
+        virtue of the module in which it is instantiated, False else."""
+        return self._is_internal
+
+    def dump(self, tab=''):
+        print(f'{tab}DUMP {type(self).__name__}[code={self._code}, name={self.name}]')
+
+# XXX we should distinguish between Dummy (no meaningful
+# representation, e.g. None or a builtin function) and Opaque (has a
+# meaningful representation, e.g. ExternalFunctionPointer)
+
+class Dummy(Type):
+    """
+    Base class for types that do not really have a representation and are
+    compatible with a void*.
+    """
+
+
+class Hashable(Type):
+    """
+    Base class for hashable types.
+    """
+
+
+class Number(Hashable):
+    """
+    Base class for number types.
+    """
+
+    def unify(self, typingctx, other):
+        """
+        Unify the two number types using Numpy's rules.
+        """
+        from numba.np import numpy_support
+        if isinstance(other, Number):
+            # XXX: this can produce unsafe conversions,
+            # e.g. would unify {int64, uint64} to float64
+            a = numpy_support.as_dtype(self)
+            b = numpy_support.as_dtype(other)
+            sel = np.promote_types(a, b)
+            return numpy_support.from_dtype(sel)
+
+
+class Callable(Type):
+    """
+    Base class for callables.
+    """
+
+    @abstractmethod
+    def get_call_type(self, context, args, kws):
+        """
+        Using the typing *context*, resolve the callable's signature for
+        the given arguments.  A signature object is returned, or None.
+        """
+
+    @abstractmethod
+    def get_call_signatures(self):
+        """
+        Returns a tuple of (list of signatures, parameterized)
+        """
+
+    @abstractmethod
+    def get_impl_key(self, sig):
+        """
+        Returns the impl key for the given signature
+        """
+
+
+class DTypeSpec(Type):
+    """
+    Base class for types usable as "dtype" arguments to various Numpy APIs
+    (e.g. np.empty()).
+    """
+
+    @abstractproperty
+    def dtype(self):
+        """
+        The actual dtype denoted by this dtype spec (a Type instance).
+        """
+
+
+class IterableType(Type):
+    """
+    Base class for iterable types.
+    """
+
+    @abstractproperty
+    def iterator_type(self):
+        """
+        The iterator type obtained when calling iter() (explicitly or implicitly).
+        """
+
+
+class Sized(Type):
+    """
+    Base class for objects that support len()
+    """
+
+
+class ConstSized(Sized):
+    """
+    For types that have a constant size
+    """
+    @abstractmethod
+    def __len__(self):
+        pass
+
+
+class IteratorType(IterableType):
+    """
+    Base class for all iterator types.
+    Derived classes should implement the *yield_type* attribute.
+    """
+
+    def __init__(self, name, **kwargs):
+        super(IteratorType, self).__init__(name, **kwargs)
+
+    @abstractproperty
+    def yield_type(self):
+        """
+        The type of values yielded by the iterator.
+        """
+
+    # This is a property to avoid recursivity (for pickling)
+
+    @property
+    def iterator_type(self):
+        return self
+
+
+class Container(Sized, IterableType):
+    """
+    Base class for container types.
+    """
+
+
+class Sequence(Container):
+    """
+    Base class for 1d sequence types.  Instances should have the *dtype*
+    attribute.
+    """
+
+
+class MutableSequence(Sequence):
+    """
+    Base class for 1d mutable sequence types.  Instances should have the
+    *dtype* attribute.
+    """
+
+
+class ArrayCompatible(Type):
+    """
+    Type class for Numpy array-compatible objects (typically, objects
+    exposing an __array__ method).
+    Derived classes should implement the *as_array* attribute.
+    """
+    # If overridden by a subclass, it should also implement typing
+    # for '__array_wrap__' with arguments (input, formal result).
+    array_priority = 0.0
+
+    @abstractproperty
+    def as_array(self):
+        """
+        The equivalent array type, for operations supporting array-compatible
+        objects (such as ufuncs).
+        """
+
+    # For compatibility with types.Array
+
+    @cached_property
+    def ndim(self):
+        return self.as_array.ndim
+
+    @cached_property
+    def layout(self):
+        return self.as_array.layout
+
+    @cached_property
+    def dtype(self):
+        return self.as_array.dtype
+
+
+class Literal(Type):
+    """Base class for Literal types.
+    Literal types contain the original Python value in the type.
+
+    A literal type should always be constructed from the `literal(val)`
+    function.
+    """
+
+    # *ctor_map* is a dictionary mapping Python types to Literal subclasses
+    # for constructing a numba type for a given Python type.
+    # It is used in `literal(val)` function.
+    # To add new Literal subclass, register a new mapping to this dict.
+    ctor_map: ptDict[type, ptType['Literal']] = {}
+
+    # *_literal_type_cache* is used to cache the numba type of the given value.
+    _literal_type_cache = None
+
+    def __init__(self, value):
+        if type(self) is Literal:
+            raise TypeError(
+                "Cannot be constructed directly. "
+                "Use `numba.types.literal(value)` instead",
+            )
+        self._literal_init(value)
+        fmt = "Literal[{}]({})"
+        super(Literal, self).__init__(fmt.format(type(value).__name__, value))
+
+    def _literal_init(self, value):
+        self._literal_value = value
+        # We want to support constants of non-hashable values, therefore
+        # fall back on the value's id() if necessary.
+        self._key = get_hashable_key(value)
+
+    @property
+    def literal_value(self):
+        return self._literal_value
+
+    @property
+    def literal_type(self):
+        if self._literal_type_cache is None:
+            from numba.core import typing
+            ctx = typing.Context()
+            try:
+                res = ctx.resolve_value_type(self.literal_value)
+            except ValueError as e:
+
+                if "Int value is too large" in str(e):
+                    # If a string literal cannot create an IntegerLiteral
+                    # because of overflow we generate this message.
+                    msg = f"Cannot create literal type. {str(e)}"
+                    raise TypeError(msg)
+                # Not all literal types have a literal_value that can be
+                # resolved to a type, for example, LiteralStrKeyDict has a
+                # literal_value that is a python dict for which there's no
+                # `typeof` support.
+                msg = "{} has no attribute 'literal_type'".format(self)
+                raise AttributeError(msg)
+            self._literal_type_cache = res
+
+        return self._literal_type_cache
+
+
+
+class TypeRef(Dummy):
+    """Reference to a type.
+
+    Used when a type is passed as a value.
+    """
+    def __init__(self, instance_type):
+        self.instance_type = instance_type
+        super(TypeRef, self).__init__('typeref[{}]'.format(self.instance_type))
+
+    @property
+    def key(self):
+        return self.instance_type
+
+
+class InitialValue(object):
+    """
+    Used as a mixin for a type will potentially have an initial value that will
+    be carried in the .initial_value attribute.
+    """
+    def __init__(self, initial_value):
+        self._initial_value = initial_value
+
+    @property
+    def initial_value(self):
+        return self._initial_value
+
+
+class Poison(Type):
+    """
+    This is the "bottom" type in the type system. It won't unify and it's
+    unliteral version is Poison of itself. It's advisable for debugging purposes
+    to call the constructor with the type that's being poisoned (for whatever
+    reason) but this isn't strictly required.
+    """
+    def __init__(self, ty):
+        self.ty = ty
+        super(Poison, self).__init__(name="Poison<%s>" % ty)
+
+    def __unliteral__(self):
+        return Poison(self)
+
+    def unify(self, typingctx, other):
+        return None
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/common.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/common.py
new file mode 100644
index 000000000..129f109dd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/common.py
@@ -0,0 +1,104 @@
+"""
+Helper classes / mixins for defining types.
+"""
+
+from .abstract import ArrayCompatible, Dummy, IterableType, IteratorType
+from numba.core.errors import NumbaTypeError, NumbaValueError
+
+
+class Opaque(Dummy):
+    """
+    A type that is a opaque pointer.
+    """
+
+
+class SimpleIterableType(IterableType):
+
+    def __init__(self, name, iterator_type):
+        self._iterator_type = iterator_type
+        super(SimpleIterableType, self).__init__(name)
+
+    @property
+    def iterator_type(self):
+        return self._iterator_type
+
+
+class SimpleIteratorType(IteratorType):
+
+    def __init__(self, name, yield_type):
+        self._yield_type = yield_type
+        super(SimpleIteratorType, self).__init__(name)
+
+    @property
+    def yield_type(self):
+        return self._yield_type
+
+
+class Buffer(IterableType, ArrayCompatible):
+    """
+    Type class for objects providing the buffer protocol.
+    Derived classes exist for more specific cases.
+    """
+    mutable = True
+    slice_is_copy = False
+    aligned = True
+
+    # CS and FS are not reserved for inner contig but strided
+    LAYOUTS = frozenset(['C', 'F', 'CS', 'FS', 'A'])
+
+    def __init__(self, dtype, ndim, layout, readonly=False, name=None):
+        from .misc import unliteral
+
+        if isinstance(dtype, Buffer):
+            msg = ("The dtype of a Buffer type cannot itself be a Buffer type, "
+                   "this is unsupported behaviour."
+                   "\nThe dtype requested for the unsupported Buffer was: {}.")
+            raise NumbaTypeError(msg.format(dtype))
+        if layout not in self.LAYOUTS:
+            raise NumbaValueError("Invalid layout '%s'" % layout)
+        self.dtype = unliteral(dtype)
+        self.ndim = ndim
+        self.layout = layout
+        if readonly:
+            self.mutable = False
+        if name is None:
+            type_name = self.__class__.__name__.lower()
+            if readonly:
+                type_name = "readonly %s" % type_name
+            name = "%s(%s, %sd, %s)" % (type_name, dtype, ndim, layout)
+        super(Buffer, self).__init__(name)
+
+    @property
+    def iterator_type(self):
+        from .iterators import ArrayIterator
+        return ArrayIterator(self)
+
+    @property
+    def as_array(self):
+        return self
+
+    def copy(self, dtype=None, ndim=None, layout=None):
+        if dtype is None:
+            dtype = self.dtype
+        if ndim is None:
+            ndim = self.ndim
+        if layout is None:
+            layout = self.layout
+        return self.__class__(dtype=dtype, ndim=ndim, layout=layout,
+                              readonly=not self.mutable)
+
+    @property
+    def key(self):
+        return self.dtype, self.ndim, self.layout, self.mutable
+
+    @property
+    def is_c_contig(self):
+        return self.layout == 'C' or (self.ndim <= 1 and self.layout in 'CF')
+
+    @property
+    def is_f_contig(self):
+        return self.layout == 'F' or (self.ndim <= 1 and self.layout in 'CF')
+
+    @property
+    def is_contig(self):
+        return self.layout in 'CF'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/containers.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/containers.py
new file mode 100644
index 000000000..0930104f2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/containers.py
@@ -0,0 +1,958 @@
+from collections.abc import Iterable
+from collections.abc import Sequence as pySequence
+from types import MappingProxyType
+
+from .abstract import (
+    ConstSized,
+    Container,
+    Hashable,
+    MutableSequence,
+    Sequence,
+    Type,
+    TypeRef,
+    Literal,
+    InitialValue,
+    Poison,
+)
+from .common import (
+    Buffer,
+    IterableType,
+    SimpleIterableType,
+    SimpleIteratorType,
+)
+from .misc import Undefined, unliteral, Optional, NoneType
+from ..typeconv import Conversion
+from ..errors import TypingError
+from .. import utils
+
+
+class Pair(Type):
+    """
+    A heterogeneous pair.
+    """
+
+    def __init__(self, first_type, second_type):
+        self.first_type = first_type
+        self.second_type = second_type
+        name = "pair<%s, %s>" % (first_type, second_type)
+        super(Pair, self).__init__(name=name)
+
+    @property
+    def key(self):
+        return self.first_type, self.second_type
+
+    def unify(self, typingctx, other):
+        if isinstance(other, Pair):
+            first = typingctx.unify_pairs(self.first_type, other.first_type)
+            second = typingctx.unify_pairs(self.second_type, other.second_type)
+            if first is not None and second is not None:
+                return Pair(first, second)
+
+
+class BaseContainerIterator(SimpleIteratorType):
+    """
+    Convenience base class for some container iterators.
+
+    Derived classes must implement the *container_class* attribute.
+    """
+
+    def __init__(self, container):
+        assert isinstance(container, self.container_class), container
+        self.container = container
+        yield_type = container.dtype
+        name = "iter(%s)" % container
+        super(BaseContainerIterator, self).__init__(name, yield_type)
+
+    def unify(self, typingctx, other):
+        cls = type(self)
+        if isinstance(other, cls):
+            container = typingctx.unify_pairs(self.container, other.container)
+            if container is not None:
+                return cls(container)
+
+    @property
+    def key(self):
+        return self.container
+
+
+class BaseContainerPayload(Type):
+    """
+    Convenience base class for some container payloads.
+
+    Derived classes must implement the *container_class* attribute.
+    """
+
+    def __init__(self, container):
+        assert isinstance(container, self.container_class)
+        self.container = container
+        name = "payload(%s)" % container
+        super(BaseContainerPayload, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.container
+
+
+class Bytes(Buffer):
+    """
+    Type class for Python 3.x bytes objects.
+    """
+
+    mutable = False
+    # Actually true but doesn't matter since bytes is immutable
+    slice_is_copy = False
+
+
+class ByteArray(Buffer):
+    """
+    Type class for bytearray objects.
+    """
+
+    slice_is_copy = True
+
+
+class PyArray(Buffer):
+    """
+    Type class for array.array objects.
+    """
+
+    slice_is_copy = True
+
+
+class MemoryView(Buffer):
+    """
+    Type class for memoryview objects.
+    """
+
+
+def is_homogeneous(*tys):
+    """Are the types homogeneous?
+    """
+    if tys:
+        first, tys = tys[0], tys[1:]
+        return not any(t != first for t in tys)
+    else:
+        # *tys* is empty.
+        return False
+
+
+class BaseTuple(ConstSized, Hashable):
+    """
+    The base class for all tuple types (with a known size).
+    """
+
+    @classmethod
+    def from_types(cls, tys, pyclass=None):
+        """
+        Instantiate the right tuple type for the given element types.
+        """
+        if pyclass is not None and pyclass is not tuple:
+            # A subclass => is it a namedtuple?
+            assert issubclass(pyclass, tuple)
+            if hasattr(pyclass, "_asdict"):
+                tys = tuple(map(unliteral, tys))
+                homogeneous = is_homogeneous(*tys)
+                if homogeneous:
+                    return NamedUniTuple(tys[0], len(tys), pyclass)
+                else:
+                    return NamedTuple(tys, pyclass)
+        else:
+            dtype = utils.unified_function_type(tys)
+            if dtype is not None:
+                return UniTuple(dtype, len(tys))
+            # non-named tuple
+            homogeneous = is_homogeneous(*tys)
+            if homogeneous:
+                return cls._make_homogeneous_tuple(tys[0], len(tys))
+            else:
+                return cls._make_heterogeneous_tuple(tys)
+
+    @classmethod
+    def _make_homogeneous_tuple(cls, dtype, count):
+        return UniTuple(dtype, count)
+
+    @classmethod
+    def _make_heterogeneous_tuple(cls, tys):
+        return Tuple(tys)
+
+
+class BaseAnonymousTuple(BaseTuple):
+    """
+    Mixin for non-named tuples.
+    """
+
+    def can_convert_to(self, typingctx, other):
+        """
+        Convert this tuple to another one.  Note named tuples are rejected.
+        """
+        if not isinstance(other, BaseAnonymousTuple):
+            return
+        if len(self) != len(other):
+            return
+        if len(self) == 0:
+            return Conversion.safe
+        if isinstance(other, BaseTuple):
+            kinds = [
+                typingctx.can_convert(ta, tb) for ta, tb in zip(self, other)
+            ]
+            if any(kind is None for kind in kinds):
+                return
+            return max(kinds)
+
+    def __unliteral__(self):
+        return type(self).from_types([unliteral(t) for t in self])
+
+
+class _HomogeneousTuple(Sequence, BaseTuple):
+    @property
+    def iterator_type(self):
+        return UniTupleIter(self)
+
+    def __getitem__(self, i):
+        """
+        Return element at position i
+        """
+        return self.dtype
+
+    def __iter__(self):
+        return iter([self.dtype] * self.count)
+
+    def __len__(self):
+        return self.count
+
+    @property
+    def types(self):
+        return (self.dtype,) * self.count
+
+
+class UniTuple(BaseAnonymousTuple, _HomogeneousTuple, Sequence):
+    """
+    Type class for homogeneous tuples.
+    """
+
+    def __init__(self, dtype, count):
+        self.dtype = dtype
+        self.count = count
+        name = "%s(%s x %d)" % (self.__class__.__name__, dtype, count,)
+        super(UniTuple, self).__init__(name)
+
+    @property
+    def mangling_args(self):
+        return self.__class__.__name__, (self.dtype, self.count)
+
+    @property
+    def key(self):
+        return self.dtype, self.count
+
+    def unify(self, typingctx, other):
+        """
+        Unify UniTuples with their dtype
+        """
+        if isinstance(other, UniTuple) and len(self) == len(other):
+            dtype = typingctx.unify_pairs(self.dtype, other.dtype)
+            if dtype is not None:
+                return UniTuple(dtype=dtype, count=self.count)
+
+    def __unliteral__(self):
+        return type(self)(dtype=unliteral(self.dtype), count=self.count)
+
+
+class UniTupleIter(BaseContainerIterator):
+    """
+    Type class for homogeneous tuple iterators.
+    """
+
+    container_class = _HomogeneousTuple
+
+
+class _HeterogeneousTuple(BaseTuple):
+    def __getitem__(self, i):
+        """
+        Return element at position i
+        """
+        return self.types[i]
+
+    def __len__(self):
+        # Beware: this makes Tuple(()) false-ish
+        return len(self.types)
+
+    def __iter__(self):
+        return iter(self.types)
+
+    @staticmethod
+    def is_types_iterable(types):
+        # issue 4463 - check if argument 'types' is iterable
+        if not isinstance(types, Iterable):
+            raise TypingError("Argument 'types' is not iterable")
+
+
+class UnionType(Type):
+    def __init__(self, types):
+        self.types = tuple(sorted(set(types), key=lambda x: x.name))
+        name = "Union[{}]".format(",".join(map(str, self.types)))
+        super(UnionType, self).__init__(name=name)
+
+    def get_type_tag(self, typ):
+        return self.types.index(typ)
+
+
+class Tuple(BaseAnonymousTuple, _HeterogeneousTuple):
+    def __new__(cls, types):
+
+        t = utils.unified_function_type(types, require_precise=True)
+        if t is not None:
+            return UniTuple(dtype=t, count=len(types))
+
+        _HeterogeneousTuple.is_types_iterable(types)
+
+        if types and all(t == types[0] for t in types[1:]):
+            return UniTuple(dtype=types[0], count=len(types))
+        else:
+            return object.__new__(Tuple)
+
+    def __init__(self, types):
+        self.types = tuple(types)
+        self.count = len(self.types)
+        self.dtype = UnionType(types)
+        name = "%s(%s)" % (
+            self.__class__.__name__,
+            ", ".join(str(i) for i in self.types),
+        )
+        super(Tuple, self).__init__(name)
+
+    @property
+    def mangling_args(self):
+        return self.__class__.__name__, tuple(t for t in self.types)
+
+    @property
+    def key(self):
+        return self.types
+
+    def unify(self, typingctx, other):
+        """
+        Unify elements of Tuples/UniTuples
+        """
+        # Other is UniTuple or Tuple
+        if isinstance(other, BaseTuple) and len(self) == len(other):
+            unified = [
+                typingctx.unify_pairs(ta, tb) for ta, tb in zip(self, other)
+            ]
+
+            if all(t is not None for t in unified):
+                return Tuple(unified)
+
+
+class _StarArgTupleMixin:
+    @classmethod
+    def _make_homogeneous_tuple(cls, dtype, count):
+        return StarArgUniTuple(dtype, count)
+
+    @classmethod
+    def _make_heterogeneous_tuple(cls, tys):
+        return StarArgTuple(tys)
+
+
+class StarArgTuple(_StarArgTupleMixin, Tuple):
+    """To distinguish from Tuple() used as argument to a `*args`.
+    """
+
+    def __new__(cls, types):
+        _HeterogeneousTuple.is_types_iterable(types)
+
+        if types and all(t == types[0] for t in types[1:]):
+            return StarArgUniTuple(dtype=types[0], count=len(types))
+        else:
+            return object.__new__(StarArgTuple)
+
+
+class StarArgUniTuple(_StarArgTupleMixin, UniTuple):
+    """To distinguish from UniTuple() used as argument to a `*args`.
+    """
+
+
+class BaseNamedTuple(BaseTuple):
+    pass
+
+
+class NamedUniTuple(_HomogeneousTuple, BaseNamedTuple):
+    def __init__(self, dtype, count, cls):
+        self.dtype = dtype
+        self.count = count
+        self.fields = tuple(cls._fields)
+        self.instance_class = cls
+        name = "%s(%s x %d)" % (cls.__name__, dtype, count)
+        super(NamedUniTuple, self).__init__(name)
+
+    @property
+    def iterator_type(self):
+        return UniTupleIter(self)
+
+    @property
+    def key(self):
+        return self.instance_class, self.dtype, self.count
+
+
+class NamedTuple(_HeterogeneousTuple, BaseNamedTuple):
+    def __init__(self, types, cls):
+        _HeterogeneousTuple.is_types_iterable(types)
+
+        self.types = tuple(types)
+        self.count = len(self.types)
+        self.fields = tuple(cls._fields)
+        self.instance_class = cls
+        name = "%s(%s)" % (cls.__name__, ", ".join(str(i) for i in self.types))
+        super(NamedTuple, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.instance_class, self.types
+
+
+class List(MutableSequence, InitialValue):
+    """
+    Type class for (arbitrary-sized) homogeneous lists.
+    """
+
+    mutable = True
+
+    def __init__(self, dtype, reflected=False, initial_value=None):
+        dtype = unliteral(dtype)
+        self.dtype = dtype
+        self.reflected = reflected
+        cls_name = "reflected list" if reflected else "list"
+        name = "%s(%s)<iv=%s>" % (cls_name, self.dtype, initial_value)
+        super(List, self).__init__(name=name)
+        InitialValue.__init__(self, initial_value)
+
+    def copy(self, dtype=None, reflected=None):
+        if dtype is None:
+            dtype = self.dtype
+        if reflected is None:
+            reflected = self.reflected
+        return List(dtype, reflected, self.initial_value)
+
+    def unify(self, typingctx, other):
+        if isinstance(other, List):
+            dtype = typingctx.unify_pairs(self.dtype, other.dtype)
+            reflected = self.reflected or other.reflected
+            if dtype is not None:
+                siv = self.initial_value
+                oiv = other.initial_value
+                if siv is not None and oiv is not None:
+                    use = siv
+                    if siv is None:
+                        use = oiv
+                    return List(dtype, reflected, use)
+                else:
+                    return List(dtype, reflected)
+
+    @property
+    def key(self):
+        return self.dtype, self.reflected, str(self.initial_value)
+
+    @property
+    def iterator_type(self):
+        return ListIter(self)
+
+    def is_precise(self):
+        return self.dtype.is_precise()
+
+    def __getitem__(self, args):
+        """
+        Overrides the default __getitem__ from Type.
+        """
+        return self.dtype
+
+    def __unliteral__(self):
+        return List(self.dtype, reflected=self.reflected,
+                    initial_value=None)
+
+
+class LiteralList(Literal, ConstSized, Hashable):
+    """A heterogeneous immutable list (basically a tuple with list semantics).
+    """
+
+    mutable = False
+
+    def __init__(self, literal_value):
+        self.is_types_iterable(literal_value)
+        self._literal_init(list(literal_value))
+        self.types = tuple(literal_value)
+        self.count = len(self.types)
+        self.name = "LiteralList({})".format(literal_value)
+
+    def __getitem__(self, i):
+        """
+        Return element at position i
+        """
+        return self.types[i]
+
+    def __len__(self):
+        return len(self.types)
+
+    def __iter__(self):
+        return iter(self.types)
+
+    @classmethod
+    def from_types(cls, tys):
+        return LiteralList(tys)
+
+    @staticmethod
+    def is_types_iterable(types):
+        if not isinstance(types, Iterable):
+            raise TypingError("Argument 'types' is not iterable")
+
+    @property
+    def iterator_type(self):
+        return ListIter(self)
+
+    def __unliteral__(self):
+        return Poison(self)
+
+    def unify(self, typingctx, other):
+        """
+        Unify this with the *other* one.
+        """
+        if isinstance(other, LiteralList) and self.count == other.count:
+            tys = []
+            for i1, i2 in zip(self.types, other.types):
+                tys.append(typingctx.unify_pairs(i1, i2))
+            if all(tys):
+                return LiteralList(tys)
+
+
+class ListIter(BaseContainerIterator):
+    """
+    Type class for list iterators.
+    """
+
+    container_class = List
+
+
+class ListPayload(BaseContainerPayload):
+    """
+    Internal type class for the dynamically-allocated payload of a list.
+    """
+
+    container_class = List
+
+
+class Set(Container):
+    """
+    Type class for homogeneous sets.
+    """
+
+    mutable = True
+
+    def __init__(self, dtype, reflected=False):
+        assert isinstance(dtype, (Hashable, Undefined))
+        self.dtype = dtype
+        self.reflected = reflected
+        cls_name = "reflected set" if reflected else "set"
+        name = "%s(%s)" % (cls_name, self.dtype)
+        super(Set, self).__init__(name=name)
+
+    @property
+    def key(self):
+        return self.dtype, self.reflected
+
+    @property
+    def iterator_type(self):
+        return SetIter(self)
+
+    def is_precise(self):
+        return self.dtype.is_precise()
+
+    def copy(self, dtype=None, reflected=None):
+        if dtype is None:
+            dtype = self.dtype
+        if reflected is None:
+            reflected = self.reflected
+        return Set(dtype, reflected)
+
+    def unify(self, typingctx, other):
+        if isinstance(other, Set):
+            dtype = typingctx.unify_pairs(self.dtype, other.dtype)
+            reflected = self.reflected or other.reflected
+            if dtype is not None:
+                return Set(dtype, reflected)
+
+
+class SetIter(BaseContainerIterator):
+    """
+    Type class for set iterators.
+    """
+
+    container_class = Set
+
+
+class SetPayload(BaseContainerPayload):
+    """
+    Internal type class for the dynamically-allocated payload of a set.
+    """
+
+    container_class = Set
+
+
+class SetEntry(Type):
+    """
+    Internal type class for the entries of a Set's hash table.
+    """
+
+    def __init__(self, set_type):
+        self.set_type = set_type
+        name = "entry(%s)" % set_type
+        super(SetEntry, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.set_type
+
+
+class ListType(IterableType):
+    """List type
+    """
+
+    mutable = True
+
+    def __init__(self, itemty):
+        assert not isinstance(itemty, TypeRef)
+        itemty = unliteral(itemty)
+        if isinstance(itemty, Optional):
+            fmt = "List.item_type cannot be of type {}"
+            raise TypingError(fmt.format(itemty))
+        # FIXME: _sentry_forbidden_types(itemty)
+        self.item_type = itemty
+        self.dtype = itemty
+        name = "{}[{}]".format(self.__class__.__name__, itemty,)
+        super(ListType, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.item_type
+
+    def is_precise(self):
+        return not isinstance(self.item_type, Undefined)
+
+    @property
+    def iterator_type(self):
+        return ListTypeIterableType(self).iterator_type
+
+    @classmethod
+    def refine(cls, itemty):
+        """Refine to a precise list type
+        """
+        res = cls(itemty)
+        assert res.is_precise()
+        return res
+
+    def unify(self, typingctx, other):
+        """
+        Unify this with the *other* list.
+        """
+        # If other is list
+        if isinstance(other, ListType):
+            if not other.is_precise():
+                return self
+
+
+class ListTypeIterableType(SimpleIterableType):
+    """List iterable type
+    """
+
+    def __init__(self, parent):
+        assert isinstance(parent, ListType)
+        self.parent = parent
+        self.yield_type = self.parent.item_type
+        name = "list[{}]".format(self.parent.name)
+        iterator_type = ListTypeIteratorType(self)
+        super(ListTypeIterableType, self).__init__(name, iterator_type)
+
+
+class ListTypeIteratorType(SimpleIteratorType):
+    def __init__(self, iterable):
+        self.parent = iterable.parent
+        self.iterable = iterable
+        yield_type = iterable.yield_type
+        name = "iter[{}->{}]".format(iterable.parent, yield_type)
+        super(ListTypeIteratorType, self).__init__(name, yield_type)
+
+
+def _sentry_forbidden_types(key, value):
+    # Forbids List and Set for now
+    if isinstance(key, (Set, List)):
+        raise TypingError("{} as key is forbidden".format(key))
+    if isinstance(value, (Set, List)):
+        raise TypingError("{} as value is forbidden".format(value))
+
+
+class DictType(IterableType, InitialValue):
+    """Dictionary type
+    """
+
+    def __init__(self, keyty, valty, initial_value=None):
+        assert not isinstance(keyty, TypeRef)
+        assert not isinstance(valty, TypeRef)
+        keyty = unliteral(keyty)
+        valty = unliteral(valty)
+        if isinstance(keyty, (Optional, NoneType)):
+            fmt = "Dict.key_type cannot be of type {}"
+            raise TypingError(fmt.format(keyty))
+        if isinstance(valty, (Optional, NoneType)):
+            fmt = "Dict.value_type cannot be of type {}"
+            raise TypingError(fmt.format(valty))
+        _sentry_forbidden_types(keyty, valty)
+        self.key_type = keyty
+        self.value_type = valty
+        self.keyvalue_type = Tuple([keyty, valty])
+        name = "{}[{},{}]<iv={}>".format(
+            self.__class__.__name__, keyty, valty, initial_value
+        )
+        super(DictType, self).__init__(name)
+        InitialValue.__init__(self, initial_value)
+
+    def is_precise(self):
+        return not any(
+            (
+                isinstance(self.key_type, Undefined),
+                isinstance(self.value_type, Undefined),
+            )
+        )
+
+    @property
+    def iterator_type(self):
+        return DictKeysIterableType(self).iterator_type
+
+    @classmethod
+    def refine(cls, keyty, valty):
+        """Refine to a precise dictionary type
+        """
+        res = cls(keyty, valty)
+        assert res.is_precise()
+        return res
+
+    def unify(self, typingctx, other):
+        """
+        Unify this with the *other* dictionary.
+        """
+        # If other is dict
+        if isinstance(other, DictType):
+            if not other.is_precise():
+                return self
+            else:
+                ukey_type = self.key_type == other.key_type
+                uvalue_type = self.value_type == other.value_type
+                if ukey_type and uvalue_type:
+                    siv = self.initial_value
+                    oiv = other.initial_value
+                    siv_none = siv is None
+                    oiv_none = oiv is None
+                    if not siv_none and not oiv_none:
+                        if siv == oiv:
+                            return DictType(self.key_type, other.value_type,
+                                            siv)
+                    return DictType(self.key_type, other.value_type)
+
+    @property
+    def key(self):
+        return self.key_type, self.value_type, str(self.initial_value)
+
+    def __unliteral__(self):
+        return DictType(self.key_type, self.value_type)
+
+
+class LiteralStrKeyDict(Literal, ConstSized, Hashable):
+    """A Dictionary of string keys to heterogeneous values (basically a
+    namedtuple with dict semantics).
+    """
+
+    class FakeNamedTuple(pySequence):
+        # This is namedtuple-like and is a workaround for #6518 and #7416.
+        # This has the couple of namedtuple properties that are used by Numba's
+        # internals but avoids use of an actual namedtuple as it cannot have
+        # numeric field names, i.e. `namedtuple('foo', '0 1')` is invalid.
+        def __init__(self, name, keys):
+            self.__name__ = name
+            self._fields = tuple(keys)
+            super(LiteralStrKeyDict.FakeNamedTuple, self).__init__()
+
+        def __len__(self):
+            return len(self._fields)
+
+        def __getitem__(self, key):
+            return self._fields[key]
+
+    mutable = False
+
+    def __init__(self, literal_value, value_index=None):
+        self._literal_init(literal_value)
+        self.value_index = value_index
+        strkeys = [x.literal_value for x in literal_value.keys()]
+        self.tuple_ty = self.FakeNamedTuple("_ntclazz", strkeys)
+        tys = [x for x in literal_value.values()]
+        self.types = tuple(tys)
+        self.count = len(self.types)
+        self.fields = tuple(self.tuple_ty._fields)
+        self.instance_class = self.tuple_ty
+        self.name = "LiteralStrKey[Dict]({})".format(literal_value)
+
+    def __unliteral__(self):
+        return Poison(self)
+
+    def unify(self, typingctx, other):
+        """
+        Unify this with the *other* one.
+        """
+        if isinstance(other, LiteralStrKeyDict):
+            tys = []
+            for (k1, v1), (k2, v2) in zip(
+                self.literal_value.items(), other.literal_value.items()
+            ):
+                if k1 != k2:  # keys must be same
+                    break
+                tys.append(typingctx.unify_pairs(v1, v2))
+            else:
+                if all(tys):
+                    d = {k: v for k, v in zip(self.literal_value.keys(), tys)}
+                    return LiteralStrKeyDict(d)
+
+    def __len__(self):
+        return len(self.types)
+
+    def __iter__(self):
+        return iter(self.types)
+
+    @property
+    def key(self):
+        # use the namedtuple fields not the namedtuple itself as it's created
+        # locally in the ctor and comparison would always be False.
+        return self.tuple_ty._fields, self.types, str(self.literal_value)
+
+
+class DictItemsIterableType(SimpleIterableType):
+    """Dictionary iterable type for .items()
+    """
+
+    def __init__(self, parent):
+        assert isinstance(parent, DictType)
+        self.parent = parent
+        self.yield_type = self.parent.keyvalue_type
+        name = "items[{}]".format(self.parent.name)
+        self.name = name
+        iterator_type = DictIteratorType(self)
+        super(DictItemsIterableType, self).__init__(name, iterator_type)
+
+
+class DictKeysIterableType(SimpleIterableType):
+    """Dictionary iterable type for .keys()
+    """
+
+    def __init__(self, parent):
+        assert isinstance(parent, DictType)
+        self.parent = parent
+        self.yield_type = self.parent.key_type
+        name = "keys[{}]".format(self.parent.name)
+        self.name = name
+        iterator_type = DictIteratorType(self)
+        super(DictKeysIterableType, self).__init__(name, iterator_type)
+
+
+class DictValuesIterableType(SimpleIterableType):
+    """Dictionary iterable type for .values()
+    """
+
+    def __init__(self, parent):
+        assert isinstance(parent, DictType)
+        self.parent = parent
+        self.yield_type = self.parent.value_type
+        name = "values[{}]".format(self.parent.name)
+        self.name = name
+        iterator_type = DictIteratorType(self)
+        super(DictValuesIterableType, self).__init__(name, iterator_type)
+
+
+class DictIteratorType(SimpleIteratorType):
+    def __init__(self, iterable):
+        self.parent = iterable.parent
+        self.iterable = iterable
+        yield_type = iterable.yield_type
+        name = "iter[{}->{}],{}".format(
+            iterable.parent, yield_type, iterable.name
+        )
+        super(DictIteratorType, self).__init__(name, yield_type)
+
+
+class StructRef(Type):
+    """A mutable struct.
+    """
+
+    def __init__(self, fields):
+        """
+        Parameters
+        ----------
+        fields : Sequence
+            A sequence of field descriptions, which is a 2-tuple-like object
+            containing `(name, type)`, where `name` is a `str` for the field
+            name, and `type` is a numba type for the field type.
+        """
+
+        def check_field_pair(fieldpair):
+            name, typ = fieldpair
+            if not isinstance(name, str):
+                msg = "expecting a str for field name"
+                raise ValueError(msg)
+            if not isinstance(typ, Type):
+                msg = "expecting a Numba Type for field type"
+                raise ValueError(msg)
+            return name, typ
+
+        fields = tuple(map(check_field_pair, fields))
+        self._fields = tuple(map(check_field_pair,
+                                 self.preprocess_fields(fields)))
+        self._typename = self.__class__.__qualname__
+        name = f"numba.{self._typename}{self._fields}"
+        super().__init__(name=name)
+
+    def preprocess_fields(self, fields):
+        """Subclasses can override this to do additional clean up on fields.
+
+        The default is an identity function.
+
+        Parameters:
+        -----------
+        fields : Sequence[Tuple[str, Type]]
+        """
+        return fields
+
+    @property
+    def field_dict(self):
+        """Return an immutable mapping for the field names and their
+        corresponding types.
+        """
+        return MappingProxyType(dict(self._fields))
+
+    def get_data_type(self):
+        """Get the payload type for the actual underlying structure referred
+        to by this struct reference.
+
+        See also: `ClassInstanceType.get_data_type`
+        """
+        return StructRefPayload(
+            typename=self.__class__.__name__, fields=self._fields,
+        )
+
+
+class StructRefPayload(Type):
+    """The type of the payload of a mutable struct.
+    """
+
+    mutable = True
+
+    def __init__(self, typename, fields):
+        self._typename = typename
+        self._fields = tuple(fields)
+        super().__init__(name=f"numba.{typename}{self._fields}.payload")
+
+    @property
+    def field_dict(self):
+        return MappingProxyType(dict(self._fields))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/function_type.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/function_type.py
new file mode 100644
index 000000000..7dffd195b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/function_type.py
@@ -0,0 +1,211 @@
+
+__all__ = ['FunctionType', 'UndefinedFunctionType', 'FunctionPrototype',
+           'WrapperAddressProtocol', 'CompileResultWAP']
+
+from abc import ABC, abstractmethod
+from .abstract import Type
+from .. import types, errors
+
+
+class FunctionType(Type):
+    """
+    First-class function type.
+    """
+
+    cconv = None
+
+    def __init__(self, signature):
+        sig = types.unliteral(signature)
+        self.nargs = len(sig.args)
+        self.signature = sig
+        self.ftype = FunctionPrototype(sig.return_type, sig.args)
+        self._key = self.ftype.key
+
+    @property
+    def key(self):
+        return self._key
+
+    @property
+    def name(self):
+        return f'{type(self).__name__}[{self.key}]'
+
+    def is_precise(self):
+        return self.signature.is_precise()
+
+    def get_precise(self):
+        return self
+
+    def dump(self, tab=''):
+        print(f'{tab}DUMP {type(self).__name__}[code={self._code}]')
+        self.signature.dump(tab=tab + '  ')
+        print(f'{tab}END DUMP {type(self).__name__}')
+
+    def get_call_type(self, context, args, kws):
+        from numba.core import typing
+
+        if kws:
+            # First-class functions carry only the type signature
+            # information and function address value. So, it is not
+            # possible to determine the positional arguments
+            # corresponding to the keyword arguments in the call
+            # expression. For instance, the definition of the
+            # first-class function may not use the same argument names
+            # that the caller assumes. [numba/issues/5540].
+            raise errors.UnsupportedError(
+                'first-class function call cannot use keyword arguments')
+
+        if len(args) != self.nargs:
+            raise ValueError(
+                f'mismatch of arguments number: {len(args)} vs {self.nargs}')
+
+        sig = self.signature
+
+        # check that arguments types match with the signature types exactly
+        for atype, sig_atype in zip(args, sig.args):
+            atype = types.unliteral(atype)
+            if sig_atype.is_precise():
+                conv_score = context.context.can_convert(
+                    fromty=atype, toty=sig_atype
+                )
+                if conv_score is None \
+                   or conv_score > typing.context.Conversion.safe:
+                    raise ValueError(
+                        f'mismatch of argument types: {atype} vs {sig_atype}')
+
+        if not sig.is_precise():
+            for dispatcher in self.dispatchers:
+                template, pysig, args, kws \
+                    = dispatcher.get_call_template(args, kws)
+                new_sig = template(context.context).apply(args, kws)
+                return types.unliteral(new_sig)
+
+        return sig
+
+    def check_signature(self, other_sig):
+        """Return True if signatures match (up to being precise).
+        """
+        sig = self.signature
+        return (self.nargs == len(other_sig.args)
+                and (sig == other_sig or not sig.is_precise()))
+
+    def unify(self, context, other):
+        if isinstance(other, types.UndefinedFunctionType) \
+           and self.nargs == other.nargs:
+            return self
+
+
+class UndefinedFunctionType(FunctionType):
+
+    _counter = 0
+
+    def __init__(self, nargs, dispatchers):
+        from numba.core.typing.templates import Signature
+        signature = Signature(types.undefined,
+                              (types.undefined,) * nargs, recvr=None)
+
+        super(UndefinedFunctionType, self).__init__(signature)
+
+        self.dispatchers = dispatchers
+
+        # make the undefined function type instance unique
+        type(self)._counter += 1
+        self._key += str(type(self)._counter)
+
+    def get_precise(self):
+        """
+        Return precise function type if possible.
+        """
+        for dispatcher in self.dispatchers:
+            for cres in dispatcher.overloads.values():
+                sig = types.unliteral(cres.signature)
+                return FunctionType(sig)
+        return self
+
+
+class FunctionPrototype(Type):
+    """
+    Represents the prototype of a first-class function type.
+    Used internally.
+    """
+    cconv = None
+
+    def __init__(self, rtype, atypes):
+        self.rtype = rtype
+        self.atypes = tuple(atypes)
+
+        assert isinstance(rtype, Type), (rtype)
+        lst = []
+        for atype in self.atypes:
+            assert isinstance(atype, Type), (atype)
+            lst.append(atype.name)
+        name = '%s(%s)' % (rtype, ', '.join(lst))
+
+        super(FunctionPrototype, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.name
+
+
+class WrapperAddressProtocol(ABC):
+    """Base class for Wrapper Address Protocol.
+
+    Objects that inherit from the WrapperAddressProtocol can be passed
+    as arguments to Numba jit compiled functions where it can be used
+    as first-class functions. As a minimum, the derived types must
+    implement two methods ``__wrapper_address__`` and ``signature``.
+    """
+
+    @abstractmethod
+    def __wrapper_address__(self):
+        """Return the address of a first-class function.
+
+        Returns
+        -------
+        addr : int
+        """
+
+    @abstractmethod
+    def signature(self):
+        """Return the signature of a first-class function.
+
+        Returns
+        -------
+        sig : Signature
+          The returned Signature instance represents the type of a
+          first-class function that the given WrapperAddressProtocol
+          instance represents.
+        """
+
+
+class CompileResultWAP(WrapperAddressProtocol):
+    """Wrapper of dispatcher instance compilation result to turn it a
+    first-class function.
+    """
+
+    def __init__(self, cres):
+        """
+        Parameters
+        ----------
+        cres : CompileResult
+          Specify compilation result of a Numba jit-decorated function
+          (that is a value of dispatcher instance ``overloads``
+          attribute)
+        """
+        self.cres = cres
+        name = getattr(cres.fndesc, 'llvm_cfunc_wrapper_name')
+        self.address = cres.library.get_pointer_to_function(name)
+
+    def dump(self, tab=''):
+        print(f'{tab}DUMP {type(self).__name__} [addr={self.address}]')
+        self.cres.signature.dump(tab=tab + '  ')
+        print(f'{tab}END DUMP {type(self).__name__}')
+
+    def __wrapper_address__(self):
+        return self.address
+
+    def signature(self):
+        return self.cres.signature
+
+    def __call__(self, *args, **kwargs):  # used in object-mode
+        return self.cres.entry_point(*args, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/functions.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/functions.py
new file mode 100644
index 000000000..f3b313e2c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/functions.py
@@ -0,0 +1,745 @@
+import traceback
+from collections import namedtuple, defaultdict
+import itertools
+import logging
+import textwrap
+from shutil import get_terminal_size
+
+from .abstract import Callable, DTypeSpec, Dummy, Literal, Type, weakref
+from .common import Opaque
+from .misc import unliteral
+from numba.core import errors, utils, types, config
+from numba.core.typeconv import Conversion
+
+_logger = logging.getLogger(__name__)
+
+
+# terminal color markup
+_termcolor = errors.termcolor()
+
+_FAILURE = namedtuple('_FAILURE', 'template matched error literal')
+
+_termwidth = get_terminal_size().columns
+
+
+# pull out the lead line as unit tests often use this
+_header_lead = "No implementation of function"
+_header_template = (_header_lead + " {the_function} found for signature:\n \n "
+                    ">>> {fname}({signature})\n \nThere are {ncandidates} "
+                    "candidate implementations:")
+
+_reason_template = """
+" - Of which {nmatches} did not match due to:\n
+"""
+
+
+def _wrapper(tmp, indent=0):
+    return textwrap.indent(tmp, ' ' * indent, lambda line: True)
+
+
+_overload_template = ("- Of which {nduplicates} did not match due to:\n"
+                      "{kind} {inof} function '{function}': File: {file}: "
+                      "Line {line}.\n  With argument(s): '({args})':")
+
+
+_err_reasons = {'specific_error': "Rejected as the implementation raised a "
+                                  "specific error:\n{}"}
+
+
+def _bt_as_lines(bt):
+    """
+    Converts a backtrace into a list of lines, squashes it a bit on the way.
+    """
+    return [y for y in itertools.chain(*[x.split('\n') for x in bt]) if y]
+
+
+def argsnkwargs_to_str(args, kwargs):
+    buf = [str(a) for a in tuple(args)]
+    buf.extend(["{}={}".format(k, v) for k, v in kwargs.items()])
+    return ', '.join(buf)
+
+
+class _ResolutionFailures(object):
+    """Collect and format function resolution failures.
+    """
+    def __init__(self, context, function_type, args, kwargs, depth=0):
+        self._context = context
+        self._function_type = function_type
+        self._args = args
+        self._kwargs = kwargs
+        self._failures = defaultdict(list)
+        self._depth = depth
+        self._max_depth = 5
+        self._scale = 2
+
+    def __len__(self):
+        return len(self._failures)
+
+    def add_error(self, calltemplate, matched, error, literal):
+        """
+        Args
+        ----
+        calltemplate : CallTemplate
+        error : Exception or str
+            Error message
+        """
+        isexc = isinstance(error, Exception)
+        errclazz = '%s: ' % type(error).__name__ if isexc else ''
+
+        key = "{}{}".format(errclazz, str(error))
+        self._failures[key].append(_FAILURE(calltemplate, matched, error,
+                                            literal))
+
+    def format(self):
+        """Return a formatted error message from all the gathered errors.
+        """
+        indent = ' ' * self._scale
+        argstr = argsnkwargs_to_str(self._args, self._kwargs)
+        ncandidates = sum([len(x) for x in self._failures.values()])
+
+        # sort out a display name for the function
+        tykey = self._function_type.typing_key
+        # most things have __name__
+        fname = getattr(tykey, '__name__', None)
+        is_external_fn_ptr = isinstance(self._function_type,
+                                        ExternalFunctionPointer)
+
+        if fname is None:
+            if is_external_fn_ptr:
+                fname = "ExternalFunctionPointer"
+            else:
+                fname = "<unknown function>"
+
+        msgbuf = [_header_template.format(the_function=self._function_type,
+                                          fname=fname,
+                                          signature=argstr,
+                                          ncandidates=ncandidates)]
+        nolitargs = tuple([unliteral(a) for a in self._args])
+        nolitkwargs = {k: unliteral(v) for k, v in self._kwargs.items()}
+        nolitargstr = argsnkwargs_to_str(nolitargs, nolitkwargs)
+
+        # depth could potentially get massive, so limit it.
+        ldepth = min(max(self._depth, 0), self._max_depth)
+
+        def template_info(tp):
+            src_info = tp.get_template_info()
+            unknown = "unknown"
+            source_name = src_info.get('name', unknown)
+            source_file = src_info.get('filename', unknown)
+            source_lines = src_info.get('lines', unknown)
+            source_kind = src_info.get('kind', 'Unknown template')
+            return source_name, source_file, source_lines, source_kind
+
+        for i, (k, err_list) in enumerate(self._failures.items()):
+            err = err_list[0]
+            nduplicates = len(err_list)
+            template, error = err.template, err.error
+            ifo = template_info(template)
+            source_name, source_file, source_lines, source_kind = ifo
+            largstr = argstr if err.literal else nolitargstr
+
+            if err.error == "No match.":
+                err_dict = defaultdict(set)
+                for errs in err_list:
+                    err_dict[errs.template].add(errs.literal)
+                # if there's just one template, and it's erroring on
+                # literal/nonliteral be specific
+                if len(err_dict) == 1:
+                    template = [_ for _ in err_dict.keys()][0]
+                    source_name, source_file, source_lines, source_kind = \
+                        template_info(template)
+                    source_lines = source_lines[0]
+                else:
+                    source_file = "<numerous>"
+                    source_lines = "N/A"
+
+                msgbuf.append(_termcolor.errmsg(
+                    _wrapper(_overload_template.format(nduplicates=nduplicates,
+                                                       kind=source_kind.title(),
+                                                       function=fname,
+                                                       inof='of',
+                                                       file=source_file,
+                                                       line=source_lines,
+                                                       args=largstr),
+                             ldepth + 1)))
+                msgbuf.append(_termcolor.highlight(_wrapper(err.error,
+                                                            ldepth + 2)))
+            else:
+                # There was at least one match in this failure class, but it
+                # failed for a specific reason try and report this.
+                msgbuf.append(_termcolor.errmsg(
+                    _wrapper(_overload_template.format(nduplicates=nduplicates,
+                                                       kind=source_kind.title(),
+                                                       function=source_name,
+                                                       inof='in',
+                                                       file=source_file,
+                                                       line=source_lines[0],
+                                                       args=largstr),
+                             ldepth + 1)))
+
+                if isinstance(error, BaseException):
+                    reason = indent + self.format_error(error)
+                    errstr = _err_reasons['specific_error'].format(reason)
+                else:
+                    errstr = error
+                # if you are a developer, show the back traces
+                if config.DEVELOPER_MODE:
+                    if isinstance(error, BaseException):
+                        # if the error is an actual exception instance, trace it
+                        bt = traceback.format_exception(type(error), error,
+                                                        error.__traceback__)
+                    else:
+                        bt = [""]
+                    bt_as_lines = _bt_as_lines(bt)
+                    nd2indent = '\n{}'.format(2 * indent)
+                    errstr += _termcolor.reset(nd2indent +
+                                               nd2indent.join(bt_as_lines))
+                msgbuf.append(_termcolor.highlight(_wrapper(errstr,
+                                                            ldepth + 2)))
+                loc = self.get_loc(template, error)
+                if loc:
+                    msgbuf.append('{}raised from {}'.format(indent, loc))
+
+        # the commented bit rewraps each block, may not be helpful?!
+        return _wrapper('\n'.join(msgbuf) + '\n') # , self._scale * ldepth)
+
+    def format_error(self, error):
+        """Format error message or exception
+        """
+        if isinstance(error, Exception):
+            return '{}: {}'.format(type(error).__name__, error)
+        else:
+            return '{}'.format(error)
+
+    def get_loc(self, classtemplate, error):
+        """Get source location information from the error message.
+        """
+        if isinstance(error, Exception) and hasattr(error, '__traceback__'):
+            # traceback is unavailable in py2
+            frame = traceback.extract_tb(error.__traceback__)[-1]
+            return "{}:{}".format(frame[0], frame[1])
+
+    def raise_error(self):
+        for faillist in self._failures.values():
+            for fail in faillist:
+                if isinstance(fail.error, errors.ForceLiteralArg):
+                    raise fail.error
+        raise errors.TypingError(self.format())
+
+
+def _unlit_non_poison(ty):
+    """Apply unliteral(ty) and raise a TypingError if type is Poison.
+    """
+    out = unliteral(ty)
+    if isinstance(out, types.Poison):
+        m = f"Poison type used in arguments; got {out}"
+        raise errors.TypingError(m)
+    return out
+
+
+class BaseFunction(Callable):
+    """
+    Base type class for some function types.
+    """
+
+    def __init__(self, template):
+
+        if isinstance(template, (list, tuple)):
+            self.templates = tuple(template)
+            keys = set(temp.key for temp in self.templates)
+            if len(keys) != 1:
+                raise ValueError("incompatible templates: keys = %s"
+                                 % (keys,))
+            self.typing_key, = keys
+        else:
+            self.templates = (template,)
+            self.typing_key = template.key
+        self._impl_keys = {}
+        name = "%s(%s)" % (self.__class__.__name__, self.typing_key)
+        self._depth = 0
+        super(BaseFunction, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.typing_key, self.templates
+
+    def augment(self, other):
+        """
+        Augment this function type with the other function types' templates,
+        so as to support more input types.
+        """
+        if type(other) is type(self) and other.typing_key == self.typing_key:
+            return type(self)(self.templates + other.templates)
+
+    def get_impl_key(self, sig):
+        """
+        Get the implementation key (used by the target context) for the
+        given signature.
+        """
+        return self._impl_keys[sig.args]
+
+    def get_call_type(self, context, args, kws):
+
+        prefer_lit = [True, False]    # old behavior preferring literal
+        prefer_not = [False, True]    # new behavior preferring non-literal
+        failures = _ResolutionFailures(context, self, args, kws,
+                                       depth=self._depth)
+
+        # get the order in which to try templates
+        from numba.core.target_extension import get_local_target # circular
+        target_hw = get_local_target(context)
+        order = utils.order_by_target_specificity(target_hw, self.templates,
+                                                  fnkey=self.key[0])
+
+        self._depth += 1
+
+        for temp_cls in order:
+            temp = temp_cls(context)
+            # The template can override the default and prefer literal args
+            choice = prefer_lit if temp.prefer_literal else prefer_not
+            for uselit in choice:
+                try:
+                    if uselit:
+                        sig = temp.apply(args, kws)
+                    else:
+                        nolitargs = tuple([_unlit_non_poison(a) for a in args])
+                        nolitkws = {k: _unlit_non_poison(v)
+                                    for k, v in kws.items()}
+                        sig = temp.apply(nolitargs, nolitkws)
+                except Exception as e:
+                    if (utils.use_new_style_errors() and not
+                            isinstance(e, errors.NumbaError)):
+                        raise e
+                    else:
+                        sig = None
+                        failures.add_error(temp, False, e, uselit)
+                else:
+                    if sig is not None:
+                        self._impl_keys[sig.args] = temp.get_impl_key(sig)
+                        self._depth -= 1
+                        return sig
+                    else:
+                        registered_sigs = getattr(temp, 'cases', None)
+                        if registered_sigs is not None:
+                            msg = "No match for registered cases:\n%s"
+                            msg = msg % '\n'.join(" * {}".format(x) for x in
+                                                  registered_sigs)
+                        else:
+                            msg = 'No match.'
+                        failures.add_error(temp, True, msg, uselit)
+
+        failures.raise_error()
+
+    def get_call_signatures(self):
+        sigs = []
+        is_param = False
+        for temp in self.templates:
+            sigs += getattr(temp, 'cases', [])
+            is_param = is_param or hasattr(temp, 'generic')
+        return sigs, is_param
+
+
+class Function(BaseFunction, Opaque):
+    """
+    Type class for builtin functions implemented by Numba.
+    """
+
+
+class BoundFunction(Callable, Opaque):
+    """
+    A function with an implicit first argument (denoted as *this* below).
+    """
+
+    def __init__(self, template, this):
+        # Create a derived template with an attribute *this*
+        newcls = type(template.__name__ + '.' + str(this), (template,),
+                      dict(this=this))
+        self.template = newcls
+        self.typing_key = self.template.key
+        self.this = this
+        name = "%s(%s for %s)" % (self.__class__.__name__,
+                                  self.typing_key, self.this)
+        super(BoundFunction, self).__init__(name)
+
+    def unify(self, typingctx, other):
+        if (isinstance(other, BoundFunction) and
+                self.typing_key == other.typing_key):
+            this = typingctx.unify_pairs(self.this, other.this)
+            if this is not None:
+                # XXX is it right that both template instances are distinct?
+                return self.copy(this=this)
+
+    def copy(self, this):
+        return type(self)(self.template, this)
+
+    @property
+    def key(self):
+        # FIXME: With target-overload, the MethodTemplate can change depending
+        #        on the target.
+        unique_impl = getattr(self.template, "_overload_func", None)
+        return self.typing_key, self.this, unique_impl
+
+    def get_impl_key(self, sig):
+        """
+        Get the implementation key (used by the target context) for the
+        given signature.
+        """
+        return self.typing_key
+
+    def get_call_type(self, context, args, kws):
+        template = self.template(context)
+        literal_e = None
+        nonliteral_e = None
+        out = None
+
+        choice = [True, False] if template.prefer_literal else [False, True]
+        for uselit in choice:
+            if uselit:
+                # Try with Literal
+                try:
+                    out = template.apply(args, kws)
+                except Exception as exc:
+                    if (utils.use_new_style_errors() and not
+                            isinstance(exc, errors.NumbaError)):
+                        raise exc
+                    if isinstance(exc, errors.ForceLiteralArg):
+                        raise exc
+                    literal_e = exc
+                    out = None
+                else:
+                    break
+            else:
+                # if the unliteral_args and unliteral_kws are the same as the
+                # literal ones, set up to not bother retrying
+                unliteral_args = tuple([_unlit_non_poison(a) for a in args])
+                unliteral_kws = {k: _unlit_non_poison(v)
+                                 for k, v in kws.items()}
+                skip = unliteral_args == args and kws == unliteral_kws
+
+                # If the above template application failed and the non-literal
+                # args are different to the literal ones, try again with
+                # literals rewritten as non-literals
+                if not skip and out is None:
+                    try:
+                        out = template.apply(unliteral_args, unliteral_kws)
+                    except Exception as exc:
+                        if isinstance(exc, errors.ForceLiteralArg):
+                            if template.prefer_literal:
+                                # For template that prefers literal types,
+                                # reaching here means that the literal types
+                                # have failed typing as well.
+                                raise exc
+                        nonliteral_e = exc
+                    else:
+                        break
+
+        if out is None and (nonliteral_e is not None or literal_e is not None):
+            header = "- Resolution failure for {} arguments:\n{}\n"
+            tmplt = _termcolor.highlight(header)
+            if config.DEVELOPER_MODE:
+                indent = ' ' * 4
+
+                def add_bt(error):
+                    if isinstance(error, BaseException):
+                        # if the error is an actual exception instance, trace it
+                        bt = traceback.format_exception(type(error), error,
+                                                        error.__traceback__)
+                    else:
+                        bt = [""]
+                    nd2indent = '\n{}'.format(2 * indent)
+                    errstr = _termcolor.reset(nd2indent +
+                                              nd2indent.join(_bt_as_lines(bt)))
+                    return _termcolor.reset(errstr)
+            else:
+                add_bt = lambda X: ''
+
+            def nested_msg(literalness, e):
+                estr = str(e)
+                estr = estr if estr else (str(repr(e)) + add_bt(e))
+                new_e = errors.TypingError(textwrap.dedent(estr))
+                return tmplt.format(literalness, str(new_e))
+
+            raise errors.TypingError(nested_msg('literal', literal_e) +
+                                     nested_msg('non-literal', nonliteral_e))
+        return out
+
+    def get_call_signatures(self):
+        sigs = getattr(self.template, 'cases', [])
+        is_param = hasattr(self.template, 'generic')
+        return sigs, is_param
+
+
+class MakeFunctionLiteral(Literal, Opaque):
+    pass
+
+
+class _PickleableWeakRef(weakref.ref):
+    """
+    Allow a weakref to be pickled.
+
+    Note that if the object referred to is not kept alive elsewhere in the
+    pickle, the weakref will immediately expire after being constructed.
+    """
+    def __getnewargs__(self):
+        obj = self()
+        if obj is None:
+            raise ReferenceError("underlying object has vanished")
+        return (obj,)
+
+
+class WeakType(Type):
+    """
+    Base class for types parametered by a mortal object, to which only
+    a weak reference is kept.
+    """
+
+    def _store_object(self, obj):
+        self._wr = _PickleableWeakRef(obj)
+
+    def _get_object(self):
+        obj = self._wr()
+        if obj is None:
+            raise ReferenceError("underlying object has vanished")
+        return obj
+
+    @property
+    def key(self):
+        return self._wr
+
+    def __eq__(self, other):
+        if type(self) is type(other):
+            obj = self._wr()
+            return obj is not None and obj is other._wr()
+        return NotImplemented
+
+    def __hash__(self):
+        return Type.__hash__(self)
+
+
+class Dispatcher(WeakType, Callable, Dummy):
+    """
+    Type class for @jit-compiled functions.
+    """
+
+    def __init__(self, dispatcher):
+        self._store_object(dispatcher)
+        super(Dispatcher, self).__init__("type(%s)" % dispatcher)
+
+    def dump(self, tab=''):
+        print((f'{tab}DUMP {type(self).__name__}[code={self._code}, '
+               f'name={self.name}]'))
+        self.dispatcher.dump(tab=tab + '  ')
+        print(f'{tab}END DUMP')
+
+    def get_call_type(self, context, args, kws):
+        """
+        Resolve a call to this dispatcher using the given argument types.
+        A signature returned and it is ensured that a compiled specialization
+        is available for it.
+        """
+        template, pysig, args, kws = \
+            self.dispatcher.get_call_template(args, kws)
+        sig = template(context).apply(args, kws)
+        if sig:
+            sig = sig.replace(pysig=pysig)
+            return sig
+
+    def get_call_signatures(self):
+        sigs = self.dispatcher.nopython_signatures
+        return sigs, True
+
+    @property
+    def dispatcher(self):
+        """
+        A strong reference to the underlying numba.dispatcher.Dispatcher
+        instance.
+        """
+        disp = self._get_object()
+        # TODO: improve interface to avoid the dynamic check here
+        if hasattr(disp, "_get_dispatcher_for_current_target"):
+            return disp._get_dispatcher_for_current_target()
+        else:
+            return disp
+
+    def get_overload(self, sig):
+        """
+        Get the compiled overload for the given signature.
+        """
+        return self.dispatcher.get_overload(sig.args)
+
+    def get_impl_key(self, sig):
+        """
+        Get the implementation key for the given signature.
+        """
+        return self.get_overload(sig)
+
+    def unify(self, context, other):
+        return utils.unified_function_type((self, other), require_precise=False)
+
+    def can_convert_to(self, typingctx, other):
+        if isinstance(other, types.FunctionType):
+            if self.dispatcher.get_compile_result(other.signature):
+                return Conversion.safe
+
+
+class ObjModeDispatcher(Dispatcher):
+    """Dispatcher subclass that enters objectmode function.
+    """
+    pass
+
+
+class ExternalFunctionPointer(BaseFunction):
+    """
+    A pointer to a native function (e.g. exported via ctypes or cffi).
+    *get_pointer* is a Python function taking an object
+    and returning the raw pointer value as an int.
+    """
+    def __init__(self, sig, get_pointer, cconv=None):
+        from numba.core.typing.templates import (AbstractTemplate,
+                                                 make_concrete_template,
+                                                 signature)
+        from numba.core.types import ffi_forced_object
+        if sig.return_type == ffi_forced_object:
+            raise TypeError("Cannot return a pyobject from a external function")
+        self.sig = sig
+        self.requires_gil = any(a == ffi_forced_object for a in self.sig.args)
+        self.get_pointer = get_pointer
+        self.cconv = cconv
+        if self.requires_gil:
+            class GilRequiringDefn(AbstractTemplate):
+                key = self.sig
+
+                def generic(self, args, kws):
+                    if kws:
+                        raise TypeError("does not support keyword arguments")
+                    # Make ffi_forced_object a bottom type to allow any type to
+                    # be casted to it. This is the only place that support
+                    # ffi_forced_object.
+                    coerced = [actual if formal == ffi_forced_object else formal
+                               for actual, formal
+                               in zip(args, self.key.args)]
+                    return signature(self.key.return_type, *coerced)
+            template = GilRequiringDefn
+        else:
+            template = make_concrete_template("CFuncPtr", sig, [sig])
+        super(ExternalFunctionPointer, self).__init__(template)
+
+    @property
+    def key(self):
+        return self.sig, self.cconv, self.get_pointer
+
+
+class ExternalFunction(Function):
+    """
+    A named native function (resolvable by LLVM) accepting an explicit
+    signature. For internal use only.
+    """
+
+    def __init__(self, symbol, sig):
+        from numba.core import typing
+        self.symbol = symbol
+        self.sig = sig
+        template = typing.make_concrete_template(symbol, symbol, [sig])
+        super(ExternalFunction, self).__init__(template)
+
+    @property
+    def key(self):
+        return self.symbol, self.sig
+
+
+class NamedTupleClass(Callable, Opaque):
+    """
+    Type class for namedtuple classes.
+    """
+
+    def __init__(self, instance_class):
+        self.instance_class = instance_class
+        name = "class(%s)" % (instance_class)
+        super(NamedTupleClass, self).__init__(name)
+
+    def get_call_type(self, context, args, kws):
+        # Overridden by the __call__ constructor resolution in
+        # typing.collections
+        return None
+
+    def get_call_signatures(self):
+        return (), True
+
+    def get_impl_key(self, sig):
+        return type(self)
+
+    @property
+    def key(self):
+        return self.instance_class
+
+
+class NumberClass(Callable, DTypeSpec, Opaque):
+    """
+    Type class for number classes (e.g. "np.float64").
+    """
+
+    def __init__(self, instance_type):
+        self.instance_type = instance_type
+        name = "class(%s)" % (instance_type,)
+        super(NumberClass, self).__init__(name)
+
+    def get_call_type(self, context, args, kws):
+        # Overridden by the __call__ constructor resolution in typing.builtins
+        return None
+
+    def get_call_signatures(self):
+        return (), True
+
+    def get_impl_key(self, sig):
+        return type(self)
+
+    @property
+    def key(self):
+        return self.instance_type
+
+    @property
+    def dtype(self):
+        return self.instance_type
+
+
+_RecursiveCallOverloads = namedtuple("_RecursiveCallOverloads", "qualname,uid")
+
+
+class RecursiveCall(Opaque):
+    """
+    Recursive call to a Dispatcher.
+    """
+    _overloads = None
+
+    def __init__(self, dispatcher_type):
+        assert isinstance(dispatcher_type, Dispatcher)
+        self.dispatcher_type = dispatcher_type
+        name = "recursive(%s)" % (dispatcher_type,)
+        super(RecursiveCall, self).__init__(name)
+        # Initializing for the first time
+        if self._overloads is None:
+            self._overloads = {}
+
+    def add_overloads(self, args, qualname, uid):
+        """Add an overload of the function.
+
+        Parameters
+        ----------
+        args :
+            argument types
+        qualname :
+            function qualifying name
+        uid :
+            unique id
+        """
+        self._overloads[args] = _RecursiveCallOverloads(qualname, uid)
+
+    def get_overloads(self, args):
+        """Get the qualifying name and unique id for the overload given the
+        argument types.
+        """
+        return self._overloads[args]
+
+    @property
+    def key(self):
+        return self.dispatcher_type
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/iterators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/iterators.py
new file mode 100644
index 000000000..2baf1d42a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/iterators.py
@@ -0,0 +1,108 @@
+from .common import SimpleIterableType, SimpleIteratorType
+from ..errors import TypingError
+
+
+class RangeType(SimpleIterableType):
+
+    def __init__(self, dtype):
+        self.dtype = dtype
+        name = "range_state_%s" % (dtype,)
+        super(SimpleIterableType, self).__init__(name)
+        self._iterator_type = RangeIteratorType(self.dtype)
+
+    def unify(self, typingctx, other):
+        if isinstance(other, RangeType):
+            dtype = typingctx.unify_pairs(self.dtype, other.dtype)
+            if dtype is not None:
+                return RangeType(dtype)
+
+
+class RangeIteratorType(SimpleIteratorType):
+
+    def __init__(self, dtype):
+        name = "range_iter_%s" % (dtype,)
+        super(SimpleIteratorType, self).__init__(name)
+        self._yield_type = dtype
+
+    def unify(self, typingctx, other):
+        if isinstance(other, RangeIteratorType):
+            dtype = typingctx.unify_pairs(self.yield_type, other.yield_type)
+            if dtype is not None:
+                return RangeIteratorType(dtype)
+
+
+class Generator(SimpleIteratorType):
+    """
+    Type class for Numba-compiled generator objects.
+    """
+
+    def __init__(self, gen_func, yield_type, arg_types, state_types,
+                 has_finalizer):
+        self.gen_func = gen_func
+        self.arg_types = tuple(arg_types)
+        self.state_types = tuple(state_types)
+        self.has_finalizer = has_finalizer
+        name = "%s generator(func=%s, args=%s, has_finalizer=%s)" % (
+            yield_type, self.gen_func, self.arg_types,
+            self.has_finalizer)
+        super(Generator, self).__init__(name, yield_type)
+
+    @property
+    def key(self):
+        return (self.gen_func, self.arg_types, self.yield_type,
+                self.has_finalizer, self.state_types)
+
+
+class EnumerateType(SimpleIteratorType):
+    """
+    Type class for `enumerate` objects.
+    Type instances are parametered with the underlying source type.
+    """
+
+    def __init__(self, iterable_type):
+        from numba.core.types import Tuple, intp
+        self.source_type = iterable_type.iterator_type
+        yield_type = Tuple([intp, self.source_type.yield_type])
+        name = 'enumerate(%s)' % (self.source_type)
+        super(EnumerateType, self).__init__(name, yield_type)
+
+    @property
+    def key(self):
+        return self.source_type
+
+
+class ZipType(SimpleIteratorType):
+    """
+    Type class for `zip` objects.
+    Type instances are parametered with the underlying source types.
+    """
+
+    def __init__(self, iterable_types):
+        from numba.core.types import Tuple
+        self.source_types = tuple(tp.iterator_type for tp in iterable_types)
+        yield_type = Tuple([tp.yield_type for tp in self.source_types])
+        name = 'zip(%s)' % ', '.join(str(tp) for tp in self.source_types)
+        super(ZipType, self).__init__(name, yield_type)
+
+    @property
+    def key(self):
+        return self.source_types
+
+
+class ArrayIterator(SimpleIteratorType):
+    """
+    Type class for iterators of array and buffer objects.
+    """
+
+    def __init__(self, array_type):
+        self.array_type = array_type
+        name = "iter(%s)" % (self.array_type,)
+        nd = array_type.ndim
+        if nd == 0:
+            raise TypingError("iteration over a 0-d array")
+        elif nd == 1:
+            yield_type = array_type.dtype
+        else:
+            # iteration semantics leads to A order layout
+            yield_type = array_type.copy(ndim=array_type.ndim - 1, layout='A')
+        super(ArrayIterator, self).__init__(name, yield_type)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/misc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/misc.py
new file mode 100644
index 000000000..dcf7c4839
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/misc.py
@@ -0,0 +1,545 @@
+from numba.core.types.abstract import Callable, Literal, Type, Hashable
+from numba.core.types.common import (Dummy, IterableType, Opaque,
+                                     SimpleIteratorType)
+from numba.core.typeconv import Conversion
+from numba.core.errors import TypingError, LiteralTypingError
+from numba.core.ir import UndefinedType
+from numba.core.utils import get_hashable_key
+
+
+class PyObject(Dummy):
+    """
+    A generic CPython object.
+    """
+
+    def is_precise(self):
+        return False
+
+
+class Phantom(Dummy):
+    """
+    A type that cannot be materialized.  A Phantom cannot be used as
+    argument or return type.
+    """
+
+
+class Undefined(Dummy):
+    """
+    A type that is left imprecise.  This is used as a temporaray placeholder
+    during type inference in the hope that the type can be later refined.
+    """
+
+    def is_precise(self):
+        return False
+
+
+class RawPointer(Opaque):
+    """
+    A raw pointer without any specific meaning.
+    """
+
+
+class StringLiteral(Literal, Dummy):
+
+    def can_convert_to(self, typingctx, other):
+        if isinstance(other, UnicodeType):
+            return Conversion.safe
+
+
+Literal.ctor_map[str] = StringLiteral
+
+
+def unliteral(lit_type):
+    """
+    Get base type from Literal type.
+    """
+    if hasattr(lit_type, '__unliteral__'):
+        return lit_type.__unliteral__()
+    return getattr(lit_type, 'literal_type', lit_type)
+
+
+def literal(value):
+    """Returns a Literal instance or raise LiteralTypingError
+    """
+    ty = type(value)
+    if isinstance(value, Literal):
+        msg = "the function does not accept a Literal type; got {} ({})"
+        raise ValueError(msg.format(value, ty))
+    try:
+        ctor = Literal.ctor_map[ty]
+    except KeyError:
+        raise LiteralTypingError("{} cannot be used as a literal".format(ty))
+    else:
+        return ctor(value)
+
+
+def maybe_literal(value):
+    """Get a Literal type for the value or None.
+    """
+    try:
+        return literal(value)
+    except LiteralTypingError:
+        return
+
+
+class Omitted(Opaque):
+    """
+    An omitted function argument with a default value.
+    """
+
+    def __init__(self, value):
+        self._value = value
+        # Use helper function to support both hashable and non-hashable
+        # values. See discussion in gh #6957.
+        self._value_key = get_hashable_key(value)
+        super(Omitted, self).__init__("omitted(default=%r)" % (value,))
+
+    @property
+    def key(self):
+        return type(self._value), self._value_key
+
+    @property
+    def value(self):
+        return self._value
+
+
+class VarArg(Type):
+    """
+    Special type representing a variable number of arguments at the
+    end of a function's signature.  Only used for signature matching,
+    not for actual values.
+    """
+
+    def __init__(self, dtype):
+        self.dtype = dtype
+        super(VarArg, self).__init__("*%s" % dtype)
+
+    @property
+    def key(self):
+        return self.dtype
+
+
+class Module(Dummy):
+    def __init__(self, pymod):
+        self.pymod = pymod
+        super(Module, self).__init__("Module(%s)" % pymod)
+
+    @property
+    def key(self):
+        return self.pymod
+
+
+class MemInfoPointer(Type):
+    """
+    Pointer to a Numba "meminfo" (i.e. the information for a managed
+    piece of memory).
+    """
+    mutable = True
+
+    def __init__(self, dtype):
+        self.dtype = dtype
+        name = "memory-managed *%s" % dtype
+        super(MemInfoPointer, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.dtype
+
+
+class CPointer(Type):
+    """
+    Type class for pointers to other types.
+
+    Attributes
+    ----------
+        dtype : The pointee type
+        addrspace : int
+            The address space pointee belongs to.
+    """
+    mutable = True
+
+    def __init__(self, dtype, addrspace=None):
+        self.dtype = dtype
+        self.addrspace = addrspace
+        if addrspace is not None:
+            name = "%s_%s*" % (dtype, addrspace)
+        else:
+            name = "%s*" % dtype
+        super(CPointer, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.dtype, self.addrspace
+
+
+class EphemeralPointer(CPointer):
+    """
+    Type class for pointers which aren't guaranteed to last long - e.g.
+    stack-allocated slots.  The data model serializes such pointers
+    by copying the data pointed to.
+    """
+
+
+class EphemeralArray(Type):
+    """
+    Similar to EphemeralPointer, but pointing to an array of elements,
+    rather than a single one.  The array size must be known at compile-time.
+    """
+
+    def __init__(self, dtype, count):
+        self.dtype = dtype
+        self.count = count
+        name = "*%s[%d]" % (dtype, count)
+        super(EphemeralArray, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.dtype, self.count
+
+
+class Object(Type):
+    # XXX unused?
+    mutable = True
+
+    def __init__(self, clsobj):
+        self.cls = clsobj
+        name = "Object(%s)" % clsobj.__name__
+        super(Object, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.cls
+
+
+class Optional(Type):
+    """
+    Type class for optional types, i.e. union { some type, None }
+    """
+
+    def __init__(self, typ):
+        assert not isinstance(typ, (Optional, NoneType))
+        typ = unliteral(typ)
+        self.type = typ
+        name = "OptionalType(%s)" % self.type
+        super(Optional, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.type
+
+    def can_convert_to(self, typingctx, other):
+        if isinstance(other, Optional):
+            return typingctx.can_convert(self.type, other.type)
+        else:
+            conv = typingctx.can_convert(self.type, other)
+            if conv is not None:
+                return max(conv, Conversion.safe)
+
+    def can_convert_from(self, typingctx, other):
+        if isinstance(other, NoneType):
+            return Conversion.promote
+        elif isinstance(other, Optional):
+            return typingctx.can_convert(other.type, self.type)
+        else:
+            conv = typingctx.can_convert(other, self.type)
+            if conv is not None:
+                return max(conv, Conversion.promote)
+
+    def unify(self, typingctx, other):
+        if isinstance(other, Optional):
+            unified = typingctx.unify_pairs(self.type, other.type)
+        else:
+            unified = typingctx.unify_pairs(self.type, other)
+
+        if unified is not None:
+            if isinstance(unified, Optional):
+                return unified
+            else:
+                return Optional(unified)
+
+
+class NoneType(Opaque):
+    """
+    The type for None.
+    """
+
+    def unify(self, typingctx, other):
+        """
+        Turn anything to a Optional type;
+        """
+        if isinstance(other, (Optional, NoneType)):
+            return other
+        return Optional(other)
+
+
+class EllipsisType(Opaque):
+    """
+    The type for the Ellipsis singleton.
+    """
+
+
+class ExceptionClass(Callable, Phantom):
+    """
+    The type of exception classes (not instances).
+    """
+
+    def __init__(self, exc_class):
+        assert issubclass(exc_class, BaseException)
+        name = "%s" % (exc_class.__name__)
+        self.exc_class = exc_class
+        super(ExceptionClass, self).__init__(name)
+
+    def get_call_type(self, context, args, kws):
+        return self.get_call_signatures()[0][0]
+
+    def get_call_signatures(self):
+        from numba.core import typing
+        return_type = ExceptionInstance(self.exc_class)
+        return [typing.signature(return_type)], False
+
+    def get_impl_key(self, sig):
+        return type(self)
+
+    @property
+    def key(self):
+        return self.exc_class
+
+
+class ExceptionInstance(Phantom):
+    """
+    The type of exception instances.  *exc_class* should be the
+    exception class.
+    """
+
+    def __init__(self, exc_class):
+        assert issubclass(exc_class, BaseException)
+        name = "%s(...)" % (exc_class.__name__,)
+        self.exc_class = exc_class
+        super(ExceptionInstance, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.exc_class
+
+
+class SliceType(Type):
+
+    def __init__(self, name, members):
+        assert members in (2, 3)
+        self.members = members
+        self.has_step = members >= 3
+        super(SliceType, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.members
+
+
+class SliceLiteral(Literal, SliceType):
+    def __init__(self, value):
+        self._literal_init(value)
+        name = 'Literal[slice]({})'.format(value)
+        members = 2 if value.step is None else 3
+        SliceType.__init__(self, name=name, members=members)
+
+    @property
+    def key(self):
+        sl = self.literal_value
+        return sl.start, sl.stop, sl.step
+
+
+Literal.ctor_map[slice] = SliceLiteral
+
+
+class ClassInstanceType(Type):
+    """
+    The type of a jitted class *instance*.  It will be the return-type
+    of the constructor of the class.
+    """
+    mutable = True
+    name_prefix = "instance"
+
+    def __init__(self, class_type):
+        self.class_type = class_type
+        name = "{0}.{1}".format(self.name_prefix, self.class_type.name)
+        super(ClassInstanceType, self).__init__(name)
+
+    def get_data_type(self):
+        return ClassDataType(self)
+
+    def get_reference_type(self):
+        return self
+
+    @property
+    def key(self):
+        return self.class_type.key
+
+    @property
+    def classname(self):
+        return self.class_type.class_name
+
+    @property
+    def jit_props(self):
+        return self.class_type.jit_props
+
+    @property
+    def jit_static_methods(self):
+        return self.class_type.jit_static_methods
+
+    @property
+    def jit_methods(self):
+        return self.class_type.jit_methods
+
+    @property
+    def struct(self):
+        return self.class_type.struct
+
+    @property
+    def methods(self):
+        return self.class_type.methods
+
+    @property
+    def static_methods(self):
+        return self.class_type.static_methods
+
+
+class ClassType(Callable, Opaque):
+    """
+    The type of the jitted class (not instance).  When the type of a class
+    is called, its constructor is invoked.
+    """
+    mutable = True
+    name_prefix = "jitclass"
+    instance_type_class = ClassInstanceType
+
+    def __init__(self, class_def, ctor_template_cls, struct, jit_methods,
+                 jit_props, jit_static_methods):
+        self.class_name = class_def.__name__
+        self.class_doc = class_def.__doc__
+        self._ctor_template_class = ctor_template_cls
+        self.jit_methods = jit_methods
+        self.jit_props = jit_props
+        self.jit_static_methods = jit_static_methods
+        self.struct = struct
+        fielddesc = ','.join("{0}:{1}".format(k, v) for k, v in struct.items())
+        name = "{0}.{1}#{2:x}<{3}>".format(self.name_prefix, self.class_name,
+                                           id(self), fielddesc)
+        super(ClassType, self).__init__(name)
+
+    def get_call_type(self, context, args, kws):
+        return self.ctor_template(context).apply(args, kws)
+
+    def get_call_signatures(self):
+        return (), True
+
+    def get_impl_key(self, sig):
+        return type(self)
+
+    @property
+    def methods(self):
+        return {k: v.py_func for k, v in self.jit_methods.items()}
+
+    @property
+    def static_methods(self):
+        return {k: v.py_func for k, v in self.jit_static_methods.items()}
+
+    @property
+    def instance_type(self):
+        return ClassInstanceType(self)
+
+    @property
+    def ctor_template(self):
+        return self._specialize_template(self._ctor_template_class)
+
+    def _specialize_template(self, basecls):
+        return type(basecls.__name__, (basecls,), dict(key=self))
+
+
+class DeferredType(Type):
+    """
+    Represents a type that will be defined later.  It must be defined
+    before it is materialized (used in the compiler).  Once defined, it
+    behaves exactly as the type it is defining.
+    """
+
+    def __init__(self):
+        self._define = None
+        name = "{0}#{1}".format(type(self).__name__, id(self))
+        super(DeferredType, self).__init__(name)
+
+    def get(self):
+        if self._define is None:
+            raise RuntimeError("deferred type not defined")
+        return self._define
+
+    def define(self, typ):
+        if self._define is not None:
+            raise TypeError("deferred type already defined")
+        if not isinstance(typ, Type):
+            raise TypeError("arg is not a Type; got: {0}".format(type(typ)))
+        self._define = typ
+
+    def unify(self, typingctx, other):
+        return typingctx.unify_pairs(self.get(), other)
+
+
+class ClassDataType(Type):
+    """
+    Internal only.
+    Represents the data of the instance.  The representation of
+    ClassInstanceType contains a pointer to a ClassDataType which represents
+    a C structure that contains all the data fields of the class instance.
+    """
+
+    def __init__(self, classtyp):
+        self.class_type = classtyp
+        name = "data.{0}".format(self.class_type.name)
+        super(ClassDataType, self).__init__(name)
+
+
+class ContextManager(Callable, Phantom):
+    """
+    An overly-simple ContextManager type that cannot be materialized.
+    """
+
+    def __init__(self, cm):
+        self.cm = cm
+        super(ContextManager, self).__init__("ContextManager({})".format(cm))
+
+    def get_call_signatures(self):
+        if not self.cm.is_callable:
+            msg = "contextmanager {} is not callable".format(self.cm)
+            raise TypingError(msg)
+
+        return (), False
+
+    def get_call_type(self, context, args, kws):
+        from numba.core import typing
+
+        if not self.cm.is_callable:
+            msg = "contextmanager {} is not callable".format(self.cm)
+            raise TypingError(msg)
+
+        posargs = list(args) + [v for k, v in sorted(kws.items())]
+        return typing.signature(self, *posargs)
+
+    def get_impl_key(self, sig):
+        return type(self)
+
+
+class UnicodeType(IterableType, Hashable):
+
+    def __init__(self, name):
+        super(UnicodeType, self).__init__(name)
+
+    @property
+    def iterator_type(self):
+        return UnicodeIteratorType(self)
+
+
+class UnicodeIteratorType(SimpleIteratorType):
+
+    def __init__(self, dtype):
+        name = "iter_unicode"
+        self.data = dtype
+        super(UnicodeIteratorType, self).__init__(name, dtype)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/npytypes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/npytypes.py
new file mode 100644
index 000000000..558451c40
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/npytypes.py
@@ -0,0 +1,604 @@
+import collections
+import warnings
+
+from llvmlite import ir
+
+from .abstract import DTypeSpec, IteratorType, MutableSequence, Number, Type
+from .common import Buffer, Opaque, SimpleIteratorType
+from numba.core.typeconv import Conversion
+from numba.core import utils
+from .misc import UnicodeType
+from .containers import Bytes
+import numpy as np
+
+class CharSeq(Type):
+    """
+    A fixed-length 8-bit character sequence.
+    """
+    mutable = True
+
+    def __init__(self, count):
+        self.count = count
+        name = "[char x %d]" % count
+        super(CharSeq, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.count
+
+    def can_convert_from(self, typingctx, other):
+        if isinstance(other, Bytes):
+            return Conversion.safe
+
+
+class UnicodeCharSeq(Type):
+    """
+    A fixed-length unicode character sequence.
+    """
+    mutable = True
+
+    def __init__(self, count):
+        self.count = count
+        name = "[unichr x %d]" % count
+        super(UnicodeCharSeq, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.count
+
+    def can_convert_to(self, typingctx, other):
+        if isinstance(other, UnicodeCharSeq):
+            return Conversion.safe
+
+    def can_convert_from(self, typingctx, other):
+        if isinstance(other, UnicodeType):
+            # Assuming that unicode_type itemsize is not greater than
+            # numpy.dtype('U1').itemsize that UnicodeCharSeq is based
+            # on.
+            return Conversion.safe
+
+
+_RecordField = collections.namedtuple(
+    '_RecordField',
+    'type,offset,alignment,title',
+)
+
+
+class Record(Type):
+    """
+    A Record datatype can be mapped to a NumPy structured dtype.
+    A record is very flexible since it is laid out as a list of bytes.
+    Fields can be mapped to arbitrary points inside it, even if they overlap.
+
+    *fields* is a list of `(name:str, data:dict)`.
+        Where `data` is `{ type: Type, offset: int }`
+    *size* is an int; the record size
+    *aligned* is a boolean; whether the record is ABI aligned.
+    """
+    mutable = True
+
+    @classmethod
+    def make_c_struct(cls, name_types):
+        """Construct a Record type from a list of (name:str, type:Types).
+        The layout of the structure will follow C.
+
+        Note: only scalar types are supported currently.
+        """
+        from numba.core.registry import cpu_target
+
+        ctx = cpu_target.target_context
+        offset = 0
+        fields = []
+        lltypes = []
+        for k, ty in name_types:
+            if not isinstance(ty, (Number, NestedArray)):
+                msg = "Only Number and NestedArray types are supported, found: {}. "
+                raise TypeError(msg.format(ty))
+            if isinstance(ty, NestedArray):
+                datatype = ctx.data_model_manager[ty].as_storage_type()
+            else:
+                datatype = ctx.get_data_type(ty)
+            lltypes.append(datatype)
+            size = ctx.get_abi_sizeof(datatype)
+            align = ctx.get_abi_alignment(datatype)
+            # align
+            misaligned = offset % align
+            if misaligned:
+                offset += align - misaligned
+            fields.append((k, {
+                'type': ty, 'offset': offset, 'alignment': align,
+            }))
+            offset += size
+        # Adjust sizeof structure
+        abi_size = ctx.get_abi_sizeof(ir.LiteralStructType(lltypes))
+        return Record(fields, size=abi_size, aligned=True)
+
+    def __init__(self, fields, size, aligned):
+        fields = self._normalize_fields(fields)
+        self.fields = dict(fields)
+        self.size = size
+        self.aligned = aligned
+
+        # Create description
+        descbuf = []
+        fmt = "{}[type={};offset={}{}]"
+        for k, infos in fields:
+            extra = ""
+            if infos.alignment is not None:
+                extra += ';alignment={}'.format(infos.alignment)
+            elif infos.title is not None:
+                extra += ';title={}'.format(infos.title)
+            descbuf.append(fmt.format(k, infos.type, infos.offset, extra))
+
+        desc = ','.join(descbuf)
+        name = 'Record({};{};{})'.format(desc, self.size, self.aligned)
+        super(Record, self).__init__(name)
+
+    @classmethod
+    def _normalize_fields(cls, fields):
+        """
+        fields:
+            [name: str,
+             value: {
+                 type: Type,
+                 offset: int,
+                 [ alignment: int ],
+                 [ title : str],
+             }]
+        """
+        res = []
+        for name, infos in sorted(fields, key=lambda x: (x[1]['offset'], x[0])):
+            fd = _RecordField(
+                type=infos['type'],
+                offset=infos['offset'],
+                alignment=infos.get('alignment'),
+                title=infos.get('title'),
+            )
+            res.append((name, fd))
+        return res
+
+    @property
+    def key(self):
+        # Numpy dtype equality doesn't always succeed, use the name instead
+        # (https://github.com/numpy/numpy/issues/5715)
+        return self.name
+
+    @property
+    def mangling_args(self):
+        return self.__class__.__name__, (self._code,)
+
+    def __len__(self):
+        """Returns the number of fields
+        """
+        return len(self.fields)
+
+    def offset(self, key):
+        """Get the byte offset of a field from the start of the structure.
+        """
+        return self.fields[key].offset
+
+    def typeof(self, key):
+        """Get the type of a field.
+        """
+        return self.fields[key].type
+
+    def alignof(self, key):
+        """Get the specified alignment of the field.
+
+        Since field alignment is optional, this may return None.
+        """
+        return self.fields[key].alignment
+
+    def has_titles(self):
+        """Returns True the record uses titles.
+        """
+        return any(fd.title is not None for fd in self.fields.values())
+
+    def is_title(self, key):
+        """Returns True if the field named *key* is a title.
+        """
+        return self.fields[key].title == key
+
+    @property
+    def members(self):
+        """An ordered list of (name, type) for the fields.
+        """
+        ordered = sorted(self.fields.items(), key=lambda x: x[1].offset)
+        return [(k, v.type) for k, v in ordered]
+
+    @property
+    def dtype(self):
+        from numba.np.numpy_support import as_struct_dtype
+
+        return as_struct_dtype(self)
+
+    def can_convert_to(self, typingctx, other):
+        """
+        Convert this Record to the *other*.
+
+        This method only implements width subtyping for records.
+        """
+        from numba.core.errors import NumbaExperimentalFeatureWarning
+
+        if isinstance(other, Record):
+            if len(other.fields) > len(self.fields):
+                return
+            for other_fd, self_fd in zip(other.fields.items(),
+                                         self.fields.items()):
+                if not other_fd == self_fd:
+                    return
+            warnings.warn(f"{self} has been considered a subtype of {other} "
+                          f" This is an experimental feature.",
+                          category=NumbaExperimentalFeatureWarning)
+            return Conversion.safe
+
+
+class DType(DTypeSpec, Opaque):
+    """
+    Type class associated with the `np.dtype`.
+
+    i.e. :code:`assert type(np.dtype('int32')) == np.dtype`
+
+    np.dtype('int32')
+    """
+
+    def __init__(self, dtype):
+        assert isinstance(dtype, Type)
+        self._dtype = dtype
+        name = "dtype(%s)" % (dtype,)
+        super(DTypeSpec, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.dtype
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    def __getitem__(self, arg):
+        res = super(DType, self).__getitem__(arg)
+        return res.copy(dtype=self.dtype)
+
+
+class NumpyFlatType(SimpleIteratorType, MutableSequence):
+    """
+    Type class for `ndarray.flat()` objects.
+    """
+
+    def __init__(self, arrty):
+        self.array_type = arrty
+        yield_type = arrty.dtype
+        self.dtype = yield_type
+        name = "array.flat({arrayty})".format(arrayty=arrty)
+        super(NumpyFlatType, self).__init__(name, yield_type)
+
+    @property
+    def key(self):
+        return self.array_type
+
+
+class NumpyNdEnumerateType(SimpleIteratorType):
+    """
+    Type class for `np.ndenumerate()` objects.
+    """
+
+    def __init__(self, arrty):
+        from . import Tuple, UniTuple, intp
+        self.array_type = arrty
+        yield_type = Tuple((UniTuple(intp, arrty.ndim), arrty.dtype))
+        name = "ndenumerate({arrayty})".format(arrayty=arrty)
+        super(NumpyNdEnumerateType, self).__init__(name, yield_type)
+
+    @property
+    def key(self):
+        return self.array_type
+
+
+class NumpyNdIterType(IteratorType):
+    """
+    Type class for `np.nditer()` objects.
+
+    The layout denotes in which order the logical shape is iterated on.
+    "C" means logical order (corresponding to in-memory order in C arrays),
+    "F" means reverse logical order (corresponding to in-memory order in
+    F arrays).
+    """
+
+    def __init__(self, arrays):
+        # Note inputs arrays can also be scalars, in which case they are
+        # broadcast.
+        self.arrays = tuple(arrays)
+        self.layout = self._compute_layout(self.arrays)
+        self.dtypes = tuple(getattr(a, 'dtype', a) for a in self.arrays)
+        self.ndim = max(getattr(a, 'ndim', 0) for a in self.arrays)
+        name = "nditer(ndim={ndim}, layout={layout}, inputs={arrays})".format(
+            ndim=self.ndim, layout=self.layout, arrays=self.arrays)
+        super(NumpyNdIterType, self).__init__(name)
+
+    @classmethod
+    def _compute_layout(cls, arrays):
+        c = collections.Counter()
+        for a in arrays:
+            if not isinstance(a, Array):
+                continue
+            if a.layout in 'CF' and a.ndim == 1:
+                c['C'] += 1
+                c['F'] += 1
+            elif a.ndim >= 1:
+                c[a.layout] += 1
+        return 'F' if c['F'] > c['C'] else 'C'
+
+    @property
+    def key(self):
+        return self.arrays
+
+    @property
+    def views(self):
+        """
+        The views yielded by the iterator.
+        """
+        return [Array(dtype, 0, 'C') for dtype in self.dtypes]
+
+    @property
+    def yield_type(self):
+        from . import BaseTuple
+        views = self.views
+        if len(views) > 1:
+            return BaseTuple.from_types(views)
+        else:
+            return views[0]
+
+    @utils.cached_property
+    def indexers(self):
+        """
+        A list of (kind, start_dim, end_dim, indices) where:
+        - `kind` is either "flat", "indexed", "0d" or "scalar"
+        - `start_dim` and `end_dim` are the dimension numbers at which
+          this indexing takes place
+        - `indices` is the indices of the indexed arrays in self.arrays
+        """
+        d = collections.OrderedDict()
+        layout = self.layout
+        ndim = self.ndim
+        assert layout in 'CF'
+        for i, a in enumerate(self.arrays):
+            if not isinstance(a, Array):
+                indexer = ('scalar', 0, 0)
+            elif a.ndim == 0:
+                indexer = ('0d', 0, 0)
+            else:
+                if a.layout == layout or (a.ndim == 1 and a.layout in 'CF'):
+                    kind = 'flat'
+                else:
+                    kind = 'indexed'
+                if layout == 'C':
+                    # If iterating in C order, broadcasting is done on the outer indices
+                    indexer = (kind, ndim - a.ndim, ndim)
+                else:
+                    indexer = (kind, 0, a.ndim)
+            d.setdefault(indexer, []).append(i)
+        return list(k + (v,) for k, v in d.items())
+
+    @utils.cached_property
+    def need_shaped_indexing(self):
+        """
+        Whether iterating on this iterator requires keeping track of
+        individual indices inside the shape.  If False, only a single index
+        over the equivalent flat shape is required, which can make the
+        iterator more efficient.
+        """
+        for kind, start_dim, end_dim, _ in self.indexers:
+            if kind in ('0d', 'scalar'):
+                pass
+            elif kind == 'flat':
+                if (start_dim, end_dim) != (0, self.ndim):
+                    # Broadcast flat iteration needs shaped indexing
+                    # to know when to restart iteration.
+                    return True
+            else:
+                return True
+        return False
+
+
+class NumpyNdIndexType(SimpleIteratorType):
+    """
+    Type class for `np.ndindex()` objects.
+    """
+
+    def __init__(self, ndim):
+        from . import UniTuple, intp
+        self.ndim = ndim
+        yield_type = UniTuple(intp, self.ndim)
+        name = "ndindex(ndim={ndim})".format(ndim=ndim)
+        super(NumpyNdIndexType, self).__init__(name, yield_type)
+
+    @property
+    def key(self):
+        return self.ndim
+
+
+class Array(Buffer):
+    """
+    Type class for Numpy arrays.
+    """
+
+    def __init__(self, dtype, ndim, layout, readonly=False, name=None,
+                 aligned=True):
+        if readonly:
+            self.mutable = False
+        if (not aligned or
+            (isinstance(dtype, Record) and not dtype.aligned)):
+            self.aligned = False
+        if name is None:
+            type_name = "array"
+            if not self.mutable:
+                type_name = "readonly " + type_name
+            if not self.aligned:
+                type_name = "unaligned " + type_name
+            name = "%s(%s, %sd, %s)" % (type_name, dtype, ndim, layout)
+        super(Array, self).__init__(dtype, ndim, layout, name=name)
+
+    @property
+    def mangling_args(self):
+        args = [self.dtype, self.ndim, self.layout,
+                'mutable' if self.mutable else 'readonly',
+                'aligned' if self.aligned else 'unaligned']
+        return self.__class__.__name__, args
+
+    def copy(self, dtype=None, ndim=None, layout=None, readonly=None):
+        if dtype is None:
+            dtype = self.dtype
+        if ndim is None:
+            ndim = self.ndim
+        if layout is None:
+            layout = self.layout
+        if readonly is None:
+            readonly = not self.mutable
+        return Array(dtype=dtype, ndim=ndim, layout=layout, readonly=readonly,
+                     aligned=self.aligned)
+
+    @property
+    def key(self):
+        return self.dtype, self.ndim, self.layout, self.mutable, self.aligned
+
+    def unify(self, typingctx, other):
+        """
+        Unify this with the *other* Array.
+        """
+        # If other is array and the ndim matches
+        if isinstance(other, Array) and other.ndim == self.ndim:
+            # If dtype matches or other.dtype is undefined (inferred)
+            if other.dtype == self.dtype or not other.dtype.is_precise():
+                if self.layout == other.layout:
+                    layout = self.layout
+                else:
+                    layout = 'A'
+                readonly = not (self.mutable and other.mutable)
+                aligned = self.aligned and other.aligned
+                return Array(dtype=self.dtype, ndim=self.ndim, layout=layout,
+                             readonly=readonly, aligned=aligned)
+
+    def can_convert_to(self, typingctx, other):
+        """
+        Convert this Array to the *other*.
+        """
+        if (isinstance(other, Array) and other.ndim == self.ndim
+            and other.dtype == self.dtype):
+            if (other.layout in ('A', self.layout)
+                and (self.mutable or not other.mutable)
+                and (self.aligned or not other.aligned)):
+                return Conversion.safe
+
+    def is_precise(self):
+        return self.dtype.is_precise()
+
+    @property
+    def box_type(self):
+        """Returns the Python type to box to.
+        """
+        return np.ndarray
+
+
+class ArrayCTypes(Type):
+    """
+    This is the type for `np.ndarray.ctypes`.
+    """
+    def __init__(self, arytype):
+        # This depends on the ndim for the shape and strides attributes,
+        # even though they are not implemented, yet.
+        self.dtype = arytype.dtype
+        self.ndim = arytype.ndim
+        name = "ArrayCTypes(dtype={0}, ndim={1})".format(self.dtype, self.ndim)
+        super(ArrayCTypes, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.dtype, self.ndim
+
+    def can_convert_to(self, typingctx, other):
+        """
+        Convert this type to the corresponding pointer type.
+        This allows passing a array.ctypes object to a C function taking
+        a raw pointer.
+
+        Note that in pure Python, the array.ctypes object can only be
+        passed to a ctypes function accepting a c_void_p, not a typed
+        pointer.
+        """
+        from . import CPointer, voidptr
+        # XXX what about readonly
+        if isinstance(other, CPointer) and other.dtype == self.dtype:
+            return Conversion.safe
+        elif other == voidptr:
+            return Conversion.safe
+
+
+class ArrayFlags(Type):
+    """
+    This is the type for `np.ndarray.flags`.
+    """
+    def __init__(self, arytype):
+        self.array_type = arytype
+        name = "ArrayFlags({0})".format(self.array_type)
+        super(ArrayFlags, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.array_type
+
+
+class NestedArray(Array):
+    """
+    A NestedArray is an array nested within a structured type (which are "void"
+    type in NumPy parlance). Unlike an Array, the shape, and not just the number
+    of dimensions is part of the type of a NestedArray.
+    """
+
+    def __init__(self, dtype, shape):
+        assert dtype.bitwidth % 8 == 0, \
+            "Dtype bitwidth must be a multiple of bytes"
+        self._shape = shape
+        name = "nestedarray(%s, %s)" % (dtype, shape)
+        ndim = len(shape)
+        super(NestedArray, self).__init__(dtype, ndim, 'C', name=name)
+
+    @property
+    def shape(self):
+        return self._shape
+
+    @property
+    def nitems(self):
+        l = 1
+        for s in self.shape:
+            l = l * s
+        return l
+
+    @property
+    def size(self):
+        return self.dtype.bitwidth // 8
+
+    @property
+    def strides(self):
+        stride = self.size
+        strides = []
+        for i in reversed(self._shape):
+             strides.append(stride)
+             stride *= i
+        return tuple(reversed(strides))
+
+    @property
+    def key(self):
+        return self.dtype, self.shape
+
+
+class NumPyRandomBitGeneratorType(Type):
+    def __init__(self, *args, **kwargs):
+        super(NumPyRandomBitGeneratorType, self).__init__(*args, **kwargs)
+        self.name = 'NumPyRandomBitGeneratorType'
+
+
+class NumPyRandomGeneratorType(Type):
+    def __init__(self, *args, **kwargs):
+        super(NumPyRandomGeneratorType, self).__init__(*args, **kwargs)
+        self.name = 'NumPyRandomGeneratorType'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/scalars.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/scalars.py
new file mode 100644
index 000000000..eb52043af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/types/scalars.py
@@ -0,0 +1,270 @@
+import enum
+
+import numpy as np
+
+from .abstract import Dummy, Hashable, Literal, Number, Type
+from functools import total_ordering
+from numba.core import utils
+from numba.core.typeconv import Conversion
+from numba.np import npdatetime_helpers
+
+
+class Boolean(Hashable):
+
+    def cast_python_value(self, value):
+        return bool(value)
+
+
+def parse_integer_bitwidth(name):
+    for prefix in ('int', 'uint'):
+        if name.startswith(prefix):
+            bitwidth = int(name[len(prefix):])
+    return bitwidth
+
+
+def parse_integer_signed(name):
+    signed = name.startswith('int')
+    return signed
+
+
+@total_ordering
+class Integer(Number):
+    def __init__(self, name, bitwidth=None, signed=None):
+        super(Integer, self).__init__(name)
+        if bitwidth is None:
+            bitwidth = parse_integer_bitwidth(name)
+        if signed is None:
+            signed = parse_integer_signed(name)
+        self.bitwidth = bitwidth
+        self.signed = signed
+
+    @classmethod
+    def from_bitwidth(cls, bitwidth, signed=True):
+        name = ('int%d' if signed else 'uint%d') % bitwidth
+        return cls(name)
+
+    def cast_python_value(self, value):
+        return getattr(np, self.name)(value)
+
+    def __lt__(self, other):
+        if self.__class__ is not other.__class__:
+            return NotImplemented
+        if self.signed != other.signed:
+            return NotImplemented
+        return self.bitwidth < other.bitwidth
+
+    @property
+    def maxval(self):
+        """
+        The maximum value representable by this type.
+        """
+        if self.signed:
+            return (1 << (self.bitwidth - 1)) - 1
+        else:
+            return (1 << self.bitwidth) - 1
+
+    @property
+    def minval(self):
+        """
+        The minimal value representable by this type.
+        """
+        if self.signed:
+            return -(1 << (self.bitwidth - 1))
+        else:
+            return 0
+
+
+class IntegerLiteral(Literal, Integer):
+    def __init__(self, value):
+        self._literal_init(value)
+        name = 'Literal[int]({})'.format(value)
+        basetype = self.literal_type
+        Integer.__init__(
+            self,
+            name=name,
+            bitwidth=basetype.bitwidth,
+            signed=basetype.signed,
+            )
+
+    def can_convert_to(self, typingctx, other):
+        conv = typingctx.can_convert(self.literal_type, other)
+        if conv is not None:
+            return max(conv, Conversion.promote)
+
+
+Literal.ctor_map[int] = IntegerLiteral
+
+
+class BooleanLiteral(Literal, Boolean):
+
+    def __init__(self, value):
+        self._literal_init(value)
+        name = 'Literal[bool]({})'.format(value)
+        Boolean.__init__(
+            self,
+            name=name
+            )
+
+    def can_convert_to(self, typingctx, other):
+        conv = typingctx.can_convert(self.literal_type, other)
+        if conv is not None:
+            return max(conv, Conversion.promote)
+
+
+Literal.ctor_map[bool] = BooleanLiteral
+
+
+@total_ordering
+class Float(Number):
+    def __init__(self, *args, **kws):
+        super(Float, self).__init__(*args, **kws)
+        # Determine bitwidth
+        assert self.name.startswith('float')
+        bitwidth = int(self.name[5:])
+        self.bitwidth = bitwidth
+
+    def cast_python_value(self, value):
+        return getattr(np, self.name)(value)
+
+    def __lt__(self, other):
+        if self.__class__ is not other.__class__:
+            return NotImplemented
+        return self.bitwidth < other.bitwidth
+
+
+@total_ordering
+class Complex(Number):
+    def __init__(self, name, underlying_float, **kwargs):
+        super(Complex, self).__init__(name, **kwargs)
+        self.underlying_float = underlying_float
+        # Determine bitwidth
+        assert self.name.startswith('complex')
+        bitwidth = int(self.name[7:])
+        self.bitwidth = bitwidth
+
+    def cast_python_value(self, value):
+        return getattr(np, self.name)(value)
+
+    def __lt__(self, other):
+        if self.__class__ is not other.__class__:
+            return NotImplemented
+        return self.bitwidth < other.bitwidth
+
+
+class _NPDatetimeBase(Type):
+    """
+    Common base class for np.datetime64 and np.timedelta64.
+    """
+
+    def __init__(self, unit, *args, **kws):
+        name = '%s[%s]' % (self.type_name, unit)
+        self.unit = unit
+        self.unit_code = npdatetime_helpers.DATETIME_UNITS[self.unit]
+        super(_NPDatetimeBase, self).__init__(name, *args, **kws)
+
+    def __lt__(self, other):
+        if self.__class__ is not other.__class__:
+            return NotImplemented
+        # A coarser-grained unit is "smaller", i.e. less precise values
+        # can be represented (but the magnitude of representable values is
+        # also greater...).
+        return self.unit_code < other.unit_code
+
+    def cast_python_value(self, value):
+        cls = getattr(np, self.type_name)
+        if self.unit:
+            return cls(value, self.unit)
+        else:
+            return cls(value)
+
+
+@total_ordering
+class NPTimedelta(_NPDatetimeBase):
+    type_name = 'timedelta64'
+
+@total_ordering
+class NPDatetime(_NPDatetimeBase):
+    type_name = 'datetime64'
+
+
+class EnumClass(Dummy):
+    """
+    Type class for Enum classes.
+    """
+    basename = "Enum class"
+
+    def __init__(self, cls, dtype):
+        assert isinstance(cls, type)
+        assert isinstance(dtype, Type)
+        self.instance_class = cls
+        self.dtype = dtype
+        name = "%s<%s>(%s)" % (self.basename, self.dtype, self.instance_class.__name__)
+        super(EnumClass, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.instance_class, self.dtype
+
+    @utils.cached_property
+    def member_type(self):
+        """
+        The type of this class' members.
+        """
+        return EnumMember(self.instance_class, self.dtype)
+
+
+class IntEnumClass(EnumClass):
+    """
+    Type class for IntEnum classes.
+    """
+    basename = "IntEnum class"
+
+    @utils.cached_property
+    def member_type(self):
+        """
+        The type of this class' members.
+        """
+        return IntEnumMember(self.instance_class, self.dtype)
+
+
+class EnumMember(Type):
+    """
+    Type class for Enum members.
+    """
+    basename = "Enum"
+    class_type_class = EnumClass
+
+    def __init__(self, cls, dtype):
+        assert isinstance(cls, type)
+        assert isinstance(dtype, Type)
+        self.instance_class = cls
+        self.dtype = dtype
+        name = "%s<%s>(%s)" % (self.basename, self.dtype, self.instance_class.__name__)
+        super(EnumMember, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.instance_class, self.dtype
+
+    @property
+    def class_type(self):
+        """
+        The type of this member's class.
+        """
+        return self.class_type_class(self.instance_class, self.dtype)
+
+
+class IntEnumMember(EnumMember):
+    """
+    Type class for IntEnum members.
+    """
+    basename = "IntEnum"
+    class_type_class = IntEnumClass
+
+    def can_convert_to(self, typingctx, other):
+        """
+        Convert IntEnum members to plain integers.
+        """
+        if issubclass(self.instance_class, enum.IntEnum):
+            conv = typingctx.can_convert(self.dtype, other)
+            return max(conv, Conversion.safe)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/__init__.py
new file mode 100644
index 000000000..7a30bbd0c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/__init__.py
@@ -0,0 +1,3 @@
+from .context import BaseContext, Context
+from .templates import (signature, make_concrete_template, Signature,
+                        fold_arguments)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/arraydecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/arraydecl.py
new file mode 100644
index 000000000..629263aad
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/arraydecl.py
@@ -0,0 +1,849 @@
+import numpy as np
+import operator
+from collections import namedtuple
+
+from numba.core import types, utils
+from numba.core.typing.templates import (AttributeTemplate, AbstractTemplate,
+                                         infer, infer_global, infer_getattr,
+                                         signature, bound_function)
+# import time side effect: array operations requires typing support of sequence
+# defined in collections: e.g. array.shape[i]
+from numba.core.typing import collections
+from numba.core.errors import (TypingError, RequireLiteralValue, NumbaTypeError,
+                               NumbaNotImplementedError, NumbaAssertionError,
+                               NumbaKeyError, NumbaIndexError)
+
+
+Indexing = namedtuple("Indexing", ("index", "result", "advanced"))
+
+
+def get_array_index_type(ary, idx):
+    """
+    Returns None or a tuple-3 for the types of the input array, index, and
+    resulting type of ``array[index]``.
+
+    Note: This is shared logic for ndarray getitem and setitem.
+    """
+    if not isinstance(ary, types.Buffer):
+        return
+
+    ndim = ary.ndim
+
+    left_indices = []
+    right_indices = []
+    ellipsis_met = False
+    advanced = False
+    has_integer = False
+
+    if not isinstance(idx, types.BaseTuple):
+        idx = [idx]
+
+    # Walk indices
+    for ty in idx:
+        if ty is types.ellipsis:
+            if ellipsis_met:
+                raise NumbaTypeError("only one ellipsis allowed in array index "
+                                     "(got %s)" % (idx,))
+            ellipsis_met = True
+        elif isinstance(ty, types.SliceType):
+            pass
+        elif isinstance(ty, types.Integer):
+            # Normalize integer index
+            ty = types.intp if ty.signed else types.uintp
+            # Integer indexing removes the given dimension
+            ndim -= 1
+            has_integer = True
+        elif (isinstance(ty, types.Array) and ty.ndim == 0
+              and isinstance(ty.dtype, types.Integer)):
+            # 0-d array used as integer index
+            ndim -= 1
+            has_integer = True
+        elif (isinstance(ty, types.Array)
+              and ty.ndim == 1
+              and isinstance(ty.dtype, (types.Integer, types.Boolean))):
+            if advanced or has_integer:
+                # We don't support the complicated combination of
+                # advanced indices (and integers are considered part
+                # of them by Numpy).
+                msg = "only one advanced index supported"
+                raise NumbaNotImplementedError(msg)
+            advanced = True
+        else:
+            raise NumbaTypeError("unsupported array index type %s in %s"
+                                 % (ty, idx))
+        (right_indices if ellipsis_met else left_indices).append(ty)
+
+    # Only Numpy arrays support advanced indexing
+    if advanced and not isinstance(ary, types.Array):
+        return
+
+    # Check indices and result dimensionality
+    all_indices = left_indices + right_indices
+    if ellipsis_met:
+        assert right_indices[0] is types.ellipsis
+        del right_indices[0]
+
+    n_indices = len(all_indices) - ellipsis_met
+    if n_indices > ary.ndim:
+        raise NumbaTypeError("cannot index %s with %d indices: %s"
+                             % (ary, n_indices, idx))
+    if n_indices == ary.ndim and ndim == 0 and not ellipsis_met:
+        # Full integer indexing => scalar result
+        # (note if ellipsis is present, a 0-d view is returned instead)
+        res = ary.dtype
+
+    elif advanced:
+        # Result is a copy
+        res = ary.copy(ndim=ndim, layout='C', readonly=False)
+
+    else:
+        # Result is a view
+        if ary.slice_is_copy:
+            # Avoid view semantics when the original type creates a copy
+            # when slicing.
+            return
+
+        # Infer layout
+        layout = ary.layout
+
+        def keeps_contiguity(ty, is_innermost):
+            # A slice can only keep an array contiguous if it is the
+            # innermost index and it is not strided
+            return (ty is types.ellipsis or isinstance(ty, types.Integer)
+                    or (is_innermost and isinstance(ty, types.SliceType)
+                        and not ty.has_step))
+
+        def check_contiguity(outer_indices):
+            """
+            Whether indexing with the given indices (from outer to inner in
+            physical layout order) can keep an array contiguous.
+            """
+            for ty in outer_indices[:-1]:
+                if not keeps_contiguity(ty, False):
+                    return False
+            if outer_indices and not keeps_contiguity(outer_indices[-1], True):
+                return False
+            return True
+
+        if layout == 'C':
+            # Integer indexing on the left keeps the array C-contiguous
+            if n_indices == ary.ndim:
+                # If all indices are there, ellipsis's place is indifferent
+                left_indices = left_indices + right_indices
+                right_indices = []
+            if right_indices:
+                layout = 'A'
+            elif not check_contiguity(left_indices):
+                layout = 'A'
+        elif layout == 'F':
+            # Integer indexing on the right keeps the array F-contiguous
+            if n_indices == ary.ndim:
+                # If all indices are there, ellipsis's place is indifferent
+                right_indices = left_indices + right_indices
+                left_indices = []
+            if left_indices:
+                layout = 'A'
+            elif not check_contiguity(right_indices[::-1]):
+                layout = 'A'
+
+        if ndim == 0:
+            # Implicitly convert to a scalar if the output ndim==0
+            res = ary.dtype
+        else:
+            res = ary.copy(ndim=ndim, layout=layout)
+
+    # Re-wrap indices
+    if isinstance(idx, types.BaseTuple):
+        idx = types.BaseTuple.from_types(all_indices)
+    else:
+        idx, = all_indices
+
+    return Indexing(idx, res, advanced)
+
+
+@infer_global(operator.getitem)
+class GetItemBuffer(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        [ary, idx] = args
+        out = get_array_index_type(ary, idx)
+        if out is not None:
+            return signature(out.result, ary, out.index)
+
+
+@infer_global(operator.setitem)
+class SetItemBuffer(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        ary, idx, val = args
+        if not isinstance(ary, types.Buffer):
+            return
+        if not ary.mutable:
+            msg = f"Cannot modify readonly array of type: {ary}"
+            raise NumbaTypeError(msg)
+        out = get_array_index_type(ary, idx)
+        if out is None:
+            return
+
+        idx = out.index
+        res = out.result  # res is the result type of the access ary[idx]
+        if isinstance(res, types.Array):
+            # Indexing produces an array
+            if isinstance(val, types.Array):
+                if not self.context.can_convert(val.dtype, res.dtype):
+                    # DType conversion not possible
+                    return
+                else:
+                    res = val
+            elif isinstance(val, types.Sequence):
+                if (res.ndim == 1 and
+                    self.context.can_convert(val.dtype, res.dtype)):
+                    # Allow assignment of sequence to 1d array
+                    res = val
+                else:
+                    # NOTE: sequence-to-array broadcasting is unsupported
+                    return
+            else:
+                # Allow scalar broadcasting
+                if self.context.can_convert(val, res.dtype):
+                    res = res.dtype
+                else:
+                    # Incompatible scalar type
+                    return
+        elif not isinstance(val, types.Array):
+            # Single item assignment
+            if not self.context.can_convert(val, res):
+                # if the array dtype is not yet defined
+                if not res.is_precise():
+                    # set the array type to use the dtype of value (RHS)
+                    newary = ary.copy(dtype=val)
+                    return signature(types.none, newary, idx, res)
+                else:
+                    return
+            res = val
+        elif (isinstance(val, types.Array) and val.ndim == 0
+              and self.context.can_convert(val.dtype, res)):
+            # val is an array(T, 0d, O), where T is the type of res, O is order
+            res = val
+        else:
+            return
+        return signature(types.none, ary, idx, res)
+
+
+def normalize_shape(shape):
+    if isinstance(shape, types.UniTuple):
+        if isinstance(shape.dtype, types.Integer):
+            dimtype = types.intp if shape.dtype.signed else types.uintp
+            return types.UniTuple(dimtype, len(shape))
+
+    elif isinstance(shape, types.Tuple) and shape.count == 0:
+        # Force (0 x intp) for consistency with other shapes
+        return types.UniTuple(types.intp, 0)
+
+
+@infer_getattr
+class ArrayAttribute(AttributeTemplate):
+    key = types.Array
+
+    def resolve_dtype(self, ary):
+        return types.DType(ary.dtype)
+
+    def resolve_itemsize(self, ary):
+        return types.intp
+
+    def resolve_shape(self, ary):
+        return types.UniTuple(types.intp, ary.ndim)
+
+    def resolve_strides(self, ary):
+        return types.UniTuple(types.intp, ary.ndim)
+
+    def resolve_ndim(self, ary):
+        return types.intp
+
+    def resolve_size(self, ary):
+        return types.intp
+
+    def resolve_flat(self, ary):
+        return types.NumpyFlatType(ary)
+
+    def resolve_ctypes(self, ary):
+        return types.ArrayCTypes(ary)
+
+    def resolve_flags(self, ary):
+        return types.ArrayFlags(ary)
+
+    def resolve_T(self, ary):
+        if ary.ndim <= 1:
+            retty = ary
+        else:
+            layout = {"C": "F", "F": "C"}.get(ary.layout, "A")
+            retty = ary.copy(layout=layout)
+        return retty
+
+    def resolve_real(self, ary):
+        return self._resolve_real_imag(ary, attr='real')
+
+    def resolve_imag(self, ary):
+        return self._resolve_real_imag(ary, attr='imag')
+
+    def _resolve_real_imag(self, ary, attr):
+        if ary.dtype in types.complex_domain:
+            return ary.copy(dtype=ary.dtype.underlying_float, layout='A')
+        elif ary.dtype in types.number_domain:
+            res = ary.copy(dtype=ary.dtype)
+            if attr == 'imag':
+                res = res.copy(readonly=True)
+            return res
+        else:
+            msg = "cannot access .{} of array of {}"
+            raise TypingError(msg.format(attr, ary.dtype))
+
+    @bound_function("array.transpose")
+    def resolve_transpose(self, ary, args, kws):
+        def sentry_shape_scalar(ty):
+            if ty in types.number_domain:
+                # Guard against non integer type
+                if not isinstance(ty, types.Integer):
+                    raise TypeError("transpose() arg cannot be {0}".format(ty))
+                return True
+            else:
+                return False
+
+        assert not kws
+        if len(args) == 0:
+            return signature(self.resolve_T(ary))
+
+        if len(args) == 1:
+            shape, = args
+
+            if sentry_shape_scalar(shape):
+                assert ary.ndim == 1
+                return signature(ary, *args)
+
+            if isinstance(shape, types.NoneType):
+                return signature(self.resolve_T(ary))
+
+            shape = normalize_shape(shape)
+            if shape is None:
+                return
+
+            assert ary.ndim == shape.count
+            return signature(self.resolve_T(ary).copy(layout="A"), shape)
+
+        else:
+            if any(not sentry_shape_scalar(a) for a in args):
+                raise TypeError("transpose({0}) is not supported".format(
+                    ', '.join(args)))
+            assert ary.ndim == len(args)
+            return signature(self.resolve_T(ary).copy(layout="A"), *args)
+
+    @bound_function("array.copy")
+    def resolve_copy(self, ary, args, kws):
+        assert not args
+        assert not kws
+        retty = ary.copy(layout="C", readonly=False)
+        return signature(retty)
+
+    @bound_function("array.item")
+    def resolve_item(self, ary, args, kws):
+        assert not kws
+        # We don't support explicit arguments as that's exactly equivalent
+        # to regular indexing.  The no-argument form is interesting to
+        # allow some degree of genericity when writing functions.
+        if not args:
+            return signature(ary.dtype)
+
+    @bound_function("array.itemset")
+    def resolve_itemset(self, ary, args, kws):
+        assert not kws
+        # We don't support explicit arguments as that's exactly equivalent
+        # to regular indexing.  The no-argument form is interesting to
+        # allow some degree of genericity when writing functions.
+        if len(args) == 1:
+            return signature(types.none, ary.dtype)
+
+    @bound_function("array.nonzero")
+    def resolve_nonzero(self, ary, args, kws):
+        assert not args
+        assert not kws
+        # 0-dim arrays return one result array
+        ndim = max(ary.ndim, 1)
+        retty = types.UniTuple(types.Array(types.intp, 1, 'C'), ndim)
+        return signature(retty)
+
+    @bound_function("array.reshape")
+    def resolve_reshape(self, ary, args, kws):
+        def sentry_shape_scalar(ty):
+            if ty in types.number_domain:
+                # Guard against non integer type
+                if not isinstance(ty, types.Integer):
+                    raise TypeError("reshape() arg cannot be {0}".format(ty))
+                return True
+            else:
+                return False
+
+        assert not kws
+        if ary.layout not in 'CF':
+            # only work for contiguous array
+            raise TypeError("reshape() supports contiguous array only")
+
+        if len(args) == 1:
+            # single arg
+            shape, = args
+
+            if sentry_shape_scalar(shape):
+                ndim = 1
+            else:
+                shape = normalize_shape(shape)
+                if shape is None:
+                    return
+                ndim = shape.count
+            retty = ary.copy(ndim=ndim)
+            return signature(retty, shape)
+
+        elif len(args) == 0:
+            # no arg
+            raise TypeError("reshape() take at least one arg")
+
+        else:
+            # vararg case
+            if any(not sentry_shape_scalar(a) for a in args):
+                raise TypeError("reshape({0}) is not supported".format(
+                    ', '.join(map(str, args))))
+
+            retty = ary.copy(ndim=len(args))
+            return signature(retty, *args)
+
+    @bound_function("array.sort")
+    def resolve_sort(self, ary, args, kws):
+        assert not args
+        assert not kws
+        return signature(types.none)
+
+    @bound_function("array.argsort")
+    def resolve_argsort(self, ary, args, kws):
+        assert not args
+        kwargs = dict(kws)
+        kind = kwargs.pop('kind', types.StringLiteral('quicksort'))
+        if not isinstance(kind, types.StringLiteral):
+            raise TypingError('"kind" must be a string literal')
+        if kwargs:
+            msg = "Unsupported keywords: {!r}"
+            raise TypingError(msg.format([k for k in kwargs.keys()]))
+        if ary.ndim == 1:
+            def argsort_stub(kind='quicksort'):
+                pass
+            pysig = utils.pysignature(argsort_stub)
+            sig = signature(types.Array(types.intp, 1, 'C'), kind).replace(pysig=pysig)
+            return sig
+
+    @bound_function("array.view")
+    def resolve_view(self, ary, args, kws):
+        from .npydecl import parse_dtype
+        assert not kws
+        dtype, = args
+        dtype = parse_dtype(dtype)
+        if dtype is None:
+            return
+        retty = ary.copy(dtype=dtype)
+        return signature(retty, *args)
+
+    @bound_function("array.astype")
+    def resolve_astype(self, ary, args, kws):
+        from .npydecl import parse_dtype
+        assert not kws
+        dtype, = args
+        if isinstance(dtype, types.UnicodeType):
+            raise RequireLiteralValue(("array.astype if dtype is a string it "
+                                       "must be constant"))
+        dtype = parse_dtype(dtype)
+        if dtype is None:
+            return
+        if not self.context.can_convert(ary.dtype, dtype):
+            raise TypeError("astype(%s) not supported on %s: "
+                            "cannot convert from %s to %s"
+                            % (dtype, ary, ary.dtype, dtype))
+        layout = ary.layout if ary.layout in 'CF' else 'C'
+        # reset the write bit irrespective of whether the cast type is the same
+        # as the current dtype, this replicates numpy
+        retty = ary.copy(dtype=dtype, layout=layout, readonly=False)
+        return signature(retty, *args)
+
+    @bound_function("array.ravel")
+    def resolve_ravel(self, ary, args, kws):
+        # Only support no argument version (default order='C')
+        assert not kws
+        assert not args
+        return signature(ary.copy(ndim=1, layout='C'))
+
+    @bound_function("array.flatten")
+    def resolve_flatten(self, ary, args, kws):
+        # Only support no argument version (default order='C')
+        assert not kws
+        assert not args
+        return signature(ary.copy(ndim=1, layout='C'))
+
+    @bound_function("array.take")
+    def resolve_take(self, ary, args, kws):
+        if kws:
+            raise NumbaAssertionError("kws not supported")
+        argty, = args
+        if isinstance(argty, types.Integer):
+            sig = signature(ary.dtype, *args)
+        elif isinstance(argty, types.Array):
+            sig = signature(argty.copy(layout='C', dtype=ary.dtype), *args)
+        elif isinstance(argty, types.List):  # 1d lists only
+            sig = signature(types.Array(ary.dtype, 1, 'C'), *args)
+        elif isinstance(argty, types.BaseTuple):
+            sig = signature(types.Array(ary.dtype, np.ndim(argty), 'C'), *args)
+        else:
+            raise TypeError("take(%s) not supported for %s" % argty)
+        return sig
+
+    def generic_resolve(self, ary, attr):
+        # Resolution of other attributes, for record arrays
+        if isinstance(ary.dtype, types.Record):
+            if attr in ary.dtype.fields:
+                attr_dtype = ary.dtype.typeof(attr)
+                if isinstance(attr_dtype, types.NestedArray):
+                    return ary.copy(
+                        dtype=attr_dtype.dtype,
+                        ndim=ary.ndim + attr_dtype.ndim,
+                        layout='A'
+                    )
+                else:
+                    return ary.copy(dtype=attr_dtype, layout='A')
+
+
+@infer_getattr
+class DTypeAttr(AttributeTemplate):
+    key = types.DType
+
+    def resolve_type(self, ary):
+        # Wrap the numeric type in NumberClass
+        return types.NumberClass(ary.dtype)
+
+    def resolve_kind(self, ary):
+        if isinstance(ary.key, types.scalars.Float):
+            val = 'f'
+        elif isinstance(ary.key, types.scalars.Integer):
+            val = 'i'
+        else:
+            return None  # other types not supported yet
+        return types.StringLiteral(val)
+
+
+@infer
+class StaticGetItemArray(AbstractTemplate):
+    key = "static_getitem"
+
+    def generic(self, args, kws):
+        # Resolution of members for record and structured arrays
+        ary, idx = args
+        if (isinstance(ary, types.Array) and isinstance(idx, str) and
+                isinstance(ary.dtype, types.Record)):
+            if idx in ary.dtype.fields:
+                attr_dtype = ary.dtype.typeof(idx)
+                if isinstance(attr_dtype, types.NestedArray):
+                    ret = ary.copy(
+                        dtype=attr_dtype.dtype,
+                        ndim=ary.ndim + attr_dtype.ndim,
+                        layout='A'
+                    )
+                    return signature(ret, *args)
+                else:
+                    ret = ary.copy(dtype=attr_dtype, layout='A')
+                    return signature(ret, *args)
+
+
+@infer_getattr
+class RecordAttribute(AttributeTemplate):
+    key = types.Record
+
+    def generic_resolve(self, record, attr):
+        ret = record.typeof(attr)
+        assert ret
+        return ret
+
+
+@infer
+class StaticGetItemRecord(AbstractTemplate):
+    key = "static_getitem"
+
+    def generic(self, args, kws):
+        # Resolution of members for records
+        record, idx = args
+        if isinstance(record, types.Record) and isinstance(idx, str):
+            if idx not in record.fields:
+                raise KeyError(f"Field '{idx}' was not found in record with "
+                               f"fields {tuple(record.fields.keys())}")
+            ret = record.typeof(idx)
+            assert ret
+            return signature(ret, *args)
+
+
+@infer_global(operator.getitem)
+class StaticGetItemLiteralRecord(AbstractTemplate):
+    def generic(self, args, kws):
+        # Resolution of members for records
+        record, idx = args
+        if isinstance(record, types.Record):
+            if isinstance(idx, types.StringLiteral):
+                if idx.literal_value not in record.fields:
+                    msg = (f"Field '{idx.literal_value}' was not found in "
+                           f"record with fields {tuple(record.fields.keys())}")
+                    raise NumbaKeyError(msg)
+                ret = record.typeof(idx.literal_value)
+                assert ret
+                return signature(ret, *args)
+            elif isinstance(idx, types.IntegerLiteral):
+                if idx.literal_value >= len(record.fields):
+                    msg = f"Requested index {idx.literal_value} is out of range"
+                    raise NumbaIndexError(msg)
+                field_names = list(record.fields)
+                ret = record.typeof(field_names[idx.literal_value])
+                assert ret
+                return signature(ret, *args)
+
+
+@infer
+class StaticSetItemRecord(AbstractTemplate):
+    key = "static_setitem"
+
+    def generic(self, args, kws):
+        # Resolution of members for record and structured arrays
+        record, idx, value = args
+        if isinstance(record, types.Record):
+            if isinstance(idx, str):
+                expectedty = record.typeof(idx)
+                if self.context.can_convert(value, expectedty) is not None:
+                    return signature(types.void, record, types.literal(idx),
+                                     value)
+            elif isinstance(idx, int):
+                if idx >= len(record.fields):
+                    msg = f"Requested index {idx} is out of range"
+                    raise NumbaIndexError(msg)
+                str_field = list(record.fields)[idx]
+                expectedty = record.typeof(str_field)
+                if self.context.can_convert(value, expectedty) is not None:
+                    return signature(types.void, record, types.literal(idx),
+                                     value)
+
+
+@infer_global(operator.setitem)
+class StaticSetItemLiteralRecord(AbstractTemplate):
+    def generic(self, args, kws):
+        # Resolution of members for records
+        target, idx, value = args
+        if isinstance(target, types.Record) and isinstance(idx, types.StringLiteral):
+            if idx.literal_value not in target.fields:
+                msg = (f"Field '{idx.literal_value}' was not found in record "
+                       f"with fields {tuple(target.fields.keys())}")
+                raise NumbaKeyError(msg)
+            expectedty = target.typeof(idx.literal_value)
+            if self.context.can_convert(value, expectedty) is not None:
+                return signature(types.void, target, idx, value)
+
+
+@infer_getattr
+class ArrayCTypesAttribute(AttributeTemplate):
+    key = types.ArrayCTypes
+
+    def resolve_data(self, ctinfo):
+        return types.uintp
+
+
+@infer_getattr
+class ArrayFlagsAttribute(AttributeTemplate):
+    key = types.ArrayFlags
+
+    def resolve_contiguous(self, ctflags):
+        return types.boolean
+
+    def resolve_c_contiguous(self, ctflags):
+        return types.boolean
+
+    def resolve_f_contiguous(self, ctflags):
+        return types.boolean
+
+
+@infer_getattr
+class NestedArrayAttribute(ArrayAttribute):
+    key = types.NestedArray
+
+
+def _expand_integer(ty):
+    """
+    If *ty* is an integer, expand it to a machine int (like Numpy).
+    """
+    if isinstance(ty, types.Integer):
+        if ty.signed:
+            return max(types.intp, ty)
+        else:
+            return max(types.uintp, ty)
+    elif isinstance(ty, types.Boolean):
+        return types.intp
+    else:
+        return ty
+
+
+def generic_homog(self, args, kws):
+    if args:
+        raise NumbaAssertionError("args not supported")
+    if kws:
+        raise NumbaAssertionError("kws not supported")
+
+    return signature(self.this.dtype, recvr=self.this)
+
+
+def generic_expand(self, args, kws):
+    assert not args
+    assert not kws
+    return signature(_expand_integer(self.this.dtype), recvr=self.this)
+
+
+def sum_expand(self, args, kws):
+    """
+    sum can be called with or without an axis parameter, and with or without
+    a dtype parameter
+    """
+    pysig = None
+    if 'axis' in kws and 'dtype' not in kws:
+        def sum_stub(axis):
+            pass
+        pysig = utils.pysignature(sum_stub)
+        # rewrite args
+        args = list(args) + [kws['axis']]
+    elif 'dtype' in kws and 'axis' not in kws:
+        def sum_stub(dtype):
+            pass
+        pysig = utils.pysignature(sum_stub)
+        # rewrite args
+        args = list(args) + [kws['dtype']]
+    elif 'dtype' in kws and 'axis' in kws:
+        def sum_stub(axis, dtype):
+            pass
+        pysig = utils.pysignature(sum_stub)
+        # rewrite args
+        args = list(args) + [kws['axis'], kws['dtype']]
+
+    args_len = len(args)
+    assert args_len <= 2
+    if args_len == 0:
+        # No axis or dtype parameter so the return type of the summation is a scalar
+        # of the type of the array.
+        out = signature(_expand_integer(self.this.dtype), *args,
+                        recvr=self.this)
+    elif args_len == 1 and 'dtype' not in kws:
+        # There is an axis parameter, either arg or kwarg
+        if self.this.ndim == 1:
+            # 1d reduces to a scalar
+            return_type = self.this.dtype
+        else:
+            # the return type of this summation is  an array of dimension one
+            # less than the input array.
+            return_type = types.Array(dtype=_expand_integer(self.this.dtype),
+                                    ndim=self.this.ndim-1, layout='C')
+        out = signature(return_type, *args, recvr=self.this)
+
+    elif args_len == 1 and 'dtype' in kws:
+        # No axis parameter so the return type of the summation is a scalar
+        # of the dtype parameter.
+        from .npydecl import parse_dtype
+        dtype, = args
+        dtype = parse_dtype(dtype)
+        out = signature(dtype, *args, recvr=self.this)
+
+    elif args_len == 2:
+        # There is an axis and dtype parameter, either arg or kwarg
+        from .npydecl import parse_dtype
+        dtype = parse_dtype(args[1])
+        return_type = dtype
+        if self.this.ndim != 1:
+            # 1d reduces to a scalar, 2d and above reduce dim by 1
+            # the return type of this summation is  an array of dimension one
+            # less than the input array.
+            return_type = types.Array(dtype=return_type,
+                                    ndim=self.this.ndim-1, layout='C')
+        out = signature(return_type, *args, recvr=self.this)
+    else:
+        pass
+    return out.replace(pysig=pysig)
+
+
+def generic_expand_cumulative(self, args, kws):
+    if args:
+        raise NumbaAssertionError("args unsupported")
+    if kws:
+        raise NumbaAssertionError("kwargs unsupported")
+    assert isinstance(self.this, types.Array)
+    return_type = types.Array(dtype=_expand_integer(self.this.dtype),
+                              ndim=1, layout='C')
+    return signature(return_type, recvr=self.this)
+
+
+def generic_hetero_real(self, args, kws):
+    assert not args
+    assert not kws
+    if isinstance(self.this.dtype, (types.Integer, types.Boolean)):
+        return signature(types.float64, recvr=self.this)
+    return signature(self.this.dtype, recvr=self.this)
+
+
+def generic_hetero_always_real(self, args, kws):
+    assert not args
+    assert not kws
+    if isinstance(self.this.dtype, (types.Integer, types.Boolean)):
+        return signature(types.float64, recvr=self.this)
+    if isinstance(self.this.dtype, types.Complex):
+        return signature(self.this.dtype.underlying_float, recvr=self.this)
+    return signature(self.this.dtype, recvr=self.this)
+
+
+def generic_index(self, args, kws):
+    assert not args
+    assert not kws
+    return signature(types.intp, recvr=self.this)
+
+
+def install_array_method(name, generic, prefer_literal=True):
+    my_attr = {"key": "array." + name, "generic": generic,
+               "prefer_literal": prefer_literal}
+    temp_class = type("Array_" + name, (AbstractTemplate,), my_attr)
+
+    def array_attribute_attachment(self, ary):
+        return types.BoundFunction(temp_class, ary)
+
+    setattr(ArrayAttribute, "resolve_" + name, array_attribute_attachment)
+
+# Functions that return the same type as the array
+for fname in ["min", "max"]:
+    install_array_method(fname, generic_homog)
+
+# Functions that return a machine-width type, to avoid overflows
+install_array_method("prod", generic_expand)
+install_array_method("sum", sum_expand, prefer_literal=True)
+
+# Functions that return a machine-width type, to avoid overflows
+for fname in ["cumsum", "cumprod"]:
+    install_array_method(fname, generic_expand_cumulative)
+
+# Functions that require integer arrays get promoted to float64 return
+for fName in ["mean"]:
+    install_array_method(fName, generic_hetero_real)
+
+# var and std by definition return in real space and int arrays
+# get promoted to float64 return
+for fName in ["var", "std"]:
+    install_array_method(fName, generic_hetero_always_real)
+
+
+@infer_global(operator.eq)
+class CmpOpEqArray(AbstractTemplate):
+    #key = operator.eq
+
+    def generic(self, args, kws):
+        assert not kws
+        [va, vb] = args
+        if isinstance(va, types.Array) and va == vb:
+            return signature(va.copy(dtype=types.boolean), va, vb)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/asnumbatype.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/asnumbatype.py
new file mode 100644
index 000000000..8622f00f3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/asnumbatype.py
@@ -0,0 +1,139 @@
+import inspect
+import typing as py_typing
+
+from numba.core.typing.typeof import typeof
+from numba.core import errors, types, utils
+
+
+def _py_version_switch(py_version, new_result, old_result):
+    return new_result if utils.PYVERSION >= py_version else old_result
+
+
+class AsNumbaTypeRegistry:
+    """
+    A registry for python typing declarations.  This registry stores a lookup
+    table for simple cases (e.g. int) and a list of functions for more
+    complicated cases (e.g. generics like List[int]).
+
+    The as_numba_type registry is meant to work statically on type annotations
+    at compile type, not dynamically on instances at runtime. To check the type
+    of an object at runtime, see numba.typeof.
+    """
+
+    def __init__(self):
+        self.lookup = {
+            type(example): typeof(example)
+            for example in [
+                0,
+                0.0,
+                complex(0),
+                "numba",
+                True,
+                None,
+            ]
+        }
+
+        self.functions = [self._builtin_infer, self._numba_type_infer]
+
+    def _numba_type_infer(self, py_type):
+        if isinstance(py_type, types.Type):
+            return py_type
+
+    def _builtin_infer(self, py_type):
+        # The type hierarchy of python typing library changes in 3.7.
+        generic_type_check = _py_version_switch(
+            (3, 7),
+            lambda x: isinstance(x, py_typing._GenericAlias),
+            lambda _: True,
+        )
+        if not generic_type_check(py_type):
+            return
+
+        list_origin = _py_version_switch((3, 7), list, py_typing.List)
+        dict_origin = _py_version_switch((3, 7), dict, py_typing.Dict)
+        set_origin = _py_version_switch((3, 7), set, py_typing.Set)
+        tuple_origin = _py_version_switch((3, 7), tuple, py_typing.Tuple)
+
+        if getattr(py_type, "__origin__", None) is py_typing.Union:
+            if len(py_type.__args__) != 2:
+                raise errors.TypingError(
+                    "Cannot type Union of more than two types")
+
+            (arg_1_py, arg_2_py) = py_type.__args__
+
+            if arg_2_py is type(None): # noqa: E721
+                return types.Optional(self.infer(arg_1_py))
+            elif arg_1_py is type(None): # noqa: E721
+                return types.Optional(self.infer(arg_2_py))
+            else:
+                raise errors.TypingError(
+                    "Cannot type Union that is not an Optional "
+                    f"(neither type type {arg_2_py} is not NoneType")
+
+        if getattr(py_type, "__origin__", None) is list_origin:
+            (element_py,) = py_type.__args__
+            return types.ListType(self.infer(element_py))
+
+        if getattr(py_type, "__origin__", None) is dict_origin:
+            key_py, value_py = py_type.__args__
+            return types.DictType(self.infer(key_py), self.infer(value_py))
+
+        if getattr(py_type, "__origin__", None) is set_origin:
+            (element_py,) = py_type.__args__
+            return types.Set(self.infer(element_py))
+
+        if getattr(py_type, "__origin__", None) is tuple_origin:
+            tys = tuple(map(self.infer, py_type.__args__))
+            return types.BaseTuple.from_types(tys)
+
+    def register(self, func_or_py_type, numba_type=None):
+        """
+        Extend AsNumbaType to support new python types (e.g. a user defined
+        JitClass).  For a simple pair of a python type and a numba type, can
+        use as a function register(py_type, numba_type).  If more complex logic
+        is required (e.g. for generic types), register can also be used as a
+        decorator for a function that takes a python type as input and returns
+        a numba type or None.
+        """
+        if numba_type is not None:
+            # register used with a specific (py_type, numba_type) pair.
+            assert isinstance(numba_type, types.Type)
+            self.lookup[func_or_py_type] = numba_type
+        else:
+            # register used as a decorator.
+            assert inspect.isfunction(func_or_py_type)
+            self.functions.append(func_or_py_type)
+
+    def try_infer(self, py_type):
+        """
+        Try to determine the numba type of a given python type.
+        We first consider the lookup dictionary.  If py_type is not there, we
+        iterate through the registered functions until one returns a numba type.
+        If type inference fails, return None.
+        """
+        result = self.lookup.get(py_type, None)
+
+        for func in self.functions:
+            if result is not None:
+                break
+            result = func(py_type)
+
+        if result is not None and not isinstance(result, types.Type):
+            raise errors.TypingError(
+                f"as_numba_type should return a numba type, got {result}"
+            )
+        return result
+
+    def infer(self, py_type):
+        result = self.try_infer(py_type)
+        if result is None:
+            raise errors.TypingError(
+                f"Cannot infer numba type of python type {py_type}"
+            )
+        return result
+
+    def __call__(self, py_type):
+        return self.infer(py_type)
+
+
+as_numba_type = AsNumbaTypeRegistry()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/bufproto.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/bufproto.py
new file mode 100644
index 000000000..b5ec67aad
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/bufproto.py
@@ -0,0 +1,67 @@
+"""
+Typing support for the buffer protocol (PEP 3118).
+"""
+
+import array
+
+from numba.core import types
+
+
+_pep3118_int_types = set('bBhHiIlLqQnN')
+
+_pep3118_scalar_map = {
+    'f': types.float32,
+    'd': types.float64,
+    'Zf': types.complex64,
+    'Zd': types.complex128,
+    }
+
+_type_map = {
+    bytearray: types.ByteArray,
+    array.array: types.PyArray,
+    }
+
+_type_map[memoryview] = types.MemoryView
+_type_map[bytes] = types.Bytes
+
+
+def decode_pep3118_format(fmt, itemsize):
+    """
+    Return the Numba type for an item with format string *fmt* and size
+    *itemsize* (in bytes).
+    """
+    # XXX reuse _dtype_from_pep3118() from np.core._internal?
+    if fmt in _pep3118_int_types:
+        # Determine int width and signedness
+        name = 'int%d' % (itemsize * 8,)
+        if fmt.isupper():
+            name = 'u' + name
+        return types.Integer(name)
+    try:
+        # For the hard-coded types above, consider "=" the same as "@"
+        # (the default).  This is because Numpy sometimes adds "="
+        # in front of the PEP 3118 format string.
+        return _pep3118_scalar_map[fmt.lstrip('=')]
+    except KeyError:
+        raise ValueError("unsupported PEP 3118 format %r" % (fmt,))
+
+
+def get_type_class(typ):
+    """
+    Get the Numba type class for buffer-compatible Python *typ*.
+    """
+    try:
+        # Look up special case.
+        return _type_map[typ]
+    except KeyError:
+        # Fall back on generic one.
+        return types.Buffer
+
+
+def infer_layout(val):
+    """
+    Infer layout of the given memoryview *val*.
+    """
+    return ('C' if val.c_contiguous else
+            'F' if val.f_contiguous else
+            'A')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/builtins.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/builtins.py
new file mode 100644
index 000000000..40b46a818
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/builtins.py
@@ -0,0 +1,1157 @@
+import itertools
+
+import numpy as np
+import operator
+
+from numba.core import types, errors
+from numba import prange
+from numba.parfors.parfor import internal_prange
+
+from numba.core.typing.templates import (AttributeTemplate, ConcreteTemplate,
+                                         AbstractTemplate, infer_global, infer,
+                                         infer_getattr, signature,
+                                         bound_function, make_callable_template)
+
+from numba.cpython.builtins import get_type_min_value, get_type_max_value
+
+from numba.core.extending import (
+    typeof_impl, type_callable, models, register_model, make_attribute_wrapper,
+    )
+
+
+@infer_global(print)
+class Print(AbstractTemplate):
+    def generic(self, args, kws):
+        for a in args:
+            sig = self.context.resolve_function_type("print_item", (a,), {})
+            if sig is None:
+                raise TypeError("Type %s is not printable." % a)
+            assert sig.return_type is types.none
+        return signature(types.none, *args)
+
+@infer
+class PrintItem(AbstractTemplate):
+    key = "print_item"
+
+    def generic(self, args, kws):
+        arg, = args
+        return signature(types.none, *args)
+
+
+@infer_global(abs)
+class Abs(ConcreteTemplate):
+    int_cases = [signature(ty, ty) for ty in sorted(types.signed_domain)]
+    uint_cases = [signature(ty, ty) for ty in sorted(types.unsigned_domain)]
+    real_cases = [signature(ty, ty) for ty in sorted(types.real_domain)]
+    complex_cases = [signature(ty.underlying_float, ty)
+                     for ty in sorted(types.complex_domain)]
+    cases = int_cases + uint_cases +  real_cases + complex_cases
+
+
+@infer_global(slice)
+class Slice(ConcreteTemplate):
+    cases = [
+        signature(types.slice2_type, types.intp),
+        signature(types.slice2_type, types.none),
+        signature(types.slice2_type, types.none, types.none),
+        signature(types.slice2_type, types.none, types.intp),
+        signature(types.slice2_type, types.intp, types.none),
+        signature(types.slice2_type, types.intp, types.intp),
+        signature(types.slice3_type, types.intp, types.intp, types.intp),
+        signature(types.slice3_type, types.none, types.intp, types.intp),
+        signature(types.slice3_type, types.intp, types.none, types.intp),
+        signature(types.slice3_type, types.intp, types.intp, types.none),
+        signature(types.slice3_type, types.intp, types.none, types.none),
+        signature(types.slice3_type, types.none, types.intp, types.none),
+        signature(types.slice3_type, types.none, types.none, types.intp),
+        signature(types.slice3_type, types.none, types.none, types.none),
+    ]
+
+
+@infer_global(range, typing_key=range)
+@infer_global(prange, typing_key=prange)
+@infer_global(internal_prange, typing_key=internal_prange)
+class Range(ConcreteTemplate):
+    cases = [
+        signature(types.range_state32_type, types.int32),
+        signature(types.range_state32_type, types.int32, types.int32),
+        signature(types.range_state32_type, types.int32, types.int32,
+                  types.int32),
+        signature(types.range_state64_type, types.int64),
+        signature(types.range_state64_type, types.int64, types.int64),
+        signature(types.range_state64_type, types.int64, types.int64,
+                  types.int64),
+        signature(types.unsigned_range_state64_type, types.uint64),
+        signature(types.unsigned_range_state64_type, types.uint64, types.uint64),
+        signature(types.unsigned_range_state64_type, types.uint64, types.uint64,
+                  types.uint64),
+    ]
+
+
+@infer
+class GetIter(AbstractTemplate):
+    key = "getiter"
+
+    def generic(self, args, kws):
+        assert not kws
+        [obj] = args
+        if isinstance(obj, types.IterableType):
+            return signature(obj.iterator_type, obj)
+
+
+@infer
+class IterNext(AbstractTemplate):
+    key = "iternext"
+
+    def generic(self, args, kws):
+        assert not kws
+        [it] = args
+        if isinstance(it, types.IteratorType):
+            return signature(types.Pair(it.yield_type, types.boolean), it)
+
+
+@infer
+class PairFirst(AbstractTemplate):
+    """
+    Given a heterogeneous pair, return the first element.
+    """
+    key = "pair_first"
+
+    def generic(self, args, kws):
+        assert not kws
+        [pair] = args
+        if isinstance(pair, types.Pair):
+            return signature(pair.first_type, pair)
+
+
+@infer
+class PairSecond(AbstractTemplate):
+    """
+    Given a heterogeneous pair, return the second element.
+    """
+    key = "pair_second"
+
+    def generic(self, args, kws):
+        assert not kws
+        [pair] = args
+        if isinstance(pair, types.Pair):
+            return signature(pair.second_type, pair)
+
+
+def choose_result_bitwidth(*inputs):
+    return max(types.intp.bitwidth, *(tp.bitwidth for tp in inputs))
+
+def choose_result_int(*inputs):
+    """
+    Choose the integer result type for an operation on integer inputs,
+    according to the integer typing NBEP.
+    """
+    bitwidth = choose_result_bitwidth(*inputs)
+    signed = any(tp.signed for tp in inputs)
+    return types.Integer.from_bitwidth(bitwidth, signed)
+
+
+# The "machine" integer types to take into consideration for operator typing
+# (according to the integer typing NBEP)
+machine_ints = (
+    sorted(set((types.intp, types.int64))) +
+    sorted(set((types.uintp, types.uint64)))
+    )
+
+# Explicit integer rules for binary operators; smaller ints will be
+# automatically upcast.
+integer_binop_cases = tuple(
+    signature(choose_result_int(op1, op2), op1, op2)
+    for op1, op2 in itertools.product(machine_ints, machine_ints)
+    )
+
+
+class BinOp(ConcreteTemplate):
+    cases = list(integer_binop_cases)
+    cases += [signature(op, op, op) for op in sorted(types.real_domain)]
+    cases += [signature(op, op, op) for op in sorted(types.complex_domain)]
+
+
+@infer_global(operator.add)
+class BinOpAdd(BinOp):
+    pass
+
+
+@infer_global(operator.iadd)
+class BinOpAdd(BinOp):
+    pass
+
+
+@infer_global(operator.sub)
+class BinOpSub(BinOp):
+    pass
+
+
+@infer_global(operator.isub)
+class BinOpSub(BinOp):
+    pass
+
+
+@infer_global(operator.mul)
+class BinOpMul(BinOp):
+    pass
+
+
+@infer_global(operator.imul)
+class BinOpMul(BinOp):
+    pass
+
+
+@infer_global(operator.mod)
+class BinOpMod(ConcreteTemplate):
+    cases = list(integer_binop_cases)
+    cases += [signature(op, op, op) for op in sorted(types.real_domain)]
+
+
+@infer_global(operator.imod)
+class BinOpMod(ConcreteTemplate):
+    cases = list(integer_binop_cases)
+    cases += [signature(op, op, op) for op in sorted(types.real_domain)]
+
+
+@infer_global(operator.truediv)
+class BinOpTrueDiv(ConcreteTemplate):
+    cases = [signature(types.float64, op1, op2)
+             for op1, op2 in itertools.product(machine_ints, machine_ints)]
+    cases += [signature(op, op, op) for op in sorted(types.real_domain)]
+    cases += [signature(op, op, op) for op in sorted(types.complex_domain)]
+
+
+@infer_global(operator.itruediv)
+class BinOpTrueDiv(ConcreteTemplate):
+    cases = [signature(types.float64, op1, op2)
+             for op1, op2 in itertools.product(machine_ints, machine_ints)]
+    cases += [signature(op, op, op) for op in sorted(types.real_domain)]
+    cases += [signature(op, op, op) for op in sorted(types.complex_domain)]
+
+
+@infer_global(operator.floordiv)
+class BinOpFloorDiv(ConcreteTemplate):
+    cases = list(integer_binop_cases)
+    cases += [signature(op, op, op) for op in sorted(types.real_domain)]
+
+
+@infer_global(operator.ifloordiv)
+class BinOpFloorDiv(ConcreteTemplate):
+    cases = list(integer_binop_cases)
+    cases += [signature(op, op, op) for op in sorted(types.real_domain)]
+
+
+@infer_global(divmod)
+class DivMod(ConcreteTemplate):
+    _tys = machine_ints + sorted(types.real_domain)
+    cases = [signature(types.UniTuple(ty, 2), ty, ty) for ty in _tys]
+
+
+@infer_global(operator.pow)
+class BinOpPower(ConcreteTemplate):
+    cases = list(integer_binop_cases)
+    # Ensure that float32 ** int doesn't go through DP computations
+    cases += [signature(types.float32, types.float32, op)
+              for op in (types.int32, types.int64, types.uint64)]
+    cases += [signature(types.float64, types.float64, op)
+              for op in (types.int32, types.int64, types.uint64)]
+    cases += [signature(op, op, op)
+              for op in sorted(types.real_domain)]
+    cases += [signature(op, op, op)
+              for op in sorted(types.complex_domain)]
+
+
+@infer_global(operator.ipow)
+class BinOpPower(ConcreteTemplate):
+    cases = list(integer_binop_cases)
+    # Ensure that float32 ** int doesn't go through DP computations
+    cases += [signature(types.float32, types.float32, op)
+              for op in (types.int32, types.int64, types.uint64)]
+    cases += [signature(types.float64, types.float64, op)
+              for op in (types.int32, types.int64, types.uint64)]
+    cases += [signature(op, op, op)
+              for op in sorted(types.real_domain)]
+    cases += [signature(op, op, op)
+              for op in sorted(types.complex_domain)]
+
+
+@infer_global(pow)
+class PowerBuiltin(BinOpPower):
+    # TODO add 3 operand version
+    pass
+
+
+class BitwiseShiftOperation(ConcreteTemplate):
+    # For bitshifts, only the first operand's signedness matters
+    # to choose the operation's signedness (the second operand
+    # should always be positive but will generally be considered
+    # signed anyway, since it's often a constant integer).
+    # (also, see issue #1995 for right-shifts)
+
+    # The RHS type is fixed to 64-bit signed/unsigned ints.
+    # The implementation will always cast the operands to the width of the
+    # result type, which is the widest between the LHS type and (u)intp.
+    cases = [signature(max(op, types.intp), op, op2)
+             for op in sorted(types.signed_domain)
+             for op2 in [types.uint64, types.int64]]
+    cases += [signature(max(op, types.uintp), op, op2)
+              for op in sorted(types.unsigned_domain)
+              for op2 in [types.uint64, types.int64]]
+    unsafe_casting = False
+
+
+@infer_global(operator.lshift)
+class BitwiseLeftShift(BitwiseShiftOperation):
+    pass
+
+@infer_global(operator.ilshift)
+class BitwiseLeftShift(BitwiseShiftOperation):
+    pass
+
+
+@infer_global(operator.rshift)
+class BitwiseRightShift(BitwiseShiftOperation):
+    pass
+
+
+@infer_global(operator.irshift)
+class BitwiseRightShift(BitwiseShiftOperation):
+    pass
+
+
+class BitwiseLogicOperation(BinOp):
+    cases = [signature(types.boolean, types.boolean, types.boolean)]
+    cases += list(integer_binop_cases)
+    unsafe_casting = False
+
+
+@infer_global(operator.and_)
+class BitwiseAnd(BitwiseLogicOperation):
+    pass
+
+
+@infer_global(operator.iand)
+class BitwiseAnd(BitwiseLogicOperation):
+    pass
+
+
+@infer_global(operator.or_)
+class BitwiseOr(BitwiseLogicOperation):
+    pass
+
+
+@infer_global(operator.ior)
+class BitwiseOr(BitwiseLogicOperation):
+    pass
+
+
+@infer_global(operator.xor)
+class BitwiseXor(BitwiseLogicOperation):
+    pass
+
+
+@infer_global(operator.ixor)
+class BitwiseXor(BitwiseLogicOperation):
+    pass
+
+
+# Bitwise invert and negate are special: we must not upcast the operand
+# for unsigned numbers, as that would change the result.
+# (i.e. ~np.int8(0) == 255 but ~np.int32(0) == 4294967295).
+
+@infer_global(operator.invert)
+class BitwiseInvert(ConcreteTemplate):
+    # Note Numba follows the Numpy semantics of returning a bool,
+    # while Python returns an int.  This makes it consistent with
+    # np.invert() and makes array expressions correct.
+    cases = [signature(types.boolean, types.boolean)]
+    cases += [signature(choose_result_int(op), op) for op in sorted(types.unsigned_domain)]
+    cases += [signature(choose_result_int(op), op) for op in sorted(types.signed_domain)]
+
+    unsafe_casting = False
+
+
+class UnaryOp(ConcreteTemplate):
+    cases = [signature(choose_result_int(op), op) for op in sorted(types.unsigned_domain)]
+    cases += [signature(choose_result_int(op), op) for op in sorted(types.signed_domain)]
+    cases += [signature(op, op) for op in sorted(types.real_domain)]
+    cases += [signature(op, op) for op in sorted(types.complex_domain)]
+    cases += [signature(types.intp, types.boolean)]
+
+
+@infer_global(operator.neg)
+class UnaryNegate(UnaryOp):
+    pass
+
+
+@infer_global(operator.pos)
+class UnaryPositive(UnaryOp):
+   pass
+
+
+@infer_global(operator.not_)
+class UnaryNot(ConcreteTemplate):
+    cases = [signature(types.boolean, types.boolean)]
+    cases += [signature(types.boolean, op) for op in sorted(types.signed_domain)]
+    cases += [signature(types.boolean, op) for op in sorted(types.unsigned_domain)]
+    cases += [signature(types.boolean, op) for op in sorted(types.real_domain)]
+    cases += [signature(types.boolean, op) for op in sorted(types.complex_domain)]
+
+
+class OrderedCmpOp(ConcreteTemplate):
+    cases = [signature(types.boolean, types.boolean, types.boolean)]
+    cases += [signature(types.boolean, op, op) for op in sorted(types.signed_domain)]
+    cases += [signature(types.boolean, op, op) for op in sorted(types.unsigned_domain)]
+    cases += [signature(types.boolean, op, op) for op in sorted(types.real_domain)]
+
+
+class UnorderedCmpOp(ConcreteTemplate):
+    cases = OrderedCmpOp.cases + [
+        signature(types.boolean, op, op) for op in sorted(types.complex_domain)]
+
+
+@infer_global(operator.lt)
+class CmpOpLt(OrderedCmpOp):
+    pass
+
+
+@infer_global(operator.le)
+class CmpOpLe(OrderedCmpOp):
+    pass
+
+
+@infer_global(operator.gt)
+class CmpOpGt(OrderedCmpOp):
+    pass
+
+
+@infer_global(operator.ge)
+class CmpOpGe(OrderedCmpOp):
+    pass
+
+
+# more specific overloads should be registered first
+@infer_global(operator.eq)
+class ConstOpEq(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        (arg1, arg2) = args
+        if isinstance(arg1, types.Literal) and isinstance(arg2, types.Literal):
+            return signature(types.boolean, arg1, arg2)
+
+
+@infer_global(operator.ne)
+class ConstOpNotEq(ConstOpEq):
+    pass
+
+
+@infer_global(operator.eq)
+class CmpOpEq(UnorderedCmpOp):
+    pass
+
+
+@infer_global(operator.ne)
+class CmpOpNe(UnorderedCmpOp):
+    pass
+
+
+class TupleCompare(AbstractTemplate):
+    def generic(self, args, kws):
+        [lhs, rhs] = args
+        if isinstance(lhs, types.BaseTuple) and isinstance(rhs, types.BaseTuple):
+            for u, v in zip(lhs, rhs):
+                # Check element-wise comparability
+                res = self.context.resolve_function_type(self.key, (u, v), {})
+                if res is None:
+                    break
+            else:
+                return signature(types.boolean, lhs, rhs)
+
+
+@infer_global(operator.eq)
+class TupleEq(TupleCompare):
+    pass
+
+
+@infer_global(operator.ne)
+class TupleNe(TupleCompare):
+    pass
+
+
+@infer_global(operator.ge)
+class TupleGe(TupleCompare):
+    pass
+
+
+@infer_global(operator.gt)
+class TupleGt(TupleCompare):
+    pass
+
+
+@infer_global(operator.le)
+class TupleLe(TupleCompare):
+    pass
+
+
+@infer_global(operator.lt)
+class TupleLt(TupleCompare):
+    pass
+
+
+@infer_global(operator.add)
+class TupleAdd(AbstractTemplate):
+    def generic(self, args, kws):
+        if len(args) == 2:
+            a, b = args
+            if (isinstance(a, types.BaseTuple) and isinstance(b, types.BaseTuple)
+                and not isinstance(a, types.BaseNamedTuple)
+                and not isinstance(b, types.BaseNamedTuple)):
+                res = types.BaseTuple.from_types(tuple(a) + tuple(b))
+                return signature(res, a, b)
+
+
+class CmpOpIdentity(AbstractTemplate):
+    def generic(self, args, kws):
+        [lhs, rhs] = args
+        return signature(types.boolean, lhs, rhs)
+
+
+@infer_global(operator.is_)
+class CmpOpIs(CmpOpIdentity):
+    pass
+
+
+@infer_global(operator.is_not)
+class CmpOpIsNot(CmpOpIdentity):
+    pass
+
+
+def normalize_1d_index(index):
+    """
+    Normalize the *index* type (an integer or slice) for indexing a 1D
+    sequence.
+    """
+    if isinstance(index, types.SliceType):
+        return index
+
+    elif isinstance(index, types.Integer):
+        return types.intp if index.signed else types.uintp
+
+
+@infer_global(operator.getitem)
+class GetItemCPointer(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        ptr, idx = args
+        if isinstance(ptr, types.CPointer) and isinstance(idx, types.Integer):
+            return signature(ptr.dtype, ptr, normalize_1d_index(idx))
+
+
+@infer_global(operator.setitem)
+class SetItemCPointer(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        ptr, idx, val = args
+        if isinstance(ptr, types.CPointer) and isinstance(idx, types.Integer):
+            return signature(types.none, ptr, normalize_1d_index(idx), ptr.dtype)
+
+
+@infer_global(len)
+class Len(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        (val,) = args
+        if isinstance(val, (types.Buffer, types.BaseTuple)):
+            return signature(types.intp, val)
+        elif isinstance(val, (types.RangeType)):
+            return signature(val.dtype, val)
+
+@infer_global(tuple)
+class TupleConstructor(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        # empty tuple case
+        if len(args) == 0:
+            return signature(types.Tuple(()))
+        (val,) = args
+        # tuple as input
+        if isinstance(val, types.BaseTuple):
+            return signature(val, val)
+
+
+@infer_global(operator.contains)
+class Contains(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        (seq, val) = args
+
+        if isinstance(seq, (types.Sequence)):
+            return signature(types.boolean, seq, val)
+
+@infer_global(operator.truth)
+class TupleBool(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        (val,) = args
+        if isinstance(val, (types.BaseTuple)):
+            return signature(types.boolean, val)
+
+
+@infer
+class StaticGetItemTuple(AbstractTemplate):
+    key = "static_getitem"
+
+    def generic(self, args, kws):
+        tup, idx = args
+        ret = None
+        if not isinstance(tup, types.BaseTuple):
+            return
+        if isinstance(idx, int):
+            try:
+                ret = tup.types[idx]
+            except IndexError:
+                raise errors.NumbaIndexError("tuple index out of range")
+        elif isinstance(idx, slice):
+            ret = types.BaseTuple.from_types(tup.types[idx])
+        if ret is not None:
+            sig = signature(ret, *args)
+            return sig
+
+
+@infer
+class StaticGetItemLiteralList(AbstractTemplate):
+    key = "static_getitem"
+
+    def generic(self, args, kws):
+        tup, idx = args
+        ret = None
+        if not isinstance(tup, types.LiteralList):
+            return
+        if isinstance(idx, int):
+            ret = tup.types[idx]
+        if ret is not None:
+            sig = signature(ret, *args)
+            return sig
+
+
+@infer
+class StaticGetItemLiteralStrKeyDict(AbstractTemplate):
+    key = "static_getitem"
+
+    def generic(self, args, kws):
+        tup, idx = args
+        ret = None
+        if not isinstance(tup, types.LiteralStrKeyDict):
+            return
+        if isinstance(idx, str):
+            if idx in tup.fields:
+                lookup = tup.fields.index(idx)
+            else:
+                raise errors.NumbaKeyError(f"Key '{idx}' is not in dict.")
+            ret = tup.types[lookup]
+        if ret is not None:
+            sig = signature(ret, *args)
+            return sig
+
+# Generic implementation for "not in"
+
+@infer
+class GenericNotIn(AbstractTemplate):
+    key = "not in"
+
+    def generic(self, args, kws):
+        args = args[::-1]
+        sig = self.context.resolve_function_type(operator.contains, args, kws)
+        return signature(sig.return_type, *sig.args[::-1])
+
+
+#-------------------------------------------------------------------------------
+
+@infer_getattr
+class MemoryViewAttribute(AttributeTemplate):
+    key = types.MemoryView
+
+    def resolve_contiguous(self, buf):
+        return types.boolean
+
+    def resolve_c_contiguous(self, buf):
+        return types.boolean
+
+    def resolve_f_contiguous(self, buf):
+        return types.boolean
+
+    def resolve_itemsize(self, buf):
+        return types.intp
+
+    def resolve_nbytes(self, buf):
+        return types.intp
+
+    def resolve_readonly(self, buf):
+        return types.boolean
+
+    def resolve_shape(self, buf):
+        return types.UniTuple(types.intp, buf.ndim)
+
+    def resolve_strides(self, buf):
+        return types.UniTuple(types.intp, buf.ndim)
+
+    def resolve_ndim(self, buf):
+        return types.intp
+
+
+#-------------------------------------------------------------------------------
+
+
+@infer_getattr
+class BooleanAttribute(AttributeTemplate):
+    key = types.Boolean
+
+    def resolve___class__(self, ty):
+        return types.NumberClass(ty)
+
+    @bound_function("number.item")
+    def resolve_item(self, ty, args, kws):
+        assert not kws
+        if not args:
+            return signature(ty)
+
+
+@infer_getattr
+class NumberAttribute(AttributeTemplate):
+    key = types.Number
+
+    def resolve___class__(self, ty):
+        return types.NumberClass(ty)
+
+    def resolve_real(self, ty):
+        return getattr(ty, "underlying_float", ty)
+
+    def resolve_imag(self, ty):
+        return getattr(ty, "underlying_float", ty)
+
+    @bound_function("complex.conjugate")
+    def resolve_conjugate(self, ty, args, kws):
+        assert not args
+        assert not kws
+        return signature(ty)
+
+    @bound_function("number.item")
+    def resolve_item(self, ty, args, kws):
+        assert not kws
+        if not args:
+            return signature(ty)
+
+
+@infer_getattr
+class NPTimedeltaAttribute(AttributeTemplate):
+    key = types.NPTimedelta
+
+    def resolve___class__(self, ty):
+        return types.NumberClass(ty)
+
+
+@infer_getattr
+class NPDatetimeAttribute(AttributeTemplate):
+    key = types.NPDatetime
+
+    def resolve___class__(self, ty):
+        return types.NumberClass(ty)
+
+
+@infer_getattr
+class SliceAttribute(AttributeTemplate):
+    key = types.SliceType
+
+    def resolve_start(self, ty):
+        return types.intp
+
+    def resolve_stop(self, ty):
+        return types.intp
+
+    def resolve_step(self, ty):
+        return types.intp
+
+    @bound_function("slice.indices")
+    def resolve_indices(self, ty, args, kws):
+        assert not kws
+        if len(args) != 1:
+            raise errors.NumbaTypeError(
+                "indices() takes exactly one argument (%d given)" % len(args)
+            )
+        typ, = args
+        if not isinstance(typ, types.Integer):
+            raise errors.NumbaTypeError(
+                "'%s' object cannot be interpreted as an integer" % typ
+            )
+        return signature(types.UniTuple(types.intp, 3), types.intp)
+
+
+#-------------------------------------------------------------------------------
+
+
+@infer_getattr
+class NumberClassAttribute(AttributeTemplate):
+    key = types.NumberClass
+
+    def resolve___call__(self, classty):
+        """
+        Resolve a number class's constructor (e.g. calling int(...))
+        """
+        ty = classty.instance_type
+
+        def typer(val):
+            if isinstance(val, (types.BaseTuple, types.Sequence)):
+                # Array constructor, e.g. np.int32([1, 2])
+                fnty = self.context.resolve_value_type(np.array)
+                sig = fnty.get_call_type(self.context, (val, types.DType(ty)),
+                                         {})
+                return sig.return_type
+            elif isinstance(val, (types.Number, types.Boolean, types.IntEnumMember)):
+                 # Scalar constructor, e.g. np.int32(42)
+                 return ty
+            elif isinstance(val, (types.NPDatetime, types.NPTimedelta)):
+                # Constructor cast from datetime-like, e.g.
+                # > np.int64(np.datetime64("2000-01-01"))
+                if ty.bitwidth == 64:
+                    return ty
+                else:
+                    msg = (f"Cannot cast {val} to {ty} as {ty} is not 64 bits "
+                           "wide.")
+                    raise errors.TypingError(msg)
+            else:
+                if (isinstance(val, types.Array) and val.ndim == 0 and
+                    val.dtype == ty):
+                    # This is 0d array -> scalar degrading
+                    return ty
+                else:
+                    # unsupported
+                    msg = f"Casting {val} to {ty} directly is unsupported."
+                    if isinstance(val, types.Array):
+                        # array casts are supported a different way.
+                        msg += f" Try doing '<array>.astype(np.{ty})' instead"
+                    raise errors.TypingError(msg)
+
+        return types.Function(make_callable_template(key=ty, typer=typer))
+
+
+@infer_getattr
+class TypeRefAttribute(AttributeTemplate):
+    key = types.TypeRef
+
+    def resolve___call__(self, classty):
+        """
+        Resolve a number class's constructor (e.g. calling int(...))
+
+        Note:
+
+        This is needed because of the limitation of the current type-system
+        implementation.  Specifically, the lack of a higher-order type
+        (i.e. passing the ``DictType`` vs ``DictType(key_type, value_type)``)
+        """
+        ty = classty.instance_type
+
+        if isinstance(ty, type) and issubclass(ty, types.Type):
+            # Redirect the typing to a:
+            #   @type_callable(ty)
+            #   def typeddict_call(context):
+            #        ...
+            # For example, see numba/typed/typeddict.py
+            #   @type_callable(DictType)
+            #   def typeddict_call(context):
+            class Redirect(object):
+
+                def __init__(self, context):
+                    self.context =  context
+
+                def __call__(self, *args, **kwargs):
+                    result = self.context.resolve_function_type(ty, args, kwargs)
+                    if hasattr(result, "pysig"):
+                        self.pysig = result.pysig
+                    return result
+
+            return types.Function(make_callable_template(key=ty,
+                                                         typer=Redirect(self.context)))
+
+
+#------------------------------------------------------------------------------
+
+
+class MinMaxBase(AbstractTemplate):
+
+    def _unify_minmax(self, tys):
+        for ty in tys:
+            if not isinstance(ty, (types.Number, types.NPDatetime, types.NPTimedelta)):
+                return
+        return self.context.unify_types(*tys)
+
+    def generic(self, args, kws):
+        """
+        Resolve a min() or max() call.
+        """
+        assert not kws
+
+        if not args:
+            return
+        if len(args) == 1:
+            # max(arg) only supported if arg is an iterable
+            if isinstance(args[0], types.BaseTuple):
+                tys = list(args[0])
+                if not tys:
+                    raise TypeError("%s() argument is an empty tuple"
+                                    % (self.key.__name__,))
+            else:
+                return
+        else:
+            # max(*args)
+            tys = args
+        retty = self._unify_minmax(tys)
+        if retty is not None:
+            return signature(retty, *args)
+
+
+@infer_global(max)
+class Max(MinMaxBase):
+    pass
+
+
+@infer_global(min)
+class Min(MinMaxBase):
+    pass
+
+
+@infer_global(round)
+class Round(ConcreteTemplate):
+    cases = [
+        signature(types.intp, types.float32),
+        signature(types.int64, types.float64),
+        signature(types.float32, types.float32, types.intp),
+        signature(types.float64, types.float64, types.intp),
+    ]
+
+
+#------------------------------------------------------------------------------
+
+
+@infer_global(bool)
+class Bool(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        [arg] = args
+        if isinstance(arg, (types.Boolean, types.Number)):
+            return signature(types.boolean, arg)
+        # XXX typing for bool cannot be polymorphic because of the
+        # types.Function thing, so we redirect to the operator.truth
+        # intrinsic.
+        return self.context.resolve_function_type(operator.truth, args, kws)
+
+
+@infer_global(int)
+class Int(AbstractTemplate):
+
+    def generic(self, args, kws):
+        if kws:
+            raise errors.NumbaAssertionError('kws not supported')
+
+        [arg] = args
+
+        if isinstance(arg, types.Integer):
+            return signature(arg, arg)
+        if isinstance(arg, (types.Float, types.Boolean)):
+            return signature(types.intp, arg)
+
+
+@infer_global(float)
+class Float(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+
+        [arg] = args
+
+        if arg not in types.number_domain:
+            raise errors.NumbaTypeError("float() only support for numbers")
+
+        if arg in types.complex_domain:
+            raise errors.NumbaTypeError("float() does not support complex")
+
+        if arg in types.integer_domain:
+            return signature(types.float64, arg)
+
+        elif arg in types.real_domain:
+            return signature(arg, arg)
+
+
+@infer_global(complex)
+class Complex(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+
+        if len(args) == 1:
+            [arg] = args
+            if arg not in types.number_domain:
+                raise errors.NumbaTypeError("complex() only support for numbers")
+            if arg == types.float32:
+                return signature(types.complex64, arg)
+            else:
+                return signature(types.complex128, arg)
+
+        elif len(args) == 2:
+            [real, imag] = args
+            if (real not in types.number_domain or
+                imag not in types.number_domain):
+                raise errors.NumbaTypeError("complex() only support for numbers")
+            if real == imag == types.float32:
+                return signature(types.complex64, real, imag)
+            else:
+                return signature(types.complex128, real, imag)
+
+
+#------------------------------------------------------------------------------
+
+@infer_global(enumerate)
+class Enumerate(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        it = args[0]
+        if len(args) > 1 and not isinstance(args[1], types.Integer):
+            raise errors.NumbaTypeError("Only integers supported as start "
+                                        "value in enumerate")
+        elif len(args) > 2:
+            #let python raise its own error
+            enumerate(*args)
+
+        if isinstance(it, types.IterableType):
+            enumerate_type = types.EnumerateType(it)
+            return signature(enumerate_type, *args)
+
+
+@infer_global(zip)
+class Zip(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        if all(isinstance(it, types.IterableType) for it in args):
+            zip_type = types.ZipType(args)
+            return signature(zip_type, *args)
+
+
+@infer_global(iter)
+class Iter(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        if len(args) == 1:
+            it = args[0]
+            if isinstance(it, types.IterableType):
+                return signature(it.iterator_type, *args)
+
+
+@infer_global(next)
+class Next(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        if len(args) == 1:
+            it = args[0]
+            if isinstance(it, types.IteratorType):
+                return signature(it.yield_type, *args)
+
+
+#------------------------------------------------------------------------------
+
+@infer_global(type)
+class TypeBuiltin(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        if len(args) == 1:
+            # One-argument type() -> return the __class__
+            # Avoid literal types
+            arg = types.unliteral(args[0])
+            classty = self.context.resolve_getattr(arg, "__class__")
+            if classty is not None:
+                return signature(classty, *args)
+
+
+#------------------------------------------------------------------------------
+
+@infer_getattr
+class OptionalAttribute(AttributeTemplate):
+    key = types.Optional
+
+    def generic_resolve(self, optional, attr):
+        return self.context.resolve_getattr(optional.type, attr)
+
+#------------------------------------------------------------------------------
+
+@infer_getattr
+class DeferredAttribute(AttributeTemplate):
+    key = types.DeferredType
+
+    def generic_resolve(self, deferred, attr):
+        return self.context.resolve_getattr(deferred.get(), attr)
+
+#------------------------------------------------------------------------------
+
+@infer_global(get_type_min_value)
+@infer_global(get_type_max_value)
+class MinValInfer(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        assert len(args) == 1
+        if isinstance(args[0], (types.DType, types.NumberClass)):
+            return signature(args[0].dtype, *args)
+
+
+#------------------------------------------------------------------------------
+
+
+class IndexValue(object):
+    """
+    Index and value
+    """
+    def __init__(self, ind, val):
+        self.index = ind
+        self.value = val
+
+    def __repr__(self):
+        return 'IndexValue(%f, %f)' % (self.index, self.value)
+
+
+class IndexValueType(types.Type):
+    def __init__(self, val_typ):
+        self.val_typ = val_typ
+        super(IndexValueType, self).__init__(
+                                    name='IndexValueType({})'.format(val_typ))
+
+
+@typeof_impl.register(IndexValue)
+def typeof_index(val, c):
+    val_typ = typeof_impl(val.value, c)
+    return IndexValueType(val_typ)
+
+
+@type_callable(IndexValue)
+def type_index_value(context):
+    def typer(ind, mval):
+        if ind == types.intp or ind == types.uintp:
+            return IndexValueType(mval)
+    return typer
+
+
+@register_model(IndexValueType)
+class IndexValueModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('index', types.intp),
+            ('value', fe_type.val_typ),
+            ]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+make_attribute_wrapper(IndexValueType, 'index', 'index')
+make_attribute_wrapper(IndexValueType, 'value', 'value')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/cffi_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/cffi_utils.py
new file mode 100644
index 000000000..ee30b81a1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/cffi_utils.py
@@ -0,0 +1,229 @@
+# -*- coding: utf-8 -*-
+"""
+Support for CFFI. Allows checking whether objects are CFFI functions and
+obtaining the pointer and numba signature.
+"""
+
+from types import BuiltinFunctionType
+import ctypes
+from functools import partial
+import numpy as np
+
+from numba.core import types
+from numba.core.errors import TypingError
+from numba.core.typing import templates
+from numba.np import numpy_support
+
+try:
+    import cffi
+    ffi = cffi.FFI()
+except ImportError:
+    ffi = None
+
+SUPPORTED = ffi is not None
+_ool_func_types = {}
+_ool_func_ptr = {}
+_ffi_instances = set()
+
+
+def is_ffi_instance(obj):
+    # Compiled FFI modules have a member, ffi, which is an instance of
+    # CompiledFFI, which behaves similarly to an instance of cffi.FFI. In
+    # order to simplify handling a CompiledFFI object, we treat them as
+    # if they're cffi.FFI instances for typing and lowering purposes.
+    try:
+        return obj in _ffi_instances or isinstance(obj, cffi.FFI)
+    except TypeError: # Unhashable type possible
+        return False
+
+def is_cffi_func(obj):
+    """Check whether the obj is a CFFI function"""
+    try:
+        return ffi.typeof(obj).kind == 'function'
+    except TypeError:
+        try:
+            return obj in _ool_func_types
+        except:
+            return False
+
+def get_pointer(cffi_func):
+    """
+    Get a pointer to the underlying function for a CFFI function as an
+    integer.
+    """
+    if cffi_func in _ool_func_ptr:
+        return _ool_func_ptr[cffi_func]
+    return int(ffi.cast("uintptr_t", cffi_func))
+
+
+_cached_type_map = None
+
+def _type_map():
+    """
+    Lazily compute type map, as calling ffi.typeof() involves costly
+    parsing of C code...
+    """
+    global _cached_type_map
+    if _cached_type_map is None:
+        _cached_type_map = {
+            ffi.typeof('bool') :                types.boolean,
+            ffi.typeof('char') :                types.char,
+            ffi.typeof('short') :               types.short,
+            ffi.typeof('int') :                 types.intc,
+            ffi.typeof('long') :                types.long_,
+            ffi.typeof('long long') :           types.longlong,
+            ffi.typeof('unsigned char') :       types.uchar,
+            ffi.typeof('unsigned short') :      types.ushort,
+            ffi.typeof('unsigned int') :        types.uintc,
+            ffi.typeof('unsigned long') :       types.ulong,
+            ffi.typeof('unsigned long long') :  types.ulonglong,
+            ffi.typeof('int8_t') :              types.char,
+            ffi.typeof('uint8_t') :             types.uchar,
+            ffi.typeof('int16_t') :             types.short,
+            ffi.typeof('uint16_t') :            types.ushort,
+            ffi.typeof('int32_t') :             types.intc,
+            ffi.typeof('uint32_t') :            types.uintc,
+            ffi.typeof('int64_t') :             types.longlong,
+            ffi.typeof('uint64_t') :            types.ulonglong,
+            ffi.typeof('float') :               types.float_,
+            ffi.typeof('double') :              types.double,
+            ffi.typeof('ssize_t') :             types.intp,
+            ffi.typeof('size_t') :              types.uintp,
+            ffi.typeof('void') :                types.void,
+        }
+    return _cached_type_map
+
+
+def map_type(cffi_type, use_record_dtype=False):
+    """
+    Map CFFI type to numba type.
+
+    Parameters
+    ----------
+    cffi_type:
+        The CFFI type to be converted.
+    use_record_dtype: bool (default: False)
+        When True, struct types are mapped to a NumPy Record dtype.
+
+    """
+    primed_map_type = partial(map_type, use_record_dtype=use_record_dtype)
+    kind = getattr(cffi_type, 'kind', '')
+    if kind == 'union':
+        raise TypeError("No support for CFFI union")
+    elif kind == 'function':
+        if cffi_type.ellipsis:
+            raise TypeError("vararg function is not supported")
+        restype = primed_map_type(cffi_type.result)
+        argtypes = [primed_map_type(arg) for arg in cffi_type.args]
+        return templates.signature(restype, *argtypes)
+    elif kind == 'pointer':
+        pointee = cffi_type.item
+        if pointee.kind == 'void':
+            return types.voidptr
+        else:
+            return types.CPointer(primed_map_type(pointee))
+    elif kind == 'array':
+        dtype = primed_map_type(cffi_type.item)
+        nelem = cffi_type.length
+        return types.NestedArray(dtype=dtype, shape=(nelem,))
+    elif kind == 'struct' and use_record_dtype:
+        return map_struct_to_record_dtype(cffi_type)
+    else:
+        result = _type_map().get(cffi_type)
+        if result is None:
+            raise TypeError(cffi_type)
+        return result
+
+
+def map_struct_to_record_dtype(cffi_type):
+    """Convert a cffi type into a NumPy Record dtype
+    """
+    fields = {
+            'names': [],
+            'formats': [],
+            'offsets': [],
+            'itemsize': ffi.sizeof(cffi_type),
+    }
+    is_aligned = True
+    for k, v in cffi_type.fields:
+        # guard unsupported values
+        if v.bitshift != -1:
+            msg = "field {!r} has bitshift, this is not supported"
+            raise ValueError(msg.format(k))
+        if v.flags != 0:
+            msg = "field {!r} has flags, this is not supported"
+            raise ValueError(msg.format(k))
+        if v.bitsize != -1:
+            msg = "field {!r} has bitsize, this is not supported"
+            raise ValueError(msg.format(k))
+        dtype = numpy_support.as_dtype(
+            map_type(v.type, use_record_dtype=True),
+        )
+        fields['names'].append(k)
+        fields['formats'].append(dtype)
+        fields['offsets'].append(v.offset)
+        # Check alignment
+        is_aligned &= (v.offset % dtype.alignment == 0)
+
+    return numpy_support.from_dtype(np.dtype(fields, align=is_aligned))
+
+
+def make_function_type(cffi_func, use_record_dtype=False):
+    """
+    Return a Numba type for the given CFFI function pointer.
+    """
+    cffi_type = _ool_func_types.get(cffi_func) or ffi.typeof(cffi_func)
+    if getattr(cffi_type, 'kind', '') == 'struct':
+        raise TypeError('No support for CFFI struct values')
+    sig = map_type(cffi_type, use_record_dtype=use_record_dtype)
+    return types.ExternalFunctionPointer(sig, get_pointer=get_pointer)
+
+
+registry = templates.Registry()
+
+@registry.register
+class FFI_from_buffer(templates.AbstractTemplate):
+    key = 'ffi.from_buffer'
+
+    def generic(self, args, kws):
+        if kws or len(args) != 1:
+            return
+        [ary] = args
+        if not isinstance(ary, types.Buffer):
+            raise TypingError("from_buffer() expected a buffer object, got %s"
+                              % (ary,))
+        if ary.layout not in ('C', 'F'):
+            raise TypingError("from_buffer() unsupported on non-contiguous buffers (got %s)"
+                              % (ary,))
+        if ary.layout != 'C' and ary.ndim > 1:
+            raise TypingError("from_buffer() only supports multidimensional arrays with C layout (got %s)"
+                              % (ary,))
+        ptr = types.CPointer(ary.dtype)
+        return templates.signature(ptr, ary)
+
+@registry.register_attr
+class FFIAttribute(templates.AttributeTemplate):
+    key = types.ffi
+
+    def resolve_from_buffer(self, ffi):
+        return types.BoundFunction(FFI_from_buffer, types.ffi)
+
+
+def register_module(mod):
+    """
+    Add typing for all functions in an out-of-line CFFI module to the typemap
+    """
+    for f in dir(mod.lib):
+        f = getattr(mod.lib, f)
+        if isinstance(f, BuiltinFunctionType):
+            _ool_func_types[f] = mod.ffi.typeof(f)
+            addr = mod.ffi.addressof(mod.lib, f.__name__)
+            _ool_func_ptr[f] = int(mod.ffi.cast("uintptr_t", addr))
+        _ffi_instances.add(mod.ffi)
+
+def register_type(cffi_type, numba_type):
+    """
+    Add typing for a given CFFI type to the typemap
+    """
+    tm = _type_map()
+    tm[cffi_type] = numba_type
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/cmathdecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/cmathdecl.py
new file mode 100644
index 000000000..010d2328a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/cmathdecl.py
@@ -0,0 +1,72 @@
+import cmath
+
+from numba.core import types, utils
+from numba.core.typing.templates import (AbstractTemplate, ConcreteTemplate,
+                                    signature, Registry)
+
+registry = Registry()
+infer_global = registry.register_global
+
+# TODO: support non-complex arguments (floats and ints)
+
+
+@infer_global(cmath.acos)
+@infer_global(cmath.acosh)
+@infer_global(cmath.asin)
+@infer_global(cmath.asinh)
+@infer_global(cmath.atan)
+@infer_global(cmath.atanh)
+@infer_global(cmath.cos)
+@infer_global(cmath.cosh)
+@infer_global(cmath.exp)
+@infer_global(cmath.log10)
+@infer_global(cmath.sin)
+@infer_global(cmath.sinh)
+@infer_global(cmath.sqrt)
+@infer_global(cmath.tan)
+@infer_global(cmath.tanh)
+class CMath_unary(ConcreteTemplate):
+    cases = [signature(tp, tp) for tp in sorted(types.complex_domain)]
+
+
+@infer_global(cmath.isinf)
+@infer_global(cmath.isnan)
+class CMath_predicate(ConcreteTemplate):
+    cases = [signature(types.boolean, tp) for tp in
+             sorted(types.complex_domain)]
+
+
+@infer_global(cmath.isfinite)
+class CMath_isfinite(CMath_predicate):
+    pass
+
+
+@infer_global(cmath.log)
+class Cmath_log(ConcreteTemplate):
+    # unary cmath.log()
+    cases = [signature(tp, tp) for tp in sorted(types.complex_domain)]
+    # binary cmath.log()
+    cases += [signature(tp, tp, tp) for tp in sorted(types.complex_domain)]
+
+
+@infer_global(cmath.phase)
+class Cmath_phase(ConcreteTemplate):
+    cases = [signature(tp, types.complex128) for tp in [types.float64]]
+    cases += [signature(types.float32, types.complex64)]
+
+
+@infer_global(cmath.polar)
+class Cmath_polar(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        [tp] = args
+        if tp in types.complex_domain:
+            float_type = tp.underlying_float
+            return signature(types.UniTuple(float_type, 2), tp)
+
+
+@infer_global(cmath.rect)
+class Cmath_rect(ConcreteTemplate):
+    cases = [signature(types.complex128, tp, tp)
+             for tp in [types.float64]]
+    cases += [signature(types.complex64, types.float32, types.float32)]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/collections.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/collections.py
new file mode 100644
index 000000000..bb4639a3e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/collections.py
@@ -0,0 +1,121 @@
+from .. import types, utils, errors
+import operator
+from .templates import (AttributeTemplate, ConcreteTemplate, AbstractTemplate,
+                        infer_global, infer, infer_getattr,
+                        signature, bound_function, make_callable_template)
+from .builtins import normalize_1d_index
+
+
+@infer_global(operator.contains)
+class InContainer(AbstractTemplate):
+    key = operator.contains
+
+    def generic(self, args, kws):
+        cont, item = args
+        if isinstance(cont, types.Container):
+            return signature(types.boolean, cont, cont.dtype)
+
+@infer_global(len)
+class ContainerLen(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        (val,) = args
+        if isinstance(val, (types.Container)):
+            return signature(types.intp, val)
+
+
+@infer_global(operator.truth)
+class SequenceBool(AbstractTemplate):
+    key = operator.truth
+
+    def generic(self, args, kws):
+        assert not kws
+        (val,) = args
+        if isinstance(val, (types.Sequence)):
+            return signature(types.boolean, val)
+
+
+@infer_global(operator.getitem)
+class GetItemSequence(AbstractTemplate):
+    key = operator.getitem
+
+    def generic(self, args, kws):
+        seq, idx = args
+        if isinstance(seq, types.Sequence):
+            idx = normalize_1d_index(idx)
+            if isinstance(idx, types.SliceType):
+                # Slicing a tuple only supported with static_getitem
+                if not isinstance(seq, types.BaseTuple):
+                    return signature(seq, seq, idx)
+            elif isinstance(idx, types.Integer):
+                return signature(seq.dtype, seq, idx)
+
+@infer_global(operator.setitem)
+class SetItemSequence(AbstractTemplate):
+    def generic(self, args, kws):
+        seq, idx, value = args
+        if isinstance(seq, types.MutableSequence):
+            idx = normalize_1d_index(idx)
+            if isinstance(idx, types.SliceType):
+                return signature(types.none, seq, idx, seq)
+            elif isinstance(idx, types.Integer):
+                if not self.context.can_convert(value, seq.dtype):
+                    msg = "invalid setitem with value of {} to element of {}"
+                    raise errors.TypingError(msg.format(types.unliteral(value), seq.dtype))
+                return signature(types.none, seq, idx, seq.dtype)
+
+
+@infer_global(operator.delitem)
+class DelItemSequence(AbstractTemplate):
+    def generic(self, args, kws):
+        seq, idx = args
+        if isinstance(seq, types.MutableSequence):
+            idx = normalize_1d_index(idx)
+            return signature(types.none, seq, idx)
+
+
+# --------------------------------------------------------------------------
+# named tuples
+
+@infer_getattr
+class NamedTupleAttribute(AttributeTemplate):
+    key = types.BaseNamedTuple
+
+    def resolve___class__(self, tup):
+        return types.NamedTupleClass(tup.instance_class)
+
+    def generic_resolve(self, tup, attr):
+        # Resolution of other attributes
+        try:
+            index = tup.fields.index(attr)
+        except ValueError:
+            return
+        return tup[index]
+
+
+@infer_getattr
+class NamedTupleClassAttribute(AttributeTemplate):
+    key = types.NamedTupleClass
+
+    def resolve___call__(self, classty):
+        """
+        Resolve the named tuple constructor, aka the class's __call__ method.
+        """
+        instance_class = classty.instance_class
+        pysig = utils.pysignature(instance_class)
+
+        def typer(*args, **kws):
+            # Fold keyword args
+            try:
+                bound = pysig.bind(*args, **kws)
+            except TypeError as e:
+                msg = "In '%s': %s" % (instance_class, e)
+                e.args = (msg,)
+                raise
+            assert not bound.kwargs
+            return types.BaseTuple.from_types(bound.args, instance_class)
+
+        # Override the typer's pysig to match the namedtuple constructor's
+        typer.pysig = pysig
+        return types.Function(make_callable_template(self.key, typer))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/context.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/context.py
new file mode 100644
index 000000000..eddc081a5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/context.py
@@ -0,0 +1,720 @@
+from collections import defaultdict
+from collections.abc import Sequence
+import types as pytypes
+import weakref
+import threading
+import contextlib
+import operator
+
+import numba
+from numba.core import types, errors
+from numba.core.typeconv import Conversion, rules
+from numba.core.typing import templates
+from numba.core.utils import order_by_target_specificity
+from .typeof import typeof, Purpose
+
+from numba.core import utils
+
+
+class Rating(object):
+    __slots__ = 'promote', 'safe_convert', "unsafe_convert"
+
+    def __init__(self):
+        self.promote = 0
+        self.safe_convert = 0
+        self.unsafe_convert = 0
+
+    def astuple(self):
+        """Returns a tuple suitable for comparing with the worse situation
+        start first.
+        """
+        return (self.unsafe_convert, self.safe_convert, self.promote)
+
+    def __add__(self, other):
+        if type(self) is not type(other):
+            return NotImplemented
+        rsum = Rating()
+        rsum.promote = self.promote + other.promote
+        rsum.safe_convert = self.safe_convert + other.safe_convert
+        rsum.unsafe_convert = self.unsafe_convert + other.unsafe_convert
+        return rsum
+
+
+class CallStack(Sequence):
+    """
+    A compile-time call stack
+    """
+
+    def __init__(self):
+        self._stack = []
+        self._lock = threading.RLock()
+
+    def __getitem__(self, index):
+        """
+        Returns item in the stack where index=0 is the top and index=1 is
+        the second item from the top.
+        """
+        return self._stack[len(self) - index - 1]
+
+    def __len__(self):
+        return len(self._stack)
+
+    @contextlib.contextmanager
+    def register(self, target, typeinfer, func_id, args):
+        # guard compiling the same function with the same signature
+        if self.match(func_id.func, args):
+            msg = "compiler re-entrant to the same function signature"
+            raise errors.NumbaRuntimeError(msg)
+        self._lock.acquire()
+        self._stack.append(CallFrame(target, typeinfer, func_id, args))
+        try:
+            yield
+        finally:
+            self._stack.pop()
+            self._lock.release()
+
+    def finditer(self, py_func):
+        """
+        Yields frame that matches the function object starting from the top
+        of stack.
+        """
+        for frame in self:
+            if frame.func_id.func is py_func:
+                yield frame
+
+    def findfirst(self, py_func):
+        """
+        Returns the first result from `.finditer(py_func)`; or None if no match.
+        """
+        try:
+            return next(self.finditer(py_func))
+        except StopIteration:
+            return
+
+    def match(self, py_func, args):
+        """
+        Returns first function that matches *py_func* and the arguments types in
+        *args*; or, None if no match.
+        """
+        for frame in self.finditer(py_func):
+            if frame.args == args:
+                return frame
+
+
+class CallFrame(object):
+    """
+    A compile-time call frame
+    """
+    def __init__(self, target, typeinfer, func_id, args):
+        self.typeinfer = typeinfer
+        self.func_id = func_id
+        self.args = args
+        self.target = target
+        self._inferred_retty = set()
+
+    def __repr__(self):
+        return "CallFrame({}, {})".format(self.func_id, self.args)
+
+    def add_return_type(self, return_type):
+        """Add *return_type* to the list of inferred return-types.
+        If there are too many, raise `TypingError`.
+        """
+        # The maximum limit is picked arbitrarily.
+        # Don't think that this needs to be user configurable.
+        RETTY_LIMIT = 16
+        self._inferred_retty.add(return_type)
+        if len(self._inferred_retty) >= RETTY_LIMIT:
+            m = "Return type of recursive function does not converge"
+            raise errors.TypingError(m)
+
+
+class BaseContext(object):
+    """A typing context for storing function typing constrain template.
+    """
+
+    def __init__(self):
+        # A list of installed registries
+        self._registries = {}
+        # Typing declarations extracted from the registries or other sources
+        self._functions = defaultdict(list)
+        self._attributes = defaultdict(list)
+        self._globals = utils.UniqueDict()
+        self.tm = rules.default_type_manager
+        self.callstack = CallStack()
+
+        # Initialize
+        self.init()
+
+    def init(self):
+        """
+        Initialize the typing context.  Can be overridden by subclasses.
+        """
+
+    def refresh(self):
+        """
+        Refresh context with new declarations from known registries.
+        Useful for third-party extensions.
+        """
+        self.load_additional_registries()
+        # Some extensions may have augmented the builtin registry
+        self._load_builtins()
+
+    def explain_function_type(self, func):
+        """
+        Returns a string description of the type of a function
+        """
+        desc = []
+        defns = []
+        param = False
+        if isinstance(func, types.Callable):
+            sigs, param = func.get_call_signatures()
+            defns.extend(sigs)
+
+        elif func in self._functions:
+            for tpl in self._functions[func]:
+                param = param or hasattr(tpl, 'generic')
+                defns.extend(getattr(tpl, 'cases', []))
+
+        else:
+            msg = "No type info available for {func!r} as a callable."
+            desc.append(msg.format(func=func))
+
+        if defns:
+            desc = ['Known signatures:']
+            for sig in defns:
+                desc.append(' * {0}'.format(sig))
+
+        return '\n'.join(desc)
+
+    def resolve_function_type(self, func, args, kws):
+        """
+        Resolve function type *func* for argument types *args* and *kws*.
+        A signature is returned.
+        """
+        # Prefer user definition first
+        try:
+            res = self._resolve_user_function_type(func, args, kws)
+        except errors.TypingError as e:
+            # Capture any typing error
+            last_exception = e
+            res = None
+        else:
+            last_exception = None
+
+        # Return early we know there's a working user function
+        if res is not None:
+            return res
+
+        # Check builtin functions
+        res = self._resolve_builtin_function_type(func, args, kws)
+
+        # Re-raise last_exception if no function type has been found
+        if res is None and last_exception is not None:
+            raise last_exception
+
+        return res
+
+    def _resolve_builtin_function_type(self, func, args, kws):
+        # NOTE: we should reduce usage of this
+        if func in self._functions:
+            # Note: Duplicating code with types.Function.get_call_type().
+            #       *defns* are CallTemplates.
+            defns = self._functions[func]
+            for defn in defns:
+                for support_literals in [True, False]:
+                    if support_literals:
+                        res = defn.apply(args, kws)
+                    else:
+                        fixedargs = [types.unliteral(a) for a in args]
+                        res = defn.apply(fixedargs, kws)
+                    if res is not None:
+                        return res
+
+    def _resolve_user_function_type(self, func, args, kws, literals=None):
+        # It's not a known function type, perhaps it's a global?
+        functy = self._lookup_global(func)
+        if functy is not None:
+            func = functy
+
+        if isinstance(func, types.Type):
+            # If it's a type, it may support a __call__ method
+            func_type = self.resolve_getattr(func, "__call__")
+            if func_type is not None:
+                # The function has a __call__ method, type its call.
+                return self.resolve_function_type(func_type, args, kws)
+
+        if isinstance(func, types.Callable):
+            # XXX fold this into the __call__ attribute logic?
+            return func.get_call_type(self, args, kws)
+
+    def _get_attribute_templates(self, typ):
+        """
+        Get matching AttributeTemplates for the Numba type.
+        """
+        if typ in self._attributes:
+            for attrinfo in self._attributes[typ]:
+                yield attrinfo
+        else:
+            for cls in type(typ).__mro__:
+                if cls in self._attributes:
+                    for attrinfo in self._attributes[cls]:
+                        yield attrinfo
+
+    def resolve_getattr(self, typ, attr):
+        """
+        Resolve getting the attribute *attr* (a string) on the Numba type.
+        The attribute's type is returned, or None if resolution failed.
+        """
+        def core(typ):
+            out = self.find_matching_getattr_template(typ, attr)
+            if out:
+                return out['return_type']
+
+        out = core(typ)
+        if out is not None:
+            return out
+
+        # Try again without literals
+        out = core(types.unliteral(typ))
+        if out is not None:
+            return out
+
+        if isinstance(typ, types.Module):
+            attrty = self.resolve_module_constants(typ, attr)
+            if attrty is not None:
+                return attrty
+
+    def find_matching_getattr_template(self, typ, attr):
+
+        templates = list(self._get_attribute_templates(typ))
+
+        # get the order in which to try templates
+        from numba.core.target_extension import get_local_target # circular
+        target_hw = get_local_target(self)
+        order = order_by_target_specificity(target_hw, templates, fnkey=attr)
+
+        for template in order:
+            return_type = template.resolve(typ, attr)
+            if return_type is not None:
+                return {
+                    'template': template,
+                    'return_type': return_type,
+                }
+
+    def resolve_setattr(self, target, attr, value):
+        """
+        Resolve setting the attribute *attr* (a string) on the *target* type
+        to the given *value* type.
+        A function signature is returned, or None if resolution failed.
+        """
+        for attrinfo in self._get_attribute_templates(target):
+            expectedty = attrinfo.resolve(target, attr)
+            # NOTE: convertibility from *value* to *expectedty* is left to
+            # the caller.
+            if expectedty is not None:
+                return templates.signature(types.void, target, expectedty)
+
+    def resolve_static_getitem(self, value, index):
+        assert not isinstance(index, types.Type), index
+        args = value, index
+        kws = ()
+        return self.resolve_function_type("static_getitem", args, kws)
+
+    def resolve_static_setitem(self, target, index, value):
+        assert not isinstance(index, types.Type), index
+        args = target, index, value
+        kws = {}
+        return self.resolve_function_type("static_setitem", args, kws)
+
+    def resolve_setitem(self, target, index, value):
+        assert isinstance(index, types.Type), index
+        fnty = self.resolve_value_type(operator.setitem)
+        sig = fnty.get_call_type(self, (target, index, value), {})
+        return sig
+
+    def resolve_delitem(self, target, index):
+        args = target, index
+        kws = {}
+        fnty = self.resolve_value_type(operator.delitem)
+        sig = fnty.get_call_type(self, args, kws)
+        return sig
+
+    def resolve_module_constants(self, typ, attr):
+        """
+        Resolve module-level global constants.
+        Return None or the attribute type
+        """
+        assert isinstance(typ, types.Module)
+        attrval = getattr(typ.pymod, attr)
+        try:
+            return self.resolve_value_type(attrval)
+        except ValueError:
+            pass
+
+    def resolve_argument_type(self, val):
+        """
+        Return the numba type of a Python value that is being used
+        as a function argument.  Integer types will all be considered
+        int64, regardless of size.
+
+        ValueError is raised for unsupported types.
+        """
+        try:
+            return typeof(val, Purpose.argument)
+        except ValueError:
+            if numba.cuda.is_cuda_array(val):
+                # There's no need to synchronize on a stream when we're only
+                # determining typing - synchronization happens at launch time,
+                # so eliding sync here is safe.
+                return typeof(numba.cuda.as_cuda_array(val, sync=False),
+                              Purpose.argument)
+            else:
+                raise
+
+    def resolve_value_type(self, val):
+        """
+        Return the numba type of a Python value that is being used
+        as a runtime constant.
+        ValueError is raised for unsupported types.
+        """
+        try:
+            ty = typeof(val, Purpose.constant)
+        except ValueError as e:
+            # Make sure the exception doesn't hold a reference to the user
+            # value.
+            typeof_exc = utils.erase_traceback(e)
+        else:
+            return ty
+
+        if isinstance(val, types.ExternalFunction):
+            return val
+
+        # Try to look up target specific typing information
+        ty = self._get_global_type(val)
+        if ty is not None:
+            return ty
+
+        raise typeof_exc
+
+    def resolve_value_type_prefer_literal(self, value):
+        """Resolve value type and prefer Literal types whenever possible.
+        """
+        lit = types.maybe_literal(value)
+        if lit is None:
+            return self.resolve_value_type(value)
+        else:
+            return lit
+
+    def _get_global_type(self, gv):
+        ty = self._lookup_global(gv)
+        if ty is not None:
+            return ty
+        if isinstance(gv, pytypes.ModuleType):
+            return types.Module(gv)
+
+    def _load_builtins(self):
+        # Initialize declarations
+        from numba.core.typing import builtins, arraydecl, npdatetime  # noqa: F401, E501
+        from numba.core.typing import ctypes_utils, bufproto           # noqa: F401, E501
+        from numba.core.unsafe import eh                    # noqa: F401
+
+        self.install_registry(templates.builtin_registry)
+
+    def load_additional_registries(self):
+        """
+        Load target-specific registries.  Can be overridden by subclasses.
+        """
+
+    def install_registry(self, registry):
+        """
+        Install a *registry* (a templates.Registry instance) of function,
+        attribute and global declarations.
+        """
+        try:
+            loader = self._registries[registry]
+        except KeyError:
+            loader = templates.RegistryLoader(registry)
+            self._registries[registry] = loader
+        for ftcls in loader.new_registrations('functions'):
+            self.insert_function(ftcls(self))
+        for ftcls in loader.new_registrations('attributes'):
+            self.insert_attributes(ftcls(self))
+        for gv, gty in loader.new_registrations('globals'):
+            existing = self._lookup_global(gv)
+            if existing is None:
+                self.insert_global(gv, gty)
+            else:
+                # A type was already inserted, see if we can add to it
+                newty = existing.augment(gty)
+                if newty is None:
+                    raise TypeError("cannot augment %s with %s"
+                                    % (existing, gty))
+                self._remove_global(gv)
+                self._insert_global(gv, newty)
+
+    def _lookup_global(self, gv):
+        """
+        Look up the registered type for global value *gv*.
+        """
+        try:
+            gv = weakref.ref(gv)
+        except TypeError:
+            pass
+        try:
+            return self._globals.get(gv, None)
+        except TypeError:
+            # Unhashable type
+            return None
+
+    def _insert_global(self, gv, gty):
+        """
+        Register type *gty* for value *gv*.  Only a weak reference
+        to *gv* is kept, if possible.
+        """
+        def on_disposal(wr, pop=self._globals.pop):
+            # pop() is pre-looked up to avoid a crash late at shutdown on 3.5
+            # (https://bugs.python.org/issue25217)
+            pop(wr)
+        try:
+            gv = weakref.ref(gv, on_disposal)
+        except TypeError:
+            pass
+        self._globals[gv] = gty
+
+    def _remove_global(self, gv):
+        """
+        Remove the registered type for global value *gv*.
+        """
+        try:
+            gv = weakref.ref(gv)
+        except TypeError:
+            pass
+        del self._globals[gv]
+
+    def insert_global(self, gv, gty):
+        self._insert_global(gv, gty)
+
+    def insert_attributes(self, at):
+        key = at.key
+        self._attributes[key].append(at)
+
+    def insert_function(self, ft):
+        key = ft.key
+        self._functions[key].append(ft)
+
+    def insert_user_function(self, fn, ft):
+        """Insert a user function.
+
+        Args
+        ----
+        - fn:
+            object used as callee
+        - ft:
+            function template
+        """
+        self._insert_global(fn, types.Function(ft))
+
+    def can_convert(self, fromty, toty):
+        """
+        Check whether conversion is possible from *fromty* to *toty*.
+        If successful, return a numba.typeconv.Conversion instance;
+        otherwise None is returned.
+        """
+        if fromty == toty:
+            return Conversion.exact
+        else:
+            # First check with the type manager (some rules are registered
+            # at startup there, see numba.typeconv.rules)
+            conv = self.tm.check_compatible(fromty, toty)
+            if conv is not None:
+                return conv
+
+            # Fall back on type-specific rules
+            forward = fromty.can_convert_to(self, toty)
+            backward = toty.can_convert_from(self, fromty)
+            if backward is None:
+                return forward
+            elif forward is None:
+                return backward
+            else:
+                return min(forward, backward)
+
+    def _rate_arguments(self, actualargs, formalargs, unsafe_casting=True,
+                        exact_match_required=False):
+        """
+        Rate the actual arguments for compatibility against the formal
+        arguments.  A Rating instance is returned, or None if incompatible.
+        """
+        if len(actualargs) != len(formalargs):
+            return None
+        rate = Rating()
+        for actual, formal in zip(actualargs, formalargs):
+            conv = self.can_convert(actual, formal)
+            if conv is None:
+                return None
+            elif not unsafe_casting and conv >= Conversion.unsafe:
+                return None
+            elif exact_match_required and conv != Conversion.exact:
+                return None
+
+            if conv == Conversion.promote:
+                rate.promote += 1
+            elif conv == Conversion.safe:
+                rate.safe_convert += 1
+            elif conv == Conversion.unsafe:
+                rate.unsafe_convert += 1
+            elif conv == Conversion.exact:
+                pass
+            else:
+                raise Exception("unreachable", conv)
+
+        return rate
+
+    def install_possible_conversions(self, actualargs, formalargs):
+        """
+        Install possible conversions from the actual argument types to
+        the formal argument types in the C++ type manager.
+        Return True if all arguments can be converted.
+        """
+        if len(actualargs) != len(formalargs):
+            return False
+        for actual, formal in zip(actualargs, formalargs):
+            if self.tm.check_compatible(actual, formal) is not None:
+                # This conversion is already known
+                continue
+            conv = self.can_convert(actual, formal)
+            if conv is None:
+                return False
+            assert conv is not Conversion.exact
+            self.tm.set_compatible(actual, formal, conv)
+        return True
+
+    def resolve_overload(self, key, cases, args, kws,
+                         allow_ambiguous=True, unsafe_casting=True,
+                         exact_match_required=False):
+        """
+        Given actual *args* and *kws*, find the best matching
+        signature in *cases*, or None if none matches.
+        *key* is used for error reporting purposes.
+        If *allow_ambiguous* is False, a tie in the best matches
+        will raise an error.
+        If *unsafe_casting* is False, unsafe casting is forbidden.
+        """
+        assert not kws, "Keyword arguments are not supported, yet"
+        options = {
+            'unsafe_casting': unsafe_casting,
+            'exact_match_required': exact_match_required,
+        }
+        # Rate each case
+        candidates = []
+        for case in cases:
+            if len(args) == len(case.args):
+                rating = self._rate_arguments(args, case.args, **options)
+                if rating is not None:
+                    candidates.append((rating.astuple(), case))
+
+        # Find the best case
+        candidates.sort(key=lambda i: i[0])
+        if candidates:
+            best_rate, best = candidates[0]
+            if not allow_ambiguous:
+                # Find whether there is a tie and if so, raise an error
+                tied = []
+                for rate, case in candidates:
+                    if rate != best_rate:
+                        break
+                    tied.append(case)
+                if len(tied) > 1:
+                    args = (key, args, '\n'.join(map(str, tied)))
+                    msg = "Ambiguous overloading for %s %s:\n%s" % args
+                    raise TypeError(msg)
+            # Simply return the best matching candidate in order.
+            # If there is a tie, since list.sort() is stable, the first case
+            # in the original order is returned.
+            # (this can happen if e.g. a function template exposes
+            #  (int32, int32) -> int32 and (int64, int64) -> int64,
+            #  and you call it with (int16, int16) arguments)
+            return best
+
+    def unify_types(self, *typelist):
+        # Sort the type list according to bit width before doing
+        # pairwise unification (with thanks to aterrel).
+        def keyfunc(obj):
+            """Uses bitwidth to order numeric-types.
+            Fallback to stable, deterministic sort.
+            """
+            return getattr(obj, 'bitwidth', 0)
+        typelist = sorted(typelist, key=keyfunc)
+        unified = typelist[0]
+        for tp in typelist[1:]:
+            unified = self.unify_pairs(unified, tp)
+            if unified is None:
+                break
+        return unified
+
+    def unify_pairs(self, first, second):
+        """
+        Try to unify the two given types.  A third type is returned,
+        or None in case of failure.
+        """
+        if first == second:
+            return first
+
+        if first is types.undefined:
+            return second
+        elif second is types.undefined:
+            return first
+
+        # Types with special unification rules
+        unified = first.unify(self, second)
+        if unified is not None:
+            return unified
+
+        unified = second.unify(self, first)
+        if unified is not None:
+            return unified
+
+        # Other types with simple conversion rules
+        conv = self.can_convert(fromty=first, toty=second)
+        if conv is not None and conv <= Conversion.safe:
+            # Can convert from first to second
+            return second
+
+        conv = self.can_convert(fromty=second, toty=first)
+        if conv is not None and conv <= Conversion.safe:
+            # Can convert from second to first
+            return first
+
+        if isinstance(first, types.Literal) or \
+           isinstance(second, types.Literal):
+            first = types.unliteral(first)
+            second = types.unliteral(second)
+            return self.unify_pairs(first, second)
+
+        # Cannot unify
+        return None
+
+
+class Context(BaseContext):
+
+    def load_additional_registries(self):
+        from . import (
+            cffi_utils,
+            cmathdecl,
+            enumdecl,
+            listdecl,
+            mathdecl,
+            npydecl,
+            randomdecl,
+            setdecl,
+            dictdecl,
+        )
+        self.install_registry(cffi_utils.registry)
+        self.install_registry(cmathdecl.registry)
+        self.install_registry(enumdecl.registry)
+        self.install_registry(listdecl.registry)
+        self.install_registry(mathdecl.registry)
+        self.install_registry(npydecl.registry)
+        self.install_registry(randomdecl.registry)
+        self.install_registry(setdecl.registry)
+        self.install_registry(dictdecl.registry)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/ctypes_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/ctypes_utils.py
new file mode 100644
index 000000000..91d194f3a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/ctypes_utils.py
@@ -0,0 +1,128 @@
+"""
+Support for typing ctypes function pointers.
+"""
+
+
+import ctypes
+import sys
+
+from numba.core import types
+from numba.core.typing import templates
+from .typeof import typeof_impl
+
+
+_FROM_CTYPES = {
+    ctypes.c_bool: types.boolean,
+
+    ctypes.c_int8:  types.int8,
+    ctypes.c_int16: types.int16,
+    ctypes.c_int32: types.int32,
+    ctypes.c_int64: types.int64,
+
+    ctypes.c_uint8: types.uint8,
+    ctypes.c_uint16: types.uint16,
+    ctypes.c_uint32: types.uint32,
+    ctypes.c_uint64: types.uint64,
+
+    ctypes.c_float: types.float32,
+    ctypes.c_double: types.float64,
+
+    ctypes.c_void_p: types.voidptr,
+    ctypes.py_object: types.ffi_forced_object,
+}
+
+_TO_CTYPES = {v: k for (k, v) in _FROM_CTYPES.items()}
+
+
+def from_ctypes(ctypeobj):
+    """
+    Convert the given ctypes type to a Numba type.
+    """
+    if ctypeobj is None:
+        # Special case for the restype of void-returning functions
+        return types.none
+
+    assert isinstance(ctypeobj, type), ctypeobj
+
+    def _convert_internal(ctypeobj):
+        # Recursive helper
+        if issubclass(ctypeobj, ctypes._Pointer):
+            valuety = _convert_internal(ctypeobj._type_)
+            if valuety is not None:
+                return types.CPointer(valuety)
+        else:
+            return _FROM_CTYPES.get(ctypeobj)
+
+    ty = _convert_internal(ctypeobj)
+    if ty is None:
+        raise TypeError("Unsupported ctypes type: %s" % ctypeobj)
+    return ty
+
+
+def to_ctypes(ty):
+    """
+    Convert the given Numba type to a ctypes type.
+    """
+    assert isinstance(ty, types.Type), ty
+
+    if ty is types.none:
+        # Special case for the restype of void-returning functions
+        return None
+
+    def _convert_internal(ty):
+        if isinstance(ty, types.CPointer):
+            return ctypes.POINTER(_convert_internal(ty.dtype))
+        else:
+            return _TO_CTYPES.get(ty)
+
+    ctypeobj = _convert_internal(ty)
+    if ctypeobj is None:
+        raise TypeError("Cannot convert Numba type '%s' to ctypes type"
+                        % (ty,))
+    return ctypeobj
+
+
+def is_ctypes_funcptr(obj):
+    try:
+        # Is it something of which we can get the address
+        ctypes.cast(obj, ctypes.c_void_p)
+    except ctypes.ArgumentError:
+        return False
+    else:
+        # Does it define argtypes and restype
+        return hasattr(obj, 'argtypes') and hasattr(obj, 'restype')
+
+
+def get_pointer(ctypes_func):
+    """
+    Get a pointer to the underlying function for a ctypes function as an
+    integer.
+    """
+    return ctypes.cast(ctypes_func, ctypes.c_void_p).value
+
+
+def make_function_type(cfnptr):
+    """
+    Return a Numba type for the given ctypes function pointer.
+    """
+    if cfnptr.argtypes is None:
+        raise TypeError("ctypes function %r doesn't define its argument types; "
+                        "consider setting the `argtypes` attribute"
+                        % (cfnptr.__name__,))
+    cargs = [from_ctypes(a)
+             for a in cfnptr.argtypes]
+    cret = from_ctypes(cfnptr.restype)
+    # void* return type is a int/long on 32 bit platforms and an int on 64 bit
+    # platforms, explicit conversion to a int64 should match.
+    if cret == types.voidptr:
+        cret = types.uintp
+    if sys.platform == 'win32' and not cfnptr._flags_ & ctypes._FUNCFLAG_CDECL:
+        # 'stdcall' calling convention under Windows
+        cconv = 'x86_stdcallcc'
+    else:
+        # Default C calling convention
+        cconv = None
+
+    sig = templates.signature(cret, *cargs)
+    return types.ExternalFunctionPointer(sig, cconv=cconv,
+                                         get_pointer=get_pointer)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/dictdecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/dictdecl.py
new file mode 100644
index 000000000..73187be5d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/dictdecl.py
@@ -0,0 +1,29 @@
+"""
+This implements the typing template for `dict()`.
+"""
+
+from .. import types, errors
+from .templates import (
+    AbstractTemplate,
+    Registry,
+    signature,
+)
+
+registry = Registry()
+infer = registry.register
+infer_global = registry.register_global
+infer_getattr = registry.register_attr
+
+
+_message_dict_support = """
+Unsupported use of `dict()` with positional or keyword argument(s). \
+The only supported use is `dict()`.
+""".strip()
+
+
+@infer_global(dict)
+class DictBuiltin(AbstractTemplate):
+    def generic(self, args, kws):
+        if args or kws:
+            raise errors.TypingError(_message_dict_support)
+        return signature(types.DictType(types.undefined, types.undefined))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/enumdecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/enumdecl.py
new file mode 100644
index 000000000..ce3c4d6c9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/enumdecl.py
@@ -0,0 +1,64 @@
+"""
+Typing for enums.
+"""
+import operator
+from numba.core import types
+from numba.core.typing.templates import (AbstractTemplate, AttributeTemplate,
+                                         signature, Registry)
+
+registry = Registry()
+infer = registry.register
+infer_global = registry.register_global
+infer_getattr = registry.register_attr
+
+
+@infer_getattr
+class EnumAttribute(AttributeTemplate):
+    key = types.EnumMember
+
+    def resolve_value(self, ty):
+        return ty.dtype
+
+
+@infer_getattr
+class EnumClassAttribute(AttributeTemplate):
+    key = types.EnumClass
+
+    def generic_resolve(self, ty, attr):
+        """
+        Resolve attributes of an enum class as enum members.
+        """
+        if attr in ty.instance_class.__members__:
+            return ty.member_type
+
+
+@infer
+class EnumClassStaticGetItem(AbstractTemplate):
+    key = "static_getitem"
+
+    def generic(self, args, kws):
+        enum, idx = args
+        if (isinstance(enum, types.EnumClass)
+                and idx in enum.instance_class.__members__):
+            return signature(enum.member_type, *args)
+
+
+class EnumCompare(AbstractTemplate):
+
+    def generic(self, args, kws):
+        [lhs, rhs] = args
+        if (isinstance(lhs, types.EnumMember)
+                and isinstance(rhs, types.EnumMember)
+                and lhs == rhs):
+            return signature(types.boolean, lhs, rhs)
+
+
+@infer_global(operator.eq)
+class EnumEq(EnumCompare):
+    pass
+
+
+
+@infer_global(operator.ne)
+class EnumNe(EnumCompare):
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/listdecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/listdecl.py
new file mode 100644
index 000000000..9ceddcff6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/listdecl.py
@@ -0,0 +1,198 @@
+import operator
+from numba.core import types
+from .templates import (ConcreteTemplate, AbstractTemplate, AttributeTemplate,
+                        CallableTemplate,  Registry, signature, bound_function,
+                        make_callable_template)
+# Ensure list is typed as a collection as well
+from numba.core.typing import collections
+
+
+registry = Registry()
+infer = registry.register
+infer_global = registry.register_global
+infer_getattr = registry.register_attr
+
+
+@infer_global(list)
+class ListBuiltin(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        if args:
+            iterable, = args
+            if isinstance(iterable, types.IterableType):
+                dtype = iterable.iterator_type.yield_type
+                return signature(types.List(dtype), iterable)
+        else:
+            return signature(types.List(types.undefined))
+
+
+@infer_getattr
+class ListAttribute(AttributeTemplate):
+    key = types.List
+
+    # NOTE: some of these should be Sequence / MutableSequence methods
+
+    @bound_function("list.append")
+    def resolve_append(self, list, args, kws):
+        item, = args
+        assert not kws
+        unified = self.context.unify_pairs(list.dtype, item)
+        if unified is not None:
+            sig = signature(types.none, unified)
+            sig = sig.replace(recvr=list.copy(dtype=unified))
+            return sig
+
+    @bound_function("list.clear")
+    def resolve_clear(self, list, args, kws):
+        assert not args
+        assert not kws
+        return signature(types.none)
+
+    @bound_function("list.copy")
+    def resolve_copy(self, list, args, kws):
+        assert not args
+        assert not kws
+        return signature(list)
+
+    @bound_function("list.count")
+    def resolve_count(self, list, args, kws):
+        item, = args
+        assert not kws
+        return signature(types.intp, list.dtype)
+
+    @bound_function("list.extend")
+    def resolve_extend(self, list, args, kws):
+        iterable, = args
+        assert not kws
+        if not isinstance(iterable, types.IterableType):
+            return
+
+        dtype = iterable.iterator_type.yield_type
+        unified = self.context.unify_pairs(list.dtype, dtype)
+        if unified is not None:
+            sig = signature(types.none, iterable)
+            sig = sig.replace(recvr = list.copy(dtype=unified))
+            return sig
+
+    @bound_function("list.index")
+    def resolve_index(self, list, args, kws):
+        assert not kws
+        if len(args) == 1:
+            return signature(types.intp, list.dtype)
+        elif len(args) == 2:
+            if isinstance(args[1], types.Integer):
+                return signature(types.intp, list.dtype, types.intp)
+        elif len(args) == 3:
+            if (isinstance(args[1], types.Integer)
+                and isinstance(args[2], types.Integer)):
+                return signature(types.intp, list.dtype, types.intp, types.intp)
+
+    @bound_function("list.insert")
+    def resolve_insert(self, list, args, kws):
+        idx, item = args
+        assert not kws
+        if isinstance(idx, types.Integer):
+            unified = self.context.unify_pairs(list.dtype, item)
+            if unified is not None:
+                sig = signature(types.none, types.intp, unified)
+                sig = sig.replace(recvr = list.copy(dtype=unified))
+                return sig
+
+    @bound_function("list.pop")
+    def resolve_pop(self, list, args, kws):
+        assert not kws
+        if not args:
+            return signature(list.dtype)
+        else:
+            idx, = args
+            if isinstance(idx, types.Integer):
+                return signature(list.dtype, types.intp)
+
+    @bound_function("list.remove")
+    def resolve_remove(self, list, args, kws):
+        assert not kws
+        if len(args) == 1:
+            return signature(types.none, list.dtype)
+
+    @bound_function("list.reverse")
+    def resolve_reverse(self, list, args, kws):
+        assert not args
+        assert not kws
+        return signature(types.none)
+
+
+@infer_global(operator.add)
+class AddList(AbstractTemplate):
+
+    def generic(self, args, kws):
+        if len(args) == 2:
+            a, b = args
+            if isinstance(a, types.List) and isinstance(b, types.List):
+                unified = self.context.unify_pairs(a, b)
+                if unified is not None:
+                    return signature(unified, a, b)
+
+
+@infer_global(operator.iadd)
+class InplaceAddList(AbstractTemplate):
+
+    def generic(self, args, kws):
+        if len(args) == 2:
+            a, b = args
+            if isinstance(a, types.List) and isinstance(b, types.List):
+                if self.context.can_convert(b.dtype, a.dtype):
+                    return signature(a, a, b)
+
+
+@infer_global(operator.mul)
+class MulList(AbstractTemplate):
+    #key = operator.mul
+
+    def generic(self, args, kws):
+        a, b = args
+        if isinstance(a, types.List) and isinstance(b, types.Integer):
+            return signature(a, a, types.intp)
+        elif isinstance(a, types.Integer) and isinstance(b, types.List):
+            return signature(b, types.intp, b)
+
+
+@infer_global(operator.imul)
+class InplaceMulList(MulList): pass
+    #key = operator.imul
+
+
+class ListCompare(AbstractTemplate):
+
+    def generic(self, args, kws):
+        [lhs, rhs] = args
+        if isinstance(lhs, types.List) and isinstance(rhs, types.List):
+            # Check element-wise comparability
+            res = self.context.resolve_function_type(self.key,
+                                                     (lhs.dtype, rhs.dtype), {})
+            if res is not None:
+                return signature(types.boolean, lhs, rhs)
+
+@infer_global(operator.eq)
+class ListEq(ListCompare): pass
+    #key = operator.eq
+
+@infer_global(operator.ne)
+class ListNe(ListCompare): pass
+    #key = operator.ne
+
+@infer_global(operator.lt)
+class ListLt(ListCompare): pass
+    #key = operator.lt
+
+@infer_global(operator.le)
+class ListLe(ListCompare): pass
+    #key = operator.le
+
+@infer_global(operator.gt)
+class ListGt(ListCompare): pass
+    #key = operator.gt
+
+@infer_global(operator.ge)
+class ListGe(ListCompare): pass
+    #key = operator.ge
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/mathdecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/mathdecl.py
new file mode 100644
index 000000000..c788d6d99
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/mathdecl.py
@@ -0,0 +1,141 @@
+import math
+from numba.core import types, utils
+from numba.core.typing.templates import (AttributeTemplate, ConcreteTemplate,
+                                         signature, Registry)
+
+registry = Registry()
+infer_global = registry.register_global
+
+
+@infer_global(math.exp)
+@infer_global(math.expm1)
+@infer_global(math.fabs)
+@infer_global(math.sqrt)
+@infer_global(math.log)
+@infer_global(math.log1p)
+@infer_global(math.log10)
+@infer_global(math.sin)
+@infer_global(math.cos)
+@infer_global(math.tan)
+@infer_global(math.sinh)
+@infer_global(math.cosh)
+@infer_global(math.tanh)
+@infer_global(math.asin)
+@infer_global(math.acos)
+@infer_global(math.atan)
+@infer_global(math.asinh)
+@infer_global(math.acosh)
+@infer_global(math.atanh)
+@infer_global(math.degrees)
+@infer_global(math.radians)
+@infer_global(math.erf)
+@infer_global(math.erfc)
+@infer_global(math.gamma)
+@infer_global(math.lgamma)
+class Math_unary(ConcreteTemplate):
+    cases = [
+        signature(types.float64, types.int64),
+        signature(types.float64, types.uint64),
+        signature(types.float32, types.float32),
+        signature(types.float64, types.float64),
+    ]
+
+
+@infer_global(math.atan2)
+class Math_atan2(ConcreteTemplate):
+    cases = [
+        signature(types.float64, types.int64, types.int64),
+        signature(types.float64, types.uint64, types.uint64),
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+    ]
+
+
+@infer_global(math.trunc)
+class Math_converter(ConcreteTemplate):
+    cases = [
+        signature(types.intp, types.intp),
+        signature(types.int64, types.int64),
+        signature(types.uint64, types.uint64),
+        signature(types.int64, types.float32),
+        signature(types.int64, types.float64),
+    ]
+
+
+@infer_global(math.floor)
+@infer_global(math.ceil)
+class Math_floor_ceil(Math_converter):
+    pass
+
+
+@infer_global(math.copysign)
+class Math_copysign(ConcreteTemplate):
+    cases = [
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+    ]
+
+
+@infer_global(math.hypot)
+class Math_hypot(ConcreteTemplate):
+    cases = [
+        signature(types.float64, types.int64, types.int64),
+        signature(types.float64, types.uint64, types.uint64),
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+    ]
+
+
+@infer_global(math.isinf)
+@infer_global(math.isnan)
+class Math_predicate(ConcreteTemplate):
+    cases = [
+        signature(types.boolean, types.int64),
+        signature(types.boolean, types.uint64),
+        signature(types.boolean, types.float32),
+        signature(types.boolean, types.float64),
+    ]
+
+
+@infer_global(math.isfinite)
+class Math_isfinite(Math_predicate):
+    pass
+
+
+@infer_global(math.pow)
+class Math_pow(ConcreteTemplate):
+    cases = [
+        signature(types.float64, types.float64, types.int64),
+        signature(types.float64, types.float64, types.uint64),
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+    ]
+
+
+@infer_global(math.gcd)
+class Math_gcd(ConcreteTemplate):
+    cases = [
+        signature(types.int64, types.int64, types.int64),
+        signature(types.int32, types.int32, types.int32),
+        signature(types.int16, types.int16, types.int16),
+        signature(types.int8, types.int8, types.int8),
+        signature(types.uint64, types.uint64, types.uint64),
+        signature(types.uint32, types.uint32, types.uint32),
+        signature(types.uint16, types.uint16, types.uint16),
+        signature(types.uint8, types.uint8, types.uint8),
+    ]
+
+
+@infer_global(math.frexp)
+class Math_frexp(ConcreteTemplate):
+    cases = [
+        signature(types.Tuple((types.float64, types.intc)), types.float64),
+        signature(types.Tuple((types.float32, types.intc)), types.float32),
+    ]
+
+@infer_global(math.ldexp)
+class Math_ldexp(ConcreteTemplate):
+    cases = [
+        signature(types.float64, types.float64, types.intc),
+        signature(types.float32, types.float32, types.intc),
+    ]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/npdatetime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/npdatetime.py
new file mode 100644
index 000000000..4ab05f439
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/npdatetime.py
@@ -0,0 +1,285 @@
+"""
+Typing declarations for np.timedelta64.
+"""
+
+
+from itertools import product
+import operator
+
+from numba.core import types
+from numba.core.typing.templates import (AttributeTemplate, ConcreteTemplate,
+                                         AbstractTemplate, infer_global, infer,
+                                         infer_getattr, signature)
+from numba.np import npdatetime_helpers
+
+
+# timedelta64-only operations
+
+class TimedeltaUnaryOp(AbstractTemplate):
+
+    def generic(self, args, kws):
+        if len(args) == 2:
+            # Guard against binary + and -
+            return
+        op, = args
+        if not isinstance(op, types.NPTimedelta):
+            return
+        return signature(op, op)
+
+
+class TimedeltaBinOp(AbstractTemplate):
+
+    def generic(self, args, kws):
+        if len(args) == 1:
+            # Guard against unary + and -
+            return
+        left, right = args
+        if not all(isinstance(tp, types.NPTimedelta) for tp in args):
+            return
+        if npdatetime_helpers.can_cast_timedelta_units(left.unit, right.unit):
+            return signature(right, left, right)
+        elif npdatetime_helpers.can_cast_timedelta_units(right.unit, left.unit):
+            return signature(left, left, right)
+
+
+class TimedeltaCmpOp(AbstractTemplate):
+
+    def generic(self, args, kws):
+        # For equality comparisons, all units are inter-comparable
+        left, right = args
+        if not all(isinstance(tp, types.NPTimedelta) for tp in args):
+            return
+        return signature(types.boolean, left, right)
+
+
+class TimedeltaOrderedCmpOp(AbstractTemplate):
+
+    def generic(self, args, kws):
+        # For ordered comparisons, units must be compatible
+        left, right = args
+        if not all(isinstance(tp, types.NPTimedelta) for tp in args):
+            return
+        if (npdatetime_helpers.can_cast_timedelta_units(left.unit, right.unit) or
+            npdatetime_helpers.can_cast_timedelta_units(right.unit, left.unit)):
+            return signature(types.boolean, left, right)
+
+
+class TimedeltaMixOp(AbstractTemplate):
+
+    def generic(self, args, kws):
+        """
+        (timedelta64, {int, float}) -> timedelta64
+        ({int, float}, timedelta64) -> timedelta64
+        """
+        left, right = args
+        if isinstance(right, types.NPTimedelta):
+            td, other = right, left
+            sig_factory = lambda other: signature(td, other, td)
+        elif isinstance(left, types.NPTimedelta):
+            td, other = left, right
+            sig_factory = lambda other: signature(td, td, other)
+        else:
+            return
+        if not isinstance(other, (types.Float, types.Integer)):
+            return
+        # Force integer types to convert to signed because it matches
+        # timedelta64 semantics better.
+        if isinstance(other, types.Integer):
+            other = types.int64
+        return sig_factory(other)
+
+
+class TimedeltaDivOp(AbstractTemplate):
+
+    def generic(self, args, kws):
+        """
+        (timedelta64, {int, float}) -> timedelta64
+        (timedelta64, timedelta64) -> float
+        """
+        left, right = args
+        if not isinstance(left, types.NPTimedelta):
+            return
+        if isinstance(right, types.NPTimedelta):
+            if (npdatetime_helpers.can_cast_timedelta_units(left.unit, right.unit)
+                or npdatetime_helpers.can_cast_timedelta_units(right.unit, left.unit)):
+                return signature(types.float64, left, right)
+        elif isinstance(right, (types.Float)):
+            return signature(left, left, right)
+        elif isinstance(right, (types.Integer)):
+            # Force integer types to convert to signed because it matches
+            # timedelta64 semantics better.
+            return signature(left, left, types.int64)
+
+
+@infer_global(operator.pos)
+class TimedeltaUnaryPos(TimedeltaUnaryOp):
+    key = operator.pos
+
+@infer_global(operator.neg)
+class TimedeltaUnaryNeg(TimedeltaUnaryOp):
+    key = operator.neg
+
+@infer_global(operator.add)
+@infer_global(operator.iadd)
+class TimedeltaBinAdd(TimedeltaBinOp):
+    key = operator.add
+
+@infer_global(operator.sub)
+@infer_global(operator.isub)
+class TimedeltaBinSub(TimedeltaBinOp):
+    key = operator.sub
+
+@infer_global(operator.mul)
+@infer_global(operator.imul)
+class TimedeltaBinMult(TimedeltaMixOp):
+    key = operator.mul
+
+@infer_global(operator.truediv)
+@infer_global(operator.itruediv)
+class TimedeltaTrueDiv(TimedeltaDivOp):
+    key = operator.truediv
+
+@infer_global(operator.floordiv)
+@infer_global(operator.ifloordiv)
+class TimedeltaFloorDiv(TimedeltaDivOp):
+    key = operator.floordiv
+
+@infer_global(operator.eq)
+class TimedeltaCmpEq(TimedeltaCmpOp):
+    key = operator.eq
+
+@infer_global(operator.ne)
+class TimedeltaCmpNe(TimedeltaCmpOp):
+    key = operator.ne
+
+@infer_global(operator.lt)
+class TimedeltaCmpLt(TimedeltaOrderedCmpOp):
+    key = operator.lt
+
+@infer_global(operator.le)
+class TimedeltaCmpLE(TimedeltaOrderedCmpOp):
+    key = operator.le
+
+@infer_global(operator.gt)
+class TimedeltaCmpGt(TimedeltaOrderedCmpOp):
+    key = operator.gt
+
+@infer_global(operator.ge)
+class TimedeltaCmpGE(TimedeltaOrderedCmpOp):
+    key = operator.ge
+
+
+@infer_global(abs)
+class TimedeltaAbs(TimedeltaUnaryOp):
+    pass
+
+
+# datetime64 operations
+
+@infer_global(operator.add)
+@infer_global(operator.iadd)
+class DatetimePlusTimedelta(AbstractTemplate):
+    key = operator.add
+
+    def generic(self, args, kws):
+        if len(args) == 1:
+            # Guard against unary +
+            return
+        left, right = args
+        if isinstance(right, types.NPTimedelta):
+            dt = left
+            td = right
+        elif isinstance(left, types.NPTimedelta):
+            dt = right
+            td = left
+        else:
+            return
+        if isinstance(dt, types.NPDatetime):
+            unit = npdatetime_helpers.combine_datetime_timedelta_units(dt.unit,
+                                                                       td.unit)
+            if unit is not None:
+                return signature(types.NPDatetime(unit), left, right)
+
+@infer_global(operator.sub)
+@infer_global(operator.isub)
+class DatetimeMinusTimedelta(AbstractTemplate):
+    key = operator.sub
+
+    def generic(self, args, kws):
+        if len(args) == 1:
+            # Guard against unary -
+            return
+        dt, td = args
+        if isinstance(dt, types.NPDatetime) and isinstance(td,
+                                                           types.NPTimedelta):
+            unit = npdatetime_helpers.combine_datetime_timedelta_units(dt.unit,
+                                                                       td.unit)
+            if unit is not None:
+                return signature(types.NPDatetime(unit), dt, td)
+
+@infer_global(operator.sub)
+class DatetimeMinusDatetime(AbstractTemplate):
+    key = operator.sub
+
+    def generic(self, args, kws):
+        if len(args) == 1:
+            # Guard against unary -
+            return
+        left, right = args
+        if isinstance(left, types.NPDatetime) and isinstance(right,
+                                                             types.NPDatetime):
+            # All units compatible...
+            unit = npdatetime_helpers.get_best_unit(left.unit, right.unit)
+            return signature(types.NPTimedelta(unit), left, right)
+
+
+class DatetimeCmpOp(AbstractTemplate):
+
+    def generic(self, args, kws):
+        # For datetime64 comparisons, all units are inter-comparable
+        left, right = args
+        if not all(isinstance(tp, types.NPDatetime) for tp in args):
+            return
+        return signature(types.boolean, left, right)
+
+
+@infer_global(operator.eq)
+class DatetimeCmpEq(DatetimeCmpOp):
+    key = operator.eq
+
+@infer_global(operator.ne)
+class DatetimeCmpNe(DatetimeCmpOp):
+    key = operator.ne
+
+@infer_global(operator.lt)
+class DatetimeCmpLt(DatetimeCmpOp):
+    key = operator.lt
+
+@infer_global(operator.le)
+class DatetimeCmpLE(DatetimeCmpOp):
+    key = operator.le
+
+@infer_global(operator.gt)
+class DatetimeCmpGt(DatetimeCmpOp):
+    key = operator.gt
+
+@infer_global(operator.ge)
+class DatetimeCmpGE(DatetimeCmpOp):
+    key = operator.ge
+
+
+@infer_global(npdatetime_helpers.datetime_minimum)
+@infer_global(npdatetime_helpers.datetime_maximum)
+class DatetimeMinMax(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        assert len(args) == 2
+        error_msg = "DatetimeMinMax requires both arguments to be NPDatetime type or both arguments to be NPTimedelta types"
+        assert isinstance(args[0], (types.NPDatetime, types.NPTimedelta)), error_msg
+        if isinstance(args[0], types.NPDatetime):
+            if not isinstance(args[1], types.NPDatetime):
+                raise TypeError(error_msg)
+        else:
+            if not isinstance(args[1], types.NPTimedelta):
+                raise TypeError(error_msg)
+        return signature(args[0], *args)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/npydecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/npydecl.py
new file mode 100644
index 000000000..e3c612286
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/npydecl.py
@@ -0,0 +1,1239 @@
+import warnings
+
+import numpy as np
+import operator
+
+from numba.core import types, utils, config
+from numba.core.typing.templates import (AttributeTemplate, AbstractTemplate,
+                                         CallableTemplate, Registry, signature)
+
+from numba.np.numpy_support import (ufunc_find_matching_loop,
+                             supported_ufunc_loop, as_dtype,
+                             from_dtype, as_dtype, resolve_output_type,
+                             carray, farray, _ufunc_loop_sig)
+from numba.core.errors import (TypingError, NumbaPerformanceWarning,
+                               NumbaTypeError, NumbaAssertionError)
+from numba import pndindex
+from numba.core.overload_glue import glue_typing
+
+registry = Registry()
+infer = registry.register
+infer_global = registry.register_global
+infer_getattr = registry.register_attr
+
+
+class Numpy_rules_ufunc(AbstractTemplate):
+    @classmethod
+    def _handle_inputs(cls, ufunc, args, kws):
+        """
+        Process argument types to a given *ufunc*.
+        Returns a (base types, explicit outputs, ndims, layout) tuple where:
+        - `base types` is a tuple of scalar types for each input
+        - `explicit outputs` is a tuple of explicit output types (arrays)
+        - `ndims` is the number of dimensions of the loop and also of
+          any outputs, explicit or implicit
+        - `layout` is the layout for any implicit output to be allocated
+        """
+        nin = ufunc.nin
+        nout = ufunc.nout
+        nargs = ufunc.nargs
+
+        # preconditions
+        assert nargs == nin + nout
+
+        if len(args) < nin:
+            msg = "ufunc '{0}': not enough arguments ({1} found, {2} required)"
+            raise TypingError(msg=msg.format(ufunc.__name__, len(args), nin))
+
+        if len(args) > nargs:
+            msg = "ufunc '{0}': too many arguments ({1} found, {2} maximum)"
+            raise TypingError(msg=msg.format(ufunc.__name__, len(args), nargs))
+
+        args = [a.as_array if isinstance(a, types.ArrayCompatible) else a
+                for a in args]
+        arg_ndims = [a.ndim if isinstance(a, types.ArrayCompatible) else 0
+                     for a in args]
+        ndims = max(arg_ndims)
+
+        # explicit outputs must be arrays (no explicit scalar return values supported)
+        explicit_outputs = args[nin:]
+
+        # all the explicit outputs must match the number max number of dimensions
+        if not all(d == ndims for d in arg_ndims[nin:]):
+            msg = "ufunc '{0}' called with unsuitable explicit output arrays."
+            raise TypingError(msg=msg.format(ufunc.__name__))
+
+        if not all(isinstance(output, types.ArrayCompatible)
+                   for output in explicit_outputs):
+            msg = "ufunc '{0}' called with an explicit output that is not an array"
+            raise TypingError(msg=msg.format(ufunc.__name__))
+
+        if not all(output.mutable for output in explicit_outputs):
+            msg = "ufunc '{0}' called with an explicit output that is read-only"
+            raise TypingError(msg=msg.format(ufunc.__name__))
+
+        # find the kernel to use, based only in the input types (as does NumPy)
+        base_types = [x.dtype if isinstance(x, types.ArrayCompatible) else x
+                      for x in args]
+
+        # Figure out the output array layout, if needed.
+        layout = None
+        if ndims > 0 and (len(explicit_outputs) < ufunc.nout):
+            layout = 'C'
+            layouts = [x.layout if isinstance(x, types.ArrayCompatible) else ''
+                       for x in args]
+
+            # Prefer C contig if any array is C contig.
+            # Next, prefer F contig.
+            # Defaults to C contig if not layouts are C/F.
+            if 'C' not in layouts and 'F' in layouts:
+                layout = 'F'
+
+        return base_types, explicit_outputs, ndims, layout
+
+    @property
+    def ufunc(self):
+        return self.key
+
+    def generic(self, args, kws):
+        # First, strip optional types, ufunc loops are typed on concrete types
+        args = [x.type if isinstance(x, types.Optional) else x for x in args]
+
+        ufunc = self.ufunc
+        base_types, explicit_outputs, ndims, layout = self._handle_inputs(
+            ufunc, args, kws)
+        ufunc_loop = ufunc_find_matching_loop(ufunc, base_types)
+        if ufunc_loop is None:
+            raise TypingError("can't resolve ufunc {0} for types {1}".format(ufunc.__name__, args))
+
+        # check if all the types involved in the ufunc loop are supported in this mode
+        if not supported_ufunc_loop(ufunc, ufunc_loop):
+            msg = "ufunc '{0}' using the loop '{1}' not supported in this mode"
+            raise TypingError(msg=msg.format(ufunc.__name__, ufunc_loop.ufunc_sig))
+
+        # if there is any explicit output type, check that it is valid
+        explicit_outputs_np = [as_dtype(tp.dtype) for tp in explicit_outputs]
+
+        # Numpy will happily use unsafe conversions (although it will actually warn)
+        if not all (np.can_cast(fromty, toty, 'unsafe') for (fromty, toty) in
+                    zip(ufunc_loop.numpy_outputs, explicit_outputs_np)):
+            msg = "ufunc '{0}' can't cast result to explicit result type"
+            raise TypingError(msg=msg.format(ufunc.__name__))
+
+        # A valid loop was found that is compatible. The result of type inference should
+        # be based on the explicit output types, and when not available with the type given
+        # by the selected NumPy loop
+        out = list(explicit_outputs)
+        implicit_output_count = ufunc.nout - len(explicit_outputs)
+        if implicit_output_count > 0:
+            # XXX this is sometimes wrong for datetime64 and timedelta64,
+            # as ufunc_find_matching_loop() doesn't do any type inference
+            ret_tys = ufunc_loop.outputs[-implicit_output_count:]
+            if ndims > 0:
+                assert layout is not None
+                # If either of the types involved in the ufunc operation have a
+                # __array_ufunc__ method then invoke the first such one to
+                # determine the output type of the ufunc.
+                array_ufunc_type = None
+                for a in args:
+                    if hasattr(a, "__array_ufunc__"):
+                        array_ufunc_type = a
+                        break
+                output_type = types.Array
+                if array_ufunc_type is not None:
+                    output_type = array_ufunc_type.__array_ufunc__(ufunc, "__call__", *args, **kws)
+                    if output_type is NotImplemented:
+                        msg = (f"unsupported use of ufunc {ufunc} on "
+                               f"{array_ufunc_type}")
+                        # raise TypeError here because
+                        # NumpyRulesArrayOperator.generic is capturing
+                        # TypingError
+                        raise NumbaTypeError(msg)
+                    elif not issubclass(output_type, types.Array):
+                        msg = (f"ufunc {ufunc} on {array_ufunc_type}"
+                               f"cannot return non-array {output_type}")
+                        # raise TypeError here because
+                        # NumpyRulesArrayOperator.generic is capturing
+                        # TypingError
+                        raise TypeError(msg)
+
+                ret_tys = [output_type(dtype=ret_ty, ndim=ndims, layout=layout)
+                           for ret_ty in ret_tys]
+                ret_tys = [resolve_output_type(self.context, args, ret_ty)
+                           for ret_ty in ret_tys]
+            out.extend(ret_tys)
+
+        return _ufunc_loop_sig(out, args)
+
+
+class NumpyRulesArrayOperator(Numpy_rules_ufunc):
+    _op_map = {
+        operator.add: "add",
+        operator.sub: "subtract",
+        operator.mul: "multiply",
+        operator.truediv: "true_divide",
+        operator.floordiv: "floor_divide",
+        operator.mod: "remainder",
+        operator.pow: "power",
+        operator.lshift: "left_shift",
+        operator.rshift: "right_shift",
+        operator.and_: "bitwise_and",
+        operator.or_: "bitwise_or",
+        operator.xor: "bitwise_xor",
+        operator.eq: "equal",
+        operator.gt: "greater",
+        operator.ge: "greater_equal",
+        operator.lt: "less",
+        operator.le: "less_equal",
+        operator.ne: "not_equal",
+    }
+
+    @property
+    def ufunc(self):
+        return getattr(np, self._op_map[self.key])
+
+    @classmethod
+    def install_operations(cls):
+        for op, ufunc_name in cls._op_map.items():
+            infer_global(op)(
+                type("NumpyRulesArrayOperator_" + ufunc_name, (cls,), dict(key=op))
+            )
+
+    def generic(self, args, kws):
+        '''Overloads and calls base class generic() method, returning
+        None if a TypingError occurred.
+
+        Returning None for operators is important since operators are
+        heavily overloaded, and by suppressing type errors, we allow
+        type inference to check other possibilities before giving up
+        (particularly user-defined operators).
+        '''
+        try:
+            sig = super(NumpyRulesArrayOperator, self).generic(args, kws)
+        except TypingError:
+            return None
+        if sig is None:
+            return None
+        args = sig.args
+        # Only accept at least one array argument, otherwise the operator
+        # doesn't involve Numpy's ufunc machinery.
+        if not any(isinstance(arg, types.ArrayCompatible)
+                   for arg in args):
+            return None
+        return sig
+
+
+_binop_map = NumpyRulesArrayOperator._op_map
+
+class NumpyRulesInplaceArrayOperator(NumpyRulesArrayOperator):
+    _op_map = {
+        operator.iadd: "add",
+        operator.isub: "subtract",
+        operator.imul: "multiply",
+        operator.itruediv: "true_divide",
+        operator.ifloordiv: "floor_divide",
+        operator.imod: "remainder",
+        operator.ipow: "power",
+        operator.ilshift: "left_shift",
+        operator.irshift: "right_shift",
+        operator.iand: "bitwise_and",
+        operator.ior: "bitwise_or",
+        operator.ixor: "bitwise_xor",
+    }
+
+    def generic(self, args, kws):
+        # Type the inplace operator as if an explicit output was passed,
+        # to handle type resolution correctly.
+        # (for example int8[:] += int16[:] should use an int8[:] output,
+        #  not int16[:])
+        lhs, rhs = args
+        if not isinstance(lhs, types.ArrayCompatible):
+            return
+        args = args + (lhs,)
+        sig = super(NumpyRulesInplaceArrayOperator, self).generic(args, kws)
+        # Strip off the fake explicit output
+        assert len(sig.args) == 3
+        real_sig = signature(sig.return_type, *sig.args[:2])
+        return real_sig
+
+
+class NumpyRulesUnaryArrayOperator(NumpyRulesArrayOperator):
+    _op_map = {
+        operator.pos: "positive",
+        operator.neg: "negative",
+        operator.invert: "invert",
+    }
+
+    def generic(self, args, kws):
+        assert not kws
+        if len(args) == 1 and isinstance(args[0], types.ArrayCompatible):
+            return super(NumpyRulesUnaryArrayOperator, self).generic(args, kws)
+
+
+# list of unary ufuncs to register
+
+_math_operations = [ "add", "subtract", "multiply",
+                     "logaddexp", "logaddexp2", "true_divide",
+                     "floor_divide", "negative", "positive", "power",
+                     "float_power", "remainder", "fmod", "absolute",
+                     "rint", "sign", "conjugate", "exp", "exp2",
+                     "log", "log2", "log10", "expm1", "log1p",
+                     "sqrt", "square", "cbrt", "reciprocal",
+                     "divide", "mod", "divmod", "abs", "fabs" , "gcd", "lcm"]
+
+_trigonometric_functions = [ "sin", "cos", "tan", "arcsin",
+                             "arccos", "arctan", "arctan2",
+                             "hypot", "sinh", "cosh", "tanh",
+                             "arcsinh", "arccosh", "arctanh",
+                             "deg2rad", "rad2deg", "degrees",
+                             "radians" ]
+
+_bit_twiddling_functions = ["bitwise_and", "bitwise_or",
+                            "bitwise_xor", "invert",
+                            "left_shift", "right_shift",
+                            "bitwise_not" ]
+
+_comparison_functions = [ "greater", "greater_equal", "less",
+                          "less_equal", "not_equal", "equal",
+                          "logical_and", "logical_or",
+                          "logical_xor", "logical_not",
+                          "maximum", "minimum", "fmax", "fmin" ]
+
+_floating_functions = [ "isfinite", "isinf", "isnan", "signbit",
+                        "copysign", "nextafter", "modf", "ldexp",
+                        "frexp", "floor", "ceil", "trunc",
+                        "spacing" ]
+
+_logic_functions = [ "isnat" ]
+
+
+# This is a set of the ufuncs that are not yet supported by Lowering. In order
+# to trigger no-python mode we must not register them until their Lowering is
+# implemented.
+#
+# It also works as a nice TODO list for ufunc support :)
+_unsupported = set([ 'frexp',
+                     'modf',
+                 ])
+
+# A list of ufuncs that are in fact aliases of other ufuncs. They need to insert the
+# resolve method, but not register the ufunc itself
+_aliases = set(["bitwise_not", "mod", "abs"])
+
+# In python3 np.divide is mapped to np.true_divide
+if np.divide == np.true_divide:
+    _aliases.add("divide")
+
+def _numpy_ufunc(name):
+    func = getattr(np, name)
+    class typing_class(Numpy_rules_ufunc):
+        key = func
+
+    typing_class.__name__ = "resolve_{0}".format(name)
+
+    if not name in _aliases:
+        infer_global(func, types.Function(typing_class))
+
+all_ufuncs = sum([_math_operations, _trigonometric_functions,
+                  _bit_twiddling_functions, _comparison_functions,
+                  _floating_functions, _logic_functions], [])
+
+supported_ufuncs = [x for x in all_ufuncs if x not in _unsupported]
+
+for func in supported_ufuncs:
+    _numpy_ufunc(func)
+
+all_ufuncs = [getattr(np, name) for name in all_ufuncs]
+supported_ufuncs = [getattr(np, name) for name in supported_ufuncs]
+
+NumpyRulesUnaryArrayOperator.install_operations()
+NumpyRulesArrayOperator.install_operations()
+NumpyRulesInplaceArrayOperator.install_operations()
+
+supported_array_operators = set(
+    NumpyRulesUnaryArrayOperator._op_map.keys()
+).union(
+    NumpyRulesArrayOperator._op_map.keys()
+).union(
+    NumpyRulesInplaceArrayOperator._op_map.keys()
+)
+
+del _math_operations, _trigonometric_functions, _bit_twiddling_functions
+del _comparison_functions, _floating_functions, _unsupported
+del _aliases, _numpy_ufunc
+
+
+# -----------------------------------------------------------------------------
+# Install global helpers for array methods.
+
+class Numpy_method_redirection(AbstractTemplate):
+    """
+    A template redirecting a Numpy global function (e.g. np.sum) to an
+    array method of the same name (e.g. ndarray.sum).
+    """
+
+    # Arguments like *axis* can specialize on literals but also support
+    # non-literals
+    prefer_literal = True
+
+    def generic(self, args, kws):
+        pysig = None
+        if kws:
+            if self.method_name == 'sum':
+                if 'axis' in kws and 'dtype' not in kws:
+                    def sum_stub(arr, axis):
+                        pass
+                    pysig = utils.pysignature(sum_stub)
+                elif 'dtype' in kws and 'axis' not in kws:
+                    def sum_stub(arr, dtype):
+                        pass
+                    pysig = utils.pysignature(sum_stub)
+                elif 'dtype' in kws and 'axis' in kws:
+                    def sum_stub(arr, axis, dtype):
+                        pass
+                    pysig = utils.pysignature(sum_stub)
+            elif self.method_name == 'argsort':
+                def argsort_stub(arr, kind='quicksort'):
+                    pass
+                pysig = utils.pysignature(argsort_stub)
+            else:
+                fmt = "numba doesn't support kwarg for {}"
+                raise TypingError(fmt.format(self.method_name))
+
+        arr = args[0]
+        # This will return a BoundFunction
+        meth_ty = self.context.resolve_getattr(arr, self.method_name)
+        # Resolve arguments on the bound function
+        meth_sig = self.context.resolve_function_type(meth_ty, args[1:], kws)
+        if meth_sig is not None:
+            return meth_sig.as_function().replace(pysig=pysig)
+
+
+# Function to glue attributes onto the numpy-esque object
+def _numpy_redirect(fname):
+    numpy_function = getattr(np, fname)
+    cls = type("Numpy_redirect_{0}".format(fname), (Numpy_method_redirection,),
+               dict(key=numpy_function, method_name=fname))
+    infer_global(numpy_function, types.Function(cls))
+
+for func in ['min', 'max', 'sum', 'prod', 'mean', 'var', 'std',
+             'cumsum', 'cumprod', 'argsort', 'nonzero', 'ravel']:
+    _numpy_redirect(func)
+
+
+# -----------------------------------------------------------------------------
+# Numpy scalar constructors
+
+# Register np.int8, etc. as converters to the equivalent Numba types
+np_types = set(getattr(np, str(nb_type)) for nb_type in types.number_domain)
+np_types.add(np.bool_)
+# Those may or may not be aliases (depending on the Numpy build / version)
+np_types.add(np.intc)
+np_types.add(np.intp)
+np_types.add(np.uintc)
+np_types.add(np.uintp)
+
+
+def register_number_classes(register_global):
+    for np_type in np_types:
+        nb_type = getattr(types, np_type.__name__)
+
+        register_global(np_type, types.NumberClass(nb_type))
+
+
+register_number_classes(infer_global)
+
+
+# -----------------------------------------------------------------------------
+# Numpy array constructors
+
+def parse_shape(shape):
+    """
+    Given a shape, return the number of dimensions.
+    """
+    ndim = None
+    if isinstance(shape, types.Integer):
+        ndim = 1
+    elif isinstance(shape, (types.Tuple, types.UniTuple)):
+        int_tys = (types.Integer, types.IntEnumMember)
+        if all(isinstance(s, int_tys) for s in shape):
+            ndim = len(shape)
+    return ndim
+
+def parse_dtype(dtype):
+    """
+    Return the dtype of a type, if it is either a DtypeSpec (used for most
+    dtypes) or a TypeRef (used for record types).
+    """
+    if isinstance(dtype, types.DTypeSpec):
+        return dtype.dtype
+    elif isinstance(dtype, types.TypeRef):
+        return dtype.instance_type
+    elif isinstance(dtype, types.StringLiteral):
+        dtstr = dtype.literal_value
+        try:
+            dt = np.dtype(dtstr)
+        except TypeError:
+            msg = f"Invalid NumPy dtype specified: '{dtstr}'"
+            raise TypingError(msg)
+        return from_dtype(dt)
+
+def _parse_nested_sequence(context, typ):
+    """
+    Parse a (possibly 0d) nested sequence type.
+    A (ndim, dtype) tuple is returned.  Note the sequence may still be
+    heterogeneous, as long as it converts to the given dtype.
+    """
+    if isinstance(typ, (types.Buffer,)):
+        raise TypingError("%r not allowed in a homogeneous sequence" % typ)
+    elif isinstance(typ, (types.Sequence,)):
+        n, dtype = _parse_nested_sequence(context, typ.dtype)
+        return n + 1, dtype
+    elif isinstance(typ, (types.BaseTuple,)):
+        if typ.count == 0:
+            # Mimic Numpy's behaviour
+            return 1, types.float64
+        n, dtype = _parse_nested_sequence(context, typ[0])
+        dtypes = [dtype]
+        for i in range(1, typ.count):
+            _n, dtype = _parse_nested_sequence(context, typ[i])
+            if _n != n:
+                raise TypingError("type %r does not have a regular shape"
+                                  % (typ,))
+            dtypes.append(dtype)
+        dtype = context.unify_types(*dtypes)
+        if dtype is None:
+            raise TypingError("cannot convert %r to a homogeneous type" % typ)
+        return n + 1, dtype
+    else:
+        # Scalar type => check it's valid as a Numpy array dtype
+        as_dtype(typ)
+        return 0, typ
+
+
+@glue_typing(np.array)
+class NpArray(CallableTemplate):
+    """
+    Typing template for np.array().
+    """
+
+    def generic(self):
+        def typer(object, dtype=None):
+            ndim, seq_dtype = _parse_nested_sequence(self.context, object)
+            if dtype is None:
+                dtype = seq_dtype
+            else:
+                dtype = parse_dtype(dtype)
+                if dtype is None:
+                    return
+            return types.Array(dtype, ndim, 'C')
+
+        return typer
+
+@glue_typing(np.full)
+class NdFull(CallableTemplate):
+
+    def generic(self):
+        def typer(shape, fill_value, dtype=None):
+            if dtype is None:
+                nb_dtype = fill_value
+            else:
+                nb_dtype = parse_dtype(dtype)
+
+            ndim = parse_shape(shape)
+            if nb_dtype is not None and ndim is not None:
+                return types.Array(dtype=nb_dtype, ndim=ndim, layout='C')
+
+        return typer
+
+@glue_typing(np.full_like)
+class NdFullLike(CallableTemplate):
+
+    def generic(self):
+        """
+        np.full_like(array, val) -> array of the same shape and layout
+        np.full_like(scalar, val) -> 0-d array of the scalar type
+        """
+        def typer(arg, fill_value, dtype=None):
+            if dtype is not None:
+                nb_dtype = parse_dtype(dtype)
+            elif isinstance(arg, types.Array):
+                nb_dtype = arg.dtype
+            else:
+                nb_dtype = arg
+            if nb_dtype is not None:
+                if isinstance(arg, types.Array):
+                    return arg.copy(dtype=nb_dtype, readonly=False)
+                else:
+                    return types.Array(dtype=nb_dtype, ndim=0, layout='C')
+
+        return typer
+
+
+@glue_typing(np.identity)
+class NdIdentity(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        n = args[0]
+        if not isinstance(n, types.Integer):
+            return
+        if len(args) >= 2:
+            nb_dtype = parse_dtype(args[1])
+        else:
+            nb_dtype = types.float64
+
+        if nb_dtype is not None:
+            return_type = types.Array(ndim=2, dtype=nb_dtype, layout='C')
+            return signature(return_type, *args)
+
+
+def _infer_dtype_from_inputs(inputs):
+    return dtype
+
+
+@glue_typing(np.linspace)
+class NdLinspace(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        bounds = args[:2]
+        if not all(isinstance(arg, types.Number) for arg in bounds):
+            return
+        if len(args) >= 3:
+            num = args[2]
+            if not isinstance(num, types.Integer):
+                return
+        if len(args) >= 4:
+            # Not supporting the other arguments as it would require
+            # keyword arguments for reasonable use.
+            return
+        if any(isinstance(arg, types.Complex) for arg in bounds):
+            dtype = types.complex128
+        else:
+            dtype = types.float64
+        return_type = types.Array(ndim=1, dtype=dtype, layout='C')
+        return signature(return_type, *args)
+
+
+@glue_typing(np.frombuffer)
+class NdFromBuffer(CallableTemplate):
+
+    def generic(self):
+        def typer(buffer, dtype=None):
+            if not isinstance(buffer, types.Buffer) or buffer.layout != 'C':
+                return
+            if dtype is None:
+                nb_dtype = types.float64
+            else:
+                nb_dtype = parse_dtype(dtype)
+
+            if nb_dtype is not None:
+                return types.Array(dtype=nb_dtype, ndim=1, layout='C',
+                                   readonly=not buffer.mutable)
+
+        return typer
+
+
+@glue_typing(np.sort)
+class NdSort(CallableTemplate):
+
+    def generic(self):
+        def typer(a):
+            if isinstance(a, types.Array):
+                return a
+
+        return typer
+
+
+@glue_typing(np.asfortranarray)
+class AsFortranArray(CallableTemplate):
+
+    def generic(self):
+        def typer(a):
+            if isinstance(a, types.Array):
+                return a.copy(layout='F', ndim=max(a.ndim, 1))
+
+        return typer
+
+
+@glue_typing(np.ascontiguousarray)
+class AsContiguousArray(CallableTemplate):
+
+    def generic(self):
+        def typer(a):
+            if isinstance(a, types.Array):
+                return a.copy(layout='C', ndim=max(a.ndim, 1))
+
+        return typer
+
+
+@glue_typing(np.copy)
+class NdCopy(CallableTemplate):
+
+    def generic(self):
+        def typer(a):
+            if isinstance(a, types.Array):
+                layout = 'F' if a.layout == 'F' else 'C'
+                return a.copy(layout=layout, readonly=False)
+
+        return typer
+
+
+@glue_typing(np.expand_dims)
+class NdExpandDims(CallableTemplate):
+
+    def generic(self):
+        def typer(a, axis):
+            if (not isinstance(a, types.Array)
+                or not isinstance(axis, types.Integer)):
+                return
+
+            layout = a.layout if a.ndim <= 1 else 'A'
+            return a.copy(ndim=a.ndim + 1, layout=layout)
+
+        return typer
+
+
+class BaseAtLeastNdTemplate(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        if not args or not all(isinstance(a, types.Array) for a in args):
+            return
+
+        rets = [self.convert_array(a) for a in args]
+        if len(rets) > 1:
+            retty = types.BaseTuple.from_types(rets)
+        else:
+            retty = rets[0]
+        return signature(retty, *args)
+
+
+@glue_typing(np.atleast_1d)
+class NdAtLeast1d(BaseAtLeastNdTemplate):
+
+    def convert_array(self, a):
+        return a.copy(ndim=max(a.ndim, 1))
+
+
+@glue_typing(np.atleast_2d)
+class NdAtLeast2d(BaseAtLeastNdTemplate):
+
+    def convert_array(self, a):
+        return a.copy(ndim=max(a.ndim, 2))
+
+
+@glue_typing(np.atleast_3d)
+class NdAtLeast3d(BaseAtLeastNdTemplate):
+
+    def convert_array(self, a):
+        return a.copy(ndim=max(a.ndim, 3))
+
+
+def _homogeneous_dims(context, func_name, arrays):
+    ndim = arrays[0].ndim
+    for a in arrays:
+        if a.ndim != ndim:
+            msg = (f"{func_name}(): all the input arrays must have same number "
+                   "of dimensions")
+            raise NumbaTypeError(msg)
+    return ndim
+
+def _sequence_of_arrays(context, func_name, arrays,
+                        dim_chooser=_homogeneous_dims):
+    if (not isinstance(arrays, types.BaseTuple)
+        or not len(arrays)
+        or not all(isinstance(a, types.Array) for a in arrays)):
+        raise TypeError("%s(): expecting a non-empty tuple of arrays, "
+                        "got %s" % (func_name, arrays))
+
+    ndim = dim_chooser(context, func_name, arrays)
+
+    dtype = context.unify_types(*(a.dtype for a in arrays))
+    if dtype is None:
+        raise TypeError("%s(): input arrays must have "
+                        "compatible dtypes" % func_name)
+
+    return dtype, ndim
+
+def _choose_concatenation_layout(arrays):
+    # Only create a F array if all input arrays have F layout.
+    # This is a simplified version of Numpy's behaviour,
+    # while Numpy's actually processes the input strides to
+    # decide on optimal output strides
+    # (see PyArray_CreateMultiSortedStridePerm()).
+    return 'F' if all(a.layout == 'F' for a in arrays) else 'C'
+
+
+@glue_typing(np.concatenate)
+class NdConcatenate(CallableTemplate):
+
+    def generic(self):
+        def typer(arrays, axis=None):
+            if axis is not None and not isinstance(axis, types.Integer):
+                # Note Numpy allows axis=None, but it isn't documented:
+                # https://github.com/numpy/numpy/issues/7968
+                return
+
+            dtype, ndim = _sequence_of_arrays(self.context,
+                                              "np.concatenate", arrays)
+            if ndim == 0:
+                raise TypeError("zero-dimensional arrays cannot be concatenated")
+
+            layout = _choose_concatenation_layout(arrays)
+
+            return types.Array(dtype, ndim, layout)
+
+        return typer
+
+
+@glue_typing(np.stack)
+class NdStack(CallableTemplate):
+
+    def generic(self):
+        def typer(arrays, axis=None):
+            if axis is not None and not isinstance(axis, types.Integer):
+                # Note Numpy allows axis=None, but it isn't documented:
+                # https://github.com/numpy/numpy/issues/7968
+                return
+
+            dtype, ndim = _sequence_of_arrays(self.context,
+                                                "np.stack", arrays)
+
+            # This diverges from Numpy's behaviour, which simply inserts
+            # a new stride at the requested axis (therefore can return
+            # a 'A' array).
+            layout = 'F' if all(a.layout == 'F' for a in arrays) else 'C'
+
+            return types.Array(dtype, ndim + 1, layout)
+
+        return typer
+
+
+class BaseStackTemplate(CallableTemplate):
+
+    def generic(self):
+        def typer(arrays):
+            dtype, ndim = _sequence_of_arrays(self.context,
+                                              self.func_name, arrays)
+
+            ndim = max(ndim, self.ndim_min)
+            layout = _choose_concatenation_layout(arrays)
+
+            return types.Array(dtype, ndim, layout)
+
+        return typer
+
+
+@glue_typing(np.hstack)
+class NdStack(BaseStackTemplate):
+    func_name = "np.hstack"
+    ndim_min = 1
+
+@glue_typing(np.vstack)
+class NdStack(BaseStackTemplate):
+    func_name = "np.vstack"
+    ndim_min = 2
+
+@glue_typing(np.dstack)
+class NdStack(BaseStackTemplate):
+    func_name = "np.dstack"
+    ndim_min = 3
+
+
+
+def _column_stack_dims(context, func_name, arrays):
+    # column_stack() allows stacking 1-d and 2-d arrays together
+    for a in arrays:
+        if a.ndim < 1 or a.ndim > 2:
+            raise TypeError("np.column_stack() is only defined on "
+                            "1-d and 2-d arrays")
+    return 2
+
+
+@glue_typing(np.column_stack)
+class NdColumnStack(CallableTemplate):
+
+    def generic(self):
+        def typer(arrays):
+            dtype, ndim = _sequence_of_arrays(self.context,
+                                              "np.column_stack", arrays,
+                                              dim_chooser=_column_stack_dims)
+
+            layout = _choose_concatenation_layout(arrays)
+
+            return types.Array(dtype, ndim, layout)
+
+        return typer
+
+
+# -----------------------------------------------------------------------------
+# Linear algebra
+
+
+class MatMulTyperMixin(object):
+
+    def matmul_typer(self, a, b, out=None):
+        """
+        Typer function for Numpy matrix multiplication.
+        """
+        if not isinstance(a, types.Array) or not isinstance(b, types.Array):
+            return
+        if not all(x.ndim in (1, 2) for x in (a, b)):
+            raise TypingError("%s only supported on 1-D and 2-D arrays"
+                              % (self.func_name, ))
+        # Output dimensionality
+        ndims = set([a.ndim, b.ndim])
+        if ndims == set([2]):
+            # M * M
+            out_ndim = 2
+        elif ndims == set([1, 2]):
+            # M* V and V * M
+            out_ndim = 1
+        elif ndims == set([1]):
+            # V * V
+            out_ndim = 0
+
+        if out is not None:
+            if out_ndim == 0:
+                raise TypeError("explicit output unsupported for vector * vector")
+            elif out.ndim != out_ndim:
+                raise TypeError("explicit output has incorrect dimensionality")
+            if not isinstance(out, types.Array) or out.layout != 'C':
+                raise TypeError("output must be a C-contiguous array")
+            all_args = (a, b, out)
+        else:
+            all_args = (a, b)
+
+        if not (config.DISABLE_PERFORMANCE_WARNINGS or
+                all(x.layout in 'CF' for x in (a, b))):
+            msg = ("%s is faster on contiguous arrays, called on %s" %
+                   (self.func_name, (a, b)))
+            warnings.warn(NumbaPerformanceWarning(msg))
+        if not all(x.dtype == a.dtype for x in all_args):
+            raise TypingError("%s arguments must all have "
+                              "the same dtype" % (self.func_name,))
+        if not isinstance(a.dtype, (types.Float, types.Complex)):
+            raise TypingError("%s only supported on "
+                              "float and complex arrays"
+                              % (self.func_name,))
+        if out:
+            return out
+        elif out_ndim > 0:
+            return types.Array(a.dtype, out_ndim, 'C')
+        else:
+            return a.dtype
+
+
+@glue_typing(np.dot)
+class Dot(MatMulTyperMixin, CallableTemplate):
+    func_name = "np.dot()"
+
+    def generic(self):
+        def typer(a, b, out=None):
+            # NOTE: np.dot() and the '@' operator have distinct semantics
+            # for >2-D arrays, but we don't support them.
+            return self.matmul_typer(a, b, out)
+
+        return typer
+
+
+@glue_typing(np.vdot)
+class VDot(CallableTemplate):
+
+    def generic(self):
+        def typer(a, b):
+            if not isinstance(a, types.Array) or not isinstance(b, types.Array):
+                return
+            if not all(x.ndim == 1 for x in (a, b)):
+                raise TypingError("np.vdot() only supported on 1-D arrays")
+            if not all(x.layout in 'CF' for x in (a, b)):
+                warnings.warn("np.vdot() is faster on contiguous arrays, called on %s"
+                              % ((a, b),), NumbaPerformanceWarning)
+            if not all(x.dtype == a.dtype for x in (a, b)):
+                raise TypingError("np.vdot() arguments must all have "
+                                  "the same dtype")
+            if not isinstance(a.dtype, (types.Float, types.Complex)):
+                raise TypingError("np.vdot() only supported on "
+                                  "float and complex arrays")
+            return a.dtype
+
+        return typer
+
+
+@infer_global(operator.matmul)
+class MatMul(MatMulTyperMixin, AbstractTemplate):
+    key = operator.matmul
+    func_name = "'@'"
+
+    def generic(self, args, kws):
+        assert not kws
+        restype = self.matmul_typer(*args)
+        if restype is not None:
+            return signature(restype, *args)
+
+
+def _check_linalg_matrix(a, func_name):
+    if not isinstance(a, types.Array):
+        return
+    if not a.ndim == 2:
+        raise TypingError("np.linalg.%s() only supported on 2-D arrays"
+                          % func_name)
+    if not isinstance(a.dtype, (types.Float, types.Complex)):
+        raise TypingError("np.linalg.%s() only supported on "
+                          "float and complex arrays" % func_name)
+
+# -----------------------------------------------------------------------------
+# Miscellaneous functions
+
+@infer_global(np.ndenumerate)
+class NdEnumerate(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        arr, = args
+
+        if isinstance(arr, types.Array):
+            enumerate_type = types.NumpyNdEnumerateType(arr)
+            return signature(enumerate_type, *args)
+
+
+@infer_global(np.nditer)
+class NdIter(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        if len(args) != 1:
+            return
+        arrays, = args
+
+        if isinstance(arrays, types.BaseTuple):
+            if not arrays:
+                return
+            arrays = list(arrays)
+        else:
+            arrays = [arrays]
+        nditerty = types.NumpyNdIterType(arrays)
+        return signature(nditerty, *args)
+
+
+@infer_global(pndindex)
+@infer_global(np.ndindex)
+class NdIndex(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+
+        # Either ndindex(shape) or ndindex(*shape)
+        if len(args) == 1 and isinstance(args[0], types.BaseTuple):
+            tup = args[0]
+            if tup.count > 0 and not isinstance(tup, types.UniTuple):
+                # Heterogeneous tuple
+                return
+            shape = list(tup)
+        else:
+            shape = args
+
+        if all(isinstance(x, types.Integer) for x in shape):
+            iterator_type = types.NumpyNdIndexType(len(shape))
+            return signature(iterator_type, *args)
+
+
+# We use the same typing key for np.round() and np.around() to
+# re-use the implementations automatically.
+@glue_typing(np.round)
+@glue_typing(np.around)
+class Round(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        assert 1 <= len(args) <= 3
+
+        arg = args[0]
+        if len(args) == 1:
+            decimals = types.intp
+            out = None
+        else:
+            decimals = args[1]
+            if len(args) == 2:
+                out = None
+            else:
+                out = args[2]
+
+        supported_scalars = (types.Integer, types.Float, types.Complex)
+        if isinstance(arg, supported_scalars):
+            assert out is None
+            return signature(arg, *args)
+        if (isinstance(arg, types.Array) and isinstance(arg.dtype, supported_scalars) and
+            isinstance(out, types.Array) and isinstance(out.dtype, supported_scalars) and
+            out.ndim == arg.ndim):
+            # arg can only be complex if out is complex too
+            if (not isinstance(arg.dtype, types.Complex)
+                or isinstance(out.dtype, types.Complex)):
+                return signature(out, *args)
+
+
+@glue_typing(np.where)
+class Where(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+
+        if len(args) == 1:
+            # 0-dim arrays return one result array
+            ary = args[0]
+            ndim = max(ary.ndim, 1)
+            retty = types.UniTuple(types.Array(types.intp, 1, 'C'), ndim)
+            return signature(retty, ary)
+
+        elif len(args) == 3:
+            cond, x, y = args
+            retdty = from_dtype(np.promote_types(
+                        as_dtype(getattr(args[1], 'dtype', args[1])),
+                        as_dtype(getattr(args[2], 'dtype', args[2]))))
+            if isinstance(cond, types.Array):
+                # array where()
+                if isinstance(x, types.Array) and isinstance(y, types.Array):
+                    if (cond.ndim == x.ndim == y.ndim):
+                        if x.layout == y.layout == cond.layout:
+                            retty = types.Array(retdty, x.ndim, x.layout)
+                        else:
+                            retty = types.Array(retdty, x.ndim, 'C')
+                        return signature(retty, *args)
+                else:
+                    # x and y both scalar
+                    retty = types.Array(retdty, cond.ndim, cond.layout)
+                    return signature(retty, *args)
+            else:
+                # scalar where()
+                if not isinstance(x, types.Array):
+                    retty = types.Array(retdty, 0, 'C')
+                    return signature(retty, *args)
+
+
+@glue_typing(np.sinc)
+class Sinc(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        assert len(args) == 1
+        arg = args[0]
+        supported_scalars = (types.Float, types.Complex)
+        if (isinstance(arg, supported_scalars) or
+              (isinstance(arg, types.Array) and
+               isinstance(arg.dtype, supported_scalars))):
+            return signature(arg, arg)
+
+
+@glue_typing(np.angle)
+class Angle(CallableTemplate):
+    """
+    Typing template for np.angle()
+    """
+    def generic(self):
+        def typer(z, deg=False):
+            if isinstance(z, types.Array):
+                dtype = z.dtype
+            else:
+                dtype = z
+            if isinstance(dtype, types.Complex):
+                ret_dtype = dtype.underlying_float
+            elif isinstance(dtype, types.Float):
+                ret_dtype = dtype
+            else:
+                return
+            if isinstance(z, types.Array):
+                return z.copy(dtype=ret_dtype)
+            else:
+                return ret_dtype
+        return typer
+
+
+@glue_typing(np.diag)
+class DiagCtor(CallableTemplate):
+    """
+    Typing template for np.diag()
+    """
+    def generic(self):
+        def typer(ref, k=0):
+            if isinstance(ref, types.Array):
+                if ref.ndim == 1:
+                    rdim = 2
+                elif ref.ndim == 2:
+                    rdim = 1
+                else:
+                    return None
+                if isinstance(k, (int, types.Integer)):
+                    return types.Array(ndim=rdim, dtype=ref.dtype, layout='C')
+        return typer
+
+
+@glue_typing(np.take)
+class Take(AbstractTemplate):
+
+    def generic(self, args, kws):
+        if kws:
+            raise NumbaAssertionError("kws not supported")
+        if len(args) != 2:
+            raise NumbaAssertionError("two arguments are required")
+        arr, ind = args
+        if isinstance(ind, types.Number):
+            retty = arr.dtype
+        elif isinstance(ind, types.Array):
+            retty = types.Array(ndim=ind.ndim, dtype=arr.dtype, layout='C')
+        elif isinstance(ind, types.List):
+            retty = types.Array(ndim=1, dtype=arr.dtype, layout='C')
+        elif isinstance(ind, types.BaseTuple):
+            retty = types.Array(ndim=np.ndim(ind), dtype=arr.dtype, layout='C')
+        else:
+            return None
+
+        return signature(retty, *args)
+
+# -----------------------------------------------------------------------------
+# Numba helpers
+
+@glue_typing(carray)
+class NumbaCArray(CallableTemplate):
+    layout = 'C'
+
+    def generic(self):
+        func_name = self.key.__name__
+
+        def typer(ptr, shape, dtype=None):
+            if ptr is types.voidptr:
+                ptr_dtype = None
+            elif isinstance(ptr, types.CPointer):
+                ptr_dtype = ptr.dtype
+            else:
+                raise NumbaTypeError("%s(): pointer argument expected, got '%s'"
+                                     % (func_name, ptr))
+
+            if dtype is None:
+                if ptr_dtype is None:
+                    raise NumbaTypeError("%s(): explicit dtype required for void* argument"
+                                         % (func_name,))
+                dtype = ptr_dtype
+            elif isinstance(dtype, types.DTypeSpec):
+                dtype = dtype.dtype
+                if ptr_dtype is not None and dtype != ptr_dtype:
+                    raise NumbaTypeError("%s(): mismatching dtype '%s' for pointer type '%s'"
+                                         % (func_name, dtype, ptr))
+            else:
+                raise NumbaTypeError("%s(): invalid dtype spec '%s'"
+                                     % (func_name, dtype))
+
+            ndim = parse_shape(shape)
+            if ndim is None:
+                raise NumbaTypeError("%s(): invalid shape '%s'"
+                                     % (func_name, shape))
+
+            return types.Array(dtype, ndim, self.layout)
+
+        return typer
+
+
+@glue_typing(farray)
+class NumbaFArray(NumbaCArray):
+    layout = 'F'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/randomdecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/randomdecl.py
new file mode 100644
index 000000000..0dc9cb439
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/randomdecl.py
@@ -0,0 +1,300 @@
+import random
+
+import numpy as np
+
+from numba.core import types
+from .templates import (ConcreteTemplate, AbstractTemplate, AttributeTemplate,
+                        CallableTemplate, Registry, signature)
+from numba.np.numpy_support import numpy_version
+from numba.core.overload_glue import glue_typing
+
+
+registry = Registry()
+infer = registry.register
+infer_global = registry.register_global
+infer_getattr = registry.register_attr
+
+
+# random.random(), random.seed() etc. are not plain functions, they are bound
+# methods of a private object.  We have to be careful to use a well-known
+# object (e.g. the string "random.seed") as a key, not the bound method itself.
+# (same for np.random.random(), etc.)
+
+_int_types = sorted(set((types.intp, types.int64)))
+# Should we support float32?
+_float_types = [types.float64]
+
+
+#
+# Basics
+#
+
+def normalize_shape(shape):
+    if isinstance(shape, types.Integer):
+        return types.intp, 1
+    elif (isinstance(shape, types.BaseTuple) and
+          all(isinstance(v, types.Integer)) for v in shape):
+        ndim = len(shape)
+        return types.UniTuple(types.intp, ndim), ndim
+    else:
+        raise TypeError("invalid size type %s" % (shape,))
+
+
+class RandomTemplate(CallableTemplate):
+    """
+    A template helper to transparently handle the typing of array-returning
+    np.random.* functions.
+    """
+
+    def array_typer(self, scalar_typer, size=None):
+        prefix = self.key.split('.')[0]
+        assert prefix in ('np', 'random'), self.key
+
+        if size is None:
+            # Scalar variant
+            def typer(*args, **kwargs):
+                return scalar_typer(*args, **kwargs)
+        else:
+            # Array variant (only for the 'np.random.*' namespace)
+            def typer(*args, **kwargs):
+                if prefix == 'random':
+                    raise TypeError("unexpected size parameter for %r"
+                                    % (self.key,))
+                shape, ndim = normalize_shape(size)
+                # Type the scalar variant and wrap the result in an array
+                # of the appropriate dimensionality.
+                sig = scalar_typer(*args, **kwargs)
+                if sig is not None:
+                    return signature(
+                        types.Array(sig.return_type, ndim, 'C'),
+                        *(sig.args + (shape,)))
+
+        return typer
+
+
+class ConcreteRandomTemplate(RandomTemplate):
+    """
+    A RandomTemplate subclass using the `cases` attribute as a list of
+    allowed scalar signatures.
+    """
+
+    def array_typer(self, size=None):
+        key = self.key
+        cases = self.cases
+        context = self.context
+
+        def concrete_scalar_typer(*args, **kwargs):
+            # Filter out omitted args
+            while args and args[-1] is None:
+                args = args[:-1]
+            return context.resolve_overload(key, cases, args, kwargs)
+
+        return RandomTemplate.array_typer(self, concrete_scalar_typer, size)
+
+
+@glue_typing(random.getrandbits, typing_key="random.getrandbits")
+class Random_getrandbits(ConcreteTemplate):
+    cases = [signature(types.uint64, types.int32)]
+
+@glue_typing(random.random, typing_key="random.random")
+@glue_typing(np.random.random, typing_key="np.random.random")
+class Random_random(ConcreteRandomTemplate):
+    cases = [signature(types.float64)]
+
+    def generic(self):
+        def typer(size=None):
+            return self.array_typer(size)()
+        return typer
+
+if numpy_version >= (1, 17):
+    glue_typing(
+        np.random.random_sample,
+        typing_key="np.random.random_sample",
+    )(Random_random)
+    glue_typing(
+        np.random.sample,
+        typing_key="np.random.sample",
+    )(Random_random)
+    glue_typing(
+        np.random.ranf,
+        typing_key="np.random.ranf",
+    )(Random_random)
+
+
+@glue_typing(random.randint, typing_key="random.randint")
+class Random_randint(ConcreteTemplate):
+    cases = [signature(tp, tp, tp) for tp in _int_types]
+
+@glue_typing(np.random.randint, typing_key="np.random.randint")
+class Random_randint(ConcreteRandomTemplate):
+    cases = [signature(tp, tp) for tp in _int_types]
+    cases += [signature(tp, tp, tp) for tp in _int_types]
+
+    def generic(self):
+        def typer(low, high=None, size=None):
+            return self.array_typer(size)(low, high)
+        return typer
+
+
+@glue_typing(random.randrange, typing_key="random.randrange")
+class Random_randrange(ConcreteTemplate):
+    cases = [signature(tp, tp) for tp in _int_types]
+    cases += [signature(tp, tp, tp) for tp in _int_types]
+    cases += [signature(tp, tp, tp, tp) for tp in _int_types]
+
+@glue_typing(random.seed, typing_key="random.seed")
+@glue_typing(np.random.seed, typing_key="np.random.seed")
+class Random_seed(ConcreteTemplate):
+    cases = [signature(types.void, types.uint32)]
+
+
+#
+# Distributions
+#
+
+@glue_typing(np.random.geometric, typing_key="np.random.geometric")
+@glue_typing(np.random.logseries, typing_key="np.random.logseries")
+@glue_typing(np.random.zipf, typing_key="np.random.zipf")
+class Numpy_geometric(ConcreteRandomTemplate):
+    cases = [signature(types.int64, tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(a, size=None):
+            return self.array_typer(size)(a)
+        return typer
+
+@glue_typing(np.random.binomial, typing_key="np.random.binomial")
+@glue_typing(np.random.negative_binomial,
+                          typing_key="np.random.negative_binomial")
+class Numpy_negative_binomial(ConcreteRandomTemplate):
+    cases = [signature(types.int64, types.int64, tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(n, p, size=None):
+            return self.array_typer(size)(n, p)
+        return typer
+
+@glue_typing(np.random.poisson, typing_key="np.random.poisson")
+class Numpy_poisson(ConcreteRandomTemplate):
+    cases = [signature(types.int64, tp) for tp in _float_types]
+    cases += [signature(types.int64)]
+
+    def generic(self):
+        def typer(lam=None, size=None):
+            return self.array_typer(size)(lam)
+        return typer
+
+@glue_typing(np.random.exponential, typing_key="np.random.exponential")
+@glue_typing(np.random.rayleigh, typing_key="np.random.rayleigh")
+class Numpy_exponential(ConcreteRandomTemplate):
+    cases = [signature(tp, tp) for tp in _float_types]
+    cases += [signature(tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(scale=None, size=None):
+            return self.array_typer(size)(scale)
+        return typer
+
+@glue_typing(np.random.hypergeometric, typing_key="np.random.hypergeometric")
+class Numpy_hypergeometric(ConcreteRandomTemplate):
+    cases = [signature(tp, tp, tp, tp) for tp in _int_types]
+
+    def generic(self):
+        def typer(ngood, nbad, nsample, size=None):
+            return self.array_typer(size)(ngood, nbad, nsample)
+        return typer
+
+@glue_typing(np.random.laplace, typing_key="np.random.laplace")
+@glue_typing(np.random.logistic, typing_key="np.random.logistic")
+@glue_typing(np.random.lognormal, typing_key="np.random.lognormal")
+@glue_typing(np.random.normal, typing_key="np.random.normal")
+class Numpy_normal(ConcreteRandomTemplate):
+    cases = [signature(tp, tp, tp) for tp in _float_types]
+    cases += [signature(tp, tp) for tp in _float_types]
+    cases += [signature(tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(loc=None, scale=None, size=None):
+            return self.array_typer(size)(loc, scale)
+        return typer
+
+@glue_typing(np.random.gamma, typing_key="np.random.gamma")
+class Numpy_gamma(ConcreteRandomTemplate):
+    cases = [signature(tp, tp, tp) for tp in _float_types]
+    cases += [signature(tp, tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(shape, scale=None, size=None):
+            return self.array_typer(size)(shape, scale)
+        return typer
+
+@glue_typing(np.random.triangular, typing_key="np.random.triangular")
+class Random_ternary_distribution(ConcreteRandomTemplate):
+    cases = [signature(tp, tp, tp, tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(left, mode, right, size=None):
+            return self.array_typer(size)(left, mode, right)
+        return typer
+
+
+@glue_typing(np.random.beta, typing_key="np.random.beta")
+@glue_typing(np.random.f, typing_key="np.random.f")
+@glue_typing(np.random.gumbel, typing_key="np.random.gumbel")
+@glue_typing(np.random.uniform, typing_key="np.random.uniform")
+@glue_typing(np.random.vonmises, typing_key="np.random.vonmises")
+@glue_typing(np.random.wald, typing_key="np.random.wald")
+@glue_typing(random.betavariate, typing_key="random.betavariate")
+@glue_typing(random.gammavariate, typing_key="random.gammavariate")
+@glue_typing(random.gauss, typing_key="random.gauss")
+@glue_typing(random.lognormvariate, typing_key="random.lognormvariate")
+@glue_typing(random.normalvariate, typing_key="random.normalvariate")
+@glue_typing(random.uniform, typing_key="random.uniform")
+@glue_typing(random.vonmisesvariate, typing_key="random.vonmisesvariate")
+@glue_typing(random.weibullvariate, typing_key="random.weibullvariate")
+class Random_binary_distribution(ConcreteRandomTemplate):
+    cases = [signature(tp, tp, tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(a, b, size=None):
+            return self.array_typer(size)(a, b)
+        return typer
+
+
+@glue_typing(np.random.chisquare, typing_key="np.random.chisquare")
+@glue_typing(np.random.pareto, typing_key="np.random.pareto")
+@glue_typing(np.random.power, typing_key="np.random.power")
+@glue_typing(np.random.standard_gamma, typing_key="np.random.standard_gamma")
+@glue_typing(np.random.standard_t, typing_key="np.random.standard_t")
+@glue_typing(np.random.weibull, typing_key="np.random.weibull")
+@glue_typing(random.expovariate, typing_key="random.expovariate")
+@glue_typing(random.paretovariate, typing_key="random.paretovariate")
+class Random_unary_distribution(ConcreteRandomTemplate):
+    cases = [signature(tp, tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(a, size=None):
+            return self.array_typer(size)(a)
+        return typer
+
+
+@glue_typing(np.random.standard_cauchy, typing_key="np.random.standard_cauchy")
+@glue_typing(np.random.standard_normal, typing_key="np.random.standard_normal")
+@glue_typing(np.random.standard_exponential,
+             typing_key="np.random.standard_exponential")
+class Random_nullary_distribution(ConcreteRandomTemplate):
+    cases = [signature(tp) for tp in _float_types]
+
+    def generic(self):
+        def typer(size=None):
+            return self.array_typer(size)()
+        return typer
+
+
+@glue_typing(random.triangular, typing_key="random.triangular")
+class Random_triangular(ConcreteTemplate):
+    cases = [signature(tp, tp, tp) for tp in _float_types]
+    cases += [signature(tp, tp, tp, tp) for tp in _float_types]
+
+# NOTE: some functions can have @overloads in numba.targets.randomimpl,
+# and therefore don't need a typing declaration here.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/setdecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/setdecl.py
new file mode 100644
index 000000000..120703915
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/setdecl.py
@@ -0,0 +1,192 @@
+import operator
+
+from numba.core import types
+from .templates import (ConcreteTemplate, AbstractTemplate, AttributeTemplate,
+                        CallableTemplate,  Registry, signature, bound_function,
+                        make_callable_template)
+# Ensure set is typed as a collection as well
+from numba.core.typing import collections
+
+
+registry = Registry()
+infer = registry.register
+infer_global = registry.register_global
+infer_getattr = registry.register_attr
+
+
+@infer_global(set)
+class SetBuiltin(AbstractTemplate):
+
+    def generic(self, args, kws):
+        assert not kws
+        if args:
+            # set(iterable)
+            iterable, = args
+            if isinstance(iterable, types.IterableType):
+                dtype = iterable.iterator_type.yield_type
+                if isinstance(dtype, types.Hashable):
+                    return signature(types.Set(dtype), iterable)
+        else:
+            # set()
+            return signature(types.Set(types.undefined))
+
+
+@infer_getattr
+class SetAttribute(AttributeTemplate):
+    key = types.Set
+
+    @bound_function("set.add")
+    def resolve_add(self, set, args, kws):
+        item, = args
+        assert not kws
+        unified = self.context.unify_pairs(set.dtype, item)
+        if unified is not None:
+            sig = signature(types.none, unified)
+            sig = sig.replace(recvr=set.copy(dtype=unified))
+            return sig
+
+    @bound_function("set.clear")
+    def resolve_clear(self, set, args, kws):
+        assert not kws
+        if not args:
+            return signature(types.none)
+
+    @bound_function("set.copy")
+    def resolve_copy(self, set, args, kws):
+        assert not kws
+        if not args:
+            return signature(set)
+
+    @bound_function("set.discard")
+    def resolve_discard(self, set, args, kws):
+        item, = args
+        assert not kws
+        return signature(types.none, set.dtype)
+
+    @bound_function("set.pop")
+    def resolve_pop(self, set, args, kws):
+        assert not kws
+        if not args:
+            return signature(set.dtype)
+
+    @bound_function("set.remove")
+    def resolve_remove(self, set, args, kws):
+        item, = args
+        assert not kws
+        return signature(types.none, set.dtype)
+
+    @bound_function("set.update")
+    def resolve_update(self, set, args, kws):
+        iterable, = args
+        assert not kws
+        if not isinstance(iterable, types.IterableType):
+            return
+
+        dtype = iterable.iterator_type.yield_type
+        unified = self.context.unify_pairs(set.dtype, dtype)
+        if unified is not None:
+            sig = signature(types.none, iterable)
+            sig = sig.replace(recvr=set.copy(dtype=unified))
+            return sig
+
+    def _resolve_xxx_update(self, set, args, kws):
+        assert not kws
+        iterable, = args
+        # Set arguments only supported for now
+        # (note we can mix non-reflected and reflected arguments)
+        if isinstance(iterable, types.Set) and iterable.dtype == set.dtype:
+            return signature(types.none, iterable)
+
+    @bound_function("set.difference_update")
+    def resolve_difference_update(self, set, args, kws):
+        return self._resolve_xxx_update(set, args, kws)
+
+    @bound_function("set.intersection_update")
+    def resolve_intersection_update(self, set, args, kws):
+        return self._resolve_xxx_update(set, args, kws)
+
+    @bound_function("set.symmetric_difference_update")
+    def resolve_symmetric_difference_update(self, set, args, kws):
+        return self._resolve_xxx_update(set, args, kws)
+
+    def _resolve_operator(self, set, args, kws):
+        assert not kws
+        iterable, = args
+        # Set arguments only supported for now
+        # (note we can mix non-reflected and reflected arguments)
+        if isinstance(iterable, types.Set) and iterable.dtype == set.dtype:
+            return signature(set, iterable)
+
+    @bound_function("set.difference")
+    def resolve_difference(self, set, args, kws):
+        return self._resolve_operator(set, args, kws)
+
+    @bound_function("set.intersection")
+    def resolve_intersection(self, set, args, kws):
+        return self._resolve_operator(set, args, kws)
+
+    @bound_function("set.symmetric_difference")
+    def resolve_symmetric_difference(self, set, args, kws):
+        return self._resolve_operator(set, args, kws)
+
+    @bound_function("set.union")
+    def resolve_union(self, set, args, kws):
+        return self._resolve_operator(set, args, kws)
+
+    def _resolve_comparator(self, set, args, kws):
+        assert not kws
+        arg, = args
+        if arg == set:
+            return signature(types.boolean, arg)
+
+    @bound_function("set.isdisjoint")
+    def resolve_isdisjoint(self, set, args, kws):
+        return self._resolve_comparator(set, args, kws)
+
+    @bound_function("set.issubset")
+    def resolve_issubset(self, set, args, kws):
+        return self._resolve_comparator(set, args, kws)
+
+    @bound_function("set.issuperset")
+    def resolve_issuperset(self, set, args, kws):
+        return self._resolve_comparator(set, args, kws)
+
+
+class SetOperator(AbstractTemplate):
+
+    def generic(self, args, kws):
+        if len(args) != 2:
+            return
+        a, b = args
+        if (isinstance(a, types.Set) and isinstance(b, types.Set)
+            and a.dtype == b.dtype):
+            return signature(a, *args)
+
+
+class SetComparison(AbstractTemplate):
+
+    def generic(self, args, kws):
+        if len(args) != 2:
+            return
+        a, b = args
+        if isinstance(a, types.Set) and isinstance(b, types.Set) and a == b:
+            return signature(types.boolean, *args)
+
+
+for op_key in (operator.add, operator.sub, operator.and_, operator.or_, operator.xor, operator.invert):
+    @infer_global(op_key)
+    class ConcreteSetOperator(SetOperator):
+        key = op_key
+
+
+for op_key in (operator.iadd, operator.isub, operator.iand, operator.ior, operator.ixor):
+    @infer_global(op_key)
+    class ConcreteInplaceSetOperator(SetOperator):
+        key = op_key
+
+
+for op_key in (operator.eq, operator.ne, operator.lt, operator.le, operator.ge, operator.gt):
+    @infer_global(op_key)
+    class ConcreteSetComparison(SetComparison):
+        key = op_key
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/templates.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/templates.py
new file mode 100644
index 000000000..caf88fe17
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/templates.py
@@ -0,0 +1,1322 @@
+"""
+Define typing templates
+"""
+
+from abc import ABC, abstractmethod
+import functools
+import sys
+import inspect
+import os.path
+from collections import namedtuple
+from collections.abc import Sequence
+from types import MethodType, FunctionType
+
+import numba
+from numba.core import types, utils, targetconfig
+from numba.core.errors import (
+    TypingError,
+    InternalError,
+    InternalTargetMismatchError,
+)
+from numba.core.cpu_options import InlineOptions
+
+# info store for inliner callback functions e.g. cost model
+_inline_info = namedtuple('inline_info',
+                          'func_ir typemap calltypes signature')
+
+
+class Signature(object):
+    """
+    The signature of a function call or operation, i.e. its argument types
+    and return type.
+    """
+
+    # XXX Perhaps the signature should be a BoundArguments, instead
+    # of separate args and pysig...
+    __slots__ = '_return_type', '_args', '_recvr', '_pysig'
+
+    def __init__(self, return_type, args, recvr, pysig=None):
+        if isinstance(args, list):
+            args = tuple(args)
+        self._return_type = return_type
+        self._args = args
+        self._recvr = recvr
+        self._pysig = pysig
+
+    @property
+    def return_type(self):
+        return self._return_type
+
+    @property
+    def args(self):
+        return self._args
+
+    @property
+    def recvr(self):
+        return self._recvr
+
+    @property
+    def pysig(self):
+        return self._pysig
+
+    def replace(self, **kwargs):
+        """Copy and replace the given attributes provided as keyword arguments.
+        Returns an updated copy.
+        """
+        curstate = dict(return_type=self.return_type,
+                        args=self.args,
+                        recvr=self.recvr,
+                        pysig=self.pysig)
+        curstate.update(kwargs)
+        return Signature(**curstate)
+
+    def __getstate__(self):
+        """
+        Needed because of __slots__.
+        """
+        return self._return_type, self._args, self._recvr, self._pysig
+
+    def __setstate__(self, state):
+        """
+        Needed because of __slots__.
+        """
+        self._return_type, self._args, self._recvr, self._pysig = state
+
+    def __hash__(self):
+        return hash((self.args, self.return_type))
+
+    def __eq__(self, other):
+        if isinstance(other, Signature):
+            return (self.args == other.args and
+                    self.return_type == other.return_type and
+                    self.recvr == other.recvr and
+                    self.pysig == other.pysig)
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __repr__(self):
+        return "%s -> %s" % (self.args, self.return_type)
+
+    @property
+    def is_method(self):
+        """
+        Whether this signature represents a bound method or a regular
+        function.
+        """
+        return self.recvr is not None
+
+    def as_method(self):
+        """
+        Convert this signature to a bound method signature.
+        """
+        if self.recvr is not None:
+            return self
+        sig = signature(self.return_type, *self.args[1:],
+                        recvr=self.args[0])
+
+        # Adjust the python signature
+        params = list(self.pysig.parameters.values())[1:]
+        sig = sig.replace(
+            pysig=utils.pySignature(
+                parameters=params,
+                return_annotation=self.pysig.return_annotation,
+            ),
+        )
+        return sig
+
+    def as_function(self):
+        """
+        Convert this signature to a regular function signature.
+        """
+        if self.recvr is None:
+            return self
+        sig = signature(self.return_type, *((self.recvr,) + self.args))
+        return sig
+
+    def as_type(self):
+        """
+        Convert this signature to a first-class function type.
+        """
+        return types.FunctionType(self)
+
+    def __unliteral__(self):
+        return signature(types.unliteral(self.return_type),
+                         *map(types.unliteral, self.args))
+
+    def dump(self, tab=''):
+        c = self.as_type()._code
+        print(f'{tab}DUMP {type(self).__name__} [type code: {c}]')
+        print(f'{tab}  Argument types:')
+        for a in self.args:
+            a.dump(tab=tab + '  | ')
+        print(f'{tab}  Return type:')
+        self.return_type.dump(tab=tab + '  | ')
+        print(f'{tab}END DUMP')
+
+    def is_precise(self):
+        for atype in self.args:
+            if not atype.is_precise():
+                return False
+        return self.return_type.is_precise()
+
+
+def make_concrete_template(name, key, signatures):
+    baseclasses = (ConcreteTemplate,)
+    gvars = dict(key=key, cases=list(signatures))
+    return type(name, baseclasses, gvars)
+
+
+def make_callable_template(key, typer, recvr=None):
+    """
+    Create a callable template with the given key and typer function.
+    """
+    def generic(self):
+        return typer
+
+    name = "%s_CallableTemplate" % (key,)
+    bases = (CallableTemplate,)
+    class_dict = dict(key=key, generic=generic, recvr=recvr)
+    return type(name, bases, class_dict)
+
+
+def signature(return_type, *args, **kws):
+    recvr = kws.pop('recvr', None)
+    assert not kws
+    return Signature(return_type, args, recvr=recvr)
+
+
+def fold_arguments(pysig, args, kws, normal_handler, default_handler,
+                   stararg_handler):
+    """
+    Given the signature *pysig*, explicit *args* and *kws*, resolve
+    omitted arguments and keyword arguments. A tuple of positional
+    arguments is returned.
+    Various handlers allow to process arguments:
+    - normal_handler(index, param, value) is called for normal arguments
+    - default_handler(index, param, default) is called for omitted arguments
+    - stararg_handler(index, param, values) is called for a "*args" argument
+    """
+    if isinstance(kws, Sequence):
+        # Normalize dict kws
+        kws = dict(kws)
+
+    # deal with kwonly args
+    params = pysig.parameters
+    kwonly = []
+    for name, p in params.items():
+        if p.kind == p.KEYWORD_ONLY:
+            kwonly.append(name)
+
+    if kwonly:
+        bind_args = args[:-len(kwonly)]
+    else:
+        bind_args = args
+    bind_kws = kws.copy()
+    if kwonly:
+        for idx, n in enumerate(kwonly):
+            bind_kws[n] = args[len(kwonly) + idx]
+
+    # now bind
+    ba = pysig.bind(*bind_args, **bind_kws)
+    for i, param in enumerate(pysig.parameters.values()):
+        name = param.name
+        default = param.default
+        if param.kind == param.VAR_POSITIONAL:
+            # stararg may be omitted, in which case its "default" value
+            # is simply the empty tuple
+            if name in ba.arguments:
+                argval = ba.arguments[name]
+                # NOTE: avoid wrapping the tuple type for stararg in another
+                #       tuple.
+                if (len(argval) == 1 and
+                        isinstance(argval[0], (types.StarArgTuple,
+                                               types.StarArgUniTuple))):
+                    argval = tuple(argval[0])
+            else:
+                argval = ()
+            out = stararg_handler(i, param, argval)
+
+            ba.arguments[name] = out
+        elif name in ba.arguments:
+            # Non-stararg, present
+            ba.arguments[name] = normal_handler(i, param, ba.arguments[name])
+        else:
+            # Non-stararg, omitted
+            assert default is not param.empty
+            ba.arguments[name] = default_handler(i, param, default)
+    # Collect args in the right order
+    args = tuple(ba.arguments[param.name]
+                 for param in pysig.parameters.values())
+    return args
+
+
+class FunctionTemplate(ABC):
+    # Set to true to disable unsafe cast.
+    # subclass overide-able
+    unsafe_casting = True
+    # Set to true to require exact match without casting.
+    # subclass overide-able
+    exact_match_required = False
+    # Set to true to prefer literal arguments.
+    # Useful for definitions that specialize on literal but also support
+    # non-literals.
+    # subclass overide-able
+    prefer_literal = False
+    # metadata
+    metadata = {}
+
+    def __init__(self, context):
+        self.context = context
+
+    def _select(self, cases, args, kws):
+        options = {
+            'unsafe_casting': self.unsafe_casting,
+            'exact_match_required': self.exact_match_required,
+        }
+        selected = self.context.resolve_overload(self.key, cases, args, kws,
+                                                 **options)
+        return selected
+
+    def get_impl_key(self, sig):
+        """
+        Return the key for looking up the implementation for the given
+        signature on the target context.
+        """
+        # Lookup the key on the class, to avoid binding it with `self`.
+        key = type(self).key
+        # On Python 2, we must also take care about unbound methods
+        if isinstance(key, MethodType):
+            assert key.im_self is None
+            key = key.im_func
+        return key
+
+    @classmethod
+    def get_source_code_info(cls, impl):
+        """
+        Gets the source information about function impl.
+        Returns:
+
+        code - str: source code as a string
+        firstlineno - int: the first line number of the function impl
+        path - str: the path to file containing impl
+
+        if any of the above are not available something generic is returned
+        """
+        try:
+            code, firstlineno = inspect.getsourcelines(impl)
+        except OSError: # missing source, probably a string
+            code = "None available (built from string?)"
+            firstlineno = 0
+        path = inspect.getsourcefile(impl)
+        if path is None:
+            path = "<unknown> (built from string?)"
+        return code, firstlineno, path
+
+    @abstractmethod
+    def get_template_info(self):
+        """
+        Returns a dictionary with information specific to the template that will
+        govern how error messages are displayed to users. The dictionary must
+        be of the form:
+        info = {
+            'kind': "unknown", # str: The kind of template, e.g. "Overload"
+            'name': "unknown", # str: The name of the source function
+            'sig': "unknown",  # str: The signature(s) of the source function
+            'filename': "unknown", # str: The filename of the source function
+            'lines': ("start", "end"), # tuple(int, int): The start and
+                                         end line of the source function.
+            'docstring': "unknown" # str: The docstring of the source function
+        }
+        """
+        pass
+
+    def __str__(self):
+        info = self.get_template_info()
+        srcinfo = f"{info['filename']}:{info['lines'][0]}"
+        return f"<{self.__class__.__name__} {srcinfo}>"
+
+    __repr__ = __str__
+
+
+class AbstractTemplate(FunctionTemplate):
+    """
+    Defines method ``generic(self, args, kws)`` which compute a possible
+    signature base on input types.  The signature does not have to match the
+    input types. It is compared against the input types afterwards.
+    """
+
+    def apply(self, args, kws):
+        generic = getattr(self, "generic")
+        sig = generic(args, kws)
+        # Enforce that *generic()* must return None or Signature
+        if sig is not None:
+            if not isinstance(sig, Signature):
+                raise AssertionError(
+                    "generic() must return a Signature or None. "
+                    "{} returned {}".format(generic, type(sig)),
+                )
+
+        # Unpack optional type if no matching signature
+        if not sig and any(isinstance(x, types.Optional) for x in args):
+            def unpack_opt(x):
+                if isinstance(x, types.Optional):
+                    return x.type
+                else:
+                    return x
+
+            args = list(map(unpack_opt, args))
+            assert not kws  # Not supported yet
+            sig = generic(args, kws)
+
+        return sig
+
+    def get_template_info(self):
+        impl = getattr(self, "generic")
+        basepath = os.path.dirname(os.path.dirname(numba.__file__))
+
+        code, firstlineno, path = self.get_source_code_info(impl)
+        sig = str(utils.pysignature(impl))
+        info = {
+            'kind': "overload",
+            'name': getattr(impl, '__qualname__', impl.__name__),
+            'sig': sig,
+            'filename': utils.safe_relpath(path, start=basepath),
+            'lines': (firstlineno, firstlineno + len(code) - 1),
+            'docstring': impl.__doc__
+        }
+        return info
+
+
+class CallableTemplate(FunctionTemplate):
+    """
+    Base class for a template defining a ``generic(self)`` method
+    returning a callable to be called with the actual ``*args`` and
+    ``**kwargs`` representing the call signature.  The callable has
+    to return a return type, a full signature, or None.  The signature
+    does not have to match the input types. It is compared against the
+    input types afterwards.
+    """
+    recvr = None
+
+    def apply(self, args, kws):
+        generic = getattr(self, "generic")
+        typer = generic()
+        match_sig = inspect.signature(typer)
+        try:
+            match_sig.bind(*args, **kws)
+        except TypeError as e:
+            # bind failed, raise, if there's a
+            # ValueError then there's likely unrecoverable
+            # problems
+            raise TypingError(str(e)) from e
+
+        sig = typer(*args, **kws)
+
+        # Unpack optional type if no matching signature
+        if sig is None:
+            if any(isinstance(x, types.Optional) for x in args):
+                def unpack_opt(x):
+                    if isinstance(x, types.Optional):
+                        return x.type
+                    else:
+                        return x
+
+                args = list(map(unpack_opt, args))
+                sig = typer(*args, **kws)
+            if sig is None:
+                return
+
+        # Get the pysig
+        try:
+            pysig = typer.pysig
+        except AttributeError:
+            pysig = utils.pysignature(typer)
+
+        # Fold any keyword arguments
+        bound = pysig.bind(*args, **kws)
+        if bound.kwargs:
+            raise TypingError("unsupported call signature")
+        if not isinstance(sig, Signature):
+            # If not a signature, `sig` is assumed to be the return type
+            if not isinstance(sig, types.Type):
+                raise TypeError("invalid return type for callable template: "
+                                "got %r" % (sig,))
+            sig = signature(sig, *bound.args)
+        if self.recvr is not None:
+            sig = sig.replace(recvr=self.recvr)
+        # Hack any omitted parameters out of the typer's pysig,
+        # as lowering expects an exact match between formal signature
+        # and actual args.
+        if len(bound.args) < len(pysig.parameters):
+            parameters = list(pysig.parameters.values())[:len(bound.args)]
+            pysig = pysig.replace(parameters=parameters)
+        sig = sig.replace(pysig=pysig)
+        cases = [sig]
+        return self._select(cases, bound.args, bound.kwargs)
+
+    def get_template_info(self):
+        impl = getattr(self, "generic")
+        basepath = os.path.dirname(os.path.dirname(numba.__file__))
+        code, firstlineno, path = self.get_source_code_info(impl)
+        sig = str(utils.pysignature(impl))
+        info = {
+            'kind': "overload",
+            'name': getattr(self.key, '__name__',
+                            getattr(impl, '__qualname__', impl.__name__),),
+            'sig': sig,
+            'filename': utils.safe_relpath(path, start=basepath),
+            'lines': (firstlineno, firstlineno + len(code) - 1),
+            'docstring': impl.__doc__
+        }
+        return info
+
+
+class ConcreteTemplate(FunctionTemplate):
+    """
+    Defines attributes "cases" as a list of signature to match against the
+    given input types.
+    """
+
+    def apply(self, args, kws):
+        cases = getattr(self, 'cases')
+        return self._select(cases, args, kws)
+
+    def get_template_info(self):
+        import operator
+        name = getattr(self.key, '__name__', "unknown")
+        op_func = getattr(operator, name, None)
+
+        kind = "Type restricted function"
+        if op_func is not None:
+            if self.key is op_func:
+                kind = "operator overload"
+        info = {
+            'kind': kind,
+            'name': name,
+            'sig': "unknown",
+            'filename': "unknown",
+            'lines': ("unknown", "unknown"),
+            'docstring': "unknown"
+        }
+        return info
+
+
+class _EmptyImplementationEntry(InternalError):
+    def __init__(self, reason):
+        super(_EmptyImplementationEntry, self).__init__(
+            "_EmptyImplementationEntry({!r})".format(reason),
+        )
+
+
+class _OverloadFunctionTemplate(AbstractTemplate):
+    """
+    A base class of templates for overload functions.
+    """
+
+    def _validate_sigs(self, typing_func, impl_func):
+        # check that the impl func and the typing func have the same signature!
+        typing_sig = utils.pysignature(typing_func)
+        impl_sig = utils.pysignature(impl_func)
+        # the typing signature is considered golden and must be adhered to by
+        # the implementation...
+        # Things that are valid:
+        # 1. args match exactly
+        # 2. kwargs match exactly in name and default value
+        # 3. Use of *args in the same location by the same name in both typing
+        #    and implementation signature
+        # 4. Use of *args in the implementation signature to consume any number
+        #    of arguments in the typing signature.
+        # Things that are invalid:
+        # 5. Use of *args in the typing signature that is not replicated
+        #    in the implementing signature
+        # 6. Use of **kwargs
+
+        def get_args_kwargs(sig):
+            kws = []
+            args = []
+            pos_arg = None
+            for x in sig.parameters.values():
+                if x.default == utils.pyParameter.empty:
+                    args.append(x)
+                    if x.kind == utils.pyParameter.VAR_POSITIONAL:
+                        pos_arg = x
+                    elif x.kind == utils.pyParameter.VAR_KEYWORD:
+                        msg = ("The use of VAR_KEYWORD (e.g. **kwargs) is "
+                               "unsupported. (offending argument name is '%s')")
+                        raise InternalError(msg % x)
+                else:
+                    kws.append(x)
+            return args, kws, pos_arg
+
+        ty_args, ty_kws, ty_pos = get_args_kwargs(typing_sig)
+        im_args, im_kws, im_pos = get_args_kwargs(impl_sig)
+
+        sig_fmt = ("Typing signature:         %s\n"
+                   "Implementation signature: %s")
+        sig_str = sig_fmt % (typing_sig, impl_sig)
+
+        err_prefix = "Typing and implementation arguments differ in "
+
+        a = ty_args
+        b = im_args
+        if ty_pos:
+            if not im_pos:
+                # case 5. described above
+                msg = ("VAR_POSITIONAL (e.g. *args) argument kind (offending "
+                       "argument name is '%s') found in the typing function "
+                       "signature, but is not in the implementing function "
+                       "signature.\n%s") % (ty_pos, sig_str)
+                raise InternalError(msg)
+        else:
+            if im_pos:
+                # no *args in typing but there's a *args in the implementation
+                # this is case 4. described above
+                b = im_args[:im_args.index(im_pos)]
+                try:
+                    a = ty_args[:ty_args.index(b[-1]) + 1]
+                except ValueError:
+                    # there's no b[-1] arg name in the ty_args, something is
+                    # very wrong, we can't work out a diff (*args consumes
+                    # unknown quantity of args) so just report first error
+                    specialized = "argument names.\n%s\nFirst difference: '%s'"
+                    msg = err_prefix + specialized % (sig_str, b[-1])
+                    raise InternalError(msg)
+
+        def gen_diff(typing, implementing):
+            diff = set(typing) ^ set(implementing)
+            return "Difference: %s" % diff
+
+        if a != b:
+            specialized = "argument names.\n%s\n%s" % (sig_str, gen_diff(a, b))
+            raise InternalError(err_prefix + specialized)
+
+        # ensure kwargs are the same
+        ty = [x.name for x in ty_kws]
+        im = [x.name for x in im_kws]
+        if ty != im:
+            specialized = "keyword argument names.\n%s\n%s"
+            msg = err_prefix + specialized % (sig_str, gen_diff(ty_kws, im_kws))
+            raise InternalError(msg)
+        same = [x.default for x in ty_kws] == [x.default for x in im_kws]
+        if not same:
+            specialized = "keyword argument default values.\n%s\n%s"
+            msg = err_prefix + specialized % (sig_str, gen_diff(ty_kws, im_kws))
+            raise InternalError(msg)
+
+    def generic(self, args, kws):
+        """
+        Type the overloaded function by compiling the appropriate
+        implementation for the given args.
+        """
+        from numba.core.typed_passes import PreLowerStripPhis
+
+        disp, new_args = self._get_impl(args, kws)
+        if disp is None:
+            return
+        # Compile and type it for the given types
+        disp_type = types.Dispatcher(disp)
+        # Store the compiled overload for use in the lowering phase if there's
+        # no inlining required (else functions are being compiled which will
+        # never be used as they are inlined)
+        if not self._inline.is_never_inline:
+            # need to run the compiler front end up to type inference to compute
+            # a signature
+            from numba.core import typed_passes, compiler
+            from numba.core.inline_closurecall import InlineWorker
+            fcomp = disp._compiler
+            flags = compiler.Flags()
+
+            # Updating these causes problems?!
+            #fcomp.targetdescr.options.parse_as_flags(flags,
+            #                                         fcomp.targetoptions)
+            #flags = fcomp._customize_flags(flags)
+
+            # spoof a compiler pipline like the one that will be in use
+            tyctx = fcomp.targetdescr.typing_context
+            tgctx = fcomp.targetdescr.target_context
+            compiler_inst = fcomp.pipeline_class(tyctx, tgctx, None, None, None,
+                                                 flags, None, )
+            inline_worker = InlineWorker(tyctx, tgctx, fcomp.locals,
+                                         compiler_inst, flags, None,)
+
+            # If the inlinee contains something to trigger literal arg dispatch
+            # then the pipeline call will unconditionally fail due to a raised
+            # ForceLiteralArg exception. Therefore `resolve` is run first, as
+            # type resolution must occur at some point, this will hit any
+            # `literally` calls and because it's going via the dispatcher will
+            # handle them correctly i.e. ForceLiteralArg propagates. This having
+            # the desired effect of ensuring the pipeline call is only made in
+            # situations that will succeed. For context see #5887.
+            resolve = disp_type.dispatcher.get_call_template
+            template, pysig, folded_args, kws = resolve(new_args, kws)
+            ir = inline_worker.run_untyped_passes(
+                disp_type.dispatcher.py_func, enable_ssa=True
+            )
+
+            (
+                typemap,
+                return_type,
+                calltypes,
+                _
+            ) = typed_passes.type_inference_stage(
+                self.context, tgctx, ir, folded_args, None)
+            ir = PreLowerStripPhis()._strip_phi_nodes(ir)
+            ir._definitions = numba.core.ir_utils.build_definitions(ir.blocks)
+
+            sig = Signature(return_type, folded_args, None)
+            # this stores a load of info for the cost model function if supplied
+            # it by default is None
+            self._inline_overloads[sig.args] = {'folded_args': folded_args}
+            # this stores the compiled overloads, if there's no compiled
+            # overload available i.e. function is always inlined, the key still
+            # needs to exist for type resolution
+
+            # NOTE: If lowering is failing on a `_EmptyImplementationEntry`,
+            #       the inliner has failed to inline this entry correctly.
+            impl_init = _EmptyImplementationEntry('always inlined')
+            self._compiled_overloads[sig.args] = impl_init
+            if not self._inline.is_always_inline:
+                # this branch is here because a user has supplied a function to
+                # determine whether to inline or not. As a result both compiled
+                # function and inliner info needed, delaying the computation of
+                # this leads to an internal state mess at present. TODO: Fix!
+                sig = disp_type.get_call_type(self.context, new_args, kws)
+                self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
+                # store the inliner information, it's used later in the cost
+                # model function call
+            iinfo = _inline_info(ir, typemap, calltypes, sig)
+            self._inline_overloads[sig.args] = {'folded_args': folded_args,
+                                                'iinfo': iinfo}
+        else:
+            sig = disp_type.get_call_type(self.context, new_args, kws)
+            if sig is None: # can't resolve for this target
+                return None
+            self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
+        return sig
+
+    def _get_impl(self, args, kws):
+        """Get implementation given the argument types.
+
+        Returning a Dispatcher object.  The Dispatcher object is cached
+        internally in `self._impl_cache`.
+        """
+        flags = targetconfig.ConfigStack.top_or_none()
+        cache_key = self.context, tuple(args), tuple(kws.items()), flags
+        try:
+            impl, args = self._impl_cache[cache_key]
+            return impl, args
+        except KeyError:
+            # pass and try outside the scope so as to not have KeyError with a
+            # nested addition error in the case the _build_impl fails
+            pass
+        impl, args = self._build_impl(cache_key, args, kws)
+        return impl, args
+
+    def _get_jit_decorator(self):
+        """Gets a jit decorator suitable for the current target"""
+
+        jitter_str = self.metadata.get('target', None)
+        if jitter_str is None:
+            from numba import jit
+            # There is no target requested, use default, this preserves
+            # original behaviour
+            jitter = lambda *args, **kwargs: jit(*args, nopython=True, **kwargs)
+        else:
+            from numba.core.target_extension import (target_registry,
+                                                     get_local_target,
+                                                     jit_registry)
+
+            # target has been requested, see what it is...
+            jitter = jit_registry.get(jitter_str, None)
+
+            if jitter is None:
+                # No JIT known for target string, see if something is
+                # registered for the string and report if not.
+                target_class = target_registry.get(jitter_str, None)
+                if target_class is None:
+                    msg = ("Unknown target '{}', has it been ",
+                           "registered?")
+                    raise ValueError(msg.format(jitter_str))
+
+                target_hw = get_local_target(self.context)
+
+                # check that the requested target is in the hierarchy for the
+                # current frame's target.
+                if not issubclass(target_hw, target_class):
+                    msg = "No overloads exist for the requested target: {}."
+
+                jitter = jit_registry[target_hw]
+
+        if jitter is None:
+            raise ValueError("Cannot find a suitable jit decorator")
+
+        return jitter
+
+    def _build_impl(self, cache_key, args, kws):
+        """Build and cache the implementation.
+
+        Given the positional (`args`) and keyword arguments (`kws`), obtains
+        the `overload` implementation and wrap it in a Dispatcher object.
+        The expected argument types are returned for use by type-inference.
+        The expected argument types are only different from the given argument
+        types if there is an imprecise type in the given argument types.
+
+        Parameters
+        ----------
+        cache_key : hashable
+            The key used for caching the implementation.
+        args : Tuple[Type]
+            Types of positional argument.
+        kws : Dict[Type]
+            Types of keyword argument.
+
+        Returns
+        -------
+        disp, args :
+            On success, returns `(Dispatcher, Tuple[Type])`.
+            On failure, returns `(None, None)`.
+
+        """
+        jitter = self._get_jit_decorator()
+
+        # Get the overload implementation for the given types
+        ov_sig = inspect.signature(self._overload_func)
+        try:
+            ov_sig.bind(*args, **kws)
+        except TypeError as e:
+            # bind failed, raise, if there's a
+            # ValueError then there's likely unrecoverable
+            # problems
+            raise TypingError(str(e)) from e
+        else:
+            ovf_result = self._overload_func(*args, **kws)
+
+        if ovf_result is None:
+            # No implementation => fail typing
+            self._impl_cache[cache_key] = None, None
+            return None, None
+        elif isinstance(ovf_result, tuple):
+            # The implementation returned a signature that the type-inferencer
+            # should be using.
+            sig, pyfunc = ovf_result
+            args = sig.args
+            kws = {}
+            cache_key = None            # don't cache
+        else:
+            # Regular case
+            pyfunc = ovf_result
+
+        # Check type of pyfunc
+        if not isinstance(pyfunc, FunctionType):
+            msg = ("Implementation function returned by `@overload` "
+                   "has an unexpected type.  Got {}")
+            raise AssertionError(msg.format(pyfunc))
+
+        # check that the typing and impl sigs match up
+        if self._strict:
+            self._validate_sigs(self._overload_func, pyfunc)
+        # Make dispatcher
+        jitdecor = jitter(**self._jit_options)
+        disp = jitdecor(pyfunc)
+        # Make sure that the implementation can be fully compiled
+        disp_type = types.Dispatcher(disp)
+        disp_type.get_call_type(self.context, args, kws)
+        if cache_key is not None:
+            self._impl_cache[cache_key] = disp, args
+        return disp, args
+
+    def get_impl_key(self, sig):
+        """
+        Return the key for looking up the implementation for the given
+        signature on the target context.
+        """
+        return self._compiled_overloads[sig.args]
+
+    @classmethod
+    def get_source_info(cls):
+        """Return a dictionary with information about the source code of the
+        implementation.
+
+        Returns
+        -------
+        info : dict
+            - "kind" : str
+                The implementation kind.
+            - "name" : str
+                The name of the function that provided the definition.
+            - "sig" : str
+                The formatted signature of the function.
+            - "filename" : str
+                The name of the source file.
+            - "lines": tuple (int, int)
+                First and list line number.
+            - "docstring": str
+                The docstring of the definition.
+        """
+        basepath = os.path.dirname(os.path.dirname(numba.__file__))
+        impl = cls._overload_func
+        code, firstlineno, path = cls.get_source_code_info(impl)
+        sig = str(utils.pysignature(impl))
+        info = {
+            'kind': "overload",
+            'name': getattr(impl, '__qualname__', impl.__name__),
+            'sig': sig,
+            'filename': utils.safe_relpath(path, start=basepath),
+            'lines': (firstlineno, firstlineno + len(code) - 1),
+            'docstring': impl.__doc__
+        }
+        return info
+
+    def get_template_info(self):
+        basepath = os.path.dirname(os.path.dirname(numba.__file__))
+        impl = self._overload_func
+        code, firstlineno, path = self.get_source_code_info(impl)
+        sig = str(utils.pysignature(impl))
+        info = {
+            'kind': "overload",
+            'name': getattr(impl, '__qualname__', impl.__name__),
+            'sig': sig,
+            'filename': utils.safe_relpath(path, start=basepath),
+            'lines': (firstlineno, firstlineno + len(code) - 1),
+            'docstring': impl.__doc__
+        }
+        return info
+
+
+def make_overload_template(func, overload_func, jit_options, strict,
+                           inline, prefer_literal=False, **kwargs):
+    """
+    Make a template class for function *func* overloaded by *overload_func*.
+    Compiler options are passed as a dictionary to *jit_options*.
+    """
+    func_name = getattr(func, '__name__', str(func))
+    name = "OverloadTemplate_%s" % (func_name,)
+    base = _OverloadFunctionTemplate
+    dct = dict(key=func, _overload_func=staticmethod(overload_func),
+               _impl_cache={}, _compiled_overloads={}, _jit_options=jit_options,
+               _strict=strict, _inline=staticmethod(InlineOptions(inline)),
+               _inline_overloads={}, prefer_literal=prefer_literal,
+               metadata=kwargs)
+    return type(base)(name, (base,), dct)
+
+
+class _TemplateTargetHelperMixin(object):
+    """Mixin for helper methods that assist with target/registry resolution"""
+
+    def _get_target_registry(self, reason):
+        """Returns the registry for the current target.
+
+        Parameters
+        ----------
+        reason: str
+            Reason for the resolution. Expects a noun.
+        Returns
+        -------
+        reg : a registry suitable for the current target.
+        """
+        from numba.core.target_extension import (_get_local_target_checked,
+                                                 dispatcher_registry)
+        hwstr = self.metadata.get('target', 'generic')
+        target_hw = _get_local_target_checked(self.context, hwstr, reason)
+        # Get registry for the current hardware
+        disp = dispatcher_registry[target_hw]
+        tgtctx = disp.targetdescr.target_context
+        # This is all workarounds...
+        # The issue is that whilst targets shouldn't care about which registry
+        # in which to register lowering implementations, the CUDA target
+        # "borrows" implementations from the CPU from specific registries. This
+        # means that if some impl is defined via @intrinsic, e.g. numba.*unsafe
+        # modules, _AND_ CUDA also makes use of the same impl, then it's
+        # required that the registry in use is one that CUDA borrows from. This
+        # leads to the following expression where by the CPU builtin_registry is
+        # used if it is in the target context as a known registry (i.e. the
+        # target installed it) and if it is not then it is assumed that the
+        # registries for the target are unbound to any other target and so it's
+        # fine to use any of them as a place to put lowering impls.
+        #
+        # NOTE: This will need subsequently fixing again when targets use solely
+        # the extension APIs to describe their implementation. The issue will be
+        # that the builtin_registry should contain _just_ the stack allocated
+        # implementations and low level target invariant things and should not
+        # be modified further. It should be acceptable to remove the `then`
+        # branch and just keep the `else`.
+
+        # In case the target has swapped, e.g. cuda borrowing cpu, refresh to
+        # populate.
+        tgtctx.refresh()
+        if builtin_registry in tgtctx._registries:
+            reg = builtin_registry
+        else:
+            # Pick a registry in which to install intrinsics
+            registries = iter(tgtctx._registries)
+            reg = next(registries)
+        return reg
+
+
+class _IntrinsicTemplate(_TemplateTargetHelperMixin, AbstractTemplate):
+    """
+    A base class of templates for intrinsic definition
+    """
+
+    def generic(self, args, kws):
+        """
+        Type the intrinsic by the arguments.
+        """
+        lower_builtin = self._get_target_registry('intrinsic').lower
+        cache_key = self.context, args, tuple(kws.items())
+        try:
+            return self._impl_cache[cache_key]
+        except KeyError:
+            pass
+        result = self._definition_func(self.context, *args, **kws)
+        if result is None:
+            return
+        [sig, imp] = result
+        pysig = utils.pysignature(self._definition_func)
+        # omit context argument from user function
+        parameters = list(pysig.parameters.values())[1:]
+        sig = sig.replace(pysig=pysig.replace(parameters=parameters))
+        self._impl_cache[cache_key] = sig
+        self._overload_cache[sig.args] = imp
+        # register the lowering
+        lower_builtin(imp, *sig.args)(imp)
+        return sig
+
+    def get_impl_key(self, sig):
+        """
+        Return the key for looking up the implementation for the given
+        signature on the target context.
+        """
+        return self._overload_cache[sig.args]
+
+    def get_template_info(self):
+        basepath = os.path.dirname(os.path.dirname(numba.__file__))
+        impl = self._definition_func
+        code, firstlineno, path = self.get_source_code_info(impl)
+        sig = str(utils.pysignature(impl))
+        info = {
+            'kind': "intrinsic",
+            'name': getattr(impl, '__qualname__', impl.__name__),
+            'sig': sig,
+            'filename': utils.safe_relpath(path, start=basepath),
+            'lines': (firstlineno, firstlineno + len(code) - 1),
+            'docstring': impl.__doc__
+        }
+        return info
+
+
+def make_intrinsic_template(handle, defn, name, kwargs):
+    """
+    Make a template class for a intrinsic handle *handle* defined by the
+    function *defn*.  The *name* is used for naming the new template class.
+    """
+    base = _IntrinsicTemplate
+    name = "_IntrinsicTemplate_%s" % (name)
+    dct = dict(key=handle, _definition_func=staticmethod(defn),
+               _impl_cache={}, _overload_cache={}, metadata=kwargs)
+    return type(base)(name, (base,), dct)
+
+
+class AttributeTemplate(object):
+    def __init__(self, context):
+        self.context = context
+
+    def resolve(self, value, attr):
+        return self._resolve(value, attr)
+
+    def _resolve(self, value, attr):
+        fn = getattr(self, "resolve_%s" % attr, None)
+        if fn is None:
+            fn = self.generic_resolve
+            if fn is NotImplemented:
+                if isinstance(value, types.Module):
+                    return self.context.resolve_module_constants(value, attr)
+                else:
+                    return None
+            else:
+                return fn(value, attr)
+        else:
+            return fn(value)
+
+    generic_resolve = NotImplemented
+
+
+class _OverloadAttributeTemplate(_TemplateTargetHelperMixin, AttributeTemplate):
+    """
+    A base class of templates for @overload_attribute functions.
+    """
+    is_method = False
+
+    def __init__(self, context):
+        super(_OverloadAttributeTemplate, self).__init__(context)
+        self.context = context
+        self._init_once()
+
+    def _init_once(self):
+        cls = type(self)
+        attr = cls._attr
+
+        lower_getattr = self._get_target_registry('attribute').lower_getattr
+
+        @lower_getattr(cls.key, attr)
+        def getattr_impl(context, builder, typ, value):
+            typingctx = context.typing_context
+            fnty = cls._get_function_type(typingctx, typ)
+            sig = cls._get_signature(typingctx, fnty, (typ,), {})
+            call = context.get_function(fnty, sig)
+            return call(builder, (value,))
+
+    def _resolve(self, typ, attr):
+        if self._attr != attr:
+            return None
+        fnty = self._get_function_type(self.context, typ)
+        sig = self._get_signature(self.context, fnty, (typ,), {})
+        # There should only be one template
+        for template in fnty.templates:
+            self._inline_overloads.update(template._inline_overloads)
+        return sig.return_type
+
+    @classmethod
+    def _get_signature(cls, typingctx, fnty, args, kws):
+        sig = fnty.get_call_type(typingctx, args, kws)
+        sig = sig.replace(pysig=utils.pysignature(cls._overload_func))
+        return sig
+
+    @classmethod
+    def _get_function_type(cls, typingctx, typ):
+        return typingctx.resolve_value_type(cls._overload_func)
+
+
+class _OverloadMethodTemplate(_OverloadAttributeTemplate):
+    """
+    A base class of templates for @overload_method functions.
+    """
+    is_method = True
+
+    def _init_once(self):
+        """
+        Overriding parent definition
+        """
+        attr = self._attr
+
+        try:
+            registry = self._get_target_registry('method')
+        except InternalTargetMismatchError:
+            # Target mismatch. Do not register attribute lookup here.
+            pass
+        else:
+            lower_builtin = registry.lower
+
+            @lower_builtin((self.key, attr), self.key, types.VarArg(types.Any))
+            def method_impl(context, builder, sig, args):
+                typ = sig.args[0]
+                typing_context = context.typing_context
+                fnty = self._get_function_type(typing_context, typ)
+                sig = self._get_signature(typing_context, fnty, sig.args, {})
+                call = context.get_function(fnty, sig)
+                # Link dependent library
+                context.add_linking_libs(getattr(call, 'libs', ()))
+                return call(builder, args)
+
+    def _resolve(self, typ, attr):
+        if self._attr != attr:
+            return None
+
+        if isinstance(typ, types.TypeRef):
+            assert typ == self.key
+        else:
+            assert isinstance(typ, self.key)
+
+        class MethodTemplate(AbstractTemplate):
+            key = (self.key, attr)
+            _inline = self._inline
+            _overload_func = staticmethod(self._overload_func)
+            _inline_overloads = self._inline_overloads
+            prefer_literal = self.prefer_literal
+
+            def generic(_, args, kws):
+                args = (typ,) + tuple(args)
+                fnty = self._get_function_type(self.context, typ)
+                sig = self._get_signature(self.context, fnty, args, kws)
+                sig = sig.replace(pysig=utils.pysignature(self._overload_func))
+                for template in fnty.templates:
+                    self._inline_overloads.update(template._inline_overloads)
+                if sig is not None:
+                    return sig.as_method()
+
+        return types.BoundFunction(MethodTemplate, typ)
+
+
+def make_overload_attribute_template(typ, attr, overload_func, inline,
+                                     prefer_literal=False,
+                                     base=_OverloadAttributeTemplate,
+                                     **kwargs):
+    """
+    Make a template class for attribute *attr* of *typ* overloaded by
+    *overload_func*.
+    """
+    assert isinstance(typ, types.Type) or issubclass(typ, types.Type)
+    name = "OverloadAttributeTemplate_%s_%s" % (typ, attr)
+    # Note the implementation cache is subclass-specific
+    dct = dict(key=typ, _attr=attr, _impl_cache={},
+               _inline=staticmethod(InlineOptions(inline)),
+               _inline_overloads={},
+               _overload_func=staticmethod(overload_func),
+               prefer_literal=prefer_literal,
+               metadata=kwargs,
+               )
+    obj = type(base)(name, (base,), dct)
+    return obj
+
+
+def make_overload_method_template(typ, attr, overload_func, inline,
+                                  prefer_literal=False, **kwargs):
+    """
+    Make a template class for method *attr* of *typ* overloaded by
+    *overload_func*.
+    """
+    return make_overload_attribute_template(
+        typ, attr, overload_func, inline=inline,
+        base=_OverloadMethodTemplate, prefer_literal=prefer_literal,
+        **kwargs,
+    )
+
+
+def bound_function(template_key):
+    """
+    Wrap an AttributeTemplate resolve_* method to allow it to
+    resolve an instance method's signature rather than a instance attribute.
+    The wrapped method must return the resolved method's signature
+    according to the given self type, args, and keywords.
+
+    It is used thusly:
+
+        class ComplexAttributes(AttributeTemplate):
+            @bound_function("complex.conjugate")
+            def resolve_conjugate(self, ty, args, kwds):
+                return ty
+
+    *template_key* (e.g. "complex.conjugate" above) will be used by the
+    target to look up the method's implementation, as a regular function.
+    """
+    def wrapper(method_resolver):
+        @functools.wraps(method_resolver)
+        def attribute_resolver(self, ty):
+            class MethodTemplate(AbstractTemplate):
+                key = template_key
+
+                def generic(_, args, kws):
+                    sig = method_resolver(self, ty, args, kws)
+                    if sig is not None and sig.recvr is None:
+                        sig = sig.replace(recvr=ty)
+                    return sig
+
+            return types.BoundFunction(MethodTemplate, ty)
+        return attribute_resolver
+    return wrapper
+
+
+# -----------------------------
+
+class Registry(object):
+    """
+    A registry of typing declarations.  The registry stores such declarations
+    for functions, attributes and globals.
+    """
+
+    def __init__(self):
+        self.functions = []
+        self.attributes = []
+        self.globals = []
+
+    def register(self, item):
+        assert issubclass(item, FunctionTemplate)
+        self.functions.append(item)
+        return item
+
+    def register_attr(self, item):
+        assert issubclass(item, AttributeTemplate)
+        self.attributes.append(item)
+        return item
+
+    def register_global(self, val=None, typ=None, **kwargs):
+        """
+        Register the typing of a global value.
+        Functional usage with a Numba type::
+            register_global(value, typ)
+
+        Decorator usage with a template class::
+            @register_global(value, typing_key=None)
+            class Template:
+                ...
+        """
+        if typ is not None:
+            # register_global(val, typ)
+            assert val is not None
+            assert not kwargs
+            self.globals.append((val, typ))
+        else:
+            def decorate(cls, typing_key):
+                class Template(cls):
+                    key = typing_key
+                if callable(val):
+                    typ = types.Function(Template)
+                else:
+                    raise TypeError("cannot infer type for global value %r")
+                self.globals.append((val, typ))
+                return cls
+
+            # register_global(val, typing_key=None)(<template class>)
+            assert val is not None
+            typing_key = kwargs.pop('typing_key', val)
+            assert not kwargs
+            if typing_key is val:
+                # Check the value is globally reachable, as it is going
+                # to be used as the key.
+                mod = sys.modules[val.__module__]
+                if getattr(mod, val.__name__) is not val:
+                    raise ValueError("%r is not globally reachable as '%s.%s'"
+                                     % (mod, val.__module__, val.__name__))
+
+            def decorator(cls):
+                return decorate(cls, typing_key)
+            return decorator
+
+
+class BaseRegistryLoader(object):
+    """
+    An incremental loader for a registry.  Each new call to
+    new_registrations() will iterate over the not yet seen registrations.
+
+    The reason for this object is multiple:
+    - there can be several contexts
+    - each context wants to install all registrations
+    - registrations can be added after the first installation, so contexts
+      must be able to get the "new" installations
+
+    Therefore each context maintains its own loaders for each existing
+    registry, without duplicating the registries themselves.
+    """
+
+    def __init__(self, registry):
+        self._registrations = dict(
+            (name, utils.stream_list(getattr(registry, name)))
+            for name in self.registry_items)
+
+    def new_registrations(self, name):
+        for item in next(self._registrations[name]):
+            yield item
+
+
+class RegistryLoader(BaseRegistryLoader):
+    """
+    An incremental loader for a typing registry.
+    """
+    registry_items = ('functions', 'attributes', 'globals')
+
+
+builtin_registry = Registry()
+infer = builtin_registry.register
+infer_getattr = builtin_registry.register_attr
+infer_global = builtin_registry.register_global
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/typeof.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/typeof.py
new file mode 100644
index 000000000..1510898e3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/typing/typeof.py
@@ -0,0 +1,284 @@
+from collections import namedtuple
+from functools import singledispatch
+import ctypes
+import enum
+
+import numpy as np
+
+from numba.core import types, utils, errors
+from numba.np import numpy_support
+from numba.np.numpy_support import numpy_version
+# terminal color markup
+_termcolor = errors.termcolor()
+
+# Note that the BitGenerator class exists in _bit_generator.pxd in
+# NumPy 1.18 (has underscore) and then bit_generator.pxd in NumPy 1.19
+if numpy_version < (1, 19):
+    from numpy.random._bit_generator import BitGenerator
+else:
+    from numpy.random.bit_generator import BitGenerator
+
+
+class Purpose(enum.Enum):
+    # Value being typed is used as an argument
+    argument = 1
+    # Value being typed is used as a constant
+    constant = 2
+
+
+_TypeofContext = namedtuple("_TypeofContext", ("purpose",))
+
+
+def typeof(val, purpose=Purpose.argument):
+    """
+    Get the Numba type of a Python value for the given purpose.
+    """
+    # Note the behaviour for Purpose.argument must match _typeof.c.
+    c = _TypeofContext(purpose)
+    ty = typeof_impl(val, c)
+    if ty is None:
+        msg = _termcolor.errmsg(
+            f"Cannot determine Numba type of {type(val)}")
+        raise ValueError(msg)
+    return ty
+
+
+@singledispatch
+def typeof_impl(val, c):
+    """
+    Generic typeof() implementation.
+    """
+    tp = _typeof_buffer(val, c)
+    if tp is not None:
+        return tp
+
+    # cffi is handled here as it does not expose a public base class
+    # for exported functions or CompiledFFI instances.
+    from numba.core.typing import cffi_utils
+    if cffi_utils.SUPPORTED:
+        if cffi_utils.is_cffi_func(val):
+            return cffi_utils.make_function_type(val)
+        if cffi_utils.is_ffi_instance(val):
+            return types.ffi
+
+    return getattr(val, "_numba_type_", None)
+
+
+def _typeof_buffer(val, c):
+    from numba.core.typing import bufproto
+    try:
+        m = memoryview(val)
+    except TypeError:
+        return
+    # Object has the buffer protocol
+    try:
+        dtype = bufproto.decode_pep3118_format(m.format, m.itemsize)
+    except ValueError:
+        return
+    type_class = bufproto.get_type_class(type(val))
+    layout = bufproto.infer_layout(m)
+    return type_class(dtype, m.ndim, layout=layout,
+                      readonly=m.readonly)
+
+
+@typeof_impl.register(ctypes._CFuncPtr)
+def _typeof_ctypes_function(val, c):
+    from .ctypes_utils import is_ctypes_funcptr, make_function_type
+    if is_ctypes_funcptr(val):
+        return make_function_type(val)
+
+
+@typeof_impl.register(type)
+def _typeof_type(val, c):
+    """
+    Type various specific Python types.
+    """
+    if issubclass(val, BaseException):
+        return types.ExceptionClass(val)
+    if issubclass(val, tuple) and hasattr(val, "_asdict"):
+        return types.NamedTupleClass(val)
+
+    if issubclass(val, np.generic):
+        return types.NumberClass(numpy_support.from_dtype(val))
+
+    if issubclass(val, types.Type):
+        return types.TypeRef(val)
+
+    from numba.typed import Dict
+    if issubclass(val, Dict):
+        return types.TypeRef(types.DictType)
+
+    from numba.typed import List
+    if issubclass(val, List):
+        return types.TypeRef(types.ListType)
+
+
+@typeof_impl.register(bool)
+def _typeof_bool(val, c):
+    return types.boolean
+
+
+@typeof_impl.register(float)
+def _typeof_float(val, c):
+    return types.float32
+
+
+@typeof_impl.register(complex)
+def _typeof_complex(val, c):
+    return types.complex64
+
+
+@typeof_impl.register(int)
+def _typeof_int(val, c):
+    # As in _typeof.c
+    nbits = utils.bit_length(val)
+    if nbits < 32:
+        typ = types.intp
+    elif nbits < 64:
+        typ = types.int64
+    elif nbits == 64 and val >= 0:
+        typ = types.uint64
+    else:
+        raise ValueError("Int value is too large: %s" % val)
+    return typ
+
+
+@typeof_impl.register(np.generic)
+def _typeof_numpy_scalar(val, c):
+    try:
+        return numpy_support.map_arrayscalar_type(val)
+    except NotImplementedError:
+        pass
+
+
+@typeof_impl.register(str)
+def _typeof_str(val, c):
+    return types.string
+
+
+@typeof_impl.register(type((lambda a: a).__code__))
+def _typeof_code(val, c):
+    return types.code_type
+
+
+@typeof_impl.register(type(None))
+def _typeof_none(val, c):
+    return types.none
+
+
+@typeof_impl.register(type(Ellipsis))
+def _typeof_ellipsis(val, c):
+    return types.ellipsis
+
+
+@typeof_impl.register(tuple)
+def _typeof_tuple(val, c):
+    tys = [typeof_impl(v, c) for v in val]
+    if any(ty is None for ty in tys):
+        return
+    return types.BaseTuple.from_types(tys, type(val))
+
+
+@typeof_impl.register(list)
+def _typeof_list(val, c):
+    if len(val) == 0:
+        raise ValueError("Cannot type empty list")
+    ty = typeof_impl(val[0], c)
+    if ty is None:
+        raise ValueError(
+            f"Cannot type list element type {type(val[0])}")
+    return types.List(ty, reflected=True)
+
+
+@typeof_impl.register(set)
+def _typeof_set(val, c):
+    if len(val) == 0:
+        raise ValueError("Cannot type empty set")
+    item = next(iter(val))
+    ty = typeof_impl(item, c)
+    if ty is None:
+        raise ValueError(
+            f"Cannot type set element type {type(item)}")
+    return types.Set(ty, reflected=True)
+
+
+@typeof_impl.register(slice)
+def _typeof_slice(val, c):
+    return types.slice2_type if val.step in (None, 1) else types.slice3_type
+
+
+@typeof_impl.register(enum.Enum)
+@typeof_impl.register(enum.IntEnum)
+def _typeof_enum(val, c):
+    clsty = typeof_impl(type(val), c)
+    return clsty.member_type
+
+
+@typeof_impl.register(enum.EnumMeta)
+def _typeof_enum_class(val, c):
+    cls = val
+    members = list(cls.__members__.values())
+    if len(members) == 0:
+        raise ValueError("Cannot type enum with no members")
+    dtypes = {typeof_impl(mem.value, c) for mem in members}
+    if len(dtypes) > 1:
+        raise ValueError("Cannot type heterogeneous enum: "
+                         "got value types %s"
+                         % ", ".join(sorted(str(ty) for ty in dtypes)))
+    if issubclass(val, enum.IntEnum):
+        typecls = types.IntEnumClass
+    else:
+        typecls = types.EnumClass
+    return typecls(cls, dtypes.pop())
+
+
+@typeof_impl.register(np.dtype)
+def _typeof_dtype(val, c):
+    tp = numpy_support.from_dtype(val)
+    return types.DType(tp)
+
+
+@typeof_impl.register(np.ndarray)
+def _typeof_ndarray(val, c):
+    try:
+        dtype = numpy_support.from_dtype(val.dtype)
+    except errors.NumbaNotImplementedError:
+        raise errors.NumbaValueError(f"Unsupported array dtype: {val.dtype}")
+    layout = numpy_support.map_layout(val)
+    readonly = not val.flags.writeable
+    return types.Array(dtype, val.ndim, layout, readonly=readonly)
+
+
+@typeof_impl.register(types.NumberClass)
+def _typeof_number_class(val, c):
+    return val
+
+
+@typeof_impl.register(types.Literal)
+def _typeof_literal(val, c):
+    return val
+
+
+@typeof_impl.register(types.TypeRef)
+def _typeof_typeref(val, c):
+    return val
+
+
+@typeof_impl.register(types.Type)
+def _typeof_nb_type(val, c):
+    if isinstance(val, types.BaseFunction):
+        return val
+    elif isinstance(val, (types.Number, types.Boolean)):
+        return types.NumberClass(val)
+    else:
+        return types.TypeRef(val)
+
+
+@typeof_impl.register(BitGenerator)
+def typeof_numpy_random_bitgen(val, c):
+    return types.NumPyRandomBitGeneratorType(val)
+
+
+@typeof_impl.register(np.random.Generator)
+def typeof_random_generator(val, c):
+    return types.NumPyRandomGeneratorType(val)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/bytes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/bytes.py
new file mode 100644
index 000000000..3c99be1bd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/bytes.py
@@ -0,0 +1,49 @@
+"""
+This file provides internal compiler utilities that support certain special
+operations with bytes and workarounds for limitations enforced in userland.
+"""
+
+from numba.core.extending import intrinsic
+from llvmlite import ir
+from numba.core import types, cgutils
+
+
+@intrinsic
+def grab_byte(typingctx, data, offset):
+    # returns a byte at a given offset in data
+    def impl(context, builder, signature, args):
+        data, idx = args
+        ptr = builder.bitcast(data, ir.IntType(8).as_pointer())
+        ch = builder.load(builder.gep(ptr, [idx]))
+        return ch
+
+    sig = types.uint8(types.voidptr, types.intp)
+    return sig, impl
+
+
+@intrinsic
+def grab_uint64_t(typingctx, data, offset):
+    # returns a uint64_t at a given offset in data
+    def impl(context, builder, signature, args):
+        data, idx = args
+        ptr = builder.bitcast(data, ir.IntType(64).as_pointer())
+        ch = builder.load(builder.gep(ptr, [idx]))
+        return ch
+    sig = types.uint64(types.voidptr, types.intp)
+    return sig, impl
+
+
+@intrinsic
+def memcpy_region(typingctx, dst, dst_offset, src, src_offset, nbytes, align):
+    '''Copy nbytes from *(src + src_offset) to *(dst + dst_offset)'''
+    def codegen(context, builder, signature, args):
+        [dst_val, dst_offset_val, src_val, src_offset_val, nbytes_val,
+         align_val] = args
+        src_ptr = builder.gep(src_val, [src_offset_val])
+        dst_ptr = builder.gep(dst_val, [dst_offset_val])
+        cgutils.raw_memcpy(builder, dst_ptr, src_ptr, nbytes_val, align_val)
+        return context.get_dummy_value()
+
+    sig = types.void(types.voidptr, types.intp, types.voidptr, types.intp,
+                     types.intp, types.intp)
+    return sig, codegen
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/eh.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/eh.py
new file mode 100644
index 000000000..56132d50f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/eh.py
@@ -0,0 +1,62 @@
+"""
+Exception handling intrinsics.
+"""
+
+from numba.core import types, errors, cgutils
+from numba.core.extending import intrinsic
+
+
+@intrinsic
+def exception_check(typingctx):
+    """An intrinsic to check if an exception is raised
+    """
+    def codegen(context, builder, signature, args):
+        nrt = context.nrt
+        return nrt.eh_check(builder)
+
+    restype = types.boolean
+    return restype(), codegen
+
+
+@intrinsic
+def mark_try_block(typingctx):
+    """An intrinsic to mark the start of a *try* block.
+    """
+    def codegen(context, builder, signature, args):
+        nrt = context.nrt
+        nrt.eh_try(builder)
+        return context.get_dummy_value()
+
+    restype = types.none
+    return restype(), codegen
+
+
+@intrinsic
+def end_try_block(typingctx):
+    """An intrinsic to mark the end of a *try* block.
+    """
+    def codegen(context, builder, signature, args):
+        nrt = context.nrt
+        nrt.eh_end_try(builder)
+        return context.get_dummy_value()
+
+    restype = types.none
+    return restype(), codegen
+
+
+@intrinsic
+def exception_match(typingctx, exc_value, exc_class):
+    """Basically do ``isinstance(exc_value, exc_class)`` for exception objects.
+    Used in ``except Exception:`` syntax.
+    """
+    # Check for our limitation
+    if exc_class.exc_class is not Exception:
+        msg = "Exception matching is limited to {}"
+        raise errors.UnsupportedError(msg.format(Exception))
+
+    def codegen(context, builder, signature, args):
+        # Intentionally always True.
+        return cgutils.true_bit
+
+    restype = types.boolean
+    return restype(exc_value, exc_class), codegen
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/nrt.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/nrt.py
new file mode 100644
index 000000000..673f0130f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/nrt.py
@@ -0,0 +1,20 @@
+"""
+Contains unsafe intrinsic that calls NRT C API
+"""
+
+from numba.core import types
+from numba.core.typing import signature
+from numba.core.extending import intrinsic
+
+
+@intrinsic
+def NRT_get_api(tyctx):
+    """NRT_get_api()
+
+    Calls NRT_get_api() from the NRT C API
+    Returns LLVM Type i8* (void pointer)
+    """
+    def codegen(cgctx, builder, sig, args):
+        return cgctx.nrt.get_nrt_api(builder)
+    sig = signature(types.voidptr)
+    return sig, codegen
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/refcount.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/refcount.py
new file mode 100644
index 000000000..3e7931460
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/unsafe/refcount.py
@@ -0,0 +1,80 @@
+"""
+Helpers to see the refcount information of an object
+"""
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core.extending import intrinsic
+
+from numba.core.runtime.nrtdynmod import _meminfo_struct_type
+
+
+@intrinsic
+def dump_refcount(typingctx, obj):
+    """Dump the refcount of an object to stdout.
+
+    Returns True if and only if object is reference-counted and NRT is enabled.
+    """
+    def codegen(context, builder, signature, args):
+        [obj] = args
+        [ty] = signature.args
+        # A sequence of (type, meminfo)
+        meminfos = []
+        if context.enable_nrt:
+            tmp_mis = context.nrt.get_meminfos(builder, ty, obj)
+            meminfos.extend(tmp_mis)
+
+        if meminfos:
+            pyapi = context.get_python_api(builder)
+            gil_state = pyapi.gil_ensure()
+            pyapi.print_string("dump refct of {}".format(ty))
+            for ty, mi in meminfos:
+                miptr = builder.bitcast(mi, _meminfo_struct_type.as_pointer())
+                refctptr = cgutils.gep_inbounds(builder, miptr, 0, 0)
+                refct = builder.load(refctptr)
+
+                pyapi.print_string(" | {} refct=".format(ty))
+                # "%zu" is not portable.  just truncate refcount to 32-bit.
+                # that's good enough for a debugging util.
+                refct_32bit = builder.trunc(refct, ir.IntType(32))
+                printed = cgutils.snprintf_stackbuffer(
+                    builder, 30, "%d [%p]", refct_32bit, miptr
+                )
+                pyapi.sys_write_stdout(printed)
+
+            pyapi.print_string(";\n")
+            pyapi.gil_release(gil_state)
+            return cgutils.true_bit
+        else:
+            return cgutils.false_bit
+
+    sig = types.bool_(obj)
+    return sig, codegen
+
+
+@intrinsic
+def get_refcount(typingctx, obj):
+    """Get the current refcount of an object.
+
+    FIXME: only handles the first object
+    """
+    def codegen(context, builder, signature, args):
+        [obj] = args
+        [ty] = signature.args
+        # A sequence of (type, meminfo)
+        meminfos = []
+        if context.enable_nrt:
+            tmp_mis = context.nrt.get_meminfos(builder, ty, obj)
+            meminfos.extend(tmp_mis)
+        refcounts = []
+        if meminfos:
+            for ty, mi in meminfos:
+                miptr = builder.bitcast(mi, _meminfo_struct_type.as_pointer())
+                refctptr = cgutils.gep_inbounds(builder, miptr, 0, 0)
+                refct = builder.load(refctptr)
+                refct_32bit = builder.trunc(refct, ir.IntType(32))
+                refcounts.append(refct_32bit)
+        return refcounts[0]
+
+    sig = types.int32(obj)
+    return sig, codegen
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/untyped_passes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/untyped_passes.py
new file mode 100644
index 000000000..d73f18d41
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/untyped_passes.py
@@ -0,0 +1,1722 @@
+from collections import defaultdict, namedtuple
+from contextlib import contextmanager
+from copy import deepcopy, copy
+import warnings
+
+from numba.core.compiler_machinery import (FunctionPass, AnalysisPass,
+                                           SSACompliantMixin, register_pass)
+from numba.core import (errors, types, ir, bytecode, postproc, rewrites, config,
+                        transforms)
+from numba.misc.special import literal_unroll
+from numba.core.analysis import (dead_branch_prune, rewrite_semantic_constants,
+                                 find_literally_calls, compute_cfg_from_blocks,
+                                 compute_use_defs)
+from numba.core.ir_utils import (guard, resolve_func_from_module, simplify_CFG,
+                                 GuardException, convert_code_obj_to_function,
+                                 mk_unique_var, build_definitions,
+                                 replace_var_names, get_name_var_table,
+                                 compile_to_numba_ir, get_definition,
+                                 find_max_label, rename_labels,
+                                 transfer_scope, fixup_var_define_in_scope,
+                                 )
+from numba.core.ssa import reconstruct_ssa
+from numba.core import interpreter
+
+
+@contextmanager
+def fallback_context(state, msg):
+    """
+    Wraps code that would signal a fallback to object mode
+    """
+    try:
+        yield
+    except Exception as e:
+        if not state.status.can_fallback:
+            raise
+        else:
+            # Clear all references attached to the traceback
+            e = e.with_traceback(None)
+            # this emits a warning containing the error message body in the
+            # case of fallback from npm to objmode
+            loop_lift = '' if state.flags.enable_looplift else 'OUT'
+            msg_rewrite = ("\nCompilation is falling back to object mode "
+                           "WITH%s looplifting enabled because %s"
+                           % (loop_lift, msg))
+            warnings.warn_explicit('%s due to: %s' % (msg_rewrite, e),
+                                   errors.NumbaWarning,
+                                   state.func_id.filename,
+                                   state.func_id.firstlineno)
+            raise
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class ExtractByteCode(FunctionPass):
+    _name = "extract_bytecode"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Extract bytecode from function
+        """
+        func_id = state['func_id']
+        bc = bytecode.ByteCode(func_id)
+        if config.DUMP_BYTECODE:
+            print(bc.dump())
+
+        state['bc'] = bc
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class TranslateByteCode(FunctionPass):
+    _name = "translate_bytecode"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Analyze bytecode and translating to Numba IR
+        """
+        func_id = state['func_id']
+        bc = state['bc']
+        interp = interpreter.Interpreter(func_id)
+        func_ir = interp.interpret(bc)
+        state["func_ir"] = func_ir
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class FixupArgs(FunctionPass):
+    _name = "fixup_args"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        state['nargs'] = state['func_ir'].arg_count
+        if not state['args'] and state['flags'].force_pyobject:
+            # Allow an empty argument types specification when object mode
+            # is explicitly requested.
+            state['args'] = (types.pyobject,) * state['nargs']
+        elif len(state['args']) != state['nargs']:
+            raise TypeError("Signature mismatch: %d argument types given, "
+                            "but function takes %d arguments"
+                            % (len(state['args']), state['nargs']))
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class IRProcessing(FunctionPass):
+    _name = "ir_processing"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        func_ir = state['func_ir']
+        post_proc = postproc.PostProcessor(func_ir)
+        post_proc.run()
+
+        if config.DEBUG or config.DUMP_IR:
+            name = func_ir.func_id.func_qualname
+            print(("IR DUMP: %s" % name).center(80, "-"))
+            func_ir.dump()
+            if func_ir.is_generator:
+                print(("GENERATOR INFO: %s" % name).center(80, "-"))
+                func_ir.dump_generator_info()
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class RewriteSemanticConstants(FunctionPass):
+    _name = "rewrite_semantic_constants"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        This prunes dead branches, a dead branch is one which is derivable as
+        not taken at compile time purely based on const/literal evaluation.
+        """
+        assert state.func_ir
+        msg = ('Internal error in pre-inference dead branch pruning '
+               'pass encountered during compilation of '
+               'function "%s"' % (state.func_id.func_name,))
+        with fallback_context(state, msg):
+            rewrite_semantic_constants(state.func_ir, state.args)
+
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class DeadBranchPrune(SSACompliantMixin, FunctionPass):
+    _name = "dead_branch_prune"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        This prunes dead branches, a dead branch is one which is derivable as
+        not taken at compile time purely based on const/literal evaluation.
+        """
+
+        # purely for demonstration purposes, obtain the analysis from a pass
+        # declare as a required dependent
+        semantic_const_analysis = self.get_analysis(type(self))  # noqa
+
+        assert state.func_ir
+        msg = ('Internal error in pre-inference dead branch pruning '
+               'pass encountered during compilation of '
+               'function "%s"' % (state.func_id.func_name,))
+        with fallback_context(state, msg):
+            dead_branch_prune(state.func_ir, state.args)
+
+        return True
+
+    def get_analysis_usage(self, AU):
+        AU.add_required(RewriteSemanticConstants)
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class InlineClosureLikes(FunctionPass):
+    _name = "inline_closure_likes"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        # Ensure we have an IR and type information.
+        assert state.func_ir
+
+        # if the return type is a pyobject, there's no type info available and
+        # no ability to resolve certain typed function calls in the array
+        # inlining code, use this variable to indicate
+        typed_pass = not isinstance(state.return_type, types.misc.PyObject)
+        from numba.core.inline_closurecall import InlineClosureCallPass
+        inline_pass = InlineClosureCallPass(
+            state.func_ir,
+            state.flags.auto_parallel,
+            state.parfor_diagnostics.replaced_fns,
+            typed_pass)
+        inline_pass.run()
+
+        # Remove all Dels, and re-run postproc
+        post_proc = postproc.PostProcessor(state.func_ir)
+        post_proc.run()
+
+        fixup_var_define_in_scope(state.func_ir.blocks)
+
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class GenericRewrites(FunctionPass):
+    _name = "generic_rewrites"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Perform any intermediate representation rewrites before type
+        inference.
+        """
+        assert state.func_ir
+        msg = ('Internal error in pre-inference rewriting '
+               'pass encountered during compilation of '
+               'function "%s"' % (state.func_id.func_name,))
+        with fallback_context(state, msg):
+            rewrites.rewrite_registry.apply('before-inference', state)
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class WithLifting(FunctionPass):
+    _name = "with_lifting"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Extract with-contexts
+        """
+        main, withs = transforms.with_lifting(
+            func_ir=state.func_ir,
+            typingctx=state.typingctx,
+            targetctx=state.targetctx,
+            flags=state.flags,
+            locals=state.locals,
+        )
+        if withs:
+            from numba.core.compiler import compile_ir, _EarlyPipelineCompletion
+            cres = compile_ir(state.typingctx, state.targetctx, main,
+                              state.args, state.return_type,
+                              state.flags, state.locals,
+                              lifted=tuple(withs), lifted_from=None,
+                              pipeline_class=type(state.pipeline))
+            raise _EarlyPipelineCompletion(cres)
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class InlineInlinables(FunctionPass):
+    """
+    This pass will inline a function wrapped by the numba.jit decorator directly
+    into the site of its call depending on the value set in the 'inline' kwarg
+    to the decorator.
+
+    This is an untyped pass. CFG simplification is performed at the end of the
+    pass but no block level clean up is performed on the mutated IR (typing
+    information is not available to do so).
+    """
+    _name = "inline_inlinables"
+    _DEBUG = False
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        """Run inlining of inlinables
+        """
+        if self._DEBUG:
+            print('before inline'.center(80, '-'))
+            print(state.func_ir.dump())
+            print(''.center(80, '-'))
+
+        from numba.core.inline_closurecall import (InlineWorker,
+                                                   callee_ir_validator)
+        inline_worker = InlineWorker(state.typingctx,
+                                     state.targetctx,
+                                     state.locals,
+                                     state.pipeline,
+                                     state.flags,
+                                     validator=callee_ir_validator)
+
+        modified = False
+        # use a work list, look for call sites via `ir.Expr.op == call` and
+        # then pass these to `self._do_work` to make decisions about inlining.
+        work_list = list(state.func_ir.blocks.items())
+        while work_list:
+            label, block = work_list.pop()
+            for i, instr in enumerate(block.body):
+                if isinstance(instr, ir.Assign):
+                    expr = instr.value
+                    if isinstance(expr, ir.Expr) and expr.op == 'call':
+                        if guard(self._do_work, state, work_list, block, i,
+                                 expr, inline_worker):
+                            modified = True
+                            break  # because block structure changed
+
+        if modified:
+            # clean up unconditional branches that appear due to inlined
+            # functions introducing blocks
+            cfg = compute_cfg_from_blocks(state.func_ir.blocks)
+            for dead in cfg.dead_nodes():
+                del state.func_ir.blocks[dead]
+            post_proc = postproc.PostProcessor(state.func_ir)
+            post_proc.run()
+            state.func_ir.blocks = simplify_CFG(state.func_ir.blocks)
+
+        if self._DEBUG:
+            print('after inline'.center(80, '-'))
+            print(state.func_ir.dump())
+            print(''.center(80, '-'))
+        return True
+
+    def _do_work(self, state, work_list, block, i, expr, inline_worker):
+        from numba.core.compiler import run_frontend
+        from numba.core.cpu import InlineOptions
+
+        # try and get a definition for the call, this isn't always possible as
+        # it might be a eval(str)/part generated awaiting update etc. (parfors)
+        to_inline = None
+        try:
+            to_inline = state.func_ir.get_definition(expr.func)
+        except Exception:
+            if self._DEBUG:
+                print("Cannot find definition for %s" % expr.func)
+            return False
+        # do not handle closure inlining here, another pass deals with that.
+        if getattr(to_inline, 'op', False) == 'make_function':
+            return False
+
+        # see if the definition is a "getattr", in which case walk the IR to
+        # try and find the python function via the module from which it's
+        # imported, this should all be encoded in the IR.
+        if getattr(to_inline, 'op', False) == 'getattr':
+            val = resolve_func_from_module(state.func_ir, to_inline)
+        else:
+            # This is likely a freevar or global
+            #
+            # NOTE: getattr 'value' on a call may fail if it's an ir.Expr as
+            # getattr is overloaded to look in _kws.
+            try:
+                val = getattr(to_inline, 'value', False)
+            except Exception:
+                raise GuardException
+
+        # if something was found...
+        if val:
+            # check it's dispatcher-like, the targetoptions attr holds the
+            # kwargs supplied in the jit decorator and is where 'inline' will
+            # be if it is present.
+            topt = getattr(val, 'targetoptions', False)
+            if topt:
+                inline_type = topt.get('inline', None)
+                # has 'inline' been specified?
+                if inline_type is not None:
+                    inline_opt = InlineOptions(inline_type)
+                    # Could this be inlinable?
+                    if not inline_opt.is_never_inline:
+                        # yes, it could be inlinable
+                        do_inline = True
+                        pyfunc = val.py_func
+                        # Has it got an associated cost model?
+                        if inline_opt.has_cost_model:
+                            # yes, it has a cost model, use it to determine
+                            # whether to do the inline
+                            py_func_ir = run_frontend(pyfunc)
+                            do_inline = inline_type(expr, state.func_ir,
+                                                    py_func_ir)
+                        # if do_inline is True then inline!
+                        if do_inline:
+                            _, _, _, new_blocks = \
+                                inline_worker.inline_function(state.func_ir,
+                                                              block, i, pyfunc,)
+                            if work_list is not None:
+                                for blk in new_blocks:
+                                    work_list.append(blk)
+                            return True
+        return False
+
+
+@register_pass(mutates_CFG=False, analysis_only=False)
+class PreserveIR(AnalysisPass):
+    """
+    Preserves the IR in the metadata
+    """
+
+    _name = "preserve_ir"
+
+    def __init__(self):
+        AnalysisPass.__init__(self)
+
+    def run_pass(self, state):
+        state.metadata['preserved_ir'] = state.func_ir.copy()
+        return False
+
+
+@register_pass(mutates_CFG=False, analysis_only=True)
+class FindLiterallyCalls(FunctionPass):
+    """Find calls to `numba.literally()` and signal if its requirement is not
+    satisfied.
+    """
+    _name = "find_literally"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        find_literally_calls(state.func_ir, state.args)
+        return False
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class CanonicalizeLoopExit(FunctionPass):
+    """A pass to canonicalize loop exit by splitting it from function exit.
+    """
+    _name = "canonicalize_loop_exit"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        fir = state.func_ir
+        cfg = compute_cfg_from_blocks(fir.blocks)
+        status = False
+        for loop in cfg.loops().values():
+            for exit_label in loop.exits:
+                if exit_label in cfg.exit_points():
+                    self._split_exit_block(fir, cfg, exit_label)
+                    status = True
+
+        fir._reset_analysis_variables()
+
+        vlt = postproc.VariableLifetime(fir.blocks)
+        fir.variable_lifetime = vlt
+        return status
+
+    def _split_exit_block(self, fir, cfg, exit_label):
+        curblock = fir.blocks[exit_label]
+        newlabel = exit_label + 1
+        newlabel = find_max_label(fir.blocks) + 1
+        fir.blocks[newlabel] = curblock
+        newblock = ir.Block(scope=curblock.scope, loc=curblock.loc)
+        newblock.append(ir.Jump(newlabel, loc=curblock.loc))
+        fir.blocks[exit_label] = newblock
+        # Rename all labels
+        fir.blocks = rename_labels(fir.blocks)
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class CanonicalizeLoopEntry(FunctionPass):
+    """A pass to canonicalize loop header by splitting it from function entry.
+
+    This is needed for loop-lifting; esp in py3.8
+    """
+    _name = "canonicalize_loop_entry"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        fir = state.func_ir
+        cfg = compute_cfg_from_blocks(fir.blocks)
+        status = False
+        for loop in cfg.loops().values():
+            if len(loop.entries) == 1:
+                [entry_label] = loop.entries
+                if entry_label == cfg.entry_point():
+                    self._split_entry_block(fir, cfg, loop, entry_label)
+                    status = True
+        fir._reset_analysis_variables()
+
+        vlt = postproc.VariableLifetime(fir.blocks)
+        fir.variable_lifetime = vlt
+        return status
+
+    def _split_entry_block(self, fir, cfg, loop, entry_label):
+        # Find iterator inputs into the for-loop header
+        header_block = fir.blocks[loop.header]
+        deps = set()
+        for expr in header_block.find_exprs(op="iternext"):
+            deps.add(expr.value)
+        # Find the getiter for each iterator
+        entry_block = fir.blocks[entry_label]
+
+        # Find the start of loop entry statement that needs to be included.
+        startpt = None
+        list_of_insts = list(entry_block.find_insts(ir.Assign))
+        for assign in reversed(list_of_insts):
+            if assign.target in deps:
+                rhs = assign.value
+                if isinstance(rhs, ir.Var):
+                    if rhs.is_temp:
+                        deps.add(rhs)
+                elif isinstance(rhs, ir.Expr):
+                    expr = rhs
+                    if expr.op == 'getiter':
+                        startpt = assign
+                        if expr.value.is_temp:
+                            deps.add(expr.value)
+                    elif expr.op == 'call':
+                        defn = guard(get_definition, fir, expr.func)
+                        if isinstance(defn, ir.Global):
+                            if expr.func.is_temp:
+                                deps.add(expr.func)
+                elif isinstance(rhs, ir.Global) and rhs.value is range:
+                    startpt = assign
+
+        if startpt is None:
+            return
+
+        splitpt = entry_block.body.index(startpt)
+        new_block = entry_block.copy()
+        new_block.body = new_block.body[splitpt:]
+        new_block.loc = new_block.body[0].loc
+        new_label = find_max_label(fir.blocks) + 1
+        entry_block.body = entry_block.body[:splitpt]
+        entry_block.append(ir.Jump(new_label, loc=new_block.loc))
+
+        fir.blocks[new_label] = new_block
+        # Rename all labels
+        fir.blocks = rename_labels(fir.blocks)
+
+
+@register_pass(mutates_CFG=False, analysis_only=True)
+class PrintIRCFG(FunctionPass):
+    _name = "print_ir_cfg"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+        self._ver = 0
+
+    def run_pass(self, state):
+        fir = state.func_ir
+        self._ver += 1
+        fir.render_dot(filename_prefix='v{}'.format(self._ver)).render()
+        return False
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class MakeFunctionToJitFunction(FunctionPass):
+    """
+    This swaps an ir.Expr.op == "make_function" i.e. a closure, for a compiled
+    function containing the closure body and puts it in ir.Global. It's a 1:1
+    statement value swap. `make_function` is already untyped
+    """
+    _name = "make_function_op_code_to_jit_function"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        from numba import njit
+        func_ir = state.func_ir
+        mutated = False
+        for idx, blk in func_ir.blocks.items():
+            for stmt in blk.body:
+                if isinstance(stmt, ir.Assign):
+                    if isinstance(stmt.value, ir.Expr):
+                        if stmt.value.op == "make_function":
+                            node = stmt.value
+                            getdef = func_ir.get_definition
+                            kw_default = getdef(node.defaults)
+                            ok = False
+                            if (kw_default is None or
+                                    isinstance(kw_default, ir.Const)):
+                                ok = True
+                            elif isinstance(kw_default, tuple):
+                                ok = all([isinstance(getdef(x), ir.Const)
+                                          for x in kw_default])
+                            elif isinstance(kw_default, ir.Expr):
+                                if kw_default.op != "build_tuple":
+                                    continue
+                                ok = all([isinstance(getdef(x), ir.Const)
+                                          for x in kw_default.items])
+                            if not ok:
+                                continue
+
+                            pyfunc = convert_code_obj_to_function(node, func_ir)
+                            func = njit()(pyfunc)
+                            new_node = ir.Global(node.code.co_name, func,
+                                                 stmt.loc)
+                            stmt.value = new_node
+                            mutated |= True
+
+        # if a change was made the del ordering is probably wrong, patch up
+        if mutated:
+            post_proc = postproc.PostProcessor(func_ir)
+            post_proc.run()
+
+        return mutated
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class TransformLiteralUnrollConstListToTuple(FunctionPass):
+    """ This pass spots a `literal_unroll([<constant values>])` and rewrites it
+    as a `literal_unroll(tuple(<constant values>))`.
+    """
+    _name = "transform_literal_unroll_const_list_to_tuple"
+
+    _accepted_types = (types.BaseTuple, types.LiteralList)
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        mutated = False
+        func_ir = state.func_ir
+        for label, blk in func_ir.blocks.items():
+            calls = [_ for _ in blk.find_exprs('call')]
+            for call in calls:
+                glbl = guard(get_definition, func_ir, call.func)
+                if glbl and isinstance(glbl, (ir.Global, ir.FreeVar)):
+                    # find a literal_unroll
+                    if glbl.value is literal_unroll:
+                        if len(call.args) > 1:
+                            msg = "literal_unroll takes one argument, found %s"
+                            raise errors.UnsupportedError(msg % len(call.args),
+                                                          call.loc)
+                        # get the arg, make sure its a build_list
+                        unroll_var = call.args[0]
+                        to_unroll = guard(get_definition, func_ir, unroll_var)
+                        if (isinstance(to_unroll, ir.Expr) and
+                                to_unroll.op == "build_list"):
+                            # make sure they are all const items in the list
+                            for i, item in enumerate(to_unroll.items):
+                                val = guard(get_definition, func_ir, item)
+                                if not val:
+                                    msg = ("multiple definitions for variable "
+                                           "%s, cannot resolve constant")
+                                    raise errors.UnsupportedError(msg % item,
+                                                                  to_unroll.loc)
+                                if not isinstance(val, ir.Const):
+                                    msg = ("Found non-constant value at "
+                                           "position %s in a list argument to "
+                                           "literal_unroll" % i)
+                                    raise errors.UnsupportedError(msg,
+                                                                  to_unroll.loc)
+                            # The above appears ok, now swap the build_list for
+                            # a built tuple.
+
+                            # find the assignment for the unroll target
+                            to_unroll_lhs = guard(get_definition, func_ir,
+                                                  unroll_var, lhs_only=True)
+
+                            if to_unroll_lhs is None:
+                                msg = ("multiple definitions for variable "
+                                       "%s, cannot resolve constant")
+                                raise errors.UnsupportedError(msg % unroll_var,
+                                                              to_unroll.loc)
+                            # scan all blocks looking for the LHS
+                            for b in func_ir.blocks.values():
+                                asgn = b.find_variable_assignment(
+                                    to_unroll_lhs.name)
+                                if asgn is not None:
+                                    break
+                            else:
+                                msg = ("Cannot find assignment for known "
+                                       "variable %s") % to_unroll_lhs.name
+                                raise errors.CompilerError(msg, to_unroll.loc)
+
+                            # Create a tuple with the list items as contents
+                            tup = ir.Expr.build_tuple(to_unroll.items,
+                                                      to_unroll.loc)
+
+                            # swap the list for the tuple
+                            asgn.value = tup
+                            mutated = True
+                        elif (isinstance(to_unroll, ir.Expr) and
+                              to_unroll.op == "build_tuple"):
+                            # this is fine, do nothing
+                            pass
+                        elif (isinstance(to_unroll, (ir.Global, ir.FreeVar)) and
+                              isinstance(to_unroll.value, tuple)):
+                            # this is fine, do nothing
+                            pass
+                        elif isinstance(to_unroll, ir.Arg):
+                            # this is only fine if the arg is a tuple
+                            ty = state.typemap[to_unroll.name]
+                            if not isinstance(ty, self._accepted_types):
+                                msg = ("Invalid use of literal_unroll with a "
+                                       "function argument, only tuples are "
+                                       "supported as function arguments, found "
+                                       "%s") % ty
+                                raise errors.UnsupportedError(msg,
+                                                              to_unroll.loc)
+                        else:
+                            extra = None
+                            if isinstance(to_unroll, ir.Expr):
+                                # probably a slice
+                                if to_unroll.op == "getitem":
+                                    ty = state.typemap[to_unroll.value.name]
+                                    # check if this is a tuple slice
+                                    if not isinstance(ty, self._accepted_types):
+                                        extra = "operation %s" % to_unroll.op
+                                        loc = to_unroll.loc
+                            elif isinstance(to_unroll, ir.Arg):
+                                extra = "non-const argument %s" % to_unroll.name
+                                loc = to_unroll.loc
+                            else:
+                                if to_unroll is None:
+                                    extra = ('multiple definitions of '
+                                             'variable "%s".' % unroll_var.name)
+                                    loc = unroll_var.loc
+                                else:
+                                    loc = to_unroll.loc
+                                    extra = "unknown problem"
+
+                            if extra:
+                                msg = ("Invalid use of literal_unroll, "
+                                       "argument should be a tuple or a list "
+                                       "of constant values. Failure reason: "
+                                       "found %s" % extra)
+                                raise errors.UnsupportedError(msg, loc)
+        return mutated
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class MixedContainerUnroller(FunctionPass):
+    _name = "mixed_container_unroller"
+
+    _DEBUG = False
+
+    _accepted_types = (types.BaseTuple, types.LiteralList)
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def analyse_tuple(self, tup):
+        """
+        Returns a map of type->list(indexes) for a typed tuple
+        """
+        d = defaultdict(list)
+        for i, ty in enumerate(tup):
+            d[ty].append(i)
+        return d
+
+    def add_offset_to_labels_w_ignore(self, blocks, offset, ignore=None):
+        """add an offset to all block labels and jump/branch targets
+        don't add an offset to anything in the ignore list
+        """
+        if ignore is None:
+            ignore = set()
+
+        new_blocks = {}
+        for l, b in blocks.items():
+            # some parfor last blocks might be empty
+            term = None
+            if b.body:
+                term = b.body[-1]
+            if isinstance(term, ir.Jump):
+                if term.target not in ignore:
+                    b.body[-1] = ir.Jump(term.target + offset, term.loc)
+            if isinstance(term, ir.Branch):
+                if term.truebr not in ignore:
+                    new_true = term.truebr + offset
+                else:
+                    new_true = term.truebr
+
+                if term.falsebr not in ignore:
+                    new_false = term.falsebr + offset
+                else:
+                    new_false = term.falsebr
+                b.body[-1] = ir.Branch(term.cond, new_true, new_false, term.loc)
+            new_blocks[l + offset] = b
+        return new_blocks
+
+    def inject_loop_body(self, switch_ir, loop_ir, caller_max_label,
+                         dont_replace, switch_data):
+        """
+        Injects the "loop body" held in `loop_ir` into `switch_ir` where ever
+        there is a statement of the form `SENTINEL.<int> = RHS`. It also:
+        * Finds and then deliberately does not relabel non-local jumps so as to
+          make the switch table suitable for injection into the IR from which
+          the loop body was derived.
+        * Looks for `typed_getitem` and wires them up to loop body version
+          specific variables or, if possible, directly writes in their constant
+          value at their use site.
+
+        Args:
+        - switch_ir, the switch table with SENTINELS as generated by
+          self.gen_switch
+        - loop_ir, the IR of the loop blocks (derived from the original func_ir)
+        - caller_max_label, the maximum label in the func_ir caller
+        - dont_replace, variables that should not be renamed (to handle
+          references to variables that are incoming at the loop head/escaping at
+          the loop exit.
+        - switch_data, the switch table data used to generated the switch_ir,
+          can be generated by self.analyse_tuple.
+
+        Returns:
+        - A type specific switch table with each case containing a versioned
+          loop body suitable for injection as a replacement for the loop_ir.
+        """
+
+        # Switch IR came from code gen, immediately relabel to prevent
+        # collisions with IR derived from the user code (caller)
+        switch_ir.blocks = self.add_offset_to_labels_w_ignore(
+            switch_ir.blocks, caller_max_label + 1)
+
+        # Find the sentinels and validate the form
+        sentinel_exits = set()
+        sentinel_blocks = []
+        for lbl, blk in switch_ir.blocks.items():
+            for i, stmt in enumerate(blk.body):
+                if isinstance(stmt, ir.Assign):
+                    if "SENTINEL" in stmt.target.name:
+                        sentinel_blocks.append(lbl)
+                        sentinel_exits.add(blk.body[-1].target)
+                        break
+
+        assert len(sentinel_exits) == 1  # should only be 1 exit
+        switch_ir.blocks.pop(sentinel_exits.pop())  # kill the exit, it's dead
+
+        # find jumps that are non-local, we won't relabel these
+        ignore_set = set()
+        local_lbl = [x for x in loop_ir.blocks.keys()]
+        for lbl, blk in loop_ir.blocks.items():
+            for i, stmt in enumerate(blk.body):
+                if isinstance(stmt, ir.Jump):
+                    if stmt.target not in local_lbl:
+                        ignore_set.add(stmt.target)
+                if isinstance(stmt, ir.Branch):
+                    if stmt.truebr not in local_lbl:
+                        ignore_set.add(stmt.truebr)
+                    if stmt.falsebr not in local_lbl:
+                        ignore_set.add(stmt.falsebr)
+
+        # make sure the generated switch table matches the switch data
+        assert len(sentinel_blocks) == len(switch_data)
+
+        # replace the sentinel_blocks with the loop body
+        for lbl, branch_ty in zip(sentinel_blocks, switch_data.keys()):
+            loop_blocks = deepcopy(loop_ir.blocks)
+            # relabel blocks WRT switch table, each block replacement will shift
+            # the maximum label
+            max_label = max(switch_ir.blocks.keys())
+            loop_blocks = self.add_offset_to_labels_w_ignore(
+                loop_blocks, max_label + 1, ignore_set)
+
+            # start label
+            loop_start_lbl = min(loop_blocks.keys())
+
+            # fix the typed_getitem locations in the loop blocks
+            for blk in loop_blocks.values():
+                new_body = []
+                for stmt in blk.body:
+                    if isinstance(stmt, ir.Assign):
+                        if (isinstance(stmt.value, ir.Expr) and
+                                stmt.value.op == "typed_getitem"):
+                            if isinstance(branch_ty, types.Literal):
+                                new_const_name = mk_unique_var("branch_const")
+                                new_const_var = ir.Var(
+                                    blk.scope, new_const_name, stmt.loc)
+                                new_const_val = ir.Const(
+                                    branch_ty.literal_value, stmt.loc)
+                                const_assign = ir.Assign(
+                                    new_const_val, new_const_var, stmt.loc)
+                                new_assign = ir.Assign(
+                                    new_const_var, stmt.target, stmt.loc)
+                                new_body.append(const_assign)
+                                new_body.append(new_assign)
+                                dont_replace.append(new_const_name)
+                            else:
+                                orig = stmt.value
+                                new_typed_getitem = ir.Expr.typed_getitem(
+                                    value=orig.value, dtype=branch_ty,
+                                    index=orig.index, loc=orig.loc)
+                                new_assign = ir.Assign(
+                                    new_typed_getitem, stmt.target, stmt.loc)
+                                new_body.append(new_assign)
+                        else:
+                            new_body.append(stmt)
+                    else:
+                        new_body.append(stmt)
+                blk.body = new_body
+
+            # rename
+            var_table = get_name_var_table(loop_blocks)
+            drop_keys = []
+            for k, v in var_table.items():
+                if v.name in dont_replace:
+                    drop_keys.append(k)
+            for k in drop_keys:
+                var_table.pop(k)
+
+            new_var_dict = {}
+            for name, var in var_table.items():
+                new_var_dict[name] = mk_unique_var(name)
+            replace_var_names(loop_blocks, new_var_dict)
+
+            # clobber the sentinel body and then stuff in the rest
+            switch_ir.blocks[lbl] = deepcopy(loop_blocks[loop_start_lbl])
+            remaining_keys = [y for y in loop_blocks.keys()]
+            remaining_keys.remove(loop_start_lbl)
+            for k in remaining_keys:
+                switch_ir.blocks[k] = deepcopy(loop_blocks[k])
+
+        if self._DEBUG:
+            print("-" * 80 + "EXIT STUFFER")
+            switch_ir.dump()
+            print("-" * 80)
+
+        return switch_ir
+
+    def gen_switch(self, data, index):
+        """
+        Generates a function with a switch table like
+        def foo():
+            if PLACEHOLDER_INDEX in (<integers>):
+                SENTINEL = None
+            elif PLACEHOLDER_INDEX in (<integers>):
+                SENTINEL = None
+            ...
+            else:
+                raise RuntimeError
+
+        The data is a map of (type : indexes) for example:
+        (int64, int64, float64)
+        might give:
+        {int64: [0, 1], float64: [2]}
+
+        The index is the index variable for the driving range loop over the
+        mixed tuple.
+        """
+        elif_tplt = "\n\telif PLACEHOLDER_INDEX in (%s,):\n\t\tSENTINEL = None"
+
+        # Note regarding the insertion of the garbage/defeat variables below:
+        # These values have been designed and inserted to defeat a specific
+        # behaviour of the cpython optimizer. The optimization was introduced
+        # in Python 3.10.
+
+        # The URL for the BPO is:
+        # https://bugs.python.org/issue44626
+        # The code for the optimization can be found at:
+        # https://github.com/python/cpython/blob/d41abe8/Python/compile.c#L7533-L7557
+
+        # Essentially the CPython optimizer will inline the exit block under
+        # certain circumstances and thus replace the jump with a return if the
+        # exit block is small enough.  This is an issue for unroller, as it
+        # looks for a jump, not a return, when it inserts the generated switch
+        # table.
+
+        # Part of the condition for this optimization to be applied is that the
+        # exit block not exceed a certain (4 at the time of writing) number of
+        # bytecode instructions. We defeat the optimizer by inserting a
+        # sufficient number of instructions so that the exit block is big
+        # enough. We don't care about this garbage, because the generated exit
+        # block is discarded anyway when we smash the switch table into the
+        # original function and so all the inserted garbage is dropped again.
+
+        # The final lines of the stacktrace w/o this will look like:
+        #
+        #  File "/numba/numba/core/untyped_passes.py", line 830, \
+        #       in inject_loop_body
+        #   sentinel_exits.add(blk.body[-1].target)
+        # AttributeError: Failed in nopython mode pipeline \
+        #       (step: handles literal_unroll)
+        # Failed in literal_unroll_subpipeline mode pipeline \
+        #       (step: performs mixed container unroll)
+        # 'Return' object has no attribute 'target'
+        #
+        # Which indicates that a Return has been found instead of a Jump
+
+        b = ('def foo():\n\tif PLACEHOLDER_INDEX in (%s,):\n\t\t'
+             'SENTINEL = None\n%s\n\telse:\n\t\t'
+             'raise RuntimeError("Unreachable")\n\t'
+             'py310_defeat1 = 1\n\t'
+             'py310_defeat2 = 2\n\t'
+             'py310_defeat3 = 3\n\t'
+             'py310_defeat4 = 4\n\t'
+             )
+        keys = [k for k in data.keys()]
+
+        elifs = []
+        for i in range(1, len(keys)):
+            elifs.append(elif_tplt % ','.join(map(str, data[keys[i]])))
+        src = b % (','.join(map(str, data[keys[0]])), ''.join(elifs))
+        wstr = src
+        l = {}
+        exec(wstr, {}, l)
+        bfunc = l['foo']
+        branches = compile_to_numba_ir(bfunc, {})
+        for lbl, blk in branches.blocks.items():
+            for stmt in blk.body:
+                if isinstance(stmt, ir.Assign):
+                    if isinstance(stmt.value, ir.Global):
+                        if stmt.value.name == "PLACEHOLDER_INDEX":
+                            stmt.value = index
+        return branches
+
+    def apply_transform(self, state):
+        # compute new CFG
+        func_ir = state.func_ir
+        cfg = compute_cfg_from_blocks(func_ir.blocks)
+        # find loops
+        loops = cfg.loops()
+
+        # 0. Find the loops containing literal_unroll and store this
+        #    information
+        literal_unroll_info = dict()
+        unroll_info = namedtuple(
+            "unroll_info", [
+                "loop", "call", "arg", "getitem"])
+
+        def get_call_args(init_arg, want):
+            # Chases the assignment of a called value back through a specific
+            # call to a global function "want" and returns the arguments
+            # supplied to that function's call
+            some_call = get_definition(func_ir, init_arg)
+            if not isinstance(some_call, ir.Expr):
+                raise GuardException
+            if not some_call.op == "call":
+                raise GuardException
+            the_global = get_definition(func_ir, some_call.func)
+            if not isinstance(the_global, ir.Global):
+                raise GuardException
+            if the_global.value is not want:
+                raise GuardException
+            return some_call
+
+        for lbl, loop in loops.items():
+            # TODO: check the loop head has literal_unroll, if it does but
+            # does not conform to the following then raise
+
+            # scan loop header
+            iternexts = [_ for _ in
+                         func_ir.blocks[loop.header].find_exprs('iternext')]
+            if len(iternexts) != 1:
+                return False
+            for iternext in iternexts:
+                # Walk the canonicalised loop structure and check it
+                # Check loop form range(literal_unroll(container)))
+                phi = guard(get_definition, func_ir,  iternext.value)
+                if phi is None:
+                    continue
+
+                # check call global "range"
+                range_call = guard(get_call_args, phi.value, range)
+                if range_call is None:
+                    continue
+                range_arg = range_call.args[0]
+
+                # check call global "len"
+                len_call = guard(get_call_args, range_arg, len)
+                if len_call is None:
+                    continue
+                len_arg = len_call.args[0]
+
+                # check literal_unroll
+                literal_unroll_call = guard(get_definition, func_ir, len_arg)
+                if literal_unroll_call is None:
+                    continue
+                if not isinstance(literal_unroll_call, ir.Expr):
+                    continue
+                if literal_unroll_call.op != "call":
+                    continue
+                literal_func = getattr(literal_unroll_call, 'func', None)
+                if not literal_func:
+                    continue
+                call_func = guard(get_definition, func_ir,
+                                  literal_unroll_call.func)
+                if call_func is None:
+                    continue
+                call_func = call_func.value
+
+                if call_func is literal_unroll:
+                    assert len(literal_unroll_call.args) == 1
+                    arg = literal_unroll_call.args[0]
+                    typemap = state.typemap
+                    resolved_arg = guard(get_definition, func_ir, arg,
+                                         lhs_only=True)
+                    ty = typemap[resolved_arg.name]
+                    assert isinstance(ty, self._accepted_types)
+                    # loop header is spelled ok, now make sure the body
+                    # actually contains a getitem
+
+                    # find a "getitem"
+                    tuple_getitem = None
+                    for lbl in loop.body:
+                        blk = func_ir.blocks[lbl]
+                        for stmt in blk.body:
+                            if isinstance(stmt, ir.Assign):
+                                if (isinstance(stmt.value, ir.Expr) and
+                                        stmt.value.op == "getitem"):
+                                    # check for something like a[i]
+                                    if stmt.value.value != arg:
+                                        # that failed, so check for the
+                                        # definition
+                                        dfn = guard(get_definition, func_ir,
+                                                    stmt.value.value)
+                                        if dfn is None:
+                                            continue
+                                        try:
+                                            args = getattr(dfn, 'args', False)
+                                        except KeyError:
+                                            continue
+                                        if not args:
+                                            continue
+                                        if not args[0] == arg:
+                                            continue
+                                    target_ty = state.typemap[arg.name]
+                                    if not isinstance(target_ty,
+                                                      self._accepted_types):
+                                        continue
+                                    tuple_getitem = stmt
+                                    break
+                        if tuple_getitem:
+                            break
+                    else:
+                        continue  # no getitem in this loop
+
+                    ui = unroll_info(loop, literal_unroll_call, arg,
+                                     tuple_getitem)
+                    literal_unroll_info[lbl] = ui
+
+        if not literal_unroll_info:
+            return False
+
+        # 1. Validate loops, must not have any calls to literal_unroll
+        for test_lbl, test_loop in literal_unroll_info.items():
+            for ref_lbl, ref_loop in literal_unroll_info.items():
+                if test_lbl == ref_lbl:  # comparing to self! skip
+                    continue
+                if test_loop.loop.header in ref_loop.loop.body:
+                    msg = ("Nesting of literal_unroll is unsupported")
+                    loc = func_ir.blocks[test_loop.loop.header].loc
+                    raise errors.UnsupportedError(msg, loc)
+
+        # 2. Do the unroll, get a loop and process it!
+        lbl, info = literal_unroll_info.popitem()
+        self.unroll_loop(state, info)
+
+        # 3. Rebuild the state, the IR has taken a hammering
+        func_ir.blocks = simplify_CFG(func_ir.blocks)
+        post_proc = postproc.PostProcessor(func_ir)
+        post_proc.run()
+        if self._DEBUG:
+            print('-' * 80 + "END OF PASS, SIMPLIFY DONE")
+            func_ir.dump()
+        func_ir._definitions = build_definitions(func_ir.blocks)
+        return True
+
+    def unroll_loop(self, state, loop_info):
+        # The general idea here is to:
+        # 1. Find *a* getitem that conforms to the literal_unroll semantic,
+        #    i.e. one that is targeting a tuple with a loop induced index
+        # 2. Compute a structure from the tuple that describes which
+        #    iterations of a loop will have which type
+        # 3. Generate a switch table in IR form for the structure in 2
+        # 4. Switch out getitems for the tuple for a `typed_getitem`
+        # 5. Inject switch table as replacement loop body
+        # 6. Patch up
+        func_ir = state.func_ir
+        getitem_target = loop_info.arg
+        target_ty = state.typemap[getitem_target.name]
+        assert isinstance(target_ty, self._accepted_types)
+
+        # 1. find a "getitem" that conforms
+        tuple_getitem = []
+        for lbl in loop_info.loop.body:
+            blk = func_ir.blocks[lbl]
+            for stmt in blk.body:
+                if isinstance(stmt, ir.Assign):
+                    if isinstance(stmt.value,
+                                  ir.Expr) and stmt.value.op == "getitem":
+                        # try a couple of spellings... a[i] and ref(a)[i]
+                        if stmt.value.value != getitem_target:
+                            dfn = func_ir.get_definition(stmt.value.value)
+                            try:
+                                args = getattr(dfn, 'args', False)
+                            except KeyError:
+                                continue
+                            if not args:
+                                continue
+                            if not args[0] == getitem_target:
+                                continue
+                        target_ty = state.typemap[getitem_target.name]
+                        if not isinstance(target_ty, self._accepted_types):
+                            continue
+                        tuple_getitem.append(stmt)
+
+        if not tuple_getitem:
+            msg = ("Loop unrolling analysis has failed, there's no getitem "
+                   "in loop body that conforms to literal_unroll "
+                   "requirements.")
+            LOC = func_ir.blocks[loop_info.loop.header].loc
+            raise errors.CompilerError(msg, LOC)
+
+        # 2. get switch data
+        switch_data = self.analyse_tuple(target_ty)
+
+        # 3. generate switch IR
+        index = func_ir._definitions[tuple_getitem[0].value.index.name][0]
+        branches = self.gen_switch(switch_data, index)
+
+        # 4. swap getitems for a typed_getitem, these are actually just
+        # placeholders at this point. When the loop is duplicated they can
+        # be swapped for a typed_getitem of the correct type or if the item
+        # is literal it can be shoved straight into the duplicated loop body
+        for item in tuple_getitem:
+            old = item.value
+            new = ir.Expr.typed_getitem(
+                old.value, types.void, old.index, old.loc)
+            item.value = new
+
+        # 5. Inject switch table
+
+        # Find the actual loop without the header (that won't get replaced)
+        # and derive some new IR for this set of blocks
+        this_loop = loop_info.loop
+        this_loop_body = this_loop.body - \
+            set([this_loop.header])
+        loop_blocks = {
+            x: func_ir.blocks[x] for x in this_loop_body}
+        new_ir = func_ir.derive(loop_blocks)
+
+        # Work out what is live on entry and exit so as to prevent
+        # replacement (defined vars can escape, used vars live at the header
+        # need to remain as-is so their references are correct, they can
+        # also escape).
+
+        usedefs = compute_use_defs(func_ir.blocks)
+        idx = this_loop.header
+        keep = set()
+        keep |= usedefs.usemap[idx] | usedefs.defmap[idx]
+        keep |= func_ir.variable_lifetime.livemap[idx]
+        dont_replace = [x for x in (keep)]
+
+        # compute the unrolled body
+        unrolled_body = self.inject_loop_body(
+            branches, new_ir, max(func_ir.blocks.keys()) + 1,
+            dont_replace, switch_data)
+
+        # 6. Patch in the unrolled body and fix up
+        blks = state.func_ir.blocks
+        the_scope = next(iter(blks.values())).scope
+        orig_lbl = tuple(this_loop_body)
+
+        replace, *delete = orig_lbl
+        unroll, header_block = unrolled_body, this_loop.header
+        unroll_lbl = [x for x in sorted(unroll.blocks.keys())]
+        blks[replace] = transfer_scope(unroll.blocks[unroll_lbl[0]], the_scope)
+        [blks.pop(d) for d in delete]
+        for k in unroll_lbl[1:]:
+            blks[k] = transfer_scope(unroll.blocks[k], the_scope)
+        # stitch up the loop predicate true -> new loop body jump
+        blks[header_block].body[-1].truebr = replace
+
+    def run_pass(self, state):
+        mutated = False
+        func_ir = state.func_ir
+        # first limit the work by squashing the CFG if possible
+        func_ir.blocks = simplify_CFG(func_ir.blocks)
+
+        if self._DEBUG:
+            print("-" * 80 + "PASS ENTRY")
+            func_ir.dump()
+            print("-" * 80)
+
+        # limitations:
+        # 1. No nested unrolls
+        # 2. Opt in via `numba.literal_unroll`
+        # 3. No multiple mix-tuple use
+
+        # keep running the transform loop until it reports no more changes
+        while(True):
+            stat = self.apply_transform(state)
+            mutated |= stat
+            if not stat:
+                break
+
+        # reset type inference now we are done with the partial results
+        state.typemap = {}
+        state.calltypes = None
+
+        return mutated
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class IterLoopCanonicalization(FunctionPass):
+    """ Transforms loops that are induced by `getiter` into range() driven loops
+    If the typemap is available this will only impact Tuple and UniTuple, if it
+    is not available it will impact all matching loops.
+    """
+    _name = "iter_loop_canonicalisation"
+
+    _DEBUG = False
+
+    # if partial typing info is available it will only look at these types
+    _accepted_types = (types.BaseTuple, types.LiteralList)
+    _accepted_calls = (literal_unroll,)
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def assess_loop(self, loop, func_ir, partial_typemap=None):
+        # it's a iter loop if:
+        # - loop header is driven by an iternext
+        # - the iternext value is a phi derived from getiter()
+
+        # check header
+        iternexts = [_ for _ in
+                     func_ir.blocks[loop.header].find_exprs('iternext')]
+        if len(iternexts) != 1:
+            return False
+        for iternext in iternexts:
+            phi = guard(get_definition, func_ir,  iternext.value)
+            if phi is None:
+                return False
+            if getattr(phi, 'op', False) == 'getiter':
+                if partial_typemap:
+                    # check that the call site is accepted, until we're
+                    # confident that tuple unrolling is behaving require opt-in
+                    # guard of `literal_unroll`, remove this later!
+                    phi_val_defn = guard(get_definition, func_ir,  phi.value)
+                    if not isinstance(phi_val_defn, ir.Expr):
+                        return False
+                    if not phi_val_defn.op == "call":
+                        return False
+                    call = guard(get_definition, func_ir,  phi_val_defn)
+                    if call is None or len(call.args) != 1:
+                        return False
+                    func_var = guard(get_definition, func_ir,  call.func)
+                    func = guard(get_definition, func_ir,  func_var)
+                    if func is None or not isinstance(func,
+                                                      (ir.Global, ir.FreeVar)):
+                        return False
+                    if (func.value is None or
+                            func.value not in self._accepted_calls):
+                        return False
+
+                    # now check the type is supported
+                    ty = partial_typemap.get(call.args[0].name, None)
+                    if ty and isinstance(ty, self._accepted_types):
+                        return len(loop.entries) == 1
+                else:
+                    return len(loop.entries) == 1
+
+    def transform(self, loop, func_ir, cfg):
+        def get_range(a):
+            return range(len(a))
+
+        def tokenise(x):
+            return mk_unique_var("CANONICALISER_%s" % x)
+
+        iternext = [_ for _ in
+                    func_ir.blocks[loop.header].find_exprs('iternext')][0]
+        LOC = func_ir.blocks[loop.header].loc
+        get_range_var = ir.Var(func_ir.blocks[loop.header].scope,
+                               tokenise('get_range_gbl'), LOC)
+        get_range_global = ir.Global('get_range', get_range, LOC)
+        assgn = ir.Assign(get_range_global, get_range_var, LOC)
+
+        loop_entry = tuple(loop.entries)[0]
+        entry_block = func_ir.blocks[loop_entry]
+        entry_block.body.insert(0, assgn)
+
+        iterarg = guard(get_definition, func_ir,  iternext.value)
+        if iterarg is not None:
+            iterarg = iterarg.value
+
+        # look for iternext
+        idx = 0
+        for stmt in entry_block.body:
+            if isinstance(stmt, ir.Assign):
+                if isinstance(stmt.value,
+                              ir.Expr) and stmt.value.op == 'getiter':
+                    break
+            idx += 1
+        else:
+            raise ValueError("problem")
+
+        # create a range(len(tup)) and inject it
+        call_get_range_var = ir.Var(entry_block.scope,
+                                    tokenise('call_get_range'), LOC)
+        make_call = ir.Expr.call(get_range_var, (stmt.value.value,), (), LOC)
+        assgn_call = ir.Assign(make_call, call_get_range_var, LOC)
+        entry_block.body.insert(idx, assgn_call)
+        entry_block.body[idx + 1].value.value = call_get_range_var
+
+        glbls = copy(func_ir.func_id.func.__globals__)
+        from numba.core.inline_closurecall import inline_closure_call
+        inline_closure_call(func_ir, glbls, entry_block, idx, get_range,)
+        kill = entry_block.body.index(assgn)
+        entry_block.body.pop(kill)
+
+        # find the induction variable + references in the loop header
+        # fixed point iter to do this, it's a bit clunky
+        induction_vars = set()
+        header_block = func_ir.blocks[loop.header]
+
+        # find induction var
+        ind = [x for x in header_block.find_exprs('pair_first')]
+        for x in ind:
+            induction_vars.add(func_ir.get_assignee(x, loop.header))
+        # find aliases of the induction var
+        tmp = set()
+        for x in induction_vars:
+            try:  # there's not always an alias, e.g. loop from inlined closure
+                tmp.add(func_ir.get_assignee(x, loop.header))
+            except ValueError:
+                pass
+        induction_vars |= tmp
+        induction_var_names = set([x.name for x in induction_vars])
+
+        # Find the downstream blocks that might reference the induction var
+        succ = set()
+        for lbl in loop.exits:
+            succ |= set([x[0] for x in cfg.successors(lbl)])
+        check_blocks = (loop.body | loop.exits | succ) ^ {loop.header}
+
+        # replace RHS use of induction var with getitem
+        for lbl in check_blocks:
+            for stmt in func_ir.blocks[lbl].body:
+                if isinstance(stmt, ir.Assign):
+                    # check for aliases
+                    try:
+                        lookup = getattr(stmt.value, 'name', None)
+                    except KeyError:
+                        continue
+                    if lookup and lookup in induction_var_names:
+                        stmt.value = ir.Expr.getitem(
+                            iterarg, stmt.value, stmt.loc)
+
+        post_proc = postproc.PostProcessor(func_ir)
+        post_proc.run()
+
+    def run_pass(self, state):
+        func_ir = state.func_ir
+        cfg = compute_cfg_from_blocks(func_ir.blocks)
+        loops = cfg.loops()
+
+        mutated = False
+        for header, loop in loops.items():
+            stat = self.assess_loop(loop, func_ir, state.typemap)
+            if stat:
+                if self._DEBUG:
+                    print("Canonicalising loop", loop)
+                self.transform(loop, func_ir, cfg)
+                mutated = True
+            else:
+                if self._DEBUG:
+                    print("NOT Canonicalising loop", loop)
+
+        func_ir.blocks = simplify_CFG(func_ir.blocks)
+        return mutated
+
+
+@register_pass(mutates_CFG=False, analysis_only=False)
+class PropagateLiterals(FunctionPass):
+    """Implement literal propagation based on partial type inference"""
+    _name = "PropagateLiterals"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def get_analysis_usage(self, AU):
+        AU.add_required(ReconstructSSA)
+
+    def run_pass(self, state):
+        func_ir = state.func_ir
+        typemap = state.typemap
+        flags = state.flags
+
+        if not hasattr(func_ir, '_definitions') \
+                and not flags.enable_ssa:
+            func_ir._definitions = build_definitions(func_ir.blocks)
+
+        changed = False
+
+        for block in func_ir.blocks.values():
+            for assign in block.find_insts(ir.Assign):
+                value = assign.value
+                if isinstance(value, (ir.Arg, ir.Const, ir.FreeVar, ir.Global)):
+                    continue
+
+                # 1) Don't change return stmt in the form
+                # $return_xyz = cast(value=ABC)
+                # 2) Don't propagate literal values that are not primitives
+                if isinstance(value, ir.Expr) and \
+                        value.op in ('cast', 'build_map', 'build_list',
+                                     'build_tuple', 'build_set'):
+                    continue
+
+                target = assign.target
+                if not flags.enable_ssa:
+                    # SSA is disabled when doing inlining
+                    if guard(get_definition, func_ir, target.name) is None:  # noqa: E501
+                        continue
+
+                # Numba cannot safely determine if an isinstance call
+                # with a PHI node is True/False. For instance, in
+                # the case below, the partial type inference step can coerce
+                # '$z' to float, so any call to 'isinstance(z, int)' would fail.
+                #
+                #   def fn(x):
+                #       if x > 4:
+                #           z = 1
+                #       else:
+                #           z = 3.14
+                #       if isinstance(z, int):
+                #           print('int')
+                #       else:
+                #           print('float')
+                #
+                # At the moment, one avoid propagating the literal
+                # value if the argument is a PHI node
+
+                if isinstance(value, ir.Expr) and value.op == 'call':
+
+                    fn = guard(get_definition, func_ir, value.func.name)
+                    if fn is None:
+                        continue
+                    if not (isinstance(fn, ir.Global) and fn.name == 'isinstance'):  # noqa: E501
+                        continue
+
+                    for arg in value.args:
+                        # check if any of the args to isinstance is a PHI node
+                        iv = func_ir._definitions[arg.name]
+                        assert len(iv) == 1  # SSA!
+                        if isinstance(iv[0], ir.Expr) and iv[0].op == 'phi':
+                            msg = ('isinstance() cannot determine the '
+                                   f'type of variable "{arg.unversioned_name}" '
+                                   'due to a branch.')
+                            raise errors.NumbaTypeError(msg, loc=assign.loc)
+
+                lit = typemap.get(target.name, None)
+                if lit and isinstance(lit, types.Literal):
+                    # replace assign instruction by ir.Const(lit) iff
+                    # lit is a literal value
+                    rhs = ir.Const(lit.literal_value, assign.loc)
+                    new_assign = ir.Assign(rhs, target, assign.loc)
+
+                    # replace instruction
+                    block.insert_after(new_assign, assign)
+                    block.remove(assign)
+
+                    changed = True
+
+        # reset type inference now we are done with the partial results
+        state.typemap = None
+        state.calltypes = None
+
+        if changed:
+            # Rebuild definitions
+            func_ir._definitions = build_definitions(func_ir.blocks)
+
+        return changed
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class LiteralPropagationSubPipelinePass(FunctionPass):
+    """Implement literal propagation based on partial type inference"""
+    _name = "LiteralPropagation"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        # Determine whether to even attempt this pass... if there's no
+        # `isinstance` as a global or as a freevar then just skip.
+        found = False
+        func_ir = state.func_ir
+        for blk in func_ir.blocks.values():
+            for asgn in blk.find_insts(ir.Assign):
+                if isinstance(asgn.value, (ir.Global, ir.FreeVar)):
+                    if asgn.value.value is isinstance:
+                        found = True
+                        break
+            if found:
+                break
+        if not found:
+            return False
+
+        # run as subpipeline
+        from numba.core.compiler_machinery import PassManager
+        from numba.core.typed_passes import PartialTypeInference
+        pm = PassManager("literal_propagation_subpipeline")
+
+        pm.add_pass(PartialTypeInference, "performs partial type inference")
+        pm.add_pass(PropagateLiterals, "performs propagation of literal values")
+
+        # rewrite consts / dead branch pruning
+        pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
+        pm.add_pass(DeadBranchPrune, "dead branch pruning")
+
+        pm.finalize()
+        pm.run(state)
+        return True
+
+    def get_analysis_usage(self, AU):
+        AU.add_required(ReconstructSSA)
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class LiteralUnroll(FunctionPass):
+    """Implement the literal_unroll semantics"""
+    _name = "literal_unroll"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        # Determine whether to even attempt this pass... if there's no
+        # `literal_unroll` as a global or as a freevar then just skip.
+        found = False
+        func_ir = state.func_ir
+        for blk in func_ir.blocks.values():
+            for asgn in blk.find_insts(ir.Assign):
+                if isinstance(asgn.value, (ir.Global, ir.FreeVar)):
+                    if asgn.value.value is literal_unroll:
+                        found = True
+                        break
+            if found:
+                break
+        if not found:
+            return False
+
+        # run as subpipeline
+        from numba.core.compiler_machinery import PassManager
+        from numba.core.typed_passes import PartialTypeInference
+        pm = PassManager("literal_unroll_subpipeline")
+        # get types where possible to help with list->tuple change
+        pm.add_pass(PartialTypeInference, "performs partial type inference")
+        # make const lists tuples
+        pm.add_pass(TransformLiteralUnrollConstListToTuple,
+                    "switch const list for tuples")
+        # recompute partial typemap following IR change
+        pm.add_pass(PartialTypeInference, "performs partial type inference")
+        # canonicalise loops
+        pm.add_pass(IterLoopCanonicalization,
+                    "switch iter loops for range driven loops")
+        # rewrite consts
+        pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
+        # do the unroll
+        pm.add_pass(MixedContainerUnroller, "performs mixed container unroll")
+        # rewrite dynamic getitem to static getitem as it's possible some more
+        # getitems will now be statically resolvable
+        pm.add_pass(GenericRewrites, "Generic Rewrites")
+        pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
+        pm.finalize()
+        pm.run(state)
+        return True
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class SimplifyCFG(FunctionPass):
+    """Perform CFG simplification"""
+    _name = "simplify_cfg"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        blks = state.func_ir.blocks
+        new_blks = simplify_CFG(blks)
+        state.func_ir.blocks = new_blks
+        mutated = blks != new_blks
+        return mutated
+
+
+@register_pass(mutates_CFG=False, analysis_only=False)
+class ReconstructSSA(FunctionPass):
+    """Perform SSA-reconstruction
+
+    Produces minimal SSA.
+    """
+    _name = "reconstruct_ssa"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        state.func_ir = reconstruct_ssa(state.func_ir)
+        self._patch_locals(state)
+
+        # Rebuild definitions
+        state.func_ir._definitions = build_definitions(state.func_ir.blocks)
+
+        # Rerun postprocessor to update metadata
+        # example generator_info
+        post_proc = postproc.PostProcessor(state.func_ir)
+        post_proc.run(emit_dels=False)
+
+        if config.DEBUG or config.DUMP_SSA:
+            name = state.func_ir.func_id.func_qualname
+            print(f"SSA IR DUMP: {name}".center(80, "-"))
+            state.func_ir.dump()
+
+        return True      # XXX detect if it actually got changed
+
+    def _patch_locals(self, state):
+        # Fix dispatcher locals dictionary type annotation
+        locals_dict = state.get('locals')
+        if locals_dict is None:
+            return
+
+        first_blk, *_ = state.func_ir.blocks.values()
+        scope = first_blk.scope
+        for parent, redefs in scope.var_redefinitions.items():
+            if parent in locals_dict:
+                typ = locals_dict[parent]
+                for derived in redefs:
+                    locals_dict[derived] = typ
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/utils.py
new file mode 100644
index 000000000..2e2528185
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/utils.py
@@ -0,0 +1,722 @@
+import atexit
+import builtins
+import functools
+import inspect
+import os
+import operator
+import timeit
+import math
+import sys
+import traceback
+import weakref
+import warnings
+import threading
+import contextlib
+
+from types import ModuleType
+from importlib import import_module
+from collections.abc import Mapping, Sequence
+import numpy as np
+
+from inspect import signature as pysignature # noqa: F401
+from inspect import Signature as pySignature # noqa: F401
+from inspect import Parameter as pyParameter # noqa: F401
+
+from numba.core.config import (PYVERSION, MACHINE_BITS, # noqa: F401
+                               DEVELOPER_MODE) # noqa: F401
+from numba.core import config
+from numba.core import types
+
+
+def erase_traceback(exc_value):
+    """
+    Erase the traceback and hanging locals from the given exception instance.
+    """
+    if exc_value.__traceback__ is not None:
+        traceback.clear_frames(exc_value.__traceback__)
+    return exc_value.with_traceback(None)
+
+
+def safe_relpath(path, start=os.curdir):
+    """
+    Produces a "safe" relative path, on windows relpath doesn't work across
+    drives as technically they don't share the same root.
+    See: https://bugs.python.org/issue7195 for details.
+    """
+    # find the drive letters for path and start and if they are not the same
+    # then don't use relpath!
+    drive_letter = lambda x: os.path.splitdrive(os.path.abspath(x))[0]
+    drive_path = drive_letter(path)
+    drive_start = drive_letter(start)
+    if drive_path != drive_start:
+        return os.path.abspath(path)
+    else:
+        return os.path.relpath(path, start=start)
+
+
+# Mapping between operator module functions and the corresponding built-in
+# operators.
+
+BINOPS_TO_OPERATORS = {
+    '+': operator.add,
+    '-': operator.sub,
+    '*': operator.mul,
+    '//': operator.floordiv,
+    '/': operator.truediv,
+    '%': operator.mod,
+    '**': operator.pow,
+    '&': operator.and_,
+    '|': operator.or_,
+    '^': operator.xor,
+    '<<': operator.lshift,
+    '>>': operator.rshift,
+    '==': operator.eq,
+    '!=': operator.ne,
+    '<': operator.lt,
+    '<=': operator.le,
+    '>': operator.gt,
+    '>=': operator.ge,
+    'is': operator.is_,
+    'is not': operator.is_not,
+    # This one has its args reversed!
+    'in': operator.contains,
+    '@': operator.matmul,
+}
+
+INPLACE_BINOPS_TO_OPERATORS = {
+    '+=': operator.iadd,
+    '-=': operator.isub,
+    '*=': operator.imul,
+    '//=': operator.ifloordiv,
+    '/=': operator.itruediv,
+    '%=': operator.imod,
+    '**=': operator.ipow,
+    '&=': operator.iand,
+    '|=': operator.ior,
+    '^=': operator.ixor,
+    '<<=': operator.ilshift,
+    '>>=': operator.irshift,
+    '@=': operator.imatmul,
+}
+
+UNARY_BUITINS_TO_OPERATORS = {
+    '+': operator.pos,
+    '-': operator.neg,
+    '~': operator.invert,
+    'not': operator.not_,
+    'is_true': operator.truth
+}
+
+OPERATORS_TO_BUILTINS = {
+    operator.add: '+',
+    operator.iadd: '+=',
+    operator.sub: '-',
+    operator.isub: '-=',
+    operator.mul: '*',
+    operator.imul: '*=',
+    operator.floordiv: '//',
+    operator.ifloordiv: '//=',
+    operator.truediv: '/',
+    operator.itruediv: '/=',
+    operator.mod: '%',
+    operator.imod: '%=',
+    operator.pow: '**',
+    operator.ipow: '**=',
+    operator.and_: '&',
+    operator.iand: '&=',
+    operator.or_: '|',
+    operator.ior: '|=',
+    operator.xor: '^',
+    operator.ixor: '^=',
+    operator.lshift: '<<',
+    operator.ilshift: '<<=',
+    operator.rshift: '>>',
+    operator.irshift: '>>=',
+    operator.eq: '==',
+    operator.ne: '!=',
+    operator.lt: '<',
+    operator.le: '<=',
+    operator.gt: '>',
+    operator.ge: '>=',
+    operator.is_: 'is',
+    operator.is_not: 'is not',
+    # This one has its args reversed!
+    operator.contains: 'in',
+    # Unary
+    operator.pos: '+',
+    operator.neg: '-',
+    operator.invert: '~',
+    operator.not_: 'not',
+    operator.truth: 'is_true',
+}
+
+
+_shutting_down = False
+
+
+def _at_shutdown():
+    global _shutting_down
+    _shutting_down = True
+
+
+def shutting_down(globals=globals):
+    """
+    Whether the interpreter is currently shutting down.
+    For use in finalizers, __del__ methods, and similar; it is advised
+    to early bind this function rather than look it up when calling it,
+    since at shutdown module globals may be cleared.
+    """
+    # At shutdown, the attribute may have been cleared or set to None.
+    v = globals().get('_shutting_down')
+    return v is True or v is None
+
+
+# weakref.finalize registers an exit function that runs all finalizers for
+# which atexit is True. Some of these finalizers may call shutting_down() to
+# check whether the interpreter is shutting down. For this to behave correctly,
+# we need to make sure that _at_shutdown is called before the finalizer exit
+# function. Since atexit operates as a LIFO stack, we first construct a dummy
+# finalizer then register atexit to ensure this ordering.
+weakref.finalize(lambda: None, lambda: None)
+atexit.register(_at_shutdown)
+
+
+def use_new_style_errors():
+    """Returns True if new style errors are to be used, false otherwise"""
+    # This uses `config` so as to make sure it gets the current value from the
+    # module as e.g. some tests mutate the config with `override_config`.
+    return config.CAPTURED_ERRORS == 'new_style'
+
+
+def use_old_style_errors():
+    """Returns True if old style errors are to be used, false otherwise"""
+    # This uses `config` so as to make sure it gets the current value from the
+    # module as e.g. some tests mutate the config with `override_config`.
+    return config.CAPTURED_ERRORS == 'old_style'
+
+
+class ThreadLocalStack:
+    """A TLS stack container.
+
+    Uses the BORG pattern and stores states in threadlocal storage.
+    """
+    _tls = threading.local()
+    stack_name: str
+    _registered = {}
+
+    def __init_subclass__(cls, *, stack_name, **kwargs):
+        super().__init_subclass__(**kwargs)
+        # Register stack_name mapping to the new subclass
+        assert stack_name not in cls._registered, \
+            f"stack_name: '{stack_name}' already in use"
+        cls.stack_name = stack_name
+        cls._registered[stack_name] = cls
+
+    def __init__(self):
+        # This class must not be used directly.
+        assert type(self) is not ThreadLocalStack
+        tls = self._tls
+        attr = f"stack_{self.stack_name}"
+        try:
+            tls_stack = getattr(tls, attr)
+        except AttributeError:
+            tls_stack = list()
+            setattr(tls, attr, tls_stack)
+
+        self._stack = tls_stack
+
+    def push(self, state):
+        """Push to the stack
+        """
+        self._stack.append(state)
+
+    def pop(self):
+        """Pop from the stack
+        """
+        return self._stack.pop()
+
+    def top(self):
+        """Get the top item on the stack.
+
+        Raises IndexError if the stack is empty. Users should check the size
+        of the stack beforehand.
+        """
+        return self._stack[-1]
+
+    def __len__(self):
+        return len(self._stack)
+
+    @contextlib.contextmanager
+    def enter(self, state):
+        """A contextmanager that pushes ``state`` for the duration of the
+        context.
+        """
+        self.push(state)
+        try:
+            yield
+        finally:
+            self.pop()
+
+
+class ConfigOptions(object):
+    OPTIONS = {}
+
+    def __init__(self):
+        self._values = self.OPTIONS.copy()
+
+    def set(self, name, value=True):
+        if name not in self.OPTIONS:
+            raise NameError("Invalid flag: %s" % name)
+        self._values[name] = value
+
+    def unset(self, name):
+        self.set(name, False)
+
+    def _check_attr(self, name):
+        if name not in self.OPTIONS:
+            raise AttributeError("Invalid flag: %s" % name)
+
+    def __getattr__(self, name):
+        self._check_attr(name)
+        return self._values[name]
+
+    def __setattr__(self, name, value):
+        if name.startswith('_'):
+            super(ConfigOptions, self).__setattr__(name, value)
+        else:
+            self._check_attr(name)
+            self._values[name] = value
+
+    def __repr__(self):
+        return "Flags(%s)" % ', '.join('%s=%s' % (k, v)
+                                       for k, v in self._values.items()
+                                       if v is not False)
+
+    def copy(self):
+        copy = type(self)()
+        copy._values = self._values.copy()
+        return copy
+
+    def __eq__(self, other):
+        return (isinstance(other, ConfigOptions) and
+                other._values == self._values)
+
+    def __ne__(self, other):
+        return not self == other
+
+    def __hash__(self):
+        return hash(tuple(sorted(self._values.items())))
+
+
+def order_by_target_specificity(target, templates, fnkey=''):
+    """This orders the given templates from most to least specific against the
+    current "target". "fnkey" is an indicative typing key for use in the
+    exception message in the case that there's no usable templates for the
+    current "target".
+    """
+    # No templates... return early!
+    if templates == []:
+        return []
+
+    from numba.core.target_extension import target_registry
+
+    # fish out templates that are specific to the target if a target is
+    # specified
+    DEFAULT_TARGET = 'generic'
+    usable = []
+    for ix, temp_cls in enumerate(templates):
+        # ? Need to do something about this next line
+        md = getattr(temp_cls, "metadata", {})
+        hw = md.get('target', DEFAULT_TARGET)
+        if hw is not None:
+            hw_clazz = target_registry[hw]
+            if target.inherits_from(hw_clazz):
+                usable.append((temp_cls, hw_clazz, ix))
+
+    # sort templates based on target specificity
+    def key(x):
+        return target.__mro__.index(x[1])
+    order = [x[0] for x in sorted(usable, key=key)]
+
+    if not order:
+        msg = (f"Function resolution cannot find any matches for function "
+               f"'{fnkey}' for the current target: '{target}'.")
+        from numba.core.errors import UnsupportedError
+        raise UnsupportedError(msg)
+
+    return order
+
+
+class SortedMap(Mapping):
+    """Immutable
+    """
+
+    def __init__(self, seq):
+        self._values = []
+        self._index = {}
+        for i, (k, v) in enumerate(sorted(seq)):
+            self._index[k] = i
+            self._values.append((k, v))
+
+    def __getitem__(self, k):
+        i = self._index[k]
+        return self._values[i][1]
+
+    def __len__(self):
+        return len(self._values)
+
+    def __iter__(self):
+        return iter(k for k, v in self._values)
+
+
+class UniqueDict(dict):
+    def __setitem__(self, key, value):
+        if key in self:
+            raise AssertionError("key already in dictionary: %r" % (key,))
+        super(UniqueDict, self).__setitem__(key, value)
+
+
+if PYVERSION > (3, 7):
+    from functools import cached_property
+else:
+    from threading import RLock
+
+    # The following cached_property() implementation is adapted from CPython:
+    # https://github.com/python/cpython/blob/3.8/Lib/functools.py#L924-L976
+    # commit SHA: 12b714391e485d0150b343b114999bae4a0d34dd
+
+    ###########################################################################
+    ### cached_property() - computed once per instance, cached as attribute
+    ###########################################################################
+
+    _NOT_FOUND = object()
+
+    class cached_property:
+        def __init__(self, func):
+            self.func = func
+            self.attrname = None
+            self.__doc__ = func.__doc__
+            self.lock = RLock()
+
+        def __set_name__(self, owner, name):
+            if self.attrname is None:
+                self.attrname = name
+            elif name != self.attrname:
+                raise TypeError(
+                    "Cannot assign the same cached_property to two different names " # noqa: E501
+                    f"({self.attrname!r} and {name!r})."
+                )
+
+        def __get__(self, instance, owner=None):
+            if instance is None:
+                return self
+            if self.attrname is None:
+                raise TypeError(
+                    "Cannot use cached_property instance without calling __set_name__ on it.") # noqa: E501
+            try:
+                cache = instance.__dict__
+            except AttributeError:  # not all objects have __dict__ (e.g. class defines slots) # noqa: E501
+                msg = (
+                    f"No '__dict__' attribute on {type(instance).__name__!r} "
+                    f"instance to cache {self.attrname!r} property."
+                )
+                raise TypeError(msg) from None
+            val = cache.get(self.attrname, _NOT_FOUND)
+            if val is _NOT_FOUND:
+                with self.lock:
+                    # check if another thread filled cache while we awaited lock
+                    val = cache.get(self.attrname, _NOT_FOUND)
+                    if val is _NOT_FOUND:
+                        val = self.func(instance)
+                        try:
+                            cache[self.attrname] = val
+                        except TypeError:
+                            msg = (
+                                f"The '__dict__' attribute on {type(instance).__name__!r} instance "    # noqa: E501
+                                f"does not support item assignment for caching {self.attrname!r} property." # noqa: E501
+                            )
+                            raise TypeError(msg) from None
+            return val
+
+
+def runonce(fn):
+    @functools.wraps(fn)
+    def inner():
+        if not inner._ran:
+            res = fn()
+            inner._result = res
+            inner._ran = True
+        return inner._result
+
+    inner._ran = False
+    return inner
+
+
+def bit_length(intval):
+    """
+    Return the number of bits necessary to represent integer `intval`.
+    """
+    assert isinstance(intval, int)
+    if intval >= 0:
+        return len(bin(intval)) - 2
+    else:
+        return len(bin(-intval - 1)) - 2
+
+
+def stream_list(lst):
+    """
+    Given a list, return an infinite iterator of iterators.
+    Each iterator iterates over the list from the last seen point up to
+    the current end-of-list.
+
+    In effect, each iterator will give the newly appended elements from the
+    previous iterator instantiation time.
+    """
+    def sublist_iterator(start, stop):
+        return iter(lst[start:stop])
+
+    start = 0
+    while True:
+        stop = len(lst)
+        yield sublist_iterator(start, stop)
+        start = stop
+
+
+class BenchmarkResult(object):
+    def __init__(self, func, records, loop):
+        self.func = func
+        self.loop = loop
+        self.records = np.array(records) / loop
+        self.best = np.min(self.records)
+
+    def __repr__(self):
+        name = getattr(self.func, "__name__", self.func)
+        args = (name, self.loop, self.records.size, format_time(self.best))
+        return "%20s: %10d loops, best of %d: %s per loop" % args
+
+
+def format_time(tm):
+    units = "s ms us ns ps".split()
+    base = 1
+    for unit in units[:-1]:
+        if tm >= base:
+            break
+        base /= 1000
+    else:
+        unit = units[-1]
+    return "%.1f%s" % (tm / base, unit)
+
+
+def benchmark(func, maxsec=1):
+    timer = timeit.Timer(func)
+    number = 1
+    result = timer.repeat(1, number)
+    # Too fast to be measured
+    while min(result) / number == 0:
+        number *= 10
+        result = timer.repeat(3, number)
+    best = min(result) / number
+    if best >= maxsec:
+        return BenchmarkResult(func, result, number)
+        # Scale it up to make it close the maximum time
+    max_per_run_time = maxsec / 3 / number
+    number = max(max_per_run_time / best / 3, 1)
+    # Round to the next power of 10
+    number = int(10 ** math.ceil(math.log10(number)))
+    records = timer.repeat(3, number)
+    return BenchmarkResult(func, records, number)
+
+
+# A dummy module for dynamically-generated functions
+_dynamic_modname = '<dynamic>'
+_dynamic_module = ModuleType(_dynamic_modname)
+_dynamic_module.__builtins__ = builtins
+
+
+def chain_exception(new_exc, old_exc):
+    """Set the __cause__ attribute on *new_exc* for explicit exception
+    chaining.  Returns the inplace modified *new_exc*.
+    """
+    if DEVELOPER_MODE:
+        new_exc.__cause__ = old_exc
+    return new_exc
+
+
+def get_nargs_range(pyfunc):
+    """Return the minimal and maximal number of Python function
+    positional arguments.
+    """
+    sig = pysignature(pyfunc)
+    min_nargs = 0
+    max_nargs = 0
+    for p in sig.parameters.values():
+        max_nargs += 1
+        if p.default == inspect._empty:
+            min_nargs += 1
+    return min_nargs, max_nargs
+
+
+def unify_function_types(numba_types):
+    """Return a normalized tuple of Numba function types so that
+
+        Tuple(numba_types)
+
+    becomes
+
+        UniTuple(dtype=<unified function type>, count=len(numba_types))
+
+    If the above transformation would be incorrect, return the
+    original input as given. For instance, if the input tuple contains
+    types that are not function or dispatcher type, the transformation
+    is considered incorrect.
+    """
+    dtype = unified_function_type(numba_types)
+    if dtype is None:
+        return numba_types
+    return (dtype,) * len(numba_types)
+
+
+def unified_function_type(numba_types, require_precise=True):
+    """Returns a unified Numba function type if possible.
+
+    Parameters
+    ----------
+    numba_types : Sequence of numba Type instances.
+    require_precise : bool
+      If True, the returned Numba function type must be precise.
+
+    Returns
+    -------
+    typ : {numba.core.types.Type, None}
+      A unified Numba function type. Or ``None`` when the Numba types
+      cannot be unified, e.g. when the ``numba_types`` contains at
+      least two different Numba function type instances.
+
+    If ``numba_types`` contains a Numba dispatcher type, the unified
+    Numba function type will be an imprecise ``UndefinedFunctionType``
+    instance, or None when ``require_precise=True`` is specified.
+
+    Specifying ``require_precise=False`` enables unifying imprecise
+    Numba dispatcher instances when used in tuples or if-then branches
+    when the precise Numba function cannot be determined on the first
+    occurrence that is not a call expression.
+    """
+    from numba.core.errors import NumbaExperimentalFeatureWarning
+
+    if not (isinstance(numba_types, Sequence) and
+            len(numba_types) > 0 and
+            isinstance(numba_types[0],
+                       (types.Dispatcher, types.FunctionType))):
+        return
+
+    warnings.warn("First-class function type feature is experimental",
+                  category=NumbaExperimentalFeatureWarning)
+
+    mnargs, mxargs = None, None
+    dispatchers = set()
+    function = None
+    undefined_function = None
+
+    for t in numba_types:
+        if isinstance(t, types.Dispatcher):
+            mnargs1, mxargs1 = get_nargs_range(t.dispatcher.py_func)
+            if mnargs is None:
+                mnargs, mxargs = mnargs1, mxargs1
+            elif not (mnargs, mxargs) == (mnargs1, mxargs1):
+                return
+            dispatchers.add(t.dispatcher)
+            t = t.dispatcher.get_function_type()
+            if t is None:
+                continue
+        if isinstance(t, types.FunctionType):
+            if mnargs is None:
+                mnargs = mxargs = t.nargs
+            elif not (mnargs == mxargs == t.nargs):
+                return
+            if isinstance(t, types.UndefinedFunctionType):
+                if undefined_function is None:
+                    undefined_function = t
+                else:
+                    # Refuse to unify using function type
+                    return
+                dispatchers.update(t.dispatchers)
+            else:
+                if function is None:
+                    function = t
+                else:
+                    assert function == t
+        else:
+            return
+    if require_precise and (function is None or undefined_function is not None):
+        return
+    if function is not None:
+        if undefined_function is not None:
+            assert function.nargs == undefined_function.nargs
+            function = undefined_function
+    elif undefined_function is not None:
+        undefined_function.dispatchers.update(dispatchers)
+        function = undefined_function
+    else:
+        function = types.UndefinedFunctionType(mnargs, dispatchers)
+
+    return function
+
+
+class _RedirectSubpackage(ModuleType):
+    """Redirect a subpackage to a subpackage.
+
+    This allows all references like:
+
+    >>> from numba.old_subpackage import module
+    >>> module.item
+
+    >>> import numba.old_subpackage.module
+    >>> numba.old_subpackage.module.item
+
+    >>> from numba.old_subpackage.module import item
+    """
+    def __init__(self, old_module_locals, new_module):
+        old_module = old_module_locals['__name__']
+        super().__init__(old_module)
+
+        self.__old_module_states = {}
+        self.__new_module = new_module
+
+        new_mod_obj = import_module(new_module)
+
+        # Map all sub-modules over
+        for k, v in new_mod_obj.__dict__.items():
+            # Get attributes so that `subpackage.xyz` and
+            # `from subpackage import xyz` work
+            setattr(self, k, v)
+            if isinstance(v, ModuleType):
+                # Map modules into the interpreter so that
+                # `import subpackage.xyz` works
+                sys.modules[f"{old_module}.{k}"] = sys.modules[v.__name__]
+
+        # copy across dunders so that package imports work too
+        for attr, value in old_module_locals.items():
+            if attr.startswith('__') and attr.endswith('__'):
+                if attr != "__builtins__":
+                    setattr(self, attr, value)
+                    self.__old_module_states[attr] = value
+
+    def __reduce__(self):
+        args = (self.__old_module_states, self.__new_module)
+        return _RedirectSubpackage, args
+
+
+def get_hashable_key(value):
+    """
+        Given a value, returns a key that can be used
+        as a hash. If the value is hashable, we return
+        the value, otherwise we return id(value).
+
+        See discussion in gh #6957
+    """
+    try:
+        hash(value)
+    except TypeError:
+        return id(value)
+    else:
+        return value
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/withcontexts.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/withcontexts.py
new file mode 100644
index 000000000..e61cca969
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/core/withcontexts.py
@@ -0,0 +1,535 @@
+from numba.core import types, errors, ir, sigutils, ir_utils
+from numba.core.typing.typeof import typeof_impl
+from numba.core.transforms import find_region_inout_vars
+from numba.core.ir_utils import build_definitions
+import numba
+
+
+class WithContext(object):
+    """A dummy object for use as contextmanager.
+    This can be used as a contextmanager.
+    """
+    is_callable = False
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, typ, val, tb):
+        pass
+
+    def mutate_with_body(self, func_ir, blocks, blk_start, blk_end,
+                         body_blocks, dispatcher_factory, extra):
+        """Mutate the *blocks* to implement this contextmanager.
+
+        Parameters
+        ----------
+        func_ir : FunctionIR
+        blocks : dict[ir.Block]
+        blk_start, blk_end : int
+            labels of the starting and ending block of the context-manager.
+        body_block: sequence[int]
+            A sequence of int's representing labels of the with-body
+        dispatcher_factory : callable
+            A callable that takes a `FunctionIR` and returns a `Dispatcher`.
+        """
+        raise NotImplementedError
+
+
+@typeof_impl.register(WithContext)
+def typeof_contextmanager(val, c):
+    return types.ContextManager(val)
+
+
+def _get_var_parent(name):
+    """Get parent of the variable given its name
+    """
+    # If not a temporary variable
+    if not name.startswith('$'):
+        # Return the base component of the name
+        return name.split('.', )[0]
+
+
+def _clear_blocks(blocks, to_clear):
+    """Remove keys in *to_clear* from *blocks*.
+    """
+    for b in to_clear:
+        del blocks[b]
+
+
+class _ByPassContextType(WithContext):
+    """A simple context-manager that tells the compiler to bypass the body
+    of the with-block.
+    """
+    def mutate_with_body(self, func_ir, blocks, blk_start, blk_end,
+                         body_blocks, dispatcher_factory, extra):
+        assert extra is None
+        # Determine variables that need forwarding
+        vlt = func_ir.variable_lifetime
+        inmap = {_get_var_parent(k): k for k in vlt.livemap[blk_start]}
+        outmap = {_get_var_parent(k): k for k in vlt.livemap[blk_end]}
+        forwardvars = {inmap[k]: outmap[k] for k in filter(bool, outmap)}
+        # Transform the block
+        _bypass_with_context(blocks, blk_start, blk_end, forwardvars)
+        _clear_blocks(blocks, body_blocks)
+
+
+bypass_context = _ByPassContextType()
+
+
+class _CallContextType(WithContext):
+    """A simple context-manager that tells the compiler to lift the body of the
+    with-block as another function.
+    """
+    def mutate_with_body(self, func_ir, blocks, blk_start, blk_end,
+                         body_blocks, dispatcher_factory, extra):
+        assert extra is None
+        vlt = func_ir.variable_lifetime
+
+        inputs, outputs = find_region_inout_vars(
+            blocks=blocks,
+            livemap=vlt.livemap,
+            callfrom=blk_start,
+            returnto=blk_end,
+            body_block_ids=set(body_blocks),
+            )
+
+        lifted_blks = {k: blocks[k] for k in body_blocks}
+        _mutate_with_block_callee(lifted_blks, blk_start, blk_end,
+                                  inputs, outputs)
+
+        # XXX: transform body-blocks to return the output variables
+        lifted_ir = func_ir.derive(
+            blocks=lifted_blks,
+            arg_names=tuple(inputs),
+            arg_count=len(inputs),
+            force_non_generator=True,
+            )
+
+        dispatcher = dispatcher_factory(lifted_ir)
+
+        newblk = _mutate_with_block_caller(
+            dispatcher, blocks, blk_start, blk_end, inputs, outputs,
+            )
+
+        blocks[blk_start] = newblk
+        _clear_blocks(blocks, body_blocks)
+        return dispatcher
+
+
+call_context = _CallContextType()
+
+
+class _ObjModeContextType(WithContext):
+    """Creates a contextmanager to be used inside jitted functions to enter
+    *object-mode* for using interpreter features.  The body of the with-context
+    is lifted into a function that is compiled in *object-mode*.  This
+    transformation process is limited and cannot process all possible
+    Python code.  However, users can wrap complicated logic in another
+    Python function, which will then be executed by the interpreter.
+
+    Use this as a function that takes keyword arguments only.
+    The argument names must correspond to the output variables from the
+    with-block.  Their respective values can be:
+
+    1. strings representing the expected types; i.e. ``"float32"``.
+    2. compile-time bound global or nonlocal variables referring to the
+       expected type. The variables are read at compile time.
+
+    When exiting the with-context, the output variables are converted
+    to the expected nopython types according to the annotation.  This process
+    is the same as passing Python objects into arguments of a nopython
+    function.
+
+    Example::
+
+        import numpy as np
+        from numba import njit, objmode, types
+
+        def bar(x):
+            # This code is executed by the interpreter.
+            return np.asarray(list(reversed(x.tolist())))
+
+        # Output type as global variable
+        out_ty = types.intp[:]
+
+        @njit
+        def foo():
+            x = np.arange(5)
+            y = np.zeros_like(x)
+            with objmode(y='intp[:]', z=out_ty):  # annotate return type
+                # this region is executed by object-mode.
+                y += bar(x)
+                z = y
+            return y, z
+
+    .. note:: Known limitations:
+
+        - with-block cannot use incoming list objects.
+        - with-block cannot use incoming function objects.
+        - with-block cannot ``yield``, ``break``, ``return`` or ``raise`` \
+          such that the execution will leave the with-block immediately.
+        - with-block cannot contain `with` statements.
+        - random number generator states do not synchronize; i.e. \
+          nopython-mode and object-mode uses different RNG states.
+
+    .. note:: When used outside of no-python mode, the context-manager has no
+        effect.
+
+    .. warning:: This feature is experimental.  The supported features may
+        change with or without notice.
+
+    """
+    is_callable = True
+
+    def _legalize_args(self, func_ir, args, kwargs, loc, func_globals,
+                       func_closures):
+        """
+        Legalize arguments to the context-manager
+
+        Parameters
+        ----------
+        func_ir: FunctionIR
+        args: tuple
+            Positional arguments to the with-context call as IR nodes.
+        kwargs: dict
+            Keyword arguments to the with-context call as IR nodes.
+        loc: numba.core.ir.Loc
+            Source location of the with-context call.
+        func_globals: dict
+            The globals dictionary of the calling function.
+        func_closures: dict
+            The resolved closure variables of the calling function.
+        """
+        if args:
+            raise errors.CompilerError(
+                "objectmode context doesn't take any positional arguments",
+                )
+        typeanns = {}
+
+        def report_error(varname, msg, loc):
+            raise errors.CompilerError(
+                    f"Error handling objmode argument {varname!r}. {msg}",
+                    loc=loc,
+                )
+
+        for k, v in kwargs.items():
+            if isinstance(v, ir.Const) and isinstance(v.value, str):
+                typeanns[k] = sigutils._parse_signature_string(v.value)
+            elif isinstance(v, ir.FreeVar):
+                try:
+                    v = func_closures[v.name]
+                except KeyError:
+                    report_error(
+                        varname=k,
+                        msg=f"Freevar {v.name!r} is not defined.",
+                        loc=loc,
+                    )
+                typeanns[k] = v
+            elif isinstance(v, ir.Global):
+                try:
+                    v = func_globals[v.name]
+                except KeyError:
+                    report_error(
+                        varname=k,
+                        msg=f"Global {v.name!r} is not defined.",
+                        loc=loc,
+                    )
+                typeanns[k] = v
+            elif isinstance(v, ir.Expr) and v.op == "getattr":
+                try:
+                    base_obj = func_ir.infer_constant(v.value)
+                    typ = getattr(base_obj, v.attr)
+                except (errors.ConstantInferenceError, AttributeError):
+                    report_error(
+                        varname=k,
+                        msg="Getattr cannot be resolved at compile-time.",
+                        loc=loc,
+                    )
+                else:
+                    typeanns[k] = typ
+            else:
+                report_error(
+                    varname=k,
+                    msg=("The value must be a compile-time constant either as "
+                         "a non-local variable or a getattr expression that "
+                         "refers to a Numba type."),
+                    loc=loc
+                )
+
+        # Legalize the types for objmode
+        for name, typ in typeanns.items():
+            self._legalize_arg_type(name, typ, loc)
+
+        return typeanns
+
+    def _legalize_arg_type(self, name, typ, loc):
+        """Legalize the argument type
+
+        Parameters
+        ----------
+        name: str
+            argument name.
+        typ: numba.core.types.Type
+            argument type.
+        loc: numba.core.ir.Loc
+            source location for error reporting.
+        """
+        if getattr(typ, "reflected", False):
+            msgbuf = [
+                "Objmode context failed.",
+                f"Argument {name!r} is declared as "
+                f"an unsupported type: {typ}.",
+                f"Reflected types are not supported.",
+            ]
+            raise errors.CompilerError(" ".join(msgbuf), loc=loc)
+
+    def mutate_with_body(self, func_ir, blocks, blk_start, blk_end,
+                         body_blocks, dispatcher_factory, extra):
+        cellnames = func_ir.func_id.func.__code__.co_freevars
+        closures = func_ir.func_id.func.__closure__
+        func_globals = func_ir.func_id.func.__globals__
+        if closures is not None:
+            # Resolve free variables
+            func_closures = {}
+            for cellname, closure in zip(cellnames, closures):
+                try:
+                    cellval = closure.cell_contents
+                except ValueError as e:
+                    # empty cell will raise
+                    if str(e) != "Cell is empty":
+                        raise
+                else:
+                    func_closures[cellname] = cellval
+        else:
+            # Missing closure object
+            func_closures = {}
+        args = extra['args'] if extra else ()
+        kwargs = extra['kwargs'] if extra else {}
+
+        typeanns = self._legalize_args(func_ir=func_ir,
+                                       args=args,
+                                       kwargs=kwargs,
+                                       loc=blocks[blk_start].loc,
+                                       func_globals=func_globals,
+                                       func_closures=func_closures,
+                                       )
+        vlt = func_ir.variable_lifetime
+
+        inputs, outputs = find_region_inout_vars(
+            blocks=blocks,
+            livemap=vlt.livemap,
+            callfrom=blk_start,
+            returnto=blk_end,
+            body_block_ids=set(body_blocks),
+            )
+
+        # Determine types in the output tuple
+        def strip_var_ver(x):
+            return x.split('.', 1)[0]
+
+        stripped_outs = list(map(strip_var_ver, outputs))
+
+        # Verify that only outputs are annotated
+        extra_annotated = set(typeanns) - set(stripped_outs)
+        if extra_annotated:
+            msg = (
+                'Invalid type annotation on non-outgoing variables: {}.'
+                'Suggestion: remove annotation of the listed variables'
+            )
+            raise errors.TypingError(msg.format(extra_annotated))
+
+        # Verify that all outputs are annotated
+
+        # Note on "$cp" variable:
+        # ``transforms.consolidate_multi_exit_withs()`` introduces the variable
+        # for the control-point to determine the correct exit block. This
+        # variable crosses the with-region boundary. Thus, it will be consider
+        # an output variable leaving the lifted with-region.
+        typeanns["$cp"] = types.int32
+        not_annotated = set(stripped_outs) - set(typeanns)
+        if not_annotated:
+            msg = (
+                'Missing type annotation on outgoing variable(s): {0}\n\n'
+                'Example code: with objmode({1}=\'<'
+                'add_type_as_string_here>\')\n'
+            )
+            stable_ann = sorted(not_annotated)
+            raise errors.TypingError(msg.format(stable_ann, stable_ann[0]))
+
+        # Get output types
+        outtup = types.Tuple([typeanns[v] for v in stripped_outs])
+
+        lifted_blks = {k: blocks[k] for k in body_blocks}
+        _mutate_with_block_callee(lifted_blks, blk_start, blk_end,
+                                  inputs, outputs)
+
+        lifted_ir = func_ir.derive(
+            blocks=lifted_blks,
+            arg_names=tuple(inputs),
+            arg_count=len(inputs),
+            force_non_generator=True,
+            )
+
+        dispatcher = dispatcher_factory(lifted_ir, objectmode=True,
+                                        output_types=outtup)
+
+        newblk = _mutate_with_block_caller(
+            dispatcher, blocks, blk_start, blk_end, inputs, outputs,
+            )
+
+        blocks[blk_start] = newblk
+        _clear_blocks(blocks, body_blocks)
+        return dispatcher
+
+    def __call__(self, *args, **kwargs):
+        # No effect when used in pure-python
+        return self
+
+
+objmode_context = _ObjModeContextType()
+
+
+def _bypass_with_context(blocks, blk_start, blk_end, forwardvars):
+    """Given the starting and ending block of the with-context,
+    replaces the head block with a new block that jumps to the end.
+
+    *blocks* is modified inplace.
+    """
+    sblk = blocks[blk_start]
+    scope = sblk.scope
+    loc = sblk.loc
+    newblk = ir.Block(scope=scope, loc=loc)
+    for k, v in forwardvars.items():
+        newblk.append(ir.Assign(value=scope.get_exact(k),
+                                target=scope.get_exact(v),
+                                loc=loc))
+    newblk.append(ir.Jump(target=blk_end, loc=loc))
+    blocks[blk_start] = newblk
+
+
+def _mutate_with_block_caller(dispatcher, blocks, blk_start, blk_end,
+                              inputs, outputs):
+    """Make a new block that calls into the lifeted with-context.
+
+    Parameters
+    ----------
+    dispatcher : Dispatcher
+    blocks : dict[ir.Block]
+    blk_start, blk_end : int
+        labels of the starting and ending block of the context-manager.
+    inputs: sequence[str]
+        Input variable names
+    outputs: sequence[str]
+        Output variable names
+    """
+    sblk = blocks[blk_start]
+    scope = sblk.scope
+    loc = sblk.loc
+    newblock = ir.Block(scope=scope, loc=loc)
+
+    ir_utils.fill_block_with_call(
+        newblock=newblock,
+        callee=dispatcher,
+        label_next=blk_end,
+        inputs=inputs,
+        outputs=outputs,
+        )
+    return newblock
+
+
+def _mutate_with_block_callee(blocks, blk_start, blk_end, inputs, outputs):
+    """Mutate *blocks* for the callee of a with-context.
+
+    Parameters
+    ----------
+    blocks : dict[ir.Block]
+    blk_start, blk_end : int
+        labels of the starting and ending block of the context-manager.
+    inputs: sequence[str]
+        Input variable names
+    outputs: sequence[str]
+        Output variable names
+    """
+    if not blocks:
+        raise errors.NumbaValueError("No blocks in with-context block")
+    head_blk = min(blocks)
+    temp_blk = blocks[head_blk]
+    scope = temp_blk.scope
+    loc = temp_blk.loc
+
+    blocks[blk_start] = ir_utils.fill_callee_prologue(
+        block=ir.Block(scope=scope, loc=loc),
+        inputs=inputs,
+        label_next=head_blk,
+        )
+    blocks[blk_end] = ir_utils.fill_callee_epilogue(
+        block=ir.Block(scope=scope, loc=loc),
+        outputs=outputs,
+    )
+
+class _ParallelChunksize(WithContext):
+    is_callable = True
+
+    """A context-manager that on entry stores the current chunksize
+    for the executing parfors and then changes the current chunksize
+    to the programmer specified value. On exit the original
+    chunksize is restored.
+    """
+    def mutate_with_body(self, func_ir, blocks, blk_start, blk_end,
+                         body_blocks, dispatcher_factory, extra):
+        ir_utils.dprint_func_ir(func_ir, "Before with changes", blocks=blocks)
+        assert extra is not None
+        args = extra["args"]
+        assert len(args) == 1
+        arg = args[0]
+        scope = blocks[blk_start].scope
+        loc = blocks[blk_start].loc
+        if isinstance(arg, ir.Arg):
+            arg = ir.Var(scope, arg.name, loc)
+
+        set_state = []
+        restore_state = []
+
+        # global for Numba itself
+        gvar = scope.redefine("$ngvar", loc)
+        set_state.append(ir.Assign(ir.Global('numba', numba, loc), gvar, loc))
+        # getattr for set chunksize function in Numba
+        spcattr = ir.Expr.getattr(gvar, 'set_parallel_chunksize', loc)
+        spcvar = scope.redefine("$spc", loc)
+        set_state.append(ir.Assign(spcattr, spcvar, loc))
+        # call set_parallel_chunksize
+        orig_pc_var = scope.redefine("$save_pc", loc)
+        cs_var = scope.redefine("$cs_var", loc)
+        set_state.append(ir.Assign(arg, cs_var, loc))
+        spc_call = ir.Expr.call(spcvar, [cs_var], (), loc)
+        set_state.append(ir.Assign(spc_call, orig_pc_var, loc))
+
+        restore_spc_call = ir.Expr.call(spcvar, [orig_pc_var], (), loc)
+        restore_state.append(ir.Assign(restore_spc_call, orig_pc_var, loc))
+
+        blocks[blk_start].body = (blocks[blk_start].body[1:-1] + 
+                                  set_state + 
+                                  [blocks[blk_start].body[-1]])
+        blocks[blk_end].body = restore_state + blocks[blk_end].body
+        func_ir._definitions = build_definitions(blocks)
+        ir_utils.dprint_func_ir(func_ir, "After with changes", blocks=blocks)
+
+    def __call__(self, *args, **kwargs):
+        """Act like a function and enforce the contract that
+        setting the chunksize takes only one integer input.
+        """
+        if len(args) != 1 or kwargs or not isinstance(args[0], int):
+            raise ValueError("parallel_chunksize takes only a "
+                             "single integer argument.")
+
+        self.chunksize = args[0]
+        return self
+
+    def __enter__(self):
+        self.orig_chunksize = numba.get_parallel_chunksize()
+        numba.set_parallel_chunksize(self.chunksize)
+
+    def __exit__(self, typ, val, tb):
+        numba.set_parallel_chunksize(self.orig_chunksize)
+
+parallel_chunksize = _ParallelChunksize()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/builtins.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/builtins.py
new file mode 100644
index 000000000..6784793f6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/builtins.py
@@ -0,0 +1,825 @@
+import math
+from functools import reduce
+
+import numpy as np
+import operator
+import warnings
+
+from llvmlite import ir
+
+from numba.core.imputils import (lower_builtin, lower_getattr,
+                                 lower_getattr_generic, lower_cast,
+                                 lower_constant, iternext_impl,
+                                 call_getiter, call_iternext, impl_ret_borrowed,
+                                 impl_ret_untracked, numba_typeref_ctor)
+from numba.core import typing, types, utils, cgutils
+from numba.core.extending import overload, intrinsic
+from numba.core.typeconv import Conversion
+from numba.core.errors import (TypingError, LoweringError,
+                               NumbaExperimentalFeatureWarning)
+from numba.misc.special import literal_unroll
+from numba.core.typing.asnumbatype import as_numba_type
+from numba.core.errors import NumbaTypeError
+
+
+@overload(operator.truth)
+def ol_truth(val):
+    if isinstance(val, types.Boolean):
+        def impl(val):
+            return val
+        return impl
+
+
+@lower_builtin(operator.is_not, types.Any, types.Any)
+def generic_is_not(context, builder, sig, args):
+    """
+    Implement `x is not y` as `not (x is y)`.
+    """
+    is_impl = context.get_function(operator.is_, sig)
+    return builder.not_(is_impl(builder, args))
+
+
+@lower_builtin(operator.is_, types.Any, types.Any)
+def generic_is(context, builder, sig, args):
+    """
+    Default implementation for `x is y`
+    """
+    lhs_type, rhs_type = sig.args
+    # the lhs and rhs have the same type
+    if lhs_type == rhs_type:
+            # mutable types
+            if lhs_type.mutable:
+                msg = 'no default `is` implementation'
+                raise LoweringError(msg)
+            # immutable types
+            else:
+                # fallbacks to `==`
+                try:
+                    eq_impl = context.get_function(operator.eq, sig)
+                except NotImplementedError:
+                    # no `==` implemented for this type
+                    return cgutils.false_bit
+                else:
+                    return eq_impl(builder, args)
+    else:
+        return cgutils.false_bit
+
+
+@lower_builtin(operator.is_, types.Opaque, types.Opaque)
+def opaque_is(context, builder, sig, args):
+    """
+    Implementation for `x is y` for Opaque types.
+    """
+    lhs_type, rhs_type = sig.args
+    # the lhs and rhs have the same type
+    if lhs_type == rhs_type:
+        lhs_ptr = builder.ptrtoint(args[0], cgutils.intp_t)
+        rhs_ptr = builder.ptrtoint(args[1], cgutils.intp_t)
+
+        return builder.icmp_unsigned('==', lhs_ptr, rhs_ptr)
+    else:
+        return cgutils.false_bit
+
+
+@lower_builtin(operator.is_, types.Boolean, types.Boolean)
+def bool_is_impl(context, builder, sig, args):
+    """
+    Implementation for `x is y` for types derived from types.Boolean
+    (e.g. BooleanLiteral), and cross-checks between literal and non-literal
+    booleans, to satisfy Python's behavior preserving identity for bools.
+    """
+    arg1, arg2 = args
+    arg1_type, arg2_type = sig.args
+    _arg1 = context.cast(builder, arg1, arg1_type, types.boolean)
+    _arg2 = context.cast(builder, arg2, arg2_type, types.boolean)
+    eq_impl = context.get_function(
+        operator.eq,
+        typing.signature(types.boolean, types.boolean, types.boolean)
+    )
+    return eq_impl(builder, (_arg1, _arg2))
+
+
+# keep types.IntegerLiteral, as otherwise there's ambiguity between this and int_eq_impl
+@lower_builtin(operator.eq, types.Literal, types.Literal)
+@lower_builtin(operator.eq, types.IntegerLiteral, types.IntegerLiteral)
+def const_eq_impl(context, builder, sig, args):
+    arg1, arg2 = sig.args
+    val = 0
+    if arg1.literal_value == arg2.literal_value:
+        val = 1
+    res = ir.Constant(ir.IntType(1), val)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+# keep types.IntegerLiteral, as otherwise there's ambiguity between this and int_ne_impl
+@lower_builtin(operator.ne, types.Literal, types.Literal)
+@lower_builtin(operator.ne, types.IntegerLiteral, types.IntegerLiteral)
+def const_ne_impl(context, builder, sig, args):
+    arg1, arg2 = sig.args
+    val = 0
+    if arg1.literal_value != arg2.literal_value:
+        val = 1
+    res = ir.Constant(ir.IntType(1), val)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def gen_non_eq(val):
+    def none_equality(a, b):
+        a_none = isinstance(a, types.NoneType)
+        b_none = isinstance(b, types.NoneType)
+        if a_none and b_none:
+            def impl(a, b):
+                return val
+            return impl
+        elif a_none ^ b_none:
+            def impl(a, b):
+                return not val
+            return impl
+    return none_equality
+
+overload(operator.eq)(gen_non_eq(True))
+overload(operator.ne)(gen_non_eq(False))
+
+#-------------------------------------------------------------------------------
+
+@lower_getattr_generic(types.DeferredType)
+def deferred_getattr(context, builder, typ, value, attr):
+    """
+    Deferred.__getattr__ => redirect to the actual type.
+    """
+    inner_type = typ.get()
+    val = context.cast(builder, value, typ, inner_type)
+    imp = context.get_getattr(inner_type, attr)
+    return imp(context, builder, inner_type, val, attr)
+
+@lower_cast(types.Any, types.DeferredType)
+@lower_cast(types.Optional, types.DeferredType)
+@lower_cast(types.Boolean, types.DeferredType)
+def any_to_deferred(context, builder, fromty, toty, val):
+    actual = context.cast(builder, val, fromty, toty.get())
+    model = context.data_model_manager[toty]
+    return model.set(builder, model.make_uninitialized(), actual)
+
+@lower_cast(types.DeferredType, types.Any)
+@lower_cast(types.DeferredType, types.Boolean)
+@lower_cast(types.DeferredType, types.Optional)
+def deferred_to_any(context, builder, fromty, toty, val):
+    model = context.data_model_manager[fromty]
+    val = model.get(builder, val)
+    return context.cast(builder, val, fromty.get(), toty)
+
+
+#------------------------------------------------------------------------------
+
+@lower_builtin(operator.getitem, types.CPointer, types.Integer)
+def getitem_cpointer(context, builder, sig, args):
+    base_ptr, idx = args
+    elem_ptr = builder.gep(base_ptr, [idx])
+    res = builder.load(elem_ptr)
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.setitem, types.CPointer, types.Integer, types.Any)
+def setitem_cpointer(context, builder, sig, args):
+    base_ptr, idx, val = args
+    elem_ptr = builder.gep(base_ptr, [idx])
+    builder.store(val, elem_ptr)
+
+
+#-------------------------------------------------------------------------------
+
+def do_minmax(context, builder, argtys, args, cmpop):
+    assert len(argtys) == len(args), (argtys, args)
+    assert len(args) > 0
+
+    def binary_minmax(accumulator, value):
+        # This is careful to reproduce Python's algorithm, e.g.
+        # max(1.5, nan, 2.5) should return 2.5 (not nan or 1.5)
+        accty, acc = accumulator
+        vty, v = value
+        ty = context.typing_context.unify_types(accty, vty)
+        assert ty is not None
+        acc = context.cast(builder, acc, accty, ty)
+        v = context.cast(builder, v, vty, ty)
+        cmpsig = typing.signature(types.boolean, ty, ty)
+        ge = context.get_function(cmpop, cmpsig)
+        pred = ge(builder, (v, acc))
+        res = builder.select(pred, v, acc)
+        return ty, res
+
+    typvals = zip(argtys, args)
+    resty, resval = reduce(binary_minmax, typvals)
+    return resval
+
+
+@lower_builtin(max, types.BaseTuple)
+def max_iterable(context, builder, sig, args):
+    argtys = list(sig.args[0])
+    args = cgutils.unpack_tuple(builder, args[0])
+    return do_minmax(context, builder, argtys, args, operator.gt)
+
+@lower_builtin(max, types.VarArg(types.Any))
+def max_vararg(context, builder, sig, args):
+    return do_minmax(context, builder, sig.args, args, operator.gt)
+
+@lower_builtin(min, types.BaseTuple)
+def min_iterable(context, builder, sig, args):
+    argtys = list(sig.args[0])
+    args = cgutils.unpack_tuple(builder, args[0])
+    return do_minmax(context, builder, argtys, args, operator.lt)
+
+@lower_builtin(min, types.VarArg(types.Any))
+def min_vararg(context, builder, sig, args):
+    return do_minmax(context, builder, sig.args, args, operator.lt)
+
+
+def _round_intrinsic(tp):
+    # round() rounds half to even
+    return "llvm.rint.f%d" % (tp.bitwidth,)
+
+@lower_builtin(round, types.Float)
+def round_impl_unary(context, builder, sig, args):
+    fltty = sig.args[0]
+    llty = context.get_value_type(fltty)
+    module = builder.module
+    fnty = ir.FunctionType(llty, [llty])
+    fn = cgutils.get_or_insert_function(module, fnty, _round_intrinsic(fltty))
+    res = builder.call(fn, args)
+    # unary round() returns an int
+    res = builder.fptosi(res, context.get_value_type(sig.return_type))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower_builtin(round, types.Float, types.Integer)
+def round_impl_binary(context, builder, sig, args):
+    fltty = sig.args[0]
+    # Allow calling the intrinsic from the Python implementation below.
+    # This avoids the conversion to an int in Python 3's unary round().
+    _round = types.ExternalFunction(
+        _round_intrinsic(fltty), typing.signature(fltty, fltty))
+
+    def round_ndigits(x, ndigits):
+        if math.isinf(x) or math.isnan(x):
+            return x
+
+        if ndigits >= 0:
+            if ndigits > 22:
+                # pow1 and pow2 are each safe from overflow, but
+                # pow1*pow2 ~= pow(10.0, ndigits) might overflow.
+                pow1 = 10.0 ** (ndigits - 22)
+                pow2 = 1e22
+            else:
+                pow1 = 10.0 ** ndigits
+                pow2 = 1.0
+            y = (x * pow1) * pow2
+            if math.isinf(y):
+                return x
+            return (_round(y) / pow2) / pow1
+
+        else:
+            pow1 = 10.0 ** (-ndigits)
+            y = x / pow1
+            return _round(y) * pow1
+
+    res = context.compile_internal(builder, round_ndigits, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+#-------------------------------------------------------------------------------
+# Numeric constructors
+
+@lower_builtin(int, types.Any)
+@lower_builtin(float, types.Any)
+def int_impl(context, builder, sig, args):
+    [ty] = sig.args
+    [val] = args
+    res = context.cast(builder, val, ty, sig.return_type)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(complex, types.VarArg(types.Any))
+def complex_impl(context, builder, sig, args):
+    complex_type = sig.return_type
+    float_type = complex_type.underlying_float
+    if len(sig.args) == 1:
+        [argty] = sig.args
+        [arg] = args
+        if isinstance(argty, types.Complex):
+            # Cast Complex* to Complex*
+            res = context.cast(builder, arg, argty, complex_type)
+            return impl_ret_untracked(context, builder, sig.return_type, res)
+        else:
+            real = context.cast(builder, arg, argty, float_type)
+            imag = context.get_constant(float_type, 0)
+
+    elif len(sig.args) == 2:
+        [realty, imagty] = sig.args
+        [real, imag] = args
+        real = context.cast(builder, real, realty, float_type)
+        imag = context.cast(builder, imag, imagty, float_type)
+
+    cmplx = context.make_complex(builder, complex_type)
+    cmplx.real = real
+    cmplx.imag = imag
+    res = cmplx._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(types.NumberClass, types.Any)
+def number_constructor(context, builder, sig, args):
+    """
+    Call a number class, e.g. np.int32(...)
+    """
+    if isinstance(sig.return_type, types.Array):
+        # Array constructor
+        dt = sig.return_type.dtype
+        def foo(*arg_hack):
+            return np.array(arg_hack, dtype=dt)
+        res = context.compile_internal(builder, foo, sig, args)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+    else:
+        # Scalar constructor
+        [val] = args
+        [valty] = sig.args
+        return context.cast(builder, val, valty, sig.return_type)
+
+
+#-------------------------------------------------------------------------------
+# Constants
+
+@lower_constant(types.Dummy)
+def constant_dummy(context, builder, ty, pyval):
+    # This handles None, etc.
+    return context.get_dummy_value()
+
+@lower_constant(types.ExternalFunctionPointer)
+def constant_function_pointer(context, builder, ty, pyval):
+    ptrty = context.get_function_pointer_type(ty)
+    ptrval = context.add_dynamic_addr(builder, ty.get_pointer(pyval),
+                                      info=str(pyval))
+    return builder.bitcast(ptrval, ptrty)
+
+
+@lower_constant(types.Optional)
+def constant_optional(context, builder, ty, pyval):
+    if pyval is None:
+        return context.make_optional_none(builder, ty.type)
+    else:
+        return context.make_optional_value(builder, ty.type, pyval)
+
+
+# -----------------------------------------------------------------------------
+
+@lower_builtin(type, types.Any)
+def type_impl(context, builder, sig, args):
+    """
+    One-argument type() builtin.
+    """
+    return context.get_dummy_value()
+
+
+@lower_builtin(iter, types.IterableType)
+def iter_impl(context, builder, sig, args):
+    ty, = sig.args
+    val, = args
+    iterval = call_getiter(context, builder, ty, val)
+    return iterval
+
+
+@lower_builtin(next, types.IteratorType)
+def next_impl(context, builder, sig, args):
+    iterty, = sig.args
+    iterval, = args
+
+    res = call_iternext(context, builder, iterty, iterval)
+
+    with builder.if_then(builder.not_(res.is_valid()), likely=False):
+        context.call_conv.return_user_exc(builder, StopIteration, ())
+
+    return res.yielded_value()
+
+
+# -----------------------------------------------------------------------------
+
+@lower_builtin("not in", types.Any, types.Any)
+def not_in(context, builder, sig, args):
+    def in_impl(a, b):
+        return operator.contains(b, a)
+
+    res = context.compile_internal(builder, in_impl, sig, args)
+    return builder.not_(res)
+
+
+# -----------------------------------------------------------------------------
+
+@lower_builtin(len, types.ConstSized)
+def constsized_len(context, builder, sig, args):
+    [ty] = sig.args
+    retty = sig.return_type
+    res = context.get_constant(retty, len(ty.types))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(bool, types.Sized)
+def sized_bool(context, builder, sig, args):
+    [ty] = sig.args
+    if len(ty):
+        return cgutils.true_bit
+    else:
+        return cgutils.false_bit
+
+@lower_builtin(tuple)
+def lower_empty_tuple(context, builder, sig, args):
+    retty = sig.return_type
+    res = context.get_constant_undef(retty)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower_builtin(tuple, types.BaseTuple)
+def lower_tuple(context, builder, sig, args):
+    val, = args
+    return impl_ret_borrowed(context, builder, sig.return_type, val)
+
+@overload(bool)
+def bool_sequence(x):
+    valid_types = (
+        types.CharSeq,
+        types.UnicodeCharSeq,
+        types.DictType,
+        types.ListType,
+        types.UnicodeType,
+        types.Set,
+    )
+
+    if isinstance(x, valid_types):
+        def bool_impl(x):
+            return len(x) > 0
+        return bool_impl
+
+@overload(bool, inline='always')
+def bool_none(x):
+    if isinstance(x, types.NoneType) or x is None:
+        return lambda x: False
+
+# -----------------------------------------------------------------------------
+
+def get_type_max_value(typ):
+    if isinstance(typ, types.Float):
+        return np.inf
+    if isinstance(typ, types.Integer):
+        return typ.maxval
+    raise NotImplementedError("Unsupported type")
+
+def get_type_min_value(typ):
+    if isinstance(typ, types.Float):
+        return -np.inf
+    if isinstance(typ, types.Integer):
+        return typ.minval
+    raise NotImplementedError("Unsupported type")
+
+@lower_builtin(get_type_min_value, types.NumberClass)
+@lower_builtin(get_type_min_value, types.DType)
+def lower_get_type_min_value(context, builder, sig, args):
+    typ = sig.args[0].dtype
+
+    if isinstance(typ, types.Integer):
+        bw = typ.bitwidth
+        lty = ir.IntType(bw)
+        val = typ.minval
+        res = ir.Constant(lty, val)
+    elif isinstance(typ, types.Float):
+        bw = typ.bitwidth
+        if bw == 32:
+            lty = ir.FloatType()
+        elif bw == 64:
+            lty = ir.DoubleType()
+        else:
+            raise NotImplementedError("llvmlite only supports 32 and 64 bit floats")
+        npty = getattr(np, 'float{}'.format(bw))
+        res = ir.Constant(lty, -np.inf)
+    elif isinstance(typ, (types.NPDatetime, types.NPTimedelta)):
+        bw = 64
+        lty = ir.IntType(bw)
+        val = types.int64.minval + 1 # minval is NaT, so minval + 1 is the smallest value
+        res = ir.Constant(lty, val)
+    return impl_ret_untracked(context, builder, lty, res)
+
+@lower_builtin(get_type_max_value, types.NumberClass)
+@lower_builtin(get_type_max_value, types.DType)
+def lower_get_type_max_value(context, builder, sig, args):
+    typ = sig.args[0].dtype
+
+    if isinstance(typ, types.Integer):
+        bw = typ.bitwidth
+        lty = ir.IntType(bw)
+        val = typ.maxval
+        res = ir.Constant(lty, val)
+    elif isinstance(typ, types.Float):
+        bw = typ.bitwidth
+        if bw == 32:
+            lty = ir.FloatType()
+        elif bw == 64:
+            lty = ir.DoubleType()
+        else:
+            raise NotImplementedError("llvmlite only supports 32 and 64 bit floats")
+        npty = getattr(np, 'float{}'.format(bw))
+        res = ir.Constant(lty, np.inf)
+    elif isinstance(typ, (types.NPDatetime, types.NPTimedelta)):
+        bw = 64
+        lty = ir.IntType(bw)
+        val = types.int64.maxval
+        res = ir.Constant(lty, val)
+    return impl_ret_untracked(context, builder, lty, res)
+
+# -----------------------------------------------------------------------------
+
+from numba.core.typing.builtins import IndexValue, IndexValueType
+from numba.extending import overload, register_jitable
+
+@lower_builtin(IndexValue, types.intp, types.Type)
+@lower_builtin(IndexValue, types.uintp, types.Type)
+def impl_index_value(context, builder, sig, args):
+    typ = sig.return_type
+    index, value = args
+    index_value = cgutils.create_struct_proxy(typ)(context, builder)
+    index_value.index = index
+    index_value.value = value
+    return index_value._getvalue()
+
+
+@overload(min)
+def indval_min(indval1, indval2):
+    if isinstance(indval1, IndexValueType) and \
+       isinstance(indval2, IndexValueType):
+        def min_impl(indval1, indval2):
+            if np.isnan(indval1.value):
+                if np.isnan(indval2.value):
+                    # both indval1 and indval2 are nans so order by index
+                    if indval1.index < indval2.index:
+                        return indval1
+                    else:
+                        return indval2
+                else:
+                    # comparing against one nan always considered less
+                    return indval1
+            elif np.isnan(indval2.value):
+                # indval1 not a nan but indval2 is so consider indval2 less
+                return indval2
+            elif indval1.value > indval2.value:
+                return indval2
+            elif indval1.value == indval2.value:
+                if indval1.index < indval2.index:
+                    return indval1
+                else:
+                    return indval2
+            return indval1
+        return min_impl
+
+
+@overload(max)
+def indval_max(indval1, indval2):
+    if isinstance(indval1, IndexValueType) and \
+       isinstance(indval2, IndexValueType):
+        def max_impl(indval1, indval2):
+            if np.isnan(indval1.value):
+                if np.isnan(indval2.value):
+                    # both indval1 and indval2 are nans so order by index
+                    if indval1.index < indval2.index:
+                        return indval1
+                    else:
+                        return indval2
+                else:
+                    # comparing against one nan always considered larger
+                    return indval1
+            elif np.isnan(indval2.value):
+                # indval1 not a nan but indval2 is so consider indval2 larger
+                return indval2
+            elif indval2.value > indval1.value:
+                return indval2
+            elif indval1.value == indval2.value:
+                if indval1.index < indval2.index:
+                    return indval1
+                else:
+                    return indval2
+            return indval1
+        return max_impl
+
+
+greater_than = register_jitable(lambda a, b: a > b)
+less_than = register_jitable(lambda a, b: a < b)
+
+
+@register_jitable
+def min_max_impl(iterable, op):
+    if isinstance(iterable, types.IterableType):
+        def impl(iterable):
+            it = iter(iterable)
+            return_val = next(it)
+            for val in it:
+                if op(val, return_val):
+                    return_val = val
+            return return_val
+        return impl
+
+
+@overload(min)
+def iterable_min(iterable):
+    return min_max_impl(iterable, less_than)
+
+
+@overload(max)
+def iterable_max(iterable):
+    return min_max_impl(iterable, greater_than)
+
+
+@lower_builtin(types.TypeRef, types.VarArg(types.Any))
+def redirect_type_ctor(context, builder, sig, args):
+    """Redirect constructor implementation to `numba_typeref_ctor(cls, *args)`,
+    which should be overloaded by the type's implementation.
+
+    For example:
+
+        d = Dict()
+
+    `d` will be typed as `TypeRef[DictType]()`.  Thus, it will call into this
+    implementation.  We need to redirect the lowering to a function
+    named ``numba_typeref_ctor``.
+    """
+    cls = sig.return_type
+
+    def call_ctor(cls, *args):
+        return numba_typeref_ctor(cls, *args)
+
+    # Pack arguments into a tuple for `*args`
+    ctor_args = types.Tuple.from_types(sig.args)
+    # Make signature T(TypeRef[T], *args) where T is cls
+    sig = typing.signature(cls, types.TypeRef(cls), ctor_args)
+    if len(ctor_args) > 0:
+        args = (context.get_dummy_value(),   # Type object has no runtime repr.
+                context.make_tuple(builder, ctor_args, args))
+    else:
+        args = (context.get_dummy_value(),   # Type object has no runtime repr.
+                context.make_tuple(builder, ctor_args, ()))
+
+    return context.compile_internal(builder, call_ctor, sig, args)
+
+
+@overload(sum)
+def ol_sum(iterable, start=0):
+    # Cpython explicitly rejects strings, bytes and bytearrays
+    # https://github.com/python/cpython/blob/3.9/Python/bltinmodule.c#L2310-L2329 # noqa: E501
+    error = None
+    if isinstance(start, types.UnicodeType):
+        error = ('strings', '')
+    elif isinstance(start, types.Bytes):
+        error = ('bytes', 'b')
+    elif isinstance(start, types.ByteArray):
+        error = ('bytearray', 'b')
+
+    if error is not None:
+        msg = "sum() can't sum {} [use {}''.join(seq) instead]".format(*error)
+        raise TypingError(msg)
+
+    # if the container is homogeneous then it's relatively easy to handle.
+    if isinstance(iterable, (types.containers._HomogeneousTuple, types.List,
+                             types.ListType, types.Array, types.RangeType)):
+        iterator = iter
+    elif isinstance(iterable, (types.containers._HeterogeneousTuple)):
+        # if container is heterogeneous then literal unroll and hope for the
+        # best.
+        iterator = literal_unroll
+    else:
+        return None
+
+    def impl(iterable, start=0):
+        acc = start
+        for x in iterator(iterable):
+            # This most likely widens the type, this is expected Numba behaviour
+            acc = acc + x
+        return acc
+    return impl
+
+
+# ------------------------------------------------------------------------------
+# map, filter, reduce
+
+
+@overload(map)
+def ol_map(func, iterable, *args):
+    def impl(func, iterable, *args):
+        for x in zip(iterable, *args):
+            yield func(*x)
+    return impl
+
+
+@overload(filter)
+def ol_filter(func, iterable):
+    if (func is None) or isinstance(func, types.NoneType):
+        def impl(func, iterable):
+            for x in iterable:
+                if x:
+                    yield x
+    else:
+        def impl(func, iterable):
+            for x in iterable:
+                if func(x):
+                    yield x
+    return impl
+
+
+@overload(isinstance)
+def ol_isinstance(var, typs):
+
+    def true_impl(var, typs):
+        return True
+
+    def false_impl(var, typs):
+        return False
+
+    var_ty = as_numba_type(var)
+
+    if isinstance(var_ty, types.Optional):
+        msg = f'isinstance cannot handle optional types. Found: "{var_ty}"'
+        raise NumbaTypeError(msg)
+
+    # NOTE: The current implementation of `isinstance` restricts the type of the
+    # instance variable to types that are well known and in common use. The
+    # danger of unrestricted tyoe comparison is that a "default" of `False` is
+    # required and this means that if there is a bug in the logic of the
+    # comparison tree `isinstance` returns False! It's therefore safer to just
+    # reject the compilation as untypable!
+    supported_var_ty = (types.Number, types.Bytes, types.RangeType,
+                        types.DictType, types.LiteralStrKeyDict, types.List,
+                        types.ListType, types.Tuple, types.UniTuple, types.Set,
+                        types.Function, types.ClassType, types.UnicodeType,
+                        types.ClassInstanceType, types.NoneType, types.Array)
+    if not isinstance(var_ty, supported_var_ty):
+        msg = f'isinstance() does not support variables of type "{var_ty}".'
+        raise NumbaTypeError(msg)
+
+    # Warn about the experimental nature of this feature.
+    msg = "Use of isinstance() detected. This is an experimental feature."
+    warnings.warn(msg, category=NumbaExperimentalFeatureWarning)
+
+    t_typs = typs
+
+    # Check the types that the var can be an instance of, it'll be a scalar,
+    # a unituple or a tuple.
+    if isinstance(t_typs, types.UniTuple):
+        # corner case - all types in isinstance are the same
+        t_typs = (t_typs.key[0])
+
+    if not isinstance(t_typs, types.Tuple):
+        t_typs = (t_typs, )
+
+    for typ in t_typs:
+
+        if isinstance(typ, types.Function):
+            key = typ.key[0]  # functions like int(..), float(..), str(..)
+        elif isinstance(typ, types.ClassType):
+            key = typ  # jitclasses
+        else:
+            key = typ.key
+
+        # corner cases for bytes, range, ...
+        # avoid registering those types on `as_numba_type`
+        types_not_registered = {
+            bytes: types.Bytes,
+            range: types.RangeType,
+            dict: (types.DictType, types.LiteralStrKeyDict),
+            list: types.List,
+            tuple: types.BaseTuple,
+            set: types.Set,
+        }
+        if key in types_not_registered:
+            if isinstance(var_ty, types_not_registered[key]):
+                return true_impl
+            continue
+
+        if isinstance(typ, types.TypeRef):
+            # Use of Numba type classes is in general not supported as they do
+            # not work when the jit is disabled.
+            if key not in (types.ListType, types.DictType):
+                msg = ("Numba type classes (except numba.typed.* container "
+                       "types) are not supported.")
+                raise NumbaTypeError(msg)
+            # Case for TypeRef (i.e. isinstance(var, typed.List))
+            #      var_ty == ListType[int64] (instance)
+            #         typ == types.ListType  (class)
+            return true_impl if type(var_ty) is key else false_impl
+        else:
+            numba_typ = as_numba_type(key)
+            if var_ty == numba_typ:
+                return true_impl
+            elif isinstance(numba_typ, types.ClassType) and \
+                    isinstance(var_ty, types.ClassInstanceType) and \
+                    var_ty.key == numba_typ.instance_type.key:
+                # check for jitclasses
+                return true_impl
+            elif isinstance(numba_typ, types.Container) and \
+                    numba_typ.key[0] == types.undefined:
+                # check for containers (list, tuple, set, ...)
+                if isinstance(var_ty, numba_typ.__class__) or \
+                    (isinstance(var_ty, types.BaseTuple) and \
+                        isinstance(numba_typ, types.BaseTuple)):
+                    return true_impl
+
+    return false_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/charseq.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/charseq.py
new file mode 100644
index 000000000..68a33bcc0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/charseq.py
@@ -0,0 +1,1020 @@
+"""Implements operations on bytes and str (unicode) array items."""
+import operator
+import numpy as np
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core.extending import (overload, intrinsic, overload_method,
+                                  lower_cast, register_jitable)
+from numba.core.cgutils import is_nonelike
+from numba.cpython import unicode
+
+# bytes and str arrays items are of type CharSeq and UnicodeCharSeq,
+# respectively.  See numpy/types/npytypes.py for CharSeq,
+# UnicodeCharSeq definitions.  The corresponding data models are
+# defined in numpy/datamodel/models.py. Boxing/unboxing of item types
+# are defined in numpy/targets/boxing.py, see box_unicodecharseq,
+# unbox_unicodecharseq, box_charseq, unbox_charseq.
+
+s1_dtype = np.dtype('S1')
+assert s1_dtype.itemsize == 1
+bytes_type = types.Bytes(types.uint8, 1, "C", readonly=True)
+
+# Currently, NumPy supports only UTF-32 arrays but this may change in
+# future and the approach used here for supporting str arrays may need
+# a revision depending on how NumPy will support UTF-8 and UTF-16
+# arrays.
+u1_dtype = np.dtype('U1')
+unicode_byte_width = u1_dtype.itemsize
+unicode_uint = {1: np.uint8, 2: np.uint16, 4: np.uint32}[unicode_byte_width]
+unicode_kind = {1: unicode.PY_UNICODE_1BYTE_KIND,
+                2: unicode.PY_UNICODE_2BYTE_KIND,
+                4: unicode.PY_UNICODE_4BYTE_KIND}[unicode_byte_width]
+
+
+# this is modified version of numba.unicode.make_deref_codegen
+def make_deref_codegen(bitsize):
+    def codegen(context, builder, signature, args):
+        data, idx = args
+        rawptr = cgutils.alloca_once_value(builder, value=data)
+        ptr = builder.bitcast(rawptr, ir.IntType(bitsize).as_pointer())
+        ch = builder.load(builder.gep(ptr, [idx]))
+        return builder.zext(ch, ir.IntType(32))
+    return codegen
+
+
+@intrinsic
+def deref_uint8(typingctx, data, offset):
+    sig = types.uint32(data, types.intp)
+    return sig, make_deref_codegen(8)
+
+
+@intrinsic
+def deref_uint16(typingctx, data, offset):
+    sig = types.uint32(data, types.intp)
+    return sig, make_deref_codegen(16)
+
+
+@intrinsic
+def deref_uint32(typingctx, data, offset):
+    sig = types.uint32(data, types.intp)
+    return sig, make_deref_codegen(32)
+
+
+@register_jitable(_nrt=False)
+def charseq_get_code(a, i):
+    """Access i-th item of CharSeq object via code value
+    """
+    return deref_uint8(a, i)
+
+
+@register_jitable
+def charseq_get_value(a, i):
+    """Access i-th item of CharSeq object via code value.
+
+    null code is interpreted as IndexError
+    """
+    code = charseq_get_code(a, i)
+    if code == 0:
+        raise IndexError('index out of range')
+    return code
+
+
+@register_jitable(_nrt=False)
+def unicode_charseq_get_code(a, i):
+    """Access i-th item of UnicodeCharSeq object via code value
+    """
+    if unicode_byte_width == 4:
+        return deref_uint32(a, i)
+    elif unicode_byte_width == 2:
+        return deref_uint16(a, i)
+    elif unicode_byte_width == 1:
+        return deref_uint8(a, i)
+    else:
+        raise NotImplementedError(
+            'unicode_charseq_get_code: unicode_byte_width not in [1, 2, 4]')
+
+
+@register_jitable
+def unicode_get_code(a, i):
+    """Access i-th item of UnicodeType object.
+    """
+    return unicode._get_code_point(a, i)
+
+
+@register_jitable
+def bytes_get_code(a, i):
+    """Access i-th item of Bytes object.
+        """
+    return a[i]
+
+
+def _get_code_impl(a):
+    if isinstance(a, types.CharSeq):
+        return charseq_get_code
+    elif isinstance(a, types.Bytes):
+        return bytes_get_code
+    elif isinstance(a, types.UnicodeCharSeq):
+        return unicode_charseq_get_code
+    elif isinstance(a, types.UnicodeType):
+        return unicode_get_code
+
+
+def _same_kind(a, b):
+    for t in [(types.CharSeq, types.Bytes),
+              (types.UnicodeCharSeq, types.UnicodeType)]:
+        if isinstance(a, t) and isinstance(b, t):
+            return True
+    return False
+
+
+def _is_bytes(a):
+    return isinstance(a, (types.CharSeq, types.Bytes))
+
+
+def is_default(x, default):
+    return x == default or isinstance(x, types.Omitted)
+
+
+@register_jitable
+def unicode_charseq_get_value(a, i):
+    """Access i-th item of UnicodeCharSeq object via unicode value
+
+    null code is interpreted as IndexError
+    """
+    code = unicode_charseq_get_code(a, i)
+    if code == 0:
+        raise IndexError('index out of range')
+    # Return numpy equivalent of `chr(code)`
+    return np.array(code, unicode_uint).view(u1_dtype)[()]
+
+
+#
+# CAST
+#
+# Currently, the following casting operations are supported:
+#   Bytes -> CharSeq                 (ex: a=np.array(b'abc'); a[()] = b'123')
+#   UnicodeType -> UnicodeCharSeq    (ex: a=np.array('abc'); a[()] = '123')
+#   CharSeq -> Bytes                 (ex: a=np.array(b'abc'); b = bytes(a[()]))
+#   UnicodeType -> Bytes             (ex: str('123')._to_bytes())
+#
+# The following casting operations can be implemented when required:
+#   Bytes -> UnicodeCharSeq   (ex: a=np.array('abc'); a[()] = b'123')
+#   UnicodeType -> CharSeq    (ex: a=np.array(b'abc'); a[()] = '123')
+#   UnicodeType -> Bytes      (ex: bytes('123', 'utf8'))
+#
+
+
+@lower_cast(types.Bytes, types.CharSeq)
+def bytes_to_charseq(context, builder, fromty, toty, val):
+    barr = cgutils.create_struct_proxy(fromty)(context, builder, value=val)
+    src = builder.bitcast(barr.data, ir.IntType(8).as_pointer())
+    src_length = barr.nitems
+
+    lty = context.get_value_type(toty)
+    dstint_t = ir.IntType(8)
+    dst_ptr = cgutils.alloca_once(builder, lty)
+    dst = builder.bitcast(dst_ptr, dstint_t.as_pointer())
+
+    dst_length = ir.Constant(src_length.type, toty.count)
+    is_shorter_value = builder.icmp_unsigned('<', src_length, dst_length)
+    count = builder.select(is_shorter_value, src_length, dst_length)
+    with builder.if_then(is_shorter_value):
+        cgutils.memset(builder,
+                       dst,
+                       ir.Constant(src_length.type,
+                                   toty.count), 0)
+    with cgutils.for_range(builder, count) as loop:
+        in_ptr = builder.gep(src, [loop.index])
+        in_val = builder.zext(builder.load(in_ptr), dstint_t)
+        builder.store(in_val, builder.gep(dst, [loop.index]))
+
+    return builder.load(dst_ptr)
+
+
+def _make_constant_bytes(context, builder, nbytes):
+    bstr_ctor = cgutils.create_struct_proxy(bytes_type)
+    bstr = bstr_ctor(context, builder)
+
+    if isinstance(nbytes, int):
+        nbytes = ir.Constant(bstr.nitems.type, nbytes)
+
+    bstr.meminfo = context.nrt.meminfo_alloc(builder, nbytes)
+    bstr.nitems = nbytes
+    bstr.itemsize = ir.Constant(bstr.itemsize.type, 1)
+    bstr.data = context.nrt.meminfo_data(builder, bstr.meminfo)
+    bstr.parent = cgutils.get_null_value(bstr.parent.type)
+    # bstr.shape and bstr.strides are not used
+    bstr.shape = cgutils.get_null_value(bstr.shape.type)
+    bstr.strides = cgutils.get_null_value(bstr.strides.type)
+    return bstr
+
+
+@lower_cast(types.CharSeq, types.Bytes)
+def charseq_to_bytes(context, builder, fromty, toty, val):
+    bstr = _make_constant_bytes(context, builder, val.type.count)
+    rawptr = cgutils.alloca_once_value(builder, value=val)
+    ptr = builder.bitcast(rawptr, bstr.data.type)
+    cgutils.memcpy(builder, bstr.data, ptr, bstr.nitems)
+    return bstr
+
+
+@lower_cast(types.UnicodeType, types.Bytes)
+def unicode_to_bytes_cast(context, builder, fromty, toty, val):
+    uni_str = cgutils.create_struct_proxy(fromty)(context, builder, value=val)
+    src1 = builder.bitcast(uni_str.data, ir.IntType(8).as_pointer())
+    notkind1 = builder.icmp_unsigned('!=', uni_str.kind,
+                                     ir.Constant(uni_str.kind.type, 1))
+    src_length = uni_str.length
+
+    with builder.if_then(notkind1):
+        context.call_conv.return_user_exc(
+            builder, ValueError,
+            ("cannot cast higher than 8-bit unicode_type to bytes",))
+
+    bstr = _make_constant_bytes(context, builder, src_length)
+    cgutils.memcpy(builder, bstr.data, src1, bstr.nitems)
+    return bstr
+
+
+@intrinsic
+def _unicode_to_bytes(typingctx, s):
+    # used in _to_bytes method
+    assert s == types.unicode_type
+    sig = bytes_type(s)
+
+    def codegen(context, builder, signature, args):
+        return unicode_to_bytes_cast(
+            context, builder, s, bytes_type, args[0])._getvalue()
+    return sig, codegen
+
+
+@lower_cast(types.UnicodeType, types.UnicodeCharSeq)
+def unicode_to_unicode_charseq(context, builder, fromty, toty, val):
+    uni_str = cgutils.create_struct_proxy(fromty)(context, builder, value=val)
+    src1 = builder.bitcast(uni_str.data, ir.IntType(8).as_pointer())
+    src2 = builder.bitcast(uni_str.data, ir.IntType(16).as_pointer())
+    src4 = builder.bitcast(uni_str.data, ir.IntType(32).as_pointer())
+    kind1 = builder.icmp_unsigned('==', uni_str.kind,
+                                  ir.Constant(uni_str.kind.type, 1))
+    kind2 = builder.icmp_unsigned('==', uni_str.kind,
+                                  ir.Constant(uni_str.kind.type, 2))
+    kind4 = builder.icmp_unsigned('==', uni_str.kind,
+                                  ir.Constant(uni_str.kind.type, 4))
+    src_length = uni_str.length
+
+    lty = context.get_value_type(toty)
+    dstint_t = ir.IntType(8 * unicode_byte_width)
+    dst_ptr = cgutils.alloca_once(builder, lty)
+    dst = builder.bitcast(dst_ptr, dstint_t.as_pointer())
+
+    dst_length = ir.Constant(src_length.type, toty.count)
+    is_shorter_value = builder.icmp_unsigned('<', src_length, dst_length)
+    count = builder.select(is_shorter_value, src_length, dst_length)
+    with builder.if_then(is_shorter_value):
+        cgutils.memset(builder,
+                       dst,
+                       ir.Constant(src_length.type,
+                                   toty.count * unicode_byte_width), 0)
+
+    with builder.if_then(kind1):
+        with cgutils.for_range(builder, count) as loop:
+            in_ptr = builder.gep(src1, [loop.index])
+            in_val = builder.zext(builder.load(in_ptr), dstint_t)
+            builder.store(in_val, builder.gep(dst, [loop.index]))
+
+    with builder.if_then(kind2):
+        if unicode_byte_width >= 2:
+            with cgutils.for_range(builder, count) as loop:
+                in_ptr = builder.gep(src2, [loop.index])
+                in_val = builder.zext(builder.load(in_ptr), dstint_t)
+                builder.store(in_val, builder.gep(dst, [loop.index]))
+        else:
+            context.call_conv.return_user_exc(
+                builder, ValueError,
+                ("cannot cast 16-bit unicode_type to %s-bit %s"
+                 % (unicode_byte_width * 8, toty)))
+
+    with builder.if_then(kind4):
+        if unicode_byte_width >= 4:
+            with cgutils.for_range(builder, count) as loop:
+                in_ptr = builder.gep(src4, [loop.index])
+                in_val = builder.zext(builder.load(in_ptr), dstint_t)
+                builder.store(in_val, builder.gep(dst, [loop.index]))
+        else:
+            context.call_conv.return_user_exc(
+                builder, ValueError,
+                ("cannot cast 32-bit unicode_type to %s-bit %s"
+                 % (unicode_byte_width * 8, toty)))
+
+    return builder.load(dst_ptr)
+
+#
+#   Operations on bytes/str array items
+#
+# Implementation note: while some operations need
+# CharSeq/UnicodeCharSeq specific implementations (getitem, len, str,
+# etc), many operations can be supported by casting
+# CharSeq/UnicodeCharSeq objects to Bytes/UnicodeType objects and
+# re-use existing operations.
+#
+# However, in numba more operations are implemented for UnicodeType
+# than for Bytes objects, hence the support for operations with bytes
+# array items will be less complete than for str arrays. Although, in
+# some cases (hash, contains, etc) the UnicodeType implementations can
+# be reused for Bytes objects via using `_to_str` method.
+#
+
+
+@overload(operator.getitem)
+def charseq_getitem(s, i):
+    get_value = None
+    if isinstance(i, types.Integer):
+        if isinstance(s, types.CharSeq):
+            get_value = charseq_get_value
+        if isinstance(s, types.UnicodeCharSeq):
+            get_value = unicode_charseq_get_value
+    if get_value is not None:
+        max_i = s.count
+        msg = 'index out of range [0, %s]' % (max_i - 1)
+
+        def getitem_impl(s, i):
+            if i < max_i and i >= 0:
+                return get_value(s, i)
+            raise IndexError(msg)
+        return getitem_impl
+
+
+@overload(len)
+def charseq_len(s):
+    if isinstance(s, (types.CharSeq, types.UnicodeCharSeq)):
+        get_code = _get_code_impl(s)
+        n = s.count
+        if n == 0:
+            def len_impl(s):
+                return 0
+            return len_impl
+        else:
+            def len_impl(s):
+                # return the index of the last non-null value (numpy
+                # behavior)
+                i = n
+                code = 0
+                while code == 0:
+                    i = i - 1
+                    if i < 0:
+                        break
+                    code = get_code(s, i)
+                return i + 1
+            return len_impl
+
+
+@overload(operator.add)
+@overload(operator.iadd)
+def charseq_concat(a, b):
+    if not _same_kind(a, b):
+        return
+    if (isinstance(a, types.UnicodeCharSeq) and
+            isinstance(b, types.UnicodeType)):
+        def impl(a, b):
+            return str(a) + b
+        return impl
+    if (isinstance(b, types.UnicodeCharSeq) and
+            isinstance(a, types.UnicodeType)):
+        def impl(a, b):
+            return a + str(b)
+        return impl
+    if (isinstance(a, types.UnicodeCharSeq) and
+            isinstance(b, types.UnicodeCharSeq)):
+        def impl(a, b):
+            return str(a) + str(b)
+        return impl
+    if (isinstance(a, (types.CharSeq, types.Bytes)) and
+            isinstance(b, (types.CharSeq, types.Bytes))):
+        def impl(a, b):
+            return (a._to_str() + b._to_str())._to_bytes()
+        return impl
+
+
+@overload(operator.mul)
+def charseq_repeat(a, b):
+    if isinstance(a, types.UnicodeCharSeq):
+        def wrap(a, b):
+            return str(a) * b
+        return wrap
+    if isinstance(b, types.UnicodeCharSeq):
+        def wrap(a, b):
+            return a * str(b)
+        return wrap
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        def wrap(a, b):
+            return (a._to_str() * b)._to_bytes()
+        return wrap
+    if isinstance(b, (types.CharSeq, types.Bytes)):
+        def wrap(a, b):
+            return (a * b._to_str())._to_bytes()
+        return wrap
+
+
+@overload(operator.not_)
+def charseq_not(a):
+    if isinstance(a, (types.UnicodeCharSeq, types.CharSeq, types.Bytes)):
+        def impl(a):
+            return len(a) == 0
+        return impl
+
+
+@overload(operator.eq)
+def charseq_eq(a, b):
+    if not _same_kind(a, b):
+        return
+    left_code = _get_code_impl(a)
+    right_code = _get_code_impl(b)
+    if left_code is not None and right_code is not None:
+        def eq_impl(a, b):
+            n = len(a)
+            if n != len(b):
+                return False
+            for i in range(n):
+                if left_code(a, i) != right_code(b, i):
+                    return False
+            return True
+        return eq_impl
+
+
+@overload(operator.ne)
+def charseq_ne(a, b):
+    if not _same_kind(a, b):
+        return
+    left_code = _get_code_impl(a)
+    right_code = _get_code_impl(b)
+    if left_code is not None and right_code is not None:
+        def ne_impl(a, b):
+            return not (a == b)
+        return ne_impl
+
+
+@overload(operator.lt)
+def charseq_lt(a, b):
+    if not _same_kind(a, b):
+        return
+    left_code = _get_code_impl(a)
+    right_code = _get_code_impl(b)
+    if left_code is not None and right_code is not None:
+        def lt_impl(a, b):
+            na = len(a)
+            nb = len(b)
+            n = min(na, nb)
+            for i in range(n):
+                ca, cb = left_code(a, i), right_code(b, i)
+                if ca != cb:
+                    return ca < cb
+            return na < nb
+        return lt_impl
+
+
+@overload(operator.gt)
+def charseq_gt(a, b):
+    if not _same_kind(a, b):
+        return
+    left_code = _get_code_impl(a)
+    right_code = _get_code_impl(b)
+    if left_code is not None and right_code is not None:
+        def gt_impl(a, b):
+            return b < a
+        return gt_impl
+
+
+@overload(operator.le)
+def charseq_le(a, b):
+    if not _same_kind(a, b):
+        return
+    left_code = _get_code_impl(a)
+    right_code = _get_code_impl(b)
+    if left_code is not None and right_code is not None:
+        def le_impl(a, b):
+            return not (a > b)
+        return le_impl
+
+
+@overload(operator.ge)
+def charseq_ge(a, b):
+    if not _same_kind(a, b):
+        return
+    left_code = _get_code_impl(a)
+    right_code = _get_code_impl(b)
+    if left_code is not None and right_code is not None:
+        def ge_impl(a, b):
+            return not (a < b)
+        return ge_impl
+
+
+@overload(operator.contains)
+def charseq_contains(a, b):
+    if not _same_kind(a, b):
+        return
+    left_code = _get_code_impl(a)
+    right_code = _get_code_impl(b)
+    if left_code is not None and right_code is not None:
+        if _is_bytes(a):
+            def contains_impl(a, b):
+                # Ideally, `return bytes(b) in bytes(a)` would be used
+                # here, but numba Bytes does not implement
+                # contains. So, using `unicode_type` implementation
+                # here:
+                return b._to_str() in a._to_str()
+        else:
+            def contains_impl(a, b):
+                return str(b) in str(a)
+        return contains_impl
+
+
+@overload_method(types.UnicodeCharSeq, 'isascii')
+@overload_method(types.CharSeq, 'isascii')
+@overload_method(types.Bytes, 'isascii')
+def charseq_isascii(s):
+    get_code = _get_code_impl(s)
+
+    def impl(s):
+        for i in range(len(s)):
+            if get_code(s, i) > 127:
+                return False
+        return True
+    return impl
+
+
+@overload_method(types.UnicodeCharSeq, '_get_kind')
+@overload_method(types.CharSeq, '_get_kind')
+def charseq_get_kind(s):
+    get_code = _get_code_impl(s)
+
+    def impl(s):
+        max_code = 0
+        for i in range(len(s)):
+            code = get_code(s, i)
+            if code > max_code:
+                max_code = code
+        if max_code > 0xffff:
+            return unicode.PY_UNICODE_4BYTE_KIND
+        if max_code > 0xff:
+            return unicode.PY_UNICODE_2BYTE_KIND
+        return unicode.PY_UNICODE_1BYTE_KIND
+    return impl
+
+
+@overload_method(types.UnicodeType, '_to_bytes')
+def unicode_to_bytes_mth(s):
+    """Convert unicode_type object to Bytes object.
+
+    Note: The usage of _to_bytes method can be eliminated once all
+    Python bytes operations are implemented for numba Bytes objects.
+
+    """
+    def impl(s):
+        return _unicode_to_bytes(s)
+    return impl
+
+
+@overload_method(types.CharSeq, '_to_str')
+@overload_method(types.Bytes, '_to_str')
+def charseq_to_str_mth(s):
+    """Convert bytes array item or bytes instance to UTF-8 str.
+
+    Note: The usage of _to_str method can be eliminated once all
+    Python bytes operations are implemented for numba Bytes objects.
+    """
+    get_code = _get_code_impl(s)
+
+    def tostr_impl(s):
+        n = len(s)
+        is_ascii = s.isascii()
+        result = unicode._empty_string(
+            unicode.PY_UNICODE_1BYTE_KIND, n, is_ascii)
+        for i in range(n):
+            code = get_code(s, i)
+            unicode._set_code_point(result, i, code)
+        return result
+    return tostr_impl
+
+
+@overload(str)
+def charseq_str(s):
+    if isinstance(s, types.UnicodeCharSeq):
+        get_code = _get_code_impl(s)
+
+        def str_impl(s):
+            n = len(s)
+            kind = s._get_kind()
+            is_ascii = kind == 1 and s.isascii()
+            result = unicode._empty_string(kind, n, is_ascii)
+            for i in range(n):
+                code = get_code(s, i)
+                unicode._set_code_point(result, i, code)
+            return result
+        return str_impl
+
+
+@overload(bytes)
+def charseq_bytes(s):
+    if isinstance(s, types.CharSeq):
+        return lambda s: s
+
+
+@overload_method(types.UnicodeCharSeq, '__hash__')
+def unicode_charseq_hash(s):
+    def impl(s):
+        return hash(str(s))
+    return impl
+
+
+@overload_method(types.CharSeq, '__hash__')
+def charseq_hash(s):
+    def impl(s):
+        # Ideally, `return hash(bytes(s))` would be used here but
+        # numba Bytes does not implement hash (yet). However, for a
+        # UTF-8 string `s`, we have hash(bytes(s)) == hash(s), hence,
+        # we can convert CharSeq object to unicode_type and reuse its
+        # hash implementation:
+        return hash(s._to_str())
+    return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'isupper')
+def unicode_charseq_isupper(s):
+    def impl(s):
+        # workaround unicode_type.isupper bug: it returns int value
+        return not not str(s).isupper()
+    return impl
+
+
+@overload_method(types.CharSeq, 'isupper')
+def charseq_isupper(s):
+    def impl(s):
+        # return bytes(s).isupper()  # TODO: implement isupper for Bytes
+        return not not s._to_str().isupper()
+    return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'upper')
+def unicode_charseq_upper(s):
+    def impl(s):
+        return str(s).upper()
+    return impl
+
+
+@overload_method(types.CharSeq, 'upper')
+def charseq_upper(s):
+    def impl(s):
+        # return bytes(s).upper()  # TODO: implement upper for Bytes
+        return s._to_str().upper()._to_bytes()
+    return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'find')
+@overload_method(types.CharSeq, 'find')
+@overload_method(types.Bytes, 'find')
+def unicode_charseq_find(a, b):
+    if isinstance(a, types.UnicodeCharSeq):
+        if isinstance(b, types.UnicodeCharSeq):
+            def impl(a, b):
+                return str(a).find(str(b))
+            return impl
+        if isinstance(b, types.UnicodeType):
+            def impl(a, b):
+                return str(a).find(b)
+            return impl
+    if isinstance(a, types.CharSeq):
+        if isinstance(b, (types.CharSeq, types.Bytes)):
+            def impl(a, b):
+                return a._to_str().find(b._to_str())
+            return impl
+    if isinstance(a, types.UnicodeType):
+        if isinstance(b, types.UnicodeCharSeq):
+            def impl(a, b):
+                return a.find(str(b))
+            return impl
+    if isinstance(a, types.Bytes):
+        if isinstance(b, types.CharSeq):
+            def impl(a, b):
+                return a._to_str().find(b._to_str())
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'rfind')
+@overload_method(types.CharSeq, 'rfind')
+@overload_method(types.Bytes, 'rfind')
+def unicode_charseq_rfind(a, b):
+    if isinstance(a, types.UnicodeCharSeq):
+        if isinstance(b, types.UnicodeCharSeq):
+            def impl(a, b):
+                return str(a).rfind(str(b))
+            return impl
+        if isinstance(b, types.UnicodeType):
+            def impl(a, b):
+                return str(a).rfind(b)
+            return impl
+    if isinstance(a, types.CharSeq):
+        if isinstance(b, (types.CharSeq, types.Bytes)):
+            def impl(a, b):
+                return a._to_str().rfind(b._to_str())
+            return impl
+    if isinstance(a, types.UnicodeType):
+        if isinstance(b, types.UnicodeCharSeq):
+            def impl(a, b):
+                return a.rfind(str(b))
+            return impl
+    if isinstance(a, types.Bytes):
+        if isinstance(b, types.CharSeq):
+            def impl(a, b):
+                return a._to_str().rfind(b._to_str())
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'startswith')
+@overload_method(types.CharSeq, 'startswith')
+@overload_method(types.Bytes, 'startswith')
+def unicode_charseq_startswith(a, b):
+    if isinstance(a, types.UnicodeCharSeq):
+        if isinstance(b, types.UnicodeCharSeq):
+            def impl(a, b):
+                return str(a).startswith(str(b))
+            return impl
+        if isinstance(b, types.UnicodeType):
+            def impl(a, b):
+                return str(a).startswith(b)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if isinstance(b, (types.CharSeq, types.Bytes)):
+            def impl(a, b):
+                return a._to_str().startswith(b._to_str())
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'endswith')
+@overload_method(types.CharSeq, 'endswith')
+@overload_method(types.Bytes, 'endswith')
+def unicode_charseq_endswith(a, b):
+    if isinstance(a, types.UnicodeCharSeq):
+        if isinstance(b, types.UnicodeCharSeq):
+            def impl(a, b):
+                return str(a).endswith(str(b))
+            return impl
+        if isinstance(b, types.UnicodeType):
+            def impl(a, b):
+                return str(a).endswith(b)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if isinstance(b, (types.CharSeq, types.Bytes)):
+            def impl(a, b):
+                return a._to_str().endswith(b._to_str())
+            return impl
+
+
+@register_jitable
+def _map_bytes(seq):
+    return [s._to_bytes() for s in seq]
+
+
+@overload_method(types.UnicodeCharSeq, 'split')
+@overload_method(types.CharSeq, 'split')
+@overload_method(types.Bytes, 'split')
+def unicode_charseq_split(a, sep=None, maxsplit=-1):
+    if not (maxsplit == -1 or
+            isinstance(maxsplit, (types.Omitted, types.Integer,
+                                  types.IntegerLiteral))):
+        return None
+    if isinstance(a, types.UnicodeCharSeq):
+        if isinstance(sep, types.UnicodeCharSeq):
+            def impl(a, sep=None, maxsplit=-1):
+                return str(a).split(sep=str(sep), maxsplit=maxsplit)
+            return impl
+        if isinstance(sep, types.UnicodeType):
+            def impl(a, sep=None, maxsplit=-1):
+                return str(a).split(sep=sep, maxsplit=maxsplit)
+            return impl
+        if is_nonelike(sep):
+            if is_default(maxsplit, -1):
+                def impl(a, sep=None, maxsplit=-1):
+                    return str(a).split()
+            else:
+                def impl(a, sep=None, maxsplit=-1):
+                    return str(a).split(maxsplit=maxsplit)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if isinstance(sep, (types.CharSeq, types.Bytes)):
+            def impl(a, sep=None, maxsplit=-1):
+                return _map_bytes(a._to_str().split(sep._to_str(),
+                                                    maxsplit=maxsplit))
+            return impl
+        if is_nonelike(sep):
+            if is_default(maxsplit, -1):
+                def impl(a, sep=None, maxsplit=-1):
+                    return _map_bytes(a._to_str().split())
+            else:
+                def impl(a, sep=None, maxsplit=-1):
+                    return _map_bytes(a._to_str().split(maxsplit=maxsplit))
+            return impl
+
+# NOT IMPLEMENTED: rsplit
+
+
+@overload_method(types.UnicodeCharSeq, 'ljust')
+@overload_method(types.CharSeq, 'ljust')
+@overload_method(types.Bytes, 'ljust')
+def unicode_charseq_ljust(a, width, fillchar=' '):
+    if isinstance(a, types.UnicodeCharSeq):
+        if is_default(fillchar, ' '):
+            def impl(a, width, fillchar=' '):
+                return str(a).ljust(width)
+            return impl
+        elif isinstance(fillchar, types.UnicodeCharSeq):
+            def impl(a, width, fillchar=' '):
+                return str(a).ljust(width, str(fillchar))
+            return impl
+        elif isinstance(fillchar, types.UnicodeType):
+            def impl(a, width, fillchar=' '):
+                return str(a).ljust(width, fillchar)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if is_default(fillchar, ' ') or is_default(fillchar, b' '):
+            def impl(a, width, fillchar=' '):
+                return a._to_str().ljust(width)._to_bytes()
+            return impl
+        elif isinstance(fillchar, (types.CharSeq, types.Bytes)):
+            def impl(a, width, fillchar=' '):
+                return a._to_str().ljust(width, fillchar._to_str())._to_bytes()
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'rjust')
+@overload_method(types.CharSeq, 'rjust')
+@overload_method(types.Bytes, 'rjust')
+def unicode_charseq_rjust(a, width, fillchar=' '):
+    if isinstance(a, types.UnicodeCharSeq):
+        if is_default(fillchar, ' '):
+            def impl(a, width, fillchar=' '):
+                return str(a).rjust(width)
+            return impl
+        elif isinstance(fillchar, types.UnicodeCharSeq):
+            def impl(a, width, fillchar=' '):
+                return str(a).rjust(width, str(fillchar))
+            return impl
+        elif isinstance(fillchar, types.UnicodeType):
+            def impl(a, width, fillchar=' '):
+                return str(a).rjust(width, fillchar)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if is_default(fillchar, ' ') or is_default(fillchar, b' '):
+            def impl(a, width, fillchar=' '):
+                return a._to_str().rjust(width)._to_bytes()
+            return impl
+        elif isinstance(fillchar, (types.CharSeq, types.Bytes)):
+            def impl(a, width, fillchar=' '):
+                return a._to_str().rjust(width, fillchar._to_str())._to_bytes()
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'center')
+@overload_method(types.CharSeq, 'center')
+@overload_method(types.Bytes, 'center')
+def unicode_charseq_center(a, width, fillchar=' '):
+    if isinstance(a, types.UnicodeCharSeq):
+        if is_default(fillchar, ' '):
+            def impl(a, width, fillchar=' '):
+                return str(a).center(width)
+            return impl
+        elif isinstance(fillchar, types.UnicodeCharSeq):
+            def impl(a, width, fillchar=' '):
+                return str(a).center(width, str(fillchar))
+            return impl
+        elif isinstance(fillchar, types.UnicodeType):
+            def impl(a, width, fillchar=' '):
+                return str(a).center(width, fillchar)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if is_default(fillchar, ' ') or is_default(fillchar, b' '):
+            def impl(a, width, fillchar=' '):
+                return a._to_str().center(width)._to_bytes()
+            return impl
+        elif isinstance(fillchar, (types.CharSeq, types.Bytes)):
+            def impl(a, width, fillchar=' '):
+                return a._to_str().center(width, fillchar._to_str())._to_bytes()
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'zfill')
+@overload_method(types.CharSeq, 'zfill')
+@overload_method(types.Bytes, 'zfill')
+def unicode_charseq_zfill(a, width):
+    if isinstance(a, types.UnicodeCharSeq):
+        def impl(a, width):
+            return str(a).zfill(width)
+        return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        def impl(a, width):
+            return a._to_str().zfill(width)._to_bytes()
+        return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'lstrip')
+@overload_method(types.CharSeq, 'lstrip')
+@overload_method(types.Bytes, 'lstrip')
+def unicode_charseq_lstrip(a, chars=None):
+    if isinstance(a, types.UnicodeCharSeq):
+        if is_nonelike(chars):
+            def impl(a, chars=None):
+                return str(a).lstrip()
+            return impl
+        elif isinstance(chars, types.UnicodeCharSeq):
+            def impl(a, chars=None):
+                return str(a).lstrip(str(chars))
+            return impl
+        elif isinstance(chars, types.UnicodeType):
+            def impl(a, chars=None):
+                return str(a).lstrip(chars)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if is_nonelike(chars):
+            def impl(a, chars=None):
+                return a._to_str().lstrip()._to_bytes()
+            return impl
+        elif isinstance(chars, (types.CharSeq, types.Bytes)):
+            def impl(a, chars=None):
+                return a._to_str().lstrip(chars._to_str())._to_bytes()
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'rstrip')
+@overload_method(types.CharSeq, 'rstrip')
+@overload_method(types.Bytes, 'rstrip')
+def unicode_charseq_rstrip(a, chars=None):
+    if isinstance(a, types.UnicodeCharSeq):
+        if is_nonelike(chars):
+            def impl(a, chars=None):
+                return str(a).rstrip()
+            return impl
+        elif isinstance(chars, types.UnicodeCharSeq):
+            def impl(a, chars=None):
+                return str(a).rstrip(str(chars))
+            return impl
+        elif isinstance(chars, types.UnicodeType):
+            def impl(a, chars=None):
+                return str(a).rstrip(chars)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if is_nonelike(chars):
+            def impl(a, chars=None):
+                return a._to_str().rstrip()._to_bytes()
+            return impl
+        elif isinstance(chars, (types.CharSeq, types.Bytes)):
+            def impl(a, chars=None):
+                return a._to_str().rstrip(chars._to_str())._to_bytes()
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'strip')
+@overload_method(types.CharSeq, 'strip')
+@overload_method(types.Bytes, 'strip')
+def unicode_charseq_strip(a, chars=None):
+    if isinstance(a, types.UnicodeCharSeq):
+        if is_nonelike(chars):
+            def impl(a, chars=None):
+                return str(a).strip()
+            return impl
+        elif isinstance(chars, types.UnicodeCharSeq):
+            def impl(a, chars=None):
+                return str(a).strip(str(chars))
+            return impl
+        elif isinstance(chars, types.UnicodeType):
+            def impl(a, chars=None):
+                return str(a).strip(chars)
+            return impl
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        if is_nonelike(chars):
+            def impl(a, chars=None):
+                return a._to_str().strip()._to_bytes()
+            return impl
+        elif isinstance(chars, (types.CharSeq, types.Bytes)):
+            def impl(a, chars=None):
+                return a._to_str().strip(chars._to_str())._to_bytes()
+            return impl
+
+
+@overload_method(types.UnicodeCharSeq, 'join')
+@overload_method(types.CharSeq, 'join')
+@overload_method(types.Bytes, 'join')
+def unicode_charseq_join(a, parts):
+
+    if isinstance(a, types.UnicodeCharSeq):
+        # assuming parts contains UnicodeCharSeq or UnicodeType objects
+        def impl(a, parts):
+            _parts = [str(p) for p in parts]
+            return str(a).join(_parts)
+        return impl
+
+    if isinstance(a, (types.CharSeq, types.Bytes)):
+        # assuming parts contains CharSeq or Bytes objects
+        def impl(a, parts):
+            _parts = [p._to_str() for p in parts]
+            return a._to_str().join(_parts)._to_bytes()
+        return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/cmathimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/cmathimpl.py
new file mode 100644
index 000000000..06cc7bef3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/cmathimpl.py
@@ -0,0 +1,528 @@
+"""
+Implement the cmath module functions.
+"""
+
+
+import cmath
+import math
+
+from numba.core.imputils import Registry, impl_ret_untracked
+from numba.core import types, cgutils
+from numba.core.typing import signature
+from numba.cpython import builtins, mathimpl
+
+registry = Registry('cmathimpl')
+lower = registry.lower
+
+
+def is_nan(builder, z):
+    return builder.fcmp_unordered('uno', z.real, z.imag)
+
+def is_inf(builder, z):
+    return builder.or_(mathimpl.is_inf(builder, z.real),
+                       mathimpl.is_inf(builder, z.imag))
+
+def is_finite(builder, z):
+    return builder.and_(mathimpl.is_finite(builder, z.real),
+                        mathimpl.is_finite(builder, z.imag))
+
+
+@lower(cmath.isnan, types.Complex)
+def isnan_float_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [value] = args
+    z = context.make_complex(builder, typ, value=value)
+    res = is_nan(builder, z)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower(cmath.isinf, types.Complex)
+def isinf_float_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [value] = args
+    z = context.make_complex(builder, typ, value=value)
+    res = is_inf(builder, z)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(cmath.isfinite, types.Complex)
+def isfinite_float_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [value] = args
+    z = context.make_complex(builder, typ, value=value)
+    res = is_finite(builder, z)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(cmath.rect, types.Float, types.Float)
+def rect_impl(context, builder, sig, args):
+    [r, phi] = args
+    # We can't call math.isfinite() inside rect() below because it
+    # only exists on 3.2+.
+    phi_is_finite = mathimpl.is_finite(builder, phi)
+
+    def rect(r, phi, phi_is_finite):
+        if not phi_is_finite:
+            if not r:
+                # cmath.rect(0, phi={inf, nan}) = 0
+                return abs(r)
+            if math.isinf(r):
+                # cmath.rect(inf, phi={inf, nan}) = inf + j phi
+                return complex(r, phi)
+        real = math.cos(phi)
+        imag = math.sin(phi)
+        if real == 0. and math.isinf(r):
+            # 0 * inf would return NaN, we want to keep 0 but xor the sign
+            real /= r
+        else:
+            real *= r
+        if imag == 0. and math.isinf(r):
+            # ditto
+            imag /= r
+        else:
+            imag *= r
+        return complex(real, imag)
+
+    inner_sig = signature(sig.return_type, *sig.args + (types.boolean,))
+    res = context.compile_internal(builder, rect, inner_sig,
+                                    args + [phi_is_finite])
+    return impl_ret_untracked(context, builder, sig, res)
+
+def intrinsic_complex_unary(inner_func):
+    def wrapper(context, builder, sig, args):
+        [typ] = sig.args
+        [value] = args
+        z = context.make_complex(builder, typ, value=value)
+        x = z.real
+        y = z.imag
+        # Same as above: math.isfinite() is unavailable on 2.x so we precompute
+        # its value and pass it to the pure Python implementation.
+        x_is_finite = mathimpl.is_finite(builder, x)
+        y_is_finite = mathimpl.is_finite(builder, y)
+        inner_sig = signature(sig.return_type,
+                              *(typ.underlying_float,) * 2 + (types.boolean,) * 2)
+        res = context.compile_internal(builder, inner_func, inner_sig,
+                                        (x, y, x_is_finite, y_is_finite))
+        return impl_ret_untracked(context, builder, sig, res)
+    return wrapper
+
+
+NAN = float('nan')
+INF = float('inf')
+
+@lower(cmath.exp, types.Complex)
+@intrinsic_complex_unary
+def exp_impl(x, y, x_is_finite, y_is_finite):
+    """cmath.exp(x + y j)"""
+    if x_is_finite:
+        if y_is_finite:
+            c = math.cos(y)
+            s = math.sin(y)
+            r = math.exp(x)
+            return complex(r * c, r * s)
+        else:
+            return complex(NAN, NAN)
+    elif math.isnan(x):
+        if y:
+            return complex(x, x)  # nan + j nan
+        else:
+            return complex(x, y)  # nan + 0j
+    elif x > 0.0:
+        # x == +inf
+        if y_is_finite:
+            real = math.cos(y)
+            imag = math.sin(y)
+            # Avoid NaNs if math.cos(y) or math.sin(y) == 0
+            # (e.g. cmath.exp(inf + 0j) == inf + 0j)
+            if real != 0:
+                real *= x
+            if imag != 0:
+                imag *= x
+            return complex(real, imag)
+        else:
+            return complex(x, NAN)
+    else:
+        # x == -inf
+        if y_is_finite:
+            r = math.exp(x)
+            c = math.cos(y)
+            s = math.sin(y)
+            return complex(r * c, r * s)
+        else:
+            r = 0
+            return complex(r, r)
+
+@lower(cmath.log, types.Complex)
+@intrinsic_complex_unary
+def log_impl(x, y, x_is_finite, y_is_finite):
+    """cmath.log(x + y j)"""
+    a = math.log(math.hypot(x, y))
+    b = math.atan2(y, x)
+    return complex(a, b)
+
+
+@lower(cmath.log, types.Complex, types.Complex)
+def log_base_impl(context, builder, sig, args):
+    """cmath.log(z, base)"""
+    [z, base] = args
+
+    def log_base(z, base):
+        return cmath.log(z) / cmath.log(base)
+
+    res = context.compile_internal(builder, log_base, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@lower(cmath.log10, types.Complex)
+def log10_impl(context, builder, sig, args):
+    LN_10 = 2.302585092994045684
+
+    def log10_impl(z):
+        """cmath.log10(z)"""
+        z = cmath.log(z)
+        # This formula gives better results on +/-inf than cmath.log(z, 10)
+        # See http://bugs.python.org/issue22544
+        return complex(z.real / LN_10, z.imag / LN_10)
+
+    res = context.compile_internal(builder, log10_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@lower(cmath.phase, types.Complex)
+@intrinsic_complex_unary
+def phase_impl(x, y, x_is_finite, y_is_finite):
+    """cmath.phase(x + y j)"""
+    return math.atan2(y, x)
+
+@lower(cmath.polar, types.Complex)
+@intrinsic_complex_unary
+def polar_impl(x, y, x_is_finite, y_is_finite):
+    """cmath.polar(x + y j)"""
+    return math.hypot(x, y), math.atan2(y, x)
+
+
+@lower(cmath.sqrt, types.Complex)
+def sqrt_impl(context, builder, sig, args):
+    # We risk spurious overflow for components >= FLT_MAX / (1 + sqrt(2)).
+
+    SQRT2 = 1.414213562373095048801688724209698079E0
+    ONE_PLUS_SQRT2 = (1. + SQRT2)
+    theargflt = sig.args[0].underlying_float
+    # Get a type specific maximum value so scaling for overflow is based on that
+    MAX = mathimpl.DBL_MAX if theargflt.bitwidth == 64 else mathimpl.FLT_MAX
+    # THRES will be double precision, should not impact typing as it's just
+    # used for comparison, there *may* be a few values near THRES which
+    # deviate from e.g. NumPy due to rounding that occurs in the computation
+    # of this value in the case of a 32bit argument.
+    THRES = MAX / ONE_PLUS_SQRT2
+
+    def sqrt_impl(z):
+        """cmath.sqrt(z)"""
+        # This is NumPy's algorithm, see npy_csqrt() in npy_math_complex.c.src
+        a = z.real
+        b = z.imag
+        if a == 0.0 and b == 0.0:
+            return complex(abs(b), b)
+        if math.isinf(b):
+            return complex(abs(b), b)
+        if math.isnan(a):
+            return complex(a, a)
+        if math.isinf(a):
+            if a < 0.0:
+                return complex(abs(b - b), math.copysign(a, b))
+            else:
+                return complex(a, math.copysign(b - b, b))
+
+        # The remaining special case (b is NaN) is handled just fine by
+        # the normal code path below.
+
+        # Scale to avoid overflow
+        if abs(a) >= THRES or abs(b) >= THRES:
+            a *= 0.25
+            b *= 0.25
+            scale = True
+        else:
+            scale = False
+        # Algorithm 312, CACM vol 10, Oct 1967
+        if a >= 0:
+            t = math.sqrt((a + math.hypot(a, b)) * 0.5)
+            real = t
+            imag = b / (2 * t)
+        else:
+            t = math.sqrt((-a + math.hypot(a, b)) * 0.5)
+            real = abs(b) / (2 * t)
+            imag = math.copysign(t, b)
+        # Rescale
+        if scale:
+            return complex(real * 2, imag)
+        else:
+            return complex(real, imag)
+
+    res = context.compile_internal(builder, sqrt_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@lower(cmath.cos, types.Complex)
+def cos_impl(context, builder, sig, args):
+    def cos_impl(z):
+        """cmath.cos(z) = cmath.cosh(z j)"""
+        return cmath.cosh(complex(-z.imag, z.real))
+
+    res = context.compile_internal(builder, cos_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.cosh, types.Complex)
+def cosh_impl(context, builder, sig, args):
+    def cosh_impl(z):
+        """cmath.cosh(z)"""
+        x = z.real
+        y = z.imag
+        if math.isinf(x):
+            if math.isnan(y):
+                # x = +inf, y = NaN => cmath.cosh(x + y j) = inf + Nan * j
+                real = abs(x)
+                imag = y
+            elif y == 0.0:
+                # x = +inf, y = 0 => cmath.cosh(x + y j) = inf + 0j
+                real = abs(x)
+                imag = y
+            else:
+                real = math.copysign(x, math.cos(y))
+                imag = math.copysign(x, math.sin(y))
+            if x < 0.0:
+                # x = -inf => negate imaginary part of result
+                imag = -imag
+            return complex(real, imag)
+        return complex(math.cos(y) * math.cosh(x),
+                       math.sin(y) * math.sinh(x))
+
+    res = context.compile_internal(builder, cosh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@lower(cmath.sin, types.Complex)
+def sin_impl(context, builder, sig, args):
+    def sin_impl(z):
+        """cmath.sin(z) = -j * cmath.sinh(z j)"""
+        r = cmath.sinh(complex(-z.imag, z.real))
+        return complex(r.imag, -r.real)
+
+    res = context.compile_internal(builder, sin_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.sinh, types.Complex)
+def sinh_impl(context, builder, sig, args):
+    def sinh_impl(z):
+        """cmath.sinh(z)"""
+        x = z.real
+        y = z.imag
+        if math.isinf(x):
+            if math.isnan(y):
+                # x = +/-inf, y = NaN => cmath.sinh(x + y j) = x + NaN * j
+                real = x
+                imag = y
+            else:
+                real = math.cos(y)
+                imag = math.sin(y)
+                if real != 0.:
+                    real *= x
+                if imag != 0.:
+                    imag *= abs(x)
+            return complex(real, imag)
+        return complex(math.cos(y) * math.sinh(x),
+                       math.sin(y) * math.cosh(x))
+
+    res = context.compile_internal(builder, sinh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@lower(cmath.tan, types.Complex)
+def tan_impl(context, builder, sig, args):
+    def tan_impl(z):
+        """cmath.tan(z) = -j * cmath.tanh(z j)"""
+        r = cmath.tanh(complex(-z.imag, z.real))
+        return complex(r.imag, -r.real)
+
+    res = context.compile_internal(builder, tan_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.tanh, types.Complex)
+def tanh_impl(context, builder, sig, args):
+    def tanh_impl(z):
+        """cmath.tanh(z)"""
+        x = z.real
+        y = z.imag
+        if math.isinf(x):
+            real = math.copysign(1., x)
+            if math.isinf(y):
+                imag = 0.
+            else:
+                imag = math.copysign(0., math.sin(2. * y))
+            return complex(real, imag)
+        # This is CPython's algorithm (see c_tanh() in cmathmodule.c).
+        # XXX how to force float constants into single precision?
+        tx = math.tanh(x)
+        ty = math.tan(y)
+        cx = 1. / math.cosh(x)
+        txty = tx * ty
+        denom = 1. + txty * txty
+        return complex(
+            tx * (1. + ty * ty) / denom,
+            ((ty / denom) * cx) * cx)
+
+    res = context.compile_internal(builder, tanh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@lower(cmath.acos, types.Complex)
+def acos_impl(context, builder, sig, args):
+    LN_4 = math.log(4)
+    THRES = mathimpl.FLT_MAX / 4
+
+    def acos_impl(z):
+        """cmath.acos(z)"""
+        # CPython's algorithm (see c_acos() in cmathmodule.c)
+        if abs(z.real) > THRES or abs(z.imag) > THRES:
+            # Avoid unnecessary overflow for large arguments
+            # (also handles infinities gracefully)
+            real = math.atan2(abs(z.imag), z.real)
+            imag = math.copysign(
+                math.log(math.hypot(z.real * 0.5, z.imag * 0.5)) + LN_4,
+                -z.imag)
+            return complex(real, imag)
+        else:
+            s1 = cmath.sqrt(complex(1. - z.real, -z.imag))
+            s2 = cmath.sqrt(complex(1. + z.real, z.imag))
+            real = 2. * math.atan2(s1.real, s2.real)
+            imag = math.asinh(s2.real * s1.imag - s2.imag * s1.real)
+            return complex(real, imag)
+
+    res = context.compile_internal(builder, acos_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.acosh, types.Complex)
+def acosh_impl(context, builder, sig, args):
+    LN_4 = math.log(4)
+    THRES = mathimpl.FLT_MAX / 4
+
+    def acosh_impl(z):
+        """cmath.acosh(z)"""
+        # CPython's algorithm (see c_acosh() in cmathmodule.c)
+        if abs(z.real) > THRES or abs(z.imag) > THRES:
+            # Avoid unnecessary overflow for large arguments
+            # (also handles infinities gracefully)
+            real = math.log(math.hypot(z.real * 0.5, z.imag * 0.5)) + LN_4
+            imag = math.atan2(z.imag, z.real)
+            return complex(real, imag)
+        else:
+            s1 = cmath.sqrt(complex(z.real - 1., z.imag))
+            s2 = cmath.sqrt(complex(z.real + 1., z.imag))
+            real = math.asinh(s1.real * s2.real + s1.imag * s2.imag)
+            imag = 2. * math.atan2(s1.imag, s2.real)
+            return complex(real, imag)
+        # Condensed formula (NumPy)
+        #return cmath.log(z + cmath.sqrt(z + 1.) * cmath.sqrt(z - 1.))
+
+    res = context.compile_internal(builder, acosh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.asinh, types.Complex)
+def asinh_impl(context, builder, sig, args):
+    LN_4 = math.log(4)
+    THRES = mathimpl.FLT_MAX / 4
+
+    def asinh_impl(z):
+        """cmath.asinh(z)"""
+        # CPython's algorithm (see c_asinh() in cmathmodule.c)
+        if abs(z.real) > THRES or abs(z.imag) > THRES:
+            real = math.copysign(
+                math.log(math.hypot(z.real * 0.5, z.imag * 0.5)) + LN_4,
+                z.real)
+            imag = math.atan2(z.imag, abs(z.real))
+            return complex(real, imag)
+        else:
+            s1 = cmath.sqrt(complex(1. + z.imag, -z.real))
+            s2 = cmath.sqrt(complex(1. - z.imag, z.real))
+            real = math.asinh(s1.real * s2.imag - s2.real * s1.imag)
+            imag = math.atan2(z.imag, s1.real * s2.real - s1.imag * s2.imag)
+            return complex(real, imag)
+
+    res = context.compile_internal(builder, asinh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.asin, types.Complex)
+def asin_impl(context, builder, sig, args):
+    def asin_impl(z):
+        """cmath.asin(z) = -j * cmath.asinh(z j)"""
+        r = cmath.asinh(complex(-z.imag, z.real))
+        return complex(r.imag, -r.real)
+
+    res = context.compile_internal(builder, asin_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.atan, types.Complex)
+def atan_impl(context, builder, sig, args):
+    def atan_impl(z):
+        """cmath.atan(z) = -j * cmath.atanh(z j)"""
+        r = cmath.atanh(complex(-z.imag, z.real))
+        if math.isinf(z.real) and math.isnan(z.imag):
+            # XXX this is odd but necessary
+            return complex(r.imag, r.real)
+        else:
+            return complex(r.imag, -r.real)
+
+    res = context.compile_internal(builder, atan_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.atanh, types.Complex)
+def atanh_impl(context, builder, sig, args):
+    LN_4 = math.log(4)
+    THRES_LARGE = math.sqrt(mathimpl.FLT_MAX / 4)
+    THRES_SMALL = math.sqrt(mathimpl.FLT_MIN)
+    PI_12 = math.pi / 2
+
+    def atanh_impl(z):
+        """cmath.atanh(z)"""
+        # CPython's algorithm (see c_atanh() in cmathmodule.c)
+        if z.real < 0.:
+            # Reduce to case where z.real >= 0., using atanh(z) = -atanh(-z).
+            negate = True
+            z = -z
+        else:
+            negate = False
+
+        ay = abs(z.imag)
+        if math.isnan(z.real) or z.real > THRES_LARGE or ay > THRES_LARGE:
+            if math.isinf(z.imag):
+                real = math.copysign(0., z.real)
+            elif math.isinf(z.real):
+                real = 0.
+            else:
+                # may be safe from overflow, depending on hypot's implementation...
+                h = math.hypot(z.real * 0.5, z.imag * 0.5)
+                real = z.real/4./h/h
+            imag = -math.copysign(PI_12, -z.imag)
+        elif z.real == 1. and ay < THRES_SMALL:
+            # C99 standard says:  atanh(1+/-0.) should be inf +/- 0j
+            if ay == 0.:
+                real = INF
+                imag = z.imag
+            else:
+                real = -math.log(math.sqrt(ay) /
+                                 math.sqrt(math.hypot(ay, 2.)))
+                imag = math.copysign(math.atan2(2., -ay) / 2, z.imag)
+        else:
+            sqay = ay * ay
+            zr1 = 1 - z.real
+            real = math.log1p(4. * z.real / (zr1 * zr1 + sqay)) * 0.25
+            imag = -math.atan2(-2. * z.imag,
+                               zr1 * (1 + z.real) - sqay) * 0.5
+
+        if math.isnan(z.imag):
+            imag = NAN
+        if negate:
+            return complex(-real, -imag)
+        else:
+            return complex(real, imag)
+
+    res = context.compile_internal(builder, atanh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/enumimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/enumimpl.py
new file mode 100644
index 000000000..4d811d22e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/enumimpl.py
@@ -0,0 +1,89 @@
+"""
+Implementation of enums.
+"""
+import operator
+
+from numba.core.imputils import (lower_builtin, lower_getattr,
+                                 lower_getattr_generic, lower_cast,
+                                 lower_constant, impl_ret_untracked)
+from numba.core import types
+from numba.core.extending import overload_method
+
+
+@lower_builtin(operator.eq, types.EnumMember, types.EnumMember)
+def enum_eq(context, builder, sig, args):
+    tu, tv = sig.args
+    u, v = args
+    res = context.generic_compare(builder, operator.eq,
+                                  (tu.dtype, tv.dtype), (u, v))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.is_, types.EnumMember, types.EnumMember)
+def enum_is(context, builder, sig, args):
+    tu, tv = sig.args
+    u, v = args
+    if tu == tv:
+        res = context.generic_compare(builder, operator.eq,
+                                      (tu.dtype, tv.dtype), (u, v))
+    else:
+        res = context.get_constant(sig.return_type, False)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.ne, types.EnumMember, types.EnumMember)
+def enum_ne(context, builder, sig, args):
+    tu, tv = sig.args
+    u, v = args
+    res = context.generic_compare(builder, operator.ne,
+                                  (tu.dtype, tv.dtype), (u, v))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_getattr(types.EnumMember, 'value')
+def enum_value(context, builder, ty, val):
+    return val
+
+
+@lower_cast(types.IntEnumMember, types.Integer)
+def int_enum_to_int(context, builder, fromty, toty, val):
+    """
+    Convert an IntEnum member to its raw integer value.
+    """
+    return context.cast(builder, val, fromty.dtype, toty)
+
+
+@lower_constant(types.EnumMember)
+def enum_constant(context, builder, ty, pyval):
+    """
+    Return a LLVM constant representing enum member *pyval*.
+    """
+    return context.get_constant_generic(builder, ty.dtype, pyval.value)
+
+
+@lower_getattr_generic(types.EnumClass)
+def enum_class_getattr(context, builder, ty, val, attr):
+    """
+    Return an enum member by attribute name.
+    """
+    member = getattr(ty.instance_class, attr)
+    return context.get_constant_generic(builder, ty.dtype, member.value)
+
+
+@lower_builtin('static_getitem', types.EnumClass, types.StringLiteral)
+def enum_class_getitem(context, builder, sig, args):
+    """
+    Return an enum member by index name.
+    """
+    enum_cls_typ, idx = sig.args
+    member = enum_cls_typ.instance_class[idx.literal_value]
+    return context.get_constant_generic(builder, enum_cls_typ.dtype,
+                                        member.value)
+
+
+@overload_method(types.IntEnumMember, '__hash__')
+def intenum_hash(val):
+    # uses the hash of the value, for IntEnums this will be int.__hash__
+    def hash_impl(val):
+        return hash(val.value)
+    return hash_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/hashing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/hashing.py
new file mode 100644
index 000000000..dcb71c95e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/hashing.py
@@ -0,0 +1,759 @@
+"""
+Hash implementations for Numba types
+"""
+
+import math
+import numpy as np
+import sys
+import ctypes
+import warnings
+from collections import namedtuple
+
+import llvmlite.binding as ll
+from llvmlite import ir
+
+from numba.core.extending import (
+    overload, overload_method, intrinsic, register_jitable)
+from numba.core import errors
+from numba.core import types, utils
+from numba.core.unsafe.bytes import grab_byte, grab_uint64_t
+from numba.cpython.randomimpl import (const_int, get_next_int, get_next_int32,
+                                      get_state_ptr)
+
+_py38_or_later = utils.PYVERSION >= (3, 8)
+_py310_or_later = utils.PYVERSION >= (3, 10)
+
+# This is Py_hash_t, which is a Py_ssize_t, which has sizeof(size_t):
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Include/pyport.h#L91-L96    # noqa: E501
+_hash_width = sys.hash_info.width
+_Py_hash_t = getattr(types, 'int%s' % _hash_width)
+_Py_uhash_t = getattr(types, 'uint%s' % _hash_width)
+
+# Constants from CPython source, obtained by various means:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Include/pyhash.h    # noqa: E501
+_PyHASH_INF = sys.hash_info.inf
+_PyHASH_NAN = sys.hash_info.nan
+_PyHASH_MODULUS = _Py_uhash_t(sys.hash_info.modulus)
+_PyHASH_BITS = 31 if types.intp.bitwidth == 32 else 61  # mersenne primes
+_PyHASH_MULTIPLIER = 0xf4243  # 1000003UL
+_PyHASH_IMAG = _PyHASH_MULTIPLIER
+_PyLong_SHIFT = sys.int_info.bits_per_digit
+_Py_HASH_CUTOFF = sys.hash_info.cutoff
+_Py_hashfunc_name = sys.hash_info.algorithm
+
+
+# hash(obj) is implemented by calling obj.__hash__()
+
+
+@overload(hash)
+def hash_overload(obj):
+    def impl(obj):
+        return obj.__hash__()
+    return impl
+
+
+@register_jitable
+def process_return(val):
+    asint = _Py_hash_t(val)
+    if (asint == int(-1)):
+        asint = int(-2)
+    return asint
+
+
+# This is a translation of CPython's _Py_HashDouble:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Python/pyhash.c#L34-L129   # noqa: E501
+# NOTE: In Python 3.10 hash of nan is now hash of the pointer to the PyObject
+# containing said nan. Numba cannot replicate this as there is no object, so it
+# elects to replicate the behaviour i.e. hash of nan is something "unique" which
+# satisfies https://bugs.python.org/issue43475.
+
+@register_jitable(locals={'x': _Py_uhash_t,
+                          'y': _Py_uhash_t,
+                          'm': types.double,
+                          'e': types.intc,
+                          'sign': types.intc,
+                          '_PyHASH_MODULUS': _Py_uhash_t,
+                          '_PyHASH_BITS': types.intc})
+def _Py_HashDouble(v):
+    if not np.isfinite(v):
+        if (np.isinf(v)):
+            if (v > 0):
+                return _PyHASH_INF
+            else:
+                return -_PyHASH_INF
+        else:
+            # Python 3.10 does not use `_PyHASH_NAN`.
+            # https://github.com/python/cpython/blob/2c4792264f9218692a1bd87398a60591f756b171/Python/pyhash.c#L102   # noqa: E501
+            # Numba returns a pseudo-random number to reflect the spirit of the
+            # change.
+            if _py310_or_later:
+                x = _prng_random_hash()
+                return process_return(x)
+            else:
+                return _PyHASH_NAN
+
+    m, e = math.frexp(v)
+
+    sign = 1
+    if (m < 0):
+        sign = -1
+        m = -m
+
+    # process 28 bits at a time;  this should work well both for binary
+    #  and hexadecimal floating point.
+    x = 0
+    while (m):
+        x = ((x << 28) & _PyHASH_MODULUS) | x >> (_PyHASH_BITS - 28)
+        m *= 268435456.0  # /* 2**28 */
+        e -= 28
+        y = int(m)  # /* pull out integer part */
+        m -= y
+        x += y
+        if x >= _PyHASH_MODULUS:
+            x -= _PyHASH_MODULUS
+    # /* adjust for the exponent;  first reduce it modulo _PyHASH_BITS */
+    if e >= 0:
+        e = e % _PyHASH_BITS
+    else:
+        e = _PyHASH_BITS - 1 - ((-1 - e) % _PyHASH_BITS)
+
+    x = ((x << e) & _PyHASH_MODULUS) | x >> (_PyHASH_BITS - e)
+
+    x = x * sign
+    return process_return(x)
+
+
+@intrinsic
+def _fpext(tyctx, val):
+    def impl(cgctx, builder, signature, args):
+        val = args[0]
+        return builder.fpext(val, ir.DoubleType())
+    sig = types.float64(types.float32)
+    return sig, impl
+
+
+@intrinsic
+def _prng_random_hash(tyctx):
+
+    def impl(cgctx, builder, signature, args):
+        state_ptr = get_state_ptr(cgctx, builder, "internal")
+        bits = const_int(_hash_width)
+
+        # Why not just use get_next_int() with the correct bitwidth?
+        # get_next_int() always returns an i64, because the bitwidth it is
+        # passed may not be a compile-time constant, so it needs to allocate
+        # the largest unit of storage that may be required. Therefore, if the
+        # hash width is 32, then we need to use get_next_int32() to ensure we
+        # don't return a wider-than-expected hash, even if everything above
+        # the low 32 bits would have been zero.
+        if _hash_width == 32:
+            value = get_next_int32(cgctx, builder, state_ptr)
+        else:
+            value = get_next_int(cgctx, builder, state_ptr, bits, False)
+
+        return value
+
+    sig = _Py_hash_t()
+    return sig, impl
+
+
+# This is a translation of CPython's long_hash, but restricted to the numerical
+# domain reachable by int64/uint64 (i.e. no BigInt like support):
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/longobject.c#L2934-L2989    # noqa: E501
+# obdigit is a uint32_t which is typedef'd to digit
+# int32_t is typedef'd to sdigit
+
+
+@register_jitable(locals={'x': _Py_uhash_t,
+                          'p1': _Py_uhash_t,
+                          'p2': _Py_uhash_t,
+                          'p3': _Py_uhash_t,
+                          'p4': _Py_uhash_t,
+                          '_PyHASH_MODULUS': _Py_uhash_t,
+                          '_PyHASH_BITS': types.int32,
+                          '_PyLong_SHIFT': types.int32,})
+def _long_impl(val):
+    # This function assumes val came from a long int repr with val being a
+    # uint64_t this means having to split the input into PyLong_SHIFT size
+    # chunks in an unsigned hash wide type, max numba can handle is a 64bit int
+
+    # mask to select low _PyLong_SHIFT bits
+    _tmp_shift = 32 - _PyLong_SHIFT
+    mask_shift = (~types.uint32(0x0)) >> _tmp_shift
+
+    # a 64bit wide max means Numba only needs 3 x 30 bit values max,
+    # or 5 x 15 bit values max on 32bit platforms
+    i = (64 // _PyLong_SHIFT) + 1
+
+    # alg as per hash_long
+    x = 0
+    p3 = (_PyHASH_BITS - _PyLong_SHIFT)
+    for idx in range(i - 1, -1, -1):
+        p1 = x << _PyLong_SHIFT
+        p2 = p1 & _PyHASH_MODULUS
+        p4 = x >> p3
+        x = p2 | p4
+        # the shift and mask splits out the `ob_digit` parts of a Long repr
+        x += types.uint32((val >> idx * _PyLong_SHIFT) & mask_shift)
+        if x >= _PyHASH_MODULUS:
+            x -= _PyHASH_MODULUS
+    return _Py_hash_t(x)
+
+
+# This has no CPython equivalent, CPython uses long_hash.
+@overload_method(types.Integer, '__hash__')
+@overload_method(types.Boolean, '__hash__')
+def int_hash(val):
+
+    _HASH_I64_MIN = -2 if sys.maxsize <= 2 ** 32 else -4
+    _SIGNED_MIN = types.int64(-0x8000000000000000)
+
+    # Find a suitable type to hold a "big" value, i.e. iinfo(ty).min/max
+    # this is to ensure e.g. int32.min is handled ok as it's abs() is its value
+    _BIG = types.int64 if getattr(val, 'signed', False) else types.uint64
+
+    # this is a bit involved due to the CPython repr of ints
+    def impl(val):
+        # If the magnitude is under PyHASH_MODULUS, just return the
+        # value val as the hash, couple of special cases if val == val:
+        # 1. it's 0, in which case return 0
+        # 2. it's signed int minimum value, return the value CPython computes
+        # but Numba cannot as there's no type wide enough to hold the shifts.
+        #
+        # If the magnitude is greater than PyHASH_MODULUS then... if the value
+        # is negative then negate it switch the sign on the hash once computed
+        # and use the standard wide unsigned hash implementation
+        val = _BIG(val)
+        mag = abs(val)
+        if mag < _PyHASH_MODULUS:
+            if val == 0:
+                ret = 0
+            elif val == _SIGNED_MIN:  # e.g. int64 min, -0x8000000000000000
+                ret = _Py_hash_t(_HASH_I64_MIN)
+            else:
+                ret = _Py_hash_t(val)
+        else:
+            needs_negate = False
+            if val < 0:
+                val = -val
+                needs_negate = True
+            ret = _long_impl(val)
+            if needs_negate:
+                ret = -ret
+        return process_return(ret)
+    return impl
+
+# This is a translation of CPython's float_hash:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/floatobject.c#L528-L532    # noqa: E501
+
+
+@overload_method(types.Float, '__hash__')
+def float_hash(val):
+    if val.bitwidth == 64:
+        def impl(val):
+            hashed = _Py_HashDouble(val)
+            return hashed
+    else:
+        def impl(val):
+            # widen the 32bit float to 64bit
+            fpextended = np.float64(_fpext(val))
+            hashed = _Py_HashDouble(fpextended)
+            return hashed
+    return impl
+
+# This is a translation of CPython's complex_hash:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/complexobject.c#L408-L428    # noqa: E501
+
+
+@overload_method(types.Complex, '__hash__')
+def complex_hash(val):
+    def impl(val):
+        hashreal = hash(val.real)
+        hashimag = hash(val.imag)
+        # Note:  if the imaginary part is 0, hashimag is 0 now,
+        # so the following returns hashreal unchanged.  This is
+        # important because numbers of different types that
+        # compare equal must have the same hash value, so that
+        # hash(x + 0*j) must equal hash(x).
+        combined = hashreal + _PyHASH_IMAG * hashimag
+        return process_return(combined)
+    return impl
+
+
+if _py38_or_later:
+    # Python 3.8 strengthened its hash alg for tuples.
+    # This is a translation of CPython's tuplehash for Python >=3.8
+    # https://github.com/python/cpython/blob/b738237d6792acba85b1f6e6c8993a812c7fd815/Objects/tupleobject.c#L338-L391    # noqa: E501
+
+    # These consts are needed for this alg variant, they are from:
+    # https://github.com/python/cpython/blob/b738237d6792acba85b1f6e6c8993a812c7fd815/Objects/tupleobject.c#L353-L363    # noqa: E501
+    if _Py_uhash_t.bitwidth // 8 > 4:
+        _PyHASH_XXPRIME_1 = _Py_uhash_t(11400714785074694791)
+        _PyHASH_XXPRIME_2 = _Py_uhash_t(14029467366897019727)
+        _PyHASH_XXPRIME_5 = _Py_uhash_t(2870177450012600261)
+
+        @register_jitable(locals={'x': types.uint64})
+        def _PyHASH_XXROTATE(x):
+            # Rotate left 31 bits
+            return ((x << types.uint64(31)) | (x >> types.uint64(33)))
+    else:
+        _PyHASH_XXPRIME_1 = _Py_uhash_t(2654435761)
+        _PyHASH_XXPRIME_2 = _Py_uhash_t(2246822519)
+        _PyHASH_XXPRIME_5 = _Py_uhash_t(374761393)
+
+        @register_jitable(locals={'x': types.uint64})
+        def _PyHASH_XXROTATE(x):
+            # Rotate left 13 bits
+            return ((x << types.uint64(13)) | (x >> types.uint64(19)))
+
+    # Python 3.7+ has literal_unroll, this means any homogeneous and
+    # heterogeneous tuples can use the same alg and just be unrolled.
+    from numba import literal_unroll
+
+    @register_jitable(locals={'acc': _Py_uhash_t, 'lane': _Py_uhash_t,
+                              '_PyHASH_XXPRIME_5': _Py_uhash_t,
+                              '_PyHASH_XXPRIME_1': _Py_uhash_t,
+                              'tl': _Py_uhash_t})
+    def _tuple_hash(tup):
+        tl = len(tup)
+        acc = _PyHASH_XXPRIME_5
+        for x in literal_unroll(tup):
+            lane = hash(x)
+            if lane == _Py_uhash_t(-1):
+                return -1
+            acc += lane * _PyHASH_XXPRIME_2
+            acc = _PyHASH_XXROTATE(acc)
+            acc *= _PyHASH_XXPRIME_1
+
+        acc += tl ^ (_PyHASH_XXPRIME_5 ^ _Py_uhash_t(3527539))
+
+        if acc == _Py_uhash_t(-1):
+            return process_return(1546275796)
+
+        return process_return(acc)
+else:
+    # This is a translation of CPython's tuplehash:
+    # https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/tupleobject.c#L347-L369    # noqa: E501
+    @register_jitable(locals={'x': _Py_uhash_t,
+                              'y': _Py_hash_t,
+                              'mult': _Py_uhash_t,
+                              'l': _Py_hash_t, })
+    def _tuple_hash(tup):
+        tl = len(tup)
+        mult = _PyHASH_MULTIPLIER
+        x = _Py_uhash_t(0x345678)
+        # in C this is while(--l >= 0), i is indexing tup instead of *tup++
+        for i, l in enumerate(range(tl - 1, -1, -1)):
+            y = hash(tup[i])
+            xxory = (x ^ y)
+            x = xxory * mult
+            mult += _Py_hash_t((_Py_uhash_t(82520) + l + l))
+        x += _Py_uhash_t(97531)
+        return process_return(x)
+
+# This is an obfuscated translation of CPython's tuplehash:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/tupleobject.c#L347-L369    # noqa: E501
+# The obfuscation occurs for a heterogeneous tuple as each tuple member needs
+# a potentially different hash() function calling for it. This cannot be done at
+# runtime as there's no way to iterate a heterogeneous tuple, so this is
+# achieved by essentially unrolling the loop over the members and inserting a
+# per-type hash function call for each member, and then simply computing the
+# hash value in an inlined/rolling fashion.
+
+
+@intrinsic
+def _tuple_hash_resolve(tyctx, val):
+    def impl(cgctx, builder, signature, args):
+        typingctx = cgctx.typing_context
+        fnty = typingctx.resolve_value_type(hash)
+        tupty, = signature.args
+        tup, = args
+        lty = cgctx.get_value_type(signature.return_type)
+        x = ir.Constant(lty, 0x345678)
+        mult = ir.Constant(lty, _PyHASH_MULTIPLIER)
+        shift = ir.Constant(lty, 82520)
+        tl = len(tupty)
+        for i, packed in enumerate(zip(tupty.types, range(tl - 1, -1, -1))):
+            ty, l = packed
+            sig = fnty.get_call_type(tyctx, (ty,), {})
+            impl = cgctx.get_function(fnty, sig)
+            tuple_val = builder.extract_value(tup, i)
+            y = impl(builder, (tuple_val,))
+            xxory = builder.xor(x, y)
+            x = builder.mul(xxory, mult)
+            lconst = ir.Constant(lty, l)
+            mult = builder.add(mult, shift)
+            mult = builder.add(mult, lconst)
+            mult = builder.add(mult, lconst)
+        x = builder.add(x, ir.Constant(lty, 97531))
+        return x
+    sig = _Py_hash_t(val)
+    return sig, impl
+
+
+@overload_method(types.BaseTuple, '__hash__')
+def tuple_hash(val):
+    if _py38_or_later or isinstance(val, types.Sequence):
+        def impl(val):
+            return _tuple_hash(val)
+        return impl
+    else:
+        def impl(val):
+            hashed = _Py_hash_t(_tuple_hash_resolve(val))
+            return process_return(hashed)
+        return impl
+
+
+# ------------------------------------------------------------------------------
+# String/bytes hashing needs hashseed info, this is from:
+# https://stackoverflow.com/a/41088757
+# with thanks to Martijn Pieters
+#
+# Developer note:
+# CPython makes use of an internal "hashsecret" which is essentially a struct
+# containing some state that is set on CPython initialization and contains magic
+# numbers used particularly in unicode/string hashing. This code binds to the
+# Python runtime libraries in use by the current process and reads the
+# "hashsecret" state so that it can be used by Numba. As this is done at runtime
+# the behaviour and influence of the PYTHONHASHSEED environment variable is
+# accommodated.
+
+from ctypes import (  # noqa
+    c_size_t,
+    c_ubyte,
+    c_uint64,
+    pythonapi,
+    Structure,
+    Union,
+)  # noqa
+
+
+class FNV(Structure):
+    _fields_ = [
+        ('prefix', c_size_t),
+        ('suffix', c_size_t)
+    ]
+
+
+class SIPHASH(Structure):
+    _fields_ = [
+        ('k0', c_uint64),
+        ('k1', c_uint64),
+    ]
+
+
+class DJBX33A(Structure):
+    _fields_ = [
+        ('padding', c_ubyte * 16),
+        ('suffix', c_size_t),
+    ]
+
+
+class EXPAT(Structure):
+    _fields_ = [
+        ('padding', c_ubyte * 16),
+        ('hashsalt', c_size_t),
+    ]
+
+
+class _Py_HashSecret_t(Union):
+    _fields_ = [
+        # ensure 24 bytes
+        ('uc', c_ubyte * 24),
+        # two Py_hash_t for FNV
+        ('fnv', FNV),
+        # two uint64 for SipHash24
+        ('siphash', SIPHASH),
+        # a different (!) Py_hash_t for small string optimization
+        ('djbx33a', DJBX33A),
+        ('expat', EXPAT),
+    ]
+
+
+_hashsecret_entry = namedtuple('_hashsecret_entry', ['symbol', 'value'])
+
+
+# Only a few members are needed at present
+def _build_hashsecret():
+    """Read hash secret from the Python process
+
+    Returns
+    -------
+    info : dict
+        - keys are "djbx33a_suffix", "siphash_k0", siphash_k1".
+        - values are the namedtuple[symbol:str, value:int]
+    """
+    # Read hashsecret and inject it into the LLVM symbol map under the
+    # prefix `_numba_hashsecret_`.
+    pyhashsecret = _Py_HashSecret_t.in_dll(pythonapi, '_Py_HashSecret')
+    info = {}
+
+    def inject(name, val):
+        symbol_name = "_numba_hashsecret_{}".format(name)
+        val = ctypes.c_uint64(val)
+        addr = ctypes.addressof(val)
+        ll.add_symbol(symbol_name, addr)
+        info[name] = _hashsecret_entry(symbol=symbol_name, value=val)
+
+    inject('djbx33a_suffix', pyhashsecret.djbx33a.suffix)
+    inject('siphash_k0', pyhashsecret.siphash.k0)
+    inject('siphash_k1', pyhashsecret.siphash.k1)
+    return info
+
+
+_hashsecret = _build_hashsecret()
+
+
+# ------------------------------------------------------------------------------
+
+
+if _Py_hashfunc_name in ('siphash24', 'fnv'):
+
+    # Check for use of the FNV hashing alg, warn users that it's not implemented
+    # and functionality relying of properties derived from hashing will be fine
+    # but hash values themselves are likely to be different.
+    if _Py_hashfunc_name == 'fnv':
+        msg = ("FNV hashing is not implemented in Numba. See PEP 456 "
+               "https://www.python.org/dev/peps/pep-0456/ "
+               "for rationale over not using FNV. Numba will continue to work, "
+               "but hashes for built in types will be computed using "
+               "siphash24. This will permit e.g. dictionaries to continue to "
+               "behave as expected, however anything relying on the value of "
+               "the hash opposed to hash as a derived property is likely to "
+               "not work as expected.")
+        warnings.warn(msg)
+
+    # This is a translation of CPython's siphash24 function:
+    # https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Python/pyhash.c#L287-L413    # noqa: E501
+
+    # /* *********************************************************************
+    # <MIT License>
+    # Copyright (c) 2013  Marek Majkowski <marek@popcount.org>
+
+    # Permission is hereby granted, free of charge, to any person obtaining a
+    # copy of this software and associated documentation files (the "Software"),
+    # to deal in the Software without restriction, including without limitation
+    # the rights to use, copy, modify, merge, publish, distribute, sublicense,
+    # and/or sell copies of the Software, and to permit persons to whom the
+    # Software is furnished to do so, subject to the following conditions:
+
+    # The above copyright notice and this permission notice shall be included in
+    # all copies or substantial portions of the Software.
+
+    # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+    # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+    # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    # DEALINGS IN THE SOFTWARE.
+    # </MIT License>
+
+    # Original location:
+    # https://github.com/majek/csiphash/
+
+    # Solution inspired by code from:
+    # Samuel Neves (supercop/crypto_auth/siphash24/little)
+    #djb (supercop/crypto_auth/siphash24/little2)
+    # Jean-Philippe Aumasson (https://131002.net/siphash/siphash24.c)
+
+    # Modified for Python by Christian Heimes:
+    # - C89 / MSVC compatibility
+    # - _rotl64() on Windows
+    # - letoh64() fallback
+    # */
+
+    @register_jitable(locals={'x': types.uint64,
+                              'b': types.uint64, })
+    def _ROTATE(x, b):
+        return types.uint64(((x) << (b)) | ((x) >> (types.uint64(64) - (b))))
+
+    @register_jitable(locals={'a': types.uint64,
+                              'b': types.uint64,
+                              'c': types.uint64,
+                              'd': types.uint64,
+                              's': types.uint64,
+                              't': types.uint64, })
+    def _HALF_ROUND(a, b, c, d, s, t):
+        a += b
+        c += d
+        b = _ROTATE(b, s) ^ a
+        d = _ROTATE(d, t) ^ c
+        a = _ROTATE(a, 32)
+        return a, b, c, d
+
+    @register_jitable(locals={'v0': types.uint64,
+                              'v1': types.uint64,
+                              'v2': types.uint64,
+                              'v3': types.uint64, })
+    def _DOUBLE_ROUND(v0, v1, v2, v3):
+        v0, v1, v2, v3 = _HALF_ROUND(v0, v1, v2, v3, 13, 16)
+        v2, v1, v0, v3 = _HALF_ROUND(v2, v1, v0, v3, 17, 21)
+        v0, v1, v2, v3 = _HALF_ROUND(v0, v1, v2, v3, 13, 16)
+        v2, v1, v0, v3 = _HALF_ROUND(v2, v1, v0, v3, 17, 21)
+        return v0, v1, v2, v3
+
+    @register_jitable(locals={'v0': types.uint64,
+                              'v1': types.uint64,
+                              'v2': types.uint64,
+                              'v3': types.uint64,
+                              'b': types.uint64,
+                              'mi': types.uint64,
+                              'tmp': types.Array(types.uint64, 1, 'C'),
+                              't': types.uint64,
+                              'mask': types.uint64,
+                              'jmp': types.uint64,
+                              'ohexefef': types.uint64})
+    def _siphash24(k0, k1, src, src_sz):
+        b = types.uint64(src_sz) << 56
+        v0 = k0 ^ types.uint64(0x736f6d6570736575)
+        v1 = k1 ^ types.uint64(0x646f72616e646f6d)
+        v2 = k0 ^ types.uint64(0x6c7967656e657261)
+        v3 = k1 ^ types.uint64(0x7465646279746573)
+
+        idx = 0
+        while (src_sz >= 8):
+            mi = grab_uint64_t(src, idx)
+            idx += 1
+            src_sz -= 8
+            v3 ^= mi
+            v0, v1, v2, v3 = _DOUBLE_ROUND(v0, v1, v2, v3)
+            v0 ^= mi
+
+        # this is the switch fallthrough:
+        # https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Python/pyhash.c#L390-L400    # noqa: E501
+        t = types.uint64(0x0)
+        boffset = idx * 8
+        ohexefef = types.uint64(0xff)
+        if src_sz >= 7:
+            jmp = (6 * 8)
+            mask = ~types.uint64(ohexefef << jmp)
+            t = (t & mask) | (types.uint64(grab_byte(src, boffset + 6))
+                              << jmp)
+        if src_sz >= 6:
+            jmp = (5 * 8)
+            mask = ~types.uint64(ohexefef << jmp)
+            t = (t & mask) | (types.uint64(grab_byte(src, boffset + 5))
+                              << jmp)
+        if src_sz >= 5:
+            jmp = (4 * 8)
+            mask = ~types.uint64(ohexefef << jmp)
+            t = (t & mask) | (types.uint64(grab_byte(src, boffset + 4))
+                              << jmp)
+        if src_sz >= 4:
+            t &= types.uint64(0xffffffff00000000)
+            for i in range(4):
+                jmp = i * 8
+                mask = ~types.uint64(ohexefef << jmp)
+                t = (t & mask) | (types.uint64(grab_byte(src, boffset + i))
+                                  << jmp)
+        if src_sz >= 3:
+            jmp = (2 * 8)
+            mask = ~types.uint64(ohexefef << jmp)
+            t = (t & mask) | (types.uint64(grab_byte(src, boffset + 2))
+                              << jmp)
+        if src_sz >= 2:
+            jmp = (1 * 8)
+            mask = ~types.uint64(ohexefef << jmp)
+            t = (t & mask) | (types.uint64(grab_byte(src, boffset + 1))
+                              << jmp)
+        if src_sz >= 1:
+            mask = ~(ohexefef)
+            t = (t & mask) | (types.uint64(grab_byte(src, boffset + 0)))
+
+        b |= t
+        v3 ^= b
+        v0, v1, v2, v3 = _DOUBLE_ROUND(v0, v1, v2, v3)
+        v0 ^= b
+        v2 ^= ohexefef
+        v0, v1, v2, v3 = _DOUBLE_ROUND(v0, v1, v2, v3)
+        v0, v1, v2, v3 = _DOUBLE_ROUND(v0, v1, v2, v3)
+        t = (v0 ^ v1) ^ (v2 ^ v3)
+        return t
+else:
+    msg = "Unsupported hashing algorithm in use %s" % _Py_hashfunc_name
+    raise ValueError(msg)
+
+
+@intrinsic
+def _inject_hashsecret_read(tyctx, name):
+    """Emit code to load the hashsecret.
+    """
+    if not isinstance(name, types.StringLiteral):
+        raise errors.TypingError("requires literal string")
+
+    sym = _hashsecret[name.literal_value].symbol
+    resty = types.uint64
+    sig = resty(name)
+
+    def impl(cgctx, builder, sig, args):
+        mod = builder.module
+        try:
+            # Search for existing global
+            gv = mod.get_global(sym)
+        except KeyError:
+            # Inject the symbol if not already exist.
+            gv = ir.GlobalVariable(mod, ir.IntType(64), name=sym)
+        v = builder.load(gv)
+        return v
+
+    return sig, impl
+
+
+def _load_hashsecret(name):
+    return _hashsecret[name].value
+
+
+@overload(_load_hashsecret)
+def _impl_load_hashsecret(name):
+    def imp(name):
+        return _inject_hashsecret_read(name)
+    return imp
+
+
+# This is a translation of CPythons's _Py_HashBytes:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Python/pyhash.c#L145-L191    # noqa: E501
+
+
+@register_jitable(locals={'_hash': _Py_uhash_t})
+def _Py_HashBytes(val, _len):
+    if (_len == 0):
+        return process_return(0)
+
+    if (_len < _Py_HASH_CUTOFF):
+        # TODO: this branch needs testing, needs a CPython setup for it!
+        # /* Optimize hashing of very small strings with inline DJBX33A. */
+        _hash = _Py_uhash_t(5381)  # /* DJBX33A starts with 5381 */
+        for idx in range(_len):
+            _hash = ((_hash << 5) + _hash) + np.uint8(grab_byte(val, idx))
+
+        _hash ^= _len
+        _hash ^= _load_hashsecret('djbx33a_suffix')
+    else:
+        tmp = _siphash24(types.uint64(_load_hashsecret('siphash_k0')),
+                         types.uint64(_load_hashsecret('siphash_k1')),
+                         val, _len)
+        _hash = process_return(tmp)
+    return process_return(_hash)
+
+# This is an approximate translation of CPython's unicode_hash:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/unicodeobject.c#L11635-L11663    # noqa: E501
+
+
+@overload_method(types.UnicodeType, '__hash__')
+def unicode_hash(val):
+    from numba.cpython.unicode import _kind_to_byte_width
+
+    def impl(val):
+        kindwidth = _kind_to_byte_width(val._kind)
+        _len = len(val)
+        # use the cache if possible
+        current_hash = val._hash
+        if current_hash != -1:
+            return current_hash
+        else:
+            # cannot write hash value to cache in the unicode struct due to
+            # pass by value on the struct making the struct member immutable
+            return _Py_HashBytes(val._data, kindwidth * _len)
+
+    return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/heapq.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/heapq.py
new file mode 100644
index 000000000..15c8166d8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/heapq.py
@@ -0,0 +1,266 @@
+# A port of https://github.com/python/cpython/blob/e42b7051/Lib/heapq.py
+
+
+import heapq as hq
+
+from numba.core import types
+from numba.core.errors import TypingError
+from numba.core.extending import overload, register_jitable
+
+
+@register_jitable
+def _siftdown(heap, startpos, pos):
+    newitem = heap[pos]
+
+    while pos > startpos:
+        parentpos = (pos - 1) >> 1
+        parent = heap[parentpos]
+        if newitem < parent:
+            heap[pos] = parent
+            pos = parentpos
+            continue
+        break
+
+    heap[pos] = newitem
+
+
+@register_jitable
+def _siftup(heap, pos):
+    endpos = len(heap)
+    startpos = pos
+    newitem = heap[pos]
+
+    childpos = 2 * pos + 1
+    while childpos < endpos:
+
+        rightpos = childpos + 1
+        if rightpos < endpos and not heap[childpos] < heap[rightpos]:
+            childpos = rightpos
+
+        heap[pos] = heap[childpos]
+        pos = childpos
+        childpos = 2 * pos + 1
+
+    heap[pos] = newitem
+    _siftdown(heap, startpos, pos)
+
+
+@register_jitable
+def _siftdown_max(heap, startpos, pos):
+    newitem = heap[pos]
+
+    while pos > startpos:
+        parentpos = (pos - 1) >> 1
+        parent = heap[parentpos]
+        if parent < newitem:
+            heap[pos] = parent
+            pos = parentpos
+            continue
+        break
+    heap[pos] = newitem
+
+
+@register_jitable
+def _siftup_max(heap, pos):
+    endpos = len(heap)
+    startpos = pos
+    newitem = heap[pos]
+
+    childpos = 2 * pos + 1
+    while childpos < endpos:
+
+        rightpos = childpos + 1
+        if rightpos < endpos and not heap[rightpos] < heap[childpos]:
+            childpos = rightpos
+
+        heap[pos] = heap[childpos]
+        pos = childpos
+        childpos = 2 * pos + 1
+
+    heap[pos] = newitem
+    _siftdown_max(heap, startpos, pos)
+
+
+@register_jitable
+def reversed_range(x):
+    # analogous to reversed(range(x))
+    return range(x - 1, -1, -1)
+
+
+@register_jitable
+def _heapify_max(x):
+    n = len(x)
+
+    for i in reversed_range(n // 2):
+        _siftup_max(x, i)
+
+
+@register_jitable
+def _heapreplace_max(heap, item):
+    returnitem = heap[0]
+    heap[0] = item
+    _siftup_max(heap, 0)
+    return returnitem
+
+
+def assert_heap_type(heap):
+    if not isinstance(heap, (types.List, types.ListType)):
+        raise TypingError('heap argument must be a list')
+
+    dt = heap.dtype
+    if isinstance(dt, types.Complex):
+        msg = ("'<' not supported between instances "
+               "of 'complex' and 'complex'")
+        raise TypingError(msg)
+
+
+def assert_item_type_consistent_with_heap_type(heap, item):
+    if not heap.dtype == item:
+        raise TypingError('heap type must be the same as item type')
+
+
+@overload(hq.heapify)
+def hq_heapify(x):
+    assert_heap_type(x)
+
+    def hq_heapify_impl(x):
+        n = len(x)
+        for i in reversed_range(n // 2):
+            _siftup(x, i)
+
+    return hq_heapify_impl
+
+
+@overload(hq.heappop)
+def hq_heappop(heap):
+    assert_heap_type(heap)
+
+    def hq_heappop_impl(heap):
+        lastelt = heap.pop()
+        if heap:
+            returnitem = heap[0]
+            heap[0] = lastelt
+            _siftup(heap, 0)
+            return returnitem
+        return lastelt
+
+    return hq_heappop_impl
+
+
+@overload(hq.heappush)
+def heappush(heap, item):
+    assert_heap_type(heap)
+    assert_item_type_consistent_with_heap_type(heap, item)
+
+    def hq_heappush_impl(heap, item):
+        heap.append(item)
+        _siftdown(heap, 0, len(heap) - 1)
+
+    return hq_heappush_impl
+
+
+@overload(hq.heapreplace)
+def heapreplace(heap, item):
+    assert_heap_type(heap)
+    assert_item_type_consistent_with_heap_type(heap, item)
+
+    def hq_heapreplace(heap, item):
+        returnitem = heap[0]
+        heap[0] = item
+        _siftup(heap, 0)
+        return returnitem
+
+    return hq_heapreplace
+
+
+@overload(hq.heappushpop)
+def heappushpop(heap, item):
+    assert_heap_type(heap)
+    assert_item_type_consistent_with_heap_type(heap, item)
+
+    def hq_heappushpop_impl(heap, item):
+        if heap and heap[0] < item:
+            item, heap[0] = heap[0], item
+            _siftup(heap, 0)
+        return item
+
+    return hq_heappushpop_impl
+
+
+def check_input_types(n, iterable):
+
+    if not isinstance(n, (types.Integer, types.Boolean)):
+        raise TypingError("First argument 'n' must be an integer")
+        # heapq also accepts 1.0 (but not 0.0, 2.0, 3.0...) but
+        # this isn't replicated
+
+    if not isinstance(iterable, (types.Sequence, types.Array, types.ListType)):
+        raise TypingError("Second argument 'iterable' must be iterable")
+
+
+@overload(hq.nsmallest)
+def nsmallest(n, iterable):
+    check_input_types(n, iterable)
+
+    def hq_nsmallest_impl(n, iterable):
+
+        if n == 0:
+            return [iterable[0] for _ in range(0)]
+        elif n == 1:
+            out = min(iterable)
+            return [out]
+
+        size = len(iterable)
+        if n >= size:
+            return sorted(iterable)[:n]
+
+        it = iter(iterable)
+        result = [(elem, i) for i, elem in zip(range(n), it)]
+
+        _heapify_max(result)
+        top = result[0][0]
+        order = n
+
+        for elem in it:
+            if elem < top:
+                _heapreplace_max(result, (elem, order))
+                top, _order = result[0]
+                order += 1
+        result.sort()
+        return [elem for (elem, order) in result]
+
+    return hq_nsmallest_impl
+
+
+@overload(hq.nlargest)
+def nlargest(n, iterable):
+    check_input_types(n, iterable)
+
+    def hq_nlargest_impl(n, iterable):
+
+        if n == 0:
+            return [iterable[0] for _ in range(0)]
+        elif n == 1:
+            out = max(iterable)
+            return [out]
+
+        size = len(iterable)
+        if n >= size:
+            return sorted(iterable)[::-1][:n]
+
+        it = iter(iterable)
+        result = [(elem, i) for i, elem in zip(range(0, -n, -1), it)]
+
+        hq.heapify(result)
+        top = result[0][0]
+        order = -n
+
+        for elem in it:
+            if top < elem:
+                hq.heapreplace(result, (elem, order))
+                top, _order = result[0]
+                order -= 1
+        result.sort(reverse=True)
+        return [elem for (elem, order) in result]
+
+    return hq_nlargest_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/iterators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/iterators.py
new file mode 100644
index 000000000..fa55dd2b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/iterators.py
@@ -0,0 +1,140 @@
+"""
+Implementation of various iterable and iterator types.
+"""
+
+from numba.core import types, cgutils
+from numba.core.imputils import (
+    lower_builtin, iternext_impl, call_iternext, call_getiter,
+    impl_ret_borrowed, impl_ret_new_ref, RefType)
+
+
+
+@lower_builtin('getiter', types.IteratorType)
+def iterator_getiter(context, builder, sig, args):
+    [it] = args
+    return impl_ret_borrowed(context, builder, sig.return_type, it)
+
+#-------------------------------------------------------------------------------
+# builtin `enumerate` implementation
+
+@lower_builtin(enumerate, types.IterableType)
+@lower_builtin(enumerate, types.IterableType, types.Integer)
+def make_enumerate_object(context, builder, sig, args):
+    assert len(args) == 1 or len(args) == 2 # enumerate(it) or enumerate(it, start)
+    srcty = sig.args[0]
+
+    if len(args) == 1:
+        src = args[0]
+        start_val = context.get_constant(types.intp, 0)
+    elif len(args) == 2:
+        src = args[0]
+        start_val = context.cast(builder, args[1], sig.args[1], types.intp)
+
+    iterobj = call_getiter(context, builder, srcty, src)
+
+    enum = context.make_helper(builder, sig.return_type)
+
+    countptr = cgutils.alloca_once(builder, start_val.type)
+    builder.store(start_val, countptr)
+
+    enum.count = countptr
+    enum.iter = iterobj
+
+    res = enum._getvalue()
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+@lower_builtin('iternext', types.EnumerateType)
+@iternext_impl(RefType.NEW)
+def iternext_enumerate(context, builder, sig, args, result):
+    [enumty] = sig.args
+    [enum] = args
+
+    enum = context.make_helper(builder, enumty, value=enum)
+
+    count = builder.load(enum.count)
+    ncount = builder.add(count, context.get_constant(types.intp, 1))
+    builder.store(ncount, enum.count)
+
+    srcres = call_iternext(context, builder, enumty.source_type, enum.iter)
+    is_valid = srcres.is_valid()
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        srcval = srcres.yielded_value()
+        result.yield_(context.make_tuple(builder, enumty.yield_type,
+                                         [count, srcval]))
+
+
+#-------------------------------------------------------------------------------
+# builtin `zip` implementation
+
+@lower_builtin(zip, types.VarArg(types.Any))
+def make_zip_object(context, builder, sig, args):
+    zip_type = sig.return_type
+
+    assert len(args) == len(zip_type.source_types)
+
+    zipobj = context.make_helper(builder, zip_type)
+
+    for i, (arg, srcty) in enumerate(zip(args, sig.args)):
+        zipobj[i] = call_getiter(context, builder, srcty, arg)
+
+    res = zipobj._getvalue()
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+@lower_builtin('iternext', types.ZipType)
+@iternext_impl(RefType.NEW)
+def iternext_zip(context, builder, sig, args, result):
+    [zip_type] = sig.args
+    [zipobj] = args
+
+    zipobj = context.make_helper(builder, zip_type, value=zipobj)
+
+    if len(zipobj) == 0:
+        # zip() is an empty iterator
+        result.set_exhausted()
+        return
+
+    p_ret_tup = cgutils.alloca_once(builder,
+                                    context.get_value_type(zip_type.yield_type))
+    p_is_valid = cgutils.alloca_once_value(builder, value=cgutils.true_bit)
+
+    for i, (iterobj, srcty) in enumerate(zip(zipobj, zip_type.source_types)):
+        is_valid = builder.load(p_is_valid)
+        # Avoid calling the remaining iternext if a iterator has been exhausted
+        with builder.if_then(is_valid):
+            srcres = call_iternext(context, builder, srcty, iterobj)
+            is_valid = builder.and_(is_valid, srcres.is_valid())
+            builder.store(is_valid, p_is_valid)
+            val = srcres.yielded_value()
+            ptr = cgutils.gep_inbounds(builder, p_ret_tup, 0, i)
+            builder.store(val, ptr)
+
+    is_valid = builder.load(p_is_valid)
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        result.yield_(builder.load(p_ret_tup))
+
+
+#-------------------------------------------------------------------------------
+# generator implementation
+
+@lower_builtin('iternext', types.Generator)
+@iternext_impl(RefType.BORROWED)
+def iternext_zip(context, builder, sig, args, result):
+    genty, = sig.args
+    gen, = args
+    impl = context.get_generator_impl(genty)
+    status, retval = impl(context, builder, sig, args)
+    context.add_linking_libs(getattr(impl, 'libs', ()))
+
+    with cgutils.if_likely(builder, status.is_ok):
+        result.set_valid(True)
+        result.yield_(retval)
+    with cgutils.if_unlikely(builder, status.is_stop_iteration):
+        result.set_exhausted()
+    with cgutils.if_unlikely(builder,
+                             builder.and_(status.is_error,
+                                          builder.not_(status.is_stop_iteration))):
+        context.call_conv.return_status_propagate(builder, status)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/listobj.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/listobj.py
new file mode 100644
index 000000000..5ad5e7c71
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/listobj.py
@@ -0,0 +1,1260 @@
+"""
+Support for native homogeneous lists.
+"""
+
+
+import math
+import operator
+
+from llvmlite import ir
+from numba.core import types, typing, errors, cgutils
+from numba.core.imputils import (lower_builtin, lower_cast,
+                                    iternext_impl, impl_ret_borrowed,
+                                    impl_ret_new_ref, impl_ret_untracked,
+                                    RefType)
+from numba.core.extending import overload_method, overload
+from numba.core.utils import cached_property
+from numba.misc import quicksort
+from numba.cpython import slicing
+from numba import literal_unroll
+
+
+def get_list_payload(context, builder, list_type, value):
+    """
+    Given a list value and type, get its payload structure (as a
+    reference, so that mutations are seen by all).
+    """
+    payload_type = types.ListPayload(list_type)
+    payload = context.nrt.meminfo_data(builder, value.meminfo)
+    ptrty = context.get_data_type(payload_type).as_pointer()
+    payload = builder.bitcast(payload, ptrty)
+    return context.make_data_helper(builder, payload_type, ref=payload)
+
+
+def get_itemsize(context, list_type):
+    """
+    Return the item size for the given list type.
+    """
+    llty = context.get_data_type(list_type.dtype)
+    return context.get_abi_sizeof(llty)
+
+
+class _ListPayloadMixin(object):
+
+    @property
+    def size(self):
+        return self._payload.size
+
+    @size.setter
+    def size(self, value):
+        self._payload.size = value
+
+    @property
+    def dirty(self):
+        return self._payload.dirty
+
+    @property
+    def data(self):
+        return self._payload._get_ptr_by_name('data')
+
+    def _gep(self, idx):
+        return cgutils.gep(self._builder, self.data, idx)
+
+    def getitem(self, idx):
+        ptr = self._gep(idx)
+        data_item = self._builder.load(ptr)
+        return self._datamodel.from_data(self._builder, data_item)
+
+    def fix_index(self, idx):
+        """
+        Fix negative indices by adding the size to them.  Positive
+        indices are left untouched.
+        """
+        is_negative = self._builder.icmp_signed('<', idx,
+                                                ir.Constant(idx.type, 0))
+        wrapped_index = self._builder.add(idx, self.size)
+        return self._builder.select(is_negative, wrapped_index, idx)
+
+    def is_out_of_bounds(self, idx):
+        """
+        Return whether the index is out of bounds.
+        """
+        underflow = self._builder.icmp_signed('<', idx,
+                                              ir.Constant(idx.type, 0))
+        overflow = self._builder.icmp_signed('>=', idx, self.size)
+        return self._builder.or_(underflow, overflow)
+
+    def clamp_index(self, idx):
+        """
+        Clamp the index in [0, size].
+        """
+        builder = self._builder
+        idxptr = cgutils.alloca_once_value(builder, idx)
+
+        zero = ir.Constant(idx.type, 0)
+        size = self.size
+
+        underflow = self._builder.icmp_signed('<', idx, zero)
+        with builder.if_then(underflow, likely=False):
+            builder.store(zero, idxptr)
+        overflow = self._builder.icmp_signed('>=', idx, size)
+        with builder.if_then(overflow, likely=False):
+            builder.store(size, idxptr)
+
+        return builder.load(idxptr)
+
+    def guard_index(self, idx, msg):
+        """
+        Raise an error if the index is out of bounds.
+        """
+        with self._builder.if_then(self.is_out_of_bounds(idx), likely=False):
+            self._context.call_conv.return_user_exc(self._builder,
+                                                    IndexError, (msg,))
+
+    def fix_slice(self, slice):
+        """
+        Fix slice start and stop to be valid (inclusive and exclusive, resp)
+        indexing bounds.
+        """
+        return slicing.fix_slice(self._builder, slice, self.size)
+
+    def incref_value(self, val):
+        "Incref an element value"
+        self._context.nrt.incref(self._builder, self.dtype, val)
+
+    def decref_value(self, val):
+        "Decref an element value"
+        self._context.nrt.decref(self._builder, self.dtype, val)
+
+
+class ListPayloadAccessor(_ListPayloadMixin):
+    """
+    A helper object to access the list attributes given the pointer to the
+    payload type.
+    """
+    def __init__(self, context, builder, list_type, payload_ptr):
+        self._context = context
+        self._builder = builder
+        self._ty = list_type
+        self._datamodel = context.data_model_manager[list_type.dtype]
+        payload_type = types.ListPayload(list_type)
+        ptrty = context.get_data_type(payload_type).as_pointer()
+        payload_ptr = builder.bitcast(payload_ptr, ptrty)
+        payload = context.make_data_helper(builder, payload_type,
+                                           ref=payload_ptr)
+        self._payload = payload
+
+
+class ListInstance(_ListPayloadMixin):
+
+    def __init__(self, context, builder, list_type, list_val):
+        self._context = context
+        self._builder = builder
+        self._ty = list_type
+        self._list = context.make_helper(builder, list_type, list_val)
+        self._itemsize = get_itemsize(context, list_type)
+        self._datamodel = context.data_model_manager[list_type.dtype]
+
+    @property
+    def dtype(self):
+        return self._ty.dtype
+
+    @property
+    def _payload(self):
+        # This cannot be cached as it can be reallocated
+        return get_list_payload(self._context, self._builder, self._ty, self._list)
+
+    @property
+    def parent(self):
+        return self._list.parent
+
+    @parent.setter
+    def parent(self, value):
+        self._list.parent = value
+
+    @property
+    def value(self):
+        return self._list._getvalue()
+
+    @property
+    def meminfo(self):
+        return self._list.meminfo
+
+    def set_dirty(self, val):
+        if self._ty.reflected:
+            self._payload.dirty = cgutils.true_bit if val else cgutils.false_bit
+
+    def clear_value(self, idx):
+        """Remove the value at the location
+        """
+        self.decref_value(self.getitem(idx))
+        # it's necessary for the dtor which just decref every slot on it.
+        self.zfill(idx, self._builder.add(idx, idx.type(1)))
+
+    def setitem(self, idx, val, incref, decref_old_value=True):
+        # Decref old data
+        if decref_old_value:
+            self.decref_value(self.getitem(idx))
+
+        ptr = self._gep(idx)
+        data_item = self._datamodel.as_data(self._builder, val)
+        self._builder.store(data_item, ptr)
+        self.set_dirty(True)
+        if incref:
+            # Incref the underlying data
+            self.incref_value(val)
+
+    def inititem(self, idx, val, incref=True):
+        ptr = self._gep(idx)
+        data_item = self._datamodel.as_data(self._builder, val)
+        self._builder.store(data_item, ptr)
+        if incref:
+            self.incref_value(val)
+
+    def zfill(self, start, stop):
+        """Zero-fill the memory at index *start* to *stop*
+
+        *stop* MUST not be smaller than *start*.
+        """
+        builder = self._builder
+        base = self._gep(start)
+        end = self._gep(stop)
+        intaddr_t = self._context.get_value_type(types.intp)
+        size = builder.sub(builder.ptrtoint(end, intaddr_t),
+                           builder.ptrtoint(base, intaddr_t))
+        cgutils.memset(builder, base, size, ir.IntType(8)(0))
+
+    @classmethod
+    def allocate_ex(cls, context, builder, list_type, nitems):
+        """
+        Allocate a ListInstance with its storage.
+        Return a (ok, instance) tuple where *ok* is a LLVM boolean and
+        *instance* is a ListInstance object (the object's contents are
+        only valid when *ok* is true).
+        """
+        intp_t = context.get_value_type(types.intp)
+
+        if isinstance(nitems, int):
+            nitems = ir.Constant(intp_t, nitems)
+
+        payload_type = context.get_data_type(types.ListPayload(list_type))
+        payload_size = context.get_abi_sizeof(payload_type)
+
+        itemsize = get_itemsize(context, list_type)
+        # Account for the fact that the payload struct contains one entry
+        payload_size -= itemsize
+
+        ok = cgutils.alloca_once_value(builder, cgutils.true_bit)
+        self = cls(context, builder, list_type, None)
+
+        # Total allocation size = <payload header size> + nitems * itemsize
+        allocsize, ovf = cgutils.muladd_with_overflow(builder, nitems,
+                                                      ir.Constant(intp_t, itemsize),
+                                                      ir.Constant(intp_t, payload_size))
+        with builder.if_then(ovf, likely=False):
+            builder.store(cgutils.false_bit, ok)
+
+        with builder.if_then(builder.load(ok), likely=True):
+            meminfo = context.nrt.meminfo_new_varsize_dtor_unchecked(
+                builder, size=allocsize, dtor=self.get_dtor())
+            with builder.if_else(cgutils.is_null(builder, meminfo),
+                                 likely=False) as (if_error, if_ok):
+                with if_error:
+                    builder.store(cgutils.false_bit, ok)
+                with if_ok:
+                    self._list.meminfo = meminfo
+                    self._list.parent = context.get_constant_null(types.pyobject)
+                    self._payload.allocated = nitems
+                    self._payload.size = ir.Constant(intp_t, 0)  # for safety
+                    self._payload.dirty = cgutils.false_bit
+                    # Zero the allocated region
+                    self.zfill(self.size.type(0), nitems)
+
+        return builder.load(ok), self
+
+    def define_dtor(self):
+        "Define the destructor if not already defined"
+        context = self._context
+        builder = self._builder
+        mod = builder.module
+        # Declare dtor
+        fnty = ir.FunctionType(ir.VoidType(), [cgutils.voidptr_t])
+        fn = cgutils.get_or_insert_function(mod, fnty,
+                                            '.dtor.list.{}'.format(self.dtype))
+        if not fn.is_declaration:
+            # End early if the dtor is already defined
+            return fn
+        fn.linkage = 'linkonce_odr'
+        # Populate the dtor
+        builder = ir.IRBuilder(fn.append_basic_block())
+        base_ptr = fn.args[0]  # void*
+
+        # get payload
+        payload = ListPayloadAccessor(context, builder, self._ty, base_ptr)
+
+        # Loop over all data to decref
+        intp = payload.size.type
+        with cgutils.for_range_slice(
+                builder, start=intp(0), stop=payload.size, step=intp(1),
+                intp=intp) as (idx, _):
+            val = payload.getitem(idx)
+            context.nrt.decref(builder, self.dtype, val)
+        builder.ret_void()
+        return fn
+
+    def get_dtor(self):
+        """"Get the element dtor function pointer as void pointer.
+
+        It's safe to be called multiple times.
+        """
+        # Define and set the Dtor
+        dtor = self.define_dtor()
+        dtor_fnptr = self._builder.bitcast(dtor, cgutils.voidptr_t)
+        return dtor_fnptr
+
+    @classmethod
+    def allocate(cls, context, builder, list_type, nitems):
+        """
+        Allocate a ListInstance with its storage.  Same as allocate_ex(),
+        but return an initialized *instance*.  If allocation failed,
+        control is transferred to the caller using the target's current
+        call convention.
+        """
+        ok, self = cls.allocate_ex(context, builder, list_type, nitems)
+        with builder.if_then(builder.not_(ok), likely=False):
+            context.call_conv.return_user_exc(builder, MemoryError,
+                                              ("cannot allocate list",))
+        return self
+
+    @classmethod
+    def from_meminfo(cls, context, builder, list_type, meminfo):
+        """
+        Allocate a new list instance pointing to an existing payload
+        (a meminfo pointer).
+        Note the parent field has to be filled by the caller.
+        """
+        self = cls(context, builder, list_type, None)
+        self._list.meminfo = meminfo
+        self._list.parent = context.get_constant_null(types.pyobject)
+        context.nrt.incref(builder, list_type, self.value)
+        # Payload is part of the meminfo, no need to touch it
+        return self
+
+    def resize(self, new_size):
+        """
+        Ensure the list is properly sized for the new size.
+        """
+        def _payload_realloc(new_allocated):
+            payload_type = context.get_data_type(types.ListPayload(self._ty))
+            payload_size = context.get_abi_sizeof(payload_type)
+            # Account for the fact that the payload struct contains one entry
+            payload_size -= itemsize
+
+            allocsize, ovf = cgutils.muladd_with_overflow(
+                builder, new_allocated,
+                ir.Constant(intp_t, itemsize),
+                ir.Constant(intp_t, payload_size))
+            with builder.if_then(ovf, likely=False):
+                context.call_conv.return_user_exc(builder, MemoryError,
+                                                  ("cannot resize list",))
+
+            ptr = context.nrt.meminfo_varsize_realloc_unchecked(builder,
+                                                                self._list.meminfo,
+                                                                size=allocsize)
+            cgutils.guard_memory_error(context, builder, ptr,
+                                       "cannot resize list")
+            self._payload.allocated = new_allocated
+
+        context = self._context
+        builder = self._builder
+        intp_t = new_size.type
+
+        itemsize = get_itemsize(context, self._ty)
+        allocated = self._payload.allocated
+
+        two = ir.Constant(intp_t, 2)
+        eight = ir.Constant(intp_t, 8)
+
+        # allocated < new_size
+        is_too_small = builder.icmp_signed('<', allocated, new_size)
+        # (allocated >> 2) > new_size
+        is_too_large = builder.icmp_signed('>', builder.ashr(allocated, two), new_size)
+
+        with builder.if_then(is_too_large, likely=False):
+            # Exact downsize to requested size
+            # NOTE: is_too_large must be aggressive enough to avoid repeated
+            # upsizes and downsizes when growing a list.
+            _payload_realloc(new_size)
+
+        with builder.if_then(is_too_small, likely=False):
+            # Upsize with moderate over-allocation (size + size >> 2 + 8)
+            new_allocated = builder.add(eight,
+                                        builder.add(new_size,
+                                                    builder.ashr(new_size, two)))
+            _payload_realloc(new_allocated)
+            self.zfill(self.size, new_allocated)
+
+        self._payload.size = new_size
+        self.set_dirty(True)
+
+    def move(self, dest_idx, src_idx, count):
+        """
+        Move `count` elements from `src_idx` to `dest_idx`.
+        """
+        dest_ptr = self._gep(dest_idx)
+        src_ptr = self._gep(src_idx)
+        cgutils.raw_memmove(self._builder, dest_ptr, src_ptr,
+                            count, itemsize=self._itemsize)
+
+        self.set_dirty(True)
+
+class ListIterInstance(_ListPayloadMixin):
+
+    def __init__(self, context, builder, iter_type, iter_val):
+        self._context = context
+        self._builder = builder
+        self._ty = iter_type
+        self._iter = context.make_helper(builder, iter_type, iter_val)
+        self._datamodel = context.data_model_manager[iter_type.yield_type]
+
+    @classmethod
+    def from_list(cls, context, builder, iter_type, list_val):
+        list_inst = ListInstance(context, builder, iter_type.container, list_val)
+        self = cls(context, builder, iter_type, None)
+        index = context.get_constant(types.intp, 0)
+        self._iter.index = cgutils.alloca_once_value(builder, index)
+        self._iter.meminfo = list_inst.meminfo
+        return self
+
+    @property
+    def _payload(self):
+        # This cannot be cached as it can be reallocated
+        return get_list_payload(self._context, self._builder,
+                                self._ty.container, self._iter)
+
+    @property
+    def value(self):
+        return self._iter._getvalue()
+
+    @property
+    def index(self):
+        return self._builder.load(self._iter.index)
+
+    @index.setter
+    def index(self, value):
+        self._builder.store(value, self._iter.index)
+
+
+#-------------------------------------------------------------------------------
+# Constructors
+
+def build_list(context, builder, list_type, items):
+    """
+    Build a list of the given type, containing the given items.
+    """
+    nitems = len(items)
+    inst = ListInstance.allocate(context, builder, list_type, nitems)
+    # Populate list
+    inst.size = context.get_constant(types.intp, nitems)
+    for i, val in enumerate(items):
+        inst.setitem(context.get_constant(types.intp, i), val, incref=True)
+
+    return impl_ret_new_ref(context, builder, list_type, inst.value)
+
+
+@lower_builtin(list, types.IterableType)
+def list_constructor(context, builder, sig, args):
+
+    def list_impl(iterable):
+        res = []
+        res.extend(iterable)
+        return res
+
+    return context.compile_internal(builder, list_impl, sig, args)
+
+@lower_builtin(list)
+def list_constructor(context, builder, sig, args):
+    list_type = sig.return_type
+    list_len = 0
+    inst = ListInstance.allocate(context, builder, list_type, list_len)
+    return impl_ret_new_ref(context, builder, list_type, inst.value)
+
+#-------------------------------------------------------------------------------
+# Various operations
+
+@lower_builtin(len, types.List)
+def list_len(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    return inst.size
+
+@lower_builtin('getiter', types.List)
+def getiter_list(context, builder, sig, args):
+    inst = ListIterInstance.from_list(context, builder, sig.return_type, args[0])
+    return impl_ret_borrowed(context, builder, sig.return_type, inst.value)
+
+@lower_builtin('iternext', types.ListIter)
+@iternext_impl(RefType.BORROWED)
+def iternext_listiter(context, builder, sig, args, result):
+    inst = ListIterInstance(context, builder, sig.args[0], args[0])
+
+    index = inst.index
+    nitems = inst.size
+    is_valid = builder.icmp_signed('<', index, nitems)
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        result.yield_(inst.getitem(index))
+        inst.index = builder.add(index, context.get_constant(types.intp, 1))
+
+
+@lower_builtin(operator.getitem, types.List, types.Integer)
+def getitem_list(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    index = args[1]
+
+    index = inst.fix_index(index)
+    inst.guard_index(index, msg="getitem out of range")
+    result = inst.getitem(index)
+
+    return impl_ret_borrowed(context, builder, sig.return_type, result)
+
+@lower_builtin(operator.setitem, types.List, types.Integer, types.Any)
+def setitem_list(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    index = args[1]
+    value = args[2]
+
+    index = inst.fix_index(index)
+    inst.guard_index(index, msg="setitem out of range")
+    inst.setitem(index, value, incref=True)
+    return context.get_dummy_value()
+
+
+@lower_builtin(operator.getitem, types.List, types.SliceType)
+def getslice_list(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    slice = context.make_helper(builder, sig.args[1], args[1])
+    slicing.guard_invalid_slice(context, builder, sig.args[1], slice)
+    inst.fix_slice(slice)
+
+    # Allocate result and populate it
+    result_size = slicing.get_slice_length(builder, slice)
+    result = ListInstance.allocate(context, builder, sig.return_type,
+                                   result_size)
+    result.size = result_size
+    with cgutils.for_range_slice_generic(builder, slice.start, slice.stop,
+                                         slice.step) as (pos_range, neg_range):
+        with pos_range as (idx, count):
+            value = inst.getitem(idx)
+            result.inititem(count, value, incref=True)
+        with neg_range as (idx, count):
+            value = inst.getitem(idx)
+            result.inititem(count, value, incref=True)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, result.value)
+
+@lower_builtin(operator.setitem, types.List, types.SliceType, types.Any)
+def setitem_list(context, builder, sig, args):
+    dest = ListInstance(context, builder, sig.args[0], args[0])
+    src = ListInstance(context, builder, sig.args[2], args[2])
+
+    slice = context.make_helper(builder, sig.args[1], args[1])
+    slicing.guard_invalid_slice(context, builder, sig.args[1], slice)
+    dest.fix_slice(slice)
+
+    src_size = src.size
+    avail_size = slicing.get_slice_length(builder, slice)
+    size_delta = builder.sub(src.size, avail_size)
+
+    zero = ir.Constant(size_delta.type, 0)
+    one = ir.Constant(size_delta.type, 1)
+
+    with builder.if_else(builder.icmp_signed('==', slice.step, one)) as (then, otherwise):
+        with then:
+            # Slice step == 1 => we can resize
+
+            # Compute the real stop, e.g. for dest[2:0] = [...]
+            real_stop = builder.add(slice.start, avail_size)
+            # Size of the list tail, after the end of slice
+            tail_size = builder.sub(dest.size, real_stop)
+
+            with builder.if_then(builder.icmp_signed('>', size_delta, zero)):
+                # Grow list then move list tail
+                dest.resize(builder.add(dest.size, size_delta))
+                dest.move(builder.add(real_stop, size_delta), real_stop,
+                          tail_size)
+
+            with builder.if_then(builder.icmp_signed('<', size_delta, zero)):
+                # Move list tail then shrink list
+                dest.move(builder.add(real_stop, size_delta), real_stop,
+                          tail_size)
+                dest.resize(builder.add(dest.size, size_delta))
+
+            dest_offset = slice.start
+
+            with cgutils.for_range(builder, src_size) as loop:
+                value = src.getitem(loop.index)
+                dest.setitem(builder.add(loop.index, dest_offset), value, incref=True)
+
+        with otherwise:
+            with builder.if_then(builder.icmp_signed('!=', size_delta, zero)):
+                msg = "cannot resize extended list slice with step != 1"
+                context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+            with cgutils.for_range_slice_generic(
+                builder, slice.start, slice.stop, slice.step) as (pos_range, neg_range):
+                with pos_range as (index, count):
+                    value = src.getitem(count)
+                    dest.setitem(index, value, incref=True)
+                with neg_range as (index, count):
+                    value = src.getitem(count)
+                    dest.setitem(index, value, incref=True)
+
+    return context.get_dummy_value()
+
+
+
+@lower_builtin(operator.delitem, types.List, types.Integer)
+def delitem_list_index(context, builder, sig, args):
+
+    def list_delitem_impl(lst, i):
+        lst.pop(i)
+
+    return context.compile_internal(builder, list_delitem_impl, sig, args)
+
+
+@lower_builtin(operator.delitem, types.List, types.SliceType)
+def delitem_list(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    slice = context.make_helper(builder, sig.args[1], args[1])
+
+    slicing.guard_invalid_slice(context, builder, sig.args[1], slice)
+    inst.fix_slice(slice)
+
+    slice_len = slicing.get_slice_length(builder, slice)
+
+    one = ir.Constant(slice_len.type, 1)
+
+    with builder.if_then(builder.icmp_signed('!=', slice.step, one), likely=False):
+        msg = "unsupported del list[start:stop:step] with step != 1"
+        context.call_conv.return_user_exc(builder, NotImplementedError, (msg,))
+
+    # Compute the real stop, e.g. for dest[2:0]
+    start = slice.start
+    real_stop = builder.add(start, slice_len)
+    # Decref the removed range
+    with cgutils.for_range_slice(
+            builder, start, real_stop, start.type(1)
+            ) as (idx, _):
+        inst.decref_value(inst.getitem(idx))
+
+    # Size of the list tail, after the end of slice
+    tail_size = builder.sub(inst.size, real_stop)
+    inst.move(start, real_stop, tail_size)
+    inst.resize(builder.sub(inst.size, slice_len))
+
+    return context.get_dummy_value()
+
+
+# XXX should there be a specific module for Sequence or collection base classes?
+
+@lower_builtin(operator.contains, types.Sequence, types.Any)
+def in_seq(context, builder, sig, args):
+    def seq_contains_impl(lst, value):
+        for elem in lst:
+            if elem == value:
+                return True
+        return False
+
+    return context.compile_internal(builder, seq_contains_impl, sig, args)
+
+@lower_builtin(bool, types.Sequence)
+def sequence_bool(context, builder, sig, args):
+    def sequence_bool_impl(seq):
+        return len(seq) != 0
+
+    return context.compile_internal(builder, sequence_bool_impl, sig, args)
+
+
+@overload(operator.truth)
+def sequence_truth(seq):
+    if isinstance(seq, types.Sequence):
+        def impl(seq):
+            return len(seq) != 0
+        return impl
+
+
+@lower_builtin(operator.add, types.List, types.List)
+def list_add(context, builder, sig, args):
+    a = ListInstance(context, builder, sig.args[0], args[0])
+    b = ListInstance(context, builder, sig.args[1], args[1])
+
+    a_size = a.size
+    b_size = b.size
+    nitems = builder.add(a_size, b_size)
+    dest = ListInstance.allocate(context, builder, sig.return_type, nitems)
+    dest.size = nitems
+
+    with cgutils.for_range(builder, a_size) as loop:
+        value = a.getitem(loop.index)
+        value = context.cast(builder, value, a.dtype, dest.dtype)
+        dest.setitem(loop.index, value, incref=True)
+    with cgutils.for_range(builder, b_size) as loop:
+        value = b.getitem(loop.index)
+        value = context.cast(builder, value, b.dtype, dest.dtype)
+        dest.setitem(builder.add(loop.index, a_size), value, incref=True)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, dest.value)
+
+@lower_builtin(operator.iadd, types.List, types.List)
+def list_add_inplace(context, builder, sig, args):
+    assert sig.args[0].dtype == sig.return_type.dtype
+    dest = _list_extend_list(context, builder, sig, args)
+
+    return impl_ret_borrowed(context, builder, sig.return_type, dest.value)
+
+
+@lower_builtin(operator.mul, types.List, types.Integer)
+@lower_builtin(operator.mul, types.Integer, types.List)
+def list_mul(context, builder, sig, args):
+    if isinstance(sig.args[0], types.List):
+        list_idx, int_idx = 0, 1
+    else:
+        list_idx, int_idx = 1, 0
+    src = ListInstance(context, builder, sig.args[list_idx], args[list_idx])
+    src_size = src.size
+
+    mult = args[int_idx]
+    zero = ir.Constant(mult.type, 0)
+    mult = builder.select(cgutils.is_neg_int(builder, mult), zero, mult)
+    nitems = builder.mul(mult, src_size)
+
+    dest = ListInstance.allocate(context, builder, sig.return_type, nitems)
+    dest.size = nitems
+
+    with cgutils.for_range_slice(builder, zero, nitems, src_size, inc=True) as (dest_offset, _):
+        with cgutils.for_range(builder, src_size) as loop:
+            value = src.getitem(loop.index)
+            dest.setitem(builder.add(loop.index, dest_offset), value, incref=True)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, dest.value)
+
+@lower_builtin(operator.imul, types.List, types.Integer)
+def list_mul_inplace(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    src_size = inst.size
+
+    mult = args[1]
+    zero = ir.Constant(mult.type, 0)
+    mult = builder.select(cgutils.is_neg_int(builder, mult), zero, mult)
+    nitems = builder.mul(mult, src_size)
+
+    inst.resize(nitems)
+
+    with cgutils.for_range_slice(builder, src_size, nitems, src_size, inc=True) as (dest_offset, _):
+        with cgutils.for_range(builder, src_size) as loop:
+            value = inst.getitem(loop.index)
+            inst.setitem(builder.add(loop.index, dest_offset), value, incref=True)
+
+    return impl_ret_borrowed(context, builder, sig.return_type, inst.value)
+
+
+#-------------------------------------------------------------------------------
+# Comparisons
+
+@lower_builtin(operator.is_, types.List, types.List)
+def list_is(context, builder, sig, args):
+    a = ListInstance(context, builder, sig.args[0], args[0])
+    b = ListInstance(context, builder, sig.args[1], args[1])
+    ma = builder.ptrtoint(a.meminfo, cgutils.intp_t)
+    mb = builder.ptrtoint(b.meminfo, cgutils.intp_t)
+    return builder.icmp_signed('==', ma, mb)
+
+@lower_builtin(operator.eq, types.List, types.List)
+def list_eq(context, builder, sig, args):
+    aty, bty = sig.args
+    a = ListInstance(context, builder, aty, args[0])
+    b = ListInstance(context, builder, bty, args[1])
+
+    a_size = a.size
+    same_size = builder.icmp_signed('==', a_size, b.size)
+
+    res = cgutils.alloca_once_value(builder, same_size)
+
+    with builder.if_then(same_size):
+        with cgutils.for_range(builder, a_size) as loop:
+            v = a.getitem(loop.index)
+            w = b.getitem(loop.index)
+            itemres = context.generic_compare(builder, operator.eq,
+                                              (aty.dtype, bty.dtype), (v, w))
+            with builder.if_then(builder.not_(itemres)):
+                # Exit early
+                builder.store(cgutils.false_bit, res)
+                loop.do_break()
+
+    return builder.load(res)
+
+@lower_builtin(operator.ne, types.List, types.List)
+def list_ne(context, builder, sig, args):
+
+    def list_ne_impl(a, b):
+        return not (a == b)
+
+    return context.compile_internal(builder, list_ne_impl, sig, args)
+
+@lower_builtin(operator.le, types.List, types.List)
+def list_le(context, builder, sig, args):
+
+    def list_le_impl(a, b):
+        m = len(a)
+        n = len(b)
+        for i in range(min(m, n)):
+            if a[i] < b[i]:
+                return True
+            elif a[i] > b[i]:
+                return False
+        return m <= n
+
+    return context.compile_internal(builder, list_le_impl, sig, args)
+
+@lower_builtin(operator.lt, types.List, types.List)
+def list_lt(context, builder, sig, args):
+
+    def list_lt_impl(a, b):
+        m = len(a)
+        n = len(b)
+        for i in range(min(m, n)):
+            if a[i] < b[i]:
+                return True
+            elif a[i] > b[i]:
+                return False
+        return m < n
+
+    return context.compile_internal(builder, list_lt_impl, sig, args)
+
+@lower_builtin(operator.ge, types.List, types.List)
+def list_ge(context, builder, sig, args):
+
+    def list_ge_impl(a, b):
+        return b <= a
+
+    return context.compile_internal(builder, list_ge_impl, sig, args)
+
+@lower_builtin(operator.gt, types.List, types.List)
+def list_gt(context, builder, sig, args):
+
+    def list_gt_impl(a, b):
+        return b < a
+
+    return context.compile_internal(builder, list_gt_impl, sig, args)
+
+#-------------------------------------------------------------------------------
+# Methods
+
+@lower_builtin("list.append", types.List, types.Any)
+def list_append(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    item = args[1]
+
+    n = inst.size
+    new_size = builder.add(n, ir.Constant(n.type, 1))
+    inst.resize(new_size)
+    inst.setitem(n, item, incref=True)
+
+    return context.get_dummy_value()
+
+@lower_builtin("list.clear", types.List)
+def list_clear(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    inst.resize(context.get_constant(types.intp, 0))
+
+    return context.get_dummy_value()
+
+@lower_builtin("list.copy", types.List)
+def list_copy(context, builder, sig, args):
+    def list_copy_impl(lst):
+        return list(lst)
+
+    return context.compile_internal(builder, list_copy_impl, sig, args)
+
+@lower_builtin("list.count", types.List, types.Any)
+def list_count(context, builder, sig, args):
+
+    def list_count_impl(lst, value):
+        res = 0
+        for elem in lst:
+            if elem == value:
+                res += 1
+        return res
+
+    return context.compile_internal(builder, list_count_impl, sig, args)
+
+def _list_extend_list(context, builder, sig, args):
+    src = ListInstance(context, builder, sig.args[1], args[1])
+    dest = ListInstance(context, builder, sig.args[0], args[0])
+
+    src_size = src.size
+    dest_size = dest.size
+    nitems = builder.add(src_size, dest_size)
+    dest.resize(nitems)
+    dest.size = nitems
+
+    with cgutils.for_range(builder, src_size) as loop:
+        value = src.getitem(loop.index)
+        value = context.cast(builder, value, src.dtype, dest.dtype)
+        dest.setitem(builder.add(loop.index, dest_size), value, incref=True)
+
+    return dest
+
+@lower_builtin("list.extend", types.List, types.IterableType)
+def list_extend(context, builder, sig, args):
+    if isinstance(sig.args[1], types.List):
+        # Specialize for list operands, for speed.
+        _list_extend_list(context, builder, sig, args)
+        return context.get_dummy_value()
+
+    def list_extend(lst, iterable):
+        # Speed hack to avoid NRT refcount operations inside the loop
+        meth = lst.append
+        for v in iterable:
+            meth(v)
+
+    return context.compile_internal(builder, list_extend, sig, args)
+
+@lower_builtin("list.index", types.List, types.Any)
+def list_index(context, builder, sig, args):
+
+    def list_index_impl(lst, value):
+        for i in range(len(lst)):
+            if lst[i] == value:
+                return i
+        # XXX references are leaked when raising
+        raise ValueError("value not in list")
+
+    return context.compile_internal(builder, list_index_impl, sig, args)
+
+@lower_builtin("list.index", types.List, types.Any,
+           types.Integer)
+def list_index(context, builder, sig, args):
+
+    def list_index_impl(lst, value, start):
+        n = len(lst)
+        if start < 0:
+            start += n
+            if start < 0:
+                start = 0
+        for i in range(start, len(lst)):
+            if lst[i] == value:
+                return i
+        # XXX references are leaked when raising
+        raise ValueError("value not in list")
+
+    return context.compile_internal(builder, list_index_impl, sig, args)
+
+@lower_builtin("list.index", types.List, types.Any,
+           types.Integer, types.Integer)
+def list_index(context, builder, sig, args):
+
+    def list_index_impl(lst, value, start, stop):
+        n = len(lst)
+        if start < 0:
+            start += n
+            if start < 0:
+                start = 0
+        if stop < 0:
+            stop += n
+        if stop > n:
+            stop = n
+        for i in range(start, stop):
+            if lst[i] == value:
+                return i
+        # XXX references are leaked when raising
+        raise ValueError("value not in list")
+
+    return context.compile_internal(builder, list_index_impl, sig, args)
+
+@lower_builtin("list.insert", types.List, types.Integer,
+           types.Any)
+def list_insert(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    index = inst.fix_index(args[1])
+    index = inst.clamp_index(index)
+    value = args[2]
+
+    n = inst.size
+    one = ir.Constant(n.type, 1)
+    new_size = builder.add(n, one)
+    inst.resize(new_size)
+    inst.move(builder.add(index, one), index, builder.sub(n, index))
+    inst.setitem(index, value, incref=True, decref_old_value=False)
+
+    return context.get_dummy_value()
+
+@lower_builtin("list.pop", types.List)
+def list_pop(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+
+    n = inst.size
+    cgutils.guard_zero(context, builder, n,
+                       (IndexError, "pop from empty list"))
+    n = builder.sub(n, ir.Constant(n.type, 1))
+    res = inst.getitem(n)
+    inst.incref_value(res)  # incref the pop'ed element
+    inst.clear_value(n)     # clear the storage space
+    inst.resize(n)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+@lower_builtin("list.pop", types.List, types.Integer)
+def list_pop(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    idx = inst.fix_index(args[1])
+
+    n = inst.size
+    cgutils.guard_zero(context, builder, n,
+                       (IndexError, "pop from empty list"))
+    inst.guard_index(idx, "pop index out of range")
+
+    res = inst.getitem(idx)
+
+    one = ir.Constant(n.type, 1)
+    n = builder.sub(n, ir.Constant(n.type, 1))
+    inst.move(idx, builder.add(idx, one), builder.sub(n, idx))
+    inst.resize(n)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+@lower_builtin("list.remove", types.List, types.Any)
+def list_remove(context, builder, sig, args):
+
+    def list_remove_impl(lst, value):
+        for i in range(len(lst)):
+            if lst[i] == value:
+                lst.pop(i)
+                return
+        # XXX references are leaked when raising
+        raise ValueError("list.remove(x): x not in list")
+
+    return context.compile_internal(builder, list_remove_impl, sig, args)
+
+@lower_builtin("list.reverse", types.List)
+def list_reverse(context, builder, sig, args):
+
+    def list_reverse_impl(lst):
+        for a in range(0, len(lst) // 2):
+            b = -a - 1
+            lst[a], lst[b] = lst[b], lst[a]
+
+    return context.compile_internal(builder, list_reverse_impl, sig, args)
+
+
+# -----------------------------------------------------------------------------
+# Sorting
+
+def gt(a, b):
+    return a > b
+
+sort_forwards = quicksort.make_jit_quicksort().run_quicksort
+sort_backwards = quicksort.make_jit_quicksort(lt=gt).run_quicksort
+
+arg_sort_forwards = quicksort.make_jit_quicksort(is_argsort=True,
+                                                 is_list=True).run_quicksort
+arg_sort_backwards = quicksort.make_jit_quicksort(is_argsort=True, lt=gt,
+                                                  is_list=True).run_quicksort
+
+
+def _sort_check_reverse(reverse):
+    if isinstance(reverse, types.Omitted):
+        rty = reverse.value
+    elif isinstance(reverse, types.Optional):
+        rty = reverse.type
+    else:
+        rty = reverse
+    if not isinstance(rty, (types.Boolean, types.Integer, int, bool)):
+        msg = "an integer is required for 'reverse' (got type %s)" % reverse
+        raise errors.TypingError(msg)
+    return rty
+
+
+def _sort_check_key(key):
+    if isinstance(key, types.Optional):
+        msg = ("Key must concretely be None or a Numba JIT compiled function, "
+               "an Optional (union of None and a value) was found")
+        raise errors.TypingError(msg)
+    if not (cgutils.is_nonelike(key) or isinstance(key, types.Dispatcher)):
+        msg = "Key must be None or a Numba JIT compiled function"
+        raise errors.TypingError(msg)
+
+
+@overload_method(types.List, "sort")
+def ol_list_sort(lst, key=None, reverse=False):
+
+    _sort_check_key(key)
+    _sort_check_reverse(reverse)
+
+    if cgutils.is_nonelike(key):
+        KEY = False
+        sort_f = sort_forwards
+        sort_b = sort_backwards
+    elif isinstance(key, types.Dispatcher):
+        KEY = True
+        sort_f = arg_sort_forwards
+        sort_b = arg_sort_backwards
+
+    def impl(lst, key=None, reverse=False):
+        if KEY is True:
+            _lst = [key(x) for x in lst]
+        else:
+            _lst = lst
+        if reverse is False or reverse == 0:
+            tmp = sort_f(_lst)
+        else:
+            tmp = sort_b(_lst)
+        if KEY is True:
+            lst[:] = [lst[i] for i in tmp]
+    return impl
+
+
+@overload(sorted)
+def ol_sorted(iterable, key=None, reverse=False):
+
+    if not isinstance(iterable, types.IterableType):
+        return False
+
+    _sort_check_key(key)
+    _sort_check_reverse(reverse)
+
+    def impl(iterable, key=None, reverse=False):
+        lst = list(iterable)
+        lst.sort(key=key, reverse=reverse)
+        return lst
+    return impl
+
+# -----------------------------------------------------------------------------
+# Implicit casting
+
+@lower_cast(types.List, types.List)
+def list_to_list(context, builder, fromty, toty, val):
+    # Casting from non-reflected to reflected
+    assert fromty.dtype == toty.dtype
+    return val
+
+# -----------------------------------------------------------------------------
+# Implementations for types.LiteralList
+# -----------------------------------------------------------------------------
+
+_banned_error = errors.TypingError("Cannot mutate a literal list")
+
+
+# Things that mutate literal lists are banned
+@overload_method(types.LiteralList, 'append')
+def literal_list_banned_append(lst, obj):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'extend')
+def literal_list_banned_extend(lst, iterable):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'insert')
+def literal_list_banned_insert(lst, index, obj):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'remove')
+def literal_list_banned_remove(lst, value):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'pop')
+def literal_list_banned_pop(lst, index=-1):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'clear')
+def literal_list_banned_clear(lst):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'sort')
+def literal_list_banned_sort(lst, key=None, reverse=False):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'reverse')
+def literal_list_banned_reverse(lst):
+    raise _banned_error
+
+
+_index_end = types.intp.maxval
+@overload_method(types.LiteralList, 'index')
+def literal_list_index(lst, x, start=0, end=_index_end):
+    # TODO: To make this work, need consts as slice for start/end so as to
+    # be able to statically analyse the bounds, then its a just loop body
+    # versioning based iteration along with enumerate to find the item
+    if isinstance(lst, types.LiteralList):
+        msg = "list.index is unsupported for literal lists"
+        raise errors.TypingError(msg)
+
+@overload_method(types.LiteralList, 'count')
+def literal_list_count(lst, x):
+    if isinstance(lst, types.LiteralList):
+        def impl(lst, x):
+            count = 0
+            for val in literal_unroll(lst):
+                if val == x:
+                    count += 1
+            return count
+        return impl
+
+@overload_method(types.LiteralList, 'copy')
+def literal_list_count(lst):
+    if isinstance(lst, types.LiteralList):
+        def impl(lst):
+            return lst # tuples are immutable, as is this, so just return it
+        return impl
+
+@overload(operator.delitem)
+def literal_list_delitem(lst, index):
+    if isinstance(lst, types.LiteralList):
+        raise _banned_error
+
+@overload(operator.setitem)
+def literal_list_setitem(lst, index, value):
+    if isinstance(lst, types.LiteralList):
+        raise errors.TypingError("Cannot mutate a literal list")
+
+@overload(operator.getitem)
+def literal_list_getitem(lst, *args):
+    if not isinstance(lst, types.LiteralList):
+        return
+    msg = ("Cannot __getitem__ on a literal list, return type cannot be "
+           "statically determined.")
+    raise errors.TypingError(msg)
+
+@overload(len)
+def literal_list_len(lst):
+    if not isinstance(lst, types.LiteralList):
+        return
+    l = lst.count
+    return lambda lst: l
+
+@overload(operator.contains)
+def literal_list_contains(lst, item):
+    if isinstance(lst, types.LiteralList):
+        def impl(lst, item):
+            for val in literal_unroll(lst):
+                if val == item:
+                    return True
+            return False
+        return impl
+
+@lower_cast(types.LiteralList, types.LiteralList)
+def literallist_to_literallist(context, builder, fromty, toty, val):
+    if len(fromty) != len(toty):
+        # Disallowed by typing layer
+        raise NotImplementedError
+
+    olditems = cgutils.unpack_tuple(builder, val, len(fromty))
+    items = [context.cast(builder, v, f, t)
+             for v, f, t in zip(olditems, fromty, toty)]
+    return context.make_tuple(builder, toty, items)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/mathimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/mathimpl.py
new file mode 100644
index 000000000..9125700e7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/mathimpl.py
@@ -0,0 +1,453 @@
+"""
+Provide math calls that uses intrinsics or libc math functions.
+"""
+
+import math
+import operator
+import sys
+import numpy as np
+
+import llvmlite.ir
+from llvmlite.ir import Constant
+
+from numba.core.imputils import Registry, impl_ret_untracked
+from numba import typeof
+from numba.core import types, utils, config, cgutils
+from numba.core.extending import overload
+from numba.core.typing import signature
+from numba.cpython.unsafe.numbers import trailing_zeros
+
+
+registry = Registry('mathimpl')
+lower = registry.lower
+
+
+# Helpers, shared with cmathimpl.
+_NP_FLT_FINFO = np.finfo(np.dtype('float32'))
+FLT_MAX = _NP_FLT_FINFO.max
+FLT_MIN = _NP_FLT_FINFO.tiny
+
+_NP_DBL_FINFO = np.finfo(np.dtype('float64'))
+DBL_MAX = _NP_DBL_FINFO.max
+DBL_MIN = _NP_DBL_FINFO.tiny
+
+FLOAT_ABS_MASK = 0x7fffffff
+FLOAT_SIGN_MASK = 0x80000000
+DOUBLE_ABS_MASK = 0x7fffffffffffffff
+DOUBLE_SIGN_MASK = 0x8000000000000000
+
+
+def is_nan(builder, val):
+    """
+    Return a condition testing whether *val* is a NaN.
+    """
+    return builder.fcmp_unordered('uno', val, val)
+
+def is_inf(builder, val):
+    """
+    Return a condition testing whether *val* is an infinite.
+    """
+    pos_inf = Constant(val.type, float("+inf"))
+    neg_inf = Constant(val.type, float("-inf"))
+    isposinf = builder.fcmp_ordered('==', val, pos_inf)
+    isneginf = builder.fcmp_ordered('==', val, neg_inf)
+    return builder.or_(isposinf, isneginf)
+
+def is_finite(builder, val):
+    """
+    Return a condition testing whether *val* is a finite.
+    """
+    # is_finite(x)  <=>  x - x != NaN
+    val_minus_val = builder.fsub(val, val)
+    return builder.fcmp_ordered('ord', val_minus_val, val_minus_val)
+
+def f64_as_int64(builder, val):
+    """
+    Bitcast a double into a 64-bit integer.
+    """
+    assert val.type == llvmlite.ir.DoubleType()
+    return builder.bitcast(val, llvmlite.ir.IntType(64))
+
+def int64_as_f64(builder, val):
+    """
+    Bitcast a 64-bit integer into a double.
+    """
+    assert val.type == llvmlite.ir.IntType(64)
+    return builder.bitcast(val, llvmlite.ir.DoubleType())
+
+def f32_as_int32(builder, val):
+    """
+    Bitcast a float into a 32-bit integer.
+    """
+    assert val.type == llvmlite.ir.FloatType()
+    return builder.bitcast(val, llvmlite.ir.IntType(32))
+
+def int32_as_f32(builder, val):
+    """
+    Bitcast a 32-bit integer into a float.
+    """
+    assert val.type == llvmlite.ir.IntType(32)
+    return builder.bitcast(val, llvmlite.ir.FloatType())
+
+def negate_real(builder, val):
+    """
+    Negate real number *val*, with proper handling of zeros.
+    """
+    # The negative zero forces LLVM to handle signed zeros properly.
+    return builder.fsub(Constant(val.type, -0.0), val)
+
+def call_fp_intrinsic(builder, name, args):
+    """
+    Call a LLVM intrinsic floating-point operation.
+    """
+    mod = builder.module
+    intr = mod.declare_intrinsic(name, [a.type for a in args])
+    return builder.call(intr, args)
+
+
+def _unary_int_input_wrapper_impl(wrapped_impl):
+    """
+    Return an implementation factory to convert the single integral input
+    argument to a float64, then defer to the *wrapped_impl*.
+    """
+    def implementer(context, builder, sig, args):
+        val, = args
+        input_type = sig.args[0]
+        fpval = context.cast(builder, val, input_type, types.float64)
+        inner_sig = signature(types.float64, types.float64)
+        res = wrapped_impl(context, builder, inner_sig, (fpval,))
+        return context.cast(builder, res, types.float64, sig.return_type)
+
+    return implementer
+
+def unary_math_int_impl(fn, float_impl):
+    impl = _unary_int_input_wrapper_impl(float_impl)
+    lower(fn, types.Integer)(impl)
+
+def unary_math_intr(fn, intrcode):
+    """
+    Implement the math function *fn* using the LLVM intrinsic *intrcode*.
+    """
+    @lower(fn, types.Float)
+    def float_impl(context, builder, sig, args):
+        res = call_fp_intrinsic(builder, intrcode, args)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+
+    unary_math_int_impl(fn, float_impl)
+    return float_impl
+
+def unary_math_extern(fn, f32extern, f64extern, int_restype=False):
+    """
+    Register implementations of Python function *fn* using the
+    external function named *f32extern* and *f64extern* (for float32
+    and float64 inputs, respectively).
+    If *int_restype* is true, then the function's return value should be
+    integral, otherwise floating-point.
+    """
+    f_restype = types.int64 if int_restype else None
+
+    def float_impl(context, builder, sig, args):
+        """
+        Implement *fn* for a types.Float input.
+        """
+        [val] = args
+        mod = builder.module
+        input_type = sig.args[0]
+        lty = context.get_value_type(input_type)
+        func_name = {
+            types.float32: f32extern,
+            types.float64: f64extern,
+            }[input_type]
+        fnty = llvmlite.ir.FunctionType(lty, [lty])
+        fn = cgutils.insert_pure_function(builder.module, fnty, name=func_name)
+        res = builder.call(fn, (val,))
+        res = context.cast(builder, res, input_type, sig.return_type)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+
+    lower(fn, types.Float)(float_impl)
+
+    # Implement wrapper for integer inputs
+    unary_math_int_impl(fn, float_impl)
+
+    return float_impl
+
+
+unary_math_intr(math.fabs, 'llvm.fabs')
+exp_impl = unary_math_intr(math.exp, 'llvm.exp')
+log_impl = unary_math_intr(math.log, 'llvm.log')
+log10_impl = unary_math_intr(math.log10, 'llvm.log10')
+sin_impl = unary_math_intr(math.sin, 'llvm.sin')
+cos_impl = unary_math_intr(math.cos, 'llvm.cos')
+
+log1p_impl = unary_math_extern(math.log1p, "log1pf", "log1p")
+expm1_impl = unary_math_extern(math.expm1, "expm1f", "expm1")
+erf_impl = unary_math_extern(math.erf, "erff", "erf")
+erfc_impl = unary_math_extern(math.erfc, "erfcf", "erfc")
+
+tan_impl = unary_math_extern(math.tan, "tanf", "tan")
+asin_impl = unary_math_extern(math.asin, "asinf", "asin")
+acos_impl = unary_math_extern(math.acos, "acosf", "acos")
+atan_impl = unary_math_extern(math.atan, "atanf", "atan")
+
+asinh_impl = unary_math_extern(math.asinh, "asinhf", "asinh")
+acosh_impl = unary_math_extern(math.acosh, "acoshf", "acosh")
+atanh_impl = unary_math_extern(math.atanh, "atanhf", "atanh")
+sinh_impl = unary_math_extern(math.sinh, "sinhf", "sinh")
+cosh_impl = unary_math_extern(math.cosh, "coshf", "cosh")
+tanh_impl = unary_math_extern(math.tanh, "tanhf", "tanh")
+
+log2_impl = unary_math_extern(math.log2, "log2f", "log2")
+ceil_impl = unary_math_extern(math.ceil, "ceilf", "ceil", True)
+floor_impl = unary_math_extern(math.floor, "floorf", "floor", True)
+
+gamma_impl = unary_math_extern(math.gamma, "numba_gammaf", "numba_gamma") # work-around
+sqrt_impl = unary_math_extern(math.sqrt, "sqrtf", "sqrt")
+trunc_impl = unary_math_extern(math.trunc, "truncf", "trunc", True)
+lgamma_impl = unary_math_extern(math.lgamma, "lgammaf", "lgamma")
+
+
+@lower(math.isnan, types.Float)
+def isnan_float_impl(context, builder, sig, args):
+    [val] = args
+    res = is_nan(builder, val)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower(math.isnan, types.Integer)
+def isnan_int_impl(context, builder, sig, args):
+    res = cgutils.false_bit
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(math.isinf, types.Float)
+def isinf_float_impl(context, builder, sig, args):
+    [val] = args
+    res = is_inf(builder, val)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower(math.isinf, types.Integer)
+def isinf_int_impl(context, builder, sig, args):
+    res = cgutils.false_bit
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(math.isfinite, types.Float)
+def isfinite_float_impl(context, builder, sig, args):
+    [val] = args
+    res = is_finite(builder, val)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(math.isfinite, types.Integer)
+def isfinite_int_impl(context, builder, sig, args):
+    res = cgutils.true_bit
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(math.copysign, types.Float, types.Float)
+def copysign_float_impl(context, builder, sig, args):
+    lty = args[0].type
+    mod = builder.module
+    fn = cgutils.get_or_insert_function(mod, llvmlite.ir.FunctionType(lty, (lty, lty)),
+                                        'llvm.copysign.%s' % lty.intrinsic_name)
+    res = builder.call(fn, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+# -----------------------------------------------------------------------------
+
+
+@lower(math.frexp, types.Float)
+def frexp_impl(context, builder, sig, args):
+    val, = args
+    fltty = context.get_data_type(sig.args[0])
+    intty = context.get_data_type(sig.return_type[1])
+    expptr = cgutils.alloca_once(builder, intty, name='exp')
+    fnty = llvmlite.ir.FunctionType(fltty, (fltty, llvmlite.ir.PointerType(intty)))
+    fname = {
+        "float": "numba_frexpf",
+        "double": "numba_frexp",
+        }[str(fltty)]
+    fn = cgutils.get_or_insert_function(builder.module, fnty, fname)
+    res = builder.call(fn, (val, expptr))
+    res = cgutils.make_anonymous_struct(builder, (res, builder.load(expptr)))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(math.ldexp, types.Float, types.intc)
+def ldexp_impl(context, builder, sig, args):
+    val, exp = args
+    fltty, intty = map(context.get_data_type, sig.args)
+    fnty = llvmlite.ir.FunctionType(fltty, (fltty, intty))
+    fname = {
+        "float": "numba_ldexpf",
+        "double": "numba_ldexp",
+        }[str(fltty)]
+    fn = cgutils.insert_pure_function(builder.module, fnty, name=fname)
+    res = builder.call(fn, (val, exp))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+# -----------------------------------------------------------------------------
+
+
+@lower(math.atan2, types.int64, types.int64)
+def atan2_s64_impl(context, builder, sig, args):
+    [y, x] = args
+    y = builder.sitofp(y, llvmlite.ir.DoubleType())
+    x = builder.sitofp(x, llvmlite.ir.DoubleType())
+    fsig = signature(types.float64, types.float64, types.float64)
+    return atan2_float_impl(context, builder, fsig, (y, x))
+
+@lower(math.atan2, types.uint64, types.uint64)
+def atan2_u64_impl(context, builder, sig, args):
+    [y, x] = args
+    y = builder.uitofp(y, llvmlite.ir.DoubleType())
+    x = builder.uitofp(x, llvmlite.ir.DoubleType())
+    fsig = signature(types.float64, types.float64, types.float64)
+    return atan2_float_impl(context, builder, fsig, (y, x))
+
+@lower(math.atan2, types.Float, types.Float)
+def atan2_float_impl(context, builder, sig, args):
+    assert len(args) == 2
+    mod = builder.module
+    ty = sig.args[0]
+    lty = context.get_value_type(ty)
+    func_name = {
+        types.float32: "atan2f",
+        types.float64: "atan2"
+        }[ty]
+    fnty = llvmlite.ir.FunctionType(lty, (lty, lty))
+    fn = cgutils.insert_pure_function(builder.module, fnty, name=func_name)
+    res = builder.call(fn, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+# -----------------------------------------------------------------------------
+
+
+@lower(math.hypot, types.int64, types.int64)
+def hypot_s64_impl(context, builder, sig, args):
+    [x, y] = args
+    y = builder.sitofp(y, llvmlite.ir.DoubleType())
+    x = builder.sitofp(x, llvmlite.ir.DoubleType())
+    fsig = signature(types.float64, types.float64, types.float64)
+    res = hypot_float_impl(context, builder, fsig, (x, y))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(math.hypot, types.uint64, types.uint64)
+def hypot_u64_impl(context, builder, sig, args):
+    [x, y] = args
+    y = builder.sitofp(y, llvmlite.ir.DoubleType())
+    x = builder.sitofp(x, llvmlite.ir.DoubleType())
+    fsig = signature(types.float64, types.float64, types.float64)
+    res = hypot_float_impl(context, builder, fsig, (x, y))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(math.hypot, types.Float, types.Float)
+def hypot_float_impl(context, builder, sig, args):
+    xty, yty = sig.args
+    assert xty == yty == sig.return_type
+    x, y = args
+
+    # Windows has alternate names for hypot/hypotf, see
+    # https://msdn.microsoft.com/fr-fr/library/a9yb3dbt%28v=vs.80%29.aspx
+    fname = {
+        types.float32: "_hypotf" if sys.platform == 'win32' else "hypotf",
+        types.float64: "_hypot" if sys.platform == 'win32' else "hypot",
+    }[xty]
+    plat_hypot = types.ExternalFunction(fname, sig)
+
+    if sys.platform == 'win32' and config.MACHINE_BITS == 32:
+        inf = xty(float('inf'))
+
+        def hypot_impl(x, y):
+            if math.isinf(x) or math.isinf(y):
+                return inf
+            return plat_hypot(x, y)
+    else:
+        def hypot_impl(x, y):
+            return plat_hypot(x, y)
+
+    res = context.compile_internal(builder, hypot_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+# -----------------------------------------------------------------------------
+
+@lower(math.radians, types.Float)
+def radians_float_impl(context, builder, sig, args):
+    [x] = args
+    coef = context.get_constant(sig.return_type, math.pi / 180)
+    res = builder.fmul(x, coef)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+unary_math_int_impl(math.radians, radians_float_impl)
+
+# -----------------------------------------------------------------------------
+
+@lower(math.degrees, types.Float)
+def degrees_float_impl(context, builder, sig, args):
+    [x] = args
+    coef = context.get_constant(sig.return_type, 180 / math.pi)
+    res = builder.fmul(x, coef)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+unary_math_int_impl(math.degrees, degrees_float_impl)
+
+# -----------------------------------------------------------------------------
+
+@lower(math.pow, types.Float, types.Float)
+@lower(math.pow, types.Float, types.Integer)
+def pow_impl(context, builder, sig, args):
+    impl = context.get_function(operator.pow, sig)
+    return impl(builder, args)
+
+# -----------------------------------------------------------------------------
+
+
+def _unsigned(T):
+    """Convert integer to unsigned integer of equivalent width."""
+    pass
+
+@overload(_unsigned)
+def _unsigned_impl(T):
+    if T in types.unsigned_domain:
+        return lambda T: T
+    elif T in types.signed_domain:
+        newT = getattr(types, 'uint{}'.format(T.bitwidth))
+        return lambda T: newT(T)
+
+
+def gcd_impl(context, builder, sig, args):
+    xty, yty = sig.args
+    assert xty == yty == sig.return_type
+    x, y = args
+
+    def gcd(a, b):
+        """
+        Stein's algorithm, heavily cribbed from Julia implementation.
+        """
+        T = type(a)
+        if a == 0: return abs(b)
+        if b == 0: return abs(a)
+        za = trailing_zeros(a)
+        zb = trailing_zeros(b)
+        k = min(za, zb)
+        # Uses np.*_shift instead of operators due to return types
+        u = _unsigned(abs(np.right_shift(a, za)))
+        v = _unsigned(abs(np.right_shift(b, zb)))
+        while u != v:
+            if u > v:
+                u, v = v, u
+            v -= u
+            v = np.right_shift(v, trailing_zeros(v))
+        r = np.left_shift(T(u), k)
+        return r
+
+    res = context.compile_internal(builder, gcd, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+lower(math.gcd, types.Integer, types.Integer)(gcd_impl)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/numbers.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/numbers.py
new file mode 100644
index 000000000..d01152359
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/numbers.py
@@ -0,0 +1,1363 @@
+import math
+import numbers
+
+import numpy as np
+import operator
+
+from llvmlite import ir
+from llvmlite.ir import Constant
+
+from numba.core.imputils import (lower_builtin, lower_getattr,
+                                    lower_getattr_generic, lower_cast,
+                                    lower_constant, impl_ret_borrowed,
+                                    impl_ret_untracked)
+from numba.core import typing, types, utils, errors, cgutils, optional
+from numba.core.extending import intrinsic, overload_method
+from numba.cpython.unsafe.numbers import viewer
+
+def _int_arith_flags(rettype):
+    """
+    Return the modifier flags for integer arithmetic.
+    """
+    if rettype.signed:
+        # Ignore the effects of signed overflow.  This is important for
+        # optimization of some indexing operations.  For example
+        # array[i+1] could see `i+1` trigger a signed overflow and
+        # give a negative number.  With Python's indexing, a negative
+        # index is treated differently: its resolution has a runtime cost.
+        # Telling LLVM to ignore signed overflows allows it to optimize
+        # away the check for a negative `i+1` if it knows `i` is positive.
+        return ['nsw']
+    else:
+        return []
+
+
+def int_add_impl(context, builder, sig, args):
+    [va, vb] = args
+    [ta, tb] = sig.args
+    a = context.cast(builder, va, ta, sig.return_type)
+    b = context.cast(builder, vb, tb, sig.return_type)
+    res = builder.add(a, b, flags=_int_arith_flags(sig.return_type))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_sub_impl(context, builder, sig, args):
+    [va, vb] = args
+    [ta, tb] = sig.args
+    a = context.cast(builder, va, ta, sig.return_type)
+    b = context.cast(builder, vb, tb, sig.return_type)
+    res = builder.sub(a, b, flags=_int_arith_flags(sig.return_type))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_mul_impl(context, builder, sig, args):
+    [va, vb] = args
+    [ta, tb] = sig.args
+    a = context.cast(builder, va, ta, sig.return_type)
+    b = context.cast(builder, vb, tb, sig.return_type)
+    res = builder.mul(a, b, flags=_int_arith_flags(sig.return_type))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_divmod_signed(context, builder, ty, x, y):
+    """
+    Reference Objects/intobject.c
+    xdivy = x / y;
+    xmody = (long)(x - (unsigned long)xdivy * y);
+    /* If the signs of x and y differ, and the remainder is non-0,
+     * C89 doesn't define whether xdivy is now the floor or the
+     * ceiling of the infinitely precise quotient.  We want the floor,
+     * and we have it iff the remainder's sign matches y's.
+     */
+    if (xmody && ((y ^ xmody) < 0) /* i.e. and signs differ */) {
+        xmody += y;
+        --xdivy;
+        assert(xmody && ((y ^ xmody) >= 0));
+    }
+    *p_xdivy = xdivy;
+    *p_xmody = xmody;
+    """
+    assert x.type == y.type
+
+    ZERO = y.type(0)
+    ONE = y.type(1)
+
+    # NOTE: On x86 at least, dividing the lowest representable integer
+    # (e.g. 0x80000000 for int32) by -1 causes a SIFGPE (division overflow),
+    # causing the process to crash.
+    # We return 0, 0 instead (more or less like Numpy).
+
+    resdiv = cgutils.alloca_once_value(builder, ZERO)
+    resmod = cgutils.alloca_once_value(builder, ZERO)
+
+    is_overflow = builder.and_(
+        builder.icmp_signed('==', x, x.type(ty.minval)),
+        builder.icmp_signed('==', y, y.type(-1)))
+
+    with builder.if_then(builder.not_(is_overflow), likely=True):
+        # Note LLVM will optimize this to a single divmod instruction,
+        # if available on the target CPU (e.g. x86).
+        xdivy = builder.sdiv(x, y)
+        xmody = builder.srem(x, y)
+
+        y_xor_xmody_ltz = builder.icmp_signed('<', builder.xor(y, xmody), ZERO)
+        xmody_istrue = builder.icmp_signed('!=', xmody, ZERO)
+        cond = builder.and_(xmody_istrue, y_xor_xmody_ltz)
+
+        with builder.if_else(cond) as (if_different_signs, if_same_signs):
+            with if_same_signs:
+                builder.store(xdivy, resdiv)
+                builder.store(xmody, resmod)
+
+            with if_different_signs:
+                builder.store(builder.sub(xdivy, ONE), resdiv)
+                builder.store(builder.add(xmody, y), resmod)
+
+    return builder.load(resdiv), builder.load(resmod)
+
+
+def int_divmod(context, builder, ty, x, y):
+    """
+    Integer divmod(x, y).  The caller must ensure that y != 0.
+    """
+    if ty.signed:
+        return int_divmod_signed(context, builder, ty, x, y)
+    else:
+        return builder.udiv(x, y), builder.urem(x, y)
+
+
+def _int_divmod_impl(context, builder, sig, args, zerodiv_message):
+    va, vb = args
+    ta, tb = sig.args
+
+    ty = sig.return_type
+    if isinstance(ty, types.UniTuple):
+        ty = ty.dtype
+    a = context.cast(builder, va, ta, ty)
+    b = context.cast(builder, vb, tb, ty)
+    quot = cgutils.alloca_once(builder, a.type, name="quot")
+    rem = cgutils.alloca_once(builder, a.type, name="rem")
+
+    with builder.if_else(cgutils.is_scalar_zero(builder, b), likely=False
+                         ) as (if_zero, if_non_zero):
+        with if_zero:
+            if not context.error_model.fp_zero_division(
+                builder, (zerodiv_message,)):
+                # No exception raised => return 0
+                # XXX We should also set the FPU exception status, but
+                # there's no easy way to do that from LLVM.
+                builder.store(b, quot)
+                builder.store(b, rem)
+        with if_non_zero:
+            q, r = int_divmod(context, builder, ty, a, b)
+            builder.store(q, quot)
+            builder.store(r, rem)
+
+    return quot, rem
+
+
+@lower_builtin(divmod, types.Integer, types.Integer)
+def int_divmod_impl(context, builder, sig, args):
+    quot, rem = _int_divmod_impl(context, builder, sig, args,
+                                 "integer divmod by zero")
+
+    return cgutils.pack_array(builder,
+                              (builder.load(quot), builder.load(rem)))
+
+
+@lower_builtin(operator.floordiv, types.Integer, types.Integer)
+@lower_builtin(operator.ifloordiv, types.Integer, types.Integer)
+def int_floordiv_impl(context, builder, sig, args):
+    quot, rem = _int_divmod_impl(context, builder, sig, args,
+                                 "integer division by zero")
+    return builder.load(quot)
+
+
+@lower_builtin(operator.truediv, types.Integer, types.Integer)
+@lower_builtin(operator.itruediv, types.Integer, types.Integer)
+def int_truediv_impl(context, builder, sig, args):
+    [va, vb] = args
+    [ta, tb] = sig.args
+    a = context.cast(builder, va, ta, sig.return_type)
+    b = context.cast(builder, vb, tb, sig.return_type)
+    with cgutils.if_zero(builder, b):
+        context.error_model.fp_zero_division(builder, ("division by zero",))
+    res = builder.fdiv(a, b)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.mod, types.Integer, types.Integer)
+@lower_builtin(operator.imod, types.Integer, types.Integer)
+def int_rem_impl(context, builder, sig, args):
+    quot, rem = _int_divmod_impl(context, builder, sig, args,
+                                 "integer modulo by zero")
+    return builder.load(rem)
+
+
+def _get_power_zerodiv_return(context, return_type):
+    if (isinstance(return_type, types.Integer)
+        and not context.error_model.raise_on_fp_zero_division):
+        # If not raising, return 0x8000... when computing 0 ** <negative number>
+        return -1 << (return_type.bitwidth - 1)
+    else:
+        return False
+
+
+def int_power_impl(context, builder, sig, args):
+    """
+    a ^ b, where a is an integer or real, and b an integer
+    """
+    is_integer = isinstance(sig.args[0], types.Integer)
+    tp = sig.return_type
+    zerodiv_return = _get_power_zerodiv_return(context, tp)
+
+    def int_power(a, b):
+        # Ensure computations are done with a large enough width
+        r = tp(1)
+        a = tp(a)
+        if b < 0:
+            invert = True
+            exp = -b
+            if exp < 0:
+                raise OverflowError
+            if is_integer:
+                if a == 0:
+                    if zerodiv_return:
+                        return zerodiv_return
+                    else:
+                        raise ZeroDivisionError("0 cannot be raised to a negative power")
+                if a != 1 and a != -1:
+                    return 0
+        else:
+            invert = False
+            exp = b
+        if exp > 0x10000:
+            # Optimization cutoff: fallback on the generic algorithm
+            return math.pow(a, float(b))
+        while exp != 0:
+            if exp & 1:
+                r *= a
+            exp >>= 1
+            a *= a
+
+        return 1.0 / r if invert else r
+
+    res = context.compile_internal(builder, int_power, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.pow, types.Integer, types.IntegerLiteral)
+@lower_builtin(operator.ipow, types.Integer, types.IntegerLiteral)
+@lower_builtin(operator.pow, types.Float, types.IntegerLiteral)
+@lower_builtin(operator.ipow, types.Float, types.IntegerLiteral)
+def static_power_impl(context, builder, sig, args):
+    """
+    a ^ b, where a is an integer or real, and b a constant integer
+    """
+    exp = sig.args[1].value
+    if not isinstance(exp, numbers.Integral):
+        raise NotImplementedError
+    if abs(exp) > 0x10000:
+        # Optimization cutoff: fallback on the generic algorithm above
+        raise NotImplementedError
+    invert = exp < 0
+    exp = abs(exp)
+
+    tp = sig.return_type
+    is_integer = isinstance(tp, types.Integer)
+    zerodiv_return = _get_power_zerodiv_return(context, tp)
+
+    val = context.cast(builder, args[0], sig.args[0], tp)
+    lty = val.type
+
+    def mul(a, b):
+        if is_integer:
+            return builder.mul(a, b)
+        else:
+            return builder.fmul(a, b)
+
+    # Unroll the exponentiation loop
+    res = lty(1)
+    a = val
+    while exp != 0:
+        if exp & 1:
+            res = mul(res, val)
+        exp >>= 1
+        val = mul(val, val)
+
+    if invert:
+        # If the exponent was negative, fix the result by inverting it
+        if is_integer:
+            # Integer inversion
+            def invert_impl(a):
+                if a == 0:
+                    if zerodiv_return:
+                        return zerodiv_return
+                    else:
+                        raise ZeroDivisionError("0 cannot be raised to a negative power")
+                if a != 1 and a != -1:
+                    return 0
+                else:
+                    return a
+
+        else:
+            # Real inversion
+            def invert_impl(a):
+                return 1.0 / a
+
+        res = context.compile_internal(builder, invert_impl,
+                                       typing.signature(tp, tp), (res,))
+
+    return res
+
+
+def int_slt_impl(context, builder, sig, args):
+    res = builder.icmp_signed('<', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_sle_impl(context, builder, sig, args):
+    res = builder.icmp_signed('<=', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_sgt_impl(context, builder, sig, args):
+    res = builder.icmp_signed('>', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_sge_impl(context, builder, sig, args):
+    res = builder.icmp_signed('>=', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_ult_impl(context, builder, sig, args):
+    res = builder.icmp_unsigned('<', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_ule_impl(context, builder, sig, args):
+    res = builder.icmp_unsigned('<=', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_ugt_impl(context, builder, sig, args):
+    res = builder.icmp_unsigned('>', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_uge_impl(context, builder, sig, args):
+    res = builder.icmp_unsigned('>=', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_eq_impl(context, builder, sig, args):
+    res = builder.icmp_unsigned('==', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_ne_impl(context, builder, sig, args):
+    res = builder.icmp_unsigned('!=', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_abs_impl(context, builder, sig, args):
+    [x] = args
+    ZERO = Constant(x.type, None)
+    ltz = builder.icmp_signed('<', x, ZERO)
+    negated = builder.neg(x)
+    res = builder.select(ltz, negated, x)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def uint_abs_impl(context, builder, sig, args):
+    [x] = args
+    return impl_ret_untracked(context, builder, sig.return_type, x)
+
+
+def int_shl_impl(context, builder, sig, args):
+    [valty, amtty] = sig.args
+    [val, amt] = args
+    val = context.cast(builder, val, valty, sig.return_type)
+    amt = context.cast(builder, amt, amtty, sig.return_type)
+    res = builder.shl(val, amt)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_shr_impl(context, builder, sig, args):
+    [valty, amtty] = sig.args
+    [val, amt] = args
+    val = context.cast(builder, val, valty, sig.return_type)
+    amt = context.cast(builder, amt, amtty, sig.return_type)
+    if sig.return_type.signed:
+        res = builder.ashr(val, amt)
+    else:
+        res = builder.lshr(val, amt)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_and_impl(context, builder, sig, args):
+    [at, bt] = sig.args
+    [av, bv] = args
+    cav = context.cast(builder, av, at, sig.return_type)
+    cbc = context.cast(builder, bv, bt, sig.return_type)
+    res = builder.and_(cav, cbc)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_or_impl(context, builder, sig, args):
+    [at, bt] = sig.args
+    [av, bv] = args
+    cav = context.cast(builder, av, at, sig.return_type)
+    cbc = context.cast(builder, bv, bt, sig.return_type)
+    res = builder.or_(cav, cbc)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_xor_impl(context, builder, sig, args):
+    [at, bt] = sig.args
+    [av, bv] = args
+    cav = context.cast(builder, av, at, sig.return_type)
+    cbc = context.cast(builder, bv, bt, sig.return_type)
+    res = builder.xor(cav, cbc)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_negate_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [val] = args
+    # Negate before upcasting, for unsigned numbers
+    res = builder.neg(val)
+    res = context.cast(builder, res, typ, sig.return_type)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_positive_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [val] = args
+    res = context.cast(builder, val, typ, sig.return_type)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_invert_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [val] = args
+    # Invert before upcasting, for unsigned numbers
+    res = builder.xor(val, Constant(val.type, int('1' * val.type.width, 2)))
+    res = context.cast(builder, res, typ, sig.return_type)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def int_sign_impl(context, builder, sig, args):
+    """
+    np.sign(int)
+    """
+    [x] = args
+    POS = Constant(x.type, 1)
+    NEG = Constant(x.type, -1)
+    ZERO = Constant(x.type, 0)
+
+    cmp_zero = builder.icmp_unsigned('==', x, ZERO)
+    cmp_pos = builder.icmp_signed('>', x, ZERO)
+
+    presult = cgutils.alloca_once(builder, x.type)
+
+    bb_zero = builder.append_basic_block(".zero")
+    bb_postest = builder.append_basic_block(".postest")
+    bb_pos = builder.append_basic_block(".pos")
+    bb_neg = builder.append_basic_block(".neg")
+    bb_exit = builder.append_basic_block(".exit")
+
+    builder.cbranch(cmp_zero, bb_zero, bb_postest)
+
+    with builder.goto_block(bb_zero):
+        builder.store(ZERO, presult)
+        builder.branch(bb_exit)
+
+    with builder.goto_block(bb_postest):
+        builder.cbranch(cmp_pos, bb_pos, bb_neg)
+
+    with builder.goto_block(bb_pos):
+        builder.store(POS, presult)
+        builder.branch(bb_exit)
+
+    with builder.goto_block(bb_neg):
+        builder.store(NEG, presult)
+        builder.branch(bb_exit)
+
+    builder.position_at_end(bb_exit)
+    res = builder.load(presult)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def bool_negate_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [val] = args
+    res = context.cast(builder, val, typ, sig.return_type)
+    res = builder.neg(res)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def bool_unary_positive_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [val] = args
+    res = context.cast(builder, val, typ, sig.return_type)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+lower_builtin(operator.eq, types.boolean, types.boolean)(int_eq_impl)
+lower_builtin(operator.ne, types.boolean, types.boolean)(int_ne_impl)
+lower_builtin(operator.lt, types.boolean, types.boolean)(int_ult_impl)
+lower_builtin(operator.le, types.boolean, types.boolean)(int_ule_impl)
+lower_builtin(operator.gt, types.boolean, types.boolean)(int_ugt_impl)
+lower_builtin(operator.ge, types.boolean, types.boolean)(int_uge_impl)
+lower_builtin(operator.neg, types.boolean)(bool_negate_impl)
+lower_builtin(operator.pos, types.boolean)(bool_unary_positive_impl)
+
+
+def _implement_integer_operators():
+    ty = types.Integer
+
+    lower_builtin(operator.add, ty, ty)(int_add_impl)
+    lower_builtin(operator.iadd, ty, ty)(int_add_impl)
+    lower_builtin(operator.sub, ty, ty)(int_sub_impl)
+    lower_builtin(operator.isub, ty, ty)(int_sub_impl)
+    lower_builtin(operator.mul, ty, ty)(int_mul_impl)
+    lower_builtin(operator.imul, ty, ty)(int_mul_impl)
+    lower_builtin(operator.eq, ty, ty)(int_eq_impl)
+    lower_builtin(operator.ne, ty, ty)(int_ne_impl)
+
+    lower_builtin(operator.lshift, ty, ty)(int_shl_impl)
+    lower_builtin(operator.ilshift, ty, ty)(int_shl_impl)
+    lower_builtin(operator.rshift, ty, ty)(int_shr_impl)
+    lower_builtin(operator.irshift, ty, ty)(int_shr_impl)
+
+    lower_builtin(operator.neg, ty)(int_negate_impl)
+    lower_builtin(operator.pos, ty)(int_positive_impl)
+
+    lower_builtin(operator.pow, ty, ty)(int_power_impl)
+    lower_builtin(operator.ipow, ty, ty)(int_power_impl)
+    lower_builtin(pow, ty, ty)(int_power_impl)
+
+    for ty in types.unsigned_domain:
+        lower_builtin(operator.lt, ty, ty)(int_ult_impl)
+        lower_builtin(operator.le, ty, ty)(int_ule_impl)
+        lower_builtin(operator.gt, ty, ty)(int_ugt_impl)
+        lower_builtin(operator.ge, ty, ty)(int_uge_impl)
+        lower_builtin(operator.pow, types.Float, ty)(int_power_impl)
+        lower_builtin(operator.ipow, types.Float, ty)(int_power_impl)
+        lower_builtin(pow, types.Float, ty)(int_power_impl)
+        lower_builtin(abs, ty)(uint_abs_impl)
+
+    lower_builtin(operator.lt, types.IntegerLiteral, types.IntegerLiteral)(int_slt_impl)
+    lower_builtin(operator.gt, types.IntegerLiteral, types.IntegerLiteral)(int_slt_impl)
+    lower_builtin(operator.le, types.IntegerLiteral, types.IntegerLiteral)(int_slt_impl)
+    lower_builtin(operator.ge, types.IntegerLiteral, types.IntegerLiteral)(int_slt_impl)
+    for ty in types.signed_domain:
+        lower_builtin(operator.lt, ty, ty)(int_slt_impl)
+        lower_builtin(operator.le, ty, ty)(int_sle_impl)
+        lower_builtin(operator.gt, ty, ty)(int_sgt_impl)
+        lower_builtin(operator.ge, ty, ty)(int_sge_impl)
+        lower_builtin(operator.pow, types.Float, ty)(int_power_impl)
+        lower_builtin(operator.ipow, types.Float, ty)(int_power_impl)
+        lower_builtin(pow, types.Float, ty)(int_power_impl)
+        lower_builtin(abs, ty)(int_abs_impl)
+
+def _implement_bitwise_operators():
+    for ty in (types.Boolean, types.Integer):
+        lower_builtin(operator.and_, ty, ty)(int_and_impl)
+        lower_builtin(operator.iand, ty, ty)(int_and_impl)
+        lower_builtin(operator.or_, ty, ty)(int_or_impl)
+        lower_builtin(operator.ior, ty, ty)(int_or_impl)
+        lower_builtin(operator.xor, ty, ty)(int_xor_impl)
+        lower_builtin(operator.ixor, ty, ty)(int_xor_impl)
+
+        lower_builtin(operator.invert, ty)(int_invert_impl)
+
+_implement_integer_operators()
+
+_implement_bitwise_operators()
+
+
+def real_add_impl(context, builder, sig, args):
+    res = builder.fadd(*args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_sub_impl(context, builder, sig, args):
+    res = builder.fsub(*args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_mul_impl(context, builder, sig, args):
+    res = builder.fmul(*args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_div_impl(context, builder, sig, args):
+    with cgutils.if_zero(builder, args[1]):
+        context.error_model.fp_zero_division(builder, ("division by zero",))
+    res = builder.fdiv(*args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_divmod(context, builder, x, y):
+    assert x.type == y.type
+    floatty = x.type
+
+    module = builder.module
+    fname = context.mangler(".numba.python.rem", [x.type])
+    fnty = ir.FunctionType(floatty, (floatty, floatty, ir.PointerType(floatty)))
+    fn = cgutils.get_or_insert_function(module, fnty, fname)
+
+    if fn.is_declaration:
+        fn.linkage = 'linkonce_odr'
+        fnbuilder = ir.IRBuilder(fn.append_basic_block('entry'))
+        fx, fy, pmod = fn.args
+        div, mod = real_divmod_func_body(context, fnbuilder, fx, fy)
+        fnbuilder.store(mod, pmod)
+        fnbuilder.ret(div)
+
+    pmod = cgutils.alloca_once(builder, floatty)
+    quotient = builder.call(fn, (x, y, pmod))
+    return quotient, builder.load(pmod)
+
+
+def real_divmod_func_body(context, builder, vx, wx):
+    # Reference Objects/floatobject.c
+    #
+    # float_divmod(PyObject *v, PyObject *w)
+    # {
+    #     double vx, wx;
+    #     double div, mod, floordiv;
+    #     CONVERT_TO_DOUBLE(v, vx);
+    #     CONVERT_TO_DOUBLE(w, wx);
+    #     mod = fmod(vx, wx);
+    #     /* fmod is typically exact, so vx-mod is *mathematically* an
+    #        exact multiple of wx.  But this is fp arithmetic, and fp
+    #        vx - mod is an approximation; the result is that div may
+    #        not be an exact integral value after the division, although
+    #        it will always be very close to one.
+    #     */
+    #     div = (vx - mod) / wx;
+    #     if (mod) {
+    #         /* ensure the remainder has the same sign as the denominator */
+    #         if ((wx < 0) != (mod < 0)) {
+    #             mod += wx;
+    #             div -= 1.0;
+    #         }
+    #     }
+    #     else {
+    #         /* the remainder is zero, and in the presence of signed zeroes
+    #            fmod returns different results across platforms; ensure
+    #            it has the same sign as the denominator; we'd like to do
+    #            "mod = wx * 0.0", but that may get optimized away */
+    #         mod *= mod;  /* hide "mod = +0" from optimizer */
+    #         if (wx < 0.0)
+    #             mod = -mod;
+    #     }
+    #     /* snap quotient to nearest integral value */
+    #     if (div) {
+    #         floordiv = floor(div);
+    #         if (div - floordiv > 0.5)
+    #             floordiv += 1.0;
+    #     }
+    #     else {
+    #         /* div is zero - get the same sign as the true quotient */
+    #         div *= div;             /* hide "div = +0" from optimizers */
+    #         floordiv = div * vx / wx; /* zero w/ sign of vx/wx */
+    #     }
+    #     return Py_BuildValue("(dd)", floordiv, mod);
+    # }
+    pmod = cgutils.alloca_once(builder, vx.type)
+    pdiv = cgutils.alloca_once(builder, vx.type)
+    pfloordiv = cgutils.alloca_once(builder, vx.type)
+
+    mod = builder.frem(vx, wx)
+    div = builder.fdiv(builder.fsub(vx, mod), wx)
+
+    builder.store(mod, pmod)
+    builder.store(div, pdiv)
+
+    # Note the use of negative zero for proper negating with `ZERO - x`
+    ZERO = vx.type(0.0)
+    NZERO = vx.type(-0.0)
+    ONE = vx.type(1.0)
+    mod_istrue = builder.fcmp_unordered('!=', mod, ZERO)
+    wx_ltz = builder.fcmp_ordered('<', wx, ZERO)
+    mod_ltz = builder.fcmp_ordered('<', mod, ZERO)
+
+    with builder.if_else(mod_istrue, likely=True) as (if_nonzero_mod, if_zero_mod):
+        with if_nonzero_mod:
+            # `mod` is non-zero or NaN
+            # Ensure the remainder has the same sign as the denominator
+            wx_ltz_ne_mod_ltz = builder.icmp_unsigned('!=', wx_ltz, mod_ltz)
+
+            with builder.if_then(wx_ltz_ne_mod_ltz):
+                builder.store(builder.fsub(div, ONE), pdiv)
+                builder.store(builder.fadd(mod, wx), pmod)
+
+        with if_zero_mod:
+            # `mod` is zero, select the proper sign depending on
+            # the denominator's sign
+            mod = builder.select(wx_ltz, NZERO, ZERO)
+            builder.store(mod, pmod)
+
+    del mod, div
+
+    div = builder.load(pdiv)
+    div_istrue = builder.fcmp_ordered('!=', div, ZERO)
+
+    with builder.if_then(div_istrue):
+        realtypemap = {'float': types.float32,
+                       'double': types.float64}
+        realtype = realtypemap[str(wx.type)]
+        floorfn = context.get_function(math.floor,
+                                       typing.signature(realtype, realtype))
+        floordiv = floorfn(builder, [div])
+        floordivdiff = builder.fsub(div, floordiv)
+        floordivincr = builder.fadd(floordiv, ONE)
+        HALF = Constant(wx.type, 0.5)
+        pred = builder.fcmp_ordered('>', floordivdiff, HALF)
+        floordiv = builder.select(pred, floordivincr, floordiv)
+        builder.store(floordiv, pfloordiv)
+
+    with cgutils.ifnot(builder, div_istrue):
+        div = builder.fmul(div, div)
+        builder.store(div, pdiv)
+        floordiv = builder.fdiv(builder.fmul(div, vx), wx)
+        builder.store(floordiv, pfloordiv)
+
+    return builder.load(pfloordiv), builder.load(pmod)
+
+
+@lower_builtin(divmod, types.Float, types.Float)
+def real_divmod_impl(context, builder, sig, args, loc=None):
+    x, y = args
+    quot = cgutils.alloca_once(builder, x.type, name="quot")
+    rem = cgutils.alloca_once(builder, x.type, name="rem")
+
+    with builder.if_else(cgutils.is_scalar_zero(builder, y), likely=False
+                         ) as (if_zero, if_non_zero):
+        with if_zero:
+            if not context.error_model.fp_zero_division(
+                builder, ("modulo by zero",), loc):
+                # No exception raised => compute the nan result,
+                # and set the FP exception word for Numpy warnings.
+                q = builder.fdiv(x, y)
+                r = builder.frem(x, y)
+                builder.store(q, quot)
+                builder.store(r, rem)
+        with if_non_zero:
+            q, r = real_divmod(context, builder, x, y)
+            builder.store(q, quot)
+            builder.store(r, rem)
+
+    return cgutils.pack_array(builder,
+                              (builder.load(quot), builder.load(rem)))
+
+
+def real_mod_impl(context, builder, sig, args, loc=None):
+    x, y = args
+    res = cgutils.alloca_once(builder, x.type)
+    with builder.if_else(cgutils.is_scalar_zero(builder, y), likely=False
+                         ) as (if_zero, if_non_zero):
+        with if_zero:
+            if not context.error_model.fp_zero_division(
+                builder, ("modulo by zero",), loc):
+                # No exception raised => compute the nan result,
+                # and set the FP exception word for Numpy warnings.
+                rem = builder.frem(x, y)
+                builder.store(rem, res)
+        with if_non_zero:
+            _, rem = real_divmod(context, builder, x, y)
+            builder.store(rem, res)
+    return impl_ret_untracked(context, builder, sig.return_type,
+                              builder.load(res))
+
+
+def real_floordiv_impl(context, builder, sig, args, loc=None):
+    x, y = args
+    res = cgutils.alloca_once(builder, x.type)
+    with builder.if_else(cgutils.is_scalar_zero(builder, y), likely=False
+                         ) as (if_zero, if_non_zero):
+        with if_zero:
+            if not context.error_model.fp_zero_division(
+                builder, ("division by zero",), loc):
+                # No exception raised => compute the +/-inf or nan result,
+                # and set the FP exception word for Numpy warnings.
+                quot = builder.fdiv(x, y)
+                builder.store(quot, res)
+        with if_non_zero:
+            quot, _ = real_divmod(context, builder, x, y)
+            builder.store(quot, res)
+    return impl_ret_untracked(context, builder, sig.return_type,
+                              builder.load(res))
+
+
+def real_power_impl(context, builder, sig, args):
+    x, y = args
+    module = builder.module
+    if context.implement_powi_as_math_call:
+        imp = context.get_function(math.pow, sig)
+        res = imp(builder, args)
+    else:
+        fn = module.declare_intrinsic('llvm.pow', [y.type])
+        res = builder.call(fn, (x, y))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_lt_impl(context, builder, sig, args):
+    res = builder.fcmp_ordered('<', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_le_impl(context, builder, sig, args):
+    res = builder.fcmp_ordered('<=', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_gt_impl(context, builder, sig, args):
+    res = builder.fcmp_ordered('>', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_ge_impl(context, builder, sig, args):
+    res = builder.fcmp_ordered('>=', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_eq_impl(context, builder, sig, args):
+    res = builder.fcmp_ordered('==', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_ne_impl(context, builder, sig, args):
+    res = builder.fcmp_unordered('!=', *args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_abs_impl(context, builder, sig, args):
+    [ty] = sig.args
+    sig = typing.signature(ty, ty)
+    impl = context.get_function(math.fabs, sig)
+    return impl(builder, args)
+
+
+def real_negate_impl(context, builder, sig, args):
+    from numba.cpython import mathimpl
+    res = mathimpl.negate_real(builder, args[0])
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_positive_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [val] = args
+    res = context.cast(builder, val, typ, sig.return_type)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def real_sign_impl(context, builder, sig, args):
+    """
+    np.sign(float)
+    """
+    [x] = args
+    POS = Constant(x.type, 1)
+    NEG = Constant(x.type, -1)
+    ZERO = Constant(x.type, 0)
+
+    presult = cgutils.alloca_once(builder, x.type)
+
+    is_pos = builder.fcmp_ordered('>', x, ZERO)
+    is_neg = builder.fcmp_ordered('<', x, ZERO)
+
+    with builder.if_else(is_pos) as (gt_zero, not_gt_zero):
+        with gt_zero:
+            builder.store(POS, presult)
+        with not_gt_zero:
+            with builder.if_else(is_neg) as (lt_zero, not_lt_zero):
+                with lt_zero:
+                    builder.store(NEG, presult)
+                with not_lt_zero:
+                    # For both NaN and 0, the result of sign() is simply
+                    # the input value.
+                    builder.store(x, presult)
+
+    res = builder.load(presult)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+ty = types.Float
+
+lower_builtin(operator.add, ty, ty)(real_add_impl)
+lower_builtin(operator.iadd, ty, ty)(real_add_impl)
+lower_builtin(operator.sub, ty, ty)(real_sub_impl)
+lower_builtin(operator.isub, ty, ty)(real_sub_impl)
+lower_builtin(operator.mul, ty, ty)(real_mul_impl)
+lower_builtin(operator.imul, ty, ty)(real_mul_impl)
+lower_builtin(operator.floordiv, ty, ty)(real_floordiv_impl)
+lower_builtin(operator.ifloordiv, ty, ty)(real_floordiv_impl)
+lower_builtin(operator.truediv, ty, ty)(real_div_impl)
+lower_builtin(operator.itruediv, ty, ty)(real_div_impl)
+lower_builtin(operator.mod, ty, ty)(real_mod_impl)
+lower_builtin(operator.imod, ty, ty)(real_mod_impl)
+lower_builtin(operator.pow, ty, ty)(real_power_impl)
+lower_builtin(operator.ipow, ty, ty)(real_power_impl)
+lower_builtin(pow, ty, ty)(real_power_impl)
+
+lower_builtin(operator.eq, ty, ty)(real_eq_impl)
+lower_builtin(operator.ne, ty, ty)(real_ne_impl)
+lower_builtin(operator.lt, ty, ty)(real_lt_impl)
+lower_builtin(operator.le, ty, ty)(real_le_impl)
+lower_builtin(operator.gt, ty, ty)(real_gt_impl)
+lower_builtin(operator.ge, ty, ty)(real_ge_impl)
+
+lower_builtin(abs, ty)(real_abs_impl)
+
+lower_builtin(operator.neg, ty)(real_negate_impl)
+lower_builtin(operator.pos, ty)(real_positive_impl)
+
+del ty
+
+
+@lower_getattr(types.Complex, "real")
+def complex_real_impl(context, builder, typ, value):
+    cplx = context.make_complex(builder, typ, value=value)
+    res = cplx.real
+    return impl_ret_untracked(context, builder, typ, res)
+
+@lower_getattr(types.Complex, "imag")
+def complex_imag_impl(context, builder, typ, value):
+    cplx = context.make_complex(builder, typ, value=value)
+    res = cplx.imag
+    return impl_ret_untracked(context, builder, typ, res)
+
+@lower_builtin("complex.conjugate", types.Complex)
+def complex_conjugate_impl(context, builder, sig, args):
+    from numba.cpython import mathimpl
+    z = context.make_complex(builder, sig.args[0], args[0])
+    z.imag = mathimpl.negate_real(builder, z.imag)
+    res = z._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def real_real_impl(context, builder, typ, value):
+    return impl_ret_untracked(context, builder, typ, value)
+
+def real_imag_impl(context, builder, typ, value):
+    res = cgutils.get_null_value(value.type)
+    return impl_ret_untracked(context, builder, typ, res)
+
+def real_conjugate_impl(context, builder, sig, args):
+    return impl_ret_untracked(context, builder, sig.return_type, args[0])
+
+for cls in (types.Float, types.Integer):
+    lower_getattr(cls, "real")(real_real_impl)
+    lower_getattr(cls, "imag")(real_imag_impl)
+    lower_builtin("complex.conjugate", cls)(real_conjugate_impl)
+
+
+@lower_builtin(operator.pow, types.Complex, types.Complex)
+@lower_builtin(operator.ipow, types.Complex, types.Complex)
+@lower_builtin(pow, types.Complex, types.Complex)
+def complex_power_impl(context, builder, sig, args):
+    [ca, cb] = args
+    ty = sig.args[0]
+    fty = ty.underlying_float
+    a = context.make_helper(builder, ty, value=ca)
+    b = context.make_helper(builder, ty, value=cb)
+    c = context.make_helper(builder, ty)
+    module = builder.module
+    pa = a._getpointer()
+    pb = b._getpointer()
+    pc = c._getpointer()
+
+    # Optimize for square because cpow loses a lot of precision
+    TWO = context.get_constant(fty, 2)
+    ZERO = context.get_constant(fty, 0)
+
+    b_real_is_two = builder.fcmp_ordered('==', b.real, TWO)
+    b_imag_is_zero = builder.fcmp_ordered('==', b.imag, ZERO)
+    b_is_two = builder.and_(b_real_is_two, b_imag_is_zero)
+
+    with builder.if_else(b_is_two) as (then, otherwise):
+        with then:
+            # Lower as multiplication
+            res = complex_mul_impl(context, builder, sig, (ca, ca))
+            cres = context.make_helper(builder, ty, value=res)
+            c.real = cres.real
+            c.imag = cres.imag
+
+        with otherwise:
+            # Lower with call to external function
+            func_name = {
+                types.complex64: "numba_cpowf",
+                types.complex128: "numba_cpow",
+                }[ty]
+            fnty = ir.FunctionType(ir.VoidType(), [pa.type] * 3)
+            cpow = cgutils.get_or_insert_function(module, fnty, func_name)
+            builder.call(cpow, (pa, pb, pc))
+
+    res = builder.load(pc)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def complex_add_impl(context, builder, sig, args):
+    [cx, cy] = args
+    ty = sig.args[0]
+    x = context.make_complex(builder, ty, value=cx)
+    y = context.make_complex(builder, ty, value=cy)
+    z = context.make_complex(builder, ty)
+    a = x.real
+    b = x.imag
+    c = y.real
+    d = y.imag
+    z.real = builder.fadd(a, c)
+    z.imag = builder.fadd(b, d)
+    res = z._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def complex_sub_impl(context, builder, sig, args):
+    [cx, cy] = args
+    ty = sig.args[0]
+    x = context.make_complex(builder, ty, value=cx)
+    y = context.make_complex(builder, ty, value=cy)
+    z = context.make_complex(builder, ty)
+    a = x.real
+    b = x.imag
+    c = y.real
+    d = y.imag
+    z.real = builder.fsub(a, c)
+    z.imag = builder.fsub(b, d)
+    res = z._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def complex_mul_impl(context, builder, sig, args):
+    """
+    (a+bi)(c+di)=(ac-bd)+i(ad+bc)
+    """
+    [cx, cy] = args
+    ty = sig.args[0]
+    x = context.make_complex(builder, ty, value=cx)
+    y = context.make_complex(builder, ty, value=cy)
+    z = context.make_complex(builder, ty)
+    a = x.real
+    b = x.imag
+    c = y.real
+    d = y.imag
+    ac = builder.fmul(a, c)
+    bd = builder.fmul(b, d)
+    ad = builder.fmul(a, d)
+    bc = builder.fmul(b, c)
+    z.real = builder.fsub(ac, bd)
+    z.imag = builder.fadd(ad, bc)
+    res = z._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+NAN = float('nan')
+
+def complex_div_impl(context, builder, sig, args):
+    def complex_div(a, b):
+        # This is CPython's algorithm (in _Py_c_quot()).
+        areal = a.real
+        aimag = a.imag
+        breal = b.real
+        bimag = b.imag
+        if not breal and not bimag:
+            raise ZeroDivisionError("complex division by zero")
+        if abs(breal) >= abs(bimag):
+            # Divide tops and bottom by b.real
+            if not breal:
+                return complex(NAN, NAN)
+            ratio = bimag / breal
+            denom = breal + bimag * ratio
+            return complex(
+                (areal + aimag * ratio) / denom,
+                (aimag - areal * ratio) / denom)
+        else:
+            # Divide tops and bottom by b.imag
+            if not bimag:
+                return complex(NAN, NAN)
+            ratio = breal / bimag
+            denom = breal * ratio + bimag
+            return complex(
+                (a.real * ratio + a.imag) / denom,
+                (a.imag * ratio - a.real) / denom)
+
+    res = context.compile_internal(builder, complex_div, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def complex_negate_impl(context, builder, sig, args):
+    from numba.cpython import mathimpl
+    [typ] = sig.args
+    [val] = args
+    cmplx = context.make_complex(builder, typ, value=val)
+    res = context.make_complex(builder, typ)
+    res.real = mathimpl.negate_real(builder, cmplx.real)
+    res.imag = mathimpl.negate_real(builder, cmplx.imag)
+    res = res._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def complex_positive_impl(context, builder, sig, args):
+    [val] = args
+    return impl_ret_untracked(context, builder, sig.return_type, val)
+
+
+def complex_eq_impl(context, builder, sig, args):
+    [cx, cy] = args
+    typ = sig.args[0]
+    x = context.make_complex(builder, typ, value=cx)
+    y = context.make_complex(builder, typ, value=cy)
+
+    reals_are_eq = builder.fcmp_ordered('==', x.real, y.real)
+    imags_are_eq = builder.fcmp_ordered('==', x.imag, y.imag)
+    res = builder.and_(reals_are_eq, imags_are_eq)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def complex_ne_impl(context, builder, sig, args):
+    [cx, cy] = args
+    typ = sig.args[0]
+    x = context.make_complex(builder, typ, value=cx)
+    y = context.make_complex(builder, typ, value=cy)
+
+    reals_are_ne = builder.fcmp_unordered('!=', x.real, y.real)
+    imags_are_ne = builder.fcmp_unordered('!=', x.imag, y.imag)
+    res = builder.or_(reals_are_ne, imags_are_ne)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def complex_abs_impl(context, builder, sig, args):
+    """
+    abs(z) := hypot(z.real, z.imag)
+    """
+    def complex_abs(z):
+        return math.hypot(z.real, z.imag)
+
+    res = context.compile_internal(builder, complex_abs, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+ty = types.Complex
+
+lower_builtin(operator.add, ty, ty)(complex_add_impl)
+lower_builtin(operator.iadd, ty, ty)(complex_add_impl)
+lower_builtin(operator.sub, ty, ty)(complex_sub_impl)
+lower_builtin(operator.isub, ty, ty)(complex_sub_impl)
+lower_builtin(operator.mul, ty, ty)(complex_mul_impl)
+lower_builtin(operator.imul, ty, ty)(complex_mul_impl)
+lower_builtin(operator.truediv, ty, ty)(complex_div_impl)
+lower_builtin(operator.itruediv, ty, ty)(complex_div_impl)
+lower_builtin(operator.neg, ty)(complex_negate_impl)
+lower_builtin(operator.pos, ty)(complex_positive_impl)
+# Complex modulo is deprecated in python3
+
+lower_builtin(operator.eq, ty, ty)(complex_eq_impl)
+lower_builtin(operator.ne, ty, ty)(complex_ne_impl)
+
+lower_builtin(abs, ty)(complex_abs_impl)
+
+del ty
+
+
+@lower_builtin("number.item", types.Boolean)
+@lower_builtin("number.item", types.Number)
+def number_item_impl(context, builder, sig, args):
+    """
+    The no-op .item() method on booleans and numbers.
+    """
+    return args[0]
+
+
+#------------------------------------------------------------------------------
+
+
+def number_not_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [val] = args
+    istrue = context.cast(builder, val, typ, sig.return_type)
+    res = builder.not_(istrue)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower_builtin(bool, types.Boolean)
+def bool_as_bool(context, builder, sig, args):
+    [val] = args
+    return val
+
+@lower_builtin(bool, types.Integer)
+def int_as_bool(context, builder, sig, args):
+    [val] = args
+    return builder.icmp_unsigned('!=', val, Constant(val.type, 0))
+
+@lower_builtin(bool, types.Float)
+def float_as_bool(context, builder, sig, args):
+    [val] = args
+    return builder.fcmp_unordered('!=', val, Constant(val.type, 0.0))
+
+@lower_builtin(bool, types.Complex)
+def complex_as_bool(context, builder, sig, args):
+    [typ] = sig.args
+    [val] = args
+    cmplx = context.make_complex(builder, typ, val)
+    real, imag = cmplx.real, cmplx.imag
+    zero = Constant(real.type, 0.0)
+    real_istrue = builder.fcmp_unordered('!=', real, zero)
+    imag_istrue = builder.fcmp_unordered('!=', imag, zero)
+    return builder.or_(real_istrue, imag_istrue)
+
+
+for ty in (types.Integer, types.Float, types.Complex):
+    lower_builtin(operator.not_, ty)(number_not_impl)
+
+lower_builtin(operator.not_, types.boolean)(number_not_impl)
+
+
+#------------------------------------------------------------------------------
+# Hashing numbers, see hashing.py
+
+#-------------------------------------------------------------------------------
+# Implicit casts between numerics
+
+@lower_cast(types.IntegerLiteral, types.Integer)
+@lower_cast(types.IntegerLiteral, types.Float)
+@lower_cast(types.IntegerLiteral, types.Complex)
+def literal_int_to_number(context, builder, fromty, toty, val):
+    lit = context.get_constant_generic(
+        builder,
+        fromty.literal_type,
+        fromty.literal_value,
+        )
+    return context.cast(builder, lit, fromty.literal_type, toty)
+
+
+@lower_cast(types.Integer, types.Integer)
+def integer_to_integer(context, builder, fromty, toty, val):
+    if toty.bitwidth == fromty.bitwidth:
+        # Just a change of signedness
+        return val
+    elif toty.bitwidth < fromty.bitwidth:
+        # Downcast
+        return builder.trunc(val, context.get_value_type(toty))
+    elif fromty.signed:
+        # Signed upcast
+        return builder.sext(val, context.get_value_type(toty))
+    else:
+        # Unsigned upcast
+        return builder.zext(val, context.get_value_type(toty))
+
+@lower_cast(types.Integer, types.voidptr)
+def integer_to_voidptr(context, builder, fromty, toty, val):
+    return builder.inttoptr(val, context.get_value_type(toty))
+
+@lower_cast(types.Float, types.Float)
+def float_to_float(context, builder, fromty, toty, val):
+    lty = context.get_value_type(toty)
+    if fromty.bitwidth < toty.bitwidth:
+        return builder.fpext(val, lty)
+    else:
+        return builder.fptrunc(val, lty)
+
+@lower_cast(types.Integer, types.Float)
+def integer_to_float(context, builder, fromty, toty, val):
+    lty = context.get_value_type(toty)
+    if fromty.signed:
+        return builder.sitofp(val, lty)
+    else:
+        return builder.uitofp(val, lty)
+
+@lower_cast(types.Float, types.Integer)
+def float_to_integer(context, builder, fromty, toty, val):
+    lty = context.get_value_type(toty)
+    if toty.signed:
+        return builder.fptosi(val, lty)
+    else:
+        return builder.fptoui(val, lty)
+
+@lower_cast(types.Float, types.Complex)
+@lower_cast(types.Integer, types.Complex)
+def non_complex_to_complex(context, builder, fromty, toty, val):
+    real = context.cast(builder, val, fromty, toty.underlying_float)
+    imag = context.get_constant(toty.underlying_float, 0)
+
+    cmplx = context.make_complex(builder, toty)
+    cmplx.real = real
+    cmplx.imag = imag
+    return cmplx._getvalue()
+
+@lower_cast(types.Complex, types.Complex)
+def complex_to_complex(context, builder, fromty, toty, val):
+    srcty = fromty.underlying_float
+    dstty = toty.underlying_float
+
+    src = context.make_complex(builder, fromty, value=val)
+    dst = context.make_complex(builder, toty)
+    dst.real = context.cast(builder, src.real, srcty, dstty)
+    dst.imag = context.cast(builder, src.imag, srcty, dstty)
+    return dst._getvalue()
+
+@lower_cast(types.Any, types.Boolean)
+def any_to_boolean(context, builder, fromty, toty, val):
+    return context.is_true(builder, fromty, val)
+
+@lower_cast(types.Boolean, types.Number)
+def boolean_to_any(context, builder, fromty, toty, val):
+    # Casting from boolean to anything first casts to int32
+    asint = builder.zext(val, ir.IntType(32))
+    return context.cast(builder, asint, types.int32, toty)
+
+@lower_cast(types.IntegerLiteral, types.Boolean)
+@lower_cast(types.BooleanLiteral, types.Boolean)
+def literal_int_to_boolean(context, builder, fromty, toty, val):
+    lit = context.get_constant_generic(
+        builder,
+        fromty.literal_type,
+        fromty.literal_value,
+        )
+    return context.is_true(builder, fromty.literal_type, lit)
+
+#-------------------------------------------------------------------------------
+# Constants
+
+@lower_constant(types.Complex)
+def constant_complex(context, builder, ty, pyval):
+    fty = ty.underlying_float
+    real = context.get_constant_generic(builder, fty, pyval.real)
+    imag = context.get_constant_generic(builder, fty, pyval.imag)
+    return Constant.literal_struct((real, imag))
+
+@lower_constant(types.Integer)
+@lower_constant(types.Float)
+@lower_constant(types.Boolean)
+def constant_integer(context, builder, ty, pyval):
+    # See https://github.com/numba/numba/issues/6979
+    # llvmlite ir.IntType specialises the formatting of the constant for a
+    # cpython bool. A NumPy np.bool_ is not a cpython bool so force it to be one
+    # so that the constant renders correctly!
+    if isinstance(pyval, np.bool_):
+        pyval = bool(pyval)
+    lty = context.get_value_type(ty)
+    return lty(pyval)
+
+
+#-------------------------------------------------------------------------------
+# View
+
+def scalar_view(scalar, viewty):
+    """ Typing for the np scalar 'view' method. """
+    if (isinstance(scalar, (types.Float, types.Integer))
+            and isinstance(viewty, types.abstract.DTypeSpec)):
+        if scalar.bitwidth != viewty.dtype.bitwidth:
+            raise errors.TypingError(
+                "Changing the dtype of a 0d array is only supported if the "
+                "itemsize is unchanged")
+
+        def impl(scalar, viewty):
+            return viewer(scalar, viewty)
+        return impl
+
+
+overload_method(types.Float, 'view')(scalar_view)
+overload_method(types.Integer, 'view')(scalar_view)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/printimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/printimpl.py
new file mode 100644
index 000000000..8ce54004c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/printimpl.py
@@ -0,0 +1,82 @@
+"""
+This file implements print functionality for the CPU.
+"""
+from numba.core import types, typing, cgutils
+from numba.core.imputils import Registry, impl_ret_untracked
+
+registry = Registry('printimpl')
+lower = registry.lower
+
+
+# NOTE: the current implementation relies on CPython API even in
+#       nopython mode.
+
+
+@lower("print_item", types.Literal)
+def print_item_impl(context, builder, sig, args):
+    """
+    Print a single constant value.
+    """
+    ty, = sig.args
+    val = ty.literal_value
+
+    pyapi = context.get_python_api(builder)
+
+    strobj = pyapi.unserialize(pyapi.serialize_object(val))
+    pyapi.print_object(strobj)
+    pyapi.decref(strobj)
+
+    res = context.get_dummy_value()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower("print_item", types.Any)
+def print_item_impl(context, builder, sig, args):
+    """
+    Print a single native value by boxing it in a Python object and
+    invoking the Python interpreter's print routine.
+    """
+    ty, = sig.args
+    val, = args
+
+    pyapi = context.get_python_api(builder)
+    env_manager = context.get_env_manager(builder)
+
+    if context.enable_nrt:
+        context.nrt.incref(builder, ty, val)
+
+    obj = pyapi.from_native_value(ty, val, env_manager)
+    with builder.if_else(cgutils.is_not_null(builder, obj), likely=True) as (if_ok, if_error):
+        with if_ok:
+            pyapi.print_object(obj)
+            pyapi.decref(obj)
+        with if_error:
+            cstr = context.insert_const_string(builder.module,
+                                               "the print() function")
+            strobj = pyapi.string_from_string(cstr)
+            pyapi.err_write_unraisable(strobj)
+            pyapi.decref(strobj)
+
+    res = context.get_dummy_value()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(print, types.VarArg(types.Any))
+def print_varargs_impl(context, builder, sig, args):
+    """
+    A entire print() call.
+    """
+    pyapi = context.get_python_api(builder)
+    gil = pyapi.gil_ensure()
+
+    for i, (argtype, argval) in enumerate(zip(sig.args, args)):
+        signature = typing.signature(types.none, argtype)
+        imp = context.get_function("print_item", signature)
+        imp(builder, [argval])
+        if i < len(args) - 1:
+            pyapi.print_string(' ')
+    pyapi.print_string('\n')
+
+    pyapi.gil_release(gil)
+    res = context.get_dummy_value()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/randomimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/randomimpl.py
new file mode 100644
index 000000000..9a2f19b8b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/randomimpl.py
@@ -0,0 +1,1644 @@
+"""
+Implement the random and np.random module functions.
+"""
+
+
+import math
+import os
+import random
+
+import numpy as np
+
+from llvmlite import ir
+
+from numba.core.extending import overload, register_jitable
+from numba.core.imputils import (Registry, impl_ret_untracked,
+                                    impl_ret_new_ref)
+from numba.core.typing import signature
+from numba import _helperlib
+from numba.core import types, utils, cgutils
+from numba.np import arrayobj
+from numba.core.overload_glue import glue_lowering
+from numba.core.errors import NumbaTypeError
+
+
+POST_PY38 = utils.PYVERSION >= (3, 8)
+
+
+registry = Registry('randomimpl')
+lower = registry.lower
+
+int32_t = ir.IntType(32)
+int64_t = ir.IntType(64)
+def const_int(x):
+    return ir.Constant(int32_t, x)
+double = ir.DoubleType()
+
+N = 624
+N_const = ir.Constant(int32_t, N)
+
+
+# This is the same struct as rnd_state_t in _random.c.
+rnd_state_t = ir.LiteralStructType([
+    # index
+    int32_t,
+    # mt[N]
+    ir.ArrayType(int32_t, N),
+    # has_gauss
+    int32_t,
+    # gauss
+    double,
+    # is_initialized
+    int32_t,
+    ])
+rnd_state_ptr_t = ir.PointerType(rnd_state_t)
+
+def get_state_ptr(context, builder, name):
+    """
+    Get a pointer to the given thread-local random state
+    (depending on *name*: "py" or "np").
+    If the state isn't initialized, it is lazily initialized with
+    system entropy.
+    """
+    assert name in ('py', 'np', 'internal')
+    func_name = "numba_get_%s_random_state" % name
+    fnty = ir.FunctionType(rnd_state_ptr_t, ())
+    fn = cgutils.get_or_insert_function(builder.module, fnty, func_name)
+    # These two attributes allow LLVM to hoist the function call
+    # outside of loops.
+    fn.attributes.add('readnone')
+    fn.attributes.add('nounwind')
+    return builder.call(fn, ())
+
+def get_py_state_ptr(context, builder):
+    """
+    Get a pointer to the thread-local Python random state.
+    """
+    return get_state_ptr(context, builder, 'py')
+
+def get_np_state_ptr(context, builder):
+    """
+    Get a pointer to the thread-local Numpy random state.
+    """
+    return get_state_ptr(context, builder, 'np')
+
+def get_internal_state_ptr(context, builder):
+    """
+    Get a pointer to the thread-local internal random state.
+    """
+    return get_state_ptr(context, builder, 'internal')
+
+# Accessors
+def get_index_ptr(builder, state_ptr):
+    return cgutils.gep_inbounds(builder, state_ptr, 0, 0)
+
+def get_array_ptr(builder, state_ptr):
+    return cgutils.gep_inbounds(builder, state_ptr, 0, 1)
+
+def get_has_gauss_ptr(builder, state_ptr):
+    return cgutils.gep_inbounds(builder, state_ptr, 0, 2)
+
+def get_gauss_ptr(builder, state_ptr):
+    return cgutils.gep_inbounds(builder, state_ptr, 0, 3)
+
+def get_rnd_shuffle(builder):
+    """
+    Get the internal function to shuffle the MT taste.
+    """
+    fnty = ir.FunctionType(ir.VoidType(), (rnd_state_ptr_t,))
+    fn = cgutils.get_or_insert_function(builder.function.module, fnty,
+                                        "numba_rnd_shuffle")
+    fn.args[0].add_attribute("nocapture")
+    return fn
+
+
+def get_next_int32(context, builder, state_ptr):
+    """
+    Get the next int32 generated by the PRNG at *state_ptr*.
+    """
+    idxptr = get_index_ptr(builder, state_ptr)
+    idx = builder.load(idxptr)
+    need_reshuffle = builder.icmp_unsigned('>=', idx, N_const)
+    with cgutils.if_unlikely(builder, need_reshuffle):
+        fn = get_rnd_shuffle(builder)
+        builder.call(fn, (state_ptr,))
+        builder.store(const_int(0), idxptr)
+    idx = builder.load(idxptr)
+    array_ptr = get_array_ptr(builder, state_ptr)
+    y = builder.load(cgutils.gep_inbounds(builder, array_ptr, 0, idx))
+    idx = builder.add(idx, const_int(1))
+    builder.store(idx, idxptr)
+    # Tempering
+    y = builder.xor(y, builder.lshr(y, const_int(11)))
+    y = builder.xor(y, builder.and_(builder.shl(y, const_int(7)),
+                                    const_int(0x9d2c5680)))
+    y = builder.xor(y, builder.and_(builder.shl(y, const_int(15)),
+                                    const_int(0xefc60000)))
+    y = builder.xor(y, builder.lshr(y, const_int(18)))
+    return y
+
+def get_next_double(context, builder, state_ptr):
+    """
+    Get the next double generated by the PRNG at *state_ptr*.
+    """
+    # a = rk_random(state) >> 5, b = rk_random(state) >> 6;
+    a = builder.lshr(get_next_int32(context, builder, state_ptr), const_int(5))
+    b = builder.lshr(get_next_int32(context, builder, state_ptr), const_int(6))
+
+    # return (a * 67108864.0 + b) / 9007199254740992.0;
+    a = builder.uitofp(a, double)
+    b = builder.uitofp(b, double)
+    return builder.fdiv(
+        builder.fadd(b, builder.fmul(a, ir.Constant(double, 67108864.0))),
+        ir.Constant(double, 9007199254740992.0))
+
+def get_next_int(context, builder, state_ptr, nbits, is_numpy):
+    """
+    Get the next integer with width *nbits*.
+    """
+    c32 = ir.Constant(nbits.type, 32)
+    def get_shifted_int(nbits):
+        shift = builder.sub(c32, nbits)
+        y = get_next_int32(context, builder, state_ptr)
+        if is_numpy:
+            # Use the last N bits, to match np.random
+            mask = builder.not_(ir.Constant(y.type, 0))
+            mask = builder.lshr(mask, builder.zext(shift, y.type))
+            return builder.and_(y, mask)
+        else:
+            # Use the first N bits, to match CPython random
+            return builder.lshr(y, builder.zext(shift, y.type))
+
+    ret = cgutils.alloca_once_value(builder, ir.Constant(int64_t, 0))
+
+    is_32b = builder.icmp_unsigned('<=', nbits, c32)
+    with builder.if_else(is_32b) as (ifsmall, iflarge):
+        with ifsmall:
+            low = get_shifted_int(nbits)
+            builder.store(builder.zext(low, int64_t), ret)
+        with iflarge:
+            # XXX This assumes nbits <= 64
+            if is_numpy:
+                # Get the high bits first to match np.random
+                high = get_shifted_int(builder.sub(nbits, c32))
+            low = get_next_int32(context, builder, state_ptr)
+            if not is_numpy:
+                # Get the high bits second to match CPython random
+                high = get_shifted_int(builder.sub(nbits, c32))
+            total = builder.add(
+                builder.zext(low, int64_t),
+                builder.shl(builder.zext(high, int64_t), ir.Constant(int64_t, 32)))
+            builder.store(total, ret)
+
+    return builder.load(ret)
+
+
+def _fill_defaults(context, builder, sig, args, defaults):
+    """
+    Assuming a homogeneous signature (same type for result and all arguments),
+    fill in the *defaults* if missing from the arguments.
+    """
+    ty = sig.return_type
+    llty = context.get_data_type(ty)
+    args = tuple(args) + tuple(ir.Constant(llty, d) for d in defaults[len(args):])
+    sig = signature(*(ty,) * (len(args) + 1))
+    return sig, args
+
+
+@glue_lowering("random.seed", types.uint32)
+def seed_impl(context, builder, sig, args):
+    res =  _seed_impl(context, builder, sig, args, get_state_ptr(context,
+                                                                 builder, "py"))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.seed", types.uint32)
+def seed_impl(context, builder, sig, args):
+    res = _seed_impl(context, builder, sig, args, get_state_ptr(context,
+                                                               builder, "np"))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def _seed_impl(context, builder, sig, args, state_ptr):
+    seed_value, = args
+    fnty = ir.FunctionType(ir.VoidType(), (rnd_state_ptr_t, int32_t))
+    fn = cgutils.get_or_insert_function(builder.function.module, fnty,
+                                        "numba_rnd_init")
+    builder.call(fn, (state_ptr, seed_value))
+    return context.get_constant(types.none, None)
+
+@glue_lowering("random.random")
+def random_impl(context, builder, sig, args):
+    state_ptr = get_state_ptr(context, builder, "py")
+    res = get_next_double(context, builder, state_ptr)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.random")
+@glue_lowering("np.random.random_sample")
+@glue_lowering("np.random.sample")
+@glue_lowering("np.random.ranf")
+def random_impl(context, builder, sig, args):
+    state_ptr = get_state_ptr(context, builder, "np")
+    res = get_next_double(context, builder, state_ptr)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("random.gauss", types.Float, types.Float)
+@glue_lowering("random.normalvariate", types.Float, types.Float)
+def gauss_impl(context, builder, sig, args):
+    res = _gauss_impl(context, builder, sig, args, "py")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.standard_normal")
+@glue_lowering("np.random.normal")
+@glue_lowering("np.random.normal", types.Float)
+@glue_lowering("np.random.normal", types.Float, types.Float)
+def np_gauss_impl(context, builder, sig, args):
+    sig, args = _fill_defaults(context, builder, sig, args, (0.0, 1.0))
+    res = _gauss_impl(context, builder, sig, args, "np")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def _gauss_pair_impl(_random):
+    def compute_gauss_pair():
+        """
+        Compute a pair of numbers on the normal distribution.
+        """
+        while True:
+            x1 = 2.0 * _random() - 1.0
+            x2 = 2.0 * _random() - 1.0
+            r2 = x1*x1 + x2*x2
+            if r2 < 1.0 and r2 != 0.0:
+                break
+
+        # Box-Muller transform
+        f = math.sqrt(-2.0 * math.log(r2) / r2)
+        return f * x1, f * x2
+    return compute_gauss_pair
+
+def _gauss_impl(context, builder, sig, args, state):
+    # The type for all computations (either float or double)
+    ty = sig.return_type
+    llty = context.get_data_type(ty)
+
+    state_ptr = get_state_ptr(context, builder, state)
+    _random = {"py": random.random,
+               "np": np.random.random}[state]
+
+    ret = cgutils.alloca_once(builder, llty, name="result")
+
+    gauss_ptr = get_gauss_ptr(builder, state_ptr)
+    has_gauss_ptr = get_has_gauss_ptr(builder, state_ptr)
+    has_gauss = cgutils.is_true(builder, builder.load(has_gauss_ptr))
+    with builder.if_else(has_gauss) as (then, otherwise):
+        with then:
+            # if has_gauss: return it
+            builder.store(builder.load(gauss_ptr), ret)
+            builder.store(const_int(0), has_gauss_ptr)
+        with otherwise:
+            # if not has_gauss: compute a pair of numbers using the Box-Muller
+            # transform; keep one and return the other
+            pair = context.compile_internal(builder,
+                                            _gauss_pair_impl(_random),
+                                            signature(types.UniTuple(ty, 2)),
+                                            ())
+
+            first, second = cgutils.unpack_tuple(builder, pair, 2)
+            builder.store(first, gauss_ptr)
+            builder.store(second, ret)
+            builder.store(const_int(1), has_gauss_ptr)
+
+    mu, sigma = args
+    return builder.fadd(mu,
+                        builder.fmul(sigma, builder.load(ret)))
+
+@glue_lowering("random.getrandbits", types.Integer)
+def getrandbits_impl(context, builder, sig, args):
+    nbits, = args
+    too_large = builder.icmp_unsigned(">=", nbits, const_int(65))
+    too_small = builder.icmp_unsigned("==", nbits, const_int(0))
+    with cgutils.if_unlikely(builder, builder.or_(too_large, too_small)):
+        msg = "getrandbits() limited to 64 bits"
+        context.call_conv.return_user_exc(builder, OverflowError, (msg,))
+    state_ptr = get_state_ptr(context, builder, "py")
+    res = get_next_int(context, builder, state_ptr, nbits, False)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def _randrange_impl(context, builder, start, stop, step, state):
+    state_ptr = get_state_ptr(context, builder, state)
+    ty = stop.type
+    zero = ir.Constant(ty, 0)
+    one = ir.Constant(ty, 1)
+    nptr = cgutils.alloca_once(builder, ty, name="n")
+    # n = stop - start
+    builder.store(builder.sub(stop, start), nptr)
+
+    with builder.if_then(builder.icmp_signed('<', step, zero)):
+        # n = (n + step + 1) // step
+        w = builder.add(builder.add(builder.load(nptr), step), one)
+        n = builder.sdiv(w, step)
+        builder.store(n, nptr)
+    with builder.if_then(builder.icmp_signed('>', step, one)):
+        # n = (n + step - 1) // step
+        w = builder.sub(builder.add(builder.load(nptr), step), one)
+        n = builder.sdiv(w, step)
+        builder.store(n, nptr)
+
+    n = builder.load(nptr)
+    with cgutils.if_unlikely(builder, builder.icmp_signed('<=', n, zero)):
+        # n <= 0
+        msg = "empty range for randrange()"
+        context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+    fnty = ir.FunctionType(ty, [ty, cgutils.true_bit.type])
+    fn = cgutils.get_or_insert_function(builder.function.module, fnty,
+                                        "llvm.ctlz.%s" % ty)
+    # Since the upper bound is exclusive, we need to subtract one before
+    # calculating the number of bits. This leads to a special case when
+    # n == 1; there's only one possible result, so we don't need bits from
+    # the PRNG. This case is handled separately towards the end of this
+    # function. CPython's implementation is simpler and just runs another
+    # iteration of the while loop when the resulting number is too large
+    # instead of subtracting one, to avoid needing to handle a special
+    # case. Thus, we only perform this subtraction for the NumPy case.
+    nm1 = builder.sub(n, one) if state == "np" else n
+    nbits = builder.trunc(builder.call(fn, [nm1, cgutils.true_bit]), int32_t)
+    nbits = builder.sub(ir.Constant(int32_t, ty.width), nbits)
+
+    rptr = cgutils.alloca_once(builder, ty, name="r")
+
+    def get_num():
+        bbwhile = builder.append_basic_block("while")
+        bbend = builder.append_basic_block("while.end")
+        builder.branch(bbwhile)
+
+        builder.position_at_end(bbwhile)
+        r = get_next_int(context, builder, state_ptr, nbits, state == "np")
+        r = builder.trunc(r, ty)
+        too_large = builder.icmp_signed('>=', r, n)
+        builder.cbranch(too_large, bbwhile, bbend)
+
+        builder.position_at_end(bbend)
+        builder.store(r, rptr)
+
+    if state == "np":
+        # Handle n == 1 case, per previous comment.
+        with builder.if_else(builder.icmp_signed('==', n, one)) as (is_one, is_not_one):
+            with is_one:
+                builder.store(zero, rptr)
+            with is_not_one:
+                get_num()
+    else:
+        get_num()
+
+    return builder.add(start, builder.mul(builder.load(rptr), step))
+
+
+@glue_lowering("random.randrange", types.Integer)
+def randrange_impl_1(context, builder, sig, args):
+    stop, = args
+    start = ir.Constant(stop.type, 0)
+    step = ir.Constant(stop.type, 1)
+    res = _randrange_impl(context, builder, start, stop, step, "py")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("random.randrange", types.Integer, types.Integer)
+def randrange_impl_2(context, builder, sig, args):
+    start, stop = args
+    step = ir.Constant(start.type, 1)
+    res = _randrange_impl(context, builder, start, stop, step, "py")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("random.randrange", types.Integer,
+               types.Integer, types.Integer)
+def randrange_impl_3(context, builder, sig, args):
+    start, stop, step = args
+    res = _randrange_impl(context, builder, start, stop, step, "py")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("random.randint", types.Integer, types.Integer)
+def randint_impl_1(context, builder, sig, args):
+    start, stop = args
+    step = ir.Constant(start.type, 1)
+    stop = builder.add(stop, step)
+    res = _randrange_impl(context, builder, start, stop, step, "py")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.randint", types.Integer)
+def randint_impl_2(context, builder, sig, args):
+    stop, = args
+    start = ir.Constant(stop.type, 0)
+    step = ir.Constant(stop.type, 1)
+    res = _randrange_impl(context, builder, start, stop, step, "np")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.randint", types.Integer, types.Integer)
+def randrange_impl_2(context, builder, sig, args):
+    start, stop = args
+    step = ir.Constant(start.type, 1)
+    res = _randrange_impl(context, builder, start, stop, step, "np")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("random.uniform", types.Float, types.Float)
+def uniform_impl(context, builder, sig, args):
+    res = uniform_impl(context, builder, sig, args, "py")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.uniform", types.Float, types.Float)
+def uniform_impl(context, builder, sig, args):
+    res = uniform_impl(context, builder, sig, args, "np")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def uniform_impl(context, builder, sig, args, state):
+    state_ptr = get_state_ptr(context, builder, state)
+    a, b = args
+    width = builder.fsub(b, a)
+    r = get_next_double(context, builder, state_ptr)
+    return builder.fadd(a, builder.fmul(width, r))
+
+@glue_lowering("random.triangular", types.Float, types.Float)
+def triangular_impl_2(context, builder, sig, args):
+    fltty = sig.return_type
+    low, high = args
+    state_ptr = get_state_ptr(context, builder, "py")
+    randval = get_next_double(context, builder, state_ptr)
+
+    def triangular_impl_2(randval, low, high):
+        u = randval
+        c = 0.5
+        if u > c:
+            u = 1.0 - u
+            low, high = high, low
+        return low + (high - low) * math.sqrt(u * c)
+
+    res = context.compile_internal(builder, triangular_impl_2,
+                                    signature(*(fltty,) * 4),
+                                    (randval, low, high))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("random.triangular", types.Float, types.Float, types.Float)
+def triangular_impl_3(context, builder, sig, args):
+    low, high, mode = args
+    res = _triangular_impl_3(context, builder, sig, low, high, mode, "py")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.triangular", types.Float,
+           types.Float, types.Float)
+def triangular_impl_3(context, builder, sig, args):
+    low, mode, high = args
+    res = _triangular_impl_3(context, builder, sig, low, high, mode, "np")
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def _triangular_impl_3(context, builder, sig, low, high, mode, state):
+    fltty = sig.return_type
+    state_ptr = get_state_ptr(context, builder, state)
+    randval = get_next_double(context, builder, state_ptr)
+
+    def triangular_impl_3(randval, low, high, mode):
+        if high == low:
+            return low
+        u = randval
+        c = (mode - low) / (high - low)
+        if u > c:
+            u = 1.0 - u
+            c = 1.0 - c
+            low, high = high, low
+        return low + (high - low) * math.sqrt(u * c)
+
+    return context.compile_internal(builder, triangular_impl_3,
+                                    signature(*(fltty,) * 5),
+                                    (randval, low, high, mode))
+
+
+@glue_lowering("random.gammavariate", types.Float, types.Float)
+def gammavariate_impl(context, builder, sig, args):
+    res = _gammavariate_impl(context, builder, sig, args, random.random)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.standard_gamma", types.Float)
+@glue_lowering("np.random.gamma", types.Float)
+@glue_lowering("np.random.gamma", types.Float, types.Float)
+def gammavariate_impl(context, builder, sig, args):
+    sig, args = _fill_defaults(context, builder, sig, args, (None, 1.0))
+    res = _gammavariate_impl(context, builder, sig, args, np.random.random)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def _gammavariate_impl(context, builder, sig, args, _random):
+    _exp = math.exp
+    _log = math.log
+    _sqrt = math.sqrt
+    _e = math.e
+
+    TWOPI = 2.0 * math.pi
+    LOG4 = _log(4.0)
+    SG_MAGICCONST = 1.0 + _log(4.5)
+
+    def gammavariate_impl(alpha, beta):
+        """Gamma distribution.  Taken from CPython.
+        """
+        # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2
+
+        # Warning: a few older sources define the gamma distribution in terms
+        # of alpha > -1.0
+        if alpha <= 0.0 or beta <= 0.0:
+            raise ValueError('gammavariate: alpha and beta must be > 0.0')
+
+        if alpha > 1.0:
+            # Uses R.C.H. Cheng, "The generation of Gamma
+            # variables with non-integral shape parameters",
+            # Applied Statistics, (1977), 26, No. 1, p71-74
+            ainv = _sqrt(2.0 * alpha - 1.0)
+            bbb = alpha - LOG4
+            ccc = alpha + ainv
+
+            while 1:
+                u1 = _random()
+                if not 1e-7 < u1 < .9999999:
+                    continue
+                u2 = 1.0 - _random()
+                v = _log(u1/(1.0-u1))/ainv
+                x = alpha*_exp(v)
+                z = u1*u1*u2
+                r = bbb+ccc*v-x
+                if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z):
+                    return x * beta
+
+        elif alpha == 1.0:
+            # expovariate(1)
+
+            if POST_PY38:
+                # Adjust due to cpython
+                # commit 63d152232e1742660f481c04a811f824b91f6790
+                return -_log(1.0 - _random()) * beta
+            else:
+                u = _random()
+                while u <= 1e-7:
+                    u = _random()
+                return -_log(u) * beta
+
+        else:   # alpha is between 0 and 1 (exclusive)
+            # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
+            while 1:
+                u = _random()
+                b = (_e + alpha)/_e
+                p = b*u
+                if p <= 1.0:
+                    x = p ** (1.0/alpha)
+                else:
+                    x = -_log((b-p)/alpha)
+                u1 = _random()
+                if p > 1.0:
+                    if u1 <= x ** (alpha - 1.0):
+                        break
+                elif u1 <= _exp(-x):
+                    break
+            return x * beta
+
+    return context.compile_internal(builder, gammavariate_impl,
+                                    sig, args)
+
+
+@glue_lowering("random.betavariate", types.Float, types.Float)
+def betavariate_impl(context, builder, sig, args):
+    res = _betavariate_impl(context, builder, sig, args,
+                             random.gammavariate)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.beta",
+           types.Float, types.Float)
+def betavariate_impl(context, builder, sig, args):
+    res = _betavariate_impl(context, builder, sig, args,
+                             np.random.gamma)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def _betavariate_impl(context, builder, sig, args, gamma):
+
+    def betavariate_impl(alpha, beta):
+        """Beta distribution.  Taken from CPython.
+        """
+        # This version due to Janne Sinkkonen, and matches all the std
+        # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution").
+        y = gamma(alpha, 1.)
+        if y == 0.0:
+            return 0.0
+        else:
+            return y / (y + gamma(beta, 1.))
+
+    return context.compile_internal(builder, betavariate_impl,
+                                    sig, args)
+
+
+@glue_lowering("random.expovariate", types.Float)
+def expovariate_impl(context, builder, sig, args):
+    _random = random.random
+    _log = math.log
+
+    def expovariate_impl(lambd):
+        """Exponential distribution.  Taken from CPython.
+        """
+        # lambd: rate lambd = 1/mean
+        # ('lambda' is a Python reserved word)
+
+        # we use 1-random() instead of random() to preclude the
+        # possibility of taking the log of zero.
+        return -_log(1.0 - _random()) / lambd
+
+    res = context.compile_internal(builder, expovariate_impl,
+                                    sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.exponential", types.Float)
+def exponential_impl(context, builder, sig, args):
+    _random = np.random.random
+    _log = math.log
+
+    def exponential_impl(scale):
+        return -_log(1.0 - _random()) * scale
+
+    res = context.compile_internal(builder, exponential_impl,
+                                    sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.standard_exponential")
+@glue_lowering("np.random.exponential")
+def exponential_impl(context, builder, sig, args):
+    _random = np.random.random
+    _log = math.log
+
+    def exponential_impl():
+        return -_log(1.0 - _random())
+
+    res = context.compile_internal(builder, exponential_impl,
+                                    sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.lognormal")
+@glue_lowering("np.random.lognormal", types.Float)
+@glue_lowering("np.random.lognormal", types.Float, types.Float)
+def np_lognormal_impl(context, builder, sig, args):
+    sig, args = _fill_defaults(context, builder, sig, args, (0.0, 1.0))
+    res = _lognormvariate_impl(context, builder, sig, args,
+                                np.random.normal)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("random.lognormvariate",
+           types.Float, types.Float)
+def lognormvariate_impl(context, builder, sig, args):
+    res = _lognormvariate_impl(context, builder, sig, args, random.gauss)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def _lognormvariate_impl(context, builder, sig, args, _gauss):
+    _exp = math.exp
+
+    def lognormvariate_impl(mu, sigma):
+        return _exp(_gauss(mu, sigma))
+
+    return context.compile_internal(builder, lognormvariate_impl,
+                                    sig, args)
+
+
+@glue_lowering("random.paretovariate", types.Float)
+def paretovariate_impl(context, builder, sig, args):
+    _random = random.random
+
+    def paretovariate_impl(alpha):
+        """Pareto distribution.  Taken from CPython."""
+        # Jain, pg. 495
+        u = 1.0 - _random()
+        return 1.0 / u ** (1.0/alpha)
+
+    res = context.compile_internal(builder, paretovariate_impl,
+                                    sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.pareto", types.Float)
+def pareto_impl(context, builder, sig, args):
+    _random = np.random.random
+
+    def pareto_impl(alpha):
+        # Same as paretovariate() - 1.
+        u = 1.0 - _random()
+        return 1.0 / u ** (1.0/alpha) - 1
+
+    res = context.compile_internal(builder, pareto_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("random.weibullvariate",
+           types.Float, types.Float)
+def weibullvariate_impl(context, builder, sig, args):
+    _random = random.random
+    _log = math.log
+
+    def weibullvariate_impl(alpha, beta):
+        """Weibull distribution.  Taken from CPython."""
+        # Jain, pg. 499; bug fix courtesy Bill Arms
+        u = 1.0 - _random()
+        return alpha * (-_log(u)) ** (1.0/beta)
+
+    res = context.compile_internal(builder, weibullvariate_impl,
+                                    sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.weibull", types.Float)
+def weibull_impl(context, builder, sig, args):
+    _random = np.random.random
+    _log = math.log
+
+    def weibull_impl(beta):
+        # Same as weibullvariate(1.0, beta)
+        u = 1.0 - _random()
+        return (-_log(u)) ** (1.0/beta)
+
+    res = context.compile_internal(builder, weibull_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("random.vonmisesvariate",
+           types.Float, types.Float)
+def vonmisesvariate_impl(context, builder, sig, args):
+    res = _vonmisesvariate_impl(context, builder, sig, args, random.random)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.vonmises",
+           types.Float, types.Float)
+def vonmisesvariate_impl(context, builder, sig, args):
+    res = _vonmisesvariate_impl(context, builder, sig, args, np.random.random)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def _vonmisesvariate_impl(context, builder, sig, args, _random):
+    _exp = math.exp
+    _sqrt = math.sqrt
+    _cos = math.cos
+    _acos = math.acos
+    _pi = math.pi
+    TWOPI = 2.0 * _pi
+
+    def vonmisesvariate_impl(mu, kappa):
+        """Circular data distribution.  Taken from CPython.
+        Note the algorithm in Python 2.6 and Numpy is different:
+        http://bugs.python.org/issue17141
+        """
+        # mu:    mean angle (in radians between 0 and 2*pi)
+        # kappa: concentration parameter kappa (>= 0)
+        # if kappa = 0 generate uniform random angle
+
+        # Based upon an algorithm published in: Fisher, N.I.,
+        # "Statistical Analysis of Circular Data", Cambridge
+        # University Press, 1993.
+
+        # Thanks to Magnus Kessler for a correction to the
+        # implementation of step 4.
+        if kappa <= 1e-6:
+            return TWOPI * _random()
+
+        s = 0.5 / kappa
+        r = s + _sqrt(1.0 + s * s)
+
+        while 1:
+            u1 = _random()
+            z = _cos(_pi * u1)
+
+            d = z / (r + z)
+            u2 = _random()
+            if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d):
+                break
+
+        q = 1.0 / r
+        f = (q + z) / (1.0 + q * z)
+        u3 = _random()
+        if u3 > 0.5:
+            theta = (mu + _acos(f)) % TWOPI
+        else:
+            theta = (mu - _acos(f)) % TWOPI
+
+        return theta
+
+    return context.compile_internal(builder, vonmisesvariate_impl,
+                                    sig, args)
+
+
+@glue_lowering("np.random.binomial", types.Integer, types.Float)
+def binomial_impl(context, builder, sig, args):
+    intty = sig.return_type
+    _random = np.random.random
+
+    def binomial_impl(n, p):
+        """
+        Binomial distribution.  Numpy's variant of the BINV algorithm
+        is used.
+        (Numpy uses BTPE for n*p >= 30, though)
+        """
+        if n < 0:
+            raise ValueError("binomial(): n <= 0")
+        if not (0.0 <= p <= 1.0):
+            raise ValueError("binomial(): p outside of [0, 1]")
+        if p == 0.0:
+            return 0
+        if p == 1.0:
+            return n
+
+        flipped = p > 0.5
+        if flipped:
+            p = 1.0 - p
+        q = 1.0 - p
+
+        niters = 1
+        qn = q ** n
+        while qn <= 1e-308:
+            # Underflow => split into several iterations
+            # Note this is much slower than Numpy's BTPE
+            niters <<= 2
+            n >>= 2
+            qn = q ** n
+            assert n > 0
+
+        np = n * p
+        bound = min(n, np + 10.0 * math.sqrt(np * q + 1))
+
+        finished = False
+        total = 0
+        while niters > 0:
+            X = 0
+            U = _random()
+            px = qn
+            while X <= bound:
+                if U <= px:
+                    total += n - X if flipped else X
+                    niters -= 1
+                    break
+                U -= px
+                X += 1
+                px = ((n - X + 1) * p * px) / (X * q)
+
+        return total
+
+    res = context.compile_internal(builder, binomial_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.chisquare", types.Float)
+def chisquare_impl(context, builder, sig, args):
+
+    def chisquare_impl(df):
+        return 2.0 * np.random.standard_gamma(df / 2.0)
+
+    res = context.compile_internal(builder, chisquare_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.f", types.Float, types.Float)
+def f_impl(context, builder, sig, args):
+
+    def f_impl(num, denom):
+        return ((np.random.chisquare(num) * denom) /
+                (np.random.chisquare(denom) * num))
+
+    res = context.compile_internal(builder, f_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.geometric", types.Float)
+def geometric_impl(context, builder, sig, args):
+    _random = np.random.random
+    intty = sig.return_type
+
+    def geometric_impl(p):
+        # Numpy's algorithm.
+        if p <= 0.0 or p > 1.0:
+            raise ValueError("geometric(): p outside of (0, 1]")
+        q = 1.0 - p
+        if p >= 0.333333333333333333333333:
+            X = intty(1)
+            sum = prod = p
+            U = _random()
+            while U > sum:
+                prod *= q
+                sum += prod
+                X += 1
+            return X
+        else:
+            return math.ceil(math.log(1.0 - _random()) / math.log(q))
+
+    res = context.compile_internal(builder, geometric_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.gumbel", types.Float, types.Float)
+def gumbel_impl(context, builder, sig, args):
+    _random = np.random.random
+    _log = math.log
+
+    def gumbel_impl(loc, scale):
+        U = 1.0 - _random()
+        return loc - scale * _log(-_log(U))
+
+    res = context.compile_internal(builder, gumbel_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.hypergeometric", types.Integer,
+           types.Integer, types.Integer)
+def hypergeometric_impl(context, builder, sig, args):
+    _random = np.random.random
+    _floor = math.floor
+
+    def hypergeometric_impl(ngood, nbad, nsamples):
+        """Numpy's algorithm for hypergeometric()."""
+        d1 = nbad + ngood - nsamples
+        d2 = float(min(nbad, ngood))
+
+        Y = d2
+        K = nsamples
+        while Y > 0.0 and K > 0:
+            Y -= _floor(_random() + Y / (d1 + K))
+            K -= 1
+        Z = int(d2 - Y)
+        if ngood > nbad:
+            return nsamples - Z
+        else:
+            return Z
+
+    res = context.compile_internal(builder, hypergeometric_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.laplace")
+@glue_lowering("np.random.laplace", types.Float)
+@glue_lowering("np.random.laplace", types.Float, types.Float)
+def laplace_impl(context, builder, sig, args):
+    _random = np.random.random
+    _log = math.log
+
+    def laplace_impl(loc, scale):
+        U = _random()
+        if U < 0.5:
+            return loc + scale * _log(U + U)
+        else:
+            return loc - scale * _log(2.0 - U - U)
+
+    sig, args = _fill_defaults(context, builder, sig, args, (0.0, 1.0))
+    res = context.compile_internal(builder, laplace_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.logistic")
+@glue_lowering("np.random.logistic", types.Float)
+@glue_lowering("np.random.logistic", types.Float, types.Float)
+def logistic_impl(context, builder, sig, args):
+    _random = np.random.random
+    _log = math.log
+
+    def logistic_impl(loc, scale):
+        U = _random()
+        return loc + scale * _log(U / (1.0 - U))
+
+    sig, args = _fill_defaults(context, builder, sig, args, (0.0, 1.0))
+    res = context.compile_internal(builder, logistic_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@glue_lowering("np.random.logseries", types.Float)
+def logseries_impl(context, builder, sig, args):
+    intty = sig.return_type
+    _random = np.random.random
+    _log = math.log
+    _exp = math.exp
+
+    def logseries_impl(p):
+        """Numpy's algorithm for logseries()."""
+        if p <= 0.0 or p > 1.0:
+            raise ValueError("logseries(): p outside of (0, 1]")
+        r = _log(1.0 - p)
+
+        while 1:
+            V = _random()
+            if V >= p:
+                return 1
+            U = _random()
+            q = 1.0 - _exp(r * U)
+            if V <= q * q:
+                # XXX what if V == 0.0 ?
+                return intty(1.0 + _log(V) / _log(q))
+            elif V >= q:
+                return 1
+            else:
+                return 2
+
+    res = context.compile_internal(builder, logseries_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.negative_binomial", types.int64, types.Float)
+def negative_binomial_impl(context, builder, sig, args):
+    _gamma = np.random.gamma
+    _poisson = np.random.poisson
+
+    def negative_binomial_impl(n, p):
+        if n <= 0:
+            raise ValueError("negative_binomial(): n <= 0")
+        if p < 0.0 or p > 1.0:
+            raise ValueError("negative_binomial(): p outside of [0, 1]")
+        Y = _gamma(n, (1.0 - p) / p)
+        return _poisson(Y)
+
+    res = context.compile_internal(builder, negative_binomial_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.poisson")
+@glue_lowering("np.random.poisson", types.Float)
+def poisson_impl(context, builder, sig, args):
+    state_ptr = get_np_state_ptr(context, builder)
+
+    retptr = cgutils.alloca_once(builder, int64_t, name="ret")
+    bbcont = builder.append_basic_block("bbcont")
+    bbend = builder.append_basic_block("bbend")
+
+    if len(args) == 1:
+        lam, = args
+        big_lam = builder.fcmp_ordered('>=', lam, ir.Constant(double, 10.0))
+        with builder.if_then(big_lam):
+            # For lambda >= 10.0, we switch to a more accurate
+            # algorithm (see _random.c).
+            fnty = ir.FunctionType(int64_t, (rnd_state_ptr_t, double))
+            fn = cgutils.get_or_insert_function(builder.function.module, fnty,
+                                                "numba_poisson_ptrs")
+            ret = builder.call(fn, (state_ptr, lam))
+            builder.store(ret, retptr)
+            builder.branch(bbend)
+
+    builder.branch(bbcont)
+    builder.position_at_end(bbcont)
+
+    _random = np.random.random
+    _exp = math.exp
+
+    def poisson_impl(lam):
+        """Numpy's algorithm for poisson() on small *lam*.
+
+        This method is invoked only if the parameter lambda of the
+        distribution is small ( < 10 ). The algorithm used is described
+        in "Knuth, D. 1969. 'Seminumerical Algorithms. The Art of
+        Computer Programming' vol 2.
+        """
+        if lam < 0.0:
+            raise ValueError("poisson(): lambda < 0")
+        if lam == 0.0:
+            return 0
+        enlam = _exp(-lam)
+        X = 0
+        prod = 1.0
+        while 1:
+            U = _random()
+            prod *= U
+            if prod <= enlam:
+                return X
+            X += 1
+
+    if len(args) == 0:
+        sig = signature(sig.return_type, types.float64)
+        args = (ir.Constant(double, 1.0),)
+
+    ret = context.compile_internal(builder, poisson_impl, sig, args)
+    builder.store(ret, retptr)
+    builder.branch(bbend)
+    builder.position_at_end(bbend)
+    res = builder.load(retptr)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.power", types.Float)
+def power_impl(context, builder, sig, args):
+
+    def power_impl(a):
+        if a <= 0.0:
+            raise ValueError("power(): a <= 0")
+        return math.pow(1 - math.exp(-np.random.standard_exponential()),
+                        1./a)
+
+    res = context.compile_internal(builder, power_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.rayleigh")
+@glue_lowering("np.random.rayleigh", types.Float)
+def rayleigh_impl(context, builder, sig, args):
+    _random = np.random.random
+
+    def rayleigh_impl(mode):
+        if mode <= 0.0:
+            raise ValueError("rayleigh(): mode <= 0")
+        return mode * math.sqrt(-2.0 * math.log(1.0 - _random()))
+
+    sig, args = _fill_defaults(context, builder, sig, args, (1.0,))
+    res = context.compile_internal(builder, rayleigh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.standard_cauchy")
+def cauchy_impl(context, builder, sig, args):
+    _gauss = np.random.standard_normal
+
+    def cauchy_impl():
+        return _gauss() / _gauss()
+
+    res = context.compile_internal(builder, cauchy_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.standard_t", types.Float)
+def standard_t_impl(context, builder, sig, args):
+
+    def standard_t_impl(df):
+        N = np.random.standard_normal()
+        G = np.random.standard_gamma(df / 2.0)
+        X = math.sqrt(df / 2.0) * N / math.sqrt(G)
+        return X
+
+    res = context.compile_internal(builder, standard_t_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.wald", types.Float, types.Float)
+def wald_impl(context, builder, sig, args):
+
+    def wald_impl(mean, scale):
+        if mean <= 0.0:
+            raise ValueError("wald(): mean <= 0")
+        if scale <= 0.0:
+            raise ValueError("wald(): scale <= 0")
+        mu_2l = mean / (2.0 * scale)
+        Y = np.random.standard_normal()
+        Y = mean * Y * Y
+        X = mean + mu_2l * (Y - math.sqrt(4 * scale * Y + Y * Y))
+        U = np.random.random()
+        if U <= mean / (mean + X):
+            return X
+        else:
+            return mean * mean / X
+
+    res = context.compile_internal(builder, wald_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering("np.random.zipf", types.Float)
+def zipf_impl(context, builder, sig, args):
+    _random = np.random.random
+    intty = sig.return_type
+
+    def zipf_impl(a):
+        if a <= 1.0:
+            raise ValueError("zipf(): a <= 1")
+        am1 = a - 1.0
+        b = 2.0 ** am1
+        while 1:
+            U = 1.0 - _random()
+            V = _random()
+            X = intty(math.floor(U ** (-1.0 / am1)))
+            T = (1.0 + 1.0 / X) ** am1
+            if X >= 1 and V * X * (T - 1.0) / (b - 1.0) <= (T / b):
+                return X
+
+    res = context.compile_internal(builder, zipf_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+def do_shuffle_impl(arr, rng):
+
+    if not isinstance(arr, types.Buffer):
+        raise TypeError("The argument to shuffle() should be a buffer type")
+
+    if rng == "np":
+        rand = np.random.randint
+    elif rng == "py":
+        rand = random.randrange
+
+    if arr.ndim == 1:
+        def impl(arr):
+            i = arr.shape[0] - 1
+            while i > 0:
+                j = rand(i + 1)
+                arr[i], arr[j] = arr[j], arr[i]
+                i -= 1
+    else:
+        def impl(arr):
+            i = arr.shape[0] - 1
+            while i > 0:
+                j = rand(i + 1)
+                arr[i], arr[j] = np.copy(arr[j]), np.copy(arr[i])
+                i -= 1
+
+    return impl
+
+@overload(random.shuffle)
+def shuffle_impl(arr):
+    return do_shuffle_impl(arr, "py")
+
+@overload(np.random.shuffle)
+def shuffle_impl(arr):
+    return do_shuffle_impl(arr, "np")
+
+@overload(np.random.permutation)
+def permutation_impl(x):
+    if isinstance(x, types.Integer):
+        def permutation_impl(x):
+            y = np.arange(x)
+            np.random.shuffle(y)
+            return y
+    elif isinstance(x, types.Array):
+        def permutation_impl(x):
+            arr_copy = x.copy()
+            np.random.shuffle(arr_copy)
+            return arr_copy
+    else:
+        permutation_impl = None
+    return permutation_impl
+
+
+# ------------------------------------------------------------------------
+# Array-producing variants of scalar random functions
+
+for typing_key, arity in [
+    ("np.random.beta", 3),
+    ("np.random.binomial", 3),
+    ("np.random.chisquare", 2),
+    ("np.random.exponential", 2),
+    ("np.random.f", 3),
+    ("np.random.gamma", 3),
+    ("np.random.geometric", 2),
+    ("np.random.gumbel", 3),
+    ("np.random.hypergeometric", 4),
+    ("np.random.laplace", 3),
+    ("np.random.logistic", 3),
+    ("np.random.lognormal", 3),
+    ("np.random.logseries", 2),
+    ("np.random.negative_binomial", 3),
+    ("np.random.normal", 3),
+    ("np.random.pareto", 2),
+    ("np.random.poisson", 2),
+    ("np.random.power", 2),
+    ("np.random.random", 1),
+    ("np.random.random_sample", 1),
+    ("np.random.ranf", 1),
+    ("np.random.sample", 1),
+    ("np.random.randint", 3),
+    ("np.random.rayleigh", 2),
+    ("np.random.standard_cauchy", 1),
+    ("np.random.standard_exponential", 1),
+    ("np.random.standard_gamma", 2),
+    ("np.random.standard_normal", 1),
+    ("np.random.standard_t", 2),
+    ("np.random.triangular", 4),
+    ("np.random.uniform", 3),
+    ("np.random.vonmises", 3),
+    ("np.random.wald", 3),
+    ("np.random.weibull", 2),
+    ("np.random.zipf", 2),
+    ]:
+
+    @glue_lowering(typing_key, *(types.Any,) * arity)
+    def random_arr(context, builder, sig, args, typing_key=typing_key):
+
+        arrty = sig.return_type
+        dtype = arrty.dtype
+        scalar_sig = signature(dtype, *sig.args[:-1])
+        scalar_args = args[:-1]
+
+        # Allocate array...
+        shapes = arrayobj._parse_shape(context, builder, sig.args[-1], args[-1])
+        arr = arrayobj._empty_nd_impl(context, builder, arrty, shapes)
+
+        # ... and populate it in natural order
+        *mod, fname = typing_key.split('.')
+        # Module must be numpy.random
+        assert mod == ['np', 'random']
+        np_func = getattr(np.random, fname)
+        fnty = context.typing_context.resolve_value_type(np_func)
+        resolved_sig = fnty.get_call_type(context.typing_context,
+                                          scalar_sig.args, {})
+        scalar_impl = context.get_function(fnty, resolved_sig)
+        with cgutils.for_range(builder, arr.nitems) as loop:
+            val = scalar_impl(builder, scalar_args)
+            ptr = cgutils.gep(builder, arr.data, loop.index)
+            arrayobj.store_item(context, builder, arrty, val, ptr)
+
+        return impl_ret_new_ref(context, builder, sig.return_type, arr._getvalue())
+
+
+# ------------------------------------------------------------------------
+# Irregular aliases: np.random.rand, np.random.randn
+
+@overload(np.random.rand)
+def rand(*size):
+    if len(size) == 0:
+        # Scalar output
+        def rand_impl(*size):
+            return np.random.random()
+
+    else:
+        # Array output
+        def rand_impl(*size):
+            return np.random.random(size)
+
+    return rand_impl
+
+@overload(np.random.randn)
+def randn(*size):
+    if len(size) == 0:
+        # Scalar output
+        def randn_impl(*size):
+            return np.random.standard_normal()
+
+    else:
+        # Array output
+        def randn_impl(*size):
+            return np.random.standard_normal(size)
+
+    return randn_impl
+
+
+# ------------------------------------------------------------------------
+# np.random.choice
+
+@overload(np.random.choice)
+def choice(a, size=None, replace=True):
+
+    if isinstance(a, types.Array):
+        # choice() over an array population
+        assert a.ndim == 1
+        dtype = a.dtype
+
+        @register_jitable
+        def get_source_size(a):
+            return len(a)
+
+        @register_jitable
+        def copy_source(a):
+            return a.copy()
+
+        @register_jitable
+        def getitem(a, a_i):
+            return a[a_i]
+
+    elif isinstance(a, types.Integer):
+        # choice() over an implied arange() population
+        dtype = np.intp
+
+        @register_jitable
+        def get_source_size(a):
+            return a
+
+        @register_jitable
+        def copy_source(a):
+            return np.arange(a)
+
+        @register_jitable
+        def getitem(a, a_i):
+            return a_i
+
+    else:
+        raise TypeError("np.random.choice() first argument should be "
+                        "int or array, got %s" % (a,))
+
+    if size in (None, types.none):
+        def choice_impl(a, size=None, replace=True):
+            """
+            choice() implementation returning a single sample
+            (note *replace* is ignored)
+            """
+            n = get_source_size(a)
+            i = np.random.randint(0, n)
+            return getitem(a, i)
+
+    else:
+        def choice_impl(a, size=None, replace=True):
+            """
+            choice() implementation returning an array of samples
+            """
+            n = get_source_size(a)
+            if replace:
+                out = np.empty(size, dtype)
+                fl = out.flat
+                for i in range(len(fl)):
+                    j = np.random.randint(0, n)
+                    fl[i] = getitem(a, j)
+                return out
+            else:
+                # Note we have to construct the array to compute out.size
+                # (`size` can be an arbitrary int or tuple of ints)
+                out = np.empty(size, dtype)
+                if out.size > n:
+                    raise ValueError("Cannot take a larger sample than "
+                                     "population when 'replace=False'")
+                # Get a permuted copy of the source array
+                # we need this implementation in order to get the
+                # np.random.choice inside numba to match the output
+                # of np.random.choice outside numba when np.random.seed
+                # is set to the same value
+                permuted_a = np.random.permutation(a)
+                fl = out.flat
+                for i in range(len(fl)):
+                    fl[i] = permuted_a[i]
+                return out
+
+    return choice_impl
+
+
+# ------------------------------------------------------------------------
+# np.random.multinomial
+
+@overload(np.random.multinomial)
+def multinomial(n, pvals, size=None):
+
+    dtype = np.intp
+
+    @register_jitable
+    def multinomial_inner(n, pvals, out):
+        # Numpy's algorithm for multinomial()
+        fl = out.flat
+        sz = out.size
+        plen = len(pvals)
+
+        for i in range(0, sz, plen):
+            # Loop body: take a set of n experiments and fill up
+            # fl[i:i + plen] with the distribution of results.
+
+            # Current sum of outcome probabilities
+            p_sum = 1.0
+            # Current remaining number of experiments
+            n_experiments = n
+            # For each possible outcome `j`, compute the number of results
+            # with this outcome.  This is done by considering the
+            # conditional probability P(X=j | X>=j) and running a binomial
+            # distribution over the remaining number of experiments.
+            for j in range(0, plen - 1):
+                p_j = pvals[j]
+                n_j = fl[i + j] = np.random.binomial(n_experiments, p_j / p_sum)
+                n_experiments -= n_j
+                if n_experiments <= 0:
+                    # Note the output was initialized to zero
+                    break
+                p_sum -= p_j
+            if n_experiments > 0:
+                # The remaining experiments end up in the last bucket
+                fl[i + plen - 1] = n_experiments
+
+    if not isinstance(n, types.Integer):
+        raise TypeError("np.random.multinomial(): n should be an "
+                        "integer, got %s" % (n,))
+
+    if not isinstance(pvals, (types.Sequence, types.Array)):
+        raise TypeError("np.random.multinomial(): pvals should be an "
+                        "array or sequence, got %s" % (pvals,))
+
+    if size in (None, types.none):
+        def multinomial_impl(n, pvals, size=None):
+            """
+            multinomial(..., size=None)
+            """
+            out = np.zeros(len(pvals), dtype)
+            multinomial_inner(n, pvals, out)
+            return out
+
+    elif isinstance(size, types.Integer):
+        def multinomial_impl(n, pvals, size=None):
+            """
+            multinomial(..., size=int)
+            """
+            out = np.zeros((size, len(pvals)), dtype)
+            multinomial_inner(n, pvals, out)
+            return out
+
+    elif isinstance(size, types.BaseTuple):
+        def multinomial_impl(n, pvals, size=None):
+            """
+            multinomial(..., size=tuple)
+            """
+            out = np.zeros(size + (len(pvals),), dtype)
+            multinomial_inner(n, pvals, out)
+            return out
+
+    else:
+        raise TypeError("np.random.multinomial(): size should be int or "
+                        "tuple or None, got %s" % (size,))
+
+    return multinomial_impl
+
+# ------------------------------------------------------------------------
+# np.random.dirichlet
+
+@overload(np.random.dirichlet)
+def dirichlet(alpha, size=None):
+    @register_jitable
+    def dirichlet_arr(alpha, out):
+
+        # Gamma distribution method to generate a Dirichlet distribution
+
+        for a_val in iter(alpha):
+            if a_val <= 0:
+                raise ValueError("dirichlet: alpha must be > 0.0")
+
+        a_len = len(alpha)
+        size = out.size
+        flat = out.flat
+        for i in range(0, size, a_len):
+            # calculate gamma random numbers per alpha specifications
+            norm = 0  # use this to normalize every the group total to 1
+            for k, w in enumerate(alpha):
+                flat[i + k] = np.random.gamma(w, 1)
+                norm += flat[i + k].item()
+            for k, w in enumerate(alpha):
+                flat[i + k] /= norm
+
+    if not isinstance(alpha, (types.Sequence, types.Array)):
+        raise NumbaTypeError(
+            "np.random.dirichlet(): alpha should be an "
+            "array or sequence, got %s" % (alpha,)
+        )
+
+    if size in (None, types.none):
+
+        def dirichlet_impl(alpha, size=None):
+            out = np.empty(len(alpha))
+            dirichlet_arr(alpha, out)
+            return out
+
+    elif isinstance(size, types.Integer):
+
+        def dirichlet_impl(alpha, size=None):
+            """
+            dirichlet(..., size=int)
+            """
+            out = np.empty((size, len(alpha)))
+            dirichlet_arr(alpha, out)
+            return out
+
+    elif isinstance(size, (types.UniTuple)) and isinstance(size.dtype, types.Integer):
+        def dirichlet_impl(alpha, size=None):
+            """
+            dirichlet(..., size=tuple)
+            """
+            out = np.empty(size + (len(alpha),))
+            dirichlet_arr(alpha, out)
+            return out
+
+    else:
+        raise NumbaTypeError(
+            "np.random.dirichlet(): size should be int or "
+            "tuple of ints or None, got %s" % size
+        )
+
+    return dirichlet_impl
+
+# ------------------------------------------------------------------------
+# np.random.noncentral_chisquare
+
+@overload(np.random.noncentral_chisquare)
+def noncentral_chisquare(df, nonc, size=None):
+
+    @register_jitable
+    def validate_input(df, nonc):
+        if df <= 0:
+            raise ValueError("df <= 0")
+        if nonc < 0:
+            raise ValueError("nonc < 0")
+
+    @register_jitable
+    def noncentral_chisquare_single(df, nonc):
+        # identical to numpy implementation from distributions.c
+        # https://github.com/numpy/numpy/blob/c65bc212ec1987caefba0ea7efe6a55803318de9/numpy/random/src/distributions/distributions.c#L797
+        
+        if np.isnan(nonc):
+            return np.nan
+
+        if 1 < df:
+            chi2 = np.random.chisquare(df-1)
+            n = np.random.standard_normal() + np.sqrt(nonc)
+            return chi2 + n * n
+
+        else:
+            i = np.random.poisson(nonc/2.0)
+            return np.random.chisquare(df + 2 * i)
+
+    if size in (None, types.none):
+        def noncentral_chisquare_impl(df, nonc, size=None):
+            validate_input(df, nonc)
+            return noncentral_chisquare_single(df, nonc)
+
+    elif isinstance(size, types.Integer) or (
+        (isinstance(size, (types.UniTuple)) and isinstance(size.dtype, types.Integer))
+        ):
+
+        def noncentral_chisquare_impl(df, nonc, size=None):
+            validate_input(df, nonc)
+            out = np.empty(size)
+            out_flat = out.flat
+            for idx in range(out.size):
+                out_flat[idx] = noncentral_chisquare_single(df, nonc)
+            return out
+
+    else:
+        raise NumbaTypeError(
+            "np.random.noncentral_chisquare(): size should be int or "
+            "tuple of ints or None, got %s" % size
+        )
+
+    return noncentral_chisquare_impl
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/rangeobj.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/rangeobj.py
new file mode 100644
index 000000000..b2633a5ff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/rangeobj.py
@@ -0,0 +1,299 @@
+"""
+Implementation of the range object for fixed-size integers.
+"""
+
+import operator
+
+from numba import prange
+from numba.core import types, cgutils, errors
+from numba.cpython.listobj import ListIterInstance
+from numba.np.arrayobj import make_array
+from numba.core.imputils import (lower_builtin, lower_cast,
+                                    iterator_impl, impl_ret_untracked)
+from numba.core.typing import signature
+from numba.core.extending import intrinsic, overload, overload_attribute, register_jitable
+from numba.parfors.parfor import internal_prange
+
+def make_range_iterator(typ):
+    """
+    Return the Structure representation of the given *typ* (an
+    instance of types.RangeIteratorType).
+    """
+    return cgutils.create_struct_proxy(typ)
+
+
+def make_range_impl(int_type, range_state_type, range_iter_type):
+    RangeState = cgutils.create_struct_proxy(range_state_type)
+
+    @lower_builtin(range, int_type)
+    @lower_builtin(prange, int_type)
+    @lower_builtin(internal_prange, int_type)
+    def range1_impl(context, builder, sig, args):
+        """
+        range(stop: int) -> range object
+        """
+        [stop] = args
+        state = RangeState(context, builder)
+        state.start = context.get_constant(int_type, 0)
+        state.stop = stop
+        state.step = context.get_constant(int_type, 1)
+        return impl_ret_untracked(context,
+                                  builder,
+                                  range_state_type,
+                                  state._getvalue())
+
+    @lower_builtin(range, int_type, int_type)
+    @lower_builtin(prange, int_type, int_type)
+    @lower_builtin(internal_prange, int_type, int_type)
+    def range2_impl(context, builder, sig, args):
+        """
+        range(start: int, stop: int) -> range object
+        """
+        start, stop = args
+        state = RangeState(context, builder)
+        state.start = start
+        state.stop = stop
+        state.step = context.get_constant(int_type, 1)
+        return impl_ret_untracked(context,
+                                  builder,
+                                  range_state_type,
+                                  state._getvalue())
+
+    @lower_builtin(range, int_type, int_type, int_type)
+    @lower_builtin(prange, int_type, int_type, int_type)
+    @lower_builtin(internal_prange, int_type, int_type, int_type)
+    def range3_impl(context, builder, sig, args):
+        """
+        range(start: int, stop: int, step: int) -> range object
+        """
+        [start, stop, step] = args
+        state = RangeState(context, builder)
+        state.start = start
+        state.stop = stop
+        state.step = step
+        return impl_ret_untracked(context,
+                                  builder,
+                                  range_state_type,
+                                  state._getvalue())
+
+    @lower_builtin(len, range_state_type)
+    def range_len(context, builder, sig, args):
+        """
+        len(range)
+        """
+        (value,) = args
+        state = RangeState(context, builder, value)
+        res = RangeIter.from_range_state(context, builder, state)
+        return impl_ret_untracked(context, builder, int_type, builder.load(res.count))
+
+    @lower_builtin('getiter', range_state_type)
+    def getiter_range32_impl(context, builder, sig, args):
+        """
+        range.__iter__
+        """
+        (value,) = args
+        state = RangeState(context, builder, value)
+        res = RangeIter.from_range_state(context, builder, state)._getvalue()
+        return impl_ret_untracked(context, builder, range_iter_type, res)
+
+    @iterator_impl(range_state_type, range_iter_type)
+    class RangeIter(make_range_iterator(range_iter_type)):
+
+        @classmethod
+        def from_range_state(cls, context, builder, state):
+            """
+            Create a RangeIter initialized from the given RangeState *state*.
+            """
+            self = cls(context, builder)
+            start = state.start
+            stop = state.stop
+            step = state.step
+
+            startptr = cgutils.alloca_once(builder, start.type)
+            builder.store(start, startptr)
+
+            countptr = cgutils.alloca_once(builder, start.type)
+
+            self.iter = startptr
+            self.stop = stop
+            self.step = step
+            self.count = countptr
+
+            diff = builder.sub(stop, start)
+            zero = context.get_constant(int_type, 0)
+            one = context.get_constant(int_type, 1)
+            pos_diff = builder.icmp_signed('>', diff, zero)
+            pos_step = builder.icmp_signed('>', step, zero)
+            sign_differs = builder.xor(pos_diff, pos_step)
+            zero_step = builder.icmp_unsigned('==', step, zero)
+
+            with cgutils.if_unlikely(builder, zero_step):
+                # step shouldn't be zero
+                context.call_conv.return_user_exc(builder, ValueError,
+                                                  ("range() arg 3 must not be zero",))
+
+            with builder.if_else(sign_differs) as (then, orelse):
+                with then:
+                    builder.store(zero, self.count)
+
+                with orelse:
+                    rem = builder.srem(diff, step)
+                    rem = builder.select(pos_diff, rem, builder.neg(rem))
+                    uneven = builder.icmp_signed('>', rem, zero)
+                    newcount = builder.add(builder.sdiv(diff, step),
+                                           builder.select(uneven, one, zero))
+                    builder.store(newcount, self.count)
+
+            return self
+
+        def iternext(self, context, builder, result):
+            zero = context.get_constant(int_type, 0)
+            countptr = self.count
+            count = builder.load(countptr)
+            is_valid = builder.icmp_signed('>', count, zero)
+            result.set_valid(is_valid)
+
+            with builder.if_then(is_valid):
+                value = builder.load(self.iter)
+                result.yield_(value)
+                one = context.get_constant(int_type, 1)
+
+                builder.store(builder.sub(count, one, flags=["nsw"]), countptr)
+                builder.store(builder.add(value, self.step), self.iter)
+
+
+range_impl_map = {
+    types.int32 : (types.range_state32_type, types.range_iter32_type),
+    types.int64 : (types.range_state64_type, types.range_iter64_type),
+    types.uint64 : (types.unsigned_range_state64_type, types.unsigned_range_iter64_type)
+}
+
+for int_type, state_types in range_impl_map.items():
+    make_range_impl(int_type, *state_types)
+
+@lower_cast(types.RangeType, types.RangeType)
+def range_to_range(context, builder, fromty, toty, val):
+    olditems = cgutils.unpack_tuple(builder, val, 3)
+    items = [context.cast(builder, v, fromty.dtype, toty.dtype)
+             for v in olditems]
+    return cgutils.make_anonymous_struct(builder, items)
+
+@intrinsic
+def length_of_iterator(typingctx, val):
+    """
+    An implementation of len(iter) for internal use.
+    Primary use is for array comprehensions (see inline_closurecall).
+    """
+    if isinstance(val, types.RangeIteratorType):
+        val_type = val.yield_type
+        def codegen(context, builder, sig, args):
+            (value,) = args
+            iter_type = range_impl_map[val_type][1]
+            iterobj = cgutils.create_struct_proxy(iter_type)(context, builder, value)
+            int_type = iterobj.count.type
+            return impl_ret_untracked(context, builder, int_type, builder.load(iterobj.count))
+        return signature(val_type, val), codegen
+    elif isinstance(val, types.ListIter):
+        def codegen(context, builder, sig, args):
+            (value,) = args
+            intp_t = context.get_value_type(types.intp)
+            iterobj = ListIterInstance(context, builder, sig.args[0], value)
+            return impl_ret_untracked(context, builder, intp_t, iterobj.size)
+        return signature(types.intp, val), codegen
+    elif isinstance(val, types.ArrayIterator):
+        def  codegen(context, builder, sig, args):
+            (iterty,) = sig.args
+            (value,) = args
+            intp_t = context.get_value_type(types.intp)
+            iterobj = context.make_helper(builder, iterty, value=value)
+            arrayty = iterty.array_type
+            ary = make_array(arrayty)(context, builder, value=iterobj.array)
+            shape = cgutils.unpack_tuple(builder, ary.shape)
+            # array iterates along the outer dimension
+            return impl_ret_untracked(context, builder, intp_t, shape[0])
+        return signature(types.intp, val), codegen
+    elif isinstance(val, types.UniTupleIter):
+        def codegen(context, builder, sig, args):
+            (iterty,) = sig.args
+            tuplety = iterty.container
+            intp_t = context.get_value_type(types.intp)
+            count_const = intp_t(tuplety.count)
+            return impl_ret_untracked(context, builder, intp_t, count_const)
+
+        return signature(types.intp, val), codegen
+    elif isinstance(val, types.ListTypeIteratorType):
+        def codegen(context, builder, sig, args):
+            (value,) = args
+            intp_t = context.get_value_type(types.intp)
+            from numba.typed.listobject import ListIterInstance
+            iterobj = ListIterInstance(context, builder, sig.args[0], value)
+            return impl_ret_untracked(context, builder, intp_t, iterobj.size)
+        return signature(types.intp, val), codegen
+    else:
+        msg = ('Unsupported iterator found in array comprehension, try '
+               'preallocating the array and filling manually.')
+        raise errors.TypingError(msg)
+
+def make_range_attr(index, attribute):
+    @intrinsic
+    def rangetype_attr_getter(typingctx, a):
+        if isinstance(a, types.RangeType):
+            def codegen(context, builder, sig, args):
+                (val,) = args
+                items = cgutils.unpack_tuple(builder, val, 3)
+                return impl_ret_untracked(context, builder, sig.return_type,
+                                          items[index])
+            return signature(a.dtype, a), codegen
+
+    @overload_attribute(types.RangeType, attribute)
+    def range_attr(rnge):
+        def get(rnge):
+            return rangetype_attr_getter(rnge)
+        return get
+
+
+@register_jitable
+def impl_contains_helper(robj, val):
+    if robj.step > 0 and (val < robj.start or val >= robj.stop):
+        return False
+    elif robj.step < 0 and (val <= robj.stop or val > robj.start):
+        return False
+
+    return ((val - robj.start) % robj.step) == 0
+
+
+@overload(operator.contains)
+def impl_contains(robj, val):
+    def impl_false(robj, val):
+        return False
+
+    if not isinstance(robj, types.RangeType):
+        return
+
+    elif isinstance(val, (types.Integer, types.Boolean)):
+        return impl_contains_helper
+
+    elif isinstance(val, types.Float):
+        def impl(robj, val):
+            if val % 1 != 0:
+                return False
+            else:
+                return impl_contains_helper(robj, int(val))
+        return impl
+
+    elif isinstance(val, types.Complex):
+        def impl(robj, val):
+            if val.imag != 0:
+                return False
+            elif val.real % 1 != 0:
+                return False
+            else:
+                return impl_contains_helper(robj, int(val.real))
+        return impl
+
+    elif not isinstance(val, types.Number):
+        return impl_false
+
+
+for ix, attr in enumerate(('start', 'stop', 'step')):
+    make_range_attr(index=ix, attribute=attr)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/setobj.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/setobj.py
new file mode 100644
index 000000000..7dd3ba6de
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/setobj.py
@@ -0,0 +1,1543 @@
+"""
+Support for native homogeneous sets.
+"""
+
+
+import collections
+import contextlib
+import math
+import operator
+
+from llvmlite import ir
+from numba.core import types, typing, cgutils
+from numba.core.imputils import (lower_builtin, lower_cast,
+                                    iternext_impl, impl_ret_borrowed,
+                                    impl_ret_new_ref, impl_ret_untracked,
+                                    for_iter, call_len, RefType)
+from numba.core.utils import cached_property
+from numba.misc import quicksort
+from numba.cpython import slicing
+from numba.extending import intrinsic
+from numba.core.errors import NumbaValueError
+
+
+def get_payload_struct(context, builder, set_type, ptr):
+    """
+    Given a set value and type, get its payload structure (as a
+    reference, so that mutations are seen by all).
+    """
+    payload_type = types.SetPayload(set_type)
+    ptrty = context.get_data_type(payload_type).as_pointer()
+    payload = builder.bitcast(ptr, ptrty)
+    return context.make_data_helper(builder, payload_type, ref=payload)
+
+
+def get_entry_size(context, set_type):
+    """
+    Return the entry size for the given set type.
+    """
+    llty = context.get_data_type(types.SetEntry(set_type))
+    return context.get_abi_sizeof(llty)
+
+
+# Note these values are special:
+# - EMPTY is obtained by issuing memset(..., 0xFF)
+# - (unsigned) EMPTY > (unsigned) DELETED > any other hash value
+EMPTY = -1
+DELETED = -2
+FALLBACK = -43
+
+# Minimal size of entries table.  Must be a power of 2!
+MINSIZE = 16
+
+# Number of cache-friendly linear probes before switching to non-linear probing
+LINEAR_PROBES = 3
+
+DEBUG_ALLOCS = False
+
+
+def get_hash_value(context, builder, typ, value):
+    """
+    Compute the hash of the given value.
+    """
+    typingctx = context.typing_context
+    fnty = typingctx.resolve_value_type(hash)
+    sig = fnty.get_call_type(typingctx, (typ,), {})
+    fn = context.get_function(fnty, sig)
+    h = fn(builder, (value,))
+    # Fixup reserved values
+    is_ok = is_hash_used(context, builder, h)
+    fallback = ir.Constant(h.type, FALLBACK)
+    return builder.select(is_ok, h, fallback)
+
+
+@intrinsic
+def _get_hash_value_intrinsic(typingctx, value):
+    def impl(context, builder, typ, args):
+        return get_hash_value(context, builder, value, args[0])
+    fnty = typingctx.resolve_value_type(hash)
+    sig = fnty.get_call_type(typingctx, (value,), {})
+    return sig, impl
+
+
+def is_hash_empty(context, builder, h):
+    """
+    Whether the hash value denotes an empty entry.
+    """
+    empty = ir.Constant(h.type, EMPTY)
+    return builder.icmp_unsigned('==', h, empty)
+
+def is_hash_deleted(context, builder, h):
+    """
+    Whether the hash value denotes a deleted entry.
+    """
+    deleted = ir.Constant(h.type, DELETED)
+    return builder.icmp_unsigned('==', h, deleted)
+
+def is_hash_used(context, builder, h):
+    """
+    Whether the hash value denotes an active entry.
+    """
+    # Everything below DELETED is an used entry
+    deleted = ir.Constant(h.type, DELETED)
+    return builder.icmp_unsigned('<', h, deleted)
+
+
+SetLoop = collections.namedtuple('SetLoop', ('index', 'entry', 'do_break'))
+
+
+class _SetPayload(object):
+
+    def __init__(self, context, builder, set_type, ptr):
+        payload = get_payload_struct(context, builder, set_type, ptr)
+        self._context = context
+        self._builder = builder
+        self._ty = set_type
+        self._payload = payload
+        self._entries = payload._get_ptr_by_name('entries')
+        self._ptr = ptr
+
+    @property
+    def mask(self):
+        return self._payload.mask
+
+    @mask.setter
+    def mask(self, value):
+        # CAUTION: mask must be a power of 2 minus 1
+        self._payload.mask = value
+
+    @property
+    def used(self):
+        return self._payload.used
+
+    @used.setter
+    def used(self, value):
+        self._payload.used = value
+
+    @property
+    def fill(self):
+        return self._payload.fill
+
+    @fill.setter
+    def fill(self, value):
+        self._payload.fill = value
+
+    @property
+    def finger(self):
+        return self._payload.finger
+
+    @finger.setter
+    def finger(self, value):
+        self._payload.finger = value
+
+    @property
+    def dirty(self):
+        return self._payload.dirty
+
+    @dirty.setter
+    def dirty(self, value):
+        self._payload.dirty = value
+
+    @property
+    def entries(self):
+        """
+        A pointer to the start of the entries array.
+        """
+        return self._entries
+
+    @property
+    def ptr(self):
+        """
+        A pointer to the start of the NRT-allocated area.
+        """
+        return self._ptr
+
+    def get_entry(self, idx):
+        """
+        Get entry number *idx*.
+        """
+        entry_ptr = cgutils.gep(self._builder, self._entries, idx)
+        entry = self._context.make_data_helper(self._builder,
+                                               types.SetEntry(self._ty),
+                                               ref=entry_ptr)
+        return entry
+
+    def _lookup(self, item, h, for_insert=False):
+        """
+        Lookup the *item* with the given hash values in the entries.
+
+        Return a (found, entry index) tuple:
+        - If found is true, <entry index> points to the entry containing
+          the item.
+        - If found is false, <entry index> points to the empty entry that
+          the item can be written to (only if *for_insert* is true)
+        """
+        context = self._context
+        builder = self._builder
+
+        intp_t = h.type
+
+        mask = self.mask
+        dtype = self._ty.dtype
+        tyctx = context.typing_context
+        fnty = tyctx.resolve_value_type(operator.eq)
+        sig = fnty.get_call_type(tyctx, (dtype, dtype), {})
+        eqfn = context.get_function(fnty, sig)
+
+        one = ir.Constant(intp_t, 1)
+        five = ir.Constant(intp_t, 5)
+
+        # The perturbation value for probing
+        perturb = cgutils.alloca_once_value(builder, h)
+        # The index of the entry being considered: start with (hash & mask)
+        index = cgutils.alloca_once_value(builder,
+                                          builder.and_(h, mask))
+        if for_insert:
+            # The index of the first deleted entry in the lookup chain
+            free_index_sentinel = mask.type(-1)  # highest unsigned index
+            free_index = cgutils.alloca_once_value(builder, free_index_sentinel)
+
+        bb_body = builder.append_basic_block("lookup.body")
+        bb_found = builder.append_basic_block("lookup.found")
+        bb_not_found = builder.append_basic_block("lookup.not_found")
+        bb_end = builder.append_basic_block("lookup.end")
+
+        def check_entry(i):
+            """
+            Check entry *i* against the value being searched for.
+            """
+            entry = self.get_entry(i)
+            entry_hash = entry.hash
+
+            with builder.if_then(builder.icmp_unsigned('==', h, entry_hash)):
+                # Hashes are equal, compare values
+                # (note this also ensures the entry is used)
+                eq = eqfn(builder, (item, entry.key))
+                with builder.if_then(eq):
+                    builder.branch(bb_found)
+
+            with builder.if_then(is_hash_empty(context, builder, entry_hash)):
+                builder.branch(bb_not_found)
+
+            if for_insert:
+                # Memorize the index of the first deleted entry
+                with builder.if_then(is_hash_deleted(context, builder, entry_hash)):
+                    j = builder.load(free_index)
+                    j = builder.select(builder.icmp_unsigned('==', j, free_index_sentinel),
+                                       i, j)
+                    builder.store(j, free_index)
+
+        # First linear probing.  When the number of collisions is small,
+        # the lineary probing loop achieves better cache locality and
+        # is also slightly cheaper computationally.
+        with cgutils.for_range(builder, ir.Constant(intp_t, LINEAR_PROBES)):
+            i = builder.load(index)
+            check_entry(i)
+            i = builder.add(i, one)
+            i = builder.and_(i, mask)
+            builder.store(i, index)
+
+        # If not found after linear probing, switch to a non-linear
+        # perturbation keyed on the unmasked hash value.
+        # XXX how to tell LLVM this branch is unlikely?
+        builder.branch(bb_body)
+        with builder.goto_block(bb_body):
+            i = builder.load(index)
+            check_entry(i)
+
+            # Perturb to go to next entry:
+            #   perturb >>= 5
+            #   i = (i * 5 + 1 + perturb) & mask
+            p = builder.load(perturb)
+            p = builder.lshr(p, five)
+            i = builder.add(one, builder.mul(i, five))
+            i = builder.and_(mask, builder.add(i, p))
+            builder.store(i, index)
+            builder.store(p, perturb)
+            # Loop
+            builder.branch(bb_body)
+
+        with builder.goto_block(bb_not_found):
+            if for_insert:
+                # Not found => for insertion, return the index of the first
+                # deleted entry (if any), to avoid creating an infinite
+                # lookup chain (issue #1913).
+                i = builder.load(index)
+                j = builder.load(free_index)
+                i = builder.select(builder.icmp_unsigned('==', j, free_index_sentinel),
+                                   i, j)
+                builder.store(i, index)
+            builder.branch(bb_end)
+
+        with builder.goto_block(bb_found):
+            builder.branch(bb_end)
+
+        builder.position_at_end(bb_end)
+
+        found = builder.phi(ir.IntType(1), 'found')
+        found.add_incoming(cgutils.true_bit, bb_found)
+        found.add_incoming(cgutils.false_bit, bb_not_found)
+
+        return found, builder.load(index)
+
+    @contextlib.contextmanager
+    def _iterate(self, start=None):
+        """
+        Iterate over the payload's entries.  Yield a SetLoop.
+        """
+        context = self._context
+        builder = self._builder
+
+        intp_t = context.get_value_type(types.intp)
+        one = ir.Constant(intp_t, 1)
+        size = builder.add(self.mask, one)
+
+        with cgutils.for_range(builder, size, start=start) as range_loop:
+            entry = self.get_entry(range_loop.index)
+            is_used = is_hash_used(context, builder, entry.hash)
+            with builder.if_then(is_used):
+                loop = SetLoop(index=range_loop.index, entry=entry,
+                               do_break=range_loop.do_break)
+                yield loop
+
+    @contextlib.contextmanager
+    def _next_entry(self):
+        """
+        Yield a random entry from the payload.  Caller must ensure the
+        set isn't empty, otherwise the function won't end.
+        """
+        context = self._context
+        builder = self._builder
+
+        intp_t = context.get_value_type(types.intp)
+        zero = ir.Constant(intp_t, 0)
+        one = ir.Constant(intp_t, 1)
+        mask = self.mask
+
+        # Start walking the entries from the stored "search finger" and
+        # break as soon as we find a used entry.
+
+        bb_body = builder.append_basic_block('next_entry_body')
+        bb_end = builder.append_basic_block('next_entry_end')
+
+        index = cgutils.alloca_once_value(builder, self.finger)
+        builder.branch(bb_body)
+
+        with builder.goto_block(bb_body):
+            i = builder.load(index)
+            # ANDing with mask ensures we stay inside the table boundaries
+            i = builder.and_(mask, builder.add(i, one))
+            builder.store(i, index)
+            entry = self.get_entry(i)
+            is_used = is_hash_used(context, builder, entry.hash)
+            builder.cbranch(is_used, bb_end, bb_body)
+
+        builder.position_at_end(bb_end)
+
+        # Update the search finger with the next position.  This avoids
+        # O(n**2) behaviour when pop() is called in a loop.
+        i = builder.load(index)
+        self.finger = i
+        yield self.get_entry(i)
+
+
+class SetInstance(object):
+
+    def __init__(self, context, builder, set_type, set_val):
+        self._context = context
+        self._builder = builder
+        self._ty = set_type
+        self._entrysize = get_entry_size(context, set_type)
+        self._set = context.make_helper(builder, set_type, set_val)
+
+    @property
+    def dtype(self):
+        return self._ty.dtype
+
+    @property
+    def payload(self):
+        """
+        The _SetPayload for this set.
+        """
+        # This cannot be cached as the pointer can move around!
+        context = self._context
+        builder = self._builder
+
+        ptr = self._context.nrt.meminfo_data(builder, self.meminfo)
+        return _SetPayload(context, builder, self._ty, ptr)
+
+    @property
+    def value(self):
+        return self._set._getvalue()
+
+    @property
+    def meminfo(self):
+        return self._set.meminfo
+
+    @property
+    def parent(self):
+        return self._set.parent
+
+    @parent.setter
+    def parent(self, value):
+        self._set.parent = value
+
+    def get_size(self):
+        """
+        Return the number of elements in the size.
+        """
+        return self.payload.used
+
+    def set_dirty(self, val):
+        if self._ty.reflected:
+            self.payload.dirty = cgutils.true_bit if val else cgutils.false_bit
+
+    def _add_entry(self, payload, entry, item, h, do_resize=True):
+        context = self._context
+        builder = self._builder
+
+        old_hash = entry.hash
+        entry.hash = h
+        self.incref_value(item)
+        entry.key = item
+        # used++
+        used = payload.used
+        one = ir.Constant(used.type, 1)
+        used = payload.used = builder.add(used, one)
+        # fill++ if entry wasn't a deleted one
+        with builder.if_then(is_hash_empty(context, builder, old_hash),
+                             likely=True):
+            payload.fill = builder.add(payload.fill, one)
+        # Grow table if necessary
+        if do_resize:
+            self.upsize(used)
+        self.set_dirty(True)
+
+    def _add_key(self, payload, item, h, do_resize=True, do_incref=True):
+        context = self._context
+        builder = self._builder
+
+        found, i = payload._lookup(item, h, for_insert=True)
+        not_found = builder.not_(found)
+
+        with builder.if_then(not_found):
+            # Not found => add it
+            entry = payload.get_entry(i)
+            old_hash = entry.hash
+            entry.hash = h
+            if do_incref:
+                self.incref_value(item)
+            entry.key = item
+            # used++
+            used = payload.used
+            one = ir.Constant(used.type, 1)
+            used = payload.used = builder.add(used, one)
+            # fill++ if entry wasn't a deleted one
+            with builder.if_then(is_hash_empty(context, builder, old_hash),
+                                 likely=True):
+                payload.fill = builder.add(payload.fill, one)
+            # Grow table if necessary
+            if do_resize:
+                self.upsize(used)
+            self.set_dirty(True)
+
+    def _remove_entry(self, payload, entry, do_resize=True, do_decref=True):
+        # Mark entry deleted
+        entry.hash = ir.Constant(entry.hash.type, DELETED)
+        if do_decref:
+            self.decref_value(entry.key)
+        # used--
+        used = payload.used
+        one = ir.Constant(used.type, 1)
+        used = payload.used = self._builder.sub(used, one)
+        # Shrink table if necessary
+        if do_resize:
+            self.downsize(used)
+        self.set_dirty(True)
+
+    def _remove_key(self, payload, item, h, do_resize=True):
+        context = self._context
+        builder = self._builder
+
+        found, i = payload._lookup(item, h)
+
+        with builder.if_then(found):
+            entry = payload.get_entry(i)
+            self._remove_entry(payload, entry, do_resize)
+
+        return found
+
+    def add(self, item, do_resize=True):
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        h = get_hash_value(context, builder, self._ty.dtype, item)
+        self._add_key(payload, item, h, do_resize)
+
+    def add_pyapi(self, pyapi, item, do_resize=True):
+        """A version of .add for use inside functions following Python calling
+        convention.
+        """
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        h = self._pyapi_get_hash_value(pyapi, context, builder, item)
+        self._add_key(payload, item, h, do_resize)
+
+    def _pyapi_get_hash_value(self, pyapi, context, builder, item):
+        """Python API compatible version of `get_hash_value()`.
+        """
+        argtypes = [self._ty.dtype]
+        resty = types.intp
+
+        def wrapper(val):
+            return _get_hash_value_intrinsic(val)
+
+        args = [item]
+        sig = typing.signature(resty, *argtypes)
+        is_error, retval = pyapi.call_jit_code(wrapper, sig, args)
+        # Handle return status
+        with builder.if_then(is_error, likely=False):
+            # Raise nopython exception as a Python exception
+            builder.ret(pyapi.get_null_object())
+        return retval
+
+    def contains(self, item):
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        h = get_hash_value(context, builder, self._ty.dtype, item)
+        found, i = payload._lookup(item, h)
+        return found
+
+    def discard(self, item):
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        h = get_hash_value(context, builder, self._ty.dtype, item)
+        found = self._remove_key(payload, item, h)
+        return found
+
+    def pop(self):
+        context = self._context
+        builder = self._builder
+
+        lty = context.get_value_type(self._ty.dtype)
+        key = cgutils.alloca_once(builder, lty)
+
+        payload = self.payload
+        with payload._next_entry() as entry:
+            builder.store(entry.key, key)
+            # since the value is returned don't decref in _remove_entry()
+            self._remove_entry(payload, entry, do_decref=False)
+
+        return builder.load(key)
+
+    def clear(self):
+        context = self._context
+        builder = self._builder
+
+        intp_t = context.get_value_type(types.intp)
+        minsize = ir.Constant(intp_t, MINSIZE)
+        self._replace_payload(minsize)
+        self.set_dirty(True)
+
+    def copy(self):
+        """
+        Return a copy of this set.
+        """
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        used = payload.used
+        fill = payload.fill
+
+        other = type(self)(context, builder, self._ty, None)
+
+        no_deleted_entries = builder.icmp_unsigned('==', used, fill)
+        with builder.if_else(no_deleted_entries, likely=True) \
+            as (if_no_deleted, if_deleted):
+            with if_no_deleted:
+                # No deleted entries => raw copy the payload
+                ok = other._copy_payload(payload)
+                with builder.if_then(builder.not_(ok), likely=False):
+                    context.call_conv.return_user_exc(builder, MemoryError,
+                                                      ("cannot copy set",))
+
+            with if_deleted:
+                # Deleted entries => re-insert entries one by one
+                nentries = self.choose_alloc_size(context, builder, used)
+                ok = other._allocate_payload(nentries)
+                with builder.if_then(builder.not_(ok), likely=False):
+                    context.call_conv.return_user_exc(builder, MemoryError,
+                                                      ("cannot copy set",))
+
+                other_payload = other.payload
+                with payload._iterate() as loop:
+                    entry = loop.entry
+                    other._add_key(other_payload, entry.key, entry.hash,
+                                   do_resize=False)
+
+        return other
+
+    def intersect(self, other):
+        """
+        In-place intersection with *other* set.
+        """
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        with payload._iterate() as loop:
+            entry = loop.entry
+            found, _ = other_payload._lookup(entry.key, entry.hash)
+            with builder.if_then(builder.not_(found)):
+                self._remove_entry(payload, entry, do_resize=False)
+
+        # Final downsize
+        self.downsize(payload.used)
+
+    def difference(self, other):
+        """
+        In-place difference with *other* set.
+        """
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        with other_payload._iterate() as loop:
+            entry = loop.entry
+            self._remove_key(payload, entry.key, entry.hash, do_resize=False)
+
+        # Final downsize
+        self.downsize(payload.used)
+
+    def symmetric_difference(self, other):
+        """
+        In-place symmetric difference with *other* set.
+        """
+        context = self._context
+        builder = self._builder
+        other_payload = other.payload
+
+        with other_payload._iterate() as loop:
+            key = loop.entry.key
+            h = loop.entry.hash
+            # We must reload our payload as it may be resized during the loop
+            payload = self.payload
+            found, i = payload._lookup(key, h, for_insert=True)
+            entry = payload.get_entry(i)
+            with builder.if_else(found) as (if_common, if_not_common):
+                with if_common:
+                    self._remove_entry(payload, entry, do_resize=False)
+                with if_not_common:
+                    self._add_entry(payload, entry, key, h)
+
+        # Final downsize
+        self.downsize(self.payload.used)
+
+    def issubset(self, other, strict=False):
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        cmp_op = '<' if strict else '<='
+
+        res = cgutils.alloca_once_value(builder, cgutils.true_bit)
+        with builder.if_else(
+            builder.icmp_unsigned(cmp_op, payload.used, other_payload.used)
+            ) as (if_smaller, if_larger):
+            with if_larger:
+                # self larger than other => self cannot possibly a subset
+                builder.store(cgutils.false_bit, res)
+            with if_smaller:
+                # check whether each key of self is in other
+                with payload._iterate() as loop:
+                    entry = loop.entry
+                    found, _ = other_payload._lookup(entry.key, entry.hash)
+                    with builder.if_then(builder.not_(found)):
+                        builder.store(cgutils.false_bit, res)
+                        loop.do_break()
+
+        return builder.load(res)
+
+    def isdisjoint(self, other):
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        res = cgutils.alloca_once_value(builder, cgutils.true_bit)
+
+        def check(smaller, larger):
+            # Loop over the smaller of the two, and search in the larger
+            with smaller._iterate() as loop:
+                entry = loop.entry
+                found, _ = larger._lookup(entry.key, entry.hash)
+                with builder.if_then(found):
+                    builder.store(cgutils.false_bit, res)
+                    loop.do_break()
+
+        with builder.if_else(
+            builder.icmp_unsigned('>', payload.used, other_payload.used)
+            ) as (if_larger, otherwise):
+
+            with if_larger:
+                # len(self) > len(other)
+                check(other_payload, payload)
+
+            with otherwise:
+                # len(self) <= len(other)
+                check(payload, other_payload)
+
+        return builder.load(res)
+
+    def equals(self, other):
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        res = cgutils.alloca_once_value(builder, cgutils.true_bit)
+        with builder.if_else(
+            builder.icmp_unsigned('==', payload.used, other_payload.used)
+            ) as (if_same_size, otherwise):
+            with if_same_size:
+                # same sizes => check whether each key of self is in other
+                with payload._iterate() as loop:
+                    entry = loop.entry
+                    found, _ = other_payload._lookup(entry.key, entry.hash)
+                    with builder.if_then(builder.not_(found)):
+                        builder.store(cgutils.false_bit, res)
+                        loop.do_break()
+            with otherwise:
+                # different sizes => cannot possibly be equal
+                builder.store(cgutils.false_bit, res)
+
+        return builder.load(res)
+
+    @classmethod
+    def allocate_ex(cls, context, builder, set_type, nitems=None):
+        """
+        Allocate a SetInstance with its storage.
+        Return a (ok, instance) tuple where *ok* is a LLVM boolean and
+        *instance* is a SetInstance object (the object's contents are
+        only valid when *ok* is true).
+        """
+        intp_t = context.get_value_type(types.intp)
+
+        if nitems is None:
+            nentries = ir.Constant(intp_t, MINSIZE)
+        else:
+            if isinstance(nitems, int):
+                nitems = ir.Constant(intp_t, nitems)
+            nentries = cls.choose_alloc_size(context, builder, nitems)
+
+        self = cls(context, builder, set_type, None)
+        ok = self._allocate_payload(nentries)
+        return ok, self
+
+    @classmethod
+    def allocate(cls, context, builder, set_type, nitems=None):
+        """
+        Allocate a SetInstance with its storage.  Same as allocate_ex(),
+        but return an initialized *instance*.  If allocation failed,
+        control is transferred to the caller using the target's current
+        call convention.
+        """
+        ok, self = cls.allocate_ex(context, builder, set_type, nitems)
+        with builder.if_then(builder.not_(ok), likely=False):
+            context.call_conv.return_user_exc(builder, MemoryError,
+                                              ("cannot allocate set",))
+        return self
+
+    @classmethod
+    def from_meminfo(cls, context, builder, set_type, meminfo):
+        """
+        Allocate a new set instance pointing to an existing payload
+        (a meminfo pointer).
+        Note the parent field has to be filled by the caller.
+        """
+        self = cls(context, builder, set_type, None)
+        self._set.meminfo = meminfo
+        self._set.parent = context.get_constant_null(types.pyobject)
+        context.nrt.incref(builder, set_type, self.value)
+        # Payload is part of the meminfo, no need to touch it
+        return self
+
+    @classmethod
+    def choose_alloc_size(cls, context, builder, nitems):
+        """
+        Choose a suitable number of entries for the given number of items.
+        """
+        intp_t = nitems.type
+        one = ir.Constant(intp_t, 1)
+        minsize = ir.Constant(intp_t, MINSIZE)
+
+        # Ensure number of entries >= 2 * used
+        min_entries = builder.shl(nitems, one)
+        # Find out first suitable power of 2, starting from MINSIZE
+        size_p = cgutils.alloca_once_value(builder, minsize)
+
+        bb_body = builder.append_basic_block("calcsize.body")
+        bb_end = builder.append_basic_block("calcsize.end")
+
+        builder.branch(bb_body)
+
+        with builder.goto_block(bb_body):
+            size = builder.load(size_p)
+            is_large_enough = builder.icmp_unsigned('>=', size, min_entries)
+            with builder.if_then(is_large_enough, likely=False):
+                builder.branch(bb_end)
+            next_size = builder.shl(size, one)
+            builder.store(next_size, size_p)
+            builder.branch(bb_body)
+
+        builder.position_at_end(bb_end)
+        return builder.load(size_p)
+
+    def upsize(self, nitems):
+        """
+        When adding to the set, ensure it is properly sized for the given
+        number of used entries.
+        """
+        context = self._context
+        builder = self._builder
+        intp_t = nitems.type
+
+        one = ir.Constant(intp_t, 1)
+        two = ir.Constant(intp_t, 2)
+
+        payload = self.payload
+
+        # Ensure number of entries >= 2 * used
+        min_entries = builder.shl(nitems, one)
+        size = builder.add(payload.mask, one)
+        need_resize = builder.icmp_unsigned('>=', min_entries, size)
+
+        with builder.if_then(need_resize, likely=False):
+            # Find out next suitable size
+            new_size_p = cgutils.alloca_once_value(builder, size)
+
+            bb_body = builder.append_basic_block("calcsize.body")
+            bb_end = builder.append_basic_block("calcsize.end")
+
+            builder.branch(bb_body)
+
+            with builder.goto_block(bb_body):
+                # Multiply by 4 (ensuring size remains a power of two)
+                new_size = builder.load(new_size_p)
+                new_size = builder.shl(new_size, two)
+                builder.store(new_size, new_size_p)
+                is_too_small = builder.icmp_unsigned('>=', min_entries, new_size)
+                builder.cbranch(is_too_small, bb_body, bb_end)
+
+            builder.position_at_end(bb_end)
+
+            new_size = builder.load(new_size_p)
+            if DEBUG_ALLOCS:
+                context.printf(builder,
+                               "upsize to %zd items: current size = %zd, "
+                               "min entries = %zd, new size = %zd\n",
+                               nitems, size, min_entries, new_size)
+            self._resize(payload, new_size, "cannot grow set")
+
+    def downsize(self, nitems):
+        """
+        When removing from the set, ensure it is properly sized for the given
+        number of used entries.
+        """
+        context = self._context
+        builder = self._builder
+        intp_t = nitems.type
+
+        one = ir.Constant(intp_t, 1)
+        two = ir.Constant(intp_t, 2)
+        minsize = ir.Constant(intp_t, MINSIZE)
+
+        payload = self.payload
+
+        # Ensure entries >= max(2 * used, MINSIZE)
+        min_entries = builder.shl(nitems, one)
+        min_entries = builder.select(builder.icmp_unsigned('>=', min_entries, minsize),
+                                     min_entries, minsize)
+        # Shrink only if size >= 4 * min_entries && size > MINSIZE
+        max_size = builder.shl(min_entries, two)
+        size = builder.add(payload.mask, one)
+        need_resize = builder.and_(
+            builder.icmp_unsigned('<=', max_size, size),
+            builder.icmp_unsigned('<', minsize, size))
+
+        with builder.if_then(need_resize, likely=False):
+            # Find out next suitable size
+            new_size_p = cgutils.alloca_once_value(builder, size)
+
+            bb_body = builder.append_basic_block("calcsize.body")
+            bb_end = builder.append_basic_block("calcsize.end")
+
+            builder.branch(bb_body)
+
+            with builder.goto_block(bb_body):
+                # Divide by 2 (ensuring size remains a power of two)
+                new_size = builder.load(new_size_p)
+                new_size = builder.lshr(new_size, one)
+                # Keep current size if new size would be < min_entries
+                is_too_small = builder.icmp_unsigned('>', min_entries, new_size)
+                with builder.if_then(is_too_small):
+                    builder.branch(bb_end)
+                builder.store(new_size, new_size_p)
+                builder.branch(bb_body)
+
+            builder.position_at_end(bb_end)
+
+            # Ensure new_size >= MINSIZE
+            new_size = builder.load(new_size_p)
+            # At this point, new_size should be < size if the factors
+            # above were chosen carefully!
+
+            if DEBUG_ALLOCS:
+                context.printf(builder,
+                               "downsize to %zd items: current size = %zd, "
+                               "min entries = %zd, new size = %zd\n",
+                               nitems, size, min_entries, new_size)
+            self._resize(payload, new_size, "cannot shrink set")
+
+    def _resize(self, payload, nentries, errmsg):
+        """
+        Resize the payload to the given number of entries.
+
+        CAUTION: *nentries* must be a power of 2!
+        """
+        context = self._context
+        builder = self._builder
+
+        # Allocate new entries
+        old_payload = payload
+
+        ok = self._allocate_payload(nentries, realloc=True)
+        with builder.if_then(builder.not_(ok), likely=False):
+            context.call_conv.return_user_exc(builder, MemoryError,
+                                              (errmsg,))
+
+        # Re-insert old entries
+        # No incref since they already were the first time they were inserted
+        payload = self.payload
+        with old_payload._iterate() as loop:
+            entry = loop.entry
+            self._add_key(payload, entry.key, entry.hash,
+                          do_resize=False, do_incref=False)
+
+        self._free_payload(old_payload.ptr)
+
+    def _replace_payload(self, nentries):
+        """
+        Replace the payload with a new empty payload with the given number
+        of entries.
+
+        CAUTION: *nentries* must be a power of 2!
+        """
+        context = self._context
+        builder = self._builder
+
+        # decref all of the previous entries
+        with self.payload._iterate() as loop:
+            entry = loop.entry
+            self.decref_value(entry.key)
+
+        # Free old payload
+        self._free_payload(self.payload.ptr)
+
+        ok = self._allocate_payload(nentries, realloc=True)
+        with builder.if_then(builder.not_(ok), likely=False):
+            context.call_conv.return_user_exc(builder, MemoryError,
+                                              ("cannot reallocate set",))
+
+    def _allocate_payload(self, nentries, realloc=False):
+        """
+        Allocate and initialize payload for the given number of entries.
+        If *realloc* is True, the existing meminfo is reused.
+
+        CAUTION: *nentries* must be a power of 2!
+        """
+        context = self._context
+        builder = self._builder
+
+        ok = cgutils.alloca_once_value(builder, cgutils.true_bit)
+
+        intp_t = context.get_value_type(types.intp)
+        zero = ir.Constant(intp_t, 0)
+        one = ir.Constant(intp_t, 1)
+
+        payload_type = context.get_data_type(types.SetPayload(self._ty))
+        payload_size = context.get_abi_sizeof(payload_type)
+        entry_size = self._entrysize
+        # Account for the fact that the payload struct already contains an entry
+        payload_size -= entry_size
+
+        # Total allocation size = <payload header size> + nentries * entry_size
+        allocsize, ovf = cgutils.muladd_with_overflow(builder, nentries,
+                                                      ir.Constant(intp_t, entry_size),
+                                                      ir.Constant(intp_t, payload_size))
+        with builder.if_then(ovf, likely=False):
+            builder.store(cgutils.false_bit, ok)
+
+        with builder.if_then(builder.load(ok), likely=True):
+            if realloc:
+                meminfo = self._set.meminfo
+                ptr = context.nrt.meminfo_varsize_alloc_unchecked(builder,
+                                                                  meminfo,
+                                                        size=allocsize)
+                alloc_ok = cgutils.is_null(builder, ptr)
+            else:
+                # create destructor to be called upon set destruction
+                dtor = self._imp_dtor(context, builder.module)
+                meminfo = context.nrt.meminfo_new_varsize_dtor_unchecked(
+                    builder, allocsize, builder.bitcast(dtor, cgutils.voidptr_t))
+                alloc_ok = cgutils.is_null(builder, meminfo)
+
+            with builder.if_else(alloc_ok,
+                                 likely=False) as (if_error, if_ok):
+                with if_error:
+                    builder.store(cgutils.false_bit, ok)
+                with if_ok:
+                    if not realloc:
+                        self._set.meminfo = meminfo
+                        self._set.parent = context.get_constant_null(types.pyobject)
+                    payload = self.payload
+                    # Initialize entries to 0xff (EMPTY)
+                    cgutils.memset(builder, payload.ptr, allocsize, 0xFF)
+                    payload.used = zero
+                    payload.fill = zero
+                    payload.finger = zero
+                    new_mask = builder.sub(nentries, one)
+                    payload.mask = new_mask
+
+                    if DEBUG_ALLOCS:
+                        context.printf(builder,
+                                       "allocated %zd bytes for set at %p: mask = %zd\n",
+                                       allocsize, payload.ptr, new_mask)
+
+        return builder.load(ok)
+
+    def _free_payload(self, ptr):
+        """
+        Free an allocated old payload at *ptr*.
+        """
+        self._context.nrt.meminfo_varsize_free(self._builder, self.meminfo, ptr)
+
+    def _copy_payload(self, src_payload):
+        """
+        Raw-copy the given payload into self.
+        """
+        context = self._context
+        builder = self._builder
+
+        ok = cgutils.alloca_once_value(builder, cgutils.true_bit)
+
+        intp_t = context.get_value_type(types.intp)
+        zero = ir.Constant(intp_t, 0)
+        one = ir.Constant(intp_t, 1)
+
+        payload_type = context.get_data_type(types.SetPayload(self._ty))
+        payload_size = context.get_abi_sizeof(payload_type)
+        entry_size = self._entrysize
+        # Account for the fact that the payload struct already contains an entry
+        payload_size -= entry_size
+
+        mask = src_payload.mask
+        nentries = builder.add(one, mask)
+
+        # Total allocation size = <payload header size> + nentries * entry_size
+        # (note there can't be any overflow since we're reusing an existing
+        #  payload's parameters)
+        allocsize = builder.add(ir.Constant(intp_t, payload_size),
+                                builder.mul(ir.Constant(intp_t, entry_size),
+                                            nentries))
+
+        with builder.if_then(builder.load(ok), likely=True):
+            # create destructor for new meminfo
+            dtor = self._imp_dtor(context, builder.module)
+            meminfo = context.nrt.meminfo_new_varsize_dtor_unchecked(
+                builder, allocsize, builder.bitcast(dtor, cgutils.voidptr_t))
+            alloc_ok = cgutils.is_null(builder, meminfo)
+
+            with builder.if_else(alloc_ok, likely=False) as (if_error, if_ok):
+                with if_error:
+                    builder.store(cgutils.false_bit, ok)
+                with if_ok:
+                    self._set.meminfo = meminfo
+                    payload = self.payload
+                    payload.used = src_payload.used
+                    payload.fill = src_payload.fill
+                    payload.finger = zero
+                    payload.mask = mask
+
+                    # instead of using `_add_key` for every entry, since the
+                    # size of the new set is the same, we can just copy the
+                    # data directly without having to re-compute the hash
+                    cgutils.raw_memcpy(builder, payload.entries,
+                                       src_payload.entries, nentries,
+                                       entry_size)
+                    # increment the refcounts to simulate `_add_key` for each
+                    # element
+                    with src_payload._iterate() as loop:
+                        self.incref_value(loop.entry.key)
+
+                    if DEBUG_ALLOCS:
+                        context.printf(builder,
+                                       "allocated %zd bytes for set at %p: mask = %zd\n",
+                                       allocsize, payload.ptr, mask)
+
+        return builder.load(ok)
+
+    def _imp_dtor(self, context, module):
+        """Define the dtor for set
+        """
+        llvoidptr = cgutils.voidptr_t
+        llsize_t= context.get_value_type(types.size_t)
+        # create a dtor function that takes (void* set, size_t size, void* dtor_info)
+        fnty = ir.FunctionType(
+            ir.VoidType(),
+            [llvoidptr, llsize_t, llvoidptr],
+        )
+        # create type-specific name
+        fname = f".dtor.set.{self._ty.dtype}"
+    
+        fn = cgutils.get_or_insert_function(module, fnty, name=fname)
+    
+        if fn.is_declaration:
+            # Set linkage
+            fn.linkage = 'linkonce_odr'
+            # Define
+            builder = ir.IRBuilder(fn.append_basic_block())
+            payload = _SetPayload(context, builder, self._ty, fn.args[0])
+            with payload._iterate() as loop:
+                entry = loop.entry
+                context.nrt.decref(builder, self._ty.dtype, entry.key)
+            builder.ret_void()
+    
+        return fn
+
+    def incref_value(self, val):
+        """Incref an element value
+        """
+        self._context.nrt.incref(self._builder, self._ty.dtype, val)
+
+    def decref_value(self, val):
+        """Decref an element value
+        """
+        self._context.nrt.decref(self._builder, self._ty.dtype, val)
+
+
+class SetIterInstance(object):
+
+    def __init__(self, context, builder, iter_type, iter_val):
+        self._context = context
+        self._builder = builder
+        self._ty = iter_type
+        self._iter = context.make_helper(builder, iter_type, iter_val)
+        ptr = self._context.nrt.meminfo_data(builder, self.meminfo)
+        self._payload = _SetPayload(context, builder, self._ty.container, ptr)
+
+    @classmethod
+    def from_set(cls, context, builder, iter_type, set_val):
+        set_inst = SetInstance(context, builder, iter_type.container, set_val)
+        self = cls(context, builder, iter_type, None)
+        index = context.get_constant(types.intp, 0)
+        self._iter.index = cgutils.alloca_once_value(builder, index)
+        self._iter.meminfo = set_inst.meminfo
+        return self
+
+    @property
+    def value(self):
+        return self._iter._getvalue()
+
+    @property
+    def meminfo(self):
+        return self._iter.meminfo
+
+    @property
+    def index(self):
+        return self._builder.load(self._iter.index)
+
+    @index.setter
+    def index(self, value):
+        self._builder.store(value, self._iter.index)
+
+    def iternext(self, result):
+        index = self.index
+        payload = self._payload
+        one = ir.Constant(index.type, 1)
+
+        result.set_exhausted()
+
+        with payload._iterate(start=index) as loop:
+            # An entry was found
+            entry = loop.entry
+            result.set_valid()
+            result.yield_(entry.key)
+            self.index = self._builder.add(loop.index, one)
+            loop.do_break()
+
+
+#-------------------------------------------------------------------------------
+# Constructors
+
+def build_set(context, builder, set_type, items):
+    """
+    Build a set of the given type, containing the given items.
+    """
+    nitems = len(items)
+    inst = SetInstance.allocate(context, builder, set_type, nitems)
+
+    if nitems > 0:
+
+        # Populate set.  Inlining the insertion code for each item would be very
+        # costly, instead we create a LLVM array and iterate over it.
+        array = cgutils.pack_array(builder, items)
+        array_ptr = cgutils.alloca_once_value(builder, array)
+
+        count = context.get_constant(types.intp, nitems)
+        with cgutils.for_range(builder, count) as loop:
+            item = builder.load(cgutils.gep(builder, array_ptr, 0, loop.index))
+            inst.add(item)
+
+    return impl_ret_new_ref(context, builder, set_type, inst.value)
+
+
+@lower_builtin(set)
+def set_empty_constructor(context, builder, sig, args):
+    set_type = sig.return_type
+    inst = SetInstance.allocate(context, builder, set_type)
+    return impl_ret_new_ref(context, builder, set_type, inst.value)
+
+@lower_builtin(set, types.IterableType)
+def set_constructor(context, builder, sig, args):
+    set_type = sig.return_type
+    items_type, = sig.args
+    items, = args
+
+    # If the argument has a len(), preallocate the set so as to
+    # avoid resizes.
+    # `for_iter` increfs each item in the set, so a `decref` is required each
+    # iteration to balance. Because the `incref` from `.add` is dependent on
+    # the item not already existing in the set, just removing its incref is not
+    # enough to guarantee all memory is freed
+    n = call_len(context, builder, items_type, items)
+    inst = SetInstance.allocate(context, builder, set_type, n)
+    with for_iter(context, builder, items_type, items) as loop:
+        inst.add(loop.value)
+        context.nrt.decref(builder, set_type.dtype, loop.value)
+
+    return impl_ret_new_ref(context, builder, set_type, inst.value)
+
+
+#-------------------------------------------------------------------------------
+# Various operations
+
+@lower_builtin(len, types.Set)
+def set_len(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    return inst.get_size()
+
+@lower_builtin(operator.contains, types.Set, types.Any)
+def in_set(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    return inst.contains(args[1])
+
+@lower_builtin('getiter', types.Set)
+def getiter_set(context, builder, sig, args):
+    inst = SetIterInstance.from_set(context, builder, sig.return_type, args[0])
+    return impl_ret_borrowed(context, builder, sig.return_type, inst.value)
+
+@lower_builtin('iternext', types.SetIter)
+@iternext_impl(RefType.BORROWED)
+def iternext_listiter(context, builder, sig, args, result):
+    inst = SetIterInstance(context, builder, sig.args[0], args[0])
+    inst.iternext(result)
+
+
+#-------------------------------------------------------------------------------
+# Methods
+
+# One-item-at-a-time operations
+
+@lower_builtin("set.add", types.Set, types.Any)
+def set_add(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    item = args[1]
+    inst.add(item)
+
+    return context.get_dummy_value()
+
+@lower_builtin("set.discard", types.Set, types.Any)
+def set_discard(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    item = args[1]
+    inst.discard(item)
+
+    return context.get_dummy_value()
+
+@lower_builtin("set.pop", types.Set)
+def set_pop(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    used = inst.payload.used
+    with builder.if_then(cgutils.is_null(builder, used), likely=False):
+        context.call_conv.return_user_exc(builder, KeyError,
+                                          ("set.pop(): empty set",))
+
+    return inst.pop()
+
+@lower_builtin("set.remove", types.Set, types.Any)
+def set_remove(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    item = args[1]
+    found = inst.discard(item)
+    with builder.if_then(builder.not_(found), likely=False):
+        context.call_conv.return_user_exc(builder, KeyError,
+                                          ("set.remove(): key not in set",))
+
+    return context.get_dummy_value()
+
+
+# Mutating set operations
+
+@lower_builtin("set.clear", types.Set)
+def set_clear(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    inst.clear()
+    return context.get_dummy_value()
+
+@lower_builtin("set.copy", types.Set)
+def set_copy(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = inst.copy()
+    return impl_ret_new_ref(context, builder, sig.return_type, other.value)
+
+@lower_builtin("set.difference_update", types.Set, types.IterableType)
+def set_difference_update(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+
+    inst.difference(other)
+
+    return context.get_dummy_value()
+
+@lower_builtin("set.intersection_update", types.Set, types.Set)
+def set_intersection_update(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+
+    inst.intersect(other)
+
+    return context.get_dummy_value()
+
+@lower_builtin("set.symmetric_difference_update", types.Set, types.Set)
+def set_symmetric_difference_update(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+
+    inst.symmetric_difference(other)
+
+    return context.get_dummy_value()
+
+@lower_builtin("set.update", types.Set, types.IterableType)
+def set_update(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    items_type = sig.args[1]
+    items = args[1]
+
+    # If the argument has a len(), assume there are few collisions and
+    # presize to len(set) + len(items)
+    n = call_len(context, builder, items_type, items)
+    if n is not None:
+        new_size = builder.add(inst.payload.used, n)
+        inst.upsize(new_size)
+
+    with for_iter(context, builder, items_type, items) as loop:
+        # make sure that the items being added are of the same dtype as the
+        # set instance
+        casted = context.cast(builder, loop.value, items_type.dtype, inst.dtype)
+        inst.add(casted)
+        # decref each item to counter balance the incref from `for_iter`
+        # `.add` will conditionally incref when the item does not already exist
+        # in the set, therefore removing its incref is not enough to guarantee
+        # all memory is freed
+        context.nrt.decref(builder, items_type.dtype, loop.value)
+
+    if n is not None:
+        # If we pre-grew the set, downsize in case there were many collisions
+        inst.downsize(inst.payload.used)
+
+    return context.get_dummy_value()
+
+for op_, op_impl in [
+    (operator.iand, set_intersection_update),
+    (operator.ior, set_update),
+    (operator.isub, set_difference_update),
+    (operator.ixor, set_symmetric_difference_update),
+    ]:
+    @lower_builtin(op_, types.Set, types.Set)
+    def set_inplace(context, builder, sig, args, op_impl=op_impl):
+        assert sig.return_type == sig.args[0]
+        op_impl(context, builder, sig, args)
+        return impl_ret_borrowed(context, builder, sig.args[0], args[0])
+
+
+# Set operations creating a new set
+
+@lower_builtin(operator.sub, types.Set, types.Set)
+@lower_builtin("set.difference", types.Set, types.Set)
+def set_difference(context, builder, sig, args):
+    def difference_impl(a, b):
+        s = a.copy()
+        s.difference_update(b)
+        return s
+
+    return context.compile_internal(builder, difference_impl, sig, args)
+
+@lower_builtin(operator.and_, types.Set, types.Set)
+@lower_builtin("set.intersection", types.Set, types.Set)
+def set_intersection(context, builder, sig, args):
+    def intersection_impl(a, b):
+        if len(a) < len(b):
+            s = a.copy()
+            s.intersection_update(b)
+            return s
+        else:
+            s = b.copy()
+            s.intersection_update(a)
+            return s
+
+    return context.compile_internal(builder, intersection_impl, sig, args)
+
+@lower_builtin(operator.xor, types.Set, types.Set)
+@lower_builtin("set.symmetric_difference", types.Set, types.Set)
+def set_symmetric_difference(context, builder, sig, args):
+    def symmetric_difference_impl(a, b):
+        if len(a) > len(b):
+            s = a.copy()
+            s.symmetric_difference_update(b)
+            return s
+        else:
+            s = b.copy()
+            s.symmetric_difference_update(a)
+            return s
+
+    return context.compile_internal(builder, symmetric_difference_impl,
+                                    sig, args)
+
+@lower_builtin(operator.or_, types.Set, types.Set)
+@lower_builtin("set.union", types.Set, types.Set)
+def set_union(context, builder, sig, args):
+    def union_impl(a, b):
+        if len(a) > len(b):
+            s = a.copy()
+            s.update(b)
+            return s
+        else:
+            s = b.copy()
+            s.update(a)
+            return s
+
+    return context.compile_internal(builder, union_impl, sig, args)
+
+
+# Predicates
+
+@lower_builtin("set.isdisjoint", types.Set, types.Set)
+def set_isdisjoint(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+
+    return inst.isdisjoint(other)
+
+@lower_builtin(operator.le, types.Set, types.Set)
+@lower_builtin("set.issubset", types.Set, types.Set)
+def set_issubset(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+
+    return inst.issubset(other)
+
+@lower_builtin(operator.ge, types.Set, types.Set)
+@lower_builtin("set.issuperset", types.Set, types.Set)
+def set_issuperset(context, builder, sig, args):
+    def superset_impl(a, b):
+        return b.issubset(a)
+
+    return context.compile_internal(builder, superset_impl, sig, args)
+
+@lower_builtin(operator.eq, types.Set, types.Set)
+def set_isdisjoint(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+
+    return inst.equals(other)
+
+@lower_builtin(operator.ne, types.Set, types.Set)
+def set_ne(context, builder, sig, args):
+    def ne_impl(a, b):
+        return not a == b
+
+    return context.compile_internal(builder, ne_impl, sig, args)
+
+@lower_builtin(operator.lt, types.Set, types.Set)
+def set_lt(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+
+    return inst.issubset(other, strict=True)
+
+@lower_builtin(operator.gt, types.Set, types.Set)
+def set_gt(context, builder, sig, args):
+    def gt_impl(a, b):
+        return b < a
+
+    return context.compile_internal(builder, gt_impl, sig, args)
+
+@lower_builtin(operator.is_, types.Set, types.Set)
+def set_is(context, builder, sig, args):
+    a = SetInstance(context, builder, sig.args[0], args[0])
+    b = SetInstance(context, builder, sig.args[1], args[1])
+    ma = builder.ptrtoint(a.meminfo, cgutils.intp_t)
+    mb = builder.ptrtoint(b.meminfo, cgutils.intp_t)
+    return builder.icmp_signed('==', ma, mb)
+
+
+# -----------------------------------------------------------------------------
+# Implicit casting
+
+@lower_cast(types.Set, types.Set)
+def set_to_set(context, builder, fromty, toty, val):
+    # Casting from non-reflected to reflected
+    assert fromty.dtype == toty.dtype
+    return val
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/slicing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/slicing.py
new file mode 100644
index 000000000..f5c38e2d9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/slicing.py
@@ -0,0 +1,302 @@
+"""
+Implement slices and various slice computations.
+"""
+
+from itertools import zip_longest
+
+from llvmlite import ir
+from numba.core import cgutils, types, typing, utils
+from numba.core.imputils import (impl_ret_borrowed, impl_ret_new_ref,
+                                 impl_ret_untracked, iternext_impl,
+                                 lower_builtin, lower_cast, lower_constant,
+                                 lower_getattr)
+
+
+def fix_index(builder, idx, size):
+    """
+    Fix negative index by adding *size* to it.  Positive
+    indices are left untouched.
+    """
+    is_negative = builder.icmp_signed('<', idx, ir.Constant(size.type, 0))
+    wrapped_index = builder.add(idx, size)
+    return builder.select(is_negative, wrapped_index, idx)
+
+
+def fix_slice(builder, slice, size):
+    """
+    Fix *slice* start and stop to be valid (inclusive and exclusive, resp)
+    indexing bounds for a sequence of the given *size*.
+    """
+    # See PySlice_GetIndicesEx()
+    zero = ir.Constant(size.type, 0)
+    minus_one = ir.Constant(size.type, -1)
+
+    def fix_bound(bound_name, lower_repl, upper_repl):
+        bound = getattr(slice, bound_name)
+        bound = fix_index(builder, bound, size)
+        # Store value
+        setattr(slice, bound_name, bound)
+        # Still negative? => clamp to lower_repl
+        underflow = builder.icmp_signed('<', bound, zero)
+        with builder.if_then(underflow, likely=False):
+            setattr(slice, bound_name, lower_repl)
+        # Greater than size? => clamp to upper_repl
+        overflow = builder.icmp_signed('>=', bound, size)
+        with builder.if_then(overflow, likely=False):
+            setattr(slice, bound_name, upper_repl)
+
+    with builder.if_else(cgutils.is_neg_int(builder, slice.step)) as (if_neg_step, if_pos_step):
+        with if_pos_step:
+            # < 0 => 0; >= size => size
+            fix_bound('start', zero, size)
+            fix_bound('stop', zero, size)
+        with if_neg_step:
+            # < 0 => -1; >= size => size - 1
+            lower = minus_one
+            upper = builder.add(size, minus_one)
+            fix_bound('start', lower, upper)
+            fix_bound('stop', lower, upper)
+
+
+def get_slice_length(builder, slicestruct):
+    """
+    Given a slice, compute the number of indices it spans, i.e. the
+    number of iterations that for_range_slice() will execute.
+
+    Pseudo-code:
+        assert step != 0
+        if step > 0:
+            if stop <= start:
+                return 0
+            else:
+                return (stop - start - 1) // step + 1
+        else:
+            if stop >= start:
+                return 0
+            else:
+                return (stop - start + 1) // step + 1
+
+    (see PySlice_GetIndicesEx() in CPython)
+    """
+    start = slicestruct.start
+    stop = slicestruct.stop
+    step = slicestruct.step
+    one = ir.Constant(start.type, 1)
+    zero = ir.Constant(start.type, 0)
+
+    is_step_negative = cgutils.is_neg_int(builder, step)
+    delta = builder.sub(stop, start)
+
+    # Nominal case
+    pos_dividend = builder.sub(delta, one)
+    neg_dividend = builder.add(delta, one)
+    dividend  = builder.select(is_step_negative, neg_dividend, pos_dividend)
+    nominal_length = builder.add(one, builder.sdiv(dividend, step))
+
+    # Catch zero length
+    is_zero_length = builder.select(is_step_negative,
+                                    builder.icmp_signed('>=', delta, zero),
+                                    builder.icmp_signed('<=', delta, zero))
+
+    # Clamp to 0 if is_zero_length
+    return builder.select(is_zero_length, zero, nominal_length)
+
+
+def get_slice_bounds(builder, slicestruct):
+    """
+    Return the [lower, upper) indexing bounds of a slice.
+    """
+    start = slicestruct.start
+    stop = slicestruct.stop
+    zero = start.type(0)
+    one = start.type(1)
+    # This is a bit pessimal, e.g. it will return [1, 5) instead
+    # of [1, 4) for `1:5:2`
+    is_step_negative = builder.icmp_signed('<', slicestruct.step, zero)
+    lower = builder.select(is_step_negative,
+                           builder.add(stop, one), start)
+    upper = builder.select(is_step_negative,
+                           builder.add(start, one), stop)
+    return lower, upper
+
+
+def fix_stride(builder, slice, stride):
+    """
+    Fix the given stride for the slice's step.
+    """
+    return builder.mul(slice.step, stride)
+
+def guard_invalid_slice(context, builder, typ, slicestruct):
+    """
+    Guard against *slicestruct* having a zero step (and raise ValueError).
+    """
+    if typ.has_step:
+        cgutils.guard_null(context, builder, slicestruct.step,
+                           (ValueError, "slice step cannot be zero"))
+
+
+def get_defaults(context):
+    """
+    Get the default values for a slice's members:
+    (start for positive step, start for negative step,
+     stop for positive step, stop for negative step, step)
+    """
+    maxint = (1 << (context.address_size - 1)) - 1
+    return (0, maxint, maxint, - maxint - 1, 1)
+
+
+#---------------------------------------------------------------------------
+# The slice structure
+
+@lower_builtin(slice, types.VarArg(types.Any))
+def slice_constructor_impl(context, builder, sig, args):
+    (
+        default_start_pos,
+        default_start_neg,
+        default_stop_pos,
+        default_stop_neg,
+        default_step,
+    ) = [context.get_constant(types.intp, x) for x in get_defaults(context)]
+
+    slice_args = [None] * 3
+
+    # Fetch non-None arguments
+    if len(args) == 1 and sig.args[0] is not types.none:
+        slice_args[1] = args[0]
+    else:
+        for i, (ty, val) in enumerate(zip(sig.args, args)):
+            if ty is not types.none:
+                slice_args[i] = val
+
+    # Fill omitted arguments
+    def get_arg_value(i, default):
+        val = slice_args[i]
+        if val is None:
+            return default
+        else:
+            return val
+
+    step = get_arg_value(2, default_step)
+    is_step_negative = builder.icmp_signed('<', step,
+                                           context.get_constant(types.intp, 0))
+    default_stop = builder.select(is_step_negative,
+                                  default_stop_neg, default_stop_pos)
+    default_start = builder.select(is_step_negative,
+                                   default_start_neg, default_start_pos)
+    stop = get_arg_value(1, default_stop)
+    start = get_arg_value(0, default_start)
+
+    ty = sig.return_type
+    sli = context.make_helper(builder, sig.return_type)
+    sli.start = start
+    sli.stop = stop
+    sli.step = step
+
+    res = sli._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_getattr(types.SliceType, "start")
+def slice_start_impl(context, builder, typ, value):
+    sli = context.make_helper(builder, typ, value)
+    return sli.start
+
+@lower_getattr(types.SliceType, "stop")
+def slice_stop_impl(context, builder, typ, value):
+    sli = context.make_helper(builder, typ, value)
+    return sli.stop
+
+@lower_getattr(types.SliceType, "step")
+def slice_step_impl(context, builder, typ, value):
+    if typ.has_step:
+        sli = context.make_helper(builder, typ, value)
+        return sli.step
+    else:
+        return context.get_constant(types.intp, 1)
+
+
+@lower_builtin("slice.indices", types.SliceType, types.Integer)
+def slice_indices(context, builder, sig, args):
+    length = args[1]
+    sli = context.make_helper(builder, sig.args[0], args[0])
+
+    with builder.if_then(cgutils.is_neg_int(builder, length), likely=False):
+        context.call_conv.return_user_exc(
+            builder, ValueError,
+            ("length should not be negative",)
+        )
+    with builder.if_then(cgutils.is_scalar_zero(builder, sli.step), likely=False):
+        context.call_conv.return_user_exc(
+            builder, ValueError,
+            ("slice step cannot be zero",)
+        )
+
+    fix_slice(builder, sli, length)
+
+    return context.make_tuple(
+        builder,
+        sig.return_type,
+        (sli.start, sli.stop, sli.step)
+    )
+
+
+def make_slice_from_constant(context, builder, ty, pyval):
+    sli = context.make_helper(builder, ty)
+    lty = context.get_value_type(types.intp)
+
+    (
+        default_start_pos,
+        default_start_neg,
+        default_stop_pos,
+        default_stop_neg,
+        default_step,
+    ) = [context.get_constant(types.intp, x) for x in get_defaults(context)]
+
+    step = pyval.step
+    if step is None:
+        step_is_neg = False
+        step = default_step
+    else:
+        step_is_neg = step < 0
+        step = lty(step)
+
+    start = pyval.start
+    if start is None:
+        if step_is_neg:
+            start = default_start_neg
+        else:
+            start = default_start_pos
+    else:
+        start = lty(start)
+
+    stop = pyval.stop
+    if stop is None:
+        if step_is_neg:
+            stop = default_stop_neg
+        else:
+            stop = default_stop_pos
+    else:
+        stop = lty(stop)
+
+    sli.start = start
+    sli.stop = stop
+    sli.step = step
+
+    return sli._getvalue()
+
+
+@lower_constant(types.SliceType)
+def constant_slice(context, builder, ty, pyval):
+    if isinstance(ty, types.Literal):
+        typ = ty.literal_type
+    else:
+        typ = ty
+
+    return make_slice_from_constant(context, builder, typ, pyval)
+
+
+@lower_cast(types.misc.SliceLiteral, types.SliceType)
+def cast_from_literal(context, builder, fromty, toty, val):
+    return make_slice_from_constant(
+        context, builder, toty, fromty.literal_value,
+    )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/tupleobj.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/tupleobj.py
new file mode 100644
index 000000000..7a76fea0e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/tupleobj.py
@@ -0,0 +1,412 @@
+"""
+Implementation of tuple objects
+"""
+
+import operator
+
+from numba.core.imputils import (lower_builtin, lower_getattr_generic,
+                                    lower_cast, lower_constant, iternext_impl,
+                                    impl_ret_borrowed, impl_ret_untracked,
+                                    RefType)
+from numba.core import typing, types, cgutils
+from numba.core.extending import overload_method, overload, intrinsic
+
+
+@lower_builtin(types.NamedTupleClass, types.VarArg(types.Any))
+def namedtuple_constructor(context, builder, sig, args):
+    # A namedtuple has the same representation as a regular tuple
+    # the arguments need casting (lower_cast) from the types in the ctor args
+    # to those in the ctor return type, this is to handle cases such as a
+    # literal present in the args, but a type present in the return type.
+    newargs = []
+    for i, arg in enumerate(args):
+        casted = context.cast(builder, arg, sig.args[i], sig.return_type[i])
+        newargs.append(casted)
+    res = context.make_tuple(builder, sig.return_type, tuple(newargs))
+    # The tuple's contents are borrowed
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+@lower_builtin(operator.add, types.BaseTuple, types.BaseTuple)
+def tuple_add(context, builder, sig, args):
+    left, right = [cgutils.unpack_tuple(builder, x) for x in args]
+    res = context.make_tuple(builder, sig.return_type, left + right)
+    # The tuple's contents are borrowed
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+def tuple_cmp_ordered(context, builder, op, sig, args):
+    tu, tv = sig.args
+    u, v = args
+    res = cgutils.alloca_once_value(builder, cgutils.true_bit)
+    bbend = builder.append_basic_block("cmp_end")
+    for i, (ta, tb) in enumerate(zip(tu.types, tv.types)):
+        a = builder.extract_value(u, i)
+        b = builder.extract_value(v, i)
+        not_equal = context.generic_compare(builder, operator.ne, (ta, tb), (a, b))
+        with builder.if_then(not_equal):
+            pred = context.generic_compare(builder, op, (ta, tb), (a, b))
+            builder.store(pred, res)
+            builder.branch(bbend)
+    # Everything matched equal => compare lengths
+    len_compare = op(len(tu.types), len(tv.types))
+    pred = context.get_constant(types.boolean, len_compare)
+    builder.store(pred, res)
+    builder.branch(bbend)
+    builder.position_at_end(bbend)
+    return builder.load(res)
+
+
+@lower_builtin(operator.eq, types.BaseTuple, types.BaseTuple)
+def tuple_eq(context, builder, sig, args):
+    tu, tv = sig.args
+    u, v = args
+    if len(tu.types) != len(tv.types):
+        res = context.get_constant(types.boolean, False)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+    res = context.get_constant(types.boolean, True)
+    for i, (ta, tb) in enumerate(zip(tu.types, tv.types)):
+        a = builder.extract_value(u, i)
+        b = builder.extract_value(v, i)
+        pred = context.generic_compare(builder, operator.eq, (ta, tb), (a, b))
+        res = builder.and_(res, pred)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower_builtin(operator.ne, types.BaseTuple, types.BaseTuple)
+def tuple_ne(context, builder, sig, args):
+    res = builder.not_(tuple_eq(context, builder, sig, args))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower_builtin(operator.lt, types.BaseTuple, types.BaseTuple)
+def tuple_lt(context, builder, sig, args):
+    res = tuple_cmp_ordered(context, builder, operator.lt, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower_builtin(operator.le, types.BaseTuple, types.BaseTuple)
+def tuple_le(context, builder, sig, args):
+    res = tuple_cmp_ordered(context, builder, operator.le, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower_builtin(operator.gt, types.BaseTuple, types.BaseTuple)
+def tuple_gt(context, builder, sig, args):
+    res = tuple_cmp_ordered(context, builder, operator.gt, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower_builtin(operator.ge, types.BaseTuple, types.BaseTuple)
+def tuple_ge(context, builder, sig, args):
+    res = tuple_cmp_ordered(context, builder, operator.ge, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+# for hashing see hashing.py
+
+@lower_getattr_generic(types.BaseNamedTuple)
+def namedtuple_getattr(context, builder, typ, value, attr):
+    """
+    Fetch a namedtuple's field.
+    """
+    index = typ.fields.index(attr)
+    res = builder.extract_value(value, index)
+    return impl_ret_borrowed(context, builder, typ[index], res)
+
+
+@lower_constant(types.UniTuple)
+@lower_constant(types.NamedUniTuple)
+def unituple_constant(context, builder, ty, pyval):
+    """
+    Create a homogeneous tuple constant.
+    """
+    consts = [context.get_constant_generic(builder, ty.dtype, v)
+              for v in pyval]
+    return impl_ret_borrowed(
+        context, builder, ty, cgutils.pack_array(builder, consts),
+    )
+
+@lower_constant(types.Tuple)
+@lower_constant(types.NamedTuple)
+def unituple_constant(context, builder, ty, pyval):
+    """
+    Create a heterogeneous tuple constant.
+    """
+    consts = [context.get_constant_generic(builder, ty.types[i], v)
+              for i, v in enumerate(pyval)]
+    return impl_ret_borrowed(
+        context, builder, ty, cgutils.pack_struct(builder, consts),
+    )
+
+
+#------------------------------------------------------------------------------
+# Tuple iterators
+
+@lower_builtin('getiter', types.UniTuple)
+@lower_builtin('getiter', types.NamedUniTuple)
+def getiter_unituple(context, builder, sig, args):
+    [tupty] = sig.args
+    [tup] = args
+
+    iterval = context.make_helper(builder, types.UniTupleIter(tupty))
+
+    index0 = context.get_constant(types.intp, 0)
+    indexptr = cgutils.alloca_once(builder, index0.type)
+    builder.store(index0, indexptr)
+
+    iterval.index = indexptr
+    iterval.tuple = tup
+
+    res = iterval._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('iternext', types.UniTupleIter)
+@iternext_impl(RefType.BORROWED)
+def iternext_unituple(context, builder, sig, args, result):
+    [tupiterty] = sig.args
+    [tupiter] = args
+
+    iterval = context.make_helper(builder, tupiterty, value=tupiter)
+
+    tup = iterval.tuple
+    idxptr = iterval.index
+    idx = builder.load(idxptr)
+    count = context.get_constant(types.intp, tupiterty.container.count)
+
+    is_valid = builder.icmp_signed('<', idx, count)
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        getitem_sig = typing.signature(tupiterty.container.dtype,
+                                       tupiterty.container,
+                                       types.intp)
+        getitem_out = getitem_unituple(context, builder, getitem_sig,
+                                       [tup, idx])
+        # As a iternext_impl function, this will incref the yieled value.
+        # We need to release the new reference from getitem_unituple.
+        if context.enable_nrt:
+            context.nrt.decref(builder, tupiterty.container.dtype, getitem_out)
+        result.yield_(getitem_out)
+        nidx = builder.add(idx, context.get_constant(types.intp, 1))
+        builder.store(nidx, iterval.index)
+
+
+@overload(operator.getitem)
+def getitem_literal_idx(tup, idx):
+    """
+    Overloads BaseTuple getitem to cover cases where constant
+    inference and RewriteConstGetitems cannot replace it
+    with a static_getitem.
+    """
+    if not (isinstance(tup, types.BaseTuple)
+            and isinstance(idx, types.IntegerLiteral)):
+        return None
+
+    idx_val = idx.literal_value
+    def getitem_literal_idx_impl(tup, idx):
+        return tup[idx_val]
+
+    return getitem_literal_idx_impl
+
+
+@lower_builtin('typed_getitem', types.BaseTuple, types.Any)
+def getitem_typed(context, builder, sig, args):
+    tupty, _ = sig.args
+    tup, idx = args
+    errmsg_oob = ("tuple index out of range",)
+
+    if len(tupty) == 0:
+        # Empty tuple.
+
+        # Always branch and raise IndexError
+        with builder.if_then(cgutils.true_bit):
+            context.call_conv.return_user_exc(builder, IndexError,
+                                              errmsg_oob)
+        # This is unreachable in runtime,
+        # but it exists to not terminate the current basicblock.
+        res = context.get_constant_null(sig.return_type)
+        return impl_ret_untracked(context, builder,
+                                  sig.return_type, res)
+    else:
+        # The tuple is not empty
+
+        bbelse = builder.append_basic_block("typed_switch.else")
+        bbend = builder.append_basic_block("typed_switch.end")
+        switch = builder.switch(idx, bbelse)
+
+        with builder.goto_block(bbelse):
+            context.call_conv.return_user_exc(builder, IndexError,
+                                            errmsg_oob)
+
+        lrtty = context.get_value_type(sig.return_type)
+        voidptrty = context.get_value_type(types.voidptr)
+        with builder.goto_block(bbend):
+            phinode = builder.phi(voidptrty)
+
+        for i in range(tupty.count):
+            ki = context.get_constant(types.intp, i)
+            bbi = builder.append_basic_block("typed_switch.%d" % i)
+            switch.add_case(ki, bbi)
+            # handle negative indexing, create case (-tuple.count + i) to
+            # reference same block as i
+            kin = context.get_constant(types.intp, -tupty.count + i)
+            switch.add_case(kin, bbi)
+            with builder.goto_block(bbi):
+                value = builder.extract_value(tup, i)
+                # Dragon warning...
+                # The fact the code has made it this far suggests that type
+                # inference decided whatever was being done with the item pulled
+                # from the tuple was legitimate, it is not the job of lowering
+                # to argue about that. However, here lies a problem, the tuple
+                # lowering is implemented as a switch table with each case
+                # writing to a phi node slot that is returned. The type of this
+                # phi node slot needs to be "correct" for the current type but
+                # it also needs to survive stores being made to it from the
+                # other cases that will in effect never run. To do this a stack
+                # slot is made for each case for the specific type and then cast
+                # to a void pointer type, this is then added as an incoming on
+                # the phi node, at the end of the switch the phi node is then
+                # cast back to the required return type for this typed_getitem.
+                # The only further complication is that if the value is not a
+                # pointer then the void* juggle won't work so a cast is made
+                # prior to store, again, that type inference has permitted it
+                # suggests this is safe.
+                # End Dragon warning...
+                DOCAST = context.typing_context.unify_types(sig.args[0][i],
+                                        sig.return_type) == sig.return_type
+                if DOCAST:
+                    value_slot = builder.alloca(lrtty,
+                                                name="TYPED_VALUE_SLOT%s" % i)
+                    casted = context.cast(builder, value, sig.args[0][i],
+                                        sig.return_type)
+                    builder.store(casted, value_slot)
+                else:
+                    value_slot = builder.alloca(value.type,
+                                                name="TYPED_VALUE_SLOT%s" % i)
+                    builder.store(value, value_slot)
+                phinode.add_incoming(builder.bitcast(value_slot, voidptrty),
+                                     bbi)
+                builder.branch(bbend)
+
+        builder.position_at_end(bbend)
+        res = builder.bitcast(phinode, lrtty.as_pointer())
+        res = builder.load(res)
+        return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.getitem, types.UniTuple, types.intp)
+@lower_builtin(operator.getitem, types.UniTuple, types.uintp)
+@lower_builtin(operator.getitem, types.NamedUniTuple, types.intp)
+@lower_builtin(operator.getitem, types.NamedUniTuple, types.uintp)
+def getitem_unituple(context, builder, sig, args):
+    tupty, _ = sig.args
+    tup, idx = args
+
+    errmsg_oob = ("tuple index out of range",)
+
+    if len(tupty) == 0:
+        # Empty tuple.
+
+        # Always branch and raise IndexError
+        with builder.if_then(cgutils.true_bit):
+            context.call_conv.return_user_exc(builder, IndexError,
+                                              errmsg_oob)
+        # This is unreachable in runtime,
+        # but it exists to not terminate the current basicblock.
+        res = context.get_constant_null(sig.return_type)
+        return impl_ret_untracked(context, builder,
+                                  sig.return_type, res)
+    else:
+        # The tuple is not empty
+        bbelse = builder.append_basic_block("switch.else")
+        bbend = builder.append_basic_block("switch.end")
+        switch = builder.switch(idx, bbelse)
+
+        with builder.goto_block(bbelse):
+            context.call_conv.return_user_exc(builder, IndexError,
+                                              errmsg_oob)
+
+        lrtty = context.get_value_type(tupty.dtype)
+        with builder.goto_block(bbend):
+            phinode = builder.phi(lrtty)
+
+        for i in range(tupty.count):
+            ki = context.get_constant(types.intp, i)
+            bbi = builder.append_basic_block("switch.%d" % i)
+            switch.add_case(ki, bbi)
+            # handle negative indexing, create case (-tuple.count + i) to
+            # reference same block as i
+            kin = context.get_constant(types.intp, -tupty.count + i)
+            switch.add_case(kin, bbi)
+            with builder.goto_block(bbi):
+                value = builder.extract_value(tup, i)
+                builder.branch(bbend)
+                phinode.add_incoming(value, bbi)
+
+        builder.position_at_end(bbend)
+        res = phinode
+        assert sig.return_type == tupty.dtype
+        return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('static_getitem', types.LiteralStrKeyDict, types.StringLiteral)
+@lower_builtin('static_getitem', types.LiteralList, types.IntegerLiteral)
+@lower_builtin('static_getitem', types.LiteralList, types.SliceLiteral)
+@lower_builtin('static_getitem', types.BaseTuple, types.IntegerLiteral)
+@lower_builtin('static_getitem', types.BaseTuple, types.SliceLiteral)
+def static_getitem_tuple(context, builder, sig, args):
+    tupty, idxty = sig.args
+    tup, idx = args
+    if isinstance(idx, int):
+        if idx < 0:
+            idx += len(tupty)
+        if not 0 <= idx < len(tupty):
+            raise IndexError("cannot index at %d in %s" % (idx, tupty))
+        res = builder.extract_value(tup, idx)
+    elif isinstance(idx, slice):
+        items = cgutils.unpack_tuple(builder, tup)[idx]
+        res = context.make_tuple(builder, sig.return_type, items)
+    elif isinstance(tupty, types.LiteralStrKeyDict):
+        # pretend to be a dictionary
+        idx_val = idxty.literal_value
+        idx_offset = tupty.fields.index(idx_val)
+        res = builder.extract_value(tup, idx_offset)
+    else:
+        raise NotImplementedError("unexpected index %r for %s"
+                                  % (idx, sig.args[0]))
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+#------------------------------------------------------------------------------
+# Implicit conversion
+
+@lower_cast(types.BaseTuple, types.BaseTuple)
+def tuple_to_tuple(context, builder, fromty, toty, val):
+    if (isinstance(fromty, types.BaseNamedTuple)
+        or isinstance(toty, types.BaseNamedTuple)):
+        # Disallowed by typing layer
+        raise NotImplementedError
+
+    if len(fromty) != len(toty):
+        # Disallowed by typing layer
+        raise NotImplementedError
+
+    olditems = cgutils.unpack_tuple(builder, val, len(fromty))
+    items = [context.cast(builder, v, f, t)
+             for v, f, t in zip(olditems, fromty, toty)]
+    return context.make_tuple(builder, toty, items)
+
+
+#------------------------------------------------------------------------------
+# Methods
+
+@overload_method(types.BaseTuple, 'index')
+def tuple_index(tup, value):
+
+    def tuple_index_impl(tup, value):
+        for i in range(len(tup)):
+            if tup[i] == value:
+                return i
+        raise ValueError("tuple.index(x): x not in tuple")
+
+    return tuple_index_impl
+
+
+@overload(operator.contains)
+def in_seq_empty_tuple(x, y):
+    if isinstance(x, types.Tuple) and not x.types:
+        return lambda x, y: False
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unicode.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unicode.py
new file mode 100644
index 000000000..a14f31910
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unicode.py
@@ -0,0 +1,2498 @@
+import sys
+import operator
+
+import numpy as np
+from llvmlite.ir import IntType, Constant
+
+from numba.core.extending import (
+    models,
+    register_model,
+    make_attribute_wrapper,
+    unbox,
+    box,
+    NativeValue,
+    overload,
+    overload_method,
+    intrinsic,
+    register_jitable,
+)
+from numba.core.imputils import (lower_constant, lower_cast, lower_builtin,
+                                 iternext_impl, impl_ret_new_ref, RefType)
+from numba.core.datamodel import register_default, StructModel
+from numba.core import utils, types, cgutils
+from numba.core.pythonapi import (
+    PY_UNICODE_1BYTE_KIND,
+    PY_UNICODE_2BYTE_KIND,
+    PY_UNICODE_4BYTE_KIND,
+    PY_UNICODE_WCHAR_KIND,
+)
+from numba._helperlib import c_helpers
+from numba.cpython.hashing import _Py_hash_t
+from numba.core.unsafe.bytes import memcpy_region
+from numba.core.errors import TypingError
+from numba.cpython.unicode_support import (_Py_TOUPPER, _Py_TOLOWER, _Py_UCS4,
+                                           _Py_ISALNUM,
+                                           _PyUnicode_ToUpperFull,
+                                           _PyUnicode_ToLowerFull,
+                                           _PyUnicode_ToFoldedFull,
+                                           _PyUnicode_ToTitleFull,
+                                           _PyUnicode_IsPrintable,
+                                           _PyUnicode_IsSpace,
+                                           _Py_ISSPACE,
+                                           _PyUnicode_IsXidStart,
+                                           _PyUnicode_IsXidContinue,
+                                           _PyUnicode_IsCased,
+                                           _PyUnicode_IsCaseIgnorable,
+                                           _PyUnicode_IsUppercase,
+                                           _PyUnicode_IsLowercase,
+                                           _PyUnicode_IsLineBreak,
+                                           _Py_ISLINEBREAK,
+                                           _Py_ISLINEFEED,
+                                           _Py_ISCARRIAGERETURN,
+                                           _PyUnicode_IsTitlecase,
+                                           _Py_ISLOWER,
+                                           _Py_ISUPPER,
+                                           _Py_TAB,
+                                           _Py_LINEFEED,
+                                           _Py_CARRIAGE_RETURN,
+                                           _Py_SPACE,
+                                           _PyUnicode_IsAlpha,
+                                           _PyUnicode_IsNumeric,
+                                           _Py_ISALPHA,
+                                           _PyUnicode_IsDigit,
+                                           _PyUnicode_IsDecimalDigit)
+from numba.cpython import slicing
+
+
+_py38_or_later = utils.PYVERSION >= (3, 8)
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L84-L85    # noqa: E501
+_MAX_UNICODE = 0x10ffff
+
+# DATA MODEL
+
+
+@register_model(types.UnicodeType)
+class UnicodeModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('data', types.voidptr),
+            ('length', types.intp),
+            ('kind', types.int32),
+            ('is_ascii', types.uint32),
+            ('hash', _Py_hash_t),
+            ('meminfo', types.MemInfoPointer(types.voidptr)),
+            # A pointer to the owner python str/unicode object
+            ('parent', types.pyobject),
+        ]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+make_attribute_wrapper(types.UnicodeType, 'data', '_data')
+make_attribute_wrapper(types.UnicodeType, 'length', '_length')
+make_attribute_wrapper(types.UnicodeType, 'kind', '_kind')
+make_attribute_wrapper(types.UnicodeType, 'is_ascii', '_is_ascii')
+make_attribute_wrapper(types.UnicodeType, 'hash', '_hash')
+
+
+@register_default(types.UnicodeIteratorType)
+class UnicodeIteratorModel(StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [('index', types.EphemeralPointer(types.uintp)),
+                   ('data', fe_type.data)]
+        super(UnicodeIteratorModel, self).__init__(dmm, fe_type, members)
+
+# CAST
+
+
+def compile_time_get_string_data(obj):
+    """Get string data from a python string for use at compile-time to embed
+    the string data into the LLVM module.
+    """
+    from ctypes import (
+        CFUNCTYPE, c_void_p, c_int, c_uint, c_ssize_t, c_ubyte, py_object,
+        POINTER, byref,
+    )
+
+    extract_unicode_fn = c_helpers['extract_unicode']
+    proto = CFUNCTYPE(c_void_p, py_object, POINTER(c_ssize_t), POINTER(c_int),
+                      POINTER(c_uint), POINTER(c_ssize_t))
+    fn = proto(extract_unicode_fn)
+    length = c_ssize_t()
+    kind = c_int()
+    is_ascii = c_uint()
+    hashv = c_ssize_t()
+    data = fn(obj, byref(length), byref(kind), byref(is_ascii), byref(hashv))
+    if data is None:
+        raise ValueError("cannot extract unicode data from the given string")
+    length = length.value
+    kind = kind.value
+    is_ascii = is_ascii.value
+    nbytes = (length + 1) * _kind_to_byte_width(kind)
+    out = (c_ubyte * nbytes).from_address(data)
+    return bytes(out), length, kind, is_ascii, hashv.value
+
+
+def make_string_from_constant(context, builder, typ, literal_string):
+    """
+    Get string data by `compile_time_get_string_data()` and return a
+    unicode_type LLVM value
+    """
+    databytes, length, kind, is_ascii, hashv = \
+        compile_time_get_string_data(literal_string)
+    mod = builder.module
+    gv = context.insert_const_bytes(mod, databytes)
+    uni_str = cgutils.create_struct_proxy(typ)(context, builder)
+    uni_str.data = gv
+    uni_str.length = uni_str.length.type(length)
+    uni_str.kind = uni_str.kind.type(kind)
+    uni_str.is_ascii = uni_str.is_ascii.type(is_ascii)
+    # Set hash to -1 to indicate that it should be computed.
+    # We cannot bake in the hash value because of hashseed randomization.
+    uni_str.hash = uni_str.hash.type(-1)
+    return uni_str._getvalue()
+
+
+@lower_cast(types.StringLiteral, types.unicode_type)
+def cast_from_literal(context, builder, fromty, toty, val):
+    return make_string_from_constant(
+        context, builder, toty, fromty.literal_value,
+    )
+
+
+# CONSTANT
+
+@lower_constant(types.unicode_type)
+def constant_unicode(context, builder, typ, pyval):
+    return make_string_from_constant(context, builder, typ, pyval)
+
+
+# BOXING
+
+
+@unbox(types.UnicodeType)
+def unbox_unicode_str(typ, obj, c):
+    """
+    Convert a unicode str object to a native unicode structure.
+    """
+    ok, data, length, kind, is_ascii, hashv = \
+        c.pyapi.string_as_string_size_and_kind(obj)
+    uni_str = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+    uni_str.data = data
+    uni_str.length = length
+    uni_str.kind = kind
+    uni_str.is_ascii = is_ascii
+    uni_str.hash = hashv
+    uni_str.meminfo = c.pyapi.nrt_meminfo_new_from_pyobject(
+        data,  # the borrowed data pointer
+        obj,   # the owner pyobject; the call will incref it.
+    )
+    uni_str.parent = obj
+
+    is_error = cgutils.is_not_null(c.builder, c.pyapi.err_occurred())
+    return NativeValue(uni_str._getvalue(), is_error=is_error)
+
+
+@box(types.UnicodeType)
+def box_unicode_str(typ, val, c):
+    """
+    Convert a native unicode structure to a unicode string
+    """
+    uni_str = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+    res = c.pyapi.string_from_kind_and_data(
+        uni_str.kind, uni_str.data, uni_str.length)
+    # hash isn't needed now, just compute it so it ends up in the unicodeobject
+    # hash cache, cpython doesn't always do this, depends how a string was
+    # created it's safe, just burns the cycles required to hash on @box
+    c.pyapi.object_hash(res)
+    c.context.nrt.decref(c.builder, typ, val)
+    return res
+
+
+# HELPER FUNCTIONS
+
+
+def make_deref_codegen(bitsize):
+    def codegen(context, builder, signature, args):
+        data, idx = args
+        ptr = builder.bitcast(data, IntType(bitsize).as_pointer())
+        ch = builder.load(builder.gep(ptr, [idx]))
+        return builder.zext(ch, IntType(32))
+
+    return codegen
+
+
+@intrinsic
+def deref_uint8(typingctx, data, offset):
+    sig = types.uint32(types.voidptr, types.intp)
+    return sig, make_deref_codegen(8)
+
+
+@intrinsic
+def deref_uint16(typingctx, data, offset):
+    sig = types.uint32(types.voidptr, types.intp)
+    return sig, make_deref_codegen(16)
+
+
+@intrinsic
+def deref_uint32(typingctx, data, offset):
+    sig = types.uint32(types.voidptr, types.intp)
+    return sig, make_deref_codegen(32)
+
+
+@intrinsic
+def _malloc_string(typingctx, kind, char_bytes, length, is_ascii):
+    """make empty string with data buffer of size alloc_bytes.
+
+    Must set length and kind values for string after it is returned
+    """
+    def details(context, builder, signature, args):
+        [kind_val, char_bytes_val, length_val, is_ascii_val] = args
+
+        # fill the struct
+        uni_str_ctor = cgutils.create_struct_proxy(types.unicode_type)
+        uni_str = uni_str_ctor(context, builder)
+        # add null padding character
+        nbytes_val = builder.mul(char_bytes_val,
+                                 builder.add(length_val,
+                                             Constant(length_val.type, 1)))
+        uni_str.meminfo = context.nrt.meminfo_alloc(builder, nbytes_val)
+        uni_str.kind = kind_val
+        uni_str.is_ascii = is_ascii_val
+        uni_str.length = length_val
+        # empty string has hash value -1 to indicate "need to compute hash"
+        uni_str.hash = context.get_constant(_Py_hash_t, -1)
+        uni_str.data = context.nrt.meminfo_data(builder, uni_str.meminfo)
+        # Set parent to NULL
+        uni_str.parent = cgutils.get_null_value(uni_str.parent.type)
+        return uni_str._getvalue()
+
+    sig = types.unicode_type(types.int32, types.intp, types.intp, types.uint32)
+    return sig, details
+
+
+@register_jitable
+def _empty_string(kind, length, is_ascii=0):
+    char_width = _kind_to_byte_width(kind)
+    s = _malloc_string(kind, char_width, length, is_ascii)
+    _set_code_point(s, length, np.uint32(0))    # Write NULL character
+    return s
+
+
+# Disable RefCt for performance.
+@register_jitable(_nrt=False)
+def _get_code_point(a, i):
+    if a._kind == PY_UNICODE_1BYTE_KIND:
+        return deref_uint8(a._data, i)
+    elif a._kind == PY_UNICODE_2BYTE_KIND:
+        return deref_uint16(a._data, i)
+    elif a._kind == PY_UNICODE_4BYTE_KIND:
+        return deref_uint32(a._data, i)
+    else:
+        # there's also a wchar kind, but that's one of the above,
+        # so skipping for this example
+        return 0
+
+####
+
+
+def make_set_codegen(bitsize):
+    def codegen(context, builder, signature, args):
+        data, idx, ch = args
+        if bitsize < 32:
+            ch = builder.trunc(ch, IntType(bitsize))
+        ptr = builder.bitcast(data, IntType(bitsize).as_pointer())
+        builder.store(ch, builder.gep(ptr, [idx]))
+        return context.get_dummy_value()
+
+    return codegen
+
+
+@intrinsic
+def set_uint8(typingctx, data, idx, ch):
+    sig = types.void(types.voidptr, types.int64, types.uint32)
+    return sig, make_set_codegen(8)
+
+
+@intrinsic
+def set_uint16(typingctx, data, idx, ch):
+    sig = types.void(types.voidptr, types.int64, types.uint32)
+    return sig, make_set_codegen(16)
+
+
+@intrinsic
+def set_uint32(typingctx, data, idx, ch):
+    sig = types.void(types.voidptr, types.int64, types.uint32)
+    return sig, make_set_codegen(32)
+
+
+@register_jitable(_nrt=False)
+def _set_code_point(a, i, ch):
+    # WARNING: This method is very dangerous:
+    #   * Assumes that data contents can be changed (only allowed for new
+    #     strings)
+    #   * Assumes that the kind of unicode string is sufficiently wide to
+    #     accept ch.  Will truncate ch to make it fit.
+    #   * Assumes that i is within the valid boundaries of the function
+    if a._kind == PY_UNICODE_1BYTE_KIND:
+        set_uint8(a._data, i, ch)
+    elif a._kind == PY_UNICODE_2BYTE_KIND:
+        set_uint16(a._data, i, ch)
+    elif a._kind == PY_UNICODE_4BYTE_KIND:
+        set_uint32(a._data, i, ch)
+    else:
+        raise AssertionError(
+            "Unexpected unicode representation in _set_code_point")
+
+
+@register_jitable
+def _pick_kind(kind1, kind2):
+    if kind1 == PY_UNICODE_WCHAR_KIND or kind2 == PY_UNICODE_WCHAR_KIND:
+        raise AssertionError("PY_UNICODE_WCHAR_KIND unsupported")
+
+    if kind1 == PY_UNICODE_1BYTE_KIND:
+        return kind2
+    elif kind1 == PY_UNICODE_2BYTE_KIND:
+        if kind2 == PY_UNICODE_4BYTE_KIND:
+            return kind2
+        else:
+            return kind1
+    elif kind1 == PY_UNICODE_4BYTE_KIND:
+        return kind1
+    else:
+        raise AssertionError("Unexpected unicode representation in _pick_kind")
+
+
+@register_jitable
+def _pick_ascii(is_ascii1, is_ascii2):
+    if is_ascii1 == 1 and is_ascii2 == 1:
+        return types.uint32(1)
+    return types.uint32(0)
+
+
+@register_jitable
+def _kind_to_byte_width(kind):
+    if kind == PY_UNICODE_1BYTE_KIND:
+        return 1
+    elif kind == PY_UNICODE_2BYTE_KIND:
+        return 2
+    elif kind == PY_UNICODE_4BYTE_KIND:
+        return 4
+    elif kind == PY_UNICODE_WCHAR_KIND:
+        raise AssertionError("PY_UNICODE_WCHAR_KIND unsupported")
+    else:
+        raise AssertionError("Unexpected unicode encoding encountered")
+
+
+@register_jitable(_nrt=False)
+def _cmp_region(a, a_offset, b, b_offset, n):
+    if n == 0:
+        return 0
+    elif a_offset + n > a._length:
+        return -1
+    elif b_offset + n > b._length:
+        return 1
+
+    for i in range(n):
+        a_chr = _get_code_point(a, a_offset + i)
+        b_chr = _get_code_point(b, b_offset + i)
+        if a_chr < b_chr:
+            return -1
+        elif a_chr > b_chr:
+            return 1
+
+    return 0
+
+
+@register_jitable
+def _codepoint_to_kind(cp):
+    """
+    Compute the minimum unicode kind needed to hold a given codepoint
+    """
+    if cp < 256:
+        return PY_UNICODE_1BYTE_KIND
+    elif cp < 65536:
+        return PY_UNICODE_2BYTE_KIND
+    else:
+        # Maximum code point of Unicode 6.0: 0x10ffff (1,114,111)
+        MAX_UNICODE = 0x10ffff
+        if cp > MAX_UNICODE:
+            msg = "Invalid codepoint. Found value greater than Unicode maximum"
+            raise ValueError(msg)
+        return PY_UNICODE_4BYTE_KIND
+
+
+@register_jitable
+def _codepoint_is_ascii(ch):
+    """
+    Returns true if a codepoint is in the ASCII range
+    """
+    return ch < 128
+
+
+# PUBLIC API
+
+
+@overload(str)
+def unicode_str(s):
+    if isinstance(s, types.UnicodeType):
+        return lambda s: s
+
+
+@overload(len)
+def unicode_len(s):
+    if isinstance(s, types.UnicodeType):
+        def len_impl(s):
+            return s._length
+        return len_impl
+
+
+@overload(operator.eq)
+def unicode_eq(a, b):
+    if not (a.is_internal and b.is_internal):
+        return
+    if isinstance(a, types.Optional):
+        check_a = a.type
+    else:
+        check_a = a
+    if isinstance(b, types.Optional):
+        check_b = b.type
+    else:
+        check_b = b
+    accept = (types.UnicodeType, types.StringLiteral, types.UnicodeCharSeq)
+    a_unicode = isinstance(check_a, accept)
+    b_unicode = isinstance(check_b, accept)
+    if a_unicode and b_unicode:
+        def eq_impl(a, b):
+            # handle Optionals at runtime
+            a_none = a is None
+            b_none = b is None
+            if a_none or b_none:
+                if a_none and b_none:
+                    return True
+                else:
+                    return False
+            # the str() is for UnicodeCharSeq, it's a nop else
+            a = str(a)
+            b = str(b)
+            if len(a) != len(b):
+                return False
+            return _cmp_region(a, 0, b, 0, len(a)) == 0
+        return eq_impl
+    elif a_unicode ^ b_unicode:
+        # one of the things is unicode, everything compares False
+        def eq_impl(a, b):
+            return False
+        return eq_impl
+
+
+@overload(operator.ne)
+def unicode_ne(a, b):
+    if not (a.is_internal and b.is_internal):
+        return
+    accept = (types.UnicodeType, types.StringLiteral, types.UnicodeCharSeq)
+    a_unicode = isinstance(a, accept)
+    b_unicode = isinstance(b, accept)
+    if a_unicode and b_unicode:
+        def ne_impl(a, b):
+            return not (a == b)
+        return ne_impl
+    elif a_unicode ^ b_unicode:
+        # one of the things is unicode, everything compares True
+        def eq_impl(a, b):
+            return True
+        return eq_impl
+
+
+@overload(operator.lt)
+def unicode_lt(a, b):
+    a_unicode = isinstance(a, (types.UnicodeType, types.StringLiteral))
+    b_unicode = isinstance(b, (types.UnicodeType, types.StringLiteral))
+    if a_unicode and b_unicode:
+        def lt_impl(a, b):
+            minlen = min(len(a), len(b))
+            eqcode = _cmp_region(a, 0, b, 0, minlen)
+            if eqcode == -1:
+                return True
+            elif eqcode == 0:
+                return len(a) < len(b)
+            return False
+        return lt_impl
+
+
+@overload(operator.gt)
+def unicode_gt(a, b):
+    a_unicode = isinstance(a, (types.UnicodeType, types.StringLiteral))
+    b_unicode = isinstance(b, (types.UnicodeType, types.StringLiteral))
+    if a_unicode and b_unicode:
+        def gt_impl(a, b):
+            minlen = min(len(a), len(b))
+            eqcode = _cmp_region(a, 0, b, 0, minlen)
+            if eqcode == 1:
+                return True
+            elif eqcode == 0:
+                return len(a) > len(b)
+            return False
+        return gt_impl
+
+
+@overload(operator.le)
+def unicode_le(a, b):
+    a_unicode = isinstance(a, (types.UnicodeType, types.StringLiteral))
+    b_unicode = isinstance(b, (types.UnicodeType, types.StringLiteral))
+    if a_unicode and b_unicode:
+        def le_impl(a, b):
+            return not (a > b)
+        return le_impl
+
+
+@overload(operator.ge)
+def unicode_ge(a, b):
+    a_unicode = isinstance(a, (types.UnicodeType, types.StringLiteral))
+    b_unicode = isinstance(b, (types.UnicodeType, types.StringLiteral))
+    if a_unicode and b_unicode:
+        def ge_impl(a, b):
+            return not (a < b)
+        return ge_impl
+
+
+@overload(operator.contains)
+def unicode_contains(a, b):
+    if isinstance(a, types.UnicodeType) and isinstance(b, types.UnicodeType):
+        def contains_impl(a, b):
+            # note parameter swap: contains(a, b) == b in a
+            return _find(a, b) > -1
+        return contains_impl
+
+
+def unicode_idx_check_type(ty, name):
+    """Check object belongs to one of specific types
+    ty: type
+        Type of the object
+    name: str
+        Name of the object
+    """
+    thety = ty
+    # if the type is omitted, the concrete type is the value
+    if isinstance(ty, types.Omitted):
+        thety = ty.value
+    # if the type is optional, the concrete type is the captured type
+    elif isinstance(ty, types.Optional):
+        thety = ty.type
+
+    accepted = (types.Integer, types.NoneType)
+    if thety is not None and not isinstance(thety, accepted):
+        raise TypingError('"{}" must be {}, not {}'.format(name, accepted, ty))
+
+
+def unicode_sub_check_type(ty, name):
+    """Check object belongs to unicode type"""
+    if not isinstance(ty, types.UnicodeType):
+        msg = '"{}" must be {}, not {}'.format(name, types.UnicodeType, ty)
+        raise TypingError(msg)
+
+
+def generate_finder(find_func):
+    """Generate finder either left or right."""
+    def impl(data, substr, start=None, end=None):
+        length = len(data)
+        sub_length = len(substr)
+        if start is None:
+            start = 0
+        if end is None:
+            end = length
+
+        start, end = _adjust_indices(length, start, end)
+        if end - start < sub_length:
+            return -1
+
+        return find_func(data, substr, start, end)
+
+    return impl
+
+
+@register_jitable
+def _finder(data, substr, start, end):
+    """Left finder."""
+    if len(substr) == 0:
+        return start
+    for i in range(start, min(len(data), end) - len(substr) + 1):
+        if _cmp_region(data, i, substr, 0, len(substr)) == 0:
+            return i
+    return -1
+
+
+@register_jitable
+def _rfinder(data, substr, start, end):
+    """Right finder."""
+    if len(substr) == 0:
+        return end
+    for i in range(min(len(data), end) - len(substr), start - 1, -1):
+        if _cmp_region(data, i, substr, 0, len(substr)) == 0:
+            return i
+    return -1
+
+
+_find = register_jitable(generate_finder(_finder))
+_rfind = register_jitable(generate_finder(_rfinder))
+
+
+@overload_method(types.UnicodeType, 'find')
+def unicode_find(data, substr, start=None, end=None):
+    """Implements str.find()"""
+    if isinstance(substr, types.UnicodeCharSeq):
+        def find_impl(data, substr, start=None, end=None):
+            return data.find(str(substr))
+        return find_impl
+
+    unicode_idx_check_type(start, 'start')
+    unicode_idx_check_type(end, 'end')
+    unicode_sub_check_type(substr, 'substr')
+
+    return _find
+
+
+@overload_method(types.UnicodeType, 'rfind')
+def unicode_rfind(data, substr, start=None, end=None):
+    """Implements str.rfind()"""
+    if isinstance(substr, types.UnicodeCharSeq):
+        def rfind_impl(data, substr, start=None, end=None):
+            return data.rfind(str(substr))
+        return rfind_impl
+
+    unicode_idx_check_type(start, 'start')
+    unicode_idx_check_type(end, 'end')
+    unicode_sub_check_type(substr, 'substr')
+
+    return _rfind
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L12831-L12857    # noqa: E501
+@overload_method(types.UnicodeType, 'rindex')
+def unicode_rindex(s, sub, start=None, end=None):
+    """Implements str.rindex()"""
+    unicode_idx_check_type(start, 'start')
+    unicode_idx_check_type(end, 'end')
+    unicode_sub_check_type(sub, 'sub')
+
+    def rindex_impl(s, sub, start=None, end=None):
+        result = s.rfind(sub, start, end)
+        if result < 0:
+            raise ValueError('substring not found')
+
+        return result
+
+    return rindex_impl
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L11692-L11718    # noqa: E501
+@overload_method(types.UnicodeType, 'index')
+def unicode_index(s, sub, start=None, end=None):
+    """Implements str.index()"""
+    unicode_idx_check_type(start, 'start')
+    unicode_idx_check_type(end, 'end')
+    unicode_sub_check_type(sub, 'sub')
+
+    def index_impl(s, sub, start=None, end=None):
+        result = s.find(sub, start, end)
+        if result < 0:
+            raise ValueError('substring not found')
+
+        return result
+
+    return index_impl
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L12922-L12976    # noqa: E501
+@overload_method(types.UnicodeType, 'partition')
+def unicode_partition(data, sep):
+    """Implements str.partition()"""
+    thety = sep
+    # if the type is omitted, the concrete type is the value
+    if isinstance(sep, types.Omitted):
+        thety = sep.value
+    # if the type is optional, the concrete type is the captured type
+    elif isinstance(sep, types.Optional):
+        thety = sep.type
+
+    accepted = (types.UnicodeType, types.UnicodeCharSeq)
+    if thety is not None and not isinstance(thety, accepted):
+        msg = '"{}" must be {}, not {}'.format('sep', accepted, sep)
+        raise TypingError(msg)
+
+    def impl(data, sep):
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/stringlib/partition.h#L7-L60    # noqa: E501
+        sep = str(sep)
+        empty_str = _empty_string(data._kind, 0, data._is_ascii)
+        sep_length = len(sep)
+        if data._kind < sep._kind or len(data) < sep_length:
+            return data, empty_str, empty_str
+
+        if sep_length == 0:
+            raise ValueError('empty separator')
+
+        pos = data.find(sep)
+        if pos < 0:
+            return data, empty_str, empty_str
+
+        return data[0:pos], sep, data[pos + sep_length:len(data)]
+
+    return impl
+
+
+@overload_method(types.UnicodeType, 'count')
+def unicode_count(src, sub, start=None, end=None):
+
+    _count_args_types_check(start)
+    _count_args_types_check(end)
+
+    if isinstance(sub, types.UnicodeType):
+        def count_impl(src, sub, start=None, end=None):
+            count = 0
+            src_len = len(src)
+            sub_len = len(sub)
+
+            start = _normalize_slice_idx_count(start, src_len, 0)
+            end = _normalize_slice_idx_count(end, src_len, src_len)
+
+            if end - start < 0 or start > src_len:
+                return 0
+
+            src = src[start : end]
+            src_len = len(src)
+            start, end = 0, src_len
+            if sub_len == 0:
+                return src_len + 1
+
+            while(start + sub_len <= src_len):
+                if src[start : start + sub_len] == sub:
+                    count += 1
+                    start += sub_len
+                else:
+                    start += 1
+            return count
+        return count_impl
+    error_msg = "The substring must be a UnicodeType, not {}"
+    raise TypingError(error_msg.format(type(sub)))
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L12979-L13033    # noqa: E501
+@overload_method(types.UnicodeType, 'rpartition')
+def unicode_rpartition(data, sep):
+    """Implements str.rpartition()"""
+    thety = sep
+    # if the type is omitted, the concrete type is the value
+    if isinstance(sep, types.Omitted):
+        thety = sep.value
+    # if the type is optional, the concrete type is the captured type
+    elif isinstance(sep, types.Optional):
+        thety = sep.type
+
+    accepted = (types.UnicodeType, types.UnicodeCharSeq)
+    if thety is not None and not isinstance(thety, accepted):
+        msg = '"{}" must be {}, not {}'.format('sep', accepted, sep)
+        raise TypingError(msg)
+
+    def impl(data, sep):
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/stringlib/partition.h#L62-L115    # noqa: E501
+        sep = str(sep)
+        empty_str = _empty_string(data._kind, 0, data._is_ascii)
+        sep_length = len(sep)
+        if data._kind < sep._kind or len(data) < sep_length:
+            return empty_str, empty_str, data
+
+        if sep_length == 0:
+            raise ValueError('empty separator')
+
+        pos = data.rfind(sep)
+        if pos < 0:
+            return empty_str, empty_str, data
+
+        return data[0:pos], sep, data[pos + sep_length:len(data)]
+
+    return impl
+
+
+@overload_method(types.UnicodeType, 'startswith')
+def unicode_startswith(a, b):
+    if isinstance(b, types.UnicodeType):
+        def startswith_impl(a, b):
+            return _cmp_region(a, 0, b, 0, len(b)) == 0
+        return startswith_impl
+    if isinstance(b, types.UnicodeCharSeq):
+        def startswith_impl(a, b):
+            return a.startswith(str(b))
+        return startswith_impl
+
+
+# https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodeobject.c#L9342-L9354    # noqa: E501
+@register_jitable
+def _adjust_indices(length, start, end):
+    if end > length:
+        end = length
+    if end < 0:
+        end += length
+        if end < 0:
+            end = 0
+    if start < 0:
+        start += length
+        if start < 0:
+            start = 0
+
+    return start, end
+
+
+@overload_method(types.UnicodeType, 'endswith')
+def unicode_endswith(s, substr, start=None, end=None):
+    if not (start is None or isinstance(start, (types.Omitted,
+                                                types.Integer,
+                                                types.NoneType))):
+        raise TypingError('The arg must be a Integer or None')
+
+    if not (end is None or isinstance(end, (types.Omitted,
+                                            types.Integer,
+                                            types.NoneType))):
+        raise TypingError('The arg must be a Integer or None')
+
+    if isinstance(substr, (types.Tuple, types.UniTuple)):
+        def endswith_impl(s, substr, start=None, end=None):
+            for item in substr:
+                if s.endswith(item, start, end) is True:
+                    return True
+
+            return False
+        return endswith_impl
+
+    if isinstance(substr, types.UnicodeType):
+        def endswith_impl(s, substr, start=None, end=None):
+            length = len(s)
+            sub_length = len(substr)
+            if start is None:
+                start = 0
+            if end is None:
+                end = length
+
+            start, end = _adjust_indices(length, start, end)
+            if end - start < sub_length:
+                return False
+
+            if sub_length == 0:
+                return True
+
+            s = s[start:end]
+            offset = len(s) - sub_length
+
+            return _cmp_region(s, offset, substr, 0, sub_length) == 0
+        return endswith_impl
+
+    if isinstance(substr, types.UnicodeCharSeq):
+        def endswith_impl(s, substr, start=None, end=None):
+            return s.endswith(str(substr), start, end)
+        return endswith_impl
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L11519-L11595    # noqa: E501
+@overload_method(types.UnicodeType, 'expandtabs')
+def unicode_expandtabs(data, tabsize=8):
+    """Implements str.expandtabs()"""
+    thety = tabsize
+    # if the type is omitted, the concrete type is the value
+    if isinstance(tabsize, types.Omitted):
+        thety = tabsize.value
+    # if the type is optional, the concrete type is the captured type
+    elif isinstance(tabsize, types.Optional):
+        thety = tabsize.type
+
+    accepted = (types.Integer, int)
+    if thety is not None and not isinstance(thety, accepted):
+        raise TypingError(
+            '"tabsize" must be {}, not {}'.format(accepted, tabsize))
+
+    def expandtabs_impl(data, tabsize=8):
+        length = len(data)
+        j = line_pos = 0
+        found = False
+        for i in range(length):
+            code_point = _get_code_point(data, i)
+            if code_point == _Py_TAB:
+                found = True
+                if tabsize > 0:
+                    # cannot overflow
+                    incr = tabsize - (line_pos % tabsize)
+                    if j > sys.maxsize - incr:
+                        raise OverflowError('new string is too long')
+                    line_pos += incr
+                    j += incr
+            else:
+                if j > sys.maxsize - 1:
+                    raise OverflowError('new string is too long')
+                line_pos += 1
+                j += 1
+                if code_point in (_Py_LINEFEED, _Py_CARRIAGE_RETURN):
+                    line_pos = 0
+
+        if not found:
+            return data
+
+        res = _empty_string(data._kind, j, data._is_ascii)
+        j = line_pos = 0
+        for i in range(length):
+            code_point = _get_code_point(data, i)
+            if code_point == _Py_TAB:
+                if tabsize > 0:
+                    incr = tabsize - (line_pos % tabsize)
+                    line_pos += incr
+                    for idx in range(j, j + incr):
+                        _set_code_point(res, idx, _Py_SPACE)
+                    j += incr
+            else:
+                line_pos += 1
+                _set_code_point(res, j, code_point)
+                j += 1
+                if code_point in (_Py_LINEFEED, _Py_CARRIAGE_RETURN):
+                    line_pos = 0
+
+        return res
+
+    return expandtabs_impl
+
+
+@overload_method(types.UnicodeType, 'split')
+def unicode_split(a, sep=None, maxsplit=-1):
+    if not (maxsplit == -1 or
+            isinstance(maxsplit, (types.Omitted, types.Integer,
+                                  types.IntegerLiteral))):
+        return None  # fail typing if maxsplit is not an integer
+
+    if isinstance(sep, types.UnicodeCharSeq):
+        def split_impl(a, sep=None, maxsplit=-1):
+            return a.split(str(sep), maxsplit=maxsplit)
+        return split_impl
+
+    if isinstance(sep, types.UnicodeType):
+        def split_impl(a, sep=None, maxsplit=-1):
+            a_len = len(a)
+            sep_len = len(sep)
+
+            if sep_len == 0:
+                raise ValueError('empty separator')
+
+            parts = []
+            last = 0
+            idx = 0
+
+            if sep_len == 1 and maxsplit == -1:
+                sep_code_point = _get_code_point(sep, 0)
+                for idx in range(a_len):
+                    if _get_code_point(a, idx) == sep_code_point:
+                        parts.append(a[last:idx])
+                        last = idx + 1
+            else:
+                split_count = 0
+
+                while idx < a_len and (maxsplit == -1 or
+                                       split_count < maxsplit):
+                    if _cmp_region(a, idx, sep, 0, sep_len) == 0:
+                        parts.append(a[last:idx])
+                        idx += sep_len
+                        last = idx
+                        split_count += 1
+                    else:
+                        idx += 1
+
+            if last <= a_len:
+                parts.append(a[last:])
+
+            return parts
+        return split_impl
+    elif sep is None or isinstance(sep, types.NoneType) or \
+            getattr(sep, 'value', False) is None:
+        def split_whitespace_impl(a, sep=None, maxsplit=-1):
+            a_len = len(a)
+
+            parts = []
+            last = 0
+            idx = 0
+            split_count = 0
+            in_whitespace_block = True
+
+            for idx in range(a_len):
+                code_point = _get_code_point(a, idx)
+                is_whitespace = _PyUnicode_IsSpace(code_point)
+                if in_whitespace_block:
+                    if is_whitespace:
+                        pass  # keep consuming space
+                    else:
+                        last = idx  # this is the start of the next string
+                        in_whitespace_block = False
+                else:
+                    if not is_whitespace:
+                        pass  # keep searching for whitespace transition
+                    else:
+                        parts.append(a[last:idx])
+                        in_whitespace_block = True
+                        split_count += 1
+                        if maxsplit != -1 and split_count == maxsplit:
+                            break
+
+            if last <= a_len and not in_whitespace_block:
+                parts.append(a[last:])
+
+            return parts
+        return split_whitespace_impl
+
+
+def generate_rsplit_whitespace_impl(isspace_func):
+    """Generate whitespace rsplit func based on either ascii or unicode"""
+
+    def rsplit_whitespace_impl(data, sep=None, maxsplit=-1):
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/stringlib/split.h#L192-L240    # noqa: E501
+        if maxsplit < 0:
+            maxsplit = sys.maxsize
+
+        result = []
+        i = len(data) - 1
+        while maxsplit > 0:
+            while i >= 0:
+                code_point = _get_code_point(data, i)
+                if not isspace_func(code_point):
+                    break
+                i -= 1
+            if i < 0:
+                break
+            j = i
+            i -= 1
+            while i >= 0:
+                code_point = _get_code_point(data, i)
+                if isspace_func(code_point):
+                    break
+                i -= 1
+            result.append(data[i + 1:j + 1])
+            maxsplit -= 1
+
+        if i >= 0:
+            # Only occurs when maxsplit was reached
+            # Skip any remaining whitespace and copy to beginning of string
+            while i >= 0:
+                code_point = _get_code_point(data, i)
+                if not isspace_func(code_point):
+                    break
+                i -= 1
+            if i >= 0:
+                result.append(data[0:i + 1])
+
+        return result[::-1]
+
+    return rsplit_whitespace_impl
+
+
+unicode_rsplit_whitespace_impl = register_jitable(
+    generate_rsplit_whitespace_impl(_PyUnicode_IsSpace))
+ascii_rsplit_whitespace_impl = register_jitable(
+    generate_rsplit_whitespace_impl(_Py_ISSPACE))
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L13095-L13108    # noqa: E501
+@overload_method(types.UnicodeType, 'rsplit')
+def unicode_rsplit(data, sep=None, maxsplit=-1):
+    """Implements str.unicode_rsplit()"""
+
+    def _unicode_rsplit_check_type(ty, name, accepted):
+        """Check object belongs to one of specified types"""
+        thety = ty
+        # if the type is omitted, the concrete type is the value
+        if isinstance(ty, types.Omitted):
+            thety = ty.value
+        # if the type is optional, the concrete type is the captured type
+        elif isinstance(ty, types.Optional):
+            thety = ty.type
+
+        if thety is not None and not isinstance(thety, accepted):
+            raise TypingError(
+                '"{}" must be {}, not {}'.format(name, accepted, ty))
+
+    _unicode_rsplit_check_type(sep, 'sep', (types.UnicodeType,
+                                            types.UnicodeCharSeq,
+                                            types.NoneType))
+    _unicode_rsplit_check_type(maxsplit, 'maxsplit', (types.Integer, int))
+
+    if sep is None or isinstance(sep, (types.NoneType, types.Omitted)):
+
+        def rsplit_whitespace_impl(data, sep=None, maxsplit=-1):
+            if data._is_ascii:
+                return ascii_rsplit_whitespace_impl(data, sep, maxsplit)
+            return unicode_rsplit_whitespace_impl(data, sep, maxsplit)
+
+        return rsplit_whitespace_impl
+
+    def rsplit_impl(data, sep=None, maxsplit=-1):
+        sep = str(sep)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/stringlib/split.h#L286-L333    # noqa: E501
+        if data._kind < sep._kind or len(data) < len(sep):
+            return [data]
+
+        def _rsplit_char(data, ch, maxsplit):
+            # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/stringlib/split.h#L242-L284    # noqa: E501
+            result = []
+            ch_code_point = _get_code_point(ch, 0)
+            i = j = len(data) - 1
+            while i >= 0 and maxsplit > 0:
+                data_code_point = _get_code_point(data, i)
+                if data_code_point == ch_code_point:
+                    result.append(data[i + 1 : j + 1])
+                    j = i = i - 1
+                    maxsplit -= 1
+                i -= 1
+            if j >= -1:
+                result.append(data[0 : j + 1])
+
+            return result[::-1]
+
+        if maxsplit < 0:
+            maxsplit = sys.maxsize
+
+        sep_length = len(sep)
+
+        if sep_length == 0:
+            raise ValueError('empty separator')
+        if sep_length == 1:
+            return _rsplit_char(data, sep, maxsplit)
+
+        result = []
+        j = len(data)
+        while maxsplit > 0:
+            pos = data.rfind(sep, start=0, end=j)
+            if pos < 0:
+                break
+            result.append(data[pos + sep_length:j])
+            j = pos
+            maxsplit -= 1
+
+        result.append(data[0:j])
+
+        return result[::-1]
+
+    return rsplit_impl
+
+
+@overload_method(types.UnicodeType, 'center')
+def unicode_center(string, width, fillchar=' '):
+    if not isinstance(width, types.Integer):
+        raise TypingError('The width must be an Integer')
+
+    if isinstance(fillchar, types.UnicodeCharSeq):
+        def center_impl(string, width, fillchar=' '):
+            return string.center(width, str(fillchar))
+        return center_impl
+
+    if not (fillchar == ' ' or
+            isinstance(fillchar, (types.Omitted, types.UnicodeType))):
+        raise TypingError('The fillchar must be a UnicodeType')
+
+    def center_impl(string, width, fillchar=' '):
+        str_len = len(string)
+        fillchar_len = len(fillchar)
+
+        if fillchar_len != 1:
+            raise ValueError('The fill character must be exactly one '
+                             'character long')
+
+        if width <= str_len:
+            return string
+
+        allmargin = width - str_len
+        lmargin = (allmargin // 2) + (allmargin & width & 1)
+        rmargin = allmargin - lmargin
+
+        l_string = fillchar * lmargin
+        if lmargin == rmargin:
+            return l_string + string + l_string
+        else:
+            return l_string + string + (fillchar * rmargin)
+
+    return center_impl
+
+
+def gen_unicode_Xjust(STRING_FIRST):
+    def unicode_Xjust(string, width, fillchar=' '):
+        if not isinstance(width, types.Integer):
+            raise TypingError('The width must be an Integer')
+
+        if isinstance(fillchar, types.UnicodeCharSeq):
+            if STRING_FIRST:
+                def ljust_impl(string, width, fillchar=' '):
+                    return string.ljust(width, str(fillchar))
+                return ljust_impl
+            else:
+                def rjust_impl(string, width, fillchar=' '):
+                    return string.rjust(width, str(fillchar))
+                return rjust_impl
+
+        if not (fillchar == ' ' or
+                isinstance(fillchar, (types.Omitted, types.UnicodeType))):
+            raise TypingError('The fillchar must be a UnicodeType')
+
+        def impl(string, width, fillchar=' '):
+            str_len = len(string)
+            fillchar_len = len(fillchar)
+
+            if fillchar_len != 1:
+                raise ValueError('The fill character must be exactly one '
+                                 'character long')
+
+            if width <= str_len:
+                return string
+
+            newstr = (fillchar * (width - str_len))
+            if STRING_FIRST:
+                return string + newstr
+            else:
+                return newstr + string
+
+        return impl
+
+    return unicode_Xjust
+
+
+overload_method(types.UnicodeType, 'rjust')(gen_unicode_Xjust(False))
+overload_method(types.UnicodeType, 'ljust')(gen_unicode_Xjust(True))
+
+
+def generate_splitlines_func(is_line_break_func):
+    """Generate splitlines performer based on ascii or unicode line breaks."""
+    def impl(data, keepends):
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/stringlib/split.h#L335-L389    # noqa: E501
+        length = len(data)
+        result = []
+        i = j = 0
+        while i < length:
+            # find a line and append it
+            while i < length:
+                code_point = _get_code_point(data, i)
+                if is_line_break_func(code_point):
+                    break
+                i += 1
+
+            # skip the line break reading CRLF as one line break
+            eol = i
+            if i < length:
+                if i + 1 < length:
+                    cur_cp = _get_code_point(data, i)
+                    next_cp = _get_code_point(data, i + 1)
+                    if _Py_ISCARRIAGERETURN(cur_cp) and _Py_ISLINEFEED(next_cp):
+                        i += 1
+                i += 1
+                if keepends:
+                    eol = i
+
+            result.append(data[j:eol])
+            j = i
+
+        return result
+
+    return impl
+
+
+_ascii_splitlines = register_jitable(generate_splitlines_func(_Py_ISLINEBREAK))
+_unicode_splitlines = register_jitable(generate_splitlines_func(
+    _PyUnicode_IsLineBreak))
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L10196-L10229    # noqa: E501
+@overload_method(types.UnicodeType, 'splitlines')
+def unicode_splitlines(data, keepends=False):
+    """Implements str.splitlines()"""
+    thety = keepends
+    # if the type is omitted, the concrete type is the value
+    if isinstance(keepends, types.Omitted):
+        thety = keepends.value
+    # if the type is optional, the concrete type is the captured type
+    elif isinstance(keepends, types.Optional):
+        thety = keepends.type
+
+    accepted = (types.Integer, int, types.Boolean, bool)
+    if thety is not None and not isinstance(thety, accepted):
+        raise TypingError(
+            '"{}" must be {}, not {}'.format('keepends', accepted, keepends))
+
+    def splitlines_impl(data, keepends=False):
+        if data._is_ascii:
+            return _ascii_splitlines(data, keepends)
+
+        return _unicode_splitlines(data, keepends)
+
+    return splitlines_impl
+
+
+@register_jitable
+def join_list(sep, parts):
+    parts_len = len(parts)
+    if parts_len == 0:
+        return ''
+
+    # Precompute size and char_width of result
+    sep_len = len(sep)
+    length = (parts_len - 1) * sep_len
+    kind = sep._kind
+    is_ascii = sep._is_ascii
+    for p in parts:
+        length += len(p)
+        kind = _pick_kind(kind, p._kind)
+        is_ascii = _pick_ascii(is_ascii, p._is_ascii)
+
+    result = _empty_string(kind, length, is_ascii)
+
+    # populate string
+    part = parts[0]
+    _strncpy(result, 0, part, 0, len(part))
+    dst_offset = len(part)
+    for idx in range(1, parts_len):
+        _strncpy(result, dst_offset, sep, 0, sep_len)
+        dst_offset += sep_len
+        part = parts[idx]
+        _strncpy(result, dst_offset, part, 0, len(part))
+        dst_offset += len(part)
+
+    return result
+
+
+@overload_method(types.UnicodeType, 'join')
+def unicode_join(sep, parts):
+
+    if isinstance(parts, types.List):
+        if isinstance(parts.dtype, types.UnicodeType):
+            def join_list_impl(sep, parts):
+                return join_list(sep, parts)
+            return join_list_impl
+        elif isinstance(parts.dtype, types.UnicodeCharSeq):
+            def join_list_impl(sep, parts):
+                _parts = [str(p) for p in parts]
+                return join_list(sep, _parts)
+            return join_list_impl
+        else:
+            pass  # lists of any other type not supported
+    elif isinstance(parts, types.IterableType):
+        def join_iter_impl(sep, parts):
+            parts_list = [p for p in parts]
+            return sep.join(parts_list)
+        return join_iter_impl
+    elif isinstance(parts, types.UnicodeType):
+        # Temporary workaround until UnicodeType is iterable
+        def join_str_impl(sep, parts):
+            parts_list = [parts[i] for i in range(len(parts))]
+            return join_list(sep, parts_list)
+        return join_str_impl
+
+
+@overload_method(types.UnicodeType, 'zfill')
+def unicode_zfill(string, width):
+    if not isinstance(width, types.Integer):
+        raise TypingError("<width> must be an Integer")
+
+    def zfill_impl(string, width):
+
+        str_len = len(string)
+
+        if width <= str_len:
+            return string
+
+        first_char = string[0] if str_len else ''
+        padding = '0' * (width - str_len)
+
+        if first_char in ['+', '-']:
+            newstr = first_char + padding + string[1:]
+        else:
+            newstr = padding + string
+
+        return newstr
+
+    return zfill_impl
+
+
+# ------------------------------------------------------------------------------
+# Strip functions
+# ------------------------------------------------------------------------------
+@register_jitable
+def unicode_strip_left_bound(string, chars):
+    str_len = len(string)
+
+    i = 0
+    if chars is not None:
+        for i in range(str_len):
+            if string[i] not in chars:
+                return i
+    else:
+        for i in range(str_len):
+            if not _PyUnicode_IsSpace(string[i]):
+                return i
+
+    return str_len
+
+
+@register_jitable
+def unicode_strip_right_bound(string, chars):
+    str_len = len(string)
+    i = 0
+    if chars is not None:
+        for i in range(str_len - 1, -1, -1):
+            if string[i] not in chars:
+                i += 1
+                break
+    else:
+        for i in range(str_len - 1, -1, -1):
+            if not _PyUnicode_IsSpace(string[i]):
+                i += 1
+                break
+
+    return i
+
+
+def unicode_strip_types_check(chars):
+    if isinstance(chars, types.Optional):
+        chars = chars.type  # catch optional type with invalid non-None type
+    if not (chars is None or isinstance(chars, (types.Omitted,
+                                                types.UnicodeType,
+                                                types.NoneType))):
+        raise TypingError('The arg must be a UnicodeType or None')
+
+
+def _count_args_types_check(arg):
+    if isinstance(arg, types.Optional):
+        arg = arg.type
+    if not (arg is None or isinstance(arg, (types.Omitted,
+                                            types.Integer,
+                                            types.NoneType))):
+        raise TypingError("The slice indices must be an Integer or None")
+
+
+@overload_method(types.UnicodeType, 'lstrip')
+def unicode_lstrip(string, chars=None):
+
+    if isinstance(chars, types.UnicodeCharSeq):
+        def lstrip_impl(string, chars=None):
+            return string.lstrip(str(chars))
+        return lstrip_impl
+
+    unicode_strip_types_check(chars)
+
+    def lstrip_impl(string, chars=None):
+        return string[unicode_strip_left_bound(string, chars):]
+    return lstrip_impl
+
+
+@overload_method(types.UnicodeType, 'rstrip')
+def unicode_rstrip(string, chars=None):
+
+    if isinstance(chars, types.UnicodeCharSeq):
+        def rstrip_impl(string, chars=None):
+            return string.rstrip(str(chars))
+        return rstrip_impl
+
+    unicode_strip_types_check(chars)
+
+    def rstrip_impl(string, chars=None):
+        return string[:unicode_strip_right_bound(string, chars)]
+    return rstrip_impl
+
+
+@overload_method(types.UnicodeType, 'strip')
+def unicode_strip(string, chars=None):
+
+    if isinstance(chars, types.UnicodeCharSeq):
+        def strip_impl(string, chars=None):
+            return string.strip(str(chars))
+        return strip_impl
+
+    unicode_strip_types_check(chars)
+
+    def strip_impl(string, chars=None):
+        lb = unicode_strip_left_bound(string, chars)
+        rb = unicode_strip_right_bound(string, chars)
+        return string[lb:rb]
+    return strip_impl
+
+
+# ------------------------------------------------------------------------------
+# Slice functions
+# ------------------------------------------------------------------------------
+
+@register_jitable
+def normalize_str_idx(idx, length, is_start=True):
+    """
+    Parameters
+    ----------
+    idx : int or None
+        the index
+    length : int
+        the string length
+    is_start : bool; optional with defaults to True
+        Is it the *start* or the *stop* of the slice?
+
+    Returns
+    -------
+    norm_idx : int
+        normalized index
+    """
+    if idx is None:
+        if is_start:
+            return 0
+        else:
+            return length
+    elif idx < 0:
+        idx += length
+
+    if idx < 0 or idx >= length:
+        raise IndexError("string index out of range")
+
+    return idx
+
+
+@register_jitable
+def _normalize_slice_idx_count(arg, slice_len, default):
+    """
+    Used for unicode_count
+
+    If arg < -slice_len, returns 0 (prevents circle)
+
+    If arg is within slice, e.g -slice_len <= arg < slice_len
+    returns its real index via arg % slice_len
+
+    If arg > slice_len, returns arg (in this case count must
+    return 0 if it is start index)
+    """
+
+    if arg is None:
+        return default
+    if -slice_len <= arg < slice_len:
+        return arg % slice_len
+    return 0 if arg < 0 else arg
+
+
+@intrinsic
+def _normalize_slice(typingctx, sliceobj, length):
+    """Fix slice object.
+    """
+    sig = sliceobj(sliceobj, length)
+
+    def codegen(context, builder, sig, args):
+        [slicetype, lengthtype] = sig.args
+        [sliceobj, length] = args
+        slice = context.make_helper(builder, slicetype, sliceobj)
+        slicing.guard_invalid_slice(context, builder, slicetype, slice)
+        slicing.fix_slice(builder, slice, length)
+        return slice._getvalue()
+
+    return sig, codegen
+
+
+@intrinsic
+def _slice_span(typingctx, sliceobj):
+    """Compute the span from the given slice object.
+    """
+    sig = types.intp(sliceobj)
+
+    def codegen(context, builder, sig, args):
+        [slicetype] = sig.args
+        [sliceobj] = args
+        slice = context.make_helper(builder, slicetype, sliceobj)
+        result_size = slicing.get_slice_length(builder, slice)
+        return result_size
+
+    return sig, codegen
+
+
+@register_jitable(_nrt=False)
+def _strncpy(dst, dst_offset, src, src_offset, n):
+    if src._kind == dst._kind:
+        byte_width = _kind_to_byte_width(src._kind)
+        src_byte_offset = byte_width * src_offset
+        dst_byte_offset = byte_width * dst_offset
+        nbytes = n * byte_width
+        memcpy_region(dst._data, dst_byte_offset, src._data,
+                      src_byte_offset, nbytes, align=1)
+    else:
+        for i in range(n):
+            _set_code_point(dst, dst_offset + i,
+                            _get_code_point(src, src_offset + i))
+
+
+@intrinsic
+def _get_str_slice_view(typingctx, src_t, start_t, length_t):
+    """Create a slice of a unicode string using a view of its data to avoid
+    extra allocation.
+    """
+    assert src_t == types.unicode_type
+
+    def codegen(context, builder, sig, args):
+        src, start, length = args
+        in_str = cgutils.create_struct_proxy(
+            types.unicode_type)(context, builder, value=src)
+        view_str = cgutils.create_struct_proxy(
+            types.unicode_type)(context, builder)
+        view_str.meminfo = in_str.meminfo
+        view_str.kind = in_str.kind
+        view_str.is_ascii = in_str.is_ascii
+        view_str.length = length
+        # hash value -1 to indicate "need to compute hash"
+        view_str.hash = context.get_constant(_Py_hash_t, -1)
+        # get a pointer to start of slice data
+        bw_typ = context.typing_context.resolve_value_type(_kind_to_byte_width)
+        bw_sig = bw_typ.get_call_type(
+            context.typing_context, (types.int32,), {})
+        bw_impl = context.get_function(bw_typ, bw_sig)
+        byte_width = bw_impl(builder, (in_str.kind,))
+        offset = builder.mul(start, byte_width)
+        view_str.data = builder.gep(in_str.data, [offset])
+        # Set parent pyobject to NULL
+        view_str.parent = cgutils.get_null_value(view_str.parent.type)
+        # incref original string
+        if context.enable_nrt:
+            context.nrt.incref(builder, sig.args[0], src)
+        return view_str._getvalue()
+
+    sig = types.unicode_type(types.unicode_type, types.intp, types.intp)
+    return sig, codegen
+
+
+@overload(operator.getitem)
+def unicode_getitem(s, idx):
+    if isinstance(s, types.UnicodeType):
+        if isinstance(idx, types.Integer):
+            def getitem_char(s, idx):
+                idx = normalize_str_idx(idx, len(s))
+                cp = _get_code_point(s, idx)
+                kind = _codepoint_to_kind(cp)
+                is_ascii = _codepoint_is_ascii(cp)
+                ret = _empty_string(kind, 1, is_ascii)
+                _set_code_point(ret, 0, cp)
+                return ret
+            return getitem_char
+        elif isinstance(idx, types.SliceType):
+            def getitem_slice(s, idx):
+                slice_idx = _normalize_slice(idx, len(s))
+                span = _slice_span(slice_idx)
+
+                cp = _get_code_point(s, slice_idx.start)
+                kind = _codepoint_to_kind(cp)
+                is_ascii = _codepoint_is_ascii(cp)
+
+                # Check slice to see if it's homogeneous in kind
+                for i in range(slice_idx.start + slice_idx.step,
+                               slice_idx.stop, slice_idx.step):
+                    cp = _get_code_point(s, i)
+                    is_ascii &= _codepoint_is_ascii(cp)
+                    new_kind = _codepoint_to_kind(cp)
+                    if kind != new_kind:
+                        kind = _pick_kind(kind, new_kind)
+                    # TODO: it might be possible to break here if the kind
+                    # is PY_UNICODE_4BYTE_KIND but there are potentially
+                    # strings coming from other internal functions that are
+                    # this wide and also actually ASCII (i.e. kind is larger
+                    # than actually required for storing the code point), so
+                    # it's necessary to continue.
+
+                if slice_idx.step == 1 and kind == s._kind:
+                    # Can return a view, the slice has the same kind as the
+                    # string itself and it's a stride slice 1.
+                    return _get_str_slice_view(s, slice_idx.start, span)
+                else:
+                    # It's heterogeneous in kind OR stride != 1
+                    ret = _empty_string(kind, span, is_ascii)
+                    cur = slice_idx.start
+                    for i in range(span):
+                        _set_code_point(ret, i, _get_code_point(s, cur))
+                        cur += slice_idx.step
+                    return ret
+
+            return getitem_slice
+
+
+# ------------------------------------------------------------------------------
+# String operations
+# ------------------------------------------------------------------------------
+
+
+@overload(operator.add)
+@overload(operator.iadd)
+def unicode_concat(a, b):
+    if isinstance(a, types.UnicodeType) and isinstance(b, types.UnicodeType):
+        def concat_impl(a, b):
+            new_length = a._length + b._length
+            new_kind = _pick_kind(a._kind, b._kind)
+            new_ascii = _pick_ascii(a._is_ascii, b._is_ascii)
+            result = _empty_string(new_kind, new_length, new_ascii)
+            for i in range(len(a)):
+                _set_code_point(result, i, _get_code_point(a, i))
+            for j in range(len(b)):
+                _set_code_point(result, len(a) + j, _get_code_point(b, j))
+            return result
+        return concat_impl
+
+    if isinstance(a, types.UnicodeType) and isinstance(b, types.UnicodeCharSeq):
+        def concat_impl(a, b):
+            return a + str(b)
+        return concat_impl
+
+
+@register_jitable
+def _repeat_impl(str_arg, mult_arg):
+    if str_arg == '' or mult_arg < 1:
+        return ''
+    elif mult_arg == 1:
+        return str_arg
+    else:
+        new_length = str_arg._length * mult_arg
+        new_kind = str_arg._kind
+        result = _empty_string(new_kind, new_length, str_arg._is_ascii)
+        # make initial copy into result
+        len_a = len(str_arg)
+        _strncpy(result, 0, str_arg, 0, len_a)
+        # loop through powers of 2 for efficient copying
+        copy_size = len_a
+        while 2 * copy_size <= new_length:
+            _strncpy(result, copy_size, result, 0, copy_size)
+            copy_size *= 2
+
+        if not 2 * copy_size == new_length:
+            # if copy_size not an exact multiple it then needs
+            # to complete the rest of the copies
+            rest = new_length - copy_size
+            _strncpy(result, copy_size, result, copy_size - rest, rest)
+            return result
+
+
+@overload(operator.mul)
+def unicode_repeat(a, b):
+    if isinstance(a, types.UnicodeType) and isinstance(b, types.Integer):
+        def wrap(a, b):
+            return _repeat_impl(a, b)
+        return wrap
+    elif isinstance(a, types.Integer) and isinstance(b, types.UnicodeType):
+        def wrap(a, b):
+            return _repeat_impl(b, a)
+        return wrap
+
+
+@overload(operator.not_)
+def unicode_not(a):
+    if isinstance(a, types.UnicodeType):
+        def impl(a):
+            return len(a) == 0
+        return impl
+
+
+@overload_method(types.UnicodeType, 'replace')
+def unicode_replace(s, old_str, new_str, count=-1):
+    thety = count
+    if isinstance(count, types.Omitted):
+        thety = count.value
+    elif isinstance(count, types.Optional):
+        thety = count.type
+
+    if not isinstance(thety, (int, types.Integer)):
+        raise TypingError('Unsupported parameters. The parameters '
+                          'must be Integer. Given count: {}'.format(count))
+
+    if not isinstance(old_str, (types.UnicodeType, types.NoneType)):
+        raise TypingError('The object must be a UnicodeType.'
+                          ' Given: {}'.format(old_str))
+
+    if not isinstance(new_str, types.UnicodeType):
+        raise TypingError('The object must be a UnicodeType.'
+                          ' Given: {}'.format(new_str))
+
+    def impl(s, old_str, new_str, count=-1):
+        if count == 0:
+            return s
+        if old_str == '':
+            schars = list(s)
+            if count == -1:
+                return new_str + new_str.join(schars) + new_str
+            split_result = [new_str]
+            min_count = min(len(schars), count)
+            for i in range(min_count):
+                split_result.append(schars[i])
+                if i + 1 != min_count:
+                    split_result.append(new_str)
+                else:
+                    split_result.append(''.join(schars[(i + 1):]))
+            if count > len(schars):
+                split_result.append(new_str)
+            return ''.join(split_result)
+        schars = s.split(old_str, count)
+        result = new_str.join(schars)
+        return result
+
+    return impl
+
+# ------------------------------------------------------------------------------
+# String `is*()` methods
+# ------------------------------------------------------------------------------
+
+
+# generates isalpha/isalnum
+def gen_isAlX(ascii_func, unicode_func):
+    def unicode_isAlX(data):
+
+        def impl(data):
+            length = len(data)
+            if length == 0:
+                return False
+
+            if length == 1:
+                code_point = _get_code_point(data, 0)
+                if data._is_ascii:
+                    return ascii_func(code_point)
+                else:
+                    return unicode_func(code_point)
+
+            if data._is_ascii:
+                for i in range(length):
+                    code_point = _get_code_point(data, i)
+                    if not ascii_func(code_point):
+                        return False
+
+            for i in range(length):
+                code_point = _get_code_point(data, i)
+                if not unicode_func(code_point):
+                    return False
+
+            return True
+
+        return impl
+    return unicode_isAlX
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L11928-L11964    # noqa: E501
+overload_method(types.UnicodeType, 'isalpha')(gen_isAlX(_Py_ISALPHA,
+                                                        _PyUnicode_IsAlpha))
+
+_unicode_is_alnum = register_jitable(lambda x:
+                                     (_PyUnicode_IsNumeric(x) or
+                                      _PyUnicode_IsAlpha(x)))
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L11975-L12006    # noqa: E501
+overload_method(types.UnicodeType, 'isalnum')(gen_isAlX(_Py_ISALNUM,
+                                                        _unicode_is_alnum))
+
+
+def _is_upper(is_lower, is_upper, is_title):
+    # impl is an approximate translation of:
+    # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L11794-L11827    # noqa: E501
+    # mixed with:
+    # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/bytes_methods.c#L218-L242    # noqa: E501
+    def impl(a):
+        l = len(a)
+        if l == 1:
+            return is_upper(_get_code_point(a, 0))
+        if l == 0:
+            return False
+        cased = False
+        for idx in range(l):
+            code_point = _get_code_point(a, idx)
+            if is_lower(code_point) or is_title(code_point):
+                return False
+            elif(not cased and is_upper(code_point)):
+                cased = True
+        return cased
+    return impl
+
+
+_always_false = register_jitable(lambda x: False)
+_ascii_is_upper = register_jitable(_is_upper(_Py_ISLOWER, _Py_ISUPPER,
+                                             _always_false))
+_unicode_is_upper = register_jitable(_is_upper(_PyUnicode_IsLowercase,
+                                               _PyUnicode_IsUppercase,
+                                               _PyUnicode_IsTitlecase))
+
+
+@overload_method(types.UnicodeType, 'isupper')
+def unicode_isupper(a):
+    """
+    Implements .isupper()
+    """
+    def impl(a):
+        if a._is_ascii:
+            return _ascii_is_upper(a)
+        else:
+            return _unicode_is_upper(a)
+    return impl
+
+
+if utils.PYVERSION >= (3, 7):
+    @overload_method(types.UnicodeType, 'isascii')
+    def unicode_isascii(data):
+        """Implements UnicodeType.isascii()"""
+
+        def impl(data):
+            return data._is_ascii
+        return impl
+
+
+@overload_method(types.UnicodeType, 'istitle')
+def unicode_istitle(data):
+    """
+    Implements UnicodeType.istitle()
+    The algorithm is an approximate translation from CPython:
+    https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L11829-L11885 # noqa: E501
+    """
+
+    def impl(data):
+        length = len(data)
+        if length == 1:
+            char = _get_code_point(data, 0)
+            return _PyUnicode_IsUppercase(char) or _PyUnicode_IsTitlecase(char)
+
+        if length == 0:
+            return False
+
+        cased = False
+        previous_is_cased = False
+        for idx in range(length):
+            char = _get_code_point(data, idx)
+            if _PyUnicode_IsUppercase(char) or _PyUnicode_IsTitlecase(char):
+                if previous_is_cased:
+                    return False
+                previous_is_cased = True
+                cased = True
+            elif _PyUnicode_IsLowercase(char):
+                if not previous_is_cased:
+                    return False
+                previous_is_cased = True
+                cased = True
+            else:
+                previous_is_cased = False
+
+        return cased
+    return impl
+
+
+@overload_method(types.UnicodeType, 'islower')
+def unicode_islower(data):
+    """
+    impl is an approximate translation of:
+    https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodeobject.c#L11900-L11933    # noqa: E501
+    mixed with:
+    https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/bytes_methods.c#L131-L156    # noqa: E501
+    """
+
+    def impl(data):
+        length = len(data)
+        if length == 1:
+            return _PyUnicode_IsLowercase(_get_code_point(data, 0))
+        if length == 0:
+            return False
+
+        cased = False
+        for idx in range(length):
+            cp = _get_code_point(data, idx)
+            if _PyUnicode_IsUppercase(cp) or _PyUnicode_IsTitlecase(cp):
+                return False
+            elif not cased and _PyUnicode_IsLowercase(cp):
+                cased = True
+        return cased
+    return impl
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L12126-L12161    # noqa: E501
+@overload_method(types.UnicodeType, 'isidentifier')
+def unicode_isidentifier(data):
+    """Implements UnicodeType.isidentifier()"""
+
+    def impl(data):
+        length = len(data)
+        if length == 0:
+            return False
+
+        first_cp = _get_code_point(data, 0)
+        if not _PyUnicode_IsXidStart(first_cp) and first_cp != 0x5F:
+            return False
+
+        for i in range(1, length):
+            code_point = _get_code_point(data, i)
+            if not _PyUnicode_IsXidContinue(code_point):
+                return False
+
+        return True
+
+    return impl
+
+
+# generator for simple unicode "isX" methods
+def gen_isX(_PyUnicode_IS_func, empty_is_false=True):
+    def unicode_isX(data):
+        def impl(data):
+            length = len(data)
+            if length == 1:
+                return _PyUnicode_IS_func(_get_code_point(data, 0))
+
+            if empty_is_false and length == 0:
+                return False
+
+            for i in range(length):
+                code_point = _get_code_point(data, i)
+                if not _PyUnicode_IS_func(code_point):
+                    return False
+
+            return True
+
+        return impl
+    return unicode_isX
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L11896-L11925    # noqa: E501
+overload_method(types.UnicodeType, 'isspace')(gen_isX(_PyUnicode_IsSpace))
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L12096-L12124    # noqa: E501
+overload_method(types.UnicodeType, 'isnumeric')(gen_isX(_PyUnicode_IsNumeric))
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L12056-L12085    # noqa: E501
+overload_method(types.UnicodeType, 'isdigit')(gen_isX(_PyUnicode_IsDigit))
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L12017-L12045    # noqa: E501
+overload_method(types.UnicodeType, 'isdecimal')(
+    gen_isX(_PyUnicode_IsDecimalDigit))
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L12188-L12213    # noqa: E501
+overload_method(types.UnicodeType, 'isprintable')(
+    gen_isX(_PyUnicode_IsPrintable, False))
+
+# ------------------------------------------------------------------------------
+# String methods that apply a transformation to the characters themselves
+# ------------------------------------------------------------------------------
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L9863-L9908    # noqa: E501
+def case_operation(ascii_func, unicode_func):
+    """Generate common case operation performer."""
+    def impl(data):
+        length = len(data)
+        if length == 0:
+            return _empty_string(data._kind, length, data._is_ascii)
+
+        if data._is_ascii:
+            res = _empty_string(data._kind, length, 1)
+            ascii_func(data, res)
+            return res
+
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L9863-L9908    # noqa: E501
+        tmp = _empty_string(PY_UNICODE_4BYTE_KIND, 3 * length, data._is_ascii)
+        # maxchar should be inside of a list to be pass as argument by reference
+        maxchars = [0]
+        newlength = unicode_func(data, length, tmp, maxchars)
+        maxchar = maxchars[0]
+        newkind = _codepoint_to_kind(maxchar)
+        res = _empty_string(newkind, newlength, _codepoint_is_ascii(maxchar))
+        for i in range(newlength):
+            _set_code_point(res, i, _get_code_point(tmp, i))
+
+        return res
+
+    return impl
+
+
+# https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodeobject.c#L9856-L9883    # noqa: E501
+@register_jitable
+def _handle_capital_sigma(data, length, idx):
+    """This is a translation of the function that handles the capital sigma."""
+    c = 0
+    j = idx - 1
+    while j >= 0:
+        c = _get_code_point(data, j)
+        if not _PyUnicode_IsCaseIgnorable(c):
+            break
+        j -= 1
+    final_sigma = (j >= 0 and _PyUnicode_IsCased(c))
+    if final_sigma:
+        j = idx + 1
+        while j < length:
+            c = _get_code_point(data, j)
+            if not _PyUnicode_IsCaseIgnorable(c):
+                break
+            j += 1
+        final_sigma = (j == length or (not _PyUnicode_IsCased(c)))
+
+    return 0x3c2 if final_sigma else 0x3c3
+
+
+# https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodeobject.c#L9885-L9895    # noqa: E501
+@register_jitable
+def _lower_ucs4(code_point, data, length, idx, mapped):
+    """This is a translation of the function that lowers a character."""
+    if code_point == 0x3A3:
+        mapped[0] = _handle_capital_sigma(data, length, idx)
+        return 1
+    return _PyUnicode_ToLowerFull(code_point, mapped)
+
+
+# https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodeobject.c#L9946-L9965    # noqa: E501
+def _gen_unicode_upper_or_lower(lower):
+    def _do_upper_or_lower(data, length, res, maxchars):
+        k = 0
+        for idx in range(length):
+            mapped = np.zeros(3, dtype=_Py_UCS4)
+            code_point = _get_code_point(data, idx)
+            if lower:
+                n_res = _lower_ucs4(code_point, data, length, idx, mapped)
+            else:
+                # might be needed if call _do_upper_or_lower in unicode_upper
+                n_res = _PyUnicode_ToUpperFull(code_point, mapped)
+            for m in mapped[:n_res]:
+                maxchars[0] = max(maxchars[0], m)
+                _set_code_point(res, k, m)
+                k += 1
+        return k
+    return _do_upper_or_lower
+
+
+_unicode_upper = register_jitable(_gen_unicode_upper_or_lower(False))
+_unicode_lower = register_jitable(_gen_unicode_upper_or_lower(True))
+
+
+def _gen_ascii_upper_or_lower(func):
+    def _ascii_upper_or_lower(data, res):
+        for idx in range(len(data)):
+            code_point = _get_code_point(data, idx)
+            _set_code_point(res, idx, func(code_point))
+    return _ascii_upper_or_lower
+
+
+_ascii_upper = register_jitable(_gen_ascii_upper_or_lower(_Py_TOUPPER))
+_ascii_lower = register_jitable(_gen_ascii_upper_or_lower(_Py_TOLOWER))
+
+
+@overload_method(types.UnicodeType, 'lower')
+def unicode_lower(data):
+    """Implements .lower()"""
+    return case_operation(_ascii_lower, _unicode_lower)
+
+
+@overload_method(types.UnicodeType, 'upper')
+def unicode_upper(data):
+    """Implements .upper()"""
+    return case_operation(_ascii_upper, _unicode_upper)
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L9819-L9834    # noqa: E501
+@register_jitable
+def _unicode_casefold(data, length, res, maxchars):
+    k = 0
+    mapped = np.zeros(3, dtype=_Py_UCS4)
+    for idx in range(length):
+        mapped.fill(0)
+        code_point = _get_code_point(data, idx)
+        n_res = _PyUnicode_ToFoldedFull(code_point, mapped)
+        for m in mapped[:n_res]:
+            maxchar = maxchars[0]
+            maxchars[0] = max(maxchar, m)
+            _set_code_point(res, k, m)
+            k += 1
+
+    return k
+
+
+@register_jitable
+def _ascii_casefold(data, res):
+    for idx in range(len(data)):
+        code_point = _get_code_point(data, idx)
+        _set_code_point(res, idx, _Py_TOLOWER(code_point))
+
+
+@overload_method(types.UnicodeType, 'casefold')
+def unicode_casefold(data):
+    """Implements str.casefold()"""
+    return case_operation(_ascii_casefold, _unicode_casefold)
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L9737-L9759    # noqa: E501
+@register_jitable
+def _unicode_capitalize(data, length, res, maxchars):
+    k = 0
+    maxchar = 0
+    mapped = np.zeros(3, dtype=_Py_UCS4)
+    code_point = _get_code_point(data, 0)
+
+    # https://github.com/python/cpython/commit/b015fc86f7b1f35283804bfee788cce0a5495df7/Objects/unicodeobject.c#diff-220e5da0d1c8abf508b25c02da6ca16c    # noqa: E501
+    if _py38_or_later:
+        n_res = _PyUnicode_ToTitleFull(code_point, mapped)
+    else:
+        n_res = _PyUnicode_ToUpperFull(code_point, mapped)
+
+    for m in mapped[:n_res]:
+        maxchar = max(maxchar, m)
+        _set_code_point(res, k, m)
+        k += 1
+    for idx in range(1, length):
+        mapped.fill(0)
+        code_point = _get_code_point(data, idx)
+        n_res = _lower_ucs4(code_point, data, length, idx, mapped)
+        for m in mapped[:n_res]:
+            maxchar = max(maxchar, m)
+            _set_code_point(res, k, m)
+            k += 1
+    maxchars[0] = maxchar
+    return k
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/bytes_methods.c#L361-L382    # noqa: E501
+@register_jitable
+def _ascii_capitalize(data, res):
+    code_point = _get_code_point(data, 0)
+    _set_code_point(res, 0, _Py_TOUPPER(code_point))
+    for idx in range(1, len(data)):
+        code_point = _get_code_point(data, idx)
+        _set_code_point(res, idx, _Py_TOLOWER(code_point))
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L10765-L10774    # noqa: E501
+@overload_method(types.UnicodeType, 'capitalize')
+def unicode_capitalize(data):
+    return case_operation(_ascii_capitalize, _unicode_capitalize)
+
+
+# https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodeobject.c#L9996-L10021    # noqa: E501
+@register_jitable
+def _unicode_title(data, length, res, maxchars):
+    """This is a translation of the function that titles a unicode string."""
+    k = 0
+    previous_cased = False
+    mapped = np.empty(3, dtype=_Py_UCS4)
+    for idx in range(length):
+        mapped.fill(0)
+        code_point = _get_code_point(data, idx)
+        if previous_cased:
+            n_res = _lower_ucs4(code_point, data, length, idx, mapped)
+        else:
+            n_res = _PyUnicode_ToTitleFull(_Py_UCS4(code_point), mapped)
+        for m in mapped[:n_res]:
+            maxchar, = maxchars
+            maxchars[0] = max(maxchar, m)
+            _set_code_point(res, k, m)
+            k += 1
+        previous_cased = _PyUnicode_IsCased(_Py_UCS4(code_point))
+    return k
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/bytes_methods.c#L332-L352    # noqa: E501
+@register_jitable
+def _ascii_title(data, res):
+    """ Does .title() on an ASCII string """
+    previous_is_cased = False
+    for idx in range(len(data)):
+        code_point = _get_code_point(data, idx)
+        if _Py_ISLOWER(code_point):
+            if not previous_is_cased:
+                code_point = _Py_TOUPPER(code_point)
+            previous_is_cased = True
+        elif _Py_ISUPPER(code_point):
+            if previous_is_cased:
+                code_point = _Py_TOLOWER(code_point)
+            previous_is_cased = True
+        else:
+            previous_is_cased = False
+        _set_code_point(res, idx, code_point)
+
+
+# https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodeobject.c#L10023-L10069    # noqa: E501
+@overload_method(types.UnicodeType, 'title')
+def unicode_title(data):
+    """Implements str.title()"""
+    # https://docs.python.org/3/library/stdtypes.html#str.title
+    return case_operation(_ascii_title, _unicode_title)
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/bytes_methods.c#L391-L408    # noqa: E501
+@register_jitable
+def _ascii_swapcase(data, res):
+    for idx in range(len(data)):
+        code_point = _get_code_point(data, idx)
+        if _Py_ISUPPER(code_point):
+            code_point = _Py_TOLOWER(code_point)
+        elif _Py_ISLOWER(code_point):
+            code_point = _Py_TOUPPER(code_point)
+        _set_code_point(res, idx, code_point)
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L9761-L9784    # noqa: E501
+@register_jitable
+def _unicode_swapcase(data, length, res, maxchars):
+    k = 0
+    maxchar = 0
+    mapped = np.empty(3, dtype=_Py_UCS4)
+    for idx in range(length):
+        mapped.fill(0)
+        code_point = _get_code_point(data, idx)
+        if _PyUnicode_IsUppercase(code_point):
+            n_res = _lower_ucs4(code_point, data, length, idx, mapped)
+        elif _PyUnicode_IsLowercase(code_point):
+            n_res = _PyUnicode_ToUpperFull(code_point, mapped)
+        else:
+            n_res = 1
+            mapped[0] = code_point
+        for m in mapped[:n_res]:
+            maxchar = max(maxchar, m)
+            _set_code_point(res, k, m)
+            k += 1
+    maxchars[0] = maxchar
+    return k
+
+
+@overload_method(types.UnicodeType, 'swapcase')
+def unicode_swapcase(data):
+    return case_operation(_ascii_swapcase, _unicode_swapcase)
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Python/bltinmodule.c#L1781-L1824    # noqa: E501
+@overload(ord)
+def ol_ord(c):
+    if isinstance(c, types.UnicodeType):
+        def impl(c):
+            lc = len(c)
+            if lc != 1:
+                # CPython does TypeError
+                raise TypeError("ord() expected a character")
+            return _get_code_point(c, 0)
+        return impl
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L2005-L2028    # noqa: E501
+# This looks a bit different to the cpython implementation but, with the
+# exception of a latin1 fast path is logically the same. It finds the "kind" of
+# the codepoint `ch`, creates a length 1 string of that kind and then injects
+# the code point into the zero position of that string. Cpython does similar but
+# branches for each kind (this is encapsulated in Numba's _set_code_point).
+@register_jitable
+def _unicode_char(ch):
+    assert ch <= _MAX_UNICODE
+    kind = _codepoint_to_kind(ch)
+    ret = _empty_string(kind, 1, kind == PY_UNICODE_1BYTE_KIND)
+    _set_code_point(ret, 0, ch)
+    return ret
+
+
+_out_of_range_msg = "chr() arg not in range(0x%hx)" % _MAX_UNICODE
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodeobject.c#L3045-L3055    # noqa: E501
+@register_jitable
+def _PyUnicode_FromOrdinal(ordinal):
+    if (ordinal < 0 or ordinal > _MAX_UNICODE):
+        raise ValueError(_out_of_range_msg)
+
+    return _unicode_char(_Py_UCS4(ordinal))
+
+
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Python/bltinmodule.c#L715-L720    # noqa: E501
+@overload(chr)
+def ol_chr(i):
+    if isinstance(i, types.Integer):
+        def impl(i):
+            return _PyUnicode_FromOrdinal(i)
+        return impl
+
+
+@overload(str)
+def integer_str(n):
+    if isinstance(n, types.Integer):
+
+        ten = n(10)
+
+        def impl(n):
+            flag = False
+            if n < 0:
+                n = -n
+                flag = True
+            if n == 0:
+                return '0'
+            length = flag + 1 + int(np.floor(np.log10(n)))
+            kind = PY_UNICODE_1BYTE_KIND
+            char_width = _kind_to_byte_width(kind)
+            s = _malloc_string(kind, char_width, length, True)
+            if flag:
+                _set_code_point(s, 0, ord('-'))
+            idx = length - 1
+            while n > 0:
+                n, digit = divmod(n, ten)
+                c = ord('0') + digit
+                _set_code_point(s, idx, c)
+                idx -= 1
+            return s
+        return impl
+
+
+@overload(str)
+def boolean_str(b):
+    if isinstance(b, types.Boolean):
+        return lambda b: "True" if b else "False"
+
+
+# ------------------------------------------------------------------------------
+# iteration
+# ------------------------------------------------------------------------------
+
+
+@lower_builtin('getiter', types.UnicodeType)
+def getiter_unicode(context, builder, sig, args):
+    [ty] = sig.args
+    [data] = args
+
+    iterobj = context.make_helper(builder, sig.return_type)
+
+    # set the index to zero
+    zero = context.get_constant(types.uintp, 0)
+    indexptr = cgutils.alloca_once_value(builder, zero)
+
+    iterobj.index = indexptr
+
+    # wire in the unicode type data
+    iterobj.data = data
+
+    # incref as needed
+    if context.enable_nrt:
+        context.nrt.incref(builder, ty, data)
+
+    res = iterobj._getvalue()
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin('iternext', types.UnicodeIteratorType)
+# a new ref counted object is put into result._yield so set the new_ref to True!
+@iternext_impl(RefType.NEW)
+def iternext_unicode(context, builder, sig, args, result):
+    [iterty] = sig.args
+    [iter] = args
+
+    tyctx = context.typing_context
+
+    # get ref to unicode.__getitem__
+    fnty = tyctx.resolve_value_type(operator.getitem)
+    getitem_sig = fnty.get_call_type(tyctx, (types.unicode_type, types.uintp),
+                                     {})
+    getitem_impl = context.get_function(fnty, getitem_sig)
+
+    # get ref to unicode.__len__
+    fnty = tyctx.resolve_value_type(len)
+    len_sig = fnty.get_call_type(tyctx, (types.unicode_type,), {})
+    len_impl = context.get_function(fnty, len_sig)
+
+    # grab unicode iterator struct
+    iterobj = context.make_helper(builder, iterty, value=iter)
+
+    # find the length of the string
+    strlen = len_impl(builder, (iterobj.data,))
+
+    # find the current index
+    index = builder.load(iterobj.index)
+
+    # see if the index is in range
+    is_valid = builder.icmp_unsigned('<', index, strlen)
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        # return value at index
+        gotitem = getitem_impl(builder, (iterobj.data, index,))
+        result.yield_(gotitem)
+
+        # bump index for next cycle
+        nindex = cgutils.increment_index(builder, index)
+        builder.store(nindex, iterobj.index)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unicode_support.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unicode_support.py
new file mode 100644
index 000000000..533ba070f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unicode_support.py
@@ -0,0 +1,765 @@
+"""
+This module contains support functions for more advanced unicode operations.
+This is not a public API and is for Numba internal use only. Most of the
+functions are relatively straightforward translations of the functions with the
+same name in CPython.
+"""
+from collections import namedtuple
+from enum import IntEnum
+
+import llvmlite.ir
+import numpy as np
+
+from numba.core import types, cgutils
+from numba.core.imputils import (impl_ret_untracked)
+
+from numba.core.extending import overload, intrinsic, register_jitable
+from numba.core.errors import TypingError
+
+# This is equivalent to the struct `_PyUnicode_TypeRecord defined in CPython's
+# Objects/unicodectype.c
+typerecord = namedtuple('typerecord',
+                        'upper lower title decimal digit flags')
+
+# The Py_UCS4 type from CPython:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/unicodeobject.h#L112    # noqa: E501
+_Py_UCS4 = types.uint32
+
+# ------------------------------------------------------------------------------
+# Start code related to/from CPython's unicodectype impl
+#
+# NOTE: the original source at:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c   # noqa: E501
+# contains this statement:
+#
+# /*
+#   Unicode character type helpers.
+#
+#   Written by Marc-Andre Lemburg (mal@lemburg.com).
+#   Modified for Python 2.0 by Fredrik Lundh (fredrik@pythonware.com)
+#
+#   Copyright (c) Corporation for National Research Initiatives.
+#
+# */
+
+
+# This enum contains the values defined in CPython's Objects/unicodectype.c that
+# provide masks for use against the various members of the typerecord
+#
+# See: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L13-L27    # noqa: E501
+#
+
+
+_Py_TAB = 0x9
+_Py_LINEFEED = 0xa
+_Py_CARRIAGE_RETURN = 0xd
+_Py_SPACE = 0x20
+
+
+class _PyUnicode_TyperecordMasks(IntEnum):
+    ALPHA_MASK = 0x01
+    DECIMAL_MASK = 0x02
+    DIGIT_MASK = 0x04
+    LOWER_MASK = 0x08
+    LINEBREAK_MASK = 0x10
+    SPACE_MASK = 0x20
+    TITLE_MASK = 0x40
+    UPPER_MASK = 0x80
+    XID_START_MASK = 0x100
+    XID_CONTINUE_MASK = 0x200
+    PRINTABLE_MASK = 0x400
+    NUMERIC_MASK = 0x800
+    CASE_IGNORABLE_MASK = 0x1000
+    CASED_MASK = 0x2000
+    EXTENDED_CASE_MASK = 0x4000
+
+
+def _PyUnicode_gettyperecord(a):
+    raise RuntimeError("Calling the Python definition is invalid")
+
+
+@intrinsic
+def _gettyperecord_impl(typingctx, codepoint):
+    """
+    Provides the binding to numba_gettyperecord, returns a `typerecord`
+    namedtuple of properties from the codepoint.
+    """
+    if not isinstance(codepoint, types.Integer):
+        raise TypingError("codepoint must be an integer")
+
+    def details(context, builder, signature, args):
+        ll_void = context.get_value_type(types.void)
+        ll_Py_UCS4 = context.get_value_type(_Py_UCS4)
+        ll_intc = context.get_value_type(types.intc)
+        ll_intc_ptr = ll_intc.as_pointer()
+        ll_uchar = context.get_value_type(types.uchar)
+        ll_uchar_ptr = ll_uchar.as_pointer()
+        ll_ushort = context.get_value_type(types.ushort)
+        ll_ushort_ptr = ll_ushort.as_pointer()
+        fnty = llvmlite.ir.FunctionType(ll_void, [
+            ll_Py_UCS4,    # code
+            ll_intc_ptr,   # upper
+            ll_intc_ptr,   # lower
+            ll_intc_ptr,   # title
+            ll_uchar_ptr,  # decimal
+            ll_uchar_ptr,  # digit
+            ll_ushort_ptr, # flags
+        ])
+        fn = cgutils.get_or_insert_function(
+            builder.module,
+            fnty, name="numba_gettyperecord")
+        upper = cgutils.alloca_once(builder, ll_intc, name='upper')
+        lower = cgutils.alloca_once(builder, ll_intc, name='lower')
+        title = cgutils.alloca_once(builder, ll_intc, name='title')
+        decimal = cgutils.alloca_once(builder, ll_uchar, name='decimal')
+        digit = cgutils.alloca_once(builder, ll_uchar, name='digit')
+        flags = cgutils.alloca_once(builder, ll_ushort, name='flags')
+
+        byref = [ upper, lower, title, decimal, digit, flags]
+        builder.call(fn, [args[0]] + byref)
+        buf = []
+        for x in byref:
+            buf.append(builder.load(x))
+
+        res = context.make_tuple(builder, signature.return_type, tuple(buf))
+        return impl_ret_untracked(context, builder, signature.return_type, res)
+
+    tupty = types.NamedTuple([types.intc, types.intc, types.intc, types.uchar,
+                              types.uchar, types.ushort], typerecord)
+    sig = tupty(_Py_UCS4)
+    return sig, details
+
+
+@overload(_PyUnicode_gettyperecord)
+def gettyperecord_impl(a):
+    """
+    Provides a _PyUnicode_gettyperecord binding, for convenience it will accept
+    single character strings and code points.
+    """
+    if isinstance(a, types.UnicodeType):
+        from numba.cpython.unicode import _get_code_point
+
+        def impl(a):
+            if len(a) > 1:
+                msg = "gettyperecord takes a single unicode character"
+                raise ValueError(msg)
+            code_point = _get_code_point(a, 0)
+            data = _gettyperecord_impl(_Py_UCS4(code_point))
+            return data
+        return impl
+    if isinstance(a, types.Integer):
+        return lambda a: _gettyperecord_impl(_Py_UCS4(a))
+
+
+# whilst it's possible to grab the _PyUnicode_ExtendedCase symbol as it's global
+# it is safer to use a defined api:
+@intrinsic
+def _PyUnicode_ExtendedCase(typingctx, index):
+    """
+    Accessor function for the _PyUnicode_ExtendedCase array, binds to
+    numba_get_PyUnicode_ExtendedCase which wraps the array and does the lookup
+    """
+    if not isinstance(index, types.Integer):
+        raise TypingError("Expected an index")
+
+    def details(context, builder, signature, args):
+        ll_Py_UCS4 = context.get_value_type(_Py_UCS4)
+        ll_intc = context.get_value_type(types.intc)
+        fnty = llvmlite.ir.FunctionType(ll_Py_UCS4, [ll_intc])
+        fn = cgutils.get_or_insert_function(
+            builder.module,
+            fnty, name="numba_get_PyUnicode_ExtendedCase")
+        return builder.call(fn, [args[0]])
+
+    sig = _Py_UCS4(types.intc)
+    return sig, details
+
+# The following functions are replications of the functions with the same name
+# in CPython's Objects/unicodectype.c
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L64-L71    # noqa: E501
+@register_jitable
+def _PyUnicode_ToTitlecase(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if (ctype.flags & _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK):
+        return _PyUnicode_ExtendedCase(ctype.title & 0xFFFF)
+    return ch + ctype.title
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L76-L81    # noqa: E501
+@register_jitable
+def _PyUnicode_IsTitlecase(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.TITLE_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L86-L91    # noqa: E501
+@register_jitable
+def _PyUnicode_IsXidStart(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.XID_START_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L96-L101    # noqa: E501
+@register_jitable
+def _PyUnicode_IsXidContinue(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.XID_CONTINUE_MASK != 0
+
+
+@register_jitable
+def _PyUnicode_ToDecimalDigit(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if ctype.flags & _PyUnicode_TyperecordMasks.DECIMAL_MASK:
+        return ctype.decimal
+    return -1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L123-L1128  # noqa: E501
+@register_jitable
+def _PyUnicode_ToDigit(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if ctype.flags & _PyUnicode_TyperecordMasks.DIGIT_MASK:
+        return ctype.digit
+    return -1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L140-L145    # noqa: E501
+@register_jitable
+def _PyUnicode_IsNumeric(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.NUMERIC_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L160-L165    # noqa: E501
+@register_jitable
+def _PyUnicode_IsPrintable(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.PRINTABLE_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L170-L175    # noqa: E501
+@register_jitable
+def _PyUnicode_IsLowercase(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.LOWER_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L180-L185    # noqa: E501
+@register_jitable
+def _PyUnicode_IsUppercase(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.UPPER_MASK != 0
+
+
+@register_jitable
+def _PyUnicode_IsLineBreak(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.LINEBREAK_MASK != 0
+
+
+@register_jitable
+def _PyUnicode_ToUppercase(ch):
+    raise NotImplementedError
+
+
+@register_jitable
+def _PyUnicode_ToLowercase(ch):
+    raise NotImplementedError
+
+
+# From: https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodectype.c#L211-L225    # noqa: E501
+@register_jitable
+def _PyUnicode_ToLowerFull(ch, res):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if (ctype.flags & _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK):
+        index = ctype.lower & 0xFFFF
+        n = ctype.lower >> 24
+        for i in range(n):
+            res[i] = _PyUnicode_ExtendedCase(index + i)
+        return n
+    res[0] = ch + ctype.lower
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodectype.c#L227-L241    # noqa: E501
+@register_jitable
+def _PyUnicode_ToTitleFull(ch, res):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if (ctype.flags & _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK):
+        index = ctype.title & 0xFFFF
+        n = ctype.title >> 24
+        for i in range(n):
+            res[i] = _PyUnicode_ExtendedCase(index + i)
+        return n
+    res[0] = ch + ctype.title
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L243-L257    # noqa: E501
+@register_jitable
+def _PyUnicode_ToUpperFull(ch, res):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if (ctype.flags & _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK):
+        index = ctype.upper & 0xFFFF
+        n = ctype.upper >> 24
+        for i in range(n):
+            # Perhaps needed to use unicode._set_code_point() here
+            res[i] = _PyUnicode_ExtendedCase(index + i)
+        return n
+    res[0] = ch + ctype.upper
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L259-L272    # noqa: E501
+@register_jitable
+def _PyUnicode_ToFoldedFull(ch, res):
+    ctype = _PyUnicode_gettyperecord(ch)
+    extended_case_mask = _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK
+    if ctype.flags & extended_case_mask and (ctype.lower >> 20) & 7:
+        index = (ctype.lower & 0xFFFF) + (ctype.lower >> 24)
+        n = (ctype.lower >> 20) & 7
+        for i in range(n):
+            res[i] = _PyUnicode_ExtendedCase(index + i)
+        return n
+    return _PyUnicode_ToLowerFull(ch, res)
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L274-L279    # noqa: E501
+@register_jitable
+def _PyUnicode_IsCased(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.CASED_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L281-L286    # noqa: E501
+@register_jitable
+def _PyUnicode_IsCaseIgnorable(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.CASE_IGNORABLE_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L123-L135    # noqa: E501
+@register_jitable
+def _PyUnicode_IsDigit(ch):
+    if _PyUnicode_ToDigit(ch) < 0:
+        return 0
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L106-L118    # noqa: E501
+@register_jitable
+def _PyUnicode_IsDecimalDigit(ch):
+    if _PyUnicode_ToDecimalDigit(ch) < 0:
+        return 0
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L291-L296    # noqa: E501
+@register_jitable
+def _PyUnicode_IsSpace(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.SPACE_MASK != 0
+
+
+@register_jitable
+def _PyUnicode_IsAlpha(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.ALPHA_MASK != 0
+
+
+# End code related to/from CPython's unicodectype impl
+# ------------------------------------------------------------------------------
+
+
+# ------------------------------------------------------------------------------
+# Start code related to/from CPython's pyctype
+
+# From the definition in CPython's Include/pyctype.h
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L5-L11    # noqa: E501
+class _PY_CTF(IntEnum):
+    LOWER = 0x01
+    UPPER = 0x02
+    ALPHA = 0x01 | 0x02
+    DIGIT = 0x04
+    ALNUM = 0x01 | 0x02 | 0x04
+    SPACE = 0x08
+    XDIGIT = 0x10
+
+
+# From the definition in CPython's Python/pyctype.c
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Python/pyctype.c#L5    # noqa: E501
+_Py_ctype_table = np.array([
+    0,  # 0x0 '\x00'
+    0,  # 0x1 '\x01'
+    0,  # 0x2 '\x02'
+    0,  # 0x3 '\x03'
+    0,  # 0x4 '\x04'
+    0,  # 0x5 '\x05'
+    0,  # 0x6 '\x06'
+    0,  # 0x7 '\x07'
+    0,  # 0x8 '\x08'
+    _PY_CTF.SPACE,  # 0x9 '\t'
+    _PY_CTF.SPACE,  # 0xa '\n'
+    _PY_CTF.SPACE,  # 0xb '\v'
+    _PY_CTF.SPACE,  # 0xc '\f'
+    _PY_CTF.SPACE,  # 0xd '\r'
+    0,  # 0xe '\x0e'
+    0,  # 0xf '\x0f'
+    0,  # 0x10 '\x10'
+    0,  # 0x11 '\x11'
+    0,  # 0x12 '\x12'
+    0,  # 0x13 '\x13'
+    0,  # 0x14 '\x14'
+    0,  # 0x15 '\x15'
+    0,  # 0x16 '\x16'
+    0,  # 0x17 '\x17'
+    0,  # 0x18 '\x18'
+    0,  # 0x19 '\x19'
+    0,  # 0x1a '\x1a'
+    0,  # 0x1b '\x1b'
+    0,  # 0x1c '\x1c'
+    0,  # 0x1d '\x1d'
+    0,  # 0x1e '\x1e'
+    0,  # 0x1f '\x1f'
+    _PY_CTF.SPACE,  # 0x20 ' '
+    0,  # 0x21 '!'
+    0,  # 0x22 '"'
+    0,  # 0x23 '#'
+    0,  # 0x24 '$'
+    0,  # 0x25 '%'
+    0,  # 0x26 '&'
+    0,  # 0x27 "'"
+    0,  # 0x28 '('
+    0,  # 0x29 ')'
+    0,  # 0x2a '*'
+    0,  # 0x2b '+'
+    0,  # 0x2c ','
+    0,  # 0x2d '-'
+    0,  # 0x2e '.'
+    0,  # 0x2f '/'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x30 '0'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x31 '1'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x32 '2'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x33 '3'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x34 '4'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x35 '5'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x36 '6'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x37 '7'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x38 '8'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x39 '9'
+    0,  # 0x3a ':'
+    0,  # 0x3b ';'
+    0,  # 0x3c '<'
+    0,  # 0x3d '='
+    0,  # 0x3e '>'
+    0,  # 0x3f '?'
+    0,  # 0x40 '@'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x41 'A'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x42 'B'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x43 'C'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x44 'D'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x45 'E'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x46 'F'
+    _PY_CTF.UPPER,  # 0x47 'G'
+    _PY_CTF.UPPER,  # 0x48 'H'
+    _PY_CTF.UPPER,  # 0x49 'I'
+    _PY_CTF.UPPER,  # 0x4a 'J'
+    _PY_CTF.UPPER,  # 0x4b 'K'
+    _PY_CTF.UPPER,  # 0x4c 'L'
+    _PY_CTF.UPPER,  # 0x4d 'M'
+    _PY_CTF.UPPER,  # 0x4e 'N'
+    _PY_CTF.UPPER,  # 0x4f 'O'
+    _PY_CTF.UPPER,  # 0x50 'P'
+    _PY_CTF.UPPER,  # 0x51 'Q'
+    _PY_CTF.UPPER,  # 0x52 'R'
+    _PY_CTF.UPPER,  # 0x53 'S'
+    _PY_CTF.UPPER,  # 0x54 'T'
+    _PY_CTF.UPPER,  # 0x55 'U'
+    _PY_CTF.UPPER,  # 0x56 'V'
+    _PY_CTF.UPPER,  # 0x57 'W'
+    _PY_CTF.UPPER,  # 0x58 'X'
+    _PY_CTF.UPPER,  # 0x59 'Y'
+    _PY_CTF.UPPER,  # 0x5a 'Z'
+    0,  # 0x5b '['
+    0,  # 0x5c '\\'
+    0,  # 0x5d ']'
+    0,  # 0x5e '^'
+    0,  # 0x5f '_'
+    0,  # 0x60 '`'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x61 'a'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x62 'b'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x63 'c'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x64 'd'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x65 'e'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x66 'f'
+    _PY_CTF.LOWER,  # 0x67 'g'
+    _PY_CTF.LOWER,  # 0x68 'h'
+    _PY_CTF.LOWER,  # 0x69 'i'
+    _PY_CTF.LOWER,  # 0x6a 'j'
+    _PY_CTF.LOWER,  # 0x6b 'k'
+    _PY_CTF.LOWER,  # 0x6c 'l'
+    _PY_CTF.LOWER,  # 0x6d 'm'
+    _PY_CTF.LOWER,  # 0x6e 'n'
+    _PY_CTF.LOWER,  # 0x6f 'o'
+    _PY_CTF.LOWER,  # 0x70 'p'
+    _PY_CTF.LOWER,  # 0x71 'q'
+    _PY_CTF.LOWER,  # 0x72 'r'
+    _PY_CTF.LOWER,  # 0x73 's'
+    _PY_CTF.LOWER,  # 0x74 't'
+    _PY_CTF.LOWER,  # 0x75 'u'
+    _PY_CTF.LOWER,  # 0x76 'v'
+    _PY_CTF.LOWER,  # 0x77 'w'
+    _PY_CTF.LOWER,  # 0x78 'x'
+    _PY_CTF.LOWER,  # 0x79 'y'
+    _PY_CTF.LOWER,  # 0x7a 'z'
+    0,  # 0x7b '{'
+    0,  # 0x7c '|'
+    0,  # 0x7d '}'
+    0,  # 0x7e '~'
+    0,  # 0x7f '\x7f'
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+], dtype=np.intc)
+
+
+# From the definition in CPython's Python/pyctype.c
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Python/pyctype.c#L145    # noqa: E501
+_Py_ctype_tolower = np.array([
+    0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07,
+    0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f,
+    0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16, 0x17,
+    0x18, 0x19, 0x1a, 0x1b, 0x1c, 0x1d, 0x1e, 0x1f,
+    0x20, 0x21, 0x22, 0x23, 0x24, 0x25, 0x26, 0x27,
+    0x28, 0x29, 0x2a, 0x2b, 0x2c, 0x2d, 0x2e, 0x2f,
+    0x30, 0x31, 0x32, 0x33, 0x34, 0x35, 0x36, 0x37,
+    0x38, 0x39, 0x3a, 0x3b, 0x3c, 0x3d, 0x3e, 0x3f,
+    0x40, 0x61, 0x62, 0x63, 0x64, 0x65, 0x66, 0x67,
+    0x68, 0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f,
+    0x70, 0x71, 0x72, 0x73, 0x74, 0x75, 0x76, 0x77,
+    0x78, 0x79, 0x7a, 0x5b, 0x5c, 0x5d, 0x5e, 0x5f,
+    0x60, 0x61, 0x62, 0x63, 0x64, 0x65, 0x66, 0x67,
+    0x68, 0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f,
+    0x70, 0x71, 0x72, 0x73, 0x74, 0x75, 0x76, 0x77,
+    0x78, 0x79, 0x7a, 0x7b, 0x7c, 0x7d, 0x7e, 0x7f,
+    0x80, 0x81, 0x82, 0x83, 0x84, 0x85, 0x86, 0x87,
+    0x88, 0x89, 0x8a, 0x8b, 0x8c, 0x8d, 0x8e, 0x8f,
+    0x90, 0x91, 0x92, 0x93, 0x94, 0x95, 0x96, 0x97,
+    0x98, 0x99, 0x9a, 0x9b, 0x9c, 0x9d, 0x9e, 0x9f,
+    0xa0, 0xa1, 0xa2, 0xa3, 0xa4, 0xa5, 0xa6, 0xa7,
+    0xa8, 0xa9, 0xaa, 0xab, 0xac, 0xad, 0xae, 0xaf,
+    0xb0, 0xb1, 0xb2, 0xb3, 0xb4, 0xb5, 0xb6, 0xb7,
+    0xb8, 0xb9, 0xba, 0xbb, 0xbc, 0xbd, 0xbe, 0xbf,
+    0xc0, 0xc1, 0xc2, 0xc3, 0xc4, 0xc5, 0xc6, 0xc7,
+    0xc8, 0xc9, 0xca, 0xcb, 0xcc, 0xcd, 0xce, 0xcf,
+    0xd0, 0xd1, 0xd2, 0xd3, 0xd4, 0xd5, 0xd6, 0xd7,
+    0xd8, 0xd9, 0xda, 0xdb, 0xdc, 0xdd, 0xde, 0xdf,
+    0xe0, 0xe1, 0xe2, 0xe3, 0xe4, 0xe5, 0xe6, 0xe7,
+    0xe8, 0xe9, 0xea, 0xeb, 0xec, 0xed, 0xee, 0xef,
+    0xf0, 0xf1, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7,
+    0xf8, 0xf9, 0xfa, 0xfb, 0xfc, 0xfd, 0xfe, 0xff,
+], dtype=np.uint8)
+
+
+# From the definition in CPython's Python/pyctype.c
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Python/pyctype.c#L180
+_Py_ctype_toupper = np.array([
+    0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07,
+    0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f,
+    0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16, 0x17,
+    0x18, 0x19, 0x1a, 0x1b, 0x1c, 0x1d, 0x1e, 0x1f,
+    0x20, 0x21, 0x22, 0x23, 0x24, 0x25, 0x26, 0x27,
+    0x28, 0x29, 0x2a, 0x2b, 0x2c, 0x2d, 0x2e, 0x2f,
+    0x30, 0x31, 0x32, 0x33, 0x34, 0x35, 0x36, 0x37,
+    0x38, 0x39, 0x3a, 0x3b, 0x3c, 0x3d, 0x3e, 0x3f,
+    0x40, 0x41, 0x42, 0x43, 0x44, 0x45, 0x46, 0x47,
+    0x48, 0x49, 0x4a, 0x4b, 0x4c, 0x4d, 0x4e, 0x4f,
+    0x50, 0x51, 0x52, 0x53, 0x54, 0x55, 0x56, 0x57,
+    0x58, 0x59, 0x5a, 0x5b, 0x5c, 0x5d, 0x5e, 0x5f,
+    0x60, 0x41, 0x42, 0x43, 0x44, 0x45, 0x46, 0x47,
+    0x48, 0x49, 0x4a, 0x4b, 0x4c, 0x4d, 0x4e, 0x4f,
+    0x50, 0x51, 0x52, 0x53, 0x54, 0x55, 0x56, 0x57,
+    0x58, 0x59, 0x5a, 0x7b, 0x7c, 0x7d, 0x7e, 0x7f,
+    0x80, 0x81, 0x82, 0x83, 0x84, 0x85, 0x86, 0x87,
+    0x88, 0x89, 0x8a, 0x8b, 0x8c, 0x8d, 0x8e, 0x8f,
+    0x90, 0x91, 0x92, 0x93, 0x94, 0x95, 0x96, 0x97,
+    0x98, 0x99, 0x9a, 0x9b, 0x9c, 0x9d, 0x9e, 0x9f,
+    0xa0, 0xa1, 0xa2, 0xa3, 0xa4, 0xa5, 0xa6, 0xa7,
+    0xa8, 0xa9, 0xaa, 0xab, 0xac, 0xad, 0xae, 0xaf,
+    0xb0, 0xb1, 0xb2, 0xb3, 0xb4, 0xb5, 0xb6, 0xb7,
+    0xb8, 0xb9, 0xba, 0xbb, 0xbc, 0xbd, 0xbe, 0xbf,
+    0xc0, 0xc1, 0xc2, 0xc3, 0xc4, 0xc5, 0xc6, 0xc7,
+    0xc8, 0xc9, 0xca, 0xcb, 0xcc, 0xcd, 0xce, 0xcf,
+    0xd0, 0xd1, 0xd2, 0xd3, 0xd4, 0xd5, 0xd6, 0xd7,
+    0xd8, 0xd9, 0xda, 0xdb, 0xdc, 0xdd, 0xde, 0xdf,
+    0xe0, 0xe1, 0xe2, 0xe3, 0xe4, 0xe5, 0xe6, 0xe7,
+    0xe8, 0xe9, 0xea, 0xeb, 0xec, 0xed, 0xee, 0xef,
+    0xf0, 0xf1, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7,
+    0xf8, 0xf9, 0xfa, 0xfb, 0xfc, 0xfd, 0xfe, 0xff,
+], dtype=np.uint8)
+
+
+class _PY_CTF_LB(IntEnum):
+    LINE_BREAK = 0x01
+    LINE_FEED = 0x02
+    CARRIAGE_RETURN = 0x04
+
+
+_Py_ctype_islinebreak = np.array([
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    _PY_CTF_LB.LINE_BREAK | _PY_CTF_LB.LINE_FEED,  # 0xa '\n'
+    _PY_CTF_LB.LINE_BREAK,  # 0xb '\v'
+    _PY_CTF_LB.LINE_BREAK,  # 0xc '\f'
+    _PY_CTF_LB.LINE_BREAK | _PY_CTF_LB.CARRIAGE_RETURN,  # 0xd '\r'
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    _PY_CTF_LB.LINE_BREAK,  # 0x1c '\x1c'
+    _PY_CTF_LB.LINE_BREAK,  # 0x1d '\x1d'
+    _PY_CTF_LB.LINE_BREAK,  # 0x1e '\x1e'
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    _PY_CTF_LB.LINE_BREAK,  # 0x85 '\x85'
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0,
+], dtype=np.intc)
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pymacro.h#L25    # noqa: E501
+@register_jitable
+def _Py_CHARMASK(ch):
+    """
+    Equivalent to the CPython macro `Py_CHARMASK()`, masks off all but the
+    lowest 256 bits of ch.
+    """
+    return types.uint8(ch) & types.uint8(0xff)
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L30    # noqa: E501
+@register_jitable
+def _Py_TOUPPER(ch):
+    """
+    Equivalent to the CPython macro `Py_TOUPPER()` converts an ASCII range
+    code point to the upper equivalent
+    """
+    return _Py_ctype_toupper[_Py_CHARMASK(ch)]
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L29    # noqa: E501
+@register_jitable
+def _Py_TOLOWER(ch):
+    """
+    Equivalent to the CPython macro `Py_TOLOWER()` converts an ASCII range
+    code point to the lower equivalent
+    """
+    return _Py_ctype_tolower[_Py_CHARMASK(ch)]
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L18    # noqa: E501
+@register_jitable
+def _Py_ISLOWER(ch):
+    """
+    Equivalent to the CPython macro `Py_ISLOWER()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.LOWER
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L19    # noqa: E501
+@register_jitable
+def _Py_ISUPPER(ch):
+    """
+    Equivalent to the CPython macro `Py_ISUPPER()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.UPPER
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L20    # noqa: E501
+@register_jitable
+def _Py_ISALPHA(ch):
+    """
+    Equivalent to the CPython macro `Py_ISALPHA()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.ALPHA
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L21    # noqa: E501
+@register_jitable
+def _Py_ISDIGIT(ch):
+    """
+    Equivalent to the CPython macro `Py_ISDIGIT()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.DIGIT
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L22    # noqa: E501
+@register_jitable
+def _Py_ISXDIGIT(ch):
+    """
+    Equivalent to the CPython macro `Py_ISXDIGIT()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.XDIGIT
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L23    # noqa: E501
+@register_jitable
+def _Py_ISALNUM(ch):
+    """
+    Equivalent to the CPython macro `Py_ISALNUM()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.ALNUM
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L24    # noqa: E501
+@register_jitable
+def _Py_ISSPACE(ch):
+    """
+    Equivalent to the CPython macro `Py_ISSPACE()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.SPACE
+
+
+@register_jitable
+def _Py_ISLINEBREAK(ch):
+    """Check if character is ASCII line break"""
+    return _Py_ctype_islinebreak[_Py_CHARMASK(ch)] & _PY_CTF_LB.LINE_BREAK
+
+
+@register_jitable
+def _Py_ISLINEFEED(ch):
+    """Check if character is line feed `\n`"""
+    return _Py_ctype_islinebreak[_Py_CHARMASK(ch)] & _PY_CTF_LB.LINE_FEED
+
+
+@register_jitable
+def _Py_ISCARRIAGERETURN(ch):
+    """Check if character is carriage return `\r`"""
+    return _Py_ctype_islinebreak[_Py_CHARMASK(ch)] & _PY_CTF_LB.CARRIAGE_RETURN
+
+
+# End code related to/from CPython's pyctype
+# ------------------------------------------------------------------------------
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/numbers.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/numbers.py
new file mode 100644
index 000000000..81a9e88c6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/numbers.py
@@ -0,0 +1,53 @@
+""" This module provides the unsafe things for targets/numbers.py
+"""
+from numba.core import types, errors
+from numba.core.extending import intrinsic
+
+from llvmlite import ir
+
+
+@intrinsic
+def viewer(tyctx, val, viewty):
+    """ Bitcast a scalar 'val' to the given type 'viewty'. """
+    bits = val.bitwidth
+    if isinstance(viewty.dtype, types.Integer):
+        bitcastty = ir.IntType(bits)
+    elif isinstance(viewty.dtype, types.Float):
+        bitcastty = ir.FloatType() if bits == 32 else ir.DoubleType()
+    else:
+        assert 0, "unreachable"
+
+    def codegen(cgctx, builder, typ, args):
+        flt = args[0]
+        return builder.bitcast(flt, bitcastty)
+    retty = viewty.dtype
+    sig = retty(val, viewty)
+    return sig, codegen
+
+
+@intrinsic
+def trailing_zeros(typeingctx, src):
+    """Counts trailing zeros in the binary representation of an integer."""
+    if not isinstance(src, types.Integer):
+        msg = ("trailing_zeros is only defined for integers, but value passed "
+               f"was '{src}'.")
+        raise errors.NumbaTypeError(msg)
+
+    def codegen(context, builder, signature, args):
+        [src] = args
+        return builder.cttz(src, ir.Constant(ir.IntType(1), 0))
+    return src(src), codegen
+
+
+@intrinsic
+def leading_zeros(typeingctx, src):
+    """Counts leading zeros in the binary representation of an integer."""
+    if not isinstance(src, types.Integer):
+        msg = ("leading_zeros is only defined for integers, but value passed "
+               f"was '{src}'.")
+        raise errors.NumbaTypeError(msg)
+
+    def codegen(context, builder, signature, args):
+        [src] = args
+        return builder.ctlz(src, ir.Constant(ir.IntType(1), 0))
+    return src(src), codegen
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/tuple.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/tuple.py
new file mode 100644
index 000000000..ef243fec5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cpython/unsafe/tuple.py
@@ -0,0 +1,84 @@
+"""
+This file provides internal compiler utilities that support certain special
+operations with tuple and workarounds for limitations enforced in userland.
+"""
+
+from numba.core import types, typing, errors
+from numba.core.cgutils import alloca_once
+from numba.core.extending import intrinsic
+
+
+@intrinsic
+def tuple_setitem(typingctx, tup, idx, val):
+    """Return a copy of the tuple with item at *idx* replaced with *val*.
+
+    Operation: ``out = tup[:idx] + (val,) + tup[idx + 1:]
+
+    **Warning**
+
+    - No boundchecking.
+    - The dtype of the tuple cannot be changed.
+      *val* is always cast to the existing dtype of the tuple.
+    """
+    def codegen(context, builder, signature, args):
+        tup, idx, val = args
+        stack = alloca_once(builder, tup.type)
+        builder.store(tup, stack)
+        # Unsafe load on unchecked bounds.  Poison value maybe returned.
+        offptr = builder.gep(stack, [idx.type(0), idx], inbounds=True)
+        builder.store(val, offptr)
+        return builder.load(stack)
+
+    sig = tup(tup, idx, tup.dtype)
+    return sig, codegen
+
+
+@intrinsic
+def build_full_slice_tuple(tyctx, sz):
+    """Creates a sz-tuple of full slices."""
+    if not isinstance(sz, types.IntegerLiteral):
+        raise errors.RequireLiteralValue(sz)
+
+    size = int(sz.literal_value)
+    tuple_type = types.UniTuple(dtype=types.slice2_type, count=size)
+    sig = tuple_type(sz)
+
+    def codegen(context, builder, signature, args):
+        def impl(length, empty_tuple):
+            out = empty_tuple
+            for i in range(length):
+                out = tuple_setitem(out, i, slice(None, None))
+            return out
+
+        inner_argtypes = [types.intp, tuple_type]
+        inner_sig = typing.signature(tuple_type, *inner_argtypes)
+        ll_idx_type = context.get_value_type(types.intp)
+        # Allocate an empty tuple
+        empty_tuple = context.get_constant_undef(tuple_type)
+        inner_args = [ll_idx_type(size), empty_tuple]
+
+        res = context.compile_internal(builder, impl, inner_sig, inner_args)
+        return res
+
+    return sig, codegen
+
+
+@intrinsic
+def unpack_single_tuple(tyctx, tup):
+    """This exists to handle the situation y = (*x,), the interpreter injects a
+    call to it in the case of a single value unpack. It's not possible at
+    interpreting time to differentiate between an unpack on a variable sized
+    container e.g. list and a fixed one, e.g. tuple. This function handles the
+    situation should it arise.
+    """
+    # See issue #6534
+    if not isinstance(tup, types.BaseTuple):
+        msg = (f"Only tuples are supported when unpacking a single item, "
+               f"got type: {tup}")
+        raise errors.UnsupportedError(msg)
+
+    sig = tup(tup)
+
+    def codegen(context, builder, signature, args):
+        return args[0] # there's only one tuple and it's a simple pass through
+    return sig, codegen
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/__init__.py
new file mode 100644
index 000000000..d08ea8793
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/__init__.py
@@ -0,0 +1,16 @@
+from numba import runtests
+from numba.core import config
+
+if config.ENABLE_CUDASIM:
+    from .simulator_init import *
+else:
+    from .device_init import *
+    from .device_init import _auto_device
+
+from numba.cuda.compiler import compile_ptx, compile_ptx_for_current_device
+
+def test(*args, **kwargs):
+    if not is_available():
+        raise cuda_error()
+
+    return runtests.main("numba.cuda.tests", *args, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/api.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/api.py
new file mode 100644
index 000000000..24636185b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/api.py
@@ -0,0 +1,517 @@
+"""
+API that are reported to numba.cuda
+"""
+
+
+import contextlib
+import os
+
+import numpy as np
+
+from .cudadrv import devicearray, devices, driver
+from numba.core import config
+from numba.cuda.api_util import prepare_shape_strides_dtype
+
+# NDarray device helper
+
+require_context = devices.require_context
+current_context = devices.get_context
+gpus = devices.gpus
+
+
+@require_context
+def from_cuda_array_interface(desc, owner=None, sync=True):
+    """Create a DeviceNDArray from a cuda-array-interface description.
+    The ``owner`` is the owner of the underlying memory.
+    The resulting DeviceNDArray will acquire a reference from it.
+
+    If ``sync`` is ``True``, then the imported stream (if present) will be
+    synchronized.
+    """
+    version = desc.get('version')
+    # Mask introduced in version 1
+    if 1 <= version:
+        mask = desc.get('mask')
+        # Would ideally be better to detect if the mask is all valid
+        if mask is not None:
+            raise NotImplementedError('Masked arrays are not supported')
+
+    shape = desc['shape']
+    strides = desc.get('strides')
+    dtype = np.dtype(desc['typestr'])
+
+    shape, strides, dtype = prepare_shape_strides_dtype(
+        shape, strides, dtype, order='C')
+    size = driver.memory_size_from_info(shape, strides, dtype.itemsize)
+
+    devptr = driver.get_devptr_for_active_ctx(desc['data'][0])
+    data = driver.MemoryPointer(
+        current_context(), devptr, size=size, owner=owner)
+    stream_ptr = desc.get('stream', None)
+    if stream_ptr is not None:
+        stream = external_stream(stream_ptr)
+        if sync and config.CUDA_ARRAY_INTERFACE_SYNC:
+            stream.synchronize()
+    else:
+        stream = 0 # No "Numba default stream", not the CUDA default stream
+    da = devicearray.DeviceNDArray(shape=shape, strides=strides,
+                                   dtype=dtype, gpu_data=data,
+                                   stream=stream)
+    return da
+
+
+def as_cuda_array(obj, sync=True):
+    """Create a DeviceNDArray from any object that implements
+    the :ref:`cuda array interface <cuda-array-interface>`.
+
+    A view of the underlying GPU buffer is created.  No copying of the data
+    is done.  The resulting DeviceNDArray will acquire a reference from `obj`.
+
+    If ``sync`` is ``True``, then the imported stream (if present) will be
+    synchronized.
+    """
+    if not is_cuda_array(obj):
+        raise TypeError("*obj* doesn't implement the cuda array interface.")
+    else:
+        return from_cuda_array_interface(obj.__cuda_array_interface__,
+                                         owner=obj, sync=sync)
+
+
+def is_cuda_array(obj):
+    """Test if the object has defined the `__cuda_array_interface__` attribute.
+
+    Does not verify the validity of the interface.
+    """
+    return hasattr(obj, '__cuda_array_interface__')
+
+
+@require_context
+def to_device(obj, stream=0, copy=True, to=None):
+    """to_device(obj, stream=0, copy=True, to=None)
+
+    Allocate and transfer a numpy ndarray or structured scalar to the device.
+
+    To copy host->device a numpy array::
+
+        ary = np.arange(10)
+        d_ary = cuda.to_device(ary)
+
+    To enqueue the transfer to a stream::
+
+        stream = cuda.stream()
+        d_ary = cuda.to_device(ary, stream=stream)
+
+    The resulting ``d_ary`` is a ``DeviceNDArray``.
+
+    To copy device->host::
+
+        hary = d_ary.copy_to_host()
+
+    To copy device->host to an existing array::
+
+        ary = np.empty(shape=d_ary.shape, dtype=d_ary.dtype)
+        d_ary.copy_to_host(ary)
+
+    To enqueue the transfer to a stream::
+
+        hary = d_ary.copy_to_host(stream=stream)
+    """
+    if to is None:
+        to, new = devicearray.auto_device(obj, stream=stream, copy=copy,
+                                          user_explicit=True)
+        return to
+    if copy:
+        to.copy_to_device(obj, stream=stream)
+    return to
+
+
+@require_context
+def device_array(shape, dtype=np.float_, strides=None, order='C', stream=0):
+    """device_array(shape, dtype=np.float_, strides=None, order='C', stream=0)
+
+    Allocate an empty device ndarray. Similar to :meth:`numpy.empty`.
+    """
+    shape, strides, dtype = prepare_shape_strides_dtype(shape, strides, dtype,
+                                                        order)
+    return devicearray.DeviceNDArray(shape=shape, strides=strides, dtype=dtype,
+                                     stream=stream)
+
+
+@require_context
+def managed_array(shape, dtype=np.float_, strides=None, order='C', stream=0,
+                  attach_global=True):
+    """managed_array(shape, dtype=np.float_, strides=None, order='C', stream=0,
+                     attach_global=True)
+
+    Allocate a np.ndarray with a buffer that is managed.
+    Similar to np.empty().
+
+    Managed memory is supported on Linux / x86 and PowerPC, and is considered
+    experimental on Windows and Linux / AArch64.
+
+    :param attach_global: A flag indicating whether to attach globally. Global
+                          attachment implies that the memory is accessible from
+                          any stream on any device. If ``False``, attachment is
+                          *host*, and memory is only accessible by devices
+                          with Compute Capability 6.0 and later.
+    """
+    shape, strides, dtype = prepare_shape_strides_dtype(shape, strides, dtype,
+                                                        order)
+    bytesize = driver.memory_size_from_info(shape, strides, dtype.itemsize)
+    buffer = current_context().memallocmanaged(bytesize,
+                                               attach_global=attach_global)
+    npary = np.ndarray(shape=shape, strides=strides, dtype=dtype, order=order,
+                       buffer=buffer)
+    managedview = np.ndarray.view(npary, type=devicearray.ManagedNDArray)
+    managedview.device_setup(buffer, stream=stream)
+    return managedview
+
+
+@require_context
+def pinned_array(shape, dtype=np.float_, strides=None, order='C'):
+    """pinned_array(shape, dtype=np.float_, strides=None, order='C')
+
+    Allocate an :class:`ndarray <numpy.ndarray>` with a buffer that is pinned
+    (pagelocked).  Similar to :func:`np.empty() <numpy.empty>`.
+    """
+    shape, strides, dtype = prepare_shape_strides_dtype(shape, strides, dtype,
+                                                        order)
+    bytesize = driver.memory_size_from_info(shape, strides,
+                                            dtype.itemsize)
+    buffer = current_context().memhostalloc(bytesize)
+    return np.ndarray(shape=shape, strides=strides, dtype=dtype, order=order,
+                      buffer=buffer)
+
+
+@require_context
+def mapped_array(shape, dtype=np.float_, strides=None, order='C', stream=0,
+                 portable=False, wc=False):
+    """mapped_array(shape, dtype=np.float_, strides=None, order='C', stream=0,
+                    portable=False, wc=False)
+
+    Allocate a mapped ndarray with a buffer that is pinned and mapped on
+    to the device. Similar to np.empty()
+
+    :param portable: a boolean flag to allow the allocated device memory to be
+              usable in multiple devices.
+    :param wc: a boolean flag to enable writecombined allocation which is faster
+        to write by the host and to read by the device, but slower to
+        write by the host and slower to write by the device.
+    """
+    shape, strides, dtype = prepare_shape_strides_dtype(shape, strides, dtype,
+                                                        order)
+    bytesize = driver.memory_size_from_info(shape, strides, dtype.itemsize)
+    buffer = current_context().memhostalloc(bytesize, mapped=True)
+    npary = np.ndarray(shape=shape, strides=strides, dtype=dtype, order=order,
+                       buffer=buffer)
+    mappedview = np.ndarray.view(npary, type=devicearray.MappedNDArray)
+    mappedview.device_setup(buffer, stream=stream)
+    return mappedview
+
+
+@contextlib.contextmanager
+@require_context
+def open_ipc_array(handle, shape, dtype, strides=None, offset=0):
+    """
+    A context manager that opens a IPC *handle* (*CUipcMemHandle*) that is
+    represented as a sequence of bytes (e.g. *bytes*, tuple of int)
+    and represent it as an array of the given *shape*, *strides* and *dtype*.
+    The *strides* can be omitted.  In that case, it is assumed to be a 1D
+    C contiguous array.
+
+    Yields a device array.
+
+    The IPC handle is closed automatically when context manager exits.
+    """
+    dtype = np.dtype(dtype)
+    # compute size
+    size = np.prod(shape) * dtype.itemsize
+    # manually recreate the IPC mem handle
+    if driver.USE_NV_BINDING:
+        driver_handle = driver.binding.CUipcMemHandle()
+        driver_handle.reserved = handle
+    else:
+        driver_handle = driver.drvapi.cu_ipc_mem_handle(*handle)
+    # use *IpcHandle* to open the IPC memory
+    ipchandle = driver.IpcHandle(None, driver_handle, size, offset=offset)
+    yield ipchandle.open_array(current_context(), shape=shape,
+                               strides=strides, dtype=dtype)
+    ipchandle.close()
+
+
+def synchronize():
+    "Synchronize the current context."
+    return current_context().synchronize()
+
+
+def _contiguous_strides_like_array(ary):
+    """
+    Given an array, compute strides for a new contiguous array of the same
+    shape.
+    """
+    # Don't recompute strides if the default strides will be sufficient to
+    # create a contiguous array.
+    if ary.flags['C_CONTIGUOUS'] or ary.flags['F_CONTIGUOUS'] or ary.ndim <= 1:
+        return None
+
+    # Otherwise, we need to compute new strides using an algorithm adapted from
+    # NumPy v1.17.4's PyArray_NewLikeArrayWithShape in
+    # core/src/multiarray/ctors.c. We permute the strides in ascending order
+    # then compute the stride for the dimensions with the same permutation.
+
+    # Stride permutation. E.g. a stride array (4, -2, 12) becomes
+    # [(1, -2), (0, 4), (2, 12)]
+    strideperm = [ x for x in enumerate(ary.strides) ]
+    strideperm.sort(key=lambda x: x[1])
+
+    # Compute new strides using permutation
+    strides = [0] * len(ary.strides)
+    stride = ary.dtype.itemsize
+    for i_perm, _ in strideperm:
+        strides[i_perm] = stride
+        stride *= ary.shape[i_perm]
+    return tuple(strides)
+
+
+def _order_like_array(ary):
+    if ary.flags['F_CONTIGUOUS'] and not ary.flags['C_CONTIGUOUS']:
+        return 'F'
+    else:
+        return 'C'
+
+
+def device_array_like(ary, stream=0):
+    """
+    Call :func:`device_array() <numba.cuda.device_array>` with information from
+    the array.
+    """
+    strides = _contiguous_strides_like_array(ary)
+    order = _order_like_array(ary)
+    return device_array(shape=ary.shape, dtype=ary.dtype, strides=strides,
+                        order=order, stream=stream)
+
+
+def mapped_array_like(ary, stream=0, portable=False, wc=False):
+    """
+    Call :func:`mapped_array() <numba.cuda.mapped_array>` with the information
+    from the array.
+    """
+    strides = _contiguous_strides_like_array(ary)
+    order = _order_like_array(ary)
+    return mapped_array(shape=ary.shape, dtype=ary.dtype, strides=strides,
+                        order=order, stream=stream, portable=portable, wc=wc)
+
+
+def pinned_array_like(ary):
+    """
+    Call :func:`pinned_array() <numba.cuda.pinned_array>` with the information
+    from the array.
+    """
+    strides = _contiguous_strides_like_array(ary)
+    order = _order_like_array(ary)
+    return pinned_array(shape=ary.shape, dtype=ary.dtype, strides=strides,
+                        order=order)
+
+
+# Stream helper
+@require_context
+def stream():
+    """
+    Create a CUDA stream that represents a command queue for the device.
+    """
+    return current_context().create_stream()
+
+
+@require_context
+def default_stream():
+    """
+    Get the default CUDA stream. CUDA semantics in general are that the default
+    stream is either the legacy default stream or the per-thread default stream
+    depending on which CUDA APIs are in use. In Numba, the APIs for the legacy
+    default stream are always the ones in use, but an option to use APIs for
+    the per-thread default stream may be provided in future.
+    """
+    return current_context().get_default_stream()
+
+
+@require_context
+def legacy_default_stream():
+    """
+    Get the legacy default CUDA stream.
+    """
+    return current_context().get_legacy_default_stream()
+
+
+@require_context
+def per_thread_default_stream():
+    """
+    Get the per-thread default CUDA stream.
+    """
+    return current_context().get_per_thread_default_stream()
+
+
+@require_context
+def external_stream(ptr):
+    """Create a Numba stream object for a stream allocated outside Numba.
+
+    :param ptr: Pointer to the external stream to wrap in a Numba Stream
+    :type ptr: int
+    """
+    return current_context().create_external_stream(ptr)
+
+
+# Page lock
+@require_context
+@contextlib.contextmanager
+def pinned(*arylist):
+    """A context manager for temporary pinning a sequence of host ndarrays.
+    """
+    pmlist = []
+    for ary in arylist:
+        pm = current_context().mempin(ary, driver.host_pointer(ary),
+                                      driver.host_memory_size(ary),
+                                      mapped=False)
+        pmlist.append(pm)
+    yield
+
+
+@require_context
+@contextlib.contextmanager
+def mapped(*arylist, **kws):
+    """A context manager for temporarily mapping a sequence of host ndarrays.
+    """
+    assert not kws or 'stream' in kws, "Only accept 'stream' as keyword."
+    stream = kws.get('stream', 0)
+    pmlist = []
+    devarylist = []
+    for ary in arylist:
+        pm = current_context().mempin(ary, driver.host_pointer(ary),
+                                      driver.host_memory_size(ary),
+                                      mapped=True)
+        pmlist.append(pm)
+        devary = devicearray.from_array_like(ary, gpu_data=pm, stream=stream)
+        devarylist.append(devary)
+    try:
+        if len(devarylist) == 1:
+            yield devarylist[0]
+        else:
+            yield devarylist
+    finally:
+        # When exiting from `with cuda.mapped(*arrs) as mapped_arrs:`, the name
+        # `mapped_arrs` stays in scope, blocking automatic unmapping based on
+        # reference count. We therefore invoke the finalizer manually.
+        for pm in pmlist:
+            pm.free()
+
+
+def event(timing=True):
+    """
+    Create a CUDA event. Timing data is only recorded by the event if it is
+    created with ``timing=True``.
+    """
+    evt = current_context().create_event(timing=timing)
+    return evt
+
+
+event_elapsed_time = driver.event_elapsed_time
+
+
+# Device selection
+
+def select_device(device_id):
+    """
+    Make the context associated with device *device_id* the current context.
+
+    Returns a Device instance.
+
+    Raises exception on error.
+    """
+    context = devices.get_context(device_id)
+    return context.device
+
+
+def get_current_device():
+    "Get current device associated with the current thread"
+    return current_context().device
+
+
+def list_devices():
+    "Return a list of all detected devices"
+    return devices.gpus
+
+
+def close():
+    """
+    Explicitly clears all contexts in the current thread, and destroys all
+    contexts if the current thread is the main thread.
+    """
+    devices.reset()
+
+
+def _auto_device(ary, stream=0, copy=True):
+    return devicearray.auto_device(ary, stream=stream, copy=copy)
+
+
+def detect():
+    """
+    Detect supported CUDA hardware and print a summary of the detected hardware.
+
+    Returns a boolean indicating whether any supported devices were detected.
+    """
+    devlist = list_devices()
+    print('Found %d CUDA devices' % len(devlist))
+    supported_count = 0
+    for dev in devlist:
+        attrs = []
+        cc = dev.compute_capability
+        kernel_timeout = dev.KERNEL_EXEC_TIMEOUT
+        tcc = dev.TCC_DRIVER
+        fp32_to_fp64_ratio = dev.SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO
+        attrs += [('Compute Capability', '%d.%d' % cc)]
+        attrs += [('PCI Device ID', dev.PCI_DEVICE_ID)]
+        attrs += [('PCI Bus ID', dev.PCI_BUS_ID)]
+        attrs += [('UUID', dev.uuid)]
+        attrs += [('Watchdog', 'Enabled' if kernel_timeout else 'Disabled')]
+        if os.name == "nt":
+            attrs += [('Compute Mode', 'TCC' if tcc else 'WDDM')]
+        attrs += [('FP32/FP64 Performance Ratio', fp32_to_fp64_ratio)]
+        if cc < (3, 5):
+            support = '[NOT SUPPORTED: CC < 3.5]'
+        elif cc < (5, 3):
+            support = '[SUPPORTED (DEPRECATED)]'
+            supported_count += 1
+        else:
+            support = '[SUPPORTED]'
+            supported_count += 1
+
+        print('id %d    %20s %40s' % (dev.id, dev.name, support))
+        for key, val in attrs:
+            print('%40s: %s' % (key, val))
+
+    print('Summary:')
+    print('\t%d/%d devices are supported' % (supported_count, len(devlist)))
+    return supported_count > 0
+
+
+@contextlib.contextmanager
+def defer_cleanup():
+    """
+    Temporarily disable memory deallocation.
+    Use this to prevent resource deallocation breaking asynchronous execution.
+
+    For example::
+
+        with defer_cleanup():
+            # all cleanup is deferred in here
+            do_speed_critical_code()
+        # cleanup can occur here
+
+    Note: this context manager can be nested.
+    """
+    with current_context().defer_cleanup():
+        yield
+
+
+profiling = require_context(driver.profiling)
+profile_start = require_context(driver.profile_start)
+profile_stop = require_context(driver.profile_stop)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/api_util.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/api_util.py
new file mode 100644
index 000000000..b8bffb7c1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/api_util.py
@@ -0,0 +1,30 @@
+import numpy as np
+
+
+def prepare_shape_strides_dtype(shape, strides, dtype, order):
+    dtype = np.dtype(dtype)
+    if isinstance(shape, int):
+        shape = (shape,)
+    if isinstance(strides, int):
+        strides = (strides,)
+    else:
+        strides = strides or _fill_stride_by_order(shape, dtype, order)
+    return shape, strides, dtype
+
+
+def _fill_stride_by_order(shape, dtype, order):
+    nd = len(shape)
+    if nd == 0:
+        return ()
+    strides = [0] * nd
+    if order == 'C':
+        strides[-1] = dtype.itemsize
+        for d in reversed(range(nd - 1)):
+            strides[d] = strides[d + 1] * shape[d + 1]
+    elif order == 'F':
+        strides[0] = dtype.itemsize
+        for d in range(1, nd):
+            strides[d] = strides[d - 1] * shape[d - 1]
+    else:
+        raise ValueError('must be either C/F order')
+    return tuple(strides)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/args.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/args.py
new file mode 100644
index 000000000..472bd0b87
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/args.py
@@ -0,0 +1,77 @@
+"""
+Hints to wrap Kernel arguments to indicate how to manage host-device
+memory transfers before & after the kernel call.
+"""
+import abc
+
+from numba.core.typing.typeof import typeof, Purpose
+
+
+class ArgHint(metaclass=abc.ABCMeta):
+    def __init__(self, value):
+        self.value = value
+
+    @abc.abstractmethod
+    def to_device(self, retr, stream=0):
+        """
+        :param stream: a stream to use when copying data
+        :param retr:
+            a list of clean-up work to do after the kernel's been run.
+            Append 0-arg lambdas to it!
+        :return: a value (usually an `DeviceNDArray`) to be passed to
+            the kernel
+        """
+        pass
+
+    @property
+    def _numba_type_(self):
+        return typeof(self.value, Purpose.argument)
+
+
+class In(ArgHint):
+    def to_device(self, retr, stream=0):
+        from .cudadrv.devicearray import auto_device
+        devary, _ = auto_device(
+            self.value,
+            stream=stream)
+        # A dummy writeback functor to keep devary alive until the kernel
+        # is called.
+        retr.append(lambda: devary)
+        return devary
+
+
+class Out(ArgHint):
+    def to_device(self, retr, stream=0):
+        from .cudadrv.devicearray import auto_device
+        devary, conv = auto_device(
+            self.value,
+            copy=False,
+            stream=stream)
+        if conv:
+            retr.append(lambda: devary.copy_to_host(self.value, stream=stream))
+        return devary
+
+
+class InOut(ArgHint):
+    def to_device(self, retr, stream=0):
+        from .cudadrv.devicearray import auto_device
+        devary, conv = auto_device(
+            self.value,
+            stream=stream)
+        if conv:
+            retr.append(lambda: devary.copy_to_host(self.value, stream=stream))
+        return devary
+
+
+def wrap_arg(value, default=InOut):
+    return value if isinstance(value, ArgHint) else default(value)
+
+
+__all__ = [
+    'In',
+    'Out',
+    'InOut',
+
+    'ArgHint',
+    'wrap_arg',
+]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/codegen.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/codegen.py
new file mode 100644
index 000000000..dd0b32c76
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/codegen.py
@@ -0,0 +1,443 @@
+from llvmlite import binding as ll
+from llvmlite import ir
+from warnings import warn
+
+from numba.core import config, serialize
+from numba.core.codegen import Codegen, CodeLibrary
+from numba.core.errors import NumbaInvalidConfigWarning
+from .cudadrv import devices, driver, nvvm, runtime
+
+import ctypes
+import numpy as np
+import os
+import subprocess
+import tempfile
+
+
+# CUDA_TRIPLE = 'nvptx64-nvidia-cuda'
+CUDA_TRIPLE = 'bi-iluvatar-ilurt'
+
+import re
+
+# These are for parsing alloca instructions
+_re_alloca_quoted = re.compile('(.*)"(\$.*)".*')
+_re_alloca_parts = re.compile('(.*)=(.*alloca(.*))')
+
+def alloca_addrspace_correction(llvmir):
+    """
+    rewrites llvmir such that alloca's go into addrspace(5) and are then
+    addrspacecast back to to addrspace(0). Alloca into 5 is a requirement of
+    the datalayout specification.
+    """
+    lines = llvmir.splitlines()
+    mangle = '__tmp'
+    new_ir = []
+    for l in lines:
+        # pluck lines containing alloca
+        if 'alloca' in l:
+            assignee, alloca_match, ptrty = _re_alloca_parts.match(l).groups()
+            q_match = _re_alloca_quoted.match(assignee)
+            if q_match:
+                start, var = q_match.groups()
+                var = var.strip()
+                name_fmt = '%s"%s"'
+                old_name = name_fmt % (start, var)
+                new_name = name_fmt % (start, var + mangle)
+            else:
+                old_name = assignee.strip()
+                new_name = old_name + mangle
+            old_name = old_name.replace('"', '')
+            new_name = new_name.replace('"', '')
+            allocaline = "%s = %s, addrspace(5)" % (new_name, alloca_match)
+            castline_fmt = ("%s = addrspacecast %s addrspace(5)* "
+                            "%s to %s addrspace(0)*")
+            castline =  castline_fmt % (old_name, ptrty, new_name, ptrty)
+            new_ir.append(allocaline)
+            new_ir.append(castline)
+        else:
+            new_ir.append(l)
+    return '\n'.join(new_ir)
+
+def disassemble_cubin(cubin):
+    # nvdisasm only accepts input from a file, so we need to write out to a
+    # temp file and clean up afterwards.
+    fd = None
+    fname = None
+    try:
+        fd, fname = tempfile.mkstemp()
+        with open(fname, 'wb') as f:
+            f.write(cubin)
+
+        try:
+            cp = subprocess.run(['nvdisasm', fname], check=True,
+                                stdout=subprocess.PIPE,
+                                stderr=subprocess.PIPE)
+        except FileNotFoundError as e:
+            msg = ("nvdisasm is required for SASS inspection, and has not "
+                   "been found.\n\nYou may need to install the CUDA "
+                   "toolkit and ensure that it is available on your "
+                   "PATH.\n")
+            raise RuntimeError(msg) from e
+        return cp.stdout.decode('utf-8')
+    finally:
+        if fd is not None:
+            os.close(fd)
+        if fname is not None:
+            os.unlink(fname)
+
+
+class CUDACodeLibrary(serialize.ReduceMixin, CodeLibrary):
+    """
+    The CUDACodeLibrary generates PTX, SASS, cubins for multiple different
+    compute capabilities. It also loads cubins to multiple devices (via
+    get_cufunc), which may be of different compute capabilities.
+    """
+
+    def __init__(self, codegen, name, entry_name=None, max_registers=None,
+                 nvvm_options=None):
+        """
+        codegen:
+            Codegen object.
+        name:
+            Name of the function in the source.
+        entry_name:
+            Name of the kernel function in the binary, if this is a global
+            kernel and not a device function.
+        max_registers:
+            The maximum register usage to aim for when linking.
+        nvvm_options:
+                Dict of options to pass to NVVM.
+        """
+        super().__init__(codegen, name)
+
+        # The llvmlite module for this library.
+        self._module = None
+        # CodeLibrary objects that will be "linked" into this library. The
+        # modules within them are compiled from NVVM IR to PTX along with the
+        # IR from this module - in that sense they are "linked" by NVVM at PTX
+        # generation time, rather than at link time.
+        self._linking_libraries = set()
+        # Files to link with the generated PTX. These are linked using the
+        # Driver API at link time.
+        self._linking_files = set()
+
+        # Cache the LLVM IR string
+        self._llvm_strs = None
+        # Maps CC -> PTX string
+        self._ptx_cache = {}
+        # Maps CC -> cubin
+        self._cubin_cache = {}
+        # Maps CC -> linker info output for cubin
+        self._linkerinfo_cache = {}
+        # Maps Device numeric ID -> cufunc
+        self._cufunc_cache = {}
+
+        self._max_registers = max_registers
+        if nvvm_options is None:
+            nvvm_options = {}
+        self._nvvm_options = nvvm_options
+        self._entry_name = entry_name
+
+    @property
+    def llvm_strs(self):
+        if self._llvm_strs is None:
+            self._llvm_strs = [str(mod) for mod in self.modules]
+        return self._llvm_strs
+
+    def get_llvm_str(self):
+        return self.llvm_strs[0]
+
+    def get_asm_str(self, cc=None):
+        return self._join_ptxes(self._get_ptxes(cc=cc))
+
+    def _get_ptxes(self, cc=None):
+        return self.llvm_strs
+        # if not cc:
+        #     ctx = devices.get_context()
+        #     device = ctx.device
+        #     cc = device.compute_capability
+
+        # ptxes = self._ptx_cache.get(cc, None)
+        # if ptxes:
+        #     return ptxes
+
+        # arch = nvvm.get_arch_option(*cc)
+        # options = self._nvvm_options.copy()
+        # options['arch'] = arch
+        # if not nvvm.NVVM().is_nvvm70:
+        #     # Avoid enabling debug for NVVM 3.4 as it has various issues. We
+        #     # need to warn the user that we're doing this if any of the
+        #     # functions that they're compiling have `debug=True` set, which we
+        #     # can determine by checking the NVVM options.
+        #     for lib in self.linking_libraries:
+        #         if lib._nvvm_options.get('debug'):
+        #             msg = ("debuginfo is not generated for CUDA versions "
+        #                    f"< 11.2 (debug=True on function: {lib.name})")
+        #             warn(NumbaInvalidConfigWarning(msg))
+        #     options['debug'] = False
+
+        # irs = self.llvm_strs
+
+        # if options.get('debug', False):
+        #     # If we're compiling with debug, we need to compile modules with
+        #     # NVVM one at a time, because it does not support multiple modules
+        #     # with debug enabled:
+        #     # https://docs.nvidia.com/cuda/nvvm-ir-spec/index.html#source-level-debugging-support
+        #     ptxes = [nvvm.llvm_to_ptx(ir, **options) for ir in irs]
+        # else:
+        #     # Otherwise, we compile all modules with NVVM at once because this
+        #     # results in better optimization than separate compilation.
+        #     ptxes = [nvvm.llvm_to_ptx(irs, **options)]
+
+        # # Sometimes the result from NVVM contains trailing whitespace and
+        # # nulls, which we strip so that the assembly dump looks a little
+        # # tidier.
+        # ptxes = [x.decode().strip('\x00').strip() for x in ptxes]
+
+        # if config.DUMP_ASSEMBLY:
+        #     print(("ASSEMBLY %s" % self._name).center(80, '-'))
+        #     print(self._join_ptxes(ptxes))
+        #     print('=' * 80)
+
+        # self._ptx_cache[cc] = ptxes
+
+        # return ptxes
+
+    def _join_ptxes(self, ptxes):
+        return "\n\n".join(ptxes)
+
+    def get_cubin(self, cc=None):
+        if cc is None:
+            ctx = devices.get_context()
+            device = ctx.device
+            cc = device.compute_capability
+
+        cubin = self._cubin_cache.get(cc, None)
+        if cubin:
+            return cubin
+
+        linker = driver.Linker.new(max_registers=self._max_registers, cc=cc)
+
+        ptxes = self._get_ptxes(cc=cc)
+        for ptx in ptxes:
+            linker.add_ptx(ptx.encode())
+        for path in self._linking_files:
+            linker.add_file_guess_ext(path)
+
+        cubin_buf, size = linker.complete()
+
+        # We take a copy of the cubin because it's owned by the linker
+        cubin_ptr = ctypes.cast(cubin_buf, ctypes.POINTER(ctypes.c_char))
+        cubin = bytes(np.ctypeslib.as_array(cubin_ptr, shape=(size,)))
+        self._cubin_cache[cc] = cubin
+        self._linkerinfo_cache[cc] = linker.info_log
+
+        return cubin
+
+    def get_iluvatar_cubin(self, cc=None):
+        if cc is None:
+            ctx = devices.get_context()
+            device = ctx.device
+            cc = device.compute_capability
+
+        cubin = self._cubin_cache.get(cc, None)
+        if cubin:
+            return cubin
+        
+        llvm_modules = self.modules
+        llvm_irs = self.llvm_strs
+        module = ll.parse_assembly(alloca_addrspace_correction(str(JITCUDACodegen("numba.cuda.jit")._create_empty_module(llvm_modules[0].name))))
+        for ir in llvm_irs:
+            other_module = ll.parse_assembly(alloca_addrspace_correction(ir))
+            module.link_in(other_module)
+        cubin_buf, size = ll.cubin_creator.create_cubin(module, cc[0] * 10 + cc[1])
+
+        # We take a copy of the cubin because it's owned by the linker
+        cubin_ptr = ctypes.cast(cubin_buf, ctypes.POINTER(ctypes.c_char))
+        cubin = bytes(np.ctypeslib.as_array(cubin_ptr, shape=(size,)))
+        self._cubin_cache[cc] = cubin
+
+        return cubin
+
+    def get_cufunc(self):
+        if self._entry_name is None:
+            msg = "Missing entry_name - are you trying to get the cufunc " \
+                  "for a device function?"
+            raise RuntimeError(msg)
+
+        ctx = devices.get_context()
+        device = ctx.device
+
+        cufunc = self._cufunc_cache.get(device.id, None)
+        if cufunc:
+            return cufunc
+
+        # cubin = self.get_cubin(cc=device.compute_capability)
+        cubin = self.get_iluvatar_cubin(cc=device.compute_capability)
+        module = ctx.create_module_image(cubin)
+
+        # Load
+        cufunc = module.get_function(self._entry_name)
+
+        # Populate caches
+        self._cufunc_cache[device.id] = cufunc
+
+        return cufunc
+
+    def get_linkerinfo(self, cc):
+        try:
+            return self._linkerinfo_cache[cc]
+        except KeyError:
+            raise KeyError(f'No linkerinfo for CC {cc}')
+
+    def get_sass(self, cc=None):
+        return disassemble_cubin(self.get_cubin(cc=cc))
+
+    def add_ir_module(self, mod):
+        self._raise_if_finalized()
+        if self._module is not None:
+            raise RuntimeError('CUDACodeLibrary only supports one module')
+        self._module = mod
+
+    def add_linking_library(self, library):
+        library._ensure_finalized()
+
+        # We don't want to allow linking more libraries in after finalization
+        # because our linked libraries are modified by the finalization, and we
+        # won't be able to finalize again after adding new ones
+        self._raise_if_finalized()
+
+        self._linking_libraries.add(library)
+
+    def add_linking_file(self, filepath):
+        self._linking_files.add(filepath)
+
+    def get_function(self, name):
+        for fn in self._module.functions:
+            if fn.name == name:
+                return fn
+        raise KeyError(f'Function {name} not found')
+
+    @property
+    def modules(self):
+        return [self._module] + [mod for lib in self._linking_libraries
+                                 for mod in lib.modules]
+
+    @property
+    def linking_libraries(self):
+        # Libraries we link to may link to other libraries, so we recursively
+        # traverse the linking libraries property to build up a list of all
+        # linked libraries.
+        libs = []
+        for lib in self._linking_libraries:
+            libs.extend(lib.linking_libraries)
+            libs.append(lib)
+        return libs
+
+    def finalize(self):
+        # Unlike the CPUCodeLibrary, we don't invoke the binding layer here -
+        # we only adjust the linkage of functions. Global kernels (with
+        # external linkage) have their linkage untouched. Device functions are
+        # set linkonce_odr to prevent them appearing in the PTX.
+
+        self._raise_if_finalized()
+
+        # Note in-place modification of the linkage of functions in linked
+        # libraries. This presently causes no issues as only device functions
+        # are shared across code libraries, so they would always need their
+        # linkage set to linkonce_odr. If in a future scenario some code
+        # libraries require linkonce_odr linkage of functions in linked
+        # modules, and another code library requires another linkage, each code
+        # library will need to take its own private copy of its linked modules.
+        #
+        # See also discussion on PR #890:
+        # https://github.com/numba/numba/pull/890
+        #
+        # We don't adjust the linkage of functions when compiling for debug -
+        # because the device functions are in separate modules, we need them to
+        # be externally visible.
+        for library in self._linking_libraries:
+            for mod in library.modules:
+                for fn in mod.functions:
+                    if not fn.is_declaration:
+                        if self._nvvm_options.get('debug', False):
+                            fn.linkage = 'weak_odr'
+                        else:
+                            fn.linkage = 'linkonce_odr'
+
+        self._finalized = True
+
+    def _reduce_states(self):
+        """
+        Reduce the instance for serialization. We retain the PTX and cubins,
+        but loaded functions are discarded. They are recreated when needed
+        after deserialization.
+        """
+        if self._linking_files:
+            msg = 'Cannot pickle CUDACodeLibrary with linking files'
+            raise RuntimeError(msg)
+        if not self._finalized:
+            raise RuntimeError('Cannot pickle unfinalized CUDACodeLibrary')
+        return dict(
+            codegen=None,
+            name=self.name,
+            entry_name=self._entry_name,
+            llvm_strs=self.llvm_strs,
+            ptx_cache=self._ptx_cache,
+            cubin_cache=self._cubin_cache,
+            linkerinfo_cache=self._linkerinfo_cache,
+            max_registers=self._max_registers,
+            nvvm_options=self._nvvm_options
+        )
+
+    @classmethod
+    def _rebuild(cls, codegen, name, entry_name, llvm_strs, ptx_cache,
+                 cubin_cache, linkerinfo_cache, max_registers, nvvm_options):
+        """
+        Rebuild an instance.
+        """
+        instance = cls(codegen, name, entry_name=entry_name)
+
+        instance._llvm_strs = llvm_strs
+        instance._ptx_cache = ptx_cache
+        instance._cubin_cache = cubin_cache
+        instance._linkerinfo_cache = linkerinfo_cache
+
+        instance._max_registers = max_registers
+        instance._nvvm_options = nvvm_options
+
+        instance._finalized = True
+
+        return instance
+
+
+class JITCUDACodegen(Codegen):
+    """
+    This codegen implementation for CUDA only generates optimized LLVM IR.
+    Generation of PTX code is done separately (see numba.cuda.compiler).
+    """
+
+    _library_class = CUDACodeLibrary
+
+    def __init__(self, module_name):
+        self._data_layout = nvvm.data_layout
+        self._target_data = ll.create_target_data(self._data_layout)
+
+    def _create_empty_module(self, name):
+        ir_module = ir.Module(name)
+        ir_module.triple = CUDA_TRIPLE
+        if self._data_layout:
+            ir_module.data_layout = self._data_layout
+        nvvm.add_ir_version(ir_module)
+        return ir_module
+
+    def _add_module(self, module):
+        pass
+
+    def magic_tuple(self):
+        """
+        Return a tuple unambiguously describing the codegen behaviour.
+        """
+        ctx = devices.get_context()
+        cc = ctx.device.compute_capability
+        return (runtime.runtime.get_version(), cc)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/compiler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/compiler.py
new file mode 100644
index 000000000..a8162c0fa
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/compiler.py
@@ -0,0 +1,319 @@
+from numba.core.typing.templates import ConcreteTemplate
+from numba.core import types, typing, funcdesc, config, compiler
+from numba.core.compiler import (sanitize_compile_result_entries, CompilerBase,
+                                 DefaultPassBuilder, Flags, Option,
+                                 CompileResult)
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core.compiler_machinery import (LoweringPass, AnalysisPass,
+                                           PassManager, register_pass)
+from numba.core.errors import NumbaInvalidConfigWarning, TypingError
+from numba.core.typed_passes import (IRLegalization, NativeLowering,
+                                     AnnotateTypes)
+from warnings import warn
+from numba.cuda.api import get_current_device
+
+
+def _nvvm_options_type(x):
+    if x is None:
+        return None
+
+    else:
+        assert isinstance(x, dict)
+        return x
+
+
+class CUDAFlags(Flags):
+    nvvm_options = Option(
+        type=_nvvm_options_type,
+        default=None,
+        doc="NVVM options",
+    )
+
+
+# The CUDACompileResult (CCR) has a specially-defined entry point equal to its
+# id.  This is because the entry point is used as a key into a dict of
+# overloads by the base dispatcher. The id of the CCR is the only small and
+# unique property of a CompileResult in the CUDA target (cf. the CPU target,
+# which uses its entry_point, which is a pointer value).
+#
+# This does feel a little hackish, and there are two ways in which this could
+# be improved:
+#
+# 1. We could change the core of Numba so that each CompileResult has its own
+#    unique ID that can be used as a key - e.g. a count, similar to the way in
+#    which types have unique counts.
+# 2. At some future time when kernel launch uses a compiled function, the entry
+#    point will no longer need to be a synthetic value, but will instead be a
+#    pointer to the compiled function as in the CPU target.
+
+class CUDACompileResult(CompileResult):
+    @property
+    def entry_point(self):
+        return id(self)
+
+
+def cuda_compile_result(**entries):
+    entries = sanitize_compile_result_entries(entries)
+    return CUDACompileResult(**entries)
+
+
+@register_pass(mutates_CFG=True, analysis_only=False)
+class CUDABackend(LoweringPass):
+
+    _name = "cuda_backend"
+
+    def __init__(self):
+        LoweringPass.__init__(self)
+
+    def run_pass(self, state):
+        """
+        Back-end: Packages lowering output in a compile result
+        """
+        lowered = state['cr']
+        signature = typing.signature(state.return_type, *state.args)
+
+        state.cr = cuda_compile_result(
+            typing_context=state.typingctx,
+            target_context=state.targetctx,
+            typing_error=state.status.fail_reason,
+            type_annotation=state.type_annotation,
+            library=state.library,
+            call_helper=lowered.call_helper,
+            signature=signature,
+            fndesc=lowered.fndesc,
+        )
+        return True
+
+
+@register_pass(mutates_CFG=False, analysis_only=False)
+class CreateLibrary(LoweringPass):
+    """
+    Create a CUDACodeLibrary for the NativeLowering pass to populate. The
+    NativeLowering pass will create a code library if none exists, but we need
+    to set it up with nvvm_options from the flags if they are present.
+    """
+
+    _name = "create_library"
+
+    def __init__(self):
+        LoweringPass.__init__(self)
+
+    def run_pass(self, state):
+        codegen = state.targetctx.codegen()
+        name = state.func_id.func_qualname
+        nvvm_options = state.flags.nvvm_options
+        state.library = codegen.create_library(name, nvvm_options=nvvm_options)
+        # Enable object caching upfront so that the library can be serialized.
+        state.library.enable_object_caching()
+
+        return True
+
+
+@register_pass(mutates_CFG=False, analysis_only=True)
+class CUDALegalization(AnalysisPass):
+
+    _name = "cuda_legalization"
+
+    def __init__(self):
+        AnalysisPass.__init__(self)
+
+    def run_pass(self, state):
+        # Early return if NVVM 7
+        from numba.cuda.cudadrv.nvvm import NVVM
+        # if NVVM().is_nvvm70
+        if True:
+            return False
+        # NVVM < 7, need to check for charseq
+        typmap = state.typemap
+
+        def check_dtype(dtype):
+            if isinstance(dtype, (types.UnicodeCharSeq, types.CharSeq)):
+                msg = (f"{k} is a char sequence type. This type is not "
+                       "supported with CUDA toolkit versions < 11.2. To "
+                       "use this type, you need to update your CUDA "
+                       "toolkit - try 'conda install cudatoolkit=11' if "
+                       "you are using conda to manage your environment.")
+                raise TypingError(msg)
+            elif isinstance(dtype, types.Record):
+                for subdtype in dtype.fields.items():
+                    # subdtype is a (name, _RecordField) pair
+                    check_dtype(subdtype[1].type)
+
+        for k, v in typmap.items():
+            if isinstance(v, types.Array):
+                check_dtype(v.dtype)
+        return False
+
+
+class CUDACompiler(CompilerBase):
+    def define_pipelines(self):
+        dpb = DefaultPassBuilder
+        pm = PassManager('cuda')
+
+        untyped_passes = dpb.define_untyped_pipeline(self.state)
+        pm.passes.extend(untyped_passes.passes)
+
+        typed_passes = dpb.define_typed_pipeline(self.state)
+        pm.passes.extend(typed_passes.passes)
+        pm.add_pass(CUDALegalization, "CUDA legalization")
+
+        lowering_passes = self.define_cuda_lowering_pipeline(self.state)
+        pm.passes.extend(lowering_passes.passes)
+
+        pm.finalize()
+        return [pm]
+
+    def define_cuda_lowering_pipeline(self, state):
+        pm = PassManager('cuda_lowering')
+        # legalise
+        pm.add_pass(IRLegalization,
+                    "ensure IR is legal prior to lowering")
+        pm.add_pass(AnnotateTypes, "annotate types")
+
+        # lower
+        pm.add_pass(CreateLibrary, "create library")
+        pm.add_pass(NativeLowering, "native lowering")
+        pm.add_pass(CUDABackend, "cuda backend")
+
+        pm.finalize()
+        return pm
+
+
+@global_compiler_lock
+def compile_cuda(pyfunc, return_type, args, debug=False, lineinfo=False,
+                 inline=False, fastmath=False, nvvm_options=None):
+    from .descriptor import cuda_target
+    typingctx = cuda_target.typing_context
+    targetctx = cuda_target.target_context
+
+    flags = CUDAFlags()
+    # Do not compile (generate native code), just lower (to LLVM)
+    flags.no_compile = True
+    flags.no_cpython_wrapper = True
+    flags.no_cfunc_wrapper = True
+    if debug or lineinfo:
+        # Note both debug and lineinfo turn on debug information in the
+        # compiled code, but we keep them separate arguments in case we
+        # later want to overload some other behavior on the debug flag.
+        # In particular, -opt=3 is not supported with -g.
+        flags.debuginfo = True
+        flags.error_model = 'python'
+    else:
+        flags.error_model = 'numpy'
+    if inline:
+        flags.forceinline = True
+    if fastmath:
+        flags.fastmath = True
+    if nvvm_options:
+        flags.nvvm_options = nvvm_options
+
+    # Run compilation pipeline
+    from numba.core.target_extension import target_override
+    with target_override('cuda'):
+        cres = compiler.compile_extra(typingctx=typingctx,
+                                      targetctx=targetctx,
+                                      func=pyfunc,
+                                      args=args,
+                                      return_type=return_type,
+                                      flags=flags,
+                                      locals={},
+                                      pipeline_class=CUDACompiler)
+
+    library = cres.library
+    library.finalize()
+
+    return cres
+
+
+@global_compiler_lock
+def compile_ptx(pyfunc, args, debug=False, lineinfo=False, device=False,
+                fastmath=False, cc=None, opt=True):
+    """Compile a Python function to PTX for a given set of argument types.
+
+    :param pyfunc: The Python function to compile.
+    :param args: A tuple of argument types to compile for.
+    :param debug: Whether to include debug info in the generated PTX.
+    :type debug: bool
+    :param lineinfo: Whether to include a line mapping from the generated PTX
+                     to the source code. Usually this is used with optimized
+                     code (since debug mode would automatically include this),
+                     so we want debug info in the LLVM but only the line
+                     mapping in the final PTX.
+    :type lineinfo: bool
+    :param device: Whether to compile a device function. Defaults to ``False``,
+                   to compile global kernel functions.
+    :type device: bool
+    :param fastmath: Whether to enable fast math flags (ftz=1, prec_sqrt=0,
+                     prec_div=, and fma=1)
+    :type fastmath: bool
+    :param cc: Compute capability to compile for, as a tuple ``(MAJOR, MINOR)``.
+               Defaults to ``(5, 3)``.
+    :type cc: tuple
+    :param opt: Enable optimizations. Defaults to ``True``.
+    :type opt: bool
+    :return: (ptx, resty): The PTX code and inferred return type
+    :rtype: tuple
+    """
+    if debug and opt:
+        msg = ("debug=True with opt=True (the default) "
+               "is not supported by CUDA. This may result in a crash"
+               " - set debug=False or opt=False.")
+        warn(NumbaInvalidConfigWarning(msg))
+
+    nvvm_options = {
+        'debug': debug,
+        'lineinfo': lineinfo,
+        'fastmath': fastmath,
+        'opt': 3 if opt else 0
+    }
+
+    cres = compile_cuda(pyfunc, None, args, debug=debug, lineinfo=lineinfo,
+                        fastmath=fastmath,
+                        nvvm_options=nvvm_options)
+    resty = cres.signature.return_type
+    if device:
+        lib = cres.library
+    else:
+        tgt = cres.target_context
+        filename = cres.type_annotation.filename
+        linenum = int(cres.type_annotation.linenum)
+        lib, kernel = tgt.prepare_cuda_kernel(cres.library, cres.fndesc, debug,
+                                              nvvm_options, filename, linenum)
+
+    cc = cc or config.CUDA_DEFAULT_PTX_CC
+    ptx = lib.get_asm_str(cc=cc)
+    return ptx, resty
+
+
+def compile_ptx_for_current_device(pyfunc, args, debug=False, lineinfo=False,
+                                   device=False, fastmath=False, opt=True):
+    """Compile a Python function to PTX for a given set of argument types for
+    the current device's compute capabilility. This calls :func:`compile_ptx`
+    with an appropriate ``cc`` value for the current device."""
+    cc = get_current_device().compute_capability
+    return compile_ptx(pyfunc, args, debug=debug, lineinfo=lineinfo,
+                       device=device, fastmath=fastmath, cc=cc, opt=True)
+
+
+def declare_device_function(name, restype, argtypes):
+    from .descriptor import cuda_target
+    typingctx = cuda_target.typing_context
+    targetctx = cuda_target.target_context
+    sig = typing.signature(restype, *argtypes)
+    extfn = ExternFunction(name, sig)
+
+    class device_function_template(ConcreteTemplate):
+        key = extfn
+        cases = [sig]
+
+    fndesc = funcdesc.ExternalFunctionDescriptor(
+        name=name, restype=restype, argtypes=argtypes)
+    typingctx.insert_user_function(extfn, device_function_template)
+    targetctx.insert_user_function(extfn, fndesc)
+    return extfn
+
+
+class ExternFunction(object):
+    def __init__(self, name, sig):
+        self.name = name
+        self.sig = sig
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cuda_paths.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cuda_paths.py
new file mode 100644
index 000000000..0da435d33
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cuda_paths.py
@@ -0,0 +1,165 @@
+import sys
+import re
+import os
+from collections import namedtuple
+
+from numba.core.config import IS_WIN32
+from numba.misc.findlib import find_lib, find_file
+
+
+_env_path_tuple = namedtuple('_env_path_tuple', ['by', 'info'])
+
+
+def _find_valid_path(options):
+    """Find valid path from *options*, which is a list of 2-tuple of
+    (name, path).  Return first pair where *path* is not None.
+    If no valid path is found, return ('<unknown>', None)
+    """
+    for by, data in options:
+        if data is not None:
+            return by, data
+    else:
+        return '<unknown>', None
+
+
+def _get_libdevice_path_decision():
+    options = [
+        ('Conda environment', get_conda_ctk()),
+        ('CUDA_HOME', get_cuda_home('nvvm', 'libdevice')),
+        ('System', get_system_ctk('nvvm', 'libdevice')),
+        ('Debian package', get_debian_pkg_libdevice()),
+    ]
+    by, libdir = _find_valid_path(options)
+    return by, libdir
+
+
+def _nvvm_lib_dir():
+    if IS_WIN32:
+        return 'nvvm', 'bin'
+    else:
+        return 'nvvm', 'lib64'
+
+
+def _get_nvvm_path_decision():
+    options = [
+        ('Conda environment', get_conda_ctk()),
+        ('CUDA_HOME', get_cuda_home(*_nvvm_lib_dir())),
+        ('System', get_system_ctk(*_nvvm_lib_dir())),
+    ]
+    by, path = _find_valid_path(options)
+    return by, path
+
+
+def _get_libdevice_paths():
+    by, libdir = _get_libdevice_path_decision()
+    # Search for pattern
+    pat = r'libdevice(\.\d+)*\.bc$'
+    candidates = find_file(re.compile(pat), libdir)
+    # Keep only the max (most recent version) of the bitcode files.
+    out = max(candidates, default=None)
+    return _env_path_tuple(by, out)
+
+
+def _cudalib_path():
+    if IS_WIN32:
+        return 'bin'
+    else:
+        return 'lib64'
+
+
+def _get_cudalib_dir_path_decision():
+    options = [
+        ('Conda environment', get_conda_ctk()),
+        ('CUDA_HOME', get_cuda_home(_cudalib_path())),
+        ('System', get_system_ctk(_cudalib_path())),
+    ]
+    by, libdir = _find_valid_path(options)
+    return by, libdir
+
+
+def _get_cudalib_dir():
+    by, libdir = _get_cudalib_dir_path_decision()
+    return _env_path_tuple(by, libdir)
+
+
+def get_system_ctk(*subdirs):
+    """Return path to system-wide cudatoolkit; or, None if it doesn't exist.
+    """
+    # Linux?
+    if sys.platform.startswith('linux'):
+        # Is cuda alias to /usr/local/cuda?
+        # We are intentionally not getting versioned cuda installation.
+        base = '/usr/local/cuda'
+        if os.path.exists(base):
+            return os.path.join(base, *subdirs)
+
+
+def get_conda_ctk():
+    """Return path to directory containing the shared libraries of cudatoolkit.
+    """
+    is_conda_env = os.path.exists(os.path.join(sys.prefix, 'conda-meta'))
+    if not is_conda_env:
+        return
+    # Assume the existence of NVVM to imply cudatoolkit installed
+    paths = find_lib('nvvm')
+    if not paths:
+        return
+    # Use the directory name of the max path
+    return os.path.dirname(max(paths))
+
+
+def get_cuda_home(*subdirs):
+    """Get paths of CUDA_HOME.
+    If *subdirs* are the subdirectory name to be appended in the resulting
+    path.
+    """
+    cuda_home = os.environ.get('CUDA_HOME')
+    if cuda_home is None:
+        # Try Windows CUDA installation without Anaconda
+        cuda_home = os.environ.get('CUDA_PATH')
+    if cuda_home is not None:
+        return os.path.join(cuda_home, *subdirs)
+
+
+def _get_nvvm_path():
+    by, path = _get_nvvm_path_decision()
+    candidates = find_lib('nvvm', path)
+    path = max(candidates) if candidates else None
+    return _env_path_tuple(by, path)
+
+
+def get_cuda_paths():
+    """Returns a dictionary mapping component names to a 2-tuple
+    of (source_variable, info).
+
+    The returned dictionary will have the following keys and infos:
+    - "nvvm": file_path
+    - "libdevice": List[Tuple[arch, file_path]]
+    - "cudalib_dir": directory_path
+
+    Note: The result of the function is cached.
+    """
+    # Check cache
+    if hasattr(get_cuda_paths, '_cached_result'):
+        return get_cuda_paths._cached_result
+    else:
+        # Not in cache
+        d = {
+            'nvvm': _get_nvvm_path(),
+            'libdevice': _get_libdevice_paths(),
+            'cudalib_dir': _get_cudalib_dir(),
+        }
+        # Cache result
+        get_cuda_paths._cached_result = d
+        return d
+
+
+def get_debian_pkg_libdevice():
+    """
+    Return the Debian NVIDIA Maintainers-packaged libdevice location, if it
+    exists.
+    """
+    pkg_libdevice_location = '/usr/lib/nvidia-cuda-toolkit/libdevice'
+    if not os.path.exists(pkg_libdevice_location):
+        return None
+    return pkg_libdevice_location
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadecl.py
new file mode 100644
index 000000000..df9fdeddc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadecl.py
@@ -0,0 +1,689 @@
+from numba.core import types, errors
+from numba.core.typing.npydecl import (parse_dtype, parse_shape,
+                                       register_number_classes)
+from numba.core.typing.templates import (AttributeTemplate, ConcreteTemplate,
+                                         AbstractTemplate, CallableTemplate,
+                                         signature, Registry)
+from numba.cuda.types import dim3, grid_group
+from numba import cuda
+
+
+registry = Registry()
+register = registry.register
+register_attr = registry.register_attr
+register_global = registry.register_global
+
+register_number_classes(register_global)
+
+
+class GridFunction(CallableTemplate):
+    def generic(self):
+        def typer(ndim):
+            if not isinstance(ndim, types.IntegerLiteral):
+                raise errors.RequireLiteralValue(ndim)
+            val = ndim.literal_value
+            if val == 1:
+                restype = types.int32
+            elif val in (2, 3):
+                restype = types.UniTuple(types.int32, val)
+            else:
+                raise ValueError('argument can only be 1, 2, 3')
+            return signature(restype, types.int32)
+        return typer
+
+
+@register
+class Cuda_grid(GridFunction):
+    key = cuda.grid
+
+
+@register
+class Cuda_gridsize(GridFunction):
+    key = cuda.gridsize
+
+
+class Cuda_array_decl(CallableTemplate):
+    def generic(self):
+        def typer(shape, dtype):
+
+            # Only integer literals and tuples of integer literals are valid
+            # shapes
+            if isinstance(shape, types.Integer):
+                if not isinstance(shape, types.IntegerLiteral):
+                    return None
+            elif isinstance(shape, (types.Tuple, types.UniTuple)):
+                if any([not isinstance(s, types.IntegerLiteral)
+                        for s in shape]):
+                    return None
+            else:
+                return None
+
+            ndim = parse_shape(shape)
+            nb_dtype = parse_dtype(dtype)
+            if nb_dtype is not None and ndim is not None:
+                return types.Array(dtype=nb_dtype, ndim=ndim, layout='C')
+
+        return typer
+
+
+@register
+class Cuda_shared_array(Cuda_array_decl):
+    key = cuda.shared.array
+
+
+@register
+class Cuda_local_array(Cuda_array_decl):
+    key = cuda.local.array
+
+
+@register
+class Cuda_const_array_like(CallableTemplate):
+    key = cuda.const.array_like
+
+    def generic(self):
+        def typer(ndarray):
+            return ndarray
+        return typer
+
+
+@register
+class Cuda_syncthreads(ConcreteTemplate):
+    key = cuda.syncthreads
+    cases = [signature(types.none)]
+
+
+@register
+class Cuda_syncthreads_count(ConcreteTemplate):
+    key = cuda.syncthreads_count
+    cases = [signature(types.i4, types.i4)]
+
+
+@register
+class Cuda_syncthreads_and(ConcreteTemplate):
+    key = cuda.syncthreads_and
+    cases = [signature(types.i4, types.i4)]
+
+
+@register
+class Cuda_syncthreads_or(ConcreteTemplate):
+    key = cuda.syncthreads_or
+    cases = [signature(types.i4, types.i4)]
+
+
+@register
+class Cuda_threadfence_device(ConcreteTemplate):
+    key = cuda.threadfence
+    cases = [signature(types.none)]
+
+
+@register
+class Cuda_threadfence_block(ConcreteTemplate):
+    key = cuda.threadfence_block
+    cases = [signature(types.none)]
+
+
+@register
+class Cuda_threadfence_system(ConcreteTemplate):
+    key = cuda.threadfence_system
+    cases = [signature(types.none)]
+
+
+@register
+class Cuda_syncwarp(ConcreteTemplate):
+    key = cuda.syncwarp
+    cases = [signature(types.none), signature(types.none, types.i4)]
+
+
+@register
+class Cuda_cg_this_grid(ConcreteTemplate):
+    key = cuda.cg.this_grid
+    cases = [signature(grid_group)]
+
+
+@register_attr
+class CudaCgModuleTemplate(AttributeTemplate):
+    key = types.Module(cuda.cg)
+
+    def resolve_this_grid(self, mod):
+        return types.Function(Cuda_cg_this_grid)
+
+
+class Cuda_grid_group_sync(AbstractTemplate):
+    key = "GridGroup.sync"
+
+    def generic(self, args, kws):
+        return signature(types.int32, recvr=self.this)
+
+
+@register_attr
+class GridGroup_attrs(AttributeTemplate):
+    key = grid_group
+
+    def resolve_sync(self, mod):
+        return types.BoundFunction(Cuda_grid_group_sync, grid_group)
+
+
+@register
+class Cuda_shfl_sync_intrinsic(ConcreteTemplate):
+    key = cuda.shfl_sync_intrinsic
+    cases = [
+        signature(types.Tuple((types.i4, types.b1)),
+                  types.i4, types.i4, types.i4, types.i4, types.i4),
+        signature(types.Tuple((types.i8, types.b1)),
+                  types.i4, types.i4, types.i8, types.i4, types.i4),
+        signature(types.Tuple((types.f4, types.b1)),
+                  types.i4, types.i4, types.f4, types.i4, types.i4),
+        signature(types.Tuple((types.f8, types.b1)),
+                  types.i4, types.i4, types.f8, types.i4, types.i4),
+    ]
+
+
+@register
+class Cuda_vote_sync_intrinsic(ConcreteTemplate):
+    key = cuda.vote_sync_intrinsic
+    cases = [signature(types.Tuple((types.i4, types.b1)),
+                       types.i4, types.i4, types.b1)]
+
+
+@register
+class Cuda_match_any_sync(ConcreteTemplate):
+    key = cuda.match_any_sync
+    cases = [
+        signature(types.i4, types.i4, types.i4),
+        signature(types.i4, types.i4, types.i8),
+        signature(types.i4, types.i4, types.f4),
+        signature(types.i4, types.i4, types.f8),
+    ]
+
+
+@register
+class Cuda_match_all_sync(ConcreteTemplate):
+    key = cuda.match_all_sync
+    cases = [
+        signature(types.Tuple((types.i4, types.b1)), types.i4, types.i4),
+        signature(types.Tuple((types.i4, types.b1)), types.i4, types.i8),
+        signature(types.Tuple((types.i4, types.b1)), types.i4, types.f4),
+        signature(types.Tuple((types.i4, types.b1)), types.i4, types.f8),
+    ]
+
+
+@register
+class Cuda_activemask(ConcreteTemplate):
+    key = cuda.activemask
+    cases = [signature(types.uint32)]
+
+
+@register
+class Cuda_lanemask_lt(ConcreteTemplate):
+    key = cuda.lanemask_lt
+    cases = [signature(types.uint32)]
+
+
+@register
+class Cuda_popc(ConcreteTemplate):
+    """
+    Supported types from `llvm.popc`
+    [here](http://docs.nvidia.com/cuda/nvvm-ir-spec/index.html#bit-manipulations-intrinics)
+    """
+    key = cuda.popc
+    cases = [
+        signature(types.int8, types.int8),
+        signature(types.int16, types.int16),
+        signature(types.int32, types.int32),
+        signature(types.int64, types.int64),
+        signature(types.uint8, types.uint8),
+        signature(types.uint16, types.uint16),
+        signature(types.uint32, types.uint32),
+        signature(types.uint64, types.uint64),
+    ]
+
+
+@register
+class Cuda_fma(ConcreteTemplate):
+    """
+    Supported types from `llvm.fma`
+    [here](https://docs.nvidia.com/cuda/nvvm-ir-spec/index.html#standard-c-library-intrinics)
+    """
+    key = cuda.fma
+    cases = [
+        signature(types.float32, types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64, types.float64),
+    ]
+
+
+@register
+class Cuda_hfma(ConcreteTemplate):
+    key = cuda.fp16.hfma
+    cases = [
+        signature(types.float16, types.float16, types.float16, types.float16)
+    ]
+
+
+@register
+class Cuda_cbrt(ConcreteTemplate):
+
+    key = cuda.cbrt
+    cases = [
+        signature(types.float32, types.float32),
+        signature(types.float64, types.float64),
+    ]
+
+
+@register
+class Cuda_brev(ConcreteTemplate):
+    key = cuda.brev
+    cases = [
+        signature(types.uint32, types.uint32),
+        signature(types.uint64, types.uint64),
+    ]
+
+
+@register
+class Cuda_clz(ConcreteTemplate):
+    """
+    Supported types from `llvm.ctlz`
+    [here](http://docs.nvidia.com/cuda/nvvm-ir-spec/index.html#bit-manipulations-intrinics)
+    """
+    key = cuda.clz
+    cases = [
+        signature(types.int8, types.int8),
+        signature(types.int16, types.int16),
+        signature(types.int32, types.int32),
+        signature(types.int64, types.int64),
+        signature(types.uint8, types.uint8),
+        signature(types.uint16, types.uint16),
+        signature(types.uint32, types.uint32),
+        signature(types.uint64, types.uint64),
+    ]
+
+
+@register
+class Cuda_ffs(ConcreteTemplate):
+    """
+    Supported types from `llvm.cttz`
+    [here](http://docs.nvidia.com/cuda/nvvm-ir-spec/index.html#bit-manipulations-intrinics)
+    """
+    key = cuda.ffs
+    cases = [
+        signature(types.uint32, types.int8),
+        signature(types.uint32, types.int16),
+        signature(types.uint32, types.int32),
+        signature(types.uint32, types.int64),
+        signature(types.uint32, types.uint8),
+        signature(types.uint32, types.uint16),
+        signature(types.uint32, types.uint32),
+        signature(types.uint32, types.uint64),
+    ]
+
+
+@register
+class Cuda_selp(AbstractTemplate):
+    key = cuda.selp
+
+    def generic(self, args, kws):
+        assert not kws
+        test, a, b = args
+
+        # per docs
+        # http://docs.nvidia.com/cuda/parallel-thread-execution/index.html#comparison-and-selection-instructions-selp
+        supported_types = (types.float64, types.float32,
+                           types.int16, types.uint16,
+                           types.int32, types.uint32,
+                           types.int64, types.uint64)
+
+        if a != b or a not in supported_types:
+            return
+
+        return signature(a, test, a, a)
+
+
+def _genfp16_unary(l_key):
+    @register
+    class Cuda_fp16_unary(ConcreteTemplate):
+        key = l_key
+        cases = [signature(types.float16, types.float16)]
+
+    return Cuda_fp16_unary
+
+
+def _genfp16_binary(l_key):
+    @register
+    class Cuda_fp16_binary(ConcreteTemplate):
+        key = l_key
+        cases = [signature(types.float16, types.float16, types.float16)]
+
+    return Cuda_fp16_binary
+
+
+def _genfp16_binary_comparison(l_key):
+    @register
+    class Cuda_fp16_cmp(ConcreteTemplate):
+        key = l_key
+
+        cases = [
+            signature(types.b1, types.float16, types.float16)
+        ]
+    return Cuda_fp16_cmp
+
+
+Cuda_hadd = _genfp16_binary(cuda.fp16.hadd)
+Cuda_hsub = _genfp16_binary(cuda.fp16.hsub)
+Cuda_hmul = _genfp16_binary(cuda.fp16.hmul)
+Cuda_hmax = _genfp16_binary(cuda.fp16.hmax)
+Cuda_hmin = _genfp16_binary(cuda.fp16.hmin)
+Cuda_hneg = _genfp16_unary(cuda.fp16.hneg)
+Cuda_habs = _genfp16_unary(cuda.fp16.habs)
+Cuda_heq = _genfp16_binary_comparison(cuda.fp16.heq)
+Cuda_hne = _genfp16_binary_comparison(cuda.fp16.hne)
+Cuda_hge = _genfp16_binary_comparison(cuda.fp16.hge)
+Cuda_hgt = _genfp16_binary_comparison(cuda.fp16.hgt)
+Cuda_hle = _genfp16_binary_comparison(cuda.fp16.hle)
+Cuda_hlt = _genfp16_binary_comparison(cuda.fp16.hlt)
+
+
+# generate atomic operations
+def _gen(l_key, supported_types):
+    @register
+    class Cuda_atomic(AbstractTemplate):
+        key = l_key
+
+        def generic(self, args, kws):
+            assert not kws
+            ary, idx, val = args
+
+            if ary.dtype not in supported_types:
+                return
+
+            if ary.ndim == 1:
+                return signature(ary.dtype, ary, types.intp, ary.dtype)
+            elif ary.ndim > 1:
+                return signature(ary.dtype, ary, idx, ary.dtype)
+    return Cuda_atomic
+
+
+all_numba_types = (types.float64, types.float32,
+                   types.int32, types.uint32,
+                   types.int64, types.uint64)
+
+integer_numba_types = (types.int32, types.uint32,
+                       types.int64, types.uint64)
+
+unsigned_int_numba_types = (types.uint32, types.uint64)
+
+Cuda_atomic_add = _gen(cuda.atomic.add, all_numba_types)
+Cuda_atomic_sub = _gen(cuda.atomic.sub, all_numba_types)
+Cuda_atomic_max = _gen(cuda.atomic.max, all_numba_types)
+Cuda_atomic_min = _gen(cuda.atomic.min, all_numba_types)
+Cuda_atomic_nanmax = _gen(cuda.atomic.nanmax, all_numba_types)
+Cuda_atomic_nanmin = _gen(cuda.atomic.nanmin, all_numba_types)
+Cuda_atomic_and = _gen(cuda.atomic.and_, integer_numba_types)
+Cuda_atomic_or = _gen(cuda.atomic.or_, integer_numba_types)
+Cuda_atomic_xor = _gen(cuda.atomic.xor, integer_numba_types)
+Cuda_atomic_inc = _gen(cuda.atomic.inc, unsigned_int_numba_types)
+Cuda_atomic_dec = _gen(cuda.atomic.dec, unsigned_int_numba_types)
+Cuda_atomic_exch = _gen(cuda.atomic.exch, integer_numba_types)
+
+
+@register
+class Cuda_atomic_compare_and_swap(AbstractTemplate):
+    key = cuda.atomic.compare_and_swap
+
+    def generic(self, args, kws):
+        assert not kws
+        ary, old, val = args
+        dty = ary.dtype
+
+        if dty in integer_numba_types and ary.ndim == 1:
+            return signature(dty, ary, dty, dty)
+
+
+@register
+class Cuda_nanosleep(ConcreteTemplate):
+    key = cuda.nanosleep
+
+    cases = [signature(types.void, types.uint32)]
+
+
+@register_attr
+class Dim3_attrs(AttributeTemplate):
+    key = dim3
+
+    def resolve_x(self, mod):
+        return types.int32
+
+    def resolve_y(self, mod):
+        return types.int32
+
+    def resolve_z(self, mod):
+        return types.int32
+
+
+@register_attr
+class CudaSharedModuleTemplate(AttributeTemplate):
+    key = types.Module(cuda.shared)
+
+    def resolve_array(self, mod):
+        return types.Function(Cuda_shared_array)
+
+
+@register_attr
+class CudaConstModuleTemplate(AttributeTemplate):
+    key = types.Module(cuda.const)
+
+    def resolve_array_like(self, mod):
+        return types.Function(Cuda_const_array_like)
+
+
+@register_attr
+class CudaLocalModuleTemplate(AttributeTemplate):
+    key = types.Module(cuda.local)
+
+    def resolve_array(self, mod):
+        return types.Function(Cuda_local_array)
+
+
+@register_attr
+class CudaAtomicTemplate(AttributeTemplate):
+    key = types.Module(cuda.atomic)
+
+    def resolve_add(self, mod):
+        return types.Function(Cuda_atomic_add)
+
+    def resolve_sub(self, mod):
+        return types.Function(Cuda_atomic_sub)
+
+    def resolve_and_(self, mod):
+        return types.Function(Cuda_atomic_and)
+
+    def resolve_or_(self, mod):
+        return types.Function(Cuda_atomic_or)
+
+    def resolve_xor(self, mod):
+        return types.Function(Cuda_atomic_xor)
+
+    def resolve_inc(self, mod):
+        return types.Function(Cuda_atomic_inc)
+
+    def resolve_dec(self, mod):
+        return types.Function(Cuda_atomic_dec)
+
+    def resolve_exch(self, mod):
+        return types.Function(Cuda_atomic_exch)
+
+    def resolve_max(self, mod):
+        return types.Function(Cuda_atomic_max)
+
+    def resolve_min(self, mod):
+        return types.Function(Cuda_atomic_min)
+
+    def resolve_nanmin(self, mod):
+        return types.Function(Cuda_atomic_nanmin)
+
+    def resolve_nanmax(self, mod):
+        return types.Function(Cuda_atomic_nanmax)
+
+    def resolve_compare_and_swap(self, mod):
+        return types.Function(Cuda_atomic_compare_and_swap)
+
+
+@register_attr
+class CudaFp16Template(AttributeTemplate):
+    key = types.Module(cuda.fp16)
+
+    def resolve_hadd(self, mod):
+        return types.Function(Cuda_hadd)
+
+    def resolve_hsub(self, mod):
+        return types.Function(Cuda_hsub)
+
+    def resolve_hmul(self, mod):
+        return types.Function(Cuda_hmul)
+
+    def resolve_hneg(self, mod):
+        return types.Function(Cuda_hneg)
+
+    def resolve_habs(self, mod):
+        return types.Function(Cuda_habs)
+
+    def resolve_hfma(self, mod):
+        return types.Function(Cuda_hfma)
+
+    def resolve_heq(self, mod):
+        return types.Function(Cuda_heq)
+
+    def resolve_hne(self, mod):
+        return types.Function(Cuda_hne)
+
+    def resolve_hge(self, mod):
+        return types.Function(Cuda_hge)
+
+    def resolve_hgt(self, mod):
+        return types.Function(Cuda_hgt)
+
+    def resolve_hle(self, mod):
+        return types.Function(Cuda_hle)
+
+    def resolve_hlt(self, mod):
+        return types.Function(Cuda_hlt)
+
+    def resolve_hmax(self, mod):
+        return types.Function(Cuda_hmax)
+
+    def resolve_hmin(self, mod):
+        return types.Function(Cuda_hmin)
+
+
+@register_attr
+class CudaModuleTemplate(AttributeTemplate):
+    key = types.Module(cuda)
+
+    def resolve_grid(self, mod):
+        return types.Function(Cuda_grid)
+
+    def resolve_gridsize(self, mod):
+        return types.Function(Cuda_gridsize)
+
+    def resolve_cg(self, mod):
+        return types.Module(cuda.cg)
+
+    def resolve_threadIdx(self, mod):
+        return dim3
+
+    def resolve_blockIdx(self, mod):
+        return dim3
+
+    def resolve_blockDim(self, mod):
+        return dim3
+
+    def resolve_gridDim(self, mod):
+        return dim3
+
+    def resolve_warpsize(self, mod):
+        return types.int32
+
+    def resolve_laneid(self, mod):
+        return types.int32
+
+    def resolve_shared(self, mod):
+        return types.Module(cuda.shared)
+
+    def resolve_popc(self, mod):
+        return types.Function(Cuda_popc)
+
+    def resolve_brev(self, mod):
+        return types.Function(Cuda_brev)
+
+    def resolve_clz(self, mod):
+        return types.Function(Cuda_clz)
+
+    def resolve_ffs(self, mod):
+        return types.Function(Cuda_ffs)
+
+    def resolve_fma(self, mod):
+        return types.Function(Cuda_fma)
+
+    def resolve_cbrt(self, mod):
+        return types.Function(Cuda_cbrt)
+
+    def resolve_syncthreads(self, mod):
+        return types.Function(Cuda_syncthreads)
+
+    def resolve_syncthreads_count(self, mod):
+        return types.Function(Cuda_syncthreads_count)
+
+    def resolve_syncthreads_and(self, mod):
+        return types.Function(Cuda_syncthreads_and)
+
+    def resolve_syncthreads_or(self, mod):
+        return types.Function(Cuda_syncthreads_or)
+
+    def resolve_threadfence(self, mod):
+        return types.Function(Cuda_threadfence_device)
+
+    def resolve_threadfence_block(self, mod):
+        return types.Function(Cuda_threadfence_block)
+
+    def resolve_threadfence_system(self, mod):
+        return types.Function(Cuda_threadfence_system)
+
+    def resolve_syncwarp(self, mod):
+        return types.Function(Cuda_syncwarp)
+
+    def resolve_shfl_sync_intrinsic(self, mod):
+        return types.Function(Cuda_shfl_sync_intrinsic)
+
+    def resolve_vote_sync_intrinsic(self, mod):
+        return types.Function(Cuda_vote_sync_intrinsic)
+
+    def resolve_match_any_sync(self, mod):
+        return types.Function(Cuda_match_any_sync)
+
+    def resolve_match_all_sync(self, mod):
+        return types.Function(Cuda_match_all_sync)
+
+    def resolve_activemask(self, mod):
+        return types.Function(Cuda_activemask)
+
+    def resolve_lanemask_lt(self, mod):
+        return types.Function(Cuda_lanemask_lt)
+
+    def resolve_selp(self, mod):
+        return types.Function(Cuda_selp)
+
+    def resolve_nanosleep(self, mod):
+        return types.Function(Cuda_nanosleep)
+
+    def resolve_atomic(self, mod):
+        return types.Module(cuda.atomic)
+
+    def resolve_fp16(self, mod):
+        return types.Module(cuda.fp16)
+
+    def resolve_const(self, mod):
+        return types.Module(cuda.const)
+
+    def resolve_local(self, mod):
+        return types.Module(cuda.local)
+
+
+register_global(cuda, types.Module(cuda))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/__init__.py
new file mode 100644
index 000000000..33bfca345
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/__init__.py
@@ -0,0 +1,9 @@
+"""CUDA Driver
+
+- Driver API binding
+- NVVM API binding
+- Device array implementation
+
+"""
+from numba.core import config
+assert not config.ENABLE_CUDASIM, 'Cannot use real driver API with simulator'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/_extras.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/_extras.c
new file mode 100644
index 000000000..c28936065
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/_extras.c
@@ -0,0 +1,45 @@
+/*
+ * Helper binding to call some CUDA Runtime API that cannot be directly
+ * encoded using ctypes.
+ */
+
+#include "_pymodule.h"
+
+
+#define CUDA_IPC_HANDLE_SIZE 64
+
+typedef int CUresult;
+typedef void* CUdeviceptr;
+
+typedef struct CUipcMemHandle_st{
+    char reserved[CUDA_IPC_HANDLE_SIZE];
+} CUipcMemHandle;
+
+typedef CUresult (*cuIpcOpenMemHandle_t)(CUdeviceptr* pdptr, CUipcMemHandle handle, unsigned int flags );
+
+static
+cuIpcOpenMemHandle_t cuIpcOpenMemHandle = 0;
+
+static
+void set_cuIpcOpenMemHandle(void* fnptr)
+{
+    cuIpcOpenMemHandle = (cuIpcOpenMemHandle_t)fnptr;
+}
+
+static
+CUresult call_cuIpcOpenMemHandle(CUdeviceptr* pdptr, CUipcMemHandle* handle, unsigned int flags)
+{
+    return cuIpcOpenMemHandle(pdptr, *handle, flags);
+}
+
+
+MOD_INIT(_extras) {
+    PyObject *m;
+    MOD_DEF(m, "_extras", "No docs", NULL)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+    PyModule_AddObject(m, "set_cuIpcOpenMemHandle", PyLong_FromVoidPtr(&set_cuIpcOpenMemHandle));
+    PyModule_AddObject(m, "call_cuIpcOpenMemHandle", PyLong_FromVoidPtr(&call_cuIpcOpenMemHandle));
+    PyModule_AddIntConstant(m, "CUDA_IPC_HANDLE_SIZE", CUDA_IPC_HANDLE_SIZE);
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/devicearray.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/devicearray.py
new file mode 100644
index 000000000..21adaa94c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/devicearray.py
@@ -0,0 +1,902 @@
+"""
+A CUDA ND Array is recognized by checking the __cuda_memory__ attribute
+on the object.  If it exists and evaluate to True, it must define shape,
+strides, dtype and size attributes similar to a NumPy ndarray.
+"""
+
+import math
+import functools
+import operator
+import copy
+from ctypes import c_void_p
+
+import numpy as np
+
+import numba
+from numba import _devicearray
+from numba.cuda.cudadrv import devices
+from numba.cuda.cudadrv import driver as _driver
+from numba.core import types, config
+from numba.np.unsafe.ndarray import to_fixed_tuple
+from numba.misc import dummyarray
+from numba.np import numpy_support
+from numba.cuda.api_util import prepare_shape_strides_dtype
+from numba.core.errors import NumbaPerformanceWarning
+from warnings import warn
+
+try:
+    lru_cache = getattr(functools, 'lru_cache')(None)
+except AttributeError:
+    # Python 3.1 or lower
+    def lru_cache(func):
+        return func
+
+
+def is_cuda_ndarray(obj):
+    "Check if an object is a CUDA ndarray"
+    return getattr(obj, '__cuda_ndarray__', False)
+
+
+def verify_cuda_ndarray_interface(obj):
+    "Verify the CUDA ndarray interface for an obj"
+    require_cuda_ndarray(obj)
+
+    def requires_attr(attr, typ):
+        if not hasattr(obj, attr):
+            raise AttributeError(attr)
+        if not isinstance(getattr(obj, attr), typ):
+            raise AttributeError('%s must be of type %s' % (attr, typ))
+
+    requires_attr('shape', tuple)
+    requires_attr('strides', tuple)
+    requires_attr('dtype', np.dtype)
+    requires_attr('size', int)
+
+
+def require_cuda_ndarray(obj):
+    "Raises ValueError is is_cuda_ndarray(obj) evaluates False"
+    if not is_cuda_ndarray(obj):
+        raise ValueError('require an cuda ndarray object')
+
+
+class DeviceNDArrayBase(_devicearray.DeviceArray):
+    """A on GPU NDArray representation
+    """
+    __cuda_memory__ = True
+    __cuda_ndarray__ = True     # There must be gpu_data attribute
+
+    def __init__(self, shape, strides, dtype, stream=0, gpu_data=None):
+        """
+        Args
+        ----
+
+        shape
+            array shape.
+        strides
+            array strides.
+        dtype
+            data type as np.dtype coercible object.
+        stream
+            cuda stream.
+        gpu_data
+            user provided device memory for the ndarray data buffer
+        """
+        if isinstance(shape, int):
+            shape = (shape,)
+        if isinstance(strides, int):
+            strides = (strides,)
+        dtype = np.dtype(dtype)
+        self.ndim = len(shape)
+        if len(strides) != self.ndim:
+            raise ValueError('strides not match ndim')
+        self._dummy = dummyarray.Array.from_desc(0, shape, strides,
+                                                 dtype.itemsize)
+        self.shape = tuple(shape)
+        self.strides = tuple(strides)
+        self.dtype = dtype
+        self.size = int(functools.reduce(operator.mul, self.shape, 1))
+        # prepare gpu memory
+        if self.size > 0:
+            if gpu_data is None:
+                self.alloc_size = _driver.memory_size_from_info(
+                    self.shape, self.strides, self.dtype.itemsize)
+                gpu_data = devices.get_context().memalloc(self.alloc_size)
+            else:
+                self.alloc_size = _driver.device_memory_size(gpu_data)
+        else:
+            # Make NULL pointer for empty allocation
+            if _driver.USE_NV_BINDING:
+                null = _driver.binding.CUdeviceptr(0)
+            else:
+                null = c_void_p(0)
+            gpu_data = _driver.MemoryPointer(context=devices.get_context(),
+                                             pointer=null, size=0)
+            self.alloc_size = 0
+
+        self.gpu_data = gpu_data
+        self.stream = stream
+
+    @property
+    def __cuda_array_interface__(self):
+        if _driver.USE_NV_BINDING:
+            if self.device_ctypes_pointer is not None:
+                ptr = int(self.device_ctypes_pointer)
+            else:
+                ptr = 0
+        else:
+            if self.device_ctypes_pointer.value is not None:
+                ptr = self.device_ctypes_pointer.value
+            else:
+                ptr = 0
+
+        return {
+            'shape': tuple(self.shape),
+            'strides': None if is_contiguous(self) else tuple(self.strides),
+            'data': (ptr, False),
+            'typestr': self.dtype.str,
+            'stream': int(self.stream) if self.stream != 0 else None,
+            'version': 3,
+        }
+
+    def bind(self, stream=0):
+        """Bind a CUDA stream to this object so that all subsequent operation
+        on this array defaults to the given stream.
+        """
+        clone = copy.copy(self)
+        clone.stream = stream
+        return clone
+
+    @property
+    def T(self):
+        return self.transpose()
+
+    def transpose(self, axes=None):
+        if axes and tuple(axes) == tuple(range(self.ndim)):
+            return self
+        elif self.ndim != 2:
+            msg = "transposing a non-2D DeviceNDArray isn't supported"
+            raise NotImplementedError(msg)
+        elif axes is not None and set(axes) != set(range(self.ndim)):
+            raise ValueError("invalid axes list %r" % (axes,))
+        else:
+            from numba.cuda.kernels.transpose import transpose
+            return transpose(self)
+
+    def _default_stream(self, stream):
+        return self.stream if not stream else stream
+
+    @property
+    def _numba_type_(self):
+        """
+        Magic attribute expected by Numba to get the numba type that
+        represents this object.
+        """
+        # Typing considerations:
+        #
+        # 1. The preference is to use 'C' or 'F' layout since this enables
+        # hardcoding stride values into compiled kernels, which is more
+        # efficient than storing a passed-in value in a register.
+        #
+        # 2. If an array is both C- and F-contiguous, prefer 'C' layout as it's
+        # the more likely / common case.
+        #
+        # 3. If an array is broadcast then it must be typed as 'A' - using 'C'
+        # or 'F' does not apply for broadcast arrays, because the strides, some
+        # of which will be 0, will not match those hardcoded in for 'C' or 'F'
+        # layouts.
+
+        broadcast = 0 in self.strides
+        if self.flags['C_CONTIGUOUS'] and not broadcast:
+            layout = 'C'
+        elif self.flags['F_CONTIGUOUS'] and not broadcast:
+            layout = 'F'
+        else:
+            layout = 'A'
+
+        dtype = numpy_support.from_dtype(self.dtype)
+        return types.Array(dtype, self.ndim, layout)
+
+    @property
+    def device_ctypes_pointer(self):
+        """Returns the ctypes pointer to the GPU data buffer
+        """
+        if self.gpu_data is None:
+            if _driver.USE_NV_BINDING:
+                return _driver.binding.CUdeviceptr(0)
+            else:
+                return c_void_p(0)
+        else:
+            return self.gpu_data.device_ctypes_pointer
+
+    @devices.require_context
+    def copy_to_device(self, ary, stream=0):
+        """Copy `ary` to `self`.
+
+        If `ary` is a CUDA memory, perform a device-to-device transfer.
+        Otherwise, perform a a host-to-device transfer.
+        """
+        if ary.size == 0:
+            # Nothing to do
+            return
+
+        sentry_contiguous(self)
+        stream = self._default_stream(stream)
+
+        self_core, ary_core = array_core(self), array_core(ary)
+        if _driver.is_device_memory(ary):
+            sentry_contiguous(ary)
+            check_array_compatibility(self_core, ary_core)
+            _driver.device_to_device(self, ary, self.alloc_size, stream=stream)
+        else:
+            # Ensure same contiguity. Only makes a host-side copy if necessary
+            # (i.e., in order to materialize a writable strided view)
+            ary_core = np.array(
+                ary_core,
+                order='C' if self_core.flags['C_CONTIGUOUS'] else 'F',
+                subok=True,
+                copy=not ary_core.flags['WRITEABLE'])
+            check_array_compatibility(self_core, ary_core)
+            _driver.host_to_device(self, ary_core, self.alloc_size,
+                                   stream=stream)
+
+    @devices.require_context
+    def copy_to_host(self, ary=None, stream=0):
+        """Copy ``self`` to ``ary`` or create a new Numpy ndarray
+        if ``ary`` is ``None``.
+
+        If a CUDA ``stream`` is given, then the transfer will be made
+        asynchronously as part as the given stream.  Otherwise, the transfer is
+        synchronous: the function returns after the copy is finished.
+
+        Always returns the host array.
+
+        Example::
+
+            import numpy as np
+            from numba import cuda
+
+            arr = np.arange(1000)
+            d_arr = cuda.to_device(arr)
+
+            my_kernel[100, 100](d_arr)
+
+            result_array = d_arr.copy_to_host()
+        """
+        if any(s < 0 for s in self.strides):
+            msg = 'D->H copy not implemented for negative strides: {}'
+            raise NotImplementedError(msg.format(self.strides))
+        assert self.alloc_size >= 0, "Negative memory size"
+        stream = self._default_stream(stream)
+        if ary is None:
+            hostary = np.empty(shape=self.alloc_size, dtype=np.byte)
+        else:
+            check_array_compatibility(self, ary)
+            hostary = ary
+
+        if self.alloc_size != 0:
+            _driver.device_to_host(hostary, self, self.alloc_size,
+                                   stream=stream)
+
+        if ary is None:
+            if self.size == 0:
+                hostary = np.ndarray(shape=self.shape, dtype=self.dtype,
+                                     buffer=hostary)
+            else:
+                hostary = np.ndarray(shape=self.shape, dtype=self.dtype,
+                                     strides=self.strides, buffer=hostary)
+        return hostary
+
+    def split(self, section, stream=0):
+        """Split the array into equal partition of the `section` size.
+        If the array cannot be equally divided, the last section will be
+        smaller.
+        """
+        stream = self._default_stream(stream)
+        if self.ndim != 1:
+            raise ValueError("only support 1d array")
+        if self.strides[0] != self.dtype.itemsize:
+            raise ValueError("only support unit stride")
+        nsect = int(math.ceil(float(self.size) / section))
+        strides = self.strides
+        itemsize = self.dtype.itemsize
+        for i in range(nsect):
+            begin = i * section
+            end = min(begin + section, self.size)
+            shape = (end - begin,)
+            gpu_data = self.gpu_data.view(begin * itemsize, end * itemsize)
+            yield DeviceNDArray(shape, strides, dtype=self.dtype, stream=stream,
+                                gpu_data=gpu_data)
+
+    def as_cuda_arg(self):
+        """Returns a device memory object that is used as the argument.
+        """
+        return self.gpu_data
+
+    def get_ipc_handle(self):
+        """
+        Returns a *IpcArrayHandle* object that is safe to serialize and transfer
+        to another process to share the local allocation.
+
+        Note: this feature is only available on Linux.
+        """
+        ipch = devices.get_context().get_ipc_handle(self.gpu_data)
+        desc = dict(shape=self.shape, strides=self.strides, dtype=self.dtype)
+        return IpcArrayHandle(ipc_handle=ipch, array_desc=desc)
+
+    def squeeze(self, axis=None, stream=0):
+        """
+        Remove axes of size one from the array shape.
+
+        Parameters
+        ----------
+        axis : None or int or tuple of ints, optional
+            Subset of dimensions to remove. A `ValueError` is raised if an axis
+            with size greater than one is selected. If `None`, all axes with
+            size one are removed.
+        stream : cuda stream or 0, optional
+            Default stream for the returned view of the array.
+
+        Returns
+        -------
+        DeviceNDArray
+            Squeezed view into the array.
+
+        """
+        new_dummy, _ = self._dummy.squeeze(axis=axis)
+        return DeviceNDArray(
+            shape=new_dummy.shape,
+            strides=new_dummy.strides,
+            dtype=self.dtype,
+            stream=self._default_stream(stream),
+            gpu_data=self.gpu_data,
+        )
+
+    def view(self, dtype):
+        """Returns a new object by reinterpretting the dtype without making a
+        copy of the data.
+        """
+        dtype = np.dtype(dtype)
+        shape = list(self.shape)
+        strides = list(self.strides)
+
+        if self.dtype.itemsize != dtype.itemsize:
+            if not self.is_c_contiguous():
+                raise ValueError(
+                    "To change to a dtype of a different size,"
+                    " the array must be C-contiguous"
+                )
+
+            shape[-1], rem = divmod(
+                shape[-1] * self.dtype.itemsize,
+                dtype.itemsize
+            )
+
+            if rem != 0:
+                raise ValueError(
+                    "When changing to a larger dtype,"
+                    " its size must be a divisor of the total size in bytes"
+                    " of the last axis of the array."
+                )
+
+            strides[-1] = dtype.itemsize
+
+        return DeviceNDArray(
+            shape=shape,
+            strides=strides,
+            dtype=dtype,
+            stream=self.stream,
+            gpu_data=self.gpu_data,
+        )
+
+    @property
+    def nbytes(self):
+        # Note: not using `alloc_size`.  `alloc_size` reports memory
+        # consumption of the allocation, not the size of the array
+        # https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.nbytes.html
+        return self.dtype.itemsize * self.size
+
+
+class DeviceRecord(DeviceNDArrayBase):
+    '''
+    An on-GPU record type
+    '''
+    def __init__(self, dtype, stream=0, gpu_data=None):
+        shape = ()
+        strides = ()
+        super(DeviceRecord, self).__init__(shape, strides, dtype, stream,
+                                           gpu_data)
+
+    @property
+    def flags(self):
+        """
+        For `numpy.ndarray` compatibility. Ideally this would return a
+        `np.core.multiarray.flagsobj`, but that needs to be constructed
+        with an existing `numpy.ndarray` (as the C- and F- contiguous flags
+        aren't writeable).
+        """
+        return dict(self._dummy.flags) # defensive copy
+
+    @property
+    def _numba_type_(self):
+        """
+        Magic attribute expected by Numba to get the numba type that
+        represents this object.
+        """
+        return numpy_support.from_dtype(self.dtype)
+
+    @devices.require_context
+    def __getitem__(self, item):
+        return self._do_getitem(item)
+
+    @devices.require_context
+    def getitem(self, item, stream=0):
+        """Do `__getitem__(item)` with CUDA stream
+        """
+        return self._do_getitem(item, stream)
+
+    def _do_getitem(self, item, stream=0):
+        stream = self._default_stream(stream)
+        typ, offset = self.dtype.fields[item]
+        newdata = self.gpu_data.view(offset)
+
+        if typ.shape == ():
+            if typ.names is not None:
+                return DeviceRecord(dtype=typ, stream=stream,
+                                    gpu_data=newdata)
+            else:
+                hostary = np.empty(1, dtype=typ)
+                _driver.device_to_host(dst=hostary, src=newdata,
+                                       size=typ.itemsize,
+                                       stream=stream)
+            return hostary[0]
+        else:
+            shape, strides, dtype = \
+                prepare_shape_strides_dtype(typ.shape,
+                                            None,
+                                            typ.subdtype[0], 'C')
+            return DeviceNDArray(shape=shape, strides=strides,
+                                 dtype=dtype, gpu_data=newdata,
+                                 stream=stream)
+
+    @devices.require_context
+    def __setitem__(self, key, value):
+        return self._do_setitem(key, value)
+
+    @devices.require_context
+    def setitem(self, key, value, stream=0):
+        """Do `__setitem__(key, value)` with CUDA stream
+        """
+        return self._do_setitem(key, value, stream=stream)
+
+    def _do_setitem(self, key, value, stream=0):
+
+        stream = self._default_stream(stream)
+
+        # If the record didn't have a default stream, and the user didn't
+        # provide a stream, then we will use the default stream for the
+        # assignment kernel and synchronize on it.
+        synchronous = not stream
+        if synchronous:
+            ctx = devices.get_context()
+            stream = ctx.get_default_stream()
+
+        # (1) prepare LHS
+
+        typ, offset = self.dtype.fields[key]
+        newdata = self.gpu_data.view(offset)
+
+        lhs = type(self)(dtype=typ, stream=stream, gpu_data=newdata)
+
+        # (2) prepare RHS
+
+        rhs, _ = auto_device(lhs.dtype.type(value), stream=stream)
+
+        # (3) do the copy
+
+        _driver.device_to_device(lhs, rhs, rhs.dtype.itemsize, stream)
+
+        if synchronous:
+            stream.synchronize()
+
+
+@lru_cache
+def _assign_kernel(ndim):
+    """
+    A separate method so we don't need to compile code every assignment (!).
+
+    :param ndim: We need to have static array sizes for cuda.local.array, so
+        bake in the number of dimensions into the kernel
+    """
+    from numba import cuda  # circular!
+
+    if ndim == 0:
+        # the (2, ndim) allocation below is not yet supported, so avoid it
+        @cuda.jit
+        def kernel(lhs, rhs):
+            lhs[()] = rhs[()]
+        return kernel
+
+    @cuda.jit
+    def kernel(lhs, rhs):
+        location = cuda.grid(1)
+
+        n_elements = 1
+        for i in range(lhs.ndim):
+            n_elements *= lhs.shape[i]
+        if location >= n_elements:
+            # bake n_elements into the kernel, better than passing it in
+            # as another argument.
+            return
+
+        # [0, :] is the to-index (into `lhs`)
+        # [1, :] is the from-index (into `rhs`)
+        idx = cuda.local.array(
+            shape=(2, ndim),
+            dtype=types.int64)
+
+        for i in range(ndim - 1, -1, -1):
+            idx[0, i] = location % lhs.shape[i]
+            idx[1, i] = (location % lhs.shape[i]) * (rhs.shape[i] > 1)
+            location //= lhs.shape[i]
+
+        lhs[to_fixed_tuple(idx[0], ndim)] = rhs[to_fixed_tuple(idx[1], ndim)]
+    return kernel
+
+
+class DeviceNDArray(DeviceNDArrayBase):
+    '''
+    An on-GPU array type
+    '''
+    def is_f_contiguous(self):
+        '''
+        Return true if the array is Fortran-contiguous.
+        '''
+        return self._dummy.is_f_contig
+
+    @property
+    def flags(self):
+        """
+        For `numpy.ndarray` compatibility. Ideally this would return a
+        `np.core.multiarray.flagsobj`, but that needs to be constructed
+        with an existing `numpy.ndarray` (as the C- and F- contiguous flags
+        aren't writeable).
+        """
+        return dict(self._dummy.flags) # defensive copy
+
+    def is_c_contiguous(self):
+        '''
+        Return true if the array is C-contiguous.
+        '''
+        return self._dummy.is_c_contig
+
+    def __array__(self, dtype=None):
+        """
+        :return: an `numpy.ndarray`, so copies to the host.
+        """
+        if dtype:
+            return self.copy_to_host().__array__(dtype)
+        else:
+            return self.copy_to_host().__array__()
+
+    def __len__(self):
+        return self.shape[0]
+
+    def reshape(self, *newshape, **kws):
+        """
+        Reshape the array without changing its contents, similarly to
+        :meth:`numpy.ndarray.reshape`. Example::
+
+            d_arr = d_arr.reshape(20, 50, order='F')
+        """
+        if len(newshape) == 1 and isinstance(newshape[0], (tuple, list)):
+            newshape = newshape[0]
+
+        cls = type(self)
+        if newshape == self.shape:
+            # nothing to do
+            return cls(shape=self.shape, strides=self.strides,
+                       dtype=self.dtype, gpu_data=self.gpu_data)
+
+        newarr, extents = self._dummy.reshape(*newshape, **kws)
+
+        if extents == [self._dummy.extent]:
+            return cls(shape=newarr.shape, strides=newarr.strides,
+                       dtype=self.dtype, gpu_data=self.gpu_data)
+        else:
+            raise NotImplementedError("operation requires copying")
+
+    def ravel(self, order='C', stream=0):
+        '''
+        Flatten the array without changing its contents, similar to
+        :meth:`numpy.ndarray.ravel`.
+        '''
+        stream = self._default_stream(stream)
+        cls = type(self)
+        newarr, extents = self._dummy.ravel(order=order)
+
+        if extents == [self._dummy.extent]:
+            return cls(shape=newarr.shape, strides=newarr.strides,
+                       dtype=self.dtype, gpu_data=self.gpu_data,
+                       stream=stream)
+
+        else:
+            raise NotImplementedError("operation requires copying")
+
+    @devices.require_context
+    def __getitem__(self, item):
+        return self._do_getitem(item)
+
+    @devices.require_context
+    def getitem(self, item, stream=0):
+        """Do `__getitem__(item)` with CUDA stream
+        """
+        return self._do_getitem(item, stream)
+
+    def _do_getitem(self, item, stream=0):
+        stream = self._default_stream(stream)
+
+        arr = self._dummy.__getitem__(item)
+        extents = list(arr.iter_contiguous_extent())
+        cls = type(self)
+        if len(extents) == 1:
+            newdata = self.gpu_data.view(*extents[0])
+
+            if not arr.is_array:
+                # Check for structured array type (record)
+                if self.dtype.names is not None:
+                    return DeviceRecord(dtype=self.dtype, stream=stream,
+                                        gpu_data=newdata)
+                else:
+                    # Element indexing
+                    hostary = np.empty(1, dtype=self.dtype)
+                    _driver.device_to_host(dst=hostary, src=newdata,
+                                           size=self._dummy.itemsize,
+                                           stream=stream)
+                return hostary[0]
+            else:
+                return cls(shape=arr.shape, strides=arr.strides,
+                           dtype=self.dtype, gpu_data=newdata, stream=stream)
+        else:
+            newdata = self.gpu_data.view(*arr.extent)
+            return cls(shape=arr.shape, strides=arr.strides,
+                       dtype=self.dtype, gpu_data=newdata, stream=stream)
+
+    @devices.require_context
+    def __setitem__(self, key, value):
+        return self._do_setitem(key, value)
+
+    @devices.require_context
+    def setitem(self, key, value, stream=0):
+        """Do `__setitem__(key, value)` with CUDA stream
+        """
+        return self._do_setitem(key, value, stream=stream)
+
+    def _do_setitem(self, key, value, stream=0):
+
+        stream = self._default_stream(stream)
+
+        # If the array didn't have a default stream, and the user didn't provide
+        # a stream, then we will use the default stream for the assignment
+        # kernel and synchronize on it.
+        synchronous = not stream
+        if synchronous:
+            ctx = devices.get_context()
+            stream = ctx.get_default_stream()
+
+        # (1) prepare LHS
+
+        arr = self._dummy.__getitem__(key)
+        newdata = self.gpu_data.view(*arr.extent)
+
+        if isinstance(arr, dummyarray.Element):
+            # convert to a 0d array
+            shape = ()
+            strides = ()
+        else:
+            shape = arr.shape
+            strides = arr.strides
+
+        lhs = type(self)(
+            shape=shape,
+            strides=strides,
+            dtype=self.dtype,
+            gpu_data=newdata,
+            stream=stream)
+
+        # (2) prepare RHS
+
+        rhs, _ = auto_device(value, stream=stream, user_explicit=True)
+        if rhs.ndim > lhs.ndim:
+            raise ValueError("Can't assign %s-D array to %s-D self" % (
+                rhs.ndim,
+                lhs.ndim))
+        rhs_shape = np.ones(lhs.ndim, dtype=np.int64)
+        # negative indices would not work if rhs.ndim == 0
+        rhs_shape[lhs.ndim - rhs.ndim:] = rhs.shape
+        rhs = rhs.reshape(*rhs_shape)
+        for i, (l, r) in enumerate(zip(lhs.shape, rhs.shape)):
+            if r != 1 and l != r:
+                raise ValueError("Can't copy sequence with size %d to array "
+                                 "axis %d with dimension %d" % ( r, i, l))
+
+        # (3) do the copy
+
+        n_elements = functools.reduce(operator.mul, lhs.shape, 1)
+        _assign_kernel(lhs.ndim).forall(n_elements, stream=stream)(lhs, rhs)
+        if synchronous:
+            stream.synchronize()
+
+
+class IpcArrayHandle(object):
+    """
+    An IPC array handle that can be serialized and transfer to another process
+    in the same machine for share a GPU allocation.
+
+    On the destination process, use the *.open()* method to creates a new
+    *DeviceNDArray* object that shares the allocation from the original process.
+    To release the resources, call the *.close()* method.  After that, the
+    destination can no longer use the shared array object.  (Note: the
+    underlying weakref to the resource is now dead.)
+
+    This object implements the context-manager interface that calls the
+    *.open()* and *.close()* method automatically::
+
+        with the_ipc_array_handle as ipc_array:
+            # use ipc_array here as a normal gpu array object
+            some_code(ipc_array)
+        # ipc_array is dead at this point
+    """
+    def __init__(self, ipc_handle, array_desc):
+        self._array_desc = array_desc
+        self._ipc_handle = ipc_handle
+
+    def open(self):
+        """
+        Returns a new *DeviceNDArray* that shares the allocation from the
+        original process.  Must not be used on the original process.
+        """
+        dptr = self._ipc_handle.open(devices.get_context())
+        return DeviceNDArray(gpu_data=dptr, **self._array_desc)
+
+    def close(self):
+        """
+        Closes the IPC handle to the array.
+        """
+        self._ipc_handle.close()
+
+    def __enter__(self):
+        return self.open()
+
+    def __exit__(self, type, value, traceback):
+        self.close()
+
+
+class MappedNDArray(DeviceNDArrayBase, np.ndarray):
+    """
+    A host array that uses CUDA mapped memory.
+    """
+
+    def device_setup(self, gpu_data, stream=0):
+        self.gpu_data = gpu_data
+        self.stream = stream
+
+
+class ManagedNDArray(DeviceNDArrayBase, np.ndarray):
+    """
+    A host array that uses CUDA managed memory.
+    """
+
+    def device_setup(self, gpu_data, stream=0):
+        self.gpu_data = gpu_data
+        self.stream = stream
+
+
+def from_array_like(ary, stream=0, gpu_data=None):
+    "Create a DeviceNDArray object that is like ary."
+    return DeviceNDArray(ary.shape, ary.strides, ary.dtype, stream=stream,
+                         gpu_data=gpu_data)
+
+
+def from_record_like(rec, stream=0, gpu_data=None):
+    "Create a DeviceRecord object that is like rec."
+    return DeviceRecord(rec.dtype, stream=stream, gpu_data=gpu_data)
+
+
+def array_core(ary):
+    """
+    Extract the repeated core of a broadcast array.
+
+    Broadcast arrays are by definition non-contiguous due to repeated
+    dimensions, i.e., dimensions with stride 0. In order to ascertain memory
+    contiguity and copy the underlying data from such arrays, we must create
+    a view without the repeated dimensions.
+
+    """
+    if not ary.strides or not ary.size:
+        return ary
+    core_index = []
+    for stride in ary.strides:
+        core_index.append(0 if stride == 0 else slice(None))
+    return ary[tuple(core_index)]
+
+
+def is_contiguous(ary):
+    """
+    Returns True iff `ary` is C-style contiguous while ignoring
+    broadcasted and 1-sized dimensions.
+    As opposed to array_core(), it does not call require_context(),
+    which can be quite expensive.
+    """
+    size = ary.dtype.itemsize
+    for shape, stride in zip(reversed(ary.shape), reversed(ary.strides)):
+        if shape > 1 and stride != 0:
+            if size != stride:
+                return False
+            size *= shape
+    return True
+
+
+errmsg_contiguous_buffer = ("Array contains non-contiguous buffer and cannot "
+                            "be transferred as a single memory region. Please "
+                            "ensure contiguous buffer with numpy "
+                            ".ascontiguousarray()")
+
+
+def sentry_contiguous(ary):
+    core = array_core(ary)
+    if not core.flags['C_CONTIGUOUS'] and not core.flags['F_CONTIGUOUS']:
+        raise ValueError(errmsg_contiguous_buffer)
+
+
+def auto_device(obj, stream=0, copy=True, user_explicit=False):
+    """
+    Create a DeviceRecord or DeviceArray like obj and optionally copy data from
+    host to device. If obj already represents device memory, it is returned and
+    no copy is made.
+    """
+    if _driver.is_device_memory(obj):
+        return obj, False
+    elif hasattr(obj, '__cuda_array_interface__'):
+        return numba.cuda.as_cuda_array(obj), False
+    else:
+        if isinstance(obj, np.void):
+            devobj = from_record_like(obj, stream=stream)
+        else:
+            # This allows you to pass non-array objects like constants and
+            # objects implementing the array interface
+            # https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.interface.html
+            # into this function (with no overhead -- copies -- for `obj`s
+            # that are already `ndarray`s.
+            obj = np.array(
+                obj,
+                copy=False,
+                subok=True)
+            sentry_contiguous(obj)
+            devobj = from_array_like(obj, stream=stream)
+        if copy:
+            if config.CUDA_WARN_ON_IMPLICIT_COPY:
+                if (
+                    not user_explicit and
+                    (not isinstance(obj, DeviceNDArray)
+                     and isinstance(obj, np.ndarray))
+                ):
+                    msg = ("Host array used in CUDA kernel will incur "
+                           "copy overhead to/from device.")
+                    warn(NumbaPerformanceWarning(msg))
+            devobj.copy_to_device(obj, stream=stream)
+        return devobj, True
+
+
+def check_array_compatibility(ary1, ary2):
+    ary1sq, ary2sq = ary1.squeeze(), ary2.squeeze()
+    if ary1.dtype != ary2.dtype:
+        raise TypeError('incompatible dtype: %s vs. %s' %
+                        (ary1.dtype, ary2.dtype))
+    if ary1sq.shape != ary2sq.shape:
+        raise ValueError('incompatible shape: %s vs. %s' %
+                         (ary1.shape, ary2.shape))
+    # We check strides only if the size is nonzero, because strides are
+    # irrelevant (and can differ) for zero-length copies.
+    if ary1.size and ary1sq.strides != ary2sq.strides:
+        raise ValueError('incompatible strides: %s vs. %s' %
+                         (ary1.strides, ary2.strides))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/devices.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/devices.py
new file mode 100644
index 000000000..6cc9e2e39
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/devices.py
@@ -0,0 +1,248 @@
+"""
+Expose each GPU devices directly.
+
+This module implements a API that is like the "CUDA runtime" context manager
+for managing CUDA context stack and clean up.  It relies on thread-local globals
+to separate the context stack management of each thread. Contexts are also
+shareable among threads.  Only the main thread can destroy Contexts.
+
+Note:
+- This module must be imported by the main-thread.
+
+"""
+import functools
+import threading
+from contextlib import contextmanager
+
+from .driver import driver, USE_NV_BINDING
+
+
+class _DeviceList(object):
+    def __getattr__(self, attr):
+        # First time looking at "lst" attribute.
+        if attr == "lst":
+            # Device list is not initialized.
+            # Query all CUDA devices.
+            numdev = driver.get_device_count()
+            gpus = [_DeviceContextManager(driver.get_device(devid))
+                    for devid in range(numdev)]
+            # Define "lst" to avoid re-initialization
+            self.lst = gpus
+            return gpus
+
+        # Other attributes
+        return super(_DeviceList, self).__getattr__(attr)
+
+    def __getitem__(self, devnum):
+        '''
+        Returns the context manager for device *devnum*.
+        '''
+        return self.lst[devnum]
+
+    def __str__(self):
+        return ', '.join([str(d) for d in self.lst])
+
+    def __iter__(self):
+        return iter(self.lst)
+
+    def __len__(self):
+        return len(self.lst)
+
+    @property
+    def current(self):
+        """Returns the active device or None if there's no active device
+        """
+        with driver.get_active_context() as ac:
+            devnum = ac.devnum
+            if devnum is not None:
+                return self[devnum]
+
+
+class _DeviceContextManager(object):
+    """
+    Provides a context manager for executing in the context of the chosen
+    device. The normal use of instances of this type is from
+    ``numba.cuda.gpus``. For example, to execute on device 2::
+
+       with numba.cuda.gpus[2]:
+           d_a = numba.cuda.to_device(a)
+
+    to copy the array *a* onto device 2, referred to by *d_a*.
+    """
+
+    def __init__(self, device):
+        self._device = device
+
+    def __getattr__(self, item):
+        return getattr(self._device, item)
+
+    def __enter__(self):
+        _runtime.get_or_create_context(self._device.id)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        # this will verify that we are popping the right device context.
+        self._device.get_primary_context().pop()
+
+    def __str__(self):
+        return "<Managed Device {self.id}>".format(self=self)
+
+
+class _Runtime(object):
+    """Emulate the CUDA runtime context management.
+
+    It owns all Devices and Contexts.
+    Keeps at most one Context per Device
+    """
+
+    def __init__(self):
+        self.gpus = _DeviceList()
+
+        # For caching the attached CUDA Context
+        self._tls = threading.local()
+
+        # Remember the main thread
+        # Only the main thread can *actually* destroy
+        self._mainthread = threading.current_thread()
+
+        # Avoid mutation of runtime state in multithreaded programs
+        self._lock = threading.RLock()
+
+    @contextmanager
+    def ensure_context(self):
+        """Ensure a CUDA context is available inside the context.
+
+        On entrance, queries the CUDA driver for an active CUDA context and
+        attaches it in TLS for subsequent calls so they do not need to query
+        the CUDA driver again.  On exit, detach the CUDA context from the TLS.
+
+        This will allow us to pickup thirdparty activated CUDA context in
+        any top-level Numba CUDA API.
+        """
+        with driver.get_active_context():
+            oldctx = self._get_attached_context()
+            newctx = self.get_or_create_context(None)
+            self._set_attached_context(newctx)
+            try:
+                yield
+            finally:
+                self._set_attached_context(oldctx)
+
+    def get_or_create_context(self, devnum):
+        """Returns the primary context and push+create it if needed
+        for *devnum*.  If *devnum* is None, use the active CUDA context (must
+        be primary) or create a new one with ``devnum=0``.
+        """
+        if devnum is None:
+            attached_ctx = self._get_attached_context()
+            if attached_ctx is None:
+                return self._get_or_create_context_uncached(devnum)
+            else:
+                return attached_ctx
+        else:
+            if USE_NV_BINDING:
+                devnum = int(devnum)
+            return self._activate_context_for(devnum)
+
+    def _get_or_create_context_uncached(self, devnum):
+        """See also ``get_or_create_context(devnum)``.
+        This version does not read the cache.
+        """
+        with self._lock:
+            # Try to get the active context in the CUDA stack or
+            # activate GPU-0 with the primary context
+            with driver.get_active_context() as ac:
+                if not ac:
+                    return self._activate_context_for(0)
+                else:
+                    # Get primary context for the active device
+                    ctx = self.gpus[ac.devnum].get_primary_context()
+                    # Is active context the primary context?
+                    if USE_NV_BINDING:
+                        ctx_handle = int(ctx.handle)
+                        ac_ctx_handle = int(ac.context_handle)
+                    else:
+                        ctx_handle = ctx.handle.value
+                        ac_ctx_handle = ac.context_handle.value
+                    if ctx_handle != ac_ctx_handle:
+                        msg = ('Numba cannot operate on non-primary'
+                               ' CUDA context {:x}')
+                        raise RuntimeError(msg.format(ac_ctx_handle))
+                    # Ensure the context is ready
+                    ctx.prepare_for_use()
+                return ctx
+
+    def _activate_context_for(self, devnum):
+        with self._lock:
+            gpu = self.gpus[devnum]
+            newctx = gpu.get_primary_context()
+            # Detect unexpected context switch
+            cached_ctx = self._get_attached_context()
+            if cached_ctx is not None and cached_ctx is not newctx:
+                raise RuntimeError('Cannot switch CUDA-context.')
+            newctx.push()
+            return newctx
+
+    def _get_attached_context(self):
+        return getattr(self._tls, 'attached_context', None)
+
+    def _set_attached_context(self, ctx):
+        self._tls.attached_context = ctx
+
+    def reset(self):
+        """Clear all contexts in the thread.  Destroy the context if and only
+        if we are in the main thread.
+        """
+        # Pop all active context.
+        while driver.pop_active_context() is not None:
+            pass
+
+        # If it is the main thread
+        if threading.current_thread() == self._mainthread:
+            self._destroy_all_contexts()
+
+    def _destroy_all_contexts(self):
+        # Reset all devices
+        for gpu in self.gpus:
+            gpu.reset()
+
+
+_runtime = _Runtime()
+
+# ================================ PUBLIC API ================================
+
+gpus = _runtime.gpus
+
+
+def get_context(devnum=None):
+    """Get the current device or use a device by device number, and
+    return the CUDA context.
+    """
+    return _runtime.get_or_create_context(devnum)
+
+
+def require_context(fn):
+    """
+    A decorator that ensures a CUDA context is available when *fn* is executed.
+
+    Note: The function *fn* cannot switch CUDA-context.
+    """
+    @functools.wraps(fn)
+    def _require_cuda_context(*args, **kws):
+        with _runtime.ensure_context():
+            return fn(*args, **kws)
+
+    return _require_cuda_context
+
+
+def reset():
+    """Reset the CUDA subsystem for the current thread.
+
+    In the main thread:
+    This removes all CUDA contexts.  Only use this at shutdown or for
+    cleaning up between tests.
+
+    In non-main threads:
+    This clear the CUDA context stack only.
+
+    """
+    _runtime.reset()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/driver.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/driver.py
new file mode 100644
index 000000000..364013a66
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/driver.py
@@ -0,0 +1,3191 @@
+"""
+CUDA driver bridge implementation
+
+NOTE:
+The new driver implementation uses a *_PendingDeallocs* that help prevents a
+crashing the system (particularly OSX) when the CUDA context is corrupted at
+resource deallocation.  The old approach ties resource management directly
+into the object destructor; thus, at corruption of the CUDA context,
+subsequent deallocation could further corrupt the CUDA context and causes the
+system to freeze in some cases.
+
+"""
+
+import sys
+import os
+import ctypes
+import weakref
+import functools
+import warnings
+import logging
+import threading
+import asyncio
+from itertools import product
+from abc import ABCMeta, abstractmethod
+from ctypes import (c_int, byref, c_size_t, c_char, c_char_p, addressof,
+                    c_void_p, c_float, c_uint)
+import contextlib
+import importlib
+import numpy as np
+from collections import namedtuple, deque
+
+from numba import mviewbuf
+from numba.core import utils, serialize, config
+from .error import CudaSupportError, CudaDriverError
+from .drvapi import API_PROTOTYPES
+from .drvapi import cu_occupancy_b2d_size, cu_stream_callback_pyobj, cu_uuid
+from numba.cuda.cudadrv import enums, drvapi, _extras
+
+
+USE_NV_BINDING = config.CUDA_USE_NVIDIA_BINDING
+
+if USE_NV_BINDING:
+    from cuda import cuda as binding, nvrtc
+    # There is no definition of the default stream in the Nvidia bindings (nor
+    # is there at the C/C++ level), so we define it here so we don't need to
+    # use a magic number 0 in places where we want the default stream.
+    CU_STREAM_DEFAULT = 0
+
+MIN_REQUIRED_CC = (3, 5)
+SUPPORTS_IPC = sys.platform.startswith('linux')
+
+
+_py_decref = ctypes.pythonapi.Py_DecRef
+_py_incref = ctypes.pythonapi.Py_IncRef
+_py_decref.argtypes = [ctypes.py_object]
+_py_incref.argtypes = [ctypes.py_object]
+
+
+def make_logger():
+    logger = logging.getLogger(__name__)
+    # is logging configured?
+    if not logger.hasHandlers():
+        # read user config
+        lvl = str(config.CUDA_LOG_LEVEL).upper()
+        lvl = getattr(logging, lvl, None)
+        if not isinstance(lvl, int):
+            # default to critical level
+            lvl = logging.CRITICAL
+        logger.setLevel(lvl)
+        # did user specify a level?
+        if config.CUDA_LOG_LEVEL:
+            # create a simple handler that prints to stderr
+            handler = logging.StreamHandler(sys.stderr)
+            fmt = '== CUDA [%(relativeCreated)d] %(levelname)5s -- %(message)s'
+            handler.setFormatter(logging.Formatter(fmt=fmt))
+            logger.addHandler(handler)
+        else:
+            # otherwise, put a null handler
+            logger.addHandler(logging.NullHandler())
+    return logger
+
+
+class DeadMemoryError(RuntimeError):
+    pass
+
+
+class LinkerError(RuntimeError):
+    pass
+
+
+class NvrtcError(RuntimeError):
+    pass
+
+
+class CudaAPIError(CudaDriverError):
+    def __init__(self, code, msg):
+        self.code = code
+        self.msg = msg
+        super(CudaAPIError, self).__init__(code, msg)
+
+    def __str__(self):
+        return "[%s] %s" % (self.code, self.msg)
+
+
+def find_driver():
+
+    envpath = config.CUDA_DRIVER
+
+    if envpath == '0':
+        # Force fail
+        _raise_driver_not_found()
+
+    # Determine DLL type
+    if sys.platform == 'win32':
+        dlloader = ctypes.WinDLL
+        dldir = ['\\windows\\system32']
+        dlnames = ['nvcuda.dll']
+    elif sys.platform == 'darwin':
+        dlloader = ctypes.CDLL
+        dldir = ['/usr/local/cuda/lib']
+        dlnames = ['libcuda.dylib']
+    else:
+        # Assume to be *nix like
+        dlloader = ctypes.CDLL
+        dldir = ['/usr/lib', '/usr/lib64']
+        dlnames = ['libcuda.so', 'libcuda.so.1']
+
+    if envpath:
+        try:
+            envpath = os.path.abspath(envpath)
+        except ValueError:
+            raise ValueError("NUMBA_CUDA_DRIVER %s is not a valid path" %
+                             envpath)
+        if not os.path.isfile(envpath):
+            raise ValueError("NUMBA_CUDA_DRIVER %s is not a valid file "
+                             "path.  Note it must be a filepath of the .so/"
+                             ".dll/.dylib or the driver" % envpath)
+        candidates = [envpath]
+    else:
+        # First search for the name in the default library path.
+        # If that is not found, try the specific path.
+        candidates = dlnames + [os.path.join(x, y)
+                                for x, y in product(dldir, dlnames)]
+
+    # Load the driver; Collect driver error information
+    path_not_exist = []
+    driver_load_error = []
+
+    for path in candidates:
+        try:
+            dll = dlloader(path)
+        except OSError as e:
+            # Problem opening the DLL
+            path_not_exist.append(not os.path.isfile(path))
+            driver_load_error.append(e)
+        else:
+            return dll
+
+    # Problem loading driver
+    if all(path_not_exist):
+        _raise_driver_not_found()
+    else:
+        errmsg = '\n'.join(str(e) for e in driver_load_error)
+        _raise_driver_error(errmsg)
+
+
+DRIVER_NOT_FOUND_MSG = """
+CUDA driver library cannot be found.
+If you are sure that a CUDA driver is installed,
+try setting environment variable NUMBA_CUDA_DRIVER
+with the file path of the CUDA driver shared library.
+"""
+
+DRIVER_LOAD_ERROR_MSG = """
+Possible CUDA driver libraries are found but error occurred during load:
+%s
+"""
+
+
+def _raise_driver_not_found():
+    raise CudaSupportError(DRIVER_NOT_FOUND_MSG)
+
+
+def _raise_driver_error(e):
+    raise CudaSupportError(DRIVER_LOAD_ERROR_MSG % e)
+
+
+def _build_reverse_error_map():
+    prefix = 'CUDA_ERROR'
+    map = utils.UniqueDict()
+    for name in dir(enums):
+        if name.startswith(prefix):
+            code = getattr(enums, name)
+            map[code] = name
+    return map
+
+
+def _getpid():
+    return os.getpid()
+
+
+ERROR_MAP = _build_reverse_error_map()
+
+
+class Driver(object):
+    """
+    Driver API functions are lazily bound.
+    """
+    _singleton = None
+
+    def __new__(cls):
+        obj = cls._singleton
+        if obj is not None:
+            return obj
+        else:
+            obj = object.__new__(cls)
+            cls._singleton = obj
+        return obj
+
+    def __init__(self):
+        self.devices = utils.UniqueDict()
+        self.is_initialized = False
+        self.initialization_error = None
+        self.pid = None
+        try:
+            if config.DISABLE_CUDA:
+                msg = ("CUDA is disabled due to setting NUMBA_DISABLE_CUDA=1 "
+                       "in the environment, or because CUDA is unsupported on "
+                       "32-bit systems.")
+                raise CudaSupportError(msg)
+            self.lib = find_driver()
+        except CudaSupportError as e:
+            self.is_initialized = True
+            self.initialization_error = e.msg
+
+    def ensure_initialized(self):
+        if self.is_initialized:
+            return
+
+        # lazily initialize logger
+        global _logger
+        _logger = make_logger()
+
+        self.is_initialized = True
+        try:
+            _logger.info('init')
+            self.cuInit(0)
+        except CudaAPIError as e:
+            description = f"{e.msg} ({e.code})"
+            self.initialization_error = description
+            raise CudaSupportError(f"Error at driver init: {description}")
+        else:
+            self.pid = _getpid()
+
+        self._initialize_extras()
+
+    def _initialize_extras(self):
+        if USE_NV_BINDING:
+            # The extras are only needed when using Numba's ctypes bindings
+            return
+
+        # set pointer to original cuIpcOpenMemHandle
+        set_proto = ctypes.CFUNCTYPE(None, c_void_p)
+        set_cuIpcOpenMemHandle = set_proto(_extras.set_cuIpcOpenMemHandle)
+        set_cuIpcOpenMemHandle(self._find_api('cuIpcOpenMemHandle'))
+        # bind caller to cuIpcOpenMemHandle that fixes the ABI
+        call_proto = ctypes.CFUNCTYPE(c_int,
+                                      ctypes.POINTER(drvapi.cu_device_ptr),
+                                      ctypes.POINTER(drvapi.cu_ipc_mem_handle),
+                                      ctypes.c_uint)
+        call_cuIpcOpenMemHandle = call_proto(_extras.call_cuIpcOpenMemHandle)
+        call_cuIpcOpenMemHandle.__name__ = 'call_cuIpcOpenMemHandle'
+        safe_call = self._ctypes_wrap_fn('call_cuIpcOpenMemHandle',
+                                         call_cuIpcOpenMemHandle)
+        # override cuIpcOpenMemHandle
+        self.cuIpcOpenMemHandle = safe_call
+
+    @property
+    def is_available(self):
+        self.ensure_initialized()
+        return self.initialization_error is None
+
+    def __getattr__(self, fname):
+        # First request of a driver API function
+        self.ensure_initialized()
+
+        if self.initialization_error is not None:
+            raise CudaSupportError("Error at driver init: \n%s:" %
+                                   self.initialization_error)
+
+        if USE_NV_BINDING:
+            return self._cuda_python_wrap_fn(fname)
+        else:
+            return self._ctypes_wrap_fn(fname)
+
+    def _ctypes_wrap_fn(self, fname, libfn=None):
+        # Wrap a CUDA driver function by default
+        if libfn is None:
+            try:
+                proto = API_PROTOTYPES[fname]
+            except KeyError:
+                raise AttributeError(fname)
+            restype = proto[0]
+            argtypes = proto[1:]
+
+            # Find function in driver library
+            libfn = self._find_api(fname)
+            libfn.restype = restype
+            libfn.argtypes = argtypes
+
+        def verbose_cuda_api_call(*args):
+            argstr = ", ".join([str(arg) for arg in args])
+            _logger.debug('call driver api: %s(%s)', libfn.__name__, argstr)
+            retcode = libfn(*args)
+            self._check_ctypes_error(fname, retcode)
+
+        def safe_cuda_api_call(*args):
+            _logger.debug('call driver api: %s', libfn.__name__)
+            retcode = libfn(*args)
+            self._check_ctypes_error(fname, retcode)
+
+        if config.CUDA_LOG_API_ARGS:
+            wrapper = verbose_cuda_api_call
+        else:
+            wrapper = safe_cuda_api_call
+
+        safe_call = functools.wraps(libfn)(wrapper)
+        setattr(self, fname, safe_call)
+        return safe_call
+
+    def _cuda_python_wrap_fn(self, fname):
+        libfn = getattr(binding, fname)
+
+        def verbose_cuda_api_call(*args):
+            argstr = ", ".join([str(arg) for arg in args])
+            _logger.debug('call driver api: %s(%s)', libfn.__name__, argstr)
+            return self._check_cuda_python_error(fname, libfn(*args))
+
+        def safe_cuda_api_call(*args):
+            _logger.debug('call driver api: %s', libfn.__name__)
+            return self._check_cuda_python_error(fname, libfn(*args))
+
+        if config.CUDA_LOG_API_ARGS:
+            wrapper = verbose_cuda_api_call
+        else:
+            wrapper = safe_cuda_api_call
+
+        safe_call = functools.wraps(libfn)(wrapper)
+        setattr(self, fname, safe_call)
+        return safe_call
+
+    def _find_api(self, fname):
+        # We use alternatively-named functions for PTDS with the Numba ctypes
+        # binding. For the NVidia binding, it handles linking to the correct
+        # variant.
+        if config.CUDA_PER_THREAD_DEFAULT_STREAM and not USE_NV_BINDING:
+            variants = ('_v2_ptds', '_v2_ptsz', '_ptds', '_ptsz', '_v2', '')
+        else:
+            variants = ('_v2', '')
+
+        for variant in variants:
+            try:
+                return getattr(self.lib, f'{fname}{variant}')
+            except AttributeError:
+                pass
+
+        # Not found.
+        # Delay missing function error to use
+        def absent_function(*args, **kws):
+            raise CudaDriverError(f'Driver missing function: {fname}')
+
+        setattr(self, fname, absent_function)
+        return absent_function
+
+    def _detect_fork(self):
+        if self.pid is not None and _getpid() != self.pid:
+            msg = 'pid %s forked from pid %s after CUDA driver init'
+            _logger.critical(msg, _getpid(), self.pid)
+            raise CudaDriverError("CUDA initialized before forking")
+
+    def _check_ctypes_error(self, fname, retcode):
+        if retcode != enums.CUDA_SUCCESS:
+            errname = ERROR_MAP.get(retcode, "UNKNOWN_CUDA_ERROR")
+            msg = "Call to %s results in %s" % (fname, errname)
+            _logger.error(msg)
+            if retcode == enums.CUDA_ERROR_NOT_INITIALIZED:
+                self._detect_fork()
+            raise CudaAPIError(retcode, msg)
+
+    def _check_cuda_python_error(self, fname, returned):
+        retcode = returned[0]
+        retval = returned[1:]
+        if len(retval) == 1:
+            retval = retval[0]
+
+        if retcode != binding.CUresult.CUDA_SUCCESS:
+            msg = "Call to %s results in %s" % (fname, retcode.name)
+            _logger.error(msg)
+            if retcode == binding.CUresult.CUDA_ERROR_NOT_INITIALIZED:
+                self._detect_fork()
+            raise CudaAPIError(retcode, msg)
+
+        return retval
+
+    def get_device(self, devnum=0):
+        dev = self.devices.get(devnum)
+        if dev is None:
+            dev = Device(devnum)
+            self.devices[devnum] = dev
+        return weakref.proxy(dev)
+
+    def get_device_count(self):
+        if USE_NV_BINDING:
+            return self.cuDeviceGetCount()
+
+        count = c_int()
+        self.cuDeviceGetCount(byref(count))
+        return count.value
+
+    def list_devices(self):
+        """Returns a list of active devices
+        """
+        return list(self.devices.values())
+
+    def reset(self):
+        """Reset all devices
+        """
+        for dev in self.devices.values():
+            dev.reset()
+
+    def pop_active_context(self):
+        """Pop the active CUDA context and return the handle.
+        If no CUDA context is active, return None.
+        """
+        with self.get_active_context() as ac:
+            if ac.devnum is not None:
+                if USE_NV_BINDING:
+                    return driver.cuCtxPopCurrent()
+                else:
+                    popped = drvapi.cu_context()
+                    driver.cuCtxPopCurrent(byref(popped))
+                    return popped
+
+    def get_active_context(self):
+        """Returns an instance of ``_ActiveContext``.
+        """
+        return _ActiveContext()
+
+    def get_version(self):
+        """
+        Returns the CUDA Runtime version as a tuple (major, minor).
+        """
+        if USE_NV_BINDING:
+            version = driver.cuDriverGetVersion()
+        else:
+            dv = ctypes.c_int(0)
+            driver.cuDriverGetVersion(ctypes.byref(dv))
+            version = dv.value
+
+        # The version is encoded as (1000 * major) + (10 * minor)
+        major = version // 1000
+        minor = (version - (major * 1000)) // 10
+        return (major, minor)
+
+
+class _ActiveContext(object):
+    """An contextmanager object to cache active context to reduce dependency
+    on querying the CUDA driver API.
+
+    Once entering the context, it is assumed that the active CUDA context is
+    not changed until the context is exited.
+    """
+    _tls_cache = threading.local()
+
+    def __enter__(self):
+        is_top = False
+        # check TLS cache
+        if hasattr(self._tls_cache, 'ctx_devnum'):
+            hctx, devnum = self._tls_cache.ctx_devnum
+        # Not cached. Query the driver API.
+        else:
+            if USE_NV_BINDING:
+                hctx = driver.cuCtxGetCurrent()
+                if int(hctx) == 0:
+                    hctx = None
+            else:
+                hctx = drvapi.cu_context(0)
+                driver.cuCtxGetCurrent(byref(hctx))
+                hctx = hctx if hctx.value else None
+
+            if hctx is None:
+                devnum = None
+            else:
+                if USE_NV_BINDING:
+                    devnum = int(driver.cuCtxGetDevice())
+                else:
+                    hdevice = drvapi.cu_device()
+                    driver.cuCtxGetDevice(byref(hdevice))
+                    devnum = hdevice.value
+
+                self._tls_cache.ctx_devnum = (hctx, devnum)
+                is_top = True
+
+        self._is_top = is_top
+        self.context_handle = hctx
+        self.devnum = devnum
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self._is_top:
+            delattr(self._tls_cache, 'ctx_devnum')
+
+    def __bool__(self):
+        """Returns True is there's a valid and active CUDA context.
+        """
+        return self.context_handle is not None
+
+    __nonzero__ = __bool__
+
+
+driver = Driver()
+
+
+def _build_reverse_device_attrs():
+    prefix = "CU_DEVICE_ATTRIBUTE_"
+    map = utils.UniqueDict()
+    for name in dir(enums):
+        if name.startswith(prefix):
+            map[name[len(prefix):]] = getattr(enums, name)
+    return map
+
+
+DEVICE_ATTRIBUTES = _build_reverse_device_attrs()
+
+
+class Device(object):
+    """
+    The device object owns the CUDA contexts.  This is owned by the driver
+    object.  User should not construct devices directly.
+    """
+    @classmethod
+    def from_identity(self, identity):
+        """Create Device object from device identity created by
+        ``Device.get_device_identity()``.
+        """
+        for devid in range(driver.get_device_count()):
+            d = driver.get_device(devid)
+            if d.get_device_identity() == identity:
+                return d
+        else:
+            errmsg = (
+                "No device of {} is found. "
+                "Target device may not be visible in this process."
+            ).format(identity)
+            raise RuntimeError(errmsg)
+
+    def __init__(self, devnum):
+        if USE_NV_BINDING:
+            result = driver.cuDeviceGet(devnum)
+            self.id = result
+            got_devnum = int(result)
+        else:
+            result = c_int()
+            driver.cuDeviceGet(byref(result), devnum)
+            got_devnum = result.value
+            self.id = got_devnum
+
+        msg = f"Driver returned device {got_devnum} instead of {devnum}"
+        if devnum != got_devnum:
+            raise RuntimeError(msg)
+
+        self.attributes = {}
+
+        # Read compute capability
+        self.compute_capability = (self.COMPUTE_CAPABILITY_MAJOR,
+                                   self.COMPUTE_CAPABILITY_MINOR)
+
+        # Read name
+        bufsz = 128
+
+        if USE_NV_BINDING:
+            buf = driver.cuDeviceGetName(bufsz, self.id)
+            name = buf.decode('utf-8').rstrip('\0')
+        else:
+            buf = (c_char * bufsz)()
+            driver.cuDeviceGetName(buf, bufsz, self.id)
+            name = buf.value
+
+        self.name = name
+
+        # Read UUID
+        if USE_NV_BINDING:
+            uuid = driver.cuDeviceGetUuid(self.id)
+            uuid_vals = tuple(uuid.bytes)
+        else:
+            uuid = cu_uuid()
+            driver.cuDeviceGetUuid(byref(uuid), self.id)
+            uuid_vals = tuple(bytes(uuid))
+
+        b = '%02x'
+        b2 = b * 2
+        b4 = b * 4
+        b6 = b * 6
+        fmt = f'GPU-{b4}-{b2}-{b2}-{b2}-{b6}'
+        self.uuid = fmt % uuid_vals
+
+        self.primary_context = None
+
+    def get_device_identity(self):
+        return {
+            'pci_domain_id': self.PCI_DOMAIN_ID,
+            'pci_bus_id': self.PCI_BUS_ID,
+            'pci_device_id': self.PCI_DEVICE_ID,
+        }
+
+    def __repr__(self):
+        return "<CUDA device %d '%s'>" % (self.id, self.name)
+
+    def __getattr__(self, attr):
+        """Read attributes lazily
+        """
+        if USE_NV_BINDING:
+            code = getattr(binding.CUdevice_attribute,
+                           f'CU_DEVICE_ATTRIBUTE_{attr}')
+            value = driver.cuDeviceGetAttribute(code, self.id)
+        else:
+            try:
+                code = DEVICE_ATTRIBUTES[attr]
+            except KeyError:
+                raise AttributeError(attr)
+
+            result = c_int()
+            driver.cuDeviceGetAttribute(byref(result), code, self.id)
+            value = result.value
+
+        setattr(self, attr, value)
+        return value
+
+    def __hash__(self):
+        return hash(self.id)
+
+    def __eq__(self, other):
+        if isinstance(other, Device):
+            return self.id == other.id
+        return False
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def get_primary_context(self):
+        """
+        Returns the primary context for the device.
+        Note: it is not pushed to the CPU thread.
+        """
+        if self.primary_context is not None:
+            return self.primary_context
+
+        met_requirement_for_device(self)
+        # create primary context
+        if USE_NV_BINDING:
+            hctx = driver.cuDevicePrimaryCtxRetain(self.id)
+        else:
+            hctx = drvapi.cu_context()
+            driver.cuDevicePrimaryCtxRetain(byref(hctx), self.id)
+
+        ctx = Context(weakref.proxy(self), hctx)
+        self.primary_context = ctx
+        return ctx
+
+    def release_primary_context(self):
+        """
+        Release reference to primary context if it has been retained.
+        """
+        if self.primary_context:
+            driver.cuDevicePrimaryCtxRelease(self.id)
+            self.primary_context = None
+
+    def reset(self):
+        try:
+            if self.primary_context is not None:
+                self.primary_context.reset()
+            self.release_primary_context()
+        finally:
+            # reset at the driver level
+            driver.cuDevicePrimaryCtxReset(self.id)
+
+
+def met_requirement_for_device(device):
+    if device.compute_capability < MIN_REQUIRED_CC:
+        raise CudaSupportError("%s has compute capability < %s" %
+                               (device, MIN_REQUIRED_CC))
+
+
+class BaseCUDAMemoryManager(object, metaclass=ABCMeta):
+    """Abstract base class for External Memory Management (EMM) Plugins."""
+
+    def __init__(self, *args, **kwargs):
+        if 'context' not in kwargs:
+            raise RuntimeError("Memory manager requires a context")
+        self.context = kwargs.pop('context')
+
+    @abstractmethod
+    def memalloc(self, size):
+        """
+        Allocate on-device memory in the current context.
+
+        :param size: Size of allocation in bytes
+        :type size: int
+        :return: A memory pointer instance that owns the allocated memory
+        :rtype: :class:`MemoryPointer`
+        """
+
+    @abstractmethod
+    def memhostalloc(self, size, mapped, portable, wc):
+        """
+        Allocate pinned host memory.
+
+        :param size: Size of the allocation in bytes
+        :type size: int
+        :param mapped: Whether the allocated memory should be mapped into the
+                       CUDA address space.
+        :type mapped: bool
+        :param portable: Whether the memory will be considered pinned by all
+                         contexts, and not just the calling context.
+        :type portable: bool
+        :param wc: Whether to allocate the memory as write-combined.
+        :type wc: bool
+        :return: A memory pointer instance that owns the allocated memory. The
+                 return type depends on whether the region was mapped into
+                 device memory.
+        :rtype: :class:`MappedMemory` or :class:`PinnedMemory`
+        """
+
+    @abstractmethod
+    def mempin(self, owner, pointer, size, mapped):
+        """
+        Pin a region of host memory that is already allocated.
+
+        :param owner: The object that owns the memory.
+        :param pointer: The pointer to the beginning of the region to pin.
+        :type pointer: int
+        :param size: The size of the region in bytes.
+        :type size: int
+        :param mapped: Whether the region should also be mapped into device
+                       memory.
+        :type mapped: bool
+        :return: A memory pointer instance that refers to the allocated
+                 memory.
+        :rtype: :class:`MappedMemory` or :class:`PinnedMemory`
+        """
+
+    @abstractmethod
+    def initialize(self):
+        """
+        Perform any initialization required for the EMM plugin instance to be
+        ready to use.
+
+        :return: None
+        """
+
+    @abstractmethod
+    def get_ipc_handle(self, memory):
+        """
+        Return an IPC handle from a GPU allocation.
+
+        :param memory: Memory for which the IPC handle should be created.
+        :type memory: :class:`MemoryPointer`
+        :return: IPC handle for the allocation
+        :rtype: :class:`IpcHandle`
+        """
+
+    @abstractmethod
+    def get_memory_info(self):
+        """
+        Returns ``(free, total)`` memory in bytes in the context. May raise
+        :class:`NotImplementedError`, if returning such information is not
+        practical (e.g. for a pool allocator).
+
+        :return: Memory info
+        :rtype: :class:`MemoryInfo`
+        """
+
+    @abstractmethod
+    def reset(self):
+        """
+        Clears up all memory allocated in this context.
+
+        :return: None
+        """
+
+    @abstractmethod
+    def defer_cleanup(self):
+        """
+        Returns a context manager that ensures the implementation of deferred
+        cleanup whilst it is active.
+
+        :return: Context manager
+        """
+
+    @property
+    @abstractmethod
+    def interface_version(self):
+        """
+        Returns an integer specifying the version of the EMM Plugin interface
+        supported by the plugin implementation. Should always return 1 for
+        implementations of this version of the specification.
+        """
+
+
+class HostOnlyCUDAMemoryManager(BaseCUDAMemoryManager):
+    """Base class for External Memory Management (EMM) Plugins that only
+    implement on-device allocation. A subclass need not implement the
+    ``memhostalloc`` and ``mempin`` methods.
+
+    This class also implements ``reset`` and ``defer_cleanup`` (see
+    :class:`numba.cuda.BaseCUDAMemoryManager`) for its own internal state
+    management. If an EMM Plugin based on this class also implements these
+    methods, then its implementations of these must also call the method from
+    ``super()`` to give ``HostOnlyCUDAMemoryManager`` an opportunity to do the
+    necessary work for the host allocations it is managing.
+
+    This class does not implement ``interface_version``, as it will always be
+    consistent with the version of Numba in which it is implemented. An EMM
+    Plugin subclassing this class should implement ``interface_version``
+    instead.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.allocations = utils.UniqueDict()
+        self.deallocations = _PendingDeallocs()
+
+    def _attempt_allocation(self, allocator):
+        """
+        Attempt allocation by calling *allocator*.  If an out-of-memory error
+        is raised, the pending deallocations are flushed and the allocation
+        is retried.  If it fails in the second attempt, the error is reraised.
+        """
+        try:
+            return allocator()
+        except CudaAPIError as e:
+            # is out-of-memory?
+            if USE_NV_BINDING:
+                oom_code = binding.CUresult.CUDA_ERROR_OUT_OF_MEMORY
+            else:
+                oom_code = enums.CUDA_ERROR_OUT_OF_MEMORY
+
+            if e.code == oom_code:
+                # clear pending deallocations
+                self.deallocations.clear()
+                # try again
+                return allocator()
+            else:
+                raise
+
+    def memhostalloc(self, size, mapped=False, portable=False,
+                     wc=False):
+        """Implements the allocation of pinned host memory.
+
+        It is recommended that this method is not overridden by EMM Plugin
+        implementations - instead, use the :class:`BaseCUDAMemoryManager`.
+        """
+        flags = 0
+        if mapped:
+            flags |= enums.CU_MEMHOSTALLOC_DEVICEMAP
+        if portable:
+            flags |= enums.CU_MEMHOSTALLOC_PORTABLE
+        if wc:
+            flags |= enums.CU_MEMHOSTALLOC_WRITECOMBINED
+
+        if USE_NV_BINDING:
+            def allocator():
+                return driver.cuMemHostAlloc(size, flags)
+
+            if mapped:
+                pointer = self._attempt_allocation(allocator)
+            else:
+                pointer = allocator()
+
+            alloc_key = pointer
+        else:
+            pointer = c_void_p()
+
+            def allocator():
+                driver.cuMemHostAlloc(byref(pointer), size, flags)
+
+            if mapped:
+                self._attempt_allocation(allocator)
+            else:
+                allocator()
+
+            alloc_key = pointer.value
+
+        finalizer = _hostalloc_finalizer(self, pointer, alloc_key, size, mapped)
+        ctx = weakref.proxy(self.context)
+
+        if mapped:
+            mem = MappedMemory(ctx, pointer, size, finalizer=finalizer)
+            self.allocations[alloc_key] = mem
+            return mem.own()
+        else:
+            return PinnedMemory(ctx, pointer, size, finalizer=finalizer)
+
+    def mempin(self, owner, pointer, size, mapped=False):
+        """Implements the pinning of host memory.
+
+        It is recommended that this method is not overridden by EMM Plugin
+        implementations - instead, use the :class:`BaseCUDAMemoryManager`.
+        """
+        if isinstance(pointer, int) and not USE_NV_BINDING:
+            pointer = c_void_p(pointer)
+
+        if USE_NV_BINDING:
+            alloc_key = pointer
+        else:
+            alloc_key = pointer.value
+
+        # possible flags are "portable" (between context)
+        # and "device-map" (map host memory to device thus no need
+        # for memory transfer).
+        flags = 0
+
+        if mapped:
+            flags |= enums.CU_MEMHOSTREGISTER_DEVICEMAP
+
+        def allocator():
+            driver.cuMemHostRegister(pointer, size, flags)
+
+        if mapped:
+            self._attempt_allocation(allocator)
+        else:
+            allocator()
+
+        finalizer = _pin_finalizer(self, pointer, alloc_key, mapped)
+        ctx = weakref.proxy(self.context)
+
+        if mapped:
+            mem = MappedMemory(ctx, pointer, size, owner=owner,
+                               finalizer=finalizer)
+            self.allocations[alloc_key] = mem
+            return mem.own()
+        else:
+            return PinnedMemory(ctx, pointer, size, owner=owner,
+                                finalizer=finalizer)
+
+    def memallocmanaged(self, size, attach_global):
+        if USE_NV_BINDING:
+            def allocator():
+                ma_flags = binding.CUmemAttach_flags
+
+                if attach_global:
+                    flags = ma_flags.CU_MEM_ATTACH_GLOBAL.value
+                else:
+                    flags = ma_flags.CU_MEM_ATTACH_HOST.value
+
+                return driver.cuMemAllocManaged(size, flags)
+
+            ptr = self._attempt_allocation(allocator)
+
+            alloc_key = ptr
+
+        else:
+            ptr = drvapi.cu_device_ptr()
+
+            def allocator():
+                flags = c_uint()
+                if attach_global:
+                    flags = enums.CU_MEM_ATTACH_GLOBAL
+                else:
+                    flags = enums.CU_MEM_ATTACH_HOST
+
+                driver.cuMemAllocManaged(byref(ptr), size, flags)
+
+            self._attempt_allocation(allocator)
+
+            alloc_key = ptr.value
+
+        finalizer = _alloc_finalizer(self, ptr, alloc_key, size)
+        ctx = weakref.proxy(self.context)
+        mem = ManagedMemory(ctx, ptr, size, finalizer=finalizer)
+        self.allocations[alloc_key] = mem
+        return mem.own()
+
+    def reset(self):
+        """Clears up all host memory (mapped and/or pinned) in the current
+        context.
+
+        EMM Plugins that override this method must call ``super().reset()`` to
+        ensure that host allocations are also cleaned up."""
+        self.allocations.clear()
+        self.deallocations.clear()
+
+    @contextlib.contextmanager
+    def defer_cleanup(self):
+        """Returns a context manager that disables cleanup of mapped or pinned
+        host memory in the current context whilst it is active.
+
+        EMM Plugins that override this method must obtain the context manager
+        from this method before yielding to ensure that cleanup of host
+        allocations is also deferred."""
+        with self.deallocations.disable():
+            yield
+
+
+class GetIpcHandleMixin:
+    """A class that provides a default implementation of ``get_ipc_handle()``.
+    """
+
+    def get_ipc_handle(self, memory):
+        """Open an IPC memory handle by using ``cuMemGetAddressRange`` to
+        determine the base pointer of the allocation. An IPC handle of type
+        ``cu_ipc_mem_handle`` is constructed and initialized with
+        ``cuIpcGetMemHandle``. A :class:`numba.cuda.IpcHandle` is returned,
+        populated with the underlying ``ipc_mem_handle``.
+        """
+        base, end = device_extents(memory)
+        if USE_NV_BINDING:
+            ipchandle = driver.cuIpcGetMemHandle(base)
+            offset = int(memory.handle) - int(base)
+        else:
+            ipchandle = drvapi.cu_ipc_mem_handle()
+            driver.cuIpcGetMemHandle(byref(ipchandle), base)
+            offset = memory.handle.value - base
+        source_info = self.context.device.get_device_identity()
+
+        return IpcHandle(memory, ipchandle, memory.size, source_info,
+                         offset=offset)
+
+
+class NumbaCUDAMemoryManager(GetIpcHandleMixin, HostOnlyCUDAMemoryManager):
+    """Internal on-device memory management for Numba. This is implemented using
+    the EMM Plugin interface, but is not part of the public API."""
+
+    def initialize(self):
+        # Set the memory capacity of *deallocations* as the memory manager
+        # becomes active for the first time
+        if self.deallocations.memory_capacity == _SizeNotSet:
+            self.deallocations.memory_capacity = self.get_memory_info().total
+
+    def memalloc(self, size):
+        if USE_NV_BINDING:
+            def allocator():
+                return driver.cuMemAlloc(size)
+
+            ptr = self._attempt_allocation(allocator)
+            alloc_key = ptr
+        else:
+            ptr = drvapi.cu_device_ptr()
+
+            def allocator():
+                driver.cuMemAlloc(byref(ptr), size)
+
+            self._attempt_allocation(allocator)
+            alloc_key = ptr.value
+
+        finalizer = _alloc_finalizer(self, ptr, alloc_key, size)
+        ctx = weakref.proxy(self.context)
+        mem = AutoFreePointer(ctx, ptr, size, finalizer=finalizer)
+        self.allocations[alloc_key] = mem
+        return mem.own()
+
+    def get_memory_info(self):
+        if USE_NV_BINDING:
+            free, total = driver.cuMemGetInfo()
+        else:
+            free = c_size_t()
+            total = c_size_t()
+            driver.cuMemGetInfo(byref(free), byref(total))
+            free = free.value
+            total = total.value
+
+        return MemoryInfo(free=free, total=total)
+
+    @property
+    def interface_version(self):
+        return _SUPPORTED_EMM_INTERFACE_VERSION
+
+
+_SUPPORTED_EMM_INTERFACE_VERSION = 1
+
+_memory_manager = None
+
+
+def _ensure_memory_manager():
+    global _memory_manager
+
+    if _memory_manager:
+        return
+
+    if config.CUDA_MEMORY_MANAGER == 'default':
+        _memory_manager = NumbaCUDAMemoryManager
+        return
+
+    try:
+        mgr_module = importlib.import_module(config.CUDA_MEMORY_MANAGER)
+        set_memory_manager(mgr_module._numba_memory_manager)
+    except Exception:
+        raise RuntimeError("Failed to use memory manager from %s" %
+                           config.CUDA_MEMORY_MANAGER)
+
+
+def set_memory_manager(mm_plugin):
+    """Configure Numba to use an External Memory Management (EMM) Plugin. If
+    the EMM Plugin version does not match one supported by this version of
+    Numba, a RuntimeError will be raised.
+
+    :param mm_plugin: The class implementing the EMM Plugin.
+    :type mm_plugin: BaseCUDAMemoryManager
+    :return: None
+    """
+    global _memory_manager
+
+    dummy = mm_plugin(context=None)
+    iv = dummy.interface_version
+    if iv != _SUPPORTED_EMM_INTERFACE_VERSION:
+        err = "EMM Plugin interface has version %d - version %d required" \
+              % (iv, _SUPPORTED_EMM_INTERFACE_VERSION)
+        raise RuntimeError(err)
+
+    _memory_manager = mm_plugin
+
+
+class _SizeNotSet(int):
+    """
+    Dummy object for _PendingDeallocs when *size* is not set.
+    """
+
+    def __new__(cls, *args, **kwargs):
+        return super().__new__(cls, 0)
+
+    def __str__(self):
+        return '?'
+
+
+_SizeNotSet = _SizeNotSet()
+
+
+class _PendingDeallocs(object):
+    """
+    Pending deallocations of a context (or device since we are using the primary
+    context). The capacity defaults to being unset (_SizeNotSet) but can be
+    modified later once the driver is initialized and the total memory capacity
+    known.
+    """
+    def __init__(self, capacity=_SizeNotSet):
+        self._cons = deque()
+        self._disable_count = 0
+        self._size = 0
+        self.memory_capacity = capacity
+
+    @property
+    def _max_pending_bytes(self):
+        return int(self.memory_capacity * config.CUDA_DEALLOCS_RATIO)
+
+    def add_item(self, dtor, handle, size=_SizeNotSet):
+        """
+        Add a pending deallocation.
+
+        The *dtor* arg is the destructor function that takes an argument,
+        *handle*.  It is used as ``dtor(handle)``.  The *size* arg is the
+        byte size of the resource added.  It is an optional argument.  Some
+        resources (e.g. CUModule) has an unknown memory footprint on the device.
+        """
+        _logger.info('add pending dealloc: %s %s bytes', dtor.__name__, size)
+        self._cons.append((dtor, handle, size))
+        self._size += int(size)
+        if (len(self._cons) > config.CUDA_DEALLOCS_COUNT or
+                self._size > self._max_pending_bytes):
+            self.clear()
+
+    def clear(self):
+        """
+        Flush any pending deallocations unless it is disabled.
+        Do nothing if disabled.
+        """
+        if not self.is_disabled:
+            while self._cons:
+                [dtor, handle, size] = self._cons.popleft()
+                _logger.info('dealloc: %s %s bytes', dtor.__name__, size)
+                dtor(handle)
+            self._size = 0
+
+    @contextlib.contextmanager
+    def disable(self):
+        """
+        Context manager to temporarily disable flushing pending deallocation.
+        This can be nested.
+        """
+        self._disable_count += 1
+        try:
+            yield
+        finally:
+            self._disable_count -= 1
+            assert self._disable_count >= 0
+
+    @property
+    def is_disabled(self):
+        return self._disable_count > 0
+
+    def __len__(self):
+        """
+        Returns number of pending deallocations.
+        """
+        return len(self._cons)
+
+
+MemoryInfo = namedtuple("MemoryInfo", "free,total")
+"""Free and total memory for a device.
+
+.. py:attribute:: free
+
+   Free device memory in bytes.
+
+.. py:attribute:: total
+
+    Total device memory in bytes.
+"""
+
+
+class Context(object):
+    """
+    This object wraps a CUDA Context resource.
+
+    Contexts should not be constructed directly by user code.
+    """
+
+    def __init__(self, device, handle):
+        self.device = device
+        self.handle = handle
+        self.allocations = utils.UniqueDict()
+        self.deallocations = _PendingDeallocs()
+        _ensure_memory_manager()
+        self.memory_manager = _memory_manager(context=self)
+        self.modules = utils.UniqueDict()
+        # For storing context specific data
+        self.extras = {}
+
+    def reset(self):
+        """
+        Clean up all owned resources in this context.
+        """
+        # Free owned resources
+        _logger.info('reset context of device %s', self.device.id)
+        self.memory_manager.reset()
+        self.modules.clear()
+        # Clear trash
+        self.deallocations.clear()
+
+    def get_memory_info(self):
+        """Returns (free, total) memory in bytes in the context.
+        """
+        return self.memory_manager.get_memory_info()
+
+    def get_active_blocks_per_multiprocessor(self, func, blocksize, memsize,
+                                             flags=None):
+        """Return occupancy of a function.
+        :param func: kernel for which occupancy is calculated
+        :param blocksize: block size the kernel is intended to be launched with
+        :param memsize: per-block dynamic shared memory usage intended, in bytes
+        """
+        args = (func, blocksize, memsize, flags)
+        if USE_NV_BINDING:
+            return self._cuda_python_active_blocks_per_multiprocessor(*args)
+        else:
+            return self._ctypes_active_blocks_per_multiprocessor(*args)
+
+    def _cuda_python_active_blocks_per_multiprocessor(self, func, blocksize,
+                                                      memsize, flags):
+        ps = [func.handle, blocksize, memsize]
+
+        if not flags:
+            return driver.cuOccupancyMaxActiveBlocksPerMultiprocessor(*ps)
+
+        ps.append(flags)
+        return driver.cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(*ps)
+
+    def _ctypes_active_blocks_per_multiprocessor(self, func, blocksize,
+                                                 memsize, flags):
+        retval = c_int()
+        args = (byref(retval), func.handle, blocksize, memsize)
+
+        if not flags:
+            driver.cuOccupancyMaxActiveBlocksPerMultiprocessor(*args)
+        else:
+            driver.cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(*args)
+
+        return retval.value
+
+    def get_max_potential_block_size(self, func, b2d_func, memsize,
+                                     blocksizelimit, flags=None):
+        """Suggest a launch configuration with reasonable occupancy.
+        :param func: kernel for which occupancy is calculated
+        :param b2d_func: function that calculates how much per-block dynamic
+                         shared memory 'func' uses based on the block size.
+                         Can also be the address of a C function.
+                         Use `0` to pass `NULL` to the underlying CUDA API.
+        :param memsize: per-block dynamic shared memory usage intended, in bytes
+        :param blocksizelimit: maximum block size the kernel is designed to
+                               handle
+        """
+        args = (func, b2d_func, memsize, blocksizelimit, flags)
+        if USE_NV_BINDING:
+            return self._cuda_python_max_potential_block_size(*args)
+        else:
+            return self._ctypes_max_potential_block_size(*args)
+
+    def _ctypes_max_potential_block_size(self, func, b2d_func, memsize,
+                                         blocksizelimit, flags):
+        gridsize = c_int()
+        blocksize = c_int()
+        b2d_cb = cu_occupancy_b2d_size(b2d_func)
+        args = [byref(gridsize), byref(blocksize), func.handle, b2d_cb,
+                memsize, blocksizelimit]
+
+        if not flags:
+            driver.cuOccupancyMaxPotentialBlockSize(*args)
+        else:
+            args.append(flags)
+            driver.cuOccupancyMaxPotentialBlockSizeWithFlags(*args)
+
+        return (gridsize.value, blocksize.value)
+
+    def _cuda_python_max_potential_block_size(self, func, b2d_func, memsize,
+                                              blocksizelimit, flags):
+        b2d_cb = ctypes.CFUNCTYPE(c_size_t, c_int)(b2d_func)
+        ptr = int.from_bytes(b2d_cb, byteorder='little')
+        driver_b2d_cb = binding.CUoccupancyB2DSize(ptr)
+        args = [func.handle, driver_b2d_cb, memsize, blocksizelimit]
+
+        if not flags:
+            return driver.cuOccupancyMaxPotentialBlockSize(*args)
+        else:
+            args.append(flags)
+            return driver.cuOccupancyMaxPotentialBlockSizeWithFlags(*args)
+
+    def prepare_for_use(self):
+        """Initialize the context for use.
+        It's safe to be called multiple times.
+        """
+        self.memory_manager.initialize()
+
+    def push(self):
+        """
+        Pushes this context on the current CPU Thread.
+        """
+        driver.cuCtxPushCurrent(self.handle)
+        self.prepare_for_use()
+
+    def pop(self):
+        """
+        Pops this context off the current CPU thread. Note that this context
+        must be at the top of the context stack, otherwise an error will occur.
+        """
+        popped = driver.pop_active_context()
+        if USE_NV_BINDING:
+            assert int(popped) == int(self.handle)
+        else:
+            assert popped.value == self.handle.value
+
+    def memalloc(self, bytesize):
+        return self.memory_manager.memalloc(bytesize)
+
+    def memallocmanaged(self, bytesize, attach_global=True):
+        return self.memory_manager.memallocmanaged(bytesize, attach_global)
+
+    def memhostalloc(self, bytesize, mapped=False, portable=False, wc=False):
+        return self.memory_manager.memhostalloc(bytesize, mapped, portable, wc)
+
+    def mempin(self, owner, pointer, size, mapped=False):
+        if mapped and not self.device.CAN_MAP_HOST_MEMORY:
+            raise CudaDriverError("%s cannot map host memory" % self.device)
+        return self.memory_manager.mempin(owner, pointer, size, mapped)
+
+    def get_ipc_handle(self, memory):
+        """
+        Returns an *IpcHandle* from a GPU allocation.
+        """
+        if not SUPPORTS_IPC:
+            raise OSError('OS does not support CUDA IPC')
+        return self.memory_manager.get_ipc_handle(memory)
+
+    def open_ipc_handle(self, handle, size):
+        # open the IPC handle to get the device pointer
+        flags = 1  # CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS
+        if USE_NV_BINDING:
+            dptr = driver.cuIpcOpenMemHandle(handle, flags)
+        else:
+            dptr = drvapi.cu_device_ptr()
+            driver.cuIpcOpenMemHandle(byref(dptr), handle, flags)
+
+        # wrap it
+        return MemoryPointer(context=weakref.proxy(self), pointer=dptr,
+                             size=size)
+
+    def enable_peer_access(self, peer_context, flags=0):
+        """Enable peer access between the current context and the peer context
+        """
+        assert flags == 0, '*flags* is reserved and MUST be zero'
+        driver.cuCtxEnablePeerAccess(peer_context, flags)
+
+    def can_access_peer(self, peer_device):
+        """Returns a bool indicating whether the peer access between the
+        current and peer device is possible.
+        """
+        if USE_NV_BINDING:
+            peer_device = binding.CUdevice(peer_device)
+            can_access_peer = driver.cuDeviceCanAccessPeer(self.device.id,
+                                                           peer_device)
+        else:
+            can_access_peer = c_int()
+            driver.cuDeviceCanAccessPeer(byref(can_access_peer),
+                                         self.device.id, peer_device,)
+
+        return bool(can_access_peer)
+
+    def create_module_ptx(self, ptx):
+        if isinstance(ptx, str):
+            ptx = ptx.encode('utf8')
+        if USE_NV_BINDING:
+            image = ptx
+        else:
+            image = c_char_p(ptx)
+        return self.create_module_image(image)
+
+    def create_module_image(self, image):
+        module = load_module_image(self, image)
+        if USE_NV_BINDING:
+            key = module.handle
+        else:
+            key = module.handle.value
+        self.modules[key] = module
+        return weakref.proxy(module)
+
+    def unload_module(self, module):
+        if USE_NV_BINDING:
+            key = module.handle
+        else:
+            key = module.handle.value
+        del self.modules[key]
+
+    def get_default_stream(self):
+        if USE_NV_BINDING:
+            handle = binding.CUstream(CU_STREAM_DEFAULT)
+        else:
+            handle = drvapi.cu_stream(drvapi.CU_STREAM_DEFAULT)
+        return Stream(weakref.proxy(self), handle, None)
+
+    def get_legacy_default_stream(self):
+        if USE_NV_BINDING:
+            handle = binding.CUstream(binding.CU_STREAM_LEGACY)
+        else:
+            handle = drvapi.cu_stream(drvapi.CU_STREAM_LEGACY)
+        return Stream(weakref.proxy(self), handle, None)
+
+    def get_per_thread_default_stream(self):
+        if USE_NV_BINDING:
+            handle = binding.CUstream(binding.CU_STREAM_PER_THREAD)
+        else:
+            handle = drvapi.cu_stream(drvapi.CU_STREAM_PER_THREAD)
+        return Stream(weakref.proxy(self), handle, None)
+
+    def create_stream(self):
+        if USE_NV_BINDING:
+            # The default stream creation flag, specifying that the created
+            # stream synchronizes with stream 0 (this is different from the
+            # default stream, which we define also as CU_STREAM_DEFAULT when
+            # the NV binding is in use).
+            flags = binding.CUstream_flags.CU_STREAM_DEFAULT.value
+            handle = driver.cuStreamCreate(flags)
+        else:
+            handle = drvapi.cu_stream()
+            driver.cuStreamCreate(byref(handle), 0)
+        return Stream(weakref.proxy(self), handle,
+                      _stream_finalizer(self.deallocations, handle))
+
+    def create_external_stream(self, ptr):
+        if not isinstance(ptr, int):
+            raise TypeError("ptr for external stream must be an int")
+        if USE_NV_BINDING:
+            handle = binding.CUstream(ptr)
+        else:
+            handle = drvapi.cu_stream(ptr)
+        return Stream(weakref.proxy(self), handle, None,
+                      external=True)
+
+    def create_event(self, timing=True):
+        flags = 0
+        if not timing:
+            flags |= enums.CU_EVENT_DISABLE_TIMING
+        if USE_NV_BINDING:
+            handle = driver.cuEventCreate(flags)
+        else:
+            handle = drvapi.cu_event()
+            driver.cuEventCreate(byref(handle), flags)
+        return Event(weakref.proxy(self), handle,
+                     finalizer=_event_finalizer(self.deallocations, handle))
+
+    def synchronize(self):
+        driver.cuCtxSynchronize()
+
+    @contextlib.contextmanager
+    def defer_cleanup(self):
+        with self.memory_manager.defer_cleanup():
+            with self.deallocations.disable():
+                yield
+
+    def __repr__(self):
+        return "<CUDA context %s of device %d>" % (self.handle, self.device.id)
+
+    def __eq__(self, other):
+        if isinstance(other, Context):
+            return self.handle == other.handle
+        else:
+            return NotImplemented
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+
+def load_module_image(context, image):
+    """
+    image must be a pointer
+    """
+    if USE_NV_BINDING:
+        return load_module_image_cuda_python(context, image)
+    else:
+        return load_module_image_ctypes(context, image)
+
+
+def load_module_image_ctypes(context, image):
+    logsz = config.CUDA_LOG_SIZE
+
+    jitinfo = (c_char * logsz)()
+    jiterrors = (c_char * logsz)()
+
+    options = {
+        enums.CU_JIT_INFO_LOG_BUFFER: addressof(jitinfo),
+        enums.CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES: c_void_p(logsz),
+        enums.CU_JIT_ERROR_LOG_BUFFER: addressof(jiterrors),
+        enums.CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES: c_void_p(logsz),
+        enums.CU_JIT_LOG_VERBOSE: c_void_p(config.CUDA_VERBOSE_JIT_LOG),
+    }
+
+    option_keys = (drvapi.cu_jit_option * len(options))(*options.keys())
+    option_vals = (c_void_p * len(options))(*options.values())
+
+    handle = drvapi.cu_module()
+    try:
+        driver.cuModuleLoadDataEx(byref(handle), image, len(options),
+                                  option_keys, option_vals)
+    except CudaAPIError as e:
+        msg = "cuModuleLoadDataEx error:\n%s" % jiterrors.value.decode("utf8")
+        raise CudaAPIError(e.code, msg)
+
+    info_log = jitinfo.value
+
+    return CtypesModule(weakref.proxy(context), handle, info_log,
+                        _module_finalizer(context, handle))
+
+
+def load_module_image_cuda_python(context, image):
+    """
+    image must be a pointer
+    """
+    logsz = config.CUDA_LOG_SIZE
+
+    jitinfo = bytearray(logsz)
+    jiterrors = bytearray(logsz)
+
+    jit_option = binding.CUjit_option
+    options = {
+        jit_option.CU_JIT_INFO_LOG_BUFFER: jitinfo,
+        jit_option.CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES: logsz,
+        jit_option.CU_JIT_ERROR_LOG_BUFFER: jiterrors,
+        jit_option.CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES: logsz,
+        jit_option.CU_JIT_LOG_VERBOSE: config.CUDA_VERBOSE_JIT_LOG,
+    }
+
+    option_keys = [k for k in options.keys()]
+    option_vals = [v for v in options.values()]
+
+    try:
+        handle = driver.cuModuleLoadDataEx(image, len(options), option_keys,
+                                           option_vals)
+    except CudaAPIError as e:
+        err_string = jiterrors.decode('utf-8')
+        msg = "cuModuleLoadDataEx error:\n%s" % err_string
+        raise CudaAPIError(e.code, msg)
+
+    info_log = jitinfo.decode('utf-8')
+
+    return CudaPythonModule(weakref.proxy(context), handle, info_log,
+                            _module_finalizer(context, handle))
+
+
+def _alloc_finalizer(memory_manager, ptr, alloc_key, size):
+    allocations = memory_manager.allocations
+    deallocations = memory_manager.deallocations
+
+    def core():
+        if allocations:
+            del allocations[alloc_key]
+        deallocations.add_item(driver.cuMemFree, ptr, size)
+
+    return core
+
+
+def _hostalloc_finalizer(memory_manager, ptr, alloc_key, size, mapped):
+    """
+    Finalize page-locked host memory allocated by `context.memhostalloc`.
+
+    This memory is managed by CUDA, and finalization entails deallocation. The
+    issues noted in `_pin_finalizer` are not relevant in this case, and the
+    finalization is placed in the `context.deallocations` queue along with
+    finalization of device objects.
+
+    """
+    allocations = memory_manager.allocations
+    deallocations = memory_manager.deallocations
+    if not mapped:
+        size = _SizeNotSet
+
+    def core():
+        if mapped and allocations:
+            del allocations[alloc_key]
+        deallocations.add_item(driver.cuMemFreeHost, ptr, size)
+
+    return core
+
+
+def _pin_finalizer(memory_manager, ptr, alloc_key, mapped):
+    """
+    Finalize temporary page-locking of host memory by `context.mempin`.
+
+    This applies to memory not otherwise managed by CUDA. Page-locking can
+    be requested multiple times on the same memory, and must therefore be
+    lifted as soon as finalization is requested, otherwise subsequent calls to
+    `mempin` may fail with `CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED`, leading
+    to unexpected behavior for the context managers `cuda.{pinned,mapped}`.
+    This function therefore carries out finalization immediately, bypassing the
+    `context.deallocations` queue.
+
+    """
+    allocations = memory_manager.allocations
+
+    def core():
+        if mapped and allocations:
+            del allocations[alloc_key]
+        driver.cuMemHostUnregister(ptr)
+
+    return core
+
+
+def _event_finalizer(deallocs, handle):
+    def core():
+        deallocs.add_item(driver.cuEventDestroy, handle)
+
+    return core
+
+
+def _stream_finalizer(deallocs, handle):
+    def core():
+        deallocs.add_item(driver.cuStreamDestroy, handle)
+
+    return core
+
+
+def _module_finalizer(context, handle):
+    dealloc = context.deallocations
+    modules = context.modules
+
+    if USE_NV_BINDING:
+        key = handle
+    else:
+        key = handle.value
+
+    def core():
+        shutting_down = utils.shutting_down  # early bind
+
+        def module_unload(handle):
+            # If we are not shutting down, we must be called due to
+            # Context.reset() of Context.unload_module().  Both must have
+            # cleared the module reference from the context.
+            assert shutting_down() or key not in modules
+            driver.cuModuleUnload(handle)
+
+        dealloc.add_item(module_unload, handle)
+
+    return core
+
+
+class _CudaIpcImpl(object):
+    """Implementation of GPU IPC using CUDA driver API.
+    This requires the devices to be peer accessible.
+    """
+    def __init__(self, parent):
+        self.base = parent.base
+        self.handle = parent.handle
+        self.size = parent.size
+        self.offset = parent.offset
+        # remember if the handle is already opened
+        self._opened_mem = None
+
+    def open(self, context):
+        """
+        Import the IPC memory and returns a raw CUDA memory pointer object
+        """
+        if self.base is not None:
+            raise ValueError('opening IpcHandle from original process')
+
+        if self._opened_mem is not None:
+            raise ValueError('IpcHandle is already opened')
+
+        mem = context.open_ipc_handle(self.handle, self.offset + self.size)
+        # this object owns the opened allocation
+        # note: it is required the memory be freed after the ipc handle is
+        #       closed by the importing context.
+        self._opened_mem = mem
+        return mem.own().view(self.offset)
+
+    def close(self):
+        if self._opened_mem is None:
+            raise ValueError('IpcHandle not opened')
+        driver.cuIpcCloseMemHandle(self._opened_mem.handle)
+        self._opened_mem = None
+
+
+class _StagedIpcImpl(object):
+    """Implementation of GPU IPC using custom staging logic to workaround
+    CUDA IPC limitation on peer accessibility between devices.
+    """
+    def __init__(self, parent, source_info):
+        self.parent = parent
+        self.base = parent.base
+        self.handle = parent.handle
+        self.size = parent.size
+        self.source_info = source_info
+
+    def open(self, context):
+        from numba import cuda
+
+        srcdev = Device.from_identity(self.source_info)
+        if USE_NV_BINDING:
+            srcdev_id = int(srcdev.id)
+        else:
+            srcdev_id = srcdev.id
+
+        impl = _CudaIpcImpl(parent=self.parent)
+        # Open context on the source device.
+        with cuda.gpus[srcdev_id]:
+            source_ptr = impl.open(cuda.devices.get_context())
+
+        # Allocate GPU buffer.
+        newmem = context.memalloc(self.size)
+        # Do D->D from the source peer-context
+        # This performs automatic host staging
+        device_to_device(newmem, source_ptr, self.size)
+
+        # Cleanup source context
+        with cuda.gpus[srcdev_id]:
+            impl.close()
+
+        return newmem
+
+    def close(self):
+        # Nothing has to be done here
+        pass
+
+
+class IpcHandle(object):
+    """
+    CUDA IPC handle. Serialization of the CUDA IPC handle object is implemented
+    here.
+
+    :param base: A reference to the original allocation to keep it alive
+    :type base: MemoryPointer
+    :param handle: The CUDA IPC handle, as a ctypes array of bytes.
+    :param size: Size of the original allocation
+    :type size: int
+    :param source_info: The identity of the device on which the IPC handle was
+                        opened.
+    :type source_info: dict
+    :param offset: The offset into the underlying allocation of the memory
+                   referred to by this IPC handle.
+    :type offset: int
+    """
+    def __init__(self, base, handle, size, source_info=None, offset=0):
+        self.base = base
+        self.handle = handle
+        self.size = size
+        self.source_info = source_info
+        self._impl = None
+        self.offset = offset
+
+    def _sentry_source_info(self):
+        if self.source_info is None:
+            raise RuntimeError("IPC handle doesn't have source info")
+
+    def can_access_peer(self, context):
+        """Returns a bool indicating whether the active context can peer
+        access the IPC handle
+        """
+        self._sentry_source_info()
+        if self.source_info == context.device.get_device_identity():
+            return True
+        source_device = Device.from_identity(self.source_info)
+        return context.can_access_peer(source_device.id)
+
+    def open_staged(self, context):
+        """Open the IPC by allowing staging on the host memory first.
+        """
+        self._sentry_source_info()
+
+        if self._impl is not None:
+            raise ValueError('IpcHandle is already opened')
+
+        self._impl = _StagedIpcImpl(self, self.source_info)
+        return self._impl.open(context)
+
+    def open_direct(self, context):
+        """
+        Import the IPC memory and returns a raw CUDA memory pointer object
+        """
+        if self._impl is not None:
+            raise ValueError('IpcHandle is already opened')
+
+        self._impl = _CudaIpcImpl(self)
+        return self._impl.open(context)
+
+    def open(self, context):
+        """Open the IPC handle and import the memory for usage in the given
+        context.  Returns a raw CUDA memory pointer object.
+
+        This is enhanced over CUDA IPC that it will work regardless of whether
+        the source device is peer-accessible by the destination device.
+        If the devices are peer-accessible, it uses .open_direct().
+        If the devices are not peer-accessible, it uses .open_staged().
+        """
+        if self.source_info is None or self.can_access_peer(context):
+            fn = self.open_direct
+        else:
+            fn = self.open_staged
+        return fn(context)
+
+    def open_array(self, context, shape, dtype, strides=None):
+        """
+        Similar to `.open()` but returns an device array.
+        """
+        from . import devicearray
+
+        # by default, set strides to itemsize
+        if strides is None:
+            strides = dtype.itemsize
+        dptr = self.open(context)
+        # read the device pointer as an array
+        return devicearray.DeviceNDArray(shape=shape, strides=strides,
+                                         dtype=dtype, gpu_data=dptr)
+
+    def close(self):
+        if self._impl is None:
+            raise ValueError('IpcHandle not opened')
+        self._impl.close()
+        self._impl = None
+
+    def __reduce__(self):
+        # Preprocess the IPC handle, which is defined as a byte array.
+        if USE_NV_BINDING:
+            preprocessed_handle = self.handle.reserved
+        else:
+            preprocessed_handle = tuple(self.handle)
+        args = (
+            self.__class__,
+            preprocessed_handle,
+            self.size,
+            self.source_info,
+            self.offset,
+        )
+        return (serialize._rebuild_reduction, args)
+
+    @classmethod
+    def _rebuild(cls, handle_ary, size, source_info, offset):
+        if USE_NV_BINDING:
+            handle = binding.CUipcMemHandle()
+            handle.reserved = handle_ary
+        else:
+            handle = drvapi.cu_ipc_mem_handle(*handle_ary)
+        return cls(base=None, handle=handle, size=size,
+                   source_info=source_info, offset=offset)
+
+
+class MemoryPointer(object):
+    """A memory pointer that owns a buffer, with an optional finalizer. Memory
+    pointers provide reference counting, and instances are initialized with a
+    reference count of 1.
+
+    The base ``MemoryPointer`` class does not use the
+    reference count for managing the buffer lifetime. Instead, the buffer
+    lifetime is tied to the memory pointer instance's lifetime:
+
+    - When the instance is deleted, the finalizer will be called.
+    - When the reference count drops to 0, no action is taken.
+
+    Subclasses of ``MemoryPointer`` may modify these semantics, for example to
+    tie the buffer lifetime to the reference count, so that the buffer is freed
+    when there are no more references.
+
+    :param context: The context in which the pointer was allocated.
+    :type context: Context
+    :param pointer: The address of the buffer.
+    :type pointer: ctypes.c_void_p
+    :param size: The size of the allocation in bytes.
+    :type size: int
+    :param owner: The owner is sometimes set by the internals of this class, or
+                  used for Numba's internal memory management. It should not be
+                  provided by an external user of the ``MemoryPointer`` class
+                  (e.g. from within an EMM Plugin); the default of `None`
+                  should always suffice.
+    :type owner: NoneType
+    :param finalizer: A function that is called when the buffer is to be freed.
+    :type finalizer: function
+    """
+    __cuda_memory__ = True
+
+    def __init__(self, context, pointer, size, owner=None, finalizer=None):
+        self.context = context
+        self.device_pointer = pointer
+        self.size = size
+        self._cuda_memsize_ = size
+        self.is_managed = finalizer is not None
+        self.refct = 1
+        self.handle = self.device_pointer
+        self._owner = owner
+
+        if finalizer is not None:
+            self._finalizer = weakref.finalize(self, finalizer)
+
+    @property
+    def owner(self):
+        return self if self._owner is None else self._owner
+
+    def own(self):
+        return OwnedPointer(weakref.proxy(self))
+
+    def free(self):
+        """
+        Forces the device memory to the trash.
+        """
+        if self.is_managed:
+            if not self._finalizer.alive:
+                raise RuntimeError("Freeing dead memory")
+            self._finalizer()
+            assert not self._finalizer.alive
+
+    def memset(self, byte, count=None, stream=0):
+        count = self.size if count is None else count
+        if stream:
+            driver.cuMemsetD8Async(self.device_pointer, byte, count,
+                                   stream.handle)
+        else:
+            driver.cuMemsetD8(self.device_pointer, byte, count)
+
+    def view(self, start, stop=None):
+        if stop is None:
+            size = self.size - start
+        else:
+            size = stop - start
+
+        # Handle NULL/empty memory buffer
+        if not self.device_pointer_value:
+            if size != 0:
+                raise RuntimeError("non-empty slice into empty slice")
+            view = self      # new view is just a reference to self
+        # Handle normal case
+        else:
+            base = self.device_pointer_value + start
+            if size < 0:
+                raise RuntimeError('size cannot be negative')
+            if USE_NV_BINDING:
+                pointer = binding.CUdeviceptr()
+                ctypes_ptr = drvapi.cu_device_ptr.from_address(pointer.getPtr())
+                ctypes_ptr.value = base
+            else:
+                pointer = drvapi.cu_device_ptr(base)
+            view = MemoryPointer(self.context, pointer, size, owner=self.owner)
+
+        if isinstance(self.owner, (MemoryPointer, OwnedPointer)):
+            # Owned by a numba-managed memory segment, take an owned reference
+            return OwnedPointer(weakref.proxy(self.owner), view)
+        else:
+            # Owned by external alloc, return view with same external owner
+            return view
+
+    @property
+    def device_ctypes_pointer(self):
+        return self.device_pointer
+
+    @property
+    def device_pointer_value(self):
+        if USE_NV_BINDING:
+            return int(self.device_pointer) or None
+        else:
+            return self.device_pointer.value
+
+
+class AutoFreePointer(MemoryPointer):
+    """Modifies the ownership semantic of the MemoryPointer so that the
+    instance lifetime is directly tied to the number of references.
+
+    When the reference count reaches zero, the finalizer is invoked.
+
+    Constructor arguments are the same as for :class:`MemoryPointer`.
+    """
+    def __init__(self, *args, **kwargs):
+        super(AutoFreePointer, self).__init__(*args, **kwargs)
+        # Releease the self reference to the buffer, so that the finalizer
+        # is invoked if all the derived pointers are gone.
+        self.refct -= 1
+
+
+class MappedMemory(AutoFreePointer):
+    """A memory pointer that refers to a buffer on the host that is mapped into
+    device memory.
+
+    :param context: The context in which the pointer was mapped.
+    :type context: Context
+    :param pointer: The address of the buffer.
+    :type pointer: ctypes.c_void_p
+    :param size: The size of the buffer in bytes.
+    :type size: int
+    :param owner: The owner is sometimes set by the internals of this class, or
+                  used for Numba's internal memory management. It should not be
+                  provided by an external user of the ``MappedMemory`` class
+                  (e.g. from within an EMM Plugin); the default of `None`
+                  should always suffice.
+    :type owner: NoneType
+    :param finalizer: A function that is called when the buffer is to be freed.
+    :type finalizer: function
+    """
+
+    __cuda_memory__ = True
+
+    def __init__(self, context, pointer, size, owner=None, finalizer=None):
+        self.owned = owner
+        self.host_pointer = pointer
+
+        if USE_NV_BINDING:
+            devptr = driver.cuMemHostGetDevicePointer(pointer, 0)
+            self._bufptr_ = self.host_pointer
+        else:
+            devptr = drvapi.cu_device_ptr()
+            driver.cuMemHostGetDevicePointer(byref(devptr), pointer, 0)
+            self._bufptr_ = self.host_pointer.value
+
+        self.device_pointer = devptr
+        super(MappedMemory, self).__init__(context, devptr, size,
+                                           finalizer=finalizer)
+        self.handle = self.host_pointer
+
+        # For buffer interface
+        self._buflen_ = self.size
+
+    def own(self):
+        return MappedOwnedPointer(weakref.proxy(self))
+
+
+class PinnedMemory(mviewbuf.MemAlloc):
+    """A pointer to a pinned buffer on the host.
+
+    :param context: The context in which the pointer was mapped.
+    :type context: Context
+    :param owner: The object owning the memory. For EMM plugin implementation,
+                  this ca
+    :param pointer: The address of the buffer.
+    :type pointer: ctypes.c_void_p
+    :param size: The size of the buffer in bytes.
+    :type size: int
+    :param owner: An object owning the buffer that has been pinned. For EMM
+                  plugin implementation, the default of ``None`` suffices for
+                  memory allocated in ``memhostalloc`` - for ``mempin``, it
+                  should be the owner passed in to the ``mempin`` method.
+    :param finalizer: A function that is called when the buffer is to be freed.
+    :type finalizer: function
+    """
+
+    def __init__(self, context, pointer, size, owner=None, finalizer=None):
+        self.context = context
+        self.owned = owner
+        self.size = size
+        self.host_pointer = pointer
+        self.is_managed = finalizer is not None
+        self.handle = self.host_pointer
+
+        # For buffer interface
+        self._buflen_ = self.size
+        if USE_NV_BINDING:
+            self._bufptr_ = self.host_pointer
+        else:
+            self._bufptr_ = self.host_pointer.value
+
+        if finalizer is not None:
+            weakref.finalize(self, finalizer)
+
+    def own(self):
+        return self
+
+
+class ManagedMemory(AutoFreePointer):
+    """A memory pointer that refers to a managed memory buffer (can be accessed
+    on both host and device).
+
+    :param context: The context in which the pointer was mapped.
+    :type context: Context
+    :param pointer: The address of the buffer.
+    :type pointer: ctypes.c_void_p
+    :param size: The size of the buffer in bytes.
+    :type size: int
+    :param owner: The owner is sometimes set by the internals of this class, or
+                  used for Numba's internal memory management. It should not be
+                  provided by an external user of the ``ManagedMemory`` class
+                  (e.g. from within an EMM Plugin); the default of `None`
+                  should always suffice.
+    :type owner: NoneType
+    :param finalizer: A function that is called when the buffer is to be freed.
+    :type finalizer: function
+    """
+
+    __cuda_memory__ = True
+
+    def __init__(self, context, pointer, size, owner=None, finalizer=None):
+        self.owned = owner
+        devptr = pointer
+        super().__init__(context, devptr, size, finalizer=finalizer)
+
+        # For buffer interface
+        self._buflen_ = self.size
+        if USE_NV_BINDING:
+            self._bufptr_ = self.device_pointer
+        else:
+            self._bufptr_ = self.device_pointer.value
+
+    def own(self):
+        return ManagedOwnedPointer(weakref.proxy(self))
+
+
+class OwnedPointer(object):
+    def __init__(self, memptr, view=None):
+        self._mem = memptr
+
+        if view is None:
+            self._view = self._mem
+        else:
+            assert not view.is_managed
+            self._view = view
+
+        mem = self._mem
+
+        def deref():
+            try:
+                mem.refct -= 1
+                assert mem.refct >= 0
+                if mem.refct == 0:
+                    mem.free()
+            except ReferenceError:
+                # ignore reference error here
+                pass
+
+        self._mem.refct += 1
+        weakref.finalize(self, deref)
+
+    def __getattr__(self, fname):
+        """Proxy MemoryPointer methods
+        """
+        return getattr(self._view, fname)
+
+
+class MappedOwnedPointer(OwnedPointer, mviewbuf.MemAlloc):
+    pass
+
+
+class ManagedOwnedPointer(OwnedPointer, mviewbuf.MemAlloc):
+    pass
+
+
+class Stream(object):
+    def __init__(self, context, handle, finalizer, external=False):
+        self.context = context
+        self.handle = handle
+        self.external = external
+        if finalizer is not None:
+            weakref.finalize(self, finalizer)
+
+    def __int__(self):
+        if USE_NV_BINDING:
+            return int(self.handle)
+        else:
+            # The default stream's handle.value is 0, which gives `None`
+            return self.handle.value or drvapi.CU_STREAM_DEFAULT
+
+    def __repr__(self):
+        if USE_NV_BINDING:
+            default_streams = {
+                CU_STREAM_DEFAULT: "<Default CUDA stream on %s>",
+                binding.CU_STREAM_LEGACY:
+                    "<Legacy default CUDA stream on %s>",
+                binding.CU_STREAM_PER_THREAD:
+                    "<Per-thread default CUDA stream on %s>",
+            }
+            ptr = int(self.handle) or 0
+        else:
+            default_streams = {
+                drvapi.CU_STREAM_DEFAULT: "<Default CUDA stream on %s>",
+                drvapi.CU_STREAM_LEGACY: "<Legacy default CUDA stream on %s>",
+                drvapi.CU_STREAM_PER_THREAD:
+                    "<Per-thread default CUDA stream on %s>",
+            }
+            ptr = self.handle.value or drvapi.CU_STREAM_DEFAULT
+
+        if ptr in default_streams:
+            return default_streams[ptr] % self.context
+        elif self.external:
+            return "<External CUDA stream %d on %s>" % (ptr, self.context)
+        else:
+            return "<CUDA stream %d on %s>" % (ptr, self.context)
+
+    def synchronize(self):
+        '''
+        Wait for all commands in this stream to execute. This will commit any
+        pending memory transfers.
+        '''
+        driver.cuStreamSynchronize(self.handle)
+
+    @contextlib.contextmanager
+    def auto_synchronize(self):
+        '''
+        A context manager that waits for all commands in this stream to execute
+        and commits any pending memory transfers upon exiting the context.
+        '''
+        yield self
+        self.synchronize()
+
+    def add_callback(self, callback, arg):
+        """
+        Add a callback to a compute stream.
+        The user provided function is called from a driver thread once all
+        preceding stream operations are complete.
+
+        Callback functions are called from a CUDA driver thread, not from
+        the thread that invoked `add_callback`. No CUDA API functions may
+        be called from within the callback function.
+
+        The duration of a callback function should be kept short, as the
+        callback will block later work in the stream and may block other
+        callbacks from being executed.
+
+        Note: The driver function underlying this method is marked for
+        eventual deprecation and may be replaced in a future CUDA release.
+
+        :param callback: Callback function with arguments (stream, status, arg).
+        :param arg: User data to be passed to the callback function.
+        """
+        data = (self, callback, arg)
+        _py_incref(data)
+        if USE_NV_BINDING:
+            ptr = int.from_bytes(self._stream_callback, byteorder='little')
+            stream_callback = binding.CUstreamCallback(ptr)
+            # The callback needs to receive a pointer to the data PyObject
+            data = id(data)
+        else:
+            stream_callback = self._stream_callback
+        driver.cuStreamAddCallback(self.handle, stream_callback, data, 0)
+
+    @staticmethod
+    @cu_stream_callback_pyobj
+    def _stream_callback(handle, status, data):
+        try:
+            stream, callback, arg = data
+            callback(stream, status, arg)
+        except Exception as e:
+            warnings.warn(f"Exception in stream callback: {e}")
+        finally:
+            _py_decref(data)
+
+    def async_done(self) -> asyncio.futures.Future:
+        """
+        Return an awaitable that resolves once all preceding stream operations
+        are complete. The result of the awaitable is the current stream.
+        """
+        loop = asyncio.get_running_loop()
+        future = loop.create_future()
+
+        def resolver(future, status):
+            if future.done():
+                return
+            elif status == 0:
+                future.set_result(self)
+            else:
+                future.set_exception(Exception(f"Stream error {status}"))
+
+        def callback(stream, status, future):
+            loop.call_soon_threadsafe(resolver, future, status)
+
+        self.add_callback(callback, future)
+        return future
+
+
+class Event(object):
+    def __init__(self, context, handle, finalizer=None):
+        self.context = context
+        self.handle = handle
+        if finalizer is not None:
+            weakref.finalize(self, finalizer)
+
+    def query(self):
+        """
+        Returns True if all work before the most recent record has completed;
+        otherwise, returns False.
+        """
+        try:
+            driver.cuEventQuery(self.handle)
+        except CudaAPIError as e:
+            if e.code == enums.CUDA_ERROR_NOT_READY:
+                return False
+            else:
+                raise
+        else:
+            return True
+
+    def record(self, stream=0):
+        """
+        Set the record point of the event to the current point in the given
+        stream.
+
+        The event will be considered to have occurred when all work that was
+        queued in the stream at the time of the call to ``record()`` has been
+        completed.
+        """
+        if USE_NV_BINDING:
+            hstream = stream.handle if stream else binding.CUstream(0)
+        else:
+            hstream = stream.handle if stream else 0
+        driver.cuEventRecord(self.handle, hstream)
+
+    def synchronize(self):
+        """
+        Synchronize the host thread for the completion of the event.
+        """
+        driver.cuEventSynchronize(self.handle)
+
+    def wait(self, stream=0):
+        """
+        All future works submitted to stream will wait util the event completes.
+        """
+        if USE_NV_BINDING:
+            hstream = stream.handle if stream else binding.CUstream(0)
+        else:
+            hstream = stream.handle if stream else 0
+        flags = 0
+        driver.cuStreamWaitEvent(hstream, self.handle, flags)
+
+    def elapsed_time(self, evtend):
+        return event_elapsed_time(self, evtend)
+
+
+def event_elapsed_time(evtstart, evtend):
+    '''
+    Compute the elapsed time between two events in milliseconds.
+    '''
+    if USE_NV_BINDING:
+        return driver.cuEventElapsedTime(evtstart.handle, evtend.handle)
+    else:
+        msec = c_float()
+        driver.cuEventElapsedTime(byref(msec), evtstart.handle, evtend.handle)
+        return msec.value
+
+
+class Module(metaclass=ABCMeta):
+    """Abstract base class for modules"""
+
+    def __init__(self, context, handle, info_log, finalizer=None):
+        self.context = context
+        self.handle = handle
+        self.info_log = info_log
+        if finalizer is not None:
+            self._finalizer = weakref.finalize(self, finalizer)
+
+    def unload(self):
+        """Unload this module from the context"""
+        self.context.unload_module(self)
+
+    @abstractmethod
+    def get_function(self, name):
+        """Returns a Function object encapsulating the named function"""
+
+    @abstractmethod
+    def get_global_symbol(self, name):
+        """Return a MemoryPointer referring to the named symbol"""
+
+
+class CtypesModule(Module):
+
+    def get_function(self, name):
+        handle = drvapi.cu_function()
+        driver.cuModuleGetFunction(byref(handle), self.handle,
+                                   name.encode('utf8'))
+        return CtypesFunction(weakref.proxy(self), handle, name)
+
+    def get_global_symbol(self, name):
+        ptr = drvapi.cu_device_ptr()
+        size = drvapi.c_size_t()
+        driver.cuModuleGetGlobal(byref(ptr), byref(size), self.handle,
+                                 name.encode('utf8'))
+        return MemoryPointer(self.context, ptr, size), size.value
+
+
+class CudaPythonModule(Module):
+
+    def get_function(self, name):
+        handle = driver.cuModuleGetFunction(self.handle, name.encode('utf8'))
+        return CudaPythonFunction(weakref.proxy(self), handle, name)
+
+    def get_global_symbol(self, name):
+        ptr, size = driver.cuModuleGetGlobal(self.handle, name.encode('utf8'))
+        return MemoryPointer(self.context, ptr, size), size
+
+
+FuncAttr = namedtuple("FuncAttr", ["regs", "shared", "local", "const",
+                                   "maxthreads"])
+
+
+class Function(metaclass=ABCMeta):
+    griddim = 1, 1, 1
+    blockdim = 1, 1, 1
+    stream = 0
+    sharedmem = 0
+
+    def __init__(self, module, handle, name):
+        self.module = module
+        self.handle = handle
+        self.name = name
+        self.attrs = self.read_func_attr_all()
+
+    def __repr__(self):
+        return "<CUDA function %s>" % self.name
+
+    @property
+    def device(self):
+        return self.module.context.device
+
+    @abstractmethod
+    def cache_config(self, prefer_equal=False, prefer_cache=False,
+                     prefer_shared=False):
+        """Set the cache configuration for this function."""
+
+    @abstractmethod
+    def read_func_attr(self, attrid):
+        """Return the value of the attribute with given ID."""
+
+    @abstractmethod
+    def read_func_attr_all(self):
+        """Return a FuncAttr object with the values of various function
+        attributes."""
+
+
+class CtypesFunction(Function):
+
+    def cache_config(self, prefer_equal=False, prefer_cache=False,
+                     prefer_shared=False):
+        prefer_equal = prefer_equal or (prefer_cache and prefer_shared)
+        if prefer_equal:
+            flag = enums.CU_FUNC_CACHE_PREFER_EQUAL
+        elif prefer_cache:
+            flag = enums.CU_FUNC_CACHE_PREFER_L1
+        elif prefer_shared:
+            flag = enums.CU_FUNC_CACHE_PREFER_SHARED
+        else:
+            flag = enums.CU_FUNC_CACHE_PREFER_NONE
+        driver.cuFuncSetCacheConfig(self.handle, flag)
+
+    def read_func_attr(self, attrid):
+        retval = c_int()
+        driver.cuFuncGetAttribute(byref(retval), attrid, self.handle)
+        return retval.value
+
+    def read_func_attr_all(self):
+        nregs = self.read_func_attr(enums.CU_FUNC_ATTRIBUTE_NUM_REGS)
+        cmem = self.read_func_attr(enums.CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES)
+        lmem = self.read_func_attr(enums.CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES)
+        smem = self.read_func_attr(enums.CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES)
+        maxtpb = self.read_func_attr(
+            enums.CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK)
+        return FuncAttr(regs=nregs, const=cmem, local=lmem, shared=smem,
+                        maxthreads=maxtpb)
+
+
+class CudaPythonFunction(Function):
+
+    def cache_config(self, prefer_equal=False, prefer_cache=False,
+                     prefer_shared=False):
+        prefer_equal = prefer_equal or (prefer_cache and prefer_shared)
+        attr = binding.CUfunction_attribute
+        if prefer_equal:
+            flag = attr.CU_FUNC_CACHE_PREFER_EQUAL
+        elif prefer_cache:
+            flag = attr.CU_FUNC_CACHE_PREFER_L1
+        elif prefer_shared:
+            flag = attr.CU_FUNC_CACHE_PREFER_SHARED
+        else:
+            flag = attr.CU_FUNC_CACHE_PREFER_NONE
+        driver.cuFuncSetCacheConfig(self.handle, flag)
+
+    def read_func_attr(self, attrid):
+        return driver.cuFuncGetAttribute(attrid, self.handle)
+
+    def read_func_attr_all(self):
+        attr = binding.CUfunction_attribute
+        nregs = self.read_func_attr(attr.CU_FUNC_ATTRIBUTE_NUM_REGS)
+        cmem = self.read_func_attr(attr.CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES)
+        lmem = self.read_func_attr(attr.CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES)
+        smem = self.read_func_attr(attr.CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES)
+        maxtpb = self.read_func_attr(
+            attr.CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK)
+        return FuncAttr(regs=nregs, const=cmem, local=lmem, shared=smem,
+                        maxthreads=maxtpb)
+
+
+def launch_kernel(cufunc_handle,
+                  gx, gy, gz,
+                  bx, by, bz,
+                  sharedmem,
+                  hstream,
+                  args,
+                  cooperative=False):
+
+    param_ptrs = [addressof(arg) for arg in args]
+    params = (c_void_p * len(param_ptrs))(*param_ptrs)
+
+    if USE_NV_BINDING:
+        params_for_launch = addressof(params)
+        extra = 0
+    else:
+        params_for_launch = params
+        extra = None
+
+    if cooperative:
+        driver.cuLaunchCooperativeKernel(cufunc_handle,
+                                         gx, gy, gz,
+                                         bx, by, bz,
+                                         sharedmem,
+                                         hstream,
+                                         params_for_launch)
+    else:
+        driver.cuLaunchKernel(cufunc_handle,
+                              gx, gy, gz,
+                              bx, by, bz,
+                              sharedmem,
+                              hstream,
+                              params_for_launch,
+                              extra)
+
+
+if USE_NV_BINDING:
+    jitty = binding.CUjitInputType
+    FILE_EXTENSION_MAP = {
+        'o': jitty.CU_JIT_INPUT_OBJECT,
+        'ptx': jitty.CU_JIT_INPUT_PTX,
+        'a': jitty.CU_JIT_INPUT_LIBRARY,
+        'lib': jitty.CU_JIT_INPUT_LIBRARY,
+        'cubin': jitty.CU_JIT_INPUT_CUBIN,
+        'fatbin': jitty.CU_JIT_INPUT_FATBINARY,
+    }
+else:
+    FILE_EXTENSION_MAP = {
+        'o': enums.CU_JIT_INPUT_OBJECT,
+        'ptx': enums.CU_JIT_INPUT_PTX,
+        'a': enums.CU_JIT_INPUT_LIBRARY,
+        'lib': enums.CU_JIT_INPUT_LIBRARY,
+        'cubin': enums.CU_JIT_INPUT_CUBIN,
+        'fatbin': enums.CU_JIT_INPUT_FATBINARY,
+    }
+
+
+class Linker(metaclass=ABCMeta):
+    """Abstract base class for linkers"""
+
+    @classmethod
+    def new(cls, max_registers=0, lineinfo=False, cc=None):
+        if USE_NV_BINDING:
+            return CudaPythonLinker(max_registers, lineinfo, cc)
+        else:
+            return CtypesLinker(max_registers, lineinfo, cc)
+
+    @abstractmethod
+    def __init__(self, max_registers, lineinfo, cc):
+        pass
+
+    @property
+    @abstractmethod
+    def info_log(self):
+        """Return the info log from the linker invocation"""
+
+    @property
+    @abstractmethod
+    def error_log(self):
+        """Return the error log from the linker invocation"""
+
+    @abstractmethod
+    def add_ptx(self, ptx, name):
+        """Add PTX source in a string to the link"""
+
+    @abstractmethod
+    def add_cu(self, cu, name):
+        """Add CUDA source in a string to the link. The name of the source
+        file should be specified in `name`."""
+
+    @abstractmethod
+    def add_file(self, path, kind):
+        """Add code from a file to the link"""
+
+    def add_cu_file(self, path):
+        with open(path, 'rb') as f:
+            cu = f.read()
+        self.add_cu(cu, os.path.basename(path))
+
+    def add_file_guess_ext(self, path):
+        """Add a file to the link, guessing its type from its extension."""
+        ext = os.path.splitext(path)[1][1:]
+        if ext == '':
+            raise RuntimeError("Don't know how to link file with no extension")
+        elif ext == 'cu':
+            self.add_cu_file(path)
+        else:
+            kind = FILE_EXTENSION_MAP.get(ext, None)
+            if kind is None:
+                raise RuntimeError("Don't know how to link file with extension "
+                                   f".{ext}")
+            self.add_file(path, kind)
+
+    @abstractmethod
+    def complete(self):
+        """Complete the link. Returns (cubin, size)
+
+        cubin is a pointer to a internal buffer of cubin owned by the linker;
+        thus, it should be loaded before the linker is destroyed.
+        """
+
+
+class CtypesLinker(Linker):
+    """
+    Links for current device if no CC given
+    """
+    def __init__(self, max_registers=0, lineinfo=False, cc=None):
+        logsz = config.CUDA_LOG_SIZE
+        linkerinfo = (c_char * logsz)()
+        linkererrors = (c_char * logsz)()
+
+        options = {
+            enums.CU_JIT_INFO_LOG_BUFFER: addressof(linkerinfo),
+            enums.CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES: c_void_p(logsz),
+            enums.CU_JIT_ERROR_LOG_BUFFER: addressof(linkererrors),
+            enums.CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES: c_void_p(logsz),
+            enums.CU_JIT_LOG_VERBOSE: c_void_p(1),
+        }
+        if max_registers:
+            options[enums.CU_JIT_MAX_REGISTERS] = c_void_p(max_registers)
+        if lineinfo:
+            options[enums.CU_JIT_GENERATE_LINE_INFO] = c_void_p(1)
+
+        if cc is None:
+            # No option value is needed, but we need something as a placeholder
+            options[enums.CU_JIT_TARGET_FROM_CUCONTEXT] = 1
+        else:
+            cc_val = cc[0] * 10 + cc[1]
+            options[enums.CU_JIT_TARGET] = c_void_p(cc_val)
+
+        raw_keys = list(options.keys())
+        raw_values = list(options.values())
+
+        option_keys = (drvapi.cu_jit_option * len(raw_keys))(*raw_keys)
+        option_vals = (c_void_p * len(raw_values))(*raw_values)
+
+        self.handle = handle = drvapi.cu_link_state()
+        driver.cuLinkCreate(len(raw_keys), option_keys, option_vals,
+                            byref(self.handle))
+
+        weakref.finalize(self, driver.cuLinkDestroy, handle)
+
+        self.linker_info_buf = linkerinfo
+        self.linker_errors_buf = linkererrors
+
+        self._keep_alive = [linkerinfo, linkererrors, option_keys, option_vals]
+
+    @property
+    def info_log(self):
+        return self.linker_info_buf.value.decode('utf8')
+
+    @property
+    def error_log(self):
+        return self.linker_errors_buf.value.decode('utf8')
+
+    def add_ptx(self, ptx, name='<cudapy-ptx>'):
+        ptxbuf = c_char_p(ptx)
+        namebuf = c_char_p(name.encode('utf8'))
+        self._keep_alive += [ptxbuf, namebuf]
+        try:
+            driver.cuLinkAddData(self.handle, enums.CU_JIT_INPUT_PTX,
+                                 ptxbuf, len(ptx), namebuf, 0, None, None)
+        except CudaAPIError as e:
+            raise LinkerError("%s\n%s" % (e, self.error_log))
+
+    def add_file(self, path, kind):
+        pathbuf = c_char_p(path.encode("utf8"))
+        self._keep_alive.append(pathbuf)
+
+        try:
+            driver.cuLinkAddFile(self.handle, kind, pathbuf, 0, None, None)
+        except CudaAPIError as e:
+            if e.code == enums.CUDA_ERROR_FILE_NOT_FOUND:
+                msg = f'{path} not found'
+            else:
+                msg = "%s\n%s" % (e, self.error_log)
+            raise LinkerError(msg)
+
+    def add_cu(self, path, name):
+        raise NotImplementedError("Linking CUDA source files is not supported "
+                                  "with the ctypes binding. ")
+
+    def complete(self):
+        cubin = c_void_p(0)
+        size = c_size_t(0)
+
+        try:
+            driver.cuLinkComplete(self.handle, byref(cubin), byref(size))
+        except CudaAPIError as e:
+            raise LinkerError("%s\n%s" % (e, self.error_log))
+
+        size = size.value
+        assert size > 0, 'linker returned a zero sized cubin'
+        del self._keep_alive[:]
+        return cubin, size
+
+
+class NvrtcProgram:
+    """NvrtcProgram is for the managing the lifetime of nvrtcProgram objects.
+
+    If an error occurs during an NVRTC call, an exception is raised. When an
+    instance of this class is deleted, it attempts to delete the underlying
+    nvrtcProgram."""
+
+    def __init__(self, src, name):
+        # Create an nvrtcProgram
+        err, program = nvrtc.nvrtcCreateProgram(src, name.encode(), 0, [], [])
+        self.check(err)
+        self._program = program
+
+        with driver.get_active_context() as ac:
+            dev = driver.get_device(ac.devnum)
+            major, minor = dev.compute_capability
+
+        # Compilation options:
+        # - Compile for the current device's compute capability.
+        # - The CUDA include path is added.
+        # - Relocatable Device Code (rdc) is needed to prevent device functions
+        #   being optimized away.
+        arch = f'--gpu-architecture=compute_{major}{minor}'.encode()
+        include = f'-I{config.CUDA_INCLUDE_PATH}'.encode()
+        opts = [arch, include, b'-rdc', b'true']
+
+        # Compile the program
+        err, = nvrtc.nvrtcCompileProgram(self._program, len(opts), opts)
+
+        # First check whether the call failed due to a "normal" compiler error
+        compile_error = (err == nvrtc.nvrtcResult.NVRTC_ERROR_COMPILATION)
+        if not compile_error:
+            # Check for any other error
+            self.check(err)
+
+        # Get log from compilation
+        err, log_size = nvrtc.nvrtcGetProgramLogSize(self._program)
+        self.check(err)
+        log = b' ' * log_size
+        err, = nvrtc.nvrtcGetProgramLog(self._program, log)
+        self.check(err)
+
+        # If the compile failed, provide the log in an exception
+        if compile_error:
+            msg = (f'NVRTC Compilation failure whilst compiling {name}:\n\n'
+                   f'{log.decode()}')
+            raise NvrtcError(msg)
+
+        # Otherwise, if there's any content in the log, present it as a warning
+        if log_size > 1:
+            msg = (f"NVRTC log messages whilst compiling {name}:\n\n"
+                   f"{log.decode()}")
+            warnings.warn(msg)
+
+        # Get and cache the PTX
+        err, ptx_len = nvrtc.nvrtcGetPTXSize(self._program)
+        self.check(err)
+        ptx = b' ' * ptx_len
+        err, = nvrtc.nvrtcGetPTX(self._program, ptx)
+        self.check(err)
+
+        self._ptx = ptx
+
+    def __del__(self):
+        if self._program:
+            err, = nvrtc.nvrtcDestroyProgram(self._program)
+            self.check(err)
+
+    @property
+    def ptx(self):
+        return self._ptx
+
+    def check(self, err):
+        if isinstance(err, binding.CUresult):
+            if err != binding.CUresult.CUDA_SUCCESS:
+                raise RuntimeError('CUDA Error calling NVRTC: {}'.format(err))
+        elif isinstance(err, nvrtc.nvrtcResult):
+            if err != nvrtc.nvrtcResult.NVRTC_SUCCESS:
+                raise RuntimeError('NVRTC Error: {}'.format(err))
+        else:
+            raise RuntimeError('Unknown error type: {}'.format(err))
+
+
+class CudaPythonLinker(Linker):
+    """
+    Links for current device if no CC given
+    """
+    def __init__(self, max_registers=0, lineinfo=False, cc=None):
+        logsz = config.CUDA_LOG_SIZE
+        linkerinfo = bytearray(logsz)
+        linkererrors = bytearray(logsz)
+
+        jit_option = binding.CUjit_option
+
+        options = {
+            jit_option.CU_JIT_INFO_LOG_BUFFER: linkerinfo,
+            jit_option.CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES: logsz,
+            jit_option.CU_JIT_ERROR_LOG_BUFFER: linkererrors,
+            jit_option.CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES: logsz,
+            jit_option.CU_JIT_LOG_VERBOSE: 1,
+        }
+        if max_registers:
+            options[jit_option.CU_JIT_MAX_REGISTERS] = max_registers
+        if lineinfo:
+            options[jit_option.CU_JIT_GENERATE_LINE_INFO] = 1
+
+        if cc is None:
+            # No option value is needed, but we need something as a placeholder
+            options[jit_option.CU_JIT_TARGET_FROM_CUCONTEXT] = 1
+        else:
+            cc_val = cc[0] * 10 + cc[1]
+            cc_enum = getattr(binding.CUjit_target,
+                              f'CU_TARGET_COMPUTE_{cc_val}')
+            options[jit_option.CU_JIT_TARGET] = cc_enum
+
+        raw_keys = list(options.keys())
+        raw_values = list(options.values())
+
+        self.handle = driver.cuLinkCreate(len(raw_keys), raw_keys, raw_values)
+
+        weakref.finalize(self, driver.cuLinkDestroy, self.handle)
+
+        self.linker_info_buf = linkerinfo
+        self.linker_errors_buf = linkererrors
+
+        self._keep_alive = [linkerinfo, linkererrors, raw_keys, raw_values]
+
+    @property
+    def info_log(self):
+        return self.linker_info_buf.decode('utf8')
+
+    @property
+    def error_log(self):
+        return self.linker_errors_buf.decode('utf8')
+
+    def add_ptx(self, ptx, name='<cudapy-ptx>'):
+        namebuf = name.encode('utf8')
+        self._keep_alive += [ptx, namebuf]
+        try:
+            input_ptx = binding.CUjitInputType.CU_JIT_INPUT_PTX
+            driver.cuLinkAddData(self.handle, input_ptx, ptx, len(ptx),
+                                 namebuf, 0, [], [])
+        except CudaAPIError as e:
+            raise LinkerError("%s\n%s" % (e, self.error_log))
+
+    def add_cu(self, cu, name):
+        program = NvrtcProgram(cu, name)
+
+        if config.DUMP_ASSEMBLY:
+            print(("ASSEMBLY %s" % name).center(80, '-'))
+            print(program.ptx.decode())
+            print('=' * 80)
+
+        # Link the program's PTX using the normal linker mechanism
+        ptx_name = os.path.splitext(name)[0] + ".ptx"
+        self.add_ptx(program.ptx, ptx_name)
+
+    def add_file(self, path, kind):
+        pathbuf = path.encode("utf8")
+        self._keep_alive.append(pathbuf)
+
+        try:
+            driver.cuLinkAddFile(self.handle, kind, pathbuf, 0, [], [])
+        except CudaAPIError as e:
+            if e.code == binding.CUresult.CUDA_ERROR_FILE_NOT_FOUND:
+                msg = f'{path} not found'
+            else:
+                msg = "%s\n%s" % (e, self.error_log)
+            raise LinkerError(msg)
+
+    def complete(self):
+        try:
+            cubin, size = driver.cuLinkComplete(self.handle)
+        except CudaAPIError as e:
+            raise LinkerError("%s\n%s" % (e, self.error_log))
+
+        assert size > 0, 'linker returned a zero sized cubin'
+        del self._keep_alive[:]
+        return cubin, size
+
+
+# -----------------------------------------------------------------------------
+
+
+def get_devptr_for_active_ctx(ptr):
+    """Query the device pointer usable in the current context from an arbitrary
+    pointer.
+    """
+    if ptr != 0:
+        if USE_NV_BINDING:
+            ptr_attrs = binding.CUpointer_attribute
+            attr = ptr_attrs.CU_POINTER_ATTRIBUTE_DEVICE_POINTER
+            ptrobj = binding.CUdeviceptr(ptr)
+            return driver.cuPointerGetAttribute(attr, ptrobj)
+        else:
+            devptr = drvapi.cu_device_ptr()
+            attr = enums.CU_POINTER_ATTRIBUTE_DEVICE_POINTER
+            driver.cuPointerGetAttribute(byref(devptr), attr, ptr)
+            return devptr
+    else:
+        if USE_NV_BINDING:
+            return binding.CUdeviceptr()
+        else:
+            return drvapi.cu_device_ptr()
+
+
+def device_extents(devmem):
+    """Find the extents (half open begin and end pointer) of the underlying
+    device memory allocation.
+
+    NOTE: it always returns the extents of the allocation but the extents
+    of the device memory view that can be a subsection of the entire allocation.
+    """
+    devptr = device_ctypes_pointer(devmem)
+    if USE_NV_BINDING:
+        s, n = driver.cuMemGetAddressRange(devptr)
+        return s, binding.CUdeviceptr(int(s) + n)
+    else:
+        s = drvapi.cu_device_ptr()
+        n = c_size_t()
+        driver.cuMemGetAddressRange(byref(s), byref(n), devptr)
+        s, n = s.value, n.value
+        return s, s + n
+
+
+def device_memory_size(devmem):
+    """Check the memory size of the device memory.
+    The result is cached in the device memory object.
+    It may query the driver for the memory size of the device memory allocation.
+    """
+    sz = getattr(devmem, '_cuda_memsize_', None)
+    if sz is None:
+        s, e = device_extents(devmem)
+        if USE_NV_BINDING:
+            sz = int(e) - int(s)
+        else:
+            sz = e - s
+        devmem._cuda_memsize_ = sz
+    assert sz >= 0, "{} length array".format(sz)
+    return sz
+
+
+def _is_datetime_dtype(obj):
+    """Returns True if the obj.dtype is datetime64 or timedelta64
+    """
+    dtype = getattr(obj, 'dtype', None)
+    return dtype is not None and dtype.char in 'Mm'
+
+
+def _workaround_for_datetime(obj):
+    """Workaround for numpy#4983: buffer protocol doesn't support
+    datetime64 or timedelta64.
+    """
+    if _is_datetime_dtype(obj):
+        obj = obj.view(np.int64)
+    return obj
+
+
+def host_pointer(obj, readonly=False):
+    """Get host pointer from an obj.
+
+    If `readonly` is False, the buffer must be writable.
+
+    NOTE: The underlying data pointer from the host data buffer is used and
+    it should not be changed until the operation which can be asynchronous
+    completes.
+    """
+    if isinstance(obj, int):
+        return obj
+
+    forcewritable = False
+    if not readonly:
+        forcewritable = isinstance(obj, np.void) or _is_datetime_dtype(obj)
+
+    obj = _workaround_for_datetime(obj)
+    return mviewbuf.memoryview_get_buffer(obj, forcewritable, readonly)
+
+
+def host_memory_extents(obj):
+    "Returns (start, end) the start and end pointer of the array (half open)."
+    obj = _workaround_for_datetime(obj)
+    return mviewbuf.memoryview_get_extents(obj)
+
+
+def memory_size_from_info(shape, strides, itemsize):
+    """Get the byte size of a contiguous memory buffer given the shape, strides
+    and itemsize.
+    """
+    assert len(shape) == len(strides), "# dim mismatch"
+    ndim = len(shape)
+    s, e = mviewbuf.memoryview_get_extents_info(shape, strides, ndim, itemsize)
+    return e - s
+
+
+def host_memory_size(obj):
+    "Get the size of the memory"
+    s, e = host_memory_extents(obj)
+    assert e >= s, "memory extend of negative size"
+    return e - s
+
+
+def device_pointer(obj):
+    "Get the device pointer as an integer"
+    if USE_NV_BINDING:
+        return obj.device_ctypes_pointer
+    else:
+        return device_ctypes_pointer(obj).value
+
+
+def device_ctypes_pointer(obj):
+    "Get the ctypes object for the device pointer"
+    if obj is None:
+        return c_void_p(0)
+    require_device_memory(obj)
+    return obj.device_ctypes_pointer
+
+
+def is_device_memory(obj):
+    """All CUDA memory object is recognized as an instance with the attribute
+    "__cuda_memory__" defined and its value evaluated to True.
+
+    All CUDA memory object should also define an attribute named
+    "device_pointer" which value is an int object carrying the pointer
+    value of the device memory address.  This is not tested in this method.
+    """
+    return getattr(obj, '__cuda_memory__', False)
+
+
+def require_device_memory(obj):
+    """A sentry for methods that accept CUDA memory object.
+    """
+    if not is_device_memory(obj):
+        raise Exception("Not a CUDA memory object.")
+
+
+def device_memory_depends(devmem, *objs):
+    """Add dependencies to the device memory.
+
+    Mainly used for creating structures that points to other device memory,
+    so that the referees are not GC and released.
+    """
+    depset = getattr(devmem, "_depends_", [])
+    depset.extend(objs)
+
+
+def host_to_device(dst, src, size, stream=0):
+    """
+    NOTE: The underlying data pointer from the host data buffer is used and
+    it should not be changed until the operation which can be asynchronous
+    completes.
+    """
+    varargs = []
+
+    if stream:
+        assert isinstance(stream, Stream)
+        fn = driver.cuMemcpyHtoDAsync
+        varargs.append(stream.handle)
+    else:
+        fn = driver.cuMemcpyHtoD
+
+    fn(device_pointer(dst), host_pointer(src, readonly=True), size, *varargs)
+
+
+def device_to_host(dst, src, size, stream=0):
+    """
+    NOTE: The underlying data pointer from the host data buffer is used and
+    it should not be changed until the operation which can be asynchronous
+    completes.
+    """
+    varargs = []
+
+    if stream:
+        assert isinstance(stream, Stream)
+        fn = driver.cuMemcpyDtoHAsync
+        varargs.append(stream.handle)
+    else:
+        fn = driver.cuMemcpyDtoH
+
+    fn(host_pointer(dst), device_pointer(src), size, *varargs)
+
+
+def device_to_device(dst, src, size, stream=0):
+    """
+    NOTE: The underlying data pointer from the host data buffer is used and
+    it should not be changed until the operation which can be asynchronous
+    completes.
+    """
+    varargs = []
+
+    if stream:
+        assert isinstance(stream, Stream)
+        fn = driver.cuMemcpyDtoDAsync
+        varargs.append(stream.handle)
+    else:
+        fn = driver.cuMemcpyDtoD
+
+    fn(device_pointer(dst), device_pointer(src), size, *varargs)
+
+
+def device_memset(dst, val, size, stream=0):
+    """Memset on the device.
+    If stream is not zero, asynchronous mode is used.
+
+    dst: device memory
+    val: byte value to be written
+    size: number of byte to be written
+    stream: a CUDA stream
+    """
+    varargs = []
+
+    if stream:
+        assert isinstance(stream, Stream)
+        fn = driver.cuMemsetD8Async
+        varargs.append(stream.handle)
+    else:
+        fn = driver.cuMemsetD8
+
+    fn(device_pointer(dst), val, size, *varargs)
+
+
+def profile_start():
+    '''
+    Enable profile collection in the current context.
+    '''
+    driver.cuProfilerStart()
+
+
+def profile_stop():
+    '''
+    Disable profile collection in the current context.
+    '''
+    driver.cuProfilerStop()
+
+
+@contextlib.contextmanager
+def profiling():
+    """
+    Context manager that enables profiling on entry and disables profiling on
+    exit.
+    """
+    profile_start()
+    yield
+    profile_stop()
+
+
+def get_version():
+    """
+    Return the driver version as a tuple of (major, minor)
+    """
+    return driver.get_version()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/drvapi.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/drvapi.py
new file mode 100644
index 000000000..cbbd792d3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/drvapi.py
@@ -0,0 +1,394 @@
+from ctypes import (c_byte, c_char_p, c_float, c_int, c_size_t, c_uint,
+                    c_uint8, c_void_p, py_object, CFUNCTYPE, POINTER)
+
+from numba.cuda.cudadrv import _extras
+
+cu_device = c_int
+cu_device_attribute = c_int     # enum
+cu_context = c_void_p           # an opaque handle
+cu_module = c_void_p            # an opaque handle
+cu_jit_option = c_int           # enum
+cu_jit_input_type = c_int       # enum
+cu_function = c_void_p          # an opaque handle
+cu_device_ptr = c_size_t        # defined as unsigned long long
+cu_stream = c_void_p            # an opaque handle
+cu_event = c_void_p
+cu_link_state = c_void_p
+cu_function_attribute = c_int
+cu_ipc_mem_handle = (c_byte * _extras.CUDA_IPC_HANDLE_SIZE)   # 64 bytes wide
+cu_uuid = (c_byte * 16)         # Device UUID
+
+cu_stream_callback_pyobj = CFUNCTYPE(None, cu_stream, c_int, py_object)
+
+cu_occupancy_b2d_size = CFUNCTYPE(c_size_t, c_int)
+
+# See https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__TYPES.html
+CU_STREAM_DEFAULT = 0
+CU_STREAM_LEGACY = 1
+CU_STREAM_PER_THREAD = 2
+
+API_PROTOTYPES = {
+    # CUresult cuInit(unsigned int Flags);
+    'cuInit' : (c_int, c_uint),
+
+    # CUresult cuDriverGetVersion (int* driverVersion )
+    'cuDriverGetVersion': (c_int, POINTER(c_int)),
+
+    # CUresult cuDeviceGetCount(int *count);
+    'cuDeviceGetCount': (c_int, POINTER(c_int)),
+
+    # CUresult cuDeviceGet(CUdevice *device, int ordinal);
+    'cuDeviceGet': (c_int, POINTER(cu_device), c_int),
+
+    # CUresult cuDeviceGetName ( char* name, int  len, CUdevice dev )
+    'cuDeviceGetName': (c_int, c_char_p, c_int, cu_device),
+
+    # CUresult cuDeviceGetAttribute(int *pi, CUdevice_attribute attrib,
+    #                               CUdevice dev);
+    'cuDeviceGetAttribute': (c_int, POINTER(c_int), cu_device_attribute,
+                             cu_device),
+
+    # CUresult cuDeviceComputeCapability(int *major, int *minor,
+    #                                    CUdevice dev);
+    'cuDeviceComputeCapability': (c_int, POINTER(c_int), POINTER(c_int),
+                                  cu_device),
+
+    # CUresult cuDevicePrimaryCtxGetState(
+    #              CUdevice dev,
+    #              unsigned int* flags,
+    #              int* active)
+    'cuDevicePrimaryCtxGetState': (c_int,
+                                   cu_device, POINTER(c_uint), POINTER(c_int)),
+
+    # CUresult cuDevicePrimaryCtxRelease ( CUdevice dev )
+    'cuDevicePrimaryCtxRelease': (c_int, cu_device),
+
+    # CUresult cuDevicePrimaryCtxReset ( CUdevice dev )
+    'cuDevicePrimaryCtxReset': (c_int, cu_device),
+
+    # CUresult cuDevicePrimaryCtxRetain ( CUcontext* pctx, CUdevice dev )
+    'cuDevicePrimaryCtxRetain': (c_int, POINTER(cu_context), cu_device),
+
+    # CUresult cuDevicePrimaryCtxSetFlags ( CUdevice dev, unsigned int  flags )
+    'cuDevicePrimaryCtxSetFlags': (c_int, cu_device, c_uint),
+
+    # CUresult cuCtxCreate(CUcontext *pctx, unsigned int flags,
+    #                      CUdevice dev);
+    'cuCtxCreate': (c_int, POINTER(cu_context), c_uint, cu_device),
+
+    # CUresult cuCtxGetDevice (	CUdevice * 	device	 )
+    'cuCtxGetDevice': (c_int, POINTER(cu_device)),
+
+    # CUresult cuCtxGetCurrent (CUcontext *pctx);
+    'cuCtxGetCurrent': (c_int, POINTER(cu_context)),
+
+    # CUresult cuCtxPushCurrent (CUcontext pctx);
+    'cuCtxPushCurrent': (c_int, cu_context),
+
+    # CUresult cuCtxPopCurrent (CUcontext *pctx);
+    'cuCtxPopCurrent': (c_int, POINTER(cu_context)),
+
+    # CUresult cuCtxDestroy(CUcontext pctx);
+    'cuCtxDestroy': (c_int, cu_context),
+
+    # CUresult cuModuleLoadDataEx(CUmodule *module, const void *image,
+    #                             unsigned int numOptions,
+    #                             CUjit_option *options,
+    #                             void **optionValues);
+    'cuModuleLoadDataEx': (c_int, cu_module, c_void_p, c_uint,
+                           POINTER(cu_jit_option), POINTER(c_void_p)),
+
+    # CUresult cuModuleUnload(CUmodule hmod);
+    'cuModuleUnload': (c_int, cu_module),
+
+    # CUresult cuModuleGetFunction(CUfunction *hfunc, CUmodule hmod,
+    #                              const char *name);
+    'cuModuleGetFunction': (c_int, cu_function, cu_module, c_char_p),
+
+    # CUresult cuModuleGetGlobal ( CUdeviceptr* dptr, size_t* bytes, CUmodule
+    #                              hmod, const char* name )
+    'cuModuleGetGlobal': (c_int, POINTER(cu_device_ptr), POINTER(c_size_t),
+                          cu_module, c_char_p),
+
+    # CUresult CUDAAPI cuFuncSetCacheConfig(CUfunction hfunc,
+    #                                       CUfunc_cache config);
+    'cuFuncSetCacheConfig': (c_int, cu_function, c_uint),
+
+    # CUresult cuMemAlloc(CUdeviceptr *dptr, size_t bytesize);
+    'cuMemAlloc': (c_int, POINTER(cu_device_ptr), c_size_t),
+
+    # CUresult cuMemAllocManaged(CUdeviceptr *dptr, size_t bytesize,
+    #                            unsigned int flags);
+    'cuMemAllocManaged': (c_int, c_void_p, c_size_t, c_uint),
+
+    # CUresult cuMemsetD8(CUdeviceptr dstDevice, unsigned char uc, size_t N)
+    'cuMemsetD8': (c_int, cu_device_ptr, c_uint8, c_size_t),
+
+    # CUresult cuMemsetD8Async(CUdeviceptr dstDevice, unsigned char uc,
+    #                          size_t N, CUstream hStream);
+    'cuMemsetD8Async': (c_int,
+                        cu_device_ptr, c_uint8, c_size_t, cu_stream),
+
+    # CUresult cuMemcpyHtoD(CUdeviceptr dstDevice, const void *srcHost,
+    #                       size_t ByteCount);
+    'cuMemcpyHtoD': (c_int, cu_device_ptr, c_void_p, c_size_t),
+
+    # CUresult cuMemcpyHtoDAsync(CUdeviceptr dstDevice, const void *srcHost,
+    #                            size_t ByteCount, CUstream hStream);
+    'cuMemcpyHtoDAsync': (c_int, cu_device_ptr, c_void_p, c_size_t,
+                          cu_stream),
+
+    # CUresult cuMemcpyDtoD(CUdeviceptr dstDevice, const void *srcDevice,
+    #                       size_t ByteCount);
+    'cuMemcpyDtoD': (c_int, cu_device_ptr, cu_device_ptr, c_size_t),
+
+    # CUresult cuMemcpyDtoDAsync(CUdeviceptr dstDevice, const void *srcDevice,
+    #                            size_t ByteCount, CUstream hStream);
+    'cuMemcpyDtoDAsync': (c_int, cu_device_ptr, cu_device_ptr, c_size_t,
+                          cu_stream),
+
+
+    # CUresult cuMemcpyDtoH(void *dstHost, CUdeviceptr srcDevice,
+    #                       size_t ByteCount);
+    'cuMemcpyDtoH': (c_int, c_void_p, cu_device_ptr, c_size_t),
+
+    # CUresult cuMemcpyDtoHAsync(void *dstHost, CUdeviceptr srcDevice,
+    #                            size_t ByteCount, CUstream hStream);
+    'cuMemcpyDtoHAsync': (c_int, c_void_p, cu_device_ptr, c_size_t,
+                          cu_stream),
+
+    # CUresult cuMemFree(CUdeviceptr dptr);
+    'cuMemFree': (c_int, cu_device_ptr),
+
+    # CUresult cuStreamCreate(CUstream *phStream, unsigned int Flags);
+    'cuStreamCreate': (c_int, POINTER(cu_stream), c_uint),
+
+    # CUresult cuStreamDestroy(CUstream hStream);
+    'cuStreamDestroy': (c_int, cu_stream),
+
+    # CUresult cuStreamSynchronize(CUstream hStream);
+    'cuStreamSynchronize': (c_int, cu_stream),
+
+    # CUresult cuStreamAddCallback(
+    #              CUstream hStream,
+    #              CUstreamCallback callback,
+    #              void* userData,
+    #              unsigned int flags)
+    'cuStreamAddCallback': (c_int, cu_stream, cu_stream_callback_pyobj,
+                            py_object, c_uint),
+
+    # CUresult cuLaunchKernel(CUfunction f, unsigned int gridDimX,
+    #                        unsigned int gridDimY,
+    #                        unsigned int gridDimZ,
+    #                        unsigned int blockDimX,
+    #                        unsigned int blockDimY,
+    #                        unsigned int blockDimZ,
+    #                        unsigned int sharedMemBytes,
+    #                        CUstream hStream, void **kernelParams,
+    #                        void ** extra)
+    'cuLaunchKernel': (c_int, cu_function, c_uint, c_uint, c_uint,
+                       c_uint, c_uint, c_uint, c_uint, cu_stream,
+                       POINTER(c_void_p), POINTER(c_void_p)),
+
+    # CUresult cuLaunchCooperativeKernel(CUfunction f, unsigned int gridDimX,
+    #                                   unsigned int gridDimY,
+    #                                   unsigned int gridDimZ,
+    #                                   unsigned int blockDimX,
+    #                                   unsigned int blockDimY,
+    #                                   unsigned int blockDimZ,
+    #                                   unsigned int sharedMemBytes,
+    #                                   CUstream hStream, void **kernelParams)
+    'cuLaunchCooperativeKernel': (c_int, cu_function, c_uint, c_uint, c_uint,
+                                  c_uint, c_uint, c_uint, c_uint, cu_stream,
+                                  POINTER(c_void_p)),
+
+    #  CUresult cuMemHostAlloc (	void ** 	pp,
+    #                               size_t 	bytesize,
+    #                               unsigned int 	Flags
+    #                           )
+    'cuMemHostAlloc': (c_int, c_void_p, c_size_t, c_uint),
+
+    #  CUresult cuMemFreeHost (	void * 	p	 )
+    'cuMemFreeHost': (c_int, c_void_p),
+
+    # CUresult cuMemHostRegister(void * 	p,
+    #                            size_t 	bytesize,
+    #                            unsigned int 	Flags)
+    'cuMemHostRegister': (c_int, c_void_p, c_size_t, c_uint),
+
+    # CUresult cuMemHostUnregister(void * 	p)
+    'cuMemHostUnregister': (c_int, c_void_p),
+
+    # CUresult cuMemHostGetDevicePointer(CUdeviceptr * pdptr,
+    #                                    void *        p,
+    #                                    unsigned int  Flags)
+    'cuMemHostGetDevicePointer': (c_int, POINTER(cu_device_ptr),
+                                  c_void_p, c_uint),
+
+    # CUresult cuMemGetInfo(size_t * free, size_t * total)
+    'cuMemGetInfo' : (c_int, POINTER(c_size_t), POINTER(c_size_t)),
+
+    # CUresult cuEventCreate (	CUevent * 	phEvent,
+    #                               unsigned int 	Flags )
+    'cuEventCreate': (c_int, POINTER(cu_event), c_uint),
+
+    # CUresult cuEventDestroy (	CUevent 	hEvent	 )
+    'cuEventDestroy': (c_int, cu_event),
+
+    # CUresult cuEventElapsedTime (	float * 	pMilliseconds,
+    #                                   CUevent 	hStart,
+    #                                   CUevent 	hEnd )
+    'cuEventElapsedTime': (c_int, POINTER(c_float), cu_event, cu_event),
+
+    # CUresult cuEventQuery (	CUevent 	hEvent	 )
+    'cuEventQuery': (c_int, cu_event),
+
+    # CUresult cuEventRecord (	CUevent 	hEvent,
+    #                               CUstream 	hStream )
+    'cuEventRecord': (c_int, cu_event, cu_stream),
+
+    # CUresult cuEventSynchronize (	CUevent 	hEvent	 )
+    'cuEventSynchronize': (c_int, cu_event),
+
+
+    # CUresult cuStreamWaitEvent (	CUstream        hStream,
+    #                                   CUevent         hEvent,
+    #                                	unsigned int 	Flags )
+    'cuStreamWaitEvent': (c_int, cu_stream, cu_event, c_uint),
+
+    # CUresult 	cuPointerGetAttribute (
+    #               void *data,
+    #               CUpointer_attribute attribute,
+    #               CUdeviceptr ptr)
+    'cuPointerGetAttribute': (c_int, c_void_p, c_uint, cu_device_ptr),
+
+    #    CUresult cuMemGetAddressRange (	CUdeviceptr * 	pbase,
+    #                                        size_t * 	psize,
+    #                                        CUdeviceptr 	dptr
+    #                                        )
+    'cuMemGetAddressRange': (c_int,
+                             POINTER(cu_device_ptr),
+                             POINTER(c_size_t),
+                             cu_device_ptr),
+
+    #    CUresult cuMemHostGetFlags (	unsigned int * 	pFlags,
+    #                                   void * 	p )
+    'cuMemHostGetFlags': (c_int,
+                          POINTER(c_uint),
+                          c_void_p),
+
+    #   CUresult cuCtxSynchronize ( void )
+    'cuCtxSynchronize' : (c_int,),
+
+    #    CUresult
+    #    cuLinkCreate(unsigned int numOptions, CUjit_option *options,
+    #                 void **optionValues, CUlinkState *stateOut);
+    'cuLinkCreate': (c_int,
+                     c_uint, POINTER(cu_jit_option),
+                     POINTER(c_void_p), POINTER(cu_link_state)),
+
+    #    CUresult
+    #    cuLinkAddData(CUlinkState state, CUjitInputType type, void *data,
+    #                  size_t size, const char *name, unsigned
+    #                  int numOptions, CUjit_option *options,
+    #                  void **optionValues);
+    'cuLinkAddData': (c_int,
+                      cu_link_state, cu_jit_input_type, c_void_p,
+                      c_size_t, c_char_p, c_uint, POINTER(cu_jit_option),
+                      POINTER(c_void_p)),
+
+    #    CUresult
+    #    cuLinkAddFile(CUlinkState state, CUjitInputType type,
+    #                  const char *path, unsigned int numOptions,
+    #                  CUjit_option *options, void **optionValues);
+
+    'cuLinkAddFile': (c_int,
+                      cu_link_state, cu_jit_input_type, c_char_p, c_uint,
+                      POINTER(cu_jit_option), POINTER(c_void_p)),
+
+    #    CUresult CUDAAPI
+    #    cuLinkComplete(CUlinkState state, void **cubinOut, size_t *sizeOut)
+    'cuLinkComplete': (c_int,
+                       cu_link_state, POINTER(c_void_p), POINTER(c_size_t)),
+
+    #    CUresult CUDAAPI
+    #    cuLinkDestroy(CUlinkState state)
+    'cuLinkDestroy': (c_int, cu_link_state),
+
+    # cuProfilerStart ( void )
+    'cuProfilerStart': (c_int,),
+
+    # cuProfilerStop ( void )
+    'cuProfilerStop': (c_int,),
+
+    # CUresult cuFuncGetAttribute ( int* pi, CUfunction_attribute attrib,
+    #                              CUfunction hfunc )
+    'cuFuncGetAttribute': (c_int,
+                           POINTER(c_int), cu_function_attribute, cu_function),
+
+    # CUresult CUDAAPI cuOccupancyMaxActiveBlocksPerMultiprocessor(
+    #                      int *numBlocks,
+    #                      CUfunction func,
+    #                      int blockSize,
+    #                      size_t dynamicSMemSize);
+    'cuOccupancyMaxActiveBlocksPerMultiprocessor': (c_int, POINTER(c_int),
+                                                    cu_function, c_size_t,
+                                                    c_uint),
+
+    # CUresult CUDAAPI cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(
+    #                      int *numBlocks,
+    #                      CUfunction func,
+    #                      int blockSize,
+    #                      size_t dynamicSMemSize,
+    #                      unsigned int flags);
+    'cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags': (c_int,
+                                                             POINTER(c_int),
+                                                             cu_function,
+                                                             c_size_t, c_uint),
+
+    # CUresult CUDAAPI cuOccupancyMaxPotentialBlockSize(
+    #                      int *minGridSize, int *blockSize,
+    #                      CUfunction func,
+    #                      CUoccupancyB2DSize blockSizeToDynamicSMemSize,
+    #                      size_t dynamicSMemSize, int blockSizeLimit);
+    'cuOccupancyMaxPotentialBlockSize': (c_int, POINTER(c_int), POINTER(c_int),
+                                         cu_function, cu_occupancy_b2d_size,
+                                         c_size_t, c_int),
+
+    # CUresult CUDAAPI cuOccupancyMaxPotentialBlockSizeWithFlags(
+    #                      int *minGridSize, int *blockSize,
+    #                      CUfunction func,
+    #                      CUoccupancyB2DSize blockSizeToDynamicSMemSize,
+    #                      size_t dynamicSMemSize, int blockSizeLimit,
+    #                      unsigned int flags);
+    'cuOccupancyMaxPotentialBlockSizeWithFlags': (c_int, POINTER(c_int),
+                                                  POINTER(c_int), cu_function,
+                                                  cu_occupancy_b2d_size,
+                                                  c_size_t, c_int, c_uint),
+
+    # CUresult cuIpcGetMemHandle ( CUipcMemHandle* pHandle, CUdeviceptr dptr )
+    'cuIpcGetMemHandle': (c_int,
+                          POINTER(cu_ipc_mem_handle), cu_device_ptr),
+
+    # CUresult cuIpcOpenMemHandle(
+    #              CUdeviceptr* pdptr,
+    #              CUipcMemHandle handle,
+    #              unsigned int Flags)
+    'cuIpcOpenMemHandle': (c_int, POINTER(cu_device_ptr), cu_ipc_mem_handle,
+                           c_uint),
+
+    # CUresult cuIpcCloseMemHandle ( CUdeviceptr dptr )
+
+    'cuIpcCloseMemHandle': (c_int, cu_device_ptr),
+
+    # CUresult cuCtxEnablePeerAccess (CUcontext peerContext, unsigned int Flags)
+    'cuCtxEnablePeerAccess': (c_int, cu_context, c_int),
+
+    # CUresult cuDeviceCanAccessPeer ( int* canAccessPeer,
+    #                                  CUdevice dev, CUdevice peerDev )
+    'cuDeviceCanAccessPeer': (c_int,
+                              POINTER(c_int), cu_device, cu_device),
+
+    # CUresult cuDeviceGetUuid ( CUuuid* uuid, CUdevice dev )
+    'cuDeviceGetUuid': (c_int, POINTER(cu_uuid), cu_device),
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/enums.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/enums.py
new file mode 100644
index 000000000..3a584ded6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/enums.py
@@ -0,0 +1,444 @@
+"""
+Enum values for CUDA driver
+"""
+
+
+CUDA_SUCCESS = 0
+CUDA_ERROR_INVALID_VALUE = 1
+CUDA_ERROR_OUT_OF_MEMORY = 2
+CUDA_ERROR_NOT_INITIALIZED = 3
+CUDA_ERROR_DEINITIALIZED = 4
+CUDA_ERROR_PROFILER_DISABLED = 5
+CUDA_ERROR_PROFILER_NOT_INITIALIZED = 6
+CUDA_ERROR_PROFILER_ALREADY_STARTED = 7
+CUDA_ERROR_PROFILER_ALREADY_STOPPED = 8
+CUDA_ERROR_NO_DEVICE = 100
+CUDA_ERROR_INVALID_DEVICE = 101
+CUDA_ERROR_INVALID_IMAGE = 200
+CUDA_ERROR_INVALID_CONTEXT = 201
+CUDA_ERROR_CONTEXT_ALREADY_CURRENT = 202
+CUDA_ERROR_MAP_FAILED = 205
+CUDA_ERROR_UNMAP_FAILED = 206
+CUDA_ERROR_ARRAY_IS_MAPPED = 207
+CUDA_ERROR_ALREADY_MAPPED = 208
+CUDA_ERROR_NO_BINARY_FOR_GPU = 209
+CUDA_ERROR_ALREADY_ACQUIRED = 210
+CUDA_ERROR_NOT_MAPPED = 211
+CUDA_ERROR_NOT_MAPPED_AS_ARRAY = 212
+CUDA_ERROR_NOT_MAPPED_AS_POINTER = 213
+CUDA_ERROR_ECC_UNCORRECTABLE = 214
+CUDA_ERROR_UNSUPPORTED_LIMIT = 215
+CUDA_ERROR_CONTEXT_ALREADY_IN_USE = 216
+CUDA_ERROR_INVALID_SOURCE = 300
+CUDA_ERROR_FILE_NOT_FOUND = 301
+CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND = 302
+CUDA_ERROR_SHARED_OBJECT_INIT_FAILED = 303
+CUDA_ERROR_OPERATING_SYSTEM = 304
+CUDA_ERROR_INVALID_HANDLE = 400
+CUDA_ERROR_NOT_FOUND = 500
+CUDA_ERROR_NOT_READY = 600
+CUDA_ERROR_LAUNCH_FAILED = 700
+CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES = 701
+CUDA_ERROR_LAUNCH_TIMEOUT = 702
+CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING = 703
+CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED = 704
+CUDA_ERROR_PEER_ACCESS_NOT_ENABLED = 705
+CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE = 708
+CUDA_ERROR_CONTEXT_IS_DESTROYED = 709
+CUDA_ERROR_ASSERT = 710
+CUDA_ERROR_TOO_MANY_PEERS = 711
+CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED = 712
+CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED = 713
+CUDA_ERROR_HARDWARE_STACK_ERROR = 714
+CUDA_ERROR_ILLEGAL_INSTRUCTION = 715
+CUDA_ERROR_MISALIGNED_ADDRESS = 716
+CUDA_ERROR_INVALID_ADDRESS_SPACE = 717
+CUDA_ERROR_INVALID_PC = 718
+CUDA_ERROR_LAUNCH_FAILED = 719
+CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE = 720
+CUDA_ERROR_NOT_PERMITTED = 800
+CUDA_ERROR_NOT_SUPPORTED = 801
+CUDA_ERROR_UNKNOWN = 999
+
+
+# no preference for shared memory or L1 (default)
+CU_FUNC_CACHE_PREFER_NONE = 0x00
+# prefer larger shared memory and smaller L1 cache
+CU_FUNC_CACHE_PREFER_SHARED = 0x01
+# prefer larger L1 cache and smaller shared memory
+CU_FUNC_CACHE_PREFER_L1 = 0x02
+# prefer equal sized L1 cache and shared memory
+CU_FUNC_CACHE_PREFER_EQUAL = 0x03
+
+# Automatic scheduling
+CU_CTX_SCHED_AUTO = 0x00
+# Set spin as default scheduling
+CU_CTX_SCHED_SPIN = 0x01
+# Set yield as default scheduling
+CU_CTX_SCHED_YIELD = 0x02
+# Set blocking synchronization as default scheduling
+CU_CTX_SCHED_BLOCKING_SYNC = 0x04
+
+CU_CTX_SCHED_MASK = 0x07
+
+# Support mapped pinned allocations
+CU_CTX_MAP_HOST = 0x08
+# Keep local memory allocation after launch
+CU_CTX_LMEM_RESIZE_TO_MAX = 0x10
+
+CU_CTX_FLAGS_MASK = 0x1f
+
+
+# If set, host memory is portable between CUDA contexts.
+# Flag for cuMemHostAlloc()
+CU_MEMHOSTALLOC_PORTABLE = 0x01
+
+# If set, host memory is mapped into CUDA address space and
+# cuMemHostGetDevicePointer() may be called on the host pointer.
+# Flag for cuMemHostAlloc()
+CU_MEMHOSTALLOC_DEVICEMAP = 0x02
+
+# If set, host memory is allocated as write-combined - fast to write,
+# faster to DMA, slow to read except via SSE4 streaming load instruction
+# (MOVNTDQA).
+# Flag for cuMemHostAlloc()
+CU_MEMHOSTALLOC_WRITECOMBINED = 0x04
+
+# If set, host memory is portable between CUDA contexts.
+# Flag for cuMemHostRegister()
+CU_MEMHOSTREGISTER_PORTABLE = 0x01
+
+# If set, host memory is mapped into CUDA address space and
+# cuMemHostGetDevicePointer() may be called on the host pointer.
+# Flag for cuMemHostRegister()
+CU_MEMHOSTREGISTER_DEVICEMAP = 0x02
+
+
+# If set, managed memory is accessible from all streams on all devices.
+CU_MEM_ATTACH_GLOBAL = 0x01
+
+# If set on a platform where the device attribute
+# cudaDevAttrConcurrentManagedAccess is zero, then managed memory is
+# only accessible on the host (unless explicitly attached to a stream
+# with cudaStreamAttachMemAsync, in which case it can be used in kernels
+# launched on that stream).
+CU_MEM_ATTACH_HOST = 0x02
+
+# If set on a platform where the device attribute
+# cudaDevAttrConcurrentManagedAccess is zero, then managed memory accesses
+# on the associated device must only be from a single stream.
+CU_MEM_ATTACH_SINGLE = 0x04
+
+
+# Default event flag
+CU_EVENT_DEFAULT = 0x0
+# Event uses blocking synchronization
+CU_EVENT_BLOCKING_SYNC = 0x1
+# Event will not record timing data
+CU_EVENT_DISABLE_TIMING = 0x2
+# Event is suitable for interprocess use. CU_EVENT_DISABLE_TIMING must be set
+CU_EVENT_INTERPROCESS = 0x4
+
+# The CUcontext on which a pointer was allocated or registered
+CU_POINTER_ATTRIBUTE_CONTEXT = 1
+# The CUmemorytype describing the physical location of a pointer
+CU_POINTER_ATTRIBUTE_MEMORY_TYPE = 2
+# The address at which a pointer's memory may be accessed on the device
+CU_POINTER_ATTRIBUTE_DEVICE_POINTER = 3
+# The address at which a pointer's memory may be accessed on the host
+CU_POINTER_ATTRIBUTE_HOST_POINTER = 4
+# A pair of tokens for use with the nv-p2p.h Linux kernel interface
+CU_POINTER_ATTRIBUTE_P2P_TOKENS = 5
+
+# Host memory
+CU_MEMORYTYPE_HOST = 0x01
+# Device memory
+CU_MEMORYTYPE_DEVICE = 0x02
+# Array memory
+CU_MEMORYTYPE_ARRAY = 0x03
+# Unified device or host memory
+CU_MEMORYTYPE_UNIFIED = 0x04
+
+
+# Compiled device-class-specific device code
+# Applicable options: none
+CU_JIT_INPUT_CUBIN = 0
+
+# PTX source code
+# Applicable options: PTX compiler options
+CU_JIT_INPUT_PTX = 1
+
+# Bundle of multiple cubins and/or PTX of some device code
+# Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY
+CU_JIT_INPUT_FATBINARY = 2
+
+# Host object with embedded device code
+# Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY
+CU_JIT_INPUT_OBJECT = 3
+
+# Archive of host objects with embedded device code
+# Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY
+CU_JIT_INPUT_LIBRARY = 4
+
+
+# Max number of registers that a thread may use.
+# Option type: unsigned int
+# Applies to: compiler only
+
+CU_JIT_MAX_REGISTERS = 0
+
+
+# IN: Specifies minimum number of threads per block to target compilation
+# for
+# OUT: Returns the number of threads the compiler actually targeted.
+# This restricts the resource utilization fo the compiler (e.g. max
+# registers) such that a block with the given number of threads should be
+# able to launch based on register limitations. Note, this option does not
+# currently take into account any other resource limitations, such as
+# shared memory utilization.
+# Cannot be combined with ::CU_JIT_TARGET.
+# Option type: unsigned int
+# Applies to: compiler only
+
+CU_JIT_THREADS_PER_BLOCK = 1
+
+
+# Overwrites the option value with the total wall clock time, in
+# milliseconds, spent in the compiler and linker
+# Option type: float
+# Applies to: compiler and linker
+
+CU_JIT_WALL_TIME = 2
+
+
+# Pointer to a buffer in which to print any log messages
+# that are informational in nature (the buffer size is specified via
+# option ::CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES)
+# Option type: char *
+# Applies to: compiler and linker
+
+CU_JIT_INFO_LOG_BUFFER = 3
+
+
+# IN: Log buffer size in bytes.  Log messages will be capped at this size
+# (including null terminator)
+# OUT: Amount of log buffer filled with messages
+# Option type: unsigned int
+# Applies to: compiler and linker
+
+CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES = 4
+
+
+# Pointer to a buffer in which to print any log messages that
+# reflect errors (the buffer size is specified via option
+# ::CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES)
+# Option type: char *
+# Applies to: compiler and linker
+
+CU_JIT_ERROR_LOG_BUFFER = 5
+
+
+# IN: Log buffer size in bytes.  Log messages will be capped at this size
+# (including null terminator)
+# OUT: Amount of log buffer filled with messages
+# Option type: unsigned int
+# Applies to: compiler and linker
+
+CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES = 6
+
+
+# Level of optimizations to apply to generated code (0 - 4), with 4
+# being the default and highest level of optimizations.
+# Option type: unsigned int
+# Applies to: compiler only
+
+CU_JIT_OPTIMIZATION_LEVEL = 7
+
+
+# No option value required. Determines the target based on the current
+# attached context (default)
+# Option type: No option value needed
+# Applies to: compiler and linker
+
+CU_JIT_TARGET_FROM_CUCONTEXT = 8
+
+
+# Target is chosen based on supplied ::CUjit_target.  Cannot be
+# combined with ::CU_JIT_THREADS_PER_BLOCK.
+# Option type: unsigned int for enumerated type ::CUjit_target
+# Applies to: compiler and linker
+
+CU_JIT_TARGET = 9
+
+
+# Specifies choice of fallback strategy if matching cubin is not found.
+# Choice is based on supplied ::CUjit_fallback.
+# Option type: unsigned int for enumerated type ::CUjit_fallback
+# Applies to: compiler only
+
+CU_JIT_FALLBACK_STRATEGY = 10
+
+
+# Specifies whether to create debug information in output (-g)
+# (0: false, default)
+# Option type: int
+# Applies to: compiler and linker
+
+CU_JIT_GENERATE_DEBUG_INFO = 11
+
+
+# Generate verbose log messages (0: false, default)
+# Option type: int
+# Applies to: compiler and linker
+
+CU_JIT_LOG_VERBOSE = 12
+
+
+# Generate line number information (-lineinfo) (0: false, default)
+# Option type: int
+# Applies to: compiler only
+
+CU_JIT_GENERATE_LINE_INFO = 13
+
+
+# Specifies whether to enable caching explicitly (-dlcm)
+# Choice is based on supplied ::CUjit_cacheMode_enum.
+# Option type: unsigned int for enumerated type ::CUjit_cacheMode_enum
+# Applies to: compiler only
+
+CU_JIT_CACHE_MODE = 14
+
+
+# Device attributes
+
+
+CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 1
+CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X = 2
+CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y = 3
+CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z = 4
+CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X = 5
+CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y = 6
+CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z = 7
+CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK = 8
+CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY = 9
+CU_DEVICE_ATTRIBUTE_WARP_SIZE = 10
+CU_DEVICE_ATTRIBUTE_MAX_PITCH = 11
+CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK = 12
+CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13
+CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT = 14
+CU_DEVICE_ATTRIBUTE_GPU_OVERLAP = 15
+CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16
+CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT = 17
+CU_DEVICE_ATTRIBUTE_INTEGRATED = 18
+CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY = 19
+CU_DEVICE_ATTRIBUTE_COMPUTE_MODE = 20
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_1D_WIDTH = 21
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_WIDTH = 22
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_HEIGHT = 23
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_3D_WIDTH = 24
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_3D_HEIGHT = 25
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_3D_DEPTH = 26
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_LAYERED_WIDTH = 27
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_LAYERED_HEIGHT = 28
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_LAYERED_LAYERS = 29
+CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT = 30
+CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS = 31
+CU_DEVICE_ATTRIBUTE_ECC_ENABLED = 32
+CU_DEVICE_ATTRIBUTE_PCI_BUS_ID = 33
+CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID = 34
+CU_DEVICE_ATTRIBUTE_TCC_DRIVER = 35
+CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36
+CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH = 37
+CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE = 38
+CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTI_PROCESSOR = 39
+CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT = 40
+CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING = 41
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_1D_LAYERED_WIDTH = 42
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_1D_LAYERED_LAYERS = 43
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_GATHER_WIDTH = 45
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_GATHER_HEIGHT = 46
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_3D_WIDTH_ALT = 47
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_3D_HEIGHT_ALT = 48
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_3D_DEPTH_ALT = 49
+CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID = 50
+CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT = 51
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_CUBEMAP_WIDTH = 52
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_CUBEMAP_LAYERED_WIDTH = 53
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_CUBEMAP_LAYERED_LAYERS = 54
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_1D_WIDTH = 55
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_2D_WIDTH = 56
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_2D_HEIGHT = 57
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_3D_WIDTH = 58
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_3D_HEIGHT = 59
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_3D_DEPTH = 60
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_1D_LAYERED_WIDTH = 61
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_1D_LAYERED_LAYERS = 62
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_2D_LAYERED_WIDTH = 63
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_2D_LAYERED_HEIGHT = 64
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_2D_LAYERED_LAYERS = 65
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_CUBEMAP_WIDTH = 66
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_CUBEMAP_LAYERED_WIDTH = 67
+CU_DEVICE_ATTRIBUTE_MAX_SURFACE_CUBEMAP_LAYERED_LAYERS = 68
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_1D_LINEAR_WIDTH = 69
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_LINEAR_WIDTH = 70
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_LINEAR_HEIGHT = 71
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_LINEAR_PITCH = 72
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_2D_MIPMAPPED_WIDTH = 73
+CU_DEVICE_ATTRIBUTE_MAX_MAX_TEXTURE_2D_MIPMAPPED_HEIGHT = 74
+CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR = 75
+CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR = 76
+CU_DEVICE_ATTRIBUTE_MAX_TEXTURE_1D_MIPMAPPED_WIDTH = 77
+CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED = 78
+CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED = 79
+CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED = 80
+CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR = 81
+CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR = 82
+CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY = 83
+CU_DEVICE_ATTRIBUTE_IS_MULTI_GPU_BOARD = 84
+CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID = 85
+CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED = 86
+CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO = 87
+CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS = 88
+CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS = 89
+CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED = 90
+CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM = 91
+CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH = 95
+CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH = 96
+CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN = 97
+
+
+# CUfunction_attribute
+
+# The maximum number of threads per block, beyond which a launch of the
+# function would fail. This number depends on both the function and the
+# device on which the function is currently loaded.
+CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 0
+
+# The size in bytes of statically-allocated shared memory required by
+# this function. This does not include dynamically-allocated shared
+# memory requested by the user at runtime.
+CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES = 1
+
+# The size in bytes of user-allocated constant memory required by this
+# function.
+CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES = 2
+
+# The size in bytes of local memory used by each thread of this function.
+CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES = 3
+
+# The number of registers used by each thread of this function.
+CU_FUNC_ATTRIBUTE_NUM_REGS = 4
+
+# The PTX virtual architecture version for which the function was
+# compiled. This value is the major PTX version * 10 + the minor PTX
+# version, so a PTX version 1.3 function would return the value 13.
+# Note that this may return the undefined value of 0 for cubins
+# compiled prior to CUDA 3.0.
+CU_FUNC_ATTRIBUTE_PTX_VERSION = 5
+
+# The binary architecture version for which the function was compiled.
+# This value is the major binary version * 10 + the minor binary version,
+# so a binary version 1.3 function would return the value 13. Note that
+# this will return a value of 10 for legacy cubins that do not have a
+# properly-encoded binary architecture version.
+CU_FUNC_ATTRIBUTE_BINARY_VERSION = 6
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/error.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/error.py
new file mode 100644
index 000000000..c82cebd54
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/error.py
@@ -0,0 +1,19 @@
+class CudaDriverError(Exception):
+    pass
+
+
+class CudaRuntimeError(Exception):
+    pass
+
+
+class CudaSupportError(ImportError):
+    pass
+
+
+class NvvmError(Exception):
+    def __str__(self):
+        return '\n'.join(map(str, self.args))
+
+
+class NvvmSupportError(ImportError):
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/libs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/libs.py
new file mode 100644
index 000000000..0489fcd7c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/libs.py
@@ -0,0 +1,127 @@
+"""Cudatoolkit libraries lookup utilities.
+
+Cudatoolkit libraries can be available via either:
+
+- the `cudatoolkit` conda package,
+- a user supplied location from CUDA_HOME,
+- a system wide location,
+- package-specific locations (e.g. the Debian NVIDIA packages),
+- or can be discovered by the system loader.
+"""
+
+import os
+import sys
+import ctypes
+
+from numba.misc.findlib import find_lib
+from numba.cuda.cuda_paths import get_cuda_paths
+
+if sys.platform == 'win32':
+    _dllnamepattern = '%s.dll'
+    _staticnamepattern = '%s.lib'
+elif sys.platform == 'darwin':
+    _dllnamepattern = 'lib%s.dylib'
+    _staticnamepattern = 'lib%s.a'
+else:
+    _dllnamepattern = 'lib%s.so'
+    _staticnamepattern = 'lib%s.a'
+
+
+def get_libdevice():
+    d = get_cuda_paths()
+    paths = d['libdevice'].info
+    return paths
+
+
+def open_libdevice():
+    with open(get_libdevice(), 'rb') as bcfile:
+        return bcfile.read()
+
+
+def get_cudalib(lib, platform=None, static=False):
+    """
+    Find the path of a CUDA library based on a search of known locations. If
+    the search fails, return a generic filename for the library (e.g.
+    'libnvvm.so' for 'nvvm') so that we may attempt to load it using the system
+    loader's search mechanism.
+    """
+    if lib == 'nvvm':
+        return get_cuda_paths()['nvvm'].info or _dllnamepattern % 'nvvm'
+    else:
+        libdir = get_cuda_paths()['cudalib_dir'].info
+
+    candidates = find_lib(lib, libdir, platform=platform, static=static)
+    namepattern = _staticnamepattern if static else _dllnamepattern
+    return max(candidates) if candidates else namepattern % lib
+
+
+def open_cudalib(lib):
+    path = get_cudalib(lib)
+    return ctypes.CDLL(path)
+
+
+def check_static_lib(lib):
+    path = get_cudalib(lib, static=True)
+    if not os.path.isfile(path):
+        raise FileNotFoundError(f'{path} not found')
+
+
+def _get_source_variable(lib):
+    if lib == 'nvvm':
+        return get_cuda_paths()['nvvm'].by
+    elif lib == 'libdevice':
+        return get_cuda_paths()['libdevice'].by
+    else:
+        return get_cuda_paths()['cudalib_dir'].by
+
+
+def test(_platform=None, print_paths=True):
+    """Test library lookup.  Path info is printed to stdout.
+    """
+    failed = False
+
+    # Checks for dynamic libraries
+    libs = 'nvvm cudart'.split()
+    for lib in libs:
+        path = get_cudalib(lib, _platform)
+        print('Finding {} from {}'.format(lib, _get_source_variable(lib)))
+        if print_paths:
+            print('\tlocated at', path)
+        else:
+            print('\tnamed ', os.path.basename(path))
+
+        if _platform in (None, sys.platform):
+            try:
+                print('\ttrying to open library', end='...')
+                open_cudalib(lib)
+                print('\tok')
+            except OSError as e:
+                print('\tERROR: failed to open %s:\n%s' % (lib, e))
+                failed = True
+
+    # Check for cudadevrt (the only static library)
+    lib = 'cudadevrt'
+    path = get_cudalib(lib, _platform, static=True)
+    print('Finding {} from {}'.format(lib, _get_source_variable(lib)))
+    if print_paths:
+        print('\tlocated at', path)
+    else:
+        print('\tnamed ', os.path.basename(path))
+
+    try:
+        check_static_lib(lib)
+    except FileNotFoundError as e:
+        print('\tERROR: failed to find %s:\n%s' % (lib, e))
+        failed = True
+
+    # Check for libdevice
+    where = _get_source_variable('libdevice')
+    print(f'Finding libdevice from {where}')
+    print('\ttrying to open library', end='...')
+    path = get_libdevice()
+    if path:
+        print('\tok')
+    else:
+        print('\tERROR: can\'t open libdevice')
+        failed = True
+    return not failed
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/ndarray.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/ndarray.py
new file mode 100644
index 000000000..bca40dfd9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/ndarray.py
@@ -0,0 +1,20 @@
+from numba.cuda.cudadrv import devices, driver
+from numba.core.registry import cpu_target
+
+
+def _calc_array_sizeof(ndim):
+    """
+    Use the ABI size in the CPU target
+    """
+    ctx = cpu_target.target_context
+    return ctx.calc_array_sizeof(ndim)
+
+
+def ndarray_device_allocate_data(ary):
+    """
+    Allocate gpu data buffer
+    """
+    datasize = driver.host_memory_size(ary)
+    # allocate
+    gpu_data = devices.get_context().memalloc(datasize)
+    return gpu_data
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/nvvm.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/nvvm.py
new file mode 100644
index 000000000..f8a7158cb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/nvvm.py
@@ -0,0 +1,909 @@
+"""
+This is a direct translation of nvvm.h
+"""
+import logging
+import re
+import sys
+import warnings
+from ctypes import (c_void_p, c_int, POINTER, c_char_p, c_size_t, byref,
+                    c_char)
+
+import threading
+
+from llvmlite import ir
+
+from .error import NvvmError, NvvmSupportError
+from .libs import get_libdevice, open_libdevice, open_cudalib
+from numba.core import cgutils, config
+
+CC_ILUVATAR_KERNEL = "iluvatar_kernel"
+
+logger = logging.getLogger(__name__)
+
+ADDRSPACE_GENERIC = 0
+ADDRSPACE_GLOBAL = 1
+ADDRSPACE_SHARED = 3
+ADDRSPACE_CONSTANT = 4
+ADDRSPACE_LOCAL = 5
+
+# Opaque handle for compilation unit
+nvvm_program = c_void_p
+
+# Result code
+nvvm_result = c_int
+
+RESULT_CODE_NAMES = '''
+NVVM_SUCCESS
+NVVM_ERROR_OUT_OF_MEMORY
+NVVM_ERROR_PROGRAM_CREATION_FAILURE
+NVVM_ERROR_IR_VERSION_MISMATCH
+NVVM_ERROR_INVALID_INPUT
+NVVM_ERROR_INVALID_PROGRAM
+NVVM_ERROR_INVALID_IR
+NVVM_ERROR_INVALID_OPTION
+NVVM_ERROR_NO_MODULE_IN_PROGRAM
+NVVM_ERROR_COMPILATION
+'''.split()
+
+for i, k in enumerate(RESULT_CODE_NAMES):
+    setattr(sys.modules[__name__], k, i)
+
+
+def is_available():
+    """
+    Return if libNVVM is available
+    """
+    try:
+        NVVM()
+    except NvvmSupportError:
+        return False
+    else:
+        return True
+
+
+_nvvm_lock = threading.Lock()
+
+
+class NVVM(object):
+    pass
+    '''Process-wide singleton.
+    '''
+    # _PROTOTYPES = {
+
+    #     # nvvmResult nvvmVersion(int *major, int *minor)
+    #     'nvvmVersion': (nvvm_result, POINTER(c_int), POINTER(c_int)),
+
+    #     # nvvmResult nvvmCreateProgram(nvvmProgram *cu)
+    #     'nvvmCreateProgram': (nvvm_result, POINTER(nvvm_program)),
+
+    #     # nvvmResult nvvmDestroyProgram(nvvmProgram *cu)
+    #     'nvvmDestroyProgram': (nvvm_result, POINTER(nvvm_program)),
+
+    #     # nvvmResult nvvmAddModuleToProgram(nvvmProgram cu, const char *buffer,
+    #     #                                   size_t size, const char *name)
+    #     'nvvmAddModuleToProgram': (
+    #         nvvm_result, nvvm_program, c_char_p, c_size_t, c_char_p),
+
+    #     # nvvmResult nvvmLazyAddModuleToProgram(nvvmProgram cu,
+    #     #                                       const char* buffer,
+    #     #                                       size_t size,
+    #     #                                       const char *name)
+    #     'nvvmLazyAddModuleToProgram': (
+    #         nvvm_result, nvvm_program, c_char_p, c_size_t, c_char_p),
+
+    #     # nvvmResult nvvmCompileProgram(nvvmProgram cu, int numOptions,
+    #     #                          const char **options)
+    #     'nvvmCompileProgram': (
+    #         nvvm_result, nvvm_program, c_int, POINTER(c_char_p)),
+
+    #     # nvvmResult nvvmGetCompiledResultSize(nvvmProgram cu,
+    #     #                                      size_t *bufferSizeRet)
+    #     'nvvmGetCompiledResultSize': (
+    #         nvvm_result, nvvm_program, POINTER(c_size_t)),
+
+    #     # nvvmResult nvvmGetCompiledResult(nvvmProgram cu, char *buffer)
+    #     'nvvmGetCompiledResult': (nvvm_result, nvvm_program, c_char_p),
+
+    #     # nvvmResult nvvmGetProgramLogSize(nvvmProgram cu,
+    #     #                                      size_t *bufferSizeRet)
+    #     'nvvmGetProgramLogSize': (nvvm_result, nvvm_program, POINTER(c_size_t)),
+
+    #     # nvvmResult nvvmGetProgramLog(nvvmProgram cu, char *buffer)
+    #     'nvvmGetProgramLog': (nvvm_result, nvvm_program, c_char_p),
+
+    #     # nvvmResult nvvmIRVersion (int* majorIR, int* minorIR, int* majorDbg,
+    #     #                           int* minorDbg )
+    #     'nvvmIRVersion': (nvvm_result, POINTER(c_int), POINTER(c_int),
+    #                       POINTER(c_int), POINTER(c_int)),
+    # }
+
+    # # Singleton reference
+    # __INSTANCE = None
+
+    # def __new__(cls):
+    #     with _nvvm_lock:
+    #         if cls.__INSTANCE is None:
+    #             cls.__INSTANCE = inst = object.__new__(cls)
+    #             try:
+    #                 inst.driver = open_cudalib('nvvm')
+    #             except OSError as e:
+    #                 cls.__INSTANCE = None
+    #                 errmsg = ("libNVVM cannot be found. Do `conda install "
+    #                           "cudatoolkit`:\n%s")
+    #                 raise NvvmSupportError(errmsg % e)
+
+    #             # Find & populate functions
+    #             for name, proto in inst._PROTOTYPES.items():
+    #                 func = getattr(inst.driver, name)
+    #                 func.restype = proto[0]
+    #                 func.argtypes = proto[1:]
+    #                 setattr(inst, name, func)
+
+    #     return cls.__INSTANCE
+
+    # def __init__(self):
+    #     ir_versions = self.get_ir_version()
+    #     self._majorIR = ir_versions[0]
+    #     self._minorIR = ir_versions[1]
+    #     self._majorDbg = ir_versions[2]
+    #     self._minorDbg = ir_versions[3]
+
+    # @property
+    # def is_nvvm70(self):
+    #     # NVVM70 uses NVVM IR version 1.6. See the documentation for
+    #     # nvvmAddModuleToProgram in
+    #     # https://docs.nvidia.com/cuda/libnvvm-api/group__compilation.html
+    #     return (self._majorIR, self._minorIR) >= (1, 6)
+
+    # def get_version(self):
+    #     major = c_int()
+    #     minor = c_int()
+    #     err = self.nvvmVersion(byref(major), byref(minor))
+    #     self.check_error(err, 'Failed to get version.')
+    #     return major.value, minor.value
+
+    # def get_ir_version(self):
+    #     majorIR = c_int()
+    #     minorIR = c_int()
+    #     majorDbg = c_int()
+    #     minorDbg = c_int()
+    #     err = self.nvvmIRVersion(byref(majorIR), byref(minorIR),
+    #                              byref(majorDbg), byref(minorDbg))
+    #     self.check_error(err, 'Failed to get IR version.')
+    #     return majorIR.value, minorIR.value, majorDbg.value, minorDbg.value
+
+    # def check_error(self, error, msg, exit=False):
+    #     if error:
+    #         exc = NvvmError(msg, RESULT_CODE_NAMES[error])
+    #         if exit:
+    #             print(exc)
+    #             sys.exit(1)
+    #         else:
+    #             raise exc
+
+
+class CompilationUnit(object):
+    def __init__(self):
+        self.driver = NVVM()
+        self._handle = nvvm_program()
+        err = self.driver.nvvmCreateProgram(byref(self._handle))
+        self.driver.check_error(err, 'Failed to create CU')
+
+    def __del__(self):
+        driver = NVVM()
+        err = driver.nvvmDestroyProgram(byref(self._handle))
+        driver.check_error(err, 'Failed to destroy CU', exit=True)
+
+    def add_module(self, buffer):
+        """
+         Add a module level NVVM IR to a compilation unit.
+         - The buffer should contain an NVVM module IR either in the bitcode
+           representation (LLVM3.0) or in the text representation.
+        """
+        err = self.driver.nvvmAddModuleToProgram(self._handle, buffer,
+                                                 len(buffer), None)
+        self.driver.check_error(err, 'Failed to add module')
+
+    def lazy_add_module(self, buffer):
+        """
+        Lazily add an NVVM IR module to a compilation unit.
+        The buffer should contain NVVM module IR either in the bitcode
+        representation or in the text representation.
+        """
+        err = self.driver.nvvmLazyAddModuleToProgram(self._handle, buffer,
+                                                     len(buffer), None)
+        self.driver.check_error(err, 'Failed to add module')
+
+    def compile(self, **options):
+        """Perform Compilation
+
+        The valid compiler options are
+
+         *   - -g (enable generation of debugging information)
+         *   - -opt=
+         *     - 0 (disable optimizations)
+         *     - 3 (default, enable optimizations)
+         *   - -arch=
+         *     - compute_20 (default)
+         *     - compute_30
+         *     - compute_35
+         *   - -ftz=
+         *     - 0 (default, preserve denormal values, when performing
+         *          single-precision floating-point operations)
+         *     - 1 (flush denormal values to zero, when performing
+         *          single-precision floating-point operations)
+         *   - -prec-sqrt=
+         *     - 0 (use a faster approximation for single-precision
+         *          floating-point square root)
+         *     - 1 (default, use IEEE round-to-nearest mode for
+         *          single-precision floating-point square root)
+         *   - -prec-div=
+         *     - 0 (use a faster approximation for single-precision
+         *          floating-point division and reciprocals)
+         *     - 1 (default, use IEEE round-to-nearest mode for
+         *          single-precision floating-point division and reciprocals)
+         *   - -fma=
+         *     - 0 (disable FMA contraction)
+         *     - 1 (default, enable FMA contraction)
+         *
+         """
+
+        # stringify options
+        opts = []
+        if 'debug' in options:
+            if options.pop('debug'):
+                opts.append('-g')
+
+        if options.pop('lineinfo', False):
+            opts.append('-generate-line-info')
+
+        if 'opt' in options:
+            opts.append('-opt=%d' % options.pop('opt'))
+
+        if options.get('arch'):
+            opts.append('-arch=%s' % options.pop('arch'))
+
+        other_options = (
+            'ftz',
+            'prec_sqrt',
+            'prec_div',
+            'fma',
+        )
+
+        for k in other_options:
+            if k in options:
+                v = int(bool(options.pop(k)))
+                opts.append('-%s=%d' % (k.replace('_', '-'), v))
+
+        # If there are any option left
+        if options:
+            optstr = ', '.join(map(repr, options.keys()))
+            raise NvvmError("unsupported option {0}".format(optstr))
+
+        # compile
+        c_opts = (c_char_p * len(opts))(*[c_char_p(x.encode('utf8'))
+                                          for x in opts])
+        err = self.driver.nvvmCompileProgram(self._handle, len(opts), c_opts)
+        self._try_error(err, 'Failed to compile\n')
+
+        # get result
+        reslen = c_size_t()
+        err = self.driver.nvvmGetCompiledResultSize(self._handle, byref(reslen))
+
+        self._try_error(err, 'Failed to get size of compiled result.')
+
+        ptxbuf = (c_char * reslen.value)()
+        err = self.driver.nvvmGetCompiledResult(self._handle, ptxbuf)
+        self._try_error(err, 'Failed to get compiled result.')
+
+        # get log
+        self.log = self.get_log()
+
+        return ptxbuf[:]
+
+    def _try_error(self, err, msg):
+        self.driver.check_error(err, "%s\n%s" % (msg, self.get_log()))
+
+    def get_log(self):
+        reslen = c_size_t()
+        err = self.driver.nvvmGetProgramLogSize(self._handle, byref(reslen))
+        self.driver.check_error(err, 'Failed to get compilation log size.')
+
+        if reslen.value > 1:
+            logbuf = (c_char * reslen.value)()
+            err = self.driver.nvvmGetProgramLog(self._handle, logbuf)
+            self.driver.check_error(err, 'Failed to get compilation log.')
+
+            return logbuf.value.decode('utf8')  # populate log attribute
+
+        return ''
+
+
+data_layout = (
+    'e-p:64:64-p1:64:64-p2:32:32-p3:32:32-p4:64:64-p5:32:32-p6:32:32-i64:64-v16:16-v24:32-'
+    'v32:32-v48:64-v96:128-v192:256-v256:256-v512:512-v1024:1024-v2048:2048-n32:64-S32-A5'
+)
+
+_supported_cc = None
+
+
+def get_supported_ccs():
+    global _supported_cc
+
+    if _supported_cc:
+        return _supported_cc
+
+    try:
+        from numba.cuda.cudadrv.runtime import runtime
+        cudart_version = runtime.get_version()
+    except: # noqa: E722
+        # The CUDA Runtime may not be present
+        cudart_version = (0, 0)
+
+    ctk_ver = f"{cudart_version[0]}.{cudart_version[1]}"
+    unsupported_ver = f"CUDA Toolkit {ctk_ver} is unsupported by Numba - " \
+                      + "10.2 is the minimum required version."
+
+    # List of supported compute capability in sorted order
+    if cudart_version == (0, 0):
+        _supported_cc = ()
+    elif cudart_version == (10, 2):
+        _supported_cc = ((3, 5), (3, 7),
+                         (5, 0), (5, 2), (5, 3),
+                         (6, 0), (6, 1), (6, 2),
+                         (7, 0), (7, 2), (7, 5))
+    elif cudart_version == (11, 0):
+        _supported_cc = ((3, 5), (3, 7),
+                         (5, 0), (5, 2), (5, 3),
+                         (6, 0), (6, 1), (6, 2),
+                         (7, 0), (7, 2), (7, 5),
+                         (8, 0))
+    elif cudart_version > (11, 0):
+        _supported_cc = ((3, 5), (3, 7),
+                         (5, 0), (5, 2), (5, 3),
+                         (6, 0), (6, 1), (6, 2),
+                         (7, 0), (7, 2), (7, 5),
+                         (8, 0), (8, 6))
+    elif cudart_version > (11, 4):
+        _supported_cc = ((3, 5), (3, 7),
+                         (5, 0), (5, 2), (5, 3),
+                         (6, 0), (6, 1), (6, 2),
+                         (7, 0), (7, 2), (7, 5),
+                         (8, 0), (8, 6), (8, 7))
+    else:
+        _supported_cc = ()
+        warnings.warn(unsupported_ver)
+
+    return _supported_cc
+
+
+def find_closest_arch(mycc):
+    """
+    Given a compute capability, return the closest compute capability supported
+    by the CUDA toolkit.
+
+    :param mycc: Compute capability as a tuple ``(MAJOR, MINOR)``
+    :return: Closest supported CC as a tuple ``(MAJOR, MINOR)``
+    """
+    supported_cc = get_supported_ccs()
+
+    if not supported_cc:
+        msg = "No supported GPU compute capabilities found. " \
+              "Please check your cudatoolkit version matches your CUDA version."
+        raise NvvmSupportError(msg)
+
+    for i, cc in enumerate(supported_cc):
+        if cc == mycc:
+            # Matches
+            return cc
+        elif cc > mycc:
+            # Exceeded
+            if i == 0:
+                # CC lower than supported
+                msg = "GPU compute capability %d.%d is not supported" \
+                      "(requires >=%d.%d)" % (mycc + cc)
+                raise NvvmSupportError(msg)
+            else:
+                # return the previous CC
+                return supported_cc[i - 1]
+
+    # CC higher than supported
+    return supported_cc[-1]  # Choose the highest
+
+
+def get_arch_option(major, minor):
+    """Matches with the closest architecture option
+    """
+    if config.FORCE_CUDA_CC:
+        arch = config.FORCE_CUDA_CC
+    else:
+        arch = find_closest_arch((major, minor))
+    return 'compute_%d%d' % arch
+
+
+MISSING_LIBDEVICE_FILE_MSG = '''Missing libdevice file.
+Please ensure you have package cudatoolkit >= 10.2
+Install package by:
+
+    conda install cudatoolkit
+'''
+
+
+class LibDevice(object):
+    _cache_ = None
+
+    def __init__(self):
+        if self._cache_ is None:
+            if get_libdevice() is None:
+                raise RuntimeError(MISSING_LIBDEVICE_FILE_MSG)
+            self._cache_ = open_libdevice()
+
+        self.bc = self._cache_
+
+    def get(self):
+        return self.bc
+
+
+ir_numba_cas_hack = """
+define internal {T} @___numba_atomic_{T}_cas_hack({T}* %ptr, {T} %cmp, {T} %val) alwaysinline {{
+    %out = cmpxchg volatile {T}* %ptr, {T} %cmp, {T} %val monotonic
+    ret {T} %out
+}}
+""" # noqa: E501
+
+cas_nvvm70 = """
+    %cas_success = cmpxchg volatile {Ti}* %iptr, {Ti} %old, {Ti} %new monotonic monotonic
+    %cas = extractvalue {{ {Ti}, i1 }} %cas_success, 0
+""" # noqa: E501
+
+
+cas_nvvm34 = """
+    %cas = cmpxchg volatile {Ti}* %iptr, {Ti} %old, {Ti} %new monotonic
+""" # noqa: E501
+
+
+# Translation of code from CUDA Programming Guide v6.5, section B.12
+ir_numba_atomic_binary_template = """
+define internal {T} @___numba_atomic_{T}_{FUNC}({T}* %ptr, {T} %val) alwaysinline {{
+entry:
+    %iptr = bitcast {T}* %ptr to {Ti}*
+    %old2 = load volatile {Ti}, {Ti}* %iptr
+    br label %attempt
+
+attempt:
+    %old = phi {Ti} [ %old2, %entry ], [ %cas, %attempt ]
+    %dold = bitcast {Ti} %old to {T}
+    %dnew = {OP} {T} %dold, %val
+    %new = bitcast {T} %dnew to {Ti}
+    {CAS}
+    %repeat = icmp ne {Ti} %cas, %old
+    br i1 %repeat, label %attempt, label %done
+
+done:
+    %result = bitcast {Ti} %old to {T}
+    ret {T} %result
+}}
+""" # noqa: E501
+
+ir_numba_atomic_inc_template = """
+define internal {T} @___numba_atomic_{Tu}_inc({T}* %iptr, {T} %val) alwaysinline {{
+entry:
+    %old2 = load volatile {T}, {T}* %iptr
+    br label %attempt
+
+attempt:
+    %old = phi {T} [ %old2, %entry ], [ %cas, %attempt ]
+    %bndchk = icmp ult {T} %old, %val
+    %inc = add {T} %old, 1
+    %new = select i1 %bndchk, {T} %inc, {T} 0
+    {CAS}
+    %repeat = icmp ne {T} %cas, %old
+    br i1 %repeat, label %attempt, label %done
+
+done:
+    ret {T} %old
+}}
+""" # noqa: E501
+
+ir_numba_atomic_dec_template = """
+define internal {T} @___numba_atomic_{Tu}_dec({T}* %iptr, {T} %val) alwaysinline {{
+entry:
+    %old2 = load volatile {T}, {T}* %iptr
+    br label %attempt
+
+attempt:
+    %old = phi {T} [ %old2, %entry ], [ %cas, %attempt ]
+    %dec = add {T} %old, -1
+    %bndchk = icmp ult {T} %dec, %val
+    %new = select i1 %bndchk, {T} %dec, {T} %val
+    {CAS}
+    %repeat = icmp ne {T} %cas, %old
+    br i1 %repeat, label %attempt, label %done
+
+done:
+    ret {T} %old
+}}
+""" # noqa: E501
+
+ir_numba_atomic_minmax_template = """
+define internal {T} @___numba_atomic_{T}_{NAN}{FUNC}({T}* %ptr, {T} %val) alwaysinline {{
+entry:
+    %ptrval = load volatile {T}, {T}* %ptr
+    ; Return early when:
+    ; - For nanmin / nanmax when val is a NaN
+    ; - For min / max when val or ptr is a NaN
+    %early_return = fcmp uno {T} %val, %{PTR_OR_VAL}val
+    br i1 %early_return, label %done, label %lt_check
+
+lt_check:
+    %dold = phi {T} [ %ptrval, %entry ], [ %dcas, %attempt ]
+    ; Continue attempts if dold less or greater than val (depending on whether min or max)
+    ; or if dold is NaN (for nanmin / nanmax)
+    %cmp = fcmp {OP} {T} %dold, %val
+    br i1 %cmp, label %attempt, label %done
+
+attempt:
+    ; Attempt to swap in the value
+    %old = bitcast {T} %dold to {Ti}
+    %iptr = bitcast {T}* %ptr to {Ti}*
+    %new = bitcast {T} %val to {Ti}
+    {CAS}
+    %dcas = bitcast {Ti} %cas to {T}
+    br label %lt_check
+
+done:
+    ret {T} %ptrval
+}}
+""" # noqa: E501
+
+
+def ir_cas(Ti):
+    # if NVVM().is_nvvm70:
+    if True:
+        return cas_nvvm70.format(Ti=Ti)
+    else:
+        return cas_nvvm34.format(Ti=Ti)
+
+
+def ir_numba_atomic_binary(T, Ti, OP, FUNC):
+    params = dict(T=T, Ti=Ti, OP=OP, FUNC=FUNC, CAS=ir_cas(Ti))
+    return ir_numba_atomic_binary_template.format(**params)
+
+
+def ir_numba_atomic_minmax(T, Ti, NAN, OP, PTR_OR_VAL, FUNC):
+    params = dict(T=T, Ti=Ti, NAN=NAN, OP=OP, PTR_OR_VAL=PTR_OR_VAL,
+                  FUNC=FUNC, CAS=ir_cas(Ti))
+
+    return ir_numba_atomic_minmax_template.format(**params)
+
+
+def ir_numba_atomic_inc(T, Tu):
+    return ir_numba_atomic_inc_template.format(T=T, Tu=Tu, CAS=ir_cas(T))
+
+
+def ir_numba_atomic_dec(T, Tu):
+    return ir_numba_atomic_dec_template.format(T=T, Tu=Tu, CAS=ir_cas(T))
+
+
+def _replace_datalayout(llvmir):
+    """
+    Find the line containing the datalayout and replace it
+    """
+    lines = llvmir.splitlines()
+    for i, ln in enumerate(lines):
+        if ln.startswith("target datalayout"):
+            tmp = 'target datalayout = "{0}"'
+            lines[i] = tmp.format(data_layout)
+            break
+    return '\n'.join(lines)
+
+
+def llvm_replace(llvmir):
+    replacements = [
+        ('declare double @"___numba_atomic_double_add"(double* %".1", double %".2")',     # noqa: E501
+         ir_numba_atomic_binary(T='double', Ti='i64', OP='fadd', FUNC='add')),
+        ('declare float @"___numba_atomic_float_sub"(float* %".1", float %".2")',         # noqa: E501
+         ir_numba_atomic_binary(T='float', Ti='i32', OP='fsub', FUNC='sub')),
+        ('declare double @"___numba_atomic_double_sub"(double* %".1", double %".2")',     # noqa: E501
+         ir_numba_atomic_binary(T='double', Ti='i64', OP='fsub', FUNC='sub')),
+        ('declare i64 @"___numba_atomic_u64_inc"(i64* %".1", i64 %".2")',
+         ir_numba_atomic_inc(T='i64', Tu='u64')),
+        ('declare i64 @"___numba_atomic_u64_dec"(i64* %".1", i64 %".2")',
+         ir_numba_atomic_dec(T='i64', Tu='u64')),
+        ('declare float @"___numba_atomic_float_max"(float* %".1", float %".2")',         # noqa: E501
+         ir_numba_atomic_minmax(T='float', Ti='i32', NAN='', OP='nnan olt',
+                                PTR_OR_VAL='ptr', FUNC='max')),
+        ('declare double @"___numba_atomic_double_max"(double* %".1", double %".2")',     # noqa: E501
+         ir_numba_atomic_minmax(T='double', Ti='i64', NAN='', OP='nnan olt',
+                                PTR_OR_VAL='ptr', FUNC='max')),
+        ('declare float @"___numba_atomic_float_min"(float* %".1", float %".2")',         # noqa: E501
+         ir_numba_atomic_minmax(T='float', Ti='i32', NAN='', OP='nnan ogt',
+                                PTR_OR_VAL='ptr', FUNC='min')),
+        ('declare double @"___numba_atomic_double_min"(double* %".1", double %".2")',     # noqa: E501
+         ir_numba_atomic_minmax(T='double', Ti='i64', NAN='', OP='nnan ogt',
+                                PTR_OR_VAL='ptr', FUNC='min')),
+        ('declare float @"___numba_atomic_float_nanmax"(float* %".1", float %".2")',      # noqa: E501
+         ir_numba_atomic_minmax(T='float', Ti='i32', NAN='nan', OP='ult',
+                                PTR_OR_VAL='', FUNC='max')),
+        ('declare double @"___numba_atomic_double_nanmax"(double* %".1", double %".2")',  # noqa: E501
+         ir_numba_atomic_minmax(T='double', Ti='i64', NAN='nan', OP='ult',
+                                PTR_OR_VAL='', FUNC='max')),
+        ('declare float @"___numba_atomic_float_nanmin"(float* %".1", float %".2")',      # noqa: E501
+         ir_numba_atomic_minmax(T='float', Ti='i32', NAN='nan', OP='ugt',
+                                PTR_OR_VAL='', FUNC='min')),
+        ('declare double @"___numba_atomic_double_nanmin"(double* %".1", double %".2")',  # noqa: E501
+         ir_numba_atomic_minmax(T='double', Ti='i64', NAN='nan', OP='ugt',
+                                PTR_OR_VAL='', FUNC='min')),
+        ('immarg', '')
+    ]
+
+    # if not NVVM().is_nvvm70:
+    if False:
+        # Replace with our cmpxchg implementation because LLVM 3.5 has a new
+        # semantic for cmpxchg.
+        replacements += [
+            ('declare i32 @"___numba_atomic_i32_cas_hack"(i32* %".1", i32 %".2", i32 %".3")',  # noqa: E501
+             ir_numba_cas_hack.format(T='i32')),
+            ('declare i64 @"___numba_atomic_i64_cas_hack"(i64* %".1", i64 %".2", i64 %".3")',  # noqa: E501
+             ir_numba_cas_hack.format(T='i64'))
+        ]
+        # Newer LLVMs generate a shorthand for datalayout that NVVM34 does not
+        # know
+        llvmir = _replace_datalayout(llvmir)
+
+    for decl, fn in replacements:
+        llvmir = llvmir.replace(decl, fn)
+
+    # llvm.numba_nvvm.atomic is used to prevent LLVM 9 onwards auto-upgrading
+    # these intrinsics into atomicrmw instructions, which are not recognized by
+    # NVVM. We can now replace them with the real intrinsic names, ready to
+    # pass to NVVM.
+    llvmir = llvmir.replace('llvm.numba_nvvm.atomic', 'llvm.nvvm.atomic')
+
+    # if NVVM().is_nvvm70:
+    # if True:
+    #     llvmir = llvm100_to_70_ir(llvmir)
+    # else:
+    #     llvmir = llvm100_to_34_ir(llvmir)
+    return llvmir
+
+
+def llvm_to_ptx(llvmir, **opts):
+    if isinstance(llvmir, str):
+        llvmir = [llvmir]
+
+    if opts.pop('fastmath', False):
+        opts.update({
+            'ftz': True,
+            'fma': True,
+            'prec_div': False,
+            'prec_sqrt': False,
+        })
+
+    cu = CompilationUnit()
+    libdevice = LibDevice()
+
+    for mod in llvmir:
+        mod = llvm_replace(mod)
+        cu.add_module(mod.encode('utf8'))
+    cu.lazy_add_module(libdevice.get())
+
+    return cu.compile(**opts)
+
+
+re_metadata_def = re.compile(r"\!\d+\s*=")
+re_metadata_correct_usage = re.compile(r"metadata\s*\![{'\"0-9]")
+re_metadata_ref = re.compile(r"\!\d+")
+
+debuginfo_pattern = r"\!{i32 \d, \!\"Debug Info Version\", i32 \d}"
+re_metadata_debuginfo = re.compile(debuginfo_pattern.replace(' ', r'\s+'))
+
+re_attributes_def = re.compile(r"^attributes #\d+ = \{ ([\w\s]+)\ }")
+supported_attributes = {'alwaysinline', 'cold', 'inlinehint', 'minsize',
+                        'noduplicate', 'noinline', 'noreturn', 'nounwind',
+                        'optnone', 'optisze', 'readnone', 'readonly'}
+
+re_getelementptr = re.compile(r"\bgetelementptr\s(?:inbounds )?\(?")
+
+re_load = re.compile(r"=\s*\bload\s(?:\bvolatile\s)?")
+
+re_call = re.compile(r"(call\s[^@]+\))(\s@)")
+re_range = re.compile(r"\s*!range\s+!\d+")
+
+re_type_tok = re.compile(r"[,{}()[\]]")
+
+re_annotations = re.compile(r"\bnonnull\b")
+
+re_unsupported_keywords = re.compile(r"\b(local_unnamed_addr|writeonly)\b")
+
+re_parenthesized_list = re.compile(r"\((.*)\)")
+
+re_spflags = re.compile(r"spFlags: (.*),")
+
+spflagmap = {
+    'DISPFlagDefinition': 'isDefinition',
+    'DISPFlagOptimized': 'isOptimized',
+}
+
+
+def llvm100_to_70_ir(ir):
+    """
+    Convert LLVM 10.0 IR for LLVM 7.0.
+    """
+    buf = []
+    for line in ir.splitlines():
+        if line.startswith('attributes #'):
+            # Remove function attributes unsupported by LLVM 7.0
+            m = re_attributes_def.match(line)
+            attrs = m.group(1).split()
+            attrs = ' '.join(a for a in attrs if a != 'willreturn')
+            line = line.replace(m.group(1), attrs)
+
+        buf.append(line)
+
+    return '\n'.join(buf)
+
+
+def llvm100_to_34_ir(ir):
+    """
+    Convert LLVM 10.0 IR for LLVM 3.4.
+    """
+    def parse_out_leading_type(s):
+        par_level = 0
+        pos = 0
+        # Parse out the first <ty> (which may be an aggregate type)
+        while True:
+            m = re_type_tok.search(s, pos)
+            if m is None:
+                # End of line
+                raise RuntimeError("failed parsing leading type: %s" % (s,))
+                break
+            pos = m.end()
+            tok = m.group(0)
+            if tok == ',':
+                if par_level == 0:
+                    # End of operand
+                    break
+            elif tok in '{[(':
+                par_level += 1
+            elif tok in ')]}':
+                par_level -= 1
+        return s[pos:].lstrip()
+
+    buf = []
+    for line in ir.splitlines():
+
+        # Fix llvm.dbg.cu
+        if line.startswith('!numba.llvm.dbg.cu'):
+            line = line.replace('!numba.llvm.dbg.cu', '!llvm.dbg.cu')
+
+        # We insert a dummy inlineasm to put debuginfo
+        if (line.lstrip().startswith('tail call void asm sideeffect "// dbg')
+                and '!numba.dbg' in line):
+            # Fix the metadata
+            line = line.replace('!numba.dbg', '!dbg')
+        if re_metadata_def.match(line):
+            # Rewrite metadata since LLVM 3.7 dropped the "metadata" type prefix
+            if None is re_metadata_correct_usage.search(line):
+                # Reintroduce the "metadata" prefix
+                line = line.replace('!{', 'metadata !{')
+                line = line.replace('!"', 'metadata !"')
+
+                assigpos = line.find('=')
+                lhs, rhs = line[:assigpos + 1], line[assigpos + 1:]
+
+                # Fix metadata reference
+                def fix_metadata_ref(m):
+                    return 'metadata ' + m.group(0)
+                line = ' '.join((lhs,
+                                 re_metadata_ref.sub(fix_metadata_ref, rhs)))
+        if line.startswith('source_filename ='):
+            continue    # skip line
+        if re_unsupported_keywords.search(line) is not None:
+            line = re_unsupported_keywords.sub(lambda m: '', line)
+
+        if line.startswith('attributes #'):
+            # Remove function attributes unsupported pre-3.8
+            m = re_attributes_def.match(line)
+            attrs = m.group(1).split()
+            attrs = ' '.join(a for a in attrs if a in supported_attributes)
+            line = line.replace(m.group(1), attrs)
+        if 'getelementptr ' in line:
+            # Rewrite "getelementptr ty, ty* ptr, ..."
+            # to "getelementptr ty *ptr, ..."
+            m = re_getelementptr.search(line)
+            if m is None:
+                raise RuntimeError("failed parsing getelementptr: %s" % (line,))
+            pos = m.end()
+            line = line[:pos] + parse_out_leading_type(line[pos:])
+        if 'load ' in line:
+            # Rewrite "load ty, ty* ptr"
+            # to "load ty *ptr"
+            m = re_load.search(line)
+            if m:
+                pos = m.end()
+                line = line[:pos] + parse_out_leading_type(line[pos:])
+        if 'call ' in line:
+            # Rewrite "call ty (...) @foo"
+            # to "call ty (...)* @foo"
+            line = re_call.sub(r"\1*\2", line)
+
+            # no !range metadata on calls
+            line = re_range.sub('', line).rstrip(',')
+
+            if '@llvm.memset' in line:
+                line = re_parenthesized_list.sub(
+                    _replace_llvm_memset_usage,
+                    line,
+                )
+        if 'declare' in line:
+            if '@llvm.memset' in line:
+                line = re_parenthesized_list.sub(
+                    _replace_llvm_memset_declaration,
+                    line,
+                )
+
+        # Remove unknown annotations
+        line = re_annotations.sub('', line)
+
+        buf.append(line)
+
+    return '\n'.join(buf)
+
+
+def _replace_llvm_memset_usage(m):
+    """Replace `llvm.memset` usage for llvm7+.
+
+    Used as functor for `re.sub.
+    """
+    params = list(m.group(1).split(','))
+    align_attr = re.search(r'align (\d+)', params[0])
+    if not align_attr:
+        raise ValueError("No alignment attribute found on memset dest")
+    else:
+        align = align_attr.group(1)
+    params.insert(-1, 'i32 {}'.format(align))
+    out = ', '.join(params)
+    return '({})'.format(out)
+
+
+def _replace_llvm_memset_declaration(m):
+    """Replace `llvm.memset` declaration for llvm7+.
+
+    Used as functor for `re.sub.
+    """
+    params = list(m.group(1).split(','))
+    params.insert(-1, 'i32')
+    out = ', '.join(params)
+    return '({})'.format(out)
+
+
+def set_cuda_kernel(lfunc):
+    mod = lfunc.module
+
+    lfunc.calling_convention = CC_ILUVATAR_KERNEL
+    # mdstr = ir.MetaDataString(mod, "kernel")
+    # mdvalue = ir.Constant(ir.IntType(32), 1)
+    # md = mod.add_metadata((lfunc, mdstr, mdvalue))
+
+    # nmd = cgutils.get_or_insert_named_metadata(mod, 'nvvm.annotations')
+    # nmd.add(md)
+
+
+def add_ir_version(mod):
+    """Add NVVM IR version to module"""
+    i32 = ir.IntType(32)
+    # if NVVM().is_nvvm70:
+    if True:
+        # NVVM IR 1.6, DWARF 3.0
+        ir_versions = [i32(1), i32(6), i32(3), i32(0)]
+    else:
+        # NVVM IR 1.1, DWARF 2.0
+        ir_versions = [i32(1), i32(2), i32(2), i32(0)]
+
+    md_ver = mod.add_metadata(ir_versions)
+    mod.add_named_metadata('nvvmir.version', md_ver)
+
+
+def fix_data_layout(module):
+    module.data_layout = data_layout
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/rtapi.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/rtapi.py
new file mode 100644
index 000000000..4a88457f9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/rtapi.py
@@ -0,0 +1,10 @@
+"""
+Declarations of the Runtime API functions.
+"""
+
+from ctypes import c_int, POINTER
+
+API_PROTOTYPES = {
+    # cudaError_t cudaRuntimeGetVersion ( int* runtimeVersion )
+    'cudaRuntimeGetVersion': (c_int, POINTER(c_int)),
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/runtime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/runtime.py
new file mode 100644
index 000000000..4d6f5c7cb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudadrv/runtime.py
@@ -0,0 +1,143 @@
+"""
+CUDA Runtime wrapper.
+
+This provides a very minimal set of bindings, since the Runtime API is not
+really used in Numba except for querying the Runtime version.
+"""
+
+import ctypes
+import functools
+import sys
+
+from numba.core import config
+from numba.cuda.cudadrv.driver import ERROR_MAP, make_logger
+from numba.cuda.cudadrv.error import CudaSupportError, CudaRuntimeError
+from numba.cuda.cudadrv.libs import open_cudalib
+from numba.cuda.cudadrv.rtapi import API_PROTOTYPES
+from numba.cuda.cudadrv import enums
+
+
+class CudaRuntimeAPIError(CudaRuntimeError):
+    """
+    Raised when there is an error accessing a C API from the CUDA Runtime.
+    """
+    def __init__(self, code, msg):
+        self.code = code
+        self.msg = msg
+        super().__init__(code, msg)
+
+    def __str__(self):
+        return "[%s] %s" % (self.code, self.msg)
+
+
+class Runtime:
+    """
+    Runtime object that lazily binds runtime API functions.
+    """
+
+    def __init__(self):
+        self.is_initialized = False
+
+    def _initialize(self):
+        # lazily initialize logger
+        global _logger
+        _logger = make_logger()
+
+        if config.DISABLE_CUDA:
+            msg = ("CUDA is disabled due to setting NUMBA_DISABLE_CUDA=1 "
+                   "in the environment, or because CUDA is unsupported on "
+                   "32-bit systems.")
+            raise CudaSupportError(msg)
+        self.lib = open_cudalib('cudart')
+
+        self.is_initialized = True
+
+    def __getattr__(self, fname):
+        # First request of a runtime API function
+        try:
+            proto = API_PROTOTYPES[fname]
+        except KeyError:
+            raise AttributeError(fname)
+        restype = proto[0]
+        argtypes = proto[1:]
+
+        if not self.is_initialized:
+            self._initialize()
+
+        # Find function in runtime library
+        libfn = self._find_api(fname)
+        libfn.restype = restype
+        libfn.argtypes = argtypes
+
+        safe_call = self._wrap_api_call(fname, libfn)
+        setattr(self, fname, safe_call)
+        return safe_call
+
+    def _wrap_api_call(self, fname, libfn):
+        @functools.wraps(libfn)
+        def safe_cuda_api_call(*args):
+            _logger.debug('call runtime api: %s', libfn.__name__)
+            retcode = libfn(*args)
+            self._check_error(fname, retcode)
+        return safe_cuda_api_call
+
+    def _check_error(self, fname, retcode):
+        if retcode != enums.CUDA_SUCCESS:
+            errname = ERROR_MAP.get(retcode, "cudaErrorUnknown")
+            msg = "Call to %s results in %s" % (fname, errname)
+            _logger.error(msg)
+            raise CudaRuntimeAPIError(retcode, msg)
+
+    def _find_api(self, fname):
+        try:
+            return getattr(self.lib, fname)
+        except AttributeError:
+            pass
+
+        # Not found.
+        # Delay missing function error to use
+        def absent_function(*args, **kws):
+            msg = "runtime missing function: %s."
+            raise CudaRuntimeError(msg % fname)
+
+        setattr(self, fname, absent_function)
+        return absent_function
+
+    def get_version(self):
+        """
+        Returns the CUDA Runtime version as a tuple (major, minor).
+        """
+        rtver = ctypes.c_int()
+        self.cudaRuntimeGetVersion(ctypes.byref(rtver))
+        # The version is encoded as (1000 * major) + (10 * minor)
+        major = rtver.value // 1000
+        minor = (rtver.value - (major * 1000)) // 10
+        return (major, minor)
+
+    def is_supported_version(self):
+        """
+        Returns True if the CUDA Runtime is a supported version.
+        """
+
+        return self.get_version() in self.supported_versions
+
+    @property
+    def supported_versions(self):
+        """A tuple of all supported CUDA toolkit versions. Versions are given in
+        the form ``(major_version, minor_version)``."""
+        if sys.platform not in ('linux', 'win32') or config.MACHINE_BITS != 64:
+            # Only 64-bit Linux and Windows are supported
+            return ()
+        return ((9, 2),
+                (10, 0), (10, 1), (10, 2),
+                (11, 0), (11, 1), (11, 2))
+
+
+runtime = Runtime()
+
+
+def get_version():
+    """
+    Return the runtime version as a tuple of (major, minor)
+    """
+    return runtime.get_version()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudaimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudaimpl.py
new file mode 100644
index 000000000..11160d3d4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudaimpl.py
@@ -0,0 +1,1116 @@
+from functools import reduce
+import operator
+import math
+
+from llvmlite import ir
+import llvmlite.binding as ll
+
+from numba.core.imputils import Registry, lower_cast
+from numba.core.typing.npydecl import parse_dtype, signature
+from numba.core.datamodel import models
+from numba.core import types, cgutils
+from .cudadrv import nvvm
+from numba import cuda
+from numba.cuda import nvvmutils, stubs, errors
+from numba.cuda.types import dim3, grid_group, CUDADispatcher
+
+
+registry = Registry()
+lower = registry.lower
+lower_attr = registry.lower_getattr
+lower_constant = registry.lower_constant
+
+
+def initialize_dim3(builder, prefix):
+    x = nvvmutils.call_sreg(builder, "%s.x" % prefix)
+    y = nvvmutils.call_sreg(builder, "%s.y" % prefix)
+    z = nvvmutils.call_sreg(builder, "%s.z" % prefix)
+    return cgutils.pack_struct(builder, (x, y, z))
+
+
+@lower_attr(types.Module(cuda), 'threadIdx')
+def cuda_threadIdx(context, builder, sig, args):
+    return initialize_dim3(builder, 'tid')
+
+
+@lower_attr(types.Module(cuda), 'blockDim')
+def cuda_blockDim(context, builder, sig, args):
+    return initialize_dim3(builder, 'ntid')
+
+
+@lower_attr(types.Module(cuda), 'blockIdx')
+def cuda_blockIdx(context, builder, sig, args):
+    return initialize_dim3(builder, 'ctaid')
+
+
+@lower_attr(types.Module(cuda), 'gridDim')
+def cuda_gridDim(context, builder, sig, args):
+    return initialize_dim3(builder, 'nctaid')
+
+
+@lower_attr(types.Module(cuda), 'laneid')
+def cuda_laneid(context, builder, sig, args):
+    return nvvmutils.call_sreg(builder, 'laneid')
+
+
+@lower_attr(types.Module(cuda), 'warpsize')
+def cuda_warpsize(context, builder, sig, args):
+    return nvvmutils.call_sreg(builder, 'warpsize')
+
+
+@lower_attr(dim3, 'x')
+def dim3_x(context, builder, sig, args):
+    return builder.extract_value(args, 0)
+
+
+@lower_attr(dim3, 'y')
+def dim3_y(context, builder, sig, args):
+    return builder.extract_value(args, 1)
+
+
+@lower_attr(dim3, 'z')
+def dim3_z(context, builder, sig, args):
+    return builder.extract_value(args, 2)
+
+
+@lower(cuda.cg.this_grid)
+def cg_this_grid(context, builder, sig, args):
+    one = context.get_constant(types.int32, 1)
+    lmod = builder.module
+    return builder.call(
+        nvvmutils.declare_cudaCGGetIntrinsicHandle(lmod),
+        (one,))
+
+
+@lower('GridGroup.sync', grid_group)
+def ptx_sync_group(context, builder, sig, args):
+    flags = context.get_constant(types.int32, 0)
+    lmod = builder.module
+    return builder.call(
+        nvvmutils.declare_cudaCGSynchronize(lmod),
+        (*args, flags))
+
+
+# -----------------------------------------------------------------------------
+
+@lower(cuda.grid, types.int32)
+def cuda_grid(context, builder, sig, args):
+    restype = sig.return_type
+    if restype == types.int32:
+        return nvvmutils.get_global_id(builder, dim=1)
+    elif isinstance(restype, types.UniTuple):
+        ids = nvvmutils.get_global_id(builder, dim=restype.count)
+        return cgutils.pack_array(builder, ids)
+    else:
+        raise ValueError('Unexpected return type %s from cuda.grid' % restype)
+
+
+def _nthreads_for_dim(builder, dim):
+    ntid = nvvmutils.call_sreg(builder, f"ntid.{dim}")
+    nctaid = nvvmutils.call_sreg(builder, f"nctaid.{dim}")
+    return builder.mul(ntid, nctaid)
+
+
+@lower(cuda.gridsize, types.int32)
+def cuda_gridsize(context, builder, sig, args):
+    restype = sig.return_type
+    nx = _nthreads_for_dim(builder, 'x')
+
+    if restype == types.int32:
+        return nx
+    elif isinstance(restype, types.UniTuple):
+        ny = _nthreads_for_dim(builder, 'y')
+
+        if restype.count == 2:
+            return cgutils.pack_array(builder, (nx, ny))
+        elif restype.count == 3:
+            nz = _nthreads_for_dim(builder, 'z')
+            return cgutils.pack_array(builder, (nx, ny, nz))
+
+    # Fallthrough to here indicates unexpected return type or tuple length
+    raise ValueError('Unexpected return type %s of cuda.gridsize' % restype)
+
+
+# -----------------------------------------------------------------------------
+
+@lower(cuda.const.array_like, types.Array)
+def cuda_const_array_like(context, builder, sig, args):
+    # This is a no-op because CUDATargetContext.make_constant_array already
+    # created the constant array.
+    return args[0]
+
+
+_unique_smem_id = 0
+
+
+def _get_unique_smem_id(name):
+    """Due to bug with NVVM invalid internalizing of shared memory in the
+    PTX output.  We can't mark shared memory to be internal. We have to
+    ensure unique name is generated for shared memory symbol.
+    """
+    global _unique_smem_id
+    _unique_smem_id += 1
+    return "{0}_{1}".format(name, _unique_smem_id)
+
+
+@lower(cuda.shared.array, types.IntegerLiteral, types.Any)
+def cuda_shared_array_integer(context, builder, sig, args):
+    length = sig.args[0].literal_value
+    dtype = parse_dtype(sig.args[1])
+    return _generic_array(context, builder, shape=(length,), dtype=dtype,
+                          symbol_name=_get_unique_smem_id('_cudapy_smem'),
+                          addrspace=nvvm.ADDRSPACE_SHARED,
+                          can_dynsized=True)
+
+
+@lower(cuda.shared.array, types.Tuple, types.Any)
+@lower(cuda.shared.array, types.UniTuple, types.Any)
+def cuda_shared_array_tuple(context, builder, sig, args):
+    shape = [ s.literal_value for s in sig.args[0] ]
+    dtype = parse_dtype(sig.args[1])
+    return _generic_array(context, builder, shape=shape, dtype=dtype,
+                          symbol_name=_get_unique_smem_id('_cudapy_smem'),
+                          addrspace=nvvm.ADDRSPACE_SHARED,
+                          can_dynsized=True)
+
+
+@lower(cuda.local.array, types.IntegerLiteral, types.Any)
+def cuda_local_array_integer(context, builder, sig, args):
+    length = sig.args[0].literal_value
+    dtype = parse_dtype(sig.args[1])
+    return _generic_array(context, builder, shape=(length,), dtype=dtype,
+                          symbol_name='_cudapy_lmem',
+                          addrspace=nvvm.ADDRSPACE_LOCAL,
+                          can_dynsized=False)
+
+
+@lower(cuda.local.array, types.Tuple, types.Any)
+@lower(cuda.local.array, types.UniTuple, types.Any)
+def ptx_lmem_alloc_array(context, builder, sig, args):
+    shape = [ s.literal_value for s in sig.args[0] ]
+    dtype = parse_dtype(sig.args[1])
+    return _generic_array(context, builder, shape=shape, dtype=dtype,
+                          symbol_name='_cudapy_lmem',
+                          addrspace=nvvm.ADDRSPACE_LOCAL,
+                          can_dynsized=False)
+
+
+@lower(stubs.syncthreads)
+def ptx_syncthreads(context, builder, sig, args):
+    assert not args
+    fname = 'llvm.nvvm.barrier0'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.VoidType(), ())
+    sync = cgutils.get_or_insert_function(lmod, fnty, fname)
+    builder.call(sync, ())
+    return context.get_dummy_value()
+
+
+@lower(stubs.syncthreads_count, types.i4)
+def ptx_syncthreads_count(context, builder, sig, args):
+    fname = 'llvm.nvvm.barrier0.popc'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.IntType(32), (ir.IntType(32),))
+    sync = cgutils.get_or_insert_function(lmod, fnty, fname)
+    return builder.call(sync, args)
+
+
+@lower(stubs.syncthreads_and, types.i4)
+def ptx_syncthreads_and(context, builder, sig, args):
+    fname = 'llvm.nvvm.barrier0.and'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.IntType(32), (ir.IntType(32),))
+    sync = cgutils.get_or_insert_function(lmod, fnty, fname)
+    return builder.call(sync, args)
+
+
+@lower(stubs.syncthreads_or, types.i4)
+def ptx_syncthreads_or(context, builder, sig, args):
+    fname = 'llvm.nvvm.barrier0.or'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.IntType(32), (ir.IntType(32),))
+    sync = cgutils.get_or_insert_function(lmod, fnty, fname)
+    return builder.call(sync, args)
+
+
+@lower(stubs.threadfence_block)
+def ptx_threadfence_block(context, builder, sig, args):
+    assert not args
+    fname = 'llvm.nvvm.membar.cta'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.VoidType(), ())
+    sync = cgutils.get_or_insert_function(lmod, fnty, fname)
+    builder.call(sync, ())
+    return context.get_dummy_value()
+
+
+@lower(stubs.threadfence_system)
+def ptx_threadfence_system(context, builder, sig, args):
+    assert not args
+    fname = 'llvm.nvvm.membar.sys'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.VoidType(), ())
+    sync = cgutils.get_or_insert_function(lmod, fnty, fname)
+    builder.call(sync, ())
+    return context.get_dummy_value()
+
+
+@lower(stubs.threadfence)
+def ptx_threadfence_device(context, builder, sig, args):
+    assert not args
+    fname = 'llvm.nvvm.membar.gl'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.VoidType(), ())
+    sync = cgutils.get_or_insert_function(lmod, fnty, fname)
+    builder.call(sync, ())
+    return context.get_dummy_value()
+
+
+@lower(stubs.syncwarp)
+def ptx_syncwarp(context, builder, sig, args):
+    mask = context.get_constant(types.int32, 0xFFFFFFFF)
+    mask_sig = signature(types.none, types.int32)
+    return ptx_syncwarp_mask(context, builder, mask_sig, [mask])
+
+
+@lower(stubs.syncwarp, types.i4)
+def ptx_syncwarp_mask(context, builder, sig, args):
+    fname = 'llvm.nvvm.bar.warp.sync'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.VoidType(), (ir.IntType(32),))
+    sync = cgutils.get_or_insert_function(lmod, fnty, fname)
+    builder.call(sync, args)
+    return context.get_dummy_value()
+
+
+@lower(stubs.shfl_sync_intrinsic, types.i4, types.i4, types.i4, types.i4,
+       types.i4)
+@lower(stubs.shfl_sync_intrinsic, types.i4, types.i4, types.i8, types.i4,
+       types.i4)
+@lower(stubs.shfl_sync_intrinsic, types.i4, types.i4, types.f4, types.i4,
+       types.i4)
+@lower(stubs.shfl_sync_intrinsic, types.i4, types.i4, types.f8, types.i4,
+       types.i4)
+def ptx_shfl_sync_i32(context, builder, sig, args):
+    """
+    The NVVM intrinsic for shfl only supports i32, but the cuda intrinsic
+    function supports both 32 and 64 bit ints and floats, so for feature parity,
+    i64, f32, and f64 are implemented. Floats by way of bitcasting the float to
+    an int, then shuffling, then bitcasting back. And 64-bit values by packing
+    them into 2 32bit values, shuffling thoose, and then packing back together.
+    """
+    mask, mode, value, index, clamp = args
+    value_type = sig.args[2]
+    if value_type in types.real_domain:
+        value = builder.bitcast(value, ir.IntType(value_type.bitwidth))
+    fname = 'llvm.nvvm.shfl.sync.i32'
+    lmod = builder.module
+    fnty = ir.FunctionType(
+        ir.LiteralStructType((ir.IntType(32), ir.IntType(1))),
+                            (ir.IntType(32), ir.IntType(32), ir.IntType(32),
+                             ir.IntType(32), ir.IntType(32))
+    )
+    func = cgutils.get_or_insert_function(lmod, fnty, fname)
+    if value_type.bitwidth == 32:
+        ret = builder.call(func, (mask, mode, value, index, clamp))
+        if value_type == types.float32:
+            rv = builder.extract_value(ret, 0)
+            pred = builder.extract_value(ret, 1)
+            fv = builder.bitcast(rv, ir.FloatType())
+            ret = cgutils.make_anonymous_struct(builder, (fv, pred))
+    else:
+        value1 = builder.trunc(value, ir.IntType(32))
+        value_lshr = builder.lshr(value, context.get_constant(types.i8, 32))
+        value2 = builder.trunc(value_lshr, ir.IntType(32))
+        ret1 = builder.call(func, (mask, mode, value1, index, clamp))
+        ret2 = builder.call(func, (mask, mode, value2, index, clamp))
+        rv1 = builder.extract_value(ret1, 0)
+        rv2 = builder.extract_value(ret2, 0)
+        pred = builder.extract_value(ret1, 1)
+        rv1_64 = builder.zext(rv1, ir.IntType(64))
+        rv2_64 = builder.zext(rv2, ir.IntType(64))
+        rv_shl = builder.shl(rv2_64, context.get_constant(types.i8, 32))
+        rv = builder.or_(rv_shl, rv1_64)
+        if value_type == types.float64:
+            rv = builder.bitcast(rv, ir.DoubleType())
+        ret = cgutils.make_anonymous_struct(builder, (rv, pred))
+    return ret
+
+
+@lower(stubs.vote_sync_intrinsic, types.i4, types.i4, types.boolean)
+def ptx_vote_sync(context, builder, sig, args):
+    fname = 'llvm.nvvm.vote.sync'
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.LiteralStructType((ir.IntType(32),
+                                                 ir.IntType(1))),
+                           (ir.IntType(32), ir.IntType(32), ir.IntType(1)))
+    func = cgutils.get_or_insert_function(lmod, fnty, fname)
+    return builder.call(func, args)
+
+
+@lower(stubs.match_any_sync, types.i4, types.i4)
+@lower(stubs.match_any_sync, types.i4, types.i8)
+@lower(stubs.match_any_sync, types.i4, types.f4)
+@lower(stubs.match_any_sync, types.i4, types.f8)
+def ptx_match_any_sync(context, builder, sig, args):
+    mask, value = args
+    width = sig.args[1].bitwidth
+    if sig.args[1] in types.real_domain:
+        value = builder.bitcast(value, ir.IntType(width))
+    fname = 'llvm.nvvm.match.any.sync.i{}'.format(width)
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.IntType(32), (ir.IntType(32), ir.IntType(width)))
+    func = cgutils.get_or_insert_function(lmod, fnty, fname)
+    return builder.call(func, (mask, value))
+
+
+@lower(stubs.match_all_sync, types.i4, types.i4)
+@lower(stubs.match_all_sync, types.i4, types.i8)
+@lower(stubs.match_all_sync, types.i4, types.f4)
+@lower(stubs.match_all_sync, types.i4, types.f8)
+def ptx_match_all_sync(context, builder, sig, args):
+    mask, value = args
+    width = sig.args[1].bitwidth
+    if sig.args[1] in types.real_domain:
+        value = builder.bitcast(value, ir.IntType(width))
+    fname = 'llvm.nvvm.match.all.sync.i{}'.format(width)
+    lmod = builder.module
+    fnty = ir.FunctionType(ir.LiteralStructType((ir.IntType(32),
+                                                 ir.IntType(1))),
+                           (ir.IntType(32), ir.IntType(width)))
+    func = cgutils.get_or_insert_function(lmod, fnty, fname)
+    return builder.call(func, (mask, value))
+
+
+@lower(stubs.activemask)
+def ptx_activemask(context, builder, sig, args):
+    activemask = ir.InlineAsm(ir.FunctionType(ir.IntType(32), []),
+                              "activemask.b32 $0;", '=r', side_effect=True)
+    return builder.call(activemask, [])
+
+
+@lower(stubs.lanemask_lt)
+def ptx_lanemask_lt(context, builder, sig, args):
+    activemask = ir.InlineAsm(ir.FunctionType(ir.IntType(32), []),
+                              "mov.u32 $0, %lanemask_lt;", '=r',
+                              side_effect=True)
+    return builder.call(activemask, [])
+
+
+@lower(stubs.popc, types.Any)
+def ptx_popc(context, builder, sig, args):
+    return builder.ctpop(args[0])
+
+
+@lower(stubs.fma, types.Any, types.Any, types.Any)
+def ptx_fma(context, builder, sig, args):
+    return builder.fma(*args)
+
+
+def float16_float_ty_constraint(bitwidth):
+    typemap = {32: ('f32', 'f'), 64: ('f64', 'd')}
+
+    try:
+        return typemap[bitwidth]
+    except KeyError:
+        msg = f"Conversion between float16 and float{bitwidth} unsupported"
+        raise errors.CudaLoweringError(msg)
+
+
+@lower_cast(types.float16, types.Float)
+def float16_to_float_cast(context, builder, fromty, toty, val):
+    if fromty.bitwidth == toty.bitwidth:
+        return val
+
+    ty, constraint = float16_float_ty_constraint(toty.bitwidth)
+
+    fnty = ir.FunctionType(context.get_value_type(toty), [ir.IntType(16)])
+    asm = ir.InlineAsm(fnty, f"cvt.{ty}.f16 $0, $1;", f"={constraint},h")
+    return builder.call(asm, [val])
+
+
+@lower_cast(types.Float, types.float16)
+def float_to_float16_cast(context, builder, fromty, toty, val):
+    if fromty.bitwidth == toty.bitwidth:
+        return val
+
+    ty, constraint = float16_float_ty_constraint(fromty.bitwidth)
+
+    fnty = ir.FunctionType(ir.IntType(16), [context.get_value_type(fromty)])
+    asm = ir.InlineAsm(fnty, f"cvt.rn.f16.{ty} $0, $1;", f"=h,{constraint}")
+    return builder.call(asm, [val])
+
+
+def float16_int_constraint(bitwidth):
+    typemap = { 8: 'c', 16: 'h', 32: 'r', 64: 'l' }
+
+    try:
+        return typemap[bitwidth]
+    except KeyError:
+        msg = f"Conversion between float16 and int{bitwidth} unsupported"
+        raise errors.CudaLoweringError(msg)
+
+
+@lower_cast(types.float16, types.Integer)
+def float16_to_integer_cast(context, builder, fromty, toty, val):
+    bitwidth = toty.bitwidth
+    constraint = float16_int_constraint(bitwidth)
+    signedness = 's' if toty.signed else 'u'
+
+    fnty = ir.FunctionType(context.get_value_type(toty), [ir.IntType(16)])
+    asm = ir.InlineAsm(fnty,
+                       f"cvt.rni.{signedness}{bitwidth}.f16 $0, $1;",
+                       f"={constraint},h")
+    return builder.call(asm, [val])
+
+
+@lower_cast(types.Integer, types.float16)
+@lower_cast(types.IntegerLiteral, types.float16)
+def integer_to_float16_cast(context, builder, fromty, toty, val):
+    bitwidth = fromty.bitwidth
+    constraint = float16_int_constraint(bitwidth)
+    signedness = 's' if fromty.signed else 'u'
+
+    fnty = ir.FunctionType(ir.IntType(16),
+                           [context.get_value_type(fromty)])
+    asm = ir.InlineAsm(fnty,
+                       f"cvt.rn.f16.{signedness}{bitwidth} $0, $1;",
+                       f"=h,{constraint}")
+    return builder.call(asm, [val])
+
+
+def lower_fp16_binary(fn, op):
+    @lower(fn, types.float16, types.float16)
+    def ptx_fp16_binary(context, builder, sig, args):
+        fnty = ir.FunctionType(ir.IntType(16),
+                               [ir.IntType(16), ir.IntType(16)])
+        asm = ir.InlineAsm(fnty, f'{op}.f16 $0,$1,$2;', '=h,h,h')
+        return builder.call(asm, args)
+
+
+lower_fp16_binary(stubs.fp16.hadd, 'add')
+lower_fp16_binary(stubs.fp16.hsub, 'sub')
+lower_fp16_binary(stubs.fp16.hmul, 'mul')
+
+
+@lower(stubs.fp16.hneg, types.float16)
+def ptx_fp16_hneg(context, builder, sig, args):
+    fnty = ir.FunctionType(ir.IntType(16), [ir.IntType(16)])
+    asm = ir.InlineAsm(fnty, 'neg.f16 $0, $1;', '=h,h')
+    return builder.call(asm, args)
+
+
+@lower(stubs.fp16.habs, types.float16)
+def ptx_fp16_habs(context, builder, sig, args):
+    if cuda.runtime.get_version() < (10, 2):
+        # CUDA < 10.2 does not support abs.f16. For these versions, we mask
+        # off the sign bit to compute abs instead. We determine whether or
+        # not to do this based on the runtime version so that our behaviour
+        # is consistent with the version of NVVM we're using to go from
+        # NVVM IR -> PTX.
+        inst = 'and.b16 $0, $1, 0x7FFF;'
+    else:
+        inst = 'abs.f16 $0, $1;'
+
+    fnty = ir.FunctionType(ir.IntType(16), [ir.IntType(16)])
+    asm = ir.InlineAsm(fnty, inst, '=h,h')
+    return builder.call(asm, args)
+
+
+@lower(stubs.fp16.hfma, types.float16, types.float16, types.float16)
+def ptx_hfma(context, builder, sig, args):
+    argtys = [ir.IntType(16), ir.IntType(16), ir.IntType(16)]
+    fnty = ir.FunctionType(ir.IntType(16), argtys)
+    asm = ir.InlineAsm(fnty, "fma.rn.f16 $0,$1,$2,$3;", "=h,h,h,h")
+    return builder.call(asm, args)
+
+
+_fp16_cmp = """{{
+          .reg .pred __$$f16_cmp_tmp;
+          setp.{op}.f16 __$$f16_cmp_tmp, $1, $2;
+          selp.u16 $0, 1, 0, __$$f16_cmp_tmp;
+        }}"""
+
+
+def _gen_fp16_cmp(op):
+    def ptx_fp16_comparison(context, builder, sig, args):
+        fnty = ir.FunctionType(ir.IntType(16), [ir.IntType(16), ir.IntType(16)])
+        asm = ir.InlineAsm(fnty, _fp16_cmp.format(op=op), '=h,h,h')
+        result = builder.call(asm, args)
+
+        zero = context.get_constant(types.int16, 0)
+        int_result = builder.bitcast(result, ir.IntType(16))
+        return builder.icmp_unsigned("!=", int_result, zero)
+    return ptx_fp16_comparison
+
+
+lower(stubs.fp16.heq, types.float16, types.float16)(_gen_fp16_cmp('eq'))
+lower(stubs.fp16.hne, types.float16, types.float16)(_gen_fp16_cmp('ne'))
+lower(stubs.fp16.hge, types.float16, types.float16)(_gen_fp16_cmp('ge'))
+lower(stubs.fp16.hgt, types.float16, types.float16)(_gen_fp16_cmp('gt'))
+lower(stubs.fp16.hle, types.float16, types.float16)(_gen_fp16_cmp('le'))
+lower(stubs.fp16.hlt, types.float16, types.float16)(_gen_fp16_cmp('lt'))
+
+
+def lower_fp16_minmax(fn, fname, op):
+    @lower(fn, types.float16, types.float16)
+    def ptx_fp16_minmax(context, builder, sig, args):
+        choice = _gen_fp16_cmp(op)(context, builder, sig, args)
+        return builder.select(choice, args[0], args[1])
+
+
+lower_fp16_minmax(stubs.fp16.hmax, 'max', 'gt')
+lower_fp16_minmax(stubs.fp16.hmin, 'min', 'lt')
+
+# See:
+# https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cbrt.html#__nv_cbrt
+# https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cbrtf.html#__nv_cbrtf
+
+
+cbrt_funcs = {
+    types.float32: '__nv_cbrtf',
+    types.float64: '__nv_cbrt',
+}
+
+
+@lower(stubs.cbrt, types.float32)
+@lower(stubs.cbrt, types.float64)
+def ptx_cbrt(context, builder, sig, args):
+    ty = sig.return_type
+    fname = cbrt_funcs[ty]
+    fty = context.get_value_type(ty)
+    lmod = builder.module
+    fnty = ir.FunctionType(fty, [fty])
+    fn = cgutils.get_or_insert_function(lmod, fnty, fname)
+    return builder.call(fn, args)
+
+
+@lower(stubs.brev, types.u4)
+def ptx_brev_u4(context, builder, sig, args):
+    # FIXME the llvm.bitreverse.i32 intrinsic isn't supported by nvcc
+    # return builder.bitreverse(args[0])
+
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(ir.IntType(32), (ir.IntType(32),)),
+        '__nv_brev')
+    return builder.call(fn, args)
+
+
+@lower(stubs.brev, types.u8)
+def ptx_brev_u8(context, builder, sig, args):
+    # FIXME the llvm.bitreverse.i64 intrinsic isn't supported by nvcc
+    # return builder.bitreverse(args[0])
+
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(ir.IntType(64), (ir.IntType(64),)),
+        '__nv_brevll')
+    return builder.call(fn, args)
+
+
+@lower(stubs.clz, types.Any)
+def ptx_clz(context, builder, sig, args):
+    return builder.ctlz(
+        args[0],
+        context.get_constant(types.boolean, 0))
+
+
+@lower(stubs.ffs, types.i4)
+@lower(stubs.ffs, types.u4)
+def ptx_ffs_32(context, builder, sig, args):
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(ir.IntType(32), (ir.IntType(32),)),
+        '__nv_ffs')
+    return builder.call(fn, args)
+
+
+@lower(stubs.ffs, types.i8)
+@lower(stubs.ffs, types.u8)
+def ptx_ffs_64(context, builder, sig, args):
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(ir.IntType(32), (ir.IntType(64),)),
+        '__nv_ffsll')
+    return builder.call(fn, args)
+
+
+@lower(stubs.selp, types.Any, types.Any, types.Any)
+def ptx_selp(context, builder, sig, args):
+    test, a, b = args
+    return builder.select(test, a, b)
+
+
+@lower(max, types.f4, types.f4)
+def ptx_max_f4(context, builder, sig, args):
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(
+            ir.FloatType(),
+            (ir.FloatType(), ir.FloatType())),
+        '__nv_fmaxf')
+    return builder.call(fn, args)
+
+
+@lower(max, types.f8, types.f4)
+@lower(max, types.f4, types.f8)
+@lower(max, types.f8, types.f8)
+def ptx_max_f8(context, builder, sig, args):
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(
+            ir.DoubleType(),
+            (ir.DoubleType(), ir.DoubleType())),
+        '__nv_fmax')
+
+    return builder.call(fn, [
+        context.cast(builder, args[0], sig.args[0], types.double),
+        context.cast(builder, args[1], sig.args[1], types.double),
+    ])
+
+
+@lower(min, types.f4, types.f4)
+def ptx_min_f4(context, builder, sig, args):
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(
+            ir.FloatType(),
+            (ir.FloatType(), ir.FloatType())),
+        '__nv_fminf')
+    return builder.call(fn, args)
+
+
+@lower(min, types.f8, types.f4)
+@lower(min, types.f4, types.f8)
+@lower(min, types.f8, types.f8)
+def ptx_min_f8(context, builder, sig, args):
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(
+            ir.DoubleType(),
+            (ir.DoubleType(), ir.DoubleType())),
+        '__nv_fmin')
+
+    return builder.call(fn, [
+        context.cast(builder, args[0], sig.args[0], types.double),
+        context.cast(builder, args[1], sig.args[1], types.double),
+    ])
+
+
+@lower(round, types.f4)
+@lower(round, types.f8)
+def ptx_round(context, builder, sig, args):
+    fn = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(
+            ir.IntType(64),
+            (ir.DoubleType(),)),
+        '__nv_llrint')
+    return builder.call(fn, [
+        context.cast(builder, args[0], sig.args[0], types.double),
+    ])
+
+
+# This rounding implementation follows the algorithm used in the "fallback
+# version" of double_round in CPython.
+# https://github.com/python/cpython/blob/a755410e054e1e2390de5830befc08fe80706c66/Objects/floatobject.c#L964-L1007
+
+@lower(round, types.f4, types.Integer)
+@lower(round, types.f8, types.Integer)
+def round_to_impl(context, builder, sig, args):
+    def round_ndigits(x, ndigits):
+        if math.isinf(x) or math.isnan(x):
+            return x
+
+        if ndigits >= 0:
+            if ndigits > 22:
+                # pow1 and pow2 are each safe from overflow, but
+                # pow1*pow2 ~= pow(10.0, ndigits) might overflow.
+                pow1 = 10.0 ** (ndigits - 22)
+                pow2 = 1e22
+            else:
+                pow1 = 10.0 ** ndigits
+                pow2 = 1.0
+            y = (x * pow1) * pow2
+            if math.isinf(y):
+                return x
+
+        else:
+            pow1 = 10.0 ** (-ndigits)
+            y = x / pow1
+
+        z = round(y)
+        if (math.fabs(y - z) == 0.5):
+            # halfway between two integers; use round-half-even
+            z = 2.0 * round(y / 2.0)
+
+        if ndigits >= 0:
+            z = (z / pow2) / pow1
+        else:
+            z *= pow1
+
+        return z
+
+    return context.compile_internal(builder, round_ndigits, sig, args, )
+
+
+def gen_deg_rad(const):
+    def impl(context, builder, sig, args):
+        argty, = sig.args
+        factor = context.get_constant(argty, const)
+        return builder.fmul(factor, args[0])
+    return impl
+
+
+_deg2rad = math.pi / 180.
+_rad2deg = 180. / math.pi
+lower(math.radians, types.f4)(gen_deg_rad(_deg2rad))
+lower(math.radians, types.f8)(gen_deg_rad(_deg2rad))
+lower(math.degrees, types.f4)(gen_deg_rad(_rad2deg))
+lower(math.degrees, types.f8)(gen_deg_rad(_rad2deg))
+
+
+def _normalize_indices(context, builder, indty, inds):
+    """
+    Convert integer indices into tuple of intp
+    """
+    if indty in types.integer_domain:
+        indty = types.UniTuple(dtype=indty, count=1)
+        indices = [inds]
+    else:
+        indices = cgutils.unpack_tuple(builder, inds, count=len(indty))
+    indices = [context.cast(builder, i, t, types.intp)
+               for t, i in zip(indty, indices)]
+    return indty, indices
+
+
+def _atomic_dispatcher(dispatch_fn):
+    def imp(context, builder, sig, args):
+        # The common argument handling code
+        aryty, indty, valty = sig.args
+        ary, inds, val = args
+        dtype = aryty.dtype
+
+        indty, indices = _normalize_indices(context, builder, indty, inds)
+
+        if dtype != valty:
+            raise TypeError("expect %s but got %s" % (dtype, valty))
+
+        if aryty.ndim != len(indty):
+            raise TypeError("indexing %d-D array with %d-D index" %
+                            (aryty.ndim, len(indty)))
+
+        lary = context.make_array(aryty)(context, builder, ary)
+        ptr = cgutils.get_item_pointer(context, builder, aryty, lary, indices,
+                                       wraparound=True)
+        # dispatcher to implementation base on dtype
+        return dispatch_fn(context, builder, dtype, ptr, val)
+    return imp
+
+
+@lower(stubs.atomic.add, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.add, types.Array, types.UniTuple, types.Any)
+@lower(stubs.atomic.add, types.Array, types.Tuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_add_tuple(context, builder, dtype, ptr, val):
+    if dtype == types.float32:
+        lmod = builder.module
+        return builder.call(nvvmutils.declare_atomic_add_float32(lmod),
+                            (ptr, val))
+    elif dtype == types.float64:
+        lmod = builder.module
+        return builder.call(nvvmutils.declare_atomic_add_float64(lmod),
+                            (ptr, val))
+    else:
+        return builder.atomic_rmw('add', ptr, val, 'monotonic')
+
+
+@lower(stubs.atomic.sub, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.sub, types.Array, types.UniTuple, types.Any)
+@lower(stubs.atomic.sub, types.Array, types.Tuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_sub(context, builder, dtype, ptr, val):
+    if dtype == types.float32:
+        lmod = builder.module
+        return builder.call(nvvmutils.declare_atomic_sub_float32(lmod),
+                            (ptr, val))
+    elif dtype == types.float64:
+        lmod = builder.module
+        return builder.call(nvvmutils.declare_atomic_sub_float64(lmod),
+                            (ptr, val))
+    else:
+        return builder.atomic_rmw('sub', ptr, val, 'monotonic')
+
+
+@lower(stubs.atomic.inc, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.inc, types.Array, types.UniTuple, types.Any)
+@lower(stubs.atomic.inc, types.Array, types.Tuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_inc(context, builder, dtype, ptr, val):
+    if dtype in cuda.cudadecl.unsigned_int_numba_types:
+        bw = dtype.bitwidth
+        lmod = builder.module
+        fn = getattr(nvvmutils, f'declare_atomic_inc_int{bw}')
+        return builder.call(fn(lmod), (ptr, val))
+    else:
+        raise TypeError(f'Unimplemented atomic inc with {dtype} array')
+
+
+@lower(stubs.atomic.dec, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.dec, types.Array, types.UniTuple, types.Any)
+@lower(stubs.atomic.dec, types.Array, types.Tuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_dec(context, builder, dtype, ptr, val):
+    if dtype in cuda.cudadecl.unsigned_int_numba_types:
+        bw = dtype.bitwidth
+        lmod = builder.module
+        fn = getattr(nvvmutils, f'declare_atomic_dec_int{bw}')
+        return builder.call(fn(lmod), (ptr, val))
+    else:
+        raise TypeError(f'Unimplemented atomic dec with {dtype} array')
+
+
+def ptx_atomic_bitwise(stub, op):
+    @_atomic_dispatcher
+    def impl_ptx_atomic(context, builder, dtype, ptr, val):
+        if dtype in (cuda.cudadecl.integer_numba_types):
+            return builder.atomic_rmw(op, ptr, val, 'monotonic')
+        else:
+            raise TypeError(f'Unimplemented atomic {op} with {dtype} array')
+
+    for ty in (types.intp, types.UniTuple, types.Tuple):
+        lower(stub, types.Array, ty, types.Any)(impl_ptx_atomic)
+
+
+ptx_atomic_bitwise(stubs.atomic.and_, 'and')
+ptx_atomic_bitwise(stubs.atomic.or_, 'or')
+ptx_atomic_bitwise(stubs.atomic.xor, 'xor')
+
+
+@lower(stubs.atomic.exch, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.exch, types.Array, types.UniTuple, types.Any)
+@lower(stubs.atomic.exch, types.Array, types.Tuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_exch(context, builder, dtype, ptr, val):
+    if dtype in (cuda.cudadecl.integer_numba_types):
+        return builder.atomic_rmw('xchg', ptr, val, 'monotonic')
+    else:
+        raise TypeError(f'Unimplemented atomic exch with {dtype} array')
+
+
+@lower(stubs.atomic.max, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.max, types.Array, types.Tuple, types.Any)
+@lower(stubs.atomic.max, types.Array, types.UniTuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_max(context, builder, dtype, ptr, val):
+    lmod = builder.module
+    if dtype == types.float64:
+        return builder.call(nvvmutils.declare_atomic_max_float64(lmod),
+                            (ptr, val))
+    elif dtype == types.float32:
+        return builder.call(nvvmutils.declare_atomic_max_float32(lmod),
+                            (ptr, val))
+    elif dtype in (types.int32, types.int64):
+        return builder.atomic_rmw('max', ptr, val, ordering='monotonic')
+    elif dtype in (types.uint32, types.uint64):
+        return builder.atomic_rmw('umax', ptr, val, ordering='monotonic')
+    else:
+        raise TypeError('Unimplemented atomic max with %s array' % dtype)
+
+
+@lower(stubs.atomic.min, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.min, types.Array, types.Tuple, types.Any)
+@lower(stubs.atomic.min, types.Array, types.UniTuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_min(context, builder, dtype, ptr, val):
+    lmod = builder.module
+    if dtype == types.float64:
+        return builder.call(nvvmutils.declare_atomic_min_float64(lmod),
+                            (ptr, val))
+    elif dtype == types.float32:
+        return builder.call(nvvmutils.declare_atomic_min_float32(lmod),
+                            (ptr, val))
+    elif dtype in (types.int32, types.int64):
+        return builder.atomic_rmw('min', ptr, val, ordering='monotonic')
+    elif dtype in (types.uint32, types.uint64):
+        return builder.atomic_rmw('umin', ptr, val, ordering='monotonic')
+    else:
+        raise TypeError('Unimplemented atomic min with %s array' % dtype)
+
+
+@lower(stubs.atomic.nanmax, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.nanmax, types.Array, types.Tuple, types.Any)
+@lower(stubs.atomic.nanmax, types.Array, types.UniTuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_nanmax(context, builder, dtype, ptr, val):
+    lmod = builder.module
+    if dtype == types.float64:
+        return builder.call(nvvmutils.declare_atomic_nanmax_float64(lmod),
+                            (ptr, val))
+    elif dtype == types.float32:
+        return builder.call(nvvmutils.declare_atomic_nanmax_float32(lmod),
+                            (ptr, val))
+    elif dtype in (types.int32, types.int64):
+        return builder.atomic_rmw('max', ptr, val, ordering='monotonic')
+    elif dtype in (types.uint32, types.uint64):
+        return builder.atomic_rmw('umax', ptr, val, ordering='monotonic')
+    else:
+        raise TypeError('Unimplemented atomic max with %s array' % dtype)
+
+
+@lower(stubs.atomic.nanmin, types.Array, types.intp, types.Any)
+@lower(stubs.atomic.nanmin, types.Array, types.Tuple, types.Any)
+@lower(stubs.atomic.nanmin, types.Array, types.UniTuple, types.Any)
+@_atomic_dispatcher
+def ptx_atomic_nanmin(context, builder, dtype, ptr, val):
+    lmod = builder.module
+    if dtype == types.float64:
+        return builder.call(nvvmutils.declare_atomic_nanmin_float64(lmod),
+                            (ptr, val))
+    elif dtype == types.float32:
+        return builder.call(nvvmutils.declare_atomic_nanmin_float32(lmod),
+                            (ptr, val))
+    elif dtype in (types.int32, types.int64):
+        return builder.atomic_rmw('min', ptr, val, ordering='monotonic')
+    elif dtype in (types.uint32, types.uint64):
+        return builder.atomic_rmw('umin', ptr, val, ordering='monotonic')
+    else:
+        raise TypeError('Unimplemented atomic min with %s array' % dtype)
+
+
+@lower(stubs.atomic.compare_and_swap, types.Array, types.Any, types.Any)
+def ptx_atomic_cas_tuple(context, builder, sig, args):
+    aryty, oldty, valty = sig.args
+    ary, old, val = args
+    dtype = aryty.dtype
+
+    lary = context.make_array(aryty)(context, builder, ary)
+    zero = context.get_constant(types.intp, 0)
+    ptr = cgutils.get_item_pointer(context, builder, aryty, lary, (zero,))
+
+    if aryty.dtype in (cuda.cudadecl.integer_numba_types):
+        lmod = builder.module
+        bitwidth = aryty.dtype.bitwidth
+        return nvvmutils.atomic_cmpxchg(builder, lmod, bitwidth, ptr, old, val)
+    else:
+        raise TypeError('Unimplemented atomic compare_and_swap '
+                        'with %s array' % dtype)
+
+
+# -----------------------------------------------------------------------------
+
+@lower(stubs.nanosleep, types.uint32)
+def ptx_nanosleep(context, builder, sig, args):
+    nanosleep = ir.InlineAsm(ir.FunctionType(ir.VoidType(), [ir.IntType(32)]),
+                             "nanosleep.u32 $0;", 'r', side_effect=True)
+    ns = args[0]
+    builder.call(nanosleep, [ns])
+
+
+# -----------------------------------------------------------------------------
+
+
+def _generic_array(context, builder, shape, dtype, symbol_name, addrspace,
+                   can_dynsized=False):
+    elemcount = reduce(operator.mul, shape, 1)
+
+    # Check for valid shape for this type of allocation.
+    # Only 1d arrays can be dynamic.
+    dynamic_smem = elemcount <= 0 and can_dynsized and len(shape) == 1
+    if elemcount <= 0 and not dynamic_smem:
+        raise ValueError("array length <= 0")
+
+    # Check that we support the requested dtype
+    data_model = context.data_model_manager[dtype]
+    other_supported_type = (
+        isinstance(dtype, (types.Record, types.Boolean))
+        or isinstance(data_model, models.StructModel)
+        or dtype == types.float16
+    )
+    if dtype not in types.number_domain and not other_supported_type:
+        raise TypeError("unsupported type: %s" % dtype)
+
+    lldtype = context.get_data_type(dtype)
+    laryty = ir.ArrayType(lldtype, elemcount)
+
+    if addrspace == nvvm.ADDRSPACE_LOCAL:
+        # Special case local address space allocation to use alloca
+        # NVVM is smart enough to only use local memory if no register is
+        # available
+        dataptr = cgutils.alloca_once(builder, laryty, name=symbol_name)
+    else:
+        lmod = builder.module
+
+        # Create global variable in the requested address space
+        gvmem = cgutils.add_global_variable(lmod, laryty, symbol_name,
+                                            addrspace)
+        # Specify alignment to avoid misalignment bug
+        align = context.get_abi_sizeof(lldtype)
+        # Alignment is required to be a power of 2 for shared memory. If it is
+        # not a power of 2 (e.g. for a Record array) then round up accordingly.
+        gvmem.align = 1 << (align - 1 ).bit_length()
+
+        if dynamic_smem:
+            gvmem.linkage = 'external'
+        else:
+            ## Comment out the following line to workaround a NVVM bug
+            ## which generates a invalid symbol name when the linkage
+            ## is internal and in some situation.
+            ## See _get_unique_smem_id()
+            # gvmem.linkage = lc.LINKAGE_INTERNAL
+
+            gvmem.initializer = ir.Constant(laryty, ir.Undefined)
+
+        # Convert to generic address-space
+        ptras = ir.PointerType(gvmem.type.pointee, nvvm.ADDRSPACE_GENERIC)
+        dataptr = builder.addrspacecast(gvmem, ptras)
+
+    targetdata = ll.create_target_data(nvvm.data_layout)
+    lldtype = context.get_data_type(dtype)
+    itemsize = lldtype.get_abi_size(targetdata)
+
+    # Compute strides
+    laststride = itemsize
+    rstrides = []
+    for i, lastsize in enumerate(reversed(shape)):
+        rstrides.append(laststride)
+        laststride *= lastsize
+    strides = [s for s in reversed(rstrides)]
+    kstrides = [context.get_constant(types.intp, s) for s in strides]
+
+    # Compute shape
+    if dynamic_smem:
+        # Compute the shape based on the dynamic shared memory configuration.
+        # Unfortunately NVVM does not provide an intrinsic for the
+        # %dynamic_smem_size register, so we must read it using inline
+        # assembly.
+        get_dynshared_size = ir.InlineAsm(ir.FunctionType(ir.IntType(32), []),
+                                          "mov.u32 $0, %dynamic_smem_size;",
+                                          '=r', side_effect=True)
+        dynsmem_size = builder.zext(builder.call(get_dynshared_size, []),
+                                    ir.IntType(64))
+        # Only 1-D dynamic shared memory is supported so the following is a
+        # sufficient construction of the shape
+        kitemsize = context.get_constant(types.intp, itemsize)
+        kshape = [builder.udiv(dynsmem_size, kitemsize)]
+    else:
+        kshape = [context.get_constant(types.intp, s) for s in shape]
+
+    # Create array object
+    ndim = len(shape)
+    aryty = types.Array(dtype=dtype, ndim=ndim, layout='C')
+    ary = context.make_array(aryty)(context, builder)
+
+    context.populate_array(ary,
+                           data=builder.bitcast(dataptr, ary.data.type),
+                           shape=kshape,
+                           strides=kstrides,
+                           itemsize=context.get_constant(types.intp, itemsize),
+                           meminfo=None)
+    return ary._getvalue()
+
+
+@lower_constant(CUDADispatcher)
+def cuda_dispatcher_const(context, builder, ty, pyval):
+    return context.get_dummy_value()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudamath.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudamath.py
new file mode 100644
index 000000000..3cfcb2bc2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/cudamath.py
@@ -0,0 +1,130 @@
+import math
+from numba.core import types
+from numba.core.typing.templates import ConcreteTemplate, signature, Registry
+
+
+registry = Registry()
+infer_global = registry.register_global
+
+
+@infer_global(math.acos)
+@infer_global(math.acosh)
+@infer_global(math.asin)
+@infer_global(math.asinh)
+@infer_global(math.atan)
+@infer_global(math.atanh)
+@infer_global(math.ceil)
+@infer_global(math.cos)
+@infer_global(math.cosh)
+@infer_global(math.degrees)
+@infer_global(math.erf)
+@infer_global(math.erfc)
+@infer_global(math.exp)
+@infer_global(math.expm1)
+@infer_global(math.fabs)
+@infer_global(math.floor)
+@infer_global(math.gamma)
+@infer_global(math.lgamma)
+@infer_global(math.log)
+@infer_global(math.log2)
+@infer_global(math.log10)
+@infer_global(math.log1p)
+@infer_global(math.radians)
+@infer_global(math.sin)
+@infer_global(math.sinh)
+@infer_global(math.sqrt)
+@infer_global(math.tan)
+@infer_global(math.tanh)
+@infer_global(math.trunc)
+class Math_unary(ConcreteTemplate):
+    cases = [
+        signature(types.float64, types.int64),
+        signature(types.float64, types.uint64),
+        signature(types.float32, types.float32),
+        signature(types.float64, types.float64),
+    ]
+
+
+@infer_global(math.atan2)
+class Math_atan2(ConcreteTemplate):
+    key = math.atan2
+    cases = [
+        signature(types.float64, types.int64, types.int64),
+        signature(types.float64, types.uint64, types.uint64),
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+    ]
+
+
+@infer_global(math.hypot)
+class Math_hypot(ConcreteTemplate):
+    key = math.hypot
+    cases = [
+        signature(types.float64, types.int64, types.int64),
+        signature(types.float64, types.uint64, types.uint64),
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+    ]
+
+
+@infer_global(math.copysign)
+@infer_global(math.fmod)
+class Math_binary(ConcreteTemplate):
+    cases = [
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+    ]
+
+
+@infer_global(math.remainder)
+class Math_remainder(ConcreteTemplate):
+    cases = [
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+    ]
+
+
+@infer_global(math.pow)
+class Math_pow(ConcreteTemplate):
+    cases = [
+        signature(types.float32, types.float32, types.float32),
+        signature(types.float64, types.float64, types.float64),
+        signature(types.float32, types.float32, types.int32),
+        signature(types.float64, types.float64, types.int32),
+    ]
+
+
+@infer_global(math.frexp)
+class Math_frexp(ConcreteTemplate):
+    cases = [
+        signature(types.Tuple([types.float32, types.int32]), types.float32),
+        signature(types.Tuple([types.float64, types.int32]), types.float64),
+    ]
+
+
+@infer_global(math.ldexp)
+class Math_ldexp(ConcreteTemplate):
+    cases = [
+        signature(types.float32, types.float32, types.int32),
+        signature(types.float64, types.float64, types.int32),
+    ]
+
+
+@infer_global(math.isinf)
+@infer_global(math.isnan)
+@infer_global(math.isfinite)
+class Math_isnan(ConcreteTemplate):
+    cases = [
+        signature(types.boolean, types.int64),
+        signature(types.boolean, types.uint64),
+        signature(types.boolean, types.float32),
+        signature(types.boolean, types.float64),
+    ]
+
+
+@infer_global(math.modf)
+class Math_modf(ConcreteTemplate):
+    cases = [
+        signature(types.UniTuple(types.float64, 2), types.float64),
+        signature(types.UniTuple(types.float32, 2), types.float32)
+    ]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/decorators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/decorators.py
new file mode 100644
index 000000000..6703a6fea
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/decorators.py
@@ -0,0 +1,170 @@
+from warnings import warn
+from numba.core import types, config, sigutils
+from numba.core.errors import DeprecationError, NumbaInvalidConfigWarning
+from numba.cuda.compiler import declare_device_function
+from numba.cuda.dispatcher import CUDADispatcher
+from numba.cuda.simulator.kernel import FakeCUDAKernel
+
+
+_msg_deprecated_signature_arg = ("Deprecated keyword argument `{0}`. "
+                                 "Signatures should be passed as the first "
+                                 "positional argument.")
+
+
+def jit(func_or_sig=None, device=False, inline=False, link=[], debug=None,
+        opt=True, cache=False, **kws):
+    """
+    JIT compile a python function conforming to the CUDA Python specification.
+    If a signature is supplied, then a function is returned that takes a
+    function to compile.
+
+    :param func_or_sig: A function to JIT compile, or a signature of a function
+       to compile. If a function is supplied, then a
+       :class:`numba.cuda.compiler.AutoJitCUDAKernel` is returned. If a
+       signature is supplied, then a function is returned. The returned
+       function accepts another function, which it will compile and then return
+       a :class:`numba.cuda.compiler.AutoJitCUDAKernel`.
+
+       .. note:: A kernel cannot have any return value.
+    :param device: Indicates whether this is a device function.
+    :type device: bool
+    :param link: A list of files containing PTX source to link with the function
+    :type link: list
+    :param debug: If True, check for exceptions thrown when executing the
+       kernel. Since this degrades performance, this should only be used for
+       debugging purposes. If set to True, then ``opt`` should be set to False.
+       Defaults to False.  (The default value can be overridden by setting
+       environment variable ``NUMBA_CUDA_DEBUGINFO=1``.)
+    :param fastmath: When True, enables fastmath optimizations as outlined in
+       the :ref:`CUDA Fast Math documentation <cuda-fast-math>`.
+    :param max_registers: Request that the kernel is limited to using at most
+       this number of registers per thread. The limit may not be respected if
+       the ABI requires a greater number of registers than that requested.
+       Useful for increasing occupancy.
+    :param opt: Whether to compile from LLVM IR to PTX with optimization
+                enabled. When ``True``, ``-opt=3`` is passed to NVVM. When
+                ``False``, ``-opt=0`` is passed to NVVM. Defaults to ``True``.
+    :type opt: bool
+    :param lineinfo: If True, generate a line mapping between source code and
+       assembly code. This enables inspection of the source code in NVIDIA
+       profiling tools and correlation with program counter sampling.
+    :type lineinfo: bool
+    :param cache: If True, enables the file-based cache for this function.
+    :type cache: bool
+    """
+
+    if link and config.ENABLE_CUDASIM:
+        raise NotImplementedError('Cannot link PTX in the simulator')
+
+    if kws.get('boundscheck'):
+        raise NotImplementedError("bounds checking is not supported for CUDA")
+
+    if kws.get('argtypes') is not None:
+        msg = _msg_deprecated_signature_arg.format('argtypes')
+        raise DeprecationError(msg)
+    if kws.get('restype') is not None:
+        msg = _msg_deprecated_signature_arg.format('restype')
+        raise DeprecationError(msg)
+    if kws.get('bind') is not None:
+        msg = _msg_deprecated_signature_arg.format('bind')
+        raise DeprecationError(msg)
+
+    debug = config.CUDA_DEBUGINFO_DEFAULT if debug is None else debug
+    fastmath = kws.get('fastmath', False)
+    extensions = kws.get('extensions', [])
+
+    if debug and opt:
+        msg = ("debug=True with opt=True (the default) "
+               "is not supported by CUDA. This may result in a crash"
+               " - set debug=False or opt=False.")
+        warn(NumbaInvalidConfigWarning(msg))
+
+    if device and kws.get('link'):
+        raise ValueError("link keyword invalid for device function")
+
+    if sigutils.is_signature(func_or_sig):
+        if config.ENABLE_CUDASIM:
+            def jitwrapper(func):
+                return FakeCUDAKernel(func, device=device, fastmath=fastmath)
+            return jitwrapper
+
+        argtypes, restype = sigutils.normalize_signature(func_or_sig)
+
+        if restype and not device and restype != types.void:
+            raise TypeError("CUDA kernel must have void return type.")
+
+        def _jit(func):
+            targetoptions = kws.copy()
+            targetoptions['debug'] = debug
+            targetoptions['link'] = link
+            targetoptions['opt'] = opt
+            targetoptions['fastmath'] = fastmath
+            targetoptions['device'] = device
+            targetoptions['extensions'] = extensions
+
+            disp = CUDADispatcher(func, targetoptions=targetoptions)
+
+            if cache:
+                disp.enable_caching()
+
+            if device:
+                from numba.core import typeinfer
+                with typeinfer.register_dispatcher(disp):
+                    disp.compile_device(argtypes)
+            else:
+                disp.compile(argtypes)
+
+            disp._specialized = True
+            disp.disable_compile()
+
+            return disp
+
+        return _jit
+    else:
+        if func_or_sig is None:
+            if config.ENABLE_CUDASIM:
+                def autojitwrapper(func):
+                    return FakeCUDAKernel(func, device=device,
+                                          fastmath=fastmath)
+            else:
+                def autojitwrapper(func):
+                    return jit(func, device=device, debug=debug, opt=opt,
+                               link=link, cache=cache, **kws)
+
+            return autojitwrapper
+        # func_or_sig is a function
+        else:
+            if config.ENABLE_CUDASIM:
+                return FakeCUDAKernel(func_or_sig, device=device,
+                                      fastmath=fastmath)
+            else:
+                targetoptions = kws.copy()
+                targetoptions['debug'] = debug
+                targetoptions['opt'] = opt
+                targetoptions['link'] = link
+                targetoptions['fastmath'] = fastmath
+                targetoptions['device'] = device
+                targetoptions['extensions'] = extensions
+                disp = CUDADispatcher(func_or_sig, targetoptions=targetoptions)
+
+                if cache:
+                    disp.enable_caching()
+
+                return disp
+
+
+def declare_device(name, sig):
+    """
+    Declare the signature of a foreign function. Returns a descriptor that can
+    be used to call the function from a Python kernel.
+
+    :param name: The name of the foreign function.
+    :type name: str
+    :param sig: The Numba signature of the function.
+    """
+    argtypes, restype = sigutils.normalize_signature(sig)
+    if restype is None:
+        msg = 'Return type must be provided for device declarations'
+        raise TypeError(msg)
+
+    return declare_device_function(name, restype, argtypes)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/descriptor.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/descriptor.py
new file mode 100644
index 000000000..b91ddf7a1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/descriptor.py
@@ -0,0 +1,33 @@
+from numba.core.descriptors import TargetDescriptor
+from numba.core.options import TargetOptions
+from .target import CUDATargetContext, CUDATypingContext
+
+
+class CUDATargetOptions(TargetOptions):
+    pass
+
+
+class CUDATarget(TargetDescriptor):
+    def __init__(self, name):
+        self.options = CUDATargetOptions
+        # The typing and target contexts are initialized only when needed -
+        # this prevents an attempt to load CUDA libraries at import time on
+        # systems that might not have them present.
+        self._typingctx = None
+        self._targetctx = None
+        super().__init__(name)
+
+    @property
+    def typing_context(self):
+        if self._typingctx is None:
+            self._typingctx = CUDATypingContext()
+        return self._typingctx
+
+    @property
+    def target_context(self):
+        if self._targetctx is None:
+            self._targetctx = CUDATargetContext(self._typingctx)
+        return self._targetctx
+
+
+cuda_target = CUDATarget('cuda')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/device_init.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/device_init.py
new file mode 100644
index 000000000..2a0c915f6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/device_init.py
@@ -0,0 +1,88 @@
+# Re export
+import sys
+from .stubs import (threadIdx, blockIdx, blockDim, gridDim, laneid,
+                    warpsize, syncthreads, syncthreads_count, syncwarp,
+                    syncthreads_and, syncthreads_or, shared, local,
+                    const, grid, gridsize, atomic, shfl_sync_intrinsic,
+                    vote_sync_intrinsic, match_any_sync, match_all_sync,
+                    threadfence_block, threadfence_system,
+                    threadfence, selp, popc, brev, clz, ffs, fma, cbrt,
+                    cg, activemask, lanemask_lt, nanosleep, fp16,
+                    _vector_type_stubs)
+from .cudadrv.error import CudaSupportError
+from numba.cuda.cudadrv.driver import (BaseCUDAMemoryManager,
+                                       HostOnlyCUDAMemoryManager,
+                                       GetIpcHandleMixin, MemoryPointer,
+                                       MappedMemory, PinnedMemory, MemoryInfo,
+                                       IpcHandle, set_memory_manager)
+from numba.cuda.cudadrv.runtime import runtime
+from .cudadrv import nvvm
+from numba.cuda import initialize
+from .errors import KernelRuntimeError
+
+from .decorators import jit, declare_device
+from .api import *
+from .api import _auto_device
+from .args import In, Out, InOut
+
+from .intrinsic_wrapper import (all_sync, any_sync, eq_sync, ballot_sync,
+                                shfl_sync, shfl_up_sync, shfl_down_sync,
+                                shfl_xor_sync)
+
+from .kernels import reduction
+
+reduce = Reduce = reduction.Reduce
+
+# Expose vector type constructors and aliases as module level attributes.
+for vector_type_stub in _vector_type_stubs:
+    setattr(sys.modules[__name__], vector_type_stub.__name__, vector_type_stub)
+    for alias in vector_type_stub.aliases:
+        setattr(sys.modules[__name__], alias, vector_type_stub)
+del vector_type_stub, _vector_type_stubs
+
+
+def is_available():
+    """Returns a boolean to indicate the availability of a CUDA GPU.
+
+    This will initialize the driver if it hasn't been initialized.
+    """
+    # whilst `driver.is_available` will init the driver itself,
+    # the driver initialization may raise and as a result break
+    # test discovery/orchestration as `cuda.is_available` is often
+    # used as a guard for whether to run a CUDA test, the try/except
+    # below is to handle this case.
+    driver_is_available = False
+    try:
+        driver_is_available = driver.driver.is_available
+    except CudaSupportError:
+        pass
+
+    return driver_is_available and nvvm.is_available()
+
+
+def is_supported_version():
+    """Returns True if the CUDA Runtime is a supported version.
+
+    Unsupported versions (e.g. newer versions than those known to Numba)
+    may still work; this function provides a facility to check whether the
+    current Numba version is tested and known to work with the current
+    runtime version. If the current version is unsupported, the caller can
+    decide how to act. Options include:
+
+    - Continuing silently,
+    - Emitting a warning,
+    - Generating an error or otherwise preventing the use of CUDA.
+    """
+
+    return runtime.is_supported_version()
+
+
+def cuda_error():
+    """Returns None if there was no error initializing the CUDA driver.
+    If there was an error initializing the driver, a string describing the
+    error is returned.
+    """
+    return driver.driver.initialization_error
+
+
+initialize.initialize_all()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/dispatcher.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/dispatcher.py
new file mode 100644
index 000000000..c0e9336d3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/dispatcher.py
@@ -0,0 +1,902 @@
+import numpy as np
+import os
+import sys
+import ctypes
+import functools
+
+from numba.core import config, serialize, sigutils, types, typing, utils
+from numba.core.caching import Cache, CacheImpl
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core.dispatcher import Dispatcher
+from numba.core.errors import NumbaPerformanceWarning
+from numba.core.typing.typeof import Purpose, typeof
+
+from numba.cuda.api import get_current_device
+from numba.cuda.args import wrap_arg
+from numba.cuda.compiler import compile_cuda, CUDACompiler
+from numba.cuda.cudadrv import driver
+from numba.cuda.cudadrv.devices import get_context
+from numba.cuda.cudadrv.libs import get_cudalib
+from numba.cuda.descriptor import cuda_target
+from numba.cuda.errors import (missing_launch_config_msg,
+                               normalize_kernel_dimensions)
+from numba.cuda import types as cuda_types
+
+from numba import cuda
+from numba import _dispatcher
+
+from warnings import warn
+
+
+class _Kernel(serialize.ReduceMixin):
+    '''
+    CUDA Kernel specialized for a given set of argument types. When called, this
+    object launches the kernel on the device.
+    '''
+
+    @global_compiler_lock
+    def __init__(self, py_func, argtypes, link=None, debug=False,
+                 lineinfo=False, inline=False, fastmath=False, extensions=None,
+                 max_registers=None, opt=True, device=False):
+
+        if device:
+            raise RuntimeError('Cannot compile a device function as a kernel')
+
+        super().__init__()
+
+        # _DispatcherBase.nopython_signatures() expects this attribute to be
+        # present, because it assumes an overload is a CompileResult. In the
+        # CUDA target, _Kernel instances are stored instead, so we provide this
+        # attribute here to avoid duplicating nopython_signatures() in the CUDA
+        # target with slight modifications.
+        self.objectmode = False
+
+        # The finalizer constructed by _DispatcherBase._make_finalizer also
+        # expects overloads to be a CompileResult. It uses the entry_point to
+        # remove a CompileResult from a target context. However, since we never
+        # insert kernels into a target context (there is no need because they
+        # cannot be called by other functions, only through the dispatcher) it
+        # suffices to pretend we have an entry point of None.
+        self.entry_point = None
+
+        self.py_func = py_func
+        self.argtypes = argtypes
+        self.debug = debug
+        self.lineinfo = lineinfo
+        self.extensions = extensions or []
+
+        nvvm_options = {
+            'debug': self.debug,
+            'lineinfo': self.lineinfo,
+            'fastmath': fastmath,
+            'opt': 3 if opt else 0
+        }
+
+        cres = compile_cuda(self.py_func, types.void, self.argtypes,
+                            debug=self.debug,
+                            lineinfo=self.lineinfo,
+                            inline=inline,
+                            fastmath=fastmath,
+                            nvvm_options=nvvm_options)
+        tgt_ctx = cres.target_context
+        code = self.py_func.__code__
+        filename = code.co_filename
+        linenum = code.co_firstlineno
+        lib, kernel = tgt_ctx.prepare_cuda_kernel(cres.library, cres.fndesc,
+                                                  debug, nvvm_options,
+                                                  filename, linenum,
+                                                  max_registers)
+
+        if not link:
+            link = []
+
+        # A kernel needs cooperative launch if grid_sync is being used.
+        self.cooperative = 'cudaCGGetIntrinsicHandle' in lib.get_asm_str()
+        # We need to link against cudadevrt if grid sync is being used.
+        if self.cooperative:
+            link.append(get_cudalib('cudadevrt', static=True))
+
+        for filepath in link:
+            lib.add_linking_file(filepath)
+
+        # populate members
+        self.entry_name = kernel.name
+        self.signature = cres.signature
+        self._type_annotation = cres.type_annotation
+        self._codelibrary = lib
+        self.call_helper = cres.call_helper
+
+        # The following are referred to by the cache implementation. Note:
+        # - There are no referenced environments in CUDA.
+        # - Kernels don't have lifted code.
+        # - reload_init is only for parfors.
+        self.target_context = tgt_ctx
+        self.fndesc = cres.fndesc
+        self.environment = cres.environment
+        self._referenced_environments = []
+        self.lifted = []
+        self.reload_init = []
+
+    @property
+    def library(self):
+        return self._codelibrary
+
+    @property
+    def type_annotation(self):
+        return self._type_annotation
+
+    def _find_referenced_environments(self):
+        return self._referenced_environments
+
+    @property
+    def codegen(self):
+        return self.target_context.codegen()
+
+    @property
+    def argument_types(self):
+        return tuple(self.signature.args)
+
+    @classmethod
+    def _rebuild(cls, cooperative, name, signature, codelibrary,
+                 debug, lineinfo, call_helper, extensions):
+        """
+        Rebuild an instance.
+        """
+        instance = cls.__new__(cls)
+        # invoke parent constructor
+        super(cls, instance).__init__()
+        # populate members
+        instance.entry_point = None
+        instance.cooperative = cooperative
+        instance.entry_name = name
+        instance.signature = signature
+        instance._type_annotation = None
+        instance._codelibrary = codelibrary
+        instance.debug = debug
+        instance.lineinfo = lineinfo
+        instance.call_helper = call_helper
+        instance.extensions = extensions
+        return instance
+
+    def _reduce_states(self):
+        """
+        Reduce the instance for serialization.
+        Compiled definitions are serialized in PTX form.
+        Type annotation are discarded.
+        Thread, block and shared memory configuration are serialized.
+        Stream information is discarded.
+        """
+        return dict(cooperative=self.cooperative, name=self.entry_name,
+                    signature=self.signature, codelibrary=self._codelibrary,
+                    debug=self.debug, lineinfo=self.lineinfo,
+                    call_helper=self.call_helper, extensions=self.extensions)
+
+    def bind(self):
+        """
+        Force binding to current CUDA context
+        """
+        self._codelibrary.get_cufunc()
+
+    @property
+    def device(self):
+        """
+        Get current active context
+        """
+        return get_current_device()
+
+    @property
+    def regs_per_thread(self):
+        '''
+        The number of registers used by each thread for this kernel.
+        '''
+        return self._codelibrary.get_cufunc().attrs.regs
+
+    def inspect_llvm(self):
+        '''
+        Returns the LLVM IR for this kernel.
+        '''
+        return self._codelibrary.get_llvm_str()
+
+    def inspect_asm(self, cc):
+        '''
+        Returns the PTX code for this kernel.
+        '''
+        return self._codelibrary.get_asm_str(cc=cc)
+
+    def inspect_sass(self):
+        '''
+        Returns the SASS code for this kernel.
+
+        Requires nvdisasm to be available on the PATH.
+        '''
+        return self._codelibrary.get_sass()
+
+    def inspect_types(self, file=None):
+        '''
+        Produce a dump of the Python source of this function annotated with the
+        corresponding Numba IR and type information. The dump is written to
+        *file*, or *sys.stdout* if *file* is *None*.
+        '''
+        if self._type_annotation is None:
+            raise ValueError("Type annotation is not available")
+
+        if file is None:
+            file = sys.stdout
+
+        print("%s %s" % (self.entry_name, self.argument_types), file=file)
+        print('-' * 80, file=file)
+        print(self._type_annotation, file=file)
+        print('=' * 80, file=file)
+
+    def max_cooperative_grid_blocks(self, blockdim, dynsmemsize=0):
+        '''
+        Calculates the maximum number of blocks that can be launched for this
+        kernel in a cooperative grid in the current context, for the given block
+        and dynamic shared memory sizes.
+
+        :param blockdim: Block dimensions, either as a scalar for a 1D block, or
+                         a tuple for 2D or 3D blocks.
+        :param dynsmemsize: Dynamic shared memory size in bytes.
+        :return: The maximum number of blocks in the grid.
+        '''
+        ctx = get_context()
+        cufunc = self._codelibrary.get_cufunc()
+
+        if isinstance(blockdim, tuple):
+            blockdim = functools.reduce(lambda x, y: x * y, blockdim)
+        active_per_sm = ctx.get_active_blocks_per_multiprocessor(cufunc,
+                                                                 blockdim,
+                                                                 dynsmemsize)
+        sm_count = ctx.device.MULTIPROCESSOR_COUNT
+        return active_per_sm * sm_count
+
+    def launch(self, args, griddim, blockdim, stream=0, sharedmem=0):
+        # Prepare kernel
+        cufunc = self._codelibrary.get_cufunc()
+
+        if self.debug:
+            excname = cufunc.name + "__errcode__"
+            excmem, excsz = cufunc.module.get_global_symbol(excname)
+            assert excsz == ctypes.sizeof(ctypes.c_int)
+            excval = ctypes.c_int()
+            excmem.memset(0, stream=stream)
+
+        # Prepare arguments
+        retr = []                       # hold functors for writeback
+
+        kernelargs = []
+        for t, v in zip(self.argument_types, args):
+            self._prepare_args(t, v, stream, retr, kernelargs)
+
+        if driver.USE_NV_BINDING:
+            zero_stream = driver.binding.CUstream(0)
+        else:
+            zero_stream = None
+
+        stream_handle = stream and stream.handle or zero_stream
+
+        # Invoke kernel
+        driver.launch_kernel(cufunc.handle,
+                             *griddim,
+                             *blockdim,
+                             sharedmem,
+                             stream_handle,
+                             kernelargs,
+                             cooperative=self.cooperative)
+
+        if self.debug:
+            driver.device_to_host(ctypes.addressof(excval), excmem, excsz)
+            if excval.value != 0:
+                # An error occurred
+                def load_symbol(name):
+                    mem, sz = cufunc.module.get_global_symbol("%s__%s__" %
+                                                              (cufunc.name,
+                                                               name))
+                    val = ctypes.c_int()
+                    driver.device_to_host(ctypes.addressof(val), mem, sz)
+                    return val.value
+
+                tid = [load_symbol("tid" + i) for i in 'zyx']
+                ctaid = [load_symbol("ctaid" + i) for i in 'zyx']
+                code = excval.value
+                exccls, exc_args, loc = self.call_helper.get_exception(code)
+                # Prefix the exception message with the source location
+                if loc is None:
+                    locinfo = ''
+                else:
+                    sym, filepath, lineno = loc
+                    filepath = os.path.abspath(filepath)
+                    locinfo = 'In function %r, file %s, line %s, ' % (sym,
+                                                                      filepath,
+                                                                      lineno,)
+                # Prefix the exception message with the thread position
+                prefix = "%stid=%s ctaid=%s" % (locinfo, tid, ctaid)
+                if exc_args:
+                    exc_args = ("%s: %s" % (prefix, exc_args[0]),) + \
+                        exc_args[1:]
+                else:
+                    exc_args = prefix,
+                raise exccls(*exc_args)
+
+        # retrieve auto converted arrays
+        for wb in retr:
+            wb()
+
+    def _prepare_args(self, ty, val, stream, retr, kernelargs):
+        """
+        Convert arguments to ctypes and append to kernelargs
+        """
+
+        # map the arguments using any extension you've registered
+        for extension in reversed(self.extensions):
+            ty, val = extension.prepare_args(
+                ty,
+                val,
+                stream=stream,
+                retr=retr)
+
+        if isinstance(ty, types.Array):
+            devary = wrap_arg(val).to_device(retr, stream)
+
+            c_intp = ctypes.c_ssize_t
+
+            meminfo = ctypes.c_void_p(0)
+            parent = ctypes.c_void_p(0)
+            nitems = c_intp(devary.size)
+            itemsize = c_intp(devary.dtype.itemsize)
+
+            ptr = driver.device_pointer(devary)
+
+            if driver.USE_NV_BINDING:
+                ptr = int(ptr)
+
+            data = ctypes.c_void_p(ptr)
+
+            kernelargs.append(meminfo)
+            kernelargs.append(parent)
+            kernelargs.append(nitems)
+            kernelargs.append(itemsize)
+            kernelargs.append(data)
+            for ax in range(devary.ndim):
+                kernelargs.append(c_intp(devary.shape[ax]))
+            for ax in range(devary.ndim):
+                kernelargs.append(c_intp(devary.strides[ax]))
+
+        elif isinstance(ty, types.Integer):
+            cval = getattr(ctypes, "c_%s" % ty)(val)
+            kernelargs.append(cval)
+
+        elif ty == types.float16:
+            cval = ctypes.c_uint16(np.float16(val).view(np.uint16))
+            kernelargs.append(cval)
+
+        elif ty == types.float64:
+            cval = ctypes.c_double(val)
+            kernelargs.append(cval)
+
+        elif ty == types.float32:
+            cval = ctypes.c_float(val)
+            kernelargs.append(cval)
+
+        elif ty == types.boolean:
+            cval = ctypes.c_uint8(int(val))
+            kernelargs.append(cval)
+
+        elif ty == types.complex64:
+            kernelargs.append(ctypes.c_float(val.real))
+            kernelargs.append(ctypes.c_float(val.imag))
+
+        elif ty == types.complex128:
+            kernelargs.append(ctypes.c_double(val.real))
+            kernelargs.append(ctypes.c_double(val.imag))
+
+        elif isinstance(ty, (types.NPDatetime, types.NPTimedelta)):
+            kernelargs.append(ctypes.c_int64(val.view(np.int64)))
+
+        elif isinstance(ty, types.Record):
+            devrec = wrap_arg(val).to_device(retr, stream)
+            ptr = devrec.device_ctypes_pointer
+            if driver.USE_NV_BINDING:
+                ptr = ctypes.c_void_p(int(ptr))
+            kernelargs.append(ptr)
+
+        elif isinstance(ty, types.BaseTuple):
+            assert len(ty) == len(val)
+            for t, v in zip(ty, val):
+                self._prepare_args(t, v, stream, retr, kernelargs)
+
+        elif isinstance(ty, types.EnumMember):
+            try:
+                self._prepare_args(
+                    ty.dtype, val.value, stream, retr, kernelargs
+                )
+            except NotImplementedError:
+                raise NotImplementedError(ty, val)
+
+        else:
+            raise NotImplementedError(ty, val)
+
+
+class ForAll(object):
+    def __init__(self, dispatcher, ntasks, tpb, stream, sharedmem):
+        if ntasks < 0:
+            raise ValueError("Can't create ForAll with negative task count: %s"
+                             % ntasks)
+        self.dispatcher = dispatcher
+        self.ntasks = ntasks
+        self.thread_per_block = tpb
+        self.stream = stream
+        self.sharedmem = sharedmem
+
+    def __call__(self, *args):
+        if self.ntasks == 0:
+            return
+
+        if self.dispatcher.specialized:
+            specialized = self.dispatcher
+        else:
+            specialized = self.dispatcher.specialize(*args)
+        blockdim = self._compute_thread_per_block(specialized)
+        griddim = (self.ntasks + blockdim - 1) // blockdim
+
+        return specialized[griddim, blockdim, self.stream,
+                           self.sharedmem](*args)
+
+    def _compute_thread_per_block(self, dispatcher):
+        tpb = self.thread_per_block
+        # Prefer user-specified config
+        if tpb != 0:
+            return tpb
+        # Else, ask the driver to give a good config
+        else:
+            ctx = get_context()
+            # Dispatcher is specialized, so there's only one definition - get
+            # it so we can get the cufunc from the code library
+            kernel = next(iter(dispatcher.overloads.values()))
+            kwargs = dict(
+                func=kernel._codelibrary.get_cufunc(),
+                b2d_func=0,     # dynamic-shared memory is constant to blksz
+                memsize=self.sharedmem,
+                blocksizelimit=1024,
+            )
+            _, tpb = ctx.get_max_potential_block_size(**kwargs)
+            return tpb
+
+
+class _LaunchConfiguration:
+    def __init__(self, dispatcher, griddim, blockdim, stream, sharedmem):
+        self.dispatcher = dispatcher
+        self.griddim = griddim
+        self.blockdim = blockdim
+        self.stream = stream
+        self.sharedmem = sharedmem
+
+        if config.CUDA_LOW_OCCUPANCY_WARNINGS:
+            # Warn when the grid has fewer than 128 blocks. This number is
+            # chosen somewhat heuristically - ideally the minimum is 2 times
+            # the number of SMs, but the number of SMs varies between devices -
+            # some very small GPUs might only have 4 SMs, but an H100-SXM5 has
+            # 132. In general kernels should be launched with large grids
+            # (hundreds or thousands of blocks), so warning when fewer than 128
+            # blocks are used will likely catch most beginner errors, where the
+            # grid tends to be very small (single-digit or low tens of blocks).
+            min_grid_size = 128
+            grid_size = griddim[0] * griddim[1] * griddim[2]
+            if grid_size < min_grid_size:
+                msg = (f"Grid size {grid_size} will likely result in GPU "
+                       "under-utilization due to low occupancy.")
+                warn(NumbaPerformanceWarning(msg))
+
+    def __call__(self, *args):
+        return self.dispatcher.call(args, self.griddim, self.blockdim,
+                                    self.stream, self.sharedmem)
+
+
+class CUDACacheImpl(CacheImpl):
+    def reduce(self, kernel):
+        return kernel._reduce_states()
+
+    def rebuild(self, target_context, payload):
+        return _Kernel._rebuild(**payload)
+
+    def check_cachable(self, cres):
+        # CUDA Kernels are always cachable - the reasons for an entity not to
+        # be cachable are:
+        #
+        # - The presence of lifted loops, or
+        # - The presence of dynamic globals.
+        #
+        # neither of which apply to CUDA kernels.
+        return True
+
+
+class CUDACache(Cache):
+    """
+    Implements a cache that saves and loads CUDA kernels and compile results.
+    """
+    _impl_class = CUDACacheImpl
+
+
+class CUDADispatcher(Dispatcher, serialize.ReduceMixin):
+    '''
+    CUDA Dispatcher object. When configured and called, the dispatcher will
+    specialize itself for the given arguments (if no suitable specialized
+    version already exists) & compute capability, and launch on the device
+    associated with the current context.
+
+    Dispatcher objects are not to be constructed by the user, but instead are
+    created using the :func:`numba.cuda.jit` decorator.
+    '''
+
+    # Whether to fold named arguments and default values. Default values are
+    # presently unsupported on CUDA, so we can leave this as False in all
+    # cases.
+    _fold_args = False
+
+    targetdescr = cuda_target
+
+    def __init__(self, py_func, targetoptions, pipeline_class=CUDACompiler):
+        super().__init__(py_func, targetoptions=targetoptions,
+                         pipeline_class=pipeline_class)
+
+        # The following properties are for specialization of CUDADispatchers. A
+        # specialized CUDADispatcher is one that is compiled for exactly one
+        # set of argument types, and bypasses some argument type checking for
+        # faster kernel launches.
+
+        # Is this a specialized dispatcher?
+        self._specialized = False
+
+        # If we produced specialized dispatchers, we cache them for each set of
+        # argument types
+        self.specializations = {}
+
+    @property
+    def _numba_type_(self):
+        return cuda_types.CUDADispatcher(self)
+
+    def enable_caching(self):
+        self._cache = CUDACache(self.py_func)
+
+    @functools.lru_cache(maxsize=128)
+    def configure(self, griddim, blockdim, stream=0, sharedmem=0):
+        griddim, blockdim = normalize_kernel_dimensions(griddim, blockdim)
+        return _LaunchConfiguration(self, griddim, blockdim, stream, sharedmem)
+
+    def __getitem__(self, args):
+        if len(args) not in [2, 3, 4]:
+            raise ValueError('must specify at least the griddim and blockdim')
+        return self.configure(*args)
+
+    def forall(self, ntasks, tpb=0, stream=0, sharedmem=0):
+        """Returns a 1D-configured dispatcher for a given number of tasks.
+
+        This assumes that:
+
+        - the kernel maps the Global Thread ID ``cuda.grid(1)`` to tasks on a
+          1-1 basis.
+        - the kernel checks that the Global Thread ID is upper-bounded by
+          ``ntasks``, and does nothing if it is not.
+
+        :param ntasks: The number of tasks.
+        :param tpb: The size of a block. An appropriate value is chosen if this
+                    parameter is not supplied.
+        :param stream: The stream on which the configured dispatcher will be
+                       launched.
+        :param sharedmem: The number of bytes of dynamic shared memory required
+                          by the kernel.
+        :return: A configured dispatcher, ready to launch on a set of
+                 arguments."""
+
+        return ForAll(self, ntasks, tpb=tpb, stream=stream, sharedmem=sharedmem)
+
+    @property
+    def extensions(self):
+        '''
+        A list of objects that must have a `prepare_args` function. When a
+        specialized kernel is called, each argument will be passed through
+        to the `prepare_args` (from the last object in this list to the
+        first). The arguments to `prepare_args` are:
+
+        - `ty` the numba type of the argument
+        - `val` the argument value itself
+        - `stream` the CUDA stream used for the current call to the kernel
+        - `retr` a list of zero-arg functions that you may want to append
+          post-call cleanup work to.
+
+        The `prepare_args` function must return a tuple `(ty, val)`, which
+        will be passed in turn to the next right-most `extension`. After all
+        the extensions have been called, the resulting `(ty, val)` will be
+        passed into Numba's default argument marshalling logic.
+        '''
+        return self.targetoptions.get('extensions')
+
+    def __call__(self, *args, **kwargs):
+        # An attempt to launch an unconfigured kernel
+        raise ValueError(missing_launch_config_msg)
+
+    def call(self, args, griddim, blockdim, stream, sharedmem):
+        '''
+        Compile if necessary and invoke this kernel with *args*.
+        '''
+        if self.specialized:
+            kernel = next(iter(self.overloads.values()))
+        else:
+            kernel = _dispatcher.Dispatcher._cuda_call(self, *args)
+
+        kernel.launch(args, griddim, blockdim, stream, sharedmem)
+
+    def _compile_for_args(self, *args, **kws):
+        # Based on _DispatcherBase._compile_for_args.
+        assert not kws
+        argtypes = [self.typeof_pyval(a) for a in args]
+        return self.compile(tuple(argtypes))
+
+    def typeof_pyval(self, val):
+        # Based on _DispatcherBase.typeof_pyval, but differs from it to support
+        # the CUDA Array Interface.
+        try:
+            return typeof(val, Purpose.argument)
+        except ValueError:
+            if cuda.is_cuda_array(val):
+                # When typing, we don't need to synchronize on the array's
+                # stream - this is done when the kernel is launched.
+                return typeof(cuda.as_cuda_array(val, sync=False),
+                              Purpose.argument)
+            else:
+                raise
+
+    def specialize(self, *args):
+        '''
+        Create a new instance of this dispatcher specialized for the given
+        *args*.
+        '''
+        cc = get_current_device().compute_capability
+        argtypes = tuple(
+            [self.typingctx.resolve_argument_type(a) for a in args])
+        if self.specialized:
+            raise RuntimeError('Dispatcher already specialized')
+
+        specialization = self.specializations.get((cc, argtypes))
+        if specialization:
+            return specialization
+
+        targetoptions = self.targetoptions
+        specialization = CUDADispatcher(self.py_func,
+                                        targetoptions=targetoptions)
+        specialization.compile(argtypes)
+        specialization.disable_compile()
+        specialization._specialized = True
+        self.specializations[cc, argtypes] = specialization
+        return specialization
+
+    @property
+    def specialized(self):
+        """
+        True if the Dispatcher has been specialized.
+        """
+        return self._specialized
+
+    def get_regs_per_thread(self, signature=None):
+        '''
+        Returns the number of registers used by each thread in this kernel for
+        the device in the current context.
+
+        :param signature: The signature of the compiled kernel to get register
+                          usage for. This may be omitted for a specialized
+                          kernel.
+        :return: The number of registers used by the compiled variant of the
+                 kernel for the given signature and current device.
+        '''
+        if signature is not None:
+            return self.overloads[signature.args].regs_per_thread
+        if self.specialized:
+            return next(iter(self.overloads.values())).regs_per_thread
+        else:
+            return {sig: overload.regs_per_thread
+                    for sig, overload in self.overloads.items()}
+
+    def get_call_template(self, args, kws):
+        # Originally copied from _DispatcherBase.get_call_template. This
+        # version deviates slightly from the _DispatcherBase version in order
+        # to force casts when calling device functions. See e.g.
+        # TestDeviceFunc.test_device_casting, added in PR #7496.
+        """
+        Get a typing.ConcreteTemplate for this dispatcher and the given
+        *args* and *kws* types.  This allows resolution of the return type.
+
+        A (template, pysig, args, kws) tuple is returned.
+        """
+        # Ensure an exactly-matching overload is available if we can
+        # compile. We proceed with the typing even if we can't compile
+        # because we may be able to force a cast on the caller side.
+        if self._can_compile:
+            self.compile_device(tuple(args))
+
+        # Create function type for typing
+        func_name = self.py_func.__name__
+        name = "CallTemplate({0})".format(func_name)
+
+        call_template = typing.make_concrete_template(
+            name, key=func_name, signatures=self.nopython_signatures)
+        pysig = utils.pysignature(self.py_func)
+
+        return call_template, pysig, args, kws
+
+    def compile_device(self, args):
+        """Compile the device function for the given argument types.
+
+        Each signature is compiled once by caching the compiled function inside
+        this object.
+
+        Returns the `CompileResult`.
+        """
+        if args not in self.overloads:
+            with self._compiling_counter:
+
+                debug = self.targetoptions.get('debug')
+                inline = self.targetoptions.get('inline')
+                fastmath = self.targetoptions.get('fastmath')
+
+                nvvm_options = {
+                    'debug': debug,
+                    'opt': 3 if self.targetoptions.get('opt') else 0,
+                    'fastmath': fastmath
+                }
+
+                cres = compile_cuda(self.py_func, None, args,
+                                    debug=debug,
+                                    inline=inline,
+                                    fastmath=fastmath,
+                                    nvvm_options=nvvm_options)
+                self.overloads[args] = cres
+
+                cres.target_context.insert_user_function(cres.entry_point,
+                                                         cres.fndesc,
+                                                         [cres.library])
+        else:
+            cres = self.overloads[args]
+
+        return cres
+
+    def add_overload(self, kernel, argtypes):
+        c_sig = [a._code for a in argtypes]
+        self._insert(c_sig, kernel, cuda=True)
+        self.overloads[argtypes] = kernel
+
+    def compile(self, sig):
+        '''
+        Compile and bind to the current context a version of this kernel
+        specialized for the given signature.
+        '''
+        argtypes, return_type = sigutils.normalize_signature(sig)
+        assert return_type is None or return_type == types.none
+
+        # Do we already have an in-memory compiled kernel?
+        if self.specialized:
+            return next(iter(self.overloads.values()))
+        else:
+            kernel = self.overloads.get(argtypes)
+            if kernel is not None:
+                return kernel
+
+        # Can we load from the disk cache?
+        kernel = self._cache.load_overload(sig, self.targetctx)
+
+        if kernel is not None:
+            self._cache_hits[sig] += 1
+        else:
+            # We need to compile a new kernel
+            self._cache_misses[sig] += 1
+            if not self._can_compile:
+                raise RuntimeError("Compilation disabled")
+
+            kernel = _Kernel(self.py_func, argtypes, **self.targetoptions)
+            # We call bind to force codegen, so that there is a cubin to cache
+            kernel.bind()
+            self._cache.save_overload(sig, kernel)
+
+        self.add_overload(kernel, argtypes)
+
+        return kernel
+
+    def inspect_llvm(self, signature=None):
+        '''
+        Return the LLVM IR for this kernel.
+
+        :param signature: A tuple of argument types.
+        :return: The LLVM IR for the given signature, or a dict of LLVM IR
+                 for all previously-encountered signatures.
+
+        '''
+        device = self.targetoptions.get('device')
+        if signature is not None:
+            if device:
+                return self.overloads[signature].library.get_llvm_str()
+            else:
+                return self.overloads[signature].inspect_llvm()
+        else:
+            if device:
+                return {sig: overload.library.get_llvm_str()
+                        for sig, overload in self.overloads.items()}
+            else:
+                return {sig: overload.inspect_llvm()
+                        for sig, overload in self.overloads.items()}
+
+    def inspect_asm(self, signature=None):
+        '''
+        Return this kernel's PTX assembly code for for the device in the
+        current context.
+
+        :param signature: A tuple of argument types.
+        :return: The PTX code for the given signature, or a dict of PTX codes
+                 for all previously-encountered signatures.
+        '''
+        cc = get_current_device().compute_capability
+        device = self.targetoptions.get('device')
+        if signature is not None:
+            if device:
+                return self.overloads[signature].library.get_asm_str(cc)
+            else:
+                return self.overloads[signature].inspect_asm(cc)
+        else:
+            if device:
+                return {sig: overload.library.get_asm_str(cc)
+                        for sig, overload in self.overloads.items()}
+            else:
+                return {sig: overload.inspect_asm(cc)
+                        for sig, overload in self.overloads.items()}
+
+    def inspect_sass(self, signature=None):
+        '''
+        Return this kernel's SASS assembly code for for the device in the
+        current context.
+
+        :param signature: A tuple of argument types.
+        :return: The SASS code for the given signature, or a dict of SASS codes
+                 for all previously-encountered signatures.
+
+        SASS for the device in the current context is returned.
+
+        Requires nvdisasm to be available on the PATH.
+        '''
+        if self.targetoptions.get('device'):
+            raise RuntimeError('Cannot inspect SASS of a device function')
+
+        if signature is not None:
+            return self.overloads[signature].inspect_sass()
+        else:
+            return {sig: defn.inspect_sass()
+                    for sig, defn in self.overloads.items()}
+
+    def inspect_types(self, file=None):
+        '''
+        Produce a dump of the Python source of this function annotated with the
+        corresponding Numba IR and type information. The dump is written to
+        *file*, or *sys.stdout* if *file* is *None*.
+        '''
+        if file is None:
+            file = sys.stdout
+
+        for _, defn in self.overloads.items():
+            defn.inspect_types(file=file)
+
+    def bind(self):
+        for defn in self.overloads.values():
+            defn.bind()
+
+    @classmethod
+    def _rebuild(cls, py_func, targetoptions):
+        """
+        Rebuild an instance.
+        """
+        instance = cls(py_func, targetoptions)
+        return instance
+
+    def _reduce_states(self):
+        """
+        Reduce the instance for serialization.
+        Compiled definitions are discarded.
+        """
+        return dict(py_func=self.py_func,
+                    targetoptions=self.targetoptions)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/errors.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/errors.py
new file mode 100644
index 000000000..653a0db6e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/errors.py
@@ -0,0 +1,59 @@
+import numbers
+from numba.core.errors import LoweringError
+
+
+class KernelRuntimeError(RuntimeError):
+    def __init__(self, msg, tid=None, ctaid=None):
+        self.tid = tid
+        self.ctaid = ctaid
+        self.msg = msg
+        t = ("An exception was raised in thread=%s block=%s\n"
+             "\t%s")
+        msg = t % (self.tid, self.ctaid, self.msg)
+        super(KernelRuntimeError, self).__init__(msg)
+
+
+class CudaLoweringError(LoweringError):
+    pass
+
+
+_launch_help_url = ("https://numba.readthedocs.io/en/stable/cuda/"
+                    "kernels.html#kernel-invocation")
+missing_launch_config_msg = """
+Kernel launch configuration was not specified. Use the syntax:
+
+kernel_function[blockspergrid, threadsperblock](arg0, arg1, ..., argn)
+
+See {} for help.
+
+""".format(_launch_help_url)
+
+
+def normalize_kernel_dimensions(griddim, blockdim):
+    """
+    Normalize and validate the user-supplied kernel dimensions.
+    """
+
+    def check_dim(dim, name):
+        if not isinstance(dim, (tuple, list)):
+            dim = [dim]
+        else:
+            dim = list(dim)
+        if len(dim) > 3:
+            raise ValueError('%s must be a sequence of 1, 2 or 3 integers, '
+                             'got %r' % (name, dim))
+        for v in dim:
+            if not isinstance(v, numbers.Integral):
+                raise TypeError('%s must be a sequence of integers, got %r'
+                                % (name, dim))
+        while len(dim) < 3:
+            dim.append(1)
+        return tuple(dim)
+
+    if None in (griddim, blockdim):
+        raise ValueError(missing_launch_config_msg)
+
+    griddim = check_dim(griddim, 'griddim')
+    blockdim = check_dim(blockdim, 'blockdim')
+
+    return griddim, blockdim
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/initialize.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/initialize.py
new file mode 100644
index 000000000..0c9343aaa
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/initialize.py
@@ -0,0 +1,17 @@
+def initialize_all():
+    # Import models to register them with the data model manager
+    import numba.cuda.models  # noqa: F401
+
+    from numba import cuda
+    from numba.cuda.dispatcher import CUDADispatcher
+    from numba.core.target_extension import (target_registry,
+                                             dispatcher_registry,
+                                             jit_registry)
+
+    def cuda_jit_device(*args, **kwargs):
+        kwargs['device'] = True
+        return cuda.jit(*args, **kwargs)
+
+    cuda_target = target_registry["cuda"]
+    jit_registry[cuda_target] = cuda_jit_device
+    dispatcher_registry[cuda_target] = CUDADispatcher
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/intrinsic_wrapper.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/intrinsic_wrapper.py
new file mode 100644
index 000000000..e02639f21
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/intrinsic_wrapper.py
@@ -0,0 +1,77 @@
+from .decorators import jit
+import numba
+
+
+@jit(device=True)
+def all_sync(mask, predicate):
+    """
+    If for all threads in the masked warp the predicate is true, then
+    a non-zero value is returned, otherwise 0 is returned.
+    """
+    return numba.cuda.vote_sync_intrinsic(mask, 0, predicate)[1]
+
+
+@jit(device=True)
+def any_sync(mask, predicate):
+    """
+    If for any thread in the masked warp the predicate is true, then
+    a non-zero value is returned, otherwise 0 is returned.
+    """
+    return numba.cuda.vote_sync_intrinsic(mask, 1, predicate)[1]
+
+
+@jit(device=True)
+def eq_sync(mask, predicate):
+    """
+    If for all threads in the masked warp the boolean predicate is the same,
+    then a non-zero value is returned, otherwise 0 is returned.
+    """
+    return numba.cuda.vote_sync_intrinsic(mask, 2, predicate)[1]
+
+
+@jit(device=True)
+def ballot_sync(mask, predicate):
+    """
+    Returns a mask of all threads in the warp whose predicate is true,
+    and are within the given mask.
+    """
+    return numba.cuda.vote_sync_intrinsic(mask, 3, predicate)[0]
+
+
+@jit(device=True)
+def shfl_sync(mask, value, src_lane):
+    """
+    Shuffles value across the masked warp and returns the value
+    from src_lane. If this is outside the warp, then the
+    given value is returned.
+    """
+    return numba.cuda.shfl_sync_intrinsic(mask, 0, value, src_lane, 0x1f)[0]
+
+
+@jit(device=True)
+def shfl_up_sync(mask, value, delta):
+    """
+    Shuffles value across the masked warp and returns the value
+    from (laneid - delta). If this is outside the warp, then the
+    given value is returned.
+    """
+    return numba.cuda.shfl_sync_intrinsic(mask, 1, value, delta, 0)[0]
+
+
+@jit(device=True)
+def shfl_down_sync(mask, value, delta):
+    """
+    Shuffles value across the masked warp and returns the value
+    from (laneid + delta). If this is outside the warp, then the
+    given value is returned.
+    """
+    return numba.cuda.shfl_sync_intrinsic(mask, 2, value, delta, 0x1f)[0]
+
+
+@jit(device=True)
+def shfl_xor_sync(mask, value, lane_mask):
+    """
+    Shuffles value across the masked warp and returns the value
+    from (laneid ^ lane_mask).
+    """
+    return numba.cuda.shfl_sync_intrinsic(mask, 3, value, lane_mask, 0x1f)[0]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/reduction.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/reduction.py
new file mode 100644
index 000000000..f733935b6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/reduction.py
@@ -0,0 +1,262 @@
+"""
+A library written in CUDA Python for generating reduction kernels
+"""
+
+from numba.np.numpy_support import from_dtype
+
+
+_WARPSIZE = 32
+_NUMWARPS = 4
+
+
+def _gpu_reduce_factory(fn, nbtype):
+    from numba import cuda
+
+    reduce_op = cuda.jit(device=True)(fn)
+    inner_sm_size = _WARPSIZE + 1   # plus one to avoid SM collision
+    max_blocksize = _NUMWARPS * _WARPSIZE
+
+    @cuda.jit(device=True)
+    def inner_warp_reduction(sm_partials, init):
+        """
+        Compute reduction within a single warp
+        """
+        tid = cuda.threadIdx.x
+        warpid = tid // _WARPSIZE
+        laneid = tid % _WARPSIZE
+
+        sm_this = sm_partials[warpid, :]
+        sm_this[laneid] = init
+        cuda.syncwarp()
+
+        width = _WARPSIZE // 2
+        while width:
+            if laneid < width:
+                old = sm_this[laneid]
+                sm_this[laneid] = reduce_op(old, sm_this[laneid + width])
+            cuda.syncwarp()
+            width //= 2
+
+    @cuda.jit(device=True)
+    def device_reduce_full_block(arr, partials, sm_partials):
+        """
+        Partially reduce `arr` into `partials` using `sm_partials` as working
+        space.  The algorithm goes like:
+
+            array chunks of 128:  |   0 | 128 | 256 | 384 | 512 |
+                        block-0:  |   x |     |     |   x |     |
+                        block-1:  |     |   x |     |     |   x |
+                        block-2:  |     |     |   x |     |     |
+
+        The array is divided into chunks of 128 (size of a threadblock).
+        The threadblocks consumes the chunks in roundrobin scheduling.
+        First, a threadblock loads a chunk into temp memory.  Then, all
+        subsequent chunks are combined into the temp memory.
+
+        Once all chunks are processed.  Inner-block reduction is performed
+        on the temp memory.  So that, there will just be one scalar result
+        per block.  The result from each block is stored to `partials` at
+        the dedicated slot.
+        """
+        tid = cuda.threadIdx.x
+        blkid = cuda.blockIdx.x
+        blksz = cuda.blockDim.x
+        gridsz = cuda.gridDim.x
+
+        # block strided loop to compute the reduction
+        start = tid + blksz * blkid
+        stop = arr.size
+        step = blksz * gridsz
+
+        # load first value
+        tmp = arr[start]
+        # loop over all values in block-stride
+        for i in range(start + step, stop, step):
+            tmp = reduce_op(tmp, arr[i])
+
+        cuda.syncthreads()
+        # inner-warp reduction
+        inner_warp_reduction(sm_partials, tmp)
+
+        cuda.syncthreads()
+        # at this point, only the first slot for each warp in tsm_partials
+        # is valid.
+
+        # finish up block reduction
+        # warning: this is assuming 4 warps.
+        # assert numwarps == 4
+        if tid < 2:
+            sm_partials[tid, 0] = reduce_op(sm_partials[tid, 0],
+                                            sm_partials[tid + 2, 0])
+            cuda.syncwarp()
+        if tid == 0:
+            partials[blkid] = reduce_op(sm_partials[0, 0], sm_partials[1, 0])
+
+    @cuda.jit(device=True)
+    def device_reduce_partial_block(arr, partials, sm_partials):
+        """
+        This computes reduction on `arr`.
+        This device function must be used by 1 threadblock only.
+        The blocksize must match `arr.size` and must not be greater than 128.
+        """
+        tid = cuda.threadIdx.x
+        blkid = cuda.blockIdx.x
+        blksz = cuda.blockDim.x
+        warpid = tid // _WARPSIZE
+        laneid = tid % _WARPSIZE
+
+        size = arr.size
+        # load first value
+        tid = cuda.threadIdx.x
+        value = arr[tid]
+        sm_partials[warpid, laneid] = value
+
+        cuda.syncthreads()
+
+        if (warpid + 1) * _WARPSIZE < size:
+            # fully populated warps
+            inner_warp_reduction(sm_partials, value)
+        else:
+            # partially populated warps
+            # NOTE: this uses a very inefficient sequential algorithm
+            if laneid == 0:
+                sm_this = sm_partials[warpid, :]
+                base = warpid * _WARPSIZE
+                for i in range(1, size - base):
+                    sm_this[0] = reduce_op(sm_this[0], sm_this[i])
+
+        cuda.syncthreads()
+        # finish up
+        if tid == 0:
+            num_active_warps = (blksz + _WARPSIZE - 1) // _WARPSIZE
+
+            result = sm_partials[0, 0]
+            for i in range(1, num_active_warps):
+                result = reduce_op(result, sm_partials[i, 0])
+
+            partials[blkid] = result
+
+    def gpu_reduce_block_strided(arr, partials, init, use_init):
+        """
+        Perform reductions on *arr* and writing out partial reduction result
+        into *partials*.  The length of *partials* is determined by the
+        number of threadblocks. The initial value is set with *init*.
+
+        Launch config:
+
+        Blocksize must be multiple of warpsize and it is limited to 4 warps.
+        """
+        tid = cuda.threadIdx.x
+
+        sm_partials = cuda.shared.array((_NUMWARPS, inner_sm_size),
+                                        dtype=nbtype)
+        if cuda.blockDim.x == max_blocksize:
+            device_reduce_full_block(arr, partials, sm_partials)
+        else:
+            device_reduce_partial_block(arr, partials, sm_partials)
+        # deal with the initializer
+        if use_init and tid == 0 and cuda.blockIdx.x == 0:
+            partials[0] = reduce_op(partials[0], init)
+
+    return cuda.jit(gpu_reduce_block_strided)
+
+
+class Reduce(object):
+    """Create a reduction object that reduces values using a given binary
+    function. The binary function is compiled once and cached inside this
+    object. Keeping this object alive will prevent re-compilation.
+    """
+
+    _cache = {}
+
+    def __init__(self, functor):
+        """
+        :param functor: A function implementing a binary operation for
+                        reduction. It will be compiled as a CUDA device
+                        function using ``cuda.jit(device=True)``.
+        """
+        self._functor = functor
+
+    def _compile(self, dtype):
+        key = self._functor, dtype
+        if key in self._cache:
+            kernel = self._cache[key]
+        else:
+            kernel = _gpu_reduce_factory(self._functor, from_dtype(dtype))
+            self._cache[key] = kernel
+        return kernel
+
+    def __call__(self, arr, size=None, res=None, init=0, stream=0):
+        """Performs a full reduction.
+
+        :param arr: A host or device array.
+        :param size: Optional integer specifying the number of elements in
+                    ``arr`` to reduce. If this parameter is not specified, the
+                    entire array is reduced.
+        :param res: Optional device array into which to write the reduction
+                    result to. The result is written into the first element of
+                    this array. If this parameter is specified, then no
+                    communication of the reduction output takes place from the
+                    device to the host.
+        :param init: Optional initial value for the reduction, the type of which
+                    must match ``arr.dtype``.
+        :param stream: Optional CUDA stream in which to perform the reduction.
+                    If no stream is specified, the default stream of 0 is
+                    used.
+        :return: If ``res`` is specified, ``None`` is returned. Otherwise, the
+                result of the reduction is returned.
+        """
+        from numba import cuda
+
+        # ensure 1d array
+        if arr.ndim != 1:
+            raise TypeError("only support 1D array")
+
+        # adjust array size
+        if size is not None:
+            arr = arr[:size]
+
+        init = arr.dtype.type(init)  # ensure the right type
+
+        # return `init` if `arr` is empty
+        if arr.size < 1:
+            return init
+
+        kernel = self._compile(arr.dtype)
+
+        # Perform the reduction on the GPU
+        blocksize = _NUMWARPS * _WARPSIZE
+        size_full = (arr.size // blocksize) * blocksize
+        size_partial = arr.size - size_full
+        full_blockct = min(size_full // blocksize, _WARPSIZE * 2)
+
+        # allocate size of partials array
+        partials_size = full_blockct
+        if size_partial:
+            partials_size += 1
+        partials = cuda.device_array(shape=partials_size, dtype=arr.dtype)
+
+        if size_full:
+            # kernel for the fully populated threadblocks
+            kernel[full_blockct, blocksize, stream](arr[:size_full],
+                                                    partials[:full_blockct],
+                                                    init,
+                                                    True)
+
+        if size_partial:
+            # kernel for partially populated threadblocks
+            kernel[1, size_partial, stream](arr[size_full:],
+                                            partials[full_blockct:],
+                                            init,
+                                            not full_blockct)
+
+        if partials.size > 1:
+            # finish up
+            kernel[1, partials_size, stream](partials, partials, init, False)
+
+        # handle return value
+        if res is not None:
+            res[:1].copy_to_device(partials[:1], stream=stream)
+            return
+        else:
+            return partials[0]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/transpose.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/transpose.py
new file mode 100644
index 000000000..b1df36e04
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/kernels/transpose.py
@@ -0,0 +1,65 @@
+from numba import cuda
+from numba.cuda.cudadrv.driver import driver
+import math
+from numba.np import numpy_support as nps
+
+
+def transpose(a, b=None):
+    """Compute the transpose of 'a' and store it into 'b', if given,
+    and return it. If 'b' is not given, allocate a new array
+    and return that.
+
+    This implements the algorithm documented in
+    http://devblogs.nvidia.com/parallelforall/efficient-matrix-transpose-cuda-cc/
+
+    :param a: an `np.ndarray` or a `DeviceNDArrayBase` subclass. If already on
+        the device its stream will be used to perform the transpose (and to copy
+        `b` to the device if necessary).
+    """
+
+    # prefer `a`'s stream if
+    stream = getattr(a, 'stream', 0)
+
+    if not b:
+        cols, rows = a.shape
+        strides = a.dtype.itemsize * cols, a.dtype.itemsize
+        b = cuda.cudadrv.devicearray.DeviceNDArray(
+            (rows, cols),
+            strides,
+            dtype=a.dtype,
+            stream=stream)
+
+    dt = nps.from_dtype(a.dtype)
+
+    tpb = driver.get_device().MAX_THREADS_PER_BLOCK
+    # we need to factor available threads into x and y axis
+    tile_width = int(math.pow(2, math.log(tpb, 2) / 2))
+    tile_height = int(tpb / tile_width)
+
+    tile_shape = (tile_height, tile_width + 1)
+
+    @cuda.jit
+    def kernel(input, output):
+
+        tile = cuda.shared.array(shape=tile_shape, dtype=dt)
+
+        tx = cuda.threadIdx.x
+        ty = cuda.threadIdx.y
+        bx = cuda.blockIdx.x * cuda.blockDim.x
+        by = cuda.blockIdx.y * cuda.blockDim.y
+        x = by + tx
+        y = bx + ty
+
+        if by + ty < input.shape[0] and bx + tx < input.shape[1]:
+            tile[ty, tx] = input[by + ty, bx + tx]
+        cuda.syncthreads()
+        if y < output.shape[0] and x < output.shape[1]:
+            output[y, x] = tile[tx, ty]
+
+    # one block per tile, plus one for remainders
+    blocks = int(b.shape[0] / tile_height + 1), int(b.shape[1] / tile_width + 1)
+    # one thread per tile element
+    threads = tile_height, tile_width
+    kernel[blocks, threads, stream](a, b)
+
+    return b
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevice.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevice.py
new file mode 100644
index 000000000..303ade74b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevice.py
@@ -0,0 +1,3382 @@
+def abs(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_abs.html
+
+    :param x: Argument.
+    :type x: int32
+    :rtype: int32
+"""
+
+
+def acos(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_acos.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def acosf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_acosf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def acosh(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_acosh.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def acoshf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_acoshf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def asin(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_asin.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def asinf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_asinf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def asinh(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_asinh.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def asinhf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_asinhf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def atan(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_atan.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def atan2(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_atan2.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def atan2f(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_atan2f.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def atanf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_atanf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def atanh(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_atanh.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def atanhf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_atanhf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def brev(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_brev.html
+
+    :param x: Argument.
+    :type x: int32
+    :rtype: int32
+"""
+
+
+def brevll(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_brevll.html
+
+    :param x: Argument.
+    :type x: int64
+    :rtype: int64
+"""
+
+
+def byte_perm(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_byte_perm.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :param z: Argument.
+    :type z: int32
+    :rtype: int32
+"""
+
+
+def cbrt(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cbrt.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def cbrtf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cbrtf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def ceil(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ceil.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def ceilf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ceilf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def clz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_clz.html
+
+    :param x: Argument.
+    :type x: int32
+    :rtype: int32
+"""
+
+
+def clzll(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_clzll.html
+
+    :param x: Argument.
+    :type x: int64
+    :rtype: int32
+"""
+
+
+def copysign(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_copysign.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def copysignf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_copysignf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def cos(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cos.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def cosf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cosf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def cosh(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cosh.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def coshf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_coshf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def cospi(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cospi.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def cospif(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_cospif.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def dadd_rd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dadd_rd.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def dadd_rn(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dadd_rn.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def dadd_ru(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dadd_ru.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def dadd_rz(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dadd_rz.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def ddiv_rd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ddiv_rd.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def ddiv_rn(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ddiv_rn.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def ddiv_ru(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ddiv_ru.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def ddiv_rz(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ddiv_rz.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def dmul_rd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dmul_rd.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def dmul_rn(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dmul_rn.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def dmul_ru(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dmul_ru.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def dmul_rz(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dmul_rz.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def double2float_rd(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2float_rd.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: float32
+"""
+
+
+def double2float_rn(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2float_rn.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: float32
+"""
+
+
+def double2float_ru(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2float_ru.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: float32
+"""
+
+
+def double2float_rz(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2float_rz.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: float32
+"""
+
+
+def double2hiint(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2hiint.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2int_rd(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2int_rd.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2int_rn(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2int_rn.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2int_ru(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2int_ru.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2int_rz(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2int_rz.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2ll_rd(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2ll_rd.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: int64
+"""
+
+
+def double2ll_rn(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2ll_rn.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: int64
+"""
+
+
+def double2ll_ru(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2ll_ru.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: int64
+"""
+
+
+def double2ll_rz(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2ll_rz.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: int64
+"""
+
+
+def double2loint(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2loint.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2uint_rd(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2uint_rd.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2uint_rn(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2uint_rn.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2uint_ru(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2uint_ru.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2uint_rz(d):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2uint_rz.html
+
+    :param d: Argument.
+    :type d: float64
+    :rtype: int32
+"""
+
+
+def double2ull_rd(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2ull_rd.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: int64
+"""
+
+
+def double2ull_rn(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2ull_rn.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: int64
+"""
+
+
+def double2ull_ru(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2ull_ru.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: int64
+"""
+
+
+def double2ull_rz(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double2ull_rz.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: int64
+"""
+
+
+def double_as_longlong(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_double_as_longlong.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: int64
+"""
+
+
+def drcp_rd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_drcp_rd.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def drcp_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_drcp_rn.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def drcp_ru(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_drcp_ru.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def drcp_rz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_drcp_rz.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def dsqrt_rd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dsqrt_rd.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def dsqrt_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dsqrt_rn.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def dsqrt_ru(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dsqrt_ru.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def dsqrt_rz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_dsqrt_rz.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def erf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erf.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def erfc(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erfc.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def erfcf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erfcf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def erfcinv(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erfcinv.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def erfcinvf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erfcinvf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def erfcx(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erfcx.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def erfcxf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erfcxf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def erff(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erff.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def erfinv(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erfinv.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def erfinvf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_erfinvf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def exp(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_exp.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def exp10(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_exp10.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def exp10f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_exp10f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def exp2(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_exp2.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def exp2f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_exp2f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def expf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_expf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def expm1(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_expm1.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def expm1f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_expm1f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fabs(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fabs.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: float64
+"""
+
+
+def fabsf(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fabsf.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: float32
+"""
+
+
+def fadd_rd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fadd_rd.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fadd_rn(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fadd_rn.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fadd_ru(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fadd_ru.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fadd_rz(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fadd_rz.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fast_cosf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_cosf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fast_exp10f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_exp10f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fast_expf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_expf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fast_fdividef(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_fdividef.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fast_log10f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_log10f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fast_log2f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_log2f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fast_logf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_logf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fast_powf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_powf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fast_sincosf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_sincosf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: UniTuple(float32 x 2)
+"""
+
+
+def fast_sinf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_sinf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fast_tanf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fast_tanf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fdim(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fdim.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def fdimf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fdimf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fdiv_rd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fdiv_rd.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fdiv_rn(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fdiv_rn.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fdiv_ru(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fdiv_ru.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fdiv_rz(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fdiv_rz.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def ffs(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ffs.html
+
+    :param x: Argument.
+    :type x: int32
+    :rtype: int32
+"""
+
+
+def ffsll(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ffsll.html
+
+    :param x: Argument.
+    :type x: int64
+    :rtype: int32
+"""
+
+
+def finitef(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_finitef.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: int32
+"""
+
+
+def float2half_rn(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2half_rn.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int16
+"""
+
+
+def float2int_rd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2int_rd.html
+
+    :param in: Argument.
+    :type in: float32
+    :rtype: int32
+"""
+
+
+def float2int_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2int_rn.html
+
+    :param in: Argument.
+    :type in: float32
+    :rtype: int32
+"""
+
+
+def float2int_ru(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2int_ru.html
+
+    :param in: Argument.
+    :type in: float32
+    :rtype: int32
+"""
+
+
+def float2int_rz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2int_rz.html
+
+    :param in: Argument.
+    :type in: float32
+    :rtype: int32
+"""
+
+
+def float2ll_rd(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2ll_rd.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int64
+"""
+
+
+def float2ll_rn(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2ll_rn.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int64
+"""
+
+
+def float2ll_ru(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2ll_ru.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int64
+"""
+
+
+def float2ll_rz(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2ll_rz.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int64
+"""
+
+
+def float2uint_rd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2uint_rd.html
+
+    :param in: Argument.
+    :type in: float32
+    :rtype: int32
+"""
+
+
+def float2uint_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2uint_rn.html
+
+    :param in: Argument.
+    :type in: float32
+    :rtype: int32
+"""
+
+
+def float2uint_ru(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2uint_ru.html
+
+    :param in: Argument.
+    :type in: float32
+    :rtype: int32
+"""
+
+
+def float2uint_rz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2uint_rz.html
+
+    :param in: Argument.
+    :type in: float32
+    :rtype: int32
+"""
+
+
+def float2ull_rd(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2ull_rd.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int64
+"""
+
+
+def float2ull_rn(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2ull_rn.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int64
+"""
+
+
+def float2ull_ru(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2ull_ru.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int64
+"""
+
+
+def float2ull_rz(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float2ull_rz.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: int64
+"""
+
+
+def float_as_int(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_float_as_int.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: int32
+"""
+
+
+def floor(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_floor.html
+
+    :param f: Argument.
+    :type f: float64
+    :rtype: float64
+"""
+
+
+def floorf(f):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_floorf.html
+
+    :param f: Argument.
+    :type f: float32
+    :rtype: float32
+"""
+
+
+def fma(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fma.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :param z: Argument.
+    :type z: float64
+    :rtype: float64
+"""
+
+
+def fma_rd(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fma_rd.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :param z: Argument.
+    :type z: float64
+    :rtype: float64
+"""
+
+
+def fma_rn(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fma_rn.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :param z: Argument.
+    :type z: float64
+    :rtype: float64
+"""
+
+
+def fma_ru(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fma_ru.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :param z: Argument.
+    :type z: float64
+    :rtype: float64
+"""
+
+
+def fma_rz(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fma_rz.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :param z: Argument.
+    :type z: float64
+    :rtype: float64
+"""
+
+
+def fmaf(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmaf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :param z: Argument.
+    :type z: float32
+    :rtype: float32
+"""
+
+
+def fmaf_rd(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmaf_rd.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :param z: Argument.
+    :type z: float32
+    :rtype: float32
+"""
+
+
+def fmaf_rn(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmaf_rn.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :param z: Argument.
+    :type z: float32
+    :rtype: float32
+"""
+
+
+def fmaf_ru(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmaf_ru.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :param z: Argument.
+    :type z: float32
+    :rtype: float32
+"""
+
+
+def fmaf_rz(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmaf_rz.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :param z: Argument.
+    :type z: float32
+    :rtype: float32
+"""
+
+
+def fmax(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmax.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def fmaxf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmaxf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fmin(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmin.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def fminf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fminf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fmod(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmod.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def fmodf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmodf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fmul_rd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmul_rd.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fmul_rn(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmul_rn.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fmul_ru(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmul_ru.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fmul_rz(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fmul_rz.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def frcp_rd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_frcp_rd.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def frcp_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_frcp_rn.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def frcp_ru(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_frcp_ru.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def frcp_rz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_frcp_rz.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def frexp(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_frexp.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: Tuple(float64, int32)
+"""
+
+
+def frexpf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_frexpf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: Tuple(float32, int32)
+"""
+
+
+def frsqrt_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_frsqrt_rn.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fsqrt_rd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fsqrt_rd.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fsqrt_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fsqrt_rn.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fsqrt_ru(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fsqrt_ru.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fsqrt_rz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fsqrt_rz.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def fsub_rd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fsub_rd.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fsub_rn(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fsub_rn.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fsub_ru(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fsub_ru.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def fsub_rz(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_fsub_rz.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def hadd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_hadd.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def half2float(h):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_half2float.html
+
+    :param h: Argument.
+    :type h: int16
+    :rtype: float32
+"""
+
+
+def hiloint2double(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_hiloint2double.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: float64
+"""
+
+
+def hypot(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_hypot.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def hypotf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_hypotf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def ilogb(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ilogb.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: int32
+"""
+
+
+def ilogbf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ilogbf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: int32
+"""
+
+
+def int2double_rn(i):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_int2double_rn.html
+
+    :param i: Argument.
+    :type i: int32
+    :rtype: float64
+"""
+
+
+def int2float_rd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_int2float_rd.html
+
+    :param in: Argument.
+    :type in: int32
+    :rtype: float32
+"""
+
+
+def int2float_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_int2float_rn.html
+
+    :param in: Argument.
+    :type in: int32
+    :rtype: float32
+"""
+
+
+def int2float_ru(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_int2float_ru.html
+
+    :param in: Argument.
+    :type in: int32
+    :rtype: float32
+"""
+
+
+def int2float_rz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_int2float_rz.html
+
+    :param in: Argument.
+    :type in: int32
+    :rtype: float32
+"""
+
+
+def int_as_float(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_int_as_float.html
+
+    :param x: Argument.
+    :type x: int32
+    :rtype: float32
+"""
+
+
+def isfinited(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_isfinited.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: int32
+"""
+
+
+def isinfd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_isinfd.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: int32
+"""
+
+
+def isinff(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_isinff.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: int32
+"""
+
+
+def isnand(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_isnand.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: int32
+"""
+
+
+def isnanf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_isnanf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: int32
+"""
+
+
+def j0(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_j0.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def j0f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_j0f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def j1(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_j1.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def j1f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_j1f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def jn(n, x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_jn.html
+
+    :param n: Argument.
+    :type n: int32
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def jnf(n, x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_jnf.html
+
+    :param n: Argument.
+    :type n: int32
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def ldexp(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ldexp.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: int32
+    :rtype: float64
+"""
+
+
+def ldexpf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ldexpf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: int32
+    :rtype: float32
+"""
+
+
+def lgamma(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_lgamma.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def lgammaf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_lgammaf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def ll2double_rd(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ll2double_rd.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float64
+"""
+
+
+def ll2double_rn(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ll2double_rn.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float64
+"""
+
+
+def ll2double_ru(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ll2double_ru.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float64
+"""
+
+
+def ll2double_rz(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ll2double_rz.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float64
+"""
+
+
+def ll2float_rd(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ll2float_rd.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float32
+"""
+
+
+def ll2float_rn(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ll2float_rn.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float32
+"""
+
+
+def ll2float_ru(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ll2float_ru.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float32
+"""
+
+
+def ll2float_rz(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ll2float_rz.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float32
+"""
+
+
+def llabs(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_llabs.html
+
+    :param x: Argument.
+    :type x: int64
+    :rtype: int64
+"""
+
+
+def llmax(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_llmax.html
+
+    :param x: Argument.
+    :type x: int64
+    :param y: Argument.
+    :type y: int64
+    :rtype: int64
+"""
+
+
+def llmin(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_llmin.html
+
+    :param x: Argument.
+    :type x: int64
+    :param y: Argument.
+    :type y: int64
+    :rtype: int64
+"""
+
+
+def llrint(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_llrint.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: int64
+"""
+
+
+def llrintf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_llrintf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: int64
+"""
+
+
+def llround(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_llround.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: int64
+"""
+
+
+def llroundf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_llroundf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: int64
+"""
+
+
+def log(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_log.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def log10(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_log10.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def log10f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_log10f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def log1p(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_log1p.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def log1pf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_log1pf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def log2(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_log2.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def log2f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_log2f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def logb(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_logb.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def logbf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_logbf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def logf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_logf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def longlong_as_double(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_longlong_as_double.html
+
+    :param x: Argument.
+    :type x: int64
+    :rtype: float64
+"""
+
+
+def max(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_max.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def min(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_min.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def modf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_modf.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: UniTuple(float64 x 2)
+"""
+
+
+def modff(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_modff.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: UniTuple(float32 x 2)
+"""
+
+
+def mul24(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_mul24.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def mul64hi(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_mul64hi.html
+
+    :param x: Argument.
+    :type x: int64
+    :param y: Argument.
+    :type y: int64
+    :rtype: int64
+"""
+
+
+def mulhi(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_mulhi.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def nearbyint(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_nearbyint.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def nearbyintf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_nearbyintf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def nextafter(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_nextafter.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def nextafterf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_nextafterf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def normcdf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_normcdf.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def normcdff(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_normcdff.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def normcdfinv(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_normcdfinv.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def normcdfinvf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_normcdfinvf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def popc(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_popc.html
+
+    :param x: Argument.
+    :type x: int32
+    :rtype: int32
+"""
+
+
+def popcll(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_popcll.html
+
+    :param x: Argument.
+    :type x: int64
+    :rtype: int32
+"""
+
+
+def pow(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_pow.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def powf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_powf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def powi(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_powi.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: int32
+    :rtype: float64
+"""
+
+
+def powif(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_powif.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: int32
+    :rtype: float32
+"""
+
+
+def rcbrt(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_rcbrt.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def rcbrtf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_rcbrtf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def remainder(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_remainder.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: float64
+"""
+
+
+def remainderf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_remainderf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: float32
+"""
+
+
+def remquo(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_remquo.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: float64
+    :rtype: Tuple(float64, int32)
+"""
+
+
+def remquof(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_remquof.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: float32
+    :rtype: Tuple(float32, int32)
+"""
+
+
+def rhadd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_rhadd.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def rint(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_rint.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def rintf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_rintf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def round(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_round.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def roundf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_roundf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def rsqrt(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_rsqrt.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def rsqrtf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_rsqrtf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def sad(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sad.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :param z: Argument.
+    :type z: int32
+    :rtype: int32
+"""
+
+
+def saturatef(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_saturatef.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def scalbn(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_scalbn.html
+
+    :param x: Argument.
+    :type x: float64
+    :param y: Argument.
+    :type y: int32
+    :rtype: float64
+"""
+
+
+def scalbnf(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_scalbnf.html
+
+    :param x: Argument.
+    :type x: float32
+    :param y: Argument.
+    :type y: int32
+    :rtype: float32
+"""
+
+
+def signbitd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_signbitd.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: int32
+"""
+
+
+def signbitf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_signbitf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: int32
+"""
+
+
+def sin(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sin.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def sincos(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sincos.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: UniTuple(float64 x 2)
+"""
+
+
+def sincosf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sincosf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: UniTuple(float32 x 2)
+"""
+
+
+def sincospi(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sincospi.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: UniTuple(float64 x 2)
+"""
+
+
+def sincospif(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sincospif.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: UniTuple(float32 x 2)
+"""
+
+
+def sinf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sinf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def sinh(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sinh.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def sinhf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sinhf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def sinpi(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sinpi.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def sinpif(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sinpif.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def sqrt(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sqrt.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def sqrtf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_sqrtf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def tan(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_tan.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def tanf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_tanf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def tanh(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_tanh.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def tanhf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_tanhf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def tgamma(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_tgamma.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def tgammaf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_tgammaf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def trunc(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_trunc.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def truncf(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_truncf.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def uhadd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_uhadd.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def uint2double_rn(i):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_uint2double_rn.html
+
+    :param i: Argument.
+    :type i: int32
+    :rtype: float64
+"""
+
+
+def uint2float_rd(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_uint2float_rd.html
+
+    :param in: Argument.
+    :type in: int32
+    :rtype: float32
+"""
+
+
+def uint2float_rn(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_uint2float_rn.html
+
+    :param in: Argument.
+    :type in: int32
+    :rtype: float32
+"""
+
+
+def uint2float_ru(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_uint2float_ru.html
+
+    :param in: Argument.
+    :type in: int32
+    :rtype: float32
+"""
+
+
+def uint2float_rz(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_uint2float_rz.html
+
+    :param in: Argument.
+    :type in: int32
+    :rtype: float32
+"""
+
+
+def ull2double_rd(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ull2double_rd.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float64
+"""
+
+
+def ull2double_rn(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ull2double_rn.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float64
+"""
+
+
+def ull2double_ru(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ull2double_ru.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float64
+"""
+
+
+def ull2double_rz(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ull2double_rz.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float64
+"""
+
+
+def ull2float_rd(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ull2float_rd.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float32
+"""
+
+
+def ull2float_rn(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ull2float_rn.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float32
+"""
+
+
+def ull2float_ru(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ull2float_ru.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float32
+"""
+
+
+def ull2float_rz(l):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ull2float_rz.html
+
+    :param l: Argument.
+    :type l: int64
+    :rtype: float32
+"""
+
+
+def ullmax(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ullmax.html
+
+    :param x: Argument.
+    :type x: int64
+    :param y: Argument.
+    :type y: int64
+    :rtype: int64
+"""
+
+
+def ullmin(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ullmin.html
+
+    :param x: Argument.
+    :type x: int64
+    :param y: Argument.
+    :type y: int64
+    :rtype: int64
+"""
+
+
+def umax(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_umax.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def umin(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_umin.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def umul24(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_umul24.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def umul64hi(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_umul64hi.html
+
+    :param x: Argument.
+    :type x: int64
+    :param y: Argument.
+    :type y: int64
+    :rtype: int64
+"""
+
+
+def umulhi(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_umulhi.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def urhadd(x, y):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_urhadd.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :rtype: int32
+"""
+
+
+def usad(x, y, z):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_usad.html
+
+    :param x: Argument.
+    :type x: int32
+    :param y: Argument.
+    :type y: int32
+    :param z: Argument.
+    :type z: int32
+    :rtype: int32
+"""
+
+
+def y0(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_y0.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def y0f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_y0f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def y1(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_y1.html
+
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def y1f(x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_y1f.html
+
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
+
+
+def yn(n, x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_yn.html
+
+    :param n: Argument.
+    :type n: int32
+    :param x: Argument.
+    :type x: float64
+    :rtype: float64
+"""
+
+
+def ynf(n, x):
+    """
+    See https://docs.nvidia.com/cuda/libdevice-users-guide/__nv_ynf.html
+
+    :param n: Argument.
+    :type n: int32
+    :param x: Argument.
+    :type x: float32
+    :rtype: float32
+"""
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevicedecl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevicedecl.py
new file mode 100644
index 000000000..e82646dca
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevicedecl.py
@@ -0,0 +1,17 @@
+from numba.cuda import libdevice, libdevicefuncs
+from numba.core.typing.templates import ConcreteTemplate, Registry
+
+registry = Registry()
+register_global = registry.register_global
+
+
+def libdevice_declare(func, retty, args):
+    class Libdevice_function(ConcreteTemplate):
+        cases = [libdevicefuncs.create_signature(retty, args)]
+
+    pyfunc = getattr(libdevice, func[5:])
+    register_global(pyfunc)(Libdevice_function)
+
+
+for func, (retty, args) in libdevicefuncs.functions.items():
+    libdevice_declare(func, retty, args)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevicefuncs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevicefuncs.py
new file mode 100644
index 000000000..ba3bb5acc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdevicefuncs.py
@@ -0,0 +1,1057 @@
+from collections import namedtuple
+from textwrap import indent
+
+from numba.types import float32, float64, int16, int32, int64, void, Tuple
+from numba.core.typing.templates import signature
+
+arg = namedtuple("arg", ("name", "ty", "is_ptr"))
+
+functions = {
+    "__nv_abs": (int32, [arg(name="x", ty=int32, is_ptr=False)]),
+    "__nv_acos": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_acosf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_acosh": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_acoshf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_asin": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_asinf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_asinh": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_asinhf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_atan": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_atan2": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_atan2f": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_atanf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_atanh": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_atanhf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_brev": (int32, [arg(name="x", ty=int32, is_ptr=False)]),
+    "__nv_brevll": (int64, [arg(name="x", ty=int64, is_ptr=False)]),
+    "__nv_byte_perm": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+            arg(name="z", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_cbrt": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_cbrtf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_ceil": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_ceilf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_clz": (int32, [arg(name="x", ty=int32, is_ptr=False)]),
+    "__nv_clzll": (int32, [arg(name="x", ty=int64, is_ptr=False)]),
+    "__nv_copysign": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_copysignf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_cos": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_cosf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_cosh": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_coshf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_cospi": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_cospif": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_dadd_rd": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_dadd_rn": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_dadd_ru": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_dadd_rz": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_ddiv_rd": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_ddiv_rn": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_ddiv_ru": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_ddiv_rz": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_dmul_rd": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_dmul_rn": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_dmul_ru": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_dmul_rz": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_double2float_rd": (
+        float32,
+        [arg(name="d", ty=float64, is_ptr=False)],
+    ),
+    "__nv_double2float_rn": (
+        float32,
+        [arg(name="d", ty=float64, is_ptr=False)],
+    ),
+    "__nv_double2float_ru": (
+        float32,
+        [arg(name="d", ty=float64, is_ptr=False)],
+    ),
+    "__nv_double2float_rz": (
+        float32,
+        [arg(name="d", ty=float64, is_ptr=False)],
+    ),
+    "__nv_double2hiint": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2int_rd": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2int_rn": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2int_ru": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2int_rz": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2ll_rd": (int64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_double2ll_rn": (int64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_double2ll_ru": (int64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_double2ll_rz": (int64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_double2loint": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2uint_rd": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2uint_rn": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2uint_ru": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2uint_rz": (int32, [arg(name="d", ty=float64, is_ptr=False)]),
+    "__nv_double2ull_rd": (int64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_double2ull_rn": (int64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_double2ull_ru": (int64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_double2ull_rz": (int64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_double_as_longlong": (
+        int64,
+        [arg(name="x", ty=float64, is_ptr=False)],
+    ),
+    "__nv_drcp_rd": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_drcp_rn": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_drcp_ru": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_drcp_rz": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_dsqrt_rd": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_dsqrt_rn": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_dsqrt_ru": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_dsqrt_rz": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_erf": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_erfc": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_erfcf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_erfcinv": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_erfcinvf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_erfcx": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_erfcxf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_erff": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_erfinv": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_erfinvf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_exp": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_exp10": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_exp10f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_exp2": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_exp2f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_expf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_expm1": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_expm1f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fabs": (float64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_fabsf": (float32, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_fadd_rd": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fadd_rn": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fadd_ru": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fadd_rz": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fast_cosf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fast_exp10f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fast_expf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fast_fdividef": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fast_log10f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fast_log2f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fast_logf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fast_powf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fast_sincosf": (
+        void,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="sptr", ty=float32, is_ptr=True),
+            arg(name="cptr", ty=float32, is_ptr=True),
+        ],
+    ),
+    "__nv_fast_sinf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fast_tanf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fdim": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fdimf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fdiv_rd": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fdiv_rn": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fdiv_ru": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fdiv_rz": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_ffs": (int32, [arg(name="x", ty=int32, is_ptr=False)]),
+    "__nv_ffsll": (int32, [arg(name="x", ty=int64, is_ptr=False)]),
+    "__nv_finitef": (int32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_float2half_rn": (int16, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float2int_rd": (int32, [arg(name="in", ty=float32, is_ptr=False)]),
+    "__nv_float2int_rn": (int32, [arg(name="in", ty=float32, is_ptr=False)]),
+    "__nv_float2int_ru": (int32, [arg(name="in", ty=float32, is_ptr=False)]),
+    "__nv_float2int_rz": (int32, [arg(name="in", ty=float32, is_ptr=False)]),
+    "__nv_float2ll_rd": (int64, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float2ll_rn": (int64, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float2ll_ru": (int64, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float2ll_rz": (int64, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float2uint_rd": (int32, [arg(name="in", ty=float32, is_ptr=False)]),
+    "__nv_float2uint_rn": (int32, [arg(name="in", ty=float32, is_ptr=False)]),
+    "__nv_float2uint_ru": (int32, [arg(name="in", ty=float32, is_ptr=False)]),
+    "__nv_float2uint_rz": (int32, [arg(name="in", ty=float32, is_ptr=False)]),
+    "__nv_float2ull_rd": (int64, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float2ull_rn": (int64, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float2ull_ru": (int64, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float2ull_rz": (int64, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_float_as_int": (int32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_floor": (float64, [arg(name="f", ty=float64, is_ptr=False)]),
+    "__nv_floorf": (float32, [arg(name="f", ty=float32, is_ptr=False)]),
+    "__nv_fma": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+            arg(name="z", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fma_rd": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+            arg(name="z", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fma_rn": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+            arg(name="z", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fma_ru": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+            arg(name="z", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fma_rz": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+            arg(name="z", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fmaf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+            arg(name="z", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmaf_rd": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+            arg(name="z", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmaf_rn": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+            arg(name="z", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmaf_ru": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+            arg(name="z", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmaf_rz": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+            arg(name="z", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmax": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fmaxf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmin": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fminf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmod": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_fmodf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmul_rd": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmul_rn": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmul_ru": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fmul_rz": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_frcp_rd": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_frcp_rn": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_frcp_ru": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_frcp_rz": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_frexp": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="b", ty=int32, is_ptr=True),
+        ],
+    ),
+    "__nv_frexpf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="b", ty=int32, is_ptr=True),
+        ],
+    ),
+    "__nv_frsqrt_rn": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fsqrt_rd": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fsqrt_rn": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fsqrt_ru": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fsqrt_rz": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_fsub_rd": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fsub_rn": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fsub_ru": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_fsub_rz": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_hadd": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_half2float": (float32, [arg(name="h", ty=int16, is_ptr=False)]),
+    "__nv_hiloint2double": (
+        float64,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_hypot": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_hypotf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_ilogb": (int32, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_ilogbf": (int32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_int2double_rn": (float64, [arg(name="i", ty=int32, is_ptr=False)]),
+    "__nv_int2float_rd": (float32, [arg(name="in", ty=int32, is_ptr=False)]),
+    "__nv_int2float_rn": (float32, [arg(name="in", ty=int32, is_ptr=False)]),
+    "__nv_int2float_ru": (float32, [arg(name="in", ty=int32, is_ptr=False)]),
+    "__nv_int2float_rz": (float32, [arg(name="in", ty=int32, is_ptr=False)]),
+    "__nv_int_as_float": (float32, [arg(name="x", ty=int32, is_ptr=False)]),
+    "__nv_isfinited": (int32, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_isinfd": (int32, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_isinff": (int32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_isnand": (int32, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_isnanf": (int32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_j0": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_j0f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_j1": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_j1f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_jn": (
+        float64,
+        [
+            arg(name="n", ty=int32, is_ptr=False),
+            arg(name="x", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_jnf": (
+        float32,
+        [
+            arg(name="n", ty=int32, is_ptr=False),
+            arg(name="x", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_ldexp": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_ldexpf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_lgamma": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_lgammaf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_ll2double_rd": (float64, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ll2double_rn": (float64, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ll2double_ru": (float64, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ll2double_rz": (float64, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ll2float_rd": (float32, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ll2float_rn": (float32, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ll2float_ru": (float32, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ll2float_rz": (float32, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_llabs": (int64, [arg(name="x", ty=int64, is_ptr=False)]),
+    "__nv_llmax": (
+        int64,
+        [
+            arg(name="x", ty=int64, is_ptr=False),
+            arg(name="y", ty=int64, is_ptr=False),
+        ],
+    ),
+    "__nv_llmin": (
+        int64,
+        [
+            arg(name="x", ty=int64, is_ptr=False),
+            arg(name="y", ty=int64, is_ptr=False),
+        ],
+    ),
+    "__nv_llrint": (int64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_llrintf": (int64, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_llround": (int64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_llroundf": (int64, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_log": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_log10": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_log10f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_log1p": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_log1pf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_log2": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_log2f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_logb": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_logbf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_logf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_longlong_as_double": (
+        float64,
+        [arg(name="x", ty=int64, is_ptr=False)],
+    ),
+    "__nv_max": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_min": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_modf": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="b", ty=float64, is_ptr=True),
+        ],
+    ),
+    "__nv_modff": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="b", ty=float32, is_ptr=True),
+        ],
+    ),
+    "__nv_mul24": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_mul64hi": (
+        int64,
+        [
+            arg(name="x", ty=int64, is_ptr=False),
+            arg(name="y", ty=int64, is_ptr=False),
+        ],
+    ),
+    "__nv_mulhi": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    # __nv_nan and __nv_nanf are excluded - they return a representation of a
+    # quiet NaN, but the argument they take seems to be undocumented, and
+    # follows a strange form - it is not an output like every other pointer
+    # argument. If a NaN is required, one can be obtained in CUDA Python by
+    # other means, e.g. `math.nan`. They are left in this list for completeness
+    # / reference.
+    # "__nv_nan": (float64, [arg(name="tagp", ty=int8, is_ptr=True)]),
+    # "__nv_nanf": (float32, [arg(name="tagp", ty=int8, is_ptr=True)]),
+    "__nv_nearbyint": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_nearbyintf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_nextafter": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_nextafterf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_normcdf": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_normcdff": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_normcdfinv": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_normcdfinvf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_popc": (int32, [arg(name="x", ty=int32, is_ptr=False)]),
+    "__nv_popcll": (int32, [arg(name="x", ty=int64, is_ptr=False)]),
+    "__nv_pow": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_powf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_powi": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_powif": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_rcbrt": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_rcbrtf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_remainder": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_remainderf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+        ],
+    ),
+    "__nv_remquo": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=float64, is_ptr=False),
+            arg(name="c", ty=int32, is_ptr=True),
+        ],
+    ),
+    "__nv_remquof": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=float32, is_ptr=False),
+            arg(name="quo", ty=int32, is_ptr=True),
+        ],
+    ),
+    "__nv_rhadd": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_rint": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_rintf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_round": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_roundf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_rsqrt": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_rsqrtf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_sad": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+            arg(name="z", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_saturatef": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_scalbn": (
+        float64,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_scalbnf": (
+        float32,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_signbitd": (int32, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_signbitf": (int32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_sin": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_sincos": (
+        void,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="sptr", ty=float64, is_ptr=True),
+            arg(name="cptr", ty=float64, is_ptr=True),
+        ],
+    ),
+    "__nv_sincosf": (
+        void,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="sptr", ty=float32, is_ptr=True),
+            arg(name="cptr", ty=float32, is_ptr=True),
+        ],
+    ),
+    "__nv_sincospi": (
+        void,
+        [
+            arg(name="x", ty=float64, is_ptr=False),
+            arg(name="sptr", ty=float64, is_ptr=True),
+            arg(name="cptr", ty=float64, is_ptr=True),
+        ],
+    ),
+    "__nv_sincospif": (
+        void,
+        [
+            arg(name="x", ty=float32, is_ptr=False),
+            arg(name="sptr", ty=float32, is_ptr=True),
+            arg(name="cptr", ty=float32, is_ptr=True),
+        ],
+    ),
+    "__nv_sinf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_sinh": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_sinhf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_sinpi": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_sinpif": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_sqrt": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_sqrtf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_tan": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_tanf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_tanh": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_tanhf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_tgamma": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_tgammaf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_trunc": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_truncf": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_uhadd": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_uint2double_rn": (float64, [arg(name="i", ty=int32, is_ptr=False)]),
+    "__nv_uint2float_rd": (float32, [arg(name="in", ty=int32, is_ptr=False)]),
+    "__nv_uint2float_rn": (float32, [arg(name="in", ty=int32, is_ptr=False)]),
+    "__nv_uint2float_ru": (float32, [arg(name="in", ty=int32, is_ptr=False)]),
+    "__nv_uint2float_rz": (float32, [arg(name="in", ty=int32, is_ptr=False)]),
+    "__nv_ull2double_rd": (float64, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ull2double_rn": (float64, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ull2double_ru": (float64, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ull2double_rz": (float64, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ull2float_rd": (float32, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ull2float_rn": (float32, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ull2float_ru": (float32, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ull2float_rz": (float32, [arg(name="l", ty=int64, is_ptr=False)]),
+    "__nv_ullmax": (
+        int64,
+        [
+            arg(name="x", ty=int64, is_ptr=False),
+            arg(name="y", ty=int64, is_ptr=False),
+        ],
+    ),
+    "__nv_ullmin": (
+        int64,
+        [
+            arg(name="x", ty=int64, is_ptr=False),
+            arg(name="y", ty=int64, is_ptr=False),
+        ],
+    ),
+    "__nv_umax": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_umin": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_umul24": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_umul64hi": (
+        int64,
+        [
+            arg(name="x", ty=int64, is_ptr=False),
+            arg(name="y", ty=int64, is_ptr=False),
+        ],
+    ),
+    "__nv_umulhi": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_urhadd": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_usad": (
+        int32,
+        [
+            arg(name="x", ty=int32, is_ptr=False),
+            arg(name="y", ty=int32, is_ptr=False),
+            arg(name="z", ty=int32, is_ptr=False),
+        ],
+    ),
+    "__nv_y0": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_y0f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_y1": (float64, [arg(name="x", ty=float64, is_ptr=False)]),
+    "__nv_y1f": (float32, [arg(name="x", ty=float32, is_ptr=False)]),
+    "__nv_yn": (
+        float64,
+        [
+            arg(name="n", ty=int32, is_ptr=False),
+            arg(name="x", ty=float64, is_ptr=False),
+        ],
+    ),
+    "__nv_ynf": (
+        float32,
+        [
+            arg(name="n", ty=int32, is_ptr=False),
+            arg(name="x", ty=float32, is_ptr=False),
+        ],
+    ),
+}
+
+
+def create_signature(retty, args):
+    """
+    Given the return type and arguments for a libdevice function, return the
+    signature of the stub function used to call it from CUDA Python.
+    """
+
+    # Any pointer arguments should be part of the return type.
+    return_types = [arg.ty for arg in args if arg.is_ptr]
+    # If the return type is void, there is no point adding it to the list of
+    # return types.
+    if retty != void:
+        return_types.insert(0, retty)
+
+    if len(return_types) > 1:
+        retty = Tuple(return_types)
+    else:
+        retty = return_types[0]
+
+    argtypes = [arg.ty for arg in args if not arg.is_ptr]
+
+    return signature(retty, *argtypes)
+
+
+# The following code generates the stubs for libdevice functions.
+#
+# Stubs can be regenerated (e.g. if the functions dict above is modified) with:
+#
+# python -c "from numba.cuda.libdevicefuncs import generate_stubs; \
+#            generate_stubs()" > numba/cuda/libdevice.py
+
+docstring_template = """
+See https://docs.nvidia.com/cuda/libdevice-users-guide/{func}.html
+
+{param_types}
+:rtype: {retty}
+"""
+
+param_template = """\
+:param {a.name}: Argument.
+:type {a.name}: {a.ty}"""
+
+
+def generate_stubs():
+    for name, (retty, args) in functions.items():
+        # Some libdevice functions have arguments called `in`, which causes a
+        # syntax error in Python, so we rename these to `x`.
+        def argname(arg):
+            if arg.name == "in":
+                return "x"
+            else:
+                return arg.name
+
+        argnames = [argname(a) for a in args if not a.is_ptr]
+        argstr = ", ".join(argnames)
+        signature = create_signature(retty, args)
+
+        param_types = "\n".join(
+            [param_template.format(a=a) for a in args if not a.is_ptr]
+        )
+        docstring = docstring_template.format(
+            param_types=param_types, retty=signature.return_type, func=name
+        )
+        docstring = indent(docstring, "    ")
+        print(f'def {name[5:]}({argstr}):\n    """{docstring}"""\n\n')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdeviceimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdeviceimpl.py
new file mode 100644
index 000000000..4bb2e905e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/libdeviceimpl.py
@@ -0,0 +1,83 @@
+from llvmlite import ir
+from numba.core import cgutils, types
+from numba.core.imputils import Registry
+from numba.cuda import libdevice, libdevicefuncs
+
+registry = Registry()
+lower = registry.lower
+
+
+def libdevice_implement(func, retty, nbargs):
+    def core(context, builder, sig, args):
+        lmod = builder.module
+        fretty = context.get_value_type(retty)
+        fargtys = [context.get_value_type(arg.ty) for arg in nbargs]
+        fnty = ir.FunctionType(fretty, fargtys)
+        fn = cgutils.get_or_insert_function(lmod, fnty, func)
+        return builder.call(fn, args)
+
+    key = getattr(libdevice, func[5:])
+
+    argtys = [arg.ty for arg in args if not arg.is_ptr]
+    lower(key, *argtys)(core)
+
+
+def libdevice_implement_multiple_returns(func, retty, prototype_args):
+    sig = libdevicefuncs.create_signature(retty, prototype_args)
+    nb_retty = sig.return_type
+
+    def core(context, builder, sig, args):
+        lmod = builder.module
+
+        fargtys = []
+        for arg in prototype_args:
+            ty = context.get_value_type(arg.ty)
+            if arg.is_ptr:
+                ty = ty.as_pointer()
+            fargtys.append(ty)
+
+        fretty = context.get_value_type(retty)
+
+        fnty = ir.FunctionType(fretty, fargtys)
+        fn = cgutils.get_or_insert_function(lmod, fnty, func)
+
+        # For returned values that are returned through a pointer, we need to
+        # allocate variables on the stack and pass a pointer to them.
+        actual_args = []
+        virtual_args = []
+        arg_idx = 0
+        for arg in prototype_args:
+            if arg.is_ptr:
+                # Allocate space for return value and add to args
+                tmp_arg = cgutils.alloca_once(builder,
+                                              context.get_value_type(arg.ty))
+                actual_args.append(tmp_arg)
+                virtual_args.append(tmp_arg)
+            else:
+                actual_args.append(args[arg_idx])
+                arg_idx += 1
+
+        ret = builder.call(fn, actual_args)
+
+        # Following the call, we need to assemble the returned values into a
+        # tuple for returning back to the caller.
+        tuple_args = []
+        if retty != types.void:
+            tuple_args.append(ret)
+        for arg in virtual_args:
+            tuple_args.append(builder.load(arg))
+
+        if isinstance(nb_retty, types.UniTuple):
+            return cgutils.pack_array(builder, tuple_args)
+        else:
+            return cgutils.pack_struct(builder, tuple_args)
+
+    key = getattr(libdevice, func[5:])
+    lower(key, *sig.args)(core)
+
+
+for func, (retty, args) in libdevicefuncs.functions.items():
+    if any([arg.is_ptr for arg in args]):
+        libdevice_implement_multiple_returns(func, retty, args)
+    else:
+        libdevice_implement(func, retty, args)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/mathimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/mathimpl.py
new file mode 100644
index 000000000..dea30a0de
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/mathimpl.py
@@ -0,0 +1,287 @@
+import math
+import operator
+from llvmlite import ir
+from numba.core import types, typing, cgutils
+from numba.core.imputils import Registry
+from numba.types import float32, float64, int64, uint64
+from numba.cuda import libdevice
+
+registry = Registry()
+lower = registry.lower
+
+
+booleans = []
+booleans += [('isnand', 'isnanf', math.isnan)]
+booleans += [('isinfd', 'isinff', math.isinf)]
+booleans += [('isfinited', 'finitef', math.isfinite)]
+
+unarys = []
+unarys += [('ceil', 'ceilf', math.ceil)]
+unarys += [('floor', 'floorf', math.floor)]
+unarys += [('fabs', 'fabsf', math.fabs)]
+unarys += [('exp', 'expf', math.exp)]
+unarys += [('expm1', 'expm1f', math.expm1)]
+unarys += [('erf', 'erff', math.erf)]
+unarys += [('erfc', 'erfcf', math.erfc)]
+unarys += [('tgamma', 'tgammaf', math.gamma)]
+unarys += [('lgamma', 'lgammaf', math.lgamma)]
+unarys += [('sqrt', 'sqrtf', math.sqrt)]
+unarys += [('log', 'logf', math.log)]
+unarys += [('log2', 'log2f', math.log2)]
+unarys += [('log10', 'log10f', math.log10)]
+unarys += [('log1p', 'log1pf', math.log1p)]
+unarys += [('acosh', 'acoshf', math.acosh)]
+unarys += [('acos', 'acosf', math.acos)]
+unarys += [('cos', 'cosf', math.cos)]
+unarys += [('cosh', 'coshf', math.cosh)]
+unarys += [('asinh', 'asinhf', math.asinh)]
+unarys += [('asin', 'asinf', math.asin)]
+unarys += [('sin', 'sinf', math.sin)]
+unarys += [('sinh', 'sinhf', math.sinh)]
+unarys += [('atan', 'atanf', math.atan)]
+unarys += [('atanh', 'atanhf', math.atanh)]
+unarys += [('tan', 'tanf', math.tan)]
+unarys += [('tanh', 'tanhf', math.tanh)]
+
+unarys_fastmath = {}
+unarys_fastmath['cosf'] = 'fast_cosf'
+unarys_fastmath['sinf'] = 'fast_sinf'
+unarys_fastmath['tanf'] = 'fast_tanf'
+unarys_fastmath['expf'] = 'fast_expf'
+unarys_fastmath['log2f'] = 'fast_log2f'
+unarys_fastmath['log10f'] = 'fast_log10f'
+unarys_fastmath['logf'] = 'fast_logf'
+
+binarys = []
+binarys += [('copysign', 'copysignf', math.copysign)]
+binarys += [('atan2', 'atan2f', math.atan2)]
+binarys += [('pow', 'powf', math.pow)]
+binarys += [('fmod', 'fmodf', math.fmod)]
+binarys += [('hypot', 'hypotf', math.hypot)]
+binarys += [('remainder', 'remainderf', math.remainder)]
+
+binarys_fastmath = {}
+binarys_fastmath['powf'] = 'fast_powf'
+
+
+@lower(math.isinf, types.Integer)
+@lower(math.isnan, types.Integer)
+def math_isinf_isnan_int(context, builder, sig, args):
+    return context.get_constant(types.boolean, 0)
+
+
+@lower(operator.truediv, types.float32, types.float32)
+def maybe_fast_truediv(context, builder, sig, args):
+    if context.fastmath:
+        sig = typing.signature(float32, float32, float32)
+        impl = context.get_function(libdevice.fast_fdividef, sig)
+        return impl(builder, args)
+    else:
+        with cgutils.if_zero(builder, args[1]):
+            context.error_model.fp_zero_division(builder, ("division by zero",))
+        res = builder.fdiv(*args)
+        return res
+
+
+@lower(math.isfinite, types.Integer)
+def math_isfinite_int(context, builder, sig, args):
+    return context.get_constant(types.boolean, 1)
+
+
+def impl_boolean(key, ty, libfunc):
+    def lower_boolean_impl(context, builder, sig, args):
+        libfunc_impl = context.get_function(libfunc,
+                                            typing.signature(types.int32, ty))
+        result = libfunc_impl(builder, args)
+        return context.cast(builder, result, types.int32, types.boolean)
+
+    lower(key, ty)(lower_boolean_impl)
+
+
+def impl_unary(key, ty, libfunc):
+    def lower_unary_impl(context, builder, sig, args):
+        actual_libfunc = libfunc
+        fast_replacement = None
+        if ty == float32 and context.fastmath:
+            fast_replacement = unarys_fastmath.get(libfunc.__name__)
+
+        if fast_replacement is not None:
+            actual_libfunc = getattr(libdevice, fast_replacement)
+
+        libfunc_impl = context.get_function(actual_libfunc,
+                                            typing.signature(ty, ty))
+        return libfunc_impl(builder, args)
+
+    lower(key, ty)(lower_unary_impl)
+
+
+def impl_unary_int(key, ty, libfunc):
+    def lower_unary_int_impl(context, builder, sig, args):
+        if sig.args[0] == int64:
+            convert = builder.sitofp
+        elif sig.args[0] == uint64:
+            convert = builder.uitofp
+        else:
+            m = 'Only 64-bit integers are supported for generic unary int ops'
+            raise TypeError(m)
+
+        arg = convert(args[0], ir.DoubleType())
+        sig = typing.signature(float64, float64)
+        libfunc_impl = context.get_function(libfunc, sig)
+        return libfunc_impl(builder, [arg])
+
+    lower(key, ty)(lower_unary_int_impl)
+
+
+def impl_binary(key, ty, libfunc):
+    def lower_binary_impl(context, builder, sig, args):
+        actual_libfunc = libfunc
+        fast_replacement = None
+        if ty == float32 and context.fastmath:
+            fast_replacement = binarys_fastmath.get(libfunc.__name__)
+
+        if fast_replacement is not None:
+            actual_libfunc = getattr(libdevice, fast_replacement)
+
+        libfunc_impl = context.get_function(actual_libfunc,
+                                            typing.signature(ty, ty, ty))
+        return libfunc_impl(builder, args)
+
+    lower(key, ty, ty)(lower_binary_impl)
+
+
+def impl_binary_int(key, ty, libfunc):
+    def lower_binary_int_impl(context, builder, sig, args):
+        if sig.args[0] == int64:
+            convert = builder.sitofp
+        elif sig.args[0] == uint64:
+            convert = builder.uitofp
+        else:
+            m = 'Only 64-bit integers are supported for generic binary int ops'
+            raise TypeError(m)
+
+        args = [convert(arg, ir.DoubleType()) for arg in args]
+        sig = typing.signature(float64, float64, float64)
+        libfunc_impl = context.get_function(libfunc, sig)
+        return libfunc_impl(builder, args)
+
+    lower(key, ty, ty)(lower_binary_int_impl)
+
+
+for fname64, fname32, key in booleans:
+    impl32 = getattr(libdevice, fname32)
+    impl64 = getattr(libdevice, fname64)
+    impl_boolean(key, float32, impl32)
+    impl_boolean(key, float64, impl64)
+
+
+for fname64, fname32, key in unarys:
+    impl32 = getattr(libdevice, fname32)
+    impl64 = getattr(libdevice, fname64)
+    impl_unary(key, float32, impl32)
+    impl_unary(key, float64, impl64)
+    impl_unary_int(key, int64, impl64)
+    impl_unary_int(key, uint64, impl64)
+
+
+for fname64, fname32, key in binarys:
+    impl32 = getattr(libdevice, fname32)
+    impl64 = getattr(libdevice, fname64)
+    impl_binary(key, float32, impl32)
+    impl_binary(key, float64, impl64)
+    impl_binary_int(key, int64, impl64)
+    impl_binary_int(key, uint64, impl64)
+
+
+def impl_pow_int(ty, libfunc):
+    def lower_pow_impl_int(context, builder, sig, args):
+        powi_sig = typing.signature(ty, ty, types.int32)
+        libfunc_impl = context.get_function(libfunc, powi_sig)
+        return libfunc_impl(builder, args)
+
+    lower(math.pow, ty, types.int32)(lower_pow_impl_int)
+
+
+impl_pow_int(types.float32, libdevice.powif)
+impl_pow_int(types.float64, libdevice.powi)
+
+
+def impl_modf(ty, libfunc):
+    retty = types.UniTuple(ty, 2)
+
+    def lower_modf_impl(context, builder, sig, args):
+        modf_sig = typing.signature(retty, ty)
+        libfunc_impl = context.get_function(libfunc, modf_sig)
+        return libfunc_impl(builder, args)
+
+    lower(math.modf, ty)(lower_modf_impl)
+
+
+impl_modf(types.float32, libdevice.modff)
+impl_modf(types.float64, libdevice.modf)
+
+
+def impl_frexp(ty, libfunc):
+    retty = types.Tuple((ty, types.int32))
+
+    def lower_frexp_impl(context, builder, sig, args):
+        frexp_sig = typing.signature(retty, ty)
+        libfunc_impl = context.get_function(libfunc, frexp_sig)
+        return libfunc_impl(builder, args)
+
+    lower(math.frexp, ty)(lower_frexp_impl)
+
+
+impl_frexp(types.float32, libdevice.frexpf)
+impl_frexp(types.float64, libdevice.frexp)
+
+
+def impl_ldexp(ty, libfunc):
+    def lower_ldexp_impl(context, builder, sig, args):
+        ldexp_sig = typing.signature(ty, ty, types.int32)
+        libfunc_impl = context.get_function(libfunc, ldexp_sig)
+        return libfunc_impl(builder, args)
+
+    lower(math.ldexp, ty, types.int32)(lower_ldexp_impl)
+
+
+impl_ldexp(types.float32, libdevice.ldexpf)
+impl_ldexp(types.float64, libdevice.ldexp)
+
+
+# Complex power implementations - translations of _Py_c_pow from CPython
+# https://github.com/python/cpython/blob/a755410e054e1e2390de5830befc08fe80706c66/Objects/complexobject.c#L123-L151
+#
+# The complex64 variant casts all constants and some variables to ensure that
+# as much computation is done in single precision as possible. A small number
+# of operations are still done in 64-bit, but these come from libdevice code.
+
+def cpow_implement(fty, cty):
+    def core(context, builder, sig, args):
+        def cpow_internal(a, b):
+
+            if b.real == fty(0.0) and b.imag == fty(0.0):
+                return cty(1.0) + cty(0.0j)
+            elif a.real == fty(0.0) and b.real == fty(0.0):
+                return cty(0.0) + cty(0.0j)
+
+            vabs = math.hypot(a.real, a.imag)
+            len = math.pow(vabs, b.real)
+            at = math.atan2(a.imag, a.real)
+            phase = at * b.real
+            if b.imag != fty(0.0):
+                len /= math.exp(at * b.imag)
+                phase += b.imag * math.log(vabs)
+
+            return len * (cty(math.cos(phase)) +
+                          cty(math.sin(phase) * cty(1.0j)))
+
+        return context.compile_internal(builder, cpow_internal, sig, args)
+
+    lower(operator.pow, cty, cty)(core)
+    lower(operator.ipow, cty, cty)(core)
+    lower(pow, cty, cty)(core)
+
+
+cpow_implement(types.float32, types.complex64)
+cpow_implement(types.float64, types.complex128)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/models.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/models.py
new file mode 100644
index 000000000..e4201dcd2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/models.py
@@ -0,0 +1,41 @@
+from llvmlite import ir
+
+from numba.core.datamodel.registry import register_default
+from numba.core.extending import register_model, models
+from numba.core import types
+from numba.cuda.types import Dim3, GridGroup, CUDADispatcher
+
+
+@register_model(Dim3)
+class Dim3Model(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('x', types.int32),
+            ('y', types.int32),
+            ('z', types.int32)
+        ]
+        super().__init__(dmm, fe_type, members)
+
+
+@register_model(GridGroup)
+class GridGroupModel(models.PrimitiveModel):
+    def __init__(self, dmm, fe_type):
+        be_type = ir.IntType(64)
+        super().__init__(dmm, fe_type, be_type)
+
+
+@register_default(types.Float)
+class FloatModel(models.PrimitiveModel):
+    def __init__(self, dmm, fe_type):
+        if fe_type == types.float16:
+            be_type = ir.IntType(16)
+        elif fe_type == types.float32:
+            be_type = ir.FloatType()
+        elif fe_type == types.float64:
+            be_type = ir.DoubleType()
+        else:
+            raise NotImplementedError(fe_type)
+        super(FloatModel, self).__init__(dmm, fe_type, be_type)
+
+
+register_model(CUDADispatcher)(models.OpaqueModel)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/nvvmutils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/nvvmutils.py
new file mode 100644
index 000000000..085f8c012
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/nvvmutils.py
@@ -0,0 +1,265 @@
+import itertools
+from llvmlite import ir
+from numba.core import cgutils
+from .cudadrv import nvvm
+from .api import current_context
+
+
+def declare_atomic_cas_int(lmod, isize):
+    fname = '___numba_atomic_i' + str(isize) + '_cas_hack'
+    fnty = ir.FunctionType(ir.IntType(isize),
+                           (ir.PointerType(ir.IntType(isize)),
+                            ir.IntType(isize),
+                            ir.IntType(isize)))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def atomic_cmpxchg(builder, lmod, isize, ptr, cmp, val):
+    # if nvvm.NVVM().is_nvvm70:
+    if True:
+        out = builder.cmpxchg(ptr, cmp, val, 'monotonic', 'monotonic')
+        return builder.extract_value(out, 0)
+    else:
+        return builder.call(declare_atomic_cas_int(lmod, isize),
+                            (ptr, cmp, val))
+
+# For atomic intrinsics, "numba_nvvm" prevents LLVM 9 onwards auto-upgrading
+# them into atomicrmw instructions that are not recognized by NVVM. It is
+# replaced with "nvvm" in llvm_to_ptx later, after the module has been parsed
+# and dumped by LLVM.
+
+
+def declare_atomic_add_float32(lmod):
+    fname = 'llvm.numba_nvvm.atomic.load.add.f32.p0f32'
+    fnty = ir.FunctionType(ir.FloatType(),
+                           (ir.PointerType(ir.FloatType(), 0), ir.FloatType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_add_float64(lmod):
+    if current_context().device.compute_capability >= (6, 0):
+        fname = 'llvm.numba_nvvm.atomic.load.add.f64.p0f64'
+    else:
+        fname = '___numba_atomic_double_add'
+    fnty = ir.FunctionType(ir.DoubleType(),
+                           (ir.PointerType(ir.DoubleType()), ir.DoubleType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_sub_float32(lmod):
+    fname = '___numba_atomic_float_sub'
+    fnty = ir.FunctionType(ir.FloatType(),
+                           (ir.PointerType(ir.FloatType()), ir.FloatType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_sub_float64(lmod):
+    fname = '___numba_atomic_double_sub'
+    fnty = ir.FunctionType(ir.DoubleType(),
+                           (ir.PointerType(ir.DoubleType()), ir.DoubleType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_inc_int32(lmod):
+    fname = 'llvm.nvvm.atomic.load.inc.32.p0i32'
+    fnty = ir.FunctionType(ir.IntType(32),
+                           (ir.PointerType(ir.IntType(32)), ir.IntType(32)))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_inc_int64(lmod):
+    fname = '___numba_atomic_u64_inc'
+    fnty = ir.FunctionType(ir.IntType(64),
+                           (ir.PointerType(ir.IntType(64)), ir.IntType(64)))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_dec_int32(lmod):
+    fname = 'llvm.nvvm.atomic.load.dec.32.p0i32'
+    fnty = ir.FunctionType(ir.IntType(32),
+                           (ir.PointerType(ir.IntType(32)), ir.IntType(32)))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_dec_int64(lmod):
+    fname = '___numba_atomic_u64_dec'
+    fnty = ir.FunctionType(ir.IntType(64),
+                           (ir.PointerType(ir.IntType(64)), ir.IntType(64)))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_max_float32(lmod):
+    fname = '___numba_atomic_float_max'
+    fnty = ir.FunctionType(ir.FloatType(),
+                           (ir.PointerType(ir.FloatType()), ir.FloatType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_max_float64(lmod):
+    fname = '___numba_atomic_double_max'
+    fnty = ir.FunctionType(ir.DoubleType(),
+                           (ir.PointerType(ir.DoubleType()), ir.DoubleType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_min_float32(lmod):
+    fname = '___numba_atomic_float_min'
+    fnty = ir.FunctionType(ir.FloatType(),
+                           (ir.PointerType(ir.FloatType()), ir.FloatType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_min_float64(lmod):
+    fname = '___numba_atomic_double_min'
+    fnty = ir.FunctionType(ir.DoubleType(),
+                           (ir.PointerType(ir.DoubleType()), ir.DoubleType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_nanmax_float32(lmod):
+    fname = '___numba_atomic_float_nanmax'
+    fnty = ir.FunctionType(ir.FloatType(),
+                           (ir.PointerType(ir.FloatType()), ir.FloatType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_nanmax_float64(lmod):
+    fname = '___numba_atomic_double_nanmax'
+    fnty = ir.FunctionType(ir.DoubleType(),
+                           (ir.PointerType(ir.DoubleType()), ir.DoubleType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_nanmin_float32(lmod):
+    fname = '___numba_atomic_float_nanmin'
+    fnty = ir.FunctionType(ir.FloatType(),
+                           (ir.PointerType(ir.FloatType()), ir.FloatType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_atomic_nanmin_float64(lmod):
+    fname = '___numba_atomic_double_nanmin'
+    fnty = ir.FunctionType(ir.DoubleType(),
+                           (ir.PointerType(ir.DoubleType()), ir.DoubleType()))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_cudaCGGetIntrinsicHandle(lmod):
+    fname = 'cudaCGGetIntrinsicHandle'
+    fnty = ir.FunctionType(ir.IntType(64),
+                           (ir.IntType(32),))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def declare_cudaCGSynchronize(lmod):
+    fname = 'cudaCGSynchronize'
+    fnty = ir.FunctionType(ir.IntType(32),
+                           (ir.IntType(64), ir.IntType(32)))
+    return cgutils.get_or_insert_function(lmod, fnty, fname)
+
+
+def insert_addrspace_conv(lmod, elemtype, addrspace):
+    addrspacename = {
+        nvvm.ADDRSPACE_SHARED: 'shared',
+        nvvm.ADDRSPACE_LOCAL: 'local',
+        nvvm.ADDRSPACE_CONSTANT: 'constant',
+    }[addrspace]
+    tyname = str(elemtype)
+    tyname = {'float': 'f32', 'double': 'f64'}.get(tyname, tyname)
+    s2g_name_fmt = 'llvm.nvvm.ptr.' + addrspacename + '.to.gen.p0%s.p%d%s'
+    s2g_name = s2g_name_fmt % (tyname, addrspace, tyname)
+    elem_ptr_ty = ir.PointerType(elemtype)
+    elem_ptr_ty_addrspace = ir.PointerType(elemtype, addrspace)
+    s2g_fnty = ir.FunctionType(elem_ptr_ty, [elem_ptr_ty_addrspace])
+    return cgutils.get_or_insert_function(lmod, s2g_fnty, s2g_name)
+
+
+def declare_string(builder, value):
+    lmod = builder.basic_block.function.module
+    cval = cgutils.make_bytearray(value.encode("utf-8") + b"\x00")
+    gl = cgutils.add_global_variable(lmod, cval.type, name="_str",
+                                     addrspace=nvvm.ADDRSPACE_CONSTANT)
+    gl.linkage = 'internal'
+    gl.global_constant = True
+    gl.initializer = cval
+
+    charty = ir.IntType(8)
+    constcharptrty = ir.PointerType(charty, nvvm.ADDRSPACE_CONSTANT)
+    charptr = builder.bitcast(gl, constcharptrty)
+
+    conv = insert_addrspace_conv(lmod, charty, nvvm.ADDRSPACE_CONSTANT)
+    return builder.call(conv, [charptr])
+
+
+def declare_vprint(lmod):
+    voidptrty = ir.PointerType(ir.IntType(8))
+    # NOTE: the second argument to vprintf() points to the variable-length
+    # array of arguments (after the format)
+    vprintfty = ir.FunctionType(ir.IntType(32), [voidptrty, voidptrty])
+    vprintf = cgutils.get_or_insert_function(lmod, vprintfty, "vprintf")
+    return vprintf
+
+
+# -----------------------------------------------------------------------------
+
+SREG_MAPPING = {
+    'tid.x': 'llvm.nvvm.read.ptx.sreg.tid.x',
+    'tid.y': 'llvm.nvvm.read.ptx.sreg.tid.y',
+    'tid.z': 'llvm.nvvm.read.ptx.sreg.tid.z',
+
+    'ntid.x': 'llvm.nvvm.read.ptx.sreg.ntid.x',
+    'ntid.y': 'llvm.nvvm.read.ptx.sreg.ntid.y',
+    'ntid.z': 'llvm.nvvm.read.ptx.sreg.ntid.z',
+
+    'ctaid.x': 'llvm.nvvm.read.ptx.sreg.ctaid.x',
+    'ctaid.y': 'llvm.nvvm.read.ptx.sreg.ctaid.y',
+    'ctaid.z': 'llvm.nvvm.read.ptx.sreg.ctaid.z',
+
+    'nctaid.x': 'llvm.nvvm.read.ptx.sreg.nctaid.x',
+    'nctaid.y': 'llvm.nvvm.read.ptx.sreg.nctaid.y',
+    'nctaid.z': 'llvm.nvvm.read.ptx.sreg.nctaid.z',
+
+    'warpsize': 'llvm.nvvm.read.ptx.sreg.warpsize',
+    'laneid': 'llvm.nvvm.read.ptx.sreg.laneid',
+}
+
+
+def call_sreg(builder, name):
+    module = builder.module
+    fnty = ir.FunctionType(ir.IntType(32), ())
+    fn = cgutils.get_or_insert_function(module, fnty, SREG_MAPPING[name])
+    return builder.call(fn, ())
+
+
+class SRegBuilder(object):
+    def __init__(self, builder):
+        self.builder = builder
+
+    def tid(self, xyz):
+        return call_sreg(self.builder, 'tid.%s' % xyz)
+
+    def ctaid(self, xyz):
+        return call_sreg(self.builder, 'ctaid.%s' % xyz)
+
+    def ntid(self, xyz):
+        return call_sreg(self.builder, 'ntid.%s' % xyz)
+
+    def nctaid(self, xyz):
+        return call_sreg(self.builder, 'nctaid.%s' % xyz)
+
+    def getdim(self, xyz):
+        tid = self.tid(xyz)
+        ntid = self.ntid(xyz)
+        nctaid = self.ctaid(xyz)
+        res = self.builder.add(self.builder.mul(ntid, nctaid), tid)
+        return res
+
+
+def get_global_id(builder, dim):
+    sreg = SRegBuilder(builder)
+    it = (sreg.getdim(xyz) for xyz in 'xyz')
+    seq = list(itertools.islice(it, None, dim))
+    if dim == 1:
+        return seq[0]
+    else:
+        return seq
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/printimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/printimpl.py
new file mode 100644
index 000000000..dac178070
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/printimpl.py
@@ -0,0 +1,86 @@
+from functools import singledispatch
+from llvmlite import ir
+from numba.core import types, cgutils
+from numba.core.errors import NumbaWarning
+from numba.core.imputils import Registry
+from numba.cuda import nvvmutils
+from warnings import warn
+
+registry = Registry()
+lower = registry.lower
+
+voidptr = ir.PointerType(ir.IntType(8))
+
+
+# NOTE: we don't use @lower here since print_item() doesn't return a LLVM value
+
+@singledispatch
+def print_item(ty, context, builder, val):
+    """
+    Handle printing of a single value of the given Numba type.
+    A (format string, [list of arguments]) is returned that will allow
+    forming the final printf()-like call.
+    """
+    raise NotImplementedError("printing unimplemented for values of type %s"
+                              % (ty,))
+
+
+@print_item.register(types.Integer)
+@print_item.register(types.IntegerLiteral)
+def int_print_impl(ty, context, builder, val):
+    if ty in types.unsigned_domain:
+        rawfmt = "%llu"
+        dsttype = types.uint64
+    else:
+        rawfmt = "%lld"
+        dsttype = types.int64
+    lld = context.cast(builder, val, ty, dsttype)
+    return rawfmt, [lld]
+
+
+@print_item.register(types.Float)
+def real_print_impl(ty, context, builder, val):
+    lld = context.cast(builder, val, ty, types.float64)
+    return "%f", [lld]
+
+
+@print_item.register(types.StringLiteral)
+def const_print_impl(ty, context, builder, sigval):
+    pyval = ty.literal_value
+    assert isinstance(pyval, str)  # Ensured by lowering
+    rawfmt = "%s"
+    val = context.insert_string_const_addrspace(builder, pyval)
+    return rawfmt, [val]
+
+
+@lower(print, types.VarArg(types.Any))
+def print_varargs(context, builder, sig, args):
+    """This function is a generic 'print' wrapper for arbitrary types.
+    It dispatches to the appropriate 'print' implementations above
+    depending on the detected real types in the signature."""
+
+    vprint = nvvmutils.declare_vprint(builder.module)
+
+    formats = []
+    values = []
+
+    for i, (argtype, argval) in enumerate(zip(sig.args, args)):
+        argfmt, argvals = print_item(argtype, context, builder, argval)
+        formats.append(argfmt)
+        values.extend(argvals)
+
+    rawfmt = " ".join(formats) + "\n"
+    if len(args) > 32:
+        msg = ('CUDA print() cannot print more than 32 items. '
+               'The raw format string will be emitted by the kernel instead.')
+        warn(msg, NumbaWarning)
+
+        rawfmt = rawfmt.replace('%', '%%')
+    fmt = context.insert_string_const_addrspace(builder, rawfmt)
+    array = cgutils.make_anonymous_struct(builder, values)
+    arrayptr = cgutils.alloca_once_value(builder, array)
+
+    vprint = nvvmutils.declare_vprint(builder.module)
+    builder.call(vprint, (fmt, builder.bitcast(arrayptr, voidptr)))
+
+    return context.get_dummy_value()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/random.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/random.py
new file mode 100644
index 000000000..7e4b66dac
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/random.py
@@ -0,0 +1,291 @@
+import math
+
+from numba import (config, cuda, float32, float64, uint32, int64, uint64,
+                   from_dtype, jit)
+
+import numpy as np
+
+# This implementation is based upon the xoroshiro128+ and splitmix64 algorithms
+# described at:
+#
+#     http://xoroshiro.di.unimi.it/
+#
+# and originally implemented by David Blackman and Sebastiano Vigna.
+#
+# The implementations below are based on the C source code:
+#
+#  * http://xoroshiro.di.unimi.it/xoroshiro128plus.c
+#  * http://xoroshiro.di.unimi.it/splitmix64.c
+#
+# Splitmix64 is used to generate the initial state of the xoroshiro128+
+# generator to ensure that small seeds don't result in predictable output.
+
+# **WARNING**: There is a lot of verbose casting in this file to ensure that
+# NumPy casting conventions (which cast uint64 [op] int32 to float64) don't
+# turn integers into floats when using these functions in the CUDA simulator.
+#
+# There are also no function type signatures to ensure that compilation is
+# deferred so that import is quick, and Sphinx autodoc works.  We are also
+# using the CPU @jit decorator everywhere to create functions that work as
+# both CPU and CUDA device functions.
+
+xoroshiro128p_dtype = np.dtype([('s0', np.uint64), ('s1', np.uint64)],
+                               align=True)
+xoroshiro128p_type = from_dtype(xoroshiro128p_dtype)
+
+# When cudasim is enabled, Fake CUDA arrays are passed to some of the
+# @jit-decorated functions. This causes a fallback to object mode and the
+# emission of a warning at present - this is visible in the test suite with
+# cudasim, but also fallback-by-default will not be permitted in future. In
+# order to avoid the warning / future error, we explicitly specify that
+# object mode with loop lifting is acceptable when using the simulator.
+_forceobj = _looplift = config.ENABLE_CUDASIM
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def init_xoroshiro128p_state(states, index, seed):
+    '''Use SplitMix64 to generate an xoroshiro128p state from 64-bit seed.
+
+    This ensures that manually set small seeds don't result in a predictable
+    initial sequence from the random number generator.
+
+    :type states: 1D array, dtype=xoroshiro128p_dtype
+    :param states: array of RNG states
+    :type index: uint64
+    :param index: offset in states to update
+    :type seed: int64
+    :param seed: seed value to use when initializing state
+    '''
+    index = int64(index)
+    seed = uint64(seed)
+
+    z = seed + uint64(0x9E3779B97F4A7C15)
+    z = (z ^ (z >> uint32(30))) * uint64(0xBF58476D1CE4E5B9)
+    z = (z ^ (z >> uint32(27))) * uint64(0x94D049BB133111EB)
+    z = z ^ (z >> uint32(31))
+
+    states[index]['s0'] = z
+    states[index]['s1'] = z
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def rotl(x, k):
+    '''Left rotate x by k bits.'''
+    x = uint64(x)
+    k = uint32(k)
+    return (x << k) | (x >> uint32(64 - k))
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def xoroshiro128p_next(states, index):
+    '''Return the next random uint64 and advance the RNG in states[index].
+
+    :type states: 1D array, dtype=xoroshiro128p_dtype
+    :param states: array of RNG states
+    :type index: int64
+    :param index: offset in states to update
+    :rtype: uint64
+    '''
+    index = int64(index)
+    s0 = states[index]['s0']
+    s1 = states[index]['s1']
+    result = s0 + s1
+
+    s1 ^= s0
+    states[index]['s0'] = uint64(rotl(s0, uint32(55))) ^ s1 ^ (s1 << uint32(14))
+    states[index]['s1'] = uint64(rotl(s1, uint32(36)))
+
+    return result
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def xoroshiro128p_jump(states, index):
+    '''Advance the RNG in ``states[index]`` by 2**64 steps.
+
+    :type states: 1D array, dtype=xoroshiro128p_dtype
+    :param states: array of RNG states
+    :type index: int64
+    :param index: offset in states to update
+    '''
+    index = int64(index)
+
+    jump = (uint64(0xbeac0467eba5facb), uint64(0xd86b048b86aa9922))
+
+    s0 = uint64(0)
+    s1 = uint64(0)
+
+    for i in range(2):
+        for b in range(64):
+            if jump[i] & (uint64(1) << uint32(b)):
+                s0 ^= states[index]['s0']
+                s1 ^= states[index]['s1']
+            xoroshiro128p_next(states, index)
+
+    states[index]['s0'] = s0
+    states[index]['s1'] = s1
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def uint64_to_unit_float64(x):
+    '''Convert uint64 to float64 value in the range [0.0, 1.0)'''
+    x = uint64(x)
+    return (x >> uint32(11)) * (float64(1) / (uint64(1) << uint32(53)))
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def uint64_to_unit_float32(x):
+    '''Convert uint64 to float32 value in the range [0.0, 1.0)'''
+    x = uint64(x)
+    return float32(uint64_to_unit_float64(x))
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def xoroshiro128p_uniform_float32(states, index):
+    '''Return a float32 in range [0.0, 1.0) and advance ``states[index]``.
+
+    :type states: 1D array, dtype=xoroshiro128p_dtype
+    :param states: array of RNG states
+    :type index: int64
+    :param index: offset in states to update
+    :rtype: float32
+    '''
+    index = int64(index)
+    return uint64_to_unit_float32(xoroshiro128p_next(states, index))
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def xoroshiro128p_uniform_float64(states, index):
+    '''Return a float64 in range [0.0, 1.0) and advance ``states[index]``.
+
+    :type states: 1D array, dtype=xoroshiro128p_dtype
+    :param states: array of RNG states
+    :type index: int64
+    :param index: offset in states to update
+    :rtype: float64
+    '''
+    index = int64(index)
+    return uint64_to_unit_float64(xoroshiro128p_next(states, index))
+
+
+TWO_PI_FLOAT32 = np.float32(2 * math.pi)
+TWO_PI_FLOAT64 = np.float64(2 * math.pi)
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def xoroshiro128p_normal_float32(states, index):
+    '''Return a normally distributed float32 and advance ``states[index]``.
+
+    The return value is drawn from a Gaussian of mean=0 and sigma=1 using the
+    Box-Muller transform.  This advances the RNG sequence by two steps.
+
+    :type states: 1D array, dtype=xoroshiro128p_dtype
+    :param states: array of RNG states
+    :type index: int64
+    :param index: offset in states to update
+    :rtype: float32
+    '''
+    index = int64(index)
+
+    u1 = xoroshiro128p_uniform_float32(states, index)
+    u2 = xoroshiro128p_uniform_float32(states, index)
+
+    z0 = math.sqrt(-float32(2.0) * math.log(u1)) * math.cos(TWO_PI_FLOAT32 * u2)
+    # discarding second normal value
+    # z1 = math.sqrt(-float32(2.0) * math.log(u1))
+    #                * math.sin(TWO_PI_FLOAT32 * u2)
+    return z0
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def xoroshiro128p_normal_float64(states, index):
+    '''Return a normally distributed float32 and advance ``states[index]``.
+
+    The return value is drawn from a Gaussian of mean=0 and sigma=1 using the
+    Box-Muller transform.  This advances the RNG sequence by two steps.
+
+    :type states: 1D array, dtype=xoroshiro128p_dtype
+    :param states: array of RNG states
+    :type index: int64
+    :param index: offset in states to update
+    :rtype: float64
+    '''
+    index = int64(index)
+
+    u1 = xoroshiro128p_uniform_float32(states, index)
+    u2 = xoroshiro128p_uniform_float32(states, index)
+
+    z0 = math.sqrt(-float64(2.0) * math.log(u1)) * math.cos(TWO_PI_FLOAT64 * u2)
+    # discarding second normal value
+    # z1 = math.sqrt(-float64(2.0) * math.log(u1))
+    #                * math.sin(TWO_PI_FLOAT64 * u2)
+    return z0
+
+
+@jit(forceobj=_forceobj, looplift=_looplift)
+def init_xoroshiro128p_states_cpu(states, seed, subsequence_start):
+    n = states.shape[0]
+    seed = uint64(seed)
+    subsequence_start = uint64(subsequence_start)
+
+    if n >= 1:
+        init_xoroshiro128p_state(states, 0, seed)
+
+        # advance to starting subsequence number
+        for _ in range(subsequence_start):
+            xoroshiro128p_jump(states, 0)
+
+        # populate the rest of the array
+        for i in range(1, n):
+            states[i] = states[i - 1]  # take state of previous generator
+            xoroshiro128p_jump(states, i)  # and jump forward 2**64 steps
+
+
+def init_xoroshiro128p_states(states, seed, subsequence_start=0, stream=0):
+    '''Initialize RNG states on the GPU for parallel generators.
+
+    This initializes the RNG states so that each state in the array corresponds
+    subsequences in the separated by 2**64 steps from each other in the main
+    sequence.  Therefore, as long no CUDA thread requests more than 2**64
+    random numbers, all of the RNG states produced by this function are
+    guaranteed to be independent.
+
+    The subsequence_start parameter can be used to advance the first RNG state
+    by a multiple of 2**64 steps.
+
+    :type states: 1D DeviceNDArray, dtype=xoroshiro128p_dtype
+    :param states: array of RNG states
+    :type seed: uint64
+    :param seed: starting seed for list of generators
+    '''
+
+    # Initialization on CPU is much faster than the GPU
+    states_cpu = np.empty(shape=states.shape, dtype=xoroshiro128p_dtype)
+    init_xoroshiro128p_states_cpu(states_cpu, seed, subsequence_start)
+
+    states.copy_to_device(states_cpu, stream=stream)
+
+
+def create_xoroshiro128p_states(n, seed, subsequence_start=0, stream=0):
+    '''Returns a new device array initialized for n random number generators.
+
+    This initializes the RNG states so that each state in the array corresponds
+    subsequences in the separated by 2**64 steps from each other in the main
+    sequence.  Therefore, as long no CUDA thread requests more than 2**64
+    random numbers, all of the RNG states produced by this function are
+    guaranteed to be independent.
+
+    The subsequence_start parameter can be used to advance the first RNG state
+    by a multiple of 2**64 steps.
+
+    :type n: int
+    :param n: number of RNG states to create
+    :type seed: uint64
+    :param seed: starting seed for list of generators
+    :type subsequence_start: uint64
+    :param subsequence_start:
+    :type stream: CUDA stream
+    :param stream: stream to run initialization kernel on
+    '''
+    states = cuda.device_array(n, dtype=xoroshiro128p_dtype, stream=stream)
+    init_xoroshiro128p_states(states, seed, subsequence_start, stream)
+    return states
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/__init__.py
new file mode 100644
index 000000000..d24aa6e7d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/__init__.py
@@ -0,0 +1,38 @@
+import sys
+
+from .api import *
+from .vector_types import vector_types
+from .reduction import Reduce
+from .cudadrv.devicearray import (device_array, device_array_like, pinned,
+                                  pinned_array, pinned_array_like,
+                                  mapped_array, to_device, auto_device)
+from .cudadrv import devicearray
+from .cudadrv.devices import require_context, gpus
+from .cudadrv.devices import get_context as current_context
+from .cudadrv.runtime import runtime
+from numba.core import config
+reduce = Reduce
+
+# Register simulated vector types as module level variables
+for name, svty in vector_types.items():
+    setattr(sys.modules[__name__], name, svty)
+    for alias in svty.aliases:
+        setattr(sys.modules[__name__], alias, svty)
+del vector_types, name, svty, alias
+
+# Ensure that any user code attempting to import cudadrv etc. gets the
+# simulator's version and not the real version if the simulator is enabled.
+if config.ENABLE_CUDASIM:
+    import sys
+    from numba.cuda.simulator import cudadrv
+    sys.modules['numba.cuda.cudadrv'] = cudadrv
+    sys.modules['numba.cuda.cudadrv.devicearray'] = cudadrv.devicearray
+    sys.modules['numba.cuda.cudadrv.devices'] = cudadrv.devices
+    sys.modules['numba.cuda.cudadrv.driver'] = cudadrv.driver
+    sys.modules['numba.cuda.cudadrv.runtime'] = cudadrv.runtime
+    sys.modules['numba.cuda.cudadrv.drvapi'] = cudadrv.drvapi
+    sys.modules['numba.cuda.cudadrv.error'] = cudadrv.error
+    sys.modules['numba.cuda.cudadrv.nvvm'] = cudadrv.nvvm
+
+    from . import compiler
+    sys.modules['numba.cuda.compiler'] = compiler
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/api.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/api.py
new file mode 100644
index 000000000..d91c99315
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/api.py
@@ -0,0 +1,103 @@
+'''
+Contains CUDA API functions
+'''
+
+# Imports here bring together parts of the API from other modules, so some of
+# them appear unused.
+from contextlib import contextmanager
+from .cudadrv.devices import require_context, reset, gpus  # noqa: F401
+from .kernel import FakeCUDAKernel
+from numba.core.typing import Signature
+from warnings import warn
+from ..args import In, Out, InOut  # noqa: F401
+
+
+def select_device(dev=0):
+    assert dev == 0, 'Only a single device supported by the simulator'
+
+
+class stream(object):
+    '''
+    The stream API is supported in the simulator - however, all execution
+    occurs synchronously, so synchronization requires no operation.
+    '''
+    @contextmanager
+    def auto_synchronize(self):
+        yield
+
+    def synchronize(self):
+        pass
+
+
+def synchronize():
+    pass
+
+
+def close():
+    gpus.closed = True
+
+
+def declare_device(*args, **kwargs):
+    pass
+
+
+def detect():
+    print('Found 1 CUDA devices')
+    print('id %d    %20s %40s' % (0, 'SIMULATOR', '[SUPPORTED]'))
+    print('%40s: 5.3' % 'compute capability')
+
+
+def list_devices():
+    return gpus
+
+
+# Events
+
+class Event(object):
+    '''
+    The simulator supports the event API, but they do not record timing info,
+    and all simulation is synchronous. Execution time is not recorded.
+    '''
+    def record(self, stream=0):
+        pass
+
+    def wait(self, stream=0):
+        pass
+
+    def synchronize(self):
+        pass
+
+    def elapsed_time(self, event):
+        warn('Simulator timings are bogus')
+        return 0.0
+
+
+event = Event
+
+
+def jit(func_or_sig=None, device=False, debug=False, argtypes=None,
+        inline=False, restype=None, fastmath=False, link=None,
+        boundscheck=None, opt=True, cache=None
+        ):
+    # Here for API compatibility
+    if boundscheck:
+        raise NotImplementedError("bounds checking is not supported for CUDA")
+
+    if link is not None:
+        raise NotImplementedError('Cannot link PTX in the simulator')
+    # Check for first argument specifying types - in that case the
+    # decorator is not being passed a function
+    if func_or_sig is None or isinstance(func_or_sig, (str, tuple, Signature)):
+        def jitwrapper(fn):
+            return FakeCUDAKernel(fn,
+                                  device=device,
+                                  fastmath=fastmath,
+                                  debug=debug)
+        return jitwrapper
+    return FakeCUDAKernel(func_or_sig, device=device, debug=debug)
+
+
+@contextmanager
+def defer_cleanup():
+    # No effect for simulator
+    yield
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/compiler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/compiler.py
new file mode 100644
index 000000000..56c88e774
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/compiler.py
@@ -0,0 +1,7 @@
+'''
+The compiler is not implemented in the simulator. This module provides a stub
+to allow tests to import successfully.
+'''
+
+compile_ptx = None
+compile_ptx_for_current_device = None
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/__init__.py
new file mode 100644
index 000000000..dde9362d4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/__init__.py
@@ -0,0 +1,2 @@
+from numba.cuda.simulator.cudadrv import (devicearray, devices, driver, drvapi,
+                                          error, nvvm)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/devicearray.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/devicearray.py
new file mode 100644
index 000000000..0b988a6d4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/devicearray.py
@@ -0,0 +1,407 @@
+'''
+The Device Array API is not implemented in the simulator. This module provides
+stubs to allow tests to import correctly.
+'''
+from contextlib import contextmanager
+
+import numpy as np
+
+
+DeviceRecord = None
+from_record_like = None
+
+
+errmsg_contiguous_buffer = ("Array contains non-contiguous buffer and cannot "
+                            "be transferred as a single memory region. Please "
+                            "ensure contiguous buffer with numpy "
+                            ".ascontiguousarray()")
+
+
+class FakeShape(tuple):
+    '''
+    The FakeShape class is used to provide a shape which does not allow negative
+    indexing, similar to the shape in CUDA Python. (Numpy shape arrays allow
+    negative indexing)
+    '''
+
+    def __getitem__(self, k):
+        if isinstance(k, int) and k < 0:
+            raise IndexError('tuple index out of range')
+        return super(FakeShape, self).__getitem__(k)
+
+
+class FakeWithinKernelCUDAArray(object):
+    '''
+    Created to emulate the behavior of arrays within kernels, where either
+    array.item or array['item'] is valid (that is, give all structured
+    arrays `numpy.recarray`-like semantics). This behaviour does not follow
+    the semantics of Python and NumPy with non-jitted code, and will be
+    deprecated and removed.
+    '''
+
+    def __init__(self, item):
+        assert isinstance(item, FakeCUDAArray)
+        self.__dict__['_item'] = item
+
+    def __wrap_if_fake(self, item):
+        if isinstance(item, FakeCUDAArray):
+            return FakeWithinKernelCUDAArray(item)
+        else:
+            return item
+
+    def __getattr__(self, attrname):
+        if attrname in dir(self._item._ary):  # For e.g. array size.
+            return self.__wrap_if_fake(getattr(self._item._ary, attrname))
+        else:
+            return self.__wrap_if_fake(self._item.__getitem__(attrname))
+
+    def __setattr__(self, nm, val):
+        self._item.__setitem__(nm, val)
+
+    def __getitem__(self, idx):
+        return self.__wrap_if_fake(self._item.__getitem__(idx))
+
+    def __setitem__(self, idx, val):
+        self._item.__setitem__(idx, val)
+
+    def __len__(self):
+        return len(self._item)
+
+
+class FakeCUDAArray(object):
+    '''
+    Implements the interface of a DeviceArray/DeviceRecord, but mostly just
+    wraps a NumPy array.
+    '''
+
+    __cuda_ndarray__ = True  # There must be gpu_data attribute
+
+    def __init__(self, ary, stream=0):
+        self._ary = ary
+        self.stream = stream
+
+    @property
+    def alloc_size(self):
+        return self._ary.nbytes
+
+    @property
+    def nbytes(self):
+        # return nbytes -- FakeCUDAArray is a wrapper around NumPy
+        return self._ary.nbytes
+
+    def __getattr__(self, attrname):
+        try:
+            attr = getattr(self._ary, attrname)
+            return attr
+        except AttributeError as e:
+            msg = "Wrapped array has no attribute '%s'" % attrname
+            raise AttributeError(msg) from e
+
+    def bind(self, stream=0):
+        return FakeCUDAArray(self._ary, stream)
+
+    @property
+    def T(self):
+        return self.transpose()
+
+    def transpose(self, axes=None):
+        return FakeCUDAArray(np.transpose(self._ary, axes=axes))
+
+    def __getitem__(self, idx):
+        ret = self._ary.__getitem__(idx)
+        if type(ret) not in [np.ndarray, np.void]:
+            return ret
+        else:
+            return FakeCUDAArray(ret, stream=self.stream)
+
+    def __setitem__(self, idx, val):
+        return self._ary.__setitem__(idx, val)
+
+    def copy_to_host(self, ary=None, stream=0):
+        if ary is None:
+            ary = np.empty_like(self._ary)
+        else:
+            check_array_compatibility(self, ary)
+        np.copyto(ary, self._ary)
+        return ary
+
+    def copy_to_device(self, ary, stream=0):
+        '''
+        Copy from the provided array into this array.
+
+        This may be less forgiving than the CUDA Python implementation, which
+        will copy data up to the length of the smallest of the two arrays,
+        whereas this expects the size of the arrays to be equal.
+        '''
+        sentry_contiguous(self)
+        self_core, ary_core = array_core(self), array_core(ary)
+        if isinstance(ary, FakeCUDAArray):
+            sentry_contiguous(ary)
+            check_array_compatibility(self_core, ary_core)
+        else:
+            ary_core = np.array(
+                ary_core,
+                order='C' if self_core.flags['C_CONTIGUOUS'] else 'F',
+                subok=True,
+                copy=False)
+            check_array_compatibility(self_core, ary_core)
+        np.copyto(self_core._ary, ary_core)
+
+    @property
+    def shape(self):
+        return FakeShape(self._ary.shape)
+
+    def ravel(self, *args, **kwargs):
+        return FakeCUDAArray(self._ary.ravel(*args, **kwargs))
+
+    def reshape(self, *args, **kwargs):
+        return FakeCUDAArray(self._ary.reshape(*args, **kwargs))
+
+    def view(self, *args, **kwargs):
+        return FakeCUDAArray(self._ary.view(*args, **kwargs))
+
+    def is_c_contiguous(self):
+        return self._ary.flags.c_contiguous
+
+    def is_f_contiguous(self):
+        return self._ary.flags.f_contiguous
+
+    def __str__(self):
+        return str(self._ary)
+
+    def __repr__(self):
+        return repr(self._ary)
+
+    def __len__(self):
+        return len(self._ary)
+
+    # TODO: Add inplace, bitwise, unary magic methods
+    #  (or maybe inherit this class from numpy)?
+    def __eq__(self, other):
+        return FakeCUDAArray(self._ary == other)
+
+    def __ne__(self, other):
+        return FakeCUDAArray(self._ary != other)
+
+    def __lt__(self, other):
+        return FakeCUDAArray(self._ary < other)
+
+    def __le__(self, other):
+        return FakeCUDAArray(self._ary <= other)
+
+    def __gt__(self, other):
+        return FakeCUDAArray(self._ary > other)
+
+    def __ge__(self, other):
+        return FakeCUDAArray(self._ary >= other)
+
+    def __add__(self, other):
+        return FakeCUDAArray(self._ary + other)
+
+    def __sub__(self, other):
+        return FakeCUDAArray(self._ary - other)
+
+    def __mul__(self, other):
+        return FakeCUDAArray(self._ary * other)
+
+    def __floordiv__(self, other):
+        return FakeCUDAArray(self._ary // other)
+
+    def __truediv__(self, other):
+        return FakeCUDAArray(self._ary / other)
+
+    def __mod__(self, other):
+        return FakeCUDAArray(self._ary % other)
+
+    def __pow__(self, other):
+        return FakeCUDAArray(self._ary ** other)
+
+    def split(self, section, stream=0):
+        return [
+            FakeCUDAArray(a)
+            for a in np.split(self._ary, range(section, len(self), section))
+        ]
+
+
+def array_core(ary):
+    """
+    Extract the repeated core of a broadcast array.
+
+    Broadcast arrays are by definition non-contiguous due to repeated
+    dimensions, i.e., dimensions with stride 0. In order to ascertain memory
+    contiguity and copy the underlying data from such arrays, we must create
+    a view without the repeated dimensions.
+
+    """
+    if not ary.strides or not ary.size:
+        return ary
+    core_index = []
+    for stride in ary.strides:
+        core_index.append(0 if stride == 0 else slice(None))
+    return ary[tuple(core_index)]
+
+
+def is_contiguous(ary):
+    """
+    Returns True iff `ary` is C-style contiguous while ignoring
+    broadcasted and 1-sized dimensions.
+    As opposed to array_core(), it does not call require_context(),
+    which can be quite expensive.
+    """
+    size = ary.dtype.itemsize
+    for shape, stride in zip(reversed(ary.shape), reversed(ary.strides)):
+        if shape > 1 and stride != 0:
+            if size != stride:
+                return False
+            size *= shape
+    return True
+
+
+def sentry_contiguous(ary):
+    core = array_core(ary)
+    if not core.flags['C_CONTIGUOUS'] and not core.flags['F_CONTIGUOUS']:
+        raise ValueError(errmsg_contiguous_buffer)
+
+
+def check_array_compatibility(ary1, ary2):
+    ary1sq, ary2sq = ary1.squeeze(), ary2.squeeze()
+    if ary1.dtype != ary2.dtype:
+        raise TypeError('incompatible dtype: %s vs. %s' %
+                        (ary1.dtype, ary2.dtype))
+    if ary1sq.shape != ary2sq.shape:
+        raise ValueError('incompatible shape: %s vs. %s' %
+                         (ary1.shape, ary2.shape))
+    if ary1sq.strides != ary2sq.strides:
+        raise ValueError('incompatible strides: %s vs. %s' %
+                         (ary1.strides, ary2.strides))
+
+
+def to_device(ary, stream=0, copy=True, to=None):
+    ary = np.array(ary, copy=False, subok=True)
+    sentry_contiguous(ary)
+    if to is None:
+        buffer_dtype = np.int64 if ary.dtype.char in 'Mm' else ary.dtype
+        return FakeCUDAArray(
+            np.ndarray(
+                buffer=np.copy(array_core(ary)).view(buffer_dtype),
+                dtype=ary.dtype,
+                shape=ary.shape,
+                strides=ary.strides,
+            ).view(type=type(ary)),
+        )
+    else:
+        to.copy_to_device(ary, stream=stream)
+
+
+@contextmanager
+def pinned(arg):
+    yield
+
+
+def mapped_array(*args, **kwargs):
+    for unused_arg in ('portable', 'wc'):
+        if unused_arg in kwargs:
+            kwargs.pop(unused_arg)
+    return device_array(*args, **kwargs)
+
+
+def pinned_array(shape, dtype=np.float_, strides=None, order='C'):
+    return np.ndarray(shape=shape, strides=strides, dtype=dtype, order=order)
+
+
+def managed_array(shape, dtype=np.float_, strides=None, order='C'):
+    return np.ndarray(shape=shape, strides=strides, dtype=dtype, order=order)
+
+
+def device_array(*args, **kwargs):
+    stream = kwargs.pop('stream') if 'stream' in kwargs else 0
+    return FakeCUDAArray(np.ndarray(*args, **kwargs), stream=stream)
+
+
+def _contiguous_strides_like_array(ary):
+    """
+    Given an array, compute strides for a new contiguous array of the same
+    shape.
+    """
+    # Don't recompute strides if the default strides will be sufficient to
+    # create a contiguous array.
+    if ary.flags['C_CONTIGUOUS'] or ary.flags['F_CONTIGUOUS'] or ary.ndim <= 1:
+        return None
+
+    # Otherwise, we need to compute new strides using an algorithm adapted from
+    # NumPy v1.17.4's PyArray_NewLikeArrayWithShape in
+    # core/src/multiarray/ctors.c. We permute the strides in ascending order
+    # then compute the stride for the dimensions with the same permutation.
+
+    # Stride permutation. E.g. a stride array (4, -2, 12) becomes
+    # [(1, -2), (0, 4), (2, 12)]
+    strideperm = [ x for x in enumerate(ary.strides) ]
+    strideperm.sort(key=lambda x: x[1])
+
+    # Compute new strides using permutation
+    strides = [0] * len(ary.strides)
+    stride = ary.dtype.itemsize
+    for i_perm, _ in strideperm:
+        strides[i_perm] = stride
+        stride *= ary.shape[i_perm]
+    return tuple(strides)
+
+
+def _order_like_array(ary):
+    if ary.flags['F_CONTIGUOUS'] and not ary.flags['C_CONTIGUOUS']:
+        return 'F'
+    else:
+        return 'C'
+
+
+def device_array_like(ary, stream=0):
+    strides = _contiguous_strides_like_array(ary)
+    order = _order_like_array(ary)
+    return device_array(shape=ary.shape, dtype=ary.dtype, strides=strides,
+                        order=order)
+
+
+def pinned_array_like(ary):
+    strides = _contiguous_strides_like_array(ary)
+    order = _order_like_array(ary)
+    return pinned_array(shape=ary.shape, dtype=ary.dtype, strides=strides,
+                        order=order)
+
+
+def auto_device(ary, stream=0, copy=True):
+    if isinstance(ary, FakeCUDAArray):
+        return ary, False
+
+    if not isinstance(ary, np.void):
+        ary = np.array(
+            ary,
+            copy=False,
+            subok=True)
+    return to_device(ary, stream, copy), True
+
+
+def is_cuda_ndarray(obj):
+    "Check if an object is a CUDA ndarray"
+    return getattr(obj, '__cuda_ndarray__', False)
+
+
+def verify_cuda_ndarray_interface(obj):
+    "Verify the CUDA ndarray interface for an obj"
+    require_cuda_ndarray(obj)
+
+    def requires_attr(attr, typ):
+        if not hasattr(obj, attr):
+            raise AttributeError(attr)
+        if not isinstance(getattr(obj, attr), typ):
+            raise AttributeError('%s must be of type %s' % (attr, typ))
+
+    requires_attr('shape', tuple)
+    requires_attr('strides', tuple)
+    requires_attr('dtype', np.dtype)
+    requires_attr('size', int)
+
+
+def require_cuda_ndarray(obj):
+    "Raises ValueError is is_cuda_ndarray(obj) evaluates False"
+    if not is_cuda_ndarray(obj):
+        raise ValueError('require an cuda ndarray object')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/devices.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/devices.py
new file mode 100644
index 000000000..bc1704599
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/devices.py
@@ -0,0 +1,111 @@
+import numpy as np
+from collections import namedtuple
+
+_MemoryInfo = namedtuple("_MemoryInfo", "free,total")
+
+
+class FakeCUDADevice:
+    def __init__(self):
+        self.uuid = 'GPU-00000000-0000-0000-0000-000000000000'
+
+
+class FakeCUDAContext:
+    '''
+    This stub implements functionality only for simulating a single GPU
+    at the moment.
+    '''
+    def __init__(self, device_id):
+        self._device_id = device_id
+        self._device = FakeCUDADevice()
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        pass
+
+    def __str__(self):
+        return "<Managed Device {self.id}>".format(self=self)
+
+    @property
+    def id(self):
+        return self._device_id
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def compute_capability(self):
+        return (5, 2)
+
+    def reset(self):
+        pass
+
+    def get_memory_info(self):
+        """
+        Cross-platform free / total host memory is hard without external
+        dependencies, e.g. `psutil` - so return infinite memory to maintain API
+        type compatibility
+        """
+        return _MemoryInfo(float('inf'), float('inf'))
+
+    def memalloc(self, sz):
+        """
+        Allocates memory on the simulated device
+        At present, there is no division between simulated
+        host memory and simulated device memory.
+        """
+        return np.ndarray(sz, dtype='u1')
+
+    def memhostalloc(self, sz, mapped=False, portable=False, wc=False):
+        '''Allocates memory on the host'''
+        return self.memalloc(sz)
+
+
+class FakeDeviceList:
+    '''
+    This stub implements a device list containing a single GPU. It also
+    keeps track of the GPU status, i.e. whether the context is closed or not,
+    which may have been set by the user calling reset()
+    '''
+    def __init__(self):
+        self.lst = (FakeCUDAContext(0),)
+        self.closed = False
+
+    def __getitem__(self, devnum):
+        self.closed = False
+        return self.lst[devnum]
+
+    def __str__(self):
+        return ', '.join([str(d) for d in self.lst])
+
+    def __iter__(self):
+        return iter(self.lst)
+
+    def __len__(self):
+        return len(self.lst)
+
+    @property
+    def current(self):
+        if self.closed:
+            return None
+        return self.lst[0]
+
+
+gpus = FakeDeviceList()
+
+
+def reset():
+    gpus[0].closed = True
+
+
+def get_context(devnum=0):
+    return FakeCUDAContext(devnum)
+
+
+def require_context(func):
+    '''
+    In the simulator, a context is always "available", so this is a no-op.
+    '''
+    return func
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/driver.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/driver.py
new file mode 100644
index 000000000..ae6b88ce5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/driver.py
@@ -0,0 +1,54 @@
+'''
+Most of the driver API is unsupported in the simulator, but some stubs are
+provided to allow tests to import correctly.
+'''
+
+
+def device_memset(dst, val, size, stream=0):
+    dst.view('u1')[:size].fill(bytes([val])[0])
+
+
+def host_to_device(dst, src, size, stream=0):
+    dst.view('u1')[:size] = src.view('u1')[:size]
+
+
+def device_to_host(dst, src, size, stream=0):
+    host_to_device(dst, src, size)
+
+
+def device_memory_size(obj):
+    return obj.itemsize * obj.size
+
+
+def device_to_device(dst, src, size, stream=0):
+    host_to_device(dst, src, size)
+
+
+class FakeDriver(object):
+    def get_device_count(self):
+        return 1
+
+
+driver = FakeDriver()
+
+Linker = None
+
+
+class LinkerError(RuntimeError):
+    pass
+
+
+class NvrtcError(RuntimeError):
+    pass
+
+
+class CudaAPIError(RuntimeError):
+    pass
+
+
+def launch_kernel(*args, **kwargs):
+    msg = 'Launching kernels directly is not supported in the simulator'
+    raise RuntimeError(msg)
+
+
+USE_NV_BINDING = False
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/drvapi.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/drvapi.py
new file mode 100644
index 000000000..44c697f37
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/drvapi.py
@@ -0,0 +1,4 @@
+'''
+drvapi is not implemented in the simulator, but this module exists to allow
+tests to import correctly.
+'''
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/error.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/error.py
new file mode 100644
index 000000000..a7b435900
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/error.py
@@ -0,0 +1,2 @@
+class CudaSupportError(RuntimeError):
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/libs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/libs.py
new file mode 100644
index 000000000..347b936c5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/libs.py
@@ -0,0 +1,2 @@
+def check_static_lib(lib):
+    raise FileNotFoundError('Linking libraries not supported by cudasim')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/nvvm.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/nvvm.py
new file mode 100644
index 000000000..98497be96
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/nvvm.py
@@ -0,0 +1,30 @@
+'''
+NVVM is not supported in the simulator, but stubs are provided to allow tests
+to import correctly.
+'''
+
+
+class NvvmSupportError(ImportError):
+    pass
+
+
+class NVVM(object):
+    def __init__(self):
+        raise NvvmSupportError('NVVM not supported in the simulator')
+
+
+CompilationUnit = None
+llvm_to_ptx = None
+set_cuda_kernel = None
+fix_data_layout = None
+get_arch_option = None
+LibDevice = None
+NvvmError = None
+
+
+def is_available():
+    return False
+
+
+def get_supported_ccs():
+    return ()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/runtime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/runtime.py
new file mode 100644
index 000000000..308d19e76
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/cudadrv/runtime.py
@@ -0,0 +1,19 @@
+'''
+The runtime API is unsupported in the simulator, but some stubs are
+provided to allow tests to import correctly.
+'''
+
+
+class FakeRuntime(object):
+    def get_version(self):
+        return (-1, -1)
+
+    def is_supported_version(self):
+        return True
+
+    @property
+    def supported_versions(self):
+        return (-1, -1),
+
+
+runtime = FakeRuntime()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/kernel.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/kernel.py
new file mode 100644
index 000000000..8297680de
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/kernel.py
@@ -0,0 +1,307 @@
+from contextlib import contextmanager
+from functools import reduce
+import sys
+import threading
+
+import numpy as np
+
+from .cudadrv.devicearray import FakeCUDAArray, FakeWithinKernelCUDAArray
+from .kernelapi import Dim3, FakeCUDAModule, swapped_cuda_module
+from ..errors import normalize_kernel_dimensions
+from ..args import wrap_arg, ArgHint
+
+
+"""
+Global variable to keep track of the current "kernel context", i.e the
+FakeCUDAModule.  We only support one kernel launch at a time.
+No support for concurrent kernel launch.
+"""
+_kernel_context = None
+
+
+@contextmanager
+def _push_kernel_context(mod):
+    """
+    Push the current kernel context.
+    """
+    global _kernel_context
+    assert _kernel_context is None, "conrrent simulated kernel not supported"
+    _kernel_context = mod
+    try:
+        yield
+    finally:
+        _kernel_context = None
+
+
+def _get_kernel_context():
+    """
+    Get the current kernel context. This is usually done by a device function.
+    """
+    return _kernel_context
+
+
+class FakeOverload:
+    '''
+    Used only to provide the max_cooperative_grid_blocks method
+    '''
+    def max_cooperative_grid_blocks(self, blockdim):
+        # We can only run one block in a cooperative grid because we have no
+        # mechanism for synchronization between different blocks
+        return 1
+
+
+class FakeOverloadDict(dict):
+    def __getitem__(self, key):
+        # Always return a fake overload for any signature, as we don't keep
+        # track of overloads in the simulator.
+        return FakeOverload()
+
+
+class FakeCUDAKernel(object):
+    '''
+    Wraps a @cuda.jit-ed function.
+    '''
+
+    def __init__(self, fn, device, fastmath=False, extensions=[], debug=False):
+        self.fn = fn
+        self._device = device
+        self._fastmath = fastmath
+        self._debug = debug
+        self.extensions = list(extensions) # defensive copy
+        # Initial configuration: grid unconfigured, stream 0, no dynamic shared
+        # memory.
+        self.grid_dim = None
+        self.block_dim = None
+        self.stream = 0
+        self.dynshared_size = 0
+
+    def __call__(self, *args):
+        if self._device:
+            with swapped_cuda_module(self.fn, _get_kernel_context()):
+                return self.fn(*args)
+
+        # Ensure we've been given a valid grid configuration
+        grid_dim, block_dim = normalize_kernel_dimensions(self.grid_dim,
+                                                          self.block_dim)
+
+        fake_cuda_module = FakeCUDAModule(grid_dim, block_dim,
+                                          self.dynshared_size)
+        with _push_kernel_context(fake_cuda_module):
+            # fake_args substitutes all numpy arrays for FakeCUDAArrays
+            # because they implement some semantics differently
+            retr = []
+
+            def fake_arg(arg):
+                # map the arguments using any extension you've registered
+                _, arg = reduce(
+                    lambda ty_val, extension: extension.prepare_args(
+                        *ty_val,
+                        stream=0,
+                        retr=retr),
+                    self.extensions,
+                    (None, arg)
+                )
+
+                if isinstance(arg, np.ndarray) and arg.ndim > 0:
+                    ret = wrap_arg(arg).to_device(retr)
+                elif isinstance(arg, ArgHint):
+                    ret = arg.to_device(retr)
+                elif isinstance(arg, np.void):
+                    ret = FakeCUDAArray(arg)  # In case a np record comes in.
+                else:
+                    ret = arg
+                if isinstance(ret, FakeCUDAArray):
+                    return FakeWithinKernelCUDAArray(ret)
+                return ret
+
+            fake_args = [fake_arg(arg) for arg in args]
+            with swapped_cuda_module(self.fn, fake_cuda_module):
+                # Execute one block at a time
+                for grid_point in np.ndindex(*grid_dim):
+                    bm = BlockManager(self.fn, grid_dim, block_dim, self._debug)
+                    bm.run(grid_point, *fake_args)
+
+            for wb in retr:
+                wb()
+
+    def __getitem__(self, configuration):
+        self.grid_dim, self.block_dim = \
+            normalize_kernel_dimensions(*configuration[:2])
+
+        if len(configuration) == 4:
+            self.dynshared_size = configuration[3]
+
+        return self
+
+    def bind(self):
+        pass
+
+    def specialize(self, *args):
+        return self
+
+    def forall(self, ntasks, tpb=0, stream=0, sharedmem=0):
+        if ntasks < 0:
+            raise ValueError("Can't create ForAll with negative task count: %s"
+                             % ntasks)
+        return self[ntasks, 1, stream, sharedmem]
+
+    @property
+    def overloads(self):
+        return FakeOverloadDict()
+
+    @property
+    def py_func(self):
+        return self.fn
+
+
+# Thread emulation
+
+class BlockThread(threading.Thread):
+    '''
+    Manages the execution of a function for a single CUDA thread.
+    '''
+    def __init__(self, f, manager, blockIdx, threadIdx, debug):
+        if debug:
+            def debug_wrapper(*args, **kwargs):
+                np.seterr(divide='raise')
+                f(*args, **kwargs)
+            target = debug_wrapper
+        else:
+            target = f
+
+        super(BlockThread, self).__init__(target=target)
+        self.syncthreads_event = threading.Event()
+        self.syncthreads_blocked = False
+        self._manager = manager
+        self.blockIdx = Dim3(*blockIdx)
+        self.threadIdx = Dim3(*threadIdx)
+        self.exception = None
+        self.daemon = True
+        self.abort = False
+        self.debug = debug
+        blockDim = Dim3(*self._manager._block_dim)
+        self.thread_id = self.threadIdx.x + (blockDim.x * (self.threadIdx.y +
+                                                           blockDim.y *
+                                                           self.threadIdx.z))
+
+    def run(self):
+        try:
+            super(BlockThread, self).run()
+        except Exception as e:
+            tid = 'tid=%s' % list(self.threadIdx)
+            ctaid = 'ctaid=%s' % list(self.blockIdx)
+            if str(e) == '':
+                msg = '%s %s' % (tid, ctaid)
+            else:
+                msg = '%s %s: %s' % (tid, ctaid, e)
+            tb = sys.exc_info()[2]
+            # Using `with_traceback` here would cause it to be mutated by
+            # future raise statements, which may or may not matter.
+            self.exception = (type(e)(msg), tb)
+
+    def syncthreads(self):
+
+        if self.abort:
+            raise RuntimeError("abort flag set on syncthreads call")
+
+        self.syncthreads_blocked = True
+        self.syncthreads_event.wait()
+        self.syncthreads_event.clear()
+
+        if self.abort:
+            raise RuntimeError("abort flag set on syncthreads clear")
+
+    def syncthreads_count(self, value):
+        idx = self.threadIdx.x, self.threadIdx.y, self.threadIdx.z
+        self._manager.block_state[idx] = value
+        self.syncthreads()
+        count = np.count_nonzero(self._manager.block_state)
+        self.syncthreads()
+        return count
+
+    def syncthreads_and(self, value):
+        idx = self.threadIdx.x, self.threadIdx.y, self.threadIdx.z
+        self._manager.block_state[idx] = value
+        self.syncthreads()
+        test = np.all(self._manager.block_state)
+        self.syncthreads()
+        return 1 if test else 0
+
+    def syncthreads_or(self, value):
+        idx = self.threadIdx.x, self.threadIdx.y, self.threadIdx.z
+        self._manager.block_state[idx] = value
+        self.syncthreads()
+        test = np.any(self._manager.block_state)
+        self.syncthreads()
+        return 1 if test else 0
+
+    def __str__(self):
+        return 'Thread <<<%s, %s>>>' % (self.blockIdx, self.threadIdx)
+
+
+class BlockManager(object):
+    '''
+    Manages the execution of a thread block.
+
+    When run() is called, all threads are started. Each thread executes until it
+    hits syncthreads(), at which point it sets its own syncthreads_blocked to
+    True so that the BlockManager knows it is blocked. It then waits on its
+    syncthreads_event.
+
+    The BlockManager polls threads to determine if they are blocked in
+    syncthreads(). If it finds a blocked thread, it adds it to the set of
+    blocked threads. When all threads are blocked, it unblocks all the threads.
+    The thread are unblocked by setting their syncthreads_blocked back to False
+    and setting their syncthreads_event.
+
+    The polling continues until no threads are alive, when execution is
+    complete.
+    '''
+    def __init__(self, f, grid_dim, block_dim, debug):
+        self._grid_dim = grid_dim
+        self._block_dim = block_dim
+        self._f = f
+        self._debug = debug
+        self.block_state = np.zeros(block_dim, dtype=np.bool_)
+
+    def run(self, grid_point, *args):
+        # Create all threads
+        threads = set()
+        livethreads = set()
+        blockedthreads = set()
+        for block_point in np.ndindex(*self._block_dim):
+            def target():
+                self._f(*args)
+            t = BlockThread(target, self, grid_point, block_point, self._debug)
+            t.start()
+            threads.add(t)
+            livethreads.add(t)
+
+        # Potential optimisations:
+        # 1. Continue the while loop immediately after finding a blocked thread
+        # 2. Don't poll already-blocked threads
+        while livethreads:
+            for t in livethreads:
+                if t.syncthreads_blocked:
+                    blockedthreads.add(t)
+                elif t.exception:
+
+                    # Abort all other simulator threads on exception,
+                    # do *not* join immediately to facilitate debugging.
+                    for t_other in threads:
+                        t_other.abort = True
+                        t_other.syncthreads_blocked = False
+                        t_other.syncthreads_event.set()
+
+                    raise t.exception[0].with_traceback(t.exception[1])
+            if livethreads == blockedthreads:
+                for t in blockedthreads:
+                    t.syncthreads_blocked = False
+                    t.syncthreads_event.set()
+                blockedthreads = set()
+            livethreads = set([ t for t in livethreads if t.is_alive() ])
+        # Final check for exceptions in case any were set prior to thread
+        # finishing, before we could check it
+        for t in threads:
+            if t.exception:
+                raise t.exception[0].with_traceback(t.exception[1])
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/kernelapi.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/kernelapi.py
new file mode 100644
index 000000000..4269481bb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/kernelapi.py
@@ -0,0 +1,439 @@
+'''
+Implements the cuda module as called from within an executing kernel
+(@cuda.jit-decorated function).
+'''
+
+from contextlib import contextmanager
+import sys
+import threading
+import traceback
+from numba.core import types
+import numpy as np
+
+from numba.np import numpy_support
+
+from .vector_types import vector_types
+
+
+class Dim3(object):
+    '''
+    Used to implement thread/block indices/dimensions
+    '''
+    def __init__(self, x, y, z):
+        self.x = x
+        self.y = y
+        self.z = z
+
+    def __str__(self):
+        return '(%s, %s, %s)' % (self.x, self.y, self.z)
+
+    def __repr__(self):
+        return 'Dim3(%s, %s, %s)' % (self.x, self.y, self.z)
+
+    def __iter__(self):
+        yield self.x
+        yield self.y
+        yield self.z
+
+
+class GridGroup:
+    '''
+    Used to implement the grid group.
+    '''
+
+    def sync(self):
+        # Synchronization of the grid group is equivalent to synchronization of
+        # the thread block, because we only support cooperative grids with one
+        # block.
+        threading.current_thread().syncthreads()
+
+
+class FakeCUDACg:
+    '''
+    CUDA Cooperative Groups
+    '''
+    def this_grid(self):
+        return GridGroup()
+
+
+class FakeCUDALocal(object):
+    '''
+    CUDA Local arrays
+    '''
+    def array(self, shape, dtype):
+        if isinstance(dtype, types.Type):
+            dtype = numpy_support.as_dtype(dtype)
+        return np.empty(shape, dtype)
+
+
+class FakeCUDAConst(object):
+    '''
+    CUDA Const arrays
+    '''
+    def array_like(self, ary):
+        return ary
+
+
+class FakeCUDAShared(object):
+    '''
+    CUDA Shared arrays.
+
+    Limitations: assumes that only one call to cuda.shared.array is on a line,
+    and that that line is only executed once per thread. i.e.::
+
+        a = cuda.shared.array(...); b = cuda.shared.array(...)
+
+    will erroneously alias a and b, and::
+
+        for i in range(10):
+            sharedarrs[i] = cuda.shared.array(...)
+
+    will alias all arrays created at that point (though it is not certain that
+    this would be supported by Numba anyway).
+    '''
+
+    def __init__(self, dynshared_size):
+        self._allocations = {}
+        self._dynshared_size = dynshared_size
+        self._dynshared = np.zeros(dynshared_size, dtype=np.byte)
+
+    def array(self, shape, dtype):
+        if isinstance(dtype, types.Type):
+            dtype = numpy_support.as_dtype(dtype)
+        # Dynamic shared memory is requested with size 0 - this all shares the
+        # same underlying memory
+        if shape == 0:
+            # Count must be the maximum number of whole elements that fit in the
+            # buffer (Numpy complains if the buffer is not a multiple of the
+            # element size)
+            count = self._dynshared_size // dtype.itemsize
+            return np.frombuffer(self._dynshared.data, dtype=dtype, count=count)
+
+        # Otherwise, identify allocations by source file and line number
+        # We pass the reference frame explicitly to work around
+        # http://bugs.python.org/issue25108
+        stack = traceback.extract_stack(sys._getframe())
+        caller = stack[-2][0:2]
+        res = self._allocations.get(caller)
+        if res is None:
+            res = np.empty(shape, dtype)
+            self._allocations[caller] = res
+        return res
+
+
+addlock = threading.Lock()
+sublock = threading.Lock()
+andlock = threading.Lock()
+orlock = threading.Lock()
+xorlock = threading.Lock()
+maxlock = threading.Lock()
+minlock = threading.Lock()
+caslock = threading.Lock()
+inclock = threading.Lock()
+declock = threading.Lock()
+exchlock = threading.Lock()
+
+
+class FakeCUDAAtomic(object):
+    def add(self, array, index, val):
+        with addlock:
+            old = array[index]
+            array[index] += val
+        return old
+
+    def sub(self, array, index, val):
+        with sublock:
+            old = array[index]
+            array[index] -= val
+        return old
+
+    def and_(self, array, index, val):
+        with andlock:
+            old = array[index]
+            array[index] &= val
+        return old
+
+    def or_(self, array, index, val):
+        with orlock:
+            old = array[index]
+            array[index] |= val
+        return old
+
+    def xor(self, array, index, val):
+        with xorlock:
+            old = array[index]
+            array[index] ^= val
+        return old
+
+    def inc(self, array, index, val):
+        with inclock:
+            old = array[index]
+            if old >= val:
+                array[index] = 0
+            else:
+                array[index] += 1
+        return old
+
+    def dec(self, array, index, val):
+        with declock:
+            old = array[index]
+            if (old == 0) or (old > val):
+                array[index] = val
+            else:
+                array[index] -= 1
+        return old
+
+    def exch(self, array, index, val):
+        with exchlock:
+            old = array[index]
+            array[index] = val
+        return old
+
+    def max(self, array, index, val):
+        with maxlock:
+            old = array[index]
+            array[index] = max(old, val)
+        return old
+
+    def min(self, array, index, val):
+        with minlock:
+            old = array[index]
+            array[index] = min(old, val)
+        return old
+
+    def nanmax(self, array, index, val):
+        with maxlock:
+            old = array[index]
+            array[index] = np.nanmax([array[index], val])
+        return old
+
+    def nanmin(self, array, index, val):
+        with minlock:
+            old = array[index]
+            array[index] = np.nanmin([array[index], val])
+        return old
+
+    def compare_and_swap(self, array, old, val):
+        with caslock:
+            index = (0,) * array.ndim
+            loaded = array[index]
+            if loaded == old:
+                array[index] = val
+            return loaded
+
+
+class FakeCUDAFp16(object):
+    def hadd(self, a, b):
+        return a + b
+
+    def hsub(self, a, b):
+        return a - b
+
+    def hmul(self, a, b):
+        return a * b
+
+    def hfma(self, a, b, c):
+        return a * b + c
+
+    def hneg(self, a):
+        return -a
+
+    def habs(self, a):
+        return abs(a)
+
+    def heq(self, a, b):
+        return a == b
+
+    def hne(self, a, b):
+        return a != b
+
+    def hge(self, a, b):
+        return a >= b
+
+    def hgt(self, a, b):
+        return a > b
+
+    def hle(self, a, b):
+        return a <= b
+
+    def hlt(self, a, b):
+        return a < b
+
+    def hmax(self, a, b):
+        return max(a, b)
+
+    def hmin(self, a, b):
+        return min(a, b)
+
+
+class FakeCUDAModule(object):
+    '''
+    An instance of this class will be injected into the __globals__ for an
+    executing function in order to implement calls to cuda.*. This will fail to
+    work correctly if the user code does::
+
+        from numba import cuda as something_else
+
+    In other words, the CUDA module must be called cuda.
+    '''
+
+    def __init__(self, grid_dim, block_dim, dynshared_size):
+        self.gridDim = Dim3(*grid_dim)
+        self.blockDim = Dim3(*block_dim)
+        self._cg = FakeCUDACg()
+        self._local = FakeCUDALocal()
+        self._shared = FakeCUDAShared(dynshared_size)
+        self._const = FakeCUDAConst()
+        self._atomic = FakeCUDAAtomic()
+        self._fp16 = FakeCUDAFp16()
+        # Insert the vector types into the kernel context
+        # Note that we need to do this in addition to exposing them as module
+        # variables in `simulator.__init__.py`, because the test cases need
+        # to access the actual cuda module as well as the fake cuda module
+        # for vector types.
+        for name, svty in vector_types.items():
+            setattr(self, name, svty)
+            for alias in svty.aliases:
+                setattr(self, alias, svty)
+
+    @property
+    def cg(self):
+        return self._cg
+
+    @property
+    def local(self):
+        return self._local
+
+    @property
+    def shared(self):
+        return self._shared
+
+    @property
+    def const(self):
+        return self._const
+
+    @property
+    def atomic(self):
+        return self._atomic
+
+    @property
+    def fp16(self):
+        return self._fp16
+
+    @property
+    def threadIdx(self):
+        return threading.current_thread().threadIdx
+
+    @property
+    def blockIdx(self):
+        return threading.current_thread().blockIdx
+
+    @property
+    def warpsize(self):
+        return 32
+
+    @property
+    def laneid(self):
+        return threading.current_thread().thread_id % 32
+
+    def syncthreads(self):
+        threading.current_thread().syncthreads()
+
+    def threadfence(self):
+        # No-op
+        pass
+
+    def threadfence_block(self):
+        # No-op
+        pass
+
+    def threadfence_system(self):
+        # No-op
+        pass
+
+    def syncthreads_count(self, val):
+        return threading.current_thread().syncthreads_count(val)
+
+    def syncthreads_and(self, val):
+        return threading.current_thread().syncthreads_and(val)
+
+    def syncthreads_or(self, val):
+        return threading.current_thread().syncthreads_or(val)
+
+    def popc(self, val):
+        return bin(val).count("1")
+
+    def fma(self, a, b, c):
+        return a * b + c
+
+    def cbrt(self, a):
+        return a ** (1 / 3)
+
+    def brev(self, val):
+        return int('{:032b}'.format(val)[::-1], 2)
+
+    def clz(self, val):
+        s = '{:032b}'.format(val)
+        return len(s) - len(s.lstrip('0'))
+
+    def ffs(self, val):
+        # The algorithm is:
+        # 1. Count the number of trailing zeros.
+        # 2. Add 1, because the LSB is numbered 1 rather than 0, and so on.
+        # 3. If we've counted 32 zeros (resulting in 33), there were no bits
+        #    set so we need to return zero.
+        s = '{:032b}'.format(val)
+        r = (len(s) - len(s.rstrip('0')) + 1) % 33
+        return r
+
+    def selp(self, a, b, c):
+        return b if a else c
+
+    def grid(self, n):
+        bdim = self.blockDim
+        bid = self.blockIdx
+        tid = self.threadIdx
+        x = bid.x * bdim.x + tid.x
+        if n == 1:
+            return x
+        y = bid.y * bdim.y + tid.y
+        if n == 2:
+            return (x, y)
+        z = bid.z * bdim.z + tid.z
+        if n == 3:
+            return (x, y, z)
+
+        raise RuntimeError("Global ID has 1-3 dimensions. %d requested" % n)
+
+    def gridsize(self, n):
+        bdim = self.blockDim
+        gdim = self.gridDim
+        x = bdim.x * gdim.x
+        if n == 1:
+            return x
+        y = bdim.y * gdim.y
+        if n == 2:
+            return (x, y)
+        z = bdim.z * gdim.z
+        if n == 3:
+            return (x, y, z)
+
+        raise RuntimeError("Global grid has 1-3 dimensions. %d requested" % n)
+
+
+@contextmanager
+def swapped_cuda_module(fn, fake_cuda_module):
+    from numba import cuda
+
+    fn_globs = fn.__globals__
+    # get all globals that is the "cuda" module
+    orig = dict((k, v) for k, v in fn_globs.items() if v is cuda)
+    # build replacement dict
+    repl = dict((k, fake_cuda_module) for k, v in orig.items())
+    # replace
+    fn_globs.update(repl)
+    try:
+        yield
+    finally:
+        # revert
+        fn_globs.update(orig)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/reduction.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/reduction.py
new file mode 100644
index 000000000..1b819c043
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/reduction.py
@@ -0,0 +1,15 @@
+from functools import reduce as pyreduce
+
+
+def Reduce(func):
+    def reduce_wrapper(seq, res=None, init=0):
+        r = pyreduce(func, seq, init)
+        if res is not None:
+            res[0] = r
+            return None
+        else:
+            return r
+    return reduce_wrapper
+
+
+reduce = Reduce
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/vector_types.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/vector_types.py
new file mode 100644
index 000000000..82a6fbe8a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator/vector_types.py
@@ -0,0 +1,58 @@
+from numba import types
+from numba.cuda.stubs import _vector_type_stubs
+
+
+class SimulatedVectorType:
+    attributes = ['x', 'y', 'z', 'w']
+
+    def __init__(self, *args):
+        args_flattened = []
+        for arg in args:
+            if isinstance(arg, SimulatedVectorType):
+                args_flattened += arg.as_list()
+            else:
+                args_flattened.append(arg)
+        self._attrs = self.attributes[:len(args_flattened)]
+        if not self.num_elements == len(args_flattened):
+            raise TypeError(
+                f"{self.name} expects {self.num_elements}"
+                f" elements, got {len(args_flattened)}"
+            )
+
+        for arg, attr in zip(args_flattened, self._attrs):
+            setattr(self, attr, arg)
+
+    @property
+    def name(self):
+        raise NotImplementedError()
+
+    @property
+    def num_elements(self):
+        raise NotImplementedError()
+
+    def as_list(self):
+        return [getattr(self, attr) for attr in self._attrs]
+
+
+def make_simulated_vector_type(num_elements, name):
+    obj = type(name, (SimulatedVectorType,), {
+        "num_elements": num_elements,
+        "base_type": types.float32,
+        "name": name
+    })
+    obj.user_facing_object = obj
+    return obj
+
+
+def _initialize():
+    _simulated_vector_types = {}
+    for stub in _vector_type_stubs:
+        num_elements = int(stub.__name__[-1])
+        _simulated_vector_types[stub.__name__] = (
+            make_simulated_vector_type(num_elements, stub.__name__)
+        )
+        _simulated_vector_types[stub.__name__].aliases = stub.aliases
+    return _simulated_vector_types
+
+
+vector_types = _initialize()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator_init.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator_init.py
new file mode 100644
index 000000000..9d7dd124a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/simulator_init.py
@@ -0,0 +1,17 @@
+# We import * from simulator here because * is imported from simulator_init by
+# numba.cuda.__init__.
+from .simulator import *  # noqa: F403, F401
+
+
+def is_available():
+    """Returns a boolean to indicate the availability of a CUDA GPU.
+    """
+    # Simulator is always available
+    return True
+
+
+def cuda_error():
+    """Returns None or an exception if the CUDA driver fails to initialize.
+    """
+    # Simulator never fails to initialize
+    return None
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/stubs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/stubs.py
new file mode 100644
index 000000000..a84e65747
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/stubs.py
@@ -0,0 +1,827 @@
+"""
+This scripts specifies all PTX special objects.
+"""
+import numpy as np
+from collections import defaultdict
+import functools
+import itertools
+from inspect import Signature, Parameter
+
+
+class Stub(object):
+    '''
+    A stub object to represent special objects that are meaningless
+    outside the context of a CUDA kernel
+    '''
+    _description_ = '<ptx special value>'
+    __slots__ = () # don't allocate __dict__
+
+    def __new__(cls):
+        raise NotImplementedError("%s is not instantiable" % cls)
+
+    def __repr__(self):
+        return self._description_
+
+
+def stub_function(fn):
+    '''
+    A stub function to represent special functions that are meaningless
+    outside the context of a CUDA kernel
+    '''
+    @functools.wraps(fn)
+    def wrapped(*args, **kwargs):
+        raise NotImplementedError("%s cannot be called from host code" % fn)
+    return wrapped
+
+
+#-------------------------------------------------------------------------------
+# Thread and grid indices and dimensions
+
+
+class Dim3(Stub):
+    '''A triple, (x, y, z)'''
+    _description_ = '<Dim3>'
+
+    @property
+    def x(self):
+        pass
+
+    @property
+    def y(self):
+        pass
+
+    @property
+    def z(self):
+        pass
+
+
+class threadIdx(Dim3):
+    '''
+    The thread indices in the current thread block. Each index is an integer
+    spanning the range from 0 inclusive to the corresponding value of the
+    attribute in :attr:`numba.cuda.blockDim` exclusive.
+    '''
+    _description_ = '<threadIdx.{x,y,z}>'
+
+
+class blockIdx(Dim3):
+    '''
+    The block indices in the grid of thread blocks. Each index is an integer
+    spanning the range from 0 inclusive to the corresponding value of the
+    attribute in :attr:`numba.cuda.gridDim` exclusive.
+    '''
+    _description_ = '<blockIdx.{x,y,z}>'
+
+
+class blockDim(Dim3):
+    '''
+    The shape of a block of threads, as declared when instantiating the kernel.
+    This value is the same for all threads in a given kernel launch, even if
+    they belong to different blocks (i.e. each block is "full").
+    '''
+    _description_ = '<blockDim.{x,y,z}>'
+
+
+class gridDim(Dim3):
+    '''
+    The shape of the grid of blocks. This value is the same for all threads in
+    a given kernel launch.
+    '''
+    _description_ = '<gridDim.{x,y,z}>'
+
+
+class warpsize(Stub):
+    '''
+    The size of a warp. All architectures implemented to date have a warp size
+    of 32.
+    '''
+    _description_ = '<warpsize>'
+
+
+class laneid(Stub):
+    '''
+    This thread's lane within a warp. Ranges from 0 to
+    :attr:`numba.cuda.warpsize` - 1.
+    '''
+    _description_ = '<laneid>'
+
+
+class grid(Stub):
+    '''grid(ndim)
+
+    Return the absolute position of the current thread in the entire grid of
+    blocks.  *ndim* should correspond to the number of dimensions declared when
+    instantiating the kernel. If *ndim* is 1, a single integer is returned.
+    If *ndim* is 2 or 3, a tuple of the given number of integers is returned.
+
+    Computation of the first integer is as follows::
+
+        cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
+
+    and is similar for the other two indices, but using the ``y`` and ``z``
+    attributes.
+    '''
+    _description_ = '<grid(ndim)>'
+
+
+class gridsize(Stub):
+    '''gridsize(ndim)
+
+    Return the absolute size (or shape) in threads of the entire grid of
+    blocks. *ndim* should correspond to the number of dimensions declared when
+    instantiating the kernel. If *ndim* is 1, a single integer is returned.
+    If *ndim* is 2 or 3, a tuple of the given number of integers is returned.
+
+    Computation of the first integer is as follows::
+
+        cuda.blockDim.x * cuda.gridDim.x
+
+    and is similar for the other two indices, but using the ``y`` and ``z``
+    attributes.
+    '''
+    _description_ = '<gridsize(ndim)>'
+
+
+#-------------------------------------------------------------------------------
+# Array creation
+
+class shared(Stub):
+    '''
+    Shared memory namespace
+    '''
+    _description_ = '<shared>'
+
+    @stub_function
+    def array(shape, dtype):
+        '''
+        Allocate a shared array of the given *shape* and *type*. *shape* is
+        either an integer or a tuple of integers representing the array's
+        dimensions.  *type* is a :ref:`Numba type <numba-types>` of the
+        elements needing to be stored in the array.
+
+        The returned array-like object can be read and written to like any
+        normal device array (e.g. through indexing).
+        '''
+
+
+class local(Stub):
+    '''
+    Local memory namespace
+    '''
+    _description_ = '<local>'
+
+    @stub_function
+    def array(shape, dtype):
+        '''
+        Allocate a local array of the given *shape* and *type*. The array is
+        private to the current thread, and resides in global memory. An
+        array-like object is returned which can be read and written to like any
+        standard array (e.g.  through indexing).
+        '''
+
+
+class const(Stub):
+    '''
+    Constant memory namespace
+    '''
+
+    @stub_function
+    def array_like(ndarray):
+        '''
+        Create a const array from *ndarry*. The resulting const array will have
+        the same shape, type, and values as *ndarray*.
+        '''
+
+
+#-------------------------------------------------------------------------------
+# Cooperative groups
+
+class cg(Stub):
+    '''
+    Cooperative groups
+    '''
+
+    @stub_function
+    def this_grid():
+        '''
+        Get the current grid group.
+        '''
+
+    class GridGroup(Stub):
+        def sync():
+            '''
+            Synchronize the current grid group.
+            '''
+
+
+#-------------------------------------------------------------------------------
+# syncthreads
+
+class syncthreads(Stub):
+    '''
+    Synchronize all threads in the same thread block.  This function implements
+    the same pattern as barriers in traditional multi-threaded programming: this
+    function waits until all threads in the block call it, at which point it
+    returns control to all its callers.
+    '''
+    _description_ = '<syncthreads()>'
+
+
+class syncthreads_count(Stub):
+    '''
+    syncthreads_count(predictate)
+
+    An extension to numba.cuda.syncthreads where the return value is a count
+    of the threads where predicate is true.
+    '''
+    _description_ = '<syncthreads_count()>'
+
+
+class syncthreads_and(Stub):
+    '''
+    syncthreads_and(predictate)
+
+    An extension to numba.cuda.syncthreads where 1 is returned if predicate is
+    true for all threads or 0 otherwise.
+    '''
+    _description_ = '<syncthreads_and()>'
+
+
+class syncthreads_or(Stub):
+    '''
+    syncthreads_or(predictate)
+
+    An extension to numba.cuda.syncthreads where 1 is returned if predicate is
+    true for any thread or 0 otherwise.
+    '''
+    _description_ = '<syncthreads_or()>'
+
+
+# -------------------------------------------------------------------------------
+# warp level operations
+
+class syncwarp(Stub):
+    '''
+    syncwarp(mask=0xFFFFFFFF)
+
+    Synchronizes a masked subset of threads in a warp.
+    '''
+    _description_ = '<warp_sync()>'
+
+
+class shfl_sync_intrinsic(Stub):
+    '''
+    shfl_sync_intrinsic(mask, mode, value, mode_offset, clamp)
+
+    Nvvm intrinsic for shuffling data across a warp
+    docs.nvidia.com/cuda/nvvm-ir-spec/index.html#nvvm-intrin-warp-level-datamove
+    '''
+    _description_ = '<shfl_sync()>'
+
+
+class vote_sync_intrinsic(Stub):
+    '''
+    vote_sync_intrinsic(mask, mode, predictate)
+
+    Nvvm intrinsic for performing a reduce and broadcast across a warp
+    docs.nvidia.com/cuda/nvvm-ir-spec/index.html#nvvm-intrin-warp-level-vote
+    '''
+    _description_ = '<vote_sync()>'
+
+
+class match_any_sync(Stub):
+    '''
+    match_any_sync(mask, value)
+
+    Nvvm intrinsic for performing a compare and broadcast across a warp.
+    Returns a mask of threads that have same value as the given value from
+    within the masked warp.
+    '''
+    _description_ = '<match_any_sync()>'
+
+
+class match_all_sync(Stub):
+    '''
+    match_all_sync(mask, value)
+
+    Nvvm intrinsic for performing a compare and broadcast across a warp.
+    Returns a tuple of (mask, pred), where mask is a mask of threads that have
+    same value as the given value from within the masked warp, if they
+    all have the same value, otherwise it is 0. Pred is a boolean of whether
+    or not all threads in the mask warp have the same warp.
+    '''
+    _description_ = '<match_all_sync()>'
+
+
+class activemask(Stub):
+    '''
+    activemask()
+
+    Returns a 32-bit integer mask of all currently active threads in the
+    calling warp. The Nth bit is set if the Nth lane in the warp is active when
+    activemask() is called. Inactive threads are represented by 0 bits in the
+    returned mask. Threads which have exited the kernel are always marked as
+    inactive.
+    '''
+    _description_ = '<activemask()>'
+
+
+class lanemask_lt(Stub):
+    '''
+    lanemask_lt()
+
+    Returns a 32-bit integer mask of all lanes (including inactive ones) with
+    ID less than the current lane.
+    '''
+    _description_ = '<lanemask_lt()>'
+
+
+# -------------------------------------------------------------------------------
+# memory fences
+
+class threadfence_block(Stub):
+    '''
+    A memory fence at thread block level
+    '''
+    _description_ = '<threadfence_block()>'
+
+
+class threadfence_system(Stub):
+    '''
+    A memory fence at system level: across devices
+    '''
+    _description_ = '<threadfence_system()>'
+
+
+class threadfence(Stub):
+    '''
+    A memory fence at device level
+    '''
+    _description_ = '<threadfence()>'
+
+
+#-------------------------------------------------------------------------------
+# bit manipulation
+
+class popc(Stub):
+    """
+    popc(x)
+
+    Returns the number of set bits in x.
+    """
+
+
+class brev(Stub):
+    """
+    brev(x)
+
+    Returns the reverse of the bit pattern of x. For example, 0b10110110
+    becomes 0b01101101.
+    """
+
+
+class clz(Stub):
+    """
+    clz(x)
+
+    Returns the number of leading zeros in z.
+    """
+
+
+class ffs(Stub):
+    """
+    ffs(x)
+
+    Returns the position of the first (least significant) bit set to 1 in x,
+    where the least significant bit position is 1. ffs(0) returns 0.
+    """
+
+
+#-------------------------------------------------------------------------------
+# comparison and selection instructions
+
+class selp(Stub):
+    """
+    selp(a, b, c)
+
+    Select between source operands, based on the value of the predicate source
+    operand.
+    """
+
+
+#-------------------------------------------------------------------------------
+# single / double precision arithmetic
+
+class fma(Stub):
+    """
+    fma(a, b, c)
+
+    Perform the fused multiply-add operation.
+    """
+
+
+class cbrt(Stub):
+    """"
+    cbrt(a)
+
+    Perform the cube root operation.
+    """
+
+
+#-------------------------------------------------------------------------------
+# atomic
+
+class atomic(Stub):
+    """Namespace for atomic operations
+    """
+    _description_ = '<atomic>'
+
+    class add(Stub):
+        """add(ary, idx, val)
+
+        Perform atomic ary[idx] += val. Supported on int32, float32, and
+        float64 operands only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class sub(Stub):
+        """sub(ary, idx, val)
+
+        Perform atomic ary[idx] -= val. Supported on int32, float32, and
+        float64 operands only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class and_(Stub):
+        """and_(ary, idx, val)
+
+        Perform atomic ary[idx] &= val. Supported on int32, int64, uint32 and
+        uint64 operands only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class or_(Stub):
+        """or_(ary, idx, val)
+
+        Perform atomic ary[idx] \|= val. Supported on int32, int64, uint32 and
+        uint64 operands only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """  # noqa: W605
+
+    class xor(Stub):
+        """xor(ary, idx, val)
+
+        Perform atomic ary[idx] ^= val. Supported on int32, int64, uint32 and
+        uint64 operands only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class inc(Stub):
+        """inc(ary, idx, val)
+
+        Perform atomic ary[idx] += 1 up to val, then reset to 0. Supported
+        on uint32, and uint64 operands only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class dec(Stub):
+        """dec(ary, idx, val)
+
+        Perform ary[idx] = (value if (array[idx] == 0) or
+        (array[idx] > value) else array[idx] - 1).
+
+        Supported on uint32, and uint64 operands only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class exch(Stub):
+        """exch(ary, idx, val)
+
+        Perform atomic ary[idx] = val. Supported on int32, int64, uint32 and
+        uint64 operands only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class max(Stub):
+        """max(ary, idx, val)
+
+        Perform atomic ary[idx] = max(ary[idx], val).
+
+        Supported on int32, int64, uint32, uint64, float32, float64 operands
+        only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class min(Stub):
+        """min(ary, idx, val)
+
+        Perform atomic ary[idx] = min(ary[idx], val).
+
+        Supported on int32, int64, uint32, uint64, float32, float64 operands
+        only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class nanmax(Stub):
+        """nanmax(ary, idx, val)
+
+        Perform atomic ary[idx] = max(ary[idx], val).
+
+        NOTE: NaN is treated as a missing value such that:
+        nanmax(NaN, n) == n, nanmax(n, NaN) == n
+
+        Supported on int32, int64, uint32, uint64, float32, float64 operands
+        only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class nanmin(Stub):
+        """nanmin(ary, idx, val)
+
+        Perform atomic ary[idx] = min(ary[idx], val).
+
+        NOTE: NaN is treated as a missing value, such that:
+        nanmin(NaN, n) == n, nanmin(n, NaN) == n
+
+        Supported on int32, int64, uint32, uint64, float32, float64 operands
+        only.
+
+        Returns the old value at the index location as if it is loaded
+        atomically.
+        """
+
+    class compare_and_swap(Stub):
+        """compare_and_swap(ary, old, val)
+
+        Conditionally assign ``val`` to the first element of an 1D array ``ary``
+        if the current value matches ``old``.
+
+        Returns the current value as if it is loaded atomically.
+        """
+
+
+#-------------------------------------------------------------------------------
+# timers
+
+class nanosleep(Stub):
+    '''
+    nanosleep(ns)
+
+    Suspends the thread for a sleep duration approximately close to the delay
+    `ns`, specified in nanoseconds.
+    '''
+    _description_ = '<nansleep()>'
+
+#-------------------------------------------------------------------------------
+# Floating point 16
+
+
+class fp16(Stub):
+    """Namespace for fp16 operations
+    """
+    _description_ = '<fp16>'
+
+    class hadd(Stub):
+        """hadd(a, b)
+
+        Perform fp16 addition, (a + b) in round to nearest mode. Supported
+        on fp16 operands only.
+
+        Returns the fp16 result of the addition.
+
+        """
+
+    class hsub(Stub):
+        """hsub(a, b)
+
+        Perform fp16 subtraction, (a - b) in round to nearest mode. Supported
+        on fp16 operands only.
+
+        Returns the fp16 result of the subtraction.
+
+        """
+
+    class hmul(Stub):
+        """hmul(a, b)
+
+        Perform fp16 multiplication, (a * b) in round to nearest mode. Supported
+        on fp16 operands only.
+
+        Returns the fp16 result of the multiplication.
+
+        """
+
+    class hfma(Stub):
+        """hfma(a, b, c)
+
+        Perform fp16 multiply and accumulate, (a * b) + c in round to nearest
+        mode. Supported on fp16 operands only.
+
+        Returns the fp16 result of the multiplication.
+
+        """
+
+    class hneg(Stub):
+        """hneg(a)
+
+        Perform fp16 negation, -(a). Supported on fp16 operands only.
+
+        Returns the fp16 result of the negation.
+
+        """
+
+    class habs(Stub):
+        """habs(a)
+
+        Perform fp16 absolute value, |a|. Supported on fp16 operands only.
+
+        Returns the fp16 result of the absolute value.
+
+        """
+
+    class heq(Stub):
+        """heq(a, b)
+
+        Perform fp16 comparison, (a == b). Supported
+        on fp16 operands only.
+
+        Returns True if a and b are equal and False otherwise.
+
+        """
+
+    class hne(Stub):
+        """hne(a, b)
+
+        Perform fp16 comparison, (a != b). Supported
+        on fp16 operands only.
+
+        Returns True if a and b are not equal and False otherwise.
+
+        """
+
+    class hge(Stub):
+        """hge(a, b)
+
+        Perform fp16 comparison, (a >= b). Supported
+        on fp16 operands only.
+
+        Returns True if a is >= b and False otherwise.
+
+        """
+
+    class hgt(Stub):
+        """hgt(a, b)
+
+        Perform fp16 comparison, (a > b). Supported
+        on fp16 operands only.
+
+        Returns True if a is > b and False otherwise.
+
+        """
+
+    class hle(Stub):
+        """hle(a, b)
+
+        Perform fp16 comparison, (a <= b). Supported
+        on fp16 operands only.
+
+        Returns True if a is <= b and False otherwise.
+
+        """
+
+    class hlt(Stub):
+        """hlt(a, b)
+
+        Perform fp16 comparison, (a < b). Supported
+        on fp16 operands only.
+
+        Returns True if a is < b and False otherwise.
+
+        """
+
+    class hmax(Stub):
+        """hmax(a, b)
+
+        Perform fp16 maximum operation, max(a,b) Supported
+        on fp16 operands only.
+
+        Returns a if a is greater than b, returns b otherwise.
+
+        """
+
+    class hmin(Stub):
+        """hmin(a, b)
+
+        Perform fp16 minimum operation, min(a,b). Supported
+        on fp16 operands only.
+
+        Returns a if a is less than b, returns b otherwise.
+
+        """
+
+
+#-------------------------------------------------------------------------------
+# vector types
+
+def make_vector_type_stubs():
+    """Make user facing objects for vector types"""
+    vector_type_stubs = []
+    vector_type_prefix = (
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+        "float32",
+        "float64"
+    )
+    vector_type_element_counts = (1, 2, 3, 4)
+    vector_type_attribute_names = ("x", "y", "z", "w")
+
+    for prefix, nelem in itertools.product(
+        vector_type_prefix, vector_type_element_counts
+    ):
+        type_name = f"{prefix}x{nelem}"
+        attr_names = vector_type_attribute_names[:nelem]
+
+        vector_type_stub = type(
+            type_name, (Stub,),
+            {
+                **{attr: lambda self: None for attr in attr_names},
+                **{
+                    "_description_": f"<{type_name}>",
+                    "__signature__": Signature(parameters=[
+                        Parameter(
+                            name=attr_name, kind=Parameter.POSITIONAL_ONLY
+                        ) for attr_name in attr_names[:nelem]
+                    ]),
+                    "__doc__": f"A stub for {type_name} to be used in "
+                    "CUDA kernels."
+                },
+                **{"aliases": []}
+            }
+        )
+        vector_type_stubs.append(vector_type_stub)
+    return vector_type_stubs
+
+
+def map_vector_type_stubs_to_alias(vector_type_stubs):
+    """For each of the stubs, create its aliases.
+
+    For example: float64x3 -> double3
+    """
+    # C-compatible type mapping, see:
+    # https://numpy.org/devdocs/reference/arrays.scalars.html#integer-types
+    base_type_to_alias = {
+        "char": f"int{np.dtype(np.byte).itemsize * 8}",
+        "short": f"int{np.dtype(np.short).itemsize * 8}",
+        "int": f"int{np.dtype(np.intc).itemsize * 8}",
+        "long": f"int{np.dtype(np.int_).itemsize * 8}",
+        "longlong": f"int{np.dtype(np.longlong).itemsize * 8}",
+        "uchar": f"uint{np.dtype(np.ubyte).itemsize * 8}",
+        "ushort": f"uint{np.dtype(np.ushort).itemsize * 8}",
+        "uint": f"uint{np.dtype(np.uintc).itemsize * 8}",
+        "ulong": f"uint{np.dtype(np.uint).itemsize * 8}",
+        "ulonglong": f"uint{np.dtype(np.ulonglong).itemsize * 8}",
+        "float": f"float{np.dtype(np.single).itemsize * 8}",
+        "double": f"float{np.dtype(np.double).itemsize * 8}"
+    }
+
+    base_type_to_vector_type = defaultdict(list)
+    for stub in vector_type_stubs:
+        base_type_to_vector_type[stub.__name__[:-2]].append(stub)
+
+    for alias, base_type in base_type_to_alias.items():
+        vector_type_stubs = base_type_to_vector_type[base_type]
+        for stub in vector_type_stubs:
+            nelem = stub.__name__[-1]
+            stub.aliases.append(f"{alias}{nelem}")
+
+
+_vector_type_stubs = make_vector_type_stubs()
+map_vector_type_stubs_to_alias(_vector_type_stubs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/target.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/target.py
new file mode 100644
index 000000000..1db5e184c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/target.py
@@ -0,0 +1,374 @@
+import re
+import llvmlite.binding as ll
+from llvmlite import ir
+
+from numba.core import typing, types, debuginfo, itanium_mangler, cgutils
+from numba.core.dispatcher import Dispatcher
+from numba.core.utils import cached_property
+from numba.core.base import BaseContext
+from numba.core.callconv import MinimalCallConv
+from numba.core.typing import cmathdecl
+
+from .cudadrv import nvvm
+from numba.cuda import codegen, nvvmutils
+
+
+# -----------------------------------------------------------------------------
+# Typing
+
+
+class CUDATypingContext(typing.BaseContext):
+    def load_additional_registries(self):
+        from . import cudadecl, cudamath, libdevicedecl, vector_types
+        from numba.core.typing import enumdecl
+
+        self.install_registry(cudadecl.registry)
+        self.install_registry(cudamath.registry)
+        self.install_registry(cmathdecl.registry)
+        self.install_registry(libdevicedecl.registry)
+        self.install_registry(enumdecl.registry)
+        self.install_registry(vector_types.typing_registry)
+
+    def resolve_value_type(self, val):
+        # treat other dispatcher object as another device function
+        from numba.cuda.dispatcher import CUDADispatcher
+        if (isinstance(val, Dispatcher) and not
+                isinstance(val, CUDADispatcher)):
+            try:
+                # use cached device function
+                val = val.__dispatcher
+            except AttributeError:
+                if not val._can_compile:
+                    raise ValueError('using cpu function on device '
+                                     'but its compilation is disabled')
+                targetoptions = val.targetoptions.copy()
+                targetoptions['device'] = True
+                targetoptions['debug'] = targetoptions.get('debug', False)
+                targetoptions['opt'] = targetoptions.get('opt', True)
+                disp = CUDADispatcher(val.py_func, targetoptions)
+                # cache the device function for future use and to avoid
+                # duplicated copy of the same function.
+                val.__dispatcher = disp
+                val = disp
+
+        # continue with parent logic
+        return super(CUDATypingContext, self).resolve_value_type(val)
+
+# -----------------------------------------------------------------------------
+# Implementation
+
+
+VALID_CHARS = re.compile(r'[^a-z0-9]', re.I)
+
+
+class CUDATargetContext(BaseContext):
+    implement_powi_as_math_call = True
+    strict_alignment = True
+
+    def __init__(self, typingctx, target='cuda'):
+        super().__init__(typingctx, target)
+
+    @property
+    def DIBuilder(self):
+        # if nvvm.NVVM().is_nvvm70:
+        if True:
+            return debuginfo.DIBuilder
+        else:
+            return debuginfo.NvvmDIBuilder
+
+    @property
+    def enable_boundscheck(self):
+        # Unconditionally disabled
+        return False
+
+    # Overrides
+    def create_module(self, name):
+        return self._internal_codegen._create_empty_module(name)
+
+    def init(self):
+        self._internal_codegen = codegen.JITCUDACodegen("numba.cuda.jit")
+        self._target_data = ll.create_target_data(nvvm.data_layout)
+
+    def load_additional_registries(self):
+        # side effect of import needed for numba.cpython.*, the builtins
+        # registry is updated at import time.
+        from numba.cpython import numbers, tupleobj, slicing # noqa: F401
+        from numba.cpython import rangeobj, iterators, enumimpl # noqa: F401
+        from numba.cpython import unicode, charseq # noqa: F401
+        from numba.cpython import cmathimpl
+        from numba.np import arrayobj # noqa: F401
+        from numba.np import npdatetime # noqa: F401
+        from . import (
+            cudaimpl, printimpl, libdeviceimpl, mathimpl, vector_types
+        )
+
+        self.install_registry(cudaimpl.registry)
+        self.install_registry(printimpl.registry)
+        self.install_registry(libdeviceimpl.registry)
+        self.install_registry(cmathimpl.registry)
+        self.install_registry(mathimpl.registry)
+        self.install_registry(vector_types.impl_registry)
+
+    def codegen(self):
+        return self._internal_codegen
+
+    @property
+    def target_data(self):
+        return self._target_data
+
+    @cached_property
+    def nonconst_module_attrs(self):
+        """
+        Some CUDA intrinsics are at the module level, but cannot be treated as
+        constants, because they are loaded from a special register in the PTX.
+        These include threadIdx, blockDim, etc.
+        """
+        from numba import cuda
+        nonconsts = ('threadIdx', 'blockDim', 'blockIdx', 'gridDim', 'laneid',
+                     'warpsize')
+        nonconsts_with_mod = tuple([(types.Module(cuda), nc)
+                                    for nc in nonconsts])
+        return nonconsts_with_mod
+
+    @cached_property
+    def call_conv(self):
+        return CUDACallConv(self)
+
+    def mangler(self, name, argtypes, *, abi_tags=(), uid=None):
+        return itanium_mangler.mangle(name, argtypes, abi_tags=abi_tags,
+                                      uid=uid)
+
+    def prepare_cuda_kernel(self, codelib, fndesc, debug,
+                            nvvm_options, filename, linenum,
+                            max_registers=None):
+        """
+        Adapt a code library ``codelib`` with the numba compiled CUDA kernel
+        with name ``fname`` and arguments ``argtypes`` for NVVM.
+        A new library is created with a wrapper function that can be used as
+        the kernel entry point for the given kernel.
+
+        Returns the new code library and the wrapper function.
+
+        Parameters:
+
+        codelib:       The CodeLibrary containing the device function to wrap
+                       in a kernel call.
+        fndesc:        The FunctionDescriptor of the source function.
+        debug:         Whether to compile with debug.
+        nvvm_options:  Dict of NVVM options used when compiling the new library.
+        filename:      The source filename that the function is contained in.
+        linenum:       The source line that the function is on.
+        max_registers: The max_registers argument for the code library.
+        """
+        kernel_name = itanium_mangler.prepend_namespace(
+            fndesc.llvm_func_name, ns='cudapy',
+        )
+        library = self.codegen().create_library(f'{codelib.name}_kernel_',
+                                                entry_name=kernel_name,
+                                                nvvm_options=nvvm_options,
+                                                max_registers=max_registers)
+        library.add_linking_library(codelib)
+        wrapper = self.generate_kernel_wrapper(library, fndesc, kernel_name,
+                                               debug, filename, linenum)
+        return library, wrapper
+
+    def generate_kernel_wrapper(self, library, fndesc, kernel_name, debug,
+                                filename, linenum):
+        """
+        Generate the kernel wrapper in the given ``library``.
+        The function being wrapped is described by ``fndesc``.
+        The wrapper function is returned.
+        """
+
+        argtypes = fndesc.argtypes
+        arginfo = self.get_arg_packer(argtypes)
+        argtys = list(arginfo.argument_types)
+        wrapfnty = ir.FunctionType(ir.VoidType(), argtys)
+        wrapper_module = self.create_module("cuda.kernel.wrapper")
+        fnty = ir.FunctionType(ir.IntType(32),
+                               [self.call_conv.get_return_type(types.pyobject)]
+                               + argtys)
+        func = ir.Function(wrapper_module, fnty, fndesc.llvm_func_name)
+
+        prefixed = itanium_mangler.prepend_namespace(func.name, ns='cudapy')
+        wrapfn = ir.Function(wrapper_module, wrapfnty, prefixed)
+        builder = ir.IRBuilder(wrapfn.append_basic_block(''))
+
+        if debug:
+            debuginfo = self.DIBuilder(
+                module=wrapper_module, filepath=filename, cgctx=self,
+            )
+            debuginfo.mark_subprogram(
+                wrapfn, kernel_name, fndesc.args, argtypes, linenum,
+            )
+            debuginfo.mark_location(builder, linenum)
+
+        # Define error handling variable
+        def define_error_gv(postfix):
+            name = wrapfn.name + postfix
+            gv = cgutils.add_global_variable(wrapper_module, ir.IntType(32),
+                                             name)
+            gv.initializer = ir.Constant(gv.type.pointee, None)
+            return gv
+
+        gv_exc = define_error_gv("__errcode__")
+        gv_tid = []
+        gv_ctaid = []
+        for i in 'xyz':
+            gv_tid.append(define_error_gv("__tid%s__" % i))
+            gv_ctaid.append(define_error_gv("__ctaid%s__" % i))
+
+        callargs = arginfo.from_arguments(builder, wrapfn.args)
+        status, _ = self.call_conv.call_function(
+            builder, func, types.void, argtypes, callargs)
+
+        if debug:
+            # Check error status
+            with cgutils.if_likely(builder, status.is_ok):
+                builder.ret_void()
+
+            with builder.if_then(builder.not_(status.is_python_exc)):
+                # User exception raised
+                old = ir.Constant(gv_exc.type.pointee, None)
+
+                # Use atomic cmpxchg to prevent rewriting the error status
+                # Only the first error is recorded
+
+                # if nvvm.NVVM().is_nvvm70:
+                if True:
+                    xchg = builder.cmpxchg(gv_exc, old, status.code,
+                                           'monotonic', 'monotonic')
+                    changed = builder.extract_value(xchg, 1)
+                else:
+                    casfnty = ir.FunctionType(old.type, [gv_exc.type, old.type,
+                                                         old.type])
+
+                    cas_hack = "___numba_atomic_i32_cas_hack"
+                    casfn = ir.Function(wrapper_module, casfnty, name=cas_hack)
+                    xchg = builder.call(casfn, [gv_exc, old, status.code])
+                    changed = builder.icmp_unsigned('==', xchg, old)
+
+                # If the xchange is successful, save the thread ID.
+                sreg = nvvmutils.SRegBuilder(builder)
+                with builder.if_then(changed):
+                    for dim, ptr, in zip("xyz", gv_tid):
+                        val = sreg.tid(dim)
+                        builder.store(val, ptr)
+
+                    for dim, ptr, in zip("xyz", gv_ctaid):
+                        val = sreg.ctaid(dim)
+                        builder.store(val, ptr)
+
+        builder.ret_void()
+
+        nvvm.set_cuda_kernel(wrapfn)
+        library.add_ir_module(wrapper_module)
+        if debug:
+            debuginfo.finalize()
+        library.finalize()
+        wrapfn = library.get_function(wrapfn.name)
+        return wrapfn
+
+    def make_constant_array(self, builder, aryty, arr):
+        """
+        Unlike the parent version.  This returns a a pointer in the constant
+        addrspace.
+        """
+
+        lmod = builder.module
+
+        constvals = [
+            self.get_constant(types.byte, i)
+            for i in iter(arr.tobytes(order='A'))
+        ]
+        constaryty = ir.ArrayType(ir.IntType(8), len(constvals))
+        constary = ir.Constant(constaryty, constvals)
+
+        addrspace = nvvm.ADDRSPACE_CONSTANT
+        gv = cgutils.add_global_variable(lmod, constary.type, "_cudapy_cmem",
+                                         addrspace=addrspace)
+        gv.linkage = 'internal'
+        gv.global_constant = True
+        gv.initializer = constary
+
+        # Preserve the underlying alignment
+        lldtype = self.get_data_type(aryty.dtype)
+        align = self.get_abi_sizeof(lldtype)
+        gv.align = 2 ** (align - 1).bit_length()
+
+        # Convert to generic address-space
+        conv = nvvmutils.insert_addrspace_conv(lmod, ir.IntType(8), addrspace)
+        addrspaceptr = gv.bitcast(ir.PointerType(ir.IntType(8), addrspace))
+        genptr = builder.call(conv, [addrspaceptr])
+
+        # Create array object
+        ary = self.make_array(aryty)(self, builder)
+        kshape = [self.get_constant(types.intp, s) for s in arr.shape]
+        kstrides = [self.get_constant(types.intp, s) for s in arr.strides]
+        self.populate_array(ary, data=builder.bitcast(genptr, ary.data.type),
+                            shape=kshape,
+                            strides=kstrides,
+                            itemsize=ary.itemsize, parent=ary.parent,
+                            meminfo=None)
+
+        return ary._getvalue()
+
+    def insert_const_string(self, mod, string):
+        """
+        Unlike the parent version.  This returns a a pointer in the constant
+        addrspace.
+        """
+        text = cgutils.make_bytearray(string.encode("utf-8") + b"\x00")
+        name = '$'.join(["__conststring__",
+                         itanium_mangler.mangle_identifier(string)])
+        # Try to reuse existing global
+        gv = mod.globals.get(name)
+        if gv is None:
+            # Not defined yet
+            gv = cgutils.add_global_variable(mod, text.type, name,
+                                             addrspace=nvvm.ADDRSPACE_CONSTANT)
+            gv.linkage = 'internal'
+            gv.global_constant = True
+            gv.initializer = text
+
+        # Cast to a i8* pointer
+        charty = gv.type.pointee.element
+        return gv.bitcast(charty.as_pointer(nvvm.ADDRSPACE_CONSTANT))
+
+    def insert_string_const_addrspace(self, builder, string):
+        """
+        Insert a constant string in the constant addresspace and return a
+        generic i8 pointer to the data.
+
+        This function attempts to deduplicate.
+        """
+        lmod = builder.module
+        gv = self.insert_const_string(lmod, string)
+        return self.insert_addrspace_conv(builder, gv,
+                                          nvvm.ADDRSPACE_CONSTANT)
+
+    def insert_addrspace_conv(self, builder, ptr, addrspace):
+        """
+        Perform addrspace conversion according to the NVVM spec
+        """
+        lmod = builder.module
+        base_type = ptr.type.pointee
+        conv = nvvmutils.insert_addrspace_conv(lmod, base_type, addrspace)
+        return builder.call(conv, [ptr])
+
+    def optimize_function(self, func):
+        """Run O1 function passes
+        """
+        pass
+        ## XXX skipped for now
+        # fpm = lp.FunctionPassManager.new(func.module)
+        #
+        # lp.PassManagerBuilder.new().populate(fpm)
+        #
+        # fpm.initialize()
+        # fpm.run(func)
+        # fpm.finalize()
+
+
+class CUDACallConv(MinimalCallConv):
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/testing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/testing.py
new file mode 100644
index 000000000..8a9ee2e92
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/testing.py
@@ -0,0 +1,170 @@
+import os
+import platform
+import shutil
+
+from numba.tests.support import SerialMixin
+from numba.cuda.cuda_paths import get_conda_ctk
+from numba.cuda.cudadrv import driver, devices, libs
+from numba.core import config
+from numba.tests.support import TestCase
+import unittest
+
+
+class CUDATestCase(SerialMixin, TestCase):
+    """
+    For tests that use a CUDA device. Test methods in a CUDATestCase must not
+    be run out of module order, because the ContextResettingTestCase may reset
+    the context and destroy resources used by a normal CUDATestCase if any of
+    its tests are run between tests from a CUDATestCase.
+    """
+
+    def setUp(self):
+        self._low_occupancy_warnings = config.CUDA_LOW_OCCUPANCY_WARNINGS
+        self._warn_on_implicit_copy = config.CUDA_WARN_ON_IMPLICIT_COPY
+
+        # Disable warnings about low gpu utilization in the test suite
+        config.CUDA_LOW_OCCUPANCY_WARNINGS = 0
+        # Disable warnings about host arrays in the test suite
+        config.CUDA_WARN_ON_IMPLICIT_COPY = 0
+
+    def tearDown(self):
+        config.CUDA_LOW_OCCUPANCY_WARNINGS = self._low_occupancy_warnings
+        config.CUDA_WARN_ON_IMPLICIT_COPY = self._warn_on_implicit_copy
+
+
+class ContextResettingTestCase(CUDATestCase):
+    """
+    For tests where the context needs to be reset after each test. Typically
+    these inspect or modify parts of the context that would usually be expected
+    to be internal implementation details (such as the state of allocations and
+    deallocations, etc.).
+    """
+
+    def tearDown(self):
+        super().tearDown()
+        from numba.cuda.cudadrv.devices import reset
+        reset()
+
+
+def ensure_supported_ccs_initialized():
+    from numba.cuda import is_available as cuda_is_available
+    from numba.cuda.cudadrv import nvvm
+
+    if cuda_is_available():
+        # Ensure that cudart.so is loaded and the list of supported compute
+        # capabilities in the nvvm module is populated before a fork. This is
+        # needed because some compilation tests don't require a CUDA context,
+        # but do use NVVM, and it is required that libcudart.so should be
+        # loaded before a fork (note that the requirement is not explicitly
+        # documented).
+        nvvm.get_supported_ccs()
+
+
+def skip_on_cudasim(reason):
+    """Skip this test if running on the CUDA simulator"""
+    return unittest.skipIf(config.ENABLE_CUDASIM, reason)
+
+
+def skip_unless_cudasim(reason):
+    """Skip this test if running on CUDA hardware"""
+    return unittest.skipUnless(config.ENABLE_CUDASIM, reason)
+
+
+def skip_unless_conda_cudatoolkit(reason):
+    """Skip test if the CUDA toolkit was not installed by Conda"""
+    return unittest.skipUnless(get_conda_ctk() is not None, reason)
+
+
+def skip_if_external_memmgr(reason):
+    """Skip test if an EMM Plugin is in use"""
+    return unittest.skipIf(config.CUDA_MEMORY_MANAGER != 'default', reason)
+
+
+def skip_under_cuda_memcheck(reason):
+    return unittest.skipIf(os.environ.get('CUDA_MEMCHECK') is not None, reason)
+
+
+def skip_without_nvdisasm(reason):
+    nvdisasm_path = shutil.which('nvdisasm')
+    return unittest.skipIf(nvdisasm_path is None, reason)
+
+
+def skip_with_nvdisasm(reason):
+    nvdisasm_path = shutil.which('nvdisasm')
+    return unittest.skipIf(nvdisasm_path is not None, reason)
+
+
+def skip_on_arm(reason):
+    cpu = platform.processor()
+    is_arm = cpu.startswith('arm') or cpu.startswith('aarch')
+    return unittest.skipIf(is_arm, reason)
+
+
+def skip_if_cuda_includes_missing(fn):
+    # Skip when cuda.h is not available - generally this should indicate
+    # whether the CUDA includes are available or not
+    cuda_h = os.path.join(config.CUDA_INCLUDE_PATH, 'cuda.h')
+    cuda_h_file = (os.path.exists(cuda_h) and os.path.isfile(cuda_h))
+    reason = 'CUDA include dir not available on this system'
+    return unittest.skipUnless(cuda_h_file, reason)(fn)
+
+
+def cc_X_or_above(major, minor):
+    if not config.ENABLE_CUDASIM:
+        cc = devices.get_context().device.compute_capability
+        return cc >= (major, minor)
+    else:
+        return True
+
+
+def skip_unless_cc_50(fn):
+    return unittest.skipUnless(cc_X_or_above(5, 0), "requires cc >= 5.0")(fn)
+
+
+def skip_unless_cc_53(fn):
+    return unittest.skipUnless(cc_X_or_above(5, 3), "requires cc >= 5.3")(fn)
+
+
+def skip_unless_cc_60(fn):
+    return unittest.skipUnless(cc_X_or_above(6, 0), "requires cc >= 6.0")(fn)
+
+
+def xfail_unless_cudasim(fn):
+    if config.ENABLE_CUDASIM:
+        return fn
+    else:
+        return unittest.expectedFailure(fn)
+
+
+def skip_with_cuda_python(reason):
+    return unittest.skipIf(driver.USE_NV_BINDING, reason)
+
+
+def skip_unless_cuda_python(reason):
+    return unittest.skipIf(not driver.USE_NV_BINDING, reason)
+
+
+def cudadevrt_missing():
+    if config.ENABLE_CUDASIM:
+        return False
+    try:
+        libs.check_static_lib('cudadevrt')
+    except FileNotFoundError:
+        return True
+    return False
+
+
+def skip_if_cudadevrt_missing(fn):
+    return unittest.skipIf(cudadevrt_missing(), 'cudadevrt missing')(fn)
+
+
+class ForeignArray(object):
+    """
+    Class for emulating an array coming from another library through the CUDA
+    Array interface. This just hides a DeviceNDArray so that it doesn't look
+    like a DeviceNDArray.
+    """
+
+    def __init__(self, arr):
+        self._arr = arr
+        self.__cuda_array_interface__ = arr.__cuda_array_interface__
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/__init__.py
new file mode 100644
index 000000000..d6171b01f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/__init__.py
@@ -0,0 +1,24 @@
+from numba.cuda.testing import ensure_supported_ccs_initialized
+from numba.testing import unittest
+from numba.testing import load_testsuite
+from numba import cuda
+from os.path import dirname, join
+
+
+def load_tests(loader, tests, pattern):
+    suite = unittest.TestSuite()
+    this_dir = dirname(__file__)
+    ensure_supported_ccs_initialized()
+    suite.addTests(load_testsuite(loader, join(this_dir, 'nocuda')))
+    if cuda.is_available():
+        suite.addTests(load_testsuite(loader, join(this_dir, 'cudasim')))
+        gpus = cuda.list_devices()
+        if gpus and gpus[0].compute_capability >= (2, 0):
+            suite.addTests(load_testsuite(loader, join(this_dir, 'cudadrv')))
+            suite.addTests(load_testsuite(loader, join(this_dir, 'cudapy')))
+            suite.addTests(load_testsuite(loader, join(this_dir, 'doc_examples')))
+        else:
+            print("skipped CUDA tests because GPU CC < 2.0")
+    else:
+        print("skipped CUDA tests")
+    return suite
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/__init__.py
new file mode 100644
index 000000000..d9e7d31af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/__init__.py
@@ -0,0 +1,8 @@
+from numba.cuda.testing import ensure_supported_ccs_initialized
+from numba.testing import load_testsuite
+import os
+
+
+def load_tests(loader, tests, pattern):
+    ensure_supported_ccs_initialized()
+    return load_testsuite(loader, os.path.dirname(__file__))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/cuda_include.cu b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/cuda_include.cu
new file mode 100644
index 000000000..69a0efd9a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/cuda_include.cu
@@ -0,0 +1,5 @@
+// Not all CUDA includes are safe to include in device code compiled by NVRTC,
+// because it does not have paths to all system include directories. Headers
+// such as cuda_device_runtime_api.h are safe to use in NVRTC without adding
+// additional includes.
+#include <cuda_device_runtime_api.h>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/error.cu b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/error.cu
new file mode 100644
index 000000000..402f3138d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/error.cu
@@ -0,0 +1,7 @@
+extern "C" __device__
+int bar(int* out, int a) {
+  // Explicitly placed to generate an error
+  SYNTAX ERROR
+  *out = a * 2;
+  return 0;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/jitlink.cu b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/jitlink.cu
new file mode 100644
index 000000000..3ba4f7969
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/jitlink.cu
@@ -0,0 +1,13 @@
+// compile with:
+//
+//  nvcc -gencode arch=compute_20,code=compute_20 -ptx jitlink.cu -o jitlink.ptx
+//
+//
+
+extern "C"{
+    __device__
+    int bar(int* out, int a) {
+        *out = a * 2;
+        return 0;
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/jitlink.ptx b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/jitlink.ptx
new file mode 100644
index 000000000..8cc1aa6d6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/jitlink.ptx
@@ -0,0 +1,30 @@
+//
+// Generated by NVIDIA NVVM Compiler
+// Compiler built on Tue Apr  1 03:34:02 2014 (1396341242)
+// Cuda compilation tools, release 6.0, V6.0.1
+//
+
+.version 4.0
+.target sm_20
+.address_size 64
+
+
+.visible .func  (.param .b32 func_retval0) bar(
+	.param .b64 bar_param_0,
+	.param .b32 bar_param_1
+)
+{
+	.reg .s32 	%r<4>;
+	.reg .s64 	%rd<2>;
+
+
+	ld.param.u64 	%rd1, [bar_param_0];
+	ld.param.u32 	%r1, [bar_param_1];
+	shl.b32 	%r2, %r1, 1;
+	st.u32 	[%rd1], %r2;
+	mov.u32 	%r3, 0;
+	st.param.b32	[func_retval0+0], %r3;
+	ret;
+}
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/warn.cu b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/warn.cu
new file mode 100644
index 000000000..4f31e951d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/data/warn.cu
@@ -0,0 +1,7 @@
+extern "C" __device__
+int bar(int* out, int a) {
+  // Explicitly placed to generate a warning for testing the NVRTC program log
+  int unused;
+  *out = a * 2;
+  return 0;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_array_attr.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_array_attr.py
new file mode 100644
index 000000000..6ac04981c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_array_attr.py
@@ -0,0 +1,92 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestArrayAttr(CUDATestCase):
+    def test_contigous_2d(self):
+        ary = np.arange(10)
+        cary = ary.reshape(2, 5)
+        fary = np.asfortranarray(cary)
+
+        dcary = cuda.to_device(cary)
+        dfary = cuda.to_device(fary)
+        self.assertTrue(dcary.is_c_contiguous())
+        self.assertTrue(not dfary.is_c_contiguous())
+        self.assertTrue(not dcary.is_f_contiguous())
+        self.assertTrue(dfary.is_f_contiguous())
+
+    def test_contigous_3d(self):
+        ary = np.arange(20)
+        cary = ary.reshape(2, 5, 2)
+        fary = np.asfortranarray(cary)
+
+        dcary = cuda.to_device(cary)
+        dfary = cuda.to_device(fary)
+        self.assertTrue(dcary.is_c_contiguous())
+        self.assertTrue(not dfary.is_c_contiguous())
+        self.assertTrue(not dcary.is_f_contiguous())
+        self.assertTrue(dfary.is_f_contiguous())
+
+    def test_contigous_4d(self):
+        ary = np.arange(60)
+        cary = ary.reshape(2, 5, 2, 3)
+        fary = np.asfortranarray(cary)
+
+        dcary = cuda.to_device(cary)
+        dfary = cuda.to_device(fary)
+        self.assertTrue(dcary.is_c_contiguous())
+        self.assertTrue(not dfary.is_c_contiguous())
+        self.assertTrue(not dcary.is_f_contiguous())
+        self.assertTrue(dfary.is_f_contiguous())
+
+    def test_ravel_c(self):
+        ary = np.arange(60)
+        reshaped = ary.reshape(2, 5, 2, 3)
+
+        expect = reshaped.ravel(order='C')
+        dary = cuda.to_device(reshaped)
+        dflat = dary.ravel()
+        flat = dflat.copy_to_host()
+        self.assertEqual(flat.ndim, 1)
+        self.assertPreciseEqual(expect, flat)
+
+        # explicit order kwarg
+        for order in 'CA':
+            expect = reshaped.ravel(order=order)
+            dary = cuda.to_device(reshaped)
+            dflat = dary.ravel(order=order)
+            flat = dflat.copy_to_host()
+            self.assertEqual(flat.ndim, 1)
+            self.assertPreciseEqual(expect, flat)
+
+    def test_ravel_f(self):
+        ary = np.arange(60)
+        reshaped = np.asfortranarray(ary.reshape(2, 5, 2, 3))
+        for order in 'FA':
+            expect = reshaped.ravel(order=order)
+            dary = cuda.to_device(reshaped)
+            dflat = dary.ravel(order=order)
+            flat = dflat.copy_to_host()
+            self.assertEqual(flat.ndim, 1)
+            self.assertPreciseEqual(expect, flat)
+
+    def test_reshape_c(self):
+        ary = np.arange(10)
+        expect = ary.reshape(2, 5)
+        dary = cuda.to_device(ary)
+        dary_reshaped = dary.reshape(2, 5)
+        got = dary_reshaped.copy_to_host()
+        self.assertPreciseEqual(expect, got)
+
+    def test_reshape_f(self):
+        ary = np.arange(10)
+        expect = ary.reshape(2, 5, order='F')
+        dary = cuda.to_device(ary)
+        dary_reshaped = dary.reshape(2, 5, order='F')
+        got = dary_reshaped.copy_to_host()
+        self.assertPreciseEqual(expect, got)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_context_stack.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_context_stack.py
new file mode 100644
index 000000000..9e255f5d8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_context_stack.py
@@ -0,0 +1,145 @@
+import numbers
+from ctypes import byref
+import weakref
+
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.cuda.cudadrv import driver
+
+
+class TestContextStack(CUDATestCase):
+    def setUp(self):
+        super().setUp()
+        # Reset before testing
+        cuda.close()
+
+    def test_gpus_current(self):
+        self.assertIs(cuda.gpus.current, None)
+        with cuda.gpus[0]:
+            self.assertEqual(int(cuda.gpus.current.id), 0)
+
+    def test_gpus_len(self):
+        self.assertGreater(len(cuda.gpus), 0)
+
+    def test_gpus_iter(self):
+        gpulist = list(cuda.gpus)
+        self.assertGreater(len(gpulist), 0)
+
+
+class TestContextAPI(CUDATestCase):
+
+    def tearDown(self):
+        super().tearDown()
+        cuda.close()
+
+    def test_context_memory(self):
+        try:
+            mem = cuda.current_context().get_memory_info()
+        except NotImplementedError:
+            self.skipTest('EMM Plugin does not implement get_memory_info()')
+
+        self.assertIsInstance(mem.free, numbers.Number)
+        self.assertEquals(mem.free, mem[0])
+
+        self.assertIsInstance(mem.total, numbers.Number)
+        self.assertEquals(mem.total, mem[1])
+
+        self.assertLessEqual(mem.free, mem.total)
+
+    @unittest.skipIf(len(cuda.gpus) < 2, "need more than 1 gpus")
+    @skip_on_cudasim('CUDA HW required')
+    def test_forbidden_context_switch(self):
+        # Cannot switch context inside a `cuda.require_context`
+        @cuda.require_context
+        def switch_gpu():
+            with cuda.gpus[1]:
+                pass
+
+        with cuda.gpus[0]:
+            with self.assertRaises(RuntimeError) as raises:
+                switch_gpu()
+
+            self.assertIn("Cannot switch CUDA-context.", str(raises.exception))
+
+    @unittest.skipIf(len(cuda.gpus) < 2, "need more than 1 gpus")
+    def test_accepted_context_switch(self):
+        def switch_gpu():
+            with cuda.gpus[1]:
+                return cuda.current_context().device.id
+
+        with cuda.gpus[0]:
+            devid = switch_gpu()
+        self.assertEqual(int(devid), 1)
+
+
+@skip_on_cudasim('CUDA HW required')
+class Test3rdPartyContext(CUDATestCase):
+    def tearDown(self):
+        super().tearDown()
+        cuda.close()
+
+    def test_attached_primary(self, extra_work=lambda: None):
+        # Emulate primary context creation by 3rd party
+        the_driver = driver.driver
+        if driver.USE_NV_BINDING:
+            dev = driver.binding.CUdevice(0)
+            hctx = the_driver.cuDevicePrimaryCtxRetain(dev)
+        else:
+            dev = 0
+            hctx = driver.drvapi.cu_context()
+            the_driver.cuDevicePrimaryCtxRetain(byref(hctx), dev)
+        try:
+            ctx = driver.Context(weakref.proxy(self), hctx)
+            ctx.push()
+            # Check that the context from numba matches the created primary
+            # context.
+            my_ctx = cuda.current_context()
+            if driver.USE_NV_BINDING:
+                self.assertEqual(int(my_ctx.handle), int(ctx.handle))
+            else:
+                self.assertEqual(my_ctx.handle.value, ctx.handle.value)
+
+            extra_work()
+        finally:
+            ctx.pop()
+            the_driver.cuDevicePrimaryCtxRelease(dev)
+
+    def test_attached_non_primary(self):
+        # Emulate non-primary context creation by 3rd party
+        the_driver = driver.driver
+        if driver.USE_NV_BINDING:
+            flags = 0
+            dev = driver.binding.CUdevice(0)
+            hctx = the_driver.cuCtxCreate(flags, dev)
+        else:
+            hctx = driver.drvapi.cu_context()
+            the_driver.cuCtxCreate(byref(hctx), 0, 0)
+        try:
+            cuda.current_context()
+        except RuntimeError as e:
+            # Expecting an error about non-primary CUDA context
+            self.assertIn("Numba cannot operate on non-primary CUDA context ",
+                          str(e))
+        else:
+            self.fail("No RuntimeError raised")
+        finally:
+            the_driver.cuCtxDestroy(hctx)
+
+    def test_cudajit_in_attached_primary_context(self):
+        def do():
+            from numba import cuda
+
+            @cuda.jit
+            def foo(a):
+                for i in range(a.size):
+                    a[i] = i
+
+            a = cuda.device_array(10)
+            foo[1, 1](a)
+            self.assertEqual(list(a.copy_to_host()), list(range(10)))
+
+        self.test_attached_primary(do)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_array_slicing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_array_slicing.py
new file mode 100644
index 000000000..7c13353f0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_array_slicing.py
@@ -0,0 +1,379 @@
+from itertools import product
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from unittest.mock import patch
+
+
+class CudaArrayIndexing(CUDATestCase):
+    def test_index_1d(self):
+        arr = np.arange(10)
+        darr = cuda.to_device(arr)
+        x, = arr.shape
+        for i in range(-x, x):
+            self.assertEqual(arr[i], darr[i])
+        with self.assertRaises(IndexError):
+            darr[-x - 1]
+        with self.assertRaises(IndexError):
+            darr[x]
+
+    def test_index_2d(self):
+        arr = np.arange(3 * 4).reshape(3, 4)
+        darr = cuda.to_device(arr)
+        x, y = arr.shape
+        for i in range(-x, x):
+            for j in range(-y, y):
+                self.assertEqual(arr[i, j], darr[i, j])
+        with self.assertRaises(IndexError):
+            darr[-x - 1, 0]
+        with self.assertRaises(IndexError):
+            darr[x, 0]
+        with self.assertRaises(IndexError):
+            darr[0, -y - 1]
+        with self.assertRaises(IndexError):
+            darr[0, y]
+
+    def test_index_3d(self):
+        arr = np.arange(3 * 4 * 5).reshape(3, 4, 5)
+        darr = cuda.to_device(arr)
+        x, y, z = arr.shape
+        for i in range(-x, x):
+            for j in range(-y, y):
+                for k in range(-z, z):
+                    self.assertEqual(arr[i, j, k], darr[i, j, k])
+        with self.assertRaises(IndexError):
+            darr[-x - 1, 0, 0]
+        with self.assertRaises(IndexError):
+            darr[x, 0, 0]
+        with self.assertRaises(IndexError):
+            darr[0, -y - 1, 0]
+        with self.assertRaises(IndexError):
+            darr[0, y, 0]
+        with self.assertRaises(IndexError):
+            darr[0, 0, -z - 1]
+        with self.assertRaises(IndexError):
+            darr[0, 0, z]
+
+
+class CudaArrayStridedSlice(CUDATestCase):
+
+    def test_strided_index_1d(self):
+        arr = np.arange(10)
+        darr = cuda.to_device(arr)
+        for i in range(arr.size):
+            np.testing.assert_equal(arr[i::2], darr[i::2].copy_to_host())
+
+    def test_strided_index_2d(self):
+        arr = np.arange(6 * 7).reshape(6, 7)
+        darr = cuda.to_device(arr)
+
+        for i in range(arr.shape[0]):
+            for j in range(arr.shape[1]):
+                np.testing.assert_equal(arr[i::2, j::2],
+                                        darr[i::2, j::2].copy_to_host())
+
+    def test_strided_index_3d(self):
+        arr = np.arange(6 * 7 * 8).reshape(6, 7, 8)
+        darr = cuda.to_device(arr)
+
+        for i in range(arr.shape[0]):
+            for j in range(arr.shape[1]):
+                for k in range(arr.shape[2]):
+                    np.testing.assert_equal(
+                        arr[i::2, j::2, k::2],
+                        darr[i::2, j::2, k::2].copy_to_host())
+
+
+class CudaArraySlicing(CUDATestCase):
+    def test_prefix_1d(self):
+        arr = np.arange(5)
+        darr = cuda.to_device(arr)
+        for i in range(arr.size):
+            expect = arr[i:]
+            got = darr[i:].copy_to_host()
+            self.assertTrue(np.all(expect == got))
+
+    def test_prefix_2d(self):
+        arr = np.arange(3 ** 2).reshape(3, 3)
+        darr = cuda.to_device(arr)
+        for i in range(arr.shape[0]):
+            for j in range(arr.shape[1]):
+                expect = arr[i:, j:]
+                sliced = darr[i:, j:]
+                self.assertEqual(expect.shape, sliced.shape)
+                self.assertEqual(expect.strides, sliced.strides)
+                got = sliced.copy_to_host()
+                self.assertTrue(np.all(expect == got))
+
+    def test_select_3d_first_two_dim(self):
+        arr = np.arange(3 * 4 * 5).reshape(3, 4, 5)
+        darr = cuda.to_device(arr)
+        # Select first dimension
+        for i in range(arr.shape[0]):
+            expect = arr[i]
+            sliced = darr[i]
+            self.assertEqual(expect.shape, sliced.shape)
+            self.assertEqual(expect.strides, sliced.strides)
+            got = sliced.copy_to_host()
+            self.assertTrue(np.all(expect == got))
+        # Select second dimension
+        for i in range(arr.shape[0]):
+            for j in range(arr.shape[1]):
+                expect = arr[i, j]
+                sliced = darr[i, j]
+                self.assertEqual(expect.shape, sliced.shape)
+                self.assertEqual(expect.strides, sliced.strides)
+                got = sliced.copy_to_host()
+                self.assertTrue(np.all(expect == got))
+
+    def test_select_f(self):
+        a = np.arange(5 * 6 * 7).reshape(5, 6, 7, order='F')
+        da = cuda.to_device(a)
+
+        for i in range(a.shape[0]):
+            for j in range(a.shape[1]):
+                self.assertTrue(np.array_equal(da[i, j, :].copy_to_host(),
+                                               a[i, j, :]))
+            for j in range(a.shape[2]):
+                self.assertTrue(np.array_equal(da[i, :, j].copy_to_host(),
+                                               a[i, :, j]))
+        for i in range(a.shape[1]):
+            for j in range(a.shape[2]):
+                self.assertTrue(np.array_equal(da[:, i, j].copy_to_host(),
+                                               a[:, i, j]))
+
+    def test_select_c(self):
+        a = np.arange(5 * 6 * 7).reshape(5, 6, 7, order='C')
+        da = cuda.to_device(a)
+
+        for i in range(a.shape[0]):
+            for j in range(a.shape[1]):
+                self.assertTrue(np.array_equal(da[i, j, :].copy_to_host(),
+                                               a[i, j, :]))
+            for j in range(a.shape[2]):
+                self.assertTrue(np.array_equal(da[i, :, j].copy_to_host(),
+                                               a[i, :, j]))
+        for i in range(a.shape[1]):
+            for j in range(a.shape[2]):
+                self.assertTrue(np.array_equal(da[:, i, j].copy_to_host(),
+                                               a[:, i, j]))
+
+    def test_prefix_select(self):
+        arr = np.arange(5 * 7).reshape(5, 7, order='F')
+
+        darr = cuda.to_device(arr)
+        self.assertTrue(np.all(darr[:1, 1].copy_to_host() == arr[:1, 1]))
+
+    def test_negative_slicing_1d(self):
+        arr = np.arange(10)
+        darr = cuda.to_device(arr)
+        for i, j in product(range(-10, 10), repeat=2):
+            np.testing.assert_array_equal(arr[i:j],
+                                          darr[i:j].copy_to_host())
+
+    def test_negative_slicing_2d(self):
+        arr = np.arange(12).reshape(3, 4)
+        darr = cuda.to_device(arr)
+        for x, y, w, s in product(range(-4, 4), repeat=4):
+            np.testing.assert_array_equal(arr[x:y, w:s],
+                                          darr[x:y, w:s].copy_to_host())
+
+    def test_empty_slice_1d(self):
+        arr = np.arange(5)
+        darr = cuda.to_device(arr)
+        for i in range(darr.shape[0]):
+            np.testing.assert_array_equal(darr[i:i].copy_to_host(), arr[i:i])
+        # empty slice of empty slice
+        self.assertFalse(darr[:0][:0].copy_to_host())
+        # out-of-bound slice just produces empty slices
+        np.testing.assert_array_equal(darr[:0][:1].copy_to_host(),
+                                      arr[:0][:1])
+        np.testing.assert_array_equal(darr[:0][-1:].copy_to_host(),
+                                      arr[:0][-1:])
+
+    def test_empty_slice_2d(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        np.testing.assert_array_equal(darr[:0].copy_to_host(), arr[:0])
+        np.testing.assert_array_equal(darr[3, :0].copy_to_host(), arr[3, :0])
+        # empty slice of empty slice
+        self.assertFalse(darr[:0][:0].copy_to_host())
+        # out-of-bound slice just produces empty slices
+        np.testing.assert_array_equal(darr[:0][:1].copy_to_host(), arr[:0][:1])
+        np.testing.assert_array_equal(darr[:0][-1:].copy_to_host(),
+                                      arr[:0][-1:])
+
+
+class CudaArraySetting(CUDATestCase):
+    """
+    Most of the slicing logic is tested in the cases above, so these
+    tests focus on the setting logic.
+    """
+
+    def test_scalar(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        arr[2, 2] = 500
+        darr[2, 2] = 500
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_rank(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        arr[2] = 500
+        darr[2] = 500
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_broadcast(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        arr[:, 2] = 500
+        darr[:, 2] = 500
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_column(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=7, fill_value=400)
+        arr[2] = _400
+        darr[2] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_row(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=5, fill_value=400)
+        arr[:, 2] = _400
+        darr[:, 2] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_subarray(self):
+        arr = np.arange(5 * 6 * 7).reshape(5, 6, 7)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=(6, 7), fill_value=400)
+        arr[2] = _400
+        darr[2] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_deep_subarray(self):
+        arr = np.arange(5 * 6 * 7 * 8).reshape(5, 6, 7, 8)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=(5, 6, 8), fill_value=400)
+        arr[:, :, 2] = _400
+        darr[:, :, 2] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_all(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=(5, 7), fill_value=400)
+        arr[:] = _400
+        darr[:] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_strides(self):
+        arr = np.ones(20)
+        darr = cuda.to_device(arr)
+        arr[::2] = 500
+        darr[::2] = 500
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_incompatible_highdim(self):
+        darr = cuda.to_device(np.arange(5 * 7))
+
+        with self.assertRaises(ValueError) as e:
+            darr[:] = np.ones(shape=(1, 2, 3))
+
+        self.assertIn(
+            member=str(e.exception),
+            container=[
+                "Can't assign 3-D array to 1-D self",  # device
+                "could not broadcast input array from shape (2,3) "
+                "into shape (35)",  # simulator, NP < 1.20
+                "could not broadcast input array from shape (2,3) "
+                "into shape (35,)",  # simulator, NP >= 1.20
+            ])
+
+    def test_incompatible_shape(self):
+        darr = cuda.to_device(np.arange(5))
+
+        with self.assertRaises(ValueError) as e:
+            darr[:] = [1, 3]
+
+        self.assertIn(
+            member=str(e.exception),
+            container=[
+                "Can't copy sequence with size 2 to array axis 0 with "
+                "dimension 5",  # device
+                "cannot copy sequence with size 2 to array axis with "
+                "dimension 5",  # simulator, NP < 1.20
+                "could not broadcast input array from shape (2,) into "
+                "shape (5,)",   # simulator, NP >= 1.20
+            ])
+
+    @skip_on_cudasim('cudasim does not use streams and operates synchronously')
+    def test_sync(self):
+        # There should be a synchronization when no stream is supplied
+        darr = cuda.to_device(np.arange(5))
+
+        with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                          return_value=None) as mock_sync:
+            darr[0] = 10
+
+        mock_sync.assert_called_once()
+
+    @skip_on_cudasim('cudasim does not use streams and operates synchronously')
+    def test_no_sync_default_stream(self):
+        # There should not be a synchronization when the array has a default
+        # stream, whether it is the default stream, the legacy default stream,
+        # the per-thread default stream, or another stream.
+        streams = (cuda.stream(), cuda.default_stream(),
+                   cuda.legacy_default_stream(),
+                   cuda.per_thread_default_stream())
+
+        for stream in streams:
+            darr = cuda.to_device(np.arange(5), stream=stream)
+
+            with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                              return_value=None) as mock_sync:
+                darr[0] = 10
+
+            mock_sync.assert_not_called()
+
+    @skip_on_cudasim('cudasim does not use streams and operates synchronously')
+    def test_no_sync_supplied_stream(self):
+        # There should not be a synchronization when a stream is supplied for
+        # the setitem call, whether it is the default stream, the legacy default
+        # stream, the per-thread default stream, or another stream.
+        streams = (cuda.stream(), cuda.default_stream(),
+                   cuda.legacy_default_stream(),
+                   cuda.per_thread_default_stream())
+
+        for stream in streams:
+            darr = cuda.to_device(np.arange(5))
+
+            with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                              return_value=None) as mock_sync:
+                darr.setitem(0, 10, stream=stream)
+
+            mock_sync.assert_not_called()
+
+    @unittest.skip('Requires PR #6367')
+    def test_issue_6505(self):
+        # On Windows, the writes to ary_v would not be visible prior to the
+        # assertion, due to the assignment being done with a kernel launch that
+        # returns asynchronously - there should now be a sync after the kernel
+        # launch to ensure that the writes are always visible.
+        ary = cuda.mapped_array(2, dtype=np.int32)
+        ary[:] = 0
+
+        ary_v = ary.view('u1')
+        ary_v[1] = 1
+        ary_v[5] = 1
+        self.assertEqual(sum(ary), 512)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_auto_context.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_auto_context.py
new file mode 100644
index 000000000..4a4d59310
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_auto_context.py
@@ -0,0 +1,21 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaAutoContext(CUDATestCase):
+    def test_auto_context(self):
+        """A problem was revealed by a customer that the use cuda.to_device
+        does not create a CUDA context.
+        This tests the problem
+        """
+        A = np.arange(10, dtype=np.float32)
+        newA = np.empty_like(A)
+        dA = cuda.to_device(A)
+
+        dA.copy_to_host(newA)
+        self.assertTrue(np.allclose(A, newA))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_devicerecord.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_devicerecord.py
new file mode 100644
index 000000000..e2acd34d7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_devicerecord.py
@@ -0,0 +1,179 @@
+import numpy as np
+import ctypes
+from numba.cuda.cudadrv.devicearray import (DeviceRecord, from_record_like,
+                                            auto_device)
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import skip_on_cudasim
+from numba.np import numpy_support
+from numba import cuda
+
+N_CHARS = 5
+
+recordtype = np.dtype(
+    [
+        ('a', np.float64),
+        ('b', np.int32),
+        ('c', np.complex64),
+        ('d', (np.str_, N_CHARS))
+    ],
+    align=True
+)
+
+recordwitharray = np.dtype(
+    [
+        ('g', np.int32),
+        ('h', np.float32, 2)
+    ],
+    align=True
+)
+
+recwithmat = np.dtype([('i', np.int32),
+                       ('j', np.float32, (3, 3))])
+
+recwithrecwithmat = np.dtype([('x', np.int32), ('y', recwithmat)])
+
+
+@skip_on_cudasim('Device Record API unsupported in the simulator')
+class TestCudaDeviceRecord(CUDATestCase):
+    """
+    Tests the DeviceRecord class with np.void host types.
+    """
+    def setUp(self):
+        super().setUp()
+        self._create_data(np.zeros)
+
+    def _create_data(self, array_ctor):
+        self.dtype = np.dtype([('a', np.int32), ('b', np.float32)], align=True)
+        self.hostz = array_ctor(1, self.dtype)[0]
+        self.hostnz = array_ctor(1, self.dtype)[0]
+        self.hostnz['a'] = 10
+        self.hostnz['b'] = 11.0
+
+    def _check_device_record(self, reference, rec):
+        self.assertEqual(rec.shape, tuple())
+        self.assertEqual(rec.strides, tuple())
+        self.assertEqual(rec.dtype, reference.dtype)
+        self.assertEqual(rec.alloc_size, reference.dtype.itemsize)
+        self.assertIsNotNone(rec.gpu_data)
+        self.assertNotEqual(rec.device_ctypes_pointer, ctypes.c_void_p(0))
+
+        numba_type = numpy_support.from_dtype(reference.dtype)
+        self.assertEqual(rec._numba_type_, numba_type)
+
+    def test_device_record_interface(self):
+        hostrec = self.hostz.copy()
+        devrec = DeviceRecord(self.dtype)
+        self._check_device_record(hostrec, devrec)
+
+    def test_device_record_copy(self):
+        hostrec = self.hostz.copy()
+        devrec = DeviceRecord(self.dtype)
+        devrec.copy_to_device(hostrec)
+
+        # Copy back and check values are all zeros
+        hostrec2 = self.hostnz.copy()
+        devrec.copy_to_host(hostrec2)
+        np.testing.assert_equal(self.hostz, hostrec2)
+
+        # Copy non-zero values to GPU and back and check values
+        hostrec3 = self.hostnz.copy()
+        devrec.copy_to_device(hostrec3)
+
+        hostrec4 = self.hostz.copy()
+        devrec.copy_to_host(hostrec4)
+        np.testing.assert_equal(hostrec4, self.hostnz)
+
+    def test_from_record_like(self):
+        # Create record from host record
+        hostrec = self.hostz.copy()
+        devrec = from_record_like(hostrec)
+        self._check_device_record(hostrec, devrec)
+
+        # Create record from device record and check for distinct data
+        devrec2 = from_record_like(devrec)
+        self._check_device_record(devrec, devrec2)
+        self.assertNotEqual(devrec.gpu_data, devrec2.gpu_data)
+
+    def test_auto_device(self):
+        # Create record from host record
+        hostrec = self.hostnz.copy()
+        devrec, new_gpu_obj = auto_device(hostrec)
+        self._check_device_record(hostrec, devrec)
+        self.assertTrue(new_gpu_obj)
+
+        # Copy data back and check it is equal to auto_device arg
+        hostrec2 = self.hostz.copy()
+        devrec.copy_to_host(hostrec2)
+        np.testing.assert_equal(hostrec2, hostrec)
+
+
+class TestCudaDeviceRecordWithRecord(TestCudaDeviceRecord):
+    """
+    Tests the DeviceRecord class with np.record host types
+    """
+    def setUp(self):
+        CUDATestCase.setUp(self)
+        self._create_data(np.recarray)
+
+
+@skip_on_cudasim('Structured array attr access not supported in simulator')
+class TestRecordDtypeWithStructArrays(CUDATestCase):
+    '''
+    Test operation of device arrays on structured arrays.
+    '''
+
+    def _createSampleArrays(self):
+        self.sample1d = cuda.device_array(3, dtype=recordtype)
+        self.samplerec1darr = cuda.device_array(1, dtype=recordwitharray)[0]
+        self.samplerecmat = cuda.device_array(1,dtype=recwithmat)[0]
+
+    def setUp(self):
+        super().setUp()
+        self._createSampleArrays()
+
+        ary = self.sample1d
+        for i in range(ary.size):
+            x = i + 1
+            ary[i]['a'] = x / 2
+            ary[i]['b'] = x
+            ary[i]['c'] = x * 1j
+            ary[i]['d'] = str(x) * N_CHARS
+
+    def test_structured_array1(self):
+        ary = self.sample1d
+        for i in range(self.sample1d.size):
+            x = i + 1
+            self.assertEqual(ary[i]['a'], x / 2)
+            self.assertEqual(ary[i]['b'], x)
+            self.assertEqual(ary[i]['c'], x * 1j)
+            self.assertEqual(ary[i]['d'], str(x) * N_CHARS)
+
+    def test_structured_array2(self):
+        ary = self.samplerec1darr
+        ary['g'] = 2
+        ary['h'][0] = 3.0
+        ary['h'][1] = 4.0
+        self.assertEqual(ary['g'], 2)
+        self.assertEqual(ary['h'][0], 3.0)
+        self.assertEqual(ary['h'][1], 4.0)
+
+    def test_structured_array3(self):
+        ary = self.samplerecmat
+        mat = np.array([[5.0, 10.0, 15.0],
+                       [20.0, 25.0, 30.0],
+                       [35.0, 40.0, 45.0]],
+                       dtype=np.float32).reshape(3,3)
+        ary['j'][:] = mat
+        np.testing.assert_equal(ary['j'], mat)
+
+    def test_structured_array4(self):
+        arr = np.zeros(1, dtype=recwithrecwithmat)
+        d_arr = cuda.to_device(arr)
+        d_arr[0]['y']['i'] = 1
+        self.assertEqual(d_arr[0]['y']['i'], 1)
+        d_arr[0]['y']['j'][0, 0] = 2.0
+        self.assertEqual(d_arr[0]['y']['j'][0, 0], 2.0)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_driver.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_driver.py
new file mode 100644
index 000000000..ea9d72fa8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_driver.py
@@ -0,0 +1,235 @@
+from ctypes import byref, c_int, c_void_p, sizeof
+
+from numba.cuda.cudadrv.driver import (host_to_device, device_to_host, driver,
+                                       launch_kernel)
+from numba.cuda.cudadrv import devices, drvapi, driver as _driver
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import skip_on_cudasim
+
+
+ptx1 = '''
+    .version 1.4
+    .target sm_10, map_f64_to_f32
+
+    .entry _Z10helloworldPi (
+    .param .u64 __cudaparm__Z10helloworldPi_A)
+    {
+    .reg .u32 %r<3>;
+    .reg .u64 %rd<6>;
+    .loc	14	4	0
+$LDWbegin__Z10helloworldPi:
+    .loc	14	6	0
+    cvt.s32.u16 	%r1, %tid.x;
+    ld.param.u64 	%rd1, [__cudaparm__Z10helloworldPi_A];
+    cvt.u64.u16 	%rd2, %tid.x;
+    mul.lo.u64 	%rd3, %rd2, 4;
+    add.u64 	%rd4, %rd1, %rd3;
+    st.global.s32 	[%rd4+0], %r1;
+    .loc	14	7	0
+    exit;
+$LDWend__Z10helloworldPi:
+    } // _Z10helloworldPi
+'''
+
+ptx2 = '''
+.version 3.0
+.target sm_20
+.address_size 64
+
+    .file	1 "/tmp/tmpxft_000012c7_00000000-9_testcuda.cpp3.i"
+    .file	2 "testcuda.cu"
+
+.entry _Z10helloworldPi(
+    .param .u64 _Z10helloworldPi_param_0
+)
+{
+    .reg .s32 	%r<3>;
+    .reg .s64 	%rl<5>;
+
+
+    ld.param.u64 	%rl1, [_Z10helloworldPi_param_0];
+    cvta.to.global.u64 	%rl2, %rl1;
+    .loc 2 6 1
+    mov.u32 	%r1, %tid.x;
+    mul.wide.u32 	%rl3, %r1, 4;
+    add.s64 	%rl4, %rl2, %rl3;
+    st.global.u32 	[%rl4], %r1;
+    .loc 2 7 2
+    ret;
+}
+'''
+
+
+@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
+class TestCudaDriver(CUDATestCase):
+    def setUp(self):
+        super().setUp()
+        self.assertTrue(len(devices.gpus) > 0)
+        self.context = devices.get_context()
+        device = self.context.device
+        ccmajor, _ = device.compute_capability
+        if ccmajor >= 2:
+            self.ptx = ptx2
+        else:
+            self.ptx = ptx1
+
+    def tearDown(self):
+        super().tearDown()
+        del self.context
+
+    def test_cuda_driver_basic(self):
+        module = self.context.create_module_ptx(self.ptx)
+        function = module.get_function('_Z10helloworldPi')
+
+        array = (c_int * 100)()
+
+        memory = self.context.memalloc(sizeof(array))
+        host_to_device(memory, array, sizeof(array))
+
+        ptr = memory.device_ctypes_pointer
+        stream = 0
+
+        if _driver.USE_NV_BINDING:
+            ptr = c_void_p(int(ptr))
+            stream = _driver.binding.CUstream(stream)
+
+        launch_kernel(function.handle,  # Kernel
+                      1,   1, 1,        # gx, gy, gz
+                      100, 1, 1,        # bx, by, bz
+                      0,                # dynamic shared mem
+                      stream,           # stream
+                      [ptr])            # arguments
+
+        device_to_host(array, memory, sizeof(array))
+        for i, v in enumerate(array):
+            self.assertEqual(i, v)
+
+        module.unload()
+
+    def test_cuda_driver_stream_operations(self):
+        module = self.context.create_module_ptx(self.ptx)
+        function = module.get_function('_Z10helloworldPi')
+
+        array = (c_int * 100)()
+
+        stream = self.context.create_stream()
+
+        with stream.auto_synchronize():
+            memory = self.context.memalloc(sizeof(array))
+            host_to_device(memory, array, sizeof(array), stream=stream)
+
+            ptr = memory.device_ctypes_pointer
+            if _driver.USE_NV_BINDING:
+                ptr = c_void_p(int(ptr))
+
+            launch_kernel(function.handle,  # Kernel
+                          1,   1, 1,        # gx, gy, gz
+                          100, 1, 1,        # bx, by, bz
+                          0,                # dynamic shared mem
+                          stream.handle,    # stream
+                          [ptr])            # arguments
+
+        device_to_host(array, memory, sizeof(array), stream=stream)
+
+        for i, v in enumerate(array):
+            self.assertEqual(i, v)
+
+    def test_cuda_driver_default_stream(self):
+        # Test properties of the default stream
+        ds = self.context.get_default_stream()
+        self.assertIn("Default CUDA stream", repr(ds))
+        self.assertEqual(0, int(ds))
+        # bool(stream) is the check that is done in memcpy to decide if async
+        # version should be used. So the default (0) stream should be true-ish
+        # even though 0 is usually false-ish in Python.
+        self.assertTrue(ds)
+        self.assertFalse(ds.external)
+
+    def test_cuda_driver_legacy_default_stream(self):
+        # Test properties of the legacy default stream
+        ds = self.context.get_legacy_default_stream()
+        self.assertIn("Legacy default CUDA stream", repr(ds))
+        self.assertEqual(1, int(ds))
+        self.assertTrue(ds)
+        self.assertFalse(ds.external)
+
+    def test_cuda_driver_per_thread_default_stream(self):
+        # Test properties of the per-thread default stream
+        ds = self.context.get_per_thread_default_stream()
+        self.assertIn("Per-thread default CUDA stream", repr(ds))
+        self.assertEqual(2, int(ds))
+        self.assertTrue(ds)
+        self.assertFalse(ds.external)
+
+    def test_cuda_driver_stream(self):
+        # Test properties of non-default streams
+        s = self.context.create_stream()
+        self.assertIn("CUDA stream", repr(s))
+        self.assertNotIn("Default", repr(s))
+        self.assertNotIn("External", repr(s))
+        self.assertNotEqual(0, int(s))
+        self.assertTrue(s)
+        self.assertFalse(s.external)
+
+    def test_cuda_driver_external_stream(self):
+        # Test properties of a stream created from an external stream object.
+        # We use the driver API directly to create a stream, to emulate an
+        # external library creating a stream
+        if _driver.USE_NV_BINDING:
+            handle = driver.cuStreamCreate(0)
+            ptr = int(handle)
+        else:
+            handle = drvapi.cu_stream()
+            driver.cuStreamCreate(byref(handle), 0)
+            ptr = handle.value
+        s = self.context.create_external_stream(ptr)
+
+        self.assertIn("External CUDA stream", repr(s))
+        # Ensure neither "Default" nor "default"
+        self.assertNotIn("efault", repr(s))
+        self.assertEqual(ptr, int(s))
+        self.assertTrue(s)
+        self.assertTrue(s.external)
+
+    def test_cuda_driver_occupancy(self):
+        module = self.context.create_module_ptx(self.ptx)
+        function = module.get_function('_Z10helloworldPi')
+
+        value = self.context.get_active_blocks_per_multiprocessor(function,
+                                                                  128, 128)
+        self.assertTrue(value > 0)
+
+        def b2d(bs):
+            return bs
+
+        grid, block = self.context.get_max_potential_block_size(function, b2d,
+                                                                128, 128)
+        self.assertTrue(grid > 0)
+        self.assertTrue(block > 0)
+
+
+class TestDevice(CUDATestCase):
+    def test_device_get_uuid(self):
+        # A device UUID looks like:
+        #
+        #     GPU-e6489c45-5b68-3b03-bab7-0e7c8e809643
+        #
+        # To test, we construct an RE that matches this form and verify that
+        # the returned UUID matches.
+        #
+        # Device UUIDs may not conform to parts of the UUID specification (RFC
+        # 4122) pertaining to versions and variants, so we do not extract and
+        # validate the values of these bits.
+
+        h = '[0-9a-f]{%d}'
+        h4 = h % 4
+        h8 = h % 8
+        h12 = h % 12
+        uuid_format = f'^GPU-{h8}-{h4}-{h4}-{h4}-{h12}$'
+
+        dev = devices.get_context().device
+        self.assertRegex(dev.uuid, uuid_format)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_libraries.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_libraries.py
new file mode 100644
index 000000000..890bf6829
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_libraries.py
@@ -0,0 +1,22 @@
+from numba.cuda.testing import unittest
+from numba.cuda.testing import skip_on_cudasim, skip_unless_conda_cudatoolkit
+from numba.misc.findlib import find_lib
+
+
+@skip_on_cudasim('Library detection unsupported in the simulator')
+@skip_unless_conda_cudatoolkit
+class TestLibraryDetection(unittest.TestCase):
+    def test_detect(self):
+        """
+        This test is solely present to ensure that shipped cudatoolkits have
+        additional core libraries in locations that Numba scans by default.
+        PyCulib (and potentially others) rely on Numba's library finding
+        capacity to find and subsequently load these libraries.
+        """
+        core_libs = ['nvvm']
+        for l in core_libs:
+            self.assertNotEqual(find_lib(l), [])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_memory.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_memory.py
new file mode 100644
index 000000000..6402f7773
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_memory.py
@@ -0,0 +1,193 @@
+import ctypes
+
+import numpy as np
+
+from numba.cuda.cudadrv import driver, drvapi, devices
+from numba.cuda.testing import unittest, ContextResettingTestCase
+from numba.cuda.testing import skip_on_cudasim
+
+
+@skip_on_cudasim('CUDA Memory API unsupported in the simulator')
+class TestCudaMemory(ContextResettingTestCase):
+    def setUp(self):
+        super().setUp()
+        self.context = devices.get_context()
+
+    def tearDown(self):
+        del self.context
+        super(TestCudaMemory, self).tearDown()
+
+    def _template(self, obj):
+        self.assertTrue(driver.is_device_memory(obj))
+        driver.require_device_memory(obj)
+        if driver.USE_NV_BINDING:
+            expected_class = driver.binding.CUdeviceptr
+        else:
+            expected_class = drvapi.cu_device_ptr
+        self.assertTrue(isinstance(obj.device_ctypes_pointer,
+                                   expected_class))
+
+    def test_device_memory(self):
+        devmem = self.context.memalloc(1024)
+        self._template(devmem)
+
+    def test_device_view(self):
+        devmem = self.context.memalloc(1024)
+        self._template(devmem.view(10))
+
+    def test_host_alloc(self):
+        devmem = self.context.memhostalloc(1024, mapped=True)
+        self._template(devmem)
+
+    def test_pinned_memory(self):
+        ary = np.arange(10)
+        devmem = self.context.mempin(ary, ary.ctypes.data,
+                                     ary.size * ary.dtype.itemsize,
+                                     mapped=True)
+        self._template(devmem)
+
+    def test_managed_memory(self):
+        devmem = self.context.memallocmanaged(1024)
+        self._template(devmem)
+
+    def test_derived_pointer(self):
+        # Use MemoryPointer.view to create derived pointer
+
+        def handle_val(mem):
+            if driver.USE_NV_BINDING:
+                return int(mem.handle)
+            else:
+                return mem.handle.value
+
+        def check(m, offset):
+            # create view
+            v1 = m.view(offset)
+            self.assertEqual(handle_val(v1.owner), handle_val(m))
+            self.assertEqual(m.refct, 2)
+            self.assertEqual(handle_val(v1) - offset, handle_val(v1.owner))
+            # create a view
+            v2 = v1.view(offset)
+            self.assertEqual(handle_val(v2.owner), handle_val(m))
+            self.assertEqual(handle_val(v2.owner), handle_val(m))
+            self.assertEqual(handle_val(v2) - offset * 2,
+                             handle_val(v2.owner))
+            self.assertEqual(m.refct, 3)
+            del v2
+            self.assertEqual(m.refct, 2)
+            del v1
+            self.assertEqual(m.refct, 1)
+
+        m = self.context.memalloc(1024)
+        check(m=m, offset=0)
+        check(m=m, offset=1)
+
+    def test_user_extension(self):
+        # User can use MemoryPointer to wrap externally defined pointers.
+        # This test checks if the finalizer is invokded at correct time
+        fake_ptr = ctypes.c_void_p(0xdeadbeef)
+        dtor_invoked = [0]
+
+        def dtor():
+            dtor_invoked[0] += 1
+
+        # Ensure finalizer is called when pointer is deleted
+        ptr = driver.MemoryPointer(context=self.context, pointer=fake_ptr,
+                                   size=40, finalizer=dtor)
+        self.assertEqual(dtor_invoked[0], 0)
+        del ptr
+        self.assertEqual(dtor_invoked[0], 1)
+
+        # Ensure removing derived pointer doesn't call finalizer
+        ptr = driver.MemoryPointer(context=self.context, pointer=fake_ptr,
+                                   size=40, finalizer=dtor)
+        owned = ptr.own()
+        del owned
+        self.assertEqual(dtor_invoked[0], 1)
+        del ptr
+        self.assertEqual(dtor_invoked[0], 2)
+
+
+class TestCudaMemoryFunctions(ContextResettingTestCase):
+    def setUp(self):
+        super().setUp()
+        self.context = devices.get_context()
+
+    def tearDown(self):
+        del self.context
+        super(TestCudaMemoryFunctions, self).tearDown()
+
+    def test_memcpy(self):
+        hstary = np.arange(100, dtype=np.uint32)
+        hstary2 = np.arange(100, dtype=np.uint32)
+        sz = hstary.size * hstary.dtype.itemsize
+        devary = self.context.memalloc(sz)
+
+        driver.host_to_device(devary, hstary, sz)
+        driver.device_to_host(hstary2, devary, sz)
+
+        self.assertTrue(np.all(hstary == hstary2))
+
+    def test_memset(self):
+        dtype = np.dtype('uint32')
+        n = 10
+        sz = dtype.itemsize * 10
+        devary = self.context.memalloc(sz)
+        driver.device_memset(devary, 0xab, sz)
+
+        hstary = np.empty(n, dtype=dtype)
+        driver.device_to_host(hstary, devary, sz)
+
+        hstary2 = np.array([0xabababab] * n, dtype=np.dtype('uint32'))
+        self.assertTrue(np.all(hstary == hstary2))
+
+    def test_d2d(self):
+        hst = np.arange(100, dtype=np.uint32)
+        hst2 = np.empty_like(hst)
+        sz = hst.size * hst.dtype.itemsize
+        dev1 = self.context.memalloc(sz)
+        dev2 = self.context.memalloc(sz)
+        driver.host_to_device(dev1, hst, sz)
+        driver.device_to_device(dev2, dev1, sz)
+        driver.device_to_host(hst2, dev2, sz)
+        self.assertTrue(np.all(hst == hst2))
+
+
+@skip_on_cudasim('CUDA Memory API unsupported in the simulator')
+class TestMVExtent(ContextResettingTestCase):
+    def test_c_contiguous_array(self):
+        ary = np.arange(100)
+        arysz = ary.dtype.itemsize * ary.size
+        s, e = driver.host_memory_extents(ary)
+        self.assertTrue(ary.ctypes.data == s)
+        self.assertTrue(arysz == driver.host_memory_size(ary))
+
+    def test_f_contiguous_array(self):
+        ary = np.asfortranarray(np.arange(100).reshape(2, 50))
+        arysz = ary.dtype.itemsize * np.prod(ary.shape)
+        s, e = driver.host_memory_extents(ary)
+        self.assertTrue(ary.ctypes.data == s)
+        self.assertTrue(arysz == driver.host_memory_size(ary))
+
+    def test_single_element_array(self):
+        ary = np.asarray(np.uint32(1234))
+        arysz = ary.dtype.itemsize
+        s, e = driver.host_memory_extents(ary)
+        self.assertTrue(ary.ctypes.data == s)
+        self.assertTrue(arysz == driver.host_memory_size(ary))
+
+    def test_ctypes_struct(self):
+        class mystruct(ctypes.Structure):
+            _fields_ = [('x', ctypes.c_int), ('y', ctypes.c_int)]
+
+        data = mystruct(x=123, y=432)
+        sz = driver.host_memory_size(data)
+        self.assertTrue(ctypes.sizeof(data) == sz)
+
+    def test_ctypes_double(self):
+        data = ctypes.c_double(1.234)
+        sz = driver.host_memory_size(data)
+        self.assertTrue(ctypes.sizeof(data) == sz)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_ndarray.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_ndarray.py
new file mode 100644
index 000000000..1e674df7a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_cuda_ndarray.py
@@ -0,0 +1,547 @@
+import itertools
+import numpy as np
+from numba.cuda.cudadrv import devicearray
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import skip_on_cudasim
+
+
+class TestCudaNDArray(CUDATestCase):
+    def test_device_array_interface(self):
+        dary = cuda.device_array(shape=100)
+        devicearray.verify_cuda_ndarray_interface(dary)
+
+        ary = np.empty(100)
+        dary = cuda.to_device(ary)
+        devicearray.verify_cuda_ndarray_interface(dary)
+
+        ary = np.asarray(1.234)
+        dary = cuda.to_device(ary)
+        self.assertEquals(dary.ndim, 0)
+        devicearray.verify_cuda_ndarray_interface(dary)
+
+    def test_device_array_from_readonly(self):
+        ary = np.arange(100, dtype=np.float32)
+        # Make the array readonly
+        ary.flags.writeable = False
+        self.assertFalse(ary.flags.writeable)
+        # Ensure that we can copy the readonly array
+        dary = cuda.to_device(ary)
+        retr = dary.copy_to_host()
+        np.testing.assert_array_equal(retr, ary)
+
+    def test_devicearray_dtype(self):
+        dary = cuda.device_array(shape=(100,), dtype="f4")
+        self.assertEqual(dary.dtype, np.dtype("f4"))
+
+    def test_devicearray_no_copy(self):
+        array = np.arange(100, dtype=np.float32)
+        cuda.to_device(array, copy=False)
+
+    def test_devicearray_shape(self):
+        ary = np.arange(2 * 3 * 4).reshape(2, 3, 4)
+        dary = cuda.to_device(ary)
+        self.assertEquals(ary.shape, dary.shape)
+        self.assertEquals(ary.shape[1:], dary.shape[1:])
+
+    def test_devicearray(self):
+        array = np.arange(100, dtype=np.int32)
+        original = array.copy()
+        gpumem = cuda.to_device(array)
+        array[:] = 0
+        gpumem.copy_to_host(array)
+
+        np.testing.assert_array_equal(array, original)
+
+    def test_stream_bind(self):
+        stream = cuda.stream()
+        with stream.auto_synchronize():
+            arr = cuda.device_array(
+                (3, 3),
+                dtype=np.float64,
+                stream=stream)
+            self.assertEqual(arr.bind(stream).stream, stream)
+            self.assertEqual(arr.stream, stream)
+
+    def test_len_1d(self):
+        ary = np.empty((3,))
+        dary = cuda.device_array(3)
+        self.assertEqual(len(ary), len(dary))
+
+    def test_len_2d(self):
+        ary = np.empty((3, 5))
+        dary = cuda.device_array((3, 5))
+        self.assertEqual(len(ary), len(dary))
+
+    def test_len_3d(self):
+        ary = np.empty((3, 5, 7))
+        dary = cuda.device_array((3, 5, 7))
+        self.assertEqual(len(ary), len(dary))
+
+    def test_devicearray_partition(self):
+        N = 100
+        array = np.arange(N, dtype=np.int32)
+        original = array.copy()
+        gpumem = cuda.to_device(array)
+        left, right = gpumem.split(N // 2)
+
+        array[:] = 0
+
+        self.assertTrue(np.all(array == 0))
+
+        right.copy_to_host(array[N // 2:])
+        left.copy_to_host(array[:N // 2])
+
+        self.assertTrue(np.all(array == original))
+
+    def test_devicearray_replace(self):
+        N = 100
+        array = np.arange(N, dtype=np.int32)
+        original = array.copy()
+        gpumem = cuda.to_device(array)
+        cuda.to_device(array * 2, to=gpumem)
+        gpumem.copy_to_host(array)
+        np.testing.assert_array_equal(array, original * 2)
+
+    @skip_on_cudasim('This works in the simulator')
+    def test_devicearray_transpose_wrongdim(self):
+        gpumem = cuda.to_device(np.array(np.arange(12)).reshape(3, 4, 1))
+
+        with self.assertRaises(NotImplementedError) as e:
+            np.transpose(gpumem)
+
+        self.assertEqual(
+            "transposing a non-2D DeviceNDArray isn't supported",
+            str(e.exception))
+
+    def test_devicearray_transpose_identity(self):
+        # any-shape identities should work
+        original = np.array(np.arange(24)).reshape(3, 4, 2)
+        array = np.transpose(cuda.to_device(original),
+                             axes=(0, 1, 2)).copy_to_host()
+        self.assertTrue(np.all(array == original))
+
+    def test_devicearray_transpose_duplicatedaxis(self):
+        gpumem = cuda.to_device(np.array(np.arange(12)).reshape(3, 4))
+
+        with self.assertRaises(ValueError) as e:
+            np.transpose(gpumem, axes=(0, 0))
+
+        self.assertIn(
+            str(e.exception),
+            container=[
+                'invalid axes list (0, 0)',  # GPU
+                'repeated axis in transpose',  # sim
+            ])
+
+    def test_devicearray_transpose_wrongaxis(self):
+        gpumem = cuda.to_device(np.array(np.arange(12)).reshape(3, 4))
+
+        with self.assertRaises(ValueError) as e:
+            np.transpose(gpumem, axes=(0, 2))
+
+        self.assertIn(
+            str(e.exception),
+            container=[
+                'invalid axes list (0, 2)',  # GPU
+                'invalid axis for this array',
+                'axis 2 is out of bounds for array of dimension 2',  # sim
+            ])
+
+    def test_devicearray_view_ok(self):
+        original = np.array(np.arange(12), dtype="i2").reshape(3, 4)
+        array = cuda.to_device(original)
+        for dtype in ("i4", "u4", "i8", "f8"):
+            with self.subTest(dtype=dtype):
+                np.testing.assert_array_equal(
+                    array.view(dtype).copy_to_host(),
+                    original.view(dtype)
+                )
+
+    def test_devicearray_view_ok_not_c_contig(self):
+        original = np.array(np.arange(32), dtype="i2").reshape(4, 8)
+        array = cuda.to_device(original)[:, ::2]
+        original = original[:, ::2]
+        np.testing.assert_array_equal(
+            array.view("u2").copy_to_host(),
+            original.view("u2")
+        )
+
+    def test_devicearray_view_bad_not_c_contig(self):
+        original = np.array(np.arange(32), dtype="i2").reshape(4, 8)
+        array = cuda.to_device(original)[:, ::2]
+        with self.assertRaises(ValueError) as e:
+            array.view("i4")
+
+        msg = str(e.exception)
+        self.assertIn('To change to a dtype of a different size,', msg)
+
+        contiguous_pre_np123 = 'the array must be C-contiguous' in msg
+        contiguous_post_np123 = 'the last axis must be contiguous' in msg
+        self.assertTrue(contiguous_pre_np123 or contiguous_post_np123,
+                        'Expected message to mention contiguity')
+
+    def test_devicearray_view_bad_itemsize(self):
+        original = np.array(np.arange(12), dtype="i2").reshape(4, 3)
+        array = cuda.to_device(original)
+        with self.assertRaises(ValueError) as e:
+            array.view("i4")
+        self.assertEqual(
+            "When changing to a larger dtype,"
+            " its size must be a divisor of the total size in bytes"
+            " of the last axis of the array.",
+            str(e.exception))
+
+    def test_devicearray_transpose_ok(self):
+        original = np.array(np.arange(12)).reshape(3, 4)
+        array = np.transpose(cuda.to_device(original)).copy_to_host()
+        self.assertTrue(np.all(array == original.T))
+
+    def test_devicearray_transpose_T(self):
+        original = np.array(np.arange(12)).reshape(3, 4)
+        array = cuda.to_device(original).T.copy_to_host()
+        self.assertTrue(np.all(array == original.T))
+
+    def test_devicearray_contiguous_slice(self):
+        # memcpys are dumb ranges of bytes, so trying to
+        # copy to a non-contiguous range shouldn't work!
+        a = np.arange(25).reshape(5, 5, order='F')
+        s = np.full(fill_value=5, shape=(5,))
+
+        d = cuda.to_device(a)
+        a[2] = s
+
+        # d is in F-order (not C-order), so d[2] is not contiguous
+        # (40-byte strides). This means we can't memcpy to it!
+        with self.assertRaises(ValueError) as e:
+            d[2].copy_to_device(s)
+        self.assertEqual(
+            devicearray.errmsg_contiguous_buffer,
+            str(e.exception))
+
+        # if d[2].copy_to_device(s), then this would pass:
+        # self.assertTrue((a == d.copy_to_host()).all())
+
+    def _test_devicearray_contiguous_host_copy(self, a_c, a_f):
+        """
+        Checks host->device memcpys
+        """
+        self.assertTrue(a_c.flags.c_contiguous)
+        self.assertTrue(a_f.flags.f_contiguous)
+
+        for original, copy in [
+            (a_f, a_f),
+            (a_f, a_c),
+            (a_c, a_f),
+            (a_c, a_c),
+        ]:
+            msg = '%s => %s' % (
+                'C' if original.flags.c_contiguous else 'F',
+                'C' if copy.flags.c_contiguous else 'F',
+            )
+
+            d = cuda.to_device(original)
+            d.copy_to_device(copy)
+            self.assertTrue(np.all(d.copy_to_host() == a_c), msg=msg)
+            self.assertTrue(np.all(d.copy_to_host() == a_f), msg=msg)
+
+    def test_devicearray_contiguous_copy_host_3d(self):
+        a_c = np.arange(5 * 5 * 5).reshape(5, 5, 5)
+        a_f = np.array(a_c, order='F')
+        self._test_devicearray_contiguous_host_copy(a_c, a_f)
+
+    def test_devicearray_contiguous_copy_host_1d(self):
+        a_c = np.arange(5)
+        a_f = np.array(a_c, order='F')
+        self._test_devicearray_contiguous_host_copy(a_c, a_f)
+
+    def test_devicearray_contiguous_copy_device(self):
+        a_c = np.arange(5 * 5 * 5).reshape(5, 5, 5)
+        a_f = np.array(a_c, order='F')
+        self.assertTrue(a_c.flags.c_contiguous)
+        self.assertTrue(a_f.flags.f_contiguous)
+
+        d = cuda.to_device(a_c)
+
+        with self.assertRaises(ValueError) as e:
+            d.copy_to_device(cuda.to_device(a_f))
+        self.assertEqual(
+            "incompatible strides: {} vs. {}".format(a_c.strides, a_f.strides),
+            str(e.exception))
+
+        d.copy_to_device(cuda.to_device(a_c))
+        self.assertTrue(np.all(d.copy_to_host() == a_c))
+
+        d = cuda.to_device(a_f)
+
+        with self.assertRaises(ValueError) as e:
+            d.copy_to_device(cuda.to_device(a_c))
+        self.assertEqual(
+            "incompatible strides: {} vs. {}".format(a_f.strides, a_c.strides),
+            str(e.exception))
+
+        d.copy_to_device(cuda.to_device(a_f))
+        self.assertTrue(np.all(d.copy_to_host() == a_f))
+
+    def test_devicearray_broadcast_host_copy(self):
+        broadsize = 4
+        coreshape = (2, 3)
+        coresize = np.prod(coreshape)
+        core_c = np.arange(coresize).reshape(coreshape, order='C')
+        core_f = np.arange(coresize).reshape(coreshape, order='F')
+        for dim in range(len(coreshape)):
+            newindex = (slice(None),) * dim + (np.newaxis,)
+            broadshape = coreshape[:dim] + (broadsize,) + coreshape[dim:]
+            broad_c = np.broadcast_to(core_c[newindex], broadshape)
+            broad_f = np.broadcast_to(core_f[newindex], broadshape)
+            dbroad_c = cuda.to_device(broad_c)
+            dbroad_f = cuda.to_device(broad_f)
+            np.testing.assert_array_equal(dbroad_c.copy_to_host(), broad_c)
+            np.testing.assert_array_equal(dbroad_f.copy_to_host(), broad_f)
+            # Also test copying across different core orderings
+            dbroad_c.copy_to_device(broad_f)
+            dbroad_f.copy_to_device(broad_c)
+            np.testing.assert_array_equal(dbroad_c.copy_to_host(), broad_f)
+            np.testing.assert_array_equal(dbroad_f.copy_to_host(), broad_c)
+
+    def test_devicearray_contiguous_host_strided(self):
+        a_c = np.arange(10)
+        d = cuda.to_device(a_c)
+        arr = np.arange(20)[::2]
+        d.copy_to_device(arr)
+        np.testing.assert_array_equal(d.copy_to_host(), arr)
+
+    def test_devicearray_contiguous_device_strided(self):
+        d = cuda.to_device(np.arange(20))
+        arr = np.arange(20)
+
+        with self.assertRaises(ValueError) as e:
+            d.copy_to_device(cuda.to_device(arr)[::2])
+        self.assertEqual(
+            devicearray.errmsg_contiguous_buffer,
+            str(e.exception))
+
+    @skip_on_cudasim('DeviceNDArray class not present in simulator')
+    def test_devicearray_relaxed_strides(self):
+        # From the reproducer in Issue #6824.
+
+        # Construct a device array that is contiguous even though
+        # the strides for the first axis (800) are not equal to
+        # the strides * size (10 * 8 = 80) for the previous axis,
+        # because the first axis size is 1.
+        arr = devicearray.DeviceNDArray((1, 10), (800, 8), np.float64)
+
+        # Ensure we still believe the array to be contiguous because
+        # strides checking is relaxed.
+        self.assertTrue(arr.flags['C_CONTIGUOUS'])
+        self.assertTrue(arr.flags['F_CONTIGUOUS'])
+
+    def test_c_f_contiguity_matches_numpy(self):
+        # From the reproducer in Issue #4943.
+
+        shapes = ((1, 4), (4, 1))
+        orders = ('C', 'F')
+
+        for shape, order in itertools.product(shapes, orders):
+            arr = np.ndarray(shape, order=order)
+            d_arr = cuda.to_device(arr)
+            self.assertEqual(arr.flags['C_CONTIGUOUS'],
+                             d_arr.flags['C_CONTIGUOUS'])
+            self.assertEqual(arr.flags['F_CONTIGUOUS'],
+                             d_arr.flags['F_CONTIGUOUS'])
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_simple_c(self):
+        # C-order 1D array
+        a = np.zeros(10, order='C')
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_simple_f(self):
+        # F-order array that is also C layout.
+        a = np.zeros(10, order='F')
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_2d_c(self):
+        # C-order 2D array
+        a = np.zeros((2, 10), order='C')
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_2d_f(self):
+        # F-order array that can only be F layout
+        a = np.zeros((2, 10), order='F')
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'F')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_noncontig_slice_c(self):
+        # Non-contiguous slice of C-order array
+        a = np.zeros((5, 5), order='C')
+        d = cuda.to_device(a)[:,2]
+        self.assertEqual(d._numba_type_.layout, 'A')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_noncontig_slice_f(self):
+        # Non-contiguous slice of F-order array
+        a = np.zeros((5, 5), order='F')
+        d = cuda.to_device(a)[2,:]
+        self.assertEqual(d._numba_type_.layout, 'A')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_contig_slice_c(self):
+        # Contiguous slice of C-order array
+        a = np.zeros((5, 5), order='C')
+        d = cuda.to_device(a)[2,:]
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_contig_slice_f(self):
+        # Contiguous slice of F-order array - is both C- and F-contiguous, so
+        # types as 'C' layout
+        a = np.zeros((5, 5), order='F')
+        d = cuda.to_device(a)[:,2]
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_broadcasted(self):
+        # Broadcasted array, similar to that used for passing scalars to ufuncs
+        a = np.broadcast_to(np.array([1]), (10,))
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'A')
+
+    def test_bug6697(self):
+        ary = np.arange(10, dtype=np.int16)
+        dary = cuda.to_device(ary)
+        got = np.asarray(dary)
+        self.assertEqual(got.dtype, dary.dtype)
+
+    @skip_on_cudasim('DeviceNDArray class not present in simulator')
+    def test_issue_8477(self):
+        # Ensure that we can copy a zero-length device array to a zero-length
+        # host array when the strides of the device and host arrays differ -
+        # this should be possible because the strides are irrelevant when the
+        # length is zero. For more info see
+        # https://github.com/numba/numba/issues/8477.
+
+        # Create a device array with shape (0,) and strides (8,)
+        dev_array = devicearray.DeviceNDArray(shape=(0,), strides=(8,),
+                                              dtype=np.int8)
+
+        # Create a host array with shape (0,) and strides (0,)
+        host_array = np.ndarray(shape=(0,), strides=(0,), dtype=np.int8)
+
+        # Sanity check for this test - ensure our destination has the strides
+        # we expect, because strides can be ignored in some cases by the
+        # ndarray constructor - checking here ensures that we haven't failed to
+        # account for unexpected behaviour across different versions of NumPy
+        self.assertEqual(host_array.strides, (0,))
+
+        # Ensure that the copy succeeds in both directions
+        dev_array.copy_to_host(host_array)
+        dev_array.copy_to_device(host_array)
+
+        # Ensure that a device-to-device copy also succeeds when the strides
+        # differ - one way of doing this is to copy the host array across and
+        # use that for copies in both directions.
+        dev_array_from_host = cuda.to_device(host_array)
+        self.assertEqual(dev_array_from_host.shape, (0,))
+        self.assertEqual(dev_array_from_host.strides, (0,))
+
+        dev_array.copy_to_device(dev_array_from_host)
+        dev_array_from_host.copy_to_device(dev_array)
+
+
+class TestRecarray(CUDATestCase):
+    def test_recarray(self):
+        # From issue #4111
+        a = np.recarray((16,), dtype=[
+            ("value1", np.int64),
+            ("value2", np.float64),
+        ])
+        a.value1 = np.arange(a.size, dtype=np.int64)
+        a.value2 = np.arange(a.size, dtype=np.float64) / 100
+
+        expect1 = a.value1
+        expect2 = a.value2
+
+        def test(x, out1, out2):
+            i = cuda.grid(1)
+            if i < x.size:
+                out1[i] = x.value1[i]
+                out2[i] = x.value2[i]
+
+        got1 = np.zeros_like(expect1)
+        got2 = np.zeros_like(expect2)
+        cuda.jit(test)[1, a.size](a, got1, got2)
+
+        np.testing.assert_array_equal(expect1, got1)
+        np.testing.assert_array_equal(expect2, got2)
+
+
+class TestCoreContiguous(CUDATestCase):
+    def _test_against_array_core(self, view):
+        self.assertEqual(
+            devicearray.is_contiguous(view),
+            devicearray.array_core(view).flags['C_CONTIGUOUS']
+        )
+
+    def test_device_array_like_1d(self):
+        d_a = cuda.device_array(10, order='C')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_2d(self):
+        d_a = cuda.device_array((10, 12), order='C')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_2d_transpose(self):
+        d_a = cuda.device_array((10, 12), order='C')
+        self._test_against_array_core(d_a.T)
+
+    def test_device_array_like_3d(self):
+        d_a = cuda.device_array((10, 12, 14), order='C')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_1d_f(self):
+        d_a = cuda.device_array(10, order='F')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_2d_f(self):
+        d_a = cuda.device_array((10, 12), order='F')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_2d_f_transpose(self):
+        d_a = cuda.device_array((10, 12), order='F')
+        self._test_against_array_core(d_a.T)
+
+    def test_device_array_like_3d_f(self):
+        d_a = cuda.device_array((10, 12, 14), order='F')
+        self._test_against_array_core(d_a)
+
+    def test_1d_view(self):
+        shape = 10
+        view = np.zeros(shape)[::2]
+        self._test_against_array_core(view)
+
+    def test_1d_view_f(self):
+        shape = 10
+        view = np.zeros(shape, order='F')[::2]
+        self._test_against_array_core(view)
+
+    def test_2d_view(self):
+        shape = (10, 12)
+        view = np.zeros(shape)[::2, ::2]
+        self._test_against_array_core(view)
+
+    def test_2d_view_f(self):
+        shape = (10, 12)
+        view = np.zeros(shape, order='F')[::2, ::2]
+        self._test_against_array_core(view)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_deallocations.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_deallocations.py
new file mode 100644
index 000000000..66fbbc372
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_deallocations.py
@@ -0,0 +1,249 @@
+from contextlib import contextmanager
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import (unittest, skip_on_cudasim,
+                                skip_if_external_memmgr, CUDATestCase)
+from numba.tests.support import captured_stderr
+from numba.core import config
+
+
+@skip_on_cudasim('not supported on CUDASIM')
+@skip_if_external_memmgr('Deallocation specific to Numba memory management')
+class TestDeallocation(CUDATestCase):
+    def test_max_pending_count(self):
+        # get deallocation manager and flush it
+        deallocs = cuda.current_context().memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        # deallocate to maximum count
+        for i in range(config.CUDA_DEALLOCS_COUNT):
+            cuda.to_device(np.arange(1))
+            self.assertEqual(len(deallocs), i + 1)
+        # one more to trigger .clear()
+        cuda.to_device(np.arange(1))
+        self.assertEqual(len(deallocs), 0)
+
+    def test_max_pending_bytes(self):
+        # get deallocation manager and flush it
+        ctx = cuda.current_context()
+        deallocs = ctx.memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+        mi = ctx.get_memory_info()
+
+        max_pending = 10**6  # 1MB
+        old_ratio = config.CUDA_DEALLOCS_RATIO
+        try:
+            # change to a smaller ratio
+            config.CUDA_DEALLOCS_RATIO = max_pending / mi.total
+            # due to round off error (floor is used in calculating
+            # _max_pending_bytes) it can be off by 1.
+            self.assertAlmostEqual(deallocs._max_pending_bytes, max_pending,
+                                   delta=1)
+
+            # allocate half the max size
+            # this will not trigger deallocation
+            cuda.to_device(np.ones(max_pending // 2, dtype=np.int8))
+            self.assertEqual(len(deallocs), 1)
+
+            # allocate another remaining
+            # this will not trigger deallocation
+            cuda.to_device(np.ones(deallocs._max_pending_bytes -
+                                   deallocs._size, dtype=np.int8))
+            self.assertEqual(len(deallocs), 2)
+
+            # another byte to trigger .clear()
+            cuda.to_device(np.ones(1, dtype=np.int8))
+            self.assertEqual(len(deallocs), 0)
+        finally:
+            # restore old ratio
+            config.CUDA_DEALLOCS_RATIO = old_ratio
+
+
+@skip_on_cudasim("defer_cleanup has no effect in CUDASIM")
+@skip_if_external_memmgr('Deallocation specific to Numba memory management')
+class TestDeferCleanup(CUDATestCase):
+    def test_basic(self):
+        harr = np.arange(5)
+        darr1 = cuda.to_device(harr)
+        deallocs = cuda.current_context().memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        with cuda.defer_cleanup():
+            darr2 = cuda.to_device(harr)
+            del darr1
+            self.assertEqual(len(deallocs), 1)
+            del darr2
+            self.assertEqual(len(deallocs), 2)
+            deallocs.clear()
+            self.assertEqual(len(deallocs), 2)
+
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+    def test_nested(self):
+        harr = np.arange(5)
+        darr1 = cuda.to_device(harr)
+        deallocs = cuda.current_context().memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        with cuda.defer_cleanup():
+            with cuda.defer_cleanup():
+                darr2 = cuda.to_device(harr)
+                del darr1
+                self.assertEqual(len(deallocs), 1)
+                del darr2
+                self.assertEqual(len(deallocs), 2)
+                deallocs.clear()
+                self.assertEqual(len(deallocs), 2)
+            deallocs.clear()
+            self.assertEqual(len(deallocs), 2)
+
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+    def test_exception(self):
+        harr = np.arange(5)
+        darr1 = cuda.to_device(harr)
+        deallocs = cuda.current_context().memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+        class CustomError(Exception):
+            pass
+
+        with self.assertRaises(CustomError):
+            with cuda.defer_cleanup():
+                darr2 = cuda.to_device(harr)
+                del darr2
+                self.assertEqual(len(deallocs), 1)
+                deallocs.clear()
+                self.assertEqual(len(deallocs), 1)
+                raise CustomError
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        del darr1
+        self.assertEqual(len(deallocs), 1)
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+
+class TestDeferCleanupAvail(CUDATestCase):
+    def test_context_manager(self):
+        # just make sure the API is available
+        with cuda.defer_cleanup():
+            pass
+
+
+@skip_on_cudasim('not supported on CUDASIM')
+class TestDel(CUDATestCase):
+    """
+    Ensure resources are deleted properly without ignored exception.
+    """
+    @contextmanager
+    def check_ignored_exception(self, ctx):
+        with captured_stderr() as cap:
+            yield
+            ctx.deallocations.clear()
+        self.assertFalse(cap.getvalue())
+
+    def test_stream(self):
+        ctx = cuda.current_context()
+        stream = ctx.create_stream()
+        with self.check_ignored_exception(ctx):
+            del stream
+
+    def test_event(self):
+        ctx = cuda.current_context()
+        event = ctx.create_event()
+        with self.check_ignored_exception(ctx):
+            del event
+
+    def test_pinned_memory(self):
+        ctx = cuda.current_context()
+        mem = ctx.memhostalloc(32)
+        with self.check_ignored_exception(ctx):
+            del mem
+
+    def test_mapped_memory(self):
+        ctx = cuda.current_context()
+        mem = ctx.memhostalloc(32, mapped=True)
+        with self.check_ignored_exception(ctx):
+            del mem
+
+    def test_device_memory(self):
+        ctx = cuda.current_context()
+        mem = ctx.memalloc(32)
+        with self.check_ignored_exception(ctx):
+            del mem
+
+    def test_managed_memory(self):
+        ctx = cuda.current_context()
+        mem = ctx.memallocmanaged(32)
+        with self.check_ignored_exception(ctx):
+            del mem
+
+    def test_pinned_contextmanager(self):
+        # Check that temporarily pinned memory is unregistered immediately,
+        # such that it can be re-pinned at any time
+        class PinnedException(Exception):
+            pass
+
+        arr = np.zeros(1)
+        ctx = cuda.current_context()
+        ctx.deallocations.clear()
+        with self.check_ignored_exception(ctx):
+            with cuda.pinned(arr):
+                pass
+            with cuda.pinned(arr):
+                pass
+            # Should also work inside a `defer_cleanup` block
+            with cuda.defer_cleanup():
+                with cuda.pinned(arr):
+                    pass
+                with cuda.pinned(arr):
+                    pass
+            # Should also work when breaking out of the block due to an
+            # exception
+            try:
+                with cuda.pinned(arr):
+                    raise PinnedException
+            except PinnedException:
+                with cuda.pinned(arr):
+                    pass
+
+    def test_mapped_contextmanager(self):
+        # Check that temporarily mapped memory is unregistered immediately,
+        # such that it can be re-mapped at any time
+        class MappedException(Exception):
+            pass
+
+        arr = np.zeros(1)
+        ctx = cuda.current_context()
+        ctx.deallocations.clear()
+        with self.check_ignored_exception(ctx):
+            with cuda.mapped(arr):
+                pass
+            with cuda.mapped(arr):
+                pass
+            # Should also work inside a `defer_cleanup` block
+            with cuda.defer_cleanup():
+                with cuda.mapped(arr):
+                    pass
+                with cuda.mapped(arr):
+                    pass
+            # Should also work when breaking out of the block due to an
+            # exception
+            try:
+                with cuda.mapped(arr):
+                    raise MappedException
+            except MappedException:
+                with cuda.mapped(arr):
+                    pass
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_detect.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_detect.py
new file mode 100644
index 000000000..528e11bf8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_detect.py
@@ -0,0 +1,81 @@
+import os
+import sys
+import subprocess
+import threading
+from numba import cuda
+from numba.cuda.testing import (unittest, CUDATestCase, skip_on_cudasim,
+                                skip_under_cuda_memcheck)
+from numba.tests.support import captured_stdout
+
+
+class TestCudaDetect(CUDATestCase):
+    def test_cuda_detect(self):
+        # exercise the code path
+        with captured_stdout() as out:
+            cuda.detect()
+        output = out.getvalue()
+        self.assertIn('Found', output)
+        self.assertIn('CUDA devices', output)
+
+
+@skip_under_cuda_memcheck('Hangs cuda-memcheck')
+class TestCUDAFindLibs(CUDATestCase):
+
+    def run_cmd(self, cmdline, env):
+        popen = subprocess.Popen(cmdline,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 env=env)
+
+        # finish in 5 minutes or kill it
+        timeout = threading.Timer(5 * 60., popen.kill)
+        try:
+            timeout.start()
+            out, err = popen.communicate()
+            # the process should exit with an error
+            return out.decode(), err.decode()
+        finally:
+            timeout.cancel()
+        return None, None
+
+    def run_test_in_separate_process(self, envvar, envvar_value):
+        env_copy = os.environ.copy()
+        env_copy[envvar] = str(envvar_value)
+        code = """if 1:
+            from numba import cuda
+            @cuda.jit('(int64,)')
+            def kernel(x):
+                pass
+            kernel(1,)
+            """
+        cmdline = [sys.executable, "-c", code]
+        return self.run_cmd(cmdline, env_copy)
+
+    @skip_on_cudasim('Simulator does not hit device library search code path')
+    @unittest.skipIf(not sys.platform.startswith('linux'), "linux only")
+    def test_cuda_find_lib_errors(self):
+        """
+        This tests that the find_libs works as expected in the case of an
+        environment variable being used to set the path.
+        """
+        # one of these is likely to exist on linux, it's also unlikely that
+        # someone has extracted the contents of libdevice into here!
+        locs = ['lib', 'lib64']
+
+        looking_for = None
+        for l in locs:
+            looking_for = os.path.join(os.path.sep, l)
+            if os.path.exists(looking_for):
+                break
+
+        # This is the testing part, the test will only run if there's a valid
+        # path in which to look
+        if looking_for is not None:
+            out, err = self.run_test_in_separate_process("NUMBA_CUDA_DRIVER",
+                                                         looking_for)
+            self.assertTrue(out is not None)
+            self.assertTrue(err is not None)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_emm_plugins.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_emm_plugins.py
new file mode 100644
index 000000000..209355ed6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_emm_plugins.py
@@ -0,0 +1,192 @@
+import ctypes
+import numpy as np
+import weakref
+
+from numba import cuda
+from numba.core import config
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.tests.support import linux_only
+
+if not config.ENABLE_CUDASIM:
+    class DeviceOnlyEMMPlugin(cuda.HostOnlyCUDAMemoryManager):
+        """
+        Dummy EMM Plugin implementation for testing. It memorises which plugin
+        API methods have been called so that the tests can check that Numba
+        called into the plugin as expected.
+        """
+
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+
+            # For tracking our dummy allocations
+            self.allocations = {}
+            self.count = 0
+
+            # For tracking which methods have been called
+            self.initialized = False
+            self.memalloc_called = False
+            self.reset_called = False
+            self.get_memory_info_called = False
+            self.get_ipc_handle_called = False
+
+        def memalloc(self, size):
+            # We maintain a list of allocations and keep track of them, so that
+            # we can test that the finalizers of objects returned by memalloc
+            # get called.
+
+            # Numba should have initialized the memory manager when preparing
+            # the context for use, prior to any memalloc call.
+            if not self.initialized:
+                raise RuntimeError("memalloc called before initialize")
+            self.memalloc_called = True
+
+            # Create an allocation and record it
+            self.count += 1
+            alloc_count = self.count
+            self.allocations[alloc_count] = size
+
+            # The finalizer deletes the record from our internal dict of
+            # allocations.
+            finalizer_allocs = self.allocations
+
+            def finalizer():
+                del finalizer_allocs[alloc_count]
+
+            # We use an AutoFreePointer so that the finalizer will be run when
+            # the reference count drops to zero.
+            ctx = weakref.proxy(self.context)
+            ptr = ctypes.c_void_p(alloc_count)
+            return cuda.cudadrv.driver.AutoFreePointer(ctx, ptr, size,
+                                                       finalizer=finalizer)
+
+        def initialize(self):
+            # No special initialization needed.
+            self.initialized = True
+
+        def reset(self):
+            # We remove all allocations on reset, just as a real EMM Plugin
+            # would do. Note that our finalizers in memalloc don't check
+            # whether the allocations are still alive, so running them after
+            # reset will detect any allocations that are floating around at
+            # exit time; however, the atexit finalizer for weakref will only
+            # print a traceback, not terminate the interpreter abnormally.
+            self.reset_called = True
+
+        def get_memory_info(self):
+            # Return some dummy memory information
+            self.get_memory_info_called = True
+            return cuda.MemoryInfo(free=32, total=64)
+
+        def get_ipc_handle(self, memory):
+            # The dummy IPC handle is only a string, so it is important that
+            # the tests don't try to do too much with it (e.g. open / close
+            # it).
+            self.get_ipc_handle_called = True
+            return "Dummy IPC handle for alloc %s" % memory.device_pointer.value
+
+        @property
+        def interface_version(self):
+            # The expected version for an EMM Plugin.
+            return 1
+
+    class BadVersionEMMPlugin(DeviceOnlyEMMPlugin):
+        """A plugin that claims to implement a different interface version"""
+
+        @property
+        def interface_version(self):
+            return 2
+
+
+@skip_on_cudasim('EMM Plugins not supported on CUDA simulator')
+class TestDeviceOnlyEMMPlugin(CUDATestCase):
+    """
+    Tests that the API of an EMM Plugin that implements device allocations
+    only is used correctly by Numba.
+    """
+
+    def setUp(self):
+        super().setUp()
+        # Always start afresh with a new context and memory manager
+        cuda.close()
+        cuda.set_memory_manager(DeviceOnlyEMMPlugin)
+
+    def tearDown(self):
+        super().tearDown()
+        # Unset the memory manager for subsequent tests
+        cuda.close()
+        cuda.cudadrv.driver._memory_manager = None
+
+    def test_memalloc(self):
+        mgr = cuda.current_context().memory_manager
+
+        # Allocate an array and check that memalloc was called with the correct
+        # size.
+        arr_1 = np.arange(10)
+        d_arr_1 = cuda.device_array_like(arr_1)
+        self.assertTrue(mgr.memalloc_called)
+        self.assertEqual(mgr.count, 1)
+        self.assertEqual(mgr.allocations[1], arr_1.nbytes)
+
+        # Allocate again, with a different size, and check that it is also
+        # correct.
+        arr_2 = np.arange(5)
+        d_arr_2 = cuda.device_array_like(arr_2)
+        self.assertEqual(mgr.count, 2)
+        self.assertEqual(mgr.allocations[2], arr_2.nbytes)
+
+        # Remove the first array, and check that our finalizer was called for
+        # the first array only.
+        del d_arr_1
+        self.assertNotIn(1, mgr.allocations)
+        self.assertIn(2, mgr.allocations)
+
+        # Remove the second array and check that its finalizer was also
+        # called.
+        del d_arr_2
+        self.assertNotIn(2, mgr.allocations)
+
+    def test_initialized_in_context(self):
+        # If we have a CUDA context, it should already have initialized its
+        # memory manager.
+        self.assertTrue(cuda.current_context().memory_manager.initialized)
+
+    def test_reset(self):
+        ctx = cuda.current_context()
+        ctx.reset()
+        self.assertTrue(ctx.memory_manager.reset_called)
+
+    def test_get_memory_info(self):
+        ctx = cuda.current_context()
+        meminfo = ctx.get_memory_info()
+        self.assertTrue(ctx.memory_manager.get_memory_info_called)
+        self.assertEqual(meminfo.free, 32)
+        self.assertEqual(meminfo.total, 64)
+
+    @linux_only
+    def test_get_ipc_handle(self):
+        # We don't attempt to close the IPC handle in this test because Numba
+        # will be expecting a real IpcHandle object to have been returned from
+        # get_ipc_handle, and it would cause problems to do so.
+        arr = np.arange(2)
+        d_arr = cuda.device_array_like(arr)
+        ipch = d_arr.get_ipc_handle()
+        ctx = cuda.current_context()
+        self.assertTrue(ctx.memory_manager.get_ipc_handle_called)
+        self.assertIn("Dummy IPC handle for alloc 1", ipch._ipc_handle)
+
+
+@skip_on_cudasim('EMM Plugins not supported on CUDA simulator')
+class TestBadEMMPluginVersion(CUDATestCase):
+    """
+    Ensure that Numba rejects EMM Plugins with incompatible version
+    numbers.
+    """
+
+    def test_bad_plugin_version(self):
+        with self.assertRaises(RuntimeError) as raises:
+            cuda.set_memory_manager(BadVersionEMMPlugin)
+        self.assertIn('version 1 required', str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_events.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_events.py
new file mode 100644
index 000000000..b611a4a75
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_events.py
@@ -0,0 +1,38 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaEvent(CUDATestCase):
+    def test_event_elapsed(self):
+        N = 32
+        dary = cuda.device_array(N, dtype=np.double)
+        evtstart = cuda.event()
+        evtend = cuda.event()
+
+        evtstart.record()
+        cuda.to_device(np.arange(N, dtype=np.double), to=dary)
+        evtend.record()
+        evtend.wait()
+        evtend.synchronize()
+        # Exercise the code path
+        evtstart.elapsed_time(evtend)
+
+    def test_event_elapsed_stream(self):
+        N = 32
+        stream = cuda.stream()
+        dary = cuda.device_array(N, dtype=np.double)
+        evtstart = cuda.event()
+        evtend = cuda.event()
+
+        evtstart.record(stream=stream)
+        cuda.to_device(np.arange(N, dtype=np.double), to=dary, stream=stream)
+        evtend.record(stream=stream)
+        evtend.wait(stream=stream)
+        evtend.synchronize()
+        # Exercise the code path
+        evtstart.elapsed_time(evtend)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_host_alloc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_host_alloc.py
new file mode 100644
index 000000000..62c4ecafe
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_host_alloc.py
@@ -0,0 +1,65 @@
+import numpy as np
+from numba.cuda.cudadrv import driver
+from numba import cuda
+from numba.cuda.testing import unittest, ContextResettingTestCase
+
+
+class TestHostAlloc(ContextResettingTestCase):
+    def test_host_alloc_driver(self):
+        n = 32
+        mem = cuda.current_context().memhostalloc(n, mapped=True)
+
+        dtype = np.dtype(np.uint8)
+        ary = np.ndarray(shape=n // dtype.itemsize, dtype=dtype,
+                         buffer=mem)
+
+        magic = 0xab
+        driver.device_memset(mem, magic, n)
+
+        self.assertTrue(np.all(ary == magic))
+
+        ary.fill(n)
+
+        recv = np.empty_like(ary)
+
+        driver.device_to_host(recv, mem, ary.size)
+
+        self.assertTrue(np.all(ary == recv))
+        self.assertTrue(np.all(recv == n))
+
+    def test_host_alloc_pinned(self):
+        ary = cuda.pinned_array(10, dtype=np.uint32)
+        ary.fill(123)
+        self.assertTrue(all(ary == 123))
+        devary = cuda.to_device(ary)
+        driver.device_memset(devary, 0, driver.device_memory_size(devary))
+        self.assertTrue(all(ary == 123))
+        devary.copy_to_host(ary)
+        self.assertTrue(all(ary == 0))
+
+    def test_host_alloc_mapped(self):
+        ary = cuda.mapped_array(10, dtype=np.uint32)
+        ary.fill(123)
+        self.assertTrue(all(ary == 123))
+        driver.device_memset(ary, 0, driver.device_memory_size(ary))
+        self.assertTrue(all(ary == 0))
+        self.assertTrue(sum(ary != 0) == 0)
+
+    def test_host_operators(self):
+        for ary in [cuda.mapped_array(10, dtype=np.uint32),
+                    cuda.pinned_array(10, dtype=np.uint32)]:
+            ary[:] = range(10)
+            self.assertTrue(sum(ary + 1) == 55)
+            self.assertTrue(sum((ary + 1) * 2 - 1) == 100)
+            self.assertTrue(sum(ary < 5) == 5)
+            self.assertTrue(sum(ary <= 5) == 6)
+            self.assertTrue(sum(ary > 6) == 3)
+            self.assertTrue(sum(ary >= 6) == 4)
+            self.assertTrue(sum(ary ** 2) == 285)
+            self.assertTrue(sum(ary // 2) == 20)
+            self.assertTrue(sum(ary / 2.0) == 22.5)
+            self.assertTrue(sum(ary % 2) == 5)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_init.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_init.py
new file mode 100644
index 000000000..600687fd5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_init.py
@@ -0,0 +1,139 @@
+import multiprocessing as mp
+import os
+
+from numba import cuda
+from numba.cuda.cudadrv.driver import CudaAPIError, driver
+from numba.cuda.cudadrv.error import CudaSupportError
+from numba.cuda.testing import skip_on_cudasim, unittest, CUDATestCase
+
+
+# A mock of cuInit that always raises a CudaAPIError
+def cuInit_raising(arg):
+    raise CudaAPIError(999, 'CUDA_ERROR_UNKNOWN')
+
+
+# Test code to run in a child that patches driver.cuInit to a variant that
+# always raises. We can't use mock.patch.object here because driver.cuInit is
+# not assigned until we attempt to initialize - mock.patch.object cannot locate
+# the non-existent original method, and so fails. Instead we patch
+# driver.cuInit with our raising version prior to any attempt to initialize.
+def cuInit_raising_test(result_queue):
+    driver.cuInit = cuInit_raising
+
+    success = False
+    msg = None
+
+    try:
+        # A CUDA operation that forces initialization of the device
+        cuda.device_array(1)
+    except CudaSupportError as e:
+        success = True
+        msg = e.msg
+
+    result_queue.put((success, msg))
+
+
+# Similar to cuInit_raising_test above, but for testing that the string
+# returned by cuda_error() is as expected.
+def initialization_error_test(result_queue):
+    driver.cuInit = cuInit_raising
+
+    success = False
+    msg = None
+
+    try:
+        # A CUDA operation that forces initialization of the device
+        cuda.device_array(1)
+    except CudaSupportError:
+        success = True
+
+    msg = cuda.cuda_error()
+    result_queue.put((success, msg))
+
+
+# For testing the path where Driver.__init__() catches a CudaSupportError
+def cuda_disabled_test(result_queue):
+    success = False
+    msg = None
+
+    try:
+        # A CUDA operation that forces initialization of the device
+        cuda.device_array(1)
+    except CudaSupportError as e:
+        success = True
+        msg = e.msg
+
+    result_queue.put((success, msg))
+
+
+# Similar to cuda_disabled_test, but checks cuda.cuda_error() instead of the
+# exception raised on initialization
+def cuda_disabled_error_test(result_queue):
+    success = False
+    msg = None
+
+    try:
+        # A CUDA operation that forces initialization of the device
+        cuda.device_array(1)
+    except CudaSupportError:
+        success = True
+
+    msg = cuda.cuda_error()
+    result_queue.put((success, msg))
+
+
+@skip_on_cudasim('CUDA Simulator does not initialize driver')
+class TestInit(CUDATestCase):
+    def _test_init_failure(self, target, expected):
+        # Run the initialization failure test in a separate subprocess
+        ctx = mp.get_context('spawn')
+        result_queue = ctx.Queue()
+        proc = ctx.Process(target=target, args=(result_queue,))
+        proc.start()
+        proc.join(30) # should complete within 30s
+        success, msg = result_queue.get()
+
+        # Ensure the child process raised an exception during initialization
+        # before checking the message
+        if not success:
+            self.fail('CudaSupportError not raised')
+
+        self.assertIn(expected, msg)
+
+    def test_init_failure_raising(self):
+        expected = 'Error at driver init: CUDA_ERROR_UNKNOWN (999)'
+        self._test_init_failure(cuInit_raising_test, expected)
+
+    def test_init_failure_error(self):
+        expected = 'CUDA_ERROR_UNKNOWN (999)'
+        self._test_init_failure(initialization_error_test, expected)
+
+    def _test_cuda_disabled(self, target):
+        # Uses _test_init_failure to launch the test in a separate subprocess
+        # with CUDA disabled.
+        cuda_disabled = os.environ.get('NUMBA_DISABLE_CUDA')
+        os.environ['NUMBA_DISABLE_CUDA'] = "1"
+        try:
+            expected = 'CUDA is disabled due to setting NUMBA_DISABLE_CUDA=1'
+            self._test_init_failure(cuda_disabled_test, expected)
+        finally:
+            if cuda_disabled is not None:
+                os.environ['NUMBA_DISABLE_CUDA'] = cuda_disabled
+            else:
+                os.environ.pop('NUMBA_DISABLE_CUDA')
+
+    def test_cuda_disabled_raising(self):
+        self._test_cuda_disabled(cuda_disabled_test)
+
+    def test_cuda_disabled_error(self):
+        self._test_cuda_disabled(cuda_disabled_error_test)
+
+    def test_init_success(self):
+        # Here we assume that initialization is successful (because many bad
+        # things will happen with the test suite if it is not) and check that
+        # there is no error recorded.
+        self.assertIsNone(cuda.cuda_error())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_inline_ptx.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_inline_ptx.py
new file mode 100644
index 000000000..60cf88aef
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_inline_ptx.py
@@ -0,0 +1,35 @@
+from llvmlite import ir
+
+from numba.cuda.cudadrv import nvvm
+from numba.cuda.testing import unittest, ContextResettingTestCase
+from numba.cuda.testing import skip_on_cudasim
+
+
+@skip_on_cudasim('Inline PTX cannot be used in the simulator')
+class TestCudaInlineAsm(ContextResettingTestCase):
+    def test_inline_rsqrt(self):
+        mod = ir.Module(__name__)
+        fnty = ir.FunctionType(ir.VoidType(), [ir.PointerType(ir.FloatType())])
+        fn = ir.Function(mod, fnty, 'cu_rsqrt')
+        bldr = ir.IRBuilder(fn.append_basic_block('entry'))
+
+        rsqrt_approx_fnty = ir.FunctionType(ir.FloatType(), [ir.FloatType()])
+        inlineasm = ir.InlineAsm(rsqrt_approx_fnty,
+                                 'rsqrt.approx.f32 $0, $1;',
+                                 '=f,f', side_effect=True)
+        val = bldr.load(fn.args[0])
+        res = bldr.call(inlineasm, [val])
+
+        bldr.store(res, fn.args[0])
+        bldr.ret_void()
+
+        # generate ptx
+        nvvm.fix_data_layout(mod)
+        nvvm.set_cuda_kernel(fn)
+        nvvmir = str(mod)
+        ptx = nvvm.llvm_to_ptx(nvvmir)
+        self.assertTrue('rsqrt.approx.f32' in str(ptx))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_ir_patch.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_ir_patch.py
new file mode 100644
index 000000000..29ed43422
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_ir_patch.py
@@ -0,0 +1,26 @@
+from numba.cuda.testing import unittest
+from numba.cuda.testing import skip_on_cudasim
+
+
+@skip_on_cudasim('Linking unsupported in the simulator')
+class TestIRPatch(unittest.TestCase):
+    def patch(self, ir):
+        # Import here to avoid error in CUDASIM
+        from numba.cuda.cudadrv.nvvm import llvm100_to_34_ir
+
+        return llvm100_to_34_ir(ir)
+
+    def test_load_rewrite(self):
+        text = "%myload = not really"
+        out = self.patch(text)
+        # No rewrite
+        self.assertEqual(text, out)
+
+        text = "%myload = load i32, i32* val"
+        out = self.patch(text)
+        # Rewritten
+        self.assertEqual("%myload = load i32* val", out)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_linker.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_linker.py
new file mode 100644
index 000000000..457e526a8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_linker.py
@@ -0,0 +1,230 @@
+import os.path
+import numpy as np
+import warnings
+from numba.cuda.testing import unittest
+from numba.cuda.testing import (skip_on_cudasim, skip_unless_cuda_python,
+                                skip_if_cuda_includes_missing,
+                                skip_with_cuda_python)
+from numba.cuda.testing import CUDATestCase
+from numba.cuda.cudadrv.driver import Linker, LinkerError, NvrtcError
+from numba.cuda import require_context
+from numba.tests.support import ignore_internal_warnings
+from numba import cuda, void, float64, int64
+
+
+def func_with_lots_of_registers(x, a, b, c, d, e, f):
+    a1 = 1.0
+    a2 = 1.0
+    a3 = 1.0
+    a4 = 1.0
+    a5 = 1.0
+    b1 = 1.0
+    b2 = 1.0
+    b3 = 1.0
+    b4 = 1.0
+    b5 = 1.0
+    c1 = 1.0
+    c2 = 1.0
+    c3 = 1.0
+    c4 = 1.0
+    c5 = 1.0
+    d1 = 10
+    d2 = 10
+    d3 = 10
+    d4 = 10
+    d5 = 10
+    for i in range(a):
+        a1 += b
+        a2 += c
+        a3 += d
+        a4 += e
+        a5 += f
+        b1 *= b
+        b2 *= c
+        b3 *= d
+        b4 *= e
+        b5 *= f
+        c1 /= b
+        c2 /= c
+        c3 /= d
+        c4 /= e
+        c5 /= f
+        d1 <<= b
+        d2 <<= c
+        d3 <<= d
+        d4 <<= e
+        d5 <<= f
+    x[cuda.grid(1)] = a1 + a2 + a3 + a4 + a5
+    x[cuda.grid(1)] += b1 + b2 + b3 + b4 + b5
+    x[cuda.grid(1)] += c1 + c2 + c3 + c4 + c5
+    x[cuda.grid(1)] += d1 + d2 + d3 + d4 + d5
+
+
+@skip_on_cudasim('Linking unsupported in the simulator')
+class TestLinker(CUDATestCase):
+
+    @require_context
+    def test_linker_basic(self):
+        '''Simply go through the constructor and destructor
+        '''
+        linker = Linker.new()
+        del linker
+
+    def _test_linking(self, eager):
+        global bar  # must be a global; other it is recognized as a freevar
+        bar = cuda.declare_device('bar', 'int32(int32)')
+
+        link = os.path.join(os.path.dirname(__file__), 'data', 'jitlink.ptx')
+
+        if eager:
+            args = ['void(int32[:], int32[:])']
+        else:
+            args = []
+
+        @cuda.jit(*args, link=[link])
+        def foo(x, y):
+            i = cuda.grid(1)
+            x[i] += bar(y[i])
+
+        A = np.array([123], dtype=np.int32)
+        B = np.array([321], dtype=np.int32)
+
+        foo[1, 1](A, B)
+
+        self.assertTrue(A[0] == 123 + 2 * 321)
+
+    def test_linking_lazy_compile(self):
+        self._test_linking(eager=False)
+
+    def test_linking_eager_compile(self):
+        self._test_linking(eager=True)
+
+    @skip_unless_cuda_python('NVIDIA Binding needed for NVRTC')
+    def test_linking_cu(self):
+        bar = cuda.declare_device('bar', 'int32(int32)')
+
+        link = os.path.join(os.path.dirname(__file__), 'data', 'jitlink.cu')
+
+        @cuda.jit(link=[link])
+        def kernel(r, x):
+            i = cuda.grid(1)
+
+            if i < len(r):
+                r[i] = bar(x[i])
+
+        x = np.arange(10, dtype=np.int32)
+        r = np.zeros_like(x)
+
+        kernel[1, 32](r, x)
+
+        # Matches the operation of bar() in jitlink.cu
+        expected = x * 2
+        np.testing.assert_array_equal(r, expected)
+
+    @skip_unless_cuda_python('NVIDIA Binding needed for NVRTC')
+    def test_linking_cu_log_warning(self):
+        bar = cuda.declare_device('bar', 'int32(int32)')
+
+        link = os.path.join(os.path.dirname(__file__), 'data', 'warn.cu')
+
+        with warnings.catch_warnings(record=True) as w:
+            ignore_internal_warnings()
+
+            @cuda.jit('void(int32)', link=[link])
+            def kernel(x):
+                bar(x)
+
+        self.assertEqual(len(w), 1, 'Expected warnings from NVRTC')
+        # Check the warning refers to the log messages
+        self.assertIn('NVRTC log messages', str(w[0].message))
+        # Check the message pertaining to the unused variable is provided
+        self.assertIn('declared but never referenced', str(w[0].message))
+
+    @skip_unless_cuda_python('NVIDIA Binding needed for NVRTC')
+    def test_linking_cu_error(self):
+        bar = cuda.declare_device('bar', 'int32(int32)')
+
+        link = os.path.join(os.path.dirname(__file__), 'data', 'error.cu')
+
+        with self.assertRaises(NvrtcError) as e:
+            @cuda.jit('void(int32)', link=[link])
+            def kernel(x):
+                bar(x)
+
+        msg = e.exception.args[0]
+        # Check the error message refers to the NVRTC compile
+        self.assertIn('NVRTC Compilation failure', msg)
+        # Check the expected error in the CUDA source is reported
+        self.assertIn('identifier "SYNTAX" is undefined', msg)
+        # Check the filename is reported correctly
+        self.assertIn('in the compilation of "error.cu"', msg)
+
+    @skip_with_cuda_python
+    def test_linking_cu_ctypes_unsupported(self):
+        msg = ('Linking CUDA source files is not supported with the ctypes '
+               'binding')
+        with self.assertRaisesRegex(NotImplementedError, msg):
+            @cuda.jit('void()', link=['jitlink.cu'])
+            def f():
+                pass
+
+    def test_linking_unknown_filetype_error(self):
+        expected_err = "Don't know how to link file with extension .cuh"
+        with self.assertRaisesRegex(RuntimeError, expected_err):
+            @cuda.jit('void()', link=['header.cuh'])
+            def kernel():
+                pass
+
+    def test_linking_file_with_no_extension_error(self):
+        expected_err = "Don't know how to link file with no extension"
+        with self.assertRaisesRegex(RuntimeError, expected_err):
+            @cuda.jit('void()', link=['data'])
+            def kernel():
+                pass
+
+    @skip_if_cuda_includes_missing
+    @skip_unless_cuda_python('NVIDIA Binding needed for NVRTC')
+    def test_linking_cu_cuda_include(self):
+        link = os.path.join(os.path.dirname(__file__), 'data',
+                            'cuda_include.cu')
+
+        # An exception will be raised when linking this kernel due to the
+        # compile failure if CUDA includes cannot be found by Nvrtc.
+        @cuda.jit('void()', link=[link])
+        def kernel():
+            pass
+
+    def test_try_to_link_nonexistent(self):
+        with self.assertRaises(LinkerError) as e:
+            @cuda.jit('void(int32[::1])', link=['nonexistent.a'])
+            def f(x):
+                x[0] = 0
+        self.assertIn('nonexistent.a not found', e.exception.args)
+
+    def test_set_registers_no_max(self):
+        """Ensure that the jitted kernel used in the test_set_registers_* tests
+        uses more than 57 registers - this ensures that test_set_registers_*
+        are really checking that they reduced the number of registers used from
+        something greater than the maximum."""
+        compiled = cuda.jit(func_with_lots_of_registers)
+        compiled = compiled.specialize(np.empty(32), *range(6))
+        self.assertGreater(compiled.get_regs_per_thread(), 57)
+
+    def test_set_registers_57(self):
+        compiled = cuda.jit(max_registers=57)(func_with_lots_of_registers)
+        compiled = compiled.specialize(np.empty(32), *range(6))
+        self.assertLessEqual(compiled.get_regs_per_thread(), 57)
+
+    def test_set_registers_38(self):
+        compiled = cuda.jit(max_registers=38)(func_with_lots_of_registers)
+        compiled = compiled.specialize(np.empty(32), *range(6))
+        self.assertLessEqual(compiled.get_regs_per_thread(), 38)
+
+    def test_set_registers_eager(self):
+        sig = void(float64[::1], int64, int64, int64, int64, int64, int64)
+        compiled = cuda.jit(sig, max_registers=38)(func_with_lots_of_registers)
+        self.assertLessEqual(compiled.get_regs_per_thread(), 38)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_managed_alloc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_managed_alloc.py
new file mode 100644
index 000000000..e9cc37ca8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_managed_alloc.py
@@ -0,0 +1,127 @@
+import numpy as np
+from ctypes import byref, c_size_t
+from numba.cuda.cudadrv.driver import device_memset, driver, USE_NV_BINDING
+from numba import cuda
+from numba.cuda.testing import unittest, ContextResettingTestCase
+from numba.cuda.testing import skip_on_cudasim, skip_on_arm
+from numba.tests.support import linux_only
+
+
+@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
+@linux_only
+@skip_on_arm('Managed Alloc support is experimental/untested on ARM')
+class TestManagedAlloc(ContextResettingTestCase):
+
+    def get_total_gpu_memory(self):
+        # We use a driver function to directly get the total GPU memory because
+        # an EMM plugin may report something different (or not implement
+        # get_memory_info at all).
+        if USE_NV_BINDING:
+            free, total = driver.cuMemGetInfo()
+            return total
+        else:
+            free = c_size_t()
+            total = c_size_t()
+            driver.cuMemGetInfo(byref(free), byref(total))
+            return total.value
+
+    def skip_if_cc_major_lt(self, min_required, reason):
+        """
+        Skip the current test if the compute capability of the device is
+        less than `min_required`.
+        """
+        ctx = cuda.current_context()
+        cc_major = ctx.device.compute_capability[0]
+        if cc_major < min_required:
+            self.skipTest(reason)
+
+    # CUDA Unified Memory comes in two flavors. For GPUs in the Kepler and
+    # Maxwell generations, managed memory allocations work as opaque,
+    # contiguous segments that can either be on the device or the host. For
+    # GPUs in the Pascal or later generations, managed memory operates on a
+    # per-page basis, so we can have arrays larger than GPU memory, where only
+    # part of them is resident on the device at one time. To ensure that this
+    # test works correctly on all supported GPUs, we'll select the size of our
+    # memory such that we only oversubscribe the GPU memory if we're on a
+    # Pascal or newer GPU (compute capability at least 6.0).
+
+    def test_managed_alloc_driver_undersubscribe(self):
+        msg = "Managed memory unsupported prior to CC 3.0"
+        self.skip_if_cc_major_lt(3, msg)
+        self._test_managed_alloc_driver(0.5)
+
+    # This test is skipped by default because it is easy to hang the machine
+    # for a very long time or get OOM killed if the GPU memory size is >50% of
+    # the system memory size. Even if the system does have more than 2x the RAM
+    # of the GPU, this test runs for a very long time (in comparison to the
+    # rest of the tests in the suite).
+    #
+    # However, it is left in here for manual testing as required.
+
+    @unittest.skip
+    def test_managed_alloc_driver_oversubscribe(self):
+        msg = "Oversubscription of managed memory unsupported prior to CC 6.0"
+        self.skip_if_cc_major_lt(6, msg)
+        self._test_managed_alloc_driver(2.0)
+
+    def test_managed_alloc_driver_host_attach(self):
+        msg = "Host attached managed memory is not accessible prior to CC 6.0"
+        self.skip_if_cc_major_lt(6, msg)
+        # Only test with a small array (0.01 * memory size) to keep the test
+        # quick.
+        self._test_managed_alloc_driver(0.01, attach_global=False)
+
+    def _test_managed_alloc_driver(self, memory_factor, attach_global=True):
+        # Verify that we can allocate and operate on managed
+        # memory through the CUDA driver interface.
+
+        total_mem_size = self.get_total_gpu_memory()
+        n_bytes = int(memory_factor * total_mem_size)
+
+        ctx = cuda.current_context()
+        mem = ctx.memallocmanaged(n_bytes, attach_global=attach_global)
+
+        dtype = np.dtype(np.uint8)
+        n_elems = n_bytes // dtype.itemsize
+        ary = np.ndarray(shape=n_elems, dtype=dtype, buffer=mem)
+
+        magic = 0xab
+        device_memset(mem, magic, n_bytes)
+        ctx.synchronize()
+
+        # Note that this assertion operates on the CPU, so this
+        # test effectively drives both the CPU and the GPU on
+        # managed memory.
+
+        self.assertTrue(np.all(ary == magic))
+
+    def _test_managed_array(self, attach_global=True):
+        # Check the managed_array interface on both host and device.
+
+        ary = cuda.managed_array(100, dtype=np.double)
+        ary.fill(123.456)
+        self.assertTrue(all(ary == 123.456))
+
+        @cuda.jit('void(double[:])')
+        def kernel(x):
+            i = cuda.grid(1)
+            if i < x.shape[0]:
+                x[i] = 1.0
+
+        kernel[10, 10](ary)
+        cuda.current_context().synchronize()
+
+        self.assertTrue(all(ary == 1.0))
+
+    def test_managed_array_attach_global(self):
+        self._test_managed_array()
+
+    def test_managed_array_attach_host(self):
+        self._test_managed_array()
+        msg = "Host attached managed memory is not accessible prior to CC 6.0"
+        self.skip_if_cc_major_lt(6, msg)
+        self._test_managed_array(attach_global=False)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_nvvm_driver.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_nvvm_driver.py
new file mode 100644
index 000000000..7f7b18878
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_nvvm_driver.py
@@ -0,0 +1,147 @@
+from llvmlite import ir
+from numba.cuda.cudadrv.nvvm import (llvm_to_ptx, set_cuda_kernel,
+                                     fix_data_layout, get_arch_option,
+                                     get_supported_ccs)
+from ctypes import c_size_t, c_uint64, sizeof
+from numba.cuda.testing import unittest
+from numba.cuda.cudadrv.nvvm import LibDevice, NvvmError, NVVM
+from numba.cuda.testing import skip_on_cudasim
+
+is64bit = sizeof(c_size_t) == sizeof(c_uint64)
+
+
+@skip_on_cudasim('NVVM Driver unsupported in the simulator')
+class TestNvvmDriver(unittest.TestCase):
+    def get_nvvmir(self):
+        if NVVM().is_nvvm70:
+            metadata = metadata_nvvm70
+        else:
+            metadata = metadata_nvvm34
+
+        return nvvmir_generic + metadata
+
+    def test_nvvm_compile_simple(self):
+        nvvmir = self.get_nvvmir()
+        ptx = llvm_to_ptx(nvvmir).decode('utf8')
+        self.assertTrue('simple' in ptx)
+        self.assertTrue('ave' in ptx)
+
+    def test_nvvm_from_llvm(self):
+        m = ir.Module("test_nvvm_from_llvm")
+        fty = ir.FunctionType(ir.VoidType(), [ir.IntType(32)])
+        kernel = ir.Function(m, fty, name='mycudakernel')
+        bldr = ir.IRBuilder(kernel.append_basic_block('entry'))
+        bldr.ret_void()
+        set_cuda_kernel(kernel)
+
+        fix_data_layout(m)
+        ptx = llvm_to_ptx(str(m)).decode('utf8')
+        self.assertTrue('mycudakernel' in ptx)
+        if is64bit:
+            self.assertTrue('.address_size 64' in ptx)
+        else:
+            self.assertTrue('.address_size 32' in ptx)
+
+    def _test_nvvm_support(self, arch):
+        compute_xx = 'compute_{0}{1}'.format(*arch)
+        nvvmir = self.get_nvvmir()
+        ptx = llvm_to_ptx(nvvmir, arch=compute_xx, ftz=1, prec_sqrt=0,
+                          prec_div=0).decode('utf8')
+        self.assertIn(".target sm_{0}{1}".format(*arch), ptx)
+        self.assertIn('simple', ptx)
+        self.assertIn('ave', ptx)
+
+    def test_nvvm_support(self):
+        """Test supported CC by NVVM
+        """
+        for arch in get_supported_ccs():
+            self._test_nvvm_support(arch=arch)
+
+    @unittest.skipIf(True, "No new CC unknown to NVVM yet")
+    def test_nvvm_future_support(self):
+        """Test unsupported CC to help track the feature support
+        """
+        # List known CC but unsupported by NVVM
+        future_archs = [
+            # (5, 2),  # for example
+        ]
+        for arch in future_archs:
+            pat = r"-arch=compute_{0}{1}".format(*arch)
+            with self.assertRaises(NvvmError) as raises:
+                self._test_nvvm_support(arch=arch)
+            self.assertIn(pat, raises.msg)
+
+
+@skip_on_cudasim('NVVM Driver unsupported in the simulator')
+class TestArchOption(unittest.TestCase):
+    def test_get_arch_option(self):
+        # Test returning the nearest lowest arch.
+        self.assertEqual(get_arch_option(5, 3), 'compute_53')
+        self.assertEqual(get_arch_option(7, 5), 'compute_75')
+        self.assertEqual(get_arch_option(7, 7), 'compute_75')
+        # Test known arch.
+        supported_cc = get_supported_ccs()
+        for arch in supported_cc:
+            self.assertEqual(get_arch_option(*arch), 'compute_%d%d' % arch)
+        self.assertEqual(get_arch_option(1000, 0),
+                         'compute_%d%d' % supported_cc[-1])
+
+
+@skip_on_cudasim('NVVM Driver unsupported in the simulator')
+class TestLibDevice(unittest.TestCase):
+    def test_libdevice_load(self):
+        # Test that constructing LibDevice gives a bitcode file
+        libdevice = LibDevice()
+        self.assertEqual(libdevice.bc[:4], b'BC\xc0\xde')
+
+
+nvvmir_generic = '''\
+target triple="nvptx64-"
+target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"
+
+define i32 @ave(i32 %a, i32 %b) {
+entry:
+%add = add nsw i32 %a, %b
+%div = sdiv i32 %add, 2
+ret i32 %div
+}
+
+define void @simple(i32* %data) {
+entry:
+%0 = call i32 @llvm.nvvm.read.ptx.sreg.ctaid.x()
+%1 = call i32 @llvm.nvvm.read.ptx.sreg.ntid.x()
+%mul = mul i32 %0, %1
+%2 = call i32 @llvm.nvvm.read.ptx.sreg.tid.x()
+%add = add i32 %mul, %2
+%call = call i32 @ave(i32 %add, i32 %add)
+%idxprom = sext i32 %add to i64
+%arrayidx = getelementptr inbounds i32, i32* %data, i64 %idxprom
+store i32 %call, i32* %arrayidx, align 4
+ret void
+}
+
+declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.x() nounwind readnone
+
+declare i32 @llvm.nvvm.read.ptx.sreg.ntid.x() nounwind readnone
+
+declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() nounwind readnone
+'''  # noqa: E501
+
+
+metadata_nvvm70 = '''
+!nvvmir.version = !{!1}
+!1 = !{i32 1, i32 6, i32 3, i32 0}
+
+!nvvm.annotations = !{!2}
+!2 = !{void (i32*)* @simple, !"kernel", i32 1}
+'''  # noqa: E501
+
+
+metadata_nvvm34 = '''
+!nvvm.annotations = !{!1}
+!1 = metadata !{void (i32*)* @simple, metadata !"kernel", i32 1}
+'''
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_pinned.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_pinned.py
new file mode 100644
index 000000000..ef727c5a8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_pinned.py
@@ -0,0 +1,37 @@
+import numpy as np
+import platform
+
+from numba import cuda
+from numba.cuda.testing import unittest, ContextResettingTestCase
+
+
+class TestPinned(ContextResettingTestCase):
+
+    def _run_copies(self, A):
+        A0 = np.copy(A)
+
+        stream = cuda.stream()
+        ptr = cuda.to_device(A, copy=False, stream=stream)
+        ptr.copy_to_device(A, stream=stream)
+        ptr.copy_to_host(A, stream=stream)
+        stream.synchronize()
+
+        self.assertTrue(np.allclose(A, A0))
+
+    def test_pinned(self):
+        machine = platform.machine()
+        if machine.startswith('arm') or machine.startswith('aarch64'):
+            count = 262144   # 2MB
+        else:
+            count = 2097152  # 16MB
+        A = np.arange(count)
+        with cuda.pinned(A):
+            self._run_copies(A)
+
+    def test_unpinned(self):
+        A = np.arange(2 * 1024 * 1024) # 16 MB
+        self._run_copies(A)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_profiler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_profiler.py
new file mode 100644
index 000000000..1660d4d42
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_profiler.py
@@ -0,0 +1,20 @@
+import unittest
+from numba.cuda.testing import ContextResettingTestCase
+from numba import cuda
+from numba.cuda.testing import skip_on_cudasim
+
+
+@skip_on_cudasim('CUDA Profiler unsupported in the simulator')
+class TestProfiler(ContextResettingTestCase):
+    def test_profiling(self):
+        with cuda.profiling():
+            a = cuda.device_array(10)
+            del a
+
+        with cuda.profiling():
+            a = cuda.device_array(100)
+            del a
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_ptds.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_ptds.py
new file mode 100644
index 000000000..cfb2b018c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_ptds.py
@@ -0,0 +1,145 @@
+import multiprocessing as mp
+import logging
+import traceback
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import (skip_on_cudasim, skip_with_cuda_python,
+                                skip_under_cuda_memcheck)
+
+
+def child_test():
+    from numba import cuda, int32, void
+    from numba.core import config
+    import io
+    import numpy as np
+    import threading
+
+    # Enable PTDS before we make any CUDA driver calls.  Enabling it first
+    # ensures that PTDS APIs are used because the CUDA driver looks up API
+    # functions on first use and memoizes them.
+    config.CUDA_PER_THREAD_DEFAULT_STREAM = 1
+
+    # Set up log capture for the Driver API so we can see what API calls were
+    # used.
+    logbuf = io.StringIO()
+    handler = logging.StreamHandler(logbuf)
+    cudadrv_logger = logging.getLogger('numba.cuda.cudadrv.driver')
+    cudadrv_logger.addHandler(handler)
+    cudadrv_logger.setLevel(logging.DEBUG)
+
+    # Set up data for our test, and copy over to the device
+    N = 2 ** 16
+    N_THREADS = 10
+    N_ADDITIONS = 4096
+
+    # Seed the RNG for repeatability
+    np.random.seed(1)
+    x = np.random.randint(low=0, high=1000, size=N, dtype=np.int32)
+    r = np.zeros_like(x)
+
+    # One input and output array for each thread
+    xs = [cuda.to_device(x) for _ in range(N_THREADS)]
+    rs = [cuda.to_device(r) for _ in range(N_THREADS)]
+
+    # Compute the grid size and get the [per-thread] default stream
+    n_threads = 256
+    n_blocks = N // n_threads
+    stream = cuda.default_stream()
+
+    # A simple multiplication-by-addition kernel. What it does exactly is not
+    # too important; only that we have a kernel that does something.
+    @cuda.jit(void(int32[::1], int32[::1]))
+    def f(r, x):
+        i = cuda.grid(1)
+
+        if i > len(r):
+            return
+
+        # Accumulate x into r
+        for j in range(N_ADDITIONS):
+            r[i] += x[i]
+
+    # This function will be used to launch the kernel from each thread on its
+    # own unique data.
+    def kernel_thread(n):
+        f[n_blocks, n_threads, stream](rs[n], xs[n])
+
+    # Create threads
+    threads = [threading.Thread(target=kernel_thread, args=(i,))
+               for i in range(N_THREADS)]
+
+    # Start all threads
+    for thread in threads:
+        thread.start()
+
+    # Wait for all threads to finish, to ensure that we don't synchronize with
+    # the device until all kernels are scheduled.
+    for thread in threads:
+        thread.join()
+
+    # Synchronize with the device
+    cuda.synchronize()
+
+    # Check output is as expected
+    expected = x * N_ADDITIONS
+    for i in range(N_THREADS):
+        np.testing.assert_equal(rs[i].copy_to_host(), expected)
+
+    # Return the driver log output to the calling process for checking
+    handler.flush()
+    return logbuf.getvalue()
+
+
+def child_test_wrapper(result_queue):
+    try:
+        output = child_test()
+        success = True
+    # Catch anything raised so it can be propagated
+    except: # noqa: E722
+        output = traceback.format_exc()
+        success = False
+
+    result_queue.put((success, output))
+
+
+@skip_under_cuda_memcheck('Hangs cuda-memcheck')
+@skip_on_cudasim('Streams not supported on the simulator')
+class TestPTDS(CUDATestCase):
+    @skip_with_cuda_python('Function names unchanged for PTDS with NV Binding')
+    def test_ptds(self):
+        # Run a test with PTDS enabled in a child process
+        ctx = mp.get_context('spawn')
+        result_queue = ctx.Queue()
+        proc = ctx.Process(target=child_test_wrapper, args=(result_queue,))
+        proc.start()
+        proc.join()
+        success, output = result_queue.get()
+
+        # Ensure the child process ran to completion before checking its output
+        if not success:
+            self.fail(output)
+
+        # Functions with a per-thread default stream variant that we expect to
+        # see in the output
+        ptds_functions = ('cuMemcpyHtoD_v2_ptds', 'cuLaunchKernel_ptsz',
+                          'cuMemcpyDtoH_v2_ptds')
+
+        for fn in ptds_functions:
+            with self.subTest(fn=fn, expected=True):
+                self.assertIn(fn, output)
+
+        # Non-PTDS versions of the functions that we should not see in the
+        # output:
+        legacy_functions = ('cuMemcpyHtoD_v2', 'cuLaunchKernel',
+                            'cuMemcpyDtoH_v2')
+
+        for fn in legacy_functions:
+            with self.subTest(fn=fn, expected=False):
+                # Ensure we only spot these function names appearing without a
+                # _ptds or _ptsz suffix by checking including the end of the
+                # line in the log
+                fn_at_end = f'{fn}\n'
+                self.assertNotIn(fn_at_end, output)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_reset_device.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_reset_device.py
new file mode 100644
index 000000000..f2e0b6d10
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_reset_device.py
@@ -0,0 +1,36 @@
+import threading
+from numba import cuda
+from numba.cuda.cudadrv.driver import driver
+from numba.cuda.testing import unittest, ContextResettingTestCase
+from queue import Queue
+
+
+class TestResetDevice(ContextResettingTestCase):
+    def test_reset_device(self):
+
+        def newthread(exception_queue):
+            try:
+                devices = range(driver.get_device_count())
+                for _ in range(2):
+                    for d in devices:
+                        cuda.select_device(d)
+                        cuda.close()
+            except Exception as e:
+                exception_queue.put(e)
+
+        # Do test on a separate thread so that we don't affect
+        # the current context in the main thread.
+
+        exception_queue = Queue()
+        t = threading.Thread(target=newthread, args=(exception_queue,))
+        t.start()
+        t.join()
+
+        exceptions = []
+        while not exception_queue.empty():
+            exceptions.append(exception_queue.get())
+        self.assertEqual(exceptions, [])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_runtime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_runtime.py
new file mode 100644
index 000000000..aa6399f47
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_runtime.py
@@ -0,0 +1,86 @@
+import multiprocessing
+import os
+from numba.core import config
+from numba.cuda.cudadrv.runtime import runtime
+from numba.cuda.testing import unittest, SerialMixin, skip_on_cudasim
+from unittest.mock import patch
+
+
+def set_visible_devices_and_check(q):
+    try:
+        from numba import cuda
+        import os
+
+        os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+        q.put(len(cuda.gpus.lst))
+    except: # noqa: E722
+        # Sentinel value for error executing test code
+        q.put(-1)
+
+
+if config.ENABLE_CUDASIM:
+    SUPPORTED_VERSIONS = (-1, -1),
+else:
+    SUPPORTED_VERSIONS = ((9, 2),
+                          (10, 0), (10, 1), (10, 2),
+                          (11, 0), (11, 1), (11, 2))
+
+
+class TestRuntime(unittest.TestCase):
+    def test_is_supported_version_true(self):
+        for v in SUPPORTED_VERSIONS:
+            with patch.object(runtime, 'get_version', return_value=v):
+                self.assertTrue(runtime.is_supported_version())
+
+    @skip_on_cudasim('The simulator always simulates a supported runtime')
+    def test_is_supported_version_false(self):
+        # Check with an old unsupported version and some potential future
+        # versions
+        for v in ((8, 0), (11, 3), (12, 0)):
+            with patch.object(runtime, 'get_version', return_value=v):
+                self.assertFalse(runtime.is_supported_version())
+
+    def test_supported_versions(self):
+        self.assertEqual(SUPPORTED_VERSIONS, runtime.supported_versions)
+
+
+class TestVisibleDevices(unittest.TestCase, SerialMixin):
+    def test_visible_devices_set_after_import(self):
+        # See Issue #6149. This test checks that we can set
+        # CUDA_VISIBLE_DEVICES after importing Numba and have the value
+        # reflected in the available list of GPUs. Prior to the fix for this
+        # issue, Numba made a call to runtime.get_version() on import that
+        # initialized the driver and froze the list of available devices before
+        # CUDA_VISIBLE_DEVICES could be set by the user.
+
+        # Avoid importing cuda at the top level so that
+        # set_visible_devices_and_check gets to import it first in its process
+        from numba import cuda
+
+        if len(cuda.gpus.lst) in (0, 1):
+            self.skipTest('This test requires multiple GPUs')
+
+        if os.environ.get('CUDA_VISIBLE_DEVICES'):
+            msg = 'Cannot test when CUDA_VISIBLE_DEVICES already set'
+            self.skipTest(msg)
+
+        ctx = multiprocessing.get_context('spawn')
+        q = ctx.Queue()
+        p = ctx.Process(target=set_visible_devices_and_check, args=(q,))
+        p.start()
+        try:
+            visible_gpu_count = q.get()
+        finally:
+            p.join()
+
+        # Make an obvious distinction between an error running the test code
+        # and an incorrect number of GPUs in the list
+        msg = 'Error running set_visible_devices_and_check'
+        self.assertNotEqual(visible_gpu_count, -1, msg=msg)
+
+        # The actual check that we see only one GPU
+        self.assertEqual(visible_gpu_count, 1)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_select_device.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_select_device.py
new file mode 100644
index 000000000..aca78d94b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_select_device.py
@@ -0,0 +1,41 @@
+#
+# Test does not work on some cards.
+#
+import threading
+from queue import Queue
+
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, ContextResettingTestCase
+
+
+def newthread(exception_queue):
+    try:
+        cuda.select_device(0)
+        stream = cuda.stream()
+        A = np.arange(100)
+        dA = cuda.to_device(A, stream=stream)
+        stream.synchronize()
+        del dA
+        del stream
+        cuda.close()
+    except Exception as e:
+        exception_queue.put(e)
+
+
+class TestSelectDevice(ContextResettingTestCase):
+    def test_select_device(self):
+        exception_queue = Queue()
+        for i in range(10):
+            t = threading.Thread(target=newthread, args=(exception_queue,))
+            t.start()
+            t.join()
+
+        exceptions = []
+        while not exception_queue.empty():
+            exceptions.append(exception_queue.get())
+        self.assertEqual(exceptions, [])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_streams.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_streams.py
new file mode 100644
index 000000000..93028a374
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudadrv/test_streams.py
@@ -0,0 +1,111 @@
+import asyncio
+import functools
+import threading
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+
+
+def with_asyncio_loop(f):
+    @functools.wraps(f)
+    def runner(*args, **kwds):
+        loop = asyncio.new_event_loop()
+        loop.set_debug(True)
+        try:
+            return loop.run_until_complete(f(*args, **kwds))
+        finally:
+            loop.close()
+    return runner
+
+
+@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
+class TestCudaStream(CUDATestCase):
+    def test_add_callback(self):
+        def callback(stream, status, event):
+            event.set()
+
+        stream = cuda.stream()
+        callback_event = threading.Event()
+        stream.add_callback(callback, callback_event)
+        self.assertTrue(callback_event.wait(1.0))
+
+    @with_asyncio_loop
+    async def test_async_done(self):
+        stream = cuda.stream()
+        await stream.async_done()
+
+    @with_asyncio_loop
+    async def test_parallel_tasks(self):
+        async def async_cuda_fn(value_in: float) -> float:
+            stream = cuda.stream()
+            h_src, h_dst = cuda.pinned_array(8), cuda.pinned_array(8)
+            h_src[:] = value_in
+            d_ary = cuda.to_device(h_src, stream=stream)
+            d_ary.copy_to_host(h_dst, stream=stream)
+            done_result = await stream.async_done()
+            self.assertEqual(done_result, stream)
+            return h_dst.mean()
+
+        values_in = [1, 2, 3, 4]
+        tasks = [asyncio.create_task(async_cuda_fn(v)) for v in values_in]
+        values_out = await asyncio.gather(*tasks)
+        self.assertTrue(np.allclose(values_in, values_out))
+
+    @with_asyncio_loop
+    async def test_multiple_async_done(self):
+        stream = cuda.stream()
+        done_aws = [stream.async_done() for _ in range(4)]
+        done = await asyncio.gather(*done_aws)
+        for d in done:
+            self.assertEqual(d, stream)
+
+    @with_asyncio_loop
+    async def test_multiple_async_done_multiple_streams(self):
+        streams = [cuda.stream() for _ in range(4)]
+        done_aws = [stream.async_done() for stream in streams]
+        done = await asyncio.gather(*done_aws)
+
+        # Ensure we got the four original streams in done
+        self.assertSetEqual(set(done), set(streams))
+
+    @with_asyncio_loop
+    async def test_cancelled_future(self):
+        stream = cuda.stream()
+        done1, done2 = stream.async_done(), stream.async_done()
+        done1.cancel()
+        await done2
+        self.assertTrue(done1.cancelled())
+        self.assertTrue(done2.done())
+
+
+@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
+class TestFailingStream(CUDATestCase):
+    # This test can only be run in isolation because it corrupts the CUDA
+    # context, which cannot be recovered from within the same process. It is
+    # left here so that it can be run manually for debugging / testing purposes
+    # - or may be re-enabled if in future there is infrastructure added for
+    # running tests in a separate process (a subprocess cannot be used because
+    # CUDA will have been initialized before the fork, so it cannot be used in
+    # the child process).
+    @unittest.skip
+    @with_asyncio_loop
+    async def test_failed_stream(self):
+        ctx = cuda.current_context()
+        module = ctx.create_module_ptx("""
+            .version 6.5
+            .target sm_30
+            .address_size 64
+            .visible .entry failing_kernel() { trap; }
+        """)
+        failing_kernel = module.get_function("failing_kernel")
+
+        stream = cuda.stream()
+        failing_kernel.configure((1,), (1,), stream=stream).__call__()
+        done = stream.async_done()
+        with self.assertRaises(Exception):
+            await done
+        self.assertIsNotNone(done.exception())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/__init__.py
new file mode 100644
index 000000000..d9e7d31af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/__init__.py
@@ -0,0 +1,8 @@
+from numba.cuda.testing import ensure_supported_ccs_initialized
+from numba.testing import load_testsuite
+import os
+
+
+def load_tests(loader, tests, pattern):
+    ensure_supported_ccs_initialized()
+    return load_testsuite(loader, os.path.dirname(__file__))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/cache_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/cache_usecases.py
new file mode 100644
index 000000000..c6041c36d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/cache_usecases.py
@@ -0,0 +1,223 @@
+from numba import cuda
+from numba.cuda.testing import CUDATestCase
+import numpy as np
+import sys
+
+
+class UseCase:
+    """
+    Provide a way to call a kernel as if it were a function.
+
+    This allows the CUDA cache tests to closely match the CPU cache tests, and
+    also to support calling cache use cases as njitted functions. The class
+    wraps a function that takes an array for the return value and arguments,
+    and provides an interface that accepts arguments, launches the kernel
+    appropriately, and returns the stored return value.
+
+    The return type is inferred from the type of the first argument, unless it
+    is explicitly overridden by the ``retty`` kwarg.
+    """
+    def __init__(self, func, retty=None):
+        self._func = func
+        self._retty = retty
+
+    def __call__(self, *args):
+        array_args = [np.asarray(arg) for arg in args]
+        if self._retty:
+            array_return = np.ndarray((), dtype=self._retty)
+        else:
+            array_return = np.zeros_like(array_args[0])
+
+        self._call(array_return, *array_args)
+        return array_return[()]
+
+    @property
+    def func(self):
+        return self._func
+
+
+class CUDAUseCase(UseCase):
+    def _call(self, ret, *args):
+        self._func[1, 1](ret, *args)
+
+
+@cuda.jit(cache=True)
+def add_usecase_kernel(r, x, y):
+    r[()] = x[()] + y[()] + Z
+
+
+@cuda.jit(cache=False)
+def add_nocache_usecase_kernel(r, x, y):
+    r[()] = x[()] + y[()] + Z
+
+
+add_usecase = CUDAUseCase(add_usecase_kernel)
+add_nocache_usecase = CUDAUseCase(add_nocache_usecase_kernel)
+
+Z = 1
+
+
+# Inner / outer cached / uncached cases
+
+@cuda.jit(cache=True)
+def inner(x, y):
+    return x + y + Z
+
+
+@cuda.jit(cache=True)
+def outer_kernel(r, x, y):
+    r[()] = inner(-y[()], x[()])
+
+
+@cuda.jit(cache=False)
+def outer_uncached_kernel(r, x, y):
+    r[()] = inner(-y[()], x[()])
+
+
+outer = CUDAUseCase(outer_kernel)
+outer_uncached = CUDAUseCase(outer_uncached_kernel)
+
+
+# Exercise returning a record instance.  This used to hardcode the dtype
+# pointer's value in the bitcode.
+
+packed_record_type = np.dtype([('a', np.int8), ('b', np.float64)])
+aligned_record_type = np.dtype([('a', np.int8), ('b', np.float64)], align=True)
+
+packed_arr = np.empty(2, dtype=packed_record_type)
+for i in range(packed_arr.size):
+    packed_arr[i]['a'] = i + 1
+    packed_arr[i]['b'] = i + 42.5
+
+aligned_arr = np.array(packed_arr, dtype=aligned_record_type)
+
+
+@cuda.jit(cache=True)
+def record_return(r, ary, i):
+    r[()] = ary[i]
+
+
+record_return_packed = CUDAUseCase(record_return, retty=packed_record_type)
+record_return_aligned = CUDAUseCase(record_return, retty=aligned_record_type)
+
+
+# Closure test cases
+
+def make_closure(x):
+    @cuda.jit(cache=True)
+    def closure(r, y):
+        r[()] = x + y[()]
+
+    return CUDAUseCase(closure)
+
+
+closure1 = make_closure(3)
+closure2 = make_closure(5)
+closure3 = make_closure(7)
+closure4 = make_closure(9)
+
+
+# Ambiguous / renamed functions
+
+@cuda.jit(cache=True)
+def ambiguous_function(r, x):
+    r[()] = x[()] + 2
+
+
+renamed_function1 = CUDAUseCase(ambiguous_function)
+
+
+@cuda.jit(cache=True)
+def ambiguous_function(r, x):
+    r[()] = x[()] + 6
+
+
+renamed_function2 = CUDAUseCase(ambiguous_function)
+
+
+@cuda.jit(cache=True)
+def many_locals():
+    aa = cuda.local.array((1, 1), np.float64)
+    ab = cuda.local.array((1, 1), np.float64)
+    ac = cuda.local.array((1, 1), np.float64)
+    ad = cuda.local.array((1, 1), np.float64)
+    ae = cuda.local.array((1, 1), np.float64)
+    af = cuda.local.array((1, 1), np.float64)
+    ag = cuda.local.array((1, 1), np.float64)
+    ah = cuda.local.array((1, 1), np.float64)
+    ai = cuda.local.array((1, 1), np.float64)
+    aj = cuda.local.array((1, 1), np.float64)
+    ak = cuda.local.array((1, 1), np.float64)
+    al = cuda.local.array((1, 1), np.float64)
+    am = cuda.local.array((1, 1), np.float64)
+    an = cuda.local.array((1, 1), np.float64)
+    ao = cuda.local.array((1, 1), np.float64)
+    ap = cuda.local.array((1, 1), np.float64)
+    ar = cuda.local.array((1, 1), np.float64)
+    at = cuda.local.array((1, 1), np.float64)
+    au = cuda.local.array((1, 1), np.float64)
+    av = cuda.local.array((1, 1), np.float64)
+    aw = cuda.local.array((1, 1), np.float64)
+    ax = cuda.local.array((1, 1), np.float64)
+    ay = cuda.local.array((1, 1), np.float64)
+    az = cuda.local.array((1, 1), np.float64)
+
+    aa[:] = 0
+    ab[:] = 0
+    ac[:] = 0
+    ad[:] = 0
+    ae[:] = 0
+    af[:] = 0
+    ag[:] = 0
+    ah[:] = 0
+    ai[:] = 0
+    aj[:] = 0
+    ak[:] = 0
+    al[:] = 0
+    am[:] = 0
+    an[:] = 0
+    ao[:] = 0
+    ap[:] = 0
+    ar[:] = 0
+    at[:] = 0
+    au[:] = 0
+    av[:] = 0
+    aw[:] = 0
+    ax[:] = 0
+    ay[:] = 0
+    az[:] = 0
+
+
+# Simple use case for multiprocessing test
+
+@cuda.jit(cache=True)
+def simple_usecase_kernel(r, x):
+    r[()] = x[()]
+
+
+simple_usecase_caller = CUDAUseCase(simple_usecase_kernel)
+
+
+class _TestModule(CUDATestCase):
+    """
+    Tests for functionality of this module's functions.
+    Note this does not define any "test_*" method, instead check_module()
+    should be called by hand.
+    """
+
+    def check_module(self, mod):
+        self.assertPreciseEqual(mod.add_usecase(2, 3), 6)
+        self.assertPreciseEqual(mod.outer_uncached(3, 2), 2)
+        self.assertPreciseEqual(mod.outer(3, 2), 2)
+
+        packed_rec = mod.record_return_packed(mod.packed_arr, 1)
+        self.assertPreciseEqual(tuple(packed_rec), (2, 43.5))
+        aligned_rec = mod.record_return_aligned(mod.aligned_arr, 1)
+        self.assertPreciseEqual(tuple(aligned_rec), (2, 43.5))
+
+        mod.simple_usecase_caller(2)
+
+
+def self_test():
+    mod = sys.modules[__name__]
+    _TestModule().check_module(mod)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/cache_with_cpu_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/cache_with_cpu_usecases.py
new file mode 100644
index 000000000..07b42d755
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/cache_with_cpu_usecases.py
@@ -0,0 +1,41 @@
+import sys
+
+from numba import cuda, njit
+from numba.cuda.testing import CUDATestCase
+from numba.cuda.tests.cudapy.cache_usecases import CUDAUseCase, UseCase
+
+
+class CPUUseCase(UseCase):
+    def _call(self, ret, *args):
+        self._func(ret, *args)
+
+
+# Using the same function as a cached CPU and CUDA-jitted function
+
+def target_shared_assign(r, x):
+    r[()] = x[()]
+
+
+assign_cuda_kernel = cuda.jit(cache=True)(target_shared_assign)
+assign_cuda = CUDAUseCase(assign_cuda_kernel)
+assign_cpu_jitted = njit(cache=True)(target_shared_assign)
+assign_cpu = CPUUseCase(assign_cpu_jitted)
+
+
+class _TestModule(CUDATestCase):
+    """
+    Tests for functionality of this module's functions.
+    Note this does not define any "test_*" method, instead check_module()
+    should be called by hand.
+    """
+
+    def check_module(self, mod):
+        self.assertPreciseEqual(mod.assign_cpu(5), 5)
+        self.assertPreciseEqual(mod.assign_cpu(5.5), 5.5)
+        self.assertPreciseEqual(mod.assign_cuda(5), 5)
+        self.assertPreciseEqual(mod.assign_cuda(5.5), 5.5)
+
+
+def self_test():
+    mod = sys.modules[__name__]
+    _TestModule().check_module(mod)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/extensions_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/extensions_usecases.py
new file mode 100644
index 000000000..1e639d379
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/extensions_usecases.py
@@ -0,0 +1,58 @@
+from numba import types
+from numba.core import config
+
+
+class TestStruct:
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+
+
+class TestStructModelType(types.Type):
+    def __init__(self):
+        super().__init__(name="TestStructModelType")
+
+
+test_struct_model_type = TestStructModelType()
+
+
+if not config.ENABLE_CUDASIM:
+    from numba import int32
+    from numba.core.extending import (
+        models,
+        register_model,
+        make_attribute_wrapper,
+        typeof_impl,
+        type_callable
+    )
+    from numba.cuda.cudaimpl import lower
+    from numba.core import cgutils
+
+    @typeof_impl.register(TestStruct)
+    def typeof_teststruct(val, c):
+        return test_struct_model_type
+
+    @register_model(TestStructModelType)
+    class TestStructModel(models.StructModel):
+        def __init__(self, dmm, fe_type):
+            members = [("x", int32), ("y", int32)]
+            super().__init__(dmm, fe_type, members)
+
+    make_attribute_wrapper(TestStructModelType, 'x', 'x')
+    make_attribute_wrapper(TestStructModelType, 'y', 'y')
+
+    @type_callable(TestStruct)
+    def type_test_struct(context):
+        def typer(x, y):
+            if isinstance(x, types.Integer) and isinstance(y, types.Integer):
+                return test_struct_model_type
+        return typer
+
+    @lower(TestStruct, types.Integer, types.Integer)
+    def lower_test_type_ctor(context, builder, sig, args):
+        obj = cgutils.create_struct_proxy(
+            test_struct_model_type
+        )(context, builder)
+        obj.x = args[0]
+        obj.y = args[1]
+        return obj._getvalue()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/jitlink.ptx b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/jitlink.ptx
new file mode 100644
index 000000000..8cc1aa6d6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/jitlink.ptx
@@ -0,0 +1,30 @@
+//
+// Generated by NVIDIA NVVM Compiler
+// Compiler built on Tue Apr  1 03:34:02 2014 (1396341242)
+// Cuda compilation tools, release 6.0, V6.0.1
+//
+
+.version 4.0
+.target sm_20
+.address_size 64
+
+
+.visible .func  (.param .b32 func_retval0) bar(
+	.param .b64 bar_param_0,
+	.param .b32 bar_param_1
+)
+{
+	.reg .s32 	%r<4>;
+	.reg .s64 	%rd<2>;
+
+
+	ld.param.u64 	%rd1, [bar_param_0];
+	ld.param.u32 	%r1, [bar_param_1];
+	shl.b32 	%r2, %r1, 1;
+	st.u32 	[%rd1], %r2;
+	mov.u32 	%r3, 0;
+	st.param.b32	[func_retval0+0], %r3;
+	ret;
+}
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/recursion_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/recursion_usecases.py
new file mode 100644
index 000000000..b182359b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/recursion_usecases.py
@@ -0,0 +1,100 @@
+"""
+Usecases of recursive functions in the CUDA target, many derived from
+numba/tests/recursion_usecases.py.
+
+Some functions are compiled at import time, hence a separate module.
+"""
+
+from numba import cuda
+
+
+@cuda.jit("i8(i8)", device=True)
+def fib1(n):
+    if n < 2:
+        return n
+    # Note the second call does not use a named argument, unlike the CPU target
+    # usecase
+    return fib1(n - 1) + fib1(n - 2)
+
+
+def make_fib2():
+    @cuda.jit("i8(i8)", device=True)
+    def fib2(n):
+        if n < 2:
+            return n
+        return fib2(n - 1) + fib2(n - 2)
+
+    return fib2
+
+
+fib2 = make_fib2()
+
+
+@cuda.jit
+def type_change_self(x, y):
+    if x > 1 and y > 0:
+        return x + type_change_self(x - y, y)
+    else:
+        return y
+
+
+# Implicit signature
+@cuda.jit(device=True)
+def fib3(n):
+    if n < 2:
+        return n
+
+    return fib3(n - 1) + fib3(n - 2)
+
+
+# Run-away self recursion
+@cuda.jit(device=True)
+def runaway_self(x):
+    return runaway_self(x)
+
+
+@cuda.jit(device=True)
+def raise_self(x):
+    if x == 1:
+        raise ValueError("raise_self")
+    elif x > 0:
+        return raise_self(x - 1)
+    else:
+        return 1
+
+
+@cuda.jit(debug=True, opt=False)
+def raise_self_kernel(x):
+    raise_self(x)
+
+
+def make_optional_return_case(jit=lambda x: x):
+    @jit
+    def foo(x):
+        if x > 5:
+            return x - 1
+        else:
+            return
+
+    @jit
+    def bar(x):
+        out = foo(x)
+        if out is None:
+            return out
+        elif out < 8:
+            return out
+        else:
+            return x * bar(out)
+
+    return bar
+
+
+def make_growing_tuple_case(jit=lambda x: x):
+    # From issue #4387
+    @jit
+    def make_list(n):
+        if n <= 0:
+            return None
+
+        return (n, make_list(n - 1))
+    return make_list
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_alignment.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_alignment.py
new file mode 100644
index 000000000..7c7dff8ca
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_alignment.py
@@ -0,0 +1,42 @@
+import numpy as np
+from numba import from_dtype, cuda
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+
+
+class TestAlignment(CUDATestCase):
+    def test_record_alignment(self):
+        rec_dtype = np.dtype([('a', 'int32'), ('b', 'float64')], align=True)
+        rec = from_dtype(rec_dtype)
+
+        @cuda.jit((rec[:],))
+        def foo(a):
+            i = cuda.grid(1)
+            a[i].a = a[i].b
+
+        a_recarray = np.recarray(3, dtype=rec_dtype)
+        for i in range(a_recarray.size):
+            a_rec = a_recarray[i]
+            a_rec.a = 0
+            a_rec.b = (i + 1) * 123
+
+        foo[1, 3](a_recarray)
+
+        self.assertTrue(np.all(a_recarray.a == a_recarray.b))
+
+    @skip_on_cudasim('Simulator does not check alignment')
+    def test_record_alignment_error(self):
+        rec_dtype = np.dtype([('a', 'int32'), ('b', 'float64')])
+        rec = from_dtype(rec_dtype)
+
+        with self.assertRaises(Exception) as raises:
+            @cuda.jit((rec[:],))
+            def foo(a):
+                i = cuda.grid(1)
+                a[i].a = a[i].b
+
+        self.assertTrue('type float64 is not aligned' in str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array.py
new file mode 100644
index 000000000..79899daf1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array.py
@@ -0,0 +1,260 @@
+import numpy as np
+
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import skip_on_cudasim, skip_unless_cudasim
+from numba import config, cuda
+
+
+if config.ENABLE_CUDASIM:
+    ARRAY_LIKE_FUNCTIONS = (cuda.device_array_like, cuda.pinned_array_like)
+else:
+    ARRAY_LIKE_FUNCTIONS = (cuda.device_array_like, cuda.mapped_array_like,
+                            cuda.pinned_array_like)
+
+
+class TestCudaArray(CUDATestCase):
+    def test_gpu_array_zero_length(self):
+        x = np.arange(0)
+        dx = cuda.to_device(x)
+        hx = dx.copy_to_host()
+        self.assertEqual(x.shape, dx.shape)
+        self.assertEqual(x.size, dx.size)
+        self.assertEqual(x.shape, hx.shape)
+        self.assertEqual(x.size, hx.size)
+
+    def test_null_shape(self):
+        null_shape = ()
+        shape1 = cuda.device_array(()).shape
+        shape2 = cuda.device_array_like(np.ndarray(())).shape
+        self.assertEqual(shape1, null_shape)
+        self.assertEqual(shape2, null_shape)
+
+    def test_gpu_array_strided(self):
+
+        @cuda.jit('void(double[:])')
+        def kernel(x):
+            i = cuda.grid(1)
+            if i < x.shape[0]:
+                x[i] = i
+
+        x = np.arange(10, dtype=np.double)
+        y = np.ndarray(shape=10 * 8, buffer=x, dtype=np.byte)
+        z = np.ndarray(9, buffer=y[4:-4], dtype=np.double)
+        kernel[10, 10](z)
+        self.assertTrue(np.allclose(z, list(range(9))))
+
+    def test_gpu_array_interleaved(self):
+
+        @cuda.jit('void(double[:], double[:])')
+        def copykernel(x, y):
+            i = cuda.grid(1)
+            if i < x.shape[0]:
+                x[i] = i
+                y[i] = i
+
+        x = np.arange(10, dtype=np.double)
+        y = x[:-1:2]
+        # z = x[1::2]
+        # n = y.size
+        try:
+            cuda.devicearray.auto_device(y)
+        except ValueError:
+            pass
+        else:
+            raise AssertionError("Should raise exception complaining the "
+                                 "contiguous-ness of the array.")
+            # Should we handle this use case?
+            # assert z.size == y.size
+            # copykernel[1, n](y, x)
+            # print(y, z)
+            # assert np.all(y == z)
+            # assert np.all(y == list(range(n)))
+
+    def test_auto_device_const(self):
+        d, _ = cuda.devicearray.auto_device(2)
+        self.assertTrue(np.all(d.copy_to_host() == np.array(2)))
+
+    def _test_array_like_same(self, like_func, array):
+        """
+        Tests of *_array_like where shape, strides, dtype, and flags should
+        all be equal.
+        """
+        array_like = like_func(array)
+        self.assertEqual(array.shape, array_like.shape)
+        self.assertEqual(array.strides, array_like.strides)
+        self.assertEqual(array.dtype, array_like.dtype)
+        self.assertEqual(array.flags['C_CONTIGUOUS'],
+                         array_like.flags['C_CONTIGUOUS'])
+        self.assertEqual(array.flags['F_CONTIGUOUS'],
+                         array_like.flags['F_CONTIGUOUS'])
+
+    def test_array_like_1d(self):
+        d_a = cuda.device_array(10, order='C')
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_same(like_func, d_a)
+
+    def test_array_like_2d(self):
+        d_a = cuda.device_array((10, 12), order='C')
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_same(like_func, d_a)
+
+    def test_array_like_2d_transpose(self):
+        d_a = cuda.device_array((10, 12), order='C')
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_same(like_func, d_a)
+
+    def test_array_like_3d(self):
+        d_a = cuda.device_array((10, 12, 14), order='C')
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_same(like_func, d_a)
+
+    def test_array_like_1d_f(self):
+        d_a = cuda.device_array(10, order='F')
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_same(like_func, d_a)
+
+    def test_array_like_2d_f(self):
+        d_a = cuda.device_array((10, 12), order='F')
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_same(like_func, d_a)
+
+    def test_array_like_2d_f_transpose(self):
+        d_a = cuda.device_array((10, 12), order='F')
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_same(like_func, d_a)
+
+    def test_array_like_3d_f(self):
+        d_a = cuda.device_array((10, 12, 14), order='F')
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_same(like_func, d_a)
+
+    def _test_array_like_view(self, like_func, view, d_view):
+        """
+        Tests of device_array_like where the original array is a view - the
+        strides should not be equal because a contiguous array is expected.
+        """
+        nb_like = like_func(d_view)
+        self.assertEqual(d_view.shape, nb_like.shape)
+        self.assertEqual(d_view.dtype, nb_like.dtype)
+
+        # Use NumPy as a reference for the expected strides
+        np_like = np.zeros_like(view)
+        self.assertEqual(nb_like.strides, np_like.strides)
+        self.assertEqual(nb_like.flags['C_CONTIGUOUS'],
+                         np_like.flags['C_CONTIGUOUS'])
+        self.assertEqual(nb_like.flags['F_CONTIGUOUS'],
+                         np_like.flags['F_CONTIGUOUS'])
+
+    def test_array_like_1d_view(self):
+        shape = 10
+        view = np.zeros(shape)[::2]
+        d_view = cuda.device_array(shape)[::2]
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_view(like_func, view, d_view)
+
+    def test_array_like_1d_view_f(self):
+        shape = 10
+        view = np.zeros(shape, order='F')[::2]
+        d_view = cuda.device_array(shape, order='F')[::2]
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_view(like_func, view, d_view)
+
+    def test_array_like_2d_view(self):
+        shape = (10, 12)
+        view = np.zeros(shape)[::2, ::2]
+        d_view = cuda.device_array(shape)[::2, ::2]
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_view(like_func, view, d_view)
+
+    def test_array_like_2d_view_f(self):
+        shape = (10, 12)
+        view = np.zeros(shape, order='F')[::2, ::2]
+        d_view = cuda.device_array(shape, order='F')[::2, ::2]
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_view(like_func, view, d_view)
+
+    @skip_on_cudasim('Numba and NumPy stride semantics differ for transpose')
+    def test_array_like_2d_view_transpose_device(self):
+        shape = (10, 12)
+        d_view = cuda.device_array(shape)[::2, ::2].T
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                # This is a special case (see issue #4974) because creating the
+                # transpose creates a new contiguous allocation with different
+                # strides.  In this case, rather than comparing against NumPy,
+                # we can only compare against expected values.
+                like = like_func(d_view)
+                self.assertEqual(d_view.shape, like.shape)
+                self.assertEqual(d_view.dtype, like.dtype)
+                self.assertEqual((40, 8), like.strides)
+                self.assertTrue(like.flags['C_CONTIGUOUS'])
+                self.assertFalse(like.flags['F_CONTIGUOUS'])
+
+    @skip_unless_cudasim('Numba and NumPy stride semantics differ for '
+                         'transpose')
+    def test_array_like_2d_view_transpose_simulator(self):
+        shape = (10, 12)
+        view = np.zeros(shape)[::2, ::2].T
+        d_view = cuda.device_array(shape)[::2, ::2].T
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                # On the simulator, the transpose has different strides to on a
+                # CUDA device (See issue #4974). Here we can compare strides
+                # against NumPy as a reference.
+                np_like = np.zeros_like(view)
+                nb_like = like_func(d_view)
+                self.assertEqual(d_view.shape, nb_like.shape)
+                self.assertEqual(d_view.dtype, nb_like.dtype)
+                self.assertEqual(np_like.strides, nb_like.strides)
+                self.assertEqual(np_like.flags['C_CONTIGUOUS'],
+                                 nb_like.flags['C_CONTIGUOUS'])
+                self.assertEqual(np_like.flags['F_CONTIGUOUS'],
+                                 nb_like.flags['F_CONTIGUOUS'])
+
+    def test_array_like_2d_view_f_transpose(self):
+        shape = (10, 12)
+        view = np.zeros(shape, order='F')[::2, ::2].T
+        d_view = cuda.device_array(shape, order='F')[::2, ::2].T
+        for like_func in ARRAY_LIKE_FUNCTIONS:
+            with self.subTest(like_func=like_func):
+                self._test_array_like_view(like_func, view, d_view)
+
+    @skip_on_cudasim('Kernel overloads not created in the simulator')
+    def test_issue_4628(self):
+        # CUDA Device arrays were reported as always being typed with 'A' order
+        # so launching the kernel with a host array and then a device array
+        # resulted in two overloads being compiled - one for 'C' order from
+        # the host array, and one for 'A' order from the device array. With the
+        # resolution of this issue, the order of the device array is also 'C',
+        # so after the kernel launches there should only be one overload of
+        # the function.
+        @cuda.jit
+        def func(A, out):
+            i = cuda.grid(1)
+            out[i] = A[i] * 2
+
+        n = 128
+        a = np.ones((n,))
+        d_a = cuda.to_device(a)
+        result = np.zeros((n,))
+
+        func[1, 128](a, result)
+        func[1, 128](d_a, result)
+
+        self.assertEqual(1, len(func.overloads))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array_args.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array_args.py
new file mode 100644
index 000000000..87db4a6c7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array_args.py
@@ -0,0 +1,201 @@
+import numpy as np
+from collections import namedtuple
+
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaArrayArg(CUDATestCase):
+    def test_array_ary(self):
+
+        @cuda.jit('double(double[:],int64)', device=True, inline=True)
+        def device_function(a, c):
+            return a[c]
+
+        @cuda.jit('void(double[:],double[:])')
+        def kernel(x, y):
+            i = cuda.grid(1)
+            y[i] = device_function(x, i)
+
+        x = np.arange(10, dtype=np.double)
+        y = np.zeros_like(x)
+        kernel[10, 1](x, y)
+        self.assertTrue(np.all(x == y))
+
+    def test_unituple(self):
+        @cuda.jit
+        def f(r, x):
+            r[0] = x[0]
+            r[1] = x[1]
+            r[2] = x[2]
+
+        x = (1, 2, 3)
+        r = np.zeros(len(x), dtype=np.int64)
+        f[1, 1](r, x)
+
+        for i in range(len(x)):
+            self.assertEqual(r[i], x[i])
+
+    def test_tuple(self):
+        @cuda.jit
+        def f(r1, r2, x):
+            r1[0] = x[0]
+            r1[1] = x[1]
+            r1[2] = x[2]
+            r2[0] = x[3]
+            r2[1] = x[4]
+            r2[2] = x[5]
+
+        x = (1, 2, 3, 4.5, 5.5, 6.5)
+        r1 = np.zeros(len(x) // 2, dtype=np.int64)
+        r2 = np.zeros(len(x) // 2, dtype=np.float64)
+        f[1, 1](r1, r2, x)
+
+        for i in range(len(r1)):
+            self.assertEqual(r1[i], x[i])
+
+        for i in range(len(r2)):
+            self.assertEqual(r2[i], x[i + len(r1)])
+
+    def test_namedunituple(self):
+        @cuda.jit
+        def f(r, x):
+            r[0] = x.x
+            r[1] = x.y
+
+        Point = namedtuple('Point', ('x', 'y'))
+        x = Point(1, 2)
+        r = np.zeros(len(x), dtype=np.int64)
+        f[1, 1](r, x)
+
+        self.assertEqual(r[0], x.x)
+        self.assertEqual(r[1], x.y)
+
+    def test_namedtuple(self):
+        @cuda.jit
+        def f(r1, r2, x):
+            r1[0] = x.x
+            r1[1] = x.y
+            r2[0] = x.r
+
+        Point = namedtuple('Point', ('x', 'y', 'r'))
+        x = Point(1, 2, 2.236)
+        r1 = np.zeros(2, dtype=np.int64)
+        r2 = np.zeros(1, dtype=np.float64)
+        f[1, 1](r1, r2, x)
+
+        self.assertEqual(r1[0], x.x)
+        self.assertEqual(r1[1], x.y)
+        self.assertEqual(r2[0], x.r)
+
+    def test_empty_tuple(self):
+        @cuda.jit
+        def f(r, x):
+            r[0] = len(x)
+
+        x = tuple()
+        r = np.ones(1, dtype=np.int64)
+        f[1, 1](r, x)
+
+        self.assertEqual(r[0], 0)
+
+    def test_tuple_of_empty_tuples(self):
+        @cuda.jit
+        def f(r, x):
+            r[0] = len(x)
+            r[1] = len(x[0])
+
+        x = ((), (), ())
+        r = np.ones(2, dtype=np.int64)
+        f[1, 1](r, x)
+
+        self.assertEqual(r[0], 3)
+        self.assertEqual(r[1], 0)
+
+    def test_tuple_of_tuples(self):
+        @cuda.jit
+        def f(r, x):
+            r[0] = len(x)
+            r[1] = len(x[0])
+            r[2] = len(x[1])
+            r[3] = len(x[2])
+            r[4] = x[1][0]
+            r[5] = x[1][1]
+            r[6] = x[2][0]
+            r[7] = x[2][1]
+            r[8] = x[2][2]
+
+        x = ((), (5, 6), (8, 9, 10))
+        r = np.ones(9, dtype=np.int64)
+        f[1, 1](r, x)
+
+        self.assertEqual(r[0], 3)
+        self.assertEqual(r[1], 0)
+        self.assertEqual(r[2], 2)
+        self.assertEqual(r[3], 3)
+        self.assertEqual(r[4], 5)
+        self.assertEqual(r[5], 6)
+        self.assertEqual(r[6], 8)
+        self.assertEqual(r[7], 9)
+        self.assertEqual(r[8], 10)
+
+    def test_tuple_of_tuples_and_scalars(self):
+        @cuda.jit
+        def f(r, x):
+            r[0] = len(x)
+            r[1] = len(x[0])
+            r[2] = x[0][0]
+            r[3] = x[0][1]
+            r[4] = x[0][2]
+            r[5] = x[1]
+
+        x = ((6, 5, 4), 7)
+        r = np.ones(9, dtype=np.int64)
+        f[1, 1](r, x)
+
+        self.assertEqual(r[0], 2)
+        self.assertEqual(r[1], 3)
+        self.assertEqual(r[2], 6)
+        self.assertEqual(r[3], 5)
+        self.assertEqual(r[4], 4)
+        self.assertEqual(r[5], 7)
+
+    def test_tuple_of_arrays(self):
+        @cuda.jit
+        def f(x):
+            i = cuda.grid(1)
+            if i < len(x[0]):
+                x[0][i] = x[1][i] + x[2][i]
+
+        N = 10
+        x0 = np.zeros(N)
+        x1 = np.ones_like(x0)
+        x2 = x1 * 3
+        x = (x0, x1, x2)
+        f[1, N](x)
+
+        np.testing.assert_equal(x0, x1 + x2)
+
+    def test_tuple_of_array_scalar_tuple(self):
+        @cuda.jit
+        def f(r, x):
+            r[0] = x[0][0]
+            r[1] = x[0][1]
+            r[2] = x[1]
+            r[3] = x[2][0]
+            r[4] = x[2][1]
+
+        z = np.arange(2, dtype=np.int64)
+        x = (2 * z, 10, (4, 3))
+        r = np.zeros(5, dtype=np.int64)
+        f[1, 1](r, x)
+
+        self.assertEqual(r[0], 0)
+        self.assertEqual(r[1], 2)
+        self.assertEqual(r[2], 10)
+        self.assertEqual(r[3], 4)
+        self.assertEqual(r[4], 3)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array_methods.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array_methods.py
new file mode 100644
index 000000000..7f129b5df
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_array_methods.py
@@ -0,0 +1,35 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import CUDATestCase
+import unittest
+
+
+def reinterpret_array_type(byte_arr, start, stop, output):
+    # Tested with just one thread
+    val = byte_arr[start:stop].view(np.int32)[0]
+    output[0] = val
+
+
+class TestCudaArrayMethods(CUDATestCase):
+    def test_reinterpret_array_type(self):
+        """
+        Reinterpret byte array as int32 in the GPU.
+        """
+        pyfunc = reinterpret_array_type
+        kernel = cuda.jit(pyfunc)
+
+        byte_arr = np.arange(256, dtype=np.uint8)
+        itemsize = np.dtype(np.int32).itemsize
+        for start in range(0, 256, itemsize):
+            stop = start + itemsize
+            expect = byte_arr[start:stop].view(np.int32)[0]
+
+            output = np.zeros(1, dtype=np.int32)
+            kernel[1, 1](byte_arr, start, stop, output)
+
+            got = output[0]
+            self.assertEqual(expect, got)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_atomics.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_atomics.py
new file mode 100644
index 000000000..81a223b40
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_atomics.py
@@ -0,0 +1,1559 @@
+import numpy as np
+from textwrap import dedent
+
+from numba import cuda, uint32, uint64, float32, float64
+from numba.cuda.testing import (unittest, CUDATestCase, skip_unless_cc_50,
+                                cc_X_or_above)
+from numba.core import config
+
+
+@cuda.jit(device=True)
+def atomic_cast_to_uint64(num):
+    return uint64(num)
+
+
+@cuda.jit(device=True)
+def atomic_cast_to_int(num):
+    return int(num)
+
+
+@cuda.jit(device=True)
+def atomic_cast_none(num):
+    return num
+
+
+@cuda.jit(device=True)
+def atomic_binary_1dim_shared(ary, idx, op2, ary_dtype, ary_nelements,
+                              binop_func, cast_func, initializer,
+                              neg_idx):
+    tid = cuda.threadIdx.x
+    sm = cuda.shared.array(ary_nelements, ary_dtype)
+    sm[tid] = initializer
+    cuda.syncthreads()
+    bin = cast_func(idx[tid] % ary_nelements)
+    if neg_idx:
+        bin = bin - ary_nelements
+    binop_func(sm, bin, op2)
+    cuda.syncthreads()
+    ary[tid] = sm[tid]
+
+
+@cuda.jit(device=True)
+def atomic_binary_1dim_shared2(ary, idx, op2, ary_dtype, ary_nelements,
+                               binop_func, cast_func):
+    tid = cuda.threadIdx.x
+    sm = cuda.shared.array(ary_nelements, ary_dtype)
+    sm[tid] = ary[tid]
+    cuda.syncthreads()
+    bin = cast_func(idx[tid] % ary_nelements)
+    binop_func(sm, bin, op2)
+    cuda.syncthreads()
+    ary[tid] = sm[tid]
+
+
+@cuda.jit(device=True)
+def atomic_binary_2dim_shared(ary, op2, ary_dtype, ary_shape,
+                              binop_func, y_cast_func, neg_idx):
+    tx = cuda.threadIdx.x
+    ty = cuda.threadIdx.y
+    sm = cuda.shared.array(ary_shape, ary_dtype)
+    sm[tx, ty] = ary[tx, ty]
+    cuda.syncthreads()
+    bin = (tx, y_cast_func(ty))
+    if neg_idx:
+        bin = (bin[0] - ary_shape[0], bin[1] - ary_shape[1])
+    binop_func(sm, bin, op2)
+    cuda.syncthreads()
+    ary[tx, ty] = sm[tx, ty]
+
+
+@cuda.jit(device=True)
+def atomic_binary_2dim_global(ary, op2, binop_func, y_cast_func, neg_idx):
+    tx = cuda.threadIdx.x
+    ty = cuda.threadIdx.y
+    bin = (tx, y_cast_func(ty))
+    if neg_idx:
+        bin = (bin[0] - ary.shape[0], bin[1] - ary.shape[1])
+    binop_func(ary, bin, op2)
+
+
+@cuda.jit(device=True)
+def atomic_binary_1dim_global(ary, idx, ary_nelements, op2,
+                              binop_func, neg_idx):
+    tid = cuda.threadIdx.x
+    bin = int(idx[tid] % ary_nelements)
+    if neg_idx:
+        bin = bin - ary_nelements
+    binop_func(ary, bin, op2)
+
+
+def atomic_add(ary):
+    atomic_binary_1dim_shared(ary, ary, 1, uint32, 32,
+                              cuda.atomic.add, atomic_cast_none, 0, False)
+
+
+def atomic_add_wrap(ary):
+    atomic_binary_1dim_shared(ary, ary, 1, uint32, 32,
+                              cuda.atomic.add, atomic_cast_none, 0, True)
+
+
+def atomic_add2(ary):
+    atomic_binary_2dim_shared(ary, 1, uint32, (4, 8),
+                              cuda.atomic.add, atomic_cast_none, False)
+
+
+def atomic_add2_wrap(ary):
+    atomic_binary_2dim_shared(ary, 1, uint32, (4, 8),
+                              cuda.atomic.add, atomic_cast_none, True)
+
+
+def atomic_add3(ary):
+    atomic_binary_2dim_shared(ary, 1, uint32, (4, 8),
+                              cuda.atomic.add, atomic_cast_to_uint64, False)
+
+
+def atomic_add_float(ary):
+    atomic_binary_1dim_shared(ary, ary, 1.0, float32, 32,
+                              cuda.atomic.add, atomic_cast_to_int, 0.0, False)
+
+
+def atomic_add_float_wrap(ary):
+    atomic_binary_1dim_shared(ary, ary, 1.0, float32, 32,
+                              cuda.atomic.add, atomic_cast_to_int, 0.0, True)
+
+
+def atomic_add_float_2(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float32, (4, 8),
+                              cuda.atomic.add, atomic_cast_none, False)
+
+
+def atomic_add_float_2_wrap(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float32, (4, 8),
+                              cuda.atomic.add, atomic_cast_none, True)
+
+
+def atomic_add_float_3(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float32, (4, 8),
+                              cuda.atomic.add, atomic_cast_to_uint64, False)
+
+
+def atomic_add_double_global(idx, ary):
+    atomic_binary_1dim_global(ary, idx, 32, 1.0, cuda.atomic.add, False)
+
+
+def atomic_add_double_global_wrap(idx, ary):
+    atomic_binary_1dim_global(ary, idx, 32, 1.0, cuda.atomic.add, True)
+
+
+def atomic_add_double_global_2(ary):
+    atomic_binary_2dim_global(ary, 1, cuda.atomic.add, atomic_cast_none, False)
+
+
+def atomic_add_double_global_2_wrap(ary):
+    atomic_binary_2dim_global(ary, 1, cuda.atomic.add, atomic_cast_none, True)
+
+
+def atomic_add_double_global_3(ary):
+    atomic_binary_2dim_global(ary, 1, cuda.atomic.add, atomic_cast_to_uint64,
+                              False)
+
+
+def atomic_add_double(idx, ary):
+    atomic_binary_1dim_shared(ary, idx, 1.0, float64, 32,
+                              cuda.atomic.add, atomic_cast_none, 0.0, False)
+
+
+def atomic_add_double_wrap(idx, ary):
+    atomic_binary_1dim_shared(ary, idx, 1.0, float64, 32,
+                              cuda.atomic.add, atomic_cast_none, 0.0, True)
+
+
+def atomic_add_double_2(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float64, (4, 8),
+                              cuda.atomic.add, atomic_cast_none, False)
+
+
+def atomic_add_double_2_wrap(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float64, (4, 8),
+                              cuda.atomic.add, atomic_cast_none, True)
+
+
+def atomic_add_double_3(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float64, (4, 8),
+                              cuda.atomic.add, atomic_cast_to_uint64, False)
+
+
+def atomic_sub(ary):
+    atomic_binary_1dim_shared(ary, ary, 1, uint32, 32,
+                              cuda.atomic.sub, atomic_cast_none, 0, False)
+
+
+def atomic_sub2(ary):
+    atomic_binary_2dim_shared(ary, 1, uint32, (4, 8),
+                              cuda.atomic.sub, atomic_cast_none, False)
+
+
+def atomic_sub3(ary):
+    atomic_binary_2dim_shared(ary, 1, uint32, (4, 8),
+                              cuda.atomic.sub, atomic_cast_to_uint64, False)
+
+
+def atomic_sub_float(ary):
+    atomic_binary_1dim_shared(ary, ary, 1.0, float32, 32,
+                              cuda.atomic.sub, atomic_cast_to_int, 0.0, False)
+
+
+def atomic_sub_float_2(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float32, (4, 8),
+                              cuda.atomic.sub, atomic_cast_none, False)
+
+
+def atomic_sub_float_3(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float32, (4, 8),
+                              cuda.atomic.sub, atomic_cast_to_uint64, False)
+
+
+def atomic_sub_double(idx, ary):
+    atomic_binary_1dim_shared(ary, idx, 1.0, float64, 32,
+                              cuda.atomic.sub, atomic_cast_none, 0.0, False)
+
+
+def atomic_sub_double_2(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float64, (4, 8),
+                              cuda.atomic.sub, atomic_cast_none, False)
+
+
+def atomic_sub_double_3(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float64, (4, 8),
+                              cuda.atomic.sub, atomic_cast_to_uint64, False)
+
+
+def atomic_sub_double_global(idx, ary):
+    atomic_binary_1dim_global(ary, idx, 32, 1.0, cuda.atomic.sub, False)
+
+
+def atomic_sub_double_global_2(ary):
+    atomic_binary_2dim_global(ary, 1.0, cuda.atomic.sub, atomic_cast_none,
+                              False)
+
+
+def atomic_sub_double_global_3(ary):
+    atomic_binary_2dim_shared(ary, 1.0, float64, (4, 8),
+                              cuda.atomic.sub, atomic_cast_to_uint64, False)
+
+
+def atomic_and(ary, op2):
+    atomic_binary_1dim_shared(ary, ary, op2, uint32, 32,
+                              cuda.atomic.and_, atomic_cast_none, 1, False)
+
+
+def atomic_and2(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.and_, atomic_cast_none, False)
+
+
+def atomic_and3(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.and_, atomic_cast_to_uint64, False)
+
+
+def atomic_and_global(idx, ary, op2):
+    atomic_binary_1dim_global(ary, idx, 32, op2, cuda.atomic.and_, False)
+
+
+def atomic_and_global_2(ary, op2):
+    atomic_binary_2dim_global(ary, op2, cuda.atomic.and_,
+                              atomic_cast_none, False)
+
+
+def atomic_or(ary, op2):
+    atomic_binary_1dim_shared(ary, ary, op2, uint32, 32,
+                              cuda.atomic.or_, atomic_cast_none, 0, False)
+
+
+def atomic_or2(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.or_, atomic_cast_none, False)
+
+
+def atomic_or3(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.or_, atomic_cast_to_uint64, False)
+
+
+def atomic_or_global(idx, ary, op2):
+    atomic_binary_1dim_global(ary, idx, 32, op2, cuda.atomic.or_, False)
+
+
+def atomic_or_global_2(ary, op2):
+    atomic_binary_2dim_global(ary, op2, cuda.atomic.or_,
+                              atomic_cast_none, False)
+
+
+def atomic_xor(ary, op2):
+    atomic_binary_1dim_shared(ary, ary, op2, uint32, 32,
+                              cuda.atomic.xor, atomic_cast_none, 0, False)
+
+
+def atomic_xor2(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.xor, atomic_cast_none, False)
+
+
+def atomic_xor3(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.xor, atomic_cast_to_uint64, False)
+
+
+def atomic_xor_global(idx, ary, op2):
+    atomic_binary_1dim_global(ary, idx, 32, op2, cuda.atomic.xor, False)
+
+
+def atomic_xor_global_2(ary, op2):
+    atomic_binary_2dim_global(ary, op2, cuda.atomic.xor,
+                              atomic_cast_none, False)
+
+
+def atomic_inc32(ary, idx, op2):
+    atomic_binary_1dim_shared2(ary, idx, op2, uint32, 32,
+                               cuda.atomic.inc, atomic_cast_none)
+
+
+def atomic_inc64(ary, idx, op2):
+    atomic_binary_1dim_shared2(ary, idx, op2, uint64, 32,
+                               cuda.atomic.inc, atomic_cast_to_int)
+
+
+def atomic_inc2_32(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.inc, atomic_cast_none, False)
+
+
+def atomic_inc2_64(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint64, (4, 8),
+                              cuda.atomic.inc, atomic_cast_none, False)
+
+
+def atomic_inc3(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.inc, atomic_cast_to_uint64, False)
+
+
+def atomic_inc_global(idx, ary, op2):
+    atomic_binary_1dim_global(ary, idx, 32, op2, cuda.atomic.inc, False)
+
+
+def atomic_inc_global_2(ary, op2):
+    atomic_binary_2dim_global(ary, op2, cuda.atomic.inc,
+                              atomic_cast_none, False)
+
+
+def atomic_dec32(ary, idx, op2):
+    atomic_binary_1dim_shared2(ary, idx, op2, uint32, 32,
+                               cuda.atomic.dec, atomic_cast_none)
+
+
+def atomic_dec64(ary, idx, op2):
+    atomic_binary_1dim_shared2(ary, idx, op2, uint64, 32,
+                               cuda.atomic.dec, atomic_cast_to_int)
+
+
+def atomic_dec2_32(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.dec, atomic_cast_none, False)
+
+
+def atomic_dec2_64(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint64, (4, 8),
+                              cuda.atomic.dec, atomic_cast_none, False)
+
+
+def atomic_dec3(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.dec, atomic_cast_to_uint64, False)
+
+
+def atomic_dec_global(idx, ary, op2):
+    atomic_binary_1dim_global(ary, idx, 32, op2, cuda.atomic.dec, False)
+
+
+def atomic_dec_global_2(ary, op2):
+    atomic_binary_2dim_global(ary, op2, cuda.atomic.dec,
+                              atomic_cast_none, False)
+
+
+def atomic_exch(ary, idx, op2):
+    atomic_binary_1dim_shared2(ary, idx, op2, uint32, 32,
+                               cuda.atomic.exch, atomic_cast_none)
+
+
+def atomic_exch2(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint32, (4, 8),
+                              cuda.atomic.exch, atomic_cast_none, False)
+
+
+def atomic_exch3(ary, op2):
+    atomic_binary_2dim_shared(ary, op2, uint64, (4, 8),
+                              cuda.atomic.exch, atomic_cast_none, False)
+
+
+def atomic_exch_global(idx, ary, op2):
+    atomic_binary_1dim_global(ary, idx, 32, op2, cuda.atomic.exch, False)
+
+
+def gen_atomic_extreme_funcs(func):
+
+    fns = dedent("""
+    def atomic(res, ary):
+        tx = cuda.threadIdx.x
+        bx = cuda.blockIdx.x
+        {func}(res, 0, ary[tx, bx])
+
+    def atomic_double_normalizedindex(res, ary):
+        tx = cuda.threadIdx.x
+        bx = cuda.blockIdx.x
+        {func}(res, 0, ary[tx, uint64(bx)])
+
+    def atomic_double_oneindex(res, ary):
+        tx = cuda.threadIdx.x
+        {func}(res, 0, ary[tx])
+
+    def atomic_double_shared(res, ary):
+        tid = cuda.threadIdx.x
+        smary = cuda.shared.array(32, float64)
+        smary[tid] = ary[tid]
+        smres = cuda.shared.array(1, float64)
+        if tid == 0:
+            smres[0] = res[0]
+        cuda.syncthreads()
+        {func}(smres, 0, smary[tid])
+        cuda.syncthreads()
+        if tid == 0:
+            res[0] = smres[0]
+    """).format(func=func)
+    ld = {}
+    exec(fns, {'cuda': cuda, 'float64': float64, 'uint64': uint64}, ld)
+    return (ld['atomic'], ld['atomic_double_normalizedindex'],
+            ld['atomic_double_oneindex'], ld['atomic_double_shared'])
+
+
+(atomic_max, atomic_max_double_normalizedindex, atomic_max_double_oneindex,
+ atomic_max_double_shared) = gen_atomic_extreme_funcs('cuda.atomic.max')
+(atomic_min, atomic_min_double_normalizedindex, atomic_min_double_oneindex,
+ atomic_min_double_shared) = gen_atomic_extreme_funcs('cuda.atomic.min')
+(atomic_nanmax, atomic_nanmax_double_normalizedindex,
+ atomic_nanmax_double_oneindex, atomic_nanmax_double_shared) = \
+    gen_atomic_extreme_funcs('cuda.atomic.nanmax')
+(atomic_nanmin, atomic_nanmin_double_normalizedindex,
+ atomic_nanmin_double_oneindex, atomic_nanmin_double_shared) = \
+    gen_atomic_extreme_funcs('cuda.atomic.nanmin')
+
+
+def atomic_compare_and_swap(res, old, ary, fill_val):
+    gid = cuda.grid(1)
+    if gid < res.size:
+        out = cuda.atomic.compare_and_swap(res[gid:], fill_val, ary[gid])
+        old[gid] = out
+
+
+class TestCudaAtomics(CUDATestCase):
+    def setUp(self):
+        super().setUp()
+        np.random.seed(0)
+
+    def test_atomic_add(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32)
+        ary_wrap = ary.copy()
+        orig = ary.copy()
+
+        cuda_atomic_add = cuda.jit('void(uint32[:])')(atomic_add)
+        cuda_atomic_add[1, 32](ary)
+
+        cuda_atomic_add_wrap = cuda.jit('void(uint32[:])')(atomic_add_wrap)
+        cuda_atomic_add_wrap[1, 32](ary_wrap)
+
+        gold = np.zeros(32, dtype=np.uint32)
+        for i in range(orig.size):
+            gold[orig[i]] += 1
+
+        self.assertTrue(np.all(ary == gold))
+        self.assertTrue(np.all(ary_wrap == gold))
+
+    def test_atomic_add2(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        ary_wrap = ary.copy()
+        orig = ary.copy()
+
+        cuda_atomic_add2 = cuda.jit('void(uint32[:,:])')(atomic_add2)
+        cuda_atomic_add2[1, (4, 8)](ary)
+
+        cuda_atomic_add2_wrap = cuda.jit('void(uint32[:,:])')(atomic_add2_wrap)
+        cuda_atomic_add2_wrap[1, (4, 8)](ary_wrap)
+
+        self.assertTrue(np.all(ary == orig + 1))
+        self.assertTrue(np.all(ary_wrap == orig + 1))
+
+    def test_atomic_add3(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_add3 = cuda.jit('void(uint32[:,:])')(atomic_add3)
+        cuda_atomic_add3[1, (4, 8)](ary)
+
+        self.assertTrue(np.all(ary == orig + 1))
+
+    def test_atomic_add_float(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float32)
+        ary_wrap = ary.copy()
+        orig = ary.copy().astype(np.intp)
+
+        cuda_atomic_add_float = cuda.jit('void(float32[:])')(atomic_add_float)
+        cuda_atomic_add_float[1, 32](ary)
+
+        add_float_wrap = cuda.jit('void(float32[:])')(atomic_add_float_wrap)
+        add_float_wrap[1, 32](ary_wrap)
+
+        gold = np.zeros(32, dtype=np.uint32)
+        for i in range(orig.size):
+            gold[orig[i]] += 1.0
+
+        self.assertTrue(np.all(ary == gold))
+        self.assertTrue(np.all(ary_wrap == gold))
+
+    def test_atomic_add_float_2(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float32).reshape(4, 8)
+        ary_wrap = ary.copy()
+        orig = ary.copy()
+
+        cuda_atomic_add2 = cuda.jit('void(float32[:,:])')(atomic_add_float_2)
+        cuda_atomic_add2[1, (4, 8)](ary)
+
+        cuda_func_wrap = cuda.jit('void(float32[:,:])')(atomic_add_float_2_wrap)
+        cuda_func_wrap[1, (4, 8)](ary_wrap)
+
+        self.assertTrue(np.all(ary == orig + 1))
+        self.assertTrue(np.all(ary_wrap == orig + 1))
+
+    def test_atomic_add_float_3(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_add3 = cuda.jit('void(float32[:,:])')(atomic_add_float_3)
+        cuda_atomic_add3[1, (4, 8)](ary)
+
+        self.assertTrue(np.all(ary == orig + 1))
+
+    def assertCorrectFloat64Atomics(self, kernel, shared=True):
+        if config.ENABLE_CUDASIM:
+            return
+
+        # Use the first (and only) definition
+        asm = next(iter(kernel.inspect_asm().values()))
+        if cc_X_or_above(6, 0):
+            if shared:
+                self.assertIn('atom.shared.add.f64', asm)
+            else:
+                self.assertIn('atom.add.f64', asm)
+        else:
+            if shared:
+                self.assertIn('atom.shared.cas.b64', asm)
+            else:
+                self.assertIn('atom.cas.b64', asm)
+
+    @skip_unless_cc_50
+    def test_atomic_add_double(self):
+        idx = np.random.randint(0, 32, size=32, dtype=np.int64)
+        ary = np.zeros(32, np.float64)
+        ary_wrap = ary.copy()
+
+        cuda_fn = cuda.jit('void(int64[:], float64[:])')(atomic_add_double)
+        cuda_fn[1, 32](idx, ary)
+
+        wrap_fn = cuda.jit('void(int64[:], float64[:])')(atomic_add_double_wrap)
+        wrap_fn[1, 32](idx, ary_wrap)
+
+        gold = np.zeros(32, dtype=np.uint32)
+        for i in range(idx.size):
+            gold[idx[i]] += 1.0
+
+        np.testing.assert_equal(ary, gold)
+        np.testing.assert_equal(ary_wrap, gold)
+        self.assertCorrectFloat64Atomics(cuda_fn)
+        self.assertCorrectFloat64Atomics(wrap_fn)
+
+    def test_atomic_add_double_2(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float64).reshape(4, 8)
+        ary_wrap = ary.copy()
+        orig = ary.copy()
+
+        cuda_fn = cuda.jit('void(float64[:,:])')(atomic_add_double_2)
+        cuda_fn[1, (4, 8)](ary)
+
+        cuda_fn_wrap = cuda.jit('void(float64[:,:])')(atomic_add_double_2_wrap)
+        cuda_fn_wrap[1, (4, 8)](ary_wrap)
+
+        np.testing.assert_equal(ary, orig + 1)
+        np.testing.assert_equal(ary_wrap, orig + 1)
+        self.assertCorrectFloat64Atomics(cuda_fn)
+        self.assertCorrectFloat64Atomics(cuda_fn_wrap)
+
+    def test_atomic_add_double_3(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float64).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(float64[:,:])')(atomic_add_double_3)
+        cuda_func[1, (4, 8)](ary)
+
+        np.testing.assert_equal(ary, orig + 1)
+        self.assertCorrectFloat64Atomics(cuda_func)
+
+    @skip_unless_cc_50
+    def test_atomic_add_double_global(self):
+        idx = np.random.randint(0, 32, size=32, dtype=np.int64)
+        ary = np.zeros(32, np.float64)
+        ary_wrap = ary.copy()
+
+        sig = 'void(int64[:], float64[:])'
+        cuda_func = cuda.jit(sig)(atomic_add_double_global)
+        wrap_cuda_func = cuda.jit(sig)(atomic_add_double_global_wrap)
+
+        cuda_func[1, 32](idx, ary)
+        wrap_cuda_func[1, 32](idx, ary_wrap)
+
+        gold = np.zeros(32, dtype=np.uint32)
+        for i in range(idx.size):
+            gold[idx[i]] += 1.0
+
+        np.testing.assert_equal(ary, gold)
+        np.testing.assert_equal(ary_wrap, gold)
+        self.assertCorrectFloat64Atomics(cuda_func, shared=False)
+        self.assertCorrectFloat64Atomics(wrap_cuda_func, shared=False)
+
+    def test_atomic_add_double_global_2(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float64).reshape(4, 8)
+        ary_wrap = ary.copy()
+        orig = ary.copy()
+
+        sig = 'void(float64[:,:])'
+        cuda_func = cuda.jit(sig)(atomic_add_double_global_2)
+        wrap_cuda_func = cuda.jit(sig)(atomic_add_double_global_2_wrap)
+
+        cuda_func[1, (4, 8)](ary)
+        wrap_cuda_func[1, (4, 8)](ary_wrap)
+
+        np.testing.assert_equal(ary, orig + 1)
+        np.testing.assert_equal(ary_wrap, orig + 1)
+        self.assertCorrectFloat64Atomics(cuda_func, shared=False)
+        self.assertCorrectFloat64Atomics(wrap_cuda_func, shared=False)
+
+    def test_atomic_add_double_global_3(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float64).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(float64[:,:])')(atomic_add_double_global_3)
+        cuda_func[1, (4, 8)](ary)
+
+        np.testing.assert_equal(ary, orig + 1)
+        self.assertCorrectFloat64Atomics(cuda_func, shared=False)
+
+    def test_atomic_sub(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32)
+        orig = ary.copy()
+        cuda_atomic_sub = cuda.jit('void(uint32[:])')(atomic_sub)
+        cuda_atomic_sub[1, 32](ary)
+
+        gold = np.zeros(32, dtype=np.uint32)
+        for i in range(orig.size):
+            gold[orig[i]] -= 1
+
+        self.assertTrue(np.all(ary == gold))
+
+    def test_atomic_sub2(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_sub2 = cuda.jit('void(uint32[:,:])')(atomic_sub2)
+        cuda_atomic_sub2[1, (4, 8)](ary)
+        self.assertTrue(np.all(ary == orig - 1))
+
+    def test_atomic_sub3(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_sub3 = cuda.jit('void(uint32[:,:])')(atomic_sub3)
+        cuda_atomic_sub3[1, (4, 8)](ary)
+        self.assertTrue(np.all(ary == orig - 1))
+
+    def test_atomic_sub_float(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float32)
+        orig = ary.copy().astype(np.intp)
+        cuda_atomic_sub_float = cuda.jit('void(float32[:])')(atomic_sub_float)
+        cuda_atomic_sub_float[1, 32](ary)
+
+        gold = np.zeros(32, dtype=np.float32)
+        for i in range(orig.size):
+            gold[orig[i]] -= 1.0
+
+        self.assertTrue(np.all(ary == gold))
+
+    def test_atomic_sub_float_2(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_sub2 = cuda.jit('void(float32[:,:])')(atomic_sub_float_2)
+        cuda_atomic_sub2[1, (4, 8)](ary)
+        self.assertTrue(np.all(ary == orig - 1))
+
+    def test_atomic_sub_float_3(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_sub3 = cuda.jit('void(float32[:,:])')(atomic_sub_float_3)
+        cuda_atomic_sub3[1, (4, 8)](ary)
+        self.assertTrue(np.all(ary == orig - 1))
+
+    def test_atomic_sub_double(self):
+        idx = np.random.randint(0, 32, size=32, dtype=np.int64)
+        ary = np.zeros(32, np.float64)
+        cuda_func = cuda.jit('void(int64[:], float64[:])')(atomic_sub_double)
+        cuda_func[1, 32](idx, ary)
+
+        gold = np.zeros(32, dtype=np.float64)
+        for i in range(idx.size):
+            gold[idx[i]] -= 1.0
+
+        np.testing.assert_equal(ary, gold)
+
+    def test_atomic_sub_double_2(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float64).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(float64[:,:])')(atomic_sub_double_2)
+        cuda_func[1, (4, 8)](ary)
+        np.testing.assert_equal(ary, orig - 1)
+
+    def test_atomic_sub_double_3(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float64).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(float64[:,:])')(atomic_sub_double_3)
+        cuda_func[1, (4, 8)](ary)
+        np.testing.assert_equal(ary, orig - 1)
+
+    def test_atomic_sub_double_global(self):
+        idx = np.random.randint(0, 32, size=32, dtype=np.int64)
+        ary = np.zeros(32, np.float64)
+        sig = 'void(int64[:], float64[:])'
+        cuda_func = cuda.jit(sig)(atomic_sub_double_global)
+        cuda_func[1, 32](idx, ary)
+
+        gold = np.zeros(32, dtype=np.float64)
+        for i in range(idx.size):
+            gold[idx[i]] -= 1.0
+
+        np.testing.assert_equal(ary, gold)
+
+    def test_atomic_sub_double_global_2(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float64).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(float64[:,:])')(atomic_sub_double_global_2)
+        cuda_func[1, (4, 8)](ary)
+        np.testing.assert_equal(ary, orig - 1)
+
+    def test_atomic_sub_double_global_3(self):
+        ary = np.random.randint(0, 32, size=32).astype(np.float64).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(float64[:,:])')(atomic_sub_double_global_3)
+        cuda_func[1, (4, 8)](ary)
+        np.testing.assert_equal(ary, orig - 1)
+
+    def test_atomic_and(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(uint32[:], uint32)')(atomic_and)
+        cuda_func[1, 32](ary, rand_const)
+
+        gold = ary.copy()
+        for i in range(orig.size):
+            gold[orig[i]] &= rand_const
+
+        self.assertTrue(np.all(ary == gold))
+
+    def test_atomic_and2(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_and2 = cuda.jit('void(uint32[:,:], uint32)')(atomic_and2)
+        cuda_atomic_and2[1, (4, 8)](ary, rand_const)
+        self.assertTrue(np.all(ary == orig & rand_const))
+
+    def test_atomic_and3(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_and3 = cuda.jit('void(uint32[:,:], uint32)')(atomic_and3)
+        cuda_atomic_and3[1, (4, 8)](ary, rand_const)
+        self.assertTrue(np.all(ary == orig & rand_const))
+
+    def test_atomic_and_global(self):
+        rand_const = np.random.randint(500)
+        idx = np.random.randint(0, 32, size=32, dtype=np.int32)
+        ary = np.random.randint(0, 32, size=32, dtype=np.int32)
+        sig = 'void(int32[:], int32[:], int32)'
+        cuda_func = cuda.jit(sig)(atomic_and_global)
+        cuda_func[1, 32](idx, ary, rand_const)
+
+        gold = ary.copy()
+        for i in range(idx.size):
+            gold[idx[i]] &= rand_const
+
+        np.testing.assert_equal(ary, gold)
+
+    def test_atomic_and_global_2(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(uint32[:,:], uint32)')(atomic_and_global_2)
+        cuda_func[1, (4, 8)](ary, rand_const)
+        np.testing.assert_equal(ary, orig & rand_const)
+
+    def test_atomic_or(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(uint32[:], uint32)')(atomic_or)
+        cuda_func[1, 32](ary, rand_const)
+
+        gold = np.zeros(32, dtype=np.uint32)
+        for i in range(orig.size):
+            gold[orig[i]] |= rand_const
+
+        self.assertTrue(np.all(ary == gold))
+
+    def test_atomic_or2(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_and2 = cuda.jit('void(uint32[:,:], uint32)')(atomic_or2)
+        cuda_atomic_and2[1, (4, 8)](ary, rand_const)
+        self.assertTrue(np.all(ary == orig | rand_const))
+
+    def test_atomic_or3(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_and3 = cuda.jit('void(uint32[:,:], uint32)')(atomic_or3)
+        cuda_atomic_and3[1, (4, 8)](ary, rand_const)
+        self.assertTrue(np.all(ary == orig | rand_const))
+
+    def test_atomic_or_global(self):
+        rand_const = np.random.randint(500)
+        idx = np.random.randint(0, 32, size=32, dtype=np.int32)
+        ary = np.random.randint(0, 32, size=32, dtype=np.int32)
+        sig = 'void(int32[:], int32[:], int32)'
+        cuda_func = cuda.jit(sig)(atomic_or_global)
+        cuda_func[1, 32](idx, ary, rand_const)
+
+        gold = ary.copy()
+        for i in range(idx.size):
+            gold[idx[i]] |= rand_const
+
+        np.testing.assert_equal(ary, gold)
+
+    def test_atomic_or_global_2(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(uint32[:,:], uint32)')(atomic_or_global_2)
+        cuda_func[1, (4, 8)](ary, rand_const)
+        np.testing.assert_equal(ary, orig | rand_const)
+
+    def test_atomic_xor(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(uint32[:], uint32)')(atomic_xor)
+        cuda_func[1, 32](ary, rand_const)
+
+        gold = np.zeros(32, dtype=np.uint32)
+        for i in range(orig.size):
+            gold[orig[i]] ^= rand_const
+
+        self.assertTrue(np.all(ary == gold))
+
+    def test_atomic_xor2(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_xor2 = cuda.jit('void(uint32[:,:], uint32)')(atomic_xor2)
+        cuda_atomic_xor2[1, (4, 8)](ary, rand_const)
+        self.assertTrue(np.all(ary == orig ^ rand_const))
+
+    def test_atomic_xor3(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_atomic_xor3 = cuda.jit('void(uint32[:,:], uint32)')(atomic_xor3)
+        cuda_atomic_xor3[1, (4, 8)](ary, rand_const)
+        self.assertTrue(np.all(ary == orig ^ rand_const))
+
+    def test_atomic_xor_global(self):
+        rand_const = np.random.randint(500)
+        idx = np.random.randint(0, 32, size=32, dtype=np.int32)
+        ary = np.random.randint(0, 32, size=32, dtype=np.int32)
+        gold = ary.copy()
+        sig = 'void(int32[:], int32[:], int32)'
+        cuda_func = cuda.jit(sig)(atomic_xor_global)
+        cuda_func[1, 32](idx, ary, rand_const)
+
+        for i in range(idx.size):
+            gold[idx[i]] ^= rand_const
+
+        np.testing.assert_equal(ary, gold)
+
+    def test_atomic_xor_global_2(self):
+        rand_const = np.random.randint(500)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+        orig = ary.copy()
+        cuda_func = cuda.jit('void(uint32[:,:], uint32)')(atomic_xor_global_2)
+        cuda_func[1, (4, 8)](ary, rand_const)
+        np.testing.assert_equal(ary, orig ^ rand_const)
+
+    def inc_dec_1dim_setup(self, dtype):
+        rconst = np.random.randint(32,  dtype=dtype)
+        rary = np.random.randint(0, 32, size=32).astype(dtype)
+        ary_idx = np.arange(32, dtype=dtype)
+        return rconst, rary, ary_idx
+
+    def inc_dec_2dim_setup(self, dtype):
+        rconst = np.random.randint(32, dtype=dtype)
+        rary = np.random.randint(0, 32, size=32).astype(dtype).reshape(4, 8)
+        return rconst, rary
+
+    def check_inc_index(self, ary, idx, rconst, sig, nblocks, blksize, func):
+        orig = ary.copy()
+        cuda_func = cuda.jit(sig)(func)
+        cuda_func[nblocks, blksize](ary, idx, rconst)
+        np.testing.assert_equal(ary, np.where(orig >= rconst, 0, orig + 1))
+
+    def check_inc_index2(self, ary, idx, rconst, sig, nblocks, blksize, func):
+        orig = ary.copy()
+        cuda_func = cuda.jit(sig)(func)
+        cuda_func[nblocks, blksize](idx, ary, rconst)
+        np.testing.assert_equal(ary, np.where(orig >= rconst, 0, orig + 1))
+
+    def check_inc(self, ary, rconst, sig, nblocks, blksize, func):
+        orig = ary.copy()
+        cuda_func = cuda.jit(sig)(func)
+        cuda_func[nblocks, blksize](ary, rconst)
+        np.testing.assert_equal(ary, np.where(orig >= rconst, 0, orig + 1))
+
+    def test_atomic_inc_32(self):
+        rand_const, ary, idx = self.inc_dec_1dim_setup(dtype=np.uint32)
+        sig = 'void(uint32[:], uint32[:], uint32)'
+        self.check_inc_index(ary, idx, rand_const, sig, 1, 32, atomic_inc32)
+
+    def test_atomic_inc_64(self):
+        rand_const, ary, idx = self.inc_dec_1dim_setup(dtype=np.uint64)
+        sig = 'void(uint64[:], uint64[:], uint64)'
+        self.check_inc_index(ary, idx, rand_const, sig, 1, 32, atomic_inc64)
+
+    def test_atomic_inc2_32(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint32)
+        sig = 'void(uint32[:,:], uint32)'
+        self.check_inc(ary, rand_const, sig, 1, (4,8), atomic_inc2_32)
+
+    def test_atomic_inc2_64(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint64)
+        sig = 'void(uint64[:,:], uint64)'
+        self.check_inc(ary, rand_const, sig, 1, (4,8), atomic_inc2_64)
+
+    def test_atomic_inc3(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint32)
+        sig = 'void(uint32[:,:], uint32)'
+        self.check_inc(ary, rand_const, sig, 1, (4,8), atomic_inc3)
+
+    def test_atomic_inc_global_32(self):
+        rand_const, ary, idx = self.inc_dec_1dim_setup(dtype=np.uint32)
+        sig = 'void(uint32[:], uint32[:], uint32)'
+        self.check_inc_index2(ary, idx, rand_const, sig, 1, 32,
+                              atomic_inc_global)
+
+    def test_atomic_inc_global_64(self):
+        rand_const, ary, idx = self.inc_dec_1dim_setup(dtype=np.uint64)
+        sig = 'void(uint64[:], uint64[:], uint64)'
+        self.check_inc_index2(ary, idx, rand_const, sig, 1, 32,
+                              atomic_inc_global)
+
+    def test_atomic_inc_global_2_32(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint32)
+        sig = 'void(uint32[:,:], uint32)'
+        self.check_inc(ary, rand_const, sig, 1, (4,8), atomic_inc_global_2)
+
+    def test_atomic_inc_global_2_64(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint64)
+        sig = 'void(uint64[:,:], uint64)'
+        self.check_inc(ary, rand_const, sig, 1, (4,8), atomic_inc_global_2)
+
+    def check_dec_index(self, ary, idx, rconst, sig, nblocks, blksize, func):
+        orig = ary.copy()
+        cuda_func = cuda.jit(sig)(func)
+        cuda_func[nblocks, blksize](ary, idx, rconst)
+        np.testing.assert_equal(ary, np.where(orig == 0, rconst,
+                                              np.where(orig > rconst,
+                                                       rconst,
+                                                       orig - 1)))
+
+    def check_dec_index2(self, ary, idx, rconst, sig, nblocks, blksize, func):
+        orig = ary.copy()
+        cuda_func = cuda.jit(sig)(func)
+        cuda_func[nblocks, blksize](idx, ary, rconst)
+        np.testing.assert_equal(ary, np.where(orig == 0, rconst,
+                                              np.where(orig > rconst,
+                                                       rconst,
+                                                       orig - 1)))
+
+    def check_dec(self, ary, rconst, sig, nblocks, blksize, func):
+        orig = ary.copy()
+        cuda_func = cuda.jit(sig)(func)
+        cuda_func[nblocks, blksize](ary, rconst)
+        np.testing.assert_equal(ary, np.where(orig == 0, rconst,
+                                              np.where(orig > rconst,
+                                                       rconst,
+                                                       orig - 1)))
+
+    def test_atomic_dec_32(self):
+        rand_const, ary, idx = self.inc_dec_1dim_setup(dtype=np.uint32)
+        sig = 'void(uint32[:], uint32[:], uint32)'
+        self.check_dec_index(ary, idx, rand_const, sig, 1, 32, atomic_dec32)
+
+    def test_atomic_dec_64(self):
+        rand_const, ary, idx = self.inc_dec_1dim_setup(dtype=np.uint64)
+        sig = 'void(uint64[:], uint64[:], uint64)'
+        self.check_dec_index(ary, idx, rand_const, sig, 1, 32, atomic_dec64)
+
+    def test_atomic_dec2_32(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint32)
+        sig = 'void(uint32[:,:], uint32)'
+        self.check_dec(ary, rand_const, sig, 1, (4,8), atomic_dec2_32)
+
+    def test_atomic_dec2_64(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint64)
+        sig = 'void(uint64[:,:], uint64)'
+        self.check_dec(ary, rand_const, sig, 1, (4,8), atomic_dec2_64)
+
+    def test_atomic_dec3_new(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint32)
+        sig = 'void(uint32[:,:], uint32)'
+        self.check_dec(ary, rand_const, sig, 1, (4,8), atomic_dec3)
+
+    def test_atomic_dec_global_32(self):
+        rand_const, ary, idx = self.inc_dec_1dim_setup(dtype=np.uint32)
+        sig = 'void(uint32[:], uint32[:], uint32)'
+        self.check_dec_index2(ary, idx, rand_const, sig, 1, 32,
+                              atomic_dec_global)
+
+    def test_atomic_dec_global_64(self):
+        rand_const, ary, idx = self.inc_dec_1dim_setup(dtype=np.uint64)
+        sig = 'void(uint64[:], uint64[:], uint64)'
+        self.check_dec_index2(ary, idx, rand_const, sig, 1, 32,
+                              atomic_dec_global)
+
+    def test_atomic_dec_global2_32(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint32)
+        sig = 'void(uint32[:,:], uint32)'
+        self.check_dec(ary, rand_const, sig, 1, (4,8), atomic_dec_global_2)
+
+    def test_atomic_dec_global2_64(self):
+        rand_const, ary = self.inc_dec_2dim_setup(np.uint64)
+        sig = 'void(uint64[:,:], uint64)'
+        self.check_dec(ary, rand_const, sig, 1, (4,8), atomic_dec_global_2)
+
+    def test_atomic_exch(self):
+        rand_const = np.random.randint(50, 100, dtype=np.uint32)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32)
+        idx = np.arange(32, dtype=np.uint32)
+
+        cuda_func = cuda.jit('void(uint32[:], uint32[:], uint32)')(atomic_exch)
+        cuda_func[1, 32](ary, idx, rand_const)
+
+        np.testing.assert_equal(ary, rand_const)
+
+    def test_atomic_exch2(self):
+        rand_const = np.random.randint(50, 100, dtype=np.uint32)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint32).reshape(4, 8)
+
+        cuda_func = cuda.jit('void(uint32[:,:], uint32)')(atomic_exch2)
+        cuda_func[1, (4, 8)](ary, rand_const)
+        np.testing.assert_equal(ary, rand_const)
+
+    def test_atomic_exch3(self):
+        rand_const = np.random.randint(50, 100, dtype=np.uint64)
+        ary = np.random.randint(0, 32, size=32).astype(np.uint64).reshape(4, 8)
+
+        cuda_func = cuda.jit('void(uint64[:,:], uint64)')(atomic_exch3)
+        cuda_func[1, (4, 8)](ary, rand_const)
+        np.testing.assert_equal(ary, rand_const)
+
+    def test_atomic_exch_global(self):
+        rand_const = np.random.randint(50, 100, dtype=np.uint32)
+        idx = np.arange(32, dtype=np.uint32)
+        ary = np.random.randint(0, 32, size=32, dtype=np.uint32)
+
+        sig = 'void(uint32[:], uint32[:], uint32)'
+        cuda_func = cuda.jit(sig)(atomic_exch_global)
+        cuda_func[1, 32](idx, ary, rand_const)
+        np.testing.assert_equal(ary, rand_const)
+
+    def check_atomic_max(self, dtype, lo, hi):
+        vals = np.random.randint(lo, hi, size=(32, 32)).astype(dtype)
+        res = np.zeros(1, dtype=vals.dtype)
+        cuda_func = cuda.jit(atomic_max)
+        cuda_func[32, 32](res, vals)
+        gold = np.max(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_max_int32(self):
+        self.check_atomic_max(dtype=np.int32, lo=-65535, hi=65535)
+
+    def test_atomic_max_uint32(self):
+        self.check_atomic_max(dtype=np.uint32, lo=0, hi=65535)
+
+    def test_atomic_max_int64(self):
+        self.check_atomic_max(dtype=np.int64, lo=-65535, hi=65535)
+
+    def test_atomic_max_uint64(self):
+        self.check_atomic_max(dtype=np.uint64, lo=0, hi=65535)
+
+    def test_atomic_max_float32(self):
+        self.check_atomic_max(dtype=np.float32, lo=-65535, hi=65535)
+
+    def test_atomic_max_double(self):
+        self.check_atomic_max(dtype=np.float64, lo=-65535, hi=65535)
+
+    def test_atomic_max_double_normalizedindex(self):
+        vals = np.random.randint(0, 65535, size=(32, 32)).astype(np.float64)
+        res = np.zeros(1, np.float64)
+        cuda_func = cuda.jit('void(float64[:], float64[:,:])')(
+            atomic_max_double_normalizedindex)
+        cuda_func[32, 32](res, vals)
+
+        gold = np.max(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_max_double_oneindex(self):
+        vals = np.random.randint(0, 128, size=32).astype(np.float64)
+        res = np.zeros(1, np.float64)
+        cuda_func = cuda.jit('void(float64[:], float64[:])')(
+            atomic_max_double_oneindex)
+        cuda_func[1, 32](res, vals)
+
+        gold = np.max(vals)
+        np.testing.assert_equal(res, gold)
+
+    def check_atomic_min(self, dtype, lo, hi):
+        vals = np.random.randint(lo, hi, size=(32, 32)).astype(dtype)
+        res = np.array([65535], dtype=vals.dtype)
+        cuda_func = cuda.jit(atomic_min)
+        cuda_func[32, 32](res, vals)
+
+        gold = np.min(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_min_int32(self):
+        self.check_atomic_min(dtype=np.int32, lo=-65535, hi=65535)
+
+    def test_atomic_min_uint32(self):
+        self.check_atomic_min(dtype=np.uint32, lo=0, hi=65535)
+
+    def test_atomic_min_int64(self):
+        self.check_atomic_min(dtype=np.int64, lo=-65535, hi=65535)
+
+    def test_atomic_min_uint64(self):
+        self.check_atomic_min(dtype=np.uint64, lo=0, hi=65535)
+
+    def test_atomic_min_float(self):
+        self.check_atomic_min(dtype=np.float32, lo=-65535, hi=65535)
+
+    def test_atomic_min_double(self):
+        self.check_atomic_min(dtype=np.float64, lo=-65535, hi=65535)
+
+    def test_atomic_min_double_normalizedindex(self):
+        vals = np.random.randint(0, 65535, size=(32, 32)).astype(np.float64)
+        res = np.ones(1, np.float64) * 65535
+        cuda_func = cuda.jit('void(float64[:], float64[:,:])')(
+            atomic_min_double_normalizedindex)
+        cuda_func[32, 32](res, vals)
+
+        gold = np.min(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_min_double_oneindex(self):
+        vals = np.random.randint(0, 128, size=32).astype(np.float64)
+        res = np.ones(1, np.float64) * 128
+        cuda_func = cuda.jit('void(float64[:], float64[:])')(
+            atomic_min_double_oneindex)
+        cuda_func[1, 32](res, vals)
+
+        gold = np.min(vals)
+        np.testing.assert_equal(res, gold)
+
+    # Taken together, _test_atomic_minmax_nan_location and
+    # _test_atomic_minmax_nan_val check that NaNs are treated similarly to the
+    # way they are in Python / NumPy - that is, {min,max}(a, b) == a if either
+    # a or b is a NaN. For the atomics, this means that the max is taken as the
+    # value stored in the memory location rather than the value supplied - i.e.
+    # for:
+    #
+    #    cuda.atomic.{min,max}(ary, idx, val)
+    #
+    # the result will be ary[idx] for either of ary[idx] or val being NaN.
+
+    def _test_atomic_minmax_nan_location(self, func):
+
+        cuda_func = cuda.jit('void(float64[:], float64[:,:])')(func)
+
+        vals = np.random.randint(0, 128, size=(1,1)).astype(np.float64)
+        res = np.zeros(1, np.float64) + np.nan
+        cuda_func[1, 1](res, vals)
+        np.testing.assert_equal(res, [np.nan])
+
+    def _test_atomic_minmax_nan_val(self, func):
+        cuda_func = cuda.jit('void(float64[:], float64[:,:])')(func)
+
+        res = np.random.randint(0, 128, size=1).astype(np.float64)
+        gold = res.copy()
+        vals = np.zeros((1, 1), np.float64) + np.nan
+        cuda_func[1, 1](res, vals)
+
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_min_nan_location(self):
+        self._test_atomic_minmax_nan_location(atomic_min)
+
+    def test_atomic_max_nan_location(self):
+        self._test_atomic_minmax_nan_location(atomic_max)
+
+    def test_atomic_min_nan_val(self):
+        self._test_atomic_minmax_nan_val(atomic_min)
+
+    def test_atomic_max_nan_val(self):
+        self._test_atomic_minmax_nan_val(atomic_max)
+
+    def test_atomic_max_double_shared(self):
+        vals = np.random.randint(0, 32, size=32).astype(np.float64)
+        res = np.zeros(1, np.float64)
+        sig = 'void(float64[:], float64[:])'
+        cuda_func = cuda.jit(sig)(atomic_max_double_shared)
+        cuda_func[1, 32](res, vals)
+
+        gold = np.max(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_min_double_shared(self):
+        vals = np.random.randint(0, 32, size=32).astype(np.float64)
+        res = np.ones(1, np.float64) * 32
+        sig = 'void(float64[:], float64[:])'
+        cuda_func = cuda.jit(sig)(atomic_min_double_shared)
+        cuda_func[1, 32](res, vals)
+
+        gold = np.min(vals)
+        np.testing.assert_equal(res, gold)
+
+    def check_compare_and_swap(self, n, fill, unfill, dtype):
+        res = [fill] * (n // 2) + [unfill] * (n // 2)
+        np.random.shuffle(res)
+        res = np.asarray(res, dtype=dtype)
+        out = np.zeros_like(res)
+        ary = np.random.randint(1, 10, size=res.size).astype(res.dtype)
+
+        fill_mask = res == fill
+        unfill_mask = res == unfill
+
+        expect_res = np.zeros_like(res)
+        expect_res[fill_mask] = ary[fill_mask]
+        expect_res[unfill_mask] = unfill
+
+        expect_out = np.zeros_like(out)
+        expect_out[fill_mask] = res[fill_mask]
+        expect_out[unfill_mask] = unfill
+
+        cuda_func = cuda.jit(atomic_compare_and_swap)
+        cuda_func[10, 10](res, out, ary, fill)
+
+        np.testing.assert_array_equal(expect_res, res)
+        np.testing.assert_array_equal(expect_out, out)
+
+    def test_atomic_compare_and_swap(self):
+        self.check_compare_and_swap(n=100, fill=-99, unfill=-1, dtype=np.int32)
+
+    def test_atomic_compare_and_swap2(self):
+        self.check_compare_and_swap(n=100, fill=-45, unfill=-1, dtype=np.int64)
+
+    def test_atomic_compare_and_swap3(self):
+        rfill = np.random.randint(50, 500, dtype=np.uint32)
+        runfill = np.random.randint(1, 25, dtype=np.uint32)
+        self.check_compare_and_swap(n=100, fill=rfill, unfill=runfill,
+                                    dtype=np.uint32)
+
+    def test_atomic_compare_and_swap4(self):
+        rfill = np.random.randint(50, 500, dtype=np.uint64)
+        runfill = np.random.randint(1, 25, dtype=np.uint64)
+        self.check_compare_and_swap(n=100, fill=rfill, unfill=runfill,
+                                    dtype=np.uint64)
+
+    # Tests that the atomic add, min, and max operations return the old value -
+    # in the simulator, they did not (see Issue #5458). The max and min have
+    # special handling for NaN values, so we explicitly test with a NaN in the
+    # array being modified and the value provided.
+
+    def _test_atomic_returns_old(self, kernel, initial):
+        x = np.zeros(2, dtype=np.float32)
+        x[0] = initial
+        kernel[1, 1](x)
+        if np.isnan(initial):
+            self.assertTrue(np.isnan(x[1]))
+        else:
+            self.assertEqual(x[1], initial)
+
+    def test_atomic_add_returns_old(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.add(x, 0, 1)
+
+        self._test_atomic_returns_old(kernel, 10)
+
+    def test_atomic_max_returns_no_replace(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.max(x, 0, 1)
+
+        self._test_atomic_returns_old(kernel, 10)
+
+    def test_atomic_max_returns_old_replace(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.max(x, 0, 10)
+
+        self._test_atomic_returns_old(kernel, 1)
+
+    def test_atomic_max_returns_old_nan_in_array(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.max(x, 0, 1)
+
+        self._test_atomic_returns_old(kernel, np.nan)
+
+    def test_atomic_max_returns_old_nan_val(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.max(x, 0, np.nan)
+
+        self._test_atomic_returns_old(kernel, 10)
+
+    def test_atomic_min_returns_old_no_replace(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.min(x, 0, 11)
+
+        self._test_atomic_returns_old(kernel, 10)
+
+    def test_atomic_min_returns_old_replace(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.min(x, 0, 10)
+
+        self._test_atomic_returns_old(kernel, 11)
+
+    def test_atomic_min_returns_old_nan_in_array(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.min(x, 0, 11)
+
+        self._test_atomic_returns_old(kernel, np.nan)
+
+    def test_atomic_min_returns_old_nan_val(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.min(x, 0, np.nan)
+
+        self._test_atomic_returns_old(kernel, 11)
+
+    # Tests for atomic nanmin/nanmax
+
+    # nanmax tests
+    def check_atomic_nanmax(self, dtype, lo, hi, init_val):
+        vals = np.random.randint(lo, hi, size=(32, 32)).astype(dtype)
+        vals[1::2] = init_val
+        res = np.zeros(1, dtype=vals.dtype)
+        cuda_func = cuda.jit(atomic_nanmax)
+        cuda_func[32, 32](res, vals)
+        gold = np.nanmax(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_nanmax_int32(self):
+        self.check_atomic_nanmax(dtype=np.int32, lo=-65535, hi=65535,
+                                 init_val=0)
+
+    def test_atomic_nanmax_uint32(self):
+        self.check_atomic_nanmax(dtype=np.uint32, lo=0, hi=65535,
+                                 init_val=0)
+
+    def test_atomic_nanmax_int64(self):
+        self.check_atomic_nanmax(dtype=np.int64, lo=-65535, hi=65535,
+                                 init_val=0)
+
+    def test_atomic_nanmax_uint64(self):
+        self.check_atomic_nanmax(dtype=np.uint64, lo=0, hi=65535,
+                                 init_val=0)
+
+    def test_atomic_nanmax_float32(self):
+        self.check_atomic_nanmax(dtype=np.float32, lo=-65535, hi=65535,
+                                 init_val=np.nan)
+
+    def test_atomic_nanmax_double(self):
+        self.check_atomic_nanmax(dtype=np.float64, lo=-65535, hi=65535,
+                                 init_val=np.nan)
+
+    def test_atomic_nanmax_double_shared(self):
+        vals = np.random.randint(0, 32, size=32).astype(np.float64)
+        vals[1::2] = np.nan
+        res = np.array([0], dtype=vals.dtype)
+        sig = 'void(float64[:], float64[:])'
+        cuda_func = cuda.jit(sig)(atomic_nanmax_double_shared)
+        cuda_func[1, 32](res, vals)
+
+        gold = np.nanmax(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_nanmax_double_oneindex(self):
+        vals = np.random.randint(0, 128, size=32).astype(np.float64)
+        vals[1::2] = np.nan
+        res = np.zeros(1, np.float64)
+        cuda_func = cuda.jit('void(float64[:], float64[:])')(
+            atomic_max_double_oneindex)
+        cuda_func[1, 32](res, vals)
+
+        gold = np.nanmax(vals)
+        np.testing.assert_equal(res, gold)
+
+    # nanmin tests
+    def check_atomic_nanmin(self, dtype, lo, hi, init_val):
+        vals = np.random.randint(lo, hi, size=(32, 32)).astype(dtype)
+        vals[1::2] = init_val
+        res = np.array([65535], dtype=vals.dtype)
+        cuda_func = cuda.jit(atomic_nanmin)
+        cuda_func[32, 32](res, vals)
+
+        gold = np.nanmin(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_nanmin_int32(self):
+        self.check_atomic_nanmin(dtype=np.int32, lo=-65535, hi=65535,
+                                 init_val=0)
+
+    def test_atomic_nanmin_uint32(self):
+        self.check_atomic_nanmin(dtype=np.uint32, lo=0, hi=65535,
+                                 init_val=0)
+
+    def test_atomic_nanmin_int64(self):
+        self.check_atomic_nanmin(dtype=np.int64, lo=-65535, hi=65535,
+                                 init_val=0)
+
+    def test_atomic_nanmin_uint64(self):
+        self.check_atomic_nanmin(dtype=np.uint64, lo=0, hi=65535,
+                                 init_val=0)
+
+    def test_atomic_nanmin_float(self):
+        self.check_atomic_nanmin(dtype=np.float32, lo=-65535, hi=65535,
+                                 init_val=np.nan)
+
+    def test_atomic_nanmin_double(self):
+        self.check_atomic_nanmin(dtype=np.float64, lo=-65535, hi=65535,
+                                 init_val=np.nan)
+
+    def test_atomic_nanmin_double_shared(self):
+        vals = np.random.randint(0, 32, size=32).astype(np.float64)
+        vals[1::2] = np.nan
+        res = np.array([32], dtype=vals.dtype)
+        sig = 'void(float64[:], float64[:])'
+        cuda_func = cuda.jit(sig)(atomic_nanmin_double_shared)
+        cuda_func[1, 32](res, vals)
+
+        gold = np.nanmin(vals)
+        np.testing.assert_equal(res, gold)
+
+    def test_atomic_nanmin_double_oneindex(self):
+        vals = np.random.randint(0, 128, size=32).astype(np.float64)
+        vals[1::2] = np.nan
+        res = np.array([128], np.float64)
+        cuda_func = cuda.jit('void(float64[:], float64[:])')(
+            atomic_min_double_oneindex)
+        cuda_func[1, 32](res, vals)
+
+        gold = np.nanmin(vals)
+        np.testing.assert_equal(res, gold)
+
+    # Returning old value tests
+
+    def _test_atomic_nan_returns_old(self, kernel, initial):
+        x = np.zeros(2, dtype=np.float32)
+        x[0] = initial
+        x[1] = np.nan
+        kernel[1, 1](x)
+        if np.isnan(initial):
+            self.assertFalse(np.isnan(x[0]))
+            self.assertTrue(np.isnan(x[1]))
+        else:
+            self.assertEqual(x[1], initial)
+
+    def test_atomic_nanmax_returns_old_no_replace(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.nanmax(x, 0, 1)
+
+        self._test_atomic_nan_returns_old(kernel, 10)
+
+    def test_atomic_nanmax_returns_old_replace(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.nanmax(x, 0, 10)
+
+        self._test_atomic_nan_returns_old(kernel, 1)
+
+    def test_atomic_nanmax_returns_old_nan_in_array(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.nanmax(x, 0, 1)
+
+        self._test_atomic_nan_returns_old(kernel, np.nan)
+
+    def test_atomic_nanmax_returns_old_nan_val(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.nanmax(x, 0, np.nan)
+
+        self._test_atomic_nan_returns_old(kernel, 10)
+
+    def test_atomic_nanmin_returns_old_no_replace(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.nanmin(x, 0, 11)
+
+        self._test_atomic_nan_returns_old(kernel, 10)
+
+    def test_atomic_nanmin_returns_old_replace(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.nanmin(x, 0, 10)
+
+        self._test_atomic_nan_returns_old(kernel, 11)
+
+    def test_atomic_nanmin_returns_old_nan_in_array(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.nanmin(x, 0, 11)
+
+        self._test_atomic_nan_returns_old(kernel, np.nan)
+
+    def test_atomic_nanmin_returns_old_nan_val(self):
+        @cuda.jit
+        def kernel(x):
+            x[1] = cuda.atomic.nanmin(x, 0, np.nan)
+
+        self._test_atomic_nan_returns_old(kernel, 11)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_blackscholes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_blackscholes.py
new file mode 100644
index 000000000..1375162d9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_blackscholes.py
@@ -0,0 +1,120 @@
+import numpy as np
+import math
+from numba import cuda, double, void
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+RISKFREE = 0.02
+VOLATILITY = 0.30
+
+A1 = 0.31938153
+A2 = -0.356563782
+A3 = 1.781477937
+A4 = -1.821255978
+A5 = 1.330274429
+RSQRT2PI = 0.39894228040143267793994605993438
+
+
+def cnd(d):
+    K = 1.0 / (1.0 + 0.2316419 * np.abs(d))
+    ret_val = (RSQRT2PI * np.exp(-0.5 * d * d) *
+               (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+    return np.where(d > 0, 1.0 - ret_val, ret_val)
+
+
+def black_scholes(callResult, putResult, stockPrice, optionStrike, optionYears,
+                  Riskfree, Volatility):
+    S = stockPrice
+    X = optionStrike
+    T = optionYears
+    R = Riskfree
+    V = Volatility
+    sqrtT = np.sqrt(T)
+    d1 = (np.log(S / X) + (R + 0.5 * V * V) * T) / (V * sqrtT)
+    d2 = d1 - V * sqrtT
+    cndd1 = cnd(d1)
+    cndd2 = cnd(d2)
+
+    expRT = np.exp(- R * T)
+    callResult[:] = (S * cndd1 - X * expRT * cndd2)
+    putResult[:] = (X * expRT * (1.0 - cndd2) - S * (1.0 - cndd1))
+
+
+def randfloat(rand_var, low, high):
+    return (1.0 - rand_var) * low + rand_var * high
+
+
+class TestBlackScholes(CUDATestCase):
+    def test_blackscholes(self):
+        OPT_N = 400
+        iterations = 2
+
+        stockPrice = randfloat(np.random.random(OPT_N), 5.0, 30.0)
+        optionStrike = randfloat(np.random.random(OPT_N), 1.0, 100.0)
+        optionYears = randfloat(np.random.random(OPT_N), 0.25, 10.0)
+
+        callResultNumpy = np.zeros(OPT_N)
+        putResultNumpy = -np.ones(OPT_N)
+
+        callResultNumba = np.zeros(OPT_N)
+        putResultNumba = -np.ones(OPT_N)
+
+        # numpy
+        for i in range(iterations):
+            black_scholes(callResultNumpy, putResultNumpy, stockPrice,
+                          optionStrike, optionYears, RISKFREE, VOLATILITY)
+
+        @cuda.jit(double(double), device=True, inline=True)
+        def cnd_cuda(d):
+            K = 1.0 / (1.0 + 0.2316419 * math.fabs(d))
+            ret_val = (RSQRT2PI * math.exp(-0.5 * d * d) *
+                       (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+            if d > 0:
+                ret_val = 1.0 - ret_val
+            return ret_val
+
+        @cuda.jit(void(double[:], double[:], double[:], double[:], double[:],
+                       double, double))
+        def black_scholes_cuda(callResult, putResult, S, X, T, R, V):
+            i = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
+            if i >= S.shape[0]:
+                return
+            sqrtT = math.sqrt(T[i])
+            d1 = ((math.log(S[i] / X[i]) + (R + 0.5 * V * V) * T[i])
+                  / (V * sqrtT))
+            d2 = d1 - V * sqrtT
+            cndd1 = cnd_cuda(d1)
+            cndd2 = cnd_cuda(d2)
+
+            expRT = math.exp((-1. * R) * T[i])
+            callResult[i] = (S[i] * cndd1 - X[i] * expRT * cndd2)
+            putResult[i] = (X[i] * expRT * (1.0 - cndd2) - S[i] * (1.0 - cndd1))
+
+        # numba
+        blockdim = 512, 1
+        griddim = int(math.ceil(float(OPT_N) / blockdim[0])), 1
+        stream = cuda.stream()
+        d_callResult = cuda.to_device(callResultNumba, stream)
+        d_putResult = cuda.to_device(putResultNumba, stream)
+        d_stockPrice = cuda.to_device(stockPrice, stream)
+        d_optionStrike = cuda.to_device(optionStrike, stream)
+        d_optionYears = cuda.to_device(optionYears, stream)
+
+        for i in range(iterations):
+            black_scholes_cuda[griddim, blockdim, stream](
+                d_callResult, d_putResult, d_stockPrice, d_optionStrike,
+                d_optionYears, RISKFREE, VOLATILITY)
+        d_callResult.copy_to_host(callResultNumba, stream)
+        d_putResult.copy_to_host(putResultNumba, stream)
+        stream.synchronize()
+
+        delta = np.abs(callResultNumpy - callResultNumba)
+        L1norm = delta.sum() / np.abs(callResultNumpy).sum()
+
+        max_abs_err = delta.max()
+        self.assertTrue(L1norm < 1e-13)
+        self.assertTrue(max_abs_err < 1e-13)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_boolean.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_boolean.py
new file mode 100644
index 000000000..fc0568233
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_boolean.py
@@ -0,0 +1,24 @@
+import numpy as np
+from numba.cuda.testing import unittest, CUDATestCase
+from numba import cuda
+
+
+def boolean_func(A, vertial):
+    if vertial:
+        A[0] = 123
+    else:
+        A[0] = 321
+
+
+class TestCudaBoolean(CUDATestCase):
+    def test_boolean(self):
+        func = cuda.jit('void(float64[:], bool_)')(boolean_func)
+        A = np.array([0], dtype='float64')
+        func[1, 1](A, True)
+        self.assertTrue(A[0] == 123)
+        func[1, 1](A, False)
+        self.assertTrue(A[0] == 321)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_caching.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_caching.py
new file mode 100644
index 000000000..16119ee30
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_caching.py
@@ -0,0 +1,493 @@
+import multiprocessing
+import os
+import shutil
+import unittest
+import warnings
+
+from numba import cuda
+from numba.core.errors import NumbaWarning
+from numba.cuda.codegen import CUDACodeLibrary
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+from numba.tests.support import SerialMixin
+from numba.tests.test_caching import (DispatcherCacheUsecasesTest,
+                                      skip_bad_access)
+from pathlib import Path
+
+
+@skip_on_cudasim('Simulator does not implement caching')
+class CUDACachingTest(SerialMixin, DispatcherCacheUsecasesTest):
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cache_usecases.py")
+    modname = "cuda_caching_test_fodder"
+
+    def setUp(self):
+        DispatcherCacheUsecasesTest.setUp(self)
+        CUDATestCase.setUp(self)
+
+    def tearDown(self):
+        CUDATestCase.tearDown(self)
+        DispatcherCacheUsecasesTest.tearDown(self)
+
+    def test_caching(self):
+        self.check_pycache(0)
+        mod = self.import_module()
+        self.check_pycache(0)
+
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+        self.check_pycache(2)  # 1 index, 1 data
+        self.assertPreciseEqual(f(2.5, 3), 6.5)
+        self.check_pycache(3)  # 1 index, 2 data
+        self.check_hits(f.func, 0, 2)
+
+        f = mod.record_return_aligned
+        rec = f(mod.aligned_arr, 1)
+        self.assertPreciseEqual(tuple(rec), (2, 43.5))
+
+        f = mod.record_return_packed
+        rec = f(mod.packed_arr, 1)
+        self.assertPreciseEqual(tuple(rec), (2, 43.5))
+        self.check_pycache(6)  # 2 index, 4 data
+        self.check_hits(f.func, 0, 2)
+
+        # Check the code runs ok from another process
+        self.run_in_separate_process()
+
+    def test_no_caching(self):
+        mod = self.import_module()
+
+        f = mod.add_nocache_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+        self.check_pycache(0)
+
+    def test_many_locals(self):
+        # Declaring many local arrays creates a very large LLVM IR, which
+        # cannot be pickled due to the level of recursion it requires to
+        # pickle. This test ensures that kernels with many locals (and
+        # therefore large IR) can be cached. See Issue #8373:
+        # https://github.com/numba/numba/issues/8373
+        self.check_pycache(0)
+        mod = self.import_module()
+        f = mod.many_locals
+        f[1, 1]()
+        self.check_pycache(2) # 1 index, 1 data
+
+    def test_closure(self):
+        mod = self.import_module()
+
+        with warnings.catch_warnings():
+            warnings.simplefilter('error', NumbaWarning)
+
+            f = mod.closure1
+            self.assertPreciseEqual(f(3), 6) # 3 + 3 = 6
+            f = mod.closure2
+            self.assertPreciseEqual(f(3), 8) # 3 + 5 = 8
+            f = mod.closure3
+            self.assertPreciseEqual(f(3), 10) # 3 + 7 = 10
+            f = mod.closure4
+            self.assertPreciseEqual(f(3), 12) # 3 + 9 = 12
+            self.check_pycache(5) # 1 nbi, 4 nbc
+
+    def test_cache_reuse(self):
+        mod = self.import_module()
+        mod.add_usecase(2, 3)
+        mod.add_usecase(2.5, 3.5)
+        mod.outer_uncached(2, 3)
+        mod.outer(2, 3)
+        mod.record_return_packed(mod.packed_arr, 0)
+        mod.record_return_aligned(mod.aligned_arr, 1)
+        mod.simple_usecase_caller(2)
+        mtimes = self.get_cache_mtimes()
+        # Two signatures compiled
+        self.check_hits(mod.add_usecase.func, 0, 2)
+
+        mod2 = self.import_module()
+        self.assertIsNot(mod, mod2)
+        f = mod2.add_usecase
+        f(2, 3)
+        self.check_hits(f.func, 1, 0)
+        f(2.5, 3.5)
+        self.check_hits(f.func, 2, 0)
+
+        # The files haven't changed
+        self.assertEqual(self.get_cache_mtimes(), mtimes)
+
+        self.run_in_separate_process()
+        self.assertEqual(self.get_cache_mtimes(), mtimes)
+
+    def test_cache_invalidate(self):
+        mod = self.import_module()
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+
+        # This should change the functions' results
+        with open(self.modfile, "a") as f:
+            f.write("\nZ = 10\n")
+
+        mod = self.import_module()
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 15)
+
+    def test_recompile(self):
+        # Explicit call to recompile() should overwrite the cache
+        mod = self.import_module()
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+
+        mod = self.import_module()
+        f = mod.add_usecase
+        mod.Z = 10
+        self.assertPreciseEqual(f(2, 3), 6)
+        f.func.recompile()
+        self.assertPreciseEqual(f(2, 3), 15)
+
+        # Freshly recompiled version is re-used from other imports
+        mod = self.import_module()
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 15)
+
+    def test_same_names(self):
+        # Function with the same names should still disambiguate
+        mod = self.import_module()
+        f = mod.renamed_function1
+        self.assertPreciseEqual(f(2), 4)
+        f = mod.renamed_function2
+        self.assertPreciseEqual(f(2), 8)
+
+    def _test_pycache_fallback(self):
+        """
+        With a disabled __pycache__, test there is a working fallback
+        (e.g. on the user-wide cache dir)
+        """
+        mod = self.import_module()
+        f = mod.add_usecase
+        # Remove this function's cache files at the end, to avoid accumulation
+        # across test calls.
+        self.addCleanup(shutil.rmtree, f.func.stats.cache_path,
+                        ignore_errors=True)
+
+        self.assertPreciseEqual(f(2, 3), 6)
+        # It's a cache miss since the file was copied to a new temp location
+        self.check_hits(f.func, 0, 1)
+
+        # Test re-use
+        mod2 = self.import_module()
+        f = mod2.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+        self.check_hits(f.func, 1, 0)
+
+        # The __pycache__ is empty (otherwise the test's preconditions
+        # wouldn't be met)
+        self.check_pycache(0)
+
+    @skip_bad_access
+    @unittest.skipIf(os.name == "nt",
+                     "cannot easily make a directory read-only on Windows")
+    def test_non_creatable_pycache(self):
+        # Make it impossible to create the __pycache__ directory
+        old_perms = os.stat(self.tempdir).st_mode
+        os.chmod(self.tempdir, 0o500)
+        self.addCleanup(os.chmod, self.tempdir, old_perms)
+
+        self._test_pycache_fallback()
+
+    @skip_bad_access
+    @unittest.skipIf(os.name == "nt",
+                     "cannot easily make a directory read-only on Windows")
+    def test_non_writable_pycache(self):
+        # Make it impossible to write to the __pycache__ directory
+        pycache = os.path.join(self.tempdir, '__pycache__')
+        os.mkdir(pycache)
+        old_perms = os.stat(pycache).st_mode
+        os.chmod(pycache, 0o500)
+        self.addCleanup(os.chmod, pycache, old_perms)
+
+        self._test_pycache_fallback()
+
+    def test_cannot_cache_linking_libraries(self):
+        link = os.path.join(Path(__file__).parent.parent,
+                            'cudadrv', 'data', 'jitlink.ptx')
+        msg = 'Cannot pickle CUDACodeLibrary with linking files'
+        with self.assertRaisesRegex(RuntimeError, msg):
+            @cuda.jit('void()', cache=True, link=[link])
+            def f():
+                pass
+
+
+@skip_on_cudasim('Simulator does not implement caching')
+class CUDAAndCPUCachingTest(SerialMixin, DispatcherCacheUsecasesTest):
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cache_with_cpu_usecases.py")
+    modname = "cuda_and_cpu_caching_test_fodder"
+
+    def setUp(self):
+        DispatcherCacheUsecasesTest.setUp(self)
+        CUDATestCase.setUp(self)
+
+    def tearDown(self):
+        CUDATestCase.tearDown(self)
+        DispatcherCacheUsecasesTest.tearDown(self)
+
+    def test_cpu_and_cuda_targets(self):
+        # The same function jitted for CPU and CUDA targets should maintain
+        # separate caches for each target.
+        self.check_pycache(0)
+        mod = self.import_module()
+        self.check_pycache(0)
+
+        f_cpu = mod.assign_cpu
+        f_cuda = mod.assign_cuda
+        self.assertPreciseEqual(f_cpu(5), 5)
+        self.check_pycache(2)  # 1 index, 1 data
+        self.assertPreciseEqual(f_cuda(5), 5)
+        self.check_pycache(3)  # 1 index, 2 data
+
+        self.check_hits(f_cpu.func, 0, 1)
+        self.check_hits(f_cuda.func, 0, 1)
+
+        self.assertPreciseEqual(f_cpu(5.5), 5.5)
+        self.check_pycache(4)  # 1 index, 3 data
+        self.assertPreciseEqual(f_cuda(5.5), 5.5)
+        self.check_pycache(5)  # 1 index, 4 data
+
+        self.check_hits(f_cpu.func, 0, 2)
+        self.check_hits(f_cuda.func, 0, 2)
+
+    def test_cpu_and_cuda_reuse(self):
+        # Existing cache files for the CPU and CUDA targets are reused.
+        mod = self.import_module()
+        mod.assign_cpu(5)
+        mod.assign_cpu(5.5)
+        mod.assign_cuda(5)
+        mod.assign_cuda(5.5)
+
+        mtimes = self.get_cache_mtimes()
+
+        # Two signatures compiled
+        self.check_hits(mod.assign_cpu.func, 0, 2)
+        self.check_hits(mod.assign_cuda.func, 0, 2)
+
+        mod2 = self.import_module()
+        self.assertIsNot(mod, mod2)
+        f_cpu = mod2.assign_cpu
+        f_cuda = mod2.assign_cuda
+
+        f_cpu(2)
+        self.check_hits(f_cpu.func, 1, 0)
+        f_cpu(2.5)
+        self.check_hits(f_cpu.func, 2, 0)
+        f_cuda(2)
+        self.check_hits(f_cuda.func, 1, 0)
+        f_cuda(2.5)
+        self.check_hits(f_cuda.func, 2, 0)
+
+        # The files haven't changed
+        self.assertEqual(self.get_cache_mtimes(), mtimes)
+
+        self.run_in_separate_process()
+        self.assertEqual(self.get_cache_mtimes(), mtimes)
+
+
+def get_different_cc_gpus():
+    # Find two GPUs with different Compute Capabilities and return them as a
+    # tuple. If two GPUs with distinct Compute Capabilities cannot be found,
+    # then None is returned.
+    first_gpu = cuda.gpus[0]
+    with first_gpu:
+        first_cc = cuda.current_context().device.compute_capability
+
+    for gpu in cuda.gpus[1:]:
+        with gpu:
+            cc = cuda.current_context().device.compute_capability
+            if cc != first_cc:
+                return (first_gpu, gpu)
+
+    return None
+
+
+@skip_on_cudasim('Simulator does not implement caching')
+class TestMultiCCCaching(SerialMixin, DispatcherCacheUsecasesTest):
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cache_usecases.py")
+    modname = "cuda_multi_cc_caching_test_fodder"
+
+    def setUp(self):
+        DispatcherCacheUsecasesTest.setUp(self)
+        CUDATestCase.setUp(self)
+
+    def tearDown(self):
+        CUDATestCase.tearDown(self)
+        DispatcherCacheUsecasesTest.tearDown(self)
+
+    def test_cache(self):
+        gpus = get_different_cc_gpus()
+        if not gpus:
+            self.skipTest('Need two different CCs for multi-CC cache test')
+
+        self.check_pycache(0)
+        mod = self.import_module()
+        self.check_pycache(0)
+
+        # Step 1. Populate the cache with the first GPU
+        with gpus[0]:
+            f = mod.add_usecase
+            self.assertPreciseEqual(f(2, 3), 6)
+            self.check_pycache(2)  # 1 index, 1 data
+            self.assertPreciseEqual(f(2.5, 3), 6.5)
+            self.check_pycache(3)  # 1 index, 2 data
+            self.check_hits(f.func, 0, 2)
+
+            f = mod.record_return_aligned
+            rec = f(mod.aligned_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+
+            f = mod.record_return_packed
+            rec = f(mod.packed_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+            self.check_pycache(6)  # 2 index, 4 data
+            self.check_hits(f.func, 0, 2)
+
+        # Step 2. Run with the second GPU - under present behaviour this
+        # doesn't further populate the cache.
+        with gpus[1]:
+            f = mod.add_usecase
+            self.assertPreciseEqual(f(2, 3), 6)
+            self.check_pycache(6)  # cache unchanged
+            self.assertPreciseEqual(f(2.5, 3), 6.5)
+            self.check_pycache(6)  # cache unchanged
+            self.check_hits(f.func, 0, 2)
+
+            f = mod.record_return_aligned
+            rec = f(mod.aligned_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+
+            f = mod.record_return_packed
+            rec = f(mod.packed_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+            self.check_pycache(6)  # cache unchanged
+            self.check_hits(f.func, 0, 2)
+
+        # Step 3. Run in a separate module with the second GPU - this populates
+        # the cache for the second CC.
+        mod2 = self.import_module()
+        self.assertIsNot(mod, mod2)
+
+        with gpus[1]:
+            f = mod2.add_usecase
+            self.assertPreciseEqual(f(2, 3), 6)
+            self.check_pycache(7)  # 2 index, 5 data
+            self.assertPreciseEqual(f(2.5, 3), 6.5)
+            self.check_pycache(8)  # 2 index, 6 data
+            self.check_hits(f.func, 0, 2)
+
+            f = mod2.record_return_aligned
+            rec = f(mod.aligned_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+
+            f = mod2.record_return_packed
+            rec = f(mod.packed_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+            self.check_pycache(10)  # 2 index, 8 data
+            self.check_hits(f.func, 0, 2)
+
+        # The following steps check that we can use the NVVM IR loaded from the
+        # cache to generate PTX for a different compute capability to the
+        # cached cubin's CC. To check this, we create another module that loads
+        # the cached version containing a cubin for GPU 1. There will be no
+        # cubin for GPU 0, so when we try to use it the PTX must be generated.
+
+        mod3 = self.import_module()
+        self.assertIsNot(mod, mod3)
+
+        # Step 4. Run with GPU 1 and get a cache hit, loading the cache created
+        # during Step 3.
+        with gpus[1]:
+            f = mod3.add_usecase
+            self.assertPreciseEqual(f(2, 3), 6)
+            self.assertPreciseEqual(f(2.5, 3), 6.5)
+
+            f = mod3.record_return_aligned
+            rec = f(mod.aligned_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+
+            f = mod3.record_return_packed
+            rec = f(mod.packed_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+
+        # Step 5. Run with GPU 0 using the module from Step 4, to force PTX
+        # generation from cached NVVM IR.
+        with gpus[0]:
+            f = mod3.add_usecase
+            self.assertPreciseEqual(f(2, 3), 6)
+            self.assertPreciseEqual(f(2.5, 3), 6.5)
+
+            f = mod3.record_return_aligned
+            rec = f(mod.aligned_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+
+            f = mod3.record_return_packed
+            rec = f(mod.packed_arr, 1)
+            self.assertPreciseEqual(tuple(rec), (2, 43.5))
+
+
+def child_initializer():
+    # Disable occupancy and implicit copy warnings in processes in a
+    # multiprocessing pool.
+    from numba.core import config
+    config.CUDA_LOW_OCCUPANCY_WARNINGS = 0
+    config.CUDA_WARN_ON_IMPLICIT_COPY = 0
+
+
+@skip_on_cudasim('Simulator does not implement caching')
+class TestMultiprocessCache(SerialMixin, DispatcherCacheUsecasesTest):
+
+    # Nested multiprocessing.Pool raises AssertionError:
+    # "daemonic processes are not allowed to have children"
+    _numba_parallel_test_ = False
+
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cache_usecases.py")
+    modname = "cuda_mp_caching_test_fodder"
+
+    def setUp(self):
+        DispatcherCacheUsecasesTest.setUp(self)
+        CUDATestCase.setUp(self)
+
+    def tearDown(self):
+        CUDATestCase.tearDown(self)
+        DispatcherCacheUsecasesTest.tearDown(self)
+
+    def test_multiprocessing(self):
+        # Check caching works from multiple processes at once (#2028)
+        mod = self.import_module()
+        # Calling a pure Python caller of the JIT-compiled function is
+        # necessary to reproduce the issue.
+        f = mod.simple_usecase_caller
+        n = 3
+        try:
+            ctx = multiprocessing.get_context('spawn')
+        except AttributeError:
+            ctx = multiprocessing
+
+        pool = ctx.Pool(n, child_initializer)
+
+        try:
+            res = sum(pool.imap(f, range(n)))
+        finally:
+            pool.close()
+        self.assertEqual(res, n * (n - 1) // 2)
+
+
+@skip_on_cudasim('Simulator does not implement the CUDACodeLibrary')
+class TestCUDACodeLibrary(CUDATestCase):
+    # For tests of miscellaneous CUDACodeLibrary behaviour that we wish to
+    # explicitly check
+
+    def test_cannot_serialize_unfinalized(self):
+        # Usually a CodeLibrary requires a real CodeGen, but since we don't
+        # interact with it, anything will do
+        codegen = object()
+        name = 'library'
+        cl = CUDACodeLibrary(codegen, name)
+        with self.assertRaisesRegex(RuntimeError, 'Cannot pickle unfinalized'):
+            cl._reduce_states()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_casting.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_casting.py
new file mode 100644
index 000000000..0587950b9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_casting.py
@@ -0,0 +1,239 @@
+import numpy as np
+
+from numba.cuda import compile_ptx
+from numba.core.types import f2, i1, i2, i4, i8, u1, u2, u4, u8
+from numba import cuda
+from numba.core import types
+from numba.cuda.testing import (CUDATestCase, skip_on_cudasim,
+                                skip_unless_cc_53)
+import itertools
+import unittest
+
+
+def to_int8(x):
+    return np.int8(x)
+
+
+def to_int16(x):
+    return np.int16(x)
+
+
+def to_int32(x):
+    return np.int32(x)
+
+
+def to_int64(x):
+    return np.int64(x)
+
+
+def to_uint8(x):
+    return np.uint8(x)
+
+
+def to_uint16(x):
+    return np.uint16(x)
+
+
+def to_uint32(x):
+    return types.uint32(x)
+
+
+def to_uint64(x):
+    return types.uint64(x)
+
+
+def to_float16(x):
+    # When division and operators on float16 types are supported, this should
+    # be changed to match the implementation in to_float32.
+    return (np.float16(x) * np.float16(0.5))
+
+
+def to_float32(x):
+    return np.float32(x) / np.float32(2)
+
+
+def to_float64(x):
+    return np.float64(x) / np.float64(2)
+
+
+def to_complex64(x):
+    return np.complex64(x)
+
+
+def to_complex128(x):
+    return np.complex128(x)
+
+
+# Since multiplication of float16 is not supported via the operator * on
+# float16s yet, and the host does not implement cuda.fp16.*, we need two
+# versions of the following functions:
+#
+# - The device version uses cuda.fp16.hmul
+# - The host version uses the * operator
+
+def cuda_int_literal_to_float16(x):
+    # Note that we need to use `2` and not `np.float16(2)` to ensure that this
+    # types as a literal int and not a const float16.
+    return cuda.fp16.hmul(np.float16(x), 2)
+
+
+def reference_int_literal_to_float16(x):
+    return np.float16(x) * np.float16(2)
+
+
+def cuda_float_literal_to_float16(x):
+    # Note that `2.5` types as a const float64 and not a literal float, but
+    # this case is provided in case that changes in future.
+    return cuda.fp16.hmul(np.float16(x), 2.5)
+
+
+def reference_float_literal_to_float16(x):
+    return np.float16(x) * np.float16(2.5)
+
+
+class TestCasting(CUDATestCase):
+    def _create_wrapped(self, pyfunc, intype, outtype):
+        wrapped_func = cuda.jit(device=True)(pyfunc)
+
+        @cuda.jit
+        def cuda_wrapper_fn(arg, res):
+            res[0] = wrapped_func(arg[0])
+
+        def wrapper_fn(arg):
+            argarray = np.zeros(1, dtype=intype)
+            argarray[0] = arg
+            resarray = np.zeros(1, dtype=outtype)
+            cuda_wrapper_fn[1, 1](argarray, resarray)
+            return resarray[0]
+
+        return wrapper_fn
+
+    def test_float_to_int(self):
+        pyfuncs = (to_int8, to_int16, to_int32, to_int64)
+        totys = (np.int8, np.int16, np.int32, np.int64)
+        fromtys = (np.float16, np.float32, np.float64)
+
+        for pyfunc, toty in zip(pyfuncs, totys):
+            for fromty in fromtys:
+                with self.subTest(fromty=fromty, toty=toty):
+                    cfunc = self._create_wrapped(pyfunc, fromty, toty)
+                    self.assertEqual(cfunc(12.3), pyfunc(12.3))
+                    self.assertEqual(cfunc(12.3), int(12.3))
+                    self.assertEqual(cfunc(-12.3), pyfunc(-12.3))
+                    self.assertEqual(cfunc(-12.3), int(-12.3))
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_float16_to_int_ptx(self):
+        pyfuncs = (to_int8, to_int16, to_int32, to_int64)
+        sizes = (8, 16, 32, 64)
+
+        for pyfunc, size in zip(pyfuncs, sizes):
+            ptx, _ = compile_ptx(pyfunc, [f2], device=True)
+            self.assertIn(f"cvt.rni.s{size}.f16", ptx)
+
+    def test_float_to_uint(self):
+        pyfuncs = (to_int8, to_int16, to_int32, to_int64)
+        totys = (np.uint8, np.uint16, np.uint32, np.uint64)
+        fromtys = (np.float16, np.float32, np.float64)
+
+        for pyfunc, toty in zip(pyfuncs, totys):
+            for fromty in fromtys:
+                with self.subTest(fromty=fromty, toty=toty):
+                    cfunc = self._create_wrapped(pyfunc, fromty, toty)
+                    self.assertEqual(cfunc(12.3), pyfunc(12.3))
+                    self.assertEqual(cfunc(12.3), int(12.3))
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_float16_to_uint_ptx(self):
+        pyfuncs = (to_uint8, to_uint16, to_uint32, to_uint64)
+        sizes = (8, 16, 32, 64)
+
+        for pyfunc, size in zip(pyfuncs, sizes):
+            ptx, _ = compile_ptx(pyfunc, [f2], device=True)
+            self.assertIn(f"cvt.rni.u{size}.f16", ptx)
+
+    def test_int_to_float(self):
+        pyfuncs = (to_float16, to_float32, to_float64)
+        totys = (np.float16, np.float32, np.float64)
+
+        for pyfunc, toty in zip(pyfuncs, totys):
+            with self.subTest(toty=toty):
+                cfunc = self._create_wrapped(pyfunc, np.int64, toty)
+                self.assertEqual(cfunc(321), pyfunc(321))
+
+    @skip_unless_cc_53
+    def test_literal_to_float16(self):
+        cudafuncs = (cuda_int_literal_to_float16,
+                     cuda_float_literal_to_float16)
+        hostfuncs = (reference_int_literal_to_float16,
+                     reference_float_literal_to_float16)
+
+        for cudafunc, hostfunc in zip(cudafuncs, hostfuncs):
+            with self.subTest(func=cudafunc):
+                cfunc = self._create_wrapped(cudafunc, np.float16, np.float16)
+                self.assertEqual(cfunc(321), hostfunc(321))
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_int_to_float16_ptx(self):
+        fromtys = (i1, i2, i4, i8)
+        sizes = (8, 16, 32, 64)
+
+        for ty, size in zip(fromtys, sizes):
+            ptx, _ = compile_ptx(to_float16, [ty], device=True)
+            self.assertIn(f"cvt.rn.f16.s{size}", ptx)
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_uint_to_float16_ptx(self):
+        fromtys = (u1, u2, u4, u8)
+        sizes = (8, 16, 32, 64)
+
+        for ty, size in zip(fromtys, sizes):
+            ptx, _ = compile_ptx(to_float16, [ty], device=True)
+            self.assertIn(f"cvt.rn.f16.u{size}", ptx)
+
+    def test_float_to_float(self):
+        pyfuncs = (to_float16, to_float32, to_float64)
+        tys = (np.float16, np.float32, np.float64)
+
+        for (pyfunc, fromty), toty in itertools.product(zip(pyfuncs, tys), tys):
+            with self.subTest(fromty=fromty, toty=toty):
+                cfunc = self._create_wrapped(pyfunc, fromty, toty)
+                # For this test we cannot use the pyfunc for comparison because
+                # the CUDA target doesn't yet implement division (or operators)
+                # for float16 values, so we test by comparing with the computed
+                # expression instead.
+                np.testing.assert_allclose(cfunc(12.3),
+                                           toty(12.3) / toty(2), rtol=0.0003)
+                np.testing.assert_allclose(cfunc(-12.3),
+                                           toty(-12.3) / toty(2), rtol=0.0003)
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_float16_to_float_ptx(self):
+        pyfuncs = (to_float32, to_float64)
+        postfixes = ("f32", "f64")
+
+        for pyfunc, postfix in zip(pyfuncs, postfixes):
+            ptx, _ = compile_ptx(pyfunc, [f2], device=True)
+            self.assertIn(f"cvt.{postfix}.f16", ptx)
+
+    def test_float_to_complex(self):
+        pyfuncs = (to_complex64, to_complex128)
+        totys = (np.complex64, np.complex128)
+        fromtys = (np.float16, np.float32, np.float64)
+
+        for pyfunc, toty in zip(pyfuncs, totys):
+            for fromty in fromtys:
+                with self.subTest(fromty=fromty, toty=toty):
+                    cfunc = self._create_wrapped(pyfunc, fromty, toty)
+                    # Here we need to explicitly cast the input to the pyfunc
+                    # to match the casting that is automatically applied when
+                    # passing the input to the cfunc as part of wrapping it in
+                    # an array of type fromtype.
+                    np.testing.assert_allclose(cfunc(3.21),
+                                               pyfunc(fromty(3.21)))
+                    np.testing.assert_allclose(cfunc(-3.21),
+                                               pyfunc(fromty(-3.21)) + 0j)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_compiler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_compiler.py
new file mode 100644
index 000000000..94561511b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_compiler.py
@@ -0,0 +1,163 @@
+from math import sqrt
+from numba import cuda, float32, uint32, void
+from numba.cuda import compile_ptx, compile_ptx_for_current_device
+from numba.cuda.cudadrv.nvvm import NVVM
+
+from numba.cuda.testing import skip_on_cudasim, unittest, CUDATestCase
+
+
+@skip_on_cudasim('Compilation unsupported in the simulator')
+class TestCompileToPTX(unittest.TestCase):
+    def test_global_kernel(self):
+        def f(r, x, y):
+            i = cuda.grid(1)
+            if i < len(r):
+                r[i] = x[i] + y[i]
+
+        args = (float32[:], float32[:], float32[:])
+        ptx, resty = compile_ptx(f, args)
+
+        # Kernels should not have a func_retval parameter
+        self.assertNotIn('func_retval', ptx)
+        # .visible .func is used to denote a device function
+        self.assertNotIn('.visible .func', ptx)
+        # .visible .entry would denote the presence of a global function
+        self.assertIn('.visible .entry', ptx)
+        # Return type for kernels should always be void
+        self.assertEqual(resty, void)
+
+    def test_device_function(self):
+        def add(x, y):
+            return x + y
+
+        args = (float32, float32)
+        ptx, resty = compile_ptx(add, args, device=True)
+
+        # Device functions take a func_retval parameter for storing the
+        # returned value in by reference
+        self.assertIn('func_retval', ptx)
+        # .visible .func is used to denote a device function
+        self.assertIn('.visible .func', ptx)
+        # .visible .entry would denote the presence of a global function
+        self.assertNotIn('.visible .entry', ptx)
+        # Inferred return type as expected?
+        self.assertEqual(resty, float32)
+
+    def test_fastmath(self):
+        def f(x, y, z, d):
+            return sqrt((x * y + z) / d)
+
+        args = (float32, float32, float32, float32)
+        ptx, resty = compile_ptx(f, args, device=True)
+
+        # Without fastmath, fma contraction is enabled by default, but ftz and
+        # approximate div / sqrt is not.
+        self.assertIn('fma.rn.f32', ptx)
+        self.assertIn('div.rn.f32', ptx)
+        self.assertIn('sqrt.rn.f32', ptx)
+
+        ptx, resty = compile_ptx(f, args, device=True, fastmath=True)
+
+        # With fastmath, ftz and approximate div / sqrt are enabled
+        self.assertIn('fma.rn.ftz.f32', ptx)
+        self.assertIn('div.approx.ftz.f32', ptx)
+        self.assertIn('sqrt.approx.ftz.f32', ptx)
+
+    def check_debug_info(self, ptx):
+        if not NVVM().is_nvvm70:
+            self.skipTest('debuginfo not generated for NVVM 3.4')
+
+        # A debug_info section should exist in the PTX. Whitespace varies
+        # between CUDA toolkit versions.
+        self.assertRegex(ptx, '\\.section\\s+\\.debug_info')
+        # A .file directive should be produced and include the name of the
+        # source. The path and whitespace may vary, so we accept anything
+        # ending in the filename of this module.
+        self.assertRegex(ptx, '\\.file.*test_compiler.py"')
+
+    def test_device_function_with_debug(self):
+        # See Issue #6719 - this ensures that compilation with debug succeeds
+        # with CUDA 11.2 / NVVM 7.0 onwards. Previously it failed because NVVM
+        # IR version metadata was not added when compiling device functions,
+        # and NVVM assumed DBG version 1.0 if not specified, which is
+        # incompatible with the 3.0 IR we use. This was specified only for
+        # kernels.
+        def f():
+            pass
+
+        ptx, resty = compile_ptx(f, [], device=True, debug=True)
+        self.check_debug_info(ptx)
+
+    def test_kernel_with_debug(self):
+        # Inspired by (but not originally affected by) Issue #6719
+        def f():
+            pass
+
+        ptx, resty = compile_ptx(f, [], debug=True)
+        self.check_debug_info(ptx)
+
+    def check_line_info(self, ptx):
+        # A .file directive should be produced and include the name of the
+        # source. The path and whitespace may vary, so we accept anything
+        # ending in the filename of this module.
+        self.assertRegex(ptx, '\\.file.*test_compiler.py"')
+
+    def test_device_function_with_line_info(self):
+        def f():
+            pass
+
+        ptx, resty = compile_ptx(f, [], device=True, lineinfo=True)
+        self.check_line_info(ptx)
+
+    def test_kernel_with_line_info(self):
+        def f():
+            pass
+
+        ptx, resty = compile_ptx(f, [], lineinfo=True)
+        self.check_line_info(ptx)
+
+
+@skip_on_cudasim('Compilation unsupported in the simulator')
+class TestCompileToPTXForCurrentDevice(CUDATestCase):
+    def test_compile_ptx_for_current_device(self):
+        def add(x, y):
+            return x + y
+
+        args = (float32, float32)
+        ptx, resty = compile_ptx_for_current_device(add, args, device=True)
+
+        # Check we target the current device's compute capability, or the
+        # closest compute capability supported by the current toolkit.
+        device_cc = cuda.get_current_device().compute_capability
+        cc = cuda.cudadrv.nvvm.find_closest_arch(device_cc)
+        target = f'.target sm_{cc[0]}{cc[1]}'
+        self.assertIn(target, ptx)
+
+
+@skip_on_cudasim('Compilation unsupported in the simulator')
+class TestCompileOnlyTests(unittest.TestCase):
+    '''For tests where we can only check correctness by examining the compiler
+    output rather than observing the effects of execution.'''
+
+    def test_nanosleep(self):
+        def use_nanosleep(x):
+            # Sleep for a constant time
+            cuda.nanosleep(32)
+            # Sleep for a variable time
+            cuda.nanosleep(x)
+
+        ptx, resty = compile_ptx(use_nanosleep, (uint32,), cc=(7, 0))
+
+        nanosleep_count = 0
+        for line in ptx.split('\n'):
+            if 'nanosleep.u32' in line:
+                nanosleep_count += 1
+
+        expected = 2
+        self.assertEqual(expected, nanosleep_count,
+                         (f'Got {nanosleep_count} nanosleep instructions, '
+                          f'expected {expected}'))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_complex.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_complex.py
new file mode 100644
index 000000000..958393162
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_complex.py
@@ -0,0 +1,296 @@
+import math
+import itertools
+
+import numpy as np
+
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.core import types
+from numba import cuda
+from numba.tests.complex_usecases import (real_usecase, imag_usecase,
+                                          conjugate_usecase, phase_usecase,
+                                          polar_as_complex_usecase,
+                                          rect_usecase, isnan_usecase,
+                                          isinf_usecase, isfinite_usecase,
+                                          exp_usecase, log_usecase,
+                                          log_base_usecase, log10_usecase,
+                                          sqrt_usecase, asin_usecase,
+                                          acos_usecase, atan_usecase,
+                                          cos_usecase, sin_usecase,
+                                          tan_usecase, acosh_usecase,
+                                          asinh_usecase, atanh_usecase,
+                                          cosh_usecase, sinh_usecase,
+                                          tanh_usecase)
+from numba.np import numpy_support
+
+
+def compile_scalar_func(pyfunc, argtypes, restype):
+    # First compile a scalar device function
+    assert not any(isinstance(tp, types.Array) for tp in argtypes)
+    assert not isinstance(restype, types.Array)
+    device_func = cuda.jit(restype(*argtypes), device=True)(pyfunc)
+
+    kernel_types = [types.Array(tp, 1, "C")
+                    for tp in [restype] + list(argtypes)]
+
+    if len(argtypes) == 1:
+        def kernel_func(out, a):
+            i = cuda.grid(1)
+            if i < out.shape[0]:
+                out[i] = device_func(a[i])
+    elif len(argtypes) == 2:
+        def kernel_func(out, a, b):
+            i = cuda.grid(1)
+            if i < out.shape[0]:
+                out[i] = device_func(a[i], b[i])
+    else:
+        assert 0
+
+    kernel = cuda.jit(tuple(kernel_types))(kernel_func)
+
+    def kernel_wrapper(values):
+        n = len(values)
+        inputs = [np.empty(n, dtype=numpy_support.as_dtype(tp))
+                  for tp in argtypes]
+        output = np.empty(n, dtype=numpy_support.as_dtype(restype))
+        for i, vs in enumerate(values):
+            for v, inp in zip(vs, inputs):
+                inp[i] = v
+        args = [output] + inputs
+        kernel[int(math.ceil(n / 256)), 256](*args)
+        return list(output)
+    return kernel_wrapper
+
+
+class BaseComplexTest(CUDATestCase):
+
+    def basic_values(self):
+        reals = [-0.0, +0.0, 1, -1, +1.5, -3.5,
+                 float('-inf'), float('+inf'), float('nan')]
+        return [complex(x, y) for x, y in itertools.product(reals, reals)]
+
+    def more_values(self):
+        reals = [0.0, +0.0, 1, -1, -math.pi, +math.pi,
+                 float('-inf'), float('+inf'), float('nan')]
+        return [complex(x, y) for x, y in itertools.product(reals, reals)]
+
+    def non_nan_values(self):
+        reals = [-0.0, +0.0, 1, -1, -math.pi, +math.pi,
+                 float('inf'), float('-inf')]
+        return [complex(x, y) for x, y in itertools.product(reals, reals)]
+
+    def run_func(self, pyfunc, sigs, values, ulps=1, ignore_sign_on_zero=False):
+        for sig in sigs:
+            if isinstance(sig, types.Type):
+                sig = sig,
+            if isinstance(sig, tuple):
+                # Assume return type is the type of first argument
+                sig = sig[0](*sig)
+            prec = ('single'
+                    if sig.args[0] in (types.float32, types.complex64)
+                    else 'double')
+            cudafunc = compile_scalar_func(pyfunc, sig.args, sig.return_type)
+            ok_values = []
+            expected_list = []
+            for args in values:
+                if not isinstance(args, (list, tuple)):
+                    args = args,
+                try:
+                    expected_list.append(pyfunc(*args))
+                    ok_values.append(args)
+                except ValueError as e:
+                    self.assertIn("math domain error", str(e))
+                    continue
+            got_list = cudafunc(ok_values)
+            for got, expected, args in zip(got_list, expected_list, ok_values):
+                msg = 'for input %r with prec %r' % (args, prec)
+                self.assertPreciseEqual(got, expected, prec=prec,
+                                        ulps=ulps,
+                                        ignore_sign_on_zero=ignore_sign_on_zero,
+                                        msg=msg)
+
+    run_unary = run_func
+    run_binary = run_func
+
+
+class TestComplex(BaseComplexTest):
+
+    def check_real_image(self, pyfunc):
+        values = self.basic_values()
+        self.run_unary(pyfunc,
+                       [tp.underlying_float(tp)
+                        for tp in (types.complex64, types.complex128)],
+                       values)
+
+    def test_real(self):
+        self.check_real_image(real_usecase)
+
+    def test_imag(self):
+        self.check_real_image(imag_usecase)
+
+    def test_conjugate(self):
+        pyfunc = conjugate_usecase
+        values = self.basic_values()
+        self.run_unary(pyfunc,
+                       [types.complex64, types.complex128],
+                       values)
+
+
+class TestCMath(BaseComplexTest):
+    """
+    Tests for cmath module support.
+    """
+
+    def check_predicate_func(self, pyfunc):
+        self.run_unary(pyfunc,
+                       [types.boolean(tp)
+                        for tp in (types.complex128, types.complex64)],
+                       self.basic_values())
+
+    def check_unary_func(self, pyfunc, ulps=1, values=None,
+                         returns_float=False, ignore_sign_on_zero=False):
+        if returns_float:
+            def sig(tp):
+                return tp.underlying_float(tp)
+        else:
+            def sig(tp):
+                return tp(tp)
+        self.run_unary(pyfunc, [sig(types.complex128)],
+                       values or self.more_values(), ulps=ulps,
+                       ignore_sign_on_zero=ignore_sign_on_zero)
+        # Avoid discontinuities around pi when in single precision.
+        self.run_unary(pyfunc, [sig(types.complex64)],
+                       values or self.basic_values(), ulps=ulps,
+                       ignore_sign_on_zero=ignore_sign_on_zero)
+
+    # Conversions
+
+    def test_phase(self):
+        self.check_unary_func(phase_usecase, returns_float=True)
+
+    def test_polar(self):
+        self.check_unary_func(polar_as_complex_usecase)
+
+    def test_rect(self):
+        def do_test(tp, seed_values):
+            values = [(z.real, z.imag) for z in seed_values
+                      if not math.isinf(z.imag) or z.real == 0]
+            float_type = tp.underlying_float
+            self.run_binary(rect_usecase, [tp(float_type, float_type)],
+                            values)
+        do_test(types.complex128, self.more_values())
+        # Avoid discontinuities around pi when in single precision.
+        do_test(types.complex64, self.basic_values())
+
+    # Classification
+
+    def test_isnan(self):
+        self.check_predicate_func(isnan_usecase)
+
+    def test_isinf(self):
+        self.check_predicate_func(isinf_usecase)
+
+    def test_isfinite(self):
+        self.check_predicate_func(isfinite_usecase)
+
+    # Power and logarithms
+
+    def test_exp(self):
+        self.check_unary_func(exp_usecase, ulps=2)
+
+    def test_log(self):
+        self.check_unary_func(log_usecase)
+
+    def test_log_base(self):
+        values = list(itertools.product(self.more_values(), self.more_values()))
+        value_types = [(types.complex128, types.complex128),
+                       (types.complex64, types.complex64)]
+        self.run_binary(log_base_usecase, value_types, values,
+                        ulps=3)
+
+    def test_log10(self):
+        self.check_unary_func(log10_usecase)
+
+    def test_sqrt(self):
+        self.check_unary_func(sqrt_usecase)
+
+    # Trigonometric functions
+
+    def test_acos(self):
+        self.check_unary_func(acos_usecase, ulps=2)
+
+    def test_asin(self):
+        self.check_unary_func(asin_usecase, ulps=2)
+
+    def test_atan(self):
+        self.check_unary_func(atan_usecase, ulps=2,
+                              values=self.non_nan_values())
+
+    def test_cos(self):
+        self.check_unary_func(cos_usecase, ulps=2)
+
+    def test_sin(self):
+        # See test_sinh.
+        self.check_unary_func(sin_usecase, ulps=2)
+
+    def test_tan(self):
+        self.check_unary_func(tan_usecase, ulps=2,
+                              ignore_sign_on_zero=True)
+
+    # Hyperbolic functions
+
+    def test_acosh(self):
+        self.check_unary_func(acosh_usecase)
+
+    def test_asinh(self):
+        self.check_unary_func(asinh_usecase, ulps=2)
+
+    def test_atanh(self):
+        self.check_unary_func(atanh_usecase, ulps=2,
+                              ignore_sign_on_zero=True)
+
+    def test_cosh(self):
+        self.check_unary_func(cosh_usecase, ulps=2)
+
+    def test_sinh(self):
+        self.check_unary_func(sinh_usecase, ulps=2)
+
+    def test_tanh(self):
+        self.check_unary_func(tanh_usecase, ulps=2,
+                              ignore_sign_on_zero=True)
+
+
+class TestAtomicOnComplexComponents(CUDATestCase):
+    # Based on the reproducer from Issue #8309. array.real and array.imag could
+    # not be used because they required returning an array from a generated
+    # function, and even if this was permitted, they could not be resolved from
+    # the atomic lowering when they were overloads.
+    #
+    # See https://github.com/numba/numba/issues/8309
+
+    def test_atomic_on_real(self):
+        @cuda.jit
+        def atomic_add_one(values):
+            i = cuda.grid(1)
+            cuda.atomic.add(values.real, i, 1)
+
+        N = 32
+        arr1 = np.arange(N) + np.arange(N) * 1j
+        arr2 = arr1.copy()
+        atomic_add_one[1, N](arr2)
+        np.testing.assert_equal(arr1 + 1, arr2)
+
+    def test_atomic_on_imag(self):
+        @cuda.jit
+        def atomic_add_one_j(values):
+            i = cuda.grid(1)
+            cuda.atomic.add(values.imag, i, 1)
+
+        N = 32
+        arr1 = np.arange(N) + np.arange(N) * 1j
+        arr2 = arr1.copy()
+        atomic_add_one_j[1, N](arr2)
+        np.testing.assert_equal(arr1 + 1j, arr2)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_complex_kernel.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_complex_kernel.py
new file mode 100644
index 000000000..e72a6df00
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_complex_kernel.py
@@ -0,0 +1,20 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaComplex(CUDATestCase):
+    def test_cuda_complex_arg(self):
+        @cuda.jit('void(complex128[:], complex128)')
+        def foo(a, b):
+            i = cuda.grid(1)
+            a[i] += b
+
+        a = np.arange(5, dtype=np.complex128)
+        a0 = a.copy()
+        foo[1, a.shape](a, 2j)
+        self.assertTrue(np.allclose(a, a0 + 2j))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_const_string.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_const_string.py
new file mode 100644
index 000000000..2c1cf45ad
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_const_string.py
@@ -0,0 +1,216 @@
+import re
+import numpy as np
+from numba import config, cuda
+from numba.core.errors import TypingError
+from numba.cuda.cudadrv.nvvm import NVVM
+from numba.cuda.testing import unittest, skip_on_cudasim, CUDATestCase
+from llvmlite import ir
+
+
+@skip_on_cudasim("This is testing CUDA backend code generation")
+class TestConstStringCodegen(unittest.TestCase):
+    def test_const_string(self):
+        # These imports are incompatible with CUDASIM
+        from numba.cuda.descriptor import cuda_target
+        from numba.cuda.cudadrv.nvvm import llvm_to_ptx, ADDRSPACE_CONSTANT
+
+        targetctx = cuda_target.target_context
+        mod = targetctx.create_module("")
+        textstring = 'A Little Brown Fox'
+        gv0 = targetctx.insert_const_string(mod, textstring)
+        # Insert the same const string a second time - the first should be
+        # reused.
+        targetctx.insert_const_string(mod, textstring)
+
+        res = re.findall(r"@\"__conststring__.*internal.*constant.*\["
+                         r"19\s+x\s+i8\]", str(mod))
+        # Ensure that the const string was only inserted once
+        self.assertEqual(len(res), 1)
+
+        fnty = ir.FunctionType(ir.IntType(8).as_pointer(), [])
+
+        # Using insert_const_string
+        fn = ir.Function(mod, fnty, "test_insert_const_string")
+        builder = ir.IRBuilder(fn.append_basic_block())
+        res = targetctx.insert_addrspace_conv(builder, gv0,
+                                              addrspace=ADDRSPACE_CONSTANT)
+        builder.ret(res)
+
+        matches = re.findall(r"@\"__conststring__.*internal.*constant.*\["
+                             r"19\s+x\s+i8\]", str(mod))
+        self.assertEqual(len(matches), 1)
+
+        # Using insert_string_const_addrspace
+        fn = ir.Function(mod, fnty, "test_insert_string_const_addrspace")
+        builder = ir.IRBuilder(fn.append_basic_block())
+        res = targetctx.insert_string_const_addrspace(builder, textstring)
+        builder.ret(res)
+
+        matches = re.findall(r"@\"__conststring__.*internal.*constant.*\["
+                             r"19\s+x\s+i8\]", str(mod))
+        self.assertEqual(len(matches), 1)
+
+        ptx = llvm_to_ptx(str(mod)).decode('ascii')
+        matches = list(re.findall(r"\.const.*__conststring__", ptx))
+
+        self.assertEqual(len(matches), 1)
+
+
+# Inspired by the reproducer from Issue #7041.
+class TestConstString(CUDATestCase):
+
+    def skip_on_nvvm34(self):
+        # There is no NVVM on cudasim, but we might as well run this test.
+        if not config.ENABLE_CUDASIM and not NVVM().is_nvvm70:
+            self.skipTest('Character sequences unsupported on NVVM 3.4')
+
+    def skip_on_nvvm70(self):
+        if NVVM().is_nvvm70:
+            self.skipTest('Character sequences are permitted with NVVM 7.0')
+
+    def test_assign_const_unicode_string(self):
+        self.skip_on_nvvm34()
+
+        @cuda.jit
+        def str_assign(arr):
+            i = cuda.grid(1)
+            if i < len(arr):
+                arr[i] = "XYZ"
+
+        n_strings = 8
+        arr = np.zeros(n_strings + 1, dtype="<U12")
+        str_assign[1, n_strings](arr)
+
+        # Expected result, e.g.:
+        #     ['XYZ' 'XYZ' 'XYZ' 'XYZ' 'XYZ' 'XYZ' 'XYZ' 'XYZ' '']
+        expected = np.zeros_like(arr)
+        expected[:-1] = 'XYZ'
+        expected[-1] = ''
+        np.testing.assert_equal(arr, expected)
+
+    def test_assign_const_byte_string(self):
+        self.skip_on_nvvm34()
+
+        @cuda.jit
+        def bytes_assign(arr):
+            i = cuda.grid(1)
+            if i < len(arr):
+                arr[i] = b"XYZ"
+
+        n_strings = 8
+        arr = np.zeros(n_strings + 1, dtype="S12")
+        bytes_assign[1, n_strings](arr)
+
+        # Expected result, e.g.:
+        #     [b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'']
+        expected = np.zeros_like(arr)
+        expected[:-1] = b'XYZ'
+        expected[-1] = b''
+        np.testing.assert_equal(arr, expected)
+
+    def test_assign_const_string_in_record(self):
+        self.skip_on_nvvm34()
+
+        @cuda.jit
+        def f(a):
+            a[0]['x'] = 1
+            a[0]['y'] = 'ABC'
+            a[1]['x'] = 2
+            a[1]['y'] = 'XYZ'
+
+        dt = np.dtype([('x', np.int32), ('y', np.dtype('<U12'))])
+        a = np.zeros(2, dt)
+
+        f[1, 1](a)
+
+        reference = np.asarray([(1, 'ABC'), (2, 'XYZ')], dtype=dt)
+        np.testing.assert_array_equal(reference, a)
+
+    def test_assign_const_bytes_in_record(self):
+        self.skip_on_nvvm34()
+
+        @cuda.jit
+        def f(a):
+            a[0]['x'] = 1
+            a[0]['y'] = b'ABC'
+            a[1]['x'] = 2
+            a[1]['y'] = b'XYZ'
+
+        dt = np.dtype([('x', np.float32), ('y', np.dtype('S12'))])
+        a = np.zeros(2, dt)
+
+        f[1, 1](a)
+
+        reference = np.asarray([(1, b'ABC'), (2, b'XYZ')], dtype=dt)
+        np.testing.assert_array_equal(reference, a)
+
+    @skip_on_cudasim('Const strings not disallowed on cudasim')
+    def test_const_unicode_string_disallowed_nvvm34(self):
+        self.skip_on_nvvm70()
+
+        @cuda.jit
+        def str_assign(arr):
+            arr[0] = "XYZ"
+
+        arr = np.zeros(1, dtype="<U12")
+        with self.assertRaisesRegex(TypingError, '.*is a char sequence.*'):
+            str_assign[1, 1](arr)
+
+    @skip_on_cudasim('Const strings not disallowed on cudasim')
+    def test_const_byte_string_disallowed_nvvm34(self):
+        self.skip_on_nvvm70()
+
+        @cuda.jit
+        def bytes_assign(arr):
+            arr[0] = b"XYZ"
+
+        arr = np.zeros(1, dtype="S12")
+        with self.assertRaisesRegex(TypingError, '.*is a char sequence.*'):
+            bytes_assign[1, 1](arr)
+
+    @skip_on_cudasim('Const strings not disallowed on cudasim')
+    def test_record_const_unicode_string_disallowed_nvvm34(self):
+        self.skip_on_nvvm70()
+
+        @cuda.jit
+        def str_assign(arr):
+            arr[0]['y'] = "XYZ"
+
+        dt = np.dtype([('x', np.int32), ('y', np.dtype('<U12'))])
+        arr = np.zeros(1, dtype=dt)
+
+        with self.assertRaisesRegex(TypingError, '.*is a char sequence.*'):
+            str_assign[1, 1](arr)
+
+    @skip_on_cudasim('Const strings not disallowed on cudasim')
+    def test_record_const_bytes_disallowed_nvvm34(self):
+        self.skip_on_nvvm70()
+
+        @cuda.jit
+        def str_assign(arr):
+            arr[0]['y'] = b"XYZ"
+
+        dt = np.dtype([('x', np.int32), ('y', np.dtype('S12'))])
+        arr = np.zeros(1, dtype=dt)
+
+        with self.assertRaisesRegex(TypingError, '.*is a char sequence.*'):
+            str_assign[1, 1](arr)
+
+    @skip_on_cudasim('Const strings not disallowed on cudasim')
+    def test_nested_record_const_unicode_string_disallowed_nvvm34(self):
+        self.skip_on_nvvm70()
+
+        @cuda.jit
+        def str_assign(arr):
+            arr[0]['y']['z'] = "XYZ"
+
+        subdt = np.dtype([('z', np.dtype('<U12')), ('a', np.int32)])
+        dt = np.dtype([('x', np.int32), ('y', subdt)])
+        arr = np.zeros(1, dtype=dt)
+
+        with self.assertRaisesRegex(TypingError, '.*is a char sequence.*'):
+            str_assign[1, 1](arr)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_constmem.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_constmem.py
new file mode 100644
index 000000000..b5f4c31ff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_constmem.py
@@ -0,0 +1,185 @@
+import numpy as np
+
+from numba import cuda, complex64, int32, float64
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.core.config import ENABLE_CUDASIM
+
+CONST_EMPTY = np.array([])
+CONST1D = np.arange(10, dtype=np.float64) / 2.
+CONST2D = np.asfortranarray(
+    np.arange(100, dtype=np.int32).reshape(10, 10))
+CONST3D = ((np.arange(5 * 5 * 5, dtype=np.complex64).reshape(5, 5, 5) + 1j) /
+           2j)
+CONST3BYTES = np.arange(3, dtype=np.uint8)
+
+CONST_RECORD_EMPTY = np.array(
+    [],
+    dtype=[('x', float), ('y', int)])
+CONST_RECORD = np.array(
+    [(1.0, 2), (3.0, 4)],
+    dtype=[('x', float), ('y', int)])
+CONST_RECORD_ALIGN = np.array(
+    [(1, 2, 3, 0xDEADBEEF, 8), (4, 5, 6, 0xBEEFDEAD, 10)],
+    dtype=np.dtype(
+        dtype=[
+            ('a', np.uint8),
+            ('b', np.uint8),
+            ('x', np.uint8),
+            ('y', np.uint32),
+            ('z', np.uint8),
+        ],
+        align=True))
+
+
+def cuconstEmpty(A):
+    C = cuda.const.array_like(CONST_EMPTY)
+    i = cuda.grid(1)
+    A[i] = len(C)
+
+
+def cuconst(A):
+    C = cuda.const.array_like(CONST1D)
+    i = cuda.grid(1)
+
+    # +1 or it'll be loaded & stored as a u32
+    A[i] = C[i] + 1.0
+
+
+def cuconst2d(A):
+    C = cuda.const.array_like(CONST2D)
+    i, j = cuda.grid(2)
+    A[i, j] = C[i, j]
+
+
+def cuconst3d(A):
+    C = cuda.const.array_like(CONST3D)
+    i = cuda.threadIdx.x
+    j = cuda.threadIdx.y
+    k = cuda.threadIdx.z
+    A[i, j, k] = C[i, j, k]
+
+
+def cuconstRecEmpty(A):
+    C = cuda.const.array_like(CONST_RECORD_EMPTY)
+    i = cuda.grid(1)
+    A[i] = len(C)
+
+
+def cuconstRec(A, B):
+    C = cuda.const.array_like(CONST_RECORD)
+    i = cuda.grid(1)
+    A[i] = C[i]['x']
+    B[i] = C[i]['y']
+
+
+def cuconstRecAlign(A, B, C, D, E):
+    Z = cuda.const.array_like(CONST_RECORD_ALIGN)
+    i = cuda.grid(1)
+    A[i] = Z[i]['a']
+    B[i] = Z[i]['b']
+    C[i] = Z[i]['x']
+    D[i] = Z[i]['y']
+    E[i] = Z[i]['z']
+
+
+def cuconstAlign(z):
+    a = cuda.const.array_like(CONST3BYTES)
+    b = cuda.const.array_like(CONST1D)
+    i = cuda.grid(1)
+    z[i] = a[i] + b[i]
+
+
+class TestCudaConstantMemory(CUDATestCase):
+    def test_const_array(self):
+        sig = (float64[:],)
+        jcuconst = cuda.jit(sig)(cuconst)
+        A = np.zeros_like(CONST1D)
+        jcuconst[2, 5](A)
+        self.assertTrue(np.all(A == CONST1D + 1))
+
+        if not ENABLE_CUDASIM:
+            self.assertIn(
+                'ld.const.f64',
+                jcuconst.inspect_asm(sig),
+                "as we're adding to it, load as a double")
+
+    def test_const_empty(self):
+        jcuconstEmpty = cuda.jit('void(int64[:])')(cuconstEmpty)
+        A = np.full(1, fill_value=-1, dtype=np.int64)
+        jcuconstEmpty[1, 1](A)
+        self.assertTrue(np.all(A == 0))
+
+    def test_const_align(self):
+        jcuconstAlign = cuda.jit('void(float64[:])')(cuconstAlign)
+        A = np.full(3, fill_value=np.nan, dtype=float)
+        jcuconstAlign[1, 3](A)
+        self.assertTrue(np.all(A == (CONST3BYTES + CONST1D[:3])))
+
+    def test_const_array_2d(self):
+        sig = (int32[:,:],)
+        jcuconst2d = cuda.jit(sig)(cuconst2d)
+        A = np.zeros_like(CONST2D, order='C')
+        jcuconst2d[(2, 2), (5, 5)](A)
+        self.assertTrue(np.all(A == CONST2D))
+
+        if not ENABLE_CUDASIM:
+            self.assertIn(
+                'ld.const.u32',
+                jcuconst2d.inspect_asm(sig),
+                "load the ints as ints")
+
+    def test_const_array_3d(self):
+        sig = (complex64[:,:,:],)
+        jcuconst3d = cuda.jit(sig)(cuconst3d)
+        A = np.zeros_like(CONST3D, order='F')
+        jcuconst3d[1, (5, 5, 5)](A)
+        self.assertTrue(np.all(A == CONST3D))
+
+        if not ENABLE_CUDASIM:
+            # CUDA <= 11.1 uses two f32 loads to load the complex. CUDA >= 11.2
+            # uses a vector of 2x f32. The root cause of these codegen
+            # differences is not known, but must be accounted for in this test.
+            if cuda.runtime.get_version() > (11, 1):
+                complex_load = 'ld.const.v2.f32'
+                description = 'Load the complex as a vector of 2x f32'
+            else:
+                complex_load = 'ld.const.f32'
+                description = 'load each half of the complex as f32'
+
+            self.assertIn(
+                complex_load, jcuconst3d.inspect_asm(sig), description
+            )
+
+    def test_const_record_empty(self):
+        jcuconstRecEmpty = cuda.jit('void(int64[:])')(cuconstRecEmpty)
+        A = np.full(1, fill_value=-1, dtype=np.int64)
+        jcuconstRecEmpty[1, 1](A)
+        self.assertTrue(np.all(A == 0))
+
+    def test_const_record(self):
+        A = np.zeros(2, dtype=float)
+        B = np.zeros(2, dtype=int)
+        jcuconst = cuda.jit(cuconstRec).specialize(A, B)
+
+        jcuconst[2, 1](A, B)
+        np.testing.assert_allclose(A, CONST_RECORD['x'])
+        np.testing.assert_allclose(B, CONST_RECORD['y'])
+
+    def test_const_record_align(self):
+        A = np.zeros(2, dtype=np.float64)
+        B = np.zeros(2, dtype=np.float64)
+        C = np.zeros(2, dtype=np.float64)
+        D = np.zeros(2, dtype=np.float64)
+        E = np.zeros(2, dtype=np.float64)
+        jcuconst = cuda.jit(cuconstRecAlign).specialize(A, B, C, D, E)
+
+        jcuconst[2, 1](A, B, C, D, E)
+        np.testing.assert_allclose(A, CONST_RECORD_ALIGN['a'])
+        np.testing.assert_allclose(B, CONST_RECORD_ALIGN['b'])
+        np.testing.assert_allclose(C, CONST_RECORD_ALIGN['x'])
+        np.testing.assert_allclose(D, CONST_RECORD_ALIGN['y'])
+        np.testing.assert_allclose(E, CONST_RECORD_ALIGN['z'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cooperative_groups.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cooperative_groups.py
new file mode 100644
index 000000000..f1ffb6e41
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cooperative_groups.py
@@ -0,0 +1,145 @@
+from __future__ import print_function
+
+import numpy as np
+
+from numba import config, cuda, int32
+from numba.cuda.testing import (unittest, CUDATestCase, skip_on_cudasim,
+                                skip_unless_cc_60, skip_if_cudadevrt_missing)
+
+
+@cuda.jit
+def this_grid(A):
+    cuda.cg.this_grid()
+    A[0] = 1.0
+
+
+@cuda.jit
+def sync_group(A):
+    g = cuda.cg.this_grid()
+    g.sync()
+    A[0] = 1.0
+
+
+@cuda.jit
+def no_sync(A):
+    A[0] = cuda.grid(1)
+
+
+def sequential_rows(M):
+    # The grid writes rows one at a time. Each thread reads an element from
+    # the previous row written by its "opposite" thread.
+    #
+    # A failure to sync the grid at each row would result in an incorrect
+    # result as some threads could run ahead of threads in other blocks, or
+    # fail to see the update to the previous row from their opposite thread.
+
+    col = cuda.grid(1)
+    g = cuda.cg.this_grid()
+
+    rows = M.shape[0]
+    cols = M.shape[1]
+
+    for row in range(1, rows):
+        opposite = cols - col - 1
+        M[row, col] = M[row - 1, opposite] + 1
+        g.sync()
+
+
+@skip_if_cudadevrt_missing
+class TestCudaCooperativeGroups(CUDATestCase):
+    @skip_unless_cc_60
+    def test_this_grid(self):
+        A = np.full(1, fill_value=np.nan)
+        this_grid[1, 1](A)
+
+        # Ensure the kernel executed beyond the call to cuda.this_grid()
+        self.assertFalse(np.isnan(A[0]), 'Value was not set')
+
+    @skip_unless_cc_60
+    @skip_on_cudasim("Simulator doesn't differentiate between normal and "
+                     "cooperative kernels")
+    def test_this_grid_is_cooperative(self):
+        A = np.full(1, fill_value=np.nan)
+        this_grid[1, 1](A)
+
+        # this_grid should have been determined to be cooperative
+        for key, overload in this_grid.overloads.items():
+            self.assertTrue(overload.cooperative)
+
+    @skip_unless_cc_60
+    def test_sync_group(self):
+        A = np.full(1, fill_value=np.nan)
+        sync_group[1, 1](A)
+
+        # Ensure the kernel executed beyond the call to cuda.sync_group()
+        self.assertFalse(np.isnan(A[0]), 'Value was not set')
+
+    @skip_unless_cc_60
+    @skip_on_cudasim("Simulator doesn't differentiate between normal and "
+                     "cooperative kernels")
+    def test_sync_group_is_cooperative(self):
+        A = np.full(1, fill_value=np.nan)
+        sync_group[1, 1](A)
+        # sync_group should have been determined to be cooperative
+        for key, overload in sync_group.overloads.items():
+            self.assertTrue(overload.cooperative)
+
+    @skip_on_cudasim("Simulator does not implement linking")
+    def test_false_cooperative_doesnt_link_cudadevrt(self):
+        """
+        We should only mark a kernel as cooperative and link cudadevrt if the
+        kernel uses grid sync. Here we ensure that one that doesn't use grid
+        synsync isn't marked as such.
+        """
+        A = np.full(1, fill_value=np.nan)
+        no_sync[1, 1](A)
+
+        for key, overload in no_sync.overloads.items():
+            self.assertFalse(overload.cooperative)
+            for link in overload._codelibrary._linking_files:
+                self.assertNotIn('cudadevrt', link)
+
+    @skip_unless_cc_60
+    def test_sync_at_matrix_row(self):
+        if config.ENABLE_CUDASIM:
+            # Use a small matrix to compute using a single block in a
+            # reasonable amount of time
+            shape = (32, 32)
+        else:
+            shape = (1024, 1024)
+        A = np.zeros(shape, dtype=np.int32)
+        blockdim = 32
+        griddim = A.shape[1] // blockdim
+
+        sig = (int32[:,::1],)
+        c_sequential_rows = cuda.jit(sig)(sequential_rows)
+
+        overload = c_sequential_rows.overloads[sig]
+        mb = overload.max_cooperative_grid_blocks(blockdim)
+        if griddim > mb:
+            unittest.skip("GPU cannot support enough cooperative grid blocks")
+
+        c_sequential_rows[griddim, blockdim](A)
+
+        reference = np.tile(np.arange(shape[0]), (shape[1], 1)).T
+        np.testing.assert_equal(A, reference)
+
+    @skip_unless_cc_60
+    def test_max_cooperative_grid_blocks(self):
+        # The maximum number of blocks will vary based on the device so we
+        # can't test for an expected value, but we can check that the function
+        # doesn't error, and that varying the number of dimensions of the block
+        # whilst keeping the total number of threads constant doesn't change
+        # the maximum to validate some of the logic.
+        sig = (int32[:,::1],)
+        c_sequential_rows = cuda.jit(sig)(sequential_rows)
+        overload = c_sequential_rows.overloads[sig]
+        blocks1d = overload.max_cooperative_grid_blocks(256)
+        blocks2d = overload.max_cooperative_grid_blocks((16, 16))
+        blocks3d = overload.max_cooperative_grid_blocks((16, 4, 4))
+        self.assertEqual(blocks1d, blocks2d)
+        self.assertEqual(blocks1d, blocks3d)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cuda_array_interface.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cuda_array_interface.py
new file mode 100644
index 000000000..6448f450a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cuda_array_interface.py
@@ -0,0 +1,435 @@
+import numpy as np
+
+from numba import vectorize, guvectorize
+from numba import cuda
+from numba.cuda.cudadrv import driver
+from numba.cuda.testing import unittest, ContextResettingTestCase, ForeignArray
+from numba.cuda.testing import skip_on_cudasim, skip_if_external_memmgr
+from numba.tests.support import linux_only, override_config
+from unittest.mock import call, patch
+
+
+@skip_on_cudasim('CUDA Array Interface is not supported in the simulator')
+class TestCudaArrayInterface(ContextResettingTestCase):
+    def assertPointersEqual(self, a, b):
+        if driver.USE_NV_BINDING:
+            self.assertEqual(int(a.device_ctypes_pointer),
+                             int(b.device_ctypes_pointer))
+
+    def test_as_cuda_array(self):
+        h_arr = np.arange(10)
+        self.assertFalse(cuda.is_cuda_array(h_arr))
+        d_arr = cuda.to_device(h_arr)
+        self.assertTrue(cuda.is_cuda_array(d_arr))
+        my_arr = ForeignArray(d_arr)
+        self.assertTrue(cuda.is_cuda_array(my_arr))
+        wrapped = cuda.as_cuda_array(my_arr)
+        self.assertTrue(cuda.is_cuda_array(wrapped))
+        # Their values must equal the original array
+        np.testing.assert_array_equal(wrapped.copy_to_host(), h_arr)
+        np.testing.assert_array_equal(d_arr.copy_to_host(), h_arr)
+        # d_arr and wrapped must be the same buffer
+        self.assertPointersEqual(wrapped, d_arr)
+
+    def get_stream_value(self, stream):
+        if driver.USE_NV_BINDING:
+            return int(stream.handle)
+        else:
+            return stream.handle.value
+
+    @skip_if_external_memmgr('Ownership not relevant with external memmgr')
+    def test_ownership(self):
+        # Get the deallocation queue
+        ctx = cuda.current_context()
+        deallocs = ctx.memory_manager.deallocations
+        # Flush all deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        # Make new device array
+        d_arr = cuda.to_device(np.arange(100))
+        # Convert it
+        cvted = cuda.as_cuda_array(d_arr)
+        # Drop reference to the original object such that
+        # only `cvted` has a reference to it.
+        del d_arr
+        # There shouldn't be any new deallocations
+        self.assertEqual(len(deallocs), 0)
+        # Try to access the memory and verify its content
+        np.testing.assert_equal(cvted.copy_to_host(), np.arange(100))
+        # Drop last reference to the memory
+        del cvted
+        self.assertEqual(len(deallocs), 1)
+        # Flush
+        deallocs.clear()
+
+    def test_kernel_arg(self):
+        h_arr = np.arange(10)
+        d_arr = cuda.to_device(h_arr)
+        my_arr = ForeignArray(d_arr)
+        wrapped = cuda.as_cuda_array(my_arr)
+
+        @cuda.jit
+        def mutate(arr, val):
+            i = cuda.grid(1)
+            if i >= len(arr):
+                return
+            arr[i] += val
+
+        val = 7
+        mutate.forall(wrapped.size)(wrapped, val)
+
+        np.testing.assert_array_equal(wrapped.copy_to_host(), h_arr + val)
+        np.testing.assert_array_equal(d_arr.copy_to_host(), h_arr + val)
+
+    def test_ufunc_arg(self):
+        @vectorize(['f8(f8, f8)'], target='cuda')
+        def vadd(a, b):
+            return a + b
+
+        # Case 1: use custom array as argument
+        h_arr = np.random.random(10)
+        arr = ForeignArray(cuda.to_device(h_arr))
+        val = 6
+        out = vadd(arr, val)
+        np.testing.assert_array_equal(out.copy_to_host(), h_arr + val)
+
+        # Case 2: use custom array as return
+        out = ForeignArray(cuda.device_array(h_arr.shape))
+        returned = vadd(h_arr, val, out=out)
+        np.testing.assert_array_equal(returned.copy_to_host(), h_arr + val)
+
+    def test_gufunc_arg(self):
+        @guvectorize(['(f8, f8, f8[:])'], '(),()->()', target='cuda')
+        def vadd(inp, val, out):
+            out[0] = inp + val
+
+        # Case 1: use custom array as argument
+        h_arr = np.random.random(10)
+        arr = ForeignArray(cuda.to_device(h_arr))
+        val = np.float64(7)
+        out = vadd(arr, val)
+        np.testing.assert_array_equal(out.copy_to_host(), h_arr + val)
+
+        # Case 2: use custom array as return
+        out = ForeignArray(cuda.device_array(h_arr.shape))
+        returned = vadd(h_arr, val, out=out)
+        np.testing.assert_array_equal(returned.copy_to_host(), h_arr + val)
+        self.assertPointersEqual(returned, out._arr)
+
+    def test_array_views(self):
+        """Views created via array interface support:
+            - Strided slices
+            - Strided slices
+        """
+        h_arr = np.random.random(10)
+        c_arr = cuda.to_device(h_arr)
+
+        arr = cuda.as_cuda_array(c_arr)
+
+        # __getitem__ interface accesses expected data
+
+        # Direct views
+        np.testing.assert_array_equal(arr.copy_to_host(), h_arr)
+        np.testing.assert_array_equal(arr[:].copy_to_host(), h_arr)
+
+        # Slicing
+        np.testing.assert_array_equal(arr[:5].copy_to_host(), h_arr[:5])
+
+        # Strided view
+        np.testing.assert_array_equal(arr[::2].copy_to_host(), h_arr[::2])
+
+        # View of strided array
+        arr_strided = cuda.as_cuda_array(c_arr[::2])
+        np.testing.assert_array_equal(arr_strided.copy_to_host(), h_arr[::2])
+
+        # A strided-view-of-array and view-of-strided-array have the same
+        # shape, strides, itemsize, and alloc_size
+        self.assertEqual(arr[::2].shape, arr_strided.shape)
+        self.assertEqual(arr[::2].strides, arr_strided.strides)
+        self.assertEqual(arr[::2].dtype.itemsize, arr_strided.dtype.itemsize)
+        self.assertEqual(arr[::2].alloc_size, arr_strided.alloc_size)
+        self.assertEqual(arr[::2].nbytes,
+                         arr_strided.size * arr_strided.dtype.itemsize)
+
+        # __setitem__ interface propagates into external array
+
+        # Writes to a slice
+        arr[:5] = np.pi
+        np.testing.assert_array_equal(
+            c_arr.copy_to_host(),
+            np.concatenate((np.full(5, np.pi), h_arr[5:]))
+        )
+
+        # Writes to a slice from a view
+        arr[:5] = arr[5:]
+        np.testing.assert_array_equal(
+            c_arr.copy_to_host(),
+            np.concatenate((h_arr[5:], h_arr[5:]))
+        )
+
+        # Writes through a view
+        arr[:] = cuda.to_device(h_arr)
+        np.testing.assert_array_equal(c_arr.copy_to_host(), h_arr)
+
+        # Writes to a strided slice
+        arr[::2] = np.pi
+        np.testing.assert_array_equal(
+            c_arr.copy_to_host()[::2],
+            np.full(5, np.pi),
+        )
+        np.testing.assert_array_equal(
+            c_arr.copy_to_host()[1::2],
+            h_arr[1::2]
+        )
+
+    def test_negative_strided_issue(self):
+        # issue #3705
+        h_arr = np.random.random(10)
+        c_arr = cuda.to_device(h_arr)
+
+        def base_offset(orig, sliced):
+            return sliced['data'][0] - orig['data'][0]
+
+        h_ai = h_arr.__array_interface__
+        c_ai = c_arr.__cuda_array_interface__
+
+        h_ai_sliced = h_arr[::-1].__array_interface__
+        c_ai_sliced = c_arr[::-1].__cuda_array_interface__
+
+        # Check data offset is correct
+        self.assertEqual(
+            base_offset(h_ai, h_ai_sliced),
+            base_offset(c_ai, c_ai_sliced),
+        )
+        # Check shape and strides are correct
+        self.assertEqual(h_ai_sliced['shape'], c_ai_sliced['shape'])
+        self.assertEqual(h_ai_sliced['strides'], c_ai_sliced['strides'])
+
+    def test_negative_strided_copy_to_host(self):
+        # issue #3705
+        h_arr = np.random.random(10)
+        c_arr = cuda.to_device(h_arr)
+        sliced = c_arr[::-1]
+        with self.assertRaises(NotImplementedError) as raises:
+            sliced.copy_to_host()
+        expected_msg = 'D->H copy not implemented for negative strides'
+        self.assertIn(expected_msg, str(raises.exception))
+
+    def test_masked_array(self):
+        h_arr = np.random.random(10)
+        h_mask = np.random.randint(2, size=10, dtype='bool')
+        c_arr = cuda.to_device(h_arr)
+        c_mask = cuda.to_device(h_mask)
+
+        # Manually create a masked CUDA Array Interface dictionary
+        masked_cuda_array_interface = c_arr.__cuda_array_interface__.copy()
+        masked_cuda_array_interface['mask'] = c_mask
+
+        with self.assertRaises(NotImplementedError) as raises:
+            cuda.from_cuda_array_interface(masked_cuda_array_interface)
+        expected_msg = 'Masked arrays are not supported'
+        self.assertIn(expected_msg, str(raises.exception))
+
+    def test_zero_size_array(self):
+        # for #4175
+        c_arr = cuda.device_array(0)
+        self.assertEqual(c_arr.__cuda_array_interface__['data'][0], 0)
+
+        @cuda.jit
+        def add_one(arr):
+            x = cuda.grid(1)
+            N = arr.shape[0]
+            if x < N:
+                arr[x] += 1
+
+        d_arr = ForeignArray(c_arr)
+        add_one[1, 10](d_arr)  # this should pass
+
+    def test_strides(self):
+        # for #4175
+        # First, test C-contiguous array
+        c_arr = cuda.device_array((2, 3, 4))
+        self.assertEqual(c_arr.__cuda_array_interface__['strides'], None)
+
+        # Second, test non C-contiguous array
+        c_arr = c_arr[:, 1, :]
+        self.assertNotEqual(c_arr.__cuda_array_interface__['strides'], None)
+
+    def test_consuming_strides(self):
+        hostarray = np.arange(10).reshape(2, 5)
+        devarray = cuda.to_device(hostarray)
+        face = devarray.__cuda_array_interface__
+        self.assertIsNone(face['strides'])
+        got = cuda.from_cuda_array_interface(face).copy_to_host()
+        np.testing.assert_array_equal(got, hostarray)
+        self.assertTrue(got.flags['C_CONTIGUOUS'])
+        # Try non-NULL strides
+        face['strides'] = hostarray.strides
+        self.assertIsNotNone(face['strides'])
+        got = cuda.from_cuda_array_interface(face).copy_to_host()
+        np.testing.assert_array_equal(got, hostarray)
+        self.assertTrue(got.flags['C_CONTIGUOUS'])
+
+    def test_produce_no_stream(self):
+        c_arr = cuda.device_array(10)
+        self.assertIsNone(c_arr.__cuda_array_interface__['stream'])
+
+        mapped_arr = cuda.mapped_array(10)
+        self.assertIsNone(mapped_arr.__cuda_array_interface__['stream'])
+
+    @linux_only
+    def test_produce_managed_no_stream(self):
+        managed_arr = cuda.managed_array(10)
+        self.assertIsNone(managed_arr.__cuda_array_interface__['stream'])
+
+    def test_produce_stream(self):
+        s = cuda.stream()
+        c_arr = cuda.device_array(10, stream=s)
+        cai_stream = c_arr.__cuda_array_interface__['stream']
+        stream_value = self.get_stream_value(s)
+        self.assertEqual(stream_value, cai_stream)
+
+        s = cuda.stream()
+        mapped_arr = cuda.mapped_array(10, stream=s)
+        cai_stream = mapped_arr.__cuda_array_interface__['stream']
+        stream_value = self.get_stream_value(s)
+        self.assertEqual(stream_value, cai_stream)
+
+    @linux_only
+    def test_produce_managed_stream(self):
+        s = cuda.stream()
+        managed_arr = cuda.managed_array(10, stream=s)
+        cai_stream = managed_arr.__cuda_array_interface__['stream']
+        stream_value = self.get_stream_value(s)
+        self.assertEqual(stream_value, cai_stream)
+
+    def test_consume_no_stream(self):
+        # Create a foreign array with no stream
+        f_arr = ForeignArray(cuda.device_array(10))
+
+        # Ensure that the imported array has no default stream
+        c_arr = cuda.as_cuda_array(f_arr)
+        self.assertEqual(c_arr.stream, 0)
+
+    def test_consume_stream(self):
+        # Create a foreign array with a stream
+        s = cuda.stream()
+        f_arr = ForeignArray(cuda.device_array(10, stream=s))
+
+        # Ensure that an imported array has the stream as its default stream
+        c_arr = cuda.as_cuda_array(f_arr)
+        self.assertTrue(c_arr.stream.external)
+        stream_value = self.get_stream_value(s)
+        imported_stream_value = self.get_stream_value(c_arr.stream)
+        self.assertEqual(stream_value, imported_stream_value)
+
+    def test_consume_no_sync(self):
+        # Create a foreign array with no stream
+        f_arr = ForeignArray(cuda.device_array(10))
+
+        with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                          return_value=None) as mock_sync:
+            cuda.as_cuda_array(f_arr)
+
+        # Ensure the synchronize method of a stream was not called
+        mock_sync.assert_not_called()
+
+    def test_consume_sync(self):
+        # Create a foreign array with a stream
+        s = cuda.stream()
+        f_arr = ForeignArray(cuda.device_array(10, stream=s))
+
+        with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                          return_value=None) as mock_sync:
+            cuda.as_cuda_array(f_arr)
+
+        # Ensure the synchronize method of a stream was called
+        mock_sync.assert_called_once_with()
+
+    def test_consume_sync_disabled(self):
+        # Create a foreign array with a stream
+        s = cuda.stream()
+        f_arr = ForeignArray(cuda.device_array(10, stream=s))
+
+        # Set sync to false before testing. The test suite should generally be
+        # run with sync enabled, but stash the old value just in case it is
+        # not.
+        with override_config('CUDA_ARRAY_INTERFACE_SYNC', False):
+            with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                              return_value=None) as mock_sync:
+                cuda.as_cuda_array(f_arr)
+
+            # Ensure the synchronize method of a stream was not called
+            mock_sync.assert_not_called()
+
+    def test_launch_no_sync(self):
+        # Create a foreign array with no stream
+        f_arr = ForeignArray(cuda.device_array(10))
+
+        @cuda.jit
+        def f(x):
+            pass
+
+        with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                          return_value=None) as mock_sync:
+            f[1, 1](f_arr)
+
+        # Ensure the synchronize method of a stream was not called
+        mock_sync.assert_not_called()
+
+    def test_launch_sync(self):
+        # Create a foreign array with a stream
+        s = cuda.stream()
+        f_arr = ForeignArray(cuda.device_array(10, stream=s))
+
+        @cuda.jit
+        def f(x):
+            pass
+
+        with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                          return_value=None) as mock_sync:
+            f[1, 1](f_arr)
+
+        # Ensure the synchronize method of a stream was called
+        mock_sync.assert_called_once_with()
+
+    def test_launch_sync_two_streams(self):
+        # Create two foreign arrays with streams
+        s1 = cuda.stream()
+        s2 = cuda.stream()
+        f_arr1 = ForeignArray(cuda.device_array(10, stream=s1))
+        f_arr2 = ForeignArray(cuda.device_array(10, stream=s2))
+
+        @cuda.jit
+        def f(x, y):
+            pass
+
+        with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                          return_value=None) as mock_sync:
+            f[1, 1](f_arr1, f_arr2)
+
+        # Ensure that synchronize was called twice
+        mock_sync.assert_has_calls([call(), call()])
+
+    def test_launch_sync_disabled(self):
+        # Create two foreign arrays with streams
+        s1 = cuda.stream()
+        s2 = cuda.stream()
+        f_arr1 = ForeignArray(cuda.device_array(10, stream=s1))
+        f_arr2 = ForeignArray(cuda.device_array(10, stream=s2))
+
+        with override_config('CUDA_ARRAY_INTERFACE_SYNC', False):
+            @cuda.jit
+            def f(x, y):
+                pass
+
+            with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                              return_value=None) as mock_sync:
+                f[1, 1](f_arr1, f_arr2)
+
+            # Ensure that synchronize was not called
+            mock_sync.assert_not_called()
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cuda_jit_no_types.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cuda_jit_no_types.py
new file mode 100644
index 000000000..b57e7649c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_cuda_jit_no_types.py
@@ -0,0 +1,90 @@
+from numba import cuda
+import numpy as np
+from numba.cuda.testing import CUDATestCase
+from numba.tests.support import override_config
+import unittest
+
+
+class TestCudaJitNoTypes(CUDATestCase):
+    """
+    Tests the jit decorator with no types provided.
+    """
+
+    def test_device_array(self):
+        @cuda.jit
+        def foo(x, y):
+            i = cuda.grid(1)
+            y[i] = x[i]
+
+        x = np.arange(10)
+        y = np.empty_like(x)
+
+        dx = cuda.to_device(x)
+        dy = cuda.to_device(y)
+
+        foo[10, 1](dx, dy)
+
+        dy.copy_to_host(y)
+
+        self.assertTrue(np.all(x == y))
+
+    def test_device_jit(self):
+        @cuda.jit(device=True)
+        def mapper(args):
+            a, b, c = args
+            return a + b + c
+
+        @cuda.jit(device=True)
+        def reducer(a, b):
+            return a + b
+
+        @cuda.jit
+        def driver(A, B):
+            i = cuda.grid(1)
+            if i < B.size:
+                args = A[i], A[i] + B[i], B[i]
+                B[i] = reducer(mapper(args), 1)
+
+        A = np.arange(100, dtype=np.float32)
+        B = np.arange(100, dtype=np.float32)
+
+        Acopy = A.copy()
+        Bcopy = B.copy()
+
+        driver[1, 100](A, B)
+
+        np.testing.assert_allclose(Acopy + Acopy + Bcopy + Bcopy + 1, B)
+
+    def test_device_jit_2(self):
+        @cuda.jit(device=True)
+        def inner(arg):
+            return arg + 1
+
+        @cuda.jit
+        def outer(argin, argout):
+            argout[0] = inner(argin[0]) + inner(2)
+
+        a = np.zeros(1)
+        b = np.zeros(1)
+
+        stream = cuda.stream()
+        d_a = cuda.to_device(a, stream)
+        d_b = cuda.to_device(b, stream)
+
+        outer[1, 1, stream](d_a, d_b)
+
+        d_b.copy_to_host(b, stream)
+
+        self.assertEqual(b[0], (a[0] + 1) + (2 + 1))
+
+    def test_jit_debug_simulator(self):
+        # Ensure that the jit decorator accepts the debug kwarg when the
+        # simulator is in use - see Issue #6615.
+        with override_config('ENABLE_CUDASIM', 1):
+            @cuda.jit(debug=True)
+            def f(x):
+                pass
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_datetime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_datetime.py
new file mode 100644
index 000000000..7921f9e9b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_datetime.py
@@ -0,0 +1,94 @@
+import numpy as np
+
+from numba import cuda, vectorize, guvectorize
+from numba.np.numpy_support import from_dtype
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+import unittest
+
+
+class TestCudaDateTime(CUDATestCase):
+    def test_basic_datetime_kernel(self):
+        @cuda.jit
+        def foo(start, end, delta):
+            for i in range(cuda.grid(1), delta.size, cuda.gridsize(1)):
+                delta[i] = end[i] - start[i]
+
+        arr1 = np.arange('2005-02', '2006-02', dtype='datetime64[D]')
+        arr2 = arr1 + np.random.randint(0, 10000, arr1.size)
+        delta = np.zeros_like(arr1, dtype='timedelta64[D]')
+
+        foo[1, 32](arr1, arr2, delta)
+
+        self.assertPreciseEqual(delta, arr2 - arr1)
+
+    def test_scalar_datetime_kernel(self):
+        @cuda.jit
+        def foo(dates, target, delta, matches, outdelta):
+            for i in range(cuda.grid(1), matches.size, cuda.gridsize(1)):
+                matches[i] = dates[i] == target
+                outdelta[i] = dates[i] - delta
+        arr1 = np.arange('2005-02', '2006-02', dtype='datetime64[D]')
+        target = arr1[5]           # datetime
+        delta = arr1[6] - arr1[5]  # timedelta
+        matches = np.zeros_like(arr1, dtype=np.bool_)
+        outdelta = np.zeros_like(arr1, dtype='datetime64[D]')
+
+        foo[1, 32](arr1, target, delta, matches, outdelta)
+        where = matches.nonzero()
+
+        self.assertEqual(list(where), [5])
+        self.assertPreciseEqual(outdelta, arr1 - delta)
+
+    @skip_on_cudasim('ufunc API unsupported in the simulator')
+    def test_ufunc(self):
+        datetime_t = from_dtype(np.dtype('datetime64[D]'))
+
+        @vectorize([(datetime_t, datetime_t)], target='cuda')
+        def timediff(start, end):
+            return end - start
+
+        arr1 = np.arange('2005-02', '2006-02', dtype='datetime64[D]')
+        arr2 = arr1 + np.random.randint(0, 10000, arr1.size)
+
+        delta = timediff(arr1, arr2)
+
+        self.assertPreciseEqual(delta, arr2 - arr1)
+
+    @skip_on_cudasim('ufunc API unsupported in the simulator')
+    def test_gufunc(self):
+        datetime_t = from_dtype(np.dtype('datetime64[D]'))
+        timedelta_t = from_dtype(np.dtype('timedelta64[D]'))
+
+        @guvectorize([(datetime_t, datetime_t, timedelta_t[:])], '(),()->()',
+                     target='cuda')
+        def timediff(start, end, out):
+            out[0] = end - start
+
+        arr1 = np.arange('2005-02', '2006-02', dtype='datetime64[D]')
+        arr2 = arr1 + np.random.randint(0, 10000, arr1.size)
+
+        delta = timediff(arr1, arr2)
+
+        self.assertPreciseEqual(delta, arr2 - arr1)
+
+    @skip_on_cudasim('no .copy_to_host() in the simulator')
+    def test_datetime_view_as_int64(self):
+        arr = np.arange('2005-02', '2006-02', dtype='datetime64[D]')
+        darr = cuda.to_device(arr)
+        viewed = darr.view(np.int64)
+        self.assertPreciseEqual(arr.view(np.int64), viewed.copy_to_host())
+        self.assertEqual(viewed.gpu_data, darr.gpu_data)
+
+    @skip_on_cudasim('no .copy_to_host() in the simulator')
+    def test_timedelta_view_as_int64(self):
+        arr = np.arange('2005-02', '2006-02', dtype='datetime64[D]')
+        arr = arr - (arr - 1)
+        self.assertEqual(arr.dtype, np.dtype('timedelta64[D]'))
+        darr = cuda.to_device(arr)
+        viewed = darr.view(np.int64)
+        self.assertPreciseEqual(arr.view(np.int64), viewed.copy_to_host())
+        self.assertEqual(viewed.gpu_data, darr.gpu_data)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_debug.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_debug.py
new file mode 100644
index 000000000..ee432091c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_debug.py
@@ -0,0 +1,93 @@
+import numpy as np
+
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+from numba.tests.support import (override_config, captured_stderr,
+                                 captured_stdout)
+from numba import cuda, float64
+import unittest
+
+
+def simple_cuda(A, B):
+    i = cuda.grid(1)
+    B[i] = A[i] + 1.5
+
+
+@skip_on_cudasim('Simulator does not produce debug dumps')
+class TestDebugOutput(CUDATestCase):
+
+    def compile_simple_cuda(self):
+        with captured_stderr() as err:
+            with captured_stdout() as out:
+                cfunc = cuda.jit((float64[:], float64[:]))(simple_cuda)
+                # Call compiled function (to ensure PTX is generated)
+                # and sanity-check results.
+                A = np.linspace(0, 1, 10).astype(np.float64)
+                B = np.zeros_like(A)
+                cfunc[1, 10](A, B)
+                self.assertTrue(np.allclose(A + 1.5, B))
+        # stderr shouldn't be affected by debug output
+        self.assertFalse(err.getvalue())
+        return out.getvalue()
+
+    def assert_fails(self, *args, **kwargs):
+        self.assertRaises(AssertionError, *args, **kwargs)
+
+    def check_debug_output(self, out, enabled_dumps):
+        all_dumps = dict.fromkeys(['bytecode', 'cfg', 'ir', 'llvm',
+                                   'assembly'],
+                                  False)
+        for name in enabled_dumps:
+            assert name in all_dumps
+            all_dumps[name] = True
+        for name, enabled in sorted(all_dumps.items()):
+            check_meth = getattr(self, '_check_dump_%s' % name)
+            if enabled:
+                check_meth(out)
+            else:
+                self.assertRaises(AssertionError, check_meth, out)
+
+    def _check_dump_bytecode(self, out):
+        self.assertIn('BINARY_ADD', out)
+
+    def _check_dump_cfg(self, out):
+        self.assertIn('CFG dominators', out)
+
+    def _check_dump_ir(self, out):
+        self.assertIn('--IR DUMP: simple_cuda--', out)
+        self.assertIn('const(float, 1.5)', out)
+
+    def _check_dump_llvm(self, out):
+        self.assertIn('--LLVM DUMP', out)
+
+    def _check_dump_assembly(self, out):
+        self.assertIn('--ASSEMBLY simple_cuda', out)
+        self.assertIn('Generated by NVIDIA NVVM Compiler', out)
+
+    def test_dump_bytecode(self):
+        with override_config('DUMP_BYTECODE', True):
+            out = self.compile_simple_cuda()
+        self.check_debug_output(out, ['bytecode'])
+
+    def test_dump_ir(self):
+        with override_config('DUMP_IR', True):
+            out = self.compile_simple_cuda()
+        self.check_debug_output(out, ['ir'])
+
+    def test_dump_cfg(self):
+        with override_config('DUMP_CFG', True):
+            out = self.compile_simple_cuda()
+        self.check_debug_output(out, ['cfg'])
+
+    def test_dump_llvm(self):
+        with override_config('DUMP_LLVM', True):
+            out = self.compile_simple_cuda()
+        self.check_debug_output(out, ['llvm'])
+
+    def test_dump_assembly(self):
+        with override_config('DUMP_ASSEMBLY', True):
+            out = self.compile_simple_cuda()
+        self.check_debug_output(out, ['assembly'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_debuginfo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_debuginfo.py
new file mode 100644
index 000000000..4264d9e81
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_debuginfo.py
@@ -0,0 +1,274 @@
+from numba.tests.support import override_config
+from numba.cuda.testing import skip_on_cudasim
+from numba import cuda
+from numba.cuda.cudadrv.nvvm import NVVM
+from numba.core import types
+from numba.core.errors import NumbaInvalidConfigWarning
+from numba.cuda.testing import CUDATestCase
+import itertools
+import re
+import unittest
+import warnings
+
+
+@skip_on_cudasim('Simulator does not produce debug dumps')
+class TestCudaDebugInfo(CUDATestCase):
+    """
+    These tests only checks the compiled PTX for debuginfo section
+    """
+    def _getasm(self, fn, sig):
+        fn.compile(sig)
+        return fn.inspect_asm(sig)
+
+    def _check(self, fn, sig, expect):
+        if not NVVM().is_nvvm70:
+            self.skipTest("debuginfo not generated for NVVM 3.4")
+
+        asm = self._getasm(fn, sig=sig)
+        re_section_dbginfo = re.compile(r"\.section\s+\.debug_info\s+{")
+        match = re_section_dbginfo.search(asm)
+        assertfn = self.assertIsNotNone if expect else self.assertIsNone
+        assertfn(match, msg=asm)
+
+    def test_no_debuginfo_in_asm(self):
+        @cuda.jit(debug=False)
+        def foo(x):
+            x[0] = 1
+
+        self._check(foo, sig=(types.int32[:],), expect=False)
+
+    def test_debuginfo_in_asm(self):
+        @cuda.jit(debug=True, opt=False)
+        def foo(x):
+            x[0] = 1
+
+        self._check(foo, sig=(types.int32[:],), expect=True)
+
+    def test_environment_override(self):
+        with override_config('CUDA_DEBUGINFO_DEFAULT', 1):
+            # Using default value
+            @cuda.jit(opt=False)
+            def foo(x):
+                x[0] = 1
+
+            self._check(foo, sig=(types.int32[:],), expect=True)
+
+            # User override default value
+            @cuda.jit(debug=False)
+            def bar(x):
+                x[0] = 1
+
+            self._check(bar, sig=(types.int32[:],), expect=False)
+
+    def test_issue_5835(self):
+        # Invalid debug metadata would segfault NVVM when any function was
+        # compiled with debug turned on and optimization off. This eager
+        # compilation should not crash anything.
+        @cuda.jit((types.int32[::1],), debug=True, opt=False)
+        def f(x):
+            x[0] = 0
+
+    def test_wrapper_has_debuginfo(self):
+        sig = (types.int32[::1],)
+
+        @cuda.jit(sig, debug=True, opt=0)
+        def f(x):
+            x[0] = 1
+
+        llvm_ir = f.inspect_llvm(sig)
+
+        if NVVM().is_nvvm70:
+            # NNVM 7.0 IR attaches a debug metadata reference to the
+            # definition
+            defines = [line for line in llvm_ir.splitlines()
+                       if 'define void @"_ZN6cudapy' in line]
+
+            # Make sure we only found one definition
+            self.assertEqual(len(defines), 1)
+
+            wrapper_define = defines[0]
+            self.assertIn('noinline !dbg', wrapper_define)
+        else:
+            # NVVM 3.4 subprogram debuginfo refers to the definition.
+            # '786478' is a constant referring to a subprogram.
+            disubprograms = [line for line in llvm_ir.splitlines()
+                             if '786478' in line]
+
+            # Make sure we only found one subprogram
+            self.assertEqual(len(disubprograms), 1)
+
+            wrapper_disubprogram = disubprograms[0]
+            # Check that the subprogram points to a wrapper (these are all in
+            # the "cudapy::" namespace).
+            self.assertIn('_ZN6cudapy', wrapper_disubprogram)
+
+    def test_debug_function_calls_internal_impl(self):
+        # Calling a function in a module generated from an implementation
+        # internal to Numba requires multiple modules to be compiled with NVVM -
+        # the internal implementation, and the caller. This example uses two
+        # modules because the `in (2, 3)` is implemented with:
+        #
+        # numba::cpython::listobj::in_seq::$3clocals$3e::seq_contains_impl$242(
+        #     UniTuple<long long, 2>,
+        #     int
+        # )
+        #
+        # This is condensed from this reproducer in Issue 5311:
+        # https://github.com/numba/numba/issues/5311#issuecomment-674206587
+
+        @cuda.jit((types.int32[:], types.int32[:]), debug=True, opt=False)
+        def f(inp, outp):
+            outp[0] = 1 if inp[0] in (2, 3) else 3
+
+    def test_debug_function_calls_device_function(self):
+        # Calling a device function requires compilation of multiple modules
+        # with NVVM - one for the caller and one for the callee. This checks
+        # that we don't cause an NVVM error in this case.
+
+        @cuda.jit(device=True, debug=True, opt=0)
+        def threadid():
+            return cuda.blockDim.x * cuda.blockIdx.x + cuda.threadIdx.x
+
+        @cuda.jit((types.int32[:],), debug=True, opt=0)
+        def kernel(arr):
+            i = cuda.grid(1)
+            if i < len(arr):
+                arr[i] = threadid()
+
+    def _test_chained_device_function(self, kernel_debug, f1_debug, f2_debug):
+        @cuda.jit(device=True, debug=f2_debug, opt=False)
+        def f2(x):
+            return x + 1
+
+        @cuda.jit(device=True, debug=f1_debug, opt=False)
+        def f1(x, y):
+            return x - f2(y)
+
+        @cuda.jit((types.int32, types.int32), debug=kernel_debug, opt=False)
+        def kernel(x, y):
+            f1(x, y)
+
+        kernel[1, 1](1, 2)
+
+    def test_chained_device_function(self):
+        # Calling a device function that calls another device function from a
+        # kernel with should succeed regardless of which jit decorators have
+        # debug=True. See Issue #7159.
+
+        debug_opts = itertools.product(*[(True, False)] * 3)
+
+        for kernel_debug, f1_debug, f2_debug in debug_opts:
+            with self.subTest(kernel_debug=kernel_debug,
+                              f1_debug=f1_debug,
+                              f2_debug=f2_debug):
+                self._test_chained_device_function(kernel_debug,
+                                                   f1_debug,
+                                                   f2_debug)
+
+    def _test_chained_device_function_two_calls(self, kernel_debug, f1_debug,
+                                                f2_debug):
+
+        @cuda.jit(device=True, debug=f2_debug, opt=False)
+        def f2(x):
+            return x + 1
+
+        @cuda.jit(device=True, debug=f1_debug, opt=False)
+        def f1(x, y):
+            return x - f2(y)
+
+        @cuda.jit(debug=kernel_debug, opt=False)
+        def kernel(x, y):
+            f1(x, y)
+            f2(x)
+
+        kernel[1, 1](1, 2)
+
+    def test_chained_device_function_two_calls(self):
+        # Calling a device function that calls a leaf device function from a
+        # kernel, and calling the leaf device function from the kernel should
+        # succeed, regardless of which jit decorators have debug=True. See
+        # Issue #7159.
+
+        debug_opts = itertools.product(*[(True, False)] * 3)
+
+        for kernel_debug, f1_debug, f2_debug in debug_opts:
+            with self.subTest(kernel_debug=kernel_debug,
+                              f1_debug=f1_debug,
+                              f2_debug=f2_debug):
+                self._test_chained_device_function_two_calls(kernel_debug,
+                                                             f1_debug,
+                                                             f2_debug)
+
+    def check_warnings(self, warnings, warn_count):
+        if NVVM().is_nvvm70:
+            # We should not warn on NVVM 7.0.
+            self.assertEqual(len(warnings), 0)
+        else:
+            self.assertEqual(len(warnings), warn_count)
+            # Each warning should warn about not generating debug info.
+            for warning in warnings:
+                self.assertIs(warning.category, NumbaInvalidConfigWarning)
+                self.assertIn('debuginfo is not generated',
+                              str(warning.message))
+
+    def test_debug_warning(self):
+        # We don't generate debug info for NVVM 3.4, and warn accordingly. Here
+        # we check that no warnings appear with NVVM 7.0, and that warnings
+        # appear as appropriate with NVVM 3.4
+        debug_opts = itertools.product(*[(True, False)] * 3)
+
+        for kernel_debug, f1_debug, f2_debug in debug_opts:
+            with self.subTest(kernel_debug=kernel_debug,
+                              f1_debug=f1_debug,
+                              f2_debug=f2_debug):
+                with warnings.catch_warnings(record=True) as w:
+                    self._test_chained_device_function_two_calls(kernel_debug,
+                                                                 f1_debug,
+                                                                 f2_debug)
+                warn_count = kernel_debug + f1_debug + f2_debug
+                self.check_warnings(w, warn_count)
+
+    def test_chained_device_three_functions(self):
+        # Like test_chained_device_function, but with enough functions (three)
+        # to ensure that the recursion visits all the way down the call tree
+        # when fixing linkage of functions for debug.
+        def three_device_fns(kernel_debug, leaf_debug):
+            @cuda.jit(device=True, debug=leaf_debug, opt=False)
+            def f3(x):
+                return x * x
+
+            @cuda.jit(device=True)
+            def f2(x):
+                return f3(x) + 1
+
+            @cuda.jit(device=True)
+            def f1(x, y):
+                return x - f2(y)
+
+            @cuda.jit(debug=kernel_debug, opt=False)
+            def kernel(x, y):
+                f1(x, y)
+
+            kernel[1, 1](1, 2)
+
+        # Check when debug on the kernel, on the leaf, and not on any function.
+
+        with warnings.catch_warnings(record=True) as w:
+            three_device_fns(kernel_debug=True, leaf_debug=True)
+        self.check_warnings(w, 2)
+
+        with warnings.catch_warnings(record=True) as w:
+            three_device_fns(kernel_debug=True, leaf_debug=False)
+        self.check_warnings(w, 1)
+
+        with warnings.catch_warnings(record=True) as w:
+            three_device_fns(kernel_debug=False, leaf_debug=True)
+        self.check_warnings(w, 1)
+
+        with warnings.catch_warnings(record=True) as w:
+            three_device_fns(kernel_debug=False, leaf_debug=False)
+        self.check_warnings(w, 0)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_device_func.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_device_func.py
new file mode 100644
index 000000000..5583aa5e2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_device_func.py
@@ -0,0 +1,222 @@
+import re
+import types
+
+import numpy as np
+
+from numba.cuda.testing import unittest, skip_on_cudasim, CUDATestCase
+from numba import cuda, jit, float32, int32
+from numba.core.errors import TypingError
+
+
+class TestDeviceFunc(CUDATestCase):
+
+    def test_use_add2f(self):
+
+        @cuda.jit("float32(float32, float32)", device=True)
+        def add2f(a, b):
+            return a + b
+
+        def use_add2f(ary):
+            i = cuda.grid(1)
+            ary[i] = add2f(ary[i], ary[i])
+
+        compiled = cuda.jit("void(float32[:])")(use_add2f)
+
+        nelem = 10
+        ary = np.arange(nelem, dtype=np.float32)
+        exp = ary + ary
+        compiled[1, nelem](ary)
+
+        self.assertTrue(np.all(ary == exp), (ary, exp))
+
+    def test_indirect_add2f(self):
+
+        @cuda.jit("float32(float32, float32)", device=True)
+        def add2f(a, b):
+            return a + b
+
+        @cuda.jit("float32(float32, float32)", device=True)
+        def indirect(a, b):
+            return add2f(a, b)
+
+        def indirect_add2f(ary):
+            i = cuda.grid(1)
+            ary[i] = indirect(ary[i], ary[i])
+
+        compiled = cuda.jit("void(float32[:])")(indirect_add2f)
+
+        nelem = 10
+        ary = np.arange(nelem, dtype=np.float32)
+        exp = ary + ary
+        compiled[1, nelem](ary)
+
+        self.assertTrue(np.all(ary == exp), (ary, exp))
+
+    def _check_cpu_dispatcher(self, add):
+        @cuda.jit
+        def add_kernel(ary):
+            i = cuda.grid(1)
+            ary[i] = add(ary[i], 1)
+
+        ary = np.arange(10)
+        expect = ary + 1
+        add_kernel[1, ary.size](ary)
+        np.testing.assert_equal(expect, ary)
+
+    def test_cpu_dispatcher(self):
+        # Test correct usage
+        @jit
+        def add(a, b):
+            return a + b
+
+        self._check_cpu_dispatcher(add)
+
+    @skip_on_cudasim('not supported in cudasim')
+    def test_cpu_dispatcher_invalid(self):
+        # Test invalid usage
+        # Explicit signature disables compilation, which also disable
+        # compiling on CUDA.
+        @jit('(i4, i4)')
+        def add(a, b):
+            return a + b
+
+        # Check that the right error message is provided.
+        with self.assertRaises(TypingError) as raises:
+            self._check_cpu_dispatcher(add)
+        msg = "Untyped global name 'add':.*using cpu function on device"
+        expected = re.compile(msg)
+        self.assertTrue(expected.search(str(raises.exception)) is not None)
+
+    def test_cpu_dispatcher_other_module(self):
+        @jit
+        def add(a, b):
+            return a + b
+
+        mymod = types.ModuleType(name='mymod')
+        mymod.add = add
+        del add
+
+        @cuda.jit
+        def add_kernel(ary):
+            i = cuda.grid(1)
+            ary[i] = mymod.add(ary[i], 1)
+
+        ary = np.arange(10)
+        expect = ary + 1
+        add_kernel[1, ary.size](ary)
+        np.testing.assert_equal(expect, ary)
+
+    @skip_on_cudasim('not supported in cudasim')
+    def test_inspect_llvm(self):
+        @cuda.jit(device=True)
+        def foo(x, y):
+            return x + y
+
+        args = (int32, int32)
+        cres = foo.compile_device(args)
+
+        fname = cres.fndesc.mangled_name
+        # Verify that the function name has "foo" in it as in the python name
+        self.assertIn('foo', fname)
+
+        llvm = foo.inspect_llvm(args)
+        # Check that the compiled function name is in the LLVM.
+        self.assertIn(fname, llvm)
+
+    @skip_on_cudasim('not supported in cudasim')
+    def test_inspect_asm(self):
+        @cuda.jit(device=True)
+        def foo(x, y):
+            return x + y
+
+        args = (int32, int32)
+        cres = foo.compile_device(args)
+
+        fname = cres.fndesc.mangled_name
+        # Verify that the function name has "foo" in it as in the python name
+        self.assertIn('foo', fname)
+
+        ptx = foo.inspect_asm(args)
+        # Check that the compiled function name is in the PTX
+        self.assertIn(fname, ptx)
+
+    @skip_on_cudasim('not supported in cudasim')
+    def test_inspect_sass_disallowed(self):
+        @cuda.jit(device=True)
+        def foo(x, y):
+            return x + y
+
+        with self.assertRaises(RuntimeError) as raises:
+            foo.inspect_sass((int32, int32))
+
+        self.assertIn('Cannot inspect SASS of a device function',
+                      str(raises.exception))
+
+    @skip_on_cudasim('cudasim will allow calling any function')
+    def test_device_func_as_kernel_disallowed(self):
+        @cuda.jit(device=True)
+        def f():
+            pass
+
+        with self.assertRaises(RuntimeError) as raises:
+            f[1, 1]()
+
+        self.assertIn('Cannot compile a device function as a kernel',
+                      str(raises.exception))
+
+    @skip_on_cudasim('cudasim ignores casting by jit decorator signature')
+    def test_device_casting(self):
+        # Ensure that casts to the correct type are forced when calling a
+        # device function with a signature. This test ensures that:
+        #
+        # - We don't compile a new specialization of rgba for float32 when we
+        #   shouldn't
+        # - We insert a cast when calling rgba, as opposed to failing to type.
+
+        @cuda.jit('int32(int32, int32, int32, int32)', device=True)
+        def rgba(r, g, b, a):
+            return (((r & 0xFF) << 16) |
+                    ((g & 0xFF) << 8) |
+                    ((b & 0xFF) << 0) |
+                    ((a & 0xFF) << 24))
+
+        @cuda.jit
+        def rgba_caller(x, channels):
+            x[0] = rgba(channels[0], channels[1], channels[2], channels[3])
+
+        x = cuda.device_array(1, dtype=np.int32)
+        channels = cuda.to_device(np.asarray([1.0, 2.0, 3.0, 4.0],
+                                             dtype=np.float32))
+
+        rgba_caller[1, 1](x, channels)
+
+        self.assertEqual(0x04010203, x[0])
+
+    def _test_declare_device(self, decl):
+        self.assertEqual(decl.name, 'f1')
+        self.assertEqual(decl.sig.args, (float32[:],))
+        self.assertEqual(decl.sig.return_type, int32)
+
+    @skip_on_cudasim('cudasim does not check signatures')
+    def test_declare_device_signature(self):
+        f1 = cuda.declare_device('f1', int32(float32[:]))
+        self._test_declare_device(f1)
+
+    @skip_on_cudasim('cudasim does not check signatures')
+    def test_declare_device_string(self):
+        f1 = cuda.declare_device('f1', 'int32(float32[:])')
+        self._test_declare_device(f1)
+
+    @skip_on_cudasim('cudasim does not check signatures')
+    def test_bad_declare_device_tuple(self):
+        with self.assertRaisesRegex(TypeError, 'Return type'):
+            cuda.declare_device('f1', (float32[:],))
+
+    @skip_on_cudasim('cudasim does not check signatures')
+    def test_bad_declare_device_string(self):
+        with self.assertRaisesRegex(TypeError, 'Return type'):
+            cuda.declare_device('f1', '(float32[:],)')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_dispatcher.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_dispatcher.py
new file mode 100644
index 000000000..7090db221
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_dispatcher.py
@@ -0,0 +1,292 @@
+import numpy as np
+import threading
+
+from numba import cuda, float32, float64, int32, int64, void
+from numba.cuda.testing import skip_on_cudasim, unittest, CUDATestCase
+import math
+
+
+def add(x, y):
+    return x + y
+
+
+def add_kernel(r, x, y):
+    r[0] = x + y
+
+
+@skip_on_cudasim('Dispatcher objects not used in the simulator')
+class TestDispatcher(CUDATestCase):
+    def _test_no_double_specialize(self, dispatcher, ty):
+
+        with self.assertRaises(RuntimeError) as e:
+            dispatcher.specialize(ty)
+
+        self.assertIn('Dispatcher already specialized', str(e.exception))
+
+    def test_no_double_specialize_sig_same_types(self):
+        # Attempting to specialize a kernel jitted with a signature is illegal,
+        # even for the same types the kernel is already specialized for.
+        @cuda.jit('void(float32[::1])')
+        def f(x):
+            pass
+
+        self._test_no_double_specialize(f, float32[::1])
+
+    def test_no_double_specialize_no_sig_same_types(self):
+        # Attempting to specialize an already-specialized kernel is illegal,
+        # even for the same types the kernel is already specialized for.
+        @cuda.jit
+        def f(x):
+            pass
+
+        f_specialized = f.specialize(float32[::1])
+        self._test_no_double_specialize(f_specialized, float32[::1])
+
+    def test_no_double_specialize_sig_diff_types(self):
+        # Attempting to specialize a kernel jitted with a signature is illegal.
+        @cuda.jit('void(int32[::1])')
+        def f(x):
+            pass
+
+        self._test_no_double_specialize(f, float32[::1])
+
+    def test_no_double_specialize_no_sig_diff_types(self):
+        # Attempting to specialize an already-specialized kernel is illegal.
+        @cuda.jit
+        def f(x):
+            pass
+
+        f_specialized = f.specialize(int32[::1])
+        self._test_no_double_specialize(f_specialized, float32[::1])
+
+    def test_specialize_cache_same(self):
+        # Ensure that the same dispatcher is returned for the same argument
+        # types, and that different dispatchers are returned for different
+        # argument types.
+        @cuda.jit
+        def f(x):
+            pass
+
+        self.assertEqual(len(f.specializations), 0)
+
+        f_float32 = f.specialize(float32[::1])
+        self.assertEqual(len(f.specializations), 1)
+
+        f_float32_2 = f.specialize(float32[::1])
+        self.assertEqual(len(f.specializations), 1)
+        self.assertIs(f_float32, f_float32_2)
+
+        f_int32 = f.specialize(int32[::1])
+        self.assertEqual(len(f.specializations), 2)
+        self.assertIsNot(f_int32, f_float32)
+
+    def test_specialize_cache_same_with_ordering(self):
+        # Ensure that the same dispatcher is returned for the same argument
+        # types, and that different dispatchers are returned for different
+        # argument types, taking into account array ordering and multiple
+        # arguments.
+        @cuda.jit
+        def f(x, y):
+            pass
+
+        self.assertEqual(len(f.specializations), 0)
+
+        # 'A' order specialization
+        f_f32a_f32a = f.specialize(float32[:], float32[:])
+        self.assertEqual(len(f.specializations), 1)
+
+        # 'C' order specialization
+        f_f32c_f32c = f.specialize(float32[::1], float32[::1])
+        self.assertEqual(len(f.specializations), 2)
+        self.assertIsNot(f_f32a_f32a, f_f32c_f32c)
+
+        # Reuse 'C' order specialization
+        f_f32c_f32c_2 = f.specialize(float32[::1], float32[::1])
+        self.assertEqual(len(f.specializations), 2)
+        self.assertIs(f_f32c_f32c, f_f32c_f32c_2)
+
+    # The following tests are based on those in numba.tests.test_dispatcher
+
+    def test_coerce_input_types(self):
+        # Do not allow unsafe conversions if we can still compile other
+        # specializations.
+        c_add = cuda.jit(add_kernel)
+
+        # Using a complex128 allows us to represent any result produced by the
+        # test
+        r = np.zeros(1, dtype=np.complex128)
+
+        c_add[1, 1](r, 123, 456)
+        self.assertEqual(r[0], add(123, 456))
+
+        c_add[1, 1](r, 12.3, 45.6)
+        self.assertEqual(r[0], add(12.3, 45.6))
+
+        c_add[1, 1](r, 12.3, 45.6j)
+        self.assertEqual(r[0], add(12.3, 45.6j))
+
+        c_add[1, 1](r, 12300000000, 456)
+        self.assertEqual(r[0], add(12300000000, 456))
+
+        # Now force compilation of only a single specialization
+        c_add = cuda.jit('(i4[::1], i4, i4)')(add_kernel)
+        r = np.zeros(1, dtype=np.int32)
+
+        c_add[1, 1](r, 123, 456)
+        self.assertPreciseEqual(r[0], add(123, 456))
+
+    @unittest.expectedFailure
+    def test_coerce_input_types_unsafe(self):
+        # Implicit (unsafe) conversion of float to int, originally from
+        # test_coerce_input_types. This test presently fails with the CUDA
+        # Dispatcher because argument preparation is done by
+        # _Kernel._prepare_args, which is currently inflexible with respect to
+        # the types it can accept when preparing.
+        #
+        # This test is marked as xfail until future changes enable this
+        # behavior.
+        c_add = cuda.jit('(i4[::1], i4, i4)')(add_kernel)
+        r = np.zeros(1, dtype=np.int32)
+
+        c_add[1, 1](r, 12.3, 45.6)
+        self.assertPreciseEqual(r[0], add(12, 45))
+
+    def test_coerce_input_types_unsafe_complex(self):
+        # Implicit conversion of complex to int disallowed
+        c_add = cuda.jit('(i4[::1], i4, i4)')(add_kernel)
+        r = np.zeros(1, dtype=np.int32)
+
+        with self.assertRaises(TypeError):
+            c_add[1, 1](r, 12.3, 45.6j)
+
+    def test_ambiguous_new_version(self):
+        """Test compiling new version in an ambiguous case
+        """
+        c_add = cuda.jit(add_kernel)
+
+        r = np.zeros(1, dtype=np.float64)
+        INT = 1
+        FLT = 1.5
+
+        c_add[1, 1](r, INT, FLT)
+        self.assertAlmostEqual(r[0], INT + FLT)
+        self.assertEqual(len(c_add.overloads), 1)
+
+        c_add[1, 1](r, FLT, INT)
+        self.assertAlmostEqual(r[0], FLT + INT)
+        self.assertEqual(len(c_add.overloads), 2)
+
+        c_add[1, 1](r, FLT, FLT)
+        self.assertAlmostEqual(r[0], FLT + FLT)
+        self.assertEqual(len(c_add.overloads), 3)
+
+        # The following call is ambiguous because (int, int) can resolve
+        # to (float, int) or (int, float) with equal weight.
+        c_add[1, 1](r, 1, 1)
+        self.assertAlmostEqual(r[0], INT + INT)
+        self.assertEqual(len(c_add.overloads), 4, "didn't compile a new "
+                                                  "version")
+
+    def test_lock(self):
+        """
+        Test that (lazy) compiling from several threads at once doesn't
+        produce errors (see issue #908).
+        """
+        errors = []
+
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        def wrapper():
+            try:
+                r = np.zeros(1, dtype=np.int64)
+                foo[1, 1](r, 1)
+                self.assertEqual(r[0], 2)
+            except Exception as e:
+                errors.append(e)
+
+        threads = [threading.Thread(target=wrapper) for i in range(16)]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+        self.assertFalse(errors)
+
+    def test_get_regs_per_thread_unspecialized(self):
+        # A kernel where the register usage per thread is likely to differ
+        # between different specializations
+        @cuda.jit
+        def pi_sin_array(x, n):
+            i = cuda.grid(1)
+            if i < n:
+                x[i] = 3.14 * math.sin(x[i])
+
+        # Call the kernel with different arguments to create two different
+        # definitions within the Dispatcher object
+        N = 10
+        arr_f32 = np.zeros(N, dtype=np.float32)
+        arr_f64 = np.zeros(N, dtype=np.float64)
+
+        pi_sin_array[1, N](arr_f32, N)
+        pi_sin_array[1, N](arr_f64, N)
+
+        # Check we get a positive integer for the two different variations
+        sig_f32 = void(float32[::1], int64)
+        sig_f64 = void(float64[::1], int64)
+        regs_per_thread_f32 = pi_sin_array.get_regs_per_thread(sig_f32)
+        regs_per_thread_f64 = pi_sin_array.get_regs_per_thread(sig_f64)
+
+        self.assertIsInstance(regs_per_thread_f32, int)
+        self.assertIsInstance(regs_per_thread_f64, int)
+
+        self.assertGreater(regs_per_thread_f32, 0)
+        self.assertGreater(regs_per_thread_f64, 0)
+
+        # Check that getting the registers per thread for all signatures
+        # provides the same values as getting the registers per thread for
+        # individual signatures.
+        regs_per_thread_all = pi_sin_array.get_regs_per_thread()
+        self.assertEqual(regs_per_thread_all[sig_f32.args],
+                         regs_per_thread_f32)
+        self.assertEqual(regs_per_thread_all[sig_f64.args],
+                         regs_per_thread_f64)
+
+        if regs_per_thread_f32 == regs_per_thread_f64:
+            # If the register usage is the same for both variants, there may be
+            # a bug, but this may also be an artifact of the compiler / driver
+            # / device combination, so produce an informational message only.
+            print('f32 and f64 variant thread usages are equal.')
+            print('This may warrant some investigation. Devices:')
+            cuda.detect()
+
+    def test_get_regs_per_thread_specialized(self):
+        @cuda.jit(void(float32[::1], int64))
+        def pi_sin_array(x, n):
+            i = cuda.grid(1)
+            if i < n:
+                x[i] = 3.14 * math.sin(x[i])
+
+        # Check we get a positive integer for the specialized variation
+        regs_per_thread = pi_sin_array.get_regs_per_thread()
+        self.assertIsInstance(regs_per_thread, int)
+        self.assertGreater(regs_per_thread, 0)
+
+    def test_dispatcher_docstring(self):
+        # Ensure that CUDA-jitting a function preserves its docstring. See
+        # Issue #5902: https://github.com/numba/numba/issues/5902
+
+        @cuda.jit
+        def add_kernel(a, b):
+            """Add two integers, kernel version"""
+
+        @cuda.jit(device=True)
+        def add_device(a, b):
+            """Add two integers, device version"""
+
+        self.assertEqual("Add two integers, kernel version", add_kernel.__doc__)
+        self.assertEqual("Add two integers, device version", add_device.__doc__)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_enums.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_enums.py
new file mode 100644
index 000000000..5232634f7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_enums.py
@@ -0,0 +1,121 @@
+"""
+Test cases adapted from numba/tests/test_enums.py
+"""
+
+import numpy as np
+
+from numba import int8, int16, int32
+from numba import cuda, vectorize, njit
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.tests.enum_usecases import (
+    Color,
+    Shape,
+    Planet,
+    RequestError,
+    IntEnumWithNegatives
+)
+
+
+class EnumTest(CUDATestCase):
+
+    pairs = [
+        (Color.red, Color.red),
+        (Color.red, Color.green),
+        (Planet.EARTH, Planet.EARTH),
+        (Planet.VENUS, Planet.MARS),
+        (Shape.circle, IntEnumWithNegatives.two) # IntEnum, same value
+    ]
+
+    def test_compare(self):
+        def f(a, b, out):
+            out[0] = a == b
+            out[1] = a != b
+            out[2] = a is b
+            out[3] = a is not b
+
+        cuda_f = cuda.jit(f)
+        for a, b in self.pairs:
+            got = np.zeros((4,), dtype=np.bool_)
+            expected = got.copy()
+            cuda_f[1, 1](a, b, got)
+            f(a, b, expected)
+            self.assertPreciseEqual(expected, got)
+
+    def test_getattr_getitem(self):
+        def f(out):
+            # Lookup of an enum member on its class
+            out[0] = Color.red == Color.green
+            out[1] = Color['red'] == Color['green']
+
+        cuda_f = cuda.jit(f)
+        got = np.zeros((2,), dtype=np.bool_)
+        expected = got.copy()
+        cuda_f[1, 1](got)
+        f(expected)
+        self.assertPreciseEqual(expected, got)
+
+    def test_return_from_device_func(self):
+        @njit
+        def inner(pred):
+            return Color.red if pred else Color.green
+
+        def f(pred, out):
+            out[0] = inner(pred) == Color.red
+            out[1] = inner(not pred) == Color.green
+
+        cuda_f = cuda.jit(f)
+        got = np.zeros((2,), dtype=np.bool_)
+        expected = got.copy()
+        f(True, expected)
+        cuda_f[1, 1](True, got)
+        self.assertPreciseEqual(expected, got)
+
+    def test_int_coerce(self):
+        def f(x, out):
+            # Implicit coercion of intenums to ints
+            if x > RequestError.internal_error:
+                out[0] = x - RequestError.not_found
+            else:
+                out[0] = x + Shape.circle
+
+        cuda_f = cuda.jit(f)
+        for x in [300, 450, 550]:
+            got = np.zeros((1,), dtype=np.int32)
+            expected = got.copy()
+            cuda_f[1, 1](x, got)
+            f(x, expected)
+            self.assertPreciseEqual(expected, got)
+
+    def test_int_cast(self):
+        def f(x, out):
+            # Explicit coercion of intenums to ints
+            if x > int16(RequestError.internal_error):
+                out[0] = x - int32(RequestError.not_found)
+            else:
+                out[0] = x + int8(Shape.circle)
+
+        cuda_f = cuda.jit(f)
+        for x in [300, 450, 550]:
+            got = np.zeros((1,), dtype=np.int32)
+            expected = got.copy()
+            cuda_f[1, 1](x, got)
+            f(x, expected)
+            self.assertEqual(expected, got)
+
+    @skip_on_cudasim("ufuncs are unsupported on simulator.")
+    def test_vectorize(self):
+        def f(x):
+            if x != RequestError.not_found:
+                return RequestError['internal_error']
+            else:
+                return RequestError.dummy
+
+        cuda_func = vectorize("int64(int64)", target='cuda')(f)
+        arr = np.array([2, 404, 500, 404], dtype=np.int64)
+        expected = np.array([f(x) for x in arr], dtype=np.int64)
+        got = cuda_func(arr)
+        self.assertPreciseEqual(expected, got)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_errors.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_errors.py
new file mode 100644
index 000000000..c20fb8dcc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_errors.py
@@ -0,0 +1,79 @@
+from numba import cuda
+from numba.core.errors import TypingError
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+
+
+def noop(x):
+    pass
+
+
+class TestJitErrors(CUDATestCase):
+    """
+    Test compile-time errors with @jit.
+    """
+
+    def test_too_many_dims(self):
+        kernfunc = cuda.jit(noop)
+
+        with self.assertRaises(ValueError) as raises:
+            kernfunc[(1, 2, 3, 4), (5, 6)]
+        self.assertIn("griddim must be a sequence of 1, 2 or 3 integers, "
+                      "got [1, 2, 3, 4]",
+                      str(raises.exception))
+
+        with self.assertRaises(ValueError) as raises:
+            kernfunc[(1, 2,), (3, 4, 5, 6)]
+        self.assertIn("blockdim must be a sequence of 1, 2 or 3 integers, "
+                      "got [3, 4, 5, 6]",
+                      str(raises.exception))
+
+    def test_non_integral_dims(self):
+        kernfunc = cuda.jit(noop)
+
+        with self.assertRaises(TypeError) as raises:
+            kernfunc[2.0, 3]
+        self.assertIn("griddim must be a sequence of integers, got [2.0]",
+                      str(raises.exception))
+
+        with self.assertRaises(TypeError) as raises:
+            kernfunc[2, 3.0]
+        self.assertIn("blockdim must be a sequence of integers, got [3.0]",
+                      str(raises.exception))
+
+    def _test_unconfigured(self, kernfunc):
+        with self.assertRaises(ValueError) as raises:
+            kernfunc(0)
+        self.assertIn("launch configuration was not specified",
+                      str(raises.exception))
+
+    def test_unconfigured_typed_cudakernel(self):
+        kernfunc = cuda.jit("void(int32)")(noop)
+        self._test_unconfigured(kernfunc)
+
+    def test_unconfigured_untyped_cudakernel(self):
+        kernfunc = cuda.jit(noop)
+        self._test_unconfigured(kernfunc)
+
+    @skip_on_cudasim('TypingError does not occur on simulator')
+    def test_typing_error(self):
+        # see #5860, this is present to catch changes to error reporting
+        # accidentally breaking the CUDA target
+
+        @cuda.jit(device=True)
+        def dev_func(x):
+            # floor is deliberately not imported for the purpose of this test.
+            return floor(x)  # noqa: F821
+
+        @cuda.jit
+        def kernel_func():
+            dev_func(1.5)
+
+        with self.assertRaises(TypingError) as raises:
+            kernel_func[1, 1]()
+        excstr = str(raises.exception)
+        self.assertIn("resolving callee type: type(CUDADispatcher", excstr)
+        self.assertIn("NameError: name 'floor' is not defined", excstr)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_exception.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_exception.py
new file mode 100644
index 000000000..18719ef0b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_exception.py
@@ -0,0 +1,168 @@
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import unittest, xfail_unless_cudasim, CUDATestCase
+from numba.core import config
+
+
+class TestException(CUDATestCase):
+    def test_exception(self):
+        def foo(ary):
+            x = cuda.threadIdx.x
+            if x == 2:
+                # NOTE: indexing with a out-of-bounds constant can fail at
+                # compile-time instead (because the getitem is rewritten as a
+                # static_getitem)
+                ary.shape[-x]
+
+        unsafe_foo = cuda.jit(foo)
+        safe_foo = cuda.jit(debug=True, opt=False)(foo)
+
+        if not config.ENABLE_CUDASIM:
+            # Simulator throws exceptions regardless of debug
+            # setting
+            unsafe_foo[1, 3](np.array([0, 1]))
+
+        with self.assertRaises(IndexError) as cm:
+            safe_foo[1, 3](np.array([0, 1]))
+        self.assertIn("tuple index out of range", str(cm.exception))
+
+    def test_user_raise(self):
+        @cuda.jit(debug=True, opt=False)
+        def foo(do_raise):
+            if do_raise:
+                raise ValueError
+
+        foo[1, 1](False)
+        with self.assertRaises(ValueError):
+            foo[1, 1](True)
+
+    def case_raise_causing_warp_diverge(self, with_debug_mode):
+        """Testing issue #2655.
+
+        Exception raising code can cause the compiler to miss location
+        of unifying branch target and resulting in unexpected warp
+        divergence.
+        """
+        with_opt_mode = not with_debug_mode
+
+        @cuda.jit(debug=with_debug_mode, opt=with_opt_mode)
+        def problematic(x, y):
+            tid = cuda.threadIdx.x
+            ntid = cuda.blockDim.x
+
+            if tid > 12:
+                for i in range(ntid):
+                    y[i] += x[i] // y[i]
+
+            cuda.syncthreads()
+            if tid < 17:
+                for i in range(ntid):
+                    x[i] += x[i] // y[i]
+
+        @cuda.jit
+        def oracle(x, y):
+            tid = cuda.threadIdx.x
+            ntid = cuda.blockDim.x
+
+            if tid > 12:
+                for i in range(ntid):
+                    if y[i] != 0:
+                        y[i] += x[i] // y[i]
+
+            cuda.syncthreads()
+            if tid < 17:
+                for i in range(ntid):
+                    if y[i] != 0:
+                        x[i] += x[i] // y[i]
+
+        n = 32
+        got_x = 1. / (np.arange(n) + 0.01)
+        got_y = 1. / (np.arange(n) + 0.01)
+        problematic[1, n](got_x, got_y)
+
+        expect_x = 1. / (np.arange(n) + 0.01)
+        expect_y = 1. / (np.arange(n) + 0.01)
+        oracle[1, n](expect_x, expect_y)
+
+        np.testing.assert_almost_equal(expect_x, got_x)
+        np.testing.assert_almost_equal(expect_y, got_y)
+
+    def test_raise_causing_warp_diverge(self):
+        """Test case for issue #2655.
+        """
+        self.case_raise_causing_warp_diverge(with_debug_mode=False)
+
+    # The following two cases relate to Issue #7806: Division by zero stops the
+    # kernel. https://github.com/numba/numba/issues/7806.
+
+    def test_no_zero_division_error(self):
+        # When debug is False:
+        # - Division by zero raises no exception
+        # - Execution proceeds after a divide by zero
+        @cuda.jit
+        def f(r, x, y):
+            r[0] = y[0] / x[0]
+            r[1] = y[0]
+
+        r = np.zeros(2)
+        x = np.zeros(1)
+        y = np.ones(1)
+
+        f[1, 1](r, x, y)
+
+        self.assertTrue(np.isinf(r[0]), 'Expected inf from div by zero')
+        self.assertEqual(r[1], y[0], 'Expected execution to continue')
+
+    def test_zero_division_error_in_debug(self):
+        # When debug is True:
+        # - Zero by division raises an exception
+        # - Execution halts at the point of division by zero
+        @cuda.jit(debug=True, opt=False)
+        def f(r, x, y):
+            r[0] = y[0] / x[0]
+            r[1] = y[0]
+
+        r = np.zeros(2)
+        x = np.zeros(1)
+        y = np.ones(1)
+
+        # Simulator and device behaviour differs slightly in the exception
+        # raised - in debug mode, the CUDA target uses the Python error model,
+        # which gives a ZeroDivision error. The simulator uses NumPy with the
+        # error mode for division by zero set to raise, which results in a
+        # FloatingPointError instead.
+        if config.ENABLE_CUDASIM:
+            exc = FloatingPointError
+        else:
+            exc = ZeroDivisionError
+
+        with self.assertRaises(exc):
+            f[1, 1](r, x, y)
+
+        self.assertEqual(r[0], 0, 'Expected result to be left unset')
+        self.assertEqual(r[1], 0, 'Expected execution to stop')
+
+    @xfail_unless_cudasim
+    def test_raise_in_device_function(self):
+        # This is an expected failure because reporting of exceptions raised in
+        # device functions does not work correctly - see Issue #8036:
+        # https://github.com/numba/numba/issues/8036
+        msg = 'Device Function Error'
+
+        @cuda.jit(device=True)
+        def f():
+            raise ValueError(msg)
+
+        @cuda.jit(debug=True)
+        def kernel():
+            f()
+
+        with self.assertRaises(ValueError) as raises:
+            kernel[1, 1]()
+
+        self.assertIn(msg, str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_extending.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_extending.py
new file mode 100644
index 000000000..142d917c0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_extending.py
@@ -0,0 +1,155 @@
+from numba.cuda.testing import skip_on_cudasim, unittest, CUDATestCase
+
+import numpy as np
+from numba import config, cuda, njit, types
+
+
+class Interval:
+    """
+    A half-open interval on the real number line.
+    """
+    def __init__(self, lo, hi):
+        self.lo = lo
+        self.hi = hi
+
+    def __repr__(self):
+        return 'Interval(%f, %f)' % (self.lo, self.hi)
+
+    @property
+    def width(self):
+        return self.hi - self.lo
+
+
+@njit
+def interval_width(interval):
+    return interval.width
+
+
+@njit
+def sum_intervals(i, j):
+    return Interval(i.lo + j.lo, i.hi + j.hi)
+
+
+if not config.ENABLE_CUDASIM:
+    from numba.core import cgutils
+    from numba.core.extending import (lower_builtin, make_attribute_wrapper,
+                                      models, register_model, type_callable,
+                                      typeof_impl)
+    from numba.core.typing.templates import AttributeTemplate
+    from numba.cuda.cudadecl import registry as cuda_registry
+    from numba.cuda.cudaimpl import lower_attr as cuda_lower_attr
+
+    class IntervalType(types.Type):
+        def __init__(self):
+            super().__init__(name='Interval')
+
+    interval_type = IntervalType()
+
+    @typeof_impl.register(Interval)
+    def typeof_interval(val, c):
+        return interval_type
+
+    @type_callable(Interval)
+    def type_interval(context):
+        def typer(lo, hi):
+            if isinstance(lo, types.Float) and isinstance(hi, types.Float):
+                return interval_type
+        return typer
+
+    @register_model(IntervalType)
+    class IntervalModel(models.StructModel):
+        def __init__(self, dmm, fe_type):
+            members = [
+                ('lo', types.float64),
+                ('hi', types.float64),
+            ]
+            models.StructModel.__init__(self, dmm, fe_type, members)
+
+    make_attribute_wrapper(IntervalType, 'lo', 'lo')
+    make_attribute_wrapper(IntervalType, 'hi', 'hi')
+
+    @lower_builtin(Interval, types.Float, types.Float)
+    def impl_interval(context, builder, sig, args):
+        typ = sig.return_type
+        lo, hi = args
+        interval = cgutils.create_struct_proxy(typ)(context, builder)
+        interval.lo = lo
+        interval.hi = hi
+        return interval._getvalue()
+
+    @cuda_registry.register_attr
+    class Interval_attrs(AttributeTemplate):
+        key = IntervalType
+
+        def resolve_width(self, mod):
+            return types.float64
+
+    @cuda_lower_attr(IntervalType, 'width')
+    def cuda_Interval_width(context, builder, sig, arg):
+        lo = builder.extract_value(arg, 0)
+        hi = builder.extract_value(arg, 1)
+        return builder.fsub(hi, lo)
+
+
+@skip_on_cudasim('Extensions not supported in the simulator')
+class TestExtending(CUDATestCase):
+    def test_attributes(self):
+        @cuda.jit
+        def f(r, x):
+            iv = Interval(x[0], x[1])
+            r[0] = iv.lo
+            r[1] = iv.hi
+
+        x = np.asarray((1.5, 2.5))
+        r = np.zeros_like(x)
+
+        f[1, 1](r, x)
+
+        np.testing.assert_equal(r, x)
+
+    def test_property(self):
+        @cuda.jit
+        def f(r, x):
+            iv = Interval(x[0], x[1])
+            r[0] = iv.width
+
+        x = np.asarray((1.5, 2.5))
+        r = np.zeros(1)
+
+        f[1, 1](r, x)
+
+        np.testing.assert_allclose(r[0], x[1] - x[0])
+
+    def test_extension_type_as_arg(self):
+        @cuda.jit
+        def f(r, x):
+            iv = Interval(x[0], x[1])
+            r[0] = interval_width(iv)
+
+        x = np.asarray((1.5, 2.5))
+        r = np.zeros(1)
+
+        f[1, 1](r, x)
+
+        np.testing.assert_allclose(r[0], x[1] - x[0])
+
+    def test_extension_type_as_retvalue(self):
+        @cuda.jit
+        def f(r, x):
+            iv1 = Interval(x[0], x[1])
+            iv2 = Interval(x[2], x[3])
+            iv_sum = sum_intervals(iv1, iv2)
+            r[0] = iv_sum.lo
+            r[1] = iv_sum.hi
+
+        x = np.asarray((1.5, 2.5, 3.0, 4.0))
+        r = np.zeros(2)
+
+        f[1, 1](r, x)
+
+        expected = np.asarray((x[0] + x[2], x[1] + x[3]))
+        np.testing.assert_allclose(r, expected)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_fastmath.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_fastmath.py
new file mode 100644
index 000000000..f0158846f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_fastmath.py
@@ -0,0 +1,204 @@
+from typing import List
+from dataclasses import dataclass, field
+from numba import cuda, float32
+from numba.cuda.compiler import compile_ptx_for_current_device
+from math import cos, sin, tan, exp, log, log10, log2, pow
+from operator import truediv
+import numpy as np
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+import unittest
+
+
+@dataclass
+class FastMathCriterion:
+    fast_expected: List[str] = field(default_factory=list)
+    fast_unexpected: List[str] = field(default_factory=list)
+    prec_expected: List[str] = field(default_factory=list)
+    prec_unexpected: List[str] = field(default_factory=list)
+
+    def check(self, test: CUDATestCase, fast: str, prec: str):
+        test.assertTrue(all(i in fast for i in self.fast_expected))
+        test.assertTrue(all(i not in fast for i in self.fast_unexpected))
+        test.assertTrue(all(i in prec for i in self.prec_expected))
+        test.assertTrue(all(i not in prec for i in self.prec_unexpected))
+
+
+@skip_on_cudasim('Fastmath and PTX inspection not available on cudasim')
+class TestFastMathOption(CUDATestCase):
+    def _test_fast_math_common(self, pyfunc, sig, device, criterion):
+
+        # Test jit code path
+        fastver = cuda.jit(sig, device=device, fastmath=True)(pyfunc)
+        precver = cuda.jit(sig, device=device)(pyfunc)
+        criterion.check(
+            self, fastver.inspect_asm(sig), precver.inspect_asm(sig)
+        )
+
+        # Test compile_ptx code path
+        fastptx, _ = compile_ptx_for_current_device(
+            pyfunc, sig, device=device, fastmath=True
+        )
+        precptx, _ = compile_ptx_for_current_device(
+            pyfunc, sig, device=device
+        )
+        criterion.check(self, fastptx, precptx)
+
+    def _test_fast_math_unary(self, op, criterion: FastMathCriterion):
+        def kernel(r, x):
+            r[0] = op(x)
+
+        def device_function(x):
+            return op(x)
+
+        self._test_fast_math_common(
+            kernel, (float32[::1], float32), device=False, criterion=criterion
+        )
+        self._test_fast_math_common(
+            device_function, (float32,), device=True, criterion=criterion
+        )
+
+    def _test_fast_math_binary(self, op, criterion: FastMathCriterion):
+        def kernel(r, x, y):
+            r[0] = op(x, y)
+
+        def device(x, y):
+            return op(x, y)
+
+        self._test_fast_math_common(
+            kernel,
+            (float32[::1], float32, float32), device=False, criterion=criterion
+        )
+        self._test_fast_math_common(
+            device, (float32, float32), device=True, criterion=criterion
+        )
+
+    def test_cosf(self):
+        self._test_fast_math_unary(
+            cos,
+            FastMathCriterion(
+                fast_expected=['cos.approx.ftz.f32 '],
+                prec_unexpected=['cos.approx.ftz.f32 ']
+            )
+        )
+
+    def test_sinf(self):
+        self._test_fast_math_unary(
+            sin,
+            FastMathCriterion(
+                fast_expected=['sin.approx.ftz.f32 '],
+                prec_unexpected=['sin.approx.ftz.f32 ']
+            )
+        )
+
+    def test_tanf(self):
+        self._test_fast_math_unary(
+            tan,
+            FastMathCriterion(fast_expected=[
+                'sin.approx.ftz.f32 ',
+                'cos.approx.ftz.f32 ',
+                'div.approx.ftz.f32 '
+            ], prec_unexpected=['sin.approx.ftz.f32 '])
+        )
+
+    def test_expf(self):
+        self._test_fast_math_unary(
+            exp,
+            FastMathCriterion(
+                fast_unexpected=['fma.rn.f32 '],
+                prec_expected=['fma.rn.f32 ']
+            )
+        )
+
+    def test_logf(self):
+        # Look for constant used to convert from log base 2 to log base e
+        self._test_fast_math_unary(
+            log, FastMathCriterion(
+                fast_expected=['lg2.approx.ftz.f32 ', '0f3F317218'],
+                prec_unexpected=['lg2.approx.ftz.f32 '],
+            )
+        )
+
+    def test_log10f(self):
+        # Look for constant used to convert from log base 2 to log base 10
+        self._test_fast_math_unary(
+            log10, FastMathCriterion(
+                fast_expected=['lg2.approx.ftz.f32 ', '0f3E9A209B'],
+                prec_unexpected=['lg2.approx.ftz.f32 ']
+            )
+        )
+
+    def test_log2f(self):
+        self._test_fast_math_unary(
+            log2, FastMathCriterion(
+                fast_expected=['lg2.approx.ftz.f32 '],
+                prec_unexpected=['lg2.approx.ftz.f32 ']
+            )
+        )
+
+    def test_powf(self):
+        self._test_fast_math_binary(
+            pow, FastMathCriterion(
+                fast_expected=['lg2.approx.ftz.f32 '],
+                prec_unexpected=['lg2.approx.ftz.f32 '],
+            )
+        )
+
+    def test_divf(self):
+        self._test_fast_math_binary(
+            truediv, FastMathCriterion(
+                fast_expected=['div.approx.ftz.f32 '],
+                fast_unexpected=['div.rn.f32'],
+                prec_expected=['div.rn.f32'],
+                prec_unexpected=['div.approx.ftz.f32 '],
+            )
+        )
+
+    def test_divf_exception(self):
+        def f10(r, x, y):
+            r[0] = x / y
+
+        sig = (float32[::1], float32, float32)
+        fastver = cuda.jit(sig, fastmath=True, debug=True)(f10)
+        precver = cuda.jit(sig, debug=True)(f10)
+        nelem = 10
+        ary = np.empty(nelem, dtype=np.float32)
+        with self.assertRaises(ZeroDivisionError):
+            precver[1, nelem](ary, 10.0, 0.0)
+
+        try:
+            fastver[1, nelem](ary, 10.0, 0.0)
+        except ZeroDivisionError:
+            self.fail("Divide in fastmath should not throw ZeroDivisionError")
+
+    @unittest.expectedFailure
+    def test_device_fastmath_propagation(self):
+        # The fastmath option doesn't presently propagate to device functions
+        # from their callees - arguably it should do, so this test is presently
+        # an xfail.
+        @cuda.jit("float32(float32, float32)", device=True)
+        def foo(a, b):
+            return a / b
+
+        def bar(arr, val):
+            i = cuda.grid(1)
+            if i < arr.size:
+                arr[i] = foo(i, val)
+
+        sig = (float32[::1], float32)
+        fastver = cuda.jit(sig, fastmath=True)(bar)
+        precver = cuda.jit(sig)(bar)
+
+        # Variants of the div instruction are further documented at:
+        # https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#floating-point-instructions-div
+
+        # The fast version should use the "fast, approximate divide" variant
+        self.assertIn('div.approx.f32', fastver.inspect_asm(sig))
+        # The precise version should use the "IEEE 754 compliant rounding"
+        # variant, and neither of the "approximate divide" variants.
+        self.assertIn('div.rn.f32', precver.inspect_asm(sig))
+        self.assertNotIn('div.approx.f32', precver.inspect_asm(sig))
+        self.assertNotIn('div.full.f32', precver.inspect_asm(sig))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_forall.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_forall.py
new file mode 100644
index 000000000..23286c22c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_forall.py
@@ -0,0 +1,52 @@
+import numpy as np
+
+from numba import cuda
+import unittest
+from numba.cuda.testing import CUDATestCase
+
+
+@cuda.jit
+def foo(x):
+    i = cuda.grid(1)
+    if i < x.size:
+        x[i] += 1
+
+
+class TestForAll(CUDATestCase):
+    def test_forall_1(self):
+        arr = np.arange(11)
+        orig = arr.copy()
+        foo.forall(arr.size)(arr)
+        np.testing.assert_array_almost_equal(arr, orig + 1)
+
+    def test_forall_2(self):
+        @cuda.jit("void(float32, float32[:], float32[:])")
+        def bar(a, x, y):
+            i = cuda.grid(1)
+            if i < x.size:
+                y[i] = a * x[i] + y[i]
+
+        x = np.arange(13, dtype=np.float32)
+        y = np.arange(13, dtype=np.float32)
+        oldy = y.copy()
+        a = 1.234
+        bar.forall(y.size)(a, x, y)
+        np.testing.assert_array_almost_equal(y, a * x + oldy, decimal=3)
+
+    def test_forall_no_work(self):
+        # Ensure that forall doesn't launch a kernel with no blocks when called
+        # with 0 elements. See Issue #5017.
+        arr = np.arange(11)
+        foo.forall(0)(arr)
+
+    def test_forall_negative_work(self):
+        # Ensure that forall doesn't allow the creation of a forall with a
+        # negative element count.
+        with self.assertRaises(ValueError) as raises:
+            foo.forall(-1)
+        self.assertIn("Can't create ForAll with negative task count",
+                      str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_freevar.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_freevar.py
new file mode 100644
index 000000000..6b7b2d2ab
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_freevar.py
@@ -0,0 +1,29 @@
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestFreeVar(CUDATestCase):
+    def test_freevar(self):
+        """Make sure we can compile the following kernel with freevar reference
+        in arguments to shared.array
+        """
+        from numba import float32
+
+        size = 1024
+        nbtype = float32
+
+        @cuda.jit("(float32[::1], intp)")
+        def foo(A, i):
+            "Dummy function"
+            sdata = cuda.shared.array(size,   # size is freevar
+                                      dtype=nbtype)  # nbtype is freevar
+            A[i] = sdata[i]
+
+        A = np.arange(2, dtype="float32")
+        foo[1, 1](A, 0)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_frexp_ldexp.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_frexp_ldexp.py
new file mode 100644
index 000000000..dad9e9765
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_frexp_ldexp.py
@@ -0,0 +1,66 @@
+import numpy as np
+import math
+from numba import cuda
+from numba.types import float32, float64, int32, void
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+def simple_frexp(aryx, aryexp, arg):
+    aryx[0], aryexp[0] = math.frexp(arg)
+
+
+def simple_ldexp(aryx, arg, exp):
+    aryx[0] = math.ldexp(arg, exp)
+
+
+class TestCudaFrexpLdexp(CUDATestCase):
+    def template_test_frexp(self, nptype, nbtype):
+        compiled = cuda.jit(void(nbtype[:], int32[:], nbtype))(simple_frexp)
+        arg = 3.1415
+        aryx = np.zeros(1, dtype=nptype)
+        aryexp = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](aryx, aryexp, arg)
+        np.testing.assert_array_equal(aryx, nptype(0.785375))
+        self.assertEquals(aryexp, 2)
+
+        arg = np.inf
+        compiled[1, 1](aryx, aryexp, arg)
+        np.testing.assert_array_equal(aryx, nptype(np.inf))
+        self.assertEquals(aryexp, 0)  # np.frexp gives -1
+
+        arg = np.nan
+        compiled[1, 1](aryx, aryexp, arg)
+        np.testing.assert_array_equal(aryx, nptype(np.nan))
+        self.assertEquals(aryexp, 0)  # np.frexp gives -1
+
+    def template_test_ldexp(self, nptype, nbtype):
+        compiled = cuda.jit(void(nbtype[:], nbtype, int32))(simple_ldexp)
+        arg = 0.785375
+        exp = 2
+        aryx = np.zeros(1, dtype=nptype)
+        compiled[1, 1](aryx, arg, exp)
+        np.testing.assert_array_equal(aryx, nptype(3.1415))
+
+        arg = np.inf
+        compiled[1, 1](aryx, arg, exp)
+        np.testing.assert_array_equal(aryx, nptype(np.inf))
+
+        arg = np.nan
+        compiled[1, 1](aryx, arg, exp)
+        np.testing.assert_array_equal(aryx, nptype(np.nan))
+
+    def test_frexp_f4(self):
+        self.template_test_frexp(np.float32, float32)
+
+    def test_ldexp_f4(self):
+        self.template_test_ldexp(np.float32, float32)
+
+    def test_frexp_f8(self):
+        self.template_test_frexp(np.float64, float64)
+
+    def test_ldexp_f8(self):
+        self.template_test_ldexp(np.float64, float64)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_globals.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_globals.py
new file mode 100644
index 000000000..a2406e665
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_globals.py
@@ -0,0 +1,60 @@
+import numpy as np
+from numba import cuda, int32, float32
+from numba.cuda.testing import unittest, CUDATestCase
+
+N = 100
+
+
+def simple_smem(ary):
+    sm = cuda.shared.array(N, int32)
+    i = cuda.grid(1)
+    if i == 0:
+        for j in range(N):
+            sm[j] = j
+    cuda.syncthreads()
+    ary[i] = sm[i]
+
+
+S0 = 10
+S1 = 20
+
+
+def coop_smem2d(ary):
+    i, j = cuda.grid(2)
+    sm = cuda.shared.array((S0, S1), float32)
+    sm[i, j] = (i + 1) / (j + 1)
+    cuda.syncthreads()
+    ary[i, j] = sm[i, j]
+
+
+class TestCudaTestGlobal(CUDATestCase):
+    def test_global_int_const(self):
+        """Test simple_smem
+        """
+        compiled = cuda.jit("void(int32[:])")(simple_smem)
+
+        nelem = 100
+        ary = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary)
+
+        self.assertTrue(np.all(ary == np.arange(nelem, dtype=np.int32)))
+
+    @unittest.SkipTest
+    def test_global_tuple_const(self):
+        """Test coop_smem2d
+        """
+        compiled = cuda.jit("void(float32[:,:])")(coop_smem2d)
+
+        shape = 10, 20
+        ary = np.empty(shape, dtype=np.float32)
+        compiled[1, shape](ary)
+
+        exp = np.empty_like(ary)
+        for i in range(ary.shape[0]):
+            for j in range(ary.shape[1]):
+                exp[i, j] = float(i + 1) / (j + 1)
+        self.assertTrue(np.allclose(ary, exp))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc.py
new file mode 100644
index 000000000..aeda5dac7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc.py
@@ -0,0 +1,309 @@
+import numpy as np
+import numpy.core.umath_tests as ut
+
+from collections import namedtuple
+from numba import void, float32, float64
+from numba import guvectorize
+from numba import cuda
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+import warnings
+from numba.core.errors import NumbaPerformanceWarning
+from numba.tests.support import override_config
+
+
+def _get_matmulcore_gufunc(dtype=float32, max_blocksize=None):
+    @guvectorize([void(dtype[:, :], dtype[:, :], dtype[:, :])],
+                 '(m,n),(n,p)->(m,p)',
+                 target='cuda')
+    def matmulcore(A, B, C):
+        m, n = A.shape
+        n, p = B.shape
+        for i in range(m):
+            for j in range(p):
+                C[i, j] = 0
+                for k in range(n):
+                    C[i, j] += A[i, k] * B[k, j]
+
+    gufunc = matmulcore
+    if max_blocksize:
+        gufunc.max_blocksize = max_blocksize
+    return gufunc
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestCUDAGufunc(CUDATestCase):
+
+    def test_gufunc_small(self):
+
+        gufunc = _get_matmulcore_gufunc(max_blocksize=512)
+
+        matrix_ct = 2
+        A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2,
+                                                                   4)
+        B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4,
+                                                                   5)
+
+        C = gufunc(A, B)
+        Gold = ut.matrix_multiply(A, B)
+        self.assertTrue(np.allclose(C, Gold))
+
+    def test_gufunc_auto_transfer(self):
+
+        gufunc = _get_matmulcore_gufunc(max_blocksize=512)
+
+        matrix_ct = 2
+        A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2,
+                                                                   4)
+        B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4,
+                                                                   5)
+
+        dB = cuda.to_device(B)
+
+        C = gufunc(A, dB).copy_to_host()
+        Gold = ut.matrix_multiply(A, B)
+        self.assertTrue(np.allclose(C, Gold))
+
+    def test_gufunc(self):
+
+        gufunc = _get_matmulcore_gufunc(max_blocksize=512)
+
+        matrix_ct = 1001 # an odd number to test thread/block division in CUDA
+        A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2,
+                                                                   4)
+        B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4,
+                                                                   5)
+
+        C = gufunc(A, B)
+        Gold = ut.matrix_multiply(A, B)
+        self.assertTrue(np.allclose(C, Gold))
+
+    def test_gufunc_hidim(self):
+
+        gufunc = _get_matmulcore_gufunc(max_blocksize=512)
+
+        matrix_ct = 100 # an odd number to test thread/block division in CUDA
+        A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(4, 25, 2, 4)
+        B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(4, 25, 4, 5)
+
+        C = gufunc(A, B)
+        Gold = ut.matrix_multiply(A, B)
+        self.assertTrue(np.allclose(C, Gold))
+
+    def test_gufunc_new_axis(self):
+
+        gufunc = _get_matmulcore_gufunc(dtype=float64)
+
+        X = np.random.randn(10, 3, 3)
+        Y = np.random.randn(3, 3)
+
+        gold = ut.matrix_multiply(X, Y)
+
+        res1 = gufunc(X, Y)
+        np.testing.assert_allclose(gold, res1)
+
+        res2 = gufunc(X, np.tile(Y, (10, 1, 1)))
+        np.testing.assert_allclose(gold, res2)
+
+    def test_gufunc_adjust_blocksize(self):
+
+        gufunc = _get_matmulcore_gufunc(max_blocksize=512)
+
+        matrix_ct = 1001 # an odd number to test thread/block division in CUDA
+        A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2,
+                                                                   4)
+        B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4,
+                                                                   5)
+
+        gufunc.max_blocksize = 32
+        C = gufunc(A, B)
+        Gold = ut.matrix_multiply(A, B)
+        self.assertTrue(np.allclose(C, Gold))
+
+    def test_gufunc_stream(self):
+
+        gufunc = _get_matmulcore_gufunc(max_blocksize=512)
+
+        #cuda.driver.flush_pending_free()
+        matrix_ct = 1001 # an odd number to test thread/block division in CUDA
+        A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2,
+                                                                   4)
+        B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4,
+                                                                   5)
+
+        stream = cuda.stream()
+        dA = cuda.to_device(A, stream)
+        dB = cuda.to_device(B, stream)
+
+        dC = cuda.device_array(shape=(1001, 2, 5), dtype=A.dtype, stream=stream)
+        dC = gufunc(dA, dB, out=dC, stream=stream)
+        C = dC.copy_to_host(stream=stream)
+        stream.synchronize()
+
+        Gold = ut.matrix_multiply(A, B)
+
+        self.assertTrue(np.allclose(C, Gold))
+
+    def test_copy(self):
+
+        @guvectorize([void(float32[:], float32[:])],
+                     '(x)->(x)',
+                     target='cuda')
+        def copy(A, B):
+            for i in range(B.size):
+                B[i] = A[i]
+
+        A = np.arange(10, dtype=np.float32) + 1
+        B = np.zeros_like(A)
+        copy(A, out=B)
+        self.assertTrue(np.allclose(A, B))
+
+    def test_copy_odd(self):
+
+        @guvectorize([void(float32[:], float32[:])],
+                     '(x)->(x)',
+                     target='cuda')
+        def copy(A, B):
+            for i in range(B.size):
+                B[i] = A[i]
+
+        A = np.arange(11, dtype=np.float32) + 1
+        B = np.zeros_like(A)
+        copy(A, out=B)
+        self.assertTrue(np.allclose(A, B))
+
+    def test_copy2d(self):
+
+        @guvectorize([void(float32[:, :], float32[:, :])],
+                     '(x, y)->(x, y)',
+                     target='cuda')
+        def copy2d(A, B):
+            for x in range(B.shape[0]):
+                for y in range(B.shape[1]):
+                    B[x, y] = A[x, y]
+
+        A = np.arange(30, dtype=np.float32).reshape(5, 6) + 1
+        B = np.zeros_like(A)
+        copy2d(A, out=B)
+        self.assertTrue(np.allclose(A, B))
+
+    # Test inefficient use of the GPU where the inputs are all mapped onto a
+    # single thread in a single block.
+    def test_inefficient_launch_configuration(self):
+        @guvectorize(['void(float32[:], float32[:], float32[:])'],
+                     '(n),(n)->(n)', target='cuda')
+        def numba_dist_cuda(a, b, dist):
+            len = a.shape[0]
+            for i in range(len):
+                dist[i] = a[i] * b[i]
+
+        a = np.random.rand(1024 * 32).astype('float32')
+        b = np.random.rand(1024 * 32).astype('float32')
+        dist = np.zeros(a.shape[0]).astype('float32')
+
+        with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
+            with warnings.catch_warnings(record=True) as w:
+                numba_dist_cuda(a, b, dist)
+                self.assertEqual(w[0].category, NumbaPerformanceWarning)
+                self.assertIn('Grid size', str(w[0].message))
+                self.assertIn('low occupancy', str(w[0].message))
+
+    def test_efficient_launch_configuration(self):
+        @guvectorize(['void(float32[:], float32[:], float32[:])'],
+                     '(n),(n)->(n)', nopython=True, target='cuda')
+        def numba_dist_cuda2(a, b, dist):
+            len = a.shape[0]
+            for i in range(len):
+                dist[i] = a[i] * b[i]
+
+        a = np.random.rand(524288 * 2).astype('float32').\
+            reshape((524288, 2))
+        b = np.random.rand(524288 * 2).astype('float32').\
+            reshape((524288, 2))
+        dist = np.zeros_like(a)
+
+        with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
+            with warnings.catch_warnings(record=True) as w:
+                numba_dist_cuda2(a, b, dist)
+                self.assertEqual(len(w), 0)
+
+    def test_nopython_flag(self):
+
+        def foo(A, B):
+            pass
+
+        # nopython = True is fine
+        guvectorize([void(float32[:], float32[:])], '(x)->(x)', target='cuda',
+                    nopython=True)(foo)
+
+        # nopython = False is bad
+        with self.assertRaises(TypeError) as raises:
+            guvectorize([void(float32[:], float32[:])], '(x)->(x)',
+                        target='cuda', nopython=False)(foo)
+        self.assertEqual("nopython flag must be True", str(raises.exception))
+
+    def test_invalid_flags(self):
+        # Check invalid flags
+        def foo(A, B):
+            pass
+
+        with self.assertRaises(TypeError) as raises:
+            guvectorize([void(float32[:], float32[:])], '(x)->(x)',
+                        target='cuda', what1=True, ever2=False)(foo)
+        head = "The following target options are not supported:"
+        msg = str(raises.exception)
+        self.assertEqual(msg[:len(head)], head)
+        items = msg[len(head):].strip().split(',')
+        items = [i.strip("'\" ") for i in items]
+        self.assertEqual(set(['what1', 'ever2']), set(items))
+
+    def test_duplicated_output(self):
+        @guvectorize([void(float32[:], float32[:])], '(x)->(x)', target='cuda')
+        def foo(inp, out):
+            pass  # intentionally empty; never executed
+
+        inp = out = np.zeros(10, dtype=np.float32)
+        with self.assertRaises(ValueError) as raises:
+            foo(inp, out, out=out)
+
+        msg = "cannot specify 'out' as both a positional and keyword argument"
+        self.assertEqual(str(raises.exception), msg)
+
+    def check_tuple_arg(self, a, b):
+        @guvectorize([(float64[:], float64[:], float64[:])], '(n),(n)->()',
+                     target='cuda')
+        def gu_reduce(x, y, r):
+            s = 0
+            for i in range(len(x)):
+                s += x[i] * y[i]
+            r[0] = s
+
+        r = gu_reduce(a, b)
+        expected = np.sum(np.asarray(a) * np.asarray(b), axis=1)
+        np.testing.assert_equal(expected, r)
+
+    def test_tuple_of_tuple_arg(self):
+        a = ((1.0, 2.0, 3.0),
+             (4.0, 5.0, 6.0))
+        b = ((1.5, 2.5, 3.5),
+             (4.5, 5.5, 6.5))
+        self.check_tuple_arg(a, b)
+
+    def test_tuple_of_namedtuple_arg(self):
+        Point = namedtuple('Point', ('x', 'y', 'z'))
+        a = (Point(x=1.0, y=2.0, z=3.0),
+             Point(x=4.0, y=5.0, z=6.0))
+        b = (Point(x=1.5, y=2.5, z=3.5),
+             Point(x=4.5, y=5.5, z=6.5))
+        self.check_tuple_arg(a, b)
+
+    def test_tuple_of_array_arg(self):
+        a = (np.asarray((1.0, 2.0, 3.0)),
+             np.asarray((4.0, 5.0, 6.0)))
+        b = (np.asarray((1.5, 2.5, 3.5)),
+             np.asarray((4.5, 5.5, 6.5)))
+        self.check_tuple_arg(a, b)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc_scalar.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc_scalar.py
new file mode 100644
index 000000000..493a9ceec
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc_scalar.py
@@ -0,0 +1,159 @@
+"""Example: sum each row using guvectorize
+
+See Numpy documentation for detail about gufunc:
+    http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html
+"""
+import numpy as np
+from numba import guvectorize, cuda
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestGUFuncScalar(CUDATestCase):
+    def test_gufunc_scalar_output(self):
+        #    function type:
+        #        - has no void return type
+        #        - array argument is one dimension fewer than the source array
+        #        - scalar output is passed as a 1-element array.
+        #
+        #    signature: (n)->()
+        #        - the function takes an array of n-element and output a scalar.
+
+        @guvectorize(['void(int32[:], int32[:])'], '(n)->()', target='cuda')
+        def sum_row(inp, out):
+            tmp = 0.
+            for i in range(inp.shape[0]):
+                tmp += inp[i]
+            out[0] = tmp
+
+        # inp is (10000, 3)
+        # out is (10000)
+        # The outer (leftmost) dimension must match or numpy broadcasting
+        # is performed. But, broadcasting on CUDA arrays is not supported.
+
+        inp = np.arange(300, dtype=np.int32).reshape(100, 3)
+
+        # invoke on CUDA with manually managed memory
+        out1 = np.empty(100, dtype=inp.dtype)
+        out2 = np.empty(100, dtype=inp.dtype)
+
+        dev_inp = cuda.to_device(
+            inp)                 # alloc and copy input data
+        dev_out1 = cuda.to_device(out1, copy=False)   # alloc only
+
+        sum_row(dev_inp, out=dev_out1)                # invoke the gufunc
+        dev_out2 = sum_row(dev_inp)                   # invoke the gufunc
+
+        dev_out1.copy_to_host(out1)                 # retrieve the result
+        dev_out2.copy_to_host(out2)                 # retrieve the result
+
+        # verify result
+        for i in range(inp.shape[0]):
+            self.assertTrue(out1[i] == inp[i].sum())
+            self.assertTrue(out2[i] == inp[i].sum())
+
+    def test_gufunc_scalar_output_bug(self):
+        # Issue 2812: Error due to using input argument types as output argument
+        @guvectorize(['void(int32, int32[:])'], '()->()', target='cuda')
+        def twice(inp, out):
+            out[0] = inp * 2
+
+        self.assertEqual(twice(10), 20)
+        arg = np.arange(10).astype(np.int32)
+        self.assertPreciseEqual(twice(arg), arg * 2)
+
+    def test_gufunc_scalar_input_saxpy(self):
+        @guvectorize(['void(float32, float32[:], float32[:], float32[:])'],
+                     '(),(t),(t)->(t)', target='cuda')
+        def saxpy(a, x, y, out):
+            for i in range(out.shape[0]):
+                out[i] = a * x[i] + y[i]
+
+        A = np.float32(2)
+        X = np.arange(10, dtype=np.float32).reshape(5, 2)
+        Y = np.arange(10, dtype=np.float32).reshape(5, 2)
+        out = saxpy(A, X, Y)
+
+        for j in range(5):
+            for i in range(2):
+                exp = A * X[j, i] + Y[j, i]
+                self.assertTrue(exp == out[j, i])
+
+        X = np.arange(10, dtype=np.float32)
+        Y = np.arange(10, dtype=np.float32)
+        out = saxpy(A, X, Y)
+
+        for j in range(10):
+            exp = A * X[j] + Y[j]
+            self.assertTrue(exp == out[j], (exp, out[j]))
+
+        A = np.arange(5, dtype=np.float32)
+        X = np.arange(10, dtype=np.float32).reshape(5, 2)
+        Y = np.arange(10, dtype=np.float32).reshape(5, 2)
+        out = saxpy(A, X, Y)
+
+        for j in range(5):
+            for i in range(2):
+                exp = A[j] * X[j, i] + Y[j, i]
+                self.assertTrue(exp == out[j, i], (exp, out[j, i]))
+
+    def test_gufunc_scalar_cast(self):
+        @guvectorize(['void(int32, int32[:], int32[:])'], '(),(t)->(t)',
+                     target='cuda')
+        def foo(a, b, out):
+            for i in range(b.size):
+                out[i] = a * b[i]
+
+        a = np.int64(2)  # type does not match signature (int32)
+        b = np.arange(10).astype(np.int32)
+        out = foo(a, b)
+        np.testing.assert_equal(out, a * b)
+
+        # test error
+        a = np.array(a)
+        da = cuda.to_device(a)
+        self.assertEqual(da.dtype, np.int64)
+        with self.assertRaises(TypeError) as raises:
+            foo(da, b)
+
+        self.assertIn("does not support .astype()", str(raises.exception))
+
+    def test_gufunc_old_style_scalar_as_array(self):
+        # Example from issue #2579
+        @guvectorize(['void(int32[:],int32[:],int32[:])'], '(n),()->(n)',
+                     target='cuda')
+        def gufunc(x, y, res):
+            for i in range(x.shape[0]):
+                res[i] = x[i] + y[0]
+
+        # Case 1
+        a = np.array([1, 2, 3, 4], dtype=np.int32)
+        b = np.array([2], dtype=np.int32)
+
+        res = np.zeros(4, dtype=np.int32)
+
+        expected = res.copy()
+        expected = a + b
+
+        gufunc(a, b, out=res)
+
+        np.testing.assert_almost_equal(expected, res)
+
+        # Case 2
+        a = np.array([1, 2, 3, 4] * 2, dtype=np.int32).reshape(2, 4)
+        b = np.array([2, 10], dtype=np.int32)
+
+        res = np.zeros((2, 4), dtype=np.int32)
+
+        expected = res.copy()
+        expected[0] = a[0] + b[0]
+        expected[1] = a[1] + b[1]
+
+        gufunc(a, b, res)
+
+        np.testing.assert_almost_equal(expected, res)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc_scheduling.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc_scheduling.py
new file mode 100644
index 000000000..2a240ef29
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_gufunc_scheduling.py
@@ -0,0 +1,95 @@
+from numba.np.ufunc.deviceufunc import GUFuncEngine
+import unittest
+
+
+def template(signature, shapes, expects):
+    gufb = GUFuncEngine.from_signature(signature)
+    sch = gufb.schedule(shapes)
+    for k, v in expects.items():
+        got = getattr(sch, k)
+        if got != v:
+            fmt = 'error for %s: got=%s but expect=%s'
+            raise AssertionError(fmt % (k, got, v))
+
+
+class TestGUFuncScheduling(unittest.TestCase):
+    def test_signature_1(self):
+        signature = '(m, n), (n, p) -> (m, p)'
+        shapes = (100, 4, 5), (1, 5, 7)
+        expects = dict(
+            ishapes=[(4, 5), (5, 7)],
+            oshapes=[(4, 7)],
+            loopdims=(100,),
+            pinned=[False, True]
+        )
+        template(signature, shapes, expects)
+
+    def test_signature_2(self):
+        signature = '(m, n), (n, p) -> (m, p)'
+        shapes = (100, 4, 5), (100, 5, 7)
+        expects = dict(
+            ishapes=[(4, 5), (5, 7)],
+            oshapes=[(4, 7)],
+            loopdims=(100,),
+            pinned=[False, False]
+        )
+        template(signature, shapes, expects)
+
+    def test_signature_3(self):
+        signature = '(m, n), (n, p) -> (m, p)'
+        shapes = (12, 34, 4, 5), (12, 34, 5, 7)
+        expects = dict(
+            ishapes=[(4, 5), (5, 7)],
+            oshapes=[(4, 7)],
+            loopdims=(12, 34),
+            pinned=[False, False]
+        )
+        template(signature, shapes, expects)
+
+    def test_signature_4(self):
+        signature = '(m, n), (n, p) -> (m, p)'
+        shapes = (4, 5), (5, 7)
+        expects = dict(
+            ishapes=[(4, 5), (5, 7)],
+            oshapes=[(4, 7)],
+            loopdims=(),
+            pinned=[False, False]
+        )
+        template(signature, shapes, expects)
+
+    def test_signature_5(self):
+        signature = '(a), (a) -> (a)'
+        shapes = (5,), (5,)
+        expects = dict(
+            ishapes=[(5,), (5,)],
+            oshapes=[(5,)],
+            loopdims=(),
+            pinned=[False, False]
+        )
+        template(signature, shapes, expects)
+
+    def test_signature_6(self):
+        signature = '(), () -> ()'
+        shapes = (5,), (5,)
+        expects = dict(
+            ishapes=[(), ()],
+            oshapes=[()],
+            loopdims=(5,),
+            pinned=[False, False]
+        )
+        template(signature, shapes, expects)
+
+    def test_signature_7(self):
+        signature = '(), () -> ()'
+        shapes = (5,), ()
+        expects = dict(
+            ishapes=[(), ()],
+            oshapes=[()],
+            loopdims=(5,),
+            pinned=[False, True]
+        )
+        template(signature, shapes, expects)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_idiv.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_idiv.py
new file mode 100644
index 000000000..44b770f42
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_idiv.py
@@ -0,0 +1,37 @@
+import numpy as np
+from numba import cuda, float32, float64, int32, void
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaIDiv(CUDATestCase):
+    def test_inplace_div(self):
+
+        @cuda.jit(void(float32[:, :], int32, int32))
+        def div(grid, l_x, l_y):
+            for x in range(l_x):
+                for y in range(l_y):
+                    grid[x, y] /= 2.0
+
+        x = np.ones((2, 2), dtype=np.float32)
+        grid = cuda.to_device(x)
+        div[1, 1](grid, 2, 2)
+        y = grid.copy_to_host()
+        self.assertTrue(np.all(y == 0.5))
+
+    def test_inplace_div_double(self):
+
+        @cuda.jit(void(float64[:, :], int32, int32))
+        def div_double(grid, l_x, l_y):
+            for x in range(l_x):
+                for y in range(l_y):
+                    grid[x, y] /= 2.0
+
+        x = np.ones((2, 2), dtype=np.float64)
+        grid = cuda.to_device(x)
+        div_double[1, 1](grid, 2, 2)
+        y = grid.copy_to_host()
+        self.assertTrue(np.all(y == 0.5))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_inspect.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_inspect.py
new file mode 100644
index 000000000..53f39753c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_inspect.py
@@ -0,0 +1,133 @@
+import numpy as np
+
+from io import StringIO
+from numba import cuda, float32, float64, int32, intp
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import (skip_on_cudasim, skip_with_nvdisasm,
+                                skip_without_nvdisasm)
+
+
+@skip_on_cudasim('Simulator does not generate code to be inspected')
+class TestInspect(CUDATestCase):
+    @property
+    def cc(self):
+        return cuda.current_context().device.compute_capability
+
+    def test_monotyped(self):
+        sig = (float32, int32)
+
+        @cuda.jit(sig)
+        def foo(x, y):
+            pass
+
+        file = StringIO()
+        foo.inspect_types(file=file)
+        typeanno = file.getvalue()
+        # Function name in annotation
+        self.assertIn("foo", typeanno)
+        # Signature in annotation
+        self.assertIn("(float32, int32)", typeanno)
+        file.close()
+        # Function name in LLVM
+        self.assertIn("foo", foo.inspect_llvm(sig))
+
+        asm = foo.inspect_asm(sig)
+
+        # Function name in PTX
+        self.assertIn("foo", asm)
+        # NVVM inserted comments in PTX
+        self.assertIn("Generated by NVIDIA NVVM Compiler", asm)
+
+    def test_polytyped(self):
+        @cuda.jit
+        def foo(x, y):
+            pass
+
+        foo[1, 1](1, 1)
+        foo[1, 1](1.2, 2.4)
+
+        file = StringIO()
+        foo.inspect_types(file=file)
+        typeanno = file.getvalue()
+        file.close()
+        # Signature in annotation
+        self.assertIn("({0}, {0})".format(intp), typeanno)
+        self.assertIn("(float64, float64)", typeanno)
+
+        # Signature in LLVM dict
+        llvmirs = foo.inspect_llvm()
+        self.assertEqual(2, len(llvmirs), )
+        self.assertIn((intp, intp), llvmirs)
+        self.assertIn((float64, float64), llvmirs)
+
+        # Function name in LLVM
+        self.assertIn("foo", llvmirs[intp, intp])
+        self.assertIn("foo", llvmirs[float64, float64])
+
+        asmdict = foo.inspect_asm()
+
+        # Signature in assembly dict
+        self.assertEqual(2, len(asmdict), )
+        self.assertIn((intp, intp), asmdict)
+        self.assertIn((float64, float64), asmdict)
+
+        # NVVM inserted in PTX
+        self.assertIn("foo", asmdict[intp, intp])
+        self.assertIn("foo", asmdict[float64, float64])
+
+    def _test_inspect_sass(self, kernel, name, sass):
+        # Ensure function appears in output
+        seen_function = False
+        for line in sass.split():
+            if '.text' in line and name in line:
+                seen_function = True
+        self.assertTrue(seen_function)
+
+        # Some instructions common to all supported architectures that should
+        # appear in the output
+        self.assertIn('S2R', sass)   # Special register to register
+        self.assertIn('BRA', sass)   # Branch
+        self.assertIn('EXIT', sass)  # Exit program
+
+    @skip_without_nvdisasm('nvdisasm needed for inspect_sass()')
+    def test_inspect_sass_eager(self):
+        sig = (float32[::1], int32[::1])
+
+        @cuda.jit(sig)
+        def add(x, y):
+            i = cuda.grid(1)
+            if i < len(x):
+                x[i] += y[i]
+
+        self._test_inspect_sass(add, 'add', add.inspect_sass(sig))
+
+    @skip_without_nvdisasm('nvdisasm needed for inspect_sass()')
+    def test_inspect_sass_lazy(self):
+        @cuda.jit
+        def add(x, y):
+            i = cuda.grid(1)
+            if i < len(x):
+                x[i] += y[i]
+
+        x = np.arange(10).astype(np.int32)
+        y = np.arange(10).astype(np.float32)
+        add[1, 10](x, y)
+
+        signature = (int32[::1], float32[::1])
+        self._test_inspect_sass(add, 'add', add.inspect_sass(signature))
+
+    @skip_with_nvdisasm('Missing nvdisasm exception only generated when it is '
+                        'not present')
+    def test_inspect_sass_nvdisasm_missing(self):
+        @cuda.jit((float32[::1],))
+        def f(x):
+            x[0] = 0
+
+        with self.assertRaises(RuntimeError) as raises:
+            f.inspect_sass()
+
+        self.assertIn('nvdisasm is required', str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_intrinsics.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_intrinsics.py
new file mode 100644
index 000000000..6e5fc0a0e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_intrinsics.py
@@ -0,0 +1,870 @@
+import itertools
+import numpy as np
+import operator
+import re
+from numba import cuda, int64
+from numba.cuda import compile_ptx
+from numba.core.types import f2
+from numba.cuda.testing import (unittest, CUDATestCase, skip_on_cudasim,
+                                skip_unless_cc_53)
+
+
+def simple_threadidx(ary):
+    i = cuda.threadIdx.x
+    ary[0] = i
+
+
+def fill_threadidx(ary):
+    i = cuda.threadIdx.x
+    ary[i] = i
+
+
+def fill3d_threadidx(ary):
+    i = cuda.threadIdx.x
+    j = cuda.threadIdx.y
+    k = cuda.threadIdx.z
+
+    ary[i, j, k] = (i + 1) * (j + 1) * (k + 1)
+
+
+def simple_grid1d(ary):
+    i = cuda.grid(1)
+    ary[i] = i
+
+
+def simple_grid2d(ary):
+    i, j = cuda.grid(2)
+    ary[i, j] = i + j
+
+
+def simple_gridsize1d(ary):
+    i = cuda.grid(1)
+    x = cuda.gridsize(1)
+    if i == 0:
+        ary[0] = x
+
+
+def simple_gridsize2d(ary):
+    i, j = cuda.grid(2)
+    x, y = cuda.gridsize(2)
+    if i == 0 and j == 0:
+        ary[0] = x
+        ary[1] = y
+
+
+def intrinsic_forloop_step(c):
+    startX, startY = cuda.grid(2)
+    gridX = cuda.gridDim.x * cuda.blockDim.x
+    gridY = cuda.gridDim.y * cuda.blockDim.y
+    height, width = c.shape
+
+    for x in range(startX, width, gridX):
+        for y in range(startY, height, gridY):
+            c[y, x] = x + y
+
+
+def simple_popc(ary, c):
+    ary[0] = cuda.popc(c)
+
+
+def simple_fma(ary, a, b, c):
+    ary[0] = cuda.fma(a, b, c)
+
+
+def simple_hadd(ary, a, b):
+    ary[0] = cuda.fp16.hadd(a[0], b[0])
+
+
+def simple_hadd_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hadd(a, b)
+
+
+def simple_hfma(ary, a, b, c):
+    ary[0] = cuda.fp16.hfma(a[0], b[0], c[0])
+
+
+def simple_hfma_scalar(ary, a, b, c):
+    ary[0] = cuda.fp16.hfma(a, b, c)
+
+
+def simple_hsub(ary, a, b):
+    ary[0] = cuda.fp16.hsub(a[0], b[0])
+
+
+def simple_hsub_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hsub(a, b)
+
+
+def simple_hmul(ary, a, b):
+    ary[0] = cuda.fp16.hmul(a[0], b[0])
+
+
+def simple_hmul_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hmul(a, b)
+
+
+def simple_hneg(ary, a):
+    ary[0] = cuda.fp16.hneg(a[0])
+
+
+def simple_hneg_scalar(ary, a):
+    ary[0] = cuda.fp16.hneg(a)
+
+
+def simple_habs(ary, a):
+    ary[0] = cuda.fp16.habs(a[0])
+
+
+def simple_habs_scalar(ary, a):
+    ary[0] = cuda.fp16.habs(a)
+
+
+def simple_heq_scalar(ary, a, b):
+    ary[0] = cuda.fp16.heq(a, b)
+
+
+def simple_hne_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hne(a, b)
+
+
+def simple_hge_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hge(a, b)
+
+
+def simple_hgt_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hgt(a, b)
+
+
+def simple_hle_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hle(a, b)
+
+
+def simple_hlt_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hlt(a, b)
+
+
+@cuda.jit(device=True)
+def hlt_func_1(x, y):
+    return cuda.fp16.hlt(x, y)
+
+
+@cuda.jit(device=True)
+def hlt_func_2(x, y):
+    return cuda.fp16.hlt(x, y)
+
+
+def test_multiple_hcmp_1(r, a, b, c):
+    # float16 predicates used in two separate functions
+    r[0] = hlt_func_1(a, b) and hlt_func_2(b, c)
+
+
+def test_multiple_hcmp_2(r, a, b, c):
+    # The same float16 predicate used in the caller and callee
+    r[0] = hlt_func_1(a, b) and cuda.fp16.hlt(b, c)
+
+
+def test_multiple_hcmp_3(r, a, b, c):
+    # Different float16 predicates used in the caller and callee
+    r[0] = hlt_func_1(a, b) and cuda.fp16.hge(c, b)
+
+
+def test_multiple_hcmp_4(r, a, b, c):
+    # The same float16 predicates used twice in a function
+    r[0] = cuda.fp16.hlt(a, b) and cuda.fp16.hlt(b, c)
+
+
+def test_multiple_hcmp_5(r, a, b, c):
+    # Different float16 predicates used in a function
+    r[0] = cuda.fp16.hlt(a, b) and cuda.fp16.hge(c, b)
+
+
+def simple_hmax_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hmax(a, b)
+
+
+def simple_hmin_scalar(ary, a, b):
+    ary[0] = cuda.fp16.hmin(a, b)
+
+
+def simple_cbrt(ary, a):
+    ary[0] = cuda.cbrt(a)
+
+
+def simple_brev(ary, c):
+    ary[0] = cuda.brev(c)
+
+
+def simple_clz(ary, c):
+    ary[0] = cuda.clz(c)
+
+
+def simple_ffs(ary, c):
+    ary[0] = cuda.ffs(c)
+
+
+def simple_round(ary, c):
+    ary[0] = round(c)
+
+
+def simple_round_to(ary, c, ndigits):
+    ary[0] = round(c, ndigits)
+
+
+def branching_with_ifs(a, b, c):
+    i = cuda.grid(1)
+
+    if a[i] > 4:
+        if b % 2 == 0:
+            a[i] = c[i]
+        else:
+            a[i] = 13
+    else:
+        a[i] = 3
+
+
+def branching_with_selps(a, b, c):
+    i = cuda.grid(1)
+
+    inner = cuda.selp(b % 2 == 0, c[i], 13)
+    a[i] = cuda.selp(a[i] > 4, inner, 3)
+
+
+def simple_laneid(ary):
+    i = cuda.grid(1)
+    ary[i] = cuda.laneid
+
+
+def simple_warpsize(ary):
+    ary[0] = cuda.warpsize
+
+
+class TestCudaIntrinsic(CUDATestCase):
+    def test_simple_threadidx(self):
+        compiled = cuda.jit("void(int32[:])")(simple_threadidx)
+        ary = np.ones(1, dtype=np.int32)
+        compiled[1, 1](ary)
+        self.assertTrue(ary[0] == 0)
+
+    def test_fill_threadidx(self):
+        compiled = cuda.jit("void(int32[:])")(fill_threadidx)
+        N = 10
+        ary = np.ones(N, dtype=np.int32)
+        exp = np.arange(N, dtype=np.int32)
+        compiled[1, N](ary)
+        self.assertTrue(np.all(ary == exp))
+
+    def test_fill3d_threadidx(self):
+        X, Y, Z = 4, 5, 6
+
+        def c_contigous():
+            compiled = cuda.jit("void(int32[:,:,::1])")(fill3d_threadidx)
+            ary = np.zeros((X, Y, Z), dtype=np.int32)
+            compiled[1, (X, Y, Z)](ary)
+            return ary
+
+        def f_contigous():
+            compiled = cuda.jit("void(int32[::1,:,:])")(fill3d_threadidx)
+            ary = np.asfortranarray(np.zeros((X, Y, Z), dtype=np.int32))
+            compiled[1, (X, Y, Z)](ary)
+            return ary
+
+        c_res = c_contigous()
+        f_res = f_contigous()
+        self.assertTrue(np.all(c_res == f_res))
+
+    def test_simple_grid1d(self):
+        compiled = cuda.jit("void(int32[::1])")(simple_grid1d)
+        ntid, nctaid = 3, 7
+        nelem = ntid * nctaid
+        ary = np.empty(nelem, dtype=np.int32)
+        compiled[nctaid, ntid](ary)
+        self.assertTrue(np.all(ary == np.arange(nelem)))
+
+    def test_simple_grid2d(self):
+        compiled = cuda.jit("void(int32[:,::1])")(simple_grid2d)
+        ntid = (4, 3)
+        nctaid = (5, 6)
+        shape = (ntid[0] * nctaid[0], ntid[1] * nctaid[1])
+        ary = np.empty(shape, dtype=np.int32)
+        exp = ary.copy()
+        compiled[nctaid, ntid](ary)
+
+        for i in range(ary.shape[0]):
+            for j in range(ary.shape[1]):
+                exp[i, j] = i + j
+
+        self.assertTrue(np.all(ary == exp))
+
+    def test_simple_gridsize1d(self):
+        compiled = cuda.jit("void(int32[::1])")(simple_gridsize1d)
+        ntid, nctaid = 3, 7
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[nctaid, ntid](ary)
+        self.assertEqual(ary[0], nctaid * ntid)
+
+    @skip_on_cudasim('Tests PTX emission')
+    def test_selp(self):
+        sig = (int64[:], int64, int64[:])
+        cu_branching_with_ifs = cuda.jit(sig)(branching_with_ifs)
+        cu_branching_with_selps = cuda.jit(sig)(branching_with_selps)
+
+        n = 32
+        b = 6
+        c = np.full(shape=32, fill_value=17, dtype=np.int64)
+
+        expected = c.copy()
+        expected[:5] = 3
+
+        a = np.arange(n, dtype=np.int64)
+        cu_branching_with_ifs[n, 1](a, b, c)
+        ptx = cu_branching_with_ifs.inspect_asm(sig)
+        self.assertEqual(2, len(re.findall(r'\s+bra\s+', ptx)))
+        np.testing.assert_array_equal(a, expected, err_msg='branching')
+
+        a = np.arange(n, dtype=np.int64)
+        cu_branching_with_selps[n, 1](a, b, c)
+        ptx = cu_branching_with_selps.inspect_asm(sig)
+        self.assertEqual(0, len(re.findall(r'\s+bra\s+', ptx)))
+        np.testing.assert_array_equal(a, expected, err_msg='selp')
+
+    def test_simple_gridsize2d(self):
+        compiled = cuda.jit("void(int32[::1])")(simple_gridsize2d)
+        ntid = (4, 3)
+        nctaid = (5, 6)
+        ary = np.zeros(2, dtype=np.int32)
+        compiled[nctaid, ntid](ary)
+
+        self.assertEqual(ary[0], nctaid[0] * ntid[0])
+        self.assertEqual(ary[1], nctaid[1] * ntid[1])
+
+    def test_intrinsic_forloop_step(self):
+        compiled = cuda.jit("void(int32[:,::1])")(intrinsic_forloop_step)
+        ntid = (4, 3)
+        nctaid = (5, 6)
+        shape = (ntid[0] * nctaid[0], ntid[1] * nctaid[1])
+        ary = np.empty(shape, dtype=np.int32)
+
+        compiled[nctaid, ntid](ary)
+
+        gridX, gridY = shape
+        height, width = ary.shape
+        for i, j in zip(range(ntid[0]), range(ntid[1])):
+            startX, startY = gridX + i, gridY + j
+            for x in range(startX, width, gridX):
+                for y in range(startY, height, gridY):
+                    self.assertTrue(ary[y, x] == x + y, (ary[y, x], x + y))
+
+    def test_3dgrid(self):
+        @cuda.jit
+        def foo(out):
+            x, y, z = cuda.grid(3)
+            a, b, c = cuda.gridsize(3)
+            out[x, y, z] = a * b * c
+
+        arr = np.zeros(9 ** 3, dtype=np.int32).reshape(9, 9, 9)
+        foo[(3, 3, 3), (3, 3, 3)](arr)
+
+        np.testing.assert_equal(arr, 9 ** 3)
+
+    def test_3dgrid_2(self):
+        @cuda.jit
+        def foo(out):
+            x, y, z = cuda.grid(3)
+            a, b, c = cuda.gridsize(3)
+            grid_is_right = (
+                x == cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x and
+                y == cuda.threadIdx.y + cuda.blockIdx.y * cuda.blockDim.y and
+                z == cuda.threadIdx.z + cuda.blockIdx.z * cuda.blockDim.z
+            )
+            gridsize_is_right = (a == cuda.blockDim.x * cuda.gridDim.x and
+                                 b == cuda.blockDim.y * cuda.gridDim.y and
+                                 c == cuda.blockDim.z * cuda.gridDim.z)
+            out[x, y, z] = grid_is_right and gridsize_is_right
+
+        x, y, z = (4 * 3, 3 * 2, 2 * 4)
+        arr = np.zeros((x * y * z), dtype=np.bool_).reshape(x, y, z)
+        foo[(4, 3, 2), (3, 2, 4)](arr)
+
+        self.assertTrue(np.all(arr))
+
+    def test_popc_u4(self):
+        compiled = cuda.jit("void(int32[:], uint32)")(simple_popc)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0xF0)
+        self.assertEquals(ary[0], 4)
+
+    def test_popc_u8(self):
+        compiled = cuda.jit("void(int32[:], uint64)")(simple_popc)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0xF00000000000)
+        self.assertEquals(ary[0], 4)
+
+    def test_fma_f4(self):
+        compiled = cuda.jit("void(f4[:], f4, f4, f4)")(simple_fma)
+        ary = np.zeros(1, dtype=np.float32)
+        compiled[1, 1](ary, 2., 3., 4.)
+        np.testing.assert_allclose(ary[0], 2 * 3 + 4)
+
+    def test_fma_f8(self):
+        compiled = cuda.jit("void(f8[:], f8, f8, f8)")(simple_fma)
+        ary = np.zeros(1, dtype=np.float64)
+        compiled[1, 1](ary, 2., 3., 4.)
+        np.testing.assert_allclose(ary[0], 2 * 3 + 4)
+
+    @skip_unless_cc_53
+    def test_hadd(self):
+        compiled = cuda.jit("void(f2[:], f2[:], f2[:])")(simple_hadd)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.array([3.], dtype=np.float16)
+        arg2 = np.array([4.], dtype=np.float16)
+        compiled[1, 1](ary, arg1, arg2)
+        np.testing.assert_allclose(ary[0], arg1 + arg2)
+
+    @skip_unless_cc_53
+    def test_hadd_scalar(self):
+        compiled = cuda.jit("void(f2[:], f2, f2)")(simple_hadd_scalar)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.float16(3.1415926)
+        arg2 = np.float16(3.)
+        compiled[1, 1](ary, arg1, arg2)
+        ref = arg1 + arg2
+        np.testing.assert_allclose(ary[0], ref)
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_hadd_ptx(self):
+        args = (f2[:], f2, f2)
+        ptx, _ = compile_ptx(simple_hadd_scalar, args, cc=(5, 3))
+        self.assertIn('add.f16', ptx)
+
+    @skip_unless_cc_53
+    def test_hfma(self):
+        compiled = cuda.jit("void(f2[:], f2[:], f2[:], f2[:])")(simple_hfma)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.array([2.], dtype=np.float16)
+        arg2 = np.array([3.], dtype=np.float16)
+        arg3 = np.array([4.], dtype=np.float16)
+        compiled[1, 1](ary, arg1, arg2, arg3)
+        np.testing.assert_allclose(ary[0], arg1 * arg2 + arg3)
+
+    @skip_unless_cc_53
+    def test_hfma_scalar(self):
+        compiled = cuda.jit("void(f2[:], f2, f2, f2)")(simple_hfma_scalar)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.float16(2.)
+        arg2 = np.float16(3.)
+        arg3 = np.float16(4.)
+        compiled[1, 1](ary, arg1, arg2, arg3)
+        ref = arg1 * arg2 + arg3
+        np.testing.assert_allclose(ary[0], ref)
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_hfma_ptx(self):
+        args = (f2[:], f2, f2, f2)
+        ptx, _ = compile_ptx(simple_hfma_scalar, args, cc=(5, 3))
+        self.assertIn('fma.rn.f16', ptx)
+
+    @skip_unless_cc_53
+    def test_hsub(self):
+        compiled = cuda.jit("void(f2[:], f2[:], f2[:])")(simple_hsub)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.array([3.], dtype=np.float16)
+        arg2 = np.array([4.], dtype=np.float16)
+        compiled[1, 1](ary, arg1, arg2)
+        np.testing.assert_allclose(ary[0], arg1 - arg2)
+
+    @skip_unless_cc_53
+    def test_hsub_scalar(self):
+        compiled = cuda.jit("void(f2[:], f2, f2)")(simple_hsub_scalar)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.float16(3.1415926)
+        arg2 = np.float16(1.57)
+        compiled[1, 1](ary, arg1, arg2)
+        ref = arg1 - arg2
+        np.testing.assert_allclose(ary[0], ref)
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_hsub_ptx(self):
+        args = (f2[:], f2, f2)
+        ptx, _ = compile_ptx(simple_hsub_scalar, args, cc=(5, 3))
+        self.assertIn('sub.f16', ptx)
+
+    @skip_unless_cc_53
+    def test_hmul(self):
+        compiled = cuda.jit()(simple_hmul)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.array([3.], dtype=np.float16)
+        arg2 = np.array([4.], dtype=np.float16)
+        compiled[1, 1](ary, arg1, arg2)
+        np.testing.assert_allclose(ary[0], arg1 * arg2)
+
+    @skip_unless_cc_53
+    def test_hmul_scalar(self):
+        compiled = cuda.jit("void(f2[:], f2, f2)")(simple_hmul_scalar)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.float16(3.1415926)
+        arg2 = np.float16(1.57)
+        compiled[1, 1](ary, arg1, arg2)
+        ref = arg1 * arg2
+        np.testing.assert_allclose(ary[0], ref)
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_hmul_ptx(self):
+        args = (f2[:], f2, f2)
+        ptx, _ = compile_ptx(simple_hmul_scalar, args, cc=(5, 3))
+        self.assertIn('mul.f16', ptx)
+
+    @skip_unless_cc_53
+    def test_hneg(self):
+        compiled = cuda.jit("void(f2[:], f2[:])")(simple_hneg)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.array([3.], dtype=np.float16)
+        compiled[1, 1](ary, arg1)
+        np.testing.assert_allclose(ary[0], -arg1)
+
+    @skip_unless_cc_53
+    def test_hneg_scalar(self):
+        compiled = cuda.jit("void(f2[:], f2)")(simple_hneg_scalar)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.float16(3.1415926)
+        compiled[1, 1](ary, arg1)
+        ref = -arg1
+        np.testing.assert_allclose(ary[0], ref)
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_hneg_ptx(self):
+        args = (f2[:], f2)
+        ptx, _ = compile_ptx(simple_hneg_scalar, args, cc=(5, 3))
+        self.assertIn('neg.f16', ptx)
+
+    @skip_unless_cc_53
+    def test_habs(self):
+        compiled = cuda.jit()(simple_habs)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.array([-3.], dtype=np.float16)
+        compiled[1, 1](ary, arg1)
+        np.testing.assert_allclose(ary[0], abs(arg1))
+
+    @skip_unless_cc_53
+    def test_habs_scalar(self):
+        compiled = cuda.jit("void(f2[:], f2)")(simple_habs_scalar)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.float16(-3.1415926)
+        compiled[1, 1](ary, arg1)
+        ref = abs(arg1)
+        np.testing.assert_allclose(ary[0], ref)
+
+    @skip_on_cudasim('Compilation unsupported in the simulator')
+    def test_habs_ptx(self):
+        args = (f2[:], f2)
+        ptx, _ = compile_ptx(simple_habs_scalar, args, cc=(5, 3))
+        if cuda.runtime.get_version() < (10, 2):
+            self.assertRegex(ptx, r'and\.b16.*0x7FFF;')
+        else:
+            self.assertIn('abs.f16', ptx)
+
+    @skip_unless_cc_53
+    def test_fp16_comparison(self):
+        fns = (simple_heq_scalar, simple_hne_scalar, simple_hge_scalar,
+               simple_hgt_scalar, simple_hle_scalar, simple_hlt_scalar)
+        ops = (operator.eq, operator.ne, operator.ge,
+               operator.gt, operator.le, operator.lt)
+
+        for fn, op in zip(fns, ops):
+            with self.subTest(op=op):
+                kernel = cuda.jit("void(b1[:], f2, f2)")(fn)
+
+                expected = np.zeros(1, dtype=np.bool8)
+                got = np.zeros(1, dtype=np.bool8)
+                arg2 = np.float16(2)
+                arg3 = np.float16(3)
+                arg4 = np.float16(4)
+
+                # Check with equal arguments
+                kernel[1, 1](got, arg3, arg3)
+                expected = op(arg3, arg3)
+                self.assertEqual(expected, got[0])
+
+                # Check with LHS < RHS
+                kernel[1, 1](got, arg3, arg4)
+                expected = op(arg3, arg4)
+                self.assertEqual(expected, got[0])
+
+                # Check with LHS > RHS
+                kernel[1, 1](got, arg3, arg2)
+                expected = op(arg3, arg2)
+                self.assertEqual(expected, got[0])
+
+    @skip_unless_cc_53
+    def test_multiple_float16_comparisons(self):
+        functions = (test_multiple_hcmp_1,
+                     test_multiple_hcmp_2,
+                     test_multiple_hcmp_3,
+                     test_multiple_hcmp_4,
+                     test_multiple_hcmp_5)
+        for fn in functions:
+            with self.subTest(fn=fn):
+                compiled = cuda.jit("void(b1[:], f2, f2, f2)")(fn)
+                ary = np.zeros(1, dtype=np.bool8)
+                arg1 = np.float16(2.)
+                arg2 = np.float16(3.)
+                arg3 = np.float16(4.)
+                compiled[1, 1](ary, arg1, arg2, arg3)
+                self.assertTrue(ary[0])
+
+    @skip_unless_cc_53
+    def test_hmax(self):
+        compiled = cuda.jit("void(f2[:], f2, f2)")(simple_hmax_scalar)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.float16(3.)
+        arg2 = np.float16(4.)
+        compiled[1, 1](ary, arg1, arg2)
+        np.testing.assert_allclose(ary[0], arg2)
+        arg1 = np.float(5.)
+        compiled[1, 1](ary, arg1, arg2)
+        np.testing.assert_allclose(ary[0], arg1)
+
+    @skip_unless_cc_53
+    def test_hmin(self):
+        compiled = cuda.jit("void(f2[:], f2, f2)")(simple_hmin_scalar)
+        ary = np.zeros(1, dtype=np.float16)
+        arg1 = np.float16(3.)
+        arg2 = np.float16(4.)
+        compiled[1, 1](ary, arg1, arg2)
+        np.testing.assert_allclose(ary[0], arg1)
+        arg1 = np.float(5.)
+        compiled[1, 1](ary, arg1, arg2)
+        np.testing.assert_allclose(ary[0], arg2)
+
+    def test_cbrt_f32(self):
+        compiled = cuda.jit("void(float32[:], float32)")(simple_cbrt)
+        ary = np.zeros(1, dtype=np.float32)
+        cbrt_arg = 2.
+        compiled[1, 1](ary, cbrt_arg)
+        np.testing.assert_allclose(ary[0], cbrt_arg ** (1 / 3))
+
+    def test_cbrt_f64(self):
+        compiled = cuda.jit("void(float64[:], float64)")(simple_cbrt)
+        ary = np.zeros(1, dtype=np.float64)
+        cbrt_arg = 6.
+        compiled[1, 1](ary, cbrt_arg)
+        np.testing.assert_allclose(ary[0], cbrt_arg ** (1 / 3))
+
+    def test_brev_u4(self):
+        compiled = cuda.jit("void(uint32[:], uint32)")(simple_brev)
+        ary = np.zeros(1, dtype=np.uint32)
+        compiled[1, 1](ary, 0x000030F0)
+        self.assertEquals(ary[0], 0x0F0C0000)
+
+    @skip_on_cudasim('only get given a Python "int", assumes 32 bits')
+    def test_brev_u8(self):
+        compiled = cuda.jit("void(uint64[:], uint64)")(simple_brev)
+        ary = np.zeros(1, dtype=np.uint64)
+        compiled[1, 1](ary, 0x000030F0000030F0)
+        self.assertEquals(ary[0], 0x0F0C00000F0C0000)
+
+    def test_clz_i4(self):
+        compiled = cuda.jit("void(int32[:], int32)")(simple_clz)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0x00100000)
+        self.assertEquals(ary[0], 11)
+
+    def test_clz_u4(self):
+        """
+        Although the CUDA Math API
+        (http://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH__INTRINSIC__INT.html)
+        only says int32 & int64 arguments are supported in C code, the LLVM
+        IR input supports i8, i16, i32 & i64 (LLVM doesn't have a concept of
+        unsigned integers, just unsigned operations on integers).
+        http://docs.nvidia.com/cuda/nvvm-ir-spec/index.html#bit-manipulations-intrinics
+        """
+        compiled = cuda.jit("void(int32[:], uint32)")(simple_clz)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0x00100000)
+        self.assertEquals(ary[0], 11)
+
+    def test_clz_i4_1s(self):
+        compiled = cuda.jit("void(int32[:], int32)")(simple_clz)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0xFFFFFFFF)
+        self.assertEquals(ary[0], 0)
+
+    def test_clz_i4_0s(self):
+        compiled = cuda.jit("void(int32[:], int32)")(simple_clz)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0x0)
+        self.assertEquals(ary[0], 32, "CUDA semantics")
+
+    @skip_on_cudasim('only get given a Python "int", assumes 32 bits')
+    def test_clz_i8(self):
+        compiled = cuda.jit("void(int32[:], int64)")(simple_clz)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0x000000000010000)
+        self.assertEquals(ary[0], 47)
+
+    def test_ffs_i4(self):
+        compiled = cuda.jit("void(int32[:], int32)")(simple_ffs)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0x00100000)
+        self.assertEquals(ary[0], 21)
+        compiled[1, 1](ary, 0x80000000)
+        self.assertEquals(ary[0], 32)
+
+    def test_ffs_u4(self):
+        compiled = cuda.jit("void(int32[:], uint32)")(simple_ffs)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0x00100000)
+        self.assertEquals(ary[0], 21)
+        compiled[1, 1](ary, 0x80000000)
+        self.assertEquals(ary[0], 32)
+
+    def test_ffs_i4_1s(self):
+        compiled = cuda.jit("void(int32[:], int32)")(simple_ffs)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0xFFFFFFFF)
+        self.assertEquals(ary[0], 1)
+
+    def test_ffs_i4_0s(self):
+        compiled = cuda.jit("void(int32[:], int32)")(simple_ffs)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0x0)
+        self.assertEquals(ary[0], 0)
+
+    @skip_on_cudasim('only get given a Python "int", assumes 32 bits')
+    def test_ffs_i8(self):
+        compiled = cuda.jit("void(int32[:], int64)")(simple_ffs)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary, 0x000000000010000)
+        self.assertEquals(ary[0], 17)
+        compiled[1, 1](ary, 0x100000000)
+        self.assertEquals(ary[0], 33)
+
+    def test_simple_laneid(self):
+        compiled = cuda.jit("void(int32[:])")(simple_laneid)
+        count = 2
+        ary = np.zeros(count * 32, dtype=np.int32)
+        exp = np.tile(np.arange(32, dtype=np.int32), count)
+        compiled[1, count * 32](ary)
+        self.assertTrue(np.all(ary == exp))
+
+    def test_simple_warpsize(self):
+        compiled = cuda.jit("void(int32[:])")(simple_warpsize)
+        ary = np.zeros(1, dtype=np.int32)
+        compiled[1, 1](ary)
+        self.assertEquals(ary[0], 32, "CUDA semantics")
+
+    def test_round_f4(self):
+        compiled = cuda.jit("void(int64[:], float32)")(simple_round)
+        ary = np.zeros(1, dtype=np.int64)
+
+        for i in [-3.0, -2.5, -2.25, -1.5, 1.5, 2.25, 2.5, 2.75]:
+            compiled[1, 1](ary, i)
+            self.assertEquals(ary[0], round(i))
+
+    def test_round_f8(self):
+        compiled = cuda.jit("void(int64[:], float64)")(simple_round)
+        ary = np.zeros(1, dtype=np.int64)
+
+        for i in [-3.0, -2.5, -2.25, -1.5, 1.5, 2.25, 2.5, 2.75]:
+            compiled[1, 1](ary, i)
+            self.assertEquals(ary[0], round(i))
+
+    def test_round_to_f4(self):
+        compiled = cuda.jit("void(float32[:], float32, int32)")(simple_round_to)
+        ary = np.zeros(1, dtype=np.float32)
+        np.random.seed(123)
+        vals = np.random.random(32).astype(np.float32)
+        np.concatenate((vals, np.array([np.inf, -np.inf, np.nan])))
+        digits = (
+            # Common case branch of round_to_impl
+            -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5,
+            # The algorithm currently implemented can only round to 13 digits
+            # with single precision. Note that this doesn't trigger the
+            # "overflow safe" branch of the implementation, which can only be
+            # hit when using double precision.
+            13
+        )
+        for val, ndigits in itertools.product(vals, digits):
+            with self.subTest(val=val, ndigits=ndigits):
+                compiled[1, 1](ary, val, ndigits)
+                self.assertPreciseEqual(ary[0], round(val, ndigits),
+                                        prec='single')
+
+    # CPython on most platforms uses rounding based on dtoa.c, whereas the CUDA
+    # round-to implementation uses CPython's fallback implementation, which has
+    # slightly different behavior at the edges of the domain. Since the CUDA
+    # simulator executes using CPython, we need to skip this test when the
+    # simulator is active.
+    @skip_on_cudasim('Overflow behavior differs on CPython')
+    def test_round_to_f4_overflow(self):
+        # Test that the input value is returned when y in round_ndigits
+        # overflows.
+        compiled = cuda.jit("void(float32[:], float32, int32)")(simple_round_to)
+        ary = np.zeros(1, dtype=np.float32)
+        val = np.finfo(np.float32).max
+        # An unusually large number of digits is required to hit the "y
+        # overflows" branch of the implementation because the typing results in
+        # the computation of y as float64.
+        ndigits = 300
+        compiled[1, 1](ary, val, ndigits)
+        self.assertEqual(ary[0], val)
+
+    def test_round_to_f4_halfway(self):
+        compiled = cuda.jit("void(float32[:], float32, int32)")(simple_round_to)
+        ary = np.zeros(1, dtype=np.float32)
+        # Value chosen to trigger the "round to even" branch of the
+        # implementation
+        val = 0.3425
+        ndigits = 3
+        compiled[1, 1](ary, val, ndigits)
+        self.assertPreciseEqual(ary[0], round(val, ndigits), prec='single')
+
+    def test_round_to_f8(self):
+        compiled = cuda.jit("void(float64[:], float64, int32)")(simple_round_to)
+        ary = np.zeros(1, dtype=np.float64)
+        np.random.seed(123)
+        vals = np.random.random(32)
+        np.concatenate((vals, np.array([np.inf, -np.inf, np.nan])))
+        digits = (-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5)
+
+        for val, ndigits in itertools.product(vals, digits):
+            with self.subTest(val=val, ndigits=ndigits):
+                compiled[1, 1](ary, val, ndigits)
+                self.assertPreciseEqual(ary[0], round(val, ndigits),
+                                        prec='exact')
+
+        # Trigger the "overflow safe" branch of the implementation
+        val = 0.12345678987654321 * 10e-15
+        ndigits = 23
+        with self.subTest(val=val, ndigits=ndigits):
+            compiled[1, 1](ary, val, ndigits)
+            self.assertPreciseEqual(ary[0], round(val, ndigits),
+                                    prec='double')
+
+    # Skipped on cudasim for the same reasons as test_round_to_f4 above.
+    @skip_on_cudasim('Overflow behavior differs on CPython')
+    def test_round_to_f8_overflow(self):
+        # Test that the input value is returned when y in round_ndigits
+        # overflows.
+        compiled = cuda.jit("void(float64[:], float64, int32)")(simple_round_to)
+        ary = np.zeros(1, dtype=np.float64)
+        val = np.finfo(np.float64).max
+        # Unlike test_round_to_f4_overflow, a reasonable number of digits can
+        # be used for this test to overflow y in round_ndigits.
+        ndigits = 12
+        compiled[1, 1](ary, val, ndigits)
+        self.assertEqual(ary[0], val)
+
+    def test_round_to_f8_halfway(self):
+        compiled = cuda.jit("void(float64[:], float64, int32)")(simple_round_to)
+        ary = np.zeros(1, dtype=np.float64)
+        # Value chosen to trigger the "round to even" branch of the
+        # implementation, with a value that is not exactly representable with a
+        # float32, but only a float64.
+        val = 0.5425
+        ndigits = 3
+        compiled[1, 1](ary, val, ndigits)
+        self.assertPreciseEqual(ary[0], round(val, ndigits), prec='double')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_ipc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_ipc.py
new file mode 100644
index 000000000..e88bbc9a0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_ipc.py
@@ -0,0 +1,314 @@
+import multiprocessing as mp
+import itertools
+import traceback
+import pickle
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.cudadrv import driver
+from numba.cuda.testing import (skip_on_arm, skip_on_cudasim,
+                                skip_under_cuda_memcheck,
+                                ContextResettingTestCase, ForeignArray)
+from numba.tests.support import linux_only, windows_only
+import unittest
+
+
+def core_ipc_handle_test(the_work, result_queue):
+    try:
+        arr = the_work()
+    # Catch anything going wrong in the worker function
+    except:  # noqa: E722
+        # FAILED. propagate the exception as a string
+        succ = False
+        out = traceback.format_exc()
+    else:
+        # OK. send the ndarray back
+        succ = True
+        out = arr
+    result_queue.put((succ, out))
+
+
+def base_ipc_handle_test(handle, size, result_queue):
+    def the_work():
+        dtype = np.dtype(np.intp)
+        with cuda.open_ipc_array(handle, shape=size // dtype.itemsize,
+                                 dtype=dtype) as darr:
+            # copy the data to host
+            return darr.copy_to_host()
+
+    core_ipc_handle_test(the_work, result_queue)
+
+
+def serialize_ipc_handle_test(handle, result_queue):
+    def the_work():
+        dtype = np.dtype(np.intp)
+        darr = handle.open_array(cuda.current_context(),
+                                 shape=handle.size // dtype.itemsize,
+                                 dtype=dtype)
+        # copy the data to host
+        arr = darr.copy_to_host()
+        handle.close()
+        return arr
+
+    core_ipc_handle_test(the_work, result_queue)
+
+
+def ipc_array_test(ipcarr, result_queue):
+    try:
+        with ipcarr as darr:
+            arr = darr.copy_to_host()
+            try:
+                # should fail to reopen
+                with ipcarr:
+                    pass
+            except ValueError as e:
+                if str(e) != 'IpcHandle is already opened':
+                    raise AssertionError('invalid exception message')
+            else:
+                raise AssertionError('did not raise on reopen')
+    # Catch any exception so we can propagate it
+    except:  # noqa: E722
+        # FAILED. propagate the exception as a string
+        succ = False
+        out = traceback.format_exc()
+    else:
+        # OK. send the ndarray back
+        succ = True
+        out = arr
+    result_queue.put((succ, out))
+
+
+@linux_only
+@skip_under_cuda_memcheck('Hangs cuda-memcheck')
+@skip_on_cudasim('Ipc not available in CUDASIM')
+@skip_on_arm('CUDA IPC not supported on ARM in Numba')
+class TestIpcMemory(ContextResettingTestCase):
+
+    def test_ipc_handle(self):
+        # prepare data for IPC
+        arr = np.arange(10, dtype=np.intp)
+        devarr = cuda.to_device(arr)
+
+        # create IPC handle
+        ctx = cuda.current_context()
+        ipch = ctx.get_ipc_handle(devarr.gpu_data)
+
+        # manually prepare for serialization as bytes
+        if driver.USE_NV_BINDING:
+            handle_bytes = ipch.handle.reserved
+        else:
+            handle_bytes = bytes(ipch.handle)
+        size = ipch.size
+
+        # spawn new process for testing
+        ctx = mp.get_context('spawn')
+        result_queue = ctx.Queue()
+        args = (handle_bytes, size, result_queue)
+        proc = ctx.Process(target=base_ipc_handle_test, args=args)
+        proc.start()
+        succ, out = result_queue.get()
+        if not succ:
+            self.fail(out)
+        else:
+            np.testing.assert_equal(arr, out)
+        proc.join(3)
+
+    def variants(self):
+        # Test with no slicing and various different slices
+        indices = (None, slice(3, None), slice(3, 8), slice(None, 8))
+        # Test with a Numba DeviceNDArray, or an array from elsewhere through
+        # the CUDA Array Interface
+        foreigns = (False, True)
+        return itertools.product(indices, foreigns)
+
+    def check_ipc_handle_serialization(self, index_arg=None, foreign=False):
+        # prepare data for IPC
+        arr = np.arange(10, dtype=np.intp)
+        devarr = cuda.to_device(arr)
+        if index_arg is not None:
+            devarr = devarr[index_arg]
+        if foreign:
+            devarr = cuda.as_cuda_array(ForeignArray(devarr))
+        expect = devarr.copy_to_host()
+
+        # create IPC handle
+        ctx = cuda.current_context()
+        ipch = ctx.get_ipc_handle(devarr.gpu_data)
+
+        # pickle
+        buf = pickle.dumps(ipch)
+        ipch_recon = pickle.loads(buf)
+        self.assertIs(ipch_recon.base, None)
+        self.assertEqual(ipch_recon.size, ipch.size)
+
+        if driver.USE_NV_BINDING:
+            self.assertEqual(ipch_recon.handle.reserved, ipch.handle.reserved)
+        else:
+            self.assertEqual(tuple(ipch_recon.handle), tuple(ipch.handle))
+
+        # spawn new process for testing
+        ctx = mp.get_context('spawn')
+        result_queue = ctx.Queue()
+        args = (ipch, result_queue)
+        proc = ctx.Process(target=serialize_ipc_handle_test, args=args)
+        proc.start()
+        succ, out = result_queue.get()
+        if not succ:
+            self.fail(out)
+        else:
+            np.testing.assert_equal(expect, out)
+        proc.join(3)
+
+    def test_ipc_handle_serialization(self):
+        for index, foreign, in self.variants():
+            with self.subTest(index=index, foreign=foreign):
+                self.check_ipc_handle_serialization(index, foreign)
+
+    def check_ipc_array(self, index_arg=None, foreign=False):
+        # prepare data for IPC
+        arr = np.arange(10, dtype=np.intp)
+        devarr = cuda.to_device(arr)
+        # Slice
+        if index_arg is not None:
+            devarr = devarr[index_arg]
+        if foreign:
+            devarr = cuda.as_cuda_array(ForeignArray(devarr))
+        expect = devarr.copy_to_host()
+        ipch = devarr.get_ipc_handle()
+
+        # spawn new process for testing
+        ctx = mp.get_context('spawn')
+        result_queue = ctx.Queue()
+        args = (ipch, result_queue)
+        proc = ctx.Process(target=ipc_array_test, args=args)
+        proc.start()
+        succ, out = result_queue.get()
+        if not succ:
+            self.fail(out)
+        else:
+            np.testing.assert_equal(expect, out)
+        proc.join(3)
+
+    def test_ipc_array(self):
+        for index, foreign, in self.variants():
+            with self.subTest(index=index, foreign=foreign):
+                self.check_ipc_array(index, foreign)
+
+
+def staged_ipc_handle_test(handle, device_num, result_queue):
+    def the_work():
+        with cuda.gpus[device_num]:
+            this_ctx = cuda.devices.get_context()
+            deviceptr = handle.open_staged(this_ctx)
+            arrsize = handle.size // np.dtype(np.intp).itemsize
+            hostarray = np.zeros(arrsize, dtype=np.intp)
+            cuda.driver.device_to_host(
+                hostarray, deviceptr, size=handle.size,
+            )
+            handle.close()
+        return hostarray
+
+    core_ipc_handle_test(the_work, result_queue)
+
+
+def staged_ipc_array_test(ipcarr, device_num, result_queue):
+    try:
+        with cuda.gpus[device_num]:
+            with ipcarr as darr:
+                arr = darr.copy_to_host()
+                try:
+                    # should fail to reopen
+                    with ipcarr:
+                        pass
+                except ValueError as e:
+                    if str(e) != 'IpcHandle is already opened':
+                        raise AssertionError('invalid exception message')
+                else:
+                    raise AssertionError('did not raise on reopen')
+    # Catch any exception so we can propagate it
+    except:  # noqa: E722
+        # FAILED. propagate the exception as a string
+        succ = False
+        out = traceback.format_exc()
+    else:
+        # OK. send the ndarray back
+        succ = True
+        out = arr
+    result_queue.put((succ, out))
+
+
+@linux_only
+@skip_under_cuda_memcheck('Hangs cuda-memcheck')
+@skip_on_cudasim('Ipc not available in CUDASIM')
+@skip_on_arm('CUDA IPC not supported on ARM in Numba')
+class TestIpcStaged(ContextResettingTestCase):
+    def test_staged(self):
+        # prepare data for IPC
+        arr = np.arange(10, dtype=np.intp)
+        devarr = cuda.to_device(arr)
+
+        # spawn new process for testing
+        mpctx = mp.get_context('spawn')
+        result_queue = mpctx.Queue()
+
+        # create IPC handle
+        ctx = cuda.current_context()
+        ipch = ctx.get_ipc_handle(devarr.gpu_data)
+        # pickle
+        buf = pickle.dumps(ipch)
+        ipch_recon = pickle.loads(buf)
+        self.assertIs(ipch_recon.base, None)
+        if driver.USE_NV_BINDING:
+            self.assertEqual(ipch_recon.handle.reserved, ipch.handle.reserved)
+        else:
+            self.assertEqual(tuple(ipch_recon.handle), tuple(ipch.handle))
+        self.assertEqual(ipch_recon.size, ipch.size)
+
+        # Test on every CUDA devices
+        for device_num in range(len(cuda.gpus)):
+            args = (ipch, device_num, result_queue)
+            proc = mpctx.Process(target=staged_ipc_handle_test, args=args)
+            proc.start()
+            succ, out = result_queue.get()
+            proc.join(3)
+            if not succ:
+                self.fail(out)
+            else:
+                np.testing.assert_equal(arr, out)
+
+    def test_ipc_array(self):
+        for device_num in range(len(cuda.gpus)):
+            # prepare data for IPC
+            arr = np.random.random(10)
+            devarr = cuda.to_device(arr)
+            ipch = devarr.get_ipc_handle()
+
+            # spawn new process for testing
+            ctx = mp.get_context('spawn')
+            result_queue = ctx.Queue()
+            args = (ipch, device_num, result_queue)
+            proc = ctx.Process(target=staged_ipc_array_test, args=args)
+            proc.start()
+            succ, out = result_queue.get()
+            proc.join(3)
+            if not succ:
+                self.fail(out)
+            else:
+                np.testing.assert_equal(arr, out)
+
+
+@windows_only
+@skip_on_cudasim('Ipc not available in CUDASIM')
+class TestIpcNotSupported(ContextResettingTestCase):
+    def test_unsupported(self):
+        arr = np.arange(10, dtype=np.intp)
+        devarr = cuda.to_device(arr)
+        with self.assertRaises(OSError) as raises:
+            devarr.get_ipc_handle()
+        errmsg = str(raises.exception)
+        self.assertIn('OS does not support CUDA IPC', errmsg)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_iterators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_iterators.py
new file mode 100644
index 000000000..47366f380
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_iterators.py
@@ -0,0 +1,99 @@
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+import numpy as np
+
+
+class TestIterators(CUDATestCase):
+
+    def test_enumerate(self):
+        @cuda.jit
+        def enumerator(x, error):
+            count = 0
+
+            for i, v in enumerate(x):
+                if count != i:
+                    error[0] = 1
+                if v != x[i]:
+                    error[0] = 2
+
+                count += 1
+
+            if count != len(x):
+                error[0] = 3
+
+        x = np.asarray((10, 9, 8, 7, 6))
+        error = np.zeros(1, dtype=np.int32)
+
+        enumerator[1, 1](x, error)
+        self.assertEqual(error[0], 0)
+
+    def _test_twoarg_function(self, f):
+        x = np.asarray((10, 9, 8, 7, 6))
+        y = np.asarray((1, 2, 3, 4, 5))
+        error = np.zeros(1, dtype=np.int32)
+
+        f[1, 1](x, y, error)
+        self.assertEqual(error[0], 0)
+
+    def test_zip(self):
+        @cuda.jit
+        def zipper(x, y, error):
+            i = 0
+
+            for xv, yv in zip(x, y):
+                if xv != x[i]:
+                    error[0] = 1
+                if yv != y[i]:
+                    error[0] = 2
+
+                i += 1
+
+            if i != len(x):
+                error[0] = 3
+
+        self._test_twoarg_function(zipper)
+
+    def test_enumerate_zip(self):
+        @cuda.jit
+        def enumerator_zipper(x, y, error):
+            count = 0
+
+            for i, (xv, yv) in enumerate(zip(x, y)):
+                if i != count:
+                    error[0] = 1
+                if xv != x[i]:
+                    error[0] = 2
+                if yv != y[i]:
+                    error[0] = 3
+
+                count += 1
+
+            if count != len(x):
+                error[0] = 4
+
+        self._test_twoarg_function(enumerator_zipper)
+
+    def test_zip_enumerate(self):
+        @cuda.jit
+        def zipper_enumerator(x, y, error):
+            count = 0
+
+            for (i, xv), yv in zip(enumerate(x), y):
+                if i != count:
+                    error[0] = 1
+                if xv != x[i]:
+                    error[0] = 2
+                if yv != y[i]:
+                    error[0] = 3
+
+                count += 1
+
+            if count != len(x):
+                error[0] = 4
+
+        self._test_twoarg_function(zipper_enumerator)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_lang.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_lang.py
new file mode 100644
index 000000000..0241c1e40
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_lang.py
@@ -0,0 +1,64 @@
+"""
+Test basic language features
+
+"""
+
+import numpy as np
+from numba import cuda, float64
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestLang(CUDATestCase):
+    def test_enumerate(self):
+        tup = (1., 2.5, 3.)
+
+        @cuda.jit("void(float64[:])")
+        def foo(a):
+            for i, v in enumerate(tup):
+                a[i] = v
+
+        a = np.zeros(len(tup))
+        foo[1, 1](a)
+        self.assertTrue(np.all(a == tup))
+
+    def test_zip(self):
+        t1 = (1, 2, 3)
+        t2 = (4.5, 5.6, 6.7)
+
+        @cuda.jit("void(float64[:])")
+        def foo(a):
+            c = 0
+            for i, j in zip(t1, t2):
+                c += i + j
+            a[0] = c
+
+        a = np.zeros(1)
+        foo[1, 1](a)
+        b = np.array(t1)
+        c = np.array(t2)
+        self.assertTrue(np.all(a == (b + c).sum()))
+
+    def test_issue_872(self):
+        '''
+        Ensure that typing and lowering of CUDA kernel API primitives works in
+        more than one block. Was originally to ensure that macro expansion works
+        for more than one block (issue #872), but macro expansion has been
+        replaced by a "proper" implementation of all kernel API functions.
+        '''
+
+        @cuda.jit("void(float64[:,:])")
+        def cuda_kernel_api_in_multiple_blocks(ary):
+            for i in range(2):
+                tx = cuda.threadIdx.x
+            for j in range(3):
+                ty = cuda.threadIdx.y
+            sm = cuda.shared.array((2, 3), float64)
+            sm[tx, ty] = 1.0
+            ary[tx, ty] = sm[tx, ty]
+
+        a = np.zeros((2, 3))
+        cuda_kernel_api_in_multiple_blocks[1, (2, 3)](a)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_laplace.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_laplace.py
new file mode 100644
index 000000000..d868b0297
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_laplace.py
@@ -0,0 +1,119 @@
+import numpy as np
+from numba import cuda, float64, void
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.core import config
+
+# NOTE: CUDA kernel does not return any value
+
+if config.ENABLE_CUDASIM:
+    tpb = 4
+else:
+    tpb = 16
+SM_SIZE = tpb, tpb
+
+
+class TestCudaLaplace(CUDATestCase):
+    def test_laplace_small(self):
+
+        @cuda.jit(float64(float64, float64), device=True, inline=True)
+        def get_max(a, b):
+            if a > b:
+                return a
+            else:
+                return b
+
+        @cuda.jit(void(float64[:, :], float64[:, :], float64[:, :]))
+        def jocabi_relax_core(A, Anew, error):
+            err_sm = cuda.shared.array(SM_SIZE, dtype=float64)
+
+            ty = cuda.threadIdx.x
+            tx = cuda.threadIdx.y
+            bx = cuda.blockIdx.x
+            by = cuda.blockIdx.y
+
+            n = A.shape[0]
+            m = A.shape[1]
+
+            i, j = cuda.grid(2)
+
+            err_sm[ty, tx] = 0
+            if j >= 1 and j < n - 1 and i >= 1 and i < m - 1:
+                Anew[j, i] = 0.25 * ( A[j, i + 1] + A[j, i - 1]
+                                      + A[j - 1, i] + A[j + 1, i])
+                err_sm[ty, tx] = Anew[j, i] - A[j, i]
+
+            cuda.syncthreads()
+
+            # max-reduce err_sm vertically
+            t = tpb // 2
+            while t > 0:
+                if ty < t:
+                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty + t, tx])
+                t //= 2
+                cuda.syncthreads()
+
+            # max-reduce err_sm horizontally
+            t = tpb // 2
+            while t > 0:
+                if tx < t and ty == 0:
+                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty, tx + t])
+                t //= 2
+                cuda.syncthreads()
+
+            if tx == 0 and ty == 0:
+                error[by, bx] = err_sm[0, 0]
+
+        if config.ENABLE_CUDASIM:
+            NN, NM = 4, 4
+            iter_max = 20
+        else:
+            NN, NM = 256, 256
+            iter_max = 1000
+
+        A = np.zeros((NN, NM), dtype=np.float64)
+        Anew = np.zeros((NN, NM), dtype=np.float64)
+
+        n = NN
+
+        tol = 1.0e-6
+        error = 1.0
+
+        for j in range(n):
+            A[j, 0] = 1.0
+            Anew[j, 0] = 1.0
+
+        iter = 0
+
+        blockdim = (tpb, tpb)
+        griddim = (NN // blockdim[0], NM // blockdim[1])
+
+        error_grid = np.zeros(griddim)
+
+        stream = cuda.stream()
+
+        dA = cuda.to_device(A, stream)          # to device and don't come back
+        dAnew = cuda.to_device(Anew, stream)    # to device and don't come back
+        derror_grid = cuda.to_device(error_grid, stream)
+
+        while error > tol and iter < iter_max:
+            self.assertTrue(error_grid.dtype == np.float64)
+
+            jocabi_relax_core[griddim, blockdim, stream](dA, dAnew, derror_grid)
+
+            derror_grid.copy_to_host(error_grid, stream=stream)
+
+            # error_grid is available on host
+            stream.synchronize()
+
+            error = np.abs(error_grid).max()
+
+            # swap dA and dAnew
+            tmp = dA
+            dA = dAnew
+            dAnew = tmp
+
+            iter += 1
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_libdevice.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_libdevice.py
new file mode 100644
index 000000000..89e9b24f1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_libdevice.py
@@ -0,0 +1,186 @@
+import numpy as np
+from numba.core import types
+from numba.cuda.testing import skip_on_cudasim, unittest, CUDATestCase
+from numba import cuda
+from numba.cuda import libdevice, compile_ptx
+from numba.cuda.libdevicefuncs import functions, create_signature
+
+
+def use_sincos(s, c, x):
+    i = cuda.grid(1)
+
+    if i < len(x):
+        sr, cr = libdevice.sincos(x[i])
+        s[i] = sr
+        c[i] = cr
+
+
+def use_frexp(frac, exp, x):
+    i = cuda.grid(1)
+
+    if i < len(x):
+        fracr, expr = libdevice.frexp(x[i])
+        frac[i] = fracr
+        exp[i] = expr
+
+
+def use_sad(r, x, y, z):
+    i = cuda.grid(1)
+
+    if i < len(x):
+        r[i] = libdevice.sad(x[i], y[i], z[i])
+
+
+@skip_on_cudasim('Libdevice functions are not supported on cudasim')
+class TestLibdevice(CUDATestCase):
+    """
+    Some tests of libdevice function wrappers that check the returned values.
+
+    These are mainly to check that the generation of the implementations
+    results in correct typing and lowering for each type of function return
+    (e.g. scalar return, UniTuple return, Tuple return, etc.).
+    """
+
+    def test_sincos(self):
+        # Tests return of a UniTuple from a libdevice function
+        arr = np.arange(100, dtype=np.float64)
+        sres = np.zeros_like(arr)
+        cres = np.zeros_like(arr)
+
+        cufunc = cuda.jit(use_sincos)
+        cufunc[4, 32](sres, cres, arr)
+
+        np.testing.assert_allclose(np.cos(arr), cres)
+        np.testing.assert_allclose(np.sin(arr), sres)
+
+    def test_frexp(self):
+        # Test return of a Tuple from a libdevice function
+        arr = np.linspace(start=1.0, stop=10.0, num=100, dtype=np.float64)
+        fracres = np.zeros_like(arr)
+        expres = np.zeros(shape=arr.shape, dtype=np.int32)
+
+        cufunc = cuda.jit(use_frexp)
+        cufunc[4, 32](fracres, expres, arr)
+
+        frac_expect, exp_expect = np.frexp(arr)
+
+        np.testing.assert_array_equal(frac_expect, fracres)
+        np.testing.assert_array_equal(exp_expect, expres)
+
+    def test_sad(self):
+        # Test return of a scalar from a libdevice function
+        x = np.arange(0, 200, 2)
+        y = np.arange(50, 150)
+        z = np.arange(15, 115)
+        r = np.zeros_like(x)
+
+        cufunc = cuda.jit(use_sad)
+        cufunc[4, 32](r, x, y, z)
+
+        np.testing.assert_array_equal(np.abs(x - y) + z, r)
+
+
+# A template for generating tests of compiling calls to libdevice functions.
+# The purpose of the call and assignment of the return variables is to ensure
+# the actual function implementations are not thrown away resulting in a PTX
+# implementation that only contains the ret instruction - this may hide certain
+# errors.
+function_template = """\
+from numba.cuda import libdevice
+
+def pyfunc(%(pyargs)s):
+    ret = libdevice.%(func)s(%(funcargs)s)
+    %(retvars)s = ret
+"""
+
+
+def make_test_call(libname):
+    """
+    Generates a test function for each libdevice function.
+    """
+
+    def _test_call_functions(self):
+        # Strip off '__nv_' from libdevice name to get Python name
+        apiname = libname[5:]
+        apifunc = getattr(libdevice, apiname)
+        retty, args = functions[libname]
+        sig = create_signature(retty, args)
+
+        # Construct arguments to the libdevice function. These are all
+        # non-pointer arguments to the underlying bitcode function.
+        funcargs = ", ".join(['a%d' % i for i, arg in enumerate(args) if not
+                              arg.is_ptr])
+
+        # Arguments to the Python function (`pyfunc` in the template above) are
+        # the arguments to the libdevice function, plus as many extra arguments
+        # as there are in the return type of the libdevice function - one for
+        # scalar-valued returns, or the length of the tuple for tuple-valued
+        # returns.
+        if isinstance(sig.return_type, (types.Tuple, types.UniTuple)):
+            # Start with the parameters for the return values
+            pyargs = ", ".join(['r%d' % i for i in
+                                range(len(sig.return_type))])
+            # Add the parameters for the argument values
+            pyargs += ", " + funcargs
+            # Generate the unpacking of the return value from the libdevice
+            # function into the Python function return values (`r0`, `r1`,
+            # etc.).
+            retvars = ", ".join(['r%d[0]' % i for i in
+                                 range(len(sig.return_type))])
+        else:
+            # Scalar return is a more straightforward case
+            pyargs = "r0, " + funcargs
+            retvars = "r0[0]"
+
+        # Create the string containing the function to compile
+        d = { 'func': apiname,
+              'pyargs': pyargs,
+              'funcargs': funcargs,
+              'retvars': retvars }
+        code = function_template % d
+
+        # Convert the string to a Python function
+        locals = {}
+        exec(code, globals(), locals)
+        pyfunc = locals['pyfunc']
+
+        # Compute the signature for compilation. This mirrors the creation of
+        # arguments to the Python function above.
+        pyargs = [ arg.ty for arg in args if not arg.is_ptr ]
+        if isinstance(sig.return_type, (types.Tuple, types.UniTuple)):
+            pyreturns = [ret[::1] for ret in sig.return_type]
+            pyargs = pyreturns + pyargs
+        else:
+            pyargs.insert(0, sig.return_type[::1])
+
+        ptx, resty = compile_ptx(pyfunc, pyargs)
+
+        # If the function body was discarded by optimization (therefore making
+        # the test a bit weak), there won't be any loading of parameters -
+        # ensure that a load from parameters occurs somewhere in the PTX
+        self.assertIn('ld.param', ptx)
+
+        # Returning the result (through a passed-in array) should also require
+        # a store to global memory, so check for at least one of those too.
+        self.assertIn('st.global', ptx)
+
+    return _test_call_functions
+
+
+@skip_on_cudasim('Compilation to PTX is not supported on cudasim')
+class TestLibdeviceCompilation(unittest.TestCase):
+    """
+    Class for holding all tests of compiling calls to libdevice functions. We
+    generate the actual tests in this class (as opposed to using subTest and
+    one test within this class) because there are a lot of tests, and it makes
+    the test suite appear frozen to test them all as subTests in one test.
+    """
+
+
+for libname in functions:
+    setattr(TestLibdeviceCompilation, 'test_%s' % libname,
+            make_test_call(libname))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_lineinfo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_lineinfo.py
new file mode 100644
index 000000000..9ebdcc484
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_lineinfo.py
@@ -0,0 +1,43 @@
+from numba.cuda.testing import skip_on_cudasim
+from numba import cuda
+from numba.core import types
+from numba.cuda.testing import CUDATestCase
+import re
+import unittest
+
+
+@skip_on_cudasim('Simulator does not produce lineinfo')
+class TestCudaLineInfo(CUDATestCase):
+    """
+    These tests only check the compiled PTX for line mappings
+    """
+    def _getasm(self, fn, sig):
+        fn.compile(sig)
+        return fn.inspect_asm(sig)
+
+    def _check(self, fn, sig, expect):
+        asm = self._getasm(fn, sig=sig)
+        # The name of this file should be present in the line mapping
+        # if lineinfo was propagated through correctly.
+        re_section_lineinfo = re.compile(r"test_lineinfo.py")
+        match = re_section_lineinfo.search(asm)
+        assertfn = self.assertIsNotNone if expect else self.assertIsNone
+        assertfn(match, msg=asm)
+
+    def test_no_lineinfo_in_asm(self):
+        @cuda.jit(lineinfo=False)
+        def foo(x):
+            x[0] = 1
+
+        self._check(foo, sig=(types.int32[:],), expect=False)
+
+    def test_lineinfo_in_asm(self):
+        @cuda.jit(lineinfo=True)
+        def foo(x):
+            x[0] = 1
+
+        self._check(foo, sig=(types.int32[:],), expect=True)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_localmem.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_localmem.py
new file mode 100644
index 000000000..26b3469a7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_localmem.py
@@ -0,0 +1,164 @@
+import numpy as np
+
+from numba import cuda, int32, complex128, void
+from numba.core import types
+from numba.core.errors import TypingError
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from .extensions_usecases import test_struct_model_type, TestStruct
+
+
+def culocal(A, B):
+    C = cuda.local.array(1000, dtype=int32)
+    for i in range(C.shape[0]):
+        C[i] = A[i]
+    for i in range(C.shape[0]):
+        B[i] = C[i]
+
+
+def culocalcomplex(A, B):
+    C = cuda.local.array(100, dtype=complex128)
+    for i in range(C.shape[0]):
+        C[i] = A[i]
+    for i in range(C.shape[0]):
+        B[i] = C[i]
+
+
+def culocal1tuple(A, B):
+    C = cuda.local.array((5,), dtype=int32)
+    for i in range(C.shape[0]):
+        C[i] = A[i]
+    for i in range(C.shape[0]):
+        B[i] = C[i]
+
+
+@skip_on_cudasim('PTX inspection not available in cudasim')
+class TestCudaLocalMem(CUDATestCase):
+    def test_local_array(self):
+        sig = (int32[:], int32[:])
+        jculocal = cuda.jit(sig)(culocal)
+        self.assertTrue('.local' in jculocal.inspect_asm(sig))
+        A = np.arange(1000, dtype='int32')
+        B = np.zeros_like(A)
+        jculocal[1, 1](A, B)
+        self.assertTrue(np.all(A == B))
+
+    def test_local_array_1_tuple(self):
+        """Ensure that local arrays can be constructed with 1-tuple shape
+        """
+        jculocal = cuda.jit('void(int32[:], int32[:])')(culocal1tuple)
+        # Don't check if .local is in the ptx because the optimizer
+        # may reduce it to registers.
+        A = np.arange(5, dtype='int32')
+        B = np.zeros_like(A)
+        jculocal[1, 1](A, B)
+        self.assertTrue(np.all(A == B))
+
+    def test_local_array_complex(self):
+        sig = 'void(complex128[:], complex128[:])'
+        jculocalcomplex = cuda.jit(sig)(culocalcomplex)
+        A = (np.arange(100, dtype='complex128') - 1) / 2j
+        B = np.zeros_like(A)
+        jculocalcomplex[1, 1](A, B)
+        self.assertTrue(np.all(A == B))
+
+    def check_dtype(self, f, dtype):
+        # Find the typing of the dtype argument to cuda.local.array
+        annotation = next(iter(f.overloads.values()))._type_annotation
+        l_dtype = annotation.typemap['l'].dtype
+        # Ensure that the typing is correct
+        self.assertEqual(l_dtype, dtype)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_numba_dtype(self):
+        # Check that Numba types can be used as the dtype of a local array
+        @cuda.jit(void(int32[::1]))
+        def f(x):
+            l = cuda.local.array(10, dtype=int32)
+            l[0] = x[0]
+            x[0] = l[0]
+
+        self.check_dtype(f, int32)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_numpy_dtype(self):
+        # Check that NumPy types can be used as the dtype of a local array
+        @cuda.jit(void(int32[::1]))
+        def f(x):
+            l = cuda.local.array(10, dtype=np.int32)
+            l[0] = x[0]
+            x[0] = l[0]
+
+        self.check_dtype(f, int32)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_string_dtype(self):
+        # Check that strings can be used to specify the dtype of a local array
+        @cuda.jit(void(int32[::1]))
+        def f(x):
+            l = cuda.local.array(10, dtype='int32')
+            l[0] = x[0]
+            x[0] = l[0]
+
+        self.check_dtype(f, int32)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_invalid_string_dtype(self):
+        # Check that strings of invalid dtypes cause a typing error
+        re = ".*Invalid NumPy dtype specified: 'int33'.*"
+        with self.assertRaisesRegex(TypingError, re):
+            @cuda.jit(void(int32[::1]))
+            def f(x):
+                l = cuda.local.array(10, dtype='int33')
+                l[0] = x[0]
+                x[0] = l[0]
+
+    def test_type_with_struct_data_model(self):
+        @cuda.jit(void(test_struct_model_type[::1]))
+        def f(x):
+            l = cuda.local.array(10, dtype=test_struct_model_type)
+            l[0] = x[0]
+            x[0] = l[0]
+
+        self.check_dtype(f, test_struct_model_type)
+
+    def test_struct_model_type_arr(self):
+        @cuda.jit(void(int32[::1], int32[::1]))
+        def f(outx, outy):
+            # Test creation
+            arr = cuda.local.array(10, dtype=test_struct_model_type)
+            # Test set to arr
+            for i in range(len(arr)):
+                obj = TestStruct(int32(i), int32(i * 2))
+                arr[i] = obj
+            # Test get from arr
+            for i in range(len(arr)):
+                outx[i] = arr[i].x
+                outy[i] = arr[i].y
+
+        arrx = np.array((10,), dtype="int32")
+        arry = np.array((10,), dtype="int32")
+
+        f[1, 1](arrx, arry)
+
+        for i, x in enumerate(arrx):
+            self.assertEqual(x, i)
+        for i, y in enumerate(arry):
+            self.assertEqual(y, i * 2)
+
+    def _check_local_array_size_fp16(self, shape, expected, ty):
+        @cuda.jit
+        def s(a):
+            arr = cuda.local.array(shape, dtype=ty)
+            a[0] = arr.size
+
+        result = np.zeros(1, dtype=np.float16)
+        s[1, 1](result)
+        self.assertEqual(result[0], expected)
+
+    def test_issue_fp16_support(self):
+        self._check_local_array_size_fp16(2, 2, types.float16)
+        self._check_local_array_size_fp16(2, 2, np.float16)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_mandel.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_mandel.py
new file mode 100644
index 000000000..5ea5740fe
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_mandel.py
@@ -0,0 +1,33 @@
+from numba import cuda
+from numba.cuda.testing import unittest
+
+
+class TestCudaMandel(unittest.TestCase):
+    def test_mandel(self):
+        """Just make sure we can compile this
+        """
+
+        @cuda.jit('(uint32, float64, float64, float64, '
+                  'float64, uint32, uint32, uint32)', device=True)
+        def mandel(tid, min_x, max_x, min_y, max_y, width, height, iters):
+            pixel_size_x = (max_x - min_x) / width
+            pixel_size_y = (max_y - min_y) / height
+
+            x = tid % width
+            y = tid / width
+
+            real = min_x + x * pixel_size_x
+            imag = min_y + y * pixel_size_y
+
+            c = complex(real, imag)
+            z = 0.0j
+
+            for i in range(iters):
+                z = z * z + c
+                if (z.real * z.real + z.imag * z.imag) >= 4:
+                    return i
+            return iters
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_math.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_math.py
new file mode 100644
index 000000000..043d9d440
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_math.py
@@ -0,0 +1,739 @@
+import numpy as np
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.np import numpy_support
+from numba import cuda, float32, float64, int32, vectorize, void, int64
+import math
+
+
+def math_acos(A, B):
+    i = cuda.grid(1)
+    B[i] = math.acos(A[i])
+
+
+def math_asin(A, B):
+    i = cuda.grid(1)
+    B[i] = math.asin(A[i])
+
+
+def math_atan(A, B):
+    i = cuda.grid(1)
+    B[i] = math.atan(A[i])
+
+
+def math_acosh(A, B):
+    i = cuda.grid(1)
+    B[i] = math.acosh(A[i])
+
+
+def math_asinh(A, B):
+    i = cuda.grid(1)
+    B[i] = math.asinh(A[i])
+
+
+def math_atanh(A, B):
+    i = cuda.grid(1)
+    B[i] = math.atanh(A[i])
+
+
+def math_cos(A, B):
+    i = cuda.grid(1)
+    B[i] = math.cos(A[i])
+
+
+def math_sin(A, B):
+    i = cuda.grid(1)
+    B[i] = math.sin(A[i])
+
+
+def math_tan(A, B):
+    i = cuda.grid(1)
+    B[i] = math.tan(A[i])
+
+
+def math_cosh(A, B):
+    i = cuda.grid(1)
+    B[i] = math.cosh(A[i])
+
+
+def math_sinh(A, B):
+    i = cuda.grid(1)
+    B[i] = math.sinh(A[i])
+
+
+def math_tanh(A, B):
+    i = cuda.grid(1)
+    B[i] = math.tanh(A[i])
+
+
+def math_atan2(A, B, C):
+    i = cuda.grid(1)
+    C[i] = math.atan2(A[i], B[i])
+
+
+def math_exp(A, B):
+    i = cuda.grid(1)
+    B[i] = math.exp(A[i])
+
+
+def math_erf(A, B):
+    i = cuda.grid(1)
+    B[i] = math.erf(A[i])
+
+
+def math_erfc(A, B):
+    i = cuda.grid(1)
+    B[i] = math.erfc(A[i])
+
+
+def math_expm1(A, B):
+    i = cuda.grid(1)
+    B[i] = math.expm1(A[i])
+
+
+def math_fabs(A, B):
+    i = cuda.grid(1)
+    B[i] = math.fabs(A[i])
+
+
+def math_gamma(A, B):
+    i = cuda.grid(1)
+    B[i] = math.gamma(A[i])
+
+
+def math_lgamma(A, B):
+    i = cuda.grid(1)
+    B[i] = math.lgamma(A[i])
+
+
+def math_log(A, B):
+    i = cuda.grid(1)
+    B[i] = math.log(A[i])
+
+
+def math_log2(A, B):
+    i = cuda.grid(1)
+    B[i] = math.log2(A[i])
+
+
+def math_log10(A, B):
+    i = cuda.grid(1)
+    B[i] = math.log10(A[i])
+
+
+def math_log1p(A, B):
+    i = cuda.grid(1)
+    B[i] = math.log1p(A[i])
+
+
+def math_remainder(A, B, C):
+    i = cuda.grid(1)
+    C[i] = math.remainder(A[i], B[i])
+
+
+def math_sqrt(A, B):
+    i = cuda.grid(1)
+    B[i] = math.sqrt(A[i])
+
+
+def math_hypot(A, B, C):
+    i = cuda.grid(1)
+    C[i] = math.hypot(A[i], B[i])
+
+
+def math_pow(A, B, C):
+    i = cuda.grid(1)
+    C[i] = math.pow(A[i], B[i])
+
+
+def math_ceil(A, B):
+    i = cuda.grid(1)
+    B[i] = math.ceil(A[i])
+
+
+def math_floor(A, B):
+    i = cuda.grid(1)
+    B[i] = math.floor(A[i])
+
+
+def math_copysign(A, B, C):
+    i = cuda.grid(1)
+    C[i] = math.copysign(A[i], B[i])
+
+
+def math_fmod(A, B, C):
+    i = cuda.grid(1)
+    C[i] = math.fmod(A[i], B[i])
+
+
+def math_modf(A, B, C):
+    i = cuda.grid(1)
+    B[i], C[i] = math.modf(A[i])
+
+
+def math_isnan(A, B):
+    i = cuda.grid(1)
+    B[i] = math.isnan(A[i])
+
+
+def math_isinf(A, B):
+    i = cuda.grid(1)
+    B[i] = math.isinf(A[i])
+
+
+def math_isfinite(A, B):
+    i = cuda.grid(1)
+    B[i] = math.isfinite(A[i])
+
+
+def math_degrees(A, B):
+    i = cuda.grid(1)
+    B[i] = math.degrees(A[i])
+
+
+def math_radians(A, B):
+    i = cuda.grid(1)
+    B[i] = math.radians(A[i])
+
+
+def math_pow_binop(A, B, C):
+    i = cuda.grid(1)
+    C[i] = A[i] ** B[i]
+
+
+def math_mod_binop(A, B, C):
+    i = cuda.grid(1)
+    C[i] = A[i] % B[i]
+
+
+class TestCudaMath(CUDATestCase):
+    def unary_template_float32(self, func, npfunc, start=0, stop=1):
+        self.unary_template(func, npfunc, np.float32, np.float32, start, stop)
+
+    def unary_template_float64(self, func, npfunc, start=0, stop=1):
+        self.unary_template(func, npfunc, np.float64, np.float64, start, stop)
+
+    def unary_template_int64(self, func, npfunc, start=0, stop=50):
+        self.unary_template(func, npfunc, np.int64, np.float64, start, stop)
+
+    def unary_template_uint64(self, func, npfunc, start=0, stop=50):
+        self.unary_template(func, npfunc, np.uint64, np.float64, start, stop)
+
+    def unary_template(self, func, npfunc, npdtype, nprestype, start, stop):
+        nelem = 50
+        A = np.linspace(start, stop, nelem).astype(npdtype)
+        B = np.empty_like(A).astype(nprestype)
+        arytype = numpy_support.from_dtype(npdtype)[::1]
+        restype = numpy_support.from_dtype(nprestype)[::1]
+        cfunc = cuda.jit((arytype, restype))(func)
+        cfunc[1, nelem](A, B)
+
+        # When this test was originally written it used
+        # assertTrue(np.allclose(...), which has different default tolerance
+        # values to assert_allclose. The tolerance values here are chosen as
+        # the tightest under which the tests will pass.
+        if npdtype == np.float64:
+            rtol = 1e-13
+        else:
+            rtol = 1e-6
+        np.testing.assert_allclose(npfunc(A), B, rtol=rtol)
+
+    def unary_bool_special_values(self, func, npfunc, npdtype, npmtype):
+        fi = np.finfo(npdtype)
+        denorm = fi.tiny / 4
+        A = np.array([0., denorm, fi.tiny, 0.5, 1., fi.max, np.inf, np.nan],
+                     dtype=npdtype)
+        B = np.empty_like(A, dtype=np.int32)
+        cfunc = cuda.jit((npmtype[::1], int32[::1]))(func)
+
+        cfunc[1, A.size](A, B)
+        np.testing.assert_array_equal(B, npfunc(A))
+
+        cfunc[1, A.size](-A, B)
+        np.testing.assert_array_equal(B, npfunc(-A))
+
+    def unary_bool_special_values_float32(self, func, npfunc):
+        self.unary_bool_special_values(func, npfunc, np.float32, float32)
+
+    def unary_bool_special_values_float64(self, func, npfunc):
+        self.unary_bool_special_values(func, npfunc, np.float64, float64)
+
+    def unary_bool_template_float32(self, func, npfunc, start=0, stop=1):
+        self.unary_template(func, npfunc, np.float32, np.float32, start, stop)
+
+    def unary_bool_template_float64(self, func, npfunc, start=0, stop=1):
+        self.unary_template(func, npfunc, np.float64, np.float64, start, stop)
+
+    def unary_bool_template_int32(self, func, npfunc, start=0, stop=49):
+        self.unary_template(func, npfunc, np.int32, np.int32, start, stop)
+
+    def unary_bool_template_int64(self, func, npfunc, start=0, stop=49):
+        self.unary_template(func, npfunc, np.int64, np.int64, start, stop)
+
+    def unary_bool_template(self, func, npfunc, npdtype, npmtype, start, stop):
+        nelem = 50
+        A = np.linspace(start, stop, nelem).astype(npdtype)
+        B = np.empty(A.shape, dtype=np.int32)
+        iarytype = npmtype[::1]
+        oarytype = int32[::1]
+        cfunc = cuda.jit((iarytype, oarytype))(func)
+        cfunc[1, nelem](A, B)
+        np.testing.assert_allclose(npfunc(A), B)
+
+    def binary_template_float32(self, func, npfunc, start=0, stop=1):
+        self.binary_template(func, npfunc, np.float32, np.float32, start, stop)
+
+    def binary_template_float64(self, func, npfunc, start=0, stop=1):
+        self.binary_template(func, npfunc, np.float64, np.float64, start, stop)
+
+    def binary_template_int64(self, func, npfunc, start=0, stop=50):
+        self.binary_template(func, npfunc, np.int64, np.float64, start, stop)
+
+    def binary_template_uint64(self, func, npfunc, start=0, stop=50):
+        self.binary_template(func, npfunc, np.uint64, np.float64, start, stop)
+
+    def binary_template(self, func, npfunc, npdtype, nprestype, start, stop):
+        nelem = 50
+        A = np.linspace(start, stop, nelem).astype(npdtype)
+        B = np.empty_like(A).astype(nprestype)
+        arytype = numpy_support.from_dtype(npdtype)[::1]
+        restype = numpy_support.from_dtype(nprestype)[::1]
+        cfunc = cuda.jit((arytype, arytype, restype))(func)
+        cfunc.bind()
+        cfunc[1, nelem](A, A, B)
+        np.testing.assert_allclose(npfunc(A, A), B)
+
+    #---------------------------------------------------------------------------
+    # test_math_acos
+
+    def test_math_acos(self):
+        self.unary_template_float32(math_acos, np.arccos)
+        self.unary_template_float64(math_acos, np.arccos)
+        # For integers we can only test with zero, since <=-1 and >=1 result in
+        # invalid values.
+        self.unary_template_int64(math_acos, np.arccos, start=0, stop=0)
+        self.unary_template_uint64(math_acos, np.arccos, start=0, stop=0)
+
+    #---------------------------------------------------------------------------
+    # test_math_asin
+
+    def test_math_asin(self):
+        self.unary_template_float32(math_asin, np.arcsin)
+        self.unary_template_float64(math_asin, np.arcsin)
+        # For integers we can only test with zero, since <=-1 and >=1 result in
+        # invalid values.
+        self.unary_template_int64(math_asin, np.arcsin, start=0, stop=0)
+        self.unary_template_uint64(math_asin, np.arcsin, start=0, stop=0)
+
+    #---------------------------------------------------------------------------
+    # test_math_atan
+
+    def test_math_atan(self):
+        self.unary_template_float32(math_atan, np.arctan)
+        self.unary_template_float64(math_atan, np.arctan)
+        self.unary_template_int64(math_atan, np.arctan)
+        self.unary_template_uint64(math_atan, np.arctan)
+
+    #---------------------------------------------------------------------------
+    # test_math_acosh
+
+    def test_math_acosh(self):
+        self.unary_template_float32(math_acosh, np.arccosh, start=1, stop=2)
+        self.unary_template_float64(math_acosh, np.arccosh, start=1, stop=2)
+        self.unary_template_int64(math_acosh, np.arccosh, start=1, stop=2)
+        self.unary_template_uint64(math_acosh, np.arccosh, start=1, stop=2)
+
+    #---------------------------------------------------------------------------
+    # test_math_asinh
+
+    def test_math_asinh(self):
+        self.unary_template_float32(math_asinh, np.arcsinh)
+        self.unary_template_float64(math_asinh, np.arcsinh)
+        self.unary_template_int64(math_asinh, np.arcsinh)
+        self.unary_template_uint64(math_asinh, np.arcsinh)
+
+    #---------------------------------------------------------------------------
+    # test_math_atanh
+
+    def test_math_atanh(self):
+        self.unary_template_float32(math_atanh, np.arctanh, start=0, stop=.9)
+        self.unary_template_float64(math_atanh, np.arctanh, start=0, stop=.9)
+        self.unary_template_int64(math_atanh, np.arctanh, start=0, stop=.9)
+        self.unary_template_uint64(math_atanh, np.arctanh, start=0, stop=.9)
+
+    #---------------------------------------------------------------------------
+    # test_math_cos
+
+    def test_math_cos(self):
+        self.unary_template_float32(math_cos, np.cos)
+        self.unary_template_float64(math_cos, np.cos)
+        self.unary_template_int64(math_cos, np.cos)
+        self.unary_template_uint64(math_cos, np.cos)
+
+    #---------------------------------------------------------------------------
+    # test_math_sin
+
+    def test_math_sin(self):
+        self.unary_template_float32(math_sin, np.sin)
+        self.unary_template_float64(math_sin, np.sin)
+        self.unary_template_int64(math_sin, np.sin)
+        self.unary_template_uint64(math_sin, np.sin)
+
+    #---------------------------------------------------------------------------
+    # test_math_tan
+
+    def test_math_tan(self):
+        self.unary_template_float32(math_tan, np.tan)
+        self.unary_template_float64(math_tan, np.tan)
+        self.unary_template_int64(math_tan, np.tan)
+        self.unary_template_uint64(math_tan, np.tan)
+
+    #---------------------------------------------------------------------------
+    # test_math_cosh
+
+    def test_math_cosh(self):
+        self.unary_template_float32(math_cosh, np.cosh)
+        self.unary_template_float64(math_cosh, np.cosh)
+        self.unary_template_int64(math_cosh, np.cosh)
+        self.unary_template_uint64(math_cosh, np.cosh)
+
+    #---------------------------------------------------------------------------
+    # test_math_sinh
+
+    def test_math_sinh(self):
+        self.unary_template_float32(math_sinh, np.sinh)
+        self.unary_template_float64(math_sinh, np.sinh)
+        self.unary_template_int64(math_sinh, np.sinh)
+        self.unary_template_uint64(math_sinh, np.sinh)
+
+    #---------------------------------------------------------------------------
+    # test_math_tanh
+
+    def test_math_tanh(self):
+        self.unary_template_float32(math_tanh, np.tanh)
+        self.unary_template_float64(math_tanh, np.tanh)
+        self.unary_template_int64(math_tanh, np.tanh)
+        self.unary_template_uint64(math_tanh, np.tanh)
+
+    #---------------------------------------------------------------------------
+    # test_math_atan2
+
+    def test_math_atan2(self):
+        self.binary_template_float32(math_atan2, np.arctan2)
+        self.binary_template_float64(math_atan2, np.arctan2)
+        self.binary_template_int64(math_atan2, np.arctan2)
+        self.binary_template_uint64(math_atan2, np.arctan2)
+
+    #---------------------------------------------------------------------------
+    # test_math_erf
+
+    def test_math_erf(self):
+        @vectorize
+        def ufunc(x):
+            return math.erf(x)
+        self.unary_template_float32(math_erf, ufunc)
+        self.unary_template_float64(math_erf, ufunc)
+        self.unary_template_int64(math_erf, ufunc)
+        self.unary_template_uint64(math_erf, ufunc)
+
+    #---------------------------------------------------------------------------
+    # test_math_erfc
+
+    def test_math_erfc(self):
+        @vectorize
+        def ufunc(x):
+            return math.erfc(x)
+        self.unary_template_float32(math_erfc, ufunc)
+        self.unary_template_float64(math_erfc, ufunc)
+        self.unary_template_int64(math_erfc, ufunc)
+        self.unary_template_uint64(math_erfc, ufunc)
+
+    #---------------------------------------------------------------------------
+    # test_math_exp
+
+    def test_math_exp(self):
+        self.unary_template_float32(math_exp, np.exp)
+        self.unary_template_float64(math_exp, np.exp)
+        self.unary_template_int64(math_exp, np.exp)
+        self.unary_template_uint64(math_exp, np.exp)
+
+    #---------------------------------------------------------------------------
+    # test_math_expm1
+
+    def test_math_expm1(self):
+        self.unary_template_float32(math_expm1, np.expm1)
+        self.unary_template_float64(math_expm1, np.expm1)
+        self.unary_template_int64(math_expm1, np.expm1)
+        self.unary_template_uint64(math_expm1, np.expm1)
+
+    #---------------------------------------------------------------------------
+    # test_math_fabs
+
+    def test_math_fabs(self):
+        self.unary_template_float32(math_fabs, np.fabs, start=-1)
+        self.unary_template_float64(math_fabs, np.fabs, start=-1)
+        self.unary_template_int64(math_fabs, np.fabs, start=-1)
+        self.unary_template_uint64(math_fabs, np.fabs, start=-1)
+
+    #---------------------------------------------------------------------------
+    # test_math_gamma
+
+    def test_math_gamma(self):
+        @vectorize
+        def ufunc(x):
+            return math.gamma(x)
+        self.unary_template_float32(math_gamma, ufunc, start=0.1)
+        self.unary_template_float64(math_gamma, ufunc, start=0.1)
+        self.unary_template_int64(math_gamma, ufunc, start=1)
+        self.unary_template_uint64(math_gamma, ufunc, start=1)
+
+    #---------------------------------------------------------------------------
+    # test_math_lgamma
+
+    def test_math_lgamma(self):
+        @vectorize
+        def ufunc(x):
+            return math.lgamma(x)
+        self.unary_template_float32(math_lgamma, ufunc, start=0.1)
+        self.unary_template_float64(math_lgamma, ufunc, start=0.1)
+        self.unary_template_int64(math_lgamma, ufunc, start=1)
+        self.unary_template_uint64(math_lgamma, ufunc, start=1)
+
+    #---------------------------------------------------------------------------
+    # test_math_log
+
+    def test_math_log(self):
+        self.unary_template_float32(math_log, np.log, start=1)
+        self.unary_template_float64(math_log, np.log, start=1)
+        self.unary_template_int64(math_log, np.log, start=1)
+        self.unary_template_uint64(math_log, np.log, start=1)
+
+    #---------------------------------------------------------------------------
+    # test_math_log2
+
+    def test_math_log2(self):
+        self.unary_template_float32(math_log2, np.log2, start=1)
+        self.unary_template_float64(math_log2, np.log2, start=1)
+        self.unary_template_int64(math_log2, np.log2, start=1)
+        self.unary_template_uint64(math_log2, np.log2, start=1)
+
+    #---------------------------------------------------------------------------
+    # test_math_log10
+
+    def test_math_log10(self):
+        self.unary_template_float32(math_log10, np.log10, start=1)
+        self.unary_template_float64(math_log10, np.log10, start=1)
+        self.unary_template_int64(math_log10, np.log10, start=1)
+        self.unary_template_uint64(math_log10, np.log10, start=1)
+
+    #---------------------------------------------------------------------------
+    # test_math_log1p
+
+    def test_math_log1p(self):
+        self.unary_template_float32(math_log1p, np.log1p)
+        self.unary_template_float64(math_log1p, np.log1p)
+        self.unary_template_int64(math_log1p, np.log1p)
+        self.unary_template_uint64(math_log1p, np.log1p)
+
+    #---------------------------------------------------------------------------
+    # test_math_remainder
+
+    def test_math_remainder(self):
+        self.binary_template_float32(math_remainder, np.remainder, start=1e-11)
+        self.binary_template_float64(math_remainder, np.remainder, start=1e-11)
+        self.binary_template_int64(math_remainder, np.remainder, start=1)
+        self.binary_template_uint64(math_remainder, np.remainder, start=1)
+
+    @skip_on_cudasim('math.remainder(0, 0) raises a ValueError on CUDASim')
+    def test_math_remainder_0_0(self):
+        @cuda.jit(void(float64[::1], int64, int64))
+        def test_0_0(r, x, y):
+            r[0] = math.remainder(x, y)
+        r = np.zeros(1, np.float64)
+        test_0_0[1, 1](r, 0, 0)
+        self.assertTrue(np.isnan(r[0]))
+
+    #---------------------------------------------------------------------------
+    # test_math_sqrt
+
+    def test_math_sqrt(self):
+        self.unary_template_float32(math_sqrt, np.sqrt)
+        self.unary_template_float64(math_sqrt, np.sqrt)
+        self.unary_template_int64(math_sqrt, np.sqrt)
+        self.unary_template_uint64(math_sqrt, np.sqrt)
+
+    #---------------------------------------------------------------------------
+    # test_math_hypot
+
+    def test_math_hypot(self):
+        self.binary_template_float32(math_hypot, np.hypot)
+        self.binary_template_float64(math_hypot, np.hypot)
+        self.binary_template_int64(math_hypot, np.hypot)
+        self.binary_template_uint64(math_hypot, np.hypot)
+
+    #---------------------------------------------------------------------------
+    # test_math_pow
+
+    def pow_template_int32(self, npdtype):
+        nelem = 50
+        A = np.linspace(0, 25, nelem).astype(npdtype)
+        B = np.arange(nelem, dtype=np.int32)
+        C = np.empty_like(A)
+        arytype = numpy_support.from_dtype(npdtype)[::1]
+        cfunc = cuda.jit((arytype, int32[::1], arytype))(math_pow)
+        cfunc.bind()
+        cfunc[1, nelem](A, B, C)
+
+        # NumPy casting rules result in a float64 output always, which doesn't
+        # match the overflow to inf of math.pow and libdevice.powi for large
+        # values of float32, so we compute the reference result with math.pow.
+        Cref = np.empty_like(A)
+        for i in range(len(A)):
+            Cref[i] = math.pow(A[i], B[i])
+        np.testing.assert_allclose(np.power(A, B).astype(npdtype), C, rtol=1e-6)
+
+    def test_math_pow(self):
+        self.binary_template_float32(math_pow, np.power)
+        self.binary_template_float64(math_pow, np.power)
+        self.pow_template_int32(np.float32)
+        self.pow_template_int32(np.float64)
+
+    #---------------------------------------------------------------------------
+    # test_math_pow_binop
+
+    def test_math_pow_binop(self):
+        self.binary_template_float32(math_pow_binop, np.power)
+        self.binary_template_float64(math_pow_binop, np.power)
+
+    #---------------------------------------------------------------------------
+    # test_math_ceil
+
+    def test_math_ceil(self):
+        self.unary_template_float32(math_ceil, np.ceil)
+        self.unary_template_float64(math_ceil, np.ceil)
+        self.unary_template_int64(math_ceil, np.ceil)
+        self.unary_template_uint64(math_ceil, np.ceil)
+
+    #---------------------------------------------------------------------------
+    # test_math_floor
+
+    def test_math_floor(self):
+        self.unary_template_float32(math_floor, np.floor)
+        self.unary_template_float64(math_floor, np.floor)
+        self.unary_template_int64(math_floor, np.floor)
+        self.unary_template_uint64(math_floor, np.floor)
+
+    #---------------------------------------------------------------------------
+    # test_math_copysign
+
+    def test_math_copysign(self):
+        self.binary_template_float32(math_copysign, np.copysign, start=-1)
+        self.binary_template_float64(math_copysign, np.copysign, start=-1)
+
+    #---------------------------------------------------------------------------
+    # test_math_modf
+
+    def test_math_modf(self):
+        def modf_template_nan(dtype, arytype):
+            A = np.array([np.nan], dtype=dtype)
+            B = np.zeros_like(A)
+            C = np.zeros_like(A)
+            cfunc = cuda.jit((arytype, arytype, arytype))(math_modf)
+            cfunc[1, len(A)](A, B, C)
+            self.assertTrue(np.isnan(B))
+            self.assertTrue(np.isnan(C))
+
+        def modf_template_compare(A, dtype, arytype):
+            A = A.astype(dtype)
+            B = np.zeros_like(A)
+            C = np.zeros_like(A)
+            cfunc = cuda.jit((arytype, arytype, arytype))(math_modf)
+            cfunc[1, len(A)](A, B, C)
+            D, E = np.modf(A)
+            self.assertTrue(np.array_equal(B,D))
+            self.assertTrue(np.array_equal(C,E))
+
+        nelem = 50
+        #32 bit float
+        with self.subTest("float32 modf on simple float"):
+            modf_template_compare(np.linspace(0, 10, nelem), dtype=np.float32,
+                                  arytype=float32[:])
+        with self.subTest("float32 modf on +- infinity"):
+            modf_template_compare(np.array([np.inf, -np.inf]), dtype=np.float32,
+                                  arytype=float32[:])
+        with self.subTest("float32 modf on nan"):
+            modf_template_nan(dtype=np.float32, arytype=float32[:])
+
+        #64 bit float
+        with self.subTest("float64 modf on simple float"):
+            modf_template_compare(np.linspace(0, 10, nelem), dtype=np.float64,
+                                  arytype=float64[:])
+        with self.subTest("float64 modf on +- infinity"):
+            modf_template_compare(np.array([np.inf, -np.inf]), dtype=np.float64,
+                                  arytype=float64[:])
+        with self.subTest("float64 modf on nan"):
+            modf_template_nan(dtype=np.float64, arytype=float64[:])
+
+    #---------------------------------------------------------------------------
+    # test_math_fmod
+
+    def test_math_fmod(self):
+        self.binary_template_float32(math_fmod, np.fmod, start=1)
+        self.binary_template_float64(math_fmod, np.fmod, start=1)
+
+    #---------------------------------------------------------------------------
+    # test_math_mod_binop
+
+    def test_math_mod_binop(self):
+        self.binary_template_float32(math_mod_binop, np.fmod, start=1)
+        self.binary_template_float64(math_mod_binop, np.fmod, start=1)
+
+    #---------------------------------------------------------------------------
+    # test_math_isnan
+
+    def test_math_isnan(self):
+        self.unary_bool_template_float32(math_isnan, np.isnan)
+        self.unary_bool_template_float64(math_isnan, np.isnan)
+        self.unary_bool_template_int32(math_isnan, np.isnan)
+        self.unary_bool_template_int64(math_isnan, np.isnan)
+        self.unary_bool_special_values_float32(math_isnan, np.isnan)
+        self.unary_bool_special_values_float64(math_isnan, np.isnan)
+
+    #---------------------------------------------------------------------------
+    # test_math_isinf
+
+    def test_math_isinf(self):
+        self.unary_bool_template_float32(math_isinf, np.isinf)
+        self.unary_bool_template_float64(math_isinf, np.isinf)
+        self.unary_bool_template_int32(math_isinf, np.isinf)
+        self.unary_bool_template_int64(math_isinf, np.isinf)
+        self.unary_bool_special_values_float32(math_isinf, np.isinf)
+        self.unary_bool_special_values_float64(math_isinf, np.isinf)
+
+    #---------------------------------------------------------------------------
+    # test_math_isfinite
+
+    def test_math_isfinite(self):
+        self.unary_bool_template_float32(math_isfinite, np.isfinite)
+        self.unary_bool_template_float64(math_isfinite, np.isfinite)
+        self.unary_bool_template_int32(math_isfinite, np.isfinite)
+        self.unary_bool_template_int64(math_isfinite, np.isfinite)
+        self.unary_bool_special_values_float32(math_isfinite, np.isfinite)
+        self.unary_bool_special_values_float64(math_isfinite, np.isfinite)
+
+    #---------------------------------------------------------------------------
+    # test_math_degrees
+
+    def test_math_degrees(self):
+        self.unary_bool_template_float32(math_degrees, np.degrees)
+        self.unary_bool_template_float64(math_degrees, np.degrees)
+
+    #---------------------------------------------------------------------------
+    # test_math_radians
+
+    def test_math_radians(self):
+        self.unary_bool_template_float32(math_radians, np.radians)
+        self.unary_bool_template_float64(math_radians, np.radians)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_matmul.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_matmul.py
new file mode 100644
index 000000000..51f1181a3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_matmul.py
@@ -0,0 +1,74 @@
+import numpy as np
+
+from numba import cuda, float32, void
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.core import config
+
+# Ensure the test takes a reasonable amount of time in the simulator
+if config.ENABLE_CUDASIM:
+    bpg, tpb = 2, 8
+else:
+    bpg, tpb = 50, 32
+
+n = bpg * tpb
+SM_SIZE = (tpb, tpb)
+
+
+class TestCudaMatMul(CUDATestCase):
+
+    def test_func(self):
+
+        @cuda.jit(void(float32[:, ::1], float32[:, ::1], float32[:, ::1]))
+        def cu_square_matrix_mul(A, B, C):
+            sA = cuda.shared.array(shape=SM_SIZE, dtype=float32)
+            sB = cuda.shared.array(shape=(tpb, tpb), dtype=float32)
+
+            tx = cuda.threadIdx.x
+            ty = cuda.threadIdx.y
+            bx = cuda.blockIdx.x
+            by = cuda.blockIdx.y
+            bw = cuda.blockDim.x
+            bh = cuda.blockDim.y
+
+            x = tx + bx * bw
+            y = ty + by * bh
+
+            acc = float32(0)  # forces all the math to be f32
+            for i in range(bpg):
+                if x < n and y < n:
+                    sA[ty, tx] = A[y, tx + i * tpb]
+                    sB[ty, tx] = B[ty + i * tpb, x]
+
+                cuda.syncthreads()
+
+                if x < n and y < n:
+                    for j in range(tpb):
+                        acc += sA[ty, j] * sB[j, tx]
+
+                cuda.syncthreads()
+
+            if x < n and y < n:
+                C[y, x] = acc
+
+        np.random.seed(42)
+        A = np.array(np.random.random((n, n)), dtype=np.float32)
+        B = np.array(np.random.random((n, n)), dtype=np.float32)
+        C = np.empty_like(A)
+
+        stream = cuda.stream()
+        with stream.auto_synchronize():
+            dA = cuda.to_device(A, stream)
+            dB = cuda.to_device(B, stream)
+            dC = cuda.to_device(C, stream)
+            cu_square_matrix_mul[(bpg, bpg), (tpb, tpb), stream](dA, dB, dC)
+            dC.copy_to_host(C, stream)
+
+        # Host compute
+        Cans = np.dot(A, B)
+
+        # Check result
+        np.testing.assert_allclose(C, Cans, rtol=1e-5)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_minmax.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_minmax.py
new file mode 100644
index 000000000..aee97fd63
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_minmax.py
@@ -0,0 +1,113 @@
+import numpy as np
+
+from numba import cuda, float64
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+
+
+def builtin_max(A, B, C):
+    i = cuda.grid(1)
+
+    if i >= len(C):
+        return
+
+    C[i] = float64(max(A[i], B[i]))
+
+
+def builtin_min(A, B, C):
+    i = cuda.grid(1)
+
+    if i >= len(C):
+        return
+
+    C[i] = float64(min(A[i], B[i]))
+
+
+@skip_on_cudasim('Tests PTX emission')
+class TestCudaMinMax(CUDATestCase):
+    def _run(
+            self,
+            kernel,
+            numpy_equivalent,
+            ptx_instruction,
+            dtype_left,
+            dtype_right,
+            n=5):
+        kernel = cuda.jit(kernel)
+
+        c = np.zeros(n, dtype=np.float64)
+        a = np.arange(n, dtype=dtype_left) + .5
+        b = np.full(n, fill_value=2, dtype=dtype_right)
+
+        kernel[1, c.shape](a, b, c)
+        np.testing.assert_allclose(c, numpy_equivalent(a, b))
+
+        ptx = next(p for p in kernel.inspect_asm().values())
+        self.assertIn(ptx_instruction, ptx)
+
+    def test_max_f8f8(self):
+        self._run(
+            builtin_max,
+            np.maximum,
+            'max.f64',
+            np.float64,
+            np.float64)
+
+    def test_max_f4f8(self):
+        self._run(
+            builtin_max,
+            np.maximum,
+            'max.f64',
+            np.float32,
+            np.float64)
+
+    def test_max_f8f4(self):
+        self._run(
+            builtin_max,
+            np.maximum,
+            'max.f64',
+            np.float64,
+            np.float32)
+
+    def test_max_f4f4(self):
+        self._run(
+            builtin_max,
+            np.maximum,
+            'max.f32',
+            np.float32,
+            np.float32)
+
+    def test_min_f8f8(self):
+        self._run(
+            builtin_min,
+            np.minimum,
+            'min.f64',
+            np.float64,
+            np.float64)
+
+    def test_min_f4f8(self):
+        self._run(
+            builtin_min,
+            np.minimum,
+            'min.f64',
+            np.float32,
+            np.float64)
+
+    def test_min_f8f4(self):
+        self._run(
+            builtin_min,
+            np.minimum,
+            'min.f64',
+            np.float64,
+            np.float32)
+
+    def test_min_f4f4(self):
+        self._run(
+            builtin_min,
+            np.minimum,
+            'min.f32',
+            np.float32,
+            np.float32)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_montecarlo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_montecarlo.py
new file mode 100644
index 000000000..181a80a69
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_montecarlo.py
@@ -0,0 +1,22 @@
+import math
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaMonteCarlo(CUDATestCase):
+    def test_montecarlo(self):
+        """Just make sure we can compile this
+        """
+
+        @cuda.jit(
+            'void(double[:], double[:], double, double, double, double[:])')
+        def step(last, paths, dt, c0, c1, normdist):
+            i = cuda.grid(1)
+            if i >= paths.shape[0]:
+                return
+            noise = normdist[i]
+            paths[i] = last[i] * math.exp(c0 * dt + c1 * noise)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multigpu.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multigpu.py
new file mode 100644
index 000000000..01b8a63ea
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multigpu.py
@@ -0,0 +1,140 @@
+from numba import cuda
+import numpy as np
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import threading
+import unittest
+
+
+class TestMultiGPUContext(CUDATestCase):
+    @unittest.skipIf(len(cuda.gpus) < 2, "need more than 1 gpus")
+    def test_multigpu_context(self):
+        @cuda.jit("void(float64[:], float64[:])")
+        def copy_plus_1(inp, out):
+            i = cuda.grid(1)
+            if i < out.size:
+                out[i] = inp[i] + 1
+
+        def check(inp, out):
+            np.testing.assert_equal(inp + 1, out)
+
+        N = 32
+        A = np.arange(N, dtype=np.float64)
+        B = np.arange(N, dtype=np.float64)
+
+        with cuda.gpus[0]:
+            copy_plus_1[1, N](A, B)
+
+        check(A, B)
+
+        copy_plus_1[1, N](A, B)
+        check(A, B)
+
+        with cuda.gpus[0]:
+            A0 = np.arange(N, dtype=np.float64)
+            B0 = np.arange(N, dtype=np.float64)
+            copy_plus_1[1, N](A0, B0)
+
+            with cuda.gpus[1]:
+                A1 = np.arange(N, dtype=np.float64)
+                B1 = np.arange(N, dtype=np.float64)
+                copy_plus_1[1, N](A1, B1)
+
+        check(A0, B0)
+        check(A1, B1)
+
+        A = np.arange(N, dtype=np.float64)
+        B = np.arange(N, dtype=np.float64)
+        copy_plus_1[1, N](A, B)
+        check(A, B)
+
+    @skip_on_cudasim('Simulator does not support multiple threads')
+    def test_multithreaded(self):
+        def work(gpu, dA, results, ridx):
+            try:
+                with gpu:
+                    arr = dA.copy_to_host()
+
+            except Exception as e:
+                results[ridx] = e
+
+            else:
+                results[ridx] = np.all(arr == np.arange(10))
+
+        dA = cuda.to_device(np.arange(10))
+
+        nthreads = 10
+        results = [None] * nthreads
+        threads = [threading.Thread(target=work, args=(cuda.gpus.current,
+                                                       dA, results, i))
+                   for i in range(nthreads)]
+        for th in threads:
+            th.start()
+
+        for th in threads:
+            th.join()
+
+        for r in results:
+            if isinstance(r, BaseException):
+                raise r
+            else:
+                self.assertTrue(r)
+
+    @unittest.skipIf(len(cuda.gpus) < 2, "need more than 1 gpus")
+    def test_with_context(self):
+
+        @cuda.jit
+        def vector_add_scalar(arr, val):
+            i = cuda.grid(1)
+            if i < arr.size:
+                arr[i] += val
+
+        hostarr = np.arange(10, dtype=np.float32)
+        with cuda.gpus[0]:
+            arr1 = cuda.to_device(hostarr)
+
+        with cuda.gpus[1]:
+            arr2 = cuda.to_device(hostarr)
+
+        with cuda.gpus[0]:
+            vector_add_scalar[1, 10](arr1, 1)
+
+        with cuda.gpus[1]:
+            vector_add_scalar[1, 10](arr2, 2)
+
+        with cuda.gpus[0]:
+            np.testing.assert_equal(arr1.copy_to_host(), (hostarr + 1))
+
+        with cuda.gpus[1]:
+            np.testing.assert_equal(arr2.copy_to_host(), (hostarr + 2))
+
+    @unittest.skipIf(len(cuda.gpus) < 2, "need more than 1 gpus")
+    def test_with_context_peer_copy(self):
+        # Peer access is not always possible - for example, with one GPU in TCC
+        # mode and one in WDDM - if that is the case, this test would fail so
+        # we need to skip it.
+        with cuda.gpus[0]:
+            ctx = cuda.current_context()
+            if not ctx.can_access_peer(1):
+                self.skipTest('Peer access between GPUs disabled')
+
+        # 1. Create a range in an array
+        hostarr = np.arange(10, dtype=np.float32)
+
+        # 2. Copy range array from host -> GPU 0
+        with cuda.gpus[0]:
+            arr1 = cuda.to_device(hostarr)
+
+        # 3. Initialize a zero-filled array on GPU 1
+        with cuda.gpus[1]:
+            arr2 = cuda.to_device(np.zeros_like(hostarr))
+
+        with cuda.gpus[0]:
+            # 4. Copy range from GPU 0 -> GPU 1
+            arr2.copy_to_device(arr1)
+
+            # 5. Copy range from GPU 1 -> host and check contents
+            np.testing.assert_equal(arr2.copy_to_host(), hostarr)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multiprocessing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multiprocessing.py
new file mode 100644
index 000000000..04a1234b4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multiprocessing.py
@@ -0,0 +1,46 @@
+import os
+import multiprocessing as mp
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+
+has_mp_get_context = hasattr(mp, 'get_context')
+is_unix = os.name == 'posix'
+
+
+def fork_test(q):
+    from numba.cuda.cudadrv.error import CudaDriverError
+    try:
+        cuda.to_device(np.arange(1))
+    except CudaDriverError as e:
+        q.put(e)
+    else:
+        q.put(None)
+
+
+@skip_on_cudasim('disabled for cudasim')
+class TestMultiprocessing(CUDATestCase):
+    @unittest.skipUnless(has_mp_get_context, 'requires mp.get_context')
+    @unittest.skipUnless(is_unix, 'requires Unix')
+    def test_fork(self):
+        """
+        Test fork detection.
+        """
+        cuda.current_context()  # force cuda initialize
+        # fork in process that also uses CUDA
+        ctx = mp.get_context('fork')
+        q = ctx.Queue()
+        proc = ctx.Process(target=fork_test, args=[q])
+        proc.start()
+        exc = q.get()
+        proc.join()
+        # there should be an exception raised in the child process
+        self.assertIsNotNone(exc)
+        self.assertIn('CUDA initialized before forking', str(exc))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multithreads.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multithreads.py
new file mode 100644
index 000000000..30afd3eb0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_multithreads.py
@@ -0,0 +1,101 @@
+import traceback
+import threading
+import multiprocessing
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import (skip_on_cudasim, skip_under_cuda_memcheck,
+                                CUDATestCase)
+import unittest
+
+try:
+    from concurrent.futures import ThreadPoolExecutor
+except ImportError:
+    has_concurrent_futures = False
+else:
+    has_concurrent_futures = True
+
+
+has_mp_get_context = hasattr(multiprocessing, 'get_context')
+
+
+def check_concurrent_compiling():
+    @cuda.jit
+    def foo(x):
+        x[0] += 1
+
+    def use_foo(x):
+        foo[1, 1](x)
+        return x
+
+    arrays = [cuda.to_device(np.arange(10)) for i in range(10)]
+    expected = np.arange(10)
+    expected[0] += 1
+    with ThreadPoolExecutor(max_workers=4) as e:
+        for ary in e.map(use_foo, arrays):
+            np.testing.assert_equal(ary, expected)
+
+
+def spawn_process_entry(q):
+    try:
+        check_concurrent_compiling()
+    # Catch anything that goes wrong in the threads
+    except:  # noqa: E722
+        msg = traceback.format_exc()
+        q.put('\n'.join(['', '=' * 80, msg]))
+    else:
+        q.put(None)
+
+
+@skip_under_cuda_memcheck('Hangs cuda-memcheck')
+@skip_on_cudasim('disabled for cudasim')
+class TestMultiThreadCompiling(CUDATestCase):
+
+    @unittest.skipIf(not has_concurrent_futures, "no concurrent.futures")
+    def test_concurrent_compiling(self):
+        check_concurrent_compiling()
+
+    @unittest.skipIf(not has_mp_get_context, "no multiprocessing.get_context")
+    def test_spawn_concurrent_compilation(self):
+        # force CUDA context init
+        cuda.get_current_device()
+        # use "spawn" to avoid inheriting the CUDA context
+        ctx = multiprocessing.get_context('spawn')
+
+        q = ctx.Queue()
+        p = ctx.Process(target=spawn_process_entry, args=(q,))
+        p.start()
+        try:
+            err = q.get()
+        finally:
+            p.join()
+        if err is not None:
+            raise AssertionError(err)
+        self.assertEqual(p.exitcode, 0, 'test failed in child process')
+
+    def test_invalid_context_error_with_d2h(self):
+        def d2h(arr, out):
+            out[:] = arr.copy_to_host()
+
+        arr = np.arange(1, 4)
+        out = np.zeros_like(arr)
+        darr = cuda.to_device(arr)
+        th = threading.Thread(target=d2h, args=[darr, out])
+        th.start()
+        th.join()
+        np.testing.assert_equal(arr, out)
+
+    def test_invalid_context_error_with_d2d(self):
+        def d2d(dst, src):
+            dst.copy_to_device(src)
+
+        arr = np.arange(100)
+        common = cuda.to_device(arr)
+        darr = cuda.to_device(np.zeros(common.shape, dtype=common.dtype))
+        th = threading.Thread(target=d2d, args=[darr, common])
+        th.start()
+        th.join()
+        np.testing.assert_equal(darr.copy_to_host(), arr)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_nondet.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_nondet.py
new file mode 100644
index 000000000..d768c8cb6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_nondet.py
@@ -0,0 +1,49 @@
+import numpy as np
+from numba import cuda, float32, void
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+def generate_input(n):
+    A = np.array(np.arange(n * n).reshape(n, n), dtype=np.float32)
+    B = np.array(np.arange(n) + 0, dtype=A.dtype)
+    return A, B
+
+
+class TestCudaNonDet(CUDATestCase):
+    def test_for_pre(self):
+        """Test issue with loop not running due to bad sign-extension at the for loop
+        precondition.
+        """
+
+        @cuda.jit(void(float32[:, :], float32[:, :], float32[:]))
+        def diagproduct(c, a, b):
+            startX, startY = cuda.grid(2)
+            gridX = cuda.gridDim.x * cuda.blockDim.x
+            gridY = cuda.gridDim.y * cuda.blockDim.y
+            height = c.shape[0]
+            width = c.shape[1]
+
+            for x in range(startX, width, (gridX)):
+                for y in range(startY, height, (gridY)):
+                    c[y, x] = a[y, x] * b[x]
+
+        N = 8
+
+        A, B = generate_input(N)
+
+        F = np.empty(A.shape, dtype=A.dtype)
+
+        blockdim = (32, 8)
+        griddim = (1, 1)
+
+        dA = cuda.to_device(A)
+        dB = cuda.to_device(B)
+        dF = cuda.to_device(F, copy=False)
+        diagproduct[griddim, blockdim](dF, dA, dB)
+
+        E = np.dot(A, np.diag(B))
+        np.testing.assert_array_almost_equal(dF.copy_to_host(), E)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_operator.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_operator.py
new file mode 100644
index 000000000..008054081
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_operator.py
@@ -0,0 +1,41 @@
+import numpy as np
+from numba.cuda.testing import unittest, CUDATestCase
+from numba import cuda
+import operator
+
+
+class TestOperatorModule(CUDATestCase):
+    """
+    Test if operator module is supported by the CUDA target.
+    """
+    def operator_template(self, op):
+        @cuda.jit
+        def foo(a, b):
+            i = 0
+            a[i] = op(a[i], b[i])
+
+        a = np.ones(1)
+        b = np.ones(1)
+        res = a.copy()
+        foo[1, 1](res, b)
+
+        np.testing.assert_equal(res, op(a, b))
+
+    def test_add(self):
+        self.operator_template(operator.add)
+
+    def test_sub(self):
+        self.operator_template(operator.sub)
+
+    def test_mul(self):
+        self.operator_template(operator.mul)
+
+    def test_truediv(self):
+        self.operator_template(operator.truediv)
+
+    def test_floordiv(self):
+        self.operator_template(operator.floordiv)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_optimization.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_optimization.py
new file mode 100644
index 000000000..812b1cfa3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_optimization.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+from numba import cuda, float64
+import unittest
+
+
+def kernel_func(x):
+    x[0] = 1
+
+
+def device_func(x, y, z):
+    return x * y + z
+
+
+# Fragments of code that are removed from kernel_func's PTX when optimization
+# is on
+removed_by_opt = ( '__local_depot0', 'call.uni', 'st.param.b64')
+
+
+@skip_on_cudasim('Simulator does not optimize code')
+class TestOptimization(CUDATestCase):
+    def test_eager_opt(self):
+        # Optimization should occur by default
+        sig = (float64[::1],)
+        kernel = cuda.jit(sig)(kernel_func)
+        ptx = kernel.inspect_asm()
+
+        for fragment in removed_by_opt:
+            with self.subTest(fragment=fragment):
+                self.assertNotIn(fragment, ptx[sig])
+
+    def test_eager_noopt(self):
+        # Optimization disabled
+        sig = (float64[::1],)
+        kernel = cuda.jit(sig, opt=False)(kernel_func)
+        ptx = kernel.inspect_asm()
+
+        for fragment in removed_by_opt:
+            with self.subTest(fragment=fragment):
+                self.assertIn(fragment, ptx[sig])
+
+    def test_lazy_opt(self):
+        # Optimization should occur by default
+        kernel = cuda.jit(kernel_func)
+        x = np.zeros(1, dtype=np.float64)
+        kernel[1, 1](x)
+
+        # Grab the PTX for the one definition that has just been jitted
+        ptx = next(iter(kernel.inspect_asm().items()))[1]
+
+        for fragment in removed_by_opt:
+            with self.subTest(fragment=fragment):
+                self.assertNotIn(fragment, ptx)
+
+    def test_lazy_noopt(self):
+        # Optimization disabled
+        kernel = cuda.jit(opt=False)(kernel_func)
+        x = np.zeros(1, dtype=np.float64)
+        kernel[1, 1](x)
+
+        # Grab the PTX for the one definition that has just been jitted
+        ptx = next(iter(kernel.inspect_asm().items()))[1]
+
+        for fragment in removed_by_opt:
+            with self.subTest(fragment=fragment):
+                self.assertIn(fragment, ptx)
+
+    def test_device_opt(self):
+        # Optimization should occur by default
+        sig = (float64, float64, float64)
+        device = cuda.jit(sig, device=True)(device_func)
+        ptx = device.inspect_asm(sig)
+        self.assertIn('fma.rn.f64', ptx)
+
+    def test_device_noopt(self):
+        # Optimization disabled
+        sig = (float64, float64, float64)
+        device = cuda.jit(sig, device=True, opt=False)(device_func)
+        ptx = device.inspect_asm(sig)
+        # Fused-multiply adds should be disabled when not optimizing
+        self.assertNotIn('fma.rn.f64', ptx)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_overload.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_overload.py
new file mode 100644
index 000000000..2764678ec
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_overload.py
@@ -0,0 +1,300 @@
+from numba import cuda, njit
+from numba.core.extending import overload
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim, unittest
+
+import numpy as np
+
+
+# Dummy function definitions to overload
+
+def generic_func_1():
+    pass
+
+
+def cuda_func_1():
+    pass
+
+
+def generic_func_2():
+    pass
+
+
+def cuda_func_2():
+    pass
+
+
+def generic_calls_generic():
+    pass
+
+
+def generic_calls_cuda():
+    pass
+
+
+def cuda_calls_generic():
+    pass
+
+
+def cuda_calls_cuda():
+    pass
+
+
+def target_overloaded():
+    pass
+
+
+def generic_calls_target_overloaded():
+    pass
+
+
+def cuda_calls_target_overloaded():
+    pass
+
+
+def target_overloaded_calls_target_overloaded():
+    pass
+
+
+# To recognise which functions are resolved for a call, we identify each with a
+# prime number. Each function called multiplies a value by its prime (starting
+# with the value 1), and we can check that the result is as expected based on
+# the final value after all multiplications.
+
+GENERIC_FUNCTION_1 = 2
+CUDA_FUNCTION_1 = 3
+GENERIC_FUNCTION_2 = 5
+CUDA_FUNCTION_2 = 7
+GENERIC_CALLS_GENERIC = 11
+GENERIC_CALLS_CUDA = 13
+CUDA_CALLS_GENERIC = 17
+CUDA_CALLS_CUDA = 19
+GENERIC_TARGET_OL = 23
+CUDA_TARGET_OL = 29
+GENERIC_CALLS_TARGET_OL = 31
+CUDA_CALLS_TARGET_OL = 37
+GENERIC_TARGET_OL_CALLS_TARGET_OL = 41
+CUDA_TARGET_OL_CALLS_TARGET_OL = 43
+
+
+# Overload implementations
+
+@overload(generic_func_1, target='generic')
+def ol_generic_func_1(x):
+    def impl(x):
+        x[0] *= GENERIC_FUNCTION_1
+    return impl
+
+
+@overload(cuda_func_1, target='cuda')
+def ol_cuda_func_1(x):
+    def impl(x):
+        x[0] *= CUDA_FUNCTION_1
+    return impl
+
+
+@overload(generic_func_2, target='generic')
+def ol_generic_func_2(x):
+    def impl(x):
+        x[0] *= GENERIC_FUNCTION_2
+    return impl
+
+
+@overload(cuda_func_2, target='cuda')
+def ol_cuda_func(x):
+    def impl(x):
+        x[0] *= CUDA_FUNCTION_2
+    return impl
+
+
+@overload(generic_calls_generic, target='generic')
+def ol_generic_calls_generic(x):
+    def impl(x):
+        x[0] *= GENERIC_CALLS_GENERIC
+        generic_func_1(x)
+    return impl
+
+
+@overload(generic_calls_cuda, target='generic')
+def ol_generic_calls_cuda(x):
+    def impl(x):
+        x[0] *= GENERIC_CALLS_CUDA
+        cuda_func_1(x)
+    return impl
+
+
+@overload(cuda_calls_generic, target='cuda')
+def ol_cuda_calls_generic(x):
+    def impl(x):
+        x[0] *= CUDA_CALLS_GENERIC
+        generic_func_1(x)
+    return impl
+
+
+@overload(cuda_calls_cuda, target='cuda')
+def ol_cuda_calls_cuda(x):
+    def impl(x):
+        x[0] *= CUDA_CALLS_CUDA
+        cuda_func_1(x)
+    return impl
+
+
+@overload(target_overloaded, target='generic')
+def ol_target_overloaded_generic(x):
+    def impl(x):
+        x[0] *= GENERIC_TARGET_OL
+    return impl
+
+
+@overload(target_overloaded, target='cuda')
+def ol_target_overloaded_cuda(x):
+    def impl(x):
+        x[0] *= CUDA_TARGET_OL
+    return impl
+
+
+@overload(generic_calls_target_overloaded, target='generic')
+def ol_generic_calls_target_overloaded(x):
+    def impl(x):
+        x[0] *= GENERIC_CALLS_TARGET_OL
+        target_overloaded(x)
+    return impl
+
+
+@overload(cuda_calls_target_overloaded, target='cuda')
+def ol_cuda_calls_target_overloaded(x):
+    def impl(x):
+        x[0] *= CUDA_CALLS_TARGET_OL
+        target_overloaded(x)
+    return impl
+
+
+@overload(target_overloaded_calls_target_overloaded, target='generic')
+def ol_generic_calls_target_overloaded_generic(x):
+    def impl(x):
+        x[0] *= GENERIC_TARGET_OL_CALLS_TARGET_OL
+        target_overloaded(x)
+    return impl
+
+
+@overload(target_overloaded_calls_target_overloaded, target='cuda')
+def ol_generic_calls_target_overloaded_cuda(x):
+    def impl(x):
+        x[0] *= CUDA_TARGET_OL_CALLS_TARGET_OL
+        target_overloaded(x)
+    return impl
+
+
+@skip_on_cudasim('Overloading not supported in cudasim')
+class TestOverload(CUDATestCase):
+    def check_overload(self, kernel, expected):
+        x = np.ones(1, dtype=np.int32)
+        cuda.jit(kernel)[1, 1](x)
+        self.assertEqual(x[0], expected)
+
+    def check_overload_cpu(self, kernel, expected):
+        x = np.ones(1, dtype=np.int32)
+        njit(kernel)(x)
+        self.assertEqual(x[0], expected)
+
+    def test_generic(self):
+        def kernel(x):
+            generic_func_1(x)
+
+        expected = GENERIC_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_cuda(self):
+        def kernel(x):
+            cuda_func_1(x)
+
+        expected = CUDA_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_generic_and_cuda(self):
+        def kernel(x):
+            generic_func_1(x)
+            cuda_func_1(x)
+
+        expected = GENERIC_FUNCTION_1 * CUDA_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_call_two_generic_calls(self):
+        def kernel(x):
+            generic_func_1(x)
+            generic_func_2(x)
+
+        expected = GENERIC_FUNCTION_1 * GENERIC_FUNCTION_2
+        self.check_overload(kernel, expected)
+
+    def test_call_two_cuda_calls(self):
+        def kernel(x):
+            cuda_func_1(x)
+            cuda_func_2(x)
+
+        expected = CUDA_FUNCTION_1 * CUDA_FUNCTION_2
+        self.check_overload(kernel, expected)
+
+    def test_generic_calls_generic(self):
+        def kernel(x):
+            generic_calls_generic(x)
+
+        expected = GENERIC_CALLS_GENERIC * GENERIC_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_generic_calls_cuda(self):
+        def kernel(x):
+            generic_calls_cuda(x)
+
+        expected = GENERIC_CALLS_CUDA * CUDA_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_cuda_calls_generic(self):
+        def kernel(x):
+            cuda_calls_generic(x)
+
+        expected = CUDA_CALLS_GENERIC * GENERIC_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_cuda_calls_cuda(self):
+        def kernel(x):
+            cuda_calls_cuda(x)
+
+        expected = CUDA_CALLS_CUDA * CUDA_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_call_target_overloaded(self):
+        def kernel(x):
+            target_overloaded(x)
+
+        expected = CUDA_TARGET_OL
+        self.check_overload(kernel, expected)
+
+    def test_generic_calls_target_overloaded(self):
+        def kernel(x):
+            generic_calls_target_overloaded(x)
+
+        expected = GENERIC_CALLS_TARGET_OL * CUDA_TARGET_OL
+        self.check_overload(kernel, expected)
+
+    def test_cuda_calls_target_overloaded(self):
+        def kernel(x):
+            cuda_calls_target_overloaded(x)
+
+        expected = CUDA_CALLS_TARGET_OL * CUDA_TARGET_OL
+        self.check_overload(kernel, expected)
+
+    def test_target_overloaded_calls_target_overloaded(self):
+        def kernel(x):
+            target_overloaded_calls_target_overloaded(x)
+
+        # Check the CUDA overloads are used on CUDA
+        expected = CUDA_TARGET_OL_CALLS_TARGET_OL * CUDA_TARGET_OL
+        self.check_overload(kernel, expected)
+
+        # Also check that the CPU overloads are used on the CPU
+        expected = GENERIC_TARGET_OL_CALLS_TARGET_OL * GENERIC_TARGET_OL
+        self.check_overload_cpu(kernel, expected)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_powi.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_powi.py
new file mode 100644
index 000000000..1932b3165
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_powi.py
@@ -0,0 +1,124 @@
+import math
+import numpy as np
+from numba import cuda, float64, int8, int32, void
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+def cu_mat_power(A, power, power_A):
+    y, x = cuda.grid(2)
+
+    m, n = power_A.shape
+    if x >= n or y >= m:
+        return
+
+    power_A[y, x] = math.pow(A[y, x], int32(power))
+
+
+def cu_mat_power_binop(A, power, power_A):
+    y, x = cuda.grid(2)
+
+    m, n = power_A.shape
+    if x >= n or y >= m:
+        return
+
+    power_A[y, x] = A[y, x] ** power
+
+
+def vec_pow(r, x, y):
+    i = cuda.grid(1)
+
+    if i < len(r):
+        r[i] = pow(x[i], y[i])
+
+
+def vec_pow_binop(r, x, y):
+    i = cuda.grid(1)
+
+    if i < len(r):
+        r[i] = x[i] ** y[i]
+
+
+def vec_pow_inplace_binop(r, x):
+    i = cuda.grid(1)
+
+    if i < len(r):
+        r[i] **= x[i]
+
+
+def random_complex(N):
+    np.random.seed(123)
+    return (np.random.random(1) + np.random.random(1) * 1j)
+
+
+class TestCudaPowi(CUDATestCase):
+    def test_powi(self):
+        dec = cuda.jit(void(float64[:, :], int8, float64[:, :]))
+        kernel = dec(cu_mat_power)
+
+        power = 2
+        A = np.arange(10, dtype=np.float64).reshape(2, 5)
+        Aout = np.empty_like(A)
+        kernel[1, A.shape](A, power, Aout)
+        self.assertTrue(np.allclose(Aout, A ** power))
+
+    def test_powi_binop(self):
+        dec = cuda.jit(void(float64[:, :], int8, float64[:, :]))
+        kernel = dec(cu_mat_power_binop)
+
+        power = 2
+        A = np.arange(10, dtype=np.float64).reshape(2, 5)
+        Aout = np.empty_like(A)
+        kernel[1, A.shape](A, power, Aout)
+        self.assertTrue(np.allclose(Aout, A ** power))
+
+    # Relative tolerance kwarg is provided because 1.0e-7 (the default for
+    # assert_allclose) is a bit tight for single precision.
+    def _test_cpow(self, dtype, func, rtol=1.0e-7):
+        N = 32
+        x = random_complex(N).astype(dtype)
+        y = random_complex(N).astype(dtype)
+        r = np.zeros_like(x)
+
+        cfunc = cuda.jit(func)
+        cfunc[1, N](r, x, y)
+        np.testing.assert_allclose(r, x ** y, rtol=rtol)
+
+        # Checks special cases
+        x = np.asarray([0.0j, 1.0j], dtype=dtype)
+        y = np.asarray([0.0j, 1.0], dtype=dtype)
+        r = np.zeros_like(x)
+
+        cfunc[1, 2](r, x, y)
+        np.testing.assert_allclose(r, x ** y, rtol=rtol)
+
+    def test_cpow_complex64_pow(self):
+        self._test_cpow(np.complex64, vec_pow, rtol=3.0e-7)
+
+    def test_cpow_complex64_binop(self):
+        self._test_cpow(np.complex64, vec_pow_binop, rtol=3.0e-7)
+
+    def test_cpow_complex128_pow(self):
+        self._test_cpow(np.complex128, vec_pow)
+
+    def test_cpow_complex128_binop(self):
+        self._test_cpow(np.complex128, vec_pow_binop)
+
+    def _test_cpow_inplace_binop(self, dtype, rtol=1.0e-7):
+        N = 32
+        x = random_complex(N).astype(dtype)
+        y = random_complex(N).astype(dtype)
+        r = x ** y
+
+        cfunc = cuda.jit(vec_pow_inplace_binop)
+        cfunc[1, N](x, y)
+        np.testing.assert_allclose(x, r, rtol=rtol)
+
+    def test_cpow_complex64_inplace_binop(self):
+        self._test_cpow_inplace_binop(np.complex64, rtol=3.0e-7)
+
+    def test_cpow_complex128_inplace_binop(self):
+        self._test_cpow_inplace_binop(np.complex128, rtol=3.0e-7)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_print.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_print.py
new file mode 100644
index 000000000..d8dca831a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_print.py
@@ -0,0 +1,128 @@
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+import subprocess
+import sys
+import unittest
+
+
+cuhello_usecase = """\
+from numba import cuda
+
+@cuda.jit
+def cuhello():
+    i = cuda.grid(1)
+    print(i, 999)
+    print(-42)
+
+cuhello[2, 3]()
+cuda.synchronize()
+"""
+
+
+printfloat_usecase = """\
+from numba import cuda
+
+@cuda.jit
+def printfloat():
+    i = cuda.grid(1)
+    print(i, 23, 34.75, 321)
+
+printfloat[1, 1]()
+cuda.synchronize()
+"""
+
+
+printstring_usecase = """\
+from numba import cuda
+
+@cuda.jit
+def printstring():
+    i = cuda.grid(1)
+    print(i, "hop!", 999)
+
+printstring[1, 3]()
+cuda.synchronize()
+"""
+
+printempty_usecase = """\
+from numba import cuda
+
+@cuda.jit
+def printempty():
+    print()
+
+printempty[1, 1]()
+cuda.synchronize()
+"""
+
+
+print_too_many_usecase = """\
+from numba import cuda
+import numpy as np
+
+@cuda.jit
+def print_too_many(r):
+    print(r[0], r[1], r[2], r[3], r[4], r[5], r[6], r[7], r[8], r[9], r[10],
+          r[11], r[12], r[13], r[14], r[15], r[16], r[17], r[18], r[19], r[20],
+          r[21], r[22], r[23], r[24], r[25], r[26], r[27], r[28], r[29], r[30],
+          r[31], r[32])
+
+print_too_many[1, 1](np.arange(33))
+cuda.synchronize()
+"""
+
+
+class TestPrint(CUDATestCase):
+    # Note that in these tests we generally strip the output to avoid dealing
+    # with platform-specific line ending issues, e.g. '\r\n' vs '\n' etc.
+
+    def run_code(self, code):
+        """Runs code in a subprocess and returns the captured output"""
+        cmd = [sys.executable, "-c", code]
+        cp = subprocess.run(cmd, timeout=60, capture_output=True, check=True)
+        return cp.stdout.decode(), cp.stderr.decode()
+
+    def test_cuhello(self):
+        output, _ = self.run_code(cuhello_usecase)
+        actual = [line.strip() for line in output.splitlines()]
+        expected = ['-42'] * 6 + ['%d 999' % i for i in range(6)]
+        # The output of GPU threads is intermingled, but each print()
+        # call is still atomic
+        self.assertEqual(sorted(actual), expected)
+
+    def test_printfloat(self):
+        output, _ = self.run_code(printfloat_usecase)
+        # CUDA and the simulator use different formats for float formatting
+        expected_cases = ["0 23 34.750000 321", "0 23 34.75 321"]
+        self.assertIn(output.strip(), expected_cases)
+
+    def test_printempty(self):
+        output, _ = self.run_code(printempty_usecase)
+        self.assertEqual(output.strip(), "")
+
+    def test_string(self):
+        output, _ = self.run_code(printstring_usecase)
+        lines = [line.strip() for line in output.splitlines(True)]
+        expected = ['%d hop! 999' % i for i in range(3)]
+        self.assertEqual(sorted(lines), expected)
+
+    @skip_on_cudasim('cudasim can print unlimited output')
+    def test_too_many_args(self):
+        # Tests that we emit the format string and warn when there are more
+        # than 32 arguments, in common with CUDA C/C++ printf - this is due to
+        # a limitation in CUDA vprintf, see:
+        # https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#limitations
+
+        output, errors = self.run_code(print_too_many_usecase)
+
+        # Check that the format string was printed instead of formatted garbage
+        expected_fmt_string = ' '.join(['%lld' for _ in range(33)])
+        self.assertIn(expected_fmt_string, output)
+
+        # Check for the expected warning about formatting more than 32 items
+        warn_msg = ('CUDA print() cannot print more than 32 items. The raw '
+                    'format string will be emitted by the kernel instead.')
+        self.assertIn(warn_msg, errors)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_py2_div_issue.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_py2_div_issue.py
new file mode 100644
index 000000000..298a5b747
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_py2_div_issue.py
@@ -0,0 +1,33 @@
+import numpy as np
+from numba import cuda, float32, int32, void
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaPy2Div(CUDATestCase):
+    def test_py2_div_issue(self):
+        @cuda.jit(void(float32[:], float32[:], float32[:], int32))
+        def preCalc(y, yA, yB, numDataPoints):
+            i = cuda.grid(1)
+            # k is unused, but may be part of the trigger for the bug this
+            # tests for.
+            k = i % numDataPoints  # noqa: F841
+
+            ans = float32(1.001 * float32(i))
+
+            y[i] = ans
+            yA[i] = ans * 1.0
+            yB[i] = ans / 1.0
+
+        numDataPoints = 15
+
+        y = np.zeros(numDataPoints, dtype=np.float32)
+        yA = np.zeros(numDataPoints, dtype=np.float32)
+        yB = np.zeros(numDataPoints, dtype=np.float32)
+        preCalc[1, 15](y, yA, yB, numDataPoints)
+
+        self.assertTrue(np.all(y == yA))
+        self.assertTrue(np.all(y == yB))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_random.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_random.py
new file mode 100644
index 000000000..11bbf95aa
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_random.py
@@ -0,0 +1,104 @@
+import math
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import unittest
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+
+from numba.cuda.random import \
+    xoroshiro128p_uniform_float32, xoroshiro128p_normal_float32, \
+    xoroshiro128p_uniform_float64, xoroshiro128p_normal_float64
+
+
+# Distributions
+UNIFORM = 1
+NORMAL = 2
+
+
+@cuda.jit
+def rng_kernel_float32(states, out, count, distribution):
+    thread_id = cuda.grid(1)
+
+    for i in range(count):
+        idx = thread_id * count + i
+
+        if distribution == UNIFORM:
+            out[idx] = xoroshiro128p_uniform_float32(states, thread_id)
+        elif distribution == NORMAL:
+            out[idx] = xoroshiro128p_normal_float32(states, thread_id)
+
+
+@cuda.jit
+def rng_kernel_float64(states, out, count, distribution):
+    thread_id = cuda.grid(1)
+
+    for i in range(count):
+        idx = thread_id * count + i
+
+        if distribution == UNIFORM:
+            out[idx] = xoroshiro128p_uniform_float64(states, thread_id)
+        elif distribution == NORMAL:
+            out[idx] = xoroshiro128p_normal_float64(states, thread_id)
+
+
+class TestCudaRandomXoroshiro128p(CUDATestCase):
+    def test_create(self):
+        states = cuda.random.create_xoroshiro128p_states(10, seed=1)
+        s = states.copy_to_host()
+        self.assertEqual(len(np.unique(s)), 10)
+
+    def test_create_subsequence_start(self):
+        states = cuda.random.create_xoroshiro128p_states(10, seed=1)
+        s1 = states.copy_to_host()
+
+        states = cuda.random.create_xoroshiro128p_states(10, seed=1,
+                                                         subsequence_start=3)
+        s2 = states.copy_to_host()
+
+        # Starting seeds should match up with offset of 3
+        np.testing.assert_array_equal(s1[3:], s2[:-3])
+
+    def test_create_stream(self):
+        stream = cuda.stream()
+        states = cuda.random.create_xoroshiro128p_states(10, seed=1,
+                                                         stream=stream)
+        s = states.copy_to_host()
+        self.assertEqual(len(np.unique(s)), 10)
+
+    def check_uniform(self, kernel_func, dtype):
+        states = cuda.random.create_xoroshiro128p_states(32 * 2, seed=1)
+        out = np.zeros(2 * 32 * 32, dtype=np.float32)
+
+        kernel_func[2, 32](states, out, 32, UNIFORM)
+        self.assertAlmostEqual(out.min(), 0.0, delta=1e-3)
+        self.assertAlmostEqual(out.max(), 1.0, delta=1e-3)
+        self.assertAlmostEqual(out.mean(), 0.5, delta=1.5e-2)
+        self.assertAlmostEqual(out.std(), 1.0 / (2 * math.sqrt(3)), delta=6e-3)
+
+    def test_uniform_float32(self):
+        self.check_uniform(rng_kernel_float32, np.float32)
+
+    @skip_on_cudasim('skip test for speed under cudasim')
+    def test_uniform_float64(self):
+        self.check_uniform(rng_kernel_float64, np.float64)
+
+    def check_normal(self, kernel_func, dtype):
+        states = cuda.random.create_xoroshiro128p_states(32 * 2, seed=1)
+        out = np.zeros(2 * 32 * 32, dtype=dtype)
+
+        kernel_func[2, 32](states, out, 32, NORMAL)
+
+        self.assertAlmostEqual(out.mean(), 0.0, delta=4e-3)
+        self.assertAlmostEqual(out.std(), 1.0, delta=2e-3)
+
+    def test_normal_float32(self):
+        self.check_normal(rng_kernel_float32, np.float32)
+
+    @skip_on_cudasim('skip test for speed under cudasim')
+    def test_normal_float64(self):
+        self.check_normal(rng_kernel_float64, np.float64)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_record_dtype.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_record_dtype.py
new file mode 100644
index 000000000..75651488e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_record_dtype.py
@@ -0,0 +1,610 @@
+import numpy as np
+from numba import cuda
+from numba.core import types
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+from numba.np import numpy_support
+
+
+def set_a(ary, i, v):
+    ary[i].a = v
+
+
+def set_b(ary, i, v):
+    ary[i].b = v
+
+
+def set_c(ary, i, v):
+    ary[i].c = v
+
+
+def set_record(ary, i, j):
+    ary[i] = ary[j]
+
+
+def record_set_a(r, v):
+    r.a = v
+
+
+def record_set_b(r, v):
+    r.b = v
+
+
+def record_set_c(r, v):
+    r.c = v
+
+
+def record_read_a(r, arr):
+    arr[0] = r.a
+
+
+def record_read_b(r, arr):
+    arr[0] = r.b
+
+
+def record_read_c(r, arr):
+    arr[0] = r.c
+
+
+def record_write_array(r):
+    r.g = 2
+    r.h[0] = 3.0
+    r.h[1] = 4.0
+
+
+def record_write_2d_array(r):
+    r.i = 3
+    r.j[0, 0] = 5.0
+    r.j[0, 1] = 6.0
+    r.j[1, 0] = 7.0
+    r.j[1, 1] = 8.0
+    r.j[2, 0] = 9.0
+    r.j[2, 1] = 10.0
+
+
+def record_read_array(r, a):
+    a[0] = r.h[0]
+    a[1] = r.h[1]
+
+
+def record_read_2d_array(r, a):
+    a[0, 0] = r.j[0, 0]
+    a[0, 1] = r.j[0, 1]
+    a[1, 0] = r.j[1, 0]
+    a[1, 1] = r.j[1, 1]
+    a[2, 0] = r.j[2, 0]
+    a[2, 1] = r.j[2, 1]
+
+
+recordtype = np.dtype(
+    [
+        ('a', np.float64),
+        ('b', np.int32),
+        ('c', np.complex64),
+        ('d', (np.uint8, 5))
+    ],
+    align=True
+)
+
+recordwitharray = np.dtype(
+    [
+        ('g', np.int32),
+        ('h', np.float32, 2)
+    ],
+    align=True
+)
+
+recordwith2darray = np.dtype([('i', np.int32),
+                              ('j', np.float32, (3, 2))])
+
+nested_array1_dtype = np.dtype([("array1", np.int16, (3,))], align=True)
+
+nested_array2_dtype = np.dtype([("array2", np.int16, (3, 2))], align=True)
+
+
+# Functions used for "full array" tests
+
+def record_write_full_array(rec):
+    rec.j[:, :] = np.ones((3, 2))
+
+
+def record_write_full_array_alt(rec):
+    rec['j'][:, :] = np.ones((3, 2))
+
+
+def recarray_set_record(ary, rec):
+    ary[0] = rec
+
+
+def recarray_write_array_of_nestedarray_broadcast(ary):
+    ary.j[:, :, :] = 1
+    return ary
+
+
+def record_setitem_array(rec_source, rec_dest):
+    rec_dest['j'] = rec_source['j']
+
+
+def recarray_write_array_of_nestedarray(ary):
+    ary.j[:, :, :] = np.ones((2, 3, 2))
+    return ary
+
+
+def recarray_getitem_return(ary):
+    return ary[0]
+
+
+def recarray_getitem_field_return(ary):
+    return ary['h']
+
+
+def recarray_getitem_field_return2(ary):
+    return ary.h
+
+
+def recarray_getitem_field_return2_2d(ary):
+    return ary.j
+
+
+def record_read_array0(ary):
+    return ary.h[0]
+
+
+def record_read_array1(ary):
+    return ary.h[1]
+
+
+def record_read_whole_array(ary):
+    return ary.h
+
+
+def record_read_2d_array00(ary):
+    return ary.j[0, 0]
+
+
+def record_read_2d_array10(ary):
+    return ary.j[1, 0]
+
+
+def record_read_2d_array01(ary):
+    return ary.j[0, 1]
+
+
+def assign_array_to_nested(dest, src):
+    dest['array1'] = src
+
+
+def assign_array_to_nested_2d(dest, src):
+    dest['array2'] = src
+
+
+class TestRecordDtype(CUDATestCase):
+
+    def _createSampleArrays(self):
+        self.sample1d = np.recarray(3, dtype=recordtype)
+        self.samplerec1darr = np.recarray(1, dtype=recordwitharray)[0]
+        self.samplerec2darr = np.recarray(1, dtype=recordwith2darray)[0]
+
+    def setUp(self):
+        super().setUp()
+        self._createSampleArrays()
+
+        ary = self.sample1d
+        for i in range(ary.size):
+            x = i + 1
+            ary[i]['a'] = x / 2
+            ary[i]['b'] = x
+            ary[i]['c'] = x * 1j
+            ary[i]['d'] = "%d" % x
+
+    def get_cfunc(self, pyfunc, argspec):
+        return cuda.jit()(pyfunc)
+
+    def _test_set_equal(self, pyfunc, value, valuetype):
+        rec = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (rec[:], types.intp, valuetype))
+
+        for i in range(self.sample1d.size):
+            got = self.sample1d.copy()
+
+            # Force the argument to the pure Python function to be
+            # a recarray, as attribute access isn't supported on
+            # structured arrays.
+            expect = got.copy().view(np.recarray)
+
+            cfunc[1, 1](got, i, value)
+            pyfunc(expect, i, value)
+
+            # Match the entire array to ensure no memory corruption
+            self.assertTrue(np.all(expect == got))
+
+    def test_set_a(self):
+        self._test_set_equal(set_a, 3.1415, types.float64)
+        # Test again to check if coercion works
+        self._test_set_equal(set_a, 3., types.float32)
+
+    def test_set_b(self):
+        self._test_set_equal(set_b, 123, types.int32)
+        # Test again to check if coercion works
+        self._test_set_equal(set_b, 123, types.float64)
+
+    def test_set_c(self):
+        self._test_set_equal(set_c, 43j, types.complex64)
+        # Test again to check if coercion works
+        self._test_set_equal(set_c, 43j, types.complex128)
+
+    def test_set_record(self):
+        pyfunc = set_record
+        rec = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (rec[:], types.intp, types.intp))
+
+        test_indices = [(0, 1), (1, 2), (0, 2)]
+        for i, j in test_indices:
+            expect = self.sample1d.copy()
+            pyfunc(expect, i, j)
+
+            got = self.sample1d.copy()
+            cfunc[1, 1](got, i, j)
+
+            # Match the entire array to ensure no memory corruption
+            self.assertEqual(expect[i], expect[j])
+            self.assertEqual(got[i], got[j])
+            self.assertTrue(np.all(expect == got))
+
+    def _test_rec_set(self, v, pyfunc, f):
+        rec = self.sample1d.copy()[0]
+        nbrecord = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (nbrecord,))
+        cfunc[1, 1](rec, v)
+        np.testing.assert_equal(rec[f], v)
+
+    def test_rec_set_a(self):
+        self._test_rec_set(np.float64(1.5), record_set_a, 'a')
+
+    def test_rec_set_b(self):
+        self._test_rec_set(np.int32(2), record_set_b, 'b')
+
+    def test_rec_set_c(self):
+        self._test_rec_set(np.complex64(4.0 + 5.0j), record_set_c, 'c')
+
+    def _test_rec_read(self, v, pyfunc, f):
+        rec = self.sample1d.copy()[0]
+        rec[f] = v
+        arr = np.zeros(1, v.dtype)
+        nbrecord = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (nbrecord,))
+        cfunc[1, 1](rec, arr)
+        np.testing.assert_equal(arr[0], v)
+
+    def test_rec_read_a(self):
+        self._test_rec_read(np.float64(1.5), record_read_a, 'a')
+
+    def test_rec_read_b(self):
+        self._test_rec_read(np.int32(2), record_read_b, 'b')
+
+    def test_rec_read_c(self):
+        self._test_rec_read(np.complex64(4.0 + 5.0j), record_read_c, 'c')
+
+    def test_record_write_1d_array(self):
+        '''
+        Test writing to a 1D array within a structured type
+        '''
+        rec = self.samplerec1darr.copy()
+        nbrecord = numpy_support.from_dtype(recordwitharray)
+        cfunc = self.get_cfunc(record_write_array, (nbrecord,))
+
+        cfunc[1, 1](rec)
+        expected = self.samplerec1darr.copy()
+        expected['g'] = 2
+        expected['h'][0] = 3.0
+        expected['h'][1] = 4.0
+
+        np.testing.assert_equal(expected, rec)
+
+    def test_record_write_2d_array(self):
+        '''
+        Test writing to a 2D array within a structured type
+        '''
+        rec = self.samplerec2darr.copy()
+        nbrecord = numpy_support.from_dtype(recordwith2darray)
+        cfunc = self.get_cfunc(record_write_2d_array, (nbrecord,))
+        cfunc[1, 1](rec)
+
+        expected = self.samplerec2darr.copy()
+        expected['i'] = 3
+        expected['j'][:] = np.asarray([5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
+                                      np.float32).reshape(3, 2)
+        np.testing.assert_equal(expected, rec)
+
+    def test_record_read_1d_array(self):
+        '''
+        Test reading from a 1D array within a structured type
+        '''
+        rec = self.samplerec1darr.copy()
+        rec['h'][0] = 4.0
+        rec['h'][1] = 5.0
+
+        nbrecord = numpy_support.from_dtype(recordwitharray)
+        cfunc = self.get_cfunc(record_read_array, (nbrecord,))
+        arr = np.zeros(2, dtype=rec['h'].dtype)
+        cfunc[1, 1](rec, arr)
+
+        np.testing.assert_equal(rec['h'], arr)
+
+    def test_record_read_2d_array(self):
+        '''
+        Test reading from a 2D array within a structured type
+        '''
+        rec = self.samplerec2darr.copy()
+        rec['j'][:] = np.asarray([5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
+                                 np.float32).reshape(3, 2)
+
+        nbrecord = numpy_support.from_dtype(recordwith2darray)
+        cfunc = self.get_cfunc(record_read_2d_array, (nbrecord,))
+        arr = np.zeros((3,2), dtype=rec['j'].dtype)
+        cfunc[1, 1](rec, arr)
+
+        np.testing.assert_equal(rec['j'], arr)
+
+
+@skip_on_cudasim('Structured array attr access not supported in simulator')
+class TestRecordDtypeWithStructArrays(TestRecordDtype):
+    '''
+    Same as TestRecordDtype, but using structured arrays instead of recarrays.
+    '''
+
+    def _createSampleArrays(self):
+        self.sample1d = np.zeros(3, dtype=recordtype)
+        self.samplerec1darr = np.zeros(1, dtype=recordwitharray)[0]
+        self.samplerec2darr = np.zeros(1, dtype=recordwith2darray)[0]
+
+
+class TestNestedArrays(CUDATestCase):
+
+    # These tests mirror those from
+    # numba.tests.test_record_dtype.TestNestedArrays added in PR
+    # #7359: https://github.com/numba/numba/pull/7359
+
+    # The code cannot be shared between the two classes without modification,
+    # as the CUDA test implementations need to be launched (and in some cases
+    # wrapped in an outer function to handle the return value). Otherwise, the
+    # code here is kept as similar to that in the equivalent CPU tests as
+    # possible.
+
+    # Reading records / recarrays
+
+    def get_cfunc(self, pyfunc, retty):
+        # Create a host-callable function for testing CUDA device functions
+        # that get a value from a record array
+        inner = cuda.jit(device=True)(pyfunc)
+
+        @cuda.jit
+        def outer(arg0, res):
+            res[0] = inner(arg0)
+
+        def host(arg0):
+            res = np.zeros(1, dtype=retty)
+            outer[1, 1](arg0, res)
+            return res[0]
+
+        return host
+
+    def test_record_read_array(self):
+        # Test reading from a 1D array within a structured type
+        nbval = np.recarray(1, dtype=recordwitharray)
+        nbval[0].h[0] = 15.0
+        nbval[0].h[1] = 25.0
+        cfunc = self.get_cfunc(record_read_array0, np.float32)
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].h[0])
+
+        cfunc = self.get_cfunc(record_read_array1, np.float32)
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].h[1])
+
+    def test_record_read_2d_array(self):
+        # Test reading from a 2D array within a structured type
+        nbval = np.recarray(1, dtype=recordwith2darray)
+        nbval[0].j = np.asarray([1.5, 2.5, 3.5, 4.5, 5.5, 6.5],
+                                np.float32).reshape(3, 2)
+        cfunc = self.get_cfunc(record_read_2d_array00, np.float32)
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].j[0, 0])
+
+        cfunc = self.get_cfunc(record_read_2d_array01, np.float32)
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].j[0, 1])
+
+        cfunc = self.get_cfunc(record_read_2d_array10, np.float32)
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].j[1, 0])
+
+    def test_setitem(self):
+        def gen():
+            nbarr1 = np.recarray(1, dtype=recordwith2darray)
+            nbarr1[0] = np.array([(1, ((1, 2), (4, 5), (2, 3)))],
+                                 dtype=recordwith2darray)[0]
+            nbarr2 = np.recarray(1, dtype=recordwith2darray)
+            nbarr2[0] = np.array([(10, ((10, 20), (40, 50), (20, 30)))],
+                                 dtype=recordwith2darray)[0]
+            return nbarr1[0], nbarr2[0]
+        pyfunc = record_setitem_array
+        pyargs = gen()
+        pyfunc(*pyargs)
+
+        cfunc = cuda.jit(pyfunc)
+        cuargs = gen()
+        cfunc[1, 1](*cuargs)
+        np.testing.assert_equal(pyargs, cuargs)
+
+    def test_getitem_idx(self):
+        # Test __getitem__ with numerical index
+
+        # This tests returning a record when passing an array and
+        # returning the first item when passing a record
+        nbarr = np.recarray(2, dtype=recordwitharray)
+        nbarr[0] = np.array([(1, (2, 3))], dtype=recordwitharray)[0]
+        for arg, retty in [(nbarr, recordwitharray), (nbarr[0], np.int32)]:
+            pyfunc = recarray_getitem_return
+            arr_expected = pyfunc(arg)
+            cfunc = self.get_cfunc(pyfunc, retty)
+            arr_res = cfunc(arg)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+    # Writing to records / recarrays
+
+    @skip_on_cudasim('Structured array attr access not supported in simulator')
+    def test_set_record(self):
+        # Test setting an entire record
+        rec = np.ones(2, dtype=recordwith2darray).view(np.recarray)[0]
+        nbarr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        arr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        pyfunc = recarray_set_record
+        pyfunc(arr, rec)
+        kernel = cuda.jit(pyfunc)
+        kernel[1, 1](nbarr, rec)
+        np.testing.assert_equal(nbarr, arr)
+
+    def test_assign_array_to_nested(self):
+        src = (np.arange(3) + 1).astype(np.int16)
+        got = np.zeros(2, dtype=nested_array1_dtype)
+        expected = np.zeros(2, dtype=nested_array1_dtype)
+
+        pyfunc = assign_array_to_nested
+        kernel = cuda.jit(pyfunc)
+
+        kernel[1, 1](got[0], src)
+        pyfunc(expected[0], src)
+
+        np.testing.assert_array_equal(expected, got)
+
+    def test_assign_array_to_nested_2d(self):
+        src = (np.arange(6) + 1).astype(np.int16).reshape((3, 2))
+        got = np.zeros(2, dtype=nested_array2_dtype)
+        expected = np.zeros(2, dtype=nested_array2_dtype)
+
+        pyfunc = assign_array_to_nested_2d
+        kernel = cuda.jit(pyfunc)
+
+        kernel[1, 1](got[0], src)
+        pyfunc(expected[0], src)
+
+        np.testing.assert_array_equal(expected, got)
+
+    def test_issue_7693(self):
+        src_dtype = np.dtype([
+            ("user", np.float64),
+            ("array", np.int16, (3,))],
+            align=True)
+
+        dest_dtype = np.dtype([
+            ("user1", np.float64),
+            ("array1", np.int16, (3,))],
+            align=True)
+
+        @cuda.jit
+        def copy(index, src, dest):
+            dest['user1'] = src[index]['user']
+            dest['array1'] = src[index]['array']
+
+        source = np.zeros(2, dtype=src_dtype)
+        got = np.zeros(2, dtype=dest_dtype)
+        expected = np.zeros(2, dtype=dest_dtype)
+
+        source[0] = (1.2, [1, 2, 3])
+        copy[1, 1](0, source, got[0])
+        copy.py_func(0, source, expected[0])
+
+        np.testing.assert_array_equal(expected, got)
+
+    # Reading and returning arrays from recarrays - the following functions are
+    # all xfailed because CUDA cannot handle returning arrays from device
+    # functions (or creating arrays in general).
+
+    @unittest.expectedFailure
+    def test_getitem_idx_2darray(self):
+        # Test __getitem__ with numerical index
+        #
+        # This test returning a record when passing an array and
+        # return the first item when passing a record
+        nbarr = np.recarray(2, dtype=recordwith2darray)
+        nbarr[0] = np.array([(1, ((1,2),(4,5),(2,3)))],
+                            dtype=recordwith2darray)[0]
+        for arg, retty in [(nbarr, recordwith2darray),
+                           (nbarr[0], (np.float32, (3, 2)))]:
+            pyfunc = recarray_getitem_field_return2_2d
+            arr_expected = pyfunc(arg)
+            cfunc = self.get_cfunc(pyfunc, retty)
+            arr_res = cfunc(arg)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+    @unittest.expectedFailure
+    def test_return_getattr_getitem_fieldname(self):
+        # Test __getitem__ with field name and getattr .field_name
+        #
+        # This tests returning a array of nestedarrays when passing an array and
+        # returning a nestedarray when passing a record
+        nbarr = np.recarray(2, dtype=recordwitharray)
+        nbarr[0] = np.array([(1, (2,3))], dtype=recordwitharray)[0]
+        for arg, retty in [(nbarr, recordwitharray), (nbarr[0], np.float32)]:
+            for pyfunc in [recarray_getitem_field_return,
+                           recarray_getitem_field_return2]:
+                arr_expected = pyfunc(arg)
+                cfunc = self.get_cfunc(pyfunc, retty)
+                arr_res = cfunc(arg)
+                np.testing.assert_equal(arr_res, arr_expected)
+
+    @unittest.expectedFailure
+    def test_record_read_arrays(self):
+        # Test reading from a 1D array within a structured type
+        nbval = np.recarray(2, dtype=recordwitharray)
+        nbval[0].h[0] = 15.0
+        nbval[0].h[1] = 25.0
+        nbval[1].h[0] = 35.0
+        nbval[1].h[1] = 45.4
+        cfunc = self.get_cfunc(record_read_whole_array, np.float32)
+        res = cfunc(nbval)
+        np.testing.assert_equal(res, nbval.h)
+
+    @unittest.expectedFailure
+    def test_return_array(self):
+        # Test getitem record AND array within record and returning it
+        nbval = np.recarray(2, dtype=recordwitharray)
+        nbval[0] = np.array([(1, (2,3))], dtype=recordwitharray)[0]
+        pyfunc = record_read_array0
+        arr_expected = pyfunc(nbval)
+        cfunc = self.get_cfunc(pyfunc, np.float32)
+        arr_res = cfunc(nbval)
+        np.testing.assert_equal(arr_expected, arr_res)
+
+    @skip_on_cudasim('Will unexpectedly pass on cudasim')
+    @unittest.expectedFailure
+    def test_set_array(self):
+        #Test setting an entire array within one record
+        arr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        rec = arr[0]
+        nbarr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        nbrec = nbarr[0]
+        for pyfunc in (record_write_full_array, record_write_full_array_alt):
+            pyfunc(rec)
+            kernel = cuda.jit(pyfunc)
+            kernel[1, 1](nbrec)
+            np.testing.assert_equal(nbarr, arr)
+
+    @unittest.expectedFailure
+    def test_set_arrays(self):
+        # Test setting an entire array of arrays (multiple records)
+        arr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        nbarr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        for pyfunc in (
+                recarray_write_array_of_nestedarray_broadcast,
+                recarray_write_array_of_nestedarray,
+        ):
+            arr_expected = pyfunc(arr)
+            cfunc = self.get_cfunc(pyfunc, nbarr.dtype)
+            arr_res = cfunc(nbarr)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_recursion.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_recursion.py
new file mode 100644
index 000000000..579275330
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_recursion.py
@@ -0,0 +1,125 @@
+from numba import cuda
+from numba.core.errors import TypingError
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+import numpy as np
+import unittest
+
+
+class TestSelfRecursion(CUDATestCase):
+
+    def setUp(self):
+        # Avoid importing this module at the top level, as it triggers
+        # compilation and can therefore fail
+        from numba.cuda.tests.cudapy import recursion_usecases
+        self.mod = recursion_usecases
+        super().setUp()
+
+    def check_fib(self, cfunc):
+        @cuda.jit
+        def kernel(r, x):
+            r[0] = cfunc(x[0])
+
+        x = np.asarray([10], dtype=np.int64)
+        r = np.zeros_like(x)
+        kernel[1, 1](r, x)
+
+        actual = r[0]
+        expected = 55
+        self.assertPreciseEqual(actual, expected)
+
+    def test_global_explicit_sig(self):
+        self.check_fib(self.mod.fib1)
+
+    def test_inner_explicit_sig(self):
+        self.check_fib(self.mod.fib2)
+
+    def test_global_implicit_sig(self):
+        self.check_fib(self.mod.fib3)
+
+    @skip_on_cudasim('Simulator does not compile')
+    def test_runaway(self):
+        with self.assertRaises(TypingError) as raises:
+            cfunc = self.mod.runaway_self
+
+            @cuda.jit('void()')
+            def kernel():
+                cfunc(1)
+
+        self.assertIn("cannot type infer runaway recursion",
+                      str(raises.exception))
+
+    @unittest.skip('Needs insert_unresolved_ref support in target')
+    def test_type_change(self):
+        pfunc = self.mod.type_change_self.py_func
+        cfunc = self.mod.type_change_self
+
+        @cuda.jit
+        def kernel(r, x, y):
+            r[0] = cfunc(x[0], y[0])
+
+        args = 13, 0.125
+        x = np.asarray([args[0]], dtype=np.int64)
+        y = np.asarray([args[1]], dtype=np.float64)
+        r = np.zeros_like(x)
+
+        kernel[1, 1](r, x, y)
+
+        expected = pfunc(*args)
+        actual = r[0]
+
+        self.assertPreciseEqual(actual, expected)
+
+    @unittest.expectedFailure
+    def test_raise(self):
+        # This is an expected failure because reporting of exceptions raised in
+        # device functions does not work correctly - see Issue #8036:
+        # https://github.com/numba/numba/issues/8036
+        with self.assertRaises(ValueError) as raises:
+            self.mod.raise_self_kernel[1, 1](3)
+
+        self.assertEqual(str(raises.exception), "raise_self")
+
+    @unittest.skip('Needs insert_unresolved_ref support in target')
+    def test_optional_return(self):
+        pfunc = self.mod.make_optional_return_case()
+        cfunc = self.mod.make_optional_return_case(cuda.jit)
+
+        @cuda.jit
+        def kernel(r, x):
+            res = cfunc(x[0])
+            if res is None:
+                res = 999
+            r[0] = res
+
+        def cpu_kernel(x):
+            res = pfunc(x)
+            if res is None:
+                res = 999
+            return res
+
+        for arg in (0, 5, 10, 15):
+            expected = cpu_kernel(arg)
+            x = np.asarray([arg], dtype=np.int64)
+            r = np.zeros_like(x)
+            kernel[1, 1](r, x)
+            actual = r[0]
+
+            self.assertEqual(expected, actual)
+
+    @skip_on_cudasim('Recursion handled because simulator does not compile')
+    def test_growing_return_tuple(self):
+        cfunc = self.mod.make_growing_tuple_case(cuda.jit)
+
+        with self.assertRaises(TypingError) as raises:
+            @cuda.jit('void()')
+            def kernel():
+                cfunc(100)
+
+        self.assertIn(
+            "Return type of recursive function does not converge",
+            str(raises.exception),
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_reduction.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_reduction.py
new file mode 100644
index 000000000..420fc7516
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_reduction.py
@@ -0,0 +1,76 @@
+import numpy as np
+from numba import cuda
+from numba.core.config import ENABLE_CUDASIM
+from numba.cuda.testing import CUDATestCase
+import unittest
+
+# Avoid recompilation of the sum_reduce function by keeping it at global scope
+sum_reduce = cuda.Reduce(lambda a, b: a + b)
+
+
+class TestReduction(CUDATestCase):
+    def _sum_reduce(self, n):
+        A = (np.arange(n, dtype=np.float64) + 1)
+        expect = A.sum()
+        got = sum_reduce(A)
+        self.assertEqual(expect, got)
+
+    def test_sum_reduce(self):
+        if ENABLE_CUDASIM:
+            # Minimal test set for the simulator (which only wraps
+            # functools.reduce)
+            test_sizes = [ 1, 16 ]
+        else:
+            # Tests around the points where blocksize changes, and around larger
+            # powers of two, sums of powers of two, and some "random" sizes
+            test_sizes = [ 1, 15, 16, 17, 127, 128, 129, 1023, 1024,
+                           1025, 1536, 1048576, 1049600, 1049728, 34567 ]
+        # Avoid recompilation by keeping sum_reduce here
+        for n in test_sizes:
+            self._sum_reduce(n)
+
+    def test_empty_array_host(self):
+        A = (np.arange(0, dtype=np.float64) + 1)
+        expect = A.sum()
+        got = sum_reduce(A)
+        self.assertEqual(expect, got)
+
+    def test_empty_array_device(self):
+        A = (np.arange(0, dtype=np.float64) + 1)
+        dA = cuda.to_device(A)
+        expect = A.sum()
+        got = sum_reduce(dA)
+        self.assertEqual(expect, got)
+
+    def test_prod_reduce(self):
+        prod_reduce = cuda.reduce(lambda a, b: a * b)
+        A = (np.arange(64, dtype=np.float64) + 1)
+        expect = A.prod()
+        got = prod_reduce(A, init=1)
+        np.testing.assert_allclose(expect, got)
+
+    def test_max_reduce(self):
+        max_reduce = cuda.Reduce(lambda a, b: max(a, b))
+        A = (np.arange(3717, dtype=np.float64) + 1)
+        expect = A.max()
+        got = max_reduce(A, init=0)
+        self.assertEqual(expect, got)
+
+    def test_non_identity_init(self):
+        init = 3
+        A = (np.arange(10, dtype=np.float64) + 1)
+        expect = A.sum() + init
+        got = sum_reduce(A, init=init)
+        self.assertEqual(expect, got)
+
+    def test_result_on_device(self):
+        A = (np.arange(10, dtype=np.float64) + 1)
+        got = cuda.to_device(np.zeros(1, dtype=np.float64))
+        expect = A.sum()
+        res = sum_reduce(A, res=got)
+        self.assertIsNone(res)
+        self.assertEqual(expect, got[0])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_retrieve_autoconverted_arrays.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_retrieve_autoconverted_arrays.py
new file mode 100644
index 000000000..640efcac3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_retrieve_autoconverted_arrays.py
@@ -0,0 +1,83 @@
+import numpy as np
+
+from numba import cuda
+from numba.cuda.args import wrap_arg
+from numba.cuda.testing import CUDATestCase
+import unittest
+
+
+class DefaultIn(object):
+    def prepare_args(self, ty, val, **kwargs):
+        return ty, wrap_arg(val, default=cuda.In)
+
+
+def nocopy(kernel):
+    kernel.extensions.append(DefaultIn())
+    return kernel
+
+
+def set_array_to_three(arr):
+    arr[0] = 3
+
+
+def set_record_to_three(rec):
+    rec[0]['b'] = 3
+
+
+recordtype = np.dtype(
+    [('b', np.int32)],
+    align=True
+)
+
+
+class TestRetrieveAutoconvertedArrays(CUDATestCase):
+    def setUp(self):
+        super().setUp()
+        self.set_array_to_three = cuda.jit(set_array_to_three)
+        self.set_array_to_three_nocopy = nocopy(cuda.jit(set_array_to_three))
+        self.set_record_to_three = cuda.jit(set_record_to_three)
+        self.set_record_to_three_nocopy = nocopy(cuda.jit(set_record_to_three))
+
+    def test_array_inout(self):
+        host_arr = np.zeros(1, dtype=np.int64)
+        self.set_array_to_three[1, 1](cuda.InOut(host_arr))
+        self.assertEqual(3, host_arr[0])
+
+    def test_array_in(self):
+        host_arr = np.zeros(1, dtype=np.int64)
+        self.set_array_to_three[1, 1](cuda.In(host_arr))
+        self.assertEqual(0, host_arr[0])
+
+    def test_array_in_from_config(self):
+        host_arr = np.zeros(1, dtype=np.int64)
+        self.set_array_to_three_nocopy[1, 1](host_arr)
+        self.assertEqual(0, host_arr[0])
+
+    def test_array_default(self):
+        host_arr = np.zeros(1, dtype=np.int64)
+        self.set_array_to_three[1, 1](host_arr)
+        self.assertEqual(3, host_arr[0])
+
+    def test_record_in(self):
+        host_rec = np.zeros(1, dtype=recordtype)
+        self.set_record_to_three[1, 1](cuda.In(host_rec))
+        self.assertEqual(0, host_rec[0]['b'])
+
+    def test_record_inout(self):
+        host_rec = np.zeros(1, dtype=recordtype)
+        self.set_record_to_three[1, 1](cuda.InOut(host_rec))
+        self.assertEqual(3, host_rec[0]['b'])
+
+    def test_record_default(self):
+        host_rec = np.zeros(1, dtype=recordtype)
+        self.set_record_to_three[1, 1](host_rec)
+        self.assertEqual(3, host_rec[0]['b'])
+
+    def test_record_in_from_config(self):
+        host_rec = np.zeros(1, dtype=recordtype)
+        self.set_record_to_three_nocopy[1, 1](host_rec)
+        self.assertEqual(0, host_rec[0]['b'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_serialize.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_serialize.py
new file mode 100644
index 000000000..b98aa85a0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_serialize.py
@@ -0,0 +1,85 @@
+import pickle
+import numpy as np
+from numba import cuda, vectorize
+from numba.core import types
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+from numba.np import numpy_support
+
+
+@skip_on_cudasim('pickling not supported in CUDASIM')
+class TestPickle(CUDATestCase):
+
+    def check_call(self, callee):
+        arr = np.array([100])
+        expected = callee[1, 1](arr)
+
+        # serialize and rebuild
+        foo1 = pickle.loads(pickle.dumps(callee))
+        del callee
+        # call rebuild function
+        got1 = foo1[1, 1](arr)
+        np.testing.assert_equal(got1, expected)
+        del got1
+
+        # test serialization of previously serialized object
+        foo2 = pickle.loads(pickle.dumps(foo1))
+        del foo1
+        # call rebuild function
+        got2 = foo2[1, 1](arr)
+        np.testing.assert_equal(got2, expected)
+        del got2
+
+        # test propagation of thread, block config
+        foo3 = pickle.loads(pickle.dumps(foo2[5, 8]))
+        del foo2
+        self.assertEqual(foo3.griddim, (5, 1, 1))
+        self.assertEqual(foo3.blockdim, (8, 1, 1))
+
+    def test_pickling_jit_typing(self):
+        @cuda.jit(device=True)
+        def inner(a):
+            return a + 1
+
+        @cuda.jit('void(intp[:])')
+        def foo(arr):
+            arr[0] = inner(arr[0])
+
+        self.check_call(foo)
+
+    def test_pickling_jit(self):
+
+        @cuda.jit(device=True)
+        def inner(a):
+            return a + 1
+
+        @cuda.jit
+        def foo(arr):
+            arr[0] = inner(arr[0])
+
+        self.check_call(foo)
+
+    def test_pickling_vectorize(self):
+        @vectorize(['intp(intp)', 'float64(float64)'], target='cuda')
+        def cuda_vect(x):
+            return x * 2
+
+        # accommodate int representations in np.arange
+        npty = numpy_support.as_dtype(types.intp)
+        # get expected result
+        ary = np.arange(10, dtype=npty)
+        expected = cuda_vect(ary)
+        # first pickle
+        foo1 = pickle.loads(pickle.dumps(cuda_vect))
+        del cuda_vect
+        got1 = foo1(ary)
+        np.testing.assert_equal(expected, got1)
+        # second pickle
+        foo2 = pickle.loads(pickle.dumps(foo1))
+        del foo1
+        got2 = foo2(ary)
+        np.testing.assert_equal(expected, got2)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_slicing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_slicing.py
new file mode 100644
index 000000000..f5a3df7f3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_slicing.py
@@ -0,0 +1,37 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+def foo(inp, out):
+    for i in range(out.shape[0]):
+        out[i] = inp[i]
+
+
+def copy(inp, out):
+    i = cuda.grid(1)
+    cufoo(inp[i, :], out[i, :])
+
+
+class TestCudaSlicing(CUDATestCase):
+    def test_slice_as_arg(self):
+        global cufoo
+        cufoo = cuda.jit("void(int32[:], int32[:])", device=True)(foo)
+        cucopy = cuda.jit("void(int32[:,:], int32[:,:])")(copy)
+
+        inp = np.arange(100, dtype=np.int32).reshape(10, 10)
+        out = np.zeros_like(inp)
+
+        cucopy[1, 10](inp, out)
+
+    def test_assign_empty_slice(self):
+        # Issue #5017. Assigning to an empty slice should not result in a
+        # CudaAPIError.
+        N = 0
+        a = range(N)
+        arr = cuda.device_array(len(a))
+        arr[:] = cuda.to_device(a)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sm.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sm.py
new file mode 100644
index 000000000..b61784a73
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sm.py
@@ -0,0 +1,444 @@
+from numba import cuda, int32, float64, void
+from numba.core.errors import TypingError
+from numba.core import types
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+
+import numpy as np
+from numba.np import numpy_support as nps
+
+from .extensions_usecases import test_struct_model_type, TestStruct
+
+recordwith2darray = np.dtype([('i', np.int32),
+                              ('j', np.float32, (3, 2))])
+
+
+class TestSharedMemoryIssue(CUDATestCase):
+    def test_issue_953_sm_linkage_conflict(self):
+        @cuda.jit(device=True)
+        def inner():
+            inner_arr = cuda.shared.array(1, dtype=int32)  # noqa: F841
+
+        @cuda.jit
+        def outer():
+            outer_arr = cuda.shared.array(1, dtype=int32)  # noqa: F841
+            inner()
+
+        outer[1, 1]()
+
+    def _check_shared_array_size(self, shape, expected):
+        @cuda.jit
+        def s(a):
+            arr = cuda.shared.array(shape, dtype=int32)
+            a[0] = arr.size
+
+        result = np.zeros(1, dtype=np.int32)
+        s[1, 1](result)
+        self.assertEqual(result[0], expected)
+
+    def test_issue_1051_shared_size_broken_1d(self):
+        self._check_shared_array_size(2, 2)
+
+    def test_issue_1051_shared_size_broken_2d(self):
+        self._check_shared_array_size((2, 3), 6)
+
+    def test_issue_1051_shared_size_broken_3d(self):
+
+        self._check_shared_array_size((2, 3, 4), 24)
+
+    def _check_shared_array_size_fp16(self, shape, expected, ty):
+        @cuda.jit
+        def s(a):
+            arr = cuda.shared.array(shape, dtype=ty)
+            a[0] = arr.size
+
+        result = np.zeros(1, dtype=np.float16)
+        s[1, 1](result)
+        self.assertEqual(result[0], expected)
+
+    def test_issue_fp16_support(self):
+        self._check_shared_array_size_fp16(2, 2, types.float16)
+        self._check_shared_array_size_fp16(2, 2, np.float16)
+
+    def test_issue_2393(self):
+        """
+        Test issue of warp misalign address due to nvvm not knowing the
+        alignment(? but it should have taken the natural alignment of the type)
+        """
+        num_weights = 2
+        num_blocks = 48
+        examples_per_block = 4
+        threads_per_block = 1
+
+        @cuda.jit
+        def costs_func(d_block_costs):
+            s_features = cuda.shared.array((examples_per_block, num_weights),
+                                           float64)
+            s_initialcost = cuda.shared.array(7, float64)  # Bug
+
+            threadIdx = cuda.threadIdx.x
+
+            prediction = 0
+            for j in range(num_weights):
+                prediction += s_features[threadIdx, j]
+
+            d_block_costs[0] = s_initialcost[0] + prediction
+
+        block_costs = np.zeros(num_blocks, dtype=np.float64)
+        d_block_costs = cuda.to_device(block_costs)
+
+        costs_func[num_blocks, threads_per_block](d_block_costs)
+
+        cuda.synchronize()
+
+
+class TestSharedMemory(CUDATestCase):
+    def _test_shared(self, arr):
+        # Use a kernel that copies via shared memory to check loading and
+        # storing different dtypes with shared memory. All threads in a block
+        # collaborate to load in values, then the output values are written
+        # only by the first thread in the block after synchronization.
+
+        nelem = len(arr)
+        nthreads = 16
+        nblocks = int(nelem / nthreads)
+        dt = nps.from_dtype(arr.dtype)
+
+        @cuda.jit
+        def use_sm_chunk_copy(x, y):
+            sm = cuda.shared.array(nthreads, dtype=dt)
+
+            tx = cuda.threadIdx.x
+            bx = cuda.blockIdx.x
+            bd = cuda.blockDim.x
+
+            # Load this block's chunk into shared
+            i = bx * bd + tx
+            if i < len(x):
+                sm[tx] = x[i]
+
+            cuda.syncthreads()
+
+            # One thread per block writes this block's chunk
+            if tx == 0:
+                for j in range(nthreads):
+                    y[bd * bx + j] = sm[j]
+
+        d_result = cuda.device_array_like(arr)
+        use_sm_chunk_copy[nblocks, nthreads](arr, d_result)
+        host_result = d_result.copy_to_host()
+        np.testing.assert_array_equal(arr, host_result)
+
+    def test_shared_recarray(self):
+        arr = np.recarray(128, dtype=recordwith2darray)
+        for x in range(len(arr)):
+            arr[x].i = x
+            j = np.arange(3 * 2, dtype=np.float32)
+            arr[x].j = j.reshape(3, 2) * x
+
+        self._test_shared(arr)
+
+    def test_shared_bool(self):
+        arr = np.random.randint(2, size=(1024,), dtype=np.bool_)
+        self._test_shared(arr)
+
+    def _test_dynshared_slice(self, func, arr, expected):
+        # Check that slices of shared memory are correct
+        # (See Bug #5073 - prior to the addition of these tests and
+        # corresponding fix, slices of dynamic shared arrays all aliased each
+        # other)
+        nshared = arr.size * arr.dtype.itemsize
+        func[1, 1, 0, nshared](arr)
+        np.testing.assert_array_equal(expected, arr)
+
+    def test_dynshared_slice_write(self):
+        # Test writing values into disjoint slices of dynamic shared memory
+        @cuda.jit
+        def slice_write(x):
+            dynsmem = cuda.shared.array(0, dtype=int32)
+            sm1 = dynsmem[0:1]
+            sm2 = dynsmem[1:2]
+
+            sm1[0] = 1
+            sm2[0] = 2
+            x[0] = dynsmem[0]
+            x[1] = dynsmem[1]
+
+        arr = np.zeros(2, dtype=np.int32)
+        expected = np.array([1, 2], dtype=np.int32)
+        self._test_dynshared_slice(slice_write, arr, expected)
+
+    def test_dynshared_slice_read(self):
+        # Test reading values from disjoint slices of dynamic shared memory
+        @cuda.jit
+        def slice_read(x):
+            dynsmem = cuda.shared.array(0, dtype=int32)
+            sm1 = dynsmem[0:1]
+            sm2 = dynsmem[1:2]
+
+            dynsmem[0] = 1
+            dynsmem[1] = 2
+            x[0] = sm1[0]
+            x[1] = sm2[0]
+
+        arr = np.zeros(2, dtype=np.int32)
+        expected = np.array([1, 2], dtype=np.int32)
+        self._test_dynshared_slice(slice_read, arr, expected)
+
+    def test_dynshared_slice_diff_sizes(self):
+        # Test reading values from disjoint slices of dynamic shared memory
+        # with different sizes
+        @cuda.jit
+        def slice_diff_sizes(x):
+            dynsmem = cuda.shared.array(0, dtype=int32)
+            sm1 = dynsmem[0:1]
+            sm2 = dynsmem[1:3]
+
+            dynsmem[0] = 1
+            dynsmem[1] = 2
+            dynsmem[2] = 3
+            x[0] = sm1[0]
+            x[1] = sm2[0]
+            x[2] = sm2[1]
+
+        arr = np.zeros(3, dtype=np.int32)
+        expected = np.array([1, 2, 3], dtype=np.int32)
+        self._test_dynshared_slice(slice_diff_sizes, arr, expected)
+
+    def test_dynshared_slice_overlap(self):
+        # Test reading values from overlapping slices of dynamic shared memory
+        @cuda.jit
+        def slice_overlap(x):
+            dynsmem = cuda.shared.array(0, dtype=int32)
+            sm1 = dynsmem[0:2]
+            sm2 = dynsmem[1:4]
+
+            dynsmem[0] = 1
+            dynsmem[1] = 2
+            dynsmem[2] = 3
+            dynsmem[3] = 4
+            x[0] = sm1[0]
+            x[1] = sm1[1]
+            x[2] = sm2[0]
+            x[3] = sm2[1]
+            x[4] = sm2[2]
+
+        arr = np.zeros(5, dtype=np.int32)
+        expected = np.array([1, 2, 2, 3, 4], dtype=np.int32)
+        self._test_dynshared_slice(slice_overlap, arr, expected)
+
+    def test_dynshared_slice_gaps(self):
+        # Test writing values to slices of dynamic shared memory doesn't write
+        # outside the slice
+        @cuda.jit
+        def slice_gaps(x):
+            dynsmem = cuda.shared.array(0, dtype=int32)
+            sm1 = dynsmem[1:3]
+            sm2 = dynsmem[4:6]
+
+            # Initial values for dynamic shared memory, some to be overwritten
+            dynsmem[0] = 99
+            dynsmem[1] = 99
+            dynsmem[2] = 99
+            dynsmem[3] = 99
+            dynsmem[4] = 99
+            dynsmem[5] = 99
+            dynsmem[6] = 99
+
+            sm1[0] = 1
+            sm1[1] = 2
+            sm2[0] = 3
+            sm2[1] = 4
+
+            x[0] = dynsmem[0]
+            x[1] = dynsmem[1]
+            x[2] = dynsmem[2]
+            x[3] = dynsmem[3]
+            x[4] = dynsmem[4]
+            x[5] = dynsmem[5]
+            x[6] = dynsmem[6]
+
+        arr = np.zeros(7, dtype=np.int32)
+        expected = np.array([99, 1, 2, 99, 3, 4, 99], dtype=np.int32)
+        self._test_dynshared_slice(slice_gaps, arr, expected)
+
+    def test_dynshared_slice_write_backwards(self):
+        # Test writing values into disjoint slices of dynamic shared memory
+        # with negative steps
+        @cuda.jit
+        def slice_write_backwards(x):
+            dynsmem = cuda.shared.array(0, dtype=int32)
+            sm1 = dynsmem[1::-1]
+            sm2 = dynsmem[3:1:-1]
+
+            sm1[0] = 1
+            sm1[1] = 2
+            sm2[0] = 3
+            sm2[1] = 4
+            x[0] = dynsmem[0]
+            x[1] = dynsmem[1]
+            x[2] = dynsmem[2]
+            x[3] = dynsmem[3]
+
+        arr = np.zeros(4, dtype=np.int32)
+        expected = np.array([2, 1, 4, 3], dtype=np.int32)
+        self._test_dynshared_slice(slice_write_backwards, arr, expected)
+
+    def test_dynshared_slice_nonunit_stride(self):
+        # Test writing values into slice of dynamic shared memory with
+        # non-unit stride
+        @cuda.jit
+        def slice_nonunit_stride(x):
+            dynsmem = cuda.shared.array(0, dtype=int32)
+            sm1 = dynsmem[::2]
+
+            # Initial values for dynamic shared memory, some to be overwritten
+            dynsmem[0] = 99
+            dynsmem[1] = 99
+            dynsmem[2] = 99
+            dynsmem[3] = 99
+            dynsmem[4] = 99
+            dynsmem[5] = 99
+
+            sm1[0] = 1
+            sm1[1] = 2
+            sm1[2] = 3
+
+            x[0] = dynsmem[0]
+            x[1] = dynsmem[1]
+            x[2] = dynsmem[2]
+            x[3] = dynsmem[3]
+            x[4] = dynsmem[4]
+            x[5] = dynsmem[5]
+
+        arr = np.zeros(6, dtype=np.int32)
+        expected = np.array([1, 99, 2, 99, 3, 99], dtype=np.int32)
+        self._test_dynshared_slice(slice_nonunit_stride, arr, expected)
+
+    def test_dynshared_slice_nonunit_reverse_stride(self):
+        # Test writing values into slice of dynamic shared memory with
+        # reverse non-unit stride
+        @cuda.jit
+        def slice_nonunit_reverse_stride(x):
+            dynsmem = cuda.shared.array(0, dtype=int32)
+            sm1 = dynsmem[-1::-2]
+
+            # Initial values for dynamic shared memory, some to be overwritten
+            dynsmem[0] = 99
+            dynsmem[1] = 99
+            dynsmem[2] = 99
+            dynsmem[3] = 99
+            dynsmem[4] = 99
+            dynsmem[5] = 99
+
+            sm1[0] = 1
+            sm1[1] = 2
+            sm1[2] = 3
+
+            x[0] = dynsmem[0]
+            x[1] = dynsmem[1]
+            x[2] = dynsmem[2]
+            x[3] = dynsmem[3]
+            x[4] = dynsmem[4]
+            x[5] = dynsmem[5]
+
+        arr = np.zeros(6, dtype=np.int32)
+        expected = np.array([99, 3, 99, 2, 99, 1], dtype=np.int32)
+        self._test_dynshared_slice(slice_nonunit_reverse_stride, arr, expected)
+
+    def test_issue_5073(self):
+        # An example with which Bug #5073 (slices of dynamic shared memory all
+        # alias) was discovered. The kernel uses all threads in the block to
+        # load values into slices of dynamic shared memory. One thread per
+        # block then writes the loaded values back to a global array after
+        # syncthreads().
+
+        arr = np.arange(1024)
+        nelem = len(arr)
+        nthreads = 16
+        nblocks = int(nelem / nthreads)
+        dt = nps.from_dtype(arr.dtype)
+        nshared = nthreads * arr.dtype.itemsize
+        chunksize = int(nthreads / 2)
+
+        @cuda.jit
+        def sm_slice_copy(x, y, chunksize):
+            dynsmem = cuda.shared.array(0, dtype=dt)
+            sm1 = dynsmem[0:chunksize]
+            sm2 = dynsmem[chunksize:chunksize * 2]
+
+            tx = cuda.threadIdx.x
+            bx = cuda.blockIdx.x
+            bd = cuda.blockDim.x
+
+            # load this block's chunk into shared
+            i = bx * bd + tx
+            if i < len(x):
+                if tx < chunksize:
+                    sm1[tx] = x[i]
+                else:
+                    sm2[tx - chunksize] = x[i]
+
+            cuda.syncthreads()
+
+            # one thread per block writes this block's chunk
+            if tx == 0:
+                for j in range(chunksize):
+                    y[bd * bx + j] = sm1[j]
+                    y[bd * bx + j + chunksize] = sm2[j]
+
+        d_result = cuda.device_array_like(arr)
+        sm_slice_copy[nblocks, nthreads, 0, nshared](arr, d_result, chunksize)
+        host_result = d_result.copy_to_host()
+        np.testing.assert_array_equal(arr, host_result)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_invalid_array_type(self):
+        rgx = ".*Cannot infer the type of variable 'arr'.*"
+
+        def unsupported_type():
+            arr = cuda.shared.array(10, dtype=np.dtype('O')) # noqa: F841
+        with self.assertRaisesRegex(TypingError, rgx):
+            cuda.jit(void())(unsupported_type)
+
+        rgx = ".*Invalid NumPy dtype specified: 'int33'.*"
+
+        def invalid_string_type():
+            arr = cuda.shared.array(10, dtype='int33') # noqa: F841
+        with self.assertRaisesRegex(TypingError, rgx):
+            cuda.jit(void())(invalid_string_type)
+
+    @skip_on_cudasim("Struct model array unsupported in simulator")
+    def test_struct_model_type_static(self):
+        nthreads = 64
+
+        @cuda.jit(void(int32[::1], int32[::1]))
+        def write_then_reverse_read_static(outx, outy):
+            # Test creation
+            arr = cuda.shared.array(nthreads, dtype=test_struct_model_type)
+
+            i = cuda.grid(1)
+            ri = nthreads - i - 1
+
+            if i < len(outx) and i < len(outy):
+                # Test set to arr
+                obj = TestStruct(int32(i), int32(i * 2))
+                arr[i] = obj
+
+                cuda.syncthreads()
+                # Test get from arr
+                outx[i] = arr[ri].x
+                outy[i] = arr[ri].y
+
+        arrx = np.zeros((nthreads,), dtype="int32")
+        arry = np.zeros((nthreads,), dtype="int32")
+
+        write_then_reverse_read_static[1, nthreads](arrx, arry)
+
+        for i, x in enumerate(arrx):
+            self.assertEqual(x, nthreads - i - 1)
+        for i, y in enumerate(arry):
+            self.assertEqual(y, (nthreads - i - 1) * 2)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sm_creation.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sm_creation.py
new file mode 100644
index 000000000..bff48e642
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sm_creation.py
@@ -0,0 +1,205 @@
+import numpy as np
+from numba import cuda, float32, int32, void
+from numba.core.errors import TypingError
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import skip_on_cudasim
+from .extensions_usecases import test_struct_model_type
+
+GLOBAL_CONSTANT = 5
+GLOBAL_CONSTANT_2 = 6
+GLOBAL_CONSTANT_TUPLE = 5, 6
+
+
+def udt_global_constants(A):
+    sa = cuda.shared.array(shape=GLOBAL_CONSTANT, dtype=float32)
+    i = cuda.grid(1)
+    A[i] = sa[i]
+
+
+def udt_global_build_tuple(A):
+    sa = cuda.shared.array(shape=(GLOBAL_CONSTANT, GLOBAL_CONSTANT_2),
+                           dtype=float32)
+    i, j = cuda.grid(2)
+    A[i, j] = sa[i, j]
+
+
+def udt_global_build_list(A):
+    sa = cuda.shared.array(shape=[GLOBAL_CONSTANT, GLOBAL_CONSTANT_2],
+                           dtype=float32)
+    i, j = cuda.grid(2)
+    A[i, j] = sa[i, j]
+
+
+def udt_global_constant_tuple(A):
+    sa = cuda.shared.array(shape=GLOBAL_CONSTANT_TUPLE, dtype=float32)
+    i, j = cuda.grid(2)
+    A[i, j] = sa[i, j]
+
+
+def udt_invalid_1(A):
+    sa = cuda.shared.array(shape=A[0], dtype=float32)
+    i = cuda.grid(1)
+    A[i] = sa[i]
+
+
+def udt_invalid_2(A):
+    sa = cuda.shared.array(shape=(1, A[0]), dtype=float32)
+    i, j = cuda.grid(2)
+    A[i, j] = sa[i, j]
+
+
+def udt_invalid_3(A):
+    sa = cuda.shared.array(shape=(1, A[0]), dtype=float32)
+    i = cuda.grid(1)
+    A[i] = sa[i, 0]
+
+
+class TestSharedMemoryCreation(CUDATestCase):
+    def getarg(self):
+        return np.array(100, dtype=np.float32, ndmin=1)
+
+    def getarg2(self):
+        return self.getarg().reshape(1,1)
+
+    def test_global_constants(self):
+        udt = cuda.jit((float32[:],))(udt_global_constants)
+        udt[1, 1](self.getarg())
+
+    def test_global_build_tuple(self):
+        udt = cuda.jit((float32[:, :],))(udt_global_build_tuple)
+        udt[1, 1](self.getarg2())
+
+    @skip_on_cudasim('Simulator does not prohibit lists for shared array shape')
+    def test_global_build_list(self):
+        with self.assertRaises(TypingError) as raises:
+            cuda.jit((float32[:, :],))(udt_global_build_list)
+
+        self.assertIn("No implementation of function "
+                      "Function(<function shared.array",
+                      str(raises.exception))
+        self.assertIn("found for signature:\n \n "
+                      ">>> array(shape=list(int64)<iv=[5, 6]>, "
+                      "dtype=class(float32)",
+                      str(raises.exception))
+
+    def test_global_constant_tuple(self):
+        udt = cuda.jit((float32[:, :],))(udt_global_constant_tuple)
+        udt[1, 1](self.getarg2())
+
+    @skip_on_cudasim("Can't check for constants in simulator")
+    def test_invalid_1(self):
+        # Scalar shape cannot be a floating point value
+        with self.assertRaises(TypingError) as raises:
+            cuda.jit((float32[:],))(udt_invalid_1)
+
+        self.assertIn("No implementation of function "
+                      "Function(<function shared.array",
+                      str(raises.exception))
+        self.assertIn("found for signature:\n \n "
+                      ">>> array(shape=float32, dtype=class(float32))",
+                      str(raises.exception))
+
+    @skip_on_cudasim("Can't check for constants in simulator")
+    def test_invalid_2(self):
+        # Tuple shape cannot contain a floating point value
+        with self.assertRaises(TypingError) as raises:
+            cuda.jit((float32[:, :],))(udt_invalid_2)
+
+        self.assertIn("No implementation of function "
+                      "Function(<function shared.array",
+                      str(raises.exception))
+        self.assertIn("found for signature:\n \n "
+                      ">>> array(shape=Tuple(Literal[int](1), "
+                      "array(float32, 1d, A)), dtype=class(float32))",
+                      str(raises.exception))
+
+    @skip_on_cudasim("Can't check for constants in simulator")
+    def test_invalid_3(self):
+        # Scalar shape must be literal
+        with self.assertRaises(TypingError) as raises:
+            cuda.jit((int32[:],))(udt_invalid_1)
+
+        self.assertIn("No implementation of function "
+                      "Function(<function shared.array",
+                      str(raises.exception))
+        self.assertIn("found for signature:\n \n "
+                      ">>> array(shape=int32, dtype=class(float32))",
+                      str(raises.exception))
+
+    @skip_on_cudasim("Can't check for constants in simulator")
+    def test_invalid_4(self):
+        # Tuple shape must contain only literals
+        with self.assertRaises(TypingError) as raises:
+            cuda.jit((int32[:],))(udt_invalid_3)
+
+        self.assertIn("No implementation of function "
+                      "Function(<function shared.array",
+                      str(raises.exception))
+        self.assertIn("found for signature:\n \n "
+                      ">>> array(shape=Tuple(Literal[int](1), int32), "
+                      "dtype=class(float32))",
+                      str(raises.exception))
+
+    def check_dtype(self, f, dtype):
+        # Find the typing of the dtype argument to cuda.shared.array
+        annotation = next(iter(f.overloads.values()))._type_annotation
+        l_dtype = annotation.typemap['s'].dtype
+        # Ensure that the typing is correct
+        self.assertEqual(l_dtype, dtype)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_numba_dtype(self):
+        # Check that Numba types can be used as the dtype of a shared array
+        @cuda.jit(void(int32[::1]))
+        def f(x):
+            s = cuda.shared.array(10, dtype=int32)
+            s[0] = x[0]
+            x[0] = s[0]
+
+        self.check_dtype(f, int32)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_numpy_dtype(self):
+        # Check that NumPy types can be used as the dtype of a shared array
+        @cuda.jit(void(int32[::1]))
+        def f(x):
+            s = cuda.shared.array(10, dtype=np.int32)
+            s[0] = x[0]
+            x[0] = s[0]
+
+        self.check_dtype(f, int32)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_string_dtype(self):
+        # Check that strings can be used to specify the dtype of a shared array
+        @cuda.jit(void(int32[::1]))
+        def f(x):
+            s = cuda.shared.array(10, dtype='int32')
+            s[0] = x[0]
+            x[0] = s[0]
+
+        self.check_dtype(f, int32)
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_invalid_string_dtype(self):
+        # Check that strings of invalid dtypes cause a typing error
+        re = ".*Invalid NumPy dtype specified: 'int33'.*"
+        with self.assertRaisesRegex(TypingError, re):
+            @cuda.jit(void(int32[::1]))
+            def f(x):
+                s = cuda.shared.array(10, dtype='int33')
+                s[0] = x[0]
+                x[0] = s[0]
+
+    @skip_on_cudasim("Can't check typing in simulator")
+    def test_type_with_struct_data_model(self):
+        @cuda.jit(void(test_struct_model_type[::1]))
+        def f(x):
+            s = cuda.shared.array(10, dtype=test_struct_model_type)
+            s[0] = x[0]
+            x[0] = s[0]
+        self.check_dtype(f, test_struct_model_type)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sync.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sync.py
new file mode 100644
index 000000000..0f00e878e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_sync.py
@@ -0,0 +1,244 @@
+import numpy as np
+from numba import cuda, int32, float32
+from numba.cuda.testing import skip_on_cudasim, unittest, CUDATestCase
+from numba.core.config import ENABLE_CUDASIM
+
+
+def useless_syncthreads(ary):
+    i = cuda.grid(1)
+    cuda.syncthreads()
+    ary[i] = i
+
+
+def useless_syncwarp(ary):
+    i = cuda.grid(1)
+    cuda.syncwarp()
+    ary[i] = i
+
+
+def useless_syncwarp_with_mask(ary):
+    i = cuda.grid(1)
+    cuda.syncwarp(0xFFFF)
+    ary[i] = i
+
+
+def coop_syncwarp(res):
+    sm = cuda.shared.array(32, int32)
+    i = cuda.grid(1)
+
+    sm[i] = i
+    cuda.syncwarp()
+
+    if i < 16:
+        sm[i] = sm[i] + sm[i + 16]
+        cuda.syncwarp(0xFFFF)
+
+    if i < 8:
+        sm[i] = sm[i] + sm[i + 8]
+        cuda.syncwarp(0xFF)
+
+    if i < 4:
+        sm[i] = sm[i] + sm[i + 4]
+        cuda.syncwarp(0xF)
+
+    if i < 2:
+        sm[i] = sm[i] + sm[i + 2]
+        cuda.syncwarp(0x3)
+
+    if i == 0:
+        res[0] = sm[0] + sm[1]
+
+
+def simple_smem(ary):
+    N = 100
+    sm = cuda.shared.array(N, int32)
+    i = cuda.grid(1)
+    if i == 0:
+        for j in range(N):
+            sm[j] = j
+    cuda.syncthreads()
+    ary[i] = sm[i]
+
+
+def coop_smem2d(ary):
+    i, j = cuda.grid(2)
+    sm = cuda.shared.array((10, 20), float32)
+    sm[i, j] = (i + 1) / (j + 1)
+    cuda.syncthreads()
+    ary[i, j] = sm[i, j]
+
+
+def dyn_shared_memory(ary):
+    i = cuda.grid(1)
+    sm = cuda.shared.array(0, float32)
+    sm[i] = i * 2
+    cuda.syncthreads()
+    ary[i] = sm[i]
+
+
+def use_threadfence(ary):
+    ary[0] += 123
+    cuda.threadfence()
+    ary[0] += 321
+
+
+def use_threadfence_block(ary):
+    ary[0] += 123
+    cuda.threadfence_block()
+    ary[0] += 321
+
+
+def use_threadfence_system(ary):
+    ary[0] += 123
+    cuda.threadfence_system()
+    ary[0] += 321
+
+
+def use_syncthreads_count(ary_in, ary_out):
+    i = cuda.grid(1)
+    ary_out[i] = cuda.syncthreads_count(ary_in[i])
+
+
+def use_syncthreads_and(ary_in, ary_out):
+    i = cuda.grid(1)
+    ary_out[i] = cuda.syncthreads_and(ary_in[i])
+
+
+def use_syncthreads_or(ary_in, ary_out):
+    i = cuda.grid(1)
+    ary_out[i] = cuda.syncthreads_or(ary_in[i])
+
+
+def _safe_cc_check(cc):
+    if ENABLE_CUDASIM:
+        return True
+    else:
+        return cuda.get_current_device().compute_capability >= cc
+
+
+class TestCudaSync(CUDATestCase):
+    def _test_useless(self, kernel):
+        compiled = cuda.jit("void(int32[::1])")(kernel)
+        nelem = 10
+        ary = np.empty(nelem, dtype=np.int32)
+        exp = np.arange(nelem, dtype=np.int32)
+        compiled[1, nelem](ary)
+        np.testing.assert_equal(ary, exp)
+
+    def test_useless_syncthreads(self):
+        self._test_useless(useless_syncthreads)
+
+    @skip_on_cudasim("syncwarp not implemented on cudasim")
+    def test_useless_syncwarp(self):
+        self._test_useless(useless_syncwarp)
+
+    @skip_on_cudasim("syncwarp not implemented on cudasim")
+    @unittest.skipUnless(_safe_cc_check((7, 0)),
+                         "Partial masks require CC 7.0 or greater")
+    def test_useless_syncwarp_with_mask(self):
+        self._test_useless(useless_syncwarp_with_mask)
+
+    @skip_on_cudasim("syncwarp not implemented on cudasim")
+    @unittest.skipUnless(_safe_cc_check((7, 0)),
+                         "Partial masks require CC 7.0 or greater")
+    def test_coop_syncwarp(self):
+        # coop_syncwarp computes the sum of all integers from 0 to 31 (496)
+        # using a single warp
+        expected = 496
+        nthreads = 32
+        nblocks = 1
+
+        compiled = cuda.jit("void(int32[::1])")(coop_syncwarp)
+        res = np.zeros(1, dtype=np.int32)
+        compiled[nblocks, nthreads](res)
+        np.testing.assert_equal(expected, res[0])
+
+    def test_simple_smem(self):
+        compiled = cuda.jit("void(int32[::1])")(simple_smem)
+        nelem = 100
+        ary = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary)
+        self.assertTrue(np.all(ary == np.arange(nelem, dtype=np.int32)))
+
+    def test_coop_smem2d(self):
+        compiled = cuda.jit("void(float32[:,::1])")(coop_smem2d)
+        shape = 10, 20
+        ary = np.empty(shape, dtype=np.float32)
+        compiled[1, shape](ary)
+        exp = np.empty_like(ary)
+        for i in range(ary.shape[0]):
+            for j in range(ary.shape[1]):
+                exp[i, j] = (i + 1) / (j + 1)
+        self.assertTrue(np.allclose(ary, exp))
+
+    def test_dyn_shared_memory(self):
+        compiled = cuda.jit("void(float32[::1])")(dyn_shared_memory)
+        shape = 50
+        ary = np.empty(shape, dtype=np.float32)
+        compiled[1, shape, 0, ary.size * 4](ary)
+        self.assertTrue(np.all(ary == 2 * np.arange(ary.size, dtype=np.int32)))
+
+    def test_threadfence_codegen(self):
+        # Does not test runtime behavior, just the code generation.
+        sig = (int32[:],)
+        compiled = cuda.jit(sig)(use_threadfence)
+        ary = np.zeros(10, dtype=np.int32)
+        compiled[1, 1](ary)
+        self.assertEqual(123 + 321, ary[0])
+        if not ENABLE_CUDASIM:
+            self.assertIn("membar.gl;", compiled.inspect_asm(sig))
+
+    def test_threadfence_block_codegen(self):
+        # Does not test runtime behavior, just the code generation.
+        sig = (int32[:],)
+        compiled = cuda.jit(sig)(use_threadfence_block)
+        ary = np.zeros(10, dtype=np.int32)
+        compiled[1, 1](ary)
+        self.assertEqual(123 + 321, ary[0])
+        if not ENABLE_CUDASIM:
+            self.assertIn("membar.cta;", compiled.inspect_asm(sig))
+
+    def test_threadfence_system_codegen(self):
+        # Does not test runtime behavior, just the code generation.
+        sig = (int32[:],)
+        compiled = cuda.jit(sig)(use_threadfence_system)
+        ary = np.zeros(10, dtype=np.int32)
+        compiled[1, 1](ary)
+        self.assertEqual(123 + 321, ary[0])
+        if not ENABLE_CUDASIM:
+            self.assertIn("membar.sys;", compiled.inspect_asm(sig))
+
+    def test_syncthreads_count(self):
+        compiled = cuda.jit("void(int32[:], int32[:])")(use_syncthreads_count)
+        ary_in = np.ones(72, dtype=np.int32)
+        ary_out = np.zeros(72, dtype=np.int32)
+        ary_in[31] = 0
+        ary_in[42] = 0
+        compiled[1, 72](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 70))
+
+    def test_syncthreads_and(self):
+        compiled = cuda.jit("void(int32[:], int32[:])")(use_syncthreads_and)
+        nelem = 100
+        ary_in = np.ones(nelem, dtype=np.int32)
+        ary_out = np.zeros(nelem, dtype=np.int32)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 1))
+        ary_in[31] = 0
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0))
+
+    def test_syncthreads_or(self):
+        compiled = cuda.jit("void(int32[:], int32[:])")(use_syncthreads_or)
+        nelem = 100
+        ary_in = np.zeros(nelem, dtype=np.int32)
+        ary_out = np.zeros(nelem, dtype=np.int32)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0))
+        ary_in[31] = 1
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 1))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_transpose.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_transpose.py
new file mode 100644
index 000000000..9c13db534
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_transpose.py
@@ -0,0 +1,80 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.kernels.transpose import transpose
+from numba.cuda.testing import unittest
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+
+
+recordwith2darray = np.dtype([('i', np.int32),
+                              ('j', np.float32, (3, 2))])
+
+
+@skip_on_cudasim('Device Array API unsupported in the simulator')
+class TestTranspose(CUDATestCase):
+
+    def test_transpose(self):
+        variants = ((5, 6, np.float64),
+                    (128, 128, np.complex128),
+                    (1025, 512, np.float64))
+
+        for rows, cols, dtype in variants:
+            with self.subTest(rows=rows, cols=cols, dtype=dtype):
+                x = np.arange(rows * cols, dtype=dtype).reshape(cols, rows)
+                y = np.zeros(rows * cols, dtype=dtype).reshape(rows, cols)
+                dx = cuda.to_device(x)
+                dy = cuda.cudadrv.devicearray.from_array_like(y)
+                transpose(dx, dy)
+                dy.copy_to_host(y)
+                np.testing.assert_array_equal(x.transpose(), y)
+
+    small_variants = ((2, 3), (16, 16), (16, 17), (17, 16), (14, 15), (15, 14),
+                      (14, 14))
+
+    def test_transpose_record(self):
+        for rows, cols in self.small_variants:
+            with self.subTest(rows=rows, cols=cols):
+                arr = np.recarray((rows, cols), dtype=recordwith2darray)
+                for x in range(rows):
+                    for y in range(cols):
+                        arr[x, y].i = x ** 2 + y
+                        j = np.arange(3 * 2, dtype=np.float32)
+                        arr[x, y].j = j.reshape(3, 2) * x + y
+
+                transposed = arr.T
+                d_arr = cuda.to_device(arr)
+                d_transposed = cuda.device_array_like(transposed)
+                transpose(d_arr, d_transposed)
+                host_transposed = d_transposed.copy_to_host()
+                np.testing.assert_array_equal(transposed, host_transposed)
+
+    def test_transpose_bool(self):
+        for rows, cols in self.small_variants:
+            with self.subTest(rows=rows, cols=cols):
+                arr = np.random.randint(2, size=(rows, cols), dtype=np.bool_)
+                transposed = arr.T
+
+                d_arr = cuda.to_device(arr)
+                d_transposed = cuda.device_array_like(transposed)
+                transpose(d_arr, d_transposed)
+
+                host_transposed = d_transposed.copy_to_host()
+                np.testing.assert_array_equal(transposed, host_transposed)
+
+    def test_transpose_view(self):
+        # Because the strides of transposes of views differ to those in NumPy
+        # (see issue #4974), we test the shape and strides of a transpose.
+        a = np.arange(120, dtype=np.int64).reshape((10, 12))
+        a_view_t = a[::2, ::2].T
+
+        d_a = cuda.to_device(a)
+        d_a_view_t = d_a[::2, ::2].T
+
+        self.assertEqual(d_a_view_t.shape, (6, 5))
+        self.assertEqual(d_a_view_t.strides, (40, 8))
+
+        h_a_view_t = d_a_view_t.copy_to_host()
+        np.testing.assert_array_equal(a_view_t, h_a_view_t)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_userexc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_userexc.py
new file mode 100644
index 000000000..9748b7522
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_userexc.py
@@ -0,0 +1,40 @@
+from numba.cuda.testing import unittest, CUDATestCase
+from numba import cuda
+from numba.core import config
+
+
+class MyError(Exception):
+    pass
+
+
+regex_pattern = (
+    r'In function [\'"]test_exc[\'"], file [\:\.\/\\\-a-zA-Z_0-9]+, line \d+'
+)
+
+
+class TestUserExc(CUDATestCase):
+
+    def test_user_exception(self):
+        @cuda.jit("void(int32)", debug=True)
+        def test_exc(x):
+            if x == 1:
+                raise MyError
+            elif x == 2:
+                raise MyError("foo")
+
+        test_exc[1, 1](0)    # no raise
+        with self.assertRaises(MyError) as cm:
+            test_exc[1, 1](1)
+        if not config.ENABLE_CUDASIM:
+            self.assertRegexpMatches(str(cm.exception), regex_pattern)
+        self.assertIn("tid=[0, 0, 0] ctaid=[0, 0, 0]", str(cm.exception))
+        with self.assertRaises(MyError) as cm:
+            test_exc[1, 1](2)
+        if not config.ENABLE_CUDASIM:
+            self.assertRegexpMatches(str(cm.exception), regex_pattern)
+            self.assertRegexpMatches(str(cm.exception), regex_pattern)
+        self.assertIn("tid=[0, 0, 0] ctaid=[0, 0, 0]: foo", str(cm.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vector_type.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vector_type.py
new file mode 100644
index 000000000..1ee72f2d3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vector_type.py
@@ -0,0 +1,307 @@
+"""
+CUDA vector type tests. Note that this test file imports
+`cuda.vector_type` module to programmatically test all the
+vector types. However, `vector_type` module is internal
+and should not be imported by user, user should only import the
+corresponding vector type from `cuda` module in kernel to use them.
+"""
+
+import numpy as np
+
+from numba.core import config
+from numba.cuda.testing import CUDATestCase
+
+from numba import cuda
+
+if config.ENABLE_CUDASIM:
+    from numba.cuda.simulator.vector_types import vector_types
+else:
+    from numba.cuda.vector_types import vector_types
+
+
+def make_kernel(vtype):
+    """
+    Returns a jit compiled kernel that constructs a vector types of
+    the given type, using the exact number of primitive types to
+    construct the vector type.
+    """
+    vobj = vtype.user_facing_object
+    base_type = vtype.base_type
+
+    def kernel_1elem(res):
+        v = vobj(base_type(0))
+        res[0] = v.x
+
+    def kernel_2elem(res):
+        v = vobj(base_type(0), base_type(1))
+        res[0] = v.x
+        res[1] = v.y
+
+    def kernel_3elem(res):
+        v = vobj(base_type(0), base_type(1), base_type(2))
+        res[0] = v.x
+        res[1] = v.y
+        res[2] = v.z
+
+    def kernel_4elem(res):
+        v = vobj(
+            base_type(0),
+            base_type(1),
+            base_type(2),
+            base_type(3)
+        )
+        res[0] = v.x
+        res[1] = v.y
+        res[2] = v.z
+        res[3] = v.w
+
+    host_function = {
+        1: kernel_1elem,
+        2: kernel_2elem,
+        3: kernel_3elem,
+        4: kernel_4elem
+    }[vtype.num_elements]
+    return cuda.jit(host_function)
+
+
+def make_fancy_creation_kernel(vtype):
+    """
+    Returns a jit compiled kernel that constructs a vector type using the
+    "fancy" construction, that is, with arbitrary combinations of primitive
+    types and vector types, as long as the total element of the construction
+    is the same as the number of elements of the vector type.
+    """
+    base_type = vtype.base_type
+    v1 = getattr(cuda, f"{vtype.name[:-1]}1")
+    v2 = getattr(cuda, f"{vtype.name[:-1]}2")
+    v3 = getattr(cuda, f"{vtype.name[:-1]}3")
+    v4 = getattr(cuda, f"{vtype.name[:-1]}4")
+
+    def kernel(res):
+        one = base_type(1.0)
+        two = base_type(2.0)
+        three = base_type(3.0)
+        four = base_type(4.0)
+
+        j = 0   # index of the result array
+
+        # Construct a 1-component vector type, possible combination includes:
+        # 2C1 = 2 combinations.
+
+        f1_1 = v1(one)  # 1
+        f1_2 = v1(f1_1) # 1
+
+        res[0] = f1_1.x
+        res[1] = f1_2.x
+        j += 2
+
+        # Construct a 2-component vector type, possible combination includes:
+        # 1 + 2C1 * 2 = 5 combinations
+
+        f2_1 = v2(two, three)       # 2 3
+        f2_2 = v2(f1_1, three)      # 1 3
+        f2_3 = v2(two, f1_1)        # 2 1
+        f2_4 = v2(f1_1, f1_1)       # 1 1
+        f2_5 = v2(f2_1)             # 2 3
+
+        for v in (f2_1, f2_2, f2_3, f2_4, f2_5):
+            res[j] = v.x
+            res[j + 1] = v.y
+            j += 2
+
+        # Construct a 3-component vector type, possible combination includes:
+        # 1 + 2C1 * 2 + 2^3 = 13 combinations
+
+        f3_1 = v3(f2_1, one)            # 2 3 1
+        f3_2 = v3(f2_1, f1_1)           # 2 3 1
+        f3_3 = v3(one, f2_1)            # 1 2 3
+        f3_4 = v3(f1_1, f2_1)           # 1 2 3
+
+        f3_5 = v3(one, two, three)      # 1 2 3
+        f3_6 = v3(f1_1, two, three)     # 1 2 3
+        f3_7 = v3(one, f1_1, three)     # 1 1 3
+        f3_8 = v3(one, two, f1_1)       # 1 2 1
+        f3_9 = v3(f1_1, f1_1, three)    # 1 1 3
+        f3_10 = v3(one, f1_1, f1_1)     # 1 1 1
+        f3_11 = v3(f1_1, two, f1_1)     # 1 2 1
+        f3_12 = v3(f1_1, f1_1, f1_1)    # 1 1 1
+
+        f3_13 = v3(f3_1)                # 2 3 1
+
+        for v in (f3_1, f3_2, f3_3, f3_4, f3_5, f3_6, f3_7, f3_8, f3_9,
+                  f3_10, f3_11, f3_12, f3_13):
+            res[j] = v.x
+            res[j + 1] = v.y
+            res[j + 2] = v.z
+            j += 3
+
+        # Construct a 4-component vector type, possible combination includes:
+        # 1 + (2C1 * 2 + 1) + 3C1 * 2^2 + 2^4 = 34 combinations
+
+        f4_1 = v4(one, two, three, four)    # 1 2 3 4
+        f4_2 = v4(f1_1, two, three, four)   # 1 2 3 4
+        f4_3 = v4(one, f1_1, three, four)   # 1 1 3 4
+        f4_4 = v4(one, two, f1_1, four)     # 1 2 1 4
+        f4_5 = v4(one, two, three, f1_1)    # 1 2 3 1
+        f4_6 = v4(f1_1, f1_1, three, four)  # 1 1 3 4
+        f4_7 = v4(f1_1, two, f1_1, four)    # 1 2 1 4
+        f4_8 = v4(f1_1, two, three, f1_1)   # 1 2 3 1
+        f4_9 = v4(one, f1_1, f1_1, four)    # 1 1 1 4
+        f4_10 = v4(one, f1_1, three, f1_1)  # 1 1 3 1
+        f4_11 = v4(one, two, f1_1, f1_1)    # 1 2 1 1
+        f4_12 = v4(f1_1, f1_1, f1_1, four)  # 1 1 1 4
+        f4_13 = v4(f1_1, f1_1, three, f1_1) # 1 1 3 1
+        f4_14 = v4(f1_1, two, f1_1, f1_1)   # 1 2 1 1
+        f4_15 = v4(one, f1_1, f1_1, f1_1)   # 1 1 1 1
+        f4_16 = v4(f1_1, f1_1, f1_1, f1_1)  # 1 1 1 1
+
+        f4_17 = v4(f2_1, two, three)        # 2 3 2 3
+        f4_18 = v4(f2_1, f1_1, three)       # 2 3 1 3
+        f4_19 = v4(f2_1, two, f1_1)         # 2 3 2 1
+        f4_20 = v4(f2_1, f1_1, f1_1)        # 2 3 1 1
+        f4_21 = v4(one, f2_1, three)        # 1 2 3 3
+        f4_22 = v4(f1_1, f2_1, three)       # 1 2 3 3
+        f4_23 = v4(one, f2_1, f1_1)         # 1 2 3 1
+        f4_24 = v4(f1_1, f2_1, f1_1)        # 1 2 3 1
+        f4_25 = v4(one, four, f2_1)         # 1 4 2 3
+        f4_26 = v4(f1_1, four, f2_1)        # 1 4 2 3
+        f4_27 = v4(one, f1_1, f2_1)         # 1 1 2 3
+        f4_28 = v4(f1_1, f1_1, f2_1)        # 1 1 2 3
+
+        f4_29 = v4(f2_1, f2_1)              # 2 3 2 3
+        f4_30 = v4(f3_1, four)              # 2 3 1 4
+        f4_31 = v4(f3_1, f1_1)              # 2 3 1 1
+        f4_32 = v4(four, f3_1)              # 4 2 3 1
+        f4_33 = v4(f1_1, f3_1)              # 1 2 3 1
+
+        f4_34 = v4(f4_1)                    # 1 2 3 4
+
+        for v in (f4_1, f4_2, f4_3, f4_4, f4_5, f4_6, f4_7, f4_8, f4_9, f4_10,
+                  f4_11, f4_12, f4_13, f4_14, f4_15, f4_16, f4_17, f4_18, f4_19,
+                  f4_20, f4_21, f4_22, f4_23, f4_24, f4_25, f4_26, f4_27, f4_28,
+                  f4_29, f4_30, f4_31, f4_32, f4_33, f4_34):
+            res[j] = v.x
+            res[j + 1] = v.y
+            res[j + 2] = v.z
+            res[j + 3] = v.w
+            j += 4
+
+    return cuda.jit(kernel)
+
+
+class TestCudaVectorType(CUDATestCase):
+
+    def test_basic(self):
+        """Basic test that makes sure that vector type and aliases
+        are available within the cuda module from both device and
+        simulator mode. This is an important sanity check, since other
+        tests below tests the vector type objects programmatically.
+        """
+        @cuda.jit("void(float64[:])")
+        def kernel(arr):
+            v1 = cuda.float64x4(1.0, 3.0, 5.0, 7.0)
+            v2 = cuda.short2(10, 11)
+            arr[0] = v1.x
+            arr[1] = v1.y
+            arr[2] = v1.z
+            arr[3] = v1.w
+            arr[4] = v2.x
+            arr[5] = v2.y
+
+        res = np.zeros(6, dtype=np.float64)
+        kernel[1, 1](res)
+        self.assertTrue(np.allclose(res, [1.0, 3.0, 5.0, 7.0, 10, 11]))
+
+    def test_creation_readout(self):
+        for vty in vector_types.values():
+            with self.subTest(vty=vty):
+                arr = np.zeros((vty.num_elements,))
+                kernel = make_kernel(vty)
+                kernel[1, 1](arr)
+                np.testing.assert_almost_equal(
+                    arr, np.array(range(vty.num_elements))
+                )
+
+    def test_fancy_creation_readout(self):
+        for vty in vector_types.values():
+            with self.subTest(vty=vty):
+                kernel = make_fancy_creation_kernel(vty)
+
+                expected = np.array([
+                    # 1-component vectors
+                    1,
+                    1,
+                    # 2-component vectors
+                    2, 3,
+                    1, 3,
+                    2, 1,
+                    1, 1,
+                    2, 3,
+                    # 3-component vectors
+                    2, 3, 1,
+                    2, 3, 1,
+                    1, 2, 3,
+                    1, 2, 3,
+                    1, 2, 3,
+                    1, 2, 3,
+                    1, 1, 3,
+                    1, 2, 1,
+                    1, 1, 3,
+                    1, 1, 1,
+                    1, 2, 1,
+                    1, 1, 1,
+                    2, 3, 1,
+                    # 4-component vectors
+                    1, 2, 3, 4,
+                    1, 2, 3, 4,
+                    1, 1, 3, 4,
+                    1, 2, 1, 4,
+                    1, 2, 3, 1,
+                    1, 1, 3, 4,
+                    1, 2, 1, 4,
+                    1, 2, 3, 1,
+                    1, 1, 1, 4,
+                    1, 1, 3, 1,
+                    1, 2, 1, 1,
+                    1, 1, 1, 4,
+                    1, 1, 3, 1,
+                    1, 2, 1, 1,
+                    1, 1, 1, 1,
+                    1, 1, 1, 1,
+                    2, 3, 2, 3,
+                    2, 3, 1, 3,
+                    2, 3, 2, 1,
+                    2, 3, 1, 1,
+                    1, 2, 3, 3,
+                    1, 2, 3, 3,
+                    1, 2, 3, 1,
+                    1, 2, 3, 1,
+                    1, 4, 2, 3,
+                    1, 4, 2, 3,
+                    1, 1, 2, 3,
+                    1, 1, 2, 3,
+                    2, 3, 2, 3,
+                    2, 3, 1, 4,
+                    2, 3, 1, 1,
+                    4, 2, 3, 1,
+                    1, 2, 3, 1,
+                    1, 2, 3, 4
+                ])
+                arr = np.zeros(expected.shape)
+                kernel[1, 1](arr)
+                np.testing.assert_almost_equal(arr, expected)
+
+    def test_vector_type_alias(self):
+        """Tests that `cuda.<vector_type.alias>` are importable and
+        that is the same as `cuda.<vector_type.name>`.
+
+        `test_fancy_creation_readout` only test vector types imported
+        with its name. This test makes sure that construction with
+        objects imported with alias should work the same.
+        """
+        for vty in vector_types.values():
+            for alias in vty.user_facing_object.aliases:
+                with self.subTest(vty=vty.name, alias=alias):
+                    self.assertEqual(
+                        id(getattr(cuda, vty.name)), id(getattr(cuda, alias))
+                    )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize.py
new file mode 100644
index 000000000..11195101a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize.py
@@ -0,0 +1,226 @@
+import numpy as np
+
+from collections import namedtuple
+from numba import vectorize
+from numba import cuda, int32, float32, float64
+from numba.cuda.testing import skip_on_cudasim
+from numba.cuda.testing import CUDATestCase
+import unittest
+
+sig = [int32(int32, int32),
+       float32(float32, float32),
+       float64(float64, float64)]
+
+test_dtypes = np.float32, np.int32
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestCUDAVectorize(CUDATestCase):
+    N = 1000001
+
+    def test_scalar(self):
+
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        a = 1.2
+        b = 2.3
+        c = vector_add(a, b)
+        self.assertEqual(c, a + b)
+
+    def test_1d(self):
+
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        cuda_ufunc = vector_add
+
+        # build python ufunc
+        np_ufunc = np.add
+
+        # test it out
+        def test(ty):
+            data = np.array(np.random.random(self.N), dtype=ty)
+
+            result = cuda_ufunc(data, data)
+            gold = np_ufunc(data, data)
+            self.assertTrue(np.allclose(gold, result), (gold, result))
+
+        test(np.double)
+        test(np.float32)
+        test(np.int32)
+
+    def test_1d_async(self):
+
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        cuda_ufunc = vector_add
+
+        # build python ufunc
+        np_ufunc = np.add
+
+        # test it out
+        def test(ty):
+            data = np.array(np.random.random(self.N), dtype=ty)
+
+            stream = cuda.stream()
+            device_data = cuda.to_device(data, stream)
+            dresult = cuda_ufunc(device_data, device_data, stream=stream)
+            result = dresult.copy_to_host()
+            stream.synchronize()
+
+            gold = np_ufunc(data, data)
+
+            self.assertTrue(np.allclose(gold, result), (gold, result))
+
+        test(np.double)
+        test(np.float32)
+        test(np.int32)
+
+    def test_nd(self):
+
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        cuda_ufunc = vector_add
+
+        def test(dtype, order, nd, size=4):
+            data = np.random.random((size,) * nd).astype(dtype)
+            data[data != data] = 2.4
+            data[data == float('inf')] = 3.8
+            data[data == float('-inf')] = -3.8
+            data2 = np.array(data.T, order=order)  # .copy(order=order)
+
+            result = data + data2
+            our_result = cuda_ufunc(data, data2)
+            self.assertTrue(np.allclose(result, our_result),
+                            (dtype, order, result, our_result))
+
+        for nd in range(1, 8):
+            for dtype in test_dtypes:
+                for order in ('C', 'F'):
+                    test(dtype, order, nd)
+
+    def test_ufunc_attrib(self):
+        self.reduce_test(8)
+        self.reduce_test(100)
+        self.reduce_test(2 ** 10 + 1)
+        self.reduce_test2(8)
+        self.reduce_test2(100)
+        self.reduce_test2(2 ** 10 + 1)
+
+    def test_output_arg(self):
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        A = np.arange(10, dtype=np.float32)
+        B = np.arange(10, dtype=np.float32)
+        C = np.empty_like(A)
+        vector_add(A, B, out=C)
+        self.assertTrue(np.allclose(A + B, C))
+
+    def reduce_test(self, n):
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        cuda_ufunc = vector_add
+        x = np.arange(n, dtype=np.int32)
+        gold = np.add.reduce(x)
+        result = cuda_ufunc.reduce(x)
+        self.assertEqual(result, gold)
+
+    def reduce_test2(self, n):
+
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        cuda_ufunc = vector_add
+
+        x = np.arange(n, dtype=np.int32)
+        gold = np.add.reduce(x)
+        stream = cuda.stream()
+        dx = cuda.to_device(x, stream)
+        result = cuda_ufunc.reduce(dx, stream=stream)
+        self.assertEqual(result, gold)
+
+    def test_auto_transfer(self):
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        cuda_ufunc = vector_add
+
+        n = 10
+        x = np.arange(n, dtype=np.int32)
+        dx = cuda.to_device(x)
+        y = cuda_ufunc(x, dx).copy_to_host()
+        np.testing.assert_equal(y, x + x)
+
+    def test_ufunc_output_ravel(self):
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        cuda_ufunc = vector_add
+
+        n = 10
+        x = np.arange(n, dtype=np.int32).reshape(2, 5)
+        dx = cuda.to_device(x)
+        cuda_ufunc(dx, dx, out=dx)
+
+        got = dx.copy_to_host()
+        expect = x + x
+        np.testing.assert_equal(got, expect)
+
+    def check_tuple_arg(self, a, b):
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        r = vector_add(a, b)
+        np.testing.assert_equal(np.asarray(a) + np.asarray(b), r)
+
+    def test_tuple_arg(self):
+        a = (1.0, 2.0, 3.0)
+        b = (4.0, 5.0, 6.0)
+        self.check_tuple_arg(a, b)
+
+    def test_namedtuple_arg(self):
+        Point = namedtuple('Point', ('x', 'y', 'z'))
+        a = Point(x=1.0, y=2.0, z=3.0)
+        b = Point(x=4.0, y=5.0, z=6.0)
+        self.check_tuple_arg(a, b)
+
+    def test_tuple_of_array_arg(self):
+        arr = np.arange(10, dtype=np.int32)
+        a = (arr, arr + 1)
+        b = (arr + 2, arr + 2)
+        self.check_tuple_arg(a, b)
+
+    def test_tuple_of_namedtuple_arg(self):
+        Point = namedtuple('Point', ('x', 'y', 'z'))
+        a = (Point(x=1.0, y=2.0, z=3.0), Point(x=1.5, y=2.5, z=3.5))
+        b = (Point(x=4.0, y=5.0, z=6.0), Point(x=4.5, y=5.5, z=6.5))
+        self.check_tuple_arg(a, b)
+
+    def test_namedtuple_of_array_arg(self):
+        xs1 = np.arange(10, dtype=np.int32)
+        ys1 = xs1 + 2
+        xs2 = np.arange(10, dtype=np.int32) * 2
+        ys2 = xs2 + 1
+        Points = namedtuple('Points', ('xs', 'ys'))
+        a = Points(xs=xs1, ys=ys1)
+        b = Points(xs=xs2, ys=ys2)
+        self.check_tuple_arg(a, b)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_complex.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_complex.py
new file mode 100644
index 000000000..82c7ca8f8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_complex.py
@@ -0,0 +1,20 @@
+import numpy as np
+from numba import vectorize
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestVectorizeComplex(CUDATestCase):
+    def test_vectorize_complex(self):
+        @vectorize(['complex128(complex128)'], target='cuda')
+        def vcomp(a):
+            return a * a + 1.
+
+        A = np.arange(5, dtype=np.complex128)
+        B = vcomp(A)
+        self.assertTrue(np.allclose(A * A + 1., B))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_decor.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_decor.py
new file mode 100644
index 000000000..12b8fa03c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_decor.py
@@ -0,0 +1,69 @@
+import numpy as np
+
+from numba import vectorize, cuda
+from numba.tests.npyufunc.test_vectorize_decor import BaseVectorizeDecor, \
+    BaseVectorizeNopythonArg, BaseVectorizeUnrecognizedArg
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestVectorizeDecor(CUDATestCase, BaseVectorizeDecor):
+    """
+    Runs the tests from BaseVectorizeDecor with the CUDA target.
+    """
+    target = 'cuda'
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestGPUVectorizeBroadcast(CUDATestCase):
+    def test_broadcast(self):
+        a = np.random.randn(100, 3, 1)
+        b = a.transpose(2, 1, 0)
+
+        def fn(a, b):
+            return a - b
+
+        @vectorize(['float64(float64,float64)'], target='cuda')
+        def fngpu(a, b):
+            return a - b
+
+        expect = fn(a, b)
+        got = fngpu(a, b)
+        np.testing.assert_almost_equal(expect, got)
+
+    def test_device_broadcast(self):
+        """
+        Same test as .test_broadcast() but with device array as inputs
+        """
+
+        a = np.random.randn(100, 3, 1)
+        b = a.transpose(2, 1, 0)
+
+        def fn(a, b):
+            return a - b
+
+        @vectorize(['float64(float64,float64)'], target='cuda')
+        def fngpu(a, b):
+            return a - b
+
+        expect = fn(a, b)
+        got = fngpu(cuda.to_device(a), cuda.to_device(b))
+        np.testing.assert_almost_equal(expect, got.copy_to_host())
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestVectorizeNopythonArg(BaseVectorizeNopythonArg, CUDATestCase):
+    def test_target_cuda_nopython(self):
+        warnings = ["nopython kwarg for cuda target is redundant"]
+        self._test_target_nopython('cuda', warnings)
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestVectorizeUnrecognizedArg(BaseVectorizeUnrecognizedArg, CUDATestCase):
+    def test_target_cuda_unrecognized_arg(self):
+        self._test_target_unrecognized_arg('cuda')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_device.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_device.py
new file mode 100644
index 000000000..e33598d8b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_device.py
@@ -0,0 +1,36 @@
+from numba import vectorize
+from numba import cuda, float32
+import numpy as np
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestCudaVectorizeDeviceCall(CUDATestCase):
+    def test_cuda_vectorize_device_call(self):
+
+        @cuda.jit(float32(float32, float32, float32), device=True)
+        def cu_device_fn(x, y, z):
+            return x ** y / z
+
+        def cu_ufunc(x, y, z):
+            return cu_device_fn(x, y, z)
+
+        ufunc = vectorize([float32(float32, float32, float32)], target='cuda')(
+            cu_ufunc)
+
+        N = 100
+
+        X = np.array(np.random.sample(N), dtype=np.float32)
+        Y = np.array(np.random.sample(N), dtype=np.float32)
+        Z = np.array(np.random.sample(N), dtype=np.float32) + 0.1
+
+        out = ufunc(X, Y, Z)
+
+        gold = (X ** Y) / Z
+
+        self.assertTrue(np.allclose(out, gold))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_scalar_arg.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_scalar_arg.py
new file mode 100644
index 000000000..1c65a41d7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_vectorize_scalar_arg.py
@@ -0,0 +1,37 @@
+import numpy as np
+from numba import vectorize
+from numba import cuda, float64
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+
+sig = [float64(float64, float64)]
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestCUDAVectorizeScalarArg(CUDATestCase):
+
+    def test_vectorize_scalar_arg(self):
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        A = np.arange(10, dtype=np.float64)
+        dA = cuda.to_device(A)
+        v = vector_add(1.0, dA)
+
+        np.testing.assert_array_almost_equal(
+            v.copy_to_host(),
+            np.arange(1, 11, dtype=np.float64))
+
+    def test_vectorize_all_scalars(self):
+        @vectorize(sig, target='cuda')
+        def vector_add(a, b):
+            return a + b
+
+        v = vector_add(1.0, 1.0)
+
+        np.testing.assert_almost_equal(2.0, v)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_warning.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_warning.py
new file mode 100644
index 000000000..e1b32dbb0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_warning.py
@@ -0,0 +1,139 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.tests.support import linux_only, override_config
+from numba.core.errors import NumbaPerformanceWarning
+import warnings
+
+
+@skip_on_cudasim('cudasim does not raise performance warnings')
+class TestWarnings(CUDATestCase):
+    def test_inefficient_launch_configuration(self):
+        @cuda.jit
+        def kernel():
+            pass
+
+        with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
+            with warnings.catch_warnings(record=True) as w:
+                kernel[1, 1]()
+
+        self.assertEqual(w[0].category, NumbaPerformanceWarning)
+        self.assertIn('Grid size', str(w[0].message))
+        self.assertIn('low occupancy', str(w[0].message))
+
+    def test_efficient_launch_configuration(self):
+        @cuda.jit
+        def kernel():
+            pass
+
+        with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
+            with warnings.catch_warnings(record=True) as w:
+                kernel[256, 256]()
+
+        self.assertEqual(len(w), 0)
+
+    def test_warn_on_host_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        arr_f32 = np.zeros(N, dtype=np.float32)
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](arr_f32, N)
+
+        self.assertEqual(w[0].category, NumbaPerformanceWarning)
+        self.assertIn('Host array used in CUDA kernel will incur',
+                      str(w[0].message))
+        self.assertIn('copy overhead', str(w[0].message))
+
+    def test_pinned_warn_on_host_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        ary = cuda.pinned_array(N, dtype=np.float32)
+
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](ary, N)
+
+        self.assertEqual(w[0].category, NumbaPerformanceWarning)
+        self.assertIn('Host array used in CUDA kernel will incur',
+                      str(w[0].message))
+        self.assertIn('copy overhead', str(w[0].message))
+
+    def test_nowarn_on_mapped_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        ary = cuda.mapped_array(N, dtype=np.float32)
+
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](ary, N)
+
+        self.assertEqual(len(w), 0)
+
+    @linux_only
+    def test_nowarn_on_managed_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        ary = cuda.managed_array(N, dtype=np.float32)
+
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](ary, N)
+
+        self.assertEqual(len(w), 0)
+
+    def test_nowarn_on_device_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        ary = cuda.device_array(N, dtype=np.float32)
+
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](ary, N)
+
+        self.assertEqual(len(w), 0)
+
+    def test_warn_on_debug_and_opt(self):
+        with warnings.catch_warnings(record=True) as w:
+            cuda.jit(debug=True, opt=True)
+
+        self.assertEqual(len(w), 1)
+        self.assertIn('not supported by CUDA', str(w[0].message))
+
+    def test_warn_on_debug_and_opt_default(self):
+        with warnings.catch_warnings(record=True) as w:
+            cuda.jit(debug=True)
+
+        self.assertEqual(len(w), 1)
+        self.assertIn('not supported by CUDA', str(w[0].message))
+
+    def test_no_warn_on_debug_and_no_opt(self):
+        with warnings.catch_warnings(record=True) as w:
+            cuda.jit(debug=True, opt=False)
+
+        self.assertEqual(len(w), 0)
+
+    def test_no_warn_with_no_debug_and_opt_kwargs(self):
+        with warnings.catch_warnings(record=True) as w:
+            cuda.jit()
+
+        self.assertEqual(len(w), 0)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_warp_ops.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_warp_ops.py
new file mode 100644
index 000000000..2fc157d07
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudapy/test_warp_ops.py
@@ -0,0 +1,276 @@
+import numpy as np
+from numba import cuda, int32, int64, float32, float64
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.core import config
+
+
+def useful_syncwarp(ary):
+    i = cuda.grid(1)
+    if i == 0:
+        ary[0] = 42
+    cuda.syncwarp(0xffffffff)
+    ary[i] = ary[0]
+
+
+def use_shfl_sync_idx(ary, idx):
+    i = cuda.grid(1)
+    val = cuda.shfl_sync(0xffffffff, i, idx)
+    ary[i] = val
+
+
+def use_shfl_sync_up(ary, delta):
+    i = cuda.grid(1)
+    val = cuda.shfl_up_sync(0xffffffff, i, delta)
+    ary[i] = val
+
+
+def use_shfl_sync_down(ary, delta):
+    i = cuda.grid(1)
+    val = cuda.shfl_down_sync(0xffffffff, i, delta)
+    ary[i] = val
+
+
+def use_shfl_sync_xor(ary, xor):
+    i = cuda.grid(1)
+    val = cuda.shfl_xor_sync(0xffffffff, i, xor)
+    ary[i] = val
+
+
+def use_shfl_sync_with_val(ary, into):
+    i = cuda.grid(1)
+    val = cuda.shfl_sync(0xffffffff, into, 0)
+    ary[i] = val
+
+
+def use_vote_sync_all(ary_in, ary_out):
+    i = cuda.grid(1)
+    pred = cuda.all_sync(0xffffffff, ary_in[i])
+    ary_out[i] = pred
+
+
+def use_vote_sync_any(ary_in, ary_out):
+    i = cuda.grid(1)
+    pred = cuda.any_sync(0xffffffff, ary_in[i])
+    ary_out[i] = pred
+
+
+def use_vote_sync_eq(ary_in, ary_out):
+    i = cuda.grid(1)
+    pred = cuda.eq_sync(0xffffffff, ary_in[i])
+    ary_out[i] = pred
+
+
+def use_vote_sync_ballot(ary):
+    i = cuda.threadIdx.x
+    ballot = cuda.ballot_sync(0xffffffff, True)
+    ary[i] = ballot
+
+
+def use_match_any_sync(ary_in, ary_out):
+    i = cuda.grid(1)
+    ballot = cuda.match_any_sync(0xffffffff, ary_in[i])
+    ary_out[i] = ballot
+
+
+def use_match_all_sync(ary_in, ary_out):
+    i = cuda.grid(1)
+    ballot, pred = cuda.match_all_sync(0xffffffff, ary_in[i])
+    ary_out[i] = ballot if pred else 0
+
+
+def use_independent_scheduling(arr):
+    i = cuda.threadIdx.x
+    if i % 4 == 0:
+        ballot = cuda.ballot_sync(0x11111111, True)
+    elif i % 4 == 1:
+        ballot = cuda.ballot_sync(0x22222222, True)
+    elif i % 4 == 2:
+        ballot = cuda.ballot_sync(0x44444444, True)
+    elif i % 4 == 3:
+        ballot = cuda.ballot_sync(0x88888888, True)
+    arr[i] = ballot
+
+
+def _safe_cc_check(cc):
+    if config.ENABLE_CUDASIM:
+        return True
+    else:
+        return cuda.get_current_device().compute_capability >= cc
+
+
+@skip_on_cudasim("Warp Operations are not yet implemented on cudasim")
+class TestCudaWarpOperations(CUDATestCase):
+    def test_useful_syncwarp(self):
+        compiled = cuda.jit("void(int32[:])")(useful_syncwarp)
+        nelem = 32
+        ary = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary)
+        self.assertTrue(np.all(ary == 42))
+
+    def test_shfl_sync_idx(self):
+        compiled = cuda.jit("void(int32[:], int32)")(use_shfl_sync_idx)
+        nelem = 32
+        idx = 4
+        ary = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary, idx)
+        self.assertTrue(np.all(ary == idx))
+
+    def test_shfl_sync_up(self):
+        compiled = cuda.jit("void(int32[:], int32)")(use_shfl_sync_up)
+        nelem = 32
+        delta = 4
+        ary = np.empty(nelem, dtype=np.int32)
+        exp = np.arange(nelem, dtype=np.int32)
+        exp[delta:] -= delta
+        compiled[1, nelem](ary, delta)
+        self.assertTrue(np.all(ary == exp))
+
+    def test_shfl_sync_down(self):
+        compiled = cuda.jit("void(int32[:], int32)")(use_shfl_sync_down)
+        nelem = 32
+        delta = 4
+        ary = np.empty(nelem, dtype=np.int32)
+        exp = np.arange(nelem, dtype=np.int32)
+        exp[:-delta] += delta
+        compiled[1, nelem](ary, delta)
+        self.assertTrue(np.all(ary == exp))
+
+    def test_shfl_sync_xor(self):
+        compiled = cuda.jit("void(int32[:], int32)")(use_shfl_sync_xor)
+        nelem = 32
+        xor = 16
+        ary = np.empty(nelem, dtype=np.int32)
+        exp = np.arange(nelem, dtype=np.int32) ^ xor
+        compiled[1, nelem](ary, xor)
+        self.assertTrue(np.all(ary == exp))
+
+    def test_shfl_sync_types(self):
+        types = int32, int64, float32, float64
+        values = (np.int32(-1), np.int64(1 << 42),
+                  np.float32(np.pi), np.float64(np.pi))
+        for typ, val in zip(types, values):
+            compiled = cuda.jit((typ[:], typ))(use_shfl_sync_with_val)
+            nelem = 32
+            ary = np.empty(nelem, dtype=val.dtype)
+            compiled[1, nelem](ary, val)
+            self.assertTrue(np.all(ary == val))
+
+    def test_vote_sync_all(self):
+        compiled = cuda.jit("void(int32[:], int32[:])")(use_vote_sync_all)
+        nelem = 32
+        ary_in = np.ones(nelem, dtype=np.int32)
+        ary_out = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 1))
+        ary_in[-1] = 0
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0))
+
+    def test_vote_sync_any(self):
+        compiled = cuda.jit("void(int32[:], int32[:])")(use_vote_sync_any)
+        nelem = 32
+        ary_in = np.zeros(nelem, dtype=np.int32)
+        ary_out = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0))
+        ary_in[2] = 1
+        ary_in[5] = 1
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 1))
+
+    def test_vote_sync_eq(self):
+        compiled = cuda.jit("void(int32[:], int32[:])")(use_vote_sync_eq)
+        nelem = 32
+        ary_in = np.zeros(nelem, dtype=np.int32)
+        ary_out = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 1))
+        ary_in[1] = 1
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0))
+        ary_in[:] = 1
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 1))
+
+    def test_vote_sync_ballot(self):
+        compiled = cuda.jit("void(uint32[:])")(use_vote_sync_ballot)
+        nelem = 32
+        ary = np.empty(nelem, dtype=np.uint32)
+        compiled[1, nelem](ary)
+        self.assertTrue(np.all(ary == np.uint32(0xffffffff)))
+
+    @unittest.skipUnless(_safe_cc_check((7, 0)),
+                         "Matching requires at least Volta Architecture")
+    def test_match_any_sync(self):
+        compiled = cuda.jit("void(int32[:], int32[:])")(use_match_any_sync)
+        nelem = 10
+        ary_in = np.arange(nelem, dtype=np.int32) % 2
+        ary_out = np.empty(nelem, dtype=np.int32)
+        exp = np.tile((0b0101010101, 0b1010101010), 5)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == exp))
+
+    @unittest.skipUnless(_safe_cc_check((7, 0)),
+                         "Matching requires at least Volta Architecture")
+    def test_match_all_sync(self):
+        compiled = cuda.jit("void(int32[:], int32[:])")(use_match_all_sync)
+        nelem = 10
+        ary_in = np.zeros(nelem, dtype=np.int32)
+        ary_out = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0b1111111111))
+        ary_in[1] = 4
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0))
+
+    @unittest.skipUnless(_safe_cc_check((7, 0)),
+                         "Independent scheduling requires at least Volta "
+                         "Architecture")
+    def test_independent_scheduling(self):
+        compiled = cuda.jit("void(uint32[:])")(use_independent_scheduling)
+        arr = np.empty(32, dtype=np.uint32)
+        exp = np.tile((0x11111111, 0x22222222, 0x44444444, 0x88888888), 8)
+        compiled[1, 32](arr)
+        self.assertTrue(np.all(arr == exp))
+
+    def test_activemask(self):
+        @cuda.jit
+        def use_activemask(x):
+            i = cuda.grid(1)
+            if (i % 2) == 0:
+                # Even numbered threads fill in even numbered array entries
+                # with binary "...01010101"
+                x[i] = cuda.activemask()
+            else:
+                # Odd numbered threads fill in odd numbered array entries
+                # with binary "...10101010"
+                x[i] = cuda.activemask()
+
+        out = np.zeros(32, dtype=np.uint32)
+        use_activemask[1, 32](out)
+
+        # 0x5 = 0101: The pattern from even-numbered threads
+        # 0xA = 1010: The pattern from odd-numbered threads
+        expected = np.tile((0x55555555, 0xAAAAAAAA), 16)
+        np.testing.assert_equal(expected, out)
+
+    def test_lanemask_lt(self):
+        @cuda.jit
+        def use_lanemask_lt(x):
+            i = cuda.grid(1)
+            x[i] = cuda.lanemask_lt()
+
+        out = np.zeros(32, dtype=np.uint32)
+        use_lanemask_lt[1, 32](out)
+
+        # A string of 1s that grows from the LSB for each entry:
+        # 0, 1, 3, 7, F, 1F, 3F, 7F, FF, 1FF, etc.
+        # or in binary:
+        # ...0001, ....0011, ...0111, etc.
+        expected = np.asarray([(2 ** i) - 1 for i in range(32)],
+                              dtype=np.uint32)
+        np.testing.assert_equal(expected, out)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/__init__.py
new file mode 100644
index 000000000..0465337eb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/__init__.py
@@ -0,0 +1,6 @@
+from numba.testing import load_testsuite
+import os
+
+
+def load_tests(loader, tests, pattern):
+    return load_testsuite(loader, os.path.dirname(__file__))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/support.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/support.py
new file mode 100644
index 000000000..4fca39cad
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/support.py
@@ -0,0 +1,6 @@
+from numba import cuda
+
+
+@cuda.jit(device=True)
+def cuda_module_in_device_function():
+    return cuda.threadIdx.x
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/test_cudasim_issues.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/test_cudasim_issues.py
new file mode 100644
index 000000000..0f544821a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/cudasim/test_cudasim_issues.py
@@ -0,0 +1,102 @@
+import threading
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import CUDATestCase, skip_unless_cudasim
+import numba.cuda.simulator as simulator
+import unittest
+
+
+class TestCudaSimIssues(CUDATestCase):
+    def test_record_access(self):
+        backyard_type = [('statue', np.float64),
+                         ('newspaper', np.float64, (6,))]
+
+        goose_type = [('garden', np.float64, (12,)),
+                      ('town', np.float64, (42,)),
+                      ('backyard', backyard_type)]
+
+        goose_np_type = np.dtype(goose_type, align=True)
+
+        @cuda.jit
+        def simple_kernel(f):
+            f.garden[0] = 45.0
+            f.backyard.newspaper[3] = 2.0
+            f.backyard.newspaper[3] = f.backyard.newspaper[3] + 3.0
+
+        item = np.recarray(1, dtype=goose_np_type)
+        simple_kernel[1, 1](item[0])
+        np.testing.assert_equal(item[0]['garden'][0], 45)
+        np.testing.assert_equal(item[0]['backyard']['newspaper'][3], 5)
+
+    def test_recarray_setting(self):
+        recordwith2darray = np.dtype([('i', np.int32),
+                                      ('j', np.float32, (3, 2))])
+        rec = np.recarray(2, dtype=recordwith2darray)
+        rec[0]['i'] = 45
+
+        @cuda.jit
+        def simple_kernel(f):
+            f[1] = f[0]
+        simple_kernel[1, 1](rec)
+        np.testing.assert_equal(rec[0]['i'], rec[1]['i'])
+
+    def test_cuda_module_in_device_function(self):
+        """
+        Discovered in https://github.com/numba/numba/issues/1837.
+        When the `cuda` module is referenced in a device function,
+        it does not have the kernel API (e.g. cuda.threadIdx, cuda.shared)
+        """
+        from numba.cuda.tests.cudasim import support
+
+        inner = support.cuda_module_in_device_function
+
+        @cuda.jit
+        def outer(out):
+            tid = inner()
+            if tid < out.size:
+                out[tid] = tid
+
+        arr = np.zeros(10, dtype=np.int32)
+        outer[1, 11](arr)
+        expected = np.arange(arr.size, dtype=np.int32)
+        np.testing.assert_equal(expected, arr)
+
+    @skip_unless_cudasim('Only works on CUDASIM')
+    def test_deadlock_on_exception(self):
+        def assert_no_blockthreads():
+            blockthreads = []
+            for t in threading.enumerate():
+                if not isinstance(t, simulator.kernel.BlockThread):
+                    continue
+
+                # join blockthreads with a short timeout to allow aborted
+                # threads to exit
+                t.join(1)
+                if t.is_alive():
+                    self.fail("Blocked kernel thread: %s" % t)
+
+            self.assertListEqual(blockthreads, [])
+
+        @simulator.jit
+        def assign_with_sync(x, y):
+            i = cuda.grid(1)
+            y[i] = x[i]
+
+            cuda.syncthreads()
+            cuda.syncthreads()
+
+        x = np.arange(3)
+        y = np.empty(3)
+        assign_with_sync[1, 3](x, y)
+        np.testing.assert_array_equal(x, y)
+        assert_no_blockthreads()
+
+        with self.assertRaises(IndexError):
+            assign_with_sync[1, 6](x, y)
+        assert_no_blockthreads()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/__init__.py
new file mode 100644
index 000000000..0465337eb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/__init__.py
@@ -0,0 +1,6 @@
+from numba.testing import load_testsuite
+import os
+
+
+def load_tests(loader, tests, pattern):
+    return load_testsuite(loader, os.path.dirname(__file__))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/ffi/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/ffi/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/ffi/functions.cu b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/ffi/functions.cu
new file mode 100644
index 000000000..02adee5fc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/ffi/functions.cu
@@ -0,0 +1,15 @@
+// Foreign function example: multiplication of a pair of floats
+
+extern "C" __device__ int
+mul_f32_f32(
+  float* return_value,
+  float x,
+  float y)
+{
+  // Compute result and store in caller-provided slot
+  *return_value = x * y;
+
+  // Signal that no Python exception occurred
+  return 0;
+}
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_cg.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_cg.py
new file mode 100644
index 000000000..b1b99f83b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_cg.py
@@ -0,0 +1,75 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.cuda.testing import (CUDATestCase, skip_on_cudasim,
+                                skip_if_cudadevrt_missing, skip_unless_cc_60)
+
+
+@skip_if_cudadevrt_missing
+@skip_unless_cc_60
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestCooperativeGroups(CUDATestCase):
+    def test_ex_grid_sync(self):
+        # magictoken.ex_grid_sync_kernel.begin
+        from numba import cuda, int32
+        import numpy as np
+
+        sig = (int32[:,::1],)
+
+        @cuda.jit(sig)
+        def sequential_rows(M):
+            col = cuda.grid(1)
+            g = cuda.cg.this_grid()
+
+            rows = M.shape[0]
+            cols = M.shape[1]
+
+            for row in range(1, rows):
+                opposite = cols - col - 1
+                # Each row's elements are one greater than the previous row
+                M[row, col] = M[row - 1, opposite] + 1
+                # Wait until all threads have written their column element,
+                # and that the write is visible to all other threads
+                g.sync()
+        # magictoken.ex_grid_sync_kernel.end
+
+        # magictoken.ex_grid_sync_data.begin
+        # Empty input data
+        A = np.zeros((1024, 1024), dtype=np.int32)
+        # A somewhat arbitrary choice (one warp), but generally smaller block sizes
+        # allow more blocks to be launched (noting that other limitations on
+        # occupancy apply such as shared memory size)
+        blockdim = 32
+        griddim = A.shape[1] // blockdim
+        # magictoken.ex_grid_sync_data.end
+
+        # Skip this test if the grid size used in the example is too large for
+        # a cooperative launch on the current GPU
+        mb = sequential_rows.overloads[sig].max_cooperative_grid_blocks(blockdim)
+        if mb < griddim:
+            self.skipTest('Device does not support a large enough coop grid')
+
+        # magictoken.ex_grid_sync_launch.begin
+        # Kernel launch - this is implicitly a cooperative launch
+        sequential_rows[griddim, blockdim](A)
+
+        # What do the results look like?
+        # print(A)
+        #
+        # [[   0    0    0 ...    0    0    0]
+        #  [   1    1    1 ...    1    1    1]
+        #  [   2    2    2 ...    2    2    2]
+        #  ...
+        #  [1021 1021 1021 ... 1021 1021 1021]
+        #  [1022 1022 1022 ... 1022 1022 1022]
+        #  [1023 1023 1023 ... 1023 1023 1023]]
+        # magictoken.ex_grid_sync_launch.end
+
+        # Sanity check - are the results what we expect?
+        reference = np.tile(np.arange(1024), (1024, 1)).T
+        np.testing.assert_equal(A, reference)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py
new file mode 100644
index 000000000..b879a12d2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_cpu_gpu_compat.py
@@ -0,0 +1,76 @@
+import unittest
+
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+from numba.tests.support import captured_stdout
+import numpy as np
+
+
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestCpuGpuCompat(CUDATestCase):
+    """
+    Test compatibility of CPU and GPU functions
+    """
+
+    def setUp(self):
+        # Prevent output from this test showing up when running the test suite
+        self._captured_stdout = captured_stdout()
+        self._captured_stdout.__enter__()
+        super().setUp()
+
+    def tearDown(self):
+        # No exception type, value, or traceback
+        self._captured_stdout.__exit__(None, None, None)
+        super().tearDown()
+
+    def test_ex_cpu_gpu_compat(self):
+        # ex_cpu_gpu_compat.import.begin
+        from math import pi
+
+        import numba
+        from numba import cuda
+        # ex_cpu_gpu_compat.import.end
+
+        # ex_cpu_gpu_compat.allocate.begin
+        X = cuda.to_device([1, 10, 234])
+        Y = cuda.to_device([2, 2, 4014])
+        Z = cuda.to_device([3, 14, 2211])
+        results = cuda.to_device([0.0, 0.0, 0.0])
+        # ex_cpu_gpu_compat.allocate.end
+
+        # ex_cpu_gpu_compat.define.begin
+        @numba.jit
+        def business_logic(x, y, z):
+            return 4 * z * (2 * x - (4 * y) / 2 * pi)
+        # ex_cpu_gpu_compat.define.end
+
+        # ex_cpu_gpu_compat.cpurun.begin
+        print(business_logic(1, 2, 3))  # -126.79644737231007
+        # ex_cpu_gpu_compat.cpurun.end
+
+        # ex_cpu_gpu_compat.usegpu.begin
+        @cuda.jit
+        def f(res, xarr, yarr, zarr):
+            tid = cuda.grid(1)
+            if tid < len(xarr):
+                # The function decorated with numba.jit may be directly reused
+                res[tid] = business_logic(xarr[tid], yarr[tid], zarr[tid])
+        # ex_cpu_gpu_compat.usegpu.end
+
+        # ex_cpu_gpu_compat.launch.begin
+        f.forall(len(X))(results, X, Y, Z)
+        print(results)
+        # [-126.79644737231007, 416.28324559588634, -218912930.2987788]
+        # ex_cpu_gpu_compat.launch.end
+
+        expect = [
+            business_logic(x, y, z) for x, y, z in zip(X, Y, Z)
+        ]
+
+        np.testing.assert_equal(
+            expect,
+            results.copy_to_host()
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_ffi.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_ffi.py
new file mode 100644
index 000000000..1dca8d042
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_ffi.py
@@ -0,0 +1,50 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.cuda.testing import (CUDATestCase, skip_on_cudasim,
+                                skip_unless_cuda_python)
+
+
+@skip_unless_cuda_python('NVIDIA Binding needed for NVRTC')
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestFFI(CUDATestCase):
+    def test_ex_linking_cu(self):
+        # magictoken.ex_linking_cu.begin
+        from numba import cuda
+        import numpy as np
+        import os
+
+        # Declaration of the foreign function
+        mul = cuda.declare_device('mul_f32_f32', 'float32(float32, float32)')
+
+        # Path to the source containing the foreign function
+        # (here assumed to be in a subdirectory called "ffi")
+        basedir = os.path.dirname(os.path.abspath(__file__))
+        functions_cu = os.path.join(basedir, 'ffi', 'functions.cu')
+
+        # Kernel that links in functions.cu and calls mul
+        @cuda.jit(link=[functions_cu])
+        def multiply_vectors(r, x, y):
+            i = cuda.grid(1)
+
+            if i < len(r):
+                r[i] = mul(x[i], y[i])
+
+        # Generate random data
+        N = 32
+        np.random.seed(1)
+        x = np.random.rand(N).astype(np.float32)
+        y = np.random.rand(N).astype(np.float32)
+        r = np.zeros_like(x)
+
+        # Run the kernel
+        multiply_vectors[1, 32](r, x, y)
+
+        # Sanity check - ensure the results match those expected
+        np.testing.assert_array_equal(r, x * y)
+        # magictoken.ex_linking_cu.end
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_laplace.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_laplace.py
new file mode 100644
index 000000000..41cb309c7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_laplace.py
@@ -0,0 +1,153 @@
+import unittest
+
+from numba.cuda.testing import (CUDATestCase, skip_if_cudadevrt_missing,
+                                skip_on_cudasim, skip_unless_cc_60)
+from numba.tests.support import captured_stdout
+
+
+@skip_if_cudadevrt_missing
+@skip_unless_cc_60
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestLaplace(CUDATestCase):
+    """
+    Test simple vector addition
+    """
+
+    def setUp(self):
+        # Prevent output from this test showing up when running the test suite
+        self._captured_stdout = captured_stdout()
+        self._captured_stdout.__enter__()
+        super().setUp()
+
+    def tearDown(self):
+        # No exception type, value, or traceback
+        self._captured_stdout.__exit__(None, None, None)
+        super().tearDown()
+
+    def test_ex_laplace(self):
+
+        # set True to regenerate the figures that
+        # accompany this example
+        plot = False
+
+        # ex_laplace.import.begin
+        import numpy as np
+        from numba import cuda
+        # ex_laplace.import.end
+
+        # ex_laplace.allocate.begin
+        # Use an odd problem size.
+        # This is so there can be an element truly in the "middle" for symmetry.
+        size = 1001
+        data = np.zeros(size)
+
+        # Middle element is made very hot
+        data[500] = 10000
+        buf_0 = cuda.to_device(data)
+
+        # This extra array is used for synchronization purposes
+        buf_1 = cuda.device_array_like(buf_0)
+
+        niter = 10000
+        # ex_laplace.allocate.end
+
+        if plot:
+            import matplotlib.pyplot as plt
+            fig, ax = plt.subplots(figsize=(16 * 0.66, 9 * 0.66))
+            plt.plot(
+                np.arange(len(buf_0)),
+                buf_0.copy_to_host(),
+                lw=3,
+                marker="*",
+                color='black'
+            )
+
+            plt.title('Initial State', fontsize=24)
+            plt.xlabel('Position', fontsize=24)
+            plt.ylabel('Temperature', fontsize=24)
+
+            ax.set_xticks(ax.get_xticks(), fontsize=16)
+            ax.set_yticks(ax.get_yticks(), fontsize=16)
+            plt.xlim(0, len(data))
+            plt.ylim(0, 10001)
+            plt.savefig('laplace_initial.svg')
+
+        # ex_laplace.kernel.begin
+        @cuda.jit
+        def solve_heat_equation(buf_0, buf_1, timesteps, k):
+            i = cuda.grid(1)
+
+            # Don't continue if our index is outside the domain
+            if i >= len(buf_0):
+                return
+
+            # Prepare to do a grid-wide synchronization later
+            grid = cuda.cg.this_grid()
+
+            for step in range(timesteps):
+                # Select the buffer from the previous timestep
+                if (step % 2) == 0:
+                    data = buf_0
+                    next_data = buf_1
+                else:
+                    data = buf_1
+                    next_data = buf_0
+
+                # Get the current temperature associated with this point
+                curr_temp = data[i]
+
+                # Apply formula from finite difference equation
+                if i == 0:
+                    # Left wall is held at T = 0
+                    next_temp = curr_temp + k * (data[i + 1] - (2 * curr_temp))
+                elif i == len(data) - 1:
+                    # Right wall is held at T = 0
+                    next_temp = curr_temp + k * (data[i - 1] - (2 * curr_temp))
+                else:
+                    # Interior points are a weighted average of their neighbors
+                    next_temp = curr_temp + k * (
+                        data[i - 1] - (2 * curr_temp) + data[i + 1]
+                    )
+
+                # Write new value to the next buffer
+                next_data[i] = next_temp
+
+                # Wait for every thread to write before moving on
+                grid.sync()
+        # ex_laplace.kernel.end
+
+        # ex_laplace.launch.begin
+        solve_heat_equation.forall(len(data))(
+            buf_0, buf_1, niter, 0.25
+        )
+        # ex_laplace.launch.end
+
+        results = buf_1.copy_to_host()
+        if plot:
+            fig, ax = plt.subplots(figsize=(16 * 0.66, 9 * 0.66))
+            plt.plot(
+                np.arange(len(results)),
+                results, lw=3,
+                marker="*",
+                color='black'
+            )
+            plt.title(f"T = {niter}", fontsize=24)
+            plt.xlabel('Position', fontsize=24)
+            plt.ylabel('Temperature', fontsize=24)
+
+            ax.set_xticks(ax.get_xticks(), fontsize=16)
+            ax.set_yticks(ax.get_yticks(), fontsize=16)
+
+            plt.ylim(0, max(results))
+            plt.xlim(0, len(results))
+            plt.savefig('laplace_final.svg')
+
+        # Integral over the domain should be equal to its initial value.
+        # Note that this should match the initial value of data[500] above, but
+        # we don't assign it to a variable because that would make the example
+        # code look a bit oddly verbose.
+        np.testing.assert_allclose(results.sum(), 10000)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_matmul.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_matmul.py
new file mode 100644
index 000000000..6e0dd44c1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_matmul.py
@@ -0,0 +1,173 @@
+"""
+Matrix multiplication example via `cuda.jit`.
+
+Reference: https://stackoverflow.com/a/64198479/13697228 by @RobertCrovella
+
+Contents in this file are referenced from the sphinx-generated docs.
+"magictoken" is used for markers as beginning and ending of example text.
+"""
+import unittest
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+from numba.tests.support import captured_stdout
+
+
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestMatMul(CUDATestCase):
+    """
+    Text matrix multiplication using simple, shared memory/square, and shared
+    memory/nonsquare cases.
+    """
+
+    def setUp(self):
+        # Prevent output from this test showing up when running the test suite
+        self._captured_stdout = captured_stdout()
+        self._captured_stdout.__enter__()
+        super().setUp()
+
+    def tearDown(self):
+        # No exception type, value, or traceback
+        self._captured_stdout.__exit__(None, None, None)
+        super().tearDown()
+
+    def test_ex_matmul(self):
+        """Test of matrix multiplication on various cases."""
+        # magictoken.ex_import.begin
+        from numba import cuda, float32
+        import numpy as np
+        import math
+        # magictoken.ex_import.end
+
+        # magictoken.ex_matmul.begin
+        @cuda.jit
+        def matmul(A, B, C):
+            """Perform square matrix multiplication of C = A * B."""
+            i, j = cuda.grid(2)
+            if i < C.shape[0] and j < C.shape[1]:
+                tmp = 0.
+                for k in range(A.shape[1]):
+                    tmp += A[i, k] * B[k, j]
+                C[i, j] = tmp
+        # magictoken.ex_matmul.end
+
+        # magictoken.ex_run_matmul.begin
+        x_h = np.arange(16).reshape([4, 4])
+        y_h = np.ones([4, 4])
+        z_h = np.zeros([4, 4])
+
+        x_d = cuda.to_device(x_h)
+        y_d = cuda.to_device(y_h)
+        z_d = cuda.to_device(z_h)
+
+        threadsperblock = (16, 16)
+        blockspergrid_x = math.ceil(z_h.shape[0] / threadsperblock[0])
+        blockspergrid_y = math.ceil(z_h.shape[1] / threadsperblock[1])
+        blockspergrid = (blockspergrid_x, blockspergrid_y)
+
+        matmul[blockspergrid, threadsperblock](x_d, y_d, z_d)
+        z_h = z_d.copy_to_host()
+        print(z_h)
+        print(x_h @ y_h)
+        # magictoken.ex_run_matmul.end
+
+        # magictoken.ex_fast_matmul.begin
+        # Controls threads per block and shared memory usage.
+        # The computation will be done on blocks of TPBxTPB elements.
+        # TPB should not be larger than 32 in this example
+        TPB = 16
+
+        @cuda.jit
+        def fast_matmul(A, B, C):
+            """
+            Perform matrix multiplication of C = A * B using CUDA shared memory.
+
+            Reference: https://stackoverflow.com/a/64198479/13697228 by @RobertCrovella
+            """
+            # Define an array in the shared memory
+            # The size and type of the arrays must be known at compile time
+            sA = cuda.shared.array(shape=(TPB, TPB), dtype=float32)
+            sB = cuda.shared.array(shape=(TPB, TPB), dtype=float32)
+
+            x, y = cuda.grid(2)
+
+            tx = cuda.threadIdx.x
+            ty = cuda.threadIdx.y
+            bpg = cuda.gridDim.x    # blocks per grid
+
+            # Each thread computes one element in the result matrix.
+            # The dot product is chunked into dot products of TPB-long vectors.
+            tmp = float32(0.)
+            for i in range(bpg):
+                # Preload data into shared memory
+                sA[ty, tx] = 0
+                sB[ty, tx] = 0
+                if y < A.shape[0] and (tx + i * TPB) < A.shape[1]:
+                    sA[ty, tx] = A[y, tx + i * TPB]
+                if x < B.shape[1] and (ty + i * TPB) < B.shape[0]:
+                    sB[ty, tx] = B[ty + i * TPB, x]
+
+                # Wait until all threads finish preloading
+                cuda.syncthreads()
+
+                # Computes partial product on the shared memory
+                for j in range(TPB):
+                    tmp += sA[ty, j] * sB[j, tx]
+
+                # Wait until all threads finish computing
+                cuda.syncthreads()
+            if y < C.shape[0] and x < C.shape[1]:
+                C[y, x] = tmp
+        # magictoken.ex_fast_matmul.end
+
+        # magictoken.ex_run_fast_matmul.begin
+        x_h = np.arange(16).reshape([4, 4])
+        y_h = np.ones([4, 4])
+        z_h = np.zeros([4, 4])
+
+        x_d = cuda.to_device(x_h)
+        y_d = cuda.to_device(y_h)
+        z_d = cuda.to_device(z_h)
+
+        threadsperblock = (TPB, TPB)
+        blockspergrid_x = math.ceil(z_h.shape[0] / threadsperblock[0])
+        blockspergrid_y = math.ceil(z_h.shape[1] / threadsperblock[1])
+        blockspergrid = (blockspergrid_x, blockspergrid_y)
+
+        fast_matmul[blockspergrid, threadsperblock](x_d, y_d, z_d)
+        z_h = z_d.copy_to_host()
+        print(z_h)
+        print(x_h @ y_h)
+        # magictoken.ex_run_fast_matmul.end
+
+        # fast_matmul test(s)
+        msg = "fast_matmul incorrect for shared memory, square case."
+        self.assertTrue(np.all(z_h == x_h @ y_h), msg=msg)
+
+        # magictoken.ex_run_nonsquare.begin
+        x_h = np.arange(115).reshape([5, 23])
+        y_h = np.ones([23, 7])
+        z_h = np.zeros([5, 7])
+
+        x_d = cuda.to_device(x_h)
+        y_d = cuda.to_device(y_h)
+        z_d = cuda.to_device(z_h)
+
+        threadsperblock = (TPB, TPB)
+        grid_y_max = max(x_h.shape[0], y_h.shape[0])
+        grid_x_max = max(x_h.shape[1], y_h.shape[1])
+        blockspergrid_x = math.ceil(grid_x_max / threadsperblock[0])
+        blockspergrid_y = math.ceil(grid_y_max / threadsperblock[1])
+        blockspergrid = (blockspergrid_x, blockspergrid_y)
+
+        fast_matmul[blockspergrid, threadsperblock](x_d, y_d, z_d)
+        z_h = z_d.copy_to_host()
+        print(z_h)
+        print(x_h @ y_h)
+        # magictoken.ex_run_nonsquare.end
+
+        # nonsquare fast_matmul test(s)
+        msg = "fast_matmul incorrect for shared memory, non-square case."
+        self.assertTrue(np.all(z_h == x_h @ y_h), msg=msg)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_montecarlo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_montecarlo.py
new file mode 100644
index 000000000..92627084f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_montecarlo.py
@@ -0,0 +1,109 @@
+import unittest
+
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+from numba.tests.support import captured_stdout
+
+
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestMonteCarlo(CUDATestCase):
+    """
+    Test monte-carlo integration
+    """
+
+    def setUp(self):
+        # Prevent output from this test showing up when running the test suite
+        self._captured_stdout = captured_stdout()
+        self._captured_stdout.__enter__()
+        super().setUp()
+
+    def tearDown(self):
+        # No exception type, value, or traceback
+        self._captured_stdout.__exit__(None, None, None)
+        super().tearDown()
+
+    def test_ex_montecarlo(self):
+        # ex_montecarlo.import.begin
+        import numba
+        import numpy as np
+        from numba import cuda
+        from numba.cuda.random import (
+            create_xoroshiro128p_states,
+            xoroshiro128p_uniform_float32,
+        )
+        # ex_montecarlo.import.end
+
+        # ex_montecarlo.define.begin
+        # number of samples, higher will lead to a more accurate answer
+        nsamps = 1000000
+        # ex_montecarlo.define.end
+
+        # ex_montecarlo.kernel.begin
+        @cuda.jit
+        def mc_integrator_kernel(out, rng_states, lower_lim, upper_lim):
+            """
+            kernel to draw random samples and evaluate the function to
+            be integrated at those sample values
+            """
+            size = len(out)
+
+            gid = cuda.grid(1)
+            if gid < size:
+                # draw a sample between 0 and 1 on this thread
+                samp = xoroshiro128p_uniform_float32(rng_states, gid)
+
+                # normalize this sample to the limit range
+                samp = samp * (upper_lim - lower_lim) + lower_lim
+
+                # evaluate the function to be
+                # integrated at the normalized
+                # value of the sample
+                y = func(samp)
+                out[gid] = y
+        # ex_montecarlo.kernel.end
+
+        # ex_montecarlo.callfunc.begin
+        @cuda.reduce
+        def sum_reduce(a, b):
+            return a + b
+
+        def mc_integrate(lower_lim, upper_lim, nsamps):
+            """
+            approximate the definite integral of `func` from
+            `lower_lim` to `upper_lim`
+            """
+            out = cuda.to_device(np.zeros(nsamps, dtype="float32"))
+            rng_states = create_xoroshiro128p_states(nsamps, seed=42)
+
+            # jit the function for use in CUDA kernels
+
+            mc_integrator_kernel.forall(nsamps)(
+                out, rng_states, lower_lim, upper_lim
+            )
+            # normalization factor to convert
+            # to the average: (b - a)/(N - 1)
+            factor = (upper_lim - lower_lim) / (nsamps - 1)
+
+            return sum_reduce(out) * factor
+        # ex_montecarlo.callfunc.end
+
+        # ex_montecarlo.launch.begin
+        # define a function to integrate
+        @numba.jit
+        def func(x):
+            return 1.0 / x
+
+        mc_integrate(1, 2, nsamps)  # array(0.6929643, dtype=float32)
+        mc_integrate(2, 3, nsamps)  # array(0.4054021, dtype=float32)
+        # ex_montecarlo.launch.end
+
+        # values computed independently using maple
+        np.testing.assert_allclose(
+            mc_integrate(1, 2, nsamps), 0.69315, atol=0.001
+        )
+        np.testing.assert_allclose(
+            mc_integrate(2, 3, nsamps), 0.4055, atol=0.001
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_random.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_random.py
new file mode 100644
index 000000000..0e93a1f17
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_random.py
@@ -0,0 +1,59 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+
+
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestRandom(CUDATestCase):
+    def test_ex_3d_grid(self):
+        # magictoken.ex_3d_grid.begin
+        from numba import cuda
+        from numba.cuda.random import (create_xoroshiro128p_states,
+                                       xoroshiro128p_uniform_float32)
+        import numpy as np
+
+        @cuda.jit
+        def random_3d(arr, rng_states):
+            # Per-dimension thread indices and strides
+            startx, starty, startz = cuda.grid(3)
+            stridex, stridey, stridez = cuda.gridsize(3)
+
+            # Linearized thread index
+            tid = (startz * stridey * stridex) + (starty * stridex) + startx
+
+            # Use strided loops over the array to assign a random value to each entry
+            for i in range(startz, arr.shape[0], stridez):
+                for j in range(starty, arr.shape[1], stridey):
+                    for k in range(startx, arr.shape[2], stridex):
+                        arr[i, j, k] = xoroshiro128p_uniform_float32(rng_states, tid)
+
+        # Array dimensions
+        X, Y, Z = 701, 900, 719
+
+        # Block and grid dimensions
+        bx, by, bz = 8, 8, 8
+        gx, gy, gz = 16, 16, 16
+
+        # Total number of threads
+        nthreads = bx * by * bz * gx * gy * gz
+
+        # Initialize a state for each thread
+        rng_states = create_xoroshiro128p_states(nthreads, seed=1)
+
+        # Generate random numbers
+        arr = cuda.device_array((X, Y, Z), dtype=np.float32)
+        random_3d[(gx, gy, gz), (bx, by, bz)](arr, rng_states)
+        # magictoken.ex_3d_grid.end
+
+        # Some basic tests of the randomly-generated numbers
+        host_arr = arr.copy_to_host()
+        self.assertGreater(np.mean(host_arr), 0.49)
+        self.assertLess(np.mean(host_arr), 0.51)
+        self.assertTrue(np.all(host_arr <= 1.0))
+        self.assertTrue(np.all(host_arr >= 0.0))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_reduction.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_reduction.py
new file mode 100644
index 000000000..c118fbf15
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_reduction.py
@@ -0,0 +1,76 @@
+import unittest
+
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+from numba.tests.support import captured_stdout
+
+
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestReduction(CUDATestCase):
+    """
+    Test shared memory reduction
+    """
+
+    def setUp(self):
+        # Prevent output from this test showing up when running the test suite
+        self._captured_stdout = captured_stdout()
+        self._captured_stdout.__enter__()
+        super().setUp()
+
+    def tearDown(self):
+        # No exception type, value, or traceback
+        self._captured_stdout.__exit__(None, None, None)
+        super().tearDown()
+
+    def test_ex_reduction(self):
+        # ex_reduction.import.begin
+        import numpy as np
+        from numba import cuda
+        from numba.types import int32
+        # ex_reduction.import.end
+
+        # ex_reduction.allocate.begin
+        # generate data
+        a = cuda.to_device(np.arange(1024))
+        nelem = len(a)
+        # ex_reduction.allocate.end
+
+        # ex_reduction.kernel.begin
+        @cuda.jit
+        def array_sum(data):
+            tid = cuda.threadIdx.x
+            size = len(data)
+            if tid < size:
+                i = cuda.grid(1)
+
+                # Declare an array in shared memory
+                shr = cuda.shared.array(nelem, int32)
+                shr[tid] = data[i]
+
+                # Ensure writes to shared memory are visible
+                # to all threads before reducing
+                cuda.syncthreads()
+
+                s = 1
+                while s < cuda.blockDim.x:
+                    if tid % (2 * s) == 0:
+                        # Stride by `s` and add
+                        shr[tid] += shr[tid + s]
+                    s *= 2
+                    cuda.syncthreads()
+
+                # After the loop, the zeroth  element contains the sum
+                if tid == 0:
+                    data[tid] = shr[tid]
+        # ex_reduction.kernel.end
+
+        # ex_reduction.launch.begin
+        array_sum[1, nelem](a)
+        print(a[0])                  # 523776
+        print(sum(np.arange(1024)))  # 523776
+        # ex_reduction.launch.end
+
+        np.testing.assert_equal(a[0], sum(np.arange(1024)))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_sessionize.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_sessionize.py
new file mode 100644
index 000000000..c36ae2bba
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_sessionize.py
@@ -0,0 +1,128 @@
+import unittest
+
+from numba.cuda.testing import (CUDATestCase, skip_if_cudadevrt_missing,
+                                skip_on_cudasim, skip_unless_cc_60)
+from numba.tests.support import captured_stdout
+
+
+@skip_if_cudadevrt_missing
+@skip_unless_cc_60
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestSessionization(CUDATestCase):
+    """
+    Test click stream sessionization
+    """
+
+    def setUp(self):
+        # Prevent output from this test showing up when running the test suite
+        self._captured_stdout = captured_stdout()
+        self._captured_stdout.__enter__()
+        super().setUp()
+
+    def tearDown(self):
+        # No exception type, value, or traceback
+        self._captured_stdout.__exit__(None, None, None)
+        super().tearDown()
+
+    def test_ex_sessionize(self):
+        # ex_sessionize.import.begin
+        import numpy as np
+        from numba import cuda
+
+        # Set the timeout to one hour
+        session_timeout = np.int64(np.timedelta64("3600", "s"))
+        # ex_sessionize.import.end
+
+        # ex_sessionize.allocate.begin
+        # Generate data
+        ids = cuda.to_device(
+            np.array(
+                [
+                    1, 1, 1, 1, 1, 1,
+                    2, 2, 2,
+                    3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
+                    4, 4, 4, 4, 4, 4, 4, 4, 4,
+                ]
+            )
+        )
+        sec = cuda.to_device(
+            np.array(
+                [
+                    1, 2, 3, 5000, 5001, 5002, 1,
+                    2, 3, 1, 2, 5000, 5001, 10000,
+                    10001, 10002, 10003, 15000, 150001,
+                    1, 5000, 50001, 15000, 20000,
+                    25000, 25001, 25002, 25003,
+                ],
+                dtype="datetime64[ns]",
+            ).astype(
+                "int64"
+            )  # Cast to int64 for compatibility
+        )
+        # Create a vector to hold the results
+        results = cuda.to_device(np.zeros(len(ids)))
+        # ex_sessionize.allocate.end
+
+        # ex_sessionize.kernel.begin
+        @cuda.jit
+        def sessionize(user_id, timestamp, results):
+            gid = cuda.grid(1)
+            size = len(user_id)
+
+            if gid >= size:
+                return
+
+            # Determine session boundaries
+            is_first_datapoint = gid == 0
+            if not is_first_datapoint:
+                new_user = user_id[gid] != user_id[gid - 1]
+                timed_out = (
+                    timestamp[gid] - timestamp[gid - 1] > session_timeout
+                )
+                is_sess_boundary = new_user or timed_out
+            else:
+                is_sess_boundary = True
+
+            # Determine session labels
+            if is_sess_boundary:
+                # This thread marks the start of a session
+                results[gid] = gid
+
+                # Make sure all session boundaries are written
+                # before populating the session id
+                grid = cuda.cg.this_grid()
+                grid.sync()
+
+                look_ahead = 1
+                # Check elements 'forward' of this one
+                # until a new session boundary is found
+                while results[gid + look_ahead] == 0:
+                    results[gid + look_ahead] = gid
+                    look_ahead += 1
+                    # Avoid out-of-bounds accesses by the last thread
+                    if gid + look_ahead == size - 1:
+                        results[gid + look_ahead] = gid
+                        break
+        # ex_sessionize.kernel.end
+
+        # ex_sessionize.launch.begin
+        sessionize.forall(len(ids))(ids, sec, results)
+
+        print(results.copy_to_host())
+        # array([ 0.,  0.,  0.,  3.,  3.,  3.,
+        #         6.,  6.,  6.,  9.,  9., 11.,
+        #         11., 13., 13., 13., 13., 17.,
+        #         18., 19., 20., 21., 21., 23.,
+        #         24., 24., 24., 24.])
+        # ex_sessionize.launch.end
+
+        expect = [
+            0, 0, 0, 3, 3, 3, 6, 6, 6, 9, 9,
+            11, 11, 13, 13, 13, 13, 17, 18, 19, 20, 21,
+            21, 23, 24, 24, 24, 24
+        ]
+        np.testing.assert_equal(expect, results.copy_to_host())
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_vecadd.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_vecadd.py
new file mode 100644
index 000000000..c6ae197ee
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/doc_examples/test_vecadd.py
@@ -0,0 +1,73 @@
+import unittest
+
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim
+from numba.tests.support import captured_stdout
+
+
+@skip_on_cudasim("cudasim doesn't support cuda import at non-top-level")
+class TestVecAdd(CUDATestCase):
+    """
+    Test simple vector addition
+    """
+
+    def setUp(self):
+        # Prevent output from this test showing
+        # up when running the test suite
+        self._captured_stdout = captured_stdout()
+        self._captured_stdout.__enter__()
+        super().setUp()
+
+    def tearDown(self):
+        # No exception type, value, or traceback
+        self._captured_stdout.__exit__(None, None, None)
+        super().tearDown()
+
+    def test_ex_vecadd(self):
+        # ex_vecadd.import.begin
+        import numpy as np
+        from numba import cuda
+        # ex_vecadd.import.end
+
+        # ex_vecadd.kernel.begin
+        @cuda.jit
+        def f(a, b, c):
+            # like threadIdx.x + (blockIdx.x * blockDim.x)
+            tid = cuda.grid(1)
+            size = len(c)
+
+            if tid < size:
+                c[tid] = a[tid] + b[tid]
+        # ex_vecadd.kernel.end
+
+        # Seed RNG for test repeatability
+        np.random.seed(1)
+
+        # ex_vecadd.allocate.begin
+        N = 100000
+        a = cuda.to_device(np.random.random(N))
+        b = cuda.to_device(np.random.random(N))
+        c = cuda.device_array_like(a)
+        # ex_vecadd.allocate.end
+
+        # ex_vecadd.forall.begin
+        f.forall(len(a))(a, b, c)
+        print(c.copy_to_host())
+        # ex_vecadd.forall.end
+
+        # ex_vecadd.launch.begin
+        # Enough threads per block for several warps per block
+        nthreads = 256
+        # Enough blocks to cover the entire vector depending on its length
+        nblocks = (len(a) // nthreads) + 1
+        f[nblocks, nthreads](a, b, c)
+        print(c.copy_to_host())
+        # ex_vecadd.launch.end
+
+        np.testing.assert_equal(
+            c.copy_to_host(),
+            a.copy_to_host() + b.copy_to_host()
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/__init__.py
new file mode 100644
index 000000000..d9e7d31af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/__init__.py
@@ -0,0 +1,8 @@
+from numba.cuda.testing import ensure_supported_ccs_initialized
+from numba.testing import load_testsuite
+import os
+
+
+def load_tests(loader, tests, pattern):
+    ensure_supported_ccs_initialized()
+    return load_testsuite(loader, os.path.dirname(__file__))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_import.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_import.py
new file mode 100644
index 000000000..73126cd6e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_import.py
@@ -0,0 +1,49 @@
+from numba.tests.support import run_in_subprocess
+import unittest
+
+
+class TestImport(unittest.TestCase):
+    def test_no_impl_import(self):
+        """
+        Tests that importing cuda doesn't trigger the import of modules
+        containing lowering implementation that would likely install things in
+        the builtins registry and have side effects impacting other targets.
+        """
+
+        banlist = (
+            'numba.cpython.slicing',
+            'numba.cpython.tupleobj',
+            'numba.cpython.enumimpl',
+            'numba.cpython.hashing',
+            'numba.cpython.heapq',
+            'numba.cpython.iterators',
+            'numba.cpython.numbers',
+            'numba.cpython.rangeobj',
+            'numba.cpython.cmathimpl',
+            'numba.cpython.mathimpl',
+            'numba.cpython.printimpl',
+            'numba.cpython.randomimpl',
+            'numba.core.optional',
+            'numba.misc.gdb_hook',
+            'numba.misc.literal',
+            'numba.misc.cffiimpl',
+            'numba.np.linalg',
+            'numba.np.polynomial',
+            'numba.np.arraymath',
+            'numba.np.npdatetime',
+            'numba.np.npyimpl',
+            'numba.typed.typeddict',
+            'numba.typed.typedlist',
+            'numba.experimental.jitclass.base',
+        )
+
+        code = "import sys; from numba import cuda; print(list(sys.modules))"
+
+        out, _ = run_in_subprocess(code)
+        modlist = set(eval(out.strip()))
+        unexpected = set(banlist) & set(modlist)
+        self.assertFalse(unexpected, "some modules unexpectedly imported")
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_library_lookup.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_library_lookup.py
new file mode 100644
index 000000000..acf670829
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_library_lookup.py
@@ -0,0 +1,238 @@
+import sys
+import os
+import multiprocessing as mp
+import warnings
+
+from numba.core.config import IS_WIN32, IS_OSX
+from numba.core.errors import NumbaWarning
+from numba.cuda.cudadrv import nvvm
+from numba.cuda.testing import (
+    unittest,
+    skip_on_cudasim,
+    SerialMixin,
+    skip_unless_conda_cudatoolkit,
+)
+from numba.cuda.cuda_paths import (
+    _get_libdevice_path_decision,
+    _get_nvvm_path_decision,
+    _get_cudalib_dir_path_decision,
+    get_system_ctk,
+)
+
+
+has_cuda = nvvm.is_available()
+has_mp_get_context = hasattr(mp, 'get_context')
+
+
+class LibraryLookupBase(SerialMixin, unittest.TestCase):
+    def setUp(self):
+        ctx = mp.get_context('spawn')
+
+        qrecv = ctx.Queue()
+        qsend = ctx.Queue()
+        self.qsend = qsend
+        self.qrecv = qrecv
+        self.child_process = ctx.Process(
+            target=check_lib_lookup,
+            args=(qrecv, qsend),
+            daemon=True,
+        )
+        self.child_process.start()
+
+    def tearDown(self):
+        self.qsend.put(self.do_terminate)
+        self.child_process.join(3)
+        # Ensure the process is terminated
+        self.assertIsNotNone(self.child_process)
+
+    def remote_do(self, action):
+        self.qsend.put(action)
+        out = self.qrecv.get()
+        self.assertNotIsInstance(out, BaseException)
+        return out
+
+    @staticmethod
+    def do_terminate():
+        return False, None
+
+
+def remove_env(name):
+    try:
+        del os.environ[name]
+    except KeyError:
+        return False
+    else:
+        return True
+
+
+def check_lib_lookup(qout, qin):
+    status = True
+    while status:
+        try:
+            action = qin.get()
+        except Exception as e:
+            qout.put(e)
+            status = False
+        else:
+            try:
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter("always", NumbaWarning)
+                    status, result = action()
+                qout.put(result + (w,))
+            except Exception as e:
+                qout.put(e)
+                status = False
+
+
+@skip_on_cudasim('Library detection unsupported in the simulator')
+@unittest.skipUnless(has_mp_get_context, 'mp.get_context not available')
+@skip_unless_conda_cudatoolkit('test assumes conda installed cudatoolkit')
+class TestLibDeviceLookUp(LibraryLookupBase):
+    def test_libdevice_path_decision(self):
+        # Check that the default is using conda environment
+        by, info, warns = self.remote_do(self.do_clear_envs)
+        if has_cuda:
+            self.assertEqual(by, 'Conda environment')
+        else:
+            self.assertEqual(by, "<unknown>")
+            self.assertIsNone(info)
+        self.assertFalse(warns)
+        # Check that CUDA_HOME works by removing conda-env
+        by, info, warns = self.remote_do(self.do_set_cuda_home)
+        self.assertEqual(by, 'CUDA_HOME')
+        self.assertEqual(info, os.path.join('mycudahome', 'nvvm', 'libdevice'))
+        self.assertFalse(warns)
+
+        if get_system_ctk() is None:
+            # Fake remove conda environment so no cudatoolkit is available
+            by, info, warns = self.remote_do(self.do_clear_envs)
+            self.assertEqual(by, '<unknown>')
+            self.assertIsNone(info)
+            self.assertFalse(warns)
+        else:
+            # Use system available cudatoolkit
+            by, info, warns = self.remote_do(self.do_clear_envs)
+            self.assertEqual(by, 'System')
+            self.assertFalse(warns)
+
+    @staticmethod
+    def do_clear_envs():
+        remove_env('CUDA_HOME')
+        remove_env('CUDA_PATH')
+        return True, _get_libdevice_path_decision()
+
+    @staticmethod
+    def do_set_cuda_home():
+        os.environ['CUDA_HOME'] = os.path.join('mycudahome')
+        _fake_non_conda_env()
+        return True, _get_libdevice_path_decision()
+
+
+@skip_on_cudasim('Library detection unsupported in the simulator')
+@unittest.skipUnless(has_mp_get_context, 'mp.get_context not available')
+@skip_unless_conda_cudatoolkit('test assumes conda installed cudatoolkit')
+class TestNvvmLookUp(LibraryLookupBase):
+    def test_nvvm_path_decision(self):
+        # Check that the default is using conda environment
+        by, info, warns = self.remote_do(self.do_clear_envs)
+        if has_cuda:
+            self.assertEqual(by, 'Conda environment')
+        else:
+            self.assertEqual(by, "<unknown>")
+            self.assertIsNone(info)
+        self.assertFalse(warns)
+        # Check that CUDA_HOME works by removing conda-env
+        by, info, warns = self.remote_do(self.do_set_cuda_home)
+        self.assertEqual(by, 'CUDA_HOME')
+        self.assertFalse(warns)
+        if IS_WIN32:
+            self.assertEqual(info, os.path.join('mycudahome', 'nvvm', 'bin'))
+        elif IS_OSX:
+            self.assertEqual(info, os.path.join('mycudahome', 'nvvm', 'lib'))
+        else:
+            self.assertEqual(info, os.path.join('mycudahome', 'nvvm', 'lib64'))
+
+        if get_system_ctk() is None:
+            # Fake remove conda environment so no cudatoolkit is available
+            by, info, warns = self.remote_do(self.do_clear_envs)
+            self.assertEqual(by, '<unknown>')
+            self.assertIsNone(info)
+            self.assertFalse(warns)
+        else:
+            # Use system available cudatoolkit
+            by, info, warns = self.remote_do(self.do_clear_envs)
+            self.assertEqual(by, 'System')
+            self.assertFalse(warns)
+
+    @staticmethod
+    def do_clear_envs():
+        remove_env('CUDA_HOME')
+        remove_env('CUDA_PATH')
+        return True, _get_nvvm_path_decision()
+
+    @staticmethod
+    def do_set_cuda_home():
+        os.environ['CUDA_HOME'] = os.path.join('mycudahome')
+        _fake_non_conda_env()
+        return True, _get_nvvm_path_decision()
+
+
+@skip_on_cudasim('Library detection unsupported in the simulator')
+@unittest.skipUnless(has_mp_get_context, 'mp.get_context not available')
+@skip_unless_conda_cudatoolkit('test assumes conda installed cudatoolkit')
+class TestCudaLibLookUp(LibraryLookupBase):
+    def test_cudalib_path_decision(self):
+        # Check that the default is using conda environment
+        by, info, warns = self.remote_do(self.do_clear_envs)
+        if has_cuda:
+            self.assertEqual(by, 'Conda environment')
+        else:
+            self.assertEqual(by, "<unknown>")
+            self.assertIsNone(info)
+        self.assertFalse(warns)
+
+        # Check that CUDA_HOME works by removing conda-env
+        self.remote_do(self.do_clear_envs)
+        by, info, warns = self.remote_do(self.do_set_cuda_home)
+        self.assertEqual(by, 'CUDA_HOME')
+        self.assertFalse(warns)
+        if IS_WIN32:
+            self.assertEqual(info, os.path.join('mycudahome', 'bin'))
+        elif IS_OSX:
+            self.assertEqual(info, os.path.join('mycudahome', 'lib'))
+        else:
+            self.assertEqual(info, os.path.join('mycudahome', 'lib64'))
+        if get_system_ctk() is None:
+            # Fake remove conda environment so no cudatoolkit is available
+            by, info, warns = self.remote_do(self.do_clear_envs)
+            self.assertEqual(by, "<unknown>")
+            self.assertIsNone(info)
+            self.assertFalse(warns)
+        else:
+            # Use system available cudatoolkit
+            by, info, warns = self.remote_do(self.do_clear_envs)
+            self.assertEqual(by, 'System')
+            self.assertFalse(warns)
+
+    @staticmethod
+    def do_clear_envs():
+        remove_env('CUDA_HOME')
+        remove_env('CUDA_PATH')
+        return True, _get_cudalib_dir_path_decision()
+
+    @staticmethod
+    def do_set_cuda_home():
+        os.environ['CUDA_HOME'] = os.path.join('mycudahome')
+        _fake_non_conda_env()
+        return True, _get_cudalib_dir_path_decision()
+
+
+def _fake_non_conda_env():
+    """
+    Monkeypatch sys.prefix to hide the fact we are in a conda-env
+    """
+    sys.prefix = ''
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_nvvm.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_nvvm.py
new file mode 100644
index 000000000..bccc995ab
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/tests/nocuda/test_nvvm.py
@@ -0,0 +1,80 @@
+from numba.cuda.cudadrv import nvvm
+from numba.cuda.testing import skip_on_cudasim
+from numba.core import utils
+
+from llvmlite import ir
+from llvmlite import binding as llvm
+
+import unittest
+
+
+original = "call void @llvm.memset.p0i8.i64(" \
+           "i8* align 4 %arg.x.41, i8 0, i64 %0, i1 false)"
+
+missing_align = "call void @llvm.memset.p0i8.i64(" \
+                "i8* %arg.x.41, i8 0, i64 %0, i1 false)"
+
+
+@skip_on_cudasim('libNVVM not supported in simulator')
+@unittest.skipIf(utils.MACHINE_BITS == 32, "CUDA not support for 32-bit")
+@unittest.skipIf(not nvvm.is_available(), "No libNVVM")
+class TestNvvmWithoutCuda(unittest.TestCase):
+    def test_nvvm_memset_fixup_for_34(self):
+        """
+        Test llvm.memset changes in llvm7.
+        In LLVM7 the alignment parameter can be implicitly provided as
+        an attribute to pointer in the first argument.
+        """
+        fixed = nvvm.llvm100_to_34_ir(original)
+        self.assertIn("call void @llvm.memset", fixed)
+
+        for ln in fixed.splitlines():
+            if 'call void @llvm.memset' in ln:
+                # The i32 4 is the alignment
+                self.assertRegexpMatches(
+                    ln,
+                    r'i32 4, i1 false\)'.replace(' ', r'\s+'),
+                )
+
+    def test_nvvm_memset_fixup_for_34_missing_align(self):
+        """
+        We require alignment to be specified as a parameter attribute to the
+        dest argument of a memset.
+        """
+        with self.assertRaises(ValueError) as e:
+            nvvm.llvm100_to_34_ir(missing_align)
+
+        self.assertIn(str(e.exception),
+                      "No alignment attribute found on memset dest")
+
+    def test_nvvm_accepts_encoding(self):
+        # Test that NVVM will accept a constant containing all possible 8-bit
+        # characters. Taken from the test case added in llvmlite PR #53:
+        #
+        #     https://github.com/numba/llvmlite/pull/53
+        #
+        # This test case is included in Numba to ensure that the encoding used
+        # by llvmlite (e.g. utf-8, latin1, etc.) does not result in an input to
+        # NVVM that it cannot parse correctly
+
+        # Create a module with a constant containing all 8-bit characters
+        c = ir.Constant(ir.ArrayType(ir.IntType(8), 256),
+                        bytearray(range(256)))
+        m = ir.Module()
+        gv = ir.GlobalVariable(m, c.type, "myconstant")
+        gv.global_constant = True
+        gv.initializer = c
+        nvvm.fix_data_layout(m)
+
+        # Parse with LLVM then dump the parsed module into NVVM
+        parsed = llvm.parse_assembly(str(m))
+        ptx = nvvm.llvm_to_ptx(str(parsed))
+
+        # Ensure all characters appear in the generated constant array.
+        elements = ", ".join([str(i) for i in range(256)])
+        myconstant = f"myconstant[256] = {{{elements}}}".encode('utf-8')
+        self.assertIn(myconstant, ptx)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/types.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/types.py
new file mode 100644
index 000000000..531dcb2cc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/types.py
@@ -0,0 +1,37 @@
+from numba.core import types
+
+
+class Dim3(types.Type):
+    """
+    A 3-tuple (x, y, z) representing the position of a block or thread.
+    """
+    def __init__(self):
+        super().__init__(name='Dim3')
+
+
+class GridGroup(types.Type):
+    """
+    The grid of all threads in a cooperative kernel launch.
+    """
+    def __init__(self):
+        super().__init__(name='GridGroup')
+
+
+dim3 = Dim3()
+grid_group = GridGroup()
+
+
+class CUDADispatcher(types.Dispatcher):
+    """The type of CUDA dispatchers"""
+    # This type exists (instead of using types.Dispatcher as the type of CUDA
+    # dispatchers) so that we can have an alternative lowering for them to the
+    # lowering of CPU dispatchers - the CPU target lowers all dispatchers as a
+    # constant address, but we need to lower to a dummy value because it's not
+    # generally valid to use the address of CUDA kernels and functions.
+    #
+    # Notes: it may be a bug in the CPU target that it lowers all dispatchers to
+    # a constant address - it should perhaps only lower dispatchers acting as
+    # first-class functions to a constant address. Even if that bug is fixed, it
+    # is still probably a good idea to have a separate type for CUDA
+    # dispatchers, and this type might get other differentiation from the CPU
+    # dispatcher type in future.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/vector_types.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/vector_types.py
new file mode 100644
index 000000000..5174e2b20
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/vector_types.py
@@ -0,0 +1,209 @@
+# CUDA built-in Vector Types
+# https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#built-in-vector-types
+
+from typing import List, Tuple, Dict
+
+from numba import types
+from numba.core import cgutils
+from numba.core.extending import make_attribute_wrapper, models, register_model
+from numba.core.imputils import Registry as ImplRegistry
+from numba.core.typing.templates import ConcreteTemplate
+from numba.core.typing.templates import Registry as TypingRegistry
+from numba.core.typing.templates import signature
+from numba.cuda import stubs
+from numba.cuda.errors import CudaLoweringError
+
+typing_registry = TypingRegistry()
+impl_registry = ImplRegistry()
+
+register = typing_registry.register
+register_attr = typing_registry.register_attr
+register_global = typing_registry.register_global
+lower = impl_registry.lower
+
+
+class VectorType(types.Type):
+    def __init__(self, name, base_type, attr_names, user_facing_object):
+        self._base_type = base_type
+        self._attr_names = attr_names
+        self._user_facing_object = user_facing_object
+        super().__init__(name=name)
+
+    @property
+    def base_type(self):
+        return self._base_type
+
+    @property
+    def attr_names(self):
+        return self._attr_names
+
+    @property
+    def num_elements(self):
+        return len(self._attr_names)
+
+    @property
+    def user_facing_object(self):
+        return self._user_facing_object
+
+
+def make_vector_type(
+    name: str,
+    base_type: types.Type,
+    attr_names: Tuple[str, ...],
+    user_facing_object
+) -> types.Type:
+    """Create a vector type.
+
+    Parameters
+    ----------
+    name: str
+        The name of the type.
+    base_type: numba.types.Type
+        The primitive type for each element in the vector.
+    attr_names: tuple of str
+        Name for each attribute.
+    user_facing_object: object
+        The handle to be used in cuda kernel.
+    """
+
+    class _VectorType(VectorType):
+        """Internal instantiation of VectorType."""
+
+        pass
+
+    class VectorTypeModel(models.StructModel):
+        def __init__(self, dmm, fe_type):
+            members = [(attr_name, base_type) for attr_name in attr_names]
+            super().__init__(dmm, fe_type, members)
+
+    vector_type = _VectorType(name, base_type, attr_names, user_facing_object)
+    register_model(_VectorType)(VectorTypeModel)
+    for attr_name in attr_names:
+        make_attribute_wrapper(_VectorType, attr_name, attr_name)
+
+    return vector_type
+
+
+def enable_vector_type_ctor(
+    vector_type: VectorType, overloads: List[List[types.Type]]
+):
+    """Create typing and lowering for vector type constructor.
+
+    Parameters
+    ----------
+    vector_type: VectorType
+        The type whose constructor to type and lower.
+    overloads: List of argument types
+        A list containing different overloads of the constructor. Each base type
+        in the argument list should either be primitive type or VectorType.
+    """
+    ctor = vector_type.user_facing_object
+
+    @register
+    class CtorTemplate(ConcreteTemplate):
+        key = ctor
+        cases = [signature(vector_type, *arglist) for arglist in overloads]
+
+    register_global(ctor, types.Function(CtorTemplate))
+
+    # Lowering
+
+    def make_lowering(fml_arg_list):
+        """Meta function to create a lowering for the constructor. Flattens
+        the arguments by converting vector_type into load instructions for each
+        of its attributes. Such as float2 -> float2.x, float2.y.
+        """
+
+        def lowering(context, builder, sig, actual_args):
+            # A list of elements to assign from
+            source_list = []
+            # Convert the list of argument types to a list of load IRs.
+            for argidx, fml_arg in enumerate(fml_arg_list):
+                if isinstance(fml_arg, VectorType):
+                    pxy = cgutils.create_struct_proxy(fml_arg)(
+                        context, builder, actual_args[argidx]
+                    )
+                    source_list += [
+                        getattr(pxy, attr) for attr in fml_arg.attr_names
+                    ]
+                else:
+                    # assumed primitive type
+                    source_list.append(actual_args[argidx])
+
+            if len(source_list) != vector_type.num_elements:
+                raise CudaLoweringError(
+                    f"Unmatched number of source elements ({len(source_list)}) "
+                    "and target elements ({vector_type.num_elements})."
+                )
+
+            out = cgutils.create_struct_proxy(vector_type)(context, builder)
+
+            for attr_name, source in zip(vector_type.attr_names, source_list):
+                setattr(out, attr_name, source)
+            return out._getvalue()
+
+        return lowering
+
+    for arglist in overloads:
+        lowering = make_lowering(arglist)
+        lower(ctor, *arglist)(lowering)
+
+
+vector_types : Dict[str, VectorType] = {}
+
+
+def build_constructor_overloads(base_type, vty_name, num_elements, arglists, l):
+    """
+    For a given vector type, build a list of overloads for its constructor.
+    """
+
+    # TODO: speed up with memoization
+    if num_elements == 0:
+        arglists.append(l[:])
+
+    for i in range(1, num_elements + 1):
+        if i == 1:
+            # For 1-element component, it can construct with either a
+            # primitive type or other 1-element component.
+            l.append(base_type)
+            build_constructor_overloads(
+                base_type, vty_name, num_elements - i, arglists, l
+            )
+            l.pop(-1)
+
+            l.append(vector_types[f"{vty_name[:-1]}1"])
+            build_constructor_overloads(
+                base_type, vty_name, num_elements - i, arglists, l
+            )
+            l.pop(-1)
+        else:
+            l.append(vector_types[f"{vty_name[:-1]}{i}"])
+            build_constructor_overloads(
+                base_type, vty_name, num_elements - i, arglists, l
+            )
+            l.pop(-1)
+
+
+def _initialize():
+    """
+    Construct the vector types, populate `vector_types` dictionary, and
+    enable the constructors.
+    """
+    vector_type_attribute_names = ("x", "y", "z", "w")
+    for stub in stubs._vector_type_stubs:
+        type_name = stub.__name__
+        base_type = getattr(types, type_name[:-2])
+        num_elements = int(type_name[-1])
+        attributes = vector_type_attribute_names[:num_elements]
+        vector_type = make_vector_type(type_name, base_type, attributes, stub)
+        vector_types[type_name] = vector_type
+
+    for vty in vector_types.values():
+        arglists, l = [], []
+        build_constructor_overloads(
+            vty.base_type, vty.name, vty.num_elements, arglists, l
+        )
+        enable_vector_type_ctor(vty, arglists)
+
+
+_initialize()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/vectorizers.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/vectorizers.py
new file mode 100644
index 000000000..9d0da8785
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/cuda/vectorizers.py
@@ -0,0 +1,253 @@
+from numba import cuda
+from numpy import array as np_array
+from numba.np.ufunc import deviceufunc
+from numba.np.ufunc.deviceufunc import (UFuncMechanism, GenerializedUFunc,
+                                        GUFuncCallSteps)
+
+
+class CUDAUFuncDispatcher(object):
+    """
+    Invoke the CUDA ufunc specialization for the given inputs.
+    """
+
+    def __init__(self, types_to_retty_kernels):
+        self.functions = types_to_retty_kernels
+        self._maxblocksize = 0  # ignored
+
+    @property
+    def max_blocksize(self):
+        return self._maxblocksize
+
+    @max_blocksize.setter
+    def max_blocksize(self, blksz):
+        self._max_blocksize = blksz
+
+    def __call__(self, *args, **kws):
+        """
+        *args: numpy arrays or DeviceArrayBase (created by cuda.to_device).
+               Cannot mix the two types in one call.
+
+        **kws:
+            stream -- cuda stream; when defined, asynchronous mode is used.
+            out    -- output array. Can be a numpy array or DeviceArrayBase
+                      depending on the input arguments.  Type must match
+                      the input arguments.
+        """
+        return CUDAUFuncMechanism.call(self.functions, args, kws)
+
+    def reduce(self, arg, stream=0):
+        assert len(list(self.functions.keys())[0]) == 2, "must be a binary " \
+                                                         "ufunc"
+        assert arg.ndim == 1, "must use 1d array"
+
+        n = arg.shape[0]
+        gpu_mems = []
+
+        if n == 0:
+            raise TypeError("Reduction on an empty array.")
+        elif n == 1:  # nothing to do
+            return arg[0]
+
+        # always use a stream
+        stream = stream or cuda.stream()
+        with stream.auto_synchronize():
+            # transfer memory to device if necessary
+            if cuda.cudadrv.devicearray.is_cuda_ndarray(arg):
+                mem = arg
+            else:
+                mem = cuda.to_device(arg, stream)
+                # do reduction
+            out = self.__reduce(mem, gpu_mems, stream)
+            # use a small buffer to store the result element
+            buf = np_array((1,), dtype=arg.dtype)
+            out.copy_to_host(buf, stream=stream)
+
+        return buf[0]
+
+    def __reduce(self, mem, gpu_mems, stream):
+        n = mem.shape[0]
+        if n % 2 != 0:  # odd?
+            fatcut, thincut = mem.split(n - 1)
+            # prevent freeing during async mode
+            gpu_mems.append(fatcut)
+            gpu_mems.append(thincut)
+            # execute the kernel
+            out = self.__reduce(fatcut, gpu_mems, stream)
+            gpu_mems.append(out)
+            return self(out, thincut, out=out, stream=stream)
+        else:  # even?
+            left, right = mem.split(n // 2)
+            # prevent freeing during async mode
+            gpu_mems.append(left)
+            gpu_mems.append(right)
+            # execute the kernel
+            self(left, right, out=left, stream=stream)
+            if n // 2 > 1:
+                return self.__reduce(left, gpu_mems, stream)
+            else:
+                return left
+
+
+class _CUDAGUFuncCallSteps(GUFuncCallSteps):
+    __slots__ = [
+        '_stream',
+    ]
+
+    def is_device_array(self, obj):
+        return cuda.is_cuda_array(obj)
+
+    def as_device_array(self, obj):
+        # We don't want to call as_cuda_array on objects that are already Numba
+        # device arrays, because this results in exporting the array as a
+        # Producer then importing it as a Consumer, which causes a
+        # synchronization on the array's stream (if it has one) by default.
+        # When we have a Numba device array, we can simply return it.
+        if cuda.cudadrv.devicearray.is_cuda_ndarray(obj):
+            return obj
+        return cuda.as_cuda_array(obj)
+
+    def to_device(self, hostary):
+        return cuda.to_device(hostary, stream=self._stream)
+
+    def to_host(self, devary, hostary):
+        out = devary.copy_to_host(hostary, stream=self._stream)
+        return out
+
+    def device_array(self, shape, dtype):
+        return cuda.device_array(shape=shape, dtype=dtype, stream=self._stream)
+
+    def prepare_inputs(self):
+        self._stream = self.kwargs.get('stream', 0)
+
+    def launch_kernel(self, kernel, nelem, args):
+        kernel.forall(nelem, stream=self._stream)(*args)
+
+
+class CUDAGenerializedUFunc(GenerializedUFunc):
+    @property
+    def _call_steps(self):
+        return _CUDAGUFuncCallSteps
+
+    def _broadcast_scalar_input(self, ary, shape):
+        return cuda.cudadrv.devicearray.DeviceNDArray(shape=shape,
+                                                      strides=(0,),
+                                                      dtype=ary.dtype,
+                                                      gpu_data=ary.gpu_data)
+
+    def _broadcast_add_axis(self, ary, newshape):
+        newax = len(newshape) - len(ary.shape)
+        # Add 0 strides for missing dimension
+        newstrides = (0,) * newax + ary.strides
+        return cuda.cudadrv.devicearray.DeviceNDArray(shape=newshape,
+                                                      strides=newstrides,
+                                                      dtype=ary.dtype,
+                                                      gpu_data=ary.gpu_data)
+
+
+class CUDAUFuncMechanism(UFuncMechanism):
+    """
+    Provide CUDA specialization
+    """
+    DEFAULT_STREAM = 0
+
+    def launch(self, func, count, stream, args):
+        func.forall(count, stream=stream)(*args)
+
+    def is_device_array(self, obj):
+        return cuda.is_cuda_array(obj)
+
+    def as_device_array(self, obj):
+        # We don't want to call as_cuda_array on objects that are already Numba
+        # device arrays, because this results in exporting the array as a
+        # Producer then importing it as a Consumer, which causes a
+        # synchronization on the array's stream (if it has one) by default.
+        # When we have a Numba device array, we can simply return it.
+        if cuda.cudadrv.devicearray.is_cuda_ndarray(obj):
+            return obj
+        return cuda.as_cuda_array(obj)
+
+    def to_device(self, hostary, stream):
+        return cuda.to_device(hostary, stream=stream)
+
+    def to_host(self, devary, stream):
+        return devary.copy_to_host(stream=stream)
+
+    def device_array(self, shape, dtype, stream):
+        return cuda.device_array(shape=shape, dtype=dtype, stream=stream)
+
+    def broadcast_device(self, ary, shape):
+        ax_differs = [ax for ax in range(len(shape))
+                      if ax >= ary.ndim
+                      or ary.shape[ax] != shape[ax]]
+
+        missingdim = len(shape) - len(ary.shape)
+        strides = [0] * missingdim + list(ary.strides)
+
+        for ax in ax_differs:
+            strides[ax] = 0
+
+        return cuda.cudadrv.devicearray.DeviceNDArray(shape=shape,
+                                                      strides=strides,
+                                                      dtype=ary.dtype,
+                                                      gpu_data=ary.gpu_data)
+
+
+vectorizer_stager_source = '''
+def __vectorized_{name}({args}, __out__):
+    __tid__ = __cuda__.grid(1)
+    if __tid__ < __out__.shape[0]:
+        __out__[__tid__] = __core__({argitems})
+'''
+
+
+class CUDAVectorize(deviceufunc.DeviceVectorize):
+    def _compile_core(self, sig):
+        cudevfn = cuda.jit(sig, device=True, inline=True)(self.pyfunc)
+        return cudevfn, cudevfn.overloads[sig.args].signature.return_type
+
+    def _get_globals(self, corefn):
+        glbl = self.pyfunc.__globals__.copy()
+        glbl.update({'__cuda__': cuda,
+                     '__core__': corefn})
+        return glbl
+
+    def _compile_kernel(self, fnobj, sig):
+        return cuda.jit(fnobj)
+
+    def build_ufunc(self):
+        return CUDAUFuncDispatcher(self.kernelmap)
+
+    @property
+    def _kernel_template(self):
+        return vectorizer_stager_source
+
+
+# ------------------------------------------------------------------------------
+# Generalized CUDA ufuncs
+
+_gufunc_stager_source = '''
+def __gufunc_{name}({args}):
+    __tid__ = __cuda__.grid(1)
+    if __tid__ < {checkedarg}:
+        __core__({argitems})
+'''
+
+
+class CUDAGUFuncVectorize(deviceufunc.DeviceGUFuncVectorize):
+    def build_ufunc(self):
+        engine = deviceufunc.GUFuncEngine(self.inputsig, self.outputsig)
+        return CUDAGenerializedUFunc(kernelmap=self.kernelmap, engine=engine)
+
+    def _compile_kernel(self, fnobj, sig):
+        return cuda.jit(sig)(fnobj)
+
+    @property
+    def _kernel_template(self):
+        return _gufunc_stager_source
+
+    def _get_globals(self, sig):
+        corefn = cuda.jit(sig, device=True)(self.pyfunc)
+        glbls = self.py_func.__globals__.copy()
+        glbls.update({'__cuda__': cuda,
+                      '__core__': corefn})
+        return glbls
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/__init__.py
new file mode 100644
index 000000000..3bc3359e9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/__init__.py
@@ -0,0 +1 @@
+from .jitclass import jitclass
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/function_type.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/function_type.py
new file mode 100644
index 000000000..32c26a95c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/function_type.py
@@ -0,0 +1,265 @@
+"""Provides Numba type, FunctionType, that makes functions as
+instances of a first-class function type.
+"""
+
+from numba.extending import typeof_impl
+from numba.extending import models, register_model
+from numba.extending import unbox, NativeValue, box
+from numba.core.imputils import lower_constant, lower_cast
+from numba.core.ccallback import CFunc
+from numba.core import cgutils
+from llvmlite import ir
+from numba.core import types
+from numba.core.types import (FunctionType, UndefinedFunctionType,
+                              FunctionPrototype, WrapperAddressProtocol)
+from numba.core.dispatcher import Dispatcher
+
+
+@typeof_impl.register(WrapperAddressProtocol)
+@typeof_impl.register(CFunc)
+def typeof_function_type(val, c):
+    if isinstance(val, CFunc):
+        sig = val._sig
+    elif isinstance(val, WrapperAddressProtocol):
+        sig = val.signature()
+    else:
+        raise NotImplementedError(
+            f'function type from {type(val).__name__}')
+    return FunctionType(sig)
+
+
+@register_model(FunctionPrototype)
+class FunctionProtoModel(models.PrimitiveModel):
+    """FunctionProtoModel describes the signatures of first-class functions
+    """
+    def __init__(self, dmm, fe_type):
+        if isinstance(fe_type, FunctionType):
+            ftype = fe_type.ftype
+        elif isinstance(fe_type, FunctionPrototype):
+            ftype = fe_type
+        else:
+            raise NotImplementedError((type(fe_type)))
+        retty = dmm.lookup(ftype.rtype).get_value_type()
+        args = [dmm.lookup(t).get_value_type() for t in ftype.atypes]
+        be_type = ir.PointerType(ir.FunctionType(retty, args))
+        super(FunctionProtoModel, self).__init__(dmm, fe_type, be_type)
+
+
+@register_model(FunctionType)
+@register_model(UndefinedFunctionType)
+class FunctionModel(models.StructModel):
+    """FunctionModel holds addresses of function implementations
+    """
+    def __init__(self, dmm, fe_type):
+        members = [
+            # address of cfunc wrapper function:
+            ('addr', types.voidptr),
+            # address of PyObject* referencing the Python function
+            # object:
+            ('pyaddr', types.voidptr),
+        ]
+        super(FunctionModel, self).__init__(dmm, fe_type, members)
+
+
+@lower_constant(types.Dispatcher)
+def lower_constant_dispatcher(context, builder, typ, pyval):
+    return context.add_dynamic_addr(builder, id(pyval),
+                                    info=type(pyval).__name__)
+
+
+@lower_constant(FunctionType)
+def lower_constant_function_type(context, builder, typ, pyval):
+    typ = typ.get_precise()
+
+    if isinstance(pyval, CFunc):
+        addr = pyval._wrapper_address
+        sfunc = cgutils.create_struct_proxy(typ)(context, builder)
+        sfunc.addr = context.add_dynamic_addr(builder, addr,
+                                              info=str(typ))
+        sfunc.pyaddr = context.add_dynamic_addr(builder, id(pyval),
+                                                info=type(pyval).__name__)
+        return sfunc._getvalue()
+
+    if isinstance(pyval, Dispatcher):
+        sfunc = cgutils.create_struct_proxy(typ)(context, builder)
+        sfunc.pyaddr = context.add_dynamic_addr(builder, id(pyval),
+                                                info=type(pyval).__name__)
+        return sfunc._getvalue()
+
+    if isinstance(pyval, WrapperAddressProtocol):
+        addr = pyval.__wrapper_address__()
+        assert typ.check_signature(pyval.signature())
+        sfunc = cgutils.create_struct_proxy(typ)(context, builder)
+        sfunc.addr = context.add_dynamic_addr(builder, addr,
+                                              info=str(typ))
+        sfunc.pyaddr = context.add_dynamic_addr(builder, id(pyval),
+                                                info=type(pyval).__name__)
+        return sfunc._getvalue()
+
+    # TODO: implement support for pytypes.FunctionType, ctypes.CFUNCTYPE
+    raise NotImplementedError(
+        'lower_constant_struct_function_type({}, {}, {}, {})'
+        .format(context, builder, typ, pyval))
+
+
+def _get_wrapper_address(func, sig):
+    """Return the address of a compiled cfunc wrapper function of `func`.
+
+    Warning: The compiled function must be compatible with the given
+    signature `sig`. If it is not, then result of calling the compiled
+    function is undefined. The compatibility is ensured when passing
+    in a first-class function to a Numba njit compiled function either
+    as an argument or via namespace scoping.
+
+    Parameters
+    ----------
+    func : object
+      A Numba cfunc or jit decoreated function or an object that
+      implements the wrapper address protocol (see note below).
+    sig : Signature
+      The expected function signature.
+
+    Returns
+    -------
+    addr : int
+      An address in memory (pointer value) of the compiled function
+      corresponding to the specified signature.
+
+    Note: wrapper address protocol
+    ------------------------------
+
+    An object implements the wrapper address protocol iff the object
+    provides a callable attribute named __wrapper_address__ that takes
+    a Signature instance as the argument, and returns an integer
+    representing the address or pointer value of a compiled function
+    for the given signature.
+
+    """
+    if not sig.is_precise():
+        # addr==-1 will indicate that no implementation is available
+        # for cases where type-inference did not identified the
+        # function type. For example, the type of an unused
+        # jit-decorated function argument will be undefined but also
+        # irrelevant.
+        addr = -1
+    elif hasattr(func, '__wrapper_address__'):
+        # func can be any object that implements the
+        # __wrapper_address__ protocol.
+        addr = func.__wrapper_address__()
+    elif isinstance(func, CFunc):
+        assert sig == func._sig
+        addr = func.address
+    elif isinstance(func, Dispatcher):
+        cres = func.get_compile_result(sig)
+        wrapper_name = cres.fndesc.llvm_cfunc_wrapper_name
+        addr = cres.library.get_pointer_to_function(wrapper_name)
+    else:
+        raise NotImplementedError(
+            f'get wrapper address of {type(func)} instance with {sig!r}')
+    if not isinstance(addr, int):
+        raise TypeError(
+            f'wrapper address must be integer, got {type(addr)} instance')
+    if addr <= 0 and addr != -1:
+        raise ValueError(f'wrapper address of {type(func)} instance must be'
+                         f' a positive integer but got {addr} [sig={sig}]')
+    # print(f'_get_wrapper_address[{func}]({sig=}) -> {addr}')
+    return addr
+
+
+def lower_get_wrapper_address(context, builder, func, sig,
+                              failure_mode='return_exc'):
+    """Low-level call to _get_wrapper_address(func, sig).
+
+    When calling this function, GIL must be acquired.
+    """
+    pyapi = context.get_python_api(builder)
+
+    # Get the cfunc wrapper address. The code below trusts that the
+    # function numba.function._get_wrapper_address exists and can be
+    # called with two arguments. However, if an exception is raised in
+    # the function, then it will be caught and propagated to the
+    # caller.
+
+    modname = context.insert_const_string(builder.module, __name__)
+    numba_mod = pyapi.import_module_noblock(modname)
+    numba_func = pyapi.object_getattr_string(
+        numba_mod, '_get_wrapper_address')
+    pyapi.decref(numba_mod)
+    sig_obj = pyapi.unserialize(pyapi.serialize_object(sig))
+
+    addr = pyapi.call_function_objargs(numba_func, (func, sig_obj))
+
+    if failure_mode != 'ignore':
+        with builder.if_then(cgutils.is_null(builder, addr), likely=False):
+            # _get_wrapper_address has raised an exception, propagate it
+            # to the caller.
+            if failure_mode == 'return_exc':
+                context.call_conv.return_exc(builder)
+            elif failure_mode == 'return_null':
+                builder.ret(pyapi.get_null_object())
+            else:
+                raise NotImplementedError(failure_mode)
+    # else the caller will handle addr == NULL
+
+    return addr  # new reference or NULL
+
+
+@unbox(FunctionType)
+def unbox_function_type(typ, obj, c):
+    typ = typ.get_precise()
+
+    sfunc = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+
+    addr = lower_get_wrapper_address(
+        c.context, c.builder, obj, typ.signature, failure_mode='return_null')
+    sfunc.addr = c.pyapi.long_as_voidptr(addr)
+    c.pyapi.decref(addr)
+
+    llty = c.context.get_value_type(types.voidptr)
+    sfunc.pyaddr = c.builder.ptrtoint(obj, llty)
+
+    return NativeValue(sfunc._getvalue())
+
+
+@box(FunctionType)
+def box_function_type(typ, val, c):
+    typ = typ.get_precise()
+
+    sfunc = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+    pyaddr_ptr = cgutils.alloca_once(c.builder, c.pyapi.pyobj)
+    raw_ptr = c.builder.inttoptr(sfunc.pyaddr, c.pyapi.pyobj)
+    with c.builder.if_then(cgutils.is_null(c.builder, raw_ptr),
+                           likely=False):
+        cstr = f"first-class function {typ} parent object not set"
+        c.pyapi.err_set_string("PyExc_MemoryError", cstr)
+        c.builder.ret(c.pyapi.get_null_object())
+    c.builder.store(raw_ptr, pyaddr_ptr)
+    cfunc = c.builder.load(pyaddr_ptr)
+    c.pyapi.incref(cfunc)
+    return cfunc
+
+
+@lower_cast(UndefinedFunctionType, FunctionType)
+def lower_cast_function_type_to_function_type(
+        context, builder, fromty, toty, val):
+    return val
+
+
+@lower_cast(types.Dispatcher, FunctionType)
+def lower_cast_dispatcher_to_function_type(context, builder, fromty, toty, val):
+    toty = toty.get_precise()
+
+    pyapi = context.get_python_api(builder)
+    sfunc = cgutils.create_struct_proxy(toty)(context, builder)
+
+    gil_state = pyapi.gil_ensure()
+    addr = lower_get_wrapper_address(
+        context, builder, val, toty.signature,
+        failure_mode='return_exc')
+    sfunc.addr = pyapi.long_as_voidptr(addr)
+    pyapi.decref(addr)
+    pyapi.gil_release(gil_state)
+
+    llty = context.get_value_type(types.voidptr)
+    sfunc.pyaddr = builder.ptrtoint(val, llty)
+    return sfunc._getvalue()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/__init__.py
new file mode 100644
index 000000000..981282f65
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/__init__.py
@@ -0,0 +1,3 @@
+from numba.experimental.jitclass.decorators import jitclass
+from numba.experimental.jitclass import boxing  # Has import-time side effect
+from numba.experimental.jitclass import overloads  # Has import-time side effect
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/_box.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/_box.c
new file mode 100644
index 000000000..df9473ff3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/_box.c
@@ -0,0 +1,179 @@
+/*
+Implements jitclass Box type in python c-api level.
+*/
+#include "../../_pymodule.h"
+
+typedef struct {
+    PyObject_HEAD
+    void *meminfoptr, *dataptr;
+} BoxObject;
+
+
+/* Store function defined in numba.core.runtime._nrt_python for use in box_dealloc.
+ * It points to a function is code segment that does not need user deallocation
+ * and does not disappear while the process is still running.
+ */
+static void (*MemInfo_release)(void*) = NULL;
+
+
+/*
+ * Box.__init__()
+ * Takes no arguments.
+ * meminfoptr and dataptr are set to NULL.
+ */
+static
+int Box_init(BoxObject *self, PyObject *args, PyObject *kwds) {
+    static char *keywords[] = {NULL};
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "", keywords))
+    {
+        return -1;
+    }
+    /* Initialize attributes to NULL */
+    self->meminfoptr = NULL;
+    self->dataptr = NULL;
+    return 0;
+}
+
+/*
+ * Box destructor
+ * Release MemInfo pointed by meminfoptr.
+ * Free the instance.
+ */
+static
+void box_dealloc(BoxObject *box)
+{
+    if (box->meminfoptr) MemInfo_release((void*)box->meminfoptr);
+    Py_TYPE(box)->tp_free((PyObject *) box);
+}
+
+
+static const char Box_doc[] = "A box for numba created jit-class instance";
+
+
+static PyTypeObject BoxType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "_box.Box",                /*tp_name*/
+    sizeof(BoxObject),         /*tp_basicsize*/
+    0,                         /*tp_itemsize*/
+    (destructor)box_dealloc,   /*tp_dealloc*/
+    0,                         /*tp_print*/
+    0,                         /*tp_getattr*/
+    0,                         /*tp_setattr*/
+    0,                         /*tp_compare*/
+    0,                         /*tp_repr*/
+    0,                         /*tp_as_number*/
+    0,                         /*tp_as_sequence*/
+    0,                         /*tp_as_mapping*/
+    0,                         /*tp_hash */
+    0,                         /*tp_call*/
+    0,                         /*tp_str*/
+    0,                         /*tp_getattro*/
+    0,                         /*tp_setattro*/
+    0,                         /*tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/
+    Box_doc,                   /* tp_doc */
+    0,                         /* tp_traverse */
+    0,                         /* tp_clear */
+    0,                         /* tp_richcompare */
+    0,                         /* tp_weaklistoffset */
+    0,                         /* tp_iter */
+    0,                         /* tp_iternext */
+    0,                         /* tp_methods */
+    0,                         /* tp_members */
+    0,                         /* tp_getset */
+    0,                         /* tp_base */
+    0,                         /* tp_dict */
+    0,                         /* tp_descr_get */
+    0,                         /* tp_descr_set */
+    0,                         /* tp_dictoffset */
+    (initproc)Box_init,        /* tp_init */
+    0,                         /* tp_alloc */
+    PyType_GenericNew,         /* tp_new */
+};
+
+
+/* Import MemInfo_Release from numba.core.runtime._nrt_python once for use in
+ * Box_dealloc.
+ */
+static void *
+import_meminfo_release(void) {
+    PyObject *nrtmod = NULL;
+    PyObject *helperdct = NULL;
+    PyObject *mi_rel_fn = NULL;
+    void *fnptr = NULL;
+    /* from numba.core.runtime import _nrt_python */
+    nrtmod = PyImport_ImportModule("numba.core.runtime._nrt_python");
+    if (!nrtmod) goto cleanup;
+    /* helperdct = _nrt_python.c_helpers */
+    helperdct = PyObject_GetAttrString(nrtmod, "c_helpers");
+    if (!helperdct) goto cleanup;
+    /* helperdct['MemInfo_release'] */
+    mi_rel_fn = PyDict_GetItemString(helperdct, "MemInfo_release");
+    if (!mi_rel_fn) goto cleanup;
+    fnptr = PyLong_AsVoidPtr(mi_rel_fn);
+
+cleanup:
+    Py_XDECREF(nrtmod);
+    Py_XDECREF(helperdct);
+    return fnptr;
+}
+
+/* Debug utils.
+ * Get internal dataptr field from Box.
+ */
+static
+PyObject* box_get_dataptr(PyObject *self, PyObject *args) {
+    BoxObject *box;
+    if (!PyArg_ParseTuple(args, "O!", &BoxType, (PyObject*)&box))
+        return NULL;
+    return PyLong_FromVoidPtr(box->dataptr);
+}
+
+/* Debug utils.
+ * Get internal meminfoptr field from Box.
+ */
+static
+PyObject* box_get_meminfoptr(PyObject *self, PyObject *args) {
+    BoxObject *box;
+    if (!PyArg_ParseTuple(args, "O!", &BoxType, (PyObject*)&box))
+        return NULL;
+    return PyLong_FromVoidPtr(box->meminfoptr);
+}
+
+
+static PyMethodDef ext_methods[] = {
+#define declmethod(func) { #func , ( PyCFunction )func , METH_VARARGS , NULL }
+    declmethod(box_get_dataptr),
+    declmethod(box_get_meminfoptr),
+    { NULL },
+#undef declmethod
+};
+
+
+MOD_INIT(_box) {
+    PyObject *m;
+
+    MOD_DEF(m, "_box", "No docs", ext_methods)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    /* init BoxType */
+    if (PyType_Ready(&BoxType))
+        return MOD_ERROR_VAL;
+
+    /* import and cache NRT_MemInfo_release function pointer */
+    MemInfo_release = import_meminfo_release();
+    if (!MemInfo_release) return MOD_ERROR_VAL;
+
+    /* bind BoxType */
+    Py_INCREF(&BoxType);
+    PyModule_AddObject(m, "Box", (PyObject *) (&BoxType));
+
+    /* bind address to direct access utils */;
+    PyModule_AddObject(m, "box_meminfoptr_offset",
+                       PyLong_FromSsize_t(offsetof(BoxObject, meminfoptr)));
+    PyModule_AddObject(m, "box_dataptr_offset",
+                       PyLong_FromSsize_t(offsetof(BoxObject, dataptr)));
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/base.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/base.py
new file mode 100644
index 000000000..105c66bfd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/base.py
@@ -0,0 +1,592 @@
+import inspect
+import operator
+import types as pytypes
+import typing as pt
+from collections import OrderedDict
+from collections.abc import Sequence
+
+from llvmlite import ir as llvmir
+from numba import njit
+from numba.core import cgutils, errors, imputils, types, utils
+from numba.core.datamodel import default_manager, models
+from numba.core.registry import cpu_target
+from numba.core.typing import templates
+from numba.core.typing.asnumbatype import as_numba_type
+from numba.core.serialize import disable_pickling
+from numba.experimental.jitclass import _box
+
+##############################################################################
+# Data model
+
+
+class InstanceModel(models.StructModel):
+    def __init__(self, dmm, fe_typ):
+        cls_data_ty = types.ClassDataType(fe_typ)
+        # MemInfoPointer uses the `dtype` attribute to traverse for nested
+        # NRT MemInfo.  Since we handle nested NRT MemInfo ourselves,
+        # we will replace provide MemInfoPointer with an opaque type
+        # so that it does not raise exception for nested meminfo.
+        dtype = types.Opaque('Opaque.' + str(cls_data_ty))
+        members = [
+            ('meminfo', types.MemInfoPointer(dtype)),
+            ('data', types.CPointer(cls_data_ty)),
+        ]
+        super(InstanceModel, self).__init__(dmm, fe_typ, members)
+
+
+class InstanceDataModel(models.StructModel):
+    def __init__(self, dmm, fe_typ):
+        clsty = fe_typ.class_type
+        members = [(_mangle_attr(k), v) for k, v in clsty.struct.items()]
+        super(InstanceDataModel, self).__init__(dmm, fe_typ, members)
+
+
+default_manager.register(types.ClassInstanceType, InstanceModel)
+default_manager.register(types.ClassDataType, InstanceDataModel)
+default_manager.register(types.ClassType, models.OpaqueModel)
+
+
+def _mangle_attr(name):
+    """
+    Mangle attributes.
+    The resulting name does not startswith an underscore '_'.
+    """
+    return 'm_' + name
+
+
+##############################################################################
+# Class object
+
+_ctor_template = """
+def ctor({args}):
+    return __numba_cls_({args})
+"""
+
+
+def _getargs(fn_sig):
+    """
+    Returns list of positional and keyword argument names in order.
+    """
+    params = fn_sig.parameters
+    args = []
+    for k, v in params.items():
+        if (v.kind & v.POSITIONAL_OR_KEYWORD) == v.POSITIONAL_OR_KEYWORD:
+            args.append(k)
+        else:
+            msg = "%s argument type unsupported in jitclass" % v.kind
+            raise errors.UnsupportedError(msg)
+    return args
+
+
+@disable_pickling
+class JitClassType(type):
+    """
+    The type of any jitclass.
+    """
+    def __new__(cls, name, bases, dct):
+        if len(bases) != 1:
+            raise TypeError("must have exactly one base class")
+        [base] = bases
+        if isinstance(base, JitClassType):
+            raise TypeError("cannot subclass from a jitclass")
+        assert 'class_type' in dct, 'missing "class_type" attr'
+        outcls = type.__new__(cls, name, bases, dct)
+        outcls._set_init()
+        return outcls
+
+    def _set_init(cls):
+        """
+        Generate a wrapper for calling the constructor from pure Python.
+        Note the wrapper will only accept positional arguments.
+        """
+        init = cls.class_type.instance_type.methods['__init__']
+        init_sig = utils.pysignature(init)
+        # get postitional and keyword arguments
+        # offset by one to exclude the `self` arg
+        args = _getargs(init_sig)[1:]
+        cls._ctor_sig = init_sig
+        ctor_source = _ctor_template.format(args=', '.join(args))
+        glbls = {"__numba_cls_": cls}
+        exec(ctor_source, glbls)
+        ctor = glbls['ctor']
+        cls._ctor = njit(ctor)
+
+    def __instancecheck__(cls, instance):
+        if isinstance(instance, _box.Box):
+            return instance._numba_type_.class_type is cls.class_type
+        return False
+
+    def __call__(cls, *args, **kwargs):
+        # The first argument of _ctor_sig is `cls`, which here
+        # is bound to None and then skipped when invoking the constructor.
+        bind = cls._ctor_sig.bind(None, *args, **kwargs)
+        bind.apply_defaults()
+        return cls._ctor(*bind.args[1:], **bind.kwargs)
+
+
+##############################################################################
+# Registration utils
+
+def _validate_spec(spec):
+    for k, v in spec.items():
+        if not isinstance(k, str):
+            raise TypeError("spec keys should be strings, got %r" % (k,))
+        if not isinstance(v, types.Type):
+            raise TypeError("spec values should be Numba type instances, got %r"
+                            % (v,))
+
+
+def _fix_up_private_attr(clsname, spec):
+    """
+    Apply the same changes to dunder names as CPython would.
+    """
+    out = OrderedDict()
+    for k, v in spec.items():
+        if k.startswith('__') and not k.endswith('__'):
+            k = '_' + clsname + k
+        out[k] = v
+    return out
+
+
+def _add_linking_libs(context, call):
+    """
+    Add the required libs for the callable to allow inlining.
+    """
+    libs = getattr(call, "libs", ())
+    if libs:
+        context.add_linking_libs(libs)
+
+
+def register_class_type(cls, spec, class_ctor, builder):
+    """
+    Internal function to create a jitclass.
+
+    Args
+    ----
+    cls: the original class object (used as the prototype)
+    spec: the structural specification contains the field types.
+    class_ctor: the numba type to represent the jitclass
+    builder: the internal jitclass builder
+    """
+    # Normalize spec
+    if spec is None:
+        spec = OrderedDict()
+    elif isinstance(spec, Sequence):
+        spec = OrderedDict(spec)
+
+    # Extend spec with class annotations.
+    for attr, py_type in pt.get_type_hints(cls).items():
+        if attr not in spec:
+            spec[attr] = as_numba_type(py_type)
+
+    _validate_spec(spec)
+
+    # Fix up private attribute names
+    spec = _fix_up_private_attr(cls.__name__, spec)
+
+    # Copy methods from base classes
+    clsdct = {}
+    for basecls in reversed(inspect.getmro(cls)):
+        clsdct.update(basecls.__dict__)
+
+    methods, props, static_methods, others = {}, {}, {}, {}
+    for k, v in clsdct.items():
+        if isinstance(v, pytypes.FunctionType):
+            methods[k] = v
+        elif isinstance(v, property):
+            props[k] = v
+        elif isinstance(v, staticmethod):
+            static_methods[k] = v
+        else:
+            others[k] = v
+
+    # Check for name shadowing
+    shadowed = (set(methods) | set(props) | set(static_methods)) & set(spec)
+    if shadowed:
+        raise NameError("name shadowing: {0}".format(', '.join(shadowed)))
+
+    docstring = others.pop('__doc__', "")
+    _drop_ignored_attrs(others)
+    if others:
+        msg = "class members are not yet supported: {0}"
+        members = ', '.join(others.keys())
+        raise TypeError(msg.format(members))
+
+    for k, v in props.items():
+        if v.fdel is not None:
+            raise TypeError("deleter is not supported: {0}".format(k))
+
+    jit_methods = {k: njit(v) for k, v in methods.items()}
+
+    jit_props = {}
+    for k, v in props.items():
+        dct = {}
+        if v.fget:
+            dct['get'] = njit(v.fget)
+        if v.fset:
+            dct['set'] = njit(v.fset)
+        jit_props[k] = dct
+
+    jit_static_methods = {
+        k: njit(v.__func__) for k, v in static_methods.items()}
+
+    # Instantiate class type
+    class_type = class_ctor(
+        cls,
+        ConstructorTemplate,
+        spec,
+        jit_methods,
+        jit_props,
+        jit_static_methods)
+
+    jit_class_dct = dict(class_type=class_type, __doc__=docstring)
+    jit_class_dct.update(jit_static_methods)
+    cls = JitClassType(cls.__name__, (cls,), jit_class_dct)
+
+    # Register resolution of the class object
+    typingctx = cpu_target.typing_context
+    typingctx.insert_global(cls, class_type)
+
+    # Register class
+    targetctx = cpu_target.target_context
+    builder(class_type, typingctx, targetctx).register()
+    as_numba_type.register(cls, class_type.instance_type)
+
+    return cls
+
+
+class ConstructorTemplate(templates.AbstractTemplate):
+    """
+    Base class for jitclass constructor templates.
+    """
+
+    def generic(self, args, kws):
+        # Redirect resolution to __init__
+        instance_type = self.key.instance_type
+        ctor = instance_type.jit_methods['__init__']
+        boundargs = (instance_type.get_reference_type(),) + args
+        disp_type = types.Dispatcher(ctor)
+        sig = disp_type.get_call_type(self.context, boundargs, kws)
+
+        if not isinstance(sig.return_type, types.NoneType):
+            raise errors.NumbaTypeError(
+                f"__init__() should return None, not '{sig.return_type}'")
+
+        # Actual constructor returns an instance value (not None)
+        out = templates.signature(instance_type, *sig.args[1:])
+        return out
+
+
+def _drop_ignored_attrs(dct):
+    # ignore anything defined by object
+    drop = set(['__weakref__',
+                '__module__',
+                '__dict__'])
+
+    if '__annotations__' in dct:
+        drop.add('__annotations__')
+
+    for k, v in dct.items():
+        if isinstance(v, (pytypes.BuiltinFunctionType,
+                          pytypes.BuiltinMethodType)):
+            drop.add(k)
+        elif getattr(v, '__objclass__', None) is object:
+            drop.add(k)
+
+    # If a class defines __eq__ but not __hash__, __hash__ is implicitly set to
+    # None. This is a class member, and class members are not presently
+    # supported.
+    if '__hash__' in dct and dct['__hash__'] is None:
+        drop.add('__hash__')
+
+    for k in drop:
+        del dct[k]
+
+
+class ClassBuilder(object):
+    """
+    A jitclass builder for a mutable jitclass.  This will register
+    typing and implementation hooks to the given typing and target contexts.
+    """
+    class_impl_registry = imputils.Registry('jitclass builder')
+    implemented_methods = set()
+
+    def __init__(self, class_type, typingctx, targetctx):
+        self.class_type = class_type
+        self.typingctx = typingctx
+        self.targetctx = targetctx
+
+    def register(self):
+        """
+        Register to the frontend and backend.
+        """
+        # Register generic implementations for all jitclasses
+        self._register_methods(self.class_impl_registry,
+                               self.class_type.instance_type)
+        # NOTE other registrations are done at the top-level
+        # (see ctor_impl and attr_impl below)
+        self.targetctx.install_registry(self.class_impl_registry)
+
+    def _register_methods(self, registry, instance_type):
+        """
+        Register method implementations.
+        This simply registers that the method names are valid methods.  Inside
+        of imp() below we retrieve the actual method to run from the type of
+        the receiver argument (i.e. self).
+        """
+        to_register = list(instance_type.jit_methods) + \
+            list(instance_type.jit_static_methods)
+        for meth in to_register:
+
+            # There's no way to retrieve the particular method name
+            # inside the implementation function, so we have to register a
+            # specific closure for each different name
+            if meth not in self.implemented_methods:
+                self._implement_method(registry, meth)
+                self.implemented_methods.add(meth)
+
+    def _implement_method(self, registry, attr):
+        # create a separate instance of imp method to avoid closure clashing
+        def get_imp():
+            def imp(context, builder, sig, args):
+                instance_type = sig.args[0]
+
+                if attr in instance_type.jit_methods:
+                    method = instance_type.jit_methods[attr]
+                elif attr in instance_type.jit_static_methods:
+                    method = instance_type.jit_static_methods[attr]
+                    # imp gets called as a method, where the first argument is
+                    # self.  We drop this for a static method.
+                    sig = sig.replace(args=sig.args[1:])
+                    args = args[1:]
+
+                disp_type = types.Dispatcher(method)
+                call = context.get_function(disp_type, sig)
+                out = call(builder, args)
+                _add_linking_libs(context, call)
+                return imputils.impl_ret_new_ref(context, builder,
+                                                 sig.return_type, out)
+            return imp
+
+        def _getsetitem_gen(getset):
+            _dunder_meth = "__%s__" % getset
+            op = getattr(operator, getset)
+
+            @templates.infer_global(op)
+            class GetSetItem(templates.AbstractTemplate):
+                def generic(self, args, kws):
+                    instance = args[0]
+                    if isinstance(instance, types.ClassInstanceType) and \
+                            _dunder_meth in instance.jit_methods:
+                        meth = instance.jit_methods[_dunder_meth]
+                        disp_type = types.Dispatcher(meth)
+                        sig = disp_type.get_call_type(self.context, args, kws)
+                        return sig
+
+            # lower both {g,s}etitem and __{g,s}etitem__ to catch the calls
+            # from python and numba
+            imputils.lower_builtin((types.ClassInstanceType, _dunder_meth),
+                                   types.ClassInstanceType,
+                                   types.VarArg(types.Any))(get_imp())
+            imputils.lower_builtin(op,
+                                   types.ClassInstanceType,
+                                   types.VarArg(types.Any))(get_imp())
+
+        dunder_stripped = attr.strip('_')
+        if dunder_stripped in ("getitem", "setitem"):
+            _getsetitem_gen(dunder_stripped)
+        else:
+            registry.lower((types.ClassInstanceType, attr),
+                           types.ClassInstanceType,
+                           types.VarArg(types.Any))(get_imp())
+
+
+@templates.infer_getattr
+class ClassAttribute(templates.AttributeTemplate):
+    key = types.ClassInstanceType
+
+    def generic_resolve(self, instance, attr):
+        if attr in instance.struct:
+            # It's a struct field => the type is well-known
+            return instance.struct[attr]
+
+        elif attr in instance.jit_methods:
+            # It's a jitted method => typeinfer it
+            meth = instance.jit_methods[attr]
+            disp_type = types.Dispatcher(meth)
+
+            class MethodTemplate(templates.AbstractTemplate):
+                key = (self.key, attr)
+
+                def generic(self, args, kws):
+                    args = (instance,) + tuple(args)
+                    sig = disp_type.get_call_type(self.context, args, kws)
+                    return sig.as_method()
+
+            return types.BoundFunction(MethodTemplate, instance)
+
+        elif attr in instance.jit_static_methods:
+            # It's a jitted method => typeinfer it
+            meth = instance.jit_static_methods[attr]
+            disp_type = types.Dispatcher(meth)
+
+            class StaticMethodTemplate(templates.AbstractTemplate):
+                key = (self.key, attr)
+
+                def generic(self, args, kws):
+                    # Don't add instance as the first argument for a static
+                    # method.
+                    sig = disp_type.get_call_type(self.context, args, kws)
+                    # sig itself does not include ClassInstanceType as it's
+                    # first argument, so instead of calling sig.as_method()
+                    # we insert the recvr. This is equivalent to
+                    # sig.replace(args=(instance,) + sig.args).as_method().
+                    return sig.replace(recvr=instance)
+
+            return types.BoundFunction(StaticMethodTemplate, instance)
+
+        elif attr in instance.jit_props:
+            # It's a jitted property => typeinfer its getter
+            impdct = instance.jit_props[attr]
+            getter = impdct['get']
+            disp_type = types.Dispatcher(getter)
+            sig = disp_type.get_call_type(self.context, (instance,), {})
+            return sig.return_type
+
+
+@ClassBuilder.class_impl_registry.lower_getattr_generic(types.ClassInstanceType)
+def get_attr_impl(context, builder, typ, value, attr):
+    """
+    Generic getattr() for @jitclass instances.
+    """
+    if attr in typ.struct:
+        # It's a struct field
+        inst = context.make_helper(builder, typ, value=value)
+        data_pointer = inst.data
+        data = context.make_data_helper(builder, typ.get_data_type(),
+                                        ref=data_pointer)
+        return imputils.impl_ret_borrowed(context, builder,
+                                          typ.struct[attr],
+                                          getattr(data, _mangle_attr(attr)))
+    elif attr in typ.jit_props:
+        # It's a jitted property
+        getter = typ.jit_props[attr]['get']
+        sig = templates.signature(None, typ)
+        dispatcher = types.Dispatcher(getter)
+        sig = dispatcher.get_call_type(context.typing_context, [typ], {})
+        call = context.get_function(dispatcher, sig)
+        out = call(builder, [value])
+        _add_linking_libs(context, call)
+        return imputils.impl_ret_new_ref(context, builder, sig.return_type, out)
+
+    raise NotImplementedError('attribute {0!r} not implemented'.format(attr))
+
+
+@ClassBuilder.class_impl_registry.lower_setattr_generic(types.ClassInstanceType)
+def set_attr_impl(context, builder, sig, args, attr):
+    """
+    Generic setattr() for @jitclass instances.
+    """
+    typ, valty = sig.args
+    target, val = args
+
+    if attr in typ.struct:
+        # It's a struct member
+        inst = context.make_helper(builder, typ, value=target)
+        data_ptr = inst.data
+        data = context.make_data_helper(builder, typ.get_data_type(),
+                                        ref=data_ptr)
+
+        # Get old value
+        attr_type = typ.struct[attr]
+        oldvalue = getattr(data, _mangle_attr(attr))
+
+        # Store n
+        setattr(data, _mangle_attr(attr), val)
+        context.nrt.incref(builder, attr_type, val)
+
+        # Delete old value
+        context.nrt.decref(builder, attr_type, oldvalue)
+
+    elif attr in typ.jit_props:
+        # It's a jitted property
+        setter = typ.jit_props[attr]['set']
+        disp_type = types.Dispatcher(setter)
+        sig = disp_type.get_call_type(context.typing_context,
+                                      (typ, valty), {})
+        call = context.get_function(disp_type, sig)
+        call(builder, (target, val))
+        _add_linking_libs(context, call)
+    else:
+        raise NotImplementedError(
+            'attribute {0!r} not implemented'.format(attr))
+
+
+def imp_dtor(context, module, instance_type):
+    llvoidptr = context.get_value_type(types.voidptr)
+    llsize = context.get_value_type(types.uintp)
+    dtor_ftype = llvmir.FunctionType(llvmir.VoidType(),
+                                     [llvoidptr, llsize, llvoidptr])
+
+    fname = "_Dtor.{0}".format(instance_type.name)
+    dtor_fn = cgutils.get_or_insert_function(module, dtor_ftype, fname)
+    if dtor_fn.is_declaration:
+        # Define
+        builder = llvmir.IRBuilder(dtor_fn.append_basic_block())
+
+        alloc_fe_type = instance_type.get_data_type()
+        alloc_type = context.get_value_type(alloc_fe_type)
+
+        ptr = builder.bitcast(dtor_fn.args[0], alloc_type.as_pointer())
+        data = context.make_helper(builder, alloc_fe_type, ref=ptr)
+
+        context.nrt.decref(builder, alloc_fe_type, data._getvalue())
+
+        builder.ret_void()
+
+    return dtor_fn
+
+
+@ClassBuilder.class_impl_registry.lower(types.ClassType,
+                                        types.VarArg(types.Any))
+def ctor_impl(context, builder, sig, args):
+    """
+    Generic constructor (__new__) for jitclasses.
+    """
+    # Allocate the instance
+    inst_typ = sig.return_type
+    alloc_type = context.get_data_type(inst_typ.get_data_type())
+    alloc_size = context.get_abi_sizeof(alloc_type)
+
+    meminfo = context.nrt.meminfo_alloc_dtor(
+        builder,
+        context.get_constant(types.uintp, alloc_size),
+        imp_dtor(context, builder.module, inst_typ),
+    )
+    data_pointer = context.nrt.meminfo_data(builder, meminfo)
+    data_pointer = builder.bitcast(data_pointer,
+                                   alloc_type.as_pointer())
+
+    # Nullify all data
+    builder.store(cgutils.get_null_value(alloc_type),
+                  data_pointer)
+
+    inst_struct = context.make_helper(builder, inst_typ)
+    inst_struct.meminfo = meminfo
+    inst_struct.data = data_pointer
+
+    # Call the jitted __init__
+    # TODO: extract the following into a common util
+    init_sig = (sig.return_type,) + sig.args
+
+    init = inst_typ.jit_methods['__init__']
+    disp_type = types.Dispatcher(init)
+    call = context.get_function(disp_type, types.void(*init_sig))
+    _add_linking_libs(context, call)
+    realargs = [inst_struct._getvalue()] + list(args)
+    call(builder, realargs)
+
+    # Prepare return value
+    ret = inst_struct._getvalue()
+
+    return imputils.impl_ret_new_ref(context, builder, inst_typ, ret)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/boxing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/boxing.py
new file mode 100644
index 000000000..7d70f561a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/boxing.py
@@ -0,0 +1,256 @@
+"""
+Implement logic relating to wrapping (box) and unwrapping (unbox) instances
+of jitclasses for use inside the python interpreter.
+"""
+
+from functools import wraps, partial
+
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core.decorators import njit
+from numba.core.pythonapi import box, unbox, NativeValue
+from numba.core.typing.typeof import typeof_impl
+from numba.experimental.jitclass import _box
+
+
+_getter_code_template = """
+def accessor(__numba_self_):
+    return __numba_self_.{0}
+"""
+
+_setter_code_template = """
+def mutator(__numba_self_, __numba_val):
+    __numba_self_.{0} = __numba_val
+"""
+
+_method_code_template = """
+def method(__numba_self_, *args):
+    return __numba_self_.{method}(*args)
+"""
+
+
+def _generate_property(field, template, fname):
+    """
+    Generate simple function that get/set a field of the instance
+    """
+    source = template.format(field)
+    glbls = {}
+    exec(source, glbls)
+    return njit(glbls[fname])
+
+
+_generate_getter = partial(_generate_property, template=_getter_code_template,
+                           fname='accessor')
+_generate_setter = partial(_generate_property, template=_setter_code_template,
+                           fname='mutator')
+
+
+def _generate_method(name, func):
+    """
+    Generate a wrapper for calling a method.  Note the wrapper will only
+    accept positional arguments.
+    """
+    source = _method_code_template.format(method=name)
+    glbls = {}
+    exec(source, glbls)
+    method = njit(glbls['method'])
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        return method(*args, **kwargs)
+
+    return wrapper
+
+
+_cache_specialized_box = {}
+
+
+def _specialize_box(typ):
+    """
+    Create a subclass of Box that is specialized to the jitclass.
+
+    This function caches the result to avoid code bloat.
+    """
+    # Check cache
+    if typ in _cache_specialized_box:
+        return _cache_specialized_box[typ]
+    dct = {'__slots__': (),
+           '_numba_type_': typ,
+           '__doc__': typ.class_type.class_doc,
+           }
+    # Inject attributes as class properties
+    for field in typ.struct:
+        getter = _generate_getter(field)
+        setter = _generate_setter(field)
+        dct[field] = property(getter, setter)
+    # Inject properties as class properties
+    for field, impdct in typ.jit_props.items():
+        getter = None
+        setter = None
+        if 'get' in impdct:
+            getter = _generate_getter(field)
+        if 'set' in impdct:
+            setter = _generate_setter(field)
+        # get docstring from either the fget or fset
+        imp = impdct.get('get') or impdct.get('set') or None
+        doc = getattr(imp, '__doc__', None)
+        dct[field] = property(getter, setter, doc=doc)
+    # Inject methods as class members
+    supported_dunders = {
+        "__abs__",
+        "__bool__",
+        "__complex__",
+        "__contains__",
+        "__float__",
+        "__getitem__",
+        "__hash__",
+        "__index__",
+        "__int__",
+        "__len__",
+        "__setitem__",
+        "__str__",
+        "__eq__",
+        "__ne__",
+        "__ge__",
+        "__gt__",
+        "__le__",
+        "__lt__",
+        "__add__",
+        "__floordiv__",
+        "__lshift__",
+        "__mod__",
+        "__mul__",
+        "__neg__",
+        "__pos__",
+        "__pow__",
+        "__rshift__",
+        "__sub__",
+        "__truediv__",
+        "__and__",
+        "__or__",
+        "__xor__",
+        "__iadd__",
+        "__ifloordiv__",
+        "__ilshift__",
+        "__imod__",
+        "__imul__",
+        "__ipow__",
+        "__irshift__",
+        "__isub__",
+        "__itruediv__",
+        "__iand__",
+        "__ior__",
+        "__ixor__",
+    }
+    for name, func in typ.methods.items():
+        if (
+            name.startswith("__")
+            and name.endswith("__")
+            and name not in supported_dunders
+        ):
+            continue
+        dct[name] = _generate_method(name, func)
+
+    # Inject static methods as class members
+    for name, func in typ.static_methods.items():
+        dct[name] = _generate_method(name, func)
+
+    # Create subclass
+    subcls = type(typ.classname, (_box.Box,), dct)
+    # Store to cache
+    _cache_specialized_box[typ] = subcls
+
+    # Pre-compile attribute getter.
+    # Note: This must be done after the "box" class is created because
+    #       compiling the getter requires the "box" class to be defined.
+    for k, v in dct.items():
+        if isinstance(v, property):
+            prop = getattr(subcls, k)
+            if prop.fget is not None:
+                fget = prop.fget
+                fast_fget = fget.compile((typ,))
+                fget.disable_compile()
+                setattr(subcls, k,
+                        property(fast_fget, prop.fset, prop.fdel,
+                                 doc=prop.__doc__))
+
+    return subcls
+
+
+###############################################################################
+# Implement box/unbox for call wrapper
+
+@box(types.ClassInstanceType)
+def _box_class_instance(typ, val, c):
+    meminfo, dataptr = cgutils.unpack_tuple(c.builder, val)
+
+    # Create Box instance
+    box_subclassed = _specialize_box(typ)
+    # Note: the ``box_subclassed`` is kept alive by the cache
+    voidptr_boxcls = c.context.add_dynamic_addr(
+        c.builder,
+        id(box_subclassed),
+        info="box_class_instance",
+    )
+    box_cls = c.builder.bitcast(voidptr_boxcls, c.pyapi.pyobj)
+
+    box = c.pyapi.call_function_objargs(box_cls, ())
+
+    # Initialize Box instance
+    llvoidptr = ir.IntType(8).as_pointer()
+    addr_meminfo = c.builder.bitcast(meminfo, llvoidptr)
+    addr_data = c.builder.bitcast(dataptr, llvoidptr)
+
+    def set_member(member_offset, value):
+        # Access member by byte offset
+        offset = c.context.get_constant(types.uintp, member_offset)
+        ptr = cgutils.pointer_add(c.builder, box, offset)
+        casted = c.builder.bitcast(ptr, llvoidptr.as_pointer())
+        c.builder.store(value, casted)
+
+    set_member(_box.box_meminfoptr_offset, addr_meminfo)
+    set_member(_box.box_dataptr_offset, addr_data)
+    return box
+
+
+@unbox(types.ClassInstanceType)
+def _unbox_class_instance(typ, val, c):
+    def access_member(member_offset):
+        # Access member by byte offset
+        offset = c.context.get_constant(types.uintp, member_offset)
+        llvoidptr = ir.IntType(8).as_pointer()
+        ptr = cgutils.pointer_add(c.builder, val, offset)
+        casted = c.builder.bitcast(ptr, llvoidptr.as_pointer())
+        return c.builder.load(casted)
+
+    struct_cls = cgutils.create_struct_proxy(typ)
+    inst = struct_cls(c.context, c.builder)
+
+    # load from Python object
+    ptr_meminfo = access_member(_box.box_meminfoptr_offset)
+    ptr_dataptr = access_member(_box.box_dataptr_offset)
+
+    # store to native structure
+    inst.meminfo = c.builder.bitcast(ptr_meminfo, inst.meminfo.type)
+    inst.data = c.builder.bitcast(ptr_dataptr, inst.data.type)
+
+    ret = inst._getvalue()
+
+    c.context.nrt.incref(c.builder, typ, ret)
+
+    return NativeValue(ret, is_error=c.pyapi.c_api_error())
+
+
+# Add a typeof_impl implementation for boxed jitclasses to short-circut the
+# various tests in typeof. This is needed for jitclasses which implement a
+# custom hash method. Without this, typeof_impl will return None, and one of the
+# later attempts to determine the type of the jitclass (before checking for
+# _numba_type_) will look up the object in a dictionary, triggering the hash
+# method. This will cause the dispatcher to determine the call signature of the
+# jit decorated obj.__hash__ method, which will call typeof(obj), and thus
+# infinite loop.
+# This implementation is here instead of in typeof.py to avoid circular imports.
+@typeof_impl.register(_box.Box)
+def _typeof_jitclass_box(val, c):
+    return getattr(type(val), "_numba_type_")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/decorators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/decorators.py
new file mode 100644
index 000000000..c9abde166
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/decorators.py
@@ -0,0 +1,82 @@
+from numba.core import types, config
+
+
+def jitclass(cls_or_spec=None, spec=None):
+    """
+    A function for creating a jitclass.
+    Can be used as a decorator or function.
+
+    Different use cases will cause different arguments to be set.
+
+    If specified, ``spec`` gives the types of class fields.
+    It must be a dictionary or sequence.
+    With a dictionary, use collections.OrderedDict for stable ordering.
+    With a sequence, it must contain 2-tuples of (fieldname, fieldtype).
+
+    Any class annotations for field names not listed in spec will be added.
+    For class annotation `x: T` we will append ``("x", as_numba_type(T))`` to
+    the spec if ``x`` is not already a key in spec.
+
+
+    Examples
+    --------
+
+    1) ``cls_or_spec = None``, ``spec = None``
+
+    >>> @jitclass()
+    ... class Foo:
+    ...     ...
+
+    2) ``cls_or_spec = None``, ``spec = spec``
+
+    >>> @jitclass(spec=spec)
+    ... class Foo:
+    ...     ...
+
+    3) ``cls_or_spec = Foo``, ``spec = None``
+
+    >>> @jitclass
+    ... class Foo:
+    ...     ...
+
+    4) ``cls_or_spec = spec``, ``spec = None``
+    In this case we update ``cls_or_spec, spec = None, cls_or_spec``.
+
+    >>> @jitclass(spec)
+    ... class Foo:
+    ...     ...
+
+    5) ``cls_or_spec = Foo``, ``spec = spec``
+
+    >>> JitFoo = jitclass(Foo, spec)
+
+    Returns
+    -------
+    If used as a decorator, returns a callable that takes a class object and
+    returns a compiled version.
+    If used as a function, returns the compiled class (an instance of
+    ``JitClassType``).
+    """
+
+    if (cls_or_spec is not None and
+        spec is None and
+            not isinstance(cls_or_spec, type)):
+        # Used like
+        # @jitclass([("x", intp)])
+        # class Foo:
+        #     ...
+        spec = cls_or_spec
+        cls_or_spec = None
+
+    def wrap(cls):
+        if config.DISABLE_JIT:
+            return cls
+        else:
+            from numba.experimental.jitclass.base import (register_class_type,
+                                                          ClassBuilder)
+            return register_class_type(cls, spec, types.ClassType, ClassBuilder)
+
+    if cls_or_spec is None:
+        return wrap
+    else:
+        return wrap(cls_or_spec)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/overloads.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/overloads.py
new file mode 100644
index 000000000..cf0e0ff3e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/jitclass/overloads.py
@@ -0,0 +1,227 @@
+"""
+Overloads for ClassInstanceType for built-in functions that call dunder methods
+on an object.
+"""
+from functools import wraps
+import inspect
+import operator
+
+from numba.core.extending import overload
+from numba.core.types import ClassInstanceType
+from numba.core.utils import PYVERSION
+
+
+def _get_args(n_args):
+    assert n_args in (1, 2)
+    return list("xy")[:n_args]
+
+
+def class_instance_overload(target):
+    """
+    Decorator to add an overload for target that applies when the first argument
+    is a ClassInstanceType.
+    """
+    def decorator(func):
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+            if not isinstance(args[0], ClassInstanceType):
+                return
+            return func(*args, **kwargs)
+
+        params = list(inspect.signature(wrapped).parameters)
+        assert params == _get_args(len(params))
+        return overload(target)(wrapped)
+
+    return decorator
+
+
+def extract_template(template, name):
+    """
+    Extract a code-generated function from a string template.
+    """
+    namespace = {}
+    exec(template, namespace)
+    return namespace[name]
+
+
+def register_simple_overload(func, *attrs, n_args=1,):
+    """
+    Register an overload for func that checks for methods __attr__ for each
+    attr in attrs.
+    """
+    # Use a template to set the signature correctly.
+    arg_names = _get_args(n_args)
+    template = f"""
+def func({','.join(arg_names)}):
+    pass
+"""
+
+    @wraps(extract_template(template, "func"))
+    def overload_func(*args, **kwargs):
+        options = [
+            try_call_method(args[0], f"__{attr}__", n_args)
+            for attr in attrs
+        ]
+        return take_first(*options)
+
+    return class_instance_overload(func)(overload_func)
+
+
+def try_call_method(cls_type, method, n_args=1):
+    """
+    If method is defined for cls_type, return a callable that calls this method.
+    If not, return None.
+    """
+    if method in cls_type.jit_methods:
+        arg_names = _get_args(n_args)
+        template = f"""
+def func({','.join(arg_names)}):
+    return {arg_names[0]}.{method}({','.join(arg_names[1:])})
+"""
+        return extract_template(template, "func")
+
+
+def take_first(*options):
+    """
+    Take the first non-None option.
+    """
+    assert all(o is None or inspect.isfunction(o) for o in options), options
+    for o in options:
+        if o is not None:
+            return o
+
+
+@class_instance_overload(bool)
+def class_bool(x):
+    using_bool_impl = try_call_method(x, "__bool__")
+
+    if '__len__' in x.jit_methods:
+        def using_len_impl(x):
+            return bool(len(x))
+    else:
+        using_len_impl = None
+
+    always_true_impl = lambda x: True
+
+    return take_first(using_bool_impl, using_len_impl, always_true_impl)
+
+
+@class_instance_overload(complex)
+def class_complex(x):
+    return take_first(
+        try_call_method(x, "__complex__"),
+        lambda x: complex(float(x))
+    )
+
+
+@class_instance_overload(operator.contains)
+def class_contains(x, y):
+    # https://docs.python.org/3/reference/expressions.html#membership-test-operations
+    return try_call_method(x, "__contains__", 2)
+    # TODO: use __iter__ if defined.
+
+
+@class_instance_overload(float)
+def class_float(x):
+    options = [try_call_method(x, "__float__")]
+
+    if (
+        PYVERSION >= (3, 8)
+        and "__index__" in x.jit_methods
+    ):
+        options.append(lambda x: float(x.__index__()))
+
+    return take_first(*options)
+
+
+@class_instance_overload(int)
+def class_int(x):
+    options = [try_call_method(x, "__int__")]
+
+    if PYVERSION >= (3, 8):
+        options.append(try_call_method(x, "__index__"))
+
+    return take_first(*options)
+
+
+@class_instance_overload(str)
+def class_str(x):
+    return take_first(
+        try_call_method(x, "__str__"),
+        lambda x: repr(x),
+    )
+
+
+@class_instance_overload(operator.ne)
+def class_ne(x, y):
+    # This doesn't use register_reflected_overload like the other operators
+    # because it falls back to inverting __eq__ rather than reflecting its
+    # arguments (as per the definition of the Python data model).
+    return take_first(
+        try_call_method(x, "__ne__", 2),
+        lambda x, y: not (x == y),
+    )
+
+
+def register_reflected_overload(func, meth_forward, meth_reflected):
+    def class_lt(x, y):
+        normal_impl = try_call_method(x, f"__{meth_forward}__", 2)
+
+        if f"__{meth_reflected}__" in y.jit_methods:
+            def reflected_impl(x, y):
+                return y > x
+        else:
+            reflected_impl = None
+
+        return take_first(normal_impl, reflected_impl)
+
+    class_instance_overload(func)(class_lt)
+
+
+register_simple_overload(abs, "abs")
+register_simple_overload(len, "len")
+register_simple_overload(hash, "hash")
+
+# Comparison operators.
+register_reflected_overload(operator.ge, "ge", "le")
+register_reflected_overload(operator.gt, "gt", "lt")
+register_reflected_overload(operator.le, "le", "ge")
+register_reflected_overload(operator.lt, "lt", "gt")
+
+# Note that eq is missing support for fallback to `x is y`, but `is` and
+# `operator.is` are presently unsupported in general.
+register_reflected_overload(operator.eq, "eq", "eq")
+
+# Arithmetic operators.
+register_simple_overload(operator.add, "add", n_args=2)
+register_simple_overload(operator.floordiv, "floordiv", n_args=2)
+register_simple_overload(operator.lshift, "lshift", n_args=2)
+register_simple_overload(operator.mul, "mul", n_args=2)
+register_simple_overload(operator.mod, "mod", n_args=2)
+register_simple_overload(operator.neg, "neg")
+register_simple_overload(operator.pos, "pos")
+register_simple_overload(operator.pow, "pow", n_args=2)
+register_simple_overload(operator.rshift, "rshift", n_args=2)
+register_simple_overload(operator.sub, "sub", n_args=2)
+register_simple_overload(operator.truediv, "truediv", n_args=2)
+
+# Inplace arithmetic operators.
+register_simple_overload(operator.iadd, "iadd", "add", n_args=2)
+register_simple_overload(operator.ifloordiv, "ifloordiv", "floordiv", n_args=2)
+register_simple_overload(operator.ilshift, "ilshift", "lshift", n_args=2)
+register_simple_overload(operator.imul, "imul", "mul", n_args=2)
+register_simple_overload(operator.imod, "imod", "mod", n_args=2)
+register_simple_overload(operator.ipow, "ipow", "pow", n_args=2)
+register_simple_overload(operator.irshift, "irshift", "rshift", n_args=2)
+register_simple_overload(operator.isub, "isub", "sub", n_args=2)
+register_simple_overload(operator.itruediv, "itruediv", "truediv", n_args=2)
+
+# Logical operators.
+register_simple_overload(operator.and_, "and", n_args=2)
+register_simple_overload(operator.or_, "or", n_args=2)
+register_simple_overload(operator.xor, "xor", n_args=2)
+
+# Inplace logical operators.
+register_simple_overload(operator.iand, "iand", "and", n_args=2)
+register_simple_overload(operator.ior, "ior", "or", n_args=2)
+register_simple_overload(operator.ixor, "ixor", "xor", n_args=2)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/structref.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/structref.py
new file mode 100644
index 000000000..4d63ff4f0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/experimental/structref.py
@@ -0,0 +1,384 @@
+"""Utilities for defining a mutable struct.
+
+A mutable struct is passed by reference;
+hence, structref (a reference to a struct).
+
+"""
+from numba import njit
+from numba.core import types, imputils, cgutils
+from numba.core.datamodel import default_manager, models
+from numba.core.extending import (
+    infer_getattr,
+    lower_getattr_generic,
+    lower_setattr_generic,
+    box,
+    unbox,
+    NativeValue,
+    intrinsic,
+    overload,
+)
+from numba.core.typing.templates import AttributeTemplate
+
+
+class _Utils:
+    """Internal builder-code utils for structref definitions.
+    """
+    def __init__(self, context, builder, struct_type):
+        """
+        Parameters
+        ----------
+        context :
+            a numba target context
+        builder :
+            a llvmlite IRBuilder
+        struct_type : numba.core.types.StructRef
+        """
+        self.context = context
+        self.builder = builder
+        self.struct_type = struct_type
+
+    def new_struct_ref(self, mi):
+        """Encapsulate the MemInfo from a `StructRefPayload` in a `StructRef`
+        """
+        context = self.context
+        builder = self.builder
+        struct_type = self.struct_type
+
+        st = cgutils.create_struct_proxy(struct_type)(context, builder)
+        st.meminfo = mi
+        return st
+
+    def get_struct_ref(self, val):
+        """Return a helper for accessing a StructRefType
+        """
+        context = self.context
+        builder = self.builder
+        struct_type = self.struct_type
+
+        return cgutils.create_struct_proxy(struct_type)(
+            context, builder, value=val
+        )
+
+    def get_data_pointer(self, val):
+        """Get the data pointer to the payload from a `StructRefType`.
+        """
+        context = self.context
+        builder = self.builder
+        struct_type = self.struct_type
+
+        structval = self.get_struct_ref(val)
+        meminfo = structval.meminfo
+        data_ptr = context.nrt.meminfo_data(builder, meminfo)
+
+        valtype = struct_type.get_data_type()
+        model = context.data_model_manager[valtype]
+        alloc_type = model.get_value_type()
+        data_ptr = builder.bitcast(data_ptr, alloc_type.as_pointer())
+        return data_ptr
+
+    def get_data_struct(self, val):
+        """Get a getter/setter helper for accessing a `StructRefPayload`
+        """
+        context = self.context
+        builder = self.builder
+        struct_type = self.struct_type
+
+        data_ptr = self.get_data_pointer(val)
+        valtype = struct_type.get_data_type()
+        dataval = cgutils.create_struct_proxy(valtype)(
+            context, builder, ref=data_ptr
+        )
+        return dataval
+
+
+def define_attributes(struct_typeclass):
+    """Define attributes on `struct_typeclass`.
+
+    Defines both setters and getters in jit-code.
+
+    This is called directly in `register()`.
+    """
+    @infer_getattr
+    class StructAttribute(AttributeTemplate):
+        key = struct_typeclass
+
+        def generic_resolve(self, typ, attr):
+            if attr in typ.field_dict:
+                attrty = typ.field_dict[attr]
+                return attrty
+
+    @lower_getattr_generic(struct_typeclass)
+    def struct_getattr_impl(context, builder, typ, val, attr):
+        utils = _Utils(context, builder, typ)
+        dataval = utils.get_data_struct(val)
+        ret = getattr(dataval, attr)
+        fieldtype = typ.field_dict[attr]
+        return imputils.impl_ret_borrowed(context, builder, fieldtype, ret)
+
+    @lower_setattr_generic(struct_typeclass)
+    def struct_setattr_impl(context, builder, sig, args, attr):
+        [inst_type, val_type] = sig.args
+        [instance, val] = args
+        utils = _Utils(context, builder, inst_type)
+        dataval = utils.get_data_struct(instance)
+        # cast val to the correct type
+        field_type = inst_type.field_dict[attr]
+        casted = context.cast(builder, val, val_type, field_type)
+        # read old
+        old_value = getattr(dataval, attr)
+        # incref new value
+        context.nrt.incref(builder, val_type, casted)
+        # decref old value (must be last in case new value is old value)
+        context.nrt.decref(builder, val_type, old_value)
+        # write new
+        setattr(dataval, attr, casted)
+
+
+def define_boxing(struct_type, obj_class):
+    """Define the boxing & unboxing logic for `struct_type` to `obj_class`.
+
+    Defines both boxing and unboxing.
+
+    - boxing turns an instance of `struct_type` into a PyObject of `obj_class`
+    - unboxing turns an instance of `obj_class` into an instance of
+      `struct_type` in jit-code.
+
+
+    Use this directly instead of `define_proxy()` when the user does not
+    want any constructor to be defined.
+    """
+    if struct_type is types.StructRef:
+        raise ValueError(f"cannot register {types.StructRef}")
+
+    obj_ctor = obj_class._numba_box_
+
+    @box(struct_type)
+    def box_struct_ref(typ, val, c):
+        """
+        Convert a raw pointer to a Python int.
+        """
+        utils = _Utils(c.context, c.builder, typ)
+        struct_ref = utils.get_struct_ref(val)
+        meminfo = struct_ref.meminfo
+
+        mip_type = types.MemInfoPointer(types.voidptr)
+        boxed_meminfo = c.box(mip_type, meminfo)
+
+        ctor_pyfunc = c.pyapi.unserialize(c.pyapi.serialize_object(obj_ctor))
+        ty_pyobj = c.pyapi.unserialize(c.pyapi.serialize_object(typ))
+
+        res = c.pyapi.call_function_objargs(
+            ctor_pyfunc, [ty_pyobj, boxed_meminfo],
+        )
+        c.pyapi.decref(ctor_pyfunc)
+        c.pyapi.decref(ty_pyobj)
+        c.pyapi.decref(boxed_meminfo)
+        return res
+
+    @unbox(struct_type)
+    def unbox_struct_ref(typ, obj, c):
+        mi_obj = c.pyapi.object_getattr_string(obj, "_meminfo")
+
+        mip_type = types.MemInfoPointer(types.voidptr)
+
+        mi = c.unbox(mip_type, mi_obj).value
+
+        utils = _Utils(c.context, c.builder, typ)
+        struct_ref = utils.new_struct_ref(mi)
+        out = struct_ref._getvalue()
+
+        c.pyapi.decref(mi_obj)
+        return NativeValue(out)
+
+
+def define_constructor(py_class, struct_typeclass, fields):
+    """Define the jit-code constructor for `struct_typeclass` using the
+    Python type `py_class` and the required `fields`.
+
+    Use this instead of `define_proxy()` if the user does not want boxing
+    logic defined.
+    """
+    # Build source code for the constructor
+    params = ', '.join(fields)
+    indent = ' ' * 8
+    init_fields_buf = []
+    for k in fields:
+        init_fields_buf.append(f"st.{k} = {k}")
+    init_fields = f'\n{indent}'.join(init_fields_buf)
+
+    source = f"""
+def ctor({params}):
+    struct_type = struct_typeclass(list(zip({list(fields)}, [{params}])))
+    def impl({params}):
+        st = new(struct_type)
+        {init_fields}
+        return st
+    return impl
+"""
+
+    glbs = dict(struct_typeclass=struct_typeclass, new=new)
+    exec(source, glbs)
+    ctor = glbs['ctor']
+    # Make it an overload
+    overload(py_class)(ctor)
+
+
+def define_proxy(py_class, struct_typeclass, fields):
+    """Defines a PyObject proxy for a structref.
+
+    This makes `py_class` a valid constructor for creating a instance of
+    `struct_typeclass` that contains the members as defined by `fields`.
+
+    Parameters
+    ----------
+    py_class : type
+        The Python class for constructing an instance of `struct_typeclass`.
+    struct_typeclass : numba.core.types.Type
+        The structref type class to bind to.
+    fields : Sequence[str]
+        A sequence of field names.
+
+    Returns
+    -------
+    None
+    """
+    define_constructor(py_class, struct_typeclass, fields)
+    define_boxing(struct_typeclass, py_class)
+
+
+def register(struct_type):
+    """Register a `numba.core.types.StructRef` for use in jit-code.
+
+    This defines the data-model for lowering an instance of `struct_type`.
+    This defines attributes accessor and mutator for an instance of
+    `struct_type`.
+
+    Parameters
+    ----------
+    struct_type : type
+        A subclass of `numba.core.types.StructRef`.
+
+    Returns
+    -------
+    struct_type : type
+        Returns the input argument so this can act like a decorator.
+
+    Examples
+    --------
+
+    .. code-block::
+
+        class MyStruct(numba.core.types.StructRef):
+            ...  # the simplest subclass can be empty
+
+        numba.experimental.structref.register(MyStruct)
+
+    """
+    if struct_type is types.StructRef:
+        raise ValueError(f"cannot register {types.StructRef}")
+    default_manager.register(struct_type, models.StructRefModel)
+    define_attributes(struct_type)
+    return struct_type
+
+
+@intrinsic
+def new(typingctx, struct_type):
+    """new(struct_type)
+
+    A jit-code only intrinsic. Used to allocate an **empty** mutable struct.
+    The fields are zero-initialized and must be set manually after calling
+    the function.
+
+    Example:
+
+        instance = new(MyStruct)
+        instance.field = field_value
+    """
+    from numba.experimental.jitclass.base import imp_dtor
+
+    inst_type = struct_type.instance_type
+
+    def codegen(context, builder, signature, args):
+        # FIXME: mostly the same as jitclass ctor_impl()
+        model = context.data_model_manager[inst_type.get_data_type()]
+        alloc_type = model.get_value_type()
+        alloc_size = context.get_abi_sizeof(alloc_type)
+
+        meminfo = context.nrt.meminfo_alloc_dtor(
+            builder,
+            context.get_constant(types.uintp, alloc_size),
+            imp_dtor(context, builder.module, inst_type),
+        )
+        data_pointer = context.nrt.meminfo_data(builder, meminfo)
+        data_pointer = builder.bitcast(data_pointer, alloc_type.as_pointer())
+
+        # Nullify all data
+        builder.store(cgutils.get_null_value(alloc_type), data_pointer)
+
+        inst_struct = context.make_helper(builder, inst_type)
+        inst_struct.meminfo = meminfo
+
+        return inst_struct._getvalue()
+
+    sig = inst_type(struct_type)
+    return sig, codegen
+
+
+class StructRefProxy:
+    """A PyObject proxy to the Numba allocated structref data structure.
+
+    Notes
+    -----
+
+    * Subclasses should not define ``__init__``.
+    * Subclasses can override ``__new__``.
+    """
+    __slots__ = ('_type', '_meminfo')
+
+    @classmethod
+    def _numba_box_(cls, ty, mi):
+        """Called by boxing logic, the conversion of Numba internal
+        representation into a PyObject.
+
+        Parameters
+        ----------
+        ty :
+            a Numba type instance.
+        mi :
+            a wrapped MemInfoPointer.
+
+        Returns
+        -------
+        instance :
+             a StructRefProxy instance.
+        """
+        instance = super().__new__(cls)
+        instance._type = ty
+        instance._meminfo = mi
+        return instance
+
+    def __new__(cls, *args):
+        """Construct a new instance of the structref.
+
+        This takes positional-arguments only due to limitation of the compiler.
+        The arguments are mapped to ``cls(*args)`` in jit-code.
+        """
+        try:
+            # use cached ctor if available
+            ctor = cls.__numba_ctor
+        except AttributeError:
+            # lazily define the ctor
+            @njit
+            def ctor(*args):
+                return cls(*args)
+            # cache it to attribute to avoid recompilation
+            cls.__numba_ctor = ctor
+        return ctor(*args)
+
+    @property
+    def _numba_type_(self):
+        """Returns the Numba type instance for this structref instance.
+
+        Subclasses should NOT override.
+        """
+        return self._type
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/extending.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/extending.py
new file mode 100644
index 000000000..ae727f759
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/extending.py
@@ -0,0 +1,3 @@
+# Re-export symbols
+from numba.core.extending import *  # noqa: F403, F401
+from numba.core.extending import _Intrinsic  # noqa: F401
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/mathnames.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/mathnames.h
new file mode 100644
index 000000000..73d45b18d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/mathnames.h
@@ -0,0 +1,77 @@
+MATH_UNARY(sin, double, double)
+MATH_UNARY(sinf, float, float)
+
+MATH_UNARY(cos, double, double)
+MATH_UNARY(cosf, float, float)
+
+MATH_UNARY(tan, double, double)
+MATH_UNARY(tanf, float, float)
+
+MATH_UNARY(sinh, double, double)
+MATH_UNARY(sinhf, float, float)
+
+MATH_UNARY(cosh, double, double)
+MATH_UNARY(coshf, float, float)
+
+MATH_UNARY(tanh, double, double)
+MATH_UNARY(tanhf, float, float)
+
+MATH_UNARY(asin, double, double)
+MATH_UNARY(asinf, float, float)
+
+MATH_UNARY(acos, double, double)
+MATH_UNARY(acosf, float, float)
+
+MATH_UNARY(atan, double, double)
+MATH_UNARY(atanf, float, float)
+
+MATH_UNARY(asinh, double, double)
+MATH_UNARY(asinhf, float, float)
+
+MATH_UNARY(acosh, double, double)
+MATH_UNARY(acoshf, float, float)
+
+MATH_UNARY(atanh, double, double)
+MATH_UNARY(atanhf, float, float)
+
+MATH_UNARY(exp, double, double)
+MATH_UNARY(expf, float, float)
+
+MATH_UNARY(expm1, double, double)
+MATH_UNARY(expm1f, float, float)
+
+MATH_UNARY(sqrt, double, double)
+MATH_UNARY(sqrtf, float, float)
+
+MATH_UNARY(fabs, double, double)
+MATH_UNARY(fabsf, float, float)
+
+MATH_UNARY(floor, double, double)
+MATH_UNARY(floorf, float, float)
+
+MATH_UNARY(ceil, double, double)
+MATH_UNARY(ceilf, float, float)
+
+MATH_UNARY(log, double, double)
+MATH_UNARY(logf, float, float)
+
+MATH_UNARY(log10, double, double)
+MATH_UNARY(log10f, float, float)
+
+MATH_UNARY(log1p, double, double)
+MATH_UNARY(log1pf, float, float)
+
+MATH_UNARY(round, double, double)
+MATH_UNARY(roundf, float, float)
+
+MATH_UNARY(trunc, double, double)
+MATH_UNARY(truncf, float, float)
+
+MATH_BINARY(pow, double, double, double)
+MATH_BINARY(powf, float, float, float)
+
+MATH_BINARY(fmod, double, double, double)
+MATH_BINARY(fmodf, float, float, float)
+
+MATH_BINARY(atan2, double, double, double)
+MATH_BINARY(atan2f, float, float, float)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/appdirs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/appdirs.py
new file mode 100644
index 000000000..e4ce1ab5b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/appdirs.py
@@ -0,0 +1,551 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright (c) 2005-2010 ActiveState Software Inc.
+# Copyright (c) 2013 Eddy Petrișor
+
+"""Utilities for determining application-specific dirs.
+
+See <http://github.com/ActiveState/appdirs> for details and usage.
+"""
+# Dev Notes:
+# - MSDN on where to store app data files:
+#   http://support.microsoft.com/default.aspx?scid=kb;en-us;310294#XSLTH3194121123120121120120
+# - Mac OS X: http://developer.apple.com/documentation/MacOSX/Conceptual/BPFileSystem/index.html
+# - XDG spec for Un*x: http://standards.freedesktop.org/basedir-spec/basedir-spec-latest.html
+
+__version_info__ = (1, 4, 1)
+__version__ = '.'.join(map(str, __version_info__))
+
+
+import sys
+import os
+
+unicode = str
+
+if sys.platform.startswith('java'):
+    import platform
+    os_name = platform.java_ver()[3][0]
+    if os_name.startswith('Windows'): # "Windows XP", "Windows 7", etc.
+        system = 'win32'
+    elif os_name.startswith('Mac'): # "Mac OS X", etc.
+        system = 'darwin'
+    else: # "Linux", "SunOS", "FreeBSD", etc.
+        # Setting this to "linux2" is not ideal, but only Windows or Mac
+        # are actually checked for and the rest of the module expects
+        # *sys.platform* style strings.
+        system = 'linux2'
+else:
+    system = sys.platform
+
+
+
+def user_data_dir(appname=None, appauthor=None, version=None, roaming=False):
+    r"""Return full path to the user-specific data dir for this application.
+
+        "appname" is the name of application.
+            If None, just the system directory is returned.
+        "appauthor" (only used on Windows) is the name of the
+            appauthor or distributing body for this application. Typically
+            it is the owning company name. This falls back to appname. You may
+            pass False to disable it.
+        "version" is an optional version path element to append to the
+            path. You might want to use this if you want multiple versions
+            of your app to be able to run independently. If used, this
+            would typically be "<major>.<minor>".
+            Only applied when appname is present.
+        "roaming" (boolean, default False) can be set True to use the Windows
+            roaming appdata directory. That means that for users on a Windows
+            network setup for roaming profiles, this user data will be
+            sync'd on login. See
+            <http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
+            for a discussion of issues.
+
+    Typical user data directories are:
+        Mac OS X:               ~/Library/Application Support/<AppName>
+        Unix:                   ~/.local/share/<AppName>    # or in $XDG_DATA_HOME, if defined
+        Win XP (not roaming):   C:\Documents and Settings\<username>\Application Data\<AppAuthor>\<AppName>
+        Win XP (roaming):       C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>
+        Win 7  (not roaming):   C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>
+        Win 7  (roaming):       C:\Users\<username>\AppData\Roaming\<AppAuthor>\<AppName>
+
+    For Unix, we follow the XDG spec and support $XDG_DATA_HOME.
+    That means, by default "~/.local/share/<AppName>".
+    """
+    if system == "win32":
+        if appauthor is None:
+            appauthor = appname
+        const = roaming and "CSIDL_APPDATA" or "CSIDL_LOCAL_APPDATA"
+        path = os.path.normpath(_get_win_folder(const))
+        if appname:
+            if appauthor is not False:
+                path = os.path.join(path, appauthor, appname)
+            else:
+                path = os.path.join(path, appname)
+    elif system == 'darwin':
+        path = os.path.expanduser('~/Library/Application Support/')
+        if appname:
+            path = os.path.join(path, appname)
+    else:
+        path = os.getenv('XDG_DATA_HOME', os.path.expanduser("~/.local/share"))
+        if appname:
+            path = os.path.join(path, appname)
+    if appname and version:
+        path = os.path.join(path, version)
+    return path
+
+
+def site_data_dir(appname=None, appauthor=None, version=None, multipath=False):
+    r"""Return full path to the user-shared data dir for this application.
+
+        "appname" is the name of application.
+            If None, just the system directory is returned.
+        "appauthor" (only used on Windows) is the name of the
+            appauthor or distributing body for this application. Typically
+            it is the owning company name. This falls back to appname. You may
+            pass False to disable it.
+        "version" is an optional version path element to append to the
+            path. You might want to use this if you want multiple versions
+            of your app to be able to run independently. If used, this
+            would typically be "<major>.<minor>".
+            Only applied when appname is present.
+        "multipath" is an optional parameter only applicable to *nix
+            which indicates that the entire list of data dirs should be
+            returned. By default, the first item from XDG_DATA_DIRS is
+            returned, or '/usr/local/share/<AppName>',
+            if XDG_DATA_DIRS is not set
+
+    Typical user data directories are:
+        Mac OS X:   /Library/Application Support/<AppName>
+        Unix:       /usr/local/share/<AppName> or /usr/share/<AppName>
+        Win XP:     C:\Documents and Settings\All Users\Application Data\<AppAuthor>\<AppName>
+        Vista:      (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
+        Win 7:      C:\ProgramData\<AppAuthor>\<AppName>   # Hidden, but writeable on Win 7.
+
+    For Unix, this is using the $XDG_DATA_DIRS[0] default.
+
+    WARNING: Do not use this on Windows. See the Vista-Fail note above for why.
+    """
+    if system == "win32":
+        if appauthor is None:
+            appauthor = appname
+        path = os.path.normpath(_get_win_folder("CSIDL_COMMON_APPDATA"))
+        if appname:
+            if appauthor is not False:
+                path = os.path.join(path, appauthor, appname)
+            else:
+                path = os.path.join(path, appname)
+    elif system == 'darwin':
+        path = os.path.expanduser('/Library/Application Support')
+        if appname:
+            path = os.path.join(path, appname)
+    else:
+        # XDG default for $XDG_DATA_DIRS
+        # only first, if multipath is False
+        path = os.getenv('XDG_DATA_DIRS',
+                         os.pathsep.join(['/usr/local/share', '/usr/share']))
+        pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)]
+        if appname:
+            if version:
+                appname = os.path.join(appname, version)
+            pathlist = [os.sep.join([x, appname]) for x in pathlist]
+
+        if multipath:
+            path = os.pathsep.join(pathlist)
+        else:
+            path = pathlist[0]
+        return path
+
+    if appname and version:
+        path = os.path.join(path, version)
+    return path
+
+
+def user_config_dir(appname=None, appauthor=None, version=None, roaming=False):
+    r"""Return full path to the user-specific config dir for this application.
+
+        "appname" is the name of application.
+            If None, just the system directory is returned.
+        "appauthor" (only used on Windows) is the name of the
+            appauthor or distributing body for this application. Typically
+            it is the owning company name. This falls back to appname. You may
+            pass False to disable it.
+        "version" is an optional version path element to append to the
+            path. You might want to use this if you want multiple versions
+            of your app to be able to run independently. If used, this
+            would typically be "<major>.<minor>".
+            Only applied when appname is present.
+        "roaming" (boolean, default False) can be set True to use the Windows
+            roaming appdata directory. That means that for users on a Windows
+            network setup for roaming profiles, this user data will be
+            sync'd on login. See
+            <http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
+            for a discussion of issues.
+
+    Typical user data directories are:
+        Mac OS X:               same as user_data_dir
+        Unix:                   ~/.config/<AppName>     # or in $XDG_CONFIG_HOME, if defined
+        Win *:                  same as user_data_dir
+
+    For Unix, we follow the XDG spec and support $XDG_CONFIG_HOME.
+    That means, by default "~/.config/<AppName>".
+    """
+    if system in ["win32", "darwin"]:
+        path = user_data_dir(appname, appauthor, None, roaming)
+    else:
+        path = os.getenv('XDG_CONFIG_HOME', os.path.expanduser("~/.config"))
+        if appname:
+            path = os.path.join(path, appname)
+    if appname and version:
+        path = os.path.join(path, version)
+    return path
+
+
+def site_config_dir(appname=None, appauthor=None, version=None, multipath=False):
+    r"""Return full path to the user-shared data dir for this application.
+
+        "appname" is the name of application.
+            If None, just the system directory is returned.
+        "appauthor" (only used on Windows) is the name of the
+            appauthor or distributing body for this application. Typically
+            it is the owning company name. This falls back to appname. You may
+            pass False to disable it.
+        "version" is an optional version path element to append to the
+            path. You might want to use this if you want multiple versions
+            of your app to be able to run independently. If used, this
+            would typically be "<major>.<minor>".
+            Only applied when appname is present.
+        "multipath" is an optional parameter only applicable to *nix
+            which indicates that the entire list of config dirs should be
+            returned. By default, the first item from XDG_CONFIG_DIRS is
+            returned, or '/etc/xdg/<AppName>', if XDG_CONFIG_DIRS is not set
+
+    Typical user data directories are:
+        Mac OS X:   same as site_data_dir
+        Unix:       /etc/xdg/<AppName> or $XDG_CONFIG_DIRS[i]/<AppName> for each value in
+                    $XDG_CONFIG_DIRS
+        Win *:      same as site_data_dir
+        Vista:      (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
+
+    For Unix, this is using the $XDG_CONFIG_DIRS[0] default, if multipath=False
+
+    WARNING: Do not use this on Windows. See the Vista-Fail note above for why.
+    """
+    if system in ["win32", "darwin"]:
+        path = site_data_dir(appname, appauthor)
+        if appname and version:
+            path = os.path.join(path, version)
+    else:
+        # XDG default for $XDG_CONFIG_DIRS
+        # only first, if multipath is False
+        path = os.getenv('XDG_CONFIG_DIRS', '/etc/xdg')
+        pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)]
+        if appname:
+            if version:
+                appname = os.path.join(appname, version)
+            pathlist = [os.sep.join([x, appname]) for x in pathlist]
+
+        if multipath:
+            path = os.pathsep.join(pathlist)
+        else:
+            path = pathlist[0]
+    return path
+
+
+def user_cache_dir(appname=None, appauthor=None, version=None, opinion=True):
+    r"""Return full path to the user-specific cache dir for this application.
+
+        "appname" is the name of application.
+            If None, just the system directory is returned.
+        "appauthor" (only used on Windows) is the name of the
+            appauthor or distributing body for this application. Typically
+            it is the owning company name. This falls back to appname. You may
+            pass False to disable it.
+        "version" is an optional version path element to append to the
+            path. You might want to use this if you want multiple versions
+            of your app to be able to run independently. If used, this
+            would typically be "<major>.<minor>".
+            Only applied when appname is present.
+        "opinion" (boolean) can be False to disable the appending of
+            "Cache" to the base app data dir for Windows. See
+            discussion below.
+
+    Typical user cache directories are:
+        Mac OS X:   ~/Library/Caches/<AppName>
+        Unix:       ~/.cache/<AppName> (XDG default)
+        Win XP:     C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>\Cache
+        Vista:      C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>\Cache
+
+    On Windows the only suggestion in the MSDN docs is that local settings go in
+    the `CSIDL_LOCAL_APPDATA` directory. This is identical to the non-roaming
+    app data dir (the default returned by `user_data_dir` above). Apps typically
+    put cache data somewhere *under* the given dir here. Some examples:
+        ...\Mozilla\Firefox\Profiles\<ProfileName>\Cache
+        ...\Acme\SuperApp\Cache\1.0
+    OPINION: This function appends "Cache" to the `CSIDL_LOCAL_APPDATA` value.
+    This can be disabled with the `opinion=False` option.
+    """
+    if system == "win32":
+        if appauthor is None:
+            appauthor = appname
+        path = os.path.normpath(_get_win_folder("CSIDL_LOCAL_APPDATA"))
+        if appname:
+            if appauthor is not False:
+                path = os.path.join(path, appauthor, appname)
+            else:
+                path = os.path.join(path, appname)
+            if opinion:
+                path = os.path.join(path, "Cache")
+    elif system == 'darwin':
+        path = os.path.expanduser('~/Library/Caches')
+        if appname:
+            path = os.path.join(path, appname)
+    else:
+        path = os.getenv('XDG_CACHE_HOME', os.path.expanduser('~/.cache'))
+        if appname:
+            path = os.path.join(path, appname)
+    if appname and version:
+        path = os.path.join(path, version)
+    return path
+
+
+def user_log_dir(appname=None, appauthor=None, version=None, opinion=True):
+    r"""Return full path to the user-specific log dir for this application.
+
+        "appname" is the name of application.
+            If None, just the system directory is returned.
+        "appauthor" (only used on Windows) is the name of the
+            appauthor or distributing body for this application. Typically
+            it is the owning company name. This falls back to appname. You may
+            pass False to disable it.
+        "version" is an optional version path element to append to the
+            path. You might want to use this if you want multiple versions
+            of your app to be able to run independently. If used, this
+            would typically be "<major>.<minor>".
+            Only applied when appname is present.
+        "opinion" (boolean) can be False to disable the appending of
+            "Logs" to the base app data dir for Windows, and "log" to the
+            base cache dir for Unix. See discussion below.
+
+    Typical user cache directories are:
+        Mac OS X:   ~/Library/Logs/<AppName>
+        Unix:       ~/.cache/<AppName>/log  # or under $XDG_CACHE_HOME if defined
+        Win XP:     C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>\Logs
+        Vista:      C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>\Logs
+
+    On Windows the only suggestion in the MSDN docs is that local settings
+    go in the `CSIDL_LOCAL_APPDATA` directory. (Note: I'm interested in
+    examples of what some windows apps use for a logs dir.)
+
+    OPINION: This function appends "Logs" to the `CSIDL_LOCAL_APPDATA`
+    value for Windows and appends "log" to the user cache dir for Unix.
+    This can be disabled with the `opinion=False` option.
+    """
+    if system == "darwin":
+        path = os.path.join(
+            os.path.expanduser('~/Library/Logs'),
+            appname)
+    elif system == "win32":
+        path = user_data_dir(appname, appauthor, version)
+        version = False
+        if opinion:
+            path = os.path.join(path, "Logs")
+    else:
+        path = user_cache_dir(appname, appauthor, version)
+        version = False
+        if opinion:
+            path = os.path.join(path, "log")
+    if appname and version:
+        path = os.path.join(path, version)
+    return path
+
+
+class AppDirs(object):
+    """Convenience wrapper for getting application dirs."""
+    def __init__(self, appname, appauthor=None, version=None, roaming=False,
+                 multipath=False):
+        self.appname = appname
+        self.appauthor = appauthor
+        self.version = version
+        self.roaming = roaming
+        self.multipath = multipath
+
+    @property
+    def user_data_dir(self):
+        return user_data_dir(self.appname, self.appauthor,
+                             version=self.version, roaming=self.roaming)
+
+    @property
+    def site_data_dir(self):
+        return site_data_dir(self.appname, self.appauthor,
+                             version=self.version, multipath=self.multipath)
+
+    @property
+    def user_config_dir(self):
+        return user_config_dir(self.appname, self.appauthor,
+                               version=self.version, roaming=self.roaming)
+
+    @property
+    def site_config_dir(self):
+        return site_config_dir(self.appname, self.appauthor,
+                             version=self.version, multipath=self.multipath)
+
+    @property
+    def user_cache_dir(self):
+        return user_cache_dir(self.appname, self.appauthor,
+                              version=self.version)
+
+    @property
+    def user_log_dir(self):
+        return user_log_dir(self.appname, self.appauthor,
+                            version=self.version)
+
+
+#---- internal support stuff
+
+def _get_win_folder_from_registry(csidl_name):
+    """This is a fallback technique at best. I'm not sure if using the
+    registry for this guarantees us the correct answer for all CSIDL_*
+    names.
+    """
+    import winreg as _winreg
+
+    shell_folder_name = {
+        "CSIDL_APPDATA": "AppData",
+        "CSIDL_COMMON_APPDATA": "Common AppData",
+        "CSIDL_LOCAL_APPDATA": "Local AppData",
+    }[csidl_name]
+
+    key = _winreg.OpenKey(
+        _winreg.HKEY_CURRENT_USER,
+        r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders"
+    )
+    dir, type = _winreg.QueryValueEx(key, shell_folder_name)
+    return dir
+
+
+def _get_win_folder_with_pywin32(csidl_name):
+    from win32com.shell import shellcon, shell
+    dir = shell.SHGetFolderPath(0, getattr(shellcon, csidl_name), 0, 0)
+    # Try to make this a unicode path because SHGetFolderPath does
+    # not return unicode strings when there is unicode data in the
+    # path.
+    try:
+        dir = unicode(dir)
+
+        # Downgrade to short path name if have highbit chars. See
+        # <http://bugs.activestate.com/show_bug.cgi?id=85099>.
+        has_high_char = False
+        for c in dir:
+            if ord(c) > 255:
+                has_high_char = True
+                break
+        if has_high_char:
+            try:
+                import win32api
+                dir = win32api.GetShortPathName(dir)
+            except ImportError:
+                pass
+    except UnicodeError:
+        pass
+    return dir
+
+
+def _get_win_folder_with_ctypes(csidl_name):
+    import ctypes
+
+    csidl_const = {
+        "CSIDL_APPDATA": 26,
+        "CSIDL_COMMON_APPDATA": 35,
+        "CSIDL_LOCAL_APPDATA": 28,
+    }[csidl_name]
+
+    buf = ctypes.create_unicode_buffer(1024)
+    ctypes.windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf)
+
+    # Downgrade to short path name if have highbit chars. See
+    # <http://bugs.activestate.com/show_bug.cgi?id=85099>.
+    has_high_char = False
+    for c in buf:
+        if ord(c) > 255:
+            has_high_char = True
+            break
+    if has_high_char:
+        buf2 = ctypes.create_unicode_buffer(1024)
+        if ctypes.windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024):
+            buf = buf2
+
+    return buf.value
+
+def _get_win_folder_with_jna(csidl_name):
+    import array
+    from com.sun import jna
+    from com.sun.jna.platform import win32
+
+    buf_size = win32.WinDef.MAX_PATH * 2
+    buf = array.zeros('c', buf_size)
+    shell = win32.Shell32.INSTANCE
+    shell.SHGetFolderPath(None, getattr(win32.ShlObj, csidl_name), None, win32.ShlObj.SHGFP_TYPE_CURRENT, buf)
+    dir = jna.Native.toString(buf.tostring()).rstrip("\0")
+
+    # Downgrade to short path name if have highbit chars. See
+    # <http://bugs.activestate.com/show_bug.cgi?id=85099>.
+    has_high_char = False
+    for c in dir:
+        if ord(c) > 255:
+            has_high_char = True
+            break
+    if has_high_char:
+        buf = array.zeros('c', buf_size)
+        kernel = win32.Kernel32.INSTANCE
+        if kernel.GetShortPathName(dir, buf, buf_size):
+            dir = jna.Native.toString(buf.tostring()).rstrip("\0")
+
+    return dir
+
+if system == "win32":
+    try:
+        import win32com.shell
+        _get_win_folder = _get_win_folder_with_pywin32
+    except ImportError:
+        try:
+            from ctypes import windll
+            _get_win_folder = _get_win_folder_with_ctypes
+        except ImportError:
+            try:
+                import com.sun.jna
+                _get_win_folder = _get_win_folder_with_jna
+            except ImportError:
+                _get_win_folder = _get_win_folder_from_registry
+
+
+#---- self test code
+
+if __name__ == "__main__":
+    appname = "MyApp"
+    appauthor = "MyCompany"
+
+    props = ("user_data_dir", "site_data_dir",
+             "user_config_dir", "site_config_dir",
+             "user_cache_dir", "user_log_dir")
+
+    print("-- app dirs %s --" % __version__)
+
+    print("-- app dirs (with optional 'version')")
+    dirs = AppDirs(appname, appauthor, version="1.0")
+    for prop in props:
+        print("%s: %s" % (prop, getattr(dirs, prop)))
+
+    print("\n-- app dirs (without optional 'version')")
+    dirs = AppDirs(appname, appauthor)
+    for prop in props:
+        print("%s: %s" % (prop, getattr(dirs, prop)))
+
+    print("\n-- app dirs (without optional 'appauthor')")
+    dirs = AppDirs(appname)
+    for prop in props:
+        print("%s: %s" % (prop, getattr(dirs, prop)))
+
+    print("\n-- app dirs (with disabled 'appauthor')")
+    dirs = AppDirs(appname, appauthor=False)
+    for prop in props:
+        print("%s: %s" % (prop, getattr(dirs, prop)))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/cffiimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/cffiimpl.py
new file mode 100644
index 000000000..226646566
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/cffiimpl.py
@@ -0,0 +1,22 @@
+"""
+Implementation of some CFFI functions
+"""
+
+
+from numba.core.imputils import Registry
+from numba.core import types
+from numba.np import arrayobj
+
+registry = Registry('cffiimpl')
+
+@registry.lower('ffi.from_buffer', types.Buffer)
+def from_buffer(context, builder, sig, args):
+    assert len(sig.args) == 1
+    assert len(args) == 1
+    [fromty] = sig.args
+    [val] = args
+    # Type inference should have prevented passing a buffer from an
+    # array to a pointer of the wrong type
+    assert fromty.dtype == sig.return_type.dtype
+    ary = arrayobj.make_array(fromty)(context, builder, val)
+    return ary.data
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/cmdlang.gdb b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/cmdlang.gdb
new file mode 100644
index 000000000..f5c1eed2c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/cmdlang.gdb
@@ -0,0 +1,5 @@
+# this file is used with the numba.gdb* functionality
+break numba_gdb_breakpoint
+commands
+return
+end
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/dummyarray.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/dummyarray.py
new file mode 100644
index 000000000..8debc87b3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/dummyarray.py
@@ -0,0 +1,456 @@
+from collections import namedtuple
+import itertools
+import functools
+import operator
+import ctypes
+
+import numpy as np
+
+from numba import _helperlib
+from numba.core import config
+
+
+Extent = namedtuple("Extent", ["begin", "end"])
+
+
+attempt_nocopy_reshape = ctypes.CFUNCTYPE(
+    ctypes.c_int,
+    ctypes.c_long,  # nd
+    np.ctypeslib.ndpointer(np.ctypeslib.c_intp, ndim=1),  # dims
+    np.ctypeslib.ndpointer(np.ctypeslib.c_intp, ndim=1),  # strides
+    ctypes.c_long,  # newnd
+    np.ctypeslib.ndpointer(np.ctypeslib.c_intp, ndim=1),  # newdims
+    np.ctypeslib.ndpointer(np.ctypeslib.c_intp, ndim=1),  # newstrides
+    ctypes.c_long,  # itemsize
+    ctypes.c_int,  # is_f_order
+)(_helperlib.c_helpers['attempt_nocopy_reshape'])
+
+class Dim(object):
+    """A single dimension of the array
+
+    Attributes
+    ----------
+    start:
+        start offset
+    stop:
+        stop offset
+    size:
+        number of items
+    stride:
+        item stride
+    """
+    __slots__ = 'start', 'stop', 'size', 'stride', 'single'
+
+    def __init__(self, start, stop, size, stride, single):
+        self.start = start
+        self.stop = stop
+        self.size = size
+        self.stride = stride
+        self.single = single
+        assert not single or size == 1
+
+    def __getitem__(self, item):
+        if isinstance(item, slice):
+            start, stop, step = item.indices(self.size)
+            stride = step * self.stride
+            start = self.start + start * abs(self.stride)
+            stop = self.start + stop * abs(self.stride)
+            if stride == 0:
+                size = 1
+            else:
+                size = _compute_size(start, stop, stride)
+            ret = Dim(
+                start=start,
+                stop=stop,
+                size=size,
+                stride=stride,
+                single=False
+            )
+            return ret
+        else:
+            sliced = self[item:item + 1] if item != -1 else self[-1:]
+            if sliced.size != 1:
+                raise IndexError
+            return Dim(
+                start=sliced.start,
+                stop=sliced.stop,
+                size=sliced.size,
+                stride=sliced.stride,
+                single=True,
+            )
+
+    def get_offset(self, idx):
+        return self.start + idx * self.stride
+
+    def __repr__(self):
+        strfmt = "Dim(start=%s, stop=%s, size=%s, stride=%s)"
+        return strfmt % (self.start, self.stop, self.size, self.stride)
+
+    def normalize(self, base):
+        return Dim(start=self.start - base, stop=self.stop - base,
+                   size=self.size, stride=self.stride, single=self.single)
+
+    def copy(self, start=None, stop=None, size=None, stride=None, single=None):
+        if start is None:
+            start = self.start
+        if stop is None:
+            stop = self.stop
+        if size is None:
+            size = self.size
+        if stride is None:
+            stride = self.stride
+        if single is None:
+            single = self.single
+        return Dim(start, stop, size, stride, single)
+
+    def is_contiguous(self, itemsize):
+        return self.stride == itemsize
+
+
+def compute_index(indices, dims):
+    return sum(d.get_offset(i) for i, d in zip(indices, dims))
+
+
+class Element(object):
+    is_array = False
+
+    def __init__(self, extent):
+        self.extent = extent
+
+    def iter_contiguous_extent(self):
+        yield self.extent
+
+
+class Array(object):
+    """A dummy numpy array-like object.  Consider it an array without the
+    actual data, but offset from the base data pointer.
+
+    Attributes
+    ----------
+    dims: tuple of Dim
+        describing each dimension of the array
+
+    ndim: int
+        number of dimension
+
+    shape: tuple of int
+        size of each dimension
+
+    strides: tuple of int
+        stride of each dimension
+
+    itemsize: int
+        itemsize
+
+    extent: (start, end)
+        start and end offset containing the memory region
+    """
+    is_array = True
+
+    @classmethod
+    def from_desc(cls, offset, shape, strides, itemsize):
+        dims = []
+        for ashape, astride in zip(shape, strides):
+            dim = Dim(offset, offset + ashape * astride, ashape, astride,
+                      single=False)
+            dims.append(dim)
+            offset = 0  # offset only applies to first dimension
+        return cls(dims, itemsize)
+
+    def __init__(self, dims, itemsize):
+        self.dims = tuple(dims)
+        self.ndim = len(self.dims)
+        self.shape = tuple(dim.size for dim in self.dims)
+        self.strides = tuple(dim.stride for dim in self.dims)
+        self.itemsize = itemsize
+        self.size = functools.reduce(operator.mul, self.shape, 1)
+        self.extent = self._compute_extent()
+        self.flags = self._compute_layout()
+
+    def _compute_layout(self):
+        # The logic here is based on that in _UpdateContiguousFlags from
+        # numpy/core/src/multiarray/flagsobject.c in NumPy v1.19.1 (commit
+        # 13661ac70).
+        # https://github.com/numpy/numpy/blob/maintenance/1.19.x/numpy/core/src/multiarray/flagsobject.c#L123-L191
+
+        # Records have no dims, and we can treat them as contiguous
+        if not self.dims:
+            return {'C_CONTIGUOUS': True, 'F_CONTIGUOUS': True}
+
+        # If this is a broadcast array then it is not contiguous
+        if any([dim.stride == 0 for dim in self.dims]):
+            return {'C_CONTIGUOUS': False, 'F_CONTIGUOUS': False}
+
+        flags = {'C_CONTIGUOUS': True, 'F_CONTIGUOUS': True}
+
+        # Check C contiguity
+        sd = self.itemsize
+        for dim in reversed(self.dims):
+            if dim.size == 0:
+                # Contiguous by definition
+                return {'C_CONTIGUOUS': True, 'F_CONTIGUOUS': True}
+            if dim.size != 1:
+                if dim.stride != sd:
+                    flags['C_CONTIGUOUS'] = False
+                sd *= dim.size
+
+        # Check F contiguity
+        sd = self.itemsize
+        for dim in self.dims:
+            if dim.size != 1:
+                if dim.stride != sd:
+                    flags['F_CONTIGUOUS'] = False
+                    return flags
+                sd *= dim.size
+
+        return flags
+
+    def _compute_extent(self):
+        firstidx = [0] * self.ndim
+        lastidx = [s - 1 for s in self.shape]
+        start = compute_index(firstidx, self.dims)
+        stop = compute_index(lastidx, self.dims) + self.itemsize
+        stop = max(stop, start)   # ensure positive extent
+        return Extent(start, stop)
+
+    def __repr__(self):
+        return '<Array dims=%s itemsize=%s>' % (self.dims, self.itemsize)
+
+    def __getitem__(self, item):
+        if not isinstance(item, tuple):
+            item = [item]
+        else:
+            item = list(item)
+
+        nitem = len(item)
+        ndim = len(self.dims)
+        if nitem > ndim:
+            raise IndexError("%d extra indices given" % (nitem - ndim,))
+
+        # Add empty slices for missing indices
+        while len(item) < ndim:
+            item.append(slice(None, None))
+
+        dims = [dim.__getitem__(it) for dim, it in zip(self.dims, item)]
+        newshape = [d.size for d in dims if not d.single]
+
+        arr = Array(dims, self.itemsize)
+        if newshape:
+            return arr.reshape(*newshape)[0]
+        else:
+            return Element(arr.extent)
+
+    @property
+    def is_c_contig(self):
+        return self.flags['C_CONTIGUOUS']
+
+    @property
+    def is_f_contig(self):
+        return self.flags['F_CONTIGUOUS']
+
+    def iter_contiguous_extent(self):
+        """ Generates extents
+        """
+        if self.is_c_contig or self.is_f_contig:
+            yield self.extent
+        else:
+            if self.dims[0].stride < self.dims[-1].stride:
+                innerdim = self.dims[0]
+                outerdims = self.dims[1:]
+                outershape = self.shape[1:]
+            else:
+                innerdim = self.dims[-1]
+                outerdims = self.dims[:-1]
+                outershape = self.shape[:-1]
+
+            if innerdim.is_contiguous(self.itemsize):
+                oslen = [range(s) for s in outershape]
+                for indices in itertools.product(*oslen):
+                    base = compute_index(indices, outerdims)
+                    yield base + innerdim.start, base + innerdim.stop
+            else:
+                oslen = [range(s) for s in self.shape]
+                for indices in itertools.product(*oslen):
+                    offset = compute_index(indices, self.dims)
+                    yield offset, offset + self.itemsize
+
+    def reshape(self, *newdims, **kws):
+        oldnd = self.ndim
+        newnd = len(newdims)
+
+        if newdims == self.shape:
+            return self, None
+
+        order = kws.pop('order', 'C')
+        if kws:
+            raise TypeError('unknown keyword arguments %s' % kws.keys())
+        if order not in 'CFA':
+            raise ValueError('order not C|F|A')
+
+        # check for exactly one instance of -1 in newdims
+        # https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/core/src/multiarray/shape.c#L470-L515   # noqa: E501
+        unknownidx = -1
+        knownsize = 1
+        for i, dim in enumerate(newdims):
+            if dim < 0:
+                if unknownidx == -1:
+                    unknownidx = i
+                else:
+                    raise ValueError("can only specify one unknown dimension")
+            else:
+                knownsize *= dim
+
+        # compute the missing dimension
+        if unknownidx >= 0:
+            if knownsize == 0 or self.size % knownsize != 0:
+                raise ValueError("cannot infer valid shape for unknown dimension")
+            else:
+                newdims = newdims[0:unknownidx] \
+                        + (self.size // knownsize,) \
+                        + newdims[unknownidx + 1:]
+
+        newsize = functools.reduce(operator.mul, newdims, 1)
+
+        if order == 'A':
+            order = 'F' if self.is_f_contig else 'C'
+
+        if newsize != self.size:
+            raise ValueError("reshape changes the size of the array")
+
+        if self.is_c_contig or self.is_f_contig:
+            if order == 'C':
+                newstrides = list(iter_strides_c_contig(self, newdims))
+            elif order == 'F':
+                newstrides = list(iter_strides_f_contig(self, newdims))
+            else:
+                raise AssertionError("unreachable")
+        else:
+            newstrides = np.empty(newnd, np.ctypeslib.c_intp)
+
+            # need to keep these around in variables, not temporaries, so they
+            # don't get GC'ed before we call into the C code
+            olddims = np.array(self.shape, dtype=np.ctypeslib.c_intp)
+            oldstrides = np.array(self.strides, dtype=np.ctypeslib.c_intp)
+            newdims = np.array(newdims, dtype=np.ctypeslib.c_intp)
+
+            if not attempt_nocopy_reshape(
+                oldnd,
+                olddims,
+                oldstrides,
+                newnd,
+                newdims,
+                newstrides,
+                self.itemsize,
+                order == 'F',
+            ):
+                raise NotImplementedError('reshape would require copy')
+
+        ret = self.from_desc(self.extent.begin, shape=newdims,
+                             strides=newstrides, itemsize=self.itemsize)
+
+        return ret, list(self.iter_contiguous_extent())
+
+    def squeeze(self, axis=None):
+        newshape, newstrides = [], []
+        if axis is None:
+            for length, stride in zip(self.shape, self.strides):
+                if length != 1:
+                    newshape.append(length)
+                    newstrides.append(stride)
+        else:
+            if not isinstance(axis, tuple):
+                axis = (axis,)
+            for ax in axis:
+                if self.shape[ax] != 1:
+                    raise ValueError(
+                        "cannot select an axis to squeeze out which has size not equal "
+                        "to one"
+                    )
+            for i, (length, stride) in enumerate(zip(self.shape, self.strides)):
+                if i not in axis:
+                    newshape.append(length)
+                    newstrides.append(stride)
+        newarr = self.from_desc(
+            self.extent.begin,
+            shape=newshape,
+            strides=newstrides,
+            itemsize=self.itemsize,
+        )
+        return newarr, list(self.iter_contiguous_extent())
+
+    def ravel(self, order='C'):
+        if order not in 'CFA':
+            raise ValueError('order not C|F|A')
+
+        if self.ndim <= 1:
+            return self
+
+        elif (order in 'CA' and self.is_c_contig or
+                          order in 'FA' and self.is_f_contig):
+            newshape = (self.size,)
+            newstrides = (self.itemsize,)
+            arr = self.from_desc(self.extent.begin, newshape, newstrides,
+                                 self.itemsize)
+            return arr, list(self.iter_contiguous_extent())
+
+        else:
+            raise NotImplementedError("ravel on non-contiguous array")
+
+
+def iter_strides_f_contig(arr, shape=None):
+    """yields the f-contiguous strides
+    """
+    shape = arr.shape if shape is None else shape
+    itemsize = arr.itemsize
+    yield itemsize
+    sum = 1
+    for s in shape[:-1]:
+        sum *= s
+        yield sum * itemsize
+
+
+def iter_strides_c_contig(arr, shape=None):
+    """yields the c-contiguous strides
+    """
+    shape = arr.shape if shape is None else shape
+    itemsize = arr.itemsize
+
+    def gen():
+        yield itemsize
+        sum = 1
+        for s in reversed(shape[1:]):
+            sum *= s
+            yield sum * itemsize
+
+    for i in reversed(list(gen())):
+        yield i
+
+
+def is_element_indexing(item, ndim):
+    if isinstance(item, slice):
+        return False
+
+    elif isinstance(item, tuple):
+        if len(item) == ndim:
+            if not any(isinstance(it, slice) for it in item):
+                return True
+
+    else:
+        return True
+
+    return False
+
+
+def _compute_size(start, stop, step):
+    """Algorithm adapted from cpython rangeobject.c
+    """
+    if step > 0:
+        lo = start
+        hi = stop
+    else:
+        lo = stop
+        hi = start
+        step = -step
+    if lo >= hi:
+        return 0
+    return (hi - lo - 1) // step + 1
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/dump_style.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/dump_style.py
new file mode 100644
index 000000000..95b5ed5be
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/dump_style.py
@@ -0,0 +1,84 @@
+try:
+    from pygments.styles.default import DefaultStyle
+except ImportError:
+    msg = "Please install pygments to see highlighted dumps"
+    raise ImportError(msg)
+
+import numba.core.config
+from pygments.styles.manni import ManniStyle
+from pygments.styles.monokai import MonokaiStyle
+from pygments.styles.native import NativeStyle
+
+from pygments.lexer import RegexLexer, include, bygroups, words
+from pygments.token import Text, Name, String,  Punctuation, Keyword, \
+    Operator, Number
+
+from pygments.style import Style
+
+
+class NumbaIRLexer(RegexLexer):
+    """
+    Pygments style lexer for Numba IR (for use with highlighting etc).
+    """
+    name = 'Numba_IR'
+    aliases = ['numba_ir']
+    filenames = ['*.numba_ir']
+
+    identifier = r'\$[a-zA-Z0-9._]+'
+    fun_or_var = r'([a-zA-Z_]+[a-zA-Z0-9]*)'
+
+    tokens = {
+        'root' : [
+            (r'(label)(\ [0-9]+)(:)$',
+                bygroups(Keyword, Name.Label, Punctuation)),
+
+            (r' = ', Operator),
+            include('whitespace'),
+            include('keyword'),
+
+            (identifier, Name.Variable),
+            (fun_or_var + r'(\()',
+                bygroups(Name.Function, Punctuation)),
+            (fun_or_var + r'(\=)',
+                bygroups(Name.Attribute, Punctuation)),
+            (fun_or_var, Name.Constant),
+            (r'[0-9]+', Number),
+
+            # <built-in function some>
+            (r'<[^>\n]*>', String),
+
+            (r'[=<>{}\[\]()*.,!\':]|x\b', Punctuation)
+        ],
+
+        'keyword':[
+            (words((
+                'del', 'jump', 'call', 'branch',
+            ), suffix=' '), Keyword),
+        ],
+
+        'whitespace': [
+            (r'(\n|\s)', Text),
+        ],
+    }
+
+
+def by_colorscheme():
+    """
+    Get appropriate style for highlighting according to
+    NUMBA_COLOR_SCHEME setting
+    """
+    styles = DefaultStyle.styles.copy()
+    styles.update({
+        Name.Variable:        "#888888",
+    })
+    custom_default = type('CustomDefaultStyle', (Style, ), {'styles': styles})
+
+    style_map = {
+        'no_color' : custom_default,
+        'dark_bg' : MonokaiStyle,
+        'light_bg' : ManniStyle,
+        'blue_bg' : NativeStyle,
+        'jupyter_nb' : DefaultStyle,
+    }
+
+    return style_map[numba.core.config.COLOR_SCHEME]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/findlib.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/findlib.py
new file mode 100644
index 000000000..1a1f63029
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/findlib.py
@@ -0,0 +1,60 @@
+import sys
+import os
+import re
+
+
+def get_lib_dirs():
+    """
+    Anaconda specific
+    """
+    if sys.platform == 'win32':
+        # on windows, historically `DLLs` has been used for CUDA libraries,
+        # since approximately CUDA 9.2, `Library\bin` has been used.
+        dirnames = ['DLLs', os.path.join('Library', 'bin')]
+    else:
+        dirnames = ['lib', ]
+    libdirs = [os.path.join(sys.prefix, x) for x in dirnames]
+    return libdirs
+
+
+DLLNAMEMAP = {
+    'linux': r'lib%(name)s\.so\.%(ver)s$',
+    'linux2': r'lib%(name)s\.so\.%(ver)s$',
+    'linux-static': r'lib%(name)s\.a$',
+    'darwin': r'lib%(name)s\.%(ver)s\.dylib$',
+    'win32': r'%(name)s%(ver)s\.dll$',
+    'win32-static': r'%(name)s\.lib$',
+    'bsd': r'lib%(name)s\.so\.%(ver)s$',
+}
+
+RE_VER = r'[0-9]*([_\.][0-9]+)*'
+
+
+def find_lib(libname, libdir=None, platform=None, static=False):
+    platform = platform or sys.platform
+    platform = 'bsd' if 'bsd' in platform else platform
+    if static:
+        platform = f"{platform}-static"
+    if platform not in DLLNAMEMAP:
+        # Return empty list if platform name is undefined.
+        # Not all platforms define their static library paths.
+        return []
+    pat = DLLNAMEMAP[platform] % {"name": libname, "ver": RE_VER}
+    regex = re.compile(pat)
+    return find_file(regex, libdir)
+
+
+def find_file(pat, libdir=None):
+    if libdir is None:
+        libdirs = get_lib_dirs()
+    elif isinstance(libdir, str):
+        libdirs = [libdir,]
+    else:
+        libdirs = list(libdir)
+    files = []
+    for ldir in libdirs:
+        entries = os.listdir(ldir)
+        candidates = [os.path.join(ldir, ent)
+                      for ent in entries if pat.match(ent)]
+        files.extend([c for c in candidates if os.path.isfile(c)])
+    return files
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/firstlinefinder.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/firstlinefinder.py
new file mode 100644
index 000000000..41a7ecf18
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/firstlinefinder.py
@@ -0,0 +1,96 @@
+"""
+This module provides helper functions to find the first line of a function
+body.
+"""
+
+import ast
+
+from numba.core.utils import PYVERSION
+
+
+class FindDefFirstLine(ast.NodeVisitor):
+    """
+    Attributes
+    ----------
+    first_stmt_line : int or None
+        This stores the first statement line number if the definition is found.
+        Or, ``None`` if the definition is not found.
+    """
+
+    def __init__(self, code):
+        """
+        Parameters
+        ----------
+        code :
+            The function's code object.
+        """
+        self._co_name = code.co_name
+        self._co_firstlineno = code.co_firstlineno
+        self.first_stmt_line = None
+
+    def _visit_children(self, node):
+        for child in ast.iter_child_nodes(node):
+            super().visit(child)
+
+    def visit_FunctionDef(self, node: ast.FunctionDef):
+        if node.name == self._co_name:
+            # Name of function matches.
+
+            # The `def` line may match co_firstlineno.
+            possible_start_lines = set([node.lineno])
+            if node.decorator_list:
+                # Has decorators.
+                # The first decorator line may match co_firstlineno.
+                first_decor = node.decorator_list[0]
+                possible_start_lines.add(first_decor.lineno)
+            # Does the first lineno match?
+            if self._co_firstlineno in possible_start_lines:
+                # Yes, we found the function.
+                # So, use the first statement line as the first line.
+                if node.body:
+                    first_stmt = node.body[0]
+                    if _is_docstring(first_stmt):
+                        # Skip docstring
+                        first_stmt = node.body[1]
+                    self.first_stmt_line = first_stmt.lineno
+                    return
+                else:
+                    # This is probably unreachable.
+                    # Function body cannot be bare. It must at least have
+                    # A const string for docstring or a `pass`.
+                    pass
+        self._visit_children(node)
+
+
+def _is_docstring(node):
+    if isinstance(node, ast.Expr):
+        if PYVERSION <= (3, 7):
+            return isinstance(node.value, ast.Str)
+        elif (isinstance(node.value, ast.Constant)
+              and isinstance(node.value.value, str)):
+            return True
+    return False
+
+
+def get_func_body_first_lineno(pyfunc):
+    """
+    Look up the first line of function body using the file in
+    ``pyfunc.__code__.co_filename``.
+
+    Returns
+    -------
+    lineno : int; or None
+        The first line number of the function body; or ``None`` if the first
+        line cannot be determined.
+    """
+    co = pyfunc.__code__
+    try:
+        with open(co.co_filename) as fin:
+            file_content = fin.read()
+    except (FileNotFoundError, OSError):
+        return
+    else:
+        tree = ast.parse(file_content)
+        finder = FindDefFirstLine(co)
+        finder.visit(tree)
+        return finder.first_stmt_line
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/gdb_hook.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/gdb_hook.py
new file mode 100644
index 000000000..2ae877ec5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/gdb_hook.py
@@ -0,0 +1,228 @@
+import os
+import sys
+
+from llvmlite import ir
+
+from numba.core import types, utils, config, cgutils, errors
+from numba import gdb, gdb_init, gdb_breakpoint
+from numba.core.extending import overload, intrinsic
+
+_path = os.path.dirname(__file__)
+
+_platform = sys.platform
+_unix_like = (_platform.startswith('linux') or
+              _platform.startswith('darwin') or
+              ('bsd' in _platform))
+
+
+def _confirm_gdb(need_ptrace_attach=True):
+    """
+    Set need_ptrace_attach to True/False to indicate whether the ptrace attach
+    permission is needed for this gdb use case. Mode 0 (classic) or 1
+    (restricted ptrace) is required if need_ptrace_attach is True. See:
+    https://www.kernel.org/doc/Documentation/admin-guide/LSM/Yama.rst
+    for details on the modes.
+    """
+    if not _unix_like:
+        msg = 'gdb support is only available on unix-like systems'
+        raise errors.NumbaRuntimeError(msg)
+    gdbloc = config.GDB_BINARY
+    if not (os.path.exists(gdbloc) and os.path.isfile(gdbloc)):
+        msg = ('Is gdb present? Location specified (%s) does not exist. The gdb'
+               ' binary location can be set using Numba configuration, see: '
+               'https://numba.readthedocs.io/en/stable/reference/envvars.html'  # noqa: E501
+               )
+        raise RuntimeError(msg % config.GDB_BINARY)
+    # Is Yama being used as a kernel security module and if so is ptrace_scope
+    # limited? In this case ptracing non-child processes requires special
+    # permission so raise an exception.
+    ptrace_scope_file = os.path.join(os.sep, 'proc', 'sys', 'kernel', 'yama',
+                                     'ptrace_scope')
+    has_ptrace_scope = os.path.exists(ptrace_scope_file)
+    if has_ptrace_scope:
+        with open(ptrace_scope_file, 'rt') as f:
+            value = f.readline().strip()
+        if need_ptrace_attach and value not in ("0", "1"):
+            msg = ("gdb can launch but cannot attach to the executing program"
+                   " because ptrace permissions have been restricted at the "
+                   "system level by the Linux security module 'Yama'.\n\n"
+                   "Documentation for this module and the security "
+                   "implications of making changes to its behaviour can be "
+                   "found in the Linux Kernel documentation "
+                   "https://www.kernel.org/doc/Documentation/admin-guide/LSM/Yama.rst"    # noqa: E501
+                   "\n\nDocumentation on how to adjust the behaviour of Yama "
+                   "on Ubuntu Linux with regards to 'ptrace_scope' can be "
+                   "found here "
+                   "https://wiki.ubuntu.com/Security/Features#ptrace.")
+            raise RuntimeError(msg)
+
+
+@overload(gdb)
+def hook_gdb(*args):
+    _confirm_gdb()
+    gdbimpl = gen_gdb_impl(args, True)
+
+    def impl(*args):
+        gdbimpl()
+    return impl
+
+
+@overload(gdb_init)
+def hook_gdb_init(*args):
+    _confirm_gdb()
+    gdbimpl = gen_gdb_impl(args, False)
+
+    def impl(*args):
+        gdbimpl()
+    return impl
+
+
+def init_gdb_codegen(cgctx, builder, signature, args,
+                     const_args, do_break=False):
+
+    int8_t = ir.IntType(8)
+    int32_t = ir.IntType(32)
+    intp_t = ir.IntType(utils.MACHINE_BITS)
+    char_ptr = ir.PointerType(ir.IntType(8))
+    zero_i32t = int32_t(0)
+
+    mod = builder.module
+    pid = cgutils.alloca_once(builder, int32_t, size=1)
+
+    # 32bit pid, 11 char max + terminator
+    pidstr = cgutils.alloca_once(builder, int8_t, size=12)
+
+    # str consts
+    intfmt = cgctx.insert_const_string(mod, '%d')
+    gdb_str = cgctx.insert_const_string(mod, config.GDB_BINARY)
+    attach_str = cgctx.insert_const_string(mod, 'attach')
+
+    new_args = []
+    # add break point command to known location
+    # this command file thing is due to commands attached to a breakpoint
+    # requiring an interactive prompt
+    # https://sourceware.org/bugzilla/show_bug.cgi?id=10079
+    new_args.extend(['-x', os.path.join(_path, 'cmdlang.gdb')])
+    # issue command to continue execution from sleep function
+    new_args.extend(['-ex', 'c'])
+    # then run the user defined args if any
+    if any([not isinstance(x, types.StringLiteral) for x in const_args]):
+        raise errors.RequireLiteralValue(const_args)
+    new_args.extend([x.literal_value for x in const_args])
+    cmdlang = [cgctx.insert_const_string(mod, x) for x in new_args]
+
+    # insert getpid, getpid is always successful, call without concern!
+    fnty = ir.FunctionType(int32_t, tuple())
+    getpid = cgutils.get_or_insert_function(mod, fnty, "getpid")
+
+    # insert snprintf
+    # int snprintf(char *str, size_t size, const char *format, ...);
+    fnty = ir.FunctionType(
+        int32_t, (char_ptr, intp_t, char_ptr), var_arg=True)
+    snprintf = cgutils.get_or_insert_function(mod, fnty, "snprintf")
+
+    # insert fork
+    fnty = ir.FunctionType(int32_t, tuple())
+    fork = cgutils.get_or_insert_function(mod, fnty, "fork")
+
+    # insert execl
+    fnty = ir.FunctionType(int32_t, (char_ptr, char_ptr), var_arg=True)
+    execl = cgutils.get_or_insert_function(mod, fnty, "execl")
+
+    # insert sleep
+    fnty = ir.FunctionType(int32_t, (int32_t,))
+    sleep = cgutils.get_or_insert_function(mod, fnty, "sleep")
+
+    # insert break point
+    fnty = ir.FunctionType(ir.VoidType(), tuple())
+    breakpoint = cgutils.get_or_insert_function(mod, fnty,
+                                                "numba_gdb_breakpoint")
+
+    # do the work
+    parent_pid = builder.call(getpid, tuple())
+    builder.store(parent_pid, pid)
+    pidstr_ptr = builder.gep(pidstr, [zero_i32t], inbounds=True)
+    pid_val = builder.load(pid)
+
+    # call snprintf to write the pid into a char *
+    stat = builder.call(
+        snprintf, (pidstr_ptr, intp_t(12), intfmt, pid_val))
+    invalid_write = builder.icmp_signed('>', stat, int32_t(12))
+    with builder.if_then(invalid_write, likely=False):
+        msg = "Internal error: `snprintf` buffer would have overflowed."
+        cgctx.call_conv.return_user_exc(builder, RuntimeError, (msg,))
+
+    # fork, check pids etc
+    child_pid = builder.call(fork, tuple())
+    fork_failed = builder.icmp_signed('==', child_pid, int32_t(-1))
+    with builder.if_then(fork_failed, likely=False):
+        msg = "Internal error: `fork` failed."
+        cgctx.call_conv.return_user_exc(builder, RuntimeError, (msg,))
+
+    is_child = builder.icmp_signed('==', child_pid, zero_i32t)
+    with builder.if_else(is_child) as (then, orelse):
+        with then:
+            # is child
+            nullptr = ir.Constant(char_ptr, None)
+            gdb_str_ptr = builder.gep(
+                gdb_str, [zero_i32t], inbounds=True)
+            attach_str_ptr = builder.gep(
+                attach_str, [zero_i32t], inbounds=True)
+            cgutils.printf(
+                builder, "Attaching to PID: %s\n", pidstr)
+            buf = (
+                gdb_str_ptr,
+                gdb_str_ptr,
+                attach_str_ptr,
+                pidstr_ptr)
+            buf = buf + tuple(cmdlang) + (nullptr,)
+            builder.call(execl, buf)
+        with orelse:
+            # is parent
+            builder.call(sleep, (int32_t(10),))
+            # if breaking is desired, break now
+            if do_break is True:
+                builder.call(breakpoint, tuple())
+
+
+def gen_gdb_impl(const_args, do_break):
+    @intrinsic
+    def gdb_internal(tyctx):
+        function_sig = types.void()
+
+        def codegen(cgctx, builder, signature, args):
+            init_gdb_codegen(cgctx, builder, signature, args, const_args,
+                             do_break=do_break)
+            return cgctx.get_constant(types.none, None)
+        return function_sig, codegen
+    return gdb_internal
+
+
+@overload(gdb_breakpoint)
+def hook_gdb_breakpoint():
+    """
+    Adds the Numba break point into the source
+    """
+    if not sys.platform.startswith('linux'):
+        raise RuntimeError('gdb is only available on linux')
+    bp_impl = gen_bp_impl()
+
+    def impl():
+        bp_impl()
+    return impl
+
+
+def gen_bp_impl():
+    @intrinsic
+    def bp_internal(tyctx):
+        function_sig = types.void()
+
+        def codegen(cgctx, builder, signature, args):
+            mod = builder.module
+            fnty = ir.FunctionType(ir.VoidType(), tuple())
+            breakpoint = cgutils.get_or_insert_function(mod, fnty,
+                                                        "numba_gdb_breakpoint")
+            builder.call(breakpoint, tuple())
+            return cgctx.get_constant(types.none, None)
+        return function_sig, codegen
+    return bp_internal
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/gdb_print_extension.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/gdb_print_extension.py
new file mode 100644
index 000000000..95ea9f1e6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/gdb_print_extension.py
@@ -0,0 +1,212 @@
+"""gdb printing extension for Numba types.
+"""
+import re
+import sys
+_PYVERSION = sys.version_info[:2]
+
+try:
+    import gdb.printing
+    import gdb
+except ImportError:
+    raise ImportError("GDB python support is not available.")
+
+
+class NumbaArrayPrinter:
+
+    def __init__(self, val):
+        self.val = val
+
+    def to_string(self):
+        try:
+            import numpy as np
+            HAVE_NUMPY = True
+        except ImportError:
+            HAVE_NUMPY = False
+
+        try:
+            NULL = 0x0
+
+            # Raw data references, these need unpacking/interpreting.
+
+            # Member "data" is...
+            # DW_TAG_member of DIDerivedType, tag of DW_TAG_pointer_type
+            # encoding e.g. DW_ATE_float
+            data = self.val["data"]
+
+            # Member "itemsize" is...
+            # DW_TAG_member of DIBasicType encoding DW_ATE_signed
+            itemsize = self.val["itemsize"]
+
+            # Members "shape" and "strides" are...
+            # DW_TAG_member of DIDerivedType, the type is a DICompositeType
+            # (it's a Numba UniTuple) with tag: DW_TAG_array_type, i.e. it's an
+            # array repr, it has a basetype of e.g. DW_ATE_unsigned and also
+            # "elements" which are referenced with a DISubrange(count: <const>)
+            # to say how many elements are in the array.
+            rshp = self.val["shape"]
+            rstrides = self.val["strides"]
+
+            # bool on whether the data is aligned.
+            is_aligned = False
+
+            # type information decode, simple type:
+            ty_str = str(self.val.type)
+            if HAVE_NUMPY and ('aligned' in ty_str or 'Record' in ty_str):
+                ty_str = ty_str.replace('unaligned ','').strip()
+                matcher = re.compile(r"array\((Record.*), (.*), (.*)\)\ \(.*")
+                # NOTE: need to deal with "Alignment" else dtype size is wrong
+                arr_info = [x.strip() for x in matcher.match(ty_str).groups()]
+                dtype_str, ndim_str, order_str = arr_info
+                rstr = 'Record\\((.*\\[.*\\]);([0-9]+);(True|False)'
+                rstr_match = re.match(rstr, dtype_str)
+                # balign is unused, it's the alignment
+                fields, balign, is_aligned_str = rstr_match.groups()
+                is_aligned = is_aligned_str == 'True'
+                field_dts = fields.split(',')
+                struct_entries = []
+                for f in field_dts:
+                    splitted = f.split('[')
+                    name = splitted[0]
+                    dt_part = splitted[1:]
+                    if len(dt_part) > 1:
+                        raise TypeError('Unsupported sub-type: %s' % f)
+                    else:
+                        dt_part = dt_part[0]
+                        if "nestedarray" in dt_part:
+                            raise TypeError('Unsupported sub-type: %s' % f)
+                        dt_as_str = dt_part.split(';')[0].split('=')[1]
+                        dtype = np.dtype(dt_as_str)
+                    struct_entries.append((name, dtype))
+                    # The dtype is actually a record of some sort
+                dtype_str = struct_entries
+            else:  # simple type
+                matcher = re.compile(r"array\((.*),(.*),(.*)\)\ \(.*")
+                arr_info = [x.strip() for x in matcher.match(ty_str).groups()]
+                dtype_str, ndim_str, order_str = arr_info
+                # fix up unichr dtype
+                if 'unichr x ' in dtype_str:
+                    dtype_str = dtype_str[1:-1].replace('unichr x ', '<U')
+
+            def dwarr2inttuple(dwarr):
+                # Converts a gdb handle to a dwarf array to a tuple of ints
+                fields = dwarr.type.fields()
+                lo, hi = fields[0].type.range()
+                return tuple([int(dwarr[x]) for x in range(lo, hi + 1)])
+
+            # shape/strides extraction
+            shape = dwarr2inttuple(rshp)
+            strides = dwarr2inttuple(rstrides)
+
+            # if data is not NULL
+            if data != NULL:
+                if HAVE_NUMPY:
+                    # The data extent in bytes is:
+                    # sum(shape * strides)
+                    # get the data, then wire to as_strided
+                    shp_arr = np.array([max(0, x - 1) for x in shape])
+                    strd_arr = np.array(strides)
+                    extent = np.sum(shp_arr * strd_arr)
+                    extent += int(itemsize)
+                    dtype_clazz = np.dtype(dtype_str, align=is_aligned)
+                    dtype = dtype_clazz
+                    this_proc = gdb.selected_inferior()
+                    mem = this_proc.read_memory(int(data), extent)
+                    arr_data = np.frombuffer(mem, dtype=dtype)
+                    new_arr = np.lib.stride_tricks.as_strided(arr_data,
+                                                              shape=shape,
+                                                              strides=strides,)
+                    return '\n' + str(new_arr)
+                # Catch all for no NumPy
+                return "array([...], dtype=%s, shape=%s)" % (dtype_str, shape)
+            else:
+                # Not yet initialized or NULLed out data
+                buf = list(["NULL/Uninitialized"])
+                return "array([" + ', '.join(buf) + "]" + ")"
+        except Exception as e:
+            return 'array[Exception: Failed to parse. %s]' % e
+
+
+class NumbaComplexPrinter:
+
+    def __init__(self, val):
+        self.val = val
+
+    def to_string(self):
+        return "%s+%sj" % (self.val['real'], self.val['imag'])
+
+
+class NumbaTuplePrinter:
+
+    def __init__(self, val):
+        self.val = val
+
+    def to_string(self):
+        buf = []
+        fields = self.val.type.fields()
+        for f in fields:
+            buf.append(str(self.val[f.name]))
+        return "(%s)" % ', '.join(buf)
+
+
+class NumbaUniTuplePrinter:
+
+    def __init__(self, val):
+        self.val = val
+
+    def to_string(self):
+        # unituples are arrays
+        fields = self.val.type.fields()
+        lo, hi = fields[0].type.range()
+        buf = []
+        for i in range(lo, hi + 1):
+            buf.append(str(self.val[i]))
+        return "(%s)" % ', '.join(buf)
+
+
+class NumbaUnicodeTypePrinter:
+
+    def __init__(self, val):
+        self.val = val
+
+    def to_string(self):
+        NULL = 0x0
+        data = self.val["data"]
+        nitems = self.val["length"]
+        kind = self.val["kind"]
+        if data != NULL:
+            # This needs sorting out, encoding is wrong
+            this_proc = gdb.selected_inferior()
+            mem = this_proc.read_memory(int(data), nitems * kind)
+            if _PYVERSION < (3, 0):
+                try:
+                    buf = unicode(mem, 'utf-8') # noqa F821
+                except UnicodeDecodeError as e:
+                    buf = "ERROR: %s" % str(e)
+            else:
+                if isinstance(mem, memoryview):
+                    buf = bytes(mem).decode()
+                else:
+                    buf = mem.decode('utf-8')
+        else:
+            buf = str(data)
+        return "'%s'" % buf
+
+
+def _create_printers():
+    printer = gdb.printing.RegexpCollectionPrettyPrinter("Numba")
+    printer.add_printer('Numba unaligned array printer', '^unaligned array\\(',
+                        NumbaArrayPrinter)
+    printer.add_printer('Numba array printer', '^array\\(', NumbaArrayPrinter)
+    printer.add_printer('Numba complex printer', '^complex[0-9]+\\ ',
+                        NumbaComplexPrinter)
+    printer.add_printer('Numba Tuple printer', '^Tuple\\(',
+                        NumbaTuplePrinter)
+    printer.add_printer('Numba UniTuple printer', '^UniTuple\\(',
+                        NumbaUniTuplePrinter)
+    printer.add_printer('Numba unicode_type printer', '^unicode_type\\s+\\(',
+                        NumbaUnicodeTypePrinter)
+    return printer
+
+
+# register the Numba pretty printers for the current object
+gdb.printing.register_pretty_printer(gdb.current_objfile(), _create_printers())
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/help/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/help/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/help/inspector.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/help/inspector.py
new file mode 100644
index 000000000..67cfc44f2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/help/inspector.py
@@ -0,0 +1,433 @@
+"""
+This file contains `__main__` so that it can be run as a commandline tool.
+
+This file contains functions to inspect Numba's support for a given Python
+module or a Python package.
+"""
+
+import argparse
+import pkgutil
+import warnings
+import types as pytypes
+
+from numba.core import errors
+from numba._version import get_versions
+from numba.core.registry import cpu_target
+from numba.tests.support import captured_stdout
+
+
+def _get_commit():
+    full = get_versions()['full'].split('.')[0]
+    if not full:
+        warnings.warn(
+            "Cannot find git commit hash. Source links could be inaccurate.",
+            category=errors.NumbaWarning,
+        )
+        return 'main'
+    return full
+
+
+commit = _get_commit()
+github_url = 'https://github.com/numba/numba/blob/{commit}/{path}#L{firstline}-L{lastline}'    # noqa: E501
+
+
+def inspect_function(function, target=None):
+    """Return information about the support of a function.
+
+    Returns
+    -------
+    info : dict
+        Defined keys:
+        - "numba_type": str or None
+            The numba type object of the function if supported.
+        - "explained": str
+            A textual description of the support.
+        - "source_infos": dict
+            A dictionary containing the source location of each definition.
+    """
+    target = target or cpu_target
+    tyct = target.typing_context
+    # Make sure we have loaded all extensions
+    tyct.refresh()
+    target.target_context.refresh()
+
+    info = {}
+    # Try getting the function type
+    source_infos = {}
+    try:
+        nbty = tyct.resolve_value_type(function)
+    except ValueError:
+        nbty = None
+        explained = 'not supported'
+    else:
+        # Make a longer explanation of the type
+        explained = tyct.explain_function_type(nbty)
+        for temp in nbty.templates:
+            try:
+                source_infos[temp] = temp.get_source_info()
+            except AttributeError:
+                source_infos[temp] = None
+
+    info['numba_type'] = nbty
+    info['explained'] = explained
+    info['source_infos'] = source_infos
+    return info
+
+
+def inspect_module(module, target=None, alias=None):
+    """Inspect a module object and yielding results from `inspect_function()`
+    for each function object in the module.
+    """
+    alias = {} if alias is None else alias
+    # Walk the module
+    for name in dir(module):
+        if name.startswith('_'):
+            # Skip
+            continue
+        obj = getattr(module, name)
+        supported_types = (pytypes.FunctionType, pytypes.BuiltinFunctionType)
+
+        if not isinstance(obj, supported_types):
+            # Skip if it's not a function
+            continue
+
+        info = dict(module=module, name=name, obj=obj)
+        if obj in alias:
+            info['alias'] = alias[obj]
+        else:
+            alias[obj] = "{module}.{name}".format(module=module.__name__,
+                                                  name=name)
+        info.update(inspect_function(obj, target=target))
+        yield info
+
+
+class _Stat(object):
+    """For gathering simple statistic of (un)supported functions"""
+    def __init__(self):
+        self.supported = 0
+        self.unsupported = 0
+
+    @property
+    def total(self):
+        total = self.supported + self.unsupported
+        return total
+
+    @property
+    def ratio(self):
+        ratio = self.supported / self.total * 100
+        return ratio
+
+    def describe(self):
+        if self.total == 0:
+            return "empty"
+        return "supported = {supported} / {total} = {ratio:.2f}%".format(
+            supported=self.supported,
+            total=self.total,
+            ratio=self.ratio,
+        )
+
+    def __repr__(self):
+        return "{clsname}({describe})".format(
+            clsname=self.__class__.__name__,
+            describe=self.describe(),
+        )
+
+
+def filter_private_module(module_components):
+    return not any(x.startswith('_') for x in module_components)
+
+
+def filter_tests_module(module_components):
+    return not any(x == 'tests' for x in module_components)
+
+
+_default_module_filters = (
+    filter_private_module,
+    filter_tests_module,
+)
+
+
+def list_modules_in_package(package, module_filters=_default_module_filters):
+    """Yield all modules in a given package.
+
+    Recursively walks the package tree.
+    """
+    onerror_ignore = lambda _: None
+
+    prefix = package.__name__ + "."
+    package_walker = pkgutil.walk_packages(
+        package.__path__,
+        prefix,
+        onerror=onerror_ignore,
+    )
+
+    def check_filter(modname):
+        module_components = modname.split('.')
+        return any(not filter_fn(module_components)
+                   for filter_fn in module_filters)
+
+    modname = package.__name__
+    if not check_filter(modname):
+        yield package
+
+    for pkginfo in package_walker:
+        modname = pkginfo[1]
+        if check_filter(modname):
+            continue
+        # In case importing of the module print to stdout
+        with captured_stdout():
+            try:
+                # Import the module
+                mod = __import__(modname)
+            except Exception:
+                continue
+
+            # Extract the module
+            for part in modname.split('.')[1:]:
+                try:
+                    mod = getattr(mod, part)
+                except AttributeError:
+                    # Suppress error in getting the attribute
+                    mod = None
+                    break
+
+        # Ignore if mod is not a module
+        if not isinstance(mod, pytypes.ModuleType):
+            # Skip non-module
+            continue
+
+        yield mod
+
+
+class Formatter(object):
+    """Base class for formatters.
+    """
+    def __init__(self, fileobj):
+        self._fileobj = fileobj
+
+    def print(self, *args, **kwargs):
+        kwargs.setdefault('file', self._fileobj)
+        print(*args, **kwargs)
+
+
+class HTMLFormatter(Formatter):
+    """Formatter that outputs HTML
+    """
+
+    def escape(self, text):
+        import html
+        return html.escape(text)
+
+    def title(self, text):
+        self.print('<h1>', text, '</h2>')
+
+    def begin_module_section(self, modname):
+        self.print('<h2>', modname, '</h2>')
+        self.print('<ul>')
+
+    def end_module_section(self):
+        self.print('</ul>')
+
+    def write_supported_item(self, modname, itemname, typename, explained,
+                             sources, alias):
+        self.print('<li>')
+        self.print('{}.<b>{}</b>'.format(
+            modname,
+            itemname,
+        ))
+        self.print(': <b>{}</b>'.format(typename))
+        self.print('<div><pre>', explained, '</pre></div>')
+
+        self.print("<ul>")
+        for tcls, source in sources.items():
+            if source:
+                self.print("<li>")
+                impl = source['name']
+                sig = source['sig']
+                filename = source['filename']
+                lines = source['lines']
+                self.print(
+                    "<p>defined by <b>{}</b>{} at {}:{}-{}</p>".format(
+                        self.escape(impl), self.escape(sig),
+                        self.escape(filename), lines[0], lines[1],
+                    ),
+                )
+                self.print('<p>{}</p>'.format(
+                    self.escape(source['docstring'] or '')
+                ))
+            else:
+                self.print("<li>{}".format(self.escape(str(tcls))))
+            self.print("</li>")
+        self.print("</ul>")
+        self.print('</li>')
+
+    def write_unsupported_item(self, modname, itemname):
+        self.print('<li>')
+        self.print('{}.<b>{}</b>: UNSUPPORTED'.format(
+            modname,
+            itemname,
+        ))
+        self.print('</li>')
+
+    def write_statistic(self, stats):
+        self.print('<p>{}</p>'.format(stats.describe()))
+
+
+class ReSTFormatter(Formatter):
+    """Formatter that output ReSTructured text format for Sphinx docs.
+    """
+    def escape(self, text):
+        return text
+
+    def title(self, text):
+        self.print(text)
+        self.print('=' * len(text))
+        self.print()
+
+    def begin_module_section(self, modname):
+        self.print(modname)
+        self.print('-' * len(modname))
+        self.print()
+
+    def end_module_section(self):
+        self.print()
+
+    def write_supported_item(self, modname, itemname, typename, explained,
+                             sources, alias):
+        self.print('.. function:: {}.{}'.format(modname, itemname))
+        self.print('   :noindex:')
+        self.print()
+
+        if alias:
+            self.print("   Alias to: ``{}``".format(alias))
+        self.print()
+
+        for tcls, source in sources.items():
+            if source:
+                impl = source['name']
+                sig = source['sig']
+                filename = source['filename']
+                lines = source['lines']
+                source_link = github_url.format(
+                    commit=commit,
+                    path=filename,
+                    firstline=lines[0],
+                    lastline=lines[1],
+                )
+                self.print(
+                    "   - defined by ``{}{}`` at `{}:{}-{} <{}>`_".format(
+                        impl, sig, filename, lines[0], lines[1], source_link,
+                    ),
+                )
+
+            else:
+                self.print("   - defined by ``{}``".format(str(tcls)))
+        self.print()
+
+    def write_unsupported_item(self, modname, itemname):
+        pass
+
+    def write_statistic(self, stat):
+        if stat.supported == 0:
+            self.print("This module is not supported.")
+        else:
+            msg = "Not showing {} unsupported functions."
+            self.print(msg.format(stat.unsupported))
+            self.print()
+            self.print(stat.describe())
+        self.print()
+
+
+def _format_module_infos(formatter, package_name, mod_sequence, target=None):
+    """Format modules.
+    """
+    formatter.title('Listings for {}'.format(package_name))
+    alias_map = {}  # remember object seen to track alias
+    for mod in mod_sequence:
+        stat = _Stat()
+        modname = mod.__name__
+        formatter.begin_module_section(formatter.escape(modname))
+        for info in inspect_module(mod, target=target, alias=alias_map):
+            nbtype = info['numba_type']
+            if nbtype is not None:
+                stat.supported += 1
+                formatter.write_supported_item(
+                    modname=formatter.escape(info['module'].__name__),
+                    itemname=formatter.escape(info['name']),
+                    typename=formatter.escape(str(nbtype)),
+                    explained=formatter.escape(info['explained']),
+                    sources=info['source_infos'],
+                    alias=info.get('alias'),
+                )
+
+            else:
+                stat.unsupported += 1
+                formatter.write_unsupported_item(
+                    modname=formatter.escape(info['module'].__name__),
+                    itemname=formatter.escape(info['name']),
+                )
+
+        formatter.write_statistic(stat)
+        formatter.end_module_section()
+
+
+def write_listings(package_name, filename, output_format):
+    """Write listing information into a file.
+
+    Parameters
+    ----------
+    package_name : str
+        Name of the package to inspect.
+    filename : str
+        Output filename. Always overwrite.
+    output_format : str
+        Support formats are "html" and "rst".
+    """
+    package = __import__(package_name)
+    if hasattr(package, '__path__'):
+        mods = list_modules_in_package(package)
+    else:
+        mods = [package]
+
+    if output_format == 'html':
+        with open(filename + '.html', 'w') as fout:
+            fmtr = HTMLFormatter(fileobj=fout)
+            _format_module_infos(fmtr, package_name, mods)
+    elif output_format == 'rst':
+        with open(filename + '.rst', 'w') as fout:
+            fmtr = ReSTFormatter(fileobj=fout)
+            _format_module_infos(fmtr, package_name, mods)
+    else:
+        raise ValueError(
+            "Output format '{}' is not supported".format(output_format))
+
+
+program_description = """
+Inspect Numba support for a given top-level package.
+""".strip()
+
+
+def main():
+    parser = argparse.ArgumentParser(description=program_description)
+    parser.add_argument(
+        'package', metavar='package', type=str,
+        help='Package to inspect',
+    )
+    parser.add_argument(
+        '--format', dest='format', default='html',
+        help='Output format; i.e. "html", "rst"',
+    )
+    parser.add_argument(
+        '--file', dest='file', default='inspector_output',
+        help='Output filename. Defaults to "inspector_output.<format>"',
+    )
+
+    args = parser.parse_args()
+    package_name = args.package
+    output_format = args.format
+    filename = args.file
+    write_listings(package_name, filename, output_format)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/init_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/init_utils.py
new file mode 100644
index 000000000..4b6dc9f0e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/init_utils.py
@@ -0,0 +1,44 @@
+"""Collection of miscellaneous initialization utilities."""
+
+from collections import namedtuple
+
+version_info = namedtuple('version_info',
+                          ('major minor patch short full '
+                           'string tuple git_revision'))
+
+
+def generate_version_info(version):
+    """Process a version string into a structured version_info object.
+
+    Parameters
+    ----------
+    version: str
+        a string describing the current version
+
+    Returns
+    -------
+    version_info: tuple
+        structured version information
+
+    See also
+    --------
+    Look at the definition of 'version_info' in this module for details.
+
+    """
+    parts = version.split('.')
+
+    def try_int(x):
+        try:
+            return int(x)
+        except ValueError:
+            return None
+    major = try_int(parts[0]) if len(parts) >= 1 else None
+    minor = try_int(parts[1]) if len(parts) >= 2 else None
+    patch = try_int(parts[2]) if len(parts) >= 3 else None
+    short = (major, minor)
+    full = (major, minor, patch)
+    string = version
+    tup = tuple(parts)
+    git_revision = tup[3] if len(tup) >= 4 else None
+    return version_info(major, minor, patch, short, full, string, tup,
+                        git_revision)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/inspection.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/inspection.py
new file mode 100644
index 000000000..a92c94308
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/inspection.py
@@ -0,0 +1,103 @@
+"""Miscellaneous inspection tools
+"""
+from tempfile import NamedTemporaryFile, TemporaryDirectory
+import os
+import warnings
+
+from numba.core.errors import NumbaWarning
+
+
+def disassemble_elf_to_cfg(elf, mangled_symbol):
+    """
+    Gets the CFG of the disassembly of an ELF object, elf, at mangled name,
+    mangled_symbol, and renders it appropriately depending on the execution
+    environment (terminal/notebook).
+    """
+    try:
+        import r2pipe
+    except ImportError:
+        raise RuntimeError("r2pipe package needed for disasm CFG")
+
+    def get_rendering(cmd=None):
+        from numba.pycc.platform import Toolchain # import local, circular ref
+        if cmd is None:
+            raise ValueError("No command given")
+
+        with TemporaryDirectory() as tmpdir:
+            # Write ELF as a temporary file in the temporary dir, do not delete!
+            with NamedTemporaryFile(delete=False, dir=tmpdir) as f:
+                f.write(elf)
+                f.flush()  # force write, radare2 needs a binary blob on disk
+
+            # Now try and link the ELF, this helps radare2 _a lot_
+            linked = False
+            try:
+                raw_dso_name = f'{os.path.basename(f.name)}.so'
+                linked_dso = os.path.join(tmpdir, raw_dso_name)
+                tc = Toolchain()
+                tc.link_shared(linked_dso, (f.name,))
+                obj_to_analyse = linked_dso
+                linked = True
+            except Exception as e:
+                # link failed, mention it to user, radare2 will still be able to
+                # analyse the object, but things like dwarf won't appear in the
+                # asm as comments.
+                msg = ('Linking the ELF object with the distutils toolchain '
+                       f'failed with: {e}. Disassembly will still work but '
+                       'might be less accurate and will not use DWARF '
+                       'information.')
+                warnings.warn(NumbaWarning(msg))
+                obj_to_analyse = f.name
+
+            # catch if r2pipe can actually talk to radare2
+            try:
+                flags = ['-2', # close stderr to hide warnings
+                         '-e io.cache=true', # fix relocations in disassembly
+                         '-e scr.color=1',  # 16 bit ANSI colour terminal
+                         '-e asm.dwarf=true', # DWARF decode
+                         '-e scr.utf8=true', # UTF8 output looks better
+                         ]
+                r = r2pipe.open(obj_to_analyse, flags=flags)
+                r.cmd('aaaaaa') # analyse as much as possible
+                # If the elf is linked then it's necessary to seek as the
+                # DSO ctor/dtor is at the default position
+                if linked:
+                    # r2 only matches up to 61 chars?! found this by experiment!
+                    mangled_symbol_61char = mangled_symbol[:61]
+                    # switch off demangle, the seek is on a mangled symbol
+                    r.cmd('e bin.demangle=false')
+                    # seek to the mangled symbol address
+                    r.cmd(f's `is~ {mangled_symbol_61char}[1]`')
+                    # switch demangling back on for output purposes
+                    r.cmd('e bin.demangle=true')
+                data = r.cmd('%s' % cmd) # print graph
+                r.quit()
+            except Exception as e:
+                if "radare2 in PATH" in str(e):
+                    msg = ("This feature requires 'radare2' to be "
+                           "installed and available on the system see: "
+                           "https://github.com/radareorg/radare2. "
+                           "Cannot find 'radare2' in $PATH.")
+                    raise RuntimeError(msg)
+                else:
+                    raise e
+        return data
+
+    class DisasmCFG(object):
+
+        def _repr_svg_(self):
+            try:
+                import graphviz
+            except ImportError:
+                raise RuntimeError("graphviz package needed for disasm CFG")
+            jupyter_rendering = get_rendering(cmd='agfd')
+            # this just makes it read slightly better in jupyter notebooks
+            jupyter_rendering.replace('fontname="Courier",',
+                                      'fontname="Courier",fontsize=6,')
+            src = graphviz.Source(jupyter_rendering)
+            return src.pipe('svg').decode('UTF-8')
+
+        def __repr__(self):
+            return get_rendering(cmd='agf')
+
+    return DisasmCFG()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/literal.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/literal.py
new file mode 100644
index 000000000..2bc1225b7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/literal.py
@@ -0,0 +1,24 @@
+from numba.core.extending import overload
+from numba.core import types
+from numba.misc.special import literally, literal_unroll
+from numba.core.errors import TypingError
+
+
+@overload(literally)
+def _ov_literally(obj):
+    if isinstance(obj, (types.Literal, types.InitialValue)):
+        return lambda obj: obj
+    else:
+        m = "Invalid use of non-Literal type in literally({})".format(obj)
+        raise TypingError(m)
+
+
+@overload(literal_unroll)
+def literal_unroll_impl(container):
+    if isinstance(container, types.Poison):
+        m = f"Invalid use of non-Literal type in literal_unroll({container})"
+        raise TypingError(m)
+
+    def impl(container):
+        return container
+    return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/llvm_pass_timings.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/llvm_pass_timings.py
new file mode 100644
index 000000000..b3263dd45
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/llvm_pass_timings.py
@@ -0,0 +1,402 @@
+import re
+import operator
+import heapq
+from collections import namedtuple
+from collections.abc import Sequence
+from contextlib import contextmanager
+
+from numba.core.utils import cached_property
+from numba.core import config
+
+import llvmlite.binding as llvm
+
+
+class RecordLLVMPassTimings:
+    """A helper context manager to track LLVM pass timings.
+    """
+
+    __slots__ = ["_data"]
+
+    def __enter__(self):
+        """Enables the pass timing in LLVM.
+        """
+        llvm.set_time_passes(True)
+        return self
+
+    def __exit__(self, exc_val, exc_type, exc_tb):
+        """Reset timings and save report internally.
+        """
+        self._data = llvm.report_and_reset_timings()
+        llvm.set_time_passes(False)
+        return
+
+    def get(self):
+        """Retrieve timing data for processing.
+
+        Returns
+        -------
+        timings: ProcessedPassTimings
+        """
+        return ProcessedPassTimings(self._data)
+
+
+PassTimingRecord = namedtuple(
+    "PassTimingRecord",
+    [
+        "user_time",
+        "user_percent",
+        "system_time",
+        "system_percent",
+        "user_system_time",
+        "user_system_percent",
+        "wall_time",
+        "wall_percent",
+        "pass_name",
+    ],
+)
+
+
+def _adjust_timings(records):
+    """Adjust timing records because of truncated information.
+
+    Details: The percent information can be used to improve the timing
+    information.
+
+    Returns
+    -------
+    res: List[PassTimingRecord]
+    """
+    total_rec = records[-1]
+    assert total_rec.pass_name == "Total"  # guard for implementation error
+
+    def make_adjuster(attr):
+        time_attr = f"{attr}_time"
+        percent_attr = f"{attr}_percent"
+        time_getter = operator.attrgetter(time_attr)
+
+        def adjust(d):
+            """Compute percent x total_time = adjusted"""
+            total = time_getter(total_rec)
+            adjusted = total * d[percent_attr] * 0.01
+            d[time_attr] = adjusted
+            return d
+
+        return adjust
+
+    # Make adjustment functions for each field
+    adj_fns = [
+        make_adjuster(x) for x in ["user", "system", "user_system", "wall"]
+    ]
+
+    # Extract dictionaries from the namedtuples
+    dicts = map(lambda x: x._asdict(), records)
+
+    def chained(d):
+        # Chain the adjustment functions
+        for fn in adj_fns:
+            d = fn(d)
+        # Reconstruct the namedtuple
+        return PassTimingRecord(**d)
+
+    return list(map(chained, dicts))
+
+
+class ProcessedPassTimings:
+    """A class for processing raw timing report from LLVM.
+
+    The processing is done lazily so we don't waste time processing unused
+    timing information.
+    """
+
+    def __init__(self, raw_data):
+        self._raw_data = raw_data
+
+    def __bool__(self):
+        return bool(self._raw_data)
+
+    def get_raw_data(self):
+        """Returns the raw string data.
+
+        Returns
+        -------
+        res: str
+        """
+        return self._raw_data
+
+    def get_total_time(self):
+        """Compute the total time spend in all passes.
+
+        Returns
+        -------
+        res: float
+        """
+        return self.list_records()[-1].wall_time
+
+    def list_records(self):
+        """Get the processed data for the timing report.
+
+        Returns
+        -------
+        res: List[PassTimingRecord]
+        """
+        return self._processed
+
+    def list_top(self, n):
+        """Returns the top(n) most time-consuming (by wall-time) passes.
+
+        Parameters
+        ----------
+        n: int
+            This limits the maximum number of items to show.
+            This function will show the ``n`` most time-consuming passes.
+
+        Returns
+        -------
+        res: List[PassTimingRecord]
+            Returns the top(n) most time-consuming passes in descending order.
+        """
+        records = self.list_records()
+        key = operator.attrgetter("wall_time")
+        return heapq.nlargest(n, records[:-1], key)
+
+    def summary(self, topn=5, indent=0):
+        """Return a string summarizing the timing information.
+
+        Parameters
+        ----------
+        topn: int; optional
+            This limits the maximum number of items to show.
+            This function will show the ``topn`` most time-consuming passes.
+        indent: int; optional
+            Set the indentation level. Defaults to 0 for no indentation.
+
+        Returns
+        -------
+        res: str
+        """
+        buf = []
+        prefix = " " * indent
+
+        def ap(arg):
+            buf.append(f"{prefix}{arg}")
+
+        ap(f"Total {self.get_total_time():.4f}s")
+        ap("Top timings:")
+        for p in self.list_top(topn):
+            ap(f"  {p.wall_time:.4f}s ({p.wall_percent:5}%) {p.pass_name}")
+        return "\n".join(buf)
+
+    @cached_property
+    def _processed(self):
+        """A cached property for lazily processing the data and returning it.
+
+        See ``_process()`` for details.
+        """
+        return self._process()
+
+    def _process(self):
+        """Parses the raw string data from LLVM timing report and attempts
+        to improve the data by recomputing the times
+        (See `_adjust_timings()``).
+        """
+
+        def parse(raw_data):
+            """A generator that parses the raw_data line-by-line to extract
+            timing information for each pass.
+            """
+            lines = raw_data.splitlines()
+            colheader = r"[a-zA-Z+ ]+"
+            # Take at least one column header.
+            multicolheaders = fr"(?:\s*-+{colheader}-+)+"
+
+            line_iter = iter(lines)
+            # find column headers
+            header_map = {
+                "User Time": "user",
+                "System Time": "system",
+                "User+System": "user_system",
+                "Wall Time": "wall",
+                "Name": "pass_name",
+            }
+            for ln in line_iter:
+                m = re.match(multicolheaders, ln)
+                if m:
+                    # Get all the column headers
+                    raw_headers = re.findall(r"[a-zA-Z][a-zA-Z+ ]+", ln)
+                    headers = [header_map[k.strip()] for k in raw_headers]
+                    break
+
+            assert headers[-1] == 'pass_name'
+            # compute the list of available attributes from the column headers
+            attrs = []
+            for k in headers[:-1]:
+                attrs.append(f"{k}_time")
+                attrs.append(f"{k}_percent")
+            # put default value 0.0 to all missing attributes
+            missing = {}
+            for k in PassTimingRecord._fields:
+                if k not in attrs and k != 'pass_name':
+                    missing[k] = 0.0
+            # parse timings
+            n = r"\s*((?:[0-9]+\.)?[0-9]+)"
+            pat = f"\\s+(?:{n}\\s*\\({n}%\\)|-+)" * (len(headers) - 1)
+            pat += r"\s*(.*)"
+            for ln in line_iter:
+                m = re.match(pat, ln)
+                if m is not None:
+                    raw_data = list(m.groups())
+                    data = {k: float(v) if v is not None else 0.0
+                            for k, v in zip(attrs, raw_data)}
+                    data.update(missing)
+                    pass_name = raw_data[-1]
+                    rec = PassTimingRecord(
+                        pass_name=pass_name, **data,
+                    )
+                    yield rec
+                    if rec.pass_name == "Total":
+                        # "Total" means the report has ended
+                        break
+            # Check that we have reach the end of the report
+            remaining = '\n'.join(line_iter)
+            if remaining:
+                raise ValueError(
+                    f"unexpected text after parser finished:\n{remaining}"
+                )
+
+        # Parse raw data
+        records = list(parse(self._raw_data))
+        return _adjust_timings(records)
+
+
+NamedTimings = namedtuple("NamedTimings", ["name", "timings"])
+
+
+class PassTimingsCollection(Sequence):
+    """A collection of pass timings.
+
+    This class implements the ``Sequence`` protocol for accessing the
+    individual timing records.
+    """
+
+    def __init__(self, name):
+        self._name = name
+        self._records = []
+
+    @contextmanager
+    def record(self, name):
+        """Record new timings and append to this collection.
+
+        Note: this is mainly for internal use inside the compiler pipeline.
+
+        See also ``RecordLLVMPassTimings``
+
+        Parameters
+        ----------
+        name: str
+            Name for the records.
+        """
+        if config.LLVM_PASS_TIMINGS:
+            # Recording of pass timings is enabled
+            with RecordLLVMPassTimings() as timings:
+                yield
+            rec = timings.get()
+            # Only keep non-empty records
+            if rec:
+                self._append(name, rec)
+        else:
+            # Do nothing. Recording of pass timings is disabled.
+            yield
+
+    def _append(self, name, timings):
+        """Append timing records
+
+        Parameters
+        ----------
+        name: str
+            Name for the records.
+        timings: ProcessedPassTimings
+            the timing records.
+        """
+        self._records.append(NamedTimings(name, timings))
+
+    def get_total_time(self):
+        """Computes the sum of the total time across all contained timings.
+
+        Returns
+        -------
+        res: float or None
+            Returns the total number of seconds or None if no timings were
+            recorded
+        """
+        if self._records:
+            return sum(r.timings.get_total_time() for r in self._records)
+        else:
+            return None
+
+    def list_longest_first(self):
+        """Returns the timings in descending order of total time duration.
+
+        Returns
+        -------
+        res: List[ProcessedPassTimings]
+        """
+        return sorted(self._records,
+                      key=lambda x: x.timings.get_total_time(),
+                      reverse=True)
+
+    @property
+    def is_empty(self):
+        """
+        """
+        return not self._records
+
+    def summary(self, topn=5):
+        """Return a string representing the summary of the timings.
+
+        Parameters
+        ----------
+        topn: int; optional, default=5.
+            This limits the maximum number of items to show.
+            This function will show the ``topn`` most time-consuming passes.
+
+        Returns
+        -------
+        res: str
+
+        See also ``ProcessedPassTimings.summary()``
+        """
+        if self.is_empty:
+            return "No pass timings were recorded"
+        else:
+            buf = []
+            ap = buf.append
+            ap(f"Printing pass timings for {self._name}")
+            overall_time = self.get_total_time()
+            ap(f"Total time: {overall_time:.4f}")
+            for i, r in enumerate(self._records):
+                ap(f"== #{i} {r.name}")
+                percent = r.timings.get_total_time() / overall_time * 100
+                ap(f" Percent: {percent:.1f}%")
+                ap(r.timings.summary(topn=topn, indent=1))
+            return "\n".join(buf)
+
+    def __getitem__(self, i):
+        """Get the i-th timing record.
+
+        Returns
+        -------
+        res: (name, timings)
+            A named tuple with two fields:
+
+            - name: str
+            - timings: ProcessedPassTimings
+        """
+        return self._records[i]
+
+    def __len__(self):
+        """Length of this collection.
+        """
+        return len(self._records)
+
+    def __str__(self):
+        return self.summary()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/mergesort.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/mergesort.py
new file mode 100644
index 000000000..cba5f3318
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/mergesort.py
@@ -0,0 +1,126 @@
+"""
+The same algorithm as translated from numpy.
+See numpy/core/src/npysort/mergesort.c.src.
+The high-level numba code is adding a little overhead comparing to
+the pure-C implementation in numpy.
+"""
+import numpy as np
+from collections import namedtuple
+
+# Array size smaller than this will be sorted by insertion sort
+SMALL_MERGESORT = 20
+
+
+MergesortImplementation = namedtuple('MergesortImplementation', [
+    'run_mergesort',
+])
+
+
+def make_mergesort_impl(wrap, lt=None, is_argsort=False):
+    kwargs_lite = dict(no_cpython_wrapper=True, _nrt=False)
+
+    # The less than
+    if lt is None:
+        @wrap(**kwargs_lite)
+        def lt(a, b):
+            return a < b
+    else:
+        lt = wrap(**kwargs_lite)(lt)
+
+    if is_argsort:
+        @wrap(**kwargs_lite)
+        def lessthan(a, b, vals):
+            return lt(vals[a], vals[b])
+    else:
+        @wrap(**kwargs_lite)
+        def lessthan(a, b, vals):
+            return lt(a, b)
+
+    @wrap(**kwargs_lite)
+    def argmergesort_inner(arr, vals, ws):
+        """The actual mergesort function
+
+        Parameters
+        ----------
+        arr : array [read+write]
+            The values being sorted inplace.  For argsort, this is the
+            indices.
+        vals : array [readonly]
+            ``None`` for normal sort.  In argsort, this is the actual array values.
+        ws : array [write]
+            The workspace.  Must be of size ``arr.size // 2``
+        """
+        if arr.size > SMALL_MERGESORT:
+            # Merge sort
+            mid = arr.size // 2
+
+            argmergesort_inner(arr[:mid], vals, ws)
+            argmergesort_inner(arr[mid:], vals, ws)
+
+            # Copy left half into workspace so we don't overwrite it
+            for i in range(mid):
+                ws[i] = arr[i]
+
+            # Merge
+            left = ws[:mid]
+            right = arr[mid:]
+            out = arr
+
+            i = j = k = 0
+            while i < left.size and j < right.size:
+                if not lessthan(right[j], left[i], vals):
+                    out[k] = left[i]
+                    i += 1
+                else:
+                    out[k] = right[j]
+                    j += 1
+                k += 1
+
+            # Leftovers
+            while i < left.size:
+                out[k] = left[i]
+                i += 1
+                k += 1
+
+            while j < right.size:
+                out[k] = right[j]
+                j += 1
+                k += 1
+        else:
+            # Insertion sort
+            i = 1
+            while i < arr.size:
+                j = i
+                while j > 0 and lessthan(arr[j], arr[j - 1], vals):
+                    arr[j - 1], arr[j] = arr[j], arr[j - 1]
+                    j -= 1
+                i += 1
+
+    # The top-level entry points
+
+    @wrap(no_cpython_wrapper=True)
+    def mergesort(arr):
+        "Inplace"
+        ws = np.empty(arr.size // 2, dtype=arr.dtype)
+        argmergesort_inner(arr, None, ws)
+        return arr
+
+
+    @wrap(no_cpython_wrapper=True)
+    def argmergesort(arr):
+        "Out-of-place"
+        idxs = np.arange(arr.size)
+        ws = np.empty(arr.size // 2, dtype=idxs.dtype)
+        argmergesort_inner(idxs, arr, ws)
+        return idxs
+
+    return MergesortImplementation(
+        run_mergesort=(argmergesort if is_argsort else mergesort)
+        )
+
+
+def make_jit_mergesort(*args, **kwargs):
+    from numba import njit
+    # NOTE: wrap with njit to allow recursion
+    #       because @register_jitable => @overload doesn't support recursion
+    return make_mergesort_impl(njit, *args, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_entry.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_entry.py
new file mode 100644
index 000000000..b95f9dd06
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_entry.py
@@ -0,0 +1,72 @@
+import sys
+import argparse
+import os
+import subprocess
+import json
+
+from .numba_sysinfo import display_sysinfo, get_sysinfo
+from .numba_gdbinfo import display_gdbinfo
+
+
+def make_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--annotate', help='Annotate source',
+                        action='store_true')
+    parser.add_argument('--dump-llvm', action="store_true",
+                        help='Print generated llvm assembly')
+    parser.add_argument('--dump-optimized', action='store_true',
+                        help='Dump the optimized llvm assembly')
+    parser.add_argument('--dump-assembly', action='store_true',
+                        help='Dump the LLVM generated assembly')
+    parser.add_argument('--annotate-html', nargs=1,
+                        help='Output source annotation as html')
+    parser.add_argument('-s', '--sysinfo', action="store_true",
+                        help='Output system information for bug reporting')
+    parser.add_argument('-g', '--gdbinfo', action="store_true",
+                        help='Output system information about gdb')
+    parser.add_argument('--sys-json', nargs=1,
+                        help='Saves the system info dict as a json file')
+    parser.add_argument('filename', nargs='?', help='Python source filename')
+    return parser
+
+
+def main():
+    parser = make_parser()
+    args = parser.parse_args()
+
+    if args.sysinfo:
+        print("System info:")
+        display_sysinfo()
+
+    if args.gdbinfo:
+        print("GDB info:")
+        display_gdbinfo()
+
+    if args.sysinfo or args.gdbinfo:
+        sys.exit(0)
+
+    if args.sys_json:
+        info = get_sysinfo()
+        info.update({'Start': info['Start'].isoformat()})
+        info.update({'Start UTC': info['Start UTC'].isoformat()})
+        with open(args.sys_json[0], 'w') as f:
+            json.dump(info, f, indent=4)
+        sys.exit(0)
+
+    os.environ['NUMBA_DUMP_ANNOTATION'] = str(int(args.annotate))
+    if args.annotate_html is not None:
+        try:
+            from jinja2 import Template
+        except ImportError:
+            raise ImportError("Please install the 'jinja2' package")
+        os.environ['NUMBA_DUMP_HTML'] = str(args.annotate_html[0])
+    os.environ['NUMBA_DUMP_LLVM'] = str(int(args.dump_llvm))
+    os.environ['NUMBA_DUMP_OPTIMIZED'] = str(int(args.dump_optimized))
+    os.environ['NUMBA_DUMP_ASSEMBLY'] = str(int(args.dump_assembly))
+
+    if args.filename:
+        cmd = [sys.executable, args.filename]
+        subprocess.call(cmd)
+    else:
+        print("numba: error: the following arguments are required: filename")
+        sys.exit(1)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_gdbinfo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_gdbinfo.py
new file mode 100644
index 000000000..db92cd24b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_gdbinfo.py
@@ -0,0 +1,161 @@
+"""Module for displaying information about Numba's gdb set up"""
+from collections import namedtuple
+import os
+import re
+import subprocess
+from textwrap import dedent
+from numba import config
+
+# Container for the output of the gdb info data collection
+_fields = ('binary_loc, extension_loc, py_ver, np_ver, supported')
+_gdb_info = namedtuple('_gdb_info', _fields)
+
+
+class _GDBTestWrapper():
+    """Wraps the gdb binary and has methods for checking what the gdb binary
+    has support for (Python and NumPy)."""
+
+    def __init__(self,):
+        gdb_binary = config.GDB_BINARY
+        if gdb_binary is None:
+            msg = ("No valid binary could be found for gdb named: "
+                   f"{config.GDB_BINARY}")
+            raise ValueError(msg)
+        self._gdb_binary = gdb_binary
+
+    def _run_cmd(self, cmd=()):
+        gdb_call = [self.gdb_binary, '-q',]
+        for x in cmd:
+            gdb_call.append('-ex')
+            gdb_call.append(x)
+        gdb_call.extend(['-ex', 'q'])
+        return subprocess.run(gdb_call, capture_output=True, timeout=10,
+                              text=True)
+
+    @property
+    def gdb_binary(self):
+        return self._gdb_binary
+
+    @classmethod
+    def success(cls, status):
+        return status.returncode == 0
+
+    def check_launch(self):
+        """Checks that gdb will launch ok"""
+        return self._run_cmd()
+
+    def check_python(self):
+        cmd = ("python from __future__ import print_function; "
+               "import sys; print(sys.version_info[:2])")
+        return self._run_cmd((cmd,))
+
+    def check_numpy(self):
+        cmd = ("python from __future__ import print_function; "
+               "import types; import numpy; "
+               "print(isinstance(numpy, types.ModuleType))")
+        return self._run_cmd((cmd,))
+
+    def check_numpy_version(self):
+        cmd = ("python from __future__ import print_function; "
+               "import types; import numpy;"
+               "print(numpy.__version__)")
+        return self._run_cmd((cmd,))
+
+
+def collect_gdbinfo():
+    """Prints information to stdout about the gdb setup that Numba has found"""
+
+    # State flags:
+    gdb_state = None
+    gdb_has_python = False
+    gdb_has_numpy = False
+    gdb_python_version = 'No Python support'
+    gdb_python_numpy_version = "No NumPy support"
+
+    # There are so many ways for gdb to not be working as expected. Surround
+    # the "is it working" tests with try/except and if there's an exception
+    # store it for processing later.
+    try:
+        # Check gdb exists
+        gdb_wrapper = _GDBTestWrapper()
+
+        # Check gdb works
+        status = gdb_wrapper.check_launch()
+        if not gdb_wrapper.success(status):
+            msg = (f"gdb at '{gdb_wrapper.gdb_binary}' does not appear to work."
+                   f"\nstdout: {status.stdout}\nstderr: {status.stderr}")
+            raise ValueError(msg)
+        gdb_state = gdb_wrapper.gdb_binary
+    except Exception as e:
+        gdb_state = f"Testing gdb binary failed. Reported Error: {e}"
+    else:
+        # Got this far, so gdb works, start checking what it supports
+        status = gdb_wrapper.check_python()
+        if gdb_wrapper.success(status):
+            version_match = re.match(r'\((\d+),\s+(\d+)\)',
+                                     status.stdout.strip())
+            if version_match is not None:
+                pymajor, pyminor = version_match.groups()
+                gdb_python_version = f"{pymajor}.{pyminor}"
+                gdb_has_python = True
+
+                status = gdb_wrapper.check_numpy()
+                if gdb_wrapper.success(status):
+                    if "Traceback" not in status.stderr.strip():
+                        if status.stdout.strip() == 'True':
+                            gdb_has_numpy = True
+                            gdb_python_numpy_version = "Unknown"
+                            # NumPy is present find the version
+                            status = gdb_wrapper.check_numpy_version()
+                            if gdb_wrapper.success(status):
+                                if "Traceback" not in status.stderr.strip():
+                                    gdb_python_numpy_version = \
+                                        status.stdout.strip()
+
+    # Work out what level of print-extension support is present in this gdb
+    if gdb_has_python:
+        if gdb_has_numpy:
+            print_ext_supported = "Full (Python and NumPy supported)"
+        else:
+            print_ext_supported = "Partial (Python only, no NumPy support)"
+    else:
+        print_ext_supported = "None"
+
+    # Work out print ext location
+    print_ext_file = "gdb_print_extension.py"
+    print_ext_path = os.path.join(os.path.dirname(__file__), print_ext_file)
+
+    # return!
+    return _gdb_info(gdb_state, print_ext_path, gdb_python_version,
+                     gdb_python_numpy_version, print_ext_supported)
+
+
+def display_gdbinfo(sep_pos=45):
+    """Displays the information collected by collect_gdbinfo.
+    """
+    gdb_info = collect_gdbinfo()
+    print('-' * 80)
+    fmt = f'%-{sep_pos}s : %-s'
+    # Display the information
+    print(fmt % ("Binary location", gdb_info.binary_loc))
+    print(fmt % ("Print extension location", gdb_info.extension_loc))
+    print(fmt % ("Python version", gdb_info.py_ver))
+    print(fmt % ("NumPy version", gdb_info.np_ver))
+    print(fmt % ("Numba printing extension support", gdb_info.supported))
+
+    print("")
+    print("To load the Numba gdb printing extension, execute the following "
+          "from the gdb prompt:")
+    print(f"\nsource {gdb_info.extension_loc}\n")
+    print('-' * 80)
+    warn = """
+    =============================================================
+    IMPORTANT: Before sharing you should remove any information
+    in the above that you wish to keep private e.g. paths.
+    =============================================================
+    """
+    print(dedent(warn))
+
+
+if __name__ == '__main__':
+    display_gdbinfo()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_sysinfo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_sysinfo.py
new file mode 100644
index 000000000..79860fac6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/numba_sysinfo.py
@@ -0,0 +1,667 @@
+import json
+import locale
+import multiprocessing
+import os
+import platform
+import textwrap
+import sys
+from contextlib import redirect_stdout
+from datetime import datetime
+from io import StringIO
+from subprocess import check_output, PIPE, CalledProcessError
+import numpy as np
+import llvmlite.binding as llvmbind
+from llvmlite import __version__ as llvmlite_version
+from numba import cuda as cu, __version__ as version_number
+from numba.cuda import cudadrv
+from numba.cuda.cudadrv.driver import driver as cudriver
+from numba.cuda.cudadrv.runtime import runtime as curuntime
+from numba.core import config
+
+_psutil_import = False
+try:
+    import psutil
+except ImportError:
+    pass
+else:
+    _psutil_import = True
+
+__all__ = ['get_sysinfo', 'display_sysinfo']
+
+# Keys of a `sysinfo` dictionary
+
+# Time info
+_start, _start_utc, _runtime = 'Start', 'Start UTC', 'Runtime'
+_numba_version = 'Numba Version'
+# Hardware info
+_machine = 'Machine'
+_cpu_name, _cpu_count = 'CPU Name', 'CPU Count'
+_cpus_allowed, _cpus_list = 'CPUs Allowed', 'List CPUs Allowed'
+_cpu_features = 'CPU Features'
+_cfs_quota, _cfs_period = 'CFS Quota', 'CFS Period',
+_cfs_restrict = 'CFS Restriction'
+_mem_total, _mem_available = 'Mem Total', 'Mem Available'
+# OS info
+_platform_name, _platform_release = 'Platform Name', 'Platform Release'
+_os_name, _os_version = 'OS Name', 'OS Version'
+_os_spec_version = 'OS Specific Version'
+_libc_version = 'Libc Version'
+# Python info
+_python_comp = 'Python Compiler'
+_python_impl = 'Python Implementation'
+_python_version = 'Python Version'
+_python_locale = 'Python Locale'
+# LLVM info
+_llvmlite_version = 'llvmlite Version'
+_llvm_version = 'LLVM Version'
+# CUDA info
+_cu_dev_init = 'CUDA Device Init'
+_cu_drv_ver = 'CUDA Driver Version'
+_cu_rt_ver = 'CUDA Runtime Version'
+_cu_nvidia_bindings = 'NVIDIA CUDA Bindings'
+_cu_nvidia_bindings_used = 'NVIDIA CUDA Bindings In Use'
+_cu_detect_out, _cu_lib_test = 'CUDA Detect Output', 'CUDA Lib Test'
+# NumPy info
+_numpy_version = 'NumPy Version'
+_numpy_supported_simd_features = 'NumPy Supported SIMD features'
+_numpy_supported_simd_dispatch = 'NumPy Supported SIMD dispatch'
+_numpy_supported_simd_baseline = 'NumPy Supported SIMD baseline'
+_numpy_AVX512_SKX_detected = 'NumPy AVX512_SKX detected'
+# SVML info
+_svml_state, _svml_loaded = 'SVML State', 'SVML Lib Loaded'
+_llvm_svml_patched = 'LLVM SVML Patched'
+_svml_operational = 'SVML Operational'
+# Threading layer info
+_tbb_thread, _tbb_error = 'TBB Threading', 'TBB Threading Error'
+_openmp_thread, _openmp_error = 'OpenMP Threading', 'OpenMP Threading Error'
+_openmp_vendor = 'OpenMP vendor'
+_wkq_thread, _wkq_error = 'Workqueue Threading', 'Workqueue Threading Error'
+# Numba info
+_numba_env_vars = 'Numba Env Vars'
+# Conda info
+_conda_build_ver, _conda_env_ver = 'Conda Build', 'Conda Env'
+_conda_platform, _conda_python_ver = 'Conda Platform', 'Conda Python Version'
+_conda_root_writable = 'Conda Root Writable'
+# Packages info
+_inst_pkg = 'Installed Packages'
+# Psutil info
+_psutil = 'Psutil Available'
+# Errors and warnings
+_errors = 'Errors'
+_warnings = 'Warnings'
+
+# Error and warning log
+_error_log = []
+_warning_log = []
+
+
+def get_os_spec_info(os_name):
+    # Linux man page for `/proc`:
+    # http://man7.org/linux/man-pages/man5/proc.5.html
+
+    # Windows documentation for `wmic OS`:
+    # https://docs.microsoft.com/en-us/windows/win32/cimwin32prov/cim-operatingsystem
+
+    # MacOS man page for `sysctl`:
+    # https://www.unix.com/man-page/osx/3/sysctl/
+    # MacOS man page for `vm_stat`:
+    # https://www.unix.com/man-page/osx/1/vm_stat/
+
+    class CmdBufferOut(tuple):
+        buffer_output_flag = True
+
+    class CmdReadFile(tuple):
+        read_file_flag = True
+
+    shell_params = {
+        'Linux': {
+            'cmd': (
+                CmdReadFile(('/sys/fs/cgroup/cpuacct/cpu.cfs_quota_us',)),
+                CmdReadFile(('/sys/fs/cgroup/cpuacct/cpu.cfs_period_us',)),
+            ),
+            'cmd_optional': (
+                CmdReadFile(('/proc/meminfo',)),
+                CmdReadFile(('/proc/self/status',)),
+            ),
+            'kwds': {
+                # output string fragment -> result dict key
+                'MemTotal:': _mem_total,
+                'MemAvailable:': _mem_available,
+                'Cpus_allowed:': _cpus_allowed,
+                'Cpus_allowed_list:': _cpus_list,
+                '/sys/fs/cgroup/cpuacct/cpu.cfs_quota_us': _cfs_quota,
+                '/sys/fs/cgroup/cpuacct/cpu.cfs_period_us': _cfs_period,
+            },
+        },
+        'Windows': {
+            'cmd': (),
+            'cmd_optional': (
+                CmdBufferOut(('wmic', 'OS', 'get', 'TotalVirtualMemorySize')),
+                CmdBufferOut(('wmic', 'OS', 'get', 'FreeVirtualMemory')),
+            ),
+            'kwds': {
+                # output string fragment -> result dict key
+                'TotalVirtualMemorySize': _mem_total,
+                'FreeVirtualMemory': _mem_available,
+            },
+        },
+        'Darwin': {
+            'cmd': (),
+            'cmd_optional': (
+                ('sysctl', 'hw.memsize'),
+                ('vm_stat'),
+            ),
+            'kwds': {
+                # output string fragment -> result dict key
+                'hw.memsize:': _mem_total,
+                'free:': _mem_available,
+            },
+            'units': {
+                _mem_total: 1,  # Size is given in bytes.
+                _mem_available: 4096,  # Size is given in 4kB pages.
+            },
+        },
+    }
+
+    os_spec_info = {}
+    params = shell_params.get(os_name, {})
+    cmd_selected = params.get('cmd', ())
+
+    if _psutil_import:
+        vm = psutil.virtual_memory()
+        os_spec_info.update({
+            _mem_total: vm.total,
+            _mem_available: vm.available,
+        })
+        p = psutil.Process()
+        cpus_allowed = p.cpu_affinity() if hasattr(p, 'cpu_affinity') else []
+        if cpus_allowed:
+            os_spec_info[_cpus_allowed] = len(cpus_allowed)
+            os_spec_info[_cpus_list] = ' '.join(str(n) for n in cpus_allowed)
+
+    else:
+        _warning_log.append(
+            "Warning (psutil): psutil cannot be imported. "
+            "For more accuracy, consider installing it.")
+        # Fallback to internal heuristics
+        cmd_selected += params.get('cmd_optional', ())
+
+    # Assuming the shell cmd returns a unique (k, v) pair per line
+    # or a unique (k, v) pair spread over several lines:
+    # Gather output in a list of strings containing a keyword and some value.
+    output = []
+    for cmd in cmd_selected:
+        if hasattr(cmd, 'read_file_flag'):
+            # Open file within Python
+            if os.path.exists(cmd[0]):
+                try:
+                    with open(cmd[0], 'r') as f:
+                        out = f.readlines()
+                        if out:
+                            out[0] = ' '.join((cmd[0], out[0]))
+                            output.extend(out)
+                except OSError as e:
+                    _error_log.append(f'Error (file read): {e}')
+                    continue
+            else:
+                _warning_log.append('Warning (no file): {}'.format(cmd[0]))
+                continue
+        else:
+            # Spawn a subprocess
+            try:
+                out = check_output(cmd, stderr=PIPE)
+            except (OSError, CalledProcessError) as e:
+                _error_log.append(f'Error (subprocess): {e}')
+                continue
+            if hasattr(cmd, 'buffer_output_flag'):
+                out = b' '.join(line for line in out.splitlines()) + b'\n'
+            output.extend(out.decode().splitlines())
+
+    # Extract (k, output) pairs by searching for keywords in output
+    kwds = params.get('kwds', {})
+    for line in output:
+        match = kwds.keys() & line.split()
+        if match and len(match) == 1:
+            k = kwds[match.pop()]
+            os_spec_info[k] = line
+        elif len(match) > 1:
+            print(f'Ambiguous output: {line}')
+
+    # Try to extract something meaningful from output string
+    def format():
+        # CFS restrictions
+        split = os_spec_info.get(_cfs_quota, '').split()
+        if split:
+            os_spec_info[_cfs_quota] = float(split[-1])
+        split = os_spec_info.get(_cfs_period, '').split()
+        if split:
+            os_spec_info[_cfs_period] = float(split[-1])
+        if os_spec_info.get(_cfs_quota, -1) != -1:
+            cfs_quota = os_spec_info.get(_cfs_quota, '')
+            cfs_period = os_spec_info.get(_cfs_period, '')
+            runtime_amount = cfs_quota / cfs_period
+            os_spec_info[_cfs_restrict] = runtime_amount
+
+    def format_optional():
+        # Memory
+        units = {_mem_total: 1024, _mem_available: 1024}
+        units.update(params.get('units', {}))
+        for k in (_mem_total, _mem_available):
+            digits = ''.join(d for d in os_spec_info.get(k, '') if d.isdigit())
+            os_spec_info[k] = int(digits or 0) * units[k]
+        # Accessible CPUs
+        split = os_spec_info.get(_cpus_allowed, '').split()
+        if split:
+            n = split[-1]
+            n = n.split(',')[-1]
+            os_spec_info[_cpus_allowed] = str(bin(int(n or 0, 16))).count('1')
+        split = os_spec_info.get(_cpus_list, '').split()
+        if split:
+            os_spec_info[_cpus_list] = split[-1]
+
+    try:
+        format()
+        if not _psutil_import:
+            format_optional()
+    except Exception as e:
+        _error_log.append(f'Error (format shell output): {e}')
+
+    # Call OS specific functions
+    os_specific_funcs = {
+        'Linux': {
+            _libc_version: lambda: ' '.join(platform.libc_ver())
+        },
+        'Windows': {
+            _os_spec_version: lambda: ' '.join(
+                s for s in platform.win32_ver()),
+        },
+        'Darwin': {
+            _os_spec_version: lambda: ''.join(
+                i or ' ' for s in tuple(platform.mac_ver()) for i in s),
+        },
+    }
+    key_func = os_specific_funcs.get(os_name, {})
+    os_spec_info.update({k: f() for k, f in key_func.items()})
+    return os_spec_info
+
+
+def get_sysinfo():
+
+    # Gather the information that shouldn't raise exceptions
+    sys_info = {
+        _start: datetime.now(),
+        _start_utc: datetime.utcnow(),
+        _machine: platform.machine(),
+        _cpu_name: llvmbind.get_host_cpu_name(),
+        _cpu_count: multiprocessing.cpu_count(),
+        _platform_name: platform.platform(aliased=True),
+        _platform_release: platform.release(),
+        _os_name: platform.system(),
+        _os_version: platform.version(),
+        _python_comp: platform.python_compiler(),
+        _python_impl: platform.python_implementation(),
+        _python_version: platform.python_version(),
+        _numba_env_vars: {k: v for (k, v) in os.environ.items()
+                          if k.startswith('NUMBA_')},
+        _numba_version: version_number,
+        _llvm_version: '.'.join(str(i) for i in llvmbind.llvm_version_info),
+        _llvmlite_version: llvmlite_version,
+        _psutil: _psutil_import,
+    }
+
+    # CPU features
+    try:
+        feature_map = llvmbind.get_host_cpu_features()
+    except RuntimeError as e:
+        _error_log.append(f'Error (CPU features): {e}')
+    else:
+        features = sorted([key for key, value in feature_map.items() if value])
+        sys_info[_cpu_features] = ' '.join(features)
+
+    # Python locale
+    # On MacOSX, getdefaultlocale can raise. Check again if Py > 3.7.5
+    try:
+        # If $LANG is unset, getdefaultlocale() can return (None, None), make
+        # sure we can encode this as strings by casting explicitly.
+        sys_info[_python_locale] = '.'.join([str(i) for i in
+                                             locale.getdefaultlocale()])
+    except Exception as e:
+        _error_log.append(f'Error (locale): {e}')
+
+    # CUDA information
+    try:
+        cu.list_devices()[0]  # will a device initialise?
+    except Exception as e:
+        sys_info[_cu_dev_init] = False
+        msg_not_found = "CUDA driver library cannot be found"
+        msg_disabled_by_user = "CUDA is disabled"
+        msg_end = " or no CUDA enabled devices are present."
+        msg_generic_problem = "CUDA device initialisation problem."
+        msg = getattr(e, 'msg', None)
+        if msg is not None:
+            if msg_not_found in msg:
+                err_msg = msg_not_found + msg_end
+            elif msg_disabled_by_user in msg:
+                err_msg = msg_disabled_by_user + msg_end
+            else:
+                err_msg = msg_generic_problem + " Message:" + msg
+        else:
+            err_msg = msg_generic_problem + " " + str(e)
+        # Best effort error report
+        _warning_log.append("Warning (cuda): %s\nException class: %s" %
+                            (err_msg, str(type(e))))
+    else:
+        try:
+            sys_info[_cu_dev_init] = True
+
+            output = StringIO()
+            with redirect_stdout(output):
+                cu.detect()
+            sys_info[_cu_detect_out] = output.getvalue()
+            output.close()
+
+            sys_info[_cu_drv_ver] = '%s.%s' % cudriver.get_version()
+            sys_info[_cu_rt_ver] = '%s.%s' % curuntime.get_version()
+
+            output = StringIO()
+            with redirect_stdout(output):
+                cudadrv.libs.test(sys.platform, print_paths=False)
+            sys_info[_cu_lib_test] = output.getvalue()
+            output.close()
+
+            try:
+                from cuda import cuda  # noqa: F401
+                nvidia_bindings_available = True
+            except ImportError:
+                nvidia_bindings_available = False
+            sys_info[_cu_nvidia_bindings] = nvidia_bindings_available
+
+            nv_binding_used = bool(cudadrv.driver.USE_NV_BINDING)
+            sys_info[_cu_nvidia_bindings_used] = nv_binding_used
+        except Exception as e:
+            _warning_log.append(
+                "Warning (cuda): Probing CUDA failed "
+                "(device and driver present, runtime problem?)\n"
+                f"(cuda) {type(e)}: {e}")
+
+    # NumPy information
+    sys_info[_numpy_version] = np.version.full_version
+    try:
+        # NOTE: These consts were added in NumPy 1.20
+        from numpy.core._multiarray_umath import (__cpu_features__,
+                                                  __cpu_dispatch__,
+                                                  __cpu_baseline__,)
+    except ImportError:
+        sys_info[_numpy_AVX512_SKX_detected] = False
+    else:
+        feat_filtered = [k for k, v in __cpu_features__.items() if v]
+        sys_info[_numpy_supported_simd_features] = feat_filtered
+        sys_info[_numpy_supported_simd_dispatch] = __cpu_dispatch__
+        sys_info[_numpy_supported_simd_baseline] = __cpu_baseline__
+        sys_info[_numpy_AVX512_SKX_detected] = \
+            __cpu_features__.get("AVX512_SKX", False)
+
+    # SVML information
+    # Replicate some SVML detection logic from numba.__init__ here.
+    # If SVML load fails in numba.__init__ the splitting of the logic
+    # here will help diagnosing the underlying issue.
+    svml_lib_loaded = True
+    try:
+        if sys.platform.startswith('linux'):
+            llvmbind.load_library_permanently("libsvml.so")
+        elif sys.platform.startswith('darwin'):
+            llvmbind.load_library_permanently("libsvml.dylib")
+        elif sys.platform.startswith('win'):
+            llvmbind.load_library_permanently("svml_dispmd")
+        else:
+            svml_lib_loaded = False
+    except Exception:
+        svml_lib_loaded = False
+    func = getattr(llvmbind.targets, "has_svml", None)
+    sys_info[_llvm_svml_patched] = func() if func else False
+    sys_info[_svml_state] = config.USING_SVML
+    sys_info[_svml_loaded] = svml_lib_loaded
+    sys_info[_svml_operational] = all((
+        sys_info[_svml_state],
+        sys_info[_svml_loaded],
+        sys_info[_llvm_svml_patched],
+    ))
+
+    # Check which threading backends are available.
+    def parse_error(e, backend):
+        # parses a linux based error message, this is to provide feedback
+        # and hide user paths etc
+        try:
+            path, problem, symbol = [x.strip() for x in e.msg.split(':')]
+            extn_dso = os.path.split(path)[1]
+            if backend in extn_dso:
+                return "%s: %s" % (problem, symbol)
+        except Exception:
+            pass
+        return "Unknown import problem."
+
+    try:
+        # check import is ok, this means the DSO linkage is working
+        from numba.np.ufunc import tbbpool  # NOQA
+        # check that the version is compatible, this is a check performed at
+        # runtime (well, compile time), it will also ImportError if there's
+        # a problem.
+        from numba.np.ufunc.parallel import _check_tbb_version_compatible
+        _check_tbb_version_compatible()
+        sys_info[_tbb_thread] = True
+    except ImportError as e:
+        # might be a missing symbol due to e.g. tbb libraries missing
+        sys_info[_tbb_thread] = False
+        sys_info[_tbb_error] = parse_error(e, 'tbbpool')
+
+    try:
+        from numba.np.ufunc import omppool
+        sys_info[_openmp_thread] = True
+        sys_info[_openmp_vendor] = omppool.openmp_vendor
+    except ImportError as e:
+        sys_info[_openmp_thread] = False
+        sys_info[_openmp_error] = parse_error(e, 'omppool')
+
+    try:
+        from numba.np.ufunc import workqueue  # NOQA
+        sys_info[_wkq_thread] = True
+    except ImportError as e:
+        sys_info[_wkq_thread] = True
+        sys_info[_wkq_error] = parse_error(e, 'workqueue')
+
+    # Look for conda and installed packages information
+    cmd = ('conda', 'info', '--json')
+    try:
+        conda_out = check_output(cmd)
+    except Exception as e:
+        _warning_log.append(f'Warning: Conda not available.\n Error was {e}\n')
+        # Conda is not available, try pip list to list installed packages
+        cmd = (sys.executable, '-m', 'pip', 'list')
+        try:
+            reqs = check_output(cmd)
+        except Exception as e:
+            _error_log.append(f'Error (pip): {e}')
+        else:
+            sys_info[_inst_pkg] = reqs.decode().splitlines()
+
+    else:
+        jsond = json.loads(conda_out.decode())
+        keys = {
+            'conda_build_version': _conda_build_ver,
+            'conda_env_version': _conda_env_ver,
+            'platform': _conda_platform,
+            'python_version': _conda_python_ver,
+            'root_writable': _conda_root_writable,
+        }
+        for conda_k, sysinfo_k in keys.items():
+            sys_info[sysinfo_k] = jsond.get(conda_k, 'N/A')
+
+        # Get info about packages in current environment
+        cmd = ('conda', 'list')
+        try:
+            conda_out = check_output(cmd)
+        except CalledProcessError as e:
+            _error_log.append(f'Error (conda): {e}')
+        else:
+            data = conda_out.decode().splitlines()
+            sys_info[_inst_pkg] = [l for l in data if not l.startswith('#')]
+
+    sys_info.update(get_os_spec_info(sys_info[_os_name]))
+    sys_info[_errors] = _error_log
+    sys_info[_warnings] = _warning_log
+    sys_info[_runtime] = (datetime.now() - sys_info[_start]).total_seconds()
+    return sys_info
+
+
+def display_sysinfo(info=None, sep_pos=45):
+    class DisplayMap(dict):
+        display_map_flag = True
+
+    class DisplaySeq(tuple):
+        display_seq_flag = True
+
+    class DisplaySeqMaps(tuple):
+        display_seqmaps_flag = True
+
+    if info is None:
+        info = get_sysinfo()
+
+    fmt = f'%-{sep_pos}s : %-s'
+    MB = 1024**2
+    template = (
+        ("-" * 80,),
+        ("__Time Stamp__",),
+        ("Report started (local time)", info.get(_start, '?')),
+        ("UTC start time", info.get(_start_utc, '?')),
+        ("Running time (s)", info.get(_runtime, '?')),
+        ("",),
+        ("__Hardware Information__",),
+        ("Machine", info.get(_machine, '?')),
+        ("CPU Name", info.get(_cpu_name, '?')),
+        ("CPU Count", info.get(_cpu_count, '?')),
+        ("Number of accessible CPUs", info.get(_cpus_allowed, '?')),
+        ("List of accessible CPUs cores", info.get(_cpus_list, '?')),
+        ("CFS Restrictions (CPUs worth of runtime)",
+            info.get(_cfs_restrict, 'None')),
+        ("",),
+        ("CPU Features", '\n'.join(
+            ' ' * (sep_pos + 3) + l if i else l
+            for i, l in enumerate(
+                textwrap.wrap(
+                    info.get(_cpu_features, '?'),
+                    width=79 - sep_pos
+                )
+            )
+        )),
+        ("",),
+        ("Memory Total (MB)", info.get(_mem_total, 0) // MB or '?'),
+        ("Memory Available (MB)"
+            if info.get(_os_name, '') != 'Darwin' or info.get(_psutil, False)
+            else "Free Memory (MB)", info.get(_mem_available, 0) // MB or '?'),
+        ("",),
+        ("__OS Information__",),
+        ("Platform Name", info.get(_platform_name, '?')),
+        ("Platform Release", info.get(_platform_release, '?')),
+        ("OS Name", info.get(_os_name, '?')),
+        ("OS Version", info.get(_os_version, '?')),
+        ("OS Specific Version", info.get(_os_spec_version, '?')),
+        ("Libc Version", info.get(_libc_version, '?')),
+        ("",),
+        ("__Python Information__",),
+        DisplayMap({k: v for k, v in info.items() if k.startswith('Python')}),
+        ("",),
+        ("__Numba Toolchain Versions__",),
+        ("Numba Version", info.get(_numba_version, '?')),
+        ("llvmlite Version", info.get(_llvmlite_version, '?')),
+        ("",),
+        ("__LLVM Information__",),
+        ("LLVM Version", info.get(_llvm_version, '?')),
+        ("",),
+        ("__CUDA Information__",),
+        ("CUDA Device Initialized", info.get(_cu_dev_init, '?')),
+        ("CUDA Driver Version", info.get(_cu_drv_ver, '?')),
+        ("CUDA Runtime Version", info.get(_cu_rt_ver, '?')),
+        ("CUDA NVIDIA Bindings Available", info.get(_cu_nvidia_bindings, '?')),
+        ("CUDA NVIDIA Bindings In Use",
+         info.get(_cu_nvidia_bindings_used, '?')),
+        ("CUDA Detect Output:",),
+        (info.get(_cu_detect_out, "None"),),
+        ("CUDA Libraries Test Output:",),
+        (info.get(_cu_lib_test, "None"),),
+        ("",),
+        ("__NumPy Information__",),
+        ("NumPy Version", info.get(_numpy_version, '?')),
+        ("NumPy Supported SIMD features",
+         DisplaySeq(info.get(_numpy_supported_simd_features, [])
+                    or ('None found.',))),
+        ("NumPy Supported SIMD dispatch",
+         DisplaySeq(info.get(_numpy_supported_simd_dispatch, [])
+                    or ('None found.',))),
+        ("NumPy Supported SIMD baseline",
+         DisplaySeq(info.get(_numpy_supported_simd_baseline, [])
+                    or ('None found.',))),
+        ("NumPy AVX512_SKX support detected",
+         info.get(_numpy_AVX512_SKX_detected, '?')),
+        ("",),
+        ("__SVML Information__",),
+        ("SVML State, config.USING_SVML", info.get(_svml_state, '?')),
+        ("SVML Library Loaded", info.get(_svml_loaded, '?')),
+        ("llvmlite Using SVML Patched LLVM", info.get(_llvm_svml_patched, '?')),
+        ("SVML Operational", info.get(_svml_operational, '?')),
+        ("",),
+        ("__Threading Layer Information__",),
+        ("TBB Threading Layer Available", info.get(_tbb_thread, '?')),
+        ("+-->TBB imported successfully." if info.get(_tbb_thread, '?')
+            else f"+--> Disabled due to {info.get(_tbb_error, '?')}",),
+        ("OpenMP Threading Layer Available", info.get(_openmp_thread, '?')),
+        (f"+-->Vendor: {info.get(_openmp_vendor, '?')}"
+            if info.get(_openmp_thread, False)
+            else f"+--> Disabled due to {info.get(_openmp_error, '?')}",),
+        ("Workqueue Threading Layer Available", info.get(_wkq_thread, '?')),
+        ("+-->Workqueue imported successfully." if info.get(_wkq_thread, False)
+            else f"+--> Disabled due to {info.get(_wkq_error, '?')}",),
+        ("",),
+        ("__Numba Environment Variable Information__",),
+        (DisplayMap(info.get(_numba_env_vars, {})) or ('None found.',)),
+        ("",),
+        ("__Conda Information__",),
+        (DisplayMap({k: v for k, v in info.items()
+                     if k.startswith('Conda')}) or ("Conda not available.",)),
+        ("",),
+        ("__Installed Packages__",),
+        DisplaySeq(info.get(_inst_pkg, ("Couldn't retrieve packages info.",))),
+        ("",),
+        ("__Error log__" if info.get(_errors, [])
+            else "No errors reported.",),
+        DisplaySeq(info.get(_errors, [])),
+        ("",),
+        ("__Warning log__" if info.get(_warnings, [])
+            else "No warnings reported.",),
+        DisplaySeq(info.get(_warnings, [])),
+        ("-" * 80,),
+        ("If requested, please copy and paste the information between\n"
+         "the dashed (----) lines, or from a given specific section as\n"
+         "appropriate.\n\n"
+         "=============================================================\n"
+         "IMPORTANT: Please ensure that you are happy with sharing the\n"
+         "contents of the information present, any information that you\n"
+         "wish to keep private you should remove before sharing.\n"
+         "=============================================================\n",),
+    )
+    for t in template:
+        if hasattr(t, 'display_seq_flag'):
+            print(*t, sep='\n')
+        elif hasattr(t, 'display_map_flag'):
+            print(*tuple(fmt % (k, v) for (k, v) in t.items()), sep='\n')
+        elif hasattr(t, 'display_seqmaps_flag'):
+            for d in t:
+                print(*tuple(fmt % ('\t' + k, v) for (k, v) in d.items()),
+                      sep='\n', end='\n')
+        elif len(t) == 2:
+            print(fmt % t)
+        else:
+            print(*t)
+
+
+if __name__ == '__main__':
+    display_sysinfo()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/quicksort.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/quicksort.py
new file mode 100644
index 000000000..91425a80b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/quicksort.py
@@ -0,0 +1,258 @@
+import collections
+
+import numpy as np
+
+from numba.core import types
+
+
+QuicksortImplementation = collections.namedtuple(
+    'QuicksortImplementation',
+    (# The compile function itself
+     'compile',
+     # All subroutines exercised by test_sort
+     'partition', 'partition3', 'insertion_sort',
+     # The top-level function
+     'run_quicksort',
+     ))
+
+
+Partition = collections.namedtuple('Partition', ('start', 'stop'))
+
+# Under this size, switch to a simple insertion sort
+SMALL_QUICKSORT = 15
+
+MAX_STACK = 100
+
+
+def make_quicksort_impl(wrap, lt=None, is_argsort=False, is_list=False, is_np_array=False):
+
+    intp = types.intp
+    zero = intp(0)
+
+    # Two subroutines to make the core algorithm generic wrt. argsort
+    # or normal sorting.  Note the genericity may make basic sort()
+    # slightly slower (~5%)
+    if is_argsort:
+        if is_list:
+            @wrap
+            def make_res(A):
+                return [x for x in range(len(A))]
+        else:
+            @wrap
+            def make_res(A):
+                return np.arange(A.size)
+
+        @wrap
+        def GET(A, idx_or_val):
+            return A[idx_or_val]
+
+    else:
+        @wrap
+        def make_res(A):
+            return A
+
+        @wrap
+        def GET(A, idx_or_val):
+            return idx_or_val
+
+    def default_lt(a, b):
+        """
+        Trivial comparison function between two keys.
+        """
+        return a < b
+
+    LT = wrap(lt if lt is not None else default_lt)
+
+    @wrap
+    def insertion_sort(A, R, low, high):
+        """
+        Insertion sort A[low:high + 1]. Note the inclusive bounds.
+        """
+        assert low >= 0
+        if high <= low:
+            return
+
+        for i in range(low + 1, high + 1):
+            k = R[i]
+            v = GET(A, k)
+            # Insert v into A[low:i]
+            j = i
+            while j > low and LT(v, GET(A, R[j - 1])):
+                # Make place for moving A[i] downwards
+                R[j] = R[j - 1]
+                j -= 1
+            R[j] = k
+
+    @wrap
+    def partition(A, R, low, high):
+        """
+        Partition A[low:high + 1] around a chosen pivot.  The pivot's index
+        is returned.
+        """
+        assert low >= 0
+        assert high > low
+
+        mid = (low + high) >> 1
+        # NOTE: the pattern of swaps below for the pivot choice and the
+        # partitioning gives good results (i.e. regular O(n log n))
+        # on sorted, reverse-sorted, and uniform arrays.  Subtle changes
+        # risk breaking this property.
+
+        # median of three {low, middle, high}
+        if LT(GET(A, R[mid]), GET(A, R[low])):
+            R[low], R[mid] = R[mid], R[low]
+        if LT(GET(A, R[high]), GET(A, R[mid])):
+            R[high], R[mid] = R[mid], R[high]
+        if LT(GET(A, R[mid]), GET(A, R[low])):
+            R[low], R[mid] = R[mid], R[low]
+        pivot = GET(A, R[mid])
+
+        # Temporarily stash the pivot at the end
+        R[high], R[mid] = R[mid], R[high]
+        i = low
+        j = high - 1
+        while True:
+            while i < high and LT(GET(A, R[i]), pivot):
+                i += 1
+            while j >= low and LT(pivot, GET(A, R[j])):
+                j -= 1
+            if i >= j:
+                break
+            R[i], R[j] = R[j], R[i]
+            i += 1
+            j -= 1
+        # Put the pivot back in its final place (all items before `i`
+        # are smaller than the pivot, all items at/after `i` are larger)
+        R[i], R[high] = R[high], R[i]
+        return i
+
+    @wrap
+    def partition3(A, low, high):
+        """
+        Three-way partition [low, high) around a chosen pivot.
+        A tuple (lt, gt) is returned such that:
+            - all elements in [low, lt) are < pivot
+            - all elements in [lt, gt] are == pivot
+            - all elements in (gt, high] are > pivot
+        """
+        mid = (low + high) >> 1
+        # median of three {low, middle, high}
+        if LT(A[mid], A[low]):
+            A[low], A[mid] = A[mid], A[low]
+        if LT(A[high], A[mid]):
+            A[high], A[mid] = A[mid], A[high]
+        if LT(A[mid], A[low]):
+            A[low], A[mid] = A[mid], A[low]
+        pivot = A[mid]
+
+        A[low], A[mid] = A[mid], A[low]
+        lt = low
+        gt = high
+        i = low + 1
+        while i <= gt:
+            if LT(A[i], pivot):
+                A[lt], A[i] = A[i], A[lt]
+                lt += 1
+                i += 1
+            elif LT(pivot, A[i]):
+                A[gt], A[i] = A[i], A[gt]
+                gt -= 1
+            else:
+                i += 1
+        return lt, gt
+
+    @wrap
+    def run_quicksort1(A):
+        R = make_res(A)
+
+        if len(A) < 2:
+            return R
+
+        stack = [Partition(zero, zero)] * MAX_STACK
+        stack[0] = Partition(zero, len(A) - 1)
+        n = 1
+
+        while n > 0:
+            n -= 1
+            low, high = stack[n]
+            # Partition until it becomes more efficient to do an insertion sort
+            while high - low >= SMALL_QUICKSORT:
+                assert n < MAX_STACK
+                i = partition(A, R, low, high)
+                # Push largest partition on the stack
+                if high - i > i - low:
+                    # Right is larger
+                    if high > i:
+                        stack[n] = Partition(i + 1, high)
+                        n += 1
+                    high = i - 1
+                else:
+                    if i > low:
+                        stack[n] = Partition(low, i - 1)
+                        n += 1
+                    low = i + 1
+
+            insertion_sort(A, R, low, high)
+
+        return R
+
+    if is_np_array:
+        @wrap
+        def run_quicksort(A):
+            if A.ndim == 1:
+                return run_quicksort1(A)
+            else:
+                for idx in np.ndindex(A.shape[:-1]):
+                    run_quicksort1(A[idx])
+                return A
+    else:
+        @wrap
+        def run_quicksort(A):
+            return run_quicksort1(A)
+
+    # Unused quicksort implementation based on 3-way partitioning; the
+    # partitioning scheme turns out exhibiting bad behaviour on sorted arrays.
+    @wrap
+    def _run_quicksort(A):
+        stack = [Partition(zero, zero)] * 100
+        stack[0] = Partition(zero, len(A) - 1)
+        n = 1
+
+        while n > 0:
+            n -= 1
+            low, high = stack[n]
+            # Partition until it becomes more efficient to do an insertion sort
+            while high - low >= SMALL_QUICKSORT:
+                assert n < MAX_STACK
+                l, r = partition3(A, low, high)
+                # One trivial (empty) partition => iterate on the other
+                if r == high:
+                    high = l - 1
+                elif l == low:
+                    low = r + 1
+                # Push largest partition on the stack
+                elif high - r > l - low:
+                    # Right is larger
+                    stack[n] = Partition(r + 1, high)
+                    n += 1
+                    high = l - 1
+                else:
+                    stack[n] = Partition(low, l - 1)
+                    n += 1
+                    low = r + 1
+
+            insertion_sort(A, low, high)
+
+
+    return QuicksortImplementation(wrap,
+                                   partition, partition3, insertion_sort,
+                                   run_quicksort)
+
+
+def make_py_quicksort(*args, **kwargs):
+    return make_quicksort_impl((lambda f: f), *args, **kwargs)
+
+def make_jit_quicksort(*args, **kwargs):
+    from numba.core.extending import register_jitable
+    return make_quicksort_impl((lambda f: register_jitable(f)),
+                               *args, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/special.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/special.py
new file mode 100644
index 000000000..36a44cebe
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/special.py
@@ -0,0 +1,104 @@
+import numpy as np
+
+from numba.core.typing.typeof import typeof
+from numba.core.typing.asnumbatype import as_numba_type
+
+
+def pndindex(*args):
+    """ Provides an n-dimensional parallel iterator that generates index tuples
+    for each iteration point. Sequentially, pndindex is identical to np.ndindex.
+    """
+    return np.ndindex(*args)
+
+
+class prange(object):
+    """ Provides a 1D parallel iterator that generates a sequence of integers.
+    In non-parallel contexts, prange is identical to range.
+    """
+    def __new__(cls, *args):
+        return range(*args)
+
+
+def _gdb_python_call_gen(func_name, *args):
+    # generates a call to a function containing a compiled in gdb command,
+    # this is to make `numba.gdb*` work in the interpreter.
+    import numba
+    fn = getattr(numba, func_name)
+    argstr = ','.join(['"%s"' for _ in args]) % args
+    defn = """def _gdb_func_injection():\n\t%s(%s)\n
+    """ % (func_name, argstr)
+    l = {}
+    exec(defn, {func_name: fn}, l)
+    return numba.njit(l['_gdb_func_injection'])
+
+
+def gdb(*args):
+    """
+    Calling this function will invoke gdb and attach it to the current process
+    at the call site. Arguments are strings in the gdb command language syntax
+    which will be executed by gdb once initialisation has occurred.
+    """
+    _gdb_python_call_gen('gdb', *args)()
+
+
+def gdb_breakpoint():
+    """
+    Calling this function will inject a breakpoint at the call site that is
+    recognised by both `gdb` and `gdb_init`, this is to allow breaking at
+    multiple points. gdb will stop in the user defined code just after the frame
+    employed by the breakpoint returns.
+    """
+    _gdb_python_call_gen('gdb_breakpoint')()
+
+
+def gdb_init(*args):
+    """
+    Calling this function will invoke gdb and attach it to the current process
+    at the call site, then continue executing the process under gdb's control.
+    Arguments are strings in the gdb command language syntax which will be
+    executed by gdb once initialisation has occurred.
+    """
+    _gdb_python_call_gen('gdb_init', *args)()
+
+
+def literally(obj):
+    """Forces Numba to interpret *obj* as an Literal value.
+
+    *obj* must be either a literal or an argument of the caller function, where
+    the argument must be bound to a literal. The literal requirement
+    propagates up the call stack.
+
+    This function is intercepted by the compiler to alter the compilation
+    behavior to wrap the corresponding function parameters as ``Literal``.
+    It has **no effect** outside of nopython-mode (interpreter, and objectmode).
+
+    The current implementation detects literal arguments in two ways:
+
+    1. Scans for uses of ``literally`` via a compiler pass.
+    2. ``literally`` is overloaded to raise ``numba.errors.ForceLiteralArg``
+       to signal the dispatcher to treat the corresponding parameter
+       differently. This mode is to support indirect use (via a function call).
+
+    The execution semantic of this function is equivalent to an identity
+    function.
+
+    See :ghfile:`numba/tests/test_literal_dispatch.py` for examples.
+    """
+    return obj
+
+
+def literal_unroll(container):
+    return container
+
+
+__all__ = [
+    'typeof',
+    'as_numba_type',
+    'prange',
+    'pndindex',
+    'gdb',
+    'gdb_breakpoint',
+    'gdb_init',
+    'literally',
+    'literal_unroll',
+]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/timsort.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/timsort.py
new file mode 100644
index 000000000..ba1560e39
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/misc/timsort.py
@@ -0,0 +1,943 @@
+"""
+Timsort implementation.  Mostly adapted from CPython's listobject.c.
+
+For more information, see listsort.txt in CPython's source tree.
+"""
+
+
+import collections
+
+from numba.core import types
+
+
+TimsortImplementation = collections.namedtuple(
+    'TimsortImplementation',
+    (# The compile function itself
+     'compile',
+     # All subroutines exercised by test_sort
+     'count_run', 'binarysort', 'gallop_left', 'gallop_right',
+     'merge_init', 'merge_append', 'merge_pop',
+     'merge_compute_minrun', 'merge_lo', 'merge_hi', 'merge_at',
+     'merge_force_collapse', 'merge_collapse',
+     # The top-level functions
+     'run_timsort', 'run_timsort_with_values'
+     ))
+
+
+# The maximum number of entries in a MergeState's pending-runs stack.
+# This is enough to sort arrays of size up to about
+#    32 * phi ** MAX_MERGE_PENDING
+# where phi ~= 1.618.  85 is ridiculously large enough, good for an array
+# with 2**64 elements.
+# NOTE this implementation doesn't depend on it (the stack is dynamically
+# allocated), but it's still good to check as an invariant.
+MAX_MERGE_PENDING  = 85
+
+# When we get into galloping mode, we stay there until both runs win less
+# often than MIN_GALLOP consecutive times.  See listsort.txt for more info.
+MIN_GALLOP = 7
+
+# Start size for temp arrays.
+MERGESTATE_TEMP_SIZE = 256
+
+# A mergestate is a named tuple with the following members:
+#  - *min_gallop* is an integer controlling when we get into galloping mode
+#  - *keys* is a temp list for merging keys
+#  - *values* is a temp list for merging values, if needed
+#  - *pending* is a stack of pending runs to be merged
+#  - *n* is the current stack length of *pending*
+
+MergeState = collections.namedtuple(
+    'MergeState', ('min_gallop', 'keys', 'values', 'pending', 'n'))
+
+
+MergeRun = collections.namedtuple('MergeRun', ('start', 'size'))
+
+
+def make_timsort_impl(wrap, make_temp_area):
+
+    make_temp_area = wrap(make_temp_area)
+    intp = types.intp
+    zero = intp(0)
+
+    @wrap
+    def has_values(keys, values):
+        return values is not keys
+
+    @wrap
+    def merge_init(keys):
+        """
+        Initialize a MergeState for a non-keyed sort.
+        """
+        temp_size = min(len(keys) // 2 + 1, MERGESTATE_TEMP_SIZE)
+        temp_keys = make_temp_area(keys, temp_size)
+        temp_values = temp_keys
+        pending = [MergeRun(zero, zero)] * MAX_MERGE_PENDING
+        return MergeState(intp(MIN_GALLOP), temp_keys, temp_values, pending, zero)
+
+    @wrap
+    def merge_init_with_values(keys, values):
+        """
+        Initialize a MergeState for a keyed sort.
+        """
+        temp_size = min(len(keys) // 2 + 1, MERGESTATE_TEMP_SIZE)
+        temp_keys = make_temp_area(keys, temp_size)
+        temp_values = make_temp_area(values, temp_size)
+        pending = [MergeRun(zero, zero)] * MAX_MERGE_PENDING
+        return MergeState(intp(MIN_GALLOP), temp_keys, temp_values, pending, zero)
+
+    @wrap
+    def merge_append(ms, run):
+        """
+        Append a run on the merge stack.
+        """
+        n = ms.n
+        assert n < MAX_MERGE_PENDING
+        ms.pending[n] = run
+        return MergeState(ms.min_gallop, ms.keys, ms.values, ms.pending, n + 1)
+
+    @wrap
+    def merge_pop(ms):
+        """
+        Pop the top run from the merge stack.
+        """
+        return MergeState(ms.min_gallop, ms.keys, ms.values, ms.pending, ms.n - 1)
+
+    @wrap
+    def merge_getmem(ms, need):
+        """
+        Ensure enough temp memory for 'need' items is available.
+        """
+        alloced = len(ms.keys)
+        if need <= alloced:
+            return ms
+        # Over-allocate
+        while alloced < need:
+            alloced = alloced << 1
+        # Don't realloc!  That can cost cycles to copy the old data, but
+        # we don't care what's in the block.
+        temp_keys = make_temp_area(ms.keys, alloced)
+        if has_values(ms.keys, ms.values):
+            temp_values = make_temp_area(ms.values, alloced)
+        else:
+            temp_values = temp_keys
+        return MergeState(ms.min_gallop, temp_keys, temp_values, ms.pending, ms.n)
+
+    @wrap
+    def merge_adjust_gallop(ms, new_gallop):
+        """
+        Modify the MergeState's min_gallop.
+        """
+        return MergeState(intp(new_gallop), ms.keys, ms.values, ms.pending, ms.n)
+
+
+    @wrap
+    def LT(a, b):
+        """
+        Trivial comparison function between two keys.  This is factored out to
+        make it clear where comparisons occur.
+        """
+        return a < b
+
+    @wrap
+    def binarysort(keys, values, lo, hi, start):
+        """
+        binarysort is the best method for sorting small arrays: it does
+        few compares, but can do data movement quadratic in the number of
+        elements.
+        [lo, hi) is a contiguous slice of a list, and is sorted via
+        binary insertion.  This sort is stable.
+        On entry, must have lo <= start <= hi, and that [lo, start) is already
+        sorted (pass start == lo if you don't know!).
+        """
+        assert lo <= start and start <= hi
+        _has_values = has_values(keys, values)
+        if lo == start:
+            start += 1
+        while start < hi:
+            pivot = keys[start]
+            # Bisect to find where to insert `pivot`
+            # NOTE: bisection only wins over linear search if the comparison
+            # function is much more expensive than simply moving data.
+            l = lo
+            r = start
+            # Invariants:
+            # pivot >= all in [lo, l).
+            # pivot  < all in [r, start).
+            # The second is vacuously true at the start.
+            while l < r:
+                p = l + ((r - l) >> 1)
+                if LT(pivot, keys[p]):
+                    r = p
+                else:
+                    l = p+1
+
+            # The invariants still hold, so pivot >= all in [lo, l) and
+            # pivot < all in [l, start), so pivot belongs at l.  Note
+            # that if there are elements equal to pivot, l points to the
+            # first slot after them -- that's why this sort is stable.
+            # Slide over to make room (aka memmove()).
+            for p in range(start, l, -1):
+                keys[p] = keys[p - 1]
+            keys[l] = pivot
+            if _has_values:
+                pivot_val = values[start]
+                for p in range(start, l, -1):
+                    values[p] = values[p - 1]
+                values[l] = pivot_val
+
+            start += 1
+
+
+    @wrap
+    def count_run(keys, lo, hi):
+        """
+        Return the length of the run beginning at lo, in the slice [lo, hi).
+        lo < hi is required on entry.  "A run" is the longest ascending sequence, with
+
+            lo[0] <= lo[1] <= lo[2] <= ...
+
+        or the longest descending sequence, with
+
+            lo[0] > lo[1] > lo[2] > ...
+
+        A tuple (length, descending) is returned, where boolean *descending*
+        is set to 0 in the former case, or to 1 in the latter.
+        For its intended use in a stable mergesort, the strictness of the defn of
+        "descending" is needed so that the caller can safely reverse a descending
+        sequence without violating stability (strict > ensures there are no equal
+        elements to get out of order).
+        """
+        assert lo < hi
+        if lo + 1 == hi:
+            # Trivial 1-long run
+            return 1, False
+        if LT(keys[lo + 1], keys[lo]):
+            # Descending run
+            for k in range(lo + 2, hi):
+                if not LT(keys[k], keys[k - 1]):
+                    return k - lo, True
+            return hi - lo, True
+        else:
+            # Ascending run
+            for k in range(lo + 2, hi):
+                if LT(keys[k], keys[k - 1]):
+                    return k - lo, False
+            return hi - lo, False
+
+
+    @wrap
+    def gallop_left(key, a, start, stop, hint):
+        """
+        Locate the proper position of key in a sorted vector; if the vector contains
+        an element equal to key, return the position immediately to the left of
+        the leftmost equal element.  [gallop_right() does the same except returns
+        the position to the right of the rightmost equal element (if any).]
+
+        "a" is a sorted vector with stop elements, starting at a[start].
+        stop must be > start.
+
+        "hint" is an index at which to begin the search, start <= hint < stop.
+        The closer hint is to the final result, the faster this runs.
+
+        The return value is the int k in start..stop such that
+
+            a[k-1] < key <= a[k]
+
+        pretending that a[start-1] is minus infinity and a[stop] is plus infinity.
+        IOW, key belongs at index k; or, IOW, the first k elements of a should
+        precede key, and the last stop-start-k should follow key.
+
+        See listsort.txt for info on the method.
+        """
+        assert stop > start
+        assert hint >= start and hint < stop
+        n = stop - start
+
+        # First, gallop from the hint to find a "good" subinterval for bisecting
+        lastofs = 0
+        ofs = 1
+        if LT(a[hint], key):
+            # a[hint] < key => gallop right, until
+            #                  a[hint + lastofs] < key <= a[hint + ofs]
+            maxofs = stop - hint
+            while ofs < maxofs:
+                if LT(a[hint + ofs], key):
+                    lastofs = ofs
+                    ofs = (ofs << 1) + 1
+                    if ofs <= 0:
+                        # Int overflow
+                        ofs = maxofs
+                else:
+                    # key <= a[hint + ofs]
+                    break
+            if ofs > maxofs:
+                ofs = maxofs
+            # Translate back to offsets relative to a[0]
+            lastofs += hint
+            ofs += hint
+        else:
+            # key <= a[hint] => gallop left, until
+            #                   a[hint - ofs] < key <= a[hint - lastofs]
+            maxofs = hint - start + 1
+            while ofs < maxofs:
+                if LT(a[hint - ofs], key):
+                    break
+                else:
+                    # key <= a[hint - ofs]
+                    lastofs = ofs
+                    ofs = (ofs << 1) + 1
+                    if ofs <= 0:
+                        # Int overflow
+                        ofs = maxofs
+            if ofs > maxofs:
+                ofs = maxofs
+            # Translate back to positive offsets relative to a[0]
+            lastofs, ofs = hint - ofs, hint - lastofs
+
+        assert start - 1 <= lastofs and lastofs < ofs and ofs <= stop
+        # Now a[lastofs] < key <= a[ofs], so key belongs somewhere to the
+        # right of lastofs but no farther right than ofs.  Do a binary
+        # search, with invariant a[lastofs-1] < key <= a[ofs].
+        lastofs += 1
+        while lastofs < ofs:
+            m = lastofs + ((ofs - lastofs) >> 1)
+            if LT(a[m], key):
+                # a[m] < key
+                lastofs = m + 1
+            else:
+                # key <= a[m]
+                ofs = m
+        # Now lastofs == ofs, so a[ofs - 1] < key <= a[ofs]
+        return ofs
+
+
+    @wrap
+    def gallop_right(key, a, start, stop, hint):
+        """
+        Exactly like gallop_left(), except that if key already exists in a[start:stop],
+        finds the position immediately to the right of the rightmost equal value.
+
+        The return value is the int k in start..stop such that
+
+            a[k-1] <= key < a[k]
+
+        The code duplication is massive, but this is enough different given that
+        we're sticking to "<" comparisons that it's much harder to follow if
+        written as one routine with yet another "left or right?" flag.
+        """
+        assert stop > start
+        assert hint >= start and hint < stop
+        n = stop - start
+
+        # First, gallop from the hint to find a "good" subinterval for bisecting
+        lastofs = 0
+        ofs = 1
+        if LT(key, a[hint]):
+            # key < a[hint] => gallop left, until
+            #                  a[hint - ofs] <= key < a[hint - lastofs]
+            maxofs = hint - start + 1
+            while ofs < maxofs:
+                if LT(key, a[hint - ofs]):
+                    lastofs = ofs
+                    ofs = (ofs << 1) + 1
+                    if ofs <= 0:
+                        # Int overflow
+                        ofs = maxofs
+                else:
+                    # a[hint - ofs] <= key
+                    break
+            if ofs > maxofs:
+                ofs = maxofs
+            # Translate back to positive offsets relative to a[0]
+            lastofs, ofs = hint - ofs, hint - lastofs
+        else:
+            # a[hint] <= key -- gallop right, until
+            # a[hint + lastofs] <= key < a[hint + ofs]
+            maxofs = stop - hint
+            while ofs < maxofs:
+                if LT(key, a[hint + ofs]):
+                    break
+                else:
+                    # a[hint + ofs] <= key
+                    lastofs = ofs
+                    ofs = (ofs << 1) + 1
+                    if ofs <= 0:
+                        # Int overflow
+                        ofs = maxofs
+            if ofs > maxofs:
+                ofs = maxofs
+            # Translate back to offsets relative to a[0]
+            lastofs += hint
+            ofs += hint
+
+        assert start - 1 <= lastofs and lastofs < ofs and ofs <= stop
+        # Now a[lastofs] <= key < a[ofs], so key belongs somewhere to the
+        # right of lastofs but no farther right than ofs.  Do a binary
+        # search, with invariant a[lastofs-1] <= key < a[ofs].
+        lastofs += 1
+        while lastofs < ofs:
+            m = lastofs + ((ofs - lastofs) >> 1)
+            if LT(key, a[m]):
+                # key < a[m]
+                ofs = m
+            else:
+                # a[m] <= key
+                lastofs = m + 1
+        # Now lastofs == ofs, so a[ofs - 1] <= key < a[ofs]
+        return ofs
+
+
+    @wrap
+    def merge_compute_minrun(n):
+        """
+        Compute a good value for the minimum run length; natural runs shorter
+        than this are boosted artificially via binary insertion.
+
+        If n < 64, return n (it's too small to bother with fancy stuff).
+        Else if n is an exact power of 2, return 32.
+        Else return an int k, 32 <= k <= 64, such that n/k is close to, but
+        strictly less than, an exact power of 2.
+
+        See listsort.txt for more info.
+        """
+        r = 0
+        assert n >= 0
+        while n >= 64:
+            r |= n & 1
+            n >>= 1
+        return n + r
+
+
+    @wrap
+    def sortslice_copy(dest_keys, dest_values, dest_start,
+                       src_keys, src_values, src_start,
+                       nitems):
+        """
+        Upwards memcpy().
+        """
+        assert src_start >= 0
+        assert dest_start >= 0
+        for i in range(nitems):
+            dest_keys[dest_start + i] = src_keys[src_start + i]
+        if has_values(src_keys, src_values):
+            for i in range(nitems):
+                dest_values[dest_start + i] = src_values[src_start + i]
+
+    @wrap
+    def sortslice_copy_down(dest_keys, dest_values, dest_start,
+                            src_keys, src_values, src_start,
+                            nitems):
+        """
+        Downwards memcpy().
+        """
+        assert src_start >= 0
+        assert dest_start >= 0
+        for i in range(nitems):
+            dest_keys[dest_start - i] = src_keys[src_start - i]
+        if has_values(src_keys, src_values):
+            for i in range(nitems):
+                dest_values[dest_start - i] = src_values[src_start - i]
+
+
+    # Disable this for debug or perf comparison
+    DO_GALLOP = 1
+
+    @wrap
+    def merge_lo(ms, keys, values, ssa, na, ssb, nb):
+        """
+        Merge the na elements starting at ssa with the nb elements starting at
+        ssb = ssa + na in a stable way, in-place.  na and nb must be > 0,
+        and should have na <= nb. See listsort.txt for more info.
+
+        An updated MergeState is returned (with possibly a different min_gallop
+        or larger temp arrays).
+
+        NOTE: compared to CPython's timsort, the requirement that
+            "Must also have that keys[ssa + na - 1] belongs at the end of the merge"
+
+        is removed. This makes the code a bit simpler and easier to reason about.
+        """
+        assert na > 0 and nb > 0 and na <= nb
+        assert ssb == ssa + na
+        # First copy [ssa, ssa + na) into the temp space
+        ms = merge_getmem(ms, na)
+        sortslice_copy(ms.keys, ms.values, 0,
+                       keys, values, ssa,
+                       na)
+        a_keys = ms.keys
+        a_values = ms.values
+        b_keys = keys
+        b_values = values
+        dest = ssa
+        ssa = 0
+
+        _has_values = has_values(a_keys, a_values)
+        min_gallop = ms.min_gallop
+
+        # Now start merging into the space left from [ssa, ...)
+
+        while nb > 0 and na > 0:
+            # Do the straightforward thing until (if ever) one run
+            # appears to win consistently.
+            acount = 0
+            bcount = 0
+
+            while True:
+                if LT(b_keys[ssb], a_keys[ssa]):
+                    keys[dest] = b_keys[ssb]
+                    if _has_values:
+                        values[dest] = b_values[ssb]
+                    dest += 1
+                    ssb += 1
+                    nb -= 1
+                    if nb == 0:
+                        break
+                    # It's a B run
+                    bcount += 1
+                    acount = 0
+                    if bcount >= min_gallop:
+                        break
+                else:
+                    keys[dest] = a_keys[ssa]
+                    if _has_values:
+                        values[dest] = a_values[ssa]
+                    dest += 1
+                    ssa += 1
+                    na -= 1
+                    if na == 0:
+                        break
+                    # It's a A run
+                    acount += 1
+                    bcount = 0
+                    if acount >= min_gallop:
+                        break
+
+            # One run is winning so consistently that galloping may
+            # be a huge win.  So try that, and continue galloping until
+            # (if ever) neither run appears to be winning consistently
+            # anymore.
+            if DO_GALLOP and na > 0 and nb > 0:
+                min_gallop += 1
+
+                while acount >= MIN_GALLOP or bcount >= MIN_GALLOP:
+                    # As long as we gallop without leaving this loop, make
+                    # the heuristic more likely
+                    min_gallop -= min_gallop > 1
+
+                    # Gallop in A to find where keys[ssb] should end up
+                    k = gallop_right(b_keys[ssb], a_keys, ssa, ssa + na, ssa)
+                    # k is an index, make it a size
+                    k -= ssa
+                    acount = k
+                    if k > 0:
+                        # Copy everything from A before k
+                        sortslice_copy(keys, values, dest,
+                                       a_keys, a_values, ssa,
+                                       k)
+                        dest += k
+                        ssa += k
+                        na -= k
+                        if na == 0:
+                            # Finished merging
+                            break
+                    # Copy keys[ssb]
+                    keys[dest] = b_keys[ssb]
+                    if _has_values:
+                        values[dest] = b_values[ssb]
+                    dest += 1
+                    ssb += 1
+                    nb -= 1
+                    if nb == 0:
+                        # Finished merging
+                        break
+
+                    # Gallop in B to find where keys[ssa] should end up
+                    k = gallop_left(a_keys[ssa], b_keys, ssb, ssb + nb, ssb)
+                    # k is an index, make it a size
+                    k -= ssb
+                    bcount = k
+                    if k > 0:
+                        # Copy everything from B before k
+                        # NOTE: source and dest are the same buffer, but the
+                        # destination index is below the source index
+                        sortslice_copy(keys, values, dest,
+                                       b_keys, b_values, ssb,
+                                       k)
+                        dest += k
+                        ssb += k
+                        nb -= k
+                        if nb == 0:
+                            # Finished merging
+                            break
+                    # Copy keys[ssa]
+                    keys[dest] = a_keys[ssa]
+                    if _has_values:
+                        values[dest] = a_values[ssa]
+                    dest += 1
+                    ssa += 1
+                    na -= 1
+                    if na == 0:
+                        # Finished merging
+                        break
+
+                # Penalize it for leaving galloping mode
+                min_gallop += 1
+
+        # Merge finished, now handle the remaining areas
+        if nb == 0:
+            # Only A remaining to copy at the end of the destination area
+            sortslice_copy(keys, values, dest,
+                           a_keys, a_values, ssa,
+                           na)
+        else:
+            assert na == 0
+            assert dest == ssb
+            # B's tail is already at the right place, do nothing
+
+        return merge_adjust_gallop(ms, min_gallop)
+
+
+    @wrap
+    def merge_hi(ms, keys, values, ssa, na, ssb, nb):
+        """
+        Merge the na elements starting at ssa with the nb elements starting at
+        ssb = ssa + na in a stable way, in-place.  na and nb must be > 0,
+        and should have na >= nb.  See listsort.txt for more info.
+
+        An updated MergeState is returned (with possibly a different min_gallop
+        or larger temp arrays).
+
+        NOTE: compared to CPython's timsort, the requirement that
+            "Must also have that keys[ssa + na - 1] belongs at the end of the merge"
+
+        is removed. This makes the code a bit simpler and easier to reason about.
+        """
+        assert na > 0 and nb > 0 and na >= nb
+        assert ssb == ssa + na
+        # First copy [ssb, ssb + nb) into the temp space
+        ms = merge_getmem(ms, nb)
+        sortslice_copy(ms.keys, ms.values, 0,
+                       keys, values, ssb,
+                       nb)
+        a_keys = keys
+        a_values = values
+        b_keys = ms.keys
+        b_values = ms.values
+
+        # Now start merging *in descending order* into the space left
+        # from [..., ssb + nb).
+        dest = ssb + nb - 1
+        ssb = nb - 1
+        ssa = ssa + na - 1
+
+        _has_values = has_values(b_keys, b_values)
+        min_gallop = ms.min_gallop
+
+        while nb > 0 and na > 0:
+            # Do the straightforward thing until (if ever) one run
+            # appears to win consistently.
+            acount = 0
+            bcount = 0
+
+            while True:
+                if LT(b_keys[ssb], a_keys[ssa]):
+                    # We merge in descending order, so copy the larger value
+                    keys[dest] = a_keys[ssa]
+                    if _has_values:
+                        values[dest] = a_values[ssa]
+                    dest -= 1
+                    ssa -= 1
+                    na -= 1
+                    if na == 0:
+                        break
+                    # It's a A run
+                    acount += 1
+                    bcount = 0
+                    if acount >= min_gallop:
+                        break
+                else:
+                    keys[dest] = b_keys[ssb]
+                    if _has_values:
+                        values[dest] = b_values[ssb]
+                    dest -= 1
+                    ssb -= 1
+                    nb -= 1
+                    if nb == 0:
+                        break
+                    # It's a B run
+                    bcount += 1
+                    acount = 0
+                    if bcount >= min_gallop:
+                        break
+
+            # One run is winning so consistently that galloping may
+            # be a huge win.  So try that, and continue galloping until
+            # (if ever) neither run appears to be winning consistently
+            # anymore.
+            if DO_GALLOP and na > 0 and nb > 0:
+                min_gallop += 1
+
+                while acount >= MIN_GALLOP or bcount >= MIN_GALLOP:
+                    # As long as we gallop without leaving this loop, make
+                    # the heuristic more likely
+                    min_gallop -= min_gallop > 1
+
+                    # Gallop in A to find where keys[ssb] should end up
+                    k = gallop_right(b_keys[ssb], a_keys, ssa - na + 1, ssa + 1, ssa)
+                    # k is an index, make it a size from the end
+                    k = ssa + 1 - k
+                    acount = k
+                    if k > 0:
+                        # Copy everything from A after k.
+                        # Destination and source are the same buffer, and destination
+                        # index is greater, so copy from the end to the start.
+                        sortslice_copy_down(keys, values, dest,
+                                            a_keys, a_values, ssa,
+                                            k)
+                        dest -= k
+                        ssa -= k
+                        na -= k
+                        if na == 0:
+                            # Finished merging
+                            break
+                    # Copy keys[ssb]
+                    keys[dest] = b_keys[ssb]
+                    if _has_values:
+                        values[dest] = b_values[ssb]
+                    dest -= 1
+                    ssb -= 1
+                    nb -= 1
+                    if nb == 0:
+                        # Finished merging
+                        break
+
+                    # Gallop in B to find where keys[ssa] should end up
+                    k = gallop_left(a_keys[ssa], b_keys, ssb - nb + 1, ssb + 1, ssb)
+                    # k is an index, make it a size from the end
+                    k = ssb + 1 - k
+                    bcount = k
+                    if k > 0:
+                        # Copy everything from B before k
+                        sortslice_copy_down(keys, values, dest,
+                                            b_keys, b_values, ssb,
+                                            k)
+                        dest -= k
+                        ssb -= k
+                        nb -= k
+                        if nb == 0:
+                            # Finished merging
+                            break
+                    # Copy keys[ssa]
+                    keys[dest] = a_keys[ssa]
+                    if _has_values:
+                        values[dest] = a_values[ssa]
+                    dest -= 1
+                    ssa -= 1
+                    na -= 1
+                    if na == 0:
+                        # Finished merging
+                        break
+
+                # Penalize it for leaving galloping mode
+                min_gallop += 1
+
+        # Merge finished, now handle the remaining areas
+        if na == 0:
+            # Only B remaining to copy at the front of the destination area
+            sortslice_copy(keys, values, dest - nb + 1,
+                           b_keys, b_values, ssb - nb + 1,
+                           nb)
+        else:
+            assert nb == 0
+            assert dest == ssa
+            # A's front is already at the right place, do nothing
+
+        return merge_adjust_gallop(ms, min_gallop)
+
+
+    @wrap
+    def merge_at(ms, keys, values, i):
+        """
+        Merge the two runs at stack indices i and i+1.
+
+        An updated MergeState is returned.
+        """
+        n = ms.n
+        assert n >= 2
+        assert i >= 0
+        assert i == n - 2 or i == n - 3
+
+        ssa, na = ms.pending[i]
+        ssb, nb = ms.pending[i + 1]
+        assert na > 0 and nb > 0
+        assert ssa + na == ssb
+
+        # Record the length of the combined runs; if i is the 3rd-last
+        # run now, also slide over the last run (which isn't involved
+        # in this merge).  The current run i+1 goes away in any case.
+        ms.pending[i] = MergeRun(ssa, na + nb)
+        if i == n - 3:
+            ms.pending[i + 1] = ms.pending[i + 2]
+        ms = merge_pop(ms)
+
+        # Where does b start in a?  Elements in a before that can be
+        # ignored (already in place).
+        k = gallop_right(keys[ssb], keys, ssa, ssa + na, ssa)
+        # [k, ssa + na) remains to be merged
+        na -= k - ssa
+        ssa = k
+        if na == 0:
+            return ms
+
+        # Where does a end in b?  Elements in b after that can be
+        # ignored (already in place).
+        k = gallop_left(keys[ssa + na - 1], keys, ssb, ssb + nb, ssb + nb - 1)
+        # [ssb, k) remains to be merged
+        nb = k - ssb
+
+        # Merge what remains of the runs, using a temp array with
+        # min(na, nb) elements.
+        if na <= nb:
+            return merge_lo(ms, keys, values, ssa, na, ssb, nb)
+        else:
+            return merge_hi(ms, keys, values, ssa, na, ssb, nb)
+
+
+    @wrap
+    def merge_collapse(ms, keys, values):
+        """
+        Examine the stack of runs waiting to be merged, merging adjacent runs
+        until the stack invariants are re-established:
+
+        1. len[-3] > len[-2] + len[-1]
+        2. len[-2] > len[-1]
+
+        An updated MergeState is returned.
+
+        See listsort.txt for more info.
+        """
+        while ms.n > 1:
+            pending = ms.pending
+            n = ms.n - 2
+            if ((n > 0 and pending[n-1].size <= pending[n].size + pending[n+1].size) or
+                (n > 1 and pending[n-2].size <= pending[n-1].size + pending[n].size)):
+                if pending[n - 1].size < pending[n + 1].size:
+                    # Merge smaller one first
+                    n -= 1
+                ms = merge_at(ms, keys, values, n)
+            elif pending[n].size < pending[n + 1].size:
+                ms = merge_at(ms, keys, values, n)
+            else:
+                break
+        return ms
+
+    @wrap
+    def merge_force_collapse(ms, keys, values):
+        """
+        Regardless of invariants, merge all runs on the stack until only one
+        remains.  This is used at the end of the mergesort.
+
+        An updated MergeState is returned.
+        """
+        while ms.n > 1:
+            pending = ms.pending
+            n = ms.n - 2
+            if n > 0:
+                if pending[n - 1].size < pending[n + 1].size:
+                    # Merge the smaller one first
+                    n -= 1
+            ms = merge_at(ms, keys, values, n)
+        return ms
+
+
+    @wrap
+    def reverse_slice(keys, values, start, stop):
+        """
+        Reverse a slice, in-place.
+        """
+        i = start
+        j = stop - 1
+        while i < j:
+            keys[i], keys[j] = keys[j], keys[i]
+            i += 1
+            j -= 1
+        if has_values(keys, values):
+            i = start
+            j = stop - 1
+            while i < j:
+                values[i], values[j] = values[j], values[i]
+                i += 1
+                j -= 1
+
+
+    @wrap
+    def run_timsort_with_mergestate(ms, keys, values):
+        """
+        Run timsort with the mergestate.
+        """
+        nremaining = len(keys)
+        if nremaining < 2:
+            return
+
+        # March over the array once, left to right, finding natural runs,
+        # and extending short natural runs to minrun elements.
+        minrun = merge_compute_minrun(nremaining)
+
+        lo = zero
+        while nremaining > 0:
+            n, desc = count_run(keys, lo, lo + nremaining)
+            if desc:
+                # Descending run => reverse
+                reverse_slice(keys, values, lo, lo + n)
+            # If short, extend to min(minrun, nremaining)
+            if n < minrun:
+                force = min(minrun, nremaining)
+                binarysort(keys, values, lo, lo + force, lo + n)
+                n = force
+            # Push run onto stack, and maybe merge.
+            ms = merge_append(ms, MergeRun(lo, n))
+            ms = merge_collapse(ms, keys, values)
+            # Advance to find next run.
+            lo += n
+            nremaining -= n
+
+        # All initial runs have been discovered, now finish merging.
+        ms = merge_force_collapse(ms, keys, values)
+        assert ms.n == 1
+        assert ms.pending[0] == (0, len(keys))
+
+
+    @wrap
+    def run_timsort(keys):
+        """
+        Run timsort over the given keys.
+        """
+        values = keys
+        run_timsort_with_mergestate(merge_init(keys), keys, values)
+
+
+    @wrap
+    def run_timsort_with_values(keys, values):
+        """
+        Run timsort over the given keys and values.
+        """
+        run_timsort_with_mergestate(merge_init_with_values(keys, values),
+                                    keys, values)
+
+    return TimsortImplementation(
+        wrap,
+        count_run, binarysort, gallop_left, gallop_right,
+        merge_init, merge_append, merge_pop,
+        merge_compute_minrun, merge_lo, merge_hi, merge_at,
+        merge_force_collapse, merge_collapse,
+        run_timsort, run_timsort_with_values)
+
+
+def make_py_timsort(*args):
+    return make_timsort_impl((lambda f: f), *args)
+
+def make_jit_timsort(*args):
+    from numba import jit
+    return make_timsort_impl((lambda f: jit(nopython=True)(f)),
+                              *args)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/mviewbuf.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/mviewbuf.c
new file mode 100644
index 000000000..734c3b396
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/mviewbuf.c
@@ -0,0 +1,370 @@
+#include "_pymodule.h"
+
+static int get_writable_buffer(PyObject* obj, Py_buffer *buf, int force)
+{
+    Py_buffer read_buf;
+    int flags = PyBUF_ND|PyBUF_STRIDES|PyBUF_FORMAT;
+    int ret;
+
+    /* Attempt to get a writable buffer */
+    if (!PyObject_GetBuffer(obj, buf, flags|PyBUF_WRITABLE))
+        return 0;
+    if (!force)
+        return -1;
+
+    /* Make a writable buffer from a read-only buffer */
+    PyErr_Clear();
+    if(-1 == PyObject_GetBuffer(obj, &read_buf, flags))
+        return -1;
+    ret = PyBuffer_FillInfo(buf, NULL, read_buf.buf, read_buf.len, 0,
+                             flags|PyBUF_WRITABLE);
+    PyBuffer_Release(&read_buf);
+    return ret;
+}
+
+static int get_readonly_buffer(PyObject* obj, Py_buffer *buf)
+{
+    int flags = PyBUF_ND|PyBUF_STRIDES|PyBUF_FORMAT;
+
+    return PyObject_GetBuffer(obj, buf, flags);
+}
+
+
+static void free_buffer(Py_buffer * buf)
+{
+    PyBuffer_Release(buf);
+}
+
+/**
+ * Return a pointer to the data of a writable buffer from obj. If only a
+ * read-only buffer is available and force is True, a read-write buffer based on
+ * the read-only buffer is obtained. Note that this may have some surprising
+ * effects on buffers which expect the data from their read-only buffer not to
+ * be modified.
+ */
+static PyObject*
+memoryview_get_buffer(PyObject *self, PyObject *args){
+    PyObject *obj = NULL;
+    int force = 0;
+    int readonly = 0;
+    PyObject *ret = NULL;
+    Py_buffer buf;
+
+    if (!PyArg_ParseTuple(args, "O|ii", &obj, &force, &readonly))
+        return NULL;
+
+    if (readonly) {
+        if (get_readonly_buffer(obj, &buf))
+            return NULL;
+    } else {
+        if (get_writable_buffer(obj, &buf, force))
+            return NULL;
+    }
+
+    ret = PyLong_FromVoidPtr(buf.buf);
+    free_buffer(&buf);
+    return ret;
+}
+
+/**
+ * Gets a half-open range [start, end) which contains the array data
+ * Modified from numpy/core/src/multiarray/array_assign.c
+ */
+static PyObject*
+get_extents(Py_ssize_t *shape, Py_ssize_t *strides, int ndim,
+            Py_ssize_t itemsize, Py_ssize_t ptr)
+{
+    Py_ssize_t start, end;
+    int idim;
+    Py_ssize_t *dimensions = shape;
+    PyObject *ret = NULL;
+
+    if (ndim < 0 ){
+        PyErr_SetString(PyExc_ValueError, "buffer ndim < 0");
+        return NULL;
+    }
+
+    if (!dimensions) {
+        if (ndim == 0) {
+            start = end = ptr;
+            end += itemsize;
+            return Py_BuildValue("nn", start, end);
+        }
+        PyErr_SetString(PyExc_ValueError, "buffer shape is not defined");
+        return NULL;
+    }
+
+    if (!strides) {
+        PyErr_SetString(PyExc_ValueError, "buffer strides is not defined");
+        return NULL;
+    }
+
+    /* Calculate with a closed range [start, end] */
+    start = end = ptr;
+    for (idim = 0; idim < ndim; ++idim) {
+        Py_ssize_t stride = strides[idim], dim = dimensions[idim];
+        /* If the array size is zero, return an empty range */
+        if (dim == 0) {
+            start = end = ptr;
+            ret = Py_BuildValue("nn", start, end);
+            break;
+        }
+        /* Expand either upwards or downwards depending on stride */
+        else {
+            if (stride > 0) {
+                end += stride * (dim - 1);
+            }
+            else if (stride < 0) {
+                start += stride * (dim - 1);
+            }
+        }
+    }
+
+    if (!ret) {
+        /* Return a half-open range */
+        Py_ssize_t out_start = start;
+        Py_ssize_t out_end = end + itemsize;
+
+        ret = Py_BuildValue("nn", out_start, out_end);
+    }
+
+    return ret;
+}
+
+static PyObject*
+memoryview_get_extents(PyObject *self, PyObject *args)
+{
+    PyObject *obj = NULL;
+    PyObject *ret = NULL;
+    Py_buffer b;
+    if (!PyArg_ParseTuple(args, "O", &obj))
+        return NULL;
+
+    if (get_readonly_buffer(obj, &b))
+        return NULL;
+
+    ret = get_extents(b.shape, b.strides, b.ndim, b.itemsize,
+                      (Py_ssize_t)b.buf);
+    free_buffer(&b);
+    return ret;
+}
+
+static PyObject*
+memoryview_get_extents_info(PyObject *self, PyObject *args)
+{
+    int i;
+    Py_ssize_t *shape_ary = NULL;
+    Py_ssize_t *strides_ary = NULL;
+    PyObject *shape_tuple = NULL;
+    PyObject *strides_tuple = NULL;
+    PyObject *shape = NULL, *strides = NULL;
+    Py_ssize_t itemsize = 0;
+    int ndim = 0;
+    PyObject* res = NULL;
+
+    if (!PyArg_ParseTuple(args, "OOin", &shape, &strides, &ndim, &itemsize))
+        goto cleanup;
+
+    if (ndim < 0) {
+        PyErr_SetString(PyExc_ValueError, "ndim is negative");
+        goto cleanup;
+    }
+
+    if (itemsize <= 0) {
+        PyErr_SetString(PyExc_ValueError, "ndim <= 0");
+        goto cleanup;
+    }
+
+    shape_ary = malloc(sizeof(Py_ssize_t) * ndim + 1);
+    strides_ary = malloc(sizeof(Py_ssize_t) * ndim + 1);
+
+    shape_tuple = PySequence_Fast(shape, "shape is not a sequence");
+    if (!shape_tuple) goto cleanup;
+
+    for (i = 0; i < ndim; ++i) {
+        shape_ary[i] = PyNumber_AsSsize_t(
+                           PySequence_Fast_GET_ITEM(shape_tuple, i),
+                           PyExc_OverflowError);
+    }
+
+    strides_tuple = PySequence_Fast(strides, "strides is not a sequence");
+    if (!strides_tuple) goto cleanup;
+
+    for (i = 0; i < ndim; ++i) {
+        strides_ary[i] = PyNumber_AsSsize_t(
+                           PySequence_Fast_GET_ITEM(strides_tuple, i),
+                           PyExc_OverflowError);
+    }
+
+    res = get_extents(shape_ary, strides_ary, ndim, itemsize, 0);
+cleanup:
+    free(shape_ary);
+    free(strides_ary);
+    Py_XDECREF(shape_tuple);
+    Py_XDECREF(strides_tuple);
+    return res;
+}
+
+
+/* new type to expose buffer interface */
+typedef struct {
+    PyObject_HEAD
+    /* Type-specific fields go here. */
+} MemAllocObject;
+
+
+static int
+get_bufinfo(PyObject *self, Py_ssize_t *psize, void **pptr)
+{
+    PyObject *buflen = NULL;
+    PyObject *bufptr = NULL;
+    Py_ssize_t size = 0;
+    void* ptr = NULL;
+    int ret = -1;
+
+    buflen = PyObject_GetAttrString(self, "_buflen_");
+    if (!buflen) goto cleanup;
+
+    bufptr = PyObject_GetAttrString(self, "_bufptr_");
+    if (!bufptr) goto cleanup;
+
+    size = PyNumber_AsSsize_t(buflen, PyExc_OverflowError);
+    if (size == -1 && PyErr_Occurred()) goto cleanup;
+    else if (size < 0) {
+        PyErr_SetString(PyExc_ValueError, "negative buffer size");
+        goto cleanup;
+    }
+
+    ptr = PyLong_AsVoidPtr(PyNumber_Long(bufptr));
+    if (PyErr_Occurred())
+        goto cleanup;
+    else if (!ptr) {
+        PyErr_SetString(PyExc_ValueError, "null buffer pointer");
+        goto cleanup;
+    }
+
+    *psize = size;
+    *pptr = ptr;
+    ret = 0;
+cleanup:
+    Py_XDECREF(buflen);
+    Py_XDECREF(bufptr);
+    return ret;
+}
+
+
+static int
+MemAllocObject_getbuffer(PyObject *self, Py_buffer *view, int flags)
+{
+    Py_ssize_t size = 0;
+    void *ptr = 0;
+    int readonly;
+
+    if(-1 == get_bufinfo(self, &size, &ptr))
+        return -1;
+
+    readonly = (PyBUF_WRITABLE & flags) != PyBUF_WRITABLE;
+
+    /* fill buffer */
+    if (-1 == PyBuffer_FillInfo(view, self, (void*)ptr, size, readonly, flags))
+        return -1;
+
+    return 0;
+}
+
+static void
+MemAllocObject_releasebuffer(PyObject *self, Py_buffer *view)
+{
+    /* Do nothing */
+}
+
+static PyBufferProcs MemAlloc_as_buffer = {
+    MemAllocObject_getbuffer,
+    MemAllocObject_releasebuffer,
+};
+
+
+static PyTypeObject MemAllocType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "mviewbuf.MemAlloc",                            /* tp_name */
+    sizeof(MemAllocObject),                   /* tp_basicsize */
+    0,                                          /* tp_itemsize */
+    /* methods */
+    0,                  /* tp_dealloc */
+    0,                            /* tp_print */
+    0,                                          /* tp_getattr */
+    0,                                          /* tp_setattr */
+#if PY_MAJOR_VERSION >= 3
+    0,                                          /* tp_reserved */
+#else
+    0,                                          /* tp_compare */
+#endif
+    0,                         /*tp_repr*/
+    0,                         /*tp_as_number*/
+    0,                         /*tp_as_sequence*/
+    0,                         /*tp_as_mapping*/
+    0,                         /*tp_hash */
+    0,                         /*tp_call*/
+    0,                         /*tp_str*/
+    0,                         /*tp_getattro*/
+    0,                         /*tp_setattro*/
+    &MemAlloc_as_buffer,        /*tp_as_buffer*/
+    (Py_TPFLAGS_DEFAULT| Py_TPFLAGS_BASETYPE),                    /* tp_flags */
+    0,                                          /* tp_doc */
+
+    0,                                          /* tp_traverse */
+    0,                                          /* tp_clear */
+    0,                                          /* tp_richcompare */
+    0,                                          /* tp_weaklistoffset */
+    0,                                          /* tp_iter */
+    0,                                          /* tp_iternext */
+    0,                                          /* tp_methods */
+    0,                                          /* tp_members */
+    0,                                          /* tp_getset */
+    0,                                          /* tp_base */
+    0,                                          /* tp_dict */
+    0,                                          /* tp_descr_get */
+    0,                                          /* tp_descr_set */
+    0,                                          /* tp_dictoffset */
+    0,                                          /* tp_init */
+    0,                                          /* tp_alloc */
+    0,                                          /* tp_new */
+    0,                                          /* tp_free */
+    0,                                          /* tp_is_gc */
+    0,                                          /* tp_bases */
+    0,                                          /* tp_mro */
+    0,                                          /* tp_cache */
+    0,                                          /* tp_subclasses */
+    0,                                          /* tp_weaklist */
+    0,                                          /* tp_del */
+    0,                                          /* tp_version_tag */
+};
+
+
+static PyMethodDef core_methods[] = {
+#define declmethod(func) { #func , ( PyCFunction )func , METH_VARARGS , NULL }
+    declmethod(memoryview_get_buffer),
+    declmethod(memoryview_get_extents),
+    declmethod(memoryview_get_extents_info),
+    { NULL },
+#undef declmethod
+};
+
+
+MOD_INIT(mviewbuf) {
+    PyObject *module;
+    MOD_DEF(module, "mviewbuf", "No docs", core_methods)
+    if (module == NULL)
+        return MOD_ERROR_VAL;
+
+    MemAllocType.tp_new = PyType_GenericNew;
+    if (PyType_Ready(&MemAllocType) < 0){
+        return MOD_ERROR_VAL;
+    }
+
+    Py_INCREF(&MemAllocType);
+    PyModule_AddObject(module, "MemAlloc", (PyObject*)&MemAllocType);
+
+    return MOD_SUCCESS_VAL(module);
+}
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/arraymath.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/arraymath.py
new file mode 100644
index 000000000..9885526ee
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/arraymath.py
@@ -0,0 +1,4881 @@
+"""
+Implementation of math operations on Array objects.
+"""
+
+
+import math
+from collections import namedtuple
+from enum import IntEnum
+from functools import partial
+import operator
+import warnings
+
+import llvmlite.ir
+import numpy as np
+
+from numba import generated_jit
+from numba.core import types, cgutils
+from numba.core.extending import overload, overload_method, register_jitable
+from numba.np.numpy_support import as_dtype, type_can_asarray
+from numba.np.numpy_support import numpy_version
+from numba.np.numpy_support import is_nonelike, check_is_integer
+from numba.core.imputils import (lower_builtin, impl_ret_borrowed,
+                                 impl_ret_new_ref, impl_ret_untracked)
+from numba.core.typing import signature
+from numba.np.arrayobj import make_array, load_item, store_item, _empty_nd_impl
+from numba.np.linalg import ensure_blas
+
+from numba.core.extending import intrinsic
+from numba.core.errors import (RequireLiteralValue, TypingError,
+                               NumbaValueError, NumbaNotImplementedError,
+                               NumbaTypeError, NumbaDeprecationWarning)
+from numba.core.overload_glue import glue_lowering
+from numba.cpython.unsafe.tuple import tuple_setitem
+
+
+def _check_blas():
+    # Checks if a BLAS is available so e.g. dot will work
+    try:
+        ensure_blas()
+    except ImportError:
+        return False
+    return True
+
+
+_HAVE_BLAS = _check_blas()
+
+
+@intrinsic
+def _create_tuple_result_shape(tyctx, shape_list, shape_tuple):
+    """
+    This routine converts shape list where the axis dimension has already
+    been popped to a tuple for indexing of the same size.  The original shape
+    tuple is also required because it contains a length field at compile time
+    whereas the shape list does not.
+    """
+
+    # The new tuple's size is one less than the original tuple since axis
+    # dimension removed.
+    nd = len(shape_tuple) - 1
+    # The return type of this intrinsic is an int tuple of length nd.
+    tupty = types.UniTuple(types.intp, nd)
+    # The function signature for this intrinsic.
+    function_sig = tupty(shape_list, shape_tuple)
+
+    def codegen(cgctx, builder, signature, args):
+        lltupty = cgctx.get_value_type(tupty)
+        # Create an empty int tuple.
+        tup = cgutils.get_null_value(lltupty)
+
+        # Get the shape list from the args and we don't need shape tuple.
+        [in_shape, _] = args
+
+        def array_indexer(a, i):
+            return a[i]
+
+        # loop to fill the tuple
+        for i in range(nd):
+            dataidx = cgctx.get_constant(types.intp, i)
+            # compile and call array_indexer
+            data = cgctx.compile_internal(builder, array_indexer,
+                                          types.intp(shape_list, types.intp),
+                                          [in_shape, dataidx])
+            tup = builder.insert_value(tup, data, i)
+        return tup
+
+    return function_sig, codegen
+
+
+@intrinsic
+def _gen_index_tuple(tyctx, shape_tuple, value, axis):
+    """
+    Generates a tuple that can be used to index a specific slice from an
+    array for sum with axis.  shape_tuple is the size of the dimensions of
+    the input array.  'value' is the value to put in the indexing tuple
+    in the axis dimension and 'axis' is that dimension.  For this to work,
+    axis has to be a const.
+    """
+    if not isinstance(axis, types.Literal):
+        raise RequireLiteralValue('axis argument must be a constant')
+    # Get the value of the axis constant.
+    axis_value = axis.literal_value
+    # The length of the indexing tuple to be output.
+    nd = len(shape_tuple)
+
+    # If the axis value is impossible for the given size array then
+    # just fake it like it was for axis 0.  This will stop compile errors
+    # when it looks like it could be called from array_sum_axis but really
+    # can't because that routine checks the axis mismatch and raise an
+    # exception.
+    if axis_value >= nd:
+        axis_value = 0
+
+    # Calculate the type of the indexing tuple.  All the non-axis
+    # dimensions have slice2 type and the axis dimension has int type.
+    before = axis_value
+    after = nd - before - 1
+
+    types_list = []
+    types_list += [types.slice2_type] * before
+    types_list += [types.intp]
+    types_list += [types.slice2_type] * after
+
+    # Creates the output type of the function.
+    tupty = types.Tuple(types_list)
+    # Defines the signature of the intrinsic.
+    function_sig = tupty(shape_tuple, value, axis)
+
+    def codegen(cgctx, builder, signature, args):
+        lltupty = cgctx.get_value_type(tupty)
+        # Create an empty indexing tuple.
+        tup = cgutils.get_null_value(lltupty)
+
+        # We only need value of the axis dimension here.
+        # The rest are constants defined above.
+        [_, value_arg, _] = args
+
+        def create_full_slice():
+            return slice(None, None)
+
+        # loop to fill the tuple with slice(None,None) before
+        # the axis dimension.
+
+        # compile and call create_full_slice
+        slice_data = cgctx.compile_internal(builder, create_full_slice,
+                                            types.slice2_type(),
+                                            [])
+        for i in range(0, axis_value):
+            tup = builder.insert_value(tup, slice_data, i)
+
+        # Add the axis dimension 'value'.
+        tup = builder.insert_value(tup, value_arg, axis_value)
+
+        # loop to fill the tuple with slice(None,None) after
+        # the axis dimension.
+        for i in range(axis_value + 1, nd):
+            tup = builder.insert_value(tup, slice_data, i)
+        return tup
+
+    return function_sig, codegen
+
+
+#----------------------------------------------------------------------------
+# Basic stats and aggregates
+
+@lower_builtin(np.sum, types.Array)
+@lower_builtin("array.sum", types.Array)
+def array_sum(context, builder, sig, args):
+    zero = sig.return_type(0)
+
+    def array_sum_impl(arr):
+        c = zero
+        for v in np.nditer(arr):
+            c += v.item()
+        return c
+
+    res = context.compile_internal(builder, array_sum_impl, sig, args,
+                                   locals=dict(c=sig.return_type))
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@register_jitable
+def _array_sum_axis_nop(arr, v):
+    return arr
+
+
+def gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero):
+    def inner(arr, axis):
+        """
+        function that performs sums over one specific axis
+
+        The third parameter to gen_index_tuple that generates the indexing
+        tuples has to be a const so we can't just pass "axis" through since
+        that isn't const.  We can check for specific values and have
+        different instances that do take consts.  Supporting axis summation
+        only up to the fourth dimension for now.
+
+        typing/arraydecl.py:sum_expand defines the return type for sum with
+        axis. It is one dimension less than the input array.
+        """
+        ndim = arr.ndim
+
+        if not is_axis_const:
+            # Catch where axis is negative or greater than 3.
+            if axis < 0 or axis > 3:
+                raise ValueError("Numba does not support sum with axis "
+                                 "parameter outside the range 0 to 3.")
+
+        # Catch the case where the user misspecifies the axis to be
+        # more than the number of the array's dimensions.
+        if axis >= ndim:
+            raise ValueError("axis is out of bounds for array")
+
+        # Convert the shape of the input array to a list.
+        ashape = list(arr.shape)
+        # Get the length of the axis dimension.
+        axis_len = ashape[axis]
+        # Remove the axis dimension from the list of dimensional lengths.
+        ashape.pop(axis)
+        # Convert this shape list back to a tuple using above intrinsic.
+        ashape_without_axis = _create_tuple_result_shape(ashape, arr.shape)
+        # Tuple needed here to create output array with correct size.
+        result = np.full(ashape_without_axis, zero, type(zero))
+
+        # Iterate through the axis dimension.
+        for axis_index in range(axis_len):
+            if is_axis_const:
+                # constant specialized version works for any valid axis value
+                index_tuple_generic = _gen_index_tuple(arr.shape, axis_index,
+                                                       const_axis_val)
+                result += arr[index_tuple_generic]
+            else:
+                # Generate a tuple used to index the input array.
+                # The tuple is ":" in all dimensions except the axis
+                # dimension where it is "axis_index".
+                if axis == 0:
+                    index_tuple1 = _gen_index_tuple(arr.shape, axis_index, 0)
+                    result += arr[index_tuple1]
+                elif axis == 1:
+                    index_tuple2 = _gen_index_tuple(arr.shape, axis_index, 1)
+                    result += arr[index_tuple2]
+                elif axis == 2:
+                    index_tuple3 = _gen_index_tuple(arr.shape, axis_index, 2)
+                    result += arr[index_tuple3]
+                elif axis == 3:
+                    index_tuple4 = _gen_index_tuple(arr.shape, axis_index, 3)
+                    result += arr[index_tuple4]
+        return op(result, 0)
+    return inner
+
+
+@lower_builtin(np.sum, types.Array, types.intp, types.DTypeSpec)
+@lower_builtin(np.sum, types.Array, types.IntegerLiteral, types.DTypeSpec)
+@lower_builtin("array.sum", types.Array, types.intp, types.DTypeSpec)
+@lower_builtin("array.sum", types.Array, types.IntegerLiteral, types.DTypeSpec)
+def array_sum_axis_dtype(context, builder, sig, args):
+    retty = sig.return_type
+    zero = getattr(retty, 'dtype', retty)(0)
+    # if the return is scalar in type then "take" the 0th element of the
+    # 0d array accumulator as the return value
+    if getattr(retty, 'ndim', None) is None:
+        op = np.take
+    else:
+        op = _array_sum_axis_nop
+    [ty_array, ty_axis, ty_dtype] = sig.args
+    is_axis_const = False
+    const_axis_val = 0
+    if isinstance(ty_axis, types.Literal):
+        # this special-cases for constant axis
+        const_axis_val = ty_axis.literal_value
+        # fix negative axis
+        if const_axis_val < 0:
+            const_axis_val = ty_array.ndim + const_axis_val
+        if const_axis_val < 0 or const_axis_val > ty_array.ndim:
+            raise ValueError("'axis' entry is out of bounds")
+
+        ty_axis = context.typing_context.resolve_value_type(const_axis_val)
+        axis_val = context.get_constant(ty_axis, const_axis_val)
+        # rewrite arguments
+        args = args[0], axis_val, args[2]
+        # rewrite sig
+        sig = sig.replace(args=[ty_array, ty_axis, ty_dtype])
+        is_axis_const = True
+
+    gen_impl = gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero)
+    compiled = register_jitable(gen_impl)
+
+    def array_sum_impl_axis(arr, axis, dtype):
+        return compiled(arr, axis)
+
+    res = context.compile_internal(builder, array_sum_impl_axis, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.sum, types.Array,  types.DTypeSpec)
+@lower_builtin("array.sum", types.Array, types.DTypeSpec)
+def array_sum_dtype(context, builder, sig, args):
+    zero = sig.return_type(0)
+
+    def array_sum_impl(arr, dtype):
+        c = zero
+        for v in np.nditer(arr):
+            c += v.item()
+        return c
+
+    res = context.compile_internal(builder, array_sum_impl, sig, args,
+                                   locals=dict(c=sig.return_type))
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.sum, types.Array, types.intp)
+@lower_builtin(np.sum, types.Array, types.IntegerLiteral)
+@lower_builtin("array.sum", types.Array, types.intp)
+@lower_builtin("array.sum", types.Array, types.IntegerLiteral)
+def array_sum_axis(context, builder, sig, args):
+    retty = sig.return_type
+    zero = getattr(retty, 'dtype', retty)(0)
+    # if the return is scalar in type then "take" the 0th element of the
+    # 0d array accumulator as the return value
+    if getattr(retty, 'ndim', None) is None:
+        op = np.take
+    else:
+        op = _array_sum_axis_nop
+    [ty_array, ty_axis] = sig.args
+    is_axis_const = False
+    const_axis_val = 0
+    if isinstance(ty_axis, types.Literal):
+        # this special-cases for constant axis
+        const_axis_val = ty_axis.literal_value
+        # fix negative axis
+        if const_axis_val < 0:
+            const_axis_val = ty_array.ndim + const_axis_val
+        if const_axis_val < 0 or const_axis_val > ty_array.ndim:
+            msg = f"'axis' entry ({const_axis_val}) is out of bounds"
+            raise NumbaValueError(msg)
+
+        ty_axis = context.typing_context.resolve_value_type(const_axis_val)
+        axis_val = context.get_constant(ty_axis, const_axis_val)
+        # rewrite arguments
+        args = args[0], axis_val
+        # rewrite sig
+        sig = sig.replace(args=[ty_array, ty_axis])
+        is_axis_const = True
+
+    gen_impl = gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero)
+    compiled = register_jitable(gen_impl)
+
+    def array_sum_impl_axis(arr, axis):
+        return compiled(arr, axis)
+
+    res = context.compile_internal(builder, array_sum_impl_axis, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.prod, types.Array)
+@lower_builtin("array.prod", types.Array)
+def array_prod(context, builder, sig, args):
+
+    def array_prod_impl(arr):
+        c = 1
+        for v in np.nditer(arr):
+            c *= v.item()
+        return c
+
+    res = context.compile_internal(builder, array_prod_impl, sig, args,
+                                   locals=dict(c=sig.return_type))
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.cumsum, types.Array)
+@lower_builtin("array.cumsum", types.Array)
+def array_cumsum(context, builder, sig, args):
+    scalar_dtype = sig.return_type.dtype
+    dtype = as_dtype(scalar_dtype)
+    zero = scalar_dtype(0)
+
+    def array_cumsum_impl(arr):
+        out = np.empty(arr.size, dtype)
+        c = zero
+        for idx, v in enumerate(arr.flat):
+            c += v
+            out[idx] = c
+        return out
+
+    res = context.compile_internal(builder, array_cumsum_impl, sig, args,
+                                   locals=dict(c=scalar_dtype))
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.cumprod, types.Array)
+@lower_builtin("array.cumprod", types.Array)
+def array_cumprod(context, builder, sig, args):
+    scalar_dtype = sig.return_type.dtype
+    dtype = as_dtype(scalar_dtype)
+
+    def array_cumprod_impl(arr):
+        out = np.empty(arr.size, dtype)
+        c = 1
+        for idx, v in enumerate(arr.flat):
+            c *= v
+            out[idx] = c
+        return out
+
+    res = context.compile_internal(builder, array_cumprod_impl, sig, args,
+                                   locals=dict(c=scalar_dtype))
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.mean, types.Array)
+@lower_builtin("array.mean", types.Array)
+def array_mean(context, builder, sig, args):
+    zero = sig.return_type(0)
+
+    def array_mean_impl(arr):
+        # Can't use the naive `arr.sum() / arr.size`, as it would return
+        # a wrong result on integer sum overflow.
+        c = zero
+        for v in np.nditer(arr):
+            c += v.item()
+        return c / arr.size
+
+    res = context.compile_internal(builder, array_mean_impl, sig, args,
+                                   locals=dict(c=sig.return_type))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.var, types.Array)
+@lower_builtin("array.var", types.Array)
+def array_var(context, builder, sig, args):
+    def array_var_impl(arr):
+        # Compute the mean
+        m = arr.mean()
+
+        # Compute the sum of square diffs
+        ssd = 0
+        for v in np.nditer(arr):
+            val = (v.item() - m)
+            ssd += np.real(val * np.conj(val))
+        return ssd / arr.size
+
+    res = context.compile_internal(builder, array_var_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.std, types.Array)
+@lower_builtin("array.std", types.Array)
+def array_std(context, builder, sig, args):
+    def array_std_impl(arry):
+        return arry.var() ** 0.5
+    res = context.compile_internal(builder, array_std_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def zero_dim_msg(fn_name):
+    msg = ("zero-size array to reduction operation "
+           "{0} which has no identity".format(fn_name))
+    return msg
+
+
+def _is_nat(x):
+    pass
+
+
+@overload(_is_nat)
+def ol_is_nat(x):
+    if numpy_version >= (1, 18):
+        return lambda x: np.isnat(x)
+    else:
+        nat = x('NaT')
+        return lambda x: x == nat
+
+
+@lower_builtin(np.min, types.Array)
+@lower_builtin("array.min", types.Array)
+def array_min(context, builder, sig, args):
+    ty = sig.args[0].dtype
+    MSG = zero_dim_msg('minimum')
+
+    if isinstance(ty, (types.NPDatetime, types.NPTimedelta)):
+        # NP < 1.18: NaT is smaller than every other value, but it is
+        # ignored as far as min() is concerned.
+        # NP >= 1.18: NaT dominates like NaN
+        def array_min_impl(arry):
+            if arry.size == 0:
+                raise ValueError(MSG)
+
+            it = np.nditer(arry)
+            min_value = next(it).take(0)
+            if _is_nat(min_value):
+                return min_value
+
+            for view in it:
+                v = view.item()
+                if _is_nat(v):
+                    if numpy_version >= (1, 18):
+                        return v
+                    else:
+                        continue
+                if v < min_value:
+                    min_value = v
+            return min_value
+
+    elif isinstance(ty, types.Complex):
+        def array_min_impl(arry):
+            if arry.size == 0:
+                raise ValueError(MSG)
+
+            it = np.nditer(arry)
+            min_value = next(it).take(0)
+
+            for view in it:
+                v = view.item()
+                if v.real < min_value.real:
+                    min_value = v
+                elif v.real == min_value.real:
+                    if v.imag < min_value.imag:
+                        min_value = v
+            return min_value
+
+    elif isinstance(ty, types.Float):
+        def array_min_impl(arry):
+            if arry.size == 0:
+                raise ValueError(MSG)
+
+            it = np.nditer(arry)
+            min_value = next(it).take(0)
+            if np.isnan(min_value):
+                return min_value
+
+            for view in it:
+                v = view.item()
+                if np.isnan(v):
+                    return v
+                if v < min_value:
+                    min_value = v
+            return min_value
+
+    else:
+        def array_min_impl(arry):
+            if arry.size == 0:
+                raise ValueError(MSG)
+
+            it = np.nditer(arry)
+            min_value = next(it).take(0)
+
+            for view in it:
+                v = view.item()
+                if v < min_value:
+                    min_value = v
+            return min_value
+
+    res = context.compile_internal(builder, array_min_impl, sig, args)
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.max, types.Array)
+@lower_builtin("array.max", types.Array)
+def array_max(context, builder, sig, args):
+    ty = sig.args[0].dtype
+    MSG = zero_dim_msg('maximum')
+
+    if isinstance(ty, (types.NPDatetime, types.NPTimedelta)):
+        # NP < 1.18: NaT is smaller than every other value, but it is
+        # ignored as far as min() is concerned.
+        # NP >= 1.18: NaT dominates like NaN
+        def array_max_impl(arry):
+            if arry.size == 0:
+                raise ValueError(MSG)
+
+            it = np.nditer(arry)
+            max_value = next(it).take(0)
+            if _is_nat(max_value):
+                return max_value
+
+            for view in it:
+                v = view.item()
+                if _is_nat(v):
+                    if numpy_version >= (1, 18):
+                        return v
+                    else:
+                        continue
+                if v > max_value:
+                    max_value = v
+            return max_value
+
+    elif isinstance(ty, types.Complex):
+        def array_max_impl(arry):
+            if arry.size == 0:
+                raise ValueError(MSG)
+
+            it = np.nditer(arry)
+            max_value = next(it).take(0)
+
+            for view in it:
+                v = view.item()
+                if v.real > max_value.real:
+                    max_value = v
+                elif v.real == max_value.real:
+                    if v.imag > max_value.imag:
+                        max_value = v
+            return max_value
+
+    elif isinstance(ty, types.Float):
+        def array_max_impl(arry):
+            if arry.size == 0:
+                raise ValueError(MSG)
+
+            it = np.nditer(arry)
+            max_value = next(it).take(0)
+            if np.isnan(max_value):
+                return max_value
+
+            for view in it:
+                v = view.item()
+                if np.isnan(v):
+                    return v
+                if v > max_value:
+                    max_value = v
+            return max_value
+
+    else:
+        def array_max_impl(arry):
+            if arry.size == 0:
+                raise ValueError(MSG)
+
+            it = np.nditer(arry)
+            max_value = next(it).take(0)
+
+            for view in it:
+                v = view.item()
+                if v > max_value:
+                    max_value = v
+            return max_value
+
+    res = context.compile_internal(builder, array_max_impl, sig, args)
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@register_jitable
+def array_argmin_impl_datetime(arry):
+    if arry.size == 0:
+        raise ValueError("attempt to get argmin of an empty sequence")
+    it = np.nditer(arry)
+    min_value = next(it).take(0)
+    min_idx = 0
+    if _is_nat(min_value):
+        return min_idx
+
+    idx = 1
+    for view in it:
+        v = view.item()
+        if _is_nat(v):
+            if numpy_version >= (1, 18):
+                return idx
+            else:
+                idx += 1
+                continue
+        if v < min_value:
+            min_value = v
+            min_idx = idx
+        idx += 1
+    return min_idx
+
+
+@register_jitable
+def array_argmin_impl_float(arry):
+    if arry.size == 0:
+        raise ValueError("attempt to get argmin of an empty sequence")
+    for v in arry.flat:
+        min_value = v
+        min_idx = 0
+        break
+    if np.isnan(min_value):
+        return min_idx
+
+    idx = 0
+    for v in arry.flat:
+        if np.isnan(v):
+            return idx
+        if v < min_value:
+            min_value = v
+            min_idx = idx
+        idx += 1
+    return min_idx
+
+
+@register_jitable
+def array_argmin_impl_generic(arry):
+    if arry.size == 0:
+        raise ValueError("attempt to get argmin of an empty sequence")
+    for v in arry.flat:
+        min_value = v
+        min_idx = 0
+        break
+    else:
+        raise RuntimeError('unreachable')
+
+    idx = 0
+    for v in arry.flat:
+        if v < min_value:
+            min_value = v
+            min_idx = idx
+        idx += 1
+    return min_idx
+
+
+@overload(np.argmin)
+@overload_method(types.Array, "argmin")
+def array_argmin(arr, axis=None):
+    if isinstance(arr.dtype, (types.NPDatetime, types.NPTimedelta)):
+        flatten_impl = array_argmin_impl_datetime
+    elif isinstance(arr.dtype, types.Float):
+        flatten_impl = array_argmin_impl_float
+    else:
+        flatten_impl = array_argmin_impl_generic
+
+    if is_nonelike(axis):
+        def array_argmin_impl(arr, axis=None):
+            return flatten_impl(arr)
+    else:
+        array_argmin_impl = build_argmax_or_argmin_with_axis_impl(
+            arr, axis, flatten_impl
+        )
+    return array_argmin_impl
+
+
+@register_jitable
+def array_argmax_impl_datetime(arry):
+    if arry.size == 0:
+        raise ValueError("attempt to get argmax of an empty sequence")
+    it = np.nditer(arry)
+    max_value = next(it).take(0)
+    max_idx = 0
+    if _is_nat(max_value):
+        return max_idx
+
+    idx = 1
+    for view in it:
+        v = view.item()
+        if _is_nat(v):
+            if numpy_version >= (1, 18):
+                return idx
+            else:
+                idx += 1
+                continue
+        if v > max_value:
+            max_value = v
+            max_idx = idx
+        idx += 1
+    return max_idx
+
+
+@register_jitable
+def array_argmax_impl_float(arry):
+    if arry.size == 0:
+        raise ValueError("attempt to get argmax of an empty sequence")
+    for v in arry.flat:
+        max_value = v
+        max_idx = 0
+        break
+    if np.isnan(max_value):
+        return max_idx
+
+    idx = 0
+    for v in arry.flat:
+        if np.isnan(v):
+            return idx
+        if v > max_value:
+            max_value = v
+            max_idx = idx
+        idx += 1
+    return max_idx
+
+
+@register_jitable
+def array_argmax_impl_generic(arry):
+    if arry.size == 0:
+        raise ValueError("attempt to get argmax of an empty sequence")
+    for v in arry.flat:
+        max_value = v
+        max_idx = 0
+        break
+
+    idx = 0
+    for v in arry.flat:
+        if v > max_value:
+            max_value = v
+            max_idx = idx
+        idx += 1
+    return max_idx
+
+
+def build_argmax_or_argmin_with_axis_impl(arr, axis, flatten_impl):
+    """
+    Given a function that implements the logic for handling a flattened
+    array, return the implementation function.
+    """
+    check_is_integer(axis, "axis")
+    retty = types.intp
+
+    tuple_buffer = tuple(range(arr.ndim))
+
+    def impl(arr, axis=None):
+        if axis < 0:
+            axis = arr.ndim + axis
+
+        if axis < 0 or axis >= arr.ndim:
+            raise ValueError("axis is out of bounds")
+
+        # Short circuit 1-dimensional arrays:
+        if arr.ndim == 1:
+            return flatten_impl(arr)
+
+        # Make chosen axis the last axis:
+        tmp = tuple_buffer
+        for i in range(axis, arr.ndim - 1):
+            tmp = tuple_setitem(tmp, i, i + 1)
+        transpose_index = tuple_setitem(tmp, arr.ndim - 1, axis)
+        transposed_arr = arr.transpose(transpose_index)
+
+        # Flatten along that axis; since we've transposed, we can just get
+        # batches off the overall flattened array.
+        m = transposed_arr.shape[-1]
+        raveled = transposed_arr.ravel()
+        assert raveled.size == arr.size
+        assert transposed_arr.size % m == 0
+        out = np.empty(transposed_arr.size // m, retty)
+        for i in range(out.size):
+            out[i] = flatten_impl(raveled[i * m:(i + 1) * m])
+
+        # Reshape based on axis we didn't flatten over:
+        return out.reshape(transposed_arr.shape[:-1])
+
+    return impl
+
+
+@overload(np.argmax)
+@overload_method(types.Array, "argmax")
+def array_argmax(arr, axis=None):
+    if isinstance(arr.dtype, (types.NPDatetime, types.NPTimedelta)):
+        flatten_impl = array_argmax_impl_datetime
+    elif isinstance(arr.dtype, types.Float):
+        flatten_impl = array_argmax_impl_float
+    else:
+        flatten_impl = array_argmax_impl_generic
+
+    if is_nonelike(axis):
+        def array_argmax_impl(arr, axis=None):
+            return flatten_impl(arr)
+    else:
+        array_argmax_impl = build_argmax_or_argmin_with_axis_impl(
+            arr, axis, flatten_impl
+        )
+    return array_argmax_impl
+
+
+@overload(np.all)
+@overload_method(types.Array, "all")
+def np_all(a):
+    def flat_all(a):
+        for v in np.nditer(a):
+            if not v.item():
+                return False
+        return True
+
+    return flat_all
+
+
+@register_jitable
+def _allclose_scalars(a_v, b_v, rtol=1e-05, atol=1e-08, equal_nan=False):
+    a_v_isnan = np.isnan(a_v)
+    b_v_isnan = np.isnan(b_v)
+
+    # only one of the values is NaN and the
+    # other is not.
+    if ( (not a_v_isnan and b_v_isnan) or
+            (a_v_isnan and not b_v_isnan) ):
+        return False
+
+    # either both of the values are NaN
+    # or both are numbers
+    if a_v_isnan and b_v_isnan:
+        if not equal_nan:
+            return False
+    else:
+        if np.isinf(a_v) or np.isinf(b_v):
+            return a_v == b_v
+
+        if np.abs(a_v - b_v) > atol + rtol * np.abs(b_v * 1.0):
+            return False
+
+    return True
+
+
+@overload(np.allclose)
+@overload_method(types.Array, "allclose")
+def np_allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
+
+    if not type_can_asarray(a):
+        raise TypeError('The first argument "a" must be array-like')
+
+    if not type_can_asarray(b):
+        raise TypeError('The second argument "b" must be array-like')
+
+    if not isinstance(rtol, types.Float):
+        raise TypeError('The third argument "rtol" must be a '
+                        'floating point')
+
+    if not isinstance(atol, types.Float):
+        raise TypingError('The fourth argument "atol" must be a '
+                          'floating point')
+
+    if not isinstance(equal_nan, types.Boolean):
+        raise TypeError('The fifth argument "equal_nan" must be a '
+                        'boolean')
+
+    is_a_scalar = isinstance(a, types.Number)
+    is_b_scalar = isinstance(b, types.Number)
+
+    if is_a_scalar and is_b_scalar:
+        def np_allclose_impl_scalar_scalar(a, b, rtol=1e-05, atol=1e-08,
+                                           equal_nan=False):
+            return _allclose_scalars(a, b, rtol=rtol, atol=atol,
+                                     equal_nan=equal_nan)
+        return np_allclose_impl_scalar_scalar
+    elif is_a_scalar and not is_b_scalar:
+        def np_allclose_impl_scalar_array(a, b, rtol=1e-05, atol=1e-08,
+                                          equal_nan=False):
+            b = np.asarray(b)
+            for bv in np.nditer(b):
+                if not _allclose_scalars(a, bv.item(), rtol=rtol, atol=atol,
+                                         equal_nan=equal_nan):
+                    return False
+            return True
+        return np_allclose_impl_scalar_array
+    elif not is_a_scalar and is_b_scalar:
+        def np_allclose_impl_array_scalar(a, b, rtol=1e-05, atol=1e-08,
+                                          equal_nan=False):
+            a = np.asarray(a)
+            for av in np.nditer(a):
+                if not _allclose_scalars(av.item(), b, rtol=rtol, atol=atol,
+                                         equal_nan=equal_nan):
+                    return False
+            return True
+        return np_allclose_impl_array_scalar
+    elif not is_a_scalar and not is_b_scalar:
+        def np_allclose_impl_array_array(a, b, rtol=1e-05, atol=1e-08,
+                                         equal_nan=False):
+            a = np.asarray(a)
+            b = np.asarray(b)
+            a_a, b_b = np.broadcast_arrays(a, b)
+
+            for av, bv in np.nditer((a_a, b_b)):
+                if not _allclose_scalars(av.item(), bv.item(), rtol=rtol,
+                                         atol=atol, equal_nan=equal_nan):
+                    return False
+
+            return True
+
+        return np_allclose_impl_array_array
+
+
+@overload(np.any)
+@overload_method(types.Array, "any")
+def np_any(a):
+    def flat_any(a):
+        for v in np.nditer(a):
+            if v.item():
+                return True
+        return False
+
+    return flat_any
+
+
+@overload(np.average)
+def np_average(arr, axis=None, weights=None):
+
+    if weights is None or isinstance(weights, types.NoneType):
+        def np_average_impl(arr, axis=None, weights=None):
+            arr = np.asarray(arr)
+            return np.mean(arr)
+    else:
+        if axis is None or isinstance(axis, types.NoneType):
+            def np_average_impl(arr, axis=None, weights=None):
+                arr = np.asarray(arr)
+                weights = np.asarray(weights)
+
+                if arr.shape != weights.shape:
+                    if axis is None:
+                        raise TypeError(
+                            "Numba does not support average when shapes of "
+                            "a and weights differ.")
+                    if weights.ndim != 1:
+                        raise TypeError(
+                            "1D weights expected when shapes of "
+                            "a and weights differ.")
+
+                scl = np.sum(weights)
+                if scl == 0.0:
+                    raise ZeroDivisionError(
+                        "Weights sum to zero, can't be normalized.")
+
+                avg = np.sum(np.multiply(arr, weights)) / scl
+                return avg
+        else:
+            def np_average_impl(arr, axis=None, weights=None):
+                raise TypeError("Numba does not support average with axis.")
+
+    return np_average_impl
+
+
+def get_isnan(dtype):
+    """
+    A generic isnan() function
+    """
+    if isinstance(dtype, (types.Float, types.Complex)):
+        return np.isnan
+    else:
+        @register_jitable
+        def _trivial_isnan(x):
+            return False
+        return _trivial_isnan
+
+
+@overload(np.iscomplex)
+def np_iscomplex(x):
+    if type_can_asarray(x):
+        # NumPy uses asanyarray here!
+        return lambda x: np.asarray(x).imag != 0
+    return None
+
+
+@overload(np.isreal)
+def np_isreal(x):
+    if type_can_asarray(x):
+        # NumPy uses asanyarray here!
+        return lambda x: np.asarray(x).imag == 0
+    return None
+
+
+@overload(np.iscomplexobj)
+def iscomplexobj(x):
+    # Implementation based on NumPy
+    # https://github.com/numpy/numpy/blob/d9b1e32cb8ef90d6b4a47853241db2a28146a57d/numpy/lib/type_check.py#L282-L320
+    dt = determine_dtype(x)
+    if isinstance(x, types.Optional):
+        dt = determine_dtype(x.type)
+    iscmplx = np.issubdtype(dt, np.complexfloating)
+
+    if isinstance(x, types.Optional):
+        def impl(x):
+            if x is None:
+                return False
+            return iscmplx
+    else:
+        def impl(x):
+            return iscmplx
+    return impl
+
+
+@overload(np.isrealobj)
+def isrealobj(x):
+    # Return True if x is not a complex type.
+    # Implementation based on NumPy
+    # https://github.com/numpy/numpy/blob/ccfbcc1cd9a4035a467f2e982a565ab27de25b6b/numpy/lib/type_check.py#L290-L322
+    def impl(x):
+        return not np.iscomplexobj(x)
+    return impl
+
+
+@overload(np.isscalar)
+def np_isscalar(num):
+    res = isinstance(num, (types.Number, types.UnicodeType, types.Boolean))
+
+    def impl(num):
+        return res
+    return impl
+
+
+def is_np_inf_impl(x, out, fn):
+
+    # if/else branch should be unified after PR #5606 is merged
+    if is_nonelike(out):
+        def impl(x, out=None):
+            return np.logical_and(np.isinf(x), fn(np.signbit(x)))
+    else:
+        def impl(x, out=None):
+            return np.logical_and(np.isinf(x), fn(np.signbit(x)), out)
+
+    return impl
+
+
+@overload(np.isneginf)
+def isneginf(x, out=None):
+    fn = register_jitable(lambda x: x)
+    return is_np_inf_impl(x, out, fn)
+
+
+@overload(np.isposinf)
+def isposinf(x, out=None):
+    fn = register_jitable(lambda x: ~x)
+    return is_np_inf_impl(x, out, fn)
+
+
+@register_jitable
+def less_than(a, b):
+    return a < b
+
+
+@register_jitable
+def greater_than(a, b):
+    return a > b
+
+
+@register_jitable
+def check_array(a):
+    if a.size == 0:
+        raise ValueError('zero-size array to reduction operation not possible')
+
+
+def nan_min_max_factory(comparison_op, is_complex_dtype):
+    if is_complex_dtype:
+        def impl(a):
+            arr = np.asarray(a)
+            check_array(arr)
+            it = np.nditer(arr)
+            return_val = next(it).take(0)
+            for view in it:
+                v = view.item()
+                if np.isnan(return_val.real) and not np.isnan(v.real):
+                    return_val = v
+                else:
+                    if comparison_op(v.real, return_val.real):
+                        return_val = v
+                    elif v.real == return_val.real:
+                        if comparison_op(v.imag, return_val.imag):
+                            return_val = v
+            return return_val
+    else:
+        def impl(a):
+            arr = np.asarray(a)
+            check_array(arr)
+            it = np.nditer(arr)
+            return_val = next(it).take(0)
+            for view in it:
+                v = view.item()
+                if not np.isnan(v):
+                    if not comparison_op(return_val, v):
+                        return_val = v
+            return return_val
+
+    return impl
+
+
+real_nanmin = register_jitable(
+    nan_min_max_factory(less_than, is_complex_dtype=False)
+)
+real_nanmax = register_jitable(
+    nan_min_max_factory(greater_than, is_complex_dtype=False)
+)
+complex_nanmin = register_jitable(
+    nan_min_max_factory(less_than, is_complex_dtype=True)
+)
+complex_nanmax = register_jitable(
+    nan_min_max_factory(greater_than, is_complex_dtype=True)
+)
+
+
+@overload(np.nanmin)
+def np_nanmin(a):
+    dt = determine_dtype(a)
+    if np.issubdtype(dt, np.complexfloating):
+        return complex_nanmin
+    else:
+        return real_nanmin
+
+
+@overload(np.nanmax)
+def np_nanmax(a):
+    dt = determine_dtype(a)
+    if np.issubdtype(dt, np.complexfloating):
+        return complex_nanmax
+    else:
+        return real_nanmax
+
+
+@overload(np.nanmean)
+def np_nanmean(a):
+    if not isinstance(a, types.Array):
+        return
+    isnan = get_isnan(a.dtype)
+
+    def nanmean_impl(a):
+        c = 0.0
+        count = 0
+        for view in np.nditer(a):
+            v = view.item()
+            if not isnan(v):
+                c += v.item()
+                count += 1
+        # np.divide() doesn't raise ZeroDivisionError
+        return np.divide(c, count)
+
+    return nanmean_impl
+
+
+@overload(np.nanvar)
+def np_nanvar(a):
+    if not isinstance(a, types.Array):
+        return
+    isnan = get_isnan(a.dtype)
+
+    def nanvar_impl(a):
+        # Compute the mean
+        m = np.nanmean(a)
+
+        # Compute the sum of square diffs
+        ssd = 0.0
+        count = 0
+        for view in np.nditer(a):
+            v = view.item()
+            if not isnan(v):
+                val = (v.item() - m)
+                ssd += np.real(val * np.conj(val))
+                count += 1
+        # np.divide() doesn't raise ZeroDivisionError
+        return np.divide(ssd, count)
+
+    return nanvar_impl
+
+
+@overload(np.nanstd)
+def np_nanstd(a):
+    if not isinstance(a, types.Array):
+        return
+
+    def nanstd_impl(a):
+        return np.nanvar(a) ** 0.5
+
+    return nanstd_impl
+
+
+@overload(np.nansum)
+def np_nansum(a):
+    if not isinstance(a, types.Array):
+        return
+    if isinstance(a.dtype, types.Integer):
+        retty = types.intp
+    else:
+        retty = a.dtype
+    zero = retty(0)
+    isnan = get_isnan(a.dtype)
+
+    def nansum_impl(a):
+        c = zero
+        for view in np.nditer(a):
+            v = view.item()
+            if not isnan(v):
+                c += v
+        return c
+
+    return nansum_impl
+
+
+@overload(np.nanprod)
+def np_nanprod(a):
+    if not isinstance(a, types.Array):
+        return
+    if isinstance(a.dtype, types.Integer):
+        retty = types.intp
+    else:
+        retty = a.dtype
+    one = retty(1)
+    isnan = get_isnan(a.dtype)
+
+    def nanprod_impl(a):
+        c = one
+        for view in np.nditer(a):
+            v = view.item()
+            if not isnan(v):
+                c *= v
+        return c
+
+    return nanprod_impl
+
+
+@overload(np.nancumprod)
+def np_nancumprod(a):
+    if not isinstance(a, types.Array):
+        return
+
+    if isinstance(a.dtype, (types.Boolean, types.Integer)):
+        # dtype cannot possibly contain NaN
+        return lambda a: np.cumprod(a)
+    else:
+        retty = a.dtype
+        is_nan = get_isnan(retty)
+        one = retty(1)
+
+        def nancumprod_impl(a):
+            out = np.empty(a.size, retty)
+            c = one
+            for idx, v in enumerate(a.flat):
+                if ~is_nan(v):
+                    c *= v
+                out[idx] = c
+            return out
+
+        return nancumprod_impl
+
+
+@overload(np.nancumsum)
+def np_nancumsum(a):
+    if not isinstance(a, types.Array):
+        return
+
+    if isinstance(a.dtype, (types.Boolean, types.Integer)):
+        # dtype cannot possibly contain NaN
+        return lambda a: np.cumsum(a)
+    else:
+        retty = a.dtype
+        is_nan = get_isnan(retty)
+        zero = retty(0)
+
+        def nancumsum_impl(a):
+            out = np.empty(a.size, retty)
+            c = zero
+            for idx, v in enumerate(a.flat):
+                if ~is_nan(v):
+                    c += v
+                out[idx] = c
+            return out
+
+        return nancumsum_impl
+
+
+@register_jitable
+def prepare_ptp_input(a):
+    arr = _asarray(a)
+    if len(arr) == 0:
+        raise ValueError('zero-size array reduction not possible')
+    else:
+        return arr
+
+
+def _compute_current_val_impl_gen(op):
+    def _compute_current_val_impl(current_val, val):
+        if isinstance(current_val, types.Complex):
+            # The sort order for complex numbers is lexicographic. If both the
+            # real and imaginary parts are non-nan then the order is determined
+            # by the real parts except when they are equal, in which case the
+            # order is determined by the imaginary parts.
+            # https://github.com/numpy/numpy/blob/577a86e/numpy/core/fromnumeric.py#L874-L877    # noqa: E501
+            def impl(current_val, val):
+                if op(val.real, current_val.real):
+                    return val
+                elif (val.real == current_val.real
+                        and op(val.imag, current_val.imag)):
+                    return val
+                return current_val
+        else:
+            def impl(current_val, val):
+                return val if op(val, current_val) else current_val
+        return impl
+    return _compute_current_val_impl
+
+
+_compute_a_max = generated_jit(_compute_current_val_impl_gen(greater_than))
+_compute_a_min = generated_jit(_compute_current_val_impl_gen(less_than))
+
+
+@generated_jit
+def _early_return(val):
+    UNUSED = 0
+    if isinstance(val, types.Complex):
+        def impl(val):
+            if np.isnan(val.real):
+                if np.isnan(val.imag):
+                    return True, np.nan + np.nan * 1j
+                else:
+                    return True, np.nan + 0j
+            else:
+                return False, UNUSED
+    elif isinstance(val, types.Float):
+        def impl(val):
+            if np.isnan(val):
+                return True, np.nan
+            else:
+                return False, UNUSED
+    else:
+        def impl(val):
+            return False, UNUSED
+    return impl
+
+
+@overload_method(types.Array, 'ptp')
+@overload(np.ptp)
+def np_ptp(a):
+
+    if hasattr(a, 'dtype'):
+        if isinstance(a.dtype, types.Boolean):
+            raise TypingError("Boolean dtype is unsupported (as per NumPy)")
+            # Numpy raises a TypeError
+
+    def np_ptp_impl(a):
+        arr = prepare_ptp_input(a)
+
+        a_flat = arr.flat
+        a_min = a_flat[0]
+        a_max = a_flat[0]
+
+        for i in range(arr.size):
+            val = a_flat[i]
+            take_branch, retval = _early_return(val)
+            if take_branch:
+                return retval
+            a_max = _compute_a_max(a_max, val)
+            a_min = _compute_a_min(a_min, val)
+
+        return a_max - a_min
+
+    return np_ptp_impl
+
+
+#----------------------------------------------------------------------------
+# Median and partitioning
+
+@register_jitable
+def nan_aware_less_than(a, b):
+    if np.isnan(a):
+        return False
+    else:
+        if np.isnan(b):
+            return True
+        else:
+            return a < b
+
+
+def _partition_factory(pivotimpl):
+    def _partition(A, low, high):
+        mid = (low + high) >> 1
+        # NOTE: the pattern of swaps below for the pivot choice and the
+        # partitioning gives good results (i.e. regular O(n log n))
+        # on sorted, reverse-sorted, and uniform arrays.  Subtle changes
+        # risk breaking this property.
+
+        # Use median of three {low, middle, high} as the pivot
+        if pivotimpl(A[mid], A[low]):
+            A[low], A[mid] = A[mid], A[low]
+        if pivotimpl(A[high], A[mid]):
+            A[high], A[mid] = A[mid], A[high]
+        if pivotimpl(A[mid], A[low]):
+            A[low], A[mid] = A[mid], A[low]
+        pivot = A[mid]
+
+        A[high], A[mid] = A[mid], A[high]
+        i = low
+        j = high - 1
+        while True:
+            while i < high and pivotimpl(A[i], pivot):
+                i += 1
+            while j >= low and pivotimpl(pivot, A[j]):
+                j -= 1
+            if i >= j:
+                break
+            A[i], A[j] = A[j], A[i]
+            i += 1
+            j -= 1
+        # Put the pivot back in its final place (all items before `i`
+        # are smaller than the pivot, all items at/after `i` are larger)
+        A[i], A[high] = A[high], A[i]
+        return i
+    return _partition
+
+
+_partition = register_jitable(_partition_factory(less_than))
+_partition_w_nan = register_jitable(_partition_factory(nan_aware_less_than))
+
+
+def _select_factory(partitionimpl):
+    def _select(arry, k, low, high):
+        """
+        Select the k'th smallest element in array[low:high + 1].
+        """
+        i = partitionimpl(arry, low, high)
+        while i != k:
+            if i < k:
+                low = i + 1
+                i = partitionimpl(arry, low, high)
+            else:
+                high = i - 1
+                i = partitionimpl(arry, low, high)
+        return arry[k]
+    return _select
+
+
+_select = register_jitable(_select_factory(_partition))
+_select_w_nan = register_jitable(_select_factory(_partition_w_nan))
+
+
+@register_jitable
+def _select_two(arry, k, low, high):
+    """
+    Select the k'th and k+1'th smallest elements in array[low:high + 1].
+
+    This is significantly faster than doing two independent selections
+    for k and k+1.
+    """
+    while True:
+        assert high > low  # by construction
+        i = _partition(arry, low, high)
+        if i < k:
+            low = i + 1
+        elif i > k + 1:
+            high = i - 1
+        elif i == k:
+            _select(arry, k + 1, i + 1, high)
+            break
+        else:  # i == k + 1
+            _select(arry, k, low, i - 1)
+            break
+
+    return arry[k], arry[k + 1]
+
+
+@register_jitable
+def _median_inner(temp_arry, n):
+    """
+    The main logic of the median() call.  *temp_arry* must be disposable,
+    as this function will mutate it.
+    """
+    low = 0
+    high = n - 1
+    half = n >> 1
+    if n & 1 == 0:
+        a, b = _select_two(temp_arry, half - 1, low, high)
+        return (a + b) / 2
+    else:
+        return _select(temp_arry, half, low, high)
+
+
+@overload(np.median)
+def np_median(a):
+    if not isinstance(a, types.Array):
+        return
+
+    def median_impl(a):
+        # np.median() works on the flattened array, and we need a temporary
+        # workspace anyway
+        temp_arry = a.flatten()
+        n = temp_arry.shape[0]
+        return _median_inner(temp_arry, n)
+
+    return median_impl
+
+
+@register_jitable
+def _collect_percentiles_inner(a, q):
+    #TODO: This needs rewriting to be closer to NumPy, particularly the nan/inf
+    # handling which is generally subject to algorithmic changes.
+    n = len(a)
+
+    if n == 1:
+        # single element array; output same for all percentiles
+        out = np.full(len(q), a[0], dtype=np.float64)
+    else:
+        out = np.empty(len(q), dtype=np.float64)
+        for i in range(len(q)):
+            percentile = q[i]
+
+            # bypass pivoting where requested percentile is 100
+            if percentile == 100:
+                val = np.max(a)
+                # heuristics to handle infinite values a la NumPy
+                if ~np.all(np.isfinite(a)):
+                    if ~np.isfinite(val):
+                        val = np.nan
+
+            # bypass pivoting where requested percentile is 0
+            elif percentile == 0:
+                val = np.min(a)
+                # convoluted heuristics to handle infinite values a la NumPy
+                if ~np.all(np.isfinite(a)):
+                    num_pos_inf = np.sum(a == np.inf)
+                    num_neg_inf = np.sum(a == -np.inf)
+                    num_finite = n - (num_neg_inf + num_pos_inf)
+                    if num_finite == 0:
+                        val = np.nan
+                    if num_pos_inf == 1 and n == 2:
+                        val = np.nan
+                    if num_neg_inf > 1:
+                        val = np.nan
+                    if num_finite == 1:
+                        if num_pos_inf > 1:
+                            if num_neg_inf != 1:
+                                val = np.nan
+
+            else:
+                # linear interp between closest ranks
+                rank = 1 + (n - 1) * np.true_divide(percentile, 100.0)
+                f = math.floor(rank)
+                m = rank - f
+                lower, upper = _select_two(a, k=int(f - 1), low=0, high=(n - 1))
+                val = lower * (1 - m) + upper * m
+            out[i] = val
+
+    return out
+
+
+@register_jitable
+def _can_collect_percentiles(a, nan_mask, skip_nan):
+    if skip_nan:
+        a = a[~nan_mask]
+        if len(a) == 0:
+            return False  # told to skip nan, but no elements remain
+    else:
+        if np.any(nan_mask):
+            return False  # told *not* to skip nan, but nan encountered
+
+    if len(a) == 1:  # single element array
+        val = a[0]
+        return np.isfinite(val)  # can collect percentiles if element is finite
+    else:
+        return True
+
+
+@register_jitable
+def check_valid(q, q_upper_bound):
+    valid = True
+
+    # avoid expensive reductions where possible
+    if q.ndim == 1 and q.size < 10:
+        for i in range(q.size):
+            if q[i] < 0.0 or q[i] > q_upper_bound or np.isnan(q[i]):
+                valid = False
+                break
+    else:
+        if np.any(np.isnan(q)) or np.any(q < 0.0) or np.any(q > q_upper_bound):
+            valid = False
+
+    return valid
+
+
+@register_jitable
+def percentile_is_valid(q):
+    if not check_valid(q, q_upper_bound=100.0):
+        raise ValueError('Percentiles must be in the range [0, 100]')
+
+
+@register_jitable
+def quantile_is_valid(q):
+    if not check_valid(q, q_upper_bound=1.0):
+        raise ValueError('Quantiles must be in the range [0, 1]')
+
+
+@register_jitable
+def _collect_percentiles(a, q, check_q, factor, skip_nan):
+    q = np.asarray(q, dtype=np.float64).flatten()
+    check_q(q)
+    q = q * factor
+
+    temp_arry = np.asarray(a, dtype=np.float64).flatten()
+    nan_mask = np.isnan(temp_arry)
+
+    if _can_collect_percentiles(temp_arry, nan_mask, skip_nan):
+        temp_arry = temp_arry[~nan_mask]
+        out = _collect_percentiles_inner(temp_arry, q)
+    else:
+        out = np.full(len(q), np.nan)
+
+    return out
+
+
+def _percentile_quantile_inner(a, q, skip_nan, factor, check_q):
+    """
+    The underlying algorithm to find percentiles and quantiles
+    is the same, hence we converge onto the same code paths
+    in this inner function implementation
+    """
+    dt = determine_dtype(a)
+    if np.issubdtype(dt, np.complexfloating):
+        raise TypingError('Not supported for complex dtype')
+        # this could be supported, but would require a
+        # lexicographic comparison
+
+    def np_percentile_q_scalar_impl(a, q):
+        return _collect_percentiles(a, q, check_q, factor, skip_nan)[0]
+
+    def np_percentile_impl(a, q):
+        return _collect_percentiles(a, q, check_q, factor, skip_nan)
+
+    if isinstance(q, (types.Number, types.Boolean)):
+        return np_percentile_q_scalar_impl
+    elif isinstance(q, types.Array) and q.ndim == 0:
+        return np_percentile_q_scalar_impl
+    else:
+        return np_percentile_impl
+
+
+@overload(np.percentile)
+def np_percentile(a, q):
+    return _percentile_quantile_inner(
+        a, q, skip_nan=False, factor=1.0, check_q=percentile_is_valid
+    )
+
+
+@overload(np.nanpercentile)
+def np_nanpercentile(a, q):
+    return _percentile_quantile_inner(
+        a, q, skip_nan=True, factor=1.0, check_q=percentile_is_valid
+    )
+
+
+@overload(np.quantile)
+def np_quantile(a, q):
+    return _percentile_quantile_inner(
+        a, q, skip_nan=False, factor=100.0, check_q=quantile_is_valid
+    )
+
+
+@overload(np.nanquantile)
+def np_nanquantile(a, q):
+    return _percentile_quantile_inner(
+        a, q, skip_nan=True, factor=100.0, check_q=quantile_is_valid
+    )
+
+
+@overload(np.nanmedian)
+def np_nanmedian(a):
+    if not isinstance(a, types.Array):
+        return
+    isnan = get_isnan(a.dtype)
+
+    def nanmedian_impl(a):
+        # Create a temporary workspace with only non-NaN values
+        temp_arry = np.empty(a.size, a.dtype)
+        n = 0
+        for view in np.nditer(a):
+            v = view.item()
+            if not isnan(v):
+                temp_arry[n] = v
+                n += 1
+
+        # all NaNs
+        if n == 0:
+            return np.nan
+
+        return _median_inner(temp_arry, n)
+
+    return nanmedian_impl
+
+
+@register_jitable
+def np_partition_impl_inner(a, kth_array):
+
+    # allocate and fill empty array rather than copy a and mutate in place
+    # as the latter approach fails to preserve strides
+    out = np.empty_like(a)
+
+    idx = np.ndindex(a.shape[:-1])  # Numpy default partition axis is -1
+    for s in idx:
+        arry = a[s].copy()
+        low = 0
+        high = len(arry) - 1
+
+        for kth in kth_array:
+            _select_w_nan(arry, kth, low, high)
+            low = kth  # narrow span of subsequent partition
+
+        out[s] = arry
+    return out
+
+
+@register_jitable
+def valid_kths(a, kth):
+    """
+    Returns a sorted, unique array of kth values which serve
+    as indexers for partitioning the input array, a.
+
+    If the absolute value of any of the provided values
+    is greater than a.shape[-1] an exception is raised since
+    we are partitioning along the last axis (per Numpy default
+    behaviour).
+
+    Values less than 0 are transformed to equivalent positive
+    index values.
+    """
+    # cast boolean to int, where relevant
+    kth_array = _asarray(kth).astype(np.int64)
+
+    if kth_array.ndim != 1:
+        raise ValueError('kth must be scalar or 1-D')
+        # numpy raises ValueError: object too deep for desired array
+
+    if np.any(np.abs(kth_array) >= a.shape[-1]):
+        raise ValueError("kth out of bounds")
+
+    out = np.empty_like(kth_array)
+
+    for index, val in np.ndenumerate(kth_array):
+        if val < 0:
+            out[index] = val + a.shape[-1]  # equivalent positive index
+        else:
+            out[index] = val
+
+    return np.unique(out)
+
+
+@overload(np.partition)
+def np_partition(a, kth):
+
+    if not isinstance(a, (types.Array, types.Sequence, types.Tuple)):
+        raise TypeError('The first argument must be an array-like')
+
+    if isinstance(a, types.Array) and a.ndim == 0:
+        raise TypeError('The first argument must be at least 1-D (found 0-D)')
+
+    kthdt = getattr(kth, 'dtype', kth)
+    if not isinstance(kthdt, (types.Boolean, types.Integer)):
+        # bool gets cast to int subsequently
+        raise TypeError('Partition index must be integer')
+
+    def np_partition_impl(a, kth):
+        a_tmp = _asarray(a)
+        if a_tmp.size == 0:
+            return a_tmp.copy()
+        else:
+            kth_array = valid_kths(a_tmp, kth)
+            return np_partition_impl_inner(a_tmp, kth_array)
+
+    return np_partition_impl
+
+
+#----------------------------------------------------------------------------
+# Building matrices
+
+@register_jitable
+def _tri_impl(N, M, k):
+    shape = max(0, N), max(0, M)  # numpy floors each dimension at 0
+    out = np.empty(shape, dtype=np.float64)  # numpy default dtype
+
+    for i in range(shape[0]):
+        m_max = min(max(0, i + k + 1), shape[1])
+        out[i, :m_max] = 1
+        out[i, m_max:] = 0
+
+    return out
+
+
+@overload(np.tri)
+def np_tri(N, M=None, k=0):
+
+    # we require k to be integer, unlike numpy
+    check_is_integer(k, 'k')
+
+    def tri_impl(N, M=None, k=0):
+        if M is None:
+            M = N
+        return _tri_impl(N, M, k)
+
+    return tri_impl
+
+
+@register_jitable
+def _make_square(m):
+    """
+    Takes a 1d array and tiles it to form a square matrix
+    - i.e. a facsimile of np.tile(m, (len(m), 1))
+    """
+    assert m.ndim == 1
+
+    len_m = len(m)
+    out = np.empty((len_m, len_m), dtype=m.dtype)
+
+    for i in range(len_m):
+        out[i] = m
+
+    return out
+
+
+@register_jitable
+def np_tril_impl_2d(m, k=0):
+    mask = np.tri(m.shape[-2], M=m.shape[-1], k=k).astype(np.uint)
+    return np.where(mask, m, np.zeros_like(m, dtype=m.dtype))
+
+
+@overload(np.tril)
+def my_tril(m, k=0):
+
+    # we require k to be integer, unlike numpy
+    check_is_integer(k, 'k')
+
+    def np_tril_impl_1d(m, k=0):
+        m_2d = _make_square(m)
+        return np_tril_impl_2d(m_2d, k)
+
+    def np_tril_impl_multi(m, k=0):
+        mask = np.tri(m.shape[-2], M=m.shape[-1], k=k).astype(np.uint)
+        idx = np.ndindex(m.shape[:-2])
+        z = np.empty_like(m)
+        zero_opt = np.zeros_like(mask, dtype=m.dtype)
+        for sel in idx:
+            z[sel] = np.where(mask, m[sel], zero_opt)
+        return z
+
+    if m.ndim == 1:
+        return np_tril_impl_1d
+    elif m.ndim == 2:
+        return np_tril_impl_2d
+    else:
+        return np_tril_impl_multi
+
+
+@overload(np.tril_indices)
+def np_tril_indices(n, k=0, m=None):
+
+    # we require integer arguments, unlike numpy
+    check_is_integer(n, 'n')
+    check_is_integer(k, 'k')
+    if not is_nonelike(m):
+        check_is_integer(m, 'm')
+
+    def np_tril_indices_impl(n, k=0, m=None):
+        return np.nonzero(np.tri(n, m, k=k))
+    return np_tril_indices_impl
+
+
+@overload(np.tril_indices_from)
+def np_tril_indices_from(arr, k=0):
+
+    # we require k to be integer, unlike numpy
+    check_is_integer(k, 'k')
+
+    if arr.ndim != 2:
+        raise TypingError("input array must be 2-d")
+
+    def np_tril_indices_from_impl(arr, k=0):
+        return np.tril_indices(arr.shape[0], k=k, m=arr.shape[1])
+    return np_tril_indices_from_impl
+
+
+@register_jitable
+def np_triu_impl_2d(m, k=0):
+    mask = np.tri(m.shape[-2], M=m.shape[-1], k=k - 1).astype(np.uint)
+    return np.where(mask, np.zeros_like(m, dtype=m.dtype), m)
+
+
+@overload(np.triu)
+def my_triu(m, k=0):
+    # we require k to be integer, unlike numpy
+    check_is_integer(k, 'k')
+
+    def np_triu_impl_1d(m, k=0):
+        m_2d = _make_square(m)
+        return np_triu_impl_2d(m_2d, k)
+
+    def np_triu_impl_multi(m, k=0):
+        mask = np.tri(m.shape[-2], M=m.shape[-1], k=k - 1).astype(np.uint)
+        idx = np.ndindex(m.shape[:-2])
+        z = np.empty_like(m)
+        zero_opt = np.zeros_like(mask, dtype=m.dtype)
+        for sel in idx:
+            z[sel] = np.where(mask, zero_opt, m[sel])
+        return z
+
+    if m.ndim == 1:
+        return np_triu_impl_1d
+    elif m.ndim == 2:
+        return np_triu_impl_2d
+    else:
+        return np_triu_impl_multi
+
+
+@overload(np.triu_indices)
+def np_triu_indices(n, k=0, m=None):
+
+    # we require integer arguments, unlike numpy
+    check_is_integer(n, 'n')
+    check_is_integer(k, 'k')
+    if not is_nonelike(m):
+        check_is_integer(m, 'm')
+
+    def np_triu_indices_impl(n, k=0, m=None):
+        return np.nonzero(1 - np.tri(n, m, k=k - 1))
+    return np_triu_indices_impl
+
+
+@overload(np.triu_indices_from)
+def np_triu_indices_from(arr, k=0):
+
+    # we require k to be integer, unlike numpy
+    check_is_integer(k, 'k')
+
+    if arr.ndim != 2:
+        raise TypingError("input array must be 2-d")
+
+    def np_triu_indices_from_impl(arr, k=0):
+        return np.triu_indices(arr.shape[0], k=k, m=arr.shape[1])
+    return np_triu_indices_from_impl
+
+
+def _prepare_array(arr):
+    pass
+
+
+@overload(_prepare_array)
+def _prepare_array_impl(arr):
+    if arr in (None, types.none):
+        return lambda arr: np.array(())
+    else:
+        return lambda arr: _asarray(arr).ravel()
+
+
+def _dtype_of_compound(inobj):
+    obj = inobj
+    while True:
+        if isinstance(obj, (types.Number, types.Boolean)):
+            return as_dtype(obj)
+        l = getattr(obj, '__len__', None)
+        if l is not None and l() == 0: # empty tuple or similar
+            return np.float64
+        dt = getattr(obj, 'dtype', None)
+        if dt is None:
+            raise TypeError("type has no dtype attr")
+        if isinstance(obj, types.Sequence):
+            obj = obj.dtype
+        else:
+            return as_dtype(dt)
+
+
+@overload(np.ediff1d)
+def np_ediff1d(ary, to_end=None, to_begin=None):
+
+    if isinstance(ary, types.Array):
+        if isinstance(ary.dtype, types.Boolean):
+            raise NumbaTypeError("Boolean dtype is unsupported (as per NumPy)")
+            # Numpy tries to do this: return ary[1:] - ary[:-1] which
+            # results in a TypeError exception being raised
+
+    # since np 1.16 there are casting checks for to_end and to_begin to make
+    # sure they are compatible with the ary
+    if numpy_version >= (1, 16):
+        ary_dt = _dtype_of_compound(ary)
+        to_begin_dt = None
+        if not(is_nonelike(to_begin)):
+            to_begin_dt = _dtype_of_compound(to_begin)
+        to_end_dt = None
+        if not(is_nonelike(to_end)):
+            to_end_dt = _dtype_of_compound(to_end)
+
+        if to_begin_dt is not None and not np.can_cast(to_begin_dt, ary_dt):
+            msg = "dtype of to_begin must be compatible with input ary"
+            raise NumbaTypeError(msg)
+
+        if to_end_dt is not None and not np.can_cast(to_end_dt, ary_dt):
+            msg = "dtype of to_end must be compatible with input ary"
+            raise NumbaTypeError(msg)
+
+    def np_ediff1d_impl(ary, to_end=None, to_begin=None):
+        # transform each input into an equivalent 1d array
+        start = _prepare_array(to_begin)
+        mid = _prepare_array(ary)
+        end = _prepare_array(to_end)
+
+        out_dtype = mid.dtype
+        # output array dtype determined by ary dtype, per NumPy
+        # (for the most part); an exception to the rule is a zero length
+        # array-like, where NumPy falls back to np.float64; this behaviour
+        # is *not* replicated
+
+        if len(mid) > 0:
+            out = np.empty((len(start) + len(mid) + len(end) - 1),
+                           dtype=out_dtype)
+            start_idx = len(start)
+            mid_idx = len(start) + len(mid) - 1
+            out[:start_idx] = start
+            out[start_idx:mid_idx] = np.diff(mid)
+            out[mid_idx:] = end
+        else:
+            out = np.empty((len(start) + len(end)), dtype=out_dtype)
+            start_idx = len(start)
+            out[:start_idx] = start
+            out[start_idx:] = end
+        return out
+
+    return np_ediff1d_impl
+
+
+def _select_element(arr):
+    pass
+
+
+@overload(_select_element)
+def _select_element_impl(arr):
+    zerod = getattr(arr, 'ndim', None) == 0
+    if zerod:
+        def impl(arr):
+            x = np.array((1,), dtype=arr.dtype)
+            x[:] = arr
+            return x[0]
+        return impl
+    else:
+        def impl(arr):
+            return arr
+        return impl
+
+
+def _get_d(dx, x):
+    pass
+
+
+@overload(_get_d)
+def get_d_impl(x, dx):
+    if is_nonelike(x):
+        def impl(x, dx):
+            return np.asarray(dx)
+    else:
+        def impl(x, dx):
+            return np.diff(np.asarray(x))
+    return impl
+
+
+@overload(np.trapz)
+def np_trapz(y, x=None, dx=1.0):
+
+    if isinstance(y, (types.Number, types.Boolean)):
+        raise TypingError('y cannot be a scalar')
+    elif isinstance(y, types.Array) and y.ndim == 0:
+        raise TypingError('y cannot be 0D')
+        # NumPy raises IndexError: list assignment index out of range
+
+    # inspired by:
+    # https://github.com/numpy/numpy/blob/7ee52003/numpy/lib/function_base.py#L4040-L4065    # noqa: E501
+    def impl(y, x=None, dx=1.0):
+        yarr = np.asarray(y)
+        d = _get_d(x, dx)
+        y_ave = (yarr[..., slice(1, None)] + yarr[..., slice(None, -1)]) / 2.0
+        ret = np.sum(d * y_ave, -1)
+        processed = _select_element(ret)
+        return processed
+
+    return impl
+
+
+@register_jitable
+def _np_vander(x, N, increasing, out):
+    """
+    Generate an N-column Vandermonde matrix from a supplied 1-dimensional
+    array, x. Store results in an output matrix, out, which is assumed to
+    be of the required dtype.
+
+    Values are accumulated using np.multiply to match the floating point
+    precision behaviour of numpy.vander.
+    """
+    m, n = out.shape
+    assert m == len(x)
+    assert n == N
+
+    if increasing:
+        for i in range(N):
+            if i == 0:
+                out[:, i] = 1
+            else:
+                out[:, i] = np.multiply(x, out[:, (i - 1)])
+    else:
+        for i in range(N - 1, -1, -1):
+            if i == N - 1:
+                out[:, i] = 1
+            else:
+                out[:, i] = np.multiply(x, out[:, (i + 1)])
+
+
+@register_jitable
+def _check_vander_params(x, N):
+    if x.ndim > 1:
+        raise ValueError('x must be a one-dimensional array or sequence.')
+    if N < 0:
+        raise ValueError('Negative dimensions are not allowed')
+
+
+@overload(np.vander)
+def np_vander(x, N=None, increasing=False):
+    if N not in (None, types.none):
+        if not isinstance(N, types.Integer):
+            raise TypingError('Second argument N must be None or an integer')
+
+    def np_vander_impl(x, N=None, increasing=False):
+        if N is None:
+            N = len(x)
+
+        _check_vander_params(x, N)
+
+        # allocate output matrix using dtype determined in closure
+        out = np.empty((len(x), int(N)), dtype=dtype)
+
+        _np_vander(x, N, increasing, out)
+        return out
+
+    def np_vander_seq_impl(x, N=None, increasing=False):
+        if N is None:
+            N = len(x)
+
+        x_arr = np.array(x)
+        _check_vander_params(x_arr, N)
+
+        # allocate output matrix using dtype inferred when x_arr was created
+        out = np.empty((len(x), int(N)), dtype=x_arr.dtype)
+
+        _np_vander(x_arr, N, increasing, out)
+        return out
+
+    if isinstance(x, types.Array):
+        x_dt = as_dtype(x.dtype)
+        # replicate numpy behaviour w.r.t.type promotion
+        dtype = np.promote_types(x_dt, int)
+        return np_vander_impl
+    elif isinstance(x, (types.Tuple, types.Sequence)):
+        return np_vander_seq_impl
+
+
+@overload(np.roll)
+def np_roll(a, shift):
+    if not isinstance(shift, (types.Integer, types.Boolean)):
+        raise TypingError('shift must be an integer')
+
+    def np_roll_impl(a, shift):
+        arr = np.asarray(a)
+        out = np.empty(arr.shape, dtype=arr.dtype)
+        # empty_like might result in different contiguity vs NumPy
+
+        arr_flat = arr.flat
+        for i in range(arr.size):
+            idx = (i + shift) % arr.size
+            out.flat[idx] = arr_flat[i]
+
+        return out
+
+    if isinstance(a, (types.Number, types.Boolean)):
+        return lambda a, shift: np.asarray(a)
+    else:
+        return np_roll_impl
+
+
+#----------------------------------------------------------------------------
+# Mathematical functions
+
+LIKELY_IN_CACHE_SIZE = 8
+
+
+@register_jitable
+def binary_search_with_guess(key, arr, length, guess):
+    # NOTE: Do not refactor... see note in np_interp function impl below
+    # this is a facsimile of binary_search_with_guess prior to 1.15:
+    # https://github.com/numpy/numpy/blob/maintenance/1.15.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
+    # Permanent reference:
+    # https://github.com/numpy/numpy/blob/3430d78c01a3b9a19adad75f1acb5ae18286da73/numpy/core/src/multiarray/compiled_base.c#L447    # noqa: E501
+    imin = 0
+    imax = length
+
+    # Handle keys outside of the arr range first
+    if key > arr[length - 1]:
+        return length
+    elif key < arr[0]:
+        return -1
+
+    # If len <= 4 use linear search.
+    # From above we know key >= arr[0] when we start.
+    if length <= 4:
+        i = 1
+        while i < length and key >= arr[i]:
+            i += 1
+        return i - 1
+
+    if guess > length - 3:
+        guess = length - 3
+
+    if guess < 1:
+        guess = 1
+
+    # check most likely values: guess - 1, guess, guess + 1
+    if key < arr[guess]:
+        if key < arr[guess - 1]:
+            imax = guess - 1
+
+            # last attempt to restrict search to items in cache
+            if guess > LIKELY_IN_CACHE_SIZE and \
+                    key >= arr[guess - LIKELY_IN_CACHE_SIZE]:
+                imin = guess - LIKELY_IN_CACHE_SIZE
+        else:
+            # key >= arr[guess - 1]
+            return guess - 1
+    else:
+        # key >= arr[guess]
+        if key < arr[guess + 1]:
+            return guess
+        else:
+            # key >= arr[guess + 1]
+            if key < arr[guess + 2]:
+                return guess + 1
+            else:
+                # key >= arr[guess + 2]
+                imin = guess + 2
+                # last attempt to restrict search to items in cache
+                if (guess < (length - LIKELY_IN_CACHE_SIZE - 1)) and \
+                        (key < arr[guess + LIKELY_IN_CACHE_SIZE]):
+                    imax = guess + LIKELY_IN_CACHE_SIZE
+
+    # finally, find index by bisection
+    while imin < imax:
+        imid = imin + ((imax - imin) >> 1)
+        if key >= arr[imid]:
+            imin = imid + 1
+        else:
+            imax = imid
+
+    return imin - 1
+
+
+@register_jitable
+def np_interp_impl_complex_fp_inner(x, xp, fp, dtype):
+    # NOTE: Do not refactor... see note in np_interp function impl below
+    # this is a facsimile of arr_interp_complex prior to 1.16:
+    # https://github.com/numpy/numpy/blob/maintenance/1.15.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
+    # Permanent reference:
+    # https://github.com/numpy/numpy/blob/3430d78c01a3b9a19adad75f1acb5ae18286da73/numpy/core/src/multiarray/compiled_base.c#L683    # noqa: E501
+    dz = np.asarray(x)
+    dx = np.asarray(xp)
+    dy = np.asarray(fp)
+
+    if len(dx) == 0:
+        raise ValueError('array of sample points is empty')
+
+    if len(dx) != len(dy):
+        raise ValueError('fp and xp are not of the same size.')
+
+    if dx.size == 1:
+        return np.full(dz.shape, fill_value=dy[0], dtype=dtype)
+
+    dres = np.empty(dz.shape, dtype=dtype)
+
+    lenx = dz.size
+    lenxp = len(dx)
+    lval = dy[0]
+    rval = dy[lenxp - 1]
+
+    if lenxp == 1:
+        xp_val = dx[0]
+        fp_val = dy[0]
+
+        for i in range(lenx):
+            x_val = dz.flat[i]
+            if x_val < xp_val:
+                dres.flat[i] = lval
+            elif x_val > xp_val:
+                dres.flat[i] = rval
+            else:
+                dres.flat[i] = fp_val
+
+    else:
+        j = 0
+
+        # only pre-calculate slopes if there are relatively few of them.
+        if lenxp <= lenx:
+            slopes = np.empty((lenxp - 1), dtype=dtype)
+        else:
+            slopes = np.empty(0, dtype=dtype)
+
+        if slopes.size:
+            for i in range(lenxp - 1):
+                inv_dx = 1 / (dx[i + 1] - dx[i])
+                real = (dy[i + 1].real - dy[i].real) * inv_dx
+                imag = (dy[i + 1].imag - dy[i].imag) * inv_dx
+                slopes[i] = real + 1j * imag
+
+        for i in range(lenx):
+            x_val = dz.flat[i]
+
+            if np.isnan(x_val):
+                real = x_val
+                imag = 0.0
+                dres.flat[i] = real + 1j * imag
+                continue
+
+            j = binary_search_with_guess(x_val, dx, lenxp, j)
+
+            if j == -1:
+                dres.flat[i] = lval
+            elif j == lenxp:
+                dres.flat[i] = rval
+            elif j == lenxp - 1:
+                dres.flat[i] = dy[j]
+            else:
+                if slopes.size:
+                    slope = slopes[j]
+                else:
+                    inv_dx = 1 / (dx[j + 1] - dx[j])
+                    real = (dy[j + 1].real - dy[j].real) * inv_dx
+                    imag = (dy[j + 1].imag - dy[j].imag) * inv_dx
+                    slope = real + 1j * imag
+
+                real = slope.real * (x_val - dx[j]) + dy[j].real
+                imag = slope.imag * (x_val - dx[j]) + dy[j].imag
+                dres.flat[i] = real + 1j * imag
+
+                # NOTE: there's a change in master which is not
+                # in any released version of 1.16.x yet... as
+                # per the real value implementation, but
+                # interpolate real and imaginary parts
+                # independently; this will need to be added in
+                # due course
+
+    return dres
+
+
+def np_interp_impl_complex_fp_inner_factory(np117_nan_handling):
+    @register_jitable
+    def impl(x, xp, fp, dtype):
+        # NOTE: Do not refactor... see note in np_interp function impl below
+        # this is a facsimile of arr_interp_complex post 1.16 with added
+        # branching to support np1.17 style NaN handling.  (see
+        # `np117_nan_handling` use)
+        # https://github.com/numpy/numpy/blob/maintenance/1.16.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
+        # Permanent reference:
+        # https://github.com/numpy/numpy/blob/971e2e89d08deeae0139d3011d15646fdac13c92/numpy/core/src/multiarray/compiled_base.c#L628    # noqa: E501
+        dz = np.asarray(x)
+        dx = np.asarray(xp)
+        dy = np.asarray(fp)
+
+        if len(dx) == 0:
+            raise ValueError('array of sample points is empty')
+
+        if len(dx) != len(dy):
+            raise ValueError('fp and xp are not of the same size.')
+
+        if dx.size == 1:
+            return np.full(dz.shape, fill_value=dy[0], dtype=dtype)
+
+        dres = np.empty(dz.shape, dtype=dtype)
+
+        lenx = dz.size
+        lenxp = len(dx)
+        lval = dy[0]
+        rval = dy[lenxp - 1]
+
+        if lenxp == 1:
+            xp_val = dx[0]
+            fp_val = dy[0]
+
+            for i in range(lenx):
+                x_val = dz.flat[i]
+                if x_val < xp_val:
+                    dres.flat[i] = lval
+                elif x_val > xp_val:
+                    dres.flat[i] = rval
+                else:
+                    dres.flat[i] = fp_val
+
+        else:
+            j = 0
+
+            # only pre-calculate slopes if there are relatively few of them.
+            if lenxp <= lenx:
+                slopes = np.empty((lenxp - 1), dtype=dtype)
+            else:
+                slopes = np.empty(0, dtype=dtype)
+
+            if slopes.size:
+                for i in range(lenxp - 1):
+                    inv_dx = 1 / (dx[i + 1] - dx[i])
+                    real = (dy[i + 1].real - dy[i].real) * inv_dx
+                    imag = (dy[i + 1].imag - dy[i].imag) * inv_dx
+                    slopes[i] = real + 1j * imag
+
+            for i in range(lenx):
+                x_val = dz.flat[i]
+
+                if np.isnan(x_val):
+                    real = x_val
+                    imag = 0.0
+                    dres.flat[i] = real + 1j * imag
+                    continue
+
+                j = binary_search_with_guess(x_val, dx, lenxp, j)
+
+                if j == -1:
+                    dres.flat[i] = lval
+                elif j == lenxp:
+                    dres.flat[i] = rval
+                elif j == lenxp - 1:
+                    dres.flat[i] = dy[j]
+                elif dx[j] == x_val:
+                    # Avoid potential non-finite interpolation
+                    dres.flat[i] = dy[j]
+                else:
+                    if slopes.size:
+                        slope = slopes[j]
+                    else:
+                        inv_dx = 1 / (dx[j + 1] - dx[j])
+                        real = (dy[j + 1].real - dy[j].real) * inv_dx
+                        imag = (dy[j + 1].imag - dy[j].imag) * inv_dx
+                        slope = real + 1j * imag
+
+                    # The following branches mimic the behavior of
+                    # different numpy version with regard to handling NaNs.
+                    if np117_nan_handling:
+                        # Numpy 1.17 handles NaN correctly
+                        result = np_interp_impl_complex_fp_innermost_117(
+                            x, slope, x_val, dx, dy, i, j,
+                        )
+                        dres.flat[i] = result
+                    else:
+                        # Numpy 1.16 does not handles NaN correctly.
+                        real = slope.real * (x_val - dx[j]) + dy[j].real
+                        imag = slope.imag * (x_val - dx[j]) + dy[j].imag
+                        dres.flat[i] = real + 1j * imag
+        return dres
+    return impl
+
+
+@register_jitable
+def np_interp_impl_complex_fp_innermost_117(x, slope, x_val, dx, dy, i, j):
+    # NOTE: Do not refactor... see note in np_interp function impl below
+    # this is a copy of innermost part of arr_interp_complex post 1.17:
+    # https://github.com/numpy/numpy/blob/maintenance/1.17.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
+    # Permanent reference:
+    # https://github.com/numpy/numpy/blob/91fbe4dde246559fa5b085ebf4bc268e2b89eea8/numpy/core/src/multiarray/compiled_base.c#L798-L812    # noqa: E501
+
+    # If we get nan in one direction, try the other
+    real = slope.real * (x_val - dx[j]) + dy[j].real
+    if np.isnan(real):
+        real = slope.real * (x_val - dx[j + 1]) + dy[j + 1].real
+        if np.isnan(real) and dy[j].real == dy[j + 1].real:
+            real = dy[j].real
+
+    imag = slope.imag * (x_val - dx[j]) + dy[j].imag
+    if np.isnan(imag):
+        imag = slope.imag * (x_val - dx[j + 1]) + dy[j + 1].imag
+        if np.isnan(imag) and dy[j].imag == dy[j + 1].imag:
+            imag = dy[j].imag
+
+    return real + 1j * imag
+
+
+@register_jitable
+def np_interp_impl_inner(x, xp, fp, dtype):
+    # NOTE: Do not refactor... see note in np_interp function impl below
+    # this is a facsimile of arr_interp prior to 1.16:
+    # https://github.com/numpy/numpy/blob/maintenance/1.15.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
+    # Permanent reference:
+    # https://github.com/numpy/numpy/blob/3430d78c01a3b9a19adad75f1acb5ae18286da73/numpy/core/src/multiarray/compiled_base.c#L532    # noqa: E501
+    dz = np.asarray(x, dtype=np.float64)
+    dx = np.asarray(xp, dtype=np.float64)
+    dy = np.asarray(fp, dtype=np.float64)
+
+    if len(dx) == 0:
+        raise ValueError('array of sample points is empty')
+
+    if len(dx) != len(dy):
+        raise ValueError('fp and xp are not of the same size.')
+
+    if dx.size == 1:
+        return np.full(dz.shape, fill_value=dy[0], dtype=dtype)
+
+    dres = np.empty(dz.shape, dtype=dtype)
+
+    lenx = dz.size
+    lenxp = len(dx)
+    lval = dy[0]
+    rval = dy[lenxp - 1]
+
+    if lenxp == 1:
+        xp_val = dx[0]
+        fp_val = dy[0]
+
+        for i in range(lenx):
+            x_val = dz.flat[i]
+            if x_val < xp_val:
+                dres.flat[i] = lval
+            elif x_val > xp_val:
+                dres.flat[i] = rval
+            else:
+                dres.flat[i] = fp_val
+
+    else:
+        j = 0
+
+        # only pre-calculate slopes if there are relatively few of them.
+        if lenxp <= lenx:
+            slopes = (dy[1:] - dy[:-1]) / (dx[1:] - dx[:-1])
+        else:
+            slopes = np.empty(0, dtype=dtype)
+
+        for i in range(lenx):
+            x_val = dz.flat[i]
+
+            if np.isnan(x_val):
+                dres.flat[i] = x_val
+                continue
+
+            j = binary_search_with_guess(x_val, dx, lenxp, j)
+
+            if j == -1:
+                dres.flat[i] = lval
+            elif j == lenxp:
+                dres.flat[i] = rval
+            elif j == lenxp - 1:
+                dres.flat[i] = dy[j]
+            else:
+                if slopes.size:
+                    slope = slopes[j]
+                else:
+                    slope = (dy[j + 1] - dy[j]) / (dx[j + 1] - dx[j])
+
+                dres.flat[i] = slope * (x_val - dx[j]) + dy[j]
+
+                # NOTE: this is in master but not in any released
+                # version of 1.16.x yet...
+                #
+                # If we get nan in one direction, try the other
+                # if np.isnan(dres.flat[i]):
+                #     dres.flat[i] = slope * (x_val - dx[j + 1]) + dy[j + 1]
+                #
+                #     if np.isnan(dres.flat[i]) and dy[j] == dy[j + 1]:
+                #         dres.flat[i] = dy[j]
+
+    return dres
+
+
+def np_interp_impl_inner_factory(np117_nan_handling):
+    def impl(x, xp, fp, dtype):
+        # NOTE: Do not refactor... see note in np_interp function impl below
+        # this is a facsimile of arr_interp post 1.16:
+        # https://github.com/numpy/numpy/blob/maintenance/1.16.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
+        # Permanent reference:
+        # https://github.com/numpy/numpy/blob/971e2e89d08deeae0139d3011d15646fdac13c92/numpy/core/src/multiarray/compiled_base.c#L473     # noqa: E501
+        dz = np.asarray(x, dtype=np.float64)
+        dx = np.asarray(xp, dtype=np.float64)
+        dy = np.asarray(fp, dtype=np.float64)
+
+        if len(dx) == 0:
+            raise ValueError('array of sample points is empty')
+
+        if len(dx) != len(dy):
+            raise ValueError('fp and xp are not of the same size.')
+
+        if dx.size == 1:
+            return np.full(dz.shape, fill_value=dy[0], dtype=dtype)
+
+        dres = np.empty(dz.shape, dtype=dtype)
+
+        lenx = dz.size
+        lenxp = len(dx)
+        lval = dy[0]
+        rval = dy[lenxp - 1]
+
+        if lenxp == 1:
+            xp_val = dx[0]
+            fp_val = dy[0]
+
+            for i in range(lenx):
+                x_val = dz.flat[i]
+                if x_val < xp_val:
+                    dres.flat[i] = lval
+                elif x_val > xp_val:
+                    dres.flat[i] = rval
+                else:
+                    dres.flat[i] = fp_val
+
+        else:
+            j = 0
+
+            # only pre-calculate slopes if there are relatively few of them.
+            if lenxp <= lenx:
+                slopes = (dy[1:] - dy[:-1]) / (dx[1:] - dx[:-1])
+            else:
+                slopes = np.empty(0, dtype=dtype)
+
+            for i in range(lenx):
+                x_val = dz.flat[i]
+
+                if np.isnan(x_val):
+                    dres.flat[i] = x_val
+                    continue
+
+                j = binary_search_with_guess(x_val, dx, lenxp, j)
+
+                if j == -1:
+                    dres.flat[i] = lval
+                elif j == lenxp:
+                    dres.flat[i] = rval
+                elif j == lenxp - 1:
+                    dres.flat[i] = dy[j]
+                elif dx[j] == x_val:
+                    # Avoid potential non-finite interpolation
+                    dres.flat[i] = dy[j]
+                else:
+                    if slopes.size:
+                        slope = slopes[j]
+                    else:
+                        slope = (dy[j + 1] - dy[j]) / (dx[j + 1] - dx[j])
+
+                    dres.flat[i] = slope * (x_val - dx[j]) + dy[j]
+
+                    # NOTE: this is in np1.17
+                    # https://github.com/numpy/numpy/blob/maintenance/1.17.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
+                    # Permanent reference:
+                    # https://github.com/numpy/numpy/blob/91fbe4dde246559fa5b085ebf4bc268e2b89eea8/numpy/core/src/multiarray/compiled_base.c#L610-L616    # noqa: E501
+                    #
+                    # If we get nan in one direction, try the other
+                    if np117_nan_handling:
+                        if np.isnan(dres.flat[i]):
+                            dres.flat[i] = slope * (x_val - dx[j + 1]) + dy[j + 1]    # noqa: E501
+                            if np.isnan(dres.flat[i]) and dy[j] == dy[j + 1]:
+                                dres.flat[i] = dy[j]
+
+        return dres
+    return impl
+
+
+np_interp_impl_inner_post_np117 = register_jitable(
+    np_interp_impl_inner_factory(np117_nan_handling=True)
+)
+np_interp_impl_complex_inner_post_np117 = register_jitable(
+    np_interp_impl_complex_fp_inner_factory(np117_nan_handling=True)
+)
+np_interp_impl_inner_pre_np117 = register_jitable(
+    np_interp_impl_inner_factory(np117_nan_handling=False)
+)
+np_interp_impl_complex_inner_pre_np117 = register_jitable(
+    np_interp_impl_complex_fp_inner_factory(np117_nan_handling=False)
+)
+
+
+@overload(np.interp)
+def np_interp(x, xp, fp):
+    # NOTE: there is considerable duplication present in the functions:
+    # np_interp_impl_complex_fp_inner_116
+    # np_interp_impl_complex_fp_inner
+    # np_interp_impl_inner_116
+    # np_interp_impl_inner
+    #
+    # This is because:
+    # 1. Replicating basic interp is relatively simple, however matching the
+    #    behaviour of NumPy for edge cases is really quite hard, after a
+    #    couple of attempts trying to avoid translation of the C source it
+    #    was deemed unavoidable.
+    # 2. Due to 1. it is much easier to keep track of changes if the Numba
+    #    source reflects the NumPy C source, so the duplication is kept.
+    # 3. There are significant changes that happened in the NumPy 1.16
+    #    release series, hence functions with `np116` appended, they behave
+    #    slightly differently!
+
+    if hasattr(xp, 'ndim') and xp.ndim > 1:
+        raise TypingError('xp must be 1D')
+    if hasattr(fp, 'ndim') and fp.ndim > 1:
+        raise TypingError('fp must be 1D')
+
+    complex_dtype_msg = (
+        "Cannot cast array data from complex dtype to float64 dtype"
+    )
+
+    xp_dt = determine_dtype(xp)
+    if np.issubdtype(xp_dt, np.complexfloating):
+        raise TypingError(complex_dtype_msg)
+
+    if numpy_version >= (1, 17):
+        impl = np_interp_impl_inner_post_np117
+        impl_complex = np_interp_impl_complex_inner_post_np117
+    elif numpy_version >= (1, 16):
+        impl = np_interp_impl_inner_pre_np117
+        impl_complex = np_interp_impl_complex_inner_pre_np117
+    else:
+        impl = np_interp_impl_inner
+        impl_complex = np_interp_impl_complex_fp_inner
+
+    fp_dt = determine_dtype(fp)
+    dtype = np.result_type(fp_dt, np.float64)
+
+    if np.issubdtype(dtype, np.complexfloating):
+        inner = impl_complex
+    else:
+        inner = impl
+
+    def np_interp_impl(x, xp, fp):
+        return inner(x, xp, fp, dtype)
+
+    def np_interp_scalar_impl(x, xp, fp):
+        return inner(x, xp, fp, dtype).flat[0]
+
+    if isinstance(x, types.Number):
+        if isinstance(x, types.Complex):
+            raise TypingError(complex_dtype_msg)
+        return np_interp_scalar_impl
+
+    return np_interp_impl
+
+
+#----------------------------------------------------------------------------
+# Statistics
+
+@register_jitable
+def row_wise_average(a):
+    assert a.ndim == 2
+
+    m, n = a.shape
+    out = np.empty((m, 1), dtype=a.dtype)
+
+    for i in range(m):
+        out[i, 0] = np.sum(a[i, :]) / n
+
+    return out
+
+
+@register_jitable
+def np_cov_impl_inner(X, bias, ddof):
+
+    # determine degrees of freedom
+    if ddof is None:
+        if bias:
+            ddof = 0
+        else:
+            ddof = 1
+
+    # determine the normalization factor
+    fact = X.shape[1] - ddof
+
+    # numpy warns if less than 0 and floors at 0
+    fact = max(fact, 0.0)
+
+    # de-mean
+    X -= row_wise_average(X)
+
+    # calculate result - requires blas
+    c = np.dot(X, np.conj(X.T))
+    c *= np.true_divide(1, fact)
+    return c
+
+
+def _prepare_cov_input_inner():
+    pass
+
+
+@overload(_prepare_cov_input_inner)
+def _prepare_cov_input_impl(m, y, rowvar, dtype):
+    if y in (None, types.none):
+        def _prepare_cov_input_inner(m, y, rowvar, dtype):
+            m_arr = np.atleast_2d(_asarray(m))
+
+            if not rowvar:
+                m_arr = m_arr.T
+
+            return m_arr
+    else:
+        def _prepare_cov_input_inner(m, y, rowvar, dtype):
+            m_arr = np.atleast_2d(_asarray(m))
+            y_arr = np.atleast_2d(_asarray(y))
+
+            # transpose if asked to and not a (1, n) vector - this looks
+            # wrong as you might end up transposing one and not the other,
+            # but it's what numpy does
+            if not rowvar:
+                if m_arr.shape[0] != 1:
+                    m_arr = m_arr.T
+                if y_arr.shape[0] != 1:
+                    y_arr = y_arr.T
+
+            m_rows, m_cols = m_arr.shape
+            y_rows, y_cols = y_arr.shape
+
+            if m_cols != y_cols:
+                raise ValueError("m and y have incompatible dimensions")
+
+            # allocate and fill output array
+            out = np.empty((m_rows + y_rows, m_cols), dtype=dtype)
+            out[:m_rows, :] = m_arr
+            out[-y_rows:, :] = y_arr
+
+            return out
+
+    return _prepare_cov_input_inner
+
+
+@register_jitable
+def _handle_m_dim_change(m):
+    if m.ndim == 2 and m.shape[0] == 1:
+        msg = ("2D array containing a single row is unsupported due to "
+               "ambiguity in type inference. To use numpy.cov in this case "
+               "simply pass the row as a 1D array, i.e. m[0].")
+        raise RuntimeError(msg)
+
+
+_handle_m_dim_nop = register_jitable(lambda x: x)
+
+
+def determine_dtype(array_like):
+    array_like_dt = np.float64
+    if isinstance(array_like, types.Array):
+        array_like_dt = as_dtype(array_like.dtype)
+    elif isinstance(array_like, (types.Number, types.Boolean)):
+        array_like_dt = as_dtype(array_like)
+    elif isinstance(array_like, (types.UniTuple, types.Tuple)):
+        coltypes = set()
+        for val in array_like:
+            if hasattr(val, 'count'):
+                [coltypes.add(v) for v in val]
+            else:
+                coltypes.add(val)
+        if len(coltypes) > 1:
+            array_like_dt = np.promote_types(*[as_dtype(ty) for ty in coltypes])
+        elif len(coltypes) == 1:
+            array_like_dt = as_dtype(coltypes.pop())
+
+    return array_like_dt
+
+
+def check_dimensions(array_like, name):
+    if isinstance(array_like, types.Array):
+        if array_like.ndim > 2:
+            raise TypeError("{0} has more than 2 dimensions".format(name))
+    elif isinstance(array_like, types.Sequence):
+        if isinstance(array_like.key[0], types.Sequence):
+            if isinstance(array_like.key[0].key[0], types.Sequence):
+                raise TypeError("{0} has more than 2 dimensions".format(name))
+
+
+@register_jitable
+def _handle_ddof(ddof):
+    if not np.isfinite(ddof):
+        raise ValueError('Cannot convert non-finite ddof to integer')
+    if ddof - int(ddof) != 0:
+        raise ValueError('ddof must be integral value')
+
+
+_handle_ddof_nop = register_jitable(lambda x: x)
+
+
+@register_jitable
+def _prepare_cov_input(m, y, rowvar, dtype, ddof, _DDOF_HANDLER,
+                       _M_DIM_HANDLER):
+    _M_DIM_HANDLER(m)
+    _DDOF_HANDLER(ddof)
+    return _prepare_cov_input_inner(m, y, rowvar, dtype)
+
+
+def scalar_result_expected(mandatory_input, optional_input):
+    opt_is_none = optional_input in (None, types.none)
+
+    if isinstance(mandatory_input, types.Array) and mandatory_input.ndim == 1:
+        return opt_is_none
+
+    if isinstance(mandatory_input, types.BaseTuple):
+        if all(isinstance(x, (types.Number, types.Boolean))
+               for x in mandatory_input.types):
+            return opt_is_none
+        else:
+            if (len(mandatory_input.types) == 1 and
+                    isinstance(mandatory_input.types[0], types.BaseTuple)):
+                return opt_is_none
+
+    if isinstance(mandatory_input, (types.Number, types.Boolean)):
+        return opt_is_none
+
+    if isinstance(mandatory_input, types.Sequence):
+        if (not isinstance(mandatory_input.key[0], types.Sequence) and
+                opt_is_none):
+            return True
+
+    return False
+
+
+@register_jitable
+def _clip_corr(x):
+    return np.where(np.fabs(x) > 1, np.sign(x), x)
+
+
+@register_jitable
+def _clip_complex(x):
+    real = _clip_corr(x.real)
+    imag = _clip_corr(x.imag)
+    return real + 1j * imag
+
+
+@overload(np.cov)
+def np_cov(m, y=None, rowvar=True, bias=False, ddof=None):
+
+    # reject problem if m and / or y are more than 2D
+    check_dimensions(m, 'm')
+    check_dimensions(y, 'y')
+
+    # reject problem if ddof invalid (either upfront if type is
+    # obviously invalid, or later if value found to be non-integral)
+    if ddof in (None, types.none):
+        _DDOF_HANDLER = _handle_ddof_nop
+    else:
+        if isinstance(ddof, (types.Integer, types.Boolean)):
+            _DDOF_HANDLER = _handle_ddof_nop
+        elif isinstance(ddof, types.Float):
+            _DDOF_HANDLER = _handle_ddof
+        else:
+            raise TypingError('ddof must be a real numerical scalar type')
+
+    # special case for 2D array input with 1 row of data - select
+    # handler function which we'll call later when we have access
+    # to the shape of the input array
+    _M_DIM_HANDLER = _handle_m_dim_nop
+    if isinstance(m, types.Array):
+        _M_DIM_HANDLER = _handle_m_dim_change
+
+    # infer result dtype
+    m_dt = determine_dtype(m)
+    y_dt = determine_dtype(y)
+    dtype = np.result_type(m_dt, y_dt, np.float64)
+
+    def np_cov_impl(m, y=None, rowvar=True, bias=False, ddof=None):
+        X = _prepare_cov_input(m, y, rowvar, dtype, ddof, _DDOF_HANDLER,
+                               _M_DIM_HANDLER).astype(dtype)
+
+        if np.any(np.array(X.shape) == 0):
+            return np.full((X.shape[0], X.shape[0]), fill_value=np.nan,
+                           dtype=dtype)
+        else:
+            return np_cov_impl_inner(X, bias, ddof)
+
+    def np_cov_impl_single_variable(m, y=None, rowvar=True, bias=False,
+                                    ddof=None):
+        X = _prepare_cov_input(m, y, rowvar, ddof, dtype, _DDOF_HANDLER,
+                               _M_DIM_HANDLER).astype(dtype)
+
+        if np.any(np.array(X.shape) == 0):
+            variance = np.nan
+        else:
+            variance = np_cov_impl_inner(X, bias, ddof).flat[0]
+
+        return np.array(variance)
+
+    if scalar_result_expected(m, y):
+        return np_cov_impl_single_variable
+    else:
+        return np_cov_impl
+
+
+@overload(np.corrcoef)
+def np_corrcoef(x, y=None, rowvar=True):
+
+    x_dt = determine_dtype(x)
+    y_dt = determine_dtype(y)
+    dtype = np.result_type(x_dt, y_dt, np.float64)
+
+    if dtype == np.complex_:
+        clip_fn = _clip_complex
+    else:
+        clip_fn = _clip_corr
+
+    def np_corrcoef_impl(x, y=None, rowvar=True):
+        c = np.cov(x, y, rowvar)
+        d = np.diag(c)
+        stddev = np.sqrt(d.real)
+
+        for i in range(c.shape[0]):
+            c[i, :] /= stddev
+            c[:, i] /= stddev
+
+        return clip_fn(c)
+
+    def np_corrcoef_impl_single_variable(x, y=None, rowvar=True):
+        c = np.cov(x, y, rowvar)
+        return c / c
+
+    if scalar_result_expected(x, y):
+        return np_corrcoef_impl_single_variable
+    else:
+        return np_corrcoef_impl
+
+
+#----------------------------------------------------------------------------
+# Element-wise computations
+
+
+@overload(np.argwhere)
+def np_argwhere(a):
+    # needs to be much more array-like for the array impl to work, Numba bug
+    # in one of the underlying function calls?
+
+    use_scalar = (numpy_version >= (1, 18) and
+                  isinstance(a, (types.Number, types.Boolean)))
+    if type_can_asarray(a) and not use_scalar:
+        if numpy_version < (1, 18):
+            check = register_jitable(lambda x: not np.any(x))
+        else:
+            check = register_jitable(lambda x: True)
+
+        def impl(a):
+            arr = np.asarray(a)
+            if arr.shape == () and check(arr):
+                return np.zeros((0, 1), dtype=types.intp)
+            return np.transpose(np.vstack(np.nonzero(arr)))
+    else:
+        if numpy_version < (1, 18):
+            falseish = (0, 1)
+            trueish = (1, 1)
+        else:
+            falseish = (0, 0)
+            trueish = (1, 0)
+
+        def impl(a):
+            if a is not None and bool(a):
+                return np.zeros(trueish, dtype=types.intp)
+            else:
+                return np.zeros(falseish, dtype=types.intp)
+
+    return impl
+
+
+@overload(np.flatnonzero)
+def np_flatnonzero(a):
+
+    if type_can_asarray(a):
+        def impl(a):
+            arr = np.asarray(a)
+            return np.nonzero(np.ravel(arr))[0]
+    else:
+        def impl(a):
+            if a is not None and bool(a):
+                data = [0]
+            else:
+                data = [x for x in range(0)]
+            return np.array(data, dtype=types.intp)
+
+    return impl
+
+
+@register_jitable
+def _fill_diagonal_params(a, wrap):
+    if a.ndim == 2:
+        m = a.shape[0]
+        n = a.shape[1]
+        step = 1 + n
+        if wrap:
+            end = n * m
+        else:
+            end = n * min(m, n)
+    else:
+        shape = np.array(a.shape)
+
+        if not np.all(np.diff(shape) == 0):
+            raise ValueError("All dimensions of input must be of equal length")
+
+        step = 1 + (np.cumprod(shape[:-1])).sum()
+        end = shape.prod()
+
+    return end, step
+
+
+@register_jitable
+def _fill_diagonal_scalar(a, val, wrap):
+    end, step = _fill_diagonal_params(a, wrap)
+
+    for i in range(0, end, step):
+        a.flat[i] = val
+
+
+@register_jitable
+def _fill_diagonal(a, val, wrap):
+    end, step = _fill_diagonal_params(a, wrap)
+    ctr = 0
+    v_len = len(val)
+
+    for i in range(0, end, step):
+        a.flat[i] = val[ctr]
+        ctr += 1
+        ctr = ctr % v_len
+
+
+@register_jitable
+def _check_val_int(a, val):
+    iinfo = np.iinfo(a.dtype)
+    v_min = iinfo.min
+    v_max = iinfo.max
+
+    # check finite values are within bounds
+    if np.any(~np.isfinite(val)) or np.any(val < v_min) or np.any(val > v_max):
+        raise ValueError('Unable to safely conform val to a.dtype')
+
+
+@register_jitable
+def _check_val_float(a, val):
+    finfo = np.finfo(a.dtype)
+    v_min = finfo.min
+    v_max = finfo.max
+
+    # check finite values are within bounds
+    finite_vals = val[np.isfinite(val)]
+    if np.any(finite_vals < v_min) or np.any(finite_vals > v_max):
+        raise ValueError('Unable to safely conform val to a.dtype')
+
+
+# no check performed, needed for pathway where no check is required
+_check_nop = register_jitable(lambda x, y: x)
+
+
+def _asarray(x):
+    pass
+
+
+@overload(_asarray)
+def _asarray_impl(x):
+    if isinstance(x, types.Array):
+        return lambda x: x
+    elif isinstance(x, (types.Sequence, types.Tuple)):
+        return lambda x: np.array(x)
+    elif isinstance(x, (types.Number, types.Boolean)):
+        ty = as_dtype(x)
+        return lambda x: np.array([x], dtype=ty)
+
+
+@overload(np.fill_diagonal)
+def np_fill_diagonal(a, val, wrap=False):
+
+    if a.ndim > 1:
+        # the following can be simplified after #3088; until then, employ
+        # a basic mechanism for catching cases where val is of a type/value
+        # which cannot safely be cast to a.dtype
+        if isinstance(a.dtype, types.Integer):
+            checker = _check_val_int
+        elif isinstance(a.dtype, types.Float):
+            checker = _check_val_float
+        else:
+            checker = _check_nop
+
+        def scalar_impl(a, val, wrap=False):
+            tmpval = _asarray(val).flatten()
+            checker(a, tmpval)
+            _fill_diagonal_scalar(a, val, wrap)
+
+        def non_scalar_impl(a, val, wrap=False):
+            tmpval = _asarray(val).flatten()
+            checker(a, tmpval)
+            _fill_diagonal(a, tmpval, wrap)
+
+        if isinstance(val, (types.Float, types.Integer, types.Boolean)):
+            return scalar_impl
+        elif isinstance(val, (types.Tuple, types.Sequence, types.Array)):
+            return non_scalar_impl
+    else:
+        msg = "The first argument must be at least 2-D (found %s-D)" % a.ndim
+        raise TypingError(msg)
+
+
+def _np_round_intrinsic(tp):
+    # np.round() always rounds half to even
+    return "llvm.rint.f%d" % (tp.bitwidth,)
+
+
+def _np_round_float(context, builder, tp, val):
+    llty = context.get_value_type(tp)
+    module = builder.module
+    fnty = llvmlite.ir.FunctionType(llty, [llty])
+    fn = cgutils.get_or_insert_function(module, fnty, _np_round_intrinsic(tp))
+    return builder.call(fn, (val,))
+
+
+@glue_lowering(np.around, types.Float)
+@glue_lowering(np.round, types.Float)
+def scalar_round_unary_float(context, builder, sig, args):
+    res = _np_round_float(context, builder, sig.args[0], args[0])
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.around, types.Integer)
+@glue_lowering(np.round, types.Integer)
+def scalar_round_unary_integer(context, builder, sig, args):
+    res = args[0]
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.around, types.Complex)
+@glue_lowering(np.round, types.Complex)
+def scalar_round_unary_complex(context, builder, sig, args):
+    fltty = sig.args[0].underlying_float
+    z = context.make_complex(builder, sig.args[0], args[0])
+    z.real = _np_round_float(context, builder, fltty, z.real)
+    z.imag = _np_round_float(context, builder, fltty, z.imag)
+    res = z._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.around, types.Float, types.Integer)
+@glue_lowering(np.round, types.Float, types.Integer)
+@glue_lowering(np.around, types.Integer, types.Integer)
+@glue_lowering(np.round, types.Integer, types.Integer)
+def scalar_round_binary_float(context, builder, sig, args):
+    def round_ndigits(x, ndigits):
+        if math.isinf(x) or math.isnan(x):
+            return x
+
+        # NOTE: this is CPython's algorithm, but perhaps this is overkill
+        # when emulating Numpy's behaviour.
+        if ndigits >= 0:
+            if ndigits > 22:
+                # pow1 and pow2 are each safe from overflow, but
+                # pow1*pow2 ~= pow(10.0, ndigits) might overflow.
+                pow1 = 10.0 ** (ndigits - 22)
+                pow2 = 1e22
+            else:
+                pow1 = 10.0 ** ndigits
+                pow2 = 1.0
+            y = (x * pow1) * pow2
+            if math.isinf(y):
+                return x
+            return (np.round(y) / pow2) / pow1
+
+        else:
+            pow1 = 10.0 ** (-ndigits)
+            y = x / pow1
+            return np.round(y) * pow1
+
+    res = context.compile_internal(builder, round_ndigits, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.around, types.Complex, types.Integer)
+@glue_lowering(np.round, types.Complex, types.Integer)
+def scalar_round_binary_complex(context, builder, sig, args):
+    def round_ndigits(z, ndigits):
+        return complex(np.round(z.real, ndigits),
+                       np.round(z.imag, ndigits))
+
+    res = context.compile_internal(builder, round_ndigits, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.around, types.Array, types.Integer, types.Array)
+@glue_lowering(np.round, types.Array, types.Integer, types.Array)
+def array_round(context, builder, sig, args):
+    def array_round_impl(arr, decimals, out):
+        if arr.shape != out.shape:
+            raise ValueError("invalid output shape")
+        for index, val in np.ndenumerate(arr):
+            out[index] = np.round(val, decimals)
+        return out
+
+    res = context.compile_internal(builder, array_round_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.sinc, types.Array)
+def array_sinc(context, builder, sig, args):
+    def array_sinc_impl(arr):
+        out = np.zeros_like(arr)
+        for index, val in np.ndenumerate(arr):
+            out[index] = np.sinc(val)
+        return out
+    res = context.compile_internal(builder, array_sinc_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.sinc, types.Number)
+def scalar_sinc(context, builder, sig, args):
+    scalar_dtype = sig.return_type
+
+    def scalar_sinc_impl(val):
+        if val == 0.e0: # to match np impl
+            val = 1e-20
+        val *= np.pi # np sinc is the normalised variant
+        return np.sin(val) / val
+    res = context.compile_internal(builder, scalar_sinc_impl, sig, args,
+                                   locals=dict(c=scalar_dtype))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.angle, types.Number)
+@glue_lowering(np.angle, types.Number, types.Boolean)
+def scalar_angle_kwarg(context, builder, sig, args):
+    deg_mult = sig.return_type(180 / np.pi)
+
+    def scalar_angle_impl(val, deg):
+        if deg:
+            return np.arctan2(val.imag, val.real) * deg_mult
+        else:
+            return np.arctan2(val.imag, val.real)
+
+    if len(args) == 1:
+        args = args + (cgutils.false_bit,)
+        sig = signature(sig.return_type, *(sig.args + (types.boolean,)))
+    res = context.compile_internal(builder, scalar_angle_impl,
+                                   sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.angle, types.Array)
+@glue_lowering(np.angle, types.Array, types.Boolean)
+def array_angle_kwarg(context, builder, sig, args):
+    ret_dtype = sig.return_type.dtype
+
+    def array_angle_impl(arr, deg):
+        out = np.zeros_like(arr, dtype=ret_dtype)
+        for index, val in np.ndenumerate(arr):
+            out[index] = np.angle(val, deg)
+        return out
+
+    if len(args) == 1:
+        args = args + (cgutils.false_bit,)
+        sig = signature(sig.return_type, *(sig.args + (types.boolean,)))
+
+    res = context.compile_internal(builder, array_angle_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.nonzero, types.Array)
+@lower_builtin("array.nonzero", types.Array)
+@glue_lowering(np.where, types.Array)
+def array_nonzero(context, builder, sig, args):
+    aryty = sig.args[0]
+    # Return type is a N-tuple of 1D C-contiguous arrays
+    retty = sig.return_type
+    outaryty = retty.dtype
+    nouts = retty.count
+
+    ary = make_array(aryty)(context, builder, args[0])
+    shape = cgutils.unpack_tuple(builder, ary.shape)
+    strides = cgutils.unpack_tuple(builder, ary.strides)
+    data = ary.data
+    layout = aryty.layout
+
+    # First count the number of non-zero elements
+    zero = context.get_constant(types.intp, 0)
+    one = context.get_constant(types.intp, 1)
+    count = cgutils.alloca_once_value(builder, zero)
+    with cgutils.loop_nest(builder, shape, zero.type) as indices:
+        ptr = cgutils.get_item_pointer2(context, builder, data, shape, strides,
+                                        layout, indices)
+        val = load_item(context, builder, aryty, ptr)
+        nz = context.is_true(builder, aryty.dtype, val)
+        with builder.if_then(nz):
+            builder.store(builder.add(builder.load(count), one), count)
+
+    # Then allocate output arrays of the right size
+    out_shape = (builder.load(count),)
+    outs = [_empty_nd_impl(context, builder, outaryty, out_shape)._getvalue()
+            for i in range(nouts)]
+    outarys = [make_array(outaryty)(context, builder, out) for out in outs]
+    out_datas = [out.data for out in outarys]
+
+    # And fill them up
+    index = cgutils.alloca_once_value(builder, zero)
+    with cgutils.loop_nest(builder, shape, zero.type) as indices:
+        ptr = cgutils.get_item_pointer2(context, builder, data, shape, strides,
+                                        layout, indices)
+        val = load_item(context, builder, aryty, ptr)
+        nz = context.is_true(builder, aryty.dtype, val)
+        with builder.if_then(nz):
+            # Store element indices in output arrays
+            if not indices:
+                # For a 0-d array, store 0 in the unique output array
+                indices = (zero,)
+            cur = builder.load(index)
+            for i in range(nouts):
+                ptr = cgutils.get_item_pointer2(context, builder, out_datas[i],
+                                                out_shape, (),
+                                                'C', [cur])
+                store_item(context, builder, outaryty, indices[i], ptr)
+            builder.store(builder.add(cur, one), index)
+
+    tup = context.make_tuple(builder, sig.return_type, outs)
+    return impl_ret_new_ref(context, builder, sig.return_type, tup)
+
+
+def array_where(context, builder, sig, args):
+    """
+    np.where(array, array, array)
+    """
+    layouts = set(a.layout for a in sig.args)
+
+    npty = np.promote_types(as_dtype(sig.args[1].dtype),
+                            as_dtype(sig.args[2].dtype))
+
+    if layouts == set('C') or layouts == set('F'):
+        # Faster implementation for C-contiguous arrays
+        def where_impl(cond, x, y):
+            shape = cond.shape
+            if x.shape != shape or y.shape != shape:
+                raise ValueError("all inputs should have the same shape")
+            res = np.empty_like(x, dtype=npty)
+            cf = cond.flat
+            xf = x.flat
+            yf = y.flat
+            rf = res.flat
+            for i in range(cond.size):
+                rf[i] = xf[i] if cf[i] else yf[i]
+            return res
+    else:
+        def where_impl(cond, x, y):
+            shape = cond.shape
+            if x.shape != shape or y.shape != shape:
+                raise ValueError("all inputs should have the same shape")
+            res = np.empty(cond.shape, dtype=npty)
+            for idx, c in np.ndenumerate(cond):
+                res[idx] = x[idx] if c else y[idx]
+            return res
+
+    res = context.compile_internal(builder, where_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@register_jitable
+def _where_x_y_scalar(cond, x, y, res):
+    for idx, c in np.ndenumerate(cond):
+        res[idx] = x if c else y
+    return res
+
+
+@register_jitable
+def _where_x_scalar(cond, x, y, res):
+    for idx, c in np.ndenumerate(cond):
+        res[idx] = x if c else y[idx]
+    return res
+
+
+@register_jitable
+def _where_y_scalar(cond, x, y, res):
+    for idx, c in np.ndenumerate(cond):
+        res[idx] = x[idx] if c else y
+    return res
+
+
+def _where_inner(context, builder, sig, args, impl):
+    cond, x, y = sig.args
+
+    x_dt = determine_dtype(x)
+    y_dt = determine_dtype(y)
+    npty = np.promote_types(x_dt, y_dt)
+
+    if cond.layout == 'F':
+        def where_impl(cond, x, y):
+            res = np.asfortranarray(np.empty(cond.shape, dtype=npty))
+            return impl(cond, x, y, res)
+    else:
+        def where_impl(cond, x, y):
+            res = np.empty(cond.shape, dtype=npty)
+            return impl(cond, x, y, res)
+
+    res = context.compile_internal(builder, where_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+array_scalar_scalar_where = partial(_where_inner, impl=_where_x_y_scalar)
+array_array_scalar_where = partial(_where_inner, impl=_where_y_scalar)
+array_scalar_array_where = partial(_where_inner, impl=_where_x_scalar)
+
+
+@glue_lowering(np.where, types.Any, types.Any, types.Any)
+def any_where(context, builder, sig, args):
+    cond, x, y = sig.args
+
+    if isinstance(cond, types.Array):
+        if isinstance(x, types.Array):
+            if isinstance(y, types.Array):
+                impl = array_where
+            elif isinstance(y, (types.Number, types.Boolean)):
+                impl = array_array_scalar_where
+        elif isinstance(x, (types.Number, types.Boolean)):
+            if isinstance(y, types.Array):
+                impl = array_scalar_array_where
+            elif isinstance(y, (types.Number, types.Boolean)):
+                impl = array_scalar_scalar_where
+
+        return impl(context, builder, sig, args)
+
+    def scalar_where_impl(cond, x, y):
+        """
+        np.where(scalar, scalar, scalar): return a 0-dim array
+        """
+        scal = x if cond else y
+        # This is the equivalent of np.full_like(scal, scal),
+        # for compatibility with Numpy < 1.8
+        arr = np.empty_like(scal)
+        arr[()] = scal
+        return arr
+
+    res = context.compile_internal(builder, scalar_where_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@overload(np.real)
+def np_real(a):
+    def np_real_impl(a):
+        return a.real
+
+    return np_real_impl
+
+
+@overload(np.imag)
+def np_imag(a):
+    def np_imag_impl(a):
+        return a.imag
+
+    return np_imag_impl
+
+
+#----------------------------------------------------------------------------
+# Misc functions
+
+@overload(operator.contains)
+def np_contains(arr, key):
+    if not isinstance(arr, types.Array):
+        return
+
+    def np_contains_impl(arr, key):
+        for x in np.nditer(arr):
+            if x == key:
+                return True
+        return False
+
+    return np_contains_impl
+
+
+@overload(np.count_nonzero)
+def np_count_nonzero(arr, axis=None):
+    if not type_can_asarray(arr):
+        raise TypingError("The argument to np.count_nonzero must be array-like")
+
+    if is_nonelike(axis):
+        def impl(arr, axis=None):
+            arr2 = np.ravel(arr)
+            return np.sum(arr2 != 0)
+    else:
+        def impl(arr, axis=None):
+            arr2 = arr.astype(np.bool_)
+            return np.sum(arr2, axis=axis)
+
+    return impl
+
+
+np_delete_handler_isslice = register_jitable(lambda x : x)
+np_delete_handler_isarray = register_jitable(lambda x : np.asarray(x))
+
+
+@overload(np.delete)
+def np_delete(arr, obj):
+    # Implementation based on numpy
+    # https://github.com/numpy/numpy/blob/af66e487a57bfd4850f4306e3b85d1dac3c70412/numpy/lib/function_base.py#L4065-L4267    # noqa: E501
+
+    if not isinstance(arr, (types.Array, types.Sequence)):
+        raise TypingError("arr must be either an Array or a Sequence")
+
+    if isinstance(obj, (types.Array, types.Sequence, types.SliceType)):
+        if isinstance(obj, (types.SliceType)):
+            handler = np_delete_handler_isslice
+        else:
+            if not isinstance(obj.dtype, types.Integer):
+                raise TypingError('obj should be of Integer dtype')
+            handler = np_delete_handler_isarray
+
+        def np_delete_impl(arr, obj):
+            arr = np.ravel(np.asarray(arr))
+            N = arr.size
+
+            keep = np.ones(N, dtype=np.bool_)
+            obj = handler(obj)
+            keep[obj] = False
+            return arr[keep]
+        return np_delete_impl
+
+    else: # scalar value
+        if not isinstance(obj, types.Integer):
+            raise TypingError('obj should be of Integer dtype')
+
+        def np_delete_scalar_impl(arr, obj):
+            arr = np.ravel(np.asarray(arr))
+            N = arr.size
+            pos = obj
+
+            if (pos < -N or pos >= N):
+                raise IndexError('obj must be less than the len(arr)')
+                # NumPy raises IndexError: index 'i' is out of
+                # bounds for axis 'x' with size 'n'
+
+            if (pos < 0):
+                pos += N
+
+            return np.concatenate((arr[:pos], arr[pos + 1:]))
+        return np_delete_scalar_impl
+
+
+@overload(np.diff)
+def np_diff_impl(a, n=1):
+    if not isinstance(a, types.Array) or a.ndim == 0:
+        return
+
+    def diff_impl(a, n=1):
+        if n == 0:
+            return a.copy()
+        if n < 0:
+            raise ValueError("diff(): order must be non-negative")
+        size = a.shape[-1]
+        out_shape = a.shape[:-1] + (max(size - n, 0),)
+        out = np.empty(out_shape, a.dtype)
+        if out.size == 0:
+            return out
+
+        # np.diff() works on each last dimension subarray independently.
+        # To make things easier, normalize input and output into 2d arrays
+        a2 = a.reshape((-1, size))
+        out2 = out.reshape((-1, out.shape[-1]))
+        # A scratchpad for subarrays
+        work = np.empty(size, a.dtype)
+
+        for major in range(a2.shape[0]):
+            # First iteration: diff a2 into work
+            for i in range(size - 1):
+                work[i] = a2[major, i + 1] - a2[major, i]
+            # Other iterations: diff work into itself
+            for niter in range(1, n):
+                for i in range(size - niter - 1):
+                    work[i] = work[i + 1] - work[i]
+            # Copy final diff into out2
+            out2[major] = work[:size - n]
+
+        return out
+
+    return diff_impl
+
+
+@overload(np.array_equal)
+def np_array_equal(a, b):
+
+    if not (type_can_asarray(a) and type_can_asarray(b)):
+        raise TypingError('Both arguments to "array_equals" must be array-like')
+
+    accepted = (types.Boolean, types.Number)
+    if isinstance(a, accepted) and isinstance(b, accepted):
+        # special case
+        def impl(a, b):
+            return a == b
+    else:
+        def impl(a, b):
+            a = np.asarray(a)
+            b = np.asarray(b)
+            if a.shape == b.shape:
+                return np.all(a == b)
+            return False
+
+    return impl
+
+
+@overload(np.intersect1d)
+def jit_np_intersect1d(ar1, ar2):
+    # Not implemented to support assume_unique or return_indices
+    # https://github.com/numpy/numpy/blob/v1.19.0/numpy/lib
+    # /arraysetops.py#L347-L441
+    if not (type_can_asarray(ar1) or type_can_asarray(ar2)):
+        raise TypingError('intersect1d: first two args must be array-like')
+
+    def np_intersects1d_impl(ar1, ar2):
+        ar1 = np.asarray(ar1)
+        ar2 = np.asarray(ar2)
+
+        ar1 = np.unique(ar1)
+        ar2 = np.unique(ar2)
+
+        aux = np.concatenate((ar1, ar2))
+        aux.sort()
+        mask = aux[1:] == aux[:-1]
+        int1d = aux[:-1][mask]
+        return int1d
+    return np_intersects1d_impl
+
+
+def validate_1d_array_like(func_name, seq):
+    if isinstance(seq, types.Array):
+        if seq.ndim != 1:
+            raise TypeError("{0}(): input should have dimension 1"
+                            .format(func_name))
+    elif not isinstance(seq, types.Sequence):
+        raise TypeError("{0}(): input should be an array or sequence"
+                        .format(func_name))
+
+
+@overload(np.bincount)
+def np_bincount(a, weights=None, minlength=0):
+    validate_1d_array_like("bincount", a)
+
+    if not isinstance(a.dtype, types.Integer):
+        return
+
+    check_is_integer(minlength, 'minlength')
+
+    if weights not in (None, types.none):
+        validate_1d_array_like("bincount", weights)
+        # weights is promoted to double in C impl
+        # https://github.com/numpy/numpy/blob/maintenance/1.16.x/numpy/core/src/multiarray/compiled_base.c#L93-L95    # noqa: E501
+        out_dtype = np.float64
+
+        @register_jitable
+        def validate_inputs(a, weights, minlength):
+            if len(a) != len(weights):
+                raise ValueError("bincount(): weights and list don't have "
+                                 "the same length")
+
+        @register_jitable
+        def count_item(out, idx, val, weights):
+            out[val] += weights[idx]
+
+    else:
+        out_dtype = types.intp
+
+        @register_jitable
+        def validate_inputs(a, weights, minlength):
+            pass
+
+        @register_jitable
+        def count_item(out, idx, val, weights):
+            out[val] += 1
+
+    def bincount_impl(a, weights=None, minlength=0):
+        validate_inputs(a, weights, minlength)
+        if minlength < 0:
+            raise ValueError("'minlength' must not be negative")
+
+        n = len(a)
+        a_max = a[0] if n > 0 else -1
+        for i in range(1, n):
+            if a[i] < 0:
+                raise ValueError("bincount(): first argument must be "
+                                 "non-negative")
+            a_max = max(a_max, a[i])
+
+        out_length = max(a_max + 1, minlength)
+        out = np.zeros(out_length, out_dtype)
+        for i in range(n):
+            count_item(out, i, a[i], weights)
+        return out
+
+    return bincount_impl
+
+
+def _searchsorted(func):
+    def searchsorted_inner(a, v, v_last, lo, hi, n):
+        """Perform inner loop of searchsorted (i.e. a binary search).
+
+        This is loosely based on the NumPy implementation in [1]_.
+
+        Parameters
+        ----------
+        a: 1-D array_like
+            The input array.
+        v: array_like
+            The current value to insert into `a`.
+        v_last: array_like
+            The previous value inserted into `a`.
+        lo: int
+            The initial/previous "low" value of the binary search.
+        hi: int
+            The initial/previous "high" value of the binary search.
+        n: int
+            The length of `a`.
+
+
+        .. [1] https://github.com/numpy/numpy/blob/809e8d26b03f549fd0b812a17b8a166bcd966889/numpy/core/src/npysort/binsearch.cpp#L173
+        """  # noqa: E501
+        if np.isnan(v):
+            # Find the first nan (i.e. the last from the end of a,
+            # since there shouldn't be many of them in practice)
+            for i in range(n, 0, -1):
+                if not np.isnan(a[i - 1]):
+                    return i
+            return 0
+
+        if v_last < v:
+            hi = n
+        else:
+            lo = 0
+            hi = hi + 1 if hi < n else n
+
+        while hi > lo:
+            mid = (lo + hi) >> 1
+            if func(a[mid], (v)):
+                # mid is too low => go up
+                lo = mid + 1
+            else:
+                # mid is too high, or is a NaN => go down
+                hi = mid
+        return lo
+    return searchsorted_inner
+
+
+_lt = less_than
+_le = register_jitable(lambda x, y: x <= y)
+_searchsorted_left = register_jitable(_searchsorted(_lt))
+_searchsorted_right = register_jitable(_searchsorted(_le))
+
+
+@overload(np.searchsorted)
+def searchsorted(a, v, side='left'):
+    side_val = getattr(side, 'literal_value', side)
+    if side_val == 'left':
+        loop_impl = _searchsorted_left
+    elif side_val == 'right':
+        loop_impl = _searchsorted_right
+    else:
+        raise NumbaValueError(f"Invalid value given for 'side': {side_val}")
+
+    if isinstance(v, types.Array):
+        # N-d array and output
+        def searchsorted_impl(a, v, side='left'):
+            n = len(a)
+            lo = 0
+            hi = n
+            out = np.empty(v.shape, np.intp)
+            v_last = v.flat[0]
+            for view, outview in np.nditer((v, out)):
+                lo = loop_impl(a, view.item(), v_last, lo, hi, n)
+                v_last = view.item()
+                outview.itemset(lo)
+            return out
+
+    elif isinstance(v, types.Sequence):
+        # 1-d sequence and output
+        def searchsorted_impl(a, v, side='left'):
+            n = len(a)
+            lo = 0
+            hi = n
+            out = np.empty(len(v), np.intp)
+            v_last = v[0]
+            for i in range(len(v)):
+                lo = loop_impl(a, v[i], v_last, lo, hi, n)
+                out[i] = lo
+                v_last = v[i]
+            return out
+    else:
+        # Scalar value and output
+        # Note: NaNs come last in Numpy-sorted arrays
+        def searchsorted_impl(a, v, side='left'):
+            n = len(a)
+            return loop_impl(a, v, v, 0, n, n)
+
+    return searchsorted_impl
+
+
+@overload(np.digitize)
+def np_digitize(x, bins, right=False):
+    @register_jitable
+    def are_bins_increasing(bins):
+        n = len(bins)
+        is_increasing = True
+        is_decreasing = True
+        if n > 1:
+            prev = bins[0]
+            for i in range(1, n):
+                cur = bins[i]
+                is_increasing = is_increasing and not prev > cur
+                is_decreasing = is_decreasing and not prev < cur
+                if not is_increasing and not is_decreasing:
+                    raise ValueError("bins must be monotonically increasing "
+                                     "or decreasing")
+                prev = cur
+        return is_increasing
+
+    # NOTE: the algorithm is slightly different from searchsorted's,
+    # as the edge cases (bin boundaries, NaN) give different results.
+
+    @register_jitable
+    def digitize_scalar(x, bins, right):
+        # bins are monotonically-increasing
+        n = len(bins)
+        lo = 0
+        hi = n
+
+        if right:
+            if np.isnan(x):
+                # Find the first nan (i.e. the last from the end of bins,
+                # since there shouldn't be many of them in practice)
+                for i in range(n, 0, -1):
+                    if not np.isnan(bins[i - 1]):
+                        return i
+                return 0
+            while hi > lo:
+                mid = (lo + hi) >> 1
+                if bins[mid] < x:
+                    # mid is too low => narrow to upper bins
+                    lo = mid + 1
+                else:
+                    # mid is too high, or is a NaN => narrow to lower bins
+                    hi = mid
+        else:
+            if np.isnan(x):
+                # NaNs end up in the last bin
+                return n
+            while hi > lo:
+                mid = (lo + hi) >> 1
+                if bins[mid] <= x:
+                    # mid is too low => narrow to upper bins
+                    lo = mid + 1
+                else:
+                    # mid is too high, or is a NaN => narrow to lower bins
+                    hi = mid
+
+        return lo
+
+    @register_jitable
+    def digitize_scalar_decreasing(x, bins, right):
+        # bins are monotonically-decreasing
+        n = len(bins)
+        lo = 0
+        hi = n
+
+        if right:
+            if np.isnan(x):
+                # Find the last nan
+                for i in range(0, n):
+                    if not np.isnan(bins[i]):
+                        return i
+                return n
+            while hi > lo:
+                mid = (lo + hi) >> 1
+                if bins[mid] < x:
+                    # mid is too high => narrow to lower bins
+                    hi = mid
+                else:
+                    # mid is too low, or is a NaN => narrow to upper bins
+                    lo = mid + 1
+        else:
+            if np.isnan(x):
+                # NaNs end up in the first bin
+                return 0
+            while hi > lo:
+                mid = (lo + hi) >> 1
+                if bins[mid] <= x:
+                    # mid is too high => narrow to lower bins
+                    hi = mid
+                else:
+                    # mid is too low, or is a NaN => narrow to upper bins
+                    lo = mid + 1
+
+        return lo
+
+    if isinstance(x, types.Array):
+        # N-d array and output
+
+        def digitize_impl(x, bins, right=False):
+            is_increasing = are_bins_increasing(bins)
+            out = np.empty(x.shape, np.intp)
+            for view, outview in np.nditer((x, out)):
+                if is_increasing:
+                    index = digitize_scalar(view.item(), bins, right)
+                else:
+                    index = digitize_scalar_decreasing(view.item(), bins, right)
+                outview.itemset(index)
+            return out
+
+        return digitize_impl
+
+    elif isinstance(x, types.Sequence):
+        # 1-d sequence and output
+
+        def digitize_impl(x, bins, right=False):
+            is_increasing = are_bins_increasing(bins)
+            out = np.empty(len(x), np.intp)
+            for i in range(len(x)):
+                if is_increasing:
+                    out[i] = digitize_scalar(x[i], bins, right)
+                else:
+                    out[i] = digitize_scalar_decreasing(x[i], bins, right)
+            return out
+
+        return digitize_impl
+
+
+_range = range
+
+
+@overload(np.histogram)
+def np_histogram(a, bins=10, range=None):
+    if isinstance(bins, (int, types.Integer)):
+        # With a uniform distribution of bins, use a fast algorithm
+        # independent of the number of bins
+
+        if range in (None, types.none):
+            inf = float('inf')
+
+            def histogram_impl(a, bins=10, range=None):
+                bin_min = inf
+                bin_max = -inf
+                for view in np.nditer(a):
+                    v = view.item()
+                    if bin_min > v:
+                        bin_min = v
+                    if bin_max < v:
+                        bin_max = v
+                return np.histogram(a, bins, (bin_min, bin_max))
+
+        else:
+            def histogram_impl(a, bins=10, range=None):
+                if bins <= 0:
+                    raise ValueError("histogram(): `bins` should be a "
+                                     "positive integer")
+                bin_min, bin_max = range
+                if not bin_min <= bin_max:
+                    raise ValueError("histogram(): max must be larger than "
+                                     "min in range parameter")
+
+                hist = np.zeros(bins, np.intp)
+                if bin_max > bin_min:
+                    bin_ratio = bins / (bin_max - bin_min)
+                    for view in np.nditer(a):
+                        v = view.item()
+                        b = math.floor((v - bin_min) * bin_ratio)
+                        if 0 <= b < bins:
+                            hist[int(b)] += 1
+                        elif v == bin_max:
+                            hist[bins - 1] += 1
+
+                bins_array = np.linspace(bin_min, bin_max, bins + 1)
+                return hist, bins_array
+
+    else:
+        # With a custom bins array, use a bisection search
+
+        def histogram_impl(a, bins=10, range=None):
+            nbins = len(bins) - 1
+            for i in _range(nbins):
+                # Note this also catches NaNs
+                if not bins[i] <= bins[i + 1]:
+                    raise ValueError("histogram(): bins must increase "
+                                     "monotonically")
+
+            bin_min = bins[0]
+            bin_max = bins[nbins]
+            hist = np.zeros(nbins, np.intp)
+
+            if nbins > 0:
+                for view in np.nditer(a):
+                    v = view.item()
+                    if not bin_min <= v <= bin_max:
+                        # Value is out of bounds, ignore (also catches NaNs)
+                        continue
+                    # Bisect in bins[:-1]
+                    lo = 0
+                    hi = nbins - 1
+                    while lo < hi:
+                        # Note the `+ 1` is necessary to avoid an infinite
+                        # loop where mid = lo => lo = mid
+                        mid = (lo + hi + 1) >> 1
+                        if v < bins[mid]:
+                            hi = mid - 1
+                        else:
+                            lo = mid
+                    hist[lo] += 1
+
+            return hist, bins
+
+    return histogram_impl
+
+
+# Create np.finfo, np.iinfo and np.MachAr
+# machar
+_mach_ar_supported = ('ibeta', 'it', 'machep', 'eps', 'negep', 'epsneg',
+                      'iexp', 'minexp', 'xmin', 'maxexp', 'xmax', 'irnd',
+                      'ngrd', 'epsilon', 'tiny', 'huge', 'precision',
+                      'resolution',)
+MachAr = namedtuple('MachAr', _mach_ar_supported)
+
+# Do not support MachAr field
+# finfo
+_finfo_supported = ('eps', 'epsneg', 'iexp', 'machep', 'max', 'maxexp', 'min',
+                    'minexp', 'negep', 'nexp', 'nmant', 'precision',
+                    'resolution', 'tiny', 'bits',)
+
+
+finfo = namedtuple('finfo', _finfo_supported)
+
+# iinfo
+_iinfo_supported = ('min', 'max', 'bits',)
+
+iinfo = namedtuple('iinfo', _iinfo_supported)
+
+
+# This module is imported under the compiler lock which should deal with the
+# lack of thread safety in the warning filter.
+def _gen_np_machar():
+    np122plus = numpy_version >= (1, 22)
+    w = None
+    with warnings.catch_warnings(record=True) as w:
+        msg = r'`np.MachAr` is deprecated \(NumPy 1.22\)'
+        warnings.filterwarnings("always", message=msg,
+                                category=DeprecationWarning,
+                                module=r'.*numba.*arraymath')
+        np_MachAr = np.MachAr
+
+    @overload(np_MachAr)
+    def MachAr_impl():
+        f = np_MachAr()
+        _mach_ar_data = tuple([getattr(f, x) for x in _mach_ar_supported])
+
+        if np122plus and w:
+            wmsg = w[0]
+            warnings.warn_explicit(wmsg.message.args[0],
+                                   NumbaDeprecationWarning,
+                                   wmsg.filename,
+                                   wmsg.lineno)
+
+        def impl():
+            return MachAr(*_mach_ar_data)
+        return impl
+
+
+_gen_np_machar()
+
+
+def generate_xinfo(np_func, container, attr):
+    @overload(np_func)
+    def xinfo_impl(arg):
+        nbty = getattr(arg, 'dtype', arg)
+        np_dtype = as_dtype(nbty)
+        try:
+            f = np_func(np_dtype)
+        except ValueError: # This exception instance comes from NumPy
+            # The np function might not support the dtype
+            return None
+        data = tuple([getattr(f, x) for x in attr])
+
+        def impl(arg):
+            return container(*data)
+        return impl
+
+
+generate_xinfo(np.finfo, finfo, _finfo_supported)
+generate_xinfo(np.iinfo, iinfo, _iinfo_supported)
+
+
+def _get_inner_prod(dta, dtb):
+    # gets an inner product implementation, if both types are float then
+    # BLAS is used else a local function
+
+    @register_jitable
+    def _innerprod(a, b):
+        acc = 0
+        for i in range(len(a)):
+            acc = acc + a[i] * b[i]
+        return acc
+
+    # no BLAS... use local function regardless
+    if not _HAVE_BLAS:
+        return _innerprod
+
+    flty = types.real_domain | types.complex_domain
+    floats = dta in flty and dtb in flty
+    if not floats:
+        return _innerprod
+    else:
+        a_dt = as_dtype(dta)
+        b_dt = as_dtype(dtb)
+        dt = np.promote_types(a_dt, b_dt)
+
+        @register_jitable
+        def _dot_wrap(a, b):
+            return np.dot(a.astype(dt), b.astype(dt))
+        return _dot_wrap
+
+
+def _assert_1d(a, func_name):
+    if isinstance(a, types.Array):
+        if not a.ndim <= 1:
+            raise TypingError("%s() only supported on 1D arrays " % func_name)
+
+
+def _np_correlate_core(ap1, ap2, mode, direction):
+    pass
+
+
+class _corr_conv_Mode(IntEnum):
+    """
+    Enumerated modes for correlate/convolve as per:
+    https://github.com/numpy/numpy/blob/ac6b1a902b99e340cf7eeeeb7392c91e38db9dd8/numpy/core/numeric.py#L862-L870    # noqa: E501
+    """
+    VALID = 0
+    SAME = 1
+    FULL = 2
+
+
+@overload(_np_correlate_core)
+def _np_correlate_core_impl(ap1, ap2, mode, direction):
+    a_dt = as_dtype(ap1.dtype)
+    b_dt = as_dtype(ap2.dtype)
+    dt = np.promote_types(a_dt, b_dt)
+    innerprod = _get_inner_prod(ap1.dtype, ap2.dtype)
+
+    Mode = _corr_conv_Mode
+
+    def impl(ap1, ap2, mode, direction):
+        # Implementation loosely based on `_pyarray_correlate` from
+        # https://github.com/numpy/numpy/blob/3bce2be74f228684ca2895ad02b63953f37e2a9d/numpy/core/src/multiarray/multiarraymodule.c#L1191    # noqa: E501
+        # For "Mode":
+        # Convolve uses 'full' by default, this is denoted by the number 2
+        # Correlate uses 'valid' by default, this is denoted by the number 0
+        # For "direction", +1 to write the return values out in order 0->N
+        # -1 to write them out N->0.
+
+        if not (mode == Mode.VALID or mode == Mode.FULL):
+            raise ValueError("Invalid mode")
+
+        n1 = len(ap1)
+        n2 = len(ap2)
+        length = n1
+        n = n2
+        if mode == Mode.VALID: # mode == valid == 0, correlate default
+            length = length - n + 1
+            n_left = 0
+            n_right = 0
+        elif mode == Mode.FULL: # mode == full == 2, convolve default
+            n_right = n - 1
+            n_left = n - 1
+            length = length + n - 1
+        else:
+            raise ValueError("Invalid mode")
+
+        ret = np.zeros(length, dt)
+        n = n - n_left
+
+        if direction == 1:
+            idx = 0
+            inc = 1
+        elif direction == -1:
+            idx = length - 1
+            inc = -1
+        else:
+            raise ValueError("Invalid direction")
+
+        for i in range(n_left):
+            ret[idx] = innerprod(ap1[:idx + 1], ap2[-(idx + 1):])
+            idx = idx + inc
+
+        for i in range(n1 - n2 + 1):
+            ret[idx] = innerprod(ap1[i : i + n2], ap2)
+            idx = idx + inc
+
+        for i in range(n_right, 0, -1):
+            ret[idx] = innerprod(ap1[-i:], ap2[:i])
+            idx = idx + inc
+        return ret
+
+    return impl
+
+
+@overload(np.correlate)
+def _np_correlate(a, v):
+    _assert_1d(a, 'np.correlate')
+    _assert_1d(v, 'np.correlate')
+
+    @register_jitable
+    def op_conj(x):
+        return np.conj(x)
+
+    @register_jitable
+    def op_nop(x):
+        return x
+
+    Mode = _corr_conv_Mode
+
+    if a.dtype in types.complex_domain:
+        if v.dtype in types.complex_domain:
+            a_op = op_nop
+            b_op = op_conj
+        else:
+            a_op = op_nop
+            b_op = op_nop
+    else:
+        if v.dtype in types.complex_domain:
+            a_op = op_nop
+            b_op = op_conj
+        else:
+            a_op = op_conj
+            b_op = op_nop
+
+    _NP_PRED = numpy_version > (1, 17)
+
+    def impl(a, v):
+        la = len(a)
+        lv = len(v)
+        if _NP_PRED is True:
+            if la == 0:
+                raise ValueError("'a' cannot be empty")
+            if lv == 0:
+                raise ValueError("'v' cannot be empty")
+        if la < lv:
+            return _np_correlate_core(b_op(v), a_op(a), Mode.VALID, -1)
+        else:
+            return _np_correlate_core(a_op(a), b_op(v), Mode.VALID, 1)
+
+    return impl
+
+
+@overload(np.convolve)
+def np_convolve(a, v):
+    _assert_1d(a, 'np.convolve')
+    _assert_1d(v, 'np.convolve')
+
+    Mode = _corr_conv_Mode
+
+    def impl(a, v):
+        la = len(a)
+        lv = len(v)
+
+        if la == 0:
+            raise ValueError("'a' cannot be empty")
+        if lv == 0:
+            raise ValueError("'v' cannot be empty")
+
+        if la < lv:
+            return _np_correlate_core(v, a[::-1], Mode.FULL, 1)
+        else:
+            return _np_correlate_core(a, v[::-1], Mode.FULL, 1)
+
+    return impl
+
+
+@overload(np.asarray)
+def np_asarray(a, dtype=None):
+
+    # developer note... keep this function (type_can_asarray) in sync with the
+    # accepted types implementations below!
+    if not type_can_asarray(a):
+        return None
+
+    impl = None
+    if isinstance(a, types.Array):
+        if is_nonelike(dtype) or a.dtype == dtype.dtype:
+            def impl(a, dtype=None):
+                return a
+        else:
+            def impl(a, dtype=None):
+                return a.astype(dtype)
+    elif isinstance(a, (types.Sequence, types.Tuple)):
+        # Nested lists cannot be unpacked, therefore only single lists are
+        # permitted and these conform to Sequence and can be unpacked along on
+        # the same path as Tuple.
+        if is_nonelike(dtype):
+            def impl(a, dtype=None):
+                return np.array(a)
+        else:
+            def impl(a, dtype=None):
+                return np.array(a, dtype)
+    elif isinstance(a, (types.Number, types.Boolean)):
+        dt_conv = a if is_nonelike(dtype) else dtype
+        ty = as_dtype(dt_conv)
+
+        def impl(a, dtype=None):
+            return np.array(a, ty)
+    elif isinstance(a, types.containers.ListType):
+        if not isinstance(a.dtype, (types.Number, types.Boolean)):
+            raise TypingError(
+                "asarray support for List is limited "
+                "to Boolean and Number types")
+
+        target_dtype = a.dtype if is_nonelike(dtype) else dtype
+
+        def impl(a, dtype=None):
+            l = len(a)
+            ret = np.empty(l, dtype=target_dtype)
+            for i, v in enumerate(a):
+                ret[i] = v
+            return ret
+    elif isinstance(a, types.StringLiteral):
+        arr = np.asarray(a.literal_value)
+
+        def impl(a, dtype=None):
+            return arr.copy()
+
+    return impl
+
+
+@overload(np.asfarray)
+def np_asfarray(a, dtype=np.float64):
+    # convert numba dtype types into NumPy dtype
+    if isinstance(dtype, types.Type):
+        dtype = as_dtype(dtype)
+    if not np.issubdtype(dtype, np.inexact):
+        dx = types.float64
+    else:
+        dx = dtype
+
+    def impl(a, dtype=np.float64):
+        return np.asarray(a, dx)
+    return impl
+
+
+@overload(np.extract)
+def np_extract(condition, arr):
+
+    def np_extract_impl(condition, arr):
+        cond = np.asarray(condition).flatten()
+        a = np.asarray(arr)
+
+        if a.size == 0:
+            raise ValueError('Cannot extract from an empty array')
+
+        # the following looks odd but replicates NumPy...
+        # https://github.com/numpy/numpy/issues/12859
+        if np.any(cond[a.size:]) and cond.size > a.size:
+            msg = 'condition shape inconsistent with arr shape'
+            raise ValueError(msg)
+            # NumPy raises IndexError: index 'm' is out of
+            # bounds for size 'n'
+
+        max_len = min(a.size, cond.size)
+        out = [a.flat[idx] for idx in range(max_len) if cond[idx]]
+
+        return np.array(out)
+
+    return np_extract_impl
+
+
+@overload(np.select)
+def np_select(condlist, choicelist, default=0):
+
+    def np_select_arr_impl(condlist, choicelist, default=0):
+        if len(condlist) != len(choicelist):
+            raise ValueError('list of cases must be same length as list '
+                             'of conditions')
+        out = default * np.ones(choicelist[0].shape, choicelist[0].dtype)
+        # should use reversed+zip, but reversed is not available
+        for i in range(len(condlist) - 1, -1, -1):
+            cond = condlist[i]
+            choice = choicelist[i]
+            out = np.where(cond, choice, out)
+        return out
+
+    # first we check the types of the input parameters
+    if not isinstance(condlist, (types.List, types.UniTuple)):
+        raise NumbaTypeError('condlist must be a List or a Tuple')
+    if not isinstance(choicelist, (types.List, types.UniTuple)):
+        raise NumbaTypeError('choicelist must be a List or a Tuple')
+    if not isinstance(default, (int, types.Number, types.Boolean)):
+        raise NumbaTypeError('default must be a scalar (number or boolean)')
+    # the types of the parameters have been checked, now we test the types
+    # of the content of the parameters
+    # implementation note: if in the future numba's np.where accepts tuples
+    # as elements of condlist, then the check below should be extended to
+    # accept tuples
+    if not isinstance(condlist[0], types.Array):
+        raise NumbaTypeError('items of condlist must be arrays')
+    if not isinstance(choicelist[0], types.Array):
+        raise NumbaTypeError('items of choicelist must be arrays')
+    # the types of the parameters and their contents have been checked,
+    # now we test the dtypes of the content of parameters
+    if isinstance(condlist[0], types.Array):
+        if not isinstance(condlist[0].dtype, types.Boolean):
+            raise NumbaTypeError('condlist arrays must contain booleans')
+    if isinstance(condlist[0], types.UniTuple):
+        if not (isinstance(condlist[0], types.UniTuple)
+                and isinstance(condlist[0][0], types.Boolean)):
+            raise NumbaTypeError('condlist tuples must only contain booleans')
+    # the input types are correct, now we perform checks on the dimensions
+    if (isinstance(condlist[0], types.Array) and
+            condlist[0].ndim != choicelist[0].ndim):
+        raise NumbaTypeError('condlist and choicelist elements must have the '
+                             'same number of dimensions')
+    if isinstance(condlist[0], types.Array) and condlist[0].ndim < 1:
+        raise NumbaTypeError('condlist arrays must be of at least dimension 1')
+
+    return np_select_arr_impl
+
+
+@overload(np.asarray_chkfinite)
+def np_asarray_chkfinite(a, dtype=None):
+
+    msg = "The argument to np.asarray_chkfinite must be array-like"
+    if not isinstance(a, (types.Array, types.Sequence, types.Tuple)):
+        raise TypingError(msg)
+
+    if is_nonelike(dtype):
+        dt = a.dtype
+    else:
+        try:
+            dt = as_dtype(dtype)
+        except NumbaNotImplementedError:
+            raise TypingError('dtype must be a valid Numpy dtype')
+
+    def impl(a, dtype=None):
+        a = np.asarray(a, dtype=dt)
+        for i in np.nditer(a):
+            if not np.isfinite(i):
+                raise ValueError("array must not contain infs or NaNs")
+        return a
+
+    return impl
+
+#----------------------------------------------------------------------------
+# Windowing functions
+#   - translated from the numpy implementations found in:
+#   https://github.com/numpy/numpy/blob/v1.16.1/numpy/lib/function_base.py#L2543-L3233    # noqa: E501
+#   at commit: f1c4c758e1c24881560dd8ab1e64ae750
+#   - and also, for NumPy >= 1.20, translated from implementations in
+#   https://github.com/numpy/numpy/blob/156cd054e007b05d4ac4829e10a369d19dd2b0b1/numpy/lib/function_base.py#L2655-L3065  # noqa: E501
+
+
+@register_jitable
+def np_bartlett_impl(M):
+    if numpy_version >= (1, 20):
+        n = np.arange(1. - M, M, 2)
+        return np.where(np.less_equal(n, 0), 1 + n / (M - 1), 1 - n / (M - 1))
+    else:
+        n = np.arange(M)
+        return np.where(np.less_equal(n, (M - 1) / 2.0), 2.0 * n / (M - 1),
+                        2.0 - 2.0 * n / (M - 1))
+
+
+@register_jitable
+def np_blackman_impl(M):
+    if numpy_version >= (1, 20):
+        n = np.arange(1. - M, M, 2)
+        return (0.42 + 0.5 * np.cos(np.pi * n / (M - 1)) +
+                0.08 * np.cos(2.0 * np.pi * n / (M - 1)))
+    else:
+        n = np.arange(M)
+        return (0.42 - 0.5 * np.cos(2.0 * np.pi * n / (M - 1)) +
+                0.08 * np.cos(4.0 * np.pi * n / (M - 1)))
+
+
+@register_jitable
+def np_hamming_impl(M):
+    if numpy_version >= (1, 20):
+        n = np.arange(1 - M, M, 2)
+        return 0.54 + 0.46 * np.cos(np.pi * n / (M - 1))
+    else:
+        n = np.arange(M)
+        return 0.54 - 0.46 * np.cos(2.0 * np.pi * n / (M - 1))
+
+
+@register_jitable
+def np_hanning_impl(M):
+    if numpy_version >= (1, 20):
+        n = np.arange(1 - M, M, 2)
+        return 0.5 + 0.5 * np.cos(np.pi * n / (M - 1))
+    else:
+        n = np.arange(M)
+        return 0.5 - 0.5 * np.cos(2.0 * np.pi * n / (M - 1))
+
+
+def window_generator(func):
+    def window_overload(M):
+        if not isinstance(M, types.Integer):
+            raise TypingError('M must be an integer')
+
+        def window_impl(M):
+
+            if M < 1:
+                return np.array((), dtype=np.float_)
+            if M == 1:
+                return np.ones(1, dtype=np.float_)
+            return func(M)
+
+        return window_impl
+    return window_overload
+
+
+overload(np.bartlett)(window_generator(np_bartlett_impl))
+overload(np.blackman)(window_generator(np_blackman_impl))
+overload(np.hamming)(window_generator(np_hamming_impl))
+overload(np.hanning)(window_generator(np_hanning_impl))
+
+
+_i0A = np.array([
+    -4.41534164647933937950E-18,
+    3.33079451882223809783E-17,
+    -2.43127984654795469359E-16,
+    1.71539128555513303061E-15,
+    -1.16853328779934516808E-14,
+    7.67618549860493561688E-14,
+    -4.85644678311192946090E-13,
+    2.95505266312963983461E-12,
+    -1.72682629144155570723E-11,
+    9.67580903537323691224E-11,
+    -5.18979560163526290666E-10,
+    2.65982372468238665035E-9,
+    -1.30002500998624804212E-8,
+    6.04699502254191894932E-8,
+    -2.67079385394061173391E-7,
+    1.11738753912010371815E-6,
+    -4.41673835845875056359E-6,
+    1.64484480707288970893E-5,
+    -5.75419501008210370398E-5,
+    1.88502885095841655729E-4,
+    -5.76375574538582365885E-4,
+    1.63947561694133579842E-3,
+    -4.32430999505057594430E-3,
+    1.05464603945949983183E-2,
+    -2.37374148058994688156E-2,
+    4.93052842396707084878E-2,
+    -9.49010970480476444210E-2,
+    1.71620901522208775349E-1,
+    -3.04682672343198398683E-1,
+    6.76795274409476084995E-1,
+])
+
+_i0B = np.array([
+    -7.23318048787475395456E-18,
+    -4.83050448594418207126E-18,
+    4.46562142029675999901E-17,
+    3.46122286769746109310E-17,
+    -2.82762398051658348494E-16,
+    -3.42548561967721913462E-16,
+    1.77256013305652638360E-15,
+    3.81168066935262242075E-15,
+    -9.55484669882830764870E-15,
+    -4.15056934728722208663E-14,
+    1.54008621752140982691E-14,
+    3.85277838274214270114E-13,
+    7.18012445138366623367E-13,
+    -1.79417853150680611778E-12,
+    -1.32158118404477131188E-11,
+    -3.14991652796324136454E-11,
+    1.18891471078464383424E-11,
+    4.94060238822496958910E-10,
+    3.39623202570838634515E-9,
+    2.26666899049817806459E-8,
+    2.04891858946906374183E-7,
+    2.89137052083475648297E-6,
+    6.88975834691682398426E-5,
+    3.36911647825569408990E-3,
+    8.04490411014108831608E-1,
+])
+
+
+@register_jitable
+def _chbevl(x, vals):
+    b0 = vals[0]
+    b1 = 0.0
+
+    for i in range(1, len(vals)):
+        b2 = b1
+        b1 = b0
+        b0 = x * b1 - b2 + vals[i]
+
+    return 0.5 * (b0 - b2)
+
+
+@register_jitable
+def _i0(x):
+    if x < 0:
+        x = -x
+    if x <= 8.0:
+        y = (0.5 * x) - 2.0
+        return np.exp(x) * _chbevl(y, _i0A)
+
+    return np.exp(x) * _chbevl(32.0 / x - 2.0, _i0B) / np.sqrt(x)
+
+
+@register_jitable
+def _i0n(n, alpha, beta):
+    y = np.empty_like(n, dtype=np.float_)
+    t = _i0(np.float_(beta))
+    for i in range(len(y)):
+        y[i] = _i0(beta * np.sqrt(1 - ((n[i] - alpha) / alpha)**2.0)) / t
+
+    return y
+
+
+@overload(np.kaiser)
+def np_kaiser(M, beta):
+    if not isinstance(M, types.Integer):
+        raise TypingError('M must be an integer')
+
+    if not isinstance(beta, (types.Integer, types.Float)):
+        raise TypingError('beta must be an integer or float')
+
+    def np_kaiser_impl(M, beta):
+        if M < 1:
+            return np.array((), dtype=np.float_)
+        if M == 1:
+            return np.ones(1, dtype=np.float_)
+
+        n = np.arange(0, M)
+        alpha = (M - 1) / 2.0
+
+        return _i0n(n, alpha, beta)
+
+    return np_kaiser_impl
+
+
+@register_jitable
+def _cross_operation(a, b, out):
+
+    def _cross_preprocessing(x):
+        x0 = x[..., 0]
+        x1 = x[..., 1]
+        if x.shape[-1] == 3:
+            x2 = x[..., 2]
+        else:
+            x2 = np.multiply(x.dtype.type(0), x0)
+        return x0, x1, x2
+
+    a0, a1, a2 = _cross_preprocessing(a)
+    b0, b1, b2 = _cross_preprocessing(b)
+
+    cp0 = np.multiply(a1, b2) - np.multiply(a2, b1)
+    cp1 = np.multiply(a2, b0) - np.multiply(a0, b2)
+    cp2 = np.multiply(a0, b1) - np.multiply(a1, b0)
+
+    out[..., 0] = cp0
+    out[..., 1] = cp1
+    out[..., 2] = cp2
+
+
+@generated_jit
+def _cross_impl(a, b):
+    dtype = np.promote_types(as_dtype(a.dtype), as_dtype(b.dtype))
+    if a.ndim == 1 and b.ndim == 1:
+        def impl(a, b):
+            cp = np.empty((3,), dtype)
+            _cross_operation(a, b, cp)
+            return cp
+    else:
+        def impl(a, b):
+            shape = np.add(a[..., 0], b[..., 0]).shape
+            cp = np.empty(shape + (3,), dtype)
+            _cross_operation(a, b, cp)
+            return cp
+    return impl
+
+
+@overload(np.cross)
+def np_cross(a, b):
+    if not type_can_asarray(a) or not type_can_asarray(b):
+        raise TypingError("Inputs must be array-like.")
+
+    def impl(a, b):
+        a_ = np.asarray(a)
+        b_ = np.asarray(b)
+        if a_.shape[-1] not in (2, 3) or b_.shape[-1] not in (2, 3):
+            raise ValueError((
+                "Incompatible dimensions for cross product\n"
+                "(dimension must be 2 or 3)"
+            ))
+
+        if a_.shape[-1] == 3 or b_.shape[-1] == 3:
+            return _cross_impl(a_, b_)
+        else:
+            raise ValueError((
+                "Dimensions for both inputs is 2.\n"
+                "Please replace your numpy.cross(a, b) call with "
+                "a call to `cross2d(a, b)` from `numba.np.extensions`."
+            ))
+    return impl
+
+
+@register_jitable
+def _cross2d_operation(a, b):
+
+    def _cross_preprocessing(x):
+        x0 = x[..., 0]
+        x1 = x[..., 1]
+        return x0, x1
+
+    a0, a1 = _cross_preprocessing(a)
+    b0, b1 = _cross_preprocessing(b)
+
+    cp = np.multiply(a0, b1) - np.multiply(a1, b0)
+    # If ndim of a and b is 1, cp is a scalar.
+    # In this case np.cross returns a 0-D array, containing the scalar.
+    # np.asarray is used to reconcile this case, without introducing
+    # overhead in the case where cp is an actual N-D array.
+    # (recall that np.asarray does not copy existing arrays)
+    return np.asarray(cp)
+
+
+@generated_jit
+def cross2d(a, b):
+    if not type_can_asarray(a) or not type_can_asarray(b):
+        raise TypingError("Inputs must be array-like.")
+
+    def impl(a, b):
+        a_ = np.asarray(a)
+        b_ = np.asarray(b)
+        if a_.shape[-1] != 2 or b_.shape[-1] != 2:
+            raise ValueError((
+                "Incompatible dimensions for 2D cross product\n"
+                "(dimension must be 2 for both inputs)"
+            ))
+        return _cross2d_operation(a_, b_)
+
+    return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/arrayobj.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/arrayobj.py
new file mode 100644
index 000000000..278d9a7f7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/arrayobj.py
@@ -0,0 +1,6075 @@
+"""
+Implementation of operations on Array objects and objects supporting
+the buffer protocol.
+"""
+
+import functools
+import math
+import operator
+
+from llvmlite import ir
+from llvmlite.ir import Constant
+
+import numpy as np
+
+from numba import pndindex, literal_unroll
+from numba.core import types, utils, typing, errors, cgutils, extending
+from numba.np.numpy_support import (as_dtype, carray, farray, is_contiguous,
+                                    is_fortran, check_is_integer)
+from numba.np.numpy_support import type_can_asarray, is_nonelike, numpy_version
+from numba.core.imputils import (lower_builtin, lower_getattr,
+                                 lower_getattr_generic,
+                                 lower_setattr_generic,
+                                 lower_cast, lower_constant,
+                                 iternext_impl, impl_ret_borrowed,
+                                 impl_ret_new_ref, impl_ret_untracked,
+                                 RefType)
+from numba.core.typing import signature
+from numba.core.extending import (register_jitable, overload, overload_method,
+                                  intrinsic)
+from numba.misc import quicksort, mergesort
+from numba.cpython import slicing
+from numba.cpython.unsafe.tuple import tuple_setitem, build_full_slice_tuple
+from numba.core.overload_glue import glue_lowering
+from numba.core.extending import overload_classmethod
+from numba.core.typing.npydecl import (parse_dtype as ty_parse_dtype,
+                                       parse_shape as ty_parse_shape)
+
+
+def set_range_metadata(builder, load, lower_bound, upper_bound):
+    """
+    Set the "range" metadata on a load instruction.
+    Note the interval is in the form [lower_bound, upper_bound).
+    """
+    range_operands = [Constant(load.type, lower_bound),
+                      Constant(load.type, upper_bound)]
+    md = builder.module.add_metadata(range_operands)
+    load.set_metadata("range", md)
+
+
+def mark_positive(builder, load):
+    """
+    Mark the result of a load instruction as positive (or zero).
+    """
+    upper_bound = (1 << (load.type.width - 1)) - 1
+    set_range_metadata(builder, load, 0, upper_bound)
+
+
+def make_array(array_type):
+    """
+    Return the Structure representation of the given *array_type*
+    (an instance of types.ArrayCompatible).
+
+    Note this does not call __array_wrap__ in case a new array structure
+    is being created (rather than populated).
+    """
+    real_array_type = array_type.as_array
+    base = cgutils.create_struct_proxy(real_array_type)
+    ndim = real_array_type.ndim
+
+    class ArrayStruct(base):
+
+        def _make_refs(self, ref):
+            sig = signature(real_array_type, array_type)
+            try:
+                array_impl = self._context.get_function('__array__', sig)
+            except NotImplementedError:
+                return super(ArrayStruct, self)._make_refs(ref)
+
+            # Return a wrapped structure and its unwrapped reference
+            datamodel = self._context.data_model_manager[array_type]
+            be_type = self._get_be_type(datamodel)
+            if ref is None:
+                outer_ref = cgutils.alloca_once(self._builder, be_type,
+                                                zfill=True)
+            else:
+                outer_ref = ref
+            # NOTE: __array__ is called with a pointer and expects a pointer
+            # in return!
+            ref = array_impl(self._builder, (outer_ref,))
+            return outer_ref, ref
+
+        @property
+        def shape(self):
+            """
+            Override .shape to inform LLVM that its elements are all positive.
+            """
+            builder = self._builder
+            if ndim == 0:
+                return base.__getattr__(self, "shape")
+
+            # Unfortunately, we can't use llvm.assume as its presence can
+            # seriously pessimize performance,
+            # *and* the range metadata currently isn't improving anything here,
+            # see https://llvm.org/bugs/show_bug.cgi?id=23848 !
+            ptr = self._get_ptr_by_name("shape")
+            dims = []
+            for i in range(ndim):
+                dimptr = cgutils.gep_inbounds(builder, ptr, 0, i)
+                load = builder.load(dimptr)
+                dims.append(load)
+                mark_positive(builder, load)
+
+            return cgutils.pack_array(builder, dims)
+
+    return ArrayStruct
+
+
+def get_itemsize(context, array_type):
+    """
+    Return the item size for the given array or buffer type.
+    """
+    llty = context.get_data_type(array_type.dtype)
+    return context.get_abi_sizeof(llty)
+
+
+def load_item(context, builder, arrayty, ptr):
+    """
+    Load the item at the given array pointer.
+    """
+    align = None if arrayty.aligned else 1
+    return context.unpack_value(builder, arrayty.dtype, ptr,
+                                align=align)
+
+
+def store_item(context, builder, arrayty, val, ptr):
+    """
+    Store the item at the given array pointer.
+    """
+    align = None if arrayty.aligned else 1
+    return context.pack_value(builder, arrayty.dtype, val, ptr, align=align)
+
+
+def fix_integer_index(context, builder, idxty, idx, size):
+    """
+    Fix the integer index' type and value for the given dimension size.
+    """
+    if idxty.signed:
+        ind = context.cast(builder, idx, idxty, types.intp)
+        ind = slicing.fix_index(builder, ind, size)
+    else:
+        ind = context.cast(builder, idx, idxty, types.uintp)
+    return ind
+
+
+def normalize_index(context, builder, idxty, idx):
+    """
+    Normalize the index type and value.  0-d arrays are converted to scalars.
+    """
+    if isinstance(idxty, types.Array) and idxty.ndim == 0:
+        assert isinstance(idxty.dtype, types.Integer)
+        idxary = make_array(idxty)(context, builder, idx)
+        idxval = load_item(context, builder, idxty, idxary.data)
+        return idxty.dtype, idxval
+    else:
+        return idxty, idx
+
+
+def normalize_indices(context, builder, index_types, indices):
+    """
+    Same as normalize_index(), but operating on sequences of
+    index types and values.
+    """
+    if len(indices):
+        index_types, indices = zip(*[normalize_index(context, builder, idxty,
+                                                     idx)
+                                     for idxty, idx in zip(index_types, indices)
+                                     ])
+    return index_types, indices
+
+
+def populate_array(array, data, shape, strides, itemsize, meminfo,
+                   parent=None):
+    """
+    Helper function for populating array structures.
+    This avoids forgetting to set fields.
+
+    *shape* and *strides* can be Python tuples or LLVM arrays.
+    """
+    context = array._context
+    builder = array._builder
+    datamodel = array._datamodel
+    # doesn't matter what this array type instance is, it's just to get the
+    # fields for the datamodel of the standard array type in this context
+    standard_array = types.Array(types.float64, 1, 'C')
+    standard_array_type_datamodel = context.data_model_manager[standard_array]
+    required_fields = set(standard_array_type_datamodel._fields)
+    datamodel_fields = set(datamodel._fields)
+    # Make sure that the presented array object has a data model that is close
+    # enough to an array for this function to proceed.
+    if (required_fields & datamodel_fields) != required_fields:
+        missing = required_fields - datamodel_fields
+        msg = (f"The datamodel for type {array._fe_type} is missing "
+               f"field{'s' if len(missing) > 1 else ''} {missing}.")
+        raise ValueError(msg)
+
+    if meminfo is None:
+        meminfo = Constant(context.get_value_type(
+            datamodel.get_type('meminfo')), None)
+
+    intp_t = context.get_value_type(types.intp)
+    if isinstance(shape, (tuple, list)):
+        shape = cgutils.pack_array(builder, shape, intp_t)
+    if isinstance(strides, (tuple, list)):
+        strides = cgutils.pack_array(builder, strides, intp_t)
+    if isinstance(itemsize, int):
+        itemsize = intp_t(itemsize)
+
+    attrs = dict(shape=shape,
+                 strides=strides,
+                 data=data,
+                 itemsize=itemsize,
+                 meminfo=meminfo,)
+
+    # Set `parent` attribute
+    if parent is None:
+        attrs['parent'] = Constant(context.get_value_type(
+            datamodel.get_type('parent')), None)
+    else:
+        attrs['parent'] = parent
+    # Calc num of items from shape
+    nitems = context.get_constant(types.intp, 1)
+    unpacked_shape = cgutils.unpack_tuple(builder, shape, shape.type.count)
+    # (note empty shape => 0d array therefore nitems = 1)
+    for axlen in unpacked_shape:
+        nitems = builder.mul(nitems, axlen, flags=['nsw'])
+    attrs['nitems'] = nitems
+
+    # Make sure that we have all the fields
+    got_fields = set(attrs.keys())
+    if got_fields != required_fields:
+        raise ValueError("missing {0}".format(required_fields - got_fields))
+
+    # Set field value
+    for k, v in attrs.items():
+        setattr(array, k, v)
+
+    return array
+
+
+def update_array_info(aryty, array):
+    """
+    Update some auxiliary information in *array* after some of its fields
+    were changed.  `itemsize` and `nitems` are updated.
+    """
+    context = array._context
+    builder = array._builder
+
+    # Calc num of items from shape
+    nitems = context.get_constant(types.intp, 1)
+    unpacked_shape = cgutils.unpack_tuple(builder, array.shape, aryty.ndim)
+    for axlen in unpacked_shape:
+        nitems = builder.mul(nitems, axlen, flags=['nsw'])
+    array.nitems = nitems
+
+    array.itemsize = context.get_constant(types.intp,
+                                          get_itemsize(context, aryty))
+
+
+@lower_builtin('getiter', types.Buffer)
+def getiter_array(context, builder, sig, args):
+    [arrayty] = sig.args
+    [array] = args
+
+    iterobj = context.make_helper(builder, sig.return_type)
+
+    zero = context.get_constant(types.intp, 0)
+    indexptr = cgutils.alloca_once_value(builder, zero)
+
+    iterobj.index = indexptr
+    iterobj.array = array
+
+    # Incref array
+    if context.enable_nrt:
+        context.nrt.incref(builder, arrayty, array)
+
+    res = iterobj._getvalue()
+
+    # Note: a decref on the iterator will dereference all internal MemInfo*
+    out = impl_ret_new_ref(context, builder, sig.return_type, res)
+    return out
+
+
+def _getitem_array_single_int(context, builder, return_type, aryty, ary, idx):
+    """ Evaluate `ary[idx]`, where idx is a single int. """
+    # optimized form of _getitem_array_generic
+    shapes = cgutils.unpack_tuple(builder, ary.shape, count=aryty.ndim)
+    strides = cgutils.unpack_tuple(builder, ary.strides, count=aryty.ndim)
+    offset = builder.mul(strides[0], idx)
+    dataptr = cgutils.pointer_add(builder, ary.data, offset)
+    view_shapes = shapes[1:]
+    view_strides = strides[1:]
+
+    if isinstance(return_type, types.Buffer):
+        # Build array view
+        retary = make_view(context, builder, aryty, ary, return_type,
+                           dataptr, view_shapes, view_strides)
+        return retary._getvalue()
+    else:
+        # Load scalar from 0-d result
+        assert not view_shapes
+        return load_item(context, builder, aryty, dataptr)
+
+
+@lower_builtin('iternext', types.ArrayIterator)
+@iternext_impl(RefType.BORROWED)
+def iternext_array(context, builder, sig, args, result):
+    [iterty] = sig.args
+    [iter] = args
+    arrayty = iterty.array_type
+
+    iterobj = context.make_helper(builder, iterty, value=iter)
+    ary = make_array(arrayty)(context, builder, value=iterobj.array)
+
+    nitems, = cgutils.unpack_tuple(builder, ary.shape, count=1)
+
+    index = builder.load(iterobj.index)
+    is_valid = builder.icmp_signed('<', index, nitems)
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        value = _getitem_array_single_int(
+            context, builder, iterty.yield_type, arrayty, ary, index
+        )
+        result.yield_(value)
+        nindex = cgutils.increment_index(builder, index)
+        builder.store(nindex, iterobj.index)
+
+
+# ------------------------------------------------------------------------------
+# Basic indexing (with integers and slices only)
+
+def basic_indexing(context, builder, aryty, ary, index_types, indices,
+                   boundscheck=None):
+    """
+    Perform basic indexing on the given array.
+    A (data pointer, shapes, strides) tuple is returned describing
+    the corresponding view.
+    """
+    zero = context.get_constant(types.intp, 0)
+
+    shapes = cgutils.unpack_tuple(builder, ary.shape, aryty.ndim)
+    strides = cgutils.unpack_tuple(builder, ary.strides, aryty.ndim)
+
+    output_indices = []
+    output_shapes = []
+    output_strides = []
+
+    ax = 0
+    for indexval, idxty in zip(indices, index_types):
+        if idxty is types.ellipsis:
+            # Fill up missing dimensions at the middle
+            n_missing = aryty.ndim - len(indices) + 1
+            for i in range(n_missing):
+                output_indices.append(zero)
+                output_shapes.append(shapes[ax])
+                output_strides.append(strides[ax])
+                ax += 1
+            continue
+        # Regular index value
+        if isinstance(idxty, types.SliceType):
+            slice = context.make_helper(builder, idxty, value=indexval)
+            slicing.guard_invalid_slice(context, builder, idxty, slice)
+            slicing.fix_slice(builder, slice, shapes[ax])
+            output_indices.append(slice.start)
+            sh = slicing.get_slice_length(builder, slice)
+            st = slicing.fix_stride(builder, slice, strides[ax])
+            output_shapes.append(sh)
+            output_strides.append(st)
+        elif isinstance(idxty, types.Integer):
+            ind = fix_integer_index(context, builder, idxty, indexval,
+                                    shapes[ax])
+            if boundscheck:
+                cgutils.do_boundscheck(context, builder, ind, shapes[ax], ax)
+            output_indices.append(ind)
+        else:
+            raise NotImplementedError("unexpected index type: %s" % (idxty,))
+        ax += 1
+
+    # Fill up missing dimensions at the end
+    assert ax <= aryty.ndim
+    while ax < aryty.ndim:
+        output_shapes.append(shapes[ax])
+        output_strides.append(strides[ax])
+        ax += 1
+
+    # No need to check wraparound, as negative indices were already
+    # fixed in the loop above.
+    dataptr = cgutils.get_item_pointer(context, builder, aryty, ary,
+                                       output_indices,
+                                       wraparound=False, boundscheck=False)
+    return (dataptr, output_shapes, output_strides)
+
+
+def make_view(context, builder, aryty, ary, return_type,
+              data, shapes, strides):
+    """
+    Build a view over the given array with the given parameters.
+    """
+    retary = make_array(return_type)(context, builder)
+    populate_array(retary,
+                   data=data,
+                   shape=shapes,
+                   strides=strides,
+                   itemsize=ary.itemsize,
+                   meminfo=ary.meminfo,
+                   parent=ary.parent)
+    return retary
+
+
+def _getitem_array_generic(context, builder, return_type, aryty, ary,
+                           index_types, indices):
+    """
+    Return the result of indexing *ary* with the given *indices*,
+    returning either a scalar or a view.
+    """
+    dataptr, view_shapes, view_strides = \
+        basic_indexing(context, builder, aryty, ary, index_types, indices,
+                       boundscheck=context.enable_boundscheck)
+
+    if isinstance(return_type, types.Buffer):
+        # Build array view
+        retary = make_view(context, builder, aryty, ary, return_type,
+                           dataptr, view_shapes, view_strides)
+        return retary._getvalue()
+    else:
+        # Load scalar from 0-d result
+        assert not view_shapes
+        return load_item(context, builder, aryty, dataptr)
+
+
+@lower_builtin(operator.getitem, types.Buffer, types.Integer)
+@lower_builtin(operator.getitem, types.Buffer, types.SliceType)
+def getitem_arraynd_intp(context, builder, sig, args):
+    """
+    Basic indexing with an integer or a slice.
+    """
+    aryty, idxty = sig.args
+    ary, idx = args
+
+    assert aryty.ndim >= 1
+    ary = make_array(aryty)(context, builder, ary)
+
+    res = _getitem_array_generic(context, builder, sig.return_type,
+                                 aryty, ary, (idxty,), (idx,))
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.getitem, types.Buffer, types.BaseTuple)
+def getitem_array_tuple(context, builder, sig, args):
+    """
+    Basic or advanced indexing with a tuple.
+    """
+    aryty, tupty = sig.args
+    ary, tup = args
+    ary = make_array(aryty)(context, builder, ary)
+
+    index_types = tupty.types
+    indices = cgutils.unpack_tuple(builder, tup, count=len(tupty))
+
+    index_types, indices = normalize_indices(context, builder,
+                                             index_types, indices)
+
+    if any(isinstance(ty, types.Array) for ty in index_types):
+        # Advanced indexing
+        return fancy_getitem(context, builder, sig, args,
+                             aryty, ary, index_types, indices)
+
+    res = _getitem_array_generic(context, builder, sig.return_type,
+                                 aryty, ary, index_types, indices)
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.setitem, types.Buffer, types.Any, types.Any)
+def setitem_array(context, builder, sig, args):
+    """
+    array[a] = scalar_or_array
+    array[a,..,b] = scalar_or_array
+    """
+    aryty, idxty, valty = sig.args
+    ary, idx, val = args
+
+    if isinstance(idxty, types.BaseTuple):
+        index_types = idxty.types
+        indices = cgutils.unpack_tuple(builder, idx, count=len(idxty))
+    else:
+        index_types = (idxty,)
+        indices = (idx,)
+
+    ary = make_array(aryty)(context, builder, ary)
+
+    # First try basic indexing to see if a single array location is denoted.
+    index_types, indices = normalize_indices(context, builder,
+                                             index_types, indices)
+    try:
+        dataptr, shapes, strides = \
+            basic_indexing(context, builder, aryty, ary, index_types, indices,
+                           boundscheck=context.enable_boundscheck)
+    except NotImplementedError:
+        use_fancy_indexing = True
+    else:
+        use_fancy_indexing = bool(shapes)
+
+    if use_fancy_indexing:
+        # Index describes a non-trivial view => use generic slice assignment
+        # (NOTE: this also handles scalar broadcasting)
+        return fancy_setslice(context, builder, sig, args,
+                              index_types, indices)
+
+    # Store source value the given location
+    val = context.cast(builder, val, valty, aryty.dtype)
+    store_item(context, builder, aryty, val, dataptr)
+
+
+@lower_builtin(len, types.Buffer)
+def array_len(context, builder, sig, args):
+    (aryty,) = sig.args
+    (ary,) = args
+    arystty = make_array(aryty)
+    ary = arystty(context, builder, ary)
+    shapeary = ary.shape
+    res = builder.extract_value(shapeary, 0)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin("array.item", types.Array)
+def array_item(context, builder, sig, args):
+    aryty, = sig.args
+    ary, = args
+    ary = make_array(aryty)(context, builder, ary)
+
+    nitems = ary.nitems
+    with builder.if_then(builder.icmp_signed('!=', nitems, nitems.type(1)),
+                         likely=False):
+        msg = "item(): can only convert an array of size 1 to a Python scalar"
+        context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+    return load_item(context, builder, aryty, ary.data)
+
+
+@lower_builtin("array.itemset", types.Array, types.Any)
+def array_itemset(context, builder, sig, args):
+    aryty, valty = sig.args
+    ary, val = args
+    assert valty == aryty.dtype
+    ary = make_array(aryty)(context, builder, ary)
+
+    nitems = ary.nitems
+    with builder.if_then(builder.icmp_signed('!=', nitems, nitems.type(1)),
+                         likely=False):
+        msg = "itemset(): can only write to an array of size 1"
+        context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+    store_item(context, builder, aryty, val, ary.data)
+    return context.get_dummy_value()
+
+
+# ------------------------------------------------------------------------------
+# Advanced / fancy indexing
+
+
+class Indexer(object):
+    """
+    Generic indexer interface, for generating indices over a fancy indexed
+    array on a single dimension.
+    """
+
+    def prepare(self):
+        """
+        Prepare the indexer by initializing any required variables, basic
+        blocks...
+        """
+        raise NotImplementedError
+
+    def get_size(self):
+        """
+        Return this dimension's size as an integer.
+        """
+        raise NotImplementedError
+
+    def get_shape(self):
+        """
+        Return this dimension's shape as a tuple.
+        """
+        raise NotImplementedError
+
+    def get_index_bounds(self):
+        """
+        Return a half-open [lower, upper) range of indices this dimension
+        is guaranteed not to step out of.
+        """
+        raise NotImplementedError
+
+    def loop_head(self):
+        """
+        Start indexation loop.  Return a (index, count) tuple.
+        *index* is an integer LLVM value representing the index over this
+        dimension.
+        *count* is either an integer LLVM value representing the current
+        iteration count, or None if this dimension should be omitted from
+        the indexation result.
+        """
+        raise NotImplementedError
+
+    def loop_tail(self):
+        """
+        Finish indexation loop.
+        """
+        raise NotImplementedError
+
+
+class EntireIndexer(Indexer):
+    """
+    Compute indices along an entire array dimension.
+    """
+
+    def __init__(self, context, builder, aryty, ary, dim):
+        self.context = context
+        self.builder = builder
+        self.aryty = aryty
+        self.ary = ary
+        self.dim = dim
+        self.ll_intp = self.context.get_value_type(types.intp)
+
+    def prepare(self):
+        builder = self.builder
+        self.size = builder.extract_value(self.ary.shape, self.dim)
+        self.index = cgutils.alloca_once(builder, self.ll_intp)
+        self.bb_start = builder.append_basic_block()
+        self.bb_end = builder.append_basic_block()
+
+    def get_size(self):
+        return self.size
+
+    def get_shape(self):
+        return (self.size,)
+
+    def get_index_bounds(self):
+        # [0, size)
+        return (self.ll_intp(0), self.size)
+
+    def loop_head(self):
+        builder = self.builder
+        # Initialize loop variable
+        self.builder.store(Constant(self.ll_intp, 0), self.index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_start)
+        cur_index = builder.load(self.index)
+        with builder.if_then(builder.icmp_signed('>=', cur_index, self.size),
+                             likely=False):
+            builder.branch(self.bb_end)
+        return cur_index, cur_index
+
+    def loop_tail(self):
+        builder = self.builder
+        next_index = cgutils.increment_index(builder, builder.load(self.index))
+        builder.store(next_index, self.index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_end)
+
+
+class IntegerIndexer(Indexer):
+    """
+    Compute indices from a single integer.
+    """
+
+    def __init__(self, context, builder, idx):
+        self.context = context
+        self.builder = builder
+        self.idx = idx
+        self.ll_intp = self.context.get_value_type(types.intp)
+
+    def prepare(self):
+        pass
+
+    def get_size(self):
+        return Constant(self.ll_intp, 1)
+
+    def get_shape(self):
+        return ()
+
+    def get_index_bounds(self):
+        # [idx, idx+1)
+        return (self.idx, self.builder.add(self.idx, self.get_size()))
+
+    def loop_head(self):
+        return self.idx, None
+
+    def loop_tail(self):
+        pass
+
+
+class IntegerArrayIndexer(Indexer):
+    """
+    Compute indices from an array of integer indices.
+    """
+
+    def __init__(self, context, builder, idxty, idxary, size):
+        self.context = context
+        self.builder = builder
+        self.idxty = idxty
+        self.idxary = idxary
+        self.size = size
+        assert idxty.ndim == 1
+        self.ll_intp = self.context.get_value_type(types.intp)
+
+    def prepare(self):
+        builder = self.builder
+        self.idx_size = cgutils.unpack_tuple(builder, self.idxary.shape)[0]
+        self.idx_index = cgutils.alloca_once(builder, self.ll_intp)
+        self.bb_start = builder.append_basic_block()
+        self.bb_end = builder.append_basic_block()
+
+    def get_size(self):
+        return self.idx_size
+
+    def get_shape(self):
+        return (self.idx_size,)
+
+    def get_index_bounds(self):
+        # Pessimal heuristic, as we don't want to scan for the min and max
+        return (self.ll_intp(0), self.size)
+
+    def loop_head(self):
+        builder = self.builder
+        # Initialize loop variable
+        self.builder.store(Constant(self.ll_intp, 0), self.idx_index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_start)
+        cur_index = builder.load(self.idx_index)
+        with builder.if_then(
+            builder.icmp_signed('>=', cur_index, self.idx_size),
+            likely=False
+        ):
+            builder.branch(self.bb_end)
+        # Load the actual index from the array of indices
+        index = _getitem_array_single_int(
+            self.context, builder, self.idxty.dtype, self.idxty, self.idxary,
+            cur_index
+        )
+        index = fix_integer_index(self.context, builder,
+                                  self.idxty.dtype, index, self.size)
+        return index, cur_index
+
+    def loop_tail(self):
+        builder = self.builder
+        next_index = cgutils.increment_index(builder,
+                                             builder.load(self.idx_index))
+        builder.store(next_index, self.idx_index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_end)
+
+
+class BooleanArrayIndexer(Indexer):
+    """
+    Compute indices from an array of boolean predicates.
+    """
+
+    def __init__(self, context, builder, idxty, idxary):
+        self.context = context
+        self.builder = builder
+        self.idxty = idxty
+        self.idxary = idxary
+        assert idxty.ndim == 1
+        self.ll_intp = self.context.get_value_type(types.intp)
+        self.zero = Constant(self.ll_intp, 0)
+
+    def prepare(self):
+        builder = self.builder
+        self.size = cgutils.unpack_tuple(builder, self.idxary.shape)[0]
+        self.idx_index = cgutils.alloca_once(builder, self.ll_intp)
+        self.count = cgutils.alloca_once(builder, self.ll_intp)
+        self.bb_start = builder.append_basic_block()
+        self.bb_tail = builder.append_basic_block()
+        self.bb_end = builder.append_basic_block()
+
+    def get_size(self):
+        builder = self.builder
+        count = cgutils.alloca_once_value(builder, self.zero)
+        # Sum all true values
+        with cgutils.for_range(builder, self.size) as loop:
+            c = builder.load(count)
+            pred = _getitem_array_single_int(
+                self.context, builder, self.idxty.dtype,
+                self.idxty, self.idxary, loop.index
+            )
+            c = builder.add(c, builder.zext(pred, c.type))
+            builder.store(c, count)
+
+        return builder.load(count)
+
+    def get_shape(self):
+        return (self.get_size(),)
+
+    def get_index_bounds(self):
+        # Pessimal heuristic, as we don't want to scan for the
+        # first and last true items
+        return (self.ll_intp(0), self.size)
+
+    def loop_head(self):
+        builder = self.builder
+        # Initialize loop variable
+        self.builder.store(self.zero, self.idx_index)
+        self.builder.store(self.zero, self.count)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_start)
+        cur_index = builder.load(self.idx_index)
+        cur_count = builder.load(self.count)
+        with builder.if_then(builder.icmp_signed('>=', cur_index, self.size),
+                             likely=False):
+            builder.branch(self.bb_end)
+        # Load the predicate and branch if false
+        pred = _getitem_array_single_int(
+            self.context, builder, self.idxty.dtype, self.idxty, self.idxary,
+            cur_index
+        )
+        with builder.if_then(builder.not_(pred)):
+            builder.branch(self.bb_tail)
+        # Increment the count for next iteration
+        next_count = cgutils.increment_index(builder, cur_count)
+        builder.store(next_count, self.count)
+        return cur_index, cur_count
+
+    def loop_tail(self):
+        builder = self.builder
+        builder.branch(self.bb_tail)
+        builder.position_at_end(self.bb_tail)
+        next_index = cgutils.increment_index(builder,
+                                             builder.load(self.idx_index))
+        builder.store(next_index, self.idx_index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_end)
+
+
+class SliceIndexer(Indexer):
+    """
+    Compute indices along a slice.
+    """
+
+    def __init__(self, context, builder, aryty, ary, dim, idxty, slice):
+        self.context = context
+        self.builder = builder
+        self.aryty = aryty
+        self.ary = ary
+        self.dim = dim
+        self.idxty = idxty
+        self.slice = slice
+        self.ll_intp = self.context.get_value_type(types.intp)
+        self.zero = Constant(self.ll_intp, 0)
+
+    def prepare(self):
+        builder = self.builder
+        # Fix slice for the dimension's size
+        self.dim_size = builder.extract_value(self.ary.shape, self.dim)
+        slicing.guard_invalid_slice(self.context, builder, self.idxty,
+                                    self.slice)
+        slicing.fix_slice(builder, self.slice, self.dim_size)
+        self.is_step_negative = cgutils.is_neg_int(builder, self.slice.step)
+        # Create loop entities
+        self.index = cgutils.alloca_once(builder, self.ll_intp)
+        self.count = cgutils.alloca_once(builder, self.ll_intp)
+        self.bb_start = builder.append_basic_block()
+        self.bb_end = builder.append_basic_block()
+
+    def get_size(self):
+        return slicing.get_slice_length(self.builder, self.slice)
+
+    def get_shape(self):
+        return (self.get_size(),)
+
+    def get_index_bounds(self):
+        lower, upper = slicing.get_slice_bounds(self.builder, self.slice)
+        return lower, upper
+
+    def loop_head(self):
+        builder = self.builder
+        # Initialize loop variable
+        self.builder.store(self.slice.start, self.index)
+        self.builder.store(self.zero, self.count)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_start)
+        cur_index = builder.load(self.index)
+        cur_count = builder.load(self.count)
+        is_finished = builder.select(self.is_step_negative,
+                                     builder.icmp_signed('<=', cur_index,
+                                                         self.slice.stop),
+                                     builder.icmp_signed('>=', cur_index,
+                                                         self.slice.stop))
+        with builder.if_then(is_finished, likely=False):
+            builder.branch(self.bb_end)
+        return cur_index, cur_count
+
+    def loop_tail(self):
+        builder = self.builder
+        next_index = builder.add(builder.load(self.index), self.slice.step,
+                                 flags=['nsw'])
+        builder.store(next_index, self.index)
+        next_count = cgutils.increment_index(builder, builder.load(self.count))
+        builder.store(next_count, self.count)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_end)
+
+
+class FancyIndexer(object):
+    """
+    Perform fancy indexing on the given array.
+    """
+
+    def __init__(self, context, builder, aryty, ary, index_types, indices):
+        self.context = context
+        self.builder = builder
+        self.aryty = aryty
+        self.shapes = cgutils.unpack_tuple(builder, ary.shape, aryty.ndim)
+        self.strides = cgutils.unpack_tuple(builder, ary.strides, aryty.ndim)
+        self.ll_intp = self.context.get_value_type(types.intp)
+
+        indexers = []
+
+        ax = 0
+        for indexval, idxty in zip(indices, index_types):
+            if idxty is types.ellipsis:
+                # Fill up missing dimensions at the middle
+                n_missing = aryty.ndim - len(indices) + 1
+                for i in range(n_missing):
+                    indexer = EntireIndexer(context, builder, aryty, ary, ax)
+                    indexers.append(indexer)
+                    ax += 1
+                continue
+
+            # Regular index value
+            if isinstance(idxty, types.SliceType):
+                slice = context.make_helper(builder, idxty, indexval)
+                indexer = SliceIndexer(context, builder, aryty, ary, ax,
+                                       idxty, slice)
+                indexers.append(indexer)
+            elif isinstance(idxty, types.Integer):
+                ind = fix_integer_index(context, builder, idxty, indexval,
+                                        self.shapes[ax])
+                indexer = IntegerIndexer(context, builder, ind)
+                indexers.append(indexer)
+            elif isinstance(idxty, types.Array):
+                idxary = make_array(idxty)(context, builder, indexval)
+                if isinstance(idxty.dtype, types.Integer):
+                    indexer = IntegerArrayIndexer(context, builder,
+                                                  idxty, idxary,
+                                                  self.shapes[ax])
+                elif isinstance(idxty.dtype, types.Boolean):
+                    indexer = BooleanArrayIndexer(context, builder,
+                                                  idxty, idxary)
+                else:
+                    assert 0
+                indexers.append(indexer)
+            else:
+                raise AssertionError("unexpected index type: %s" % (idxty,))
+            ax += 1
+
+        # Fill up missing dimensions at the end
+        assert ax <= aryty.ndim, (ax, aryty.ndim)
+        while ax < aryty.ndim:
+            indexer = EntireIndexer(context, builder, aryty, ary, ax)
+            indexers.append(indexer)
+            ax += 1
+
+        assert len(indexers) == aryty.ndim, (len(indexers), aryty.ndim)
+        self.indexers = indexers
+
+    def prepare(self):
+        for i in self.indexers:
+            i.prepare()
+        # Compute the resulting shape
+        self.indexers_shape = sum([i.get_shape() for i in self.indexers], ())
+
+    def get_shape(self):
+        """
+        Get the resulting data shape as Python tuple.
+        """
+        return self.indexers_shape
+
+    def get_offset_bounds(self, strides, itemsize):
+        """
+        Get a half-open [lower, upper) range of byte offsets spanned by
+        the indexer with the given strides and itemsize.  The indexer is
+        guaranteed to not go past those bounds.
+        """
+        assert len(strides) == self.aryty.ndim
+        builder = self.builder
+        is_empty = cgutils.false_bit
+        zero = self.ll_intp(0)
+        one = self.ll_intp(1)
+        lower = zero
+        upper = zero
+        for indexer, shape, stride in zip(self.indexers, self.indexers_shape,
+                                          strides):
+            is_empty = builder.or_(is_empty,
+                                   builder.icmp_unsigned('==', shape, zero))
+            # Compute [lower, upper) indices on this dimension
+            lower_index, upper_index = indexer.get_index_bounds()
+            lower_offset = builder.mul(stride, lower_index)
+            upper_offset = builder.mul(stride, builder.sub(upper_index, one))
+            # Adjust total interval
+            is_downwards = builder.icmp_signed('<', stride, zero)
+            lower = builder.add(lower,
+                                builder.select(is_downwards,
+                                               upper_offset,
+                                               lower_offset))
+            upper = builder.add(upper,
+                                builder.select(is_downwards,
+                                               lower_offset,
+                                               upper_offset))
+        # Make interval half-open
+        upper = builder.add(upper, itemsize)
+        # Adjust for empty shape
+        lower = builder.select(is_empty, zero, lower)
+        upper = builder.select(is_empty, zero, upper)
+        return lower, upper
+
+    def begin_loops(self):
+        indices, counts = zip(*(i.loop_head() for i in self.indexers))
+        return indices, counts
+
+    def end_loops(self):
+        for i in reversed(self.indexers):
+            i.loop_tail()
+
+
+def fancy_getitem(context, builder, sig, args,
+                  aryty, ary, index_types, indices):
+
+    shapes = cgutils.unpack_tuple(builder, ary.shape)
+    strides = cgutils.unpack_tuple(builder, ary.strides)
+    data = ary.data
+
+    indexer = FancyIndexer(context, builder, aryty, ary,
+                           index_types, indices)
+    indexer.prepare()
+
+    # Construct output array
+    out_ty = sig.return_type
+    out_shapes = indexer.get_shape()
+
+    out = _empty_nd_impl(context, builder, out_ty, out_shapes)
+    out_data = out.data
+    out_idx = cgutils.alloca_once_value(builder,
+                                        context.get_constant(types.intp, 0))
+
+    # Loop on source and copy to destination
+    indices, _ = indexer.begin_loops()
+
+    # No need to check for wraparound, as the indexers all ensure
+    # a positive index is returned.
+    ptr = cgutils.get_item_pointer2(context, builder, data, shapes, strides,
+                                    aryty.layout, indices, wraparound=False,
+                                    boundscheck=context.enable_boundscheck)
+    val = load_item(context, builder, aryty, ptr)
+
+    # Since the destination is C-contiguous, no need for multi-dimensional
+    # indexing.
+    cur = builder.load(out_idx)
+    ptr = builder.gep(out_data, [cur])
+    store_item(context, builder, out_ty, val, ptr)
+    next_idx = cgutils.increment_index(builder, cur)
+    builder.store(next_idx, out_idx)
+
+    indexer.end_loops()
+
+    return impl_ret_new_ref(context, builder, out_ty, out._getvalue())
+
+
+@lower_builtin(operator.getitem, types.Buffer, types.Array)
+def fancy_getitem_array(context, builder, sig, args):
+    """
+    Advanced or basic indexing with an array.
+    """
+    aryty, idxty = sig.args
+    ary, idx = args
+    ary = make_array(aryty)(context, builder, ary)
+    if idxty.ndim == 0:
+        # 0-d array index acts as a basic integer index
+        idxty, idx = normalize_index(context, builder, idxty, idx)
+        res = _getitem_array_generic(context, builder, sig.return_type,
+                                     aryty, ary, (idxty,), (idx,))
+        return impl_ret_borrowed(context, builder, sig.return_type, res)
+    else:
+        # Advanced indexing
+        return fancy_getitem(context, builder, sig, args,
+                             aryty, ary, (idxty,), (idx,))
+
+
+def offset_bounds_from_strides(context, builder, arrty, arr, shapes, strides):
+    """
+    Compute a half-open range [lower, upper) of byte offsets from the
+    array's data pointer, that bound the in-memory extent of the array.
+
+    This mimics offset_bounds_from_strides() from
+    numpy/core/src/private/mem_overlap.c
+    """
+    itemsize = arr.itemsize
+    zero = itemsize.type(0)
+    one = zero.type(1)
+    if arrty.layout in 'CF':
+        # Array is contiguous: contents are laid out sequentially
+        # starting from arr.data and upwards
+        lower = zero
+        upper = builder.mul(itemsize, arr.nitems)
+    else:
+        # Non-contiguous array: need to examine strides
+        lower = zero
+        upper = zero
+        for i in range(arrty.ndim):
+            # Compute the largest byte offset on this dimension
+            #   max_axis_offset = strides[i] * (shapes[i] - 1)
+            # (shapes[i] == 0 is catered for by the empty array case below)
+            max_axis_offset = builder.mul(strides[i],
+                                          builder.sub(shapes[i], one))
+            is_upwards = builder.icmp_signed('>=', max_axis_offset, zero)
+            # Expand either upwards or downwards depending on stride
+            upper = builder.select(is_upwards,
+                                   builder.add(upper, max_axis_offset), upper)
+            lower = builder.select(is_upwards,
+                                   lower, builder.add(lower, max_axis_offset))
+        # Return a half-open range
+        upper = builder.add(upper, itemsize)
+        # Adjust for empty arrays
+        is_empty = builder.icmp_signed('==', arr.nitems, zero)
+        upper = builder.select(is_empty, zero, upper)
+        lower = builder.select(is_empty, zero, lower)
+
+    return lower, upper
+
+
+def compute_memory_extents(context, builder, lower, upper, data):
+    """
+    Given [lower, upper) byte offsets and a base data pointer,
+    compute the memory pointer bounds as pointer-sized integers.
+    """
+    data_ptr_as_int = builder.ptrtoint(data, lower.type)
+    start = builder.add(data_ptr_as_int, lower)
+    end = builder.add(data_ptr_as_int, upper)
+    return start, end
+
+
+def get_array_memory_extents(context, builder, arrty, arr, shapes, strides,
+                             data):
+    """
+    Compute a half-open range [start, end) of pointer-sized integers
+    which fully contain the array data.
+    """
+    lower, upper = offset_bounds_from_strides(context, builder, arrty, arr,
+                                              shapes, strides)
+    return compute_memory_extents(context, builder, lower, upper, data)
+
+
+def extents_may_overlap(context, builder, a_start, a_end, b_start, b_end):
+    """
+    Whether two memory extents [a_start, a_end) and [b_start, b_end)
+    may overlap.
+    """
+    # Comparisons are unsigned, since we are really comparing pointers
+    may_overlap = builder.and_(
+        builder.icmp_unsigned('<', a_start, b_end),
+        builder.icmp_unsigned('<', b_start, a_end),
+    )
+    return may_overlap
+
+
+def maybe_copy_source(context, builder, use_copy,
+                      srcty, src, src_shapes, src_strides, src_data):
+    ptrty = src_data.type
+
+    copy_layout = 'C'
+    copy_data = cgutils.alloca_once_value(builder, src_data)
+    copy_shapes = src_shapes
+    copy_strides = None  # unneeded for contiguous arrays
+
+    with builder.if_then(use_copy, likely=False):
+        # Allocate temporary scratchpad
+        # XXX: should we use a stack-allocated array for very small
+        # data sizes?
+        allocsize = builder.mul(src.itemsize, src.nitems)
+        data = context.nrt.allocate(builder, allocsize)
+        voidptrty = data.type
+        data = builder.bitcast(data, ptrty)
+        builder.store(data, copy_data)
+
+        # Copy source data into scratchpad
+        intp_t = context.get_value_type(types.intp)
+
+        with cgutils.loop_nest(builder, src_shapes, intp_t) as indices:
+            src_ptr = cgutils.get_item_pointer2(context, builder, src_data,
+                                                src_shapes, src_strides,
+                                                srcty.layout, indices)
+            dest_ptr = cgutils.get_item_pointer2(context, builder, data,
+                                                 copy_shapes, copy_strides,
+                                                 copy_layout, indices)
+            builder.store(builder.load(src_ptr), dest_ptr)
+
+    def src_getitem(source_indices):
+        assert len(source_indices) == srcty.ndim
+        src_ptr = cgutils.alloca_once(builder, ptrty)
+        with builder.if_else(use_copy, likely=False) as (if_copy, otherwise):
+            with if_copy:
+                builder.store(
+                    cgutils.get_item_pointer2(context, builder,
+                                              builder.load(copy_data),
+                                              copy_shapes, copy_strides,
+                                              copy_layout, source_indices,
+                                              wraparound=False),
+                    src_ptr)
+            with otherwise:
+                builder.store(
+                    cgutils.get_item_pointer2(context, builder, src_data,
+                                              src_shapes, src_strides,
+                                              srcty.layout, source_indices,
+                                              wraparound=False),
+                    src_ptr)
+        return load_item(context, builder, srcty, builder.load(src_ptr))
+
+    def src_cleanup():
+        # Deallocate memory
+        with builder.if_then(use_copy, likely=False):
+            data = builder.load(copy_data)
+            data = builder.bitcast(data, voidptrty)
+            context.nrt.free(builder, data)
+
+    return src_getitem, src_cleanup
+
+
+def _bc_adjust_dimension(context, builder, shapes, strides, target_shape):
+    """
+    Preprocess dimension for broadcasting.
+    Returns (shapes, strides) such that the ndim match *target_shape*.
+    When expanding to higher ndim, the returning shapes and strides are
+    prepended with ones and zeros, respectively.
+    When truncating to lower ndim, the shapes are checked (in runtime).
+    All extra dimension must have size of 1.
+    """
+    zero = context.get_constant(types.uintp, 0)
+    one = context.get_constant(types.uintp, 1)
+
+    # Adjust for broadcasting to higher dimension
+    if len(target_shape) > len(shapes):
+        nd_diff = len(target_shape) - len(shapes)
+        # Fill missing shapes with one, strides with zeros
+        shapes = [one] * nd_diff + shapes
+        strides = [zero] * nd_diff + strides
+    # Adjust for broadcasting to lower dimension
+    elif len(target_shape) < len(shapes):
+        # Accepted if all extra dims has shape 1
+        nd_diff = len(shapes) - len(target_shape)
+        dim_is_one = [builder.icmp_unsigned('==', sh, one)
+                      for sh in shapes[:nd_diff]]
+        accepted = functools.reduce(builder.and_, dim_is_one,
+                                    cgutils.true_bit)
+        # Check error
+        with builder.if_then(builder.not_(accepted), likely=False):
+            msg = "cannot broadcast source array for assignment"
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+        # Truncate extra shapes, strides
+        shapes = shapes[nd_diff:]
+        strides = strides[nd_diff:]
+
+    return shapes, strides
+
+
+def _bc_adjust_shape_strides(context, builder, shapes, strides, target_shape):
+    """
+    Broadcast shapes and strides to target_shape given that their ndim already
+    matches.  For each location where the shape is 1 and does not match the
+    dim for target, it is set to the value at the target and the stride is
+    set to zero.
+    """
+    bc_shapes = []
+    bc_strides = []
+    zero = context.get_constant(types.uintp, 0)
+    one = context.get_constant(types.uintp, 1)
+    # Adjust all mismatching ones in shape
+    mismatch = [builder.icmp_signed('!=', tar, old)
+                for tar, old in zip(target_shape, shapes)]
+    src_is_one = [builder.icmp_signed('==', old, one) for old in shapes]
+    preds = [builder.and_(x, y) for x, y in zip(mismatch, src_is_one)]
+    bc_shapes = [builder.select(p, tar, old)
+                 for p, tar, old in zip(preds, target_shape, shapes)]
+    bc_strides = [builder.select(p, zero, old)
+                  for p, old in zip(preds, strides)]
+    return bc_shapes, bc_strides
+
+
+def _broadcast_to_shape(context, builder, arrtype, arr, target_shape):
+    """
+    Broadcast the given array to the target_shape.
+    Returns (array_type, array)
+    """
+    # Compute broadcasted shape and strides
+    shapes = cgutils.unpack_tuple(builder, arr.shape)
+    strides = cgutils.unpack_tuple(builder, arr.strides)
+
+    shapes, strides = _bc_adjust_dimension(context, builder, shapes, strides,
+                                           target_shape)
+    shapes, strides = _bc_adjust_shape_strides(context, builder, shapes,
+                                               strides, target_shape)
+    new_arrtype = arrtype.copy(ndim=len(target_shape), layout='A')
+    # Create new view
+    new_arr = make_array(new_arrtype)(context, builder)
+    repl = dict(shape=cgutils.pack_array(builder, shapes),
+                strides=cgutils.pack_array(builder, strides))
+    cgutils.copy_struct(new_arr, arr, repl)
+    return new_arrtype, new_arr
+
+
+@intrinsic
+def _numpy_broadcast_to(typingctx, array, shape):
+    ret = array.copy(ndim=shape.count, layout='A', readonly=True)
+    sig = ret(array, shape)
+
+    def codegen(context, builder, sig, args):
+        src, shape_ = args
+        srcty = sig.args[0]
+
+        src = make_array(srcty)(context, builder, src)
+        shape_ = cgutils.unpack_tuple(builder, shape_)
+        _, dest = _broadcast_to_shape(context, builder, srcty, src, shape_,)
+
+        # Hack to get np.broadcast_to to return a read-only array
+        setattr(dest, 'parent', Constant(
+                context.get_value_type(dest._datamodel.get_type('parent')),
+                None))
+
+        res = dest._getvalue()
+        return impl_ret_borrowed(context, builder, sig.return_type, res)
+    return sig, codegen
+
+
+@register_jitable
+def _can_broadcast(array, dest_shape):
+    src_shape = array.shape
+    src_ndim = len(src_shape)
+    dest_ndim = len(dest_shape)
+    if src_ndim > dest_ndim:
+        raise ValueError('input operand has more dimensions than allowed '
+                         'by the axis remapping')
+    for size in dest_shape:
+        if size < 0:
+            raise ValueError('all elements of broadcast shape must be '
+                             'non-negative')
+
+    # based on _broadcast_onto function in numba/np/npyimpl.py
+    src_index = 0
+    dest_index = dest_ndim - src_ndim
+    while src_index < src_ndim:
+        src_dim = src_shape[src_index]
+        dest_dim = dest_shape[dest_index]
+        # possible cases for (src_dim, dest_dim):
+        #  * (1, 1)   -> Ok
+        #  * (>1, 1)  -> Error!
+        #  * (>1, >1) -> src_dim == dest_dim else error!
+        #  * (1, >1)  -> Ok
+        if src_dim == dest_dim or src_dim == 1:
+            src_index += 1
+            dest_index += 1
+        else:
+            raise ValueError('operands could not be broadcast together '
+                             'with remapped shapes')
+
+
+@overload(np.broadcast_to)
+def numpy_broadcast_to(array, shape):
+    if not type_can_asarray(array):
+        raise errors.TypingError('The first argument "array" must '
+                                 'be array-like')
+
+    if isinstance(shape, types.UniTuple):
+        if not isinstance(shape.dtype, types.Integer):
+            raise errors.TypingError('The second argument "shape" must '
+                                     'be a tuple of integers')
+
+        def impl(array, shape):
+            array = np.asarray(array)
+            _can_broadcast(array, shape)
+            return _numpy_broadcast_to(array, shape)
+    elif isinstance(shape, types.Integer):
+        def impl(array, shape):
+            return np.broadcast_to(array, (shape,))
+    else:
+        msg = ('The argument "shape" must be a tuple or an integer. '
+               'Got %s' % shape)
+        raise errors.TypingError(msg)
+    return impl
+
+
+@register_jitable
+def numpy_broadcast_shapes_list(r, m, shape):
+    for i in range(len(shape)):
+        k = m - len(shape) + i
+        tmp = shape[i]
+        if tmp < 0:
+            raise ValueError("negative dimensions are not allowed")
+        if tmp == 1:
+            continue
+        if r[k] == 1:
+            r[k] = tmp
+        elif r[k] != tmp:
+            raise ValueError("shape mismatch: objects"
+                             " cannot be broadcast"
+                             " to a single shape")
+
+
+def ol_numpy_broadcast_shapes(*args):
+    # Based on https://github.com/numpy/numpy/blob/f702b26fff3271ba6a6ba29a021fc19051d1f007/numpy/core/src/multiarray/iterators.c#L1129-L1212  # noqa
+    for idx, arg in enumerate(args):
+        is_int = isinstance(arg, types.Integer)
+        is_int_tuple = isinstance(arg, types.UniTuple) and \
+            isinstance(arg.dtype, types.Integer)
+        is_empty_tuple = isinstance(arg, types.Tuple) and len(arg.types) == 0
+        if not (is_int or is_int_tuple or is_empty_tuple):
+            msg = (f'Argument {idx} must be either an int or tuple[int]. '
+                   f'Got {arg}')
+            raise errors.TypingError(msg)
+
+    # discover the number of dimensions
+    m = 0
+    for arg in args:
+        if isinstance(arg, types.Integer):
+            m = max(m, 1)
+        elif isinstance(arg, types.BaseTuple):
+            m = max(m, len(arg))
+
+    if m == 0:
+        return lambda *args: ()
+    else:
+        tup_init = (1,) * m
+
+        def impl(*args):
+            # propagate args
+            r = [1] * m
+            tup = tup_init
+            for arg in literal_unroll(args):
+                if isinstance(arg, tuple) and len(arg) > 0:
+                    numpy_broadcast_shapes_list(r, m, arg)
+                elif isinstance(arg, int):
+                    numpy_broadcast_shapes_list(r, m, (arg,))
+            for idx, elem in enumerate(r):
+                tup = tuple_setitem(tup, idx, elem)
+            return tup
+        return impl
+
+
+if numpy_version >= (1, 20):
+    overload(np.broadcast_shapes)(ol_numpy_broadcast_shapes)
+
+
+@overload(np.broadcast_arrays)
+def numpy_broadcast_arrays(*args):
+
+    for idx, arg in enumerate(args):
+        if not type_can_asarray(arg):
+            raise errors.TypingError(f'Argument "{idx}" must '
+                                     'be array-like')
+
+    unified_dtype = None
+    dt = None
+    for arg in args:
+        if isinstance(arg, (types.Array, types.BaseTuple)):
+            dt = arg.dtype
+        else:
+            dt = arg
+
+        if unified_dtype is None:
+            unified_dtype = dt
+        elif unified_dtype != dt:
+            raise errors.TypingError('Mismatch of argument types. Numba cannot '
+                                     'broadcast arrays with different types. '
+                                     f'Got {args}')
+
+    # number of dimensions
+    m = 0
+    for idx, arg in enumerate(args):
+        if isinstance(arg, types.ArrayCompatible):
+            m = max(m, arg.ndim)
+        elif isinstance(arg, (types.Number, types.Boolean, types.BaseTuple)):
+            m = max(m, 1)
+        else:
+            raise errors.TypingError(f'Unhandled type {arg}')
+
+    tup_init = (0,) * m
+
+    def impl(*args):
+        # find out the output shape
+        # we can't call np.broadcast_shapes here since args may have arrays
+        # with different shapes and it is not possible to create a list
+        # with those shapes dynamically
+        shape = [1] * m
+        for array in literal_unroll(args):
+            numpy_broadcast_shapes_list(shape, m, np.asarray(array).shape)
+
+        tup = tup_init
+
+        for i in range(m):
+            tup = tuple_setitem(tup, i, shape[i])
+
+        # numpy checks if the input arrays have the same shape as `shape`
+        outs = []
+        for array in literal_unroll(args):
+            outs.append(np.broadcast_to(np.asarray(array), tup))
+        return outs
+
+    return impl
+
+
+def fancy_setslice(context, builder, sig, args, index_types, indices):
+    """
+    Implement slice assignment for arrays.  This implementation works for
+    basic as well as fancy indexing, since there's no functional difference
+    between the two for indexed assignment.
+    """
+    aryty, _, srcty = sig.args
+    ary, _, src = args
+
+    ary = make_array(aryty)(context, builder, ary)
+    dest_shapes = cgutils.unpack_tuple(builder, ary.shape)
+    dest_strides = cgutils.unpack_tuple(builder, ary.strides)
+    dest_data = ary.data
+
+    indexer = FancyIndexer(context, builder, aryty, ary,
+                           index_types, indices)
+    indexer.prepare()
+
+    if isinstance(srcty, types.Buffer):
+        # Source is an array
+        src_dtype = srcty.dtype
+        index_shape = indexer.get_shape()
+        src = make_array(srcty)(context, builder, src)
+        # Broadcast source array to shape
+        srcty, src = _broadcast_to_shape(context, builder, srcty, src,
+                                         index_shape)
+        src_shapes = cgutils.unpack_tuple(builder, src.shape)
+        src_strides = cgutils.unpack_tuple(builder, src.strides)
+        src_data = src.data
+
+        # Check shapes are equal
+        shape_error = cgutils.false_bit
+        assert len(index_shape) == len(src_shapes)
+
+        for u, v in zip(src_shapes, index_shape):
+            shape_error = builder.or_(shape_error,
+                                      builder.icmp_signed('!=', u, v))
+
+        with builder.if_then(shape_error, likely=False):
+            msg = "cannot assign slice from input of different size"
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+        # Check for array overlap
+        src_start, src_end = get_array_memory_extents(context, builder, srcty,
+                                                      src, src_shapes,
+                                                      src_strides, src_data)
+
+        dest_lower, dest_upper = indexer.get_offset_bounds(dest_strides,
+                                                           ary.itemsize)
+        dest_start, dest_end = compute_memory_extents(context, builder,
+                                                      dest_lower, dest_upper,
+                                                      dest_data)
+
+        use_copy = extents_may_overlap(context, builder, src_start, src_end,
+                                       dest_start, dest_end)
+
+        src_getitem, src_cleanup = maybe_copy_source(context, builder, use_copy,
+                                                     srcty, src, src_shapes,
+                                                     src_strides, src_data)
+
+    elif isinstance(srcty, types.Sequence):
+        src_dtype = srcty.dtype
+
+        # Check shape is equal to sequence length
+        index_shape = indexer.get_shape()
+        assert len(index_shape) == 1
+        len_impl = context.get_function(len, signature(types.intp, srcty))
+        seq_len = len_impl(builder, (src,))
+
+        shape_error = builder.icmp_signed('!=', index_shape[0], seq_len)
+
+        with builder.if_then(shape_error, likely=False):
+            msg = "cannot assign slice from input of different size"
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+        def src_getitem(source_indices):
+            idx, = source_indices
+            getitem_impl = context.get_function(
+                operator.getitem,
+                signature(src_dtype, srcty, types.intp),
+            )
+            return getitem_impl(builder, (src, idx))
+
+        def src_cleanup():
+            pass
+
+    else:
+        # Source is a scalar (broadcast or not, depending on destination
+        # shape).
+        src_dtype = srcty
+
+        def src_getitem(source_indices):
+            return src
+
+        def src_cleanup():
+            pass
+
+    # Loop on destination and copy from source to destination
+    dest_indices, counts = indexer.begin_loops()
+
+    # Source is iterated in natural order
+    source_indices = tuple(c for c in counts if c is not None)
+    val = src_getitem(source_indices)
+
+    # Cast to the destination dtype (cross-dtype slice assignment is allowed)
+    val = context.cast(builder, val, src_dtype, aryty.dtype)
+
+    # No need to check for wraparound, as the indexers all ensure
+    # a positive index is returned.
+    dest_ptr = cgutils.get_item_pointer2(context, builder, dest_data,
+                                         dest_shapes, dest_strides,
+                                         aryty.layout, dest_indices,
+                                         wraparound=False)
+    store_item(context, builder, aryty, val, dest_ptr)
+
+    indexer.end_loops()
+
+    src_cleanup()
+
+    return context.get_dummy_value()
+
+
+# ------------------------------------------------------------------------------
+# Shape / layout altering
+
+def vararg_to_tuple(context, builder, sig, args):
+    aryty = sig.args[0]
+    dimtys = sig.args[1:]
+    # values
+    ary = args[0]
+    dims = args[1:]
+    # coerce all types to intp
+    dims = [context.cast(builder, val, ty, types.intp)
+            for ty, val in zip(dimtys, dims)]
+    # make a tuple
+    shape = cgutils.pack_array(builder, dims, dims[0].type)
+
+    shapety = types.UniTuple(dtype=types.intp, count=len(dims))
+    new_sig = typing.signature(sig.return_type, aryty, shapety)
+    new_args = ary, shape
+
+    return new_sig, new_args
+
+
+@lower_builtin('array.transpose', types.Array)
+def array_transpose(context, builder, sig, args):
+    return array_T(context, builder, sig.args[0], args[0])
+
+
+def permute_arrays(axis, shape, strides):
+    if len(axis) != len(set(axis)):
+        raise ValueError("repeated axis in transpose")
+    dim = len(shape)
+    for x in axis:
+        if x >= dim or abs(x) > dim:
+            raise ValueError("axis is out of bounds for array of "
+                             "given dimension")
+
+    shape[:] = shape[axis]
+    strides[:] = strides[axis]
+
+
+# Transposing an array involves permuting the shape and strides of the array
+# based on the given axes.
+@lower_builtin('array.transpose', types.Array, types.BaseTuple)
+def array_transpose_tuple(context, builder, sig, args):
+    aryty = sig.args[0]
+    ary = make_array(aryty)(context, builder, args[0])
+
+    axisty, axis = sig.args[1], args[1]
+    num_axis, dtype = axisty.count, axisty.dtype
+
+    ll_intp = context.get_value_type(types.intp)
+    ll_ary_size = ir.ArrayType(ll_intp, num_axis)
+
+    # Allocate memory for axes, shapes, and strides arrays.
+    arys = [axis, ary.shape, ary.strides]
+    ll_arys = [cgutils.alloca_once(builder, ll_ary_size) for _ in arys]
+
+    # Store axes, shapes, and strides arrays to the allocated memory.
+    for src, dst in zip(arys, ll_arys):
+        builder.store(src, dst)
+
+    np_ary_ty = types.Array(dtype=dtype, ndim=1, layout='C')
+    np_itemsize = context.get_constant(types.intp,
+                                       context.get_abi_sizeof(ll_intp))
+
+    # Form NumPy arrays for axes, shapes, and strides arrays.
+    np_arys = [make_array(np_ary_ty)(context, builder) for _ in arys]
+
+    # Roughly, `np_ary = np.array(ll_ary)` for each of axes, shapes, and strides
+    for np_ary, ll_ary in zip(np_arys, ll_arys):
+        populate_array(np_ary,
+                       data=builder.bitcast(ll_ary, ll_intp.as_pointer()),
+                       shape=[context.get_constant(types.intp, num_axis)],
+                       strides=[np_itemsize],
+                       itemsize=np_itemsize,
+                       meminfo=None)
+
+    # Pass NumPy arrays formed above to permute_arrays function that permutes
+    # shapes and strides based on axis contents.
+    context.compile_internal(builder, permute_arrays,
+                             typing.signature(types.void,
+                                              np_ary_ty, np_ary_ty, np_ary_ty),
+                             [a._getvalue() for a in np_arys])
+
+    # Make a new array based on permuted shape and strides and return it.
+    ret = make_array(sig.return_type)(context, builder)
+    populate_array(ret,
+                   data=ary.data,
+                   shape=builder.load(ll_arys[1]),
+                   strides=builder.load(ll_arys[2]),
+                   itemsize=ary.itemsize,
+                   meminfo=ary.meminfo,
+                   parent=ary.parent)
+    res = ret._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('array.transpose', types.Array, types.VarArg(types.Any))
+def array_transpose_vararg(context, builder, sig, args):
+    new_sig, new_args = vararg_to_tuple(context, builder, sig, args)
+    return array_transpose_tuple(context, builder, new_sig, new_args)
+
+
+@overload(np.transpose)
+def numpy_transpose(a, axes=None):
+    if isinstance(a, types.BaseTuple):
+        raise errors.UnsupportedError("np.transpose does not accept tuples")
+
+    if axes is None:
+        def np_transpose_impl(a, axes=None):
+            return a.transpose()
+    else:
+        def np_transpose_impl(a, axes=None):
+            return a.transpose(axes)
+
+    return np_transpose_impl
+
+
+@lower_getattr(types.Array, 'T')
+def array_T(context, builder, typ, value):
+    if typ.ndim <= 1:
+        res = value
+    else:
+        ary = make_array(typ)(context, builder, value)
+        ret = make_array(typ)(context, builder)
+        shapes = cgutils.unpack_tuple(builder, ary.shape, typ.ndim)
+        strides = cgutils.unpack_tuple(builder, ary.strides, typ.ndim)
+        populate_array(ret,
+                       data=ary.data,
+                       shape=cgutils.pack_array(builder, shapes[::-1]),
+                       strides=cgutils.pack_array(builder, strides[::-1]),
+                       itemsize=ary.itemsize,
+                       meminfo=ary.meminfo,
+                       parent=ary.parent)
+        res = ret._getvalue()
+    return impl_ret_borrowed(context, builder, typ, res)
+
+
+@overload(np.logspace)
+def numpy_logspace(start, stop, num=50):
+    if not isinstance(start, types.Number):
+        raise errors.TypingError('The first argument "start" must be a number')
+    if not isinstance(stop, types.Number):
+        raise errors.TypingError('The second argument "stop" must be a number')
+    if not isinstance(num, (int, types.Integer)):
+        raise errors.TypingError('The third argument "num" must be an integer')
+
+    def impl(start, stop, num=50):
+        y = np.linspace(start, stop, num)
+        return np.power(10.0, y)
+
+    return impl
+
+
+@overload(np.rot90)
+def numpy_rot90(arr, k=1):
+    # supporting axes argument it needs to be included in np.flip
+    if not isinstance(k, (int, types.Integer)):
+        raise errors.TypingError('The second argument "k" must be an integer')
+    if not isinstance(arr, types.Array):
+        raise errors.TypingError('The first argument "arr" must be an array')
+
+    if arr.ndim < 2:
+        raise errors.NumbaValueError('Input must be >= 2-d.')
+
+    def impl(arr, k=1):
+        k = k % 4
+        if k == 0:
+            return arr[:]
+        elif k == 1:
+            return np.swapaxes(np.fliplr(arr), 0, 1)
+        elif k == 2:
+            return np.flipud(np.fliplr(arr))
+        elif k == 3:
+            return np.fliplr(np.swapaxes(arr, 0, 1))
+        else:
+            raise AssertionError  # unreachable
+
+    return impl
+
+
+def _attempt_nocopy_reshape(context, builder, aryty, ary,
+                            newnd, newshape, newstrides):
+    """
+    Call into Numba_attempt_nocopy_reshape() for the given array type
+    and instance, and the specified new shape.
+
+    Return value is non-zero if successful, and the array pointed to
+    by *newstrides* will be filled up with the computed results.
+    """
+    ll_intp = context.get_value_type(types.intp)
+    ll_intp_star = ll_intp.as_pointer()
+    ll_intc = context.get_value_type(types.intc)
+    fnty = ir.FunctionType(ll_intc, [
+        # nd, *dims, *strides
+        ll_intp, ll_intp_star, ll_intp_star,
+        # newnd, *newdims, *newstrides
+        ll_intp, ll_intp_star, ll_intp_star,
+        # itemsize, is_f_order
+        ll_intp, ll_intc])
+    fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                        "numba_attempt_nocopy_reshape")
+
+    nd = ll_intp(aryty.ndim)
+    shape = cgutils.gep_inbounds(builder, ary._get_ptr_by_name('shape'), 0, 0)
+    strides = cgutils.gep_inbounds(builder, ary._get_ptr_by_name('strides'),
+                                   0, 0)
+    newnd = ll_intp(newnd)
+    newshape = cgutils.gep_inbounds(builder, newshape, 0, 0)
+    newstrides = cgutils.gep_inbounds(builder, newstrides, 0, 0)
+    is_f_order = ll_intc(0)
+    res = builder.call(fn, [nd, shape, strides,
+                            newnd, newshape, newstrides,
+                            ary.itemsize, is_f_order])
+    return res
+
+
+def normalize_reshape_value(origsize, shape):
+    num_neg_value = 0
+    known_size = 1
+    for ax, s in enumerate(shape):
+        if s < 0:
+            num_neg_value += 1
+            neg_ax = ax
+        else:
+            known_size *= s
+
+    if num_neg_value == 0:
+        if origsize != known_size:
+            raise ValueError("total size of new array must be unchanged")
+
+    elif num_neg_value == 1:
+        # Infer negative dimension
+        if known_size == 0:
+            inferred = 0
+            ok = origsize == 0
+        else:
+            inferred = origsize // known_size
+            ok = origsize % known_size == 0
+        if not ok:
+            raise ValueError("total size of new array must be unchanged")
+        shape[neg_ax] = inferred
+
+    else:
+        raise ValueError("multiple negative shape values")
+
+
+@lower_builtin('array.reshape', types.Array, types.BaseTuple)
+def array_reshape(context, builder, sig, args):
+    aryty = sig.args[0]
+    retty = sig.return_type
+
+    shapety = sig.args[1]
+    shape = args[1]
+
+    ll_intp = context.get_value_type(types.intp)
+    ll_shape = ir.ArrayType(ll_intp, shapety.count)
+
+    ary = make_array(aryty)(context, builder, args[0])
+
+    # We will change the target shape in this slot
+    # (see normalize_reshape_value() below)
+    newshape = cgutils.alloca_once(builder, ll_shape)
+    builder.store(shape, newshape)
+
+    # Create a shape array pointing to the value of newshape.
+    # (roughly, `shape_ary = np.array(ary.shape)`)
+    shape_ary_ty = types.Array(dtype=shapety.dtype, ndim=1, layout='C')
+    shape_ary = make_array(shape_ary_ty)(context, builder)
+    shape_itemsize = context.get_constant(types.intp,
+                                          context.get_abi_sizeof(ll_intp))
+    populate_array(shape_ary,
+                   data=builder.bitcast(newshape, ll_intp.as_pointer()),
+                   shape=[context.get_constant(types.intp, shapety.count)],
+                   strides=[shape_itemsize],
+                   itemsize=shape_itemsize,
+                   meminfo=None)
+
+    # Compute the original array size
+    size = ary.nitems
+
+    # Call our normalizer which will fix the shape array in case of negative
+    # shape value
+    context.compile_internal(builder, normalize_reshape_value,
+                             typing.signature(types.void,
+                                              types.uintp, shape_ary_ty),
+                             [size, shape_ary._getvalue()])
+
+    # Perform reshape (nocopy)
+    newnd = shapety.count
+    newstrides = cgutils.alloca_once(builder, ll_shape)
+
+    ok = _attempt_nocopy_reshape(context, builder, aryty, ary, newnd,
+                                 newshape, newstrides)
+    fail = builder.icmp_unsigned('==', ok, ok.type(0))
+
+    with builder.if_then(fail):
+        msg = "incompatible shape for array"
+        context.call_conv.return_user_exc(builder, NotImplementedError, (msg,))
+
+    ret = make_array(retty)(context, builder)
+    populate_array(ret,
+                   data=ary.data,
+                   shape=builder.load(newshape),
+                   strides=builder.load(newstrides),
+                   itemsize=ary.itemsize,
+                   meminfo=ary.meminfo,
+                   parent=ary.parent)
+    res = ret._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('array.reshape', types.Array, types.VarArg(types.Any))
+def array_reshape_vararg(context, builder, sig, args):
+    new_sig, new_args = vararg_to_tuple(context, builder, sig, args)
+    return array_reshape(context, builder, new_sig, new_args)
+
+
+@overload(np.reshape)
+def np_reshape(a, shape):
+    def np_reshape_impl(a, shape):
+        return a.reshape(shape)
+    return np_reshape_impl
+
+
+@overload(np.append)
+def np_append(arr, values, axis=None):
+
+    if not type_can_asarray(arr):
+        raise errors.TypingError('The first argument "arr" must be array-like')
+
+    if not type_can_asarray(values):
+        raise errors.TypingError('The second argument "values" must be '
+                                 'array-like')
+
+    if is_nonelike(axis):
+        def impl(arr, values, axis=None):
+            arr = np.ravel(np.asarray(arr))
+            values = np.ravel(np.asarray(values))
+            return np.concatenate((arr, values))
+    else:
+
+        if not isinstance(axis, types.Integer):
+            raise errors.TypingError('The third argument "axis" must be an '
+                                     'integer')
+
+        def impl(arr, values, axis=None):
+            return np.concatenate((arr, values), axis=axis)
+    return impl
+
+
+@lower_builtin('array.ravel', types.Array)
+def array_ravel(context, builder, sig, args):
+    # Only support no argument version (default order='C')
+    def imp_nocopy(ary):
+        """No copy version"""
+        return ary.reshape(ary.size)
+
+    def imp_copy(ary):
+        """Copy version"""
+        return ary.flatten()
+
+    # If the input array is C layout already, use the nocopy version
+    if sig.args[0].layout == 'C':
+        imp = imp_nocopy
+    # otherwise, use flatten under-the-hood
+    else:
+        imp = imp_copy
+
+    res = context.compile_internal(builder, imp, sig, args)
+    res = impl_ret_new_ref(context, builder, sig.return_type, res)
+    return res
+
+
+@lower_builtin(np.ravel, types.Array)
+def np_ravel(context, builder, sig, args):
+    def np_ravel_impl(a):
+        return a.ravel()
+
+    return context.compile_internal(builder, np_ravel_impl, sig, args)
+
+
+@lower_builtin('array.flatten', types.Array)
+def array_flatten(context, builder, sig, args):
+    # Only support flattening to C layout currently.
+    def imp(ary):
+        return ary.copy().reshape(ary.size)
+
+    res = context.compile_internal(builder, imp, sig, args)
+    res = impl_ret_new_ref(context, builder, sig.return_type, res)
+    return res
+
+
+@register_jitable
+def _np_clip_impl(a, a_min, a_max, out):
+    # Both a_min and a_max are numpy arrays
+    ret = np.empty_like(a) if out is None else out
+    a_b, a_min_b, a_max_b = np.broadcast_arrays(a, a_min, a_max)
+    for index in np.ndindex(a_b.shape):
+        val_a = a_b[index]
+        val_a_min = a_min_b[index]
+        val_a_max = a_max_b[index]
+        ret[index] = min(max(val_a, val_a_min), val_a_max)
+
+    return ret
+
+
+@register_jitable
+def _np_clip_impl_none(a, b, use_min, out):
+    for index in np.ndindex(a.shape):
+        val_a = a[index]
+        val_b = b[index]
+        if use_min:
+            out[index] = min(val_a, val_b)
+        else:
+            out[index] = max(val_a, val_b)
+    return out
+
+
+@overload(np.clip)
+def np_clip(a, a_min, a_max, out=None):
+    if not type_can_asarray(a):
+        raise errors.TypingError('The argument "a" must be array-like')
+
+    if (not isinstance(a_min, types.NoneType) and
+            not type_can_asarray(a_min)):
+        raise errors.TypingError(('The argument "a_min" must be a number '
+                                 'or an array-like'))
+
+    if (not isinstance(a_max, types.NoneType) and
+            not type_can_asarray(a_max)):
+        raise errors.TypingError('The argument "a_max" must be a number '
+                                 'or an array-like')
+
+    if not (isinstance(out, types.Array) or is_nonelike(out)):
+        msg = 'The argument "out" must be an array if it is provided'
+        raise errors.TypingError(msg)
+
+    # TODO: support scalar a (issue #3469)
+    a_min_is_none = a_min is None or isinstance(a_min, types.NoneType)
+    a_max_is_none = a_max is None or isinstance(a_max, types.NoneType)
+
+    if a_min_is_none and a_max_is_none:
+        # Raises value error when both a_min and a_max are None
+        def np_clip_nn(a, a_min, a_max, out=None):
+            raise ValueError("array_clip: must set either max or min")
+
+        return np_clip_nn
+
+    a_min_is_scalar = isinstance(a_min, types.Number)
+    a_max_is_scalar = isinstance(a_max, types.Number)
+
+    if a_min_is_scalar and a_max_is_scalar:
+        def np_clip_ss(a, a_min, a_max, out=None):
+            # a_min and a_max are scalars
+            # since their shape will be empty
+            # so broadcasting is not needed at all
+            ret = np.empty_like(a) if out is None else out
+            for index in np.ndindex(a.shape):
+                val_a = a[index]
+                ret[index] = min(max(val_a, a_min), a_max)
+
+            return ret
+
+        return np_clip_ss
+    elif a_min_is_scalar and not a_max_is_scalar:
+        if a_max_is_none:
+            def np_clip_sn(a, a_min, a_max, out=None):
+                # a_min is a scalar
+                # since its shape will be empty
+                # so broadcasting is not needed at all
+                ret = np.empty_like(a) if out is None else out
+                for index in np.ndindex(a.shape):
+                    val_a = a[index]
+                    ret[index] = max(val_a, a_min)
+
+                return ret
+
+            return np_clip_sn
+        else:
+            def np_clip_sa(a, a_min, a_max, out=None):
+                # a_min is a scalar
+                # since its shape will be empty
+                # broadcast it to shape of a
+                # by using np.full_like
+                a_min_full = np.full_like(a, a_min)
+                return _np_clip_impl(a, a_min_full, a_max, out)
+
+            return np_clip_sa
+    elif not a_min_is_scalar and a_max_is_scalar:
+        if a_min_is_none:
+            def np_clip_ns(a, a_min, a_max, out=None):
+                # a_max is a scalar
+                # since its shape will be empty
+                # so broadcasting is not needed at all
+                ret = np.empty_like(a) if out is None else out
+                for index in np.ndindex(a.shape):
+                    val_a = a[index]
+                    ret[index] = min(val_a, a_max)
+
+                return ret
+
+            return np_clip_ns
+        else:
+            def np_clip_as(a, a_min, a_max, out=None):
+                # a_max is a scalar
+                # since its shape will be empty
+                # broadcast it to shape of a
+                # by using np.full_like
+                a_max_full = np.full_like(a, a_max)
+                return _np_clip_impl(a, a_min, a_max_full, out)
+
+            return np_clip_as
+    else:
+        # Case where exactly one of a_min or a_max is None
+        if a_min_is_none:
+            def np_clip_na(a, a_min, a_max, out=None):
+                # a_max is a numpy array but a_min is None
+                ret = np.empty_like(a) if out is None else out
+                a_b, a_max_b = np.broadcast_arrays(a, a_max)
+                return _np_clip_impl_none(a_b, a_max_b, True, ret)
+
+            return np_clip_na
+        elif a_max_is_none:
+            def np_clip_an(a, a_min, a_max, out=None):
+                # a_min is a numpy array but a_max is None
+                ret = np.empty_like(a) if out is None else out
+                a_b, a_min_b = np.broadcast_arrays(a, a_min)
+                return _np_clip_impl_none(a_b, a_min_b, False, ret)
+
+            return np_clip_an
+        else:
+            def np_clip_aa(a, a_min, a_max, out=None):
+                # Both a_min and a_max are clearly arrays
+                # because none of the above branches
+                # returned
+                return _np_clip_impl(a, a_min, a_max, out)
+
+            return np_clip_aa
+
+
+@overload_method(types.Array, 'clip')
+def array_clip(a, a_min=None, a_max=None, out=None):
+    def impl(a, a_min=None, a_max=None, out=None):
+        return np.clip(a, a_min, a_max, out)
+    return impl
+
+
+def _change_dtype(context, builder, oldty, newty, ary):
+    """
+    Attempt to fix up *ary* for switching from *oldty* to *newty*.
+
+    See Numpy's array_descr_set()
+    (np/core/src/multiarray/getset.c).
+    Attempt to fix the array's shape and strides for a new dtype.
+    False is returned on failure, True on success.
+    """
+    assert oldty.ndim == newty.ndim
+    assert oldty.layout == newty.layout
+
+    new_layout = ord(newty.layout)
+    any_layout = ord('A')
+    c_layout = ord('C')
+    f_layout = ord('F')
+
+    int8 = types.int8
+
+    def imp(nd, dims, strides, old_itemsize, new_itemsize, layout):
+        # Attempt to update the layout due to limitation of the numba
+        # type system.
+        if layout == any_layout:
+            # Test rightmost stride to be contiguous
+            if strides[-1] == old_itemsize:
+                # Process this as if it is C contiguous
+                layout = int8(c_layout)
+            # Test leftmost stride to be F contiguous
+            elif strides[0] == old_itemsize:
+                # Process this as if it is F contiguous
+                layout = int8(f_layout)
+
+        if old_itemsize != new_itemsize and (layout == any_layout or nd == 0):
+            return False
+
+        if layout == c_layout:
+            i = nd - 1
+        else:
+            i = 0
+
+        if new_itemsize < old_itemsize:
+            # If it is compatible, increase the size of the dimension
+            # at the end (or at the front if F-contiguous)
+            if (old_itemsize % new_itemsize) != 0:
+                return False
+
+            newdim = old_itemsize // new_itemsize
+            dims[i] *= newdim
+            strides[i] = new_itemsize
+
+        elif new_itemsize > old_itemsize:
+            # Determine if last (or first if F-contiguous) dimension
+            # is compatible
+            bytelength = dims[i] * old_itemsize
+            if (bytelength % new_itemsize) != 0:
+                return False
+
+            dims[i] = bytelength // new_itemsize
+            strides[i] = new_itemsize
+
+        else:
+            # Same item size: nothing to do (this also works for
+            # non-contiguous arrays).
+            pass
+
+        return True
+
+    old_itemsize = context.get_constant(types.intp,
+                                        get_itemsize(context, oldty))
+    new_itemsize = context.get_constant(types.intp,
+                                        get_itemsize(context, newty))
+
+    nd = context.get_constant(types.intp, newty.ndim)
+    shape_data = cgutils.gep_inbounds(builder, ary._get_ptr_by_name('shape'),
+                                      0, 0)
+    strides_data = cgutils.gep_inbounds(builder,
+                                        ary._get_ptr_by_name('strides'), 0, 0)
+
+    shape_strides_array_type = types.Array(dtype=types.intp, ndim=1, layout='C')
+    arycls = context.make_array(shape_strides_array_type)
+
+    shape_constant = cgutils.pack_array(builder,
+                                        [context.get_constant(types.intp,
+                                                              newty.ndim)])
+
+    sizeof_intp = context.get_abi_sizeof(context.get_data_type(types.intp))
+    sizeof_intp = context.get_constant(types.intp, sizeof_intp)
+    strides_constant = cgutils.pack_array(builder, [sizeof_intp])
+
+    shape_ary = arycls(context, builder)
+
+    populate_array(shape_ary,
+                   data=shape_data,
+                   shape=shape_constant,
+                   strides=strides_constant,
+                   itemsize=sizeof_intp,
+                   meminfo=None)
+
+    strides_ary = arycls(context, builder)
+    populate_array(strides_ary,
+                   data=strides_data,
+                   shape=shape_constant,
+                   strides=strides_constant,
+                   itemsize=sizeof_intp,
+                   meminfo=None)
+
+    shape = shape_ary._getvalue()
+    strides = strides_ary._getvalue()
+    args = [nd, shape, strides, old_itemsize, new_itemsize,
+            context.get_constant(types.int8, new_layout)]
+
+    sig = signature(types.boolean,
+                    types.intp,  # nd
+                    shape_strides_array_type,  # dims
+                    shape_strides_array_type,  # strides
+                    types.intp,  # old_itemsize
+                    types.intp,  # new_itemsize
+                    types.int8,  # layout
+                    )
+
+    res = context.compile_internal(builder, imp, sig, args)
+    update_array_info(newty, ary)
+    res = impl_ret_borrowed(context, builder, sig.return_type, res)
+    return res
+
+
+@overload(np.shape)
+def np_shape(a):
+    if not type_can_asarray(a):
+        raise errors.TypingError("The argument to np.shape must be array-like")
+
+    def impl(a):
+        return np.asarray(a).shape
+    return impl
+
+# ------------------------------------------------------------------------------
+
+
+@overload(np.unique)
+def np_unique(a):
+    def np_unique_impl(a):
+        b = np.sort(a.ravel())
+        head = list(b[:1])
+        tail = [x for i, x in enumerate(b[1:]) if b[i] != x]
+        return np.array(head + tail)
+    return np_unique_impl
+
+
+@overload(np.repeat)
+def np_repeat(a, repeats):
+    # Implementation for repeats being a scalar is a module global function
+    # (see below) because it might be called from the implementation below.
+
+    def np_repeat_impl_repeats_array_like(a, repeats):
+        # implementation if repeats is an array like
+        repeats_array = np.asarray(repeats, dtype=np.int64)
+        # if it is a singleton array, invoke the scalar implementation
+        if repeats_array.shape[0] == 1:
+            return np_repeat_impl_repeats_scaler(a, repeats_array[0])
+        if np.any(repeats_array < 0):
+            raise ValueError("negative dimensions are not allowed")
+        asa = np.asarray(a)
+        aravel = asa.ravel()
+        n = aravel.shape[0]
+        if aravel.shape != repeats_array.shape:
+            raise ValueError(
+                "operands could not be broadcast together")
+        to_return = np.empty(np.sum(repeats_array), dtype=asa.dtype)
+        pos = 0
+        for i in range(n):
+            to_return[pos : pos + repeats_array[i]] = aravel[i]
+            pos += repeats_array[i]
+        return to_return
+
+    # type checking
+    if isinstance(a, (types.Array,
+                      types.List,
+                      types.BaseTuple,
+                      types.Number,
+                      types.Boolean,
+                      )
+                  ):
+        if isinstance(repeats, types.Integer):
+            return np_repeat_impl_repeats_scaler
+        elif isinstance(repeats, (types.Array, types.List)):
+            if isinstance(repeats.dtype, types.Integer):
+                return np_repeat_impl_repeats_array_like
+
+        raise errors.TypingError(
+            "The repeats argument must be an integer "
+            "or an array-like of integer dtype")
+
+
+@register_jitable
+def np_repeat_impl_repeats_scaler(a, repeats):
+    if repeats < 0:
+        raise ValueError("negative dimensions are not allowed")
+    asa = np.asarray(a)
+    aravel = asa.ravel()
+    n = aravel.shape[0]
+    if repeats == 0:
+        return np.empty(0, dtype=asa.dtype)
+    elif repeats == 1:
+        return np.copy(aravel)
+    else:
+        to_return = np.empty(n * repeats, dtype=asa.dtype)
+        for i in range(n):
+            to_return[i * repeats : (i + 1) * repeats] = aravel[i]
+        return to_return
+
+
+@extending.overload_method(types.Array, 'repeat')
+def array_repeat(a, repeats):
+    def array_repeat_impl(a, repeats):
+        return np.repeat(a, repeats)
+
+    return array_repeat_impl
+
+
+@intrinsic
+def _intrin_get_itemsize(tyctx, dtype):
+    """Computes the itemsize of the dtype"""
+    sig = types.intp(dtype)
+
+    def codegen(cgctx, builder, sig, llargs):
+        llty = cgctx.get_data_type(sig.args[0].dtype)
+        llintp = cgctx.get_data_type(sig.return_type)
+        return llintp(cgctx.get_abi_sizeof(llty))
+    return sig, codegen
+
+
+def _compatible_view(a, dtype):
+    pass
+
+
+@overload(_compatible_view, target='generic')
+def ol_compatible_view(a, dtype):
+    """Determines if the array and dtype are compatible for forming a view."""
+    # NOTE: NumPy 1.23+ uses this check.
+    # Code based on:
+    # https://github.com/numpy/numpy/blob/750ad21258cfc00663586d5a466e24f91b48edc7/numpy/core/src/multiarray/getset.c#L500-L555  # noqa: E501
+    def impl(a, dtype):
+        dtype_size = _intrin_get_itemsize(dtype)
+        if dtype_size != a.itemsize:
+            # catch forbidden cases
+            if a.ndim == 0:
+                msg1 = ("Changing the dtype of a 0d array is only supported "
+                        "if the itemsize is unchanged")
+                raise ValueError(msg1)
+            else:
+                # NumPy has a check here for subarray type conversion which
+                # Numba doesn't support
+                pass
+
+            # Resize on last axis only
+            axis = a.ndim - 1
+            p1 = a.shape[axis] != 1
+            p2 = a.size != 0
+            p3 = a.strides[axis] != a.itemsize
+            if (p1 and p2 and p3):
+                msg2 = ("To change to a dtype of a different size, the last "
+                        "axis must be contiguous")
+                raise ValueError(msg2)
+
+            if dtype_size < a.itemsize:
+                if dtype_size == 0 or a.itemsize % dtype_size != 0:
+                    msg3 = ("When changing to a smaller dtype, its size must "
+                            "be a divisor of the size of original dtype")
+                    raise ValueError(msg3)
+            else:
+                newdim = a.shape[axis] * a.itemsize
+                if newdim % dtype_size != 0:
+                    msg4 = ("When changing to a larger dtype, its size must be "
+                            "a divisor of the total size in bytes of the last "
+                            "axis of the array.")
+                    raise ValueError(msg4)
+    return impl
+
+
+@lower_builtin('array.view', types.Array, types.DTypeSpec)
+def array_view(context, builder, sig, args):
+    aryty = sig.args[0]
+    retty = sig.return_type
+
+    ary = make_array(aryty)(context, builder, args[0])
+    ret = make_array(retty)(context, builder)
+    # Copy all fields, casting the "data" pointer appropriately
+    fields = set(ret._datamodel._fields)
+    for k in sorted(fields):
+        val = getattr(ary, k)
+        if k == 'data':
+            ptrty = ret.data.type
+            ret.data = builder.bitcast(val, ptrty)
+        else:
+            setattr(ret, k, val)
+
+    if numpy_version >= (1, 23):
+        # NumPy 1.23+ bans views using a dtype that is a different size to that
+        # of the array when the last axis is not contiguous. For example, this
+        # manifests at runtime when a dtype size altering view is requested
+        # on a Fortran ordered array.
+
+        tyctx = context.typing_context
+        fnty = tyctx.resolve_value_type(_compatible_view)
+        _compatible_view_sig = fnty.get_call_type(tyctx, (*sig.args,), {})
+        impl = context.get_function(fnty, _compatible_view_sig)
+        impl(builder, args)
+
+    ok = _change_dtype(context, builder, aryty, retty, ret)
+    fail = builder.icmp_unsigned('==', ok, Constant(ok.type, 0))
+
+    with builder.if_then(fail):
+        msg = "new type not compatible with array"
+        context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+    res = ret._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+# ------------------------------------------------------------------------------
+# Array attributes
+
+@lower_getattr(types.Array, "dtype")
+def array_dtype(context, builder, typ, value):
+    res = context.get_dummy_value()
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "shape")
+@lower_getattr(types.MemoryView, "shape")
+def array_shape(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = array.shape
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "strides")
+@lower_getattr(types.MemoryView, "strides")
+def array_strides(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = array.strides
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "ndim")
+@lower_getattr(types.MemoryView, "ndim")
+def array_ndim(context, builder, typ, value):
+    res = context.get_constant(types.intp, typ.ndim)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "size")
+def array_size(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = array.nitems
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "itemsize")
+@lower_getattr(types.MemoryView, "itemsize")
+def array_itemsize(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = array.itemsize
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "nbytes")
+def array_nbytes(context, builder, typ, value):
+    """
+    nbytes = size * itemsize
+    """
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = builder.mul(array.nitems, array.itemsize)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "contiguous")
+def array_contiguous(context, builder, typ, value):
+    res = context.get_constant(types.boolean, typ.is_contig)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "c_contiguous")
+def array_c_contiguous(context, builder, typ, value):
+    res = context.get_constant(types.boolean, typ.is_c_contig)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "f_contiguous")
+def array_f_contiguous(context, builder, typ, value):
+    res = context.get_constant(types.boolean, typ.is_f_contig)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "readonly")
+def array_readonly(context, builder, typ, value):
+    res = context.get_constant(types.boolean, not typ.mutable)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+# array.ctypes
+
+@lower_getattr(types.Array, "ctypes")
+def array_ctypes(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    # Create new ArrayCType structure
+    act = types.ArrayCTypes(typ)
+    ctinfo = context.make_helper(builder, act)
+    ctinfo.data = array.data
+    ctinfo.meminfo = array.meminfo
+    res = ctinfo._getvalue()
+    return impl_ret_borrowed(context, builder, act, res)
+
+
+@lower_getattr(types.ArrayCTypes, "data")
+def array_ctypes_data(context, builder, typ, value):
+    ctinfo = context.make_helper(builder, typ, value=value)
+    res = ctinfo.data
+    # Convert it to an integer
+    res = builder.ptrtoint(res, context.get_value_type(types.intp))
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_cast(types.ArrayCTypes, types.CPointer)
+@lower_cast(types.ArrayCTypes, types.voidptr)
+def array_ctypes_to_pointer(context, builder, fromty, toty, val):
+    ctinfo = context.make_helper(builder, fromty, value=val)
+    res = ctinfo.data
+    res = builder.bitcast(res, context.get_value_type(toty))
+    return impl_ret_untracked(context, builder, toty, res)
+
+
+def _call_contiguous_check(checker, context, builder, aryty, ary):
+    """Helper to invoke the contiguous checker function on an array
+
+    Args
+    ----
+    checker :
+        ``numba.numpy_supports.is_contiguous``, or
+        ``numba.numpy_supports.is_fortran``.
+    context : target context
+    builder : llvm ir builder
+    aryty : numba type
+    ary : llvm value
+    """
+    ary = make_array(aryty)(context, builder, value=ary)
+    tup_intp = types.UniTuple(types.intp, aryty.ndim)
+    itemsize = context.get_abi_sizeof(context.get_value_type(aryty.dtype))
+    check_sig = signature(types.bool_, tup_intp, tup_intp, types.intp)
+    check_args = [ary.shape, ary.strides,
+                  context.get_constant(types.intp, itemsize)]
+    is_contig = context.compile_internal(builder, checker, check_sig,
+                                         check_args)
+    return is_contig
+
+
+# array.flags
+
+@lower_getattr(types.Array, "flags")
+def array_flags(context, builder, typ, value):
+    flagsobj = context.make_helper(builder, types.ArrayFlags(typ))
+    flagsobj.parent = value
+    res = flagsobj._getvalue()
+    context.nrt.incref(builder, typ, value)
+    return impl_ret_new_ref(context, builder, typ, res)
+
+
+@lower_getattr(types.ArrayFlags, "contiguous")
+@lower_getattr(types.ArrayFlags, "c_contiguous")
+def array_flags_c_contiguous(context, builder, typ, value):
+    if typ.array_type.layout != 'C':
+        # any layout can still be contiguous
+        flagsobj = context.make_helper(builder, typ, value=value)
+        res = _call_contiguous_check(is_contiguous, context, builder,
+                                     typ.array_type, flagsobj.parent)
+    else:
+        val = typ.array_type.layout == 'C'
+        res = context.get_constant(types.boolean, val)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.ArrayFlags, "f_contiguous")
+def array_flags_f_contiguous(context, builder, typ, value):
+    if typ.array_type.layout != 'F':
+        # any layout can still be contiguous
+        flagsobj = context.make_helper(builder, typ, value=value)
+        res = _call_contiguous_check(is_fortran, context, builder,
+                                     typ.array_type, flagsobj.parent)
+    else:
+        layout = typ.array_type.layout
+        val = layout == 'F' if typ.array_type.ndim > 1 else layout in 'CF'
+        res = context.get_constant(types.boolean, val)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+# ------------------------------------------------------------------------------
+# .real / .imag
+
+@lower_getattr(types.Array, "real")
+def array_real_part(context, builder, typ, value):
+    if typ.dtype in types.complex_domain:
+        return array_complex_attr(context, builder, typ, value, attr='real')
+    elif typ.dtype in types.number_domain:
+        # as an identity function
+        return impl_ret_borrowed(context, builder, typ, value)
+    else:
+        raise NotImplementedError('unsupported .real for {}'.format(type.dtype))
+
+
+@lower_getattr(types.Array, "imag")
+def array_imag_part(context, builder, typ, value):
+    if typ.dtype in types.complex_domain:
+        return array_complex_attr(context, builder, typ, value, attr='imag')
+    elif typ.dtype in types.number_domain:
+        # return a readonly zero array
+        sig = signature(typ.copy(readonly=True), typ)
+        arrtype, shapes = _parse_empty_like_args(context, builder, sig, [value])
+        ary = _empty_nd_impl(context, builder, arrtype, shapes)
+        cgutils.memset(builder, ary.data, builder.mul(ary.itemsize,
+                                                      ary.nitems), 0)
+        return impl_ret_new_ref(context, builder, sig.return_type,
+                                ary._getvalue())
+    else:
+        raise NotImplementedError('unsupported .imag for {}'.format(type.dtype))
+
+
+def array_complex_attr(context, builder, typ, value, attr):
+    """
+    Given a complex array, it's memory layout is:
+
+        R C R C R C
+        ^   ^   ^
+
+    (`R` indicates a float for the real part;
+     `C` indicates a float for the imaginary part;
+     the `^` indicates the start of each element)
+
+    To get the real part, we can simply change the dtype and itemsize to that
+    of the underlying float type.  The new layout is:
+
+        R x R x R x
+        ^   ^   ^
+
+    (`x` indicates unused)
+
+    A load operation will use the dtype to determine the number of bytes to
+    load.
+
+    To get the imaginary part, we shift the pointer by 1 float offset and
+    change the dtype and itemsize.  The new layout is:
+
+        x C x C x C
+          ^   ^   ^
+    """
+    if attr not in ['real', 'imag'] or typ.dtype not in types.complex_domain:
+        raise NotImplementedError("cannot get attribute `{}`".format(attr))
+
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+
+    # sizeof underlying float type
+    flty = typ.dtype.underlying_float
+    sizeof_flty = context.get_abi_sizeof(context.get_data_type(flty))
+    itemsize = array.itemsize.type(sizeof_flty)
+
+    # cast data pointer to float type
+    llfltptrty = context.get_value_type(flty).as_pointer()
+    dataptr = builder.bitcast(array.data, llfltptrty)
+
+    # add offset
+    if attr == 'imag':
+        dataptr = builder.gep(dataptr, [ir.IntType(32)(1)])
+
+    # make result
+    resultty = typ.copy(dtype=flty, layout='A')
+    result = make_array(resultty)(context, builder)
+    repl = dict(data=dataptr, itemsize=itemsize)
+    cgutils.copy_struct(result, array, repl)
+    return impl_ret_borrowed(context, builder, resultty, result._getvalue())
+
+
+@overload_method(types.Array, 'conj')
+@overload_method(types.Array, 'conjugate')
+def array_conj(arr):
+    def impl(arr):
+        return np.conj(arr)
+    return impl
+
+# ------------------------------------------------------------------------------
+# DType attribute
+
+
+def dtype_type(context, builder, dtypety, dtypeval):
+    # Just return a dummy opaque value
+    return context.get_dummy_value()
+
+
+lower_getattr(types.DType, 'type')(dtype_type)
+lower_getattr(types.DType, 'kind')(dtype_type)
+
+
+# ------------------------------------------------------------------------------
+# Structured / record lookup
+
+@lower_getattr_generic(types.Array)
+def array_record_getattr(context, builder, typ, value, attr):
+    """
+    Generic getattr() implementation for record arrays: fetch the given
+    record member, i.e. a subarray.
+    """
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+
+    rectype = typ.dtype
+    if not isinstance(rectype, types.Record):
+        raise NotImplementedError("attribute %r of %s not defined"
+                                  % (attr, typ))
+    dtype = rectype.typeof(attr)
+    offset = rectype.offset(attr)
+
+    if isinstance(dtype, types.NestedArray):
+        resty = typ.copy(
+            dtype=dtype.dtype, ndim=typ.ndim + dtype.ndim, layout='A')
+    else:
+        resty = typ.copy(dtype=dtype, layout='A')
+
+    raryty = make_array(resty)
+
+    rary = raryty(context, builder)
+
+    constoffset = context.get_constant(types.intp, offset)
+
+    newdataptr = cgutils.pointer_add(
+        builder, array.data, constoffset,  return_type=rary.data.type,
+    )
+    if isinstance(dtype, types.NestedArray):
+        # new shape = recarray shape + inner dimension from nestedarray
+        shape = cgutils.unpack_tuple(builder, array.shape, typ.ndim)
+        shape += [context.get_constant(types.intp, i) for i in dtype.shape]
+        # new strides = recarray strides + strides of the inner nestedarray
+        strides = cgutils.unpack_tuple(builder, array.strides, typ.ndim)
+        strides += [context.get_constant(types.intp, i) for i in dtype.strides]
+        # New datasize = size of elements of the nestedarray
+        datasize = context.get_abi_sizeof(context.get_data_type(dtype.dtype))
+    else:
+        # New shape, strides, and datasize match the underlying array
+        shape = array.shape
+        strides = array.strides
+        datasize = context.get_abi_sizeof(context.get_data_type(dtype))
+    populate_array(rary,
+                   data=newdataptr,
+                   shape=shape,
+                   strides=strides,
+                   itemsize=context.get_constant(types.intp, datasize),
+                   meminfo=array.meminfo,
+                   parent=array.parent)
+    res = rary._getvalue()
+    return impl_ret_borrowed(context, builder, resty, res)
+
+
+@lower_builtin('static_getitem', types.Array, types.StringLiteral)
+def array_record_getitem(context, builder, sig, args):
+    index = args[1]
+    if not isinstance(index, str):
+        # This will fallback to normal getitem
+        raise NotImplementedError
+    return array_record_getattr(context, builder, sig.args[0], args[0], index)
+
+
+@lower_getattr_generic(types.Record)
+def record_getattr(context, builder, typ, value, attr):
+    """
+    Generic getattr() implementation for records: get the given record member.
+    """
+    context.sentry_record_alignment(typ, attr)
+    offset = typ.offset(attr)
+    elemty = typ.typeof(attr)
+
+    if isinstance(elemty, types.NestedArray):
+        # Only a nested array's *data* is stored in a structured array,
+        # so we create an array structure to point to that data.
+        aryty = make_array(elemty)
+        ary = aryty(context, builder)
+        dtype = elemty.dtype
+        newshape = [context.get_constant(types.intp, s) for s in
+                    elemty.shape]
+        newstrides = [context.get_constant(types.intp, s) for s in
+                      elemty.strides]
+        newdata = cgutils.get_record_member(builder, value, offset,
+                                            context.get_data_type(dtype))
+        populate_array(
+            ary,
+            data=newdata,
+            shape=cgutils.pack_array(builder, newshape),
+            strides=cgutils.pack_array(builder, newstrides),
+            itemsize=context.get_constant(types.intp, elemty.size),
+            meminfo=None,
+            parent=None,
+        )
+        res = ary._getvalue()
+        return impl_ret_borrowed(context, builder, typ, res)
+    else:
+        dptr = cgutils.get_record_member(builder, value, offset,
+                                         context.get_data_type(elemty))
+        align = None if typ.aligned else 1
+        res = context.unpack_value(builder, elemty, dptr, align)
+        return impl_ret_borrowed(context, builder, typ, res)
+
+
+@lower_setattr_generic(types.Record)
+def record_setattr(context, builder, sig, args, attr):
+    """
+    Generic setattr() implementation for records: set the given record member.
+    """
+    typ, valty = sig.args
+    target, val = args
+
+    context.sentry_record_alignment(typ, attr)
+    offset = typ.offset(attr)
+    elemty = typ.typeof(attr)
+
+    if isinstance(elemty, types.NestedArray):
+        # Copy the data from the RHS into the nested array
+        val_struct = cgutils.create_struct_proxy(valty)(context, builder,
+                                                        value=args[1])
+        src = val_struct.data
+        dest = cgutils.get_record_member(builder, target, offset,
+                                         src.type.pointee)
+        cgutils.memcpy(builder, dest, src,
+                       context.get_constant(types.intp, elemty.nitems))
+    else:
+        # Set the given scalar record member
+        dptr = cgutils.get_record_member(builder, target, offset,
+                                         context.get_data_type(elemty))
+        val = context.cast(builder, val, valty, elemty)
+        align = None if typ.aligned else 1
+        context.pack_value(builder, elemty, val, dptr, align=align)
+
+
+@lower_builtin('static_getitem', types.Record, types.StringLiteral)
+def record_static_getitem_str(context, builder, sig, args):
+    """
+    Record.__getitem__ redirects to getattr()
+    """
+    impl = context.get_getattr(sig.args[0], args[1])
+    return impl(context, builder, sig.args[0], args[0], args[1])
+
+
+@lower_builtin('static_getitem', types.Record, types.IntegerLiteral)
+def record_static_getitem_int(context, builder, sig, args):
+    """
+    Record.__getitem__ redirects to getattr()
+    """
+    idx = sig.args[1].literal_value
+    fields = list(sig.args[0].fields)
+    ll_field = context.insert_const_string(builder.module, fields[idx])
+    impl = context.get_getattr(sig.args[0], ll_field)
+    return impl(context, builder, sig.args[0], args[0], fields[idx])
+
+
+@lower_builtin('static_setitem', types.Record, types.StringLiteral, types.Any)
+def record_static_setitem_str(context, builder, sig, args):
+    """
+    Record.__setitem__ redirects to setattr()
+    """
+    recty, _, valty = sig.args
+    rec, idx, val = args
+    getattr_sig = signature(sig.return_type, recty, valty)
+    impl = context.get_setattr(idx, getattr_sig)
+    assert impl is not None
+    return impl(builder, (rec, val))
+
+
+@lower_builtin('static_setitem', types.Record, types.IntegerLiteral, types.Any)
+def record_static_setitem_int(context, builder, sig, args):
+    """
+    Record.__setitem__ redirects to setattr()
+    """
+    recty, _, valty = sig.args
+    rec, idx, val = args
+    getattr_sig = signature(sig.return_type, recty, valty)
+    fields = list(sig.args[0].fields)
+    impl = context.get_setattr(fields[idx], getattr_sig)
+    assert impl is not None
+    return impl(builder, (rec, val))
+
+
+# ------------------------------------------------------------------------------
+# Constant arrays and records
+
+
+@lower_constant(types.Array)
+def constant_array(context, builder, ty, pyval):
+    """
+    Create a constant array (mechanism is target-dependent).
+    """
+    return context.make_constant_array(builder, ty, pyval)
+
+
+@lower_constant(types.Record)
+def constant_record(context, builder, ty, pyval):
+    """
+    Create a record constant as a stack-allocated array of bytes.
+    """
+    lty = ir.ArrayType(ir.IntType(8), pyval.nbytes)
+    val = lty(bytearray(pyval.tostring()))
+    return cgutils.alloca_once_value(builder, val)
+
+
+@lower_constant(types.Bytes)
+def constant_bytes(context, builder, ty, pyval):
+    """
+    Create a constant array from bytes (mechanism is target-dependent).
+    """
+    buf = np.array(bytearray(pyval), dtype=np.uint8)
+    return context.make_constant_array(builder, ty, buf)
+
+# ------------------------------------------------------------------------------
+# Comparisons
+
+
+@lower_builtin(operator.is_, types.Array, types.Array)
+def array_is(context, builder, sig, args):
+    aty, bty = sig.args
+    if aty != bty:
+        return cgutils.false_bit
+
+    def array_is_impl(a, b):
+        return (a.shape == b.shape and
+                a.strides == b.strides and
+                a.ctypes.data == b.ctypes.data)
+
+    return context.compile_internal(builder, array_is_impl, sig, args)
+
+
+# ------------------------------------------------------------------------------
+# builtin `np.flat` implementation
+
+def make_array_flat_cls(flatiterty):
+    """
+    Return the Structure representation of the given *flatiterty* (an
+    instance of types.NumpyFlatType).
+    """
+    return _make_flattening_iter_cls(flatiterty, 'flat')
+
+
+def make_array_ndenumerate_cls(nditerty):
+    """
+    Return the Structure representation of the given *nditerty* (an
+    instance of types.NumpyNdEnumerateType).
+    """
+    return _make_flattening_iter_cls(nditerty, 'ndenumerate')
+
+
+def _increment_indices(context, builder, ndim, shape, indices, end_flag=None,
+                       loop_continue=None, loop_break=None):
+    zero = context.get_constant(types.intp, 0)
+
+    bbend = builder.append_basic_block('end_increment')
+
+    if end_flag is not None:
+        builder.store(cgutils.false_byte, end_flag)
+
+    for dim in reversed(range(ndim)):
+        idxptr = cgutils.gep_inbounds(builder, indices, dim)
+        idx = cgutils.increment_index(builder, builder.load(idxptr))
+
+        count = shape[dim]
+        in_bounds = builder.icmp_signed('<', idx, count)
+        with cgutils.if_likely(builder, in_bounds):
+            # New index is still in bounds
+            builder.store(idx, idxptr)
+            if loop_continue is not None:
+                loop_continue(dim)
+            builder.branch(bbend)
+        # Index out of bounds => reset it and proceed it to outer index
+        builder.store(zero, idxptr)
+        if loop_break is not None:
+            loop_break(dim)
+
+    if end_flag is not None:
+        builder.store(cgutils.true_byte, end_flag)
+    builder.branch(bbend)
+
+    builder.position_at_end(bbend)
+
+
+def _increment_indices_array(context, builder, arrty, arr, indices,
+                             end_flag=None):
+    shape = cgutils.unpack_tuple(builder, arr.shape, arrty.ndim)
+    _increment_indices(context, builder, arrty.ndim, shape, indices, end_flag)
+
+
+def make_nditer_cls(nditerty):
+    """
+    Return the Structure representation of the given *nditerty* (an
+    instance of types.NumpyNdIterType).
+    """
+    ndim = nditerty.ndim
+    layout = nditerty.layout
+    narrays = len(nditerty.arrays)
+    nshapes = ndim if nditerty.need_shaped_indexing else 1
+
+    class BaseSubIter(object):
+        """
+        Base class for sub-iterators of a nditer() instance.
+        """
+
+        def __init__(self, nditer, member_name, start_dim, end_dim):
+            self.nditer = nditer
+            self.member_name = member_name
+            self.start_dim = start_dim
+            self.end_dim = end_dim
+            self.ndim = end_dim - start_dim
+
+        def set_member_ptr(self, ptr):
+            setattr(self.nditer, self.member_name, ptr)
+
+        @utils.cached_property
+        def member_ptr(self):
+            return getattr(self.nditer, self.member_name)
+
+        def init_specific(self, context, builder):
+            pass
+
+        def loop_continue(self, context, builder, logical_dim):
+            pass
+
+        def loop_break(self, context, builder, logical_dim):
+            pass
+
+    class FlatSubIter(BaseSubIter):
+        """
+        Sub-iterator walking a contiguous array in physical order, with
+        support for broadcasting (the index is reset on the outer dimension).
+        """
+
+        def init_specific(self, context, builder):
+            zero = context.get_constant(types.intp, 0)
+            self.set_member_ptr(cgutils.alloca_once_value(builder, zero))
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            index = builder.load(self.member_ptr)
+            return builder.gep(arr.data, [index])
+
+        def loop_continue(self, context, builder, logical_dim):
+            if logical_dim == self.ndim - 1:
+                # Only increment index inside innermost logical dimension
+                index = builder.load(self.member_ptr)
+                index = cgutils.increment_index(builder, index)
+                builder.store(index, self.member_ptr)
+
+        def loop_break(self, context, builder, logical_dim):
+            if logical_dim == 0:
+                # At the exit of outermost logical dimension, reset index
+                zero = context.get_constant(types.intp, 0)
+                builder.store(zero, self.member_ptr)
+            elif logical_dim == self.ndim - 1:
+                # Inside innermost logical dimension, increment index
+                index = builder.load(self.member_ptr)
+                index = cgutils.increment_index(builder, index)
+                builder.store(index, self.member_ptr)
+
+    class TrivialFlatSubIter(BaseSubIter):
+        """
+        Sub-iterator walking a contiguous array in physical order,
+        *without* support for broadcasting.
+        """
+
+        def init_specific(self, context, builder):
+            assert not nditerty.need_shaped_indexing
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            assert len(indices) <= 1, len(indices)
+            return builder.gep(arr.data, indices)
+
+    class IndexedSubIter(BaseSubIter):
+        """
+        Sub-iterator walking an array in logical order.
+        """
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            assert len(indices) == self.ndim
+            return cgutils.get_item_pointer(context, builder, arrty, arr,
+                                            indices, wraparound=False)
+
+    class ZeroDimSubIter(BaseSubIter):
+        """
+        Sub-iterator "walking" a 0-d array.
+        """
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            return arr.data
+
+    class ScalarSubIter(BaseSubIter):
+        """
+        Sub-iterator "walking" a scalar value.
+        """
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            return arr
+
+    class NdIter(cgutils.create_struct_proxy(nditerty)):
+        """
+        .nditer() implementation.
+
+        Note: 'F' layout means the shape is iterated in reverse logical order,
+        so indices and shapes arrays have to be reversed as well.
+        """
+
+        @utils.cached_property
+        def subiters(self):
+            l = []
+            factories = {'flat': FlatSubIter if nditerty.need_shaped_indexing
+                         else TrivialFlatSubIter,
+                         'indexed': IndexedSubIter,
+                         '0d': ZeroDimSubIter,
+                         'scalar': ScalarSubIter,
+                         }
+            for i, sub in enumerate(nditerty.indexers):
+                kind, start_dim, end_dim, _ = sub
+                member_name = 'index%d' % i
+                factory = factories[kind]
+                l.append(factory(self, member_name, start_dim, end_dim))
+            return l
+
+        def init_specific(self, context, builder, arrtys, arrays):
+            """
+            Initialize the nditer() instance for the specific array inputs.
+            """
+            zero = context.get_constant(types.intp, 0)
+
+            # Store inputs
+            self.arrays = context.make_tuple(builder, types.Tuple(arrtys),
+                                             arrays)
+            # Create slots for scalars
+            for i, ty in enumerate(arrtys):
+                if not isinstance(ty, types.Array):
+                    member_name = 'scalar%d' % i
+                    # XXX as_data()?
+                    slot = cgutils.alloca_once_value(builder, arrays[i])
+                    setattr(self, member_name, slot)
+
+            arrays = self._arrays_or_scalars(context, builder, arrtys, arrays)
+
+            # Extract iterator shape (the shape of the most-dimensional input)
+            main_shape_ty = types.UniTuple(types.intp, ndim)
+            main_shape = None
+            main_nitems = None
+            for i, arrty in enumerate(arrtys):
+                if isinstance(arrty, types.Array) and arrty.ndim == ndim:
+                    main_shape = arrays[i].shape
+                    main_nitems = arrays[i].nitems
+                    break
+            else:
+                # Only scalar inputs => synthesize a dummy shape
+                assert ndim == 0
+                main_shape = context.make_tuple(builder, main_shape_ty, ())
+                main_nitems = context.get_constant(types.intp, 1)
+
+            # Validate shapes of array inputs
+            def check_shape(shape, main_shape):
+                n = len(shape)
+                for i in range(n):
+                    if shape[i] != main_shape[len(main_shape) - n + i]:
+                        raise ValueError("nditer(): operands could not be "
+                                         "broadcast together")
+
+            for arrty, arr in zip(arrtys, arrays):
+                if isinstance(arrty, types.Array) and arrty.ndim > 0:
+                    sig = signature(types.none,
+                                    types.UniTuple(types.intp, arrty.ndim),
+                                    main_shape_ty)
+                    context.compile_internal(builder, check_shape,
+                                             sig, (arr.shape, main_shape))
+
+            # Compute shape and size
+            shapes = cgutils.unpack_tuple(builder, main_shape)
+            if layout == 'F':
+                shapes = shapes[::-1]
+
+            # If shape is empty, mark iterator exhausted
+            shape_is_empty = builder.icmp_signed('==', main_nitems, zero)
+            exhausted = builder.select(shape_is_empty, cgutils.true_byte,
+                                       cgutils.false_byte)
+
+            if not nditerty.need_shaped_indexing:
+                # Flatten shape to make iteration faster on small innermost
+                # dimensions (e.g. a (100000, 3) shape)
+                shapes = (main_nitems,)
+            assert len(shapes) == nshapes
+
+            indices = cgutils.alloca_once(builder, zero.type, size=nshapes)
+            for dim in range(nshapes):
+                idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                builder.store(zero, idxptr)
+
+            self.indices = indices
+            self.shape = cgutils.pack_array(builder, shapes, zero.type)
+            self.exhausted = cgutils.alloca_once_value(builder, exhausted)
+
+            # Initialize subiterators
+            for subiter in self.subiters:
+                subiter.init_specific(context, builder)
+
+        def iternext_specific(self, context, builder, result):
+            """
+            Compute next iteration of the nditer() instance.
+            """
+            bbend = builder.append_basic_block('end')
+
+            # Branch early if exhausted
+            exhausted = cgutils.as_bool_bit(builder,
+                                            builder.load(self.exhausted))
+            with cgutils.if_unlikely(builder, exhausted):
+                result.set_valid(False)
+                builder.branch(bbend)
+
+            arrtys = nditerty.arrays
+            arrays = cgutils.unpack_tuple(builder, self.arrays)
+            arrays = self._arrays_or_scalars(context, builder, arrtys, arrays)
+            indices = self.indices
+
+            # Compute iterated results
+            result.set_valid(True)
+            views = self._make_views(context, builder, indices, arrtys, arrays)
+            views = [v._getvalue() for v in views]
+            if len(views) == 1:
+                result.yield_(views[0])
+            else:
+                result.yield_(context.make_tuple(builder, nditerty.yield_type,
+                                                 views))
+
+            shape = cgutils.unpack_tuple(builder, self.shape)
+            _increment_indices(context, builder, len(shape), shape,
+                               indices, self.exhausted,
+                               functools.partial(self._loop_continue,
+                                                 context,
+                                                 builder),
+                               functools.partial(self._loop_break,
+                                                 context,
+                                                 builder),
+                               )
+
+            builder.branch(bbend)
+            builder.position_at_end(bbend)
+
+        def _loop_continue(self, context, builder, dim):
+            for sub in self.subiters:
+                if sub.start_dim <= dim < sub.end_dim:
+                    sub.loop_continue(context, builder, dim - sub.start_dim)
+
+        def _loop_break(self, context, builder, dim):
+            for sub in self.subiters:
+                if sub.start_dim <= dim < sub.end_dim:
+                    sub.loop_break(context, builder, dim - sub.start_dim)
+
+        def _make_views(self, context, builder, indices, arrtys, arrays):
+            """
+            Compute the views to be yielded.
+            """
+            views = [None] * narrays
+            indexers = nditerty.indexers
+            subiters = self.subiters
+            rettys = nditerty.yield_type
+            if isinstance(rettys, types.BaseTuple):
+                rettys = list(rettys)
+            else:
+                rettys = [rettys]
+            indices = [builder.load(cgutils.gep_inbounds(builder, indices, i))
+                       for i in range(nshapes)]
+
+            for sub, subiter in zip(indexers, subiters):
+                _, _, _, array_indices = sub
+                sub_indices = indices[subiter.start_dim:subiter.end_dim]
+                if layout == 'F':
+                    sub_indices = sub_indices[::-1]
+                for i in array_indices:
+                    assert views[i] is None
+                    views[i] = self._make_view(context, builder, sub_indices,
+                                               rettys[i],
+                                               arrtys[i], arrays[i], subiter)
+            assert all(v for v in views)
+            return views
+
+        def _make_view(self, context, builder, indices, retty, arrty, arr,
+                       subiter):
+            """
+            Compute a 0d view for a given input array.
+            """
+            assert isinstance(retty, types.Array) and retty.ndim == 0
+
+            ptr = subiter.compute_pointer(context, builder, indices, arrty, arr)
+            view = context.make_array(retty)(context, builder)
+
+            itemsize = get_itemsize(context, retty)
+            shape = context.make_tuple(builder, types.UniTuple(types.intp, 0),
+                                       ())
+            strides = context.make_tuple(builder, types.UniTuple(types.intp, 0),
+                                         ())
+            # HACK: meminfo=None avoids expensive refcounting operations
+            # on ephemeral views
+            populate_array(view, ptr, shape, strides, itemsize, meminfo=None)
+            return view
+
+        def _arrays_or_scalars(self, context, builder, arrtys, arrays):
+            # Return a list of either array structures or pointers to
+            # scalar slots
+            l = []
+            for i, (arrty, arr) in enumerate(zip(arrtys, arrays)):
+                if isinstance(arrty, types.Array):
+                    l.append(context.make_array(arrty)(context,
+                                                       builder,
+                                                       value=arr))
+                else:
+                    l.append(getattr(self, "scalar%d" % i))
+            return l
+
+    return NdIter
+
+
+def make_ndindex_cls(nditerty):
+    """
+    Return the Structure representation of the given *nditerty* (an
+    instance of types.NumpyNdIndexType).
+    """
+    ndim = nditerty.ndim
+
+    class NdIndexIter(cgutils.create_struct_proxy(nditerty)):
+        """
+        .ndindex() implementation.
+        """
+
+        def init_specific(self, context, builder, shapes):
+            zero = context.get_constant(types.intp, 0)
+            indices = cgutils.alloca_once(builder, zero.type,
+                                          size=context.get_constant(types.intp,
+                                                                    ndim))
+            exhausted = cgutils.alloca_once_value(builder, cgutils.false_byte)
+
+            for dim in range(ndim):
+                idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                builder.store(zero, idxptr)
+                # 0-sized dimensions really indicate an empty array,
+                # but we have to catch that condition early to avoid
+                # a bug inside the iteration logic.
+                dim_size = shapes[dim]
+                dim_is_empty = builder.icmp_unsigned('==', dim_size, zero)
+                with cgutils.if_unlikely(builder, dim_is_empty):
+                    builder.store(cgutils.true_byte, exhausted)
+
+            self.indices = indices
+            self.exhausted = exhausted
+            self.shape = cgutils.pack_array(builder, shapes, zero.type)
+
+        def iternext_specific(self, context, builder, result):
+            zero = context.get_constant(types.intp, 0)
+
+            bbend = builder.append_basic_block('end')
+
+            exhausted = cgutils.as_bool_bit(builder,
+                                            builder.load(self.exhausted))
+            with cgutils.if_unlikely(builder, exhausted):
+                result.set_valid(False)
+                builder.branch(bbend)
+
+            indices = [builder.load(cgutils.gep_inbounds(builder,
+                                                         self.indices,
+                                                         dim))
+                       for dim in range(ndim)]
+            for load in indices:
+                mark_positive(builder, load)
+
+            result.yield_(cgutils.pack_array(builder, indices, zero.type))
+            result.set_valid(True)
+
+            shape = cgutils.unpack_tuple(builder, self.shape, ndim)
+            _increment_indices(context, builder, ndim, shape,
+                               self.indices, self.exhausted)
+
+            builder.branch(bbend)
+            builder.position_at_end(bbend)
+
+    return NdIndexIter
+
+
+def _make_flattening_iter_cls(flatiterty, kind):
+    assert kind in ('flat', 'ndenumerate')
+
+    array_type = flatiterty.array_type
+
+    if array_type.layout == 'C':
+        class CContiguousFlatIter(cgutils.create_struct_proxy(flatiterty)):
+            """
+            .flat() / .ndenumerate() implementation for C-contiguous arrays.
+            """
+
+            def init_specific(self, context, builder, arrty, arr):
+                zero = context.get_constant(types.intp, 0)
+                self.index = cgutils.alloca_once_value(builder, zero)
+                # We can't trust strides[-1] to always contain the right
+                # step value, see
+                # http://docs.scipy.org/doc/numpy-dev/release.html#npy-relaxed-strides-checking    # noqa: E501
+                self.stride = arr.itemsize
+
+                if kind == 'ndenumerate':
+                    # Zero-initialize the indices array.
+                    indices = cgutils.alloca_once(
+                        builder, zero.type,
+                        size=context.get_constant(types.intp, arrty.ndim))
+
+                    for dim in range(arrty.ndim):
+                        idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                        builder.store(zero, idxptr)
+
+                    self.indices = indices
+
+            # NOTE: Using gep() instead of explicit pointer addition helps
+            # LLVM vectorize the loop (since the stride is known and
+            # constant).  This is not possible in the non-contiguous case,
+            # where the strides are unknown at compile-time.
+
+            def iternext_specific(self, context, builder, arrty, arr, result):
+                ndim = arrty.ndim
+                nitems = arr.nitems
+
+                index = builder.load(self.index)
+                is_valid = builder.icmp_signed('<', index, nitems)
+                result.set_valid(is_valid)
+
+                with cgutils.if_likely(builder, is_valid):
+                    ptr = builder.gep(arr.data, [index])
+                    value = load_item(context, builder, arrty, ptr)
+                    if kind == 'flat':
+                        result.yield_(value)
+                    else:
+                        # ndenumerate(): fetch and increment indices
+                        indices = self.indices
+                        idxvals = [builder.load(cgutils.gep_inbounds(builder,
+                                                                     indices,
+                                                                     dim))
+                                   for dim in range(ndim)]
+                        idxtuple = cgutils.pack_array(builder, idxvals)
+                        result.yield_(
+                            cgutils.make_anonymous_struct(builder,
+                                                          [idxtuple, value]))
+                        _increment_indices_array(context, builder, arrty,
+                                                 arr, indices)
+
+                    index = cgutils.increment_index(builder, index)
+                    builder.store(index, self.index)
+
+            def getitem(self, context, builder, arrty, arr, index):
+                ptr = builder.gep(arr.data, [index])
+                return load_item(context, builder, arrty, ptr)
+
+            def setitem(self, context, builder, arrty, arr, index, value):
+                ptr = builder.gep(arr.data, [index])
+                store_item(context, builder, arrty, value, ptr)
+
+        return CContiguousFlatIter
+
+    else:
+        class FlatIter(cgutils.create_struct_proxy(flatiterty)):
+            """
+            Generic .flat() / .ndenumerate() implementation for
+            non-contiguous arrays.
+            It keeps track of pointers along each dimension in order to
+            minimize computations.
+            """
+
+            def init_specific(self, context, builder, arrty, arr):
+                zero = context.get_constant(types.intp, 0)
+                data = arr.data
+                ndim = arrty.ndim
+                shapes = cgutils.unpack_tuple(builder, arr.shape, ndim)
+
+                indices = cgutils.alloca_once(
+                    builder, zero.type, size=context.get_constant(types.intp,
+                                                                  arrty.ndim))
+                pointers = cgutils.alloca_once(
+                    builder, data.type, size=context.get_constant(types.intp,
+                                                                  arrty.ndim))
+                exhausted = cgutils.alloca_once_value(builder,
+                                                      cgutils.false_byte)
+
+                # Initialize indices and pointers with their start values.
+                for dim in range(ndim):
+                    idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                    ptrptr = cgutils.gep_inbounds(builder, pointers, dim)
+                    builder.store(data, ptrptr)
+                    builder.store(zero, idxptr)
+                    # 0-sized dimensions really indicate an empty array,
+                    # but we have to catch that condition early to avoid
+                    # a bug inside the iteration logic (see issue #846).
+                    dim_size = shapes[dim]
+                    dim_is_empty = builder.icmp_unsigned('==', dim_size, zero)
+                    with cgutils.if_unlikely(builder, dim_is_empty):
+                        builder.store(cgutils.true_byte, exhausted)
+
+                self.indices = indices
+                self.pointers = pointers
+                self.exhausted = exhausted
+
+            def iternext_specific(self, context, builder, arrty, arr, result):
+                ndim = arrty.ndim
+                shapes = cgutils.unpack_tuple(builder, arr.shape, ndim)
+                strides = cgutils.unpack_tuple(builder, arr.strides, ndim)
+                indices = self.indices
+                pointers = self.pointers
+
+                zero = context.get_constant(types.intp, 0)
+
+                bbend = builder.append_basic_block('end')
+
+                # Catch already computed iterator exhaustion
+                is_exhausted = cgutils.as_bool_bit(
+                    builder, builder.load(self.exhausted))
+                with cgutils.if_unlikely(builder, is_exhausted):
+                    result.set_valid(False)
+                    builder.branch(bbend)
+                result.set_valid(True)
+
+                # Current pointer inside last dimension
+                last_ptr = cgutils.gep_inbounds(builder, pointers, ndim - 1)
+                ptr = builder.load(last_ptr)
+                value = load_item(context, builder, arrty, ptr)
+                if kind == 'flat':
+                    result.yield_(value)
+                else:
+                    # ndenumerate() => yield (indices, value)
+                    idxvals = [builder.load(cgutils.gep_inbounds(builder,
+                                                                 indices,
+                                                                 dim))
+                               for dim in range(ndim)]
+                    idxtuple = cgutils.pack_array(builder, idxvals)
+                    result.yield_(
+                        cgutils.make_anonymous_struct(builder,
+                                                      [idxtuple, value]))
+
+                # Update indices and pointers by walking from inner
+                # dimension to outer.
+                for dim in reversed(range(ndim)):
+                    idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                    idx = cgutils.increment_index(builder,
+                                                  builder.load(idxptr))
+
+                    count = shapes[dim]
+                    stride = strides[dim]
+                    in_bounds = builder.icmp_signed('<', idx, count)
+                    with cgutils.if_likely(builder, in_bounds):
+                        # Index is valid => pointer can simply be incremented.
+                        builder.store(idx, idxptr)
+                        ptrptr = cgutils.gep_inbounds(builder, pointers, dim)
+                        ptr = builder.load(ptrptr)
+                        ptr = cgutils.pointer_add(builder, ptr, stride)
+                        builder.store(ptr, ptrptr)
+                        # Reset pointers in inner dimensions
+                        for inner_dim in range(dim + 1, ndim):
+                            ptrptr = cgutils.gep_inbounds(builder,
+                                                          pointers,
+                                                          inner_dim)
+                            builder.store(ptr, ptrptr)
+                        builder.branch(bbend)
+                    # Reset index and continue with next dimension
+                    builder.store(zero, idxptr)
+
+                # End of array
+                builder.store(cgutils.true_byte, self.exhausted)
+                builder.branch(bbend)
+
+                builder.position_at_end(bbend)
+
+            def _ptr_for_index(self, context, builder, arrty, arr, index):
+                ndim = arrty.ndim
+                shapes = cgutils.unpack_tuple(builder, arr.shape, count=ndim)
+                strides = cgutils.unpack_tuple(builder, arr.strides, count=ndim)
+
+                # First convert the flattened index into a regular n-dim index
+                indices = []
+                for dim in reversed(range(ndim)):
+                    indices.append(builder.urem(index, shapes[dim]))
+                    index = builder.udiv(index, shapes[dim])
+                indices.reverse()
+
+                ptr = cgutils.get_item_pointer2(context, builder, arr.data,
+                                                shapes, strides, arrty.layout,
+                                                indices)
+                return ptr
+
+            def getitem(self, context, builder, arrty, arr, index):
+                ptr = self._ptr_for_index(context, builder, arrty, arr, index)
+                return load_item(context, builder, arrty, ptr)
+
+            def setitem(self, context, builder, arrty, arr, index, value):
+                ptr = self._ptr_for_index(context, builder, arrty, arr, index)
+                store_item(context, builder, arrty, value, ptr)
+
+        return FlatIter
+
+
+@lower_getattr(types.Array, "flat")
+def make_array_flatiter(context, builder, arrty, arr):
+    flatitercls = make_array_flat_cls(types.NumpyFlatType(arrty))
+    flatiter = flatitercls(context, builder)
+
+    flatiter.array = arr
+
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, ref=flatiter._get_ptr_by_name('array'))
+
+    flatiter.init_specific(context, builder, arrty, arr)
+
+    res = flatiter._getvalue()
+    return impl_ret_borrowed(context, builder, types.NumpyFlatType(arrty), res)
+
+
+@lower_builtin('iternext', types.NumpyFlatType)
+@iternext_impl(RefType.BORROWED)
+def iternext_numpy_flatiter(context, builder, sig, args, result):
+    [flatiterty] = sig.args
+    [flatiter] = args
+
+    flatitercls = make_array_flat_cls(flatiterty)
+    flatiter = flatitercls(context, builder, value=flatiter)
+
+    arrty = flatiterty.array_type
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, value=flatiter.array)
+
+    flatiter.iternext_specific(context, builder, arrty, arr, result)
+
+
+@lower_builtin(operator.getitem, types.NumpyFlatType, types.Integer)
+def iternext_numpy_getitem(context, builder, sig, args):
+    flatiterty = sig.args[0]
+    flatiter, index = args
+
+    flatitercls = make_array_flat_cls(flatiterty)
+    flatiter = flatitercls(context, builder, value=flatiter)
+
+    arrty = flatiterty.array_type
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, value=flatiter.array)
+
+    res = flatiter.getitem(context, builder, arrty, arr, index)
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.setitem, types.NumpyFlatType, types.Integer,
+               types.Any)
+def iternext_numpy_getitem_any(context, builder, sig, args):
+    flatiterty = sig.args[0]
+    flatiter, index, value = args
+
+    flatitercls = make_array_flat_cls(flatiterty)
+    flatiter = flatitercls(context, builder, value=flatiter)
+
+    arrty = flatiterty.array_type
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, value=flatiter.array)
+
+    flatiter.setitem(context, builder, arrty, arr, index, value)
+    return context.get_dummy_value()
+
+
+@lower_builtin(len, types.NumpyFlatType)
+def iternext_numpy_getitem_flat(context, builder, sig, args):
+    flatiterty = sig.args[0]
+    flatitercls = make_array_flat_cls(flatiterty)
+    flatiter = flatitercls(context, builder, value=args[0])
+
+    arrcls = context.make_array(flatiterty.array_type)
+    arr = arrcls(context, builder, value=flatiter.array)
+    return arr.nitems
+
+
+@lower_builtin(np.ndenumerate, types.Array)
+def make_array_ndenumerate(context, builder, sig, args):
+    arrty, = sig.args
+    arr, = args
+    nditercls = make_array_ndenumerate_cls(types.NumpyNdEnumerateType(arrty))
+    nditer = nditercls(context, builder)
+
+    nditer.array = arr
+
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, ref=nditer._get_ptr_by_name('array'))
+
+    nditer.init_specific(context, builder, arrty, arr)
+
+    res = nditer._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('iternext', types.NumpyNdEnumerateType)
+@iternext_impl(RefType.BORROWED)
+def iternext_numpy_nditer(context, builder, sig, args, result):
+    [nditerty] = sig.args
+    [nditer] = args
+
+    nditercls = make_array_ndenumerate_cls(nditerty)
+    nditer = nditercls(context, builder, value=nditer)
+
+    arrty = nditerty.array_type
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, value=nditer.array)
+
+    nditer.iternext_specific(context, builder, arrty, arr, result)
+
+
+@lower_builtin(pndindex, types.VarArg(types.Integer))
+@lower_builtin(np.ndindex, types.VarArg(types.Integer))
+def make_array_ndindex(context, builder, sig, args):
+    """ndindex(*shape)"""
+    shape = [context.cast(builder, arg, argty, types.intp)
+             for argty, arg in zip(sig.args, args)]
+
+    nditercls = make_ndindex_cls(types.NumpyNdIndexType(len(shape)))
+    nditer = nditercls(context, builder)
+    nditer.init_specific(context, builder, shape)
+
+    res = nditer._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(pndindex, types.BaseTuple)
+@lower_builtin(np.ndindex, types.BaseTuple)
+def make_array_ndindex_tuple(context, builder, sig, args):
+    """ndindex(shape)"""
+    ndim = sig.return_type.ndim
+    if ndim > 0:
+        idxty = sig.args[0].dtype
+        tup = args[0]
+
+        shape = cgutils.unpack_tuple(builder, tup, ndim)
+        shape = [context.cast(builder, idx, idxty, types.intp)
+                 for idx in shape]
+    else:
+        shape = []
+
+    nditercls = make_ndindex_cls(types.NumpyNdIndexType(len(shape)))
+    nditer = nditercls(context, builder)
+    nditer.init_specific(context, builder, shape)
+
+    res = nditer._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('iternext', types.NumpyNdIndexType)
+@iternext_impl(RefType.BORROWED)
+def iternext_numpy_ndindex(context, builder, sig, args, result):
+    [nditerty] = sig.args
+    [nditer] = args
+
+    nditercls = make_ndindex_cls(nditerty)
+    nditer = nditercls(context, builder, value=nditer)
+
+    nditer.iternext_specific(context, builder, result)
+
+
+@lower_builtin(np.nditer, types.Any)
+def make_array_nditer(context, builder, sig, args):
+    """
+    nditer(...)
+    """
+    nditerty = sig.return_type
+    arrtys = nditerty.arrays
+
+    if isinstance(sig.args[0], types.BaseTuple):
+        arrays = cgutils.unpack_tuple(builder, args[0])
+    else:
+        arrays = [args[0]]
+
+    nditer = make_nditer_cls(nditerty)(context, builder)
+    nditer.init_specific(context, builder, arrtys, arrays)
+
+    res = nditer._getvalue()
+    return impl_ret_borrowed(context, builder, nditerty, res)
+
+
+@lower_builtin('iternext', types.NumpyNdIterType)
+@iternext_impl(RefType.BORROWED)
+def iternext_numpy_nditer2(context, builder, sig, args, result):
+    [nditerty] = sig.args
+    [nditer] = args
+
+    nditer = make_nditer_cls(nditerty)(context, builder, value=nditer)
+    nditer.iternext_specific(context, builder, result)
+
+
+# ------------------------------------------------------------------------------
+# Numpy array constructors
+
+def _empty_nd_impl(context, builder, arrtype, shapes):
+    """Utility function used for allocating a new array during LLVM code
+    generation (lowering).  Given a target context, builder, array
+    type, and a tuple or list of lowered dimension sizes, returns a
+    LLVM value pointing at a Numba runtime allocated array.
+    """
+    arycls = make_array(arrtype)
+    ary = arycls(context, builder)
+
+    datatype = context.get_data_type(arrtype.dtype)
+    itemsize = context.get_constant(types.intp, get_itemsize(context, arrtype))
+
+    # compute array length
+    arrlen = context.get_constant(types.intp, 1)
+    overflow = Constant(ir.IntType(1), 0)
+    for s in shapes:
+        arrlen_mult = builder.smul_with_overflow(arrlen, s)
+        arrlen = builder.extract_value(arrlen_mult, 0)
+        overflow = builder.or_(
+            overflow, builder.extract_value(arrlen_mult, 1)
+        )
+
+    if arrtype.ndim == 0:
+        strides = ()
+    elif arrtype.layout == 'C':
+        strides = [itemsize]
+        for dimension_size in reversed(shapes[1:]):
+            strides.append(builder.mul(strides[-1], dimension_size))
+        strides = tuple(reversed(strides))
+    elif arrtype.layout == 'F':
+        strides = [itemsize]
+        for dimension_size in shapes[:-1]:
+            strides.append(builder.mul(strides[-1], dimension_size))
+        strides = tuple(strides)
+    else:
+        raise NotImplementedError(
+            "Don't know how to allocate array with layout '{0}'.".format(
+                arrtype.layout))
+
+    # Check overflow, numpy also does this after checking order
+    allocsize_mult = builder.smul_with_overflow(arrlen, itemsize)
+    allocsize = builder.extract_value(allocsize_mult, 0)
+    overflow = builder.or_(overflow, builder.extract_value(allocsize_mult, 1))
+
+    with builder.if_then(overflow, likely=False):
+        # Raise same error as numpy, see:
+        # https://github.com/numpy/numpy/blob/2a488fe76a0f732dc418d03b452caace161673da/numpy/core/src/multiarray/ctors.c#L1095-L1101    # noqa: E501
+        context.call_conv.return_user_exc(
+            builder, ValueError,
+            ("array is too big; `arr.size * arr.dtype.itemsize` is larger than"
+             " the maximum possible size.",)
+        )
+
+    dtype = arrtype.dtype
+    align_val = context.get_preferred_array_alignment(dtype)
+    align = context.get_constant(types.uint32, align_val)
+    args = (context.get_dummy_value(), allocsize, align)
+
+    mip = types.MemInfoPointer(types.voidptr)
+    arytypeclass = types.TypeRef(type(arrtype))
+    argtypes = signature(mip, arytypeclass, types.intp, types.uint32)
+
+    meminfo = context.compile_internal(builder, _call_allocator, argtypes, args)
+    data = context.nrt.meminfo_data(builder, meminfo)
+
+    intp_t = context.get_value_type(types.intp)
+    shape_array = cgutils.pack_array(builder, shapes, ty=intp_t)
+    strides_array = cgutils.pack_array(builder, strides, ty=intp_t)
+
+    populate_array(ary,
+                   data=builder.bitcast(data, datatype.as_pointer()),
+                   shape=shape_array,
+                   strides=strides_array,
+                   itemsize=itemsize,
+                   meminfo=meminfo)
+
+    return ary
+
+
+@overload_classmethod(types.Array, "_allocate")
+def _ol_array_allocate(cls, allocsize, align):
+    """Implements a Numba-only default target (cpu) classmethod on the array type.
+    """
+    def impl(cls, allocsize, align):
+        return intrin_alloc(allocsize, align)
+    return impl
+
+
+def _call_allocator(arrtype, size, align):
+    """Trampoline to call the intrinsic used for allocation
+    """
+    return arrtype._allocate(size, align)
+
+
+@intrinsic
+def intrin_alloc(typingctx, allocsize, align):
+    """Intrinsic to call into the allocator for Array
+    """
+    def codegen(context, builder, signature, args):
+        [allocsize, align] = args
+        meminfo = context.nrt.meminfo_alloc_aligned(builder, allocsize, align)
+        return meminfo
+
+    mip = types.MemInfoPointer(types.voidptr)    # return untyped pointer
+    sig = signature(mip, allocsize, align)
+    return sig, codegen
+
+
+def _parse_shape(context, builder, ty, val):
+    """
+    Parse the shape argument to an array constructor.
+    """
+    def safecast_intp(context, builder, src_t, src):
+        """Cast src to intp only if value can be maintained"""
+        intp_t = context.get_value_type(types.intp)
+        intp_width = intp_t.width
+        intp_ir = ir.IntType(intp_width)
+        maxval = Constant(intp_ir, ((1 << intp_width - 1) - 1))
+        if src_t.width < intp_width:
+            res = builder.sext(src, intp_ir)
+        elif src_t.width >= intp_width:
+            is_larger = builder.icmp_signed(">", src, maxval)
+            with builder.if_then(is_larger, likely=False):
+                context.call_conv.return_user_exc(
+                    builder, ValueError,
+                    ("Cannot safely convert value to intp",)
+                )
+            if src_t.width > intp_width:
+                res = builder.trunc(src, intp_ir)
+            else:
+                res = src
+        return res
+
+    if isinstance(ty, types.Integer):
+        ndim = 1
+        passed_shapes = [context.cast(builder, val, ty, types.intp)]
+    else:
+        assert isinstance(ty, types.BaseTuple)
+        ndim = ty.count
+        passed_shapes = cgutils.unpack_tuple(builder, val, count=ndim)
+
+    shapes = []
+    for s in passed_shapes:
+        shapes.append(safecast_intp(context, builder, s.type, s))
+
+    zero = context.get_constant_generic(builder, types.intp, 0)
+    for dim in range(ndim):
+        is_neg = builder.icmp_signed('<', shapes[dim], zero)
+        with cgutils.if_unlikely(builder, is_neg):
+            context.call_conv.return_user_exc(
+                builder, ValueError, ("negative dimensions not allowed",)
+            )
+
+    return shapes
+
+
+def _parse_empty_args(context, builder, sig, args):
+    """
+    Parse the arguments of a np.empty(), np.zeros() or np.ones() call.
+    """
+    arrshapetype = sig.args[0]
+    arrshape = args[0]
+    arrtype = sig.return_type
+    return arrtype, _parse_shape(context, builder, arrshapetype, arrshape)
+
+
+def _parse_empty_like_args(context, builder, sig, args):
+    """
+    Parse the arguments of a np.empty_like(), np.zeros_like() or
+    np.ones_like() call.
+    """
+    arytype = sig.args[0]
+    if isinstance(arytype, types.Array):
+        ary = make_array(arytype)(context, builder, value=args[0])
+        shapes = cgutils.unpack_tuple(builder, ary.shape, count=arytype.ndim)
+        return sig.return_type, shapes
+    else:
+        return sig.return_type, ()
+
+
+def _check_const_str_dtype(fname, dtype):
+    if isinstance(dtype, types.UnicodeType):
+        msg = f"If np.{fname} dtype is a string it must be a string constant."
+        raise errors.TypingError(msg)
+
+
+@intrinsic
+def numpy_empty_nd(tyctx, ty_shape, ty_dtype, ty_retty_ref):
+    ty_retty = ty_retty_ref.instance_type
+    sig = ty_retty(ty_shape, ty_dtype, ty_retty_ref)
+
+    def codegen(cgctx, builder, sig, llargs):
+        arrtype, shapes = _parse_empty_args(cgctx, builder, sig, llargs)
+        ary = _empty_nd_impl(cgctx, builder, arrtype, shapes)
+        return ary._getvalue()
+    return sig, codegen
+
+
+@overload(np.empty)
+def ol_np_empty(shape, dtype=float):
+    _check_const_str_dtype("empty", dtype)
+    if (dtype is float or
+        (isinstance(dtype, types.Function) and dtype.typing_key is float) or
+            is_nonelike(dtype)): #default
+        nb_dtype = types.double
+    else:
+        nb_dtype = ty_parse_dtype(dtype)
+
+    ndim = ty_parse_shape(shape)
+    if nb_dtype is not None and ndim is not None:
+        retty = types.Array(dtype=nb_dtype, ndim=ndim, layout='C')
+
+        def impl(shape, dtype=float):
+            return numpy_empty_nd(shape, dtype, retty)
+        return impl
+    else:
+        msg = f"Cannot parse input types to function np.empty({shape}, {dtype})"
+        raise errors.TypingError(msg)
+
+
+@intrinsic
+def numpy_empty_like_nd(tyctx, ty_prototype, ty_dtype, ty_retty_ref):
+    ty_retty = ty_retty_ref.instance_type
+    sig = ty_retty(ty_prototype, ty_dtype, ty_retty_ref)
+
+    def codegen(cgctx, builder, sig, llargs):
+        arrtype, shapes = _parse_empty_like_args(cgctx, builder, sig, llargs)
+        ary = _empty_nd_impl(cgctx, builder, arrtype, shapes)
+        return ary._getvalue()
+    return sig, codegen
+
+
+@overload(np.empty_like)
+def ol_np_empty_like(arr, dtype=None):
+    _check_const_str_dtype("empty_like", dtype)
+    if not is_nonelike(dtype):
+        nb_dtype = ty_parse_dtype(dtype)
+    elif isinstance(arr, types.Array):
+        nb_dtype = arr.dtype
+    else:
+        nb_dtype = arr
+    if nb_dtype is not None:
+        if isinstance(arr, types.Array):
+            layout = arr.layout if arr.layout != 'A' else 'C'
+            retty = arr.copy(dtype=nb_dtype, layout=layout, readonly=False)
+        else:
+            retty = types.Array(nb_dtype, 0, 'C')
+    else:
+        msg = ("Cannot parse input types to function "
+               f"np.empty_like({arr}, {dtype})")
+        raise errors.TypingError(msg)
+
+    def impl(arr, dtype=None):
+        return numpy_empty_like_nd(arr, dtype, retty)
+    return impl
+
+
+@intrinsic
+def _zero_fill_array_method(tyctx, self):
+    sig = types.none(self)
+
+    def codegen(cgctx, builder, sig, llargs):
+        ary = make_array(sig.args[0])(cgctx, builder, llargs[0])
+        cgutils.memset(builder, ary.data, builder.mul(ary.itemsize, ary.nitems),
+                       0)
+    return sig, codegen
+
+
+@overload_method(types.Array, '_zero_fill')
+def ol_array_zero_fill(self):
+    """Adds a `._zero_fill` method to zero fill an array using memset."""
+    def impl(self):
+        _zero_fill_array_method(self)
+    return impl
+
+
+@overload(np.zeros)
+def ol_np_zeros(shape, dtype=float):
+    _check_const_str_dtype("zeros", dtype)
+
+    def impl(shape, dtype=float):
+        arr = np.empty(shape, dtype=dtype)
+        arr._zero_fill()
+        return arr
+    return impl
+
+
+@overload(np.zeros_like)
+def ol_np_zeros_like(a, dtype=None):
+    _check_const_str_dtype("zeros_like", dtype)
+
+    # NumPy uses 'a' as the arg name for the array-like
+    def impl(a, dtype=None):
+        arr = np.empty_like(a, dtype=dtype)
+        arr._zero_fill()
+        return arr
+    return impl
+
+
+@overload(np.ones_like)
+def ol_np_ones_like(a, dtype=None):
+    _check_const_str_dtype("ones_like", dtype)
+
+    # NumPy uses 'a' as the arg name for the array-like
+    def impl(a, dtype=None):
+        arr = np.empty_like(a, dtype=dtype)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = 1
+        return arr
+    return impl
+
+
+@glue_lowering(np.full, types.Any, types.Any)
+def numpy_full_nd(context, builder, sig, args):
+
+    def full(shape, value):
+        arr = np.empty(shape, type(value))
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = value
+        return arr
+
+    res = context.compile_internal(builder, full, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.full, types.Any, types.Any, types.DTypeSpec)
+@glue_lowering(np.full, types.Any, types.Any, types.StringLiteral)
+def numpy_full_dtype_nd(context, builder, sig, args):
+
+    def full(shape, value, dtype):
+        arr = np.empty(shape, dtype)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = value
+        return arr
+
+    res = context.compile_internal(builder, full, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.full_like, types.Any, types.Any)
+def numpy_full_like_nd(context, builder, sig, args):
+
+    def full_like(arr, value):
+        arr = np.empty_like(arr)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = value
+        return arr
+
+    res = context.compile_internal(builder, full_like, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.full_like, types.Any, types.Any, types.DTypeSpec)
+@glue_lowering(np.full_like, types.Any, types.Any, types.StringLiteral)
+def numpy_full_like_nd_type_spec(context, builder, sig, args):
+
+    def full_like(arr, value, dtype):
+        arr = np.empty_like(arr, dtype)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = value
+        return arr
+
+    res = context.compile_internal(builder, full_like, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@overload(np.ones)
+def ol_np_ones(shape, dtype=None):
+    # for some reason the NumPy default for dtype is None in the source but
+    # ends up as np.float64 by definition.
+    _check_const_str_dtype("ones", dtype)
+
+    def impl(shape, dtype=None):
+        arr = np.empty(shape, dtype=dtype)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = 1
+        return arr
+    return impl
+
+
+@glue_lowering(np.identity, types.Integer)
+def numpy_identity(context, builder, sig, args):
+
+    def identity(n):
+        arr = np.zeros((n, n))
+        for i in range(n):
+            arr[i, i] = 1
+        return arr
+
+    res = context.compile_internal(builder, identity, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.identity, types.Integer, types.DTypeSpec)
+@glue_lowering(np.identity, types.Integer, types.StringLiteral)
+def numpy_identity_type_spec(context, builder, sig, args):
+
+    def identity(n, dtype):
+        arr = np.zeros((n, n), dtype)
+        for i in range(n):
+            arr[i, i] = 1
+        return arr
+
+    res = context.compile_internal(builder, identity, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def _eye_none_handler(N, M):
+    pass
+
+
+@extending.overload(_eye_none_handler)
+def _eye_none_handler_impl(N, M):
+    if isinstance(M, types.NoneType):
+        def impl(N, M):
+            return N
+    else:
+        def impl(N, M):
+            return M
+    return impl
+
+
+@extending.overload(np.eye)
+def numpy_eye(N, M=None, k=0, dtype=float):
+
+    if dtype is None or isinstance(dtype, types.NoneType):
+        dt = np.dtype(float)
+    elif isinstance(dtype, (types.DTypeSpec, types.Number)):
+        # dtype or instance of dtype
+        dt = as_dtype(getattr(dtype, 'dtype', dtype))
+    else:
+        dt = np.dtype(dtype)
+
+    def impl(N, M=None, k=0, dtype=float):
+        _M = _eye_none_handler(N, M)
+        arr = np.zeros((N, _M), dt)
+        if k >= 0:
+            d = min(N, _M - k)
+            for i in range(d):
+                arr[i, i + k] = 1
+        else:
+            d = min(N + k, _M)
+            for i in range(d):
+                arr[i - k, i] = 1
+        return arr
+    return impl
+
+
+@glue_lowering(np.diag, types.Array)
+def numpy_diag(context, builder, sig, args):
+    def diag_impl(val):
+        return np.diag(val, k=0)
+    return context.compile_internal(builder, diag_impl, sig, args)
+
+
+@glue_lowering(np.diag, types.Array, types.Integer)
+def numpy_diag_kwarg(context, builder, sig, args):
+    arg = sig.args[0]
+    if arg.ndim == 1:
+        # vector context
+        def diag_impl(arr, k=0):
+            s = arr.shape
+            n = s[0] + abs(k)
+            ret = np.zeros((n, n), arr.dtype)
+            if k >= 0:
+                for i in range(n - k):
+                    ret[i, k + i] = arr[i]
+            else:
+                for i in range(n + k):
+                    ret[i - k, i] = arr[i]
+            return ret
+    elif arg.ndim == 2:
+        # matrix context
+        def diag_impl(arr, k=0):
+            # Will return arr.diagonal(v, k) when axis args are supported
+            rows, cols = arr.shape
+            if k < 0:
+                rows = rows + k
+            if k > 0:
+                cols = cols - k
+            n = max(min(rows, cols), 0)
+            ret = np.empty(n, arr.dtype)
+            if k >= 0:
+                for i in range(n):
+                    ret[i] = arr[i, k + i]
+            else:
+                for i in range(n):
+                    ret[i] = arr[i - k, i]
+            return ret
+    else:
+        # invalid input
+        raise ValueError("Input must be 1- or 2-d.")
+
+    res = context.compile_internal(builder, diag_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin('array.take', types.Array, types.Integer)
+@glue_lowering(np.take, types.Array, types.Integer)
+def numpy_take_1(context, builder, sig, args):
+
+    def take_impl(a, indices):
+        if indices > (a.size - 1) or indices < -a.size:
+            raise IndexError("Index out of bounds")
+        return a.ravel()[indices]
+
+    res = context.compile_internal(builder, take_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin('array.take', types.Array, types.Array)
+@glue_lowering(np.take, types.Array, types.Array)
+def numpy_take_2(context, builder, sig, args):
+
+    F_order = sig.args[1].layout == 'F'
+
+    def take_impl(a, indices):
+        ret = np.empty(indices.size, dtype=a.dtype)
+        if F_order:
+            walker = indices.copy()  # get C order
+        else:
+            walker = indices
+        it = np.nditer(walker)
+        i = 0
+        flat = a.ravel()
+        for x in it:
+            if x > (a.size - 1) or x < -a.size:
+                raise IndexError("Index out of bounds")
+            ret[i] = flat[x]
+            i = i + 1
+        return ret.reshape(indices.shape)
+
+    res = context.compile_internal(builder, take_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin('array.take', types.Array, types.List)
+@glue_lowering(np.take, types.Array, types.List)
+@lower_builtin('array.take', types.Array, types.BaseTuple)
+@glue_lowering(np.take, types.Array, types.BaseTuple)
+def numpy_take_3(context, builder, sig, args):
+
+    def take_impl(a, indices):
+        convert = np.array(indices)
+        ret = np.empty(convert.size, dtype=a.dtype)
+        it = np.nditer(convert)
+        i = 0
+        flat = a.ravel()
+        for x in it:
+            if x > (a.size - 1) or x < -a.size:
+                raise IndexError("Index out of bounds")
+            ret[i] = flat[x]
+            i = i + 1
+        return ret.reshape(convert.shape)
+
+    res = context.compile_internal(builder, take_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def _arange_dtype(*args):
+    bounds = [a for a in args if not isinstance(a, types.NoneType)]
+
+    if any(isinstance(a, types.Complex) for a in bounds):
+        dtype = types.complex128
+    elif any(isinstance(a, types.Float) for a in bounds):
+        dtype = types.float64
+    else:
+        # `np.arange(10).dtype` is always `np.dtype(int)`, aka `np.int_`, which
+        # in all released versions of numpy corresponds to the C `long` type.
+        # Windows 64 is broken by default here because Numba (as of 0.47) does
+        # not differentiate between Python and NumPy integers, so a `typeof(1)`
+        # on w64 is `int64`, i.e. `intp`. This means an arange(<some int>) will
+        # be typed as arange(int64) and the following will yield int64 opposed
+        # to int32. Example: without a load of analysis to work out of the args
+        # were wrapped in NumPy int*() calls it's not possible to detect the
+        # difference between `np.arange(10)` and `np.arange(np.int64(10)`.
+        NPY_TY = getattr(types, "int%s" % (8 * np.dtype(int).itemsize))
+
+        # unliteral these types such that `max` works.
+        unliteral_bounds = [types.unliteral(x) for x in bounds]
+        dtype = max(unliteral_bounds + [NPY_TY,])
+
+    return dtype
+
+
+@overload(np.arange)
+def np_arange(start, stop=None, step=None, dtype=None):
+    if isinstance(stop, types.Optional):
+        stop = stop.type
+    if isinstance(step, types.Optional):
+        step = step.type
+    if isinstance(dtype, types.Optional):
+        dtype = dtype.type
+
+    if stop is None:
+        stop = types.none
+    if step is None:
+        step = types.none
+    if dtype is None:
+        dtype = types.none
+
+    if (not isinstance(start, types.Number) or
+        not isinstance(stop, (types.NoneType, types.Number)) or
+        not isinstance(step, (types.NoneType, types.Number)) or
+            not isinstance(dtype, (types.NoneType, types.DTypeSpec))):
+
+        return
+
+    if isinstance(dtype, types.NoneType):
+        true_dtype = _arange_dtype(start, stop, step)
+    else:
+        true_dtype = dtype.dtype
+
+    use_complex = any([isinstance(x, types.Complex)
+                       for x in (start, stop, step)])
+
+    start_value = getattr(start, "literal_value", None)
+    stop_value = getattr(stop, "literal_value", None)
+    step_value = getattr(step, "literal_value", None)
+
+    def impl(start, stop=None, step=None, dtype=None):
+        # Allow for improved performance if given literal arguments.
+        lit_start = start_value if start_value is not None else start
+        lit_stop = stop_value if stop_value is not None else stop
+        lit_step = step_value if step_value is not None else step
+
+        _step = lit_step if lit_step is not None else 1
+        if lit_stop is None:
+            _start, _stop = 0, lit_start
+        else:
+            _start, _stop = lit_start, lit_stop
+
+        if _step == 0:
+            raise ValueError("Maximum allowed size exceeded")
+
+        nitems_c = (_stop - _start) / _step
+        nitems_r = int(math.ceil(nitems_c.real))
+
+        # Binary operator needed for compiler branch pruning.
+        if use_complex is True:
+            nitems_i = int(math.ceil(nitems_c.imag))
+            nitems = max(min(nitems_i, nitems_r), 0)
+        else:
+            nitems = max(nitems_r, 0)
+        arr = np.empty(nitems, true_dtype)
+        val = _start
+        for i in range(nitems):
+            arr[i] = val + (i * _step)
+        return arr
+
+    return impl
+
+
+@glue_lowering(np.linspace, types.Number, types.Number)
+def numpy_linspace_2(context, builder, sig, args):
+
+    def linspace(start, stop):
+        return np.linspace(start, stop, 50)
+
+    res = context.compile_internal(builder, linspace, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@glue_lowering(np.linspace, types.Number, types.Number, types.Integer)
+def numpy_linspace_3(context, builder, sig, args):
+    dtype = as_dtype(sig.return_type.dtype)
+
+    # Implementation based on https://github.com/numpy/numpy/blob/v1.20.0/numpy/core/function_base.py#L24 # noqa: E501
+    def linspace(start, stop, num):
+        arr = np.empty(num, dtype)
+        # The multiply by 1.0 mirrors
+        # https://github.com/numpy/numpy/blob/v1.20.0/numpy/core/function_base.py#L125-L128  # noqa: E501
+        # the side effect of this is important... start and stop become the same
+        # type as `dtype` i.e. 64/128 bits wide (float/complex). This is
+        # important later when used in the `np.divide`.
+        start = start * 1.0
+        stop = stop * 1.0
+        if num == 0:
+            return arr
+        div = num - 1
+        if div > 0:
+            delta = stop - start
+            step = np.divide(delta, div)
+            for i in range(0, num):
+                arr[i] = start + (i * step)
+        else:
+            arr[0] = start
+        if num > 1:
+            arr[-1] = stop
+        return arr
+
+    res = context.compile_internal(builder, linspace, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def _array_copy(context, builder, sig, args):
+    """
+    Array copy.
+    """
+    arytype = sig.args[0]
+    ary = make_array(arytype)(context, builder, value=args[0])
+    shapes = cgutils.unpack_tuple(builder, ary.shape)
+
+    rettype = sig.return_type
+    ret = _empty_nd_impl(context, builder, rettype, shapes)
+
+    src_data = ary.data
+    dest_data = ret.data
+
+    assert rettype.layout in "CF"
+    if arytype.layout == rettype.layout:
+        # Fast path: memcpy
+        cgutils.raw_memcpy(builder, dest_data, src_data, ary.nitems,
+                           ary.itemsize, align=1)
+
+    else:
+        src_strides = cgutils.unpack_tuple(builder, ary.strides)
+        dest_strides = cgutils.unpack_tuple(builder, ret.strides)
+        intp_t = context.get_value_type(types.intp)
+
+        with cgutils.loop_nest(builder, shapes, intp_t) as indices:
+            src_ptr = cgutils.get_item_pointer2(context, builder, src_data,
+                                                shapes, src_strides,
+                                                arytype.layout, indices)
+            dest_ptr = cgutils.get_item_pointer2(context, builder, dest_data,
+                                                 shapes, dest_strides,
+                                                 rettype.layout, indices)
+            builder.store(builder.load(src_ptr), dest_ptr)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, ret._getvalue())
+
+
+@lower_builtin("array.copy", types.Array)
+def array_copy(context, builder, sig, args):
+    return _array_copy(context, builder, sig, args)
+
+
+@glue_lowering(np.copy, types.Array)
+def numpy_copy(context, builder, sig, args):
+    return _array_copy(context, builder, sig, args)
+
+
+def _as_layout_array(context, builder, sig, args, output_layout):
+    """
+    Common logic for layout conversion function;
+    e.g. ascontiguousarray and asfortranarray
+    """
+    retty = sig.return_type
+    aryty = sig.args[0]
+    assert retty.layout == output_layout, 'return-type has incorrect layout'
+
+    if aryty.ndim == 0:
+        # 0-dim input => asfortranarray() returns a 1-dim array
+        assert retty.ndim == 1
+        ary = make_array(aryty)(context, builder, value=args[0])
+        ret = make_array(retty)(context, builder)
+
+        shape = context.get_constant_generic(
+            builder, types.UniTuple(types.intp, 1), (1,),
+        )
+        strides = context.make_tuple(builder,
+                                     types.UniTuple(types.intp, 1),
+                                     (ary.itemsize,))
+        populate_array(ret, ary.data, shape, strides, ary.itemsize,
+                       ary.meminfo, ary.parent)
+        return impl_ret_borrowed(context, builder, retty, ret._getvalue())
+
+    elif (retty.layout == aryty.layout
+            or (aryty.ndim == 1 and aryty.layout in 'CF')):
+        # 1-dim contiguous input => return the same array
+        return impl_ret_borrowed(context, builder, retty, args[0])
+
+    else:
+        if aryty.layout == 'A':
+            # There's still chance the array is in contiguous layout,
+            # just that we don't know at compile time.
+            # We can do a runtime check.
+
+            # Prepare and call is_contiguous or is_fortran
+            assert output_layout in 'CF'
+            check_func = is_contiguous if output_layout == 'C' else is_fortran
+            is_contig = _call_contiguous_check(check_func,
+                                               context,
+                                               builder,
+                                               aryty,
+                                               args[0])
+            with builder.if_else(is_contig) as (then, orelse):
+                # If the array is already contiguous, just return it
+                with then:
+                    out_then = impl_ret_borrowed(context, builder, retty,
+                                                 args[0])
+                    then_blk = builder.block
+                # Otherwise, copy to a new contiguous region
+                with orelse:
+                    out_orelse = _array_copy(context, builder, sig, args)
+                    orelse_blk = builder.block
+            # Phi node for the return value
+            ret_phi = builder.phi(out_then.type)
+            ret_phi.add_incoming(out_then, then_blk)
+            ret_phi.add_incoming(out_orelse, orelse_blk)
+            return ret_phi
+
+        else:
+            # Return a copy with the right layout
+            return _array_copy(context, builder, sig, args)
+
+
+@glue_lowering(np.asfortranarray, types.Array)
+def array_asfortranarray(context, builder, sig, args):
+    return _as_layout_array(context, builder, sig, args, output_layout='F')
+
+
+@glue_lowering(np.ascontiguousarray, types.Array)
+def array_ascontiguousarray(context, builder, sig, args):
+    return _as_layout_array(context, builder, sig, args, output_layout='C')
+
+
+@overload(np.ascontiguousarray)
+def array_ascontiguousarray_scalar(a):
+    """
+    This is an implementation for scalar.
+    For arrays, see `array_ascontiguousarray`.
+    """
+    if isinstance(a, (types.Number, types.Boolean,)):
+        def impl(a):
+            return np.ascontiguousarray(np.array(a))
+        return impl
+
+
+@overload(np.asfortranarray)
+def array_asfortranarray_scalar(a):
+    """
+    This is an implementation for scalar.
+    For arrays, see `array_asfortranarray`.
+    """
+    if isinstance(a, (types.Number, types.Boolean,)):
+        def impl(a):
+            return np.asfortranarray(np.array(a))
+        return impl
+
+
+@lower_builtin("array.astype", types.Array, types.DTypeSpec)
+@lower_builtin("array.astype", types.Array, types.StringLiteral)
+def array_astype(context, builder, sig, args):
+    arytype = sig.args[0]
+    ary = make_array(arytype)(context, builder, value=args[0])
+    shapes = cgutils.unpack_tuple(builder, ary.shape)
+
+    rettype = sig.return_type
+    ret = _empty_nd_impl(context, builder, rettype, shapes)
+
+    src_data = ary.data
+    dest_data = ret.data
+
+    src_strides = cgutils.unpack_tuple(builder, ary.strides)
+    dest_strides = cgutils.unpack_tuple(builder, ret.strides)
+    intp_t = context.get_value_type(types.intp)
+
+    with cgutils.loop_nest(builder, shapes, intp_t) as indices:
+        src_ptr = cgutils.get_item_pointer2(context, builder, src_data,
+                                            shapes, src_strides,
+                                            arytype.layout, indices)
+        dest_ptr = cgutils.get_item_pointer2(context, builder, dest_data,
+                                             shapes, dest_strides,
+                                             rettype.layout, indices)
+        item = load_item(context, builder, arytype, src_ptr)
+        item = context.cast(builder, item, arytype.dtype, rettype.dtype)
+        store_item(context, builder, rettype, item, dest_ptr)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, ret._getvalue())
+
+
+@glue_lowering(np.frombuffer, types.Buffer)
+@glue_lowering(np.frombuffer, types.Buffer, types.DTypeSpec)
+@glue_lowering(np.frombuffer, types.Buffer, types.StringLiteral)
+def np_frombuffer(context, builder, sig, args):
+    bufty = sig.args[0]
+    aryty = sig.return_type
+
+    buf = make_array(bufty)(context, builder, value=args[0])
+    out_ary_ty = make_array(aryty)
+    out_ary = out_ary_ty(context, builder)
+    out_datamodel = out_ary._datamodel
+
+    itemsize = get_itemsize(context, aryty)
+    ll_itemsize = Constant(buf.itemsize.type, itemsize)
+    nbytes = builder.mul(buf.nitems, buf.itemsize)
+
+    # Check that the buffer size is compatible
+    rem = builder.srem(nbytes, ll_itemsize)
+    is_incompatible = cgutils.is_not_null(builder, rem)
+    with builder.if_then(is_incompatible, likely=False):
+        msg = "buffer size must be a multiple of element size"
+        context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+    shape = cgutils.pack_array(builder, [builder.sdiv(nbytes, ll_itemsize)])
+    strides = cgutils.pack_array(builder, [ll_itemsize])
+    data = builder.bitcast(
+        buf.data, context.get_value_type(out_datamodel.get_type('data'))
+    )
+
+    populate_array(out_ary,
+                   data=data,
+                   shape=shape,
+                   strides=strides,
+                   itemsize=ll_itemsize,
+                   meminfo=buf.meminfo,
+                   parent=buf.parent,)
+
+    res = out_ary._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@glue_lowering(carray, types.Any, types.Any)
+@glue_lowering(carray, types.Any, types.Any, types.DTypeSpec)
+@glue_lowering(farray, types.Any, types.Any)
+@glue_lowering(farray, types.Any, types.Any, types.DTypeSpec)
+def np_cfarray(context, builder, sig, args):
+    """
+    numba.numpy_support.carray(...) and
+    numba.numpy_support.farray(...).
+    """
+    ptrty, shapety = sig.args[:2]
+    ptr, shape = args[:2]
+
+    aryty = sig.return_type
+    assert aryty.layout in 'CF'
+
+    out_ary = make_array(aryty)(context, builder)
+
+    itemsize = get_itemsize(context, aryty)
+    ll_itemsize = cgutils.intp_t(itemsize)
+
+    if isinstance(shapety, types.BaseTuple):
+        shapes = cgutils.unpack_tuple(builder, shape)
+    else:
+        shapety = (shapety,)
+        shapes = (shape,)
+    shapes = [context.cast(builder, value, fromty, types.intp)
+              for fromty, value in zip(shapety, shapes)]
+
+    off = ll_itemsize
+    strides = []
+    if aryty.layout == 'F':
+        for s in shapes:
+            strides.append(off)
+            off = builder.mul(off, s)
+    else:
+        for s in reversed(shapes):
+            strides.append(off)
+            off = builder.mul(off, s)
+        strides.reverse()
+
+    data = builder.bitcast(ptr,
+                           context.get_data_type(aryty.dtype).as_pointer())
+
+    populate_array(out_ary,
+                   data=data,
+                   shape=shapes,
+                   strides=strides,
+                   itemsize=ll_itemsize,
+                   # Array is not memory-managed
+                   meminfo=None,
+                   )
+
+    res = out_ary._getvalue()
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def _get_seq_size(context, builder, seqty, seq):
+    if isinstance(seqty, types.BaseTuple):
+        return context.get_constant(types.intp, len(seqty))
+    elif isinstance(seqty, types.Sequence):
+        len_impl = context.get_function(len, signature(types.intp, seqty,))
+        return len_impl(builder, (seq,))
+    else:
+        assert 0
+
+
+def _get_borrowing_getitem(context, seqty):
+    """
+    Return a getitem() implementation that doesn't incref its result.
+    """
+    retty = seqty.dtype
+    getitem_impl = context.get_function(operator.getitem,
+                                        signature(retty, seqty, types.intp))
+
+    def wrap(builder, args):
+        ret = getitem_impl(builder, args)
+        if context.enable_nrt:
+            context.nrt.decref(builder, retty, ret)
+        return ret
+
+    return wrap
+
+
+def compute_sequence_shape(context, builder, ndim, seqty, seq):
+    """
+    Compute the likely shape of a nested sequence (possibly 0d).
+    """
+    intp_t = context.get_value_type(types.intp)
+    zero = Constant(intp_t, 0)
+
+    def get_first_item(seqty, seq):
+        if isinstance(seqty, types.BaseTuple):
+            if len(seqty) == 0:
+                return None, None
+            else:
+                return seqty[0], builder.extract_value(seq, 0)
+        else:
+            getitem_impl = _get_borrowing_getitem(context, seqty)
+            return seqty.dtype, getitem_impl(builder, (seq, zero))
+
+    # Compute shape by traversing the first element of each nested
+    # sequence
+    shapes = []
+    innerty, inner = seqty, seq
+
+    for i in range(ndim):
+        if i > 0:
+            innerty, inner = get_first_item(innerty, inner)
+        shapes.append(_get_seq_size(context, builder, innerty, inner))
+
+    return tuple(shapes)
+
+
+def check_sequence_shape(context, builder, seqty, seq, shapes):
+    """
+    Check the nested sequence matches the given *shapes*.
+    """
+
+    def _fail():
+        context.call_conv.return_user_exc(builder, ValueError,
+                                          ("incompatible sequence shape",))
+
+    def check_seq_size(seqty, seq, shapes):
+        if len(shapes) == 0:
+            return
+
+        size = _get_seq_size(context, builder, seqty, seq)
+        expected = shapes[0]
+        mismatch = builder.icmp_signed('!=', size, expected)
+        with builder.if_then(mismatch, likely=False):
+            _fail()
+
+        if len(shapes) == 1:
+            return
+
+        if isinstance(seqty, types.Sequence):
+            getitem_impl = _get_borrowing_getitem(context, seqty)
+            with cgutils.for_range(builder, size) as loop:
+                innerty = seqty.dtype
+                inner = getitem_impl(builder, (seq, loop.index))
+                check_seq_size(innerty, inner, shapes[1:])
+
+        elif isinstance(seqty, types.BaseTuple):
+            for i in range(len(seqty)):
+                innerty = seqty[i]
+                inner = builder.extract_value(seq, i)
+                check_seq_size(innerty, inner, shapes[1:])
+
+        else:
+            assert 0, seqty
+
+    check_seq_size(seqty, seq, shapes)
+
+
+def assign_sequence_to_array(context, builder, data, shapes, strides,
+                             arrty, seqty, seq):
+    """
+    Assign a nested sequence contents to an array.  The shape must match
+    the sequence's structure.
+    """
+
+    def assign_item(indices, valty, val):
+        ptr = cgutils.get_item_pointer2(context, builder, data, shapes, strides,
+                                        arrty.layout, indices, wraparound=False)
+        val = context.cast(builder, val, valty, arrty.dtype)
+        store_item(context, builder, arrty, val, ptr)
+
+    def assign(seqty, seq, shapes, indices):
+        if len(shapes) == 0:
+            assert not isinstance(seqty, (types.Sequence, types.BaseTuple))
+            assign_item(indices, seqty, seq)
+            return
+
+        size = shapes[0]
+
+        if isinstance(seqty, types.Sequence):
+            getitem_impl = _get_borrowing_getitem(context, seqty)
+            with cgutils.for_range(builder, size) as loop:
+                innerty = seqty.dtype
+                inner = getitem_impl(builder, (seq, loop.index))
+                assign(innerty, inner, shapes[1:], indices + (loop.index,))
+
+        elif isinstance(seqty, types.BaseTuple):
+            for i in range(len(seqty)):
+                innerty = seqty[i]
+                inner = builder.extract_value(seq, i)
+                index = context.get_constant(types.intp, i)
+                assign(innerty, inner, shapes[1:], indices + (index,))
+
+        else:
+            assert 0, seqty
+
+    assign(seqty, seq, shapes, ())
+
+
+@glue_lowering(np.array, types.Any)
+@glue_lowering(np.array, types.Any, types.DTypeSpec)
+@glue_lowering(np.array, types.Any, types.StringLiteral)
+def np_array(context, builder, sig, args):
+    arrty = sig.return_type
+    ndim = arrty.ndim
+    seqty = sig.args[0]
+    seq = args[0]
+
+    shapes = compute_sequence_shape(context, builder, ndim, seqty, seq)
+    assert len(shapes) == ndim
+
+    check_sequence_shape(context, builder, seqty, seq, shapes)
+    arr = _empty_nd_impl(context, builder, arrty, shapes)
+    assign_sequence_to_array(context, builder, arr.data, shapes, arr.strides,
+                             arrty, seqty, seq)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, arr._getvalue())
+
+
+def _normalize_axis(context, builder, func_name, ndim, axis):
+    zero = axis.type(0)
+    ll_ndim = axis.type(ndim)
+
+    # Normalize negative axis
+    is_neg_axis = builder.icmp_signed('<', axis, zero)
+    axis = builder.select(is_neg_axis, builder.add(axis, ll_ndim), axis)
+
+    # Check axis for bounds
+    axis_out_of_bounds = builder.or_(
+        builder.icmp_signed('<', axis, zero),
+        builder.icmp_signed('>=', axis, ll_ndim))
+    with builder.if_then(axis_out_of_bounds, likely=False):
+        msg = "%s(): axis out of bounds" % func_name
+        context.call_conv.return_user_exc(builder, IndexError, (msg,))
+
+    return axis
+
+
+def _insert_axis_in_shape(context, builder, orig_shape, ndim, axis):
+    """
+    Compute shape with the new axis inserted
+    e.g. given original shape (2, 3, 4) and axis=2,
+    the returned new shape is (2, 3, 1, 4).
+    """
+    assert len(orig_shape) == ndim - 1
+
+    ll_shty = ir.ArrayType(cgutils.intp_t, ndim)
+    shapes = cgutils.alloca_once(builder, ll_shty)
+
+    one = cgutils.intp_t(1)
+
+    # 1. copy original sizes at appropriate places
+    for dim in range(ndim - 1):
+        ll_dim = cgutils.intp_t(dim)
+        after_axis = builder.icmp_signed('>=', ll_dim, axis)
+        sh = orig_shape[dim]
+        idx = builder.select(after_axis,
+                             builder.add(ll_dim, one),
+                             ll_dim)
+        builder.store(sh, cgutils.gep_inbounds(builder, shapes, 0, idx))
+
+    # 2. insert new size (1) at axis dimension
+    builder.store(one, cgutils.gep_inbounds(builder, shapes, 0, axis))
+
+    return cgutils.unpack_tuple(builder, builder.load(shapes))
+
+
+def _insert_axis_in_strides(context, builder, orig_strides, ndim, axis):
+    """
+    Same as _insert_axis_in_shape(), but with a strides array.
+    """
+    assert len(orig_strides) == ndim - 1
+
+    ll_shty = ir.ArrayType(cgutils.intp_t, ndim)
+    strides = cgutils.alloca_once(builder, ll_shty)
+
+    one = cgutils.intp_t(1)
+    zero = cgutils.intp_t(0)
+
+    # 1. copy original strides at appropriate places
+    for dim in range(ndim - 1):
+        ll_dim = cgutils.intp_t(dim)
+        after_axis = builder.icmp_signed('>=', ll_dim, axis)
+        idx = builder.select(after_axis,
+                             builder.add(ll_dim, one),
+                             ll_dim)
+        builder.store(orig_strides[dim],
+                      cgutils.gep_inbounds(builder, strides, 0, idx))
+
+    # 2. insert new stride at axis dimension
+    # (the value is indifferent for a 1-sized dimension, we use 0)
+    builder.store(zero, cgutils.gep_inbounds(builder, strides, 0, axis))
+
+    return cgutils.unpack_tuple(builder, builder.load(strides))
+
+
+def expand_dims(context, builder, sig, args, axis):
+    """
+    np.expand_dims() with the given axis.
+    """
+    retty = sig.return_type
+    ndim = retty.ndim
+    arrty = sig.args[0]
+
+    arr = make_array(arrty)(context, builder, value=args[0])
+    ret = make_array(retty)(context, builder)
+
+    shapes = cgutils.unpack_tuple(builder, arr.shape)
+    strides = cgutils.unpack_tuple(builder, arr.strides)
+
+    new_shapes = _insert_axis_in_shape(context, builder, shapes, ndim, axis)
+    new_strides = _insert_axis_in_strides(context, builder, strides, ndim, axis)
+
+    populate_array(ret,
+                   data=arr.data,
+                   shape=new_shapes,
+                   strides=new_strides,
+                   itemsize=arr.itemsize,
+                   meminfo=arr.meminfo,
+                   parent=arr.parent)
+
+    return ret._getvalue()
+
+
+@glue_lowering(np.expand_dims, types.Array, types.Integer)
+def np_expand_dims(context, builder, sig, args):
+    axis = context.cast(builder, args[1], sig.args[1], types.intp)
+    axis = _normalize_axis(context, builder, "np.expand_dims",
+                           sig.return_type.ndim, axis)
+
+    ret = expand_dims(context, builder, sig, args, axis)
+    return impl_ret_borrowed(context, builder, sig.return_type, ret)
+
+
+def _atleast_nd(context, builder, sig, args, transform):
+    arrtys = sig.args
+    arrs = args
+
+    if isinstance(sig.return_type, types.BaseTuple):
+        rettys = list(sig.return_type)
+    else:
+        rettys = [sig.return_type]
+    assert len(rettys) == len(arrtys)
+
+    rets = [transform(context, builder, arr, arrty, retty)
+            for arr, arrty, retty in zip(arrs, arrtys, rettys)]
+
+    if isinstance(sig.return_type, types.BaseTuple):
+        ret = context.make_tuple(builder, sig.return_type, rets)
+    else:
+        ret = rets[0]
+    return impl_ret_borrowed(context, builder, sig.return_type, ret)
+
+
+def _atleast_nd_transform(min_ndim, axes):
+    """
+    Return a callback successively inserting 1-sized dimensions at the
+    following axes.
+    """
+    assert min_ndim == len(axes)
+
+    def transform(context, builder, arr, arrty, retty):
+        for i in range(min_ndim):
+            ndim = i + 1
+            if arrty.ndim < ndim:
+                axis = cgutils.intp_t(axes[i])
+                newarrty = arrty.copy(ndim=arrty.ndim + 1)
+                arr = expand_dims(context, builder,
+                                  typing.signature(newarrty, arrty), (arr,),
+                                  axis)
+                arrty = newarrty
+
+        return arr
+
+    return transform
+
+
+@glue_lowering(np.atleast_1d, types.VarArg(types.Array))
+def np_atleast_1d(context, builder, sig, args):
+    transform = _atleast_nd_transform(1, [0])
+
+    return _atleast_nd(context, builder, sig, args, transform)
+
+
+@glue_lowering(np.atleast_2d, types.VarArg(types.Array))
+def np_atleast_2d(context, builder, sig, args):
+    transform = _atleast_nd_transform(2, [0, 0])
+
+    return _atleast_nd(context, builder, sig, args, transform)
+
+
+@glue_lowering(np.atleast_3d, types.VarArg(types.Array))
+def np_atleast_3d(context, builder, sig, args):
+    transform = _atleast_nd_transform(3, [0, 0, 2])
+
+    return _atleast_nd(context, builder, sig, args, transform)
+
+
+def _do_concatenate(context, builder, axis,
+                    arrtys, arrs, arr_shapes, arr_strides,
+                    retty, ret_shapes):
+    """
+    Concatenate arrays along the given axis.
+    """
+    assert len(arrtys) == len(arrs) == len(arr_shapes) == len(arr_strides)
+
+    zero = cgutils.intp_t(0)
+
+    # Allocate return array
+    ret = _empty_nd_impl(context, builder, retty, ret_shapes)
+    ret_strides = cgutils.unpack_tuple(builder, ret.strides)
+
+    # Compute the offset by which to bump the destination pointer
+    # after copying each input array.
+    # Morally, we need to copy each input array at different start indices
+    # into the destination array; bumping the destination pointer
+    # is simply easier than offsetting all destination indices.
+    copy_offsets = []
+
+    for arr_sh in arr_shapes:
+        # offset = ret_strides[axis] * input_shape[axis]
+        offset = zero
+        for dim, (size, stride) in enumerate(zip(arr_sh, ret_strides)):
+            is_axis = builder.icmp_signed('==', axis.type(dim), axis)
+            addend = builder.mul(size, stride)
+            offset = builder.select(is_axis,
+                                    builder.add(offset, addend),
+                                    offset)
+        copy_offsets.append(offset)
+
+    # Copy input arrays into the return array
+    ret_data = ret.data
+
+    for arrty, arr, arr_sh, arr_st, offset in zip(arrtys, arrs, arr_shapes,
+                                                  arr_strides, copy_offsets):
+        arr_data = arr.data
+
+        # Do the copy loop
+        # Note the loop nesting is optimized for the destination layout
+        loop_nest = cgutils.loop_nest(builder, arr_sh, cgutils.intp_t,
+                                      order=retty.layout)
+
+        with loop_nest as indices:
+            src_ptr = cgutils.get_item_pointer2(context, builder, arr_data,
+                                                arr_sh, arr_st,
+                                                arrty.layout, indices)
+            val = load_item(context, builder, arrty, src_ptr)
+            val = context.cast(builder, val, arrty.dtype, retty.dtype)
+            dest_ptr = cgutils.get_item_pointer2(context, builder, ret_data,
+                                                 ret_shapes, ret_strides,
+                                                 retty.layout, indices)
+            store_item(context, builder, retty, val, dest_ptr)
+
+        # Bump destination pointer
+        ret_data = cgutils.pointer_add(builder, ret_data, offset)
+
+    return ret
+
+
+def _np_concatenate(context, builder, arrtys, arrs, retty, axis):
+    ndim = retty.ndim
+
+    arrs = [make_array(aty)(context, builder, value=a)
+            for aty, a in zip(arrtys, arrs)]
+
+    axis = _normalize_axis(context, builder, "np.concatenate", ndim, axis)
+
+    # Get input shapes
+    arr_shapes = [cgutils.unpack_tuple(builder, arr.shape) for arr in arrs]
+    arr_strides = [cgutils.unpack_tuple(builder, arr.strides) for arr in arrs]
+
+    # Compute return shape:
+    # - the dimension for the concatenation axis is summed over all inputs
+    # - other dimensions must match exactly for each input
+    ret_shapes = [cgutils.alloca_once_value(builder, sh)
+                  for sh in arr_shapes[0]]
+
+    for dim in range(ndim):
+        is_axis = builder.icmp_signed('==', axis.type(dim), axis)
+        ret_shape_ptr = ret_shapes[dim]
+        ret_sh = builder.load(ret_shape_ptr)
+        other_shapes = [sh[dim] for sh in arr_shapes[1:]]
+
+        with builder.if_else(is_axis) as (on_axis, on_other_dim):
+            with on_axis:
+                sh = functools.reduce(
+                    builder.add,
+                    other_shapes + [ret_sh])
+                builder.store(sh, ret_shape_ptr)
+
+            with on_other_dim:
+                is_ok = cgutils.true_bit
+                for sh in other_shapes:
+                    is_ok = builder.and_(is_ok,
+                                         builder.icmp_signed('==', sh, ret_sh))
+                with builder.if_then(builder.not_(is_ok), likely=False):
+                    context.call_conv.return_user_exc(
+                        builder, ValueError,
+                        ("np.concatenate(): input sizes over "
+                         "dimension %d do not match" % dim,))
+
+    ret_shapes = [builder.load(sh) for sh in ret_shapes]
+
+    ret = _do_concatenate(context, builder, axis,
+                          arrtys, arrs, arr_shapes, arr_strides,
+                          retty, ret_shapes)
+    return impl_ret_new_ref(context, builder, retty, ret._getvalue())
+
+
+def _np_stack(context, builder, arrtys, arrs, retty, axis):
+    ndim = retty.ndim
+
+    zero = cgutils.intp_t(0)
+    one = cgutils.intp_t(1)
+    ll_narrays = cgutils.intp_t(len(arrs))
+
+    arrs = [make_array(aty)(context, builder, value=a)
+            for aty, a in zip(arrtys, arrs)]
+
+    axis = _normalize_axis(context, builder, "np.stack", ndim, axis)
+
+    # Check input arrays have the same shape
+    orig_shape = cgutils.unpack_tuple(builder, arrs[0].shape)
+
+    for arr in arrs[1:]:
+        is_ok = cgutils.true_bit
+        for sh, orig_sh in zip(cgutils.unpack_tuple(builder, arr.shape),
+                               orig_shape):
+            is_ok = builder.and_(is_ok, builder.icmp_signed('==', sh, orig_sh))
+            with builder.if_then(builder.not_(is_ok), likely=False):
+                context.call_conv.return_user_exc(
+                    builder, ValueError,
+                    ("np.stack(): all input arrays must have the same shape",))
+
+    orig_strides = [cgutils.unpack_tuple(builder, arr.strides) for arr in arrs]
+
+    # Compute input shapes and return shape with the new axis inserted
+    # e.g. given 5 input arrays of shape (2, 3, 4) and axis=1,
+    # corrected input shape is (2, 1, 3, 4) and return shape is (2, 5, 3, 4).
+    ll_shty = ir.ArrayType(cgutils.intp_t, ndim)
+
+    input_shapes = cgutils.alloca_once(builder, ll_shty)
+    ret_shapes = cgutils.alloca_once(builder, ll_shty)
+
+    # 1. copy original sizes at appropriate places
+    for dim in range(ndim - 1):
+        ll_dim = cgutils.intp_t(dim)
+        after_axis = builder.icmp_signed('>=', ll_dim, axis)
+        sh = orig_shape[dim]
+        idx = builder.select(after_axis,
+                             builder.add(ll_dim, one),
+                             ll_dim)
+        builder.store(sh, cgutils.gep_inbounds(builder, input_shapes, 0, idx))
+        builder.store(sh, cgutils.gep_inbounds(builder, ret_shapes, 0, idx))
+
+    # 2. insert new size at axis dimension
+    builder.store(one, cgutils.gep_inbounds(builder, input_shapes, 0, axis))
+    builder.store(ll_narrays, cgutils.gep_inbounds(builder,
+                                                   ret_shapes,
+                                                   0,
+                                                   axis))
+
+    input_shapes = cgutils.unpack_tuple(builder, builder.load(input_shapes))
+    input_shapes = [input_shapes] * len(arrs)
+    ret_shapes = cgutils.unpack_tuple(builder, builder.load(ret_shapes))
+
+    # Compute input strides for each array with the new axis inserted
+    input_strides = [cgutils.alloca_once(builder, ll_shty)
+                     for i in range(len(arrs))]
+
+    # 1. copy original strides at appropriate places
+    for dim in range(ndim - 1):
+        ll_dim = cgutils.intp_t(dim)
+        after_axis = builder.icmp_signed('>=', ll_dim, axis)
+        idx = builder.select(after_axis,
+                             builder.add(ll_dim, one),
+                             ll_dim)
+        for i in range(len(arrs)):
+            builder.store(orig_strides[i][dim],
+                          cgutils.gep_inbounds(builder, input_strides[i], 0,
+                                               idx))
+
+    # 2. insert new stride at axis dimension
+    # (the value is indifferent for a 1-sized dimension, we put 0)
+    for i in range(len(arrs)):
+        builder.store(zero, cgutils.gep_inbounds(builder, input_strides[i], 0,
+                                                 axis))
+
+    input_strides = [cgutils.unpack_tuple(builder, builder.load(st))
+                     for st in input_strides]
+
+    # Create concatenated array
+    ret = _do_concatenate(context, builder, axis,
+                          arrtys, arrs, input_shapes, input_strides,
+                          retty, ret_shapes)
+    return impl_ret_new_ref(context, builder, retty, ret._getvalue())
+
+
+@glue_lowering(np.concatenate, types.BaseTuple)
+def np_concatenate(context, builder, sig, args):
+    axis = context.get_constant(types.intp, 0)
+    return _np_concatenate(context, builder,
+                           list(sig.args[0]),
+                           cgutils.unpack_tuple(builder, args[0]),
+                           sig.return_type,
+                           axis)
+
+
+@glue_lowering(np.concatenate, types.BaseTuple, types.Integer)
+def np_concatenate_axis(context, builder, sig, args):
+    axis = context.cast(builder, args[1], sig.args[1], types.intp)
+    return _np_concatenate(context, builder,
+                           list(sig.args[0]),
+                           cgutils.unpack_tuple(builder, args[0]),
+                           sig.return_type,
+                           axis)
+
+
+@glue_lowering(np.column_stack, types.BaseTuple)
+def np_column_stack(context, builder, sig, args):
+    orig_arrtys = list(sig.args[0])
+    orig_arrs = cgutils.unpack_tuple(builder, args[0])
+
+    arrtys = []
+    arrs = []
+
+    axis = context.get_constant(types.intp, 1)
+
+    for arrty, arr in zip(orig_arrtys, orig_arrs):
+        if arrty.ndim == 2:
+            arrtys.append(arrty)
+            arrs.append(arr)
+        else:
+            # Convert 1d array to 2d column array: np.expand_dims(a, 1)
+            assert arrty.ndim == 1
+            newty = arrty.copy(ndim=2)
+            expand_sig = typing.signature(newty, arrty)
+            newarr = expand_dims(context, builder, expand_sig, (arr,), axis)
+
+            arrtys.append(newty)
+            arrs.append(newarr)
+
+    return _np_concatenate(context, builder, arrtys, arrs,
+                           sig.return_type, axis)
+
+
+def _np_stack_common(context, builder, sig, args, axis):
+    """
+    np.stack() with the given axis value.
+    """
+    return _np_stack(context, builder,
+                     list(sig.args[0]),
+                     cgutils.unpack_tuple(builder, args[0]),
+                     sig.return_type,
+                     axis)
+
+
+@glue_lowering(np.stack, types.BaseTuple)
+def np_stack(context, builder, sig, args):
+    axis = context.get_constant(types.intp, 0)
+    return _np_stack_common(context, builder, sig, args, axis)
+
+
+@glue_lowering(np.stack, types.BaseTuple, types.Integer)
+def np_stack_axis(context, builder, sig, args):
+    axis = context.cast(builder, args[1], sig.args[1], types.intp)
+    return _np_stack_common(context, builder, sig, args, axis)
+
+
+@glue_lowering(np.hstack, types.BaseTuple)
+def np_hstack(context, builder, sig, args):
+    tupty = sig.args[0]
+    ndim = tupty[0].ndim
+
+    if ndim == 0:
+        # hstack() on 0-d arrays returns a 1-d array
+        axis = context.get_constant(types.intp, 0)
+        return _np_stack_common(context, builder, sig, args, axis)
+
+    else:
+        # As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
+        axis = 0 if ndim == 1 else 1
+
+        def np_hstack_impl(arrays):
+            return np.concatenate(arrays, axis=axis)
+
+        return context.compile_internal(builder, np_hstack_impl, sig, args)
+
+
+@glue_lowering(np.vstack, types.BaseTuple)
+def np_vstack(context, builder, sig, args):
+    tupty = sig.args[0]
+    ndim = tupty[0].ndim
+
+    if ndim == 0:
+        def np_vstack_impl(arrays):
+            return np.expand_dims(np.hstack(arrays), 1)
+
+    elif ndim == 1:
+        # np.stack(arrays, axis=0)
+        axis = context.get_constant(types.intp, 0)
+        return _np_stack_common(context, builder, sig, args, axis)
+
+    else:
+        def np_vstack_impl(arrays):
+            return np.concatenate(arrays, axis=0)
+
+    return context.compile_internal(builder, np_vstack_impl, sig, args)
+
+
+@glue_lowering(np.dstack, types.BaseTuple)
+def np_dstack(context, builder, sig, args):
+    tupty = sig.args[0]
+    retty = sig.return_type
+    ndim = tupty[0].ndim
+
+    if ndim == 0:
+        def np_vstack_impl(arrays):
+            return np.hstack(arrays).reshape(1, 1, -1)
+
+        return context.compile_internal(builder, np_vstack_impl, sig, args)
+
+    elif ndim == 1:
+        # np.expand_dims(np.stack(arrays, axis=1), axis=0)
+        axis = context.get_constant(types.intp, 1)
+        stack_retty = retty.copy(ndim=retty.ndim - 1)
+        stack_sig = typing.signature(stack_retty, *sig.args)
+        stack_ret = _np_stack_common(context, builder, stack_sig, args, axis)
+
+        axis = context.get_constant(types.intp, 0)
+        expand_sig = typing.signature(retty, stack_retty)
+        return expand_dims(context, builder, expand_sig, (stack_ret,), axis)
+
+    elif ndim == 2:
+        # np.stack(arrays, axis=2)
+        axis = context.get_constant(types.intp, 2)
+        return _np_stack_common(context, builder, sig, args, axis)
+
+    else:
+        def np_vstack_impl(arrays):
+            return np.concatenate(arrays, axis=2)
+
+        return context.compile_internal(builder, np_vstack_impl, sig, args)
+
+
+@extending.overload_method(types.Array, 'fill')
+def arr_fill(arr, val):
+
+    def fill_impl(arr, val):
+        arr[:] = val
+        return None
+
+    return fill_impl
+
+
+@extending.overload_method(types.Array, 'dot')
+def array_dot(arr, other):
+    def dot_impl(arr, other):
+        return np.dot(arr, other)
+
+    return dot_impl
+
+
+@overload(np.fliplr)
+def np_flip_lr(a):
+
+    if not type_can_asarray(a):
+        raise errors.TypingError("Cannot np.fliplr on %s type" % a)
+
+    def impl(a):
+        A = np.asarray(a)
+        # this handling is superfluous/dead as < 2d array cannot be indexed as
+        # present below and so typing fails. If the typing doesn't fail due to
+        # some future change, this will catch it.
+        if A.ndim < 2:
+            raise ValueError('Input must be >= 2-d.')
+        return A[::, ::-1, ...]
+    return impl
+
+
+@overload(np.flipud)
+def np_flip_ud(a):
+
+    if not type_can_asarray(a):
+        raise errors.TypingError("Cannot np.flipud on %s type" % a)
+
+    def impl(a):
+        A = np.asarray(a)
+        # this handling is superfluous/dead as a 0d array cannot be indexed as
+        # present below and so typing fails. If the typing doesn't fail due to
+        # some future change, this will catch it.
+        if A.ndim < 1:
+            raise ValueError('Input must be >= 1-d.')
+        return A[::-1, ...]
+    return impl
+
+
+@intrinsic
+def _build_flip_slice_tuple(tyctx, sz):
+    """ Creates a tuple of slices for np.flip indexing like
+    `(slice(None, None, -1),) * sz` """
+    if not isinstance(sz, types.IntegerLiteral):
+        raise errors.RequireLiteralValue(sz)
+    size = int(sz.literal_value)
+    tuple_type = types.UniTuple(dtype=types.slice3_type, count=size)
+    sig = tuple_type(sz)
+
+    def codegen(context, builder, signature, args):
+        def impl(length, empty_tuple):
+            out = empty_tuple
+            for i in range(length):
+                out = tuple_setitem(out, i, slice(None, None, -1))
+            return out
+
+        inner_argtypes = [types.intp, tuple_type]
+        inner_sig = typing.signature(tuple_type, *inner_argtypes)
+        ll_idx_type = context.get_value_type(types.intp)
+        # Allocate an empty tuple
+        empty_tuple = context.get_constant_undef(tuple_type)
+        inner_args = [ll_idx_type(size), empty_tuple]
+
+        res = context.compile_internal(builder, impl, inner_sig, inner_args)
+        return res
+
+    return sig, codegen
+
+
+@overload(np.flip)
+def np_flip(a):
+    # a constant value is needed for the tuple slice, types.Array.ndim can
+    # provide this and so at presnet only type.Array is support
+    if not isinstance(a, types.Array):
+        raise errors.TypingError("Cannot np.flip on %s type" % a)
+
+    def impl(a):
+        sl = _build_flip_slice_tuple(a.ndim)
+        return a[sl]
+
+    return impl
+
+
+@overload(np.array_split)
+def np_array_split(ary, indices_or_sections, axis=0):
+    if isinstance(ary, (types.UniTuple, types.ListType, types.List)):
+        def impl(ary, indices_or_sections, axis=0):
+            return np.array_split(
+                np.asarray(ary),
+                indices_or_sections,
+                axis=axis
+            )
+
+        return impl
+
+    if isinstance(indices_or_sections, types.Integer):
+        def impl(ary, indices_or_sections, axis=0):
+            l, rem = divmod(ary.shape[axis], indices_or_sections)
+            indices = np.cumsum(np.array(
+                [l + 1] * rem +
+                [l] * (indices_or_sections - rem - 1)
+            ))
+            return np.array_split(ary, indices, axis=axis)
+
+        return impl
+
+    elif (
+        isinstance(indices_or_sections, types.IterableType)
+        and isinstance(
+            indices_or_sections.iterator_type.yield_type,
+            types.Integer
+        )
+    ):
+        def impl(ary, indices_or_sections, axis=0):
+            slice_tup = build_full_slice_tuple(ary.ndim)
+            out = list()
+            prev = 0
+            for cur in indices_or_sections:
+                idx = tuple_setitem(slice_tup, axis, slice(prev, cur))
+                out.append(ary[idx])
+                prev = cur
+            out.append(ary[tuple_setitem(slice_tup, axis, slice(cur, None))])
+            return out
+
+        return impl
+
+    elif (
+        isinstance(indices_or_sections, types.Tuple)
+        and all(isinstance(t, types.Integer) for t in indices_or_sections.types)
+    ):
+        def impl(ary, indices_or_sections, axis=0):
+            slice_tup = build_full_slice_tuple(ary.ndim)
+            out = list()
+            prev = 0
+            for cur in literal_unroll(indices_or_sections):
+                idx = tuple_setitem(slice_tup, axis, slice(prev, cur))
+                out.append(ary[idx])
+                prev = cur
+            out.append(ary[tuple_setitem(slice_tup, axis, slice(cur, None))])
+            return out
+
+        return impl
+
+
+@overload(np.split)
+def np_split(ary, indices_or_sections, axis=0):
+    # This is just a wrapper of array_split, but with an extra error if
+    # indices is an int.
+    if isinstance(ary, (types.UniTuple, types.ListType, types.List)):
+        def impl(ary, indices_or_sections, axis=0):
+            return np.split(np.asarray(ary), indices_or_sections, axis=axis)
+
+        return impl
+
+    if isinstance(indices_or_sections, types.Integer):
+        def impl(ary, indices_or_sections, axis=0):
+            _, rem = divmod(ary.shape[axis], indices_or_sections)
+            if rem != 0:
+                raise ValueError(
+                    "array split does not result in an equal division"
+                )
+            return np.array_split(
+                ary, indices_or_sections, axis=axis
+            )
+
+        return impl
+
+    else:
+        return np_array_split(ary, indices_or_sections, axis=axis)
+
+
+# -----------------------------------------------------------------------------
+# Sorting
+
+_sorts = {}
+
+
+def lt_floats(a, b):
+    return math.isnan(b) or a < b
+
+
+def get_sort_func(kind, is_float, is_argsort=False):
+    """
+    Get a sort implementation of the given kind.
+    """
+    key = kind, is_float, is_argsort
+    try:
+        return _sorts[key]
+    except KeyError:
+        if kind == 'quicksort':
+            sort = quicksort.make_jit_quicksort(
+                lt=lt_floats if is_float else None,
+                is_argsort=is_argsort,
+                is_np_array=True)
+            func = sort.run_quicksort
+        elif kind == 'mergesort':
+            sort = mergesort.make_jit_mergesort(
+                lt=lt_floats if is_float else None,
+                is_argsort=is_argsort)
+            func = sort.run_mergesort
+        _sorts[key] = func
+        return func
+
+
+@lower_builtin("array.sort", types.Array)
+def array_sort(context, builder, sig, args):
+    arytype = sig.args[0]
+    sort_func = get_sort_func(kind='quicksort',
+                              is_float=isinstance(arytype.dtype, types.Float))
+
+    def array_sort_impl(arr):
+        # Note we clobber the return value
+        sort_func(arr)
+
+    return context.compile_internal(builder, array_sort_impl, sig, args)
+
+
+@glue_lowering(np.sort, types.Array)
+def np_sort(context, builder, sig, args):
+
+    def np_sort_impl(a):
+        res = a.copy()
+        res.sort()
+        return res
+
+    return context.compile_internal(builder, np_sort_impl, sig, args)
+
+
+@lower_builtin("array.argsort", types.Array, types.StringLiteral)
+@lower_builtin(np.argsort, types.Array, types.StringLiteral)
+def array_argsort(context, builder, sig, args):
+    arytype, kind = sig.args
+    sort_func = get_sort_func(kind=kind.literal_value,
+                              is_float=isinstance(arytype.dtype, types.Float),
+                              is_argsort=True)
+
+    def array_argsort_impl(arr):
+        return sort_func(arr)
+
+    innersig = sig.replace(args=sig.args[:1])
+    innerargs = args[:1]
+    return context.compile_internal(builder, array_argsort_impl,
+                                    innersig, innerargs)
+
+
+# ------------------------------------------------------------------------------
+# Implicit cast
+
+@lower_cast(types.Array, types.Array)
+def array_to_array(context, builder, fromty, toty, val):
+    # Type inference should have prevented illegal array casting.
+    assert fromty.mutable != toty.mutable or toty.layout == 'A'
+    return val
+
+
+@lower_cast(types.Array, types.UnicodeCharSeq)
+@lower_cast(types.Array, types.Float)
+@lower_cast(types.Array, types.Integer)
+@lower_cast(types.Array, types.Complex)
+@lower_cast(types.Array, types.Boolean)
+@lower_cast(types.Array, types.NPTimedelta)
+@lower_cast(types.Array, types.NPDatetime)
+def array0d_to_scalar(context, builder, fromty, toty, val):
+    def impl(a):
+        # a is an array(T, 0d, O), T is type, O is order
+        return a.take(0)
+
+    sig = signature(toty, fromty)
+    res = context.compile_internal(builder, impl, sig, [val])
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_cast(types.Array, types.UnicodeCharSeq)
+def array_to_unichrseq(context, builder, fromty, toty, val):
+    def impl(a):
+        return str(a[()])
+
+    sig = signature(toty, fromty)
+    res = context.compile_internal(builder, impl, sig, [val])
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+# ------------------------------------------------------------------------------
+# Stride tricks
+
+def reshape_unchecked(a, shape, strides):
+    """
+    An intrinsic returning a derived array with the given shape and strides.
+    """
+    raise NotImplementedError
+
+
+@extending.type_callable(reshape_unchecked)
+def type_reshape_unchecked(context):
+    def check_shape(shape):
+        return (isinstance(shape, types.BaseTuple) and
+                all(isinstance(v, types.Integer) for v in shape))
+
+    def typer(a, shape, strides):
+        if not isinstance(a, types.Array):
+            return
+        if not check_shape(shape) or not check_shape(strides):
+            return
+        if len(shape) != len(strides):
+            return
+        return a.copy(ndim=len(shape), layout='A')
+
+    return typer
+
+
+@lower_builtin(reshape_unchecked, types.Array, types.BaseTuple, types.BaseTuple)
+def impl_shape_unchecked(context, builder, sig, args):
+    aryty = sig.args[0]
+    retty = sig.return_type
+
+    ary = make_array(aryty)(context, builder, args[0])
+    out = make_array(retty)(context, builder)
+    shape = cgutils.unpack_tuple(builder, args[1])
+    strides = cgutils.unpack_tuple(builder, args[2])
+
+    populate_array(out,
+                   data=ary.data,
+                   shape=shape,
+                   strides=strides,
+                   itemsize=ary.itemsize,
+                   meminfo=ary.meminfo,
+                   )
+
+    res = out._getvalue()
+    return impl_ret_borrowed(context, builder, retty, res)
+
+
+@extending.overload(np.lib.stride_tricks.as_strided)
+def as_strided(x, shape=None, strides=None):
+    if shape in (None, types.none):
+        @register_jitable
+        def get_shape(x, shape):
+            return x.shape
+    else:
+        @register_jitable
+        def get_shape(x, shape):
+            return shape
+
+    if strides in (None, types.none):
+        # When *strides* is not passed, as_strided() does a non-size-checking
+        # reshape(), possibly changing the original strides.  This is too
+        # cumbersome to support right now, and a Web search shows all example
+        # use cases of as_strided() pass explicit *strides*.
+        raise NotImplementedError("as_strided() strides argument is mandatory")
+    else:
+        @register_jitable
+        def get_strides(x, strides):
+            return strides
+
+    def as_strided_impl(x, shape=None, strides=None):
+        x = reshape_unchecked(x, get_shape(x, shape), get_strides(x, strides))
+        return x
+
+    return as_strided_impl
+
+
+@overload(bool)
+def ol_bool(arr):
+    if isinstance(arr, types.Array):
+        def impl(arr):
+            if arr.size == 0:
+                return False # this is deprecated
+            elif arr.size == 1:
+                return bool(arr.take(0))
+            else:
+                msg = ("The truth value of an array with more than one element "
+                       "is ambiguous. Use a.any() or a.all()")
+                raise ValueError(msg)
+        return impl
+
+
+@overload(np.swapaxes)
+def numpy_swapaxes(arr, axis1, axis2):
+    if not isinstance(axis1, (int, types.Integer)):
+        raise errors.TypingError('The second argument "axis1" must be an '
+                                 'integer')
+    if not isinstance(axis2, (int, types.Integer)):
+        raise errors.TypingError('The third argument "axis2" must be an '
+                                 'integer')
+    if not isinstance(arr, types.Array):
+        raise errors.TypingError('The first argument "arr" must be an array')
+
+    # create tuple list for transpose
+    ndim = arr.ndim
+    axes_list = tuple(range(ndim))
+
+    def impl(arr, axis1, axis2):
+        if axis1 >= ndim or abs(axis1) > ndim:
+            raise ValueError('The second argument "axis1" is out of bounds '
+                             'for array of given dimension')
+        if axis2 >= ndim or abs(axis2) > ndim:
+            raise ValueError('The third argument "axis2" is out of bounds '
+                             'for array of given dimension')
+
+        # to ensure tuple_setitem support of negative values
+        if axis1 < 0:
+            axis1 += ndim
+        if axis2 < 0:
+            axis2 += ndim
+
+        axes_tuple = tuple_setitem(axes_list, axis1, axis2)
+        axes_tuple = tuple_setitem(axes_tuple, axis2, axis1)
+        return np.transpose(arr, axes_tuple)
+
+    return impl
+
+
+@register_jitable
+def _take_along_axis_impl(
+        arr, indices, axis, Ni_orig, Nk_orig, indices_broadcast_shape
+):
+    # Based on example code in
+    # https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/shape_base.py#L90-L103
+    # With addition of pre-broadcasting:
+    # https://github.com/numpy/numpy/issues/19704
+
+    # Wrap axis, it's used in tuple_setitem so must be (axis >= 0) to ensure
+    # the GEP is in bounds.
+    if axis < 0:
+        axis = arr.ndim + axis
+
+    # Broadcast the two arrays to matching shapes:
+    arr_shape = list(arr.shape)
+    arr_shape[axis] = 1
+    for i, (d1, d2) in enumerate(zip(arr_shape, indices.shape)):
+        if d1 == 1:
+            new_val = d2
+        elif d2 == 1:
+            new_val = d1
+        else:
+            if d1 != d2:
+                raise ValueError(
+                    "`arr` and `indices` dimensions don't match"
+                )
+            new_val = d1
+        indices_broadcast_shape = tuple_setitem(
+            indices_broadcast_shape, i, new_val
+        )
+    arr_broadcast_shape = tuple_setitem(
+        indices_broadcast_shape, axis, arr.shape[axis]
+    )
+    arr = np.broadcast_to(arr, arr_broadcast_shape)
+    indices = np.broadcast_to(indices, indices_broadcast_shape)
+
+    Ni = Ni_orig
+    if len(Ni_orig) > 0:
+        for i in range(len(Ni)):
+            Ni = tuple_setitem(Ni, i, arr.shape[i])
+    Nk = Nk_orig
+    if len(Nk_orig) > 0:
+        for i in range(len(Nk)):
+            Nk = tuple_setitem(Nk, i, arr.shape[axis + 1 + i])
+
+    J = indices.shape[axis]  # Need not equal M
+    out = np.empty(Ni + (J,) + Nk, arr.dtype)
+
+    np_s_ = (slice(None, None, None),)
+
+    for ii in np.ndindex(Ni):
+        for kk in np.ndindex(Nk):
+            a_1d = arr[ii + np_s_ + kk]
+            indices_1d = indices[ii + np_s_ + kk]
+            out_1d = out[ii + np_s_ + kk]
+            for j in range(J):
+                out_1d[j] = a_1d[indices_1d[j]]
+    return out
+
+
+@overload(np.take_along_axis)
+def arr_take_along_axis(arr, indices, axis):
+    if not isinstance(arr, types.Array):
+        raise errors.TypingError('The first argument "arr" must be an array')
+    if not isinstance(indices, types.Array):
+        raise errors.TypingError(
+            'The second argument "indices" must be an array')
+    if not isinstance(indices.dtype, types.Integer):
+        raise errors.TypingError('The indices array must contain integers')
+    if is_nonelike(axis):
+        arr_ndim = 1
+    else:
+        arr_ndim = arr.ndim
+    if arr_ndim != indices.ndim:
+        # Matches NumPy error:
+        raise errors.TypingError(
+            "`indices` and `arr` must have the same number of dimensions"
+        )
+
+    indices_broadcast_shape = tuple(range(indices.ndim))
+    if is_nonelike(axis):
+        def take_along_axis_impl(arr, indices, axis):
+            return _take_along_axis_impl(arr.flatten(), indices, 0, (), (),
+                                         indices_broadcast_shape)
+    else:
+        check_is_integer(axis, "axis")
+        if not isinstance(axis, types.IntegerLiteral):
+            raise errors.NumbaValueError("axis must be a literal value")
+        axis = axis.literal_value
+        if axis < 0:
+            axis = arr.ndim + axis
+
+        if axis < 0 or axis >= arr.ndim:
+            raise errors.NumbaValueError("axis is out of bounds")
+
+        Ni = tuple(range(axis))
+        Nk = tuple(range(axis + 1, arr.ndim))
+
+        def take_along_axis_impl(arr, indices, axis):
+            return _take_along_axis_impl(arr, indices, axis, Ni, Nk,
+                                         indices_broadcast_shape)
+    return take_along_axis_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/extensions.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/extensions.py
new file mode 100644
index 000000000..e0051a1fc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/extensions.py
@@ -0,0 +1,10 @@
+"""
+NumPy extensions.
+"""
+
+from numba.np.arraymath import cross2d
+
+
+__all__ = [
+    'cross2d'
+]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/linalg.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/linalg.py
new file mode 100644
index 000000000..0f36bf28a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/linalg.py
@@ -0,0 +1,2763 @@
+"""
+Implementation of linear algebra operations.
+"""
+
+
+import contextlib
+
+from llvmlite import ir
+
+import numpy as np
+import operator
+
+from numba.core.imputils import (lower_builtin, impl_ret_borrowed,
+                                    impl_ret_new_ref, impl_ret_untracked)
+from numba.core.typing import signature
+from numba.core.extending import overload, register_jitable
+from numba.core import types, cgutils
+from numba.core.errors import TypingError, NumbaTypeError
+from .arrayobj import make_array, _empty_nd_impl, array_copy
+from numba.np import numpy_support as np_support
+from numba.core.overload_glue import glue_lowering
+
+ll_char = ir.IntType(8)
+ll_char_p = ll_char.as_pointer()
+ll_void_p = ll_char_p
+ll_intc = ir.IntType(32)
+ll_intc_p = ll_intc.as_pointer()
+intp_t = cgutils.intp_t
+ll_intp_p = intp_t.as_pointer()
+
+
+# fortran int type, this needs to match the F_INT C declaration in
+# _lapack.c and is present to accommodate potential future 64bit int
+# based LAPACK use.
+F_INT_nptype = np.int32
+F_INT_nbtype = types.int32
+
+# BLAS kinds as letters
+_blas_kinds = {
+    types.float32: 's',
+    types.float64: 'd',
+    types.complex64: 'c',
+    types.complex128: 'z',
+}
+
+
+def get_blas_kind(dtype, func_name="<BLAS function>"):
+    kind = _blas_kinds.get(dtype)
+    if kind is None:
+        raise TypeError("unsupported dtype for %s()" % (func_name,))
+    return kind
+
+
+def ensure_blas():
+    try:
+        import scipy.linalg.cython_blas
+    except ImportError:
+        raise ImportError("scipy 0.16+ is required for linear algebra")
+
+
+def ensure_lapack():
+    try:
+        import scipy.linalg.cython_lapack
+    except ImportError:
+        raise ImportError("scipy 0.16+ is required for linear algebra")
+
+
+def make_constant_slot(context, builder, ty, val):
+    const = context.get_constant_generic(builder, ty, val)
+    return cgutils.alloca_once_value(builder, const)
+
+
+class _BLAS:
+    """
+    Functions to return type signatures for wrapped
+    BLAS functions.
+    """
+
+    def __init__(self):
+        ensure_blas()
+
+    @classmethod
+    def numba_xxnrm2(cls, dtype):
+        rtype = getattr(dtype, "underlying_float", dtype)
+        sig = types.intc(types.char,             # kind
+                         types.intp,             # n
+                         types.CPointer(dtype),  # x
+                         types.intp,             # incx
+                         types.CPointer(rtype))  # returned
+
+        return types.ExternalFunction("numba_xxnrm2", sig)
+
+    @classmethod
+    def numba_xxgemm(cls, dtype):
+        sig = types.intc(
+            types.char,             # kind
+            types.char,             # transa
+            types.char,             # transb
+            types.intp,             # m
+            types.intp,             # n
+            types.intp,             # k
+            types.CPointer(dtype),  # alpha
+            types.CPointer(dtype),  # a
+            types.intp,             # lda
+            types.CPointer(dtype),  # b
+            types.intp,             # ldb
+            types.CPointer(dtype),  # beta
+            types.CPointer(dtype),  # c
+            types.intp              # ldc
+        )
+        return types.ExternalFunction("numba_xxgemm", sig)
+
+
+class _LAPACK:
+    """
+    Functions to return type signatures for wrapped
+    LAPACK functions.
+    """
+
+    def __init__(self):
+        ensure_lapack()
+
+    @classmethod
+    def numba_xxgetrf(cls, dtype):
+        sig = types.intc(types.char,                   # kind
+                         types.intp,                   # m
+                         types.intp,                   # n
+                         types.CPointer(dtype),        # a
+                         types.intp,                   # lda
+                         types.CPointer(F_INT_nbtype)  # ipiv
+                         )
+        return types.ExternalFunction("numba_xxgetrf", sig)
+
+    @classmethod
+    def numba_ez_xxgetri(cls, dtype):
+        sig = types.intc(types.char,                   # kind
+                         types.intp,                   # n
+                         types.CPointer(dtype),        # a
+                         types.intp,                   # lda
+                         types.CPointer(F_INT_nbtype)  # ipiv
+                         )
+        return types.ExternalFunction("numba_ez_xxgetri", sig)
+
+    @classmethod
+    def numba_ez_rgeev(cls, dtype):
+        sig = types.intc(types.char,             # kind
+                         types.char,             # jobvl
+                         types.char,             # jobvr
+                         types.intp,             # n
+                         types.CPointer(dtype),  # a
+                         types.intp,             # lda
+                         types.CPointer(dtype),  # wr
+                         types.CPointer(dtype),  # wi
+                         types.CPointer(dtype),  # vl
+                         types.intp,             # ldvl
+                         types.CPointer(dtype),  # vr
+                         types.intp              # ldvr
+                         )
+        return types.ExternalFunction("numba_ez_rgeev", sig)
+
+    @classmethod
+    def numba_ez_cgeev(cls, dtype):
+        sig = types.intc(types.char,             # kind
+                         types.char,             # jobvl
+                         types.char,             # jobvr
+                         types.intp,             # n
+                         types.CPointer(dtype),  # a
+                         types.intp,             # lda
+                         types.CPointer(dtype),  # w
+                         types.CPointer(dtype),  # vl
+                         types.intp,             # ldvl
+                         types.CPointer(dtype),  # vr
+                         types.intp              # ldvr
+                         )
+        return types.ExternalFunction("numba_ez_cgeev", sig)
+
+    @classmethod
+    def numba_ez_xxxevd(cls, dtype):
+        wtype = getattr(dtype, "underlying_float", dtype)
+        sig = types.intc(types.char,             # kind
+                         types.char,             # jobz
+                         types.char,             # uplo
+                         types.intp,             # n
+                         types.CPointer(dtype),  # a
+                         types.intp,             # lda
+                         types.CPointer(wtype),  # w
+                         )
+        return types.ExternalFunction("numba_ez_xxxevd", sig)
+
+    @classmethod
+    def numba_xxpotrf(cls, dtype):
+        sig = types.intc(types.char,             # kind
+                         types.char,             # uplo
+                         types.intp,             # n
+                         types.CPointer(dtype),  # a
+                         types.intp              # lda
+                         )
+        return types.ExternalFunction("numba_xxpotrf", sig)
+
+    @classmethod
+    def numba_ez_gesdd(cls, dtype):
+        stype = getattr(dtype, "underlying_float", dtype)
+        sig = types.intc(
+            types.char,             # kind
+            types.char,             # jobz
+            types.intp,             # m
+            types.intp,             # n
+            types.CPointer(dtype),  # a
+            types.intp,             # lda
+            types.CPointer(stype),  # s
+            types.CPointer(dtype),  # u
+            types.intp,             # ldu
+            types.CPointer(dtype),  # vt
+            types.intp              # ldvt
+        )
+
+        return types.ExternalFunction("numba_ez_gesdd", sig)
+
+    @classmethod
+    def numba_ez_geqrf(cls, dtype):
+        sig = types.intc(
+            types.char,             # kind
+            types.intp,             # m
+            types.intp,             # n
+            types.CPointer(dtype),  # a
+            types.intp,             # lda
+            types.CPointer(dtype),  # tau
+        )
+        return types.ExternalFunction("numba_ez_geqrf", sig)
+
+    @classmethod
+    def numba_ez_xxgqr(cls, dtype):
+        sig = types.intc(
+            types.char,             # kind
+            types.intp,             # m
+            types.intp,             # n
+            types.intp,             # k
+            types.CPointer(dtype),  # a
+            types.intp,             # lda
+            types.CPointer(dtype),  # tau
+        )
+        return types.ExternalFunction("numba_ez_xxgqr", sig)
+
+    @classmethod
+    def numba_ez_gelsd(cls, dtype):
+        rtype = getattr(dtype, "underlying_float", dtype)
+        sig = types.intc(
+            types.char,                 # kind
+            types.intp,                 # m
+            types.intp,                 # n
+            types.intp,                 # nrhs
+            types.CPointer(dtype),      # a
+            types.intp,                 # lda
+            types.CPointer(dtype),      # b
+            types.intp,                 # ldb
+            types.CPointer(rtype),      # S
+            types.float64,              # rcond
+            types.CPointer(types.intc)  # rank
+        )
+        return types.ExternalFunction("numba_ez_gelsd", sig)
+
+    @classmethod
+    def numba_xgesv(cls, dtype):
+        sig = types.intc(
+            types.char,                    # kind
+            types.intp,                    # n
+            types.intp,                    # nhrs
+            types.CPointer(dtype),         # a
+            types.intp,                    # lda
+            types.CPointer(F_INT_nbtype),  # ipiv
+            types.CPointer(dtype),         # b
+            types.intp                     # ldb
+        )
+        return types.ExternalFunction("numba_xgesv", sig)
+
+
+@contextlib.contextmanager
+def make_contiguous(context, builder, sig, args):
+    """
+    Ensure that all array arguments are contiguous, if necessary by
+    copying them.
+    A new (sig, args) tuple is yielded.
+    """
+    newtys = []
+    newargs = []
+    copies = []
+    for ty, val in zip(sig.args, args):
+        if not isinstance(ty, types.Array) or ty.layout in 'CF':
+            newty, newval = ty, val
+        else:
+            newty = ty.copy(layout='C')
+            copysig = signature(newty, ty)
+            newval = array_copy(context, builder, copysig, (val,))
+            copies.append((newty, newval))
+        newtys.append(newty)
+        newargs.append(newval)
+    yield signature(sig.return_type, *newtys), tuple(newargs)
+    for ty, val in copies:
+        context.nrt.decref(builder, ty, val)
+
+
+def check_c_int(context, builder, n):
+    """
+    Check whether *n* fits in a C `int`.
+    """
+    _maxint = 2**31 - 1
+
+    def impl(n):
+        if n > _maxint:
+            raise OverflowError("array size too large to fit in C int")
+
+    context.compile_internal(builder, impl,
+                             signature(types.none, types.intp), (n,))
+
+
+def check_blas_return(context, builder, res):
+    """
+    Check the integer error return from one of the BLAS wrappers in
+    _helperlib.c.
+    """
+    with builder.if_then(cgutils.is_not_null(builder, res), likely=False):
+        # Those errors shouldn't happen, it's easier to just abort the process
+        pyapi = context.get_python_api(builder)
+        pyapi.gil_ensure()
+        pyapi.fatal_error("BLAS wrapper returned with an error")
+
+
+def check_lapack_return(context, builder, res):
+    """
+    Check the integer error return from one of the LAPACK wrappers in
+    _helperlib.c.
+    """
+    with builder.if_then(cgutils.is_not_null(builder, res), likely=False):
+        # Those errors shouldn't happen, it's easier to just abort the process
+        pyapi = context.get_python_api(builder)
+        pyapi.gil_ensure()
+        pyapi.fatal_error("LAPACK wrapper returned with an error")
+
+
+def call_xxdot(context, builder, conjugate, dtype,
+               n, a_data, b_data, out_data):
+    """
+    Call the BLAS vector * vector product function for the given arguments.
+    """
+    fnty = ir.FunctionType(ir.IntType(32),
+                           [ll_char, ll_char, intp_t,    # kind, conjugate, n
+                            ll_void_p, ll_void_p, ll_void_p,  # a, b, out
+                            ])
+    fn = cgutils.get_or_insert_function(builder.module, fnty, "numba_xxdot")
+
+    kind = get_blas_kind(dtype)
+    kind_val = ir.Constant(ll_char, ord(kind))
+    conjugate = ir.Constant(ll_char, int(conjugate))
+
+    res = builder.call(fn, (kind_val, conjugate, n,
+                            builder.bitcast(a_data, ll_void_p),
+                            builder.bitcast(b_data, ll_void_p),
+                            builder.bitcast(out_data, ll_void_p)))
+    check_blas_return(context, builder, res)
+
+
+def call_xxgemv(context, builder, do_trans,
+                m_type, m_shapes, m_data, v_data, out_data):
+    """
+    Call the BLAS matrix * vector product function for the given arguments.
+    """
+    fnty = ir.FunctionType(ir.IntType(32),
+                           [ll_char, ll_char,                 # kind, trans
+                            intp_t, intp_t,                   # m, n
+                            ll_void_p, ll_void_p, intp_t,     # alpha, a, lda
+                            ll_void_p, ll_void_p, ll_void_p,  # x, beta, y
+                            ])
+    fn = cgutils.get_or_insert_function(builder.module, fnty, "numba_xxgemv")
+
+    dtype = m_type.dtype
+    alpha = make_constant_slot(context, builder, dtype, 1.0)
+    beta = make_constant_slot(context, builder, dtype, 0.0)
+
+    if m_type.layout == 'F':
+        m, n = m_shapes
+        lda = m_shapes[0]
+    else:
+        n, m = m_shapes
+        lda = m_shapes[1]
+
+    kind = get_blas_kind(dtype)
+    kind_val = ir.Constant(ll_char, ord(kind))
+    trans = ir.Constant(ll_char, ord('t') if do_trans else ord('n'))
+
+    res = builder.call(fn, (kind_val, trans, m, n,
+                            builder.bitcast(alpha, ll_void_p),
+                            builder.bitcast(m_data, ll_void_p), lda,
+                            builder.bitcast(v_data, ll_void_p),
+                            builder.bitcast(beta, ll_void_p),
+                            builder.bitcast(out_data, ll_void_p)))
+    check_blas_return(context, builder, res)
+
+
+def call_xxgemm(context, builder,
+                x_type, x_shapes, x_data,
+                y_type, y_shapes, y_data,
+                out_type, out_shapes, out_data):
+    """
+    Call the BLAS matrix * matrix product function for the given arguments.
+    """
+    fnty = ir.FunctionType(ir.IntType(32),
+                           [ll_char,                       # kind
+                            ll_char, ll_char,              # transa, transb
+                            intp_t, intp_t, intp_t,        # m, n, k
+                            ll_void_p, ll_void_p, intp_t,  # alpha, a, lda
+                            ll_void_p, intp_t, ll_void_p,  # b, ldb, beta
+                            ll_void_p, intp_t,             # c, ldc
+                            ])
+    fn = cgutils.get_or_insert_function(builder.module, fnty, "numba_xxgemm")
+
+    m, k = x_shapes
+    _k, n = y_shapes
+    dtype = x_type.dtype
+    alpha = make_constant_slot(context, builder, dtype, 1.0)
+    beta = make_constant_slot(context, builder, dtype, 0.0)
+
+    trans = ir.Constant(ll_char, ord('t'))
+    notrans = ir.Constant(ll_char, ord('n'))
+
+    def get_array_param(ty, shapes, data):
+        return (
+            # Transpose if layout different from result's
+            notrans if ty.layout == out_type.layout else trans,
+            # Size of the inner dimension in physical array order
+            shapes[1] if ty.layout == 'C' else shapes[0],
+            # The data pointer, unit-less
+            builder.bitcast(data, ll_void_p),
+        )
+
+    transa, lda, data_a = get_array_param(y_type, y_shapes, y_data)
+    transb, ldb, data_b = get_array_param(x_type, x_shapes, x_data)
+    _, ldc, data_c = get_array_param(out_type, out_shapes, out_data)
+
+    kind = get_blas_kind(dtype)
+    kind_val = ir.Constant(ll_char, ord(kind))
+
+    res = builder.call(fn, (kind_val, transa, transb, n, m, k,
+                            builder.bitcast(alpha, ll_void_p), data_a, lda,
+                            data_b, ldb, builder.bitcast(beta, ll_void_p),
+                            data_c, ldc))
+    check_blas_return(context, builder, res)
+
+
+def dot_2_mm(context, builder, sig, args):
+    """
+    np.dot(matrix, matrix)
+    """
+    def dot_impl(a, b):
+        m, k = a.shape
+        _k, n = b.shape
+        if k == 0:
+            return np.zeros((m, n), a.dtype)
+        out = np.empty((m, n), a.dtype)
+        return np.dot(a, b, out)
+
+    res = context.compile_internal(builder, dot_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def dot_2_vm(context, builder, sig, args):
+    """
+    np.dot(vector, matrix)
+    """
+    def dot_impl(a, b):
+        m, = a.shape
+        _m, n = b.shape
+        if m == 0:
+            return np.zeros((n, ), a.dtype)
+        out = np.empty((n, ), a.dtype)
+        return np.dot(a, b, out)
+
+    res = context.compile_internal(builder, dot_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def dot_2_mv(context, builder, sig, args):
+    """
+    np.dot(matrix, vector)
+    """
+    def dot_impl(a, b):
+        m, n = a.shape
+        _n, = b.shape
+        if n == 0:
+            return np.zeros((m, ), a.dtype)
+        out = np.empty((m, ), a.dtype)
+        return np.dot(a, b, out)
+
+    res = context.compile_internal(builder, dot_impl, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def dot_2_vv(context, builder, sig, args, conjugate=False):
+    """
+    np.dot(vector, vector)
+    np.vdot(vector, vector)
+    """
+    aty, bty = sig.args
+    dtype = sig.return_type
+    a = make_array(aty)(context, builder, args[0])
+    b = make_array(bty)(context, builder, args[1])
+    n, = cgutils.unpack_tuple(builder, a.shape)
+
+    def check_args(a, b):
+        m, = a.shape
+        n, = b.shape
+        if m != n:
+            raise ValueError("incompatible array sizes for np.dot(a, b) "
+                             "(vector * vector)")
+
+    context.compile_internal(builder, check_args,
+                             signature(types.none, *sig.args), args)
+    check_c_int(context, builder, n)
+
+    out = cgutils.alloca_once(builder, context.get_value_type(dtype))
+    call_xxdot(context, builder, conjugate, dtype, n, a.data, b.data, out)
+    return builder.load(out)
+
+
+@glue_lowering(np.dot, types.Array, types.Array)
+def dot_2(context, builder, sig, args):
+    """
+    np.dot(a, b)
+    a @ b
+    """
+    ensure_blas()
+
+    with make_contiguous(context, builder, sig, args) as (sig, args):
+        ndims = [x.ndim for x in sig.args[:2]]
+        if ndims == [2, 2]:
+            return dot_2_mm(context, builder, sig, args)
+        elif ndims == [2, 1]:
+            return dot_2_mv(context, builder, sig, args)
+        elif ndims == [1, 2]:
+            return dot_2_vm(context, builder, sig, args)
+        elif ndims == [1, 1]:
+            return dot_2_vv(context, builder, sig, args)
+        else:
+            assert 0
+
+
+lower_builtin(operator.matmul, types.Array, types.Array)(dot_2)
+
+@glue_lowering(np.vdot, types.Array, types.Array)
+def vdot(context, builder, sig, args):
+    """
+    np.vdot(a, b)
+    """
+    ensure_blas()
+
+    with make_contiguous(context, builder, sig, args) as (sig, args):
+        return dot_2_vv(context, builder, sig, args, conjugate=True)
+
+
+def dot_3_vm_check_args(a, b, out):
+    m, = a.shape
+    _m, n = b.shape
+    if m != _m:
+        raise ValueError("incompatible array sizes for "
+                         "np.dot(a, b) (vector * matrix)")
+    if out.shape != (n,):
+        raise ValueError("incompatible output array size for "
+                         "np.dot(a, b, out) (vector * matrix)")
+
+
+def dot_3_mv_check_args(a, b, out):
+    m, _n = a.shape
+    n, = b.shape
+    if n != _n:
+        raise ValueError("incompatible array sizes for np.dot(a, b) "
+                         "(matrix * vector)")
+    if out.shape != (m,):
+        raise ValueError("incompatible output array size for "
+                         "np.dot(a, b, out) (matrix * vector)")
+
+
+def dot_3_vm(context, builder, sig, args):
+    """
+    np.dot(vector, matrix, out)
+    np.dot(matrix, vector, out)
+    """
+    xty, yty, outty = sig.args
+    assert outty == sig.return_type
+    dtype = xty.dtype
+
+    x = make_array(xty)(context, builder, args[0])
+    y = make_array(yty)(context, builder, args[1])
+    out = make_array(outty)(context, builder, args[2])
+    x_shapes = cgutils.unpack_tuple(builder, x.shape)
+    y_shapes = cgutils.unpack_tuple(builder, y.shape)
+    out_shapes = cgutils.unpack_tuple(builder, out.shape)
+    if xty.ndim < yty.ndim:
+        # Vector * matrix
+        # Asked for x * y, we will compute y.T * x
+        mty = yty
+        m_shapes = y_shapes
+        v_shape = x_shapes[0]
+        lda = m_shapes[1]
+        do_trans = yty.layout == 'F'
+        m_data, v_data = y.data, x.data
+        check_args = dot_3_vm_check_args
+    else:
+        # Matrix * vector
+        # We will compute x * y
+        mty = xty
+        m_shapes = x_shapes
+        v_shape = y_shapes[0]
+        lda = m_shapes[0]
+        do_trans = xty.layout == 'C'
+        m_data, v_data = x.data, y.data
+        check_args = dot_3_mv_check_args
+
+    context.compile_internal(builder, check_args,
+                             signature(types.none, *sig.args), args)
+    for val in m_shapes:
+        check_c_int(context, builder, val)
+
+    zero = context.get_constant(types.intp, 0)
+    both_empty = builder.icmp_signed('==', v_shape, zero)
+    matrix_empty = builder.icmp_signed('==', lda, zero)
+    is_empty = builder.or_(both_empty, matrix_empty)
+    with builder.if_else(is_empty, likely=False) as (empty, nonempty):
+        with empty:
+            cgutils.memset(builder, out.data,
+                           builder.mul(out.itemsize, out.nitems), 0)
+        with nonempty:
+            call_xxgemv(context, builder, do_trans, mty, m_shapes, m_data,
+                        v_data, out.data)
+
+    return impl_ret_borrowed(context, builder, sig.return_type,
+                             out._getvalue())
+
+
+def dot_3_mm(context, builder, sig, args):
+    """
+    np.dot(matrix, matrix, out)
+    """
+    xty, yty, outty = sig.args
+    assert outty == sig.return_type
+    dtype = xty.dtype
+
+    x = make_array(xty)(context, builder, args[0])
+    y = make_array(yty)(context, builder, args[1])
+    out = make_array(outty)(context, builder, args[2])
+    x_shapes = cgutils.unpack_tuple(builder, x.shape)
+    y_shapes = cgutils.unpack_tuple(builder, y.shape)
+    out_shapes = cgutils.unpack_tuple(builder, out.shape)
+    m, k = x_shapes
+    _k, n = y_shapes
+
+    # The only case Numpy supports
+    assert outty.layout == 'C'
+
+    def check_args(a, b, out):
+        m, k = a.shape
+        _k, n = b.shape
+        if k != _k:
+            raise ValueError("incompatible array sizes for np.dot(a, b) "
+                             "(matrix * matrix)")
+        if out.shape != (m, n):
+            raise ValueError("incompatible output array size for "
+                             "np.dot(a, b, out) (matrix * matrix)")
+
+    context.compile_internal(builder, check_args,
+                             signature(types.none, *sig.args), args)
+
+    check_c_int(context, builder, m)
+    check_c_int(context, builder, k)
+    check_c_int(context, builder, n)
+
+    x_data = x.data
+    y_data = y.data
+    out_data = out.data
+
+    # If eliminated dimension is zero, set all entries to zero and return
+    zero = context.get_constant(types.intp, 0)
+    both_empty = builder.icmp_signed('==', k, zero)
+    x_empty = builder.icmp_signed('==', m, zero)
+    y_empty = builder.icmp_signed('==', n, zero)
+    is_empty = builder.or_(both_empty, builder.or_(x_empty, y_empty))
+    with builder.if_else(is_empty, likely=False) as (empty, nonempty):
+        with empty:
+            cgutils.memset(builder, out.data,
+                           builder.mul(out.itemsize, out.nitems), 0)
+        with nonempty:
+            # Check if any of the operands is really a 1-d vector represented
+            # as a (1, k) or (k, 1) 2-d array.  In those cases, it is pessimal
+            # to call the generic matrix * matrix product BLAS function.
+            one = context.get_constant(types.intp, 1)
+            is_left_vec = builder.icmp_signed('==', m, one)
+            is_right_vec = builder.icmp_signed('==', n, one)
+
+            with builder.if_else(is_right_vec) as (r_vec, r_mat):
+                with r_vec:
+                    with builder.if_else(is_left_vec) as (v_v, m_v):
+                        with v_v:
+                            # V * V
+                            call_xxdot(context, builder, False, dtype,
+                                       k, x_data, y_data, out_data)
+                        with m_v:
+                            # M * V
+                            do_trans = xty.layout == outty.layout
+                            call_xxgemv(context, builder, do_trans,
+                                        xty, x_shapes, x_data, y_data, out_data)
+                with r_mat:
+                    with builder.if_else(is_left_vec) as (v_m, m_m):
+                        with v_m:
+                            # V * M
+                            do_trans = yty.layout != outty.layout
+                            call_xxgemv(context, builder, do_trans,
+                                        yty, y_shapes, y_data, x_data, out_data)
+                        with m_m:
+                            # M * M
+                            call_xxgemm(context, builder,
+                                        xty, x_shapes, x_data,
+                                        yty, y_shapes, y_data,
+                                        outty, out_shapes, out_data)
+
+    return impl_ret_borrowed(context, builder, sig.return_type,
+                             out._getvalue())
+
+
+@glue_lowering(np.dot, types.Array, types.Array, types.Array)
+def dot_3(context, builder, sig, args):
+    """
+    np.dot(a, b, out)
+    """
+    ensure_blas()
+
+    with make_contiguous(context, builder, sig, args) as (sig, args):
+        ndims = set(x.ndim for x in sig.args[:2])
+        if ndims == set([2]):
+            return dot_3_mm(context, builder, sig, args)
+        elif ndims == set([1, 2]):
+            return dot_3_vm(context, builder, sig, args)
+        else:
+            assert 0
+
+fatal_error_sig = types.intc()
+fatal_error_func = types.ExternalFunction("numba_fatal_error", fatal_error_sig)
+
+
+@register_jitable
+def _check_finite_matrix(a):
+    for v in np.nditer(a):
+        if not np.isfinite(v.item()):
+            raise np.linalg.LinAlgError(
+                "Array must not contain infs or NaNs.")
+
+
+def _check_linalg_matrix(a, func_name, la_prefix=True):
+    # la_prefix is present as some functions, e.g. np.trace()
+    # are documented under "linear algebra" but aren't in the
+    # module
+    prefix = "np.linalg" if la_prefix else "np"
+    interp = (prefix, func_name)
+    # Unpack optional type
+    if isinstance(a, types.Optional):
+        a = a.type
+    if not isinstance(a, types.Array):
+        msg = "%s.%s() only supported for array types" % interp
+        raise TypingError(msg, highlighting=False)
+    if not a.ndim == 2:
+        msg = "%s.%s() only supported on 2-D arrays." % interp
+        raise TypingError(msg, highlighting=False)
+    if not isinstance(a.dtype, (types.Float, types.Complex)):
+        msg = "%s.%s() only supported on "\
+            "float and complex arrays." % interp
+        raise TypingError(msg, highlighting=False)
+
+
+def _check_homogeneous_types(func_name, *types):
+    t0 = types[0].dtype
+    for t in types[1:]:
+        if t.dtype != t0:
+            msg = "np.linalg.%s() only supports inputs that have homogeneous dtypes." % func_name
+            raise TypingError(msg, highlighting=False)
+
+
+def _copy_to_fortran_order():
+    pass
+
+
+@overload(_copy_to_fortran_order)
+def ol_copy_to_fortran_order(a):
+    # This function copies the array 'a' into a new array with fortran order.
+    # This exists because the copy routines don't take order flags yet.
+    F_layout = a.layout == 'F'
+    A_layout = a.layout == 'A'
+    def impl(a):
+        if F_layout:
+            # it's F ordered at compile time, just copy
+            acpy = np.copy(a)
+        elif A_layout:
+            # decide based on runtime value
+            flag_f = a.flags.f_contiguous
+            if flag_f:
+                # it's already F ordered, so copy but in a round about way to
+                # ensure that the copy is also F ordered
+                acpy = np.copy(a.T).T
+            else:
+                # it's something else ordered, so let asfortranarray deal with
+                # copying and making it fortran ordered
+                acpy = np.asfortranarray(a)
+        else:
+            # it's C ordered at compile time, asfortranarray it.
+            acpy = np.asfortranarray(a)
+        return acpy
+    return impl
+
+
+@register_jitable
+def _inv_err_handler(r):
+    if r != 0:
+        if r < 0:
+            fatal_error_func()
+            assert 0   # unreachable
+        if r > 0:
+            raise np.linalg.LinAlgError(
+                "Matrix is singular to machine precision.")
+
+@register_jitable
+def _dummy_liveness_func(a):
+    """pass a list of variables to be preserved through dead code elimination"""
+    return a[0]
+
+
+@overload(np.linalg.inv)
+def inv_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "inv")
+
+    numba_xxgetrf = _LAPACK().numba_xxgetrf(a.dtype)
+
+    numba_xxgetri = _LAPACK().numba_ez_xxgetri(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "inv"))
+
+    def inv_impl(a):
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        if n == 0:
+            return acpy
+
+        ipiv = np.empty(n, dtype=F_INT_nptype)
+
+        r = numba_xxgetrf(kind, n, n, acpy.ctypes, n, ipiv.ctypes)
+        _inv_err_handler(r)
+
+        r = numba_xxgetri(kind, n, acpy.ctypes, n, ipiv.ctypes)
+        _inv_err_handler(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, ipiv.size])
+        return acpy
+
+    return inv_impl
+
+
+@register_jitable
+def _handle_err_maybe_convergence_problem(r):
+    if r != 0:
+        if r < 0:
+            fatal_error_func()
+            assert 0   # unreachable
+        if r > 0:
+            raise ValueError("Internal algorithm failed to converge.")
+
+
+def _check_linalg_1_or_2d_matrix(a, func_name, la_prefix=True):
+    # la_prefix is present as some functions, e.g. np.trace()
+    # are documented under "linear algebra" but aren't in the
+    # module
+    prefix = "np.linalg" if la_prefix else "np"
+    interp = (prefix, func_name)
+    # checks that a matrix is 1 or 2D
+    if not isinstance(a, types.Array):
+        raise TypingError("%s.%s() only supported for array types "
+                          % interp)
+    if not a.ndim <= 2:
+        raise TypingError("%s.%s() only supported on 1 and 2-D arrays "
+                          % interp)
+    if not isinstance(a.dtype, (types.Float, types.Complex)):
+        raise TypingError("%s.%s() only supported on "
+                          "float and complex arrays." % interp)
+
+
+@overload(np.linalg.cholesky)
+def cho_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "cholesky")
+
+    numba_xxpotrf = _LAPACK().numba_xxpotrf(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "cholesky"))
+    UP = ord('U')
+    LO = ord('L')
+
+    def cho_impl(a):
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        # The output is allocated in C order
+        out = a.copy()
+
+        if n == 0:
+            return out
+
+        # Pass UP since xxpotrf() operates in F order
+        # The semantics ensure this works fine
+        # (out is really its Hermitian in F order, but UP instructs
+        #  xxpotrf to compute the Hermitian of the upper triangle
+        #  => they cancel each other)
+        r = numba_xxpotrf(kind, UP, n, out.ctypes, n)
+        if r != 0:
+            if r < 0:
+                fatal_error_func()
+                assert 0   # unreachable
+            if r > 0:
+                raise np.linalg.LinAlgError(
+                    "Matrix is not positive definite.")
+        # Zero out upper triangle, in F order
+        for col in range(n):
+            out[:col, col] = 0
+        return out
+
+    return cho_impl
+
+@overload(np.linalg.eig)
+def eig_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "eig")
+
+    numba_ez_rgeev = _LAPACK().numba_ez_rgeev(a.dtype)
+    numba_ez_cgeev = _LAPACK().numba_ez_cgeev(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "eig"))
+
+    JOBVL = ord('N')
+    JOBVR = ord('V')
+
+    def real_eig_impl(a):
+        """
+        eig() implementation for real arrays.
+        """
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldvl = 1
+        ldvr = n
+        wr = np.empty(n, dtype=a.dtype)
+        wi = np.empty(n, dtype=a.dtype)
+        vl = np.empty((n, ldvl), dtype=a.dtype)
+        vr = np.empty((n, ldvr), dtype=a.dtype)
+
+        if n == 0:
+            return (wr, vr.T)
+
+        r = numba_ez_rgeev(kind,
+                            JOBVL,
+                            JOBVR,
+                            n,
+                            acpy.ctypes,
+                            n,
+                            wr.ctypes,
+                            wi.ctypes,
+                            vl.ctypes,
+                            ldvl,
+                            vr.ctypes,
+                            ldvr)
+        _handle_err_maybe_convergence_problem(r)
+
+        # By design numba does not support dynamic return types, however,
+        # Numpy does. Numpy uses this ability in the case of returning
+        # eigenvalues/vectors of a real matrix. The return type of
+        # np.linalg.eig(), when operating on a matrix in real space
+        # depends on the values present in the matrix itself (recalling
+        # that eigenvalues are the roots of the characteristic polynomial
+        # of the system matrix, which will by construction depend on the
+        # values present in the system matrix). As numba cannot handle
+        # the case of a runtime decision based domain change relative to
+        # the input type, if it is required numba raises as below.
+        if np.any(wi):
+            raise ValueError(
+                "eig() argument must not cause a domain change.")
+
+        # put these in to help with liveness analysis,
+        # `.ctypes` doesn't keep the vars alive
+        _dummy_liveness_func([acpy.size, vl.size, vr.size, wr.size, wi.size])
+        return (wr, vr.T)
+
+    def cmplx_eig_impl(a):
+        """
+        eig() implementation for complex arrays.
+        """
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldvl = 1
+        ldvr = n
+        w = np.empty(n, dtype=a.dtype)
+        vl = np.empty((n, ldvl), dtype=a.dtype)
+        vr = np.empty((n, ldvr), dtype=a.dtype)
+
+        if n == 0:
+            return (w, vr.T)
+
+        r = numba_ez_cgeev(kind,
+                            JOBVL,
+                            JOBVR,
+                            n,
+                            acpy.ctypes,
+                            n,
+                            w.ctypes,
+                            vl.ctypes,
+                            ldvl,
+                            vr.ctypes,
+                            ldvr)
+        _handle_err_maybe_convergence_problem(r)
+
+        # put these in to help with liveness analysis,
+        # `.ctypes` doesn't keep the vars alive
+        _dummy_liveness_func([acpy.size, vl.size, vr.size, w.size])
+        return (w, vr.T)
+
+    if isinstance(a.dtype, types.scalars.Complex):
+        return cmplx_eig_impl
+    else:
+        return real_eig_impl
+
+@overload(np.linalg.eigvals)
+def eigvals_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "eigvals")
+
+    numba_ez_rgeev = _LAPACK().numba_ez_rgeev(a.dtype)
+    numba_ez_cgeev = _LAPACK().numba_ez_cgeev(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "eigvals"))
+
+    JOBVL = ord('N')
+    JOBVR = ord('N')
+
+    def real_eigvals_impl(a):
+        """
+        eigvals() implementation for real arrays.
+        """
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldvl = 1
+        ldvr = 1
+        wr = np.empty(n, dtype=a.dtype)
+
+        if n == 0:
+            return wr
+
+        wi = np.empty(n, dtype=a.dtype)
+
+        # not referenced but need setting for MKL null check
+        vl = np.empty((1), dtype=a.dtype)
+        vr = np.empty((1), dtype=a.dtype)
+
+        r = numba_ez_rgeev(kind,
+                            JOBVL,
+                            JOBVR,
+                            n,
+                            acpy.ctypes,
+                            n,
+                            wr.ctypes,
+                            wi.ctypes,
+                            vl.ctypes,
+                            ldvl,
+                            vr.ctypes,
+                            ldvr)
+        _handle_err_maybe_convergence_problem(r)
+
+        # By design numba does not support dynamic return types, however,
+        # Numpy does. Numpy uses this ability in the case of returning
+        # eigenvalues/vectors of a real matrix. The return type of
+        # np.linalg.eigvals(), when operating on a matrix in real space
+        # depends on the values present in the matrix itself (recalling
+        # that eigenvalues are the roots of the characteristic polynomial
+        # of the system matrix, which will by construction depend on the
+        # values present in the system matrix). As numba cannot handle
+        # the case of a runtime decision based domain change relative to
+        # the input type, if it is required numba raises as below.
+        if np.any(wi):
+            raise ValueError(
+                "eigvals() argument must not cause a domain change.")
+
+        # put these in to help with liveness analysis,
+        # `.ctypes` doesn't keep the vars alive
+        _dummy_liveness_func([acpy.size, vl.size, vr.size, wr.size, wi.size])
+        return wr
+
+    def cmplx_eigvals_impl(a):
+        """
+        eigvals() implementation for complex arrays.
+        """
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldvl = 1
+        ldvr = 1
+        w = np.empty(n, dtype=a.dtype)
+
+        if n == 0:
+            return w
+
+        vl = np.empty((1), dtype=a.dtype)
+        vr = np.empty((1), dtype=a.dtype)
+
+        r = numba_ez_cgeev(kind,
+                            JOBVL,
+                            JOBVR,
+                            n,
+                            acpy.ctypes,
+                            n,
+                            w.ctypes,
+                            vl.ctypes,
+                            ldvl,
+                            vr.ctypes,
+                            ldvr)
+        _handle_err_maybe_convergence_problem(r)
+
+        # put these in to help with liveness analysis,
+        # `.ctypes` doesn't keep the vars alive
+        _dummy_liveness_func([acpy.size, vl.size, vr.size, w.size])
+        return w
+
+    if isinstance(a.dtype, types.scalars.Complex):
+        return cmplx_eigvals_impl
+    else:
+        return real_eigvals_impl
+
+@overload(np.linalg.eigh)
+def eigh_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "eigh")
+
+    # convert typing floats to numpy floats for use in the impl
+    w_type = getattr(a.dtype, "underlying_float", a.dtype)
+    w_dtype = np_support.as_dtype(w_type)
+
+    numba_ez_xxxevd = _LAPACK().numba_ez_xxxevd(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "eigh"))
+
+    JOBZ = ord('V')
+    UPLO = ord('L')
+
+    def eigh_impl(a):
+        n = a.shape[-1]
+
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        w = np.empty(n, dtype=w_dtype)
+
+        if n == 0:
+            return (w, acpy)
+
+        r = numba_ez_xxxevd(kind,  # kind
+                            JOBZ,  # jobz
+                            UPLO,  # uplo
+                            n,  # n
+                            acpy.ctypes,  # a
+                            n,  # lda
+                            w.ctypes  # w
+                            )
+        _handle_err_maybe_convergence_problem(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, w.size])
+        return (w, acpy)
+
+    return eigh_impl
+
+@overload(np.linalg.eigvalsh)
+def eigvalsh_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "eigvalsh")
+
+    # convert typing floats to numpy floats for use in the impl
+    w_type = getattr(a.dtype, "underlying_float", a.dtype)
+    w_dtype = np_support.as_dtype(w_type)
+
+    numba_ez_xxxevd = _LAPACK().numba_ez_xxxevd(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "eigvalsh"))
+
+    JOBZ = ord('N')
+    UPLO = ord('L')
+
+    def eigvalsh_impl(a):
+        n = a.shape[-1]
+
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        w = np.empty(n, dtype=w_dtype)
+
+        if n == 0:
+            return w
+
+        r = numba_ez_xxxevd(kind,  # kind
+                            JOBZ,  # jobz
+                            UPLO,  # uplo
+                            n,  # n
+                            acpy.ctypes,  # a
+                            n,  # lda
+                            w.ctypes  # w
+                            )
+        _handle_err_maybe_convergence_problem(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, w.size])
+        return w
+
+    return eigvalsh_impl
+
+@overload(np.linalg.svd)
+def svd_impl(a, full_matrices=1):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "svd")
+
+    # convert typing floats to numpy floats for use in the impl
+    s_type = getattr(a.dtype, "underlying_float", a.dtype)
+    s_dtype = np_support.as_dtype(s_type)
+
+    numba_ez_gesdd = _LAPACK().numba_ez_gesdd(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "svd"))
+
+    JOBZ_A = ord('A')
+    JOBZ_S = ord('S')
+
+    def svd_impl(a, full_matrices=1):
+        n = a.shape[-1]
+        m = a.shape[-2]
+
+        if n == 0 or m == 0:
+            raise np.linalg.LinAlgError("Arrays cannot be empty")
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ldu = m
+        minmn = min(m, n)
+
+        if full_matrices:
+            JOBZ = JOBZ_A
+            ucol = m
+            ldvt = n
+        else:
+            JOBZ = JOBZ_S
+            ucol = minmn
+            ldvt = minmn
+
+        u = np.empty((ucol, ldu), dtype=a.dtype)
+        s = np.empty(minmn, dtype=s_dtype)
+        vt = np.empty((n, ldvt), dtype=a.dtype)
+
+        r = numba_ez_gesdd(
+            kind,  # kind
+            JOBZ,  # jobz
+            m,  # m
+            n,  # n
+            acpy.ctypes,  # a
+            m,  # lda
+            s.ctypes,  # s
+            u.ctypes,  # u
+            ldu,  # ldu
+            vt.ctypes,  # vt
+            ldvt          # ldvt
+        )
+        _handle_err_maybe_convergence_problem(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, vt.size, u.size, s.size])
+        return (u.T, s, vt.T)
+
+    return svd_impl
+
+
+@overload(np.linalg.qr)
+def qr_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "qr")
+
+    # Need two functions, the first computes R, storing it in the upper
+    # triangle of A with the below diagonal part of A containing elementary
+    # reflectors needed to construct Q. The second turns the below diagonal
+    # entries of A into Q, storing Q in A (creates orthonormal columns from
+    # the elementary reflectors).
+
+    numba_ez_geqrf = _LAPACK().numba_ez_geqrf(a.dtype)
+    numba_ez_xxgqr = _LAPACK().numba_ez_xxgqr(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "qr"))
+
+    def qr_impl(a):
+        n = a.shape[-1]
+        m = a.shape[-2]
+
+        if n == 0 or m == 0:
+            raise np.linalg.LinAlgError("Arrays cannot be empty")
+
+        _check_finite_matrix(a)
+
+        # copy A as it will be destroyed
+        q = _copy_to_fortran_order(a)
+
+        lda = m
+
+        minmn = min(m, n)
+        tau = np.empty((minmn), dtype=a.dtype)
+
+        ret = numba_ez_geqrf(
+            kind,  # kind
+            m,  # m
+            n,  # n
+            q.ctypes,  # a
+            m,  # lda
+            tau.ctypes  # tau
+        )
+        if ret < 0:
+            fatal_error_func()
+            assert 0   # unreachable
+
+        # pull out R, this is transposed because of Fortran
+        r = np.zeros((n, minmn), dtype=a.dtype).T
+
+        # the triangle in R
+        for i in range(minmn):
+            for j in range(i + 1):
+                r[j, i] = q[j, i]
+
+        # and the possible square in R
+        for i in range(minmn, n):
+            for j in range(minmn):
+                r[j, i] = q[j, i]
+
+        ret = numba_ez_xxgqr(
+            kind,  # kind
+            m,  # m
+            minmn,  # n
+            minmn,  # k
+            q.ctypes,  # a
+            m,  # lda
+            tau.ctypes  # tau
+        )
+        _handle_err_maybe_convergence_problem(ret)
+
+        # help liveness analysis
+        _dummy_liveness_func([tau.size, q.size])
+        return (q[:, :minmn], r)
+
+    return qr_impl
+
+
+# helpers and jitted specialisations required for np.linalg.lstsq
+# and np.linalg.solve. These functions have "system" in their name
+# as a differentiator.
+
+def _system_copy_in_b(bcpy, b, nrhs):
+    """
+    Correctly copy 'b' into the 'bcpy' scratch space.
+    """
+    raise NotImplementedError
+
+
+@overload(_system_copy_in_b)
+def _system_copy_in_b_impl(bcpy, b, nrhs):
+    if b.ndim == 1:
+        def oneD_impl(bcpy, b, nrhs):
+            bcpy[:b.shape[-1], 0] = b
+        return oneD_impl
+    else:
+        def twoD_impl(bcpy, b, nrhs):
+            bcpy[:b.shape[-2], :nrhs] = b
+        return twoD_impl
+
+
+def _system_compute_nrhs(b):
+    """
+    Compute the number of right hand sides in the system of equations
+    """
+    raise NotImplementedError
+
+
+@overload(_system_compute_nrhs)
+def _system_compute_nrhs_impl(b):
+    if b.ndim == 1:
+        def oneD_impl(b):
+            return 1
+        return oneD_impl
+    else:
+        def twoD_impl(b):
+            return b.shape[-1]
+        return twoD_impl
+
+
+def _system_check_dimensionally_valid(a, b):
+    """
+    Check that AX=B style system input is dimensionally valid.
+    """
+    raise NotImplementedError
+
+
+@overload(_system_check_dimensionally_valid)
+def _system_check_dimensionally_valid_impl(a, b):
+    ndim = b.ndim
+    if ndim == 1:
+        def oneD_impl(a, b):
+            am = a.shape[-2]
+            bm = b.shape[-1]
+            if am != bm:
+                raise np.linalg.LinAlgError(
+                    "Incompatible array sizes, system is not dimensionally valid.")
+        return oneD_impl
+    else:
+        def twoD_impl(a, b):
+            am = a.shape[-2]
+            bm = b.shape[-2]
+            if am != bm:
+                raise np.linalg.LinAlgError(
+                    "Incompatible array sizes, system is not dimensionally valid.")
+        return twoD_impl
+
+
+def _system_check_non_empty(a, b):
+    """
+    Check that AX=B style system input is not empty.
+    """
+    raise NotImplementedError
+
+
+@overload(_system_check_non_empty)
+def _system_check_non_empty_impl(a, b):
+    ndim = b.ndim
+    if ndim == 1:
+        def oneD_impl(a, b):
+            am = a.shape[-2]
+            an = a.shape[-1]
+            bm = b.shape[-1]
+            if am == 0 or bm == 0 or an == 0:
+                raise np.linalg.LinAlgError('Arrays cannot be empty')
+        return oneD_impl
+    else:
+        def twoD_impl(a, b):
+            am = a.shape[-2]
+            an = a.shape[-1]
+            bm = b.shape[-2]
+            bn = b.shape[-1]
+            if am == 0 or bm == 0 or an == 0 or bn == 0:
+                raise np.linalg.LinAlgError('Arrays cannot be empty')
+        return twoD_impl
+
+
+def _lstsq_residual(b, n, nrhs):
+    """
+    Compute the residual from the 'b' scratch space.
+    """
+    raise NotImplementedError
+
+
+@overload(_lstsq_residual)
+def _lstsq_residual_impl(b, n, nrhs):
+    ndim = b.ndim
+    dtype = b.dtype
+    real_dtype = np_support.as_dtype(getattr(dtype, "underlying_float", dtype))
+
+    if ndim == 1:
+        if isinstance(dtype, (types.Complex)):
+            def cmplx_impl(b, n, nrhs):
+                res = np.empty((1,), dtype=real_dtype)
+                res[0] = np.sum(np.abs(b[n:, 0])**2)
+                return res
+            return cmplx_impl
+        else:
+            def real_impl(b, n, nrhs):
+                res = np.empty((1,), dtype=real_dtype)
+                res[0] = np.sum(b[n:, 0]**2)
+                return res
+            return real_impl
+    else:
+        assert ndim == 2
+        if isinstance(dtype, (types.Complex)):
+            def cmplx_impl(b, n, nrhs):
+                res = np.empty((nrhs), dtype=real_dtype)
+                for k in range(nrhs):
+                    res[k] = np.sum(np.abs(b[n:, k])**2)
+                return res
+            return cmplx_impl
+        else:
+            def real_impl(b, n, nrhs):
+                res = np.empty((nrhs), dtype=real_dtype)
+                for k in range(nrhs):
+                    res[k] = np.sum(b[n:, k]**2)
+                return res
+            return real_impl
+
+
+def _lstsq_solution(b, bcpy, n):
+    """
+    Extract 'x' (the lstsq solution) from the 'bcpy' scratch space.
+    Note 'b' is only used to check the system input dimension...
+    """
+    raise NotImplementedError
+
+
+@overload(_lstsq_solution)
+def _lstsq_solution_impl(b, bcpy, n):
+    if b.ndim == 1:
+        def oneD_impl(b, bcpy, n):
+            return bcpy.T.ravel()[:n]
+        return oneD_impl
+    else:
+        def twoD_impl(b, bcpy, n):
+            return bcpy[:n, :].copy()
+        return twoD_impl
+
+
+@overload(np.linalg.lstsq)
+def lstsq_impl(a, b, rcond=-1.0):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "lstsq")
+
+    # B can be 1D or 2D.
+    _check_linalg_1_or_2d_matrix(b, "lstsq")
+
+    _check_homogeneous_types("lstsq", a, b)
+
+    np_dt = np_support.as_dtype(a.dtype)
+    nb_dt = a.dtype
+
+    # convert typing floats to np floats for use in the impl
+    r_type = getattr(nb_dt, "underlying_float", nb_dt)
+    real_dtype = np_support.as_dtype(r_type)
+
+    # lapack solver
+    numba_ez_gelsd = _LAPACK().numba_ez_gelsd(a.dtype)
+
+    kind = ord(get_blas_kind(nb_dt, "lstsq"))
+
+    # The following functions select specialisations based on
+    # information around 'b', a lot of this effort is required
+    # as 'b' can be either 1D or 2D, and then there are
+    # some optimisations available depending on real or complex
+    # space.
+
+    def lstsq_impl(a, b, rcond=-1.0):
+        n = a.shape[-1]
+        m = a.shape[-2]
+        nrhs = _system_compute_nrhs(b)
+
+        # check the systems have no inf or NaN
+        _check_finite_matrix(a)
+        _check_finite_matrix(b)
+
+        # check the system is not empty
+        _system_check_non_empty(a, b)
+
+        # check the systems are dimensionally valid
+        _system_check_dimensionally_valid(a, b)
+
+        minmn = min(m, n)
+        maxmn = max(m, n)
+
+        # a is destroyed on exit, copy it
+        acpy = _copy_to_fortran_order(a)
+
+        # b is overwritten on exit with the solution, copy allocate
+        bcpy = np.empty((nrhs, maxmn), dtype=np_dt).T
+        # specialised copy in due to b being 1 or 2D
+        _system_copy_in_b(bcpy, b, nrhs)
+
+        # Allocate returns
+        s = np.empty(minmn, dtype=real_dtype)
+        rank_ptr = np.empty(1, dtype=np.int32)
+
+        r = numba_ez_gelsd(
+            kind,  # kind
+            m,  # m
+            n,  # n
+            nrhs,  # nrhs
+            acpy.ctypes,  # a
+            m,  # lda
+            bcpy.ctypes,  # a
+            maxmn,  # ldb
+            s.ctypes,  # s
+            rcond,  # rcond
+            rank_ptr.ctypes  # rank
+        )
+        _handle_err_maybe_convergence_problem(r)
+
+        # set rank to that which was computed
+        rank = rank_ptr[0]
+
+        # compute residuals
+        if rank < n or m <= n:
+            res = np.empty((0), dtype=real_dtype)
+        else:
+            # this requires additional dispatch as there's a faster
+            # impl if the result is in the real domain (no abs() required)
+            res = _lstsq_residual(bcpy, n, nrhs)
+
+        # extract 'x', the solution
+        x = _lstsq_solution(b, bcpy, n)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, bcpy.size, s.size, rank_ptr.size])
+        return (x, res, rank, s[:minmn])
+
+    return lstsq_impl
+
+
+def _solve_compute_return(b, bcpy):
+    """
+    Extract 'x' (the solution) from the 'bcpy' scratch space.
+    Note 'b' is only used to check the system input dimension...
+    """
+    raise NotImplementedError
+
+
+@overload(_solve_compute_return)
+def _solve_compute_return_impl(b, bcpy):
+    if b.ndim == 1:
+        def oneD_impl(b, bcpy):
+            return bcpy.T.ravel()
+        return oneD_impl
+    else:
+        def twoD_impl(b, bcpy):
+            return bcpy
+        return twoD_impl
+
+
+@overload(np.linalg.solve)
+def solve_impl(a, b):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "solve")
+    _check_linalg_1_or_2d_matrix(b, "solve")
+
+    _check_homogeneous_types("solve", a, b)
+
+    np_dt = np_support.as_dtype(a.dtype)
+    nb_dt = a.dtype
+
+    # the lapack solver
+    numba_xgesv = _LAPACK().numba_xgesv(a.dtype)
+
+    kind = ord(get_blas_kind(nb_dt, "solve"))
+
+    def solve_impl(a, b):
+        n = a.shape[-1]
+        nrhs = _system_compute_nrhs(b)
+
+        # check the systems have no inf or NaN
+        _check_finite_matrix(a)
+        _check_finite_matrix(b)
+
+        # check the systems are dimensionally valid
+        _system_check_dimensionally_valid(a, b)
+
+        # a is destroyed on exit, copy it
+        acpy = _copy_to_fortran_order(a)
+
+        # b is overwritten on exit with the solution, copy allocate
+        bcpy = np.empty((nrhs, n), dtype=np_dt).T
+        if n == 0:
+            return _solve_compute_return(b, bcpy)
+
+        # specialised copy in due to b being 1 or 2D
+        _system_copy_in_b(bcpy, b, nrhs)
+
+        # allocate pivot array (needs to be fortran int size)
+        ipiv = np.empty(n, dtype=F_INT_nptype)
+
+        r = numba_xgesv(
+            kind,        # kind
+            n,           # n
+            nrhs,        # nhrs
+            acpy.ctypes,  # a
+            n,           # lda
+            ipiv.ctypes,  # ipiv
+            bcpy.ctypes,  # b
+            n            # ldb
+        )
+        _inv_err_handler(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, bcpy.size, ipiv.size])
+        return _solve_compute_return(b, bcpy)
+
+    return solve_impl
+
+
+@overload(np.linalg.pinv)
+def pinv_impl(a, rcond=1.e-15):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "pinv")
+
+    # convert typing floats to numpy floats for use in the impl
+    s_type = getattr(a.dtype, "underlying_float", a.dtype)
+    s_dtype = np_support.as_dtype(s_type)
+
+    numba_ez_gesdd = _LAPACK().numba_ez_gesdd(a.dtype)
+
+    numba_xxgemm = _BLAS().numba_xxgemm(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "pinv"))
+    JOB = ord('S')
+
+    # need conjugate transposes
+    TRANSA = ord('C')
+    TRANSB = ord('C')
+
+    # scalar constants
+    dt = np_support.as_dtype(a.dtype)
+    zero = np.array([0.], dtype=dt)
+    one = np.array([1.], dtype=dt)
+
+    def pinv_impl(a, rcond=1.e-15):
+
+        # The idea is to build the pseudo-inverse via inverting the singular
+        # value decomposition of a matrix `A`. Mathematically, this is roughly
+        # A = U*S*V^H        [The SV decomposition of A]
+        # A^+ = V*(S^+)*U^H  [The inverted SV decomposition of A]
+        # where ^+ is pseudo inversion and ^H is Hermitian transpose.
+        # As V and U are unitary, their inverses are simply their Hermitian
+        # transpose. S has singular values on its diagonal and zero elsewhere,
+        # it is inverted trivially by reciprocal of the diagonal values with
+        # the exception that zero singular values remain as zero.
+        #
+        # The practical implementation can take advantage of a few things to
+        # gain a few % performance increase:
+        # * A is destroyed by the SVD algorithm from LAPACK so a copy is
+        #   required, this memory is exactly the right size in which to return
+        #   the pseudo-inverse and so can be reused for this purpose.
+        # * The pseudo-inverse of S can be applied to either V or U^H, this
+        #   then leaves a GEMM operation to compute the inverse via either:
+        #   A^+ = (V*(S^+))*U^H
+        #   or
+        #   A^+ = V*((S^+)*U^H)
+        #   however application of S^+ to V^H or U is more convenient as they
+        #   are the result of the SVD algorithm. The application of the
+        #   diagonal system is just a matrix multiplication which results in a
+        #   row/column scaling (direction depending). To save effort, this
+        #   "matrix multiplication" is applied to the smallest of U or V^H and
+        #   only up to the point of "cut-off" (see next note) just as a direct
+        #   scaling.
+        # * The cut-off level for application of S^+ can be used to reduce
+        #   total effort, this cut-off can come via rcond or may just naturally
+        #   be present as a result of zeros in the singular values. Regardless
+        #   there's no need to multiply by zeros in the application of S^+ to
+        #   V^H or U as above. Further, the GEMM operation can be shrunk in
+        #   effort by noting that the possible zero block generated by the
+        #   presence of zeros in S^+ has no effect apart from wasting cycles as
+        #   it is all fmadd()s where one operand is zero. The inner dimension
+        #   of the GEMM operation can therefore be set as shrunk accordingly!
+
+        n = a.shape[-1]
+        m = a.shape[-2]
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        if m == 0 or n == 0:
+            return acpy.T.ravel().reshape(a.shape).T
+
+        minmn = min(m, n)
+
+        u = np.empty((minmn, m), dtype=a.dtype)
+        s = np.empty(minmn, dtype=s_dtype)
+        vt = np.empty((n, minmn), dtype=a.dtype)
+
+        r = numba_ez_gesdd(
+            kind,         # kind
+            JOB,          # job
+            m,            # m
+            n,            # n
+            acpy.ctypes,  # a
+            m,            # lda
+            s.ctypes,     # s
+            u.ctypes,     # u
+            m,            # ldu
+            vt.ctypes,    # vt
+            minmn         # ldvt
+        )
+        _handle_err_maybe_convergence_problem(r)
+
+        # Invert singular values under threshold. Also find the index of
+        # the threshold value as this is the upper limit for the application
+        # of the inverted singular values. Finding this value saves
+        # multiplication by a block of zeros that would be created by the
+        # application of these values to either U or V^H ahead of multiplying
+        # them together. This is done by simply in BLAS parlance via
+        # restricting the `k` dimension to `cut_idx` in `xgemm` whilst keeping
+        # the leading dimensions correct.
+
+        cut_at = s[0] * rcond
+        cut_idx = 0
+        for k in range(minmn):
+            if s[k] > cut_at:
+                s[k] = 1. / s[k]
+                cut_idx = k
+        cut_idx += 1
+
+        # Use cut_idx so there's no scaling by 0.
+        if m >= n:
+            # U is largest so apply S^+ to V^H.
+            for i in range(n):
+                for j in range(cut_idx):
+                    vt[i, j] = vt[i, j] * s[j]
+        else:
+            # V^H is largest so apply S^+ to U.
+            for i in range(cut_idx):
+                s_local = s[i]
+                for j in range(minmn):
+                    u[i, j] = u[i, j] * s_local
+
+        # Do (v^H)^H*U^H (obviously one of the matrices includes the S^+
+        # scaling) and write back to acpy. Note the innner dimension of cut_idx
+        # taking account of the possible zero block.
+        # We can store the result in acpy, given we had to create it
+        # for use in the SVD, and it is now redundant and the right size
+        # but wrong shape.
+
+        r = numba_xxgemm(
+            kind,
+            TRANSA,       # TRANSA
+            TRANSB,       # TRANSB
+            n,            # M
+            m,            # N
+            cut_idx,      # K
+            one.ctypes,   # ALPHA
+            vt.ctypes,    # A
+            minmn,        # LDA
+            u.ctypes,     # B
+            m,            # LDB
+            zero.ctypes,  # BETA
+            acpy.ctypes,  # C
+            n             # LDC
+        )
+
+        # help liveness analysis
+        #acpy.size
+        #vt.size
+        #u.size
+        #s.size
+        #one.size
+        #zero.size
+        _dummy_liveness_func([acpy.size, vt.size, u.size, s.size, one.size,
+            zero.size])
+        return acpy.T.ravel().reshape(a.shape).T
+
+    return pinv_impl
+
+
+def _get_slogdet_diag_walker(a):
+    """
+    Walks the diag of a LUP decomposed matrix
+    uses that det(A) = prod(diag(lup(A)))
+    and also that log(a)+log(b) = log(a*b)
+    The return sign is adjusted based on the values found
+    such that the log(value) stays in the real domain.
+    """
+    if isinstance(a.dtype, types.Complex):
+        @register_jitable
+        def cmplx_diag_walker(n, a, sgn):
+            # walk diagonal
+            csgn = sgn + 0.j
+            acc = 0.
+            for k in range(n):
+                absel = np.abs(a[k, k])
+                csgn = csgn * (a[k, k] / absel)
+                acc = acc + np.log(absel)
+            return (csgn, acc)
+        return cmplx_diag_walker
+    else:
+        @register_jitable
+        def real_diag_walker(n, a, sgn):
+            # walk diagonal
+            acc = 0.
+            for k in range(n):
+                v = a[k, k]
+                if v < 0.:
+                    sgn = -sgn
+                    v = -v
+                acc = acc + np.log(v)
+            # sgn is a float dtype
+            return (sgn + 0., acc)
+        return real_diag_walker
+
+
+@overload(np.linalg.slogdet)
+def slogdet_impl(a):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "slogdet")
+
+    numba_xxgetrf = _LAPACK().numba_xxgetrf(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "slogdet"))
+
+    diag_walker = _get_slogdet_diag_walker(a)
+
+    ONE = a.dtype(1)
+    ZERO = getattr(a.dtype, "underlying_float", a.dtype)(0)
+
+    def slogdet_impl(a):
+        n = a.shape[-1]
+        if a.shape[-2] != n:
+            msg = "Last 2 dimensions of the array must be square."
+            raise np.linalg.LinAlgError(msg)
+
+        if n == 0:
+            return (ONE, ZERO)
+
+        _check_finite_matrix(a)
+
+        acpy = _copy_to_fortran_order(a)
+
+        ipiv = np.empty(n, dtype=F_INT_nptype)
+
+        r = numba_xxgetrf(kind, n, n, acpy.ctypes, n, ipiv.ctypes)
+
+        if r > 0:
+            # factorisation failed, return same defaults as np
+            return (0., -np.inf)
+        _inv_err_handler(r)  # catch input-to-lapack problem
+
+        # The following, prior to the call to diag_walker, is present
+        # to account for the effect of possible permutations to the
+        # sign of the determinant.
+        # This is the same idea as in numpy:
+        # File name `umath_linalg.c.src` e.g.
+        # https://github.com/numpy/numpy/blob/master/numpy/linalg/umath_linalg.c.src
+        # in function `@TYPE@_slogdet_single_element`.
+        sgn = 1
+        for k in range(n):
+            sgn = sgn + (ipiv[k] != (k + 1))
+
+        sgn = sgn & 1
+        if sgn == 0:
+            sgn = -1
+
+        # help liveness analysis
+        _dummy_liveness_func([ipiv.size])
+        return diag_walker(n, acpy, sgn)
+
+    return slogdet_impl
+
+
+@overload(np.linalg.det)
+def det_impl(a):
+
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "det")
+
+    def det_impl(a):
+        (sgn, slogdet) = np.linalg.slogdet(a)
+        return sgn * np.exp(slogdet)
+
+    return det_impl
+
+
+def _compute_singular_values(a):
+    """
+    Compute singular values of *a*.
+    """
+    raise NotImplementedError
+
+
+@overload(_compute_singular_values)
+def _compute_singular_values_impl(a):
+    """
+    Returns a function to compute singular values of `a`
+    """
+    numba_ez_gesdd = _LAPACK().numba_ez_gesdd(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "svd"))
+
+    # Flag for "only compute `S`" to give to xgesdd
+    JOBZ_N = ord('N')
+
+    nb_ret_type = getattr(a.dtype, "underlying_float", a.dtype)
+    np_ret_type = np_support.as_dtype(nb_ret_type)
+    np_dtype = np_support.as_dtype(a.dtype)
+
+    # These are not referenced in the computation but must be set
+    # for MKL.
+    u = np.empty((1, 1), dtype=np_dtype)
+    vt = np.empty((1, 1), dtype=np_dtype)
+
+    def sv_function(a):
+        """
+        Computes singular values.
+        """
+        # Don't use the np.linalg.svd impl instead
+        # call LAPACK to shortcut doing the "reconstruct
+        # singular vectors from reflectors" step and just
+        # get back the singular values.
+        n = a.shape[-1]
+        m = a.shape[-2]
+        if m == 0 or n == 0:
+            raise np.linalg.LinAlgError('Arrays cannot be empty')
+        _check_finite_matrix(a)
+
+        ldu = m
+        minmn = min(m, n)
+
+        # need to be >=1 but aren't referenced
+        ucol = 1
+        ldvt = 1
+
+        acpy = _copy_to_fortran_order(a)
+
+        # u and vt are not referenced however need to be
+        # allocated (as done above) for MKL as it
+        # checks for ref is nullptr.
+        s = np.empty(minmn, dtype=np_ret_type)
+
+        r = numba_ez_gesdd(
+            kind,        # kind
+            JOBZ_N,      # jobz
+            m,           # m
+            n,           # n
+            acpy.ctypes,  # a
+            m,           # lda
+            s.ctypes,    # s
+            u.ctypes,    # u
+            ldu,         # ldu
+            vt.ctypes,   # vt
+            ldvt         # ldvt
+        )
+        _handle_err_maybe_convergence_problem(r)
+
+        # help liveness analysis
+        _dummy_liveness_func([acpy.size, vt.size, u.size, s.size])
+        return s
+
+    return sv_function
+
+
+def _oneD_norm_2(a):
+    """
+    Compute the L2-norm of 1D-array *a*.
+    """
+    raise NotImplementedError
+
+
+@overload(_oneD_norm_2)
+def _oneD_norm_2_impl(a):
+    nb_ret_type = getattr(a.dtype, "underlying_float", a.dtype)
+    np_ret_type = np_support.as_dtype(nb_ret_type)
+
+    xxnrm2 = _BLAS().numba_xxnrm2(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "norm"))
+
+    def impl(a):
+        # Just ignore order, calls are guarded to only come
+        # from cases where order=None or order=2.
+        n = len(a)
+        # Call L2-norm routine from BLAS
+        ret = np.empty((1,), dtype=np_ret_type)
+        jmp = int(a.strides[0] / a.itemsize)
+        r = xxnrm2(
+            kind,      # kind
+            n,         # n
+            a.ctypes,  # x
+            jmp,       # incx
+            ret.ctypes  # result
+        )
+        if r < 0:
+            fatal_error_func()
+            assert 0   # unreachable
+
+        # help liveness analysis
+        #ret.size
+        #a.size
+        _dummy_liveness_func([ret.size, a.size])
+        return ret[0]
+
+    return impl
+
+
+def _get_norm_impl(a, ord_flag):
+    # This function is quite involved as norm supports a large
+    # range of values to select different norm types via kwarg `ord`.
+    # The implementation below branches on dimension of the input
+    # (1D or 2D). The default for `ord` is `None` which requires
+    # special handling in numba, this is dealt with first in each of
+    # the dimension branches. Following this the various norms are
+    # computed via code that is in most cases simply a loop version
+    # of a ufunc based version as found in numpy.
+
+    # The following is common to both 1D and 2D cases.
+    # Convert typing floats to numpy floats for use in the impl.
+    # The return type is always a float, numba differs from numpy in
+    # that it returns an input precision specific value whereas numpy
+    # always returns np.float64.
+    nb_ret_type = getattr(a.dtype, "underlying_float", a.dtype)
+    np_ret_type = np_support.as_dtype(nb_ret_type)
+
+    np_dtype = np_support.as_dtype(a.dtype)
+
+    xxnrm2 = _BLAS().numba_xxnrm2(a.dtype)
+
+    kind = ord(get_blas_kind(a.dtype, "norm"))
+
+    if a.ndim == 1:
+        # 1D cases
+
+        # handle "ord" being "None", must be done separately
+        if ord_flag in (None, types.none):
+            def oneD_impl(a, ord=None):
+                return _oneD_norm_2(a)
+        else:
+            def oneD_impl(a, ord=None):
+                n = len(a)
+
+                # Shortcut to handle zero length arrays
+                # this differs slightly to numpy in that
+                # numpy raises a ValueError for kwarg ord=
+                # +/-np.inf as the reduction operations like
+                # max() and min() don't accept zero length
+                # arrays
+                if n == 0:
+                    return 0.0
+
+                # Note: on order == 2
+                # This is the same as for ord=="None" but because
+                # we have to handle "None" specially this condition
+                # is separated
+                if ord == 2:
+                    return _oneD_norm_2(a)
+                elif ord == np.inf:
+                    # max(abs(a))
+                    ret = abs(a[0])
+                    for k in range(1, n):
+                        val = abs(a[k])
+                        if val > ret:
+                            ret = val
+                    return ret
+
+                elif ord == -np.inf:
+                    # min(abs(a))
+                    ret = abs(a[0])
+                    for k in range(1, n):
+                        val = abs(a[k])
+                        if val < ret:
+                            ret = val
+                    return ret
+
+                elif ord == 0:
+                    # sum(a != 0)
+                    ret = 0.0
+                    for k in range(n):
+                        if a[k] != 0.:
+                            ret += 1.
+                    return ret
+
+                elif ord == 1:
+                    # sum(abs(a))
+                    ret = 0.0
+                    for k in range(n):
+                        ret += abs(a[k])
+                    return ret
+
+                else:
+                    # sum(abs(a)**ord)**(1./ord)
+                    ret = 0.0
+                    for k in range(n):
+                        ret += abs(a[k])**ord
+                    return ret**(1. / ord)
+        return oneD_impl
+
+    elif a.ndim == 2:
+        # 2D cases
+
+        # handle "ord" being "None"
+        if ord_flag in (None, types.none):
+            # Force `a` to be C-order, so that we can take a contiguous
+            # 1D view.
+            if a.layout == 'C':
+                @register_jitable
+                def array_prepare(a):
+                    return a
+            elif a.layout == 'F':
+                @register_jitable
+                def array_prepare(a):
+                    # Legal since L2(a) == L2(a.T)
+                    return a.T
+            else:
+                @register_jitable
+                def array_prepare(a):
+                    return a.copy()
+
+            # Compute the Frobenius norm, this is the L2,2 induced norm of `A`
+            # which is the L2-norm of A.ravel() and so can be computed via BLAS
+            def twoD_impl(a, ord=None):
+                n = a.size
+                if n == 0:
+                    # reshape() currently doesn't support zero-sized arrays
+                    return 0.0
+                a_c = array_prepare(a)
+                return _oneD_norm_2(a_c.reshape(n))
+        else:
+            # max value for this dtype
+            max_val = np.finfo(np_ret_type.type).max
+
+            def twoD_impl(a, ord=None):
+                n = a.shape[-1]
+                m = a.shape[-2]
+
+                # Shortcut to handle zero size arrays
+                # this differs slightly to numpy in that
+                # numpy raises errors for some ord values
+                # and in other cases returns zero.
+                if a.size == 0:
+                    return 0.0
+
+                if ord == np.inf:
+                    # max of sum of abs across rows
+                    # max(sum(abs(a)), axis=1)
+                    global_max = 0.
+                    for ii in range(m):
+                        tmp = 0.
+                        for jj in range(n):
+                            tmp += abs(a[ii, jj])
+                        if tmp > global_max:
+                            global_max = tmp
+                    return global_max
+
+                elif ord == -np.inf:
+                    # min of sum of abs across rows
+                    # min(sum(abs(a)), axis=1)
+                    global_min = max_val
+                    for ii in range(m):
+                        tmp = 0.
+                        for jj in range(n):
+                            tmp += abs(a[ii, jj])
+                        if tmp < global_min:
+                            global_min = tmp
+                    return global_min
+                elif ord == 1:
+                    # max of sum of abs across cols
+                    # max(sum(abs(a)), axis=0)
+                    global_max = 0.
+                    for ii in range(n):
+                        tmp = 0.
+                        for jj in range(m):
+                            tmp += abs(a[jj, ii])
+                        if tmp > global_max:
+                            global_max = tmp
+                    return global_max
+
+                elif ord == -1:
+                    # min of sum of abs across cols
+                    # min(sum(abs(a)), axis=0)
+                    global_min = max_val
+                    for ii in range(n):
+                        tmp = 0.
+                        for jj in range(m):
+                            tmp += abs(a[jj, ii])
+                        if tmp < global_min:
+                            global_min = tmp
+                    return global_min
+
+                # Results via SVD, singular values are sorted on return
+                # by definition.
+                elif ord == 2:
+                    # max SV
+                    return _compute_singular_values(a)[0]
+                elif ord == -2:
+                    # min SV
+                    return _compute_singular_values(a)[-1]
+                else:
+                    # replicate numpy error
+                    raise ValueError("Invalid norm order for matrices.")
+        return twoD_impl
+    else:
+        assert 0  # unreachable
+
+
+@overload(np.linalg.norm)
+def norm_impl(a, ord=None):
+    ensure_lapack()
+
+    _check_linalg_1_or_2d_matrix(a, "norm")
+
+    return _get_norm_impl(a, ord)
+
+
+@overload(np.linalg.cond)
+def cond_impl(a, p=None):
+    ensure_lapack()
+
+    _check_linalg_matrix(a, "cond")
+
+    def impl(a, p=None):
+        # This is extracted for performance, numpy does approximately:
+        # `condition = norm(a) * norm(inv(a))`
+        # in the cases of `p == 2` or `p ==-2` singular values are used
+        # for computing norms. This costs numpy an svd of `a` then an
+        # inversion of `a` and another svd of `a`.
+        # Below is a different approach, which also gives a more
+        # accurate answer as there is no inversion involved.
+        # Recall that the singular values of an inverted matrix are the
+        # reciprocal of singular values of the original matrix.
+        # Therefore calling `svd(a)` once yields all the information
+        # needed about both `a` and `inv(a)` without the cost or
+        # potential loss of accuracy incurred through inversion.
+        # For the case of `p == 2`, the result is just the ratio of
+        # `largest singular value/smallest singular value`, and for the
+        # case of `p==-2` the result is simply the
+        # `smallest singular value/largest singular value`.
+        # As a result of this, numba accepts non-square matrices as
+        # input when p==+/-2 as well as when p==None.
+        if p == 2 or p == -2 or p is None:
+            s = _compute_singular_values(a)
+            if p == 2 or p is None:
+                r = np.divide(s[0], s[-1])
+            else:
+                r = np.divide(s[-1], s[0])
+        else:  # cases np.inf, -np.inf, 1, -1
+            norm_a = np.linalg.norm(a, p)
+            norm_inv_a = np.linalg.norm(np.linalg.inv(a), p)
+            r = norm_a * norm_inv_a
+        # NumPy uses a NaN mask, if the input has a NaN, it will return NaN,
+        # Numba calls ban NaN through the use of _check_finite_matrix but this
+        # catches cases where NaN occurs through floating point use
+        if np.isnan(r):
+            return np.inf
+        else:
+            return r
+    return impl
+
+
+@register_jitable
+def _get_rank_from_singular_values(sv, t):
+    """
+    Gets rank from singular values with cut-off at a given tolerance
+    """
+    rank = 0
+    for k in range(len(sv)):
+        if sv[k] > t:
+            rank = rank + 1
+        else:  # sv is ordered big->small so break on condition not met
+            break
+    return rank
+
+
+@overload(np.linalg.matrix_rank)
+def matrix_rank_impl(a, tol=None):
+    """
+    Computes rank for matrices and vectors.
+    The only issue that may arise is that because numpy uses double
+    precision lapack calls whereas numba uses type specific lapack
+    calls, some singular values may differ and therefore counting the
+    number of them above a tolerance may lead to different counts,
+    and therefore rank, in some cases.
+    """
+    ensure_lapack()
+
+    _check_linalg_1_or_2d_matrix(a, "matrix_rank")
+
+    def _2d_matrix_rank_impl(a, tol):
+
+        # handle the tol==None case separately for type inference to work
+        if tol in (None, types.none):
+            nb_type = getattr(a.dtype, "underlying_float", a.dtype)
+            np_type = np_support.as_dtype(nb_type)
+            eps_val = np.finfo(np_type).eps
+
+            def _2d_tol_none_impl(a, tol=None):
+                s = _compute_singular_values(a)
+                # replicate numpy default tolerance calculation
+                r = a.shape[0]
+                c = a.shape[1]
+                l = max(r, c)
+                t = s[0] * l * eps_val
+                return _get_rank_from_singular_values(s, t)
+            return _2d_tol_none_impl
+        else:
+            def _2d_tol_not_none_impl(a, tol=None):
+                s = _compute_singular_values(a)
+                return _get_rank_from_singular_values(s, tol)
+            return _2d_tol_not_none_impl
+
+    def _get_matrix_rank_impl(a, tol):
+        ndim = a.ndim
+        if ndim == 1:
+            # NOTE: Technically, the numpy implementation could be argued as
+            # incorrect for the case of a vector (1D matrix). If a tolerance
+            # is provided and a vector with a singular value below tolerance is
+            # encountered this should report a rank of zero, the numpy
+            # implementation does not do this and instead elects to report that
+            # if any value in the vector is nonzero then the rank is 1.
+            # An example would be [0, 1e-15, 0, 2e-15] which numpy reports as
+            # rank 1 invariant of `tol`. The singular value for this vector is
+            # obviously sqrt(5)*1e-15 and so a tol of e.g. sqrt(6)*1e-15 should
+            # lead to a reported rank of 0 whereas a tol of 1e-15 should lead
+            # to a reported rank of 1, numpy reports 1 regardless.
+            # The code below replicates the numpy behaviour.
+            def _1d_matrix_rank_impl(a, tol=None):
+                for k in range(len(a)):
+                    if a[k] != 0.:
+                        return 1
+                return 0
+            return _1d_matrix_rank_impl
+        elif ndim == 2:
+            return _2d_matrix_rank_impl(a, tol)
+        else:
+            assert 0  # unreachable
+
+    return _get_matrix_rank_impl(a, tol)
+
+
+@overload(np.linalg.matrix_power)
+def matrix_power_impl(a, n):
+    """
+    Computes matrix power. Only integer powers are supported in numpy.
+    """
+
+    _check_linalg_matrix(a, "matrix_power")
+    np_dtype = np_support.as_dtype(a.dtype)
+
+    nt = getattr(n, 'dtype', n)
+    if not isinstance(nt, types.Integer):
+        raise NumbaTypeError("Exponent must be an integer.")
+
+    def matrix_power_impl(a, n):
+
+        if n == 0:
+            # this should be eye() but it doesn't support
+            # the dtype kwarg yet so do it manually to save
+            # the copy required by eye(a.shape[0]).asdtype()
+            A = np.zeros(a.shape, dtype=np_dtype)
+            for k in range(a.shape[0]):
+                A[k, k] = 1.
+            return A
+
+        am, an = a.shape[-1], a.shape[-2]
+        if am != an:
+            raise ValueError('input must be a square array')
+
+        # empty, return a copy
+        if am == 0:
+            return a.copy()
+
+        # note: to be consistent over contiguousness, C order is
+        # returned as that is what dot() produces and the most common
+        # paths through matrix_power will involve that. Therefore
+        # copies are made here to ensure the data ordering is
+        # correct for paths not going via dot().
+
+        if n < 0:
+            A = np.linalg.inv(a).copy()
+            if n == -1:  # return now
+                return A
+            n = -n
+        else:
+            if n == 1:  # return a copy now
+                return a.copy()
+            A = a  # this is safe, `a` is only read
+
+        if n < 4:
+            if n == 2:
+                return np.dot(A, A)
+            if n == 3:
+                return np.dot(np.dot(A, A), A)
+        else:
+
+            acc = A
+            exp = n
+
+            # Initialise ret, SSA cannot see the loop will execute, without this
+            # it appears as uninitialised.
+            ret = acc
+            # tried a loop split and branchless using identity matrix as
+            # input but it seems like having a "first entry" flag is quicker
+            flag = True
+            while exp != 0:
+                if exp & 1:
+                    if flag:
+                        ret = acc
+                        flag = False
+                    else:
+                        ret = np.dot(ret, acc)
+                acc = np.dot(acc, acc)
+                exp = exp >> 1
+
+            return ret
+
+    return matrix_power_impl
+
+# This is documented under linalg despite not being in the module
+
+
+@overload(np.trace)
+def matrix_trace_impl(a, offset=0):
+    """
+    Computes the trace of an array.
+    """
+
+    _check_linalg_matrix(a, "trace", la_prefix=False)
+
+    if not isinstance(offset, (int, types.Integer)):
+        raise NumbaTypeError("integer argument expected, got %s" % offset)
+
+    def matrix_trace_impl(a, offset=0):
+        rows, cols = a.shape
+        k = offset
+        if k < 0:
+            rows = rows + k
+        if k > 0:
+            cols = cols - k
+        n = max(min(rows, cols), 0)
+        ret = 0
+        if k >= 0:
+            for i in range(n):
+                ret += a[i, k + i]
+        else:
+            for i in range(n):
+                ret += a[i - k, i]
+        return ret
+
+    return matrix_trace_impl
+
+
+def _check_scalar_or_lt_2d_mat(a, func_name, la_prefix=True):
+    prefix = "np.linalg" if la_prefix else "np"
+    interp = (prefix, func_name)
+    # checks that a matrix is 1 or 2D
+    if isinstance(a, types.Array):
+        if not a.ndim <= 2:
+            raise TypingError("%s.%s() only supported on 1 and 2-D arrays "
+                              % interp, highlighting=False)
+
+
+@register_jitable
+def outer_impl_none(a, b, out):
+    aa = np.asarray(a)
+    bb = np.asarray(b)
+    return np.multiply(aa.ravel().reshape((aa.size, 1)),
+                        bb.ravel().reshape((1, bb.size)))
+
+
+@register_jitable
+def outer_impl_arr(a, b, out):
+    aa = np.asarray(a)
+    bb = np.asarray(b)
+    np.multiply(aa.ravel().reshape((aa.size, 1)),
+                bb.ravel().reshape((1, bb.size)),
+                out)
+    return out
+
+
+def _get_outer_impl(a, b, out):
+    if out in (None, types.none):
+        return outer_impl_none
+    else:
+        return outer_impl_arr
+
+
+@overload(np.outer)
+def outer_impl(a, b, out=None):
+
+    _check_scalar_or_lt_2d_mat(a, "outer", la_prefix=False)
+    _check_scalar_or_lt_2d_mat(b, "outer", la_prefix=False)
+
+    impl = _get_outer_impl(a, b, out)
+
+    def outer_impl(a, b, out=None):
+        return impl(a, b, out)
+
+    return outer_impl
+
+
+def _kron_normaliser_impl(x):
+    # makes x into a 2d array
+    if isinstance(x, types.Array):
+        if x.layout not in ('C', 'F'):
+            raise TypingError("np.linalg.kron only supports 'C' or 'F' layout "
+                              "input arrays. Received an input of "
+                              "layout '{}'.".format(x.layout))
+        elif x.ndim == 2:
+            @register_jitable
+            def nrm_shape(x):
+                xn = x.shape[-1]
+                xm = x.shape[-2]
+                return x.reshape(xm, xn)
+            return nrm_shape
+        else:
+            @register_jitable
+            def nrm_shape(x):
+                xn = x.shape[-1]
+                return x.reshape(1, xn)
+            return nrm_shape
+    else:  # assume its a scalar
+        @register_jitable
+        def nrm_shape(x):
+            a = np.empty((1, 1), type(x))
+            a[0] = x
+            return a
+        return nrm_shape
+
+
+def _kron_return(a, b):
+    # transforms c into something that kron would return
+    # based on the shapes of a and b
+    a_is_arr = isinstance(a, types.Array)
+    b_is_arr = isinstance(b, types.Array)
+    if a_is_arr and b_is_arr:
+        if a.ndim == 2 or b.ndim == 2:
+            @register_jitable
+            def ret(a, b, c):
+                return c
+            return ret
+        else:
+            @register_jitable
+            def ret(a, b, c):
+                return c.reshape(c.size)
+            return ret
+    else:  # at least one of (a, b) is a scalar
+        if a_is_arr:
+            @register_jitable
+            def ret(a, b, c):
+                return c.reshape(a.shape)
+            return ret
+        elif b_is_arr:
+            @register_jitable
+            def ret(a, b, c):
+                return c.reshape(b.shape)
+            return ret
+        else:  # both scalars
+            @register_jitable
+            def ret(a, b, c):
+                return c[0]
+            return ret
+
+
+@overload(np.kron)
+def kron_impl(a, b):
+
+    _check_scalar_or_lt_2d_mat(a, "kron", la_prefix=False)
+    _check_scalar_or_lt_2d_mat(b, "kron", la_prefix=False)
+
+    fix_a = _kron_normaliser_impl(a)
+    fix_b = _kron_normaliser_impl(b)
+    ret_c = _kron_return(a, b)
+
+    # this is fine because the ufunc for the Hadamard product
+    # will reject differing dtypes in a and b.
+    dt = getattr(a, 'dtype', a)
+
+    def kron_impl(a, b):
+
+        aa = fix_a(a)
+        bb = fix_b(b)
+
+        am = aa.shape[-2]
+        an = aa.shape[-1]
+        bm = bb.shape[-2]
+        bn = bb.shape[-1]
+
+        cm = am * bm
+        cn = an * bn
+
+        # allocate c
+        C = np.empty((cm, cn), dtype=dt)
+
+        # In practice this is runs quicker than the more obvious
+        # `each element of A multiplied by B and assigned to
+        # a block in C` like alg.
+
+        # loop over rows of A
+        for i in range(am):
+            # compute the column offset into C
+            rjmp = i * bm
+            # loop over rows of B
+            for k in range(bm):
+                # compute row the offset into C
+                irjmp = rjmp + k
+                # slice a given row of B
+                slc = bb[k, :]
+                # loop over columns of A
+                for j in range(an):
+                    # vectorized assignment of an element of A
+                    # multiplied by the current row of B into
+                    # a slice of a row of C
+                    cjmp = j * bn
+                    C[irjmp, cjmp:cjmp + bn] = aa[i, j] * slc
+
+        return ret_c(a, b, C)
+
+    return kron_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npdatetime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npdatetime.py
new file mode 100644
index 000000000..97af460ca
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npdatetime.py
@@ -0,0 +1,855 @@
+"""
+Implementation of operations on numpy timedelta64.
+"""
+
+import numpy as np
+import operator
+
+import llvmlite.ir
+from llvmlite.ir import Constant
+
+from numba.core import types, cgutils
+from numba.core.cgutils import create_constant_array
+from numba.core.imputils import (lower_builtin, lower_constant,
+                                 impl_ret_untracked, lower_cast)
+from numba.np import npdatetime_helpers, numpy_support, npyfuncs
+from numba.extending import overload_method
+from numba.core.config import IS_32BITS
+from numba.core.errors import LoweringError
+
+# datetime64 and timedelta64 use the same internal representation
+DATETIME64 = TIMEDELTA64 = llvmlite.ir.IntType(64)
+NAT = Constant(TIMEDELTA64, npdatetime_helpers.NAT)
+
+TIMEDELTA_BINOP_SIG = (types.NPTimedelta,) * 2
+
+
+def scale_by_constant(builder, val, factor):
+    """
+    Multiply *val* by the constant *factor*.
+    """
+    return builder.mul(val, Constant(TIMEDELTA64, factor))
+
+
+def unscale_by_constant(builder, val, factor):
+    """
+    Divide *val* by the constant *factor*.
+    """
+    return builder.sdiv(val, Constant(TIMEDELTA64, factor))
+
+
+def add_constant(builder, val, const):
+    """
+    Add constant *const* to *val*.
+    """
+    return builder.add(val, Constant(TIMEDELTA64, const))
+
+
+def scale_timedelta(context, builder, val, srcty, destty):
+    """
+    Scale the timedelta64 *val* from *srcty* to *destty*
+    (both numba.types.NPTimedelta instances)
+    """
+    factor = npdatetime_helpers.get_timedelta_conversion_factor(
+        srcty.unit, destty.unit)
+    if factor is None:
+        # This can happen when using explicit output in a ufunc.
+        msg = f"cannot convert timedelta64 from {srcty.unit} to {destty.unit}"
+        raise LoweringError(msg)
+    return scale_by_constant(builder, val, factor)
+
+
+def normalize_timedeltas(context, builder, left, right, leftty, rightty):
+    """
+    Scale either *left* or *right* to the other's unit, in order to have
+    homogeneous units.
+    """
+    factor = npdatetime_helpers.get_timedelta_conversion_factor(
+        leftty.unit, rightty.unit)
+    if factor is not None:
+        return scale_by_constant(builder, left, factor), right
+    factor = npdatetime_helpers.get_timedelta_conversion_factor(
+        rightty.unit, leftty.unit)
+    if factor is not None:
+        return left, scale_by_constant(builder, right, factor)
+    # Typing should not let this happen, except on == and != operators
+    raise RuntimeError("cannot normalize %r and %r" % (leftty, rightty))
+
+
+def alloc_timedelta_result(builder, name='ret'):
+    """
+    Allocate a NaT-initialized datetime64 (or timedelta64) result slot.
+    """
+    ret = cgutils.alloca_once(builder, TIMEDELTA64, name=name)
+    builder.store(NAT, ret)
+    return ret
+
+
+def alloc_boolean_result(builder, name='ret'):
+    """
+    Allocate an uninitialized boolean result slot.
+    """
+    ret = cgutils.alloca_once(builder, llvmlite.ir.IntType(1), name=name)
+    return ret
+
+
+def is_not_nat(builder, val):
+    """
+    Return a predicate which is true if *val* is not NaT.
+    """
+    return builder.icmp_unsigned('!=', val, NAT)
+
+
+def are_not_nat(builder, vals):
+    """
+    Return a predicate which is true if all of *vals* are not NaT.
+    """
+    assert len(vals) >= 1
+    pred = is_not_nat(builder, vals[0])
+    for val in vals[1:]:
+        pred = builder.and_(pred, is_not_nat(builder, val))
+    return pred
+
+
+normal_year_months = create_constant_array(
+    TIMEDELTA64,
+    [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
+leap_year_months = create_constant_array(
+    TIMEDELTA64,
+    [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
+normal_year_months_acc = create_constant_array(
+    TIMEDELTA64,
+    [0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334])
+leap_year_months_acc = create_constant_array(
+    TIMEDELTA64,
+    [0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335])
+
+
+@lower_constant(types.NPDatetime)
+@lower_constant(types.NPTimedelta)
+def datetime_constant(context, builder, ty, pyval):
+    return DATETIME64(pyval.astype(np.int64))
+
+
+# Arithmetic operators on timedelta64
+
+@lower_builtin(operator.pos, types.NPTimedelta)
+def timedelta_pos_impl(context, builder, sig, args):
+    res = args[0]
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.neg, types.NPTimedelta)
+def timedelta_neg_impl(context, builder, sig, args):
+    res = builder.neg(args[0])
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(abs, types.NPTimedelta)
+def timedelta_abs_impl(context, builder, sig, args):
+    val, = args
+    ret = alloc_timedelta_result(builder)
+    with builder.if_else(cgutils.is_scalar_neg(builder, val)) as (then, otherwise):
+        with then:
+            builder.store(builder.neg(val), ret)
+        with otherwise:
+            builder.store(val, ret)
+    res = builder.load(ret)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def timedelta_sign_impl(context, builder, sig, args):
+    """
+    np.sign(timedelta64)
+    """
+    val, = args
+    ret = alloc_timedelta_result(builder)
+    zero = Constant(TIMEDELTA64, 0)
+    with builder.if_else(builder.icmp_signed('>', val, zero)
+                         ) as (gt_zero, le_zero):
+        with gt_zero:
+            builder.store(Constant(TIMEDELTA64, 1), ret)
+        with le_zero:
+            with builder.if_else(builder.icmp_unsigned('==', val, zero)
+                                 ) as (eq_zero, lt_zero):
+                with eq_zero:
+                    builder.store(Constant(TIMEDELTA64, 0), ret)
+                with lt_zero:
+                    builder.store(Constant(TIMEDELTA64, -1), ret)
+    res = builder.load(ret)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.add, *TIMEDELTA_BINOP_SIG)
+@lower_builtin(operator.iadd, *TIMEDELTA_BINOP_SIG)
+def timedelta_add_impl(context, builder, sig, args):
+    [va, vb] = args
+    [ta, tb] = sig.args
+    ret = alloc_timedelta_result(builder)
+    with cgutils.if_likely(builder, are_not_nat(builder, [va, vb])):
+        va = scale_timedelta(context, builder, va, ta, sig.return_type)
+        vb = scale_timedelta(context, builder, vb, tb, sig.return_type)
+        builder.store(builder.add(va, vb), ret)
+    res = builder.load(ret)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.sub, *TIMEDELTA_BINOP_SIG)
+@lower_builtin(operator.isub, *TIMEDELTA_BINOP_SIG)
+def timedelta_sub_impl(context, builder, sig, args):
+    [va, vb] = args
+    [ta, tb] = sig.args
+    ret = alloc_timedelta_result(builder)
+    with cgutils.if_likely(builder, are_not_nat(builder, [va, vb])):
+        va = scale_timedelta(context, builder, va, ta, sig.return_type)
+        vb = scale_timedelta(context, builder, vb, tb, sig.return_type)
+        builder.store(builder.sub(va, vb), ret)
+    res = builder.load(ret)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+def _timedelta_times_number(context, builder, td_arg, td_type,
+                            number_arg, number_type, return_type):
+    ret = alloc_timedelta_result(builder)
+    with cgutils.if_likely(builder, is_not_nat(builder, td_arg)):
+        if isinstance(number_type, types.Float):
+            val = builder.sitofp(td_arg, number_arg.type)
+            val = builder.fmul(val, number_arg)
+            val = _cast_to_timedelta(context, builder, val)
+        else:
+            val = builder.mul(td_arg, number_arg)
+        # The scaling is required for ufunc np.multiply() with an explicit
+        # output in a different unit.
+        val = scale_timedelta(context, builder, val, td_type, return_type)
+        builder.store(val, ret)
+    return builder.load(ret)
+
+
+@lower_builtin(operator.mul, types.NPTimedelta, types.Integer)
+@lower_builtin(operator.imul, types.NPTimedelta, types.Integer)
+@lower_builtin(operator.mul, types.NPTimedelta, types.Float)
+@lower_builtin(operator.imul, types.NPTimedelta, types.Float)
+def timedelta_times_number(context, builder, sig, args):
+    res = _timedelta_times_number(context, builder,
+                                  args[0], sig.args[0], args[1], sig.args[1],
+                                  sig.return_type)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.mul, types.Integer, types.NPTimedelta)
+@lower_builtin(operator.imul, types.Integer, types.NPTimedelta)
+@lower_builtin(operator.mul, types.Float, types.NPTimedelta)
+@lower_builtin(operator.imul, types.Float, types.NPTimedelta)
+def number_times_timedelta(context, builder, sig, args):
+    res = _timedelta_times_number(context, builder,
+                                  args[1], sig.args[1], args[0], sig.args[0],
+                                  sig.return_type)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.truediv, types.NPTimedelta, types.Integer)
+@lower_builtin(operator.itruediv, types.NPTimedelta, types.Integer)
+@lower_builtin(operator.floordiv, types.NPTimedelta, types.Integer)
+@lower_builtin(operator.ifloordiv, types.NPTimedelta, types.Integer)
+@lower_builtin(operator.truediv, types.NPTimedelta, types.Float)
+@lower_builtin(operator.itruediv, types.NPTimedelta, types.Float)
+@lower_builtin(operator.floordiv, types.NPTimedelta, types.Float)
+@lower_builtin(operator.ifloordiv, types.NPTimedelta, types.Float)
+def timedelta_over_number(context, builder, sig, args):
+    td_arg, number_arg = args
+    number_type = sig.args[1]
+    ret = alloc_timedelta_result(builder)
+    ok = builder.and_(is_not_nat(builder, td_arg),
+                      builder.not_(cgutils.is_scalar_zero_or_nan(builder, number_arg)))
+    with cgutils.if_likely(builder, ok):
+        # Denominator is non-zero, non-NaN
+        if isinstance(number_type, types.Float):
+            val = builder.sitofp(td_arg, number_arg.type)
+            val = builder.fdiv(val, number_arg)
+            val = _cast_to_timedelta(context, builder, val)
+        else:
+            val = builder.sdiv(td_arg, number_arg)
+        # The scaling is required for ufuncs np.*divide() with an explicit
+        # output in a different unit.
+        val = scale_timedelta(context, builder, val,
+                              sig.args[0], sig.return_type)
+        builder.store(val, ret)
+    res = builder.load(ret)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.truediv, *TIMEDELTA_BINOP_SIG)
+@lower_builtin(operator.itruediv, *TIMEDELTA_BINOP_SIG)
+def timedelta_over_timedelta(context, builder, sig, args):
+    [va, vb] = args
+    [ta, tb] = sig.args
+    not_nan = are_not_nat(builder, [va, vb])
+    ll_ret_type = context.get_value_type(sig.return_type)
+    ret = cgutils.alloca_once(builder, ll_ret_type, name='ret')
+    builder.store(Constant(ll_ret_type, float('nan')), ret)
+    with cgutils.if_likely(builder, not_nan):
+        va, vb = normalize_timedeltas(context, builder, va, vb, ta, tb)
+        va = builder.sitofp(va, ll_ret_type)
+        vb = builder.sitofp(vb, ll_ret_type)
+        builder.store(builder.fdiv(va, vb), ret)
+    res = builder.load(ret)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+if numpy_support.numpy_version >= (1, 16):
+    # np 1.16 added support for:
+    # * np.floor_divide on mm->q
+    # * np.remainder on mm->m
+
+    @lower_builtin(operator.floordiv, *TIMEDELTA_BINOP_SIG)
+    def timedelta_floor_div_timedelta(context, builder, sig, args):
+        [va, vb] = args
+        [ta, tb] = sig.args
+        ll_ret_type = context.get_value_type(sig.return_type)
+        not_nan = are_not_nat(builder, [va, vb])
+        ret = cgutils.alloca_once(builder, ll_ret_type, name='ret')
+        zero = Constant(ll_ret_type, 0)
+        one = Constant(ll_ret_type, 1)
+        builder.store(zero, ret)
+        with cgutils.if_likely(builder, not_nan):
+            va, vb = normalize_timedeltas(context, builder, va, vb, ta, tb)
+            # is the denominator zero or NaT?
+            denom_ok = builder.not_(builder.icmp_signed('==', vb, zero))
+            with cgutils.if_likely(builder, denom_ok):
+                # is either arg negative?
+                vaneg = builder.icmp_signed('<', va, zero)
+                neg = builder.or_(vaneg, builder.icmp_signed('<', vb, zero))
+                with builder.if_else(neg) as (then, otherwise):
+                    with then:  # one or more value negative
+                        with builder.if_else(vaneg) as (negthen, negotherwise):
+                            with negthen:
+                                top = builder.sub(va, one)
+                                div = builder.sdiv(top, vb)
+                                builder.store(div, ret)
+                            with negotherwise:
+                                top = builder.add(va, one)
+                                div = builder.sdiv(top, vb)
+                                builder.store(div, ret)
+                    with otherwise:
+                        div = builder.sdiv(va, vb)
+                        builder.store(div, ret)
+        res = builder.load(ret)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+
+    def timedelta_mod_timedelta(context, builder, sig, args):
+        # inspired by https://github.com/numpy/numpy/blob/fe8072a12d65e43bd2e0b0f9ad67ab0108cc54b3/numpy/core/src/umath/loops.c.src#L1424
+        # alg is basically as `a % b`:
+        # if a or b is NaT return NaT
+        # elseif b is 0 return NaT
+        # else pretend a and b are int and do pythonic int modulus
+
+        [va, vb] = args
+        [ta, tb] = sig.args
+        not_nan = are_not_nat(builder, [va, vb])
+        ll_ret_type = context.get_value_type(sig.return_type)
+        ret = alloc_timedelta_result(builder)
+        builder.store(NAT, ret)
+        zero = Constant(ll_ret_type, 0)
+        with cgutils.if_likely(builder, not_nan):
+            va, vb = normalize_timedeltas(context, builder, va, vb, ta, tb)
+            # is the denominator zero or NaT?
+            denom_ok = builder.not_(builder.icmp_signed('==', vb, zero))
+            with cgutils.if_likely(builder, denom_ok):
+                # is either arg negative?
+                vapos = builder.icmp_signed('>', va, zero)
+                vbpos = builder.icmp_signed('>', vb, zero)
+                rem = builder.srem(va, vb)
+                cond = builder.or_(builder.and_(vapos, vbpos),
+                                   builder.icmp_signed('==', rem, zero))
+                with builder.if_else(cond) as (then, otherwise):
+                    with then:
+                        builder.store(rem, ret)
+                    with otherwise:
+                        builder.store(builder.add(rem, vb), ret)
+
+        res = builder.load(ret)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+
+# Comparison operators on timedelta64
+
+
+def _create_timedelta_comparison_impl(ll_op, default_value):
+    def impl(context, builder, sig, args):
+        [va, vb] = args
+        [ta, tb] = sig.args
+        ret = alloc_boolean_result(builder)
+        with builder.if_else(are_not_nat(builder, [va, vb])) as (then, otherwise):
+            with then:
+                try:
+                    norm_a, norm_b = normalize_timedeltas(
+                        context, builder, va, vb, ta, tb)
+                except RuntimeError:
+                    # Cannot normalize units => the values are unequal (except if NaT)
+                    builder.store(default_value, ret)
+                else:
+                    builder.store(builder.icmp_unsigned(ll_op, norm_a, norm_b), ret)
+            with otherwise:
+                if numpy_support.numpy_version < (1, 16):
+                    # No scaling when comparing NaTs
+                    builder.store(builder.icmp_unsigned(ll_op, va, vb), ret)
+                else:
+                    # NumPy >= 1.16 switched to NaT ==/>=/>/</<= NaT being
+                    # False and NaT != <anything, including NaT> being True
+                    if ll_op == '!=':
+                        builder.store(cgutils.true_bit, ret)
+                    else:
+                        builder.store(cgutils.false_bit, ret)
+        res = builder.load(ret)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+
+    return impl
+
+
+def _create_timedelta_ordering_impl(ll_op):
+    def impl(context, builder, sig, args):
+        [va, vb] = args
+        [ta, tb] = sig.args
+        ret = alloc_boolean_result(builder)
+        with builder.if_else(are_not_nat(builder, [va, vb])) as (then, otherwise):
+            with then:
+                norm_a, norm_b = normalize_timedeltas(
+                    context, builder, va, vb, ta, tb)
+                builder.store(builder.icmp_signed(ll_op, norm_a, norm_b), ret)
+            with otherwise:
+                if numpy_support.numpy_version < (1, 16):
+                    # No scaling when comparing NaT with something else
+                    # (i.e. NaT is <= everything else, since it's the smallest
+                    #  int64 value)
+                    builder.store(builder.icmp_signed(ll_op, va, vb), ret)
+                else:
+                    # NumPy >= 1.16 switched to NaT >=/>/</<= NaT being False
+                    builder.store(cgutils.false_bit, ret)
+        res = builder.load(ret)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+
+    return impl
+
+
+timedelta_eq_timedelta_impl = _create_timedelta_comparison_impl(
+    '==', cgutils.false_bit)
+timedelta_ne_timedelta_impl = _create_timedelta_comparison_impl(
+    '!=', cgutils.true_bit)
+timedelta_lt_timedelta_impl = _create_timedelta_ordering_impl('<')
+timedelta_le_timedelta_impl = _create_timedelta_ordering_impl('<=')
+timedelta_gt_timedelta_impl = _create_timedelta_ordering_impl('>')
+timedelta_ge_timedelta_impl = _create_timedelta_ordering_impl('>=')
+
+for op_, func in [(operator.eq, timedelta_eq_timedelta_impl),
+                  (operator.ne, timedelta_ne_timedelta_impl),
+                  (operator.lt,  timedelta_lt_timedelta_impl),
+                  (operator.le, timedelta_le_timedelta_impl),
+                  (operator.gt,  timedelta_gt_timedelta_impl),
+                  (operator.ge, timedelta_ge_timedelta_impl)]:
+    lower_builtin(op_, *TIMEDELTA_BINOP_SIG)(func)
+
+
+# Arithmetic on datetime64
+
+def is_leap_year(builder, year_val):
+    """
+    Return a predicate indicating whether *year_val* (offset by 1970) is a
+    leap year.
+    """
+    actual_year = builder.add(year_val, Constant(DATETIME64, 1970))
+    multiple_of_4 = cgutils.is_null(
+        builder, builder.and_(actual_year, Constant(DATETIME64, 3)))
+    not_multiple_of_100 = cgutils.is_not_null(
+        builder, builder.srem(actual_year, Constant(DATETIME64, 100)))
+    multiple_of_400 = cgutils.is_null(
+        builder, builder.srem(actual_year, Constant(DATETIME64, 400)))
+    return builder.and_(multiple_of_4,
+                        builder.or_(not_multiple_of_100, multiple_of_400))
+
+
+def year_to_days(builder, year_val):
+    """
+    Given a year *year_val* (offset to 1970), return the number of days
+    since the 1970 epoch.
+    """
+    # The algorithm below is copied from Numpy's get_datetimestruct_days()
+    # (src/multiarray/datetime.c)
+    ret = cgutils.alloca_once(builder, TIMEDELTA64)
+    # First approximation
+    days = scale_by_constant(builder, year_val, 365)
+    # Adjust for leap years
+    with builder.if_else(cgutils.is_neg_int(builder, year_val)) \
+            as (if_neg, if_pos):
+        with if_pos:
+            # At or after 1970:
+            # 1968 is the closest leap year before 1970.
+            # Exclude the current year, so add 1.
+            from_1968 = add_constant(builder, year_val, 1)
+            # Add one day for each 4 years
+            p_days = builder.add(days,
+                                 unscale_by_constant(builder, from_1968, 4))
+            # 1900 is the closest previous year divisible by 100
+            from_1900 = add_constant(builder, from_1968, 68)
+            # Subtract one day for each 100 years
+            p_days = builder.sub(p_days,
+                                 unscale_by_constant(builder, from_1900, 100))
+            # 1600 is the closest previous year divisible by 400
+            from_1600 = add_constant(builder, from_1900, 300)
+            # Add one day for each 400 years
+            p_days = builder.add(p_days,
+                                 unscale_by_constant(builder, from_1600, 400))
+            builder.store(p_days, ret)
+        with if_neg:
+            # Before 1970:
+            # NOTE `year_val` is negative, and so will be `from_1972` and `from_2000`.
+            # 1972 is the closest later year after 1970.
+            # Include the current year, so subtract 2.
+            from_1972 = add_constant(builder, year_val, -2)
+            # Subtract one day for each 4 years (`from_1972` is negative)
+            n_days = builder.add(days,
+                                 unscale_by_constant(builder, from_1972, 4))
+            # 2000 is the closest later year divisible by 100
+            from_2000 = add_constant(builder, from_1972, -28)
+            # Add one day for each 100 years
+            n_days = builder.sub(n_days,
+                                 unscale_by_constant(builder, from_2000, 100))
+            # 2000 is also the closest later year divisible by 400
+            # Subtract one day for each 400 years
+            n_days = builder.add(n_days,
+                                 unscale_by_constant(builder, from_2000, 400))
+            builder.store(n_days, ret)
+    return builder.load(ret)
+
+
+def reduce_datetime_for_unit(builder, dt_val, src_unit, dest_unit):
+    dest_unit_code = npdatetime_helpers.DATETIME_UNITS[dest_unit]
+    src_unit_code = npdatetime_helpers.DATETIME_UNITS[src_unit]
+    if dest_unit_code < 2 or src_unit_code >= 2:
+        return dt_val, src_unit
+    # Need to compute the day ordinal for *dt_val*
+    if src_unit_code == 0:
+        # Years to days
+        year_val = dt_val
+        days_val = year_to_days(builder, year_val)
+
+    else:
+        # Months to days
+        leap_array = cgutils.global_constant(builder, "leap_year_months_acc",
+                                             leap_year_months_acc)
+        normal_array = cgutils.global_constant(builder, "normal_year_months_acc",
+                                               normal_year_months_acc)
+
+        days = cgutils.alloca_once(builder, TIMEDELTA64)
+
+        # First compute year number and month number
+        year, month = cgutils.divmod_by_constant(builder, dt_val, 12)
+
+        # Then deduce the number of days
+        with builder.if_else(is_leap_year(builder, year)) as (then, otherwise):
+            with then:
+                addend = builder.load(cgutils.gep(builder, leap_array,
+                                                  0, month, inbounds=True))
+                builder.store(addend, days)
+            with otherwise:
+                addend = builder.load(cgutils.gep(builder, normal_array,
+                                                  0, month, inbounds=True))
+                builder.store(addend, days)
+
+        days_val = year_to_days(builder, year)
+        days_val = builder.add(days_val, builder.load(days))
+
+    if dest_unit_code == 2:
+        # Need to scale back to weeks
+        weeks, _ = cgutils.divmod_by_constant(builder, days_val, 7)
+        return weeks, 'W'
+    else:
+        return days_val, 'D'
+
+
+def convert_datetime_for_arith(builder, dt_val, src_unit, dest_unit):
+    """
+    Convert datetime *dt_val* from *src_unit* to *dest_unit*.
+    """
+    # First partial conversion to days or weeks, if necessary.
+    dt_val, dt_unit = reduce_datetime_for_unit(
+        builder, dt_val, src_unit, dest_unit)
+    # Then multiply by the remaining constant factor.
+    dt_factor = npdatetime_helpers.get_timedelta_conversion_factor(dt_unit, dest_unit)
+    if dt_factor is None:
+        # This can happen when using explicit output in a ufunc.
+        raise LoweringError("cannot convert datetime64 from %r to %r"
+                                       % (src_unit, dest_unit))
+    return scale_by_constant(builder, dt_val, dt_factor)
+
+
+def _datetime_timedelta_arith(ll_op_name):
+    def impl(context, builder, dt_arg, dt_unit,
+             td_arg, td_unit, ret_unit):
+        ret = alloc_timedelta_result(builder)
+        with cgutils.if_likely(builder, are_not_nat(builder, [dt_arg, td_arg])):
+            dt_arg = convert_datetime_for_arith(builder, dt_arg,
+                                                dt_unit, ret_unit)
+            td_factor = npdatetime_helpers.get_timedelta_conversion_factor(
+                td_unit, ret_unit)
+            td_arg = scale_by_constant(builder, td_arg, td_factor)
+            ret_val = getattr(builder, ll_op_name)(dt_arg, td_arg)
+            builder.store(ret_val, ret)
+        return builder.load(ret)
+    return impl
+
+
+_datetime_plus_timedelta = _datetime_timedelta_arith('add')
+_datetime_minus_timedelta = _datetime_timedelta_arith('sub')
+
+# datetime64 + timedelta64
+
+
+@lower_builtin(operator.add, types.NPDatetime, types.NPTimedelta)
+@lower_builtin(operator.iadd, types.NPDatetime, types.NPTimedelta)
+def datetime_plus_timedelta(context, builder, sig, args):
+    dt_arg, td_arg = args
+    dt_type, td_type = sig.args
+    res = _datetime_plus_timedelta(context, builder,
+                                   dt_arg, dt_type.unit,
+                                   td_arg, td_type.unit,
+                                   sig.return_type.unit)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.add, types.NPTimedelta, types.NPDatetime)
+@lower_builtin(operator.iadd, types.NPTimedelta, types.NPDatetime)
+def timedelta_plus_datetime(context, builder, sig, args):
+    td_arg, dt_arg = args
+    td_type, dt_type = sig.args
+    res = _datetime_plus_timedelta(context, builder,
+                                   dt_arg, dt_type.unit,
+                                   td_arg, td_type.unit,
+                                   sig.return_type.unit)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+# datetime64 - timedelta64
+
+
+@lower_builtin(operator.sub, types.NPDatetime, types.NPTimedelta)
+@lower_builtin(operator.isub, types.NPDatetime, types.NPTimedelta)
+def datetime_minus_timedelta(context, builder, sig, args):
+    dt_arg, td_arg = args
+    dt_type, td_type = sig.args
+    res = _datetime_minus_timedelta(context, builder,
+                                    dt_arg, dt_type.unit,
+                                    td_arg, td_type.unit,
+                                    sig.return_type.unit)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+# datetime64 - datetime64
+
+
+@lower_builtin(operator.sub, types.NPDatetime, types.NPDatetime)
+def datetime_minus_datetime(context, builder, sig, args):
+    va, vb = args
+    ta, tb = sig.args
+    unit_a = ta.unit
+    unit_b = tb.unit
+    ret_unit = sig.return_type.unit
+    ret = alloc_timedelta_result(builder)
+    with cgutils.if_likely(builder, are_not_nat(builder, [va, vb])):
+        va = convert_datetime_for_arith(builder, va, unit_a, ret_unit)
+        vb = convert_datetime_for_arith(builder, vb, unit_b, ret_unit)
+        ret_val = builder.sub(va, vb)
+        builder.store(ret_val, ret)
+    res = builder.load(ret)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+# datetime64 comparisons
+
+
+def _create_datetime_comparison_impl(ll_op):
+    def impl(context, builder, sig, args):
+        va, vb = args
+        ta, tb = sig.args
+        unit_a = ta.unit
+        unit_b = tb.unit
+        ret_unit = npdatetime_helpers.get_best_unit(unit_a, unit_b)
+        ret = alloc_boolean_result(builder)
+        with builder.if_else(are_not_nat(builder, [va, vb])) as (then, otherwise):
+            with then:
+                norm_a = convert_datetime_for_arith(
+                    builder, va, unit_a, ret_unit)
+                norm_b = convert_datetime_for_arith(
+                    builder, vb, unit_b, ret_unit)
+                ret_val = builder.icmp_signed(ll_op, norm_a, norm_b)
+                builder.store(ret_val, ret)
+            with otherwise:
+                if numpy_support.numpy_version < (1, 16):
+                    # No scaling when comparing NaTs
+                    ret_val = builder.icmp_signed(ll_op, va, vb)
+                else:
+                    if ll_op == '!=':
+                        ret_val = cgutils.true_bit
+                    else:
+                        ret_val = cgutils.false_bit
+                builder.store(ret_val, ret)
+        res = builder.load(ret)
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+
+    return impl
+
+
+datetime_eq_datetime_impl = _create_datetime_comparison_impl('==')
+datetime_ne_datetime_impl = _create_datetime_comparison_impl('!=')
+datetime_lt_datetime_impl = _create_datetime_comparison_impl('<')
+datetime_le_datetime_impl = _create_datetime_comparison_impl('<=')
+datetime_gt_datetime_impl = _create_datetime_comparison_impl('>')
+datetime_ge_datetime_impl = _create_datetime_comparison_impl('>=')
+
+for op, func in [(operator.eq, datetime_eq_datetime_impl),
+                 (operator.ne, datetime_ne_datetime_impl),
+                 (operator.lt, datetime_lt_datetime_impl),
+                 (operator.le, datetime_le_datetime_impl),
+                 (operator.gt, datetime_gt_datetime_impl),
+                 (operator.ge, datetime_ge_datetime_impl)]:
+    lower_builtin(op, *[types.NPDatetime]*2)(func)
+
+
+########################################################################
+# datetime/timedelta fmax/fmin maximum/minimum support
+
+def _gen_datetime_max_impl(NAT_DOMINATES):
+    def datetime_max_impl(context, builder, sig, args):
+        # note this could be optimizing relying on the actual value of NAT
+        # but as NumPy doesn't rely on this, this seems more resilient
+        in1, in2 = args
+        in1_not_nat = is_not_nat(builder, in1)
+        in2_not_nat = is_not_nat(builder, in2)
+        in1_ge_in2 = builder.icmp_signed('>=', in1, in2)
+        res = builder.select(in1_ge_in2, in1, in2)
+        if NAT_DOMINATES and numpy_support.numpy_version >= (1, 18):
+            # NaT now dominates, like NaN
+            in1, in2 = in2, in1
+        res = builder.select(in1_not_nat, res, in2)
+        res = builder.select(in2_not_nat, res, in1)
+
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+    return datetime_max_impl
+
+datetime_maximum_impl = _gen_datetime_max_impl(True)
+datetime_fmax_impl = _gen_datetime_max_impl(False)
+
+def _gen_datetime_min_impl(NAT_DOMINATES):
+    def datetime_min_impl(context, builder, sig, args):
+        # note this could be optimizing relying on the actual value of NAT
+        # but as NumPy doesn't rely on this, this seems more resilient
+        in1, in2 = args
+        in1_not_nat = is_not_nat(builder, in1)
+        in2_not_nat = is_not_nat(builder, in2)
+        in1_le_in2 = builder.icmp_signed('<=', in1, in2)
+        res = builder.select(in1_le_in2, in1, in2)
+        if NAT_DOMINATES and numpy_support.numpy_version >= (1, 18):
+            # NaT now dominates, like NaN
+            in1, in2 = in2, in1
+        res = builder.select(in1_not_nat, res, in2)
+        res = builder.select(in2_not_nat, res, in1)
+
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+    return datetime_min_impl
+
+datetime_minimum_impl = _gen_datetime_min_impl(True)
+datetime_fmin_impl = _gen_datetime_min_impl(False)
+
+def _gen_timedelta_max_impl(NAT_DOMINATES):
+    def timedelta_max_impl(context, builder, sig, args):
+        # note this could be optimizing relying on the actual value of NAT
+        # but as NumPy doesn't rely on this, this seems more resilient
+        in1, in2 = args
+        in1_not_nat = is_not_nat(builder, in1)
+        in2_not_nat = is_not_nat(builder, in2)
+        in1_ge_in2 = builder.icmp_signed('>=', in1, in2)
+        res = builder.select(in1_ge_in2, in1, in2)
+        if NAT_DOMINATES and numpy_support.numpy_version >= (1, 18):
+            # NaT now dominates, like NaN
+            in1, in2 = in2, in1
+        res = builder.select(in1_not_nat, res, in2)
+        res = builder.select(in2_not_nat, res, in1)
+
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+    return timedelta_max_impl
+
+timedelta_maximum_impl = _gen_timedelta_max_impl(True)
+timedelta_fmax_impl = _gen_timedelta_max_impl(False)
+
+def _gen_timedelta_min_impl(NAT_DOMINATES):
+    def timedelta_min_impl(context, builder, sig, args):
+        # note this could be optimizing relying on the actual value of NAT
+        # but as NumPy doesn't rely on this, this seems more resilient
+        in1, in2 = args
+        in1_not_nat = is_not_nat(builder, in1)
+        in2_not_nat = is_not_nat(builder, in2)
+        in1_le_in2 = builder.icmp_signed('<=', in1, in2)
+        res = builder.select(in1_le_in2, in1, in2)
+        if NAT_DOMINATES and numpy_support.numpy_version >= (1, 18):
+            # NaT now dominates, like NaN
+            in1, in2 = in2, in1
+        res = builder.select(in1_not_nat, res, in2)
+        res = builder.select(in2_not_nat, res, in1)
+
+        return impl_ret_untracked(context, builder, sig.return_type, res)
+    return timedelta_min_impl
+
+timedelta_minimum_impl = _gen_timedelta_min_impl(True)
+timedelta_fmin_impl = _gen_timedelta_min_impl(False)
+
+def _cast_to_timedelta(context, builder, val):
+    temp = builder.alloca(TIMEDELTA64)
+    val_is_nan = builder.fcmp_unordered('uno', val, val)
+    with builder.if_else(val_is_nan) as (
+            then, els):
+        with then:
+            # NaN does not guarantee to cast to NAT.
+            # We should store NAT explicitly.
+            builder.store(NAT, temp)
+        with els:
+            builder.store(builder.fptosi(val, TIMEDELTA64), temp)
+    return builder.load(temp)
+
+
+@lower_builtin(np.isnat, types.NPDatetime)
+@lower_builtin(np.isnat, types.NPTimedelta)
+def _np_isnat_impl(context, builder, sig, args):
+    return npyfuncs.np_datetime_isnat_impl(context, builder, sig, args)
+
+
+@lower_cast(types.NPDatetime, types.Integer)
+@lower_cast(types.NPTimedelta, types.Integer)
+def _cast_npdatetime_int64(context, builder, fromty, toty, val):
+    if toty.bitwidth != 64: # all date time types are 64 bit
+        msg = f"Cannot cast {fromty} to {toty} as {toty} is not 64 bits wide."
+        raise ValueError(msg)
+    return val
+
+
+@overload_method(types.NPTimedelta, '__hash__')
+@overload_method(types.NPDatetime, '__hash__')
+def ol_hash_npdatetime(x):
+    if IS_32BITS:
+        def impl(x):
+            x = np.int64(x)
+            if x < 2**31 - 1:  # x < LONG_MAX
+                y = np.int32(x)
+            else:
+                hi = (np.int64(x) & 0xffffffff00000000) >> 32
+                lo = (np.int64(x) & 0x00000000ffffffff)
+                y = np.int32(lo + (1000003) * hi)
+            if y == -1:
+                y = np.int32(-2)
+            return y
+    else:
+        def impl(x):
+            if np.int64(x) == -1:
+                return np.int64(-2)
+            return np.int64(x)
+    return impl
+
+
+lower_builtin(npdatetime_helpers.datetime_minimum, types.NPDatetime, types.NPDatetime)(datetime_minimum_impl)
+lower_builtin(npdatetime_helpers.datetime_minimum, types.NPTimedelta, types.NPTimedelta)(datetime_minimum_impl)
+lower_builtin(npdatetime_helpers.datetime_maximum, types.NPDatetime, types.NPDatetime)(datetime_maximum_impl)
+lower_builtin(npdatetime_helpers.datetime_maximum, types.NPTimedelta, types.NPTimedelta)(datetime_maximum_impl)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npdatetime_helpers.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npdatetime_helpers.py
new file mode 100644
index 000000000..4abb671b9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npdatetime_helpers.py
@@ -0,0 +1,212 @@
+"""
+Helper functions for np.timedelta64 and np.datetime64.
+For now, multiples-of-units (for example timedeltas expressed in tens
+of seconds) are not supported.
+"""
+
+
+import numpy as np
+
+
+DATETIME_UNITS = {
+    'Y': 0,   # Years
+    'M': 1,   # Months
+    'W': 2,   # Weeks
+    # Yes, there's a gap here
+    'D': 4,   # Days
+    'h': 5,   # Hours
+    'm': 6,   # Minutes
+    's': 7,   # Seconds
+    'ms': 8,  # Milliseconds
+    'us': 9,  # Microseconds
+    'ns': 10,  # Nanoseconds
+    'ps': 11,  # Picoseconds
+    'fs': 12,  # Femtoseconds
+    'as': 13,  # Attoseconds
+    '': 14,   # "generic", i.e. unit-less
+}
+
+NAT = np.timedelta64('nat').astype(np.int64)
+
+# NOTE: numpy has several inconsistent functions for timedelta casting:
+# - can_cast_timedelta64_{metadata,units}() disallows "safe" casting
+#   to and from generic units
+# - cast_timedelta_to_timedelta() allows casting from (but not to)
+#   generic units
+# - compute_datetime_metadata_greatest_common_divisor() allows casting from
+#   generic units (used for promotion)
+
+
+def same_kind(src, dest):
+    """
+    Whether the *src* and *dest* units are of the same kind.
+    """
+    return (DATETIME_UNITS[src] < 5) == (DATETIME_UNITS[dest] < 5)
+
+
+def can_cast_timedelta_units(src, dest):
+    # Mimic NumPy's "safe" casting and promotion
+    # `dest` must be more precise than `src` and they must be compatible
+    # for conversion.
+    # XXX should we switch to enforcing "same-kind" for Numpy 1.10+ ?
+    src = DATETIME_UNITS[src]
+    dest = DATETIME_UNITS[dest]
+    if src == dest:
+        return True
+    if src == 14:
+        return True
+    if src > dest:
+        return False
+    if dest == 14:
+        # unit-less timedelta64 is not compatible with anything else
+        return False
+    if src <= 1 and dest > 1:
+        # Cannot convert between months or years and other units
+        return False
+    return True
+
+
+# Exact conversion factors from one unit to the immediately more precise one
+_factors = {
+    0: (1, 12),   # Years -> Months
+    2: (4, 7),    # Weeks -> Days
+    4: (5, 24),   # Days -> Hours
+    5: (6, 60),   # Hours -> Minutes
+    6: (7, 60),   # Minutes -> Seconds
+    7: (8, 1000),
+    8: (9, 1000),
+    9: (10, 1000),
+    10: (11, 1000),
+    11: (12, 1000),
+    12: (13, 1000),
+}
+
+
+def _get_conversion_multiplier(big_unit_code, small_unit_code):
+    """
+    Return an integer multiplier allowing to convert from *big_unit_code*
+    to *small_unit_code*.
+    None is returned if the conversion is not possible through a
+    simple integer multiplication.
+    """
+    # Mimics get_datetime_units_factor() in NumPy's datetime.c,
+    # with a twist to allow no-op conversion from generic units.
+    if big_unit_code == 14:
+        return 1
+    c = big_unit_code
+    factor = 1
+    while c < small_unit_code:
+        try:
+            c, mult = _factors[c]
+        except KeyError:
+            # No possible conversion
+            return None
+        factor *= mult
+    if c == small_unit_code:
+        return factor
+    else:
+        return None
+
+
+def get_timedelta_conversion_factor(src_unit, dest_unit):
+    """
+    Return an integer multiplier allowing to convert from timedeltas
+    of *src_unit* to *dest_unit*.
+    """
+    return _get_conversion_multiplier(DATETIME_UNITS[src_unit],
+                                      DATETIME_UNITS[dest_unit])
+
+
+def get_datetime_timedelta_conversion(datetime_unit, timedelta_unit):
+    """
+    Compute a possible conversion for combining *datetime_unit* and
+    *timedelta_unit* (presumably for adding or subtracting).
+    Return (result unit, integer datetime multiplier, integer timedelta
+    multiplier). RuntimeError is raised if the combination is impossible.
+    """
+    # XXX now unused (I don't know where / how Numpy uses this)
+    dt_unit_code = DATETIME_UNITS[datetime_unit]
+    td_unit_code = DATETIME_UNITS[timedelta_unit]
+    if td_unit_code == 14 or dt_unit_code == 14:
+        return datetime_unit, 1, 1
+    if td_unit_code < 2 and dt_unit_code >= 2:
+        # Cannot combine Y or M timedelta64 with a finer-grained datetime64
+        raise RuntimeError("cannot combine datetime64(%r) and timedelta64(%r)"
+                           % (datetime_unit, timedelta_unit))
+    dt_factor, td_factor = 1, 1
+
+    # If years or months, the datetime unit is first scaled to weeks or days,
+    # then conversion continues below.  This is the same algorithm as used
+    # in Numpy's get_datetime_conversion_factor() (src/multiarray/datetime.c):
+    # """Conversions between years/months and other units use
+    # the factor averaged over the 400 year leap year cycle."""
+    if dt_unit_code == 0:
+        if td_unit_code >= 4:
+            dt_factor = 97 + 400 * 365
+            td_factor = 400
+            dt_unit_code = 4
+        elif td_unit_code == 2:
+            dt_factor = 97 + 400 * 365
+            td_factor = 400 * 7
+            dt_unit_code = 2
+    elif dt_unit_code == 1:
+        if td_unit_code >= 4:
+            dt_factor = 97 + 400 * 365
+            td_factor = 400 * 12
+            dt_unit_code = 4
+        elif td_unit_code == 2:
+            dt_factor = 97 + 400 * 365
+            td_factor = 400 * 12 * 7
+            dt_unit_code = 2
+
+    if td_unit_code >= dt_unit_code:
+        factor = _get_conversion_multiplier(dt_unit_code, td_unit_code)
+        assert factor is not None, (dt_unit_code, td_unit_code)
+        return timedelta_unit, dt_factor * factor, td_factor
+    else:
+        factor = _get_conversion_multiplier(td_unit_code, dt_unit_code)
+        assert factor is not None, (dt_unit_code, td_unit_code)
+        return datetime_unit, dt_factor, td_factor * factor
+
+
+def combine_datetime_timedelta_units(datetime_unit, timedelta_unit):
+    """
+    Return the unit result of combining *datetime_unit* with *timedelta_unit*
+    (e.g. by adding or subtracting).  None is returned if combining
+    those units is forbidden.
+    """
+    dt_unit_code = DATETIME_UNITS[datetime_unit]
+    td_unit_code = DATETIME_UNITS[timedelta_unit]
+    if dt_unit_code == 14:
+        return timedelta_unit
+    elif td_unit_code == 14:
+        return datetime_unit
+    if td_unit_code < 2 and dt_unit_code >= 2:
+        return None
+    if dt_unit_code > td_unit_code:
+        return datetime_unit
+    else:
+        return timedelta_unit
+
+
+def get_best_unit(unit_a, unit_b):
+    """
+    Get the best (i.e. finer-grained) of two units.
+    """
+    a = DATETIME_UNITS[unit_a]
+    b = DATETIME_UNITS[unit_b]
+    if a == 14:
+        return unit_b
+    if b == 14:
+        return unit_a
+    if b > a:
+        return unit_b
+    return unit_a
+
+
+def datetime_minimum(a, b):
+    pass
+
+
+def datetime_maximum(a, b):
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npyfuncs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npyfuncs.py
new file mode 100644
index 000000000..c475532f9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npyfuncs.py
@@ -0,0 +1,1624 @@
+"""Codegen for functions used as kernels in NumPy functions
+
+Typically, the kernels of several ufuncs that can't map directly to
+Python builtins
+"""
+
+
+import math
+
+import llvmlite.ir
+import numpy as np
+
+from numba.core.imputils import impl_ret_untracked
+from numba.core import typing, types, errors, lowering, cgutils
+from numba.core.extending import register_jitable
+from numba.np import npdatetime
+from numba.cpython import cmathimpl, mathimpl, numbers
+
+# some NumPy constants. Note that we could generate some of them using
+# the math library, but having the values copied from npy_math seems to
+# yield more accurate results
+_NPY_LOG2E  = 1.442695040888963407359924681001892137 # math.log(math.e, 2)
+_NPY_LOG10E = 0.434294481903251827651128918916605082 # math.log(math.e, 10)
+_NPY_LOGE2  = 0.693147180559945309417232121458176568 # math.log(2)
+
+
+def _check_arity_and_homogeneity(sig, args, arity, return_type = None):
+    """checks that the following are true:
+    - args and sig.args have arg_count elements
+    - all input types are homogeneous
+    - return type is 'return_type' if provided, otherwise it must be
+      homogeneous with the input types.
+    """
+    assert len(args) == arity
+    assert len(sig.args) == arity
+    ty = sig.args[0]
+    if return_type is None:
+        return_type = ty
+    # must have homogeneous args
+    if not( all(arg==ty for arg in sig.args) and sig.return_type == return_type):
+        import inspect
+        fname = inspect.currentframe().f_back.f_code.co_name
+        msg = '{0} called with invalid types: {1}'.format(fname, sig)
+        assert False, msg
+
+
+def _call_func_by_name_with_cast(context, builder, sig, args,
+                                 func_name, ty=types.float64):
+    # it is quite common in NumPy to have loops implemented as a call
+    # to the double version of the function, wrapped in casts. This
+    # helper function facilitates that.
+    mod = builder.module
+    lty = context.get_argument_type(ty)
+    fnty = llvmlite.ir.FunctionType(lty, [lty]*len(sig.args))
+    fn = cgutils.insert_pure_function(mod, fnty, name=func_name)
+    cast_args = [context.cast(builder, arg, argty, ty)
+                 for arg, argty in zip(args, sig.args) ]
+
+    result = builder.call(fn, cast_args)
+    return context.cast(builder, result, types.float64, sig.return_type)
+
+
+def _dispatch_func_by_name_type(context, builder, sig, args, table, user_name):
+    # for most cases the functions are homogeneous on all their types.
+    # this code dispatches on the first argument type as it is the most useful
+    # for our uses (all cases but ldexp are homogeneous in all types, and
+    # dispatching on the first argument type works of ldexp as well)
+    #
+    # assumes that the function pointed by func_name has the type
+    # signature sig (but needs translation to llvm types).
+
+    ty = sig.args[0]
+    try:
+        func_name = table[ty]
+    except KeyError as e:
+        msg = "No {0} function for real type {1}".format(user_name, str(e))
+        raise errors.LoweringError(msg)
+
+    mod = builder.module
+    if ty in types.complex_domain:
+        # In numba struct types are always passed by pointer. So the call has to
+        # be transformed from "result = func(ops...)" to "func(&result, ops...).
+        # note that the result value pointer as first argument is the convention
+        # used by numba.
+
+        # First, prepare the return value
+        out = context.make_complex(builder, ty)
+        ptrargs = [cgutils.alloca_once_value(builder, arg)
+                   for arg in args]
+        call_args = [out._getpointer()] + ptrargs
+        # get_value_as_argument for struct types like complex allocate stack space
+        # and initialize with the value, the return value is the pointer to that
+        # allocated space (ie: pointer to a copy of the value in the stack).
+        # get_argument_type returns a pointer to the struct type in consonance.
+        call_argtys = [ty] + list(sig.args)
+        call_argltys = [context.get_value_type(ty).as_pointer()
+                        for ty in call_argtys]
+        fnty = llvmlite.ir.FunctionType(llvmlite.ir.VoidType(), call_argltys)
+        # Note: the function isn't pure here (it writes to its pointer args)
+        fn = cgutils.get_or_insert_function(mod, fnty, func_name)
+        builder.call(fn, call_args)
+        retval = builder.load(call_args[0])
+    else:
+        argtypes = [context.get_argument_type(aty) for aty in sig.args]
+        restype = context.get_argument_type(sig.return_type)
+        fnty = llvmlite.ir.FunctionType(restype, argtypes)
+        fn = cgutils.insert_pure_function(mod, fnty, name=func_name)
+        retval = context.call_external_function(builder, fn, sig.args, args)
+    return retval
+
+
+
+########################################################################
+# Division kernels inspired by NumPy loops.c.src code
+#
+# The builtins are not applicable as they rely on a test for zero in the
+# denominator. If it is zero the appropriate exception is raised.
+# In NumPy, a division by zero does not raise an exception, but instead
+# generated a known value. Note that a division by zero in any of the
+# operations of a vector may raise an exception or issue a warning
+# depending on the np.seterr configuration. This is not supported
+# right now (and in any case, it won't be handled by these functions
+# either)
+
+def np_int_sdiv_impl(context, builder, sig, args):
+    # based on the actual code in NumPy loops.c.src for signed integer types
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    num, den = args
+    ty = sig.args[0]  # any arg type will do, homogeneous
+
+    ZERO = context.get_constant(ty, 0)
+    MINUS_ONE = context.get_constant(ty, -1)
+    MIN_INT = context.get_constant(ty, 1 << (den.type.width-1))
+    den_is_zero = builder.icmp_unsigned('==', ZERO, den)
+    den_is_minus_one = builder.icmp_unsigned('==', MINUS_ONE, den)
+    num_is_min_int = builder.icmp_unsigned('==', MIN_INT, num)
+    could_cause_sigfpe = builder.and_(den_is_minus_one, num_is_min_int)
+    force_zero = builder.or_(den_is_zero, could_cause_sigfpe)
+    with builder.if_else(force_zero, likely=False) as (then, otherwise):
+        with then:
+            bb_then = builder.basic_block
+        with otherwise:
+            bb_otherwise = builder.basic_block
+            div = builder.sdiv(num, den)
+            mod = builder.srem(num, den)
+            num_gt_zero = builder.icmp_signed('>', num, ZERO)
+            den_gt_zero = builder.icmp_signed('>', den, ZERO)
+            not_same_sign = builder.xor(num_gt_zero, den_gt_zero)
+            mod_not_zero = builder.icmp_unsigned('!=', mod, ZERO)
+            needs_fixing = builder.and_(not_same_sign, mod_not_zero)
+            fix_value = builder.select(needs_fixing, MINUS_ONE, ZERO)
+            result_otherwise = builder.add(div, fix_value)
+
+    result = builder.phi(ZERO.type)
+    result.add_incoming(ZERO, bb_then)
+    result.add_incoming(result_otherwise, bb_otherwise)
+
+    return result
+
+
+def np_int_srem_impl(context, builder, sig, args):
+    # based on the actual code in NumPy loops.c.src for signed integers
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    num, den = args
+    ty = sig.args[0]  # any arg type will do, homogeneous
+
+    ZERO = context.get_constant(ty, 0)
+    den_not_zero = builder.icmp_unsigned('!=', ZERO, den)
+    bb_no_if = builder.basic_block
+    with cgutils.if_unlikely(builder, den_not_zero):
+        bb_if = builder.basic_block
+        mod = builder.srem(num,den)
+        num_gt_zero = builder.icmp_signed('>', num, ZERO)
+        den_gt_zero = builder.icmp_signed('>', den, ZERO)
+        not_same_sign = builder.xor(num_gt_zero, den_gt_zero)
+        mod_not_zero = builder.icmp_unsigned('!=', mod, ZERO)
+        needs_fixing = builder.and_(not_same_sign, mod_not_zero)
+        fix_value = builder.select(needs_fixing, den, ZERO)
+        final_mod = builder.add(fix_value, mod)
+
+    result = builder.phi(ZERO.type)
+    result.add_incoming(ZERO, bb_no_if)
+    result.add_incoming(final_mod, bb_if)
+
+    return result
+
+
+def np_int_sdivrem_impl(context, builder, sig, args):
+    div = np_int_sdiv_impl(context, builder, sig.return_type[0](*sig.args), args)
+    rem = np_int_srem_impl(context, builder, sig.return_type[1](*sig.args), args)
+    return context.make_tuple(builder, sig.return_type, [div, rem])
+
+
+def np_int_udiv_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    num, den = args
+    ty = sig.args[0]  # any arg type will do, homogeneous
+
+    ZERO = context.get_constant(ty, 0)
+    div_by_zero = builder.icmp_unsigned('==', ZERO, den)
+    with builder.if_else(div_by_zero, likely=False) as (then, otherwise):
+        with then:
+            # division by zero
+            bb_then = builder.basic_block
+        with otherwise:
+            # divide!
+            div = builder.udiv(num, den)
+            bb_otherwise = builder.basic_block
+
+    result = builder.phi(ZERO.type)
+    result.add_incoming(ZERO, bb_then)
+    result.add_incoming(div, bb_otherwise)
+    return result
+
+
+def np_int_urem_impl(context, builder, sig, args):
+    # based on the actual code in NumPy loops.c.src for signed integers
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    num, den = args
+    ty = sig.args[0]  # any arg type will do, homogeneous
+
+    ZERO = context.get_constant(ty, 0)
+    den_not_zero = builder.icmp_unsigned('!=', ZERO, den)
+    bb_no_if = builder.basic_block
+    with cgutils.if_unlikely(builder, den_not_zero):
+        bb_if = builder.basic_block
+        mod = builder.urem(num,den)
+
+    result = builder.phi(ZERO.type)
+    result.add_incoming(ZERO, bb_no_if)
+    result.add_incoming(mod, bb_if)
+
+    return result
+
+
+def np_int_udivrem_impl(context, builder, sig, args):
+    div = np_int_udiv_impl(context, builder, sig.return_type[0](*sig.args), args)
+    rem = np_int_urem_impl(context, builder, sig.return_type[1](*sig.args), args)
+    return context.make_tuple(builder, sig.return_type, [div, rem])
+
+
+# implementation of int_fmod is in fact the same as the unsigned remainder,
+# that is: srem with a special case returning 0 when the denominator is 0.
+np_int_fmod_impl = np_int_urem_impl
+
+
+def np_real_div_impl(context, builder, sig, args):
+    # in NumPy real div has the same semantics as an fdiv for generating
+    # NANs, INF and NINF
+    _check_arity_and_homogeneity(sig, args, 2)
+    return builder.fdiv(*args)
+
+
+def np_real_mod_impl(context, builder, sig, args):
+    # note: this maps to NumPy remainder, which has the same semantics as Python
+    # based on code in loops.c.src
+    _check_arity_and_homogeneity(sig, args, 2)
+    in1, in2 = args
+    ty = sig.args[0]
+
+    ZERO = context.get_constant(ty, 0.0)
+    res = builder.frem(in1, in2)
+    res_ne_zero = builder.fcmp_ordered('!=', res, ZERO)
+    den_lt_zero = builder.fcmp_ordered('<', in2, ZERO)
+    res_lt_zero = builder.fcmp_ordered('<', res, ZERO)
+    needs_fixing = builder.and_(res_ne_zero,
+                                builder.xor(den_lt_zero, res_lt_zero))
+    fix_value = builder.select(needs_fixing, in2, ZERO)
+
+    return builder.fadd(res, fix_value)
+
+
+def np_real_fmod_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+    return builder.frem(*args)
+
+
+def _fabs(context, builder, arg):
+    ZERO = llvmlite.ir.Constant(arg.type, 0.0)
+    arg_negated = builder.fsub(ZERO, arg)
+    arg_is_negative = builder.fcmp_ordered('<', arg, ZERO)
+    return builder.select(arg_is_negative, arg_negated, arg)
+
+
+def np_complex_div_impl(context, builder, sig, args):
+    # Extracted from numpy/core/src/umath/loops.c.src,
+    # inspired by complex_div_impl
+    # variables named coherent with loops.c.src
+    # This is implemented using the approach described in
+    #   R.L. Smith. Algorithm 116: Complex division.
+    #   Communications of the ACM, 5(8):435, 1962
+
+    in1, in2 = [context.make_complex(builder, sig.args[0], value=arg)
+                for arg in args]
+
+    in1r = in1.real  # numerator.real
+    in1i = in1.imag  # numerator.imag
+    in2r = in2.real  # denominator.real
+    in2i = in2.imag  # denominator.imag
+    ftype = in1r.type
+    assert all([i.type==ftype for i in [in1r, in1i, in2r, in2i]]), "mismatched types"
+    out = context.make_helper(builder, sig.return_type)
+
+    ZERO = llvmlite.ir.Constant(ftype, 0.0)
+    ONE = llvmlite.ir.Constant(ftype, 1.0)
+
+    # if abs(denominator.real) >= abs(denominator.imag)
+    in2r_abs = _fabs(context, builder, in2r)
+    in2i_abs = _fabs(context, builder, in2i)
+    in2r_abs_ge_in2i_abs = builder.fcmp_ordered('>=', in2r_abs, in2i_abs)
+    with builder.if_else(in2r_abs_ge_in2i_abs) as (then, otherwise):
+        with then:
+            # if abs(denominator.real) == 0 and abs(denominator.imag) == 0
+            in2r_is_zero = builder.fcmp_ordered('==', in2r_abs, ZERO)
+            in2i_is_zero = builder.fcmp_ordered('==', in2i_abs, ZERO)
+            in2_is_zero = builder.and_(in2r_is_zero, in2i_is_zero)
+            with builder.if_else(in2_is_zero) as (inn_then, inn_otherwise):
+                with inn_then:
+                    # division by 0.
+                    # fdiv generates the appropriate NAN/INF/NINF
+                    out.real = builder.fdiv(in1r, in2r_abs)
+                    out.imag = builder.fdiv(in1i, in2i_abs)
+                with inn_otherwise:
+                    # general case for:
+                    # abs(denominator.real) > abs(denominator.imag)
+                    rat = builder.fdiv(in2i, in2r)
+                    # scl = 1.0/(in2r + in2i*rat)
+                    tmp1 = builder.fmul(in2i, rat)
+                    tmp2 = builder.fadd(in2r, tmp1)
+                    scl = builder.fdiv(ONE, tmp2)
+                    # out.real = (in1r + in1i*rat)*scl
+                    # out.imag = (in1i - in1r*rat)*scl
+                    tmp3 = builder.fmul(in1i, rat)
+                    tmp4 = builder.fmul(in1r, rat)
+                    tmp5 = builder.fadd(in1r, tmp3)
+                    tmp6 = builder.fsub(in1i, tmp4)
+                    out.real = builder.fmul(tmp5, scl)
+                    out.imag = builder.fmul(tmp6, scl)
+        with otherwise:
+            # general case for:
+            # abs(denominator.imag) > abs(denominator.real)
+            rat = builder.fdiv(in2r, in2i)
+            # scl = 1.0/(in2i + in2r*rat)
+            tmp1 = builder.fmul(in2r, rat)
+            tmp2 = builder.fadd(in2i, tmp1)
+            scl = builder.fdiv(ONE, tmp2)
+            # out.real = (in1r*rat + in1i)*scl
+            # out.imag = (in1i*rat - in1r)*scl
+            tmp3 = builder.fmul(in1r, rat)
+            tmp4 = builder.fmul(in1i, rat)
+            tmp5 = builder.fadd(tmp3, in1i)
+            tmp6 = builder.fsub(tmp4, in1r)
+            out.real = builder.fmul(tmp5, scl)
+            out.imag = builder.fmul(tmp6, scl)
+
+    return out._getvalue()
+
+
+########################################################################
+# NumPy logaddexp
+
+def np_real_logaddexp_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    dispatch_table = {
+        types.float32: 'npy_logaddexpf',
+        types.float64: 'npy_logaddexp',
+    }
+
+    return _dispatch_func_by_name_type(context, builder, sig, args,
+                                       dispatch_table, 'logaddexp')
+
+########################################################################
+# NumPy logaddexp2
+
+def np_real_logaddexp2_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    dispatch_table = {
+        types.float32: 'npy_logaddexp2f',
+        types.float64: 'npy_logaddexp2',
+    }
+
+    return _dispatch_func_by_name_type(context, builder, sig, args,
+                                       dispatch_table, 'logaddexp2')
+
+
+########################################################################
+# true div kernels
+
+def np_int_truediv_impl(context, builder, sig, args):
+    # in NumPy we don't check for 0 denominator... fdiv handles div by
+    # 0 in the way NumPy expects..
+    # integer truediv always yields double
+    num, den = args
+    lltype = num.type
+    assert all(i.type==lltype for i in args), "must have homogeneous types"
+    numty, denty = sig.args
+
+    num = context.cast(builder, num, numty, types.float64)
+    den = context.cast(builder, den, denty, types.float64)
+
+    return builder.fdiv(num,den)
+
+
+########################################################################
+# floor div kernels
+
+def np_real_floor_div_impl(context, builder, sig, args):
+    res = np_real_div_impl(context, builder, sig, args)
+    s = typing.signature(sig.return_type, sig.return_type)
+    return np_real_floor_impl(context, builder, s, (res,))
+
+
+def np_real_divmod_impl(context, builder, sig, args):
+    div = np_real_floor_div_impl(context, builder, sig.return_type[0](*sig.args), args)
+    rem = np_real_mod_impl(context, builder, sig.return_type[1](*sig.args), args)
+    return context.make_tuple(builder, sig.return_type, [div, rem])
+
+
+def np_complex_floor_div_impl(context, builder, sig, args):
+    # this is based on the complex floor divide in Numpy's loops.c.src
+    # This is basically a full complex division with a complex floor
+    # applied.
+    # The complex floor seems to be defined as the real floor applied
+    # with the real part and zero in the imaginary part. Fully developed
+    # so it avoids computing anything related to the imaginary result.
+    float_kind = sig.args[0].underlying_float
+    floor_sig = typing.signature(float_kind, float_kind)
+
+    in1, in2 = [context.make_complex(builder, sig.args[0], value=arg)
+                for arg in args]
+
+    in1r = in1.real
+    in1i = in1.imag
+    in2r = in2.real
+    in2i = in2.imag
+    ftype = in1r.type
+    assert all([i.type==ftype for i in [in1r, in1i, in2r, in2i]]), "mismatched types"
+
+    ZERO = llvmlite.ir.Constant(ftype, 0.0)
+
+    out = context.make_helper(builder, sig.return_type)
+    out.imag = ZERO
+
+    in2r_abs = _fabs(context, builder, in2r)
+    in2i_abs = _fabs(context, builder, in2i)
+    in2r_abs_ge_in2i_abs = builder.fcmp_ordered('>=', in2r_abs, in2i_abs)
+
+    with builder.if_else(in2r_abs_ge_in2i_abs) as (then, otherwise):
+        with then:
+            rat = builder.fdiv(in2i, in2r)
+            # out.real = floor((in1r+in1i*rat)/(in2r + in2i*rat))
+            tmp1 = builder.fmul(in1i, rat)
+            tmp2 = builder.fmul(in2i, rat)
+            tmp3 = builder.fadd(in1r, tmp1)
+            tmp4 = builder.fadd(in2r, tmp2)
+            tmp5 = builder.fdiv(tmp3, tmp4)
+            out.real = np_real_floor_impl(context, builder, floor_sig, (tmp5,))
+        with otherwise:
+            rat = builder.fdiv(in2r, in2i)
+            # out.real = floor((in1i + in1r*rat)/(in2i + in2r*rat))
+            tmp1 = builder.fmul(in1r, rat)
+            tmp2 = builder.fmul(in2r, rat)
+            tmp3 = builder.fadd(in1i, tmp1)
+            tmp4 = builder.fadd(in2i, tmp2)
+            tmp5 = builder.fdiv(tmp3, tmp4)
+            out.real = np_real_floor_impl(context, builder, floor_sig, (tmp5,))
+    return out._getvalue()
+
+
+########################################################################
+# numpy power funcs
+
+def np_complex_power_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    return numbers.complex_power_impl(context, builder, sig, args)
+
+
+########################################################################
+# numpy float power funcs
+
+def real_float_power_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    return numbers.real_power_impl(context, builder, sig, args)
+
+
+def np_complex_float_power_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    return numbers.complex_power_impl(context, builder, sig, args)
+
+
+########################################################################
+# numpy greatest common denominator
+
+def np_gcd_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+    return mathimpl.gcd_impl(context, builder, sig, args)
+
+
+########################################################################
+# numpy lowest common multiple
+
+def np_lcm_impl(context, builder, sig, args):
+
+    xty, yty = sig.args
+    assert xty == yty == sig.return_type
+    x, y = args
+
+    def lcm(a, b):
+        """
+        Like gcd, heavily cribbed from Julia.
+        """
+        return 0 if a == 0 else abs(a * (b // np.gcd(b, a)))
+
+    res = context.compile_internal(builder, lcm, sig, args)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+########################################################################
+# Numpy style complex sign
+
+def np_complex_sign_impl(context, builder, sig, args):
+    # equivalent to complex sign in NumPy's sign
+    # but implemented via selects, balancing the 4 cases.
+    _check_arity_and_homogeneity(sig, args, 1)
+    op = args[0]
+    ty = sig.args[0]
+    float_ty = ty.underlying_float
+
+    ZERO = context.get_constant(float_ty, 0.0)
+    ONE  = context.get_constant(float_ty, 1.0)
+    MINUS_ONE = context.get_constant(float_ty, -1.0)
+    NAN = context.get_constant(float_ty, float('nan'))
+    result = context.make_complex(builder, ty)
+    result.real = ZERO
+    result.imag = ZERO
+
+    cmp_sig = typing.signature(types.boolean, *[ty] * 2)
+    cmp_args = [op, result._getvalue()]
+    arg1_ge_arg2 = np_complex_ge_impl(context, builder, cmp_sig, cmp_args)
+    arg1_eq_arg2 = np_complex_eq_impl(context, builder, cmp_sig, cmp_args)
+    arg1_lt_arg2 = np_complex_lt_impl(context, builder, cmp_sig, cmp_args)
+
+    real_when_ge = builder.select(arg1_eq_arg2, ZERO, ONE)
+    real_when_nge = builder.select(arg1_lt_arg2, MINUS_ONE, NAN)
+    result.real = builder.select(arg1_ge_arg2, real_when_ge, real_when_nge)
+
+    return result._getvalue()
+
+
+########################################################################
+# Numpy rint
+
+def np_real_rint_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    return mathimpl.call_fp_intrinsic(builder, 'llvm.rint', args)
+
+
+def np_complex_rint_impl(context, builder, sig, args):
+    # based on code in NumPy's funcs.inc.src
+    # rint of a complex number defined as rint of its real and imag
+    # parts
+    _check_arity_and_homogeneity(sig, args, 1)
+    ty = sig.args[0]
+    float_ty = ty.underlying_float
+    in1 = context.make_complex(builder, ty, value=args[0])
+    out = context.make_complex(builder, ty)
+
+    inner_sig = typing.signature(*[float_ty]*2)
+    out.real = np_real_rint_impl(context, builder, inner_sig, [in1.real])
+    out.imag = np_real_rint_impl(context, builder, inner_sig, [in1.imag])
+    return out._getvalue()
+
+
+########################################################################
+# NumPy exp
+
+def np_real_exp_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.exp_impl(context, builder, sig, args)
+
+
+def np_complex_exp_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return cmathimpl.exp_impl(context, builder, sig, args)
+
+########################################################################
+# NumPy exp2
+
+def np_real_exp2_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    dispatch_table = {
+        types.float32: 'npy_exp2f',
+        types.float64: 'npy_exp2',
+    }
+
+    return _dispatch_func_by_name_type(context, builder, sig, args,
+                                       dispatch_table, 'exp2')
+
+
+def np_complex_exp2_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    ty = sig.args[0]
+    float_ty = ty.underlying_float
+    in1 = context.make_complex(builder, ty, value=args[0])
+    tmp = context.make_complex(builder, ty)
+    loge2 = context.get_constant(float_ty, _NPY_LOGE2)
+    tmp.real = builder.fmul(loge2, in1.real)
+    tmp.imag = builder.fmul(loge2, in1.imag)
+    return np_complex_exp_impl(context, builder, sig, [tmp._getvalue()])
+
+
+########################################################################
+# NumPy log
+
+def np_real_log_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.log_impl(context, builder, sig, args)
+
+
+def np_complex_log_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return cmathimpl.log_impl(context, builder, sig, args)
+
+########################################################################
+# NumPy log2
+
+def np_real_log2_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    dispatch_table = {
+        types.float32: 'npy_log2f',
+        types.float64: 'npy_log2',
+    }
+
+    return _dispatch_func_by_name_type(context, builder, sig, args,
+                                       dispatch_table, 'log2')
+
+def np_complex_log2_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    ty = sig.args[0]
+    float_ty = ty.underlying_float
+    tmp = np_complex_log_impl(context, builder, sig, args)
+    tmp = context.make_complex(builder, ty, value=tmp)
+    log2e = context.get_constant(float_ty, _NPY_LOG2E)
+    tmp.real = builder.fmul(log2e, tmp.real)
+    tmp.imag = builder.fmul(log2e, tmp.imag)
+    return tmp._getvalue()
+
+
+########################################################################
+# NumPy log10
+
+def np_real_log10_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.log10_impl(context, builder, sig, args)
+
+
+def np_complex_log10_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    ty = sig.args[0]
+    float_ty = ty.underlying_float
+    tmp = np_complex_log_impl(context, builder, sig, args)
+    tmp = context.make_complex(builder, ty, value=tmp)
+    log10e = context.get_constant(float_ty, _NPY_LOG10E)
+    tmp.real = builder.fmul(log10e, tmp.real)
+    tmp.imag = builder.fmul(log10e, tmp.imag)
+    return tmp._getvalue()
+
+
+########################################################################
+# NumPy expm1
+
+def np_real_expm1_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.expm1_impl(context, builder, sig, args)
+
+def np_complex_expm1_impl(context, builder, sig, args):
+    # this is based on nc_expm1 in funcs.inc.src
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    ty = sig.args[0]
+    float_ty = ty.underlying_float
+    float_unary_sig = typing.signature(*[float_ty]*2)
+
+    MINUS_ONE = context.get_constant(float_ty, -1.0)
+    in1 = context.make_complex(builder, ty, value=args[0])
+    a = np_real_exp_impl(context, builder, float_unary_sig, [in1.real])
+    out = context.make_complex(builder, ty)
+    cos_imag = np_real_cos_impl(context, builder, float_unary_sig, [in1.imag])
+    sin_imag = np_real_sin_impl(context, builder, float_unary_sig, [in1.imag])
+    tmp = builder.fmul(a, cos_imag)
+    out.imag = builder.fmul(a, sin_imag)
+    out.real = builder.fadd(tmp, MINUS_ONE)
+
+    return out._getvalue()
+
+
+########################################################################
+# NumPy log1p
+
+def np_real_log1p_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.log1p_impl(context, builder, sig, args)
+
+def np_complex_log1p_impl(context, builder, sig, args):
+    # base on NumPy's nc_log1p in funcs.inc.src
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    ty = sig.args[0]
+    float_ty = ty.underlying_float
+    float_unary_sig = typing.signature(*[float_ty]*2)
+    float_binary_sig = typing.signature(*[float_ty]*3)
+
+    ONE = context.get_constant(float_ty, 1.0)
+    in1 = context.make_complex(builder, ty, value=args[0])
+    out = context.make_complex(builder, ty)
+    real_plus_one = builder.fadd(in1.real, ONE)
+    l = np_real_hypot_impl(context, builder, float_binary_sig,
+                           [real_plus_one, in1.imag])
+    out.imag = np_real_atan2_impl(context, builder, float_binary_sig,
+                                  [in1.imag, real_plus_one])
+    out.real = np_real_log_impl(context, builder, float_unary_sig, [l])
+
+    return out._getvalue()
+
+
+########################################################################
+# NumPy sqrt
+
+def np_real_sqrt_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.sqrt_impl(context, builder, sig, args)
+
+
+def np_complex_sqrt_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return cmathimpl.sqrt_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy square
+
+def np_int_square_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return builder.mul(args[0], args[0])
+
+
+def np_real_square_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return builder.fmul(args[0], args[0])
+
+def np_complex_square_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    binary_sig = typing.signature(*[sig.return_type]*3)
+    return numbers.complex_mul_impl(context, builder, binary_sig,
+                                     [args[0], args[0]])
+
+
+########################################################################
+# NumPy cbrt
+
+def np_real_cbrt_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    # We enable fastmath here to force np.power(x, 1/3) to generate a
+    # call to libm cbrt function
+    @register_jitable(fastmath=True)
+    def cbrt(x):
+        if x < 0:
+            return -np.power(-x, 1.0 / 3.0)
+        else:
+            return np.power(x, 1.0 / 3.0)
+
+    def _cbrt(x):
+        if np.isnan(x):
+            return np.nan
+        return cbrt(x)
+
+    return context.compile_internal(builder, _cbrt, sig, args)
+
+
+########################################################################
+# NumPy reciprocal
+
+def np_int_reciprocal_impl(context, builder, sig, args):
+    # based on the implementation in loops.c.src
+    # integer versions for reciprocal are performed via promotion
+    # using double, and then converted back to the type
+    _check_arity_and_homogeneity(sig, args, 1)
+    ty = sig.return_type
+
+    binary_sig = typing.signature(*[ty]*3)
+    in_as_float = context.cast(builder, args[0], ty, types.float64)
+    ONE = context.get_constant(types.float64, 1)
+    result_as_float = builder.fdiv(ONE, in_as_float)
+    return context.cast(builder, result_as_float, types.float64, ty)
+
+
+def np_real_reciprocal_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    ONE = context.get_constant(sig.return_type, 1.0)
+    return builder.fdiv(ONE, args[0])
+
+
+def np_complex_reciprocal_impl(context, builder, sig, args):
+    # based on the implementation in loops.c.src
+    # Basically the same Smith method used for division, but with
+    # the numerator substituted by 1.0
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    ty = sig.args[0]
+    float_ty = ty.underlying_float
+
+    ZERO = context.get_constant(float_ty, 0.0)
+    ONE = context.get_constant(float_ty, 1.0)
+    in1 = context.make_complex(builder, ty, value=args[0])
+    out = context.make_complex(builder, ty)
+    in1r = in1.real
+    in1i = in1.imag
+    in1r_abs = _fabs(context, builder, in1r)
+    in1i_abs = _fabs(context, builder, in1i)
+    in1i_abs_le_in1r_abs = builder.fcmp_ordered('<=', in1i_abs, in1r_abs)
+
+    with builder.if_else(in1i_abs_le_in1r_abs) as (then, otherwise):
+        with then:
+            r = builder.fdiv(in1i, in1r)
+            tmp0 = builder.fmul(in1i, r)
+            d = builder.fadd(in1r, tmp0)
+            inv_d = builder.fdiv(ONE, d)
+            minus_r = builder.fsub(ZERO, r)
+            out.real = inv_d
+            out.imag = builder.fmul(minus_r, inv_d)
+        with otherwise:
+            r = builder.fdiv(in1r, in1i)
+            tmp0 = builder.fmul(in1r, r)
+            d = builder.fadd(tmp0, in1i)
+            inv_d = builder.fdiv(ONE, d)
+            out.real = builder.fmul(r, inv_d)
+            out.imag = builder.fsub(ZERO, inv_d)
+
+    return out._getvalue()
+
+
+########################################################################
+# NumPy sin
+
+def np_real_sin_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.sin_impl(context, builder, sig, args)
+
+
+def np_complex_sin_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return cmathimpl.sin_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy cos
+
+def np_real_cos_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.cos_impl(context, builder, sig, args)
+
+
+def np_complex_cos_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return cmathimpl.cos_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy tan
+
+def np_real_tan_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.tan_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy asin
+
+def np_real_asin_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.asin_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy acos
+
+def np_real_acos_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.acos_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy atan
+
+def np_real_atan_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.atan_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy atan2
+
+def np_real_atan2_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+    return mathimpl.atan2_float_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy hypot
+
+def np_real_hypot_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+    return mathimpl.hypot_float_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy sinh
+
+def np_real_sinh_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.sinh_impl(context, builder, sig, args)
+
+
+def np_complex_sinh_impl(context, builder, sig, args):
+    # npymath does not provide a complex sinh. The code in funcs.inc.src
+    # is translated here...
+    _check_arity_and_homogeneity(sig, args, 1)
+
+
+    ty = sig.args[0]
+    fty = ty.underlying_float
+    fsig1 = typing.signature(*[fty]*2)
+    x = context.make_complex(builder, ty, args[0])
+    out = context.make_complex(builder, ty)
+    xr = x.real
+    xi = x.imag
+
+    sxi = np_real_sin_impl(context, builder, fsig1, [xi])
+    shxr = np_real_sinh_impl(context, builder, fsig1, [xr])
+    cxi = np_real_cos_impl(context, builder, fsig1, [xi])
+    chxr = np_real_cosh_impl(context, builder, fsig1, [xr])
+
+    out.real = builder.fmul(cxi, shxr)
+    out.imag = builder.fmul(sxi, chxr)
+
+    return out._getvalue()
+
+
+########################################################################
+# NumPy cosh
+
+def np_real_cosh_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.cosh_impl(context, builder, sig, args)
+
+
+def np_complex_cosh_impl(context, builder, sig, args):
+    # npymath does not provide a complex cosh. The code in funcs.inc.src
+    # is translated here...
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    ty = sig.args[0]
+    fty = ty.underlying_float
+    fsig1 = typing.signature(*[fty]*2)
+    x = context.make_complex(builder, ty, args[0])
+    out = context.make_complex(builder, ty)
+    xr = x.real
+    xi = x.imag
+
+    cxi = np_real_cos_impl(context, builder, fsig1, [xi])
+    chxr = np_real_cosh_impl(context, builder, fsig1, [xr])
+    sxi = np_real_sin_impl(context, builder, fsig1, [xi])
+    shxr = np_real_sinh_impl(context, builder, fsig1, [xr])
+
+    out.real = builder.fmul(cxi, chxr)
+    out.imag = builder.fmul(sxi, shxr)
+
+    return out._getvalue()
+
+
+########################################################################
+# NumPy tanh
+
+def np_real_tanh_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.tanh_impl(context, builder, sig, args)
+
+
+def np_complex_tanh_impl(context, builder, sig, args):
+    # npymath does not provide complex tan functions. The code
+    # in funcs.inc.src for tanh is translated here...
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    ty = sig.args[0]
+    fty = ty.underlying_float
+    fsig1 = typing.signature(*[fty]*2)
+    ONE = context.get_constant(fty, 1.0)
+    x = context.make_complex(builder, ty, args[0])
+    out = context.make_complex(builder, ty)
+
+    xr = x.real
+    xi = x.imag
+    si = np_real_sin_impl(context, builder, fsig1, [xi])
+    ci = np_real_cos_impl(context, builder, fsig1, [xi])
+    shr = np_real_sinh_impl(context, builder, fsig1, [xr])
+    chr_ = np_real_cosh_impl(context, builder, fsig1, [xr])
+    rs = builder.fmul(ci, shr)
+    is_ = builder.fmul(si, chr_)
+    rc = builder.fmul(ci, chr_)
+    ic = builder.fmul(si, shr) # note: opposite sign from code in funcs.inc.src
+    sqr_rc = builder.fmul(rc, rc)
+    sqr_ic = builder.fmul(ic, ic)
+    d = builder.fadd(sqr_rc, sqr_ic)
+    inv_d = builder.fdiv(ONE, d)
+    rs_rc = builder.fmul(rs, rc)
+    is_ic = builder.fmul(is_, ic)
+    is_rc = builder.fmul(is_, rc)
+    rs_ic = builder.fmul(rs, ic)
+    numr = builder.fadd(rs_rc, is_ic)
+    numi = builder.fsub(is_rc, rs_ic)
+    out.real = builder.fmul(numr, inv_d)
+    out.imag = builder.fmul(numi, inv_d)
+
+    return out._getvalue()
+
+
+########################################################################
+# NumPy asinh
+
+def np_real_asinh_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.asinh_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy acosh
+
+def np_real_acosh_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.acosh_impl(context, builder, sig, args)
+
+
+def np_complex_acosh_impl(context, builder, sig, args):
+    # npymath does not provide a complex acosh. The code in funcs.inc.src
+    # is translated here...
+    # log(x + sqrt(x+1) * sqrt(x-1))
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    ty = sig.args[0]
+    csig2 = typing.signature(*[ty]*3)
+
+    ONE = context.get_constant_generic(builder, ty, 1.0 + 0.0j)
+    x = args[0]
+
+    x_plus_one = numbers.complex_add_impl(context, builder, csig2, [x,
+                                                                     ONE])
+    x_minus_one = numbers.complex_sub_impl(context, builder, csig2, [x,
+                                                                      ONE])
+    sqrt_x_plus_one = np_complex_sqrt_impl(context, builder, sig, [x_plus_one])
+    sqrt_x_minus_one = np_complex_sqrt_impl(context, builder, sig, [x_minus_one])
+    prod_sqrt = numbers.complex_mul_impl(context, builder, csig2,
+                                          [sqrt_x_plus_one,
+                                           sqrt_x_minus_one])
+    log_arg = numbers.complex_add_impl(context, builder, csig2, [x,
+                                                                  prod_sqrt])
+
+    return np_complex_log_impl(context, builder, sig, [log_arg])
+
+
+########################################################################
+# NumPy atanh
+
+def np_real_atanh_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+    return mathimpl.atanh_impl(context, builder, sig, args)
+
+
+########################################################################
+# NumPy floor
+
+def np_real_floor_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    return mathimpl.call_fp_intrinsic(builder, 'llvm.floor', args)
+
+
+########################################################################
+# NumPy ceil
+
+def np_real_ceil_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    return mathimpl.call_fp_intrinsic(builder, 'llvm.ceil', args)
+
+
+########################################################################
+# NumPy trunc
+
+def np_real_trunc_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    return mathimpl.call_fp_intrinsic(builder, 'llvm.trunc', args)
+
+
+########################################################################
+# NumPy fabs
+
+def np_real_fabs_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    return mathimpl.call_fp_intrinsic(builder, 'llvm.fabs', args)
+
+
+########################################################################
+# NumPy style predicates
+
+# For real and integer types rely on numbers... but complex ordering in
+# NumPy is lexicographic (while Python does not provide ordering).
+def np_complex_ge_impl(context, builder, sig, args):
+    # equivalent to macro CGE in NumPy's loops.c.src
+    # ((xr > yr && !npy_isnan(xi) && !npy_isnan(yi)) || (xr == yr && xi >= yi))
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+
+    ty = sig.args[0]
+    in1, in2 = [context.make_complex(builder, ty, value=arg) for arg in args]
+    xr = in1.real
+    xi = in1.imag
+    yr = in2.real
+    yi = in2.imag
+
+    xr_gt_yr = builder.fcmp_ordered('>', xr, yr)
+    no_nan_xi_yi = builder.fcmp_ordered('ord', xi, yi)
+    xr_eq_yr = builder.fcmp_ordered('==', xr, yr)
+    xi_ge_yi = builder.fcmp_ordered('>=', xi, yi)
+    first_term = builder.and_(xr_gt_yr, no_nan_xi_yi)
+    second_term = builder.and_(xr_eq_yr, xi_ge_yi)
+    return builder.or_(first_term, second_term)
+
+
+def np_complex_le_impl(context, builder, sig, args):
+    # equivalent to macro CLE in NumPy's loops.c.src
+    # ((xr < yr && !npy_isnan(xi) && !npy_isnan(yi)) || (xr == yr && xi <= yi))
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+
+    ty = sig.args[0]
+    in1, in2 = [context.make_complex(builder, ty, value=arg) for arg in args]
+    xr = in1.real
+    xi = in1.imag
+    yr = in2.real
+    yi = in2.imag
+
+    xr_lt_yr = builder.fcmp_ordered('<', xr, yr)
+    no_nan_xi_yi = builder.fcmp_ordered('ord', xi, yi)
+    xr_eq_yr = builder.fcmp_ordered('==', xr, yr)
+    xi_le_yi = builder.fcmp_ordered('<=', xi, yi)
+    first_term = builder.and_(xr_lt_yr, no_nan_xi_yi)
+    second_term = builder.and_(xr_eq_yr, xi_le_yi)
+    return builder.or_(first_term, second_term)
+
+
+def np_complex_gt_impl(context, builder, sig, args):
+    # equivalent to macro CGT in NumPy's loops.c.src
+    # ((xr > yr && !npy_isnan(xi) && !npy_isnan(yi)) || (xr == yr && xi > yi))
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+
+    ty = sig.args[0]
+    in1, in2 = [context.make_complex(builder, ty, value=arg) for arg in args]
+    xr = in1.real
+    xi = in1.imag
+    yr = in2.real
+    yi = in2.imag
+
+    xr_gt_yr = builder.fcmp_ordered('>', xr, yr)
+    no_nan_xi_yi = builder.fcmp_ordered('ord', xi, yi)
+    xr_eq_yr = builder.fcmp_ordered('==', xr, yr)
+    xi_gt_yi = builder.fcmp_ordered('>', xi, yi)
+    first_term = builder.and_(xr_gt_yr, no_nan_xi_yi)
+    second_term = builder.and_(xr_eq_yr, xi_gt_yi)
+    return builder.or_(first_term, second_term)
+
+
+def np_complex_lt_impl(context, builder, sig, args):
+    # equivalent to macro CLT in NumPy's loops.c.src
+    # ((xr < yr && !npy_isnan(xi) && !npy_isnan(yi)) || (xr == yr && xi < yi))
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+
+    ty = sig.args[0]
+    in1, in2 = [context.make_complex(builder, ty, value=arg) for arg in args]
+    xr = in1.real
+    xi = in1.imag
+    yr = in2.real
+    yi = in2.imag
+
+    xr_lt_yr = builder.fcmp_ordered('<', xr, yr)
+    no_nan_xi_yi = builder.fcmp_ordered('ord', xi, yi)
+    xr_eq_yr = builder.fcmp_ordered('==', xr, yr)
+    xi_lt_yi = builder.fcmp_ordered('<', xi, yi)
+    first_term = builder.and_(xr_lt_yr, no_nan_xi_yi)
+    second_term = builder.and_(xr_eq_yr, xi_lt_yi)
+    return builder.or_(first_term, second_term)
+
+
+def np_complex_eq_impl(context, builder, sig, args):
+    # equivalent to macro CEQ in NumPy's loops.c.src
+    # (xr == yr && xi == yi)
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+
+    ty = sig.args[0]
+    in1, in2 = [context.make_complex(builder, ty, value=arg) for arg in args]
+    xr = in1.real
+    xi = in1.imag
+    yr = in2.real
+    yi = in2.imag
+
+    xr_eq_yr = builder.fcmp_ordered('==', xr, yr)
+    xi_eq_yi = builder.fcmp_ordered('==', xi, yi)
+    return builder.and_(xr_eq_yr, xi_eq_yi)
+
+
+def np_complex_ne_impl(context, builder, sig, args):
+    # equivalent to macro CNE in NumPy's loops.c.src
+    # (xr != yr || xi != yi)
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+
+    ty = sig.args[0]
+    in1, in2 = [context.make_complex(builder, ty, value=arg) for arg in args]
+    xr = in1.real
+    xi = in1.imag
+    yr = in2.real
+    yi = in2.imag
+
+    xr_ne_yr = builder.fcmp_unordered('!=', xr, yr)
+    xi_ne_yi = builder.fcmp_unordered('!=', xi, yi)
+    return builder.or_(xr_ne_yr, xi_ne_yi)
+
+
+########################################################################
+# NumPy logical algebra
+
+# these are made generic for all types for now, assuming that
+# cgutils.is_true works in the underlying types.
+
+def _complex_is_true(context, builder, ty, val):
+    complex_val = context.make_complex(builder, ty, value=val)
+    re_true = cgutils.is_true(builder, complex_val.real)
+    im_true = cgutils.is_true(builder, complex_val.imag)
+    return builder.or_(re_true, im_true)
+
+
+def np_logical_and_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+    a = cgutils.is_true(builder, args[0])
+    b = cgutils.is_true(builder, args[1])
+    return builder.and_(a, b)
+
+
+def np_complex_logical_and_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+    a = _complex_is_true(context, builder, sig.args[0], args[0])
+    b = _complex_is_true(context, builder, sig.args[1], args[1])
+    return builder.and_(a, b)
+
+
+def np_logical_or_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+    a = cgutils.is_true(builder, args[0])
+    b = cgutils.is_true(builder, args[1])
+    return builder.or_(a, b)
+
+
+def np_complex_logical_or_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+    a = _complex_is_true(context, builder, sig.args[0], args[0])
+    b = _complex_is_true(context, builder, sig.args[1], args[1])
+    return builder.or_(a, b)
+
+
+def np_logical_xor_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+    a = cgutils.is_true(builder, args[0])
+    b = cgutils.is_true(builder, args[1])
+    return builder.xor(a, b)
+
+
+def np_complex_logical_xor_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2, return_type=types.boolean)
+    a = _complex_is_true(context, builder, sig.args[0], args[0])
+    b = _complex_is_true(context, builder, sig.args[1], args[1])
+    return builder.xor(a, b)
+
+
+def np_logical_not_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return cgutils.is_false(builder, args[0])
+
+
+def np_complex_logical_not_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    a = _complex_is_true(context, builder, sig.args[0], args[0])
+    return builder.not_(a)
+
+########################################################################
+# NumPy style max/min
+#
+# There are 2 different sets of functions to perform max and min in
+# NumPy: maximum/minimum and fmax/fmin.
+# Both differ in the way NaNs are handled, so the actual differences
+# come in action only on float/complex numbers. The functions used for
+# integers is shared. For booleans maximum is equivalent to or, and
+# minimum is equivalent to and. Datetime support will go elsewhere.
+
+def np_int_smax_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+    arg1, arg2 = args
+    arg1_sge_arg2 = builder.icmp_signed('>=', arg1, arg2)
+    return builder.select(arg1_sge_arg2, arg1, arg2)
+
+
+def np_int_umax_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+    arg1, arg2 = args
+    arg1_uge_arg2 = builder.icmp_unsigned('>=', arg1, arg2)
+    return builder.select(arg1_uge_arg2, arg1, arg2)
+
+
+def np_real_maximum_impl(context, builder, sig, args):
+    # maximum prefers nan (tries to return a nan).
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    arg1, arg2 = args
+    arg1_nan = builder.fcmp_unordered('uno', arg1, arg1)
+    any_nan = builder.fcmp_unordered('uno', arg1, arg2)
+    nan_result = builder.select(arg1_nan, arg1, arg2)
+
+    arg1_ge_arg2 = builder.fcmp_ordered('>=', arg1, arg2)
+    non_nan_result = builder.select(arg1_ge_arg2, arg1, arg2)
+
+    return builder.select(any_nan, nan_result, non_nan_result)
+
+
+def np_real_fmax_impl(context, builder, sig, args):
+    # fmax prefers non-nan (tries to return a non-nan).
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    arg1, arg2 = args
+    arg2_nan = builder.fcmp_unordered('uno', arg2, arg2)
+    any_nan = builder.fcmp_unordered('uno', arg1, arg2)
+    nan_result = builder.select(arg2_nan, arg1, arg2)
+
+    arg1_ge_arg2 = builder.fcmp_ordered('>=', arg1, arg2)
+    non_nan_result = builder.select(arg1_ge_arg2, arg1, arg2)
+
+    return builder.select(any_nan, nan_result, non_nan_result)
+
+
+def np_complex_maximum_impl(context, builder, sig, args):
+    # maximum prefers nan (tries to return a nan).
+    # There is an extra caveat with complex numbers, as there is more
+    # than one type of nan. NumPy's docs state that the nan in the
+    # first argument is returned when both arguments are nans.
+    # If only one nan is found, that nan is returned.
+    _check_arity_and_homogeneity(sig, args, 2)
+    ty = sig.args[0]
+    bc_sig = typing.signature(types.boolean, ty)
+    bcc_sig = typing.signature(types.boolean, *[ty]*2)
+    arg1, arg2 = args
+    arg1_nan = np_complex_isnan_impl(context, builder, bc_sig, [arg1])
+    arg2_nan = np_complex_isnan_impl(context, builder, bc_sig, [arg2])
+    any_nan = builder.or_(arg1_nan, arg2_nan)
+    nan_result = builder.select(arg1_nan, arg1, arg2)
+
+    arg1_ge_arg2 = np_complex_ge_impl(context, builder, bcc_sig, args)
+    non_nan_result = builder.select(arg1_ge_arg2, arg1, arg2)
+
+    return builder.select(any_nan, nan_result, non_nan_result)
+
+
+def np_complex_fmax_impl(context, builder, sig, args):
+    # fmax prefers non-nan (tries to return a non-nan).
+    # There is an extra caveat with complex numbers, as there is more
+    # than one type of nan. NumPy's docs state that the nan in the
+    # first argument is returned when both arguments are nans.
+    _check_arity_and_homogeneity(sig, args, 2)
+    ty = sig.args[0]
+    bc_sig = typing.signature(types.boolean, ty)
+    bcc_sig = typing.signature(types.boolean, *[ty]*2)
+    arg1, arg2 = args
+    arg1_nan = np_complex_isnan_impl(context, builder, bc_sig, [arg1])
+    arg2_nan = np_complex_isnan_impl(context, builder, bc_sig, [arg2])
+    any_nan = builder.or_(arg1_nan, arg2_nan)
+    nan_result = builder.select(arg2_nan, arg1, arg2)
+
+    arg1_ge_arg2 = np_complex_ge_impl(context, builder, bcc_sig, args)
+    non_nan_result = builder.select(arg1_ge_arg2, arg1, arg2)
+
+    return builder.select(any_nan, nan_result, non_nan_result)
+
+
+def np_int_smin_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+    arg1, arg2 = args
+    arg1_sle_arg2 = builder.icmp_signed('<=', arg1, arg2)
+    return builder.select(arg1_sle_arg2, arg1, arg2)
+
+
+def np_int_umin_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+    arg1, arg2 = args
+    arg1_ule_arg2 = builder.icmp_unsigned('<=', arg1, arg2)
+    return builder.select(arg1_ule_arg2, arg1, arg2)
+
+
+def np_real_minimum_impl(context, builder, sig, args):
+    # minimum prefers nan (tries to return a nan).
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    arg1, arg2 = args
+    arg1_nan = builder.fcmp_unordered('uno', arg1, arg1)
+    any_nan = builder.fcmp_unordered('uno', arg1, arg2)
+    nan_result = builder.select(arg1_nan, arg1, arg2)
+
+    arg1_le_arg2 = builder.fcmp_ordered('<=', arg1, arg2)
+    non_nan_result = builder.select(arg1_le_arg2, arg1, arg2)
+
+    return builder.select(any_nan, nan_result, non_nan_result)
+
+
+def np_real_fmin_impl(context, builder, sig, args):
+    # fmin prefers non-nan (tries to return a non-nan).
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    arg1, arg2 = args
+    arg1_nan = builder.fcmp_unordered('uno', arg1, arg1)
+    any_nan = builder.fcmp_unordered('uno', arg1, arg2)
+    nan_result = builder.select(arg1_nan, arg2, arg1)
+
+    arg1_le_arg2 = builder.fcmp_ordered('<=', arg1, arg2)
+    non_nan_result = builder.select(arg1_le_arg2, arg1, arg2)
+
+    return builder.select(any_nan, nan_result, non_nan_result)
+
+
+def np_complex_minimum_impl(context, builder, sig, args):
+    # minimum prefers nan (tries to return a nan).
+    # There is an extra caveat with complex numbers, as there is more
+    # than one type of nan. NumPy's docs state that the nan in the
+    # first argument is returned when both arguments are nans.
+    # If only one nan is found, that nan is returned.
+    _check_arity_and_homogeneity(sig, args, 2)
+    ty = sig.args[0]
+    bc_sig = typing.signature(types.boolean, ty)
+    bcc_sig = typing.signature(types.boolean, *[ty]*2)
+    arg1, arg2 = args
+    arg1_nan = np_complex_isnan_impl(context, builder, bc_sig, [arg1])
+    arg2_nan = np_complex_isnan_impl(context, builder, bc_sig, [arg2])
+    any_nan = builder.or_(arg1_nan, arg2_nan)
+    nan_result = builder.select(arg1_nan, arg1, arg2)
+
+    arg1_le_arg2 = np_complex_le_impl(context, builder, bcc_sig, args)
+    non_nan_result = builder.select(arg1_le_arg2, arg1, arg2)
+
+    return builder.select(any_nan, nan_result, non_nan_result)
+
+
+def np_complex_fmin_impl(context, builder, sig, args):
+    # fmin prefers non-nan (tries to return a non-nan).
+    # There is an extra caveat with complex numbers, as there is more
+    # than one type of nan. NumPy's docs state that the nan in the
+    # first argument is returned when both arguments are nans.
+    _check_arity_and_homogeneity(sig, args, 2)
+    ty = sig.args[0]
+    bc_sig = typing.signature(types.boolean, ty)
+    bcc_sig = typing.signature(types.boolean, *[ty]*2)
+    arg1, arg2 = args
+    arg1_nan = np_complex_isnan_impl(context, builder, bc_sig, [arg1])
+    arg2_nan = np_complex_isnan_impl(context, builder, bc_sig, [arg2])
+    any_nan = builder.or_(arg1_nan, arg2_nan)
+    nan_result = builder.select(arg2_nan, arg1, arg2)
+
+    arg1_le_arg2 = np_complex_le_impl(context, builder, bcc_sig, args)
+    non_nan_result = builder.select(arg1_le_arg2, arg1, arg2)
+
+    return builder.select(any_nan, nan_result, non_nan_result)
+
+
+########################################################################
+# NumPy floating point misc
+
+def np_int_isnan_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return cgutils.false_bit
+
+
+def np_real_isnan_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return mathimpl.is_nan(builder, args[0])
+
+
+def np_complex_isnan_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+
+    x, = args
+    ty, = sig.args
+    complex_val = context.make_complex(builder, ty, value=x)
+    return cmathimpl.is_nan(builder, complex_val)
+
+
+def np_int_isfinite_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return cgutils.true_bit
+
+
+def np_datetime_isfinite_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return builder.icmp_unsigned('!=', args[0], npdatetime.NAT)
+
+
+def np_datetime_isnat_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return builder.icmp_signed('==', args[0], npdatetime.NAT)
+
+
+def np_real_isfinite_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return mathimpl.is_finite(builder, args[0])
+
+
+def np_complex_isfinite_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    x, = args
+    ty, = sig.args
+    complex_val = context.make_complex(builder, ty, value=x)
+    return cmathimpl.is_finite(builder, complex_val)
+
+
+def np_int_isinf_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return cgutils.false_bit
+
+
+def np_real_isinf_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    return mathimpl.is_inf(builder, args[0])
+
+
+def np_complex_isinf_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+    x, = args
+    ty, = sig.args
+    complex_val = context.make_complex(builder, ty, value=x)
+    return cmathimpl.is_inf(builder, complex_val)
+
+
+def np_real_signbit_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1, return_type=types.boolean)
+
+    dispatch_table = {
+        types.float32: 'numba_signbitf',
+        types.float64: 'numba_signbit',
+    }
+    inner_sig = typing.signature(types.intc, *sig.args)
+
+    int_res = _dispatch_func_by_name_type(context, builder, inner_sig, args,
+                                          dispatch_table, 'signbit')
+    bool_res = builder.icmp_unsigned('!=', int_res, int_res.type(0))
+    return bool_res
+
+
+def np_real_copysign_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    return mathimpl.copysign_float_impl(context, builder, sig, args)
+
+def np_real_nextafter_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 2)
+
+    dispatch_table = {
+        types.float32: 'npy_nextafterf',
+        types.float64: 'npy_nextafter',
+    }
+
+    return _dispatch_func_by_name_type(context, builder, sig, args,
+                                       dispatch_table, 'nextafter')
+
+def np_real_spacing_impl(context, builder, sig, args):
+    _check_arity_and_homogeneity(sig, args, 1)
+
+    dispatch_table = {
+        types.float32: 'npy_spacingf',
+        types.float64: 'npy_spacing',
+    }
+
+    return _dispatch_func_by_name_type(context, builder, sig, args,
+                                       dispatch_table, 'spacing')
+
+
+def np_real_ldexp_impl(context, builder, sig, args):
+    # this one is slightly different to other ufuncs.
+    # arguments are not homogeneous and second arg may come as
+    # an 'i' or an 'l'.
+
+    # the function expects the second argument to be have a C int type
+    x1, x2 = args
+    ty1, ty2 = sig.args
+    # note that types.intc should be equivalent to int_ that is
+    # 'NumPy's default int')
+    x2 = context.cast(builder, x2, ty2, types.intc)
+    f_fi_sig = typing.signature(ty1, ty1, types.intc)
+    return mathimpl.ldexp_impl(context, builder, f_fi_sig, (x1, x2))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npyimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npyimpl.py
new file mode 100644
index 000000000..4c4dc5956
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/npyimpl.py
@@ -0,0 +1,613 @@
+"""
+Implementation of functions in the Numpy package.
+"""
+
+
+import math
+import sys
+import itertools
+from collections import namedtuple
+
+import llvmlite.ir as ir
+
+import numpy as np
+import operator
+
+from numba.np import arrayobj, ufunc_db, numpy_support
+from numba.core.imputils import Registry, impl_ret_new_ref, force_error_model
+from numba.core import typing, types, utils, cgutils, callconv
+from numba.np.numpy_support import (
+    ufunc_find_matching_loop, select_array_wrapper, from_dtype, _ufunc_loop_sig
+)
+from numba.core.typing import npydecl
+from numba.core.extending import overload, intrinsic
+
+from numba.core import errors
+from numba.cpython import builtins
+
+registry = Registry('npyimpl')
+lower = registry.lower
+
+
+########################################################################
+
+# In the way we generate code, ufuncs work with scalar as well as
+# with array arguments. The following helper classes help dealing
+# with scalar and array arguments in a regular way.
+#
+# In short, the classes provide a uniform interface. The interface
+# handles the indexing of as many dimensions as the array may have.
+# For scalars, all indexing is ignored and when the value is read,
+# the scalar is returned. For arrays code for actual indexing is
+# generated and reading performs the appropriate indirection.
+
+class _ScalarIndexingHelper(object):
+    def update_indices(self, loop_indices, name):
+        pass
+
+    def as_values(self):
+        pass
+
+
+class _ScalarHelper(object):
+    """Helper class to handle scalar arguments (and result).
+    Note that store_data is only used when generating code for
+    a scalar ufunc and to write the output value.
+
+    For loading, the value is directly used without having any
+    kind of indexing nor memory backing it up. This is the use
+    for input arguments.
+
+    For storing, a variable is created in the stack where the
+    value will be written.
+
+    Note that it is not supported (as it is unneeded for our
+    current use-cases) reading back a stored value. This class
+    will always "load" the original value it got at its creation.
+    """
+    def __init__(self, ctxt, bld, val, ty):
+        self.context = ctxt
+        self.builder = bld
+        self.val = val
+        self.base_type = ty
+        intpty = ctxt.get_value_type(types.intp)
+        self.shape = [ir.Constant(intpty, 1)]
+
+        lty = ctxt.get_data_type(ty) if ty != types.boolean else ir.IntType(1)
+        self._ptr = cgutils.alloca_once(bld, lty)
+
+    def create_iter_indices(self):
+        return _ScalarIndexingHelper()
+
+    def load_data(self, indices):
+        return self.val
+
+    def store_data(self, indices, val):
+        self.builder.store(val, self._ptr)
+
+    @property
+    def return_val(self):
+        return self.builder.load(self._ptr)
+
+
+class _ArrayIndexingHelper(namedtuple('_ArrayIndexingHelper',
+                                      ('array', 'indices'))):
+    def update_indices(self, loop_indices, name):
+        bld = self.array.builder
+        intpty = self.array.context.get_value_type(types.intp)
+        ONE = ir.Constant(ir.IntType(intpty.width), 1)
+
+        # we are only interested in as many inner dimensions as dimensions
+        # the indexed array has (the outer dimensions are broadcast, so
+        # ignoring the outer indices produces the desired result.
+        indices = loop_indices[len(loop_indices) - len(self.indices):]
+        for src, dst, dim in zip(indices, self.indices, self.array.shape):
+            cond = bld.icmp_unsigned('>', dim, ONE)
+            with bld.if_then(cond):
+                bld.store(src, dst)
+
+    def as_values(self):
+        """
+        The indexing helper is built using alloca for each value, so it
+        actually contains pointers to the actual indices to load. Note
+        that update_indices assumes the same. This method returns the
+        indices as values
+        """
+        bld = self.array.builder
+        return [bld.load(index) for index in self.indices]
+
+
+class _ArrayHelper(namedtuple('_ArrayHelper', ('context', 'builder',
+                                               'shape', 'strides', 'data',
+                                               'layout', 'base_type', 'ndim',
+                                               'return_val'))):
+    """Helper class to handle array arguments/result.
+    It provides methods to generate code loading/storing specific
+    items as well as support code for handling indices.
+    """
+    def create_iter_indices(self):
+        intpty = self.context.get_value_type(types.intp)
+        ZERO = ir.Constant(ir.IntType(intpty.width), 0)
+
+        indices = []
+        for i in range(self.ndim):
+            x = cgutils.alloca_once(self.builder, ir.IntType(intpty.width))
+            self.builder.store(ZERO, x)
+            indices.append(x)
+        return _ArrayIndexingHelper(self, indices)
+
+    def _load_effective_address(self, indices):
+        return cgutils.get_item_pointer2(self.context,
+                                         self.builder,
+                                         data=self.data,
+                                         shape=self.shape,
+                                         strides=self.strides,
+                                         layout=self.layout,
+                                         inds=indices)
+
+    def load_data(self, indices):
+        model = self.context.data_model_manager[self.base_type]
+        ptr = self._load_effective_address(indices)
+        return model.load_from_data_pointer(self.builder, ptr)
+
+    def store_data(self, indices, value):
+        ctx = self.context
+        bld = self.builder
+        store_value = ctx.get_value_as_data(bld, self.base_type, value)
+        assert ctx.get_data_type(self.base_type) == store_value.type
+        bld.store(store_value, self._load_effective_address(indices))
+
+
+def _prepare_argument(ctxt, bld, inp, tyinp, where='input operand'):
+    """returns an instance of the appropriate Helper (either
+    _ScalarHelper or _ArrayHelper) class to handle the argument.
+    using the polymorphic interface of the Helper classes, scalar
+    and array cases can be handled with the same code"""
+
+    # first un-Optional Optionals
+    if isinstance(tyinp, types.Optional):
+        oty = tyinp
+        tyinp = tyinp.type
+        inp = ctxt.cast(bld, inp, oty, tyinp)
+
+    # then prepare the arg for a concrete instance
+    if isinstance(tyinp, types.ArrayCompatible):
+        ary     = ctxt.make_array(tyinp)(ctxt, bld, inp)
+        shape   = cgutils.unpack_tuple(bld, ary.shape, tyinp.ndim)
+        strides = cgutils.unpack_tuple(bld, ary.strides, tyinp.ndim)
+        return _ArrayHelper(ctxt, bld, shape, strides, ary.data,
+                            tyinp.layout, tyinp.dtype, tyinp.ndim, inp)
+    elif (types.unliteral(tyinp) in types.number_domain | {types.boolean}
+          or isinstance(tyinp, types.scalars._NPDatetimeBase)):
+        return _ScalarHelper(ctxt, bld, inp, tyinp)
+    else:
+        raise NotImplementedError('unsupported type for {0}: {1}'.format(where,
+                                  str(tyinp)))
+
+
+_broadcast_onto_sig = types.intp(types.intp, types.CPointer(types.intp),
+                                 types.intp, types.CPointer(types.intp))
+def _broadcast_onto(src_ndim, src_shape, dest_ndim, dest_shape):
+    '''Low-level utility function used in calculating a shape for
+    an implicit output array.  This function assumes that the
+    destination shape is an LLVM pointer to a C-style array that was
+    already initialized to a size of one along all axes.
+
+    Returns an integer value:
+    >= 1  :  Succeeded.  Return value should equal the number of dimensions in
+             the destination shape.
+    0     :  Failed to broadcast because source shape is larger than the
+             destination shape (this case should be weeded out at type
+             checking).
+    < 0   :  Failed to broadcast onto destination axis, at axis number ==
+             -(return_value + 1).
+    '''
+    if src_ndim > dest_ndim:
+        # This check should have been done during type checking, but
+        # let's be defensive anyway...
+        return 0
+    else:
+        src_index = 0
+        dest_index = dest_ndim - src_ndim
+        while src_index < src_ndim:
+            src_dim_size = src_shape[src_index]
+            dest_dim_size = dest_shape[dest_index]
+            # Check to see if we've already mutated the destination
+            # shape along this axis.
+            if dest_dim_size != 1:
+                # If we have mutated the destination shape already,
+                # then the source axis size must either be one,
+                # or the destination axis size.
+                if src_dim_size != dest_dim_size and src_dim_size != 1:
+                    return -(dest_index + 1)
+            elif src_dim_size != 1:
+                # If the destination size is still its initial
+                dest_shape[dest_index] = src_dim_size
+            src_index += 1
+            dest_index += 1
+    return dest_index
+
+def _build_array(context, builder, array_ty, input_types, inputs):
+    """Utility function to handle allocation of an implicit output array
+    given the target context, builder, output array type, and a list of
+    _ArrayHelper instances.
+    """
+    # First, strip optional types, ufunc loops are typed on concrete types
+    input_types = [x.type if isinstance(x, types.Optional) else x
+                   for x in input_types]
+
+    intp_ty = context.get_value_type(types.intp)
+    def make_intp_const(val):
+        return context.get_constant(types.intp, val)
+
+    ZERO = make_intp_const(0)
+    ONE = make_intp_const(1)
+
+    src_shape = cgutils.alloca_once(builder, intp_ty, array_ty.ndim,
+                                    "src_shape")
+    dest_ndim = make_intp_const(array_ty.ndim)
+    dest_shape = cgutils.alloca_once(builder, intp_ty, array_ty.ndim,
+                                     "dest_shape")
+    dest_shape_addrs = tuple(cgutils.gep_inbounds(builder, dest_shape, index)
+                             for index in range(array_ty.ndim))
+
+    # Initialize the destination shape with all ones.
+    for dest_shape_addr in dest_shape_addrs:
+        builder.store(ONE, dest_shape_addr)
+
+    # For each argument, try to broadcast onto the destination shape,
+    # mutating along any axis where the argument shape is not one and
+    # the destination shape is one.
+    for arg_number, arg in enumerate(inputs):
+        if not hasattr(arg, "ndim"): # Skip scalar arguments
+            continue
+        arg_ndim = make_intp_const(arg.ndim)
+        for index in range(arg.ndim):
+            builder.store(arg.shape[index],
+                          cgutils.gep_inbounds(builder, src_shape, index))
+        arg_result = context.compile_internal(
+            builder, _broadcast_onto, _broadcast_onto_sig,
+            [arg_ndim, src_shape, dest_ndim, dest_shape])
+        with cgutils.if_unlikely(builder,
+                                 builder.icmp_signed('<', arg_result, ONE)):
+            msg = "unable to broadcast argument %d to output array" % (
+                arg_number,)
+
+            loc = errors.loc_info.get('loc', None)
+            if loc is not None:
+                msg += '\nFile "%s", line %d, ' % (loc.filename, loc.line)
+
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+    real_array_ty = array_ty.as_array
+
+    dest_shape_tup = tuple(builder.load(dest_shape_addr)
+                           for dest_shape_addr in dest_shape_addrs)
+    array_val = arrayobj._empty_nd_impl(context, builder, real_array_ty,
+                                        dest_shape_tup)
+
+    # Get the best argument to call __array_wrap__ on
+    array_wrapper_index = select_array_wrapper(input_types)
+    array_wrapper_ty = input_types[array_wrapper_index]
+    try:
+        # __array_wrap__(source wrapped array, out array) -> out wrapped array
+        array_wrap = context.get_function('__array_wrap__',
+                                          array_ty(array_wrapper_ty, real_array_ty))
+    except NotImplementedError:
+        # If it's the same priority as a regular array, assume we
+        # should use the allocated array unchanged.
+        if array_wrapper_ty.array_priority != types.Array.array_priority:
+            raise
+        out_val = array_val._getvalue()
+    else:
+        wrap_args = (inputs[array_wrapper_index].return_val, array_val._getvalue())
+        out_val = array_wrap(builder, wrap_args)
+
+    ndim = array_ty.ndim
+    shape   = cgutils.unpack_tuple(builder, array_val.shape, ndim)
+    strides = cgutils.unpack_tuple(builder, array_val.strides, ndim)
+    return _ArrayHelper(context, builder, shape, strides, array_val.data,
+                        array_ty.layout, array_ty.dtype, ndim,
+                        out_val)
+
+# ufuncs either return a single result when nout == 1, else a tuple of results
+
+def _unpack_output_types(ufunc, sig):
+    if ufunc.nout == 1:
+        return [sig.return_type]
+    else:
+        return list(sig.return_type)
+
+
+def _unpack_output_values(ufunc, builder, values):
+    if ufunc.nout == 1:
+        return [values]
+    else:
+        return cgutils.unpack_tuple(builder, values)
+
+
+def _pack_output_values(ufunc, context, builder, typ, values):
+    if ufunc.nout == 1:
+        return values[0]
+    else:
+        return context.make_tuple(builder, typ, values)
+
+
+def numpy_ufunc_kernel(context, builder, sig, args, ufunc, kernel_class):
+    # This is the code generator that builds all the looping needed
+    # to execute a numpy functions over several dimensions (including
+    # scalar cases).
+    #
+    # context - the code generation context
+    # builder - the code emitter
+    # sig - signature of the ufunc
+    # args - the args to the ufunc
+    # ufunc - the ufunc itself
+    # kernel_class -  a code generating subclass of _Kernel that provides
+
+    arguments = [_prepare_argument(context, builder, arg, tyarg)
+                 for arg, tyarg in zip(args, sig.args)]
+
+    if len(arguments) < ufunc.nin:
+        raise RuntimeError(
+            "Not enough inputs to {}, expected {} got {}"
+            .format(ufunc.__name__, ufunc.nin, len(arguments)))
+
+    for out_i, ret_ty in enumerate(_unpack_output_types(ufunc, sig)):
+        if ufunc.nin + out_i >= len(arguments):
+            # this out argument is not provided
+            if isinstance(ret_ty, types.ArrayCompatible):
+                output = _build_array(context, builder, ret_ty, sig.args, arguments)
+            else:
+                output = _prepare_argument(
+                    context, builder,
+                    ir.Constant(context.get_value_type(ret_ty), None), ret_ty)
+            arguments.append(output)
+        elif context.enable_nrt:
+            # Incref the output
+            context.nrt.incref(builder, ret_ty, args[ufunc.nin + out_i])
+
+    inputs = arguments[:ufunc.nin]
+    outputs = arguments[ufunc.nin:]
+    assert len(outputs) == ufunc.nout
+
+    outer_sig = _ufunc_loop_sig(
+        [a.base_type for a in outputs],
+        [a.base_type for a in inputs]
+    )
+    kernel = kernel_class(context, builder, outer_sig)
+    intpty = context.get_value_type(types.intp)
+
+    indices = [inp.create_iter_indices() for inp in inputs]
+
+    # assume outputs are all the same size, which numpy requires
+
+    loopshape = outputs[0].shape
+    with cgutils.loop_nest(builder, loopshape, intp=intpty) as loop_indices:
+        vals_in = []
+        for i, (index, arg) in enumerate(zip(indices, inputs)):
+            index.update_indices(loop_indices, i)
+            vals_in.append(arg.load_data(index.as_values()))
+
+        vals_out = _unpack_output_values(ufunc, builder, kernel.generate(*vals_in))
+        for val_out, output in zip(vals_out, outputs):
+            output.store_data(loop_indices, val_out)
+
+    out = _pack_output_values(ufunc, context, builder, sig.return_type, [o.return_val for o in outputs])
+    return impl_ret_new_ref(context, builder, sig.return_type, out)
+
+
+# Kernels are the code to be executed inside the multidimensional loop.
+class _Kernel(object):
+    def __init__(self, context, builder, outer_sig):
+        self.context = context
+        self.builder = builder
+        self.outer_sig = outer_sig
+
+    def cast(self, val, fromty, toty):
+        """Numpy uses cast semantics that are different from standard Python
+        (for example, it does allow casting from complex to float).
+
+        This method acts as a patch to context.cast so that it allows
+        complex to real/int casts.
+
+        """
+        if (isinstance(fromty, types.Complex) and
+            not isinstance(toty, types.Complex)):
+            # attempt conversion of the real part to the specified type.
+            # note that NumPy issues a warning in this kind of conversions
+            newty = fromty.underlying_float
+            attr = self.context.get_getattr(fromty, 'real')
+            val = attr(self.context, self.builder, fromty, val, 'real')
+            fromty = newty
+            # let the regular cast do the rest...
+
+        return self.context.cast(self.builder, val, fromty, toty)
+
+
+def _ufunc_db_function(ufunc):
+    """Use the ufunc loop type information to select the code generation
+    function from the table provided by the dict_of_kernels. The dict
+    of kernels maps the loop identifier to a function with the
+    following signature: (context, builder, signature, args).
+
+    The loop type information has the form 'AB->C'. The letters to the
+    left of '->' are the input types (specified as NumPy letter
+    types).  The letters to the right of '->' are the output
+    types. There must be 'ufunc.nin' letters to the left of '->', and
+    'ufunc.nout' letters to the right.
+
+    For example, a binary float loop resulting in a float, will have
+    the following signature: 'ff->f'.
+
+    A given ufunc implements many loops. The list of loops implemented
+    for a given ufunc can be accessed using the 'types' attribute in
+    the ufunc object. The NumPy machinery selects the first loop that
+    fits a given calling signature (in our case, what we call the
+    outer_sig). This logic is mimicked by 'ufunc_find_matching_loop'.
+    """
+
+    class _KernelImpl(_Kernel):
+        def __init__(self, context, builder, outer_sig):
+            super(_KernelImpl, self).__init__(context, builder, outer_sig)
+            loop = ufunc_find_matching_loop(
+                ufunc, outer_sig.args + tuple(_unpack_output_types(ufunc, outer_sig)))
+            self.fn = context.get_ufunc_info(ufunc).get(loop.ufunc_sig)
+            self.inner_sig = _ufunc_loop_sig(loop.outputs, loop.inputs)
+
+            if self.fn is None:
+                msg = "Don't know how to lower ufunc '{0}' for loop '{1}'"
+                raise NotImplementedError(msg.format(ufunc.__name__, loop))
+
+        def generate(self, *args):
+            isig = self.inner_sig
+            osig = self.outer_sig
+
+            cast_args = [self.cast(val, inty, outty)
+                         for val, inty, outty in zip(args, osig.args,
+                                                     isig.args)]
+            with force_error_model(self.context, 'numpy'):
+                res = self.fn(self.context, self.builder, isig, cast_args)
+            dmm = self.context.data_model_manager
+            res = dmm[isig.return_type].from_return(self.builder, res)
+            return self.cast(res, isig.return_type, osig.return_type)
+
+    return _KernelImpl
+
+
+################################################################################
+# Helper functions that register the ufuncs
+
+def register_ufunc_kernel(ufunc, kernel):
+    def do_ufunc(context, builder, sig, args):
+        return numpy_ufunc_kernel(context, builder, sig, args, ufunc, kernel)
+
+    _any = types.Any
+    in_args = (_any,) * ufunc.nin
+
+    # Add a lowering for each out argument that is missing.
+    for n_explicit_out in range(ufunc.nout + 1):
+        out_args = (types.Array,) * n_explicit_out
+        lower(ufunc, *in_args, *out_args)(do_ufunc)
+
+    return kernel
+
+
+def register_unary_operator_kernel(operator, ufunc, kernel, inplace=False):
+    assert not inplace  # are there any inplace unary operators?
+    def lower_unary_operator(context, builder, sig, args):
+        return numpy_ufunc_kernel(context, builder, sig, args, ufunc, kernel)
+    _arr_kind = types.Array
+    lower(operator, _arr_kind)(lower_unary_operator)
+
+
+def register_binary_operator_kernel(op, ufunc, kernel, inplace=False):
+    def lower_binary_operator(context, builder, sig, args):
+        return numpy_ufunc_kernel(context, builder, sig, args, ufunc, kernel)
+
+    def lower_inplace_operator(context, builder, sig, args):
+        # The visible signature is (A, B) -> A
+        # The implementation's signature (with explicit output)
+        # is (A, B, A) -> A
+        args = tuple(args) + (args[0],)
+        sig = typing.signature(sig.return_type, *sig.args + (sig.args[0],))
+        return numpy_ufunc_kernel(context, builder, sig, args, ufunc, kernel)
+
+    _any = types.Any
+    _arr_kind = types.Array
+    formal_sigs = [(_arr_kind, _arr_kind), (_any, _arr_kind), (_arr_kind, _any)]
+    for sig in formal_sigs:
+        if not inplace:
+            lower(op, *sig)(lower_binary_operator)
+        else:
+            lower(op, *sig)(lower_inplace_operator)
+
+
+################################################################################
+# Use the contents of ufunc_db to initialize the supported ufuncs
+
+@lower(operator.pos, types.Array)
+def array_positive_impl(context, builder, sig, args):
+    '''Lowering function for +(array) expressions.  Defined here
+    (numba.targets.npyimpl) since the remaining array-operator
+    lowering functions are also registered in this module.
+    '''
+    class _UnaryPositiveKernel(_Kernel):
+        def generate(self, *args):
+            [val] = args
+            return val
+
+    return numpy_ufunc_kernel(context, builder, sig, args, np.positive,
+                              _UnaryPositiveKernel)
+
+
+def _register_ufuncs():
+    kernels = {}
+    # NOTE: Assuming ufunc implementation for the CPUContext.
+    for ufunc in ufunc_db.get_ufuncs():
+        kernels[ufunc] = register_ufunc_kernel(ufunc, _ufunc_db_function(ufunc))
+
+    for _op_map in (npydecl.NumpyRulesUnaryArrayOperator._op_map,
+                    npydecl.NumpyRulesArrayOperator._op_map,
+                    ):
+        for operator, ufunc_name in _op_map.items():
+            ufunc = getattr(np, ufunc_name)
+            kernel = kernels[ufunc]
+            if ufunc.nin == 1:
+                register_unary_operator_kernel(operator, ufunc, kernel)
+            elif ufunc.nin == 2:
+                register_binary_operator_kernel(operator, ufunc, kernel)
+            else:
+                raise RuntimeError("There shouldn't be any non-unary or binary operators")
+
+    for _op_map in (npydecl.NumpyRulesInplaceArrayOperator._op_map,
+                    ):
+        for operator, ufunc_name in _op_map.items():
+            ufunc = getattr(np, ufunc_name)
+            kernel = kernels[ufunc]
+            if ufunc.nin == 1:
+                register_unary_operator_kernel(operator, ufunc, kernel, inplace=True)
+            elif ufunc.nin == 2:
+                register_binary_operator_kernel(operator, ufunc, kernel, inplace=True)
+            else:
+                raise RuntimeError("There shouldn't be any non-unary or binary operators")
+
+
+_register_ufuncs()
+
+
+@intrinsic
+def _make_dtype_object(typingctx, desc):
+    """Given a string or NumberClass description *desc*, returns the dtype object.
+    """
+    def from_nb_type(nb_type):
+        return_type = types.DType(nb_type)
+        sig = return_type(desc)
+
+        def codegen(context, builder, signature, args):
+            # All dtype objects are dummy values in LLVM.
+            # They only exist in the type level.
+            return context.get_dummy_value()
+
+        return sig, codegen
+
+    if isinstance(desc, types.Literal):
+        # Convert the str description into np.dtype then to numba type.
+        nb_type = from_dtype(np.dtype(desc.literal_value))
+        return from_nb_type(nb_type)
+    elif isinstance(desc, types.functions.NumberClass):
+        thestr = str(desc.dtype)
+        # Convert the str description into np.dtype then to numba type.
+        nb_type = from_dtype(np.dtype(thestr))
+        return from_nb_type(nb_type)
+
+@overload(np.dtype)
+def numpy_dtype(desc):
+    """Provide an implementation so that numpy.dtype function can be lowered.
+    """
+    if isinstance(desc, (types.Literal, types.functions.NumberClass)):
+        def imp(desc):
+            return _make_dtype_object(desc)
+        return imp
+    else:
+        raise errors.NumbaTypeError('unknown dtype descriptor: {}'.format(desc))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/numpy_support.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/numpy_support.py
new file mode 100644
index 000000000..eea1b612d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/numpy_support.py
@@ -0,0 +1,720 @@
+import collections
+import ctypes
+import re
+
+import numpy as np
+
+from numba.core import errors, types
+from numba.core.typing.templates import signature
+from numba.np import npdatetime_helpers
+from numba.core.errors import TypingError
+
+# re-export
+from numba.core.cgutils import is_nonelike   # noqa: F401
+
+
+numpy_version = tuple(map(int, np.__version__.split('.')[:2]))
+
+
+FROM_DTYPE = {
+    np.dtype('bool'): types.boolean,
+    np.dtype('int8'): types.int8,
+    np.dtype('int16'): types.int16,
+    np.dtype('int32'): types.int32,
+    np.dtype('int64'): types.int64,
+
+    np.dtype('uint8'): types.uint8,
+    np.dtype('uint16'): types.uint16,
+    np.dtype('uint32'): types.uint32,
+    np.dtype('uint64'): types.uint64,
+
+    np.dtype('float32'): types.float32,
+    np.dtype('float64'): types.float64,
+    np.dtype('float16'): types.float16,
+    np.dtype('complex64'): types.complex64,
+    np.dtype('complex128'): types.complex128,
+
+    np.dtype(object): types.pyobject,
+}
+
+re_typestr = re.compile(r'[<>=\|]([a-z])(\d+)?$', re.I)
+re_datetimestr = re.compile(r'[<>=\|]([mM])8?(\[([a-z]+)\])?$', re.I)
+
+sizeof_unicode_char = np.dtype('U1').itemsize
+
+
+def _from_str_dtype(dtype):
+    m = re_typestr.match(dtype.str)
+    if not m:
+        raise NotImplementedError(dtype)
+    groups = m.groups()
+    typecode = groups[0]
+    if typecode == 'U':
+        # unicode
+        if dtype.byteorder not in '=|':
+            raise NotImplementedError("Does not support non-native "
+                                      "byteorder")
+        count = dtype.itemsize // sizeof_unicode_char
+        assert count == int(groups[1]), "Unicode char size mismatch"
+        return types.UnicodeCharSeq(count)
+
+    elif typecode == 'S':
+        # char
+        count = dtype.itemsize
+        assert count == int(groups[1]), "Char size mismatch"
+        return types.CharSeq(count)
+
+    else:
+        raise NotImplementedError(dtype)
+
+
+def _from_datetime_dtype(dtype):
+    m = re_datetimestr.match(dtype.str)
+    if not m:
+        raise NotImplementedError(dtype)
+    groups = m.groups()
+    typecode = groups[0]
+    unit = groups[2] or ''
+    if typecode == 'm':
+        return types.NPTimedelta(unit)
+    elif typecode == 'M':
+        return types.NPDatetime(unit)
+    else:
+        raise NotImplementedError(dtype)
+
+
+def from_dtype(dtype):
+    """
+    Return a Numba Type instance corresponding to the given Numpy *dtype*.
+    NotImplementedError is raised on unsupported Numpy dtypes.
+    """
+    if type(dtype) == type and issubclass(dtype, np.generic):
+        dtype = np.dtype(dtype)
+    elif getattr(dtype, "fields", None) is not None:
+        return from_struct_dtype(dtype)
+
+    try:
+        return FROM_DTYPE[dtype]
+    except KeyError:
+        pass
+
+    try:
+        char = dtype.char
+    except AttributeError:
+        pass
+    else:
+        if char in 'SU':
+            return _from_str_dtype(dtype)
+        if char in 'mM':
+            return _from_datetime_dtype(dtype)
+        if char in 'V' and dtype.subdtype is not None:
+            subtype = from_dtype(dtype.subdtype[0])
+            return types.NestedArray(subtype, dtype.shape)
+
+    raise errors.NumbaNotImplementedError(dtype)
+
+
+_as_dtype_letters = {
+    types.NPDatetime: 'M8',
+    types.NPTimedelta: 'm8',
+    types.CharSeq: 'S',
+    types.UnicodeCharSeq: 'U',
+}
+
+
+def as_dtype(nbtype):
+    """
+    Return a numpy dtype instance corresponding to the given Numba type.
+    NotImplementedError is if no correspondence is known.
+    """
+    nbtype = types.unliteral(nbtype)
+    if isinstance(nbtype, (types.Complex, types.Integer, types.Float)):
+        return np.dtype(str(nbtype))
+    if nbtype is types.bool_:
+        return np.dtype('?')
+    if isinstance(nbtype, (types.NPDatetime, types.NPTimedelta)):
+        letter = _as_dtype_letters[type(nbtype)]
+        if nbtype.unit:
+            return np.dtype('%s[%s]' % (letter, nbtype.unit))
+        else:
+            return np.dtype(letter)
+    if isinstance(nbtype, (types.CharSeq, types.UnicodeCharSeq)):
+        letter = _as_dtype_letters[type(nbtype)]
+        return np.dtype('%s%d' % (letter, nbtype.count))
+    if isinstance(nbtype, types.Record):
+        return as_struct_dtype(nbtype)
+    if isinstance(nbtype, types.EnumMember):
+        return as_dtype(nbtype.dtype)
+    if isinstance(nbtype, types.npytypes.DType):
+        return as_dtype(nbtype.dtype)
+    if isinstance(nbtype, types.NumberClass):
+        return as_dtype(nbtype.dtype)
+    if isinstance(nbtype, types.NestedArray):
+        spec = (as_dtype(nbtype.dtype), tuple(nbtype.shape))
+        return np.dtype(spec)
+    if isinstance(nbtype, types.PyObject):
+        return np.dtype(object)
+
+    msg = f"{nbtype} cannot be represented as a NumPy dtype"
+    raise errors.NumbaNotImplementedError(msg)
+
+
+def as_struct_dtype(rec):
+    """Convert Numba Record type to NumPy structured dtype
+    """
+    assert isinstance(rec, types.Record)
+    names = []
+    formats = []
+    offsets = []
+    titles = []
+    # Fill the fields if they are not a title.
+    for k, t in rec.members:
+        if not rec.is_title(k):
+            names.append(k)
+            formats.append(as_dtype(t))
+            offsets.append(rec.offset(k))
+            titles.append(rec.fields[k].title)
+
+    fields = {
+        'names': names,
+        'formats': formats,
+        'offsets': offsets,
+        'itemsize': rec.size,
+        'titles': titles,
+    }
+    _check_struct_alignment(rec, fields)
+    return np.dtype(fields, align=rec.aligned)
+
+
+def _check_struct_alignment(rec, fields):
+    """Check alignment compatibility with Numpy"""
+    if rec.aligned:
+        for k, dt in zip(fields['names'], fields['formats']):
+            llvm_align = rec.alignof(k)
+            npy_align = dt.alignment
+            if llvm_align is not None and npy_align != llvm_align:
+                msg = (
+                    'NumPy is using a different alignment ({}) '
+                    'than Numba/LLVM ({}) for {}. '
+                    'This is likely a NumPy bug.'
+                )
+                raise ValueError(msg.format(npy_align, llvm_align, dt))
+
+
+def map_arrayscalar_type(val):
+    if isinstance(val, np.generic):
+        # We can't blindly call np.dtype() as it loses information
+        # on some types, e.g. datetime64 and timedelta64.
+        dtype = val.dtype
+    else:
+        try:
+            dtype = np.dtype(type(val))
+        except TypeError:
+            raise NotImplementedError("no corresponding numpy dtype "
+                                      "for %r" % type(val))
+    return from_dtype(dtype)
+
+
+def is_array(val):
+    return isinstance(val, np.ndarray)
+
+
+def map_layout(val):
+    if val.flags['C_CONTIGUOUS']:
+        layout = 'C'
+    elif val.flags['F_CONTIGUOUS']:
+        layout = 'F'
+    else:
+        layout = 'A'
+    return layout
+
+
+def select_array_wrapper(inputs):
+    """
+    Given the array-compatible input types to an operation (e.g. ufunc),
+    select the appropriate input for wrapping the operation output,
+    according to each input's __array_priority__.
+
+    An index into *inputs* is returned.
+    """
+    max_prio = float('-inf')
+    selected_index = None
+    for index, ty in enumerate(inputs):
+        # Ties are broken by choosing the first winner, as in Numpy
+        if (isinstance(ty, types.ArrayCompatible) and
+                ty.array_priority > max_prio):
+            selected_index = index
+            max_prio = ty.array_priority
+
+    assert selected_index is not None
+    return selected_index
+
+
+def resolve_output_type(context, inputs, formal_output):
+    """
+    Given the array-compatible input types to an operation (e.g. ufunc),
+    and the operation's formal output type (a types.Array instance),
+    resolve the actual output type using the typing *context*.
+
+    This uses a mechanism compatible with Numpy's __array_priority__ /
+    __array_wrap__.
+    """
+    selected_input = inputs[select_array_wrapper(inputs)]
+    args = selected_input, formal_output
+    sig = context.resolve_function_type('__array_wrap__', args, {})
+    if sig is None:
+        if selected_input.array_priority == types.Array.array_priority:
+            # If it's the same priority as a regular array, assume we
+            # should return the output unchanged.
+            # (we can't define __array_wrap__ explicitly for types.Buffer,
+            #  as that would be inherited by most array-compatible objects)
+            return formal_output
+        raise errors.TypingError("__array_wrap__ failed for %s" % (args,))
+    return sig.return_type
+
+
+def supported_ufunc_loop(ufunc, loop):
+    """Return whether the *loop* for the *ufunc* is supported -in nopython-.
+
+    *loop* should be a UFuncLoopSpec instance, and *ufunc* a numpy ufunc.
+
+    For ufuncs implemented using the ufunc_db, it is supported if the ufunc_db
+    contains a lowering definition for 'loop' in the 'ufunc' entry.
+
+    For other ufuncs, it is type based. The loop will be considered valid if it
+    only contains the following letter types: '?bBhHiIlLqQfd'. Note this is
+    legacy and when implementing new ufuncs the ufunc_db should be preferred,
+    as it allows for a more fine-grained incremental support.
+    """
+    # NOTE: Assuming ufunc for the CPUContext
+    from numba.np import ufunc_db
+    loop_sig = loop.ufunc_sig
+    try:
+        # check if the loop has a codegen description in the
+        # ufunc_db. If so, we can proceed.
+
+        # note that as of now not all ufuncs have an entry in the
+        # ufunc_db
+        supported_loop = loop_sig in ufunc_db.get_ufunc_info(ufunc)
+    except KeyError:
+        # for ufuncs not in ufunc_db, base the decision of whether the
+        # loop is supported on its types
+        loop_types = [x.char for x in loop.numpy_inputs + loop.numpy_outputs]
+        supported_types = '?bBhHiIlLqQfd'
+        # check if all the types involved in the ufunc loop are
+        # supported in this mode
+        supported_loop = all(t in supported_types for t in loop_types)
+
+    return supported_loop
+
+
+class UFuncLoopSpec(collections.namedtuple('_UFuncLoopSpec',
+                                           ('inputs', 'outputs', 'ufunc_sig'))):
+    """
+    An object describing a ufunc loop's inner types.  Properties:
+    - inputs: the inputs' Numba types
+    - outputs: the outputs' Numba types
+    - ufunc_sig: the string representing the ufunc's type signature, in
+      Numpy format (e.g. "ii->i")
+    """
+
+    __slots__ = ()
+
+    @property
+    def numpy_inputs(self):
+        return [as_dtype(x) for x in self.inputs]
+
+    @property
+    def numpy_outputs(self):
+        return [as_dtype(x) for x in self.outputs]
+
+
+def _ufunc_loop_sig(out_tys, in_tys):
+    if len(out_tys) == 1:
+        return signature(out_tys[0], *in_tys)
+    else:
+        return signature(types.Tuple(out_tys), *in_tys)
+
+
+def ufunc_can_cast(from_, to, has_mixed_inputs, casting='safe'):
+    """
+    A variant of np.can_cast() that can allow casting any integer to
+    any real or complex type, in case the operation has mixed-kind
+    inputs.
+
+    For example we want `np.power(float32, int32)` to be computed using
+    SP arithmetic and return `float32`.
+    However, `np.sqrt(int32)` should use DP arithmetic and return `float64`.
+    """
+    from_ = np.dtype(from_)
+    to = np.dtype(to)
+    if has_mixed_inputs and from_.kind in 'iu' and to.kind in 'cf':
+        # Decide that all integers can cast to any real or complex type.
+        return True
+    return np.can_cast(from_, to, casting)
+
+
+def ufunc_find_matching_loop(ufunc, arg_types):
+    """Find the appropriate loop to be used for a ufunc based on the types
+    of the operands
+
+    ufunc        - The ufunc we want to check
+    arg_types    - The tuple of arguments to the ufunc, including any
+                   explicit output(s).
+    return value - A UFuncLoopSpec identifying the loop, or None
+                   if no matching loop is found.
+    """
+
+    # Separate logical input from explicit output arguments
+    input_types = arg_types[:ufunc.nin]
+    output_types = arg_types[ufunc.nin:]
+    assert(len(input_types) == ufunc.nin)
+
+    try:
+        np_input_types = [as_dtype(x) for x in input_types]
+    except errors.NumbaNotImplementedError:
+        return None
+    try:
+        np_output_types = [as_dtype(x) for x in output_types]
+    except errors.NumbaNotImplementedError:
+        return None
+
+    # Whether the inputs are mixed integer / floating-point
+    has_mixed_inputs = (
+        any(dt.kind in 'iu' for dt in np_input_types) and
+        any(dt.kind in 'cf' for dt in np_input_types))
+
+    def choose_types(numba_types, ufunc_letters):
+        """
+        Return a list of Numba types representing *ufunc_letters*,
+        except when the letter designates a datetime64 or timedelta64,
+        in which case the type is taken from *numba_types*.
+        """
+        assert len(ufunc_letters) >= len(numba_types)
+        types = [tp if letter in 'mM' else from_dtype(np.dtype(letter))
+                 for tp, letter in zip(numba_types, ufunc_letters)]
+        # Add missing types (presumably implicit outputs)
+        types += [from_dtype(np.dtype(letter))
+                  for letter in ufunc_letters[len(numba_types):]]
+        return types
+
+    def set_output_dt_units(inputs, outputs, ufunc_inputs, ufunc_name):
+        """
+        Sets the output unit of a datetime type based on the input units
+
+        Timedelta is a special dtype that requires the time unit to be
+        specified (day, month, etc). Not every operation with timedelta inputs
+        leads to an output of timedelta output. However, for those that do,
+        the unit of output must be inferred based on the units of the inputs.
+
+        At the moment this function takes care of two cases:
+        a) where all inputs are timedelta with the same unit (mm), and
+        therefore the output has the same unit.
+        This case is used for arr.sum, and for arr1+arr2 where all arrays
+        are timedeltas.
+        If in the future this needs to be extended to a case with mixed units,
+        the rules should be implemented in `npdatetime_helpers` and called
+        from this function to set the correct output unit.
+        b) where left operand is a timedelta, i.e. the "m?" case. This case
+        is used for division, eg timedelta / int.
+
+        At the time of writing, Numba does not support addition of timedelta
+        and other types, so this function does not consider the case "?m",
+        i.e. where timedelta is the right operand to a non-timedelta left
+        operand. To extend it in the future, just add another elif clause.
+        """
+        def make_specific(outputs, unit):
+            new_outputs = []
+            for out in outputs:
+                if isinstance(out, types.NPTimedelta) and out.unit == "":
+                    new_outputs.append(types.NPTimedelta(unit))
+                else:
+                    new_outputs.append(out)
+            return new_outputs
+
+        def make_datetime_specific(outputs, dt_unit, td_unit):
+            new_outputs = []
+            for out in outputs:
+                if isinstance(out, types.NPDatetime) and out.unit == "":
+                    unit = npdatetime_helpers.combine_datetime_timedelta_units(
+                        dt_unit, td_unit)
+                    if unit is None:
+                        raise TypeError(f"ufunc '{ufunc_name}' is not " +
+                                        "supported between " +
+                                        f"datetime64[{dt_unit}] " +
+                                        f"and timedelta64[{td_unit}]"
+                                        )
+                    new_outputs.append(types.NPDatetime(unit))
+                else:
+                    new_outputs.append(out)
+            return new_outputs
+
+        if ufunc_inputs == 'mm':
+            if all(inp.unit == inputs[0].unit for inp in inputs):
+                # Case with operation on same units. Operations on different
+                # units not adjusted for now but might need to be
+                # added in the future
+                unit = inputs[0].unit
+                new_outputs = make_specific(outputs, unit)
+            else:
+                return outputs
+            return new_outputs
+        elif ufunc_inputs == 'mM':
+            # case where the left operand has timedelta type
+            # and the right operand has datetime
+            td_unit = inputs[0].unit
+            dt_unit = inputs[1].unit
+            return make_datetime_specific(outputs, dt_unit, td_unit)
+
+        elif ufunc_inputs == 'Mm':
+            # case where the right operand has timedelta type
+            # and the left operand has datetime
+            dt_unit = inputs[0].unit
+            td_unit = inputs[1].unit
+            return make_datetime_specific(outputs, dt_unit, td_unit)
+
+        elif ufunc_inputs[0] == 'm':
+            # case where the left operand has timedelta type
+            unit = inputs[0].unit
+            new_outputs = make_specific(outputs, unit)
+            return new_outputs
+
+    # In NumPy, the loops are evaluated from first to last. The first one
+    # that is viable is the one used. One loop is viable if it is possible
+    # to cast every input operand to the one expected by the ufunc.
+    # Also under NumPy 1.10+ the output must be able to be cast back
+    # to a close enough type ("same_kind").
+
+    for candidate in ufunc.types:
+        ufunc_inputs = candidate[:ufunc.nin]
+        ufunc_outputs = candidate[-ufunc.nout:] if ufunc.nout else []
+
+        if 'e' in ufunc_inputs:
+            # Skip float16 arrays since we don't have implementation for them
+            continue
+        if 'O' in ufunc_inputs:
+            # Skip object arrays
+            continue
+        found = True
+        # Skip if any input or output argument is mismatching
+        for outer, inner in zip(np_input_types, ufunc_inputs):
+            # (outer is a dtype instance, inner is a type char)
+            if outer.char in 'mM' or inner in 'mM':
+                # For datetime64 and timedelta64, we want to retain
+                # precise typing (i.e. the units); therefore we look for
+                # an exact match.
+                if outer.char != inner:
+                    found = False
+                    break
+            elif not ufunc_can_cast(outer.char, inner,
+                                    has_mixed_inputs, 'safe'):
+                found = False
+                break
+        if found:
+            # Can we cast the inner result to the outer result type?
+            for outer, inner in zip(np_output_types, ufunc_outputs):
+                if (outer.char not in 'mM' and not
+                    ufunc_can_cast(inner, outer.char,
+                                   has_mixed_inputs, 'same_kind')):
+                    found = False
+                    break
+        if found:
+            # Found: determine the Numba types for the loop's inputs and
+            # outputs.
+            try:
+                inputs = choose_types(input_types, ufunc_inputs)
+                outputs = choose_types(output_types, ufunc_outputs)
+                # if the left operand or both are timedeltas, or the first
+                # argument is datetime and the second argument is timedelta,
+                # then the output units need to be determined.
+                if ufunc_inputs[0] == 'm' or ufunc_inputs == 'Mm':
+                    outputs = set_output_dt_units(inputs, outputs,
+                                                  ufunc_inputs, ufunc.__name__)
+
+            except errors.NumbaNotImplementedError:
+                # One of the selected dtypes isn't supported by Numba
+                # (e.g. float16), try other candidates
+                continue
+            else:
+                return UFuncLoopSpec(inputs, outputs, candidate)
+
+    return None
+
+
+def _is_aligned_struct(struct):
+    return struct.isalignedstruct
+
+
+def from_struct_dtype(dtype):
+    """Convert a NumPy structured dtype to Numba Record type
+    """
+    if dtype.hasobject:
+        raise TypeError("Do not support dtype containing object")
+
+    fields = []
+    for name, info in dtype.fields.items():
+        # *info* may have 3 element
+        [elemdtype, offset] = info[:2]
+        title = info[2] if len(info) == 3 else None
+
+        ty = from_dtype(elemdtype)
+        infos = {
+            'type': ty,
+            'offset': offset,
+            'title': title,
+        }
+        fields.append((name, infos))
+
+    # Note: dtype.alignment is not consistent.
+    #       It is different after passing into a recarray.
+    #       recarray(N, dtype=mydtype).dtype.alignment != mydtype.alignment
+    size = dtype.itemsize
+    aligned = _is_aligned_struct(dtype)
+
+    return types.Record(fields, size, aligned)
+
+
+def _get_bytes_buffer(ptr, nbytes):
+    """
+    Get a ctypes array of *nbytes* starting at *ptr*.
+    """
+    if isinstance(ptr, ctypes.c_void_p):
+        ptr = ptr.value
+    arrty = ctypes.c_byte * nbytes
+    return arrty.from_address(ptr)
+
+
+def _get_array_from_ptr(ptr, nbytes, dtype):
+    return np.frombuffer(_get_bytes_buffer(ptr, nbytes), dtype)
+
+
+def carray(ptr, shape, dtype=None):
+    """
+    Return a Numpy array view over the data pointed to by *ptr* with the
+    given *shape*, in C order.  If *dtype* is given, it is used as the
+    array's dtype, otherwise the array's dtype is inferred from *ptr*'s type.
+    """
+    from numba.core.typing.ctypes_utils import from_ctypes
+
+    try:
+        # Use ctypes parameter protocol if available
+        ptr = ptr._as_parameter_
+    except AttributeError:
+        pass
+
+    # Normalize dtype, to accept e.g. "int64" or np.int64
+    if dtype is not None:
+        dtype = np.dtype(dtype)
+
+    if isinstance(ptr, ctypes.c_void_p):
+        if dtype is None:
+            raise TypeError("explicit dtype required for void* argument")
+        p = ptr
+    elif isinstance(ptr, ctypes._Pointer):
+        ptrty = from_ctypes(ptr.__class__)
+        assert isinstance(ptrty, types.CPointer)
+        ptr_dtype = as_dtype(ptrty.dtype)
+        if dtype is not None and dtype != ptr_dtype:
+            raise TypeError("mismatching dtype '%s' for pointer %s"
+                            % (dtype, ptr))
+        dtype = ptr_dtype
+        p = ctypes.cast(ptr, ctypes.c_void_p)
+    else:
+        raise TypeError("expected a ctypes pointer, got %r" % (ptr,))
+
+    nbytes = dtype.itemsize * np.product(shape, dtype=np.intp)
+    return _get_array_from_ptr(p, nbytes, dtype).reshape(shape)
+
+
+def farray(ptr, shape, dtype=None):
+    """
+    Return a Numpy array view over the data pointed to by *ptr* with the
+    given *shape*, in Fortran order.  If *dtype* is given, it is used as the
+    array's dtype, otherwise the array's dtype is inferred from *ptr*'s type.
+    """
+    if not isinstance(shape, int):
+        shape = shape[::-1]
+    return carray(ptr, shape, dtype).T
+
+
+def is_contiguous(dims, strides, itemsize):
+    """Is the given shape, strides, and itemsize of C layout?
+
+    Note: The code is usable as a numba-compiled function
+    """
+    nd = len(dims)
+    # Check and skip 1s or 0s in inner dims
+    innerax = nd - 1
+    while innerax > -1 and dims[innerax] <= 1:
+        innerax -= 1
+
+    # Early exit if all axis are 1s or 0s
+    if innerax < 0:
+        return True
+
+    # Check itemsize matches innermost stride
+    if itemsize != strides[innerax]:
+        return False
+
+    # Check and skip 1s or 0s in outer dims
+    outerax = 0
+    while outerax < innerax and dims[outerax] <= 1:
+        outerax += 1
+
+    # Check remaining strides to be contiguous
+    ax = innerax
+    while ax > outerax:
+        if strides[ax] * dims[ax] != strides[ax - 1]:
+            return False
+        ax -= 1
+    return True
+
+
+def is_fortran(dims, strides, itemsize):
+    """Is the given shape, strides, and itemsize of F layout?
+
+    Note: The code is usable as a numba-compiled function
+    """
+    nd = len(dims)
+    # Check and skip 1s or 0s in inner dims
+    firstax = 0
+    while firstax < nd and dims[firstax] <= 1:
+        firstax += 1
+
+    # Early exit if all axis are 1s or 0s
+    if firstax >= nd:
+        return True
+
+    # Check itemsize matches innermost stride
+    if itemsize != strides[firstax]:
+        return False
+
+    # Check and skip 1s or 0s in outer dims
+    lastax = nd - 1
+    while lastax > firstax and dims[lastax] <= 1:
+        lastax -= 1
+
+    # Check remaining strides to be contiguous
+    ax = firstax
+    while ax < lastax:
+        if strides[ax] * dims[ax] != strides[ax + 1]:
+            return False
+        ax += 1
+    return True
+
+
+def type_can_asarray(arr):
+    """ Returns True if the type of 'arr' is supported by the Numba `np.asarray`
+    implementation, False otherwise.
+    """
+
+    ok = (types.Array, types.Sequence, types.Tuple, types.StringLiteral,
+          types.Number, types.Boolean, types.containers.ListType)
+
+    return isinstance(arr, ok)
+
+
+def check_is_integer(v, name):
+    """Raises TypingError if the value is not an integer."""
+    if not isinstance(v, (int, types.Integer)):
+        raise TypingError('{} must be an integer'.format(name))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/polynomial.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/polynomial.py
new file mode 100644
index 000000000..c501b0408
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/polynomial.py
@@ -0,0 +1,59 @@
+"""
+Implementation of operations involving polynomials.
+"""
+
+
+import numpy as np
+
+from numba import jit
+from numba.core import types
+from numba.core.extending import overload
+from numba.np import numpy_support as np_support
+
+
+@overload(np.roots)
+def roots_impl(p):
+
+    # cast int vectors to float cf. numpy, this is a bit dicey as
+    # the roots could be complex which will fail anyway
+    ty = getattr(p, 'dtype', p)
+    if isinstance(ty, types.Integer):
+        cast_t = np.float64
+    else:
+        cast_t = np_support.as_dtype(ty)
+
+    def roots_impl(p):
+        # impl based on numpy:
+        # https://github.com/numpy/numpy/blob/master/numpy/lib/polynomial.py
+
+        if len(p.shape) != 1:
+            raise ValueError("Input must be a 1d array.")
+
+        non_zero = np.nonzero(p)[0]
+
+        if len(non_zero) == 0:
+            return np.zeros(0, dtype=cast_t)
+
+        tz = len(p) - non_zero[-1] - 1
+
+        # pull out the coeffs selecting between possible zero pads
+        p = p[int(non_zero[0]):int(non_zero[-1]) + 1]
+
+        n = len(p)
+        if n > 1:
+            # construct companion matrix, ensure fortran order
+            # to give to eigvals, write to upper diag and then
+            # transpose.
+            A = np.diag(np.ones((n - 2,), cast_t), 1).T
+            A[0, :] = -p[1:] / p[0]  # normalize
+            roots = np.linalg.eigvals(A)
+        else:
+            roots = np.zeros(0, dtype=cast_t)
+
+        # add in additional zeros on the end if needed
+        if tz > 0:
+            return np.hstack((roots, np.zeros(tz, dtype=cast_t)))
+        else:
+            return roots
+
+    return roots_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/generator_core.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/generator_core.py
new file mode 100644
index 000000000..8509ea498
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/generator_core.py
@@ -0,0 +1,124 @@
+"""
+Core Implementations for Generator/BitGenerator Models.
+"""
+
+from llvmlite import ir
+from numba.core import cgutils, types
+from numba.core.extending import (intrinsic, make_attribute_wrapper, models,
+                                  overload, register_jitable,
+                                  register_model)
+from numba.np.numpy_support import numpy_version
+
+
+@register_model(types.NumPyRandomBitGeneratorType)
+class NumPyRngBitGeneratorModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('parent', types.pyobject),
+            ('state_address', types.uintp),
+            ('state', types.uintp),
+            ('fnptr_next_uint64', types.uintp),
+            ('fnptr_next_uint32', types.uintp),
+            ('fnptr_next_double', types.uintp),
+            ('bit_generator', types.uintp),
+        ]
+        super(NumPyRngBitGeneratorModel, self).__init__(dmm, fe_type, members)
+
+
+_bit_gen_type = types.NumPyRandomBitGeneratorType('bit_generator')
+
+
+@register_model(types.NumPyRandomGeneratorType)
+class NumPyRandomGeneratorTypeModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('bit_generator', _bit_gen_type),
+            ('meminfo', types.MemInfoPointer(types.voidptr)),
+            ('parent', types.pyobject)
+        ]
+        super(
+            NumPyRandomGeneratorTypeModel,
+            self).__init__(
+            dmm,
+            fe_type,
+            members)
+
+
+# The Generator instances have a bit_generator attr
+make_attribute_wrapper(
+    types.NumPyRandomGeneratorType,
+    'bit_generator',
+    'bit_generator')
+
+
+def _generate_next_binding(overloadable_function, return_type):
+    """
+        Generate the overloads for "next_(some type)" functions.
+    """
+    @intrinsic
+    def intrin_NumPyRandomBitGeneratorType_next_ty(tyctx, inst):
+        sig = return_type(inst)
+
+        def codegen(cgctx, builder, sig, llargs):
+            name = overloadable_function.__name__
+            struct_ptr = cgutils.create_struct_proxy(inst)(cgctx, builder,
+                                                           value=llargs[0])
+
+            # Get the 'state' and 'fnptr_next_(type)' members of the struct
+            state = struct_ptr.state
+            next_double_addr = getattr(struct_ptr, f'fnptr_{name}')
+
+            # LLVM IR types needed
+            ll_void_ptr_t = cgctx.get_value_type(types.voidptr)
+            ll_return_t = cgctx.get_value_type(return_type)
+            ll_uintp_t = cgctx.get_value_type(types.uintp)
+
+            # Convert the stored Generator function address to a pointer
+            next_fn_fnptr = builder.inttoptr(
+                next_double_addr, ll_void_ptr_t)
+            # Add the function to the module
+            fnty = ir.FunctionType(ll_return_t, (ll_uintp_t,))
+            next_fn = cgutils.get_or_insert_function(
+                builder.module, fnty, name)
+            # Bit cast the function pointer to the function type
+            fnptr_as_fntype = builder.bitcast(next_fn_fnptr, next_fn.type)
+            # call it with the "state" address as the arg
+            ret = builder.call(fnptr_as_fntype, (state,))
+            return ret
+        return sig, codegen
+
+    @overload(overloadable_function)
+    def ol_next_ty(bitgen):
+        if isinstance(bitgen, types.NumPyRandomBitGeneratorType):
+            def impl(bitgen):
+                return intrin_NumPyRandomBitGeneratorType_next_ty(bitgen)
+            return impl
+
+
+# Some function stubs for "next(some type)", these will be overloaded
+def next_double(bitgen):
+    return bitgen.ctypes.next_double(bitgen.ctypes.state)
+
+
+def next_uint32(bitgen):
+    return bitgen.ctypes.next_uint32(bitgen.ctypes.state)
+
+
+def next_uint64(bitgen):
+    return bitgen.ctypes.next_uint64(bitgen.ctypes.state)
+
+
+_generate_next_binding(next_double, types.double)
+_generate_next_binding(next_uint32, types.uint32)
+_generate_next_binding(next_uint64, types.uint64)
+
+
+# This is for keeping parity with: https://github.com/numpy/numpy/pull/20314
+if numpy_version >= (1, 22):
+    @register_jitable
+    def next_float(bitgen):
+        return (next_uint32(bitgen) >> 8) * (1.0 / 16777216.0)
+else:
+    @register_jitable
+    def next_float(bitgen):
+        return (next_uint32(bitgen) >> 9) * (1.0 / 8388608.0)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/generator_methods.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/generator_methods.py
new file mode 100644
index 000000000..c0ce29210
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/random/generator_methods.py
@@ -0,0 +1,73 @@
+"""
+Implementation of method overloads for Generator objects.
+"""
+
+import numpy as np
+from numba.core import types
+from numba.core.extending import overload_method
+from numba.np.numpy_support import as_dtype, from_dtype
+from numba.np.random.generator_core import next_float, next_double
+from numba.np.numpy_support import is_nonelike
+from numba.core.errors import TypingError
+from numba.core.types.containers import Tuple, UniTuple
+
+
+def _get_proper_func(func_32, func_64, dtype, dist_name="the given"):
+    """
+        Most of the standard NumPy distributions that accept dtype argument
+        only support either np.float32 or np.float64 as dtypes.
+
+        This is a helper function that helps Numba select the proper underlying
+        implementation according to provided dtype.
+    """
+    if isinstance(dtype, types.Omitted):
+        dtype = dtype.value
+
+    if not isinstance(dtype, types.Type):
+        dt = np.dtype(dtype)
+        nb_dt = from_dtype(dt)
+        np_dt = dtype
+    else:
+        nb_dt = dtype
+        np_dt = as_dtype(nb_dt)
+
+    np_dt = np.dtype(np_dt)
+
+    if np_dt == np.float32:
+        next_func = func_32
+    elif np_dt == np.float64:
+        next_func = func_64
+    else:
+        raise TypingError(
+            f"Unsupported dtype {np_dt} for {dist_name} distribution"
+        )
+
+    return next_func, nb_dt
+
+
+# Overload the Generator().random()
+@overload_method(types.NumPyRandomGeneratorType, 'random')
+def NumPyRandomGeneratorType_random(inst, size=None, dtype=np.float64):
+    dist_func, nb_dt = _get_proper_func(next_float, next_double,
+                                        dtype, "random")
+    if isinstance(size, types.Omitted):
+        size = size.value
+
+    if is_nonelike(size):
+        def impl(inst, size=None, dtype=np.float64):
+            return nb_dt(dist_func(inst.bit_generator))
+        return impl
+    else:
+        assert any([isinstance(size, UniTuple) and
+                   isinstance(size.dtype, types.Integer),
+                   isinstance(size, Tuple) and size.count == 0,
+                   isinstance(size, types.Integer)]
+                   ), "Size argument either of None,"\
+            + " an integer, an empty tuple or a tuple of integers"
+
+        def impl(inst, size=None, dtype=np.float64):
+            out = np.empty(size, dtype=dtype)
+            for i in np.ndindex(size):
+                out[i] = dist_func(inst.bit_generator)
+            return out
+        return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/__init__.py
new file mode 100644
index 000000000..14383e17f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/__init__.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+
+from numba.np.ufunc.decorators import Vectorize, GUVectorize, vectorize, guvectorize
+from numba.np.ufunc._internal import PyUFunc_None, PyUFunc_Zero, PyUFunc_One
+from numba.np.ufunc import _internal, array_exprs
+from numba.np.ufunc.parallel import (threading_layer, get_num_threads,
+                                     set_num_threads, get_thread_id,
+                                     set_parallel_chunksize,
+                                     get_parallel_chunksize)
+
+
+if hasattr(_internal, 'PyUFunc_ReorderableNone'):
+    PyUFunc_ReorderableNone = _internal.PyUFunc_ReorderableNone
+del _internal, array_exprs
+
+
+def _init():
+
+    def init_cuda_vectorize():
+        from numba.cuda.vectorizers import CUDAVectorize
+        return CUDAVectorize
+
+    def init_cuda_guvectorize():
+        from numba.cuda.vectorizers import CUDAGUFuncVectorize
+        return CUDAGUFuncVectorize
+
+    Vectorize.target_registry.ondemand['cuda'] = init_cuda_vectorize
+    GUVectorize.target_registry.ondemand['cuda'] = init_cuda_guvectorize
+
+
+_init()
+del _init
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_internal.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_internal.c
new file mode 100644
index 000000000..98a643788
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_internal.c
@@ -0,0 +1,804 @@
+#include "_internal.h"
+#include "Python.h"
+
+/* A small object that handles deallocation of some of a PyUFunc's fields */
+typedef struct {
+    PyObject_HEAD
+    /* Borrowed reference */
+    PyUFuncObject *ufunc;
+    /* Owned reference to ancillary object */
+    PyObject *object;
+} PyUFuncCleaner;
+
+PyTypeObject PyUFuncCleaner_Type;
+
+
+static PyObject *
+cleaner_new(PyUFuncObject *ufunc, PyObject *object)
+{
+    PyUFuncCleaner *obj = PyObject_New(PyUFuncCleaner, &PyUFuncCleaner_Type);
+    if (obj != NULL) {
+        obj->ufunc = ufunc;
+        Py_XINCREF(object);
+        obj->object = object;
+    }
+    return (PyObject *) obj;
+}
+
+/* Deallocate the PyArray_malloc calls */
+static void
+cleaner_dealloc(PyUFuncCleaner *self)
+{
+    PyUFuncObject *ufunc = self->ufunc;
+    Py_XDECREF(self->object);
+    if (ufunc->functions)
+        PyArray_free(ufunc->functions);
+    if (ufunc->types)
+        PyArray_free(ufunc->types);
+    if (ufunc->data)
+        PyArray_free(ufunc->data);
+    PyObject_Del(self);
+}
+
+PyTypeObject PyUFuncCleaner_Type = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "numba._UFuncCleaner",                      /* tp_name*/
+    sizeof(PyUFuncCleaner),                     /* tp_basicsize*/
+    0,                                          /* tp_itemsize */
+    /* methods */
+    (destructor) cleaner_dealloc,               /* tp_dealloc */
+    0,                                          /* tp_print */
+    0,                                          /* tp_getattr */
+    0,                                          /* tp_setattr */
+#if defined(NPY_PY3K)
+    0,                                          /* tp_reserved */
+#else
+    0,                                          /* tp_compare */
+#endif
+    0,                                          /* tp_repr */
+    0,                                          /* tp_as_number */
+    0,                                          /* tp_as_sequence */
+    0,                                          /* tp_as_mapping */
+    0,                                          /* tp_hash */
+    0,                                          /* tp_call */
+    0,                                          /* tp_str */
+    0,                                          /* tp_getattro */
+    0,                                          /* tp_setattro */
+    0,                                          /* tp_as_buffer */
+    Py_TPFLAGS_DEFAULT,                         /* tp_flags */
+    0,                                          /* tp_doc */
+    0,                                          /* tp_traverse */
+    0,                                          /* tp_clear */
+    0,                                          /* tp_richcompare */
+    0,                                          /* tp_weaklistoffset */
+    0,                                          /* tp_iter */
+    0,                                          /* tp_iternext */
+    0,                                          /* tp_methods */
+    0,                                          /* tp_members */
+    0,                                          /* tp_getset */
+    0,                                          /* tp_base */
+    0,                                          /* tp_dict */
+    0,                                          /* tp_descr_get */
+    0,                                          /* tp_descr_set */
+    0,                                          /* tp_dictoffset */
+    0,                                          /* tp_init */
+    0,                                          /* tp_alloc */
+    0,                                          /* tp_new */
+    0,                                          /* tp_free */
+    0,                                          /* tp_is_gc */
+    0,                                          /* tp_bases */
+    0,                                          /* tp_mro */
+    0,                                          /* tp_cache */
+    0,                                          /* tp_subclasses */
+    0,                                          /* tp_weaklist */
+    0,                                          /* tp_del */
+    0,                                          /* tp_version_tag */
+};
+
+/* ______________________________________________________________________
+ * DUFunc: A call-time (hence dynamic) specializable ufunc.
+ */
+
+typedef struct {
+    PyObject_HEAD
+    PyObject      * dispatcher;
+    PyUFuncObject * ufunc;
+    PyObject      * keepalive;
+    int             frozen;
+} PyDUFuncObject;
+
+static void
+dufunc_dealloc(PyDUFuncObject *self)
+{
+    /* Note: There is no need to call PyArray_free() on
+       self->ufunc->ptr, since ufunc_dealloc() will do it for us. */
+    Py_XDECREF(self->ufunc);
+    Py_XDECREF(self->dispatcher);
+    Py_XDECREF(self->keepalive);
+    Py_TYPE(self)->tp_free((PyObject *)self);
+}
+
+static PyObject *
+dufunc_repr(PyDUFuncObject *dufunc)
+{
+    return PyString_FromFormat("<numba._DUFunc '%s'>", dufunc->ufunc->name);
+}
+
+static PyObject *
+dufunc_call(PyDUFuncObject *self, PyObject *args, PyObject *kws)
+{
+    PyObject *result=NULL, *method=NULL;
+
+    result = PyUFunc_Type.tp_call((PyObject *)self->ufunc, args, kws);
+    if ((!self->frozen) &&
+            (result == NULL) &&
+            (PyErr_Occurred()) &&
+            (PyErr_ExceptionMatches(PyExc_TypeError))) {
+
+        /* Break back into Python when we fail at dispatch. */
+        PyErr_Clear();
+        method = PyObject_GetAttrString((PyObject*)self, "_compile_for_args");
+
+        if (method) {
+            result = PyObject_Call(method, args, kws);
+            if (result) {
+                Py_DECREF(result);
+                result = PyUFunc_Type.tp_call((PyObject *)self->ufunc, args,
+                                              kws);
+            }
+        }
+        Py_XDECREF(method);
+    }
+    return result;
+}
+
+static Py_ssize_t
+_get_nin(PyObject * py_func_obj)
+{
+    int result = -1;
+    PyObject *inspect=NULL, *getargspec=NULL, *argspec=NULL, *args=NULL;
+
+    inspect = PyImport_ImportModule("inspect");
+    if (!inspect) goto _get_nin_cleanup;
+    getargspec = PyObject_GetAttrString(inspect, "getfullargspec");
+    if (!getargspec) goto _get_nin_cleanup;
+    argspec = PyObject_CallFunctionObjArgs(getargspec, py_func_obj, NULL);
+    if (!argspec) goto _get_nin_cleanup;
+    args = PyObject_GetAttrString(argspec, "args");
+    if (!args) goto _get_nin_cleanup;
+    result = PyList_Size(args);
+
+  _get_nin_cleanup:
+    Py_XDECREF(args);
+    Py_XDECREF(argspec);
+    Py_XDECREF(getargspec);
+    Py_XDECREF(inspect);
+    return result;
+}
+
+static int
+dufunc_init(PyDUFuncObject *self, PyObject *args, PyObject *kws)
+{
+    PyObject *dispatcher=NULL, *keepalive=NULL, *py_func_obj=NULL, *tmp;
+    PyUFuncObject *ufunc=NULL;
+    int identity=PyUFunc_None;
+    int nin=-1, nout=1, idx;
+    const char *name=NULL, *doc=NULL;
+
+    static char * kwlist[] = {"dispatcher", "identity", "_keepalive", "nin",
+                              "nout", NULL};
+
+    if (!PyArg_ParseTupleAndKeywords(args, kws, "O|iO!nn", kwlist,
+                                     &dispatcher, &identity,
+                                     &PyList_Type, &keepalive, &nin, &nout)) {
+        return -1;
+    }
+
+    py_func_obj = PyObject_GetAttrString(dispatcher, "py_func");
+    if (!py_func_obj) {
+        return -1;
+    }
+
+    if (nin < 0) {
+        nin = (int)_get_nin(py_func_obj);
+        if ((nin < 0) || (PyErr_Occurred())) {
+            Py_XDECREF(py_func_obj);
+            return -1;
+        }
+    }
+
+    /* Construct the UFunc. */
+    tmp = PyObject_GetAttrString(py_func_obj, "__name__");
+    if (tmp) {
+        name = PyString_AsString(tmp);
+    }
+    Py_XDECREF(tmp);
+    tmp = PyObject_GetAttrString(py_func_obj, "__doc__");
+    if (tmp && (tmp != Py_None)) {
+        doc = PyString_AsString(tmp);
+    }
+    Py_XDECREF(tmp);
+    tmp = NULL;
+    Py_XDECREF(py_func_obj);
+    py_func_obj = NULL;
+    if (!name) {
+        return -1;
+    }
+    ufunc = (PyUFuncObject *)PyUFunc_FromFuncAndData(NULL, NULL, NULL, 0,
+                                                     nin, nout, identity,
+                                                     name, doc, 0);
+    if (!ufunc) {
+        return -1;
+    }
+
+    for (idx = 0; idx < nout; idx++) {
+        ufunc->op_flags[idx + nin] |= NPY_ITER_READWRITE | NPY_ITER_UPDATEIFCOPY | NPY_ITER_ALLOCATE;
+    }
+
+    /* Construct a keepalive list if none was given. */
+    if (!keepalive) {
+        keepalive = PyList_New(0);
+        if (!keepalive) {
+            Py_XDECREF(ufunc);
+            return -1;
+        }
+    } else {
+        Py_INCREF(keepalive);
+    }
+
+    tmp = self->dispatcher;
+    Py_INCREF(dispatcher);
+    self->dispatcher = dispatcher;
+    Py_XDECREF(tmp);
+
+    tmp = (PyObject*)self->ufunc;
+    self->ufunc = ufunc;
+    Py_XDECREF(tmp);
+
+    tmp = self->keepalive;
+    /* Already incref'ed, either by PyList_New(), or else clause, both above. */
+    self->keepalive = keepalive;
+    Py_XDECREF(tmp);
+
+    self->frozen = 0;
+
+    return 0;
+}
+
+static PyMemberDef dufunc_members[] = {
+    {"_dispatcher", T_OBJECT_EX, offsetof(PyDUFuncObject, dispatcher), 0,
+         "Dispatcher object for the core Python function."},
+    {"ufunc", T_OBJECT_EX, offsetof(PyDUFuncObject, ufunc), 0,
+         "Numpy Ufunc for the dynamic ufunc."},
+    {"_keepalive", T_OBJECT_EX, offsetof(PyDUFuncObject, keepalive), 0,
+         "List of objects to keep alive during life of dufunc."},
+    {NULL}
+};
+
+/* ____________________________________________________________
+ * Shims to expose ufunc methods.
+ */
+
+static struct _ufunc_dispatch {
+    /* Note that the following may also hold `_PyCFunctionFastWithKeywords` */
+    PyCFunctionWithKeywords ufunc_reduce;
+    PyCFunctionWithKeywords ufunc_accumulate;
+    PyCFunctionWithKeywords ufunc_reduceat;
+    PyCFunctionWithKeywords ufunc_outer;
+#if NPY_API_VERSION >= 0x00000008
+    PyCFunction ufunc_at;
+#endif
+} ufunc_dispatch;
+
+static int
+init_ufunc_dispatch(int *numpy_uses_fastcall)
+{
+    int result = 0;
+    PyMethodDef * crnt = PyUFunc_Type.tp_methods;
+    const char * crnt_name = NULL;
+    for (; crnt->ml_name != NULL; crnt++) {
+        crnt_name = crnt->ml_name;
+        switch(crnt_name[0]) {
+        case 'a':
+            if (strncmp(crnt_name, "accumulate", 11) == 0) {
+                ufunc_dispatch.ufunc_accumulate =
+                    (PyCFunctionWithKeywords)crnt->ml_meth;
+#if NPY_API_VERSION >= 0x00000008
+            } else if (strncmp(crnt_name, "at", 3) == 0) {
+                ufunc_dispatch.ufunc_at = crnt->ml_meth;
+#endif
+            } else {
+                result = -1;
+            }
+            break;
+        case 'o':
+            if (strncmp(crnt_name, "outer", 6) == 0) {
+                ufunc_dispatch.ufunc_outer =
+                    (PyCFunctionWithKeywords)crnt->ml_meth;
+            } else {
+                result = -1;
+            }
+            break;
+        case 'r':
+            if (strncmp(crnt_name, "reduce", 7) == 0) {
+                ufunc_dispatch.ufunc_reduce =
+                    (PyCFunctionWithKeywords)crnt->ml_meth;
+            } else if (strncmp(crnt_name, "reduceat", 9) == 0) {
+                ufunc_dispatch.ufunc_reduceat =
+                    (PyCFunctionWithKeywords)crnt->ml_meth;
+            } else {
+                result = -1;
+            }
+            break;
+        default:
+            result = -1; /* Unknown method */
+        }
+        if (result < 0) break;
+
+        /* Check whether NumPy uses fastcall (ufunc.at never uses it) */
+        if (strncmp(crnt_name, "at", 3) != 0) {
+            if (*numpy_uses_fastcall == -1) {
+                *numpy_uses_fastcall = crnt->ml_flags & METH_FASTCALL;
+            }
+            else if (*numpy_uses_fastcall != (crnt->ml_flags & METH_FASTCALL)) {
+                return -1;
+            }
+        }
+    }
+    if (result == 0) {
+        /* Sanity check. */
+        result = ((ufunc_dispatch.ufunc_reduce != NULL)
+                  && (ufunc_dispatch.ufunc_accumulate != NULL)
+                  && (ufunc_dispatch.ufunc_reduceat != NULL)
+                  && (ufunc_dispatch.ufunc_outer != NULL)
+#if NPY_API_VERSION >= 0x00000008
+                  && (ufunc_dispatch.ufunc_at != NULL)
+#endif
+                  );
+    }
+    return result;
+}
+
+
+static PyObject *
+dufunc_reduce(PyDUFuncObject * self, PyObject * args, PyObject *kws)
+{
+    return ufunc_dispatch.ufunc_reduce((PyObject*)self->ufunc, args, kws);
+}
+
+static PyObject *
+dufunc_accumulate(PyDUFuncObject * self, PyObject * args, PyObject *kws)
+{
+    return ufunc_dispatch.ufunc_accumulate((PyObject*)self->ufunc, args, kws);
+}
+
+static PyObject *
+dufunc_reduceat(PyDUFuncObject * self, PyObject * args, PyObject *kws)
+{
+    return ufunc_dispatch.ufunc_reduceat((PyObject*)self->ufunc, args, kws);
+}
+
+static PyObject *
+dufunc_outer(PyDUFuncObject * self, PyObject * args, PyObject *kws)
+{
+    return ufunc_dispatch.ufunc_outer((PyObject*)self->ufunc, args, kws);
+}
+
+
+/*
+ * The following are the vectorcall versions of the above, since NumPy
+ * uses the FASTCALL/Vectorcall protocol starting with version 1.21.
+ * The only NumPy versions supporting vectorcall use Python 3.7 or higher.
+ */
+static PyObject *
+dufunc_reduce_fast(PyDUFuncObject * self,
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+    return ((_PyCFunctionFastWithKeywords)ufunc_dispatch.ufunc_reduce)(
+            (PyObject*)self->ufunc, args, len_args, kwnames);
+}
+
+static PyObject *
+dufunc_reduceat_fast(PyDUFuncObject * self,
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+    return ((_PyCFunctionFastWithKeywords)ufunc_dispatch.ufunc_reduceat)(
+            (PyObject*)self->ufunc, args, len_args, kwnames);
+}
+
+
+static PyObject *
+dufunc_accumulate_fast(PyDUFuncObject * self,
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+    return ((_PyCFunctionFastWithKeywords)ufunc_dispatch.ufunc_accumulate)(
+            (PyObject*)self->ufunc, args, len_args, kwnames);
+}
+
+
+static PyObject *
+dufunc_outer_fast(PyDUFuncObject * self,
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+    return ((_PyCFunctionFastWithKeywords)ufunc_dispatch.ufunc_outer)(
+            (PyObject*)self->ufunc, args, len_args, kwnames);
+}
+
+
+#if NPY_API_VERSION >= 0x00000008
+static PyObject *
+dufunc_at(PyDUFuncObject * self, PyObject * args)
+{
+    return ufunc_dispatch.ufunc_at((PyObject*)self->ufunc, args);
+}
+#endif
+
+static PyObject *
+dufunc__compile_for_args(PyDUFuncObject * self, PyObject * args,
+                        PyObject * kws)
+{
+    PyErr_SetString(PyExc_NotImplementedError,
+                    "Abstract method _DUFunc._compile_for_args() called!");
+    return NULL;
+}
+
+static int *
+_build_arg_types_array(PyObject * type_list, Py_ssize_t nargs)
+{
+    int *arg_types_array=NULL;
+    Py_ssize_t idx, arg_types_size = PyList_Size(type_list);
+
+    if (arg_types_size != nargs) {
+        PyErr_SetString(
+            PyExc_ValueError,
+            "argument type list size does not equal ufunc argument count");
+        return NULL;
+    }
+    arg_types_array = PyArray_malloc(sizeof(int) * nargs);
+    if (!arg_types_array) {
+        PyErr_NoMemory();
+        return NULL;
+    }
+    for (idx = 0; idx < nargs; idx++) {
+        arg_types_array[idx] = (int)PyLong_AsLong(PyList_GET_ITEM(type_list,
+                                                                  idx));
+    }
+    if (PyErr_Occurred()) {
+        PyArray_free(arg_types_array);
+        arg_types_array = NULL;
+    }
+    return arg_types_array;
+}
+
+static PyObject *
+dufunc__add_loop(PyDUFuncObject * self, PyObject * args)
+{
+    PyUFuncObject * ufunc=self->ufunc;
+    void *loop_ptr=NULL, *data_ptr=NULL;
+    int idx=-1, usertype=NPY_VOID;
+    int *arg_types_arr=NULL;
+    PyObject *arg_types=NULL, *loop_obj=NULL, *data_obj=NULL;
+    PyUFuncGenericFunction old_func=NULL;
+
+    if (self->frozen) {
+        PyErr_SetString(PyExc_ValueError,
+                        "_DUFunc._add_loop() called for frozen dufunc");
+        return NULL;
+    }
+
+    if (!PyArg_ParseTuple(args, "O!O!|O!",
+                          &PyLong_Type, &loop_obj, &PyList_Type, &arg_types,
+                          &PyLong_Type, &data_obj)) {
+        return NULL;
+    }
+
+    loop_ptr = PyLong_AsVoidPtr(loop_obj);
+    if (PyErr_Occurred()) {
+        return NULL;
+    }
+    if (data_obj) {
+        data_ptr = PyLong_AsVoidPtr(data_obj);
+        if (PyErr_Occurred()) {
+            return NULL;
+        }
+    }
+
+    arg_types_arr = _build_arg_types_array(arg_types, (Py_ssize_t)ufunc->nargs);
+    if (!arg_types_arr) goto _dufunc__add_loop_fail;
+
+    /* Check to see if any of the input types are user defined dtypes.
+       If they are, we should use PyUFunc_RegisterLoopForType() since
+       dispatch on a user defined dtype uses a Python dictionary
+       keyed by usertype (and not the functions array).
+
+       For more information, see how the usertype argument is used in
+       PyUFunc_RegisterLoopForType(), defined by Numpy at
+       .../numpy/core/src/umath/ufunc_object.c
+    */
+    for (idx = 0; idx < ufunc->nargs; idx++) {
+        if (arg_types_arr[idx] >= NPY_USERDEF) {
+            usertype = arg_types_arr[idx];
+        }
+    }
+
+    if (usertype != NPY_VOID) {
+        if (PyUFunc_RegisterLoopForType(ufunc, usertype,
+                                        (PyUFuncGenericFunction)loop_ptr,
+                                        arg_types_arr, data_ptr) < 0) {
+            goto _dufunc__add_loop_fail;
+        }
+    } else if (PyUFunc_ReplaceLoopBySignature(ufunc,
+                                              (PyUFuncGenericFunction)loop_ptr,
+                                              arg_types_arr, &old_func) == 0) {
+        /* TODO: Consider freeing any memory held by the old loop (somehow) */
+        for (idx = 0; idx < ufunc->ntypes; idx++) {
+            if (ufunc->functions[idx] == (PyUFuncGenericFunction)loop_ptr) {
+                ufunc->data[idx] = data_ptr;
+                break;
+            }
+        }
+    } else {
+        /* The following is an attempt to loosely follow the allocation
+           code in Numpy.  See ufunc_frompyfunc() in
+           .../numpy/core/src/umath/umathmodule.c.
+
+           The primary goal is to allocate a single chunk of memory to
+           hold the functions, data, and types loop arrays:
+
+           ptr == |<- functions ->|<- data ->|<- types ->|
+
+        */
+        int ntypes=ufunc->ntypes + 1;
+        PyUFuncGenericFunction *functions=NULL;
+        void **data=NULL;
+        char *types=NULL;
+        void *newptr=NULL, *oldptr=NULL;
+        size_t functions_size=sizeof(PyUFuncGenericFunction) * ntypes;
+        size_t data_size=sizeof(void *) * ntypes;
+        size_t type_ofs=sizeof(char) * ufunc->ntypes * ufunc->nargs;
+        size_t newsize=(functions_size + data_size +
+                        (sizeof(char) * ntypes * ufunc->nargs));
+
+        oldptr = ufunc->ptr;
+        newptr = PyArray_malloc(newsize);
+        if (!newptr) {
+            PyErr_NoMemory();
+            goto _dufunc__add_loop_fail;
+        }
+        functions = (PyUFuncGenericFunction*)newptr;
+        memcpy(functions, ufunc->functions,
+               sizeof(PyUFuncGenericFunction) * ufunc->ntypes);
+        functions[ntypes - 1] = (PyUFuncGenericFunction)loop_ptr;
+        data = (void **)((char *)functions + functions_size);
+        memcpy(data, ufunc->data, sizeof(void *) * ufunc->ntypes);
+        data[ntypes - 1] = data_ptr;
+        types = (char *)data + data_size;
+        memcpy(types, ufunc->types, sizeof(char) * ufunc->ntypes *
+               ufunc->nargs);
+        for (idx = 0; idx < ufunc->nargs; idx++) {
+            types[idx + type_ofs] = (char)arg_types_arr[idx];
+        }
+
+        ufunc->ntypes = ntypes;
+        ufunc->functions = functions;
+        ufunc->types = types;
+        ufunc->data = data;
+        ufunc->ptr = newptr;
+        PyArray_free(oldptr);
+    }
+
+    PyArray_free(arg_types_arr);
+    Py_INCREF(Py_None);
+    return Py_None;
+
+ _dufunc__add_loop_fail:
+    PyArray_free(arg_types_arr);
+    return NULL;
+}
+
+static struct PyMethodDef dufunc_methods[] = {
+    {"reduce",
+        (PyCFunction)dufunc_reduce,
+        METH_VARARGS | METH_KEYWORDS, NULL },
+    {"accumulate",
+        (PyCFunction)dufunc_accumulate,
+        METH_VARARGS | METH_KEYWORDS, NULL },
+    {"reduceat",
+        (PyCFunction)dufunc_reduceat,
+        METH_VARARGS | METH_KEYWORDS, NULL },
+    {"outer",
+        (PyCFunction)dufunc_outer,
+        METH_VARARGS | METH_KEYWORDS, NULL},
+#if NPY_API_VERSION >= 0x00000008
+    {"at",
+        (PyCFunction)dufunc_at,
+        METH_VARARGS, NULL},
+#endif
+    {"_compile_for_args",
+        (PyCFunction)dufunc__compile_for_args,
+        METH_VARARGS | METH_KEYWORDS,
+        "Abstract method: subclasses should overload _compile_for_args() to compile the ufunc at the given arguments' types."},
+    {"_add_loop",
+        (PyCFunction)dufunc__add_loop,
+        METH_VARARGS,
+        NULL},
+    {NULL, NULL, 0, NULL}           /* sentinel */
+};
+
+
+/*
+ * If Python is new enough, NumPy may use fastcall.  In that case we have to
+ * also use fastcall for simplicity and speed.
+ */
+static struct PyMethodDef dufunc_methods_fast[] = {
+    {"reduce",
+        (PyCFunction)dufunc_reduce_fast,
+        METH_FASTCALL | METH_KEYWORDS, NULL },
+    {"accumulate",
+        (PyCFunction)dufunc_accumulate_fast,
+        METH_FASTCALL | METH_KEYWORDS, NULL },
+    {"reduceat",
+        (PyCFunction)dufunc_reduceat_fast,
+        METH_FASTCALL | METH_KEYWORDS, NULL },
+    {"outer",
+        (PyCFunction)dufunc_outer_fast,
+        METH_FASTCALL | METH_KEYWORDS, NULL},
+#if NPY_API_VERSION >= 0x00000008
+    {"at",
+        (PyCFunction)dufunc_at,
+        METH_VARARGS, NULL},
+#endif
+    {"_compile_for_args",
+        (PyCFunction)dufunc__compile_for_args,
+        METH_VARARGS | METH_KEYWORDS,
+        "Abstract method: subclasses should overload _compile_for_args() to compile the ufunc at the given arguments' types."},
+    {"_add_loop",
+        (PyCFunction)dufunc__add_loop,
+        METH_VARARGS,
+        NULL},
+    {NULL, NULL, 0, NULL}           /* sentinel */
+};
+
+
+static PyObject *
+dufunc_getfrozen(PyDUFuncObject * self, void * closure)
+{
+    PyObject *result=(self->frozen) ? Py_True : Py_False;
+    Py_INCREF(result);
+    return result;
+}
+
+static int
+dufunc_setfrozen(PyDUFuncObject * self, PyObject * value, void * closure)
+{
+    int result=0;
+    if (PyObject_IsTrue(value)) {
+        self->frozen = 1;
+    } else {
+        PyErr_SetString(PyExc_ValueError,
+                        "cannot clear the _DUFunc.frozen flag");
+        result = -1;
+    }
+    return result;
+}
+
+static PyGetSetDef dufunc_getsets[] = {
+    {"_frozen",
+     (getter)dufunc_getfrozen, (setter)dufunc_setfrozen,
+     "flag indicating call-time compilation has been disabled",
+     NULL},
+    {NULL}  /* Sentinel */
+};
+
+PyTypeObject PyDUFunc_Type = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    "numba._DUFunc",                            /* tp_name*/
+    sizeof(PyDUFuncObject),                     /* tp_basicsize*/
+    0,                                          /* tp_itemsize */
+    /* methods */
+    (destructor) dufunc_dealloc,                /* tp_dealloc */
+    0,                                          /* tp_print */
+    0,                                          /* tp_getattr */
+    0,                                          /* tp_setattr */
+    0,                                          /* tp_compare/tp_reserved */
+    (reprfunc) dufunc_repr,                     /* tp_repr */
+    0,                                          /* tp_as_number */
+    0,                                          /* tp_as_sequence */
+    0,                                          /* tp_as_mapping */
+    0,                                          /* tp_hash */
+    (ternaryfunc) dufunc_call,                  /* tp_call */
+    (reprfunc) dufunc_repr,                     /* tp_str */
+    0,                                          /* tp_getattro */
+    0,                                          /* tp_setattro */
+    0,                                          /* tp_as_buffer */
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE,   /* tp_flags */
+    0,                                          /* tp_doc */
+    0,                                          /* tp_traverse */
+    0,                                          /* tp_clear */
+    0,                                          /* tp_richcompare */
+    0,                                          /* tp_weaklistoffset */
+    0,                                          /* tp_iter */
+    0,                                          /* tp_iternext */
+    dufunc_methods,                             /* tp_methods */
+    dufunc_members,                             /* tp_members */
+    dufunc_getsets,                             /* tp_getset */
+    0,                                          /* tp_base */
+    0,                                          /* tp_dict */
+    0,                                          /* tp_descr_get */
+    0,                                          /* tp_descr_set */
+    0,                                          /* tp_dictoffset */
+    (initproc) dufunc_init,                     /* tp_init */
+    0,                                          /* tp_alloc */
+    0,                                          /* tp_new */
+    0,                                          /* tp_free */
+    0,                                          /* tp_is_gc */
+    0,                                          /* tp_bases */
+    0,                                          /* tp_mro */
+    0,                                          /* tp_cache */
+    0,                                          /* tp_subclasses */
+    0,                                          /* tp_weaklist */
+    0,                                          /* tp_del */
+    0,                                          /* tp_version_tag */
+};
+
+/* ______________________________________________________________________
+ * Module initialization boilerplate follows.
+ */
+
+static PyMethodDef ext_methods[] = {
+    {"fromfunc", (PyCFunction) ufunc_fromfunc, METH_VARARGS, NULL},
+    { NULL }
+};
+
+/* Don't remove this marker, it is used for inserting licensing code */
+/*MARK1*/
+
+MOD_INIT(_internal)
+{
+    PyObject *m;
+
+    /* Don't remove this marker, it is used for inserting licensing code */
+    /*MARK2*/
+
+    import_array();
+    import_umath();
+
+    MOD_DEF(m, "_internal", "No docs",
+            ext_methods)
+
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    if (PyType_Ready(&PyUFuncCleaner_Type) < 0)
+        return MOD_ERROR_VAL;
+
+    PyDUFunc_Type.tp_new = PyType_GenericNew;
+
+    int numpy_uses_fastcall = -1;
+    if (init_ufunc_dispatch(&numpy_uses_fastcall) <= 0)
+        return MOD_ERROR_VAL;
+
+    if (numpy_uses_fastcall) {
+        PyDUFunc_Type.tp_methods = dufunc_methods_fast;
+    }
+
+    if (PyType_Ready(&PyDUFunc_Type) < 0)
+        return MOD_ERROR_VAL;
+    Py_INCREF(&PyDUFunc_Type);
+    if (PyModule_AddObject(m, "_DUFunc", (PyObject *)&PyDUFunc_Type) < 0)
+        return MOD_ERROR_VAL;
+
+    if (PyModule_AddIntMacro(m, PyUFunc_One)
+        || PyModule_AddIntMacro(m, PyUFunc_Zero)
+        || PyModule_AddIntMacro(m, PyUFunc_None)
+#if NPY_API_VERSION >= 0x00000007
+        || PyModule_AddIntMacro(m, PyUFunc_ReorderableNone)
+#endif
+        )
+        return MOD_ERROR_VAL;
+
+    return MOD_SUCCESS_VAL(m);
+}
+
+
+#include "_ufunc.c"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_internal.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_internal.h
new file mode 100644
index 000000000..2abb35873
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_internal.h
@@ -0,0 +1,27 @@
+/* Python include */
+
+#ifndef NUMBA_UFUNC_INTERNAL_H_
+#define NUMBA_UFUNC_INTERNAL_H_
+
+#include "../../_pymodule.h"
+#include <structmember.h>
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+#include "numpy/ndarrayobject.h"
+#include "numpy/ufuncobject.h"
+
+extern PyObject *ufunc_fromfunc(PyObject *NPY_UNUSED(dummy), PyObject *args);
+
+int PyUFunc_GeneralizedFunction(PyUFuncObject *ufunc,
+                                PyObject *args, PyObject *kwds,
+                                PyArrayObject **op);
+
+#define APPEND_(X, Y) X #Y
+#define APPEND(X, Y) APPEND_(X, Y)
+#define SENTRY_VALID_LONG(X) if( (X) == -1 ){                        \
+    PyErr_SetString(PyExc_RuntimeError,                              \
+                    APPEND("PyLong_AsLong overflow at ", __LINE__)); \
+    return NULL;                                                     \
+}
+
+#endif  /* NUMBA_UFUNC_INTERNAL_H_ */
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_num_threads.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_num_threads.c
new file mode 100644
index 000000000..ff671517d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_num_threads.c
@@ -0,0 +1,36 @@
+// Thread local num_threads variable for masking out the total number of
+// launched threads.
+
+#include "../../_pymodule.h"
+
+#ifdef _MSC_VER
+#define THREAD_LOCAL(ty) __declspec(thread) ty
+#else
+/* Non-standard C99 extension that's understood by gcc and clang */
+#define THREAD_LOCAL(ty) __thread ty
+#endif
+
+static THREAD_LOCAL(int) num_threads = 0;
+
+static void set_num_threads(int count)
+{
+    num_threads = count;
+}
+
+static int get_num_threads(void)
+{
+    return num_threads;
+}
+
+MOD_INIT(_num_threads)
+{
+    PyObject *m;
+    MOD_DEF(m, "_num_threads", "No docs", NULL)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    SetAttrStringFromVoidPointer(m, set_num_threads);
+    SetAttrStringFromVoidPointer(m, get_num_threads);
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_ufunc.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_ufunc.c
new file mode 100644
index 000000000..c820c6352
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/_ufunc.c
@@ -0,0 +1,222 @@
+
+/* Included by _internal.c */
+#include "_internal.h"
+
+static int
+get_string(PyObject *obj, const char **s, const char *type_error_message)
+{
+    if (!PyString_Check(obj) && obj != Py_None) {
+        PyErr_SetString(PyExc_TypeError, type_error_message);
+        return -1;
+    }
+    *s = NULL;
+    if (obj != Py_None) {
+        *s = PyString_AsString(obj);
+        if (*s == NULL)
+            return -1;
+    }
+    return 0;
+}
+
+
+PyObject *
+ufunc_fromfunc(PyObject *NPY_UNUSED(dummy), PyObject *args)
+{
+    int nin, nout;
+    int nfuncs, ntypes, ndata;
+    PyObject *func_list;
+    PyObject *type_list;
+    PyObject *data_list;
+    PyObject *func_obj;
+    PyObject *type_obj;
+    PyObject *data_obj;
+    PyObject *object; /* object to hold on to while ufunc is alive */
+    PyObject *pyname, *pydoc;
+    const char *name = NULL, *doc = NULL;
+    char *signature = NULL;
+    int identity;
+
+    int i, j, idx;
+    int custom_dtype = 0;
+    PyUFuncGenericFunction *funcs;
+    int *types;
+    void **data;
+    PyUFuncObject *ufunc;
+
+    if (!PyArg_ParseTuple(args, "OOO!O!iiOOi|s",
+                          &pyname, &pydoc,
+                          &PyList_Type, &func_list,
+                          &PyList_Type, &type_list,
+                          &nin, &nout, &data_list,
+                          &object, &identity, &signature)) {
+        return NULL;
+    }
+    if (get_string(pyname, &name, "name should be str or None"))
+        return NULL;
+    if (get_string(pydoc, &doc, "doc should be str or None"))
+        return NULL;
+    /* Ensure the pointers to C strings stay alive until the ufunc dies. */
+    object = PyTuple_Pack(3, object, pyname, pydoc);
+    if (!object)
+        return NULL;
+
+    nfuncs = PyList_Size(func_list);
+
+    ntypes = PyList_Size(type_list);
+    if (ntypes != nfuncs) {
+        PyErr_SetString(PyExc_TypeError, "length of types list must be same as length of function pointer list");
+        return NULL;
+    }
+
+    ndata = PyList_Size(data_list);
+    if (ndata != nfuncs) {
+        PyErr_SetString(PyExc_TypeError, "length of data pointer list must be same as length of function pointer list");
+        return NULL;
+    }
+
+    funcs = PyArray_malloc(nfuncs * sizeof(PyUFuncGenericFunction));
+    if (funcs == NULL) {
+        return NULL;
+    }
+
+    /* build function pointer array */
+    for (i = 0; i < nfuncs; i++) {
+        func_obj = PyList_GetItem(func_list, i);
+        /* Function pointers are passed in as long objects.
+           Is there a better way to do this? */
+        if (PyLong_Check(func_obj)) {
+            funcs[i] = (PyUFuncGenericFunction)PyLong_AsVoidPtr(func_obj);
+        }
+        else {
+            PyErr_SetString(PyExc_TypeError, "function pointer must be long object, or None");
+            return NULL;
+        }
+    }
+
+    types = PyArray_malloc(nfuncs * (nin+nout) * sizeof(int));
+    if (types == NULL) {
+        return NULL;
+    }
+
+    /* build function signatures array */
+    for (i = 0; i < nfuncs; i++) {
+        type_obj = PyList_GetItem(type_list, i);
+        if (!type_obj)
+            return NULL;
+
+        for (j = 0; j < (nin+nout); j++) {
+            int dtype_num;
+            PyObject *dtype_num_obj = PyList_GetItem(type_obj, j);
+            if (!dtype_num_obj)
+                return NULL;
+
+            SENTRY_VALID_LONG(
+                types[i*(nin+nout) + j] = PyLong_AsLong(dtype_num_obj)
+            );
+
+            dtype_num = PyLong_AsLong(PyList_GetItem(type_obj, j));
+
+            SENTRY_VALID_LONG(dtype_num);
+
+            if (dtype_num >= NPY_USERDEF) {
+                custom_dtype = dtype_num;
+            }
+        }
+    }
+
+    data = PyArray_malloc(nfuncs * sizeof(void *));
+    if (data == NULL) {
+        return NULL;
+    }
+
+    /* build function data pointers array */
+    for (i = 0; i < nfuncs; i++) {
+        if (PyList_Check(data_list)) {
+            data_obj = PyList_GetItem(data_list, i);
+            if (PyLong_Check(data_obj)) {
+                data[i] = PyLong_AsVoidPtr(data_obj);
+            }
+            else if (data_obj == Py_None) {
+                data[i] = NULL;
+            }
+            else {
+                PyErr_SetString(PyExc_TypeError, "data pointer must be long object, or None");
+                return NULL;
+            }
+        }
+        else if (data_list == Py_None) {
+            data[i] = NULL;
+        }
+        else {
+            PyErr_SetString(PyExc_TypeError, "data pointers argument must be a list of void pointers, or None");
+            return NULL;
+        }
+    }
+
+    if (!custom_dtype) {
+        char *char_types = PyArray_malloc(nfuncs * (nin+nout) * sizeof(char));
+        for (i = 0; i < nfuncs; i++) {
+            for (j = 0; j < (nin+nout); j++) {
+                char_types[i*(nin+nout) + j] = (char)types[i*(nin+nout) + j];
+            }
+        }
+        PyArray_free(types);
+        ufunc = (PyUFuncObject *) PyUFunc_FromFuncAndDataAndSignature(
+            (PyUFuncGenericFunction*) funcs, data, (char*) char_types,
+            nfuncs, nin, nout,
+            identity,
+            name, doc,
+            0 /* check_return */, signature);
+        if (!ufunc) {
+            PyArray_free(funcs);
+            PyArray_free(data);
+            Py_DECREF(object);
+            return NULL;
+        }
+        /* XXX funcs, char_types and data won't be free'ed when the ufunc dies */
+    }
+    else {
+        ufunc = (PyUFuncObject *) PyUFunc_FromFuncAndDataAndSignature(
+            0, 0, 0, 0,
+            nin,
+            nout,
+            identity,
+            name, doc,
+            0 /* check_return */, signature);
+        if (!ufunc) {
+            PyArray_free(funcs);
+            PyArray_free(data);
+            PyArray_free(types);
+            Py_DECREF(object);
+            return NULL;
+        }
+
+        PyUFunc_RegisterLoopForType(ufunc,
+                                    custom_dtype,
+                                    funcs[0],
+                                    types,
+                                    0);
+        PyArray_free(funcs);
+        PyArray_free(types);
+        PyArray_free(data);
+        funcs = NULL;
+        data = NULL;
+    }
+
+    for (idx = 0; idx < nout; idx++) {
+        ufunc->op_flags[idx + nin] |= NPY_ITER_READWRITE | NPY_ITER_UPDATEIFCOPY | NPY_ITER_ALLOCATE;
+    }
+
+    /* Create the sentinel object to clean up dynamically-allocated fields
+       when the ufunc is destroyed. */
+    ufunc->obj = cleaner_new(ufunc, object);
+    Py_DECREF(object);
+    if (ufunc->obj == NULL) {
+        PyArray_free(funcs);
+        PyArray_free(data);
+        Py_DECREF(ufunc);
+        return NULL;
+    }
+
+    return (PyObject *) ufunc;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/array_exprs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/array_exprs.py
new file mode 100644
index 000000000..bb8815ddc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/array_exprs.py
@@ -0,0 +1,406 @@
+import ast
+from collections import defaultdict, OrderedDict
+import contextlib
+import sys
+from types import SimpleNamespace
+
+import numpy as np
+import operator
+
+from numba.core import types, targetconfig, ir, rewrites, compiler
+from numba.core.typing import npydecl
+from numba.np.ufunc.dufunc import DUFunc
+
+
+def _is_ufunc(func):
+    return isinstance(func, (np.ufunc, DUFunc))
+
+
+@rewrites.register_rewrite('after-inference')
+class RewriteArrayExprs(rewrites.Rewrite):
+    '''The RewriteArrayExprs class is responsible for finding array
+    expressions in Numba intermediate representation code, and
+    rewriting those expressions to a single operation that will expand
+    into something similar to a ufunc call.
+    '''
+    def __init__(self, state, *args, **kws):
+        super(RewriteArrayExprs, self).__init__(state, *args, **kws)
+        # Install a lowering hook if we are using this rewrite.
+        special_ops = state.targetctx.special_ops
+        if 'arrayexpr' not in special_ops:
+            special_ops['arrayexpr'] = _lower_array_expr
+
+    def match(self, func_ir, block, typemap, calltypes):
+        """
+        Using typing and a basic block, search the basic block for array
+        expressions.
+        Return True when one or more matches were found, False otherwise.
+        """
+        # We can trivially reject everything if there are no
+        # calls in the type results.
+        if len(calltypes) == 0:
+            return False
+
+        self.crnt_block = block
+        self.typemap = typemap
+        # { variable name: IR assignment (of a function call or operator) }
+        self.array_assigns = OrderedDict()
+        # { variable name: IR assignment (of a constant) }
+        self.const_assigns = {}
+
+        assignments = block.find_insts(ir.Assign)
+        for instr in assignments:
+            target_name = instr.target.name
+            expr = instr.value
+            # Does it assign an expression to an array variable?
+            if (isinstance(expr, ir.Expr) and
+                isinstance(typemap.get(target_name, None), types.Array)):
+                self._match_array_expr(instr, expr, target_name)
+            elif isinstance(expr, ir.Const):
+                # Track constants since we might need them for an
+                # array expression.
+                self.const_assigns[target_name] = expr
+
+        return len(self.array_assigns) > 0
+
+    def _match_array_expr(self, instr, expr, target_name):
+        """
+        Find whether the given assignment (*instr*) of an expression (*expr*)
+        to variable *target_name* is an array expression.
+        """
+        # We've matched a subexpression assignment to an
+        # array variable.  Now see if the expression is an
+        # array expression.
+        expr_op = expr.op
+        array_assigns = self.array_assigns
+
+        if ((expr_op in ('unary', 'binop')) and (
+                expr.fn in npydecl.supported_array_operators)):
+            # It is an array operator that maps to a ufunc.
+            # check that all args have internal types
+            if all(self.typemap[var.name].is_internal
+                   for var in expr.list_vars()):
+                array_assigns[target_name] = instr
+
+        elif ((expr_op == 'call') and (expr.func.name in self.typemap)):
+            # It could be a match for a known ufunc call.
+            func_type = self.typemap[expr.func.name]
+            if isinstance(func_type, types.Function):
+                func_key = func_type.typing_key
+                if _is_ufunc(func_key):
+                    # If so, check whether an explicit output is passed.
+                    if not self._has_explicit_output(expr, func_key):
+                        # If not, match it as a (sub)expression.
+                        array_assigns[target_name] = instr
+
+    def _has_explicit_output(self, expr, func):
+        """
+        Return whether the *expr* call to *func* (a ufunc) features an
+        explicit output argument.
+        """
+        nargs = len(expr.args) + len(expr.kws)
+        if expr.vararg is not None:
+            # XXX *args unsupported here, assume there may be an explicit
+            # output
+            return True
+        return nargs > func.nin
+
+    def _get_array_operator(self, ir_expr):
+        ir_op = ir_expr.op
+        if ir_op in ('unary', 'binop'):
+            return ir_expr.fn
+        elif ir_op == 'call':
+            return self.typemap[ir_expr.func.name].typing_key
+        raise NotImplementedError(
+            "Don't know how to find the operator for '{0}' expressions.".format(
+                ir_op))
+
+    def _get_operands(self, ir_expr):
+        '''Given a Numba IR expression, return the operands to the expression
+        in order they appear in the expression.
+        '''
+        ir_op = ir_expr.op
+        if ir_op == 'binop':
+            return ir_expr.lhs, ir_expr.rhs
+        elif ir_op == 'unary':
+            return ir_expr.list_vars()
+        elif ir_op == 'call':
+            return ir_expr.args
+        raise NotImplementedError(
+            "Don't know how to find the operands for '{0}' expressions.".format(
+                ir_op))
+
+    def _translate_expr(self, ir_expr):
+        '''Translate the given expression from Numba IR to an array expression
+        tree.
+        '''
+        ir_op = ir_expr.op
+        if ir_op == 'arrayexpr':
+            return ir_expr.expr
+        operands_or_args = [self.const_assigns.get(op_var.name, op_var)
+                            for op_var in self._get_operands(ir_expr)]
+        return self._get_array_operator(ir_expr), operands_or_args
+
+    def _handle_matches(self):
+        '''Iterate over the matches, trying to find which instructions should
+        be rewritten, deleted, or moved.
+        '''
+        replace_map = {}
+        dead_vars = set()
+        used_vars = defaultdict(int)
+        for instr in self.array_assigns.values():
+            expr = instr.value
+            arr_inps = []
+            arr_expr = self._get_array_operator(expr), arr_inps
+            new_expr = ir.Expr(op='arrayexpr',
+                               loc=expr.loc,
+                               expr=arr_expr,
+                               ty=self.typemap[instr.target.name])
+            new_instr = ir.Assign(new_expr, instr.target, instr.loc)
+            replace_map[instr] = new_instr
+            self.array_assigns[instr.target.name] = new_instr
+            for operand in self._get_operands(expr):
+                operand_name = operand.name
+                if operand.is_temp and operand_name in self.array_assigns:
+                    child_assign = self.array_assigns[operand_name]
+                    child_expr = child_assign.value
+                    child_operands = child_expr.list_vars()
+                    for operand in child_operands:
+                        used_vars[operand.name] += 1
+                    arr_inps.append(self._translate_expr(child_expr))
+                    if child_assign.target.is_temp:
+                        dead_vars.add(child_assign.target.name)
+                        replace_map[child_assign] = None
+                elif operand_name in self.const_assigns:
+                    arr_inps.append(self.const_assigns[operand_name])
+                else:
+                    used_vars[operand.name] += 1
+                    arr_inps.append(operand)
+        return replace_map, dead_vars, used_vars
+
+    def _get_final_replacement(self, replacement_map, instr):
+        '''Find the final replacement instruction for a given initial
+        instruction by chasing instructions in a map from instructions
+        to replacement instructions.
+        '''
+        replacement = replacement_map[instr]
+        while replacement in replacement_map:
+            replacement = replacement_map[replacement]
+        return replacement
+
+    def apply(self):
+        '''When we've found array expressions in a basic block, rewrite that
+        block, returning a new, transformed block.
+        '''
+        # Part 1: Figure out what instructions should be rewritten
+        # based on the matches found.
+        replace_map, dead_vars, used_vars = self._handle_matches()
+        # Part 2: Using the information above, rewrite the target
+        # basic block.
+        result = self.crnt_block.copy()
+        result.clear()
+        delete_map = {}
+        for instr in self.crnt_block.body:
+            if isinstance(instr, ir.Assign):
+                if instr in replace_map:
+                    replacement = self._get_final_replacement(
+                        replace_map, instr)
+                    if replacement:
+                        result.append(replacement)
+                        for var in replacement.value.list_vars():
+                            var_name = var.name
+                            if var_name in delete_map:
+                                result.append(delete_map.pop(var_name))
+                            if used_vars[var_name] > 0:
+                                used_vars[var_name] -= 1
+
+                else:
+                    result.append(instr)
+            elif isinstance(instr, ir.Del):
+                instr_value = instr.value
+                if used_vars[instr_value] > 0:
+                    used_vars[instr_value] -= 1
+                    delete_map[instr_value] = instr
+                elif instr_value not in dead_vars:
+                    result.append(instr)
+            else:
+                result.append(instr)
+        if delete_map:
+            for instr in delete_map.values():
+                result.insert_before_terminator(instr)
+        return result
+
+
+_unaryops = {
+    operator.pos: ast.UAdd,
+    operator.neg: ast.USub,
+    operator.invert: ast.Invert,
+}
+
+_binops = {
+    operator.add: ast.Add,
+    operator.sub: ast.Sub,
+    operator.mul: ast.Mult,
+    operator.truediv: ast.Div,
+    operator.mod: ast.Mod,
+    operator.or_: ast.BitOr,
+    operator.rshift: ast.RShift,
+    operator.xor: ast.BitXor,
+    operator.lshift: ast.LShift,
+    operator.and_: ast.BitAnd,
+    operator.pow: ast.Pow,
+    operator.floordiv: ast.FloorDiv,
+}
+
+
+_cmpops = {
+    operator.eq: ast.Eq,
+    operator.ne: ast.NotEq,
+    operator.lt: ast.Lt,
+    operator.le: ast.LtE,
+    operator.gt: ast.Gt,
+    operator.ge: ast.GtE,
+}
+
+
+def _arr_expr_to_ast(expr):
+    '''Build a Python expression AST from an array expression built by
+    RewriteArrayExprs.
+    '''
+    if isinstance(expr, tuple):
+        op, arr_expr_args = expr
+        ast_args = []
+        env = {}
+        for arg in arr_expr_args:
+            ast_arg, child_env = _arr_expr_to_ast(arg)
+            ast_args.append(ast_arg)
+            env.update(child_env)
+        if op in npydecl.supported_array_operators:
+            if len(ast_args) == 2:
+                if op in _binops:
+                    return ast.BinOp(
+                        ast_args[0], _binops[op](), ast_args[1]), env
+                if op in _cmpops:
+                    return ast.Compare(
+                        ast_args[0], [_cmpops[op]()], [ast_args[1]]), env
+            else:
+                assert op in _unaryops
+                return ast.UnaryOp(_unaryops[op](), ast_args[0]), env
+        elif _is_ufunc(op):
+            fn_name = "__ufunc_or_dufunc_{0}".format(
+                hex(hash(op)).replace("-", "_"))
+            fn_ast_name = ast.Name(fn_name, ast.Load())
+            env[fn_name] = op # Stash the ufunc or DUFunc in the environment
+            ast_call = ast.Call(fn_ast_name, ast_args, [])
+            return ast_call, env
+    elif isinstance(expr, ir.Var):
+        return ast.Name(expr.name, ast.Load(),
+                        lineno=expr.loc.line,
+                        col_offset=expr.loc.col if expr.loc.col else 0), {}
+    elif isinstance(expr, ir.Const):
+        return ast.Num(expr.value), {}
+    raise NotImplementedError(
+        "Don't know how to translate array expression '%r'" % (expr,))
+
+
+@contextlib.contextmanager
+def _legalize_parameter_names(var_list):
+    """
+    Legalize names in the variable list for use as a Python function's
+    parameter names.
+    """
+    var_map = OrderedDict()
+    for var in var_list:
+        old_name = var.name
+        new_name = var.scope.redefine(old_name, loc=var.loc).name
+        new_name = new_name.replace("$", "_").replace(".", "_")
+        # Caller should ensure the names are unique
+        if new_name in var_map:
+            raise AssertionError(f"{new_name!r} not unique")
+        var_map[new_name] = var, old_name
+        var.name = new_name
+    param_names = list(var_map)
+    try:
+        yield param_names
+    finally:
+        # Make sure the old names are restored, to avoid confusing
+        # other parts of Numba (see issue #1466)
+        for var, old_name in var_map.values():
+            var.name = old_name
+
+
+def _lower_array_expr(lowerer, expr):
+    '''Lower an array expression built by RewriteArrayExprs.
+    '''
+    expr_name = "__numba_array_expr_%s" % (hex(hash(expr)).replace("-", "_"))
+    expr_filename = expr.loc.filename
+    expr_var_list = expr.list_vars()
+    # The expression may use a given variable several times, but we
+    # should only create one parameter for it.
+    expr_var_unique = sorted(set(expr_var_list), key=lambda var: var.name)
+
+    # Arguments are the names external to the new closure
+    expr_args = [var.name for var in expr_var_unique]
+
+    # 1. Create an AST tree from the array expression.
+    with _legalize_parameter_names(expr_var_unique) as expr_params:
+        ast_args = [ast.arg(param_name, None)
+                    for param_name in expr_params]
+        # Parse a stub function to ensure the AST is populated with
+        # reasonable defaults for the Python version.
+        ast_module = ast.parse('def {0}(): return'.format(expr_name),
+                               expr_filename, 'exec')
+        assert hasattr(ast_module, 'body') and len(ast_module.body) == 1
+        ast_fn = ast_module.body[0]
+        ast_fn.args.args = ast_args
+        ast_fn.body[0].value, namespace = _arr_expr_to_ast(expr.expr)
+        ast.fix_missing_locations(ast_module)
+
+    # 2. Compile the AST module and extract the Python function.
+    code_obj = compile(ast_module, expr_filename, 'exec')
+    exec(code_obj, namespace)
+    impl = namespace[expr_name]
+
+    # 3. Now compile a ufunc using the Python function as kernel.
+
+    context = lowerer.context
+    builder = lowerer.builder
+    outer_sig = expr.ty(*(lowerer.typeof(name) for name in expr_args))
+    inner_sig_args = []
+    for argty in outer_sig.args:
+        if isinstance(argty, types.Optional):
+            argty = argty.type
+        if isinstance(argty, types.Array):
+            inner_sig_args.append(argty.dtype)
+        else:
+            inner_sig_args.append(argty)
+    inner_sig = outer_sig.return_type.dtype(*inner_sig_args)
+
+    flags = targetconfig.ConfigStack().top_or_none()
+    flags = compiler.Flags() if flags is None else flags.copy() # make sure it's a clone or a fresh instance
+    # Follow the Numpy error model.  Note this also allows e.g. vectorizing
+    # division (issue #1223).
+    flags.error_model = 'numpy'
+    cres = context.compile_subroutine(builder, impl, inner_sig, flags=flags,
+                                      caching=False)
+
+    # Create kernel subclass calling our native function
+    from numba.np import npyimpl
+
+    class ExprKernel(npyimpl._Kernel):
+        def generate(self, *args):
+            arg_zip = zip(args, self.outer_sig.args, inner_sig.args)
+            cast_args = [self.cast(val, inty, outty)
+                         for val, inty, outty in arg_zip]
+            result = self.context.call_internal(
+                builder, cres.fndesc, inner_sig, cast_args)
+            return self.cast(result, inner_sig.return_type,
+                             self.outer_sig.return_type)
+
+    # create a fake ufunc object which is enough to trick numpy_ufunc_kernel
+    ufunc = SimpleNamespace(nin=len(expr_args), nout=1, __name__=expr_name)
+    ufunc.nargs = ufunc.nin + ufunc.nout
+
+    args = [lowerer.loadvar(name) for name in expr_args]
+    return npyimpl.numpy_ufunc_kernel(
+        context, builder, outer_sig, args, ufunc, ExprKernel)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/decorators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/decorators.py
new file mode 100644
index 000000000..57776a466
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/decorators.py
@@ -0,0 +1,199 @@
+import inspect
+
+from numba.np.ufunc import _internal
+from numba.np.ufunc.parallel import ParallelUFuncBuilder, ParallelGUFuncBuilder
+
+from numba.core.registry import DelayedRegistry
+from numba.np.ufunc import dufunc
+from numba.np.ufunc import gufunc
+
+
+class _BaseVectorize(object):
+
+    @classmethod
+    def get_identity(cls, kwargs):
+        return kwargs.pop('identity', None)
+
+    @classmethod
+    def get_cache(cls, kwargs):
+        return kwargs.pop('cache', False)
+
+    @classmethod
+    def get_target_implementation(cls, kwargs):
+        target = kwargs.pop('target', 'cpu')
+        try:
+            return cls.target_registry[target]
+        except KeyError:
+            raise ValueError("Unsupported target: %s" % target)
+
+
+class Vectorize(_BaseVectorize):
+    target_registry = DelayedRegistry({'cpu': dufunc.DUFunc,
+                                       'parallel': ParallelUFuncBuilder,})
+
+    def __new__(cls, func, **kws):
+        identity = cls.get_identity(kws)
+        cache = cls.get_cache(kws)
+        imp = cls.get_target_implementation(kws)
+        return imp(func, identity=identity, cache=cache, targetoptions=kws)
+
+
+class GUVectorize(_BaseVectorize):
+    target_registry = DelayedRegistry({'cpu': gufunc.GUFunc,
+                                       'parallel': ParallelGUFuncBuilder,})
+
+    def __new__(cls, func, signature, **kws):
+        identity = cls.get_identity(kws)
+        cache = cls.get_cache(kws)
+        imp = cls.get_target_implementation(kws)
+        if imp is gufunc.GUFunc:
+            is_dyn = kws.pop('is_dynamic', False)
+            return imp(func, signature, identity=identity, cache=cache,
+                       is_dynamic=is_dyn, targetoptions=kws)
+        else:
+            return imp(func, signature, identity=identity, cache=cache,
+                       targetoptions=kws)
+
+
+def vectorize(ftylist_or_function=(), **kws):
+    """vectorize(ftylist_or_function=(), target='cpu', identity=None, **kws)
+
+    A decorator that creates a Numpy ufunc object using Numba compiled
+    code.  When no arguments or only keyword arguments are given,
+    vectorize will return a Numba dynamic ufunc (DUFunc) object, where
+    compilation/specialization may occur at call-time.
+
+    Args
+    -----
+    ftylist_or_function: function or iterable
+
+        When the first argument is a function, signatures are dealt
+        with at call-time.
+
+        When the first argument is an iterable of type signatures,
+        which are either function type object or a string describing
+        the function type, signatures are finalized at decoration
+        time.
+
+    Keyword Args
+    ------------
+
+    target: str
+            A string for code generation target.  Default to "cpu".
+
+    identity: int, str, or None
+        The identity (or unit) value for the element-wise function
+        being implemented.  Allowed values are None (the default), 0, 1,
+        and "reorderable".
+
+    cache: bool
+        Turns on caching.
+
+
+    Returns
+    --------
+
+    A NumPy universal function
+
+    Examples
+    -------
+        @vectorize(['float32(float32, float32)',
+                    'float64(float64, float64)'], identity=1)
+        def sum(a, b):
+            return a + b
+
+        @vectorize
+        def sum(a, b):
+            return a + b
+
+        @vectorize(identity=1)
+        def mul(a, b):
+            return a * b
+
+    """
+    if isinstance(ftylist_or_function, str):
+        # Common user mistake
+        ftylist = [ftylist_or_function]
+    elif inspect.isfunction(ftylist_or_function):
+        return dufunc.DUFunc(ftylist_or_function, **kws)
+    elif ftylist_or_function is not None:
+        ftylist = ftylist_or_function
+
+    def wrap(func):
+        vec = Vectorize(func, **kws)
+        for sig in ftylist:
+            vec.add(sig)
+        if len(ftylist) > 0:
+            vec.disable_compile()
+        return vec.build_ufunc()
+
+    return wrap
+
+
+def guvectorize(*args, **kwargs):
+    """guvectorize(ftylist, signature, target='cpu', identity=None, **kws)
+
+    A decorator to create numpy generialized-ufunc object from Numba compiled
+    code.
+
+    Args
+    -----
+    ftylist: iterable
+        An iterable of type signatures, which are either
+        function type object or a string describing the
+        function type.
+
+    signature: str
+        A NumPy generialized-ufunc signature.
+        e.g. "(m, n), (n, p)->(m, p)"
+
+    identity: int, str, or None
+        The identity (or unit) value for the element-wise function
+        being implemented.  Allowed values are None (the default), 0, 1,
+        and "reorderable".
+
+    cache: bool
+        Turns on caching.
+
+    target: str
+            A string for code generation target.  Defaults to "cpu".
+
+    Returns
+    --------
+
+    A NumPy generialized universal-function
+
+    Example
+    -------
+        @guvectorize(['void(int32[:,:], int32[:,:], int32[:,:])',
+                      'void(float32[:,:], float32[:,:], float32[:,:])'],
+                      '(x, y),(x, y)->(x, y)')
+        def add_2d_array(a, b, c):
+            for i in range(c.shape[0]):
+                for j in range(c.shape[1]):
+                    c[i, j] = a[i, j] + b[i, j]
+
+    """
+    if len(args) == 1:
+        ftylist = []
+        signature = args[0]
+        kwargs.setdefault('is_dynamic', True)
+    elif len(args) == 2:
+        ftylist = args[0]
+        signature = args[1]
+    else:
+        raise TypeError('guvectorize() takes one or two positional arguments')
+
+    if isinstance(ftylist, str):
+        # Common user mistake
+        ftylist = [ftylist]
+
+    def wrap(func):
+        guvec = GUVectorize(func, signature, **kwargs)
+        for fty in ftylist:
+            guvec.add(fty)
+        if len(ftylist) > 0:
+            guvec.disable_compile()
+        return guvec.build_ufunc()
+
+    return wrap
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/deviceufunc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/deviceufunc.py
new file mode 100644
index 000000000..f67f28826
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/deviceufunc.py
@@ -0,0 +1,841 @@
+"""
+Implements custom ufunc dispatch mechanism for non-CPU devices.
+"""
+
+from collections import OrderedDict
+import operator
+import warnings
+from functools import reduce
+
+import numpy as np
+
+from numba.np.ufunc.ufuncbuilder import _BaseUFuncBuilder, parse_identity
+from numba.core import types, sigutils
+from numba.core.typing import signature
+from numba.np.ufunc.sigparse import parse_signature
+
+
+def _broadcast_axis(a, b):
+    """
+    Raises
+    ------
+    ValueError if broadcast fails
+    """
+    if a == b:
+        return a
+    elif a == 1:
+        return b
+    elif b == 1:
+        return a
+    else:
+        raise ValueError("failed to broadcast {0} and {1}".format(a, b))
+
+
+def _pairwise_broadcast(shape1, shape2):
+    """
+    Raises
+    ------
+    ValueError if broadcast fails
+    """
+    shape1, shape2 = map(tuple, [shape1, shape2])
+
+    while len(shape1) < len(shape2):
+        shape1 = (1,) + shape1
+
+    while len(shape1) > len(shape2):
+        shape2 = (1,) + shape2
+
+    return tuple(_broadcast_axis(a, b) for a, b in zip(shape1, shape2))
+
+
+def _multi_broadcast(*shapelist):
+    """
+    Raises
+    ------
+    ValueError if broadcast fails
+    """
+    assert shapelist
+
+    result = shapelist[0]
+    others = shapelist[1:]
+    try:
+        for i, each in enumerate(others, start=1):
+            result = _pairwise_broadcast(result, each)
+    except ValueError:
+        raise ValueError("failed to broadcast argument #{0}".format(i))
+    else:
+        return result
+
+
+class UFuncMechanism(object):
+    """
+    Prepare ufunc arguments for vectorize.
+    """
+    DEFAULT_STREAM = None
+    SUPPORT_DEVICE_SLICING = False
+
+    def __init__(self, typemap, args):
+        """Never used directly by user. Invoke by UFuncMechanism.call().
+        """
+        self.typemap = typemap
+        self.args = args
+        nargs = len(self.args)
+        self.argtypes = [None] * nargs
+        self.scalarpos = []
+        self.signature = None
+        self.arrays = [None] * nargs
+
+    def _fill_arrays(self):
+        """
+        Get all arguments in array form
+        """
+        for i, arg in enumerate(self.args):
+            if self.is_device_array(arg):
+                self.arrays[i] = self.as_device_array(arg)
+            elif isinstance(arg, (int, float, complex, np.number)):
+                # Is scalar
+                self.scalarpos.append(i)
+            else:
+                self.arrays[i] = np.asarray(arg)
+
+    def _fill_argtypes(self):
+        """
+        Get dtypes
+        """
+        for i, ary in enumerate(self.arrays):
+            if ary is not None:
+                self.argtypes[i] = np.asarray(ary).dtype
+
+    def _resolve_signature(self):
+        """Resolve signature.
+        May have ambiguous case.
+        """
+        matches = []
+        # Resolve scalar args exact match first
+        if self.scalarpos:
+            # Try resolve scalar arguments
+            for formaltys in self.typemap:
+                match_map = []
+                for i, (formal, actual) in enumerate(zip(formaltys,
+                                                         self.argtypes)):
+                    if actual is None:
+                        actual = np.asarray(self.args[i]).dtype
+
+                    match_map.append(actual == formal)
+
+                if all(match_map):
+                    matches.append(formaltys)
+
+        # No matching with exact match; try coercing the scalar arguments
+        if not matches:
+            matches = []
+            for formaltys in self.typemap:
+                all_matches = all(actual is None or formal == actual
+                                  for formal, actual in
+                                  zip(formaltys, self.argtypes))
+                if all_matches:
+                    matches.append(formaltys)
+
+        if not matches:
+            raise TypeError("No matching version.  GPU ufunc requires array "
+                            "arguments to have the exact types.  This behaves "
+                            "like regular ufunc with casting='no'.")
+
+        if len(matches) > 1:
+            raise TypeError("Failed to resolve ufunc due to ambiguous "
+                            "signature. Too many untyped scalars. "
+                            "Use numpy dtype object to type tag.")
+
+        # Try scalar arguments
+        self.argtypes = matches[0]
+
+    def _get_actual_args(self):
+        """Return the actual arguments
+        Casts scalar arguments to np.array.
+        """
+        for i in self.scalarpos:
+            self.arrays[i] = np.array([self.args[i]], dtype=self.argtypes[i])
+
+        return self.arrays
+
+    def _broadcast(self, arys):
+        """Perform numpy ufunc broadcasting
+        """
+        shapelist = [a.shape for a in arys]
+        shape = _multi_broadcast(*shapelist)
+
+        for i, ary in enumerate(arys):
+            if ary.shape == shape:
+                pass
+
+            else:
+                if self.is_device_array(ary):
+                    arys[i] = self.broadcast_device(ary, shape)
+
+                else:
+                    ax_differs = [ax for ax in range(len(shape))
+                                  if ax >= ary.ndim
+                                  or ary.shape[ax] != shape[ax]]
+
+                    missingdim = len(shape) - len(ary.shape)
+                    strides = [0] * missingdim + list(ary.strides)
+
+                    for ax in ax_differs:
+                        strides[ax] = 0
+
+                    strided = np.lib.stride_tricks.as_strided(ary,
+                                                              shape=shape,
+                                                              strides=strides)
+
+                    arys[i] = self.force_array_layout(strided)
+
+        return arys
+
+    def get_arguments(self):
+        """Prepare and return the arguments for the ufunc.
+        Does not call to_device().
+        """
+        self._fill_arrays()
+        self._fill_argtypes()
+        self._resolve_signature()
+        arys = self._get_actual_args()
+        return self._broadcast(arys)
+
+    def get_function(self):
+        """Returns (result_dtype, function)
+        """
+        return self.typemap[self.argtypes]
+
+    def is_device_array(self, obj):
+        """Is the `obj` a device array?
+        Override in subclass
+        """
+        return False
+
+    def as_device_array(self, obj):
+        """Convert the `obj` to a device array
+        Override in subclass
+
+        Default implementation is an identity function
+        """
+        return obj
+
+    def broadcast_device(self, ary, shape):
+        """Handles ondevice broadcasting
+
+        Override in subclass to add support.
+        """
+        raise NotImplementedError("broadcasting on device is not supported")
+
+    def force_array_layout(self, ary):
+        """Ensures array layout met device requirement.
+
+        Override in sublcass
+        """
+        return ary
+
+    @classmethod
+    def call(cls, typemap, args, kws):
+        """Perform the entire ufunc call mechanism.
+        """
+        # Handle keywords
+        stream = kws.pop('stream', cls.DEFAULT_STREAM)
+        out = kws.pop('out', None)
+
+        if kws:
+            warnings.warn("unrecognized keywords: %s" % ', '.join(kws))
+
+        # Begin call resolution
+        cr = cls(typemap, args)
+        args = cr.get_arguments()
+        resty, func = cr.get_function()
+
+        outshape = args[0].shape
+
+        # Adjust output value
+        if out is not None and cr.is_device_array(out):
+            out = cr.as_device_array(out)
+
+        def attempt_ravel(a):
+            if cr.SUPPORT_DEVICE_SLICING:
+                raise NotImplementedError
+
+            try:
+                # Call the `.ravel()` method
+                return a.ravel()
+            except NotImplementedError:
+                # If it is not a device array
+                if not cr.is_device_array(a):
+                    raise
+                # For device array, retry ravel on the host by first
+                # copying it back.
+                else:
+                    hostary = cr.to_host(a, stream).ravel()
+                    return cr.to_device(hostary, stream)
+
+        if args[0].ndim > 1:
+            args = [attempt_ravel(a) for a in args]
+
+        # Prepare argument on the device
+        devarys = []
+        any_device = False
+        for a in args:
+            if cr.is_device_array(a):
+                devarys.append(a)
+                any_device = True
+            else:
+                dev_a = cr.to_device(a, stream=stream)
+                devarys.append(dev_a)
+
+        # Launch
+        shape = args[0].shape
+        if out is None:
+            # No output is provided
+            devout = cr.device_array(shape, resty, stream=stream)
+
+            devarys.extend([devout])
+            cr.launch(func, shape[0], stream, devarys)
+
+            if any_device:
+                # If any of the arguments are on device,
+                # Keep output on the device
+                return devout.reshape(outshape)
+            else:
+                # Otherwise, transfer output back to host
+                return devout.copy_to_host().reshape(outshape)
+
+        elif cr.is_device_array(out):
+            # If output is provided and it is a device array,
+            # Return device array
+            if out.ndim > 1:
+                out = attempt_ravel(out)
+            devout = out
+            devarys.extend([devout])
+            cr.launch(func, shape[0], stream, devarys)
+            return devout.reshape(outshape)
+
+        else:
+            # If output is provided and it is a host array,
+            # Return host array
+            assert out.shape == shape
+            assert out.dtype == resty
+            devout = cr.device_array(shape, resty, stream=stream)
+            devarys.extend([devout])
+            cr.launch(func, shape[0], stream, devarys)
+            return devout.copy_to_host(out, stream=stream).reshape(outshape)
+
+    def to_device(self, hostary, stream):
+        """Implement to device transfer
+        Override in subclass
+        """
+        raise NotImplementedError
+
+    def to_host(self, devary, stream):
+        """Implement to host transfer
+        Override in subclass
+        """
+        raise NotImplementedError
+
+    def device_array(self, shape, dtype, stream):
+        """Implements device allocation
+        Override in subclass
+        """
+        raise NotImplementedError
+
+    def launch(self, func, count, stream, args):
+        """Implements device function invocation
+        Override in subclass
+        """
+        raise NotImplementedError
+
+
+def to_dtype(ty):
+    if isinstance(ty, types.EnumMember):
+        ty = ty.dtype
+    return np.dtype(str(ty))
+
+
+class DeviceVectorize(_BaseUFuncBuilder):
+    def __init__(self, func, identity=None, cache=False, targetoptions={}):
+        if cache:
+            raise TypeError("caching is not supported")
+        for opt in targetoptions:
+            if opt == 'nopython':
+                warnings.warn("nopython kwarg for cuda target is redundant",
+                              RuntimeWarning)
+            else:
+                fmt = "Unrecognized options. "
+                fmt += "cuda vectorize target does not support option: '%s'"
+                raise KeyError(fmt % opt)
+        self.py_func = func
+        self.identity = parse_identity(identity)
+        # { arg_dtype: (return_dtype), cudakernel }
+        self.kernelmap = OrderedDict()
+
+    @property
+    def pyfunc(self):
+        return self.py_func
+
+    def add(self, sig=None, argtypes=None, restype=None):
+        # Handle argtypes
+        if argtypes is not None:
+            warnings.warn("Keyword argument argtypes is deprecated",
+                          DeprecationWarning)
+            assert sig is None
+            if restype is None:
+                sig = tuple(argtypes)
+            else:
+                sig = restype(*argtypes)
+        del argtypes
+        del restype
+
+        # compile core as device function
+        args, return_type = sigutils.normalize_signature(sig)
+        devfnsig = signature(return_type, *args)
+
+        funcname = self.pyfunc.__name__
+        kernelsource = self._get_kernel_source(self._kernel_template,
+                                               devfnsig, funcname)
+        corefn, return_type = self._compile_core(devfnsig)
+        glbl = self._get_globals(corefn)
+        sig = signature(types.void, *([a[:] for a in args] + [return_type[:]]))
+        exec(kernelsource, glbl)
+
+        stager = glbl['__vectorized_%s' % funcname]
+        kernel = self._compile_kernel(stager, sig)
+
+        argdtypes = tuple(to_dtype(t) for t in devfnsig.args)
+        resdtype = to_dtype(return_type)
+        self.kernelmap[tuple(argdtypes)] = resdtype, kernel
+
+    def build_ufunc(self):
+        raise NotImplementedError
+
+    def _get_kernel_source(self, template, sig, funcname):
+        args = ['a%d' % i for i in range(len(sig.args))]
+        fmts = dict(name=funcname,
+                    args=', '.join(args),
+                    argitems=', '.join('%s[__tid__]' % i for i in args))
+        return template.format(**fmts)
+
+    def _compile_core(self, sig):
+        raise NotImplementedError
+
+    def _get_globals(self, corefn):
+        raise NotImplementedError
+
+    def _compile_kernel(self, fnobj, sig):
+        raise NotImplementedError
+
+
+class DeviceGUFuncVectorize(_BaseUFuncBuilder):
+    def __init__(self, func, sig, identity=None, cache=False, targetoptions={}):
+        if cache:
+            raise TypeError("caching is not supported")
+        # Allow nopython flag to be set.
+        if not targetoptions.pop('nopython', True):
+            raise TypeError("nopython flag must be True")
+        # Are there any more target options?
+        if targetoptions:
+            opts = ', '.join([repr(k) for k in targetoptions.keys()])
+            fmt = "The following target options are not supported: {0}"
+            raise TypeError(fmt.format(opts))
+
+        self.py_func = func
+        self.identity = parse_identity(identity)
+        self.signature = sig
+        self.inputsig, self.outputsig = parse_signature(self.signature)
+        assert len(self.outputsig) == 1, "only support 1 output"
+        # { arg_dtype: (return_dtype), cudakernel }
+        self.kernelmap = OrderedDict()
+
+    @property
+    def pyfunc(self):
+        return self.py_func
+
+    def add(self, sig=None, argtypes=None, restype=None):
+        # Handle argtypes
+        if argtypes is not None:
+            warnings.warn("Keyword argument argtypes is deprecated",
+                          DeprecationWarning)
+            assert sig is None
+            if restype is None:
+                sig = tuple(argtypes)
+            else:
+                sig = restype(*argtypes)
+        del argtypes
+        del restype
+
+        indims = [len(x) for x in self.inputsig]
+        outdims = [len(x) for x in self.outputsig]
+        args, return_type = sigutils.normalize_signature(sig)
+
+        funcname = self.py_func.__name__
+        src = expand_gufunc_template(self._kernel_template, indims,
+                                     outdims, funcname, args)
+
+        glbls = self._get_globals(sig)
+
+        exec(src, glbls)
+        fnobj = glbls['__gufunc_{name}'.format(name=funcname)]
+
+        outertys = list(_determine_gufunc_outer_types(args, indims + outdims))
+        kernel = self._compile_kernel(fnobj, sig=tuple(outertys))
+
+        dtypes = tuple(np.dtype(str(t.dtype)) for t in outertys)
+        self.kernelmap[tuple(dtypes[:-1])] = dtypes[-1], kernel
+
+    def _compile_kernel(self, fnobj, sig):
+        raise NotImplementedError
+
+    def _get_globals(self, sig):
+        raise NotImplementedError
+
+
+def _determine_gufunc_outer_types(argtys, dims):
+    for at, nd in zip(argtys, dims):
+        if isinstance(at, types.Array):
+            yield at.copy(ndim=nd + 1)
+        else:
+            if nd > 0:
+                raise ValueError("gufunc signature mismatch: ndim>0 for scalar")
+            yield types.Array(dtype=at, ndim=1, layout='A')
+
+
+def expand_gufunc_template(template, indims, outdims, funcname, argtypes):
+    """Expand gufunc source template
+    """
+    argdims = indims + outdims
+    argnames = ["arg{0}".format(i) for i in range(len(argdims))]
+    checkedarg = "min({0})".format(', '.join(["{0}.shape[0]".format(a)
+                                              for a in argnames]))
+    inputs = [_gen_src_for_indexing(aref, adims, atype)
+              for aref, adims, atype in zip(argnames, indims, argtypes)]
+    outputs = [_gen_src_for_indexing(aref, adims, atype)
+               for aref, adims, atype in zip(argnames[len(indims):], outdims,
+                                             argtypes[len(indims):])]
+    argitems = inputs + outputs
+    src = template.format(name=funcname, args=', '.join(argnames),
+                          checkedarg=checkedarg,
+                          argitems=', '.join(argitems))
+    return src
+
+
+def _gen_src_for_indexing(aref, adims, atype):
+    return "{aref}[{sliced}]".format(aref=aref,
+                                     sliced=_gen_src_index(adims, atype))
+
+
+def _gen_src_index(adims, atype):
+    if adims > 0:
+        return ','.join(['__tid__'] + [':'] * adims)
+    elif isinstance(atype, types.Array) and atype.ndim - 1 == adims:
+        # Special case for 0-nd in shape-signature but
+        # 1d array in type signature.
+        # Slice it so that the result has the same dimension.
+        return '__tid__:(__tid__ + 1)'
+    else:
+        return '__tid__'
+
+
+class GUFuncEngine(object):
+    '''Determine how to broadcast and execute a gufunc
+    base on input shape and signature
+    '''
+
+    @classmethod
+    def from_signature(cls, signature):
+        return cls(*parse_signature(signature))
+
+    def __init__(self, inputsig, outputsig):
+        # signatures
+        self.sin = inputsig
+        self.sout = outputsig
+        # argument count
+        self.nin = len(self.sin)
+        self.nout = len(self.sout)
+
+    def schedule(self, ishapes):
+        if len(ishapes) != self.nin:
+            raise TypeError('invalid number of input argument')
+
+        # associate symbol values for input signature
+        symbolmap = {}
+        outer_shapes = []
+        inner_shapes = []
+
+        for argn, (shape, symbols) in enumerate(zip(ishapes, self.sin)):
+            argn += 1  # start from 1 for human
+            inner_ndim = len(symbols)
+            if len(shape) < inner_ndim:
+                fmt = "arg #%d: insufficient inner dimension"
+                raise ValueError(fmt % (argn,))
+            if inner_ndim:
+                inner_shape = shape[-inner_ndim:]
+                outer_shape = shape[:-inner_ndim]
+            else:
+                inner_shape = ()
+                outer_shape = shape
+
+            for axis, (dim, sym) in enumerate(zip(inner_shape, symbols)):
+                axis += len(outer_shape)
+                if sym in symbolmap:
+                    if symbolmap[sym] != dim:
+                        fmt = "arg #%d: shape[%d] mismatch argument"
+                        raise ValueError(fmt % (argn, axis))
+                symbolmap[sym] = dim
+
+            outer_shapes.append(outer_shape)
+            inner_shapes.append(inner_shape)
+
+        # solve output shape
+        oshapes = []
+        for outsig in self.sout:
+            oshape = []
+            for sym in outsig:
+                oshape.append(symbolmap[sym])
+            oshapes.append(tuple(oshape))
+
+        # find the biggest outershape as looping dimension
+        sizes = [reduce(operator.mul, s, 1) for s in outer_shapes]
+        largest_i = np.argmax(sizes)
+        loopdims = outer_shapes[largest_i]
+
+        pinned = [False] * self.nin  # same argument for each iteration
+        for i, d in enumerate(outer_shapes):
+            if d != loopdims:
+                if d == (1,) or d == ():
+                    pinned[i] = True
+                else:
+                    fmt = "arg #%d: outer dimension mismatch"
+                    raise ValueError(fmt % (i + 1,))
+
+        return GUFuncSchedule(self, inner_shapes, oshapes, loopdims, pinned)
+
+
+class GUFuncSchedule(object):
+    def __init__(self, parent, ishapes, oshapes, loopdims, pinned):
+        self.parent = parent
+        # core shapes
+        self.ishapes = ishapes
+        self.oshapes = oshapes
+        # looping dimension
+        self.loopdims = loopdims
+        self.loopn = reduce(operator.mul, loopdims, 1)
+        # flags
+        self.pinned = pinned
+
+        self.output_shapes = [loopdims + s for s in oshapes]
+
+    def __str__(self):
+        import pprint
+
+        attrs = 'ishapes', 'oshapes', 'loopdims', 'loopn', 'pinned'
+        values = [(k, getattr(self, k)) for k in attrs]
+        return pprint.pformat(dict(values))
+
+
+class GenerializedUFunc(object):
+    def __init__(self, kernelmap, engine):
+        self.kernelmap = kernelmap
+        self.engine = engine
+        self.max_blocksize = 2 ** 30
+        assert self.engine.nout == 1, "only support single output"
+
+    def __call__(self, *args, **kws):
+        callsteps = self._call_steps(self.engine.nin, self.engine.nout,
+                                     args, kws)
+        callsteps.prepare_inputs()
+        indtypes, schedule, outdtype, kernel = self._schedule(
+            callsteps.norm_inputs, callsteps.output)
+        callsteps.adjust_input_types(indtypes)
+        callsteps.allocate_outputs(schedule, outdtype)
+        callsteps.prepare_kernel_parameters()
+        newparams, newretval = self._broadcast(schedule,
+                                               callsteps.kernel_parameters,
+                                               callsteps.kernel_returnvalue)
+        callsteps.launch_kernel(kernel, schedule.loopn, newparams + [newretval])
+        return callsteps.post_process_result()
+
+    def _schedule(self, inputs, out):
+        input_shapes = [a.shape for a in inputs]
+        schedule = self.engine.schedule(input_shapes)
+
+        # find kernel
+        idtypes = tuple(i.dtype for i in inputs)
+        try:
+            outdtype, kernel = self.kernelmap[idtypes]
+        except KeyError:
+            # No exact match, then use the first compatible.
+            # This does not match the numpy dispatching exactly.
+            # Later, we may just jit a new version for the missing signature.
+            idtypes = self._search_matching_signature(idtypes)
+            # Select kernel
+            outdtype, kernel = self.kernelmap[idtypes]
+
+        # check output
+        if out is not None and schedule.output_shapes[0] != out.shape:
+            raise ValueError('output shape mismatch')
+
+        return idtypes, schedule, outdtype, kernel
+
+    def _search_matching_signature(self, idtypes):
+        """
+        Given the input types in `idtypes`, return a compatible sequence of
+        types that is defined in `kernelmap`.
+
+        Note: Ordering is guaranteed by `kernelmap` being a OrderedDict
+        """
+        for sig in self.kernelmap.keys():
+            if all(np.can_cast(actual, desired)
+                   for actual, desired in zip(sig, idtypes)):
+                return sig
+        else:
+            raise TypeError("no matching signature")
+
+    def _broadcast(self, schedule, params, retval):
+        assert schedule.loopn > 0, "zero looping dimension"
+
+        odim = 1 if not schedule.loopdims else schedule.loopn
+        newparams = []
+        for p, cs in zip(params, schedule.ishapes):
+            if not cs and p.size == 1:
+                # Broadcast scalar input
+                devary = self._broadcast_scalar_input(p, odim)
+                newparams.append(devary)
+            else:
+                # Broadcast vector input
+                newparams.append(self._broadcast_array(p, odim, cs))
+        newretval = retval.reshape(odim, *schedule.oshapes[0])
+        return newparams, newretval
+
+    def _broadcast_array(self, ary, newdim, innerdim):
+        newshape = (newdim,) + innerdim
+        # No change in shape
+        if ary.shape == newshape:
+            return ary
+
+        # Creating new dimension
+        elif len(ary.shape) < len(newshape):
+            assert newshape[-len(ary.shape):] == ary.shape, \
+               "cannot add dim and reshape at the same time"
+            return self._broadcast_add_axis(ary, newshape)
+
+        # Collapsing dimension
+        else:
+            return ary.reshape(*newshape)
+
+    def _broadcast_add_axis(self, ary, newshape):
+        raise NotImplementedError("cannot add new axis")
+
+    def _broadcast_scalar_input(self, ary, shape):
+        raise NotImplementedError
+
+
+class GUFuncCallSteps(object):
+    __slots__ = [
+        'args',
+        'kwargs',
+        'output',
+        'norm_inputs',
+        'kernel_returnvalue',
+        'kernel_parameters',
+        '_is_device_array',
+        '_need_device_conversion',
+    ]
+
+    def __init__(self, nin, nout, args, kwargs):
+        if nout > 1:
+            raise ValueError('multiple output is not supported')
+        self.args = args
+        self.kwargs = kwargs
+
+        user_output_is_device = False
+        self.output = self.kwargs.get('out')
+        if self.output is not None:
+            user_output_is_device = self.is_device_array(self.output)
+            if user_output_is_device:
+                self.output = self.as_device_array(self.output)
+        self._is_device_array = [self.is_device_array(a) for a in self.args]
+        self._need_device_conversion = (not any(self._is_device_array) and
+                                        not user_output_is_device)
+
+        # Normalize inputs
+        inputs = []
+        for a, isdev in zip(self.args, self._is_device_array):
+            if isdev:
+                inputs.append(self.as_device_array(a))
+            else:
+                inputs.append(np.asarray(a))
+        self.norm_inputs = inputs[:nin]
+        # Check if there are extra arguments for outputs.
+        unused_inputs = inputs[nin:]
+        if unused_inputs:
+            if self.output is not None:
+                raise ValueError("cannot specify 'out' as both a positional "
+                                 "and keyword argument")
+            else:
+                [self.output] = unused_inputs
+
+    def adjust_input_types(self, indtypes):
+        """
+        Attempt to cast the inputs to the required types if necessary
+        and if they are not device array.
+
+        Side effect: Only affects the element of `norm_inputs` that requires
+        a type cast.
+        """
+        for i, (ity, val) in enumerate(zip(indtypes, self.norm_inputs)):
+            if ity != val.dtype:
+                if not hasattr(val, 'astype'):
+                    msg = ("compatible signature is possible by casting but "
+                           "{0} does not support .astype()").format(type(val))
+                    raise TypeError(msg)
+                # Cast types
+                self.norm_inputs[i] = val.astype(ity)
+
+    def allocate_outputs(self, schedule, outdtype):
+        # allocate output
+        if self._need_device_conversion or self.output is None:
+            retval = self.device_array(shape=schedule.output_shapes[0],
+                                       dtype=outdtype)
+        else:
+            retval = self.output
+        self.kernel_returnvalue = retval
+
+    def prepare_kernel_parameters(self):
+        params = []
+        for inp, isdev in zip(self.norm_inputs, self._is_device_array):
+            if isdev:
+                params.append(inp)
+            else:
+                params.append(self.to_device(inp))
+        assert all(self.is_device_array(a) for a in params)
+        self.kernel_parameters = params
+
+    def post_process_result(self):
+        if self._need_device_conversion:
+            out = self.to_host(self.kernel_returnvalue, self.output)
+        elif self.output is None:
+            out = self.kernel_returnvalue
+        else:
+            out = self.output
+        return out
+
+    def prepare_inputs(self):
+        pass
+
+    def launch_kernel(self, kernel, nelem, args):
+        raise NotImplementedError
+
+    def is_device_array(self, obj):
+        raise NotImplementedError
+
+    def as_device_array(self, obj):
+        return obj
+
+    def to_device(self, hostary):
+        raise NotImplementedError
+
+    def device_array(self, shape, dtype):
+        raise NotImplementedError
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/dufunc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/dufunc.py
new file mode 100644
index 000000000..26a7b1a41
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/dufunc.py
@@ -0,0 +1,323 @@
+from numba import jit, typeof
+from numba.core import cgutils, types, serialize, sigutils
+from numba.core.extending import is_jitted
+from numba.core.typing import npydecl
+from numba.core.typing.templates import AbstractTemplate, signature
+from numba.np.ufunc import _internal
+from numba.parfors import array_analysis
+from numba.np.ufunc import ufuncbuilder
+from numba.np import numpy_support
+
+
+def make_dufunc_kernel(_dufunc):
+    from numba.np import npyimpl
+
+    class DUFuncKernel(npyimpl._Kernel):
+        """
+        npyimpl._Kernel subclass responsible for lowering a DUFunc kernel
+        (element-wise function) inside a broadcast loop (which is
+        generated by npyimpl.numpy_ufunc_kernel()).
+        """
+        dufunc = _dufunc
+
+        def __init__(self, context, builder, outer_sig):
+            super(DUFuncKernel, self).__init__(context, builder, outer_sig)
+            self.inner_sig, self.cres = self.dufunc.find_ewise_function(
+                outer_sig.args)
+
+        def generate(self, *args):
+            isig = self.inner_sig
+            osig = self.outer_sig
+            cast_args = [self.cast(val, inty, outty)
+                         for val, inty, outty in
+                         zip(args, osig.args, isig.args)]
+            if self.cres.objectmode:
+                func_type = self.context.call_conv.get_function_type(
+                    types.pyobject, [types.pyobject] * len(isig.args))
+            else:
+                func_type = self.context.call_conv.get_function_type(
+                    isig.return_type, isig.args)
+            module = self.builder.block.function.module
+            entry_point = cgutils.get_or_insert_function(
+                module, func_type,
+                self.cres.fndesc.llvm_func_name)
+            entry_point.attributes.add("alwaysinline")
+
+            _, res = self.context.call_conv.call_function(
+                self.builder, entry_point, isig.return_type, isig.args,
+                cast_args)
+            return self.cast(res, isig.return_type, osig.return_type)
+
+    DUFuncKernel.__name__ += _dufunc.ufunc.__name__
+    return DUFuncKernel
+
+
+class DUFuncLowerer(object):
+    '''Callable class responsible for lowering calls to a specific DUFunc.
+    '''
+    def __init__(self, dufunc):
+        self.kernel = make_dufunc_kernel(dufunc)
+        self.libs = []
+
+    def __call__(self, context, builder, sig, args):
+        from numba.np import npyimpl
+        return npyimpl.numpy_ufunc_kernel(context, builder, sig, args,
+                                          self.kernel.dufunc.ufunc,
+                                          self.kernel)
+
+
+class DUFunc(serialize.ReduceMixin, _internal._DUFunc):
+    """
+    Dynamic universal function (DUFunc) intended to act like a normal
+    Numpy ufunc, but capable of call-time (just-in-time) compilation
+    of fast loops specialized to inputs.
+    """
+    # NOTE: __base_kwargs must be kept in synch with the kwlist in
+    # _internal.c:dufunc_init()
+    __base_kwargs = set(('identity', '_keepalive', 'nin', 'nout'))
+
+    def __init__(self, py_func, identity=None, cache=False, targetoptions={}):
+        if is_jitted(py_func):
+            py_func = py_func.py_func
+        dispatcher = jit(_target='npyufunc',
+                         cache=cache,
+                         **targetoptions)(py_func)
+        self._initialize(dispatcher, identity)
+
+    def _initialize(self, dispatcher, identity):
+        identity = ufuncbuilder.parse_identity(identity)
+        super(DUFunc, self).__init__(dispatcher, identity=identity)
+        # Loop over a copy of the keys instead of the keys themselves,
+        # since we're changing the dictionary while looping.
+        self._install_type()
+        self._lower_me = DUFuncLowerer(self)
+        self._install_cg()
+        self.__name__ = dispatcher.py_func.__name__
+        self.__doc__ = dispatcher.py_func.__doc__
+
+    def _reduce_states(self):
+        """
+        NOTE: part of ReduceMixin protocol
+        """
+        siglist = list(self._dispatcher.overloads.keys())
+        return dict(
+            dispatcher=self._dispatcher,
+            identity=self.identity,
+            frozen=self._frozen,
+            siglist=siglist,
+        )
+
+    @classmethod
+    def _rebuild(cls, dispatcher, identity, frozen, siglist):
+        """
+        NOTE: part of ReduceMixin protocol
+        """
+        self = _internal._DUFunc.__new__(cls)
+        self._initialize(dispatcher, identity)
+        # Re-add signatures
+        for sig in siglist:
+            self.add(sig)
+        if frozen:
+            self.disable_compile()
+        return self
+
+    def build_ufunc(self):
+        """
+        For compatibility with the various *UFuncBuilder classes.
+        """
+        return self
+
+    @property
+    def targetoptions(self):
+        return self._dispatcher.targetoptions
+
+    @property
+    def nin(self):
+        return self.ufunc.nin
+
+    @property
+    def nout(self):
+        return self.ufunc.nout
+
+    @property
+    def nargs(self):
+        return self.ufunc.nargs
+
+    @property
+    def ntypes(self):
+        return self.ufunc.ntypes
+
+    @property
+    def types(self):
+        return self.ufunc.types
+
+    @property
+    def identity(self):
+        return self.ufunc.identity
+
+    def disable_compile(self):
+        """
+        Disable the compilation of new signatures at call time.
+        """
+        # If disabling compilation then there must be at least one signature
+        assert len(self._dispatcher.overloads) > 0
+        self._frozen = True
+
+    def add(self, sig):
+        """
+        Compile the DUFunc for the given signature.
+        """
+        args, return_type = sigutils.normalize_signature(sig)
+        return self._compile_for_argtys(args, return_type)
+
+    def _compile_for_args(self, *args, **kws):
+        nin = self.ufunc.nin
+        if kws:
+            if 'out' in kws:
+                out = kws.pop('out')
+                args += (out,)
+            if kws:
+                raise TypeError("unexpected keyword arguments to ufunc: %s"
+                                % ", ".join(repr(k) for k in sorted(kws)))
+
+        args_len = len(args)
+        assert (args_len == nin) or (args_len == nin + self.ufunc.nout)
+        assert not kws
+        argtys = []
+        for arg in args[:nin]:
+            argty = typeof(arg)
+            if isinstance(argty, types.Array):
+                argty = argty.dtype
+            else:
+                # To avoid a mismatch in how Numba types scalar values as
+                # opposed to Numpy, we need special logic for scalars.
+                # For example, on 64-bit systems, numba.typeof(3) => int32, but
+                # np.array(3).dtype => int64.
+
+                # Note: this will not handle numpy "duckarrays" correctly,
+                # including but not limited to those implementing `__array__`
+                # and `__array_ufunc__`.
+                argty = numpy_support.map_arrayscalar_type(arg)
+            argtys.append(argty)
+        return self._compile_for_argtys(tuple(argtys))
+
+    def _compile_for_argtys(self, argtys, return_type=None):
+        """
+        Given a tuple of argument types (these should be the array
+        dtypes, and not the array types themselves), compile the
+        element-wise function for those inputs, generate a UFunc loop
+        wrapper, and register the loop with the Numpy ufunc object for
+        this DUFunc.
+        """
+        if self._frozen:
+            raise RuntimeError("compilation disabled for %s" % (self,))
+        assert isinstance(argtys, tuple)
+        if return_type is None:
+            sig = argtys
+        else:
+            sig = return_type(*argtys)
+        cres, argtys, return_type = ufuncbuilder._compile_element_wise_function(
+            self._dispatcher, self.targetoptions, sig)
+        actual_sig = ufuncbuilder._finalize_ufunc_signature(
+            cres, argtys, return_type)
+        dtypenums, ptr, env = ufuncbuilder._build_element_wise_ufunc_wrapper(
+            cres, actual_sig)
+        self._add_loop(int(ptr), dtypenums)
+        self._keepalive.append((ptr, cres.library, env))
+        self._lower_me.libs.append(cres.library)
+        return cres
+
+    def _install_type(self, typingctx=None):
+        """Constructs and installs a typing class for a DUFunc object in the
+        input typing context.  If no typing context is given, then
+        _install_type() installs into the typing context of the
+        dispatcher object (should be same default context used by
+        jit() and njit()).
+        """
+        if typingctx is None:
+            typingctx = self._dispatcher.targetdescr.typing_context
+        _ty_cls = type('DUFuncTyping_' + self.ufunc.__name__,
+                       (AbstractTemplate,),
+                       dict(key=self, generic=self._type_me))
+        typingctx.insert_user_function(self, _ty_cls)
+
+    def find_ewise_function(self, ewise_types):
+        """
+        Given a tuple of element-wise argument types, find a matching
+        signature in the dispatcher.
+
+        Return a 2-tuple containing the matching signature, and
+        compilation result.  Will return two None's if no matching
+        signature was found.
+        """
+        if self._frozen:
+            # If we cannot compile, coerce to the best matching loop
+            loop = numpy_support.ufunc_find_matching_loop(self, ewise_types)
+            if loop is None:
+                return None, None
+            ewise_types = tuple(loop.inputs + loop.outputs)[:len(ewise_types)]
+        for sig, cres in self._dispatcher.overloads.items():
+            if sig.args == ewise_types:
+                return sig, cres
+        return None, None
+
+    def _type_me(self, argtys, kwtys):
+        """
+        Implement AbstractTemplate.generic() for the typing class
+        built by DUFunc._install_type().
+
+        Return the call-site signature after either validating the
+        element-wise signature or compiling for it.
+        """
+        assert not kwtys
+        ufunc = self.ufunc
+        _handle_inputs_result = npydecl.Numpy_rules_ufunc._handle_inputs(
+            ufunc, argtys, kwtys)
+        base_types, explicit_outputs, ndims, layout = _handle_inputs_result
+        explicit_output_count = len(explicit_outputs)
+        if explicit_output_count > 0:
+            ewise_types = tuple(base_types[:-len(explicit_outputs)])
+        else:
+            ewise_types = tuple(base_types)
+        sig, cres = self.find_ewise_function(ewise_types)
+        if sig is None:
+            # Matching element-wise signature was not found; must
+            # compile.
+            if self._frozen:
+                raise TypeError("cannot call %s with types %s"
+                                % (self, argtys))
+            self._compile_for_argtys(ewise_types)
+            sig, cres = self.find_ewise_function(ewise_types)
+            assert sig is not None
+        if explicit_output_count > 0:
+            outtys = list(explicit_outputs)
+        elif ufunc.nout == 1:
+            if ndims > 0:
+                outtys = [types.Array(sig.return_type, ndims, layout)]
+            else:
+                outtys = [sig.return_type]
+        else:
+            raise NotImplementedError("typing gufuncs (nout > 1)")
+        outtys.extend(argtys)
+        return signature(*outtys)
+
+    def _install_cg(self, targetctx=None):
+        """
+        Install an implementation function for a DUFunc object in the
+        given target context.  If no target context is given, then
+        _install_cg() installs into the target context of the
+        dispatcher object (should be same default context used by
+        jit() and njit()).
+        """
+        if targetctx is None:
+            targetctx = self._dispatcher.targetdescr.target_context
+        _any = types.Any
+        _arr = types.Array
+        # Either all outputs are explicit or none of them are
+        sig0 = (_any,) * self.ufunc.nin + (_arr,) * self.ufunc.nout
+        sig1 = (_any,) * self.ufunc.nin
+        targetctx.insert_func_defn(
+            [(self._lower_me, self, sig) for sig in (sig0, sig1)])
+
+
+array_analysis.MAP_TYPES.append(DUFunc)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc.py
new file mode 100644
index 000000000..39190a246
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc.py
@@ -0,0 +1,192 @@
+from numba import typeof
+from numba.core import types
+from numba.np.ufunc.ufuncbuilder import GUFuncBuilder
+from numba.np.ufunc.sigparse import parse_signature
+from numba.np.numpy_support import ufunc_find_matching_loop
+from numba.core import serialize
+import functools
+
+
+class GUFunc(serialize.ReduceMixin):
+    """
+    Dynamic generalized universal function (GUFunc)
+    intended to act like a normal Numpy gufunc, but capable
+    of call-time (just-in-time) compilation of fast loops
+    specialized to inputs.
+    """
+
+    def __init__(self, py_func, signature, identity=None, cache=None,
+                 is_dynamic=False, targetoptions={}):
+        self.ufunc = None
+        self._frozen = False
+        self._is_dynamic = is_dynamic
+        self._identity = identity
+
+        # GUFunc cannot inherit from GUFuncBuilder because "identity"
+        # is a property of GUFunc. Thus, we hold a reference to a GUFuncBuilder
+        # object here
+        self.gufunc_builder = GUFuncBuilder(
+            py_func, signature, identity, cache, targetoptions)
+        self.__name__ = self.gufunc_builder.py_func.__name__
+        functools.update_wrapper(self, py_func)
+
+    def _reduce_states(self):
+        gb = self.gufunc_builder
+        dct = dict(
+            py_func=gb.py_func,
+            signature=gb.signature,
+            identity=self._identity,
+            cache=gb.cache,
+            is_dynamic=self._is_dynamic,
+            targetoptions=gb.targetoptions,
+            typesigs=gb._sigs,
+            frozen=self._frozen,
+        )
+        return dct
+
+    @classmethod
+    def _rebuild(cls, py_func, signature, identity, cache, is_dynamic,
+                 targetoptions, typesigs, frozen):
+        self = cls(py_func=py_func, signature=signature, identity=identity,
+                   cache=cache, is_dynamic=is_dynamic,
+                   targetoptions=targetoptions)
+        for sig in typesigs:
+            self.add(sig)
+        self.build_ufunc()
+        self._frozen = frozen
+        return self
+
+    def __repr__(self):
+        return f"<numba._GUFunc '{self.__name__}'>"
+
+    def add(self, fty):
+        self.gufunc_builder.add(fty)
+
+    def build_ufunc(self):
+        self.ufunc = self.gufunc_builder.build_ufunc()
+        return self
+
+    def disable_compile(self):
+        """
+        Disable the compilation of new signatures at call time.
+        """
+        # If disabling compilation then there must be at least one signature
+        assert len(self.gufunc_builder._sigs) > 0
+        self._frozen = True
+
+    @property
+    def is_dynamic(self):
+        return self._is_dynamic
+
+    @property
+    def nin(self):
+        return self.ufunc.nin
+
+    @property
+    def nout(self):
+        return self.ufunc.nout
+
+    @property
+    def nargs(self):
+        return self.ufunc.nargs
+
+    @property
+    def ntypes(self):
+        return self.ufunc.ntypes
+
+    @property
+    def types(self):
+        return self.ufunc.types
+
+    @property
+    def identity(self):
+        return self.ufunc.identity
+
+    @property
+    def signature(self):
+        return self.ufunc.signature
+
+    @property
+    def accumulate(self):
+        return self.ufunc.accumulate
+
+    @property
+    def at(self):
+        return self.ufunc.at
+
+    @property
+    def outer(self):
+        return self.ufunc.outer
+
+    @property
+    def reduce(self):
+        return self.ufunc.reduce
+
+    @property
+    def reduceat(self):
+        return self.ufunc.reduceat
+
+    def _get_ewise_dtypes(self, args):
+        argtys = map(lambda x: typeof(x), args)
+        tys = []
+        for argty in argtys:
+            if isinstance(argty, types.Array):
+                tys.append(argty.dtype)
+            else:
+                tys.append(argty)
+        return tys
+
+    def _num_args_match(self, *args):
+        parsed_sig = parse_signature(self.gufunc_builder.signature)
+        # parsed_sig[1] has always length 1
+        return len(args) == len(parsed_sig[0]) + 1
+
+    def _get_signature(self, *args):
+        parsed_sig = parse_signature(self.gufunc_builder.signature)
+        # ewise_types is a list of [int32, int32, int32, ...]
+        ewise_types = self._get_ewise_dtypes(args)
+
+        # first time calling the gufunc
+        # generate a signature based on input arguments
+        l = []
+        for idx, sig_dim in enumerate(parsed_sig[0]):
+            ndim = len(sig_dim)
+            if ndim == 0:  # append scalar
+                l.append(ewise_types[idx])
+            else:
+                l.append(types.Array(ewise_types[idx], ndim, 'A'))
+
+        # add return type to signature
+        retty = ewise_types[-1]
+        ret_ndim = len(parsed_sig[-1][0]) or 1  # small hack to return scalar
+        l.append(types.Array(retty, ret_ndim, 'A'))
+
+        return types.none(*l)
+
+    def __call__(self, *args, **kwargs):
+        # If compilation is disabled OR it is NOT a dynamic gufunc
+        # call the underlying gufunc
+        if self._frozen or not self.is_dynamic:
+            return self.ufunc(*args, **kwargs)
+        elif "out" in kwargs:
+            # If "out" argument is supplied
+            args += (kwargs.pop("out"),)
+
+        if self._num_args_match(*args) is False:
+            # It is not allowed to call a dynamic gufunc without
+            # providing all the arguments
+            # see: https://github.com/numba/numba/pull/5938#discussion_r506429392  # noqa: E501
+            msg = (
+                f"Too few arguments for function '{self.__name__}'. "
+                "Note that the pattern `out = gufunc(Arg1, Arg2, ..., ArgN)` "
+                "is not allowed. Use `gufunc(Arg1, Arg2, ..., ArgN, out) "
+                "instead.")
+            raise TypeError(msg)
+
+        # at this point we know the gufunc is a dynamic one
+        ewise = self._get_ewise_dtypes(args)
+        if not (self.ufunc and ufunc_find_matching_loop(self.ufunc, ewise)):
+            sig = self._get_signature(*args)
+            self.add(sig)
+            self.build_ufunc()
+        return self.ufunc(*args, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc_scheduler.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc_scheduler.cpp
new file mode 100644
index 000000000..b136d5004
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc_scheduler.cpp
@@ -0,0 +1,441 @@
+/*
+ * Copyright (c) 2017 Intel Corporation
+ * SPDX-License-Identifier: BSD-2-Clause
+ */
+
+#include <vector>
+#include <assert.h>
+#include <algorithm>
+#include <cmath>
+#include <iostream>
+#include <stdio.h>
+#include <stdint.h>
+#include "gufunc_scheduler.h"
+
+#ifdef _MSC_VER
+#define THREAD_LOCAL(ty) __declspec(thread) ty
+#else
+/* Non-standard C99 extension that's understood by gcc and clang */
+#define THREAD_LOCAL(ty) __thread ty
+#endif
+
+// Default 0 value means one evenly-sized chunk of work per worker thread.
+static THREAD_LOCAL(uintp) parallel_chunksize = 0;
+
+// round not available on VS2010.
+double guround (double number) {
+	return number < 0.0 ? ceil(number - 0.5) : floor(number + 0.5);
+}
+
+class RangeActual {
+public:
+    std::vector<intp> start, end;
+
+    RangeActual() {}
+
+    RangeActual(intp s, intp e) {
+        start.push_back(s);
+        end.push_back(e);
+    }
+
+    RangeActual(const std::vector<intp> &s, const std::vector<intp> &e) {
+        assert(s.size() == e.size());
+        start = s;
+        end = e;
+    }
+
+    RangeActual(const std::vector<intp> &lens) {
+        for(uintp i = 0; i < lens.size(); ++i) {
+            start.push_back(0);
+            end.push_back(lens[i] - 1);
+        }
+    }
+
+    RangeActual(uintp num_dims, intp *lens) {
+        for(uintp i = 0; i < num_dims; ++i) {
+            start.push_back(0);
+            end.push_back(lens[i] - 1);
+        }
+    }
+
+    RangeActual(uintp num_dims, intp *starts, intp *ends) {
+        for(uintp i = 0; i < num_dims; ++i) {
+            start.push_back(starts[i]);
+            end.push_back(ends[i]);
+        }
+    }
+
+    uintp ndim() const {
+        return start.size();
+    }
+
+    std::vector<intp> iters_per_dim() const {
+        std::vector<intp> ret;
+        for(uintp i = 0; i < start.size(); ++i) {
+			intp ret_val = end[i] - start[i] + 1;
+			if(end[i] < start[i])
+				ret_val = 0;
+            ret.push_back(ret_val);
+        }
+        return ret;
+    }
+
+    uintp total_size() const {
+        std::vector<intp> per_dim = iters_per_dim();
+        uintp res = 1;
+        for (size_t i = 0; i < per_dim.size(); ++i) {
+            res *= per_dim[i];
+        }
+        return res;
+    }
+
+    void print() const {
+        size_t i;
+        for(i = 0; i < start.size(); ++i) {
+            printf("%td ", start[i]);
+        }
+        for(i = 0; i < end.size(); ++i) {
+            printf("%td ", end[i]);
+        }
+    }
+};
+
+extern "C" uintp set_parallel_chunksize(uintp n) {
+    uintp orig = parallel_chunksize;
+    parallel_chunksize = n;
+    return orig;
+}
+
+extern "C" uintp get_parallel_chunksize() {
+    return parallel_chunksize;
+}
+
+extern "C" uintp get_sched_size(uintp num_threads, uintp num_dim, intp *starts, intp *ends) {
+    if (parallel_chunksize == 0) {
+        return num_threads;
+    }
+    RangeActual ra(num_dim, starts, ends);
+    uintp total_work_size = ra.total_size();
+    uintp num_divisions = total_work_size / parallel_chunksize;
+    return num_divisions < num_threads ? num_threads : num_divisions;
+}
+
+class dimlength {
+public:
+    uintp dim;
+    intp length;
+    dimlength(uintp d, intp l) : dim(d), length(l) {}
+};
+
+struct dimlength_by_dim {
+    bool operator()(const dimlength &a, const dimlength &b) const {
+        return a.dim < b.dim;
+    }
+};
+
+struct dimlength_by_length_reverse {
+    bool operator()(const dimlength &a, const dimlength &b) const {
+        return a.length > b.length;
+    }
+};
+
+class isf_range {
+public:
+    uintp dim;
+    intp lower_bound, upper_bound;
+    isf_range(uintp d, intp l, intp u) : dim(d), lower_bound(l), upper_bound(u) {}
+};
+
+struct isf_range_by_dim {
+    bool operator()(const isf_range &a, const isf_range &b) const {
+        return a.dim < b.dim;
+    }
+};
+
+/*
+ * m_a is the current start of the partition.
+ * m_b is the current end of the partition.
+ * m_c is the start of the next partition.
+ */
+class chunk_info {
+public:
+    intp m_a, m_b, m_c;
+    chunk_info(intp a, intp b, intp c) : m_a(a), m_b(b), m_c(c) {}
+};
+
+/*
+ * Split a space starting at rs and ending at re into "divisions" parts.
+ */
+chunk_info chunk(intp rs, intp re, intp divisions) {
+    assert(divisions >= 1);
+    intp total = (re - rs) + 1;
+    // If only one division then everything goes into that division.
+    if( divisions == 1) {
+        return chunk_info(rs, re, re + 1);
+    } else {
+        intp len = total / divisions;
+        intp res_end = rs + len - 1;
+        // Return the first division by starting at the beginning (rs) and going to
+        // the remaining length divided by the number of divisions.
+        return chunk_info(rs, res_end, res_end + 1);
+    }
+}
+
+chunk_info equalizing_chunk(intp rs, intp re, intp divisions, float thread_percent) {
+    assert(divisions >= 1);
+    intp total = (re - rs) + 1;
+    if (divisions == 1) {
+        return chunk_info(rs, re, re + 1);
+    }
+    else {
+        intp len = total * thread_percent;
+        intp res_end = rs + len - 1;
+        return chunk_info(rs, res_end, res_end + 1);
+    }
+}
+
+RangeActual isfRangeToActual(const std::vector<isf_range> &build) {
+    std::vector<isf_range> bunsort(build);
+    std::sort(bunsort.begin(), bunsort.end(), isf_range_by_dim());
+    std::vector<intp> lower_bounds(bunsort.size()), upper_bounds(bunsort.size());
+    for(uintp i = 0; i < bunsort.size(); ++i) {
+        lower_bounds[i] = bunsort[i].lower_bound;
+        upper_bounds[i] = bunsort[i].upper_bound;
+    }
+    return RangeActual(lower_bounds, upper_bounds);
+}
+
+/*
+ * Does the main work of splitting the iteration space between threads.
+ * In general, we start by allocating a number of threads to handle the largest dimension
+ * then call the routine recursively to allocate threads to the next largest dimension
+ * and so one.
+ */
+void divide_work(const RangeActual &full_iteration_space,
+                 std::vector<RangeActual> &assignments,
+                 std::vector<isf_range> &build,
+                 uintp start_thread,
+                 uintp end_thread,
+                 const std::vector<dimlength> &dims,
+                 uintp index) {
+    // Number of threads used for this dimension.
+    uintp num_threads = (end_thread - start_thread) + 1;
+
+    assert(num_threads >= 1);
+    // If there is only one thread left then it gets all the remaining work.
+    if(num_threads == 1) {
+        assert(build.size() <= dims.size());
+
+        // build holds the ongoing constructed range of iterations in each dimension.
+        // If the length of build is the number of dims then we have a complete allocation
+        // so store it in assignments.
+        if(build.size() == dims.size()) {
+            assignments[start_thread] = isfRangeToActual(build);
+        } else {
+            // There are still more dimensions to add.
+            // Create a copy of the incoming build.
+            std::vector<isf_range> new_build(build.begin()+0, build.begin()+index);
+            // Add an entry to new_build for this thread to handle the entire current dimension.
+            new_build.push_back(isf_range(dims[index].dim, full_iteration_space.start[dims[index].dim], full_iteration_space.end[dims[index].dim]));
+            // Recursively process.
+            divide_work(full_iteration_space, assignments, new_build, start_thread, end_thread, dims, index+1);
+        }
+    } else {
+        // There is more than 1 thread for handling this dimension so need to split the dimension between the threads.
+        assert(index < dims.size());
+        intp total_len = 0;
+        // Compute the total number of iterations in the remaining dimensions to be processed, including the current one.
+        for(uintp i = index; i < dims.size(); ++i) total_len += dims[i].length > 1 ? dims[i].length : 0;
+        uintp divisions_for_this_dim;
+        if(total_len == 0) {
+            divisions_for_this_dim = num_threads;
+        } else {
+            // We allocate the remaining threads proportionally to the ratio of the current dimension length to the total.
+            divisions_for_this_dim = intp(guround(num_threads * ((float)dims[index].length / total_len)));
+            if (divisions_for_this_dim < 1) {
+                        divisions_for_this_dim = 1;
+            }
+        }
+
+        // These are used to divide the iteration space.
+        intp chunkstart = full_iteration_space.start[dims[index].dim];
+        intp chunkend   = full_iteration_space.end[dims[index].dim];
+
+        // These are used to divide threads.
+        intp threadstart = start_thread;
+        intp threadend   = end_thread;
+
+        // for each division of the current dimension...
+        for(uintp i = 0; i < divisions_for_this_dim; ++i) {
+            chunk_info chunk_thread = chunk(threadstart, threadend, divisions_for_this_dim - i);
+            // Number of threads used for this division.
+            uintp threads_used_here = (1 + (chunk_thread.m_b - chunk_thread.m_a));
+            chunk_info chunk_index = equalizing_chunk(chunkstart, chunkend, divisions_for_this_dim - i, threads_used_here / (float)num_threads);
+            // Remember that the next division has threads_used_here fewer threads to allocate.
+            num_threads -= threads_used_here;
+            // m_c contains the next start value so update the iteration space and thread space in preparation for next iteration of this loop.
+            chunkstart = chunk_index.m_c;
+            threadstart = chunk_thread.m_c;
+            // Copy the incoming build to new_build.
+            std::vector<isf_range> new_build(build.begin()+0, build.begin()+index);
+            // Add this dimension to new_build to handle start=m_a to end=m_b.
+            new_build.push_back(isf_range(dims[index].dim, chunk_index.m_a, chunk_index.m_b));
+            // Recursively process the next dimension.
+            divide_work(full_iteration_space, assignments, new_build, chunk_thread.m_a, chunk_thread.m_b, dims, index+1);
+        }
+    }
+}
+
+/*
+ * Convert from internal format of vector of ranges to a flattened 2D-array usable by Python.
+ */
+template<class T>
+void flatten_schedule(const std::vector<RangeActual> &sched, T *out_sched) {
+    uintp outer = sched.size();
+    uintp inner = sched[0].start.size();
+    for(uintp i = 0; i < outer; ++i) {
+        for(uintp j = 0; j < inner; ++j) {
+            out_sched[(i*inner*2) + j] = sched[i].start[j];
+        }
+        for(uintp j = 0; j < inner; ++j) {
+            out_sched[(i*inner*2) + j + inner] = sched[i].end[j];
+        }
+    }
+}
+
+/*
+ * Main routine that computes a static schedule.
+ * full_space is the iteration space in each dimension.
+ * num_sched is the number of worker threads.
+ */
+std::vector<RangeActual> create_schedule(const RangeActual &full_space, uintp num_sched) {
+    // Compute the number of iterations to be run for each dimension.
+    std::vector<intp> ipd = full_space.iters_per_dim();
+
+    // We special-case one dimensional.
+    if(full_space.ndim() == 1) {
+        // Get the number of iterations for the single dimension.
+        intp ra_len = ipd[0];
+        // If there are fewer iterations for the single dimension than there are threads...
+        if(ra_len < 0 || (uintp)ra_len <= num_sched) {
+            std::vector<RangeActual> ret;
+            for(uintp i = 0; i < num_sched; ++i) {
+                // If the amount of iterations is less than the current thread then give it no work,
+                // signified by start of 1 and end of 0.
+                if(ra_len < 0 || (uintp)ra_len <= i) {
+                    ret.push_back(RangeActual((intp)1, (intp)0));
+                } else {
+                    // Give just i'th iteration to thread i.
+                    ret.push_back(RangeActual(full_space.start[0] + i, full_space.start[0] + i));
+                }
+            }
+            return ret;
+        } else {
+            // There are more iterations than threads.
+            std::vector<RangeActual> ret;
+            // cur holds the next unallocated iteration.
+            intp cur = 0;
+            // For each thread...
+            for(uintp i = 0; i < num_sched; ++i) {
+                // Compute the number of items to do in this thread as
+                // the floor of the amount of work left (ra_len-cur) divided
+                // by the number of threads left to which to allocate work.
+                intp ilen = ((ra_len-cur-1) / (num_sched-i)) + 1;
+
+                // Compute the start iteration number for that thread as the start iteration
+                // plus the modal number of iterations times the thread number.
+                intp start = full_space.start[0] + cur;
+                intp end;
+                // If this isn't the last thread then the end iteration number is one less
+                // than the start iteration number of the next thread.  If it is the last
+                // thread then assign all remaining iterations to it.
+                if(i < num_sched-1) {
+                    end = full_space.start[0] + (cur + ilen) - 1;
+                } else {
+                    end = full_space.end[0];
+                }
+                // Record the iteration start and end in the schedule.
+                ret.push_back(RangeActual(start, end));
+                // Update the next unallocated iteration.
+                cur += ilen;
+            }
+            return ret;
+        }
+    } else {
+        // Two or more dimensions are handled generically here.
+        std::vector<dimlength> dims;
+        // Create a vector of objects associating dimensional index to length.
+        for(uintp i = 0; i < ipd.size(); ++i) dims.push_back(dimlength(i, ipd[i]));
+        // Sort the dimensions in the reverse order of their length.
+        std::sort(dims.begin(), dims.end(), dimlength_by_length_reverse());
+        std::vector<RangeActual> assignments(num_sched, RangeActual((intp)1,(intp)0));
+        std::vector<isf_range> build;
+        // Compute the division of work across dimensions and threads.
+        divide_work(full_space, assignments, build, 0, num_sched-1, dims, 0);
+        return assignments;
+    }
+}
+
+/*
+ *   Print the calculated schedule when in debug mode.
+ */
+void print_schedule(const std::vector<RangeActual> &vra) {
+    size_t i;
+    for (i = 0; i < vra.size(); ++i) {
+        printf("sched[%td] = ", i);
+        vra[i].print();
+        printf("\n");
+    }
+}
+
+/*
+    num_dim (D) is the number of dimensions of the iteration space.
+    starts is the range-start of each of those dimensions, inclusive.
+    ends is the range-end of each of those dimensions, inclusive.
+    num_threads is the number (N) of chunks to break the iteration space into
+    sched is pre-allocated memory for the schedule to be stored in and is of size NxD.
+    debug is non-zero if DEBUG_ARRAY_OPT is turned on.
+*/
+extern "C" void do_scheduling_signed(uintp num_dim, intp *starts, intp *ends, uintp num_threads, intp *sched, intp debug) {
+    if (debug) {
+        printf("do_scheduling_signed\n");
+        printf("num_dim = %d\n", (int)num_dim);
+        printf("ranges = (");
+        for (unsigned i = 0; i < num_dim; i++) {
+            printf("[%d, %d], ", (int)starts[i], (int)ends[i]);
+        }
+        printf(")\n");
+        printf("num_threads = %d\n", (int)num_threads);
+    }
+
+    if (num_threads == 0) return;
+
+    RangeActual full_space(num_dim, starts, ends);
+    std::vector<RangeActual> ret = create_schedule(full_space, num_threads);
+    if (debug) {
+        print_schedule(ret);
+    }
+    flatten_schedule(ret, sched);
+}
+
+extern "C" void do_scheduling_unsigned(uintp num_dim, intp *starts, intp *ends, uintp num_threads, uintp *sched, intp debug) {
+    if (debug) {
+        printf("do_scheduling_unsigned\n");
+        printf("num_dim = %d\n", (int)num_dim);
+        printf("ranges = (");
+        for (unsigned i = 0; i < num_dim; i++) {
+            printf("[%d, %d], ", (int)starts[i], (int)ends[i]);
+        }
+        printf(")\n");
+        printf("num_threads = %d\n", (int)num_threads);
+    }
+
+    if (num_threads == 0) return;
+
+    RangeActual full_space(num_dim, starts, ends);
+    std::vector<RangeActual> ret = create_schedule(full_space, num_threads);
+    if (debug) {
+        print_schedule(ret);
+    }
+    flatten_schedule(ret, sched);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc_scheduler.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc_scheduler.h
new file mode 100644
index 000000000..314131d01
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/gufunc_scheduler.h
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2017 Intel Corporation
+ * SPDX-License-Identifier: BSD-2-Clause
+ */
+
+#ifndef GUFUNC_SCHEDULER
+#define GUFUNC_SCHEDULER
+
+/* define int64_t and uint64_t for Visual Studio, where stdint only available > VS2008 */
+#ifdef _MSC_VER
+    #define int64_t signed __int64
+    #define uint64_t unsigned __int64
+#else
+    #include <stdint.h>
+#endif
+
+#ifndef __SIZEOF_POINTER__
+    /* MSVC doesn't define __SIZEOF_POINTER__ */
+    #if defined(_WIN64)
+        #define intp int64_t
+        #define uintp uint64_t
+    #elif defined(_WIN32)
+        #define intp int
+        #define uintp unsigned
+    #else
+        #error "cannot determine size of intp"
+    #endif
+#elif __SIZEOF_POINTER__ == 8
+    #define intp int64_t
+    #define uintp uint64_t
+#else
+    #define intp int
+    #define uintp unsigned
+#endif
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+void do_scheduling_signed(uintp num_dim, intp *starts, intp *ends, uintp num_threads, intp *sched, intp debug);
+void do_scheduling_unsigned(uintp num_dim, intp *starts, intp *ends, uintp num_threads, uintp *sched, intp debug);
+uintp set_parallel_chunksize(uintp);
+uintp get_parallel_chunksize(void);
+uintp get_sched_size(uintp num_threads, uintp num_dim, intp *starts, intp *ends);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/omppool.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/omppool.cpp
new file mode 100644
index 000000000..ef73122a2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/omppool.cpp
@@ -0,0 +1,265 @@
+/*
+Threading layer on top of OpenMP.
+*/
+
+#include "../../_pymodule.h"
+#ifdef _POSIX_C_SOURCE
+#undef _POSIX_C_SOURCE
+#endif
+#ifdef _XOPEN_SOURCE
+#undef _XOPEN_SOURCE
+#endif
+#include <omp.h>
+#include <string.h>
+#include <stdio.h>
+#include "workqueue.h"
+#include "gufunc_scheduler.h"
+
+#ifdef _MSC_VER
+#include <malloc.h>
+#else
+#include <pthread.h>
+#include <sys/types.h>
+#include <unistd.h>
+#include <signal.h>
+#endif
+
+#define _DEBUG 0
+#define _DEBUG_FORK 0
+
+// OpenMP vendor strings
+#if defined(_MSC_VER)
+#define _OMP_VENDOR "MS"
+#elif defined(__clang__)
+#define _OMP_VENDOR "Intel"
+#elif defined(__GNUC__) // NOTE: clang also defines this, but it's checked above
+#define _NOT_FORKSAFE 1 // GNU OpenMP Not forksafe
+#define _OMP_VENDOR "GNU"
+#endif
+
+#if defined(_NOT_FORKSAFE)
+static pid_t parent_pid = 0; // 0 is not set, users can't own this anyway
+#endif
+
+
+#ifdef _MSC_VER
+#define THREAD_LOCAL(ty) __declspec(thread) ty
+#else
+/* Non-standard C99 extension that's understood by gcc and clang */
+#define THREAD_LOCAL(ty) __thread ty
+#endif
+
+// This is the number of threads that is default, it is set on initialisation of
+// the threading backend via the launch_threads() call
+static int _INIT_NUM_THREADS = -1;
+
+// This is the per-thread thread mask, each thread can carry its own mask.
+static THREAD_LOCAL(int) _TLS_num_threads = 0;
+
+static void
+set_num_threads(int count)
+{
+    _TLS_num_threads = count;
+}
+
+static int
+get_num_threads(void)
+{
+    if (_TLS_num_threads == 0)
+    {
+        // This is a thread that did not call launch_threads() but is still a
+        // "main" thread, probably from e.g. threading.Thread() use, it still
+        // has a TLS slot which is 0 from the lack of launch_threads() call
+        _TLS_num_threads = _INIT_NUM_THREADS;
+    }
+    return _TLS_num_threads;
+}
+
+static int
+get_thread_id(void)
+{
+    return omp_get_thread_num();
+}
+
+static void
+add_task(void *fn, void *args, void *dims, void *steps, void *data)
+{
+    puts("Running add_task() with omppool sequentially");
+    typedef void (*func_ptr_t)(void *args, void *dims, void *steps, void *data);
+    func_ptr_t func = reinterpret_cast<func_ptr_t>(fn);
+    func(args, dims, steps, data);
+}
+
+static void
+parallel_for(void *fn, char **args, size_t *dimensions, size_t *steps, void *data,
+             size_t inner_ndim, size_t array_count, int num_threads)
+{
+    typedef void (*func_ptr_t)(char **args, size_t *dims, size_t *steps, void *data);
+    func_ptr_t func = reinterpret_cast<func_ptr_t>(fn);
+    static bool printed = false;
+    if(!printed && _DEBUG)
+    {
+        puts("Using parallel_for");
+        printed = true;
+    }
+
+#if defined(_NOT_FORKSAFE)
+    // Handle GNU OpenMP not being forksafe...
+    // This checks if the pid set by the process that initialized this library
+    // matches the parent of this pid. If they do match this is a fork() from
+    // Python and not a spawn(), as spawn()s reinit the library. Forks are
+    // dangerous as GNU OpenMP is not forksafe, so warn then terminate.
+    if(_DEBUG_FORK)
+    {
+        printf("Registered parent pid=%d, my pid=%d, my parent pid=%d\n", parent_pid, getpid(), getppid());
+    }
+    if (parent_pid == getppid())
+    {
+        fprintf(stderr, "%s", "Terminating: fork() called from a process "
+                "already using GNU OpenMP, this is unsafe.\n");
+        raise(SIGTERM);
+        return;
+    }
+#endif
+
+    //     args = <ir.Argument '.1' of type i8**>,
+    //     dimensions = <ir.Argument '.2' of type i64*>
+    //     steps = <ir.Argument '.3' of type i64*>
+    //     data = <ir.Argument '.4' of type i8*>
+
+    const size_t arg_len = (inner_ndim + 1);
+    // index variable in OpenMP 'for' statement must have signed integral type for MSVC
+    const ptrdiff_t size = (ptrdiff_t)dimensions[0];
+
+    // holds the shared variable for `num_threads`, this is a bit superfluous
+    // but present to force thinking about the scope of validity
+    int agreed_nthreads = num_threads;
+
+    if(_DEBUG)
+    {
+        printf("inner_ndim: %zu\n",inner_ndim);
+        printf("arg_len: %zu\n", arg_len);
+        printf("total: %td\n", size);
+        printf("dimensions: ");
+        for(size_t j = 0; j < arg_len; j++)
+            printf("%zu, ", ((size_t *)dimensions)[j]);
+        printf("\nsteps: ");
+        for(size_t j = 0; j < array_count; j++)
+            printf("%zu, ", steps[j]);
+        printf("\n*args: ");
+        for(size_t j = 0; j < array_count; j++)
+            printf("%p, ", (void *)args[j]);
+        printf("\n");
+    }
+
+    // Set the thread mask on the pragma such that the state is scope limited
+    // and passed via a register on the OMP region call site, this limiting
+    // global state and racing
+    #pragma omp parallel num_threads(num_threads), shared(agreed_nthreads)
+    {
+        size_t * count_space = (size_t *)alloca(sizeof(size_t) * arg_len);
+        char ** array_arg_space = (char**)alloca(sizeof(char*) * array_count);
+
+        // tell the active thread team about the number of threads
+        set_num_threads(agreed_nthreads);
+
+        #pragma omp for
+        for(ptrdiff_t r = 0; r < size; r++)
+        {
+            memcpy(count_space, dimensions, arg_len * sizeof(size_t));
+            count_space[0] = 1;
+
+            if(_DEBUG)
+            {
+                printf("THREAD %p:", count_space);
+                printf("count_space: ");
+                for(size_t j = 0; j < arg_len; j++)
+                    printf("%zd, ", count_space[j]);
+                printf("\n");
+            }
+            for(size_t j = 0; j < array_count; j++)
+            {
+                char * base = args[j];
+                size_t step = steps[j];
+                ptrdiff_t offset = step * r;
+                array_arg_space[j] = base + offset;
+
+                if(0&&_DEBUG)
+                {
+                    printf("Index %zu\n", j);
+                    printf("-->Got base %p\n", (void *)base);
+                    printf("-->Got step %zu\n", step);
+                    printf("-->Got offset %td\n", offset);
+                    printf("-->Got addr %p\n", (void *)array_arg_space[j]);
+                }
+            }
+
+            if(_DEBUG)
+            {
+                printf("array_arg_space: ");
+                for(size_t j = 0; j < array_count; j++)
+                    printf("%p, ", (void *)array_arg_space[j]);
+                printf("\n");
+            }
+            func(array_arg_space, count_space, steps, data);
+        }
+    }
+}
+
+static void launch_threads(int count)
+{
+    // this must be called in a fork+thread safe region from Python
+    static bool initialized = false;
+#ifdef _NOT_FORKSAFE
+    parent_pid = getpid(); // record the parent PID for use later
+    if(_DEBUG_FORK)
+    {
+        printf("Setting parent as %d\n", parent_pid);
+    }
+#endif
+    if(initialized)
+        return;
+    if(_DEBUG)
+        puts("Using OpenMP");
+    if(count < 1)
+        return;
+    omp_set_num_threads(count);
+    omp_set_nested(0x1); // enable nesting, control depth with OMP env var
+    _INIT_NUM_THREADS = count;
+}
+
+static void synchronize(void)
+{
+}
+
+static void ready(void)
+{
+}
+
+MOD_INIT(omppool)
+{
+    PyObject *m;
+    MOD_DEF(m, "omppool", "No docs", NULL)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    SetAttrStringFromVoidPointer(m, launch_threads);
+    SetAttrStringFromVoidPointer(m, synchronize);
+    SetAttrStringFromVoidPointer(m, ready);
+    SetAttrStringFromVoidPointer(m, add_task);
+    SetAttrStringFromVoidPointer(m, parallel_for);
+    SetAttrStringFromVoidPointer(m, do_scheduling_signed);
+    SetAttrStringFromVoidPointer(m, do_scheduling_unsigned);
+    SetAttrStringFromVoidPointer(m, set_num_threads);
+    SetAttrStringFromVoidPointer(m, get_num_threads);
+    SetAttrStringFromVoidPointer(m, get_thread_id);
+    SetAttrStringFromVoidPointer(m, set_parallel_chunksize);
+    SetAttrStringFromVoidPointer(m, get_parallel_chunksize);
+    SetAttrStringFromVoidPointer(m, get_sched_size);
+
+    PyObject *tmp = PyString_FromString(_OMP_VENDOR);
+    PyObject_SetAttrString(m, "openmp_vendor", tmp);
+    Py_DECREF(tmp);
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/parallel.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/parallel.py
new file mode 100644
index 000000000..df0d05ac8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/parallel.py
@@ -0,0 +1,758 @@
+"""
+This file implements the code-generator for parallel-vectorize.
+
+ParallelUFunc is the platform independent base class for generating
+the thread dispatcher.  This thread dispatcher launches threads
+that execute the generated function of UFuncCore.
+UFuncCore is subclassed to specialize for the input/output types.
+The actual workload is invoked inside the function generated by UFuncCore.
+UFuncCore also defines a work-stealing mechanism that allows idle threads
+to steal works from other threads.
+"""
+
+import os
+import sys
+import warnings
+from threading import RLock as threadRLock
+from ctypes import CFUNCTYPE, c_int, CDLL, POINTER, c_uint
+
+import numpy as np
+
+import llvmlite.binding as ll
+from llvmlite import ir
+
+from numba.np.numpy_support import as_dtype
+from numba.core import types, cgutils, config, errors
+from numba.core.typing import signature
+from numba.np.ufunc.wrappers import _wrapper_info
+from numba.np.ufunc import ufuncbuilder
+from numba.extending import overload, intrinsic
+
+_IS_OSX = sys.platform.startswith('darwin')
+_IS_LINUX = sys.platform.startswith('linux')
+_IS_WINDOWS = sys.platform.startswith('win32')
+
+
+def get_thread_count():
+    """
+    Gets the available thread count.
+    """
+    t = config.NUMBA_NUM_THREADS
+    if t < 1:
+        raise ValueError("Number of threads specified must be > 0.")
+    return t
+
+
+NUM_THREADS = get_thread_count()
+
+
+def build_gufunc_kernel(library, ctx, info, sig, inner_ndim):
+    """Wrap the original CPU ufunc/gufunc with a parallel dispatcher.
+    This function will wrap gufuncs and ufuncs something like.
+
+    Args
+    ----
+    ctx
+        numba's codegen context
+
+    info: (library, env, name)
+        inner function info
+
+    sig
+        type signature of the gufunc
+
+    inner_ndim
+        inner dimension of the gufunc (this is len(sig.args) in the case of a
+        ufunc)
+
+    Returns
+    -------
+    wrapper_info : (library, env, name)
+        The info for the gufunc wrapper.
+
+    Details
+    -------
+
+    The kernel signature looks like this:
+
+    void kernel(char **args, npy_intp *dimensions, npy_intp* steps, void* data)
+
+    args - the input arrays + output arrays
+    dimensions - the dimensions of the arrays
+    steps - the step size for the array (this is like sizeof(type))
+    data - any additional data
+
+    The parallel backend then stages multiple calls to this kernel concurrently
+    across a number of threads. Practically, for each item of work, the backend
+    duplicates `dimensions` and adjusts the first entry to reflect the size of
+    the item of work, it also forms up an array of pointers into the args for
+    offsets to read/write from/to with respect to its position in the items of
+    work. This allows the same kernel to be used for each item of work, with
+    simply adjusted reads/writes/domain sizes and is safe by virtue of the
+    domain partitioning.
+
+    NOTE: The execution backend is passed the requested thread count, but it can
+    choose to ignore it (TBB)!
+    """
+    assert isinstance(info, tuple)  # guard against old usage
+    # Declare types and function
+    byte_t = ir.IntType(8)
+    byte_ptr_t = ir.PointerType(byte_t)
+    byte_ptr_ptr_t = ir.PointerType(byte_ptr_t)
+
+    intp_t = ctx.get_value_type(types.intp)
+    intp_ptr_t = ir.PointerType(intp_t)
+
+    fnty = ir.FunctionType(ir.VoidType(), [ir.PointerType(byte_ptr_t),
+                                           ir.PointerType(intp_t),
+                                           ir.PointerType(intp_t),
+                                           byte_ptr_t])
+    wrapperlib = ctx.codegen().create_library('parallelgufuncwrapper')
+    mod = wrapperlib.create_ir_module('parallel.gufunc.wrapper')
+    kernel_name = ".kernel.{}_{}".format(id(info.env), info.name)
+    lfunc = ir.Function(mod, fnty, name=kernel_name)
+
+    bb_entry = lfunc.append_basic_block('')
+
+    # Function body starts
+    builder = ir.IRBuilder(bb_entry)
+
+    args, dimensions, steps, data = lfunc.args
+
+    # Release the GIL (and ensure we have the GIL)
+    # Note: numpy ufunc may not always release the GIL; thus,
+    #       we need to ensure we have the GIL.
+    pyapi = ctx.get_python_api(builder)
+    gil_state = pyapi.gil_ensure()
+    thread_state = pyapi.save_thread()
+
+    def as_void_ptr(arg):
+        return builder.bitcast(arg, byte_ptr_t)
+
+    # Array count depends on whether an "output" array is needed. In the case
+    # of a void return type cf. gufunc it is the number of args, in the case of
+    # a non-void return type cf. ufunc it is the number of args + 1 so as to
+    # account for the output array.
+    array_count = len(sig.args)
+    if not isinstance(sig.return_type, types.NoneType):
+        array_count += 1
+
+    parallel_for_ty = ir.FunctionType(ir.VoidType(),
+                                      [byte_ptr_t] * 5 + [intp_t, ] * 3)
+    parallel_for = cgutils.get_or_insert_function(mod, parallel_for_ty,
+                                                  'numba_parallel_for')
+
+    # Reference inner-function and link
+    innerfunc_fnty = ir.FunctionType(
+        ir.VoidType(),
+        [byte_ptr_ptr_t, intp_ptr_t, intp_ptr_t, byte_ptr_t],
+    )
+    tmp_voidptr = cgutils.get_or_insert_function(mod, innerfunc_fnty,
+                                                 info.name,)
+    wrapperlib.add_linking_library(info.library)
+
+    get_num_threads = cgutils.get_or_insert_function(
+        builder.module,
+        ir.FunctionType(ir.IntType(types.intp.bitwidth), []),
+        "get_num_threads")
+
+    num_threads = builder.call(get_num_threads, [])
+
+    # Prepare call
+    fnptr = builder.bitcast(tmp_voidptr, byte_ptr_t)
+    innerargs = [as_void_ptr(x) for x
+                 in [args, dimensions, steps, data]]
+    builder.call(parallel_for, [fnptr] + innerargs +
+                 [intp_t(x) for x in (inner_ndim, array_count)] + [num_threads])
+
+    # Release the GIL
+    pyapi.restore_thread(thread_state)
+    pyapi.gil_release(gil_state)
+
+    builder.ret_void()
+
+    wrapperlib.add_ir_module(mod)
+    wrapperlib.add_linking_library(library)
+    return _wrapper_info(library=wrapperlib, name=lfunc.name, env=info.env)
+
+
+# ------------------------------------------------------------------------------
+
+class ParallelUFuncBuilder(ufuncbuilder.UFuncBuilder):
+    def build(self, cres, sig):
+        _launch_threads()
+
+        # Builder wrapper for ufunc entry point
+        ctx = cres.target_context
+        signature = cres.signature
+        library = cres.library
+        fname = cres.fndesc.llvm_func_name
+
+        info = build_ufunc_wrapper(library, ctx, fname, signature, cres)
+        ptr = info.library.get_pointer_to_function(info.name)
+        # Get dtypes
+        dtypenums = [np.dtype(a.name).num for a in signature.args]
+        dtypenums.append(np.dtype(signature.return_type.name).num)
+        keepalive = ()
+        return dtypenums, ptr, keepalive
+
+
+def build_ufunc_wrapper(library, ctx, fname, signature, cres):
+    innerfunc = ufuncbuilder.build_ufunc_wrapper(library, ctx, fname,
+                                                 signature, objmode=False,
+                                                 cres=cres)
+    info = build_gufunc_kernel(library, ctx, innerfunc, signature,
+                               len(signature.args))
+    return info
+
+# ---------------------------------------------------------------------------
+
+
+class ParallelGUFuncBuilder(ufuncbuilder.GUFuncBuilder):
+    def __init__(self, py_func, signature, identity=None, cache=False,
+                 targetoptions={}):
+        # Force nopython mode
+        targetoptions.update(dict(nopython=True))
+        super(
+            ParallelGUFuncBuilder,
+            self).__init__(
+            py_func=py_func,
+            signature=signature,
+            identity=identity,
+            cache=cache,
+            targetoptions=targetoptions)
+
+    def build(self, cres):
+        """
+        Returns (dtype numbers, function ptr, EnvironmentObject)
+        """
+        _launch_threads()
+
+        # Build wrapper for ufunc entry point
+        info = build_gufunc_wrapper(
+            self.py_func, cres, self.sin, self.sout, cache=self.cache,
+            is_parfors=False,
+        )
+        ptr = info.library.get_pointer_to_function(info.name)
+        env = info.env
+
+        # Get dtypes
+        dtypenums = []
+        for a in cres.signature.args:
+            if isinstance(a, types.Array):
+                ty = a.dtype
+            else:
+                ty = a
+            dtypenums.append(as_dtype(ty).num)
+
+        return dtypenums, ptr, env
+
+
+# This is not a member of the ParallelGUFuncBuilder function because it is
+# called without an enclosing instance from parfors
+
+def build_gufunc_wrapper(py_func, cres, sin, sout, cache, is_parfors):
+    """Build gufunc wrapper for the given arguments.
+    The *is_parfors* is a boolean indicating whether the gufunc is being
+    built for use as a ParFors kernel. This changes codegen and caching
+    behavior.
+    """
+    library = cres.library
+    ctx = cres.target_context
+    signature = cres.signature
+    innerinfo = ufuncbuilder.build_gufunc_wrapper(
+        py_func, cres, sin, sout, cache=cache, is_parfors=is_parfors,
+    )
+    sym_in = set(sym for term in sin for sym in term)
+    sym_out = set(sym for term in sout for sym in term)
+    inner_ndim = len(sym_in | sym_out)
+
+    info = build_gufunc_kernel(
+        library, ctx, innerinfo, signature, inner_ndim,
+    )
+    return info
+
+# ---------------------------------------------------------------------------
+
+
+_backend_init_thread_lock = threadRLock()
+
+_windows = sys.platform.startswith('win32')
+
+
+class _nop(object):
+    """A no-op contextmanager
+    """
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, *args):
+        pass
+
+
+_backend_init_process_lock = None
+
+
+def _set_init_process_lock():
+    global _backend_init_process_lock
+    try:
+        # Force the use of an RLock in the case a fork was used to start the
+        # process and thereby the init sequence, some of the threading backend
+        # init sequences are not fork safe. Also, windows global mp locks seem
+        # to be fine.
+        with _backend_init_thread_lock: # protect part-initialized module access
+            import multiprocessing
+            if "fork" in multiprocessing.get_start_method() or _windows:
+                ctx = multiprocessing.get_context()
+                _backend_init_process_lock = ctx.RLock()
+            else:
+                _backend_init_process_lock = _nop()
+
+    except OSError as e:
+
+        # probably lack of /dev/shm for semaphore writes, warn the user
+        msg = (
+            "Could not obtain multiprocessing lock due to OS level error: %s\n"
+            "A likely cause of this problem is '/dev/shm' is missing or"
+            "read-only such that necessary semaphores cannot be written.\n"
+            "*** The responsibility of ensuring multiprocessing safe access to "
+            "this initialization sequence/module import is deferred to the "
+            "user! ***\n"
+        )
+        warnings.warn(msg % str(e))
+
+        _backend_init_process_lock = _nop()
+
+
+_is_initialized = False
+
+# this is set by _launch_threads
+_threading_layer = None
+
+
+def threading_layer():
+    """
+    Get the name of the threading layer in use for parallel CPU targets
+    """
+    if _threading_layer is None:
+        raise ValueError("Threading layer is not initialized.")
+    else:
+        return _threading_layer
+
+
+def _check_tbb_version_compatible():
+    """
+    Checks that if TBB is present it is of a compatible version.
+    """
+    try:
+        # first check that the TBB version is new enough
+        if _IS_WINDOWS:
+            libtbb_name = 'tbb12.dll'
+        elif _IS_OSX:
+            libtbb_name = 'libtbb.12.dylib'
+        elif _IS_LINUX:
+            libtbb_name = 'libtbb.so.12'
+        else:
+            raise ValueError("Unknown operating system")
+        libtbb = CDLL(libtbb_name)
+        version_func = libtbb.TBB_runtime_interface_version
+        version_func.argtypes = []
+        version_func.restype = c_int
+        tbb_iface_ver = version_func()
+        if tbb_iface_ver < 12010: # magic number from TBB
+            msg = ("The TBB threading layer requires TBB "
+                   "version 2021 update 1 or later i.e., "
+                   "TBB_INTERFACE_VERSION >= 12010. Found "
+                   "TBB_INTERFACE_VERSION = %s. The TBB "
+                   "threading layer is disabled.") % tbb_iface_ver
+            problem = errors.NumbaWarning(msg)
+            warnings.warn(problem)
+            raise ImportError("Problem with TBB. Reason: %s" % msg)
+    except (ValueError, OSError) as e:
+        # Translate as an ImportError for consistent error class use, this error
+        # will never materialise
+        raise ImportError("Problem with TBB. Reason: %s" % e)
+
+
+def _launch_threads():
+    if not _backend_init_process_lock:
+        _set_init_process_lock()
+
+    with _backend_init_process_lock:
+        with _backend_init_thread_lock:
+            global _is_initialized
+            if _is_initialized:
+                return
+
+            def select_known_backend(backend):
+                """
+                Loads a specific threading layer backend based on string
+                """
+                lib = None
+                if backend.startswith("tbb"):
+                    try:
+                        # check if TBB is present and compatible
+                        _check_tbb_version_compatible()
+                        # now try and load the backend
+                        from numba.np.ufunc import tbbpool as lib
+                    except ImportError:
+                        pass
+                elif backend.startswith("omp"):
+                    # TODO: Check that if MKL is present that it is a version
+                    # that understands GNU OMP might be present
+                    try:
+                        from numba.np.ufunc import omppool as lib
+                    except ImportError:
+                        pass
+                elif backend.startswith("workqueue"):
+                    from numba.np.ufunc import workqueue as lib
+                else:
+                    msg = "Unknown value specified for threading layer: %s"
+                    raise ValueError(msg % backend)
+                return lib
+
+            def select_from_backends(backends):
+                """
+                Selects from presented backends and returns the first working
+                """
+                lib = None
+                for backend in backends:
+                    lib = select_known_backend(backend)
+                    if lib is not None:
+                        break
+                else:
+                    backend = ''
+                return lib, backend
+
+            t = str(config.THREADING_LAYER).lower()
+            namedbackends = config.THREADING_LAYER_PRIORITY
+            if not (len(namedbackends) == 3 and
+                    set(namedbackends) == {'tbb', 'omp', 'workqueue'}):
+                raise ValueError(
+                    "THREADING_LAYER_PRIORITY invalid: %s. "
+                    "It must be a permutation of "
+                    "{'tbb', 'omp', 'workqueue'}"
+                    % namedbackends
+                )
+
+            lib = None
+            err_helpers = dict()
+            err_helpers['TBB'] = ("Intel TBB is required, try:\n"
+                                  "$ conda/pip install tbb")
+            err_helpers['OSX_OMP'] = ("Intel OpenMP is required, try:\n"
+                                      "$ conda/pip install intel-openmp")
+            requirements = []
+
+            def raise_with_hint(required):
+                errmsg = "No threading layer could be loaded.\n%s"
+                hintmsg = "HINT:\n%s"
+                if len(required) == 0:
+                    hint = ''
+                if len(required) == 1:
+                    hint = hintmsg % err_helpers[required[0]]
+                if len(required) > 1:
+                    options = '\nOR\n'.join([err_helpers[x] for x in required])
+                    hint = hintmsg % ("One of:\n%s" % options)
+                raise ValueError(errmsg % hint)
+
+            if t in namedbackends:
+                # Try and load the specific named backend
+                lib = select_known_backend(t)
+                if not lib:
+                    # something is missing preventing a valid backend from
+                    # loading, set requirements for hinting
+                    if t == 'tbb':
+                        requirements.append('TBB')
+                    elif t == 'omp' and _IS_OSX:
+                        requirements.append('OSX_OMP')
+                libname = t
+            elif t in ['threadsafe', 'forksafe', 'safe']:
+                # User wants a specific behaviour...
+                available = ['tbb']
+                requirements.append('TBB')
+                if t == "safe":
+                    # "safe" is TBB, which is fork and threadsafe everywhere
+                    pass
+                elif t == "threadsafe":
+                    if _IS_OSX:
+                        requirements.append('OSX_OMP')
+                    # omp is threadsafe everywhere
+                    available.append('omp')
+                elif t == "forksafe":
+                    # everywhere apart from linux (GNU OpenMP) has a guaranteed
+                    # forksafe OpenMP, as OpenMP has better performance, prefer
+                    # this to workqueue
+                    if not _IS_LINUX:
+                        available.append('omp')
+                    if _IS_OSX:
+                        requirements.append('OSX_OMP')
+                    # workqueue is forksafe everywhere
+                    available.append('workqueue')
+                else:  # unreachable
+                    msg = "No threading layer available for purpose %s"
+                    raise ValueError(msg % t)
+                # select amongst available
+                lib, libname = select_from_backends(available)
+            elif t == 'default':
+                # If default is supplied, try them in order, tbb, omp,
+                # workqueue
+                lib, libname = select_from_backends(namedbackends)
+                if not lib:
+                    # set requirements for hinting
+                    requirements.append('TBB')
+                    if _IS_OSX:
+                        requirements.append('OSX_OMP')
+            else:
+                msg = "The threading layer requested '%s' is unknown to Numba."
+                raise ValueError(msg % t)
+
+            # No lib found, raise and hint
+            if not lib:
+                raise_with_hint(requirements)
+
+            ll.add_symbol('numba_parallel_for', lib.parallel_for)
+            ll.add_symbol('do_scheduling_signed', lib.do_scheduling_signed)
+            ll.add_symbol('do_scheduling_unsigned', lib.do_scheduling_unsigned)
+
+            launch_threads = CFUNCTYPE(None, c_int)(lib.launch_threads)
+            launch_threads(NUM_THREADS)
+
+            _load_threading_functions(lib)  # load late
+
+            # set library name so it can be queried
+            global _threading_layer
+            _threading_layer = libname
+            _is_initialized = True
+
+
+def _load_threading_functions(lib):
+
+    ll.add_symbol('get_num_threads', lib.get_num_threads)
+    ll.add_symbol('set_num_threads', lib.set_num_threads)
+    ll.add_symbol('get_thread_id', lib.get_thread_id)
+
+    global _set_num_threads
+    _set_num_threads = CFUNCTYPE(None, c_int)(lib.set_num_threads)
+    _set_num_threads(NUM_THREADS)
+
+    global _get_num_threads
+    _get_num_threads = CFUNCTYPE(c_int)(lib.get_num_threads)
+
+    global _get_thread_id
+    _get_thread_id = CFUNCTYPE(c_int)(lib.get_thread_id)
+
+    ll.add_symbol('set_parallel_chunksize', lib.set_parallel_chunksize)
+    ll.add_symbol('get_parallel_chunksize', lib.get_parallel_chunksize)
+    ll.add_symbol('get_sched_size', lib.get_sched_size)
+    global _set_parallel_chunksize
+    _set_parallel_chunksize = CFUNCTYPE(c_uint,
+                                        c_uint)(lib.set_parallel_chunksize)
+    global _get_parallel_chunksize
+    _get_parallel_chunksize = CFUNCTYPE(c_uint)(lib.get_parallel_chunksize)
+    global _get_sched_size
+    _get_sched_size = CFUNCTYPE(c_uint,
+                                c_uint,
+                                c_uint,
+                                POINTER(c_int),
+                                POINTER(c_int))(lib.get_sched_size)
+
+
+# Some helpers to make set_num_threads jittable
+
+def gen_snt_check():
+    from numba.core.config import NUMBA_NUM_THREADS
+    msg = "The number of threads must be between 1 and %s" % NUMBA_NUM_THREADS
+
+    def snt_check(n):
+        if n > NUMBA_NUM_THREADS or n < 1:
+            raise ValueError(msg)
+    return snt_check
+
+
+snt_check = gen_snt_check()
+
+
+@overload(snt_check)
+def ol_snt_check(n):
+    return snt_check
+
+
+def set_num_threads(n):
+    """
+    Set the number of threads to use for parallel execution.
+
+    By default, all :obj:`numba.config.NUMBA_NUM_THREADS` threads are used.
+
+    This functionality works by masking out threads that are not used.
+    Therefore, the number of threads *n* must be less than or equal to
+    :obj:`~.NUMBA_NUM_THREADS`, the total number of threads that are launched.
+    See its documentation for more details.
+
+    This function can be used inside of a jitted function.
+
+    Parameters
+    ----------
+    n: The number of threads. Must be between 1 and NUMBA_NUM_THREADS.
+
+    See Also
+    --------
+    get_num_threads, numba.config.NUMBA_NUM_THREADS,
+    numba.config.NUMBA_DEFAULT_NUM_THREADS, :envvar:`NUMBA_NUM_THREADS`
+
+    """
+    _launch_threads()
+    if not isinstance(n, (int, np.integer)):
+        raise TypeError("The number of threads specified must be an integer")
+    snt_check(n)
+    _set_num_threads(n)
+
+
+@overload(set_num_threads)
+def ol_set_num_threads(n):
+    _launch_threads()
+    if not isinstance(n, types.Integer):
+        msg = "The number of threads specified must be an integer"
+        raise errors.TypingError(msg)
+
+    def impl(n):
+        snt_check(n)
+        _set_num_threads(n)
+    return impl
+
+
+def get_num_threads():
+    """
+    Get the number of threads used for parallel execution.
+
+    By default (if :func:`~.set_num_threads` is never called), all
+    :obj:`numba.config.NUMBA_NUM_THREADS` threads are used.
+
+    This number is less than or equal to the total number of threads that are
+    launched, :obj:`numba.config.NUMBA_NUM_THREADS`.
+
+    This function can be used inside of a jitted function.
+
+    Returns
+    -------
+    The number of threads.
+
+    See Also
+    --------
+    set_num_threads, numba.config.NUMBA_NUM_THREADS,
+    numba.config.NUMBA_DEFAULT_NUM_THREADS, :envvar:`NUMBA_NUM_THREADS`
+
+    """
+    _launch_threads()
+    num_threads = _get_num_threads()
+    if num_threads <= 0:
+        raise RuntimeError("Invalid number of threads. "
+                           "This likely indicates a bug in Numba. "
+                           "(thread_id=%s, num_threads=%s)" %
+                           (get_thread_id(), num_threads))
+    return num_threads
+
+
+@overload(get_num_threads)
+def ol_get_num_threads():
+    _launch_threads()
+
+    def impl():
+        num_threads = _get_num_threads()
+        if num_threads <= 0:
+            print("Broken thread_id: ", get_thread_id())
+            print("num_threads: ", num_threads)
+            raise RuntimeError("Invalid number of threads. "
+                               "This likely indicates a bug in Numba.")
+        return num_threads
+    return impl
+
+
+@intrinsic
+def _iget_num_threads(typingctx):
+    _launch_threads()
+
+    def codegen(context, builder, signature, args):
+        mod = builder.module
+        fnty = ir.FunctionType(cgutils.intp_t, [])
+        fn = cgutils.get_or_insert_function(mod, fnty, "get_num_threads")
+        return builder.call(fn, [])
+    return signature(types.intp), codegen
+
+
+def get_thread_id():
+    """
+    Returns a unique ID for each thread in the range 0 (inclusive)
+    to :func:`~.get_num_threads` (exclusive).
+    """
+    # Called from the interpreter directly, this should return 0
+    # Called from a sequential JIT region, this should return 0
+    # Called from a parallel JIT region, this should return 0..N
+    # Called from objmode in a parallel JIT region, this should return 0..N
+    _launch_threads()
+    return _get_thread_id()
+
+
+@overload(get_thread_id)
+def ol_get_thread_id():
+    _launch_threads()
+
+    def impl():
+        return _iget_thread_id()
+    return impl
+
+
+@intrinsic
+def _iget_thread_id(typingctx):
+    def codegen(context, builder, signature, args):
+        mod = builder.module
+        fnty = ir.FunctionType(cgutils.intp_t, [])
+        fn = cgutils.get_or_insert_function(mod, fnty, "get_thread_id")
+        return builder.call(fn, [])
+    return signature(types.intp), codegen
+
+
+_DYLD_WORKAROUND_SET = 'NUMBA_DYLD_WORKAROUND' in os.environ
+_DYLD_WORKAROUND_VAL = int(os.environ.get('NUMBA_DYLD_WORKAROUND', 0))
+
+if _DYLD_WORKAROUND_SET and _DYLD_WORKAROUND_VAL:
+    _launch_threads()
+
+
+def set_parallel_chunksize(n):
+    _launch_threads()
+    if not isinstance(n, (int, np.integer)):
+        raise TypeError("The parallel chunksize must be an integer")
+    global _set_parallel_chunksize
+    if n < 0:
+        raise ValueError("chunksize must be greater than or equal to zero")
+    return _set_parallel_chunksize(n)
+
+
+def get_parallel_chunksize():
+    _launch_threads()
+    global _get_parallel_chunksize
+    return _get_parallel_chunksize()
+
+
+@overload(set_parallel_chunksize)
+def ol_set_parallel_chunksize(n):
+    _launch_threads()
+    if not isinstance(n, types.Integer):
+        msg = "The parallel chunksize must be an integer"
+        raise errors.TypingError(msg)
+
+    def impl(n):
+        if n < 0:
+            raise ValueError("chunksize must be greater than or equal to zero")
+        return _set_parallel_chunksize(n)
+    return impl
+
+
+@overload(get_parallel_chunksize)
+def ol_get_parallel_chunksize():
+    _launch_threads()
+
+    def impl():
+        return _get_parallel_chunksize()
+    return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/sigparse.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/sigparse.py
new file mode 100644
index 000000000..67ca346c9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/sigparse.py
@@ -0,0 +1,63 @@
+import tokenize
+import string
+
+
+def parse_signature(sig):
+    '''Parse generalized ufunc signature.
+
+    NOTE: ',' (COMMA) is a delimiter; not separator.
+          This means trailing comma is legal.
+    '''
+    def stripws(s):
+        return ''.join(c for c in s if c not in string.whitespace)
+
+    def tokenizer(src):
+        def readline():
+            yield src
+        gen = readline()
+        return tokenize.generate_tokens(lambda: next(gen))
+
+    def parse(src):
+        tokgen = tokenizer(src)
+        while True:
+            tok = next(tokgen)
+            if tok[1] == '(':
+                symbols = []
+                while True:
+                    tok = next(tokgen)
+                    if tok[1] == ')':
+                        break
+                    elif tok[0] == tokenize.NAME:
+                        symbols.append(tok[1])
+                    elif tok[1] == ',':
+                        continue
+                    else:
+                        raise ValueError('bad token in signature "%s"' % tok[1])
+                yield tuple(symbols)
+                tok = next(tokgen)
+                if tok[1] == ',':
+                    continue
+                elif tokenize.ISEOF(tok[0]):
+                    break
+            elif tokenize.ISEOF(tok[0]):
+                break
+            else:
+                raise ValueError('bad token in signature "%s"' % tok[1])
+
+    ins, _, outs = stripws(sig).partition('->')
+    inputs = list(parse(ins))
+    outputs = list(parse(outs))
+
+    # check that all output symbols are defined in the inputs
+    isym = set()
+    osym = set()
+    for grp in inputs:
+        isym |= set(grp)
+    for grp in outputs:
+        osym |= set(grp)
+
+    diff = osym.difference(isym)
+    if diff:
+        raise NameError('undefined output symbols: %s' % ','.join(sorted(diff)))
+
+    return inputs, outputs
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/tbbpool.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/tbbpool.cpp
new file mode 100644
index 000000000..2a8cb8f60
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/tbbpool.cpp
@@ -0,0 +1,352 @@
+/*
+Implement parallel vectorize workqueue on top of Intel TBB.
+*/
+
+#define TBB_PREVIEW_WAITING_FOR_WORKERS 1
+/* tbb.h redefines these */
+#include "../../_pymodule.h"
+#ifdef _POSIX_C_SOURCE
+#undef _POSIX_C_SOURCE
+#endif
+#ifdef _XOPEN_SOURCE
+#undef _XOPEN_SOURCE
+#endif
+
+#include <tbb/tbb.h>
+#include <string.h>
+#include <stdio.h>
+#include <thread>
+#include "workqueue.h"
+
+#include "gufunc_scheduler.h"
+
+/* TBB 2019 U5 is the minimum required version as this is needed:
+ * https://github.com/intel/tbb/blob/18070344d755ece04d169e6cc40775cae9288cee/CHANGES#L133-L134
+ * and therefore
+ * https://github.com/intel/tbb/blob/18070344d755ece04d169e6cc40775cae9288cee/CHANGES#L128-L129
+ * from here:
+ * https://github.com/intel/tbb/blob/2019_U5/include/tbb/tbb_stddef.h#L29
+ */
+#if (TBB_INTERFACE_VERSION >= 12060) || (TBB_INTERFACE_VERSION < 12010)
+#error "TBB version is incompatible, 2021.1 through to 2021.5 required, i.e. 12010 <= TBB_INTERFACE_VERSION < 12060"
+#endif
+
+#define _DEBUG 0
+#define _TRACE_SPLIT 0
+
+static tbb::task_group *tg = NULL;
+
+static tbb::task_scheduler_handle tsh;
+static bool tsh_was_initialized = false;
+
+#ifdef _MSC_VER
+#define THREAD_LOCAL(ty) __declspec(thread) ty
+#else
+/* Non-standard C99 extension that's understood by gcc and clang */
+#define THREAD_LOCAL(ty) __thread ty
+#endif
+
+// This is the number of threads that is default, it is set on initialisation of
+// the threading backend via the launch_threads() call
+static int _INIT_NUM_THREADS = -1;
+
+// This is the per-thread thread mask, each thread can carry its own mask.
+static THREAD_LOCAL(int) _TLS_num_threads = 0;
+
+
+static void
+set_num_threads(int count)
+{
+    _TLS_num_threads = count;
+}
+
+static int
+get_num_threads(void)
+{
+    if (_TLS_num_threads == 0)
+    {
+        // This is a thread that did not call launch_threads() but is still a
+        // "main" thread, probably from e.g. threading.Thread() use, it still
+        // has a TLS slot which is 0 from the lack of launch_threads() call
+        _TLS_num_threads = _INIT_NUM_THREADS;
+    }
+    return _TLS_num_threads;
+}
+
+static int
+get_thread_id(void)
+{
+    int tid = tbb::this_task_arena::current_thread_index();
+    // This function may be called from pure python or from a sequential JIT
+    // region, in either case, the task_arena may not be initialised. This
+    // condition is intercepted and a thread ID of 0 is returned.
+    if (tid == tbb::task_arena::not_initialized)
+    {
+        return 0;
+    } else {
+        return tid;
+    }
+}
+
+// watch the arena, if it decides to create more threads/add threads into the
+// arena then make sure they get the right thread count
+class fix_tls_observer: public tbb::task_scheduler_observer {
+    int mask_val;
+    void on_scheduler_entry( bool is_worker ) override;
+public:
+    fix_tls_observer(tbb::task_arena &arena, int mask) : tbb::task_scheduler_observer(arena), mask_val(mask)
+    {
+        observe(true);
+    }
+};
+
+void fix_tls_observer::on_scheduler_entry(bool worker) {
+    set_num_threads(mask_val);
+}
+
+static void
+add_task(void *fn, void *args, void *dims, void *steps, void *data)
+{
+    tg->run([=]
+    {
+        auto func = reinterpret_cast<void (*)(void *args, void *dims, void *steps, void *data)>(fn);
+        func(args, dims, steps, data);
+    });
+}
+
+static void
+parallel_for(void *fn, char **args, size_t *dimensions, size_t *steps, void *data,
+             size_t inner_ndim, size_t array_count, int num_threads)
+{
+    static bool printed = false;
+    if(!printed && _DEBUG)
+    {
+        puts("Using parallel_for");
+        printed = true;
+    }
+
+    //     args = <ir.Argument '.1' of type i8**>,
+    //     dimensions = <ir.Argument '.2' of type i64*>
+    //     steps = <ir.Argument '.3' of type i64*>
+    //     data = <ir.Argument '.4' of type i8*>
+
+    const size_t arg_len = (inner_ndim + 1);
+
+    if(_DEBUG && _TRACE_SPLIT)
+    {
+        printf("inner_ndim: %lu\n",inner_ndim);
+        printf("arg_len: %lu\n", arg_len);
+        printf("total: %lu\n", dimensions[0]);
+        printf("dimensions: ");
+        for(size_t j = 0; j < arg_len; j++)
+            printf("%lu, ", ((size_t *)dimensions)[j]);
+        printf("\nsteps: ");
+        for(size_t j = 0; j < array_count; j++)
+            printf("%lu, ", steps[j]);
+        printf("\n*args: ");
+        for(size_t j = 0; j < array_count; j++)
+            printf("%p, ", (void *)args[j]);
+        printf("\n");
+    }
+
+    // This is making the assumption that the calling thread knows the truth
+    // about num_threads, which should be correct via the following:
+    // program starts/reinits and the threadpool launches, num_threads TLS is
+    // set as default. Any thread spawned on init making a call to this function
+    // will have a valid num_threads TLS slot and so the task_arena is sized
+    // appropriately and it's value is used in the observer that fixes the TLS
+    // slots of any subsequent threads joining the task_arena. This leads to
+    // all threads in a task_arena having valid num_threads TLS slots prior to
+    // doing any work. Any further call to query the TLS slot value made by any
+    // thread in the arena is then safe and were any thread to create a nested
+    // parallel region the same logic applies as per program start/reinit.
+    tbb::task_arena limited(num_threads);
+    fix_tls_observer observer(limited, num_threads);
+
+    limited.execute([&]{
+        using range_t = tbb::blocked_range<size_t>;
+        tbb::parallel_for(range_t(0, dimensions[0]), [=](const range_t &range)
+        {
+            size_t * count_space = (size_t *)alloca(sizeof(size_t) * arg_len);
+            char ** array_arg_space = (char**)alloca(sizeof(char*) * array_count);
+            memcpy(count_space, dimensions, arg_len * sizeof(size_t));
+            count_space[0] = range.size();
+
+            if(_DEBUG && _TRACE_SPLIT > 1)
+            {
+                printf("THREAD %p:", count_space);
+                printf("count_space: ");
+                for(size_t j = 0; j < arg_len; j++)
+                    printf("%lu, ", count_space[j]);
+                printf("\n");
+            }
+            for(size_t j = 0; j < array_count; j++)
+            {
+                char * base = args[j];
+                size_t step = steps[j];
+                ptrdiff_t offset = step * range.begin();
+                array_arg_space[j] = base + offset;
+
+                if(_DEBUG && _TRACE_SPLIT > 2)
+                {
+                    printf("Index %ld\n", j);
+                    printf("-->Got base %p\n", (void *)base);
+                    printf("-->Got step %lu\n", step);
+                    printf("-->Got offset %ld\n", offset);
+                    printf("-->Got addr %p\n", (void *)array_arg_space[j]);
+                }
+            }
+
+            if(_DEBUG && _TRACE_SPLIT > 2)
+            {
+                printf("array_arg_space: ");
+                for(size_t j = 0; j < array_count; j++)
+                    printf("%p, ", (void *)array_arg_space[j]);
+                printf("\n");
+            }
+            auto func = reinterpret_cast<void (*)(char **args, size_t *dims, size_t *steps, void *data)>(fn);
+            func(array_arg_space, count_space, steps, data);
+        });
+    });
+}
+
+static std::thread::id init_thread_id;
+static THREAD_LOCAL(bool) need_reinit_after_fork = false;
+
+static void set_main_thread()
+{
+    init_thread_id = std::this_thread::get_id();
+}
+
+static bool is_main_thread()
+{
+    return std::this_thread::get_id() == init_thread_id;
+}
+
+static void prepare_fork(void)
+{
+    if(_DEBUG)
+    {
+        puts("Suspending TBB: prepare fork");
+    }
+    if (tsh_was_initialized)
+    {
+        if(is_main_thread())
+        {
+            if (!tbb::finalize(tsh, std::nothrow))
+            {
+                tbb::task_scheduler_handle::release(tsh);
+                puts("Unable to join threads to shut down before fork(). "
+                     "This can break multithreading in child processes.\n");
+            }
+            tsh_was_initialized = false;
+            need_reinit_after_fork = true;
+        }
+        else
+        {
+            fprintf(stderr, "Numba: Attempted to fork from a non-main thread, "
+                            "the TBB library may be in an invalid state in the "
+                            "child process.\n");
+        }
+    }
+}
+
+static void reset_after_fork(void)
+{
+    if(_DEBUG)
+    {
+        puts("Resuming TBB: after fork");
+    }
+
+    if(need_reinit_after_fork)
+    {
+        tsh = tbb::task_scheduler_handle::get();
+        set_main_thread();
+        tsh_was_initialized = true;
+        need_reinit_after_fork = false;
+    }
+}
+
+static void unload_tbb(void)
+{
+    if (tg)
+    {
+        if(_DEBUG)
+        {
+            puts("Unloading TBB");
+        }
+        tg->wait();
+        delete tg;
+        tg = NULL;
+    }
+    if (tsh_was_initialized)
+    {
+        // blocking terminate is not strictly required here, ignore return value
+        (void)tbb::finalize(tsh, std::nothrow);
+        tsh_was_initialized = false;
+    }
+}
+
+static void launch_threads(int count)
+{
+    if(tg)
+        return;
+
+    if(_DEBUG)
+        puts("Using TBB");
+
+    if(count < 1)
+        count = tbb::task_arena::automatic;
+
+    tsh = tbb::task_scheduler_handle::get();
+    tsh_was_initialized = true;
+
+    tg = new tbb::task_group;
+    tg->run([] {}); // start creating threads asynchronously
+
+    _INIT_NUM_THREADS = count;
+
+    set_main_thread();
+
+#ifndef _MSC_VER
+    pthread_atfork(prepare_fork, reset_after_fork, reset_after_fork);
+#endif
+}
+
+static void synchronize(void)
+{
+    tg->wait();
+}
+
+static void ready(void)
+{
+}
+
+MOD_INIT(tbbpool)
+{
+    PyObject *m;
+    MOD_DEF(m, "tbbpool", "No docs", NULL)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+    PyModuleDef *md = PyModule_GetDef(m);
+    if (md)
+    {
+        md->m_free = (freefunc)unload_tbb;
+    }
+
+    SetAttrStringFromVoidPointer(m, launch_threads);
+    SetAttrStringFromVoidPointer(m, synchronize);
+    SetAttrStringFromVoidPointer(m, ready);
+    SetAttrStringFromVoidPointer(m, add_task);
+    SetAttrStringFromVoidPointer(m, parallel_for);
+    SetAttrStringFromVoidPointer(m, do_scheduling_signed);
+    SetAttrStringFromVoidPointer(m, do_scheduling_unsigned);
+    SetAttrStringFromVoidPointer(m, set_num_threads);
+    SetAttrStringFromVoidPointer(m, get_num_threads);
+    SetAttrStringFromVoidPointer(m, get_thread_id);
+    SetAttrStringFromVoidPointer(m, set_parallel_chunksize);
+    SetAttrStringFromVoidPointer(m, get_parallel_chunksize);
+    SetAttrStringFromVoidPointer(m, get_sched_size);
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/ufuncbuilder.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/ufuncbuilder.py
new file mode 100644
index 000000000..a2847883a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/ufuncbuilder.py
@@ -0,0 +1,394 @@
+# -*- coding: utf-8 -*-
+
+import inspect
+from contextlib import contextmanager
+
+from numba.core import config, targetconfig
+from numba.core.decorators import jit
+from numba.core.descriptors import TargetDescriptor
+from numba.core.extending import is_jitted
+from numba.core.options import TargetOptions, include_default_options
+from numba.core.registry import cpu_target
+from numba.core.target_extension import dispatcher_registry, target_registry
+from numba.core import utils, types, serialize, compiler, sigutils
+from numba.np.numpy_support import as_dtype
+from numba.np.ufunc import _internal
+from numba.np.ufunc.sigparse import parse_signature
+from numba.np.ufunc.wrappers import build_ufunc_wrapper, build_gufunc_wrapper
+from numba.core.caching import FunctionCache, NullCache
+from numba.core.compiler_lock import global_compiler_lock
+
+
+_options_mixin = include_default_options(
+    "nopython",
+    "forceobj",
+    "boundscheck",
+    "fastmath",
+    "target_backend",
+)
+
+
+class UFuncTargetOptions(_options_mixin, TargetOptions):
+
+    def finalize(self, flags, options):
+        if not flags.is_set("enable_pyobject"):
+            flags.enable_pyobject = True
+
+        if not flags.is_set("enable_looplift"):
+            flags.enable_looplift = True
+
+        flags.inherit_if_not_set("nrt", default=True)
+
+        if not flags.is_set("debuginfo"):
+            flags.debuginfo = config.DEBUGINFO_DEFAULT
+
+        if not flags.is_set("boundscheck"):
+            flags.boundscheck = flags.debuginfo
+
+        flags.enable_pyobject_looplift = True
+
+        flags.inherit_if_not_set("fastmath")
+
+
+class UFuncTarget(TargetDescriptor):
+    options = UFuncTargetOptions
+
+    def __init__(self):
+        super().__init__('ufunc')
+
+    @property
+    def typing_context(self):
+        return cpu_target.typing_context
+
+    @property
+    def target_context(self):
+        return cpu_target.target_context
+
+
+ufunc_target = UFuncTarget()
+
+
+class UFuncDispatcher(serialize.ReduceMixin):
+    """
+    An object handling compilation of various signatures for a ufunc.
+    """
+    targetdescr = ufunc_target
+
+    def __init__(self, py_func, locals={}, targetoptions={}):
+        self.py_func = py_func
+        self.overloads = utils.UniqueDict()
+        self.targetoptions = targetoptions
+        self.locals = locals
+        self.cache = NullCache()
+
+    def _reduce_states(self):
+        """
+        NOTE: part of ReduceMixin protocol
+        """
+        return dict(
+            pyfunc=self.py_func,
+            locals=self.locals,
+            targetoptions=self.targetoptions,
+        )
+
+    @classmethod
+    def _rebuild(cls, pyfunc, locals, targetoptions):
+        """
+        NOTE: part of ReduceMixin protocol
+        """
+        return cls(py_func=pyfunc, locals=locals, targetoptions=targetoptions)
+
+    def enable_caching(self):
+        self.cache = FunctionCache(self.py_func)
+
+    def compile(self, sig, locals={}, **targetoptions):
+        locs = self.locals.copy()
+        locs.update(locals)
+
+        topt = self.targetoptions.copy()
+        topt.update(targetoptions)
+
+        flags = compiler.Flags()
+        self.targetdescr.options.parse_as_flags(flags, topt)
+
+        flags.no_cpython_wrapper = True
+        flags.error_model = "numpy"
+        # Disable loop lifting
+        # The feature requires a real
+        #  python function
+        flags.enable_looplift = False
+
+        return self._compile_core(sig, flags, locals)
+
+    def _compile_core(self, sig, flags, locals):
+        """
+        Trigger the compiler on the core function or load a previously
+        compiled version from the cache.  Returns the CompileResult.
+        """
+        typingctx = self.targetdescr.typing_context
+        targetctx = self.targetdescr.target_context
+
+        @contextmanager
+        def store_overloads_on_success():
+            # use to ensure overloads are stored on success
+            try:
+                yield
+            except Exception:
+                raise
+            else:
+                exists = self.overloads.get(cres.signature)
+                if exists is None:
+                    self.overloads[cres.signature] = cres
+
+        # Use cache and compiler in a critical section
+        with global_compiler_lock:
+            with targetconfig.ConfigStack().enter(flags.copy()):
+                with store_overloads_on_success():
+                    # attempt look up of existing
+                    cres = self.cache.load_overload(sig, targetctx)
+                    if cres is not None:
+                        return cres
+
+                    # Compile
+                    args, return_type = sigutils.normalize_signature(sig)
+                    cres = compiler.compile_extra(typingctx, targetctx,
+                                                  self.py_func, args=args,
+                                                  return_type=return_type,
+                                                  flags=flags, locals=locals)
+
+                    # cache lookup failed before so safe to save
+                    self.cache.save_overload(sig, cres)
+
+                    return cres
+
+
+dispatcher_registry[target_registry['npyufunc']] = UFuncDispatcher
+
+
+# Utility functions
+
+def _compile_element_wise_function(nb_func, targetoptions, sig):
+    # Do compilation
+    # Return CompileResult to test
+    cres = nb_func.compile(sig, **targetoptions)
+    args, return_type = sigutils.normalize_signature(sig)
+    return cres, args, return_type
+
+
+def _finalize_ufunc_signature(cres, args, return_type):
+    '''Given a compilation result, argument types, and a return type,
+    build a valid Numba signature after validating that it doesn't
+    violate the constraints for the compilation mode.
+    '''
+    if return_type is None:
+        if cres.objectmode:
+            # Object mode is used and return type is not specified
+            raise TypeError("return type must be specified for object mode")
+        else:
+            return_type = cres.signature.return_type
+
+    assert return_type != types.pyobject
+    return return_type(*args)
+
+
+def _build_element_wise_ufunc_wrapper(cres, signature):
+    '''Build a wrapper for the ufunc loop entry point given by the
+    compilation result object, using the element-wise signature.
+    '''
+    ctx = cres.target_context
+    library = cres.library
+    fname = cres.fndesc.llvm_func_name
+
+    with global_compiler_lock:
+        info = build_ufunc_wrapper(library, ctx, fname, signature,
+                                   cres.objectmode, cres)
+        ptr = info.library.get_pointer_to_function(info.name)
+    # Get dtypes
+    dtypenums = [as_dtype(a).num for a in signature.args]
+    dtypenums.append(as_dtype(signature.return_type).num)
+    return dtypenums, ptr, cres.environment
+
+
+_identities = {
+    0: _internal.PyUFunc_Zero,
+    1: _internal.PyUFunc_One,
+    None: _internal.PyUFunc_None,
+    "reorderable": _internal.PyUFunc_ReorderableNone,
+}
+
+
+def parse_identity(identity):
+    """
+    Parse an identity value and return the corresponding low-level value
+    for Numpy.
+    """
+    try:
+        identity = _identities[identity]
+    except KeyError:
+        raise ValueError("Invalid identity value %r" % (identity,))
+    return identity
+
+
+# Class definitions
+
+class _BaseUFuncBuilder(object):
+
+    def add(self, sig=None):
+        if hasattr(self, 'targetoptions'):
+            targetoptions = self.targetoptions
+        else:
+            targetoptions = self.nb_func.targetoptions
+        cres, args, return_type = _compile_element_wise_function(
+            self.nb_func, targetoptions, sig)
+        sig = self._finalize_signature(cres, args, return_type)
+        self._sigs.append(sig)
+        self._cres[sig] = cres
+        return cres
+
+    def disable_compile(self):
+        """
+        Disable the compilation of new signatures at call time.
+        """
+        # Override this for implementations that support lazy compilation
+
+
+class UFuncBuilder(_BaseUFuncBuilder):
+
+    def __init__(self, py_func, identity=None, cache=False, targetoptions={}):
+        if is_jitted(py_func):
+            py_func = py_func.py_func
+        self.py_func = py_func
+        self.identity = parse_identity(identity)
+        self.nb_func = jit(_target='npyufunc',
+                           cache=cache,
+                           **targetoptions)(py_func)
+        self._sigs = []
+        self._cres = {}
+
+    def _finalize_signature(self, cres, args, return_type):
+        '''Slated for deprecation, use ufuncbuilder._finalize_ufunc_signature()
+        instead.
+        '''
+        return _finalize_ufunc_signature(cres, args, return_type)
+
+    def build_ufunc(self):
+        with global_compiler_lock:
+            dtypelist = []
+            ptrlist = []
+            if not self.nb_func:
+                raise TypeError("No definition")
+
+            # Get signature in the order they are added
+            keepalive = []
+            cres = None
+            for sig in self._sigs:
+                cres = self._cres[sig]
+                dtypenums, ptr, env = self.build(cres, sig)
+                dtypelist.append(dtypenums)
+                ptrlist.append(int(ptr))
+                keepalive.append((cres.library, env))
+
+            datlist = [None] * len(ptrlist)
+
+            if cres is None:
+                argspec = inspect.getfullargspec(self.py_func)
+                inct = len(argspec.args)
+            else:
+                inct = len(cres.signature.args)
+            outct = 1
+
+            # Becareful that fromfunc does not provide full error checking yet.
+            # If typenum is out-of-bound, we have nasty memory corruptions.
+            # For instance, -1 for typenum will cause segfault.
+            # If elements of type-list (2nd arg) is tuple instead,
+            # there will also memory corruption. (Seems like code rewrite.)
+            ufunc = _internal.fromfunc(
+                self.py_func.__name__, self.py_func.__doc__,
+                ptrlist, dtypelist, inct, outct, datlist,
+                keepalive, self.identity,
+            )
+
+            return ufunc
+
+    def build(self, cres, signature):
+        '''Slated for deprecation, use
+        ufuncbuilder._build_element_wise_ufunc_wrapper().
+        '''
+        return _build_element_wise_ufunc_wrapper(cres, signature)
+
+
+class GUFuncBuilder(_BaseUFuncBuilder):
+
+    # TODO handle scalar
+    def __init__(self, py_func, signature, identity=None, cache=False,
+                 targetoptions={}):
+        self.py_func = py_func
+        self.identity = parse_identity(identity)
+        self.nb_func = jit(_target='npyufunc', cache=cache)(py_func)
+        self.signature = signature
+        self.sin, self.sout = parse_signature(signature)
+        self.targetoptions = targetoptions
+        self.cache = cache
+        self._sigs = []
+        self._cres = {}
+
+    def _finalize_signature(self, cres, args, return_type):
+        if not cres.objectmode and cres.signature.return_type != types.void:
+            raise TypeError("gufunc kernel must have void return type")
+
+        if return_type is None:
+            return_type = types.void
+
+        return return_type(*args)
+
+    @global_compiler_lock
+    def build_ufunc(self):
+        type_list = []
+        func_list = []
+        if not self.nb_func:
+            raise TypeError("No definition")
+
+        # Get signature in the order they are added
+        keepalive = []
+        for sig in self._sigs:
+            cres = self._cres[sig]
+            dtypenums, ptr, env = self.build(cres)
+            type_list.append(dtypenums)
+            func_list.append(int(ptr))
+            keepalive.append((cres.library, env))
+
+        datalist = [None] * len(func_list)
+
+        nin = len(self.sin)
+        nout = len(self.sout)
+
+        # Pass envs to fromfuncsig to bind to the lifetime of the ufunc object
+        ufunc = _internal.fromfunc(
+            self.py_func.__name__, self.py_func.__doc__,
+            func_list, type_list, nin, nout, datalist,
+            keepalive, self.identity, self.signature,
+        )
+        return ufunc
+
+    def build(self, cres):
+        """
+        Returns (dtype numbers, function ptr, EnvironmentObject)
+        """
+        # Builder wrapper for ufunc entry point
+        signature = cres.signature
+        info = build_gufunc_wrapper(
+            self.py_func, cres, self.sin, self.sout,
+            cache=self.cache, is_parfors=False,
+        )
+
+        env = info.env
+        ptr = info.library.get_pointer_to_function(info.name)
+        # Get dtypes
+        dtypenums = []
+        for a in signature.args:
+            if isinstance(a, types.Array):
+                ty = a.dtype
+            else:
+                ty = a
+            dtypenums.append(as_dtype(ty).num)
+        return dtypenums, ptr, env
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/workqueue.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/workqueue.c
new file mode 100644
index 000000000..c752d06e1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/workqueue.c
@@ -0,0 +1,656 @@
+/*
+Implement parallel vectorize workqueue.
+
+This keeps a set of worker threads running all the time.
+They wait and spin on a task queue for jobs.
+
+**WARNING**
+This module is not thread-safe.  Adding task to queue is not protected from
+race conditions.
+*/
+#include "../../_pymodule.h"
+#ifdef _POSIX_C_SOURCE
+#undef _POSIX_C_SOURCE
+#endif
+#ifdef _XOPEN_SOURCE
+#undef _XOPEN_SOURCE
+#endif
+
+#ifdef _MSC_VER
+/* Windows */
+#include <windows.h>
+#include <process.h>
+#include <malloc.h>
+#include <signal.h>
+#define NUMBA_WINTHREAD
+#else
+/* PThread */
+#include <pthread.h>
+#include <unistd.h>
+
+#if defined(__FreeBSD__)
+#include <stdlib.h>
+#else
+#include <alloca.h>
+#endif
+
+#include <sys/types.h>
+#include <unistd.h>
+#include <signal.h>
+#define NUMBA_PTHREAD
+#endif
+
+#include <string.h>
+#include <stddef.h>
+#include <stdio.h>
+#include "workqueue.h"
+#include "gufunc_scheduler.h"
+
+#define _DEBUG 0
+
+/* workqueue is not threadsafe, so we use DSO globals to flag and update various
+ * states.
+ */
+/* This variable is the nesting level, it's incremented at the start of each
+ * parallel region and decremented at the end, if parallel regions are nested
+ * on entry the value == 1 and workqueue will abort (this in preference to just
+ * hanging or segfaulting).
+ */
+static int _nesting_level = 0;
+
+/* As the thread-pool isn't inherited by children,
+   free the task-queue, too. */
+static void reset_after_fork(void);
+
+static void
+add_task_internal(void *fn, void *args, void *dims, void *steps, void *data, int tid);
+
+/* PThread */
+#ifdef NUMBA_PTHREAD
+
+static pthread_key_t tidkey;
+
+typedef struct
+{
+    pthread_cond_t cond;
+    pthread_mutex_t mutex;
+} queue_condition_t;
+
+static int
+queue_condition_init(queue_condition_t *qc)
+{
+    int r;
+    if ((r = pthread_cond_init(&qc->cond, NULL)))
+        return r;
+    if ((r = pthread_mutex_init(&qc->mutex, NULL)))
+        return r;
+    return 0;
+}
+
+static void
+queue_condition_lock(queue_condition_t *qc)
+{
+    /* XXX errors? */
+    pthread_mutex_lock(&qc->mutex);
+}
+
+static void
+queue_condition_unlock(queue_condition_t *qc)
+{
+    /* XXX errors? */
+    pthread_mutex_unlock(&qc->mutex);
+}
+
+static void
+queue_condition_signal(queue_condition_t *qc)
+{
+    /* XXX errors? */
+    pthread_cond_signal(&qc->cond);
+}
+
+static void
+queue_condition_wait(queue_condition_t *qc)
+{
+    /* XXX errors? */
+    pthread_cond_wait(&qc->cond, &qc->mutex);
+}
+
+static thread_pointer
+numba_new_thread(void *worker, void *arg)
+{
+    int status;
+    pthread_attr_t attr;
+    pthread_t th;
+
+    pthread_atfork(0, 0, reset_after_fork);
+
+    /* Create detached threads */
+    pthread_attr_init(&attr);
+    pthread_attr_setdetachstate(&attr, PTHREAD_CREATE_DETACHED);
+
+    status = pthread_create(&th, &attr, worker, arg);
+
+    if (status != 0)
+    {
+        return NULL;
+    }
+
+    pthread_attr_destroy(&attr);
+    return (thread_pointer)th;
+}
+
+static int
+get_thread_id(void)
+{
+    return (int)(intptr_t)pthread_getspecific(tidkey);
+}
+
+static void
+set_thread_id(int tid)
+{
+    pthread_setspecific(tidkey, (void*)(intptr_t)tid);
+}
+
+static void
+platform_launch(void)
+{
+    pthread_key_create(&tidkey, NULL);
+}
+
+#endif /* pthread threading */
+
+/* Win Thread */
+#ifdef NUMBA_WINTHREAD
+
+DWORD tidkey;
+
+typedef struct
+{
+    CONDITION_VARIABLE cv;
+    CRITICAL_SECTION cs;
+} queue_condition_t;
+
+static int
+queue_condition_init(queue_condition_t *qc)
+{
+    InitializeConditionVariable(&qc->cv);
+    InitializeCriticalSection(&qc->cs);
+    return 0;
+}
+
+static void
+queue_condition_lock(queue_condition_t *qc)
+{
+    EnterCriticalSection(&qc->cs);
+}
+
+static void
+queue_condition_unlock(queue_condition_t *qc)
+{
+    LeaveCriticalSection(&qc->cs);
+}
+
+static void
+queue_condition_signal(queue_condition_t *qc)
+{
+    WakeConditionVariable(&qc->cv);
+}
+
+static void
+queue_condition_wait(queue_condition_t *qc)
+{
+    SleepConditionVariableCS(&qc->cv, &qc->cs, INFINITE);
+}
+
+/* Adapted from Python/thread_nt.h */
+typedef struct
+{
+    void (*func)(void*);
+    void *arg;
+} callobj;
+
+static unsigned __stdcall
+bootstrap(void *call)
+{
+    callobj *obj = (callobj*)call;
+    void (*func)(void*) = obj->func;
+    void *arg = obj->arg;
+    HeapFree(GetProcessHeap(), 0, obj);
+    func(arg);
+    _endthreadex(0);
+    return 0;
+}
+
+static thread_pointer
+numba_new_thread(void *worker, void *arg)
+{
+    uintptr_t handle;
+    unsigned threadID;
+    callobj *obj;
+
+    if (sizeof(handle) > sizeof(void*))
+        return 0;
+
+    obj = (callobj*)HeapAlloc(GetProcessHeap(), 0, sizeof(*obj));
+    if (!obj)
+        return NULL;
+
+    obj->func = worker;
+    obj->arg = arg;
+
+    handle = _beginthreadex(NULL, 0, bootstrap, obj, 0, &threadID);
+    if (handle == -1)
+        return 0;
+    return (thread_pointer)handle;
+}
+
+static int
+get_thread_id(void)
+{
+    return (int)(intptr_t)TlsGetValue(tidkey);
+}
+
+static void
+set_thread_id(int tid)
+{
+    TlsSetValue(tidkey, (void*)(intptr_t)tid);
+}
+
+static void
+platform_launch(void)
+{
+    tidkey = TlsAlloc();
+}
+
+#endif /* Windows threading */
+
+typedef struct Task
+{
+    void (*func)(void *args, void *dims, void *steps, void *data);
+    void *args, *dims, *steps, *data;
+    int tid;
+} Task;
+
+typedef struct
+{
+    queue_condition_t cond;
+    int state;
+    Task task;
+} Queue;
+
+
+static Queue *queues = NULL;
+static int queue_count;
+static int queue_pivot = 0;
+static int NUM_THREADS = -1;
+
+static void
+queue_state_wait(Queue *queue, int old, int repl)
+{
+    queue_condition_t *cond = &queue->cond;
+
+    queue_condition_lock(cond);
+    while (queue->state != old)
+    {
+        queue_condition_wait(cond);
+    }
+    queue->state = repl;
+    queue_condition_signal(cond);
+    queue_condition_unlock(cond);
+}
+
+// break on this for debug
+void debug_marker(void);
+void debug_marker() {};
+
+
+#ifdef _MSC_VER
+#define THREAD_LOCAL(ty) __declspec(thread) ty
+#else
+/* Non-standard C99 extension that's understood by gcc and clang */
+#define THREAD_LOCAL(ty) __thread ty
+#endif
+
+// This is the number of threads that is default, it is set on initialisation of
+// the threading backend via the launch_threads() call
+static int _INIT_NUM_THREADS = -1;
+
+// This is the per-thread thread mask, each thread can carry its own mask.
+static THREAD_LOCAL(int) _TLS_num_threads = 0;
+
+static void
+set_num_threads(int count)
+{
+    if (!queues)
+    {
+        launch_threads(NUM_THREADS);
+    }
+    _TLS_num_threads = count;
+}
+
+static int
+get_num_threads(void)
+{
+    if (!queues)
+    {
+        launch_threads(NUM_THREADS);
+    }
+    // This is purely to permit the implementation to survive to the point
+    // where it can exit cleanly as multiple threads cannot be used with this
+    // backend
+    if (_TLS_num_threads == 0)
+    {
+        // This is a thread that did not call launch_threads() but is still a
+        // "main" thread, probably from e.g. threading.Thread() use, it still
+        // has a TLS slot which is 0 from the lack of launch_threads() call
+        _TLS_num_threads = _INIT_NUM_THREADS;
+    }
+    return _TLS_num_threads;
+}
+
+
+// this complies to a launchable function from `add_task` like:
+// add_task(nopfn, NULL, NULL, NULL, NULL)
+// useful if you want to limit the number of threads locally
+// static void nopfn(void *args, void *dims, void *steps, void *data) {};
+
+
+// synchronize the TLS num_threads slot to value args[0]
+static void sync_tls(void *args, void *dims, void *steps, void *data) {
+    int nthreads = *((int *)(args));
+    _TLS_num_threads = nthreads;
+};
+
+
+static void
+parallel_for(void *fn, char **args, size_t *dimensions, size_t *steps, void *data,
+             size_t inner_ndim, size_t array_count, int num_threads)
+{
+
+    //     args = <ir.Argument '.1' of type i8**>,
+    //     dimensions = <ir.Argument '.2' of type i64*>
+    //     steps = <ir.Argument '.3' of type i64*>
+    //     data = <ir.Argument '.4' of type i8*>
+
+    // check the nesting level, if it's already 1, abort, workqueue cannot
+    // handle nesting.
+    if (_nesting_level >= 1){
+        fprintf(stderr, "%s", "Terminating: Nested parallel kernel launch "
+                              "detected, the workqueue threading layer does "
+                              "not supported nested parallelism. Try the TBB "
+                              "threading layer.\n");
+        raise(SIGABRT);
+        return;
+    }
+
+    // increment the nest level
+    _nesting_level += 1;
+
+    size_t * count_space = NULL;
+    char ** array_arg_space = NULL;
+    const size_t arg_len = (inner_ndim + 1);
+    int i; // induction var for chunking, thread count unlikely to overflow int
+    size_t j, count, remain, total;
+
+    ptrdiff_t offset;
+    char * base;
+    int old_queue_count = -1;
+
+    size_t step;
+
+    debug_marker();
+
+    total = *((size_t *)dimensions);
+    count = total / num_threads;
+    remain = total;
+
+    if(_DEBUG)
+    {
+        printf("inner_ndim: %zd\n",inner_ndim);
+        printf("arg_len: %zd\n", arg_len);
+        printf("total: %zd\n", total);
+        printf("count: %zd\n", count);
+
+        printf("dimensions: ");
+        for(j = 0; j < arg_len; j++)
+        {
+            printf("%zd, ", ((size_t *)dimensions)[j]);
+        }
+        printf("\n");
+
+        printf("steps: ");
+        for(j = 0; j < array_count; j++)
+        {
+            printf("%zd, ", steps[j]);
+        }
+        printf("\n");
+
+        printf("*args: ");
+        for(j = 0; j < array_count; j++)
+        {
+            printf("%p, ", (void *)args[j]);
+        }
+    }
+
+    // sync the thread pool TLS slots, sync all slots, we don't know which
+    // threads will end up running.
+    for (i = 0; i < NUM_THREADS; i++)
+    {
+        add_task_internal(sync_tls, (void *)(&num_threads), NULL, NULL, NULL, i);
+    }
+    ready();
+    synchronize();
+
+    // This backend isn't threadsafe so just mutate the global
+    old_queue_count = queue_count;
+    queue_count = num_threads;
+
+    for (i = 0; i < num_threads; i++)
+    {
+        count_space = (size_t *)alloca(sizeof(size_t) * arg_len);
+        memcpy(count_space, dimensions, arg_len * sizeof(size_t));
+        if(i == num_threads - 1)
+        {
+            // Last thread takes all leftover
+            count_space[0] = remain;
+        }
+        else
+        {
+            count_space[0] = count;
+            remain = remain - count;
+        }
+
+        if(_DEBUG)
+        {
+            printf("\n=================== THREAD %d ===================\n", i);
+            printf("\ncount_space: ");
+            for(j = 0; j < arg_len; j++)
+            {
+                printf("%zd, ", count_space[j]);
+            }
+            printf("\n");
+        }
+
+        array_arg_space = alloca(sizeof(char*) * array_count);
+
+        for(j = 0; j < array_count; j++)
+        {
+            base = args[j];
+            step = steps[j];
+            offset = step * count * i;
+            array_arg_space[j] = (char *)(base + offset);
+
+            if(_DEBUG)
+            {
+                printf("Index %zd\n", j);
+                printf("-->Got base %p\n", (void *)base);
+                printf("-->Got step %zd\n", step);
+                printf("-->Got offset %td\n", offset);
+                printf("-->Got addr %p\n", (void *)array_arg_space[j]);
+            }
+        }
+
+        if(_DEBUG)
+        {
+            printf("\narray_arg_space: ");
+            for(j = 0; j < array_count; j++)
+            {
+                printf("%p, ", (void *)array_arg_space[j]);
+            }
+        }
+        add_task_internal(fn, (void *)array_arg_space, (void *)count_space, steps, data, i);
+    }
+
+    ready();
+    synchronize();
+
+    queue_count = old_queue_count;
+    // decrement the nest level
+    _nesting_level -= 1;
+}
+
+static void
+add_task_internal(void *fn, void *args, void *dims, void *steps, void *data, int tid)
+{
+    void (*func)(void *args, void *dims, void *steps, void *data) = fn;
+
+    if (!queues)
+    {
+        launch_threads(NUM_THREADS);
+    }
+
+    Queue *queue = &queues[queue_pivot];
+
+    Task *task = &queue->task;
+    task->func = func;
+    task->args = args;
+    task->dims = dims;
+    task->steps = steps;
+    task->data = data;
+    task->tid = tid;
+
+    /* Move pivot */
+    if ( ++queue_pivot == queue_count )
+    {
+        queue_pivot = 0;
+    }
+}
+
+static void
+add_task(void *fn, void *args, void *dims, void *steps, void *data)
+{
+    add_task_internal(fn, args, dims, steps, data, 0);
+}
+
+static
+void thread_worker(void *arg)
+{
+    Queue *queue = (Queue*)arg;
+    Task *task;
+
+    while (1)
+    {
+        /* Wait for the queue to be in READY state (i.e. for some task
+         * to need running), and switch it to RUNNING.
+         */
+        queue_state_wait(queue, READY, RUNNING);
+
+        task = &queue->task;
+        set_thread_id(task->tid);
+        task->func(task->args, task->dims, task->steps, task->data);
+
+        /* Task is done. */
+        queue_state_wait(queue, RUNNING, DONE);
+    }
+}
+
+static void launch_threads(int count)
+{
+    if (!queues)
+    {
+        platform_launch();
+
+        /* If queues are not yet allocated,
+           create them, one for each thread. */
+        int i;
+        size_t sz = sizeof(Queue) * count;
+
+        /* set for use in parallel_for */
+        NUM_THREADS = count;
+        queues = malloc(sz);     /* this memory will leak */
+        /* Note this initializes the state to IDLE */
+        memset(queues, 0, sz);
+        queue_count = count;
+
+        for (i = 0; i < count; ++i)
+        {
+            queue_condition_init(&queues[i].cond);
+            numba_new_thread(thread_worker, &queues[i]);
+        }
+
+        _INIT_NUM_THREADS = count;
+    }
+}
+
+static void synchronize(void)
+{
+    // This probably isn't needed, but is put in as a guard
+    if (!queues)
+    {
+        launch_threads(NUM_THREADS);
+    }
+    int i;
+    for (i = 0; i < queue_count; ++i)
+    {
+        queue_state_wait(&queues[i], DONE, IDLE);
+    }
+}
+
+static void ready(void)
+{
+    // This probably isn't needed, but is put in as a guard
+    if (!queues)
+    {
+        launch_threads(NUM_THREADS);
+    }
+    int i;
+    for (i = 0; i < queue_count; ++i)
+    {
+        queue_state_wait(&queues[i], IDLE, READY);
+    }
+}
+
+static void reset_after_fork(void)
+{
+    free(queues);
+    queues = NULL;
+    if (_INIT_NUM_THREADS != -1)
+    {
+        NUM_THREADS = _INIT_NUM_THREADS;
+    }
+    _nesting_level = 0;
+}
+
+MOD_INIT(workqueue)
+{
+    PyObject *m;
+    MOD_DEF(m, "workqueue", "No docs", NULL)
+    if (m == NULL)
+        return MOD_ERROR_VAL;
+
+    SetAttrStringFromVoidPointer(m, launch_threads);
+    SetAttrStringFromVoidPointer(m, synchronize);
+    SetAttrStringFromVoidPointer(m, ready);
+    SetAttrStringFromVoidPointer(m, add_task);
+    SetAttrStringFromVoidPointer(m, parallel_for);
+    SetAttrStringFromVoidPointer(m, do_scheduling_signed);
+    SetAttrStringFromVoidPointer(m, do_scheduling_unsigned);
+    SetAttrStringFromVoidPointer(m, set_num_threads);
+    SetAttrStringFromVoidPointer(m, get_num_threads);
+    SetAttrStringFromVoidPointer(m, get_thread_id);
+    SetAttrStringFromVoidPointer(m, set_parallel_chunksize);
+    SetAttrStringFromVoidPointer(m, get_parallel_chunksize);
+    SetAttrStringFromVoidPointer(m, get_sched_size);
+
+    return MOD_SUCCESS_VAL(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/workqueue.h b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/workqueue.h
new file mode 100644
index 000000000..80558a0e5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/workqueue.h
@@ -0,0 +1,65 @@
+typedef struct opaque_thread * thread_pointer;
+
+enum QUEUE_STATE
+{
+    /*
+    The queue has 4 states:
+
+    IDLE: not doing anything
+    READY: tasks enqueued; signal workers to start
+    RUNNING: workers running
+    DONE: workers completed
+    */
+    IDLE = 0, READY, RUNNING, DONE
+};
+
+/* Launch `count` number of threads and create the associated thread queue.
+Must invoke once before each add_task() is used.
+*Warning* queues memory are leaked at interpreter tear down!
+*/
+static
+void launch_threads(int count);
+
+/* Add task to queue
+Automatically assigned to queues of different thread in a round robin fashion.
+*/
+static
+void add_task(void *fn, void *args, void *dims, void *steps, void *data);
+
+/* Wait until all tasks are done */
+static
+void synchronize(void);
+
+/* Signal worker threads that tasks are added and it is ready to run */
+static
+void ready(void);
+
+/* parallel for loop with 1d tiling.
+
+ Args:
+
+ fn - function pointer to the gufunc
+
+ These are from the Python: args, dimensions, steps, data = lfunc.args
+ args - gufunc args
+ dims - gufunc dims
+ steps - gufunc steps
+ data - gufunc data
+
+ inner_ndim - inner dimension of the gufunc
+ array_count - the number of arrays in the signature (Python: len(sig.args) + 1)
+ the +1 is for the output array.
+
+ */
+static void
+parallel_for(void *fn, char **args, size_t *dims, size_t *steps, void *data,\
+             size_t inner_ndim, size_t array_count, int num_threads);
+
+
+/* Masking API cf. OpenMP */
+static void
+set_num_threads(int count);
+static int
+get_num_threads(void);
+static int
+get_thread_id(void);
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/wrappers.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/wrappers.py
new file mode 100644
index 000000000..ef37c4077
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc/wrappers.py
@@ -0,0 +1,743 @@
+from collections import namedtuple
+
+import numpy as np
+
+from llvmlite.ir import Constant, IRBuilder
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core.caching import make_library_cache, NullCache
+
+
+_wrapper_info = namedtuple('_wrapper_info', ['library', 'env', 'name'])
+
+
+def _build_ufunc_loop_body(load, store, context, func, builder, arrays, out,
+                           offsets, store_offset, signature, pyapi, env):
+    elems = load()
+
+    # Compute
+    status, retval = context.call_conv.call_function(builder, func,
+                                                     signature.return_type,
+                                                     signature.args, elems)
+
+    # Store
+    with builder.if_else(status.is_ok, likely=True) as (if_ok, if_error):
+        with if_ok:
+            store(retval)
+        with if_error:
+            gil = pyapi.gil_ensure()
+            context.call_conv.raise_error(builder, pyapi, status)
+            pyapi.gil_release(gil)
+
+    # increment indices
+    for off, ary in zip(offsets, arrays):
+        builder.store(builder.add(builder.load(off), ary.step), off)
+
+    builder.store(builder.add(builder.load(store_offset), out.step),
+                  store_offset)
+
+    return status.code
+
+
+def _build_ufunc_loop_body_objmode(load, store, context, func, builder,
+                                   arrays, out, offsets, store_offset,
+                                   signature, env, pyapi):
+    elems = load()
+
+    # Compute
+    _objargs = [types.pyobject] * len(signature.args)
+    # We need to push the error indicator to avoid it messing with
+    # the ufunc's execution.  We restore it unless the ufunc raised
+    # a new error.
+    with pyapi.err_push(keep_new=True):
+        status, retval = context.call_conv.call_function(builder, func,
+                                                         types.pyobject,
+                                                         _objargs, elems)
+        # Release owned reference to arguments
+        for elem in elems:
+            pyapi.decref(elem)
+    # NOTE: if an error occurred, it will be caught by the Numpy machinery
+
+    # Store
+    store(retval)
+
+    # increment indices
+    for off, ary in zip(offsets, arrays):
+        builder.store(builder.add(builder.load(off), ary.step), off)
+
+    builder.store(builder.add(builder.load(store_offset), out.step),
+                  store_offset)
+
+    return status.code
+
+
+def build_slow_loop_body(context, func, builder, arrays, out, offsets,
+                         store_offset, signature, pyapi, env):
+    def load():
+        elems = [ary.load_direct(builder.load(off))
+                 for off, ary in zip(offsets, arrays)]
+        return elems
+
+    def store(retval):
+        out.store_direct(retval, builder.load(store_offset))
+
+    return _build_ufunc_loop_body(load, store, context, func, builder, arrays,
+                                  out, offsets, store_offset, signature, pyapi,
+                                  env=env)
+
+
+def build_obj_loop_body(context, func, builder, arrays, out, offsets,
+                        store_offset, signature, pyapi, envptr, env):
+    env_body = context.get_env_body(builder, envptr)
+    env_manager = pyapi.get_env_manager(env, env_body, envptr)
+
+    def load():
+        # Load
+        elems = [ary.load_direct(builder.load(off))
+                 for off, ary in zip(offsets, arrays)]
+        # Box
+        elems = [pyapi.from_native_value(t, v, env_manager)
+                 for v, t in zip(elems, signature.args)]
+        return elems
+
+    def store(retval):
+        is_ok = cgutils.is_not_null(builder, retval)
+        # If an error is raised by the object mode ufunc, it will
+        # simply get caught by the Numpy ufunc machinery.
+        with builder.if_then(is_ok, likely=True):
+            # Unbox
+            native = pyapi.to_native_value(signature.return_type, retval)
+            assert native.cleanup is None
+            # Store
+            out.store_direct(native.value, builder.load(store_offset))
+            # Release owned reference
+            pyapi.decref(retval)
+
+    return _build_ufunc_loop_body_objmode(load, store, context, func, builder,
+                                          arrays, out, offsets, store_offset,
+                                          signature, envptr, pyapi)
+
+
+def build_fast_loop_body(context, func, builder, arrays, out, offsets,
+                         store_offset, signature, ind, pyapi, env):
+    def load():
+        elems = [ary.load_aligned(ind)
+                 for ary in arrays]
+        return elems
+
+    def store(retval):
+        out.store_aligned(retval, ind)
+
+    return _build_ufunc_loop_body(load, store, context, func, builder, arrays,
+                                  out, offsets, store_offset, signature, pyapi,
+                                  env=env)
+
+
+def build_ufunc_wrapper(library, context, fname, signature, objmode, cres):
+    """
+    Wrap the scalar function with a loop that iterates over the arguments
+
+    Returns
+    -------
+    (library, env, name)
+    """
+    assert isinstance(fname, str)
+    byte_t = ir.IntType(8)
+    byte_ptr_t = ir.PointerType(byte_t)
+    byte_ptr_ptr_t = ir.PointerType(byte_ptr_t)
+    intp_t = context.get_value_type(types.intp)
+    intp_ptr_t = ir.PointerType(intp_t)
+
+    fnty = ir.FunctionType(ir.VoidType(), [byte_ptr_ptr_t, intp_ptr_t,
+                                           intp_ptr_t, byte_ptr_t])
+
+    wrapperlib = context.codegen().create_library('ufunc_wrapper')
+    wrapper_module = wrapperlib.create_ir_module('')
+    if objmode:
+        func_type = context.call_conv.get_function_type(
+            types.pyobject, [types.pyobject] * len(signature.args))
+    else:
+        func_type = context.call_conv.get_function_type(
+            signature.return_type, signature.args)
+
+    func = ir.Function(wrapper_module, func_type, name=fname)
+    func.attributes.add("alwaysinline")
+
+    wrapper = ir.Function(wrapper_module, fnty, "__ufunc__." + func.name)
+    arg_args, arg_dims, arg_steps, arg_data = wrapper.args
+    arg_args.name = "args"
+    arg_dims.name = "dims"
+    arg_steps.name = "steps"
+    arg_data.name = "data"
+
+    builder = IRBuilder(wrapper.append_basic_block("entry"))
+
+    # Prepare Environment
+    envname = context.get_env_name(cres.fndesc)
+    env = cres.environment
+    envptr = builder.load(context.declare_env_global(builder.module, envname))
+
+    # Emit loop
+    loopcount = builder.load(arg_dims, name="loopcount")
+
+    # Prepare inputs
+    arrays = []
+    for i, typ in enumerate(signature.args):
+        arrays.append(UArrayArg(context, builder, arg_args, arg_steps, i, typ))
+
+    # Prepare output
+    out = UArrayArg(context, builder, arg_args, arg_steps, len(arrays),
+                    signature.return_type)
+
+    # Setup indices
+    offsets = []
+    zero = context.get_constant(types.intp, 0)
+    for _ in arrays:
+        p = cgutils.alloca_once(builder, intp_t)
+        offsets.append(p)
+        builder.store(zero, p)
+
+    store_offset = cgutils.alloca_once(builder, intp_t)
+    builder.store(zero, store_offset)
+
+    unit_strided = cgutils.true_bit
+    for ary in arrays:
+        unit_strided = builder.and_(unit_strided, ary.is_unit_strided)
+
+    pyapi = context.get_python_api(builder)
+    if objmode:
+        # General loop
+        gil = pyapi.gil_ensure()
+        with cgutils.for_range(builder, loopcount, intp=intp_t):
+            build_obj_loop_body(
+                context, func, builder, arrays, out, offsets,
+                store_offset, signature, pyapi, envptr, env,
+            )
+        pyapi.gil_release(gil)
+        builder.ret_void()
+
+    else:
+        with builder.if_else(unit_strided) as (is_unit_strided, is_strided):
+            with is_unit_strided:
+                with cgutils.for_range(builder, loopcount, intp=intp_t) as loop:
+                    build_fast_loop_body(
+                        context, func, builder, arrays, out, offsets,
+                        store_offset, signature, loop.index, pyapi,
+                        env=envptr,
+                    )
+
+            with is_strided:
+                # General loop
+                with cgutils.for_range(builder, loopcount, intp=intp_t):
+                    build_slow_loop_body(
+                        context, func, builder, arrays, out, offsets,
+                        store_offset, signature, pyapi,
+                        env=envptr,
+                    )
+
+        builder.ret_void()
+    del builder
+
+    # Link and finalize
+    wrapperlib.add_ir_module(wrapper_module)
+    wrapperlib.add_linking_library(library)
+    return _wrapper_info(library=wrapperlib, env=env, name=wrapper.name)
+
+
+class UArrayArg(object):
+    def __init__(self, context, builder, args, steps, i, fe_type):
+        self.context = context
+        self.builder = builder
+        self.fe_type = fe_type
+        offset = self.context.get_constant(types.intp, i)
+        offseted_args = self.builder.load(builder.gep(args, [offset]))
+        data_type = context.get_data_type(fe_type)
+        self.dataptr = self.builder.bitcast(offseted_args,
+                                            data_type.as_pointer())
+        sizeof = self.context.get_abi_sizeof(data_type)
+        self.abisize = self.context.get_constant(types.intp, sizeof)
+        offseted_step = self.builder.gep(steps, [offset])
+        self.step = self.builder.load(offseted_step)
+        self.is_unit_strided = builder.icmp_unsigned('==',
+                                                     self.abisize, self.step)
+        self.builder = builder
+
+    def load_direct(self, byteoffset):
+        """
+        Generic load from the given *byteoffset*.  load_aligned() is
+        preferred if possible.
+        """
+        ptr = cgutils.pointer_add(self.builder, self.dataptr, byteoffset)
+        return self.context.unpack_value(self.builder, self.fe_type, ptr)
+
+    def load_aligned(self, ind):
+        # Using gep() instead of explicit pointer addition helps LLVM
+        # vectorize the loop.
+        ptr = self.builder.gep(self.dataptr, [ind])
+        return self.context.unpack_value(self.builder, self.fe_type, ptr)
+
+    def store_direct(self, value, byteoffset):
+        ptr = cgutils.pointer_add(self.builder, self.dataptr, byteoffset)
+        self.context.pack_value(self.builder, self.fe_type, value, ptr)
+
+    def store_aligned(self, value, ind):
+        ptr = self.builder.gep(self.dataptr, [ind])
+        self.context.pack_value(self.builder, self.fe_type, value, ptr)
+
+
+GufWrapperCache = make_library_cache('guf')
+
+
+class _GufuncWrapper(object):
+    def __init__(self, py_func, cres, sin, sout, cache, is_parfors):
+        """
+        The *is_parfors* argument is a boolean that indicates if the GUfunc
+        being built is to be used as a ParFors kernel. If True, it disables
+        the caching on the wrapper as a separate unit because it will be linked
+        into the caller function and cached along with it.
+        """
+        self.py_func = py_func
+        self.cres = cres
+        self.sin = sin
+        self.sout = sout
+        self.is_objectmode = self.signature.return_type == types.pyobject
+        self.cache = (GufWrapperCache(py_func=self.py_func)
+                      if cache else NullCache())
+        self.is_parfors = bool(is_parfors)
+
+    @property
+    def library(self):
+        return self.cres.library
+
+    @property
+    def context(self):
+        return self.cres.target_context
+
+    @property
+    def call_conv(self):
+        return self.context.call_conv
+
+    @property
+    def signature(self):
+        return self.cres.signature
+
+    @property
+    def fndesc(self):
+        return self.cres.fndesc
+
+    @property
+    def env(self):
+        return self.cres.environment
+
+    def _wrapper_function_type(self):
+        byte_t = ir.IntType(8)
+        byte_ptr_t = ir.PointerType(byte_t)
+        byte_ptr_ptr_t = ir.PointerType(byte_ptr_t)
+        intp_t = self.context.get_value_type(types.intp)
+        intp_ptr_t = ir.PointerType(intp_t)
+
+        fnty = ir.FunctionType(ir.VoidType(), [byte_ptr_ptr_t, intp_ptr_t,
+                                               intp_ptr_t, byte_ptr_t])
+        return fnty
+
+    def _build_wrapper(self, library, name):
+        """
+        The LLVM IRBuilder code to create the gufunc wrapper.
+        The *library* arg is the CodeLibrary to which the wrapper should
+        be added.  The *name* arg is the name of the wrapper function being
+        created.
+        """
+        intp_t = self.context.get_value_type(types.intp)
+        fnty = self._wrapper_function_type()
+
+        wrapper_module = library.create_ir_module('_gufunc_wrapper')
+        func_type = self.call_conv.get_function_type(self.fndesc.restype,
+                                                     self.fndesc.argtypes)
+        fname = self.fndesc.llvm_func_name
+        func = ir.Function(wrapper_module, func_type, name=fname)
+
+        func.attributes.add("alwaysinline")
+        wrapper = ir.Function(wrapper_module, fnty, name)
+        # The use of weak_odr linkage avoids the function being dropped due
+        # to the order in which the wrappers and the user function are linked.
+        wrapper.linkage = 'weak_odr'
+        arg_args, arg_dims, arg_steps, arg_data = wrapper.args
+        arg_args.name = "args"
+        arg_dims.name = "dims"
+        arg_steps.name = "steps"
+        arg_data.name = "data"
+
+        builder = IRBuilder(wrapper.append_basic_block("entry"))
+        loopcount = builder.load(arg_dims, name="loopcount")
+        pyapi = self.context.get_python_api(builder)
+
+        # Unpack shapes
+        unique_syms = set()
+        for grp in (self.sin, self.sout):
+            for syms in grp:
+                unique_syms |= set(syms)
+
+        sym_map = {}
+        for syms in self.sin:
+            for s in syms:
+                if s not in sym_map:
+                    sym_map[s] = len(sym_map)
+
+        sym_dim = {}
+        for s, i in sym_map.items():
+            sym_dim[s] = builder.load(builder.gep(arg_dims,
+                                                  [self.context.get_constant(
+                                                      types.intp,
+                                                      i + 1)]))
+
+        # Prepare inputs
+        arrays = []
+        step_offset = len(self.sin) + len(self.sout)
+        for i, (typ, sym) in enumerate(zip(self.signature.args,
+                                           self.sin + self.sout)):
+            ary = GUArrayArg(self.context, builder, arg_args,
+                             arg_steps, i, step_offset, typ, sym, sym_dim)
+            step_offset += len(sym)
+            arrays.append(ary)
+
+        bbreturn = builder.append_basic_block('.return')
+
+        # Prologue
+        self.gen_prologue(builder, pyapi)
+
+        # Loop
+        with cgutils.for_range(builder, loopcount, intp=intp_t) as loop:
+            args = [a.get_array_at_offset(loop.index) for a in arrays]
+            innercall, error = self.gen_loop_body(builder, pyapi, func, args)
+            # If error, escape
+            cgutils.cbranch_or_continue(builder, error, bbreturn)
+
+        builder.branch(bbreturn)
+        builder.position_at_end(bbreturn)
+
+        # Epilogue
+        self.gen_epilogue(builder, pyapi)
+
+        builder.ret_void()
+
+        # Link
+        library.add_ir_module(wrapper_module)
+        library.add_linking_library(self.library)
+
+    def _compile_wrapper(self, wrapper_name):
+        # Gufunc created by Parfors?
+        if self.is_parfors:
+            # No wrapper caching for parfors
+            wrapperlib = self.context.codegen().create_library(str(self))
+            # Build wrapper
+            self._build_wrapper(wrapperlib, wrapper_name)
+        # Non-parfors?
+        else:
+            # Use cache and compiler in a critical section
+            wrapperlib = self.cache.load_overload(
+                self.cres.signature, self.cres.target_context,
+            )
+            if wrapperlib is None:
+                # Create library and enable caching
+                wrapperlib = self.context.codegen().create_library(str(self))
+                wrapperlib.enable_object_caching()
+                # Build wrapper
+                self._build_wrapper(wrapperlib, wrapper_name)
+                # Cache
+                self.cache.save_overload(self.cres.signature, wrapperlib)
+
+        return wrapperlib
+
+    @global_compiler_lock
+    def build(self):
+        wrapper_name = "__gufunc__." + self.fndesc.mangled_name
+        wrapperlib = self._compile_wrapper(wrapper_name)
+        return _wrapper_info(
+            library=wrapperlib, env=self.env, name=wrapper_name,
+        )
+
+    def gen_loop_body(self, builder, pyapi, func, args):
+        status, retval = self.call_conv.call_function(
+            builder, func, self.signature.return_type, self.signature.args,
+            args)
+
+        with builder.if_then(status.is_error, likely=False):
+            gil = pyapi.gil_ensure()
+            self.context.call_conv.raise_error(builder, pyapi, status)
+            pyapi.gil_release(gil)
+
+        return status.code, status.is_error
+
+    def gen_prologue(self, builder, pyapi):
+        pass        # Do nothing
+
+    def gen_epilogue(self, builder, pyapi):
+        pass        # Do nothing
+
+
+class _GufuncObjectWrapper(_GufuncWrapper):
+    def gen_loop_body(self, builder, pyapi, func, args):
+        innercall, error = _prepare_call_to_object_mode(self.context,
+                                                        builder, pyapi, func,
+                                                        self.signature,
+                                                        args)
+        return innercall, error
+
+    def gen_prologue(self, builder, pyapi):
+        # Acquire the GIL
+        self.gil = pyapi.gil_ensure()
+
+    def gen_epilogue(self, builder, pyapi):
+        # Release GIL
+        pyapi.gil_release(self.gil)
+
+
+def build_gufunc_wrapper(py_func, cres, sin, sout, cache, is_parfors):
+    signature = cres.signature
+    wrapcls = (_GufuncObjectWrapper
+               if signature.return_type == types.pyobject
+               else _GufuncWrapper)
+    return wrapcls(
+        py_func, cres, sin, sout, cache, is_parfors=is_parfors,
+    ).build()
+
+
+def _prepare_call_to_object_mode(context, builder, pyapi, func,
+                                 signature, args):
+    mod = builder.module
+
+    bb_core_return = builder.append_basic_block('ufunc.core.return')
+
+    # Call to
+    # PyObject* ndarray_new(int nd,
+    #       npy_intp *dims,   /* shape */
+    #       npy_intp *strides,
+    #       void* data,
+    #       int type_num,
+    #       int itemsize)
+
+    ll_int = context.get_value_type(types.int32)
+    ll_intp = context.get_value_type(types.intp)
+    ll_intp_ptr = ir.PointerType(ll_intp)
+    ll_voidptr = context.get_value_type(types.voidptr)
+    ll_pyobj = context.get_value_type(types.pyobject)
+    fnty = ir.FunctionType(ll_pyobj, [ll_int, ll_intp_ptr,
+                                      ll_intp_ptr, ll_voidptr,
+                                      ll_int, ll_int])
+
+    fn_array_new = cgutils.get_or_insert_function(mod, fnty,
+                                                  "numba_ndarray_new")
+
+    # Convert each llarray into pyobject
+    error_pointer = cgutils.alloca_once(builder, ir.IntType(1), name='error')
+    builder.store(cgutils.true_bit, error_pointer)
+
+    # The PyObject* arguments to the kernel function
+    object_args = []
+    object_pointers = []
+
+    for i, (arg, argty) in enumerate(zip(args, signature.args)):
+        # Allocate NULL-initialized slot for this argument
+        objptr = cgutils.alloca_once(builder, ll_pyobj, zfill=True)
+        object_pointers.append(objptr)
+
+        if isinstance(argty, types.Array):
+            # Special case arrays: we don't need full-blown NRT reflection
+            # since the argument will be gone at the end of the kernel
+            arycls = context.make_array(argty)
+            array = arycls(context, builder, value=arg)
+
+            zero = Constant(ll_int, 0)
+
+            # Extract members of the llarray
+            nd = Constant(ll_int, argty.ndim)
+            dims = builder.gep(array._get_ptr_by_name('shape'), [zero, zero])
+            strides = builder.gep(array._get_ptr_by_name('strides'),
+                                  [zero, zero])
+            data = builder.bitcast(array.data, ll_voidptr)
+            dtype = np.dtype(str(argty.dtype))
+
+            # Prepare other info for reconstruction of the PyArray
+            type_num = Constant(ll_int, dtype.num)
+            itemsize = Constant(ll_int, dtype.itemsize)
+
+            # Call helper to reconstruct PyArray objects
+            obj = builder.call(fn_array_new, [nd, dims, strides, data,
+                                              type_num, itemsize])
+        else:
+            # Other argument types => use generic boxing
+            obj = pyapi.from_native_value(argty, arg)
+
+        builder.store(obj, objptr)
+        object_args.append(obj)
+
+        obj_is_null = cgutils.is_null(builder, obj)
+        builder.store(obj_is_null, error_pointer)
+        cgutils.cbranch_or_continue(builder, obj_is_null, bb_core_return)
+
+    # Call ufunc core function
+    object_sig = [types.pyobject] * len(object_args)
+
+    status, retval = context.call_conv.call_function(
+        builder, func, types.pyobject, object_sig,
+        object_args)
+    builder.store(status.is_error, error_pointer)
+
+    # Release returned object
+    pyapi.decref(retval)
+
+    builder.branch(bb_core_return)
+    # At return block
+    builder.position_at_end(bb_core_return)
+
+    # Release argument objects
+    for objptr in object_pointers:
+        pyapi.decref(builder.load(objptr))
+
+    innercall = status.code
+    return innercall, builder.load(error_pointer)
+
+
+class GUArrayArg(object):
+    def __init__(self, context, builder, args, steps, i, step_offset,
+                 typ, syms, sym_dim):
+
+        self.context = context
+        self.builder = builder
+
+        offset = context.get_constant(types.intp, i)
+
+        data = builder.load(builder.gep(args, [offset], name="data.ptr"),
+                            name="data")
+        self.data = data
+
+        core_step_ptr = builder.gep(steps, [offset], name="core.step.ptr")
+        core_step = builder.load(core_step_ptr)
+
+        if isinstance(typ, types.Array):
+            as_scalar = not syms
+
+            # number of symbol in the shape spec should match the dimension
+            # of the array type.
+            if len(syms) != typ.ndim:
+                if len(syms) == 0 and typ.ndim == 1:
+                    # This is an exception for handling scalar argument.
+                    # The type can be 1D array for scalar.
+                    # In the future, we may deprecate this exception.
+                    pass
+                else:
+                    raise TypeError("type and shape signature mismatch for arg "
+                                    "#{0}".format(i + 1))
+
+            ndim = typ.ndim
+            shape = [sym_dim[s] for s in syms]
+            strides = []
+
+            for j in range(ndim):
+                stepptr = builder.gep(steps,
+                                      [context.get_constant(types.intp,
+                                                            step_offset + j)],
+                                      name="step.ptr")
+                step = builder.load(stepptr)
+                strides.append(step)
+
+            ldcls = (_ArrayAsScalarArgLoader
+                     if as_scalar
+                     else _ArrayArgLoader)
+
+            self._loader = ldcls(dtype=typ.dtype,
+                                 ndim=ndim,
+                                 core_step=core_step,
+                                 as_scalar=as_scalar,
+                                 shape=shape,
+                                 strides=strides)
+        else:
+            # If typ is not an array
+            if syms:
+                raise TypeError("scalar type {0} given for non scalar "
+                                "argument #{1}".format(typ, i + 1))
+            self._loader = _ScalarArgLoader(dtype=typ, stride=core_step)
+
+    def get_array_at_offset(self, ind):
+        return self._loader.load(context=self.context, builder=self.builder,
+                                 data=self.data, ind=ind)
+
+
+class _ScalarArgLoader(object):
+    """
+    Handle GFunc argument loading where a scalar type is used in the core
+    function.
+    Note: It still has a stride because the input to the gufunc can be an array
+          for this argument.
+    """
+
+    def __init__(self, dtype, stride):
+        self.dtype = dtype
+        self.stride = stride
+
+    def load(self, context, builder, data, ind):
+        # Load at base + ind * stride
+        data = builder.gep(data, [builder.mul(ind, self.stride)])
+        dptr = builder.bitcast(data,
+                               context.get_data_type(self.dtype).as_pointer())
+        return builder.load(dptr)
+
+
+class _ArrayArgLoader(object):
+    """
+    Handle GUFunc argument loading where an array is expected.
+    """
+
+    def __init__(self, dtype, ndim, core_step, as_scalar, shape, strides):
+        self.dtype = dtype
+        self.ndim = ndim
+        self.core_step = core_step
+        self.as_scalar = as_scalar
+        self.shape = shape
+        self.strides = strides
+
+    def load(self, context, builder, data, ind):
+        arytyp = types.Array(dtype=self.dtype, ndim=self.ndim, layout="A")
+        arycls = context.make_array(arytyp)
+
+        array = arycls(context, builder)
+        offseted_data = cgutils.pointer_add(builder,
+                                            data,
+                                            builder.mul(self.core_step,
+                                                        ind))
+
+        shape, strides = self._shape_and_strides(context, builder)
+
+        itemsize = context.get_abi_sizeof(context.get_data_type(self.dtype))
+        context.populate_array(array,
+                               data=builder.bitcast(offseted_data,
+                                                    array.data.type),
+                               shape=shape,
+                               strides=strides,
+                               itemsize=context.get_constant(types.intp,
+                                                             itemsize),
+                               meminfo=None)
+
+        return array._getvalue()
+
+    def _shape_and_strides(self, context, builder):
+        shape = cgutils.pack_array(builder, self.shape)
+        strides = cgutils.pack_array(builder, self.strides)
+        return shape, strides
+
+
+class _ArrayAsScalarArgLoader(_ArrayArgLoader):
+    """
+    Handle GUFunc argument loading where the shape signature specifies
+    a scalar "()" but a 1D array is used for the type of the core function.
+    """
+
+    def _shape_and_strides(self, context, builder):
+        # Set shape and strides for a 1D size 1 array
+        one = context.get_constant(types.intp, 1)
+        zero = context.get_constant(types.intp, 0)
+        shape = cgutils.pack_array(builder, [one])
+        strides = cgutils.pack_array(builder, [zero])
+        return shape, strides
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc_db.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc_db.py
new file mode 100644
index 000000000..0c7a2a42a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/ufunc_db.py
@@ -0,0 +1,1153 @@
+"""This file contains information on how to translate different ufuncs
+into numba. It is a database of different ufuncs and how each of its
+loops maps to a function that implements the inner kernel of that ufunc
+(the inner kernel being the per-element function).
+
+Use the function get_ufunc_info to get the information related to the
+ufunc
+"""
+
+
+import numpy as np
+
+
+# this is lazily initialized to avoid circular imports
+_ufunc_db = None
+
+
+def _lazy_init_db():
+    global _ufunc_db
+
+    if _ufunc_db is None:
+        _ufunc_db = {}
+        _fill_ufunc_db(_ufunc_db)
+
+
+def get_ufuncs():
+    """obtain a list of supported ufuncs in the db"""
+    _lazy_init_db()
+    return _ufunc_db.keys()
+
+
+def get_ufunc_info(ufunc_key):
+    """get the lowering information for the ufunc with key ufunc_key.
+
+    The lowering information is a dictionary that maps from a numpy
+    loop string (as given by the ufunc types attribute) to a function
+    that handles code generation for a scalar version of the ufunc
+    (that is, generates the "per element" operation").
+
+    raises a KeyError if the ufunc is not in the ufunc_db
+    """
+    _lazy_init_db()
+    return _ufunc_db[ufunc_key]
+
+
+def _fill_ufunc_db(ufunc_db):
+    # some of these imports would cause a problem of circular
+    # imports if done at global scope when importing the numba
+    # module.
+    from numba.np import npyfuncs
+    from numba.cpython import cmathimpl, mathimpl, numbers
+    from numba.np.numpy_support import numpy_version
+
+    ufunc_db[np.isnat] = {
+        # datetime & timedelta
+        'M->?': npyfuncs.np_datetime_isnat_impl,
+        'm->?': npyfuncs.np_datetime_isnat_impl,
+    }
+
+    ufunc_db[np.negative] = {
+        '?->?': numbers.int_invert_impl,
+        'b->b': numbers.int_negate_impl,
+        'B->B': numbers.int_negate_impl,
+        'h->h': numbers.int_negate_impl,
+        'H->H': numbers.int_negate_impl,
+        'i->i': numbers.int_negate_impl,
+        'I->I': numbers.int_negate_impl,
+        'l->l': numbers.int_negate_impl,
+        'L->L': numbers.int_negate_impl,
+        'q->q': numbers.int_negate_impl,
+        'Q->Q': numbers.int_negate_impl,
+        'f->f': numbers.real_negate_impl,
+        'd->d': numbers.real_negate_impl,
+        'F->F': numbers.complex_negate_impl,
+        'D->D': numbers.complex_negate_impl,
+    }
+
+    ufunc_db[np.positive] = {
+        '?->?': numbers.int_positive_impl,
+        'b->b': numbers.int_positive_impl,
+        'B->B': numbers.int_positive_impl,
+        'h->h': numbers.int_positive_impl,
+        'H->H': numbers.int_positive_impl,
+        'i->i': numbers.int_positive_impl,
+        'I->I': numbers.int_positive_impl,
+        'l->l': numbers.int_positive_impl,
+        'L->L': numbers.int_positive_impl,
+        'q->q': numbers.int_positive_impl,
+        'Q->Q': numbers.int_positive_impl,
+        'f->f': numbers.real_positive_impl,
+        'd->d': numbers.real_positive_impl,
+        'F->F': numbers.complex_positive_impl,
+        'D->D': numbers.complex_positive_impl,
+    }
+
+    ufunc_db[np.absolute] = {
+        '?->?': numbers.int_abs_impl,
+        'b->b': numbers.int_abs_impl,
+        'B->B': numbers.uint_abs_impl,
+        'h->h': numbers.int_abs_impl,
+        'H->H': numbers.uint_abs_impl,
+        'i->i': numbers.int_abs_impl,
+        'I->I': numbers.uint_abs_impl,
+        'l->l': numbers.int_abs_impl,
+        'L->L': numbers.uint_abs_impl,
+        'q->q': numbers.int_abs_impl,
+        'Q->Q': numbers.uint_abs_impl,
+        'f->f': numbers.real_abs_impl,
+        'd->d': numbers.real_abs_impl,
+        'F->f': numbers.complex_abs_impl,
+        'D->d': numbers.complex_abs_impl,
+    }
+
+    ufunc_db[np.sign] = {
+        'b->b': numbers.int_sign_impl,
+        'B->B': numbers.int_sign_impl,
+        'h->h': numbers.int_sign_impl,
+        'H->H': numbers.int_sign_impl,
+        'i->i': numbers.int_sign_impl,
+        'I->I': numbers.int_sign_impl,
+        'l->l': numbers.int_sign_impl,
+        'L->L': numbers.int_sign_impl,
+        'q->q': numbers.int_sign_impl,
+        'Q->Q': numbers.int_sign_impl,
+        'f->f': numbers.real_sign_impl,
+        'd->d': numbers.real_sign_impl,
+        'F->F': npyfuncs.np_complex_sign_impl,
+        'D->D': npyfuncs.np_complex_sign_impl,
+    }
+
+    ufunc_db[np.add] = {
+        '??->?': numbers.int_or_impl,
+        'bb->b': numbers.int_add_impl,
+        'BB->B': numbers.int_add_impl,
+        'hh->h': numbers.int_add_impl,
+        'HH->H': numbers.int_add_impl,
+        'ii->i': numbers.int_add_impl,
+        'II->I': numbers.int_add_impl,
+        'll->l': numbers.int_add_impl,
+        'LL->L': numbers.int_add_impl,
+        'qq->q': numbers.int_add_impl,
+        'QQ->Q': numbers.int_add_impl,
+        'ff->f': numbers.real_add_impl,
+        'dd->d': numbers.real_add_impl,
+        'FF->F': numbers.complex_add_impl,
+        'DD->D': numbers.complex_add_impl,
+    }
+
+    ufunc_db[np.subtract] = {
+        '??->?': numbers.int_xor_impl,
+        'bb->b': numbers.int_sub_impl,
+        'BB->B': numbers.int_sub_impl,
+        'hh->h': numbers.int_sub_impl,
+        'HH->H': numbers.int_sub_impl,
+        'ii->i': numbers.int_sub_impl,
+        'II->I': numbers.int_sub_impl,
+        'll->l': numbers.int_sub_impl,
+        'LL->L': numbers.int_sub_impl,
+        'qq->q': numbers.int_sub_impl,
+        'QQ->Q': numbers.int_sub_impl,
+        'ff->f': numbers.real_sub_impl,
+        'dd->d': numbers.real_sub_impl,
+        'FF->F': numbers.complex_sub_impl,
+        'DD->D': numbers.complex_sub_impl,
+    }
+
+    ufunc_db[np.multiply] = {
+        '??->?': numbers.int_and_impl,
+        'bb->b': numbers.int_mul_impl,
+        'BB->B': numbers.int_mul_impl,
+        'hh->h': numbers.int_mul_impl,
+        'HH->H': numbers.int_mul_impl,
+        'ii->i': numbers.int_mul_impl,
+        'II->I': numbers.int_mul_impl,
+        'll->l': numbers.int_mul_impl,
+        'LL->L': numbers.int_mul_impl,
+        'qq->q': numbers.int_mul_impl,
+        'QQ->Q': numbers.int_mul_impl,
+        'ff->f': numbers.real_mul_impl,
+        'dd->d': numbers.real_mul_impl,
+        'FF->F': numbers.complex_mul_impl,
+        'DD->D': numbers.complex_mul_impl,
+    }
+
+    if np.divide != np.true_divide:
+        ufunc_db[np.divide] = {
+            'bb->b': npyfuncs.np_int_sdiv_impl,
+            'BB->B': npyfuncs.np_int_udiv_impl,
+            'hh->h': npyfuncs.np_int_sdiv_impl,
+            'HH->H': npyfuncs.np_int_udiv_impl,
+            'ii->i': npyfuncs.np_int_sdiv_impl,
+            'II->I': npyfuncs.np_int_udiv_impl,
+            'll->l': npyfuncs.np_int_sdiv_impl,
+            'LL->L': npyfuncs.np_int_udiv_impl,
+            'qq->q': npyfuncs.np_int_sdiv_impl,
+            'QQ->Q': npyfuncs.np_int_udiv_impl,
+            'ff->f': npyfuncs.np_real_div_impl,
+            'dd->d': npyfuncs.np_real_div_impl,
+            'FF->F': npyfuncs.np_complex_div_impl,
+            'DD->D': npyfuncs.np_complex_div_impl,
+        }
+
+    ufunc_db[np.true_divide] = {
+        'bb->d': npyfuncs.np_int_truediv_impl,
+        'BB->d': npyfuncs.np_int_truediv_impl,
+        'hh->d': npyfuncs.np_int_truediv_impl,
+        'HH->d': npyfuncs.np_int_truediv_impl,
+        'ii->d': npyfuncs.np_int_truediv_impl,
+        'II->d': npyfuncs.np_int_truediv_impl,
+        'll->d': npyfuncs.np_int_truediv_impl,
+        'LL->d': npyfuncs.np_int_truediv_impl,
+        'qq->d': npyfuncs.np_int_truediv_impl,
+        'QQ->d': npyfuncs.np_int_truediv_impl,
+        'ff->f': npyfuncs.np_real_div_impl,
+        'dd->d': npyfuncs.np_real_div_impl,
+        'FF->F': npyfuncs.np_complex_div_impl,
+        'DD->D': npyfuncs.np_complex_div_impl,
+    }
+
+    ufunc_db[np.floor_divide] = {
+        'bb->b': npyfuncs.np_int_sdiv_impl,
+        'BB->B': npyfuncs.np_int_udiv_impl,
+        'hh->h': npyfuncs.np_int_sdiv_impl,
+        'HH->H': npyfuncs.np_int_udiv_impl,
+        'ii->i': npyfuncs.np_int_sdiv_impl,
+        'II->I': npyfuncs.np_int_udiv_impl,
+        'll->l': npyfuncs.np_int_sdiv_impl,
+        'LL->L': npyfuncs.np_int_udiv_impl,
+        'qq->q': npyfuncs.np_int_sdiv_impl,
+        'QQ->Q': npyfuncs.np_int_udiv_impl,
+        'ff->f': npyfuncs.np_real_floor_div_impl,
+        'dd->d': npyfuncs.np_real_floor_div_impl,
+    }
+    if numpy_version < (1, 22): # removed in 1.22+
+        ufunc_db[np.floor_divide].update({
+            'FF->F': npyfuncs.np_complex_floor_div_impl,
+            'DD->D': npyfuncs.np_complex_floor_div_impl,
+        })
+
+    ufunc_db[np.remainder] = {
+        'bb->b': npyfuncs.np_int_srem_impl,
+        'BB->B': npyfuncs.np_int_urem_impl,
+        'hh->h': npyfuncs.np_int_srem_impl,
+        'HH->H': npyfuncs.np_int_urem_impl,
+        'ii->i': npyfuncs.np_int_srem_impl,
+        'II->I': npyfuncs.np_int_urem_impl,
+        'll->l': npyfuncs.np_int_srem_impl,
+        'LL->L': npyfuncs.np_int_urem_impl,
+        'qq->q': npyfuncs.np_int_srem_impl,
+        'QQ->Q': npyfuncs.np_int_urem_impl,
+        'ff->f': npyfuncs.np_real_mod_impl,
+        'dd->d': npyfuncs.np_real_mod_impl,
+    }
+
+    ufunc_db[np.divmod] = {
+        'bb->bb': npyfuncs.np_int_sdivrem_impl,
+        'BB->BB': npyfuncs.np_int_udivrem_impl,
+        'hh->hh': npyfuncs.np_int_sdivrem_impl,
+        'HH->HH': npyfuncs.np_int_udivrem_impl,
+        'ii->ii': npyfuncs.np_int_sdivrem_impl,
+        'II->II': npyfuncs.np_int_udivrem_impl,
+        'll->ll': npyfuncs.np_int_sdivrem_impl,
+        'LL->LL': npyfuncs.np_int_udivrem_impl,
+        'qq->qq': npyfuncs.np_int_sdivrem_impl,
+        'QQ->QQ': npyfuncs.np_int_udivrem_impl,
+        'ff->ff': npyfuncs.np_real_divmod_impl,
+        'dd->dd': npyfuncs.np_real_divmod_impl,
+    }
+
+    ufunc_db[np.fmod] = {
+        'bb->b': npyfuncs.np_int_fmod_impl,
+        'BB->B': npyfuncs.np_int_fmod_impl,
+        'hh->h': npyfuncs.np_int_fmod_impl,
+        'HH->H': npyfuncs.np_int_fmod_impl,
+        'ii->i': npyfuncs.np_int_fmod_impl,
+        'II->I': npyfuncs.np_int_fmod_impl,
+        'll->l': npyfuncs.np_int_fmod_impl,
+        'LL->L': npyfuncs.np_int_fmod_impl,
+        'qq->q': npyfuncs.np_int_fmod_impl,
+        'QQ->Q': npyfuncs.np_int_fmod_impl,
+        'ff->f': npyfuncs.np_real_fmod_impl,
+        'dd->d': npyfuncs.np_real_fmod_impl,
+    }
+
+    ufunc_db[np.logaddexp] = {
+        'ff->f': npyfuncs.np_real_logaddexp_impl,
+        'dd->d': npyfuncs.np_real_logaddexp_impl,
+    }
+
+    ufunc_db[np.logaddexp2] = {
+        'ff->f': npyfuncs.np_real_logaddexp2_impl,
+        'dd->d': npyfuncs.np_real_logaddexp2_impl,
+    }
+
+    ufunc_db[np.power] = {
+        'bb->b': numbers.int_power_impl,
+        'BB->B': numbers.int_power_impl,
+        'hh->h': numbers.int_power_impl,
+        'HH->H': numbers.int_power_impl,
+        'ii->i': numbers.int_power_impl,
+        'II->I': numbers.int_power_impl,
+        'll->l': numbers.int_power_impl,
+        'LL->L': numbers.int_power_impl,
+        'qq->q': numbers.int_power_impl,
+        'QQ->Q': numbers.int_power_impl,
+        # XXX we would like to use `int_power_impl` for real ** integer
+        # as well (for better performance), but the current ufunc typing
+        # rules forbid that
+        'ff->f': numbers.real_power_impl,
+        'dd->d': numbers.real_power_impl,
+        'FF->F': npyfuncs.np_complex_power_impl,
+        'DD->D': npyfuncs.np_complex_power_impl,
+    }
+
+    ufunc_db[np.float_power] = {
+        'ff->f': npyfuncs.real_float_power_impl,
+        'dd->d': npyfuncs.real_float_power_impl,
+        'FF->F': npyfuncs.np_complex_float_power_impl,
+        'DD->D': npyfuncs.np_complex_float_power_impl,
+    }
+
+    ufunc_db[np.gcd] = {
+        'bb->b': npyfuncs.np_gcd_impl,
+        'BB->B': npyfuncs.np_gcd_impl,
+        'hh->h': npyfuncs.np_gcd_impl,
+        'HH->H': npyfuncs.np_gcd_impl,
+        'ii->i': npyfuncs.np_gcd_impl,
+        'II->I': npyfuncs.np_gcd_impl,
+        'll->l': npyfuncs.np_gcd_impl,
+        'LL->L': npyfuncs.np_gcd_impl,
+        'qq->q': npyfuncs.np_gcd_impl,
+        'QQ->Q': npyfuncs.np_gcd_impl,
+    }
+
+    ufunc_db[np.lcm] = {
+        'bb->b': npyfuncs.np_lcm_impl,
+        'BB->B': npyfuncs.np_lcm_impl,
+        'hh->h': npyfuncs.np_lcm_impl,
+        'HH->H': npyfuncs.np_lcm_impl,
+        'ii->i': npyfuncs.np_lcm_impl,
+        'II->I': npyfuncs.np_lcm_impl,
+        'll->l': npyfuncs.np_lcm_impl,
+        'LL->L': npyfuncs.np_lcm_impl,
+        'qq->q': npyfuncs.np_lcm_impl,
+        'QQ->Q': npyfuncs.np_lcm_impl,
+    }
+
+    ufunc_db[np.rint] = {
+        'f->f': npyfuncs.np_real_rint_impl,
+        'd->d': npyfuncs.np_real_rint_impl,
+        'F->F': npyfuncs.np_complex_rint_impl,
+        'D->D': npyfuncs.np_complex_rint_impl,
+    }
+
+    ufunc_db[np.conjugate] = {
+        'b->b': numbers.real_conjugate_impl,
+        'B->B': numbers.real_conjugate_impl,
+        'h->h': numbers.real_conjugate_impl,
+        'H->H': numbers.real_conjugate_impl,
+        'i->i': numbers.real_conjugate_impl,
+        'I->I': numbers.real_conjugate_impl,
+        'l->l': numbers.real_conjugate_impl,
+        'L->L': numbers.real_conjugate_impl,
+        'q->q': numbers.real_conjugate_impl,
+        'Q->Q': numbers.real_conjugate_impl,
+        'f->f': numbers.real_conjugate_impl,
+        'd->d': numbers.real_conjugate_impl,
+        'F->F': numbers.complex_conjugate_impl,
+        'D->D': numbers.complex_conjugate_impl,
+    }
+
+    ufunc_db[np.exp] = {
+        'f->f': npyfuncs.np_real_exp_impl,
+        'd->d': npyfuncs.np_real_exp_impl,
+        'F->F': npyfuncs.np_complex_exp_impl,
+        'D->D': npyfuncs.np_complex_exp_impl,
+    }
+
+    ufunc_db[np.exp2] = {
+        'f->f': npyfuncs.np_real_exp2_impl,
+        'd->d': npyfuncs.np_real_exp2_impl,
+        'F->F': npyfuncs.np_complex_exp2_impl,
+        'D->D': npyfuncs.np_complex_exp2_impl,
+    }
+
+    ufunc_db[np.log] = {
+        'f->f': npyfuncs.np_real_log_impl,
+        'd->d': npyfuncs.np_real_log_impl,
+        'F->F': npyfuncs.np_complex_log_impl,
+        'D->D': npyfuncs.np_complex_log_impl,
+    }
+
+    ufunc_db[np.log2] = {
+        'f->f': npyfuncs.np_real_log2_impl,
+        'd->d': npyfuncs.np_real_log2_impl,
+        'F->F': npyfuncs.np_complex_log2_impl,
+        'D->D': npyfuncs.np_complex_log2_impl,
+    }
+
+    ufunc_db[np.log10] = {
+        'f->f': npyfuncs.np_real_log10_impl,
+        'd->d': npyfuncs.np_real_log10_impl,
+        'F->F': npyfuncs.np_complex_log10_impl,
+        'D->D': npyfuncs.np_complex_log10_impl,
+    }
+
+    ufunc_db[np.expm1] = {
+        'f->f': npyfuncs.np_real_expm1_impl,
+        'd->d': npyfuncs.np_real_expm1_impl,
+        'F->F': npyfuncs.np_complex_expm1_impl,
+        'D->D': npyfuncs.np_complex_expm1_impl,
+    }
+
+    ufunc_db[np.log1p] = {
+        'f->f': npyfuncs.np_real_log1p_impl,
+        'd->d': npyfuncs.np_real_log1p_impl,
+        'F->F': npyfuncs.np_complex_log1p_impl,
+        'D->D': npyfuncs.np_complex_log1p_impl,
+    }
+
+    ufunc_db[np.sqrt] = {
+        'f->f': npyfuncs.np_real_sqrt_impl,
+        'd->d': npyfuncs.np_real_sqrt_impl,
+        'F->F': npyfuncs.np_complex_sqrt_impl,
+        'D->D': npyfuncs.np_complex_sqrt_impl,
+    }
+
+    ufunc_db[np.square] = {
+        'b->b': npyfuncs.np_int_square_impl,
+        'B->B': npyfuncs.np_int_square_impl,
+        'h->h': npyfuncs.np_int_square_impl,
+        'H->H': npyfuncs.np_int_square_impl,
+        'i->i': npyfuncs.np_int_square_impl,
+        'I->I': npyfuncs.np_int_square_impl,
+        'l->l': npyfuncs.np_int_square_impl,
+        'L->L': npyfuncs.np_int_square_impl,
+        'q->q': npyfuncs.np_int_square_impl,
+        'Q->Q': npyfuncs.np_int_square_impl,
+        'f->f': npyfuncs.np_real_square_impl,
+        'd->d': npyfuncs.np_real_square_impl,
+        'F->F': npyfuncs.np_complex_square_impl,
+        'D->D': npyfuncs.np_complex_square_impl,
+    }
+
+    ufunc_db[np.cbrt] = {
+        'f->f': npyfuncs.np_real_cbrt_impl,
+        'd->d': npyfuncs.np_real_cbrt_impl,
+    }
+
+    ufunc_db[np.reciprocal] = {
+        'b->b': npyfuncs.np_int_reciprocal_impl,
+        'B->B': npyfuncs.np_int_reciprocal_impl,
+        'h->h': npyfuncs.np_int_reciprocal_impl,
+        'H->H': npyfuncs.np_int_reciprocal_impl,
+        'i->i': npyfuncs.np_int_reciprocal_impl,
+        'I->I': npyfuncs.np_int_reciprocal_impl,
+        'l->l': npyfuncs.np_int_reciprocal_impl,
+        'L->L': npyfuncs.np_int_reciprocal_impl,
+        'q->q': npyfuncs.np_int_reciprocal_impl,
+        'Q->Q': npyfuncs.np_int_reciprocal_impl,
+        'f->f': npyfuncs.np_real_reciprocal_impl,
+        'd->d': npyfuncs.np_real_reciprocal_impl,
+        'F->F': npyfuncs.np_complex_reciprocal_impl,
+        'D->D': npyfuncs.np_complex_reciprocal_impl,
+    }
+
+    ufunc_db[np.sin] = {
+        'f->f': npyfuncs.np_real_sin_impl,
+        'd->d': npyfuncs.np_real_sin_impl,
+        'F->F': npyfuncs.np_complex_sin_impl,
+        'D->D': npyfuncs.np_complex_sin_impl,
+    }
+
+    ufunc_db[np.cos] = {
+        'f->f': npyfuncs.np_real_cos_impl,
+        'd->d': npyfuncs.np_real_cos_impl,
+        'F->F': npyfuncs.np_complex_cos_impl,
+        'D->D': npyfuncs.np_complex_cos_impl,
+    }
+
+    tan_impl = cmathimpl.tan_impl
+
+    ufunc_db[np.tan] = {
+        'f->f': npyfuncs.np_real_tan_impl,
+        'd->d': npyfuncs.np_real_tan_impl,
+        'F->F': tan_impl,
+        'D->D': tan_impl,
+    }
+
+    arcsin_impl = cmathimpl.asin_impl
+
+    ufunc_db[np.arcsin] = {
+        'f->f': npyfuncs.np_real_asin_impl,
+        'd->d': npyfuncs.np_real_asin_impl,
+        'F->F': arcsin_impl,
+        'D->D': arcsin_impl,
+    }
+
+    ufunc_db[np.arccos] = {
+        'f->f': npyfuncs.np_real_acos_impl,
+        'd->d': npyfuncs.np_real_acos_impl,
+        'F->F': cmathimpl.acos_impl,
+        'D->D': cmathimpl.acos_impl,
+    }
+
+    arctan_impl = cmathimpl.atan_impl
+
+    ufunc_db[np.arctan] = {
+        'f->f': npyfuncs.np_real_atan_impl,
+        'd->d': npyfuncs.np_real_atan_impl,
+        'F->F': arctan_impl,
+        'D->D': arctan_impl,
+    }
+
+    ufunc_db[np.arctan2] = {
+        'ff->f': npyfuncs.np_real_atan2_impl,
+        'dd->d': npyfuncs.np_real_atan2_impl,
+    }
+
+    ufunc_db[np.hypot] = {
+        'ff->f': npyfuncs.np_real_hypot_impl,
+        'dd->d': npyfuncs.np_real_hypot_impl,
+    }
+
+    ufunc_db[np.sinh] = {
+        'f->f': npyfuncs.np_real_sinh_impl,
+        'd->d': npyfuncs.np_real_sinh_impl,
+        'F->F': npyfuncs.np_complex_sinh_impl,
+        'D->D': npyfuncs.np_complex_sinh_impl,
+    }
+
+    ufunc_db[np.cosh] = {
+        'f->f': npyfuncs.np_real_cosh_impl,
+        'd->d': npyfuncs.np_real_cosh_impl,
+        'F->F': npyfuncs.np_complex_cosh_impl,
+        'D->D': npyfuncs.np_complex_cosh_impl,
+    }
+
+    ufunc_db[np.tanh] = {
+        'f->f': npyfuncs.np_real_tanh_impl,
+        'd->d': npyfuncs.np_real_tanh_impl,
+        'F->F': npyfuncs.np_complex_tanh_impl,
+        'D->D': npyfuncs.np_complex_tanh_impl,
+    }
+
+    arcsinh_impl = cmathimpl.asinh_impl
+
+    ufunc_db[np.arcsinh] = {
+        'f->f': npyfuncs.np_real_asinh_impl,
+        'd->d': npyfuncs.np_real_asinh_impl,
+        'F->F': arcsinh_impl,
+        'D->D': arcsinh_impl,
+    }
+
+    ufunc_db[np.arccosh] = {
+        'f->f': npyfuncs.np_real_acosh_impl,
+        'd->d': npyfuncs.np_real_acosh_impl,
+        'F->F': npyfuncs.np_complex_acosh_impl,
+        'D->D': npyfuncs.np_complex_acosh_impl,
+    }
+
+    arctanh_impl = cmathimpl.atanh_impl
+
+    ufunc_db[np.arctanh] = {
+        'f->f': npyfuncs.np_real_atanh_impl,
+        'd->d': npyfuncs.np_real_atanh_impl,
+        'F->F': arctanh_impl,
+        'D->D': arctanh_impl,
+    }
+
+    ufunc_db[np.deg2rad] = {
+        'f->f': mathimpl.radians_float_impl,
+        'd->d': mathimpl.radians_float_impl,
+    }
+
+    ufunc_db[np.radians] = ufunc_db[np.deg2rad]
+
+    ufunc_db[np.rad2deg] = {
+        'f->f': mathimpl.degrees_float_impl,
+        'd->d': mathimpl.degrees_float_impl,
+    }
+
+    ufunc_db[np.degrees] = ufunc_db[np.rad2deg]
+
+    ufunc_db[np.floor] = {
+        'f->f': npyfuncs.np_real_floor_impl,
+        'd->d': npyfuncs.np_real_floor_impl,
+    }
+
+    ufunc_db[np.ceil] = {
+        'f->f': npyfuncs.np_real_ceil_impl,
+        'd->d': npyfuncs.np_real_ceil_impl,
+    }
+
+    ufunc_db[np.trunc] = {
+        'f->f': npyfuncs.np_real_trunc_impl,
+        'd->d': npyfuncs.np_real_trunc_impl,
+    }
+
+    ufunc_db[np.fabs] = {
+        'f->f': npyfuncs.np_real_fabs_impl,
+        'd->d': npyfuncs.np_real_fabs_impl,
+    }
+
+    # logical ufuncs
+    ufunc_db[np.greater] = {
+        '??->?': numbers.int_ugt_impl,
+        'bb->?': numbers.int_sgt_impl,
+        'BB->?': numbers.int_ugt_impl,
+        'hh->?': numbers.int_sgt_impl,
+        'HH->?': numbers.int_ugt_impl,
+        'ii->?': numbers.int_sgt_impl,
+        'II->?': numbers.int_ugt_impl,
+        'll->?': numbers.int_sgt_impl,
+        'LL->?': numbers.int_ugt_impl,
+        'qq->?': numbers.int_sgt_impl,
+        'QQ->?': numbers.int_ugt_impl,
+        'ff->?': numbers.real_gt_impl,
+        'dd->?': numbers.real_gt_impl,
+        'FF->?': npyfuncs.np_complex_gt_impl,
+        'DD->?': npyfuncs.np_complex_gt_impl,
+    }
+
+    ufunc_db[np.greater_equal] = {
+        '??->?': numbers.int_uge_impl,
+        'bb->?': numbers.int_sge_impl,
+        'BB->?': numbers.int_uge_impl,
+        'hh->?': numbers.int_sge_impl,
+        'HH->?': numbers.int_uge_impl,
+        'ii->?': numbers.int_sge_impl,
+        'II->?': numbers.int_uge_impl,
+        'll->?': numbers.int_sge_impl,
+        'LL->?': numbers.int_uge_impl,
+        'qq->?': numbers.int_sge_impl,
+        'QQ->?': numbers.int_uge_impl,
+        'ff->?': numbers.real_ge_impl,
+        'dd->?': numbers.real_ge_impl,
+        'FF->?': npyfuncs.np_complex_ge_impl,
+        'DD->?': npyfuncs.np_complex_ge_impl,
+    }
+
+    ufunc_db[np.less] = {
+        '??->?': numbers.int_ult_impl,
+        'bb->?': numbers.int_slt_impl,
+        'BB->?': numbers.int_ult_impl,
+        'hh->?': numbers.int_slt_impl,
+        'HH->?': numbers.int_ult_impl,
+        'ii->?': numbers.int_slt_impl,
+        'II->?': numbers.int_ult_impl,
+        'll->?': numbers.int_slt_impl,
+        'LL->?': numbers.int_ult_impl,
+        'qq->?': numbers.int_slt_impl,
+        'QQ->?': numbers.int_ult_impl,
+        'ff->?': numbers.real_lt_impl,
+        'dd->?': numbers.real_lt_impl,
+        'FF->?': npyfuncs.np_complex_lt_impl,
+        'DD->?': npyfuncs.np_complex_lt_impl,
+    }
+
+    ufunc_db[np.less_equal] = {
+        '??->?': numbers.int_ule_impl,
+        'bb->?': numbers.int_sle_impl,
+        'BB->?': numbers.int_ule_impl,
+        'hh->?': numbers.int_sle_impl,
+        'HH->?': numbers.int_ule_impl,
+        'ii->?': numbers.int_sle_impl,
+        'II->?': numbers.int_ule_impl,
+        'll->?': numbers.int_sle_impl,
+        'LL->?': numbers.int_ule_impl,
+        'qq->?': numbers.int_sle_impl,
+        'QQ->?': numbers.int_ule_impl,
+        'ff->?': numbers.real_le_impl,
+        'dd->?': numbers.real_le_impl,
+        'FF->?': npyfuncs.np_complex_le_impl,
+        'DD->?': npyfuncs.np_complex_le_impl,
+    }
+
+    ufunc_db[np.not_equal] = {
+        '??->?': numbers.int_ne_impl,
+        'bb->?': numbers.int_ne_impl,
+        'BB->?': numbers.int_ne_impl,
+        'hh->?': numbers.int_ne_impl,
+        'HH->?': numbers.int_ne_impl,
+        'ii->?': numbers.int_ne_impl,
+        'II->?': numbers.int_ne_impl,
+        'll->?': numbers.int_ne_impl,
+        'LL->?': numbers.int_ne_impl,
+        'qq->?': numbers.int_ne_impl,
+        'QQ->?': numbers.int_ne_impl,
+        'ff->?': numbers.real_ne_impl,
+        'dd->?': numbers.real_ne_impl,
+        'FF->?': npyfuncs.np_complex_ne_impl,
+        'DD->?': npyfuncs.np_complex_ne_impl,
+    }
+
+    ufunc_db[np.equal] = {
+        '??->?': numbers.int_eq_impl,
+        'bb->?': numbers.int_eq_impl,
+        'BB->?': numbers.int_eq_impl,
+        'hh->?': numbers.int_eq_impl,
+        'HH->?': numbers.int_eq_impl,
+        'ii->?': numbers.int_eq_impl,
+        'II->?': numbers.int_eq_impl,
+        'll->?': numbers.int_eq_impl,
+        'LL->?': numbers.int_eq_impl,
+        'qq->?': numbers.int_eq_impl,
+        'QQ->?': numbers.int_eq_impl,
+        'ff->?': numbers.real_eq_impl,
+        'dd->?': numbers.real_eq_impl,
+        'FF->?': npyfuncs.np_complex_eq_impl,
+        'DD->?': npyfuncs.np_complex_eq_impl,
+    }
+
+    ufunc_db[np.logical_and] = {
+        '??->?': npyfuncs.np_logical_and_impl,
+        'bb->?': npyfuncs.np_logical_and_impl,
+        'BB->?': npyfuncs.np_logical_and_impl,
+        'hh->?': npyfuncs.np_logical_and_impl,
+        'HH->?': npyfuncs.np_logical_and_impl,
+        'ii->?': npyfuncs.np_logical_and_impl,
+        'II->?': npyfuncs.np_logical_and_impl,
+        'll->?': npyfuncs.np_logical_and_impl,
+        'LL->?': npyfuncs.np_logical_and_impl,
+        'qq->?': npyfuncs.np_logical_and_impl,
+        'QQ->?': npyfuncs.np_logical_and_impl,
+        'ff->?': npyfuncs.np_logical_and_impl,
+        'dd->?': npyfuncs.np_logical_and_impl,
+        'FF->?': npyfuncs.np_complex_logical_and_impl,
+        'DD->?': npyfuncs.np_complex_logical_and_impl,
+    }
+
+    ufunc_db[np.logical_or] = {
+        '??->?': npyfuncs.np_logical_or_impl,
+        'bb->?': npyfuncs.np_logical_or_impl,
+        'BB->?': npyfuncs.np_logical_or_impl,
+        'hh->?': npyfuncs.np_logical_or_impl,
+        'HH->?': npyfuncs.np_logical_or_impl,
+        'ii->?': npyfuncs.np_logical_or_impl,
+        'II->?': npyfuncs.np_logical_or_impl,
+        'll->?': npyfuncs.np_logical_or_impl,
+        'LL->?': npyfuncs.np_logical_or_impl,
+        'qq->?': npyfuncs.np_logical_or_impl,
+        'QQ->?': npyfuncs.np_logical_or_impl,
+        'ff->?': npyfuncs.np_logical_or_impl,
+        'dd->?': npyfuncs.np_logical_or_impl,
+        'FF->?': npyfuncs.np_complex_logical_or_impl,
+        'DD->?': npyfuncs.np_complex_logical_or_impl,
+    }
+
+    ufunc_db[np.logical_xor] = {
+        '??->?': npyfuncs.np_logical_xor_impl,
+        'bb->?': npyfuncs.np_logical_xor_impl,
+        'BB->?': npyfuncs.np_logical_xor_impl,
+        'hh->?': npyfuncs.np_logical_xor_impl,
+        'HH->?': npyfuncs.np_logical_xor_impl,
+        'ii->?': npyfuncs.np_logical_xor_impl,
+        'II->?': npyfuncs.np_logical_xor_impl,
+        'll->?': npyfuncs.np_logical_xor_impl,
+        'LL->?': npyfuncs.np_logical_xor_impl,
+        'qq->?': npyfuncs.np_logical_xor_impl,
+        'QQ->?': npyfuncs.np_logical_xor_impl,
+        'ff->?': npyfuncs.np_logical_xor_impl,
+        'dd->?': npyfuncs.np_logical_xor_impl,
+        'FF->?': npyfuncs.np_complex_logical_xor_impl,
+        'DD->?': npyfuncs.np_complex_logical_xor_impl,
+    }
+
+    ufunc_db[np.logical_not] = {
+        '?->?': npyfuncs.np_logical_not_impl,
+        'b->?': npyfuncs.np_logical_not_impl,
+        'B->?': npyfuncs.np_logical_not_impl,
+        'h->?': npyfuncs.np_logical_not_impl,
+        'H->?': npyfuncs.np_logical_not_impl,
+        'i->?': npyfuncs.np_logical_not_impl,
+        'I->?': npyfuncs.np_logical_not_impl,
+        'l->?': npyfuncs.np_logical_not_impl,
+        'L->?': npyfuncs.np_logical_not_impl,
+        'q->?': npyfuncs.np_logical_not_impl,
+        'Q->?': npyfuncs.np_logical_not_impl,
+        'f->?': npyfuncs.np_logical_not_impl,
+        'd->?': npyfuncs.np_logical_not_impl,
+        'F->?': npyfuncs.np_complex_logical_not_impl,
+        'D->?': npyfuncs.np_complex_logical_not_impl,
+    }
+
+    ufunc_db[np.maximum] = {
+        '??->?': npyfuncs.np_logical_or_impl,
+        'bb->b': npyfuncs.np_int_smax_impl,
+        'BB->B': npyfuncs.np_int_umax_impl,
+        'hh->h': npyfuncs.np_int_smax_impl,
+        'HH->H': npyfuncs.np_int_umax_impl,
+        'ii->i': npyfuncs.np_int_smax_impl,
+        'II->I': npyfuncs.np_int_umax_impl,
+        'll->l': npyfuncs.np_int_smax_impl,
+        'LL->L': npyfuncs.np_int_umax_impl,
+        'qq->q': npyfuncs.np_int_smax_impl,
+        'QQ->Q': npyfuncs.np_int_umax_impl,
+        'ff->f': npyfuncs.np_real_maximum_impl,
+        'dd->d': npyfuncs.np_real_maximum_impl,
+        'FF->F': npyfuncs.np_complex_maximum_impl,
+        'DD->D': npyfuncs.np_complex_maximum_impl,
+    }
+
+    ufunc_db[np.minimum] = {
+        '??->?': npyfuncs.np_logical_and_impl,
+        'bb->b': npyfuncs.np_int_smin_impl,
+        'BB->B': npyfuncs.np_int_umin_impl,
+        'hh->h': npyfuncs.np_int_smin_impl,
+        'HH->H': npyfuncs.np_int_umin_impl,
+        'ii->i': npyfuncs.np_int_smin_impl,
+        'II->I': npyfuncs.np_int_umin_impl,
+        'll->l': npyfuncs.np_int_smin_impl,
+        'LL->L': npyfuncs.np_int_umin_impl,
+        'qq->q': npyfuncs.np_int_smin_impl,
+        'QQ->Q': npyfuncs.np_int_umin_impl,
+        'ff->f': npyfuncs.np_real_minimum_impl,
+        'dd->d': npyfuncs.np_real_minimum_impl,
+        'FF->F': npyfuncs.np_complex_minimum_impl,
+        'DD->D': npyfuncs.np_complex_minimum_impl,
+    }
+
+    ufunc_db[np.fmax] = {
+        '??->?': npyfuncs.np_logical_or_impl,
+        'bb->b': npyfuncs.np_int_smax_impl,
+        'BB->B': npyfuncs.np_int_umax_impl,
+        'hh->h': npyfuncs.np_int_smax_impl,
+        'HH->H': npyfuncs.np_int_umax_impl,
+        'ii->i': npyfuncs.np_int_smax_impl,
+        'II->I': npyfuncs.np_int_umax_impl,
+        'll->l': npyfuncs.np_int_smax_impl,
+        'LL->L': npyfuncs.np_int_umax_impl,
+        'qq->q': npyfuncs.np_int_smax_impl,
+        'QQ->Q': npyfuncs.np_int_umax_impl,
+        'ff->f': npyfuncs.np_real_fmax_impl,
+        'dd->d': npyfuncs.np_real_fmax_impl,
+        'FF->F': npyfuncs.np_complex_fmax_impl,
+        'DD->D': npyfuncs.np_complex_fmax_impl,
+    }
+
+    ufunc_db[np.fmin] = {
+        '??->?': npyfuncs.np_logical_and_impl,
+        'bb->b': npyfuncs.np_int_smin_impl,
+        'BB->B': npyfuncs.np_int_umin_impl,
+        'hh->h': npyfuncs.np_int_smin_impl,
+        'HH->H': npyfuncs.np_int_umin_impl,
+        'ii->i': npyfuncs.np_int_smin_impl,
+        'II->I': npyfuncs.np_int_umin_impl,
+        'll->l': npyfuncs.np_int_smin_impl,
+        'LL->L': npyfuncs.np_int_umin_impl,
+        'qq->q': npyfuncs.np_int_smin_impl,
+        'QQ->Q': npyfuncs.np_int_umin_impl,
+        'ff->f': npyfuncs.np_real_fmin_impl,
+        'dd->d': npyfuncs.np_real_fmin_impl,
+        'FF->F': npyfuncs.np_complex_fmin_impl,
+        'DD->D': npyfuncs.np_complex_fmin_impl,
+    }
+
+    # misc floating functions
+    ufunc_db[np.isnan] = {
+        'f->?': npyfuncs.np_real_isnan_impl,
+        'd->?': npyfuncs.np_real_isnan_impl,
+        'F->?': npyfuncs.np_complex_isnan_impl,
+        'D->?': npyfuncs.np_complex_isnan_impl,
+        # int8
+        'b->?': npyfuncs.np_int_isnan_impl,
+        'B->?': npyfuncs.np_int_isnan_impl,
+        # int16
+        'h->?': npyfuncs.np_int_isnan_impl,
+        'H->?': npyfuncs.np_int_isnan_impl,
+        # int32
+        'i->?': npyfuncs.np_int_isnan_impl,
+        'I->?': npyfuncs.np_int_isnan_impl,
+        # int64
+        'l->?': npyfuncs.np_int_isnan_impl,
+        'L->?': npyfuncs.np_int_isnan_impl,
+        # intp
+        'q->?': npyfuncs.np_int_isnan_impl,
+        'Q->?': npyfuncs.np_int_isnan_impl,
+        # boolean
+        '?->?': npyfuncs.np_int_isnan_impl,
+    }
+
+    if numpy_version >= (1, 18):
+        ufunc_db[np.isnan].update({
+            'm->?': npyfuncs.np_datetime_isnat_impl,
+            'M->?': npyfuncs.np_datetime_isnat_impl,
+        })
+
+    ufunc_db[np.isinf] = {
+        'f->?': npyfuncs.np_real_isinf_impl,
+        'd->?': npyfuncs.np_real_isinf_impl,
+        'F->?': npyfuncs.np_complex_isinf_impl,
+        'D->?': npyfuncs.np_complex_isinf_impl,
+        # int8
+        'b->?': npyfuncs.np_int_isinf_impl,
+        'B->?': npyfuncs.np_int_isinf_impl,
+        # int16
+        'h->?': npyfuncs.np_int_isinf_impl,
+        'H->?': npyfuncs.np_int_isinf_impl,
+        # int32
+        'i->?': npyfuncs.np_int_isinf_impl,
+        'I->?': npyfuncs.np_int_isinf_impl,
+        # int64
+        'l->?': npyfuncs.np_int_isinf_impl,
+        'L->?': npyfuncs.np_int_isinf_impl,
+        # intp
+        'q->?': npyfuncs.np_int_isinf_impl,
+        'Q->?': npyfuncs.np_int_isinf_impl,
+        # boolean
+        '?->?': npyfuncs.np_int_isinf_impl,
+    }
+
+    if numpy_version >= (1, 18):
+        ufunc_db[np.isinf].update({
+            'm->?': npyfuncs.np_int_isinf_impl,
+            'M->?': npyfuncs.np_int_isinf_impl,
+        })
+
+    ufunc_db[np.isfinite] = {
+        'f->?': npyfuncs.np_real_isfinite_impl,
+        'd->?': npyfuncs.np_real_isfinite_impl,
+        'F->?': npyfuncs.np_complex_isfinite_impl,
+        'D->?': npyfuncs.np_complex_isfinite_impl,
+        # int8
+        'b->?': npyfuncs.np_int_isfinite_impl,
+        'B->?': npyfuncs.np_int_isfinite_impl,
+        # int16
+        'h->?': npyfuncs.np_int_isfinite_impl,
+        'H->?': npyfuncs.np_int_isfinite_impl,
+        # int32
+        'i->?': npyfuncs.np_int_isfinite_impl,
+        'I->?': npyfuncs.np_int_isfinite_impl,
+        # int64
+        'l->?': npyfuncs.np_int_isfinite_impl,
+        'L->?': npyfuncs.np_int_isfinite_impl,
+        # intp
+        'q->?': npyfuncs.np_int_isfinite_impl,
+        'Q->?': npyfuncs.np_int_isfinite_impl,
+        # boolean
+        '?->?': npyfuncs.np_int_isfinite_impl,
+        # datetime & timedelta
+        'M->?': npyfuncs.np_datetime_isfinite_impl,
+        'm->?': npyfuncs.np_datetime_isfinite_impl,
+    }
+
+    ufunc_db[np.signbit] = {
+        'f->?': npyfuncs.np_real_signbit_impl,
+        'd->?': npyfuncs.np_real_signbit_impl,
+    }
+
+    ufunc_db[np.copysign] = {
+        'ff->f': npyfuncs.np_real_copysign_impl,
+        'dd->d': npyfuncs.np_real_copysign_impl,
+    }
+
+    ufunc_db[np.nextafter] = {
+        'ff->f': npyfuncs.np_real_nextafter_impl,
+        'dd->d': npyfuncs.np_real_nextafter_impl,
+    }
+
+    ufunc_db[np.spacing] = {
+        'f->f': npyfuncs.np_real_spacing_impl,
+        'd->d': npyfuncs.np_real_spacing_impl,
+    }
+
+    ufunc_db[np.ldexp] = {
+        'fi->f': npyfuncs.np_real_ldexp_impl,
+        'fl->f': npyfuncs.np_real_ldexp_impl,
+        'di->d': npyfuncs.np_real_ldexp_impl,
+        'dl->d': npyfuncs.np_real_ldexp_impl,
+    }
+
+    # bit twiddling functions
+    ufunc_db[np.bitwise_and] = {
+        '??->?': numbers.int_and_impl,
+        'bb->b': numbers.int_and_impl,
+        'BB->B': numbers.int_and_impl,
+        'hh->h': numbers.int_and_impl,
+        'HH->H': numbers.int_and_impl,
+        'ii->i': numbers.int_and_impl,
+        'II->I': numbers.int_and_impl,
+        'll->l': numbers.int_and_impl,
+        'LL->L': numbers.int_and_impl,
+        'qq->q': numbers.int_and_impl,
+        'QQ->Q': numbers.int_and_impl,
+    }
+
+    ufunc_db[np.bitwise_or] = {
+        '??->?': numbers.int_or_impl,
+        'bb->b': numbers.int_or_impl,
+        'BB->B': numbers.int_or_impl,
+        'hh->h': numbers.int_or_impl,
+        'HH->H': numbers.int_or_impl,
+        'ii->i': numbers.int_or_impl,
+        'II->I': numbers.int_or_impl,
+        'll->l': numbers.int_or_impl,
+        'LL->L': numbers.int_or_impl,
+        'qq->q': numbers.int_or_impl,
+        'QQ->Q': numbers.int_or_impl,
+    }
+
+    ufunc_db[np.bitwise_xor] = {
+        '??->?': numbers.int_xor_impl,
+        'bb->b': numbers.int_xor_impl,
+        'BB->B': numbers.int_xor_impl,
+        'hh->h': numbers.int_xor_impl,
+        'HH->H': numbers.int_xor_impl,
+        'ii->i': numbers.int_xor_impl,
+        'II->I': numbers.int_xor_impl,
+        'll->l': numbers.int_xor_impl,
+        'LL->L': numbers.int_xor_impl,
+        'qq->q': numbers.int_xor_impl,
+        'QQ->Q': numbers.int_xor_impl,
+    }
+
+    ufunc_db[np.invert] = {  # aka np.bitwise_not
+        '?->?': numbers.int_invert_impl,
+        'b->b': numbers.int_invert_impl,
+        'B->B': numbers.int_invert_impl,
+        'h->h': numbers.int_invert_impl,
+        'H->H': numbers.int_invert_impl,
+        'i->i': numbers.int_invert_impl,
+        'I->I': numbers.int_invert_impl,
+        'l->l': numbers.int_invert_impl,
+        'L->L': numbers.int_invert_impl,
+        'q->q': numbers.int_invert_impl,
+        'Q->Q': numbers.int_invert_impl,
+    }
+
+    ufunc_db[np.left_shift] = {
+        'bb->b': numbers.int_shl_impl,
+        'BB->B': numbers.int_shl_impl,
+        'hh->h': numbers.int_shl_impl,
+        'HH->H': numbers.int_shl_impl,
+        'ii->i': numbers.int_shl_impl,
+        'II->I': numbers.int_shl_impl,
+        'll->l': numbers.int_shl_impl,
+        'LL->L': numbers.int_shl_impl,
+        'qq->q': numbers.int_shl_impl,
+        'QQ->Q': numbers.int_shl_impl,
+    }
+
+    ufunc_db[np.right_shift] = {
+        'bb->b': numbers.int_shr_impl,
+        'BB->B': numbers.int_shr_impl,
+        'hh->h': numbers.int_shr_impl,
+        'HH->H': numbers.int_shr_impl,
+        'ii->i': numbers.int_shr_impl,
+        'II->I': numbers.int_shr_impl,
+        'll->l': numbers.int_shr_impl,
+        'LL->L': numbers.int_shr_impl,
+        'qq->q': numbers.int_shr_impl,
+        'QQ->Q': numbers.int_shr_impl,
+    }
+
+    # Inject datetime64 support
+    from numba.np import npdatetime
+    ufunc_db[np.negative].update({
+        'm->m': npdatetime.timedelta_neg_impl,
+    })
+    ufunc_db[np.positive].update({
+        'm->m': npdatetime.timedelta_pos_impl,
+    })
+    ufunc_db[np.absolute].update({
+        'm->m': npdatetime.timedelta_abs_impl,
+    })
+    ufunc_db[np.sign].update({
+        'm->m': npdatetime.timedelta_sign_impl,
+    })
+    ufunc_db[np.add].update({
+        'mm->m': npdatetime.timedelta_add_impl,
+        'Mm->M': npdatetime.datetime_plus_timedelta,
+        'mM->M': npdatetime.timedelta_plus_datetime,
+    })
+    ufunc_db[np.subtract].update({
+        'mm->m': npdatetime.timedelta_sub_impl,
+        'Mm->M': npdatetime.datetime_minus_timedelta,
+        'MM->m': npdatetime.datetime_minus_datetime,
+    })
+    ufunc_db[np.multiply].update({
+        'mq->m': npdatetime.timedelta_times_number,
+        'md->m': npdatetime.timedelta_times_number,
+        'qm->m': npdatetime.number_times_timedelta,
+        'dm->m': npdatetime.number_times_timedelta,
+    })
+    if np.divide != np.true_divide:
+        ufunc_db[np.divide].update({
+            'mq->m': npdatetime.timedelta_over_number,
+            'md->m': npdatetime.timedelta_over_number,
+            'mm->d': npdatetime.timedelta_over_timedelta,
+        })
+    ufunc_db[np.true_divide].update({
+        'mq->m': npdatetime.timedelta_over_number,
+        'md->m': npdatetime.timedelta_over_number,
+        'mm->d': npdatetime.timedelta_over_timedelta,
+    })
+    ufunc_db[np.floor_divide].update({
+        'mq->m': npdatetime.timedelta_over_number,
+        'md->m': npdatetime.timedelta_over_number,
+    })
+
+    if numpy_version >= (1, 16):
+        ufunc_db[np.floor_divide].update({
+            'mm->q': npdatetime.timedelta_floor_div_timedelta,
+        })
+
+    ufunc_db[np.equal].update({
+        'MM->?': npdatetime.datetime_eq_datetime_impl,
+        'mm->?': npdatetime.timedelta_eq_timedelta_impl,
+    })
+    ufunc_db[np.not_equal].update({
+        'MM->?': npdatetime.datetime_ne_datetime_impl,
+        'mm->?': npdatetime.timedelta_ne_timedelta_impl,
+    })
+    ufunc_db[np.less].update({
+        'MM->?': npdatetime.datetime_lt_datetime_impl,
+        'mm->?': npdatetime.timedelta_lt_timedelta_impl,
+    })
+    ufunc_db[np.less_equal].update({
+        'MM->?': npdatetime.datetime_le_datetime_impl,
+        'mm->?': npdatetime.timedelta_le_timedelta_impl,
+    })
+    ufunc_db[np.greater].update({
+        'MM->?': npdatetime.datetime_gt_datetime_impl,
+        'mm->?': npdatetime.timedelta_gt_timedelta_impl,
+    })
+    ufunc_db[np.greater_equal].update({
+        'MM->?': npdatetime.datetime_ge_datetime_impl,
+        'mm->?': npdatetime.timedelta_ge_timedelta_impl,
+    })
+    ufunc_db[np.maximum].update({
+        'MM->M': npdatetime.datetime_maximum_impl,
+        'mm->m': npdatetime.timedelta_maximum_impl,
+    })
+    ufunc_db[np.minimum].update({
+        'MM->M': npdatetime.datetime_minimum_impl,
+        'mm->m': npdatetime.timedelta_minimum_impl,
+    })
+    # there is no difference for datetime/timedelta in maximum/fmax
+    # and minimum/fmin
+    ufunc_db[np.fmax].update({
+        'MM->M': npdatetime.datetime_fmax_impl,
+        'mm->m': npdatetime.timedelta_fmax_impl,
+    })
+    ufunc_db[np.fmin].update({
+        'MM->M': npdatetime.datetime_fmin_impl,
+        'mm->m': npdatetime.timedelta_fmin_impl,
+    })
+
+    if numpy_version >= (1, 16):
+        ufunc_db[np.remainder].update({
+            'mm->m': npdatetime.timedelta_mod_timedelta,
+        })
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/unsafe/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/unsafe/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/unsafe/ndarray.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/unsafe/ndarray.py
new file mode 100644
index 000000000..95085a300
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/np/unsafe/ndarray.py
@@ -0,0 +1,80 @@
+"""
+This file provides internal compiler utilities that support certain special
+operations with numpy.
+"""
+from numba.core import types, typing
+from numba.core.cgutils import unpack_tuple
+from numba.core.extending import intrinsic
+from numba.core.imputils import impl_ret_new_ref
+from numba.core.errors import RequireLiteralValue, TypingError
+
+from numba.cpython.unsafe.tuple import tuple_setitem
+
+
+@intrinsic
+def empty_inferred(typingctx, shape):
+    """A version of numpy.empty whose dtype is inferred by the type system.
+
+    Expects `shape` to be a int-tuple.
+
+    There is special logic in the type-inferencer to handle the "refine"-ing
+    of undefined dtype.
+    """
+    from numba.np.arrayobj import _empty_nd_impl
+
+    def codegen(context, builder, signature, args):
+        # check that the return type is now defined
+        arrty = signature.return_type
+        assert arrty.is_precise()
+        shapes = unpack_tuple(builder, args[0])
+        # redirect implementation to np.empty
+        res = _empty_nd_impl(context, builder, arrty, shapes)
+        return impl_ret_new_ref(context, builder, arrty, res._getvalue())
+
+    # make function signature
+    nd = len(shape)
+    array_ty = types.Array(ndim=nd, layout='C', dtype=types.undefined)
+    sig = array_ty(shape)
+    return sig, codegen
+
+
+@intrinsic
+def to_fixed_tuple(typingctx, array, length):
+    """Convert *array* into a tuple of *length*
+
+    Returns ``UniTuple(array.dtype, length)``
+
+    ** Warning **
+    - No boundchecking.
+      If *length* is longer than *array.size*, the behavior is undefined.
+    """
+    if not isinstance(length, types.IntegerLiteral):
+        raise RequireLiteralValue('*length* argument must be a constant')
+
+    if array.ndim != 1:
+        raise TypingError("Not supported on array.ndim={}".format(array.ndim))
+
+    # Determine types
+    tuple_size = int(length.literal_value)
+    tuple_type = types.UniTuple(dtype=array.dtype, count=tuple_size)
+    sig = tuple_type(array, length)
+
+    def codegen(context, builder, signature, args):
+        def impl(array, length, empty_tuple):
+            out = empty_tuple
+            for i in range(length):
+                out = tuple_setitem(out, i, array[i])
+            return out
+
+        inner_argtypes = [signature.args[0], types.intp, tuple_type]
+        inner_sig = typing.signature(tuple_type, *inner_argtypes)
+        ll_idx_type = context.get_value_type(types.intp)
+        # Allocate an empty tuple
+        empty_tuple = context.get_constant_undef(tuple_type)
+        inner_args = [args[0], ll_idx_type(tuple_size), empty_tuple]
+
+        res = context.compile_internal(builder, impl, inner_sig, inner_args)
+        return res
+
+    return sig, codegen
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/__init__.py
new file mode 100644
index 000000000..f2acbe339
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/__init__.py
@@ -0,0 +1 @@
+from numba.parfors import parfor_lowering
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/array_analysis.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/array_analysis.py
new file mode 100644
index 000000000..18a8aa115
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/array_analysis.py
@@ -0,0 +1,3260 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+import numpy
+import operator
+from numba.core import types, ir, config, cgutils, errors
+from numba.core.ir_utils import (
+    mk_unique_var,
+    find_topo_order,
+    dprint_func_ir,
+    get_global_func_typ,
+    guard,
+    require,
+    get_definition,
+    find_callname,
+    find_build_sequence,
+    find_const,
+    is_namedtuple_class,
+    build_definitions,
+    find_potential_aliases,
+    get_canonical_alias,
+    GuardException,
+)
+from numba.core.analysis import compute_cfg_from_blocks
+from numba.core.typing import npydecl, signature
+import copy
+from numba.core.extending import intrinsic, register_jitable
+import llvmlite
+from numba.np.unsafe.ndarray import to_fixed_tuple
+
+UNKNOWN_CLASS = -1
+CONST_CLASS = 0
+MAP_TYPES = [numpy.ufunc]
+
+array_analysis_extensions = {}
+
+# declaring call classes
+array_creation = ["empty", "zeros", "ones", "full"]
+
+random_int_args = ["rand", "randn"]
+
+random_1arg_size = [
+    "ranf",
+    "random_sample",
+    "sample",
+    "random",
+    "standard_normal",
+]
+
+random_2arg_sizelast = [
+    "chisquare",
+    "weibull",
+    "power",
+    "geometric",
+    "exponential",
+    "poisson",
+    "rayleigh",
+]
+
+random_3arg_sizelast = [
+    "normal",
+    "uniform",
+    "beta",
+    "binomial",
+    "f",
+    "gamma",
+    "lognormal",
+    "laplace",
+]
+
+random_calls = (
+    random_int_args
+    + random_1arg_size
+    + random_2arg_sizelast
+    + random_3arg_sizelast
+    + ["randint", "triangular"]
+)
+
+
+@intrinsic
+def wrap_index(typingctx, idx, size):
+    """
+    Calculate index value "idx" relative to a size "size" value as
+    (idx % size), where "size" is known to be positive.
+    Note that we use the mod(%) operation here instead of
+    (idx < 0 ? idx + size : idx) because we may have situations
+    where idx > size due to the way indices are calculated
+    during slice/range analysis.
+
+    Both idx and size have to be Integer types.
+    size should be from the array size vars that array_analysis
+    adds and the bitwidth should match the platform maximum.
+    """
+    require(isinstance(idx, types.scalars.Integer))
+    require(isinstance(size, types.scalars.Integer))
+
+    # We need both idx and size to be platform size so that we can compare.
+    unified_ty = types.intp if size.signed else types.uintp
+    idx_unified = types.intp if idx.signed else types.uintp
+
+    def codegen(context, builder, sig, args):
+        ll_idx_unified_ty = context.get_data_type(idx_unified)
+        ll_unified_ty = context.get_data_type(unified_ty)
+        if idx_unified.signed:
+            idx = builder.sext(args[0], ll_idx_unified_ty)
+        else:
+            idx = builder.zext(args[0], ll_idx_unified_ty)
+        if unified_ty.signed:
+            size = builder.sext(args[1], ll_unified_ty)
+        else:
+            size = builder.zext(args[1], ll_unified_ty)
+        neg_size = builder.neg(size)
+        zero = llvmlite.ir.Constant(ll_unified_ty, 0)
+        # If idx is unsigned then these signed comparisons will fail in those
+        # cases where the idx has the highest bit set, namely more than 2**63
+        # on 64-bit platforms.
+        idx_negative = builder.icmp_signed("<", idx, zero)
+        pos_oversize = builder.icmp_signed(">=", idx, size)
+        neg_oversize = builder.icmp_signed("<=", idx, neg_size)
+        pos_res = builder.select(pos_oversize, size, idx)
+        neg_res = builder.select(neg_oversize, zero, builder.add(idx, size))
+        mod = builder.select(idx_negative, neg_res, pos_res)
+        return mod
+
+    return signature(unified_ty, idx, size), codegen
+
+
+def wrap_index_literal(idx, size):
+    if idx < 0:
+        if idx <= -size:
+            return 0
+        else:
+            return idx + size
+    else:
+        if idx >= size:
+            return size
+        else:
+            return idx
+
+
+@intrinsic
+def assert_equiv(typingctx, *val):
+    """
+    A function that asserts the inputs are of equivalent size,
+    and throws runtime error when they are not. The input is
+    a vararg that contains an error message, followed by a set
+    of objects of either array, tuple or integer.
+    """
+    if len(val) > 1:
+        # Make sure argument is a single tuple type. Note that this only
+        # happens when IR containing assert_equiv call is being compiled
+        # (and going through type inference) again.
+        val = (types.StarArgTuple(val),)
+
+    assert len(val[0]) > 1
+    # Arguments must be either array, tuple, or integer
+    assert all(
+        isinstance(a, (
+            types.ArrayCompatible,
+            types.BaseTuple,
+            types.SliceType,
+            types.Integer
+        ))
+        for a in val[0][1:]
+    )
+    if not isinstance(val[0][0], types.StringLiteral):
+        raise errors.TypingError('first argument must be a StringLiteral')
+
+    def codegen(context, builder, sig, args):
+        assert len(args) == 1  # it is a vararg tuple
+        tup = cgutils.unpack_tuple(builder, args[0])
+        tup_type = sig.args[0]
+        msg = sig.args[0][0].literal_value
+
+        def unpack_shapes(a, aty):
+            if isinstance(aty, types.ArrayCompatible):
+                ary = context.make_array(aty)(context, builder, a)
+                return cgutils.unpack_tuple(builder, ary.shape)
+            elif isinstance(aty, types.BaseTuple):
+                return cgutils.unpack_tuple(builder, a)
+            else:  # otherwise it is a single integer
+                return [a]
+
+        def pairwise(a, aty, b, bty):
+            ashapes = unpack_shapes(a, aty)
+            bshapes = unpack_shapes(b, bty)
+            assert len(ashapes) == len(bshapes)
+            for (m, n) in zip(ashapes, bshapes):
+                m_eq_n = builder.icmp_unsigned('==', m, n)
+                with builder.if_else(m_eq_n) as (then, orelse):
+                    with then:
+                        pass
+                    with orelse:
+                        context.call_conv.return_user_exc(
+                            builder, AssertionError, (msg,)
+                        )
+
+        for i in range(1, len(tup_type) - 1):
+            pairwise(tup[i], tup_type[i], tup[i + 1], tup_type[i + 1])
+        r = context.get_constant_generic(builder, types.NoneType, None)
+        return r
+
+    return signature(types.none, *val), codegen
+
+
+class EquivSet(object):
+
+    """EquivSet keeps track of equivalence relations between
+    a set of objects.
+    """
+
+    def __init__(self, obj_to_ind=None, ind_to_obj=None, next_ind=0):
+        """Create a new EquivSet object. Optional keyword arguments are for
+        internal use only.
+        """
+        # obj_to_ind maps object to equivalence index (sometimes also called
+        # equivalence class) is a non-negative number that uniquely identifies
+        # a set of objects that are equivalent.
+        self.obj_to_ind = obj_to_ind if obj_to_ind else {}
+        # ind_to_obj maps equivalence index to a list of objects.
+        self.ind_to_obj = ind_to_obj if ind_to_obj else {}
+        # next index number that is incremented each time a new equivalence
+        # relation is created.
+        self.next_ind = next_ind
+
+    def empty(self):
+        """Return an empty EquivSet object.
+        """
+        return EquivSet()
+
+    def clone(self):
+        """Return a new copy.
+        """
+        return EquivSet(
+            obj_to_ind=copy.deepcopy(self.obj_to_ind),
+            ind_to_obj=copy.deepcopy(self.ind_to_obj),
+            next_id=self.next_ind,
+        )
+
+    def __repr__(self):
+        return "EquivSet({})".format(self.ind_to_obj)
+
+    def is_empty(self):
+        """Return true if the set is empty, or false otherwise.
+        """
+        return self.obj_to_ind == {}
+
+    def _get_ind(self, x):
+        """Return the internal index (greater or equal to 0) of the given
+        object, or -1 if not found.
+        """
+        return self.obj_to_ind.get(x, -1)
+
+    def _get_or_add_ind(self, x):
+        """Return the internal index (greater or equal to 0) of the given
+        object, or create a new one if not found.
+        """
+        if x in self.obj_to_ind:
+            i = self.obj_to_ind[x]
+        else:
+            i = self.next_ind
+            self.next_ind += 1
+        return i
+
+    def _insert(self, objs):
+        """Base method that inserts a set of equivalent objects by modifying
+        self.
+        """
+        assert len(objs) > 1
+
+        inds = tuple(self._get_or_add_ind(x) for x in objs)
+        ind = min(inds)
+
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print("_insert:", objs, inds)
+
+        if not (ind in self.ind_to_obj):
+            self.ind_to_obj[ind] = []
+
+        for i, obj in zip(inds, objs):
+            if i == ind:
+                if not (obj in self.ind_to_obj[ind]):
+                    self.ind_to_obj[ind].append(obj)
+                    self.obj_to_ind[obj] = ind
+            else:
+                if i in self.ind_to_obj:
+                    # those already existing are reassigned
+                    for x in self.ind_to_obj[i]:
+                        self.obj_to_ind[x] = ind
+                        self.ind_to_obj[ind].append(x)
+                    del self.ind_to_obj[i]
+                else:
+                    # those that are new are assigned.
+                    self.obj_to_ind[obj] = ind
+                    self.ind_to_obj[ind].append(obj)
+
+    def is_equiv(self, *objs):
+        """Try to derive if given objects are equivalent, return true
+        if so, or false otherwise.
+        """
+        inds = [self._get_ind(x) for x in objs]
+        ind = max(inds)
+        if ind != -1:
+            return all(i == ind for i in inds)
+        else:
+            return all([x == objs[0] for x in objs])
+
+    def get_equiv_const(self, obj):
+        """Check if obj is equivalent to some int constant, and return
+        the constant if found, or None otherwise.
+        """
+        ind = self._get_ind(obj)
+        if ind >= 0:
+            objs = self.ind_to_obj[ind]
+            for x in objs:
+                if isinstance(x, int):
+                    return x
+        return None
+
+    def get_equiv_set(self, obj):
+        """Return the set of equivalent objects.
+        """
+        ind = self._get_ind(obj)
+        if ind >= 0:
+            return set(self.ind_to_obj[ind])
+        return set()
+
+    def insert_equiv(self, *objs):
+        """Insert a set of equivalent objects by modifying self. This
+        method can be overloaded to transform object type before insertion.
+        """
+        return self._insert(objs)
+
+    def intersect(self, equiv_set):
+        """ Return the intersection of self and the given equiv_set,
+        without modifying either of them. The result will also keep
+        old equivalence indices unchanged.
+        """
+        new_set = self.empty()
+        new_set.next_ind = self.next_ind
+
+        for objs in equiv_set.ind_to_obj.values():
+            inds = tuple(self._get_ind(x) for x in objs)
+            ind_to_obj = {}
+
+            for i, x in zip(inds, objs):
+                if i in ind_to_obj:
+                    ind_to_obj[i].append(x)
+                elif i >= 0:
+                    ind_to_obj[i] = [x]
+
+            for v in ind_to_obj.values():
+                if len(v) > 1:
+                    new_set._insert(v)
+
+        return new_set
+
+
+class ShapeEquivSet(EquivSet):
+
+    """Just like EquivSet, except that it accepts only numba IR variables
+    and constants as objects, guided by their types. Arrays are considered
+    equivalent as long as their shapes are equivalent. Scalars are
+    equivalent only when they are equal in value. Tuples are equivalent
+    when they are of the same size, and their elements are equivalent.
+    """
+
+    def __init__(
+        self,
+        typemap,
+        defs=None,
+        ind_to_var=None,
+        obj_to_ind=None,
+        ind_to_obj=None,
+        next_id=0,
+        ind_to_const=None,
+    ):
+        """Create a new ShapeEquivSet object, where typemap is a dictionary
+        that maps variable names to their types, and it will not be modified.
+        Optional keyword arguments are for internal use only.
+        """
+        self.typemap = typemap
+        # defs maps variable name to an int, where
+        # 1 means the variable is defined only once, and numbers greater
+        # than 1 means defined more than once.
+        self.defs = defs if defs else {}
+        # ind_to_var maps index number to a list of variables (of ir.Var type).
+        # It is used to retrieve defined shape variables given an equivalence
+        # index.
+        self.ind_to_var = ind_to_var if ind_to_var else {}
+        # ind_to_const maps index number to a constant, if known.
+        self.ind_to_const = ind_to_const if ind_to_const else {}
+
+        super(ShapeEquivSet, self).__init__(obj_to_ind, ind_to_obj, next_id)
+
+    def empty(self):
+        """Return an empty ShapeEquivSet.
+        """
+        return ShapeEquivSet(self.typemap, {})
+
+    def clone(self):
+        """Return a new copy.
+        """
+        return ShapeEquivSet(
+            self.typemap,
+            defs=copy.copy(self.defs),
+            ind_to_var=copy.copy(self.ind_to_var),
+            obj_to_ind=copy.deepcopy(self.obj_to_ind),
+            ind_to_obj=copy.deepcopy(self.ind_to_obj),
+            next_id=self.next_ind,
+            ind_to_const=copy.deepcopy(self.ind_toconst),
+        )
+
+    def __repr__(self):
+        return "ShapeEquivSet({}, ind_to_var={}, ind_to_const={})".format(
+            self.ind_to_obj, self.ind_to_var, self.ind_to_const
+        )
+
+    def _get_names(self, obj):
+        """Return a set of names for the given obj, where array and tuples
+        are broken down to their individual shapes or elements. This is
+        safe because both Numba array shapes and Python tuples are immutable.
+        """
+        if isinstance(obj, ir.Var) or isinstance(obj, str):
+            name = obj if isinstance(obj, str) else obj.name
+            if name not in self.typemap:
+                return (name,)
+
+            typ = self.typemap[name]
+            if isinstance(typ, (types.BaseTuple, types.ArrayCompatible)):
+                ndim = (typ.ndim
+                        if isinstance(typ, types.ArrayCompatible)
+                        else len(typ))
+                # Treat 0d array as if it were a scalar.
+                if ndim == 0:
+                    return (name,)
+                else:
+                    return tuple("{}#{}".format(name, i) for i in range(ndim))
+            else:
+                return (name,)
+        elif isinstance(obj, ir.Const):
+            if isinstance(obj.value, tuple):
+                return obj.value
+            else:
+                return (obj.value,)
+        elif isinstance(obj, tuple):
+
+            def get_names(x):
+                names = self._get_names(x)
+                if len(names) != 0:
+                    return names[0]
+                return names
+
+            return tuple(get_names(x) for x in obj)
+        elif isinstance(obj, int):
+            return (obj,)
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print(
+                f"Ignoring untracked object type {type(obj)} in ShapeEquivSet")
+        return ()
+
+    def is_equiv(self, *objs):
+        """Overload EquivSet.is_equiv to handle Numba IR variables and
+        constants.
+        """
+        assert len(objs) > 1
+        obj_names = [self._get_names(x) for x in objs]
+        obj_names = [x for x in obj_names if x != ()]  # rule out 0d shape
+        if len(obj_names) <= 1:
+            return False
+        ndims = [len(names) for names in obj_names]
+        ndim = ndims[0]
+        if not all(ndim == x for x in ndims):
+            if config.DEBUG_ARRAY_OPT >= 1:
+                print("is_equiv: Dimension mismatch for {}".format(objs))
+            return False
+        for i in range(ndim):
+            names = [obj_name[i] for obj_name in obj_names]
+            if not super(ShapeEquivSet, self).is_equiv(*names):
+                return False
+        return True
+
+    def get_equiv_const(self, obj):
+        """If the given object is equivalent to a constant scalar,
+        return the scalar value, or None otherwise.
+        """
+        names = self._get_names(obj)
+        if len(names) != 1:
+            return None
+        return super(ShapeEquivSet, self).get_equiv_const(names[0])
+
+    def get_equiv_var(self, obj):
+        """If the given object is equivalent to some defined variable,
+        return the variable, or None otherwise.
+        """
+        names = self._get_names(obj)
+        if len(names) != 1:
+            return None
+        ind = self._get_ind(names[0])
+        vs = self.ind_to_var.get(ind, [])
+        return vs[0] if vs != [] else None
+
+    def get_equiv_set(self, obj):
+        """Return the set of equivalent objects.
+        """
+        names = self._get_names(obj)
+        if len(names) != 1:
+            return None
+        return super(ShapeEquivSet, self).get_equiv_set(names[0])
+
+    def _insert(self, objs):
+        """Overload EquivSet._insert to manage ind_to_var dictionary.
+        """
+        inds = []
+        for obj in objs:
+            if obj in self.obj_to_ind:
+                inds.append(self.obj_to_ind[obj])
+        varlist = []
+        constval = None
+        names = set()
+        for i in sorted(inds):
+            if i in self.ind_to_var:
+                for x in self.ind_to_var[i]:
+                    if not (x.name in names):
+                        varlist.append(x)
+                        names.add(x.name)
+            if i in self.ind_to_const:
+                assert constval is None
+                constval = self.ind_to_const[i]
+        super(ShapeEquivSet, self)._insert(objs)
+        new_ind = self.obj_to_ind[objs[0]]
+        for i in set(inds):
+            if i in self.ind_to_var:
+                del self.ind_to_var[i]
+        self.ind_to_var[new_ind] = varlist
+        if constval is not None:
+            self.ind_to_const[new_ind] = constval
+
+    def insert_equiv(self, *objs):
+        """Overload EquivSet.insert_equiv to handle Numba IR variables and
+        constants. Input objs are either variable or constant, and at least
+        one of them must be variable.
+        """
+        assert len(objs) > 1
+        obj_names = [self._get_names(x) for x in objs]
+        obj_names = [x for x in obj_names if x != ()]  # rule out 0d shape
+        if len(obj_names) <= 1:
+            return
+        names = sum([list(x) for x in obj_names], [])
+        ndims = [len(x) for x in obj_names]
+        ndim = ndims[0]
+        assert all(
+            ndim == x for x in ndims
+        ), "Dimension mismatch for {}".format(objs)
+        varlist = []
+        constlist = []
+        for obj in objs:
+            if not isinstance(obj, tuple):
+                obj = (obj,)
+            for var in obj:
+                if isinstance(var, ir.Var) and not (var.name in varlist):
+                    # favor those already defined, move to front of varlist
+                    if var.name in self.defs:
+                        varlist.insert(0, var)
+                    else:
+                        varlist.append(var)
+                if isinstance(var, ir.Const) and not (var.value in constlist):
+                    constlist.append(var.value)
+
+        # try to populate ind_to_var if variables are present
+        for obj in varlist:
+            name = obj.name
+            if name in names and not (name in self.obj_to_ind):
+                self.ind_to_obj[self.next_ind] = [name]
+                self.obj_to_ind[name] = self.next_ind
+                self.ind_to_var[self.next_ind] = [obj]
+                self.next_ind += 1
+
+        # create equivalence classes for previously unseen constants
+        for const in constlist:
+            if const in names and not (const in self.obj_to_ind):
+                self.ind_to_obj[self.next_ind] = [const]
+                self.obj_to_ind[const] = self.next_ind
+                self.ind_to_const[self.next_ind] = const
+                self.next_ind += 1
+
+        some_change = False
+
+        for i in range(ndim):
+            names = [obj_name[i] for obj_name in obj_names]
+            ie_res = super(ShapeEquivSet, self).insert_equiv(*names)
+            some_change = some_change or ie_res
+
+        return some_change
+
+    def has_shape(self, name):
+        """Return true if the shape of the given variable is available.
+        """
+        return self.get_shape(name) is not None
+
+    def get_shape(self, name):
+        """Return a tuple of variables that corresponds to the shape
+        of the given array, or None if not found.
+        """
+        return guard(self._get_shape, name)
+
+    def _get_shape(self, name):
+        """Return a tuple of variables that corresponds to the shape
+        of the given array, or raise GuardException if not found.
+        """
+        inds = self.get_shape_classes(name)
+        require(inds != ())
+        shape = []
+        for i in inds:
+            require(i in self.ind_to_var)
+            vs = self.ind_to_var[i]
+            if vs != []:
+                shape.append(vs[0])
+            else:
+                require(i in self.ind_to_const)
+                vs = self.ind_to_const[i]
+                shape.append(vs)
+        return tuple(shape)
+
+    def get_shape_classes(self, name):
+        """Instead of the shape tuple, return tuple of int, where
+        each int is the corresponding class index of the size object.
+        Unknown shapes are given class index -1. Return empty tuple
+        if the input name is a scalar variable.
+        """
+        if isinstance(name, ir.Var):
+            name = name.name
+        typ = self.typemap[name] if name in self.typemap else None
+        if not (
+            isinstance(typ, (
+                types.BaseTuple, types.SliceType, types.ArrayCompatible
+            ))
+        ):
+            return []
+        # Treat 0d arrays like scalars.
+        if isinstance(typ, types.ArrayCompatible) and typ.ndim == 0:
+            return []
+        names = self._get_names(name)
+        inds = tuple(self._get_ind(name) for name in names)
+        return inds
+
+    def intersect(self, equiv_set):
+        """Overload the intersect method to handle ind_to_var.
+        """
+        newset = super(ShapeEquivSet, self).intersect(equiv_set)
+        ind_to_var = {}
+        for i, objs in newset.ind_to_obj.items():
+            assert len(objs) > 0
+            obj = objs[0]
+            assert obj in self.obj_to_ind
+            assert obj in equiv_set.obj_to_ind
+            j = self.obj_to_ind[obj]
+            k = equiv_set.obj_to_ind[obj]
+            assert j in self.ind_to_var
+            assert k in equiv_set.ind_to_var
+            varlist = []
+            names = [x.name for x in equiv_set.ind_to_var[k]]
+            for x in self.ind_to_var[j]:
+                if x.name in names:
+                    varlist.append(x)
+            ind_to_var[i] = varlist
+        newset.ind_to_var = ind_to_var
+        return newset
+
+    def define(self, name, redefined):
+        """Increment the internal count of how many times a variable is being
+        defined. Most variables in Numba IR are SSA, i.e., defined only once,
+        but not all of them. When a variable is being re-defined, it must
+        be removed from the equivalence relation and added to the redefined
+        set but only if that redefinition is not known to have the same
+        equivalence classes. Those variables redefined are removed from all
+        the blocks' equivalence sets later.
+
+        Arrays passed to define() use their whole name but these do not
+        appear in the equivalence sets since they are stored there per
+        dimension. Calling _get_names() here converts array names to
+        dimensional names.
+
+        This function would previously invalidate if there were any multiple
+        definitions of a variable.  However, we realized that this behavior
+        is overly restrictive.  You need only invalidate on multiple
+        definitions if they are not known to be equivalent. So, the
+        equivalence insertion functions now return True if some change was
+        made (meaning the definition was not equivalent) and False
+        otherwise. If no change was made, then define() need not be
+        called. For no change to have been made, the variable must
+        already be present. If the new definition of the var has the
+        case where lhs and rhs are in the same equivalence class then
+        again, no change will be made and define() need not be called
+        or the variable invalidated.
+        """
+        if isinstance(name, ir.Var):
+            name = name.name
+        if name in self.defs:
+            self.defs[name] += 1
+            name_res = list(self._get_names(name))
+            for one_name in name_res:
+                # NOTE: variable being redefined, must invalidate previous
+                # equivalences. Believe it is a rare case, and only happens to
+                # scalar accumuators.
+                if one_name in self.obj_to_ind:
+                    redefined.add(
+                        one_name
+                    )  # remove this var from all equiv sets
+                    i = self.obj_to_ind[one_name]
+                    del self.obj_to_ind[one_name]
+                    self.ind_to_obj[i].remove(one_name)
+                    if self.ind_to_obj[i] == []:
+                        del self.ind_to_obj[i]
+                    assert i in self.ind_to_var
+                    names = [x.name for x in self.ind_to_var[i]]
+                    if name in names:
+                        j = names.index(name)
+                        del self.ind_to_var[i][j]
+                        if self.ind_to_var[i] == []:
+                            del self.ind_to_var[i]
+                            # no more size variables, remove equivalence too
+                            if i in self.ind_to_obj:
+                                for obj in self.ind_to_obj[i]:
+                                    del self.obj_to_ind[obj]
+                                del self.ind_to_obj[i]
+        else:
+            self.defs[name] = 1
+
+    def union_defs(self, defs, redefined):
+        """Union with the given defs dictionary. This is meant to handle
+        branch join-point, where a variable may have been defined in more
+        than one branches.
+        """
+        for k, v in defs.items():
+            if v > 0:
+                self.define(k, redefined)
+
+
+class SymbolicEquivSet(ShapeEquivSet):
+
+    """Just like ShapeEquivSet, except that it also reasons about variable
+    equivalence symbolically by using their arithmetic definitions.
+    The goal is to automatically derive the equivalence of array ranges
+    (slicing). For instance, a[1:m] and a[0:m-1] shall be considered
+    size-equivalence.
+    """
+
+    def __init__(
+        self,
+        typemap,
+        def_by=None,
+        ref_by=None,
+        ext_shapes=None,
+        defs=None,
+        ind_to_var=None,
+        obj_to_ind=None,
+        ind_to_obj=None,
+        next_id=0,
+    ):
+        """Create a new SymbolicEquivSet object, where typemap is a dictionary
+        that maps variable names to their types, and it will not be modified.
+        Optional keyword arguments are for internal use only.
+        """
+        # A "defined-by" table that maps A to a tuple of (B, i), which
+        # means A is defined as: A = B + i, where A,B are variable names,
+        # and i is an integer constants.
+        self.def_by = def_by if def_by else {}
+        # A "referred-by" table that maps A to a list of [(B, i), (C, j) ...],
+        # which implies a sequence of definitions: B = A - i, C = A - j, and
+        # so on, where A,B,C,... are variable names, and i,j,... are
+        # integer constants.
+        self.ref_by = ref_by if ref_by else {}
+        # A extended shape table that can map an arbitrary object to a shape,
+        # currently used to remember shapes for SetItem IR node, and wrapped
+        # indices for Slice objects.
+        self.ext_shapes = ext_shapes if ext_shapes else {}
+        # rel_map keeps a map of relative sizes that we have seen so
+        # that if we compute the same relative sizes different times
+        # in different ways we can associate those two instances
+        # of the same relative size to the same equivalence class.
+        self.rel_map = {}
+        # wrap_index() computes the effectual index given a slice and a
+        # dimension's size.  We need to be able to know that two wrap_index
+        # calls are equivalent.  They are known to be equivalent if the slice
+        # and dimension sizes of the two wrap_index calls are equivalent.
+        # wrap_map maps from a tuple of equivalence class ids for a slice and
+        # a dimension size to some new equivalence class id for the output size.
+        self.wrap_map = {}
+        super(SymbolicEquivSet, self).__init__(
+            typemap, defs, ind_to_var, obj_to_ind, ind_to_obj, next_id
+        )
+
+    def empty(self):
+        """Return an empty SymbolicEquivSet.
+        """
+        return SymbolicEquivSet(self.typemap)
+
+    def __repr__(self):
+        return (
+            "SymbolicEquivSet({}, ind_to_var={}, def_by={}, "
+            "ref_by={}, ext_shapes={})".format(
+                self.ind_to_obj,
+                self.ind_to_var,
+                self.def_by,
+                self.ref_by,
+                self.ext_shapes,
+            )
+        )
+
+    def clone(self):
+        """Return a new copy.
+        """
+        return SymbolicEquivSet(
+            self.typemap,
+            def_by=copy.copy(self.def_by),
+            ref_by=copy.copy(self.ref_by),
+            ext_shapes=copy.copy(self.ext_shapes),
+            defs=copy.copy(self.defs),
+            ind_to_var=copy.copy(self.ind_to_var),
+            obj_to_ind=copy.deepcopy(self.obj_to_ind),
+            ind_to_obj=copy.deepcopy(self.ind_to_obj),
+            next_id=self.next_ind,
+        )
+
+    def get_rel(self, name):
+        """Retrieve a definition pair for the given variable,
+        or return None if it is not available.
+        """
+        return guard(self._get_or_set_rel, name)
+
+    def _get_or_set_rel(self, name, func_ir=None):
+        """Retrieve a definition pair for the given variable,
+        and if it is not already available, try to look it up
+        in the given func_ir, and remember it for future use.
+        """
+        if isinstance(name, ir.Var):
+            name = name.name
+        require(self.defs.get(name, 0) == 1)
+        if name in self.def_by:
+            return self.def_by[name]
+        else:
+            require(func_ir is not None)
+
+            def plus(x, y):
+                x_is_const = isinstance(x, int)
+                y_is_const = isinstance(y, int)
+                if x_is_const:
+                    if y_is_const:
+                        return x + y
+                    else:
+                        (var, offset) = y
+                        return (var, x + offset)
+                else:
+                    (var, offset) = x
+                    if y_is_const:
+                        return (var, y + offset)
+                    else:
+                        return None
+
+            def minus(x, y):
+                if isinstance(y, int):
+                    return plus(x, -y)
+                elif (
+                    isinstance(x, tuple)
+                    and isinstance(y, tuple)
+                    and x[0] == y[0]
+                ):
+                    return minus(x[1], y[1])
+                else:
+                    return None
+
+            expr = get_definition(func_ir, name)
+            value = (name, 0)  # default to its own name
+            if isinstance(expr, ir.Expr):
+                if expr.op == "call":
+                    fname, mod_name = find_callname(
+                        func_ir, expr, typemap=self.typemap
+                    )
+                    if (
+                        fname == "wrap_index"
+                        and mod_name == "numba.parfors.array_analysis"
+                    ):
+                        index = tuple(
+                            self.obj_to_ind.get(x.name, -1) for x in expr.args
+                        )
+                        # If wrap_index for a slice works on a variable
+                        # that is not analyzable (e.g., multiple definitions)
+                        # then we have to return None here since we can't know
+                        # how that size will compare to others if we can't
+                        # analyze some part of the slice.
+                        if -1 in index:
+                            return None
+                        names = self.ext_shapes.get(index, [])
+                        names.append(name)
+                        if len(names) > 0:
+                            self._insert(names)
+                        self.ext_shapes[index] = names
+                elif expr.op == "binop":
+                    lhs = self._get_or_set_rel(expr.lhs, func_ir)
+                    rhs = self._get_or_set_rel(expr.rhs, func_ir)
+                    # If either the lhs or rhs is not analyzable
+                    # then don't try to record information this var.
+                    if lhs is None or rhs is None:
+                        return None
+                    elif expr.fn == operator.add:
+                        value = plus(lhs, rhs)
+                    elif expr.fn == operator.sub:
+                        value = minus(lhs, rhs)
+            elif isinstance(expr, ir.Const) and isinstance(expr.value, int):
+                value = expr.value
+            require(value is not None)
+            # update def_by table
+            self.def_by[name] = value
+            if isinstance(value, int) or (
+                isinstance(value, tuple)
+                and (value[0] != name or value[1] != 0)
+            ):
+                # update ref_by table too
+                if isinstance(value, tuple):
+                    (var, offset) = value
+                    if not (var in self.ref_by):
+                        self.ref_by[var] = []
+                    self.ref_by[var].append((name, -offset))
+                    # insert new equivalence if found
+                    ind = self._get_ind(var)
+                    if ind >= 0:
+                        objs = self.ind_to_obj[ind]
+                        names = []
+                        for obj in objs:
+                            if obj in self.ref_by:
+                                names += [
+                                    x
+                                    for (x, i) in self.ref_by[obj]
+                                    if i == -offset
+                                ]
+                        if len(names) > 1:
+                            super(SymbolicEquivSet, self)._insert(names)
+            return value
+
+    def define(self, var, redefined, func_ir=None, typ=None):
+        """Besides incrementing the definition count of the given variable
+        name, it will also retrieve and simplify its definition from func_ir,
+        and remember the result for later equivalence comparison. Supported
+        operations are:
+          1. arithmetic plus and minus with constants
+          2. wrap_index (relative to some given size)
+        """
+        if isinstance(var, ir.Var):
+            name = var.name
+        else:
+            name = var
+        super(SymbolicEquivSet, self).define(name, redefined)
+        if (
+            func_ir
+            and self.defs.get(name, 0) == 1
+            and isinstance(typ, types.Number)
+        ):
+            value = guard(self._get_or_set_rel, name, func_ir)
+            # turn constant definition into equivalence
+            if isinstance(value, int):
+                self._insert([name, value])
+            if isinstance(var, ir.Var):
+                ind = self._get_or_add_ind(name)
+                if not (ind in self.ind_to_obj):
+                    self.ind_to_obj[ind] = [name]
+                    self.obj_to_ind[name] = ind
+                if ind in self.ind_to_var:
+                    self.ind_to_var[ind].append(var)
+                else:
+                    self.ind_to_var[ind] = [var]
+            return True
+
+    def _insert(self, objs):
+        """Overload _insert method to handle ind changes between relative
+        objects.  Returns True if some change is made, false otherwise.
+        """
+        indset = set()
+        uniqs = set()
+        for obj in objs:
+            ind = self._get_ind(obj)
+            if ind == -1:
+                uniqs.add(obj)
+            elif not (ind in indset):
+                uniqs.add(obj)
+                indset.add(ind)
+        if len(uniqs) <= 1:
+            return False
+        uniqs = list(uniqs)
+        super(SymbolicEquivSet, self)._insert(uniqs)
+        objs = self.ind_to_obj[self._get_ind(uniqs[0])]
+
+        # New equivalence guided by def_by and ref_by
+        offset_dict = {}
+
+        def get_or_set(d, k):
+            if k in d:
+                v = d[k]
+            else:
+                v = []
+                d[k] = v
+            return v
+
+        for obj in objs:
+            if obj in self.def_by:
+                value = self.def_by[obj]
+                if isinstance(value, tuple):
+                    (name, offset) = value
+                    get_or_set(offset_dict, -offset).append(name)
+                    if name in self.ref_by:  # relative to name
+                        for (v, i) in self.ref_by[name]:
+                            get_or_set(offset_dict, -(offset + i)).append(v)
+            if obj in self.ref_by:
+                for (name, offset) in self.ref_by[obj]:
+                    get_or_set(offset_dict, offset).append(name)
+        for names in offset_dict.values():
+            self._insert(names)
+        return True
+
+    def set_shape_setitem(self, obj, shape):
+        """remember shapes of SetItem IR nodes.
+        """
+        assert isinstance(obj, (ir.StaticSetItem, ir.SetItem))
+        self.ext_shapes[obj] = shape
+
+    def _get_shape(self, obj):
+        """Overload _get_shape to retrieve the shape of SetItem IR nodes.
+        """
+        if isinstance(obj, (ir.StaticSetItem, ir.SetItem)):
+            require(obj in self.ext_shapes)
+            return self.ext_shapes[obj]
+        else:
+            assert isinstance(obj, ir.Var)
+            typ = self.typemap[obj.name]
+            # for slice type, return the shape variable itself
+            if isinstance(typ, types.SliceType):
+                return (obj,)
+            else:
+                return super(SymbolicEquivSet, self)._get_shape(obj)
+
+
+class WrapIndexMeta(object):
+    """
+      Array analysis should be able to analyze all the function
+      calls that it adds to the IR.  That way, array analysis can
+      be run as often as needed and you should get the same
+      equivalencies.  One modification to the IR that array analysis
+      makes is the insertion of wrap_index calls.  Thus, repeated
+      array analysis passes should be able to analyze these wrap_index
+      calls.  The difficulty of these calls is that the equivalence
+      class of the left-hand side of the assignment is not present in
+      the arguments to wrap_index in the right-hand side.  Instead,
+      the equivalence class of the wrap_index output is a combination
+      of the wrap_index args.  The important thing to
+      note is that if the equivalence classes of the slice size
+      and the dimension's size are the same for two wrap index
+      calls then we can be assured of the answer being the same.
+      So, we maintain the wrap_map dict that maps from a tuple
+      of equivalence class ids for the slice and dimension size
+      to some new equivalence class id for the output size.
+      However, when we are analyzing the first such wrap_index
+      call we don't have a variable there to associate to the
+      size since we're in the process of analyzing the instruction
+      that creates that mapping.  So, instead we return an object
+      of this special class and analyze_inst will establish the
+      connection between a tuple of the parts of this object
+      below and the left-hand side variable.
+    """
+
+    def __init__(self, slice_size, dim_size):
+        self.slice_size = slice_size
+        self.dim_size = dim_size
+
+
+@register_jitable
+def runtime_broadcast_assert_shapes(max_dim, arg0, arg1):
+    new_shape = []
+    shapes = []
+    shapes.append(list(arg0.shape))
+    shapes.append(list(arg1.shape))
+    for i in range(max_dim):
+        sizes = []
+        for shape in shapes:
+            if i < len(shape):
+                size = shape[len(shape) - 1 - i]
+                if size != 1:
+                    sizes.append(size)  # non-1 size to front
+        if len(sizes) == 0:
+            sizes.append(1)
+        new_shape.append(sizes[0])
+
+    rev = new_shape[::-1]
+    return to_fixed_tuple(rev, max_dim)
+
+
+class ArrayAnalysis(object):
+    aa_count = 0
+
+    """Analyzes Numpy array computations for properties such as
+    shape/size equivalence, and keeps track of them on a per-block
+    basis. The analysis should only be run once because it modifies
+    the incoming IR by inserting assertion statements that safeguard
+    parfor optimizations.
+    """
+
+    def __init__(self, context, func_ir, typemap, calltypes):
+        self.context = context
+        self.func_ir = func_ir
+        self.typemap = typemap
+        self.calltypes = calltypes
+
+        # EquivSet of variables, indexed by block number
+        self.equiv_sets = {}
+        # keep attr calls to arrays like t=A.sum() as {t:('sum',A)}
+        self.array_attr_calls = {}
+        # keep attrs of objects (value,attr)->shape_var
+        self.object_attrs = {}
+        # keep prepended instructions from conditional branch
+        self.prepends = {}
+        # keep track of pruned precessors when branch degenerates to jump
+        self.pruned_predecessors = {}
+
+    def get_equiv_set(self, block_label):
+        """Return the equiv_set object of an block given its label.
+        """
+        return self.equiv_sets[block_label]
+
+    def remove_redefineds(self, redefineds):
+        """Take a set of variables in redefineds and go through all
+        the currently existing equivalence sets (created in topo order)
+        and remove that variable from all of them since it is multiply
+        defined within the function.
+        """
+        unused = set()
+        for r in redefineds:
+            for eslabel in self.equiv_sets:
+                es = self.equiv_sets[eslabel]
+                es.define(r, unused)
+
+    def run(self, blocks=None, equiv_set=None):
+        """run array shape analysis on the given IR blocks, resulting in
+        modified IR and finalized EquivSet for each block.
+        """
+        if blocks is None:
+            blocks = self.func_ir.blocks
+
+        self.func_ir._definitions = build_definitions(self.func_ir.blocks)
+
+        if equiv_set is None:
+            init_equiv_set = SymbolicEquivSet(self.typemap)
+        else:
+            init_equiv_set = equiv_set
+
+        self.alias_map, self.arg_aliases = find_potential_aliases(
+            blocks,
+            self.func_ir.arg_names,
+            self.typemap,
+            self.func_ir
+        )
+
+        aa_count_save = ArrayAnalysis.aa_count
+        ArrayAnalysis.aa_count += 1
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("Starting ArrayAnalysis:", aa_count_save)
+        dprint_func_ir(self.func_ir, "before array analysis", blocks)
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print(
+                "ArrayAnalysis variable types: ", sorted(self.typemap.items())
+            )
+            print("ArrayAnalysis call types: ", self.calltypes)
+
+        cfg = compute_cfg_from_blocks(blocks)
+        topo_order = find_topo_order(blocks, cfg=cfg)
+        # Traverse blocks in topological order
+        self._run_on_blocks(topo_order, blocks, cfg, init_equiv_set)
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            self.dump()
+            print(
+                "ArrayAnalysis post variable types: ",
+                sorted(self.typemap.items()),
+            )
+            print("ArrayAnalysis post call types: ", self.calltypes)
+
+        dprint_func_ir(self.func_ir, "after array analysis", blocks)
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("Ending ArrayAnalysis:", aa_count_save)
+
+    def _run_on_blocks(self, topo_order, blocks, cfg, init_equiv_set):
+        for label in topo_order:
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print("Processing block:", label)
+            block = blocks[label]
+            scope = block.scope
+            pending_transforms = self._determine_transform(
+                cfg, block, label, scope, init_equiv_set
+            )
+            self._combine_to_new_block(block, pending_transforms)
+
+    def _combine_to_new_block(self, block, pending_transforms):
+        """Combine the new instructions from previous pass into a new block
+        body.
+        """
+        new_body = []
+        for inst, pre, post in pending_transforms:
+            for instr in pre:
+                new_body.append(instr)
+            new_body.append(inst)
+            for instr in post:
+                new_body.append(instr)
+        block.body = new_body
+
+    def _determine_transform(self, cfg, block, label, scope, init_equiv_set):
+        """Determine the transformation for each instruction in the block
+        """
+        equiv_set = None
+        # equiv_set is the intersection of predecessors
+        preds = cfg.predecessors(label)
+        # some incoming edge may be pruned due to prior analysis
+        if label in self.pruned_predecessors:
+            pruned = self.pruned_predecessors[label]
+        else:
+            pruned = []
+        # Go through each incoming edge, process prepended instructions and
+        # calculate beginning equiv_set of current block as an intersection
+        # of incoming ones.
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print("preds:", preds)
+        for (p, q) in preds:
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print("p, q:", p, q)
+            if p in pruned:
+                continue
+            if p in self.equiv_sets:
+                from_set = self.equiv_sets[p].clone()
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("p in equiv_sets", from_set)
+                if (p, label) in self.prepends:
+                    instrs = self.prepends[(p, label)]
+                    for inst in instrs:
+                        redefined = set()
+                        self._analyze_inst(
+                            label, scope, from_set, inst, redefined
+                        )
+                        # Remove anything multiply defined in this block
+                        # from every block equivs.
+                        # NOTE: necessary? can't observe effect in testsuite
+                        self.remove_redefineds(redefined)
+                if equiv_set is None:
+                    equiv_set = from_set
+                else:
+                    equiv_set = equiv_set.intersect(from_set)
+                    redefined = set()
+                    equiv_set.union_defs(from_set.defs, redefined)
+                    # Remove anything multiply defined in this block
+                    # from every block equivs.
+                    # NOTE: necessary? can't observe effect in testsuite
+                    self.remove_redefineds(redefined)
+
+        # Start with a new equiv_set if none is computed
+        if equiv_set is None:
+            equiv_set = init_equiv_set
+        self.equiv_sets[label] = equiv_set
+
+        # Go through instructions in a block, and insert pre/post
+        # instructions as we analyze them.
+        pending_transforms = []
+        for inst in block.body:
+            redefined = set()
+            pre, post = self._analyze_inst(
+                label, scope, equiv_set, inst, redefined
+            )
+            # Remove anything multiply defined in this block from every block
+            # equivs.
+            if len(redefined) > 0:
+                self.remove_redefineds(redefined)
+
+            pending_transforms.append((inst, pre, post))
+        return pending_transforms
+
+    def dump(self):
+        """dump per-block equivalence sets for debugging purposes.
+        """
+        print("Array Analysis: ", self.equiv_sets)
+
+    def _define(self, equiv_set, var, typ, value):
+        self.typemap[var.name] = typ
+        self.func_ir._definitions[var.name] = [value]
+        redefineds = set()
+        equiv_set.define(var, redefineds, self.func_ir, typ)
+
+    class AnalyzeResult(object):
+        def __init__(self, **kwargs):
+            self.kwargs = kwargs
+
+    def _analyze_inst(self, label, scope, equiv_set, inst, redefined):
+        pre = []
+        post = []
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print("analyze_inst:", inst)
+        if isinstance(inst, ir.Assign):
+            lhs = inst.target
+            typ = self.typemap[lhs.name]
+            shape = None
+            if isinstance(typ, types.ArrayCompatible) and typ.ndim == 0:
+                shape = ()
+            elif isinstance(inst.value, ir.Expr):
+                result = self._analyze_expr(scope, equiv_set, inst.value, lhs)
+                if result:
+                    require(isinstance(result, ArrayAnalysis.AnalyzeResult))
+                    if 'shape' in result.kwargs:
+                        shape = result.kwargs['shape']
+                    if 'pre' in result.kwargs:
+                        pre.extend(result.kwargs['pre'])
+                    if 'post' in result.kwargs:
+                        post.extend(result.kwargs['post'])
+                    if 'rhs' in result.kwargs:
+                        inst.value = result.kwargs['rhs']
+            elif isinstance(inst.value, (ir.Var, ir.Const)):
+                shape = inst.value
+            elif isinstance(inst.value, ir.Global):
+                gvalue = inst.value.value
+                # only integer values can be part of shape
+                # TODO: support cases with some but not all integer values or
+                # nested tuples
+                if (isinstance(gvalue, tuple)
+                        and all(isinstance(v, int) for v in gvalue)):
+                    shape = gvalue
+                elif isinstance(gvalue, int):
+                    shape = (gvalue,)
+            elif isinstance(inst.value, ir.Arg):
+                if (
+                    isinstance(typ, types.containers.UniTuple)
+                    and isinstance(typ.dtype, types.Integer)
+                ):
+                    shape = inst.value
+                elif (
+                    isinstance(typ, types.containers.Tuple)
+                    and all([isinstance(x,
+                            (types.Integer, types.IntegerLiteral))
+                        for x in typ.types]
+                    )
+                ):
+                    shape = inst.value
+
+            if isinstance(shape, ir.Const):
+                if isinstance(shape.value, tuple):
+                    loc = shape.loc
+                    shape = tuple(ir.Const(x, loc) for x in shape.value)
+                elif isinstance(shape.value, int):
+                    shape = (shape,)
+                else:
+                    shape = None
+            elif isinstance(shape, ir.Var) and isinstance(
+                self.typemap[shape.name], types.Integer
+            ):
+                shape = (shape,)
+            elif isinstance(shape, WrapIndexMeta):
+                """ Here we've got the special WrapIndexMeta object
+                    back from analyzing a wrap_index call.  We define
+                    the lhs and then get it's equivalence class then
+                    add the mapping from the tuple of slice size and
+                    dimensional size equivalence ids to the lhs
+                    equivalence id.
+                """
+                equiv_set.define(lhs, redefined, self.func_ir, typ)
+                lhs_ind = equiv_set._get_ind(lhs.name)
+                if lhs_ind != -1:
+                    equiv_set.wrap_map[
+                        (shape.slice_size, shape.dim_size)
+                    ] = lhs_ind
+                return pre, post
+
+            if isinstance(typ, types.ArrayCompatible):
+                if (
+                    shape is not None
+                    and isinstance(shape, ir.Var)
+                    and isinstance(
+                        self.typemap[shape.name], types.containers.Tuple
+                    )
+                ):
+                    pass
+                elif (
+                    shape is None
+                    or isinstance(shape, tuple)
+                    or (
+                        isinstance(shape, ir.Var)
+                        and not equiv_set.has_shape(shape)
+                    )
+                ):
+                    shape = self._gen_shape_call(
+                        equiv_set, lhs, typ.ndim, shape, post
+                    )
+            elif isinstance(typ, types.UniTuple):
+                if shape and isinstance(typ.dtype, types.Integer):
+                    shape = self._gen_shape_call(
+                        equiv_set, lhs, len(typ), shape, post
+                    )
+            elif (
+                isinstance(typ, types.containers.Tuple)
+                and all([isinstance(x,
+                        (types.Integer, types.IntegerLiteral))
+                    for x in typ.types]
+                )
+            ):
+                shape = self._gen_shape_call(
+                    equiv_set, lhs, len(typ), shape, post
+                )
+
+            """ See the comment on the define() function.
+
+                We need only call define(), which will invalidate a variable
+                from being in the equivalence sets on multiple definitions,
+                if the variable was not previously defined or if the new
+                definition would be in a conflicting equivalence class to the
+                original equivalence class for the variable.
+
+                insert_equiv() returns True if either of these conditions are
+                True and then we call define() in those cases.
+                If insert_equiv() returns False then no changes were made and
+                all equivalence classes are consistent upon a redefinition so
+                no invalidation is needed and we don't call define().
+            """
+            needs_define = True
+            if shape is not None:
+                needs_define = equiv_set.insert_equiv(lhs, shape)
+            if needs_define:
+                equiv_set.define(lhs, redefined, self.func_ir, typ)
+        elif isinstance(inst, (ir.StaticSetItem, ir.SetItem)):
+            index = (
+                inst.index if isinstance(inst, ir.SetItem) else inst.index_var
+            )
+            result = guard(
+                self._index_to_shape, scope, equiv_set, inst.target, index
+            )
+            if not result:
+                return [], []
+            if result[0] is not None:
+                assert isinstance(inst, (ir.StaticSetItem, ir.SetItem))
+                inst.index = result[0]
+            result = result[1]
+            target_shape = result.kwargs['shape']
+            if 'pre' in result.kwargs:
+                pre = result.kwargs['pre']
+            value_shape = equiv_set.get_shape(inst.value)
+            if value_shape == ():  # constant
+                equiv_set.set_shape_setitem(inst, target_shape)
+                return pre, []
+            elif value_shape is not None:
+                target_typ = self.typemap[inst.target.name]
+                require(isinstance(target_typ, types.ArrayCompatible))
+                target_ndim = target_typ.ndim
+                shapes = [target_shape, value_shape]
+                names = [inst.target.name, inst.value.name]
+                broadcast_result = self._broadcast_assert_shapes(
+                    scope, equiv_set, inst.loc, shapes, names
+                )
+                require('shape' in broadcast_result.kwargs)
+                require('pre' in broadcast_result.kwargs)
+                shape = broadcast_result.kwargs['shape']
+                asserts = broadcast_result.kwargs['pre']
+                n = len(shape)
+                # shape dimension must be within target dimension
+                assert target_ndim >= n
+                equiv_set.set_shape_setitem(inst, shape)
+                return pre + asserts, []
+            else:
+                return pre, []
+        elif isinstance(inst, ir.Branch):
+
+            def handle_call_binop(cond_def):
+                br = None
+                if cond_def.fn == operator.eq:
+                    br = inst.truebr
+                    otherbr = inst.falsebr
+                    cond_val = 1
+                elif cond_def.fn == operator.ne:
+                    br = inst.falsebr
+                    otherbr = inst.truebr
+                    cond_val = 0
+                lhs_typ = self.typemap[cond_def.lhs.name]
+                rhs_typ = self.typemap[cond_def.rhs.name]
+                if br is not None and (
+                    (
+                        isinstance(lhs_typ, types.Integer)
+                        and isinstance(rhs_typ, types.Integer)
+                    )
+                    or (
+                        isinstance(lhs_typ, types.BaseTuple)
+                        and isinstance(rhs_typ, types.BaseTuple)
+                    )
+                ):
+                    loc = inst.loc
+                    args = (cond_def.lhs, cond_def.rhs)
+                    asserts = self._make_assert_equiv(
+                        scope, loc, equiv_set, args
+                    )
+                    asserts.append(
+                        ir.Assign(ir.Const(cond_val, loc), cond_var, loc)
+                    )
+                    self.prepends[(label, br)] = asserts
+                    self.prepends[(label, otherbr)] = [
+                        ir.Assign(ir.Const(1 - cond_val, loc), cond_var, loc)
+                    ]
+
+            cond_var = inst.cond
+            cond_def = guard(get_definition, self.func_ir, cond_var)
+            if not cond_def:  # phi variable has no single definition
+                # We'll use equiv_set to try to find a cond_def instead
+                equivs = equiv_set.get_equiv_set(cond_var)
+                defs = []
+                for name in equivs:
+                    if isinstance(name, str) and name in self.typemap:
+                        var_def = guard(
+                            get_definition, self.func_ir, name, lhs_only=True
+                        )
+                        if isinstance(var_def, ir.Var):
+                            var_def = var_def.name
+                        if var_def:
+                            defs.append(var_def)
+                    else:
+                        defs.append(name)
+                defvars = set(filter(lambda x: isinstance(x, str), defs))
+                defconsts = set(defs).difference(defvars)
+                if len(defconsts) == 1:
+                    cond_def = list(defconsts)[0]
+                elif len(defvars) == 1:
+                    cond_def = guard(
+                        get_definition, self.func_ir, list(defvars)[0]
+                    )
+            if isinstance(cond_def, ir.Expr) and cond_def.op == 'binop':
+                handle_call_binop(cond_def)
+            elif isinstance(cond_def, ir.Expr) and cond_def.op == 'call':
+                # this handles bool(predicate)
+                glbl_bool = guard(get_definition, self.func_ir, cond_def.func)
+                if glbl_bool is not None and glbl_bool.value is bool:
+                    if len(cond_def.args) == 1:
+                        condition = guard(get_definition, self.func_ir,
+                                          cond_def.args[0])
+                        if (condition is not None and
+                            isinstance(condition, ir.Expr) and
+                                condition.op == 'binop'):
+                            handle_call_binop(condition)
+            else:
+                if isinstance(cond_def, ir.Const):
+                    cond_def = cond_def.value
+                if isinstance(cond_def, int) or isinstance(cond_def, bool):
+                    # condition is always true/false, prune the outgoing edge
+                    pruned_br = inst.falsebr if cond_def else inst.truebr
+                    if pruned_br in self.pruned_predecessors:
+                        self.pruned_predecessors[pruned_br].append(label)
+                    else:
+                        self.pruned_predecessors[pruned_br] = [label]
+
+        elif type(inst) in array_analysis_extensions:
+            # let external calls handle stmt if type matches
+            f = array_analysis_extensions[type(inst)]
+            pre, post = f(inst, equiv_set, self.typemap, self)
+
+        return pre, post
+
+    def _analyze_expr(self, scope, equiv_set, expr, lhs):
+        fname = "_analyze_op_{}".format(expr.op)
+        try:
+            fn = getattr(self, fname)
+        except AttributeError:
+            return None
+        return guard(fn, scope, equiv_set, expr, lhs)
+
+    def _analyze_op_getattr(self, scope, equiv_set, expr, lhs):
+        # TODO: getattr of npytypes.Record
+        if expr.attr == "T" and self._isarray(expr.value.name):
+            return self._analyze_op_call_numpy_transpose(
+                scope, equiv_set, expr.loc, [expr.value], {}
+            )
+        elif expr.attr == "shape":
+            shape = equiv_set.get_shape(expr.value)
+            return ArrayAnalysis.AnalyzeResult(shape=shape)
+        elif expr.attr in ("real", "imag") and self._isarray(expr.value.name):
+            # Shape of real or imag attr is the same as the shape of the array
+            # itself.
+            return ArrayAnalysis.AnalyzeResult(shape=expr.value)
+        elif self._isarray(lhs.name):
+            canonical_value = get_canonical_alias(
+                expr.value.name, self.alias_map
+            )
+            if (canonical_value, expr.attr) in self.object_attrs:
+                return ArrayAnalysis.AnalyzeResult(
+                    shape=self.object_attrs[(canonical_value, expr.attr)]
+                )
+            else:
+                typ = self.typemap[lhs.name]
+                post = []
+                shape = self._gen_shape_call(
+                    equiv_set, lhs, typ.ndim, None, post
+                )
+                self.object_attrs[(canonical_value, expr.attr)] = shape
+                return ArrayAnalysis.AnalyzeResult(shape=shape, post=post)
+
+        return None
+
+    def _analyze_op_cast(self, scope, equiv_set, expr, lhs):
+        return ArrayAnalysis.AnalyzeResult(shape=expr.value)
+
+    def _analyze_op_exhaust_iter(self, scope, equiv_set, expr, lhs):
+        var = expr.value
+        typ = self.typemap[var.name]
+        if isinstance(typ, types.BaseTuple):
+            require(len(typ) == expr.count)
+            require(equiv_set.has_shape(var))
+            return ArrayAnalysis.AnalyzeResult(shape=var)
+        return None
+
+    def gen_literal_slice_part(
+        self,
+        arg_val,
+        loc,
+        scope,
+        stmts,
+        equiv_set,
+        name="static_literal_slice_part",
+    ):
+        # Create var to hold the calculated slice size.
+        static_literal_slice_part_var = ir.Var(scope, mk_unique_var(name), loc)
+        static_literal_slice_part_val = ir.Const(arg_val, loc)
+        static_literal_slice_part_typ = types.IntegerLiteral(arg_val)
+        # We'll prepend this slice size calculation to the get/setitem.
+        stmts.append(
+            ir.Assign(
+                value=static_literal_slice_part_val,
+                target=static_literal_slice_part_var,
+                loc=loc,
+            )
+        )
+        self._define(
+            equiv_set,
+            static_literal_slice_part_var,
+            static_literal_slice_part_typ,
+            static_literal_slice_part_val,
+        )
+        return static_literal_slice_part_var, static_literal_slice_part_typ
+
+    def gen_static_slice_size(
+        self, lhs_rel, rhs_rel, loc, scope, stmts, equiv_set
+    ):
+        the_var, *_ = self.gen_literal_slice_part(
+            rhs_rel - lhs_rel,
+            loc,
+            scope,
+            stmts,
+            equiv_set,
+            name="static_slice_size",
+        )
+        return the_var
+
+    def gen_explicit_neg(
+        self,
+        arg,
+        arg_rel,
+        arg_typ,
+        size_typ,
+        loc,
+        scope,
+        dsize,
+        stmts,
+        equiv_set,
+    ):
+        assert not isinstance(size_typ, int)
+        # Create var to hold the calculated slice size.
+        explicit_neg_var = ir.Var(scope, mk_unique_var("explicit_neg"), loc)
+        explicit_neg_val = ir.Expr.binop(operator.add, dsize, arg, loc=loc)
+        # Determine the type of that var.  Can be literal if we know the
+        # literal size of the dimension.
+        explicit_neg_typ = types.intp
+        self.calltypes[explicit_neg_val] = signature(
+            explicit_neg_typ, size_typ, arg_typ
+        )
+        # We'll prepend this slice size calculation to the get/setitem.
+        stmts.append(
+            ir.Assign(value=explicit_neg_val, target=explicit_neg_var, loc=loc)
+        )
+        self._define(
+            equiv_set, explicit_neg_var, explicit_neg_typ, explicit_neg_val
+        )
+        return explicit_neg_var, explicit_neg_typ
+
+    def update_replacement_slice(
+        self,
+        lhs,
+        lhs_typ,
+        lhs_rel,
+        dsize_rel,
+        replacement_slice,
+        slice_index,
+        need_replacement,
+        loc,
+        scope,
+        stmts,
+        equiv_set,
+        size_typ,
+        dsize,
+    ):
+        # Do compile-time calculation of real index value if both the given
+        # index value and the array length are known at compile time.
+        known = False
+        if isinstance(lhs_rel, int):
+            # If the index and the array size are known then the real index
+            # can be calculated at compile time.
+            if lhs_rel == 0:
+                # Special-case 0 as nothing needing to be done.
+                known = True
+            elif isinstance(dsize_rel, int):
+                known = True
+                # Calculate the real index.
+                wil = wrap_index_literal(lhs_rel, dsize_rel)
+                # If the given index value is between 0 and dsize then
+                # there's no need to rewrite anything.
+                if wil != lhs_rel:
+                    if config.DEBUG_ARRAY_OPT >= 2:
+                        print("Replacing slice to hard-code known slice size.")
+                    # Indicate we will need to replace the slice var.
+                    need_replacement = True
+                    literal_var, literal_typ = self.gen_literal_slice_part(
+                        wil, loc, scope, stmts, equiv_set
+                    )
+                    assert slice_index == 0 or slice_index == 1
+                    if slice_index == 0:
+                        replacement_slice.args = (
+                            literal_var,
+                            replacement_slice.args[1],
+                        )
+                    else:
+                        replacement_slice.args = (
+                            replacement_slice.args[0],
+                            literal_var,
+                        )
+                    # Update lhs information with the negative removed.
+                    lhs = replacement_slice.args[slice_index]
+                    lhs_typ = literal_typ
+                    lhs_rel = equiv_set.get_rel(lhs)
+            elif lhs_rel < 0:
+                # Indicate we will need to replace the slice var.
+                need_replacement = True
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("Replacing slice due to known negative index.")
+                explicit_neg_var, explicit_neg_typ = self.gen_explicit_neg(
+                    lhs,
+                    lhs_rel,
+                    lhs_typ,
+                    size_typ,
+                    loc,
+                    scope,
+                    dsize,
+                    stmts,
+                    equiv_set,
+                )
+                if slice_index == 0:
+                    replacement_slice.args = (
+                        explicit_neg_var,
+                        replacement_slice.args[1],
+                    )
+                else:
+                    replacement_slice.args = (
+                        replacement_slice.args[0],
+                        explicit_neg_var,
+                    )
+                # Update lhs information with the negative removed.
+                lhs = replacement_slice.args[slice_index]
+                lhs_typ = explicit_neg_typ
+                lhs_rel = equiv_set.get_rel(lhs)
+        return (
+            lhs,
+            lhs_typ,
+            lhs_rel,
+            replacement_slice,
+            need_replacement,
+            known,
+        )
+
+    def slice_size(self, index, dsize, equiv_set, scope, stmts):
+        """Reason about the size of a slice represented by the "index"
+        variable, and return a variable that has this size data, or
+        raise GuardException if it cannot reason about it.
+
+        The computation takes care of negative values used in the slice
+        with respect to the given dimensional size ("dsize").
+
+        Extra statements required to produce the result are appended
+        to parent function's stmts list.
+        """
+        loc = index.loc
+        # Get the definition of the index variable.
+        index_def = get_definition(self.func_ir, index)
+        fname, mod_name = find_callname(
+            self.func_ir, index_def, typemap=self.typemap
+        )
+        require(fname == 'slice' and mod_name in ('builtins'))
+        require(len(index_def.args) == 2)
+        lhs = index_def.args[0]
+        rhs = index_def.args[1]
+        size_typ = self.typemap[dsize.name]
+        lhs_typ = self.typemap[lhs.name]
+        rhs_typ = self.typemap[rhs.name]
+
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print(f"slice_size index={index} dsize={dsize} "
+                  f"index_def={index_def} lhs={lhs} rhs={rhs} "
+                  f"size_typ={size_typ} lhs_typ={lhs_typ} rhs_typ={rhs_typ}")
+
+        # Make a deepcopy of the original slice to use as the
+        # replacement slice, which we will modify as necessary
+        # below to convert all negative constants in the slice
+        # to be relative to the dimension size.
+        replacement_slice = copy.deepcopy(index_def)
+        need_replacement = False
+
+        # Fill in the left side of the slice's ":" with 0 if it wasn't
+        # specified.
+        if isinstance(lhs_typ, types.NoneType):
+            zero_var = ir.Var(scope, mk_unique_var("zero"), loc)
+            zero = ir.Const(0, loc)
+            stmts.append(ir.Assign(value=zero, target=zero_var, loc=loc))
+            self._define(equiv_set, zero_var, types.IntegerLiteral(0), zero)
+            lhs = zero_var
+            lhs_typ = types.IntegerLiteral(0)
+            replacement_slice.args = (lhs, replacement_slice.args[1])
+            need_replacement = True
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print("Replacing slice because lhs is None.")
+
+        # Fill in the right side of the slice's ":" with the array
+        # length if it wasn't specified.
+        if isinstance(rhs_typ, types.NoneType):
+            rhs = dsize
+            rhs_typ = size_typ
+            replacement_slice.args = (replacement_slice.args[0], rhs)
+            need_replacement = True
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print("Replacing slice because lhs is None.")
+
+        lhs_rel = equiv_set.get_rel(lhs)
+        rhs_rel = equiv_set.get_rel(rhs)
+        dsize_rel = equiv_set.get_rel(dsize)
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print(
+                "lhs_rel", lhs_rel, "rhs_rel", rhs_rel, "dsize_rel", dsize_rel
+            )
+
+        # Update replacement slice with the real index value if we can
+        # compute it at compile time.
+        [
+            lhs,
+            lhs_typ,
+            lhs_rel,
+            replacement_slice,
+            need_replacement,
+            lhs_known,
+        ] = self.update_replacement_slice(
+            lhs,
+            lhs_typ,
+            lhs_rel,
+            dsize_rel,
+            replacement_slice,
+            0,
+            need_replacement,
+            loc,
+            scope,
+            stmts,
+            equiv_set,
+            size_typ,
+            dsize,
+        )
+        [
+            rhs,
+            rhs_typ,
+            rhs_rel,
+            replacement_slice,
+            need_replacement,
+            rhs_known,
+        ] = self.update_replacement_slice(
+            rhs,
+            rhs_typ,
+            rhs_rel,
+            dsize_rel,
+            replacement_slice,
+            1,
+            need_replacement,
+            loc,
+            scope,
+            stmts,
+            equiv_set,
+            size_typ,
+            dsize,
+        )
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print("lhs_known:", lhs_known)
+            print("rhs_known:", rhs_known)
+
+        # If neither of the parts of the slice were negative constants
+        # then we don't need to do slice replacement in the IR.
+        if not need_replacement:
+            replacement_slice_var = None
+        else:
+            # Create a new var for the replacement slice.
+            replacement_slice_var = ir.Var(
+                scope, mk_unique_var("replacement_slice"), loc
+            )
+            # Create a deepcopy of slice calltype so that when we change it
+            # below the original isn't changed.  Make the types of the parts of
+            # the slice intp.
+            new_arg_typs = (types.intp, types.intp)
+            rs_calltype = self.typemap[index_def.func.name].get_call_type(
+                self.context, new_arg_typs, {}
+            )
+            self.calltypes[replacement_slice] = rs_calltype
+            stmts.append(
+                ir.Assign(
+                    value=replacement_slice,
+                    target=replacement_slice_var,
+                    loc=loc,
+                )
+            )
+            # The type of the replacement slice is the same type as the
+            # original.
+            self.typemap[replacement_slice_var.name] = self.typemap[index.name]
+
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print(
+                "after rewriting negatives",
+                "lhs_rel",
+                lhs_rel,
+                "rhs_rel",
+                rhs_rel,
+            )
+
+        if lhs_known and rhs_known:
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print("lhs and rhs known so return static size")
+            return (
+                self.gen_static_slice_size(
+                    lhs_rel, rhs_rel, loc, scope, stmts, equiv_set
+                ),
+                replacement_slice_var,
+            )
+
+        if (
+            lhs_rel == 0
+            and isinstance(rhs_rel, tuple)
+            and equiv_set.is_equiv(dsize, rhs_rel[0])
+            and rhs_rel[1] == 0
+        ):
+            return dsize, None
+
+        slice_typ = types.intp
+        orig_slice_typ = slice_typ
+
+        size_var = ir.Var(scope, mk_unique_var("slice_size"), loc)
+        size_val = ir.Expr.binop(operator.sub, rhs, lhs, loc=loc)
+        self.calltypes[size_val] = signature(slice_typ, rhs_typ, lhs_typ)
+        self._define(equiv_set, size_var, slice_typ, size_val)
+        size_rel = equiv_set.get_rel(size_var)
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print("size_rel", size_rel, type(size_rel))
+
+        wrap_var = ir.Var(scope, mk_unique_var("wrap"), loc)
+        wrap_def = ir.Global("wrap_index", wrap_index, loc=loc)
+        fnty = get_global_func_typ(wrap_index)
+        sig = self.context.resolve_function_type(
+            fnty, (orig_slice_typ, size_typ), {}
+        )
+        self._define(equiv_set, wrap_var, fnty, wrap_def)
+
+        def gen_wrap_if_not_known(val, val_typ, known):
+            if not known:
+                var = ir.Var(scope, mk_unique_var("var"), loc)
+                var_typ = types.intp
+                new_value = ir.Expr.call(wrap_var, [val, dsize], {}, loc)
+                # def_res will be False if there is something unanalyzable
+                # that prevents a size association from being created.
+                self._define(equiv_set, var, var_typ, new_value)
+                self.calltypes[new_value] = sig
+                return (var, var_typ, new_value)
+            else:
+                return (val, val_typ, None)
+
+        var1, var1_typ, value1 = gen_wrap_if_not_known(lhs, lhs_typ, lhs_known)
+        var2, var2_typ, value2 = gen_wrap_if_not_known(rhs, rhs_typ, rhs_known)
+
+        stmts.append(ir.Assign(value=size_val, target=size_var, loc=loc))
+        stmts.append(ir.Assign(value=wrap_def, target=wrap_var, loc=loc))
+        if value1 is not None:
+            stmts.append(ir.Assign(value=value1, target=var1, loc=loc))
+        if value2 is not None:
+            stmts.append(ir.Assign(value=value2, target=var2, loc=loc))
+
+        post_wrap_size_var = ir.Var(
+            scope, mk_unique_var("post_wrap_slice_size"), loc
+        )
+        post_wrap_size_val = ir.Expr.binop(operator.sub,
+                                           var2,
+                                           var1,
+                                           loc=loc)
+        self.calltypes[post_wrap_size_val] = signature(
+            slice_typ, var2_typ, var1_typ
+        )
+        self._define(
+            equiv_set, post_wrap_size_var, slice_typ, post_wrap_size_val
+        )
+
+        stmts.append(
+            ir.Assign(
+                value=post_wrap_size_val, target=post_wrap_size_var, loc=loc
+            )
+        )
+
+        # rel_map keeps a map of relative sizes that we have seen so
+        # that if we compute the same relative sizes different times
+        # in different ways we can associate those two instances
+        # of the same relative size to the same equivalence class.
+        if isinstance(size_rel, tuple):
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print("size_rel is tuple", equiv_set.rel_map)
+            rel_map_entry = None
+            for rme, rme_tuple in equiv_set.rel_map.items():
+                if rme[1] == size_rel[1] and equiv_set.is_equiv(
+                    rme[0], size_rel[0]
+                ):
+                    rel_map_entry = rme_tuple
+                    break
+
+            if rel_map_entry is not None:
+                # We have seen this relative size before so establish
+                # equivalence to the previous variable.
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("establishing equivalence to", rel_map_entry)
+                equiv_set.insert_equiv(size_var, rel_map_entry[0])
+                equiv_set.insert_equiv(post_wrap_size_var, rel_map_entry[1])
+            else:
+                # The first time we've seen this relative size so
+                # remember the variable defining that size.
+                equiv_set.rel_map[size_rel] = (size_var, post_wrap_size_var)
+
+        return post_wrap_size_var, replacement_slice_var
+
+    def _index_to_shape(self, scope, equiv_set, var, ind_var):
+        """For indexing like var[index] (either write or read), see if
+        the index corresponds to a range/slice shape.
+        Returns a 2-tuple where the first item is either None or a ir.Var
+        to be used to replace the index variable in the outer getitem or
+        setitem instruction.  The second item is also a tuple returning
+        the shape and prepending instructions.
+        """
+        typ = self.typemap[var.name]
+        require(isinstance(typ, types.ArrayCompatible))
+        ind_typ = self.typemap[ind_var.name]
+        ind_shape = equiv_set._get_shape(ind_var)
+        var_shape = equiv_set._get_shape(var)
+        if isinstance(ind_typ, types.SliceType):
+            seq_typs = (ind_typ,)
+            seq = (ind_var,)
+        else:
+            require(isinstance(ind_typ, types.BaseTuple))
+            seq, op = find_build_sequence(self.func_ir, ind_var)
+            require(op == "build_tuple")
+            seq_typs = tuple(self.typemap[x.name] for x in seq)
+        require(len(ind_shape) == len(seq_typs) == len(var_shape))
+        stmts = []
+
+        def to_shape(typ, index, dsize):
+            if isinstance(typ, types.SliceType):
+                return self.slice_size(index, dsize, equiv_set, scope, stmts)
+            elif isinstance(typ, types.Number):
+                return None, None
+            else:
+                # unknown dimension size for this index,
+                # so we'll raise GuardException
+                require(False)
+
+        shape_list = []
+        index_var_list = []
+        replace_index = False
+        for (typ, size, dsize, orig_ind) in zip(seq_typs,
+                                                ind_shape,
+                                                var_shape,
+                                                seq):
+            # Convert the given dimension of the get/setitem index expr.
+            shape_part, index_var_part = to_shape(typ, size, dsize)
+            shape_list.append(shape_part)
+
+            # to_shape will return index_var_part as not None if a
+            # replacement of the slice is required to convert from
+            # negative indices to positive relative indices.
+            if index_var_part is not None:
+                # Remember that we need to replace the build_tuple.
+                replace_index = True
+                index_var_list.append(index_var_part)
+            else:
+                index_var_list.append(orig_ind)
+
+        # If at least one of the dimensions required a new slice variable
+        # then we'll need to replace the build_tuple for this get/setitem.
+        if replace_index:
+            # Multi-dimensional array access needs a replacement tuple built.
+            if len(index_var_list) > 1:
+                # Make a variable to hold the new build_tuple.
+                replacement_build_tuple_var = ir.Var(
+                    scope,
+                    mk_unique_var("replacement_build_tuple"),
+                    ind_shape[0].loc,
+                )
+                # Create the build tuple from the accumulated index vars above.
+                new_build_tuple = ir.Expr.build_tuple(
+                    index_var_list, ind_shape[0].loc
+                )
+                stmts.append(
+                    ir.Assign(
+                        value=new_build_tuple,
+                        target=replacement_build_tuple_var,
+                        loc=ind_shape[0].loc,
+                    )
+                )
+                # New build_tuple has same type as the original one.
+                self.typemap[replacement_build_tuple_var.name] = ind_typ
+            else:
+                replacement_build_tuple_var = index_var_list[0]
+        else:
+            replacement_build_tuple_var = None
+
+        shape = tuple(shape_list)
+        require(not all(x is None for x in shape))
+        shape = tuple(x for x in shape if x is not None)
+        return (replacement_build_tuple_var,
+                ArrayAnalysis.AnalyzeResult(shape=shape, pre=stmts))
+
+    def _analyze_op_getitem(self, scope, equiv_set, expr, lhs):
+        result = self._index_to_shape(scope, equiv_set, expr.value, expr.index)
+        if result[0] is not None:
+            expr.index = result[0]
+        return result[1]
+
+    def _analyze_op_static_getitem(self, scope, equiv_set, expr, lhs):
+        var = expr.value
+        typ = self.typemap[var.name]
+        if not isinstance(typ, types.BaseTuple):
+            result = self._index_to_shape(
+                scope, equiv_set, expr.value, expr.index_var
+            )
+            if result[0] is not None:
+                expr.index_var = result[0]
+            return result[1]
+        shape = equiv_set._get_shape(var)
+        if isinstance(expr.index, int):
+            require(expr.index < len(shape))
+            return ArrayAnalysis.AnalyzeResult(shape=shape[expr.index])
+        elif isinstance(expr.index, slice):
+            return ArrayAnalysis.AnalyzeResult(shape=shape[expr.index])
+        require(False)
+
+    def _analyze_op_unary(self, scope, equiv_set, expr, lhs):
+        require(expr.fn in UNARY_MAP_OP)
+        # for scalars, only + operator results in equivalence
+        # for example, if "m = -n", m and n are not equivalent
+        if self._isarray(expr.value.name) or expr.fn == operator.add:
+            return ArrayAnalysis.AnalyzeResult(shape=expr.value)
+        return None
+
+    def _analyze_op_binop(self, scope, equiv_set, expr, lhs):
+        require(expr.fn in BINARY_MAP_OP)
+        return self._analyze_broadcast(
+            scope, equiv_set, expr.loc, [expr.lhs, expr.rhs], expr.fn
+        )
+
+    def _analyze_op_inplace_binop(self, scope, equiv_set, expr, lhs):
+        require(expr.fn in INPLACE_BINARY_MAP_OP)
+        return self._analyze_broadcast(
+            scope, equiv_set, expr.loc, [expr.lhs, expr.rhs], expr.fn
+        )
+
+    def _analyze_op_arrayexpr(self, scope, equiv_set, expr, lhs):
+        return self._analyze_broadcast(
+            scope, equiv_set, expr.loc, expr.list_vars(), None
+        )
+
+    def _analyze_op_build_tuple(self, scope, equiv_set, expr, lhs):
+        # For the moment, we can't do anything with tuples that
+        # contain multi-dimensional arrays, compared to array dimensions.
+        # Return None to say we won't track this tuple if a part of it
+        # is an array.
+        for x in expr.items:
+            if (
+                isinstance(x, ir.Var)
+                and isinstance(self.typemap[x.name], types.ArrayCompatible)
+                and self.typemap[x.name].ndim > 1
+            ):
+                return None
+
+        consts = []
+        for var in expr.items:
+            x = guard(find_const, self.func_ir, var)
+            if x is not None:
+                consts.append(x)
+            else:
+                break
+        else:
+            out = tuple([ir.Const(x, expr.loc) for x in consts])
+            return ArrayAnalysis.AnalyzeResult(
+                shape=out,
+                rhs=ir.Const(tuple(consts), expr.loc)
+            )
+        # default return for non-const
+        return ArrayAnalysis.AnalyzeResult(shape=tuple(expr.items))
+
+    def _analyze_op_call(self, scope, equiv_set, expr, lhs):
+        from numba.stencils.stencil import StencilFunc
+
+        callee = expr.func
+        callee_def = get_definition(self.func_ir, callee)
+        if isinstance(
+            callee_def, (ir.Global, ir.FreeVar)
+        ) and is_namedtuple_class(callee_def.value):
+            return ArrayAnalysis.AnalyzeResult(shape=tuple(expr.args))
+        if isinstance(callee_def, (ir.Global, ir.FreeVar)) and isinstance(
+            callee_def.value, StencilFunc
+        ):
+            args = expr.args
+            return self._analyze_stencil(
+                scope,
+                equiv_set,
+                callee_def.value,
+                expr.loc,
+                args,
+                dict(expr.kws),
+            )
+
+        fname, mod_name = find_callname(
+            self.func_ir, expr, typemap=self.typemap
+        )
+        added_mod_name = False
+        # call via attribute (i.e. array.func)
+        if isinstance(mod_name, ir.Var) and isinstance(
+            self.typemap[mod_name.name], types.ArrayCompatible
+        ):
+            args = [mod_name] + expr.args
+            mod_name = "numpy"
+            # Remember that args and expr.args don't alias.
+            added_mod_name = True
+        else:
+            args = expr.args
+        fname = "_analyze_op_call_{}_{}".format(mod_name, fname).replace(
+            ".", "_"
+        )
+        if fname in UFUNC_MAP_OP:  # known numpy ufuncs
+            return self._analyze_broadcast(scope, equiv_set,
+                                           expr.loc, args, None)
+        else:
+            try:
+                fn = getattr(self, fname)
+            except AttributeError:
+                return None
+            result = guard(
+                fn,
+                scope=scope,
+                equiv_set=equiv_set,
+                loc=expr.loc,
+                args=args,
+                kws=dict(expr.kws),
+            )
+            # We want the ability for function fn to modify arguments.
+            # If args and expr.args don't alias then we need the extra
+            # step of assigning back into expr.args from the args that
+            # was passed to fn.
+            if added_mod_name:
+                expr.args = args[1:]
+            return result
+
+    def _analyze_op_call_builtins_len(self, scope, equiv_set, loc, args, kws):
+        # python 3 version of len()
+        require(len(args) == 1)
+        var = args[0]
+        typ = self.typemap[var.name]
+        require(isinstance(typ, types.ArrayCompatible))
+        shape = equiv_set._get_shape(var)
+        return ArrayAnalysis.AnalyzeResult(shape=shape[0], rhs=shape[0])
+
+    def _analyze_op_call_numba_parfors_array_analysis_assert_equiv(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        equiv_set.insert_equiv(*args[1:])
+        return None
+
+    def _analyze_op_call_numba_parfors_array_analysis_wrap_index(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        """ Analyze wrap_index calls added by a previous run of
+            Array Analysis
+        """
+        require(len(args) == 2)
+        # Two parts to wrap index, the specified slice size...
+        slice_size = args[0].name
+        # ...and the size of the dimension.
+        dim_size = args[1].name
+        # Get the equivalence class ids for both.
+        slice_eq = equiv_set._get_or_add_ind(slice_size)
+        dim_eq = equiv_set._get_or_add_ind(dim_size)
+        # See if a previous wrap_index calls we've analyzed maps from
+        # the same pair of equivalence class ids for slice and dim size.
+        if (slice_eq, dim_eq) in equiv_set.wrap_map:
+            wrap_ind = equiv_set.wrap_map[(slice_eq, dim_eq)]
+            require(wrap_ind in equiv_set.ind_to_var)
+            vs = equiv_set.ind_to_var[wrap_ind]
+            require(vs != [])
+            # Return the shape of the variable from the previous wrap_index.
+            return ArrayAnalysis.AnalyzeResult(shape=(vs[0],))
+        else:
+            # We haven't seen this combination of slice and dim
+            # equivalence class ids so return a WrapIndexMeta so that
+            # _analyze_inst can establish the connection to the lhs var.
+            return ArrayAnalysis.AnalyzeResult(
+                shape=WrapIndexMeta(slice_eq, dim_eq)
+            )
+
+    def _analyze_numpy_create_array(self, scope, equiv_set, loc, args, kws):
+        shape_var = None
+        if len(args) > 0:
+            shape_var = args[0]
+        elif "shape" in kws:
+            shape_var = kws["shape"]
+        if shape_var:
+            return ArrayAnalysis.AnalyzeResult(shape=shape_var)
+        raise errors.UnsupportedRewriteError(
+            "Must specify a shape for array creation",
+            loc=loc,
+        )
+
+    def _analyze_op_call_numpy_empty(self, scope, equiv_set, loc, args, kws):
+        return self._analyze_numpy_create_array(
+            scope, equiv_set, loc, args, kws
+        )
+
+    def _analyze_op_call_numba_np_unsafe_ndarray_empty_inferred(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_numpy_create_array(
+            scope, equiv_set, loc, args, kws
+        )
+
+    def _analyze_op_call_numpy_zeros(self, scope, equiv_set, loc, args, kws):
+        return self._analyze_numpy_create_array(
+            scope, equiv_set, loc, args, kws
+        )
+
+    def _analyze_op_call_numpy_ones(self, scope, equiv_set, loc, args, kws):
+        return self._analyze_numpy_create_array(
+            scope, equiv_set, loc, args, kws
+        )
+
+    def _analyze_op_call_numpy_eye(self, scope, equiv_set, loc, args, kws):
+        if len(args) > 0:
+            N = args[0]
+        elif "N" in kws:
+            N = kws["N"]
+        else:
+            raise errors.UnsupportedRewriteError(
+                "Expect one argument (or 'N') to eye function",
+                loc=loc,
+            )
+        if "M" in kws:
+            M = kws["M"]
+        else:
+            M = N
+        return ArrayAnalysis.AnalyzeResult(shape=(N, M))
+
+    def _analyze_op_call_numpy_identity(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        assert len(args) > 0
+        N = args[0]
+        return ArrayAnalysis.AnalyzeResult(shape=(N, N))
+
+    def _analyze_op_call_numpy_diag(self, scope, equiv_set, loc, args, kws):
+        # We can only reason about the output shape when the input is 1D or
+        # square 2D.
+        assert len(args) > 0
+        a = args[0]
+        assert isinstance(a, ir.Var)
+        atyp = self.typemap[a.name]
+        if isinstance(atyp, types.ArrayCompatible):
+            if atyp.ndim == 2:
+                if "k" in kws:  # will proceed only when k = 0 or absent
+                    k = kws["k"]
+                    if not equiv_set.is_equiv(k, 0):
+                        return None
+                (m, n) = equiv_set._get_shape(a)
+                if equiv_set.is_equiv(m, n):
+                    return ArrayAnalysis.AnalyzeResult(shape=(m,))
+            elif atyp.ndim == 1:
+                (m,) = equiv_set._get_shape(a)
+                return ArrayAnalysis.AnalyzeResult(shape=(m, m))
+        return None
+
+    def _analyze_numpy_array_like(self, scope, equiv_set, args, kws):
+        assert len(args) > 0
+        var = args[0]
+        typ = self.typemap[var.name]
+        if isinstance(typ, types.Integer):
+            return ArrayAnalysis.AnalyzeResult(shape=(1,))
+        elif isinstance(typ, types.ArrayCompatible) and equiv_set.has_shape(
+            var
+        ):
+            return ArrayAnalysis.AnalyzeResult(shape=var)
+        return None
+
+    def _analyze_op_call_numpy_ravel(self, scope, equiv_set, loc, args, kws):
+        assert len(args) == 1
+        var = args[0]
+        typ = self.typemap[var.name]
+        assert isinstance(typ, types.ArrayCompatible)
+        # output array is same shape as input if input is 1D
+        if typ.ndim == 1 and equiv_set.has_shape(var):
+            if typ.layout == "C":
+                # output is the same as input (no copy) for 'C' layout
+                # optimize out the call
+                return ArrayAnalysis.AnalyzeResult(shape=var, rhs=var)
+            else:
+                return ArrayAnalysis.AnalyzeResult(shape=var)
+        # TODO: handle multi-D input arrays (calc array size)
+        return None
+
+    def _analyze_op_call_numpy_copy(self, scope, equiv_set, loc, args, kws):
+        return self._analyze_numpy_array_like(scope, equiv_set, args, kws)
+
+    def _analyze_op_call_numpy_empty_like(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_numpy_array_like(scope, equiv_set, args, kws)
+
+    def _analyze_op_call_numpy_zeros_like(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_numpy_array_like(scope, equiv_set, args, kws)
+
+    def _analyze_op_call_numpy_ones_like(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_numpy_array_like(scope, equiv_set, args, kws)
+
+    def _analyze_op_call_numpy_full_like(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_numpy_array_like(scope, equiv_set, args, kws)
+
+    def _analyze_op_call_numpy_asfortranarray(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_numpy_array_like(scope, equiv_set, args, kws)
+
+    def _analyze_op_call_numpy_reshape(self, scope, equiv_set, loc, args, kws):
+        n = len(args)
+        assert n > 1
+        if n == 2:
+            typ = self.typemap[args[1].name]
+            if isinstance(typ, types.BaseTuple):
+                return ArrayAnalysis.AnalyzeResult(shape=args[1])
+
+        # Reshape is allowed to take one argument that has the value <0.
+        # This means that the size of that dimension should be inferred from
+        # the size of the array being reshaped and the other dimensions
+        # specified.  Our general approach here is to see if the reshape
+        # has any <0 arguments.  If it has more than one then throw a
+        # ValueError.  If exactly one <0 argument is found, remember its
+        # argument index.
+        stmts = []
+        neg_one_index = -1
+        for arg_index in range(1, len(args)):
+            reshape_arg = args[arg_index]
+            reshape_arg_def = guard(get_definition, self.func_ir, reshape_arg)
+            if isinstance(reshape_arg_def, ir.Const):
+                if reshape_arg_def.value < 0:
+                    if neg_one_index == -1:
+                        neg_one_index = arg_index
+                    else:
+                        msg = ("The reshape API may only include one negative"
+                               " argument.")
+                        raise errors.UnsupportedRewriteError(
+                            msg, loc=reshape_arg.loc
+                        )
+
+        if neg_one_index >= 0:
+            # If exactly one <0 argument to reshape was found, then we are
+            # going to insert code to calculate the missing dimension and then
+            # replace the negative with the calculated size.  We do this
+            # because we can't let array equivalence analysis think that some
+            # array has a negative dimension size.
+            loc = args[0].loc
+            # Create a variable to hold the size of the array being reshaped.
+            calc_size_var = ir.Var(scope, mk_unique_var("calc_size_var"), loc)
+            self.typemap[calc_size_var.name] = types.intp
+            # Assign the size of the array calc_size_var.
+            init_calc_var = ir.Assign(
+                ir.Expr.getattr(args[0], "size", loc), calc_size_var, loc
+            )
+            stmts.append(init_calc_var)
+            # For each other dimension, divide the current size by the
+            # specified dimension size.  Once all such dimensions have been
+            # done then what is left is the size of the negative dimension.
+            for arg_index in range(1, len(args)):
+                # Skip the negative dimension.
+                if arg_index == neg_one_index:
+                    continue
+                div_calc_size_var = ir.Var(
+                    scope, mk_unique_var("calc_size_var"), loc
+                )
+                self.typemap[div_calc_size_var.name] = types.intp
+                # Calculate the next size as current size // the current arg's
+                # dimension size.
+                new_binop = ir.Expr.binop(
+                    operator.floordiv, calc_size_var, args[arg_index], loc
+                )
+                div_calc = ir.Assign(new_binop, div_calc_size_var, loc)
+                self.calltypes[new_binop] = signature(
+                    types.intp, types.intp, types.intp
+                )
+                stmts.append(div_calc)
+                calc_size_var = div_calc_size_var
+            # Put the calculated value back into the reshape arguments,
+            # replacing the negative.
+            args[neg_one_index] = calc_size_var
+
+        return ArrayAnalysis.AnalyzeResult(shape=tuple(args[1:]), pre=stmts)
+
+    def _analyze_op_call_numpy_transpose(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        in_arr = args[0]
+        typ = self.typemap[in_arr.name]
+        assert isinstance(
+            typ, types.ArrayCompatible
+        ), "Invalid np.transpose argument"
+        shape = equiv_set._get_shape(in_arr)
+        if len(args) == 1:
+            return ArrayAnalysis.AnalyzeResult(shape=tuple(reversed(shape)))
+        axes = [guard(find_const, self.func_ir, a) for a in args[1:]]
+        if isinstance(axes[0], tuple):
+            axes = list(axes[0])
+        if None in axes:
+            return None
+        ret = [shape[i] for i in axes]
+        return ArrayAnalysis.AnalyzeResult(shape=tuple(ret))
+
+    def _analyze_op_call_numpy_random_rand(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        if len(args) > 0:
+            return ArrayAnalysis.AnalyzeResult(shape=tuple(args))
+        return None
+
+    def _analyze_op_call_numpy_random_randn(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_call_numpy_random_rand(
+            scope, equiv_set, loc, args, kws
+        )
+
+    def _analyze_op_numpy_random_with_size(
+        self, pos, scope, equiv_set, args, kws
+    ):
+        if "size" in kws:
+            return ArrayAnalysis.AnalyzeResult(shape=kws["size"])
+        if len(args) > pos:
+            return ArrayAnalysis.AnalyzeResult(shape=args[pos])
+        return None
+
+    def _analyze_op_call_numpy_random_ranf(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            0, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_random_sample(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            0, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_sample(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            0, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_random(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            0, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_standard_normal(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            0, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_chisquare(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            1, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_weibull(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            1, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_power(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            1, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_geometric(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            1, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_exponential(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            1, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_poisson(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            1, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_rayleigh(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            1, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_normal(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_uniform(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_beta(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_binomial(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_f(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_gamma(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_lognormal(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_laplace(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_randint(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            2, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_random_triangular(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        return self._analyze_op_numpy_random_with_size(
+            3, scope, equiv_set, args, kws
+        )
+
+    def _analyze_op_call_numpy_concatenate(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        assert len(args) > 0
+        loc = args[0].loc
+        seq, op = find_build_sequence(self.func_ir, args[0])
+        n = len(seq)
+        require(n > 0)
+        axis = 0
+        if "axis" in kws:
+            if isinstance(kws["axis"], int):  # internal use only
+                axis = kws["axis"]
+            else:
+                axis = find_const(self.func_ir, kws["axis"])
+        elif len(args) > 1:
+            axis = find_const(self.func_ir, args[1])
+        require(isinstance(axis, int))
+        require(op == "build_tuple")
+        shapes = [equiv_set._get_shape(x) for x in seq]
+        if axis < 0:
+            axis = len(shapes[0]) + axis
+        require(0 <= axis < len(shapes[0]))
+        asserts = []
+        new_shape = []
+        if n == 1:  # from one array N-dimension to (N-1)-dimension
+            shape = shapes[0]
+            # first size is the count, pop it out of shapes
+            n = equiv_set.get_equiv_const(shapes[0])
+            shape.pop(0)
+            for i in range(len(shape)):
+                if i == axis:
+                    m = equiv_set.get_equiv_const(shape[i])
+                    size = m * n if (m and n) else None
+                else:
+                    size = self._sum_size(equiv_set, shapes[0])
+            new_shape.append(size)
+        else:  # from n arrays N-dimension to N-dimension
+            for i in range(len(shapes[0])):
+                if i == axis:
+                    size = self._sum_size(
+                        equiv_set, [shape[i] for shape in shapes]
+                    )
+                else:
+                    sizes = [shape[i] for shape in shapes]
+                    asserts.append(
+                        self._call_assert_equiv(scope, loc, equiv_set, sizes)
+                    )
+                    size = sizes[0]
+                new_shape.append(size)
+        return ArrayAnalysis.AnalyzeResult(
+            shape=tuple(new_shape),
+            pre=sum(asserts, [])
+        )
+
+    def _analyze_op_call_numpy_stack(self, scope, equiv_set, loc, args, kws):
+        assert len(args) > 0
+        loc = args[0].loc
+        seq, op = find_build_sequence(self.func_ir, args[0])
+        n = len(seq)
+        require(n > 0)
+        axis = 0
+        if "axis" in kws:
+            if isinstance(kws["axis"], int):  # internal use only
+                axis = kws["axis"]
+            else:
+                axis = find_const(self.func_ir, kws["axis"])
+        elif len(args) > 1:
+            axis = find_const(self.func_ir, args[1])
+        require(isinstance(axis, int))
+        # only build_tuple can give reliable count
+        require(op == "build_tuple")
+        shapes = [equiv_set._get_shape(x) for x in seq]
+        asserts = self._call_assert_equiv(scope, loc, equiv_set, seq)
+        shape = shapes[0]
+        if axis < 0:
+            axis = len(shape) + axis + 1
+        require(0 <= axis <= len(shape))
+        new_shape = list(shape[0:axis]) + [n] + list(shape[axis:])
+        return ArrayAnalysis.AnalyzeResult(shape=tuple(new_shape), pre=asserts)
+
+    def _analyze_op_call_numpy_vstack(self, scope, equiv_set, loc, args, kws):
+        assert len(args) == 1
+        seq, op = find_build_sequence(self.func_ir, args[0])
+        n = len(seq)
+        require(n > 0)
+        typ = self.typemap[seq[0].name]
+        require(isinstance(typ, types.ArrayCompatible))
+        if typ.ndim < 2:
+            return self._analyze_op_call_numpy_stack(
+                scope, equiv_set, loc, args, kws
+            )
+        else:
+            kws["axis"] = 0
+            return self._analyze_op_call_numpy_concatenate(
+                scope, equiv_set, loc, args, kws
+            )
+
+    def _analyze_op_call_numpy_hstack(self, scope, equiv_set, loc, args, kws):
+        assert len(args) == 1
+        seq, op = find_build_sequence(self.func_ir, args[0])
+        n = len(seq)
+        require(n > 0)
+        typ = self.typemap[seq[0].name]
+        require(isinstance(typ, types.ArrayCompatible))
+        if typ.ndim < 2:
+            kws["axis"] = 0
+        else:
+            kws["axis"] = 1
+        return self._analyze_op_call_numpy_concatenate(
+            scope, equiv_set, loc, args, kws
+        )
+
+    def _analyze_op_call_numpy_dstack(self, scope, equiv_set, loc, args, kws):
+        assert len(args) == 1
+        seq, op = find_build_sequence(self.func_ir, args[0])
+        n = len(seq)
+        require(n > 0)
+        typ = self.typemap[seq[0].name]
+        require(isinstance(typ, types.ArrayCompatible))
+        if typ.ndim == 1:
+            kws["axis"] = 1
+            result = self._analyze_op_call_numpy_stack(
+                scope, equiv_set, loc, args, kws
+            )
+            require(result)
+            result.kwargs['shape'] = tuple([1] + list(result.kwargs['shape']))
+            return result
+        elif typ.ndim == 2:
+            kws["axis"] = 2
+            return self._analyze_op_call_numpy_stack(
+                scope, equiv_set, loc, args, kws
+            )
+        else:
+            kws["axis"] = 2
+            return self._analyze_op_call_numpy_concatenate(
+                scope, equiv_set, loc, args, kws
+            )
+
+    def _analyze_op_call_numpy_cumsum(self, scope, equiv_set, loc, args, kws):
+        # TODO
+        return None
+
+    def _analyze_op_call_numpy_cumprod(self, scope, equiv_set, loc, args, kws):
+        # TODO
+        return None
+
+    def _analyze_op_call_numpy_linspace(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        n = len(args)
+        num = 50
+        if n > 2:
+            num = args[2]
+        elif "num" in kws:
+            num = kws["num"]
+        return ArrayAnalysis.AnalyzeResult(shape=(num,))
+
+    def _analyze_op_call_numpy_dot(self, scope, equiv_set, loc, args, kws):
+        n = len(args)
+        assert n >= 2
+        loc = args[0].loc
+        require(all([self._isarray(x.name) for x in args]))
+        typs = [self.typemap[x.name] for x in args]
+        dims = [ty.ndim for ty in typs]
+        require(all(x > 0 for x in dims))
+        if dims[0] == 1 and dims[1] == 1:
+            return None
+        shapes = [equiv_set._get_shape(x) for x in args]
+        if dims[0] == 1:
+            asserts = self._call_assert_equiv(
+                scope, loc, equiv_set, [shapes[0][0], shapes[1][-2]]
+            )
+            return ArrayAnalysis.AnalyzeResult(
+                shape=tuple(shapes[1][0:-2] + shapes[1][-1:]),
+                pre=asserts
+            )
+        if dims[1] == 1:
+            asserts = self._call_assert_equiv(
+                scope, loc, equiv_set, [shapes[0][-1], shapes[1][0]]
+            )
+            return ArrayAnalysis.AnalyzeResult(
+                shape=tuple(shapes[0][0:-1]),
+                pre=asserts
+            )
+        if dims[0] == 2 and dims[1] == 2:
+            asserts = self._call_assert_equiv(
+                scope, loc, equiv_set, [shapes[0][1], shapes[1][0]]
+            )
+            return ArrayAnalysis.AnalyzeResult(
+                shape=(shapes[0][0], shapes[1][1]),
+                pre=asserts
+            )
+        if dims[0] > 2:  # TODO: handle higher dimension cases
+            pass
+        return None
+
+    def _analyze_stencil(self, scope, equiv_set, stencil_func, loc, args, kws):
+        # stencil requires that all relatively indexed array arguments are
+        # of same size
+        std_idx_arrs = stencil_func.options.get("standard_indexing", ())
+        kernel_arg_names = stencil_func.kernel_ir.arg_names
+        if isinstance(std_idx_arrs, str):
+            std_idx_arrs = (std_idx_arrs,)
+        rel_idx_arrs = []
+        assert len(args) > 0 and len(args) == len(kernel_arg_names)
+        for arg, var in zip(kernel_arg_names, args):
+            typ = self.typemap[var.name]
+            if isinstance(typ, types.ArrayCompatible) and not (
+                arg in std_idx_arrs
+            ):
+                rel_idx_arrs.append(var)
+        n = len(rel_idx_arrs)
+        require(n > 0)
+        asserts = self._call_assert_equiv(scope, loc, equiv_set, rel_idx_arrs)
+        shape = equiv_set.get_shape(rel_idx_arrs[0])
+        return ArrayAnalysis.AnalyzeResult(shape=shape, pre=asserts)
+
+    def _analyze_op_call_numpy_linalg_inv(
+        self, scope, equiv_set, loc, args, kws
+    ):
+        require(len(args) >= 1)
+        return ArrayAnalysis.AnalyzeResult(shape=equiv_set._get_shape(args[0]))
+
+    def _insert_runtime_broadcast_call(self, scope, loc, arrs, max_dim):
+        pre = []
+
+        typs = [self.typemap[x.name] for x in arrs]
+        dims = [self.typemap[x.name].ndim for x in arrs]
+        max_dim = max(dims)
+
+        runtime_broadcast_shape = ir.Var(
+            scope, mk_unique_var("runtime_broadcast_shape"), loc
+        )
+        runtime_broadcast_type = types.containers.List(types.intp)
+        self.typemap[runtime_broadcast_shape.name] = runtime_broadcast_type
+
+        func_var = ir.Var(scope, mk_unique_var("runtime_broadcast_call"), loc)
+        func_def = ir.Global(
+            "runtime_broadcast_assert_shapes",
+            runtime_broadcast_assert_shapes,
+            loc=loc
+        )
+        pre.append(
+            ir.Assign(value=func_def, target=func_var, loc=loc)
+        )
+        func_fnty = get_global_func_typ(runtime_broadcast_assert_shapes)
+        print("func_fnty:", func_fnty)
+        self.typemap[func_var.name] = func_fnty
+        fargs = [types.literal(max_dim)] + typs
+        print("typs:", typs)
+        print("dims:", dims, max_dim)
+        print("fargs:", fargs)
+        func_sig = self.context.resolve_function_type(func_fnty, fargs, {})
+        print("func_sig:", func_sig)
+
+        max_dim_var = ir.Var(scope, mk_unique_var("max_dim"), loc)
+        max_dim_val = ir.Const(max_dim, loc)
+        max_dim_typ = types.IntegerLiteral(max_dim)
+        self.typemap[max_dim_var.name] = max_dim_typ
+        pre.append(
+            ir.Assign(value=max_dim_val, target=max_dim_var, loc=loc)
+        )
+
+        func_call = ir.Expr.call(func_var, [max_dim_var] + arrs, {}, loc)
+
+        self.calltypes[func_call] = func_sig
+        pre.append(
+            ir.Assign(value=func_call, target=runtime_broadcast_shape, loc=loc)
+        )
+
+        return ArrayAnalysis.AnalyzeResult(
+            shape=runtime_broadcast_shape,
+            pre=pre
+        )
+
+    def _analyze_broadcast(self, scope, equiv_set, loc, args, fn):
+        """Infer shape equivalence of arguments based on Numpy broadcast rules
+        and return shape of output
+        https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
+        """
+        tups = list(filter(lambda a: self._istuple(a.name), args))
+        # Here we have a tuple concatenation.
+        if len(tups) == 2 and fn.__name__ == 'add':
+            # If either of the tuples is empty then the resulting shape
+            # is just the other tuple.
+            tup0typ = self.typemap[tups[0].name]
+            tup1typ = self.typemap[tups[1].name]
+            if tup0typ.count == 0:
+                return ArrayAnalysis.AnalyzeResult(
+                    shape=equiv_set.get_shape(tups[1])
+                )
+            if tup1typ.count == 0:
+                return ArrayAnalysis.AnalyzeResult(
+                    shape=equiv_set.get_shape(tups[0])
+                )
+
+            try:
+                shapes = [equiv_set.get_shape(x) for x in tups]
+                if None in shapes:
+                    return None
+                concat_shapes = sum(shapes, ())
+                return ArrayAnalysis.AnalyzeResult(
+                    shape=concat_shapes
+                )
+            except GuardException:
+                return None
+
+        # else arrays
+        arrs = list(filter(lambda a: self._isarray(a.name), args))
+        require(len(arrs) > 0)
+        names = [x.name for x in arrs]
+        dims = [self.typemap[x.name].ndim for x in arrs]
+        max_dim = max(dims)
+        require(max_dim > 0)
+        try:
+            shapes = [equiv_set.get_shape(x) for x in arrs]
+        except GuardException:
+            return ArrayAnalysis.AnalyzeResult(
+                shape=arrs[0],
+                pre=self._call_assert_equiv(scope, loc, equiv_set, arrs)
+            )
+        if None not in shapes:
+            return self._broadcast_assert_shapes(
+                scope, equiv_set, loc, shapes, names
+            )
+        else:
+            return self._insert_runtime_broadcast_call(
+                scope, loc, arrs, max_dim
+            )
+
+    def _broadcast_assert_shapes(self, scope, equiv_set, loc, shapes, names):
+        """Produce assert_equiv for sizes in each dimension, taking into
+        account of dimension coercion and constant size of 1.
+        """
+        asserts = []
+        new_shape = []
+        max_dim = max([len(shape) for shape in shapes])
+        const_size_one = None
+        for i in range(max_dim):
+            sizes = []
+            size_names = []
+            for name, shape in zip(names, shapes):
+                if i < len(shape):
+                    size = shape[len(shape) - 1 - i]
+                    const_size = equiv_set.get_equiv_const(size)
+                    if const_size == 1:
+                        const_size_one = size
+                    else:
+                        sizes.append(size)  # non-1 size to front
+                        size_names.append(name)
+            if sizes == []:
+                assert const_size_one is not None
+                sizes.append(const_size_one)
+                size_names.append("1")
+            asserts.append(
+                self._call_assert_equiv(
+                    scope, loc, equiv_set, sizes, names=size_names
+                )
+            )
+            new_shape.append(sizes[0])
+        return ArrayAnalysis.AnalyzeResult(
+            shape=tuple(reversed(new_shape)),
+            pre=sum(asserts, [])
+        )
+
+    def _call_assert_equiv(self, scope, loc, equiv_set, args, names=None):
+        insts = self._make_assert_equiv(
+            scope, loc, equiv_set, args, names=names
+        )
+        if len(args) > 1:
+            equiv_set.insert_equiv(*args)
+        return insts
+
+    def _make_assert_equiv(self, scope, loc, equiv_set, _args, names=None):
+        # filter out those that are already equivalent
+        if config.DEBUG_ARRAY_OPT >= 2:
+            print("make_assert_equiv:", _args, names)
+        if names is None:
+            names = [x.name for x in _args]
+        args = []
+        arg_names = []
+        for name, x in zip(names, _args):
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print("name, x:", name, x)
+            seen = False
+            for y in args:
+                if config.DEBUG_ARRAY_OPT >= 2:
+                    print("is equiv to?", y, equiv_set.is_equiv(x, y))
+                if equiv_set.is_equiv(x, y):
+                    seen = True
+                    break
+            if not seen:
+                args.append(x)
+                arg_names.append(name)
+
+        # no assertion necessary if there are less than two
+        if len(args) < 2:
+            if config.DEBUG_ARRAY_OPT >= 2:
+                print(
+                    "Will not insert assert_equiv as args are known to be "
+                    "equivalent."
+                )
+            return []
+
+        msg = "Sizes of {} do not match on {}".format(
+            ", ".join(arg_names), loc
+        )
+        msg_val = ir.Const(msg, loc)
+        msg_typ = types.StringLiteral(msg)
+        msg_var = ir.Var(scope, mk_unique_var("msg"), loc)
+        self.typemap[msg_var.name] = msg_typ
+        argtyps = tuple([msg_typ] + [self.typemap[x.name] for x in args])
+
+        # assert_equiv takes vararg, which requires a tuple as argument type
+        tup_typ = types.StarArgTuple.from_types(argtyps)
+
+        # prepare function variable whose type may vary since it takes vararg
+        assert_var = ir.Var(scope, mk_unique_var("assert"), loc)
+        assert_def = ir.Global("assert_equiv", assert_equiv, loc=loc)
+        fnty = get_global_func_typ(assert_equiv)
+        sig = self.context.resolve_function_type(fnty, (tup_typ,), {})
+        self._define(equiv_set, assert_var, fnty, assert_def)
+
+        # The return value from assert_equiv is always of none type.
+        var = ir.Var(scope, mk_unique_var("ret"), loc)
+        value = ir.Expr.call(assert_var, [msg_var] + args, {}, loc=loc)
+        self._define(equiv_set, var, types.none, value)
+        self.calltypes[value] = sig
+
+        return [
+            ir.Assign(value=msg_val, target=msg_var, loc=loc),
+            ir.Assign(value=assert_def, target=assert_var, loc=loc),
+            ir.Assign(value=value, target=var, loc=loc),
+        ]
+
+    def _gen_shape_call(self, equiv_set, var, ndims, shape, post):
+        # attr call: A_sh_attr = getattr(A, shape)
+        if isinstance(shape, ir.Var):
+            shape = equiv_set.get_shape(shape)
+
+        # already a tuple variable that contains size
+        if isinstance(shape, ir.Var):
+            attr_var = shape
+            shape_attr_call = None
+            shape = None
+        elif isinstance(shape, ir.Arg):
+            attr_var = var
+            shape_attr_call = None
+            shape = None
+        else:
+            shape_attr_call = ir.Expr.getattr(var, "shape", var.loc)
+            attr_var = ir.Var(
+                var.scope, mk_unique_var("{}_shape".format(var.name)), var.loc
+            )
+            shape_attr_typ = types.containers.UniTuple(types.intp, ndims)
+        size_vars = []
+        use_attr_var = False
+        # trim shape tuple if it is more than ndim
+        if shape:
+            nshapes = len(shape)
+            if ndims < nshapes:
+                shape = shape[(nshapes - ndims) :]
+        for i in range(ndims):
+            skip = False
+            if shape and shape[i]:
+                if isinstance(shape[i], ir.Var):
+                    typ = self.typemap[shape[i].name]
+                    if isinstance(typ, (types.Number, types.SliceType)):
+                        size_var = shape[i]
+                        skip = True
+                else:
+                    if isinstance(shape[i], int):
+                        size_val = ir.Const(shape[i], var.loc)
+                    else:
+                        size_val = shape[i]
+                    assert isinstance(size_val, ir.Const)
+                    size_var = ir.Var(
+                        var.scope,
+                        mk_unique_var("{}_size{}".format(var.name, i)),
+                        var.loc,
+                    )
+                    post.append(ir.Assign(size_val, size_var, var.loc))
+                    self._define(equiv_set, size_var, types.intp, size_val)
+                    skip = True
+            if not skip:
+                # get size: Asize0 = A_sh_attr[0]
+                size_var = ir.Var(
+                    var.scope,
+                    mk_unique_var("{}_size{}".format(var.name, i)),
+                    var.loc,
+                )
+                getitem = ir.Expr.static_getitem(attr_var, i, None, var.loc)
+                use_attr_var = True
+                self.calltypes[getitem] = None
+                post.append(ir.Assign(getitem, size_var, var.loc))
+                self._define(equiv_set, size_var, types.intp, getitem)
+            size_vars.append(size_var)
+        if use_attr_var and shape_attr_call:
+            # only insert shape call if there is any getitem call
+            post.insert(0, ir.Assign(shape_attr_call, attr_var, var.loc))
+            self._define(equiv_set, attr_var, shape_attr_typ, shape_attr_call)
+        return tuple(size_vars)
+
+    def _isarray(self, varname):
+        typ = self.typemap[varname]
+        return isinstance(typ, types.npytypes.Array) and typ.ndim > 0
+
+    def _istuple(self, varname):
+        typ = self.typemap[varname]
+        return isinstance(typ, types.BaseTuple)
+
+    def _sum_size(self, equiv_set, sizes):
+        """Return the sum of the given list of sizes if they are all equivalent
+        to some constant, or None otherwise.
+        """
+        s = 0
+        for size in sizes:
+            n = equiv_set.get_equiv_const(size)
+            if n is None:
+                return None
+            else:
+                s += n
+        return s
+
+
+UNARY_MAP_OP = list(npydecl.NumpyRulesUnaryArrayOperator._op_map.keys()) + [
+    operator.pos
+]
+BINARY_MAP_OP = npydecl.NumpyRulesArrayOperator._op_map.keys()
+INPLACE_BINARY_MAP_OP = npydecl.NumpyRulesInplaceArrayOperator._op_map.keys()
+UFUNC_MAP_OP = [f.__name__ for f in npydecl.supported_ufuncs]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor.py
new file mode 100644
index 000000000..145e9c18e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor.py
@@ -0,0 +1,5016 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+"""
+This module transforms data-parallel operations such as Numpy calls into
+'Parfor' nodes, which are nested loops that can be parallelized.
+It also implements optimizations such as loop fusion, and extends the rest of
+compiler analysis and optimizations to support Parfors.
+This is similar to ParallelAccelerator package in Julia:
+https://github.com/IntelLabs/ParallelAccelerator.jl
+'Parallelizing Julia with a Non-invasive DSL', T. Anderson et al., ECOOP'17.
+"""
+import types as pytypes  # avoid confusion with numba.types
+import sys, math
+import os
+import textwrap
+import copy
+import inspect
+import linecache
+from functools import reduce
+from collections import defaultdict, OrderedDict, namedtuple
+from contextlib import contextmanager
+import operator
+from dataclasses import make_dataclass
+
+from llvmlite import ir as lir
+from numba.core.imputils import impl_ret_untracked
+import numba.core.ir
+from numba.core import types, typing, utils, errors, ir, analysis, postproc, rewrites, typeinfer, config, ir_utils
+from numba import prange, pndindex
+from numba.np.npdatetime_helpers import datetime_minimum, datetime_maximum
+from numba.np.numpy_support import as_dtype, numpy_version
+from numba.core.typing.templates import infer_global, AbstractTemplate
+from numba.stencils.stencilparfor import StencilPass
+from numba.core.extending import register_jitable, lower_builtin
+
+from numba.core.ir_utils import (
+    mk_unique_var,
+    next_label,
+    mk_alloc,
+    get_np_ufunc_typ,
+    mk_range_block,
+    mk_loop_header,
+    get_name_var_table,
+    replace_vars,
+    replace_vars_inner,
+    visit_vars,
+    visit_vars_inner,
+    remove_dead,
+    copy_propagate,
+    get_block_copies,
+    apply_copy_propagate,
+    dprint_func_ir,
+    find_topo_order,
+    get_stmt_writes,
+    rename_labels,
+    get_call_table,
+    simplify,
+    simplify_CFG,
+    has_no_side_effect,
+    canonicalize_array_math,
+    add_offset_to_labels,
+    find_callname,
+    find_build_sequence,
+    guard,
+    require,
+    GuardException,
+    compile_to_numba_ir,
+    get_definition,
+    build_definitions,
+    replace_arg_nodes,
+    replace_returns,
+    is_getitem,
+    is_setitem,
+    is_get_setitem,
+    index_var_of_get_setitem,
+    set_index_var_of_get_setitem,
+    find_potential_aliases,
+    replace_var_names,
+    transfer_scope,
+)
+
+from numba.core.analysis import (compute_use_defs, compute_live_map,
+                            compute_dead_maps, compute_cfg_from_blocks)
+from numba.core.controlflow import CFGraph
+from numba.core.typing import npydecl, signature
+from numba.core.types.functions import Function
+from numba.parfors.array_analysis import (random_int_args, random_1arg_size,
+                                  random_2arg_sizelast, random_3arg_sizelast,
+                                  random_calls, assert_equiv)
+from numba.core.extending import overload
+import copy
+import numpy
+import numpy as np
+from numba.parfors import array_analysis
+import numba.cpython.builtins
+from numba.stencils import stencilparfor
+# circular dependency: import numba.npyufunc.dufunc.DUFunc
+
+# wrapped pretty print
+_termwidth = 80
+_txtwrapper = textwrap.TextWrapper(width=_termwidth, drop_whitespace=False)
+def print_wrapped(x):
+    for l in x.splitlines():
+        [print(y) for y in _txtwrapper.wrap(l)]
+
+sequential_parfor_lowering = False
+
+# init_prange is a sentinel call that specifies the start of the initialization
+# code for the computation in the upcoming prange call
+# This lets the prange pass to put the code in the generated parfor's init_block
+def init_prange():
+    return
+
+@overload(init_prange)
+def init_prange_overload():
+    def no_op():
+        return
+    return no_op
+
+class internal_prange(object):
+
+    def __new__(cls, *args):
+        return range(*args)
+
+
+def min_parallel_impl(return_type, arg):
+    # XXX: use prange for 1D arrays since pndindex returns a 1-tuple instead of
+    # integer. This causes type and fusion issues.
+    if arg.ndim == 0:
+        def min_1(in_arr):
+            return in_arr[()]
+    elif arg.ndim == 1:
+        if isinstance(arg.dtype, (types.NPDatetime, types.NPTimedelta)):
+            # NaT is always returned if it is in the array
+            def min_1(in_arr):
+                numba.parfors.parfor.init_prange()
+                min_checker(len(in_arr))
+                val = numba.cpython.builtins.get_type_max_value(in_arr.dtype)
+                for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                    val = datetime_minimum(val, in_arr[i])
+                return val
+        else:
+            def min_1(in_arr):
+                numba.parfors.parfor.init_prange()
+                min_checker(len(in_arr))
+                val = numba.cpython.builtins.get_type_max_value(in_arr.dtype)
+                for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                    val = min(val, in_arr[i])
+                return val
+    else:
+        def min_1(in_arr):
+            numba.parfors.parfor.init_prange()
+            min_checker(len(in_arr))
+            val = numba.cpython.builtins.get_type_max_value(in_arr.dtype)
+            for i in numba.pndindex(in_arr.shape):
+                val = min(val, in_arr[i])
+            return val
+    return min_1
+
+def max_parallel_impl(return_type, arg):
+    if arg.ndim == 0:
+        def max_1(in_arr):
+            return in_arr[()]
+    elif arg.ndim == 1:
+        if isinstance(arg.dtype, (types.NPDatetime, types.NPTimedelta)):
+            # NaT is always returned if it is in the array
+            def max_1(in_arr):
+                numba.parfors.parfor.init_prange()
+                max_checker(len(in_arr))
+                val = numba.cpython.builtins.get_type_min_value(in_arr.dtype)
+                for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                    val = datetime_maximum(val, in_arr[i])
+                return val
+        else:
+            def max_1(in_arr):
+                numba.parfors.parfor.init_prange()
+                max_checker(len(in_arr))
+                val = numba.cpython.builtins.get_type_min_value(in_arr.dtype)
+                for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                    val = max(val, in_arr[i])
+                return val
+    else:
+        def max_1(in_arr):
+            numba.parfors.parfor.init_prange()
+            max_checker(len(in_arr))
+            val = numba.cpython.builtins.get_type_min_value(in_arr.dtype)
+            for i in numba.pndindex(in_arr.shape):
+                val = max(val, in_arr[i])
+            return val
+    return max_1
+
+def argmin_parallel_impl(in_arr):
+    numba.parfors.parfor.init_prange()
+    argmin_checker(len(in_arr))
+    A = in_arr.ravel()
+    init_val = numba.cpython.builtins.get_type_max_value(A.dtype)
+    ival = typing.builtins.IndexValue(0, init_val)
+    for i in numba.parfors.parfor.internal_prange(len(A)):
+        curr_ival = typing.builtins.IndexValue(i, A[i])
+        ival = min(ival, curr_ival)
+    return ival.index
+
+def argmax_parallel_impl(in_arr):
+    numba.parfors.parfor.init_prange()
+    argmax_checker(len(in_arr))
+    A = in_arr.ravel()
+    init_val = numba.cpython.builtins.get_type_min_value(A.dtype)
+    ival = typing.builtins.IndexValue(0, init_val)
+    for i in numba.parfors.parfor.internal_prange(len(A)):
+        curr_ival = typing.builtins.IndexValue(i, A[i])
+        ival = max(ival, curr_ival)
+    return ival.index
+
+def dotvv_parallel_impl(a, b):
+    numba.parfors.parfor.init_prange()
+    l = a.shape[0]
+    m = b.shape[0]
+    # TODO: investigate assert_equiv
+    #assert_equiv("sizes of l, m do not match", l, m)
+    s = 0
+    for i in numba.parfors.parfor.internal_prange(l):
+        s += a[i] * b[i]
+    return s
+
+def dotvm_parallel_impl(a, b):
+    numba.parfors.parfor.init_prange()
+    l = a.shape
+    m, n = b.shape
+    # TODO: investigate assert_equiv
+    #assert_equiv("Sizes of l, m do not match", l, m)
+    c = np.zeros(n, a.dtype)
+    # TODO: evaluate dotvm implementation options
+    #for i in prange(n):
+    #    s = 0
+    #    for j in range(m):
+    #        s += a[j] * b[j, i]
+    #    c[i] = s
+    for i in numba.parfors.parfor.internal_prange(m):
+        c += a[i] * b[i, :]
+    return c
+
+def dotmv_parallel_impl(a, b):
+    numba.parfors.parfor.init_prange()
+    m, n = a.shape
+    l = b.shape
+    # TODO: investigate assert_equiv
+    #assert_equiv("sizes of n, l do not match", n, l)
+    c = np.empty(m, a.dtype)
+    for i in numba.parfors.parfor.internal_prange(m):
+        s = 0
+        for j in range(n):
+            s += a[i, j] * b[j]
+        c[i] = s
+    return c
+
+def dot_parallel_impl(return_type, atyp, btyp):
+    # Note that matrix matrix multiply is not translated.
+    if (isinstance(atyp, types.npytypes.Array) and
+        isinstance(btyp, types.npytypes.Array)):
+        if atyp.ndim == btyp.ndim == 1:
+            return dotvv_parallel_impl
+        # TODO: evaluate support for dotvm and enable
+        #elif atyp.ndim == 1 and btyp.ndim == 2:
+        #    return dotvm_parallel_impl
+        elif atyp.ndim == 2 and btyp.ndim == 1:
+            return dotmv_parallel_impl
+
+def sum_parallel_impl(return_type, arg):
+    zero = return_type(0)
+
+    if arg.ndim == 0:
+        def sum_1(in_arr):
+            return in_arr[()]
+    elif arg.ndim == 1:
+        def sum_1(in_arr):
+            numba.parfors.parfor.init_prange()
+            val = zero
+            for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                val += in_arr[i]
+            return val
+    else:
+        def sum_1(in_arr):
+            numba.parfors.parfor.init_prange()
+            val = zero
+            for i in numba.pndindex(in_arr.shape):
+                val += in_arr[i]
+            return val
+    return sum_1
+
+def prod_parallel_impl(return_type, arg):
+    one = return_type(1)
+
+    if arg.ndim == 0:
+        def prod_1(in_arr):
+            return in_arr[()]
+    elif arg.ndim == 1:
+        def prod_1(in_arr):
+            numba.parfors.parfor.init_prange()
+            val = one
+            for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                val *= in_arr[i]
+            return val
+    else:
+        def prod_1(in_arr):
+            numba.parfors.parfor.init_prange()
+            val = one
+            for i in numba.pndindex(in_arr.shape):
+                val *= in_arr[i]
+            return val
+    return prod_1
+
+
+def mean_parallel_impl(return_type, arg):
+    # can't reuse sum since output type is different
+    zero = return_type(0)
+
+    if arg.ndim == 0:
+        def mean_1(in_arr):
+            return in_arr[()]
+    elif arg.ndim == 1:
+        def mean_1(in_arr):
+            numba.parfors.parfor.init_prange()
+            val = zero
+            for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                val += in_arr[i]
+            return val/len(in_arr)
+    else:
+        def mean_1(in_arr):
+            numba.parfors.parfor.init_prange()
+            val = zero
+            for i in numba.pndindex(in_arr.shape):
+                val += in_arr[i]
+            return val/in_arr.size
+    return mean_1
+
+def var_parallel_impl(return_type, arg):
+    if arg.ndim == 0:
+        def var_1(in_arr):
+            return 0
+    elif arg.ndim == 1:
+        def var_1(in_arr):
+            # Compute the mean
+            m = in_arr.mean()
+            # Compute the sum of square diffs
+            numba.parfors.parfor.init_prange()
+            ssd = 0
+            for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                val = in_arr[i] - m
+                ssd += np.real(val * np.conj(val))
+            return ssd / len(in_arr)
+    else:
+        def var_1(in_arr):
+            # Compute the mean
+            m = in_arr.mean()
+            # Compute the sum of square diffs
+            numba.parfors.parfor.init_prange()
+            ssd = 0
+            for i in numba.pndindex(in_arr.shape):
+                val = in_arr[i] - m
+                ssd += np.real(val * np.conj(val))
+            return ssd / in_arr.size
+    return var_1
+
+def std_parallel_impl(return_type, arg):
+    def std_1(in_arr):
+        return in_arr.var() ** 0.5
+    return std_1
+
+def arange_parallel_impl(return_type, *args):
+    dtype = as_dtype(return_type.dtype)
+
+    def arange_1(stop):
+        return np.arange(0, stop, 1, dtype)
+
+    def arange_2(start, stop):
+        return np.arange(start, stop, 1, dtype)
+
+    def arange_3(start, stop, step):
+        return np.arange(start, stop, step, dtype)
+
+    if any(isinstance(a, types.Complex) for a in args):
+        def arange_4(start, stop, step, dtype):
+            numba.parfors.parfor.init_prange()
+            nitems_c = (stop - start) / step
+            nitems_r = math.ceil(nitems_c.real)
+            nitems_i = math.ceil(nitems_c.imag)
+            nitems = int(max(min(nitems_i, nitems_r), 0))
+            arr = np.empty(nitems, dtype)
+            for i in numba.parfors.parfor.internal_prange(nitems):
+                arr[i] = start + i * step
+            return arr
+    else:
+        def arange_4(start, stop, step, dtype):
+            numba.parfors.parfor.init_prange()
+            nitems_r = math.ceil((stop - start) / step)
+            nitems = int(max(nitems_r, 0))
+            arr = np.empty(nitems, dtype)
+            val = start
+            for i in numba.parfors.parfor.internal_prange(nitems):
+                arr[i] = start + i * step
+            return arr
+
+    if len(args) == 1:
+        return arange_1
+    elif len(args) == 2:
+        return arange_2
+    elif len(args) == 3:
+        return arange_3
+    elif len(args) == 4:
+        return arange_4
+    else:
+        raise ValueError("parallel arange with types {}".format(args))
+
+def linspace_parallel_impl(return_type, *args):
+    dtype = as_dtype(return_type.dtype)
+
+    def linspace_2(start, stop):
+        return np.linspace(start, stop, 50)
+
+    def linspace_3(start, stop, num):
+        numba.parfors.parfor.init_prange()
+        arr = np.empty(num, dtype)
+        div = num - 1
+        delta = stop - start
+        arr[0] = start
+        for i in numba.parfors.parfor.internal_prange(num):
+            arr[i] = start + delta * (i / div)
+        return arr
+
+    if len(args) == 2:
+        return linspace_2
+    elif len(args) == 3:
+        return linspace_3
+    else:
+        raise ValueError("parallel linspace with types {}".format(args))
+
+swap_functions_map = {
+    ('argmin', 'numpy'): lambda r,a: argmin_parallel_impl,
+    ('argmax', 'numpy'): lambda r,a: argmax_parallel_impl,
+    ('min', 'numpy'): min_parallel_impl,
+    ('max', 'numpy'): max_parallel_impl,
+    ('amin', 'numpy'): min_parallel_impl,
+    ('amax', 'numpy'): max_parallel_impl,
+    ('sum', 'numpy'): sum_parallel_impl,
+    ('prod', 'numpy'): prod_parallel_impl,
+    ('mean', 'numpy'): mean_parallel_impl,
+    ('var', 'numpy'): var_parallel_impl,
+    ('std', 'numpy'): std_parallel_impl,
+    ('dot', 'numpy'): dot_parallel_impl,
+    ('arange', 'numpy'): arange_parallel_impl,
+    ('linspace', 'numpy'): linspace_parallel_impl,
+}
+
+def fill_parallel_impl(return_type, arr, val):
+    """Parallel implementation of ndarray.fill.  The array on
+       which to operate is retrieved from get_call_name and
+       is passed along with the value to fill.
+    """
+    if arr.ndim == 1:
+        def fill_1(in_arr, val):
+            numba.parfors.parfor.init_prange()
+            for i in numba.parfors.parfor.internal_prange(len(in_arr)):
+                in_arr[i] = val
+            return None
+    else:
+        def fill_1(in_arr, val):
+            numba.parfors.parfor.init_prange()
+            for i in numba.pndindex(in_arr.shape):
+                in_arr[i] = val
+            return None
+    return fill_1
+
+replace_functions_ndarray = {
+    'fill': fill_parallel_impl,
+}
+
+@register_jitable
+def max_checker(arr_size):
+    if arr_size == 0:
+        raise ValueError(("zero-size array to reduction operation "
+                            "maximum which has no identity"))
+
+@register_jitable
+def min_checker(arr_size):
+    if arr_size == 0:
+        raise ValueError(("zero-size array to reduction operation "
+                            "minimum which has no identity"))
+
+@register_jitable
+def argmin_checker(arr_size):
+    if arr_size == 0:
+        raise ValueError("attempt to get argmin of an empty sequence")
+
+@register_jitable
+def argmax_checker(arr_size):
+    if arr_size == 0:
+        raise ValueError("attempt to get argmax of an empty sequence")
+
+checker_impl = namedtuple('checker_impl', ['name', 'func'])
+
+replace_functions_checkers_map = {
+    ('argmin', 'numpy') : checker_impl('argmin_checker', argmin_checker),
+    ('argmax', 'numpy') : checker_impl('argmax_checker', argmax_checker),
+    ('min', 'numpy') : checker_impl('min_checker', min_checker),
+    ('max', 'numpy') : checker_impl('max_checker', max_checker),
+    ('amin', 'numpy') : checker_impl('min_checker', min_checker),
+    ('amax', 'numpy') : checker_impl('max_checker', max_checker),
+}
+
+
+class LoopNest(object):
+
+    '''The LoopNest class holds information of a single loop including
+    the index variable (of a non-negative integer value), and the
+    range variable, e.g. range(r) is 0 to r-1 with step size 1.
+    '''
+
+    def __init__(self, index_variable, start, stop, step):
+        self.index_variable = index_variable
+        self.start = start
+        self.stop = stop
+        self.step = step
+
+
+    def __repr__(self):
+        return ("LoopNest(index_variable = {}, range = ({}, {}, {}))".
+                format(self.index_variable, self.start, self.stop, self.step))
+
+    def list_vars(self):
+       all_uses = []
+       all_uses.append(self.index_variable)
+       if isinstance(self.start, ir.Var):
+           all_uses.append(self.start)
+       if isinstance(self.stop, ir.Var):
+           all_uses.append(self.stop)
+       if isinstance(self.step, ir.Var):
+           all_uses.append(self.step)
+       return all_uses
+
+class Parfor(ir.Expr, ir.Stmt):
+
+    id_counter = 0
+
+    def __init__(
+            self,
+            loop_nests,
+            init_block,
+            loop_body,
+            loc,
+            index_var,
+            equiv_set,
+            pattern,
+            flags,
+            *,  #only specify the options below by keyword
+            no_sequential_lowering=False,
+            races=set()):
+        super(Parfor, self).__init__(
+            op='parfor',
+            loc=loc
+        )
+
+        self.id = type(self).id_counter
+        type(self).id_counter += 1
+        #self.input_info  = input_info
+        #self.output_info = output_info
+        self.loop_nests = loop_nests
+        self.init_block = init_block
+        self.loop_body = loop_body
+        self.index_var = index_var
+        self.params = None  # filled right before parallel lowering
+        self.equiv_set = equiv_set
+        # The parallel patterns this parfor was generated from and their options
+        # for example, a parfor could be from the stencil pattern with
+        # the neighborhood option
+        assert len(pattern) > 1
+        self.patterns = [pattern]
+        self.flags = flags
+        # if True, this parfor shouldn't be lowered sequentially even with the
+        # sequential lowering option
+        self.no_sequential_lowering = no_sequential_lowering
+        self.races = races
+        self.redvars = []
+        self.reddict = {}
+        if config.DEBUG_ARRAY_OPT_STATS:
+            fmt = 'Parallel for-loop #{} is produced from pattern \'{}\' at {}'
+            print(fmt.format(
+                  self.id, pattern, loc))
+
+    def __repr__(self):
+        return "id=" + str(self.id) + repr(self.loop_nests) + \
+            repr(self.loop_body) + repr(self.index_var)
+
+    def list_vars(self):
+        """list variables used (read/written) in this parfor by
+        traversing the body and combining block uses.
+        """
+        all_uses = []
+        for l, b in self.loop_body.items():
+            for stmt in b.body:
+                all_uses += stmt.list_vars()
+
+        for loop in self.loop_nests:
+            all_uses += loop.list_vars()
+
+        for stmt in self.init_block.body:
+            all_uses += stmt.list_vars()
+
+        return all_uses
+
+    def get_shape_classes(self, var, typemap=None):
+        """get the shape classes for a given variable.
+        If a typemap is specified then use it for type resolution
+        """
+        # We get shape classes from the equivalence set but that
+        # keeps its own typemap at a time prior to lowering.  So
+        # if something is added during lowering then we can pass
+        # in a type map to use.  We temporarily replace the
+        # equivalence set typemap, do the work and then restore
+        # the original on the way out.
+        if typemap is not None:
+            save_typemap = self.equiv_set.typemap
+            self.equiv_set.typemap = typemap
+        res = self.equiv_set.get_shape_classes(var)
+        if typemap is not None:
+            self.equiv_set.typemap = save_typemap
+        return res
+
+    def dump(self, file=None):
+        file = file or sys.stdout
+        print(("begin parfor {}".format(self.id)).center(20, '-'), file=file)
+        print("index_var = ", self.index_var, file=file)
+        print("params = ", self.params, file=file)
+        print("races = ", self.races, file=file)
+        for loopnest in self.loop_nests:
+            print(loopnest, file=file)
+        print("init block:", file=file)
+        self.init_block.dump(file)
+        for offset, block in sorted(self.loop_body.items()):
+            print('label %s:' % (offset,), file=file)
+            block.dump(file)
+        print(("end parfor {}".format(self.id)).center(20, '-'), file=file)
+
+    def validate_params(self, typemap):
+        """
+        Check that Parfors params are of valid types.
+        """
+        if self.params is None:
+            msg = ("Cannot run parameter validation on a Parfor with params "
+                   "not set")
+            raise ValueError(msg)
+        for p in self.params:
+            ty = typemap.get(p)
+            if ty is None:
+                msg = ("Cannot validate parameter %s, there is no type "
+                       "information available")
+                raise ValueError(msg)
+            if isinstance(ty, types.BaseTuple):
+                if ty.count > config.PARFOR_MAX_TUPLE_SIZE:
+                    msg = ("Use of a tuple (%s) of length %d in a parallel region "
+                           "exceeds the maximum supported tuple size.  Since "
+                           "Generalized Universal Functions back parallel regions "
+                           "and those do not support tuples, tuples passed to "
+                           "parallel regions are unpacked if their size is below "
+                           "a certain threshold, currently configured to be %d. "
+                           "This threshold can be modified using the Numba "
+                           "environment variable NUMBA_PARFOR_MAX_TUPLE_SIZE.")
+                    raise errors.UnsupportedParforsError(msg %
+                              (p, ty.count, config.PARFOR_MAX_TUPLE_SIZE),
+                              self.loc)
+
+
+def _analyze_parfor(parfor, equiv_set, typemap, array_analysis):
+    """Recursive array analysis for parfor nodes.
+    """
+    func_ir = array_analysis.func_ir
+    parfor_blocks = wrap_parfor_blocks(parfor)
+    # Since init_block get label 0 after wrap, we need to save
+    # the equivset for the real block label 0.
+    backup_equivset = array_analysis.equiv_sets.get(0, None)
+    array_analysis.run(parfor_blocks, equiv_set)
+    unwrap_parfor_blocks(parfor, parfor_blocks)
+    parfor.equiv_set = array_analysis.equiv_sets[0]
+    # Restore equivset for block 0 after parfor is unwrapped
+    if backup_equivset:
+        array_analysis.equiv_sets[0] = backup_equivset
+    return [], []
+
+array_analysis.array_analysis_extensions[Parfor] = _analyze_parfor
+
+class ParforDiagnostics(object):
+    """Holds parfor diagnostic info, this is accumulated throughout the
+    PreParforPass and ParforPass, also in the closure inlining!
+    """
+    def __init__(self):
+        # holds ref to the function for which this is providing diagnostics
+        self.func = None
+        # holds a map of the replaced functions
+        self.replaced_fns = dict()
+        # used to identify "internal" parfor functions
+        self.internal_name = '__numba_parfor_gufunc'
+        self.fusion_info = defaultdict(list)
+        self.nested_fusion_info = defaultdict(list)
+        self.fusion_reports = []
+        self.hoist_info = {}
+        self.has_setup = False
+
+    def setup(self, func_ir, fusion_enabled):
+        self.func_ir = func_ir
+        self.name = self.func_ir.func_id.func_qualname
+        self.line = self.func_ir.loc
+        self.fusion_enabled = fusion_enabled
+        if self.internal_name in self.name:
+            self.purpose = 'Internal parallel function'
+        else:
+            self.purpose = 'Function %s, %s' % (self.name, self.line)
+        # we store a reference to the parfors prior to fusion etc, the parfors
+        # do get mangled in the fusion process but in a predetermined manner
+        # and by holding a reference here the "before" state can be printed
+        self.initial_parfors = self.get_parfors()
+        self.has_setup = True
+
+    @property
+    def has_setup(self):
+        return self._has_setup
+
+    @has_setup.setter
+    def has_setup(self, state):
+        self._has_setup = state
+
+    def count_parfors(self, blocks=None):
+        return len(self.get_parfors())
+
+    def _get_nested_parfors(self, parfor, parfors_list):
+        blocks = wrap_parfor_blocks(parfor)
+        self._get_parfors(blocks, parfors_list)
+        unwrap_parfor_blocks(parfor)
+
+    def _get_parfors(self, blocks, parfors_list):
+        for label, blk in blocks.items():
+            for stmt in blk.body:
+                if isinstance(stmt, Parfor):
+                    parfors_list.append(stmt)
+                    self._get_nested_parfors(stmt, parfors_list)
+
+    def get_parfors(self):
+        parfors_list = []
+        self._get_parfors(self.func_ir.blocks, parfors_list)
+        return parfors_list
+
+    def hoisted_allocations(self):
+        allocs = []
+        for pf_id, data in self.hoist_info.items():
+            stmt = data.get('hoisted', [])
+            for inst in stmt:
+                if isinstance(inst.value, ir.Expr):
+                    if inst.value.op == 'call':
+                        call = guard(find_callname, self.func_ir, inst.value)
+                        if call is not None and call == ('empty', 'numpy'):
+                            allocs.append(inst)
+        return allocs
+
+    def compute_graph_info(self, _a):
+        """
+        compute adjacency list of the fused loops
+        and find the roots in of the lists
+        """
+        a = copy.deepcopy(_a)
+        if a == {}:
+            return [], set()
+
+        vtx = set()
+        for v in a.values():
+            for x in v:
+                vtx.add(x)
+
+        # find roots
+        potential_roots = set(a.keys())
+        roots = potential_roots - vtx
+        if roots is None:
+            roots = set()
+
+        # populate rest of adjacency list
+        not_roots = set()
+        for x in range(max(set(a.keys()).union(vtx)) + 1):
+            val = a.get(x)
+            if val is not None:
+                a[x] = val
+            elif val is []:
+                not_roots.add(x) # debug only
+            else:
+                a[x] = []
+
+
+        # fold adjacency list into an actual list ordered
+        # by vtx
+        l = []
+        for x in sorted(a.keys()):
+            l.append(a[x])
+
+        return l, roots #, not_roots
+
+    def get_stats(self, fadj, nadj, root):
+        """
+        Computes the number of fused and serialized loops
+        based on a fusion adjacency list `fadj` and a nested
+        parfors adjacency list `nadj` for the root, `root`
+        """
+        def count_root(fadj, nadj, root, nfused, nserial):
+            for k in nadj[root]:
+                nserial += 1
+                if nadj[k] == []:
+                    nfused += len(fadj[k])
+                else:
+                    nf, ns = count_root(fadj, nadj, k, nfused, nserial)
+                    nfused += nf
+                    nserial = ns
+            return nfused, nserial
+        nfused, nserial = count_root(fadj, nadj, root, 0, 0)
+        return nfused, nserial
+
+    def reachable_nodes(self, adj, root):
+        """
+        returns a list of nodes reachable in an adjacency list from a
+        specified root
+        """
+        fusers = []
+        fusers.extend(adj[root])
+        for k in adj[root]:
+            if adj[k] != []:
+                fusers.extend(self.reachable_nodes(adj, k))
+        return fusers
+
+    def sort_pf_by_line(self, pf_id, parfors_simple):
+        """
+        pd_id - the parfors id
+        parfors_simple - the simple parfors map
+        """
+        # this sorts parfors by source line number
+        pf = parfors_simple[pf_id][0]
+        pattern = pf.patterns[0]
+        line = max(0, pf.loc.line - 1) # why are these out by 1 ?!
+        filename = self.func_ir.loc.filename
+        nadj, nroots = self.compute_graph_info(self.nested_fusion_info)
+        fadj, froots = self.compute_graph_info(self.fusion_info)
+        graphs = [nadj, fadj]
+
+        # If the parfor is internal, like internal prange, then the
+        # default line number is from its location in the numba source
+        # To get a more accurate line number, this first checks the
+        # adjacency graph for fused parfors that might not be internal
+        # and uses the minimum line number from there. If that fails
+        # (case where there's just a single internal parfor) the IR
+        # is walked backwards from the parfor location and the first non
+        # parfor statement line number is used.
+        if isinstance(pattern, tuple):
+            if pattern[1] == 'internal':
+                reported_loc = pattern[2][1]
+                if reported_loc.filename == filename:
+                    return max(0, reported_loc.line - 1)
+                else:
+                    # first recurse and check the adjacency list for
+                    # something that is not an in internal parfor
+                    tmp = []
+                    for adj in graphs:
+                        if adj: # graph may be empty, e.g. no nesting
+                            for k in adj[pf_id]:
+                                tmp.append(self.sort_pf_by_line(k, parfors_simple))
+                            if tmp:
+                                return max(0, min(tmp) - 1)
+                    # second run through the parfor block to see if there's
+                    # and reference to a line number in the user source
+                    for blk in pf.loop_body.values():
+                        for stmt in blk.body:
+                            if stmt.loc.filename == filename:
+                                return max(0, stmt.loc.line - 1)
+                    # finally run through the func_ir and look for the
+                    # first non-parfor statement prior to this one and
+                    # grab the line from that
+                    for blk in self.func_ir.blocks.values():
+                        try:
+                            idx = blk.body.index(pf)
+                            for i in range(idx - 1, 0, -1):
+                                stmt = blk.body[i]
+                                if not isinstance(stmt, Parfor):
+                                    line = max(0, stmt.loc.line - 1)
+                                    break
+                        except ValueError:
+                            pass
+        return line
+
+    def get_parfors_simple(self, print_loop_search):
+        parfors_simple = dict()
+
+        # print in line order, parfors loop id is based on discovery order
+        for pf in sorted(self.initial_parfors, key=lambda x: x.loc.line):
+            # use 0 here, the parfors are mutated by the time this routine
+            # is called, however, fusion appends the patterns so we can just
+            # pull in the first as a "before fusion" emulation
+            r_pattern = pf.patterns[0]
+            pattern = pf.patterns[0]
+            loc = pf.loc
+            if isinstance(pattern, tuple):
+                if pattern[0] == 'prange':
+                    if pattern[1] == 'internal':
+                        replfn = '.'.join(reversed(list(pattern[2][0])))
+                        loc = pattern[2][1]
+                        r_pattern = '%s %s' % (replfn, '(internal parallel version)')
+                    elif pattern[1] == 'user':
+                        r_pattern = "user defined prange"
+                    elif pattern[1] == 'pndindex':
+                        r_pattern = "internal pndindex" #FIXME: trace this!
+                    else:
+                        assert 0
+            fmt = 'Parallel for-loop #%s: is produced from %s:\n    %s\n \n'
+            if print_loop_search:
+                print_wrapped(fmt % (pf.id, loc, r_pattern))
+            parfors_simple[pf.id] = (pf, loc, r_pattern)
+        return parfors_simple
+
+    def get_all_lines(self, parfors_simple):
+        # ensure adjacency lists are the same size for both sets of info
+        # (nests and fusion may not traverse the same space, for
+        # convenience [] is used as a condition to halt recursion)
+        fadj, froots = self.compute_graph_info(self.fusion_info)
+        nadj, _nroots = self.compute_graph_info(self.nested_fusion_info)
+
+        if len(fadj) > len(nadj):
+            lim = len(fadj)
+            tmp = nadj
+        else:
+            lim = len(nadj)
+            tmp = fadj
+        for x in range(len(tmp), lim):
+            tmp.append([])
+
+        # This computes the roots of true loop nests (i.e. loops containing
+        # loops opposed to just a loop that's a root).
+        nroots = set()
+        if _nroots:
+            for r in _nroots:
+                if nadj[r] != []:
+                    nroots.add(r)
+        all_roots = froots ^ nroots
+
+        # This computes all the parfors at the top level that are either:
+        # - roots of loop fusion
+        # - roots of true loop nests
+        # it then combines these based on source line number for ease of
+        # producing output ordered in a manner similar to the code structure
+        froots_lines = {}
+        for x in froots:
+            line = self.sort_pf_by_line(x, parfors_simple)
+            froots_lines[line] = 'fuse', x, fadj
+
+        nroots_lines = {}
+        for x in nroots:
+            line = self.sort_pf_by_line(x, parfors_simple)
+            nroots_lines[line] = 'nest', x, nadj
+
+        all_lines = froots_lines.copy()
+        all_lines.update(nroots_lines)
+        return all_lines
+
+    def source_listing(self, parfors_simple, purpose_str):
+        filename = self.func_ir.loc.filename
+        count = self.count_parfors()
+        func_name = self.func_ir.func_id.func
+        try:
+            lines = inspect.getsource(func_name).splitlines()
+        except OSError: # generated function
+            lines = None
+        if lines and parfors_simple:
+            src_width = max([len(x) for x in lines])
+            map_line_to_pf = defaultdict(list) # parfors can alias lines
+            for k, v in parfors_simple.items():
+                # TODO: do a better job of tracking parfors that are not in
+                # this file but are referred to, e.g. np.arange()
+                if parfors_simple[k][1].filename == filename:
+                    match_line = self.sort_pf_by_line(k, parfors_simple)
+                    map_line_to_pf[match_line].append(str(k))
+
+            max_pf_per_line = max([1] + [len(x) for x in map_line_to_pf.values()])
+            width = src_width + (1 + max_pf_per_line * (len(str(count)) + 2))
+            newlines = []
+            newlines.append('\n')
+            newlines.append('Parallel loop listing for %s' % purpose_str)
+            newlines.append(width * '-' + '|loop #ID')
+            fmt = '{0:{1}}| {2}'
+            # why are these off by 1?
+            lstart = max(0, self.func_ir.loc.line - 1)
+            for no, line in enumerate(lines, lstart):
+                pf_ids = map_line_to_pf.get(no, None)
+                if pf_ids is not None:
+                    pfstr = '#' + ', '.join(pf_ids)
+                else:
+                    pfstr = ''
+                stripped = line.strip('\n')
+                srclen = len(stripped)
+                if pf_ids:
+                    l = fmt.format(width * '-', width, pfstr)
+                else:
+                    l = fmt.format(width * ' ', width, pfstr)
+                newlines.append(stripped + l[srclen:])
+            print('\n'.join(newlines))
+        else:
+            print("No source available")
+
+    def print_unoptimised(self, lines):
+        # This prints the unoptimised parfors state
+        sword = '+--'
+        fac = len(sword)
+        fadj, froots = self.compute_graph_info(self.fusion_info)
+        nadj, _nroots = self.compute_graph_info(self.nested_fusion_info)
+
+        if len(fadj) > len(nadj):
+            lim = len(fadj)
+            tmp = nadj
+        else:
+            lim = len(nadj)
+            tmp = fadj
+        for x in range(len(tmp), lim):
+            tmp.append([])
+
+        def print_nest(fadj_, nadj_, theroot, reported, region_id):
+            def print_g(fadj_, nadj_, nroot, depth):
+                print_wrapped(fac * depth * ' ' + '%s%s %s' % (sword, nroot, '(parallel)'))
+                for k in nadj_[nroot]:
+                    if nadj_[k] == []:
+                        msg = []
+                        msg.append(fac * (depth + 1) * ' ' + '%s%s %s' % (sword, k, '(parallel)'))
+                        if fadj_[k] != [] and k not in reported:
+                            fused = self.reachable_nodes(fadj_, k)
+                            for i in fused:
+                                msg.append(fac * (depth + 1) * ' ' + '%s%s %s' % (sword, i, '(parallel)'))
+                        reported.append(k)
+                        print_wrapped('\n'.join(msg))
+                    else:
+                        print_g(fadj_, nadj_, k, depth + 1)
+
+            if nadj_[theroot] != []:
+                print_wrapped("Parallel region %s:" % region_id)
+                print_g(fadj_, nadj_, theroot, 0)
+                print("\n")
+                region_id = region_id + 1
+            return region_id
+
+        def print_fuse(ty, pf_id, adj, depth, region_id):
+            msg = []
+            print_wrapped("Parallel region %s:" % region_id)
+            msg.append(fac * depth * ' ' + '%s%s %s' % (sword, pf_id, '(parallel)'))
+            if adj[pf_id] != []:
+                fused = sorted(self.reachable_nodes(adj, pf_id))
+                for k in fused:
+                    msg.append(fac * depth * ' ' + '%s%s %s' % (sword, k, '(parallel)'))
+            region_id = region_id + 1
+            print_wrapped('\n'.join(msg))
+            print("\n")
+            return region_id
+
+        # Walk the parfors by src line and print optimised structure
+        region_id = 0
+        reported = []
+        for line, info in sorted(lines.items()):
+            opt_ty, pf_id, adj = info
+            if opt_ty == 'fuse':
+                if pf_id not in reported:
+                    region_id = print_fuse('f', pf_id, adj, 0, region_id)
+            elif opt_ty == 'nest':
+                region_id = print_nest(fadj, nadj, pf_id, reported, region_id)
+            else:
+                assert 0
+
+    def print_optimised(self, lines):
+        # This prints the optimised output based on the transforms that
+        # occurred during loop fusion and rewriting of loop nests
+        sword = '+--'
+        fac = len(sword)
+        fadj, froots = self.compute_graph_info(self.fusion_info)
+        nadj, _nroots = self.compute_graph_info(self.nested_fusion_info)
+
+        if len(fadj) > len(nadj):
+            lim = len(fadj)
+            tmp = nadj
+        else:
+            lim = len(nadj)
+            tmp = fadj
+        for x in range(len(tmp), lim):
+            tmp.append([])
+
+        summary = dict()
+        # region : {fused, serialized}
+
+        def print_nest(fadj_, nadj_, theroot, reported, region_id):
+            def print_g(fadj_, nadj_, nroot, depth):
+                for k in nadj_[nroot]:
+                    msg = fac * depth * ' ' + '%s%s %s' % (sword, k, '(serial')
+                    if nadj_[k] == []:
+                        fused = []
+                        if fadj_[k] != [] and k not in reported:
+                            fused = sorted(self.reachable_nodes(fadj_, k))
+                            msg += ", fused with loop(s): "
+                            msg += ', '.join([str(x) for x in fused])
+                        msg += ')'
+                        reported.append(k)
+                        print_wrapped(msg)
+                        summary[region_id]['fused'] += len(fused)
+                    else:
+                        print_wrapped(msg + ')')
+                        print_g(fadj_, nadj_, k, depth + 1)
+                    summary[region_id]['serialized'] += 1
+
+            if nadj_[theroot] != []:
+                print_wrapped("Parallel region %s:" % region_id)
+                print_wrapped('%s%s %s' % (sword, theroot, '(parallel)'))
+                summary[region_id] = {'root': theroot, 'fused': 0, 'serialized': 0}
+                print_g(fadj_, nadj_, theroot, 1)
+                print("\n")
+                region_id = region_id + 1
+            return region_id
+
+        def print_fuse(ty, pf_id, adj, depth, region_id):
+            print_wrapped("Parallel region %s:" % region_id)
+            msg = fac * depth * ' ' + '%s%s %s' % (sword, pf_id, '(parallel')
+            fused = []
+            if adj[pf_id] != []:
+                fused = sorted(self.reachable_nodes(adj, pf_id))
+                msg += ", fused with loop(s): "
+                msg += ', '.join([str(x) for x in fused])
+
+            summary[region_id] = {'root': pf_id, 'fused': len(fused), 'serialized': 0}
+            msg += ')'
+            print_wrapped(msg)
+            print("\n")
+            region_id = region_id + 1
+            return region_id
+
+        # Walk the parfors by src line and print optimised structure
+        region_id = 0
+        reported = []
+        for line, info in sorted(lines.items()):
+            opt_ty, pf_id, adj = info
+            if opt_ty == 'fuse':
+                if pf_id not in reported:
+                    region_id = print_fuse('f', pf_id, adj, 0, region_id)
+            elif opt_ty == 'nest':
+                region_id = print_nest(fadj, nadj, pf_id, reported, region_id)
+            else:
+                assert 0
+
+        # print the summary of the fuse/serialize rewrite
+        if summary:
+            for k, v in sorted(summary.items()):
+                msg = ('\n \nParallel region %s (loop #%s) had %s '
+                    'loop(s) fused')
+                root = v['root']
+                fused = v['fused']
+                serialized = v['serialized']
+                if serialized != 0:
+                    msg += (' and %s loop(s) '
+                    'serialized as part of the larger '
+                    'parallel loop (#%s).')
+                    print_wrapped(msg % (k, root, fused, serialized, root))
+                else:
+                    msg += '.'
+                    print_wrapped(msg % (k, root, fused))
+        else:
+            print_wrapped("Parallel structure is already optimal.")
+
+    def allocation_hoist(self):
+        found = False
+        print('Allocation hoisting:')
+        for pf_id, data in self.hoist_info.items():
+            stmt = data.get('hoisted', [])
+            for inst in stmt:
+                if isinstance(inst.value, ir.Expr):
+                    try:
+                        attr = inst.value.attr
+                        if attr == 'empty':
+                            msg = ("The memory allocation derived from the "
+                                "instruction at %s is hoisted out of the "
+                                "parallel loop labelled #%s (it will be "
+                                "performed before the loop is executed and "
+                                "reused inside the loop):")
+                            loc = inst.loc
+                            print_wrapped(msg % (loc, pf_id))
+                            try:
+                                path = os.path.relpath(loc.filename)
+                            except ValueError:
+                                path = os.path.abspath(loc.filename)
+                            lines = linecache.getlines(path)
+                            if lines and loc.line:
+                                print_wrapped("   Allocation:: " + lines[0 if loc.line < 2 else loc.line - 1].strip())
+                            print_wrapped("    - numpy.empty() is used for the allocation.\n")
+                            found = True
+                    except (KeyError, AttributeError):
+                        pass
+        if not found:
+            print_wrapped('No allocation hoisting found')
+
+    def instruction_hoist(self):
+        print("")
+        print('Instruction hoisting:')
+        hoist_info_printed = False
+        if self.hoist_info:
+            for pf_id, data in self.hoist_info.items():
+                hoisted = data.get('hoisted', None)
+                not_hoisted = data.get('not_hoisted', None)
+                if not hoisted and not not_hoisted:
+                    print("loop #%s has nothing to hoist." % pf_id)
+                    continue
+
+                print("loop #%s:" % pf_id)
+                if hoisted:
+                    print("  Has the following hoisted:")
+                    [print("    %s" % y) for y in hoisted]
+                    hoist_info_printed = True
+                if not_hoisted:
+                    print("  Failed to hoist the following:")
+                    [print("    %s: %s" % (y, x)) for x, y in not_hoisted]
+                    hoist_info_printed = True
+        if not hoist_info_printed:
+            print_wrapped('No instruction hoisting found')
+        print_wrapped(80 * '-')
+
+    def dump(self, level=1):
+        if not self.has_setup:
+            raise RuntimeError("self.setup has not been called")
+        name = self.func_ir.func_id.func_qualname
+        line = self.func_ir.loc
+        if self.internal_name in name:
+            purpose_str = 'Internal parallel functions '
+            purpose = 'internal'
+        else:
+            purpose_str = ' Function %s, %s ' % (name, line)
+            purpose = 'user'
+
+        print_loop_search = False
+        print_source_listing = False
+        print_fusion_search = False
+        print_fusion_summary = False
+        print_loopnest_rewrite = False
+        print_pre_optimised = False
+        print_post_optimised = False
+        print_allocation_hoist = False
+        print_instruction_hoist = False
+        print_internal = False
+
+        # each level switches on progressively more output
+        if level in (1, 2, 3, 4):
+            print_source_listing = True
+            print_post_optimised = True
+        else:
+            raise ValueError("Report level unknown, should be one of 1, 2, 3, 4")
+
+        if level in (2, 3, 4):
+            print_pre_optimised = True
+
+        if level in (3, 4):
+            print_allocation_hoist = True
+
+        if level == 3:
+            print_fusion_summary = True
+            print_loopnest_rewrite = True
+
+        if level == 4:
+            print_fusion_search = True
+            print_instruction_hoist = True
+            print_internal = True
+
+        if purpose == 'internal' and not print_internal:
+            return
+
+        print_wrapped('\n ')
+        print_wrapped(_termwidth * "=")
+        print_wrapped((" Parallel Accelerator Optimizing: %s " % purpose_str).center(_termwidth, '='))
+        print_wrapped(_termwidth * "=")
+        print_wrapped("")
+
+#----------- search section
+        if print_loop_search:
+            print_wrapped('Looking for parallel loops'.center(_termwidth, '-'))
+        parfors_simple = self.get_parfors_simple(print_loop_search)
+
+        count = self.count_parfors()
+        if print_loop_search:
+            print_wrapped("\nFound %s parallel loops." % count)
+            print_wrapped('-' * _termwidth)
+
+#----------- augmented source section
+        filename = self.func_ir.loc.filename
+        try:
+            # Try to get a relative path
+            # ipython/jupyter input just returns as filename
+            path = os.path.relpath(filename)
+        except ValueError:
+            # Fallback to absolute path if error occurred in getting the
+            # relative path.
+            # This may happen on windows if the drive is different
+            path = os.path.abspath(filename)
+
+        if print_source_listing:
+            self.source_listing(parfors_simple, purpose_str)
+
+#---------- these are used a lot here on in
+        sword = '+--'
+        parfors = self.get_parfors() # this is the mutated parfors
+        parfor_ids = [x.id for x in parfors]
+        n_parfors = len(parfor_ids)
+
+#----------- loop fusion section
+        if print_fusion_search or print_fusion_summary:
+            if not sequential_parfor_lowering:
+                print_wrapped(' Fusing loops '.center(_termwidth, '-'))
+                msg = ("Attempting fusion of parallel loops (combines loops "
+                        "with similar properties)...\n")
+                print_wrapped(msg)
+            else:
+                msg = "Performing sequential lowering of loops...\n"
+                print_wrapped(msg)
+                print_wrapped(_termwidth * '-')
+        # if there are some parfors, print information about them!
+        if n_parfors > -1:
+            def dump_graph_indented(a, root_msg, node_msg):
+                fac = len(sword)
+                def print_graph(adj, roots):
+                    def print_g(adj, root, depth):
+                        for k in adj[root]:
+                            print_wrapped(fac * depth * ' ' + '%s%s %s' % (sword, k, node_msg))
+                            if adj[k] != []:
+                                print_g(adj, k, depth + 1)
+                    for r in roots:
+                        print_wrapped('%s%s %s' % (sword, r, root_msg))
+                        print_g(l, r, 1)
+                        print_wrapped("")
+                l, roots = self.compute_graph_info(a)
+                print_graph(l, roots)
+
+            if print_fusion_search:
+                for report in self.fusion_reports:
+                    l1, l2, msg = report
+                    print_wrapped("  Trying to fuse loops #%s and #%s:" % (l1, l2))
+                    print_wrapped("    %s" % msg)
+
+            if self.fusion_info != {}:
+                if print_fusion_summary:
+                    print_wrapped("\n \nFused loop summary:\n")
+
+                    dump_graph_indented(self.fusion_info, 'has the following loops fused into it:', '(fused)')
+
+            if print_fusion_summary:
+                if self.fusion_enabled:
+                    after_fusion = "Following the attempted fusion of parallel for-loops"
+                else:
+                    after_fusion = "With fusion disabled"
+
+                print_wrapped(('\n{} there are {} parallel for-loop(s) (originating from loops labelled: {}).').format(
+                        after_fusion, n_parfors, ', '.join(['#%s' % x for x in parfor_ids])))
+                print_wrapped(_termwidth * '-')
+                print_wrapped("")
+
+#----------- loop nest section
+            if print_loopnest_rewrite:
+                if self.nested_fusion_info != {}:
+                    print_wrapped((" Optimising loop nests ").center(_termwidth, '-'))
+                    print_wrapped("Attempting loop nest rewrites (optimising for the largest parallel loops)...\n ")
+                    root_msg = 'is a parallel loop'
+                    node_msg = '--> rewritten as a serial loop'
+                    dump_graph_indented(self.nested_fusion_info, root_msg, node_msg)
+                    print_wrapped(_termwidth * '-')
+                    print_wrapped("")
+
+#---------- compute various properties and orderings in the data for subsequent use
+            all_lines = self.get_all_lines(parfors_simple)
+
+            if print_pre_optimised:
+                print(' Before Optimisation '.center(_termwidth,'-'))
+                self.print_unoptimised(all_lines)
+                print(_termwidth * '-')
+
+            if print_post_optimised:
+                print(' After Optimisation '.center(_termwidth,'-'))
+                self.print_optimised(all_lines)
+                print(_termwidth * '-')
+            print_wrapped("")
+            print_wrapped(_termwidth * '-')
+            print_wrapped("\n ")
+
+#----------- LICM section
+            if print_allocation_hoist or print_instruction_hoist:
+                print_wrapped('Loop invariant code motion'.center(80, '-'))
+
+            if print_allocation_hoist:
+                self.allocation_hoist()
+
+            if print_instruction_hoist:
+                self.instruction_hoist()
+
+        else: # there are no parfors
+            print_wrapped('Function %s, %s, has no parallel for-loops.'.format(name, line))
+
+    def __str__(self):
+        r  = "ParforDiagnostics:\n"
+        r += repr(self.replaced_fns)
+        return r
+
+    def __repr__(self):
+        r  = "ParforDiagnostics"
+        return r
+
+
+class PreParforPass(object):
+    """Preprocessing for the Parfor pass. It mostly inlines parallel
+    implementations of numpy functions if available.
+    """
+    def __init__(self, func_ir, typemap, calltypes, typingctx, targetctx,
+                 options, swapped={}, replace_functions_map=None):
+        self.func_ir = func_ir
+        self.typemap = typemap
+        self.calltypes = calltypes
+        self.typingctx = typingctx
+        self.targetctx = targetctx
+        self.options = options
+        # diagnostics
+        self.swapped = swapped
+        if replace_functions_map is None:
+            replace_functions_map = swap_functions_map
+        self.replace_functions_map = replace_functions_map
+        self.stats = {
+            'replaced_func': 0,
+            'replaced_dtype': 0,
+        }
+
+    def run(self):
+        """Run pre-parfor processing pass.
+        """
+        # e.g. convert A.sum() to np.sum(A) for easier match and optimization
+        canonicalize_array_math(self.func_ir, self.typemap,
+                                self.calltypes, self.typingctx)
+        if self.options.numpy:
+            self._replace_parallel_functions(self.func_ir.blocks)
+        self.func_ir.blocks = simplify_CFG(self.func_ir.blocks)
+
+    def _replace_parallel_functions(self, blocks):
+        """
+        Replace functions with their parallel implementation in
+        replace_functions_map if available.
+        The implementation code is inlined to enable more optimization.
+        """
+        swapped = self.swapped
+        from numba.core.inline_closurecall import inline_closure_call
+        work_list = list(blocks.items())
+        while work_list:
+            label, block = work_list.pop()
+            for i, instr in enumerate(block.body):
+                if isinstance(instr, ir.Assign):
+                    lhs = instr.target
+                    lhs_typ = self.typemap[lhs.name]
+                    expr = instr.value
+                    if isinstance(expr, ir.Expr) and expr.op == 'call':
+                        # Try and inline known calls with their parallel implementations
+                        def replace_func():
+                            func_def = get_definition(self.func_ir, expr.func)
+                            callname = find_callname(self.func_ir, expr)
+                            repl_func = self.replace_functions_map.get(callname, None)
+                            # Handle method on array type
+                            if (repl_func is None and
+                                len(callname) == 2 and
+                                isinstance(callname[1], ir.Var) and
+                                isinstance(self.typemap[callname[1].name],
+                                           types.npytypes.Array)):
+                                repl_func = replace_functions_ndarray.get(callname[0], None)
+                                if repl_func is not None:
+                                    # Add the array that the method is on to the arg list.
+                                    expr.args.insert(0, callname[1])
+
+                            require(repl_func is not None)
+                            typs = tuple(self.typemap[x.name] for x in expr.args)
+                            try:
+                                new_func =  repl_func(lhs_typ, *typs)
+                            except:
+                                new_func = None
+                            require(new_func is not None)
+                            g = copy.copy(self.func_ir.func_id.func.__globals__)
+                            g['numba'] = numba
+                            g['np'] = numpy
+                            g['math'] = math
+                            # if the function being inlined has a function
+                            # checking the inputs, find it and add it to globals
+                            check = replace_functions_checkers_map.get(callname,
+                                                                       None)
+                            if check is not None:
+                                g[check.name] = check.func
+                            # inline the parallel implementation
+                            new_blocks, _ = inline_closure_call(self.func_ir, g,
+                                            block, i, new_func, self.typingctx, self.targetctx,
+                                            typs, self.typemap, self.calltypes, work_list)
+                            call_table = get_call_table(new_blocks, topological_ordering=False)
+
+                            # find the prange in the new blocks and record it for use in diagnostics
+                            for call in call_table:
+                                for k, v in call.items():
+                                    if v[0] == 'internal_prange':
+                                        swapped[k] = [callname, repl_func.__name__, func_def, block.body[i].loc]
+                                        break
+                            return True
+                        if guard(replace_func):
+                            self.stats['replaced_func'] += 1
+                            break
+                    elif (isinstance(expr, ir.Expr) and expr.op == 'getattr' and
+                          expr.attr == 'dtype'):
+                        # Replace getattr call "A.dtype" with numpy.dtype(<actual type>).
+                        # This helps remove superfluous dependencies from parfor.
+                        typ = self.typemap[expr.value.name]
+                        if isinstance(typ, types.npytypes.Array):
+                            # Convert A.dtype to four statements.
+                            # 1) Get numpy global.
+                            # 2) Create var for known type of array as string
+                            #    constant. e.g. 'float64'
+                            # 3) Get dtype function from numpy module.
+                            # 4) Create var for numpy.dtype(var from #2).
+
+                            # Create var for numpy module.
+                            dtype = typ.dtype
+                            scope = block.scope
+                            loc = instr.loc
+                            g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc)
+                            self.typemap[g_np_var.name] = types.misc.Module(numpy)
+                            g_np = ir.Global('np', numpy, loc)
+                            g_np_assign = ir.Assign(g_np, g_np_var, loc)
+
+                            # Create var for the inferred type of the array
+                            # e.g., 'float64'
+                            dtype_str = str(dtype)
+                            if dtype_str == 'bool':
+                                dtype_str = 'bool_'
+                            typ_var = ir.Var(
+                                scope, mk_unique_var("$np_typ_var"), loc)
+                            self.typemap[typ_var.name] = types.StringLiteral(
+                                dtype_str)
+                            typ_var_assign = ir.Assign(
+                                ir.Const(dtype_str, loc), typ_var, loc)
+
+                            # Get the dtype function from the numpy module.
+                            dtype_attr_var = ir.Var(scope, mk_unique_var("$dtype_attr_var"), loc)
+                            temp = find_template(numpy.dtype)
+                            tfunc = numba.core.types.Function(temp)
+                            tfunc.get_call_type(self.typingctx, (self.typemap[typ_var.name],), {})
+                            self.typemap[dtype_attr_var.name] = types.functions.Function(temp)
+                            dtype_attr_getattr = ir.Expr.getattr(g_np_var, 'dtype', loc)
+                            dtype_attr_assign = ir.Assign(dtype_attr_getattr, dtype_attr_var, loc)
+
+                            # Call numpy.dtype on the statically coded type two steps above.
+                            dtype_var = ir.Var(scope, mk_unique_var("$dtype_var"), loc)
+                            self.typemap[dtype_var.name] = types.npytypes.DType(dtype)
+                            dtype_getattr = ir.Expr.call(dtype_attr_var, [typ_var], (), loc)
+                            dtype_assign = ir.Assign(dtype_getattr, dtype_var, loc)
+                            self.calltypes[dtype_getattr] = signature(
+                                self.typemap[dtype_var.name], self.typemap[typ_var.name])
+
+                            # The original A.dtype rhs is replaced with result of this call.
+                            instr.value = dtype_var
+                            # Add statements to body of the code.
+                            block.body.insert(0, dtype_assign)
+                            block.body.insert(0, dtype_attr_assign)
+                            block.body.insert(0, typ_var_assign)
+                            block.body.insert(0, g_np_assign)
+                            self.stats['replaced_dtype'] += 1
+                            break
+
+def find_template(op):
+    for ft in numba.core.typing.templates.builtin_registry.functions:
+        if ft.key == op:
+            return ft
+
+
+class ParforPassStates:
+    """This class encapsulates all internal states of the ParforPass.
+    """
+
+    def __init__(self, func_ir, typemap, calltypes, return_type, typingctx,
+                 targetctx,  options, flags, metadata,
+                 diagnostics=ParforDiagnostics()):
+        self.func_ir = func_ir
+        self.typemap = typemap
+        self.calltypes = calltypes
+        self.typingctx = typingctx
+        self.targetctx = targetctx
+        self.return_type = return_type
+        self.options = options
+        self.diagnostics = diagnostics
+        self.swapped_fns = diagnostics.replaced_fns
+        self.fusion_info = diagnostics.fusion_info
+        self.nested_fusion_info = diagnostics.nested_fusion_info
+
+        self.array_analysis = array_analysis.ArrayAnalysis(
+            self.typingctx, self.func_ir, self.typemap, self.calltypes,
+        )
+
+        ir_utils._the_max_label.update(max(func_ir.blocks.keys()))
+        self.flags = flags
+        self.metadata = metadata
+        if "parfors" not in metadata:
+            metadata["parfors"] = {}
+
+
+class ConvertInplaceBinop:
+    """Parfor subpass to convert setitem on Arrays
+    """
+    def __init__(self, pass_states):
+        """
+        Parameters
+        ----------
+        pass_states : ParforPassStates
+        """
+        self.pass_states = pass_states
+        self.rewritten = []
+
+    def run(self, blocks):
+        pass_states = self.pass_states
+        # convert expressions like A += ... where A is an array.
+        topo_order = find_topo_order(blocks)
+        # variables available in the program so far (used for finding map
+        # functions in array_expr lowering)
+        for label in topo_order:
+            block = blocks[label]
+            new_body = []
+            equiv_set = pass_states.array_analysis.get_equiv_set(label)
+            for instr in block.body:
+                if isinstance(instr, ir.Assign):
+                    lhs = instr.target
+                    expr = instr.value
+                    if isinstance(expr, ir.Expr) and expr.op == 'inplace_binop':
+                        loc = expr.loc
+                        target = expr.lhs
+                        value = expr.rhs
+                        target_typ = pass_states.typemap[target.name]
+                        value_typ = pass_states.typemap[value.name]
+                        # Handle A op= ...
+                        if isinstance(target_typ, types.npytypes.Array):
+                            # RHS is an array
+                            if isinstance(value_typ, types.npytypes.Array):
+                                new_instr = self._inplace_binop_to_parfor(equiv_set,
+                                        loc, expr.immutable_fn, target, value)
+                                self.rewritten.append(
+                                    dict(old=instr, new=new_instr,
+                                         reason='inplace_binop'),
+                                )
+                                instr = [new_instr, ir.Assign(target, lhs, loc)]
+                if isinstance(instr, list):
+                    new_body.extend(instr)
+                else:
+                    new_body.append(instr)
+            block.body = new_body
+
+    def _inplace_binop_to_parfor(self, equiv_set, loc, op, target, value):
+        """generate parfor from setitem node with a boolean or slice array indices.
+        The value can be either a scalar or an array variable, and if a boolean index
+        is used for the latter case, the same index must be used for the value too.
+        """
+        pass_states = self.pass_states
+        scope = target.scope
+        arr_typ = pass_states.typemap[target.name]
+        el_typ = arr_typ.dtype
+        init_block = ir.Block(scope, loc)
+        value_typ = pass_states.typemap[value.name]
+
+        size_vars = equiv_set.get_shape(target)
+
+        # generate loopnests and size variables from target correlations
+        index_vars, loopnests = _mk_parfor_loops(pass_states.typemap, size_vars, scope, loc)
+
+        # generate body
+        body_label = next_label()
+        body_block = ir.Block(scope, loc)
+        index_var, index_var_typ = _make_index_var(
+                pass_states.typemap, scope, index_vars, body_block)
+
+        # Read value.
+        value_var = ir.Var(scope, mk_unique_var("$value_var"), loc)
+        pass_states.typemap[value_var.name] = value_typ.dtype
+        getitem_call = ir.Expr.getitem(value, index_var, loc)
+        pass_states.calltypes[getitem_call] = signature(
+            value_typ.dtype, value_typ, index_var_typ)
+        body_block.body.append(ir.Assign(getitem_call, value_var, loc))
+
+        # Read target
+        target_var = ir.Var(scope, mk_unique_var("$target_var"), loc)
+        pass_states.typemap[target_var.name] = el_typ
+        getitem_call = ir.Expr.getitem(target, index_var, loc)
+        pass_states.calltypes[getitem_call] = signature(
+            el_typ, arr_typ, index_var_typ)
+        body_block.body.append(ir.Assign(getitem_call, target_var, loc))
+
+        # Create temp to hold result.
+        expr_out_var = ir.Var(scope, mk_unique_var("$expr_out_var"), loc)
+        pass_states.typemap[expr_out_var.name] = el_typ
+
+        # Create binop and assign result to temporary.
+        binop_expr = ir.Expr.binop(op, target_var, value_var, loc)
+        body_block.body.append(ir.Assign(binop_expr, expr_out_var, loc))
+        unified_type = self.pass_states.typingctx.unify_pairs(el_typ, value_typ.dtype)
+        pass_states.calltypes[binop_expr] = signature(
+            unified_type, unified_type, unified_type)
+
+        # Write to target
+        setitem_node = ir.SetItem(target, index_var, expr_out_var, loc)
+        pass_states.calltypes[setitem_node] = signature(
+            types.none, arr_typ, index_var_typ, el_typ)
+        body_block.body.append(setitem_node)
+
+        parfor = Parfor(loopnests, init_block, {}, loc, index_var, equiv_set,
+                        ('inplace_binop', ''), pass_states.flags)
+        parfor.loop_body = {body_label: body_block}
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("parfor from inplace_binop")
+            parfor.dump()
+        return parfor
+
+    def _type_getitem(self, args):
+        fnty = operator.getitem
+        return self.pass_states.typingctx.resolve_function_type(fnty, tuple(args), {})
+
+
+def get_index_var(x):
+    return x.index if isinstance(x, ir.SetItem) else x.index_var
+
+
+class ConvertSetItemPass:
+    """Parfor subpass to convert setitem on Arrays
+    """
+    def __init__(self, pass_states):
+        """
+        Parameters
+        ----------
+        pass_states : ParforPassStates
+        """
+        self.pass_states = pass_states
+        self.rewritten = []
+
+    def run(self, blocks):
+        pass_states = self.pass_states
+        # convert setitem expressions like A[C] = c or A[C] = B[C] to parfor,
+        # where C is a boolean array.
+        topo_order = find_topo_order(blocks)
+        # variables available in the program so far (used for finding map
+        # functions in array_expr lowering)
+        for label in topo_order:
+            block = blocks[label]
+            new_body = []
+            equiv_set = pass_states.array_analysis.get_equiv_set(label)
+            for instr in block.body:
+                if isinstance(instr, (ir.StaticSetItem, ir.SetItem)):
+                    loc = instr.loc
+                    target = instr.target
+                    index = get_index_var(instr)
+                    value = instr.value
+                    target_typ = pass_states.typemap[target.name]
+                    index_typ = pass_states.typemap[index.name]
+                    value_typ = pass_states.typemap[value.name]
+                    # Handle A[boolean_array] = <scalar or array>
+                    if isinstance(target_typ, types.npytypes.Array):
+                        if (isinstance(index_typ, types.npytypes.Array) and
+                            isinstance(index_typ.dtype, types.Boolean) and
+                            target_typ.ndim == index_typ.ndim):
+                            # RHS is a scalar number
+                            if isinstance(value_typ, types.Number):
+                                new_instr = self._setitem_to_parfor(equiv_set,
+                                        loc, target, index, value)
+                                self.rewritten.append(
+                                    dict(old=instr, new=new_instr,
+                                        reason='masked_assign_broadcast_scalar'),
+                                )
+                                instr = new_instr
+                            # RHS is an array
+                            elif isinstance(value_typ, types.npytypes.Array):
+                                val_def = guard(get_definition, pass_states.func_ir,
+                                                value.name)
+                                if (isinstance(val_def, ir.Expr) and
+                                    val_def.op == 'getitem' and
+                                    val_def.index.name == index.name):
+                                    new_instr = self._setitem_to_parfor(equiv_set,
+                                            loc, target, index, val_def.value)
+                                    self.rewritten.append(
+                                        dict(old=instr, new=new_instr,
+                                             reason='masked_assign_array'),
+                                    )
+                                    instr = new_instr
+                        else:
+                            # Handle A[:] = x
+                            shape = equiv_set.get_shape(instr)
+                            # Don't converted broadcasted setitems into parfors.
+                            if isinstance(index_typ, types.BaseTuple):
+                                # The sliced dims are those in the index that
+                                # are made of slices.  Count the numbers of slices
+                                # in the index tuple.
+                                sliced_dims = len(list(filter(
+                                    lambda x: isinstance(x, types.misc.SliceType),
+                                    index_typ.types)))
+                            elif isinstance(index_typ, types.misc.SliceType):
+                                # For singular indices there can be a bare slice
+                                # and if so there is one dimension being set.
+                                sliced_dims = 1
+                            else:
+                                sliced_dims = 0
+
+                            # Only create a parfor for this setitem if we know the
+                            # shape of the output and number of dimensions set is
+                            # equal to the number of dimensions on the right side.
+                            if (shape is not None and
+                                (not isinstance(value_typ, types.npytypes.Array) or
+                                 sliced_dims == value_typ.ndim)):
+                                new_instr = self._setitem_to_parfor(equiv_set,
+                                        loc, target, index, value, shape=shape)
+                                self.rewritten.append(
+                                        dict(old=instr, new=new_instr,
+                                             reason='slice'),
+                                )
+                                instr = new_instr
+                new_body.append(instr)
+            block.body = new_body
+
+    def _setitem_to_parfor(self, equiv_set, loc, target, index, value, shape=None):
+        """generate parfor from setitem node with a boolean or slice array indices.
+        The value can be either a scalar or an array variable, and if a boolean index
+        is used for the latter case, the same index must be used for the value too.
+        """
+        pass_states = self.pass_states
+        scope = target.scope
+        arr_typ = pass_states.typemap[target.name]
+        el_typ = arr_typ.dtype
+        index_typ = pass_states.typemap[index.name]
+        init_block = ir.Block(scope, loc)
+
+        if shape:
+            # Slice index is being used on the target array, we'll have to create
+            # a sub-array so that the target dimension matches the given shape.
+            assert(isinstance(index_typ, types.BaseTuple) or
+                   isinstance(index_typ, types.SliceType))
+            # setitem has a custom target shape
+            size_vars = shape
+            # create a new target array via getitem
+            subarr_var = ir.Var(scope, mk_unique_var("$subarr"), loc)
+            getitem_call = ir.Expr.getitem(target, index, loc)
+            subarr_typ = typing.arraydecl.get_array_index_type( arr_typ, index_typ).result
+            pass_states.typemap[subarr_var.name] = subarr_typ
+            pass_states.calltypes[getitem_call] = self._type_getitem((arr_typ, index_typ))
+            init_block.append(ir.Assign(getitem_call, subarr_var, loc))
+            target = subarr_var
+        else:
+            # Otherwise it is a boolean array that is used as index.
+            assert(isinstance(index_typ, types.ArrayCompatible))
+            size_vars = equiv_set.get_shape(target)
+            bool_typ = index_typ.dtype
+
+        # generate loopnests and size variables from lhs correlations
+        loopnests = []
+        index_vars = []
+        for size_var in size_vars:
+            index_var = ir.Var(scope, mk_unique_var("parfor_index"), loc)
+            index_vars.append(index_var)
+            pass_states.typemap[index_var.name] = types.uintp
+            loopnests.append(LoopNest(index_var, 0, size_var, 1))
+
+        # generate body
+        body_label = next_label()
+        body_block = ir.Block(scope, loc)
+        index_var, index_var_typ = _make_index_var(
+                pass_states.typemap, scope, index_vars, body_block)
+        parfor = Parfor(loopnests, init_block, {}, loc, index_var, equiv_set,
+                        ('setitem', ''), pass_states.flags)
+        if shape:
+            # slice subarray
+            parfor.loop_body = {body_label: body_block}
+            true_block = body_block
+            end_label = None
+        else:
+            # boolean mask
+            true_label = next_label()
+            true_block = ir.Block(scope, loc)
+            end_label = next_label()
+            end_block = ir.Block(scope, loc)
+            parfor.loop_body = {body_label: body_block,
+                                true_label: true_block,
+                                end_label:  end_block,
+                                }
+            mask_var = ir.Var(scope, mk_unique_var("$mask_var"), loc)
+            pass_states.typemap[mask_var.name] = bool_typ
+            mask_val = ir.Expr.getitem(index, index_var, loc)
+            body_block.body.extend([
+               ir.Assign(mask_val, mask_var, loc),
+               ir.Branch(mask_var, true_label, end_label, loc)
+            ])
+
+        value_typ = pass_states.typemap[value.name]
+        if isinstance(value_typ, types.npytypes.Array):
+            value_var = ir.Var(scope, mk_unique_var("$value_var"), loc)
+            pass_states.typemap[value_var.name] = value_typ.dtype
+            getitem_call = ir.Expr.getitem(value, index_var, loc)
+            pass_states.calltypes[getitem_call] = signature(
+                value_typ.dtype, value_typ, index_var_typ)
+            true_block.body.append(ir.Assign(getitem_call, value_var, loc))
+        else:
+            value_var = value
+        setitem_node = ir.SetItem(target, index_var, value_var, loc)
+        pass_states.calltypes[setitem_node] = signature(
+            types.none, pass_states.typemap[target.name], index_var_typ, el_typ)
+        true_block.body.append(setitem_node)
+        if end_label:
+            true_block.body.append(ir.Jump(end_label, loc))
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("parfor from setitem")
+            parfor.dump()
+        return parfor
+
+    def _type_getitem(self, args):
+        fnty = operator.getitem
+        return self.pass_states.typingctx.resolve_function_type(fnty, tuple(args), {})
+
+
+def _make_index_var(typemap, scope, index_vars, body_block, force_tuple=False):
+    """ When generating a SetItem call to an array in a parfor, the general
+    strategy is to generate a tuple if the array is more than 1 dimension.
+    If it is 1 dimensional then you can use a simple variable.  This routine
+    is also used when converting pndindex to parfor but pndindex requires a
+    tuple even if the iteration space is 1 dimensional.  The pndindex use of
+    this function will use force_tuple to make the output index a tuple even
+    if it is one dimensional.
+    """
+    ndims = len(index_vars)
+    loc = body_block.loc
+    if ndims > 1 or force_tuple:
+        tuple_var = ir.Var(scope, mk_unique_var(
+            "$parfor_index_tuple_var"), loc)
+        typemap[tuple_var.name] = types.containers.UniTuple(
+            types.uintp, ndims)
+        tuple_call = ir.Expr.build_tuple(list(index_vars), loc)
+        tuple_assign = ir.Assign(tuple_call, tuple_var, loc)
+        body_block.body.append(tuple_assign)
+        return tuple_var, types.containers.UniTuple(types.uintp, ndims)
+    elif ndims == 1:
+        return index_vars[0], types.uintp
+    else:
+        raise errors.UnsupportedRewriteError(
+            "Parfor does not handle arrays of dimension 0",
+            loc=loc,
+        )
+
+
+def _mk_parfor_loops(typemap, size_vars, scope, loc):
+    """
+    Create loop index variables and build LoopNest objects for a parfor.
+    """
+    loopnests = []
+    index_vars = []
+    for size_var in size_vars:
+        index_var = ir.Var(scope, mk_unique_var("parfor_index"), loc)
+        index_vars.append(index_var)
+        typemap[index_var.name] = types.uintp
+        loopnests.append(LoopNest(index_var, 0, size_var, 1))
+    return index_vars, loopnests
+
+class ConvertNumpyPass:
+    """
+    Convert supported Numpy functions, as well as arrayexpr nodes, to
+    parfor nodes.
+    """
+    def __init__(self, pass_states):
+        self.pass_states = pass_states
+        self.rewritten = []
+
+    def run(self, blocks):
+        pass_states = self.pass_states
+        topo_order = find_topo_order(blocks)
+        # variables available in the program so far (used for finding map
+        # functions in array_expr lowering)
+        avail_vars = []
+        for label in topo_order:
+            block = blocks[label]
+            new_body = []
+            equiv_set = pass_states.array_analysis.get_equiv_set(label)
+            for instr in block.body:
+                if isinstance(instr, ir.Assign):
+                    expr = instr.value
+                    lhs = instr.target
+                    lhs_typ = self.pass_states.typemap[lhs.name]
+                    if self._is_C_or_F_order(lhs_typ):
+                        if guard(self._is_supported_npycall, expr):
+                            new_instr = self._numpy_to_parfor(equiv_set, lhs, expr)
+                            if new_instr is not None:
+                                self.rewritten.append(dict(
+                                    old=instr,
+                                    new=new_instr,
+                                    reason='numpy_allocator',
+                                ))
+                                instr = new_instr
+                        elif isinstance(expr, ir.Expr) and expr.op == 'arrayexpr':
+                            new_instr = self._arrayexpr_to_parfor(
+                                equiv_set, lhs, expr, avail_vars)
+                            self.rewritten.append(dict(
+                                old=instr,
+                                new=new_instr,
+                                reason='arrayexpr',
+                            ))
+                            instr = new_instr
+                    avail_vars.append(lhs.name)
+                new_body.append(instr)
+            block.body = new_body
+
+    def _is_C_order(self, arr_name):
+        if isinstance(arr_name, types.npytypes.Array):
+            return arr_name.layout == 'C' and arr_name.ndim > 0
+        elif arr_name is str:
+            typ = self.pass_states.typemap[arr_name]
+            return (isinstance(typ, types.npytypes.Array) and
+                    typ.layout == 'C' and
+                    typ.ndim > 0)
+        else:
+            return False
+
+    def _is_C_or_F_order(self, arr_name):
+        if isinstance(arr_name, types.npytypes.Array):
+            return (arr_name.layout == 'C' or arr_name.layout == 'F') and arr_name.ndim > 0
+        elif arr_name is str:
+            typ = self.pass_states.typemap[arr_name]
+            return (isinstance(typ, types.npytypes.Array) and
+                    (typ.layout == 'C' or typ.layout == 'F') and
+                    typ.ndim > 0)
+        else:
+            return False
+
+    def _arrayexpr_to_parfor(self, equiv_set, lhs, arrayexpr, avail_vars):
+        """generate parfor from arrayexpr node, which is essentially a
+        map with recursive tree.
+        """
+        pass_states = self.pass_states
+        scope = lhs.scope
+        loc = lhs.loc
+        expr = arrayexpr.expr
+        arr_typ = pass_states.typemap[lhs.name]
+        el_typ = arr_typ.dtype
+
+        # generate loopnests and size variables from lhs correlations
+        size_vars = equiv_set.get_shape(lhs)
+        index_vars, loopnests = _mk_parfor_loops(pass_states.typemap, size_vars, scope, loc)
+
+        # generate init block and body
+        init_block = ir.Block(scope, loc)
+        init_block.body = mk_alloc(
+            pass_states.typingctx,
+            pass_states.typemap, pass_states.calltypes, lhs,
+            tuple(size_vars), el_typ, scope, loc,
+            pass_states.typemap[lhs.name])
+        body_label = next_label()
+        body_block = ir.Block(scope, loc)
+        expr_out_var = ir.Var(scope, mk_unique_var("$expr_out_var"), loc)
+        pass_states.typemap[expr_out_var.name] = el_typ
+
+        index_var, index_var_typ = _make_index_var(
+            pass_states.typemap, scope, index_vars, body_block)
+
+        body_block.body.extend(
+            _arrayexpr_tree_to_ir(
+                pass_states.func_ir,
+                pass_states.typingctx,
+                pass_states.typemap,
+                pass_states.calltypes,
+                equiv_set,
+                init_block,
+                expr_out_var,
+                expr,
+                index_var,
+                index_vars,
+                avail_vars))
+
+        pat = ('array expression {}'.format(repr_arrayexpr(arrayexpr.expr)),)
+
+        parfor = Parfor(loopnests, init_block, {}, loc, index_var, equiv_set, pat[0], pass_states.flags)
+
+        setitem_node = ir.SetItem(lhs, index_var, expr_out_var, loc)
+        pass_states.calltypes[setitem_node] = signature(
+            types.none, pass_states.typemap[lhs.name], index_var_typ, el_typ)
+        body_block.body.append(setitem_node)
+        parfor.loop_body = {body_label: body_block}
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("parfor from arrayexpr")
+            parfor.dump()
+        return parfor
+
+    def _is_supported_npycall(self, expr):
+        """check if we support parfor translation for
+        this Numpy call.
+        """
+        call_name, mod_name = find_callname(self.pass_states.func_ir, expr)
+        if not (isinstance(mod_name, str) and mod_name.startswith('numpy')):
+            return False
+        if call_name in ['zeros', 'ones']:
+            return True
+        if mod_name == 'numpy.random' and call_name in random_calls:
+            return True
+        # TODO: add more calls
+        return False
+
+    def _numpy_to_parfor(self, equiv_set, lhs, expr):
+        call_name, mod_name = find_callname(self.pass_states.func_ir, expr)
+        args = expr.args
+        kws = dict(expr.kws)
+        if call_name in ['zeros', 'ones'] or mod_name == 'numpy.random':
+            return self._numpy_map_to_parfor(equiv_set, call_name, lhs, args, kws, expr)
+        # return error if we couldn't handle it (avoid rewrite infinite loop)
+        raise errors.UnsupportedRewriteError(
+            f"parfor translation failed for {expr}", loc=expr.loc,
+        )
+
+    def _numpy_map_to_parfor(self, equiv_set, call_name, lhs, args, kws, expr):
+        """generate parfor from Numpy calls that are maps.
+        """
+        pass_states = self.pass_states
+        scope = lhs.scope
+        loc = lhs.loc
+        arr_typ = pass_states.typemap[lhs.name]
+        el_typ = arr_typ.dtype
+
+        # generate loopnests and size variables from lhs correlations
+        size_vars = equiv_set.get_shape(lhs)
+        if size_vars is None:
+            if config.DEBUG_ARRAY_OPT >= 1:
+                print("Could not convert numpy map to parfor, unknown size")
+            return None
+
+        index_vars, loopnests = _mk_parfor_loops(pass_states.typemap, size_vars, scope, loc)
+
+        # generate init block and body
+        init_block = ir.Block(scope, loc)
+        init_block.body = mk_alloc(
+            pass_states.typingctx,
+            pass_states.typemap, pass_states.calltypes, lhs,
+            tuple(size_vars), el_typ, scope, loc,
+            pass_states.typemap[lhs.name])
+        body_label = next_label()
+        body_block = ir.Block(scope, loc)
+        expr_out_var = ir.Var(scope, mk_unique_var("$expr_out_var"), loc)
+        pass_states.typemap[expr_out_var.name] = el_typ
+
+        index_var, index_var_typ = _make_index_var(
+            pass_states.typemap, scope, index_vars, body_block)
+
+        if call_name == 'zeros':
+            value = ir.Const(el_typ(0), loc)
+        elif call_name == 'ones':
+            value = ir.Const(el_typ(1), loc)
+        elif call_name in random_calls:
+            # remove size arg to reuse the call expr for single value
+            _remove_size_arg(call_name, expr)
+            # update expr type
+            new_arg_typs, new_kw_types = _get_call_arg_types(
+                expr, pass_states.typemap)
+            pass_states.calltypes.pop(expr)
+            pass_states.calltypes[expr] = pass_states.typemap[expr.func.name].get_call_type(
+                typing.Context(), new_arg_typs, new_kw_types)
+            value = expr
+        else:
+            raise NotImplementedError(
+                "Map of numpy.{} to parfor is not implemented".format(call_name))
+
+        value_assign = ir.Assign(value, expr_out_var, loc)
+        body_block.body.append(value_assign)
+
+        setitem_node = ir.SetItem(lhs, index_var, expr_out_var, loc)
+        pass_states.calltypes[setitem_node] = signature(
+            types.none, pass_states.typemap[lhs.name], index_var_typ, el_typ)
+        body_block.body.append(setitem_node)
+
+        parfor = Parfor(loopnests, init_block, {}, loc, index_var, equiv_set,
+                        ('{} function'.format(call_name,), 'NumPy mapping'),
+                        pass_states.flags)
+        parfor.loop_body = {body_label: body_block}
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("generated parfor for numpy map:")
+            parfor.dump()
+        return parfor
+
+
+class ConvertReducePass:
+    """
+    Find reduce() calls and convert them to parfors.
+    """
+    def __init__(self, pass_states):
+        self.pass_states = pass_states
+        self.rewritten = []
+
+    def run(self, blocks):
+        pass_states = self.pass_states
+
+        topo_order = find_topo_order(blocks)
+        for label in topo_order:
+            block = blocks[label]
+            new_body = []
+            equiv_set = pass_states.array_analysis.get_equiv_set(label)
+            for instr in block.body:
+                parfor = None
+                if isinstance(instr, ir.Assign):
+                    loc = instr.loc
+                    lhs = instr.target
+                    expr = instr.value
+                    callname = guard(find_callname, pass_states.func_ir, expr)
+                    if (callname == ('reduce', 'builtins')
+                        or callname == ('reduce', '_functools')):
+                        # reduce function with generic function
+                        parfor = guard(self._reduce_to_parfor, equiv_set, lhs,
+                                       expr.args, loc)
+                    if parfor:
+                        self.rewritten.append(dict(
+                            new=parfor,
+                            old=instr,
+                            reason='reduce',
+                        ))
+                        instr = parfor
+                new_body.append(instr)
+            block.body = new_body
+        return
+
+    def _reduce_to_parfor(self, equiv_set, lhs, args, loc):
+        """
+        Convert a reduce call to a parfor.
+        The call arguments should be (call_name, array, init_value).
+        """
+        pass_states = self.pass_states
+
+        scope = lhs.scope
+        call_name = args[0]
+        in_arr = args[1]
+        arr_def = get_definition(pass_states.func_ir, in_arr.name)
+
+        mask_var = None
+        mask_indices = None
+
+        # Search for array[boolean_mask]
+        mask_query_result = guard(_find_mask, pass_states.typemap, pass_states.func_ir, arr_def)
+        if mask_query_result:
+            in_arr, mask_var, mask_typ, mask_indices = mask_query_result
+
+        init_val = args[2]
+        size_vars = equiv_set.get_shape(in_arr if mask_indices is None else mask_var)
+        if size_vars is None:
+            return None
+
+        index_vars, loopnests = _mk_parfor_loops(pass_states.typemap, size_vars, scope, loc)
+        mask_index = index_vars
+        if mask_indices:
+            # the following is never tested
+            raise AssertionError("unreachable")
+            index_vars = tuple(x if x else index_vars[0] for x in mask_indices)
+        acc_var = lhs
+
+        # init block has to init the reduction variable
+        init_block = ir.Block(scope, loc)
+        init_block.body.append(ir.Assign(init_val, acc_var, loc))
+
+        # produce loop body
+        body_label = next_label()
+        index_var, loop_body = self._mk_reduction_body(call_name,
+                                scope, loc, index_vars, in_arr, acc_var)
+        if mask_indices:
+            # the following is never tested
+            raise AssertionError("unreachable")
+            index_var = mask_index[0]
+
+        if mask_var is not None:
+            true_label = min(loop_body.keys())
+            false_label = max(loop_body.keys())
+            body_block = ir.Block(scope, loc)
+            loop_body[body_label] = body_block
+            mask = ir.Var(scope, mk_unique_var("$mask_val"), loc)
+            pass_states.typemap[mask.name] = mask_typ
+            mask_val = ir.Expr.getitem(mask_var, index_var, loc)
+            body_block.body.extend([
+               ir.Assign(mask_val, mask, loc),
+               ir.Branch(mask, true_label, false_label, loc)
+            ])
+
+        parfor = Parfor(loopnests, init_block, loop_body, loc, index_var,
+                        equiv_set, ('{} function'.format(call_name),
+                                    'reduction'), pass_states.flags)
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("parfor from reduction")
+            parfor.dump()
+        return parfor
+
+    def _mk_reduction_body(self, call_name, scope, loc,
+                           index_vars, in_arr, acc_var):
+        """
+        Produce the body blocks for a reduction function indicated by call_name.
+        """
+        from numba.core.inline_closurecall import check_reduce_func
+
+        pass_states = self.pass_states
+        reduce_func = get_definition(pass_states.func_ir, call_name)
+        fcode = check_reduce_func(pass_states.func_ir, reduce_func)
+
+        arr_typ = pass_states.typemap[in_arr.name]
+        in_typ = arr_typ.dtype
+        body_block = ir.Block(scope, loc)
+        index_var, index_var_type = _make_index_var(
+            pass_states.typemap, scope, index_vars, body_block)
+
+        tmp_var = ir.Var(scope, mk_unique_var("$val"), loc)
+        pass_states.typemap[tmp_var.name] = in_typ
+        getitem_call = ir.Expr.getitem(in_arr, index_var, loc)
+        pass_states.calltypes[getitem_call] = signature(
+            in_typ, arr_typ, index_var_type)
+        body_block.append(ir.Assign(getitem_call, tmp_var, loc))
+
+        reduce_f_ir = compile_to_numba_ir(fcode,
+                                        pass_states.func_ir.func_id.func.__globals__,
+                                        pass_states.typingctx,
+                                        pass_states.targetctx,
+                                        (in_typ, in_typ),
+                                        pass_states.typemap,
+                                        pass_states.calltypes)
+        loop_body = reduce_f_ir.blocks
+        end_label = next_label()
+        end_block = ir.Block(scope, loc)
+        loop_body[end_label] = end_block
+        first_reduce_label = min(reduce_f_ir.blocks.keys())
+        first_reduce_block = reduce_f_ir.blocks[first_reduce_label]
+        body_block.body.extend(first_reduce_block.body)
+        first_reduce_block.body = body_block.body
+        replace_arg_nodes(first_reduce_block, [acc_var, tmp_var])
+        replace_returns(loop_body, acc_var, end_label)
+        return index_var, loop_body
+
+
+class ConvertLoopPass:
+    """Build Parfor nodes from prange loops.
+    """
+    def __init__(self, pass_states):
+        self.pass_states = pass_states
+        self.rewritten = []
+
+    def run(self, blocks):
+        pass_states = self.pass_states
+
+        call_table, _ = get_call_table(blocks)
+        cfg = compute_cfg_from_blocks(blocks)
+        usedefs = compute_use_defs(blocks)
+        live_map = compute_live_map(cfg, blocks, usedefs.usemap, usedefs.defmap)
+        loops = cfg.loops()
+        sized_loops = [(loops[k], len(loops[k].body)) for k in loops.keys()]
+        moved_blocks = []
+        # We go over all loops, smaller loops first (inner first)
+        for loop, s in sorted(sized_loops, key=lambda tup: tup[1]):
+            if len(loop.entries) != 1 or len(loop.exits) != 1:
+                continue
+            entry = list(loop.entries)[0]
+            for inst in blocks[entry].body:
+                # if prange or pndindex call
+                if (isinstance(inst, ir.Assign)
+                        and isinstance(inst.value, ir.Expr)
+                        and inst.value.op == 'call'
+                        and self._is_parallel_loop(inst.value.func.name, call_table)):
+                    # Here we've found a parallel loop, either prange or pndindex.
+                    # We create a parfor from this loop and then overwrite the contents
+                    # of the original loop header block to contain this parfor and then
+                    # a jump to the original loop exit block.  Other blocks in the
+                    # original loop are discarded.
+                    body_labels = [ l for l in loop.body if
+                                    l in blocks and l != loop.header ]
+                    args = inst.value.args
+                    loop_kind, loop_replacing = self._get_loop_kind(inst.value.func.name,
+                                                                    call_table)
+                    # Get the body of the header of the loops minus the branch terminator
+                    # The general approach is to prepend the header block to the first
+                    # body block and then let dead code removal handle removing unneeded
+                    # statements.  Not all statements in the header block are unnecessary.
+                    header_body = blocks[loop.header].body[:-1]
+                    # find loop index variable (pair_first in header block)
+                    loop_index = None
+                    for hbi, stmt in enumerate(header_body):
+                        if (isinstance(stmt, ir.Assign)
+                                and isinstance(stmt.value, ir.Expr)
+                                and stmt.value.op == 'pair_first'):
+                            loop_index = stmt.target.name
+                            li_index = hbi
+                            break
+                    assert(loop_index is not None)
+                    # Remove pair_first from header.
+                    # We have to remove the pair_first by hand since it causes problems
+                    # for some code below if we don't.
+                    header_body = header_body[:li_index] + header_body[li_index+1:]
+
+                    # loop_index may be assigned to other vars
+                    # get header copies to find all of them
+                    cps, _ = get_block_copies({0: blocks[loop.header]},
+                                              pass_states.typemap)
+                    cps = cps[0]
+                    loop_index_vars = set(t for t, v in cps if v == loop_index)
+                    loop_index_vars.add(loop_index)
+
+                    scope = blocks[entry].scope
+                    loc = inst.loc
+                    equiv_set = pass_states.array_analysis.get_equiv_set(loop.header)
+                    init_block = ir.Block(scope, loc)
+                    init_block.body = self._get_prange_init_block(blocks[entry],
+                                                            call_table, args)
+                    loop_body = {l: blocks[l] for l in body_labels}
+                    # Add an empty block to the end of loop body
+                    end_label = next_label()
+                    loop_body[end_label] = ir.Block(scope, loc)
+
+                    # Detect races in the prange.
+                    # Races are defs in the parfor body that are live at the exit block.
+                    bodydefs = set()
+                    for bl in body_labels:
+                        bodydefs = bodydefs.union(usedefs.defmap[bl])
+                    exit_lives = set()
+                    for bl in loop.exits:
+                        exit_lives = exit_lives.union(live_map[bl])
+                    races = bodydefs.intersection(exit_lives)
+                    # It is possible for the result of an ir.Global to be flagged
+                    # as a race if it is defined in this Parfor and then used in
+                    # a subsequent Parfor.  push_call_vars() in the Parfor pass
+                    # copies such ir.Global nodes into the Parfors in which they
+                    # are used so no need to treat things of type Module as a race.
+                    races = races.intersection({x for x in races
+                              if not isinstance(pass_states.typemap[x], types.misc.Module)})
+
+                    # replace jumps to header block with the end block
+                    for l in body_labels:
+                        last_inst = loop_body[l].body[-1]
+                        if (isinstance(last_inst, ir.Jump) and
+                            last_inst.target == loop.header):
+                            last_inst.target = end_label
+
+                    def find_indexed_arrays():
+                        """find expressions that involve getitem using the
+                        index variable. Return both the arrays and expressions.
+                        """
+                        indices = copy.copy(loop_index_vars)
+                        for block in loop_body.values():
+                            for inst in block.find_insts(ir.Assign):
+                                if (isinstance(inst.value, ir.Var) and
+                                    inst.value.name in indices):
+                                    indices.add(inst.target.name)
+                        arrs = []
+                        exprs = []
+                        for block in loop_body.values():
+                            for inst in block.body:
+                                lv = set(x.name for x in inst.list_vars())
+                                if lv & indices:
+                                    if lv.issubset(indices):
+                                        continue
+                                    require(isinstance(inst, ir.Assign))
+                                    expr = inst.value
+                                    require(isinstance(expr, ir.Expr) and
+                                       expr.op in ['getitem', 'static_getitem'])
+                                    arrs.append(expr.value.name)
+                                    exprs.append(expr)
+                        return arrs, exprs
+
+                    mask_var = None
+                    mask_indices = None
+                    def find_mask_from_size(size_var):
+                        """Find the case where size_var is defined by A[M].shape,
+                        where M is a boolean array.
+                        """
+                        size_def = get_definition(pass_states.func_ir, size_var)
+                        require(size_def and isinstance(size_def, ir.Expr) and
+                                size_def.op == 'getattr' and size_def.attr == 'shape')
+                        arr_var = size_def.value
+                        live_vars = set.union(*[live_map[l] for l in loop.exits])
+                        index_arrs, index_exprs = find_indexed_arrays()
+                        require([arr_var.name] == list(index_arrs))
+                        # input array has to be dead after loop
+                        require(arr_var.name not in live_vars)
+                        # loop for arr's definition, where size = arr.shape
+                        arr_def = get_definition(pass_states.func_ir, size_def.value)
+                        result = _find_mask(pass_states.typemap, pass_states.func_ir, arr_def)
+
+                        # The following is never tested.
+                        raise AssertionError("unreachable")
+                        # Found the mask.
+                        # Replace B[i] with A[i], where B = A[M]
+                        for expr in index_exprs:
+                            expr.value = result[0]
+                        return result
+
+                    # pndindex and prange are provably positive except when
+                    # user provides negative start to prange()
+                    unsigned_index = True
+                    # TODO: support array mask optimization for prange
+                    # TODO: refactor and simplify array mask optimization
+                    if loop_kind == 'pndindex':
+                        assert(equiv_set.has_shape(args[0]))
+                        # see if input array to pndindex is output of array
+                        # mask like B = A[M]
+                        result = guard(find_mask_from_size, args[0])
+                        if result:
+                            in_arr, mask_var, mask_typ, mask_indices = result
+                        else:
+                            in_arr = args[0]
+                        assert(isinstance(in_arr, ir.Var))
+                        in_arr_typ = pass_states.typemap[in_arr.name]
+                        if isinstance(in_arr_typ, types.Integer):
+                            index_var = ir.Var(scope, mk_unique_var("parfor_index"), loc)
+                            pass_states.typemap[index_var.name] = types.uintp
+                            loops = [LoopNest(index_var, 0, in_arr, 1)]
+                            index_vars = [index_var]
+                        else:
+                            size_vars = equiv_set.get_shape(in_arr
+                                          if mask_indices is None else mask_var)
+                            index_vars, loops = _mk_parfor_loops(
+                                pass_states.typemap, size_vars, scope, loc,
+                            )
+                        assert(len(loops) > 0)
+                        orig_index = index_vars
+                        if mask_indices:
+                            # replace mask indices if required;
+                            # integer indices of original array should be used
+                            # instead of parfor indices
+                            index_vars = tuple(x if x else index_vars[0]
+                                               for x in mask_indices)
+                        first_body_block = loop_body[min(loop_body.keys())]
+                        body_block = ir.Block(scope, loc)
+                        index_var, index_var_typ = _make_index_var(
+                            pass_states.typemap, scope, index_vars, body_block,
+                            force_tuple=True
+                        )
+                        body = body_block.body + first_body_block.body
+                        first_body_block.body = body
+                        if mask_indices:
+                            orig_index_var = orig_index[0]
+                        else:
+                            orig_index_var = index_var
+
+                        # if masked array optimization is being applied, create
+                        # the branch for array selection
+                        if mask_var is not None:
+                            # The following code are not tested
+                            raise AssertionError("unreachable")
+                            body_label = next_label()
+                            # loop_body needs new labels greater than body_label
+                            loop_body = add_offset_to_labels(loop_body,
+                                            body_label - min(loop_body.keys()) + 1)
+                            labels = loop_body.keys()
+                            true_label = min(labels)
+                            false_label = max(labels)
+                            body_block = ir.Block(scope, loc)
+                            loop_body[body_label] = body_block
+                            mask = ir.Var(scope, mk_unique_var("$mask_val"), loc)
+                            pass_states.typemap[mask.name] = mask_typ
+                            mask_val = ir.Expr.getitem(mask_var, orig_index_var, loc)
+                            body_block.body.extend([
+                               ir.Assign(mask_val, mask, loc),
+                               ir.Branch(mask, true_label, false_label, loc)
+                            ])
+                    else: # prange
+                        start = 0
+                        step = 1
+                        size_var = args[0]
+                        if len(args) == 2:
+                            start = args[0]
+                            size_var = args[1]
+                        if len(args) == 3:
+                            start = args[0]
+                            size_var = args[1]
+                            try:
+                                step = pass_states.func_ir.get_definition(args[2])
+                            except KeyError:
+                                raise errors.UnsupportedRewriteError(
+                                    "Only known step size is supported for prange",
+                                    loc=inst.loc,
+                                )
+                            if not isinstance(step, ir.Const):
+                                raise errors.UnsupportedRewriteError(
+                                    "Only constant step size is supported for prange",
+                                    loc=inst.loc,
+                                )
+                            step = step.value
+                            if step != 1:
+                                raise errors.UnsupportedRewriteError(
+                                    "Only constant step size of 1 is supported for prange",
+                                    loc=inst.loc,
+                                )
+                        index_var = ir.Var(scope, mk_unique_var("parfor_index"), loc)
+                        # assume user-provided start to prange can be negative
+                        # this is the only case parfor can have negative index
+                        if isinstance(start, int) and start >= 0:
+                            index_var_typ = types.uintp
+                        else:
+                            index_var_typ = types.intp
+                            unsigned_index = False
+                        loops = [LoopNest(index_var, start, size_var, step)]
+                        pass_states.typemap[index_var.name] = index_var_typ
+
+                        # We can't just drop the header block since there can be things
+                        # in there other than the prange looping infrastructure.
+                        # So we just add the header to the first loop body block (minus the
+                        # branch) and let dead code elimination remove the unnecessary parts.
+                        first_body_label = min(loop_body.keys())
+                        loop_body[first_body_label].body = header_body + loop_body[first_body_label].body
+
+                    index_var_map = {v: index_var for v in loop_index_vars}
+                    replace_vars(loop_body, index_var_map)
+                    if unsigned_index:
+                        # need to replace signed array access indices to enable
+                        # optimizations (see #2846)
+                        self._replace_loop_access_indices(
+                            loop_body, loop_index_vars, index_var)
+                    parfor = Parfor(loops, init_block, loop_body, loc,
+                                    orig_index_var if mask_indices else index_var,
+                                    equiv_set,
+                                    ("prange", loop_kind, loop_replacing),
+                                    pass_states.flags, races=races)
+
+                    blocks[loop.header].body = [parfor]
+                    # We have to insert the header_body after the parfor because in
+                    # a Numba loop this will be executed one more times before the
+                    # branch and may contain instructions such as variable renamings
+                    # that are relied upon later.
+                    blocks[loop.header].body.extend(header_body)
+                    blocks[loop.header].body.append(ir.Jump(list(loop.exits)[0], loc))
+                    self.rewritten.append(dict(
+                        old_loop=loop,
+                        new=parfor,
+                        reason='loop',
+                    ))
+                    # remove loop blocks from top level dict
+                    for l in body_labels:
+                        if l != loop.header:
+                            blocks.pop(l)
+                    if config.DEBUG_ARRAY_OPT >= 1:
+                        print("parfor from loop")
+                        parfor.dump()
+
+    def _is_parallel_loop(self, func_var, call_table):
+        # prange can be either getattr (numba.prange) or global (prange)
+        if func_var not in call_table:
+            return False
+        call = call_table[func_var]
+        return len(call) > 0 and (call[0] == 'prange' or call[0] == prange
+                or call[0] == 'internal_prange' or call[0] == internal_prange
+                or call[0] == 'pndindex' or call[0] == pndindex)
+
+    def _get_loop_kind(self, func_var, call_table):
+        """see if prange is user prange or internal"""
+        pass_states = self.pass_states
+        # prange can be either getattr (numba.prange) or global (prange)
+        assert func_var in call_table
+        call = call_table[func_var]
+        assert len(call) > 0
+        kind = 'user', ''
+        if call[0] == 'internal_prange' or call[0] == internal_prange:
+            try:
+                kind = 'internal', (pass_states.swapped_fns[func_var][0], pass_states.swapped_fns[func_var][-1])
+            except KeyError:
+                # FIXME: Fix this issue... the code didn't manage to trace the
+                # swapout for func_var so set the kind as internal so that the
+                # transform can occur, it's just not tracked
+                kind = 'internal', ('', '')
+        elif call[0] == 'pndindex' or call[0] == pndindex:
+            kind = 'pndindex', ''
+        return kind
+
+    def _get_prange_init_block(self, entry_block, call_table, prange_args):
+        """
+        If there is init_prange, find the code between init_prange and prange
+        calls. Remove the code from entry_block and return it.
+        """
+        init_call_ind = -1
+        prange_call_ind = -1
+        init_body = []
+        for i, inst in enumerate(entry_block.body):
+            # if init_prange call
+            if (isinstance(inst, ir.Assign) and isinstance(inst.value, ir.Expr)
+                    and inst.value.op == 'call'
+                    and self._is_prange_init(inst.value.func.name, call_table)):
+                init_call_ind = i
+            if (isinstance(inst, ir.Assign) and isinstance(inst.value, ir.Expr)
+                    and inst.value.op == 'call'
+                    and self._is_parallel_loop(inst.value.func.name, call_table)):
+                prange_call_ind = i
+        if init_call_ind != -1 and prange_call_ind != -1:
+            # we save instructions that are used to calculate prange call args
+            # in the entry block. The rest go to parfor init_block
+            arg_related_vars = {v.name for v in prange_args}
+            saved_nodes = []
+            for i in reversed(range(init_call_ind+1, prange_call_ind)):
+                inst = entry_block.body[i]
+                inst_vars = {v.name for v in inst.list_vars()}
+                if arg_related_vars & inst_vars:
+                    arg_related_vars |= inst_vars
+                    saved_nodes.append(inst)
+                else:
+                    init_body.append(inst)
+
+            init_body.reverse()
+            saved_nodes.reverse()
+            entry_block.body = (entry_block.body[:init_call_ind]
+                        + saved_nodes + entry_block.body[prange_call_ind+1:])
+
+        return init_body
+
+    def _is_prange_init(self, func_var, call_table):
+        if func_var not in call_table:
+            return False
+        call = call_table[func_var]
+        return len(call) > 0 and (call[0] == 'init_prange' or call[0] == init_prange)
+
+    def _replace_loop_access_indices(self, loop_body, index_set, new_index):
+        """
+        Replace array access indices in a loop body with a new index.
+        index_set has all the variables that are equivalent to loop index.
+        """
+        # treat new index like others since replacing it with itself is ok
+        index_set.add(new_index.name)
+
+        with dummy_return_in_loop_body(loop_body):
+            labels = find_topo_order(loop_body)
+
+        first_label = labels[0]
+        added_indices = set()
+
+        # traverse loop body and replace indices in getitem/setitem with
+        # new_index if possible.
+        # also, find equivalent indices defined in first block.
+        for l in labels:
+            block = loop_body[l]
+            for stmt in block.body:
+                if (isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Var)):
+                    # the first block dominates others so we can use copies
+                    # of indices safely
+                    if (l == first_label and stmt.value.name in index_set
+                            and stmt.target.name not in index_set):
+                        index_set.add(stmt.target.name)
+                        added_indices.add(stmt.target.name)
+                    # make sure parallel index is not overwritten
+                    else:
+                        scope = block.scope
+
+                        def unver(name):
+                            from numba.core import errors
+                            try:
+                                return scope.get_exact(name).unversioned_name
+                            except errors.NotDefinedError:
+                                return name
+
+                        if unver(stmt.target.name) in map(unver, index_set) and unver(stmt.target.name) != unver(stmt.value.name):
+                            raise errors.UnsupportedRewriteError(
+                                "Overwrite of parallel loop index",
+                                loc=stmt.target.loc,
+                            )
+
+
+
+                if is_get_setitem(stmt):
+                    index = index_var_of_get_setitem(stmt)
+                    # statics can have none indices
+                    if index is None:
+                        continue
+                    ind_def = guard(get_definition, self.pass_states.func_ir,
+                                    index, lhs_only=True)
+                    if (index.name in index_set
+                            or (ind_def is not None
+                                and ind_def.name in index_set)):
+                        set_index_var_of_get_setitem(stmt, new_index)
+                    # corner case where one dimension of a multi-dim access
+                    # should be replaced
+                    guard(self._replace_multi_dim_ind, ind_def, index_set,
+                                                                     new_index)
+
+                if isinstance(stmt, Parfor):
+                    self._replace_loop_access_indices(stmt.loop_body, index_set, new_index)
+
+        # remove added indices for correct recursive parfor handling
+        index_set -= added_indices
+        return
+
+    def _replace_multi_dim_ind(self, ind_var, index_set, new_index):
+        """
+        replace individual indices in multi-dimensional access variable, which
+        is a build_tuple
+        """
+        pass_states = self.pass_states
+        require(ind_var is not None)
+        # check for Tuple instead of UniTuple since some dims could be slices
+        require(isinstance(pass_states.typemap[ind_var.name],
+                (types.Tuple, types.UniTuple)))
+        ind_def_node = get_definition(pass_states.func_ir, ind_var)
+        require(isinstance(ind_def_node, ir.Expr)
+                and ind_def_node.op == 'build_tuple')
+        ind_def_node.items = [new_index if v.name in index_set else v
+                              for v in ind_def_node.items]
+
+
+def _find_mask(typemap, func_ir, arr_def):
+    """check if an array is of B[...M...], where M is a
+    boolean array, and other indices (if available) are ints.
+    If found, return B, M, M's type, and a tuple representing mask indices.
+    Otherwise, raise GuardException.
+    """
+    require(isinstance(arr_def, ir.Expr) and arr_def.op == 'getitem')
+    value = arr_def.value
+    index = arr_def.index
+    value_typ = typemap[value.name]
+    index_typ = typemap[index.name]
+    ndim = value_typ.ndim
+    require(isinstance(value_typ, types.npytypes.Array))
+    if (isinstance(index_typ, types.npytypes.Array) and
+        isinstance(index_typ.dtype, types.Boolean) and
+        ndim == index_typ.ndim):
+        return value, index, index_typ.dtype, None
+    elif isinstance(index_typ, types.BaseTuple):
+        # Handle multi-dimension differently by requiring
+        # all indices to be constant except the one for mask.
+        seq, op = find_build_sequence(func_ir, index)
+        require(op == 'build_tuple' and len(seq) == ndim)
+        count_consts = 0
+        mask_indices = []
+        mask_var = None
+        for ind in seq:
+            index_typ = typemap[ind.name]
+            # Handle boolean mask
+            if (isinstance(index_typ, types.npytypes.Array) and
+                isinstance(index_typ.dtype, types.Boolean)):
+                mask_var = ind
+                mask_typ = index_typ.dtype
+                mask_indices.append(None)
+            # Handle integer array selector
+            elif (isinstance(index_typ, types.npytypes.Array) and
+                isinstance(index_typ.dtype, types.Integer)):
+                mask_var = ind
+                mask_typ = index_typ.dtype
+                mask_indices.append(None)
+            # Handle integer index
+            elif isinstance(index_typ, types.Integer):
+                count_consts += 1
+                mask_indices.append(ind)
+
+        require(mask_var and count_consts == ndim - 1)
+        return value, mask_var, mask_typ, mask_indices
+    raise GuardException
+
+
+class ParforPass(ParforPassStates):
+
+    """ParforPass class is responsible for converting NumPy
+    calls in Numba intermediate representation to Parfors, which
+    will lower into either sequential or parallel loops during lowering
+    stage.
+    """
+
+    def _pre_run(self):
+        # run array analysis, a pre-requisite for parfor translation
+        self.array_analysis.run(self.func_ir.blocks)
+        # NOTE: Prepare _the_max_label. See #6102
+        ir_utils._the_max_label.update(
+            ir_utils.find_max_label(self.func_ir.blocks))
+
+    def run(self):
+        """run parfor conversion pass: replace Numpy calls
+        with Parfors when possible and optimize the IR."""
+        self._pre_run()
+        # run stencil translation to parfor
+        if self.options.stencil:
+            stencil_pass = StencilPass(self.func_ir, self.typemap,
+                                       self.calltypes, self.array_analysis,
+                                       self.typingctx, self.targetctx,
+                                       self.flags)
+            stencil_pass.run()
+        if self.options.setitem:
+            ConvertSetItemPass(self).run(self.func_ir.blocks)
+        if self.options.numpy:
+            ConvertNumpyPass(self).run(self.func_ir.blocks)
+        if self.options.reduction:
+            ConvertReducePass(self).run(self.func_ir.blocks)
+        if self.options.prange:
+            ConvertLoopPass(self).run(self.func_ir.blocks)
+        if self.options.inplace_binop:
+            ConvertInplaceBinop(self).run(self.func_ir.blocks)
+
+        # setup diagnostics now parfors are found
+        self.diagnostics.setup(self.func_ir, self.options.fusion)
+
+        dprint_func_ir(self.func_ir, "after parfor pass")
+
+        # simplify CFG of parfor body loops since nested parfors with extra
+        # jumps can be created with prange conversion
+        n_parfors = simplify_parfor_body_CFG(self.func_ir.blocks)
+        # simplify before fusion
+        simplify(self.func_ir, self.typemap, self.calltypes, self.metadata["parfors"])
+        # need two rounds of copy propagation to enable fusion of long sequences
+        # of parfors like test_fuse_argmin (some PYTHONHASHSEED values since
+        # apply_copies_parfor depends on set order for creating dummy assigns)
+        simplify(self.func_ir, self.typemap, self.calltypes, self.metadata["parfors"])
+
+        if self.options.fusion and n_parfors >= 2:
+            self.func_ir._definitions = build_definitions(self.func_ir.blocks)
+            self.array_analysis.equiv_sets = dict()
+            self.array_analysis.run(self.func_ir.blocks)
+            # reorder statements to maximize fusion
+            # push non-parfors down
+            maximize_fusion(self.func_ir, self.func_ir.blocks, self.typemap,
+                                                            up_direction=False)
+            dprint_func_ir(self.func_ir, "after maximize fusion down")
+            self.fuse_parfors(self.array_analysis,
+                              self.func_ir.blocks,
+                              self.func_ir,
+                              self.typemap)
+            dprint_func_ir(self.func_ir, "after first fuse")
+            # push non-parfors up
+            maximize_fusion(self.func_ir, self.func_ir.blocks, self.typemap)
+            dprint_func_ir(self.func_ir, "after maximize fusion up")
+            # try fuse again after maximize
+            self.fuse_parfors(self.array_analysis,
+                              self.func_ir.blocks,
+                              self.func_ir,
+                              self.typemap)
+            dprint_func_ir(self.func_ir, "after fusion")
+            # remove dead code after fusion to remove extra arrays and variables
+            simplify(self.func_ir, self.typemap, self.calltypes, self.metadata["parfors"])
+
+        # push function call variables inside parfors so gufunc function
+        # wouldn't need function variables as argument
+        push_call_vars(self.func_ir.blocks, {}, {}, self.typemap)
+        dprint_func_ir(self.func_ir, "after push call vars")
+        # simplify again
+        simplify(self.func_ir, self.typemap, self.calltypes, self.metadata["parfors"])
+        dprint_func_ir(self.func_ir, "after optimization")
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("variable types: ", sorted(self.typemap.items()))
+            print("call types: ", self.calltypes)
+
+        if config.DEBUG_ARRAY_OPT >= 3:
+            for(block_label, block) in self.func_ir.blocks.items():
+                new_block = []
+                scope = block.scope
+                for stmt in block.body:
+                    new_block.append(stmt)
+                    if isinstance(stmt, ir.Assign):
+                        loc = stmt.loc
+                        lhs = stmt.target
+                        rhs = stmt.value
+                        lhs_typ = self.typemap[lhs.name]
+                        print("Adding print for assignment to ", lhs.name, lhs_typ, type(lhs_typ))
+                        if lhs_typ in types.number_domain or isinstance(lhs_typ, types.Literal):
+                            str_var = ir.Var(scope, mk_unique_var("str_var"), loc)
+                            self.typemap[str_var.name] = types.StringLiteral(lhs.name)
+                            lhs_const = ir.Const(lhs.name, loc)
+                            str_assign = ir.Assign(lhs_const, str_var, loc)
+                            new_block.append(str_assign)
+                            str_print = ir.Print([str_var], None, loc)
+                            self.calltypes[str_print] = signature(types.none, self.typemap[str_var.name])
+                            new_block.append(str_print)
+                            ir_print = ir.Print([lhs], None, loc)
+                            self.calltypes[ir_print] = signature(types.none, lhs_typ)
+                            new_block.append(ir_print)
+                block.body = new_block
+
+        if self.func_ir.is_generator:
+            fix_generator_types(self.func_ir.generator_info, self.return_type,
+                                self.typemap)
+        if sequential_parfor_lowering:
+            lower_parfor_sequential(
+                self.typingctx, self.func_ir, self.typemap, self.calltypes, self.metadata)
+        else:
+            # prepare for parallel lowering
+            # add parfor params to parfors here since lowering is destructive
+            # changing the IR after this is not allowed
+            parfor_ids, parfors = get_parfor_params(self.func_ir.blocks,
+                                                    self.options.fusion,
+                                                    self.nested_fusion_info)
+
+            # Validate reduction in parfors.
+            for p in parfors:
+                p.redvars, p.reddict = get_parfor_reductions(self.func_ir, p, p.params, self.calltypes)
+
+            # Validate parameters:
+            for p in parfors:
+                p.validate_params(self.typemap)
+
+            if config.DEBUG_ARRAY_OPT_STATS:
+                name = self.func_ir.func_id.func_qualname
+                n_parfors = len(parfor_ids)
+                if n_parfors > 0:
+                    after_fusion = ("After fusion" if self.options.fusion
+                                    else "With fusion disabled")
+                    print(('{}, function {} has '
+                           '{} parallel for-loop(s) #{}.').format(
+                           after_fusion, name, n_parfors, parfor_ids))
+                else:
+                    print('Function {} has no Parfor.'.format(name))
+
+        return
+
+    def _find_mask(self, arr_def):
+        """check if an array is of B[...M...], where M is a
+        boolean array, and other indices (if available) are ints.
+        If found, return B, M, M's type, and a tuple representing mask indices.
+        Otherwise, raise GuardException.
+        """
+        return _find_mask(self.typemap, self.func_ir, arr_def)
+
+    def _mk_parfor_loops(self, size_vars, scope, loc):
+        """
+        Create loop index variables and build LoopNest objects for a parfor.
+        """
+        return _mk_parfor_loops(self.typemap, size_vars, scope, loc)
+
+    def fuse_parfors(self, array_analysis, blocks, func_ir, typemap):
+        for label, block in blocks.items():
+            equiv_set = array_analysis.get_equiv_set(label)
+            fusion_happened = True
+            while fusion_happened:
+                fusion_happened = False
+                new_body = []
+                i = 0
+                while i < len(block.body) - 1:
+                    stmt = block.body[i]
+                    next_stmt = block.body[i + 1]
+                    if isinstance(stmt, Parfor) and isinstance(next_stmt, Parfor):
+                        # we have to update equiv_set since they have changed due to
+                        # variables being renamed before fusion.
+                        equiv_set = array_analysis.get_equiv_set(label)
+                        stmt.equiv_set = equiv_set
+                        next_stmt.equiv_set = equiv_set
+                        fused_node, fuse_report = try_fuse(equiv_set, stmt, next_stmt,
+                            self.metadata["parfors"], func_ir, typemap)
+                        # accumulate fusion reports
+                        self.diagnostics.fusion_reports.append(fuse_report)
+                        if fused_node is not None:
+                            fusion_happened = True
+                            self.diagnostics.fusion_info[stmt.id].extend([next_stmt.id])
+                            new_body.append(fused_node)
+                            self.fuse_recursive_parfor(fused_node, equiv_set, func_ir, typemap)
+                            i += 2
+                            continue
+                    new_body.append(stmt)
+                    if isinstance(stmt, Parfor):
+                        self.fuse_recursive_parfor(stmt, equiv_set, func_ir, typemap)
+                    i += 1
+                new_body.append(block.body[-1])
+                block.body = new_body
+        return
+
+    def fuse_recursive_parfor(self, parfor, equiv_set, func_ir, typemap):
+        blocks = wrap_parfor_blocks(parfor)
+        maximize_fusion(self.func_ir, blocks, self.typemap)
+        dprint_func_ir(self.func_ir, "after recursive maximize fusion down", blocks)
+        arr_analysis = array_analysis.ArrayAnalysis(self.typingctx, self.func_ir,
+                                                self.typemap, self.calltypes)
+        arr_analysis.run(blocks, equiv_set)
+        self.fuse_parfors(arr_analysis, blocks, func_ir, typemap)
+        unwrap_parfor_blocks(parfor)
+
+
+def _remove_size_arg(call_name, expr):
+    "remove size argument from args or kws"
+    # remove size kwarg
+    kws = dict(expr.kws)
+    kws.pop('size', '')
+    expr.kws = tuple(kws.items())
+
+    # remove size arg if available
+    if call_name in random_1arg_size + random_int_args:
+        # these calls have only a "size" argument or list of ints
+        # so remove all args
+        expr.args = []
+
+    if call_name in random_3arg_sizelast:
+        # normal, uniform, ... have 3 args, last one is size
+        if len(expr.args) == 3:
+            expr.args.pop()
+
+    if call_name in random_2arg_sizelast:
+        # have 2 args, last one is size
+        if len(expr.args) == 2:
+            expr.args.pop()
+
+    if call_name == 'randint':
+        # has 4 args, 3rd one is size
+        if len(expr.args) == 3:
+            expr.args.pop()
+        if len(expr.args) == 4:
+            dt_arg = expr.args.pop()
+            expr.args.pop()  # remove size
+            expr.args.append(dt_arg)
+
+    if call_name == 'triangular':
+        # has 4 args, last one is size
+        if len(expr.args) == 4:
+            expr.args.pop()
+
+    return
+
+
+def _get_call_arg_types(expr, typemap):
+    new_arg_typs = []
+    for arg in expr.args:
+        new_arg_typs.append(typemap[arg.name])
+
+    new_kw_types = {}
+    for name, arg in expr.kws:
+        new_kw_types[name] = typemap[arg.name]
+
+    return tuple(new_arg_typs), new_kw_types
+
+
+def _arrayexpr_tree_to_ir(
+        func_ir,
+        typingctx,
+        typemap,
+        calltypes,
+        equiv_set,
+        init_block,
+        expr_out_var,
+        expr,
+        parfor_index_tuple_var,
+        all_parfor_indices,
+        avail_vars):
+    """generate IR from array_expr's expr tree recursively. Assign output to
+    expr_out_var and returns the whole IR as a list of Assign nodes.
+    """
+    el_typ = typemap[expr_out_var.name]
+    scope = expr_out_var.scope
+    loc = expr_out_var.loc
+    out_ir = []
+
+    if isinstance(expr, tuple):
+        op, arr_expr_args = expr
+        arg_vars = []
+        for arg in arr_expr_args:
+            arg_out_var = ir.Var(scope, mk_unique_var("$arg_out_var"), loc)
+            typemap[arg_out_var.name] = el_typ
+            out_ir += _arrayexpr_tree_to_ir(func_ir,
+                                            typingctx,
+                                            typemap,
+                                            calltypes,
+                                            equiv_set,
+                                            init_block,
+                                            arg_out_var,
+                                            arg,
+                                            parfor_index_tuple_var,
+                                            all_parfor_indices,
+                                            avail_vars)
+            arg_vars.append(arg_out_var)
+        if op in npydecl.supported_array_operators:
+            el_typ1 = typemap[arg_vars[0].name]
+            if len(arg_vars) == 2:
+                el_typ2 = typemap[arg_vars[1].name]
+                func_typ = typingctx.resolve_function_type(op, (el_typ1,
+                                                                el_typ2), {})
+                ir_expr = ir.Expr.binop(op, arg_vars[0], arg_vars[1], loc)
+                if op == operator.truediv:
+                    func_typ, ir_expr = _gen_np_divide(
+                        arg_vars[0], arg_vars[1], out_ir, typemap)
+            else:
+                func_typ = typingctx.resolve_function_type(op, (el_typ1,), {})
+                ir_expr = ir.Expr.unary(op, arg_vars[0], loc)
+            calltypes[ir_expr] = func_typ
+            el_typ = func_typ.return_type
+            out_ir.append(ir.Assign(ir_expr, expr_out_var, loc))
+        for T in array_analysis.MAP_TYPES:
+            if isinstance(op, T):
+                # elif isinstance(op, (np.ufunc, DUFunc)):
+                # function calls are stored in variables which are not removed
+                # op is typing_key to the variables type
+                func_var_name = _find_func_var(typemap, op, avail_vars, loc=loc)
+                func_var = ir.Var(scope, mk_unique_var(func_var_name), loc)
+                typemap[func_var.name] = typemap[func_var_name]
+                func_var_def = copy.deepcopy(func_ir.get_definition(func_var_name))
+                if isinstance(func_var_def, ir.Expr) and func_var_def.op == 'getattr' and func_var_def.attr == 'sqrt':
+                     g_math_var = ir.Var(scope, mk_unique_var("$math_g_var"), loc)
+                     typemap[g_math_var.name] = types.misc.Module(math)
+                     g_math = ir.Global('math', math, loc)
+                     g_math_assign = ir.Assign(g_math, g_math_var, loc)
+                     func_var_def = ir.Expr.getattr(g_math_var, 'sqrt', loc)
+                     out_ir.append(g_math_assign)
+#                     out_ir.append(func_var_def)
+                ir_expr = ir.Expr.call(func_var, arg_vars, (), loc)
+                call_typ = typemap[func_var.name].get_call_type(
+                    typingctx, tuple(typemap[a.name] for a in arg_vars), {})
+                calltypes[ir_expr] = call_typ
+                el_typ = call_typ.return_type
+                #signature(el_typ, el_typ)
+                out_ir.append(ir.Assign(func_var_def, func_var, loc))
+                out_ir.append(ir.Assign(ir_expr, expr_out_var, loc))
+    elif isinstance(expr, ir.Var):
+        var_typ = typemap[expr.name]
+        if isinstance(var_typ, types.Array):
+            el_typ = var_typ.dtype
+            ir_expr = _gen_arrayexpr_getitem(
+                equiv_set,
+                expr,
+                parfor_index_tuple_var,
+                all_parfor_indices,
+                el_typ,
+                calltypes,
+                typingctx,
+                typemap,
+                init_block,
+                out_ir)
+        else:
+            # assert typemap[expr.name]==el_typ
+            el_typ = var_typ
+            ir_expr = expr
+        out_ir.append(ir.Assign(ir_expr, expr_out_var, loc))
+    elif isinstance(expr, ir.Const):
+        el_typ = typing.Context().resolve_value_type(expr.value)
+        out_ir.append(ir.Assign(expr, expr_out_var, loc))
+
+    if len(out_ir) == 0:
+        raise errors.UnsupportedRewriteError(
+            f"Don't know how to translate array expression '{expr:r}'",
+            loc=expr.loc,
+        )
+    typemap.pop(expr_out_var.name, None)
+    typemap[expr_out_var.name] = el_typ
+    return out_ir
+
+
+def _gen_np_divide(arg1, arg2, out_ir, typemap):
+    """generate np.divide() instead of / for array_expr to get numpy error model
+    like inf for division by zero (test_division_by_zero).
+    """
+    scope = arg1.scope
+    loc = arg1.loc
+    # g_np_var = Global(numpy)
+    g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc)
+    typemap[g_np_var.name] = types.misc.Module(numpy)
+    g_np = ir.Global('np', numpy, loc)
+    g_np_assign = ir.Assign(g_np, g_np_var, loc)
+    # attr call: div_attr = getattr(g_np_var, divide)
+    div_attr_call = ir.Expr.getattr(g_np_var, "divide", loc)
+    attr_var = ir.Var(scope, mk_unique_var("$div_attr"), loc)
+    func_var_typ = get_np_ufunc_typ(numpy.divide)
+    typemap[attr_var.name] = func_var_typ
+    attr_assign = ir.Assign(div_attr_call, attr_var, loc)
+    # divide call:  div_attr(arg1, arg2)
+    div_call = ir.Expr.call(attr_var, [arg1, arg2], (), loc)
+    func_typ = func_var_typ.get_call_type(
+        typing.Context(), [typemap[arg1.name], typemap[arg2.name]], {})
+    out_ir.extend([g_np_assign, attr_assign])
+    return func_typ, div_call
+
+
+def _gen_arrayexpr_getitem(
+        equiv_set,
+        var,
+        parfor_index_tuple_var,
+        all_parfor_indices,
+        el_typ,
+        calltypes,
+        typingctx,
+        typemap,
+        init_block,
+        out_ir):
+    """if there is implicit dimension broadcast, generate proper access variable
+    for getitem. For example, if indices are (i1,i2,i3) but shape is (c1,0,c3),
+    generate a tuple with (i1,0,i3) for access.  Another example: for (i1,i2,i3)
+    and (c1,c2) generate (i2,i3).
+    """
+    loc = var.loc
+    index_var = parfor_index_tuple_var
+    var_typ =  typemap[var.name]
+    ndims = typemap[var.name].ndim
+    num_indices = len(all_parfor_indices)
+    size_vars = equiv_set.get_shape(var) or []
+    size_consts = [equiv_set.get_equiv_const(x) for x in size_vars]
+    # Handle array-scalar
+    if ndims == 0:
+        # call np.ravel
+        ravel_var = ir.Var(var.scope, mk_unique_var("$ravel"), loc)
+        ravel_typ = types.npytypes.Array(dtype=var_typ.dtype, ndim=1, layout='C')
+        typemap[ravel_var.name] = ravel_typ
+        stmts = ir_utils.gen_np_call('ravel', numpy.ravel, ravel_var, [var], typingctx, typemap, calltypes)
+        init_block.body.extend(stmts)
+        var = ravel_var
+        # Const(0)
+        const_node = ir.Const(0, var.loc)
+        const_var = ir.Var(var.scope, mk_unique_var("$const_ind_0"), loc)
+        typemap[const_var.name] = types.uintp
+        const_assign = ir.Assign(const_node, const_var, loc)
+        out_ir.append(const_assign)
+        index_var = const_var
+    # Handle 1d array
+    elif ndims == 1:
+        # Use last index for 1D arrays
+        index_var = all_parfor_indices[-1]
+    # Handle known constant size
+    elif any([x is not None for x in size_consts]):
+        # Need a tuple as index
+        ind_offset = num_indices - ndims
+        tuple_var = ir.Var(var.scope, mk_unique_var(
+            "$parfor_index_tuple_var_bcast"), loc)
+        typemap[tuple_var.name] = types.containers.UniTuple(types.uintp, ndims)
+        # Just in case, const var for size 1 dim access index: $const0 =
+        # Const(0)
+        const_node = ir.Const(0, var.loc)
+        const_var = ir.Var(var.scope, mk_unique_var("$const_ind_0"), loc)
+        typemap[const_var.name] = types.uintp
+        const_assign = ir.Assign(const_node, const_var, loc)
+        out_ir.append(const_assign)
+        index_vars = []
+        for i in reversed(range(ndims)):
+            size_var = size_vars[i]
+            size_const = size_consts[i]
+            if size_const == 1:
+                index_vars.append(const_var)
+            else:
+                index_vars.append(all_parfor_indices[ind_offset + i])
+        index_vars = list(reversed(index_vars))
+        tuple_call = ir.Expr.build_tuple(index_vars, loc)
+        tuple_assign = ir.Assign(tuple_call, tuple_var, loc)
+        out_ir.append(tuple_assign)
+        index_var = tuple_var
+
+    ir_expr = ir.Expr.getitem(var, index_var, loc)
+    calltypes[ir_expr] = signature(el_typ, typemap[var.name],
+                                   typemap[index_var.name])
+    return ir_expr
+
+
+def _find_func_var(typemap, func, avail_vars, loc):
+    """find variable in typemap which represents the function func.
+    """
+    for v in avail_vars:
+        t = typemap[v]
+        # Function types store actual functions in typing_key.
+        if isinstance(t, Function) and t.typing_key == func:
+            return v
+    raise errors.UnsupportedRewriteError("ufunc call variable not found", loc=loc)
+
+
+def lower_parfor_sequential(typingctx, func_ir, typemap, calltypes, metadata):
+    ir_utils._the_max_label.update(ir_utils.find_max_label(func_ir.blocks))
+    parfor_found = False
+    new_blocks = {}
+    scope = next(iter(func_ir.blocks.values())).scope
+    for (block_label, block) in func_ir.blocks.items():
+        block_label, parfor_found = _lower_parfor_sequential_block(
+            block_label, block, new_blocks, typemap, calltypes, parfor_found,
+            scope=scope)
+        # old block stays either way
+        new_blocks[block_label] = block
+    func_ir.blocks = new_blocks
+    # rename only if parfor found and replaced (avoid test_flow_control error)
+    if parfor_found:
+        func_ir.blocks = rename_labels(func_ir.blocks)
+    dprint_func_ir(func_ir, "after parfor sequential lowering")
+    simplify(func_ir, typemap, calltypes, metadata["parfors"])
+    dprint_func_ir(func_ir, "after parfor sequential simplify")
+
+
+def _lower_parfor_sequential_block(
+        block_label,
+        block,
+        new_blocks,
+        typemap,
+        calltypes,
+        parfor_found,
+        scope):
+    i = _find_first_parfor(block.body)
+    while i != -1:
+        parfor_found = True
+        inst = block.body[i]
+        loc = inst.init_block.loc
+        # split block across parfor
+        prev_block = ir.Block(scope, loc)
+        prev_block.body = block.body[:i]
+        block.body = block.body[i + 1:]
+        # previous block jump to parfor init block
+        init_label = next_label()
+        prev_block.body.append(ir.Jump(init_label, loc))
+        new_blocks[init_label] = transfer_scope(inst.init_block, scope)
+        new_blocks[block_label] = prev_block
+        block_label = next_label()
+
+        ndims = len(inst.loop_nests)
+        for i in range(ndims):
+            loopnest = inst.loop_nests[i]
+            # create range block for loop
+            range_label = next_label()
+            header_label = next_label()
+            range_block = mk_range_block(
+                typemap,
+                loopnest.start,
+                loopnest.stop,
+                loopnest.step,
+                calltypes,
+                scope,
+                loc)
+            range_block.body[-1].target = header_label  # fix jump target
+            phi_var = range_block.body[-2].target
+            new_blocks[range_label] = range_block
+            header_block = mk_loop_header(typemap, phi_var, calltypes,
+                                          scope, loc)
+            header_block.body[-2].target = loopnest.index_variable
+            new_blocks[header_label] = header_block
+            # jump to this new inner loop
+            if i == 0:
+                inst.init_block.body.append(ir.Jump(range_label, loc))
+                header_block.body[-1].falsebr = block_label
+            else:
+                new_blocks[prev_header_label].body[-1].truebr = range_label
+                header_block.body[-1].falsebr = prev_header_label
+            prev_header_label = header_label  # to set truebr next loop
+
+        # last body block jump to inner most header
+        body_last_label = max(inst.loop_body.keys())
+        inst.loop_body[body_last_label].body.append(
+            ir.Jump(header_label, loc))
+        # inner most header jumps to first body block
+        body_first_label = min(inst.loop_body.keys())
+        header_block.body[-1].truebr = body_first_label
+        # add parfor body to blocks
+        for (l, b) in inst.loop_body.items():
+            l, parfor_found = _lower_parfor_sequential_block(
+                l, b, new_blocks, typemap, calltypes, parfor_found,
+                scope=scope)
+            new_blocks[l] = transfer_scope(b, scope)
+        i = _find_first_parfor(block.body)
+    return block_label, parfor_found
+
+
+def _find_first_parfor(body):
+    for (i, inst) in enumerate(body):
+        if isinstance(inst, Parfor) and not inst.no_sequential_lowering:
+            return i
+    return -1
+
+
+def get_parfor_params(blocks, options_fusion, fusion_info):
+    """find variables used in body of parfors from outside and save them.
+    computed as live variables at entry of first block.
+    """
+
+    # since parfor wrap creates a back-edge to first non-init basic block,
+    # live_map[first_non_init_block] contains variables defined in parfor body
+    # that could be undefined before. So we only consider variables that are
+    # actually defined before the parfor body in the program.
+    parfor_ids = set()
+    parfors = []
+    pre_defs = set()
+    _, all_defs = compute_use_defs(blocks)
+    topo_order = find_topo_order(blocks)
+    for label in topo_order:
+        block = blocks[label]
+        for i, parfor in _find_parfors(block.body):
+            # find variable defs before the parfor in the same block
+            dummy_block = ir.Block(block.scope, block.loc)
+            dummy_block.body = block.body[:i]
+            before_defs = compute_use_defs({0: dummy_block}).defmap[0]
+            pre_defs |= before_defs
+            params = get_parfor_params_inner(
+                parfor, pre_defs, options_fusion, fusion_info,
+            )
+            parfor.params, parfor.races = _combine_params_races_for_ssa_names(
+                block.scope, params, parfor.races,
+            )
+            parfor_ids.add(parfor.id)
+            parfors.append(parfor)
+
+        pre_defs |= all_defs[label]
+    return parfor_ids, parfors
+
+
+def _combine_params_races_for_ssa_names(scope, params, races):
+    """Returns `(params|races1, races1)`, where `races1` contains all variables
+    in `races` are NOT referring to the same unversioned (SSA) variables in
+    `params`.
+    """
+    def unversion(k):
+        try:
+            return scope.get_exact(k).unversioned_name
+        except ir.NotDefinedError:
+            # XXX: it's a bug that something references an undefined name
+            return k
+
+    races1 = set(races)
+    unver_params = list(map(unversion, params))
+
+    for rv in races:
+        if any(unversion(rv) == pv for pv in unver_params):
+            races1.discard(rv)
+        else:
+            break
+
+    return params | races1, races1
+
+
+def get_parfor_params_inner(parfor, pre_defs, options_fusion, fusion_info):
+    blocks = wrap_parfor_blocks(parfor)
+    cfg = compute_cfg_from_blocks(blocks)
+    usedefs = compute_use_defs(blocks)
+    live_map = compute_live_map(cfg, blocks, usedefs.usemap, usedefs.defmap)
+    parfor_ids, _ = get_parfor_params(blocks, options_fusion, fusion_info)
+    n_parfors = len(parfor_ids)
+    if n_parfors > 0:
+        if config.DEBUG_ARRAY_OPT_STATS:
+            after_fusion = ("After fusion" if options_fusion
+                            else "With fusion disabled")
+            print(('{}, parallel for-loop {} has '
+                'nested Parfor(s) #{}.').format(
+                after_fusion, parfor.id, n_parfors, parfor_ids))
+        fusion_info[parfor.id] = list(parfor_ids)
+
+    unwrap_parfor_blocks(parfor)
+    keylist = sorted(live_map.keys())
+    init_block = keylist[0]
+    first_non_init_block = keylist[1]
+
+    before_defs = usedefs.defmap[init_block] | pre_defs
+    params = live_map[first_non_init_block] & before_defs
+    return params
+
+
+def _find_parfors(body):
+    for i, inst in enumerate(body):
+        if isinstance(inst, Parfor):
+            yield i, inst
+
+
+def get_parfor_outputs(parfor, parfor_params):
+    """get arrays that are written to inside the parfor and need to be passed
+    as parameters to gufunc.
+    """
+    # FIXME: The following assumes the target of all SetItem are outputs,
+    # which is wrong!
+    last_label = max(parfor.loop_body.keys())
+    outputs = []
+    for blk in parfor.loop_body.values():
+        for stmt in blk.body:
+            if (isinstance(stmt, (ir.StaticSetItem, ir.SetItem)) and
+                get_index_var(stmt).name == parfor.index_var.name):
+                outputs.append(stmt.target.name)
+    # make sure these written arrays are in parfor parameters (live coming in)
+    outputs = list(set(outputs) & set(parfor_params))
+    return sorted(outputs)
+
+
+_RedVarInfo = make_dataclass(
+    "_RedVarInfo",
+    ["init_val", "reduce_nodes", "redop"],
+    frozen=True,
+)
+
+
+def get_parfor_reductions(func_ir, parfor, parfor_params, calltypes, reductions=None,
+        reduce_varnames=None, param_uses=None, param_nodes=None,
+        var_to_param=None):
+    """find variables that are updated using their previous values and an array
+    item accessed with parfor index, e.g. s = s+A[i]
+    """
+    if reductions is None:
+        reductions = {}
+    if reduce_varnames is None:
+        reduce_varnames = []
+
+    # for each param variable, find what other variables are used to update it
+    # also, keep the related nodes
+    if param_uses is None:
+        param_uses = defaultdict(list)
+    if param_nodes is None:
+        param_nodes = defaultdict(list)
+    if var_to_param is None:
+        var_to_param = {}
+
+    blocks = wrap_parfor_blocks(parfor)
+    topo_order = find_topo_order(blocks)
+    topo_order = topo_order[1:]  # ignore init block
+    unwrap_parfor_blocks(parfor)
+
+    for label in reversed(topo_order):
+        for stmt in reversed(parfor.loop_body[label].body):
+            if (isinstance(stmt, ir.Assign)
+                    and (stmt.target.name in parfor_params
+                      or stmt.target.name in var_to_param)):
+                lhs = stmt.target
+                rhs = stmt.value
+                cur_param = lhs if lhs.name in parfor_params else var_to_param[lhs.name]
+                used_vars = []
+                if isinstance(rhs, ir.Var):
+                    used_vars = [rhs.name]
+                elif isinstance(rhs, ir.Expr):
+                    used_vars = [v.name for v in stmt.value.list_vars()]
+                param_uses[cur_param].extend(used_vars)
+                for v in used_vars:
+                    var_to_param[v] = cur_param
+                # save copy of dependent stmt
+                stmt_cp = copy.deepcopy(stmt)
+                if stmt.value in calltypes:
+                    calltypes[stmt_cp.value] = calltypes[stmt.value]
+                param_nodes[cur_param].append(stmt_cp)
+            if isinstance(stmt, Parfor):
+                # recursive parfors can have reductions like test_prange8
+                get_parfor_reductions(func_ir, stmt, parfor_params, calltypes,
+                    reductions, reduce_varnames, None, param_nodes, var_to_param)
+
+    for param, used_vars in param_uses.items():
+        # a parameter is a reduction variable if its value is used to update it
+        # check reduce_varnames since recursive parfors might have processed
+        # param already
+        param_name = param.name
+        if param_name in used_vars and param_name not in reduce_varnames:
+            param_nodes[param].reverse()
+            reduce_nodes = get_reduce_nodes(param, param_nodes[param], func_ir)
+            # Certain kinds of ill-formed Python (like potentially undefined
+            # variables) in combination with SSA can make things look like
+            # reductions except that they don't have reduction operators.
+            # If we get to this point but don't find a reduction operator
+            # then assume it is this situation and just don't treat this
+            # variable as a reduction.
+            if reduce_nodes is not None:
+                reduce_varnames.append(param_name)
+                check_conflicting_reduction_operators(param, reduce_nodes)
+                gri_out = guard(get_reduction_init, reduce_nodes)
+                if gri_out is not None:
+                    init_val, redop = gri_out
+                else:
+                    init_val = None
+                    redop = None
+                reductions[param_name] = _RedVarInfo(
+                    init_val=init_val,
+                    reduce_nodes=reduce_nodes,
+                    redop=redop,
+                )
+
+    return reduce_varnames, reductions
+
+def check_conflicting_reduction_operators(param, nodes):
+    """In prange, a user could theoretically specify conflicting
+       reduction operators.  For example, in one spot it is += and
+       another spot *=.  Here, we raise an exception if multiple
+       different reduction operators are used in one prange.
+    """
+    first_red_func = None
+    for node in nodes:
+        if (isinstance(node, ir.Assign) and
+            isinstance(node.value, ir.Expr) and
+            node.value.op=='inplace_binop'):
+            if first_red_func is None:
+                first_red_func = node.value.fn
+            else:
+                if first_red_func != node.value.fn:
+                    msg = ("Reduction variable %s has multiple conflicting "
+                           "reduction operators." % param.unversioned_name)
+                    raise errors.UnsupportedRewriteError(msg, node.loc)
+
+def get_reduction_init(nodes):
+    """
+    Get initial value for known reductions.
+    Currently, only += and *= are supported.
+    """
+    require(len(nodes) >= 1)
+    # there could be multiple extra assignments after the reduce node
+    # See: test_reduction_var_reuse
+    acc_expr = list(filter(lambda x: isinstance(x.value, ir.Expr), nodes))[-1].value
+    require(isinstance(acc_expr, ir.Expr) and acc_expr.op=='inplace_binop')
+    if acc_expr.fn == operator.iadd or acc_expr.fn == operator.isub:
+        return 0, acc_expr.fn
+    if (  acc_expr.fn == operator.imul
+       or acc_expr.fn == operator.itruediv ):
+        return 1, acc_expr.fn
+    return None, None
+
+def supported_reduction(x, func_ir):
+    if x.op == 'inplace_binop' or x.op == 'binop':
+        if x.fn == operator.ifloordiv or x.fn == operator.floordiv:
+            raise errors.NumbaValueError(("Parallel floordiv reductions are not supported. "
+                              "If all divisors are integers then a floordiv "
+                              "reduction can in some cases be parallelized as "
+                              "a multiply reduction followed by a floordiv of "
+                              "the resulting product."), x.loc)
+        supps = [operator.iadd,
+                 operator.isub,
+                 operator.imul,
+                 operator.itruediv,
+                 operator.add,
+                 operator.sub,
+                 operator.mul,
+                 operator.truediv]
+        return x.fn in supps
+    if x.op == 'call':
+        callname = guard(find_callname, func_ir, x)
+        if callname in [
+            ('max', 'builtins'),
+            ('min', 'builtins'),
+            ('datetime_minimum', 'numba.np.npdatetime_helpers'),
+            ('datetime_maximum', 'numba.np.npdatetime_helpers'),
+        ]:
+            return True
+    return False
+
+def get_reduce_nodes(reduction_node, nodes, func_ir):
+    """
+    Get nodes that combine the reduction variable with a sentinel variable.
+    Recognizes the first node that combines the reduction variable with another
+    variable.
+    """
+    reduce_nodes = None
+    defs = {}
+
+    def lookup(var, varonly=True):
+        val = defs.get(var.name, None)
+        if isinstance(val, ir.Var):
+            return lookup(val)
+        else:
+            return var if (varonly or val is None) else val
+    name = reduction_node.name
+    unversioned_name = reduction_node.unversioned_name
+    for i, stmt in enumerate(nodes):
+        lhs = stmt.target
+        rhs = stmt.value
+        defs[lhs.name] = rhs
+        if isinstance(rhs, ir.Var) and rhs.name in defs:
+            rhs = lookup(rhs)
+        if isinstance(rhs, ir.Expr):
+            in_vars = set(lookup(v, True).name for v in rhs.list_vars())
+            if name in in_vars:
+                # reductions like sum have an assignment afterwards
+                # e.g. $2 = a + $1; a = $2
+                # reductions that are functions calls like max() don't have an
+                # extra assignment afterwards
+                if (not (i+1 < len(nodes) and isinstance(nodes[i+1], ir.Assign)
+                        and nodes[i+1].target.unversioned_name == unversioned_name)
+                        and lhs.unversioned_name != unversioned_name):
+                    raise ValueError(
+                        f"Use of reduction variable {unversioned_name!r} other "
+                        "than in a supported reduction function is not "
+                        "permitted."
+                    )
+
+                if not supported_reduction(rhs, func_ir):
+                    raise ValueError(("Use of reduction variable " + unversioned_name +
+                                      " in an unsupported reduction function."))
+                args = [ (x.name, lookup(x, True)) for x in get_expr_args(rhs) ]
+                non_red_args = [ x for (x, y) in args if y.name != name ]
+                assert len(non_red_args) == 1
+                args = [ (x, y) for (x, y) in args if x != y.name ]
+                replace_dict = dict(args)
+                replace_dict[non_red_args[0]] = ir.Var(lhs.scope, name+"#init", lhs.loc)
+                replace_vars_inner(rhs, replace_dict)
+                reduce_nodes = nodes[i:]
+                break
+    return reduce_nodes
+
+def get_expr_args(expr):
+    """
+    Get arguments of an expression node
+    """
+    if expr.op in ['binop', 'inplace_binop']:
+        return [expr.lhs, expr.rhs]
+    if expr.op == 'call':
+        return [v for v in expr.args]
+    raise NotImplementedError("get arguments for expression {}".format(expr))
+
+def visit_parfor_pattern_vars(parfor, callback, cbdata):
+    # currently, only stencil pattern has variables
+    for pattern in parfor.patterns:
+        if pattern[0] == 'stencil':
+            left_lengths = pattern[1][0]
+            for i in range(len(left_lengths)):
+                if isinstance(left_lengths[i], ir.Var):
+                    left_lengths[i] = visit_vars_inner(left_lengths[i],
+                                                            callback, cbdata)
+            right_lengths = pattern[1][1]
+            for i in range(len(right_lengths)):
+                if isinstance(right_lengths[i], ir.Var):
+                    right_lengths[i] = visit_vars_inner(right_lengths[i],
+                                                            callback, cbdata)
+
+def visit_vars_parfor(parfor, callback, cbdata):
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("visiting parfor vars for:", parfor)
+        print("cbdata: ", sorted(cbdata.items()))
+    for l in parfor.loop_nests:
+        l.index_variable = visit_vars_inner(l.index_variable, callback, cbdata)
+        if isinstance(l.start, ir.Var):
+            l.start = visit_vars_inner(l.start, callback, cbdata)
+        if isinstance(l.stop, ir.Var):
+            l.stop = visit_vars_inner(l.stop, callback, cbdata)
+        if isinstance(l.step, ir.Var):
+            l.step = visit_vars_inner(l.step, callback, cbdata)
+    visit_vars({-1: parfor.init_block}, callback, cbdata)
+    visit_parfor_pattern_vars(parfor, callback, cbdata)
+    visit_vars(parfor.loop_body, callback, cbdata)
+    return
+
+
+# add call to visit parfor variable
+ir_utils.visit_vars_extensions[Parfor] = visit_vars_parfor
+
+
+def parfor_defs(parfor, use_set=None, def_set=None):
+    """list variables written in this parfor by recursively
+    calling compute_use_defs() on body and combining block defs.
+    """
+    if use_set is None:
+        use_set = set()
+    if def_set is None:
+        def_set = set()
+    blocks = wrap_parfor_blocks(parfor)
+    uses, defs = compute_use_defs(blocks)
+    cfg = compute_cfg_from_blocks(blocks)
+    last_label = max(blocks.keys())
+    unwrap_parfor_blocks(parfor)
+
+    # Conservatively, only add defs for blocks that are definitely executed
+    # Go through blocks in order, as if they are statements of the block that
+    # includes the parfor, and update uses/defs.
+
+    # no need for topo order of ir_utils
+    topo_order = cfg.topo_order()
+    # blocks that dominate last block are definitely executed
+    definitely_executed = cfg.dominators()[last_label]
+    # except loop bodies that might not execute
+    for loop in cfg.loops().values():
+        definitely_executed -= loop.body
+    for label in topo_order:
+        if label in definitely_executed:
+            # see compute_use_defs() in analysis.py
+            # variables defined in the block that includes the parfor are not
+            # uses of that block (are not potentially live in the beginning of
+            # the block)
+            use_set.update(uses[label] - def_set)
+            def_set.update(defs[label])
+        else:
+            use_set.update(uses[label] - def_set)
+
+    # treat loop variables and size variables as use
+    loop_vars = {
+        l.start.name for l in parfor.loop_nests if isinstance(
+            l.start, ir.Var)}
+    loop_vars |= {
+        l.stop.name for l in parfor.loop_nests if isinstance(
+            l.stop, ir.Var)}
+    loop_vars |= {
+        l.step.name for l in parfor.loop_nests if isinstance(
+            l.step, ir.Var)}
+    use_set.update(loop_vars - def_set)
+    use_set |= get_parfor_pattern_vars(parfor)
+
+    return analysis._use_defs_result(usemap=use_set, defmap=def_set)
+
+
+analysis.ir_extension_usedefs[Parfor] = parfor_defs
+
+
+def _parfor_use_alloca(parfor, alloca_set):
+    """
+    Reduction variables for parfors and the reduction variables within
+    nested parfors must be stack allocated.
+    """
+    alloca_set |= set(parfor.redvars)
+
+    blocks = wrap_parfor_blocks(parfor)
+    alloca_set |= analysis.must_use_alloca(blocks)
+
+    unwrap_parfor_blocks(parfor)
+
+
+analysis.ir_extension_use_alloca[Parfor] = _parfor_use_alloca
+
+
+def parfor_insert_dels(parfor, curr_dead_set):
+    """insert dels in parfor. input: dead variable set right after parfor.
+    returns the variables for which del was inserted.
+    """
+    blocks = wrap_parfor_blocks(parfor)
+    cfg = compute_cfg_from_blocks(blocks)
+    usedefs = compute_use_defs(blocks)
+    live_map = compute_live_map(cfg, blocks, usedefs.usemap, usedefs.defmap)
+    dead_map = compute_dead_maps(cfg, blocks, live_map, usedefs.defmap)
+
+    # treat loop variables and size variables as live
+    loop_vars = {
+        l.start.name for l in parfor.loop_nests if isinstance(
+            l.start, ir.Var)}
+    loop_vars |= {
+        l.stop.name for l in parfor.loop_nests if isinstance(
+            l.stop, ir.Var)}
+    loop_vars |= {
+        l.step.name for l in parfor.loop_nests if isinstance(
+            l.step, ir.Var)}
+    loop_vars |= {l.index_variable.name for l in parfor.loop_nests}
+    # for var_list in parfor.array_analysis.array_size_vars.values():
+    #    loop_vars |= {v.name for v in var_list if isinstance(v, ir.Var)}
+
+    dead_set = set()
+    for label in blocks.keys():
+        # only kill vars that are actually dead at the parfor's block
+        dead_map.internal[label] &= curr_dead_set
+        dead_map.internal[label] -= loop_vars
+        dead_set |= dead_map.internal[label]
+        dead_map.escaping[label] &= curr_dead_set
+        dead_map.escaping[label] -= loop_vars
+        dead_set |= dead_map.escaping[label]
+
+    # dummy class to replace func_ir. _patch_var_dels only accesses blocks
+    class DummyFuncIR(object):
+
+        def __init__(self, blocks):
+            self.blocks = blocks
+    post_proc = postproc.PostProcessor(DummyFuncIR(blocks))
+    post_proc._patch_var_dels(dead_map.internal, dead_map.escaping)
+    unwrap_parfor_blocks(parfor)
+
+    return dead_set | loop_vars
+
+
+postproc.ir_extension_insert_dels[Parfor] = parfor_insert_dels
+
+
+def maximize_fusion(func_ir, blocks, typemap, up_direction=True):
+    """
+    Reorder statements to maximize parfor fusion. Push all parfors up or down
+    so they are adjacent.
+    """
+    call_table, _ = get_call_table(blocks)
+    alias_map, arg_aliases = find_potential_aliases(
+                                 blocks,
+                                 func_ir.arg_names,
+                                 typemap,
+                                 func_ir
+                             )
+    for block in blocks.values():
+        order_changed = True
+        while order_changed:
+            order_changed = maximize_fusion_inner(
+                                func_ir,
+                                block,
+                                call_table,
+                                alias_map,
+                                arg_aliases,
+                                up_direction
+                            )
+
+def maximize_fusion_inner(func_ir, block, call_table, alias_map,
+                          arg_aliases, up_direction=True):
+    order_changed = False
+    i = 0
+    # i goes to body[-3] (i+1 to body[-2]) since body[-1] is terminator and
+    # shouldn't be reordered
+    while i < len(block.body) - 2:
+        stmt = block.body[i]
+        next_stmt = block.body[i+1]
+        can_reorder = (_can_reorder_stmts(stmt, next_stmt, func_ir,
+                                          call_table, alias_map, arg_aliases)
+                        if up_direction else _can_reorder_stmts(next_stmt, stmt,
+                        func_ir, call_table, alias_map, arg_aliases))
+        if can_reorder:
+            block.body[i] = next_stmt
+            block.body[i+1] = stmt
+            order_changed = True
+        i += 1
+    return order_changed
+
+def expand_aliases(the_set, alias_map, arg_aliases):
+    ret = set()
+    for i in the_set:
+        if i in alias_map:
+            ret = ret.union(alias_map[i])
+        if i in arg_aliases:
+            ret = ret.union(arg_aliases)
+        ret.add(i)
+    return ret
+
+def _can_reorder_stmts(stmt, next_stmt, func_ir, call_table,
+                       alias_map, arg_aliases):
+    """
+    Check dependencies to determine if a parfor can be reordered in the IR block
+    with a non-parfor statement.
+    """
+    # swap only parfors with non-parfors
+    # don't reorder calls with side effects (e.g. file close)
+    # only read-read dependencies are OK
+    # make sure there is no write-write, write-read dependencies
+    if (isinstance(stmt, Parfor)
+        and not isinstance(next_stmt, Parfor)
+        and not isinstance(next_stmt, ir.Print)
+        and (not isinstance(next_stmt, ir.Assign)
+            or has_no_side_effect(next_stmt.value, set(), call_table)
+            or guard(is_assert_equiv, func_ir, next_stmt.value))):
+        stmt_accesses = expand_aliases({v.name for v in stmt.list_vars()},
+                                       alias_map, arg_aliases)
+        stmt_writes = expand_aliases(get_parfor_writes(stmt),
+                                     alias_map, arg_aliases)
+        next_accesses = expand_aliases({v.name for v in next_stmt.list_vars()},
+                                       alias_map, arg_aliases)
+        next_writes = expand_aliases(get_stmt_writes(next_stmt),
+                                     alias_map, arg_aliases)
+        if len((stmt_writes & next_accesses)
+                | (next_writes & stmt_accesses)) == 0:
+            return True
+    return False
+
+def is_assert_equiv(func_ir, expr):
+    func_name, mod_name = find_callname(func_ir, expr)
+    return func_name == 'assert_equiv'
+
+
+def get_parfor_writes(parfor):
+    assert isinstance(parfor, Parfor)
+    writes = set()
+    blocks = parfor.loop_body.copy()
+    blocks[-1] = parfor.init_block
+    for block in blocks.values():
+        for stmt in block.body:
+            writes.update(get_stmt_writes(stmt))
+            if isinstance(stmt, Parfor):
+                writes.update(get_parfor_writes(stmt))
+    return writes
+
+FusionReport = namedtuple('FusionReport', ['first', 'second', 'message'])
+
+def try_fuse(equiv_set, parfor1, parfor2, metadata, func_ir, typemap):
+    """try to fuse parfors and return a fused parfor, otherwise return None
+    """
+    dprint("try_fuse: trying to fuse \n", parfor1, "\n", parfor2)
+
+    # default report is None
+    report = None
+
+    # fusion of parfors with different dimensions not supported yet
+    if len(parfor1.loop_nests) != len(parfor2.loop_nests):
+        dprint("try_fuse: parfors number of dimensions mismatch")
+        msg = "- fusion failed: number of loops mismatched, %s, %s."
+        fmt = "parallel loop #%s has a nest of %s loops"
+        l1 = fmt % (parfor1.id, len(parfor1.loop_nests))
+        l2 = fmt % (parfor2.id, len(parfor2.loop_nests))
+        report = FusionReport(parfor1.id, parfor2.id, msg % (l1, l2))
+        return None, report
+
+    ndims = len(parfor1.loop_nests)
+    # all loops should be equal length
+
+    def is_equiv(x, y):
+        return x == y or equiv_set.is_equiv(x, y)
+
+    def get_user_varname(v):
+        """get original variable name by user if possible"""
+        if not isinstance(v, ir.Var):
+            return v
+        v = v.name
+        if "var_rename_map" in metadata and v in metadata["var_rename_map"]:
+            user_varname = metadata["var_rename_map"][v]
+            return user_varname
+        return v
+
+    for i in range(ndims):
+        nest1 = parfor1.loop_nests[i]
+        nest2 = parfor2.loop_nests[i]
+        if not (is_equiv(nest1.start, nest2.start) and
+                is_equiv(nest1.stop, nest2.stop) and
+                is_equiv(nest1.step, nest2.step)):
+            dprint("try_fuse: parfor dimension correlation mismatch", i)
+            msg = "- fusion failed: loop dimension mismatched in axis %s. "
+            msg += "slice(%s, %s, %s) != " % (get_user_varname(nest1.start),
+                get_user_varname(nest1.stop), get_user_varname(nest1.step))
+            msg += "slice(%s, %s, %s)" % (get_user_varname(nest2.start),
+                get_user_varname(nest2.stop), get_user_varname(nest2.step))
+            report = FusionReport(parfor1.id, parfor2.id, msg % i)
+            return None, report
+
+    func_ir._definitions = build_definitions(func_ir.blocks)
+    # TODO: make sure parfor1's reduction output is not used in parfor2
+    # only data parallel loops
+    p1_cross_dep, p1_ip, p1_ia, p1_non_ia = has_cross_iter_dep(parfor1, func_ir, typemap)
+    if not p1_cross_dep:
+        p2_cross_dep = has_cross_iter_dep(parfor2, func_ir, typemap, p1_ip, p1_ia, p1_non_ia)[0]
+    else:
+        p2_cross_dep = True
+
+    if p1_cross_dep or p2_cross_dep:
+        dprint("try_fuse: parfor cross iteration dependency found")
+        msg = ("- fusion failed: cross iteration dependency found "
+                "between loops #%s and #%s")
+        report = FusionReport(parfor1.id, parfor2.id,
+                                msg % (parfor1.id, parfor2.id))
+        return None, report
+
+
+    # find parfor1's defs, only body is considered since init_block will run
+    # first after fusion as well
+    p1_body_usedefs = compute_use_defs(parfor1.loop_body)
+    p1_body_defs = set()
+    for defs in p1_body_usedefs.defmap.values():
+        p1_body_defs |= defs
+
+    p2_usedefs = compute_use_defs(parfor2.loop_body)
+    p2_uses = compute_use_defs({0: parfor2.init_block}).usemap[0]
+    for uses in p2_usedefs.usemap.values():
+        p2_uses |= uses
+
+    if not p1_body_defs.isdisjoint(p2_uses):
+        dprint("try_fuse: parfor2 depends on parfor1 body")
+        msg = ("- fusion failed: parallel loop %s has a dependency on the "
+                "body of parallel loop %s. ")
+        report = FusionReport(parfor1.id, parfor2.id,
+                                msg % (parfor1.id, parfor2.id))
+        return None, report
+
+    return fuse_parfors_inner(parfor1, parfor2)
+
+
+def fuse_parfors_inner(parfor1, parfor2):
+    # fuse parfor2 into parfor1
+    # append parfor2's init block on parfor1's
+    parfor1.init_block.body.extend(parfor2.init_block.body)
+
+    # append parfor2's first block to parfor1's last block
+    parfor2_first_label = min(parfor2.loop_body.keys())
+    parfor2_first_block = parfor2.loop_body[parfor2_first_label].body
+    parfor1_first_label = min(parfor1.loop_body.keys())
+    parfor1_last_label = max(parfor1.loop_body.keys())
+    parfor1.loop_body[parfor1_last_label].body.extend(parfor2_first_block)
+
+    # add parfor2 body blocks to parfor1's except first
+    parfor1.loop_body.update(parfor2.loop_body)
+    parfor1.loop_body.pop(parfor2_first_label)
+
+    # replace parfor2 indices with parfor1's
+    ndims = len(parfor1.loop_nests)
+    index_dict = {parfor2.index_var.name: parfor1.index_var}
+    for i in range(ndims):
+        index_dict[parfor2.loop_nests[i].index_variable.name] = parfor1.loop_nests[
+            i].index_variable
+    replace_vars(parfor1.loop_body, index_dict)
+
+    # re-order labels from min to max
+    blocks = wrap_parfor_blocks(parfor1, entry_label=parfor1_first_label)
+    blocks = rename_labels(blocks)
+    unwrap_parfor_blocks(parfor1, blocks)
+
+    nameset = set(x.name for x in index_dict.values())
+    remove_duplicate_definitions(parfor1.loop_body, nameset)
+    parfor1.patterns.extend(parfor2.patterns)
+    if config.DEBUG_ARRAY_OPT_STATS:
+        print('Parallel for-loop #{} is fused into for-loop #{}.'.format(
+              parfor2.id, parfor1.id))
+
+    msg = '- fusion succeeded: parallel for-loop #{} is fused into for-loop #{}.'
+    msg = msg.format(parfor2.id, parfor1.id)
+    report = FusionReport(parfor1.id, parfor2.id, msg)
+
+    return parfor1, report
+
+
+def remove_duplicate_definitions(blocks, nameset):
+    """Remove duplicated definition for variables in the given nameset, which
+    is often a result of parfor fusion.
+    """
+    for label, block in blocks.items():
+        body = block.body
+        new_body = []
+        defined = set()
+        for inst in body:
+            if isinstance(inst, ir.Assign):
+                name = inst.target.name
+                if name in nameset:
+                    if name in defined:
+                        continue
+                    defined.add(name)
+            new_body.append(inst)
+        block.body = new_body
+    return
+
+
+def has_cross_iter_dep(
+        parfor,
+        func_ir,
+        typemap,
+        index_positions=None,
+        indexed_arrays=None,
+        non_indexed_arrays=None):
+    # We should assume there is cross iteration dependency unless we can
+    # prove otherwise.  Return True if there is a cross-iter dependency
+    # that should prevent fusion, False if fusion is okay.
+    # Also returns index_positions, indexed_arrays, and non_indexed_arrays
+    # who purpose is described below so that subsequent additional
+    # has_cross_iter_dep calls for other parfors can build on the same
+    # data structures to make sure that the array accesses generate no
+    # cross-iter dependencies both within a parfor but also across parfors.
+
+    # TODO: make it more accurate using ud-chains
+
+    # Get the index variable used by this parfor.
+    # This will hold other variables with equivalent value, e.g., a = index_var
+    indices = {l.index_variable.name for l in parfor.loop_nests}
+    # This set will store variables that are (potentially recursively)
+    # defined in relation to an index variable, e.g., a = index_var + 1.
+    # A getitem that uses an index variable from this set will be considered
+    # as potentially having a cross-iter dependency and so won't fuse.
+    derived_from_indices = set()
+    # For the first parfor considered for fusion, the latter 3 args will be None
+    # and initialized to empty.  For the second parfor, the structures from the
+    # previous parfor are passed in so that cross-parfor violations of the
+    # below comments can prevent fusion.
+    #
+    # index_positions keeps track of which index positions have had an index
+    # variable used for them and which ones haven't for each possible array
+    # dimensionality.  After the first array access is seen, if subsequent
+    # ones use a parfor index for a different dimension then we conservatively
+    # say that we can't fuse.  For example, if a 2D array is accessed with
+    # a[parfor_index, 0] then index_positions[2] will be (True, False) and
+    # if a[0, parfor_index] happens later which is (False, True) then this
+    # conflicts with the previous value and will prevent fusion.
+    #
+    # indexed_arrays records arrays that are accessed with at least one
+    # parfor index.  If such an array is later accessed with indices that
+    # don't include a parfor index then conservatively assume we can't fuse.
+    #
+    # non_indexed_arrays holds arrays that are indexed without a parfor index.
+    # If an array first accessed without a parfor index is later indexed
+    # with one then conservatively assume we can't fuse.
+    if index_positions is None:
+        index_positions = {}
+    if indexed_arrays is None:
+        indexed_arrays = set()
+    if non_indexed_arrays is None:
+        non_indexed_arrays = set()
+
+    def add_check_position(new_position,
+                           array_accessed,
+                           index_positions,
+                           indexed_arrays,
+                           non_indexed_arrays):
+        """Returns True if there is a reason to prevent fusion based
+           on the rules described above.
+           new_position will be a list or tuple of booleans that
+           says whether the index in that spot is a parfor index
+           or not.  array_accessed is the array on which the access
+           is occurring."""
+
+        # Convert list indices to tuple for generality.
+        if isinstance(new_position, list):
+            new_position = tuple(new_position)
+
+        # If none of the indices are based on a parfor index.
+        if True not in new_position:
+            # See if this array has been accessed before with a
+            # a parfor index and if so say that we can't fuse.
+            if array_accessed in indexed_arrays:
+                return True
+            else:
+                # Either array is already in non_indexed arrays or we
+                # will add it.  Either way, this index usage can fuse.
+                non_indexed_arrays.add(array_accessed)
+                return False
+
+        # Fallthrough for cases where one of the indices is a parfor index.
+        # If this array was previously accessed without a parfor index then
+        # conservatively say we can't fuse.
+        if array_accessed in non_indexed_arrays:
+            return True
+
+        npsize = len(new_position)
+        # See if we have not seen a npsize dimensioned array accessed before.
+        if npsize not in index_positions:
+            # If not then add current set of parfor/non-parfor indices and
+            # indicate it is safe as it is the first usage.
+            index_positions[npsize] = new_position
+            return False
+
+        # Here we have a subsequent access to a npsize-dimensioned array.
+        # Make sure we see the same combination of parfor/non-parfor index
+        # indices that we've seen before.  If not then return True saying
+        # that we can't fuse.
+        return index_positions[npsize] != new_position
+
+    def check_index(stmt_index,
+                    array_accessed,
+                    index_positions,
+                    indexed_arrays,
+                    non_indexed_arrays,
+                    derived_from_indices):
+        """Looks at the indices of a getitem or setitem to see if there
+           is a reason that they would prevent fusion.
+           Returns True if fusion should be prohibited, False otherwise.
+        """
+        if isinstance(stmt_index, ir.Var):
+            # If the array is 2+ dimensions then the index should be a tuple.
+            if isinstance(typemap[stmt_index.name], types.BaseTuple):
+                # Get how the index tuple is constructed.
+                fbs_res = guard(find_build_sequence, func_ir, stmt_index)
+                if fbs_res is not None:
+                    ind_seq, _ = fbs_res
+                    # If any indices are derived from an index is used then
+                    # return True to say we can't fuse.
+                    if (all([x.name in indices or
+                        x.name not in derived_from_indices for x in ind_seq])):
+                        # Get position in index tuple where parfor indices used.
+                        new_index_positions = [x.name in indices for x in ind_seq]
+                        # Make sure that we aren't accessing a given array with
+                        # different indices in a different order.
+                        return add_check_position(new_index_positions,
+                                                  array_accessed,
+                                                  index_positions,
+                                                  indexed_arrays,
+                                                  non_indexed_arrays)
+                    else:
+                        # index derived from a parfor index used so no fusion
+                        return True
+                else:
+                    # Don't know how the index tuple is built so
+                    # have to assume fusion can't happen.
+                    return True
+            else:
+                # Should be for 1D arrays.
+                if stmt_index.name in indices:
+                    # Array indexed by a parfor index variable.
+                    # Make sure this 1D access is consistent with prior ones.
+                    return add_check_position((True,),
+                                              array_accessed,
+                                              index_positions,
+                                              indexed_arrays,
+                                              non_indexed_arrays)
+                elif stmt_index.name in derived_from_indices:
+                    # If we ever index an array with something calculated
+                    # from an index then no fusion.
+                    return True
+                else:
+                    # Some kind of index that isn't a parfor index or
+                    # one derived from one, e.g., a constant.  Make sure
+                    # this is consistent with prior accessed of this array.
+                    return add_check_position((False,),
+                                              array_accessed,
+                                              index_positions,
+                                              indexed_arrays,
+                                              non_indexed_arrays)
+        else:
+            # We don't know how to handle non-Var indices so no fusion.
+            return True
+
+        # All branches above should cover all the cases and each should
+        # return so we should never get here.
+        raise errors.InternalError("Some code path in the parfor fusion "
+                                   "cross-iteration dependency checker "
+                                   "check_index didn't return a result.")
+
+    # Iterate through all the statements in the parfor.
+    for b in parfor.loop_body.values():
+        for stmt in b.body:
+            # Make sure SetItem accesses are fusion safe.
+            if isinstance(stmt, (ir.SetItem, ir.StaticSetItem)):
+                if isinstance(typemap[stmt.target.name], types.npytypes.Array):
+                    # Check index safety with prior array accesses.
+                    if check_index(stmt.index,
+                                   stmt.target.name,
+                                   index_positions,
+                                   indexed_arrays,
+                                   non_indexed_arrays,
+                                   derived_from_indices):
+                        return True, index_positions, indexed_arrays, non_indexed_arrays
+                # Fusion safe so go to next statement.
+                continue
+            elif isinstance(stmt, ir.Assign):
+                # If stmt of form a = parfor_index then add "a" to set of
+                # parfor indices.
+                if isinstance(stmt.value, ir.Var):
+                    if stmt.value.name in indices:
+                        indices.add(stmt.target.name)
+                        continue
+                elif isinstance(stmt.value, ir.Expr):
+                    op = stmt.value.op
+                    # Make sure getitem accesses are fusion safe.
+                    if op in ['getitem', 'static_getitem']:
+                        if isinstance(typemap[stmt.value.value.name], types.npytypes.Array):
+                            # Check index safety with prior array accesses.
+                            if check_index(stmt.value.index,
+                                           stmt.value.value.name,
+                                           index_positions,
+                                           indexed_arrays,
+                                           non_indexed_arrays,
+                                           derived_from_indices):
+                                return True, index_positions, indexed_arrays, non_indexed_arrays
+                        # Fusion safe so go to next statement.
+                        continue
+                    elif op == 'call':
+                        # If there is a call in the parfor body that takes some
+                        # array parameter then we have no way to analyze what
+                        # that call is doing so presume it is unsafe for fusion.
+                        if (any([isinstance(typemap[x.name], types.npytypes.Array)
+                            for x in stmt.value.list_vars()])):
+                            return True, index_positions, indexed_arrays, non_indexed_arrays
+
+                    # Get the vars used by this non-setitem/getitem statement.
+                    rhs_vars = [x.name for x in stmt.value.list_vars()]
+                    # If a parfor index is used as part of this statement or
+                    # something previous determined to be derived from a parfor
+                    # index then add the target variable to the set of
+                    # variables that are derived from parfors and so should
+                    # prevent fusion if used as an index.
+                    if (not indices.isdisjoint(rhs_vars) or
+                        not derived_from_indices.isdisjoint(rhs_vars)):
+                        derived_from_indices.add(stmt.target.name)
+
+    return False, index_positions, indexed_arrays, non_indexed_arrays
+
+
+def dprint(*s):
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print(*s)
+
+def get_parfor_pattern_vars(parfor):
+    """ get the variables used in parfor pattern information
+    """
+    out = set()
+    # currently, only stencil pattern has variables
+    for pattern in parfor.patterns:
+        if pattern[0] == 'stencil':
+            left_lengths = pattern[1][0]
+            right_lengths = pattern[1][1]
+            for v in left_lengths+right_lengths:
+                if isinstance(v, ir.Var):
+                    out.add(v.name)
+    return out
+
+def remove_dead_parfor(parfor, lives, lives_n_aliases, arg_aliases, alias_map, func_ir, typemap):
+    """ remove dead code inside parfor including get/sets
+    """
+
+    with dummy_return_in_loop_body(parfor.loop_body):
+        labels = find_topo_order(parfor.loop_body)
+
+    # get/setitem replacement should ideally use dataflow to propagate setitem
+    # saved values, but for simplicity we handle the common case of propagating
+    # setitems in the first block (which is dominant) if the array is not
+    # potentially changed in any way
+    first_label = labels[0]
+    first_block_saved_values = {}
+    _update_parfor_get_setitems(
+        parfor.loop_body[first_label].body,
+        parfor.index_var, alias_map,
+        first_block_saved_values,
+        lives_n_aliases
+        )
+
+    # remove saved first block setitems if array potentially changed later
+    saved_arrs = set(first_block_saved_values.keys())
+    for l in labels:
+        if l == first_label:
+            continue
+        for stmt in parfor.loop_body[l].body:
+            if (isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Expr)
+                    and stmt.value.op == 'getitem'
+                    and stmt.value.index.name == parfor.index_var.name):
+                continue
+            varnames = set(v.name for v in stmt.list_vars())
+            rm_arrs = varnames & saved_arrs
+            for a in rm_arrs:
+                first_block_saved_values.pop(a, None)
+
+
+    # replace getitems with available value
+    # e.g. A[i] = v; ... s = A[i]  ->  s = v
+    for l in labels:
+        if l == first_label:
+            continue
+        block = parfor.loop_body[l]
+        saved_values = first_block_saved_values.copy()
+        _update_parfor_get_setitems(block.body, parfor.index_var, alias_map,
+                                        saved_values, lives_n_aliases)
+
+
+    # after getitem replacement, remove extra setitems
+    blocks = parfor.loop_body.copy()  # shallow copy is enough
+    last_label = max(blocks.keys())
+    return_label, tuple_var = _add_liveness_return_block(blocks, lives_n_aliases, typemap)
+    # jump to return label
+    jump = ir.Jump(return_label, ir.Loc("parfors_dummy", -1))
+    blocks[last_label].body.append(jump)
+    cfg = compute_cfg_from_blocks(blocks)
+    usedefs = compute_use_defs(blocks)
+    live_map = compute_live_map(cfg, blocks, usedefs.usemap, usedefs.defmap)
+    alias_set = set(alias_map.keys())
+
+    for label, block in blocks.items():
+        new_body = []
+        in_lives = {v.name for v in block.terminator.list_vars()}
+        # find live variables at the end of block
+        for out_blk, _data in cfg.successors(label):
+            in_lives |= live_map[out_blk]
+        for stmt in reversed(block.body):
+            # aliases of lives are also live for setitems
+            alias_lives = in_lives & alias_set
+            for v in alias_lives:
+                in_lives |= alias_map[v]
+            if (isinstance(stmt, (ir.StaticSetItem, ir.SetItem)) and
+                get_index_var(stmt).name == parfor.index_var.name and
+                stmt.target.name not in in_lives and
+                stmt.target.name not in arg_aliases):
+                continue
+            in_lives |= {v.name for v in stmt.list_vars()}
+            new_body.append(stmt)
+        new_body.reverse()
+        block.body = new_body
+
+    typemap.pop(tuple_var.name)  # remove dummy tuple type
+    blocks[last_label].body.pop()  # remove jump
+
+    """
+      Process parfor body recursively.
+      Note that this is the only place in this function that uses the
+      argument lives instead of lives_n_aliases.  The former does not
+      include the aliases of live variables but only the live variable
+      names themselves.  See a comment in this function for how that
+      is used.
+    """
+    remove_dead_parfor_recursive(
+        parfor, lives, arg_aliases, alias_map, func_ir, typemap)
+
+    # remove parfor if empty
+    is_empty = len(parfor.init_block.body) == 0
+    for block in parfor.loop_body.values():
+        is_empty &= len(block.body) == 0
+    if is_empty:
+        return None
+    return parfor
+
+def _update_parfor_get_setitems(block_body, index_var, alias_map,
+                                  saved_values, lives):
+    """
+    replace getitems of a previously set array in a block of parfor loop body
+    """
+    for stmt in block_body:
+        if (isinstance(stmt, (ir.StaticSetItem, ir.SetItem)) and
+            get_index_var(stmt).name == index_var.name and
+            stmt.target.name not in lives):
+            # saved values of aliases of SetItem target array are invalid
+            for w in alias_map.get(stmt.target.name, []):
+                saved_values.pop(w, None)
+            # set saved value after invalidation since alias_map may
+            # contain the array itself (e.g. pi example)
+            saved_values[stmt.target.name] = stmt.value
+            continue
+        if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Expr):
+            rhs = stmt.value
+            if rhs.op == 'getitem' and isinstance(rhs.index, ir.Var):
+                if rhs.index.name == index_var.name:
+                    # replace getitem if value saved
+                    stmt.value = saved_values.get(rhs.value.name, rhs)
+                    continue
+        # conservative assumption: array is modified if referenced
+        # remove all referenced arrays
+        for v in stmt.list_vars():
+            saved_values.pop(v.name, None)
+            # aliases are potentially modified as well
+            for w in alias_map.get(v.name, []):
+                saved_values.pop(w, None)
+
+    return
+
+ir_utils.remove_dead_extensions[Parfor] = remove_dead_parfor
+
+
+def remove_dead_parfor_recursive(parfor, lives, arg_aliases, alias_map,
+                                                             func_ir, typemap):
+    """create a dummy function from parfor and call remove dead recursively
+    """
+    blocks = parfor.loop_body.copy()  # shallow copy is enough
+    first_body_block = min(blocks.keys())
+    assert first_body_block > 0  # we are using 0 for init block here
+    last_label = max(blocks.keys())
+
+    """
+      Previously, this statement used lives_n_aliases.  That had the effect of
+      keeping variables in the init_block alive if they aliased an array that
+      was later written to.  By using just lives to indicate which variables
+      names are live at exit of the parfor but then using alias_map for the
+      actual recursive dead code removal, we keep any writes to aliased arrays
+      alive but also allow aliasing assignments (i.e., a = b) to be eliminated
+      so long as 'b' is not written to through the variable 'a' later on.
+      This makes assignment handling of remove_dead_block work properly since
+      it allows distinguishing between live variables and their aliases.
+    """
+    return_label, tuple_var = _add_liveness_return_block(blocks, lives, typemap)
+
+    # branch back to first body label to simulate loop
+    scope = blocks[last_label].scope
+
+    branchcond = ir.Var(scope, mk_unique_var("$branchcond"), ir.Loc("parfors_dummy", -1))
+    typemap[branchcond.name] = types.boolean
+
+    branch = ir.Branch(branchcond, first_body_block, return_label, ir.Loc("parfors_dummy", -1))
+    blocks[last_label].body.append(branch)
+
+    # add dummy jump in init_block for CFG to work
+    blocks[0] = parfor.init_block
+    blocks[0].body.append(ir.Jump(first_body_block, ir.Loc("parfors_dummy", -1)))
+
+    # args var including aliases is ok
+    remove_dead(blocks, arg_aliases, func_ir, typemap, alias_map, arg_aliases)
+    typemap.pop(tuple_var.name)  # remove dummy tuple type
+    blocks[0].body.pop()  # remove dummy jump
+    blocks[last_label].body.pop()  # remove branch
+    return
+
+def _add_liveness_return_block(blocks, lives, typemap):
+    last_label = max(blocks.keys())
+    return_label = last_label + 1
+
+    loc = blocks[last_label].loc
+    scope = blocks[last_label].scope
+    blocks[return_label] = ir.Block(scope, loc)
+
+    # add lives in a dummpy return to last block to avoid their removal
+    tuple_var = ir.Var(scope, mk_unique_var("$tuple_var"), loc)
+    # dummy type for tuple_var
+    typemap[tuple_var.name] = types.containers.UniTuple(
+        types.uintp, 2)
+    live_vars = [ir.Var(scope, v, loc) for v in lives]
+    tuple_call = ir.Expr.build_tuple(live_vars, loc)
+    blocks[return_label].body.append(ir.Assign(tuple_call, tuple_var, loc))
+    blocks[return_label].body.append(ir.Return(tuple_var, loc))
+    return return_label, tuple_var
+
+
+def find_potential_aliases_parfor(parfor, args, typemap, func_ir, alias_map, arg_aliases):
+    blocks = wrap_parfor_blocks(parfor)
+    ir_utils.find_potential_aliases(
+        blocks, args, typemap, func_ir, alias_map, arg_aliases)
+    unwrap_parfor_blocks(parfor)
+    return
+
+ir_utils.alias_analysis_extensions[Parfor] = find_potential_aliases_parfor
+
+def simplify_parfor_body_CFG(blocks):
+    """simplify CFG of body loops in parfors"""
+    n_parfors = 0
+    for block in blocks.values():
+        for stmt in block.body:
+            if isinstance(stmt, Parfor):
+                n_parfors += 1
+                parfor = stmt
+                # add dummy return to enable CFG creation
+                # can't use dummy_return_in_loop_body since body changes
+                last_block = parfor.loop_body[max(parfor.loop_body.keys())]
+                scope = last_block.scope
+                loc = ir.Loc("parfors_dummy", -1)
+                const = ir.Var(scope, mk_unique_var("$const"), loc)
+                last_block.body.append(ir.Assign(ir.Const(0, loc), const, loc))
+                last_block.body.append(ir.Return(const, loc))
+                parfor.loop_body = simplify_CFG(parfor.loop_body)
+                last_block = parfor.loop_body[max(parfor.loop_body.keys())]
+                last_block.body.pop()
+                # call on body recursively
+                simplify_parfor_body_CFG(parfor.loop_body)
+    return n_parfors
+
+
+def wrap_parfor_blocks(parfor, entry_label = None):
+    """wrap parfor blocks for analysis/optimization like CFG"""
+    blocks = parfor.loop_body.copy()  # shallow copy is enough
+    if entry_label is None:
+        entry_label = min(blocks.keys())
+    assert entry_label > 0  # we are using 0 for init block here
+
+    # add dummy jump in init_block for CFG to work
+    blocks[0] = parfor.init_block
+    blocks[0].body.append(ir.Jump(entry_label, blocks[0].loc))
+    for block in blocks.values():
+        if len(block.body) == 0 or (not block.body[-1].is_terminator):
+            block.body.append(ir.Jump(entry_label, block.loc))
+    return blocks
+
+
+def unwrap_parfor_blocks(parfor, blocks=None):
+    """
+    unwrap parfor blocks after analysis/optimization.
+    Allows changes to the parfor loop.
+    """
+    if blocks is not None:
+        # make sure init block isn't removed
+        init_block_label = min(blocks.keys())
+        # update loop body blocks
+        blocks.pop(init_block_label)
+        parfor.loop_body = blocks
+
+    # make sure dummy jump to loop body isn't altered
+    first_body_label = min(parfor.loop_body.keys())
+    assert isinstance(parfor.init_block.body[-1], ir.Jump)
+
+    # remove dummy jump to loop body
+    parfor.init_block.body.pop()
+
+    # make sure dummy jump back to loop body isn't altered
+    for block in parfor.loop_body.values():
+        if (isinstance(block.body[-1], ir.Jump) and
+            block.body[-1].target == first_body_label):
+            # remove dummy jump back to loop
+            block.body.pop()
+    return
+
+
+def get_copies_parfor(parfor, typemap):
+    """find copies generated/killed by parfor"""
+    blocks = wrap_parfor_blocks(parfor)
+    in_copies_parfor, out_copies_parfor = copy_propagate(blocks, typemap)
+    in_gen_copies, in_extra_kill = get_block_copies(blocks, typemap)
+    unwrap_parfor_blocks(parfor)
+
+    # parfor's extra kill is kills of its init block,
+    # and all possible gens and kills of it's body loop.
+    # body doesn't gen and only kills since it may or may not run
+    # TODO: save copies that are repeated in parfor
+    kill_set = in_extra_kill[0]
+    for label in parfor.loop_body.keys():
+        kill_set |= {l for l, r in in_gen_copies[label]}
+        kill_set |= in_extra_kill[label]
+
+    # gen copies is copies generated by init that are not killed by body
+    last_label = max(parfor.loop_body.keys())
+    gens = out_copies_parfor[last_label] & in_gen_copies[0]
+
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("copy propagate parfor gens:", gens, "kill_set", kill_set)
+    return gens, kill_set
+
+
+ir_utils.copy_propagate_extensions[Parfor] = get_copies_parfor
+
+
+def apply_copies_parfor(parfor, var_dict, name_var_table,
+                        typemap, calltypes, save_copies):
+    """apply copy propagate recursively in parfor"""
+    # replace variables in pattern metadata like stencil neighborhood
+    for i, pattern in enumerate(parfor.patterns):
+        if pattern[0] == 'stencil':
+            parfor.patterns[i] = ('stencil',
+                replace_vars_inner(pattern[1], var_dict))
+
+    # replace loop boundary variables
+    for l in parfor.loop_nests:
+        l.start = replace_vars_inner(l.start, var_dict)
+        l.stop = replace_vars_inner(l.stop, var_dict)
+        l.step = replace_vars_inner(l.step, var_dict)
+
+    blocks = wrap_parfor_blocks(parfor)
+    # add dummy assigns for each copy
+    assign_list = []
+    for lhs_name, rhs in var_dict.items():
+        assign_list.append(ir.Assign(rhs, name_var_table[lhs_name],
+                                     ir.Loc("dummy", -1)))
+    blocks[0].body = assign_list + blocks[0].body
+    in_copies_parfor, out_copies_parfor = copy_propagate(blocks, typemap)
+    apply_copy_propagate(blocks, in_copies_parfor, name_var_table, typemap,
+                         calltypes, save_copies)
+    unwrap_parfor_blocks(parfor)
+    # remove dummy assignments
+    blocks[0].body = blocks[0].body[len(assign_list):]
+    return
+
+
+ir_utils.apply_copy_propagate_extensions[Parfor] = apply_copies_parfor
+
+
+def push_call_vars(blocks, saved_globals, saved_getattrs, typemap, nested=False):
+    """push call variables to right before their call site.
+    assuming one global/getattr is created for each call site and control flow
+    doesn't change it.
+    """
+    for block in blocks.values():
+        new_body = []
+        # global/attr variables that are defined in this block already,
+        #   no need to reassign them
+        block_defs = set()
+        # Some definitions are copied right before the call but then we
+        # need to rename that symbol in that block so that typing won't
+        # generate an error trying to lock the save var twice.
+        # In rename_dict, we collect the symbols that must be renamed in
+        # this block.  We collect them then apply the renaming at the end.
+        rename_dict = {}
+        for stmt in block.body:
+            def process_assign(stmt):
+                if isinstance(stmt, ir.Assign):
+                    rhs = stmt.value
+                    lhs = stmt.target
+                    if (isinstance(rhs, ir.Global)):
+                        saved_globals[lhs.name] = stmt
+                        block_defs.add(lhs.name)
+                    elif isinstance(rhs, ir.Expr) and rhs.op == 'getattr':
+                        if (rhs.value.name in saved_globals
+                                or rhs.value.name in saved_getattrs):
+                            saved_getattrs[lhs.name] = stmt
+                            block_defs.add(lhs.name)
+
+            if not nested and isinstance(stmt, Parfor):
+                for s in stmt.init_block.body:
+                    process_assign(s)
+                pblocks = stmt.loop_body.copy()
+                push_call_vars(pblocks, saved_globals, saved_getattrs, typemap, nested=True)
+                new_body.append(stmt)
+                continue
+            else:
+                process_assign(stmt)
+            for v in stmt.list_vars():
+                new_body += _get_saved_call_nodes(v.name, saved_globals,
+                                                  saved_getattrs, block_defs, rename_dict)
+            new_body.append(stmt)
+        block.body = new_body
+        # If there is anything to rename then apply the renaming here.
+        if len(rename_dict) > 0:
+            # Fix-up the typing for the renamed vars.
+            for k, v in rename_dict.items():
+                typemap[v] = typemap[k]
+            # This is only to call replace_var_names which takes a dict.
+            temp_blocks = {0: block}
+            replace_var_names(temp_blocks, rename_dict)
+
+    return
+
+
+def _get_saved_call_nodes(fname, saved_globals, saved_getattrs, block_defs, rename_dict):
+    """ Implement the copying of globals or getattrs for the purposes noted in
+        push_call_vars.  We make a new var and assign to it a copy of the
+        global or getattr.  We remember this new assignment node and add an
+        entry in the renaming dictionary so that for this block the original
+        var name is replaced by the new var name we created.
+    """
+    nodes = []
+    while (fname not in block_defs and (fname in saved_globals
+                                        or fname in saved_getattrs)):
+        def rename_global_or_getattr(obj, var_base, nodes, block_defs, rename_dict):
+            assert(isinstance(obj, ir.Assign))
+            renamed_var = ir.Var(obj.target.scope,
+                                 mk_unique_var(var_base),
+                                 obj.target.loc)
+            renamed_assign = ir.Assign(copy.deepcopy(obj.value),
+                                       renamed_var,
+                                       obj.loc)
+            nodes.append(renamed_assign)
+            block_defs.add(obj.target.name)
+            rename_dict[obj.target.name] = renamed_assign.target.name
+
+        if fname in saved_globals:
+            rename_global_or_getattr(saved_globals[fname], "$push_global_to_block",
+                                     nodes, block_defs, rename_dict)
+            fname = '_PA_DONE'
+        elif fname in saved_getattrs:
+            rename_global_or_getattr(saved_getattrs[fname], "$push_getattr_to_block",
+                                     nodes, block_defs, rename_dict)
+            fname = saved_getattrs[fname].value.value.name
+    nodes.reverse()
+    return nodes
+
+def repr_arrayexpr(arrayexpr):
+    """Extract operators from arrayexpr to represent it abstractly as a string.
+    """
+    if isinstance(arrayexpr, tuple):
+        opr = arrayexpr[0]
+        # sometimes opr is not string like '+', but is a ufunc object
+        if not isinstance(opr, str):
+            if hasattr(opr, '__name__'):
+                opr = opr.__name__
+            else:
+                opr = '_'  # can return dummy since repr is not critical
+        args = arrayexpr[1]
+        if len(args) == 1:
+            return '({}({}))'.format(opr, repr_arrayexpr(args[0]))
+        else:
+            opr = ' ' + opr + ' '
+            return '({})'.format(opr.join([ repr_arrayexpr(x) for x in args ]))
+    elif isinstance(arrayexpr, numba.core.ir.Var):
+        name = arrayexpr.name
+        if name.startswith('$'):
+            return '\'%s\' (temporary variable)' % name
+        else:
+            return name
+    elif isinstance(arrayexpr, numba.core.ir.Const):
+        return repr(arrayexpr.value)
+    else:
+        return '_'
+
+def fix_generator_types(generator_info, return_type, typemap):
+    """postproc updates generator_info with live variables after transformations
+    but generator variables have types in return_type that are updated here.
+    """
+    new_state_types = []
+    for v in generator_info.state_vars:
+        new_state_types.append(typemap[v])
+    return_type.state_types = tuple(new_state_types)
+    return
+
+
+def get_parfor_call_table(parfor, call_table=None, reverse_call_table=None):
+    if call_table is None:
+        call_table = {}
+    if reverse_call_table is None:
+        reverse_call_table = {}
+    blocks = wrap_parfor_blocks(parfor)
+    call_table, reverse_call_table = get_call_table(blocks, call_table,
+                                                    reverse_call_table)
+    unwrap_parfor_blocks(parfor)
+    return call_table, reverse_call_table
+
+
+ir_utils.call_table_extensions[Parfor] = get_parfor_call_table
+
+
+def get_parfor_tuple_table(parfor, tuple_table=None):
+    if tuple_table is None:
+        tuple_table = {}
+    blocks = wrap_parfor_blocks(parfor)
+    tuple_table = ir_utils.get_tuple_table(blocks, tuple_table)
+    unwrap_parfor_blocks(parfor)
+    return tuple_table
+
+
+ir_utils.tuple_table_extensions[Parfor] = get_parfor_tuple_table
+
+
+def get_parfor_array_accesses(parfor, accesses=None):
+    if accesses is None:
+        accesses = set()
+    blocks = wrap_parfor_blocks(parfor)
+    accesses = ir_utils.get_array_accesses(blocks, accesses)
+    unwrap_parfor_blocks(parfor)
+    return accesses
+
+
+# parfor handler is same as
+ir_utils.array_accesses_extensions[Parfor] = get_parfor_array_accesses
+
+
+def parfor_add_offset_to_labels(parfor, offset):
+    blocks = wrap_parfor_blocks(parfor)
+    blocks = add_offset_to_labels(blocks, offset)
+    blocks[0] = blocks[offset]
+    blocks.pop(offset)
+    unwrap_parfor_blocks(parfor, blocks)
+    return
+
+
+ir_utils.add_offset_to_labels_extensions[Parfor] = parfor_add_offset_to_labels
+
+
+def parfor_find_max_label(parfor):
+    blocks = wrap_parfor_blocks(parfor)
+    max_label = ir_utils.find_max_label(blocks)
+    unwrap_parfor_blocks(parfor)
+    return max_label
+
+ir_utils.find_max_label_extensions[Parfor] = parfor_find_max_label
+
+
+def parfor_typeinfer(parfor, typeinferer):
+    save_blocks = typeinferer.blocks
+    blocks = wrap_parfor_blocks(parfor)
+    index_vars = [l.index_variable for l in parfor.loop_nests]
+    # no need to handle parfor.index_var (tuple of variables), since it will be
+    # assigned to a tuple from individual indices
+    first_block = min(blocks.keys())
+    loc = blocks[first_block].loc
+    # XXX
+    index_assigns = [ir.Assign(ir.Const(1, loc=loc, use_literal_type=False), v, loc) for v in index_vars]
+    save_first_block_body = blocks[first_block].body
+    blocks[first_block].body = index_assigns + blocks[first_block].body
+    typeinferer.blocks = blocks
+    typeinferer.build_constraint()
+    typeinferer.blocks = save_blocks
+    blocks[first_block].body = save_first_block_body
+    unwrap_parfor_blocks(parfor)
+
+
+typeinfer.typeinfer_extensions[Parfor] = parfor_typeinfer
+
+def build_parfor_definitions(parfor, definitions=None):
+    """get variable definition table for parfors"""
+    if definitions is None:
+        definitions = defaultdict(list)
+
+    # avoid wrap_parfor_blocks() since build_definitions is called inside
+    # find_potential_aliases_parfor where the parfor is already wrapped
+    build_definitions(parfor.loop_body, definitions)
+    build_definitions({0: parfor.init_block}, definitions)
+    return definitions
+
+ir_utils.build_defs_extensions[Parfor] = build_parfor_definitions
+
+@contextmanager
+def dummy_return_in_loop_body(loop_body):
+    """adds dummy return to last block of parfor loop body for CFG computation
+    """
+    # max is last block since we add it manually for prange
+    last_label = max(loop_body.keys())
+    scope = loop_body[last_label].scope
+    const = ir.Var(scope, mk_unique_var("$const"), ir.Loc("parfors_dummy", -1))
+    loop_body[last_label].body.append(
+        ir.Return(const, ir.Loc("parfors_dummy", -1)))
+    yield
+    # remove dummy return
+    loop_body[last_label].body.pop()
+
+@infer_global(reduce)
+class ReduceInfer(AbstractTemplate):
+    def generic(self, args, kws):
+        assert not kws
+        if len(args) != 3:
+            raise errors.NumbaAssertionError("len(args) != 3")
+        assert isinstance(args[1], types.Array)
+        return signature(args[1].dtype, *args)
+
+
+def ensure_parallel_support():
+    """Check if the platform supports parallel=True and raise if it does not.
+    """
+    if config.IS_32BITS:
+        msg = ("The 'parallel' target is not currently supported on 32 bit "
+               "hardware.")
+        raise errors.UnsupportedParforsError(msg)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor_lowering.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor_lowering.py
new file mode 100644
index 000000000..edee0e4ff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor_lowering.py
@@ -0,0 +1,1923 @@
+import copy
+import operator
+
+import types as pytypes
+import operator
+import warnings
+from dataclasses import make_dataclass
+
+import llvmlite.ir
+import numpy as np
+
+import numba
+from numba.parfors import parfor
+from numba.core import types, ir, config, compiler, sigutils, cgutils
+from numba.core.ir_utils import (
+    add_offset_to_labels,
+    replace_var_names,
+    remove_dels,
+    legalize_names,
+    mk_unique_var,
+    rename_labels,
+    get_name_var_table,
+    visit_vars_inner,
+    get_definition,
+    guard,
+    get_call_table,
+    is_pure,
+    get_np_ufunc_typ,
+    get_unused_var_name,
+    is_const_call,
+    fixup_var_define_in_scope,
+    transfer_scope,
+    find_max_label,
+    get_global_func_typ,
+)
+from numba.core.typing import signature
+from numba.parfors.parfor import ensure_parallel_support
+from numba.core.errors import (
+    NumbaParallelSafetyWarning, NotDefinedError, CompilerError, InternalError,
+)
+from numba.parfors.parfor_lowering_utils import ParforLoweringBuilder
+
+def _lower_parfor_parallel(lowerer, parfor):
+    """Lowerer that handles LLVM code generation for parfor.
+    This function lowers a parfor IR node to LLVM.
+    The general approach is as follows:
+    1) The code from the parfor's init block is lowered normally
+       in the context of the current function.
+    2) The body of the parfor is transformed into a gufunc function.
+    3) Code is inserted into the main function that calls do_scheduling
+       to divide the iteration space for each thread, allocates
+       reduction arrays, calls the gufunc function, and then invokes
+       the reduction function across the reduction arrays to produce
+       the final reduction values.
+    """
+    from numba.np.ufunc.parallel import get_thread_count
+
+    ensure_parallel_support()
+    typingctx = lowerer.context.typing_context
+    targetctx = lowerer.context
+    builder = lowerer.builder
+    # We copy the typemap here because for race condition variable we'll
+    # update their type to array so they can be updated by the gufunc.
+    orig_typemap = lowerer.fndesc.typemap
+    # replace original typemap with copy and restore the original at the end.
+    lowerer.fndesc.typemap = copy.copy(orig_typemap)
+    if config.DEBUG_ARRAY_OPT:
+        print("lowerer.fndesc", lowerer.fndesc, type(lowerer.fndesc))
+    typemap = lowerer.fndesc.typemap
+    varmap = lowerer.varmap
+
+    if config.DEBUG_ARRAY_OPT:
+        print("_lower_parfor_parallel")
+        parfor.dump()
+
+    loc = parfor.init_block.loc
+    scope = parfor.init_block.scope
+
+    # produce instructions for init_block
+    if config.DEBUG_ARRAY_OPT:
+        print("init_block = ", parfor.init_block, " ", type(parfor.init_block))
+    for instr in parfor.init_block.body:
+        if config.DEBUG_ARRAY_OPT:
+            print("lower init_block instr = ", instr)
+        lowerer.lower_inst(instr)
+
+    for racevar in parfor.races:
+        if racevar not in varmap:
+            rvtyp = typemap[racevar]
+            rv = ir.Var(scope, racevar, loc)
+            lowerer._alloca_var(rv.name, rvtyp)
+
+    alias_map = {}
+    arg_aliases = {}
+    numba.parfors.parfor.find_potential_aliases_parfor(parfor, parfor.params, typemap,
+                                        lowerer.func_ir, alias_map, arg_aliases)
+    if config.DEBUG_ARRAY_OPT:
+        print("alias_map", alias_map)
+        print("arg_aliases", arg_aliases)
+
+    # run get_parfor_outputs() and get_parfor_reductions() before gufunc creation
+    # since Jumps are modified so CFG of loop_body dict will become invalid
+    assert parfor.params is not None
+
+    parfor_output_arrays = numba.parfors.parfor.get_parfor_outputs(
+        parfor, parfor.params)
+    parfor_redvars, parfor_reddict = parfor.redvars, parfor.reddict
+    if config.DEBUG_ARRAY_OPT:
+        print("parfor_redvars:", parfor_redvars)
+        print("parfor_reddict:", parfor_reddict)
+
+    # init reduction array allocation here.
+    nredvars = len(parfor_redvars)
+    redarrs = {}
+    if nredvars > 0:
+        # reduction arrays outer dimension equal to thread count
+        scope = parfor.init_block.scope
+        loc = parfor.init_block.loc
+        pfbdr = ParforLoweringBuilder(lowerer=lowerer, scope=scope, loc=loc)
+
+        # Get the Numba internal function to call to get the thread count.
+        get_num_threads = pfbdr.bind_global_function(
+            fobj=numba.np.ufunc.parallel._iget_num_threads,
+            ftype=get_global_func_typ(numba.np.ufunc.parallel._iget_num_threads),
+            args=()
+        )
+
+        # Insert the call to assign the thread count to a variable.
+        num_threads_var = pfbdr.assign(
+            rhs=pfbdr.call(get_num_threads, args=[]),
+            typ=types.intp,
+            name="num_threads_var")
+
+        # For each reduction variable...
+        for i in range(nredvars):
+            red_name = parfor_redvars[i]
+            # Get the type of the reduction variable.
+            redvar_typ = lowerer.fndesc.typemap[red_name]
+            # Get the ir.Var for the reduction variable.
+            redvar = ir.Var(scope, red_name, loc)
+            # Get the type of the array that holds the per-thread
+            # reduction variables.
+            redarrvar_typ = redtyp_to_redarraytype(redvar_typ)
+            reddtype = redarrvar_typ.dtype
+            if config.DEBUG_ARRAY_OPT:
+                print(
+                    "reduction_info",
+                    red_name,
+                    redvar_typ,
+                    redarrvar_typ,
+                    reddtype,
+                    types.DType(reddtype),
+                    num_threads_var,
+                    type(num_threads_var)
+                )
+
+            # If this is reduction over an array,
+            # the reduction array has just one added per-worker dimension.
+            if isinstance(redvar_typ, types.npytypes.Array):
+                redarrdim = redvar_typ.ndim + 1
+            else:
+                redarrdim = 1
+
+            # Reduction array is created and initialized to the initial reduction value.
+
+            # First create a var for the numpy empty ufunc.
+            glbl_np_empty = pfbdr.bind_global_function(
+                fobj=np.empty,
+                ftype=get_np_ufunc_typ(np.empty),
+                args=(
+                    types.UniTuple(types.intp, redarrdim),
+                ),
+                kws={'dtype': types.DType(reddtype)}
+            )
+
+            size_var_list = [num_threads_var]
+
+            # If this is a reduction over an array...
+            if isinstance(redvar_typ, types.npytypes.Array):
+                # Add code to get the shape of the array being reduced over.
+                redshape_var = pfbdr.assign(
+                    rhs=ir.Expr.getattr(redvar, "shape", loc),
+                    typ=types.UniTuple(types.intp, redvar_typ.ndim),
+                    name="redarr_shape",
+                )
+
+                # Add the dimension sizes of the array being reduced over to the tuple of sizes pass to empty.
+                for j in range(redvar_typ.ndim):
+                    onedimvar = pfbdr.assign(
+                        rhs=ir.Expr.static_getitem(redshape_var, j, None, loc),
+                        typ=types.intp,
+                        name="redshapeonedim",
+                    )
+                    size_var_list.append(onedimvar)
+
+            # Empty call takes tuple of sizes.  Create here and fill in outer dimension (num threads).
+            size_var = pfbdr.make_tuple_variable(
+                size_var_list, name='tuple_size_var',
+            )
+
+            # Resolve dtype
+            cval = pfbdr._typingctx.resolve_value_type(reddtype)
+            dt = pfbdr.make_const_variable(cval=cval, typ=types.DType(reddtype))
+            # Add call to empty passing the size var tuple.
+            empty_call = pfbdr.call(glbl_np_empty, args=[size_var, dt])
+
+            redarr_var = pfbdr.assign(
+                rhs=empty_call, typ=redarrvar_typ, name="redarr",
+            )
+
+            # Remember mapping of original reduction array to the newly created per-worker reduction array.
+            redarrs[redvar.name] = redarr_var
+
+            init_val = parfor_reddict[red_name].init_val
+
+            if init_val is not None:
+                if isinstance(redvar_typ, types.npytypes.Array):
+                    # Create an array of identity values for the reduction.
+                    # First, create a variable for np.full.
+                    full_func_node = pfbdr.bind_global_function(
+                        fobj=np.full,
+                        ftype=get_np_ufunc_typ(np.full),
+                        args=(
+                            types.UniTuple(types.intp, redvar_typ.ndim),
+                            reddtype,
+                        ),
+                        kws={'dtype': types.DType(reddtype)},
+                    )
+
+                    # Then create a var with the identify value.
+                    init_val_var = pfbdr.make_const_variable(
+                        cval=init_val,
+                        typ=reddtype,
+                        name="init_val",
+                    )
+
+                    # Then, call np.full with the shape of the reduction array and the identity value.
+                    full_call = pfbdr.call(
+                        full_func_node, args=[redshape_var, init_val_var, dt],
+                    )
+
+                    redtoset = pfbdr.assign(
+                        rhs=full_call,
+                        typ=redvar_typ,
+                        name="redtoset",
+                    )
+                else:
+                    redtoset = pfbdr.make_const_variable(
+                        cval=init_val,
+                        typ=reddtype,
+                        name="redtoset",
+                    )
+            else:
+                redtoset = redvar
+
+                if config.DEBUG_ARRAY_OPT_RUNTIME:
+                    res_print_str = "res_print1 for redvar " + str(redvar) + ":"
+                    strconsttyp = types.StringLiteral(res_print_str)
+
+                    lhs = pfbdr.make_const_variable(
+                        cval=res_print_str,
+                        typ=strconsttyp,
+                        name="str_const",
+                    )
+
+                    res_print = ir.Print(args=[lhs, redvar],
+                                         vararg=None, loc=loc)
+                    lowerer.fndesc.calltypes[res_print] = signature(types.none,
+                                                             typemap[lhs.name],
+                                                             typemap[redvar.name])
+                    print("res_print_redvar", res_print)
+                    lowerer.lower_inst(res_print)
+
+
+            # For each thread, initialize the per-worker reduction array to
+            # the current reduction array value.
+
+            # Get the Numba type of the variable that holds the thread count.
+            num_thread_type = typemap[num_threads_var.name]
+            # Get the LLVM type of the thread count variable.
+            ntllvm_type = targetctx.get_value_type(num_thread_type)
+            # Create a LLVM variable to hold the loop index.
+            alloc_loop_var = cgutils.alloca_once(builder, ntllvm_type)
+            # Associate this LLVM variable to a Numba IR variable so that
+            # we can use setitem IR builder.
+            # Create a Numba IR variable.
+            numba_ir_loop_index_var = scope.redefine("$loop_index", loc)
+            # Give that variable the right type.
+            typemap[numba_ir_loop_index_var.name] = num_thread_type
+            # Associate this Numba variable to the LLVM variable in the
+            # lowerer's varmap.
+            lowerer.varmap[numba_ir_loop_index_var.name] = alloc_loop_var
+            # Insert a loop into the outputed LLVM that goes from 0 to
+            # the current thread count.
+            with cgutils.for_range(builder, lowerer.loadvar(num_threads_var.name), intp=ntllvm_type) as loop:
+                # Store the loop index into the alloca'd LLVM loop index variable.
+                builder.store(loop.index, alloc_loop_var)
+                # Initialize one element of the reduction array using the Numba
+                # IR variable associated with this loop's index.
+                pfbdr.setitem(obj=redarr_var, index=numba_ir_loop_index_var, val=redtoset)
+
+    # compile parfor body as a separate function to be used with GUFuncWrapper
+    flags = parfor.flags.copy()
+    flags.error_model = "numpy"
+    # Can't get here unless  flags.auto_parallel == ParallelOptions(True)
+    index_var_typ = typemap[parfor.loop_nests[0].index_variable.name]
+    # index variables should have the same type, check rest of indices
+    for l in parfor.loop_nests[1:]:
+        assert typemap[l.index_variable.name] == index_var_typ
+    numba.parfors.parfor.sequential_parfor_lowering = True
+    try:
+        (func,
+         func_args,
+         func_sig,
+         func_arg_types,
+         exp_name_to_tuple_var) = _create_gufunc_for_parfor_body(
+            lowerer, parfor, typemap, typingctx, targetctx, flags, {},
+            bool(alias_map), index_var_typ, parfor.races)
+    finally:
+        numba.parfors.parfor.sequential_parfor_lowering = False
+
+    # get the shape signature
+    func_args = ['sched'] + func_args
+    num_reductions = len(parfor_redvars)
+    num_inputs = len(func_args) - len(parfor_output_arrays) - num_reductions
+    if config.DEBUG_ARRAY_OPT:
+        print("func_args = ", func_args)
+        print("num_inputs = ", num_inputs)
+        print("parfor_outputs = ", parfor_output_arrays)
+        print("parfor_redvars = ", parfor_redvars)
+        print("num_reductions = ", num_reductions)
+    gu_signature = _create_shape_signature(
+        parfor.get_shape_classes,
+        num_inputs,
+        num_reductions,
+        func_args,
+        func_sig,
+        parfor.races,
+        typemap)
+    if config.DEBUG_ARRAY_OPT:
+        print("gu_signature = ", gu_signature)
+
+    # call the func in parallel by wrapping it with ParallelGUFuncBuilder
+    loop_ranges = [(l.start, l.stop, l.step) for l in parfor.loop_nests]
+    if config.DEBUG_ARRAY_OPT:
+        print("loop_nests = ", parfor.loop_nests)
+        print("loop_ranges = ", loop_ranges)
+    call_parallel_gufunc(
+        lowerer,
+        func,
+        gu_signature,
+        func_sig,
+        func_args,
+        func_arg_types,
+        loop_ranges,
+        parfor_redvars,
+        parfor_reddict,
+        redarrs,
+        parfor.init_block,
+        index_var_typ,
+        parfor.races,
+        exp_name_to_tuple_var)
+
+    if nredvars > 0:
+        _parfor_lowering_finalize_reduction(
+            parfor, redarrs, lowerer, parfor_reddict, num_threads_var,
+        )
+
+    # Cleanup reduction variable
+    for v in redarrs.values():
+        lowerer.lower_inst(ir.Del(v.name, loc=loc))
+    # Restore the original typemap of the function that was replaced temporarily at the
+    # Beginning of this function.
+    lowerer.fndesc.typemap = orig_typemap
+
+    if config.DEBUG_ARRAY_OPT:
+        print("_lower_parfor_parallel done")
+
+
+_ReductionInfo = make_dataclass(
+    "_ReductionInfo",
+    [
+        "redvar_info",
+        "redvar_name",
+        "redvar_typ",
+        "redarr_var",
+        "redarr_typ",
+        "init_val",
+    ],
+    frozen=True,
+)
+
+
+def _parfor_lowering_finalize_reduction(
+        parfor,
+        redarrs,
+        lowerer,
+        parfor_reddict,
+        thread_count_var,
+    ):
+    """Emit code to finalize the reduction from the intermediate values of
+    each thread.
+    """
+    # For each reduction variable
+    for redvar_name, redarr_var in redarrs.items():
+        # Pseudo-code for this loop body:
+        #     tmp = redarr[0]
+        #     for i in range(1, thread_count):
+        #         tmp = reduce_op(redarr[i], tmp)
+        #     reduction_result = tmp
+        redvar_typ = lowerer.fndesc.typemap[redvar_name]
+        redarr_typ = lowerer.fndesc.typemap[redarr_var.name]
+        init_val = lowerer.loadvar(redvar_name)
+
+        reduce_info = _ReductionInfo(
+            redvar_info = parfor_reddict[redvar_name],
+            redvar_name=redvar_name,
+            redvar_typ=redvar_typ,
+            redarr_var=redarr_var,
+            redarr_typ=redarr_typ,
+            init_val=init_val,
+        )
+        # generate code for combining reduction variable with thread output
+        handler = (_lower_trivial_inplace_binops
+                   if reduce_info.redvar_info.redop is not None
+                   else _lower_non_trivial_reduce)
+        handler(parfor, lowerer, thread_count_var, reduce_info)
+
+
+class ParforsUnexpectedReduceNodeError(InternalError):
+    def __init__(self, inst):
+        super().__init__(f"Unknown reduce instruction node: {inst}")
+
+
+def _lower_trivial_inplace_binops(parfor, lowerer, thread_count_var, reduce_info):
+    """Lower trivial inplace-binop reduction.
+    """
+    for inst in reduce_info.redvar_info.reduce_nodes:
+        # Var assigns to Var?
+        if _lower_var_to_var_assign(lowerer, inst):
+            pass
+        # Is inplace-binop for the reduction?
+        elif _is_inplace_binop_and_rhs_is_init(inst, reduce_info.redvar_name):
+            fn = inst.value.fn
+            redvar_result = _emit_binop_reduce_call(
+                fn, lowerer, thread_count_var, reduce_info,
+            )
+            lowerer.storevar(redvar_result, name=inst.target.name)
+        # Otherwise?
+        else:
+            raise ParforsUnexpectedReduceNodeError(inst)
+
+        # XXX: This seems like a hack to stop the loop with this condition.
+        if _fix_redvar_name_ssa_mismatch(parfor, lowerer, inst,
+                                   reduce_info.redvar_name):
+            break
+    if config.DEBUG_ARRAY_OPT_RUNTIME:
+        varname = reduce_info.redvar_name
+        lowerer.print_variable(
+            f"{parfor.loc}: parfor {fn.__name__} reduction {varname} =",
+            varname,
+        )
+
+
+def _lower_non_trivial_reduce(parfor, lowerer, thread_count_var, reduce_info):
+    """Lower non-trivial reduction such as call to `functools.reduce()`.
+    """
+    init_name = f"{reduce_info.redvar_name}#init"
+    # The init_name variable is not defined at this point.
+    lowerer.fndesc.typemap.setdefault(init_name, reduce_info.redvar_typ)
+    # Emit a sequence of the reduction operation for each intermediate result
+    # of each thread.
+    num_thread_llval = lowerer.loadvar(thread_count_var.name)
+    with cgutils.for_range(lowerer.builder, num_thread_llval) as loop:
+        tid = loop.index
+        for inst in reduce_info.redvar_info.reduce_nodes:
+            # Var assigns to Var?
+            if _lower_var_to_var_assign(lowerer, inst):
+                pass
+            # The reduction operation?
+            elif (isinstance(inst, ir.Assign)
+                    and any(var.name == init_name for var in inst.list_vars())):
+                elem = _emit_getitem_call(tid, lowerer, reduce_info)
+                lowerer.storevar(elem, init_name)
+                lowerer.lower_inst(inst)
+
+            # Otherwise?
+            else:
+                raise ParforsUnexpectedReduceNodeError(inst)
+
+            # XXX: This seems like a hack to stop the loop with this condition.
+            if _fix_redvar_name_ssa_mismatch(parfor, lowerer, inst,
+                                       reduce_info.redvar_name):
+                break
+
+    if config.DEBUG_ARRAY_OPT_RUNTIME:
+        varname = reduce_info.redvar_name
+        lowerer.print_variable(
+            f"{parfor.loc}: parfor non-trivial reduction {varname} =",
+            varname,
+        )
+
+def _lower_var_to_var_assign(lowerer, inst):
+    """Lower Var->Var assignment.
+
+    Returns True if-and-only-if `inst` is a Var->Var assignment.
+    """
+    if isinstance(inst, ir.Assign) and isinstance(inst.value, ir.Var):
+        loaded = lowerer.loadvar(inst.value.name)
+        lowerer.storevar(loaded, name=inst.target.name)
+        return True
+    return False
+
+def _emit_getitem_call(idx, lowerer, reduce_info):
+    """Emit call to ``redarr_var[idx]``
+    """
+    def reducer_getitem(redarr, index):
+        return redarr[index]
+
+    builder = lowerer.builder
+    ctx = lowerer.context
+    redarr_typ = reduce_info.redarr_typ
+    arg_arr = lowerer.loadvar(reduce_info.redarr_var.name)
+    args = (arg_arr, idx)
+    sig = signature(reduce_info.redvar_typ, redarr_typ, types.intp)
+    elem = ctx.compile_internal(builder, reducer_getitem, sig, args)
+    return elem
+
+
+def _emit_binop_reduce_call(binop, lowerer, thread_count_var, reduce_info):
+    """Emit call to the ``binop`` for the reduction variable.
+    """
+
+    def reduction_add(thread_count, redarr, init):
+        c = init
+        for i in range(thread_count):
+            c += redarr[i]
+        return c
+
+    def reduction_mul(thread_count, redarr, init):
+        c = init
+        for i in range(thread_count):
+            c *= redarr[i]
+        return c
+
+    kernel = {
+        operator.iadd: reduction_add,
+        operator.isub: reduction_add,
+        operator.imul: reduction_mul,
+        operator.ifloordiv: reduction_mul,
+        operator.itruediv: reduction_mul,
+    }[binop]
+
+    ctx = lowerer.context
+    builder = lowerer.builder
+    redarr_typ = reduce_info.redarr_typ
+    arg_arr = lowerer.loadvar(reduce_info.redarr_var.name)
+
+    if config.DEBUG_ARRAY_OPT_RUNTIME:
+        init_var = reduce_info.redarr_var.scope.get(reduce_info.redvar_name)
+        res_print = ir.Print(
+            args=[reduce_info.redarr_var, init_var], vararg=None,
+            loc=lowerer.loc,
+        )
+        typemap = lowerer.fndesc.typemap
+        lowerer.fndesc.calltypes[res_print] = signature(
+            types.none, typemap[reduce_info.redarr_var.name],
+            typemap[init_var.name],
+        )
+        lowerer.lower_inst(res_print)
+
+    arg_thread_count = lowerer.loadvar(thread_count_var.name)
+    args = (arg_thread_count, arg_arr, reduce_info.init_val)
+    sig = signature(
+        reduce_info.redvar_typ, types.uintp, redarr_typ, reduce_info.redvar_typ,
+    )
+
+    redvar_result = ctx.compile_internal(builder, kernel, sig, args)
+    return redvar_result
+
+
+def _is_inplace_binop_and_rhs_is_init(inst, redvar_name):
+    """Is ``inst`` an inplace-binop and the RHS is the reduction init?
+    """
+    if not isinstance(inst, ir.Assign):
+        return False
+    rhs = inst.value
+    if not isinstance(rhs, ir.Expr):
+        return False
+    if rhs.op != "inplace_binop":
+        return False
+    if rhs.rhs.name != f"{redvar_name}#init":
+        return False
+    return True
+
+
+def _fix_redvar_name_ssa_mismatch(parfor, lowerer, inst, redvar_name):
+    """Fix reduction variable name mismatch due to SSA.
+    """
+    # Only process reduction statements post-gufunc execution
+    # until we see an assignment with a left-hand side to the
+    # reduction variable's name.  This fixes problems with
+    # cases where there are multiple assignments to the
+    # reduction variable in the parfor.
+    scope = parfor.init_block.scope
+    if isinstance(inst, ir.Assign):
+        try:
+            reduction_var = scope.get_exact(redvar_name)
+        except NotDefinedError:
+            # Ideally, this shouldn't happen. The redvar name
+            # missing from scope indicates an error from
+            # other rewrite passes.
+            is_same_source_var = redvar_name == inst.target.name
+        else:
+            # Because of SSA, the redvar and target var of
+            # the current assignment would be different even
+            # though they refer to the same source-level var.
+            redvar_unver_name = reduction_var.unversioned_name
+            target_unver_name = inst.target.unversioned_name
+            is_same_source_var = redvar_unver_name == target_unver_name
+
+        if is_same_source_var:
+            # If redvar is different from target var, add an
+            # assignment to put target var into redvar.
+            if redvar_name != inst.target.name:
+                val = lowerer.loadvar(inst.target.name)
+                lowerer.storevar(val, name=redvar_name)
+                return True
+
+    return False
+
+def _create_shape_signature(
+        get_shape_classes,
+        num_inputs,
+        num_reductions,
+        args,
+        func_sig,
+        races,
+        typemap):
+    '''Create shape signature for GUFunc
+    '''
+    if config.DEBUG_ARRAY_OPT:
+        print("_create_shape_signature", num_inputs, num_reductions, args, races)
+        for i in args[1:]:
+            print("argument", i, type(i), get_shape_classes(i, typemap=typemap))
+
+    num_inouts = len(args) - num_reductions
+    # maximum class number for array shapes
+    classes = [get_shape_classes(var, typemap=typemap) if var not in races else (-1,) for var in args[1:]]
+    class_set = set()
+    for _class in classes:
+        if _class:
+            for i in _class:
+                class_set.add(i)
+    max_class = max(class_set) + 1 if class_set else 0
+    classes.insert(0, (max_class,)) # force set the class of 'sched' argument
+    class_set.add(max_class)
+    thread_num_class = max_class + 1
+    class_set.add(thread_num_class)
+    class_map = {}
+    # TODO: use prefix + class number instead of single char
+    alphabet = ord('a')
+    for n in class_set:
+       if n >= 0:
+           class_map[n] = chr(alphabet)
+           alphabet += 1
+    threadcount_ordinal = chr(alphabet)
+
+    alpha_dict = {'latest_alpha' : alphabet}
+
+    def bump_alpha(c, class_map):
+        if c >= 0:
+            return class_map[c]
+        else:
+            alpha_dict['latest_alpha'] += 1
+            return chr(alpha_dict['latest_alpha'])
+
+    gu_sin = []
+    gu_sout = []
+    count = 0
+    syms_sin = ()
+    if config.DEBUG_ARRAY_OPT:
+        print("args", args)
+        print("classes", classes)
+        print("threadcount_ordinal", threadcount_ordinal)
+    for cls, arg in zip(classes, args):
+        count = count + 1
+        if cls:
+            dim_syms = tuple(bump_alpha(c, class_map) for c in cls)
+        else:
+            dim_syms = ()
+        if (count > num_inouts):
+            # Add the threadcount_ordinal to represent the thread count
+            # to the start of the reduction array.
+            gu_sin.append(tuple([threadcount_ordinal] + list(dim_syms[1:])))
+        else:
+            gu_sin.append(dim_syms)
+            syms_sin += dim_syms
+    return (gu_sin, gu_sout)
+
+def _print_block(block):
+    for i, inst in enumerate(block.body):
+        print("    ", i, " ", inst)
+
+def _print_body(body_dict):
+    '''Pretty-print a set of IR blocks.
+    '''
+    for label, block in body_dict.items():
+        print("label: ", label)
+        _print_block(block)
+
+
+def wrap_loop_body(loop_body):
+    blocks = loop_body.copy()  # shallow copy is enough
+    first_label = min(blocks.keys())
+    last_label = max(blocks.keys())
+    loc = blocks[last_label].loc
+    blocks[last_label].body.append(ir.Jump(first_label, loc))
+    return blocks
+
+def unwrap_loop_body(loop_body):
+    last_label = max(loop_body.keys())
+    loop_body[last_label].body = loop_body[last_label].body[:-1]
+
+def add_to_def_once_sets(a_def, def_once, def_more):
+    '''If the variable is already defined more than once, do nothing.
+       Else if defined exactly once previously then transition this
+       variable to the defined more than once set (remove it from
+       def_once set and add to def_more set).
+       Else this must be the first time we've seen this variable defined
+       so add to def_once set.
+    '''
+    if a_def in def_more:
+        pass
+    elif a_def in def_once:
+        def_more.add(a_def)
+        def_once.remove(a_def)
+    else:
+        def_once.add(a_def)
+
+def compute_def_once_block(block, def_once, def_more, getattr_taken, typemap, module_assigns):
+    '''Effect changes to the set of variables defined once or more than once
+       for a single block.
+       block - the block to process
+       def_once - set of variable names known to be defined exactly once
+       def_more - set of variable names known to be defined more than once
+       getattr_taken - dict mapping variable name to tuple of object and attribute taken
+       module_assigns - dict mapping variable name to the Global that they came from
+    '''
+    # The only "defs" occur in assignments, so find such instructions.
+    assignments = block.find_insts(ir.Assign)
+    # For each assignment...
+    for one_assign in assignments:
+        # Get the LHS/target of the assignment.
+        a_def = one_assign.target.name
+        # Add variable to def sets.
+        add_to_def_once_sets(a_def, def_once, def_more)
+
+        rhs = one_assign.value
+        if isinstance(rhs, ir.Global):
+            # Remember assignments of the form "a = Global(...)"
+            # Is this a module?
+            if isinstance(rhs.value, pytypes.ModuleType):
+                module_assigns[a_def] = rhs.value.__name__
+        if isinstance(rhs, ir.Expr) and rhs.op == 'getattr' and rhs.value.name in def_once:
+            # Remember assignments of the form "a = b.c"
+            getattr_taken[a_def] = (rhs.value.name, rhs.attr)
+        if isinstance(rhs, ir.Expr) and rhs.op == 'call' and rhs.func.name in getattr_taken:
+            # If "a" is being called then lookup the getattr definition of "a"
+            # as above, getting the module variable "b" (base_obj)
+            # and the attribute "c" (base_attr).
+            base_obj, base_attr = getattr_taken[rhs.func.name]
+            if base_obj in module_assigns:
+                # If we know the definition of the module variable then get the module
+                # name from module_assigns.
+                base_mod_name = module_assigns[base_obj]
+                if not is_const_call(base_mod_name, base_attr):
+                    # Calling a method on an object could modify the object and is thus
+                    # like a def of that object.  We call is_const_call to see if this module/attribute
+                    # combination is known to not modify the module state.  If we don't know that
+                    # the combination is safe then we have to assume there could be a modification to
+                    # the module and thus add the module variable as defined more than once.
+                    add_to_def_once_sets(base_obj, def_once, def_more)
+            else:
+                # Assume the worst and say that base_obj could be modified by the call.
+                add_to_def_once_sets(base_obj, def_once, def_more)
+        if isinstance(rhs, ir.Expr) and rhs.op == 'call':
+            # If a mutable object is passed to a function, then it may be changed and
+            # therefore can't be hoisted.
+            # For each argument to the function...
+            for argvar in rhs.args:
+                # Get the argument's type.
+                if isinstance(argvar, ir.Var):
+                    argvar = argvar.name
+                avtype = typemap[argvar]
+                # If that type doesn't have a mutable attribute or it does and it's set to
+                # not mutable then this usage is safe for hoisting.
+                if getattr(avtype, 'mutable', False):
+                    # Here we have a mutable variable passed to a function so add this variable
+                    # to the def lists.
+                    add_to_def_once_sets(argvar, def_once, def_more)
+
+def compute_def_once_internal(loop_body, def_once, def_more, getattr_taken, typemap, module_assigns):
+    '''Compute the set of variables defined exactly once in the given set of blocks
+       and use the given sets for storing which variables are defined once, more than
+       once and which have had a getattr call on them.
+    '''
+    # For each block...
+    for label, block in loop_body.items():
+        # Scan this block and effect changes to def_once, def_more, and getattr_taken
+        # based on the instructions in that block.
+        compute_def_once_block(block, def_once, def_more, getattr_taken, typemap, module_assigns)
+        # Have to recursively process parfors manually here.
+        for inst in block.body:
+            if isinstance(inst, parfor.Parfor):
+                # Recursively compute for the parfor's init block.
+                compute_def_once_block(inst.init_block, def_once, def_more, getattr_taken, typemap, module_assigns)
+                # Recursively compute for the parfor's loop body.
+                compute_def_once_internal(inst.loop_body, def_once, def_more, getattr_taken, typemap, module_assigns)
+
+def compute_def_once(loop_body, typemap):
+    '''Compute the set of variables defined exactly once in the given set of blocks.
+    '''
+    def_once = set()   # set to hold variables defined exactly once
+    def_more = set()   # set to hold variables defined more than once
+    getattr_taken = {}
+    module_assigns = {}
+    compute_def_once_internal(loop_body, def_once, def_more, getattr_taken, typemap, module_assigns)
+    return def_once, def_more
+
+def find_vars(var, varset):
+    assert isinstance(var, ir.Var)
+    varset.add(var.name)
+    return var
+
+def _hoist_internal(inst, dep_on_param, call_table, hoisted, not_hoisted,
+                    typemap, stored_arrays):
+    if inst.target.name in stored_arrays:
+        not_hoisted.append((inst, "stored array"))
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("Instruction", inst, " could not be hoisted because the created array is stored.")
+        return False
+
+    uses = set()
+    visit_vars_inner(inst.value, find_vars, uses)
+    diff = uses.difference(dep_on_param)
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("_hoist_internal:", inst, "uses:", uses, "diff:", diff)
+    if len(diff) == 0 and is_pure(inst.value, None, call_table):
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("Will hoist instruction", inst, typemap[inst.target.name])
+        hoisted.append(inst)
+        if not isinstance(typemap[inst.target.name], types.npytypes.Array):
+            dep_on_param += [inst.target.name]
+        return True
+    else:
+        if len(diff) > 0:
+            not_hoisted.append((inst, "dependency"))
+            if config.DEBUG_ARRAY_OPT >= 1:
+                print("Instruction", inst, " could not be hoisted because of a dependency.")
+        else:
+            not_hoisted.append((inst, "not pure"))
+            if config.DEBUG_ARRAY_OPT >= 1:
+                print("Instruction", inst, " could not be hoisted because it isn't pure.")
+    return False
+
+def find_setitems_block(setitems, itemsset, block, typemap):
+    for inst in block.body:
+        if isinstance(inst, (ir.StaticSetItem, ir.SetItem)):
+            setitems.add(inst.target.name)
+            # If we store a non-mutable object into an array then that is safe to hoist.
+            # If the stored object is mutable and you hoist then multiple entries in the
+            # outer array could reference the same object and changing one index would then
+            # change other indices.
+            if getattr(typemap[inst.value.name], "mutable", False):
+                itemsset.add(inst.value.name)
+        elif isinstance(inst, parfor.Parfor):
+            find_setitems_block(setitems, itemsset, inst.init_block, typemap)
+            find_setitems_body(setitems, itemsset, inst.loop_body, typemap)
+
+def find_setitems_body(setitems, itemsset, loop_body, typemap):
+    """
+      Find the arrays that are written into (goes into setitems) and the
+      mutable objects (mostly arrays) that are written into other arrays
+      (goes into itemsset).
+    """
+    for label, block in loop_body.items():
+        find_setitems_block(setitems, itemsset, block, typemap)
+
+def empty_container_allocator_hoist(inst, dep_on_param, call_table, hoisted,
+                                    not_hoisted, typemap, stored_arrays):
+    if (isinstance(inst, ir.Assign) and
+        isinstance(inst.value, ir.Expr) and
+        inst.value.op == 'call' and
+        inst.value.func.name in call_table):
+        call_list = call_table[inst.value.func.name]
+        if call_list == ['empty', np]:
+            return _hoist_internal(inst, dep_on_param, call_table, hoisted,
+                                   not_hoisted, typemap, stored_arrays)
+    return False
+
+def hoist(parfor_params, loop_body, typemap, wrapped_blocks):
+    dep_on_param = copy.copy(parfor_params)
+    hoisted = []
+    not_hoisted = []
+
+    # Compute the set of variable defined exactly once in the loop body.
+    def_once, def_more = compute_def_once(loop_body, typemap)
+    (call_table, reverse_call_table) = get_call_table(wrapped_blocks)
+
+    setitems = set()
+    itemsset = set()
+    find_setitems_body(setitems, itemsset, loop_body, typemap)
+    dep_on_param = list(set(dep_on_param).difference(setitems))
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("hoist - def_once:", def_once, "setitems:", setitems, "itemsset:", itemsset, "dep_on_param:", dep_on_param, "parfor_params:", parfor_params)
+    for si in setitems:
+        add_to_def_once_sets(si, def_once, def_more)
+
+    for label, block in loop_body.items():
+        new_block = []
+        for inst in block.body:
+            if empty_container_allocator_hoist(inst, dep_on_param, call_table,
+                                   hoisted, not_hoisted, typemap, itemsset):
+                continue
+            elif isinstance(inst, ir.Assign) and inst.target.name in def_once:
+                if _hoist_internal(inst, dep_on_param, call_table,
+                                   hoisted, not_hoisted, typemap, itemsset):
+                    # don't add this instruction to the block since it is
+                    # hoisted
+                    continue
+            elif isinstance(inst, parfor.Parfor):
+                new_init_block = []
+                if config.DEBUG_ARRAY_OPT >= 1:
+                    print("parfor")
+                    inst.dump()
+                for ib_inst in inst.init_block.body:
+                    if empty_container_allocator_hoist(ib_inst, dep_on_param,
+                        call_table, hoisted, not_hoisted, typemap, itemsset):
+                        continue
+                    elif (isinstance(ib_inst, ir.Assign) and
+                        ib_inst.target.name in def_once):
+                        if _hoist_internal(ib_inst, dep_on_param, call_table,
+                                           hoisted, not_hoisted, typemap, itemsset):
+                            # don't add this instruction to the block since it is hoisted
+                            continue
+                    new_init_block.append(ib_inst)
+                inst.init_block.body = new_init_block
+
+            new_block.append(inst)
+        block.body = new_block
+    return hoisted, not_hoisted
+
+def redtyp_is_scalar(redtype):
+    return not isinstance(redtype, types.npytypes.Array)
+
+def redtyp_to_redarraytype(redtyp):
+    """Go from a reducation variable type to a reduction array type used to hold
+       per-worker results.
+    """
+    redarrdim = 1
+    # If the reduction type is an array then allocate reduction array with ndim+1 dimensions.
+    if isinstance(redtyp, types.npytypes.Array):
+        redarrdim += redtyp.ndim
+        # We don't create array of array but multi-dimensional reduction array with same dtype.
+        redtyp = redtyp.dtype
+    return types.npytypes.Array(redtyp, redarrdim, "C")
+
+def redarraytype_to_sig(redarraytyp):
+    """Given a reduction array type, find the type of the reduction argument to the gufunc.
+    """
+    assert isinstance(redarraytyp, types.npytypes.Array)
+    return types.npytypes.Array(redarraytyp.dtype, redarraytyp.ndim, redarraytyp.layout)
+
+def legalize_names_with_typemap(names, typemap):
+    """ We use ir_utils.legalize_names to replace internal IR variable names
+        containing illegal characters (e.g. period) with a legal character
+        (underscore) so as to create legal variable names.
+        The original variable names are in the typemap so we also
+        need to add the legalized name to the typemap as well.
+    """
+    outdict = legalize_names(names)
+    # For each pair in the dict of legalized names...
+    for x, y in outdict.items():
+        # If the name had some legalization change to it...
+        if x != y:
+            # Set the type of the new name the same as the type of the old name.
+            typemap[y] = typemap[x]
+    return outdict
+
+def to_scalar_from_0d(x):
+    if isinstance(x, types.ArrayCompatible):
+        if x.ndim == 0:
+            return x.dtype
+    return x
+
+def _create_gufunc_for_parfor_body(
+        lowerer,
+        parfor,
+        typemap,
+        typingctx,
+        targetctx,
+        flags,
+        locals,
+        has_aliases,
+        index_var_typ,
+        races):
+    '''
+    Takes a parfor and creates a gufunc function for its body.
+    There are two parts to this function.
+    1) Code to iterate across the iteration space as defined by the schedule.
+    2) The parfor body that does the work for a single point in the iteration space.
+    Part 1 is created as Python text for simplicity with a sentinel assignment to mark the point
+    in the IR where the parfor body should be added.
+    This Python text is 'exec'ed into existence and its IR retrieved with run_frontend.
+    The IR is scanned for the sentinel assignment where that basic block is split and the IR
+    for the parfor body inserted.
+    '''
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("starting _create_gufunc_for_parfor_body")
+
+    loc = parfor.init_block.loc
+
+    # The parfor body and the main function body share ir.Var nodes.
+    # We have to do some replacements of Var names in the parfor body to make them
+    # legal parameter names.  If we don't copy then the Vars in the main function also
+    # would incorrectly change their name.
+    loop_body = copy.copy(parfor.loop_body)
+    remove_dels(loop_body)
+
+    parfor_dim = len(parfor.loop_nests)
+    loop_indices = [l.index_variable.name for l in parfor.loop_nests]
+
+    # Get all the parfor params.
+    parfor_params = parfor.params
+    # Get just the outputs of the parfor.
+    parfor_outputs = numba.parfors.parfor.get_parfor_outputs(parfor, parfor_params)
+    # Get all parfor reduction vars, and operators.
+    typemap = lowerer.fndesc.typemap
+    parfor_redvars, parfor_reddict = numba.parfors.parfor.get_parfor_reductions(
+        lowerer.func_ir, parfor, parfor_params, lowerer.fndesc.calltypes)
+    # Compute just the parfor inputs as a set difference.
+    parfor_inputs = sorted(
+        list(
+            set(parfor_params) -
+            set(parfor_outputs) -
+            set(parfor_redvars)))
+
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("parfor_params = ", parfor_params, " ", type(parfor_params))
+        print("parfor_outputs = ", parfor_outputs, " ", type(parfor_outputs))
+        print("parfor_inputs = ", parfor_inputs, " ", type(parfor_inputs))
+        print("parfor_redvars = ", parfor_redvars, " ", type(parfor_redvars))
+
+    # -------------------------------------------------------------------------
+    # Convert tuples to individual parameters.
+    tuple_expanded_parfor_inputs = []
+    tuple_var_to_expanded_names = {}
+    expanded_name_to_tuple_var = {}
+    next_expanded_tuple_var = 0
+    parfor_tuple_params = []
+    # For each input to the parfor.
+    for pi in parfor_inputs:
+        # Get the type of the input.
+        pi_type = typemap[pi]
+        # If it is a UniTuple or Tuple we will do the conversion.
+        if isinstance(pi_type, types.UniTuple) or isinstance(pi_type, types.NamedUniTuple):
+            # Get the size and dtype of the tuple.
+            tuple_count = pi_type.count
+            tuple_dtype = pi_type.dtype
+            # Only do tuples up to config.PARFOR_MAX_TUPLE_SIZE length.
+            assert(tuple_count <= config.PARFOR_MAX_TUPLE_SIZE)
+            this_var_expansion = []
+            for i in range(tuple_count):
+                # Generate a new name for the individual part of the tuple var.
+                expanded_name = "expanded_tuple_var_" + str(next_expanded_tuple_var)
+                # Add that name to the new list of inputs to the gufunc.
+                tuple_expanded_parfor_inputs.append(expanded_name)
+                this_var_expansion.append(expanded_name)
+                # Remember a mapping from new param name to original tuple
+                # var and the index within the tuple.
+                expanded_name_to_tuple_var[expanded_name] = (pi, i)
+                next_expanded_tuple_var += 1
+                # Set the type of the new parameter.
+                typemap[expanded_name] = tuple_dtype
+            # Remember a mapping from the original tuple var to the
+            # individual parts.
+            tuple_var_to_expanded_names[pi] = this_var_expansion
+            parfor_tuple_params.append(pi)
+        elif isinstance(pi_type, types.Tuple) or isinstance(pi_type, types.NamedTuple):
+            # This is the same as above for UniTuple except that each part of
+            # the tuple can have a different type and we fetch that type with
+            # pi_type.types[offset].
+            tuple_count = pi_type.count
+            tuple_types = pi_type.types
+            # Only do tuples up to config.PARFOR_MAX_TUPLE_SIZE length.
+            assert(tuple_count <= config.PARFOR_MAX_TUPLE_SIZE)
+            this_var_expansion = []
+            for i in range(tuple_count):
+                expanded_name = "expanded_tuple_var_" + str(next_expanded_tuple_var)
+                tuple_expanded_parfor_inputs.append(expanded_name)
+                this_var_expansion.append(expanded_name)
+                expanded_name_to_tuple_var[expanded_name] = (pi, i)
+                next_expanded_tuple_var += 1
+                typemap[expanded_name] = tuple_types[i]
+            tuple_var_to_expanded_names[pi] = this_var_expansion
+            parfor_tuple_params.append(pi)
+        else:
+            tuple_expanded_parfor_inputs.append(pi)
+    parfor_inputs = tuple_expanded_parfor_inputs
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("parfor_inputs post tuple handling = ", parfor_inputs, " ", type(parfor_inputs))
+    # -------------------------------------------------------------------------
+
+    races = races.difference(set(parfor_redvars))
+    for race in races:
+        msg = ("Variable %s used in parallel loop may be written "
+               "to simultaneously by multiple workers and may result "
+               "in non-deterministic or unintended results." % race)
+        warnings.warn(NumbaParallelSafetyWarning(msg, loc))
+    replace_var_with_array(races, loop_body, typemap, lowerer.fndesc.calltypes)
+
+    # Reduction variables are represented as arrays, so they go under
+    # different names.
+    parfor_redarrs = []
+    parfor_red_arg_types = []
+    for var in parfor_redvars:
+        arr = var + "_arr"
+        parfor_redarrs.append(arr)
+        redarraytype = redtyp_to_redarraytype(typemap[var])
+        parfor_red_arg_types.append(redarraytype)
+        redarrsig = redarraytype_to_sig(redarraytype)
+        if arr in typemap:
+            assert(typemap[arr] == redarrsig)
+        else:
+            typemap[arr] = redarrsig
+
+    # Reorder all the params so that inputs go first then outputs.
+    parfor_params = parfor_inputs + parfor_outputs + parfor_redarrs
+
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("parfor_params = ", parfor_params, " ", type(parfor_params))
+        print("loop_indices = ", loop_indices, " ", type(loop_indices))
+        print("loop_body = ", loop_body, " ", type(loop_body))
+        _print_body(loop_body)
+
+    # Some Var are not legal parameter names so create a dict of potentially illegal
+    # param name to guaranteed legal name.
+    param_dict = legalize_names_with_typemap(parfor_params + parfor_redvars + parfor_tuple_params, typemap)
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print(
+            "param_dict = ",
+            sorted(
+                param_dict.items()),
+            " ",
+            type(param_dict))
+
+    # Some loop_indices are not legal parameter names so create a dict of potentially illegal
+    # loop index to guaranteed legal name.
+    ind_dict = legalize_names_with_typemap(loop_indices, typemap)
+    # Compute a new list of legal loop index names.
+    legal_loop_indices = [ind_dict[v] for v in loop_indices]
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("ind_dict = ", sorted(ind_dict.items()), " ", type(ind_dict))
+        print(
+            "legal_loop_indices = ",
+            legal_loop_indices,
+            " ",
+            type(legal_loop_indices))
+        for pd in parfor_params:
+            print("pd = ", pd)
+            print("pd type = ", typemap[pd], " ", type(typemap[pd]))
+
+    # Get the types of each parameter.
+    param_types = [to_scalar_from_0d(typemap[v]) for v in parfor_params]
+    # Calculate types of args passed to gufunc.
+    func_arg_types = [typemap[v] for v in (parfor_inputs + parfor_outputs)] + parfor_red_arg_types
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("new param_types:", param_types)
+        print("new func_arg_types:", func_arg_types)
+
+    # Replace illegal parameter names in the loop body with legal ones.
+    replace_var_names(loop_body, param_dict)
+    # remember the name before legalizing as the actual arguments
+    parfor_args = parfor_params
+    # Change parfor_params to be legal names.
+    parfor_params = [param_dict[v] for v in parfor_params]
+    parfor_params_orig = parfor_params
+
+    parfor_params = []
+    ascontig = False
+    for pindex in range(len(parfor_params_orig)):
+        if (ascontig and
+            pindex < len(parfor_inputs) and
+            isinstance(param_types[pindex], types.npytypes.Array)):
+            parfor_params.append(parfor_params_orig[pindex]+"param")
+        else:
+            parfor_params.append(parfor_params_orig[pindex])
+
+    # Change parfor body to replace illegal loop index vars with legal ones.
+    replace_var_names(loop_body, ind_dict)
+    loop_body_var_table = get_name_var_table(loop_body)
+    sentinel_name = get_unused_var_name("__sentinel__", loop_body_var_table)
+
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print(
+            "legal parfor_params = ",
+            parfor_params,
+            " ",
+            type(parfor_params))
+
+    # Determine the unique names of the scheduling and gufunc functions.
+    # sched_func_name = "__numba_parfor_sched_%s" % (hex(hash(parfor)).replace("-", "_"))
+    gufunc_name = "__numba_parfor_gufunc_%s" % (
+        hex(hash(parfor)).replace("-", "_"))
+    if config.DEBUG_ARRAY_OPT:
+        # print("sched_func_name ", type(sched_func_name), " ", sched_func_name)
+        print("gufunc_name ", type(gufunc_name), " ", gufunc_name)
+
+    gufunc_txt = ""
+
+    # Create the gufunc function.
+    gufunc_txt += "def " + gufunc_name + \
+        "(sched, " + (", ".join(parfor_params)) + "):\n"
+
+    globls = {"np": np, "numba": numba}
+
+    # First thing in the gufunc, we reconstruct tuples from their
+    # individual parts, e.g., orig_tup_name = (part1, part2,).
+    # The rest of the code of the function will use the original tuple name.
+    for tup_var, exp_names in tuple_var_to_expanded_names.items():
+        tup_type = typemap[tup_var]
+        gufunc_txt += "    " + param_dict[tup_var]
+        # Determine if the tuple is a named tuple.
+        if (isinstance(tup_type, types.NamedTuple) or
+            isinstance(tup_type, types.NamedUniTuple)):
+            named_tup = True
+        else:
+            named_tup = False
+
+        if named_tup:
+            # It is a named tuple so try to find the global that defines the
+            # named tuple.
+            func_def = guard(get_definition, lowerer.func_ir, tup_var)
+            named_tuple_def = None
+            if config.DEBUG_ARRAY_OPT:
+                print("func_def:", func_def, type(func_def))
+            if func_def is not None:
+                if (isinstance(func_def, ir.Expr) and
+                    func_def.op == 'call'):
+                    named_tuple_def = guard(get_definition, lowerer.func_ir, func_def.func)
+                    if config.DEBUG_ARRAY_OPT:
+                        print("named_tuple_def:", named_tuple_def, type(named_tuple_def))
+                elif isinstance(func_def, ir.Arg):
+                    named_tuple_def = typemap[func_def.name]
+                    if config.DEBUG_ARRAY_OPT:
+                        print("named_tuple_def:", named_tuple_def,
+                              type(named_tuple_def), named_tuple_def.name)
+            if named_tuple_def is not None:
+                if (isinstance(named_tuple_def, ir.Global) or
+                    isinstance(named_tuple_def, ir.FreeVar)):
+                    gval = named_tuple_def.value
+                    if config.DEBUG_ARRAY_OPT:
+                        print("gval:", gval, type(gval))
+                    globls[named_tuple_def.name] = gval
+                elif isinstance(named_tuple_def, types.containers.BaseNamedTuple):
+                    named_tuple_name = named_tuple_def.name.split('(')[0]
+                    if config.DEBUG_ARRAY_OPT:
+                        print("name:", named_tuple_name,
+                              named_tuple_def.instance_class,
+                              type(named_tuple_def.instance_class))
+                    globls[named_tuple_name] = named_tuple_def.instance_class
+            else:
+                if config.DEBUG_ARRAY_OPT:
+                    print("Didn't find definition of namedtuple for globls.")
+                raise CompilerError("Could not find definition of " + str(tup_var),
+                                     tup_var.loc)
+            gufunc_txt += " = " + tup_type.instance_class.__name__ + "("
+            for name, field_name in zip(exp_names, tup_type.fields):
+                gufunc_txt += field_name + "=" + param_dict[name] + ","
+        else:
+            # Just a regular tuple so use (part0, part1, ...)
+            gufunc_txt += " = (" + ", ".join([param_dict[x] for x in exp_names])
+            if len(exp_names) == 1:
+                # Add comma for tuples with singular values.  We can't unilaterally
+                # add a comma always because (,) isn't valid.
+                gufunc_txt += ","
+
+        gufunc_txt += ")\n"
+
+    for pindex in range(len(parfor_inputs)):
+        if ascontig and isinstance(param_types[pindex], types.npytypes.Array):
+            gufunc_txt += ("    " + parfor_params_orig[pindex]
+                + " = np.ascontiguousarray(" + parfor_params[pindex] + ")\n")
+
+    gufunc_thread_id_var = "ParallelAcceleratorGufuncThreadId"
+
+    if len(parfor_redarrs) > 0:
+        gufunc_txt += "    " + gufunc_thread_id_var + " = "
+        gufunc_txt += "numba.np.ufunc.parallel._iget_thread_id()\n"
+
+    # Add initialization of reduction variables
+    for arr, var in zip(parfor_redarrs, parfor_redvars):
+        gufunc_txt += "    " + param_dict[var] + \
+             "=" + param_dict[arr] + "[" + gufunc_thread_id_var + "]\n"
+        if config.DEBUG_ARRAY_OPT_RUNTIME:
+            gufunc_txt += "    print(\"thread id =\", ParallelAcceleratorGufuncThreadId)\n"
+            gufunc_txt += "    print(\"initial reduction value\",ParallelAcceleratorGufuncThreadId," + param_dict[var] + "," + param_dict[var] + ".shape)\n"
+            gufunc_txt += "    print(\"reduction array\",ParallelAcceleratorGufuncThreadId," + param_dict[arr] + "," + param_dict[arr] + ".shape)\n"
+
+    # For each dimension of the parfor, create a for loop in the generated gufunc function.
+    # Iterate across the proper values extracted from the schedule.
+    # The form of the schedule is start_dim0, start_dim1, ..., start_dimN, end_dim0,
+    # end_dim1, ..., end_dimN
+    for eachdim in range(parfor_dim):
+        for indent in range(eachdim + 1):
+            gufunc_txt += "    "
+        sched_dim = eachdim
+        gufunc_txt += ("for " +
+                       legal_loop_indices[eachdim] +
+                       " in range(sched[" +
+                       str(sched_dim) +
+                       "], sched[" +
+                       str(sched_dim +
+                           parfor_dim) +
+                       "] + np.uint8(1)):\n")
+
+    if config.DEBUG_ARRAY_OPT_RUNTIME:
+        for indent in range(parfor_dim + 1):
+            gufunc_txt += "    "
+        gufunc_txt += "print("
+        for eachdim in range(parfor_dim):
+            gufunc_txt += "\"" + legal_loop_indices[eachdim] + "\"," + legal_loop_indices[eachdim] + ","
+        gufunc_txt += ")\n"
+
+    # Add the sentinel assignment so that we can find the loop body position
+    # in the IR.
+    for indent in range(parfor_dim + 1):
+        gufunc_txt += "    "
+    gufunc_txt += sentinel_name + " = 0\n"
+    # Add assignments of reduction variables (for returning the value)
+    for arr, var in zip(parfor_redarrs, parfor_redvars):
+        if config.DEBUG_ARRAY_OPT_RUNTIME:
+            gufunc_txt += "    print(\"final reduction value\",ParallelAcceleratorGufuncThreadId," + param_dict[var] + ")\n"
+            gufunc_txt += "    print(\"final reduction array\",ParallelAcceleratorGufuncThreadId," + param_dict[arr] + ")\n"
+        # After the gufunc loops, copy the accumulated temp value back to reduction array.
+        gufunc_txt += "    " + param_dict[arr] + \
+            "[" + gufunc_thread_id_var + "] = " + param_dict[var] + "\n"
+    gufunc_txt += "    return None\n"
+
+    if config.DEBUG_ARRAY_OPT:
+        print("gufunc_txt = ", type(gufunc_txt), "\n", gufunc_txt)
+        print("globls:", globls, type(globls))
+    # Force gufunc outline into existence.
+    locls = {}
+    exec(gufunc_txt, globls, locls)
+    gufunc_func = locls[gufunc_name]
+
+    if config.DEBUG_ARRAY_OPT:
+        print("gufunc_func = ", type(gufunc_func), "\n", gufunc_func)
+    # Get the IR for the gufunc outline.
+    gufunc_ir = compiler.run_frontend(gufunc_func)
+    if config.DEBUG_ARRAY_OPT:
+        print("gufunc_ir dump ", type(gufunc_ir))
+        gufunc_ir.dump()
+        print("loop_body dump ", type(loop_body))
+        _print_body(loop_body)
+
+    # rename all variables in gufunc_ir afresh
+    var_table = get_name_var_table(gufunc_ir.blocks)
+    new_var_dict = {}
+    reserved_names = [sentinel_name] + \
+        list(param_dict.values()) + legal_loop_indices
+    for name, var in var_table.items():
+        if not (name in reserved_names):
+            new_var_dict[name] = mk_unique_var(name)
+    replace_var_names(gufunc_ir.blocks, new_var_dict)
+    if config.DEBUG_ARRAY_OPT:
+        print("gufunc_ir dump after renaming ")
+        gufunc_ir.dump()
+    gufunc_param_types = [types.npytypes.Array(
+            index_var_typ, 1, "C")] + param_types
+    if config.DEBUG_ARRAY_OPT:
+        print(
+            "gufunc_param_types = ",
+            type(gufunc_param_types),
+            "\n",
+            gufunc_param_types)
+
+    gufunc_stub_last_label = find_max_label(gufunc_ir.blocks) + 1
+
+    # Add gufunc stub last label to each parfor.loop_body label to prevent
+    # label conflicts.
+    loop_body = add_offset_to_labels(loop_body, gufunc_stub_last_label)
+    # new label for splitting sentinel block
+    new_label = find_max_label(loop_body) + 1
+
+    # If enabled, add a print statement after every assignment.
+    if config.DEBUG_ARRAY_OPT_RUNTIME:
+        for label, block in loop_body.items():
+            new_block = block.copy()
+            new_block.clear()
+            loc = block.loc
+            scope = block.scope
+            for inst in block.body:
+                new_block.append(inst)
+                # Append print after assignment
+                if isinstance(inst, ir.Assign):
+                    # Only apply to numbers
+                    if typemap[inst.target.name] not in types.number_domain:
+                        continue
+
+                    # Make constant string
+                    strval = "{} =".format(inst.target.name)
+                    strconsttyp = types.StringLiteral(strval)
+
+                    lhs = ir.Var(scope, mk_unique_var("str_const"), loc)
+                    assign_lhs = ir.Assign(value=ir.Const(value=strval, loc=loc),
+                                           target=lhs, loc=loc)
+                    typemap[lhs.name] = strconsttyp
+                    new_block.append(assign_lhs)
+
+                    # Make print node
+                    print_node = ir.Print(args=[lhs, inst.target], vararg=None, loc=loc)
+                    new_block.append(print_node)
+                    sig = numba.core.typing.signature(types.none,
+                                           typemap[lhs.name],
+                                           typemap[inst.target.name])
+                    lowerer.fndesc.calltypes[print_node] = sig
+            loop_body[label] = new_block
+
+    if config.DEBUG_ARRAY_OPT:
+        print("parfor loop body")
+        _print_body(loop_body)
+
+    wrapped_blocks = wrap_loop_body(loop_body)
+    hoisted, not_hoisted = hoist(parfor_params, loop_body, typemap, wrapped_blocks)
+    start_block = gufunc_ir.blocks[min(gufunc_ir.blocks.keys())]
+    start_block.body = start_block.body[:-1] + hoisted + [start_block.body[-1]]
+    unwrap_loop_body(loop_body)
+
+    # store hoisted into diagnostics
+    diagnostics = lowerer.metadata['parfor_diagnostics']
+    diagnostics.hoist_info[parfor.id] = {'hoisted': hoisted,
+                                         'not_hoisted': not_hoisted}
+
+    if config.DEBUG_ARRAY_OPT:
+        print("After hoisting")
+        _print_body(loop_body)
+
+    # Search all the block in the gufunc outline for the sentinel assignment.
+    for label, block in gufunc_ir.blocks.items():
+        for i, inst in enumerate(block.body):
+            if isinstance(
+                    inst,
+                    ir.Assign) and inst.target.name == sentinel_name:
+                # We found the sentinel assignment.
+                loc = inst.loc
+                scope = block.scope
+                # split block across __sentinel__
+                # A new block is allocated for the statements prior to the sentinel
+                # but the new block maintains the current block label.
+                prev_block = ir.Block(scope, loc)
+                prev_block.body = block.body[:i]
+                # The current block is used for statements after the sentinel.
+                block.body = block.body[i + 1:]
+                # But the current block gets a new label.
+                body_first_label = min(loop_body.keys())
+
+                # The previous block jumps to the minimum labelled block of the
+                # parfor body.
+                prev_block.append(ir.Jump(body_first_label, loc))
+                # Add all the parfor loop body blocks to the gufunc function's
+                # IR.
+                for (l, b) in loop_body.items():
+                    gufunc_ir.blocks[l] = transfer_scope(b, scope)
+                body_last_label = max(loop_body.keys())
+                gufunc_ir.blocks[new_label] = block
+                gufunc_ir.blocks[label] = prev_block
+                # Add a jump from the last parfor body block to the block containing
+                # statements after the sentinel.
+                gufunc_ir.blocks[body_last_label].append(
+                    ir.Jump(new_label, loc))
+                break
+        else:
+            continue
+        break
+
+    if config.DEBUG_ARRAY_OPT:
+        print("gufunc_ir last dump before renaming")
+        gufunc_ir.dump()
+
+    gufunc_ir.blocks = rename_labels(gufunc_ir.blocks)
+    remove_dels(gufunc_ir.blocks)
+
+    if config.DEBUG_ARRAY_OPT:
+        print("gufunc_ir last dump")
+        gufunc_ir.dump()
+        print("flags", flags)
+        print("typemap", typemap)
+
+    old_alias = flags.noalias
+    if not has_aliases:
+        if config.DEBUG_ARRAY_OPT:
+            print("No aliases found so adding noalias flag.")
+        flags.noalias = True
+
+    fixup_var_define_in_scope(gufunc_ir.blocks)
+    kernel_func = compiler.compile_ir(
+        typingctx,
+        targetctx,
+        gufunc_ir,
+        gufunc_param_types,
+        types.none,
+        flags,
+        locals)
+
+    flags.noalias = old_alias
+
+    kernel_sig = signature(types.none, *gufunc_param_types)
+    if config.DEBUG_ARRAY_OPT:
+        print("finished create_gufunc_for_parfor_body. kernel_sig = ", kernel_sig)
+
+    return kernel_func, parfor_args, kernel_sig, func_arg_types, expanded_name_to_tuple_var
+
+def replace_var_with_array_in_block(vars, block, typemap, calltypes):
+    new_block = []
+    for inst in block.body:
+        if isinstance(inst, ir.Assign) and inst.target.name in vars:
+            const_node = ir.Const(0, inst.loc)
+            const_var = ir.Var(inst.target.scope, mk_unique_var("$const_ind_0"), inst.loc)
+            typemap[const_var.name] = types.uintp
+            const_assign = ir.Assign(const_node, const_var, inst.loc)
+            new_block.append(const_assign)
+
+            val_var = ir.Var(inst.target.scope, mk_unique_var("$val"), inst.loc)
+            typemap[val_var.name] = typemap[inst.target.name]
+            new_block.append(ir.Assign(inst.value, val_var, inst.loc))
+            setitem_node = ir.SetItem(inst.target, const_var, val_var, inst.loc)
+            calltypes[setitem_node] = signature(
+                types.none, types.npytypes.Array(typemap[inst.target.name], 1, "C"), types.intp, typemap[inst.target.name])
+            new_block.append(setitem_node)
+            continue
+        elif isinstance(inst, parfor.Parfor):
+            replace_var_with_array_internal(vars, {0: inst.init_block}, typemap, calltypes)
+            replace_var_with_array_internal(vars, inst.loop_body, typemap, calltypes)
+
+        new_block.append(inst)
+    return new_block
+
+def replace_var_with_array_internal(vars, loop_body, typemap, calltypes):
+    for label, block in loop_body.items():
+        block.body = replace_var_with_array_in_block(vars, block, typemap, calltypes)
+
+def replace_var_with_array(vars, loop_body, typemap, calltypes):
+    replace_var_with_array_internal(vars, loop_body, typemap, calltypes)
+    for v in vars:
+        el_typ = typemap[v]
+        typemap.pop(v, None)
+        typemap[v] = types.npytypes.Array(el_typ, 1, "C")
+
+def call_parallel_gufunc(lowerer, cres, gu_signature, outer_sig, expr_args, expr_arg_types,
+                         loop_ranges, redvars, reddict, redarrdict, init_block, index_var_typ, races,
+                         exp_name_to_tuple_var):
+    '''
+    Adds the call to the gufunc function from the main function.
+    '''
+    context = lowerer.context
+    builder = lowerer.builder
+
+    from numba.np.ufunc.parallel import (build_gufunc_wrapper,
+                           _launch_threads)
+
+    if config.DEBUG_ARRAY_OPT:
+        print("make_parallel_loop")
+        print("outer_sig = ", outer_sig.args, outer_sig.return_type,
+              outer_sig.recvr, outer_sig.pysig)
+        print("loop_ranges = ", loop_ranges)
+        print("expr_args", expr_args)
+        print("expr_arg_types", expr_arg_types)
+        print("gu_signature", gu_signature)
+
+    # Build the wrapper for GUFunc
+    args, return_type = sigutils.normalize_signature(outer_sig)
+    llvm_func = cres.library.get_function(cres.fndesc.llvm_func_name)
+    sin, sout = gu_signature
+
+    # These are necessary for build_gufunc_wrapper to find external symbols
+    _launch_threads()
+
+    info = build_gufunc_wrapper(llvm_func, cres, sin, sout,
+                                cache=False, is_parfors=True)
+    wrapper_name = info.name
+    cres.library._ensure_finalized()
+
+    if config.DEBUG_ARRAY_OPT:
+        print("parallel function = ", wrapper_name, cres)
+
+    # loadvars for loop_ranges
+    def load_range(v):
+        if isinstance(v, ir.Var):
+            return lowerer.loadvar(v.name)
+        else:
+            return context.get_constant(types.uintp, v)
+
+    num_dim = len(loop_ranges)
+    for i in range(num_dim):
+        start, stop, step = loop_ranges[i]
+        start = load_range(start)
+        stop = load_range(stop)
+        assert(step == 1)  # We do not support loop steps other than 1
+        step = load_range(step)
+        loop_ranges[i] = (start, stop, step)
+
+        if config.DEBUG_ARRAY_OPT:
+            print("call_parallel_gufunc loop_ranges[{}] = ".format(i), start,
+                  stop, step)
+            cgutils.printf(builder, "loop range[{}]: %d %d (%d)\n".format(i),
+                           start, stop, step)
+
+    # Commonly used LLVM types and constants
+    byte_t = llvmlite.ir.IntType(8)
+    byte_ptr_t = llvmlite.ir.PointerType(byte_t)
+    byte_ptr_ptr_t = llvmlite.ir.PointerType(byte_ptr_t)
+    intp_t = context.get_value_type(types.intp)
+    uintp_t = context.get_value_type(types.uintp)
+    intp_ptr_t = llvmlite.ir.PointerType(intp_t)
+    uintp_ptr_t = llvmlite.ir.PointerType(uintp_t)
+    zero = context.get_constant(types.uintp, 0)
+    one = context.get_constant(types.uintp, 1)
+    one_type = one.type
+    sizeof_intp = context.get_abi_sizeof(intp_t)
+
+    # Prepare sched, first pop it out of expr_args, outer_sig, and gu_signature
+    expr_args.pop(0)
+    sched_sig = sin.pop(0)
+
+    if config.DEBUG_ARRAY_OPT:
+        print("Parfor has potentially negative start", index_var_typ.signed)
+
+    if index_var_typ.signed:
+        sched_type = intp_t
+        sched_ptr_type = intp_ptr_t
+    else:
+        sched_type = uintp_t
+        sched_ptr_type = uintp_ptr_t
+
+    # Call do_scheduling with appropriate arguments
+    dim_starts = cgutils.alloca_once(
+        builder, sched_type, size=context.get_constant(
+            types.uintp, num_dim), name="dim_starts")
+    dim_stops = cgutils.alloca_once(
+        builder, sched_type, size=context.get_constant(
+            types.uintp, num_dim), name="dim_stops")
+    for i in range(num_dim):
+        start, stop, step = loop_ranges[i]
+        if start.type != one_type:
+            start = builder.sext(start, one_type)
+        if stop.type != one_type:
+            stop = builder.sext(stop, one_type)
+        if step.type != one_type:
+            step = builder.sext(step, one_type)
+        # substract 1 because do-scheduling takes inclusive ranges
+        stop = builder.sub(stop, one)
+        builder.store(
+            start, builder.gep(
+                dim_starts, [
+                    context.get_constant(
+                        types.uintp, i)]))
+        builder.store(stop, builder.gep(dim_stops,
+                                        [context.get_constant(types.uintp, i)]))
+
+    get_chunksize = cgutils.get_or_insert_function(
+        builder.module,
+        llvmlite.ir.FunctionType(uintp_t, []),
+        name="get_parallel_chunksize")
+
+    set_chunksize = cgutils.get_or_insert_function(
+        builder.module,
+        llvmlite.ir.FunctionType(llvmlite.ir.VoidType(), [uintp_t]),
+        name="set_parallel_chunksize")
+
+    get_num_threads = cgutils.get_or_insert_function(
+        builder.module,
+        llvmlite.ir.FunctionType(llvmlite.ir.IntType(types.intp.bitwidth), []),
+        "get_num_threads")
+
+    num_threads = builder.call(get_num_threads, [])
+    current_chunksize = builder.call(get_chunksize, [])
+
+    with cgutils.if_unlikely(builder, builder.icmp_signed('<=', num_threads,
+                                                  num_threads.type(0))):
+        cgutils.printf(builder, "num_threads: %d\n", num_threads)
+        context.call_conv.return_user_exc(builder, RuntimeError,
+                                                  ("Invalid number of threads. "
+                                                   "This likely indicates a bug in Numba.",))
+
+    get_sched_size_fnty = llvmlite.ir.FunctionType(uintp_t, [uintp_t, uintp_t, intp_ptr_t, intp_ptr_t])
+    get_sched_size = cgutils.get_or_insert_function(
+        builder.module,
+        get_sched_size_fnty,
+        name="get_sched_size")
+    num_divisions = builder.call(get_sched_size, [num_threads,
+                                                  context.get_constant(types.uintp, num_dim),
+                                                  dim_starts,
+                                                  dim_stops])
+    builder.call(set_chunksize, [zero])
+
+    multiplier = context.get_constant(types.uintp, num_dim * 2)
+    sched_size = builder.mul(num_divisions, multiplier)
+    sched = builder.alloca(sched_type, size=sched_size, name="sched")
+
+    debug_flag = 1 if config.DEBUG_ARRAY_OPT else 0
+    scheduling_fnty = llvmlite.ir.FunctionType(
+        intp_ptr_t, [uintp_t, intp_ptr_t, intp_ptr_t, uintp_t, sched_ptr_type, intp_t])
+    if index_var_typ.signed:
+        do_scheduling = cgutils.get_or_insert_function(builder.module,
+                                                       scheduling_fnty,
+                                                       name="do_scheduling_signed")
+    else:
+        do_scheduling = cgutils.get_or_insert_function(builder.module,
+                                                       scheduling_fnty,
+                                                       name="do_scheduling_unsigned")
+
+    builder.call(
+        do_scheduling, [
+            context.get_constant(
+                types.uintp, num_dim), dim_starts, dim_stops, num_divisions,
+            sched, context.get_constant(
+                    types.intp, debug_flag)])
+
+    # Get the LLVM vars for the Numba IR reduction array vars.
+    redarrs = [lowerer.loadvar(redarrdict[x].name) for x in redvars]
+
+    nredvars = len(redvars)
+    ninouts = len(expr_args) - nredvars
+
+    def load_potential_tuple_var(x):
+        """Given a variable name, if that variable is not a new name
+           introduced as the extracted part of a tuple then just return
+           the variable loaded from its name.  However, if the variable
+           does represent part of a tuple, as recognized by the name of
+           the variable being present in the exp_name_to_tuple_var dict,
+           then we load the original tuple var instead that we get from
+           the dict and then extract the corresponding element of the
+           tuple, also stored and returned to use in the dict (i.e., offset).
+        """
+        if x in exp_name_to_tuple_var:
+            orig_tup, offset = exp_name_to_tuple_var[x]
+            tup_var = lowerer.loadvar(orig_tup)
+            res = builder.extract_value(tup_var, offset)
+            return res
+        else:
+            return lowerer.loadvar(x)
+
+    # ----------------------------------------------------------------------------
+    # Prepare arguments: args, shapes, steps, data
+    all_args = [load_potential_tuple_var(x) for x in expr_args[:ninouts]] + redarrs
+    num_args = len(all_args)
+    num_inps = len(sin) + 1
+    args = cgutils.alloca_once(
+        builder,
+        byte_ptr_t,
+        size=context.get_constant(
+            types.intp,
+            1 + num_args),
+        name="pargs")
+    array_strides = []
+    # sched goes first
+    builder.store(builder.bitcast(sched, byte_ptr_t), args)
+    array_strides.append(context.get_constant(types.intp, sizeof_intp))
+    rv_to_arg_dict = {}
+    # followed by other arguments
+    for i in range(num_args):
+        arg = all_args[i]
+        var = expr_args[i]
+        aty = expr_arg_types[i]
+        dst = builder.gep(args, [context.get_constant(types.intp, i + 1)])
+        if i >= ninouts:  # reduction variables
+            ary = context.make_array(aty)(context, builder, arg)
+            strides = cgutils.unpack_tuple(builder, ary.strides, aty.ndim)
+            # Start from 1 because we skip the first dimension of length num_threads just like sched.
+            for j in range(len(strides)):
+                array_strides.append(strides[j])
+            builder.store(builder.bitcast(ary.data, byte_ptr_t), dst)
+        elif isinstance(aty, types.ArrayCompatible):
+            if var in races:
+                typ = (context.get_data_type(aty.dtype)
+                       if aty.dtype != types.boolean
+                       else llvmlite.ir.IntType(1))
+
+                rv_arg = cgutils.alloca_once(builder, typ)
+                builder.store(arg, rv_arg)
+                builder.store(builder.bitcast(rv_arg, byte_ptr_t), dst)
+                rv_to_arg_dict[var] = (arg, rv_arg)
+
+                array_strides.append(context.get_constant(types.intp, context.get_abi_sizeof(typ)))
+            else:
+                ary = context.make_array(aty)(context, builder, arg)
+                strides = cgutils.unpack_tuple(builder, ary.strides, aty.ndim)
+                for j in range(len(strides)):
+                    array_strides.append(strides[j])
+                builder.store(builder.bitcast(ary.data, byte_ptr_t), dst)
+        else:
+            if i < num_inps:
+                # Scalar input, need to store the value in an array of size 1
+                typ = (context.get_data_type(aty)
+                       if not isinstance(aty, types.Boolean)
+                       else llvmlite.ir.IntType(1))
+                ptr = cgutils.alloca_once(builder, typ)
+                builder.store(arg, ptr)
+            else:
+                # Scalar output, must allocate
+                typ = (context.get_data_type(aty)
+                       if not isinstance(aty, types.Boolean)
+                       else llvmlite.ir.IntType(1))
+                ptr = cgutils.alloca_once(builder, typ)
+            builder.store(builder.bitcast(ptr, byte_ptr_t), dst)
+
+    # ----------------------------------------------------------------------------
+    # Next, we prepare the individual dimension info recorded in gu_signature
+    sig_dim_dict = {}
+    occurrences = []
+    occurrences = [sched_sig[0]]
+    sig_dim_dict[sched_sig[0]] = context.get_constant(types.intp, 2 * num_dim)
+    assert len(expr_args) == len(all_args)
+    assert len(expr_args) == len(expr_arg_types)
+    assert len(expr_args) == len(sin + sout)
+    assert len(expr_args) == len(outer_sig.args[1:])
+    for var, arg, aty, gu_sig in zip(expr_args, all_args,
+                                     expr_arg_types, sin + sout):
+        if isinstance(aty, types.npytypes.Array):
+            i = aty.ndim - len(gu_sig)
+        else:
+            i = 0
+        if config.DEBUG_ARRAY_OPT:
+            print("var =", var, "gu_sig =", gu_sig, "type =", aty, "i =", i)
+
+        for dim_sym in gu_sig:
+            if config.DEBUG_ARRAY_OPT:
+                print("var = ", var, " type = ", aty)
+            if var in races:
+                sig_dim_dict[dim_sym] = context.get_constant(types.intp, 1)
+            else:
+                ary = context.make_array(aty)(context, builder, arg)
+                shapes = cgutils.unpack_tuple(builder, ary.shape, aty.ndim)
+                sig_dim_dict[dim_sym] = shapes[i]
+
+            if not (dim_sym in occurrences):
+                if config.DEBUG_ARRAY_OPT:
+                    print("dim_sym = ", dim_sym, ", i = ", i)
+                    cgutils.printf(builder, dim_sym + " = %d\n", sig_dim_dict[dim_sym])
+                occurrences.append(dim_sym)
+            i = i + 1
+
+    # ----------------------------------------------------------------------------
+    # Prepare shapes, which is a single number (outer loop size), followed by
+    # the size of individual shape variables.
+    nshapes = len(sig_dim_dict) + 1
+    shapes = cgutils.alloca_once(builder, intp_t, size=nshapes, name="pshape")
+    # For now, outer loop size is the same as number of threads
+    builder.store(num_divisions, shapes)
+    # Individual shape variables go next
+    i = 1
+    for dim_sym in occurrences:
+        if config.DEBUG_ARRAY_OPT:
+            cgutils.printf(builder, dim_sym + " = %d\n", sig_dim_dict[dim_sym])
+        builder.store(
+            sig_dim_dict[dim_sym], builder.gep(
+                shapes, [
+                    context.get_constant(
+                        types.intp, i)]))
+        i = i + 1
+
+    # ----------------------------------------------------------------------------
+    # Prepare steps for each argument. Note that all steps are counted in
+    # bytes.
+    num_steps = num_args + 1 + len(array_strides)
+    steps = cgutils.alloca_once(
+        builder, intp_t, size=context.get_constant(
+            types.intp, num_steps), name="psteps")
+    # First goes the step size for sched, which is 2 * num_dim
+    builder.store(context.get_constant(types.intp, 2 * num_dim * sizeof_intp),
+                  steps)
+    # The steps for all others are 0, except for reduction results.
+    for i in range(num_args):
+        # steps are strides from one thread to the next
+        stepsize = zero
+
+        dst = builder.gep(steps, [context.get_constant(types.intp, 1 + i)])
+        builder.store(stepsize, dst)
+    for j in range(len(array_strides)):
+        dst = builder.gep(
+            steps, [
+                context.get_constant(
+                    types.intp, 1 + num_args + j)])
+        builder.store(array_strides[j], dst)
+
+    # ----------------------------------------------------------------------------
+    # prepare data
+    data = cgutils.get_null_value(byte_ptr_t)
+
+    fnty = llvmlite.ir.FunctionType(llvmlite.ir.VoidType(),
+                                    [byte_ptr_ptr_t, intp_ptr_t,
+                                     intp_ptr_t, byte_ptr_t])
+
+    fn = cgutils.get_or_insert_function(builder.module, fnty, wrapper_name)
+    context.active_code_library.add_linking_library(info.library)
+
+    if config.DEBUG_ARRAY_OPT:
+        cgutils.printf(builder, "before calling kernel %p\n", fn)
+    builder.call(fn, [args, shapes, steps, data])
+    if config.DEBUG_ARRAY_OPT:
+        cgutils.printf(builder, "after calling kernel %p\n", fn)
+
+    builder.call(set_chunksize, [current_chunksize])
+
+    for k, v in rv_to_arg_dict.items():
+        arg, rv_arg = v
+        only_elem_ptr = builder.gep(rv_arg, [context.get_constant(types.intp, 0)])
+        builder.store(builder.load(only_elem_ptr), lowerer.getvar(k))
+
+    context.active_code_library.add_linking_library(cres.library)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor_lowering_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor_lowering_utils.py
new file mode 100644
index 000000000..e21849ed2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/parfors/parfor_lowering_utils.py
@@ -0,0 +1,213 @@
+from collections import namedtuple
+
+from numba.core import types, ir
+from numba.core.ir_utils import mk_unique_var
+from numba.core.typing import signature
+
+
+_CallableNode = namedtuple("BoundFunc", ["func", "sig"])
+
+
+class ParforLoweringBuilder:
+    """Helper class for building Numba-IR and lowering for Parfor.
+    """
+    def __init__(self, lowerer, scope, loc):
+        self._lowerer = lowerer
+        self._scope = scope
+        self._loc = loc
+
+    @property
+    def _context(self):
+        return self._lowerer.context
+
+    @property
+    def _typingctx(self):
+        return self._context.typing_context
+
+    @property
+    def _typemap(self):
+        return self._lowerer.fndesc.typemap
+
+    @property
+    def _calltypes(self):
+        return self._lowerer.fndesc.calltypes
+
+    def bind_global_function(self, fobj, ftype, args, kws={}):
+        """Binds a global function to a variable.
+
+        Parameters
+        ----------
+        fobj : object
+            The function to be bound.
+        ftype : types.Type
+        args : Sequence[types.Type]
+        kws : Mapping[str, types.Type]
+
+        Returns
+        -------
+        callable: _CallableNode
+        """
+        loc = self._loc
+        varname = f"{fobj.__name__}_func"
+        gvname = f"{fobj.__name__}"
+
+        func_sig = self._typingctx.resolve_function_type(ftype, args, kws)
+        func_var = self.assign(
+            rhs=ir.Global(gvname, fobj, loc=loc), typ=ftype, name=varname
+        )
+        return _CallableNode(func=func_var, sig=func_sig)
+
+    def make_const_variable(self, cval, typ, name="pf_const") -> ir.Var:
+        """Makes a constant variable
+
+        Parameters
+        ----------
+        cval : object
+            The constant value
+        typ : types.Type
+            type of the value
+        name : str
+            variable name to store to
+
+        Returns
+        -------
+        res : ir.Var
+        """
+        return self.assign(
+            rhs=ir.Const(cval, loc=self._loc), typ=typ, name=name
+        )
+
+    def make_tuple_variable(self, varlist, name="pf_tuple") -> ir.Var:
+        """Makes a tuple variable
+
+        Parameters
+        ----------
+        varlist : Sequence[ir.Var]
+            Variables containing the values to be stored.
+        name : str
+            variable name to store to
+
+        Returns
+        -------
+        res : ir.Var
+        """
+        loc = self._loc
+        vartys = [self._typemap[x.name] for x in varlist]
+        tupty = types.Tuple.from_types(vartys)
+        return self.assign(
+            rhs=ir.Expr.build_tuple(varlist, loc), typ=tupty, name=name
+        )
+
+    def assign(self, rhs, typ, name="pf_assign") -> ir.Var:
+        """Assign a value to a new variable
+
+        Parameters
+        ----------
+        rhs : object
+            The value
+        typ : types.Type
+            type of the value
+        name : str
+            variable name to store to
+
+        Returns
+        -------
+        res : ir.Var
+        """
+        loc = self._loc
+        var = ir.Var(self._scope, mk_unique_var(name), loc)
+        self._typemap[var.name] = typ
+        assign = ir.Assign(rhs, var, loc)
+        self._lowerer.lower_inst(assign)
+        return var
+
+    def assign_inplace(self, rhs, typ, name) -> ir.Var:
+        """Assign a value to a new variable or inplace if it already exist
+
+        Parameters
+        ----------
+        rhs : object
+            The value
+        typ : types.Type
+            type of the value
+        name : str
+            variable name to store to
+
+        Returns
+        -------
+        res : ir.Var
+        """
+        loc = self._loc
+        var = ir.Var(self._scope, name, loc)
+        assign = ir.Assign(rhs, var, loc)
+        self._typemap.setdefault(var.name, typ)
+        self._lowerer.lower_inst(assign)
+        return var
+
+    def call(self, callable_node, args, kws={}) -> ir.Expr:
+        """Call a bound callable
+
+        Parameters
+        ----------
+        callable_node : _CallableNode
+            The callee
+        args : Sequence[ir.Var]
+        kws : Mapping[str, ir.Var]
+
+        Returns
+        -------
+        res : ir.Expr
+            The expression node for the return value of the call
+        """
+        call = ir.Expr.call(callable_node.func, args, kws, loc=self._loc)
+        self._calltypes[call] = callable_node.sig
+        return call
+
+    def setitem(self, obj, index, val) -> ir.SetItem:
+        """Makes a setitem call
+
+        Parameters
+        ----------
+        obj : ir.Var
+            the object being indexed
+        index : ir.Var
+            the index
+        val : ir.Var
+            the value to be stored
+
+        Returns
+        -------
+        res : ir.SetItem
+        """
+        loc = self._loc
+        tm = self._typemap
+        setitem = ir.SetItem(obj, index, val, loc=loc)
+        self._lowerer.fndesc.calltypes[setitem] = signature(
+            types.none, tm[obj.name], tm[index.name], tm[val.name]
+        )
+        self._lowerer.lower_inst(setitem)
+        return setitem
+
+    def getitem(self, obj, index, typ) -> ir.Expr:
+        """Makes a getitem call
+
+        Parameters
+        ----------
+        obj : ir.Var
+            the object being indexed
+        index : ir.Var
+            the index
+        val : ir.Var
+            the ty
+
+        Returns
+        -------
+        res : ir.Expr
+            the retrieved value
+        """
+        tm = self._typemap
+        getitem = ir.Expr.getitem(obj, index, loc=self._loc)
+        self._lowerer.fndesc.calltypes[getitem] = signature(
+            typ, tm[obj.name], tm[index.name],
+        )
+        return getitem
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/__init__.py
new file mode 100644
index 000000000..bda809ad1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/__init__.py
@@ -0,0 +1,100 @@
+# -*- coding: utf-8 -*-
+
+
+import os
+import logging
+import subprocess
+import tempfile
+import sys
+
+# Public API
+from .cc import CC
+from .decorators import export, exportmany
+
+
+def get_ending(args):
+    if args.llvm:
+        return ".bc"
+    elif args.olibs:
+        return ".o"
+    elif args.python:
+        return find_pyext_ending()
+    else:
+        return find_shared_ending()
+
+
+def main(args=None):
+    import argparse
+
+    from .compiler import ModuleCompiler
+    from .platform import Toolchain, find_shared_ending, find_pyext_ending
+    from numba.pycc import decorators
+
+    parser = argparse.ArgumentParser(
+        description="DEPRECATED - Compile Python modules to a single shared library")
+    parser.add_argument("inputs", nargs='+', help="Input file(s)")
+    parser.add_argument("-o", nargs=1, dest="output",
+                        help="Output file  (default is name of first input -- with new ending)")
+
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument("-c", action="store_true", dest="olibs",
+                       help="Create object file from each input instead of shared-library")
+    group.add_argument("--llvm", action="store_true",
+                       help="Emit llvm instead of native code")
+
+    parser.add_argument('--header', action="store_true",
+                        help="Emit C header file with function signatures")
+    parser.add_argument('--python', action='store_true',
+                        help='Emit additionally generated Python wrapper and '
+                        'extension module code in output')
+    parser.add_argument('-d', '--debug', action='store_true',
+                        help='Print extra debug information')
+
+    args = parser.parse_args(args)
+
+    logger = logging.getLogger(__name__)
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+
+    logger.warn("The 'pycc' script is DEPRECATED; "
+                "please use the numba.pycc.CC API instead")
+
+    if args.output:
+        args.output = args.output[0]
+        output_base = os.path.split(args.output)[1]
+        module_name = os.path.splitext(output_base)[0]
+    else:
+        input_base = os.path.splitext(args.inputs[0])[0]
+        module_name = os.path.split(input_base)[1]
+        args.output = input_base + get_ending(args)
+    logger.debug('args.output --> %s', args.output)
+
+    if args.header:
+        print('ERROR: pycc --header has been disabled in this release due to a known issue')
+        sys.exit(1)
+
+    logger.debug('inputs --> %s', args.inputs)
+    decorators.process_input_files(args.inputs)
+
+    compiler = ModuleCompiler(decorators.export_registry, module_name=module_name)
+    if args.llvm:
+        logger.debug('emit llvm')
+        compiler.write_llvm_bitcode(args.output, wrap=args.python)
+    elif args.olibs:
+        logger.debug('emit object file')
+        compiler.write_native_object(args.output, wrap=args.python)
+    else:
+        logger.debug('emit shared library')
+        logger.debug('write to temporary object file %s', tempfile.gettempdir())
+
+        toolchain = Toolchain()
+        toolchain.debug = args.debug
+        temp_obj = (tempfile.gettempdir() + os.sep +
+                    os.path.basename(args.output) + '.o')
+        compiler.write_native_object(temp_obj, wrap=args.python)
+        libraries = toolchain.get_python_libraries()
+        toolchain.link_shared(args.output, [temp_obj],
+                              toolchain.get_python_libraries(),
+                              toolchain.get_python_library_dirs(),
+                              export_symbols=compiler.dll_exports)
+        os.remove(temp_obj)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/cc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/cc.py
new file mode 100644
index 000000000..f171f28d8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/cc.py
@@ -0,0 +1,306 @@
+from distutils import dir_util, log
+from distutils.command import build_ext
+from distutils.extension import Extension
+import os
+import shutil
+import sys
+import tempfile
+
+from numba.core import typing, sigutils
+from numba.core.compiler_lock import global_compiler_lock
+from numba.pycc.compiler import ModuleCompiler, ExportEntry
+from numba.pycc.platform import Toolchain
+from numba import cext
+
+
+extension_libs = cext.get_extension_libs()
+
+
+class CC(object):
+    """
+    An ahead-of-time compiler to create extension modules that don't
+    depend on Numba.
+    """
+
+    # NOTE: using ccache can speed up repetitive builds
+    # (especially for the mixin modules)
+
+    _mixin_sources = ['modulemixin.c',]  + extension_libs
+
+    # -flto strips all unused helper functions, which 1) makes the
+    # produced output much smaller and 2) can make the linking step faster.
+    # (the Windows linker seems to do this by default, judging by the results)
+
+    _extra_cflags = {
+        # Comment out due to odd behavior with GCC 4.9+ with LTO
+        # 'posix': ['-flto'],
+        }
+
+    _extra_ldflags = {
+        # Comment out due to odd behavior with GCC 4.9+ with LTO
+        # 'posix': ['-flto'],
+        }
+
+    def __init__(self, extension_name, source_module=None):
+        if '.' in extension_name:
+            raise ValueError("basename should be a simple module name, not "
+                             "qualified name")
+
+        self._basename = extension_name
+        self._init_function = 'pycc_init_' + extension_name
+        self._exported_functions = {}
+        # Resolve source module name and directory
+        f = sys._getframe(1)
+        if source_module is None:
+            dct = f.f_globals
+            source_module = dct['__name__']
+        elif hasattr(source_module, '__name__'):
+            dct = source_module.__dict__
+            source_module = source_module.__name__
+        else:
+            dct = sys.modules[source_module].__dict__
+
+        self._source_path = dct.get('__file__', '')
+        self._source_module = source_module
+        self._toolchain = Toolchain()
+        self._verbose = False
+        # By default, output in directory of caller module
+        self._output_dir = os.path.dirname(self._source_path)
+        self._output_file = self._toolchain.get_ext_filename(extension_name)
+        self._use_nrt = True
+        self._target_cpu = ''
+
+    @property
+    def name(self):
+        """
+        The name of the extension module to create.
+        """
+        return self._basename
+
+    @property
+    def output_file(self):
+        """
+        The specific output file (a DLL) that will be generated.
+        """
+        return self._output_file
+
+    @output_file.setter
+    def output_file(self, value):
+        self._output_file = value
+
+    @property
+    def output_dir(self):
+        """
+        The directory the output file will be put in.
+        """
+        return self._output_dir
+
+    @output_dir.setter
+    def output_dir(self, value):
+        self._output_dir = value
+
+    @property
+    def use_nrt(self):
+        return self._use_nrt
+
+    @use_nrt.setter
+    def use_nrt(self, value):
+        self._use_nrt = value
+
+    @property
+    def target_cpu(self):
+        """
+        The target CPU model for code generation.
+        """
+        return self._target_cpu
+
+    @target_cpu.setter
+    def target_cpu(self, value):
+        self._target_cpu = value
+
+    @property
+    def verbose(self):
+        """
+        Whether to display detailed information when compiling.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, value):
+        self._verbose = value
+
+    def export(self, exported_name, sig):
+        """
+        Mark a function for exporting in the extension module.
+        """
+        fn_args, fn_retty = sigutils.normalize_signature(sig)
+        sig = typing.signature(fn_retty, *fn_args)
+        if exported_name in self._exported_functions:
+            raise KeyError("duplicated export symbol %s" % (exported_name))
+
+        def decorator(func):
+            entry = ExportEntry(exported_name, sig, func)
+            self._exported_functions[exported_name] = entry
+            return func
+
+        return decorator
+
+    @property
+    def _export_entries(self):
+        return sorted(self._exported_functions.values(),
+                      key=lambda entry: entry.symbol)
+
+    def _get_mixin_sources(self):
+        here = os.path.dirname(__file__)
+        mixin_sources = self._mixin_sources[:]
+        if self._use_nrt:
+            mixin_sources.append('../core/runtime/nrt.c')
+        return [os.path.join(here, f) for f in mixin_sources]
+
+    def _get_mixin_defines(self):
+        # Macro definitions required by modulemixin.c
+        return [
+            ('PYCC_MODULE_NAME', self._basename),
+            ('PYCC_USE_NRT', int(self._use_nrt)),
+            ]
+
+    def _get_extra_cflags(self):
+        extra_cflags = self._extra_cflags.get(sys.platform, [])
+        if not extra_cflags:
+            extra_cflags = self._extra_cflags.get(os.name, [])
+        return extra_cflags
+
+    def _get_extra_ldflags(self):
+        extra_ldflags = self._extra_ldflags.get(sys.platform, [])
+        if not extra_ldflags:
+            extra_ldflags = self._extra_ldflags.get(os.name, [])
+        # helperlib uses pthread on linux. make sure we are linking to it.
+        if sys.platform.startswith("linux"):
+            if "-pthread" not in extra_ldflags:
+                extra_ldflags.append('-pthread')
+        return extra_ldflags
+
+    def _compile_mixins(self, build_dir):
+        sources = self._get_mixin_sources()
+        macros = self._get_mixin_defines()
+        include_dirs = self._toolchain.get_python_include_dirs()
+
+        extra_cflags = self._get_extra_cflags()
+        # XXX distutils creates a whole subtree inside build_dir,
+        # e.g. /tmp/test_pycc/home/antoine/numba/numba/pycc/modulemixin.o
+        objects = self._toolchain.compile_objects(sources, build_dir,
+                                                  include_dirs=include_dirs,
+                                                  macros=macros,
+                                                  extra_cflags=extra_cflags)
+        return objects
+
+    @global_compiler_lock
+    def _compile_object_files(self, build_dir):
+        compiler = ModuleCompiler(self._export_entries, self._basename,
+                                self._use_nrt, cpu_name=self._target_cpu)
+        compiler.external_init_function = self._init_function
+        temp_obj = os.path.join(build_dir,
+                                os.path.splitext(self._output_file)[0] + '.o')
+        log.info("generating LLVM code for '%s' into %s",
+                self._basename, temp_obj)
+        compiler.write_native_object(temp_obj, wrap=True)
+        return [temp_obj], compiler.dll_exports
+
+    @global_compiler_lock
+    def compile(self):
+        """
+        Compile the extension module.
+        """
+        self._toolchain.verbose = self.verbose
+        build_dir = tempfile.mkdtemp(prefix='pycc-build-%s-' % self._basename)
+
+        # Compile object file
+        objects, dll_exports = self._compile_object_files(build_dir)
+
+        # Compile mixins
+        objects += self._compile_mixins(build_dir)
+
+        # Then create shared library
+        extra_ldflags = self._get_extra_ldflags()
+        output_dll = os.path.join(self._output_dir, self._output_file)
+        libraries = self._toolchain.get_python_libraries()
+        library_dirs = self._toolchain.get_python_library_dirs()
+        self._toolchain.link_shared(output_dll, objects,
+                                    libraries, library_dirs,
+                                    export_symbols=dll_exports,
+                                    extra_ldflags=extra_ldflags)
+
+        shutil.rmtree(build_dir)
+
+    def distutils_extension(self, **kwargs):
+        """
+        Create a distutils extension object that can be used in your
+        setup.py.
+        """
+        macros = kwargs.pop('macros', []) + self._get_mixin_defines()
+        depends = kwargs.pop('depends', []) + [self._source_path]
+        extra_compile_args = (kwargs.pop('extra_compile_args', [])
+                              + self._get_extra_cflags())
+        extra_link_args = (kwargs.pop('extra_link_args', [])
+                           + self._get_extra_ldflags())
+        include_dirs = (kwargs.pop('include_dirs', [])
+                        + self._toolchain.get_python_include_dirs())
+        libraries = (kwargs.pop('libraries', [])
+                     + self._toolchain.get_python_libraries())
+        library_dirs = (kwargs.pop('library_dirs', [])
+                        + self._toolchain.get_python_library_dirs())
+        python_package_path = self._source_module[:self._source_module.rfind('.')+1]
+
+        ext = _CCExtension(name=python_package_path + self._basename,
+                           sources=self._get_mixin_sources(),
+                           depends=depends,
+                           define_macros=macros,
+                           include_dirs=include_dirs,
+                           libraries=libraries,
+                           library_dirs=library_dirs,
+                           extra_compile_args=extra_compile_args,
+                           extra_link_args=extra_link_args,
+                           **kwargs)
+        ext.monkey_patch_distutils()
+        ext._cc = self
+        return ext
+
+
+class _CCExtension(Extension):
+    """
+    A Numba-specific Extension subclass to LLVM-compile pure Python code
+    to an extension module.
+    """
+
+    _cc = None
+    _distutils_monkey_patched = False
+
+    def _prepare_object_files(self, build_ext):
+        cc = self._cc
+        dir_util.mkpath(os.path.join(build_ext.build_temp, *self.name.split('.')[:-1]))
+        objects, _ = cc._compile_object_files(build_ext.build_temp)
+        # Add generated object files for linking
+        self.extra_objects = objects
+
+    @classmethod
+    def monkey_patch_distutils(cls):
+        """
+        Monkey-patch distutils with our own build_ext class knowing
+        about pycc-compiled extensions modules.
+        """
+        if cls._distutils_monkey_patched:
+            return
+
+        _orig_build_ext = build_ext.build_ext
+
+        class _CC_build_ext(_orig_build_ext):
+
+            def build_extension(self, ext):
+                if isinstance(ext, _CCExtension):
+                    ext._prepare_object_files(self)
+
+                _orig_build_ext.build_extension(self, ext)
+
+        build_ext.build_ext = _CC_build_ext
+
+        cls._distutils_monkey_patched = True
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/compiler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/compiler.py
new file mode 100644
index 000000000..f42a6be17
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/compiler.py
@@ -0,0 +1,471 @@
+# -*- coding: utf-8 -*-
+
+import logging
+import os
+import sys
+
+from llvmlite import ir
+from llvmlite.binding import Linkage
+
+from numba.pycc import llvm_types as lt
+from numba.core.cgutils import create_constant_array
+from numba.core.compiler import compile_extra, Flags
+from numba.core.compiler_lock import global_compiler_lock
+
+from numba.core.registry import cpu_target
+from numba.core.runtime import nrtdynmod
+from numba.core import cgutils
+
+
+logger = logging.getLogger(__name__)
+
+__all__ = ['Compiler']
+
+NULL = ir.Constant(lt._void_star, None)
+ZERO = ir.Constant(lt._int32, 0)
+ONE = ir.Constant(lt._int32, 1)
+METH_VARARGS_AND_KEYWORDS = ir.Constant(lt._int32, 1|2)
+
+
+def get_header():
+    import numpy
+    import textwrap
+
+    return textwrap.dedent("""\
+    #include <stdint.h>
+
+    #ifndef HAVE_LONGDOUBLE
+        #define HAVE_LONGDOUBLE %d
+    #endif
+
+    typedef struct {
+        float real;
+        float imag;
+    } complex64;
+
+    typedef struct {
+        double real;
+        double imag;
+    } complex128;
+
+    #if HAVE_LONGDOUBLE
+    typedef struct {
+        long double real;
+        long double imag;
+    } complex256;
+    #endif
+
+    typedef float float32;
+    typedef double float64;
+    #if HAVE_LONGDOUBLE
+    typedef long double float128;
+    #endif
+    """ % hasattr(numpy, 'complex256'))
+
+
+class ExportEntry(object):
+    """
+    A simple record for exporting symbols.
+    """
+
+    def __init__(self, symbol, signature, function):
+        self.symbol = symbol
+        self.signature = signature
+        self.function = function
+
+    def __repr__(self):
+        return "ExportEntry(%r, %r)" % (self.symbol, self.signature)
+
+
+class _ModuleCompiler(object):
+    """A base class to compile Python modules to a single shared library or
+    extension module.
+
+    :param export_entries: a list of ExportEntry instances.
+    :param module_name: the name of the exported module.
+    """
+
+    #: Structure used to describe a method of an extension type.
+    #: struct PyMethodDef {
+    #:     const char  *ml_name;       /* The name of the built-in function/method */
+    #:     PyCFunction  ml_meth;       /* The C function that implements it */
+    #:     int          ml_flags;      /* Combination of METH_xxx flags, which mostly
+    #:                                    describe the args expected by the C func */
+    #:     const char  *ml_doc;        /* The __doc__ attribute, or NULL */
+    #: };
+    method_def_ty = ir.LiteralStructType((lt._int8_star,
+                                          lt._void_star,
+                                          lt._int32,
+                                          lt._int8_star))
+
+    method_def_ptr = ir.PointerType(method_def_ty)
+    # The structure type constructed by PythonAPI.serialize_uncached()
+    env_def_ty = ir.LiteralStructType((lt._void_star, lt._int32, lt._void_star))
+    env_def_ptr = ir.PointerType(env_def_ty)
+
+    def __init__(self, export_entries, module_name, use_nrt=False,
+                 **aot_options):
+        self.module_name = module_name
+        self.export_python_wrap = False
+        self.dll_exports = []
+        self.export_entries = export_entries
+        # Used by the CC API but not the legacy API
+        self.external_init_function = None
+        self.use_nrt = use_nrt
+
+        self.typing_context = cpu_target.typing_context
+        self.context = cpu_target.target_context.with_aot_codegen(
+            self.module_name, **aot_options)
+
+    def _mangle_method_symbol(self, func_name):
+        return "._pycc_method_%s" % (func_name,)
+
+    def _emit_python_wrapper(self, llvm_module):
+        """Emit generated Python wrapper and extension module code.
+        """
+        raise NotImplementedError
+
+    @global_compiler_lock
+    def _cull_exports(self):
+        """Read all the exported functions/modules in the translator
+        environment, and join them into a single LLVM module.
+        """
+        self.exported_function_types = {}
+        self.function_environments = {}
+        self.environment_gvs = {}
+
+        codegen = self.context.codegen()
+        library = codegen.create_library(self.module_name)
+
+        # Generate IR for all exported functions
+        flags = Flags()
+        flags.no_compile = True
+        if not self.export_python_wrap:
+            flags.no_cpython_wrapper = True
+            flags.no_cfunc_wrapper = True
+        if self.use_nrt:
+            flags.nrt = True
+            # Compile NRT helpers
+            nrt_module, _ = nrtdynmod.create_nrt_module(self.context)
+            library.add_ir_module(nrt_module)
+
+        for entry in self.export_entries:
+            cres = compile_extra(self.typing_context, self.context,
+                                entry.function,
+                                entry.signature.args,
+                                entry.signature.return_type, flags,
+                                locals={}, library=library)
+
+            func_name = cres.fndesc.llvm_func_name
+            llvm_func = cres.library.get_function(func_name)
+
+            if self.export_python_wrap:
+                llvm_func.linkage = 'internal'
+                wrappername = cres.fndesc.llvm_cpython_wrapper_name
+                wrapper = cres.library.get_function(wrappername)
+                wrapper.name = self._mangle_method_symbol(entry.symbol)
+                wrapper.linkage = 'external'
+                fnty = cres.target_context.call_conv.get_function_type(
+                    cres.fndesc.restype, cres.fndesc.argtypes)
+                self.exported_function_types[entry] = fnty
+                self.function_environments[entry] = cres.environment
+                self.environment_gvs[entry] = cres.fndesc.env_name
+            else:
+                llvm_func.name = entry.symbol
+                self.dll_exports.append(entry.symbol)
+
+        if self.export_python_wrap:
+            wrapper_module = library.create_ir_module("wrapper")
+            self._emit_python_wrapper(wrapper_module)
+            library.add_ir_module(wrapper_module)
+
+        # Hide all functions in the DLL except those explicitly exported
+        library.finalize()
+        for fn in library.get_defined_functions():
+            if fn.name not in self.dll_exports:
+                if fn.linkage in {Linkage.private, Linkage.internal}:
+                    # Private/Internal linkage must have "default" visibility
+                    fn.visibility = "default"
+                else:
+                    fn.visibility = 'hidden'
+        return library
+
+    def write_llvm_bitcode(self, output, wrap=False, **kws):
+        self.export_python_wrap = wrap
+        library = self._cull_exports()
+        with open(output, 'wb') as fout:
+            fout.write(library.emit_bitcode())
+
+    def write_native_object(self, output, wrap=False, **kws):
+        self.export_python_wrap = wrap
+        library = self._cull_exports()
+        with open(output, 'wb') as fout:
+            fout.write(library.emit_native_object())
+
+    def emit_type(self, tyobj):
+        ret_val = str(tyobj)
+        if 'int' in ret_val:
+            if ret_val.endswith(('8', '16', '32', '64')):
+                ret_val += "_t"
+        return ret_val
+
+    def emit_header(self, output):
+        fname, ext = os.path.splitext(output)
+        with open(fname + '.h', 'w') as fout:
+            fout.write(get_header())
+            fout.write("\n/* Prototypes */\n")
+            for export_entry in self.export_entries:
+                name = export_entry.symbol
+                restype = self.emit_type(export_entry.signature.return_type)
+                args = ", ".join(self.emit_type(argtype)
+                                 for argtype in export_entry.signature.args)
+                fout.write("extern %s %s(%s);\n" % (restype, name, args))
+
+    def _emit_method_array(self, llvm_module):
+        """
+        Collect exported methods and emit a PyMethodDef array.
+
+        :returns: a pointer to the PyMethodDef array.
+        """
+        method_defs = []
+        for entry in self.export_entries:
+            name = entry.symbol
+            llvm_func_name = self._mangle_method_symbol(name)
+            fnty = self.exported_function_types[entry]
+            lfunc = ir.Function(llvm_module, fnty, llvm_func_name)
+
+            method_name = self.context.insert_const_string(llvm_module, name)
+            method_def_const = ir.Constant.literal_struct(
+                (method_name,
+                 ir.Constant.bitcast(lfunc, lt._void_star),
+                 METH_VARARGS_AND_KEYWORDS,
+                 NULL))
+            method_defs.append(method_def_const)
+
+        sentinel = ir.Constant.literal_struct([NULL, NULL, ZERO, NULL])
+        method_defs.append(sentinel)
+        method_array_init = create_constant_array(self.method_def_ty, method_defs)
+        method_array = cgutils.add_global_variable(llvm_module,
+                                                   method_array_init.type,
+                                                   '.module_methods')
+        method_array.initializer = method_array_init
+        method_array.linkage = 'internal'
+        method_array_ptr = ir.Constant.gep(method_array, [ZERO, ZERO])
+        return method_array_ptr
+
+    def _emit_environment_array(self, llvm_module, builder, pyapi):
+        """
+        Emit an array of env_def_t structures (see modulemixin.c)
+        storing the pickled environment constants for each of the
+        exported functions.
+        """
+        env_defs = []
+        for entry in self.export_entries:
+            env = self.function_environments[entry]
+            # Constants may be unhashable so avoid trying to cache them
+            env_def = pyapi.serialize_uncached(env.consts)
+            env_defs.append(env_def)
+        env_defs_init = create_constant_array(self.env_def_ty, env_defs)
+        gv = self.context.insert_unique_const(llvm_module,
+                                              '.module_environments',
+                                              env_defs_init)
+        return gv.gep([ZERO, ZERO])
+
+    def _emit_envgvs_array(self, llvm_module, builder, pyapi):
+        """
+        Emit an array of Environment pointers that needs to be filled at
+        initialization.
+        """
+        env_setters = []
+        for entry in self.export_entries:
+            envgv_name = self.environment_gvs[entry]
+            gv = self.context.declare_env_global(llvm_module, envgv_name)
+            envgv = gv.bitcast(lt._void_star)
+            env_setters.append(envgv)
+
+        env_setters_init = create_constant_array(lt._void_star, env_setters)
+        gv = self.context.insert_unique_const(llvm_module,
+                                              '.module_envgvs',
+                                              env_setters_init)
+        return gv.gep([ZERO, ZERO])
+
+    def _emit_module_init_code(self, llvm_module, builder, modobj,
+                               method_array, env_array, envgv_array):
+        """
+        Emit call to "external" init function, if any.
+        """
+        if self.external_init_function:
+            fnty = ir.FunctionType(lt._int32,
+                                   [modobj.type, self.method_def_ptr,
+                                    self.env_def_ptr, envgv_array.type])
+            fn = ir.Function(llvm_module, fnty, self.external_init_function)
+            return builder.call(fn, [modobj, method_array, env_array,
+                                     envgv_array])
+        else:
+            return None
+
+
+class ModuleCompiler(_ModuleCompiler):
+
+    _ptr_fun = lambda ret, *args: ir.PointerType(ir.FunctionType(ret, args))
+
+    #: typedef int (*visitproc)(PyObject *, void *);
+    visitproc_ty = _ptr_fun(lt._int8,
+                            lt._pyobject_head_p)
+
+    #: typedef int (*inquiry)(PyObject *);
+    inquiry_ty = _ptr_fun(lt._int8,
+                          lt._pyobject_head_p)
+
+    #: typedef int (*traverseproc)(PyObject *, visitproc, void *);
+    traverseproc_ty = _ptr_fun(lt._int8,
+                               lt._pyobject_head_p,
+                               visitproc_ty,
+                               lt._void_star)
+
+    #  typedef void (*freefunc)(void *)
+    freefunc_ty = _ptr_fun(lt._int8,
+                           lt._void_star)
+
+    # PyObject* (*m_init)(void);
+    m_init_ty = _ptr_fun(lt._int8)
+
+    _char_star = lt._int8_star
+
+    #: typedef struct PyModuleDef_Base {
+    #:   PyObject_HEAD
+    #:   PyObject* (*m_init)(void);
+    #:   Py_ssize_t m_index;
+    #:   PyObject* m_copy;
+    #: } PyModuleDef_Base;
+    module_def_base_ty = ir.LiteralStructType(
+        (
+            lt._pyobject_head,
+            m_init_ty,
+            lt._llvm_py_ssize_t,
+            lt._pyobject_head_p
+        ))
+
+    #: This struct holds all information that is needed to create a module object.
+    #: typedef struct PyModuleDef{
+    #:   PyModuleDef_Base m_base;
+    #:   const char* m_name;
+    #:   const char* m_doc;
+    #:   Py_ssize_t m_size;
+    #:   PyMethodDef *m_methods;
+    #:   inquiry m_reload;
+    #:   traverseproc m_traverse;
+    #:   inquiry m_clear;
+    #:   freefunc m_free;
+    #: }PyModuleDef;
+    module_def_ty = ir.LiteralStructType(
+        (
+            module_def_base_ty,
+            _char_star,
+            _char_star,
+            lt._llvm_py_ssize_t,
+            _ModuleCompiler.method_def_ptr,
+            inquiry_ty,
+            traverseproc_ty,
+            inquiry_ty,
+            freefunc_ty
+        ))
+
+    @property
+    def module_create_definition(self):
+        """
+        Return the signature and name of the Python C API function to
+        initialize the module.
+        """
+        signature = ir.FunctionType(lt._pyobject_head_p,
+                                    (ir.PointerType(self.module_def_ty),
+                                     lt._int32))
+
+        name = "PyModule_Create2"
+        if lt._trace_refs_:
+            name += "TraceRefs"
+
+        return signature, name
+
+    @property
+    def module_init_definition(self):
+        """
+        Return the name and signature of the module's initialization function.
+        """
+        signature = ir.FunctionType(lt._pyobject_head_p, ())
+
+        return signature, "PyInit_" + self.module_name
+
+    def _emit_python_wrapper(self, llvm_module):
+        # Figure out the Python C API module creation function, and
+        # get a LLVM function for it.
+        create_module_fn = ir.Function(llvm_module, *self.module_create_definition)
+        create_module_fn.linkage = 'external'
+
+        # Define a constant string for the module name.
+        mod_name_const = self.context.insert_const_string(llvm_module,
+                                                          self.module_name)
+
+        mod_def_base_init = ir.Constant.literal_struct(
+            (lt._pyobject_head_init,                        # PyObject_HEAD
+             ir.Constant(self.m_init_ty, None),             # m_init
+             ir.Constant(lt._llvm_py_ssize_t, None),        # m_index
+             ir.Constant(lt._pyobject_head_p, None),        # m_copy
+            )
+        )
+        mod_def_base = cgutils.add_global_variable(llvm_module,
+                                                   mod_def_base_init.type,
+                                                   '.module_def_base')
+        mod_def_base.initializer = mod_def_base_init
+        mod_def_base.linkage = 'internal'
+
+        method_array = self._emit_method_array(llvm_module)
+
+        mod_def_init = ir.Constant.literal_struct(
+            (mod_def_base_init,                              # m_base
+             mod_name_const,                                 # m_name
+             ir.Constant(self._char_star, None),             # m_doc
+             ir.Constant(lt._llvm_py_ssize_t, -1),           # m_size
+             method_array,                                   # m_methods
+             ir.Constant(self.inquiry_ty, None),             # m_reload
+             ir.Constant(self.traverseproc_ty, None),        # m_traverse
+             ir.Constant(self.inquiry_ty, None),             # m_clear
+             ir.Constant(self.freefunc_ty, None)             # m_free
+            )
+        )
+
+        # Define a constant string for the module name.
+        mod_def = cgutils.add_global_variable(llvm_module, mod_def_init.type,
+                                              '.module_def')
+        mod_def.initializer = mod_def_init
+        mod_def.linkage = 'internal'
+
+        # Define the module initialization function.
+        mod_init_fn = ir.Function(llvm_module, *self.module_init_definition)
+        entry = mod_init_fn.append_basic_block('Entry')
+        builder = ir.IRBuilder(entry)
+        pyapi = self.context.get_python_api(builder)
+
+        mod = builder.call(create_module_fn,
+                           (mod_def,
+                            ir.Constant(lt._int32, sys.api_version)))
+
+        # Test if module has been created correctly.
+        # (XXX for some reason comparing with the NULL constant fails llvm
+        #  with an assertion in pydebug mode)
+        with builder.if_then(cgutils.is_null(builder, mod)):
+            builder.ret(NULL.bitcast(mod_init_fn.type.pointee.return_type))
+
+        env_array = self._emit_environment_array(llvm_module, builder, pyapi)
+        envgv_array = self._emit_envgvs_array(llvm_module, builder, pyapi)
+        ret = self._emit_module_init_code(llvm_module, builder, mod,
+                                          method_array, env_array, envgv_array)
+        if ret is not None:
+            with builder.if_then(cgutils.is_not_null(builder, ret)):
+                # Init function errored out
+                builder.ret(ir.Constant(mod.type, None))
+
+        builder.ret(mod)
+
+        self.dll_exports.append(mod_init_fn.name)
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/decorators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/decorators.py
new file mode 100644
index 000000000..34278903d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/decorators.py
@@ -0,0 +1,72 @@
+import re
+import warnings
+
+from numba.core import typing, sigutils
+from numba.pycc.compiler import ExportEntry
+
+# Registry is okay to be a global because we are using pycc as a standalone
+# commandline tool.
+export_registry = []
+
+
+def export(prototype):
+    warnings.warn("export() is deprecated, use the numba.pycc.CC API instead",
+                  DeprecationWarning, stacklevel=2)
+
+    sym, sig = parse_prototype(prototype)
+
+    def wrappped(func):
+        fn_argtys, fn_retty = sigutils.normalize_signature(sig)
+        signature = typing.signature(fn_retty, *fn_argtys)
+        entry = ExportEntry(symbol=sym, signature=signature, function=func)
+        export_registry.append(entry)
+
+    return wrappped
+
+
+def exportmany(prototypes):
+    warnings.warn("exportmany() is deprecated, use the numba.pycc.CC API instead",
+                  DeprecationWarning, stacklevel=2)
+
+    def wrapped(func):
+        for proto in prototypes:
+            export(proto)(func)
+    return wrapped
+
+
+def process_input_files(inputs):
+    """
+    Read input source files for execution of legacy @export / @exportmany
+    decorators.
+    """
+    for ifile in inputs:
+        with open(ifile) as fin:
+            exec(compile(fin.read(), ifile, 'exec'))
+
+
+def clear_export_registry():
+    export_registry[:] = []
+
+
+# --------------------------------- Internal ---------------------------------
+
+re_symbol = re.compile(r'[_a-z][_a-z0-9]*', re.I)
+
+
+def parse_prototype(text):
+    """Separate the symbol and function-type in a a string with
+    "symbol function-type" (e.g. "mult float(float, float)")
+
+    Returns
+    ---------
+    (symbol_string, functype_string)
+    """
+    m = re_symbol.match(text)
+    if not m:
+        raise ValueError("Invalid function name for export prototype")
+    s = m.start(0)
+    e = m.end(0)
+    symbol = text[s:e]
+    functype = text[e + 1:]
+    return symbol, functype
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/llvm_types.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/llvm_types.py
new file mode 100644
index 000000000..4259ced6e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/llvm_types.py
@@ -0,0 +1,37 @@
+import sys
+import ctypes
+import struct as struct_
+import llvmlite.ir
+from llvmlite.ir import Constant
+
+_trace_refs_ = hasattr(sys, 'getobjects')
+_plat_bits = struct_.calcsize('@P') * 8
+
+_int8 = llvmlite.ir.IntType(8)
+_int32 = llvmlite.ir.IntType(32)
+
+_void_star = llvmlite.ir.PointerType(_int8)
+
+_int8_star = _void_star
+
+_sizeof_py_ssize_t = ctypes.sizeof(getattr(ctypes, 'c_size_t'))
+_llvm_py_ssize_t = llvmlite.ir.IntType(_sizeof_py_ssize_t * 8)
+
+if _trace_refs_:
+    _pyobject_head = llvmlite.ir.LiteralStructType([_void_star, _void_star,
+                                                    _llvm_py_ssize_t, _void_star])
+    _pyobject_head_init = Constant.literal_struct([
+        Constant(_void_star, None),            # _ob_next
+        Constant(_void_star, None),            # _ob_prev
+        Constant(_llvm_py_ssize_t, 1),         # ob_refcnt
+        Constant(_void_star, None),            # ob_type
+        ])
+
+else:
+    _pyobject_head = llvmlite.ir.LiteralStructType([_llvm_py_ssize_t, _void_star])
+    _pyobject_head_init = Constant.literal_struct([
+        Constant(_llvm_py_ssize_t, 1),    # ob_refcnt
+        Constant(_void_star, None),       # ob_type
+        ])
+
+_pyobject_head_p = llvmlite.ir.PointerType(_pyobject_head)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/modulemixin.c b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/modulemixin.c
new file mode 100644
index 000000000..85849a46a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/modulemixin.c
@@ -0,0 +1,205 @@
+/*
+ * This C file is compiled and linked into pycc-generated shared objects.
+ * It provides the Numba helper functions for runtime use in pycc-compiled
+ * functions.
+ */
+
+#include "../_numba_common.h"
+#include "../_pymodule.h"
+
+/* Define all runtime-required symbols in this C module, but do not
+   export them outside the shared library if possible. */
+
+#define NUMBA_EXPORT_FUNC(_rettype) VISIBILITY_HIDDEN _rettype
+#define NUMBA_EXPORT_DATA(_vartype) VISIBILITY_HIDDEN _vartype
+
+#define PYCC_COMPILING
+
+#include "../_helperlib.c"
+#include "../_dynfunc.c"
+
+#if PYCC_USE_NRT
+#include "../core/runtime/_nrt_python.c"
+#include "../core/runtime/nrt.h"
+#endif
+
+/* Defines hashsecret variables (see issue #6386) */
+int64_t _numba_hashsecret_siphash_k0;
+int64_t _numba_hashsecret_siphash_k1;
+int64_t _numba_hashsecret_djbx33a_suffix;
+
+/* NOTE: import_array() is macro, not a function.  It returns NULL on
+   failure */
+static void *
+wrap_import_array(void) {
+    import_array();
+    return (void *) 1;
+}
+
+
+static int
+init_numpy(void) {
+    return wrap_import_array() != NULL;
+}
+
+
+#ifndef PYCC_MODULE_NAME
+#error PYCC_MODULE_NAME must be defined
+#endif
+
+/* Preprocessor trick: need to use two levels of macros otherwise
+   PYCC_MODULE_NAME would not get expanded */
+#define __PYCC(prefix, modname) prefix ## modname
+#define _PYCC(prefix, modname) __PYCC(prefix, modname)
+#define PYCC(prefix) _PYCC(prefix, PYCC_MODULE_NAME)
+
+/* Silence warnings about unused functions */
+VISIBILITY_HIDDEN void **PYCC(_unused_) = {
+    (void *) Numba_make_generator,
+};
+
+/* The LLVM-generated functions for atomic refcounting */
+extern void *nrt_atomic_add, *nrt_atomic_sub;
+
+/* The structure type constructed by PythonAPI.serialize_uncached() */
+typedef struct {
+    const char *data;
+    int len;
+    const char *hashbuf;
+} env_def_t;
+
+/* Environment GlobalVariable address type */
+typedef void **env_gv_t;
+
+/*
+ * Recreate an environment object from a env_def_t structure.
+ */
+static EnvironmentObject *
+recreate_environment(PyObject *module, env_def_t env)
+{
+    EnvironmentObject *envobj;
+    PyObject *env_consts;
+
+    env_consts = numba_unpickle(env.data, env.len, env.hashbuf);
+    if (env_consts == NULL)
+        return NULL;
+    if (!PyList_Check(env_consts)) {
+        PyErr_Format(PyExc_TypeError,
+                     "environment constants should be a list, got '%s'",
+                     Py_TYPE(env_consts)->tp_name);
+        Py_DECREF(env_consts);
+        return NULL;
+    }
+
+    envobj = env_new_empty(&EnvironmentType);
+    if (envobj == NULL) {
+        Py_DECREF(env_consts);
+        return NULL;
+    }
+    envobj->consts = env_consts;
+    envobj->globals = PyModule_GetDict(module);
+    if (envobj->globals == NULL) {
+        Py_DECREF(envobj);
+        return NULL;
+    }
+    Py_INCREF(envobj->globals);
+    return envobj;
+}
+
+/*
+ * Subroutine to initialize all resources required for running the
+ * pycc-compiled functions.
+ */
+
+int
+PYCC(pycc_init_) (PyObject *module, PyMethodDef *defs,
+                                    env_def_t *envs,
+                                    env_gv_t *envgvs)
+{
+    /* Aligns hashsecret with values in current python process so that
+     * hashes computed inside the pycc module are correct if imported
+     * by the current process. Imports in a new process get the right
+     * hash secret through:
+     * `numba.cpython.hashing._load_hashsecret`.
+     */
+    _numba_hashsecret_siphash_k0 = _Py_HashSecret.siphash.k0;
+    _numba_hashsecret_siphash_k1 = _Py_HashSecret.siphash.k1;
+    _numba_hashsecret_djbx33a_suffix = _Py_HashSecret.djbx33a.suffix;
+
+    PyMethodDef *fdef;
+    PyObject *modname = NULL;
+    PyObject *docobj = NULL;
+    int i;
+
+    if (!init_numpy()) {
+        goto error;
+    }
+    if (init_dynfunc_module(module)) {
+        goto error;
+    }
+    /* Initialize random generation. */
+    numba_rnd_ensure_global_init();
+
+#if PYCC_USE_NRT
+    NRT_MemSys_init();
+    NRT_MemSys_set_atomic_inc_dec((NRT_atomic_inc_dec_func) &nrt_atomic_add,
+                                  (NRT_atomic_inc_dec_func) &nrt_atomic_sub);
+    if (init_nrt_python_module(module)) {
+        goto error;
+    }
+#endif
+
+    modname = PyObject_GetAttrString(module, "__name__");
+    if (modname == NULL) {
+        goto error;
+    }
+
+    /* Empty docstring for all compiled functions */
+    docobj = PyString_FromString("");
+    if (docobj == NULL) {
+        goto error;
+    }
+
+    /* Overwrite C method objects with our own Closure objects, in order
+     * to make their environments available to the compiled functions.
+     */
+    for (i = 0, fdef = defs; fdef->ml_name != NULL; i++, fdef++) {
+        PyObject *func;
+        PyObject *nameobj;
+        EnvironmentObject *envobj;
+
+        envobj = recreate_environment(module, envs[i]);
+        if (envobj == NULL) {
+            goto error;
+        }
+        nameobj = PyString_FromString(fdef->ml_name);
+        if (nameobj == NULL) {
+            Py_DECREF(envobj);
+            goto error;
+        }
+        // Store the environment pointer into the global
+        *envgvs[i] = envobj;
+
+        func = pycfunction_new(module, nameobj, docobj,
+                               fdef->ml_meth, envobj, NULL);
+        Py_DECREF(envobj);
+        Py_DECREF(nameobj);
+
+        if (func == NULL) {
+            goto error;
+        }
+        if (PyObject_SetAttrString(module, fdef->ml_name, func)) {
+            Py_DECREF(func);
+            goto error;
+        }
+        Py_DECREF(func);
+    }
+    Py_DECREF(docobj);
+    Py_DECREF(modname);
+    return 0;
+
+error:
+    Py_XDECREF(docobj);
+    Py_XDECREF(modname);
+    return -1;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/platform.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/platform.py
new file mode 100644
index 000000000..1aa68f29f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/platform.py
@@ -0,0 +1,273 @@
+from distutils.ccompiler import CCompiler, new_compiler
+from distutils.command.build_ext import build_ext
+from distutils.sysconfig import customize_compiler
+from distutils import log
+
+import numpy.distutils.misc_util as np_misc
+
+import functools
+import os
+import subprocess
+import sys
+from tempfile import NamedTemporaryFile, mkdtemp, gettempdir
+from contextlib import contextmanager
+
+_configs = {
+    # DLL suffix, Python C extension suffix
+    'win': ('.dll', '.pyd'),
+    'default': ('.so', '.so'),
+}
+
+
+def get_configs(arg):
+    return _configs.get(sys.platform[:3], _configs['default'])[arg]
+
+
+find_shared_ending = functools.partial(get_configs, 0)
+find_pyext_ending = functools.partial(get_configs, 1)
+
+@contextmanager
+def _gentmpfile(suffix):
+    # windows locks the tempfile so use a tempdir + file, see
+    # https://github.com/numba/numba/issues/3304
+    try:
+        tmpdir = mkdtemp()
+        ntf = open(os.path.join(tmpdir, "temp%s" % suffix), 'wt')
+        yield ntf
+    finally:
+        try:
+            ntf.close()
+            os.remove(ntf)
+        except:
+            pass
+        else:
+            os.rmdir(tmpdir)
+
+def _check_external_compiler():
+    # see if the external compiler bound in numpy.distutil is present
+    # and working
+    compiler = new_compiler()
+    customize_compiler(compiler)
+    for suffix in ['.c', '.cxx']:
+        try:
+            with _gentmpfile(suffix) as ntf:
+                simple_c = "int main(void) { return 0; }"
+                ntf.write(simple_c)
+                ntf.flush()
+                ntf.close()
+                # *output_dir* is set to avoid the compiler putting temp files
+                # in the current directory.
+                compiler.compile([ntf.name], output_dir=gettempdir())
+        except Exception: # likely CompileError or file system issue
+            return False
+    return True
+
+# boolean on whether the externally provided compiler is present and
+# functioning correctly
+_external_compiler_ok = _check_external_compiler()
+
+
+class _DummyExtension(object):
+    libraries = []
+
+
+class Toolchain(object):
+
+    def __init__(self):
+        if not _external_compiler_ok:
+            self._raise_external_compiler_error()
+
+        # Need to import it here since setuptools may monkeypatch it
+        from distutils.dist import Distribution
+        self._verbose = False
+        self._compiler = new_compiler()
+        customize_compiler(self._compiler)
+        self._build_ext = build_ext(Distribution())
+        self._build_ext.finalize_options()
+        self._py_lib_dirs = self._build_ext.library_dirs
+        self._py_include_dirs = self._build_ext.include_dirs
+        self._math_info = np_misc.get_info('npymath')
+
+    @property
+    def verbose(self):
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, value):
+        self._verbose = value
+        # DEBUG will let Numpy spew many messages, so stick to INFO
+        # to print commands executed by distutils
+        log.set_threshold(log.INFO if value else log.WARN)
+
+    def _raise_external_compiler_error(self):
+        basemsg = ("Attempted to compile AOT function without the "
+                   "compiler used by `numpy.distutils` present.")
+        conda_msg = "If using conda try:\n\n#> conda install %s"
+        plt = sys.platform
+        if plt.startswith('linux'):
+            if sys.maxsize <= 2 ** 32:
+                compilers = ['gcc_linux-32', 'gxx_linux-32']
+            else:
+                compilers = ['gcc_linux-64', 'gxx_linux-64']
+            msg = "%s %s" % (basemsg, conda_msg % ' '.join(compilers))
+        elif plt.startswith('darwin'):
+            compilers = ['clang_osx-64', 'clangxx_osx-64']
+            msg = "%s %s" % (basemsg, conda_msg % ' '.join(compilers))
+        elif plt.startswith('win32'):
+            winmsg = "Cannot find suitable msvc."
+            msg = "%s %s" % (basemsg, winmsg)
+        else:
+            msg = "Unknown platform %s" % plt
+        raise RuntimeError(msg)
+
+    def compile_objects(self, sources, output_dir,
+                        include_dirs=(), depends=(), macros=(),
+                        extra_cflags=None):
+        """
+        Compile the given source files into a separate object file each,
+        all beneath the *output_dir*.  A list of paths to object files
+        is returned.
+
+        *macros* has the same format as in distutils: a list of 1- or 2-tuples.
+        If a 1-tuple (name,), the given name is considered undefined by
+        the C preprocessor.
+        If a 2-tuple (name, value), the given name is expanded into the
+        given value by the C preprocessor.
+        """
+        objects = self._compiler.compile(sources,
+                                         output_dir=output_dir,
+                                         include_dirs=include_dirs,
+                                         depends=depends,
+                                         macros=macros or [],
+                                         extra_preargs=extra_cflags)
+        return objects
+
+    def link_shared(self, output, objects, libraries=(),
+                    library_dirs=(), export_symbols=(),
+                    extra_ldflags=None):
+        """
+        Create a shared library *output* linking the given *objects*
+        and *libraries* (all strings).
+        """
+        output_dir, output_filename = os.path.split(output)
+        self._compiler.link(CCompiler.SHARED_OBJECT, objects,
+                            output_filename, output_dir,
+                            libraries, library_dirs,
+                            export_symbols=export_symbols,
+                            extra_preargs=extra_ldflags)
+
+    def get_python_libraries(self):
+        """
+        Get the library arguments necessary to link with Python.
+        """
+        libs = self._build_ext.get_libraries(_DummyExtension())
+        if sys.platform == 'win32':
+            # Under Windows, need to link explicitly against the CRT,
+            # as the MSVC compiler would implicitly do.
+            # (XXX msvcrtd in pydebug mode?)
+            libs = libs + ['msvcrt']
+        return libs + self._math_info['libraries']
+
+    def get_python_library_dirs(self):
+        """
+        Get the library directories necessary to link with Python.
+        """
+        return list(self._py_lib_dirs) + self._math_info['library_dirs']
+
+    def get_python_include_dirs(self):
+        """
+        Get the include directories necessary to compile against the Python
+        and Numpy C APIs.
+        """
+        return list(self._py_include_dirs) + self._math_info['include_dirs']
+
+    def get_ext_filename(self, ext_name):
+        """
+        Given a C extension's module name, return its intended filename.
+        """
+        return self._build_ext.get_ext_filename(ext_name)
+
+
+#
+# Patch Numpy's exec_command() to avoid random crashes on Windows in test_pycc
+# see https://github.com/numpy/numpy/pull/7614
+# and https://github.com/numpy/numpy/pull/7862
+#
+
+def _patch_exec_command():
+    # Patch the internal worker _exec_command()
+    import numpy.distutils.exec_command as mod
+    orig_exec_command = mod._exec_command
+    mod._exec_command = _exec_command
+
+
+def _exec_command(command, use_shell=None, use_tee=None, **env):
+    """
+    Internal workhorse for exec_command().
+    Code from https://github.com/numpy/numpy/pull/7862
+    """
+    if use_shell is None:
+        use_shell = os.name == 'posix'
+    if use_tee is None:
+        use_tee = os.name == 'posix'
+
+    executable = None
+
+    if os.name == 'posix' and use_shell:
+        # On POSIX, subprocess always uses /bin/sh, override
+        sh = os.environ.get('SHELL', '/bin/sh')
+        if _is_sequence(command):
+            command = [sh, '-c', ' '.join(command)]
+        else:
+            command = [sh, '-c', command]
+        use_shell = False
+
+    elif os.name == 'nt' and _is_sequence(command):
+        # On Windows, join the string for CreateProcess() ourselves as
+        # subprocess does it a bit differently
+        command = ' '.join(_quote_arg(arg) for arg in command)
+
+    # Inherit environment by default
+    env = env or None
+    try:
+        proc = subprocess.Popen(command, shell=use_shell, env=env,
+                                stdout=subprocess.PIPE,
+                                stderr=subprocess.PIPE,
+                                universal_newlines=True)
+    except OSError:
+        # Return 127, as os.spawn*() and /bin/sh do
+        return '', 127
+    text, err = proc.communicate()
+    # Only append stderr if the command failed, as otherwise
+    # the output may become garbled for parsing
+    if proc.returncode:
+        if text:
+            text += "\n"
+        text += err
+    # Another historical oddity
+    if text[-1:] == '\n':
+        text = text[:-1]
+    if use_tee:
+        print(text)
+    return proc.returncode, text
+
+
+def _quote_arg(arg):
+    """
+    Quote the argument for safe use in a shell command line.
+    """
+    # If there is a quote in the string, assume relevants parts of the
+    # string are already quoted (e.g. '-I"C:\\Program Files\\..."')
+    if '"' not in arg and ' ' in arg:
+        return '"%s"' % arg
+    return arg
+
+
+def _is_sequence(arg):
+    if isinstance(arg, (str, bytes)):
+        return False
+    try:
+        len(arg)
+        return True
+    except Exception:
+        return False
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/pycc b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/pycc
new file mode 100644
index 000000000..272e4e2ca
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/pycc/pycc
@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+from numba.pycc import main
+main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/runtests.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/runtests.py
new file mode 100644
index 000000000..9acdd1fb0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/runtests.py
@@ -0,0 +1,9 @@
+from numba.testing._runtests import _main
+
+
+if __name__ == '__main__':
+    import sys
+    # For parallel testing under Windows
+    from multiprocessing import freeze_support
+    freeze_support()
+    sys.exit(0 if _main(sys.argv) else 1)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/scripts/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/scripts/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/scripts/generate_lower_listing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/scripts/generate_lower_listing.py
new file mode 100644
index 000000000..1a93e7a3d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/scripts/generate_lower_listing.py
@@ -0,0 +1,169 @@
+"""
+Generate documentation for all registered implementation for lowering
+using reStructured text.
+"""
+
+
+from subprocess import check_output
+
+import os.path
+try:
+    from StringIO import StringIO       # py2
+except ImportError:
+    from io import StringIO
+from collections import defaultdict
+import inspect
+from functools import partial
+
+import numba
+from numba.core.registry import cpu_target
+
+
+def git_hash():
+    out = check_output(['git', 'log', "--pretty=format:'%H'", '-n', '1'])
+    return out.decode('ascii').strip("'\"")
+
+
+def get_func_name(fn):
+    return getattr(fn, '__qualname__', fn.__name__)
+
+
+def gather_function_info(backend):
+    fninfos = defaultdict(list)
+    basepath = os.path.dirname(os.path.dirname(numba.__file__))
+    for fn, osel in backend._defns.items():
+        for sig, impl in osel.versions:
+            info = {}
+            fninfos[fn].append(info)
+            info['fn'] = fn
+            info['sig'] = sig
+            code, firstlineno = inspect.getsourcelines(impl)
+            path = inspect.getsourcefile(impl)
+            info['impl'] = {
+                'name': get_func_name(impl),
+                'filename': os.path.relpath(path, start=basepath),
+                'lines': (firstlineno, firstlineno + len(code) - 1),
+                'docstring': impl.__doc__
+            }
+
+    return fninfos
+
+
+def bind_file_to_print(fobj):
+    return partial(print, file=fobj)
+
+
+def format_signature(sig):
+    def fmt(c):
+        try:
+            return c.__name__
+        except AttributeError:
+            return repr(c).strip('\'"')
+    out = tuple(map(fmt, sig))
+    return '`({0})`'.format(', '.join(out))
+
+
+github_url = ('https://github.com/numba/numba/blob/'
+              '{commit}/{path}#L{firstline}-L{lastline}')
+
+description = """
+This lists all lowering definition registered to the CPU target.
+Each subsection corresponds to a Python function that is supported by numba
+nopython mode. These functions have one or more lower implementation with
+different signatures. The compiler chooses the most specific implementation
+from all overloads.
+"""
+
+
+def format_function_infos(fninfos):
+    buf = StringIO()
+    try:
+        print = bind_file_to_print(buf)
+
+        title_line = "Lowering Listing"
+        print(title_line)
+        print('=' * len(title_line))
+
+        print(description)
+
+        commit = git_hash()
+
+        def format_fname(fn):
+            try:
+                fname = "{0}.{1}".format(fn.__module__, get_func_name(fn))
+            except AttributeError:
+                fname = repr(fn)
+            return fn, fname
+
+        for fn, fname in sorted(map(format_fname, fninfos), key=lambda x: x[1]):
+            impinfos = fninfos[fn]
+            header_line = "``{0}``".format(fname)
+            print(header_line)
+            print('-' * len(header_line))
+            print()
+
+            formatted_sigs = map(
+                lambda x: format_signature(x['sig']), impinfos)
+            sorted_impinfos = sorted(zip(formatted_sigs, impinfos),
+                                     key=lambda x: x[0])
+
+            col_signatures = ['Signature']
+            col_urls = ['Definition']
+
+            for fmtsig, info in sorted_impinfos:
+                impl = info['impl']
+
+                filename = impl['filename']
+                lines = impl['lines']
+                fname = impl['name']
+
+                source = '{0} lines {1}-{2}'.format(filename, *lines)
+                link = github_url.format(commit=commit, path=filename,
+                                         firstline=lines[0], lastline=lines[1])
+                url = '``{0}`` `{1} <{2}>`_'.format(fname, source, link)
+
+                col_signatures.append(fmtsig)
+                col_urls.append(url)
+
+            # table formatting
+            max_width_col_sig = max(map(len, col_signatures))
+            max_width_col_url = max(map(len, col_urls))
+            padding = 2
+            width_col_sig = padding * 2 + max_width_col_sig
+            width_col_url = padding * 2 + max_width_col_url
+            line_format = "{{0:^{0}}}  {{1:^{1}}}".format(width_col_sig,
+                                                          width_col_url)
+            print(line_format.format('=' * width_col_sig, '=' * width_col_url))
+            print(line_format.format(col_signatures[0], col_urls[0]))
+            print(line_format.format('=' * width_col_sig, '=' * width_col_url))
+            for sig, url in zip(col_signatures[1:], col_urls[1:]):
+                print(line_format.format(sig, url))
+            print(line_format.format('=' * width_col_sig, '=' * width_col_url))
+            print()
+
+        return buf.getvalue()
+    finally:
+        buf.close()
+
+
+# Main routine for this module:
+
+def gen_lower_listing(path=None):
+    """
+    Generate lowering listing to ``path`` or (if None) to stdout.
+    """
+    cpu_backend = cpu_target.target_context
+    cpu_backend.refresh()
+
+    fninfos = gather_function_info(cpu_backend)
+    out = format_function_infos(fninfos)
+
+    if path is None:
+        print(out)
+    else:
+        with open(path, 'w') as fobj:
+            print(out, file=fobj)
+
+
+if __name__ == '__main__':
+    gen_lower_listing()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/stencil.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/stencil.py
new file mode 100644
index 000000000..b22a3abec
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/stencil.py
@@ -0,0 +1,837 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+import copy
+
+import numpy as np
+from llvmlite import ir as lir
+
+from numba.core import types, typing, utils, ir, config, ir_utils, registry
+from numba.core.typing.templates import (CallableTemplate, signature,
+                                         infer_global, AbstractTemplate)
+from numba.core.imputils import lower_builtin
+from numba.core.extending import register_jitable
+from numba.core.errors import NumbaValueError
+from numba.misc.special import literal_unroll
+import numba
+
+import operator
+from numba.np import numpy_support
+
+class StencilFuncLowerer(object):
+    '''Callable class responsible for lowering calls to a specific StencilFunc.
+    '''
+    def __init__(self, sf):
+        self.stencilFunc = sf
+
+    def __call__(self, context, builder, sig, args):
+        cres = self.stencilFunc.compile_for_argtys(sig.args, {},
+                    sig.return_type, None)
+        res = context.call_internal(builder, cres.fndesc, sig, args)
+        context.add_linking_libs([cres.library])
+        return res
+
+@register_jitable
+def raise_if_incompatible_array_sizes(a, *args):
+    ashape = a.shape
+
+    # We need literal_unroll here because the stencil might take
+    # multiple input arrays with different types that are not compatible
+    # (e.g. values as float[:] and flags as bool[:])
+    # When more than three total arrays are given, the second and third
+    # are iterated over in the loop below. Without literal_unroll, their
+    # types have to match.
+    # An example failing signature without literal_unroll might be
+    # (float[:], float[:], bool[:]) (Just (float[:], bool[:]) wouldn't fail)
+    for arg in literal_unroll(args):
+        if a.ndim != arg.ndim:
+            raise ValueError("Secondary stencil array does not have same number "
+                             " of dimensions as the first stencil input.")
+        argshape = arg.shape
+        for i in range(len(ashape)):
+            if ashape[i] > argshape[i]:
+                raise ValueError("Secondary stencil array has some dimension "
+                                 "smaller the same dimension in the first "
+                                 "stencil input.")
+
+def slice_addition(the_slice, addend):
+    """ Called by stencil in Python mode to add the loop index to a
+        user-specified slice.
+    """
+    return slice(the_slice.start + addend, the_slice.stop + addend)
+
+class StencilFunc(object):
+    """
+    A special type to hold stencil information for the IR.
+    """
+
+    id_counter = 0
+
+    def __init__(self, kernel_ir, mode, options):
+        self.id = type(self).id_counter
+        type(self).id_counter += 1
+        self.kernel_ir = kernel_ir
+        self.mode = mode
+        self.options = options
+        self.kws = []       # remember original kws arguments
+
+        # stencils only supported for CPU context currently
+        self._typingctx = registry.cpu_target.typing_context
+        self._targetctx = registry.cpu_target.target_context
+        self._typingctx.refresh()
+        self._targetctx.refresh()
+        self._install_type(self._typingctx)
+        self.neighborhood = self.options.get("neighborhood")
+        self._type_cache = {}
+        self._lower_me = StencilFuncLowerer(self)
+
+    def replace_return_with_setitem(self, blocks, index_vars, out_name):
+        """
+        Find return statements in the IR and replace them with a SetItem
+        call of the value "returned" by the kernel into the result array.
+        Returns the block labels that contained return statements.
+        """
+        ret_blocks = []
+
+        for label, block in blocks.items():
+            scope = block.scope
+            loc = block.loc
+            new_body = []
+            for stmt in block.body:
+                if isinstance(stmt, ir.Return):
+                    ret_blocks.append(label)
+                    # If 1D array then avoid the tuple construction.
+                    if len(index_vars) == 1:
+                        rvar = ir.Var(scope, out_name, loc)
+                        ivar = ir.Var(scope, index_vars[0], loc)
+                        new_body.append(ir.SetItem(rvar, ivar, stmt.value, loc))
+                    else:
+                        # Convert the string names of the index variables into
+                        # ir.Var's.
+                        var_index_vars = []
+                        for one_var in index_vars:
+                            index_var = ir.Var(scope, one_var, loc)
+                            var_index_vars += [index_var]
+
+                        s_index_name = ir_utils.mk_unique_var("stencil_index")
+                        s_index_var  = ir.Var(scope, s_index_name, loc)
+                        # Build a tuple from the index ir.Var's.
+                        tuple_call = ir.Expr.build_tuple(var_index_vars, loc)
+                        new_body.append(ir.Assign(tuple_call, s_index_var, loc))
+                        rvar = ir.Var(scope, out_name, loc)
+                        # Write the return statements original value into
+                        # the array using the tuple index.
+                        si = ir.SetItem(rvar, s_index_var, stmt.value, loc)
+                        new_body.append(si)
+                else:
+                    new_body.append(stmt)
+            block.body = new_body
+        return ret_blocks
+
+    def add_indices_to_kernel(self, kernel, index_names, ndim,
+                              neighborhood, standard_indexed, typemap, calltypes):
+        """
+        Transforms the stencil kernel as specified by the user into one
+        that includes each dimension's index variable as part of the getitem
+        calls.  So, in effect array[-1] becomes array[index0-1].
+        """
+        const_dict = {}
+        kernel_consts = []
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("add_indices_to_kernel", ndim, neighborhood)
+            ir_utils.dump_blocks(kernel.blocks)
+
+        if neighborhood is None:
+            need_to_calc_kernel = True
+        else:
+            need_to_calc_kernel = False
+            if len(neighborhood) != ndim:
+                raise ValueError("%d dimensional neighborhood specified for %d " \
+                    "dimensional input array" % (len(neighborhood), ndim))
+
+        tuple_table = ir_utils.get_tuple_table(kernel.blocks)
+
+        relatively_indexed = set()
+
+        for block in kernel.blocks.values():
+            scope = block.scope
+            loc = block.loc
+            new_body = []
+            for stmt in block.body:
+                if (isinstance(stmt, ir.Assign) and
+                    isinstance(stmt.value, ir.Const)):
+                    if config.DEBUG_ARRAY_OPT >= 1:
+                        print("remembering in const_dict", stmt.target.name,
+                              stmt.value.value)
+                    # Remember consts for use later.
+                    const_dict[stmt.target.name] = stmt.value.value
+                if ((isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Expr)
+                        and stmt.value.op in ['setitem', 'static_setitem']
+                        and stmt.value.value.name in kernel.arg_names) or
+                   (isinstance(stmt, ir.SetItem)
+                        and stmt.target.name in kernel.arg_names)):
+                    raise ValueError("Assignments to arrays passed to stencil " \
+                        "kernels is not allowed.")
+                if (isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Expr)
+                        and stmt.value.op in ['getitem', 'static_getitem']
+                        and stmt.value.value.name in kernel.arg_names
+                        and stmt.value.value.name not in standard_indexed):
+                    # We found a getitem from the input array.
+                    if stmt.value.op == 'getitem':
+                        stmt_index_var = stmt.value.index
+                    else:
+                        stmt_index_var = stmt.value.index_var
+                        # allow static_getitem since rewrite passes are applied
+                        #raise ValueError("Unexpected static_getitem in add_indices_to_kernel.")
+
+                    relatively_indexed.add(stmt.value.value.name)
+
+                    # Store the index used after looking up the variable in
+                    # the const dictionary.
+                    if need_to_calc_kernel:
+                        assert hasattr(stmt_index_var, 'name')
+
+                        if stmt_index_var.name in tuple_table:
+                            kernel_consts += [tuple_table[stmt_index_var.name]]
+                        elif stmt_index_var.name in const_dict:
+                            kernel_consts += [const_dict[stmt_index_var.name]]
+                        else:
+                            raise NumbaValueError("stencil kernel index is not "
+                                "constant, 'neighborhood' option required")
+
+                    if ndim == 1:
+                        # Single dimension always has index variable 'index0'.
+                        # tmpvar will hold the real index and is computed by
+                        # adding the relative offset in stmt.value.index to
+                        # the current absolute location in index0.
+                        index_var = ir.Var(scope, index_names[0], loc)
+                        tmpname = ir_utils.mk_unique_var("stencil_index")
+                        tmpvar  = ir.Var(scope, tmpname, loc)
+                        stmt_index_var_typ = typemap[stmt_index_var.name]
+                        # If the array is indexed with a slice then we
+                        # have to add the index value with a call to
+                        # slice_addition.
+                        if isinstance(stmt_index_var_typ, types.misc.SliceType):
+                            sa_var = ir.Var(scope, ir_utils.mk_unique_var("slice_addition"), loc)
+                            sa_func = numba.njit(slice_addition)
+                            sa_func_typ = types.functions.Dispatcher(sa_func)
+                            typemap[sa_var.name] = sa_func_typ
+                            g_sa = ir.Global("slice_addition", sa_func, loc)
+                            new_body.append(ir.Assign(g_sa, sa_var, loc))
+                            slice_addition_call = ir.Expr.call(sa_var, [stmt_index_var, index_var], (), loc)
+                            calltypes[slice_addition_call] = sa_func_typ.get_call_type(self._typingctx, [stmt_index_var_typ, types.intp], {})
+                            new_body.append(ir.Assign(slice_addition_call, tmpvar, loc))
+                            new_body.append(ir.Assign(
+                                           ir.Expr.getitem(stmt.value.value, tmpvar, loc),
+                                           stmt.target, loc))
+                        else:
+                            acc_call = ir.Expr.binop(operator.add, stmt_index_var,
+                                                     index_var, loc)
+                            new_body.append(ir.Assign(acc_call, tmpvar, loc))
+                            new_body.append(ir.Assign(
+                                           ir.Expr.getitem(stmt.value.value, tmpvar, loc),
+                                           stmt.target, loc))
+                    else:
+                        index_vars = []
+                        sum_results = []
+                        s_index_name = ir_utils.mk_unique_var("stencil_index")
+                        s_index_var  = ir.Var(scope, s_index_name, loc)
+                        const_index_vars = []
+                        ind_stencils = []
+
+                        stmt_index_var_typ = typemap[stmt_index_var.name]
+                        # Same idea as above but you have to extract
+                        # individual elements out of the tuple indexing
+                        # expression and add the corresponding index variable
+                        # to them and then reconstitute as a tuple that can
+                        # index the array.
+                        for dim in range(ndim):
+                            tmpname = ir_utils.mk_unique_var("const_index")
+                            tmpvar  = ir.Var(scope, tmpname, loc)
+                            new_body.append(ir.Assign(ir.Const(dim, loc),
+                                                      tmpvar, loc))
+                            const_index_vars += [tmpvar]
+                            index_var = ir.Var(scope, index_names[dim], loc)
+                            index_vars += [index_var]
+
+                            tmpname = ir_utils.mk_unique_var("ind_stencil_index")
+                            tmpvar  = ir.Var(scope, tmpname, loc)
+                            ind_stencils += [tmpvar]
+                            getitemname = ir_utils.mk_unique_var("getitem")
+                            getitemvar  = ir.Var(scope, getitemname, loc)
+                            getitemcall = ir.Expr.getitem(stmt_index_var,
+                                                       const_index_vars[dim], loc)
+                            new_body.append(ir.Assign(getitemcall, getitemvar, loc))
+                            # Get the type of this particular part of the index tuple.
+                            if isinstance(stmt_index_var_typ, types.ConstSized):
+                                one_index_typ = stmt_index_var_typ[dim]
+                            else:
+                                one_index_typ = stmt_index_var_typ[:]
+                            # If the array is indexed with a slice then we
+                            # have to add the index value with a call to
+                            # slice_addition.
+                            if isinstance(one_index_typ, types.misc.SliceType):
+                                sa_var = ir.Var(scope, ir_utils.mk_unique_var("slice_addition"), loc)
+                                sa_func = numba.njit(slice_addition)
+                                sa_func_typ = types.functions.Dispatcher(sa_func)
+                                typemap[sa_var.name] = sa_func_typ
+                                g_sa = ir.Global("slice_addition", sa_func, loc)
+                                new_body.append(ir.Assign(g_sa, sa_var, loc))
+                                slice_addition_call = ir.Expr.call(sa_var, [getitemvar, index_vars[dim]], (), loc)
+                                calltypes[slice_addition_call] = sa_func_typ.get_call_type(self._typingctx, [one_index_typ, types.intp], {})
+                                new_body.append(ir.Assign(slice_addition_call, tmpvar, loc))
+                            else:
+                                acc_call = ir.Expr.binop(operator.add, getitemvar,
+                                                         index_vars[dim], loc)
+                                new_body.append(ir.Assign(acc_call, tmpvar, loc))
+
+                        tuple_call = ir.Expr.build_tuple(ind_stencils, loc)
+                        new_body.append(ir.Assign(tuple_call, s_index_var, loc))
+                        new_body.append(ir.Assign(
+                                  ir.Expr.getitem(stmt.value.value,s_index_var,loc),
+                                  stmt.target,loc))
+                else:
+                    new_body.append(stmt)
+            block.body = new_body
+
+        if need_to_calc_kernel:
+            # Find the size of the kernel by finding the maximum absolute value
+            # index used in the kernel specification.
+            neighborhood = [[0,0] for _ in range(ndim)]
+            if len(kernel_consts) == 0:
+                raise NumbaValueError("Stencil kernel with no accesses to "
+                                      "relatively indexed arrays.")
+
+            for index in kernel_consts:
+                if isinstance(index, tuple) or isinstance(index, list):
+                    for i in range(len(index)):
+                        te = index[i]
+                        if isinstance(te, ir.Var) and te.name in const_dict:
+                            te = const_dict[te.name]
+                        if isinstance(te, int):
+                            neighborhood[i][0] = min(neighborhood[i][0], te)
+                            neighborhood[i][1] = max(neighborhood[i][1], te)
+                        else:
+                            raise NumbaValueError(
+                                "stencil kernel index is not constant,"
+                                "'neighborhood' option required")
+                    index_len = len(index)
+                elif isinstance(index, int):
+                    neighborhood[0][0] = min(neighborhood[0][0], index)
+                    neighborhood[0][1] = max(neighborhood[0][1], index)
+                    index_len = 1
+                else:
+                    raise NumbaValueError(
+                        "Non-tuple or non-integer used as stencil index.")
+                if index_len != ndim:
+                    raise NumbaValueError(
+                        "Stencil index does not match array dimensionality.")
+
+        return (neighborhood, relatively_indexed)
+
+
+    def get_return_type(self, argtys):
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("get_return_type", argtys)
+            ir_utils.dump_blocks(self.kernel_ir.blocks)
+
+        if not isinstance(argtys[0], types.npytypes.Array):
+            raise NumbaValueError("The first argument to a stencil kernel must "
+                                  "be the primary input array.")
+
+        from numba.core import typed_passes
+        typemap, return_type, calltypes, _ = typed_passes.type_inference_stage(
+                self._typingctx,
+                self._targetctx,
+                self.kernel_ir,
+                argtys,
+                None,
+                {})
+        if isinstance(return_type, types.npytypes.Array):
+            raise NumbaValueError(
+                "Stencil kernel must return a scalar and not a numpy array.")
+
+        real_ret = types.npytypes.Array(return_type, argtys[0].ndim,
+                                                     argtys[0].layout)
+        return (real_ret, typemap, calltypes)
+
+    def _install_type(self, typingctx):
+        """Constructs and installs a typing class for a StencilFunc object in
+        the input typing context.
+        """
+        _ty_cls = type('StencilFuncTyping_' +
+                       str(self.id),
+                       (AbstractTemplate,),
+                       dict(key=self, generic=self._type_me))
+        typingctx.insert_user_function(self, _ty_cls)
+
+    def compile_for_argtys(self, argtys, kwtys, return_type, sigret):
+        # look in the type cache to find if result array is passed
+        (_, result, typemap, calltypes) = self._type_cache[argtys]
+        new_func = self._stencil_wrapper(result, sigret, return_type,
+                                         typemap, calltypes, *argtys)
+        return new_func
+
+    def _type_me(self, argtys, kwtys):
+        """
+        Implement AbstractTemplate.generic() for the typing class
+        built by StencilFunc._install_type().
+        Return the call-site signature.
+        """
+        if (self.neighborhood is not None and
+            len(self.neighborhood) != argtys[0].ndim):
+            raise NumbaValueError("%d dimensional neighborhood specified "
+                                  "for %d dimensional input array" %
+                                  (len(self.neighborhood), argtys[0].ndim))
+
+        argtys_extra = argtys
+        sig_extra = ""
+        result = None
+        if 'out' in kwtys:
+            argtys_extra += (kwtys['out'],)
+            sig_extra += ", out=None"
+            result = kwtys['out']
+
+        if 'neighborhood' in kwtys:
+            argtys_extra += (kwtys['neighborhood'],)
+            sig_extra += ", neighborhood=None"
+
+        # look in the type cache first
+        if argtys_extra in self._type_cache:
+            (_sig, _, _, _) = self._type_cache[argtys_extra]
+            return _sig
+
+        (real_ret, typemap, calltypes) = self.get_return_type(argtys)
+        sig = signature(real_ret, *argtys_extra)
+        dummy_text = ("def __numba_dummy_stencil({}{}):\n    pass\n".format(
+                        ",".join(self.kernel_ir.arg_names), sig_extra))
+        exec(dummy_text) in globals(), locals()
+        dummy_func = eval("__numba_dummy_stencil")
+        sig = sig.replace(pysig=utils.pysignature(dummy_func))
+        self._targetctx.insert_func_defn([(self._lower_me, self, argtys_extra)])
+        self._type_cache[argtys_extra] = (sig, result, typemap, calltypes)
+        return sig
+
+    def copy_ir_with_calltypes(self, ir, calltypes):
+        """
+        Create a copy of a given IR along with its calltype information.
+        We need a copy of the calltypes because copy propagation applied
+        to the copied IR will change the calltypes and make subsequent
+        uses of the original IR invalid.
+        """
+        copy_calltypes = {}
+        kernel_copy = ir.copy()
+        kernel_copy.blocks = {}
+        # For each block...
+        for (block_label, block) in ir.blocks.items():
+            new_block = copy.deepcopy(ir.blocks[block_label])
+            new_block.body = []
+            # For each statement in each block...
+            for stmt in ir.blocks[block_label].body:
+                # Copy the statement to the new copy of the kernel
+                # and if the original statement is in the original
+                # calltypes then add the type associated with this
+                # statement to the calltypes copy.
+                scopy = copy.deepcopy(stmt)
+                new_block.body.append(scopy)
+                if stmt in calltypes:
+                    copy_calltypes[scopy] = calltypes[stmt]
+            kernel_copy.blocks[block_label] = new_block
+        return (kernel_copy, copy_calltypes)
+
+    def _stencil_wrapper(self, result, sigret, return_type, typemap, calltypes, *args):
+        # Overall approach:
+        # 1) Construct a string containing a function definition for the stencil function
+        #    that will execute the stencil kernel.  This function definition includes a
+        #    unique stencil function name, the parameters to the stencil kernel, loop
+        #    nests across the dimensions of the input array.  Those loop nests use the
+        #    computed stencil kernel size so as not to try to compute elements where
+        #    elements outside the bounds of the input array would be needed.
+        # 2) The but of the loop nest in this new function is a special sentinel
+        #    assignment.
+        # 3) Get the IR of this new function.
+        # 4) Split the block containing the sentinel assignment and remove the sentinel
+        #    assignment.  Insert the stencil kernel IR into the stencil function IR
+        #    after label and variable renaming of the stencil kernel IR to prevent
+        #    conflicts with the stencil function IR.
+        # 5) Compile the combined stencil function IR + stencil kernel IR into existence.
+
+        # Copy the kernel so that our changes for this callsite
+        # won't effect other callsites.
+        (kernel_copy, copy_calltypes) = self.copy_ir_with_calltypes(
+                                            self.kernel_ir, calltypes)
+        # The stencil kernel body becomes the body of a loop, for which args aren't needed.
+        ir_utils.remove_args(kernel_copy.blocks)
+        first_arg = kernel_copy.arg_names[0]
+
+        in_cps, out_cps = ir_utils.copy_propagate(kernel_copy.blocks, typemap)
+        name_var_table = ir_utils.get_name_var_table(kernel_copy.blocks)
+        ir_utils.apply_copy_propagate(
+            kernel_copy.blocks,
+            in_cps,
+            name_var_table,
+            typemap,
+            copy_calltypes)
+
+        if "out" in name_var_table:
+            raise NumbaValueError("Cannot use the reserved word 'out' in stencil kernels.")
+
+        sentinel_name = ir_utils.get_unused_var_name("__sentinel__", name_var_table)
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("name_var_table", name_var_table, sentinel_name)
+
+        the_array = args[0]
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("_stencil_wrapper", return_type, return_type.dtype,
+                                      type(return_type.dtype), args)
+            ir_utils.dump_blocks(kernel_copy.blocks)
+
+        # We generate a Numba function to execute this stencil and here
+        # create the unique name of this function.
+        stencil_func_name = "__numba_stencil_%s_%s" % (
+                                        hex(id(the_array)).replace("-", "_"),
+                                        self.id)
+
+        # We will put a loop nest in the generated function for each
+        # dimension in the input array.  Here we create the name for
+        # the index variable for each dimension.  index0, index1, ...
+        index_vars = []
+        for i in range(the_array.ndim):
+            index_var_name = ir_utils.get_unused_var_name("index" + str(i),
+                                                          name_var_table)
+            index_vars += [index_var_name]
+
+        # Create extra signature for out and neighborhood.
+        out_name = ir_utils.get_unused_var_name("out", name_var_table)
+        neighborhood_name = ir_utils.get_unused_var_name("neighborhood",
+                                                         name_var_table)
+        sig_extra = ""
+        if result is not None:
+            sig_extra += ", {}=None".format(out_name)
+        if "neighborhood" in dict(self.kws):
+            sig_extra += ", {}=None".format(neighborhood_name)
+
+        # Get a list of the standard indexed array names.
+        standard_indexed = self.options.get("standard_indexing", [])
+
+        if first_arg in standard_indexed:
+            raise NumbaValueError("The first argument to a stencil kernel must "
+                                  "use relative indexing, not standard indexing.")
+
+        if len(set(standard_indexed) - set(kernel_copy.arg_names)) != 0:
+            raise NumbaValueError("Standard indexing requested for an array name "
+                                  "not present in the stencil kernel definition.")
+
+        # Add index variables to getitems in the IR to transition the accesses
+        # in the kernel from relative to regular Python indexing.  Returns the
+        # computed size of the stencil kernel and a list of the relatively indexed
+        # arrays.
+        kernel_size, relatively_indexed = self.add_indices_to_kernel(
+                kernel_copy, index_vars, the_array.ndim,
+                self.neighborhood, standard_indexed, typemap, copy_calltypes)
+        if self.neighborhood is None:
+            self.neighborhood = kernel_size
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("After add_indices_to_kernel")
+            ir_utils.dump_blocks(kernel_copy.blocks)
+
+        # The return in the stencil kernel becomes a setitem for that
+        # particular point in the iteration space.
+        ret_blocks = self.replace_return_with_setitem(kernel_copy.blocks,
+                                                      index_vars, out_name)
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("After replace_return_with_setitem", ret_blocks)
+            ir_utils.dump_blocks(kernel_copy.blocks)
+
+        # Start to form the new function to execute the stencil kernel.
+        func_text = "def {}({}{}):\n".format(stencil_func_name,
+                        ",".join(kernel_copy.arg_names), sig_extra)
+
+        # Get loop ranges for each dimension, which could be either int
+        # or variable. In the latter case we'll use the extra neighborhood
+        # argument to the function.
+        ranges = []
+        for i in range(the_array.ndim):
+            if isinstance(kernel_size[i][0], int):
+                lo = kernel_size[i][0]
+                hi = kernel_size[i][1]
+            else:
+                lo = "{}[{}][0]".format(neighborhood_name, i)
+                hi = "{}[{}][1]".format(neighborhood_name, i)
+            ranges.append((lo, hi))
+
+        # If there are more than one relatively indexed arrays, add a call to
+        # a function that will raise an error if any of the relatively indexed
+        # arrays are of different size than the first input array.
+        if len(relatively_indexed) > 1:
+            func_text += "    raise_if_incompatible_array_sizes(" + first_arg
+            for other_array in relatively_indexed:
+                if other_array != first_arg:
+                    func_text += "," + other_array
+            func_text += ")\n"
+
+        # Get the shape of the first input array.
+        shape_name = ir_utils.get_unused_var_name("full_shape", name_var_table)
+        func_text += "    {} = {}.shape\n".format(shape_name, first_arg)
+
+        # Converts cval to a string constant
+        def cval_as_str(cval):
+            if not np.isfinite(cval):
+                # See if this is a string-repr numerical const, issue #7286
+                if np.isnan(cval):
+                    return "np.nan"
+                elif np.isinf(cval):
+                    if cval < 0:
+                        return "-np.inf"
+                    else:
+                        return "np.inf"
+            else:
+                return str(cval)
+
+        # If we have to allocate the output array (the out argument was not used)
+        # then us numpy.full if the user specified a cval stencil decorator option
+        # or np.zeros if they didn't to allocate the array.
+        if result is None:
+            return_type_name = numpy_support.as_dtype(
+                               return_type.dtype).type.__name__
+            out_init ="{} = np.empty({}, dtype=np.{})\n".format(
+                        out_name, shape_name, return_type_name)
+
+            if "cval" in self.options:
+                cval = self.options["cval"]
+                cval_ty = typing.typeof.typeof(cval)
+                if not self._typingctx.can_convert(cval_ty, return_type.dtype):
+                    msg = "cval type does not match stencil return type."
+                    raise NumbaValueError(msg)
+            else:
+                 cval = 0
+            func_text += "    " + out_init
+            for dim in range(the_array.ndim):
+                start_items = [":"] * the_array.ndim
+                end_items = [":"] * the_array.ndim
+                start_items[dim] = ":-{}".format(self.neighborhood[dim][0])
+                end_items[dim] = "-{}:".format(self.neighborhood[dim][1])
+                func_text += "    " + "{}[{}] = {}\n".format(out_name, ",".join(start_items), cval_as_str(cval))
+                func_text += "    " + "{}[{}] = {}\n".format(out_name, ",".join(end_items), cval_as_str(cval))
+        else: # result is present, if cval is set then use it
+            if "cval" in self.options:
+                cval = self.options["cval"]
+                cval_ty = typing.typeof.typeof(cval)
+                if not self._typingctx.can_convert(cval_ty, return_type.dtype):
+                    msg = "cval type does not match stencil return type."
+                    raise NumbaValueError(msg)
+                out_init = "{}[:] = {}\n".format(out_name, cval_as_str(cval))
+                func_text += "    " + out_init
+
+        offset = 1
+        # Add the loop nests to the new function.
+        for i in range(the_array.ndim):
+            for j in range(offset):
+                func_text += "    "
+            # ranges[i][0] is the minimum index used in the i'th dimension
+            # but minimum's greater than 0 don't preclude any entry in the array.
+            # So, take the minimum of 0 and the minimum index found in the kernel
+            # and this will be a negative number (potentially -0).  Then, we do
+            # unary - on that to get the positive offset in this dimension whose
+            # use is precluded.
+            # ranges[i][1] is the maximum of 0 and the observed maximum index
+            # in this dimension because negative maximums would not cause us to
+            # preclude any entry in the array from being used.
+            func_text += ("for {} in range(-min(0,{}),"
+                          "{}[{}]-max(0,{})):\n").format(
+                            index_vars[i],
+                            ranges[i][0],
+                            shape_name,
+                            i,
+                            ranges[i][1])
+            offset += 1
+
+        for j in range(offset):
+            func_text += "    "
+        # Put a sentinel in the code so we can locate it in the IR.  We will
+        # remove this sentinel assignment and replace it with the IR for the
+        # stencil kernel body.
+        func_text += "{} = 0\n".format(sentinel_name)
+        func_text += "    return {}\n".format(out_name)
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("new stencil func text")
+            print(func_text)
+
+        # Force the new stencil function into existence.
+        exec(func_text) in globals(), locals()
+        stencil_func = eval(stencil_func_name)
+        if sigret is not None:
+            pysig = utils.pysignature(stencil_func)
+            sigret.pysig = pysig
+        # Get the IR for the newly created stencil function.
+        from numba.core import compiler
+        stencil_ir = compiler.run_frontend(stencil_func)
+        ir_utils.remove_dels(stencil_ir.blocks)
+
+        # rename all variables in stencil_ir afresh
+        var_table = ir_utils.get_name_var_table(stencil_ir.blocks)
+        new_var_dict = {}
+        reserved_names = ([sentinel_name, out_name, neighborhood_name,
+                           shape_name] + kernel_copy.arg_names + index_vars)
+        for name, var in var_table.items():
+            if not name in reserved_names:
+                new_var_dict[name] = ir_utils.mk_unique_var(name)
+        ir_utils.replace_var_names(stencil_ir.blocks, new_var_dict)
+
+        stencil_stub_last_label = max(stencil_ir.blocks.keys()) + 1
+
+        # Shift labels in the kernel copy so they are guaranteed unique
+        # and don't conflict with any labels in the stencil_ir.
+        kernel_copy.blocks = ir_utils.add_offset_to_labels(
+                                kernel_copy.blocks, stencil_stub_last_label)
+        new_label = max(kernel_copy.blocks.keys()) + 1
+        # Adjust ret_blocks to account for addition of the offset.
+        ret_blocks = [x + stencil_stub_last_label for x in ret_blocks]
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("ret_blocks w/ offsets", ret_blocks, stencil_stub_last_label)
+            print("before replace sentinel stencil_ir")
+            ir_utils.dump_blocks(stencil_ir.blocks)
+            print("before replace sentinel kernel_copy")
+            ir_utils.dump_blocks(kernel_copy.blocks)
+
+        # Search all the block in the stencil outline for the sentinel.
+        for label, block in stencil_ir.blocks.items():
+            for i, inst in enumerate(block.body):
+                if (isinstance( inst, ir.Assign) and
+                    inst.target.name == sentinel_name):
+                    # We found the sentinel assignment.
+                    loc = inst.loc
+                    scope = block.scope
+                    # split block across __sentinel__
+                    # A new block is allocated for the statements prior to the
+                    # sentinel but the new block maintains the current block
+                    # label.
+                    prev_block = ir.Block(scope, loc)
+                    prev_block.body = block.body[:i]
+                    # The current block is used for statements after sentinel.
+                    block.body = block.body[i + 1:]
+                    # But the current block gets a new label.
+                    body_first_label = min(kernel_copy.blocks.keys())
+
+                    # The previous block jumps to the minimum labelled block of
+                    # the parfor body.
+                    prev_block.append(ir.Jump(body_first_label, loc))
+                    # Add all the parfor loop body blocks to the gufunc
+                    # function's IR.
+                    for (l, b) in kernel_copy.blocks.items():
+                        stencil_ir.blocks[l] = b
+
+                    stencil_ir.blocks[new_label] = block
+                    stencil_ir.blocks[label] = prev_block
+                    # Add a jump from all the blocks that previously contained
+                    # a return in the stencil kernel to the block
+                    # containing statements after the sentinel.
+                    for ret_block in ret_blocks:
+                        stencil_ir.blocks[ret_block].append(
+                            ir.Jump(new_label, loc))
+                    break
+            else:
+                continue
+            break
+
+        stencil_ir.blocks = ir_utils.rename_labels(stencil_ir.blocks)
+        ir_utils.remove_dels(stencil_ir.blocks)
+
+        assert(isinstance(the_array, types.Type))
+        array_types = args
+
+        new_stencil_param_types = list(array_types)
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("new_stencil_param_types", new_stencil_param_types)
+            ir_utils.dump_blocks(stencil_ir.blocks)
+
+        # Compile the combined stencil function with the replaced loop
+        # body in it.
+        ir_utils.fixup_var_define_in_scope(stencil_ir.blocks)
+        new_func = compiler.compile_ir(
+            self._typingctx,
+            self._targetctx,
+            stencil_ir,
+            new_stencil_param_types,
+            None,
+            compiler.DEFAULT_FLAGS,
+            {})
+        return new_func
+
+    def __call__(self, *args, **kwargs):
+        if (self.neighborhood is not None and
+            len(self.neighborhood) != args[0].ndim):
+            raise ValueError("{} dimensional neighborhood specified for {} "
+                             "dimensional input array".format(
+                                len(self.neighborhood), args[0].ndim))
+
+        if 'out' in kwargs:
+            result = kwargs['out']
+            rdtype = result.dtype
+            rttype = numpy_support.from_dtype(rdtype)
+            result_type = types.npytypes.Array(rttype, result.ndim,
+                                               numpy_support.map_layout(result))
+            array_types = tuple([typing.typeof.typeof(x) for x in args])
+            array_types_full = tuple([typing.typeof.typeof(x) for x in args] +
+                                     [result_type])
+        else:
+            result = None
+            array_types = tuple([typing.typeof.typeof(x) for x in args])
+            array_types_full = array_types
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("__call__", array_types, args, kwargs)
+
+        (real_ret, typemap, calltypes) = self.get_return_type(array_types)
+        new_func = self._stencil_wrapper(result, None, real_ret, typemap,
+                                         calltypes, *array_types_full)
+
+        if result is None:
+            return new_func.entry_point(*args)
+        else:
+            return new_func.entry_point(*(args+(result,)))
+
+def stencil(func_or_mode='constant', **options):
+    # called on function without specifying mode style
+    if not isinstance(func_or_mode, str):
+        mode = 'constant'  # default style
+        func = func_or_mode
+    else:
+        mode = func_or_mode
+        func = None
+
+    for option in options:
+        if option not in ["cval", "standard_indexing", "neighborhood"]:
+            raise ValueError("Unknown stencil option " + option)
+
+    wrapper = _stencil(mode, options)
+    if func is not None:
+        return wrapper(func)
+    return wrapper
+
+def _stencil(mode, options):
+    if mode != 'constant':
+        raise ValueError("Unsupported mode style " + mode)
+
+    def decorated(func):
+        from numba.core import compiler
+        kernel_ir = compiler.run_frontend(func)
+        return StencilFunc(kernel_ir, mode, options)
+
+    return decorated
+
+@lower_builtin(stencil)
+def stencil_dummy_lower(context, builder, sig, args):
+    "lowering for dummy stencil calls"
+    return lir.Constant(lir.IntType(types.intp.bitwidth), 0)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/stencilparfor.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/stencilparfor.py
new file mode 100644
index 000000000..5f30893b3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/stencils/stencilparfor.py
@@ -0,0 +1,944 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+import numbers
+import copy
+import types as pytypes
+from operator import add
+import operator
+
+import numpy as np
+
+import numba.parfors.parfor
+from numba.core import types, ir, rewrites, config, ir_utils
+from numba.core.typing.templates import infer_global, AbstractTemplate
+from numba.core.typing import signature
+from numba.core import  utils, typing
+from numba.core.ir_utils import (get_call_table, mk_unique_var,
+                            compile_to_numba_ir, replace_arg_nodes, guard,
+                            find_callname, require, find_const, GuardException)
+from numba.core.errors import NumbaValueError
+from numba.core.utils import OPERATORS_TO_BUILTINS
+
+
+def _compute_last_ind(dim_size, index_const):
+    if index_const > 0:
+        return dim_size - index_const
+    else:
+        return dim_size
+
+class StencilPass(object):
+    def __init__(self, func_ir, typemap, calltypes, array_analysis, typingctx,
+                 targetctx, flags):
+        self.func_ir = func_ir
+        self.typemap = typemap
+        self.calltypes = calltypes
+        self.array_analysis = array_analysis
+        self.typingctx = typingctx
+        self.targetctx = targetctx
+        self.flags = flags
+
+    def run(self):
+        """ Finds all calls to StencilFuncs in the IR and converts them to parfor.
+        """
+        from numba.stencils.stencil import StencilFunc
+
+        # Get all the calls in the function IR.
+        call_table, _ = get_call_table(self.func_ir.blocks)
+        stencil_calls = []
+        stencil_dict = {}
+        for call_varname, call_list in call_table.items():
+            for one_call in call_list:
+                if isinstance(one_call, StencilFunc):
+                    # Remember all calls to StencilFuncs.
+                    stencil_calls.append(call_varname)
+                    stencil_dict[call_varname] = one_call
+        if not stencil_calls:
+            return  # return early if no stencil calls found
+
+        # find and transform stencil calls
+        for label, block in self.func_ir.blocks.items():
+            for i, stmt in reversed(list(enumerate(block.body))):
+                # Found a call to a StencilFunc.
+                if (isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Expr)
+                        and stmt.value.op == 'call'
+                        and stmt.value.func.name in stencil_calls):
+                    kws = dict(stmt.value.kws)
+                    # Create dictionary of input argument number to
+                    # the argument itself.
+                    input_dict = {i: stmt.value.args[i] for i in
+                                                    range(len(stmt.value.args))}
+                    in_args = stmt.value.args
+                    arg_typemap = tuple(self.typemap[i.name] for i in in_args)
+                    for arg_type in arg_typemap:
+                        if isinstance(arg_type, types.BaseTuple):
+                            raise ValueError("Tuple parameters not supported " \
+                                "for stencil kernels in parallel=True mode.")
+
+                    out_arr = kws.get('out')
+
+                    # Get the StencilFunc object corresponding to this call.
+                    sf = stencil_dict[stmt.value.func.name]
+                    stencil_ir, rt, arg_to_arr_dict = get_stencil_ir(sf,
+                            self.typingctx, arg_typemap,
+                            block.scope, block.loc, input_dict,
+                            self.typemap, self.calltypes)
+                    index_offsets = sf.options.get('index_offsets', None)
+                    gen_nodes = self._mk_stencil_parfor(label, in_args, out_arr,
+                            stencil_ir, index_offsets, stmt.target, rt, sf,
+                            arg_to_arr_dict)
+                    block.body = block.body[:i] + gen_nodes + block.body[i+1:]
+                # Found a call to a stencil via numba.stencil().
+                elif (isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Expr)
+                        and stmt.value.op == 'call'
+                        and guard(find_callname, self.func_ir, stmt.value)
+                                    == ('stencil', 'numba')):
+                    # remove dummy stencil() call
+                    stmt.value = ir.Const(0, stmt.loc)
+
+    def replace_return_with_setitem(self, blocks, exit_value_var,
+                                    parfor_body_exit_label):
+        """
+        Find return statements in the IR and replace them with a SetItem
+        call of the value "returned" by the kernel into the result array.
+        Returns the block labels that contained return statements.
+        """
+        for label, block in blocks.items():
+            scope = block.scope
+            loc = block.loc
+            new_body = []
+            for stmt in block.body:
+                if isinstance(stmt, ir.Return):
+                    # previous stmt should have been a cast
+                    prev_stmt = new_body.pop()
+                    assert (isinstance(prev_stmt, ir.Assign)
+                        and isinstance(prev_stmt.value, ir.Expr)
+                        and prev_stmt.value.op == 'cast')
+
+                    new_body.append(ir.Assign(prev_stmt.value.value, exit_value_var, loc))
+                    new_body.append(ir.Jump(parfor_body_exit_label, loc))
+                else:
+                    new_body.append(stmt)
+            block.body = new_body
+
+    def _mk_stencil_parfor(self, label, in_args, out_arr, stencil_ir,
+                           index_offsets, target, return_type, stencil_func,
+                           arg_to_arr_dict):
+        """ Converts a set of stencil kernel blocks to a parfor.
+        """
+        gen_nodes = []
+        stencil_blocks = stencil_ir.blocks
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("_mk_stencil_parfor", label, in_args, out_arr, index_offsets,
+                   return_type, stencil_func, stencil_blocks)
+            ir_utils.dump_blocks(stencil_blocks)
+
+        in_arr = in_args[0]
+        # run copy propagate to replace in_args copies (e.g. a = A)
+        in_arr_typ = self.typemap[in_arr.name]
+        in_cps, out_cps = ir_utils.copy_propagate(stencil_blocks, self.typemap)
+        name_var_table = ir_utils.get_name_var_table(stencil_blocks)
+
+        ir_utils.apply_copy_propagate(
+            stencil_blocks,
+            in_cps,
+            name_var_table,
+            self.typemap,
+            self.calltypes)
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("stencil_blocks after copy_propagate")
+            ir_utils.dump_blocks(stencil_blocks)
+        ir_utils.remove_dead(stencil_blocks, self.func_ir.arg_names, stencil_ir,
+                             self.typemap)
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("stencil_blocks after removing dead code")
+            ir_utils.dump_blocks(stencil_blocks)
+
+        # create parfor vars
+        ndims = self.typemap[in_arr.name].ndim
+        scope = in_arr.scope
+        loc = in_arr.loc
+        parfor_vars = []
+        for i in range(ndims):
+            parfor_var = ir.Var(scope, mk_unique_var(
+                "$parfor_index_var"), loc)
+            self.typemap[parfor_var.name] = types.intp
+            parfor_vars.append(parfor_var)
+
+        start_lengths, end_lengths = self._replace_stencil_accesses(
+             stencil_ir, parfor_vars, in_args, index_offsets, stencil_func,
+             arg_to_arr_dict)
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("stencil_blocks after replace stencil accesses")
+            print("start_lengths:", start_lengths)
+            print("end_lengths:", end_lengths)
+            ir_utils.dump_blocks(stencil_blocks)
+
+        # create parfor loop nests
+        loopnests = []
+        equiv_set = self.array_analysis.get_equiv_set(label)
+        in_arr_dim_sizes = equiv_set.get_shape(in_arr)
+
+        assert ndims == len(in_arr_dim_sizes)
+        start_inds = []
+        last_inds = []
+        for i in range(ndims):
+            last_ind = self._get_stencil_last_ind(in_arr_dim_sizes[i],
+                                        end_lengths[i], gen_nodes, scope, loc)
+            start_ind = self._get_stencil_start_ind(
+                                        start_lengths[i], gen_nodes, scope, loc)
+            start_inds.append(start_ind)
+            last_inds.append(last_ind)
+            # start from stencil size to avoid invalid array access
+            loopnests.append(numba.parfors.parfor.LoopNest(parfor_vars[i],
+                                start_ind, last_ind, 1))
+
+        # We have to guarantee that the exit block has maximum label and that
+        # there's only one exit block for the parfor body.
+        # So, all return statements will change to jump to the parfor exit block.
+        parfor_body_exit_label = max(stencil_blocks.keys()) + 1
+        stencil_blocks[parfor_body_exit_label] = ir.Block(scope, loc)
+        exit_value_var = ir.Var(scope, mk_unique_var("$parfor_exit_value"), loc)
+        self.typemap[exit_value_var.name] = return_type.dtype
+
+        # create parfor index var
+        for_replacing_ret = []
+        if ndims == 1:
+            parfor_ind_var = parfor_vars[0]
+        else:
+            parfor_ind_var = ir.Var(scope, mk_unique_var(
+                "$parfor_index_tuple_var"), loc)
+            self.typemap[parfor_ind_var.name] = types.containers.UniTuple(
+                types.intp, ndims)
+            tuple_call = ir.Expr.build_tuple(parfor_vars, loc)
+            tuple_assign = ir.Assign(tuple_call, parfor_ind_var, loc)
+            for_replacing_ret.append(tuple_assign)
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("stencil_blocks after creating parfor index var")
+            ir_utils.dump_blocks(stencil_blocks)
+
+        # empty init block
+        init_block = ir.Block(scope, loc)
+        if out_arr is None:
+            in_arr_typ = self.typemap[in_arr.name]
+
+            shape_name = ir_utils.mk_unique_var("in_arr_shape")
+            shape_var = ir.Var(scope, shape_name, loc)
+            shape_getattr = ir.Expr.getattr(in_arr, "shape", loc)
+            self.typemap[shape_name] = types.containers.UniTuple(types.intp,
+                                                               in_arr_typ.ndim)
+            init_block.body.extend([ir.Assign(shape_getattr, shape_var, loc)])
+
+            zero_name = ir_utils.mk_unique_var("zero_val")
+            zero_var = ir.Var(scope, zero_name, loc)
+            if "cval" in stencil_func.options:
+                cval = stencil_func.options["cval"]
+                # TODO: Loosen this restriction to adhere to casting rules.
+                cval_ty = typing.typeof.typeof(cval)
+                if not self.typingctx.can_convert(cval_ty, return_type.dtype):
+                    raise ValueError("cval type does not match stencil return type.")
+
+                temp2 = return_type.dtype(cval)
+            else:
+                temp2 = return_type.dtype(0)
+            full_const = ir.Const(temp2, loc)
+            self.typemap[zero_name] = return_type.dtype
+            init_block.body.extend([ir.Assign(full_const, zero_var, loc)])
+
+            so_name = ir_utils.mk_unique_var("stencil_output")
+            out_arr = ir.Var(scope, so_name, loc)
+            self.typemap[out_arr.name] = numba.core.types.npytypes.Array(
+                                                           return_type.dtype,
+                                                           in_arr_typ.ndim,
+                                                           in_arr_typ.layout)
+            dtype_g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc)
+            self.typemap[dtype_g_np_var.name] = types.misc.Module(np)
+            dtype_g_np = ir.Global('np', np, loc)
+            dtype_g_np_assign = ir.Assign(dtype_g_np, dtype_g_np_var, loc)
+            init_block.body.append(dtype_g_np_assign)
+
+            dtype_np_attr_call = ir.Expr.getattr(dtype_g_np_var, return_type.dtype.name, loc)
+            dtype_attr_var = ir.Var(scope, mk_unique_var("$np_attr_attr"), loc)
+            self.typemap[dtype_attr_var.name] = types.functions.NumberClass(return_type.dtype)
+            dtype_attr_assign = ir.Assign(dtype_np_attr_call, dtype_attr_var, loc)
+            init_block.body.append(dtype_attr_assign)
+
+            stmts = ir_utils.gen_np_call("empty",
+                                       np.empty,
+                                       out_arr,
+                                       [shape_var, dtype_attr_var],
+                                       self.typingctx,
+                                       self.typemap,
+                                       self.calltypes)
+            # ------------------
+            # Generate the code to fill just the border with zero_var.
+
+            # Generate a none var to use in slicing.
+            none_var = ir.Var(scope, mk_unique_var("$none_var"), loc)
+            none_assign = ir.Assign(ir.Const(None, loc), none_var, loc)
+            stmts.append(none_assign)
+            self.typemap[none_var.name] = types.none
+            # Generate a zero var to use in slicing.
+            zero_index_var = ir.Var(scope, mk_unique_var("$zero_index_var"), loc)
+            zero_index_assign = ir.Assign(ir.Const(0, loc), zero_index_var, loc)
+            stmts.append(zero_index_assign)
+            self.typemap[zero_index_var.name] = types.intp
+            # Generate generic ":" slice.
+            # ---- Generate var to hold slice func var.
+            slice_func_var = ir.Var(scope, mk_unique_var("$slice_func_var"), loc)
+            slice_fn_ty = self.typingctx.resolve_value_type(slice)
+            self.typemap[slice_func_var.name] = slice_fn_ty
+            slice_g = ir.Global('slice', slice, loc)
+            slice_assign = ir.Assign(slice_g, slice_func_var, loc)
+            stmts.append(slice_assign)
+            # ---- Generate call to slice func.
+            sig = self.typingctx.resolve_function_type(slice_fn_ty,
+                                                       (types.none,) * 2,
+                                                       {})
+            slice_callexpr = ir.Expr.call(func=slice_func_var,
+                                          args=(none_var, none_var),
+                                          kws=(),
+                                          loc=loc)
+            self.calltypes[slice_callexpr] = sig
+            # ---- Generate slice var
+            slice_var = ir.Var(scope, mk_unique_var("$slice"), loc)
+            self.typemap[slice_var.name] = types.slice2_type
+            slice_assign = ir.Assign(slice_callexpr, slice_var, loc)
+            stmts.append(slice_assign)
+
+            def handle_border(slice_fn_ty,
+                              dim,
+                              scope,
+                              loc,
+                              slice_func_var,
+                              stmts,
+                              border_inds,
+                              border_tuple_items,
+                              other_arg,
+                              other_first):
+                # Handle the border for start or end of the index range.
+                # ---- Generate call to slice func.
+                sig = self.typingctx.resolve_function_type(
+                    slice_fn_ty,
+                    (types.intp,) * 2,
+                    {})
+                si = border_inds[dim]
+                assert(isinstance(si, (int, ir.Var)))
+                si_var = ir.Var(scope, mk_unique_var("$border_ind"), loc)
+                self.typemap[si_var.name] = types.intp
+                if isinstance(si, int):
+                    si_assign = ir.Assign(ir.Const(si, loc), si_var, loc)
+                else:
+                    si_assign = ir.Assign(si, si_var, loc)
+                stmts.append(si_assign)
+
+                slice_callexpr = ir.Expr.call(
+                    func=slice_func_var,
+                    args=(other_arg, si_var) if other_first else (si_var, other_arg),
+                    kws=(),
+                    loc=loc)
+                self.calltypes[slice_callexpr] = sig
+                # ---- Generate slice var
+                border_slice_var = ir.Var(scope, mk_unique_var("$slice"), loc)
+                self.typemap[border_slice_var.name] = types.slice2_type
+                slice_assign = ir.Assign(slice_callexpr, border_slice_var, loc)
+                stmts.append(slice_assign)
+
+                border_tuple_items[dim] = border_slice_var
+                border_ind_var = ir.Var(scope, mk_unique_var(
+                    "$border_index_tuple_var"), loc)
+                self.typemap[border_ind_var.name] = types.containers.UniTuple(
+                    types.slice2_type, ndims)
+                tuple_call = ir.Expr.build_tuple(border_tuple_items, loc)
+                tuple_assign = ir.Assign(tuple_call, border_ind_var, loc)
+                stmts.append(tuple_assign)
+
+                setitem_call = ir.SetItem(out_arr, border_ind_var, zero_var, loc)
+                self.calltypes[setitem_call] = signature(
+                                                types.none, self.typemap[out_arr.name],
+                                                self.typemap[border_ind_var.name],
+                                                self.typemap[out_arr.name].dtype
+                                                )
+                stmts.append(setitem_call)
+
+            # For each dimension, add setitem to set border values.
+            for dim in range(in_arr_typ.ndim):
+                # First, fill all entries with ":".
+                start_tuple_items = [slice_var] * in_arr_typ.ndim
+                last_tuple_items = [slice_var] * in_arr_typ.ndim
+
+                handle_border(slice_fn_ty,
+                              dim,
+                              scope,
+                              loc,
+                              slice_func_var,
+                              stmts,
+                              start_inds,
+                              start_tuple_items,
+                              zero_index_var,
+                              True)
+                handle_border(slice_fn_ty,
+                              dim,
+                              scope,
+                              loc,
+                              slice_func_var,
+                              stmts,
+                              last_inds,
+                              last_tuple_items,
+                              in_arr_dim_sizes[dim],
+                              False)
+
+            # ------------------
+
+            equiv_set.insert_equiv(out_arr, in_arr_dim_sizes)
+            init_block.body.extend(stmts)
+        else: # out is present
+            if "cval" in stencil_func.options: # do out[:] = cval
+                cval = stencil_func.options["cval"]
+                # TODO: Loosen this restriction to adhere to casting rules.
+                cval_ty = typing.typeof.typeof(cval)
+                if not self.typingctx.can_convert(cval_ty, return_type.dtype):
+                    msg = "cval type does not match stencil return type."
+                    raise NumbaValueError(msg)
+
+                # get slice ref
+                slice_var = ir.Var(scope, mk_unique_var("$py_g_var"), loc)
+                slice_fn_ty = self.typingctx.resolve_value_type(slice)
+                self.typemap[slice_var.name] = slice_fn_ty
+                slice_g = ir.Global('slice', slice, loc)
+                slice_assigned = ir.Assign(slice_g, slice_var, loc)
+                init_block.body.append(slice_assigned)
+
+                sig = self.typingctx.resolve_function_type(slice_fn_ty,
+                                                           (types.none,) * 2,
+                                                           {})
+
+                callexpr = ir.Expr.call(func=slice_var, args=(), kws=(),
+                                        loc=loc)
+
+                self.calltypes[callexpr] = sig
+                slice_inst_var = ir.Var(scope, mk_unique_var("$slice_inst"),
+                                        loc)
+                self.typemap[slice_inst_var.name] = types.slice2_type
+                slice_assign = ir.Assign(callexpr, slice_inst_var, loc)
+                init_block.body.append(slice_assign)
+
+                # get const val for cval
+                cval_const_val = ir.Const(return_type.dtype(cval), loc)
+                cval_const_var = ir.Var(scope, mk_unique_var("$cval_const"),
+                                            loc)
+                self.typemap[cval_const_var.name] = return_type.dtype
+                cval_const_assign = ir.Assign(cval_const_val,
+                                              cval_const_var, loc)
+                init_block.body.append(cval_const_assign)
+
+                # do setitem on `out` array
+                setitemexpr = ir.StaticSetItem(out_arr, slice(None, None),
+                                               slice_inst_var, cval_const_var,
+                                               loc)
+                init_block.body.append(setitemexpr)
+                sig = signature(types.none, self.typemap[out_arr.name],
+                                self.typemap[slice_inst_var.name],
+                                self.typemap[out_arr.name].dtype)
+                self.calltypes[setitemexpr] = sig
+
+
+        self.replace_return_with_setitem(stencil_blocks, exit_value_var,
+                                         parfor_body_exit_label)
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("stencil_blocks after replacing return")
+            ir_utils.dump_blocks(stencil_blocks)
+
+        setitem_call = ir.SetItem(out_arr, parfor_ind_var, exit_value_var, loc)
+        self.calltypes[setitem_call] = signature(
+                                        types.none, self.typemap[out_arr.name],
+                                        self.typemap[parfor_ind_var.name],
+                                        self.typemap[out_arr.name].dtype
+                                        )
+        stencil_blocks[parfor_body_exit_label].body.extend(for_replacing_ret)
+        stencil_blocks[parfor_body_exit_label].body.append(setitem_call)
+
+        # simplify CFG of parfor body (exit block could be simplified often)
+        # add dummy return to enable CFG
+        dummy_loc = ir.Loc("stencilparfor_dummy", -1)
+        ret_const_var = ir.Var(scope, mk_unique_var("$cval_const"), dummy_loc)
+        cval_const_assign = ir.Assign(ir.Const(0, loc=dummy_loc), ret_const_var, dummy_loc)
+        stencil_blocks[parfor_body_exit_label].body.append(cval_const_assign)
+
+        stencil_blocks[parfor_body_exit_label].body.append(
+            ir.Return(ret_const_var, dummy_loc),
+        )
+        stencil_blocks = ir_utils.simplify_CFG(stencil_blocks)
+        stencil_blocks[max(stencil_blocks.keys())].body.pop()
+
+        if config.DEBUG_ARRAY_OPT >= 1:
+            print("stencil_blocks after adding SetItem")
+            ir_utils.dump_blocks(stencil_blocks)
+
+        pattern = ('stencil', [start_lengths, end_lengths])
+        parfor = numba.parfors.parfor.Parfor(loopnests, init_block, stencil_blocks,
+                                     loc, parfor_ind_var, equiv_set, pattern, self.flags)
+        gen_nodes.append(parfor)
+        gen_nodes.append(ir.Assign(out_arr, target, loc))
+        return gen_nodes
+
+    def _get_stencil_last_ind(self, dim_size, end_length, gen_nodes, scope,
+                                                                        loc):
+        last_ind = dim_size
+        if end_length != 0:
+            # set last index to size minus stencil size to avoid invalid
+            # memory access
+            index_const = ir.Var(scope, mk_unique_var("stencil_const_var"),
+                                                                        loc)
+            self.typemap[index_const.name] = types.intp
+            if isinstance(end_length, numbers.Number):
+                const_assign = ir.Assign(ir.Const(end_length, loc),
+                                                        index_const, loc)
+            else:
+                const_assign = ir.Assign(end_length, index_const, loc)
+
+            gen_nodes.append(const_assign)
+            last_ind = ir.Var(scope, mk_unique_var("last_ind"), loc)
+            self.typemap[last_ind.name] = types.intp
+
+            g_var = ir.Var(scope, mk_unique_var("compute_last_ind_var"), loc)
+            check_func = numba.njit(_compute_last_ind)
+            func_typ = types.functions.Dispatcher(check_func)
+            self.typemap[g_var.name] = func_typ
+            g_obj = ir.Global("_compute_last_ind", check_func, loc)
+            g_assign = ir.Assign(g_obj, g_var, loc)
+            gen_nodes.append(g_assign)
+            index_call = ir.Expr.call(g_var, [dim_size, index_const], (), loc)
+            self.calltypes[index_call] = func_typ.get_call_type(
+                self.typingctx, [types.intp, types.intp], {})
+            index_assign = ir.Assign(index_call, last_ind, loc)
+            gen_nodes.append(index_assign)
+
+        return last_ind
+
+    def _get_stencil_start_ind(self, start_length, gen_nodes, scope, loc):
+        if isinstance(start_length, int):
+            return abs(min(start_length, 0))
+        def get_start_ind(s_length):
+            return abs(min(s_length, 0))
+        f_ir = compile_to_numba_ir(get_start_ind, {}, self.typingctx,
+                                   self.targetctx, (types.intp,), self.typemap,
+                                   self.calltypes)
+        assert len(f_ir.blocks) == 1
+        block = f_ir.blocks.popitem()[1]
+        replace_arg_nodes(block, [start_length])
+        gen_nodes += block.body[:-2]
+        ret_var = block.body[-2].value.value
+        return ret_var
+
+    def _replace_stencil_accesses(self, stencil_ir, parfor_vars, in_args,
+                                  index_offsets, stencil_func, arg_to_arr_dict):
+        """ Convert relative indexing in the stencil kernel to standard indexing
+            by adding the loop index variables to the corresponding dimensions
+            of the array index tuples.
+        """
+        stencil_blocks = stencil_ir.blocks
+        in_arr = in_args[0]
+        in_arg_names = [x.name for x in in_args]
+
+        if "standard_indexing" in stencil_func.options:
+            for x in stencil_func.options["standard_indexing"]:
+                if x not in arg_to_arr_dict:
+                    raise ValueError("Standard indexing requested for an array " \
+                        "name not present in the stencil kernel definition.")
+            standard_indexed = [arg_to_arr_dict[x] for x in
+                                     stencil_func.options["standard_indexing"]]
+        else:
+            standard_indexed = []
+
+        if in_arr.name in standard_indexed:
+            raise ValueError("The first argument to a stencil kernel must use " \
+                "relative indexing, not standard indexing.")
+
+        ndims = self.typemap[in_arr.name].ndim
+        scope = in_arr.scope
+        loc = in_arr.loc
+        # replace access indices, find access lengths in each dimension
+        need_to_calc_kernel = stencil_func.neighborhood is None
+
+        # If we need to infer the kernel size then initialize the minimum and
+        # maximum seen indices for each dimension to 0.  If we already have
+        # the neighborhood calculated then just convert from neighborhood format
+        # to the separate start and end lengths format used here.
+        if need_to_calc_kernel:
+            start_lengths = ndims*[0]
+            end_lengths = ndims*[0]
+        else:
+            start_lengths = [x[0] for x in stencil_func.neighborhood]
+            end_lengths   = [x[1] for x in stencil_func.neighborhood]
+
+        # Get all the tuples defined in the stencil blocks.
+        tuple_table = ir_utils.get_tuple_table(stencil_blocks)
+
+        found_relative_index = False
+
+        # For all blocks in the stencil kernel...
+        for label, block in stencil_blocks.items():
+            new_body = []
+            # For all statements in those blocks...
+            for stmt in block.body:
+                # Reject assignments to input arrays.
+                if ((isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Expr)
+                        and stmt.value.op in ['setitem', 'static_setitem']
+                        and stmt.value.value.name in in_arg_names) or
+                   ((isinstance(stmt, ir.SetItem) or
+                     isinstance(stmt, ir.StaticSetItem))
+                        and stmt.target.name in in_arg_names)):
+                    raise ValueError("Assignments to arrays passed to stencil kernels is not allowed.")
+                # We found a getitem for some array.  If that array is an input
+                # array and isn't in the list of standard indexed arrays then
+                # update min and max seen indices if we are inferring the
+                # kernel size and create a new tuple where the relative offsets
+                # are added to loop index vars to get standard indexing.
+                if (isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Expr)
+                        and stmt.value.op in ['static_getitem', 'getitem']
+                        and stmt.value.value.name in in_arg_names
+                        and stmt.value.value.name not in standard_indexed):
+                    index_list = stmt.value.index
+                    # handle 1D case
+                    if ndims == 1:
+                        index_list = [index_list]
+                    else:
+                        if hasattr(index_list, 'name') and index_list.name in tuple_table:
+                            index_list = tuple_table[index_list.name]
+                    # indices can be inferred as constant in simple expressions
+                    # like -c where c is constant
+                    # handled here since this is a common stencil index pattern
+                    stencil_ir._definitions = ir_utils.build_definitions(stencil_blocks)
+                    index_list = [_get_const_index_expr(
+                        stencil_ir, self.func_ir, v) for v in index_list]
+                    if index_offsets:
+                        index_list = self._add_index_offsets(index_list,
+                                    list(index_offsets), new_body, scope, loc)
+
+                    # update min and max indices
+                    if need_to_calc_kernel:
+                        # all indices should be integer to be able to calculate
+                        # neighborhood automatically
+                        if (isinstance(index_list, ir.Var) or
+                            any([not isinstance(v, int) for v in index_list])):
+                            raise ValueError("Variable stencil index only "
+                                "possible with known neighborhood")
+                        start_lengths = list(map(min, start_lengths,
+                                                                    index_list))
+                        end_lengths = list(map(max, end_lengths, index_list))
+                        found_relative_index = True
+
+                    # update access indices
+                    index_vars = self._add_index_offsets(parfor_vars,
+                                list(index_list), new_body, scope, loc)
+
+                    # new access index tuple
+                    if ndims == 1:
+                        ind_var = index_vars[0]
+                    else:
+                        ind_var = ir.Var(scope, mk_unique_var(
+                            "$parfor_index_ind_var"), loc)
+                        self.typemap[ind_var.name] = types.containers.UniTuple(
+                            types.intp, ndims)
+                        tuple_call = ir.Expr.build_tuple(index_vars, loc)
+                        tuple_assign = ir.Assign(tuple_call, ind_var, loc)
+                        new_body.append(tuple_assign)
+
+                    # getitem return type is scalar if all indices are integer
+                    if all([self.typemap[v.name] == types.intp
+                                                        for v in index_vars]):
+                        getitem_return_typ = self.typemap[
+                                                    stmt.value.value.name].dtype
+                    else:
+                        # getitem returns an array
+                        getitem_return_typ = self.typemap[stmt.value.value.name]
+                    # new getitem with the new index var
+                    getitem_call = ir.Expr.getitem(stmt.value.value, ind_var,
+                                                                            loc)
+                    self.calltypes[getitem_call] = signature(
+                        getitem_return_typ,
+                        self.typemap[stmt.value.value.name],
+                        self.typemap[ind_var.name])
+                    stmt.value = getitem_call
+
+                new_body.append(stmt)
+            block.body = new_body
+        if need_to_calc_kernel and not found_relative_index:
+            raise ValueError("Stencil kernel with no accesses to " \
+                "relatively indexed arrays.")
+
+        return start_lengths, end_lengths
+
+    def _add_index_offsets(self, index_list, index_offsets, new_body,
+                           scope, loc):
+        """ Does the actual work of adding loop index variables to the
+            relative index constants or variables.
+        """
+        assert len(index_list) == len(index_offsets)
+
+        # shortcut if all values are integer
+        if all([isinstance(v, int) for v in index_list+index_offsets]):
+            # add offsets in all dimensions
+            return list(map(add, index_list, index_offsets))
+
+        out_nodes = []
+        index_vars = []
+        for i in range(len(index_list)):
+            # new_index = old_index + offset
+            old_index_var = index_list[i]
+            if isinstance(old_index_var, int):
+                old_index_var = ir.Var(scope,
+                                mk_unique_var("old_index_var"), loc)
+                self.typemap[old_index_var.name] = types.intp
+                const_assign = ir.Assign(ir.Const(index_list[i], loc),
+                                                    old_index_var, loc)
+                out_nodes.append(const_assign)
+
+            offset_var = index_offsets[i]
+            if isinstance(offset_var, int):
+                offset_var = ir.Var(scope,
+                                mk_unique_var("offset_var"), loc)
+                self.typemap[offset_var.name] = types.intp
+                const_assign = ir.Assign(ir.Const(index_offsets[i], loc),
+                                                offset_var, loc)
+                out_nodes.append(const_assign)
+
+            if (isinstance(old_index_var, slice)
+                    or isinstance(self.typemap[old_index_var.name],
+                                    types.misc.SliceType)):
+                # only one arg can be slice
+                assert self.typemap[offset_var.name] == types.intp
+                index_var = self._add_offset_to_slice(old_index_var, offset_var,
+                                                        out_nodes, scope, loc)
+                index_vars.append(index_var)
+                continue
+
+            if (isinstance(offset_var, slice)
+                    or isinstance(self.typemap[offset_var.name],
+                                    types.misc.SliceType)):
+                # only one arg can be slice
+                assert self.typemap[old_index_var.name] == types.intp
+                index_var = self._add_offset_to_slice(offset_var, old_index_var,
+                                                        out_nodes, scope, loc)
+                index_vars.append(index_var)
+                continue
+
+            index_var = ir.Var(scope,
+                            mk_unique_var("offset_stencil_index"), loc)
+            self.typemap[index_var.name] = types.intp
+            index_call = ir.Expr.binop(operator.add, old_index_var,
+                                                offset_var, loc)
+            self.calltypes[index_call] = self.typingctx.resolve_function_type(
+                                         operator.add, (types.intp, types.intp), {})
+            index_assign = ir.Assign(index_call, index_var, loc)
+            out_nodes.append(index_assign)
+            index_vars.append(index_var)
+
+        new_body.extend(out_nodes)
+        return index_vars
+
+    def _add_offset_to_slice(self, slice_var, offset_var, out_nodes, scope,
+                                loc):
+        if isinstance(slice_var, slice):
+            f_text = """def f(offset):
+                return slice({} + offset, {} + offset)
+            """.format(slice_var.start, slice_var.stop)
+            loc = {}
+            exec(f_text, {}, loc)
+            f = loc['f']
+            args = [offset_var]
+            arg_typs = (types.intp,)
+        else:
+            def f(old_slice, offset):
+                return slice(old_slice.start + offset, old_slice.stop + offset)
+            args = [slice_var, offset_var]
+            slice_type = self.typemap[slice_var.name]
+            arg_typs = (slice_type, types.intp,)
+        _globals = self.func_ir.func_id.func.__globals__
+        f_ir = compile_to_numba_ir(f, _globals, self.typingctx, self.targetctx,
+                                   arg_typs, self.typemap, self.calltypes)
+        _, block = f_ir.blocks.popitem()
+        replace_arg_nodes(block, args)
+        new_index = block.body[-2].value.value
+        out_nodes.extend(block.body[:-2])  # ignore return nodes
+        return new_index
+
+def get_stencil_ir(sf, typingctx, args, scope, loc, input_dict, typemap,
+                                                                    calltypes):
+    """get typed IR from stencil bytecode
+    """
+    from numba.core.cpu import CPUContext
+    from numba.core.registry import cpu_target
+    from numba.core.annotations import type_annotations
+    from numba.core.typed_passes import type_inference_stage
+
+    # get untyped IR
+    stencil_func_ir = sf.kernel_ir.copy()
+    # copy the IR nodes to avoid changing IR in the StencilFunc object
+    stencil_blocks = copy.deepcopy(stencil_func_ir.blocks)
+    stencil_func_ir.blocks = stencil_blocks
+
+    name_var_table = ir_utils.get_name_var_table(stencil_func_ir.blocks)
+    if "out" in name_var_table:
+        raise ValueError("Cannot use the reserved word 'out' in stencil kernels.")
+
+    # get typed IR with a dummy pipeline (similar to test_parfors.py)
+    from numba.core.registry import cpu_target
+    targetctx = cpu_target.target_context
+    with cpu_target.nested_context(typingctx, targetctx):
+        tp = DummyPipeline(typingctx, targetctx, args, stencil_func_ir)
+
+        rewrites.rewrite_registry.apply('before-inference', tp.state)
+
+        tp.state.typemap, tp.state.return_type, tp.state.calltypes, _ = type_inference_stage(
+            tp.state.typingctx, tp.state.targetctx, tp.state.func_ir,
+            tp.state.args, None)
+
+        type_annotations.TypeAnnotation(
+            func_ir=tp.state.func_ir,
+            typemap=tp.state.typemap,
+            calltypes=tp.state.calltypes,
+            lifted=(),
+            lifted_from=None,
+            args=tp.state.args,
+            return_type=tp.state.return_type,
+            html_output=config.HTML)
+
+    # make block labels unique
+    stencil_blocks = ir_utils.add_offset_to_labels(stencil_blocks,
+                                                        ir_utils.next_label())
+    min_label = min(stencil_blocks.keys())
+    max_label = max(stencil_blocks.keys())
+    ir_utils._the_max_label.update(max_label)
+
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("Initial stencil_blocks")
+        ir_utils.dump_blocks(stencil_blocks)
+
+    # rename variables,
+    var_dict = {}
+    for v, typ in tp.state.typemap.items():
+        new_var = ir.Var(scope, mk_unique_var(v), loc)
+        var_dict[v] = new_var
+        typemap[new_var.name] = typ  # add new var type for overall function
+    ir_utils.replace_vars(stencil_blocks, var_dict)
+
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("After replace_vars")
+        ir_utils.dump_blocks(stencil_blocks)
+
+    # add call types to overall function
+    for call, call_typ in tp.state.calltypes.items():
+        calltypes[call] = call_typ
+
+    arg_to_arr_dict = {}
+    # replace arg with arr
+    for block in stencil_blocks.values():
+        for stmt in block.body:
+            if isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Arg):
+                if config.DEBUG_ARRAY_OPT >= 1:
+                    print("input_dict", input_dict, stmt.value.index,
+                               stmt.value.name, stmt.value.index in input_dict)
+                arg_to_arr_dict[stmt.value.name] = input_dict[stmt.value.index].name
+                stmt.value = input_dict[stmt.value.index]
+
+    if config.DEBUG_ARRAY_OPT >= 1:
+        print("arg_to_arr_dict", arg_to_arr_dict)
+        print("After replace arg with arr")
+        ir_utils.dump_blocks(stencil_blocks)
+
+    ir_utils.remove_dels(stencil_blocks)
+    stencil_func_ir.blocks = stencil_blocks
+    return stencil_func_ir, sf.get_return_type(args)[0], arg_to_arr_dict
+
+class DummyPipeline(object):
+    def __init__(self, typingctx, targetctx, args, f_ir):
+        from numba.core.compiler import StateDict
+        self.state = StateDict()
+        self.state.typingctx = typingctx
+        self.state.targetctx = targetctx
+        self.state.args = args
+        self.state.func_ir = f_ir
+        self.state.typemap = None
+        self.state.return_type = None
+        self.state.calltypes = None
+
+
+def _get_const_index_expr(stencil_ir, func_ir, index_var):
+    """
+    infer index_var as constant if it is of a expression form like c-1 where c
+    is a constant in the outer function.
+    index_var is assumed to be inside stencil kernel
+    """
+    const_val = guard(
+        _get_const_index_expr_inner, stencil_ir, func_ir, index_var)
+    if const_val is not None:
+        return const_val
+    return index_var
+
+def _get_const_index_expr_inner(stencil_ir, func_ir, index_var):
+    """inner constant inference function that calls constant, unary and binary
+    cases.
+    """
+    require(isinstance(index_var, ir.Var))
+    # case where the index is a const itself in outer function
+    var_const =  guard(_get_const_two_irs, stencil_ir, func_ir, index_var)
+    if var_const is not None:
+        return var_const
+    # get index definition
+    index_def = ir_utils.get_definition(stencil_ir, index_var)
+    # match inner_var = unary(index_var)
+    var_const = guard(
+        _get_const_unary_expr, stencil_ir, func_ir, index_def)
+    if var_const is not None:
+        return var_const
+    # match inner_var = arg1 + arg2
+    var_const = guard(
+        _get_const_binary_expr, stencil_ir, func_ir, index_def)
+    if var_const is not None:
+        return var_const
+    raise GuardException
+
+def _get_const_two_irs(ir1, ir2, var):
+    """get constant in either of two IRs if available
+    otherwise, throw GuardException
+    """
+    var_const = guard(find_const, ir1, var)
+    if var_const is not None:
+        return var_const
+    var_const = guard(find_const, ir2, var)
+    if var_const is not None:
+        return var_const
+    raise GuardException
+
+def _get_const_unary_expr(stencil_ir, func_ir, index_def):
+    """evaluate constant unary expr if possible
+    otherwise, raise GuardException
+    """
+    require(isinstance(index_def, ir.Expr) and index_def.op == 'unary')
+    inner_var = index_def.value
+    # return -c as constant
+    const_val = _get_const_index_expr_inner(stencil_ir, func_ir, inner_var)
+    op = OPERATORS_TO_BUILTINS[index_def.fn]
+    return eval("{}{}".format(op, const_val))
+
+def _get_const_binary_expr(stencil_ir, func_ir, index_def):
+    """evaluate constant binary expr if possible
+    otherwise, raise GuardException
+    """
+    require(isinstance(index_def, ir.Expr) and index_def.op == 'binop')
+    arg1 = _get_const_index_expr_inner(stencil_ir, func_ir, index_def.lhs)
+    arg2 = _get_const_index_expr_inner(stencil_ir, func_ir, index_def.rhs)
+    op = OPERATORS_TO_BUILTINS[index_def.fn]
+    return eval("{}{}{}".format(arg1, op, arg2))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/__init__.py
new file mode 100644
index 000000000..4eb89a92f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/__init__.py
@@ -0,0 +1,61 @@
+import os
+import sys
+import functools
+import unittest
+import traceback
+from fnmatch import fnmatch
+from os.path import join, isfile, relpath, normpath, splitext
+
+from .main import NumbaTestProgram, SerialSuite, make_tag_decorator
+from numba.core import config
+
+
+def load_testsuite(loader, dir):
+    """Find tests in 'dir'."""
+    try:
+        suite = unittest.TestSuite()
+        files = []
+        for f in os.listdir(dir):
+            path = join(dir, f)
+            if isfile(path) and fnmatch(f, 'test_*.py'):
+                files.append(f)
+            elif isfile(join(path, '__init__.py')):
+                suite.addTests(loader.discover(path))
+        for f in files:
+            # turn 'f' into a filename relative to the toplevel dir...
+            f = relpath(join(dir, f), loader._top_level_dir)
+            # ...and translate it to a module name.
+            f = splitext(normpath(f.replace(os.path.sep, '.')))[0]
+            suite.addTests(loader.loadTestsFromName(f))
+        return suite
+    except Exception:
+        traceback.print_exc(file=sys.stderr)
+        sys.exit(-1)
+
+
+def run_tests(argv=None, defaultTest=None, topleveldir=None,
+              xmloutput=None, verbosity=1, nomultiproc=False):
+    """
+    args
+    ----
+    - xmloutput [str or None]
+        Path of XML output directory (optional)
+    - verbosity [int]
+        Verbosity level of tests output
+
+    Returns the TestResult object after running the test *suite*.
+    """
+
+    if xmloutput is not None:
+        import xmlrunner
+        runner = xmlrunner.XMLTestRunner(output=xmloutput)
+    else:
+        runner = None
+    prog = NumbaTestProgram(argv=argv,
+                            module=None,
+                            defaultTest=defaultTest,
+                            topleveldir=topleveldir,
+                            testRunner=runner, exit=False,
+                            verbosity=verbosity,
+                            nomultiproc=nomultiproc)
+    return prog.result
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/__main__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/__main__.py
new file mode 100644
index 000000000..964b0ce91
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/__main__.py
@@ -0,0 +1,4 @@
+import sys
+from numba.testing import run_tests
+
+sys.exit(0 if run_tests(sys.argv).wasSuccessful() else 1)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/_runtests.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/_runtests.py
new file mode 100644
index 000000000..7df2bd743
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/_runtests.py
@@ -0,0 +1,114 @@
+import json
+import re
+import logging
+
+
+def _main(argv, **kwds):
+    from numba.testing import run_tests
+    # This helper function assumes the first element of argv
+    # is the name of the calling program.
+    # The 'main' API function is invoked in-process, and thus
+    # will synthesize that name.
+
+    if '--log' in argv:
+        logging.basicConfig(level=logging.DEBUG)
+        argv.remove('--log')
+
+    if '--failed-first' in argv:
+        # Failed first
+        argv.remove('--failed-first')
+        return _FailedFirstRunner().main(argv, kwds)
+    elif '--last-failed' in argv:
+        argv.remove('--last-failed')
+        return _FailedFirstRunner(last_failed=True).main(argv, kwds)
+    else:
+        return run_tests(argv, defaultTest='numba.tests',
+                         **kwds).wasSuccessful()
+
+
+def main(*argv, **kwds):
+    """keyword arguments are accepted for backward compatibility only.
+    See `numba.testing.run_tests()` documentation for details."""
+    return _main(['<main>'] + list(argv), **kwds)
+
+
+class _FailedFirstRunner(object):
+    """
+    Test Runner to handle the failed-first (--failed-first) option.
+    """
+    cache_filename = '.runtests_lastfailed'
+
+    def __init__(self, last_failed=False):
+        self.last_failed = last_failed
+
+    def main(self, argv, kwds):
+        from numba.testing import run_tests
+        prog = argv[0]
+        argv = argv[1:]
+        flags = [a for a in argv if a.startswith('-')]
+
+        all_tests, failed_tests = self.find_last_failed(argv)
+        # Prepare tests to run
+        if failed_tests:
+            ft = "There were {} previously failed tests"
+            print(ft.format(len(failed_tests)))
+            remaing_tests = [t for t in all_tests
+                             if t not in failed_tests]
+            if self.last_failed:
+                tests = list(failed_tests)
+            else:
+                tests = failed_tests + remaing_tests
+        else:
+            if self.last_failed:
+                tests = []
+            else:
+                tests = list(all_tests)
+
+        if not tests:
+            print("No tests to run")
+            return True
+        # Run the testsuite
+        print("Running {} tests".format(len(tests)))
+        print('Flags', flags)
+        result = run_tests([prog] + flags + tests, **kwds)
+        # Update failed tests records only if we have run the all the tests
+        # last failed.
+        if len(tests) == result.testsRun:
+            self.save_failed_tests(result, all_tests)
+        return result.wasSuccessful()
+
+    def save_failed_tests(self, result, all_tests):
+        print("Saving failed tests to {}".format(self.cache_filename))
+        cache = []
+        # Find failed tests
+        failed = set()
+        for case in result.errors + result.failures:
+            failed.add(case[0].id())
+        # Build cache
+        for t in all_tests:
+            if t in failed:
+                cache.append(t)
+        # Write cache
+        with open(self.cache_filename, 'w') as fout:
+            json.dump(cache, fout)
+
+    def find_last_failed(self, argv):
+        from numba.tests.support import captured_output
+
+        # Find all tests
+        listargv = ['-l'] + [a for a in argv if not a.startswith('-')]
+        with captured_output("stdout") as stream:
+            main(*listargv)
+
+            pat = re.compile(r"^(\w+\.)+\w+$")
+            lines = stream.getvalue().splitlines()
+        all_tests = [x for x in lines if pat.match(x) is not None]
+
+        try:
+            fobj = open(self.cache_filename)
+        except OSError:
+            failed_tests = []
+        else:
+            with fobj as fin:
+                failed_tests = json.load(fin)
+        return all_tests, failed_tests
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/loader.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/loader.py
new file mode 100644
index 000000000..66ba417d6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/loader.py
@@ -0,0 +1,26 @@
+from unittest import loader, case
+from os.path import isdir, isfile, join, dirname, basename
+
+
+class TestLoader(loader.TestLoader):
+
+    def __init__(self, topleveldir=None):
+        super(TestLoader, self).__init__()
+        self._top_level_dir = topleveldir or dirname(dirname(dirname(__file__)))
+
+    def _find_tests(self, start_dir, pattern, namespace=False):
+        # Upstream doesn't look for 'load_tests' in start_dir.
+
+        if isdir(start_dir) and not namespace and isfile(join(start_dir, '__init__.py')):
+            name = self._get_name_from_path(start_dir)
+            package = self._get_module_from_name(name)
+            load_tests = getattr(package, 'load_tests', None)
+            tests = self.loadTestsFromModule(package)
+            if load_tests is not None:
+                try:
+                    yield load_tests(self, tests, pattern)
+                except Exception as e:
+                    yield loader._make_failed_load_tests(package.__name__, e, self.suiteClass)
+        else:
+            for t in super(TestLoader, self)._find_tests(start_dir, pattern):
+                yield t
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/main.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/main.py
new file mode 100644
index 000000000..7d9a48b88
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/main.py
@@ -0,0 +1,796 @@
+import collections
+import contextlib
+import cProfile
+import inspect
+import gc
+import multiprocessing
+import os
+import random
+import sys
+import time
+import unittest
+import warnings
+
+from io import StringIO
+from unittest import result, runner, signals, suite, loader, case
+
+from .loader import TestLoader
+from numba.core import config
+
+try:
+    from multiprocessing import TimeoutError
+except ImportError:
+    from Queue import Empty as TimeoutError
+
+
+def make_tag_decorator(known_tags):
+    """
+    Create a decorator allowing tests to be tagged with the *known_tags*.
+    """
+
+    def tag(*tags):
+        """
+        Tag a test method with the given tags.
+        Can be used in conjunction with the --tags command-line argument
+        for runtests.py.
+        """
+        for t in tags:
+            if t not in known_tags:
+                raise ValueError("unknown tag: %r" % (t,))
+
+        def decorate(func):
+            if (not callable(func) or isinstance(func, type)
+                or not func.__name__.startswith('test_')):
+                raise TypeError("@tag(...) should be used on test methods")
+            try:
+                s = func.tags
+            except AttributeError:
+                s = func.tags = set()
+            s.update(tags)
+            return func
+        return decorate
+
+    return tag
+
+
+def cuda_sensitive_mtime(x):
+    """
+    Return a key for sorting tests bases on mtime and test name. For CUDA
+    tests, interleaving tests from different classes is dangerous as the CUDA
+    context might get reset unexpectedly between methods of a class, so for
+    CUDA tests the key prioritises the test module and class ahead of the
+    mtime.
+    """
+    cls = x.__class__
+    key = str(os.path.getmtime(inspect.getfile(cls))) + str(x)
+
+    from numba.cuda.testing import CUDATestCase
+    if CUDATestCase in cls.mro():
+        key = "%s.%s %s" % (str(cls.__module__), str(cls.__name__), key)
+
+    return key
+
+def parse_slice(useslice):
+    """Parses the argument string "useslice" as the arguments to the `slice()`
+    constructor and returns a slice object that's been instantiated with those
+    arguments. i.e. input useslice="1,20,2" leads to output `slice(1, 20, 2)`.
+    """
+    try:
+        l = {}
+        exec("sl = slice(%s)" % useslice, l)
+        return l['sl']
+    except Exception:
+        msg = ("Expected arguments consumable by 'slice' to follow "
+                "option `-j`, found '%s'" % useslice)
+        raise ValueError(msg)
+
+
+class TestLister(object):
+    """Simply list available tests rather than running them."""
+    def __init__(self, useslice):
+        self.useslice = parse_slice(useslice)
+
+    def run(self, test):
+        result = runner.TextTestResult(sys.stderr, descriptions=True, verbosity=1)
+        self._test_list = _flatten_suite(test)
+        masked_list = self._test_list[self.useslice]
+        self._test_list.sort(key=cuda_sensitive_mtime)
+        for t in masked_list:
+            print(t.id())
+        print('%d tests found. %s selected' % (len(self._test_list), len(masked_list)))
+        return result
+
+
+class SerialSuite(unittest.TestSuite):
+    """A simple marker to make sure tests in this suite are run serially.
+
+    Note: As the suite is going through internals of unittest,
+          it may get unpacked and stuffed into a plain TestSuite.
+          We need to set an attribute on the TestCase objects to
+          remember they should not be run in parallel.
+    """
+
+    def addTest(self, test):
+        if not isinstance(test, unittest.TestCase):
+            # It's a sub-suite, recurse
+            for t in test:
+                self.addTest(t)
+        else:
+            # It's a test case, mark it serial
+            test._numba_parallel_test_ = False
+            super(SerialSuite, self).addTest(test)
+
+
+class BasicTestRunner(runner.TextTestRunner):
+    def __init__(self, useslice, **kwargs):
+        runner.TextTestRunner.__init__(self, **kwargs)
+        self.useslice = parse_slice(useslice)
+
+    def run(self, test):
+        run = _flatten_suite(test)[self.useslice]
+        run.sort(key=cuda_sensitive_mtime)
+        wrapped = unittest.TestSuite(run)
+        return super(BasicTestRunner, self).run(wrapped)
+
+
+# "unittest.main" is really the TestProgram class!
+# (defined in a module named itself "unittest.main"...)
+
+class NumbaTestProgram(unittest.main):
+    """
+    A TestProgram subclass adding the following options:
+    * a -R option to enable reference leak detection
+    * a --profile option to enable profiling of the test run
+    * a -m option for parallel execution
+    * a -l option to (only) list tests
+
+    Currently the options are only added in 3.4+.
+    """
+
+    refleak = False
+    profile = False
+    multiprocess = False
+    useslice = None
+    list = False
+    tags = None
+    exclude_tags = None
+    random_select = None
+    random_seed = 42
+
+    def __init__(self, *args, **kwargs):
+        topleveldir = kwargs.pop('topleveldir', None)
+        kwargs['testLoader'] = TestLoader(topleveldir)
+
+        # HACK to force unittest not to change warning display options
+        # (so that NumbaWarnings don't appear all over the place)
+        sys.warnoptions.append(':x')
+        self.nomultiproc = kwargs.pop('nomultiproc', False)
+        super(NumbaTestProgram, self).__init__(*args, **kwargs)
+
+    def _getParentArgParser(self):
+        # NOTE: this hook only exists on Python 3.4+. The options won't be
+        # added in earlier versions (which use optparse - 3.3 - or getopt()
+        # - 2.x).
+        parser = super(NumbaTestProgram, self)._getParentArgParser()
+        if self.testRunner is None:
+            parser.add_argument('-R', '--refleak', dest='refleak',
+                                action='store_true',
+                                help='Detect reference / memory leaks')
+        parser.add_argument('-m', '--multiprocess', dest='multiprocess',
+                            nargs='?',
+                            type=int,
+                            const=multiprocessing.cpu_count(),
+                            help='Parallelize tests')
+        parser.add_argument('-l', '--list', dest='list',
+                            action='store_true',
+                            help='List tests without running them')
+        parser.add_argument('--tags', dest='tags', type=str,
+                            help='Comma-separated list of tags to select '
+                                 'a subset of the test suite')
+        parser.add_argument('--exclude-tags', dest='exclude_tags', type=str,
+                            help='Comma-separated list of tags to de-select '
+                                 'a subset of the test suite')
+        parser.add_argument('--random', dest='random_select', type=float,
+                            help='Random proportion of tests to select')
+        parser.add_argument('--profile', dest='profile',
+                            action='store_true',
+                            help='Profile the test run')
+        parser.add_argument('-j', '--slice', dest='useslice', nargs='?',
+                            type=str, const="None",
+                            help='Slice the test sequence')
+
+        def git_diff_str(x):
+            if x != 'ancestor':
+                raise ValueError("invalid option for --gitdiff")
+            return x
+
+        parser.add_argument('-g', '--gitdiff', dest='gitdiff', type=git_diff_str,
+                            default=False, nargs='?',
+                            help=('Run tests from changes made against '
+                                  'origin/release0.56 as identified by'
+                                  '`git diff`. '
+                                  'If set to "ancestor", the diff compares '
+                                  'against the common ancestor.'))
+        return parser
+
+    def _handle_tags(self, argv, tagstr):
+        found = None
+        for x in argv:
+            if tagstr in x:
+                if found is None:
+                    found = x
+                else:
+                    raise ValueError("argument %s supplied repeatedly" % tagstr)
+
+        if found is not None:
+            posn = argv.index(found)
+            try:
+                if found == tagstr: # --tagstr <arg>
+                    tag_args = argv[posn + 1].strip()
+                    argv.remove(tag_args)
+                else: # --tagstr=<arg>
+                    if '=' in found:
+                        tag_args =  found.split('=')[1].strip()
+                    else:
+                        raise AssertionError('unreachable')
+            except IndexError:
+                # at end of arg list, raise
+                msg = "%s requires at least one tag to be specified"
+                raise ValueError(msg % tagstr)
+            # see if next arg is "end options" or some other flag
+            if tag_args.startswith('-'):
+                raise ValueError("tag starts with '-', probably a syntax error")
+            # see if tag is something like "=<tagname>" which is likely a syntax
+            # error of form `--tags =<tagname>`, note the space prior to `=`.
+            if '=' in tag_args:
+                msg = "%s argument contains '=', probably a syntax error"
+                raise ValueError(msg % tagstr)
+            attr = tagstr[2:].replace('-', '_')
+            setattr(self, attr, tag_args)
+            argv.remove(found)
+
+
+    def parseArgs(self, argv):
+        if '-l' in argv:
+            argv.remove('-l')
+            self.list = True
+
+        super(NumbaTestProgram, self).parseArgs(argv)
+
+        # If at this point self.test doesn't exist, it is because
+        # no test ID was given in argv. Use the default instead.
+        if not hasattr(self, 'test') or not self.test.countTestCases():
+            self.testNames = (self.defaultTest,)
+            self.createTests()
+
+        if self.tags:
+            tags = [s.strip() for s in self.tags.split(',')]
+            self.test = _choose_tagged_tests(self.test, tags, mode='include')
+
+        if self.exclude_tags:
+            tags = [s.strip() for s in self.exclude_tags.split(',')]
+            self.test = _choose_tagged_tests(self.test, tags, mode='exclude')
+
+        if self.random_select:
+            self.test = _choose_random_tests(self.test, self.random_select,
+                                             self.random_seed)
+
+        if self.gitdiff is not False:
+            self.test = _choose_gitdiff_tests(
+                self.test,
+                use_common_ancestor=(self.gitdiff == 'ancestor'),
+            )
+
+        if self.verbosity <= 0:
+            # We aren't interested in informational messages / warnings when
+            # running with '-q'.
+            self.buffer = True
+
+    def _do_discovery(self, argv, Loader=None):
+        # Disable unittest's implicit test discovery when parsing
+        # CLI arguments, as it can select other tests than Numba's
+        # (e.g. some test_xxx module that may happen to be directly
+        #  reachable from sys.path)
+        return
+
+    def runTests(self):
+        if self.refleak:
+            self.testRunner = RefleakTestRunner
+
+            if not hasattr(sys, "gettotalrefcount"):
+                warnings.warn("detecting reference leaks requires a debug build "
+                              "of Python, only memory leaks will be detected")
+
+        elif self.list:
+            self.testRunner = TestLister(self.useslice)
+
+        elif self.testRunner is None:
+            self.testRunner = BasicTestRunner(self.useslice,
+                                              verbosity=self.verbosity,
+                                              failfast=self.failfast,
+                                              buffer=self.buffer)
+
+        if self.multiprocess and not self.nomultiproc:
+            if self.multiprocess < 1:
+                msg = ("Value specified for the number of processes to use in "
+                    "running the suite must be > 0")
+                raise ValueError(msg)
+            self.testRunner = ParallelTestRunner(runner.TextTestRunner,
+                                                 self.multiprocess,
+                                                 self.useslice,
+                                                 verbosity=self.verbosity,
+                                                 failfast=self.failfast,
+                                                 buffer=self.buffer)
+
+        def run_tests_real():
+            super(NumbaTestProgram, self).runTests()
+
+        if self.profile:
+            filename = os.path.splitext(
+                os.path.basename(sys.modules['__main__'].__file__)
+                )[0] + '.prof'
+            p = cProfile.Profile(timer=time.perf_counter)  # 3.3+
+            p.enable()
+            try:
+                p.runcall(run_tests_real)
+            finally:
+                p.disable()
+                print("Writing test profile data into %r" % (filename,))
+                p.dump_stats(filename)
+        else:
+            run_tests_real()
+
+
+# These are tests which are generated and injected into the test suite, what
+# gets injected depends on features of the test environment, e.g. TBB presence
+# it's important for doing the CI "slice tests" that these are run at the end
+# See notes in `_flatten_suite` for why. Simple substring matching is used to
+# determine a match.
+_GENERATED = (
+    "numba.cuda.tests.cudapy.test_libdevice.TestLibdeviceCompilation",
+    "numba.tests.test_num_threads",
+    "numba.tests.test_parallel_backend",
+    "numba.tests.test_svml",
+    "numba.tests.test_ufuncs",
+)
+
+
+def _flatten_suite_inner(test):
+    """
+    Workhorse for _flatten_suite
+    """
+    tests = []
+    if isinstance(test, (unittest.TestSuite, list, tuple)):
+        for x in test:
+            tests.extend(_flatten_suite_inner(x))
+    else:
+        tests.append(test)
+    return tests
+
+
+def _flatten_suite(test):
+    """
+    Expand nested suite into list of test cases.
+    """
+    tests = _flatten_suite_inner(test)
+    # Strip out generated tests and stick them at the end, this is to make sure
+    # that tests appear in a consistent order regardless of features available.
+    # This is so that a slice through the test suite e.g. (1::N) would likely be
+    # consistent up to the point of the generated tests, which rely on specific
+    # features.
+    generated = set()
+    for t in tests:
+        for g in _GENERATED:
+            if g in str(t):
+                generated.add(t)
+    normal = set(tests) - generated
+    def key(x):
+        return x.__module__, type(x).__name__, x._testMethodName
+    tests = sorted(normal, key=key)
+    tests.extend(sorted(list(generated), key=key))
+    return tests
+
+
+def _choose_gitdiff_tests(tests, *, use_common_ancestor=False):
+    try:
+        from git import Repo
+    except ImportError:
+        raise ValueError("gitpython needed for git functionality")
+    repo = Repo('.')
+    path = os.path.join('numba', 'tests')
+    if use_common_ancestor:
+        print(f"Git diff by common ancestor")
+        target = 'origin/release0.56...HEAD'
+    else:
+        target = 'origin/release0.56..HEAD'
+    gdiff_paths = repo.git.diff(target, path, name_only=True).split()
+    # normalise the paths as they are unix style from repo.git.diff
+    gdiff_paths = [os.path.normpath(x) for x in gdiff_paths]
+    selected = []
+    gdiff_paths = [os.path.join(repo.working_dir, x) for x in gdiff_paths]
+    for test in _flatten_suite(tests):
+        assert isinstance(test, unittest.TestCase)
+        fname = inspect.getsourcefile(test.__class__)
+        if fname in gdiff_paths:
+            selected.append(test)
+    print("Git diff identified %s tests" % len(selected))
+    return unittest.TestSuite(selected)
+
+def _choose_tagged_tests(tests, tags, mode='include'):
+    """
+    Select tests that are tagged/not tagged with at least one of the given tags.
+    Set mode to 'include' to include the tests with tags, or 'exclude' to
+    exclude the tests with the tags.
+    """
+    selected = []
+    tags = set(tags)
+    for test in _flatten_suite(tests):
+        assert isinstance(test, unittest.TestCase)
+        func = getattr(test, test._testMethodName)
+        try:
+            # Look up the method's underlying function (Python 2)
+            func = func.im_func
+        except AttributeError:
+            pass
+
+        found_tags = getattr(func, 'tags', None)
+        # only include the test if the tags *are* present
+        if mode == 'include':
+            if found_tags is not None and found_tags & tags:
+                selected.append(test)
+        elif mode == 'exclude':
+            # only include the test if the tags *are not* present
+            if found_tags is None or not (found_tags & tags):
+                selected.append(test)
+        else:
+            raise ValueError("Invalid 'mode' supplied: %s." % mode)
+    return unittest.TestSuite(selected)
+
+
+def _choose_random_tests(tests, ratio, seed):
+    """
+    Choose a given proportion of tests at random.
+    """
+    rnd = random.Random()
+    rnd.seed(seed)
+    if isinstance(tests, unittest.TestSuite):
+        tests = _flatten_suite(tests)
+    tests = rnd.sample(tests, int(len(tests) * ratio))
+    tests = sorted(tests, key=lambda case: case.id())
+    return unittest.TestSuite(tests)
+
+
+# The reference leak detection code is liberally taken and adapted from
+# Python's own Lib/test/regrtest.py.
+
+def _refleak_cleanup():
+    # Collect cyclic trash and read memory statistics immediately after.
+    func1 = sys.getallocatedblocks
+    try:
+        func2 = sys.gettotalrefcount
+    except AttributeError:
+        func2 = lambda: 42
+
+    # Flush standard output, so that buffered data is sent to the OS and
+    # associated Python objects are reclaimed.
+    for stream in (sys.stdout, sys.stderr, sys.__stdout__, sys.__stderr__):
+        if stream is not None:
+            stream.flush()
+
+    sys._clear_type_cache()
+    # This also clears the various internal CPython freelists.
+    gc.collect()
+    return func1(), func2()
+
+
+class ReferenceLeakError(RuntimeError):
+    pass
+
+
+class IntPool(collections.defaultdict):
+
+    def __missing__(self, key):
+        return key
+
+
+class RefleakTestResult(runner.TextTestResult):
+
+    warmup = 3
+    repetitions = 6
+
+    def _huntLeaks(self, test):
+        self.stream.flush()
+
+        repcount = self.repetitions
+        nwarmup = self.warmup
+        rc_deltas = [0] * (repcount - nwarmup)
+        alloc_deltas = [0] * (repcount - nwarmup)
+        # Preallocate ints likely to be stored in rc_deltas and alloc_deltas,
+        # to make sys.getallocatedblocks() less flaky.
+        _int_pool = IntPool()
+        for i in range(-200, 200):
+            _int_pool[i]
+
+        for i in range(repcount):
+            # Use a pristine, silent result object to avoid recursion
+            res = result.TestResult()
+            test.run(res)
+            # Poorly-written tests may fail when run several times.
+            # In this case, abort the refleak run and report the failure.
+            if not res.wasSuccessful():
+                self.failures.extend(res.failures)
+                self.errors.extend(res.errors)
+                raise AssertionError
+            del res
+            alloc_after, rc_after = _refleak_cleanup()
+            if i >= nwarmup:
+                rc_deltas[i - nwarmup] = _int_pool[rc_after - rc_before]
+                alloc_deltas[i - nwarmup] = _int_pool[alloc_after - alloc_before]
+            alloc_before, rc_before = alloc_after, rc_after
+        return rc_deltas, alloc_deltas
+
+    def addSuccess(self, test):
+        try:
+            rc_deltas, alloc_deltas = self._huntLeaks(test)
+        except AssertionError:
+            # Test failed when repeated
+            assert not self.wasSuccessful()
+            return
+
+        # These checkers return False on success, True on failure
+        def check_rc_deltas(deltas):
+            return any(deltas)
+
+        def check_alloc_deltas(deltas):
+            # At least 1/3rd of 0s
+            if 3 * deltas.count(0) < len(deltas):
+                return True
+            # Nothing else than 1s, 0s and -1s
+            if not set(deltas) <= set((1, 0, -1)):
+                return True
+            return False
+
+        failed = False
+
+        for deltas, item_name, checker in [
+            (rc_deltas, 'references', check_rc_deltas),
+            (alloc_deltas, 'memory blocks', check_alloc_deltas)]:
+            if checker(deltas):
+                msg = '%s leaked %s %s, sum=%s' % (
+                    test, deltas, item_name, sum(deltas))
+                failed = True
+                try:
+                    raise ReferenceLeakError(msg)
+                except Exception:
+                    exc_info = sys.exc_info()
+                if self.showAll:
+                    self.stream.write("%s = %r " % (item_name, deltas))
+                self.addFailure(test, exc_info)
+
+        if not failed:
+            super(RefleakTestResult, self).addSuccess(test)
+
+
+class RefleakTestRunner(runner.TextTestRunner):
+    resultclass = RefleakTestResult
+
+
+class ParallelTestResult(runner.TextTestResult):
+    """
+    A TestResult able to inject results from other results.
+    """
+
+    def add_results(self, result):
+        """
+        Add the results from the other *result* to this result.
+        """
+        self.stream.write(result.stream.getvalue())
+        self.stream.flush()
+        self.testsRun += result.testsRun
+        self.failures.extend(result.failures)
+        self.errors.extend(result.errors)
+        self.skipped.extend(result.skipped)
+        self.expectedFailures.extend(result.expectedFailures)
+        self.unexpectedSuccesses.extend(result.unexpectedSuccesses)
+
+
+class _MinimalResult(object):
+    """
+    A minimal, picklable TestResult-alike object.
+    """
+
+    __slots__ = (
+        'failures', 'errors', 'skipped', 'expectedFailures',
+        'unexpectedSuccesses', 'stream', 'shouldStop', 'testsRun',
+        'test_id')
+
+    def fixup_case(self, case):
+        """
+        Remove any unpicklable attributes from TestCase instance *case*.
+        """
+        # Python 3.3 doesn't reset this one.
+        case._outcomeForDoCleanups = None
+
+    def __init__(self, original_result, test_id=None):
+        for attr in self.__slots__:
+            setattr(self, attr, getattr(original_result, attr, None))
+        for case, _ in self.expectedFailures:
+            self.fixup_case(case)
+        for case, _ in self.errors:
+            self.fixup_case(case)
+        for case, _ in self.failures:
+            self.fixup_case(case)
+        self.test_id = test_id
+
+
+class _FakeStringIO(object):
+    """
+    A trivial picklable StringIO-alike for Python 2.
+    """
+
+    def __init__(self, value):
+        self._value = value
+
+    def getvalue(self):
+        return self._value
+
+
+class _MinimalRunner(object):
+    """
+    A minimal picklable object able to instantiate a runner in a
+    child process and run a test case with it.
+    """
+
+    def __init__(self, runner_cls, runner_args):
+        self.runner_cls = runner_cls
+        self.runner_args = runner_args
+
+    # Python 2 doesn't know how to pickle instance methods, so we use __call__
+    # instead.
+
+    def __call__(self, test):
+        # Executed in child process
+        kwargs = self.runner_args
+        # Force recording of output in a buffer (it will be printed out
+        # by the parent).
+        kwargs['stream'] = StringIO()
+        runner = self.runner_cls(**kwargs)
+        result = runner._makeResult()
+        # Avoid child tracebacks when Ctrl-C is pressed.
+        signals.installHandler()
+        signals.registerResult(result)
+        result.failfast = runner.failfast
+        result.buffer = runner.buffer
+        with self.cleanup_object(test):
+            test(result)
+        # HACK as cStringIO.StringIO isn't picklable in 2.x
+        result.stream = _FakeStringIO(result.stream.getvalue())
+        return _MinimalResult(result, test.id())
+
+    @contextlib.contextmanager
+    def cleanup_object(self, test):
+        """
+        A context manager which cleans up unwanted attributes on a test case
+        (or any other object).
+        """
+        vanilla_attrs = set(test.__dict__)
+        try:
+            yield test
+        finally:
+            spurious_attrs = set(test.__dict__) - vanilla_attrs
+            for name in spurious_attrs:
+                del test.__dict__[name]
+
+
+def _split_nonparallel_tests(test, sliced=slice(None)):
+    """
+    Split test suite into parallel and serial tests.
+    """
+    ptests = []
+    stests = []
+
+    flat = _flatten_suite(test)[sliced]
+
+    def is_parallelizable_test_case(test):
+        # Guard for the fake test case created by unittest when test
+        # discovery fails, as it isn't picklable (e.g. "LoadTestsFailure")
+        method_name = test._testMethodName
+        method = getattr(test, method_name)
+        if method.__name__ != method_name and method.__name__ == "testFailure":
+            return False
+        # Was parallel execution explicitly disabled?
+        return getattr(test, "_numba_parallel_test_", True)
+
+    for t in flat:
+        if is_parallelizable_test_case(t):
+            ptests.append(t)
+        else:
+            stests.append(t)
+
+    return ptests, stests
+
+# A test can't run longer than 10 minutes
+_TIMEOUT = 600
+
+class ParallelTestRunner(runner.TextTestRunner):
+    """
+    A test runner which delegates the actual running to a pool of child
+    processes.
+    """
+
+    resultclass = ParallelTestResult
+    timeout = _TIMEOUT
+
+    def __init__(self, runner_cls, nprocs, useslice, **kwargs):
+        runner.TextTestRunner.__init__(self, **kwargs)
+        self.runner_cls = runner_cls
+        self.nprocs = nprocs
+        self.useslice = parse_slice(useslice)
+        self.runner_args = kwargs
+
+    def _run_inner(self, result):
+        # We hijack TextTestRunner.run()'s inner logic by passing this
+        # method as if it were a test case.
+        child_runner = _MinimalRunner(self.runner_cls, self.runner_args)
+
+        # Split the tests and recycle the worker process to tame memory usage.
+        chunk_size = 100
+        splitted_tests = [self._ptests[i:i + chunk_size]
+                          for i in range(0, len(self._ptests), chunk_size)]
+
+        for tests in splitted_tests:
+            pool = multiprocessing.Pool(self.nprocs)
+            try:
+                self._run_parallel_tests(result, pool, child_runner, tests)
+            except:
+                # On exception, kill still active workers immediately
+                pool.terminate()
+                # Make sure exception is reported and not ignored
+                raise
+            else:
+                # Close the pool cleanly unless asked to early out
+                if result.shouldStop:
+                    pool.terminate()
+                    break
+                else:
+                    pool.close()
+            finally:
+                # Always join the pool (this is necessary for coverage.py)
+                pool.join()
+        if not result.shouldStop:
+            stests = SerialSuite(self._stests)
+            stests.run(result)
+            return result
+
+    def _run_parallel_tests(self, result, pool, child_runner, tests):
+        remaining_ids = set(t.id() for t in tests)
+        tests.sort(key=cuda_sensitive_mtime)
+        it = pool.imap_unordered(child_runner, tests)
+        while True:
+            try:
+                child_result = it.__next__(self.timeout)
+            except StopIteration:
+                return
+            except TimeoutError as e:
+                # Diagnose the names of unfinished tests
+                msg = ("Tests didn't finish before timeout (or crashed):\n%s"
+                       % "".join("- %r\n" % tid for tid in sorted(remaining_ids))
+                       )
+                e.args = (msg,) + e.args[1:]
+                raise e
+            else:
+                result.add_results(child_result)
+                remaining_ids.discard(child_result.test_id)
+                if child_result.shouldStop:
+                    result.shouldStop = True
+                    return
+
+    def run(self, test):
+        self._ptests, self._stests = _split_nonparallel_tests(test,
+                                                              sliced=
+                                                              self.useslice)
+        print("Parallel: %s. Serial: %s" % (len(self._ptests),
+                                            len(self._stests)))
+        # This will call self._run_inner() on the created result object,
+        # and print out the detailed test results at the end.
+        return super(ParallelTestRunner, self).run(self._run_inner)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/notebook.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/notebook.py
new file mode 100644
index 000000000..646a92d73
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/testing/notebook.py
@@ -0,0 +1,171 @@
+from unittest import TestCase
+
+from ipykernel.tests import utils
+from nbformat.converter import convert
+from nbformat.reader import reads
+
+import re
+import json
+from copy import copy
+import unittest
+
+try:
+    # py3
+    from queue import Empty
+
+    def isstr(s):
+        return isinstance(s, str)
+except ImportError:
+    # py2
+    from Queue import Empty
+
+    def isstr(s):
+        return isinstance(s, basestring)  # noqa
+
+class NotebookTest(TestCase):
+    """Validate a notebook. All code cells are executed in order. The output is either checked
+    for errors (if no reference output is present), or is compared against expected output.
+
+
+    Useful references:
+    http://nbformat.readthedocs.org/en/latest/format_description.html
+    http://jupyter-client.readthedocs.org/en/latest/messaging.html
+"""
+
+
+    IGNORE_TYPES = ["execute_request", "execute_input", "status", "pyin"]
+    STRIP_KEYS = ["execution_count", "traceback", "prompt_number", "source"]
+    NBFORMAT_VERSION = 4
+
+    def _test_notebook(self, notebook, test):
+
+        with open(notebook) as f:
+            nb = convert(reads(f.read()), self.NBFORMAT_VERSION)
+        _, kernel = utils.start_new_kernel()
+        for i, c in enumerate([c for c in nb.cells if c.cell_type == 'code']):
+            self._test_notebook_cell(self.sanitize_cell(c), i, kernel, test)
+
+    def _test_notebook_cell(self, cell, i, kernel, test):
+
+        if hasattr(cell, 'source'): # nbformat 4.0 and later
+            code = cell.source
+        else:
+            code = cell.input
+        iopub = kernel.iopub_channel
+        kernel.execute(code)
+        outputs = []
+        msg = None
+        no_error = True
+        first_error = -1
+        error_msg = ''
+        while self.should_continue(msg):
+            try:
+                msg = iopub.get_msg(block=True, timeout=1)
+            except Empty:
+                continue
+            if msg['msg_type'] not in self.IGNORE_TYPES:
+                if msg['msg_type'] == 'error':
+                    error_msg = '  ' + msg['content']['ename'] + '\n  ' + msg['content']['evalue']
+                    no_error = False
+                    if first_error == -1:
+                        first_error = i
+                i = len(outputs)
+                expected = i < len(cell.outputs) and cell.outputs[i] or []
+                o = self.transform_message(msg, expected)
+                outputs.append(o)
+
+        if (test == 'check_error'):
+            self.assertTrue(no_error, 'Executing cell %d resulted in an error:\n%s'%(first_error, error_msg))
+        else:
+            # Compare computed output against stored output.
+            # TODO: This doesn't work right now as the generated output is too diverse to
+            #       be verifiable.
+            scrub = lambda x: self.dump_canonical(list(self.scrub_outputs(x)))
+            scrubbed = scrub(outputs)
+            expected = scrub(cell.outputs)
+            #print('output=%s'%outputs)
+            #print('expected=%s'%expected)
+            #self.assertEqual(scrubbed, expected, "\n{}\n\n{}".format(scrubbed, expected))
+        
+    def dump_canonical(self, obj):
+        return json.dumps(obj, indent=2, sort_keys=True)
+
+    def scrub_outputs(self, outputs):
+        """
+        remove all scrubs from output data and text
+        """
+        for output in outputs:
+            out = copy(output)
+
+            for scrub, sub in []:#self.scrubs.items():
+                def _scrubLines(lines):
+                    if isstr(lines):
+                        return re.sub(scrub, sub, lines)
+                    else:
+                        return [re.sub(scrub, sub, line) for line in lines]
+
+                if "text" in out:
+                    out["text"] = _scrubLines(out["text"])
+
+                if "data" in out:
+                    if isinstance(out["data"], dict):
+                        for mime, data in out["data"].items():
+                            out["data"][mime] = _scrubLines(data)
+                    else:
+                        out["data"] = _scrubLines(out["data"])
+            yield out
+
+    def strip_keys(self, d):
+        """
+        remove keys from STRIP_KEYS to ensure comparability
+        """
+        for key in self.STRIP_KEYS:
+            d.pop(key, None)
+        return d
+
+    def sanitize_cell(self, cell):
+        """
+        remove non-reproducible things
+        """
+        for output in cell.outputs:
+            self.strip_keys(output)
+        return cell
+
+    def transform_message(self, msg, expected):
+        """
+        transform a message into something like the notebook
+        """
+        SWAP_KEYS = {
+            "output_type": {
+                "pyout": "execute_result",
+                "pyerr": "error"
+            }
+        }
+
+        output = {
+            u"output_type": msg["msg_type"]
+        }
+        output.update(msg["content"])
+
+        output = self.strip_keys(output)
+        for key, swaps in SWAP_KEYS.items():
+            if key in output and output[key] in swaps:
+                output[key] = swaps[output[key]]
+
+        if "data" in output and "data" not in expected:
+            output["text"] = output["data"]
+            del output["data"]
+
+        return output
+
+    def should_continue(self, msg):
+        """
+        determine whether the current message is the last for this cell
+        """
+        if msg is None:
+            return True
+
+        return not (msg["msg_type"] == "status" and
+                    msg["content"]["execution_state"] == "idle")
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/__init__.py
new file mode 100644
index 000000000..d2a38c690
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/__init__.py
@@ -0,0 +1,33 @@
+import gc
+from os.path import dirname, join
+import multiprocessing
+import sys
+import time
+import unittest
+import warnings
+
+from unittest.suite import TestSuite
+from numba.testing import load_testsuite
+
+
+try:
+    import faulthandler
+except ImportError:
+    faulthandler = None
+else:
+    try:
+        # May fail in IPython Notebook with UnsupportedOperation
+        faulthandler.enable()
+    except Exception as e:
+        msg = "Failed to enable faulthandler due to:\n{err}"
+        warnings.warn(msg.format(err=e))
+
+def load_tests(loader, tests, pattern):
+    suite = TestSuite()
+    suite.addTests(load_testsuite(loader, dirname(__file__)))
+    # Numba CUDA tests are located in a separate directory:
+    cuda_dir = join(dirname(dirname(__file__)), 'cuda/tests')
+    suite.addTests(loader.discover(cuda_dir))
+
+    return suite
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/annotation_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/annotation_usecases.py
new file mode 100644
index 000000000..a249ccbd5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/annotation_usecases.py
@@ -0,0 +1,16 @@
+"""
+Usecases with Python 3 function annotations.  This is a separate module
+in order to avoid syntax errors with Python 2.
+"""
+
+
+class AnnotatedClass:
+    """
+    A class with annotated methods.
+    """
+
+    def __init__(self, v: int):
+        self.x = v
+
+    def add(self, v: int) -> int:
+        return self.x + v
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cache_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cache_usecases.py
new file mode 100644
index 000000000..3f483959f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cache_usecases.py
@@ -0,0 +1,184 @@
+"""
+This file will be copied to a temporary directory in order to
+exercise caching compiled Numba functions.
+
+See test_dispatcher.py.
+"""
+
+import sys
+
+import numpy as np
+
+from numba import jit, generated_jit, prange
+from numba.core import types
+
+from numba.tests.ctypes_usecases import c_sin
+from numba.tests.support import TestCase, captured_stderr
+
+
+@jit(cache=True, nopython=True)
+def simple_usecase(x):
+    return x
+
+def simple_usecase_caller(x):
+    return simple_usecase(x)
+
+
+@jit(cache=True, nopython=True)
+def add_usecase(x, y):
+    return x + y + Z
+
+
+@jit(cache=True, forceobj=True)
+def add_objmode_usecase(x, y):
+    object()
+    return x + y + Z
+
+
+@jit(nopython=True)
+def add_nocache_usecase(x, y):
+    return x + y + Z
+
+
+@generated_jit(cache=True, nopython=True)
+def generated_usecase(x, y):
+    if isinstance(x, types.Complex):
+        def impl(x, y):
+            return x + y
+    else:
+        def impl(x, y):
+            return x - y
+    return impl
+
+
+@jit(cache=True, nopython=True)
+def inner(x, y):
+    return x + y + Z
+
+@jit(cache=True, nopython=True)
+def outer(x, y):
+    return inner(-y, x)
+
+@jit(cache=False, nopython=True)
+def outer_uncached(x, y):
+    return inner(-y, x)
+
+
+@jit(cache=True, forceobj=True)
+def looplifted(n):
+    object()
+    res = 0
+    for i in range(n):
+        res = res + i
+    return res
+
+
+@jit(cache=True, nopython=True)
+def use_c_sin(x):
+    return c_sin(x)
+
+@jit(cache=True, nopython=True)
+def use_c_sin_nest1(x):
+    return use_c_sin(x)
+
+@jit(cache=True, nopython=True)
+def use_c_sin_nest2(x):
+    return use_c_sin_nest1(x)
+
+
+@jit(cache=True, nopython=True)
+def ambiguous_function(x):
+    return x + 2
+
+renamed_function1 = ambiguous_function
+
+@jit(cache=True, nopython=True)
+def ambiguous_function(x):
+    return x + 6
+
+renamed_function2 = ambiguous_function
+
+
+def make_closure(x):
+    @jit(cache=True, nopython=True)
+    def closure(y):
+        return x + y
+
+    return closure
+
+closure1 = make_closure(3)
+closure2 = make_closure(5)
+closure3 = make_closure(7)
+closure4 = make_closure(9)
+
+
+biggie = np.arange(10**6)
+
+@jit(cache=True, nopython=True)
+def use_big_array():
+    return biggie
+
+
+Z = 1
+
+# Exercise returning a record instance.  This used to hardcode the dtype
+# pointer's value in the bitcode.
+
+packed_record_type = np.dtype([('a', np.int8), ('b', np.float64)])
+aligned_record_type = np.dtype([('a', np.int8), ('b', np.float64)], align=True)
+
+packed_arr = np.empty(2, dtype=packed_record_type)
+for i in range(packed_arr.size):
+    packed_arr[i]['a'] = i + 1
+    packed_arr[i]['b'] = i + 42.5
+
+aligned_arr = np.array(packed_arr, dtype=aligned_record_type)
+
+@jit(cache=True, nopython=True)
+def record_return(ary, i):
+    return ary[i]
+
+
+class _TestModule(TestCase):
+    """
+    Tests for functionality of this module's functions.
+    Note this does not define any "test_*" method, instead check_module()
+    should be called by hand.
+    """
+
+    def check_module(self, mod):
+        self.assertPreciseEqual(mod.add_usecase(2, 3), 6)
+        self.assertPreciseEqual(mod.add_objmode_usecase(2, 3), 6)
+        self.assertPreciseEqual(mod.outer_uncached(3, 2), 2)
+        self.assertPreciseEqual(mod.outer(3, 2), 2)
+        self.assertPreciseEqual(mod.generated_usecase(3, 2), 1)
+
+        packed_rec = mod.record_return(mod.packed_arr, 1)
+        self.assertPreciseEqual(tuple(packed_rec), (2, 43.5))
+        aligned_rec = mod.record_return(mod.aligned_arr, 1)
+        self.assertPreciseEqual(tuple(aligned_rec), (2, 43.5))
+
+
+@jit(cache=True)
+def first_class_function_mul(x):
+    return x * x
+
+
+@jit(cache=True)
+def first_class_function_add(x):
+    return x + x
+
+
+@jit(cache=True)
+def first_class_function_usecase(f, x):
+    return f(x)
+
+
+def self_test():
+    mod = sys.modules[__name__]
+    _TestModule().check_module(mod)
+
+
+@jit(parallel=True, cache=True, nopython=True)
+def parfor_usecase(ary):
+    return ary * ary + ary
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cffi_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cffi_usecases.py
new file mode 100644
index 000000000..a101b977d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cffi_usecases.py
@@ -0,0 +1,197 @@
+import sys
+
+import numpy as np
+
+import numba.core.typing.cffi_utils as cffi_support
+from numba.tests.support import import_dynamic, temp_directory
+from numba.core.types import complex128
+
+
+def load_inline_module():
+    """
+    Create an inline module, return the corresponding ffi and dll objects.
+    """
+    from cffi import FFI
+
+    # We can't rely on libc availability on Windows anymore, so we use our
+    # own compiled wrappers (see https://bugs.python.org/issue23606).
+
+    defs = """
+    double _numba_test_sin(double x);
+    double _numba_test_cos(double x);
+    double _numba_test_funcptr(double (*func)(double));
+    bool _numba_test_boolean(void);
+    """
+
+    ffi = FFI()
+    ffi.cdef(defs)
+    # Load the _helperlib namespace
+    from numba import _helperlib
+    return ffi, ffi.dlopen(_helperlib.__file__)
+
+
+def load_ool_module():
+    """
+    Compile an out-of-line module, return the corresponding ffi and
+    module objects.
+    """
+    from cffi import FFI
+
+    numba_complex = """
+    typedef struct _numba_complex {
+        double real;
+        double imag;
+    } numba_complex;
+    """
+
+    bool_define = """
+    #ifdef _MSC_VER
+        #define false 0
+        #define true 1
+        #define bool int
+    #else
+        #include <stdbool.h>
+    #endif
+    """
+
+    defs = numba_complex + """
+    bool boolean(void);
+    double sin(double x);
+    double cos(double x);
+    int foo(int a, int b, int c);
+    void vsSin(int n, float* x, float* y);
+    void vdSin(int n, double* x, double* y);
+    void vector_real(numba_complex *c, double *real, int n);
+    void vector_imag(numba_complex *c, double *imag, int n);
+    """
+
+    source = numba_complex + bool_define + """
+    static bool boolean(void)
+    {
+        return true;
+    }
+
+    static int foo(int a, int b, int c)
+    {
+        return a + b * c;
+    }
+
+    void vsSin(int n, float* x, float* y) {
+        int i;
+        for (i=0; i<n; i++)
+            y[i] = sin(x[i]);
+    }
+
+    void vdSin(int n, double* x, double* y) {
+        int i;
+        for (i=0; i<n; i++)
+            y[i] = sin(x[i]);
+    }
+
+    static void vector_real(numba_complex *c, double *real, int n) {
+        int i;
+        for (i = 0; i < n; i++)
+            real[i] = c[i].real;
+    }
+
+    static void vector_imag(numba_complex *c, double *imag, int n) {
+        int i;
+        for (i = 0; i < n; i++)
+            imag[i] = c[i].imag;
+    }
+    """
+
+    ffi = FFI()
+    ffi.set_source('cffi_usecases_ool', source)
+    ffi.cdef(defs, override=True)
+    tmpdir = temp_directory('test_cffi')
+    ffi.compile(tmpdir=tmpdir)
+    sys.path.append(tmpdir)
+    try:
+        mod = import_dynamic('cffi_usecases_ool')
+        cffi_support.register_module(mod)
+        cffi_support.register_type(mod.ffi.typeof('struct _numba_complex'),
+                                   complex128)
+        return mod.ffi, mod
+    finally:
+        sys.path.remove(tmpdir)
+
+
+def init():
+    """
+    Initialize module globals.  This can invoke external utilities, hence not
+    being executed implicitly at module import.
+    """
+    global ffi, cffi_sin, cffi_cos, cffi_bool
+
+    if ffi is None:
+        ffi, dll = load_inline_module()
+        cffi_sin = dll._numba_test_sin
+        cffi_cos = dll._numba_test_cos
+        cffi_bool = dll._numba_test_boolean
+        del dll
+
+def init_ool():
+    """
+    Same as init() for OOL mode.
+    """
+    global ffi_ool, cffi_sin_ool, cffi_cos_ool, cffi_foo, cffi_bool_ool
+    global vsSin, vdSin, vector_real, vector_imag
+
+    if ffi_ool is None:
+        ffi_ool, mod = load_ool_module()
+        cffi_sin_ool = mod.lib.sin
+        cffi_cos_ool = mod.lib.cos
+        cffi_foo = mod.lib.foo
+        cffi_bool_ool = mod.lib.boolean
+        vsSin = mod.lib.vsSin
+        vdSin = mod.lib.vdSin
+        vector_real = mod.lib.vector_real
+        vector_imag = mod.lib.vector_imag
+        del mod
+
+ffi = ffi_ool = None
+
+
+def use_cffi_sin(x):
+    return cffi_sin(x) * 2
+
+def use_two_funcs(x):
+    return cffi_sin(x) - cffi_cos(x)
+
+def use_cffi_sin_ool(x):
+    return cffi_sin_ool(x) * 2
+
+def use_cffi_boolean_true():
+    return cffi_bool_ool()
+
+def use_two_funcs_ool(x):
+    return cffi_sin_ool(x) - cffi_cos_ool(x)
+
+def use_func_pointer(fa, fb, x):
+    if x > 0:
+        return fa(x)
+    else:
+        return fb(x)
+
+def use_user_defined_symbols():
+    return cffi_foo(1, 2, 3)
+
+# The from_buffer method is member of cffi.FFI, and also of CompiledFFI objects
+# (cffi_usecases_ool.ffi is a CompiledFFI object) so we use both in these
+# functions.
+
+def vector_sin_float32(x, y):
+    vsSin(len(x), ffi.from_buffer(x), ffi_ool.from_buffer(y))
+
+def vector_sin_float64(x, y):
+    vdSin(len(x), ffi.from_buffer(x), ffi_ool.from_buffer(y))
+
+
+# For testing pointer to structs from buffers
+
+def vector_extract_real(x, y):
+    vector_real(ffi.from_buffer(x), ffi.from_buffer(y), len(x))
+
+def vector_extract_imag(x, y):
+    vector_imag(ffi.from_buffer(x), ffi.from_buffer(y), len(x))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cfunc_cache_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cfunc_cache_usecases.py
new file mode 100644
index 000000000..24c3079ce
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cfunc_cache_usecases.py
@@ -0,0 +1,70 @@
+"""
+This file will be copied to a temporary directory in order to
+exercise caching compiled C callbacks.
+
+See test_cfunc.py.
+"""
+
+import sys
+
+from numba import cfunc, jit
+from numba.tests.support import TestCase, captured_stderr
+
+
+Z = 1
+
+add_sig = "float64(float64, float64)"
+
+div_sig = "float64(int64, int64)"
+
+
+@cfunc(add_sig, cache=True, nopython=True)
+def add_usecase(x, y):
+    return x + y + Z
+
+@cfunc(add_sig, nopython=True)
+def add_nocache_usecase(x, y):
+    return x + y + Z
+
+@cfunc(div_sig, cache=True, nopython=True)
+def div_usecase(a, b):
+    return a / b
+
+
+@jit(nopython=True)
+def inner(x, y):
+    return x + y + Z
+
+@cfunc(add_sig, cache=True, nopython=True)
+def outer(x, y):
+    return inner(-y, x)
+
+
+class _TestModule(TestCase):
+    """
+    Tests for functionality of this module's cfuncs.
+    Note this does not define any "test_*" method, instead check_module()
+    should be called by hand.
+    """
+
+    def check_module(self, mod):
+        f = mod.add_usecase
+        self.assertPreciseEqual(f.ctypes(2.0, 3.0), 6.0)
+        f = mod.add_nocache_usecase
+        self.assertPreciseEqual(f.ctypes(2.0, 3.0), 6.0)
+        f = mod.outer
+        self.assertPreciseEqual(f.ctypes(5.0, 2.0), 4.0)
+
+        f = mod.div_usecase
+        with captured_stderr() as err:
+            self.assertPreciseEqual(f.ctypes(7, 2), 3.5)
+        self.assertEqual(err.getvalue(), "")
+        with captured_stderr() as err:
+            f.ctypes(7, 0)
+        err = err.getvalue()
+        self.assertIn("ZeroDivisionError", err)
+
+
+def self_test():
+    mod = sys.modules[__name__]
+    _TestModule().check_module(mod)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cloudpickle_main_class.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cloudpickle_main_class.py
new file mode 100644
index 000000000..0e29315b3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/cloudpickle_main_class.py
@@ -0,0 +1,6 @@
+# Expected to run this module as __main__
+
+
+# Cloudpickle will think this is a dynamic class when this module is __main__
+class Klass:
+    classvar = None
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/compile_with_pycc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/compile_with_pycc.py
new file mode 100644
index 000000000..3f3ae500d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/compile_with_pycc.py
@@ -0,0 +1,134 @@
+import cmath
+
+import numpy as np
+
+from numba import float32
+from numba.types import unicode_type, i8
+from numba.pycc import CC, exportmany, export
+from numba.tests.support import has_blas
+from numba import typed
+
+
+#
+# New API
+#
+
+cc = CC('pycc_test_simple')
+cc.use_nrt = False
+
+# Note the first signature omits the return type
+@cc.export('multf', (float32, float32))
+@cc.export('multi', 'i4(i4, i4)')
+def mult(a, b):
+    return a * b
+
+# Test imported C globals such as Py_None, PyExc_ZeroDivisionError
+@cc.export('get_none', 'none()')
+def get_none():
+    return None
+
+@cc.export('div', 'f8(f8, f8)')
+def div(x, y):
+    return x / y
+
+_two = 2
+
+# This one can't be compiled by the legacy API as it doesn't execute
+# the script in a proper module.
+@cc.export('square', 'i8(i8)')
+def square(u):
+    return u ** _two
+
+# These ones need helperlib
+cc_helperlib = CC('pycc_test_helperlib')
+cc_helperlib.use_nrt = False
+
+@cc_helperlib.export('power', 'i8(i8, i8)')
+def power(u, v):
+    return u ** v
+
+@cc_helperlib.export('sqrt', 'c16(c16)')
+def sqrt(u):
+    return cmath.sqrt(u)
+
+@cc_helperlib.export('size', 'i8(f8[:])')
+def size(arr):
+    return arr.size
+
+# Exercise linking to Numpy math functions
+@cc_helperlib.export('np_sqrt', 'f8(f8)')
+def np_sqrt(u):
+    return np.sqrt(u)
+
+@cc_helperlib.export('spacing', 'f8(f8)')
+def np_spacing(u):
+    return np.spacing(u)
+
+
+# This one clashes with libc random() unless pycc is careful with naming.
+@cc_helperlib.export('random', 'f8(i4)')
+def random_impl(seed):
+    if seed != -1:
+        np.random.seed(seed)
+    return np.random.random()
+
+# These ones need NRT
+cc_nrt = CC('pycc_test_nrt')
+
+@cc_nrt.export('zero_scalar', 'f8(i4)')
+def zero_scalar(n):
+    arr = np.zeros(n)
+    return arr[-1]
+
+if has_blas:
+    # This one also needs BLAS
+    @cc_nrt.export('vector_dot', 'f8(i4)')
+    def vector_dot(n):
+        a = np.linspace(1, n, n)
+        return np.dot(a, a)
+
+# This one needs an environment
+@cc_nrt.export('zeros', 'f8[:](i4)')
+def zeros(n):
+    return np.zeros(n)
+
+# requires list dtor, #issue3535
+@cc_nrt.export('np_argsort', 'intp[:](float64[:])')
+def np_argsort(arr):
+    return np.argsort(arr)
+
+#
+# Legacy API
+#
+
+exportmany(['multf f4(f4,f4)', 'multi i4(i4,i4)'])(mult)
+# Needs to link to helperlib to due with complex arguments
+# export('multc c16(c16,c16)')(mult)
+export('mult f8(f8, f8)')(mult)
+
+
+@cc_nrt.export('dict_usecase', 'intp[:](intp[:])')
+def dict_usecase(arr):
+    d = typed.Dict()
+    for i in range(arr.size):
+        d[i] = arr[i]
+    out = np.zeros_like(arr)
+    for k, v in d.items():
+        out[k] = k * v
+    return out
+
+# checks for issue #6386
+@cc_nrt.export('internal_str_dict', i8(unicode_type))
+def internal_str_dict(x):
+    d = typed.Dict.empty(unicode_type,i8)
+    if(x not in d):
+        d[x] = len(d)
+    return len(d)
+
+@cc_nrt.export('hash_str', i8(unicode_type))
+def internal_str_dict(x):
+    return hash(x)
+
+@cc_nrt.export('hash_literal_str_A', i8())
+def internal_str_dict():
+    return hash("A")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/complex_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/complex_usecases.py
new file mode 100644
index 000000000..113cd6921
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/complex_usecases.py
@@ -0,0 +1,93 @@
+import cmath
+
+
+def div_usecase(x, y):
+    return x / y
+
+
+def real_usecase(x):
+    return x.real
+
+def imag_usecase(x):
+    return x.imag
+
+def conjugate_usecase(x):
+    return x.conjugate()
+
+
+def acos_usecase(x):
+    return cmath.acos(x)
+
+def cos_usecase(x):
+    return cmath.cos(x)
+
+def asin_usecase(x):
+    return cmath.asin(x)
+
+def sin_usecase(x):
+    return cmath.sin(x)
+
+def atan_usecase(x):
+    return cmath.atan(x)
+
+def tan_usecase(x):
+    return cmath.tan(x)
+
+def acosh_usecase(x):
+    return cmath.acosh(x)
+
+def cosh_usecase(x):
+    return cmath.cosh(x)
+
+def asinh_usecase(x):
+    return cmath.asinh(x)
+
+def sinh_usecase(x):
+    return cmath.sinh(x)
+
+def atanh_usecase(x):
+    return cmath.atanh(x)
+
+def tanh_usecase(x):
+    return cmath.tanh(x)
+
+def exp_usecase(x):
+    return cmath.exp(x)
+
+def isfinite_usecase(x):
+    return cmath.isfinite(x)
+
+def isinf_usecase(x):
+    return cmath.isinf(x)
+
+def isnan_usecase(x):
+    return cmath.isnan(x)
+
+def log_usecase(x):
+    return cmath.log(x)
+
+def log_base_usecase(x, base):
+    return cmath.log(x, base)
+
+def log10_usecase(x):
+    return cmath.log10(x)
+
+def phase_usecase(x):
+    return cmath.phase(x)
+
+def polar_usecase(x):
+    return cmath.polar(x)
+
+_two = 2.0
+
+def polar_as_complex_usecase(x):
+    # HACK: clear errno by invoking float.__pow__
+    # (workaround for http://bugs.python.org/issue24489)
+    _two ** _two
+    return complex(*cmath.polar(x))
+
+def rect_usecase(r, phi):
+    return cmath.rect(r, phi)
+
+def sqrt_usecase(x):
+    return cmath.sqrt(x)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/ctypes_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/ctypes_usecases.py
new file mode 100644
index 000000000..5ed8d3ab8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/ctypes_usecases.py
@@ -0,0 +1,114 @@
+from ctypes import *
+import sys
+
+import numpy as np
+
+
+is_windows = sys.platform.startswith('win32')
+
+# We can't rely on libc availability on Windows anymore, so we use our
+# own compiled wrappers (see https://bugs.python.org/issue23606).
+
+from numba import _helperlib
+libnumba = CDLL(_helperlib.__file__)
+del _helperlib
+
+# A typed C function (cdecl under Windows)
+
+c_sin = libnumba._numba_test_sin
+c_sin.argtypes = [c_double]
+c_sin.restype = c_double
+
+def use_c_sin(x):
+    return c_sin(x)
+
+c_cos = libnumba._numba_test_cos
+c_cos.argtypes = [c_double]
+c_cos.restype = c_double
+
+def use_two_funcs(x):
+    return c_sin(x) - c_cos(x)
+
+# Typed C functions accepting an array pointer
+# (either as a "void *" or as a typed pointer)
+
+c_vsquare = libnumba._numba_test_vsquare
+c_vsquare.argtypes = [c_int, c_void_p, c_void_p]
+
+c_vcube = libnumba._numba_test_vsquare
+c_vcube.argtypes = [c_int, POINTER(c_double), POINTER(c_double)]
+
+def use_c_vsquare(x):
+    out = np.empty_like(x)
+    c_vsquare(x.size, x.ctypes, out.ctypes)
+    return out
+
+def use_c_vcube(x):
+    out = np.empty_like(x)
+    c_vcube(x.size, x.ctypes, out.ctypes)
+    return out
+
+# An untyped C function
+
+c_untyped = libnumba._numba_test_exp
+
+def use_c_untyped(x):
+    return c_untyped(x)
+
+# A C function wrapped in a CFUNCTYPE
+
+ctype_wrapping = CFUNCTYPE(c_double, c_double)(use_c_sin)
+
+def use_ctype_wrapping(x):
+    return ctype_wrapping(x)
+
+# A Python API function
+
+savethread = pythonapi.PyEval_SaveThread
+savethread.argtypes = []
+savethread.restype = c_void_p
+
+restorethread = pythonapi.PyEval_RestoreThread
+restorethread.argtypes = [c_void_p]
+restorethread.restype = None
+
+if is_windows:
+    # A function with the stdcall calling convention
+    c_sleep = windll.kernel32.Sleep
+    c_sleep.argtypes = [c_uint]
+    c_sleep.restype = None
+
+    def use_c_sleep(x):
+        c_sleep(x)
+
+
+def use_c_pointer(x):
+    """
+    Running in Python will cause a segfault.
+    """
+    threadstate = savethread()
+    x += 1
+    restorethread(threadstate)
+    return x
+
+
+def use_func_pointer(fa, fb, x):
+    if x > 0:
+        return fa(x)
+    else:
+        return fb(x)
+
+
+mydct = {'what': 1232121}
+
+def call_me_maybe(arr):
+    return mydct[arr[0].decode('ascii')]
+
+# Create a callback into the python interpreter
+py_call_back = CFUNCTYPE(c_int, py_object)(call_me_maybe)
+
+
+def take_array_ptr(ptr):
+    return ptr
+
+c_take_array_ptr = CFUNCTYPE(c_void_p, c_void_p)(take_array_ptr)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/__init__.py
new file mode 100644
index 000000000..b2958441f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/__init__.py
@@ -0,0 +1,10 @@
+from os.path import dirname
+import unittest
+from unittest.suite import TestSuite
+
+from numba.testing import load_testsuite
+
+def load_tests(loader, tests, pattern):
+    suite = TestSuite()
+    suite.addTests(load_testsuite(loader, dirname(__file__)))
+    return suite
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_examples.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_examples.py
new file mode 100644
index 000000000..e0aa2e1ae
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_examples.py
@@ -0,0 +1,202 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import sys
+import unittest
+from numba.tests.support import captured_stdout
+
+
+class MatplotlibBlocker:
+    '''Blocks the import of matplotlib, so that doc examples that attempt to
+    plot the output don't result in plots popping up and blocking testing.'''
+
+    def find_spec(self, fullname, path, target=None):
+        if fullname == 'matplotlib':
+            msg = 'Blocked import of matplotlib for test suite run'
+            raise ImportError(msg)
+
+
+class DocsExamplesTest(unittest.TestCase):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._mpl_blocker = MatplotlibBlocker()
+
+    def setUp(self):
+        sys.meta_path.insert(0, self._mpl_blocker)
+
+    def tearDown(self):
+        sys.meta_path.remove(self._mpl_blocker)
+
+    def test_mandelbrot(self):
+        with captured_stdout():
+            # magictoken.ex_mandelbrot.begin
+            from timeit import default_timer as timer
+            try:
+                from matplotlib.pylab import imshow, show
+                have_mpl = True
+            except ImportError:
+                have_mpl = False
+            import numpy as np
+            from numba import jit
+
+            @jit(nopython=True)
+            def mandel(x, y, max_iters):
+                """
+                Given the real and imaginary parts of a complex number,
+                determine if it is a candidate for membership in the Mandelbrot
+                set given a fixed number of iterations.
+                """
+                i = 0
+                c = complex(x,y)
+                z = 0.0j
+                for i in range(max_iters):
+                    z = z * z + c
+                    if (z.real * z.real + z.imag * z.imag) >= 4:
+                        return i
+
+                return 255
+
+            @jit(nopython=True)
+            def create_fractal(min_x, max_x, min_y, max_y, image, iters):
+                height = image.shape[0]
+                width = image.shape[1]
+
+                pixel_size_x = (max_x - min_x) / width
+                pixel_size_y = (max_y - min_y) / height
+                for x in range(width):
+                    real = min_x + x * pixel_size_x
+                    for y in range(height):
+                        imag = min_y + y * pixel_size_y
+                        color = mandel(real, imag, iters)
+                        image[y, x] = color
+
+                return image
+
+            image = np.zeros((500 * 2, 750 * 2), dtype=np.uint8)
+            s = timer()
+            create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20)
+            e = timer()
+            print(e - s)
+            if have_mpl:
+                imshow(image)
+                show()
+            # magictoken.ex_mandelbrot.end
+
+    def test_moving_average(self):
+        with captured_stdout():
+            # magictoken.ex_moving_average.begin
+            import numpy as np
+
+            from numba import guvectorize
+
+            @guvectorize(['void(float64[:], intp[:], float64[:])'],
+                         '(n),()->(n)')
+            def move_mean(a, window_arr, out):
+                window_width = window_arr[0]
+                asum = 0.0
+                count = 0
+                for i in range(window_width):
+                    asum += a[i]
+                    count += 1
+                    out[i] = asum / count
+                for i in range(window_width, len(a)):
+                    asum += a[i] - a[i - window_width]
+                    out[i] = asum / count
+
+            arr = np.arange(20, dtype=np.float64).reshape(2, 10)
+            print(arr)
+            print(move_mean(arr, 3))
+            # magictoken.ex_moving_average.end
+
+    def test_nogil(self):
+        with captured_stdout():
+            # magictoken.ex_no_gil.begin
+            import math
+            import threading
+            from timeit import repeat
+
+            import numpy as np
+            from numba import jit
+
+            nthreads = 4
+            size = 10**6
+
+            def func_np(a, b):
+                """
+                Control function using Numpy.
+                """
+                return np.exp(2.1 * a + 3.2 * b)
+
+            @jit('void(double[:], double[:], double[:])', nopython=True,
+                 nogil=True)
+            def inner_func_nb(result, a, b):
+                """
+                Function under test.
+                """
+                for i in range(len(result)):
+                    result[i] = math.exp(2.1 * a[i] + 3.2 * b[i])
+
+            def timefunc(correct, s, func, *args, **kwargs):
+                """
+                Benchmark *func* and print out its runtime.
+                """
+                print(s.ljust(20), end=" ")
+                # Make sure the function is compiled before the benchmark is
+                # started
+                res = func(*args, **kwargs)
+                if correct is not None:
+                    assert np.allclose(res, correct), (res, correct)
+                # time it
+                print('{:>5.0f} ms'.format(min(repeat(
+                    lambda: func(*args, **kwargs), number=5, repeat=2)) * 1000))
+                return res
+
+            def make_singlethread(inner_func):
+                """
+                Run the given function inside a single thread.
+                """
+                def func(*args):
+                    length = len(args[0])
+                    result = np.empty(length, dtype=np.float64)
+                    inner_func(result, *args)
+                    return result
+                return func
+
+            def make_multithread(inner_func, numthreads):
+                """
+                Run the given function inside *numthreads* threads, splitting
+                its arguments into equal-sized chunks.
+                """
+                def func_mt(*args):
+                    length = len(args[0])
+                    result = np.empty(length, dtype=np.float64)
+                    args = (result,) + args
+                    chunklen = (length + numthreads - 1) // numthreads
+                    # Create argument tuples for each input chunk
+                    chunks = [[arg[i * chunklen:(i + 1) * chunklen] for arg in
+                               args] for i in range(numthreads)]
+                    # Spawn one thread per chunk
+                    threads = [threading.Thread(target=inner_func, args=chunk)
+                               for chunk in chunks]
+                    for thread in threads:
+                        thread.start()
+                    for thread in threads:
+                        thread.join()
+                    return result
+                return func_mt
+
+            func_nb = make_singlethread(inner_func_nb)
+            func_nb_mt = make_multithread(inner_func_nb, nthreads)
+
+            a = np.random.rand(size)
+            b = np.random.rand(size)
+
+            correct = timefunc(None, "numpy (1 thread)", func_np, a, b)
+            timefunc(correct, "numba (1 thread)", func_nb, a, b)
+            timefunc(correct, "numba (%d threads)" % nthreads, func_nb_mt, a, b)
+            # magictoken.ex_no_gil.end
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_jitclass.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_jitclass.py
new file mode 100644
index 000000000..ca5faea42
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_jitclass.py
@@ -0,0 +1,97 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.tests.support import TestCase
+
+
+class DocsJitclassUsageTest(TestCase):
+
+    def test_ex_jitclass(self):
+        # magictoken.ex_jitclass.begin
+        import numpy as np
+        from numba import int32, float32    # import the types
+        from numba.experimental import jitclass
+
+        spec = [
+            ('value', int32),               # a simple scalar field
+            ('array', float32[:]),          # an array field
+        ]
+
+        @jitclass(spec)
+        class Bag(object):
+            def __init__(self, value):
+                self.value = value
+                self.array = np.zeros(value, dtype=np.float32)
+
+            @property
+            def size(self):
+                return self.array.size
+
+            def increment(self, val):
+                for i in range(self.size):
+                    self.array[i] += val
+                return self.array
+
+            @staticmethod
+            def add(x, y):
+                return x + y
+
+        n = 21
+        mybag = Bag(n)
+        # magictoken.ex_jitclass.end
+
+        self.assertTrue(isinstance(mybag, Bag))
+        self.assertPreciseEqual(mybag.value, n)
+        np.testing.assert_allclose(mybag.array, np.zeros(n, dtype=np.float32))
+        self.assertPreciseEqual(mybag.size, n)
+        np.testing.assert_allclose(mybag.increment(3),
+                                   3 * np.ones(n, dtype=np.float32))
+        np.testing.assert_allclose(mybag.increment(6),
+                                   9 * np.ones(n, dtype=np.float32))
+        self.assertPreciseEqual(mybag.add(1, 1), 2)
+        self.assertPreciseEqual(Bag.add(1, 2), 3)
+
+    def test_ex_jitclass_type_hints(self):
+        # magictoken.ex_jitclass_type_hints.begin
+        from typing import List
+        from numba.experimental import jitclass
+        from numba.typed import List as NumbaList
+
+        @jitclass
+        class Counter:
+            value: int
+
+            def __init__(self):
+                self.value = 0
+
+            def get(self) -> int:
+                ret = self.value
+                self.value += 1
+                return ret
+
+        @jitclass
+        class ListLoopIterator:
+            counter: Counter
+            items: List[float]
+
+            def __init__(self, items: List[float]):
+                self.items = items
+                self.counter = Counter()
+
+            def get(self) -> float:
+                idx = self.counter.get() % len(self.items)
+                return self.items[idx]
+
+        items = NumbaList([3.14, 2.718, 0.123, -4.])
+        loop_itr = ListLoopIterator(items)
+        # magictoken.ex_jitclass_type_hints.end
+
+        for idx in range(10):
+            self.assertEqual(loop_itr.counter.value, idx)
+            self.assertAlmostEqual(loop_itr.get(), items[idx % len(items)])
+            self.assertEqual(loop_itr.counter.value, idx + 1)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_literal_container_usage.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_literal_container_usage.py
new file mode 100644
index 000000000..4872faa4c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_literal_container_usage.py
@@ -0,0 +1,161 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.tests.support import captured_stdout
+from numba import typed
+
+
+class DocsLiteralContainerUsageTest(unittest.TestCase):
+
+    def test_ex_literal_dict_compile_time_consts(self):
+        with captured_stdout():
+            # magictoken.test_ex_literal_dict_compile_time_consts.begin
+            import numpy as np
+            from numba import njit, types
+            from numba.extending import overload
+
+            # overload this function
+            def specialize(x):
+                pass
+
+            @overload(specialize)
+            def ol_specialize(x):
+                ld = x.literal_value
+                const_expr = []
+                for k, v in ld.items():
+                    if isinstance(v, types.Literal):
+                        lv = v.literal_value
+                        if lv == 'cat':
+                            const_expr.append("Meow!")
+                        elif lv == 'dog':
+                            const_expr.append("Woof!")
+                        elif isinstance(lv, int):
+                            const_expr.append(k.literal_value * lv)
+                    else: # it's an array
+                        const_expr.append("Array(dim={dim}".format(dim=v.ndim))
+                const_strings = tuple(const_expr)
+
+                def impl(x):
+                    return const_strings
+                return impl
+
+            @njit
+            def foo():
+                pets_ints_and_array = {'a': 1,
+                                       'b': 2,
+                                       'c': 'cat',
+                                       'd': 'dog',
+                                       'e': np.ones(5,)}
+                return specialize(pets_ints_and_array)
+
+            result = foo()
+            print(result) # ('a', 'bb', 'Meow!', 'Woof!', 'Array(dim=1')
+            # magictoken.test_ex_literal_dict_compile_time_consts.end
+
+        self.assertEqual(result, ('a', 'bb', 'Meow!', 'Woof!', 'Array(dim=1'))
+
+    def test_ex_initial_value_dict_compile_time_consts(self):
+        with captured_stdout():
+            # magictoken.test_ex_initial_value_dict_compile_time_consts.begin
+            from numba import njit, literally
+            from numba.extending import overload
+
+            # overload this function
+            def specialize(x):
+                pass
+
+            @overload(specialize)
+            def ol_specialize(x):
+                iv = x.initial_value
+                if iv is None:
+                    return lambda x: literally(x) # Force literal dispatch
+                assert iv == {'a': 1, 'b': 2, 'c': 3} # INITIAL VALUE
+                return lambda x: literally(x)
+
+            @njit
+            def foo():
+                d = {'a': 1, 'b': 2, 'c': 3}
+                d['c'] = 20 # no impact on .initial_value
+                d['d'] = 30 # no impact on .initial_value
+                return specialize(d)
+
+            result = foo()
+            print(result) # {a: 1, b: 2, c: 20, d: 30} # NOT INITIAL VALUE!
+            # magictoken.test_ex_initial_value_dict_compile_time_consts.end
+
+        expected = typed.Dict()
+        for k, v in {'a': 1, 'b': 2, 'c': 20, 'd': 30}.items():
+            expected[k] = v
+        self.assertEqual(result, expected)
+
+    def test_ex_literal_list(self):
+        with captured_stdout():
+            # magictoken.test_ex_literal_list.begin
+            from numba import njit
+            from numba.extending import overload
+
+            # overload this function
+            def specialize(x):
+                pass
+
+            @overload(specialize)
+            def ol_specialize(x):
+                l = x.literal_value
+                const_expr = []
+                for v in l:
+                    const_expr.append(str(v))
+                const_strings = tuple(const_expr)
+
+                def impl(x):
+                    return const_strings
+                return impl
+
+            @njit
+            def foo():
+                const_list = ['a', 10, 1j, ['another', 'list']]
+                return specialize(const_list)
+
+            result = foo()
+            print(result) # ('Literal[str](a)', 'Literal[int](10)', 'complex128', 'list(unicode_type)') # noqa E501
+            # magictoken.test_ex_literal_list.end
+
+        expected = ('Literal[str](a)', 'Literal[int](10)', 'complex128',
+                    "list(unicode_type)<iv=['another', 'list']>")
+        self.assertEqual(result, expected)
+
+    def test_ex_initial_value_list_compile_time_consts(self):
+        with captured_stdout():
+            # magictoken.test_ex_initial_value_list_compile_time_consts.begin
+            from numba import njit, literally
+            from numba.extending import overload
+
+            # overload this function
+            def specialize(x):
+                pass
+
+            @overload(specialize)
+            def ol_specialize(x):
+                iv = x.initial_value
+                if iv is None:
+                    return lambda x: literally(x) # Force literal dispatch
+                assert iv == [1, 2, 3] # INITIAL VALUE
+                return lambda x: x
+
+            @njit
+            def foo():
+                l = [1, 2, 3]
+                l[2] = 20 # no impact on .initial_value
+                l.append(30) # no impact on .initial_value
+                return specialize(l)
+
+            result = foo()
+            print(result) # [1, 2, 20, 30] # NOT INITIAL VALUE!
+            # magictoken.test_ex_initial_value_list_compile_time_consts.end
+
+        expected = [1, 2, 20, 30]
+        self.assertEqual(result, expected)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_literally_usage.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_literally_usage.py
new file mode 100644
index 000000000..8fe8b45d3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_literally_usage.py
@@ -0,0 +1,59 @@
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.tests.support import captured_stdout
+
+
+class DocsLiterallyUsageTest(unittest.TestCase):
+
+    def test_literally_usage(self):
+        with captured_stdout() as stdout:
+            # magictoken.ex_literally_usage.begin
+            import numba
+
+            def power(x, n):
+                raise NotImplementedError
+
+            @numba.extending.overload(power)
+            def ov_power(x, n):
+                if isinstance(n, numba.types.Literal):
+                    # only if `n` is a literal
+                    if n.literal_value == 2:
+                        # special case: square
+                        print("square")
+                        return lambda x, n: x * x
+                    elif n.literal_value == 3:
+                        # special case: cubic
+                        print("cubic")
+                        return lambda x, n: x * x * x
+                else:
+                    # If `n` is not literal, request literal dispatch
+                    return lambda x, n: numba.literally(n)
+
+                print("generic")
+                return lambda x, n: x ** n
+
+            @numba.njit
+            def test_power(x, n):
+                return power(x, n)
+
+            # should print "square" and "9"
+            print(test_power(3, 2))
+
+            # should print "cubic" and "27"
+            print(test_power(3, 3))
+
+            # should print "generic" and "81"
+            print(test_power(3, 4))
+
+            # magictoken.ex_literally_usage.end
+            assert test_power(3, 2) == 3 ** 2
+            assert test_power(3, 3) == 3 ** 3
+            assert test_power(3, 4) == 3 ** 4
+
+        self.assertEqual('square\n9\ncubic\n27\ngeneric\n81\n',
+                         stdout.getvalue())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_llvm_pass_timings.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_llvm_pass_timings.py
new file mode 100644
index 000000000..2f607c847
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_llvm_pass_timings.py
@@ -0,0 +1,31 @@
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.tests.support import captured_stdout, override_config
+
+
+class DocsLLVMPassTimings(unittest.TestCase):
+
+    def test_pass_timings(self):
+        with override_config('LLVM_PASS_TIMINGS', True):
+            with captured_stdout() as stdout:
+                # magictoken.ex_llvm_pass_timings.begin
+                import numba
+
+                @numba.njit
+                def foo(n):
+                    c = 0
+                    for i in range(n):
+                        for j in range(i):
+                            c += j
+                    return c
+
+                foo(10)
+                md = foo.get_metadata(foo.signatures[0])
+                print(md['llvm_pass_timings'])
+                # magictoken.ex_llvm_pass_timings.end
+            self.assertIn("Finalize object", stdout.getvalue())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_numpy_generators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_numpy_generators.py
new file mode 100644
index 000000000..e10266d07
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_numpy_generators.py
@@ -0,0 +1,38 @@
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+import numpy as np
+import numba
+
+
+class NumpyGeneratorUsageTest(unittest.TestCase):
+
+    def test_numpy_gen_usage(self):
+        # magictoken.npgen_usage.begin
+        x = np.random.default_rng(1)
+        y = np.random.default_rng(1)
+
+        size = 10
+
+        @numba.njit
+        def do_stuff(gen):
+            return gen.random(size=int(size / 2))
+
+        original = x.random(size=size)
+        # [0.51182162 0.9504637  0.14415961 0.94864945 0.31183145
+        #  0.42332645 0.82770259 0.40919914 0.54959369 0.02755911]
+
+        numba_func_res = do_stuff(y)
+        # [0.51182162 0.9504637  0.14415961 0.94864945 0.31183145]
+
+        after_numba = y.random(size=int(size / 2))
+        # [0.42332645 0.82770259 0.40919914 0.54959369 0.02755911]
+
+        # magictoken.npgen_usage.end
+        numba_res = np.concatenate((numba_func_res, after_numba))
+        for _np_res, _nb_res in zip(original, numba_res):
+            self.assertEqual(_np_res, _nb_res)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_parallel_chunksize.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_parallel_chunksize.py
new file mode 100644
index 000000000..36e161a02
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_parallel_chunksize.py
@@ -0,0 +1,122 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.tests.support import captured_stdout, skip_parfors_unsupported
+from numba import set_parallel_chunksize
+from numba.tests.support import TestCase
+
+
+@skip_parfors_unsupported
+class ChunksizeExamplesTest(TestCase):
+
+    _numba_parallel_test_ = False
+
+    def setUp(self):
+        set_parallel_chunksize(0)
+
+    def tearDown(self):
+        set_parallel_chunksize(0)
+
+    def test_unbalanced_example(self):
+        with captured_stdout():
+            # magictoken.ex_unbalanced.begin
+            from numba import (njit,
+                               prange,
+                               )
+            import numpy as np
+
+            @njit(parallel=True)
+            def func1():
+                n = 100
+                vals = np.empty(n)
+                # The work in each iteration of the following prange
+                # loop is proportional to its index.
+                for i in prange(n):
+                    cur = i + 1
+                    for j in range(i):
+                        if cur % 2 == 0:
+                            cur //= 2
+                        else:
+                            cur = cur * 3 + 1
+                    vals[i] = cur
+                return vals
+
+            result = func1()
+            # magictoken.ex_unbalanced.end
+            self.assertPreciseEqual(result, func1.py_func())
+
+    def test_chunksize_manual(self):
+        with captured_stdout():
+            # magictoken.ex_chunksize_manual.begin
+            from numba import (njit,
+                               prange,
+                               set_parallel_chunksize,
+                               get_parallel_chunksize,
+                               )
+
+            @njit(parallel=True)
+            def func1(n):
+                acc = 0
+                print(get_parallel_chunksize()) # Will print 4.
+                for i in prange(n):
+                    print(get_parallel_chunksize()) # Will print 0.
+                    acc += i
+                print(get_parallel_chunksize()) # Will print 4.
+                return acc
+
+            @njit(parallel=True)
+            def func2(n):
+                acc = 0
+                # This version gets the previous chunksize explicitly.
+                old_chunksize = get_parallel_chunksize()
+                set_parallel_chunksize(8)
+                for i in prange(n):
+                    acc += i
+                set_parallel_chunksize(old_chunksize)
+                return acc
+
+            # This version saves the previous chunksize as returned
+            # by set_parallel_chunksize.
+            old_chunksize = set_parallel_chunksize(4)
+            result1 = func1(12)
+            result2 = func2(12)
+            result3 = func1(12)
+            set_parallel_chunksize(old_chunksize)
+            # magictoken.ex_chunksize_manual.end
+            self.assertPreciseEqual(result1, func1.py_func(12))
+            self.assertPreciseEqual(result2, func2.py_func(12))
+            self.assertPreciseEqual(result3, func1.py_func(12))
+
+    def test_chunksize_with(self):
+        with captured_stdout():
+            # magictoken.ex_chunksize_with.begin
+            from numba import njit, prange, parallel_chunksize
+
+            @njit(parallel=True)
+            def func1(n):
+                acc = 0
+                for i in prange(n):
+                    acc += i
+                return acc
+
+            @njit(parallel=True)
+            def func2(n):
+                acc = 0
+                with parallel_chunksize(8):
+                    for i in prange(n):
+                        acc += i
+                return acc
+
+            with parallel_chunksize(4):
+                result1 = func1(12)
+                result2 = func2(12)
+                result3 = func1(12)
+            # magictoken.ex_chunksize_with.end
+            self.assertPreciseEqual(result1, func1.py_func(12))
+            self.assertPreciseEqual(result2, func2.py_func(12))
+            self.assertPreciseEqual(result3, func1.py_func(12))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_rec_array.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_rec_array.py
new file mode 100644
index 000000000..74f7d9dc3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_rec_array.py
@@ -0,0 +1,46 @@
+import unittest
+
+
+class TestExample(unittest.TestCase):
+
+    def test_documentation_example1(self):
+        # magictoken.ex_rec_arr_const_index.begin
+        import numpy as np
+        from numba import njit
+
+        arr = np.array([(1, 2)], dtype=[('a1', 'f8'), ('a2', 'f8')])
+        fields_gl = ('a1', 'a2')
+
+        @njit
+        def get_field_sum(rec):
+            fields_lc = ('a1', 'a2')
+            field_name1 = fields_lc[0]
+            field_name2 = fields_gl[1]
+            return rec[field_name1] + rec[field_name2]
+
+        get_field_sum(arr[0])  # returns 3
+        # magictoken.ex_rec_arr_const_index.end
+        self.assertEqual(get_field_sum(arr[0]), 3)
+
+    def test_documentation_example2(self):
+        # magictoken.ex_rec_arr_lit_unroll_index.begin
+        import numpy as np
+        from numba import njit, literal_unroll
+
+        arr = np.array([(1, 2)], dtype=[('a1', 'f8'), ('a2', 'f8')])
+        fields_gl = ('a1', 'a2')
+
+        @njit
+        def get_field_sum(rec):
+            out = 0
+            for f in literal_unroll(fields_gl):
+                out += rec[f]
+            return out
+
+        get_field_sum(arr[0])   # returns 3
+        # magictoken.ex_rec_arr_lit_unroll_index.end
+        self.assertEqual(get_field_sum(arr[0]), 3)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_structref_usage.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_structref_usage.py
new file mode 100644
index 000000000..9634a9b0e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_structref_usage.py
@@ -0,0 +1,149 @@
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+
+# magictoken.ex_structref_type_definition.begin
+import numpy as np
+
+from numba import njit
+from numba.core import types
+from numba.experimental import structref
+
+from numba.tests.support import skip_unless_scipy
+
+
+# Define a StructRef.
+# `structref.register` associates the type with the default data model.
+# This will also install getters and setters to the fields of
+# the StructRef.
+@structref.register
+class MyStructType(types.StructRef):
+    def preprocess_fields(self, fields):
+        # This method is called by the type constructor for additional
+        # preprocessing on the fields.
+        # Here, we don't want the struct to take Literal types.
+        return tuple((name, types.unliteral(typ)) for name, typ in fields)
+
+
+# Define a Python type that can be use as a proxy to the StructRef
+# allocated inside Numba. Users can construct the StructRef via
+# the constructor for this type in python code and jit-code.
+class MyStruct(structref.StructRefProxy):
+    def __new__(cls, name, vector):
+        # Overriding the __new__ method is optional, doing so
+        # allows Python code to use keyword arguments,
+        # or add other customized behavior.
+        # The default __new__ takes `*args`.
+        # IMPORTANT: Users should not override __init__.
+        return structref.StructRefProxy.__new__(cls, name, vector)
+
+    # By default, the proxy type does not reflect the attributes or
+    # methods to the Python side. It is up to users to define
+    # these. (This may be automated in the future.)
+
+    @property
+    def name(self):
+        # To access a field, we can define a function that simply
+        # return the field in jit-code.
+        # The definition of MyStruct_get_name is shown later.
+        return MyStruct_get_name(self)
+
+    @property
+    def vector(self):
+        # The definition of MyStruct_get_vector is shown later.
+        return MyStruct_get_vector(self)
+
+
+@njit
+def MyStruct_get_name(self):
+    # In jit-code, the StructRef's attribute is exposed via
+    # structref.register
+    return self.name
+
+
+@njit
+def MyStruct_get_vector(self):
+    return self.vector
+
+
+# This associates the proxy with MyStructType for the given set of
+# fields. Notice how we are not constraining the type of each field.
+# Field types remain generic.
+structref.define_proxy(MyStruct, MyStructType, ["name", "vector"])
+# magictoken.ex_structref_type_definition.end
+
+
+@skip_unless_scipy
+class TestStructRefUsage(unittest.TestCase):
+    def test_type_definition(self):
+        np.random.seed(0)
+        # Redirect print
+        buf = []
+
+        def print(*args):
+            buf.append(args)
+
+        # magictoken.ex_structref_type_definition_test.begin
+        # Let's test our new StructRef.
+
+        # Define one in Python
+        alice = MyStruct("Alice", vector=np.random.random(3))
+
+        # Define one in jit-code
+        @njit
+        def make_bob():
+            bob = MyStruct("unnamed", vector=np.zeros(3))
+            # Mutate the attributes
+            bob.name = "Bob"
+            bob.vector = np.random.random(3)
+            return bob
+
+        bob = make_bob()
+
+        # Out: Alice: [0.5488135  0.71518937 0.60276338]
+        print(f"{alice.name}: {alice.vector}")
+        # Out: Bob: [0.88325739 0.73527629 0.87746707]
+        print(f"{bob.name}: {bob.vector}")
+
+        # Define a jit function to operate on the structs.
+        @njit
+        def distance(a, b):
+            return np.linalg.norm(a.vector - b.vector)
+
+        # Out: 0.4332647200356598
+        print(distance(alice, bob))
+        # magictoken.ex_structref_type_definition_test.end
+
+        self.assertEqual(len(buf), 3)
+
+    def test_overload_method(self):
+        # magictoken.ex_structref_method.begin
+        from numba.core.extending import overload_method
+        from numba.core.errors import TypingError
+
+        # Use @overload_method to add a method for
+        # MyStructType.distance(other)
+        # where *other* is an instance of MyStructType.
+        @overload_method(MyStructType, "distance")
+        def ol_distance(self, other):
+            # Guard that *other* is an instance of MyStructType
+            if not isinstance(other, MyStructType):
+                raise TypingError(
+                    f"*other* must be a {MyStructType}; got {other}"
+                )
+
+            def impl(self, other):
+                return np.linalg.norm(self.vector - other.vector)
+
+            return impl
+
+        # Test
+        @njit
+        def test():
+            alice = MyStruct("Alice", vector=np.random.random(3))
+            bob = MyStruct("Bob", vector=np.random.random(3))
+            # Use the method
+            return alice.distance(bob)
+        # magictoken.ex_structref_method.end
+
+        self.assertIsInstance(test(), float)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_typed_dict_usage.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_typed_dict_usage.py
new file mode 100644
index 000000000..93880fc92
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_typed_dict_usage.py
@@ -0,0 +1,111 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.tests.support import captured_stdout
+
+
+class DocsTypedDictUsageTest(unittest.TestCase):
+
+    def test_ex_typed_dict_from_cpython(self):
+        with captured_stdout():
+            # magictoken.ex_typed_dict_from_cpython.begin
+            import numpy as np
+            from numba import njit
+            from numba.core import types
+            from numba.typed import Dict
+
+            # The Dict.empty() constructs a typed dictionary.
+            # The key and value typed must be explicitly declared.
+            d = Dict.empty(
+                key_type=types.unicode_type,
+                value_type=types.float64[:],
+            )
+
+            # The typed-dict can be used from the interpreter.
+            d['posx'] = np.asarray([1, 0.5, 2], dtype='f8')
+            d['posy'] = np.asarray([1.5, 3.5, 2], dtype='f8')
+            d['velx'] = np.asarray([0.5, 0, 0.7], dtype='f8')
+            d['vely'] = np.asarray([0.2, -0.2, 0.1], dtype='f8')
+
+            # Here's a function that expects a typed-dict as the argument
+            @njit
+            def move(d):
+                # inplace operations on the arrays
+                d['posx'] += d['velx']
+                d['posy'] += d['vely']
+
+            print('posx: ', d['posx'])  # Out: posx:  [1.  0.5 2. ]
+            print('posy: ', d['posy'])  # Out: posy:  [1.5 3.5 2. ]
+
+            # Call move(d) to inplace update the arrays in the typed-dict.
+            move(d)
+
+            print('posx: ', d['posx'])  # Out: posx:  [1.5 0.5 2.7]
+            print('posy: ', d['posy'])  # Out: posy:  [1.7 3.3 2.1]
+            # magictoken.ex_typed_dict_from_cpython.end
+
+        # Test
+        np.testing.assert_array_equal(d['posx'], [1.5, 0.5, 2.7])
+        np.testing.assert_array_equal(d['posy'], [1.7, 3.3, 2.1])
+
+    def test_ex_typed_dict_njit(self):
+        with captured_stdout():
+            # magictoken.ex_typed_dict_njit.begin
+            import numpy as np
+            from numba import njit
+            from numba.core import types
+            from numba.typed import Dict
+
+            # Make array type.  Type-expression is not supported in jit
+            # functions.
+            float_array = types.float64[:]
+
+            @njit
+            def foo():
+                # Make dictionary
+                d = Dict.empty(
+                    key_type=types.unicode_type,
+                    value_type=float_array,
+                )
+                # Fill the dictionary
+                d["posx"] = np.arange(3).astype(np.float64)
+                d["posy"] = np.arange(3, 6).astype(np.float64)
+                return d
+
+            d = foo()
+            # Print the dictionary
+            print(d)  # Out: {posx: [0. 1. 2.], posy: [3. 4. 5.]}
+            # magictoken.ex_typed_dict_njit.end
+        np.testing.assert_array_equal(d['posx'], [0, 1, 2])
+        np.testing.assert_array_equal(d['posy'], [3, 4, 5])
+
+    def test_ex_inferred_dict_njit(self):
+        with captured_stdout():
+            # magictoken.ex_inferred_dict_njit.begin
+            from numba import njit
+            import numpy as np
+
+            @njit
+            def foo():
+                d = dict()
+                k = {1: np.arange(1), 2: np.arange(2)}
+                # The following tells the compiler what the key type and the
+                # value
+                # type are for `d`.
+                d[3] = np.arange(3)
+                d[5] = np.arange(5)
+                return d, k
+
+            d, k = foo()
+            print(d)    # {3: [0 1 2], 5: [0 1 2 3 4]}
+            print(k)    # {1: [0], 2: [0 1]}
+            # magictoken.ex_inferred_dict_njit.end
+        np.testing.assert_array_equal(d[3], [0, 1, 2])
+        np.testing.assert_array_equal(d[5], [0, 1, 2, 3, 4])
+        np.testing.assert_array_equal(k[1], [0])
+        np.testing.assert_array_equal(k[2], [0, 1])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_typed_list_usage.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_typed_list_usage.py
new file mode 100644
index 000000000..1d804625e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/doc_examples/test_typed_list_usage.py
@@ -0,0 +1,95 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.tests.support import captured_stdout
+
+
+class DocsTypedListUsageTest(unittest.TestCase):
+
+    def test_ex_inferred_list_jit(self):
+        with captured_stdout():
+
+            # magictoken.ex_inferred_list_jit.begin
+            from numba import njit
+            from numba.typed import List
+
+            @njit
+            def foo():
+                # Instantiate a typed-list
+                l = List()
+                # Append a value to it, this will set the type to int32/int64
+                # (depending on platform)
+                l.append(42)
+                # The usual list operations, getitem, pop and length are
+                # supported
+                print(l[0])   # 42
+                l[0] = 23
+                print(l[0])   # 23
+                print(len(l)) # 1
+                l.pop()
+                print(len(l)) # 0
+                return l
+
+            foo()
+
+            # magictoken.ex_inferred_list_jit.end
+
+    def test_ex_inferred_list(self):
+        with captured_stdout():
+            # magictoken.ex_inferred_list.begin
+            from numba import njit
+            from numba.typed import List
+
+            @njit
+            def foo(mylist):
+                for i in range(10, 20):
+                    mylist.append(i)
+                return mylist
+
+            # Instantiate a typed-list, outside of a jit context
+            l = List()
+            # Append a value to it, this will set the type to int32/int64
+            # (depending on platform)
+            l.append(42)
+            # The usual list operations, getitem, pop and length are supported
+            print(l[0])   # 42
+            l[0] = 23
+            print(l[0])   # 23
+            print(len(l)) # 1
+            l.pop()
+            print(len(l)) # 0
+
+            # And you can use the typed-list as an argument for a jit compiled
+            # function
+            l = foo(l)
+            print(len(l)) # 10
+
+            # You can also directly construct a typed-list from an existing
+            # Python list
+            py_list = [2, 3, 5]
+            numba_list = List(py_list)
+            print(len(numba_list)) # 3
+
+            # magictoken.ex_inferred_list.end
+
+    def test_ex_nested_list(self):
+        with captured_stdout():
+            # magictoken.ex_nested_list.begin
+            from numba.typed import List
+
+            # typed-lists can be nested in typed-lists
+            mylist = List()
+            for i in range(10):
+                l = List()
+                for i in range(10):
+                    l.append(i)
+                mylist.append(l)
+            # mylist is now a list of 10 lists, each containing 10 integers
+            print(mylist)
+
+            # magictoken.ex_nested_list.end
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/dummy_module.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/dummy_module.py
new file mode 100644
index 000000000..4152f3ff5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/dummy_module.py
@@ -0,0 +1,4 @@
+'''Dummy module'''
+
+def function():
+    '''Do nothing'''
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/enum_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/enum_usecases.py
new file mode 100644
index 000000000..995dbd917
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/enum_usecases.py
@@ -0,0 +1,55 @@
+from enum import Enum, IntEnum
+
+
+class Color(Enum):
+    red = 1
+    green = 2
+    blue = 3
+
+
+class Shake(Enum):
+    vanilla = 7
+    chocolate = 4
+    cookies = 9
+    # Same as Color.blue
+    mint = 3
+
+
+class Planet(Enum):
+    MERCURY = (3.303e+23, 2.4397e6)
+    VENUS   = (4.869e+24, 6.0518e6)
+    EARTH   = (5.976e+24, 6.37814e6)
+    MARS    = (6.421e+23, 3.3972e6)
+    JUPITER = (1.9e+27,   7.1492e7)
+    SATURN  = (5.688e+26, 6.0268e7)
+    URANUS  = (8.686e+25, 2.5559e7)
+    NEPTUNE = (1.024e+26, 2.4746e7)
+
+
+class HeterogeneousEnum(Enum):
+    red = 1.0
+    green = 2.0
+    blue = 3j
+
+
+class Shape(IntEnum):
+    # Same as Color.green
+    circle = 2
+    # Same as RequestError.internal_error
+    square = 500
+
+
+class RequestError(IntEnum):
+    dummy = 2
+    not_found = 404
+    internal_error = 500
+
+class IntEnumWithNegatives(IntEnum):
+    # Used for testing of hash, need to make sure -1 -> -2 to comply with CPy
+    one = 1
+    two = 2
+    too = 2
+    three = 3
+    negone = -1
+    negtwo = -2
+    negthree = -3
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/error_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/error_usecases.py
new file mode 100644
index 000000000..c504a11c7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/error_usecases.py
@@ -0,0 +1,6 @@
+import numba as nb
+
+
+@nb.jit(nopython=True, parallel=True)
+def foo():
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/__init__.py
new file mode 100644
index 000000000..b2958441f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/__init__.py
@@ -0,0 +1,10 @@
+from os.path import dirname
+import unittest
+from unittest.suite import TestSuite
+
+from numba.testing import load_testsuite
+
+def load_tests(loader, tests, pattern):
+    suite = TestSuite()
+    suite.addTests(load_testsuite(loader, dirname(__file__)))
+    return suite
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_array_arg.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_array_arg.py
new file mode 100644
index 000000000..cb48dae2c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_array_arg.py
@@ -0,0 +1,51 @@
+# NOTE: This test is sensitive to line numbers as it checks breakpoints
+from numba import njit, types
+import numpy as np
+from numba.tests.gdb_support import GdbMIDriver
+from numba.tests.support import TestCase, needs_subprocess
+import unittest
+
+
+@needs_subprocess
+class Test(TestCase):
+
+    def test(self):
+        @njit(debug=True)
+        def foo(x):
+            z = np.ones_like(x) # break here
+            return x, z
+
+        tmp = np.ones(5)
+        foo(tmp)
+
+        driver = GdbMIDriver(__file__)
+        driver.set_breakpoint(line=15)
+        driver.run()
+        driver.check_hit_breakpoint(1)
+        driver.stack_list_arguments(2)
+        llvm_intp = f"i{types.intp.bitwidth}"
+        expect = (
+            '[frame={level="0",args=[{name="x",type="array(float64, 1d, C) '
+            f'({{i8*, i8*, {llvm_intp}, {llvm_intp}, double*, '
+            f'[1 x {llvm_intp}], [1 x {llvm_intp}]}})"}}]}}]'
+        )
+        driver.assert_output(expect)
+        driver.stack_list_variables(1)
+        # 'z' should be zero-init
+        expect = ('{name="z",value="{meminfo = 0x0, parent = 0x0, nitems = 0, '
+                  'itemsize = 0, data = 0x0, shape = {0}, strides = {0}}"}')
+        driver.assert_output(expect)
+        driver.set_breakpoint(line=16)
+        driver.cont()
+        driver.check_hit_breakpoint(2)
+        driver.stack_list_variables(1)
+        # 'z' should be populated
+        expect = (r'^.*\{name="z",value="\{meminfo = 0x[0-9a-f]+ .*, '
+                  r'parent = 0x0, nitems = 5, itemsize = 8, '
+                  r'data = 0x[0-9a-f]+, shape = \{5\}, strides = \{8\}\}.*$')
+        driver.assert_regex_output(expect)
+        driver.quit()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_basic.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_basic.py
new file mode 100644
index 000000000..2a28d6f74
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_basic.py
@@ -0,0 +1,39 @@
+# NOTE: This test is sensitive to line numbers as it checks breakpoints
+from numba import njit, types
+from numba.tests.gdb_support import GdbMIDriver
+from numba.tests.support import TestCase, needs_subprocess
+import unittest
+
+
+@needs_subprocess
+class Test(TestCase):
+
+    def test(self):
+        @njit(debug=True)
+        def foo(x):
+            z = 7 + x # break here
+            return x, z
+
+        foo(120)
+
+        sz = types.intp.bitwidth
+        driver = GdbMIDriver(__file__)
+        driver.set_breakpoint(line=14)
+        driver.run()
+        driver.check_hit_breakpoint(1)
+        driver.stack_list_arguments(2)
+        expect = ('[frame={level="0",args=[{name="x",type="int%s",'
+                  'value="120"}]}]' % sz)
+        driver.assert_output(expect)
+        driver.stack_list_variables(1)
+        expect = '[{name="x",arg="1",value="120"},{name="z",value="0"}]'
+        driver.assert_output(expect)
+        driver.next()
+        driver.stack_list_variables(1)
+        expect = '[{name="x",arg="1",value="120"},{name="z",value="127"}]'
+        driver.assert_output(expect)
+        driver.quit()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_break_on_symbol.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_break_on_symbol.py
new file mode 100644
index 000000000..7743aafc3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_break_on_symbol.py
@@ -0,0 +1,34 @@
+# NOTE: This test is sensitive to line numbers as it checks breakpoints
+from numba import njit, types
+from numba.tests.gdb_support import GdbMIDriver
+from numba.tests.support import TestCase, needs_subprocess
+import unittest
+
+
+@njit(debug=True)
+def foo(x):
+    z = 7 + x
+    return x, z
+
+
+@needs_subprocess
+class Test(TestCase):
+
+    def test(self):
+        foo(120)
+        sz = types.intp.bitwidth
+        driver = GdbMIDriver(__file__)
+        driver.set_breakpoint(symbol="__main__::foo")
+        driver.run() # will hit cpython symbol match
+        driver.check_hit_breakpoint(number=1)
+        driver.cont() # will hit njit symbol match
+        driver.check_hit_breakpoint(number=1, line=10) # Ensure line number
+        driver.stack_list_arguments(2)
+        expect = ('[frame={level="0",args=[{name="x",type="int%s",'
+                  'value="120"}]}]' % sz)
+        driver.assert_output(expect)
+        driver.quit()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_break_on_symbol_version.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_break_on_symbol_version.py
new file mode 100644
index 000000000..72f09b19a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_break_on_symbol_version.py
@@ -0,0 +1,65 @@
+# NOTE: This test is sensitive to line numbers as it checks breakpoints
+from numba import njit
+from numba.tests.gdb_support import GdbMIDriver
+from numba.tests.support import TestCase, needs_subprocess
+import unittest
+
+
+def foo_factory(n):
+    @njit(debug=True)
+    def foo(x):
+        z = 7 + n
+        return x, z
+
+    return foo
+
+
+foo1, foo2, foo3 = [foo_factory(x) for x in range(3)]
+
+
+@njit(debug=True)
+def call_foo():
+    a = foo1(10)
+    b = foo2(20)
+    c = foo3(30)
+    return a, b, c
+
+
+@needs_subprocess
+class Test(TestCase):
+
+    def test(self):
+        call_foo()
+        driver = GdbMIDriver(__file__)
+        # A specific foo, the first one, it has uid=2
+        vsym = "__main__::foo_factory::_3clocals_3e::foo[abi:v2]"
+        driver.set_breakpoint(symbol=vsym)
+        driver.run()
+        driver.check_hit_breakpoint(number=1)
+        driver.assert_regex_output(r'^.*foo\[abi:v2\].*line="11"')
+        driver.stack_list_arguments(2)
+        expect = ('[frame={level="0",args=[{name="x",type="Literal[int](10)",'
+                  'value="10"}]}]')
+        driver.assert_output(expect)
+        # Now break on any foo
+        driver.set_breakpoint(symbol="foo")
+        driver.cont()
+        driver.check_hit_breakpoint(number=2)
+        driver.assert_regex_output(r'^.*foo\[abi:v3\].*line="11"')
+        driver.stack_list_arguments(2)
+        expect = ('[frame={level="0",args=[{name="x",type="Literal[int](20)",'
+                  'value="20"}]}]')
+        driver.assert_output(expect)
+        # and again, hit the third foo
+        driver.cont()
+        driver.check_hit_breakpoint(number=2)
+        driver.assert_regex_output(r'^.*foo\[abi:v4\].*line="11"')
+        driver.stack_list_arguments(2)
+        expect = ('[frame={level="0",args=[{name="x",type="Literal[int](30)",'
+                  'value="30"}]}]')
+        driver.assert_output(expect)
+        driver.quit()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_conditional_breakpoint.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_conditional_breakpoint.py
new file mode 100644
index 000000000..867a53eb1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_conditional_breakpoint.py
@@ -0,0 +1,45 @@
+# NOTE: This test is sensitive to line numbers as it checks breakpoints
+from numba import njit
+from numba.tests.gdb_support import GdbMIDriver
+from numba.tests.support import TestCase, needs_subprocess
+import unittest
+
+
+@needs_subprocess
+class Test(TestCase):
+
+    def test(self):
+
+        @njit(debug=True)
+        def foo(x, y):
+            c = x + y  # break-here
+            return c
+
+        @njit(debug=True)
+        def call_foo(a):
+            acc = 0
+            for i in range(10):
+                acc += foo(i, a)
+            return acc
+
+        call_foo(10)
+
+        driver = GdbMIDriver(__file__)
+        driver.set_breakpoint(line=15, condition='x == 4')
+        driver.run()
+        driver.check_hit_breakpoint(1)
+        driver.stack_list_arguments(1)
+        expect = ('[frame={level="0",args=[{name="x",value="4"},'
+                  '{name="y",value="10"}]}]')
+        driver.assert_output(expect)
+        driver.set_breakpoint(line=22, condition='i == 8')
+        driver.cont()
+        driver.check_hit_breakpoint(2)
+        driver.stack_list_variables(1)
+        # i should be 8
+        driver.assert_output('{name="i",value="8"}')
+        driver.quit()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_pretty_print.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_pretty_print.py
new file mode 100644
index 000000000..b0be5dbe8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb/test_pretty_print.py
@@ -0,0 +1,69 @@
+# NOTE: This test is sensitive to line numbers as it checks breakpoints
+from numba import njit
+import numpy as np
+from numba.tests.gdb_support import GdbMIDriver, needs_gdb_py3
+from numba.tests.support import TestCase, needs_subprocess
+from numba.misc.numba_gdbinfo import collect_gdbinfo
+import unittest
+import re
+
+
+@needs_gdb_py3
+@needs_subprocess
+class Test(TestCase):
+
+    def test(self):
+        rdt_a = np.dtype([("x", np.int16), ("y", np.float64)], align=True)
+
+        @njit(debug=True)
+        def foo():
+            a = 1.234
+            b = (1, 2, 3)
+            c = ('a', b, 4)
+            d = np.arange(5.)
+            e = np.array([[1, 3j], [2, 4j]])
+            f = "Some string" + "           L-Padded string".lstrip()
+            g = 11 + 22j
+            h = np.arange(24).reshape((4, 6))[::2, ::3]
+            i = np.zeros(2, dtype=rdt_a)
+            return a, b, c, d, e, f, g, h, i
+
+        foo()
+
+        extension = collect_gdbinfo().extension_loc
+        driver = GdbMIDriver(__file__, init_cmds=['-x', extension], debug=False)
+        driver.set_breakpoint(line=29)
+        driver.run()
+        driver.check_hit_breakpoint(1)
+
+        # Ideally the function would be run to get the string repr of locals
+        # but not everything appears in DWARF e.g. string literals. Further,
+        # str on NumPy arrays seems to vary a bit in output. Therefore a custom
+        # match is used.
+
+        driver.stack_list_variables(1)
+        output = driver._captured.after.decode('UTF-8')
+        done_str = output.splitlines()[0]
+        pat = r'^\^done,variables=\[\{(.*)\}\]$'
+        lcls_strs = re.match(pat, done_str).groups()[0].split('},{')
+        lcls = {k: v for k, v in [re.match(r'name="(.*)",value="(.*)"',
+                x).groups() for x in lcls_strs]}
+        expected = dict()
+        expected['a'] = r'1\.234'
+        expected['b'] = r'\(1, 2, 3\)'
+        expected['c'] = r'\(0x0, \(1, 2, 3\), 4\)'
+        expected['d'] = r'\\n\[0. 1. 2. 3. 4.\]'
+        expected['e'] = r'\\n\[\[1.\+0.j 0.\+3.j\]\\n \[2.\+0.j 0.\+4.j\]\]'
+        expected['f'] = "'Some stringL-Padded string'"
+        expected['g'] = r"11\+22j"
+        expected['h'] = r'\\n\[\[ 0  3\]\\n \[12 15\]\]'
+        expected['i'] = r'\\n\[\(0, 0.\) \(0, 0.\)\]'
+
+        for k, v in expected.items():
+            self.assertRegex(lcls[k], v)
+
+        driver.quit()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb_support.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb_support.py
new file mode 100644
index 000000000..7a40bea03
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/gdb_support.py
@@ -0,0 +1,197 @@
+"""Helpers for running gdb related testing"""
+import os
+import re
+import sys
+import unittest
+from numba.core import config
+from numba.misc.gdb_hook import _confirm_gdb
+from numba.misc.numba_gdbinfo import collect_gdbinfo
+
+# check if gdb is present and working
+try:
+    _confirm_gdb(need_ptrace_attach=False)  # The driver launches as `gdb EXE`.
+    _HAVE_GDB = True
+    _gdb_info = collect_gdbinfo()
+    _GDB_HAS_PY3 = _gdb_info.py_ver.startswith('3')
+except Exception:
+    _HAVE_GDB = False
+    _GDB_HAS_PY3 = False
+
+_msg = "functioning gdb with correct ptrace permissions is required"
+needs_gdb = unittest.skipUnless(_HAVE_GDB, _msg)
+
+_msg = "gdb with python 3 support needed"
+needs_gdb_py3 = unittest.skipUnless(_GDB_HAS_PY3, _msg)
+
+
+try:
+    import pexpect
+    _HAVE_PEXPECT = True
+except ImportError:
+    _HAVE_PEXPECT = False
+
+
+_msg = "pexpect module needed for test"
+skip_unless_pexpect = unittest.skipUnless(_HAVE_PEXPECT, _msg)
+
+
+class GdbMIDriver(object):
+    """
+    Driver class for the GDB machine interface:
+    https://sourceware.org/gdb/onlinedocs/gdb/GDB_002fMI.html
+    """
+    def __init__(self, file_name, debug=False, timeout=120, init_cmds=None):
+        if not _HAVE_PEXPECT:
+            msg = ("This driver requires the pexpect module. This can be "
+                   "obtained via:\n\n$ conda install pexpect")
+            raise RuntimeError(msg)
+        if not _HAVE_GDB:
+            msg = ("This driver requires a gdb binary. This can be "
+                   "obtained via the system package manager.")
+            raise RuntimeError(msg)
+        self._gdb_binary = config.GDB_BINARY
+        self._python = sys.executable
+        self._debug = debug
+        self._file_name = file_name
+        self._timeout = timeout
+        self._init_cmds = init_cmds
+        self._drive()
+
+    def _drive(self):
+        """This function sets up the caputured gdb instance"""
+        assert os.path.isfile(self._file_name)
+        cmd = [self._gdb_binary, '--interpreter', 'mi']
+        if self._init_cmds is not None:
+            cmd += list(self._init_cmds)
+        cmd += ['--args', self._python, self._file_name]
+        self._captured = pexpect.spawn(' '.join(cmd))
+        if self._debug:
+            self._captured.logfile = sys.stdout.buffer
+
+    def supports_python(self):
+        """Returns True if the underlying gdb implementation has python support
+           False otherwise"""
+        return "python" in self.list_features()
+
+    def supports_numpy(self):
+        """Returns True if the underlying gdb implementation has NumPy support
+           (and by extension Python support) False otherwise"""
+        if not self.supports_python():
+            return False
+        # Some gdb's have python 2!
+        cmd = ('python from __future__ import print_function;'
+               'import numpy; print(numpy)')
+        self.interpreter_exec('console', cmd)
+        return "module \'numpy\' from" in self._captured.before.decode()
+
+    def _captured_expect(self, expect):
+        try:
+            self._captured.expect(expect, timeout=self._timeout)
+        except pexpect.exceptions.TIMEOUT as e:
+            msg = f"Expected value did not arrive: {expect}."
+            raise ValueError(msg) from e
+
+    def assert_output(self, expected):
+        """Asserts that the current output string contains the expected."""
+        output = self._captured.after
+        decoded = output.decode('utf-8')
+        assert expected in decoded, f'decoded={decoded}\nexpected={expected})'
+
+    def assert_regex_output(self, expected):
+        """Asserts that the current output string contains the expected
+        regex."""
+        output = self._captured.after
+        decoded = output.decode('utf-8')
+        done_str = decoded.splitlines()[0]
+        found = re.match(expected, done_str)
+        assert found, f'decoded={decoded}\nexpected={expected})'
+
+    def _run_command(self, command, expect=''):
+        self._captured.sendline(command)
+        self._captured_expect(expect)
+
+    def run(self):
+        """gdb command ~= 'run'"""
+        self._run_command('-exec-run', expect=r'\^running.*\r\n')
+
+    def cont(self):
+        """gdb command ~= 'continue'"""
+        self._run_command('-exec-continue', expect=r'\^running.*\r\n')
+
+    def quit(self):
+        """gdb command ~= 'quit'"""
+        self._run_command('-gdb-exit', expect=r'-gdb-exit')
+        self._captured.terminate()
+
+    def next(self):
+        """gdb command ~= 'next'"""
+        self._run_command('-exec-next', expect=r'\*stopped,.*\r\n')
+
+    def step(self):
+        """gdb command ~= 'step'"""
+        self._run_command('-exec-step', expect=r'\*stopped,.*\r\n')
+
+    def set_breakpoint(self, line=None, symbol=None, condition=None):
+        """gdb command ~= 'break'"""
+        if line is not None and symbol is not None:
+            raise ValueError("Can only supply one of line or symbol")
+        bp = '-break-insert '
+        if condition is not None:
+            bp += f'-c "{condition}" '
+        if line is not None:
+            assert isinstance(line, int)
+            bp += f'-f {self._file_name}:{line} '
+        if symbol is not None:
+            assert isinstance(symbol, str)
+            bp += f'-f {symbol} '
+        self._run_command(bp, expect=r'\^done')
+
+    def check_hit_breakpoint(self, number=None, line=None):
+        """Checks that a breakpoint has been hit"""
+        self._captured_expect(r'\*stopped,.*\r\n')
+        self.assert_output('*stopped,reason="breakpoint-hit",')
+        if number is not None:
+            assert isinstance(number, int)
+            self.assert_output(f'bkptno="{number}"')
+        if line is not None:
+            assert isinstance(line, int)
+            self.assert_output(f'line="{line}"')
+
+    def stack_list_arguments(self, print_values=1, low_frame=0, high_frame=0):
+        """gdb command ~= 'info args'"""
+        for x in (print_values, low_frame, high_frame):
+            assert isinstance(x, int) and x in (0, 1, 2)
+        cmd = f'-stack-list-arguments {print_values} {low_frame} {high_frame}'
+        self._run_command(cmd, expect=r'\^done,.*\r\n')
+
+    def stack_list_variables(self, print_values=1):
+        """gdb command ~= 'info locals'"""
+        assert isinstance(print_values, int) and print_values in (0, 1, 2)
+        cmd = f'-stack-list-variables {print_values}'
+        self._run_command(cmd, expect=r'\^done,.*\r\n')
+
+    def interpreter_exec(self, interpreter=None, command=None):
+        """gdb command ~= 'interpreter-exec'"""
+        if interpreter is None:
+            raise ValueError("interpreter cannot be None")
+        if command is None:
+            raise ValueError("command cannot be None")
+        cmd = f'-interpreter-exec {interpreter} "{command}"'
+        self._run_command(cmd, expect=r'\^(done|error).*\r\n')  # NOTE no `,`
+
+    def _list_features_raw(self):
+        cmd = '-list-features'
+        self._run_command(cmd, expect=r'\^done,.*\r\n')
+
+    def list_features(self):
+        """No equivalent gdb command? Returns a list of supported gdb
+           features.
+        """
+        self._list_features_raw()
+        output = self._captured.after
+        decoded = output.decode('utf-8')
+        m = re.match('.*features=\\[(.*)\\].*', decoded)
+        assert m is not None, "No match found for features string"
+        g = m.groups()
+        assert len(g) == 1, "Invalid number of match groups found"
+        return g[0].replace('"', '').split(',')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/inlining_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/inlining_usecases.py
new file mode 100644
index 000000000..aefd5f589
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/inlining_usecases.py
@@ -0,0 +1,45 @@
+""" Test cases for inlining IR from another module """
+from numba import njit
+from numba.core.extending import overload
+
+_GLOBAL1 = 100
+
+
+@njit(inline='always')
+def bar():
+    return _GLOBAL1 + 10
+
+
+def baz_factory(a):
+    b = 17 + a
+
+    @njit(inline='always')
+    def baz():
+        return _GLOBAL1 + a - b
+    return baz
+
+
+def baz():
+    return _GLOBAL1 + 10
+
+
+@overload(baz, inline='always')
+def baz_ol():
+    def impl():
+        return _GLOBAL1 + 10
+    return impl
+
+
+def bop_factory(a):
+    b = 17 + a
+
+    def bop():
+        return _GLOBAL1 + a - b
+
+    @overload(bop, inline='always')
+    def baz():
+        def impl():
+            return _GLOBAL1 + a - b
+        return impl
+
+    return bop
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/matmul_usecase.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/matmul_usecase.py
new file mode 100644
index 000000000..84af4ef30
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/matmul_usecase.py
@@ -0,0 +1,24 @@
+"""Use cases for testing matmul (@)
+"""
+def matmul_usecase(x, y):
+    return x @ y
+
+def imatmul_usecase(x, y):
+    x @= y
+    return x
+
+class DumbMatrix(object):
+
+    def __init__(self, value):
+        self.value = value
+
+    def __matmul__(self, other):
+        if isinstance(other, DumbMatrix):
+            return DumbMatrix(self.value * other.value)
+        return NotImplemented
+
+    def __imatmul__(self, other):
+        if isinstance(other, DumbMatrix):
+            self.value *= other.value
+            return self
+        return NotImplemented
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/__init__.py
new file mode 100644
index 000000000..b2958441f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/__init__.py
@@ -0,0 +1,10 @@
+from os.path import dirname
+import unittest
+from unittest.suite import TestSuite
+
+from numba.testing import load_testsuite
+
+def load_tests(loader, tests, pattern):
+    suite = TestSuite()
+    suite.addTests(load_testsuite(loader, dirname(__file__)))
+    return suite
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/cache_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/cache_usecases.py
new file mode 100644
index 000000000..4d250756d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/cache_usecases.py
@@ -0,0 +1,76 @@
+import numba as nb
+
+
+#
+# UFunc
+#
+
+def direct_ufunc_cache_usecase(**kwargs):
+    @nb.vectorize(["intp(intp)", "float64(float64)"], cache=True, **kwargs)
+    def ufunc(inp):
+        return inp * 2
+
+    return ufunc
+
+
+def indirect_ufunc_cache_usecase(**kwargs):
+    @nb.njit(cache=True)
+    def indirect_ufunc_core(inp):
+        return inp * 3
+
+    @nb.vectorize(["intp(intp)", "float64(float64)", "complex64(complex64)"],
+                  **kwargs)
+    def ufunc(inp):
+        return indirect_ufunc_core(inp)
+
+    return ufunc
+
+
+#
+# DUFunc
+#
+
+def direct_dufunc_cache_usecase(**kwargs):
+    @nb.vectorize(cache=True, **kwargs)
+    def ufunc(inp):
+        return inp * 2
+
+    return ufunc
+
+
+def indirect_dufunc_cache_usecase(**kwargs):
+    @nb.njit(cache=True)
+    def indirect_ufunc_core(inp):
+        return inp * 3
+
+    @nb.vectorize(**kwargs)
+    def ufunc(inp):
+        return indirect_ufunc_core(inp)
+
+    return ufunc
+
+
+#
+# GUFunc
+#
+
+def direct_gufunc_cache_usecase(**kwargs):
+    @nb.guvectorize(["(intp, intp[:])", "(float64, float64[:])"],
+                    "()->()", cache=True, **kwargs)
+    def gufunc(inp, out):
+        out[0] = inp * 2
+
+    return gufunc
+
+
+def indirect_gufunc_cache_usecase(**kwargs):
+    @nb.njit(cache=True)
+    def core(x):
+        return x * 3
+
+    @nb.guvectorize(["(intp, intp[:])", "(float64, float64[:])",
+                     "(complex64, complex64[:])"], "()->()", **kwargs)
+    def gufunc(inp, out):
+        out[0] = core(inp)
+
+    return gufunc
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_caching.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_caching.py
new file mode 100644
index 000000000..cfb47f113
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_caching.py
@@ -0,0 +1,228 @@
+import sys
+import os.path
+import re
+import subprocess
+
+import numpy as np
+
+from numba.tests.support import capture_cache_log
+from numba.tests.test_caching import BaseCacheTest
+from numba.core import config
+import unittest
+
+
+class UfuncCacheTest(BaseCacheTest):
+    """
+    Since the cache stats is not exposed by ufunc, we test by looking at the
+    cache debug log.
+    """
+    _numba_parallel_test_ = False
+
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cache_usecases.py")
+    modname = "ufunc_caching_test_fodder"
+
+    regex_data_saved = re.compile(r'\[cache\] data saved to')
+    regex_index_saved = re.compile(r'\[cache\] index saved to')
+
+    regex_data_loaded = re.compile(r'\[cache\] data loaded from')
+    regex_index_loaded = re.compile(r'\[cache\] index loaded from')
+
+    def check_cache_saved(self, cachelog, count):
+        """
+        Check number of cache-save were issued
+        """
+        data_saved = self.regex_data_saved.findall(cachelog)
+        index_saved = self.regex_index_saved.findall(cachelog)
+        self.assertEqual(len(data_saved), count)
+        self.assertEqual(len(index_saved), count)
+
+    def check_cache_loaded(self, cachelog, count):
+        """
+        Check number of cache-load were issued
+        """
+        data_loaded = self.regex_data_loaded.findall(cachelog)
+        index_loaded = self.regex_index_loaded.findall(cachelog)
+        self.assertEqual(len(data_loaded), count)
+        self.assertEqual(len(index_loaded), count)
+
+    def check_ufunc_cache(self, usecase_name, n_overloads, **kwargs):
+        """
+        Check number of cache load/save.
+        There should be one per overloaded version.
+        """
+        mod = self.import_module()
+        usecase = getattr(mod, usecase_name)
+        # New cache entry saved
+        with capture_cache_log() as out:
+            new_ufunc = usecase(**kwargs)
+        cachelog = out.getvalue()
+        self.check_cache_saved(cachelog, count=n_overloads)
+
+        # Use cached version
+        with capture_cache_log() as out:
+            cached_ufunc = usecase(**kwargs)
+        cachelog = out.getvalue()
+        self.check_cache_loaded(cachelog, count=n_overloads)
+
+        return new_ufunc, cached_ufunc
+
+
+class TestUfuncCacheTest(UfuncCacheTest):
+
+    def test_direct_ufunc_cache(self, **kwargs):
+        new_ufunc, cached_ufunc = self.check_ufunc_cache(
+            "direct_ufunc_cache_usecase", n_overloads=2, **kwargs)
+        # Test the cached and original versions
+        inp = np.random.random(10).astype(np.float64)
+        np.testing.assert_equal(new_ufunc(inp), cached_ufunc(inp))
+        inp = np.arange(10, dtype=np.intp)
+        np.testing.assert_equal(new_ufunc(inp), cached_ufunc(inp))
+
+    def test_direct_ufunc_cache_objmode(self):
+        self.test_direct_ufunc_cache(forceobj=True)
+
+    def test_direct_ufunc_cache_parallel(self):
+        self.test_direct_ufunc_cache(target='parallel')
+
+    def test_indirect_ufunc_cache(self, **kwargs):
+        new_ufunc, cached_ufunc = self.check_ufunc_cache(
+            "indirect_ufunc_cache_usecase", n_overloads=3, **kwargs)
+        # Test the cached and original versions
+        inp = np.random.random(10).astype(np.float64)
+        np.testing.assert_equal(new_ufunc(inp), cached_ufunc(inp))
+        inp = np.arange(10, dtype=np.intp)
+        np.testing.assert_equal(new_ufunc(inp), cached_ufunc(inp))
+
+    def test_indirect_ufunc_cache_parallel(self):
+        self.test_indirect_ufunc_cache(target='parallel')
+
+
+class TestDUfuncCacheTest(UfuncCacheTest):
+    # Note: DUFunc doesn't support parallel target yet
+
+    def check_dufunc_usecase(self, usecase_name):
+        mod = self.import_module()
+        usecase = getattr(mod, usecase_name)
+        # Create dufunc
+        with capture_cache_log() as out:
+            ufunc = usecase()
+        self.check_cache_saved(out.getvalue(), count=0)
+        # Compile & cache
+        with capture_cache_log() as out:
+            ufunc(np.arange(10))
+        self.check_cache_saved(out.getvalue(), count=1)
+        self.check_cache_loaded(out.getvalue(), count=0)
+        # Use cached
+        with capture_cache_log() as out:
+            ufunc = usecase()
+            ufunc(np.arange(10))
+        self.check_cache_loaded(out.getvalue(), count=1)
+
+    def test_direct_dufunc_cache(self):
+        # We don't test for objmode because DUfunc don't support it.
+        self.check_dufunc_usecase('direct_dufunc_cache_usecase')
+
+    def test_indirect_dufunc_cache(self):
+        self.check_dufunc_usecase('indirect_dufunc_cache_usecase')
+
+
+def _fix_raw_path(rstr):
+    if config.IS_WIN32:
+        rstr = rstr.replace(r'/', r'\\\\')
+    return rstr
+
+
+class TestGUfuncCacheTest(UfuncCacheTest):
+
+    def test_filename_prefix(self):
+        mod = self.import_module()
+        usecase = getattr(mod, "direct_gufunc_cache_usecase")
+        with capture_cache_log() as out:
+            usecase()
+        cachelog = out.getvalue()
+        # find number filename with "guf-" prefix
+        fmt1 = _fix_raw_path(r'/__pycache__/guf-{}')
+        prefixed = re.findall(fmt1.format(self.modname), cachelog)
+        fmt2 = _fix_raw_path(r'/__pycache__/{}')
+        normal = re.findall(fmt2.format(self.modname), cachelog)
+        # expecting 2 overloads
+        self.assertGreater(len(normal), 2)
+        # expecting equal number of wrappers and overloads cache entries
+        self.assertEqual(len(normal), len(prefixed))
+
+    def test_direct_gufunc_cache(self, **kwargs):
+        # 2 cache entry for the 2 overloads
+        # and 2 cache entry for the gufunc wrapper
+        new_ufunc, cached_ufunc = self.check_ufunc_cache(
+            "direct_gufunc_cache_usecase", n_overloads=2 + 2, **kwargs)
+        # Test the cached and original versions
+        inp = np.random.random(10).astype(np.float64)
+        np.testing.assert_equal(new_ufunc(inp), cached_ufunc(inp))
+        inp = np.arange(10, dtype=np.intp)
+        np.testing.assert_equal(new_ufunc(inp), cached_ufunc(inp))
+
+    def test_direct_gufunc_cache_objmode(self):
+        self.test_direct_gufunc_cache(forceobj=True)
+
+    def test_direct_gufunc_cache_parallel(self):
+        self.test_direct_gufunc_cache(target='parallel')
+
+    def test_indirect_gufunc_cache(self, **kwargs):
+        # 3 cache entry for the 3 overloads
+        # and no cache entry for the gufunc wrapper
+        new_ufunc, cached_ufunc = self.check_ufunc_cache(
+            "indirect_gufunc_cache_usecase", n_overloads=3, **kwargs)
+        # Test the cached and original versions
+        inp = np.random.random(10).astype(np.float64)
+        np.testing.assert_equal(new_ufunc(inp), cached_ufunc(inp))
+        inp = np.arange(10, dtype=np.intp)
+        np.testing.assert_equal(new_ufunc(inp), cached_ufunc(inp))
+
+    def test_indirect_gufunc_cache_parallel(self, **kwargs):
+        self.test_indirect_gufunc_cache(target='parallel')
+
+
+class TestCacheSpecificIssue(UfuncCacheTest):
+
+    def run_in_separate_process(self, runcode):
+        # Based on the same name util function in test_dispatcher but modified
+        # to allow user to define what to run.
+        code = """if 1:
+            import sys
+
+            sys.path.insert(0, %(tempdir)r)
+            mod = __import__(%(modname)r)
+            mod.%(runcode)s
+            """ % dict(tempdir=self.tempdir, modname=self.modname,
+                       runcode=runcode)
+
+        popen = subprocess.Popen([sys.executable, "-c", code],
+                                 stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        out, err = popen.communicate()
+        if popen.returncode != 0:
+            raise AssertionError("process failed with code %s: stderr follows"
+                                 "\n%s\n" % (popen.returncode, err.decode()))
+
+    #
+    # The following test issue #2198 that loading cached (g)ufunc first
+    # bypasses some target context initialization.
+    #
+
+    def test_first_load_cached_ufunc(self):
+        # ensure function is cached
+        self.run_in_separate_process('direct_ufunc_cache_usecase()')
+        # use the cached function
+        # this will fail if the target context is not init'ed
+        self.run_in_separate_process('direct_ufunc_cache_usecase()')
+
+    def test_first_load_cached_gufunc(self):
+        # ensure function is cached
+        self.run_in_separate_process('direct_gufunc_cache_usecase()')
+        # use the cached function
+        # this will fail out if the target context is not init'ed
+        self.run_in_separate_process('direct_gufunc_cache_usecase()')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_dufunc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_dufunc.py
new file mode 100644
index 000000000..a9d7f1bcc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_dufunc.py
@@ -0,0 +1,125 @@
+import pickle
+
+import numpy as np
+
+from numba import njit, vectorize
+from numba.tests.support import MemoryLeakMixin
+import unittest
+from numba.np.ufunc import dufunc
+
+
+def pyuadd(a0, a1):
+    return a0 + a1
+
+
+class TestDUFunc(MemoryLeakMixin, unittest.TestCase):
+
+    def nopython_dufunc(self, pyfunc):
+        return dufunc.DUFunc(pyfunc, targetoptions=dict(nopython=True))
+
+    def test_frozen(self):
+        duadd = self.nopython_dufunc(pyuadd)
+        self.assertFalse(duadd._frozen)
+        duadd._frozen = True
+        self.assertTrue(duadd._frozen)
+        with self.assertRaises(ValueError):
+            duadd._frozen = False
+        with self.assertRaises(TypeError):
+            duadd(np.linspace(0,1,10), np.linspace(1,2,10))
+
+    def test_scalar(self):
+        duadd = self.nopython_dufunc(pyuadd)
+        self.assertEqual(pyuadd(1,2), duadd(1,2))
+
+    def test_npm_call(self):
+        duadd = self.nopython_dufunc(pyuadd)
+
+        @njit
+        def npmadd(a0, a1, o0):
+            duadd(a0, a1, o0)
+        X = np.linspace(0,1.9,20)
+        X0 = X[:10]
+        X1 = X[10:]
+        out0 = np.zeros(10)
+        npmadd(X0, X1, out0)
+        np.testing.assert_array_equal(X0 + X1, out0)
+        Y0 = X0.reshape((2,5))
+        Y1 = X1.reshape((2,5))
+        out1 = np.zeros((2,5))
+        npmadd(Y0, Y1, out1)
+        np.testing.assert_array_equal(Y0 + Y1, out1)
+        Y2 = X1[:5]
+        out2 = np.zeros((2,5))
+        npmadd(Y0, Y2, out2)
+        np.testing.assert_array_equal(Y0 + Y2, out2)
+
+    def test_npm_call_implicit_output(self):
+        duadd = self.nopython_dufunc(pyuadd)
+
+        @njit
+        def npmadd(a0, a1):
+            return duadd(a0, a1)
+        X = np.linspace(0,1.9,20)
+        X0 = X[:10]
+        X1 = X[10:]
+        out0 = npmadd(X0, X1)
+        np.testing.assert_array_equal(X0 + X1, out0)
+        Y0 = X0.reshape((2,5))
+        Y1 = X1.reshape((2,5))
+        out1 = npmadd(Y0, Y1)
+        np.testing.assert_array_equal(Y0 + Y1, out1)
+        Y2 = X1[:5]
+        out2 = npmadd(Y0, Y2)
+        np.testing.assert_array_equal(Y0 + Y2, out2)
+        out3 = npmadd(1.,2.)
+        self.assertEqual(out3, 3.)
+
+    def test_ufunc_props(self):
+        duadd = self.nopython_dufunc(pyuadd)
+        self.assertEqual(duadd.nin, 2)
+        self.assertEqual(duadd.nout, 1)
+        self.assertEqual(duadd.nargs, duadd.nin + duadd.nout)
+        self.assertEqual(duadd.ntypes, 0)
+        self.assertEqual(duadd.types, [])
+        self.assertEqual(duadd.identity, None)
+        duadd(1, 2)
+        self.assertEqual(duadd.ntypes, 1)
+        self.assertEqual(duadd.ntypes, len(duadd.types))
+
+
+class TestDUFuncPickling(MemoryLeakMixin, unittest.TestCase):
+    def check(self, ident, result_type):
+        buf = pickle.dumps(ident)
+        rebuilt = pickle.loads(buf)
+
+        # Check reconstructed dufunc
+        r = rebuilt(123)
+        self.assertEqual(123, r)
+        self.assertIsInstance(r, result_type)
+
+        # Try to use reconstructed dufunc in @jit
+        @njit
+        def foo(x):
+            return rebuilt(x)
+
+        r = foo(321)
+        self.assertEqual(321, r)
+        self.assertIsInstance(r, result_type)
+
+    def test_unrestricted(self):
+        @vectorize
+        def ident(x1):
+            return x1
+
+        self.check(ident, result_type=(int, np.integer))
+
+    def test_restricted(self):
+        @vectorize(["float64(float64)"])
+        def ident(x1):
+            return x1
+
+        self.check(ident, result_type=float)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_errors.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_errors.py
new file mode 100644
index 000000000..e80ae67eb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_errors.py
@@ -0,0 +1,174 @@
+import contextlib
+import sys
+
+import numpy as np
+
+from numba import vectorize, guvectorize
+
+from numba.tests.support import (TestCase, CheckWarningsMixin,
+                                 skip_m1_fenv_errors)
+import unittest
+
+
+def sqrt(val):
+    if val < 0.0:
+        raise ValueError('Value must be positive')
+    return val ** 0.5
+
+
+def gufunc_foo(inp, n, out):
+    for i in range(inp.shape[0]):
+        if inp[i] < 0:
+            raise ValueError('Value must be positive')
+        out[i] = inp[i] * n[0]
+
+def truediv(a, b):
+    return a / b
+
+def floordiv(a, b):
+    return a // b
+
+def remainder(a, b):
+    return a % b
+
+def power(a, b):
+    return a ** b
+
+
+class TestExceptions(TestCase):
+    """
+    Test raising exceptions inside ufuncs.
+    """
+
+    def check_ufunc_raise(self, **vectorize_args):
+        f = vectorize(['float64(float64)'], **vectorize_args)(sqrt)
+        arr = np.array([1, 4, -2, 9, -1, 16], dtype=np.float64)
+        out = np.zeros_like(arr)
+        with self.assertRaises(ValueError) as cm:
+            f(arr, out)
+        self.assertIn('Value must be positive', str(cm.exception))
+        # All values were computed except for the ones giving an error
+        self.assertEqual(list(out), [1, 2, 0, 3, 0, 4])
+
+    def test_ufunc_raise(self):
+        self.check_ufunc_raise(nopython=True)
+
+    def test_ufunc_raise_objmode(self):
+        self.check_ufunc_raise(forceobj=True)
+
+    def check_gufunc_raise(self, **vectorize_args):
+        f = guvectorize(['int32[:], int32[:], int32[:]'], '(n),()->(n)',
+                        **vectorize_args)(gufunc_foo)
+        arr = np.array([1, 2, -3, 4], dtype=np.int32)
+        out = np.zeros_like(arr)
+        with self.assertRaises(ValueError) as cm:
+            f(arr, 2, out)
+        # The gufunc bailed out after the error
+        self.assertEqual(list(out), [2, 4, 0, 0])
+
+    def test_gufunc_raise(self):
+        self.check_gufunc_raise(nopython=True)
+
+    def test_gufunc_raise_objmode(self):
+        self.check_gufunc_raise(forceobj=True)
+
+class TestFloatingPointExceptions(TestCase, CheckWarningsMixin):
+    """
+    Test floating-point exceptions inside ufuncs.
+
+    Note the warnings emitted by Numpy reflect IEEE-754 semantics.
+    """
+
+    def check_truediv_real(self, dtype):
+        """
+        Test 1 / 0 and 0 / 0.
+        """
+        f = vectorize(nopython=True)(truediv)
+        a = np.array([5., 6., 0., 8.], dtype=dtype)
+        b = np.array([1., 0., 0., 4.], dtype=dtype)
+        expected = np.array([5., float('inf'), float('nan'), 2.])
+        with self.check_warnings(["divide by zero encountered",
+                                  "invalid value encountered"]):
+            res = f(a, b)
+            self.assertPreciseEqual(res, expected)
+
+    def test_truediv_float(self):
+        self.check_truediv_real(np.float64)
+
+    def test_truediv_integer(self):
+        self.check_truediv_real(np.int32)
+
+    def check_divmod_float(self, pyfunc, values, messages):
+        """
+        Test 1 // 0 and 0 // 0.
+        """
+        f = vectorize(nopython=True)(pyfunc)
+        a = np.array([5., 6., 0., 9.])
+        b = np.array([1., 0., 0., 4.])
+        expected = np.array(values)
+        with self.check_warnings(messages):
+            res = f(a, b)
+            self.assertPreciseEqual(res, expected)
+
+    def test_floordiv_float(self):
+        self.check_divmod_float(floordiv,
+                                [5.0, float('inf'), float('nan'), 2.0],
+                                ["divide by zero encountered",
+                                 "invalid value encountered"])
+
+    @skip_m1_fenv_errors
+    def test_remainder_float(self):
+        self.check_divmod_float(remainder,
+                                [0.0, float('nan'), float('nan'), 1.0],
+                                ["invalid value encountered"])
+
+    def check_divmod_int(self, pyfunc, values):
+        """
+        Test 1 % 0 and 0 % 0.
+        """
+        f = vectorize(nopython=True)(pyfunc)
+        a = np.array([5, 6, 0, 9])
+        b = np.array([1, 0, 0, 4])
+        expected = np.array(values)
+        # No warnings raised because LLVM makes it difficult
+        with self.check_warnings([]):
+            res = f(a, b)
+            self.assertPreciseEqual(res, expected)
+
+    def test_floordiv_int(self):
+        self.check_divmod_int(floordiv, [5, 0, 0, 2])
+
+    def test_remainder_int(self):
+        self.check_divmod_int(remainder, [0, 0, 0, 1])
+
+    def test_power_float(self):
+        """
+        Test 0 ** -1 and 2 ** <big number>.
+        """
+        f = vectorize(nopython=True)(power)
+        a = np.array([5., 0., 2., 8.])
+        b = np.array([1., -1., 1e20, 4.])
+        expected = np.array([5., float('inf'), float('inf'), 4096.])
+        with self.check_warnings(["divide by zero encountered",
+                                  "overflow encountered"]):
+            res = f(a, b)
+            self.assertPreciseEqual(res, expected)
+
+    def test_power_integer(self):
+        """
+        Test 0 ** -1.
+        Note 2 ** <big number> returns an undefined value (depending
+        on the algorithm).
+        """
+        dtype = np.int64
+        f = vectorize(["int64(int64, int64)"], nopython=True)(power)
+        a = np.array([5, 0, 6], dtype=dtype)
+        b = np.array([1, -1, 2], dtype=dtype)
+        expected = np.array([5, -2**63, 36], dtype=dtype)
+        with self.check_warnings([]):
+            res = f(a, b)
+            self.assertPreciseEqual(res, expected)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_gufunc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_gufunc.py
new file mode 100644
index 000000000..c5c6f9c94
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_gufunc.py
@@ -0,0 +1,481 @@
+import unittest
+import pickle
+
+import numpy as np
+import numpy.core.umath_tests as ut
+
+from numba import void, float32, int64, jit, guvectorize
+from numba.np.ufunc import GUVectorize
+from numba.tests.support import tag, TestCase
+
+
+def matmulcore(A, B, C):
+    """docstring for matmulcore"""
+    m, n = A.shape
+    n, p = B.shape
+    for i in range(m):
+        for j in range(p):
+            C[i, j] = 0
+            for k in range(n):
+                C[i, j] += A[i, k] * B[k, j]
+
+
+def axpy(a, x, y, out):
+    out[0] = a * x  + y
+
+
+class TestGUFunc(TestCase):
+    target = 'cpu'
+
+    def check_matmul_gufunc(self, gufunc):
+        matrix_ct = 1001
+        A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2, 4)
+        B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4, 5)
+
+        C = gufunc(A, B)
+        Gold = ut.matrix_multiply(A, B)
+
+        np.testing.assert_allclose(C, Gold, rtol=1e-5, atol=1e-8)
+
+    def test_gufunc(self):
+        gufunc = GUVectorize(matmulcore, '(m,n),(n,p)->(m,p)',
+                             target=self.target)
+        gufunc.add((float32[:, :], float32[:, :], float32[:, :]))
+        gufunc = gufunc.build_ufunc()
+
+        self.check_matmul_gufunc(gufunc)
+
+    def test_guvectorize_decor(self):
+        gufunc = guvectorize([void(float32[:,:], float32[:,:], float32[:,:])],
+                             '(m,n),(n,p)->(m,p)',
+                             target=self.target)(matmulcore)
+
+        self.check_matmul_gufunc(gufunc)
+
+    def test_ufunc_like(self):
+        # Test problem that the stride of "scalar" gufunc argument not properly
+        # handled when the actual argument is an array,
+        # causing the same value (first value) being repeated.
+        gufunc = GUVectorize(axpy, '(), (), () -> ()', target=self.target)
+        gufunc.add('(intp, intp, intp, intp[:])')
+        gufunc = gufunc.build_ufunc()
+
+        x = np.arange(10, dtype=np.intp)
+        out = gufunc(x, x, x)
+
+        np.testing.assert_equal(out, x * x + x)
+
+    def test_axis(self):
+        # issue https://github.com/numba/numba/issues/6773
+        @guvectorize(["f8[:],f8[:]"], "(n)->(n)")
+        def my_cumsum(x, res):
+            acc = 0
+            for i in range(x.shape[0]):
+                acc += x[i]
+                res[i] = acc
+
+        x = np.ones((20, 30))
+        # Check regular call
+        y = my_cumsum(x, axis=0)
+        expected = np.cumsum(x, axis=0)
+        np.testing.assert_equal(y, expected)
+        # Check "out" kw
+        out_kw = np.zeros_like(y)
+        my_cumsum(x, out=out_kw, axis=0)
+        np.testing.assert_equal(out_kw, expected)
+
+    def test_docstring(self):
+        @guvectorize([(int64[:], int64, int64[:])], '(n),()->(n)')
+        def gufunc(x, y, res):
+            "docstring for gufunc"
+            for i in range(x.shape[0]):
+                res[i] = x[i] + y
+
+        self.assertEqual("numba.tests.npyufunc.test_gufunc", gufunc.__module__)
+        self.assertEqual("gufunc", gufunc.__name__)
+        self.assertEqual("TestGUFunc.test_docstring.<locals>.gufunc", gufunc.__qualname__)
+        self.assertEqual("docstring for gufunc", gufunc.__doc__)
+
+
+class TestGUFuncParallel(TestGUFunc):
+    _numba_parallel_test_ = False
+    target = 'parallel'
+
+
+class TestDynamicGUFunc(TestCase):
+    target = 'cpu'
+
+    def test_dynamic_matmul(self):
+
+        def check_matmul_gufunc(gufunc, A, B, C):
+            Gold = ut.matrix_multiply(A, B)
+            gufunc(A, B, C)
+            np.testing.assert_allclose(C, Gold, rtol=1e-5, atol=1e-8)
+
+        gufunc = GUVectorize(matmulcore, '(m,n),(n,p)->(m,p)',
+                             target=self.target, is_dynamic=True)
+        matrix_ct = 10
+        Ai64 = np.arange(matrix_ct * 2 * 4, dtype=np.int64).reshape(matrix_ct, 2, 4)
+        Bi64 = np.arange(matrix_ct * 4 * 5, dtype=np.int64).reshape(matrix_ct, 4, 5)
+        Ci64 = np.arange(matrix_ct * 2 * 5, dtype=np.int64).reshape(matrix_ct, 2, 5)
+        check_matmul_gufunc(gufunc, Ai64, Bi64, Ci64)
+
+        A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2, 4)
+        B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4, 5)
+        C = np.arange(matrix_ct * 2 * 5, dtype=np.float32).reshape(matrix_ct, 2, 5)
+        check_matmul_gufunc(gufunc, A, B, C)  # trigger compilation
+
+        self.assertEqual(len(gufunc.types), 2)  # ensure two versions of gufunc
+
+
+    def test_dynamic_ufunc_like(self):
+
+        def check_ufunc_output(gufunc, x):
+            out = np.zeros(10, dtype=x.dtype)
+            out_kw = np.zeros(10, dtype=x.dtype)
+            gufunc(x, x, x, out)
+            gufunc(x, x, x, out=out_kw)
+            golden = x * x + x
+            np.testing.assert_equal(out, golden)
+            np.testing.assert_equal(out_kw, golden)
+
+        # Test problem that the stride of "scalar" gufunc argument not properly
+        # handled when the actual argument is an array,
+        # causing the same value (first value) being repeated.
+        gufunc = GUVectorize(axpy, '(), (), () -> ()', target=self.target,
+                             is_dynamic=True)
+        x = np.arange(10, dtype=np.intp)
+        check_ufunc_output(gufunc, x)
+
+
+    def test_dynamic_scalar_output(self):
+        """
+        Note that scalar output is a 0-dimension array that acts as
+        a pointer to the output location.
+        """
+
+        @guvectorize('(n)->()', target=self.target, nopython=True)
+        def sum_row(inp, out):
+            tmp = 0.
+            for i in range(inp.shape[0]):
+                tmp += inp[i]
+            out[()] = tmp
+
+        # inp is (10000, 3)
+        # out is (10000)
+        # The outer (leftmost) dimension must match or numpy broadcasting is performed.
+
+        self.assertTrue(sum_row.is_dynamic)
+        inp = np.arange(30000, dtype=np.int32).reshape(10000, 3)
+        out = np.zeros(10000, dtype=np.int32)
+        sum_row(inp, out)
+
+        # verify result
+        for i in range(inp.shape[0]):
+            self.assertEqual(out[i], inp[i].sum())
+
+        msg = "Too few arguments for function 'sum_row'."
+        with self.assertRaisesRegex(TypeError, msg):
+            sum_row(inp)
+
+    def test_axis(self):
+        # issue https://github.com/numba/numba/issues/6773
+        @guvectorize("(n)->(n)")
+        def my_cumsum(x, res):
+            acc = 0
+            for i in range(x.shape[0]):
+                acc += x[i]
+                res[i] = acc
+
+        x = np.ones((20, 30))
+        expected = np.cumsum(x, axis=0)
+        # Check regular call
+        y = np.zeros_like(expected)
+        my_cumsum(x, y, axis=0)
+        np.testing.assert_equal(y, expected)
+        # Check "out" kw
+        out_kw = np.zeros_like(y)
+        my_cumsum(x, out=out_kw, axis=0)
+        np.testing.assert_equal(out_kw, expected)
+
+    def test_gufunc_attributes(self):
+        @guvectorize("(n)->(n)")
+        def gufunc(x, res):
+            acc = 0
+            for i in range(x.shape[0]):
+                acc += x[i]
+                res[i] = acc
+
+        # ensure gufunc exports attributes
+        attrs = ['signature', 'accumulate', 'at', 'outer', 'reduce', 'reduceat']
+        for attr in attrs:
+            contains = hasattr(gufunc, attr)
+            self.assertTrue(contains, 'dynamic gufunc not exporting "%s"' % (attr,))
+
+        a = np.array([1, 2, 3, 4])
+        res = np.array([0, 0, 0, 0])
+        gufunc(a, res)  # trigger compilation
+        self.assertPreciseEqual(res, np.array([1, 3, 6, 10]))
+
+        # other attributes are not callable from a gufunc with signature
+        # see: https://github.com/numba/numba/issues/2794
+        # note: this is a limitation in NumPy source code!
+        self.assertEqual(gufunc.signature, "(n)->(n)")
+
+        with self.assertRaises(RuntimeError) as raises:
+            gufunc.accumulate(a)
+        self.assertEqual(str(raises.exception), "Reduction not defined on ufunc with signature")
+
+        with self.assertRaises(RuntimeError) as raises:
+            gufunc.reduce(a)
+        self.assertEqual(str(raises.exception), "Reduction not defined on ufunc with signature")
+
+        with self.assertRaises(RuntimeError) as raises:
+            gufunc.reduceat(a, [0, 2])
+        self.assertEqual(str(raises.exception), "Reduction not defined on ufunc with signature")
+
+        with self.assertRaises(TypeError) as raises:
+            gufunc.outer(a, a)
+        self.assertEqual(str(raises.exception), "method outer is not allowed in ufunc with non-trivial signature")
+
+    def test_gufunc_attributes2(self):
+        @guvectorize('(),()->()')
+        def add(x, y, res):
+            res[0] = x + y
+
+        # add signature "(),() -> ()" is evaluated to None
+        self.assertIsNone(add.signature)
+
+        a = np.array([1, 2, 3, 4])
+        b = np.array([4, 3, 2, 1])
+        res = np.array([0, 0, 0, 0])
+        add(a, b, res)  # trigger compilation
+        self.assertPreciseEqual(res, np.array([5, 5, 5, 5]))
+
+        # now test other attributes
+        self.assertIsNone(add.signature)
+        self.assertEqual(add.reduce(a), 10)
+        self.assertPreciseEqual(add.accumulate(a), np.array([1, 3, 6, 10]))
+        self.assertPreciseEqual(add.outer([0, 1], [1, 2]), np.array([[1, 2], [2, 3]]))
+        self.assertPreciseEqual(add.reduceat(a, [0, 2]), np.array([3, 7]))
+
+        x = np.array([1, 2, 3, 4])
+        y = np.array([1, 2])
+        add.at(x, [0, 1], y)
+        self.assertPreciseEqual(x, np.array([2, 4, 3, 4]))
+
+
+class TestGUVectorizeScalar(TestCase):
+    """
+    Nothing keeps user from out-of-bound memory access
+    """
+    target = 'cpu'
+
+    def test_scalar_output(self):
+        """
+        Note that scalar output is a 0-dimension array that acts as
+        a pointer to the output location.
+        """
+
+        @guvectorize(['void(int32[:], int32[:])'], '(n)->()',
+                     target=self.target, nopython=True)
+        def sum_row(inp, out):
+            tmp = 0.
+            for i in range(inp.shape[0]):
+                tmp += inp[i]
+            out[()] = tmp
+
+        # inp is (10000, 3)
+        # out is (10000)
+        # The outer (leftmost) dimension must match or numpy broadcasting is performed.
+
+        inp = np.arange(30000, dtype=np.int32).reshape(10000, 3)
+        out = sum_row(inp)
+
+        # verify result
+        for i in range(inp.shape[0]):
+            self.assertEqual(out[i], inp[i].sum())
+
+    def test_scalar_input(self):
+
+        @guvectorize(['int32[:], int32[:], int32[:]'], '(n),()->(n)',
+                     target=self.target, nopython=True)
+        def foo(inp, n, out):
+            for i in range(inp.shape[0]):
+                out[i] = inp[i] * n[0]
+
+        inp = np.arange(3 * 10, dtype=np.int32).reshape(10, 3)
+        # out = np.empty_like(inp)
+        out = foo(inp, 2)
+
+        # verify result
+        self.assertPreciseEqual(inp * 2, out)
+
+    def test_scalar_input_core_type(self):
+        def pyfunc(inp, n, out):
+            for i in range(inp.size):
+                out[i] = n * (inp[i] + 1)
+
+        my_gufunc = guvectorize(['int32[:], int32, int32[:]'],
+                                '(n),()->(n)',
+                                target=self.target)(pyfunc)
+
+        # test single core loop execution
+        arr = np.arange(10).astype(np.int32)
+        got = my_gufunc(arr, 2)
+
+        expected = np.zeros_like(got)
+        pyfunc(arr, 2, expected)
+
+        np.testing.assert_equal(got, expected)
+
+        # test multiple core loop execution
+        arr = np.arange(20).astype(np.int32).reshape(10, 2)
+        got = my_gufunc(arr, 2)
+
+        expected = np.zeros_like(got)
+        for ax in range(expected.shape[0]):
+            pyfunc(arr[ax], 2, expected[ax])
+
+        np.testing.assert_equal(got, expected)
+
+    def test_scalar_input_core_type_error(self):
+        with self.assertRaises(TypeError) as raises:
+            @guvectorize(['int32[:], int32, int32[:]'], '(n),(n)->(n)',
+                         target=self.target)
+            def pyfunc(a, b, c):
+                pass
+        self.assertEqual("scalar type int32 given for non scalar argument #2",
+                         str(raises.exception))
+
+    def test_ndim_mismatch(self):
+        with self.assertRaises(TypeError) as raises:
+            @guvectorize(['int32[:], int32[:]'], '(m,n)->(n)',
+                         target=self.target)
+            def pyfunc(a, b):
+                pass
+        self.assertEqual("type and shape signature mismatch for arg #1",
+                         str(raises.exception))
+
+
+class TestGUVectorizeScalarParallel(TestGUVectorizeScalar):
+    _numba_parallel_test_ = False
+    target = 'parallel'
+
+
+class TestGUVectorizePickling(TestCase):
+    def test_pickle_gufunc_non_dyanmic(self):
+        """Non-dynamic gufunc.
+        """
+        @guvectorize(["f8,f8[:]"], "()->()")
+        def double(x, out):
+            out[:] = x * 2
+
+        # pickle
+        ser = pickle.dumps(double)
+        cloned = pickle.loads(ser)
+
+        # attributes carried over
+        self.assertEqual(cloned._frozen, double._frozen)
+        self.assertEqual(cloned.identity, double.identity)
+        self.assertEqual(cloned.is_dynamic, double.is_dynamic)
+        self.assertEqual(cloned.gufunc_builder._sigs,
+                         double.gufunc_builder._sigs)
+        # expected value of attributes
+        self.assertTrue(cloned._frozen)
+
+        cloned.disable_compile()
+        self.assertTrue(cloned._frozen)
+
+        # scalar version
+        self.assertPreciseEqual(double(0.5), cloned(0.5))
+        # array version
+        arr = np.arange(10)
+        self.assertPreciseEqual(double(arr), cloned(arr))
+
+    def test_pickle_gufunc_dyanmic_null_init(self):
+        """Dynamic gufunc w/o prepopulating before pickling.
+        """
+        @guvectorize("()->()", identity=1)
+        def double(x, out):
+            out[:] = x * 2
+
+        # pickle
+        ser = pickle.dumps(double)
+        cloned = pickle.loads(ser)
+
+        # attributes carried over
+        self.assertEqual(cloned._frozen, double._frozen)
+        self.assertEqual(cloned.identity, double.identity)
+        self.assertEqual(cloned.is_dynamic, double.is_dynamic)
+        self.assertEqual(cloned.gufunc_builder._sigs,
+                         double.gufunc_builder._sigs)
+        # expected value of attributes
+        self.assertFalse(cloned._frozen)
+
+        # scalar version
+        expect = np.zeros(1)
+        got = np.zeros(1)
+        double(0.5, out=expect)
+        cloned(0.5, out=got)
+        self.assertPreciseEqual(expect, got)
+        # array version
+        arr = np.arange(10)
+        expect = np.zeros_like(arr)
+        got = np.zeros_like(arr)
+        double(arr, out=expect)
+        cloned(arr, out=got)
+        self.assertPreciseEqual(expect, got)
+
+    def test_pickle_gufunc_dynamic_initialized(self):
+        """Dynamic gufunc prepopulated before pickling.
+
+        Once unpickled, we disable compilation to verify that the gufunc
+        compilation state is carried over.
+        """
+        @guvectorize("()->()", identity=1)
+        def double(x, out):
+            out[:] = x * 2
+
+        # prepopulate scalar
+        expect = np.zeros(1)
+        got = np.zeros(1)
+        double(0.5, out=expect)
+        # prepopulate array
+        arr = np.arange(10)
+        expect = np.zeros_like(arr)
+        got = np.zeros_like(arr)
+        double(arr, out=expect)
+
+        # pickle
+        ser = pickle.dumps(double)
+        cloned = pickle.loads(ser)
+
+        # attributes carried over
+        self.assertEqual(cloned._frozen, double._frozen)
+        self.assertEqual(cloned.identity, double.identity)
+        self.assertEqual(cloned.is_dynamic, double.is_dynamic)
+        self.assertEqual(cloned.gufunc_builder._sigs,
+                         double.gufunc_builder._sigs)
+        # expected value of attributes
+        self.assertFalse(cloned._frozen)
+
+        # disable compilation
+        cloned.disable_compile()
+        self.assertTrue(cloned._frozen)
+        # scalar version
+        expect = np.zeros(1)
+        got = np.zeros(1)
+        double(0.5, out=expect)
+        cloned(0.5, out=got)
+        self.assertPreciseEqual(expect, got)
+        # array version
+        expect = np.zeros_like(arr)
+        got = np.zeros_like(arr)
+        double(arr, out=expect)
+        cloned(arr, out=got)
+        self.assertPreciseEqual(expect, got)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_env_variable.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_env_variable.py
new file mode 100644
index 000000000..7d11692ad
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_env_variable.py
@@ -0,0 +1,38 @@
+from numba.np.ufunc.parallel import get_thread_count
+from os import environ as env
+from numba.core import config
+import unittest
+
+
+class TestParallelEnvVariable(unittest.TestCase):
+    """
+    Tests environment variables related to the underlying "parallel"
+    functions for npyufuncs.
+    """
+
+    _numba_parallel_test_ = False
+
+    def test_num_threads_variable(self):
+        """
+        Tests the NUMBA_NUM_THREADS env variable behaves as expected.
+        """
+        key = 'NUMBA_NUM_THREADS'
+        current = str(getattr(env, key, config.NUMBA_NUM_THREADS))
+        threads = "3154"
+        env[key] = threads
+        try:
+            config.reload_config()
+        except RuntimeError as e:
+            # This test should fail if threads have already been launched
+            self.assertIn("Cannot set NUMBA_NUM_THREADS", e.args[0])
+        else:
+            self.assertEqual(threads, str(get_thread_count()))
+            self.assertEqual(threads, str(config.NUMBA_NUM_THREADS))
+        finally:
+            # reset the env variable/set to default. Should not fail even if
+            # threads are launched because the value is the same.
+            env[key] = current
+            config.reload_config()
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_low_work.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_low_work.py
new file mode 100644
index 000000000..cab4d4274
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_low_work.py
@@ -0,0 +1,44 @@
+"""
+There was a deadlock problem when work count is smaller than number of threads.
+"""
+
+import numpy as np
+
+from numba import float32, float64, int32, uint32
+from numba.np.ufunc import Vectorize
+import unittest
+
+
+def vector_add(a, b):
+    return a + b
+
+
+class TestParallelLowWorkCount(unittest.TestCase):
+
+    _numba_parallel_test_ = False
+
+    def test_low_workcount(self):
+        # build parallel native code ufunc
+        pv = Vectorize(vector_add, target='parallel')
+        for ty in (int32, uint32, float32, float64):
+            pv.add(ty(ty, ty))
+        para_ufunc = pv.build_ufunc()
+
+        # build python ufunc
+        np_ufunc = np.vectorize(vector_add)
+
+        # test it out
+        def test(ty):
+            data = np.arange(1).astype(ty) # just one item
+            result = para_ufunc(data, data)
+            gold = np_ufunc(data, data)
+            np.testing.assert_allclose(gold, result)
+
+        test(np.double)
+        test(np.float32)
+        test(np.int32)
+        test(np.uint32)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_ufunc_issues.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_ufunc_issues.py
new file mode 100644
index 000000000..223712229
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_parallel_ufunc_issues.py
@@ -0,0 +1,128 @@
+import time
+import ctypes
+
+import numpy as np
+
+from numba.tests.support import captured_stdout
+from numba import vectorize, guvectorize
+import unittest
+
+
+class TestParUfuncIssues(unittest.TestCase):
+
+    _numba_parallel_test_ = False
+
+    def test_thread_response(self):
+        """
+        Related to #89.
+        This does not test #89 but tests the fix for it.
+        We want to make sure the worker threads can be used multiple times
+        and with different time gap between each execution.
+        """
+
+        @vectorize('float64(float64, float64)', target='parallel')
+        def fnv(a, b):
+            return a + b
+
+        sleep_time = 1   # 1 second
+        while sleep_time > 0.00001:    # 10us
+            time.sleep(sleep_time)
+            a = b = np.arange(10**5)
+            np.testing.assert_equal(a + b, fnv(a, b))
+            # Reduce sleep time
+            sleep_time /= 2
+
+    def test_gil_reacquire_deadlock(self):
+        """
+        Testing issue #1998 due to GIL reacquiring
+        """
+        # make a ctypes callback that requires the GIL
+        proto = ctypes.CFUNCTYPE(None, ctypes.c_int32)
+        characters = 'abcdefghij'
+
+        def bar(x):
+            print(characters[x])
+
+        cbar = proto(bar)
+
+        # our unit under test
+        @vectorize(['int32(int32)'], target='parallel', nopython=True)
+        def foo(x):
+            print(x % 10)  # this reacquires the GIL
+            cbar(x % 10)   # this reacquires the GIL
+            return x * 2
+
+        # Numpy ufunc has a heuristic to determine whether to release the GIL
+        # during execution.  Small input size (10) seems to not release the GIL.
+        # Large input size (1000) seems to release the GIL.
+        for nelem in [1, 10, 100, 1000]:
+            # inputs
+            a = np.arange(nelem, dtype=np.int32)
+            acopy = a.copy()
+            # run and capture stdout
+            with captured_stdout() as buf:
+                got = foo(a)
+            stdout = buf.getvalue()
+            buf.close()
+            # process outputs from print
+            got_output = sorted(map(lambda x: x.strip(), stdout.splitlines()))
+            # build expected output
+            expected_output = [str(x % 10) for x in range(nelem)]
+            expected_output += [characters[x % 10] for x in range(nelem)]
+            expected_output = sorted(expected_output)
+            # verify
+            self.assertEqual(got_output, expected_output)
+            np.testing.assert_equal(got, 2 * acopy)
+
+
+
+class TestParGUfuncIssues(unittest.TestCase):
+
+    _numba_parallel_test_ = False
+
+    def test_gil_reacquire_deadlock(self):
+        """
+        Testing similar issue to #1998 due to GIL reacquiring for Gufunc
+        """
+        # make a ctypes callback that requires the GIL
+        proto = ctypes.CFUNCTYPE(None, ctypes.c_int32)
+        characters = 'abcdefghij'
+
+        def bar(x):
+            print(characters[x])
+
+        cbar = proto(bar)
+
+        # our unit under test
+        @guvectorize(['(int32, int32[:])'], "()->()",
+                     target='parallel', nopython=True)
+        def foo(x, out):
+            print(x % 10)  # this reacquires the GIL
+            cbar(x % 10)   # this reacquires the GIL
+            out[0] = x * 2
+
+        # Numpy ufunc has a heuristic to determine whether to release the GIL
+        # during execution.  Small input size (10) seems to not release the GIL.
+        # Large input size (1000) seems to release the GIL.
+        for nelem in [1, 10, 100, 1000]:
+            # inputs
+            a = np.arange(nelem, dtype=np.int32)
+            acopy = a.copy()
+            # run and capture stdout
+            with captured_stdout() as buf:
+                got = foo(a)
+            stdout = buf.getvalue()
+            buf.close()
+            # process outputs from print
+            got_output = sorted(map(lambda x: x.strip(), stdout.splitlines()))
+            # build expected output
+            expected_output = [str(x % 10) for x in range(nelem)]
+            expected_output += [characters[x % 10] for x in range(nelem)]
+            expected_output = sorted(expected_output)
+            # verify
+            self.assertEqual(got_output, expected_output)
+            np.testing.assert_equal(got, 2 * acopy)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_ufunc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_ufunc.py
new file mode 100644
index 000000000..cf09d6efd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_ufunc.py
@@ -0,0 +1,145 @@
+import numpy as np
+
+from numba import float32, jit, njit
+from numba.np.ufunc import Vectorize
+from numba.core.errors import TypingError
+from numba.tests.support import TestCase
+import unittest
+
+
+dtype = np.float32
+a = np.arange(80, dtype=dtype).reshape(8, 10)
+b = a.copy()
+c = a.copy(order='F')
+d = np.arange(16 * 20, dtype=dtype).reshape(16, 20)[::2, ::2]
+
+
+def add(a, b):
+    return a + b
+
+
+def add_multiple_args(a, b, c, d):
+    return a + b + c + d
+
+
+def gufunc_add(a, b):
+    result = 0.0
+    for i in range(a.shape[0]):
+        result += a[i] * b[i]
+
+    return result
+
+
+def ufunc_reduce(ufunc, arg):
+    for i in range(arg.ndim):
+        arg = ufunc.reduce(arg)
+    return arg
+
+
+vectorizers = [
+    Vectorize,
+    # ParallelVectorize,
+    # StreamVectorize,
+    # CudaVectorize,
+    # GUFuncVectorize,
+]
+
+
+class TestUFuncs(TestCase):
+
+    def _test_ufunc_attributes(self, cls, a, b, *args):
+        "Test ufunc attributes"
+        vectorizer = cls(add, *args)
+        vectorizer.add(float32(float32, float32))
+        ufunc = vectorizer.build_ufunc()
+
+        info = (cls, a.ndim)
+        self.assertPreciseEqual(ufunc(a, b), a + b, msg=info)
+        self.assertPreciseEqual(ufunc_reduce(ufunc, a), np.sum(a), msg=info)
+        self.assertPreciseEqual(ufunc.accumulate(a), np.add.accumulate(a),
+                                msg=info)
+        self.assertPreciseEqual(ufunc.outer(a, b), np.add.outer(a, b), msg=info)
+
+    def _test_broadcasting(self, cls, a, b, c, d):
+        "Test multiple args"
+        vectorizer = cls(add_multiple_args)
+        vectorizer.add(float32(float32, float32, float32, float32))
+        ufunc = vectorizer.build_ufunc()
+
+        info = (cls, a.shape)
+        self.assertPreciseEqual(ufunc(a, b, c, d), a + b + c + d, msg=info)
+
+    def test_ufunc_attributes(self):
+        for v in vectorizers: # 1D
+            self._test_ufunc_attributes(v, a[0], b[0])
+        for v in vectorizers: # 2D
+            self._test_ufunc_attributes(v, a, b)
+        for v in vectorizers: # 3D
+            self._test_ufunc_attributes(v, a[:, np.newaxis, :],
+                                        b[np.newaxis, :, :])
+
+    def test_broadcasting(self):
+        for v in vectorizers: # 1D
+            self._test_broadcasting(v, a[0], b[0], c[0], d[0])
+        for v in vectorizers: # 2D
+            self._test_broadcasting(v, a, b, c, d)
+        for v in vectorizers: # 3D
+            self._test_broadcasting(v, a[:, np.newaxis, :], b[np.newaxis, :, :],
+                                    c[:, np.newaxis, :], d[np.newaxis, :, :])
+
+    def test_implicit_broadcasting(self):
+        for v in vectorizers:
+            vectorizer = v(add)
+            vectorizer.add(float32(float32, float32))
+            ufunc = vectorizer.build_ufunc()
+
+            broadcasting_b = b[np.newaxis, :, np.newaxis, np.newaxis, :]
+            self.assertPreciseEqual(ufunc(a, broadcasting_b),
+                                    a + broadcasting_b)
+
+    def test_ufunc_exception_on_write_to_readonly(self):
+        z = np.ones(10)
+        z.flags.writeable = False # flip write bit
+
+        tests = []
+        expect = "ufunc 'sin' called with an explicit output that is read-only"
+        tests.append((jit(nopython=True), TypingError, expect))
+        tests.append((jit(nopython=False), ValueError,
+                      "output array is read-only"))
+
+        for dec, exc, msg in tests:
+            def test(x):
+                a = np.ones(x.shape, x.dtype) # do not copy RO attribute from x
+                np.sin(a, x)
+
+            with self.assertRaises(exc) as raises:
+                dec(test)(z)
+
+            self.assertIn(msg, str(raises.exception))
+
+    def test_optional_type_handling(self):
+        # Tests ufunc compilation with Optional type
+
+        @njit
+        def inner(x, y):
+            if y > 2:
+                z = None
+            else:
+                z = np.ones(4)
+            return np.add(x, z)
+
+        # This causes `z` to be np.ones(4) at runtime, success
+        self.assertPreciseEqual(inner(np.arange(4), 1),
+                                np.arange(1, 5).astype(np.float64))
+
+        with self.assertRaises(TypeError) as raises:
+            # This causes `z` to be None at runtime, TypeError raised on the
+            # type cast of the Optional.
+            inner(np.arange(4), 3)
+
+        msg = "expected array(float64, 1d, C), got None"
+        self.assertIn(msg, str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_ufuncbuilding.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_ufuncbuilding.py
new file mode 100644
index 000000000..1d4e83e7f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_ufuncbuilding.py
@@ -0,0 +1,369 @@
+import sys
+
+import numpy as np
+
+from numba.np.ufunc.ufuncbuilder import GUFuncBuilder
+from numba import vectorize, guvectorize
+from numba.np.ufunc import PyUFunc_One
+from numba.np.ufunc.dufunc import DUFunc as UFuncBuilder
+from numba.tests.support import tag, TestCase
+from numba.core import config
+import unittest
+
+
+def add(a, b):
+    """An addition"""
+    return a + b
+
+def equals(a, b):
+    return a == b
+
+def mul(a, b):
+    """A multiplication"""
+    return a * b
+
+def guadd(a, b, c):
+    """A generalized addition"""
+    x, y = c.shape
+    for i in range(x):
+        for j in range(y):
+            c[i, j] = a[i, j] + b[i, j]
+
+@vectorize(nopython=True)
+def inner(a, b):
+    return a + b
+
+@vectorize(["int64(int64, int64)"], nopython=True)
+def inner_explicit(a, b):
+    return a + b
+
+def outer(a, b):
+    return inner(a, b)
+
+def outer_explicit(a, b):
+    return inner_explicit(a, b)
+
+
+class Dummy: pass
+
+
+def guadd_obj(a, b, c):
+    Dummy()  # to force object mode
+    x, y = c.shape
+    for i in range(x):
+        for j in range(y):
+            c[i, j] = a[i, j] + b[i, j]
+
+def guadd_scalar_obj(a, b, c):
+    Dummy()  # to force object mode
+    x, y = c.shape
+    for i in range(x):
+        for j in range(y):
+            c[i, j] = a[i, j] + b
+
+
+class MyException(Exception):
+    pass
+
+
+def guerror(a, b, c):
+    raise MyException
+
+
+class TestUfuncBuilding(TestCase):
+
+    def test_basic_ufunc(self):
+        ufb = UFuncBuilder(add)
+        cres = ufb.add("int32(int32, int32)")
+        self.assertFalse(cres.objectmode)
+        cres = ufb.add("int64(int64, int64)")
+        self.assertFalse(cres.objectmode)
+        ufunc = ufb.build_ufunc()
+
+        def check(a):
+            b = ufunc(a, a)
+            self.assertPreciseEqual(a + a, b)
+            self.assertEqual(b.dtype, a.dtype)
+
+        a = np.arange(12, dtype='int32')
+        check(a)
+        # Non-contiguous dimension
+        a = a[::2]
+        check(a)
+        a = a.reshape((2, 3))
+        check(a)
+
+        # Metadata
+        self.assertEqual(ufunc.__name__, "add")
+        self.assertIn("An addition", ufunc.__doc__)
+
+    def test_ufunc_struct(self):
+        ufb = UFuncBuilder(add)
+        cres = ufb.add("complex64(complex64, complex64)")
+        self.assertFalse(cres.objectmode)
+        ufunc = ufb.build_ufunc()
+
+        def check(a):
+            b = ufunc(a, a)
+            self.assertPreciseEqual(a + a, b)
+            self.assertEqual(b.dtype, a.dtype)
+
+        a = np.arange(12, dtype='complex64') + 1j
+        check(a)
+        # Non-contiguous dimension
+        a = a[::2]
+        check(a)
+        a = a.reshape((2, 3))
+        check(a)
+
+    def test_ufunc_forceobj(self):
+        ufb = UFuncBuilder(add, targetoptions={'forceobj': True})
+        cres = ufb.add("int32(int32, int32)")
+        self.assertTrue(cres.objectmode)
+        ufunc = ufb.build_ufunc()
+
+        a = np.arange(10, dtype='int32')
+        b = ufunc(a, a)
+        self.assertPreciseEqual(a + a, b)
+
+    def test_nested_call(self):
+        """
+        Check nested call to an implicitly-typed ufunc.
+        """
+        builder = UFuncBuilder(outer,
+                               targetoptions={'nopython': True})
+        builder.add("(int64, int64)")
+        ufunc = builder.build_ufunc()
+        self.assertEqual(ufunc(-1, 3), 2)
+
+    def test_nested_call_explicit(self):
+        """
+        Check nested call to an explicitly-typed ufunc.
+        """
+        builder = UFuncBuilder(outer_explicit,
+                               targetoptions={'nopython': True})
+        builder.add("(int64, int64)")
+        ufunc = builder.build_ufunc()
+        self.assertEqual(ufunc(-1, 3), 2)
+
+
+class TestUfuncBuildingJitDisabled(TestUfuncBuilding):
+
+    def setUp(self):
+        self.old_disable_jit = config.DISABLE_JIT
+        config.DISABLE_JIT = False
+
+    def tearDown(self):
+        config.DISABLE_JIT = self.old_disable_jit
+
+
+class TestGUfuncBuilding(TestCase):
+
+    def test_basic_gufunc(self):
+        gufb = GUFuncBuilder(guadd, "(x, y),(x, y)->(x, y)")
+        cres = gufb.add("void(int32[:,:], int32[:,:], int32[:,:])")
+        self.assertFalse(cres.objectmode)
+        ufunc = gufb.build_ufunc()
+
+        a = np.arange(10, dtype="int32").reshape(2, 5)
+        b = ufunc(a, a)
+
+        self.assertPreciseEqual(a + a, b)
+        self.assertEqual(b.dtype, np.dtype('int32'))
+
+        # Metadata
+        self.assertEqual(ufunc.__name__, "guadd")
+        self.assertIn("A generalized addition", ufunc.__doc__)
+
+    def test_gufunc_struct(self):
+        gufb = GUFuncBuilder(guadd, "(x, y),(x, y)->(x, y)")
+        cres = gufb.add("void(complex64[:,:], complex64[:,:], complex64[:,:])")
+        self.assertFalse(cres.objectmode)
+        ufunc = gufb.build_ufunc()
+
+        a = np.arange(10, dtype="complex64").reshape(2, 5) + 1j
+        b = ufunc(a, a)
+
+        self.assertPreciseEqual(a + a, b)
+
+    def test_gufunc_struct_forceobj(self):
+        gufb = GUFuncBuilder(guadd, "(x, y),(x, y)->(x, y)",
+                             targetoptions=dict(forceobj=True))
+        cres = gufb.add("void(complex64[:,:], complex64[:,:], complex64[:,"
+                        ":])")
+        self.assertTrue(cres.objectmode)
+        ufunc = gufb.build_ufunc()
+
+        a = np.arange(10, dtype="complex64").reshape(2, 5) + 1j
+        b = ufunc(a, a)
+
+        self.assertPreciseEqual(a + a, b)
+
+
+class TestGUfuncBuildingJitDisabled(TestGUfuncBuilding):
+
+    def setUp(self):
+        self.old_disable_jit = config.DISABLE_JIT
+        config.DISABLE_JIT = False
+
+    def tearDown(self):
+        config.DISABLE_JIT = self.old_disable_jit
+
+
+class TestVectorizeDecor(TestCase):
+
+    _supported_identities = [0, 1, None, "reorderable"]
+
+    def test_vectorize(self):
+        ufunc = vectorize(['int32(int32, int32)'])(add)
+        a = np.arange(10, dtype='int32')
+        b = ufunc(a, a)
+        self.assertPreciseEqual(a + a, b)
+
+    def test_vectorize_objmode(self):
+        ufunc = vectorize(['int32(int32, int32)'], forceobj=True)(add)
+        a = np.arange(10, dtype='int32')
+        b = ufunc(a, a)
+        self.assertPreciseEqual(a + a, b)
+
+    def test_vectorize_bool_return(self):
+        ufunc = vectorize(['bool_(int32, int32)'])(equals)
+        a = np.arange(10, dtype='int32')
+        r = ufunc(a,a)
+        self.assertPreciseEqual(r, np.ones(r.shape, dtype=np.bool_))
+
+    def test_vectorize_identity(self):
+        sig = 'int32(int32, int32)'
+        for identity in self._supported_identities:
+            ufunc = vectorize([sig], identity=identity)(add)
+            expected = None if identity == 'reorderable' else identity
+            self.assertEqual(ufunc.identity, expected)
+        # Default value is None
+        ufunc = vectorize([sig])(add)
+        self.assertIs(ufunc.identity, None)
+        # Invalid values
+        with self.assertRaises(ValueError):
+            vectorize([sig], identity='none')(add)
+        with self.assertRaises(ValueError):
+            vectorize([sig], identity=2)(add)
+
+    def test_vectorize_no_args(self):
+        a = np.linspace(0,1,10)
+        b = np.linspace(1,2,10)
+        ufunc = vectorize(add)
+        self.assertPreciseEqual(ufunc(a,b), a + b)
+        ufunc2 = vectorize(add)
+        c = np.empty(10)
+        ufunc2(a, b, c)
+        self.assertPreciseEqual(c, a + b)
+
+    def test_vectorize_only_kws(self):
+        a = np.linspace(0,1,10)
+        b = np.linspace(1,2,10)
+        ufunc = vectorize(identity=PyUFunc_One, nopython=True)(mul)
+        self.assertPreciseEqual(ufunc(a,b), a * b)
+
+    def test_vectorize_output_kwarg(self):
+        """
+        Passing the output array as a keyword argument (issue #1867).
+        """
+        def check(ufunc):
+            a = np.arange(10, 16, dtype='int32')
+            out = np.zeros_like(a)
+            got = ufunc(a, a, out=out)
+            self.assertIs(got, out)
+            self.assertPreciseEqual(out, a + a)
+            with self.assertRaises(TypeError):
+                ufunc(a, a, zzz=out)
+
+        # With explicit sigs
+        ufunc = vectorize(['int32(int32, int32)'], nopython=True)(add)
+        check(ufunc)
+        # With implicit sig
+        ufunc = vectorize(nopython=True)(add)
+        check(ufunc)  # compiling
+        check(ufunc)  # after compiling
+
+    def test_guvectorize(self):
+        ufunc = guvectorize(['(int32[:,:], int32[:,:], int32[:,:])'],
+                            "(x,y),(x,y)->(x,y)")(guadd)
+        a = np.arange(10, dtype='int32').reshape(2, 5)
+        b = ufunc(a, a)
+        self.assertPreciseEqual(a + a, b)
+
+    def test_guvectorize_no_output(self):
+        ufunc = guvectorize(['(int32[:,:], int32[:,:], int32[:,:])'],
+                            "(x,y),(x,y),(x,y)")(guadd)
+        a = np.arange(10, dtype='int32').reshape(2, 5)
+        out = np.zeros_like(a)
+        ufunc(a, a, out)
+        self.assertPreciseEqual(a + a, out)
+
+    def test_guvectorize_objectmode(self):
+        ufunc = guvectorize(['(int32[:,:], int32[:,:], int32[:,:])'],
+                            "(x,y),(x,y)->(x,y)")(guadd_obj)
+        a = np.arange(10, dtype='int32').reshape(2, 5)
+        b = ufunc(a, a)
+        self.assertPreciseEqual(a + a, b)
+
+    def test_guvectorize_scalar_objectmode(self):
+        """
+        Test passing of scalars to object mode gufuncs.
+        """
+        ufunc = guvectorize(['(int32[:,:], int32, int32[:,:])'],
+                            "(x,y),()->(x,y)")(guadd_scalar_obj)
+        a = np.arange(10, dtype='int32').reshape(2, 5)
+        b = ufunc(a, 3)
+        self.assertPreciseEqual(a + 3, b)
+
+    def test_guvectorize_error_in_objectmode(self):
+        ufunc = guvectorize(['(int32[:,:], int32[:,:], int32[:,:])'],
+                            "(x,y),(x,y)->(x,y)", forceobj=True)(guerror)
+        a = np.arange(10, dtype='int32').reshape(2, 5)
+        with self.assertRaises(MyException):
+            ufunc(a, a)
+
+    def test_guvectorize_identity(self):
+        args = (['(int32[:,:], int32[:,:], int32[:,:])'], "(x,y),(x,y)->(x,y)")
+        for identity in self._supported_identities:
+            ufunc = guvectorize(*args, identity=identity)(guadd)
+            expected = None if identity == 'reorderable' else identity
+            self.assertEqual(ufunc.identity, expected)
+        # Default value is None
+        ufunc = guvectorize(*args)(guadd)
+        self.assertIs(ufunc.identity, None)
+        # Invalid values
+        with self.assertRaises(ValueError):
+            guvectorize(*args, identity='none')(add)
+        with self.assertRaises(ValueError):
+            guvectorize(*args, identity=2)(add)
+
+    def test_guvectorize_invalid_layout(self):
+        sigs = ['(int32[:,:], int32[:,:], int32[:,:])']
+        # Syntax error
+        with self.assertRaises(ValueError) as raises:
+            guvectorize(sigs, ")-:")(guadd)
+        self.assertIn("bad token in signature", str(raises.exception))
+        # Output shape can't be inferred from inputs
+        with self.assertRaises(NameError) as raises:
+            guvectorize(sigs, "(x,y),(x,y)->(x,z,v)")(guadd)
+        self.assertEqual(str(raises.exception),
+                         "undefined output symbols: v,z")
+        # Arrow but no outputs
+        with self.assertRaises(ValueError) as raises:
+            guvectorize(sigs, "(x,y),(x,y),(x,y)->")(guadd)
+        # (error message depends on Numpy version)
+
+
+class TestVectorizeDecorJitDisabled(TestVectorizeDecor):
+
+    def setUp(self):
+        self.old_disable_jit = config.DISABLE_JIT
+        config.DISABLE_JIT = False
+
+    def tearDown(self):
+        config.DISABLE_JIT = self.old_disable_jit
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_vectorize_decor.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_vectorize_decor.py
new file mode 100644
index 000000000..3bb014197
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/npyufunc/test_vectorize_decor.py
@@ -0,0 +1,151 @@
+import math
+
+import numpy as np
+
+from numba import int32, uint32, float32, float64, jit, vectorize
+from numba.tests.support import tag, CheckWarningsMixin
+import unittest
+
+
+pi = math.pi
+
+
+def sinc(x):
+    if x == 0.0:
+        return 1.0
+    else:
+        return math.sin(x * pi) / (pi * x)
+
+def scaled_sinc(x, scale):
+    if x == 0.0:
+        return scale
+    else:
+        return scale * (math.sin(x * pi) / (pi * x))
+
+def vector_add(a, b):
+    return a + b
+
+
+class BaseVectorizeDecor(object):
+    target = None
+    wrapper = None
+    funcs = {
+        'func1': sinc,
+        'func2': scaled_sinc,
+        'func3': vector_add,
+    }
+
+    @classmethod
+    def _run_and_compare(cls, func, sig, A, *args, **kwargs):
+        if cls.wrapper is not None:
+            func = cls.wrapper(func)
+        numba_func = vectorize(sig, target=cls.target)(func)
+        numpy_func = np.vectorize(func)
+        result = numba_func(A, *args)
+        gold = numpy_func(A, *args)
+        np.testing.assert_allclose(result, gold, **kwargs)
+
+    def test_1(self):
+        sig = ['float64(float64)', 'float32(float32)']
+        func = self.funcs['func1']
+        A = np.arange(100, dtype=np.float64)
+        self._run_and_compare(func, sig, A)
+
+    def test_2(self):
+        sig = [float64(float64), float32(float32)]
+        func = self.funcs['func1']
+        A = np.arange(100, dtype=np.float64)
+        self._run_and_compare(func, sig, A)
+
+    def test_3(self):
+        sig = ['float64(float64, uint32)']
+        func = self.funcs['func2']
+        A = np.arange(100, dtype=np.float64)
+        scale = np.uint32(3)
+        self._run_and_compare(func, sig, A, scale, atol=1e-8)
+
+    def test_4(self):
+        sig = [
+            int32(int32, int32),
+            uint32(uint32, uint32),
+            float32(float32, float32),
+            float64(float64, float64),
+        ]
+        func = self.funcs['func3']
+        A = np.arange(100, dtype=np.float64)
+        self._run_and_compare(func, sig, A, A)
+        A = A.astype(np.float32)
+        self._run_and_compare(func, sig, A, A)
+        A = A.astype(np.int32)
+        self._run_and_compare(func, sig, A, A)
+        A = A.astype(np.uint32)
+        self._run_and_compare(func, sig, A, A)
+
+
+class TestCPUVectorizeDecor(unittest.TestCase, BaseVectorizeDecor):
+    target = 'cpu'
+
+
+class TestParallelVectorizeDecor(unittest.TestCase, BaseVectorizeDecor):
+    _numba_parallel_test_ = False
+    target = 'parallel'
+
+
+class TestCPUVectorizeJitted(unittest.TestCase, BaseVectorizeDecor):
+    target = 'cpu'
+    wrapper = jit
+
+
+class BaseVectorizeNopythonArg(unittest.TestCase, CheckWarningsMixin):
+    """
+    Test passing the nopython argument to the vectorize decorator.
+    """
+    def _test_target_nopython(self, target, warnings, with_sig=True):
+        a = np.array([2.0], dtype=np.float32)
+        b = np.array([3.0], dtype=np.float32)
+        sig = [float32(float32, float32)]
+        args = with_sig and [sig] or []
+        with self.check_warnings(warnings):
+            f = vectorize(*args, target=target, nopython=True)(vector_add)
+            f(a, b)
+
+class TestVectorizeNopythonArg(BaseVectorizeNopythonArg):
+    def test_target_cpu_nopython(self):
+        self._test_target_nopython('cpu', [])
+
+    def test_target_cpu_nopython_no_sig(self):
+        self._test_target_nopython('cpu', [], False)
+
+    def test_target_parallel_nopython(self):
+        self._test_target_nopython('parallel', [])
+
+
+class BaseVectorizeUnrecognizedArg(unittest.TestCase, CheckWarningsMixin):
+    """
+    Test passing an unrecognized argument to the vectorize decorator.
+    """
+    def _test_target_unrecognized_arg(self, target, with_sig=True):
+        a = np.array([2.0], dtype=np.float32)
+        b = np.array([3.0], dtype=np.float32)
+        sig = [float32(float32, float32)]
+        args = with_sig and [sig] or []
+        with self.assertRaises(KeyError) as raises:
+            f = vectorize(*args, target=target, nonexistent=2)(vector_add)
+            f(a, b)
+        self.assertIn("Unrecognized options", str(raises.exception))
+
+class TestVectorizeUnrecognizedArg(BaseVectorizeUnrecognizedArg):
+    def test_target_cpu_unrecognized_arg(self):
+        self._test_target_unrecognized_arg('cpu')
+
+    def test_target_cpu_unrecognized_arg_no_sig(self):
+        self._test_target_unrecognized_arg('cpu', False)
+
+    def test_target_parallel_unrecognized_arg(self):
+        self._test_target_unrecognized_arg('parallel')
+
+
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/orphaned_semaphore_usecase.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/orphaned_semaphore_usecase.py
new file mode 100644
index 000000000..e9101e2f2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/orphaned_semaphore_usecase.py
@@ -0,0 +1,24 @@
+# See issue: https://github.com/numba/numba/issues/4348
+# this file must be run in isolation to replicate, test:
+# numba.tests.test_parallel_backend.TestInitSafetyIssues.test_orphaned_semaphore
+# does this to check semaphores are not leaking.
+
+import multiprocessing as mp
+
+import numba # noqa, deliberately unused, here to test import is safe
+
+
+def w():
+    pass
+
+
+def main():
+    ps = [mp.Process(target=w) for _ in range(4)]
+    [p.start() for p in ps]
+    [p.join() for p in ps]
+
+
+if __name__ == '__main__':
+    p = mp.get_context('spawn').Process(target=main)
+    p.start()
+    p.join()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/overload_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/overload_usecases.py
new file mode 100644
index 000000000..fe1ca5799
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/overload_usecases.py
@@ -0,0 +1,28 @@
+# Python 3 syntax only use cases, used in test_extending.py
+
+# arg name is different, and there's no arg name to match before *args
+
+
+def impl4(z, *args, kw=None):
+    if z > 10:
+        return 1
+    else:
+        return -1
+
+# arg name is different but at a detectable location, with *args
+
+
+def impl5(z, b, *args, kw=None):
+    if z > 10:
+        return 1
+    else:
+        return -1
+
+
+def var_positional_impl(a, *star_args_token, kw=None, kw1=12):
+    def impl(a, b, f, kw=None, kw1=12):
+        if a > 10:
+            return 1
+        else:
+            return -1
+    return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/parfors_cache_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/parfors_cache_usecases.py
new file mode 100644
index 000000000..b650bcf5e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/parfors_cache_usecases.py
@@ -0,0 +1,66 @@
+import sys
+
+import numpy as np
+
+from numba import njit
+from numba.tests.support import TestCase
+
+
+@njit(parallel=True, cache=True)
+def arrayexprs_case(arr):
+    return arr / arr.sum()
+
+
+@njit(parallel=True, cache=True)
+def prange_case(arr):
+    out = np.zeros_like(arr)
+    c = 1 / arr.sum()
+    for i in range(arr.size):
+        out[i] = arr[i] * c
+    return out
+
+
+@njit(cache=True)
+def caller_case(arr):
+    return prange_case(arrayexprs_case(arr))
+
+
+class _TestModule(TestCase):
+    """
+    Tests for functionality of this module's functions.
+    Note this does not define any "test_*" method, instead check_module()
+    should be called by hand.
+    """
+    def check_module(self, mod):
+        total_cache_hits = 0
+        for fn in [mod.arrayexprs_case, mod.prange_case, mod.caller_case]:
+            arr = np.ones(20)
+            np.testing.assert_allclose(
+                fn(arr), fn.py_func(arr),
+            )
+            # Accumulate cache hits
+            total_cache_hits += len(fn.stats.cache_hits)
+        self.assertGreater(
+            total_cache_hits, 0,
+            msg="At least one dispatcher has used the cache",
+        )
+
+    def run_module(self, mod):
+        # This just executes the module's functionality without asserting
+        # anything about the cache, it's used in tests that ensure that
+        # properties such as thread count aren't baked in to the cached object.
+        for fn in [mod.arrayexprs_case, mod.prange_case, mod.caller_case]:
+            arr = np.ones(20)
+            np.testing.assert_allclose(
+                fn(arr), fn.py_func(arr),
+            )
+
+
+def self_test():
+    mod = sys.modules[__name__]
+    _TestModule().check_module(mod)
+
+
+def self_run():
+    mod = sys.modules[__name__]
+    _TestModule().run_module(mod)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/parfors_max_label_error.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/parfors_max_label_error.py
new file mode 100644
index 000000000..adfc97bf4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/parfors_max_label_error.py
@@ -0,0 +1,68 @@
+"""
+This is a test for an error with ir_utils._max_label not being updated
+correctly. As a result of the error, inline_closurecall will incorrectly
+overwrite an existing label, resulting in code that creates an effect-free
+infinite loop, which is an undefined behavior to LLVM. LLVM will then assume
+the function can never execute and an alternative code path will be taken
+in spite of the value of any conditional branch that is guarding the alternative
+code path.
+"""
+import numpy as np
+
+from numba import njit
+from numba.core.dispatcher import TargetConfigurationStack
+from numba.core.target_extension import (
+    dispatcher_registry,
+    CPUDispatcher,
+    CPU,
+    target_registry,
+)
+from numba.core.retarget import BasicRetarget
+
+# ------------ A "CustomCPU" target ------------
+
+
+class CustomCPU(CPU):
+    pass
+
+
+class CustomCPUDispatcher(CPUDispatcher):
+    pass
+
+
+target_registry["CustomCPU"] = CustomCPU
+dispatcher_registry[target_registry["CustomCPU"]] = CustomCPUDispatcher
+
+
+# ------------ For switching target ------------
+
+
+class MyCustomTarget(BasicRetarget):
+    @property
+    def output_target(self):
+        return "CustomCPU"
+
+    def compile_retarget(self, cpu_disp):
+        kernel = njit(_target="CustomCPU", parallel=True)(cpu_disp.py_func)
+        return kernel
+
+
+retarget = MyCustomTarget()
+
+
+# ------------ Functions being tested ------------
+@njit
+def f(a):
+    return np.arange(a.size)
+
+
+def main():
+    a = np.ones(20)
+    with TargetConfigurationStack.switch_target(retarget):
+        r = f(a)
+    np.testing.assert_equal(r, f.py_func(a))
+    print("TEST PASSED")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pdlike_usecase.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pdlike_usecase.py
new file mode 100644
index 000000000..75ac00b6f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pdlike_usecase.py
@@ -0,0 +1,306 @@
+"""
+Implementation of a minimal Pandas-like API.
+"""
+
+import numpy as np
+
+from numba.core import types, cgutils
+from numba.core.datamodel import models
+from numba.core.extending import (
+    typeof_impl, type_callable, register_model,
+    lower_builtin, box, unbox, NativeValue,
+    overload, overload_attribute, overload_method, make_attribute_wrapper)
+from numba.core.imputils import impl_ret_borrowed
+
+
+class Index(object):
+    """
+    A minimal pandas.Index-like object.
+    """
+
+    def __init__(self, data):
+        assert isinstance(data, np.ndarray)
+        assert data.ndim == 1
+        self._data = data
+
+    def __iter__(self):
+        return iter(self._data)
+
+    @property
+    def dtype(self):
+        return self._data.dtype
+
+    @property
+    def flags(self):
+        return self._data.flags
+
+
+class IndexType(types.Buffer):
+    """
+    The type class for Index objects.
+    """
+    array_priority = 1000
+
+    def __init__(self, dtype, layout, pyclass):
+        self.pyclass = pyclass
+        super(IndexType, self).__init__(dtype, 1, layout)
+
+    @property
+    def key(self):
+        return self.pyclass, self.dtype, self.layout
+
+    @property
+    def as_array(self):
+        return types.Array(self.dtype, 1, self.layout)
+
+    def copy(self, dtype=None, ndim=1, layout=None):
+        assert ndim == 1
+        if dtype is None:
+            dtype = self.dtype
+        layout = layout or self.layout
+        return type(self)(dtype, layout, self.pyclass)
+
+
+class Series(object):
+    """
+    A minimal pandas.Series-like object.
+    """
+
+    def __init__(self, data, index):
+        assert isinstance(data, np.ndarray)
+        assert isinstance(index, Index)
+        assert data.ndim == 1
+        self._values = data
+        self._index = index
+
+    def __iter__(self):
+        return iter(self._values)
+
+    @property
+    def dtype(self):
+        return self._values.dtype
+
+    @property
+    def flags(self):
+        return self._values.flags
+
+
+class SeriesType(types.ArrayCompatible):
+    """
+    The type class for Series objects.
+    """
+    array_priority = 1000
+
+    def __init__(self, dtype, index):
+        assert isinstance(index, IndexType)
+        self.dtype = dtype
+        self.index = index
+        self.values = types.Array(self.dtype, 1, 'C')
+        name = "series(%s, %s)" % (dtype, index)
+        super(SeriesType, self).__init__(name)
+
+    @property
+    def key(self):
+        return self.dtype, self.index
+
+    @property
+    def as_array(self):
+        return self.values
+
+    def copy(self, dtype=None, ndim=1, layout='C'):
+        assert ndim == 1
+        assert layout == 'C'
+        if dtype is None:
+            dtype = self.dtype
+        return type(self)(dtype, self.index)
+
+
+@typeof_impl.register(Index)
+def typeof_index(val, c):
+    arrty = typeof_impl(val._data, c)
+    assert arrty.ndim == 1
+    return IndexType(arrty.dtype, arrty.layout, type(val))
+
+@typeof_impl.register(Series)
+def typeof_series(val, c):
+    index = typeof_impl(val._index, c)
+    arrty = typeof_impl(val._values, c)
+    assert arrty.ndim == 1
+    assert arrty.layout == 'C'
+    return SeriesType(arrty.dtype, index)
+
+@type_callable('__array_wrap__')
+def type_array_wrap(context):
+    def typer(input_type, result):
+        if isinstance(input_type, (IndexType, SeriesType)):
+            return input_type.copy(dtype=result.dtype,
+                                   ndim=result.ndim,
+                                   layout=result.layout)
+
+    return typer
+
+@type_callable(Series)
+def type_series_constructor(context):
+    def typer(data, index):
+        if isinstance(index, IndexType) and isinstance(data, types.Array):
+            assert data.layout == 'C'
+            assert data.ndim == 1
+            return SeriesType(data.dtype, index)
+
+    return typer
+
+
+# Backend extensions for Index and Series
+
+@register_model(IndexType)
+class IndexModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [('data', fe_type.as_array)]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+@register_model(SeriesType)
+class SeriesModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('index', fe_type.index),
+            ('values', fe_type.as_array),
+            ]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+make_attribute_wrapper(IndexType, 'data', '_data')
+make_attribute_wrapper(SeriesType, 'index', '_index')
+make_attribute_wrapper(SeriesType, 'values', '_values')
+
+def make_index(context, builder, typ, **kwargs):
+    return cgutils.create_struct_proxy(typ)(context, builder, **kwargs)
+
+def make_series(context, builder, typ, **kwargs):
+    return cgutils.create_struct_proxy(typ)(context, builder, **kwargs)
+
+@lower_builtin('__array__', IndexType)
+def index_as_array(context, builder, sig, args):
+    val = make_index(context, builder, sig.args[0], ref=args[0])
+    return val._get_ptr_by_name('data')
+
+@lower_builtin('__array__', SeriesType)
+def series_as_array(context, builder, sig, args):
+    val = make_series(context, builder, sig.args[0], ref=args[0])
+    return val._get_ptr_by_name('values')
+
+@lower_builtin('__array_wrap__', IndexType, types.Array)
+def index_wrap_array(context, builder, sig, args):
+    dest = make_index(context, builder, sig.return_type)
+    dest.data = args[1]
+    return impl_ret_borrowed(context, builder, sig.return_type, dest._getvalue())
+
+@lower_builtin('__array_wrap__', SeriesType, types.Array)
+def series_wrap_array(context, builder, sig, args):
+    src = make_series(context, builder, sig.args[0], value=args[0])
+    dest = make_series(context, builder, sig.return_type)
+    dest.values = args[1]
+    dest.index = src.index
+    return impl_ret_borrowed(context, builder, sig.return_type, dest._getvalue())
+
+@lower_builtin(Series, types.Array, IndexType)
+def pdseries_constructor(context, builder, sig, args):
+    data, index = args
+    series = make_series(context, builder, sig.return_type)
+    series.index = index
+    series.values = data
+    return impl_ret_borrowed(context, builder, sig.return_type, series._getvalue())
+
+
+@unbox(IndexType)
+def unbox_index(typ, obj, c):
+    """
+    Convert a Index object to a native structure.
+    """
+    data = c.pyapi.object_getattr_string(obj, "_data")
+    index = make_index(c.context, c.builder, typ)
+    index.data = c.unbox(typ.as_array, data).value
+
+    return NativeValue(index._getvalue())
+
+@unbox(SeriesType)
+def unbox_series(typ, obj, c):
+    """
+    Convert a Series object to a native structure.
+    """
+    index = c.pyapi.object_getattr_string(obj, "_index")
+    values = c.pyapi.object_getattr_string(obj, "_values")
+    series = make_series(c.context, c.builder, typ)
+    series.index = c.unbox(typ.index, index).value
+    series.values = c.unbox(typ.values, values).value
+
+    return NativeValue(series._getvalue())
+
+
+@box(IndexType)
+def box_index(typ, val, c):
+    """
+    Convert a native index structure to a Index object.
+    """
+    # First build a Numpy array object, then wrap it in a Index
+    index = make_index(c.context, c.builder, typ, value=val)
+    classobj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.pyclass))
+    arrayobj = c.box(typ.as_array, index.data)
+    indexobj = c.pyapi.call_function_objargs(classobj, (arrayobj,))
+    return indexobj
+
+@box(SeriesType)
+def box_series(typ, val, c):
+    """
+    Convert a native series structure to a Series object.
+    """
+    series = make_series(c.context, c.builder, typ, value=val)
+    classobj = c.pyapi.unserialize(c.pyapi.serialize_object(Series))
+    indexobj = c.box(typ.index, series.index)
+    arrayobj = c.box(typ.as_array, series.values)
+    seriesobj = c.pyapi.call_function_objargs(classobj, (arrayobj, indexobj))
+    return seriesobj
+
+
+@overload_attribute(IndexType, 'is_monotonic_increasing')
+def index_is_monotonic_increasing(index):
+    """
+    Index.is_monotonic_increasing
+    """
+    def getter(index):
+        data = index._data
+        if len(data) == 0:
+            return True
+        u = data[0]
+        for v in data:
+            if v < u:
+                return False
+            v = u
+        return True
+
+    return getter
+
+@overload(len)
+def series_len(series):
+    """
+    len(Series)
+    """
+    if isinstance(series, SeriesType):
+        def len_impl(series):
+            return len(series._values)
+        return len_impl
+
+@overload_method(SeriesType, 'clip')
+def series_clip(series, lower, upper):
+    """
+    Series.clip(...)
+    """
+    def clip_impl(series, lower, upper):
+        data = series._values.copy()
+        for i in range(len(data)):
+            v = data[i]
+            if v < lower:
+                data[i] = lower
+            elif v > upper:
+                data[i] = upper
+        return Series(data, series._index)
+
+    return clip_impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/nested/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/nested/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/nested/source_module.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/nested/source_module.py
new file mode 100644
index 000000000..234fcdb53
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/nested/source_module.py
@@ -0,0 +1,20 @@
+import numpy as np
+
+from numba.pycc import CC
+
+
+cc = CC('pycc_compiled_module')
+
+_const = 42
+
+
+# This ones references a global variable at compile time
+@cc.export('get_const', 'i8()')
+def get_const():
+    return _const
+
+
+# This one needs NRT and an environment
+@cc.export('ones', 'f8[:](i4)')
+def ones(n):
+    return np.ones(n)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_distutils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_distutils.py
new file mode 100644
index 000000000..0a210359e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_distutils.py
@@ -0,0 +1,15 @@
+from distutils.core import setup
+
+from source_module import cc
+
+from numba.pycc.platform import _patch_exec_command
+
+
+def run_setup():
+    # Avoid sporadic crashes on Windows due to MSVCRT spawnve()
+    _patch_exec_command()
+    setup(ext_modules=[cc.distutils_extension()])
+
+
+if __name__ == '__main__':
+    run_setup()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_distutils_nested.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_distutils_nested.py
new file mode 100644
index 000000000..a64de8bbc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_distutils_nested.py
@@ -0,0 +1,15 @@
+from distutils.core import setup
+
+from nested.source_module import cc
+
+from numba.pycc.platform import _patch_exec_command
+
+
+def run_setup():
+    # Avoid sporadic crashes on Windows due to MSVCRT spawnve()
+    _patch_exec_command()
+    setup(ext_modules=[cc.distutils_extension()])
+
+
+if __name__ == '__main__':
+    run_setup()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_setuptools.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_setuptools.py
new file mode 100644
index 000000000..8e09b4d28
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_setuptools.py
@@ -0,0 +1,15 @@
+from setuptools import setup
+
+from source_module import cc
+
+from numba.pycc.platform import _patch_exec_command
+
+
+def run_setup():
+    # Avoid sporadic crashes on Windows due to MSVCRT spawnve()
+    _patch_exec_command()
+    setup(ext_modules=[cc.distutils_extension()])
+
+
+if __name__ == '__main__':
+    run_setup()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_setuptools_nested.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_setuptools_nested.py
new file mode 100644
index 000000000..3ba6434fc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/setup_setuptools_nested.py
@@ -0,0 +1,15 @@
+from setuptools import setup
+
+from nested.source_module import cc
+
+from numba.pycc.platform import _patch_exec_command
+
+
+def run_setup():
+    # Avoid sporadic crashes on Windows due to MSVCRT spawnve()
+    _patch_exec_command()
+    setup(ext_modules=[cc.distutils_extension()])
+
+
+if __name__ == '__main__':
+    run_setup()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/source_module.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/source_module.py
new file mode 100644
index 000000000..e5e6ea82f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/pycc_distutils_usecase/source_module.py
@@ -0,0 +1,18 @@
+import numpy as np
+
+from numba.pycc import CC
+
+
+cc = CC('pycc_compiled_module')
+
+_const = 42
+
+# This ones references a global variable at compile time
+@cc.export('get_const', 'i8()')
+def get_const():
+    return _const
+
+# This one needs NRT and an environment
+@cc.export('ones', 'f8[:](i4)')
+def ones(n):
+    return np.ones(n)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/recursion_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/recursion_usecases.py
new file mode 100644
index 000000000..7fa987c42
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/recursion_usecases.py
@@ -0,0 +1,228 @@
+"""
+Usecases of recursive functions.
+
+Some functions are compiled at import time, hence a separate module.
+"""
+
+from numba import jit
+
+
+@jit("i8(i8)", nopython=True)
+def fib1(n):
+    if n < 2:
+        return n
+    # Note the second call uses a named argument
+    return fib1(n - 1) + fib1(n=n - 2)
+
+
+def make_fib2():
+    @jit("i8(i8)", nopython=True)
+    def fib2(n):
+        if n < 2:
+            return n
+        return fib2(n - 1) + fib2(n=n - 2)
+
+    return fib2
+
+fib2 = make_fib2()
+
+
+def make_type_change_self(jit=lambda x: x):
+    @jit
+    def type_change_self(x, y):
+        if x > 1 and y > 0:
+            return x + type_change_self(x - y, y)
+        else:
+            return y
+    return type_change_self
+
+
+# Implicit signature
+@jit(nopython=True)
+def fib3(n):
+    if n < 2:
+        return n
+    return fib3(n - 1) + fib3(n - 2)
+
+
+# Run-away self recursion
+@jit(nopython=True)
+def runaway_self(x):
+    return runaway_self(x)
+
+
+@jit(nopython=True)
+def raise_self(x):
+    if x == 1:
+        raise ValueError("raise_self")
+    elif x > 0:
+        return raise_self(x - 1)
+    else:
+        return 1
+
+
+# Mutual recursion
+@jit(nopython=True)
+def outer_fac(n):
+    if n < 1:
+        return 1
+    return n * inner_fac(n - 1)
+
+
+@jit(nopython=True)
+def inner_fac(n):
+    if n < 1:
+        return 1
+    return n * outer_fac(n - 1)
+
+
+# Mutual recursion with different arg names
+def make_mutual2(jit=lambda x: x):
+    @jit
+    def foo(x):
+        if x > 0:
+            return 2 * bar(z=1, y=x)
+        return 1 + x
+
+    @jit
+    def bar(y, z):
+        return foo(x=y - z)
+
+    return foo, bar
+
+
+# Mutual runaway recursion
+
+@jit(nopython=True)
+def runaway_mutual(x):
+    return runaway_mutual_inner(x)
+
+
+@jit(nopython=True)
+def runaway_mutual_inner(x):
+    return runaway_mutual(x)
+
+
+# Mutual type changing recursion
+
+def make_type_change_mutual(jit=lambda x: x):
+    @jit
+    def foo(x, y):
+        if x > 1 and y > 0:
+            # call bar first to exercise partial type inference.
+            # typeinferer suspended at the call to bar() and haven't determined
+            # the potential return type from the else-branch
+            return x + bar(x - y, y)
+        else:
+            return y
+
+    @jit
+    def bar(x, y):
+        if x > 1 and y > 0:
+            return x + foo(x - y, y)
+        else:
+            return y
+
+    return foo
+
+
+# Indirect mutual recursion
+def make_four_level(jit=lambda x: x):
+    @jit
+    def first(x):
+        # The recursing call must have a path that is non-recursing.
+        if x > 0:
+            return second(x) * 2
+        else:
+            return 1
+
+    @jit
+    def second(x):
+        return third(x) * 3
+
+    @jit
+    def third(x):
+        return fourth(x) * 4
+
+    @jit
+    def fourth(x):
+        return first(x / 2 - 1)
+
+    return first
+
+
+def make_inner_error(jit=lambda x: x):
+    @jit
+    def outer(x):
+        if x > 0:
+            return inner(x)
+
+        else:
+            return 1
+
+    @jit
+    def inner(x):
+        if x > 0:
+            return outer(x - 1)
+        else:
+            # this branch is actually never executed
+            return error_fun(x)
+
+    @jit
+    def error_fun(x):
+        # to trigger an untyped attribute error
+        return x.ndim
+
+    return outer
+
+
+def make_raise_mutual(jit=lambda x: x):
+    @jit
+    def outer(x):
+        if x > 0:
+            return inner(x)
+        else:
+            return 1
+
+    @jit
+    def inner(x):
+        if x == 1:
+            raise ValueError('raise_mutual')
+        elif x > 0:
+            return outer(x - 1)
+        else:
+            return 1
+
+    return outer
+
+
+def make_optional_return_case(jit=lambda x: x):
+    @jit
+    def foo(x):
+        if x > 5:
+            return x - 1
+        else:
+            return
+
+    @jit
+    def bar(x):
+        out = foo(x)
+        if out is None:
+            return out
+        elif out < 8:
+            return out
+        else:
+            return x * bar(out)
+
+    return bar
+
+
+def make_growing_tuple_case(jit=lambda x: x):
+    # From issue #4387
+    @jit
+    def make_list(n):
+        if n <= 0:
+            return None
+
+        return (n, make_list(n - 1))
+    return make_list
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/serialize_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/serialize_usecases.py
new file mode 100644
index 000000000..f5a342e9b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/serialize_usecases.py
@@ -0,0 +1,114 @@
+"""
+Separate module with function samples for serialization tests,
+to avoid issues with __main__.
+"""
+
+import math
+
+from numba import jit, generated_jit
+from numba.core import types
+
+
+@jit((types.int32, types.int32))
+def add_with_sig(a, b):
+    return a + b
+
+@jit
+def add_without_sig(a, b):
+    return a + b
+
+@jit(nopython=True)
+def add_nopython(a, b):
+    return a + b
+
+@jit(nopython=True)
+def add_nopython_fail(a, b):
+    object()
+    return a + b
+
+def closure(a):
+    @jit(nopython=True)
+    def inner(b, c):
+        return a + b + c
+    return inner
+
+K = 3.0
+
+from math import sqrt
+
+def closure_with_globals(x, **jit_args):
+    @jit(**jit_args)
+    def inner(y):
+        # Exercise a builtin function and a module-level constant
+        k = max(K, K + 1)
+        # Exercise two functions from another module, one accessed with
+        # dotted notation, one imported explicitly.
+        return math.hypot(x, y) + sqrt(k)
+    return inner
+
+@jit(nopython=True)
+def other_function(x, y):
+    return math.hypot(x, y)
+
+@jit(forceobj=True)
+def get_global_objmode(x):
+    return K * x
+
+import numpy as np
+import numpy.random as nprand
+
+@jit(nopython=True)
+def get_renamed_module(x):
+    nprand.seed(42)
+    return np.cos(x), nprand.random()
+
+
+def closure_calling_other_function(x):
+    @jit(nopython=True)
+    def inner(y, z):
+        return other_function(x, y) + z
+    return inner
+
+def closure_calling_other_closure(x):
+    @jit(nopython=True)
+    def other_inner(y):
+        return math.hypot(x, y)
+
+    @jit(nopython=True)
+    def inner(y):
+        return other_inner(y) + x
+    return inner
+
+
+# A generated function using some globals and closure vars
+
+k1 = 5
+k2 = 42
+
+@generated_jit(nopython=True)
+def generated_add(x, y):
+    k3 = 1
+    if isinstance(x, types.Complex):
+        def impl(x, y):
+            return x + y + k1
+    else:
+        def impl(x, y):
+            return x + y + k2 + k3
+    return impl
+
+
+# A dynamic function calling a builtin function
+def _get_dyn_func(**jit_args):
+    code = """
+        def dyn_func(x):
+            res = 0
+            for i in range(x):
+                res += x
+            return res
+        """
+    ns = {}
+    exec(code.strip(), ns)
+    return jit(**jit_args)(ns['dyn_func'])
+
+dyn_func = _get_dyn_func(nopython=True)
+dyn_func_objmode = _get_dyn_func(forceobj=True)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/support.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/support.py
new file mode 100644
index 000000000..3a9232147
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/support.py
@@ -0,0 +1,1200 @@
+"""
+Assorted utilities for use in tests.
+"""
+
+import cmath
+import contextlib
+from collections import defaultdict
+import enum
+import gc
+import math
+import platform
+import os
+import signal
+import shutil
+import subprocess
+import sys
+import tempfile
+import threading
+import time
+import io
+import ctypes
+import multiprocessing as mp
+import warnings
+import traceback
+from contextlib import contextmanager
+import uuid
+import importlib
+import types as pytypes
+
+import numpy as np
+
+from numba import testing
+from numba.core import errors, typing, utils, config, cpu
+from numba.core.compiler import (compile_extra, compile_isolated, Flags,
+                                 DEFAULT_FLAGS, CompilerBase,
+                                 DefaultPassBuilder)
+from numba.core.typed_passes import IRLegalization
+from numba.core.untyped_passes import PreserveIR
+import unittest
+from numba.core.runtime import rtsys
+from numba.np import numpy_support
+from numba.pycc.platform import _external_compiler_ok
+
+
+try:
+    import scipy
+except ImportError:
+    scipy = None
+
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+nrt_flags = Flags()
+nrt_flags.nrt = True
+
+
+tag = testing.make_tag_decorator(['important', 'long_running'])
+
+_32bit = sys.maxsize <= 2 ** 32
+is_parfors_unsupported = _32bit
+skip_parfors_unsupported = unittest.skipIf(
+    is_parfors_unsupported,
+    'parfors not supported',
+)
+skip_py38_or_later = unittest.skipIf(
+    utils.PYVERSION >= (3, 8),
+    "unsupported on py3.8 or later"
+)
+
+skip_unless_py10_or_later = unittest.skipUnless(
+    utils.PYVERSION >= (3, 10),
+    "needs Python 3.10 or later"
+)
+
+skip_unless_py10 = unittest.skipUnless(
+    utils.PYVERSION == (3, 10),
+    "needs Python 3.10"
+)
+
+skip_if_32bit = unittest.skipIf(_32bit, "Not supported on 32 bit")
+
+_msg = "SciPy needed for test"
+skip_unless_scipy = unittest.skipIf(scipy is None, _msg)
+
+_lnx_reason = 'linux only test'
+linux_only = unittest.skipIf(not sys.platform.startswith('linux'), _lnx_reason)
+
+_win_reason = 'Windows-only test'
+windows_only = unittest.skipIf(not sys.platform.startswith('win'), _win_reason)
+
+_is_armv7l = platform.machine() == 'armv7l'
+
+disabled_test = unittest.skipIf(True, 'Test disabled')
+
+# See issue #4563, PPC64LE LLVM bug
+skip_ppc64le_issue4563 = unittest.skipIf(platform.machine() == 'ppc64le',
+                                         ("Hits: 'Parameter area must exist "
+                                          "to pass an argument in memory'"))
+
+# Typeguard
+has_typeguard = bool(os.environ.get('NUMBA_USE_TYPEGUARD', 0))
+
+skip_unless_typeguard = unittest.skipUnless(
+    has_typeguard, "Typeguard is not enabled",
+)
+
+skip_if_typeguard = unittest.skipIf(
+    has_typeguard, "Broken if Typeguard is enabled",
+)
+
+# See issue #6465, PPC64LE LLVM bug
+skip_ppc64le_issue6465 = unittest.skipIf(platform.machine() == 'ppc64le',
+                                         ("Hits: 'mismatch in size of "
+                                          "parameter area' in "
+                                          "LowerCall_64SVR4"))
+
+# fenv.h on M1 may have various issues:
+# https://github.com/numba/numba/issues/7822#issuecomment-1065356758
+_uname = platform.uname()
+IS_OSX_ARM64 = _uname.system == 'Darwin' and _uname.machine == 'arm64'
+skip_m1_fenv_errors = unittest.skipIf(IS_OSX_ARM64,
+    "fenv.h-like functionality unreliable on OSX arm64")
+
+try:
+    import scipy.linalg.cython_lapack
+    has_lapack = True
+except ImportError:
+    has_lapack = False
+
+needs_lapack = unittest.skipUnless(has_lapack,
+                                   "LAPACK needs SciPy 1.0+")
+
+try:
+    import scipy.linalg.cython_blas
+    has_blas = True
+except ImportError:
+    has_blas = False
+
+needs_blas = unittest.skipUnless(has_blas, "BLAS needs SciPy 1.0+")
+
+# Decorate a test with @needs_subprocess to ensure it doesn't run unless the
+# `SUBPROC_TEST` environment variable is set. Use this in conjunction with:
+# TestCase::subprocess_test_runner which will execute a given test in subprocess
+# with this environment variable set.
+_exec_cond = os.environ.get('SUBPROC_TEST', None) == '1'
+needs_subprocess = unittest.skipUnless(_exec_cond, "needs subprocess harness")
+
+
+# decorate for test needs external compilers
+needs_external_compilers = unittest.skipIf(not _external_compiler_ok,
+                                           ('Compatible external compilers are '
+                                            'missing'))
+
+
+def ignore_internal_warnings():
+    """Use in testing within a ` warnings.catch_warnings` block to filter out
+    warnings that are unrelated/internally generated by Numba.
+    """
+    # Filter out warnings from typeguard
+    warnings.filterwarnings('ignore', module="typeguard")
+    # Filter out warnings about TBB interface mismatch
+    warnings.filterwarnings(action='ignore',
+                            message=r".*TBB_INTERFACE_VERSION.*",
+                            category=errors.NumbaWarning,
+                            module=r'numba\.np\.ufunc\.parallel.*')
+
+
+class CompilationCache(object):
+    """
+    A cache of compilation results for various signatures and flags.
+    This can make tests significantly faster (or less slow).
+    """
+
+    def __init__(self):
+        self.typingctx = typing.Context()
+        self.targetctx = cpu.CPUContext(self.typingctx, 'cpu')
+        self.cr_cache = {}
+
+    def compile(self, func, args, return_type=None, flags=DEFAULT_FLAGS):
+        """
+        Compile the function or retrieve an already compiled result
+        from the cache.
+        """
+        from numba.core.registry import cpu_target
+
+        cache_key = (func, args, return_type, flags)
+        if cache_key in self.cr_cache:
+            cr = self.cr_cache[cache_key]
+        else:
+            # Register the contexts in case for nested @jit or @overload calls
+            # (same as compile_isolated())
+            with cpu_target.nested_context(self.typingctx, self.targetctx):
+                cr = compile_extra(self.typingctx, self.targetctx, func,
+                                   args, return_type, flags, locals={})
+            self.cr_cache[cache_key] = cr
+        return cr
+
+
+class TestCase(unittest.TestCase):
+
+    longMessage = True
+
+    # A random state yielding the same random numbers for any test case.
+    # Use as `self.random.<method name>`
+    @utils.cached_property
+    def random(self):
+        return np.random.RandomState(42)
+
+    def reset_module_warnings(self, module):
+        """
+        Reset the warnings registry of a module.  This can be necessary
+        as the warnings module is buggy in that regard.
+        See http://bugs.python.org/issue4180
+        """
+        if isinstance(module, str):
+            module = sys.modules[module]
+        try:
+            del module.__warningregistry__
+        except AttributeError:
+            pass
+
+    @contextlib.contextmanager
+    def assertTypingError(self):
+        """
+        A context manager that asserts the enclosed code block fails
+        compiling in nopython mode.
+        """
+        _accepted_errors = (errors.LoweringError, errors.TypingError,
+                            TypeError, NotImplementedError)
+        with self.assertRaises(_accepted_errors) as cm:
+            yield cm
+
+    @contextlib.contextmanager
+    def assertRefCount(self, *objects):
+        """
+        A context manager that asserts the given objects have the
+        same reference counts before and after executing the
+        enclosed block.
+        """
+        old_refcounts = [sys.getrefcount(x) for x in objects]
+        yield
+        new_refcounts = [sys.getrefcount(x) for x in objects]
+        for old, new, obj in zip(old_refcounts, new_refcounts, objects):
+            if old != new:
+                self.fail("Refcount changed from %d to %d for object: %r"
+                          % (old, new, obj))
+
+    @contextlib.contextmanager
+    def assertNoNRTLeak(self):
+        """
+        A context manager that asserts no NRT leak was created during
+        the execution of the enclosed block.
+        """
+        old = rtsys.get_allocation_stats()
+        yield
+        new = rtsys.get_allocation_stats()
+        total_alloc = new.alloc - old.alloc
+        total_free = new.free - old.free
+        total_mi_alloc = new.mi_alloc - old.mi_alloc
+        total_mi_free = new.mi_free - old.mi_free
+        self.assertEqual(total_alloc, total_free,
+                         "number of data allocs != number of data frees")
+        self.assertEqual(total_mi_alloc, total_mi_free,
+                         "number of meminfo allocs != number of meminfo frees")
+
+
+    _bool_types = (bool, np.bool_)
+    _exact_typesets = [_bool_types, (int,), (str,), (np.integer,),
+                       (bytes, np.bytes_)]
+    _approx_typesets = [(float,), (complex,), (np.inexact)]
+    _sequence_typesets = [(tuple, list)]
+    _float_types = (float, np.floating)
+    _complex_types = (complex, np.complexfloating)
+
+    def _detect_family(self, numeric_object):
+        """
+        This function returns a string description of the type family
+        that the object in question belongs to.  Possible return values
+        are: "exact", "complex", "approximate", "sequence", and "unknown"
+        """
+        if isinstance(numeric_object, np.ndarray):
+            return "ndarray"
+
+        if isinstance(numeric_object, enum.Enum):
+            return "enum"
+
+        for tp in self._sequence_typesets:
+            if isinstance(numeric_object, tp):
+                return "sequence"
+
+        for tp in self._exact_typesets:
+            if isinstance(numeric_object, tp):
+                return "exact"
+
+        for tp in self._complex_types:
+            if isinstance(numeric_object, tp):
+                return "complex"
+
+        for tp in self._approx_typesets:
+            if isinstance(numeric_object, tp):
+                return "approximate"
+
+        return "unknown"
+
+    def _fix_dtype(self, dtype):
+        """
+        Fix the given *dtype* for comparison.
+        """
+        # Under 64-bit Windows, Numpy may return either int32 or int64
+        # arrays depending on the function.
+        if (sys.platform == 'win32' and sys.maxsize > 2**32 and
+            dtype == np.dtype('int32')):
+            return np.dtype('int64')
+        else:
+            return dtype
+
+    def _fix_strides(self, arr):
+        """
+        Return the strides of the given array, fixed for comparison.
+        Strides for 0- or 1-sized dimensions are ignored.
+        """
+        if arr.size == 0:
+            return [0] * arr.ndim
+        else:
+            return [stride / arr.itemsize
+                    for (stride, shape) in zip(arr.strides, arr.shape)
+                    if shape > 1]
+
+    def assertStridesEqual(self, first, second):
+        """
+        Test that two arrays have the same shape and strides.
+        """
+        self.assertEqual(first.shape, second.shape, "shapes differ")
+        self.assertEqual(first.itemsize, second.itemsize, "itemsizes differ")
+        self.assertEqual(self._fix_strides(first), self._fix_strides(second),
+                         "strides differ")
+
+    def assertPreciseEqual(self, first, second, prec='exact', ulps=1,
+                           msg=None, ignore_sign_on_zero=False,
+                           abs_tol=None
+                           ):
+        """
+        Versatile equality testing function with more built-in checks than
+        standard assertEqual().
+
+        For arrays, test that layout, dtype, shape are identical, and
+        recursively call assertPreciseEqual() on the contents.
+
+        For other sequences, recursively call assertPreciseEqual() on
+        the contents.
+
+        For scalars, test that two scalars or have similar types and are
+        equal up to a computed precision.
+        If the scalars are instances of exact types or if *prec* is
+        'exact', they are compared exactly.
+        If the scalars are instances of inexact types (float, complex)
+        and *prec* is not 'exact', then the number of significant bits
+        is computed according to the value of *prec*: 53 bits if *prec*
+        is 'double', 24 bits if *prec* is single.  This number of bits
+        can be lowered by raising the *ulps* value.
+        ignore_sign_on_zero can be set to True if zeros are to be considered
+        equal regardless of their sign bit.
+        abs_tol if this is set to a float value its value is used in the
+        following. If, however, this is set to the string "eps" then machine
+        precision of the type(first) is used in the following instead. This
+        kwarg is used to check if the absolute difference in value between first
+        and second is less than the value set, if so the numbers being compared
+        are considered equal. (This is to handle small numbers typically of
+        magnitude less than machine precision).
+
+        Any value of *prec* other than 'exact', 'single' or 'double'
+        will raise an error.
+        """
+        try:
+            self._assertPreciseEqual(first, second, prec, ulps, msg,
+                ignore_sign_on_zero, abs_tol)
+        except AssertionError as exc:
+            failure_msg = str(exc)
+            # Fall off of the 'except' scope to avoid Python 3 exception
+            # chaining.
+        else:
+            return
+        # Decorate the failure message with more information
+        self.fail("when comparing %s and %s: %s" % (first, second, failure_msg))
+
+    def _assertPreciseEqual(self, first, second, prec='exact', ulps=1,
+                            msg=None, ignore_sign_on_zero=False,
+                            abs_tol=None):
+        """Recursive workhorse for assertPreciseEqual()."""
+
+        def _assertNumberEqual(first, second, delta=None):
+            if (delta is None or first == second == 0.0
+                or math.isinf(first) or math.isinf(second)):
+                self.assertEqual(first, second, msg=msg)
+                # For signed zeros
+                if not ignore_sign_on_zero:
+                    try:
+                        if math.copysign(1, first) != math.copysign(1, second):
+                            self.fail(
+                                self._formatMessage(msg,
+                                                    "%s != %s" %
+                                                    (first, second)))
+                    except TypeError:
+                        pass
+            else:
+                self.assertAlmostEqual(first, second, delta=delta, msg=msg)
+
+        first_family = self._detect_family(first)
+        second_family = self._detect_family(second)
+
+        assertion_message = "Type Family mismatch. (%s != %s)" % (first_family,
+            second_family)
+        if msg:
+            assertion_message += ': %s' % (msg,)
+        self.assertEqual(first_family, second_family, msg=assertion_message)
+
+        # We now know they are in the same comparison family
+        compare_family = first_family
+
+        # For recognized sequences, recurse
+        if compare_family == "ndarray":
+            dtype = self._fix_dtype(first.dtype)
+            self.assertEqual(dtype, self._fix_dtype(second.dtype))
+            self.assertEqual(first.ndim, second.ndim,
+                             "different number of dimensions")
+            self.assertEqual(first.shape, second.shape,
+                             "different shapes")
+            self.assertEqual(first.flags.writeable, second.flags.writeable,
+                             "different mutability")
+            # itemsize is already checked by the dtype test above
+            self.assertEqual(self._fix_strides(first),
+                self._fix_strides(second), "different strides")
+            if first.dtype != dtype:
+                first = first.astype(dtype)
+            if second.dtype != dtype:
+                second = second.astype(dtype)
+            for a, b in zip(first.flat, second.flat):
+                self._assertPreciseEqual(a, b, prec, ulps, msg,
+                                         ignore_sign_on_zero, abs_tol)
+            return
+
+        elif compare_family == "sequence":
+            self.assertEqual(len(first), len(second), msg=msg)
+            for a, b in zip(first, second):
+                self._assertPreciseEqual(a, b, prec, ulps, msg,
+                                         ignore_sign_on_zero, abs_tol)
+            return
+
+        elif compare_family == "exact":
+            exact_comparison = True
+
+        elif compare_family in ["complex", "approximate"]:
+            exact_comparison = False
+
+        elif compare_family == "enum":
+            self.assertIs(first.__class__, second.__class__)
+            self._assertPreciseEqual(first.value, second.value,
+                                     prec, ulps, msg,
+                                     ignore_sign_on_zero, abs_tol)
+            return
+
+        elif compare_family == "unknown":
+            # Assume these are non-numeric types: we will fall back
+            # on regular unittest comparison.
+            self.assertIs(first.__class__, second.__class__)
+            exact_comparison = True
+
+        else:
+            assert 0, "unexpected family"
+
+        # If a Numpy scalar, check the dtype is exactly the same too
+        # (required for datetime64 and timedelta64).
+        if hasattr(first, 'dtype') and hasattr(second, 'dtype'):
+            self.assertEqual(first.dtype, second.dtype)
+
+        # Mixing bools and non-bools should always fail
+        if (isinstance(first, self._bool_types) !=
+            isinstance(second, self._bool_types)):
+            assertion_message = ("Mismatching return types (%s vs. %s)"
+                                 % (first.__class__, second.__class__))
+            if msg:
+                assertion_message += ': %s' % (msg,)
+            self.fail(assertion_message)
+
+        try:
+            if cmath.isnan(first) and cmath.isnan(second):
+                # The NaNs will compare unequal, skip regular comparison
+                return
+        except TypeError:
+            # Not floats.
+            pass
+
+        # if absolute comparison is set, use it
+        if abs_tol is not None:
+            if abs_tol == "eps":
+                rtol = np.finfo(type(first)).eps
+            elif isinstance(abs_tol, float):
+                rtol = abs_tol
+            else:
+                raise ValueError("abs_tol is not \"eps\" or a float, found %s"
+                    % abs_tol)
+            if abs(first - second) < rtol:
+                return
+
+        exact_comparison = exact_comparison or prec == 'exact'
+
+        if not exact_comparison and prec != 'exact':
+            if prec == 'single':
+                bits = 24
+            elif prec == 'double':
+                bits = 53
+            else:
+                raise ValueError("unsupported precision %r" % (prec,))
+            k = 2 ** (ulps - bits - 1)
+            delta = k * (abs(first) + abs(second))
+        else:
+            delta = None
+        if isinstance(first, self._complex_types):
+            _assertNumberEqual(first.real, second.real, delta)
+            _assertNumberEqual(first.imag, second.imag, delta)
+        elif isinstance(first, (np.timedelta64, np.datetime64)):
+            # Since Np 1.16 NaT == NaT is False, so special comparison needed
+            if numpy_support.numpy_version >= (1, 16) and np.isnat(first):
+                self.assertEqual(np.isnat(first), np.isnat(second))
+            else:
+                _assertNumberEqual(first, second, delta)
+        else:
+            _assertNumberEqual(first, second, delta)
+
+    def run_nullary_func(self, pyfunc, flags):
+        """
+        Compile the 0-argument *pyfunc* with the given *flags*, and check
+        it returns the same result as the pure Python function.
+        The got and expected results are returned.
+        """
+        cr = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cr.entry_point
+        expected = pyfunc()
+        got = cfunc()
+        self.assertPreciseEqual(got, expected)
+        return got, expected
+
+    def subprocess_test_runner(self, test_module, test_class=None,
+                               test_name=None, envvars=None, timeout=60):
+        """
+        Runs named unit test(s) as specified in the arguments as:
+        test_module.test_class.test_name. test_module must always be supplied
+        and if no further refinement is made with test_class and test_name then
+        all tests in the module will be run. The tests will be run in a
+        subprocess with environment variables specified in `envvars`.
+        If given, envvars must be a map of form:
+            environment variable name (str) -> value (str)
+        It is most convenient to use this method in conjunction with
+        @needs_subprocess as the decorator will cause the decorated test to be
+        skipped unless the `SUBPROC_TEST` environment variable is set to 1
+        (this special environment variable is set by this method such that the
+        specified test(s) will not be skipped in the subprocess).
+
+
+        Following execution in the subprocess this method will check the test(s)
+        executed without error. The timeout kwarg can be used to allow more time
+        for longer running tests, it defaults to 60 seconds.
+        """
+        themod = self.__module__
+        thecls = type(self).__name__
+        parts = (test_module, test_class, test_name)
+        fully_qualified_test = '.'.join(x for x in parts if x is not None)
+        cmd = [sys.executable, '-m', 'numba.runtests', fully_qualified_test]
+        env_copy = os.environ.copy()
+        env_copy['SUBPROC_TEST'] = '1'
+        envvars = pytypes.MappingProxyType({} if envvars is None else envvars)
+        env_copy.update(envvars)
+        status = subprocess.run(cmd, stdout=subprocess.PIPE,
+                                stderr=subprocess.PIPE, timeout=timeout,
+                                env=env_copy, universal_newlines=True)
+        streams = (f'\ncaptured stdout: {status.stdout}\n'
+                   f'captured stderr: {status.stderr}')
+        self.assertEqual(status.returncode, 0, streams)
+        self.assertIn('OK', status.stderr)
+        self.assertNotIn('FAIL', status.stderr)
+        self.assertNotIn('ERROR', status.stderr)
+
+    def run_test_in_subprocess(maybefunc=None, timeout=60, envvars=None):
+        """Runs the decorated test in a subprocess via invoking numba's test
+        runner. kwargs timeout and envvars are passed through to
+        subprocess_test_runner."""
+        def wrapper(func):
+            def inner(self, *args, **kwargs):
+                if os.environ.get("SUBPROC_TEST", None) != "1":
+                    # Not in a subprocess test env, so stage the call to run the
+                    # test in a subprocess which will set the env var.
+                    class_name = self.__class__.__name__
+                    self.subprocess_test_runner(test_module=self.__module__,
+                                                test_class=class_name,
+                                                test_name=func.__name__,
+                                                timeout=timeout,
+                                                envvars=envvars,)
+                else:
+                    # env var is set, so we're in the subprocess, run the
+                    # actual test.
+                    func(self)
+            return inner
+
+        if isinstance(maybefunc, pytypes.FunctionType):
+            return wrapper(maybefunc)
+        else:
+            return wrapper
+
+
+class SerialMixin(object):
+    """Mixin to mark test for serial execution.
+    """
+    _numba_parallel_test_ = False
+
+
+# Various helpers
+
+@contextlib.contextmanager
+def override_config(name, value):
+    """
+    Return a context manager that temporarily sets Numba config variable
+    *name* to *value*.  *name* must be the name of an existing variable
+    in numba.config.
+    """
+    old_value = getattr(config, name)
+    setattr(config, name, value)
+    try:
+        yield
+    finally:
+        setattr(config, name, old_value)
+
+
+@contextlib.contextmanager
+def override_env_config(name, value):
+    """
+    Return a context manager that temporarily sets an Numba config environment
+    *name* to *value*.
+    """
+    old = os.environ.get(name)
+    os.environ[name] = value
+    config.reload_config()
+
+    try:
+        yield
+    finally:
+        if old is None:
+            # If it wasn't set originally, delete the environ var
+            del os.environ[name]
+        else:
+            # Otherwise, restore to the old value
+            os.environ[name] = old
+        # Always reload config
+        config.reload_config()
+
+
+def compile_function(name, code, globs):
+    """
+    Given a *code* string, compile it with globals *globs* and return
+    the function named *name*.
+    """
+    co = compile(code.rstrip(), "<string>", "single")
+    ns = {}
+    eval(co, globs, ns)
+    return ns[name]
+
+def tweak_code(func, codestring=None, consts=None):
+    """
+    Tweak the code object of the given function by replacing its
+    *codestring* (a bytes object) and *consts* tuple, optionally.
+    """
+    co = func.__code__
+    tp = type(co)
+    if codestring is None:
+        codestring = co.co_code
+    if consts is None:
+        consts = co.co_consts
+    if utils.PYVERSION >= (3, 8):
+        new_code = tp(co.co_argcount, co.co_posonlyargcount,
+                      co.co_kwonlyargcount, co.co_nlocals,
+                      co.co_stacksize, co.co_flags, codestring,
+                      consts, co.co_names, co.co_varnames,
+                      co.co_filename, co.co_name, co.co_firstlineno,
+                      co.co_lnotab)
+    else:
+        new_code = tp(co.co_argcount, co.co_kwonlyargcount, co.co_nlocals,
+                      co.co_stacksize, co.co_flags, codestring,
+                      consts, co.co_names, co.co_varnames,
+                      co.co_filename, co.co_name, co.co_firstlineno,
+                      co.co_lnotab)
+    func.__code__ = new_code
+
+
+_trashcan_dir = 'numba-tests'
+
+if os.name == 'nt':
+    # Under Windows, gettempdir() points to the user-local temp dir
+    _trashcan_dir = os.path.join(tempfile.gettempdir(), _trashcan_dir)
+else:
+    # Mix the UID into the directory name to allow different users to
+    # run the test suite without permission errors (issue #1586)
+    _trashcan_dir = os.path.join(tempfile.gettempdir(),
+                                 "%s.%s" % (_trashcan_dir, os.getuid()))
+
+# Stale temporary directories are deleted after they are older than this value.
+# The test suite probably won't ever take longer than this...
+_trashcan_timeout = 24 * 3600  # 1 day
+
+def _create_trashcan_dir():
+    try:
+        os.mkdir(_trashcan_dir)
+    except FileExistsError:
+        pass
+
+def _purge_trashcan_dir():
+    freshness_threshold = time.time() - _trashcan_timeout
+    for fn in sorted(os.listdir(_trashcan_dir)):
+        fn = os.path.join(_trashcan_dir, fn)
+        try:
+            st = os.stat(fn)
+            if st.st_mtime < freshness_threshold:
+                shutil.rmtree(fn, ignore_errors=True)
+        except OSError as e:
+            # In parallel testing, several processes can attempt to
+            # remove the same entry at once, ignore.
+            pass
+
+def _create_trashcan_subdir(prefix):
+    _purge_trashcan_dir()
+    path = tempfile.mkdtemp(prefix=prefix + '-', dir=_trashcan_dir)
+    return path
+
+def temp_directory(prefix):
+    """
+    Create a temporary directory with the given *prefix* that will survive
+    at least as long as this process invocation.  The temporary directory
+    will be eventually deleted when it becomes stale enough.
+
+    This is necessary because a DLL file can't be deleted while in use
+    under Windows.
+
+    An interesting side-effect is to be able to inspect the test files
+    shortly after a test suite run.
+    """
+    _create_trashcan_dir()
+    return _create_trashcan_subdir(prefix)
+
+
+def import_dynamic(modname):
+    """
+    Import and return a module of the given name.  Care is taken to
+    avoid issues due to Python's internal directory caching.
+    """
+    import importlib
+    importlib.invalidate_caches()
+    __import__(modname)
+    return sys.modules[modname]
+
+
+# From CPython
+
+@contextlib.contextmanager
+def captured_output(stream_name):
+    """Return a context manager used by captured_stdout/stdin/stderr
+    that temporarily replaces the sys stream *stream_name* with a StringIO."""
+    orig_stdout = getattr(sys, stream_name)
+    setattr(sys, stream_name, io.StringIO())
+    try:
+        yield getattr(sys, stream_name)
+    finally:
+        setattr(sys, stream_name, orig_stdout)
+
+def captured_stdout():
+    """Capture the output of sys.stdout:
+
+       with captured_stdout() as stdout:
+           print("hello")
+       self.assertEqual(stdout.getvalue(), "hello\n")
+    """
+    return captured_output("stdout")
+
+def captured_stderr():
+    """Capture the output of sys.stderr:
+
+       with captured_stderr() as stderr:
+           print("hello", file=sys.stderr)
+       self.assertEqual(stderr.getvalue(), "hello\n")
+    """
+    return captured_output("stderr")
+
+
+@contextlib.contextmanager
+def capture_cache_log():
+    with captured_stdout() as out:
+        with override_config('DEBUG_CACHE', True):
+            yield out
+
+
+class MemoryLeak(object):
+
+    __enable_leak_check = True
+
+    def memory_leak_setup(self):
+        # Clean up any NRT-backed objects hanging in a dead reference cycle
+        gc.collect()
+        self.__init_stats = rtsys.get_allocation_stats()
+
+    def memory_leak_teardown(self):
+        if self.__enable_leak_check:
+            self.assert_no_memory_leak()
+
+    def assert_no_memory_leak(self):
+        old = self.__init_stats
+        new = rtsys.get_allocation_stats()
+        total_alloc = new.alloc - old.alloc
+        total_free = new.free - old.free
+        total_mi_alloc = new.mi_alloc - old.mi_alloc
+        total_mi_free = new.mi_free - old.mi_free
+        self.assertEqual(total_alloc, total_free)
+        self.assertEqual(total_mi_alloc, total_mi_free)
+
+    def disable_leak_check(self):
+        # For per-test use when MemoryLeakMixin is injected into a TestCase
+        self.__enable_leak_check = False
+
+
+class MemoryLeakMixin(MemoryLeak):
+
+    def setUp(self):
+        super(MemoryLeakMixin, self).setUp()
+        self.memory_leak_setup()
+
+    def tearDown(self):
+        super(MemoryLeakMixin, self).tearDown()
+        gc.collect()
+        self.memory_leak_teardown()
+
+
+@contextlib.contextmanager
+def forbid_codegen():
+    """
+    Forbid LLVM code generation during the execution of the context
+    manager's enclosed block.
+
+    If code generation is invoked, a RuntimeError is raised.
+    """
+    from numba.core import codegen
+    patchpoints = ['CPUCodeLibrary._finalize_final_module']
+
+    old = {}
+    def fail(*args, **kwargs):
+        raise RuntimeError("codegen forbidden by test case")
+    try:
+        # XXX use the mock library instead?
+        for name in patchpoints:
+            parts = name.split('.')
+            obj = codegen
+            for attrname in parts[:-1]:
+                obj = getattr(obj, attrname)
+            attrname = parts[-1]
+            value = getattr(obj, attrname)
+            assert callable(value), ("%r should be callable" % name)
+            old[obj, attrname] = value
+            setattr(obj, attrname, fail)
+        yield
+    finally:
+        for (obj, attrname), value in old.items():
+            setattr(obj, attrname, value)
+
+
+# For details about redirection of file-descriptor, read
+# https://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/
+
+@contextlib.contextmanager
+def redirect_fd(fd):
+    """
+    Temporarily redirect *fd* to a pipe's write end and return a file object
+    wrapping the pipe's read end.
+    """
+
+    from numba import _helperlib
+    libnumba = ctypes.CDLL(_helperlib.__file__)
+
+    libnumba._numba_flush_stdout()
+    save = os.dup(fd)
+    r, w = os.pipe()
+    try:
+        os.dup2(w, fd)
+        yield io.open(r, "r")
+    finally:
+        libnumba._numba_flush_stdout()
+        os.close(w)
+        os.dup2(save, fd)
+        os.close(save)
+
+
+def redirect_c_stdout():
+    """Redirect C stdout
+    """
+    fd = sys.__stdout__.fileno()
+    return redirect_fd(fd)
+
+
+def run_in_new_process_caching(func, cache_dir_prefix=__name__, verbose=True):
+    """Spawn a new process to run `func` with a temporary cache directory.
+
+    The childprocess's stdout and stderr will be captured and redirected to
+    the current process's stdout and stderr.
+
+    Returns
+    -------
+    ret : dict
+        exitcode: 0 for success. 1 for exception-raised.
+        stdout: str
+        stderr: str
+    """
+    cache_dir = temp_directory(cache_dir_prefix)
+    return run_in_new_process_in_cache_dir(func, cache_dir, verbose=verbose)
+
+
+def run_in_new_process_in_cache_dir(func, cache_dir, verbose=True):
+    """Spawn a new process to run `func` with a temporary cache directory.
+
+    The childprocess's stdout and stderr will be captured and redirected to
+    the current process's stdout and stderr.
+
+    Similar to ``run_in_new_process_caching()`` but the ``cache_dir`` is a
+    directory path instead of a name prefix for the directory path.
+
+    Returns
+    -------
+    ret : dict
+        exitcode: 0 for success. 1 for exception-raised.
+        stdout: str
+        stderr: str
+    """
+    ctx = mp.get_context('spawn')
+    qout = ctx.Queue()
+    with override_env_config('NUMBA_CACHE_DIR', cache_dir):
+        proc = ctx.Process(target=_remote_runner, args=[func, qout])
+        proc.start()
+        proc.join()
+        stdout = qout.get_nowait()
+        stderr = qout.get_nowait()
+        if verbose and stdout.strip():
+            print()
+            print('STDOUT'.center(80, '-'))
+            print(stdout)
+        if verbose and stderr.strip():
+            print(file=sys.stderr)
+            print('STDERR'.center(80, '-'), file=sys.stderr)
+            print(stderr, file=sys.stderr)
+    return {
+        'exitcode': proc.exitcode,
+        'stdout': stdout,
+        'stderr': stderr,
+    }
+
+
+def _remote_runner(fn, qout):
+    """Used by `run_in_new_process_caching()`
+    """
+    with captured_stderr() as stderr:
+        with captured_stdout() as stdout:
+            try:
+                fn()
+            except Exception:
+                traceback.print_exc()
+                exitcode = 1
+            else:
+                exitcode = 0
+        qout.put(stdout.getvalue())
+    qout.put(stderr.getvalue())
+    sys.exit(exitcode)
+
+class CheckWarningsMixin(object):
+    @contextlib.contextmanager
+    def check_warnings(self, messages, category=RuntimeWarning):
+        with warnings.catch_warnings(record=True) as catch:
+            warnings.simplefilter("always")
+            yield
+        found = 0
+        for w in catch:
+            for m in messages:
+                if m in str(w.message):
+                    self.assertEqual(w.category, category)
+                    found += 1
+        self.assertEqual(found, len(messages))
+
+
+def _format_jit_options(**jit_options):
+    if not jit_options:
+        return ''
+    out = []
+    for key, value in jit_options.items():
+        if isinstance(value, str):
+            value = '"{}"'.format(value)
+        out.append('{}={}'.format(key, value))
+    return ', '.join(out)
+
+
+@contextlib.contextmanager
+def create_temp_module(source_lines, **jit_options):
+    """A context manager that creates and imports a temporary module
+    from sources provided in ``source_lines``.
+
+    Optionally it is possible to provide jit options for ``jit_module`` if it
+    is explicitly used in ``source_lines`` like ``jit_module({jit_options})``.
+    """
+    # Use try/finally so cleanup happens even when an exception is raised
+    try:
+        tempdir = temp_directory('test_temp_module')
+        # Generate random module name
+        temp_module_name = 'test_temp_module_{}'.format(
+            str(uuid.uuid4()).replace('-', '_'))
+        temp_module_path = os.path.join(tempdir, temp_module_name + '.py')
+
+        jit_options = _format_jit_options(**jit_options)
+        with open(temp_module_path, 'w') as f:
+            lines = source_lines.format(jit_options=jit_options)
+            f.write(lines)
+        # Add test_module to sys.path so it can be imported
+        sys.path.insert(0, tempdir)
+        test_module = importlib.import_module(temp_module_name)
+        yield test_module
+    finally:
+        sys.modules.pop(temp_module_name, None)
+        sys.path.remove(tempdir)
+        shutil.rmtree(tempdir)
+
+
+def run_in_subprocess(code, flags=None, env=None, timeout=30):
+    """Run a snippet of Python code in a subprocess with flags, if any are
+    given. 'env' is passed to subprocess.Popen(). 'timeout' is passed to
+    popen.communicate().
+
+    Returns the stdout and stderr of the subprocess after its termination.
+    """
+    if flags is None:
+        flags = []
+    cmd = [sys.executable,] + flags + ["-c", code]
+    popen = subprocess.Popen(cmd, stdout=subprocess.PIPE,
+                             stderr=subprocess.PIPE, env=env)
+    out, err = popen.communicate(timeout=timeout)
+    if popen.returncode != 0:
+        msg = "process failed with code %s: stderr follows\n%s\n"
+        raise AssertionError(msg % (popen.returncode, err.decode()))
+    return out, err
+
+
+def strace(work, syscalls, timeout=10):
+    """Runs strace whilst executing the function work() in the current process,
+    captures the listed syscalls (list of strings). Takes an optional timeout in
+    seconds, default is 10, if this is exceeded the process will be sent a
+    SIGKILL. Returns a list of lines that are output by strace.
+    """
+
+    # Open a tmpfile for strace to write into.
+    with tempfile.NamedTemporaryFile('w+t') as ntf:
+
+        parent_pid = os.getpid()
+        strace_binary = shutil.which('strace')
+        if strace_binary is None:
+            raise ValueError("No valid 'strace' binary could be found")
+        cmd = [strace_binary, # strace
+               '-q', # quietly (no attach/detach print out)
+               '-p', str(parent_pid), # this PID
+               '-e', ','.join(syscalls), # these syscalls
+               '-o', ntf.name] # put output into this file
+
+        # redirect stdout, stderr is handled by the `-o` flag to strace.
+        popen = subprocess.Popen(cmd, stdout=subprocess.PIPE,)
+        strace_pid = popen.pid
+        thread_timeout = threading.Timer(timeout, popen.kill)
+        thread_timeout.start()
+
+        def check_return(problem=''):
+            ret = popen.returncode
+            if ret != 0:
+                msg = ("strace exited non-zero, process return code was:"
+                       f"{ret}. {problem}")
+                raise RuntimeError(msg)
+        try:
+            # push the communication onto a thread so it doesn't block.
+            # start comms thread
+            thread_comms = threading.Thread(target=popen.communicate)
+            thread_comms.start()
+
+            # do work
+            work()
+            # Flush the output buffer file
+            ntf.flush()
+            # interrupt the strace process to stop it if it's still running
+            if popen.poll() is None:
+                os.kill(strace_pid, signal.SIGINT)
+            else:
+                # it's not running, probably an issue, raise
+                problem="If this is SIGKILL, increase the timeout?"
+                check_return(problem)
+            # Make sure the return code is 0, SIGINT to detach is considered
+            # a successful exit.
+            popen.wait()
+            check_return()
+            # collect the data
+            strace_data = ntf.readlines()
+        finally:
+            # join communication, should be stopped now as process has
+            # exited
+            thread_comms.join()
+            # should be stopped already
+            thread_timeout.cancel()
+
+    return strace_data
+
+
+def strace_supported():
+    """Checks if strace is supported and working"""
+
+    # Only support this on linux where the `strace` binary is likely to be the
+    # strace needed.
+    if not sys.platform.startswith('linux'):
+        return False
+
+    def force_clone(): # subprocess triggers a clone
+        subprocess.run([sys.executable, '-c', 'exit()'])
+
+    syscall = 'clone'
+    try:
+        trace = strace(force_clone, [syscall,])
+    except Exception:
+        return False
+    return syscall in ''.join(trace)
+
+
+class IRPreservingTestPipeline(CompilerBase):
+    """ Same as the standard pipeline, but preserves the func_ir into the
+    metadata store after legalisation, useful for testing IR changes"""
+
+    def define_pipelines(self):
+        pipeline = DefaultPassBuilder.define_nopython_pipeline(
+            self.state, "ir_preserving_custom_pipe")
+        # mangle the default pipeline and inject DCE and IR preservation ahead
+        # of legalisation
+
+        # TODO: add a way to not do this! un-finalizing is not a good idea
+        pipeline._finalized = False
+        pipeline.add_pass_after(PreserveIR, IRLegalization)
+
+        pipeline.finalize()
+        return [pipeline]
+
+
+def print_azure_matrix():
+    """This is a utility function that prints out the map of NumPy to Python
+    versions and how many of that combination are being tested across all the
+    declared config for azure-pipelines. It is useful to run when updating the
+    azure-pipelines config to be able to quickly see what the coverage is."""
+    import yaml
+    from yaml import Loader
+    base_path = os.path.dirname(os.path.abspath(__file__))
+    azure_pipe = os.path.join(base_path, '..', '..', 'azure-pipelines.yml')
+    if not os.path.isfile(azure_pipe):
+        self.skipTest("'azure-pipelines.yml' is not available")
+    with open(os.path.abspath(azure_pipe), 'rt') as f:
+        data = f.read()
+    pipe_yml = yaml.load(data, Loader=Loader)
+
+    templates = pipe_yml['jobs']
+    # first look at the items in the first two templates, this is osx/linux
+    py2np_map = defaultdict(lambda: defaultdict(int))
+    for tmplt in templates[:2]:
+        matrix = tmplt['parameters']['matrix']
+        for setup in matrix.values():
+            py2np_map[setup['NUMPY']][setup['PYTHON']]+=1
+
+    # next look at the items in the windows only template
+    winpath = ['..', '..', 'buildscripts', 'azure', 'azure-windows.yml']
+    azure_windows = os.path.join(base_path, *winpath)
+    if not os.path.isfile(azure_windows):
+        self.skipTest("'azure-windows.yml' is not available")
+    with open(os.path.abspath(azure_windows), 'rt') as f:
+        data = f.read()
+    windows_yml = yaml.load(data, Loader=Loader)
+
+    # There's only one template in windows and its keyed differently to the
+    # above, get its matrix.
+    matrix = windows_yml['jobs'][0]['strategy']['matrix']
+    for setup in matrix.values():
+        py2np_map[setup['NUMPY']][setup['PYTHON']]+=1
+
+    print("NumPy | Python | Count")
+    print("-----------------------")
+    for npver, pys in sorted(py2np_map.items()):
+        for pyver, count in pys.items():
+            print(f" {npver} |  {pyver:<4}  |   {count}")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_alignment.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_alignment.py
new file mode 100644
index 000000000..77eee17b2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_alignment.py
@@ -0,0 +1,40 @@
+# See also numba.cuda.tests.test_alignment
+
+import numpy as np
+from numba import from_dtype, njit, void
+from numba.tests.support import TestCase
+
+
+class TestAlignment(TestCase):
+
+    def test_record_alignment(self):
+        rec_dtype = np.dtype([('a', 'int32'), ('b', 'float64')], align=True)
+        rec = from_dtype(rec_dtype)
+
+        @njit((rec[:],))
+        def foo(a):
+            for i in range(a.size):
+                a[i].a = a[i].b
+
+        a_recarray = np.recarray(3, dtype=rec_dtype)
+        for i in range(a_recarray.size):
+            a_rec = a_recarray[i]
+            a_rec.a = 0
+            a_rec.b = (i + 1) * 123
+
+        foo(a_recarray)
+        np.testing.assert_equal(a_recarray.a, a_recarray.b)
+
+    def test_record_misaligned(self):
+        rec_dtype = np.dtype([('a', 'int32'), ('b', 'float64')])
+        rec = from_dtype(rec_dtype)
+
+        # Unlike the CUDA target, this will not generate an error
+        @njit((rec[:],))
+        def foo(a):
+            for i in range(a.size):
+                a[i].a = a[i].b
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_analysis.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_analysis.py
new file mode 100644
index 000000000..82ea42a45
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_analysis.py
@@ -0,0 +1,1050 @@
+# Tests numba.analysis functions
+import collections
+import types as pytypes
+
+import numpy as np
+from numba.core.compiler import compile_isolated, run_frontend, Flags, StateDict
+from numba import jit, njit, literal_unroll
+from numba.core import types, errors, ir, rewrites, ir_utils, utils, cpu
+from numba.core import postproc
+from numba.core.inline_closurecall import InlineClosureCallPass
+from numba.tests.support import (TestCase, MemoryLeakMixin, SerialMixin,
+                                 IRPreservingTestPipeline)
+from numba.core.analysis import dead_branch_prune, rewrite_semantic_constants
+from numba.core.untyped_passes import (ReconstructSSA, TranslateByteCode,
+                                       IRProcessing, DeadBranchPrune,
+                                       PreserveIR)
+from numba.core.compiler import DefaultPassBuilder, CompilerBase, PassManager
+
+
+_GLOBAL = 123
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+
+def compile_to_ir(func):
+    func_ir = run_frontend(func)
+    state = StateDict()
+    state.func_ir = func_ir
+    state.typemap = None
+    state.calltypes = None
+    # Transform to SSA
+    ReconstructSSA().run_pass(state)
+    # call this to get print etc rewrites
+    rewrites.rewrite_registry.apply('before-inference', state)
+    return func_ir
+
+
+class TestBranchPruneBase(MemoryLeakMixin, TestCase):
+    """
+    Tests branch pruning
+    """
+    _DEBUG = False
+
+    # find *all* branches
+    def find_branches(self, the_ir):
+        branches = []
+        for blk in the_ir.blocks.values():
+            tmp = [_ for _ in blk.find_insts(cls=ir.Branch)]
+            branches.extend(tmp)
+        return branches
+
+    def assert_prune(self, func, args_tys, prune, *args, **kwargs):
+        # This checks that the expected pruned branches have indeed been pruned.
+        # func is a python function to assess
+        # args_tys is the numba types arguments tuple
+        # prune arg is a list, one entry per branch. The value in the entry is
+        # encoded as follows:
+        # True: using constant inference only, the True branch will be pruned
+        # False: using constant inference only, the False branch will be pruned
+        # None: under no circumstances should this branch be pruned
+        # *args: the argument instances to pass to the function to check
+        #        execution is still valid post transform
+        # **kwargs:
+        #        - flags: compiler.Flags instance to pass to `compile_isolated`,
+        #          permits use of e.g. object mode
+
+        func_ir = compile_to_ir(func)
+        before = func_ir.copy()
+        if self._DEBUG:
+            print("=" * 80)
+            print("before inline")
+            func_ir.dump()
+
+        # run closure inlining to ensure that nonlocals in closures are visible
+        inline_pass = InlineClosureCallPass(func_ir,
+                                            cpu.ParallelOptions(False),)
+        inline_pass.run()
+
+        # Remove all Dels, and re-run postproc
+        post_proc = postproc.PostProcessor(func_ir)
+        post_proc.run()
+
+        rewrite_semantic_constants(func_ir, args_tys)
+        if self._DEBUG:
+            print("=" * 80)
+            print("before prune")
+            func_ir.dump()
+
+        dead_branch_prune(func_ir, args_tys)
+
+        after = func_ir
+        if self._DEBUG:
+            print("after prune")
+            func_ir.dump()
+
+        before_branches = self.find_branches(before)
+        self.assertEqual(len(before_branches), len(prune))
+
+        # what is expected to be pruned
+        expect_removed = []
+        for idx, prune in enumerate(prune):
+            branch = before_branches[idx]
+            if prune is True:
+                expect_removed.append(branch.truebr)
+            elif prune is False:
+                expect_removed.append(branch.falsebr)
+            elif prune is None:
+                pass  # nothing should be removed!
+            elif prune == 'both':
+                expect_removed.append(branch.falsebr)
+                expect_removed.append(branch.truebr)
+            else:
+                assert 0, "unreachable"
+
+        # compare labels
+        original_labels = set([_ for _ in before.blocks.keys()])
+        new_labels = set([_ for _ in after.blocks.keys()])
+        # assert that the new labels are precisely the original less the
+        # expected pruned labels
+        try:
+            self.assertEqual(new_labels, original_labels - set(expect_removed))
+        except AssertionError as e:
+            print("new_labels", sorted(new_labels))
+            print("original_labels", sorted(original_labels))
+            print("expect_removed", sorted(expect_removed))
+            raise e
+
+        supplied_flags = kwargs.pop('flags', False)
+        compiler_kws = {'flags': supplied_flags} if supplied_flags else {}
+        cres = compile_isolated(func, args_tys, **compiler_kws)
+        if args is None:
+            res = cres.entry_point()
+            expected = func()
+        else:
+            res = cres.entry_point(*args)
+            expected = func(*args)
+        self.assertEqual(res, expected)
+
+
+class TestBranchPrune(TestBranchPruneBase, SerialMixin):
+
+    def test_single_if(self):
+
+        def impl(x):
+            if 1 == 0:
+                return 3.14159
+
+        self.assert_prune(impl, (types.NoneType('none'),), [True], None)
+
+        def impl(x):
+            if 1 == 1:
+                return 3.14159
+
+        self.assert_prune(impl, (types.NoneType('none'),), [False], None)
+
+        def impl(x):
+            if x is None:
+                return 3.14159
+
+        self.assert_prune(impl, (types.NoneType('none'),), [False], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [True], 10)
+
+        def impl(x):
+            if x == 10:
+                return 3.14159
+
+        self.assert_prune(impl, (types.NoneType('none'),), [True], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [None], 10)
+
+        def impl(x):
+            if x == 10:
+                z = 3.14159  # noqa: F841 # no effect
+
+        self.assert_prune(impl, (types.NoneType('none'),), [True], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [None], 10)
+
+        def impl(x):
+            z = None
+            y = z
+            if x == y:
+                return 100
+
+        self.assert_prune(impl, (types.NoneType('none'),), [False], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [True], 10)
+
+    def test_single_if_else(self):
+
+        def impl(x):
+            if x is None:
+                return 3.14159
+            else:
+                return 1.61803
+
+        self.assert_prune(impl, (types.NoneType('none'),), [False], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [True], 10)
+
+    def test_single_if_const_val(self):
+
+        def impl(x):
+            if x == 100:
+                return 3.14159
+
+        self.assert_prune(impl, (types.NoneType('none'),), [True], None)
+        self.assert_prune(impl, (types.IntegerLiteral(100),), [None], 100)
+
+        def impl(x):
+            # switch the condition order
+            if 100 == x:
+                return 3.14159
+
+        self.assert_prune(impl, (types.NoneType('none'),), [True], None)
+        self.assert_prune(impl, (types.IntegerLiteral(100),), [None], 100)
+
+    def test_single_if_else_two_const_val(self):
+
+        def impl(x, y):
+            if x == y:
+                return 3.14159
+            else:
+                return 1.61803
+
+        self.assert_prune(impl, (types.IntegerLiteral(100),) * 2, [None], 100,
+                          100)
+        self.assert_prune(impl, (types.NoneType('none'),) * 2, [False], None,
+                          None)
+        self.assert_prune(impl, (types.IntegerLiteral(100),
+                                 types.NoneType('none'),), [True], 100, None)
+        self.assert_prune(impl, (types.IntegerLiteral(100),
+                                 types.IntegerLiteral(1000)), [None], 100, 1000)
+
+    def test_single_if_else_w_following_undetermined(self):
+
+        def impl(x):
+            x_is_none_work = False
+            if x is None:
+                x_is_none_work = True
+            else:
+                dead = 7  # noqa: F841 # no effect
+
+            if x_is_none_work:
+                y = 10
+            else:
+                y = -3
+            return y
+
+        self.assert_prune(impl, (types.NoneType('none'),), [False, None], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [True, None], 10)
+
+        def impl(x):
+            x_is_none_work = False
+            if x is None:
+                x_is_none_work = True
+            else:
+                pass
+
+            if x_is_none_work:
+                y = 10
+            else:
+                y = -3
+            return y
+
+        if utils.PYVERSION >= (3, 10):
+            # Python 3.10 creates a block with a NOP in it for the `pass` which
+            # means it gets pruned.
+            self.assert_prune(impl, (types.NoneType('none'),), [False, None],
+                              None)
+        else:
+            self.assert_prune(impl, (types.NoneType('none'),), [None, None],
+                              None)
+
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [True, None], 10)
+
+    def test_double_if_else_rt_const(self):
+
+        def impl(x):
+            one_hundred = 100
+            x_is_none_work = 4
+            if x is None:
+                x_is_none_work = 100
+            else:
+                dead = 7  # noqa: F841 # no effect
+
+            if x_is_none_work == one_hundred:
+                y = 10
+            else:
+                y = -3
+
+            return y, x_is_none_work
+
+        self.assert_prune(impl, (types.NoneType('none'),), [False, None], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [True, None], 10)
+
+    def test_double_if_else_non_literal_const(self):
+
+        def impl(x):
+            one_hundred = 100
+            if x == one_hundred:
+                y = 3.14159
+            else:
+                y = 1.61803
+            return y
+
+        # no prune as compilation specialization on literal value not permitted
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [None], 10)
+        self.assert_prune(impl, (types.IntegerLiteral(100),), [None], 100)
+
+    def test_single_two_branches_same_cond(self):
+
+        def impl(x):
+            if x is None:
+                y = 10
+            else:
+                y = 40
+
+            if x is not None:
+                z = 100
+            else:
+                z = 400
+
+            return z, y
+
+        self.assert_prune(impl, (types.NoneType('none'),), [False, True], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [True, False], 10)
+
+    def test_cond_is_kwarg_none(self):
+
+        def impl(x=None):
+            if x is None:
+                y = 10
+            else:
+                y = 40
+
+            if x is not None:
+                z = 100
+            else:
+                z = 400
+
+            return z, y
+
+        self.assert_prune(impl, (types.Omitted(None),),
+                          [False, True], None)
+        self.assert_prune(impl, (types.NoneType('none'),), [False, True], None)
+        self.assert_prune(impl, (types.IntegerLiteral(10),), [True, False], 10)
+
+    def test_cond_is_kwarg_value(self):
+
+        def impl(x=1000):
+            if x == 1000:
+                y = 10
+            else:
+                y = 40
+
+            if x != 1000:
+                z = 100
+            else:
+                z = 400
+
+            return z, y
+
+        self.assert_prune(impl, (types.Omitted(1000),), [None, None], 1000)
+        self.assert_prune(impl, (types.IntegerLiteral(1000),), [None, None],
+                          1000)
+        self.assert_prune(impl, (types.IntegerLiteral(0),), [None, None], 0)
+        self.assert_prune(impl, (types.NoneType('none'),), [True, False], None)
+
+    def test_cond_rewrite_is_correct(self):
+        # this checks that when a condition is replaced, it is replace by a
+        # true/false bit that correctly represents the evaluated condition
+        def fn(x):
+            if x is None:
+                return 10
+            return 12
+
+        def check(func, arg_tys, bit_val):
+            func_ir = compile_to_ir(func)
+
+            # check there is 1 branch
+            before_branches = self.find_branches(func_ir)
+            self.assertEqual(len(before_branches), 1)
+
+            # check the condition in the branch is a binop
+            pred_var = before_branches[0].cond
+            pred_defn = ir_utils.get_definition(func_ir, pred_var)
+            self.assertEqual(pred_defn.op, 'call')
+            condition_var = pred_defn.args[0]
+            condition_op = ir_utils.get_definition(func_ir, condition_var)
+            self.assertEqual(condition_op.op, 'binop')
+
+            # do the prune, this should kill the dead branch and rewrite the
+            #'condition to a true/false const bit
+            if self._DEBUG:
+                print("=" * 80)
+                print("before prune")
+                func_ir.dump()
+            dead_branch_prune(func_ir, arg_tys)
+            if self._DEBUG:
+                print("=" * 80)
+                print("after prune")
+                func_ir.dump()
+
+            # after mutation, the condition should be a const value `bit_val`
+            new_condition_defn = ir_utils.get_definition(func_ir, condition_var)
+            self.assertTrue(isinstance(new_condition_defn, ir.Const))
+            self.assertEqual(new_condition_defn.value, bit_val)
+
+        check(fn, (types.NoneType('none'),), 1)
+        check(fn, (types.IntegerLiteral(10),), 0)
+
+    def test_obj_mode_fallback(self):
+        # see issue #3879, this checks that object mode fall back doesn't suffer
+        # from the IR mutation
+
+        @jit
+        def bug(a, b):
+            if a.ndim == 1:
+                if b is None:
+                    return dict()
+                return 12
+            return []
+
+        self.assertEqual(bug(np.zeros(10), 4), 12)
+        self.assertEqual(bug(np.arange(10), None), dict())
+        self.assertEqual(bug(np.arange(10).reshape((2, 5)), 10), [])
+        self.assertEqual(bug(np.arange(10).reshape((2, 5)), None), [])
+        self.assertFalse(bug.nopython_signatures)
+
+    def test_global_bake_in(self):
+
+        def impl(x):
+            if _GLOBAL == 123:
+                return x
+            else:
+                return x + 10
+
+        self.assert_prune(impl, (types.IntegerLiteral(1),), [False], 1)
+
+        global _GLOBAL
+        tmp = _GLOBAL
+
+        try:
+            _GLOBAL = 5
+
+            def impl(x):
+                if _GLOBAL == 123:
+                    return x
+                else:
+                    return x + 10
+
+            self.assert_prune(impl, (types.IntegerLiteral(1),), [True], 1)
+        finally:
+            _GLOBAL = tmp
+
+    def test_freevar_bake_in(self):
+
+        _FREEVAR = 123
+
+        def impl(x):
+            if _FREEVAR == 123:
+                return x
+            else:
+                return x + 10
+
+        self.assert_prune(impl, (types.IntegerLiteral(1),), [False], 1)
+
+        _FREEVAR = 12
+
+        def impl(x):
+            if _FREEVAR == 123:
+                return x
+            else:
+                return x + 10
+
+        self.assert_prune(impl, (types.IntegerLiteral(1),), [True], 1)
+
+    def test_redefined_variables_are_not_considered_in_prune(self):
+        # see issue #4163, checks that if a variable that is an argument is
+        # redefined in the user code it is not considered const
+
+        def impl(array, a=None):
+            if a is None:
+                a = 0
+            if a < 0:
+                return 10
+            return 30
+
+        self.assert_prune(impl,
+                          (types.Array(types.float64, 2, 'C'),
+                           types.NoneType('none'),),
+                          [None, None],
+                          np.zeros((2, 3)), None)
+
+    def test_comparison_operators(self):
+        # see issue #4163, checks that a variable that is an argument and has
+        # value None survives TypeError from invalid comparison which should be
+        # dead
+
+        def impl(array, a=None):
+            x = 0
+            if a is None:
+                return 10 # dynamic exec would return here
+            # static analysis requires that this is executed with a=None,
+            # hence TypeError
+            if a < 0:
+                return 20
+            return x
+
+        self.assert_prune(impl,
+                          (types.Array(types.float64, 2, 'C'),
+                           types.NoneType('none'),),
+                          [False, 'both'],
+                          np.zeros((2, 3)), None)
+
+        self.assert_prune(impl,
+                          (types.Array(types.float64, 2, 'C'),
+                           types.float64,),
+                          [None, None],
+                          np.zeros((2, 3)), 12.)
+
+    def test_redefinition_analysis_same_block(self):
+        # checks that a redefinition in a block with prunable potential doesn't
+        # break
+
+        def impl(array, x, a=None):
+            b = 2
+            if x < 4:
+                b = 12
+            if a is None: # known true
+                a = 7 # live
+            else:
+                b = 15 # dead
+            if a < 0: # valid as a result of the redefinition of 'a'
+                return 10
+            return 30 + b + a
+
+        self.assert_prune(impl,
+                          (types.Array(types.float64, 2, 'C'),
+                           types.float64, types.NoneType('none'),),
+                          [None, False, None],
+                          np.zeros((2, 3)), 1., None)
+
+    def test_redefinition_analysis_different_block_can_exec(self):
+        # checks that a redefinition in a block that may be executed prevents
+        # pruning
+
+        def impl(array, x, a=None):
+            b = 0
+            if x > 5:
+                a = 11 # a redefined, cannot tell statically if this will exec
+            if x < 4:
+                b = 12
+            if a is None: # cannot prune, cannot determine if re-defn occurred
+                b += 5
+            else:
+                b += 7
+                if a < 0:
+                    return 10
+            return 30 + b
+
+        self.assert_prune(impl,
+                          (types.Array(types.float64, 2, 'C'),
+                           types.float64, types.NoneType('none'),),
+                          [None, None, None, None],
+                          np.zeros((2, 3)), 1., None)
+
+    def test_redefinition_analysis_different_block_cannot_exec(self):
+        # checks that a redefinition in a block guarded by something that
+        # has prune potential
+
+        def impl(array, x=None, a=None):
+            b = 0
+            if x is not None:
+                a = 11
+            if a is None:
+                b += 5
+            else:
+                b += 7
+            return 30 + b
+
+        self.assert_prune(impl,
+                          (types.Array(types.float64, 2, 'C'),
+                           types.NoneType('none'), types.NoneType('none')),
+                          [True, None],
+                          np.zeros((2, 3)), None, None)
+
+        self.assert_prune(impl,
+                          (types.Array(types.float64, 2, 'C'),
+                           types.NoneType('none'), types.float64),
+                          [True, None],
+                          np.zeros((2, 3)), None, 1.2)
+
+        self.assert_prune(impl,
+                          (types.Array(types.float64, 2, 'C'),
+                           types.float64, types.NoneType('none')),
+                          [None, None],
+                          np.zeros((2, 3)), 1.2, None)
+
+    def test_closure_and_nonlocal_can_prune(self):
+        # Closures must be inlined ahead of branch pruning in case nonlocal
+        # is used. See issue #6585.
+        def impl():
+            x = 1000
+
+            def closure():
+                nonlocal x
+                x = 0
+
+            closure()
+
+            if x == 0:
+                return True
+            else:
+                return False
+
+        self.assert_prune(impl, (), [False,],)
+
+    def test_closure_and_nonlocal_cannot_prune(self):
+        # Closures must be inlined ahead of branch pruning in case nonlocal
+        # is used. See issue #6585.
+        def impl(n):
+            x = 1000
+
+            def closure(t):
+                nonlocal x
+                x = t
+
+            closure(n)
+
+            if x == 0:
+                return True
+            else:
+                return False
+
+        self.assert_prune(impl, (types.int64,), [None,], 1)
+
+
+class TestBranchPrunePredicates(TestBranchPruneBase, SerialMixin):
+    # Really important thing to remember... the branch on predicates end up as
+    # POP_JUMP_IF_<bool> and the targets are backwards compared to normal, i.e.
+    # the true condition is far jump and the false the near i.e. `if x` would
+    # end up in Numba IR as e.g. `branch x 10, 6`.
+
+    _TRUTHY = (1, "String", True, 7.4, 3j)
+    _FALSEY = (0, "", False, 0.0, 0j, None)
+
+    def _literal_const_sample_generator(self, pyfunc, consts):
+        """
+        This takes a python function, pyfunc, and manipulates its co_const
+        __code__ member to create a new function with different co_consts as
+        supplied in argument consts.
+
+        consts is a dict {index: value} of co_const tuple index to constant
+        value used to update a pyfunc clone's co_const.
+        """
+        pyfunc_code = pyfunc.__code__
+
+        # translate consts spec to update the constants
+        co_consts = {k: v for k, v in enumerate(pyfunc_code.co_consts)}
+        for k, v in consts.items():
+            co_consts[k] = v
+        new_consts = tuple([v for _, v in sorted(co_consts.items())])
+
+        # create new code parts
+        co_args = [pyfunc_code.co_argcount]
+
+        if utils.PYVERSION >= (3, 8):
+            co_args.append(pyfunc_code.co_posonlyargcount)
+        co_args.append(pyfunc_code.co_kwonlyargcount)
+        co_args.extend([pyfunc_code.co_nlocals,
+                        pyfunc_code.co_stacksize,
+                        pyfunc_code.co_flags,
+                        pyfunc_code.co_code,
+                        new_consts,
+                        pyfunc_code.co_names,
+                        pyfunc_code.co_varnames,
+                        pyfunc_code.co_filename,
+                        pyfunc_code.co_name,
+                        pyfunc_code.co_firstlineno,
+                        pyfunc_code.co_lnotab,
+                        pyfunc_code.co_freevars,
+                        pyfunc_code.co_cellvars
+                        ])
+
+        # create code object with mutation
+        new_code = pytypes.CodeType(*co_args)
+
+        # get function
+        return pytypes.FunctionType(new_code, globals())
+
+    def test_literal_const_code_gen(self):
+        def impl(x):
+            _CONST1 = "PLACEHOLDER1"
+            if _CONST1:
+                return 3.14159
+            else:
+                _CONST2 = "PLACEHOLDER2"
+            return _CONST2 + 4
+
+        new = self._literal_const_sample_generator(impl, {1:0, 3:20})
+        iconst = impl.__code__.co_consts
+        nconst = new.__code__.co_consts
+        self.assertEqual(iconst, (None, "PLACEHOLDER1", 3.14159,
+                                  "PLACEHOLDER2", 4))
+        self.assertEqual(nconst, (None, 0, 3.14159,  20, 4))
+        self.assertEqual(impl(None), 3.14159)
+        self.assertEqual(new(None), 24)
+
+    def test_single_if_const(self):
+
+        def impl(x):
+            _CONST1 = "PLACEHOLDER1"
+            if _CONST1:
+                return 3.14159
+
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for const in c_inp:
+                func = self._literal_const_sample_generator(impl, {1: const})
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_negate_const(self):
+
+        def impl(x):
+            _CONST1 = "PLACEHOLDER1"
+            if not _CONST1:
+                return 3.14159
+
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for const in c_inp:
+                func = self._literal_const_sample_generator(impl, {1: const})
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_else_const(self):
+
+        def impl(x):
+            _CONST1 = "PLACEHOLDER1"
+            if _CONST1:
+                return 3.14159
+            else:
+                return 1.61803
+
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for const in c_inp:
+                func = self._literal_const_sample_generator(impl, {1: const})
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_else_negate_const(self):
+
+        def impl(x):
+            _CONST1 = "PLACEHOLDER1"
+            if not _CONST1:
+                return 3.14159
+            else:
+                return 1.61803
+
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for const in c_inp:
+                func = self._literal_const_sample_generator(impl, {1: const})
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_freevar(self):
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for const in c_inp:
+
+                def func(x):
+                    if const:
+                        return 3.14159, const
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_negate_freevar(self):
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for const in c_inp:
+
+                def func(x):
+                    if not const:
+                        return 3.14159, const
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_else_freevar(self):
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for const in c_inp:
+
+                def func(x):
+                    if const:
+                        return 3.14159, const
+                    else:
+                        return 1.61803, const
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_else_negate_freevar(self):
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for const in c_inp:
+
+                def func(x):
+                    if not const:
+                        return 3.14159, const
+                    else:
+                        return 1.61803, const
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    # globals in this section have absurd names after their test usecase names
+    # so as to prevent collisions and permit tests to run in parallel
+    def test_single_if_global(self):
+        global c_test_single_if_global
+
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for c in c_inp:
+                c_test_single_if_global = c
+
+                def func(x):
+                    if c_test_single_if_global:
+                        return 3.14159, c_test_single_if_global
+
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_negate_global(self):
+        global c_test_single_if_negate_global
+
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for c in c_inp:
+                c_test_single_if_negate_global = c
+
+                def func(x):
+                    if c_test_single_if_negate_global:
+                        return 3.14159, c_test_single_if_negate_global
+
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_else_global(self):
+        global c_test_single_if_else_global
+
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for c in c_inp:
+                c_test_single_if_else_global = c
+
+                def func(x):
+                    if c_test_single_if_else_global:
+                        return 3.14159, c_test_single_if_else_global
+                    else:
+                        return 1.61803, c_test_single_if_else_global
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_single_if_else_negate_global(self):
+        global c_test_single_if_else_negate_global
+
+        for c_inp, prune in (self._TRUTHY, False), (self._FALSEY, True):
+            for c in c_inp:
+                c_test_single_if_else_negate_global = c
+
+                def func(x):
+                    if not c_test_single_if_else_negate_global:
+                        return 3.14159, c_test_single_if_else_negate_global
+                    else:
+                        return 1.61803, c_test_single_if_else_negate_global
+                self.assert_prune(func, (types.NoneType('none'),), [prune],
+                                  None)
+
+    def test_issue_5618(self):
+
+        @njit
+        def foo():
+            values = np.zeros(1)
+            tmp = 666
+            if tmp:
+                values[0] = tmp
+            return values
+
+        self.assertPreciseEqual(foo.py_func()[0], 666.)
+        self.assertPreciseEqual(foo()[0], 666.)
+
+
+class TestBranchPruneSSA(MemoryLeakMixin, TestCase):
+    # Tests SSA rewiring of phi nodes after branch pruning.
+
+    class SSAPrunerCompiler(CompilerBase):
+        def define_pipelines(self):
+            # This is a simple pipeline that does branch pruning on IR in SSA
+            # form, then types and lowers as per the standard nopython pipeline.
+            pm = PassManager("testing pm")
+            pm.add_pass(TranslateByteCode, "analyzing bytecode")
+            pm.add_pass(IRProcessing, "processing IR")
+            # SSA early
+            pm.add_pass(ReconstructSSA, "ssa")
+            pm.add_pass(DeadBranchPrune, "dead branch pruning")
+            # type and then lower as usual
+            pm.add_pass(PreserveIR, "preserves the IR as metadata")
+            dpb = DefaultPassBuilder
+            typed_passes = dpb.define_typed_pipeline(self.state)
+            pm.passes.extend(typed_passes.passes)
+            lowering_passes = dpb.define_nopython_lowering_pipeline(self.state)
+            pm.passes.extend(lowering_passes.passes)
+            pm.finalize()
+            return [pm]
+
+    def test_ssa_update_phi(self):
+        # This checks that dead branch pruning is rewiring phi nodes correctly
+        # after a block containing an incoming for a phi is removed.
+
+        @njit(pipeline_class=self.SSAPrunerCompiler)
+        def impl(p=None, q=None):
+            z = 1
+            r = False
+            if p is None:
+                r = True # live
+
+            if r and q is not None:
+                z = 20 # dead
+
+            # one of the incoming blocks for z is dead, the phi needs an update
+            # were this not done, it would refer to variables that do not exist
+            # and result in a lowering error.
+            return z, r
+
+        self.assertPreciseEqual(impl(), impl.py_func())
+
+    def test_ssa_replace_phi(self):
+        # This checks that when a phi only has one incoming, because the other
+        # has been pruned, that a direct assignment is used instead.
+
+        @njit(pipeline_class=self.SSAPrunerCompiler)
+        def impl(p=None):
+            z = 0
+            if p is None:
+                z = 10
+            else:
+                z = 20
+
+            return z
+
+        self.assertPreciseEqual(impl(), impl.py_func())
+        func_ir = impl.overloads[impl.signatures[0]].metadata['preserved_ir']
+
+        # check the func_ir, make sure there's no phi nodes
+        for blk in func_ir.blocks.values():
+            self.assertFalse([*blk.find_exprs('phi')])
+
+
+class TestBranchPrunePostSemanticConstRewrites(TestBranchPruneBase):
+    # Tests that semantic constants rewriting works by virtue of branch pruning
+
+    def test_array_ndim_attr(self):
+
+        def impl(array):
+            if array.ndim == 2:
+                if array.shape[1] == 2:
+                    return 1
+            else:
+                return 10
+
+        self.assert_prune(impl, (types.Array(types.float64, 2, 'C'),), [False,
+                                                                        None],
+                          np.zeros((2, 3)))
+        self.assert_prune(impl, (types.Array(types.float64, 1, 'C'),), [True,
+                                                                        'both'],
+                          np.zeros((2,)))
+
+    def test_tuple_len(self):
+
+        def impl(tup):
+            if len(tup) == 3:
+                if tup[2] == 2:
+                    return 1
+            else:
+                return 0
+
+        self.assert_prune(impl, (types.UniTuple(types.int64, 3),), [False,
+                                                                    None],
+                          tuple([1, 2, 3]))
+        self.assert_prune(impl, (types.UniTuple(types.int64, 2),), [True,
+                                                                    'both'],
+                          tuple([1, 2]))
+
+    def test_attr_not_len(self):
+        # The purpose of this test is to make sure that the conditions guarding
+        # the rewrite part do not themselves raise exceptions.
+        # This produces an `ir.Expr` call node for `float.as_integer_ratio`,
+        # which is a getattr() on `float`.
+
+        @njit
+        def test():
+            float.as_integer_ratio(1.23)
+
+        # this should raise a TypingError
+        with self.assertRaises(errors.TypingError) as e:
+            test()
+
+        self.assertIn("Unknown attribute 'as_integer_ratio'", str(e.exception))
+
+    def test_ndim_not_on_array(self):
+
+        FakeArray = collections.namedtuple('FakeArray', ['ndim'])
+        fa = FakeArray(ndim=2)
+
+        def impl(fa):
+            if fa.ndim == 2:
+                return fa.ndim
+            else:
+                object()
+
+        # check prune works for array ndim
+        self.assert_prune(impl, (types.Array(types.float64, 2, 'C'),), [False],
+                          np.zeros((2, 3)))
+
+        # check prune fails for something with `ndim` attr that is not array
+        FakeArrayType = types.NamedUniTuple(types.int64, 1, FakeArray)
+        self.assert_prune(impl, (FakeArrayType,), [None], fa,
+                          flags=enable_pyobj_flags)
+
+    def test_semantic_const_propagates_before_static_rewrites(self):
+        # see issue #5015, the ndim needs writing in as a const before
+        # the rewrite passes run to make e.g. getitems static where possible
+        @njit
+        def impl(a, b):
+            return a.shape[:b.ndim]
+
+        args = (np.zeros((5, 4, 3, 2)), np.zeros((1, 1)))
+
+        self.assertPreciseEqual(impl(*args), impl.py_func(*args))
+
+    def test_tuple_const_propagation(self):
+        @njit(pipeline_class=IRPreservingTestPipeline)
+        def impl(*args):
+            s = 0
+            for arg in literal_unroll(args):
+                s += len(arg)
+            return s
+
+        inp = ((), (1, 2, 3), ())
+        self.assertPreciseEqual(impl(*inp), impl.py_func(*inp))
+
+        ol = impl.overloads[impl.signatures[0]]
+        func_ir = ol.metadata['preserved_ir']
+        # make sure one of the inplace binop args is a Const
+        binop_consts = set()
+        for blk in func_ir.blocks.values():
+            for expr in blk.find_exprs('inplace_binop'):
+                inst = blk.find_variable_assignment(expr.rhs.name)
+                self.assertIsInstance(inst.value, ir.Const)
+                binop_consts.add(inst.value.value)
+        self.assertEqual(binop_consts, {len(x) for x in inp})
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_annotations.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_annotations.py
new file mode 100644
index 000000000..15bc4e611
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_annotations.py
@@ -0,0 +1,251 @@
+import re
+from io import StringIO
+
+import numba
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types
+from numba import njit
+from numba.tests.support import override_config, TestCase
+import unittest
+
+try:
+    import jinja2
+except ImportError:
+    jinja2 = None
+
+try:
+    import pygments
+except ImportError:
+    pygments = None
+
+
+@unittest.skipIf(jinja2 is None, "please install the 'jinja2' package")
+class TestAnnotation(TestCase):
+
+    def test_exercise_code_path(self):
+        """
+        Ensures template.html is available
+        """
+
+        def foo(n, a):
+            s = a
+            for i in range(n):
+                s += i
+            return s
+
+        cres = compile_isolated(foo, [types.int32, types.int32])
+        ta = cres.type_annotation
+
+        buf = StringIO()
+        ta.html_annotate(buf)
+        output = buf.getvalue()
+        buf.close()
+        self.assertIn("foo", output)
+
+    def test_exercise_code_path_with_lifted_loop(self):
+        """
+        Ensures that lifted loops are handled correctly in obj mode
+        """
+        # the functions to jit
+        def bar(x):
+            return x
+
+        def foo(x):
+            h = 0.
+            for i in range(x): # py 38 needs two loops for one to lift?!
+                h = h + i
+            for k in range(x):
+                h = h + k
+            if x:
+                h = h - bar(x)
+            return h
+
+        # compile into an isolated context
+        flags = Flags()
+        flags.enable_pyobject = True
+        flags.enable_looplift = True
+        cres = compile_isolated(foo, [types.intp], flags=flags)
+
+        ta = cres.type_annotation
+
+        buf = StringIO()
+        ta.html_annotate(buf)
+        output = buf.getvalue()
+        buf.close()
+        self.assertIn("bar", output)
+        self.assertIn("foo", output)
+        self.assertIn("LiftedLoop", output)
+
+    def test_html_output_with_lifted_loop(self):
+        """
+        Test some format and behavior of the html annotation with lifted loop
+        """
+        @numba.jit
+        def udt(x):
+            object()  # to force object mode
+            z = 0
+            for i in range(x):  # this line is tagged
+                z += i
+            return z
+
+        # Regex pattern to check for the "lifted_tag" in the line of the loop
+        re_lifted_tag = re.compile(
+            r'<td class="lifted_tag">\s*'
+            r'\s*<details>'
+            r'\s*<summary>'
+            r'\s*<code>'
+            r'\s*[0-9]+:'
+            r'\s*[&nbsp;]+for i in range\(x\):  # this line is tagged\s*',
+            re.MULTILINE)
+
+        # Compile int64 version
+        sig_i64 = (types.int64,)
+        udt.compile(sig_i64)  # compile with lifted loop
+        cres = udt.overloads[sig_i64]
+
+        # Make html output
+        buf = StringIO()
+        cres.type_annotation.html_annotate(buf)
+        output = buf.getvalue()
+        buf.close()
+
+        # There should be only one function output.
+        self.assertEqual(output.count("Function name: udt"), 1)
+
+        sigfmt = "with signature: {} -&gt; pyobject"
+        self.assertEqual(output.count(sigfmt.format(sig_i64)), 1)
+        # Ensure the loop is tagged
+        self.assertEqual(len(re.findall(re_lifted_tag, output)), 1,
+                         msg='%s not found in %s' % (re_lifted_tag, output))
+
+        # Compile float64 version
+        sig_f64 = (types.float64,)
+        udt.compile(sig_f64)
+        cres = udt.overloads[sig_f64]
+
+        # Make html output
+        buf = StringIO()
+        cres.type_annotation.html_annotate(buf)
+        output = buf.getvalue()
+        buf.close()
+
+        # There should be two function output
+        self.assertEqual(output.count("Function name: udt"), 2)
+        self.assertEqual(output.count(sigfmt.format(sig_i64)), 1)
+        self.assertEqual(output.count(sigfmt.format(sig_f64)), 1)
+        # Ensure the loop is tagged in both output
+        self.assertEqual(len(re.findall(re_lifted_tag, output)), 2)
+
+    @unittest.skipIf(pygments is None, "please install the 'pygments' package")
+    def test_pretty_print(self):
+
+        @numba.njit
+        def foo(x, y):
+            return x, y
+
+        foo(1, 2)
+        # Exercise the method
+        foo.inspect_types(pretty=True)
+
+        # Exercise but supply a not None file kwarg, this is invalid
+        with self.assertRaises(ValueError) as raises:
+            foo.inspect_types(pretty=True, file='should be None')
+        self.assertIn('`file` must be None if `pretty=True`',
+                      str(raises.exception))
+
+
+class TestTypeAnnotation(unittest.TestCase):
+
+    def findpatloc(self, lines, pat):
+        for i, ln in enumerate(lines):
+            if pat in ln:
+                return i
+        raise ValueError("can't find {!r}".format(pat))
+
+    def getlines(self, func):
+        strbuf = StringIO()
+        func.inspect_types(strbuf)
+        return strbuf.getvalue().splitlines()
+
+    def test_delete(self):
+        @numba.njit
+        def foo(appleorange, berrycherry):
+            return appleorange + berrycherry
+
+        foo(1, 2)
+
+        lines = self.getlines(foo)
+
+        # Ensure deletion show up after their use
+        sa = self.findpatloc(lines, 'appleorange = arg(0, name=appleorange)')
+        sb = self.findpatloc(lines, 'berrycherry = arg(1, name=berrycherry)')
+
+        ea = self.findpatloc(lines, 'del appleorange')
+        eb = self.findpatloc(lines, 'del berrycherry')
+
+        self.assertLess(sa, ea)
+        self.assertLess(sb, eb)
+
+    def _lifetimes_impl(self, extend):
+        with override_config('EXTEND_VARIABLE_LIFETIMES', extend):
+            @njit
+            def foo(a):
+                b = a
+                return b
+            x = 10
+            b = foo(x)
+            self.assertEqual(b, x)
+
+        lines = self.getlines(foo)
+
+        sa = self.findpatloc(lines, 'a = arg(0, name=a)')
+        sb = self.findpatloc(lines, 'b = a')
+
+        cast_ret = self.findpatloc(lines, 'cast(value=b)')
+
+        dela = self.findpatloc(lines, 'del a')
+        delb = self.findpatloc(lines, 'del b')
+
+        return sa, sb, cast_ret, dela, delb
+
+    def test_delete_standard_lifetimes(self):
+        # without extended lifetimes, dels occur as soon as dead
+        #
+        # label 0
+        #   a = arg(0, name=a)  :: int64
+        #   b = a  :: int64
+        #   del a
+        #   $8return_value.2 = cast(value=b)  :: int64
+        #   del b
+        #   return $8return_value.2
+
+        sa, sb, cast_ret, dela, delb = self._lifetimes_impl(extend=0)
+
+        self.assertLess(sa, dela)
+        self.assertLess(sb, delb)
+        # del a is before cast and del b is after
+        self.assertLess(dela, cast_ret)
+        self.assertGreater(delb, cast_ret)
+
+    def test_delete_extended_lifetimes(self):
+        # with extended lifetimes, dels are last in block:
+        #
+        # label 0
+        #   a = arg(0, name=a)  :: int64
+        #   b = a  :: int64
+        #   $8return_value.2 = cast(value=b)  :: int64
+        #   del a
+        #   del b
+        #   return $8return_value.2
+
+        sa, sb, cast_ret, dela, delb = self._lifetimes_impl(extend=1)
+
+        self.assertLess(sa, dela)
+        self.assertLess(sb, delb)
+        # dels are after the cast
+        self.assertGreater(dela, cast_ret)
+        self.assertGreater(delb, cast_ret)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_api.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_api.py
new file mode 100644
index 000000000..b156ceb6a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_api.py
@@ -0,0 +1,90 @@
+import warnings
+
+import numba
+from numba import jit, njit
+
+from numba.tests.support import TestCase
+import unittest
+
+
+class TestNumbaModule(TestCase):
+    """
+    Test the APIs exposed by the top-level `numba` module.
+    """
+
+    def check_member(self, name):
+        self.assertTrue(hasattr(numba, name), name)
+        self.assertIn(name, numba.__all__)
+
+    def test_numba_module(self):
+        # jit
+        self.check_member("jit")
+        self.check_member("vectorize")
+        self.check_member("guvectorize")
+        self.check_member("njit")
+        # errors
+        self.check_member("NumbaError")
+        self.check_member("TypingError")
+        # types
+        self.check_member("int32")
+        # misc
+        numba.__version__  # not in __all__
+
+
+class TestJitDecorator(TestCase):
+    """
+    Test the jit and njit decorators
+    """
+    def test_jit_nopython_forceobj(self):
+        with self.assertRaises(ValueError) as cm:
+            jit(nopython=True, forceobj=True)
+        self.assertIn(
+            "Only one of 'nopython' or 'forceobj' can be True.",
+            str(cm.exception)
+        )
+
+        def py_func(x):
+            return x
+
+        jit_func = jit(nopython=True)(py_func)
+        jit_func(1)
+        # Check length of nopython_signatures to check
+        # which mode the function was compiled in
+        self.assertEqual(len(jit_func.nopython_signatures), 1)
+
+        jit_func = jit(forceobj=True)(py_func)
+        jit_func(1)
+        self.assertEqual(len(jit_func.nopython_signatures), 0)
+
+    def test_njit_nopython_forceobj(self):
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', RuntimeWarning)
+            njit(forceobj=True)
+        self.assertEqual(len(w), 1)
+        self.assertIn(
+            'forceobj is set for njit and is ignored', str(w[0].message)
+        )
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', RuntimeWarning)
+            njit(nopython=True)
+        self.assertEqual(len(w), 1)
+        self.assertIn(
+            'nopython is set for njit and is ignored', str(w[0].message)
+        )
+
+        def py_func(x):
+            return x
+
+        jit_func = njit(nopython=True)(py_func)
+        jit_func(1)
+        self.assertEqual(len(jit_func.nopython_signatures), 1)
+
+        jit_func = njit(forceobj=True)(py_func)
+        jit_func(1)
+        # Since forceobj is ignored this has to compile in nopython mode
+        self.assertEqual(len(jit_func.nopython_signatures), 1)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_analysis.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_analysis.py
new file mode 100644
index 000000000..27bcbb303
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_analysis.py
@@ -0,0 +1,1140 @@
+import itertools
+
+import numpy as np
+import sys
+from collections import namedtuple
+from io import StringIO
+
+from numba import njit, typeof, prange
+from numba.core import (
+    types,
+    typing,
+    ir,
+    bytecode,
+    postproc,
+    cpu,
+    registry,
+    utils,
+)
+from numba.tests.support import (TestCase, tag, skip_parfors_unsupported,
+                                 skip_unless_scipy)
+from numba.parfors.array_analysis import EquivSet, ArrayAnalysis
+from numba.core.compiler import Compiler, Flags, PassManager
+from numba.core.ir_utils import remove_dead
+from numba.core.untyped_passes import (ExtractByteCode, TranslateByteCode, FixupArgs,
+                             IRProcessing, DeadBranchPrune,
+                             RewriteSemanticConstants, GenericRewrites,
+                             WithLifting, PreserveIR, InlineClosureLikes)
+
+from numba.core.typed_passes import (NopythonTypeInference, AnnotateTypes,
+                                NopythonRewrites, IRLegalization)
+
+from numba.core.compiler_machinery import FunctionPass, PassManager, register_pass
+from numba.experimental import jitclass
+import unittest
+
+
+skip_unsupported = skip_parfors_unsupported
+
+
+# test class for #3700
+@jitclass([('L', types.int32), ('T', types.int32)])
+class ExampleClass3700(object):
+    def __init__(self, n):
+        self.L = n
+        self.T = n + 1
+
+
+# test value for test_global_tuple
+GVAL = (1.2,)
+GVAL2 = (3, 4)
+
+
+class TestEquivSet(TestCase):
+
+    """
+    Test array_analysis.EquivSet.
+    """
+    def test_insert_equiv(self):
+        s1 = EquivSet()
+        s1.insert_equiv('a', 'b')
+        self.assertTrue(s1.is_equiv('a', 'b'))
+        self.assertTrue(s1.is_equiv('b', 'a'))
+        s1.insert_equiv('c', 'd')
+        self.assertTrue(s1.is_equiv('c', 'd'))
+        self.assertFalse(s1.is_equiv('c', 'a'))
+        s1.insert_equiv('a', 'c')
+        self.assertTrue(s1.is_equiv('a', 'b', 'c', 'd'))
+        self.assertFalse(s1.is_equiv('a', 'e'))
+
+    def test_intersect(self):
+        s1 = EquivSet()
+        s2 = EquivSet()
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_empty())
+        s1.insert_equiv('a', 'b')
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_empty())
+        s2.insert_equiv('b', 'c')
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_empty())
+        s2.insert_equiv('d', 'a')
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_empty())
+        s1.insert_equiv('a', 'e')
+        s2.insert_equiv('c', 'd')
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_equiv('a', 'b'))
+        self.assertFalse(r.is_equiv('a', 'e'))
+        self.assertFalse(r.is_equiv('c', 'd'))
+
+
+@register_pass(analysis_only=False, mutates_CFG=True)
+class ArrayAnalysisPass(FunctionPass):
+    _name = "array_analysis_pass"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        state.array_analysis = ArrayAnalysis(state.typingctx, state.func_ir,
+                                             state.typemap, state.calltypes)
+        state.array_analysis.run(state.func_ir.blocks)
+        post_proc = postproc.PostProcessor(state.func_ir)
+        post_proc.run()
+        state.func_ir_copies.append(state.func_ir.copy())
+        if state.test_idempotence and len(state.func_ir_copies) > 1:
+            state.test_idempotence(state.func_ir_copies)
+        return False
+
+
+class ArrayAnalysisTester(Compiler):
+
+    @classmethod
+    def mk_pipeline(cls, args, return_type=None, flags=None, locals={},
+                    library=None, typing_context=None, target_context=None):
+        if not flags:
+            flags = Flags()
+        flags.nrt = True
+        if typing_context is None:
+            typing_context = registry.cpu_target.typing_context
+        if target_context is None:
+            target_context =  registry.cpu_target.target_context
+        return cls(typing_context, target_context, library, args, return_type,
+                   flags, locals)
+
+    def compile_to_ir(self, func, test_idempotence=None):
+        """
+        Populate and run compiler pipeline
+        """
+        self.state.func_id = bytecode.FunctionIdentity.from_function(func)
+        ExtractByteCode().run_pass(self.state)
+
+        self.state.lifted = ()
+        self.state.lifted_from = None
+        state = self.state
+        state.func_ir_copies = []
+        state.test_idempotence = test_idempotence
+
+        name = 'array_analysis_testing'
+        pm = PassManager(name)
+        pm.add_pass(TranslateByteCode, "analyzing bytecode")
+        pm.add_pass(FixupArgs, "fix up args")
+        pm.add_pass(IRProcessing, "processing IR")
+        # pre typing
+        if not state.flags.no_rewrites:
+            pm.add_pass(GenericRewrites, "nopython rewrites")
+            pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
+            pm.add_pass(DeadBranchPrune, "dead branch pruning")
+        pm.add_pass(InlineClosureLikes,
+                    "inline calls to locally defined closures")
+        # typing
+        pm.add_pass(NopythonTypeInference, "nopython frontend")
+
+        if not state.flags.no_rewrites:
+            pm.add_pass(NopythonRewrites, "nopython rewrites")
+
+        # Array Analysis pass
+        pm.add_pass(ArrayAnalysisPass, "array analysis")
+        if test_idempotence:
+            # Do another pass of array analysis to test idempotence
+            pm.add_pass(ArrayAnalysisPass, "idempotence array analysis")
+        # legalise
+        pm.add_pass(IRLegalization, "ensure IR is legal prior to lowering")
+        pm.add_pass(AnnotateTypes, "annotate types")
+
+        # partial compile
+        pm.finalize()
+        pm.run(state)
+        return state.array_analysis
+
+
+class TestArrayAnalysis(TestCase):
+
+    def compare_ir(self, ir_list):
+        outputs = []
+        for func_ir in ir_list:
+            remove_dead(func_ir.blocks, func_ir.arg_names, func_ir)
+            output = StringIO()
+            func_ir.dump(file=output)
+            outputs.append(output.getvalue())
+        self.assertTrue(len(set(outputs)) == 1)  # assert all outputs are equal
+
+    def _compile_and_test(self, fn, arg_tys, asserts=[], equivs=[], idempotent=True):
+        """
+        Compile the given function and get its IR.
+        """
+        test_pipeline = ArrayAnalysisTester.mk_pipeline(arg_tys)
+        test_idempotence = self.compare_ir if idempotent else lambda x:()
+        analysis = test_pipeline.compile_to_ir(fn, test_idempotence)
+        if equivs:
+            for func in equivs:
+                # only test the equiv_set of the first block
+                func(analysis.equiv_sets[0])
+        if asserts is None:
+            self.assertTrue(self._has_no_assertcall(analysis.func_ir))
+        else:
+            for func in asserts:
+                func(analysis.func_ir, analysis.typemap)
+
+    def _has_assertcall(self, func_ir, typemap, args):
+        msg = "Sizes of {} do not match".format(', '.join(args))
+        for label, block in func_ir.blocks.items():
+            for expr in block.find_exprs(op='call'):
+                fn = func_ir.get_definition(expr.func.name)
+                if isinstance(fn, ir.Global) and fn.name == 'assert_equiv':
+                    typ = typemap[expr.args[0].name]
+                    if typ.literal_value.startswith(msg):
+                        return True
+        return False
+
+    def _has_shapecall(self, func_ir, x):
+        for label, block in func_ir.blocks.items():
+            for expr in block.find_exprs(op='getattr'):
+                if expr.attr == 'shape':
+                    y = func_ir.get_definition(expr.value, lhs_only=True)
+                    z = func_ir.get_definition(x, lhs_only=True)
+                    y = y.name if isinstance(y, ir.Var) else y
+                    z = z.name if isinstance(z, ir.Var) else z
+                    if y == z:
+                        return True
+        return False
+
+    def _has_no_assertcall(self, func_ir):
+        for label, block in func_ir.blocks.items():
+            for expr in block.find_exprs(op='call'):
+                fn = func_ir.get_definition(expr.func.name)
+                if isinstance(fn, ir.Global) and fn.name == 'assert_equiv':
+                    return False
+        return True
+
+    def with_assert(self, *args):
+        return lambda func_ir, typemap: self.assertTrue(
+            self._has_assertcall(func_ir, typemap, args))
+
+    def without_assert(self, *args):
+        return lambda func_ir, typemap: self.assertFalse(
+            self._has_assertcall(func_ir, typemap, args))
+
+    def with_equiv(self, *args):
+        def check(equiv_set):
+            n = len(args)
+            for i in range(n - 1):
+                if not equiv_set.is_equiv(args[i], args[n - 1]):
+                    return False
+            return True
+        return lambda equiv_set: self.assertTrue(check(equiv_set))
+
+    def without_equiv(self, *args):
+        def check(equiv_set):
+            n = len(args)
+            for i in range(n - 1):
+                if equiv_set.is_equiv(args[i], args[n - 1]):
+                    return False
+            return True
+        return lambda equiv_set: self.assertTrue(check(equiv_set))
+
+    def with_shapecall(self, x):
+        return lambda func_ir, s: self.assertTrue(self._has_shapecall(func_ir, x))
+
+    def without_shapecall(self, x):
+        return lambda func_ir, s: self.assertFalse(self._has_shapecall(func_ir, x))
+
+    def test_base_cases(self):
+        def test_0():
+            a = np.zeros(0)
+            b = np.zeros(1)
+            m = 0
+            n = 1
+            c = np.zeros((m, n))
+            return
+        self._compile_and_test(test_0, (),
+                               equivs=[self.with_equiv('a', (0,)),
+                                       self.with_equiv('b', (1,)),
+                                       self.with_equiv('c', (0, 1))])
+
+        def test_1(n):
+            a = np.zeros(n)
+            b = np.zeros(n)
+            return a + b
+        self._compile_and_test(test_1, (types.intp,), asserts=None)
+
+        def test_2(m, n):
+            a = np.zeros(n)
+            b = np.zeros(m)
+            return a + b
+        self._compile_and_test(test_2, (types.intp, types.intp),
+                               asserts=[self.with_assert('a', 'b')])
+
+        def test_3(n):
+            a = np.zeros(n)
+            return a + n
+        self._compile_and_test(test_3, (types.intp,), asserts=None)
+
+        def test_4(n):
+            a = np.zeros(n)
+            b = a + 1
+            c = a + 2
+            return a + c
+        self._compile_and_test(test_4, (types.intp,), asserts=None)
+
+        def test_5(n):
+            a = np.zeros((n, n))
+            m = n
+            b = np.zeros((m, n))
+            return a + b
+        self._compile_and_test(test_5, (types.intp,), asserts=None)
+
+        def test_6(m, n):
+            a = np.zeros(n)
+            b = np.zeros(m)
+            d = a + b
+            e = a - b
+            return d + e
+        self._compile_and_test(test_6, (types.intp, types.intp),
+                               asserts=[self.with_assert('a', 'b'),
+                                        self.without_assert('d', 'e')])
+
+        def test_7(m, n):
+            a = np.zeros(n)
+            b = np.zeros(m)
+            if m == 10:
+                d = a + b
+            else:
+                d = a - b
+            return d + a
+        self._compile_and_test(test_7, (types.intp, types.intp),
+                               asserts=[self.with_assert('a', 'b'),
+                                        self.without_assert('d', 'a')])
+
+        def test_8(m, n):
+            a = np.zeros(n)
+            b = np.zeros(m)
+            if m == 10:
+                d = b + a
+            else:
+                d = a + a
+            return b + d
+        self._compile_and_test(test_8, (types.intp, types.intp),
+                               asserts=[self.with_assert('b', 'a'),
+                                        self.with_assert('b', 'd')])
+
+        def test_9(m):
+            A = np.ones(m)
+            s = 0
+            while m < 2:
+                m += 1
+                B = np.ones(m)
+                s += np.sum(A + B)
+            return s
+        self._compile_and_test(test_9, (types.intp,),
+                               asserts=[self.with_assert('A', 'B')])
+
+        def test_10(m, n):
+            p = m - 1
+            q = n + 1
+            r = q + 1
+            A = np.zeros(p)
+            B = np.zeros(q)
+            C = np.zeros(r)
+            D = np.zeros(m)
+            s = np.sum(A + B)
+            t = np.sum(C + D)
+            return s + t
+        self._compile_and_test(test_10, (types.intp,types.intp,),
+                               asserts=[self.with_assert('A', 'B'),
+                                        self.without_assert('C', 'D')])
+
+        def test_11():
+            a = np.ones(5)
+            b = np.ones(5)
+            c = a[1:]
+            d = b[:-1]
+            e = len(c)
+            f = len(d)
+            return e == f
+        self._compile_and_test(test_11, (),
+                               equivs=[self.with_equiv('e', 'f')])
+
+        def test_12():
+            a = np.ones(25).reshape((5,5))
+            b = np.ones(25).reshape((5,5))
+            c = a[1:,:]
+            d = b[:-1,:]
+            e = c.shape[0]
+            f = d.shape[0]
+            g = len(d)
+            return e == f
+        self._compile_and_test(test_12, (),
+                               equivs=[self.with_equiv('e', 'f', 'g')])
+
+        def test_tup_arg(T):
+            T2 = T
+            return T2[0]
+
+        int_arr_typ = types.Array(types.intp, 1, 'C')
+        self._compile_and_test(test_tup_arg,
+            (types.Tuple((int_arr_typ, int_arr_typ)),), asserts=None)
+
+        def test_arr_in_tup(m):
+            A = np.ones(m)
+            S = (A,)
+            B = np.ones(len(S[0]))
+            return B
+
+        self._compile_and_test(test_arr_in_tup, (types.intp,),
+                               equivs=[self.with_equiv("A", "B")])
+
+        T = namedtuple("T", ['a','b'])
+        def test_namedtuple(n):
+            r = T(n, n)
+            return r[0]
+        self._compile_and_test(test_namedtuple, (types.intp,),
+                                equivs=[self.with_equiv('r', ('n', 'n'))],)
+
+        # np.where is tricky since it returns tuple of arrays
+        def test_np_where_tup_return(A):
+            c = np.where(A)
+            return len(c[0])
+
+        self._compile_and_test(test_np_where_tup_return,
+            (types.Array(types.intp, 1, 'C'),), asserts=None)
+
+        def test_shape(A):
+            (m, n) = A.shape
+            B = np.ones((m, n))
+            return A + B
+        self._compile_and_test(test_shape, (types.Array(types.intp, 2, 'C'),),
+                               asserts=None)
+
+        def test_cond(l, m, n):
+            A = np.ones(l)
+            B = np.ones(m)
+            C = np.ones(n)
+            if l == m:
+                r = np.sum(A + B)
+            else:
+                r = 0
+            if m != n:
+                s = 0
+            else:
+                s = np.sum(B + C)
+            t = 0
+            if l == m:
+                if m == n:
+                    t = np.sum(A + B + C)
+            return r + s + t
+        self._compile_and_test(test_cond, (types.intp, types.intp, types.intp),
+                               asserts=None)
+
+        def test_assert_1(m, n):
+            assert(m == n)
+            A = np.ones(m)
+            B = np.ones(n)
+            return np.sum(A + B)
+        self._compile_and_test(test_assert_1, (types.intp, types.intp),
+                               asserts=None)
+
+        def test_assert_2(A, B):
+            assert(A.shape == B.shape)
+            return np.sum(A + B)
+
+        self._compile_and_test(test_assert_2, (types.Array(types.intp, 1, 'C'),
+                                               types.Array(types.intp, 1, 'C'),),
+                               asserts=None)
+        self._compile_and_test(test_assert_2, (types.Array(types.intp, 2, 'C'),
+                                               types.Array(types.intp, 2, 'C'),),
+                               asserts=None)
+        # expected failure
+        with self.assertRaises(AssertionError) as raises:
+            self._compile_and_test(test_assert_2, (types.Array(types.intp, 1, 'C'),
+                                                   types.Array(types.intp, 2, 'C'),),
+                                   asserts=None)
+        msg = "Dimension mismatch"
+        self.assertIn(msg, str(raises.exception))
+
+
+    def test_stencilcall(self):
+        from numba.stencils.stencil import stencil
+        @stencil
+        def kernel_1(a):
+            return 0.25 * (a[0,1] + a[1,0] + a[0,-1] + a[-1,0])
+
+        def test_1(n):
+            a = np.ones((n,n))
+            b = kernel_1(a)
+            return a + b
+
+        self._compile_and_test(test_1, (types.intp,),
+                               equivs=[self.with_equiv('a', 'b')],
+                               asserts=[self.without_assert('a', 'b')])
+
+        def test_2(n):
+            a = np.ones((n,n))
+            b = np.ones((n+1,n+1))
+            kernel_1(a, out=b)
+            return a
+
+        self._compile_and_test(test_2, (types.intp,),
+                               equivs=[self.without_equiv('a', 'b')])
+
+        @stencil(standard_indexing=('c',))
+        def kernel_2(a, b, c):
+            return a[0,1,0] + b[0,-1,0] + c[0]
+
+        def test_3(n):
+            a = np.arange(64).reshape(4,8,2)
+            b = np.arange(64).reshape(n,8,2)
+            u = np.zeros(1)
+            v = kernel_2(a, b, u)
+            return v
+
+        # standard indexed arrays are not considered in size equivalence
+        self._compile_and_test(test_3, (types.intp,),
+                               equivs=[self.with_equiv('a', 'b', 'v'),
+                                       self.without_equiv('a', 'u')],
+                               asserts=[self.with_assert('a', 'b')])
+
+    def test_slice(self):
+        def test_1(m, n):
+            A = np.zeros(m)
+            B = np.zeros(n)
+            s = np.sum(A + B)
+            C = A[1:m-1]
+            D = B[1:n-1]
+            t = np.sum(C + D)
+            return s + t
+        self._compile_and_test(test_1, (types.intp,types.intp,),
+                               asserts=[self.with_assert('A', 'B'),
+                                        self.without_assert('C', 'D')],
+                               idempotent=False)
+
+        def test_2(m):
+            A = np.zeros(m)
+            B = A[0:m-3]
+            C = A[1:m-2]
+            D = A[2:m-1]
+            E = B + C
+            return D + E
+        self._compile_and_test(test_2, (types.intp,),
+                               asserts=[self.without_assert('B', 'C'),
+                                        self.without_assert('D', 'E')],
+                               idempotent=False)
+
+        def test_3(m):
+            A = np.zeros((m,m))
+            B = A[0:m-2,0:m-2]
+            C = A[1:m-1,1:m-1]
+            E = B + C
+            return E
+        self._compile_and_test(test_3, (types.intp,),
+                               asserts=[self.without_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_4(m):
+            A = np.zeros((m,m))
+            B = A[0:m-2,:]
+            C = A[1:m-1,:]
+            E = B + C
+            return E
+        self._compile_and_test(test_4, (types.intp,),
+                               asserts=[self.without_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_5(m,n):
+            A = np.zeros(m)
+            B = np.zeros(m)
+            B[0:m-2] = A[1:m-1]
+            C = np.zeros(n)
+            D = A[1:m-1]
+            C[0:n-2] = D
+            # B and C are not necessarily of the same size because we can't
+            # derive m == n from (m-2) % m == (n-2) % n
+            return B + C
+        self._compile_and_test(test_5, (types.intp,types.intp),
+                               asserts=[self.without_assert('B', 'A'),
+                                        self.with_assert('C', 'D'),
+                                        self.with_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_6(m):
+            A = np.zeros((m,m))
+            B = A[0:m-2,:-1]
+            C = A[1:m-1,:-1]
+            E = B + C
+            return E
+        self._compile_and_test(test_6, (types.intp,),
+                               asserts=[self.without_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_7(m):
+            A = np.zeros((m,m))
+            B = A[0:m-2,-3:-1]
+            C = A[1:m-1,-4:-2]
+            E = B + C
+            return E
+        self._compile_and_test(test_7, (types.intp,),
+                               asserts=[self.with_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_8(m):
+            A = np.zeros((m,m))
+            B = A[:m-2,0:]
+            C = A[1:-1,:]
+            E = B + C
+            return E
+        self._compile_and_test(test_8, (types.intp,),
+                               asserts=[self.without_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_9(m):
+            # issues #3461 and #3554, checks equivalence on empty slices
+            # and across binop
+            A = np.zeros((m))
+            B = A[:0] # B = array([], dtype=int64)
+            C = A[1:]
+            D = A[:-1:-1] # D = array([], dtype=int64)
+            E = B + D
+            F = E
+            F += 1 # F = array([], dtype=int64)
+            return A, C, F
+        self._compile_and_test(test_9, (types.intp,),
+                               equivs=[self.without_equiv('B', 'C'),
+                                       self.with_equiv('A', 'm'),
+                                       self.with_equiv('B', 'D'),
+                                       self.with_equiv('F', 'D'),],
+                               idempotent=False)
+
+    @skip_unless_scipy
+    def test_numpy_calls(self):
+        def test_zeros(n):
+            a = np.zeros(n)
+            b = np.zeros((n, n))
+            c = np.zeros(shape=(n, n))
+        self._compile_and_test(test_zeros, (types.intp,),
+                               equivs=[self.with_equiv('a', 'n'),
+                                       self.with_equiv('b', ('n', 'n')),
+                                       self.with_equiv('b', 'c')])
+
+        def test_0d_array(n):
+            a = np.array(1)
+            b = np.ones(2)
+            return a + b
+        self._compile_and_test(test_0d_array, (types.intp,),
+                               equivs=[self.without_equiv('a', 'b')],
+                               asserts=[self.without_shapecall('a')])
+
+        def test_ones(n):
+            a = np.ones(n)
+            b = np.ones((n, n))
+            c = np.ones(shape=(n, n))
+        self._compile_and_test(test_ones, (types.intp,),
+                               equivs=[self.with_equiv('a', 'n'),
+                                       self.with_equiv('b', ('n', 'n')),
+                                       self.with_equiv('b', 'c')])
+
+        def test_empty(n):
+            a = np.empty(n)
+            b = np.empty((n, n))
+            c = np.empty(shape=(n, n))
+        self._compile_and_test(test_empty, (types.intp,),
+                               equivs=[self.with_equiv('a', 'n'),
+                                       self.with_equiv('b', ('n', 'n')),
+                                       self.with_equiv('b', 'c')])
+
+        def test_eye(n):
+            a = np.eye(n)
+            b = np.eye(N=n)
+            c = np.eye(N=n, M=n)
+            d = np.eye(N=n, M=n + 1)
+        self._compile_and_test(test_eye, (types.intp,),
+                               equivs=[self.with_equiv('a', ('n', 'n')),
+                                       self.with_equiv('b', ('n', 'n')),
+                                       self.with_equiv('b', 'c'),
+                                       self.without_equiv('b', 'd')])
+
+        def test_identity(n):
+            a = np.identity(n)
+        self._compile_and_test(test_identity, (types.intp,),
+                               equivs=[self.with_equiv('a', ('n', 'n'))])
+
+        def test_diag(n):
+            a = np.identity(n)
+            b = np.diag(a)
+            c = np.diag(b)
+            d = np.diag(a, k=1)
+        self._compile_and_test(test_diag, (types.intp,),
+                               equivs=[self.with_equiv('b', ('n',)),
+                                       self.with_equiv('c', ('n', 'n'))],
+                               asserts=[self.with_shapecall('d'),
+                                        self.without_shapecall('c')])
+
+        def test_array_like(a):
+            b = np.empty_like(a)
+            c = np.zeros_like(a)
+            d = np.ones_like(a)
+            e = np.full_like(a, 1)
+            f = np.asfortranarray(a)
+
+        self._compile_and_test(test_array_like, (types.Array(types.intp, 2, 'C'),),
+                               equivs=[
+                                   self.with_equiv('a', 'b', 'd', 'e', 'f')],
+                               asserts=[self.with_shapecall('a'),
+                                        self.without_shapecall('b')])
+
+        def test_reshape(n):
+            a = np.ones(n * n)
+            b = a.reshape((n, n))
+            return a.sum() + b.sum()
+        self._compile_and_test(test_reshape, (types.intp,),
+                               equivs=[self.with_equiv('b', ('n', 'n'))],
+                               asserts=[self.without_shapecall('b')])
+
+
+        def test_transpose(m, n):
+            a = np.ones((m, n))
+            b = a.T
+            c = a.transpose()
+            # Numba njit cannot compile explicit transpose call!
+            # c = np.transpose(b)
+        self._compile_and_test(test_transpose, (types.intp, types.intp),
+                               equivs=[self.with_equiv('a', ('m', 'n')),
+                                       self.with_equiv('b', ('n', 'm')),
+                                       self.with_equiv('c', ('n', 'm'))])
+
+
+        def test_transpose_3d(m, n, k):
+            a = np.ones((m, n, k))
+            b = a.T
+            c = a.transpose()
+            d = a.transpose(2,0,1)
+            dt = a.transpose((2,0,1))
+            e = a.transpose(0,2,1)
+            et = a.transpose((0,2,1))
+            # Numba njit cannot compile explicit transpose call!
+            # c = np.transpose(b)
+        self._compile_and_test(test_transpose_3d, (types.intp, types.intp, types.intp),
+                               equivs=[self.with_equiv('a', ('m', 'n', 'k')),
+                                       self.with_equiv('b', ('k', 'n', 'm')),
+                                       self.with_equiv('c', ('k', 'n', 'm')),
+                                       self.with_equiv('d', ('k', 'm', 'n')),
+                                       self.with_equiv('dt', ('k', 'm', 'n')),
+                                       self.with_equiv('e', ('m', 'k', 'n')),
+                                       self.with_equiv('et', ('m', 'k', 'n'))])
+
+        def test_real_imag_attr(m, n):
+            a = np.ones((m, n))
+            b = a.real
+            c = a.imag
+
+        self._compile_and_test(test_real_imag_attr, (types.intp, types.intp),
+                               equivs=[self.with_equiv('a', ('m', 'n')),
+                                       self.with_equiv('b', ('m', 'n')),
+                                       self.with_equiv('c', ('m', 'n')),
+                                       ])
+
+        def test_random(n):
+            a0 = np.random.rand(n)
+            a1 = np.random.rand(n, n)
+            b0 = np.random.randn(n)
+            b1 = np.random.randn(n, n)
+            c0 = np.random.ranf(n)
+            c1 = np.random.ranf((n, n))
+            c2 = np.random.ranf(size=(n, n))
+            d0 = np.random.random_sample(n)
+            d1 = np.random.random_sample((n, n))
+            d2 = np.random.random_sample(size=(n, n))
+            e0 = np.random.sample(n)
+            e1 = np.random.sample((n, n))
+            e2 = np.random.sample(size=(n, n))
+            f0 = np.random.random(n)
+            f1 = np.random.random((n, n))
+            f2 = np.random.random(size=(n, n))
+            g0 = np.random.standard_normal(n)
+            g1 = np.random.standard_normal((n, n))
+            g2 = np.random.standard_normal(size=(n, n))
+            h0 = np.random.chisquare(10, n)
+            h1 = np.random.chisquare(10, (n, n))
+            h2 = np.random.chisquare(10, size=(n, n))
+            i0 = np.random.weibull(10, n)
+            i1 = np.random.weibull(10, (n, n))
+            i2 = np.random.weibull(10, size=(n, n))
+            j0 = np.random.power(10, n)
+            j1 = np.random.power(10, (n, n))
+            j2 = np.random.power(10, size=(n, n))
+            k0 = np.random.geometric(0.1, n)
+            k1 = np.random.geometric(0.1, (n, n))
+            k2 = np.random.geometric(0.1, size=(n, n))
+            l0 = np.random.exponential(10, n)
+            l1 = np.random.exponential(10, (n, n))
+            l2 = np.random.exponential(10, size=(n, n))
+            m0 = np.random.poisson(10, n)
+            m1 = np.random.poisson(10, (n, n))
+            m2 = np.random.poisson(10, size=(n, n))
+            n0 = np.random.rayleigh(10, n)
+            n1 = np.random.rayleigh(10, (n, n))
+            n2 = np.random.rayleigh(10, size=(n, n))
+            o0 = np.random.normal(0, 1, n)
+            o1 = np.random.normal(0, 1, (n, n))
+            o2 = np.random.normal(0, 1, size=(n, n))
+            p0 = np.random.uniform(0, 1, n)
+            p1 = np.random.uniform(0, 1, (n, n))
+            p2 = np.random.uniform(0, 1, size=(n, n))
+            q0 = np.random.beta(0.1, 1, n)
+            q1 = np.random.beta(0.1, 1, (n, n))
+            q2 = np.random.beta(0.1, 1, size=(n, n))
+            r0 = np.random.binomial(0, 1, n)
+            r1 = np.random.binomial(0, 1, (n, n))
+            r2 = np.random.binomial(0, 1, size=(n, n))
+            s0 = np.random.f(0.1, 1, n)
+            s1 = np.random.f(0.1, 1, (n, n))
+            s2 = np.random.f(0.1, 1, size=(n, n))
+            t0 = np.random.gamma(0.1, 1, n)
+            t1 = np.random.gamma(0.1, 1, (n, n))
+            t2 = np.random.gamma(0.1, 1, size=(n, n))
+            u0 = np.random.lognormal(0, 1, n)
+            u1 = np.random.lognormal(0, 1, (n, n))
+            u2 = np.random.lognormal(0, 1, size=(n, n))
+            v0 = np.random.laplace(0, 1, n)
+            v1 = np.random.laplace(0, 1, (n, n))
+            v2 = np.random.laplace(0, 1, size=(n, n))
+            w0 = np.random.randint(0, 10, n)
+            w1 = np.random.randint(0, 10, (n, n))
+            w2 = np.random.randint(0, 10, size=(n, n))
+            x0 = np.random.triangular(-3, 0, 10, n)
+            x1 = np.random.triangular(-3, 0, 10, (n, n))
+            x2 = np.random.triangular(-3, 0, 10, size=(n, n))
+
+        last = ord('x') + 1
+        vars1d = [('n',)] + [chr(x) + '0' for x in range(ord('a'), last)]
+        vars2d = [('n', 'n')] + [chr(x) + '1' for x in range(ord('a'), last)]
+        vars2d += [chr(x) + '1' for x in range(ord('c'), last)]
+        self._compile_and_test(test_random, (types.intp,),
+                               equivs=[self.with_equiv(*vars1d),
+                                       self.with_equiv(*vars2d)])
+
+        def test_concatenate(m, n):
+            a = np.ones(m)
+            b = np.ones(n)
+            c = np.concatenate((a, b))
+            d = np.ones((2, n))
+            e = np.ones((3, n))
+            f = np.concatenate((d, e))
+            # Numba njit cannot compile concatenate with single array!
+            # g = np.ones((3,4,5))
+            # h = np.concatenate(g)
+            i = np.ones((m, 2))
+            j = np.ones((m, 3))
+            k = np.concatenate((i, j), axis=1)
+            l = np.ones((m, n))
+            o = np.ones((m, n))
+            p = np.concatenate((l, o))
+            # Numba njit cannot support list argument!
+            # q = np.concatenate([d, e])
+        self._compile_and_test(test_concatenate, (types.intp, types.intp),
+                               equivs=[self.with_equiv('f', (5, 'n')),
+                                       #self.with_equiv('h', (3 + 4 + 5, )),
+                                       self.with_equiv('k', ('m', 5))],
+                               asserts=[self.with_shapecall('c'),
+                                        self.without_shapecall('f'),
+                                        self.without_shapecall('k'),
+                                        self.with_shapecall('p')])
+
+        def test_vsd_stack():
+            k = np.ones((2,))
+            l = np.ones((2, 3))
+            o = np.ones((2, 3, 4))
+            p = np.vstack((k, k))
+            q = np.vstack((l, l))
+            r = np.hstack((k, k))
+            s = np.hstack((l, l))
+            t = np.dstack((k, k))
+            u = np.dstack((l, l))
+            v = np.dstack((o, o))
+
+        self._compile_and_test(test_vsd_stack, (),
+                               equivs=[self.with_equiv('p', (2, 2)),
+                                       self.with_equiv('q', (4, 3)),
+                                       self.with_equiv('r', (4,)),
+                                       self.with_equiv('s', (2, 6)),
+                                       self.with_equiv('t', (1, 2, 2)),
+                                       self.with_equiv('u', (2, 3, 2)),
+                                       self.with_equiv('v', (2, 3, 8)),
+                                       ])
+
+        def test_stack(m, n):
+            a = np.ones(m)
+            b = np.ones(n)
+            c = np.stack((a, b))
+            d = np.ones((m, n))
+            e = np.ones((m, n))
+            f = np.stack((d, e))
+            g = np.stack((d, e), axis=0)
+            h = np.stack((d, e), axis=1)
+            i = np.stack((d, e), axis=2)
+            j = np.stack((d, e), axis=-1)
+
+        self._compile_and_test(test_stack, (types.intp, types.intp),
+                                equivs=[self.with_equiv('m', 'n'),
+                                        self.with_equiv('c', (2, 'm')),
+                                        self.with_equiv(
+                                    'f', 'g', (2, 'm', 'n')),
+            self.with_equiv(
+                                    'h', ('m', 2, 'n')),
+            self.with_equiv(
+                                    'i', 'j', ('m', 'n', 2)),
+        ])
+
+        def test_linspace(m, n):
+            a = np.linspace(m, n)
+            b = np.linspace(m, n, 10)
+            # Numba njit does not support num keyword to linspace call!
+            # c = np.linspace(m,n,num=10)
+        self._compile_and_test(test_linspace, (types.float64, types.float64),
+                               equivs=[self.with_equiv('a', (50,)),
+                                       self.with_equiv('b', (10,))])
+
+        def test_dot(l, m, n):
+            a = np.dot(np.ones(1), np.ones(1))
+            b = np.dot(np.ones(2), np.ones((2, 3)))
+            # Numba njit does not support higher dimensional inputs
+            #c = np.dot(np.ones(2),np.ones((3,2,4)))
+            #d = np.dot(np.ones(2),np.ones((3,5,2,4)))
+            e = np.dot(np.ones((1, 2)), np.ones(2,))
+            #f = np.dot(np.ones((1,2,3)),np.ones(3,))
+            #g = np.dot(np.ones((1,2,3,4)),np.ones(4,))
+            h = np.dot(np.ones((2, 3)), np.ones((3, 4)))
+            i = np.dot(np.ones((m, n)), np.ones((n, m)))
+            j = np.dot(np.ones((m, m)), np.ones((l, l)))
+
+        self._compile_and_test(test_dot, (types.intp, types.intp, types.intp),
+                               equivs=[self.without_equiv('a', (1,)),  # not array
+                                       self.with_equiv('b', (3,)),
+                                       self.with_equiv('e', (1,)),
+                                       self.with_equiv('h', (2, 4)),
+                                       self.with_equiv('i', ('m', 'm')),
+                                       self.with_equiv('j', ('m', 'm')),
+                                       ],
+                               asserts=[self.with_assert('m', 'l')])
+
+        def test_broadcast(m, n):
+            a = np.ones((m, n))
+            b = np.ones(n)
+            c = a + b
+            d = np.ones((1, n))
+            e = a + c - d
+        self._compile_and_test(test_broadcast, (types.intp, types.intp),
+                               equivs=[self.with_equiv('a', 'c', 'e')],
+                               asserts=None)
+
+        # make sure shape of a global tuple of ints is handled properly
+        def test_global_tuple():
+            a = np.ones(GVAL2)
+            b = np.ones(GVAL2)
+
+        self._compile_and_test(test_global_tuple, (),
+                               equivs=[self.with_equiv('a', 'b')],
+                               asserts=None)
+
+
+class TestArrayAnalysisParallelRequired(TestCase):
+    """This is to just split out tests that need the parallel backend and
+    therefore serialised execution.
+    """
+
+    _numba_parallel_test_ = False
+
+    @skip_unsupported
+    def test_misc(self):
+
+        @njit
+        def swap(x, y):
+            return(y, x)
+
+        def test_bug2537(m):
+            a = np.ones(m)
+            b = np.ones(m)
+            for i in range(m):
+                a[i], b[i] = swap(a[i], b[i])
+
+        try:
+            njit(test_bug2537, parallel=True)(10)
+        except IndexError:
+            self.fail("test_bug2537 raised IndexError!")
+
+    @skip_unsupported
+    def test_global_namedtuple(self):
+        Row = namedtuple('Row', ['A'])
+        row = Row(3)
+
+        def test_impl():
+            rr = row
+            res = rr.A
+            if res == 2:
+                res = 3
+            return res
+
+        self.assertEqual(njit(test_impl, parallel=True)(), test_impl())
+
+    @skip_unsupported
+    def test_array_T_issue_3700(self):
+
+        def test_impl(t_obj, X):
+            for i in prange(t_obj.T):
+                X[i] = i
+            return X.sum()
+
+        n = 5
+        t_obj = ExampleClass3700(n)
+        X1 = np.zeros(t_obj.T)
+        X2 = np.zeros(t_obj.T)
+        self.assertEqual(
+            njit(test_impl, parallel=True)(t_obj, X1), test_impl(t_obj, X2))
+
+    @skip_unsupported
+    def test_slice_shape_issue_3380(self):
+        # these tests shouldn't throw error in array analysis
+        def test_impl1():
+            a = slice(None, None)
+            return True
+
+        self.assertEqual(njit(test_impl1, parallel=True)(), test_impl1())
+
+        def test_impl2(A, a):
+            b = a
+            return A[b]
+
+        A = np.arange(10)
+        a = slice(None)
+        np.testing.assert_array_equal(
+            njit(test_impl2, parallel=True)(A, a), test_impl2(A, a))
+
+    @skip_unsupported
+    def test_slice_dtype_issue_5056(self):
+        # see issue 5056
+
+        @njit(parallel=True)
+        def test_impl(data):
+            N = data.shape[0]
+            sums = np.zeros(N)
+            for i in prange(N):
+                sums[i] = np.sum(data[np.int32(0):np.int32(1)])
+            return sums
+
+        data = np.arange(10.)
+        np.testing.assert_array_equal(test_impl(data), test_impl.py_func(data))
+
+    @skip_unsupported
+    def test_global_tuple(self):
+        """make sure a global tuple with non-integer values does not cause errors
+        (test for #6726).
+        """
+
+        def test_impl():
+            d = GVAL[0]
+            return d
+
+        self.assertEqual(njit(test_impl, parallel=True)(), test_impl())
+
+
+class TestArrayAnalysisInterface(TestCase):
+    def test_analyze_op_call_interface(self):
+        # gather _analyze_op_call_*
+        aoc = {}
+        for fname in dir(ArrayAnalysis):
+            if fname.startswith('_analyze_op_call_'):
+                aoc[fname] = getattr(ArrayAnalysis, fname)
+        # check interface
+        def iface_stub(self, scope, equiv_set, loc, args, kws):
+            pass
+        expected = utils.pysignature(iface_stub)
+        for k, v in aoc.items():
+            got = utils.pysignature(v)
+            with self.subTest(fname=k, sig=got):
+                self.assertEqual(got, expected)
+
+    @skip_unsupported
+    def test_array_analysis_extensions(self):
+        # Test that the `array_analysis` object in `array_analysis_extensions`
+        # can perform analysis on the scope using `equiv_sets`.
+        from numba.parfors.parfor import Parfor
+        from numba.parfors import array_analysis
+
+        orig_parfor = array_analysis.array_analysis_extensions[Parfor]
+
+        shared = {'counter': 0}
+
+        def testcode(array_analysis):
+            # Find call node corresponding to the ``A = empty(n)``
+            func_ir = array_analysis.func_ir
+            for call in func_ir.blocks[0].find_exprs('call'):
+                callee = func_ir.get_definition(call.func)
+                if getattr(callee, "value", None) is empty:
+                    if getattr(call.args[0], 'name', None) == 'n':
+                        break
+            else:
+                return
+
+            variable_A = func_ir.get_assignee(call)
+            # n must be equiv to
+            es = array_analysis.equiv_sets[0]
+            self.assertTrue(es.is_equiv('n', variable_A.name))
+            shared['counter'] += 1
+
+        def new_parfor(parfor, equiv_set, typemap, array_analysis):
+            """Recursive array analysis for parfor nodes.
+            """
+            testcode(array_analysis)
+            # Call original
+            return orig_parfor(
+                parfor, equiv_set, typemap, array_analysis,
+            )
+
+        try:
+            # Replace the array-analysis extension for Parfor node
+            array_analysis.array_analysis_extensions[Parfor] = new_parfor
+
+            empty = np.empty   # avoid scanning a getattr in the IR
+            def f(n):
+                A = empty(n)
+                for i in prange(n):
+                    S = np.arange(i)
+                    A[i] = S.sum()
+                return A + 1
+
+            got = njit(parallel=True)(f)(10)
+            executed_count = shared['counter']
+            self.assertGreater(executed_count, 0)
+        finally:
+            # Re-install the original handler
+            array_analysis.array_analysis_extensions[Parfor] = orig_parfor
+
+        # Check normal execution
+        expected = njit(parallel=True)(f)(10)
+        self.assertPreciseEqual(got, expected)
+        # Make sure we have uninstalled the handler
+        self.assertEqual(executed_count, shared['counter'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_attr.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_attr.py
new file mode 100644
index 000000000..55176e132
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_attr.py
@@ -0,0 +1,389 @@
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated
+from numba.np.numpy_support import from_dtype
+from numba import njit, typeof
+from numba.core import types
+from numba.tests.support import (TestCase, CompilationCache, MemoryLeakMixin,
+                                 tag, skip_parfors_unsupported)
+from numba.core.errors import TypingError
+from numba.experimental import jitclass
+
+
+def array_dtype(a):
+    return a.dtype
+
+
+def use_dtype(a, b):
+    return a.view(b.dtype)
+
+
+def array_itemsize(a):
+    return a.itemsize
+
+
+def array_shape(a, i):
+    return a.shape[i]
+
+
+def array_strides(a, i):
+    return a.strides[i]
+
+
+def array_ndim(a):
+    return a.ndim
+
+
+def array_size(a):
+    return a.size
+
+
+def array_flags_contiguous(a):
+    return a.flags.contiguous
+
+def array_flags_c_contiguous(a):
+    return a.flags.c_contiguous
+
+def array_flags_f_contiguous(a):
+    return a.flags.f_contiguous
+
+
+def nested_array_itemsize(a):
+    return a.f.itemsize
+
+
+def nested_array_shape(a):
+    return a.f.shape
+
+
+def nested_array_strides(a):
+    return a.f.strides
+
+
+def nested_array_ndim(a):
+    return a.f.ndim
+
+
+def nested_array_size(a):
+    return a.f.size
+
+
+def size_after_slicing_usecase(buf, i):
+    sliced = buf[i]
+    # Make sure size attribute is not lost
+    return sliced.size
+
+
+def array_ctypes_data(arr):
+    return arr.ctypes.data
+
+
+def array_real(arr):
+    return arr.real
+
+
+def array_imag(arr):
+    return arr.imag
+
+
+class TestArrayAttr(MemoryLeakMixin, TestCase):
+
+    def setUp(self):
+        super(TestArrayAttr, self).setUp()
+        self.ccache = CompilationCache()
+        self.a = np.arange(20, dtype=np.int32).reshape(4, 5)
+
+    def check_unary(self, pyfunc, arr):
+        aryty = typeof(arr)
+        cfunc = self.get_cfunc(pyfunc, (aryty,))
+        expected = pyfunc(arr)
+        self.assertPreciseEqual(cfunc(arr), expected)
+        # Retry with forced any layout
+        cfunc = self.get_cfunc(pyfunc, (aryty.copy(layout='A'),))
+        self.assertPreciseEqual(cfunc(arr), expected)
+
+    def check_unary_with_arrays(self, pyfunc,):
+        self.check_unary(pyfunc, self.a)
+        self.check_unary(pyfunc, self.a.T)
+        self.check_unary(pyfunc, self.a[::2])
+        # 0-d array
+        arr = np.array([42]).reshape(())
+        self.check_unary(pyfunc, arr)
+        # array with an empty dimension
+        arr = np.zeros(0)
+        self.check_unary(pyfunc, arr)
+
+        # check with reshape
+        self.check_unary(pyfunc, arr.reshape((1, 0, 2)))
+
+    def get_cfunc(self, pyfunc, argspec):
+        cres = self.ccache.compile(pyfunc, argspec)
+        return cres.entry_point
+
+    def test_shape(self):
+        pyfunc = array_shape
+        cfunc = self.get_cfunc(pyfunc, (types.int32[:,:], types.int32))
+
+        for i in range(self.a.ndim):
+            self.assertEqual(pyfunc(self.a, i), cfunc(self.a, i))
+
+    def test_strides(self):
+        pyfunc = array_strides
+        cfunc = self.get_cfunc(pyfunc, (types.int32[:,:], types.int32))
+
+        for i in range(self.a.ndim):
+            self.assertEqual(pyfunc(self.a, i), cfunc(self.a, i))
+
+    def test_ndim(self):
+        self.check_unary_with_arrays(array_ndim)
+
+    def test_size(self):
+        self.check_unary_with_arrays(array_size)
+
+    def test_itemsize(self):
+        self.check_unary_with_arrays(array_itemsize)
+
+    def test_dtype(self):
+        pyfunc = array_dtype
+        self.check_unary(pyfunc, self.a)
+        dtype = np.dtype([('x', np.int8), ('y', np.int8)])
+        arr = np.zeros(4, dtype=dtype)
+        self.check_unary(pyfunc, arr)
+
+    def test_use_dtype(self):
+        # Test using the dtype attribute inside the Numba function itself
+        b = np.empty(1, dtype=np.int16)
+        pyfunc = use_dtype
+        cfunc = self.get_cfunc(pyfunc, (typeof(self.a), typeof(b)))
+        expected = pyfunc(self.a, b)
+        self.assertPreciseEqual(cfunc(self.a, b), expected)
+
+    def test_flags_contiguous(self):
+        self.check_unary_with_arrays(array_flags_contiguous)
+
+    def test_flags_c_contiguous(self):
+        self.check_unary_with_arrays(array_flags_c_contiguous)
+
+    def test_flags_f_contiguous(self):
+        self.check_unary_with_arrays(array_flags_f_contiguous)
+
+
+class TestNestedArrayAttr(MemoryLeakMixin, unittest.TestCase):
+    def setUp(self):
+        super(TestNestedArrayAttr, self).setUp()
+        dtype = np.dtype([('a', np.int32), ('f', np.int32, (2, 5))])
+        self.a = np.recarray(1, dtype)[0]
+        self.nbrecord = from_dtype(self.a.dtype)
+
+    def get_cfunc(self, pyfunc):
+        cres = compile_isolated(pyfunc, (self.nbrecord,))
+        return cres.entry_point
+
+    def test_shape(self):
+        pyfunc = nested_array_shape
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_strides(self):
+        pyfunc = nested_array_strides
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_ndim(self):
+        pyfunc = nested_array_ndim
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_size(self):
+        pyfunc = nested_array_size
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+    def test_itemsize(self):
+        pyfunc = nested_array_itemsize
+        cfunc = self.get_cfunc(pyfunc)
+
+        self.assertEqual(pyfunc(self.a), cfunc(self.a))
+
+
+class TestSlicedArrayAttr(MemoryLeakMixin, unittest.TestCase):
+    def test_size_after_slicing(self):
+        pyfunc = size_after_slicing_usecase
+        cfunc = njit(pyfunc)
+        arr = np.arange(2 * 5).reshape(2, 5)
+        for i in range(arr.shape[0]):
+            self.assertEqual(pyfunc(arr, i), cfunc(arr, i))
+        arr = np.arange(2 * 5 * 3).reshape(2, 5, 3)
+        for i in range(arr.shape[0]):
+            self.assertEqual(pyfunc(arr, i), cfunc(arr, i))
+
+
+class TestArrayCTypes(MemoryLeakMixin, TestCase):
+
+    _numba_parallel_test_ = False
+
+    def test_array_ctypes_data(self):
+        pyfunc = array_ctypes_data
+        cfunc = njit(pyfunc)
+        arr = np.arange(3)
+        self.assertEqual(pyfunc(arr), cfunc(arr))
+
+    @skip_parfors_unsupported
+    def test_array_ctypes_ref_error_in_parallel(self):
+        # Issue #2887
+        from ctypes import CFUNCTYPE, c_void_p, c_int32, c_double, c_bool
+
+        @CFUNCTYPE(c_bool, c_void_p, c_int32, c_void_p)
+        def callback(inptr, size, outptr):
+            # A ctypes callback that manipulate the incoming pointers.
+            try:
+                inbuf = (c_double * size).from_address(inptr)
+                outbuf = (c_double * 1).from_address(outptr)
+                a = np.ndarray(size, buffer=inbuf, dtype=np.float64)
+                b = np.ndarray(1, buffer=outbuf, dtype=np.float64)
+                b[0] = (a + a.size)[0]
+                return True
+            except:
+                import traceback
+                traceback.print_exception()
+                return False
+
+
+        # parallel=True is required to reproduce the error.
+        @njit(parallel=True)
+        def foo(size):
+            arr = np.ones(size)
+            out = np.empty(1)
+            # Exercise array.ctypes
+            inct = arr.ctypes
+            outct = out.ctypes
+            # The reference to `arr` is dead by now
+            status = callback(inct.data, size, outct.data)
+            return status, out[0]
+
+        size = 3
+        status, got = foo(size)
+        self.assertTrue(status)
+        self.assertPreciseEqual(got, (np.ones(size) + size)[0])
+
+
+class TestRealImagAttr(MemoryLeakMixin, TestCase):
+    def check_complex(self, pyfunc):
+        cfunc = njit(pyfunc)
+        # test 1D
+        size = 10
+        arr = np.arange(size) + np.arange(size) * 10j
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test 2D
+        arr = arr.reshape(2, 5)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+
+    def test_complex_real(self):
+        self.check_complex(array_real)
+
+    def test_complex_imag(self):
+        self.check_complex(array_imag)
+
+    def check_number_real(self, dtype):
+        pyfunc = array_real
+        cfunc = njit(pyfunc)
+        # test 1D
+        size = 10
+        arr = np.arange(size, dtype=dtype)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test 2D
+        arr = arr.reshape(2, 5)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test identity
+        self.assertEqual(arr.data, pyfunc(arr).data)
+        self.assertEqual(arr.data, cfunc(arr).data)
+        # test writable
+        real = cfunc(arr)
+        self.assertNotEqual(arr[0, 0], 5)
+        real[0, 0] = 5
+        self.assertEqual(arr[0, 0], 5)
+
+    def test_number_real(self):
+        """
+        Testing .real of non-complex dtypes
+        """
+        for dtype in [np.uint8, np.int32, np.float32, np.float64]:
+            self.check_number_real(dtype)
+
+    def check_number_imag(self, dtype):
+        pyfunc = array_imag
+        cfunc = njit(pyfunc)
+        # test 1D
+        size = 10
+        arr = np.arange(size, dtype=dtype)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test 2D
+        arr = arr.reshape(2, 5)
+        self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+        # test are zeros
+        self.assertEqual(cfunc(arr).tolist(), np.zeros_like(arr).tolist())
+        # test readonly
+        imag = cfunc(arr)
+        with self.assertRaises(ValueError) as raises:
+            imag[0] = 1
+        self.assertEqual('assignment destination is read-only',
+                         str(raises.exception))
+
+    def test_number_imag(self):
+        """
+        Testing .imag of non-complex dtypes
+        """
+        for dtype in [np.uint8, np.int32, np.float32, np.float64]:
+            self.check_number_imag(dtype)
+
+    def test_record_real(self):
+        rectyp = np.dtype([('real', np.float32), ('imag', np.complex64)])
+        arr = np.zeros(3, dtype=rectyp)
+        arr['real'] = np.random.random(arr.size)
+        arr['imag'] = np.random.random(arr.size) * 1.3j
+
+        # check numpy behavior
+        # .real is identity
+        self.assertIs(array_real(arr), arr)
+        # .imag is zero_like
+        self.assertEqual(array_imag(arr).tolist(), np.zeros_like(arr).tolist())
+
+        # check numba behavior
+        # it's most likely a user error, anyway
+        jit_array_real = njit(array_real)
+        jit_array_imag = njit(array_imag)
+
+        with self.assertRaises(TypingError) as raises:
+            jit_array_real(arr)
+        self.assertIn("cannot access .real of array of Record",
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            jit_array_imag(arr)
+        self.assertIn("cannot access .imag of array of Record",
+                      str(raises.exception))
+
+class TestJitclassFlagsSegfault(MemoryLeakMixin, TestCase):
+    """Regression test for: https://github.com/numba/numba/issues/4775 """
+
+    def test(self):
+
+        @jitclass(dict())
+        class B(object):
+
+            def __init__(self):
+                pass
+
+            def foo(self, X):
+                X.flags
+
+        Z = B()
+        Z.foo(np.ones(4))
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_constants.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_constants.py
new file mode 100644
index 000000000..ac5a7e39f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_constants.py
@@ -0,0 +1,193 @@
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated
+from numba.core.errors import TypingError
+from numba import jit, typeof
+from numba.core import types
+
+
+a0 = np.array(42)
+
+s1 = np.int32(64)
+
+a1 = np.arange(12)
+a2 = a1[::2]
+a3 = a1.reshape((3, 4)).T
+
+dt = np.dtype([('x', np.int8), ('y', 'S3')])
+
+a4 = np.arange(32, dtype=np.int8).view(dt)
+a5 = a4[::-2]
+
+# A recognizable data string
+a6 = np.frombuffer(b"XXXX_array_contents_XXXX", dtype=np.float32)
+
+
+myarray = np.array([1, ])
+
+
+def getitem0(i):
+    return a0[()]
+
+
+def getitem1(i):
+    return a1[i]
+
+
+def getitem2(i):
+    return a2[i]
+
+
+def getitem3(i):
+    return a3[i]
+
+
+def getitem4(i):
+    return a4[i]
+
+
+def getitem5(i):
+    return a5[i]
+
+
+def getitem6(i):
+    return a6[i]
+
+
+def use_arrayscalar_const():
+    return s1
+
+
+def write_to_global_array():
+    myarray[0] = 1
+
+
+def bytes_as_const_array():
+    return np.frombuffer(b'foo', dtype=np.uint8)
+
+
+class TestConstantArray(unittest.TestCase):
+    """
+    Test array constants.
+    """
+
+    def check_array_const(self, pyfunc):
+        cres = compile_isolated(pyfunc, (types.int32,))
+        cfunc = cres.entry_point
+        for i in [0, 1, 2]:
+            np.testing.assert_array_equal(pyfunc(i), cfunc(i))
+
+    def test_array_const_0d(self):
+        self.check_array_const(getitem0)
+
+    def test_array_const_1d_contig(self):
+        self.check_array_const(getitem1)
+
+    def test_array_const_1d_noncontig(self):
+        self.check_array_const(getitem2)
+
+    def test_array_const_2d(self):
+        self.check_array_const(getitem3)
+
+    def test_record_array_const_contig(self):
+        self.check_array_const(getitem4)
+
+    def test_record_array_const_noncontig(self):
+        self.check_array_const(getitem5)
+
+    def test_array_const_alignment(self):
+        """
+        Issue #1933: the array declaration in the LLVM IR must have
+        the right alignment specified.
+        """
+        sig = (types.intp,)
+        cfunc = jit(sig, nopython=True)(getitem6)
+        ir = cfunc.inspect_llvm(sig)
+        for line in ir.splitlines():
+            if 'XXXX_array_contents_XXXX' in line:
+                self.assertIn("constant [24 x i8]", line)  # sanity check
+                # Should be the ABI-required alignment for float32
+                # on most platforms...
+                self.assertIn(", align 4", line)
+                break
+        else:
+            self.fail("could not find array declaration in LLVM IR")
+
+    def test_arrayscalar_const(self):
+        pyfunc = use_arrayscalar_const
+        cres = compile_isolated(pyfunc, ())
+        cfunc = cres.entry_point
+
+        self.assertEqual(pyfunc(), cfunc())
+
+    def test_write_to_global_array(self):
+        pyfunc = write_to_global_array
+        with self.assertRaises(TypingError):
+            compile_isolated(pyfunc, ())
+
+    def test_issue_1850(self):
+        """
+        This issue is caused by an unresolved bug in numpy since version 1.6.
+        See numpy GH issue #3147.
+        """
+        constarr = np.array([86])
+
+        def pyfunc():
+            return constarr[0]
+
+        cres = compile_isolated(pyfunc, ())
+        out = cres.entry_point()
+        self.assertEqual(out, 86)
+
+    def test_too_big_to_freeze(self):
+        """
+        Test issue https://github.com/numba/numba/issues/2188 where freezing
+        a constant array into the code that's prohibitively long and consumes
+        too much RAM.
+        """
+        def test(biggie):
+            expect = np.copy(biggie)
+            self.assertEqual(typeof(biggie), typeof(expect))
+
+            def pyfunc():
+                return biggie
+
+            cres = compile_isolated(pyfunc, ())
+            # Check that the array is not frozen into the LLVM IR.
+            # LLVM size must be less than the array size.
+            self.assertLess(len(cres.library.get_llvm_str()), biggie.nbytes)
+            # Run and test result
+            out = cres.entry_point()
+            self.assertIs(biggie, out)
+            # Remove all local references to biggie
+            del out
+            biggie = None  # del biggie is syntax error in py2
+            # Run again and verify result
+            out = cres.entry_point()
+            np.testing.assert_equal(expect, out)
+            self.assertEqual(typeof(expect), typeof(out))
+
+        nelem = 10**7   # 10 million items
+
+        c_array = np.arange(nelem).reshape(nelem)
+        f_array = np.asfortranarray(np.random.random((2, nelem // 2)))
+        self.assertEqual(typeof(c_array).layout, 'C')
+        self.assertEqual(typeof(f_array).layout, 'F')
+        # Test C contig
+        test(c_array)
+        # Test F contig
+        test(f_array)
+
+
+class TestConstantBytes(unittest.TestCase):
+    def test_constant_bytes(self):
+        pyfunc = bytes_as_const_array
+        cres = compile_isolated(pyfunc, ())
+        cfunc = cres.entry_point
+
+        np.testing.assert_array_equal(pyfunc(), cfunc())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_exprs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_exprs.py
new file mode 100644
index 000000000..090bbd5ad
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_exprs.py
@@ -0,0 +1,742 @@
+import gc
+from io import StringIO
+
+import numpy as np
+
+from numba import njit, vectorize
+from numba import typeof
+from numba.core import utils, types, typing, ir, compiler, cpu, cgutils
+from numba.core.compiler import Compiler, Flags
+from numba.core.registry import cpu_target
+from numba.tests.support import (MemoryLeakMixin, TestCase, temp_directory,
+                                 create_temp_module)
+from numba.extending import (
+    overload,
+    models,
+    lower_builtin,
+    register_model,
+    make_attribute_wrapper,
+    type_callable,
+    typeof_impl
+)
+import operator
+import textwrap
+
+import unittest
+
+
+class Namespace(dict):
+    def __getattr__(s, k):
+        return s[k] if k in s else super(Namespace, s).__getattr__(k)
+
+def axy(a, x, y):
+    return a * x + y
+
+def ax2(a, x, y):
+    return a * x + y
+
+def pos_root(As, Bs, Cs):
+    return (-Bs + (((Bs ** 2.) - (4. * As * Cs)) ** 0.5)) / (2. * As)
+
+def neg_root_common_subexpr(As, Bs, Cs):
+    _2As = 2. * As
+    _4AsCs = 2. * _2As * Cs
+    _Bs2_4AsCs = (Bs ** 2. - _4AsCs)
+    return (-Bs - (_Bs2_4AsCs ** 0.5)) / _2As
+
+def neg_root_complex_subexpr(As, Bs, Cs):
+    _2As = 2. * As
+    _4AsCs = 2. * _2As * Cs
+    _Bs2_4AsCs = (Bs ** 2. - _4AsCs) + 0j # Force into the complex domain.
+    return (-Bs - (_Bs2_4AsCs ** 0.5)) / _2As
+
+vaxy = vectorize(axy)
+
+def call_stuff(a0, a1):
+    return np.cos(vaxy(a0, np.sin(a1) - 1., 1.))
+
+def are_roots_imaginary(As, Bs, Cs):
+    return (Bs ** 2 - 4 * As * Cs) < 0
+
+def div_add(As, Bs, Cs):
+    return As / Bs + Cs
+
+def cube(As):
+    return As ** 3
+
+def explicit_output(a, b, out):
+    np.cos(a, out)
+    return np.add(out, b, out)
+
+def variable_name_reuse(a, b, c, d):
+    u = a + b
+    u = u - a * b
+    u = u * c + d
+    return u
+
+
+# From issue #1264
+def distance_matrix(vectors):
+    n_vectors = vectors.shape[0]
+    result = np.empty((n_vectors, n_vectors), dtype=np.float64)
+
+    for i in range(n_vectors):
+        for j in range(i, n_vectors):
+            result[i,j] = result[j,i] = np.sum(
+                (vectors[i] - vectors[j]) ** 2) ** 0.5
+
+    return result
+
+
+class RewritesTester(Compiler):
+    @classmethod
+    def mk_pipeline(cls, args, return_type=None, flags=None, locals={},
+                    library=None, typing_context=None, target_context=None):
+        if not flags:
+            flags = Flags()
+        flags.nrt = True
+        if typing_context is None:
+            typing_context = cpu_target.typing_context
+        if target_context is None:
+            target_context = cpu_target.target_context
+        return cls(typing_context, target_context, library, args, return_type,
+                   flags, locals)
+
+    @classmethod
+    def mk_no_rw_pipeline(cls, args, return_type=None, flags=None, locals={},
+                          library=None, **kws):
+        if not flags:
+            flags = Flags()
+        flags.no_rewrites = True
+        return cls.mk_pipeline(args, return_type, flags, locals, library, **kws)
+
+
+class TestArrayExpressions(MemoryLeakMixin, TestCase):
+
+    def _compile_function(self, fn, arg_tys):
+        """
+        Compile the given function both without and with rewrites enabled.
+        """
+        control_pipeline = RewritesTester.mk_no_rw_pipeline(arg_tys)
+        cres_0 = control_pipeline.compile_extra(fn)
+        control_cfunc = cres_0.entry_point
+
+        test_pipeline = RewritesTester.mk_pipeline(arg_tys)
+        cres_1 = test_pipeline.compile_extra(fn)
+        test_cfunc = cres_1.entry_point
+
+        return control_pipeline, control_cfunc, test_pipeline, test_cfunc
+
+    def test_simple_expr(self):
+        '''
+        Using a simple array expression, verify that rewriting is taking
+        place, and is fusing loops.
+        '''
+        A = np.linspace(0,1,10)
+        X = np.linspace(2,1,10)
+        Y = np.linspace(1,2,10)
+        arg_tys = [typeof(arg) for arg in (A, X, Y)]
+
+        control_pipeline, nb_axy_0, test_pipeline, nb_axy_1 = \
+            self._compile_function(axy, arg_tys)
+
+        control_pipeline2 = RewritesTester.mk_no_rw_pipeline(arg_tys)
+        cres_2 = control_pipeline2.compile_extra(ax2)
+        nb_ctl = cres_2.entry_point
+
+        expected = nb_axy_0(A, X, Y)
+        actual = nb_axy_1(A, X, Y)
+        control = nb_ctl(A, X, Y)
+        np.testing.assert_array_equal(expected, actual)
+        np.testing.assert_array_equal(control, actual)
+
+        ir0 = control_pipeline.state.func_ir.blocks
+        ir1 = test_pipeline.state.func_ir.blocks
+        ir2 = control_pipeline2.state.func_ir.blocks
+        self.assertEqual(len(ir0), len(ir1))
+        self.assertEqual(len(ir0), len(ir2))
+        # The rewritten IR should be smaller than the original.
+        self.assertGreater(len(ir0[0].body), len(ir1[0].body))
+        self.assertEqual(len(ir0[0].body), len(ir2[0].body))
+
+    def _get_array_exprs(self, block):
+        for instr in block:
+            if isinstance(instr, ir.Assign):
+                if isinstance(instr.value, ir.Expr):
+                    if instr.value.op == 'arrayexpr':
+                        yield instr
+
+    def _array_expr_to_set(self, expr, out=None):
+        '''
+        Convert an array expression tree into a set of operators.
+        '''
+        if out is None:
+            out = set()
+        if not isinstance(expr, tuple):
+            raise ValueError("{0} not a tuple".format(expr))
+        operation, operands = expr
+        processed_operands = []
+        for operand in operands:
+            if isinstance(operand, tuple):
+                operand, _ = self._array_expr_to_set(operand, out)
+            processed_operands.append(operand)
+        processed_expr = operation, tuple(processed_operands)
+        out.add(processed_expr)
+        return processed_expr, out
+
+    def _test_root_function(self, fn=pos_root):
+        A = np.random.random(10)
+        B = np.random.random(10) + 1. # Increase likelihood of real
+                                      # root (could add 2 to force all
+                                      # roots to be real).
+        C = np.random.random(10)
+        arg_tys = [typeof(arg) for arg in (A, B, C)]
+
+        control_pipeline = RewritesTester.mk_no_rw_pipeline(arg_tys)
+        control_cres = control_pipeline.compile_extra(fn)
+        nb_fn_0 = control_cres.entry_point
+
+        test_pipeline = RewritesTester.mk_pipeline(arg_tys)
+        test_cres = test_pipeline.compile_extra(fn)
+        nb_fn_1 = test_cres.entry_point
+
+        np_result = fn(A, B, C)
+        nb_result_0 = nb_fn_0(A, B, C)
+        nb_result_1 = nb_fn_1(A, B, C)
+        np.testing.assert_array_almost_equal(np_result, nb_result_0)
+        np.testing.assert_array_almost_equal(nb_result_0, nb_result_1)
+
+        return Namespace(locals())
+
+    def _test_cube_function(self, fn=cube):
+        A = np.arange(10, dtype=np.float64)
+        arg_tys = (typeof(A),)
+
+        control_pipeline = RewritesTester.mk_no_rw_pipeline(arg_tys)
+        control_cres = control_pipeline.compile_extra(fn)
+        nb_fn_0 = control_cres.entry_point
+
+        test_pipeline = RewritesTester.mk_pipeline(arg_tys)
+        test_cres = test_pipeline.compile_extra(fn)
+        nb_fn_1 = test_cres.entry_point
+
+        expected = A ** 3
+        self.assertPreciseEqual(expected, nb_fn_0(A))
+        self.assertPreciseEqual(expected, nb_fn_1(A))
+
+        return Namespace(locals())
+
+    def _test_explicit_output_function(self, fn):
+        """
+        Test function having a (a, b, out) signature where *out* is
+        an output array the function writes into.
+        """
+        A = np.arange(10, dtype=np.float64)
+        B = A + 1
+        arg_tys = (typeof(A),) * 3
+
+        control_pipeline, control_cfunc, test_pipeline, test_cfunc = \
+            self._compile_function(fn, arg_tys)
+
+        def run_func(fn):
+            out = np.zeros_like(A)
+            fn(A, B, out)
+            return out
+
+        expected = run_func(fn)
+        self.assertPreciseEqual(expected, run_func(control_cfunc))
+        self.assertPreciseEqual(expected, run_func(test_cfunc))
+
+        return Namespace(locals())
+
+    def _assert_array_exprs(self, block, expected_count):
+        """
+        Assert the *block* has the expected number of array expressions
+        in it.
+        """
+        rewrite_count = len(list(self._get_array_exprs(block)))
+        self.assertEqual(rewrite_count, expected_count)
+
+    def _assert_total_rewrite(self, control_ir, test_ir, trivial=False):
+        """
+        Given two dictionaries of Numba IR blocks, check to make sure the
+        control IR has no array expressions, while the test IR
+        contains one and only one.
+        """
+        # Both IRs have the same number of blocks (presumably 1)
+        self.assertEqual(len(control_ir), len(test_ir))
+        control_block = control_ir[0].body
+        test_block = test_ir[0].body
+        self._assert_array_exprs(control_block, 0)
+        self._assert_array_exprs(test_block, 1)
+        if not trivial:
+            # If the expression wasn't trivial, the block length should
+            # have decreased (since a sequence of exprs was replaced
+            # with a single nested array expr).
+            self.assertGreater(len(control_block), len(test_block))
+
+    def _assert_no_rewrite(self, control_ir, test_ir):
+        """
+        Given two dictionaries of Numba IR blocks, check to make sure
+        the control IR and the test IR both have no array expressions.
+        """
+        self.assertEqual(len(control_ir), len(test_ir))
+        # All blocks should be identical, and not rewritten
+        for k, v in control_ir.items():
+            control_block = v.body
+            test_block = test_ir[k].body
+            self.assertEqual(len(control_block), len(test_block))
+            self._assert_array_exprs(control_block, 0)
+            self._assert_array_exprs(test_block, 0)
+
+    def test_trivial_expr(self):
+        """
+        Ensure even a non-nested expression is rewritten, as it can enable
+        scalar optimizations such as rewriting `x ** 2`.
+        """
+        ns = self._test_cube_function()
+        self._assert_total_rewrite(ns.control_pipeline.state.func_ir.blocks,
+                                   ns.test_pipeline.state.func_ir.blocks,
+                                   trivial=True)
+
+    def test_complicated_expr(self):
+        '''
+        Using the polynomial root function, ensure the full expression is
+        being put in the same kernel with no remnants of intermediate
+        array expressions.
+        '''
+        ns = self._test_root_function()
+        self._assert_total_rewrite(ns.control_pipeline.state.func_ir.blocks,
+                                   ns.test_pipeline.state.func_ir.blocks)
+
+    def test_common_subexpressions(self, fn=neg_root_common_subexpr):
+        '''
+        Attempt to verify that rewriting will incorporate user common
+        subexpressions properly.
+        '''
+        ns = self._test_root_function(fn)
+        ir0 = ns.control_pipeline.state.func_ir.blocks
+        ir1 = ns.test_pipeline.state.func_ir.blocks
+        self.assertEqual(len(ir0), len(ir1))
+        self.assertGreater(len(ir0[0].body), len(ir1[0].body))
+        self.assertEqual(len(list(self._get_array_exprs(ir0[0].body))), 0)
+        # Verify that we didn't rewrite everything into a monolithic
+        # array expression since we stored temporary values in
+        # variables that might be used later (from the optimization's
+        # point of view).
+        array_expr_instrs = list(self._get_array_exprs(ir1[0].body))
+        self.assertGreater(len(array_expr_instrs), 1)
+        # Now check that we haven't duplicated any subexpressions in
+        # the rewritten code.
+        array_sets = list(self._array_expr_to_set(instr.value.expr)[1]
+                          for instr in array_expr_instrs)
+        for expr_set_0, expr_set_1 in zip(array_sets[:-1], array_sets[1:]):
+            intersections = expr_set_0.intersection(expr_set_1)
+            if intersections:
+                self.fail("Common subexpressions detected in array "
+                          "expressions ({0})".format(intersections))
+
+    def test_complex_subexpression(self):
+        return self.test_common_subexpressions(neg_root_complex_subexpr)
+
+    def test_ufunc_and_dufunc_calls(self):
+        '''
+        Verify that ufunc and DUFunc calls are being properly included in
+        array expressions.
+        '''
+        A = np.random.random(10)
+        B = np.random.random(10)
+        arg_tys = [typeof(arg) for arg in (A, B)]
+
+        vaxy_descr = vaxy._dispatcher.targetdescr
+        control_pipeline = RewritesTester.mk_no_rw_pipeline(
+            arg_tys,
+            typing_context=vaxy_descr.typing_context,
+            target_context=vaxy_descr.target_context)
+        cres_0 = control_pipeline.compile_extra(call_stuff)
+        nb_call_stuff_0 = cres_0.entry_point
+
+        test_pipeline = RewritesTester.mk_pipeline(
+            arg_tys,
+            typing_context=vaxy_descr.typing_context,
+            target_context=vaxy_descr.target_context)
+        cres_1 = test_pipeline.compile_extra(call_stuff)
+        nb_call_stuff_1 = cres_1.entry_point
+
+        expected = call_stuff(A, B)
+        control = nb_call_stuff_0(A, B)
+        actual = nb_call_stuff_1(A, B)
+        np.testing.assert_array_almost_equal(expected, control)
+        np.testing.assert_array_almost_equal(expected, actual)
+
+        self._assert_total_rewrite(control_pipeline.state.func_ir.blocks,
+                                   test_pipeline.state.func_ir.blocks)
+
+    def test_cmp_op(self):
+        '''
+        Verify that comparison operators are supported by the rewriter.
+        '''
+        ns = self._test_root_function(are_roots_imaginary)
+        self._assert_total_rewrite(ns.control_pipeline.state.func_ir.blocks,
+                                   ns.test_pipeline.state.func_ir.blocks)
+
+    def test_explicit_output(self):
+        """
+        Check that ufunc calls with explicit outputs are not rewritten.
+        """
+        ns = self._test_explicit_output_function(explicit_output)
+        self._assert_no_rewrite(ns.control_pipeline.state.func_ir.blocks,
+                                ns.test_pipeline.state.func_ir.blocks)
+
+
+class TestRewriteIssues(MemoryLeakMixin, TestCase):
+
+    def test_issue_1184(self):
+        from numba import jit
+        import numpy as np
+
+        @jit(nopython=True)
+        def foo(arr):
+            return arr
+
+        @jit(nopython=True)
+        def bar(arr):
+            c = foo(arr)
+            d = foo(arr)   # two calls to trigger rewrite
+            return c, d
+
+        arr = np.arange(10)
+        out_c, out_d = bar(arr)
+        self.assertIs(out_c, out_d)
+        self.assertIs(out_c, arr)
+
+    def test_issue_1264(self):
+        n = 100
+        x = np.random.uniform(size=n*3).reshape((n,3))
+        expected = distance_matrix(x)
+        actual = njit(distance_matrix)(x)
+        np.testing.assert_array_almost_equal(expected, actual)
+        # Avoid sporadic failures in MemoryLeakMixin.tearDown()
+        gc.collect()
+
+    def test_issue_1372(self):
+        """Test array expression with duplicated term"""
+        from numba import njit
+
+        @njit
+        def foo(a, b):
+            b = np.sin(b)
+            return b + b + a
+
+        a = np.random.uniform(10)
+        b = np.random.uniform(10)
+        expect = foo.py_func(a, b)
+        got = foo(a, b)
+        np.testing.assert_allclose(got, expect)
+
+    def test_unary_arrayexpr(self):
+        """
+        Typing of unary array expression (np.negate) can be incorrect.
+        """
+        @njit
+        def foo(a, b):
+            return b - a + -a
+
+        b = 1.5
+        a = np.arange(10, dtype=np.int32)
+
+        expect = foo.py_func(a, b)
+        got = foo(a, b)
+        self.assertPreciseEqual(got, expect)
+
+    def test_bitwise_arrayexpr(self):
+        """
+        Typing of bitwise boolean array expression can be incorrect
+        (issue #1813).
+        """
+        @njit
+        def foo(a, b):
+            return ~(a & (~b))
+
+        a = np.array([True, True, False, False])
+        b = np.array([False, True, False, True])
+
+        expect = foo.py_func(a, b)
+        got = foo(a, b)
+        self.assertPreciseEqual(got, expect)
+
+    def test_annotations(self):
+        """
+        Type annotation of array expressions with disambiguated
+        variable names (issue #1466).
+        """
+        cfunc = njit(variable_name_reuse)
+
+        a = np.linspace(0, 1, 10)
+        cfunc(a, a, a, a)
+
+        buf = StringIO()
+        cfunc.inspect_types(buf)
+        res = buf.getvalue()
+        self.assertIn("#   u.1 = ", res)
+        self.assertIn("#   u.2 = ", res)
+
+    def test_issue_5599_name_collision(self):
+        # The original error will fail in lowering of the array_expr
+        @njit
+        def f(x):
+            arr = np.ones(x)
+
+            for _ in range(2):
+                val =  arr * arr
+                arr = arr.copy()
+            return arr
+
+        got = f(5)
+        expect = f.py_func(5)
+        np.testing.assert_array_equal(got, expect)
+
+
+class TestSemantics(MemoryLeakMixin, unittest.TestCase):
+
+    def test_division_by_zero(self):
+        # Array expressions should follow the Numpy error model
+        # i.e. 1./0. returns +inf instead of raising ZeroDivisionError
+        pyfunc = div_add
+        cfunc = njit(pyfunc)
+
+        a = np.float64([0.0, 1.0, float('inf')])
+        b = np.float64([0.0, 0.0, 1.0])
+        c = np.ones_like(a)
+
+        expect = pyfunc(a, b, c)
+        got = cfunc(a, b, c)
+        np.testing.assert_array_equal(expect, got)
+
+
+class TestOptionals(MemoryLeakMixin, unittest.TestCase):
+    """ Tests the arrival and correct lowering of Optional types at a arrayexpr
+    derived ufunc, see #3972"""
+
+    def test_optional_scalar_type(self):
+
+        @njit
+        def arr_expr(x, y):
+            return x + y
+
+        @njit
+        def do_call(x, y):
+            if y > 0:
+                z = None
+            else:
+                z = y
+            return arr_expr(x, z)
+
+        args = (np.arange(5), -1.2)
+
+        # check result
+        res = do_call(*args)
+        expected = do_call.py_func(*args)
+        np.testing.assert_allclose(res, expected)
+
+        # check type
+        s = arr_expr.signatures
+        oty = s[0][1]
+        self.assertTrue(isinstance(oty, types.Optional))
+        self.assertTrue(isinstance(oty.type, types.Float))
+
+    def test_optional_array_type(self):
+
+        @njit
+        def arr_expr(x, y):
+            return x + y
+
+        @njit
+        def do_call(x, y):
+            if y[0] > 0:
+                z = None
+            else:
+                z = y
+            return arr_expr(x, z)
+
+        args = (np.arange(5), np.arange(5.))
+
+        # check result
+        res = do_call(*args)
+        expected = do_call.py_func(*args)
+        np.testing.assert_allclose(res, expected)
+
+        # check type
+        s = arr_expr.signatures
+        oty = s[0][1]
+        self.assertTrue(isinstance(oty, types.Optional))
+        self.assertTrue(isinstance(oty.type, types.Array))
+        self.assertTrue(isinstance(oty.type.dtype, types.Float))
+
+
+class TestOptionalsExceptions(MemoryLeakMixin, unittest.TestCase):
+    # same as above, but the Optional resolves to None and TypeError's
+
+    def test_optional_scalar_type_exception_on_none(self):
+
+        self.disable_leak_check()
+
+        @njit
+        def arr_expr(x, y):
+            return x + y
+
+        @njit
+        def do_call(x, y):
+            if y > 0:
+                z = None
+            else:
+                z = y
+            return arr_expr(x, z)
+
+        args = (np.arange(5), 1.0)
+
+        # check result
+        with self.assertRaises(TypeError) as raises:
+            do_call(*args)
+
+        self.assertIn("expected float64, got None", str(raises.exception))
+
+        # check type
+        s = arr_expr.signatures
+        oty = s[0][1]
+        self.assertTrue(isinstance(oty, types.Optional))
+        self.assertTrue(isinstance(oty.type, types.Float))
+
+    def test_optional_array_type_exception_on_none(self):
+
+        self.disable_leak_check()
+
+        @njit
+        def arr_expr(x, y):
+            return x + y
+
+        @njit
+        def do_call(x, y):
+            if y[0] > 0:
+                z = None
+            else:
+                z = y
+            return arr_expr(x, z)
+
+        args = (np.arange(5), np.arange(1., 5.))
+
+        # check result
+        with self.assertRaises(TypeError) as raises:
+            do_call(*args)
+
+        excstr = str(raises.exception)
+        self.assertIn("expected array(float64,", excstr)
+        self.assertIn("got None", excstr)
+
+        # check type
+        s = arr_expr.signatures
+        oty = s[0][1]
+        self.assertTrue(isinstance(oty, types.Optional))
+        self.assertTrue(isinstance(oty.type, types.Array))
+        self.assertTrue(isinstance(oty.type.dtype, types.Float))
+
+
+class TestExternalTypes(MemoryLeakMixin, unittest.TestCase):
+    """ Tests RewriteArrayExprs with external (user defined) types,
+    see #5157"""
+
+    source_lines = textwrap.dedent("""
+        from numba.core import types
+
+        class FooType(types.Type):
+            def __init__(self):
+                super(FooType, self).__init__(name='Foo')
+        """)
+
+    def make_foo_type(self, FooType):
+        class Foo(object):
+            def __init__(self, value):
+                self.value = value
+
+        @register_model(FooType)
+        class FooModel(models.StructModel):
+            def __init__(self, dmm, fe_type):
+                members = [("value", types.intp)]
+                models.StructModel.__init__(self, dmm, fe_type, members)
+
+        make_attribute_wrapper(FooType, "value", "value")
+
+        @type_callable(Foo)
+        def type_foo(context):
+            def typer(value):
+                return FooType()
+
+            return typer
+
+        @lower_builtin(Foo, types.intp)
+        def impl_foo(context, builder, sig, args):
+            typ = sig.return_type
+            [value] = args
+            foo = cgutils.create_struct_proxy(typ)(context, builder)
+            foo.value = value
+            return foo._getvalue()
+
+        @typeof_impl.register(Foo)
+        def typeof_foo(val, c):
+            return FooType()
+
+        return Foo, FooType
+
+    def test_external_type(self):
+        with create_temp_module(self.source_lines) as test_module:
+            Foo, FooType = self.make_foo_type(test_module.FooType)
+
+            # sum of foo class instance and array return an array
+            # binary operation with foo class instance as one of args
+            @overload(operator.add)
+            def overload_foo_add(lhs, rhs):
+                if isinstance(lhs, FooType) and isinstance(rhs, types.Array):
+                    def imp(lhs, rhs):
+                        return np.array([lhs.value, rhs[0]])
+
+                    return imp
+
+            # sum of 2 foo class instances return an array
+            # binary operation with 2 foo class instances as args
+            @overload(operator.add)
+            def overload_foo_add(lhs, rhs):
+                if isinstance(lhs, FooType) and isinstance(rhs, FooType):
+                    def imp(lhs, rhs):
+                        return np.array([lhs.value, rhs.value])
+
+                    return imp
+
+            # neg of foo class instance return an array
+            # unary operation with foo class instance arg
+            @overload(operator.neg)
+            def overload_foo_neg(x):
+                if isinstance(x, FooType):
+                    def imp(x):
+                        return np.array([-x.value])
+
+                    return imp
+
+            @njit
+            def arr_expr_sum1(x, y):
+                return Foo(x) + np.array([y])
+
+            @njit
+            def arr_expr_sum2(x, y):
+                return Foo(x) + Foo(y)
+
+            @njit
+            def arr_expr_neg(x):
+                return -Foo(x)
+
+            np.testing.assert_array_equal(arr_expr_sum1(0, 1), np.array([0, 1]))
+            np.testing.assert_array_equal(arr_expr_sum2(2, 3), np.array([2, 3]))
+            np.testing.assert_array_equal(arr_expr_neg(4), np.array([-4]))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_iterators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_iterators.py
new file mode 100644
index 000000000..041ff29d1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_iterators.py
@@ -0,0 +1,549 @@
+import itertools
+
+import numpy as np
+
+from numba import jit, typeof
+from numba.core import types
+from numba.core.compiler import compile_isolated
+from numba.tests.support import TestCase, CompilationCache, MemoryLeakMixin, tag
+import unittest
+
+
+def array_iter(arr):
+    total = 0
+    for i, v in enumerate(arr):
+        total += i * v
+    return total
+
+def array_iter_items(arr):
+    return list(iter(arr))
+
+def array_view_iter(arr, idx):
+    total = 0
+    for i, v in enumerate(arr[idx]):
+        total += i * v
+    return total
+
+def array_flat(arr, out):
+    for i, v in enumerate(arr.flat):
+        out[i] = v
+
+def array_flat_getitem(arr, ind):
+    return arr.flat[ind]
+
+def array_flat_setitem(arr, ind, val):
+    arr.flat[ind] = val
+
+def array_flat_sum(arr):
+    s = 0
+    for i, v in enumerate(arr.flat):
+        s = s + (i + 1) * v
+    return s
+
+def array_flat_len(arr):
+    return len(arr.flat)
+
+def array_ndenumerate_sum(arr):
+    s = 0
+    for (i, j), v in np.ndenumerate(arr):
+        s = s + (i + 1) * (j + 1) * v
+    return s
+
+def np_ndindex_empty():
+    s = 0
+    for ind in np.ndindex(()):
+        s += s + len(ind) + 1
+    return s
+
+def np_ndindex(x, y):
+    s = 0
+    n = 0
+    for i, j in np.ndindex(x, y):
+        s = s + (i + 1) * (j + 1)
+    return s
+
+def np_ndindex_array(arr):
+    s = 0
+    n = 0
+    for indices in np.ndindex(arr.shape):
+        for i, j in enumerate(indices):
+            s = s + (i + 1) * (j + 1)
+    return s
+
+def np_nditer1(a):
+    res = []
+    for u in np.nditer(a):
+        res.append(u.item())
+    return res
+
+def np_nditer2(a, b):
+    res = []
+    for u, v in np.nditer((a, b)):
+        res.append((u.item(), v.item()))
+    return res
+
+def np_nditer3(a, b, c):
+    res = []
+    for u, v, w in np.nditer((a, b, c)):
+        res.append((u.item(), v.item(), w.item()))
+    return res
+
+def iter_next(arr):
+    it = iter(arr)
+    it2 = iter(arr)
+    return next(it), next(it), next(it2)
+
+
+#
+# Test premature free (see issue #2112).
+# The following test allocates an array ``x`` inside the body.
+# The compiler will put a ``del x`` right after the last use of ``x``,
+# which is right after the creation of the array iterator and
+# before the loop is entered.  If the iterator does not incref the array,
+# the iterator will be reading garbage data of free'ed memory.
+#
+
+def array_flat_premature_free(size):
+    x = np.arange(size)
+    res = np.zeros_like(x, dtype=np.intp)
+    for i, v in enumerate(x.flat):
+        res[i] = v
+    return res
+
+def array_ndenumerate_premature_free(size):
+    x = np.arange(size)
+    res = np.zeros_like(x, dtype=np.intp)
+    for i, v in np.ndenumerate(x):
+        res[i] = v
+    return res
+
+
+class TestArrayIterators(MemoryLeakMixin, TestCase):
+    """
+    Test array.flat, np.ndenumerate(), etc.
+    """
+
+    def setUp(self):
+        super(TestArrayIterators, self).setUp()
+        self.ccache = CompilationCache()
+
+    def check_array_iter_1d(self, arr):
+        pyfunc = array_iter
+        cres = compile_isolated(pyfunc, [typeof(arr)])
+        cfunc = cres.entry_point
+        expected = pyfunc(arr)
+        self.assertPreciseEqual(cfunc(arr), expected)
+
+    def check_array_iter_items(self, arr):
+        pyfunc = array_iter_items
+        cres = compile_isolated(pyfunc, [typeof(arr)])
+        cfunc = cres.entry_point
+        expected = pyfunc(arr)
+        self.assertPreciseEqual(cfunc(arr), expected)
+
+    def check_array_view_iter(self, arr, index):
+        pyfunc = array_view_iter
+        cres = compile_isolated(pyfunc, [typeof(arr), typeof(index)])
+        cfunc = cres.entry_point
+        expected = pyfunc(arr, index)
+        self.assertPreciseEqual(cfunc(arr, index), expected)
+
+    def check_array_flat(self, arr, arrty=None):
+        out = np.zeros(arr.size, dtype=arr.dtype)
+        nb_out = out.copy()
+        if arrty is None:
+            arrty = typeof(arr)
+
+        cres = compile_isolated(array_flat, [arrty, typeof(out)])
+        cfunc = cres.entry_point
+
+        array_flat(arr, out)
+        cfunc(arr, nb_out)
+
+        self.assertPreciseEqual(out, nb_out)
+
+    def check_array_unary(self, arr, arrty, func):
+        cres = compile_isolated(func, [arrty])
+        cfunc = cres.entry_point
+        self.assertPreciseEqual(cfunc(arr), func(arr))
+
+    def check_array_flat_sum(self, arr, arrty):
+        self.check_array_unary(arr, arrty, array_flat_sum)
+
+    def check_array_ndenumerate_sum(self, arr, arrty):
+        self.check_array_unary(arr, arrty, array_ndenumerate_sum)
+
+    def test_array_iter(self):
+        # Test iterating over arrays
+        arr = np.arange(6)
+        self.check_array_iter_1d(arr)
+        self.check_array_iter_items(arr)
+        arr = arr[::2]
+        self.assertFalse(arr.flags.c_contiguous)
+        self.assertFalse(arr.flags.f_contiguous)
+        self.check_array_iter_1d(arr)
+        self.check_array_iter_items(arr)
+        arr = np.bool_([1, 0, 0, 1])
+        self.check_array_iter_1d(arr)
+        self.check_array_iter_items(arr)
+        arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        self.check_array_iter_items(arr)
+        self.check_array_iter_items(arr.T)
+
+    def test_array_iter_yielded_order(self):
+        # See issue #5692
+        @jit(nopython=True)
+        def foo(arr):
+            t = []
+            for y1 in arr:
+                for y2 in y1:
+                    t.append(y2.ravel())
+            return t
+
+        # 'F' ordered
+        arr = np.arange(24).reshape((2, 3, 4), order='F')
+        expected = foo.py_func(arr)
+        got = foo(arr)
+        self.assertPreciseEqual(expected, got)
+
+        # 'A' ordered, outer strided
+        arr = np.arange(64).reshape((4, 8, 2), order='F')[::2, :, :]
+        expected = foo.py_func(arr)
+        got = foo(arr)
+        self.assertPreciseEqual(expected, got)
+
+        # 'A' ordered, middle strided
+        arr = np.arange(64).reshape((4, 8, 2), order='F')[:, ::2, :]
+        expected = foo.py_func(arr)
+        got = foo(arr)
+        self.assertPreciseEqual(expected, got)
+
+        # 'A' ordered, inner strided
+        arr = np.arange(64).reshape((4, 8, 2), order='F')[:, :, ::2]
+        expected = foo.py_func(arr)
+        got = foo(arr)
+        self.assertPreciseEqual(expected, got)
+
+        @jit(nopython=True)
+        def flag_check(arr):
+            out = []
+            for sub in arr:
+                out.append((sub, sub.flags.c_contiguous,
+                            sub.flags.f_contiguous))
+            return out
+
+        arr = np.arange(10).reshape((2, 5), order='F')
+        expected = flag_check.py_func(arr)
+        got = flag_check(arr)
+
+        self.assertEqual(len(expected), len(got))
+        ex_arr, e_flag_c, e_flag_f = expected[0]
+        go_arr, g_flag_c, g_flag_f = got[0]
+        np.testing.assert_allclose(ex_arr, go_arr)
+        self.assertEqual(e_flag_c, g_flag_c)
+        self.assertEqual(e_flag_f, g_flag_f)
+
+    def test_array_view_iter(self):
+        # Test iterating over a 1d view over a 2d array
+        arr = np.arange(12).reshape((3, 4))
+        self.check_array_view_iter(arr, 1)
+        self.check_array_view_iter(arr.T, 1)
+        arr = arr[::2]
+        self.check_array_view_iter(arr, 1)
+        arr = np.bool_([1, 0, 0, 1]).reshape((2, 2))
+        self.check_array_view_iter(arr, 1)
+
+    def test_array_flat_3d(self):
+        arr = np.arange(24).reshape(4, 2, 3)
+
+        arrty = typeof(arr)
+        self.assertEqual(arrty.ndim, 3)
+        self.assertEqual(arrty.layout, 'C')
+        self.assertTrue(arr.flags.c_contiguous)
+        # Test with C-contiguous array
+        self.check_array_flat(arr)
+        # Test with Fortran-contiguous array
+        arr = arr.transpose()
+        self.assertFalse(arr.flags.c_contiguous)
+        self.assertTrue(arr.flags.f_contiguous)
+        self.assertEqual(typeof(arr).layout, 'F')
+        self.check_array_flat(arr)
+        # Test with non-contiguous array
+        arr = arr[::2]
+        self.assertFalse(arr.flags.c_contiguous)
+        self.assertFalse(arr.flags.f_contiguous)
+        self.assertEqual(typeof(arr).layout, 'A')
+        self.check_array_flat(arr)
+        # Boolean array
+        arr = np.bool_([1, 0, 0, 1] * 2).reshape((2, 2, 2))
+        self.check_array_flat(arr)
+
+    def test_array_flat_empty(self):
+        # Test .flat with various shapes of empty arrays, contiguous
+        # and non-contiguous (see issue #846).
+        arr = np.zeros(0, dtype=np.int32)
+        arr = arr.reshape(0, 2)
+        arrty = types.Array(types.int32, 2, layout='C')
+        self.check_array_flat_sum(arr, arrty)
+        arrty = types.Array(types.int32, 2, layout='F')
+        self.check_array_flat_sum(arr, arrty)
+        arrty = types.Array(types.int32, 2, layout='A')
+        self.check_array_flat_sum(arr, arrty)
+        arr = arr.reshape(2, 0)
+        arrty = types.Array(types.int32, 2, layout='C')
+        self.check_array_flat_sum(arr, arrty)
+        arrty = types.Array(types.int32, 2, layout='F')
+        self.check_array_flat_sum(arr, arrty)
+        arrty = types.Array(types.int32, 2, layout='A')
+        self.check_array_flat_sum(arr, arrty)
+
+    def test_array_flat_getitem(self):
+        # Test indexing of array.flat object
+        pyfunc = array_flat_getitem
+        def check(arr, ind):
+            cr = self.ccache.compile(pyfunc, (typeof(arr), typeof(ind)))
+            expected = pyfunc(arr, ind)
+            self.assertEqual(cr.entry_point(arr, ind), expected)
+
+        arr = np.arange(24).reshape(4, 2, 3)
+        for i in range(arr.size):
+            check(arr, i)
+        arr = arr.T
+        for i in range(arr.size):
+            check(arr, i)
+        arr = arr[::2]
+        for i in range(arr.size):
+            check(arr, i)
+        arr = np.array([42]).reshape(())
+        for i in range(arr.size):
+            check(arr, i)
+        # Boolean array
+        arr = np.bool_([1, 0, 0, 1])
+        for i in range(arr.size):
+            check(arr, i)
+        arr = arr[::2]
+        for i in range(arr.size):
+            check(arr, i)
+
+    def test_array_flat_setitem(self):
+        # Test indexing of array.flat object
+        pyfunc = array_flat_setitem
+        def check(arr, ind):
+            arrty = typeof(arr)
+            cr = self.ccache.compile(pyfunc, (arrty, typeof(ind), arrty.dtype))
+            # Use np.copy() to keep the layout
+            expected = np.copy(arr)
+            got = np.copy(arr)
+            pyfunc(expected, ind, 123)
+            cr.entry_point(got, ind, 123)
+            self.assertPreciseEqual(got, expected)
+
+        arr = np.arange(24).reshape(4, 2, 3)
+        for i in range(arr.size):
+            check(arr, i)
+        arr = arr.T
+        for i in range(arr.size):
+            check(arr, i)
+        arr = arr[::2]
+        for i in range(arr.size):
+            check(arr, i)
+        arr = np.array([42]).reshape(())
+        for i in range(arr.size):
+            check(arr, i)
+        # Boolean array
+        arr = np.bool_([1, 0, 0, 1])
+        for i in range(arr.size):
+            check(arr, i)
+        arr = arr[::2]
+        for i in range(arr.size):
+            check(arr, i)
+
+    def test_array_flat_len(self):
+        # Test len(array.flat)
+        pyfunc = array_flat_len
+        def check(arr):
+            cr = self.ccache.compile(pyfunc, (typeof(arr),))
+            expected = pyfunc(arr)
+            self.assertPreciseEqual(cr.entry_point(arr), expected)
+
+        arr = np.arange(24).reshape(4, 2, 3)
+        check(arr)
+        arr = arr.T
+        check(arr)
+        arr = arr[::2]
+        check(arr)
+        arr = np.array([42]).reshape(())
+        check(arr)
+
+    def test_array_flat_premature_free(self):
+        cres = compile_isolated(array_flat_premature_free, [types.intp])
+        cfunc = cres.entry_point
+        expect = array_flat_premature_free(6)
+        got = cfunc(6)
+        self.assertTrue(got.sum())
+        self.assertPreciseEqual(expect, got)
+
+    def test_array_ndenumerate_2d(self):
+        arr = np.arange(12).reshape(4, 3)
+        arrty = typeof(arr)
+        self.assertEqual(arrty.ndim, 2)
+        self.assertEqual(arrty.layout, 'C')
+        self.assertTrue(arr.flags.c_contiguous)
+        # Test with C-contiguous array
+        self.check_array_ndenumerate_sum(arr, arrty)
+        # Test with Fortran-contiguous array
+        arr = arr.transpose()
+        self.assertFalse(arr.flags.c_contiguous)
+        self.assertTrue(arr.flags.f_contiguous)
+        arrty = typeof(arr)
+        self.assertEqual(arrty.layout, 'F')
+        self.check_array_ndenumerate_sum(arr, arrty)
+        # Test with non-contiguous array
+        arr = arr[::2]
+        self.assertFalse(arr.flags.c_contiguous)
+        self.assertFalse(arr.flags.f_contiguous)
+        arrty = typeof(arr)
+        self.assertEqual(arrty.layout, 'A')
+        self.check_array_ndenumerate_sum(arr, arrty)
+        # Boolean array
+        arr = np.bool_([1, 0, 0, 1]).reshape((2, 2))
+        self.check_array_ndenumerate_sum(arr, typeof(arr))
+
+    def test_array_ndenumerate_empty(self):
+        arr = np.zeros(0, dtype=np.int32)
+        arr = arr.reshape(0, 2)
+        arrty = types.Array(types.int32, 2, layout='C')
+        self.check_array_ndenumerate_sum(arr, arrty)
+        arrty = types.Array(types.int32, 2, layout='F')
+        self.check_array_ndenumerate_sum(arr, arrty)
+        arrty = types.Array(types.int32, 2, layout='A')
+        self.check_array_ndenumerate_sum(arr, arrty)
+        arr = arr.reshape(2, 0)
+        arrty = types.Array(types.int32, 2, layout='C')
+        self.check_array_flat_sum(arr, arrty)
+        arrty = types.Array(types.int32, 2, layout='F')
+        self.check_array_flat_sum(arr, arrty)
+        arrty = types.Array(types.int32, 2, layout='A')
+        self.check_array_flat_sum(arr, arrty)
+
+    def test_array_ndenumerate_premature_free(self):
+        cres = compile_isolated(array_ndenumerate_premature_free, [types.intp])
+        cfunc = cres.entry_point
+        expect = array_ndenumerate_premature_free(6)
+        got = cfunc(6)
+        self.assertTrue(got.sum())
+        self.assertPreciseEqual(expect, got)
+
+    def test_np_ndindex(self):
+        func = np_ndindex
+        cres = compile_isolated(func, [types.int32, types.int32])
+        cfunc = cres.entry_point
+        self.assertPreciseEqual(cfunc(3, 4), func(3, 4))
+        self.assertPreciseEqual(cfunc(3, 0), func(3, 0))
+        self.assertPreciseEqual(cfunc(0, 3), func(0, 3))
+        self.assertPreciseEqual(cfunc(0, 0), func(0, 0))
+
+    def test_np_ndindex_array(self):
+        func = np_ndindex_array
+        arr = np.arange(12, dtype=np.int32) + 10
+        self.check_array_unary(arr, typeof(arr), func)
+        arr = arr.reshape((4, 3))
+        self.check_array_unary(arr, typeof(arr), func)
+        arr = arr.reshape((2, 2, 3))
+        self.check_array_unary(arr, typeof(arr), func)
+
+    def test_np_ndindex_empty(self):
+        func = np_ndindex_empty
+        cres = compile_isolated(func, [])
+        cfunc = cres.entry_point
+        self.assertPreciseEqual(cfunc(), func())
+
+    def test_iter_next(self):
+        # This also checks memory management with iter() and next()
+        func = iter_next
+        arr = np.arange(12, dtype=np.int32) + 10
+        self.check_array_unary(arr, typeof(arr), func)
+
+
+class TestNdIter(MemoryLeakMixin, TestCase):
+    """
+    Test np.nditer()
+    """
+
+    def inputs(self):
+        # All those inputs are compatible with a (3, 4) main shape
+
+        # scalars
+        yield np.float32(100)
+
+        # 0-d arrays
+        yield np.array(102, dtype=np.int16)
+
+        # 1-d arrays
+        yield np.arange(4).astype(np.complex64)
+        yield np.arange(8)[::2]
+
+        # 2-d arrays
+        a = np.arange(12).reshape((3, 4))
+        yield a
+        yield a.copy(order='F')
+        a = np.arange(24).reshape((6, 4))[::2]
+        yield a
+
+    def basic_inputs(self):
+        yield np.arange(4).astype(np.complex64)
+        yield np.arange(8)[::2]
+        a = np.arange(12).reshape((3, 4))
+        yield a
+        yield a.copy(order='F')
+
+    def check_result(self, got, expected):
+        self.assertEqual(set(got), set(expected), (got, expected))
+
+    def test_nditer1(self):
+        pyfunc = np_nditer1
+        cfunc = jit(nopython=True)(pyfunc)
+        for a in self.inputs():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.check_result(got, expected)
+
+    def test_nditer2(self):
+        pyfunc = np_nditer2
+        cfunc = jit(nopython=True)(pyfunc)
+        for a, b in itertools.product(self.inputs(), self.inputs()):
+            expected = pyfunc(a, b)
+            got = cfunc(a, b)
+            self.check_result(got, expected)
+
+    def test_nditer3(self):
+        pyfunc = np_nditer3
+        cfunc = jit(nopython=True)(pyfunc)
+        # Use a restricted set of inputs, to shorten test time
+        inputs = self.basic_inputs
+        for a, b, c in itertools.product(inputs(), inputs(), inputs()):
+            expected = pyfunc(a, b, c)
+            got = cfunc(a, b, c)
+            self.check_result(got, expected)
+
+    def test_errors(self):
+        # Incompatible shapes
+        pyfunc = np_nditer2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.disable_leak_check()
+
+        def check_incompatible(a, b):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(a, b)
+            self.assertIn("operands could not be broadcast together",
+                          str(raises.exception))
+
+        check_incompatible(np.arange(2), np.arange(3))
+        a = np.arange(12).reshape((3, 4))
+        b = np.arange(3)
+        check_incompatible(a, b)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_manipulation.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_manipulation.py
new file mode 100644
index 000000000..d3e578936
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_manipulation.py
@@ -0,0 +1,1256 @@
+from functools import partial
+from itertools import permutations
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit, njit, from_dtype, typeof
+from numba.np.numpy_support import numpy_version
+from numba.core.errors import TypingError
+from numba.core import types, errors
+from numba.tests.support import (TestCase, MemoryLeakMixin, CompilationCache,
+                                 tag)
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+no_pyobj_flags = Flags()
+no_pyobj_flags.nrt = True
+
+
+def from_generic(pyfuncs_to_use):
+    """Decorator for generic check functions.
+        Iterates over 'pyfuncs_to_use', calling 'func' with the iterated
+        item as first argument. Example:
+
+        @from_generic(numpy_array_reshape, array_reshape)
+        def check_only_shape(pyfunc, arr, shape, expected_shape):
+            # Only check Numba result to avoid Numpy bugs
+            self.memory_leak_setup()
+            got = generic_run(pyfunc, arr, shape)
+            self.assertEqual(got.shape, expected_shape)
+            self.assertEqual(got.size, arr.size)
+            del got
+            self.memory_leak_teardown()
+    """
+    def decorator(func):
+        def result(*args, **kwargs):
+            return [func(pyfunc, *args, **kwargs) for pyfunc in pyfuncs_to_use]
+        return result
+    return decorator
+
+
+def array_reshape(arr, newshape):
+    return arr.reshape(newshape)
+
+
+def numpy_array_reshape(arr, newshape):
+    return np.reshape(arr, newshape)
+
+
+def numpy_broadcast_to(arr, shape):
+    return np.broadcast_to(arr, shape)
+
+
+def numpy_broadcast_shapes(*args):
+    return np.broadcast_shapes(*args)
+
+
+def numpy_broadcast_arrays(*args):
+    return np.broadcast_arrays(*args)
+
+
+def numpy_broadcast_to_indexing(arr, shape, idx):
+    return np.broadcast_to(arr, shape)[idx]
+
+
+def flatten_array(a):
+    return a.flatten()
+
+
+def ravel_array(a):
+    return a.ravel()
+
+
+def ravel_array_size(a):
+    return a.ravel().size
+
+
+def numpy_ravel_array(a):
+    return np.ravel(a)
+
+
+def transpose_array(a):
+    return a.transpose()
+
+
+def numpy_transpose_array(a):
+    return np.transpose(a)
+
+def numpy_transpose_array_axes_kwarg(arr, axes):
+    return np.transpose(arr, axes=axes)
+
+
+def numpy_transpose_array_axes_kwarg_copy(arr, axes):
+    return np.transpose(arr, axes=axes).copy()
+
+
+def array_transpose_axes(arr, axes):
+    return arr.transpose(axes)
+
+
+def array_transpose_axes_copy(arr, axes):
+    return arr.transpose(axes).copy()
+
+
+def transpose_issue_4708(m, n):
+    r1 = np.reshape(np.arange(m * n * 3), (m, 3, n))
+    r2 = np.reshape(np.arange(n * 3), (n, 3))
+    r_dif = (r1 - r2.T).T
+    r_dif = np.transpose(r_dif, (2, 0, 1))
+    z = r_dif + 1
+    return z
+
+
+def squeeze_array(a):
+    return a.squeeze()
+
+
+def expand_dims(a, axis):
+    return np.expand_dims(a, axis)
+
+
+def atleast_1d(*args):
+    return np.atleast_1d(*args)
+
+
+def atleast_2d(*args):
+    return np.atleast_2d(*args)
+
+
+def atleast_3d(*args):
+    return np.atleast_3d(*args)
+
+
+def as_strided1(a):
+    # as_strided() with implicit shape
+    strides = (a.strides[0] // 2,) + a.strides[1:]
+    return np.lib.stride_tricks.as_strided(a, strides=strides)
+
+
+def as_strided2(a):
+    # Rolling window example as in https://github.com/numba/numba/issues/1884
+    window = 3
+    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
+    strides = a.strides + (a.strides[-1],)
+    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
+
+
+def add_axis2(a):
+    return a[np.newaxis, :]
+
+
+def bad_index(arr, arr2d):
+    x = arr.x,
+    y = arr.y
+    # note that `x` is a tuple, which causes a new axis to be created.
+    arr2d[x, y] = 1.0
+
+
+def bad_float_index(arr):
+    # 2D index required for this function because 1D index
+    # fails typing
+    return arr[1, 2.0]
+
+
+def numpy_fill_diagonal(arr, val, wrap=False):
+    return np.fill_diagonal(arr, val, wrap)
+
+
+def numpy_shape(arr):
+    return np.shape(arr)
+
+
+def numpy_flatnonzero(a):
+    return np.flatnonzero(a)
+
+
+def numpy_argwhere(a):
+    return np.argwhere(a)
+
+
+class TestArrayManipulation(MemoryLeakMixin, TestCase):
+    """
+    Check shape-changing operations on arrays.
+    """
+
+    def setUp(self):
+        super(TestArrayManipulation, self).setUp()
+        self.ccache = CompilationCache()
+
+    def test_array_reshape(self):
+        pyfuncs_to_use = [array_reshape, numpy_array_reshape]
+
+        def generic_run(pyfunc, arr, shape):
+            cres = compile_isolated(pyfunc, (typeof(arr), typeof(shape)))
+            return cres.entry_point(arr, shape)
+
+        @from_generic(pyfuncs_to_use)
+        def check(pyfunc, arr, shape):
+            expected = pyfunc(arr, shape)
+            self.memory_leak_setup()
+            got = generic_run(pyfunc, arr, shape)
+            self.assertPreciseEqual(got, expected)
+            del got
+            self.memory_leak_teardown()
+
+        @from_generic(pyfuncs_to_use)
+        def check_only_shape(pyfunc, arr, shape, expected_shape):
+            # Only check Numba result to avoid Numpy bugs
+            self.memory_leak_setup()
+            got = generic_run(pyfunc, arr, shape)
+            self.assertEqual(got.shape, expected_shape)
+            self.assertEqual(got.size, arr.size)
+            del got
+            self.memory_leak_teardown()
+
+        @from_generic(pyfuncs_to_use)
+        def check_err_shape(pyfunc, arr, shape):
+            with self.assertRaises(NotImplementedError) as raises:
+                generic_run(pyfunc, arr, shape)
+            self.assertEqual(str(raises.exception),
+                             "incompatible shape for array")
+
+        @from_generic(pyfuncs_to_use)
+        def check_err_size(pyfunc, arr, shape):
+            with self.assertRaises(ValueError) as raises:
+                generic_run(pyfunc, arr, shape)
+            self.assertEqual(str(raises.exception),
+                             "total size of new array must be unchanged")
+
+        @from_generic(pyfuncs_to_use)
+        def check_err_multiple_negative(pyfunc, arr, shape):
+            with self.assertRaises(ValueError) as raises:
+                generic_run(pyfunc, arr, shape)
+            self.assertEqual(str(raises.exception),
+                             "multiple negative shape values")
+
+
+        # C-contiguous
+        arr = np.arange(24)
+        check(arr, (24,))
+        check(arr, (4, 6))
+        check(arr, (8, 3))
+        check(arr, (8, 1, 3))
+        check(arr, (1, 8, 1, 1, 3, 1))
+        arr = np.arange(24).reshape((2, 3, 4))
+        check(arr, (24,))
+        check(arr, (4, 6))
+        check(arr, (8, 3))
+        check(arr, (8, 1, 3))
+        check(arr, (1, 8, 1, 1, 3, 1))
+        check_err_size(arr, ())
+        check_err_size(arr, (25,))
+        check_err_size(arr, (8, 4))
+        arr = np.arange(24).reshape((1, 8, 1, 1, 3, 1))
+        check(arr, (24,))
+        check(arr, (4, 6))
+        check(arr, (8, 3))
+        check(arr, (8, 1, 3))
+
+        # F-contiguous
+        arr = np.arange(24).reshape((2, 3, 4)).T
+        check(arr, (4, 3, 2))
+        check(arr, (1, 4, 1, 3, 1, 2, 1))
+        check_err_shape(arr, (2, 3, 4))
+        check_err_shape(arr, (6, 4))
+        check_err_shape(arr, (2, 12))
+
+        # Test negative shape value
+        arr = np.arange(25).reshape(5,5)
+        check(arr, -1)
+        check(arr, (-1,))
+        check(arr, (-1, 5))
+        check(arr, (5, -1, 5))
+        check(arr, (5, 5, -1))
+        check_err_size(arr, (-1, 4))
+        check_err_multiple_negative(arr, (-1, -2, 5, 5))
+        check_err_multiple_negative(arr, (5, 5, -1, -1))
+
+        # 0-sized arrays
+        def check_empty(arr):
+            check(arr, 0)
+            check(arr, (0,))
+            check(arr, (1, 0, 2))
+            check(arr, (0, 55, 1, 0, 2))
+            # -1 is buggy in Numpy with 0-sized arrays
+            check_only_shape(arr, -1, (0,))
+            check_only_shape(arr, (-1,), (0,))
+            check_only_shape(arr, (0, -1), (0, 0))
+            check_only_shape(arr, (4, -1), (4, 0))
+            check_only_shape(arr, (-1, 0, 4), (0, 0, 4))
+            check_err_size(arr, ())
+            check_err_size(arr, 1)
+            check_err_size(arr, (1, 2))
+
+        arr = np.array([])
+        check_empty(arr)
+        check_empty(arr.reshape((3, 2, 0)))
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+    def test_array_transpose_axes(self):
+        pyfuncs_to_use = [numpy_transpose_array_axes_kwarg,
+                          numpy_transpose_array_axes_kwarg_copy,
+                          array_transpose_axes,
+                          array_transpose_axes_copy]
+
+        def run(pyfunc, arr, axes):
+            cres = self.ccache.compile(pyfunc, (typeof(arr), typeof(axes)))
+            return cres.entry_point(arr, axes)
+
+        @from_generic(pyfuncs_to_use)
+        def check(pyfunc, arr, axes):
+            expected = pyfunc(arr, axes)
+            got = run(pyfunc, arr, axes)
+            self.assertPreciseEqual(got, expected)
+            self.assertEqual(got.flags.f_contiguous,
+                             expected.flags.f_contiguous)
+            self.assertEqual(got.flags.c_contiguous,
+                             expected.flags.c_contiguous)
+
+        @from_generic(pyfuncs_to_use)
+        def check_err_axis_repeated(pyfunc, arr, axes):
+            with self.assertRaises(ValueError) as raises:
+                run(pyfunc, arr, axes)
+            self.assertEqual(str(raises.exception),
+                             "repeated axis in transpose")
+
+        @from_generic(pyfuncs_to_use)
+        def check_err_axis_oob(pyfunc, arr, axes):
+            with self.assertRaises(ValueError) as raises:
+                run(pyfunc, arr, axes)
+            self.assertEqual(str(raises.exception),
+                             "axis is out of bounds for array of given dimension")
+
+        @from_generic(pyfuncs_to_use)
+        def check_err_invalid_args(pyfunc, arr, axes):
+            with self.assertRaises((TypeError, TypingError)):
+                run(pyfunc, arr, axes)
+
+        arrs = [np.arange(24),
+                np.arange(24).reshape(4, 6),
+                np.arange(24).reshape(2, 3, 4),
+                np.arange(24).reshape(1, 2, 3, 4),
+                np.arange(64).reshape(8, 4, 2)[::3,::2,:]]
+
+        for i in range(len(arrs)):
+            # First check `None`, the default, which is to reverse dims
+            check(arrs[i], None)
+            # Check supplied axis permutations
+            for axes in permutations(tuple(range(arrs[i].ndim))):
+                ndim = len(axes)
+                neg_axes = tuple([x - ndim for x in axes])
+                check(arrs[i], axes)
+                check(arrs[i], neg_axes)
+
+        @from_generic([transpose_issue_4708])
+        def check_issue_4708(pyfunc, m, n):
+            expected = pyfunc(m, n)
+            got = njit(pyfunc)(m, n)
+            # values in arrays are equals,
+            # but stronger assertions not hold (layout and strides equality)
+            np.testing.assert_equal(got, expected)
+
+        check_issue_4708(3, 2)
+        check_issue_4708(2, 3)
+        check_issue_4708(5, 4)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        check_err_invalid_args(arrs[1], "foo")
+        check_err_invalid_args(arrs[1], ("foo",))
+        check_err_invalid_args(arrs[1], 5.3)
+        check_err_invalid_args(arrs[2], (1.2, 5))
+
+        check_err_axis_repeated(arrs[1], (0, 0))
+        check_err_axis_repeated(arrs[2], (2, 0, 0))
+        check_err_axis_repeated(arrs[3], (3, 2, 1, 1))
+
+        check_err_axis_oob(arrs[0], (1,))
+        check_err_axis_oob(arrs[0], (-2,))
+        check_err_axis_oob(arrs[1], (0, 2))
+        check_err_axis_oob(arrs[1], (-3, 2))
+        check_err_axis_oob(arrs[1], (0, -3))
+        check_err_axis_oob(arrs[2], (3, 1, 2))
+        check_err_axis_oob(arrs[2], (-4, 1, 2))
+        check_err_axis_oob(arrs[3], (3, 1, 2, 5))
+        check_err_axis_oob(arrs[3], (3, 1, 2, -5))
+
+        with self.assertRaises(TypingError) as e:
+            jit(nopython=True)(numpy_transpose_array)((np.array([0, 1]),))
+        self.assertIn("np.transpose does not accept tuples",
+                        str(e.exception))
+
+    def test_expand_dims(self):
+        pyfunc = expand_dims
+
+        def run(arr, axis):
+            cres = self.ccache.compile(pyfunc, (typeof(arr), typeof(axis)))
+            return cres.entry_point(arr, axis)
+
+        def check(arr, axis):
+            expected = pyfunc(arr, axis)
+            self.memory_leak_setup()
+            got = run(arr, axis)
+            self.assertPreciseEqual(got, expected)
+            del got
+            self.memory_leak_teardown()
+
+        def check_all_axes(arr):
+            for axis in range(-arr.ndim - 1, arr.ndim + 1):
+                check(arr, axis)
+
+        # 1d
+        arr = np.arange(5)
+        check_all_axes(arr)
+        # 3d (C, F, A)
+        arr = np.arange(24).reshape((2, 3, 4))
+        check_all_axes(arr)
+        check_all_axes(arr.T)
+        check_all_axes(arr[::-1])
+        # 0d
+        arr = np.array(42)
+        check_all_axes(arr)
+
+    def check_atleast_nd(self, pyfunc, cfunc):
+        def check_result(got, expected):
+            # We would like to check the result has the same contiguity,
+            # but we can't rely on the "flags" attribute when there are
+            # 1-sized dimensions.
+            self.assertStridesEqual(got, expected)
+            self.assertPreciseEqual(got.flatten(), expected.flatten())
+
+        def check_single(arg):
+            check_result(cfunc(arg), pyfunc(arg))
+
+        def check_tuple(*args):
+            expected_tuple = pyfunc(*args)
+            got_tuple = cfunc(*args)
+            self.assertEqual(len(got_tuple), len(expected_tuple))
+            for got, expected in zip(got_tuple, expected_tuple):
+                check_result(got, expected)
+
+        # 0d
+        a1 = np.array(42)
+        a2 = np.array(5j)
+        check_single(a1)
+        check_tuple(a1, a2)
+        # 1d
+        b1 = np.arange(5)
+        b2 = np.arange(6) + 1j
+        b3 = b1[::-1]
+        check_single(b1)
+        check_tuple(b1, b2, b3)
+        # 2d
+        c1 = np.arange(6).reshape((2, 3))
+        c2 = c1.T
+        c3 = c1[::-1]
+        check_single(c1)
+        check_tuple(c1, c2, c3)
+        # 3d
+        d1 = np.arange(24).reshape((2, 3, 4))
+        d2 = d1.T
+        d3 = d1[::-1]
+        check_single(d1)
+        check_tuple(d1, d2, d3)
+        # 4d
+        e = np.arange(16).reshape((2, 2, 2, 2))
+        check_single(e)
+        # mixed dimensions
+        check_tuple(a1, b2, c3, d2)
+
+    def test_atleast_1d(self):
+        pyfunc = atleast_1d
+        cfunc = jit(nopython=True)(pyfunc)
+        self.check_atleast_nd(pyfunc, cfunc)
+
+    def test_atleast_2d(self):
+        pyfunc = atleast_2d
+        cfunc = jit(nopython=True)(pyfunc)
+        self.check_atleast_nd(pyfunc, cfunc)
+
+    def test_atleast_3d(self):
+        pyfunc = atleast_3d
+        cfunc = jit(nopython=True)(pyfunc)
+        self.check_atleast_nd(pyfunc, cfunc)
+
+    def check_as_strided(self, pyfunc):
+        def run(arr):
+            cres = self.ccache.compile(pyfunc, (typeof(arr),))
+            return cres.entry_point(arr)
+        def check(arr):
+            expected = pyfunc(arr)
+            got = run(arr)
+            self.assertPreciseEqual(got, expected)
+
+        arr = np.arange(24)
+        check(arr)
+        check(arr.reshape((6, 4)))
+        check(arr.reshape((4, 1, 6)))
+
+    def test_as_strided(self):
+        self.check_as_strided(as_strided1)
+        self.check_as_strided(as_strided2)
+
+    def test_flatten_array(self, flags=enable_pyobj_flags, layout='C'):
+        a = np.arange(9).reshape(3, 3)
+        if layout == 'F':
+            a = a.T
+
+        pyfunc = flatten_array
+        arraytype1 = typeof(a)
+        if layout == 'A':
+            # Force A layout
+            arraytype1 = arraytype1.copy(layout='A')
+
+        self.assertEqual(arraytype1.layout, layout)
+        cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
+        cfunc = cr.entry_point
+
+        expected = pyfunc(a)
+        got = cfunc(a)
+        np.testing.assert_equal(expected, got)
+
+    def test_flatten_array_npm(self):
+        self.test_flatten_array(flags=no_pyobj_flags)
+        self.test_flatten_array(flags=no_pyobj_flags, layout='F')
+        self.test_flatten_array(flags=no_pyobj_flags, layout='A')
+
+    def test_ravel_array(self, flags=enable_pyobj_flags):
+        def generic_check(pyfunc, a, assume_layout):
+            # compile
+            arraytype1 = typeof(a)
+            self.assertEqual(arraytype1.layout, assume_layout)
+            cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
+            cfunc = cr.entry_point
+
+            expected = pyfunc(a)
+            got = cfunc(a)
+            # Check result matches
+            np.testing.assert_equal(expected, got)
+            # Check copying behavior
+            py_copied = (a.ctypes.data != expected.ctypes.data)
+            nb_copied = (a.ctypes.data != got.ctypes.data)
+            self.assertEqual(py_copied, assume_layout != 'C')
+            self.assertEqual(py_copied, nb_copied)
+
+        check_method = partial(generic_check, ravel_array)
+        check_function = partial(generic_check, numpy_ravel_array)
+
+        def check(*args, **kwargs):
+            check_method(*args, **kwargs)
+            check_function(*args, **kwargs)
+
+        # Check 2D
+        check(np.arange(9).reshape(3, 3), assume_layout='C')
+        check(np.arange(9).reshape(3, 3, order='F'), assume_layout='F')
+        check(np.arange(18).reshape(3, 3, 2)[:, :, 0], assume_layout='A')
+
+        # Check 3D
+        check(np.arange(18).reshape(2, 3, 3), assume_layout='C')
+        check(np.arange(18).reshape(2, 3, 3, order='F'), assume_layout='F')
+        check(np.arange(36).reshape(2, 3, 3, 2)[:, :, :, 0], assume_layout='A')
+
+    def test_ravel_array_size(self, flags=enable_pyobj_flags):
+        a = np.arange(9).reshape(3, 3)
+
+        pyfunc = ravel_array_size
+        arraytype1 = typeof(a)
+        cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
+        cfunc = cr.entry_point
+
+        expected = pyfunc(a)
+        got = cfunc(a)
+        np.testing.assert_equal(expected, got)
+
+    def test_ravel_array_npm(self):
+        self.test_ravel_array(flags=no_pyobj_flags)
+
+    def test_ravel_array_size_npm(self):
+        self.test_ravel_array_size(flags=no_pyobj_flags)
+
+    def test_transpose_array(self, flags=enable_pyobj_flags):
+        @from_generic([transpose_array, numpy_transpose_array])
+        def check(pyfunc):
+            a = np.arange(9).reshape(3, 3)
+
+            arraytype1 = typeof(a)
+            cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
+            cfunc = cr.entry_point
+
+            expected = pyfunc(a)
+            got = cfunc(a)
+            np.testing.assert_equal(expected, got)
+
+        check()
+
+    def test_transpose_array_npm(self):
+        self.test_transpose_array(flags=no_pyobj_flags)
+
+    def test_squeeze_array(self, flags=enable_pyobj_flags):
+        a = np.arange(2 * 1 * 3 * 1 * 4).reshape(2, 1, 3, 1, 4)
+
+        pyfunc = squeeze_array
+        arraytype1 = typeof(a)
+        cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
+        cfunc = cr.entry_point
+
+        expected = pyfunc(a)
+        got = cfunc(a)
+        np.testing.assert_equal(expected, got)
+
+    def test_squeeze_array_npm(self):
+        with self.assertRaises(errors.TypingError) as raises:
+            self.test_squeeze_array(flags=no_pyobj_flags)
+
+        self.assertIn("squeeze", str(raises.exception))
+
+    def test_add_axis2(self, flags=enable_pyobj_flags):
+        a = np.arange(9).reshape(3, 3)
+
+        pyfunc = add_axis2
+        arraytype1 = typeof(a)
+        cr = compile_isolated(pyfunc, (arraytype1,), flags=flags)
+        cfunc = cr.entry_point
+
+        expected = pyfunc(a)
+        got = cfunc(a)
+        np.testing.assert_equal(expected, got)
+
+    def test_add_axis2_npm(self):
+        with self.assertTypingError() as raises:
+            self.test_add_axis2(flags=no_pyobj_flags)
+        self.assertIn("unsupported array index type none in",
+                      str(raises.exception))
+
+    def test_bad_index_npm(self):
+        with self.assertTypingError() as raises:
+            arraytype1 = from_dtype(np.dtype([('x', np.int32),
+                                              ('y', np.int32)]))
+            arraytype2 = types.Array(types.int32, 2, 'C')
+            compile_isolated(bad_index, (arraytype1, arraytype2),
+                             flags=no_pyobj_flags)
+        self.assertIn('unsupported array index type', str(raises.exception))
+
+    def test_bad_float_index_npm(self):
+        with self.assertTypingError() as raises:
+            compile_isolated(bad_float_index,
+                             (types.Array(types.float64, 2, 'C'),))
+        self.assertIn('unsupported array index type float64',
+                      str(raises.exception))
+
+    def test_fill_diagonal_basic(self):
+        pyfunc = numpy_fill_diagonal
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def _shape_variations(n):
+            # square
+            yield (n, n)
+            # tall and thin
+            yield (2 * n, n)
+            # short and fat
+            yield (n, 2 * n)
+            # a bit taller than wide; odd numbers of rows and cols
+            yield ((2 * n + 1), (2 * n - 1))
+            # 4d, all dimensions same
+            yield (n, n, n, n)
+            # weird edge case
+            yield (1, 1, 1)
+
+        def _val_variations():
+            yield 1
+            yield 3.142
+            yield np.nan
+            yield -np.inf
+            yield True
+            yield np.arange(4)
+            yield (4,)
+            yield [8, 9]
+            yield np.arange(54).reshape(9, 3, 2, 1)  # contiguous C
+            yield np.asfortranarray(np.arange(9).reshape(3, 3))  # contiguous F
+            yield np.arange(9).reshape(3, 3)[::-1]  # non-contiguous
+
+        # contiguous arrays
+        def _multi_dimensional_array_variations(n):
+            for shape in _shape_variations(n):
+                yield np.zeros(shape, dtype=np.float64)
+                yield np.asfortranarray(np.ones(shape, dtype=np.float64))
+
+        # non-contiguous arrays
+        def _multi_dimensional_array_variations_strided(n):
+            for shape in _shape_variations(n):
+                tmp = np.zeros(tuple([x * 2 for x in shape]), dtype=np.float64)
+                slicer = tuple(slice(0, x * 2, 2) for x in shape)
+                yield tmp[slicer]
+
+        def _check_fill_diagonal(arr, val):
+            for wrap in None, True, False:
+                a = arr.copy()
+                b = arr.copy()
+
+                if wrap is None:
+                    params = {}
+                else:
+                    params = {'wrap': wrap}
+
+                pyfunc(a, val, **params)
+                cfunc(b, val, **params)
+                self.assertPreciseEqual(a, b)
+
+        for arr in _multi_dimensional_array_variations(3):
+            for val in _val_variations():
+                _check_fill_diagonal(arr, val)
+
+        for arr in _multi_dimensional_array_variations_strided(3):
+            for val in _val_variations():
+                _check_fill_diagonal(arr, val)
+
+        # non-numeric input arrays
+        arr = np.array([True] * 9).reshape(3, 3)
+        _check_fill_diagonal(arr, False)
+        _check_fill_diagonal(arr, [False, True, False])
+        _check_fill_diagonal(arr, np.array([True, False, True]))
+
+    def test_fill_diagonal_exception_cases(self):
+        pyfunc = numpy_fill_diagonal
+        cfunc = jit(nopython=True)(pyfunc)
+        val = 1
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # first argument unsupported number of dimensions
+        for a in np.array([]), np.ones(5):
+            with self.assertRaises(TypingError) as raises:
+                cfunc(a, val)
+            assert "The first argument must be at least 2-D" in str(raises.exception)
+
+        # multi-dimensional input where dimensions are not all equal
+        with self.assertRaises(ValueError) as raises:
+            a = np.zeros((3, 3, 4))
+            cfunc(a, val)
+            self.assertEqual("All dimensions of input must be of equal length", str(raises.exception))
+
+        # cases where val has incompatible type / value
+        def _assert_raises(arr, val):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(arr, val)
+            self.assertEqual("Unable to safely conform val to a.dtype", str(raises.exception))
+
+        arr = np.zeros((3, 3), dtype=np.int32)
+        val = np.nan
+        _assert_raises(arr, val)
+
+        val = [3.3, np.inf]
+        _assert_raises(arr, val)
+
+        val = np.array([1, 2, 1e10], dtype=np.int64)
+        _assert_raises(arr, val)
+
+        arr = np.zeros((3, 3), dtype=np.float32)
+        val = [1.4, 2.6, -1e100]
+        _assert_raises(arr, val)
+
+        val = 1.1e100
+        _assert_raises(arr, val)
+
+        val = np.array([-1e100])
+        _assert_raises(arr, val)
+
+    def test_broadcast_to(self):
+        pyfunc = numpy_broadcast_to
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Tests taken from
+        # https://github.com/numpy/numpy/blob/75f852edf94a7293e7982ad516bee314d7187c2d/numpy/lib/tests/test_stride_tricks.py#L234-L257  # noqa: E501
+        data = [
+            [np.array(0), (0,)],
+            [np.array(0), (1,)],
+            [np.array(0), (3,)],
+            [np.ones(1), (1,)],
+            [np.ones(1), (2,)],
+            [np.ones(1), (1, 2, 3)],
+            [np.arange(3), (3,)],
+            [np.arange(3), (1, 3)],
+            [np.arange(3), (2, 3)],
+            # test if shape is not a tuple
+            [np.ones(0), 0],
+            [np.ones(1), 1],
+            [np.ones(1), 2],
+            # these cases with size 0 are strange, but they reproduce the behavior
+            # of broadcasting with ufuncs
+            [np.ones(1), (0,)],
+            [np.ones((1, 2)), (0, 2)],
+            [np.ones((2, 1)), (2, 0)],
+            # numpy accepts scalar values as first argument to np.broadcast_to
+            [2, (2, 2)],
+            # tuple input
+            [(1, 2), (2, 2)],
+        ]
+        for input_array, shape in data:
+            expected = pyfunc(input_array, shape)
+            got = cfunc(input_array, shape)
+            self.assertPreciseEqual(got, expected)
+
+    def test_broadcast_to_raises(self):
+        pyfunc = numpy_broadcast_to
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Tests taken from
+        # https://github.com/numpy/numpy/blob/75f852edf94a7293e7982ad516bee314d7187c2d/numpy/lib/tests/test_stride_tricks.py#L260-L276  # noqa: E501
+        data = [
+            [np.zeros((0,)), (), TypingError,
+             'The argument "shape" must be a tuple or an integer.'],
+            [np.zeros((1,)), (), TypingError,
+             'The argument "shape" must be a tuple or an integer.'],
+            [np.zeros((3,)), (), TypingError,
+             'The argument "shape" must be a tuple or an integer.'],
+            [np.zeros((3,)), (1,), ValueError,
+             'operands could not be broadcast together with remapped shapes'],
+            [np.zeros((3,)), (2,), ValueError,
+             'operands could not be broadcast together with remapped shapes'],
+            [np.zeros((3,)), (4,), ValueError,
+             'operands could not be broadcast together with remapped shapes'],
+            [np.zeros((1, 2)), (2, 1), ValueError,
+             'operands could not be broadcast together with remapped shapes'],
+            [np.zeros((1, 1)), (1,), ValueError,
+             'input operand has more dimensions than allowed by the axis remapping'],
+            [np.zeros((2, 2)), (3,), ValueError,
+             'input operand has more dimensions than allowed by the axis remapping'],
+            [np.zeros((1,)), -1, ValueError,
+             'all elements of broadcast shape must be non-negative'],
+            [np.zeros((1,)), (-1,), ValueError,
+             'all elements of broadcast shape must be non-negative'],
+            [np.zeros((1, 2)), (-1, 2), ValueError,
+             'all elements of broadcast shape must be non-negative'],
+            [np.zeros((1, 2)), (1.1, 2.2), TypingError,
+             'The second argument "shape" must be a tuple of integers'],
+            ['hello', (3,), TypingError,
+             'The first argument "array" must be array-like'],
+        ]
+        self.disable_leak_check()
+        for arr, target_shape, err, msg in data:
+            with self.assertRaises(err) as raises:
+                cfunc(arr, target_shape)
+            self.assertIn(msg, str(raises.exception))
+
+    def test_broadcast_to_change_view(self):
+        pyfunc = numpy_broadcast_to
+        cfunc = jit(nopython=True)(pyfunc)
+        input_array = np.zeros(2, dtype=np.int32)
+        shape = (2, 2)
+        view = cfunc(input_array, shape)
+        input_array[0] = 10
+
+        self.assertEqual(input_array.sum(), 10)
+        self.assertEqual(view.sum(), 20)
+
+    def test_broadcast_to_indexing(self):
+        pyfunc = numpy_broadcast_to_indexing
+        cfunc = jit(nopython=True)(pyfunc)
+        data = [
+            [np.ones(2), (2, 2), (1,)],
+        ]
+        for input_array, shape, idx in data:
+            expected = pyfunc(input_array, shape, idx)
+            got = cfunc(input_array, shape, idx)
+            self.assertPreciseEqual(got, expected)
+
+    @unittest.skipIf(numpy_version < (1, 20), "requires NumPy 1.20 or newer")
+    def test_broadcast_shapes(self):
+        pyfunc = numpy_broadcast_shapes
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Tests taken from
+        # https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L296-L334
+        data = [
+            # [[], ()],  # cannot compute fingerprint of empty list
+            [()],
+            [(), ()],
+            [(7,)],
+            [(1, 2),],
+            [(1, 1)],
+            [(1, 1), (3, 4)],
+            [(6, 7), (5, 6, 1), (7,), (5, 1, 7)],
+            [(5, 6, 1)],
+            [(1, 3), (3, 1)],
+            [(1, 0), (0, 0)],
+            [(0, 1), (0, 0)],
+            [(1, 0), (0, 1)],
+            [(1, 1), (0, 0)],
+            [(1, 1), (1, 0)],
+            [(1, 1), (0, 1)],
+            [(), (0,)],
+            [(0,), (0, 0)],
+            [(0,), (0, 1)],
+            [(1,), (0, 0)],
+            [(), (0, 0)],
+            [(1, 1), (0,)],
+            [(1,), (0, 1)],
+            [(1,), (1, 0)],
+            [(), (1, 0)],
+            [(), (0, 1)],
+            [(1,), (3,)],
+            [2, (3, 2)],
+        ]
+        for input_shape in data:
+            expected = pyfunc(*input_shape)
+            got = cfunc(*input_shape)
+            self.assertIsInstance(got, tuple)
+            self.assertPreciseEqual(expected, got)
+
+    @unittest.skipIf(numpy_version < (1, 20), "requires NumPy 1.20 or newer")
+    def test_broadcast_shapes_raises(self):
+        pyfunc = numpy_broadcast_shapes
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.disable_leak_check()
+
+        # Tests taken from
+        # https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L337-L351
+        data = [
+            [(3,), (4,)],
+            [(2, 3), (2,)],
+            [(3,), (3,), (4,)],
+            [(1, 3, 4), (2, 3, 3)],
+            [(1, 2), (3, 1), (3, 2), (10, 5)],
+            [2, (2, 3)],
+        ]
+        for input_shape in data:
+            with self.assertRaises(ValueError) as raises:
+                cfunc(*input_shape)
+
+            self.assertIn("shape mismatch: objects cannot be broadcast to a single shape",
+                          str(raises.exception))
+
+    @unittest.skipIf(numpy_version < (1, 20), "requires NumPy 1.20 or newer")
+    def test_broadcast_shapes_negative_dimension(self):
+        pyfunc = numpy_broadcast_shapes
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.disable_leak_check()
+        with self.assertRaises(ValueError) as raises:
+            cfunc((1, 2), (2), (-2))
+
+        self.assertIn("negative dimensions are not allowed", str(raises.exception))
+
+    @unittest.skipIf(numpy_version < (1, 20), "requires NumPy 1.20 or newer")
+    def test_broadcast_shapes_invalid_type(self):
+        pyfunc = numpy_broadcast_shapes
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.disable_leak_check()
+
+        inps = [
+            ((1, 2), ('hello',)),
+            (3.4,),
+            ('string',),
+            ((1.2, 'a')),
+            (1, ((1.2, 'a'))),
+        ]
+
+        for inp in inps:
+            with self.assertRaises(TypingError) as raises:
+                cfunc(*inp)
+
+            self.assertIn("must be either an int or tuple[int]", str(raises.exception))
+
+    def test_shape(self):
+        pyfunc = numpy_shape
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(x):
+            expected = pyfunc(x)
+            got = cfunc(x)
+            self.assertPreciseEqual(got, expected)
+
+        # check arrays
+        for t in [(), (1,), (2, 3,), (4, 5, 6)]:
+            arr = np.empty(t)
+            check(arr)
+
+        # check some types that go via asarray
+        for t in [1, False, [1,], [[1, 2,],[3, 4]], (1,), (1, 2, 3)]:
+            check(arr)
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc('a')
+
+        self.assertIn("The argument to np.shape must be array-like",
+                      str(raises.exception))
+
+    def test_flatnonzero_basic(self):
+        pyfunc = numpy_flatnonzero
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield np.arange(-5, 5)
+            yield np.full(5, fill_value=0)
+            yield np.array([])
+            a = self.random.randn(100)
+            a[np.abs(a) > 0.2] = 0.0
+            yield a
+            yield a.reshape(5, 5, 4)
+            yield a.reshape(50, 2, order='F')
+            yield a.reshape(25, 4)[1::2]
+            yield a * 1j
+
+        for a in a_variations():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+    def test_argwhere_basic(self):
+        pyfunc = numpy_argwhere
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield np.arange(-5, 5) > 2
+            yield np.full(5, fill_value=0)
+            yield np.full(5, fill_value=1)
+            yield np.array([])
+            yield np.array([-1.0, 0.0, 1.0])
+            a = self.random.randn(100)
+            yield a > 0.2
+            yield a.reshape(5, 5, 4) > 0.5
+            yield a.reshape(50, 2, order='F') > 0.5
+            yield a.reshape(25, 4)[1::2] > 0.5
+            yield a == a - 1
+            yield a > -a
+
+        for a in a_variations():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+    @staticmethod
+    def array_like_variations():
+        yield ((1.1, 2.2), (3.3, 4.4), (5.5, 6.6))
+        yield (0.0, 1.0, 0.0, -6.0)
+        yield ([0, 1], [2, 3])
+        yield ()
+        yield np.nan
+        yield 0
+        yield 1
+        yield False
+        yield True
+        yield (True, False, True)
+        yield 2 + 1j
+        # the following are not array-like, but NumPy does not raise
+        yield None
+        yield 'a_string'
+        yield ''
+
+
+    def test_flatnonzero_array_like(self):
+        pyfunc = numpy_flatnonzero
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for a in self.array_like_variations():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+    def test_argwhere_array_like(self):
+        pyfunc = numpy_argwhere
+        cfunc = jit(nopython=True)(pyfunc)
+        for a in self.array_like_variations():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+    def broadcast_arrays_assert_correct_shape(self, input_shapes, expected_shape):
+        # Broadcast a list of arrays with the given input shapes and check the
+        # common output shape.
+        pyfunc = numpy_broadcast_arrays
+        cfunc = jit(nopython=True)(pyfunc)
+
+        inarrays = [np.zeros(s) for s in input_shapes]
+        outarrays = cfunc(*inarrays)
+        expected = [expected_shape] * len(inarrays)
+        got = [a.shape for a in outarrays]
+        self.assertPreciseEqual(expected, got)
+
+    def test_broadcast_arrays_same_input_shapes(self):
+        # Tests taken from
+        # https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L83-L107  # noqa: E501
+        # Check that the final shape is just the input shape.
+        pyfunc = numpy_broadcast_arrays
+        cfunc = jit(nopython=True)(pyfunc)
+
+        data = [
+            # (),
+            (1,),
+            (3,),
+            (0, 1),
+            (0, 3),
+            (1, 0),
+            (3, 0),
+            (1, 3),
+            (3, 1),
+            (3, 3),
+        ]
+        for shape in data:
+            input_shapes = [shape]
+            # Single input.
+            self.broadcast_arrays_assert_correct_shape(input_shapes, shape)
+            # Double input.
+            input_shapes2 = [shape, shape]
+            self.broadcast_arrays_assert_correct_shape(input_shapes2, shape)
+            # Triple input.
+            input_shapes3 = [shape, shape, shape]
+            self.broadcast_arrays_assert_correct_shape(input_shapes3, shape)
+
+    def test_broadcast_arrays_two_compatible_by_ones_input_shapes(self):
+        # Tests taken from
+        # https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L110-L132
+        # Check that two different input shapes of the same length, but some have
+        # ones, broadcast to the correct shape.
+
+        data = [
+            [[(1,), (3,)], (3,)],
+            [[(1, 3), (3, 3)], (3, 3)],
+            [[(3, 1), (3, 3)], (3, 3)],
+            [[(1, 3), (3, 1)], (3, 3)],
+            [[(1, 1), (3, 3)], (3, 3)],
+            [[(1, 1), (1, 3)], (1, 3)],
+            [[(1, 1), (3, 1)], (3, 1)],
+            [[(1, 0), (0, 0)], (0, 0)],
+            [[(0, 1), (0, 0)], (0, 0)],
+            [[(1, 0), (0, 1)], (0, 0)],
+            [[(1, 1), (0, 0)], (0, 0)],
+            [[(1, 1), (1, 0)], (1, 0)],
+            [[(1, 1), (0, 1)], (0, 1)],
+        ]
+        for input_shapes, expected_shape in data:
+            self.broadcast_arrays_assert_correct_shape(input_shapes, expected_shape)
+            # Reverse the input shapes since broadcasting should be symmetric.
+            self.broadcast_arrays_assert_correct_shape(input_shapes[::-1], expected_shape)
+
+    def test_broadcast_arrays_two_compatible_by_prepending_ones_input_shapes(self):
+        # Tests taken from
+        # https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/tests/test_stride_tricks.py#L135-L164
+        # Check that two different input shapes (of different lengths) broadcast
+        # to the correct shape.
+
+        data = [
+            [[(), (3,)], (3,)],
+            [[(3,), (3, 3)], (3, 3)],
+            [[(3,), (3, 1)], (3, 3)],
+            [[(1,), (3, 3)], (3, 3)],
+            [[(), (3, 3)], (3, 3)],
+            [[(1, 1), (3,)], (1, 3)],
+            [[(1,), (3, 1)], (3, 1)],
+            [[(1,), (1, 3)], (1, 3)],
+            [[(), (1, 3)], (1, 3)],
+            [[(), (3, 1)], (3, 1)],
+            [[(), (0,)], (0,)],
+            [[(0,), (0, 0)], (0, 0)],
+            [[(0,), (0, 1)], (0, 0)],
+            [[(1,), (0, 0)], (0, 0)],
+            [[(), (0, 0)], (0, 0)],
+            [[(1, 1), (0,)], (1, 0)],
+            [[(1,), (0, 1)], (0, 1)],
+            [[(1,), (1, 0)], (1, 0)],
+            [[(), (1, 0)], (1, 0)],
+            [[(), (0, 1)], (0, 1)],
+        ]
+        for input_shapes, expected_shape in data:
+            self.broadcast_arrays_assert_correct_shape(input_shapes, expected_shape)
+            # Reverse the input shapes since broadcasting should be symmetric.
+            self.broadcast_arrays_assert_correct_shape(input_shapes[::-1], expected_shape)
+
+    def test_broadcast_arrays_scalar_input(self):
+        pyfunc = numpy_broadcast_arrays
+        cfunc = jit(nopython=True)(pyfunc)
+        data = [
+            [[True, False], (1,)],
+            [[1, 2], (1,)],
+            [[(1, 2), 2], (2,)],
+        ]
+        for inarrays, expected_shape in data:
+            outarrays = cfunc(*inarrays)
+            got = [a.shape for a in outarrays]
+            expected = [expected_shape] * len(inarrays)
+            self.assertPreciseEqual(expected, got)
+
+    def test_broadcast_arrays_tuple_input(self):
+        pyfunc = numpy_broadcast_arrays
+        cfunc = jit(nopython=True)(pyfunc)
+        outarrays = cfunc((123, 456), (789,))
+        expected = [(2,), (2,)]
+        got = [a.shape for a in outarrays]
+        self.assertPreciseEqual(expected, got)
+
+    def test_broadcast_arrays_non_array_input(self):
+        pyfunc = numpy_broadcast_arrays
+        cfunc = jit(nopython=True)(pyfunc)
+        outarrays = cfunc(np.intp(2), np.zeros((1, 3), dtype=np.intp))
+        expected = [(1, 3), (1, 3)]
+        got = [a.shape for a in outarrays]
+        self.assertPreciseEqual(expected, got)
+
+    def test_broadcast_arrays_invalid_mixed_input_types(self):
+        pyfunc = numpy_broadcast_arrays
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            arr = np.arange(6).reshape((2, 3))
+            b = True
+            cfunc(arr, b)
+        self.assertIn('Mismatch of argument types', str(raises.exception))
+
+    def test_broadcast_arrays_invalid_input(self):
+        pyfunc = numpy_broadcast_arrays
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            arr = np.zeros(3, dtype=np.int64)
+            s = 'hello world'
+            cfunc(arr, s)
+        self.assertIn('Argument "1" must be array-like', str(raises.exception))
+
+    def test_broadcast_arrays_incompatible_shapes_raise_valueerror(self):
+        # Check that a ValueError is raised for incompatible shapes.
+        pyfunc = numpy_broadcast_arrays
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.disable_leak_check()
+
+        data = [
+            [(3,), (4,)],
+            [(2, 3), (2,)],
+            [(3,), (3,), (4,)],
+            [(1, 3, 4), (2, 3, 3)],
+        ]
+        for input_shapes in data:
+            for shape in [input_shapes, input_shapes[::-1]]:
+                # Reverse the input shapes since broadcasting should be symmetric.
+                with self.assertRaises(ValueError) as raises:
+                    inarrays = [np.zeros(s) for s in shape]
+                    cfunc(*inarrays)
+                self.assertIn("shape mismatch: objects cannot be broadcast to a single shape",
+                              str(raises.exception))
+
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_methods.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_methods.py
new file mode 100644
index 000000000..eee5cfeff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_methods.py
@@ -0,0 +1,1646 @@
+from itertools import product, cycle, permutations
+import gc
+import sys
+import warnings
+
+import numpy as np
+
+from numba import jit, typeof
+from numba.core import types
+from numba.core.compiler import compile_isolated
+from numba.core.errors import TypingError, LoweringError, NumbaValueError
+from numba.np.numpy_support import as_dtype, numpy_version
+from numba.tests.support import (TestCase, CompilationCache, MemoryLeak,
+                                 MemoryLeakMixin, tag, needs_blas)
+import unittest
+
+TIMEDELTA_M = 'timedelta64[M]'
+TIMEDELTA_Y = 'timedelta64[Y]'
+
+
+def np_around_array(arr, decimals, out):
+    np.around(arr, decimals, out)
+
+def np_around_binary(val, decimals):
+    return np.around(val, decimals)
+
+def np_around_unary(val):
+    return np.around(val)
+
+def np_round_array(arr, decimals, out):
+    np.round(arr, decimals, out)
+
+def np_round_binary(val, decimals):
+    return np.round(val, decimals)
+
+def np_round_unary(val):
+    return np.round(val)
+
+def _fixed_np_round(arr, decimals=0, out=None):
+    """
+    A slightly bugfixed version of np.round().
+    """
+    if out is not None and arr.dtype.kind == 'c':
+        # workaround for https://github.com/numpy/numpy/issues/5779
+        _fixed_np_round(arr.real, decimals, out.real)
+        _fixed_np_round(arr.imag, decimals, out.imag)
+        return out
+    else:
+        res = np.round(arr, decimals, out)
+        if out is None:
+            # workaround for https://github.com/numpy/numpy/issues/5780
+            def fixup_signed_zero(arg, res):
+                if res == 0.0 and arg < 0:
+                    return -np.abs(res)
+                else:
+                    return res
+            if isinstance(arr, (complex, np.complexfloating)):
+                res = complex(fixup_signed_zero(arr.real, res.real),
+                              fixup_signed_zero(arr.imag, res.imag))
+            else:
+                res = fixup_signed_zero(arr, res)
+        return res
+
+
+def array_T(arr):
+    return arr.T
+
+def array_transpose(arr):
+    return arr.transpose()
+
+def array_copy(arr):
+    return arr.copy()
+
+def np_copy(arr):
+    return np.copy(arr)
+
+def np_asfortranarray(arr):
+    return np.asfortranarray(arr)
+
+def np_ascontiguousarray(arr):
+    return np.ascontiguousarray(arr)
+
+def array_view(arr, newtype):
+    return arr.view(newtype)
+
+def array_take(arr, indices):
+    return arr.take(indices)
+
+def array_take_kws(arr, indices, axis):
+    return arr.take(indices, axis=axis)
+
+def np_arange_1(arg0):
+    return np.arange(arg0)
+
+def np_arange_2(arg0, arg1):
+    return np.arange(arg0, arg1)
+
+def np_arange_3(arg0, arg1, arg2):
+    return np.arange(arg0, arg1, arg2)
+
+def np_arange_4(arg0, arg1, arg2, arg3):
+    return np.arange(arg0, arg1, arg2, arg3)
+
+def np_arange_1_stop(arg0, stop):
+    return np.arange(arg0, stop=stop)
+
+def np_arange_1_step(arg0, step):
+    return np.arange(arg0, step=step)
+
+def np_arange_1_dtype(arg0, dtype):
+    return np.arange(arg0, dtype=dtype)
+
+def np_arange_2_step(arg0, arg1, step):
+    return np.arange(arg0, arg1, step=step)
+
+def np_arange_2_dtype(arg0, arg1, dtype):
+    return np.arange(arg0, arg1, dtype=dtype)
+
+def np_arange_start_stop(start, stop):
+    return np.arange(start=start, stop=stop)
+
+def np_arange_start_stop_step(start, stop, step):
+    return np.arange(start=start, stop=stop, step=step)
+
+def np_arange_start_stop_step_dtype(start, stop, step, dtype):
+    return np.arange(start=start, stop=stop, step=step, dtype=dtype)
+
+def array_fill(arr, val):
+    return arr.fill(val)
+
+# XXX Can't pass a dtype as a Dispatcher argument for now
+def make_array_view(newtype):
+    def array_view(arr):
+        return arr.view(newtype)
+    return array_view
+
+def array_sliced_view(arr, ):
+    return arr[0:4].view(np.float32)[0]
+
+def make_array_astype(newtype):
+    def array_astype(arr):
+        return arr.astype(newtype)
+    return array_astype
+
+
+def np_frombuffer(b):
+    """
+    np.frombuffer() on a Python-allocated buffer.
+    """
+    return np.frombuffer(b)
+
+def np_frombuffer_dtype(b):
+    return np.frombuffer(b, dtype=np.complex64)
+
+def np_frombuffer_dtype_str(b):
+    return np.frombuffer(b, dtype='complex64')
+
+def np_frombuffer_allocated(shape):
+    """
+    np.frombuffer() on a Numba-allocated buffer.
+    """
+    arr = np.ones(shape, dtype=np.int32)
+    return np.frombuffer(arr)
+
+def np_frombuffer_allocated_dtype(shape):
+    arr = np.ones(shape, dtype=np.int32)
+    return np.frombuffer(arr, dtype=np.complex64)
+
+def identity_usecase(a, b):
+    return (a is b), (a is not b)
+
+def array_nonzero(a):
+    return a.nonzero()
+
+def np_nonzero(a):
+    return np.nonzero(a)
+
+def np_where_1(c):
+    return np.where(c)
+
+def np_where_3(c, x, y):
+    return np.where(c, x, y)
+
+def array_item(a):
+    return a.item()
+
+def array_itemset(a, v):
+    a.itemset(v)
+
+def array_sum(a, *args):
+    return a.sum(*args)
+
+def array_sum_axis_kws(a, axis):
+    return a.sum(axis=axis)
+
+def array_sum_dtype_kws(a, dtype):
+    return a.sum(dtype=dtype)
+
+def array_sum_axis_dtype_kws(a, dtype, axis):
+    return a.sum(axis=axis, dtype=dtype)
+
+def array_sum_axis_dtype_pos(a, a1, a2):
+    return a.sum(a1, a2)
+
+def array_sum_const_multi(arr, axis):
+    # use np.sum with different constant args multiple times to check
+    # for internal compile cache to see if constant-specialization is
+    # applied properly.
+    a = np.sum(arr, axis=4)
+    b = np.sum(arr, 3)
+    # the last invocation uses runtime-variable
+    c = np.sum(arr, axis)
+    # as method
+    d = arr.sum(axis=5)
+    # negative const axis
+    e = np.sum(arr, axis=-1)
+    return a, b, c, d, e
+
+def array_sum_const_axis_neg_one(a, axis):
+    # use .sum with -1 axis, this is for use with 1D arrays where the above
+    # "const_multi" variant would raise errors
+    return a.sum(axis=-1)
+
+def array_cumsum(a, *args):
+    return a.cumsum(*args)
+
+def array_cumsum_kws(a, axis):
+    return a.cumsum(axis=axis)
+
+def array_real(a):
+    return np.real(a)
+
+def array_imag(a):
+    return np.imag(a)
+
+def np_clip_no_out(a, a_min, a_max):
+    return np.clip(a, a_min, a_max)
+
+def np_clip(a, a_min, a_max, out=None):
+    return np.clip(a, a_min, a_max, out)
+
+def np_clip_kwargs(a, a_min, a_max, out=None):
+    return np.clip(a, a_min, a_max, out=out)
+
+def array_clip(a, a_min=None, a_max=None, out=None):
+    return a.clip(a_min, a_max, out)
+
+def array_clip_kwargs(a, a_min=None, a_max=None, out=None):
+    return a.clip(a_min, a_max, out=out)
+
+def array_clip_no_out(a, a_min, a_max):
+    return a.clip(a_min, a_max)
+
+def array_conj(a):
+    return a.conj()
+
+def array_conjugate(a):
+    return a.conjugate()
+
+def np_unique(a):
+    return np.unique(a)
+
+
+def array_dot(a, b):
+    return a.dot(b)
+
+def array_dot_chain(a, b):
+    return a.dot(b).dot(b)
+
+def array_ctor(n, dtype):
+    return np.ones(n, dtype=dtype)
+
+class TestArrayMethods(MemoryLeakMixin, TestCase):
+    """
+    Test various array methods and array-related functions.
+    """
+
+    def setUp(self):
+        super(TestArrayMethods, self).setUp()
+        self.ccache = CompilationCache()
+
+    def check_round_scalar(self, unary_pyfunc, binary_pyfunc):
+        base_values = [-3.0, -2.5, -2.25, -1.5, 1.5, 2.25, 2.5, 2.75]
+        complex_values = [x * (1 - 1j) for x in base_values]
+        int_values = [int(x) for x in base_values]
+        argtypes = (types.float64, types.float32, types.int32,
+                    types.complex64, types.complex128)
+        argvalues = [base_values, base_values, int_values,
+                     complex_values, complex_values]
+
+        pyfunc = binary_pyfunc
+        for ty, values in zip(argtypes, argvalues):
+            cres = compile_isolated(pyfunc, (ty, types.int32))
+            cfunc = cres.entry_point
+            for decimals in (1, 0, -1):
+                for v in values:
+                    if decimals > 0:
+                        v *= 10
+                    expected = _fixed_np_round(v, decimals)
+                    got = cfunc(v, decimals)
+                    self.assertPreciseEqual(got, expected)
+
+        pyfunc = unary_pyfunc
+        for ty, values in zip(argtypes, argvalues):
+            cres = compile_isolated(pyfunc, (ty,))
+            cfunc = cres.entry_point
+            for v in values:
+                expected = _fixed_np_round(v)
+                got = cfunc(v)
+                self.assertPreciseEqual(got, expected)
+
+    def test_round_scalar(self):
+        self.check_round_scalar(np_round_unary, np_round_binary)
+
+    def test_around_scalar(self):
+        self.check_round_scalar(np_around_unary, np_around_binary)
+
+    def check_round_array(self, pyfunc):
+        def check_round(cfunc, values, inty, outty, decimals):
+            # Create input and output arrays of the right type
+            arr = values.astype(as_dtype(inty))
+            out = np.zeros_like(arr).astype(as_dtype(outty))
+            pyout = out.copy()
+            _fixed_np_round(arr, decimals, pyout)
+            self.memory_leak_setup()
+            cfunc(arr, decimals, out)
+            self.memory_leak_teardown()
+            np.testing.assert_allclose(out, pyout)
+            # Output shape mismatch
+            with self.assertRaises(ValueError) as raises:
+                cfunc(arr, decimals, out[1:])
+            self.assertEqual(str(raises.exception),
+                             "invalid output shape")
+
+        def check_types(argtypes, outtypes, values):
+            for inty, outty in product(argtypes, outtypes):
+                cres = compile_isolated(pyfunc,
+                                        (types.Array(inty, 1, 'A'),
+                                         types.int32,
+                                         types.Array(outty, 1, 'A')))
+                cfunc = cres.entry_point
+                check_round(cres.entry_point, values, inty, outty, 0)
+                check_round(cres.entry_point, values, inty, outty, 1)
+                if not isinstance(outty, types.Integer):
+                    check_round(cres.entry_point, values * 10, inty, outty, -1)
+                else:
+                    # Avoid Numpy bug when output is an int:
+                    # https://github.com/numpy/numpy/issues/5777
+                    pass
+
+        values = np.array([-3.0, -2.5, -2.25, -1.5, 1.5, 2.25, 2.5, 2.75])
+
+        argtypes = (types.float64, types.float32)
+        check_types(argtypes, argtypes, values)
+
+        argtypes = (types.complex64, types.complex128)
+        check_types(argtypes, argtypes, values * (1 - 1j))
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+    def test_round_array(self):
+        self.check_round_array(np_round_array)
+
+    def test_around_array(self):
+        self.check_round_array(np_around_array)
+
+    def test_array_view(self):
+
+        def run(arr, dtype):
+            pyfunc = make_array_view(dtype)
+            cres = self.ccache.compile(pyfunc, (typeof(arr),))
+            return cres.entry_point(arr)
+        def check(arr, dtype):
+            expected = arr.view(dtype)
+            self.memory_leak_setup()
+            got = run(arr, dtype)
+            self.assertPreciseEqual(got, expected)
+            del got
+            self.memory_leak_teardown()
+        def check_err(arr, dtype):
+            with self.assertRaises(ValueError) as raises:
+                run(arr, dtype)
+            self.assertEqual(str(raises.exception),
+                             "new type not compatible with array")
+        def check_err_noncontig_last_axis(arr, dtype):
+            # check NumPy interpreted version raises
+            msg = ("To change to a dtype of a different size, the last axis "
+                   "must be contiguous")
+            with self.assertRaises(ValueError) as raises:
+                make_array_view(dtype)(arr)
+            self.assertEqual(str(raises.exception), msg)
+            # check Numba version raises
+            with self.assertRaises(ValueError) as raises:
+                run(arr, dtype)
+            self.assertEqual(str(raises.exception), msg)
+
+        def check_err_0d(arr, dtype):
+            # check NumPy interpreted version raises
+            msg = ("Changing the dtype of a 0d array is only supported "
+                   "if the itemsize is unchanged")
+            with self.assertRaises(ValueError) as raises:
+                make_array_view(dtype)(arr)
+            self.assertEqual(str(raises.exception), msg)
+            # check Numba version raises
+            with self.assertRaises(ValueError) as raises:
+                run(arr, dtype)
+            self.assertEqual(str(raises.exception), msg)
+
+        def check_err_smaller_dtype(arr, dtype):
+            # check NumPy interpreted version raises
+            msg = ("When changing to a smaller dtype, its size must be a "
+                   "divisor of the size of original dtype")
+            with self.assertRaises(ValueError) as raises:
+                make_array_view(dtype)(arr)
+            self.assertEqual(str(raises.exception), msg)
+            # check Numba version raises
+            with self.assertRaises(ValueError) as raises:
+                run(arr, dtype)
+            self.assertEqual(str(raises.exception), msg)
+
+        def check_err_larger_dtype(arr, dtype):
+            # check NumPy interpreted version raises
+            msg = ("When changing to a larger dtype, its size must be a "
+                   "divisor of the total size in bytes of the last axis "
+                   "of the array.")
+            with self.assertRaises(ValueError) as raises:
+                make_array_view(dtype)(arr)
+            self.assertEqual(str(raises.exception), msg)
+            # check Numba version raises
+            with self.assertRaises(ValueError) as raises:
+                run(arr, dtype)
+            self.assertEqual(str(raises.exception), msg)
+
+        dt1 = np.dtype([('a', np.int8), ('b', np.int8)])
+        dt2 = np.dtype([('u', np.int16), ('v', np.int8)])
+        dt3 = np.dtype([('x', np.int16), ('y', np.int16)])
+
+        # The checking routines are much more specific from NumPy 1.23 onwards
+        # as the granularity of error reporing is improved in Numba to match
+        # that of NumPy.
+        if numpy_version >= (1, 23):
+            check_error_larger_dt = check_err_larger_dtype
+            check_error_smaller_dt = check_err_smaller_dtype
+            check_error_noncontig = check_err_noncontig_last_axis
+            check_error_0d = check_err_0d
+        else:
+            check_error_larger_dt = check_err
+            check_error_smaller_dt = check_err
+            check_error_noncontig = check_err
+            check_error_0d = check_err
+
+        # C-contiguous
+        arr = np.arange(24, dtype=np.int8)
+        check(arr, np.dtype('int16'))
+        check(arr, np.int16)
+        check(arr, np.int8)
+        check(arr, np.float32)
+        check(arr, np.complex64)
+        check(arr, dt1)
+        check(arr, dt2)
+        check_error_larger_dt(arr, np.complex128)
+
+        # Last dimension must have a compatible size
+        arr = arr.reshape((3, 8))
+        check(arr, np.int8)
+        check(arr, np.float32)
+        check(arr, np.complex64)
+        check(arr, dt1)
+        check_error_larger_dt(arr, dt2)
+        check_error_larger_dt(arr, np.complex128)
+
+        # F-contiguous
+        f_arr = np.arange(24, dtype=np.int8).reshape((3, 8)).T
+        # neither F or C contiguous
+        not_f_or_c_arr = np.zeros((4, 4)).T[::2, ::2]
+
+        # NumPy 1.23 does not allow views with different size dtype for
+        # non-contiguous last axis.
+        if numpy_version >= (1, 23):
+            check_maybe_error = check_err_noncontig_last_axis
+        else:
+            check_maybe_error = check
+
+        check(f_arr, np.int8)
+        check(not_f_or_c_arr, np.uint64)
+        check_maybe_error(f_arr, np.float32)
+        check_maybe_error(f_arr, np.complex64)
+        check_maybe_error(f_arr, dt1)
+
+        check_error_noncontig(f_arr, dt2)
+        check_error_noncontig(f_arr, np.complex128)
+        check_error_noncontig(not_f_or_c_arr, np.int8)
+
+        # Non-contiguous: only a type with the same itemsize can be used
+        arr = np.arange(16, dtype=np.int32)[::2]
+        check(arr, np.uint32)
+        check(arr, np.float32)
+        check(arr, dt3)
+        check_error_noncontig(arr, np.int8)
+        check_error_noncontig(arr, np.int16)
+        check_error_noncontig(arr, np.int64)
+        check_error_noncontig(arr, dt1)
+        check_error_noncontig(arr, dt2)
+
+        ## Zero-dim array: only a type with the same itemsize can be used
+        arr = np.array([42], dtype=np.int32).reshape(())
+        check(arr, np.uint32)
+        check(arr, np.float32)
+        check(arr, dt3)
+        check_error_0d(arr, np.int8)
+        check_error_0d(arr, np.int16)
+        check_error_0d(arr, np.int64)
+        check_error_0d(arr, dt1)
+        check_error_0d(arr, dt2)
+
+        # Changing to smaller dtype
+        arr = np.array(['abcdef'])
+        check_error_smaller_dt(arr, np.complex128)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+    def test_array_sliced_view(self):
+        """
+        Test .view() on A layout array but has contiguous innermost dimension.
+        """
+        pyfunc = array_sliced_view
+        cres = self.ccache.compile(pyfunc, (types.uint8[:],))
+        cfunc = cres.entry_point
+
+        orig = np.array([1.5, 2], dtype=np.float32)
+        byteary = orig.view(np.uint8)
+
+        expect = pyfunc(byteary)
+        got = cfunc(byteary)
+
+        self.assertEqual(expect, got)
+
+    def test_array_astype(self):
+
+        def run(arr, dtype):
+            pyfunc = make_array_astype(dtype)
+            cres = self.ccache.compile(pyfunc, (typeof(arr),))
+            return cres.entry_point(arr)
+        def check(arr, dtype):
+            expected = arr.astype(dtype).copy(order='A')
+            got = run(arr, dtype)
+            self.assertPreciseEqual(got, expected)
+
+        # C-contiguous
+        arr = np.arange(24, dtype=np.int8)
+        check(arr, np.dtype('int16'))
+        check(arr, np.int32)
+        check(arr, np.float32)
+        check(arr, np.complex128)
+        check(arr, "float32")
+
+        # F-contiguous
+        arr = np.arange(24, dtype=np.int8).reshape((3, 8)).T
+        check(arr, np.float32)
+
+        # Non-contiguous
+        arr = np.arange(16, dtype=np.int32)[::2]
+        check(arr, np.uint64)
+
+        # check read only attr does not get copied
+        arr = np.arange(16, dtype=np.int32)
+        arr.flags.writeable = False
+        check(arr, np.int32)
+
+        # Invalid conversion
+        dt = np.dtype([('x', np.int8)])
+        with self.assertTypingError() as raises:
+            check(arr, dt)
+        self.assertIn('cannot convert from int32 to Record',
+                      str(raises.exception))
+        # Check non-Literal string raises
+        unicode_val = "float32"
+        with self.assertTypingError() as raises:
+            @jit(nopython=True)
+            def foo(dtype):
+                np.array([1]).astype(dtype)
+            foo(unicode_val)
+        self.assertIn('array.astype if dtype is a string it must be constant',
+                      str(raises.exception))
+
+    def check_np_frombuffer(self, pyfunc):
+        def run(buf):
+            cres = self.ccache.compile(pyfunc, (typeof(buf),))
+            return cres.entry_point(buf)
+        def check(buf):
+            old_refcnt = sys.getrefcount(buf)
+            expected = pyfunc(buf)
+            self.memory_leak_setup()
+            got = run(buf)
+            self.assertPreciseEqual(got, expected)
+            del expected
+            # Note gc.collect is due to references in `except ... as e` that
+            # aren't immediately cleared
+            gc.collect()
+            self.assertEqual(sys.getrefcount(buf), old_refcnt + 1)
+            del got
+            gc.collect()
+            self.assertEqual(sys.getrefcount(buf), old_refcnt)
+            self.memory_leak_teardown()
+
+        b = bytearray(range(16))
+        check(b)
+        check(bytes(b))
+        check(memoryview(b))
+        check(np.arange(12))
+        b = np.arange(12).reshape((3, 4))
+        check(b)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertRaises(ValueError) as raises:
+            run(bytearray(b"xxx"))
+        self.assertEqual("buffer size must be a multiple of element size",
+                         str(raises.exception))
+
+    def test_np_frombuffer(self):
+        self.check_np_frombuffer(np_frombuffer)
+
+    def test_np_frombuffer_dtype(self):
+        self.check_np_frombuffer(np_frombuffer_dtype)
+
+    def test_np_frombuffer_dtype_str(self):
+        self.check_np_frombuffer(np_frombuffer_dtype_str)
+
+    def test_np_frombuffer_dtype_non_const_str(self):
+        @jit(nopython=True)
+        def func(buf, dt):
+            np.frombuffer(buf, dtype=dt)
+
+        with self.assertRaises(TypingError) as raises:
+            func(bytearray(range(16)), 'int32')
+
+        excstr = str(raises.exception)
+        self.assertIn('No match', excstr)
+        self.assertIn('frombuffer(bytearray(uint8, 1d, C), dtype=unicode_type)',
+                      excstr)
+
+    def check_layout_dependent_func(self, pyfunc, fac=np.arange):
+        def is_same(a, b):
+            return a.ctypes.data == b.ctypes.data
+        def check_arr(arr):
+            cres = compile_isolated(pyfunc, (typeof(arr),))
+            expected = pyfunc(arr)
+            got = cres.entry_point(arr)
+            self.assertPreciseEqual(expected, got)
+            self.assertEqual(is_same(expected, arr), is_same(got, arr))
+        arr = fac(24)
+        check_arr(arr)
+        check_arr(arr.reshape((3, 8)))
+        check_arr(arr.reshape((3, 8)).T)
+        check_arr(arr.reshape((3, 8))[::2])
+        check_arr(arr.reshape((2, 3, 4)))
+        check_arr(arr.reshape((2, 3, 4)).T)
+        check_arr(arr.reshape((2, 3, 4))[::2])
+        arr = np.array([0]).reshape(())
+        check_arr(arr)
+
+    def test_array_transpose(self):
+        self.check_layout_dependent_func(array_transpose)
+
+    def test_array_T(self):
+        self.check_layout_dependent_func(array_T)
+
+    def test_array_copy(self):
+        self.check_layout_dependent_func(array_copy)
+
+    def test_np_copy(self):
+        self.check_layout_dependent_func(np_copy)
+
+    def check_ascontiguousarray_scalar(self, pyfunc):
+        def check_scalar(x):
+            cres = compile_isolated(pyfunc, (typeof(x), ))
+            expected = pyfunc(x)
+            got = cres.entry_point(x)
+            self.assertPreciseEqual(expected, got)
+        for x in [42, 42.0, 42j, np.float32(42), np.float64(42), True]:
+            check_scalar(x)
+
+    def test_np_asfortranarray(self):
+        self.check_layout_dependent_func(np_asfortranarray)
+        self.check_ascontiguousarray_scalar(np_asfortranarray)
+
+    def test_np_ascontiguousarray(self):
+        self.check_layout_dependent_func(np_ascontiguousarray)
+        self.check_ascontiguousarray_scalar(np_ascontiguousarray)
+
+    def check_np_frombuffer_allocated(self, pyfunc):
+        def run(shape):
+            cres = self.ccache.compile(pyfunc, (typeof(shape),))
+            return cres.entry_point(shape)
+        def check(shape):
+            expected = pyfunc(shape)
+            got = run(shape)
+            self.assertPreciseEqual(got, expected)
+
+        check((16,))
+        check((4, 4))
+        check((1, 0, 1))
+
+    def test_np_frombuffer_allocated(self):
+        self.check_np_frombuffer_allocated(np_frombuffer_allocated)
+
+    def test_np_frombuffer_allocated2(self):
+        self.check_np_frombuffer_allocated(np_frombuffer_allocated_dtype)
+
+    def check_nonzero(self, pyfunc):
+        def fac(N):
+            np.random.seed(42)
+            arr = np.random.random(N)
+            arr[arr < 0.3] = 0.0
+            arr[arr > 0.7] = float('nan')
+            return arr
+
+        def check_arr(arr):
+            cres = compile_isolated(pyfunc, (typeof(arr),))
+            expected = pyfunc(arr)
+            expected = [a.copy() for a in expected]
+            self.assertPreciseEqual(cres.entry_point(arr), expected)
+
+        arr = np.int16([1, 0, -1, 0])
+        check_arr(arr)
+        arr = np.bool_([1, 0, 1])
+        check_arr(arr)
+
+        arr = fac(24)
+        check_arr(arr)
+        check_arr(arr.reshape((3, 8)))
+        check_arr(arr.reshape((3, 8)).T)
+        check_arr(arr.reshape((3, 8))[::2])
+        check_arr(arr.reshape((2, 3, 4)))
+        check_arr(arr.reshape((2, 3, 4)).T)
+        check_arr(arr.reshape((2, 3, 4))[::2])
+        for v in (0.0, 1.5, float('nan')):
+            arr = np.array([v]).reshape(())
+            check_arr(arr)
+
+        arr = np.array(["Hello", "", "world"])
+        check_arr(arr)
+
+    def test_array_nonzero(self):
+        self.check_nonzero(array_nonzero)
+
+    def test_np_nonzero(self):
+        self.check_nonzero(np_nonzero)
+
+    def test_np_where_1(self):
+        self.check_nonzero(np_where_1)
+
+    def test_np_where_3(self):
+        pyfunc = np_where_3
+        def fac(N):
+            np.random.seed(42)
+            arr = np.random.random(N)
+            arr[arr < 0.3] = 0.0
+            arr[arr > 0.7] = float('nan')
+            return arr
+
+        layouts = cycle(['C', 'F', 'A'])
+        _types = [np.int32, np.int64, np.float32, np.float64, np.complex64,
+                  np.complex128]
+
+        np.random.seed(42)
+
+        def check_arr(arr, layout=False):
+            np.random.shuffle(_types)
+            if layout != False:
+                x = np.zeros_like(arr, dtype=_types[0], order=layout)
+                y = np.zeros_like(arr, dtype=_types[1], order=layout)
+                arr = arr.copy(order=layout)
+            else:
+                x = np.zeros_like(arr, dtype=_types[0], order=next(layouts))
+                y = np.zeros_like(arr, dtype=_types[1], order=next(layouts))
+            x.fill(4)
+            y.fill(9)
+            cres = compile_isolated(pyfunc, (typeof(arr), typeof(x), typeof(y)))
+            expected = pyfunc(arr, x, y)
+            got = cres.entry_point(arr, x, y)
+            self.assertPreciseEqual(got, expected)
+
+        def check_scal(scal):
+            x = 4
+            y = 5
+            np.random.shuffle(_types)
+            x = _types[0](4)
+            y = _types[1](5)
+            cres = compile_isolated(pyfunc, (typeof(scal), typeof(x), typeof(y)))
+            expected = pyfunc(scal, x, y)
+            got = cres.entry_point(scal, x, y)
+            self.assertPreciseEqual(got, expected)
+
+        arr = np.int16([1, 0, -1, 0])
+        check_arr(arr)
+        arr = np.bool_([1, 0, 1])
+        check_arr(arr)
+
+        arr = fac(24)
+        check_arr(arr)
+        check_arr(arr.reshape((3, 8)))
+        check_arr(arr.reshape((3, 8)).T)
+        check_arr(arr.reshape((3, 8))[::2])
+        check_arr(arr.reshape((2, 3, 4)))
+        check_arr(arr.reshape((2, 3, 4)).T)
+        check_arr(arr.reshape((2, 3, 4))[::2])
+
+        check_arr(arr.reshape((2, 3, 4)), layout='F')
+        check_arr(arr.reshape((2, 3, 4)).T, layout='F')
+        check_arr(arr.reshape((2, 3, 4))[::2], layout='F')
+
+        for v in (0.0, 1.5, float('nan')):
+            arr = np.array([v]).reshape(())
+            check_arr(arr)
+
+        for x in (0, 1, True, False, 2.5, 0j):
+            check_scal(x)
+
+    def test_np_where_3_broadcast_x_y_scalar(self):
+        pyfunc = np_where_3
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check_ok(args):
+            expected = pyfunc(*args)
+            got = cfunc(*args)
+            self.assertPreciseEqual(got, expected)
+
+        def a_variations():
+            a = np.linspace(-2, 4, 20)
+            self.random.shuffle(a)
+            yield a
+            yield a.reshape(2, 5, 2)
+            yield a.reshape(4, 5, order='F')
+            yield a.reshape(2, 5, 2)[::-1]
+
+        for a in a_variations():
+            params = (a > 0, 0, 1)
+            check_ok(params)
+
+            params = (a < 0, np.nan, 1 + 4j)
+            check_ok(params)
+
+            params = (a > 1, True, False)
+            check_ok(params)
+
+    def test_np_where_3_broadcast_x_or_y_scalar(self):
+        pyfunc = np_where_3
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check_ok(args):
+            condition, x, y = args
+
+            expected = pyfunc(condition, x, y)
+            got = cfunc(condition, x, y)
+            self.assertPreciseEqual(got, expected)
+
+            # swap x and y
+            expected = pyfunc(condition, y, x)
+            got = cfunc(condition, y, x)
+            self.assertPreciseEqual(got, expected)
+
+        def array_permutations():
+            x = np.arange(9).reshape(3, 3)
+            yield x
+            yield x * 1.1
+            yield np.asfortranarray(x)
+            yield x[::-1]
+            yield np.linspace(-10, 10, 60).reshape(3, 4, 5) * 1j
+
+        def scalar_permutations():
+            yield 0
+            yield 4.3
+            yield np.nan
+            yield True
+            yield 8 + 4j
+
+        for x in array_permutations():
+            for y in scalar_permutations():
+                x_mean = np.mean(x)
+                condition = x > x_mean
+                params = (condition, x, y)
+                check_ok(params)
+
+    def test_arange_1_arg(self):
+
+        all_pyfuncs = (
+            np_arange_1,
+            lambda x: np.arange(x, 10),
+            lambda x: np.arange(7, step=max(1, abs(x)))
+        )
+
+        for pyfunc in all_pyfuncs:
+            cfunc = jit(nopython=True)(pyfunc)
+
+            def check_ok(arg0):
+                expected = pyfunc(arg0)
+                got = cfunc(arg0)
+                np.testing.assert_allclose(expected, got)
+
+            check_ok(0)
+            check_ok(1)
+            check_ok(4)
+            check_ok(5.5)
+            check_ok(-3)
+            check_ok(complex(4, 4))
+            check_ok(np.int8(0))
+
+    def test_arange_2_arg(self):
+        def check_ok(arg0, arg1, pyfunc, cfunc):
+            expected = pyfunc(arg0, arg1)
+            got = cfunc(arg0, arg1)
+            np.testing.assert_allclose(expected, got)
+
+        all_pyfuncs = (
+            np_arange_2,
+            np_arange_start_stop,
+            np_arange_1_stop,
+            np_arange_1_step,
+            lambda x, y: np.arange(x, y, 5),
+            lambda x, y: np.arange(2, y, step=x),
+        )
+
+        for pyfunc in all_pyfuncs:
+            cfunc = jit(nopython=True)(pyfunc)
+
+            check_ok(-1, 5, pyfunc, cfunc)
+            check_ok(-8, -1, pyfunc, cfunc)
+            check_ok(4, 0.5, pyfunc, cfunc)
+            check_ok(0.5, 4, pyfunc, cfunc)
+            check_ok(complex(1, 1), complex(4, 4), pyfunc, cfunc)
+            check_ok(complex(4, 4), complex(1, 1), pyfunc, cfunc)
+            check_ok(3, None, pyfunc, cfunc)
+
+        pyfunc = np_arange_1_dtype
+        cfunc = jit(nopython=True)(pyfunc)
+
+        check_ok(5, np.float32, pyfunc, cfunc)
+        check_ok(2.0, np.int32, pyfunc, cfunc)
+        check_ok(10, np.complex128, pyfunc, cfunc)
+        check_ok(np.complex64(10), np.complex128, pyfunc, cfunc)
+        check_ok(7, None, pyfunc, cfunc)
+        check_ok(np.int8(0), None, pyfunc, cfunc)
+
+    def test_arange_3_arg(self):
+        windows64 = sys.platform.startswith('win32') and sys.maxsize > 2 ** 32
+
+        def check_ok(arg0, arg1, arg2, pyfunc, cfunc, check_dtype=False):
+            expected = pyfunc(arg0, arg1, arg2)
+            got = cfunc(arg0, arg1, arg2)
+            np.testing.assert_allclose(expected, got)
+            # windows 64 cannot differentiate between a python int and a
+            # np.int64 which means the result from numba is int64 more often
+            # than in NumPy.
+            if not windows64:
+                self.assertEqual(expected.dtype, got.dtype)
+
+        for pyfunc in (np_arange_3, np_arange_2_step, np_arange_start_stop_step):
+            cfunc = jit(nopython=True)(pyfunc)
+
+            check_ok(0, 5, 1, pyfunc, cfunc)
+            check_ok(-8, -1, 3, pyfunc, cfunc)
+            check_ok(0, -10, -2, pyfunc, cfunc)
+            check_ok(0.5, 4, 2, pyfunc, cfunc)
+            check_ok(0, 1, 0.1, pyfunc, cfunc)
+            check_ok(0, complex(4, 4), complex(1, 1), pyfunc, cfunc)
+            check_ok(3, 6, None, pyfunc, cfunc)
+            check_ok(3, None, None, pyfunc, cfunc)
+            check_ok(np.int8(0), np.int8(5), np.int8(1), pyfunc, cfunc)
+            check_ok(np.int8(0), np.int16(5), np.int32(1), pyfunc, cfunc)
+            # check upcasting logic, this matters most on windows
+            i8 = np.int8
+            check_ok(i8(0), i8(5), i8(1), pyfunc, cfunc, True) # C int
+            check_ok(np.int64(0), i8(5), i8(1), pyfunc, cfunc, True) # int64
+
+        pyfunc = np_arange_2_dtype
+        cfunc = jit(nopython=True)(pyfunc)
+
+        check_ok(1, 5, np.float32, pyfunc, cfunc)
+        check_ok(2.0, 8, np.int32, pyfunc, cfunc)
+        check_ok(-2, 10, np.complex128, pyfunc, cfunc)
+        check_ok(3, np.complex64(10), np.complex128, pyfunc, cfunc)
+        check_ok(1, 7, None, pyfunc, cfunc)
+        check_ok(np.int8(0), np.int32(5), None, pyfunc, cfunc, True)
+
+    def test_arange_4_arg(self):
+        for pyfunc in (np_arange_4, np_arange_start_stop_step_dtype):
+            cfunc = jit(nopython=True)(pyfunc)
+
+            def check_ok(arg0, arg1, arg2, arg3):
+                expected = pyfunc(arg0, arg1, arg2, arg3)
+                got = cfunc(arg0, arg1, arg2, arg3)
+                np.testing.assert_allclose(expected, got)
+
+            check_ok(0, 5, 1, np.float64)
+            check_ok(-8, -1, 3, np.int32)
+            check_ok(0, -10, -2, np.float32)
+            check_ok(0.5, 4, 2, None)
+            check_ok(0, 1, 0.1, np.complex128)
+            check_ok(0, complex(4, 4), complex(1, 1), np.complex128)
+            check_ok(3, 6, None, None)
+            check_ok(3, None, None, None)
+
+    def test_arange_throws(self):
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        bad_funcs_1 = [
+            lambda x: np.arange(stop=x),
+            lambda x: np.arange(step=x),
+            lambda x: np.arange(dtype=x),
+        ]
+        bad_funcs_2 = [
+            lambda x, y: np.arange(stop=x, step=y),
+            lambda x, y: np.arange(stop=x, dtype=y),
+        ]
+
+        for pyfunc in bad_funcs_1:
+            with self.assertRaises(TypingError) as raises:
+                cfunc = jit(nopython=True)(pyfunc)
+                cfunc(2)
+        for pyfunc in bad_funcs_2:
+            with self.assertRaises(TypingError) as raises:
+                cfunc = jit(nopython=True)(pyfunc)
+                cfunc(2, 6)
+
+        # check step size = 0, this is nonsense
+        pyfunc = np_arange_3
+        cfunc = jit(nopython=True)(pyfunc)
+        for f in (pyfunc, cfunc,):
+            for inputs in [(1, np.int16(2), 0), (1, 2, 0)]:
+                # there's a different error depending on whether any of the
+                # input values are np scalars
+                permitted_errors = (ZeroDivisionError, ValueError)
+                with self.assertRaises(permitted_errors) as raises:
+                    # this will raise RuntimeWarning's about zero division
+                    with warnings.catch_warnings():
+                        warnings.simplefilter("ignore")
+                        f(*inputs)
+                    self.assertIn("Maximum allowed size exceeded",
+                                str(raises.exception))
+
+    def test_arange_accuracy(self):
+        # Checking arange reasonably replicates NumPy's algorithm
+        # see https://github.com/numba/numba/issues/6768
+        @jit(nopython=True)
+        def foo(step):
+            return np.arange(0, 1 + step, step)
+
+        x = 0.010101010101010102
+        self.assertPreciseEqual(foo(x), foo.py_func(x))
+
+    def test_item(self):
+        pyfunc = array_item
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check_ok(arg):
+            expected = pyfunc(arg)
+            got = cfunc(arg)
+            self.assertPreciseEqual(got, expected)
+
+        def check_err(arg):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(arg)
+            self.assertIn("item(): can only convert an array of size 1 to a Python scalar",
+                          str(raises.exception))
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # Test on different kinds of scalars and 1-item arrays
+        check_ok(np.float32([1.5]))
+        check_ok(np.complex128([[1.5j]]))
+        check_ok(np.array(1.5))
+        check_ok(np.bool_(True))
+        check_ok(np.float32(1.5))
+
+        check_err(np.array([1, 2]))
+        check_err(np.array([]))
+
+    def test_itemset(self):
+        pyfunc = array_itemset
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check_ok(a, v):
+            expected = a.copy()
+            got = a.copy()
+            pyfunc(expected, v)
+            cfunc(got, v)
+            self.assertPreciseEqual(got, expected)
+
+        def check_err(a):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(a, 42)
+            self.assertIn("itemset(): can only write to an array of size 1",
+                          str(raises.exception))
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # Test on different kinds of 1-item arrays
+        check_ok(np.float32([1.5]), 42)
+        check_ok(np.complex128([[1.5j]]), 42)
+        check_ok(np.array(1.5), 42)
+
+        check_err(np.array([1, 2]))
+        check_err(np.array([]))
+
+    def test_sum(self):
+        """ test sum over a whole range of dtypes, no axis or dtype parameter
+        """
+        pyfunc = array_sum
+        cfunc = jit(nopython=True)(pyfunc)
+        all_dtypes = [np.float64, np.float32, np.int64, np.int32,
+                      np.complex64, np.complex128, np.uint32, np.uint64, np.timedelta64]
+        all_test_arrays = [
+            [np.ones((7, 6, 5, 4, 3), arr_dtype),
+             np.ones(1, arr_dtype),
+             np.ones((7, 3), arr_dtype) * -5]
+            for arr_dtype in all_dtypes]
+
+        for arr_list in all_test_arrays:
+            for arr in arr_list:
+                with self.subTest("Test np.sum with {} input ".format(arr.dtype)):
+                    self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+
+    def test_sum_axis_kws1(self):
+        """ test sum with axis parameter over a whole range of dtypes  """
+        pyfunc = array_sum_axis_kws
+        cfunc = jit(nopython=True)(pyfunc)
+        all_dtypes = [np.float64, np.float32, np.int64, np.uint64, np.complex64,
+                      np.complex128, TIMEDELTA_M]
+        all_test_arrays = [
+            [np.ones((7, 6, 5, 4, 3), arr_dtype),
+             np.ones(1, arr_dtype),
+             np.ones((7, 3), arr_dtype) * -5]
+            for arr_dtype in all_dtypes]
+        for arr_list in all_test_arrays:
+            for arr in arr_list:
+                for axis in (0, 1, 2):
+                    if axis > len(arr.shape)-1:
+                        continue
+                    with self.subTest("Testing np.sum(axis) with {} "
+                                      "input ".format(arr.dtype)):
+                        self.assertPreciseEqual(pyfunc(arr, axis=axis),
+                                                cfunc(arr, axis=axis))
+
+    def test_sum_axis_kws2(self):
+        """  testing uint32 and int32 separately
+
+        uint32 and int32 must be tested separately because Numpy's current
+        behaviour is different in 64bits Windows (accumulates as int32)
+        and 64bits Linux (accumulates as int64), while Numba has decided to always
+        accumulate as int64, when the OS is 64bits. No testing has been done
+        for behaviours in 32 bits platforms.
+        """
+        pyfunc = array_sum_axis_kws
+        cfunc = jit(nopython=True)(pyfunc)
+        all_dtypes = [np.int32, np.uint32, ]
+        # expected return dtypes in Numba
+        out_dtypes = {np.dtype('int32'): np.int64, np.dtype('uint32'): np.uint64,
+                      np.dtype('int64'): np.int64,
+                      np.dtype(TIMEDELTA_M): np.dtype(TIMEDELTA_M)}
+        all_test_arrays = [
+            [np.ones((7, 6, 5, 4, 3), arr_dtype),
+             np.ones(1, arr_dtype),
+             np.ones((7, 3), arr_dtype) * -5]
+            for arr_dtype in all_dtypes]
+
+        for arr_list in all_test_arrays:
+            for arr in arr_list:
+                for axis in (0, 1, 2):
+                    if axis > len(arr.shape)-1:
+                        continue
+                    with self.subTest("Testing np.sum(axis) with {} "
+                                      "input ".format(arr.dtype)):
+                        npy_res = pyfunc(arr, axis=axis)
+                        numba_res = cfunc(arr, axis=axis)
+                        if isinstance(numba_res, np.ndarray):
+                            self.assertPreciseEqual(
+                                npy_res.astype(out_dtypes[arr.dtype]),
+                                numba_res.astype(out_dtypes[arr.dtype]))
+                        else:
+                            # the results are scalars
+                            self.assertEqual(npy_res, numba_res)
+
+    def test_sum_dtype_kws(self):
+        """ test sum with dtype parameter over a whole range of dtypes """
+        pyfunc = array_sum_dtype_kws
+        cfunc = jit(nopython=True)(pyfunc)
+        all_dtypes = [np.float64, np.float32, np.int64, np.int32, np.uint32,
+                      np.uint64, np.complex64, np.complex128, TIMEDELTA_M]
+        all_test_arrays = [
+            [np.ones((7, 6, 5, 4, 3), arr_dtype),
+             np.ones(1, arr_dtype),
+             np.ones((7, 3), arr_dtype) * -5]
+            for arr_dtype in all_dtypes]
+
+        out_dtypes = {np.dtype('float64'): [np.float64],
+                      np.dtype('float32'): [np.float64, np.float32],
+                      np.dtype('int64'): [np.float64, np.int64, np.float32],
+                      np.dtype('int32'): [np.float64, np.int64, np.float32, np.int32],
+                      np.dtype('uint32'): [np.float64, np.int64, np.float32],
+                      np.dtype('uint64'): [np.float64, np.int64],
+                      np.dtype('complex64'): [np.complex64, np.complex128],
+                      np.dtype('complex128'): [np.complex128],
+                      np.dtype(TIMEDELTA_M): [np.dtype(TIMEDELTA_M)]}
+
+        for arr_list in all_test_arrays:
+            for arr in arr_list:
+                for out_dtype in out_dtypes[arr.dtype]:
+                    subtest_str = ("Testing np.sum with {} input and {} output"
+                                   .format(arr.dtype, out_dtype))
+                    with self.subTest(subtest_str):
+                        self.assertPreciseEqual(pyfunc(arr, dtype=out_dtype),
+                                                cfunc(arr, dtype=out_dtype))
+
+    def test_sum_axis_dtype_kws(self):
+        """ test sum with axis and dtype parameters over a whole range of dtypes """
+        pyfunc = array_sum_axis_dtype_kws
+        cfunc = jit(nopython=True)(pyfunc)
+        all_dtypes = [np.float64, np.float32, np.int64, np.int32, np.uint32,
+                      np.uint64, np.complex64, np.complex128, TIMEDELTA_M]
+        all_test_arrays = [
+            [np.ones((7, 6, 5, 4, 3), arr_dtype),
+             np.ones(1, arr_dtype),
+             np.ones((7, 3), arr_dtype) * -5]
+            for arr_dtype in all_dtypes]
+
+        out_dtypes = {np.dtype('float64'): [np.float64],
+                      np.dtype('float32'): [np.float64, np.float32],
+                      np.dtype('int64'): [np.float64, np.int64, np.float32],
+                      np.dtype('int32'): [np.float64, np.int64, np.float32, np.int32],
+                      np.dtype('uint32'): [np.float64, np.int64, np.float32],
+                      np.dtype('uint64'): [np.float64, np.uint64],
+                      np.dtype('complex64'): [np.complex64, np.complex128],
+                      np.dtype('complex128'): [np.complex128],
+                      np.dtype(TIMEDELTA_M): [np.dtype(TIMEDELTA_M)],
+                      np.dtype(TIMEDELTA_Y): [np.dtype(TIMEDELTA_Y)]}
+
+        for arr_list in all_test_arrays:
+            for arr in arr_list:
+                for out_dtype in out_dtypes[arr.dtype]:
+                    for axis in (0, 1, 2):
+                        if axis > len(arr.shape) - 1:
+                            continue
+                        subtest_str = ("Testing np.sum with {} input and {} output "
+                                       .format(arr.dtype, out_dtype))
+                        with self.subTest(subtest_str):
+                            py_res = pyfunc(arr, axis=axis, dtype=out_dtype)
+                            nb_res = cfunc(arr, axis=axis, dtype=out_dtype)
+                            self.assertPreciseEqual(py_res, nb_res)
+
+    def test_sum_axis_dtype_pos_arg(self):
+        """ testing that axis and dtype inputs work when passed as positional """
+        pyfunc = array_sum_axis_dtype_pos
+        cfunc = jit(nopython=True)(pyfunc)
+        dtype = np.float64
+        # OK
+        a = np.ones((7, 6, 5, 4, 3))
+        self.assertPreciseEqual(pyfunc(a, 1, dtype),
+                                cfunc(a,  1, dtype))
+
+        self.assertPreciseEqual(pyfunc(a, 2, dtype),
+                                cfunc(a, 2, dtype))
+
+    def test_sum_1d_kws(self):
+        # check 1d reduces to scalar
+        pyfunc = array_sum_axis_kws
+        cfunc = jit(nopython=True)(pyfunc)
+        a = np.arange(10.)
+        self.assertPreciseEqual(pyfunc(a, axis=0), cfunc(a, axis=0))
+        pyfunc = array_sum_const_axis_neg_one
+        cfunc = jit(nopython=True)(pyfunc)
+        a = np.arange(10.)
+        self.assertPreciseEqual(pyfunc(a, axis=-1), cfunc(a, axis=-1))
+
+    def test_sum_const(self):
+        pyfunc = array_sum_const_multi
+        cfunc = jit(nopython=True)(pyfunc)
+
+        arr = np.ones((3, 4, 5, 6, 7, 8))
+        axis = 1
+        self.assertPreciseEqual(pyfunc(arr, axis), cfunc(arr, axis))
+        axis = 2
+        self.assertPreciseEqual(pyfunc(arr, axis), cfunc(arr, axis))
+
+    def test_sum_exceptions(self):
+        # Exceptions leak references
+        self.disable_leak_check()
+        pyfunc = array_sum
+        cfunc = jit(nopython=True)(pyfunc)
+
+        a = np.ones((7, 6, 5, 4, 3))
+        b = np.ones((4, 3))
+        # BAD: axis > dimensions
+        with self.assertRaises(ValueError):
+            cfunc(b, 2)
+        # BAD: negative axis
+        with self.assertRaises(ValueError):
+            cfunc(a, -1)
+        # BAD: axis greater than 3
+        with self.assertRaises(ValueError):
+            cfunc(a, 4)
+
+    def test_sum_const_negative(self):
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        @jit(nopython=True)
+        def foo(arr):
+            return arr.sum(axis=-3)
+
+        # ndim == 4, axis == -3, OK
+        a = np.ones((1, 2, 3, 4))
+        self.assertPreciseEqual(foo(a), foo.py_func(a))
+        # ndim == 3, axis == -3, OK
+        a = np.ones((1, 2, 3))
+        self.assertPreciseEqual(foo(a), foo.py_func(a))
+        # ndim == 2, axis == -3, BAD
+        a = np.ones((1, 2))
+        with self.assertRaises(NumbaValueError) as raises:
+            foo(a)
+        errmsg = "'axis' entry (-1) is out of bounds"
+        self.assertIn(errmsg, str(raises.exception))
+        with self.assertRaises(ValueError) as raises:
+            foo.py_func(a)
+        self.assertIn("out of bounds", str(raises.exception))
+
+    def test_cumsum(self):
+        pyfunc = array_cumsum
+        cfunc = jit(nopython=True)(pyfunc)
+        # OK
+        a = np.ones((2, 3))
+        self.assertPreciseEqual(pyfunc(a), cfunc(a))
+        # BAD: with axis
+        with self.assertRaises(TypingError):
+            cfunc(a, 1)
+        # BAD: with kw axis
+        pyfunc = array_cumsum_kws
+        cfunc = jit(nopython=True)(pyfunc)
+        with self.assertRaises(TypingError):
+            cfunc(a, axis=1)
+
+    def test_take(self):
+        pyfunc = array_take
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(arr, ind):
+            expected = pyfunc(arr, ind)
+            got = cfunc(arr, ind)
+            self.assertPreciseEqual(expected, got)
+            if hasattr(expected, 'order'):
+                self.assertEqual(expected.order == got.order)
+
+        # need to check:
+        # 1. scalar index
+        # 2. 1d array index
+        # 3. nd array index, >2d and F order
+        # 4. reflected list
+        # 5. tuples
+
+        test_indices = []
+        test_indices.append(1)
+        test_indices.append(5)
+        test_indices.append(11)
+        test_indices.append(-2)
+        test_indices.append(np.array([1, 5, 1, 11, 3]))
+        test_indices.append(np.array([[1, 5, 1], [11, 3, 0]], order='F'))
+        test_indices.append(np.array([[[1, 5, 1], [11, 3, 0]]]))
+        test_indices.append(np.array([[[[1, 5]], [[11, 0]], [[1, 2]]]]))
+        test_indices.append([1, 5, 1, 11, 3])
+        test_indices.append((1, 5, 1))
+        test_indices.append(((1, 5, 1), (11, 3, 2)))
+        test_indices.append((((1,), (5,), (1,)), ((11,), (3,), (2,))))
+
+        layouts = cycle(['C', 'F', 'A'])
+
+        for dt in [np.float64, np.int64, np.complex128]:
+            A = np.arange(12, dtype=dt).reshape((4, 3), order=next(layouts))
+            for ind in test_indices:
+                check(A, ind)
+
+        #check illegal access raises
+        A = np.arange(12, dtype=dt).reshape((4, 3), order=next(layouts))
+        szA = A.size
+        illegal_indices = [szA, -szA - 1, np.array(szA), np.array(-szA - 1),
+                           [szA], [-szA - 1]]
+        for x in illegal_indices:
+            with self.assertRaises(IndexError):
+                cfunc(A, x) #  oob raises
+
+        # check float indexing raises
+        with self.assertRaises(TypingError):
+            cfunc(A, [1.7])
+
+        # check unsupported arg raises
+        with self.assertRaises(TypingError):
+            take_kws = jit(nopython=True)(array_take_kws)
+            take_kws(A, 1, 1)
+
+        # check kwarg unsupported raises
+        with self.assertRaises(TypingError):
+            take_kws = jit(nopython=True)(array_take_kws)
+            take_kws(A, 1, axis=1)
+
+        #exceptions leak refs
+        self.disable_leak_check()
+
+    def test_fill(self):
+        pyfunc = array_fill
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(arr, val):
+            expected = np.copy(arr)
+            erv = pyfunc(expected, val)
+            self.assertTrue(erv is None)
+            got = np.copy(arr)
+            grv = cfunc(got, val)
+            self.assertTrue(grv is None)
+            # check mutation is the same
+            self.assertPreciseEqual(expected, got)
+
+        # scalar
+        A = np.arange(1)
+        for x in [np.float64, np.bool_]:
+            check(A, x(10))
+
+        # 2d
+        A = np.arange(12).reshape(3, 4)
+        for x in [np.float64, np.bool_]:
+            check(A, x(10))
+
+        # 4d
+        A = np.arange(48, dtype=np.complex64).reshape(2, 3, 4, 2)
+        for x in [np.float64, np.complex128, np.bool_]:
+            check(A, x(10))
+
+    def test_real(self):
+        pyfunc = array_real
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.linspace(-10, 10)
+        np.testing.assert_equal(pyfunc(x), cfunc(x))
+
+        x, y = np.meshgrid(x, x)
+        z = x + 1j*y
+        np.testing.assert_equal(pyfunc(z), cfunc(z))
+
+    def test_imag(self):
+        pyfunc = array_imag
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.linspace(-10, 10)
+        np.testing.assert_equal(pyfunc(x), cfunc(x))
+
+        x, y = np.meshgrid(x, x)
+        z = x + 1j*y
+        np.testing.assert_equal(pyfunc(z), cfunc(z))
+
+    def _lower_clip_result_test_util(self, func, a, a_min, a_max):
+        # verifies that type-inference is working on the return value
+        # this used to trigger issue #3489
+        def lower_clip_result(a):
+            return np.expm1(func(a, a_min, a_max))
+
+        np.testing.assert_almost_equal(
+            lower_clip_result(a),
+            jit(nopython=True)(lower_clip_result)(a))
+
+    def test_clip(self):
+        has_out = (np_clip, np_clip_kwargs, array_clip, array_clip_kwargs)
+        has_no_out = (np_clip_no_out, array_clip_no_out)
+        # TODO: scalars are not tested (issue #3469)
+        for a in (np.linspace(-10, 10, 101),
+                  np.linspace(-10, 10, 40).reshape(5, 2, 4)):
+            for pyfunc in has_out + has_no_out:
+                cfunc = jit(nopython=True)(pyfunc)
+
+                msg = "array_clip: must set either max or min"
+                with self.assertRaisesRegex(ValueError, msg):
+                    cfunc(a, None, None)
+
+                np.testing.assert_equal(pyfunc(a, 0, None), cfunc(a, 0, None))
+                np.testing.assert_equal(pyfunc(a, None, 0), cfunc(a, None, 0))
+
+                np.testing.assert_equal(pyfunc(a, -5, 5), cfunc(a, -5, 5))
+
+                if pyfunc in has_out:
+                    pyout = np.empty_like(a)
+                    cout = np.empty_like(a)
+                    np.testing.assert_equal(pyfunc(a, -5, 5, pyout),
+                                            cfunc(a, -5, 5, cout))
+                    np.testing.assert_equal(pyout, cout)
+
+                self._lower_clip_result_test_util(cfunc, a, -5, 5)
+
+    def test_clip_array_min_max(self):
+        has_out = (np_clip, np_clip_kwargs, array_clip, array_clip_kwargs)
+        has_no_out = (np_clip_no_out, array_clip_no_out)
+        # TODO: scalars are not tested (issue #3469)
+        a = np.linspace(-10, 10, 40).reshape(5, 2, 4)
+        a_min_arr = np.arange(-8, 0).astype(a.dtype).reshape(2, 4)
+        a_max_arr = np.arange(0, 8).astype(a.dtype).reshape(2, 4)
+        mins = [0, -5, a_min_arr, None]
+        maxs = [0, 5, a_max_arr, None]
+        for pyfunc in has_out + has_no_out:
+            cfunc = jit(nopython=True)(pyfunc)
+
+            for a_min in mins:
+                for a_max in maxs:
+
+                    if a_min is None and a_max is None:
+                        msg = "array_clip: must set either max or min"
+                        with self.assertRaisesRegex(ValueError, msg):
+                            cfunc(a, None, None)
+                        continue
+
+                    np.testing.assert_equal(pyfunc(a, a_min, a_max), cfunc(a, a_min, a_max))
+
+                    if pyfunc in has_out:
+                        pyout = np.empty_like(a)
+                        cout = np.empty_like(a)
+                        np.testing.assert_equal(pyfunc(a, a_min, a_max, pyout),
+                                                cfunc(a, a_min, a_max, cout))
+                        np.testing.assert_equal(pyout, cout)
+
+                    self._lower_clip_result_test_util(cfunc, a, a_min, a_max)
+
+    def test_clip_bad_array(self):
+        cfunc = jit(nopython=True)(np_clip)
+        msg = '.*The argument "a" must be array-like.*'
+        with self.assertRaisesRegex(TypingError, msg):
+            cfunc(None, 0, 10)
+
+    def test_clip_bad_min(self):
+        cfunc = jit(nopython=True)(np_clip)
+        msg = '.*The argument "a_min" must be a number.*'
+        with self.assertRaisesRegex(TypingError, msg):
+            cfunc(1, 'a', 10)
+
+    def test_clip_bad_max(self):
+        cfunc = jit(nopython=True)(np_clip)
+        msg = '.*The argument "a_max" must be a number.*'
+        with self.assertRaisesRegex(TypingError, msg):
+            cfunc(1, 1, 'b')
+
+    def test_clip_bad_out(self):
+        cfunc = jit(nopython=True)(np_clip)
+        msg = '.*The argument "out" must be an array if it is provided.*'
+        with self.assertRaisesRegex(TypingError, msg):
+            cfunc(5, 1, 10, out=6)
+
+    def test_clip_no_broadcast(self):
+        self.disable_leak_check()
+        cfunc = jit(nopython=True)(np_clip)
+        msg = ".*shape mismatch: objects cannot be broadcast to a single shape.*"
+        a = np.linspace(-10, 10, 40).reshape(5, 2, 4)
+        a_min_arr = np.arange(-5, 0).astype(a.dtype).reshape(5, 1)
+        a_max_arr = np.arange(0, 5).astype(a.dtype).reshape(5, 1)
+        min_max = [(0, a_max_arr), (-5, a_max_arr),
+                   (a_min_arr, a_max_arr),
+                   (a_min_arr, 0), (a_min_arr, 5)]
+        for a_min, a_max in min_max:
+            with self.assertRaisesRegex(ValueError, msg):
+                cfunc(a, a_min, a_max)
+
+    def test_conj(self):
+        for pyfunc in [array_conj, array_conjugate]:
+            cfunc = jit(nopython=True)(pyfunc)
+
+            x = np.linspace(-10, 10)
+            np.testing.assert_equal(pyfunc(x), cfunc(x))
+
+            x, y = np.meshgrid(x, x)
+            z = x + 1j*y
+            np.testing.assert_equal(pyfunc(z), cfunc(z))
+
+    def test_unique(self):
+        pyfunc = np_unique
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(a):
+            np.testing.assert_equal(pyfunc(a), cfunc(a))
+
+        check(np.array([[1, 1, 3], [3, 4, 5]]))
+        check(np.array(np.zeros(5)))
+        check(np.array([[3.1, 3.1], [1.7, 2.29], [3.3, 1.7]]))
+        check(np.array([]))
+
+    @needs_blas
+    def test_array_dot(self):
+        # just ensure that the dot impl dispatches correctly, do
+        # not test dot itself, this is done in test_linalg.
+        pyfunc = array_dot
+        cfunc = jit(nopython=True)(pyfunc)
+        a = np.arange(20.).reshape(4, 5)
+        b = np.arange(5.)
+        np.testing.assert_equal(pyfunc(a, b), cfunc(a, b))
+
+        # check that chaining works
+        pyfunc = array_dot_chain
+        cfunc = jit(nopython=True)(pyfunc)
+        a = np.arange(16.).reshape(4, 4)
+        np.testing.assert_equal(pyfunc(a, a), cfunc(a, a))
+
+    def test_array_ctor_with_dtype_arg(self):
+        # Test using np.dtype and np.generic (i.e. np.dtype.type) has args
+        pyfunc = array_ctor
+        cfunc = jit(nopython=True)(pyfunc)
+        n = 2
+        args = n, np.int32
+        np.testing.assert_array_equal(pyfunc(*args), cfunc(*args))
+        args = n, np.dtype('int32')
+        np.testing.assert_array_equal(pyfunc(*args), cfunc(*args))
+        args = n, np.float32
+        np.testing.assert_array_equal(pyfunc(*args), cfunc(*args))
+        args = n, np.dtype('f4')
+        np.testing.assert_array_equal(pyfunc(*args), cfunc(*args))
+
+class TestArrayComparisons(TestCase):
+
+    def test_identity(self):
+        def check(a, b, expected):
+            cres = compile_isolated(pyfunc, (typeof(a), typeof(b)))
+            self.assertPreciseEqual(cres.entry_point(a, b),
+                                    (expected, not expected))
+
+        pyfunc = identity_usecase
+
+        arr = np.zeros(10, dtype=np.int32).reshape((2, 5))
+        check(arr, arr, True)
+        check(arr, arr[:], True)
+        check(arr, arr.copy(), False)
+        check(arr, arr.view('uint32'), False)
+        check(arr, arr.T, False)
+        check(arr, arr[:-1], False)
+
+    # Other comparison operators ('==', etc.) are tested in test_ufuncs
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_reductions.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_reductions.py
new file mode 100644
index 000000000..36e5a67d7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_reductions.py
@@ -0,0 +1,1214 @@
+from itertools import product, combinations_with_replacement
+
+import numpy as np
+
+from numba import jit, typeof
+from numba.core.compiler import compile_isolated
+from numba.np.numpy_support import numpy_version
+from numba.tests.support import TestCase, MemoryLeakMixin, tag
+import unittest
+
+
+def array_all(arr):
+    return arr.all()
+
+def array_all_global(arr):
+    return np.all(arr)
+
+def array_any(arr):
+    return arr.any()
+
+def array_any_global(arr):
+    return np.any(arr)
+
+def array_cumprod(arr):
+    return arr.cumprod()
+
+def array_cumprod_global(arr):
+    return np.cumprod(arr)
+
+def array_nancumprod(arr):
+    return np.nancumprod(arr)
+
+def array_cumsum(arr):
+    return arr.cumsum()
+
+def array_cumsum_global(arr):
+    return np.cumsum(arr)
+
+def array_nancumsum(arr):
+    return np.nancumsum(arr)
+
+def array_sum(arr):
+    return arr.sum()
+
+def array_sum_global(arr):
+    return np.sum(arr)
+
+def array_prod(arr):
+    return arr.prod()
+
+def array_prod_global(arr):
+    return np.prod(arr)
+
+def array_mean(arr):
+    return arr.mean()
+
+def array_mean_global(arr):
+    return np.mean(arr)
+
+def array_var(arr):
+    return arr.var()
+
+def array_var_global(arr):
+    return np.var(arr)
+
+def array_std(arr):
+    return arr.std()
+
+def array_std_global(arr):
+    return np.std(arr)
+
+def array_min(arr):
+    return arr.min()
+
+def array_min_global(arr):
+    return np.min(arr)
+
+def array_max(arr):
+    return arr.max()
+
+def array_max_global(arr):
+    return np.max(arr)
+
+def array_argmin(arr):
+    return arr.argmin()
+
+def array_argmin_global(arr):
+    return np.argmin(arr)
+
+def array_argmax(arr):
+    return arr.argmax()
+
+def array_argmax_global(arr):
+    return np.argmax(arr)
+
+def array_median_global(arr):
+    return np.median(arr)
+
+def array_nanmin(arr):
+    return np.nanmin(arr)
+
+def array_nanmax(arr):
+    return np.nanmax(arr)
+
+def array_nanmean(arr):
+    return np.nanmean(arr)
+
+def array_nansum(arr):
+    return np.nansum(arr)
+
+def array_nanprod(arr):
+    return np.nanprod(arr)
+
+def array_nanstd(arr):
+    return np.nanstd(arr)
+
+def array_nanvar(arr):
+    return np.nanvar(arr)
+
+def array_nanmedian_global(arr):
+    return np.nanmedian(arr)
+
+def array_percentile_global(arr, q):
+    return np.percentile(arr, q)
+
+def array_nanpercentile_global(arr, q):
+    return np.nanpercentile(arr, q)
+
+def array_ptp_global(a):
+    return np.ptp(a)
+
+def array_ptp(a):
+    return a.ptp()
+
+def array_quantile_global(arr, q):
+    return np.quantile(arr, q)
+
+def array_nanquantile_global(arr, q):
+    return np.nanquantile(arr, q)
+
+def base_test_arrays(dtype):
+    if dtype == np.bool_:
+        def factory(n):
+            assert n % 2 == 0
+            return np.bool_([0, 1] * (n // 2))
+    else:
+        def factory(n):
+            return np.arange(n, dtype=dtype) + 1
+
+    a1 = factory(10)
+    a2 = factory(10).reshape(2, 5)
+    # The prod() of this array fits in a 32-bit int
+    a3 = (factory(12))[::-1].reshape((2, 3, 2), order='A')
+    assert not (a3.flags.c_contiguous or a3.flags.f_contiguous)
+
+    return [a1, a2, a3]
+
+def full_test_arrays(dtype):
+    array_list = base_test_arrays(dtype)
+
+    # Add floats with some mantissa
+    if dtype == np.float32:
+        array_list += [a / 10 for a in array_list]
+
+    # add imaginary part
+    if dtype == np.complex64:
+        acc = []
+        for a in array_list:
+            tmp = a / 10 + 1j * a / 11
+            tmp[::2] = np.conj(tmp[::2])
+            acc.append(tmp)
+        array_list.extend(acc)
+
+    for a in array_list:
+        assert a.dtype == np.dtype(dtype)
+    return array_list
+
+def run_comparative(compare_func, test_array):
+    arrty = typeof(test_array)
+    cres = compile_isolated(compare_func, [arrty])
+    numpy_result = compare_func(test_array)
+    numba_result = cres.entry_point(test_array)
+
+    return numpy_result, numba_result
+
+
+class TestArrayReductions(MemoryLeakMixin, TestCase):
+    """
+    Test array reduction methods and functions such as .sum(), .max(), etc.
+    """
+
+    def setUp(self):
+        super(TestArrayReductions, self).setUp()
+        np.random.seed(42)
+
+    def check_reduction_basic(self, pyfunc, **kwargs):
+        # Basic reduction checks on 1-d float64 arrays
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(arr):
+            self.assertPreciseEqual(pyfunc(arr), cfunc(arr), **kwargs)
+
+        arr = np.float64([1.0, 2.0, 0.0, -0.0, 1.0, -1.5])
+        check(arr)
+        arr = np.float64([-0.0, -1.5])
+        check(arr)
+        arr = np.float64([-1.5, 2.5, 'inf'])
+        check(arr)
+        arr = np.float64([-1.5, 2.5, '-inf'])
+        check(arr)
+        arr = np.float64([-1.5, 2.5, 'inf', '-inf'])
+        check(arr)
+        arr = np.float64(['nan', -1.5, 2.5, 'nan', 3.0])
+        check(arr)
+        arr = np.float64(['nan', -1.5, 2.5, 'nan', 'inf', '-inf', 3.0])
+        check(arr)
+        arr = np.float64([5.0, 'nan', -1.5, 'nan'])
+        check(arr)
+        # Only NaNs
+        arr = np.float64(['nan', 'nan'])
+        check(arr)
+
+    def test_all_basic(self, pyfunc=array_all):
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(arr):
+            self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+
+        arr = np.float64([1.0, 0.0, float('inf'), float('nan')])
+        check(arr)
+        arr[1] = -0.0
+        check(arr)
+        arr[1] = 1.5
+        check(arr)
+        arr = arr.reshape((2, 2))
+        check(arr)
+        check(arr[::-1])
+
+    def test_any_basic(self, pyfunc=array_any):
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(arr):
+            self.assertPreciseEqual(pyfunc(arr), cfunc(arr))
+
+        arr = np.float64([0.0, -0.0, 0.0, 0.0])
+        check(arr)
+        arr[2] = float('nan')
+        check(arr)
+        arr[2] = float('inf')
+        check(arr)
+        arr[2] = 1.5
+        check(arr)
+        arr = arr.reshape((2, 2))
+        check(arr)
+        check(arr[::-1])
+
+    def test_sum_basic(self):
+        self.check_reduction_basic(array_sum)
+
+    def test_mean_basic(self):
+        self.check_reduction_basic(array_mean)
+
+    def test_var_basic(self):
+        self.check_reduction_basic(array_var, prec='double')
+
+    def test_std_basic(self):
+        self.check_reduction_basic(array_std)
+
+    def test_min_basic(self):
+        self.check_reduction_basic(array_min)
+
+    def test_max_basic(self):
+        self.check_reduction_basic(array_max)
+
+    def test_argmin_basic(self):
+        self.check_reduction_basic(array_argmin)
+
+    def test_argmax_basic(self):
+        self.check_reduction_basic(array_argmax)
+
+    def test_nanmin_basic(self):
+        self.check_reduction_basic(array_nanmin)
+
+    def test_nanmax_basic(self):
+        self.check_reduction_basic(array_nanmax)
+
+    def test_nanmean_basic(self):
+        self.check_reduction_basic(array_nanmean)
+
+    def test_nansum_basic(self):
+        self.check_reduction_basic(array_nansum)
+
+    def test_nanprod_basic(self):
+        self.check_reduction_basic(array_nanprod)
+
+    def test_nanstd_basic(self):
+        self.check_reduction_basic(array_nanstd)
+
+    def test_nanvar_basic(self):
+        self.check_reduction_basic(array_nanvar, prec='double')
+
+    def check_median_basic(self, pyfunc, array_variations):
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(arr):
+            expected = pyfunc(arr)
+            got = cfunc(arr)
+            self.assertPreciseEqual(got, expected)
+
+        # Odd sizes
+        def check_odd(a):
+            check(a)
+            a = a.reshape((9, 7))
+            check(a)
+            check(a.T)
+        for a in array_variations(np.arange(63) + 10.5):
+            check_odd(a)
+
+        # Even sizes
+        def check_even(a):
+            check(a)
+            a = a.reshape((4, 16))
+            check(a)
+            check(a.T)
+        for a in array_variations(np.arange(64) + 10.5):
+            check_even(a)
+
+    @staticmethod
+    def _array_variations(a):
+        # Sorted, reversed, random, many duplicates, many NaNs, all NaNs
+        yield a
+        a = a[::-1].copy()
+        yield a
+        np.random.shuffle(a)
+        yield a
+        a[a % 4 >= 1] = 3.5
+        yield a
+        a[a % 4 >= 2] = np.nan
+        yield a
+        a[:] = np.nan
+        yield a
+
+    def test_median_basic(self):
+        pyfunc = array_median_global
+
+        def variations(a):
+            # Sorted, reversed, random, many duplicates
+            yield a
+            a = a[::-1].copy()
+            yield a
+            np.random.shuffle(a)
+            yield a
+            a[a % 4 >= 1] = 3.5
+            yield a
+
+        self.check_median_basic(pyfunc, variations)
+
+    def check_percentile_and_quantile(self, pyfunc, q_upper_bound):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(a, q, abs_tol=1e-12):
+            expected = pyfunc(a, q)
+            got = cfunc(a, q)
+            # NOTE: inf/nan is not checked, seems to be susceptible to upstream
+            # changes
+            finite = np.isfinite(expected)
+            if np.all(finite):
+                self.assertPreciseEqual(got, expected, abs_tol=abs_tol)
+            else:
+                self.assertPreciseEqual(got[finite], expected[finite],
+                                        abs_tol=abs_tol)
+
+        a = self.random.randn(27).reshape(3, 3, 3)
+        q = np.linspace(0, q_upper_bound, 14)[::-1]
+        check(a, q)
+        check(a, 0)
+        check(a, q_upper_bound / 2)
+        check(a, q_upper_bound)
+
+        not_finite = [np.nan, -np.inf, np.inf]
+        a.flat[:10] = self.random.choice(not_finite, 10)
+        self.random.shuffle(a)
+        self.random.shuffle(q)
+        check(a, q)
+
+        a = a.flatten().tolist()
+        q = q.flatten().tolist()
+        check(a, q)
+        check(tuple(a), tuple(q))
+
+        a = self.random.choice([1, 2, 3, 4], 10)
+        q = np.linspace(0, q_upper_bound, 5)
+        check(a, q)
+
+        # tests inspired by
+        # https://github.com/numpy/numpy/blob/345b2f6e/numpy/lib/tests/test_function_base.py
+        x = np.arange(8) * 0.5
+        np.testing.assert_equal(cfunc(x, 0), 0.)
+        np.testing.assert_equal(cfunc(x, q_upper_bound), 3.5)
+        np.testing.assert_equal(cfunc(x, q_upper_bound / 2), 1.75)
+
+        x = np.arange(12).reshape(3, 4)
+        q = np.array((0.25, 0.5, 1.0)) * q_upper_bound
+        np.testing.assert_equal(cfunc(x, q), [2.75, 5.5, 11.0])
+
+        x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
+        q = np.array((0.25, 0.50)) * q_upper_bound
+        np.testing.assert_equal(cfunc(x, q).shape, (2,))
+
+        q = np.array((0.25, 0.50, 0.75)) * q_upper_bound
+        np.testing.assert_equal(cfunc(x, q).shape, (3,))
+
+        x = np.arange(12).reshape(3, 4)
+        np.testing.assert_equal(cfunc(x, q_upper_bound / 2), 5.5)
+        self.assertTrue(np.isscalar(cfunc(x, q_upper_bound / 2)))
+
+        np.testing.assert_equal(cfunc([1, 2, 3], 0), 1)
+
+        a = np.array([2, 3, 4, 1])
+        cfunc(a, [q_upper_bound / 2])
+        np.testing.assert_equal(a, np.array([2, 3, 4, 1]))
+
+    def check_percentile_edge_cases(self, pyfunc, q_upper_bound=100):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(a, q, abs_tol=1e-14):
+            expected = pyfunc(a, q)
+            got = cfunc(a, q)
+            # NOTE: inf/nan is not checked, seems to be susceptible to upstream
+            # changes
+            finite = np.isfinite(expected)
+            if np.all(finite):
+                self.assertPreciseEqual(got, expected, abs_tol=abs_tol)
+            else:
+                self.assertPreciseEqual(got[finite], expected[finite],
+                                        abs_tol=abs_tol)
+
+        def convert_to_float_and_check(a, q, abs_tol=1e-14):
+            expected = pyfunc(a, q).astype(np.float64)
+            got = cfunc(a, q)
+            self.assertPreciseEqual(got, expected, abs_tol=abs_tol)
+
+        def _array_combinations(elements):
+            for i in range(1, 10):
+                for comb in combinations_with_replacement(elements, i):
+                    yield np.array(comb)
+
+        # high number of combinations, many including non-finite values
+        q = (0, 0.1 * q_upper_bound, 0.2 * q_upper_bound, q_upper_bound)
+        element_pool = (1, -1, np.nan, np.inf, -np.inf)
+        for a in _array_combinations(element_pool):
+            check(a, q)
+
+        # edge cases - numpy exhibits behavioural differences across
+        # platforms, see: https://github.com/numpy/numpy/issues/13272
+        if q_upper_bound == 1:
+            _check = convert_to_float_and_check
+        else:
+            _check = check
+
+        a = np.array(5)
+        q = np.array(1)
+        _check(a, q)
+
+        if numpy_version < (1, 20):
+            # NumPy 1.20+ rewrites the interpolation part of percentile/quantile
+            # to use np.subtract which doesn't support bools.
+            a = True
+            q = False
+            _check(a, q)
+
+            a = np.array([False, True, True])
+            q = a
+            _check(a, q)
+
+        a = 5
+        q = q_upper_bound / 2
+        _check(a, q)
+
+    def check_percentile_exceptions(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check_err(a, q):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(a, q)
+            self.assertEqual(
+                "Percentiles must be in the range [0, 100]",
+                str(raises.exception)
+            )
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        a = np.arange(5)
+        check_err(a, -5)  # q less than 0
+        check_err(a, (1, 10, 105))  # q contains value greater than 100
+        check_err(a, (1, 10, np.nan))  # q contains nan
+
+        with self.assertTypingError() as e:
+            a = np.arange(5) * 1j
+            q = 0.1
+            cfunc(a, q)
+
+        self.assertIn('Not supported for complex dtype', str(e.exception))
+
+    def check_quantile_exceptions(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check_err(a, q):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(a, q)
+            self.assertEqual(
+                "Quantiles must be in the range [0, 1]",
+                str(raises.exception)
+            )
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        a = np.arange(5)
+        check_err(a, -0.5)  # q less than 0
+        check_err(a, (0.1, 0.10, 1.05))  # q contains value greater than 1
+        check_err(a, (0.1, 0.10, np.nan))  # q contains nan
+
+        with self.assertTypingError() as e:
+            a = np.arange(5) * 1j
+            q = 0.1
+            cfunc(a, q)
+
+        self.assertIn('Not supported for complex dtype', str(e.exception))
+
+    def test_percentile_basic(self):
+        pyfunc = array_percentile_global
+        self.check_percentile_and_quantile(pyfunc, q_upper_bound=100)
+        self.check_percentile_edge_cases(pyfunc, q_upper_bound=100)
+        self.check_percentile_exceptions(pyfunc)
+
+    def test_nanpercentile_basic(self):
+        pyfunc = array_nanpercentile_global
+        self.check_percentile_and_quantile(pyfunc, q_upper_bound=100)
+        self.check_percentile_edge_cases(pyfunc, q_upper_bound=100)
+        self.check_percentile_exceptions(pyfunc)
+
+    def test_quantile_basic(self):
+        pyfunc = array_quantile_global
+        self.check_percentile_and_quantile(pyfunc, q_upper_bound=1)
+        self.check_percentile_edge_cases(pyfunc, q_upper_bound=1)
+        self.check_quantile_exceptions(pyfunc)
+
+    def test_nanquantile_basic(self):
+        pyfunc = array_nanquantile_global
+        self.check_percentile_and_quantile(pyfunc, q_upper_bound=1)
+        self.check_percentile_edge_cases(pyfunc, q_upper_bound=1)
+        self.check_quantile_exceptions(pyfunc)
+
+    def test_nanmedian_basic(self):
+        pyfunc = array_nanmedian_global
+        self.check_median_basic(pyfunc, self._array_variations)
+
+    def test_array_sum_global(self):
+        arr = np.arange(10, dtype=np.int32)
+        arrty = typeof(arr)
+        self.assertEqual(arrty.ndim, 1)
+        self.assertEqual(arrty.layout, 'C')
+
+        cres = compile_isolated(array_sum_global, [arrty])
+        cfunc = cres.entry_point
+
+        self.assertEqual(np.sum(arr), cfunc(arr))
+
+    def test_array_prod_int_1d(self):
+        arr = np.arange(10, dtype=np.int32) + 1
+        arrty = typeof(arr)
+        self.assertEqual(arrty.ndim, 1)
+        self.assertEqual(arrty.layout, 'C')
+
+        cres = compile_isolated(array_prod, [arrty])
+        cfunc = cres.entry_point
+
+        self.assertEqual(arr.prod(), cfunc(arr))
+
+    def test_array_prod_float_1d(self):
+        arr = np.arange(10, dtype=np.float32) + 1 / 10
+        arrty = typeof(arr)
+        self.assertEqual(arrty.ndim, 1)
+        self.assertEqual(arrty.layout, 'C')
+
+        cres = compile_isolated(array_prod, [arrty])
+        cfunc = cres.entry_point
+
+        np.testing.assert_allclose(arr.prod(), cfunc(arr))
+
+    def test_array_prod_global(self):
+        arr = np.arange(10, dtype=np.int32)
+        arrty = typeof(arr)
+        self.assertEqual(arrty.ndim, 1)
+        self.assertEqual(arrty.layout, 'C')
+
+        cres = compile_isolated(array_prod_global, [arrty])
+        cfunc = cres.entry_point
+
+        np.testing.assert_allclose(np.prod(arr), cfunc(arr))
+
+    def check_cumulative(self, pyfunc):
+        arr = np.arange(2, 10, dtype=np.int16)
+        expected, got = run_comparative(pyfunc, arr)
+        self.assertPreciseEqual(got, expected)
+        arr = np.linspace(2, 8, 6)
+        expected, got = run_comparative(pyfunc, arr)
+        self.assertPreciseEqual(got, expected)
+        arr = arr.reshape((3, 2))
+        expected, got = run_comparative(pyfunc, arr)
+        self.assertPreciseEqual(got, expected)
+
+    def test_array_cumsum(self):
+        self.check_cumulative(array_cumsum)
+
+    def test_array_cumsum_global(self):
+        self.check_cumulative(array_cumsum_global)
+
+    def test_array_cumprod(self):
+        self.check_cumulative(array_cumprod)
+
+    def test_array_cumprod_global(self):
+        self.check_cumulative(array_cumprod_global)
+
+    def check_aggregation_magnitude(self, pyfunc, is_prod=False):
+        """
+        Check that integer overflows are avoided (issue #931).
+        """
+        # Overflows are avoided here (ints are cast either to intp
+        # or float64).
+        n_items = 2 if is_prod else 10  # avoid overflow on prod()
+        arr = (np.arange(n_items) + 40000).astype('int16')
+        npr, nbr = run_comparative(pyfunc, arr)
+        self.assertPreciseEqual(npr, nbr)
+        # Overflows are avoided for functions returning floats here.
+        # Other functions may wrap around.
+        arr = (np.arange(10) + 2**60).astype('int64')
+        npr, nbr = run_comparative(pyfunc, arr)
+        self.assertPreciseEqual(npr, nbr)
+        arr = arr.astype('uint64')
+        npr, nbr = run_comparative(pyfunc, arr)
+        self.assertPreciseEqual(npr, nbr)
+
+    def test_sum_magnitude(self):
+        self.check_aggregation_magnitude(array_sum)
+        self.check_aggregation_magnitude(array_sum_global)
+
+    def test_cumsum_magnitude(self):
+        self.check_aggregation_magnitude(array_cumsum)
+        self.check_aggregation_magnitude(array_cumsum_global)
+
+    def test_nancumsum_magnitude(self):
+        self.check_aggregation_magnitude(array_nancumsum, is_prod=True)
+
+    def test_prod_magnitude(self):
+        self.check_aggregation_magnitude(array_prod, is_prod=True)
+        self.check_aggregation_magnitude(array_prod_global, is_prod=True)
+
+    def test_cumprod_magnitude(self):
+        self.check_aggregation_magnitude(array_cumprod, is_prod=True)
+        self.check_aggregation_magnitude(array_cumprod_global, is_prod=True)
+
+    def test_nancumprod_magnitude(self):
+        self.check_aggregation_magnitude(array_nancumprod, is_prod=True)
+
+    def test_mean_magnitude(self):
+        self.check_aggregation_magnitude(array_mean)
+        self.check_aggregation_magnitude(array_mean_global)
+
+    def test_var_magnitude(self):
+        self.check_aggregation_magnitude(array_var)
+        self.check_aggregation_magnitude(array_var_global)
+
+    def test_std_magnitude(self):
+        self.check_aggregation_magnitude(array_std)
+        self.check_aggregation_magnitude(array_std_global)
+
+    def _do_check_nptimedelta(self, pyfunc, arr):
+        arrty = typeof(arr)
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
+        # Even vs. odd size, for np.median
+        self.assertPreciseEqual(cfunc(arr[:-1]), pyfunc(arr[:-1]))
+        # Test with different orders, for np.median
+        arr = arr[::-1].copy()  # Keep 'C' layout
+        self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
+        np.random.shuffle(arr)
+        self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
+        # Test with a NaT
+        if numpy_version != (1, 21) and 'median' not in pyfunc.__name__:
+            # There's problems with NaT handling in "median" on at least NumPy
+            # 1.21.{3, 4}. See https://github.com/numpy/numpy/issues/20376
+            arr[arr.size // 2] = 'NaT'
+            self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
+        if 'median' not in pyfunc.__name__:
+            # Test with (val, NaT)^N (and with the random NaT from above)
+            # use a loop, there's some weird thing/bug with arr[1::2] = 'NaT'
+
+            # Further Numba has bug(s) relating to NaN/NaT handling in anything
+            # using a partition such as np.median
+            for x in range(1, len(arr), 2):
+                arr[x] = 'NaT'
+            self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
+        # Test with all NaTs
+        arr.fill(arrty.dtype('NaT'))
+        self.assertPreciseEqual(cfunc(arr), pyfunc(arr))
+
+    def check_npdatetime(self, pyfunc):
+        arr = np.arange(10).astype(dtype='M8[Y]')
+        self._do_check_nptimedelta(pyfunc, arr)
+
+    def check_nptimedelta(self, pyfunc):
+        arr = np.arange(10).astype(dtype='m8[s]')
+        self._do_check_nptimedelta(pyfunc, arr)
+
+    def test_min_npdatetime(self):
+        self.check_npdatetime(array_min)
+        self.check_nptimedelta(array_min)
+
+    def test_max_npdatetime(self):
+        self.check_npdatetime(array_max)
+        self.check_nptimedelta(array_max)
+
+    def test_argmin_npdatetime(self):
+        self.check_npdatetime(array_argmin)
+        self.check_nptimedelta(array_argmin)
+
+    def test_argmax_npdatetime(self):
+        self.check_npdatetime(array_argmax)
+        self.check_nptimedelta(array_argmax)
+
+    def test_median_npdatetime(self):
+        self.check_nptimedelta(array_median_global)
+
+    def test_sum_npdatetime(self):
+        self.check_nptimedelta(array_sum)
+
+    def test_cumsum_npdatetime(self):
+        self.check_nptimedelta(array_cumsum)
+
+    def test_mean_npdatetime(self):
+        self.check_nptimedelta(array_mean)
+
+    def check_nan_cumulative(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(a):
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+        def _set_some_values_to_nan(a):
+            p = a.size // 2  # set approx half elements to NaN
+            np.put(a, np.random.choice(range(a.size), p, replace=False), np.nan)
+            return a
+
+        def a_variations():
+            yield np.linspace(-1, 3, 60).reshape(3, 4, 5)
+            yield np.array([np.inf, 3, 4])
+            yield np.array([True, True, True, False])
+            yield np.arange(1, 10)
+            yield np.asfortranarray(np.arange(1, 64) - 33.3)
+            yield np.arange(1, 10, dtype=np.float32)[::-1]
+
+        for a in a_variations():
+            check(a)  # no nans
+            check(_set_some_values_to_nan(a.astype(np.float64)))  # about 50% nans
+
+        # edge cases
+        check(np.array([]))
+        check(np.full(10, np.nan))
+
+        parts = np.array([np.nan, 2, np.nan, 4, 5, 6, 7, 8, 9])
+
+        a = parts + 1j * parts[::-1]
+        a = a.reshape(3, 3)
+        check(a)
+
+    def test_nancumprod_basic(self):
+        self.check_cumulative(array_nancumprod)
+        self.check_nan_cumulative(array_nancumprod)
+
+    def test_nancumsum_basic(self):
+        self.check_cumulative(array_nancumsum)
+        self.check_nan_cumulative(array_nancumsum)
+
+    def test_ptp_basic(self):
+        pyfunc = array_ptp_global
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(a):
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+        def a_variations():
+            yield np.arange(10)
+            yield np.array([-1.1, np.nan, 2.2])
+            yield np.array([-np.inf, 5])
+            yield (4, 2, 5)
+            yield (1,)
+            yield np.full(5, 5)
+            yield [2.2, -2.3, 0.1]
+            a = np.linspace(-10, 10, 16).reshape(4, 2, 2)
+            yield a
+            yield np.asfortranarray(a)
+            yield a[::-1]
+            np.random.RandomState(0).shuffle(a)
+            yield a
+            yield 6
+            yield 6.5
+            yield -np.inf
+            yield 1 + 4j
+            yield [2.2, np.nan]
+            yield [2.2, np.inf]
+            yield ((4.1, 2.0, -7.6), (4.3, 2.7, 5.2))
+            yield np.full(5, np.nan)
+            yield 1 + np.nan * 1j
+            yield np.nan + np.nan * 1j
+            yield np.nan
+
+        for a in a_variations():
+            check(a)
+
+    def test_ptp_method(self):
+        # checks wiring of np.ndarray.ptp() only, `np.ptp` test above checks
+        # the actual alg
+        pyfunc = array_ptp
+        cfunc = jit(nopython=True)(pyfunc)
+
+        a = np.arange(10)
+        expected = pyfunc(a)
+        got = cfunc(a)
+        self.assertPreciseEqual(expected, got)
+
+    def test_ptp_complex(self):
+        pyfunc = array_ptp_global
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(a):
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+        def make_array(real_nan=False, imag_nan=False):
+            real = np.linspace(-4, 4, 25)
+            if real_nan:
+                real[4:9] = np.nan
+            imag = np.linspace(-5, 5, 25)
+            if imag_nan:
+                imag[7:12] = np.nan
+            return (real + 1j * imag).reshape(5, 5)
+
+        for real_nan, imag_nan in product([True, False], repeat=2):
+            comp = make_array(real_nan, imag_nan)
+            check(comp)
+
+        real = np.ones(8)
+        imag = np.arange(-4, 4)
+        comp = real + 1j * imag
+        check(comp)
+        comp = real - 1j * imag
+        check(comp)
+
+        comp = np.full((4, 4), fill_value=(1 - 1j))
+        check(comp)
+
+    def test_ptp_exceptions(self):
+        pyfunc = array_ptp_global
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertTypingError() as e:
+            cfunc(np.array((True, True, False)))
+
+        msg = "Boolean dtype is unsupported (as per NumPy)"
+        self.assertIn(msg, str(e.exception))
+
+        with self.assertRaises(ValueError) as e:
+            cfunc(np.array([]))
+
+        msg = "zero-size array reduction not possible"
+        self.assertIn(msg, str(e.exception))
+
+    def test_min_max_complex_basic(self):
+        pyfuncs = array_min_global, array_max_global
+
+        for pyfunc in pyfuncs:
+            cfunc = jit(nopython=True)(pyfunc)
+
+            def check(a):
+                expected = pyfunc(a)
+                got = cfunc(a)
+                self.assertPreciseEqual(expected, got)
+
+            real = np.linspace(-10, 10, 40)
+            real[:4] = real[-1]
+            imag = real * 2
+            a = real - imag * 1j
+            check(a)
+
+            for _ in range(10):
+                self.random.shuffle(real)
+                self.random.shuffle(imag)
+                dtype = self.random.choice([np.complex64, np.complex128])
+                a = real - imag * 1j
+                a[:4] = a[-1]
+                check(a.astype(dtype))
+
+    def test_nanmin_nanmax_complex_basic(self):
+        pyfuncs = array_nanmin, array_nanmax
+
+        for pyfunc in pyfuncs:
+            cfunc = jit(nopython=True)(pyfunc)
+
+            def check(a):
+                expected = pyfunc(a)
+                got = cfunc(a)
+                self.assertPreciseEqual(expected, got)
+
+            real = np.linspace(-10, 10, 40)
+            real[:4] = real[-1]
+            real[5:9] = np.nan
+            imag = real * 2
+            imag[7:12] = np.nan
+            a = real - imag * 1j
+            check(a)
+
+            for _ in range(10):
+                self.random.shuffle(real)
+                self.random.shuffle(imag)
+                a = real - imag * 1j
+                a[:4] = a[-1]
+                check(a)
+
+    def test_nanmin_nanmax_non_array_inputs(self):
+        pyfuncs = array_nanmin, array_nanmax
+
+        def check(a):
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+        def a_variations():
+            yield [1, 6, 4, 2]
+            yield ((-10, 4, -12), (5, 200, -30))
+            yield np.array(3)
+            yield (2,)
+            yield 3.142
+            yield False
+            yield (np.nan, 3.142, -5.2, 3.0)
+            yield [np.inf, np.nan, -np.inf]
+            yield [(np.nan, 1.1), (-4.4, 8.7)]
+
+        for pyfunc in pyfuncs:
+            cfunc = jit(nopython=True)(pyfunc)
+
+            for a in a_variations():
+                check(a)
+
+    def test_argmax_axis_1d_2d_4d(self):
+        arr1d = np.array([0, 20, 3, 4])
+        arr2d = np.arange(6).reshape(2, 3)
+        arr2d[0,1] += 100
+
+        arr4d = np.arange(120).reshape(2, 3, 4, 5) + 10
+        arr4d[0, 1, 1, 2] += 100
+        arr4d[1, 0, 0, 0] -= 51
+
+        for arr in [arr1d, arr2d, arr4d]:
+            axes = list(range(arr.ndim)) + [
+                -(i+1) for i in range(arr.ndim)
+            ]
+            py_functions = [
+                lambda a, _axis=axis: np.argmax(a, axis=_axis)
+                for axis in axes
+            ]
+            c_functions = [
+                jit(nopython=True)(pyfunc) for pyfunc in py_functions
+            ]
+            for cfunc in c_functions:
+                self.assertPreciseEqual(cfunc.py_func(arr), cfunc(arr))
+
+    def test_argmax_axis_out_of_range(self):
+        arr1d = np.arange(6)
+        arr2d = np.arange(6).reshape(2, 3)
+
+        @jit(nopython=True)
+        def jitargmax(arr, axis):
+            return np.argmax(arr, axis)
+
+        def assert_raises(arr, axis):
+            with self.assertRaisesRegex(ValueError, "axis.*out of bounds"):
+                jitargmax.py_func(arr, axis)
+            with self.assertRaisesRegex(ValueError, "axis.*out of bounds"):
+                jitargmax(arr, axis)
+
+        assert_raises(arr1d, 1)
+        assert_raises(arr1d, -2)
+        assert_raises(arr2d, -3)
+        assert_raises(arr2d, 2)
+
+    def test_argmax_axis_must_be_integer(self):
+        arr = np.arange(6)
+
+        @jit(nopython=True)
+        def jitargmax(arr, axis):
+            return np.argmax(arr, axis)
+
+        with self.assertTypingError() as e:
+            jitargmax(arr, "foo")
+        self.assertIn("axis must be an integer", str(e.exception))
+
+    def test_argmax_method_axis(self):
+        arr2d = np.arange(6).reshape(2, 3)
+
+        def argmax(arr):
+            return arr2d.argmax(axis=0)
+
+        self.assertPreciseEqual(argmax(arr2d),
+                                jit(nopython=True)(argmax)(arr2d))
+
+    def test_argmax_return_type(self):
+        # See issue #7853, return type should be intp not based on input type
+        arr2d = np.arange(6, dtype=np.uint8).reshape(2, 3)
+
+        def argmax(arr):
+            return arr2d.argmax(axis=0)
+
+        self.assertPreciseEqual(argmax(arr2d),
+                                jit(nopython=True)(argmax)(arr2d))
+
+    def test_argmin_axis_1d_2d_4d(self):
+        arr1d = np.array([0, 20, 3, 4])
+        arr2d = np.arange(6).reshape(2, 3)
+        arr2d[0,1] += 100
+
+        arr4d = np.arange(120).reshape(2, 3, 4, 5) + 10
+        arr4d[0, 1, 1, 2] += 100
+        arr4d[1, 0, 0, 0] -= 51
+
+        for arr in [arr1d, arr2d, arr4d]:
+            axes = list(range(arr.ndim)) + [
+                -(i+1) for i in range(arr.ndim)
+            ]
+            py_functions = [
+                lambda a, _axis=axis: np.argmin(a, axis=_axis)
+                for axis in axes
+            ]
+            c_functions = [
+                jit(nopython=True)(pyfunc) for pyfunc in py_functions
+            ]
+            for cfunc in c_functions:
+                self.assertPreciseEqual(cfunc.py_func(arr), cfunc(arr))
+
+    def test_argmin_axis_out_of_range(self):
+        arr1d = np.arange(6)
+        arr2d = np.arange(6).reshape(2, 3)
+
+        @jit(nopython=True)
+        def jitargmin(arr, axis):
+            return np.argmin(arr, axis)
+
+        def assert_raises(arr, axis):
+            with self.assertRaisesRegex(ValueError, "axis.*out of bounds"):
+                jitargmin.py_func(arr, axis)
+            with self.assertRaisesRegex(ValueError, "axis.*out of bounds"):
+                jitargmin(arr, axis)
+
+        assert_raises(arr1d, 1)
+        assert_raises(arr1d, -2)
+        assert_raises(arr2d, -3)
+        assert_raises(arr2d, 2)
+
+    def test_argmin_axis_must_be_integer(self):
+        arr = np.arange(6)
+
+        @jit(nopython=True)
+        def jitargmin(arr, axis):
+            return np.argmin(arr, axis)
+
+        with self.assertTypingError() as e:
+            jitargmin(arr, "foo")
+        self.assertIn("axis must be an integer", str(e.exception))
+
+    def test_argmin_method_axis(self):
+        arr2d = np.arange(6).reshape(2, 3)
+
+        def argmin(arr):
+            return arr2d.argmin(axis=0)
+
+        self.assertPreciseEqual(argmin(arr2d),
+                                jit(nopython=True)(argmin)(arr2d))
+
+    def test_argmin_return_type(self):
+        # See issue #7853, return type should be intp not based on input type
+        arr2d = np.arange(6, dtype=np.uint8).reshape(2, 3)
+
+        def argmin(arr):
+            return arr2d.argmin(axis=0)
+
+        self.assertPreciseEqual(argmin(arr2d),
+                                jit(nopython=True)(argmin)(arr2d))
+
+    @classmethod
+    def install_generated_tests(cls):
+        # These form a testing product where each of the combinations are tested
+
+        # these function are tested in real and complex space
+        reduction_funcs = [array_sum, array_sum_global,
+                           array_prod, array_prod_global,
+                           array_mean, array_mean_global,
+                           array_var, array_var_global,
+                           array_std, array_std_global,
+                           array_all, array_all_global,
+                           array_any, array_any_global,
+                           array_min, array_min_global,
+                           array_max, array_max_global,
+                           array_nanmax, array_nanmin,
+                           array_nansum,
+                           ]
+
+        # these functions only work in real space as no complex comparison
+        # operator is implemented
+        reduction_funcs_rspace = [array_argmin, array_argmin_global,
+                                  array_argmax, array_argmax_global]
+
+        reduction_funcs += [array_nanmean, array_nanstd, array_nanvar]
+        reduction_funcs += [array_nanprod]
+
+        dtypes_to_test = [np.int32, np.float32, np.bool_, np.complex64]
+
+        def install_tests(dtypes, funcs):
+            # Install tests on class
+            for dt in dtypes:
+                test_arrays = full_test_arrays(dt)
+                for red_func, test_array in product(funcs, test_arrays):
+                    # Create the name for the test function
+                    test_name = "test_{0}_{1}_{2}d"
+                    test_name = test_name.format(red_func.__name__,
+                                                 test_array.dtype.name,
+                                                 test_array.ndim)
+
+                    def new_test_function(self, redFunc=red_func,
+                                          testArray=test_array,
+                                          testName=test_name):
+                        ulps = 1
+                        if 'prod' in red_func.__name__ and \
+                            np.iscomplexobj(testArray):
+                            # prod family accumulate slightly more error on
+                            # some architectures (power, 32bit) for complex input
+                            ulps = 3
+                        npr, nbr = run_comparative(redFunc, testArray)
+                        self.assertPreciseEqual(npr, nbr, msg=testName,
+                                                prec="single", ulps=ulps)
+
+                    # Install it into the class
+                    setattr(cls, test_name, new_test_function)
+
+        # install tests for reduction functions that only work in real space
+        install_tests(dtypes_to_test[:-1], reduction_funcs_rspace)
+
+        # install tests for reduction functions
+        install_tests(dtypes_to_test, reduction_funcs)
+
+
+TestArrayReductions.install_generated_tests()
+
+
+class TestArrayReductionsExceptions(MemoryLeakMixin, TestCase):
+
+    # int64, size 0
+    zero_size = np.arange(0)
+
+    def check_exception(self, pyfunc, msg):
+        cfunc = jit(nopython=True)(pyfunc)
+        # make sure NumPy raises consistently/no behaviour change
+        with self.assertRaises(BaseException):
+            pyfunc(self.zero_size)
+        # check numba impl raises expected
+        with self.assertRaises(ValueError) as e:
+            cfunc(self.zero_size)
+        self.assertIn(msg, str(e.exception))
+
+    @classmethod
+    def install(cls):
+
+        fn_to_msg = dict()
+        empty_seq = "attempt to get {0} of an empty sequence"
+        op_no_ident = ("zero-size array to reduction operation "
+                       "{0}")
+        for x in [array_argmax, array_argmax_global, array_argmin,
+                  array_argmin_global]:
+            fn_to_msg[x] = empty_seq
+        for x in [array_max, array_max, array_min, array_min]:
+            fn_to_msg[x] = op_no_ident
+
+        name_template = "test_zero_size_array_{0}"
+        for fn, msg in fn_to_msg.items():
+            test_name = name_template.format(fn.__name__)
+
+            lmsg = msg.format(fn.__name__)
+            lmsg = lmsg.replace('array_','').replace('_global','')
+            def test_fn(self, func=fn, message=lmsg):
+                self.check_exception(func, message)
+
+            setattr(cls, test_name, test_fn)
+
+TestArrayReductionsExceptions.install()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_return.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_return.py
new file mode 100644
index 000000000..b5fd3cf32
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_array_return.py
@@ -0,0 +1,41 @@
+import numpy as np
+
+from numba.core.compiler import compile_isolated
+from numba import typeof
+from numba.tests.support import MemoryLeakMixin
+import unittest
+
+
+def array_return(a, i):
+    a[i] = 123
+    return a
+
+
+def array_return_start_with_loop(a):
+    for i in range(a.size):
+        a[i] += 1
+    return a
+
+
+class TestArrayReturn(MemoryLeakMixin, unittest.TestCase):
+    def test_array_return(self):
+        a = np.arange(10)
+        i = 2
+        at, it = typeof(a), typeof(i)
+        cres = compile_isolated(array_return, (at, it))
+        cfunc = cres.entry_point
+        self.assertIs(a, cfunc(a, i))
+
+    def test_array_return_start_with_loop(self):
+        """
+        A bug breaks array return if the function starts with a loop
+        """
+        a = np.arange(10)
+        at = typeof(a)
+        cres = compile_isolated(array_return_start_with_loop, [at])
+        cfunc = cres.entry_point
+        self.assertIs(a, cfunc(a))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_asnumbatype.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_asnumbatype.py
new file mode 100644
index 000000000..868487460
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_asnumbatype.py
@@ -0,0 +1,170 @@
+"""
+Tests for the as_numba_type() machinery.
+"""
+import typing as py_typing
+
+
+import unittest
+
+from numba.core import types
+from numba.core.errors import TypingError
+from numba.core.typing.typeof import typeof
+from numba.core.typing.asnumbatype import as_numba_type, AsNumbaTypeRegistry
+from numba.experimental.jitclass import jitclass
+from numba.tests.support import TestCase
+
+
+class TestAsNumbaType(TestCase):
+
+    int_nb_type = typeof(0)
+    float_nb_type = typeof(0.0)
+    complex_nb_type = typeof(complex(0))
+    str_nb_type = typeof("numba")
+    bool_nb_type = typeof(True)
+    none_nb_type = typeof(None)
+
+    def test_simple_types(self):
+        self.assertEqual(as_numba_type(int), self.int_nb_type)
+        self.assertEqual(as_numba_type(float), self.float_nb_type)
+        self.assertEqual(as_numba_type(complex), self.complex_nb_type)
+        self.assertEqual(as_numba_type(str), self.str_nb_type)
+        self.assertEqual(as_numba_type(bool), self.bool_nb_type)
+        self.assertEqual(as_numba_type(type(None)), self.none_nb_type)
+
+    def test_numba_types(self):
+        numba_types = [
+            types.intp,
+            types.boolean,
+            types.ListType(types.float64),
+            types.DictType(
+                types.intp, types.Tuple([types.float32, types.float32])
+            ),
+        ]
+
+        for ty in numba_types:
+            self.assertEqual(as_numba_type(ty), ty)
+
+    def test_single_containers(self):
+        self.assertEqual(
+            as_numba_type(py_typing.List[float]),
+            types.ListType(self.float_nb_type),
+        )
+        self.assertEqual(
+            as_numba_type(py_typing.Dict[float, str]),
+            types.DictType(self.float_nb_type, self.str_nb_type),
+        )
+        self.assertEqual(
+            as_numba_type(py_typing.Set[complex]),
+            types.Set(self.complex_nb_type),
+        )
+        self.assertEqual(
+            as_numba_type(py_typing.Tuple[float, float]),
+            types.Tuple([self.float_nb_type, self.float_nb_type]),
+        )
+        self.assertEqual(
+            as_numba_type(py_typing.Tuple[float, complex]),
+            types.Tuple([self.float_nb_type, self.complex_nb_type]),
+        )
+
+    def test_optional(self):
+        self.assertEqual(
+            as_numba_type(py_typing.Optional[float]),
+            types.Optional(self.float_nb_type),
+        )
+        self.assertEqual(
+            as_numba_type(py_typing.Union[str, None]),
+            types.Optional(self.str_nb_type),
+        )
+        self.assertEqual(
+            as_numba_type(py_typing.Union[None, bool]),
+            types.Optional(self.bool_nb_type),
+        )
+
+        # Optional[x] is a special case of Union[x, None].  We raise a
+        # TypingError if the right type is not NoneType.
+        with self.assertRaises(TypingError) as raises:
+            as_numba_type(py_typing.Union[int, float])
+        self.assertIn("Cannot type Union that is not an Optional",
+                      str(raises.exception))
+
+    def test_nested_containers(self):
+        IntList = py_typing.List[int]
+        self.assertEqual(
+            as_numba_type(py_typing.List[IntList]),
+            types.ListType(types.ListType(self.int_nb_type)),
+        )
+        self.assertEqual(
+            as_numba_type(py_typing.List[py_typing.Dict[float, bool]]),
+            types.ListType(
+                types.DictType(self.float_nb_type, self.bool_nb_type)
+            ),
+        )
+        self.assertEqual(
+            as_numba_type(
+                py_typing.Set[py_typing.Tuple[py_typing.Optional[int], float]]),
+            types.Set(types.Tuple(
+                [types.Optional(self.int_nb_type), self.float_nb_type])),
+        )
+
+    def test_jitclass_registers(self):
+
+        @jitclass
+        class MyInt:
+            x: int
+
+            def __init__(self, value):
+                self.x = value
+
+        self.assertEqual(as_numba_type(MyInt), MyInt.class_type.instance_type)
+
+    def test_type_alias(self):
+        Pair = py_typing.Tuple[int, int]
+        ListOfPairs = py_typing.List[Pair]
+
+        pair_nb_type = types.Tuple((self.int_nb_type, self.int_nb_type))
+        self.assertEqual(as_numba_type(Pair), pair_nb_type)
+        self.assertEqual(
+            as_numba_type(ListOfPairs), types.ListType(pair_nb_type)
+        )
+
+    def test_overwrite_type(self):
+        as_numba_type = AsNumbaTypeRegistry()
+        self.assertEqual(as_numba_type(float), self.float_nb_type)
+        as_numba_type.register(float, types.float32)
+        self.assertEqual(as_numba_type(float), types.float32)
+        self.assertNotEqual(as_numba_type(float), self.float_nb_type)
+
+    def test_any_throws(self):
+        Any = py_typing.Any
+
+        any_types = [
+            py_typing.Optional[Any],
+            py_typing.List[Any],
+            py_typing.Set[Any],
+            py_typing.Dict[float, Any],
+            py_typing.Dict[Any, float],
+            py_typing.Tuple[int, Any],
+        ]
+
+        for bad_py_type in any_types:
+            with self.assertRaises(TypingError) as raises:
+                as_numba_type(bad_py_type)
+            self.assertIn(
+                "Cannot infer numba type of python type",
+                str(raises.exception),
+            )
+
+    def test_bad_union_throws(self):
+        bad_unions = [
+            py_typing.Union[str, int],
+            py_typing.Union[int, type(None), py_typing.Tuple[bool, bool]],
+        ]
+
+        for bad_py_type in bad_unions:
+            with self.assertRaises(TypingError) as raises:
+                as_numba_type(bad_py_type)
+            self.assertIn("Cannot type Union", str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_auto_constants.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_auto_constants.py
new file mode 100644
index 000000000..938fa0187
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_auto_constants.py
@@ -0,0 +1,40 @@
+import math
+import sys
+
+import numpy as np
+
+from numba import njit
+from numba.core.compiler import compile_isolated
+import numba.tests.usecases as uc
+import unittest
+
+
+class TestAutoConstants(unittest.TestCase):
+    def test_numpy_nan(self):
+        def pyfunc():
+            return np.nan
+
+        cres = compile_isolated(pyfunc, ())
+        cfunc = cres.entry_point
+
+        self.assertTrue(math.isnan(pyfunc()))
+        self.assertTrue(math.isnan(cfunc()))
+
+    def test_sys_constant(self):
+        def pyfunc():
+            return sys.hexversion
+
+        cres = compile_isolated(pyfunc, ())
+        cfunc = cres.entry_point
+
+        self.assertEqual(pyfunc(), cfunc())
+
+    def test_module_string_constant(self):
+        @njit
+        def f():
+            return uc._GLOBAL_STR
+        self.assertEqual(f(), f.py_func())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_blackscholes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_blackscholes.py
new file mode 100644
index 000000000..d12846117
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_blackscholes.py
@@ -0,0 +1,125 @@
+import math
+
+import numpy as np
+
+import unittest
+from numba import njit
+from numba.extending import register_jitable
+from numba.tests.support import TestCase
+
+
+RISKFREE = 0.02
+VOLATILITY = 0.30
+
+
+A1 = 0.31938153
+A2 = -0.356563782
+A3 = 1.781477937
+A4 = -1.821255978
+A5 = 1.330274429
+RSQRT2PI = 0.39894228040143267793994605993438
+
+
+@register_jitable
+def cnd_array(d):
+    K = 1.0 / (1.0 + 0.2316419 * np.abs(d))
+    ret_val = (RSQRT2PI * np.exp(-0.5 * d * d) *
+               (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+    return np.where(d > 0, 1.0 - ret_val, ret_val)
+
+
+@register_jitable
+def cnd(d):
+    K = 1.0 / (1.0 + 0.2316419 * math.fabs(d))
+    ret_val = (RSQRT2PI * math.exp(-0.5 * d * d) *
+               (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+    if d > 0:
+        ret_val = 1.0 - ret_val
+    return ret_val
+
+
+@njit
+def blackscholes_arrayexpr(stockPrice, optionStrike, optionYears, Riskfree,
+                           Volatility):
+    S = stockPrice
+    X = optionStrike
+    T = optionYears
+    R = Riskfree
+    V = Volatility
+    sqrtT = np.sqrt(T)
+    d1 = (np.log(S / X) + (R + 0.5 * V * V) * T) / (V * sqrtT)
+    d2 = d1 - V * sqrtT
+    cndd1 = cnd_array(d1)
+    cndd2 = cnd_array(d2)
+
+    expRT = np.exp(- R * T)
+
+    callResult = (S * cndd1 - X * expRT * cndd2)
+    putResult = (X * expRT * (1.0 - cndd2) - S * (1.0 - cndd1))
+    return callResult, putResult
+
+
+@njit
+def blackscholes_scalar(callResult, putResult, stockPrice, optionStrike,
+                        optionYears, Riskfree, Volatility):
+    S = stockPrice
+    X = optionStrike
+    T = optionYears
+    R = Riskfree
+    V = Volatility
+    for i in range(len(S)):
+        sqrtT = math.sqrt(T[i])
+        d1 = (math.log(S[i] / X[i]) + (R + 0.5 * V * V) * T[i]) / (V * sqrtT)
+        d2 = d1 - V * sqrtT
+        cndd1 = cnd(d1)
+        cndd2 = cnd(d2)
+
+        expRT = math.exp((-1. * R) * T[i])
+        callResult[i] = (S[i] * cndd1 - X[i] * expRT * cndd2)
+        putResult[i] = (X[i] * expRT * (1.0 - cndd2) - S[i] * (1.0 - cndd1))
+
+
+def randfloat(rand_var, low, high):
+    return (1.0 - rand_var) * low + rand_var * high
+
+
+class TestBlackScholes(TestCase):
+    def test_array_expr(self):
+        OPT_N = 400
+
+        stockPrice = randfloat(self.random.random_sample(OPT_N), 5.0, 30.0)
+        optionStrike = randfloat(self.random.random_sample(OPT_N), 1.0, 100.0)
+        optionYears = randfloat(self.random.random_sample(OPT_N), 0.25, 10.0)
+
+        args = stockPrice, optionStrike, optionYears, RISKFREE, VOLATILITY
+
+        callResultGold, putResultGold = blackscholes_arrayexpr.py_func(*args)
+        callResultNumba, putResultNumba = blackscholes_arrayexpr(*args)
+
+        delta = np.abs(callResultGold - callResultNumba)
+        self.assertAlmostEqual(delta.max(), 0)
+
+    def test_scalar(self):
+        OPT_N = 400
+
+        callResultGold = np.zeros(OPT_N)
+        putResultGold = np.zeros(OPT_N)
+
+        callResultNumba = np.zeros(OPT_N)
+        putResultNumba = np.zeros(OPT_N)
+
+        stockPrice = randfloat(self.random.random_sample(OPT_N), 5.0, 30.0)
+        optionStrike = randfloat(self.random.random_sample(OPT_N), 1.0, 100.0)
+        optionYears = randfloat(self.random.random_sample(OPT_N), 0.25, 10.0)
+
+        args = stockPrice, optionStrike, optionYears, RISKFREE, VOLATILITY
+
+        blackscholes_scalar.py_func(callResultGold, putResultGold, *args)
+        blackscholes_scalar(callResultNumba, putResultNumba, *args)
+
+        delta = np.abs(callResultGold - callResultNumba)
+        self.assertAlmostEqual(delta.max(), 0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_boundscheck.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_boundscheck.py
new file mode 100644
index 000000000..8dbb18107
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_boundscheck.py
@@ -0,0 +1,266 @@
+import numpy as np
+
+from numba.core.compiler import compile_isolated, DEFAULT_FLAGS
+from numba.cuda.testing import SerialMixin
+from numba import typeof, cuda, njit
+from numba.core.types import float64
+from numba.tests.support import MemoryLeakMixin, override_env_config
+from numba.core import config
+import unittest
+
+BOUNDSCHECK_FLAGS = DEFAULT_FLAGS.copy()
+BOUNDSCHECK_FLAGS.boundscheck = True
+
+
+def basic_array_access(a):
+    return a[10]
+
+
+def slice_array_access(a):
+    # The first index (slice) is not bounds checked
+    return a[10:, 10]
+
+
+def fancy_array_access(x):
+    a = np.array([1, 2, 3])
+    return x[a]
+
+
+class TestBoundsCheckNoError(MemoryLeakMixin, unittest.TestCase):
+    def setUp(self):
+        self.old_boundscheck = config.BOUNDSCHECK
+        config.BOUNDSCHECK = None
+
+    def test_basic_array_boundscheck(self):
+        a = np.arange(5)
+        # Check the numpy behavior to make sure the test is correct
+        with self.assertRaises(IndexError):
+            # TODO: When we raise the same error message as numpy, test that
+            # they are the same
+            basic_array_access(a)
+
+        at = typeof(a)
+        c_noboundscheck = compile_isolated(basic_array_access, [at],
+                                           flags=DEFAULT_FLAGS)
+        noboundscheck = c_noboundscheck.entry_point
+        # Check that the default flag doesn't raise
+        noboundscheck(a)
+        # boundscheck(a) is tested in TestBoundsCheckError below
+
+    def test_slice_array_boundscheck(self):
+        a = np.ones((5, 5))
+        b = np.ones((5, 20))
+        with self.assertRaises(IndexError):
+            # TODO: When we raise the same error message as numpy, test that
+            # they are the same
+            slice_array_access(a)
+        # Out of bounds on a slice doesn't raise
+        slice_array_access(b)
+
+        at = typeof(a)
+        rt = float64[:]
+        c_noboundscheck = compile_isolated(slice_array_access, [at],
+                                           return_type=rt,
+                                           flags=DEFAULT_FLAGS)
+        noboundscheck = c_noboundscheck.entry_point
+        c_boundscheck = compile_isolated(slice_array_access, [at],
+                                         return_type=rt,
+                                         flags=BOUNDSCHECK_FLAGS)
+        boundscheck = c_boundscheck.entry_point
+        # Check that the default flag doesn't raise
+        noboundscheck(a)
+        noboundscheck(b)
+        # boundscheck(a) is tested in TestBoundsCheckError below
+
+        # Doesn't raise
+        boundscheck(b)
+
+    def test_fancy_indexing_boundscheck(self):
+        a = np.arange(3)
+        b = np.arange(4)
+
+        # Check the numpy behavior to ensure the test is correct.
+        with self.assertRaises(IndexError):
+            # TODO: When we raise the same error message as numpy, test that
+            # they are the same
+            fancy_array_access(a)
+        fancy_array_access(b)
+
+        at = typeof(a)
+        rt = at.dtype[:]
+        c_noboundscheck = compile_isolated(fancy_array_access, [at],
+                                           return_type=rt,
+                                           flags=DEFAULT_FLAGS)
+        noboundscheck = c_noboundscheck.entry_point
+        c_boundscheck = compile_isolated(fancy_array_access, [at],
+                                         return_type=rt,
+                                         flags=BOUNDSCHECK_FLAGS)
+        boundscheck = c_boundscheck.entry_point
+        # Check that the default flag doesn't raise
+        noboundscheck(a)
+        noboundscheck(b)
+        # boundscheck(a) is tested in TestBoundsCheckError below
+
+        # Doesn't raise
+        boundscheck(b)
+
+    def tearDown(self):
+        config.BOUNDSCHECK = self.old_boundscheck
+
+
+class TestNoCudaBoundsCheck(SerialMixin, unittest.TestCase):
+    def setUp(self):
+        self.old_boundscheck = config.BOUNDSCHECK
+        config.BOUNDSCHECK = None
+
+    @unittest.skipIf(not cuda.is_available(), "NO CUDA")
+    def test_no_cuda_boundscheck(self):
+        with self.assertRaises(NotImplementedError):
+            @cuda.jit(boundscheck=True)
+            def func():
+                pass
+
+        # Make sure we aren't raising "not supported" error if we aren't
+        # requesting bounds checking anyway. Related pull request: #5257
+        @cuda.jit(boundscheck=False)
+        def func3():
+            pass
+
+        with override_env_config('NUMBA_BOUNDSCHECK', '1'):
+            @cuda.jit
+            def func2(x, a):
+                a[1] = x[1]
+
+            a = np.ones((1,))
+            x = np.zeros((1,))
+            # Out of bounds but doesn't raise (it does raise in the simulator,
+            # so skip there)
+            if not config.ENABLE_CUDASIM:
+                func2[1, 1](x, a)
+
+    def tearDown(self):
+        config.BOUNDSCHECK = self.old_boundscheck
+
+
+# This is a separate test because the jitted functions that raise exceptions
+# have memory leaks.
+class TestBoundsCheckError(unittest.TestCase):
+    def setUp(self):
+        self.old_boundscheck = config.BOUNDSCHECK
+        config.BOUNDSCHECK = None
+
+    def test_basic_array_boundscheck(self):
+        a = np.arange(5)
+        # Check the numpy behavior to make sure the test is correct
+        with self.assertRaises(IndexError):
+            # TODO: When we raise the same error message as numpy, test that
+            # they are the same
+            basic_array_access(a)
+
+        at = typeof(a)
+        c_boundscheck = compile_isolated(basic_array_access, [at],
+                                         flags=BOUNDSCHECK_FLAGS)
+        boundscheck = c_boundscheck.entry_point
+
+        with self.assertRaises(IndexError):
+            boundscheck(a)
+
+    def test_slice_array_boundscheck(self):
+        a = np.ones((5, 5))
+        b = np.ones((5, 20))
+        with self.assertRaises(IndexError):
+            # TODO: When we raise the same error message as numpy, test that
+            # they are the same
+            slice_array_access(a)
+        # Out of bounds on a slice doesn't raise
+        slice_array_access(b)
+
+        at = typeof(a)
+        rt = float64[:]
+        c_boundscheck = compile_isolated(slice_array_access, [at],
+                                         return_type=rt,
+                                         flags=BOUNDSCHECK_FLAGS)
+        boundscheck = c_boundscheck.entry_point
+        with self.assertRaises(IndexError):
+            boundscheck(a)
+
+    def test_fancy_indexing_boundscheck(self):
+        a = np.arange(3)
+        b = np.arange(4)
+
+        # Check the numpy behavior to ensure the test is correct.
+        with self.assertRaises(IndexError):
+            # TODO: When we raise the same error message as numpy, test that
+            # they are the same
+            fancy_array_access(a)
+        fancy_array_access(b)
+
+        at = typeof(a)
+        rt = at.dtype[:]
+        c_boundscheck = compile_isolated(fancy_array_access, [at],
+                                         return_type=rt,
+                                         flags=BOUNDSCHECK_FLAGS)
+        boundscheck = c_boundscheck.entry_point
+        with self.assertRaises(IndexError):
+            boundscheck(a)
+
+    def tearDown(self):
+        config.BOUNDSCHECK = self.old_boundscheck
+
+
+class TestBoundsEnvironmentVariable(unittest.TestCase):
+    def setUp(self):
+        self.old_boundscheck = config.BOUNDSCHECK
+        config.BOUNDSCHECK = None
+
+        @njit
+        def default(x):
+            return x[1]
+
+        @njit(boundscheck=False)
+        def off(x):
+            return x[1]
+
+        @njit(boundscheck=True)
+        def on(x):
+            return x[1]
+
+        self.default = default
+        self.off = off
+        self.on = on
+
+    def test_boundscheck_unset(self):
+        with override_env_config('NUMBA_BOUNDSCHECK', ''):
+            a = np.array([1])
+
+            # Doesn't raise
+            self.default(a)
+            self.off(a)
+
+            with self.assertRaises(IndexError):
+                self.on(a)
+
+    def test_boundscheck_enabled(self):
+        with override_env_config('NUMBA_BOUNDSCHECK', '1'):
+            a = np.array([1])
+
+            with self.assertRaises(IndexError):
+                self.default(a)
+                self.off(a)
+                self.on(a)
+
+    def test_boundscheck_disabled(self):
+        with override_env_config('NUMBA_BOUNDSCHECK', '0'):
+            a = np.array([1])
+
+            # Doesn't raise
+            self.default(a)
+            self.off(a)
+            self.on(a)
+
+    def tearDown(self):
+        config.BOUNDSCHECK = self.old_boundscheck
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_buffer_protocol.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_buffer_protocol.py
new file mode 100644
index 000000000..b680d819e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_buffer_protocol.py
@@ -0,0 +1,289 @@
+import array
+
+import numpy as np
+
+from numba import jit
+from numba.tests.support import TestCase, compile_function, MemoryLeakMixin
+import unittest
+
+
+@jit(nopython=True)
+def len_usecase(buf):
+    return len(buf)
+
+
+@jit(nopython=True)
+def getitem_usecase(buf, i):
+    return buf[i]
+
+
+@jit(nopython=True)
+def getslice_usecase(buf, i, j):
+    s = buf[i:j]
+    return s[0] + 2 * s[-1]
+
+
+@jit(nopython=True)
+def setitem_usecase(buf, i, v):
+    buf[i] = v
+
+
+@jit(nopython=True)
+def iter_usecase(buf):
+    res = 0.0
+    for i, x in enumerate(buf):
+        res += x
+        res *= i + 1
+    return res
+
+
+def attrgetter(attr):
+    code = """def func(x):
+        return x.%(attr)s
+""" % locals()
+    pyfunc = compile_function("func", code, globals())
+    return jit(nopython=True)(pyfunc)
+
+
+contiguous_usecase = attrgetter("contiguous")
+c_contiguous_usecase = attrgetter("c_contiguous")
+f_contiguous_usecase = attrgetter("f_contiguous")
+itemsize_usecase = attrgetter("itemsize")
+nbytes_usecase = attrgetter("nbytes")
+ndim_usecase = attrgetter("ndim")
+readonly_usecase = attrgetter("readonly")
+shape_usecase = attrgetter("shape")
+strides_usecase = attrgetter("strides")
+
+
+class TestBufferProtocol(MemoryLeakMixin, TestCase):
+    """
+    Test operations on buffer-providing objects.
+    """
+
+    def _arrays(self):
+        n = 10
+        for letter, offset in [
+            ('b', -3),
+            ('B', 0),
+            ('h', -5000),
+            ('H', 40000),
+            ('i', -100000),
+            ('I', 1000000),
+            ('l', -100000),
+            ('L', 1000000),
+            ('q', -2**60),
+            ('Q', 2**63 + 1),
+            ('f', 1.5),
+            ('d', -1.5),
+            ]:
+            yield array.array(letter, [i + offset for i in range(n)])
+
+    def _memoryviews(self):
+        n = 10
+        yield memoryview(bytearray(b"abcdefghi"))
+        yield memoryview(b"abcdefghi")
+        # Different item types
+        for dtype, start, stop in [
+            ('int8', -10, 10),
+            ('uint8', 0, 10),
+            ('int16', -5000, 1000),
+            ('uint16', 40000, 50000),
+            ('int32', -100000, 100000),
+            ('uint32', 0, 1000000),
+            ('int64', -2**60, 10),
+            ('uint64', 0, 2**64 - 10),
+            ('float32', 1.5, 3.5),
+            ('float64', 1.5, 3.5),
+            ('complex64', -8j, 12 + 5j),
+            ('complex128', -8j, 12 + 5j),
+            ]:
+            yield memoryview(np.linspace(start, stop, n).astype(dtype))
+        # Different layouts
+        arr = np.arange(12).reshape((3, 4))
+        assert arr.flags.c_contiguous and not arr.flags.f_contiguous
+        yield memoryview(arr)
+        arr = arr.T
+        assert arr.flags.f_contiguous and not arr.flags.c_contiguous
+        yield memoryview(arr)
+        arr = arr[::2]
+        assert not arr.flags.f_contiguous and not arr.flags.c_contiguous
+        yield memoryview(arr)
+
+    def _readonlies(self):
+        yield b"xyz"
+        yield memoryview(b"abcdefghi")
+        arr = np.arange(5)
+        arr.setflags(write=False)
+        yield memoryview(arr)
+
+    def _check_unary(self, jitfunc, *args):
+        pyfunc = jitfunc.py_func
+        self.assertPreciseEqual(jitfunc(*args), pyfunc(*args))
+
+    def check_len(self, obj):
+        self._check_unary(len_usecase, obj)
+
+    def check_iter(self, obj):
+        self._check_unary(iter_usecase, obj)
+
+    def check_getitem(self, obj):
+        # Be careful to index all dimensions, since we don't support
+        # partial indexing yet.
+        def yield_indices(obj):
+            try:
+                shape = obj.shape
+            except AttributeError:
+                shape = len(obj),
+            for tup in np.ndindex(shape):
+                # Simple 1d buffer-providing objects usually don't support
+                # tuple indexing.
+                if len(tup) == 1:
+                    yield tup[0]
+                else:
+                    yield tup
+
+        for i in yield_indices(obj):
+            try:
+                expected = obj[i]
+            except (NotImplementedError, TypeError):
+                if isinstance(obj, memoryview):
+                    # The memoryview object doesn't support all codes yet,
+                    # fall back on the underlying object.
+                    expected = obj.obj[i]
+                else:
+                    raise
+            self.assertPreciseEqual(getitem_usecase(obj, i), expected)
+
+    def check_setitem(self, obj):
+        for i in range(len(obj)):
+            orig = list(obj)
+            val = obj[i] // 2 + 1
+            setitem_usecase(obj, i, val)
+            self.assertEqual(obj[i], val)
+            for j, val in enumerate(orig):
+                if j != i:
+                    self.assertEqual(obj[j], val)
+
+    def check_getslice(self, obj):
+        self._check_unary(getslice_usecase, obj, 1, len(obj) - 1)
+
+    def test_len(self):
+        self.check_len(bytearray(5))
+        self.check_len(b"xyz")
+        for mem in self._memoryviews():
+            self.check_len(mem)
+        for arr in self._arrays():
+            self.check_len(arr)
+        for buf in self._readonlies():
+            self.check_getitem(buf)
+
+    def test_getitem(self):
+        self.check_getitem(bytearray(b"abc"))
+        self.check_getitem(b"xyz")
+        for mem in self._memoryviews():
+            self.check_getitem(mem)
+        for arr in self._arrays():
+            self.check_getitem(arr)
+        for buf in self._readonlies():
+            self.check_getitem(buf)
+
+    def test_getslice(self):
+        with self.assertTypingError():
+            self.check_getslice(bytearray(b"abcde"))
+        self.check_getslice(b"xyzuvw")
+        self.check_getslice(memoryview(b"xyzuvw"))
+        with self.assertTypingError():
+            self.check_getslice(array.array('i', range(10)))
+        for buf in self._readonlies():
+            self.check_getitem(buf)
+
+    def test_setitem(self):
+        self.check_setitem(bytearray(b"abcdefghi"))
+        for arr in self._arrays():
+            self.check_setitem(arr)
+        for mem in self._memoryviews():
+            self.check_getitem(mem)
+        # Read-only buffers
+        for buf in self._readonlies():
+            with self.assertTypingError():
+                self.check_setitem(buf)
+
+    def test_iter(self):
+        self.check_iter(bytearray(b"abc"))
+        self.check_iter(b"xyz")
+        self.check_iter(memoryview(b"xyz"))
+        for arr in self._arrays():
+            self.check_iter(arr)
+        for buf in self._readonlies():
+            self.check_getitem(buf)
+
+
+class TestMemoryView(MemoryLeakMixin, TestCase):
+    """
+    Test memoryview-specific attributes and operations.
+    """
+
+    def _arrays(self):
+        arr = np.arange(12)
+        yield arr
+        arr = arr.reshape((3, 4))
+        yield arr
+        yield arr.T
+        yield arr[::2]
+        arr.setflags(write=False)
+        yield arr
+        arr = np.zeros(())
+        assert arr.ndim == 0
+        yield arr
+
+    def test_ndim(self):
+        for arr in self._arrays():
+            m = memoryview(arr)
+            self.assertPreciseEqual(ndim_usecase(m), arr.ndim)
+
+    def test_shape(self):
+        for arr in self._arrays():
+            m = memoryview(arr)
+            self.assertPreciseEqual(shape_usecase(m), arr.shape)
+
+    def test_strides(self):
+        for arr in self._arrays():
+            m = memoryview(arr)
+            self.assertPreciseEqual(strides_usecase(m), arr.strides)
+
+    def test_itemsize(self):
+        for arr in self._arrays():
+            m = memoryview(arr)
+            self.assertPreciseEqual(itemsize_usecase(m), arr.itemsize)
+
+    def test_nbytes(self):
+        for arr in self._arrays():
+            m = memoryview(arr)
+            self.assertPreciseEqual(nbytes_usecase(m), arr.size * arr.itemsize)
+
+    def test_readonly(self):
+        for arr in self._arrays():
+            m = memoryview(arr)
+            self.assertIs(readonly_usecase(m), not arr.flags.writeable)
+        m = memoryview(b"xyz")
+        self.assertIs(readonly_usecase(m), True)
+        m = memoryview(bytearray(b"xyz"))
+        self.assertIs(readonly_usecase(m), False)
+
+    def test_contiguous(self):
+        m = memoryview(bytearray(b"xyz"))
+        self.assertIs(contiguous_usecase(m), True)
+        self.assertIs(c_contiguous_usecase(m), True)
+        self.assertIs(f_contiguous_usecase(m), True)
+        for arr in self._arrays():
+            m = memoryview(arr)
+            # Note `arr.flags.contiguous` is wrong (it mimics c_contiguous)
+            self.assertIs(contiguous_usecase(m),
+                          arr.flags.f_contiguous or arr.flags.c_contiguous)
+            self.assertIs(c_contiguous_usecase(m), arr.flags.c_contiguous)
+            self.assertIs(f_contiguous_usecase(m), arr.flags.f_contiguous)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_builtins.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_builtins.py
new file mode 100644
index 000000000..ceea67af9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_builtins.py
@@ -0,0 +1,1404 @@
+import itertools
+import functools
+import sys
+import operator
+from collections import namedtuple
+
+import numpy as np
+
+import unittest
+import warnings
+
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit, typeof, njit, typed
+from numba.core import errors, types, utils, config
+from numba.tests.support import TestCase, tag, ignore_internal_warnings
+
+py38orlater = utils.PYVERSION >= (3, 8)
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+forceobj_flags = Flags()
+forceobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+nrt_no_pyobj_flags = Flags()
+nrt_no_pyobj_flags.nrt = True
+
+
+def abs_usecase(x):
+    return abs(x)
+
+def all_usecase(x, y):
+    if x == None and y == None:
+        return all([])
+    elif x == None:
+        return all([y])
+    elif y == None:
+        return all([x])
+    else:
+        return all([x, y])
+
+def any_usecase(x, y):
+    if x == None and y == None:
+        return any([])
+    elif x == None:
+        return any([y])
+    elif y == None:
+        return any([x])
+    else:
+        return any([x, y])
+
+def bool_usecase(x):
+    return bool(x)
+
+def complex_usecase(x, y):
+    return complex(x, y)
+
+def divmod_usecase(x, y):
+    return divmod(x, y)
+
+def enumerate_usecase():
+    result = 0
+    for i, j in enumerate((1., 2.5, 3.)):
+        result += i * j
+    return result
+
+def enumerate_start_usecase():
+    result = 0
+    for i, j in enumerate((1., 2.5, 3.), 42):
+        result += i * j
+    return result
+
+def enumerate_invalid_start_usecase():
+    result = 0
+    for i, j in enumerate((1., 2.5, 3.), 3.14159):
+        result += i * j
+    return result
+
+def filter_usecase(x, filter_func):
+    return filter(filter_func, x)
+
+def float_usecase(x):
+    return float(x)
+
+def format_usecase(x, y):
+    return x.format(y)
+
+def globals_usecase():
+    return globals()
+
+# NOTE: hash() is tested in test_hashing
+
+def hex_usecase(x):
+    return hex(x)
+
+def str_usecase(x):
+    return str(x)
+
+def int_usecase(x, base):
+    return int(x, base=base)
+
+def iter_next_usecase(x):
+    it = iter(x)
+    return next(it), next(it)
+
+def locals_usecase(x):
+    y = 5
+    return locals()['y']
+
+def long_usecase(x, base):
+    return long(x, base=base)
+
+def map_usecase(x, map_func):
+    return map(map_func, x)
+
+
+def max_usecase1(x, y):
+    return max(x, y)
+
+def max_usecase2(x, y):
+    return max([x, y])
+
+def max_usecase3(x):
+    return max(x)
+
+def max_usecase4():
+    return max(())
+
+
+def min_usecase1(x, y):
+    return min(x, y)
+
+def min_usecase2(x, y):
+    return min([x, y])
+
+def min_usecase3(x):
+    return min(x)
+
+def min_usecase4():
+    return min(())
+
+def oct_usecase(x):
+    return oct(x)
+
+def reduce_usecase(reduce_func, x):
+    return functools.reduce(reduce_func, x)
+
+def round_usecase1(x):
+    return round(x)
+
+def round_usecase2(x, n):
+    return round(x, n)
+
+def sum_usecase(x):
+    return sum(x)
+
+def type_unary_usecase(a, b):
+    return type(a)(b)
+
+def truth_usecase(p):
+    return operator.truth(p)
+
+def unichr_usecase(x):
+    return unichr(x)
+
+def zip_usecase():
+    result = 0
+    for i, j in zip((1, 2, 3), (4.5, 6.7)):
+        result += i * j
+    return result
+
+def zip_0_usecase():
+    result = 0
+    for i in zip():
+        result += 1
+    return result
+
+def zip_1_usecase():
+    result = 0
+    for i, in zip((1, 2)):
+        result += i
+    return result
+
+
+def zip_3_usecase():
+    result = 0
+    for i, j, k in zip((1, 2), (3, 4, 5), (6.7, 8.9)):
+        result += i * j * k
+    return result
+
+
+def zip_first_exhausted():
+    iterable = range(7)
+    n = 3
+    it = iter(iterable)
+    # 1st iterator is shorter
+    front = list(zip(range(n), it))
+    # Make sure that we didn't skip one in `it`
+    back = list(it)
+    return front, back
+
+
+def pow_op_usecase(x, y):
+    return x ** y
+
+
+def pow_usecase(x, y):
+    return pow(x, y)
+
+
+def sum_usecase(x):
+    return sum(x)
+
+
+def sum_kwarg_usecase(x, start=0):
+    ret = sum(x, start)
+    if py38orlater:
+        return sum(x, start=start), ret
+    else:
+        return ret
+
+
+def isinstance_usecase(a):
+    if isinstance(a, (int, float)):
+        if isinstance(a, int):
+            return a + 1, 'int'
+        if isinstance(a, float):
+            return a + 2.0, 'float'
+    elif isinstance(a, str):
+        return a + ", world!", 'str'
+    elif isinstance(a, complex):
+        return a.imag, 'complex'
+    elif isinstance(a, (tuple, list)):
+        if isinstance(a, tuple):
+            return 'tuple'
+        else:
+            return 'list'
+    elif isinstance(a, set):
+        return 'set'
+    elif isinstance(a, bytes):
+        return 'bytes'
+    return 'no match'
+
+
+def isinstance_dict():
+    a = {1: 2, 3: 4}
+    b = {'a': 10, 'b': np.zeros(3)}
+    if isinstance(a, dict) and isinstance(b, dict):
+        return 'dict'
+    else:
+        return 'not dict'
+
+
+def isinstance_usecase_numba_types(a):
+    if isinstance(a, typed.List):
+        return 'typed list'
+    elif isinstance(a, (types.int32, types.int64)):
+        if isinstance(a, types.int32):
+            return 'int32'
+        else:
+            return 'int64'
+    elif isinstance(a, (types.float32, types.float64)):
+        if isinstance(a, types.float32):
+            return 'float32'
+        elif isinstance(a, types.float64):
+            return 'float64'
+    elif isinstance(a, typed.Dict):
+        return 'typed dict'
+    else:
+        return 'no match'
+
+
+def isinstance_usecase_numba_types_2():
+    # some types cannot be passed as argument to njit functions
+    a = b'hello'
+    b = range(1, 2)
+    c = dict()
+    c[2] = 3
+    if isinstance(a, bytes) and \
+            isinstance(b, range) and \
+            isinstance(c, dict):
+        return True
+    return False
+
+
+def invalid_isinstance_usecase(x):
+    if isinstance(x, ('foo',)):
+        return 'true branch'
+    else:
+        return 'false branch'
+
+
+def isinstance_usecase_invalid_type(x):
+    # this should be a valid call when x := float
+    if isinstance(x, (float, 'not a type')):
+        return True
+    else:
+        return False
+
+
+def invalid_isinstance_usecase_phi_nopropagate(x):
+    if x > 4:
+        z = 10
+    else:
+        z = 'a'
+    if isinstance(z, int):
+        return True
+    else:
+        return False
+
+
+def invalid_isinstance_optional_usecase(x):
+    if x > 4:
+        z = 10
+    else:
+        z = None
+    if isinstance(z, int):
+        return True
+    else:
+        return False
+
+def invalid_isinstance_unsupported_type_usecase():
+    ntpl = namedtuple('ntpl', ['a', 'b'])
+    inst = ntpl(1, 2)
+    def impl(x):
+        return isinstance(inst, ntpl)
+    return impl
+
+class TestBuiltins(TestCase):
+
+    def run_nullary_func(self, pyfunc, flags):
+        cr = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cr.entry_point
+        expected = pyfunc()
+        self.assertPreciseEqual(cfunc(), expected)
+
+    def test_abs(self, flags=enable_pyobj_flags):
+        pyfunc = abs_usecase
+
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-1, 0, 1]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+        cr = compile_isolated(pyfunc, (types.float32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-1.1, 0.0, 1.1]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x), prec='single')
+
+        complex_values = [-1.1 + 0.5j, 0.0 + 0j, 1.1 + 3j,
+                          float('inf') + 1j * float('nan'),
+                          float('nan') - 1j * float('inf')]
+        cr = compile_isolated(pyfunc, (types.complex64,), flags=flags)
+        cfunc = cr.entry_point
+        for x in complex_values:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x), prec='single')
+        cr = compile_isolated(pyfunc, (types.complex128,), flags=flags)
+        cfunc = cr.entry_point
+        for x in complex_values:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+        for unsigned_type in types.unsigned_domain:
+            unsigned_values = [0, 10, 2, 2 ** unsigned_type.bitwidth - 1]
+            cr = compile_isolated(pyfunc, (unsigned_type,), flags=flags)
+            cfunc = cr.entry_point
+            for x in unsigned_values:
+                self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+    def test_abs_npm(self):
+        self.test_abs(flags=no_pyobj_flags)
+
+    def test_all(self, flags=enable_pyobj_flags):
+        pyfunc = all_usecase
+
+        cr = compile_isolated(pyfunc, (types.int32,types.int32), flags=flags)
+        cfunc = cr.entry_point
+        x_operands = [-1, 0, 1, None]
+        y_operands = [-1, 0, 1, None]
+        for x, y in itertools.product(x_operands, y_operands):
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+    def test_all_npm(self):
+        with self.assertTypingError():
+            self.test_all(flags=no_pyobj_flags)
+
+    def test_any(self, flags=enable_pyobj_flags):
+        pyfunc = any_usecase
+
+        cr = compile_isolated(pyfunc, (types.int32,types.int32), flags=flags)
+        cfunc = cr.entry_point
+        x_operands = [-1, 0, 1, None]
+        y_operands = [-1, 0, 1, None]
+        for x, y in itertools.product(x_operands, y_operands):
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+    def test_any_npm(self):
+        with self.assertTypingError():
+            self.test_any(flags=no_pyobj_flags)
+
+    def test_bool(self, flags=enable_pyobj_flags):
+        pyfunc = bool_usecase
+
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-1, 0, 1]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+        cr = compile_isolated(pyfunc, (types.float64,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [0.0, -0.0, 1.5, float('inf'), float('nan')]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+        cr = compile_isolated(pyfunc, (types.complex128,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [complex(0, float('inf')), complex(0, float('nan'))]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+    def test_bool_npm(self):
+        self.test_bool(flags=no_pyobj_flags)
+
+    def test_bool_nonnumber(self, flags=enable_pyobj_flags):
+        pyfunc = bool_usecase
+
+        cr = compile_isolated(pyfunc, (types.string,), flags=flags)
+        cfunc = cr.entry_point
+        for x in ['x', '']:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+        cr = compile_isolated(pyfunc, (types.Dummy('list'),), flags=flags)
+        cfunc = cr.entry_point
+        for x in [[1], []]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+    def test_bool_nonnumber_npm(self):
+        with self.assertTypingError():
+            self.test_bool_nonnumber(flags=no_pyobj_flags)
+
+    def test_complex(self, flags=enable_pyobj_flags):
+        pyfunc = complex_usecase
+
+        cr = compile_isolated(pyfunc, (types.int32, types.int32), flags=flags)
+        cfunc = cr.entry_point
+
+        x_operands = [-1, 0, 1]
+        y_operands = [-1, 0, 1]
+        for x, y in itertools.product(x_operands, y_operands):
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+    def test_complex_npm(self):
+        self.test_complex(flags=no_pyobj_flags)
+
+    def test_divmod_ints(self, flags=enable_pyobj_flags):
+        pyfunc = divmod_usecase
+
+        cr = compile_isolated(pyfunc, (types.int64, types.int64),
+                              flags=flags)
+        cfunc = cr.entry_point
+
+        def truncate_result(x, bits=64):
+            # Remove any extraneous bits (since Numba will return
+            # a 64-bit result by definition)
+            if x >= 0:
+                x &= (1 << (bits - 1)) - 1
+            return x
+
+        denominators = [1, 3, 7, 15, -1, -3, -7, -15, 2**63 - 1, -2**63]
+        numerators = denominators + [0]
+        for x, y, in itertools.product(numerators, denominators):
+            expected_quot, expected_rem = pyfunc(x, y)
+            quot, rem = cfunc(x, y)
+            f = truncate_result
+            self.assertPreciseEqual((f(quot), f(rem)),
+                                    (f(expected_quot), f(expected_rem)))
+
+        for x in numerators:
+            with self.assertRaises(ZeroDivisionError):
+                cfunc(x, 0)
+
+    def test_divmod_ints_npm(self):
+        self.test_divmod_ints(flags=no_pyobj_flags)
+
+    def test_divmod_floats(self, flags=enable_pyobj_flags):
+        pyfunc = divmod_usecase
+
+        cr = compile_isolated(pyfunc, (types.float64, types.float64),
+                              flags=flags)
+        cfunc = cr.entry_point
+
+        denominators = [1., 3.5, 1e100, -2., -7.5, -1e101,
+                        np.inf, -np.inf, np.nan]
+        numerators = denominators + [-0.0, 0.0]
+        for x, y, in itertools.product(numerators, denominators):
+            expected_quot, expected_rem = pyfunc(x, y)
+            quot, rem = cfunc(x, y)
+            self.assertPreciseEqual((quot, rem), (expected_quot, expected_rem))
+
+        for x in numerators:
+            with self.assertRaises(ZeroDivisionError):
+                cfunc(x, 0.0)
+
+    def test_divmod_floats_npm(self):
+        self.test_divmod_floats(flags=no_pyobj_flags)
+
+    def test_enumerate(self, flags=enable_pyobj_flags):
+        self.run_nullary_func(enumerate_usecase, flags)
+
+    def test_enumerate_npm(self):
+        self.test_enumerate(flags=no_pyobj_flags)
+
+    def test_enumerate_start(self, flags=enable_pyobj_flags):
+        self.run_nullary_func(enumerate_start_usecase, flags)
+
+    def test_enumerate_start_npm(self):
+        self.test_enumerate_start(flags=no_pyobj_flags)
+
+    def test_enumerate_start_invalid_start_type(self):
+        pyfunc = enumerate_invalid_start_usecase
+        cr = compile_isolated(pyfunc, (), flags=enable_pyobj_flags)
+        with self.assertRaises(TypeError) as raises:
+            cr.entry_point()
+
+        msg = "'float' object cannot be interpreted as an integer"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_enumerate_start_invalid_start_type_npm(self):
+        pyfunc = enumerate_invalid_start_usecase
+        with self.assertRaises(errors.TypingError) as raises:
+            cr = compile_isolated(pyfunc, (), flags=no_pyobj_flags)
+        msg = "Only integers supported as start value in enumerate"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_filter(self, flags=enable_pyobj_flags):
+        pyfunc = filter_usecase
+        cr = compile_isolated(pyfunc, (types.Dummy('list'),
+                                       types.Dummy('function_ptr')),
+                                       flags=flags)
+        cfunc = cr.entry_point
+
+        filter_func = lambda x: x % 2
+        x = [0, 1, 2, 3, 4]
+        self.assertSequenceEqual(list(cfunc(x, filter_func)),
+                                 list(pyfunc(x, filter_func)))
+
+    def test_filter_npm(self):
+        with self.assertTypingError():
+            self.test_filter(flags=no_pyobj_flags)
+
+    def test_float(self, flags=enable_pyobj_flags):
+        pyfunc = float_usecase
+
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-1, 0, 1]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+        cr = compile_isolated(pyfunc, (types.float32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-1.1, 0.0, 1.1]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x), prec='single')
+
+        cr = compile_isolated(pyfunc, (types.string,), flags=flags)
+        cfunc = cr.entry_point
+        for x in ['-1.1', '0.0', '1.1']:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+    def test_float_npm(self):
+        with self.assertTypingError():
+            self.test_float(flags=no_pyobj_flags)
+
+    def test_format(self, flags=enable_pyobj_flags):
+        pyfunc = format_usecase
+
+        cr = compile_isolated(pyfunc, (types.string, types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        x = '{0}'
+        for y in [-1, 0, 1]:
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+        cr = compile_isolated(pyfunc, (types.string,
+                                       types.float32,), flags=flags)
+        cfunc = cr.entry_point
+        x = '{0}'
+        for y in [-1.1, 0.0, 1.1]:
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+        cr = compile_isolated(pyfunc, (types.string,
+                                       types.string,), flags=flags)
+        cfunc = cr.entry_point
+        x = '{0}'
+        for y in ['a', 'b', 'c']:
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+    def test_format_npm(self):
+        with self.assertTypingError():
+            self.test_format(flags=no_pyobj_flags)
+
+    def test_globals(self, flags=enable_pyobj_flags):
+        pyfunc = globals_usecase
+        cr = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cr.entry_point
+        g = cfunc()
+        self.assertIs(g, globals())
+
+    def test_globals_npm(self):
+        with self.assertTypingError():
+            self.test_globals(flags=no_pyobj_flags)
+
+    def test_globals_jit(self, **jit_flags):
+        # Issue #416: weird behaviour of globals() in combination with
+        # the @jit decorator.
+        pyfunc = globals_usecase
+        jitted = jit(**jit_flags)(pyfunc)
+        self.assertIs(jitted(), globals())
+        self.assertIs(jitted(), globals())
+
+    def test_globals_jit_npm(self):
+        with self.assertTypingError():
+            self.test_globals_jit(nopython=True)
+
+    def test_hex(self, flags=enable_pyobj_flags):
+        pyfunc = hex_usecase
+
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-1, 0, 1]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+    def test_hex_npm(self):
+        with self.assertTypingError():
+            self.test_hex(flags=no_pyobj_flags)
+
+    def test_int_str(self, flags=nrt_no_pyobj_flags):
+        pyfunc = str_usecase
+
+        small_inputs = [
+            1234,
+            1,
+            0,
+            10,
+            1000,
+        ]
+
+        large_inputs = [
+            123456789,
+            2222222,
+            1000000,
+            ~0x0
+        ]
+
+        args = [*small_inputs, *large_inputs]
+
+        typs = [
+            types.int8,
+            types.int16,
+            types.int32,
+            types.int64,
+            types.uint,
+            types.uint8,
+            types.uint16,
+            types.uint32,
+            types.uint64,
+        ]
+
+        for typ in typs:
+            cr = compile_isolated(pyfunc, (typ,), flags=flags)
+            cfunc = cr.entry_point
+            for v in args:
+                self.assertPreciseEqual(cfunc(typ(v)), pyfunc(typ(v)))
+
+                if typ.signed:
+                    self.assertPreciseEqual(cfunc(typ(-v)), pyfunc(typ(-v)))
+
+    def test_int(self, flags=enable_pyobj_flags):
+        pyfunc = int_usecase
+
+        cr = compile_isolated(pyfunc, (types.string, types.int32), flags=flags)
+        cfunc = cr.entry_point
+
+        x_operands = ['-1', '0', '1', '10']
+        y_operands = [2, 8, 10, 16]
+        for x, y in itertools.product(x_operands, y_operands):
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+    def test_int_npm(self):
+        with self.assertTypingError():
+            self.test_int(flags=no_pyobj_flags)
+
+    def test_iter_next(self, flags=enable_pyobj_flags):
+        pyfunc = iter_next_usecase
+        cr = compile_isolated(pyfunc, (types.UniTuple(types.int32, 3),),
+                              flags=flags)
+        cfunc = cr.entry_point
+        self.assertPreciseEqual(cfunc((1, 42, 5)), (1, 42))
+
+        cr = compile_isolated(pyfunc, (types.UniTuple(types.int32, 1),),
+                              flags=flags)
+        cfunc = cr.entry_point
+        with self.assertRaises(StopIteration):
+            cfunc((1,))
+
+    def test_iter_next_npm(self):
+        self.test_iter_next(flags=no_pyobj_flags)
+
+    def test_locals(self, flags=enable_pyobj_flags):
+        pyfunc = locals_usecase
+        with self.assertRaises(errors.ForbiddenConstruct):
+            cr = compile_isolated(pyfunc, (types.int64,), flags=flags)
+
+    def test_locals_forceobj(self):
+        self.test_locals(flags=forceobj_flags)
+
+    def test_locals_npm(self):
+        with self.assertTypingError():
+            self.test_locals(flags=no_pyobj_flags)
+
+    def test_map(self, flags=enable_pyobj_flags):
+        pyfunc = map_usecase
+        cr = compile_isolated(pyfunc, (types.Dummy('list'),
+                                       types.Dummy('function_ptr')),
+                                       flags=flags)
+        cfunc = cr.entry_point
+
+        map_func = lambda x: x * 2
+        x = [0, 1, 2, 3, 4]
+        self.assertSequenceEqual(list(cfunc(x, map_func)),
+                                 list(pyfunc(x, map_func)))
+
+    def test_map_npm(self):
+        with self.assertTypingError():
+            self.test_map(flags=no_pyobj_flags)
+
+    #
+    # min() and max()
+    #
+
+    def check_minmax_1(self, pyfunc, flags):
+        cr = compile_isolated(pyfunc, (types.int32, types.int32), flags=flags)
+        cfunc = cr.entry_point
+
+        x_operands = [-1, 0, 1]
+        y_operands = [-1, 0, 1]
+        for x, y in itertools.product(x_operands, y_operands):
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+    def test_max_1(self, flags=enable_pyobj_flags):
+        """
+        max(*args)
+        """
+        self.check_minmax_1(max_usecase1, flags)
+
+    def test_min_1(self, flags=enable_pyobj_flags):
+        """
+        min(*args)
+        """
+        self.check_minmax_1(min_usecase1, flags)
+
+    def test_max_npm_1(self):
+        self.test_max_1(flags=no_pyobj_flags)
+
+    def test_min_npm_1(self):
+        self.test_min_1(flags=no_pyobj_flags)
+
+    def check_minmax_2(self, pyfunc, flags):
+        cr = compile_isolated(pyfunc, (types.int32, types.int32), flags=flags)
+        cfunc = cr.entry_point
+
+        x_operands = [-1, 0, 1]
+        y_operands = [-1, 0, 1]
+        for x, y in itertools.product(x_operands, y_operands):
+            self.assertPreciseEqual(cfunc(x, y), pyfunc(x, y))
+
+    def test_max_2(self, flags=enable_pyobj_flags):
+        """
+        max(list)
+        """
+        self.check_minmax_2(max_usecase2, flags)
+
+    def test_min_2(self, flags=enable_pyobj_flags):
+        """
+        min(list)
+        """
+        self.check_minmax_2(min_usecase2, flags)
+
+    def test_max_npm_2(self):
+        with self.assertTypingError():
+            self.test_max_2(flags=no_pyobj_flags)
+
+    def test_min_npm_2(self):
+        with self.assertTypingError():
+            self.test_min_2(flags=no_pyobj_flags)
+
+    def check_minmax_3(self, pyfunc, flags):
+        def check(argty):
+            cr = compile_isolated(pyfunc, (argty,), flags=flags)
+            cfunc = cr.entry_point
+            # Check that the algorithm matches Python's with a non-total order
+            tup = (1.5, float('nan'), 2.5)
+            for val in [tup, tup[::-1]]:
+                self.assertPreciseEqual(cfunc(val), pyfunc(val))
+
+        check(types.UniTuple(types.float64, 3))
+        check(types.Tuple((types.float32, types.float64, types.float32)))
+
+    def test_max_3(self, flags=enable_pyobj_flags):
+        """
+        max(tuple)
+        """
+        self.check_minmax_3(max_usecase3, flags)
+
+    def test_min_3(self, flags=enable_pyobj_flags):
+        """
+        min(tuple)
+        """
+        self.check_minmax_3(min_usecase3, flags)
+
+    def test_max_npm_3(self):
+        self.test_max_3(flags=no_pyobj_flags)
+
+    def test_min_npm_3(self):
+        self.test_min_3(flags=no_pyobj_flags)
+
+    def check_min_max_invalid_types(self, pyfunc, flags=enable_pyobj_flags):
+        cr = compile_isolated(pyfunc, (types.int32, types.Dummy('list')),
+                              flags=flags)
+        cfunc = cr.entry_point
+        cfunc(1, [1])
+
+    def test_max_1_invalid_types(self):
+        with self.assertRaises(TypeError):
+            self.check_min_max_invalid_types(max_usecase1)
+
+    def test_max_1_invalid_types_npm(self):
+        with self.assertTypingError():
+            self.check_min_max_invalid_types(max_usecase1, flags=no_pyobj_flags)
+
+    def test_min_1_invalid_types(self):
+        with self.assertRaises(TypeError):
+            self.check_min_max_invalid_types(min_usecase1)
+
+    def test_min_1_invalid_types_npm(self):
+        with self.assertTypingError():
+            self.check_min_max_invalid_types(min_usecase1, flags=no_pyobj_flags)
+
+    # Test that max(1) and min(1) fail
+
+    def check_min_max_unary_non_iterable(self, pyfunc, flags=enable_pyobj_flags):
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        cfunc(1)
+
+    def test_max_unary_non_iterable(self):
+        with self.assertRaises(TypeError):
+            self.check_min_max_unary_non_iterable(max_usecase3)
+
+    def test_max_unary_non_iterable_npm(self):
+        with self.assertTypingError():
+            self.check_min_max_unary_non_iterable(max_usecase3)
+
+    def test_min_unary_non_iterable(self):
+        with self.assertRaises(TypeError):
+            self.check_min_max_unary_non_iterable(min_usecase3)
+
+    def test_min_unary_non_iterable_npm(self):
+        with self.assertTypingError():
+            self.check_min_max_unary_non_iterable(min_usecase3)
+
+    # Test that max(()) and min(()) fail
+
+    def check_min_max_empty_tuple(self, pyfunc, func_name):
+        with self.assertTypingError() as raises:
+            compile_isolated(pyfunc, (), flags=no_pyobj_flags)
+        self.assertIn("%s() argument is an empty tuple" % func_name,
+                      str(raises.exception))
+
+    def test_max_empty_tuple(self):
+        self.check_min_max_empty_tuple(max_usecase4, "max")
+
+    def test_min_empty_tuple(self):
+        self.check_min_max_empty_tuple(min_usecase4, "min")
+
+
+    def test_oct(self, flags=enable_pyobj_flags):
+        pyfunc = oct_usecase
+
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-8, -1, 0, 1, 8]:
+            self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+    def test_oct_npm(self):
+        with self.assertTypingError():
+            self.test_oct(flags=no_pyobj_flags)
+
+    def test_reduce(self, flags=enable_pyobj_flags):
+        pyfunc = reduce_usecase
+        cr = compile_isolated(pyfunc, (types.Dummy('function_ptr'),
+                                       types.Dummy('list')),
+                                       flags=flags)
+        cfunc = cr.entry_point
+
+        reduce_func = lambda x, y: x + y
+
+        x = range(10)
+        self.assertPreciseEqual(cfunc(reduce_func, x), pyfunc(reduce_func, x))
+
+        x = [x + x/10.0 for x in range(10)]
+        self.assertPreciseEqual(cfunc(reduce_func, x), pyfunc(reduce_func, x))
+
+        x = [complex(x, x) for x in range(10)]
+        self.assertPreciseEqual(cfunc(reduce_func, x), pyfunc(reduce_func, x))
+
+    def test_reduce_npm(self):
+        with self.assertTypingError():
+            self.test_reduce(flags=no_pyobj_flags)
+
+    def test_round1(self, flags=enable_pyobj_flags):
+        pyfunc = round_usecase1
+
+        for tp in (types.float64, types.float32):
+            cr = compile_isolated(pyfunc, (tp,), flags=flags)
+            cfunc = cr.entry_point
+            values = [-1.6, -1.5, -1.4, -0.5, 0.0, 0.1, 0.5, 0.6, 1.4, 1.5, 5.0]
+            values += [-0.1, -0.0]
+            for x in values:
+                self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+    def test_round1_npm(self):
+        self.test_round1(flags=no_pyobj_flags)
+
+    def test_round2(self, flags=enable_pyobj_flags):
+        pyfunc = round_usecase2
+
+        for tp in (types.float64, types.float32):
+            prec = 'single' if tp is types.float32 else 'exact'
+            cr = compile_isolated(pyfunc, (tp, types.int32), flags=flags)
+            cfunc = cr.entry_point
+            for x in [0.0, 0.1, 0.125, 0.25, 0.5, 0.75, 1.25,
+                      1.5, 1.75, 2.25, 2.5, 2.75, 12.5, 15.0, 22.5]:
+                for n in (-1, 0, 1, 2):
+                    self.assertPreciseEqual(cfunc(x, n), pyfunc(x, n),
+                                            prec=prec)
+                    expected = pyfunc(-x, n)
+                    self.assertPreciseEqual(cfunc(-x, n), pyfunc(-x, n),
+                                            prec=prec)
+
+    def test_round2_npm(self):
+        self.test_round2(flags=no_pyobj_flags)
+
+    def test_sum_objmode(self, flags=enable_pyobj_flags):
+        pyfunc = sum_usecase
+
+        cr = compile_isolated(pyfunc, (types.Dummy('list'),), flags=flags)
+        cfunc = cr.entry_point
+
+        x = range(10)
+        self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+        x = [x + x/10.0 for x in range(10)]
+        self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+        x = [complex(x, x) for x in range(10)]
+        self.assertPreciseEqual(cfunc(x), pyfunc(x))
+
+    def test_sum(self):
+        # In Python 3.8 "start" can be specified as a kwarg, so test that too
+        sum_default = njit(sum_usecase)
+        sum_kwarg = njit(sum_kwarg_usecase)
+
+        @njit
+        def sum_range(sz, start=0):
+            tmp = range(sz)
+            ret = sum(tmp, start)
+            if py38orlater:
+                return sum(tmp, start=start), ret
+            else:
+                return ret
+
+        ntpl = namedtuple('ntpl', ['a', 'b'])
+
+        # check call with default kwarg, start=0
+        def args():
+            yield [*range(10)]
+            yield [x + x/10.0 for x in range(10)]
+            yield [x * 1j for x in range(10)]
+            yield (1, 2, 3)
+            yield (1, 2, 3j)
+            # uints will likely end up as floats as `start` is signed, so just
+            # test mixed signed ints
+            yield (np.int64(32), np.int32(2), np.int8(3))
+            tl = typed.List(range(5))
+            yield tl
+            yield np.ones(5)
+            yield ntpl(100, 200)
+            yield ntpl(100, 200j)
+
+        for x in args():
+            self.assertPreciseEqual(sum_default(x), sum_default.py_func(x))
+
+        # Check the uint use case, as start is signed, NumPy will end up with
+        # a float result whereas Numba will end up with an int (see integer
+        # typing NBEP).
+        x = (np.uint64(32), np.uint32(2), np.uint8(3))
+        self.assertEqual(sum_default(x), sum_default.py_func(x))
+
+        # check call with changing default kwarg, start
+        def args_kws():
+            yield [*range(10)], 12
+            yield [x + x/10.0 for x in range(10)], 19j
+            yield [x * 1j for x in range(10)], -2
+            yield (1, 2, 3), 9
+            yield (1, 2, 3j), -0
+            # uints will likely end up as floats as `start` is signed, so just
+            # test mixed signed ints
+            yield (np.int64(32), np.int32(2), np.int8(3)), np.uint32(7)
+            tl = typed.List(range(5))
+            yield tl, 100
+            yield np.ones((5, 5)), 10 * np.ones((5,))
+            yield ntpl(100, 200), -50
+            yield ntpl(100, 200j), 9
+
+        for x, start in args_kws():
+            self.assertPreciseEqual(sum_kwarg(x, start=start),
+                                    sum_kwarg.py_func(x, start=start))
+
+        # check call with range()
+        for start in range(-3, 4):
+            for sz in range(-3, 4):
+                self.assertPreciseEqual(sum_range(sz, start=start),
+                                        sum_range.py_func(sz, start=start))
+
+    def test_sum_exceptions(self):
+        sum_default = njit(sum_usecase)
+        sum_kwarg = njit(sum_kwarg_usecase)
+
+        # check start as string/bytes/bytearray is error
+        msg = "sum() can't sum {}"
+
+        with self.assertRaises(errors.TypingError) as raises:
+            sum_kwarg((1, 2, 3), 'a')
+
+        self.assertIn(msg.format('strings'), str(raises.exception))
+
+        with self.assertRaises(errors.TypingError) as raises:
+            sum_kwarg((1, 2, 3), b'123')
+
+        self.assertIn(msg.format('bytes'), str(raises.exception))
+
+        with self.assertRaises(errors.TypingError) as raises:
+            sum_kwarg((1, 2, 3), bytearray(b'123'))
+
+        self.assertIn(msg.format('bytearray'), str(raises.exception))
+
+        # check invalid type has no impl
+        with self.assertRaises(errors.TypingError) as raises:
+            sum_default('abcd')
+
+        self.assertIn('No implementation', str(raises.exception))
+
+    def test_truth(self):
+        pyfunc = truth_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.assertEqual(pyfunc(True), cfunc(True))
+        self.assertEqual(pyfunc(False), cfunc(False))
+
+    def test_type_unary(self):
+        # Test type(val) and type(val)(other_val)
+        pyfunc = type_unary_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(*args):
+            expected = pyfunc(*args)
+            self.assertPreciseEqual(cfunc(*args), expected)
+
+        check(1.5, 2)
+        check(1, 2.5)
+        check(1.5j, 2)
+        check(True, 2)
+        check(2.5j, False)
+
+    def test_zip(self, flags=forceobj_flags):
+        self.run_nullary_func(zip_usecase, flags)
+
+    def test_zip_npm(self):
+        self.test_zip(flags=no_pyobj_flags)
+
+    def test_zip_1(self, flags=forceobj_flags):
+        self.run_nullary_func(zip_1_usecase, flags)
+
+    def test_zip_1_npm(self):
+        self.test_zip_1(flags=no_pyobj_flags)
+
+    def test_zip_3(self, flags=forceobj_flags):
+        self.run_nullary_func(zip_3_usecase, flags)
+
+    def test_zip_3_npm(self):
+        self.test_zip_3(flags=no_pyobj_flags)
+
+    def test_zip_0(self, flags=forceobj_flags):
+        self.run_nullary_func(zip_0_usecase, flags)
+
+    def test_zip_0_npm(self):
+        self.test_zip_0(flags=no_pyobj_flags)
+
+    def test_zip_first_exhausted(self, flags=forceobj_flags):
+        """
+        Test side effect to the input iterators when a left iterator has been
+        exhausted before the ones on the right.
+        """
+        self.run_nullary_func(zip_first_exhausted, flags)
+
+    def test_zip_first_exhausted_npm(self):
+        self.test_zip_first_exhausted(flags=nrt_no_pyobj_flags)
+
+    def test_pow_op_usecase(self):
+        args = [
+            (2, 3),
+            (2.0, 3),
+            (2, 3.0),
+            (2j, 3.0j),
+        ]
+
+        for x, y in args:
+            cres = compile_isolated(pow_op_usecase, (typeof(x), typeof(y)),
+                                    flags=no_pyobj_flags)
+            r = cres.entry_point(x, y)
+            self.assertPreciseEqual(r, pow_op_usecase(x, y))
+
+    def test_pow_usecase(self):
+        args = [
+            (2, 3),
+            (2.0, 3),
+            (2, 3.0),
+            (2j, 3.0j),
+        ]
+
+        for x, y in args:
+            cres = compile_isolated(pow_usecase, (typeof(x), typeof(y)),
+                                    flags=no_pyobj_flags)
+            r = cres.entry_point(x, y)
+            self.assertPreciseEqual(r, pow_usecase(x, y))
+
+    def _check_min_max(self, pyfunc):
+        cfunc = njit()(pyfunc)
+        expected = pyfunc()
+        got = cfunc()
+        self.assertPreciseEqual(expected, got)
+
+    def test_min_max_iterable_input(self):
+
+        @njit
+        def frange(start, stop, step):
+            i = start
+            while i < stop:
+                yield i
+                i += step
+
+        def sample_functions(op):
+            yield lambda: op(range(10))
+            yield lambda: op(range(4, 12))
+            yield lambda: op(range(-4, -15, -1))
+            yield lambda: op([6.6, 5.5, 7.7])
+            yield lambda: op([(3, 4), (1, 2)])
+            yield lambda: op(frange(1.1, 3.3, 0.1))
+            yield lambda: op([np.nan, -np.inf, np.inf, np.nan])
+            yield lambda: op([(3,), (1,), (2,)])
+
+        for fn in sample_functions(op=min):
+            self._check_min_max(fn)
+
+        for fn in sample_functions(op=max):
+            self._check_min_max(fn)
+
+
+class TestOperatorMixedTypes(TestCase):
+
+    def test_eq_ne(self):
+        for opstr in ('eq', 'ne'):
+            op = getattr(operator, opstr)
+
+            @njit
+            def func(a, b):
+                return op(a, b)
+
+            # all these things should evaluate to being equal or not, all should
+            # survive typing.
+            things = (1, 0, True, False, 1.0, 2.0, 1.1, 1j, None, "", "1")
+            for x, y in itertools.product(things, things):
+                self.assertPreciseEqual(func.py_func(x, y), func(x, y))
+
+    def test_cmp(self):
+        for opstr in ('gt', 'lt', 'ge', 'le', 'eq', 'ne'):
+            op = getattr(operator, opstr)
+            @njit
+            def func(a, b):
+                return op(a, b)
+
+            # numerical things should all be comparable
+            things = (1, 0, True, False, 1.0, 0.0, 1.1)
+            for x, y in itertools.product(things, things):
+                expected = func.py_func(x, y)
+                got = func(x, y)
+                message = ("%s %s %s does not match between Python and Numba"
+                           % (x, opstr, y))
+                self.assertEqual(expected, got, message)
+
+
+class TestIsinstanceBuiltin(TestCase):
+    def test_isinstance(self):
+        pyfunc = isinstance_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        inputs = (
+            3,              # int
+            5.0,            # float
+            "Hello",        # string
+            b'world',       # bytes
+            1j,             # complex
+            [1, 2, 3],      # list
+            (1, 3, 3, 3),   # UniTuple
+            set([1, 2]),    # set
+            (1, 'nba', 2),  # Heterogeneous Tuple
+            # {'hello': 2},   # dict - doesn't work as input
+            None,
+        )
+
+        for inpt in inputs:
+            expected = pyfunc(inpt)
+            got = cfunc(inpt)
+            self.assertEqual(expected, got)
+
+    def test_isinstance_dict(self):
+        # Tests typed.Dict and LiteralStrKeyDict
+        pyfunc = isinstance_dict
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertEqual(pyfunc(), cfunc())
+
+    def test_isinstance_numba_types(self):
+        # This makes use of type aliasing between python scalars and NumPy
+        # scalars, see also test_numba_types()
+        pyfunc = isinstance_usecase_numba_types
+        cfunc = jit(nopython=True)(pyfunc)
+
+        inputs = (
+            (types.int32(1), 'int32'),
+            (types.int64(2), 'int64'),
+            (types.float32(3.0), 'float32'),
+            (types.float64(4.0), 'float64'),
+            (types.complex64(5j), 'no match'),
+            (typed.List([1, 2]), 'typed list'),
+            (typed.Dict.empty(types.int64, types.int64), 'typed dict')
+        )
+
+        for inpt, expected in inputs:
+            got = cfunc(inpt)
+            self.assertEqual(expected, got)
+
+    def test_isinstance_numba_types_2(self):
+        pyfunc = isinstance_usecase_numba_types_2
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertEqual(pyfunc(), cfunc())
+
+    def test_isinstance_invalid_type(self):
+        pyfunc = isinstance_usecase_invalid_type
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # valid type
+        self.assertTrue(cfunc(3.4))
+
+        # invalid type
+        msg = 'Cannot infer numba type of python type'
+
+        with self.assertRaises(errors.TypingError) as raises:
+            cfunc(100)
+
+        self.assertIn(msg, str(raises.exception))
+
+    def test_isinstance_exceptions(self):
+        fns = [
+            (invalid_isinstance_usecase,
+             'Cannot infer numba type of python type'),
+            (invalid_isinstance_usecase_phi_nopropagate,
+             ('isinstance() cannot determine the type of variable "z" due to a '
+             'branch.')),
+            (invalid_isinstance_optional_usecase,
+             ('isinstance() cannot determine the type of variable "z" due to a '
+             'branch.')),
+            (invalid_isinstance_unsupported_type_usecase(),
+             ('isinstance() does not support variables of type "ntpl(')),
+        ]
+
+        for fn, msg in fns:
+            fn = njit(fn)
+
+            with self.assertRaises(errors.TypingError) as raises:
+                fn(100)
+
+            self.assertIn(msg, str(raises.exception))
+
+    def test_combinations(self):
+        # Combinatorically test common classes and instances
+        def gen_w_arg(clazz_type):
+            def impl(x):
+                return isinstance(x, clazz_type)
+            return impl
+
+        clazz_types = (int, float, complex, str, list, tuple, bytes, set, range,
+                       np.int8, np.float32,)
+        instances = (1, 2.3, 4j, '5', [6,], (7,), b'8', {9,}, None,
+                     (10, 11, 12), (13, 'a', 14j), np.array([15, 16, 17]),
+                     np.int8(18), np.float32(19),
+                     typed.Dict.empty(types.unicode_type, types.float64),
+                     typed.List.empty_list(types.complex128), np.ones(4))
+
+        for ct in clazz_types:
+            fn = njit(gen_w_arg(ct))
+            for x in instances:
+                expected = fn.py_func(x)
+                got = fn(x)
+                self.assertEqual(got, expected)
+
+    def test_numba_types(self):
+        # Check types which are Numba types, this would break without the jit
+        # decorator in all cases except numba.typed containers.
+        def gen_w_arg(clazz_type):
+            def impl():
+                return isinstance(1, clazz_type)
+            return impl
+
+        clazz_types = (types.Integer, types.Float, types.Array,)
+
+        msg = "Numba type classes.*are not supported"
+        for ct in clazz_types:
+            fn = njit(gen_w_arg(ct))
+            with self.assertRaises(errors.TypingError) as raises:
+                fn()
+            self.assertRegex(str(raises.exception), msg)
+
+    def test_python_numpy_scalar_alias_problem(self):
+        # There's a problem due to Python and NumPy scalars being aliased in the
+        # type system. This is because e.g. int scalar values and NumPy np.intp
+        # type alias to types.intp. This test merely records this fact.
+
+        @njit
+        def foo():
+            return isinstance(np.intp(10), int)
+
+        self.assertEqual(foo(), True)
+        self.assertEqual(foo.py_func(), False)
+
+        @njit
+        def bar():
+            return isinstance(1, np.intp)
+
+        self.assertEqual(bar(), True)
+        self.assertEqual(bar.py_func(), False)
+
+    def test_branch_prune(self):
+        # Check that isinstance branches are pruned allowing otherwise
+        # impossible type specific specialisation.
+
+        @njit
+        def foo(x):
+            if isinstance(x, str):
+                return x + 'some_string'
+            elif isinstance(x, complex):
+                return np.imag(x)
+            elif isinstance(x, tuple):
+                return len(x)
+            else:
+                assert 0
+
+        for x in ('string', 1 + 2j, ('a', 3, 4j)):
+            expected = foo.py_func(x)
+            got = foo(x)
+            self.assertEqual(got, expected)
+
+    @TestCase.run_test_in_subprocess
+    def test_experimental_warning(self):
+        # Check that if the isinstance feature is in use then an experimental
+        # warning is raised. Needs subproc as something else in the test suite
+        # might triggered the warning already.
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', errors.NumbaWarning)
+            ignore_internal_warnings()
+
+            @njit
+            def foo(x):
+                return isinstance(x, float)
+
+            foo(1.234)
+
+            self.assertEqual(len(w), 1)
+
+            self.assertEqual(w[0].category,
+                             errors.NumbaExperimentalFeatureWarning)
+            msg = ("Use of isinstance() detected. This is an experimental "
+                   "feature.")
+            self.assertIn(msg, str(w[0].message))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_byteflow.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_byteflow.py
new file mode 100644
index 000000000..041e1d65b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_byteflow.py
@@ -0,0 +1,94 @@
+"""
+Test byteflow.py specific issues
+"""
+import unittest
+
+from numba.tests.support import TestCase
+from numba.core.compiler import run_frontend
+
+
+class TestByteFlowIssues(TestCase):
+    def test_issue_5087(self):
+        # This is an odd issue. The exact number of print below is
+        # necessary to trigger it. Too many or too few will alter the behavior.
+        # Also note that the function below will not be executed. The problem
+        # occurs at compilation. The definition below is invalid for execution.
+        # The problem occurs in the bytecode analysis.
+        def udt():
+            print
+            print
+            print
+
+            for i in range:
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+                print
+
+                for j in range:
+                    print
+                    print
+                    print
+                    print
+                    print
+                    print
+                    print
+                    for k in range:
+                        for l in range:
+                            print
+
+                    print
+                    print
+                    print
+                    print
+                    print
+                    print
+                    print
+                    print
+                    print
+                    if print:
+                        for n in range:
+                            print
+                    else:
+                        print
+
+        run_frontend(udt)
+
+    def test_issue_5097(self):
+        # Inspired by https://github.com/numba/numba/issues/5097
+        def udt():
+            for i in range(0):
+                if i > 0:
+                    pass
+                a = None # noqa: F841
+
+        run_frontend(udt)
+
+    def test_issue_5680(self):
+        # From https://github.com/numba/numba/issues/5680#issuecomment-625351336
+        def udt():
+            for k in range(0):
+                if 1 == 1:
+                    ...
+                if 'a' == 'a':
+                    ...
+
+        run_frontend(udt)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_caching.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_caching.py
new file mode 100644
index 000000000..8f9a82dfd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_caching.py
@@ -0,0 +1,1076 @@
+import inspect
+import llvmlite.binding as ll
+import multiprocessing
+import numpy as np
+import os
+import shutil
+import subprocess
+import sys
+import traceback
+import unittest
+import warnings
+from numba import njit
+from numba.core import codegen
+from numba.core.caching import _UserWideCacheLocator
+from numba.core.errors import NumbaWarning
+from numba.parfors import parfor
+from numba.tests.support import (
+    TestCase,
+    SerialMixin,
+    capture_cache_log,
+    import_dynamic,
+    override_config,
+    override_env_config,
+    run_in_new_process_caching,
+    skip_if_typeguard,
+    skip_parfors_unsupported,
+    temp_directory,
+)
+
+try:
+    import ipykernel
+except ImportError:
+    ipykernel = None
+
+
+def check_access_is_preventable():
+    # This exists to check whether it is possible to prevent access to
+    # a file/directory through the use of `chmod 500`. If a user has
+    # elevated rights (e.g. root) then writes are likely to be possible
+    # anyway. Tests that require functioning access prevention are
+    # therefore skipped based on the result of this check.
+    tempdir = temp_directory('test_cache')
+    test_dir = (os.path.join(tempdir, 'writable_test'))
+    os.mkdir(test_dir)
+    # check a write is possible
+    with open(os.path.join(test_dir, 'write_ok'), 'wt') as f:
+        f.write('check1')
+    # now forbid access
+    os.chmod(test_dir, 0o500)
+    try:
+        with open(os.path.join(test_dir, 'write_forbidden'), 'wt') as f:
+            f.write('check2')
+        # access prevention is not possible
+        return False
+    except PermissionError:
+        # Check that the cause of the exception is due to access/permission
+        # as per
+        # https://github.com/conda/conda/blob/4.5.0/conda/gateways/disk/permissions.py#L35-L37  # noqa: E501
+        # errno reports access/perm fail so access prevention via
+        # `chmod 500` works for this user.
+        return True
+    finally:
+        os.chmod(test_dir, 0o775)
+        shutil.rmtree(test_dir)
+
+
+_access_preventable = check_access_is_preventable()
+_access_msg = "Cannot create a directory to which writes are preventable"
+skip_bad_access = unittest.skipUnless(_access_preventable, _access_msg)
+
+
+def constant_unicode_cache():
+    c = "abcd"
+    return hash(c), c
+
+
+def check_constant_unicode_cache():
+    pyfunc = constant_unicode_cache
+    cfunc = njit(cache=True)(pyfunc)
+    exp_hv, exp_str = pyfunc()
+    got_hv, got_str = cfunc()
+    assert exp_hv == got_hv
+    assert exp_str == got_str
+
+
+def dict_cache():
+    return {'a': 1, 'b': 2}
+
+
+def check_dict_cache():
+    pyfunc = dict_cache
+    cfunc = njit(cache=True)(pyfunc)
+    exp = pyfunc()
+    got = cfunc()
+    assert exp == got
+
+
+def generator_cache():
+    for v in (1, 2, 3):
+        yield v
+
+
+def check_generator_cache():
+    pyfunc = generator_cache
+    cfunc = njit(cache=True)(pyfunc)
+    exp = list(pyfunc())
+    got = list(cfunc())
+    assert exp == got
+
+
+class TestCaching(SerialMixin, TestCase):
+    def run_test(self, func):
+        func()
+        res = run_in_new_process_caching(func)
+        self.assertEqual(res['exitcode'], 0)
+
+    def test_constant_unicode_cache(self):
+        self.run_test(check_constant_unicode_cache)
+
+    def test_dict_cache(self):
+        self.run_test(check_dict_cache)
+
+    def test_generator_cache(self):
+        self.run_test(check_generator_cache)
+
+    def test_omitted(self):
+
+        # Test in a new directory
+        cache_dir = temp_directory(self.__class__.__name__)
+        ctx = multiprocessing.get_context()
+        result_queue = ctx.Queue()
+        proc = ctx.Process(
+            target=omitted_child_test_wrapper,
+            args=(result_queue, cache_dir, False),
+        )
+        proc.start()
+        proc.join()
+        success, output = result_queue.get()
+
+        # Ensure the child process is completed before checking its output
+        if not success:
+            self.fail(output)
+
+        self.assertEqual(
+            output,
+            1000,
+            "Omitted function returned an incorrect output"
+        )
+
+        proc = ctx.Process(
+            target=omitted_child_test_wrapper,
+            args=(result_queue, cache_dir, True)
+        )
+        proc.start()
+        proc.join()
+        success, output = result_queue.get()
+
+        # Ensure the child process is completed before checking its output
+        if not success:
+            self.fail(output)
+
+        self.assertEqual(
+            output,
+            1000,
+            "Omitted function returned an incorrect output"
+        )
+
+
+def omitted_child_test_wrapper(result_queue, cache_dir, second_call):
+    with override_config("CACHE_DIR", cache_dir):
+        @njit(cache=True)
+        def test(num=1000):
+            return num
+
+        try:
+            output = test()
+            # If we have a second call, we should have a cache hit.
+            # Otherwise, we expect a cache miss.
+            if second_call:
+                assert test._cache_hits[test.signatures[0]] == 1, \
+                    "Cache did not hit as expected"
+                assert test._cache_misses[test.signatures[0]] == 0, \
+                    "Cache has an unexpected miss"
+            else:
+                assert test._cache_misses[test.signatures[0]] == 1, \
+                    "Cache did not miss as expected"
+                assert test._cache_hits[test.signatures[0]] == 0, \
+                    "Cache has an unexpected hit"
+            success = True
+        # Catch anything raised so it can be propagated
+        except: # noqa: E722
+            output = traceback.format_exc()
+            success = False
+        result_queue.put((success, output))
+
+
+class BaseCacheTest(TestCase):
+    # The source file that will be copied
+    usecases_file = None
+    # Make sure this doesn't conflict with another module
+    modname = None
+
+    def setUp(self):
+        self.tempdir = temp_directory('test_cache')
+        sys.path.insert(0, self.tempdir)
+        self.modfile = os.path.join(self.tempdir, self.modname + ".py")
+        self.cache_dir = os.path.join(self.tempdir, "__pycache__")
+        shutil.copy(self.usecases_file, self.modfile)
+        self.maxDiff = None
+
+    def tearDown(self):
+        sys.modules.pop(self.modname, None)
+        sys.path.remove(self.tempdir)
+
+    def import_module(self):
+        # Import a fresh version of the test module.  All jitted functions
+        # in the test module will start anew and load overloads from
+        # the on-disk cache if possible.
+        old = sys.modules.pop(self.modname, None)
+        if old is not None:
+            # Make sure cached bytecode is removed
+            cached = [old.__cached__]
+            for fn in cached:
+                try:
+                    os.unlink(fn)
+                except FileNotFoundError:
+                    pass
+        mod = import_dynamic(self.modname)
+        self.assertEqual(mod.__file__.rstrip('co'), self.modfile)
+        return mod
+
+    def cache_contents(self):
+        try:
+            return [fn for fn in os.listdir(self.cache_dir)
+                    if not fn.endswith(('.pyc', ".pyo"))]
+        except FileNotFoundError:
+            return []
+
+    def get_cache_mtimes(self):
+        return dict((fn, os.path.getmtime(os.path.join(self.cache_dir, fn)))
+                    for fn in sorted(self.cache_contents()))
+
+    def check_pycache(self, n):
+        c = self.cache_contents()
+        self.assertEqual(len(c), n, c)
+
+    def dummy_test(self):
+        pass
+
+
+class DispatcherCacheUsecasesTest(BaseCacheTest):
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cache_usecases.py")
+    modname = "dispatcher_caching_test_fodder"
+
+    def run_in_separate_process(self):
+        # Cached functions can be run from a distinct process.
+        # Also stresses issue #1603: uncached function calling cached function
+        # shouldn't fail compiling.
+        code = """if 1:
+            import sys
+
+            sys.path.insert(0, %(tempdir)r)
+            mod = __import__(%(modname)r)
+            mod.self_test()
+            """ % dict(tempdir=self.tempdir, modname=self.modname)
+
+        popen = subprocess.Popen([sys.executable, "-c", code],
+                                 stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        out, err = popen.communicate()
+        if popen.returncode != 0:
+            raise AssertionError(
+                "process failed with code %s: \n"
+                "stdout follows\n%s\n"
+                "stderr follows\n%s\n"
+                % (popen.returncode, out.decode(), err.decode()),
+            )
+
+    def check_hits(self, func, hits, misses=None):
+        st = func.stats
+        self.assertEqual(sum(st.cache_hits.values()), hits, st.cache_hits)
+        if misses is not None:
+            self.assertEqual(sum(st.cache_misses.values()), misses,
+                             st.cache_misses)
+
+
+class TestCache(DispatcherCacheUsecasesTest):
+
+    def test_caching(self):
+        self.check_pycache(0)
+        mod = self.import_module()
+        self.check_pycache(0)
+
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+        self.check_pycache(2)  # 1 index, 1 data
+        self.assertPreciseEqual(f(2.5, 3), 6.5)
+        self.check_pycache(3)  # 1 index, 2 data
+        self.check_hits(f, 0, 2)
+
+        f = mod.add_objmode_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+        self.check_pycache(5)  # 2 index, 3 data
+        self.assertPreciseEqual(f(2.5, 3), 6.5)
+        self.check_pycache(6)  # 2 index, 4 data
+        self.check_hits(f, 0, 2)
+
+        f = mod.record_return
+        rec = f(mod.aligned_arr, 1)
+        self.assertPreciseEqual(tuple(rec), (2, 43.5))
+        rec = f(mod.packed_arr, 1)
+        self.assertPreciseEqual(tuple(rec), (2, 43.5))
+        self.check_pycache(9)  # 3 index, 6 data
+        self.check_hits(f, 0, 2)
+
+        f = mod.generated_usecase
+        self.assertPreciseEqual(f(3, 2), 1)
+        self.assertPreciseEqual(f(3j, 2), 2 + 3j)
+
+        # Check the code runs ok from another process
+        self.run_in_separate_process()
+
+    def test_caching_nrt_pruned(self):
+        self.check_pycache(0)
+        mod = self.import_module()
+        self.check_pycache(0)
+
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+        self.check_pycache(2)  # 1 index, 1 data
+        # NRT pruning may affect cache
+        self.assertPreciseEqual(f(2, np.arange(3)), 2 + np.arange(3) + 1)
+        self.check_pycache(3)  # 1 index, 2 data
+        self.check_hits(f, 0, 2)
+
+    def test_inner_then_outer(self):
+        # Caching inner then outer function is ok
+        mod = self.import_module()
+        self.assertPreciseEqual(mod.inner(3, 2), 6)
+        self.check_pycache(2)  # 1 index, 1 data
+        # Uncached outer function shouldn't fail (issue #1603)
+        f = mod.outer_uncached
+        self.assertPreciseEqual(f(3, 2), 2)
+        self.check_pycache(2)  # 1 index, 1 data
+        mod = self.import_module()
+        f = mod.outer_uncached
+        self.assertPreciseEqual(f(3, 2), 2)
+        self.check_pycache(2)  # 1 index, 1 data
+        # Cached outer will create new cache entries
+        f = mod.outer
+        self.assertPreciseEqual(f(3, 2), 2)
+        self.check_pycache(4)  # 2 index, 2 data
+        self.assertPreciseEqual(f(3.5, 2), 2.5)
+        self.check_pycache(6)  # 2 index, 4 data
+
+    def test_outer_then_inner(self):
+        # Caching outer then inner function is ok
+        mod = self.import_module()
+        self.assertPreciseEqual(mod.outer(3, 2), 2)
+        self.check_pycache(4)  # 2 index, 2 data
+        self.assertPreciseEqual(mod.outer_uncached(3, 2), 2)
+        self.check_pycache(4)  # same
+        mod = self.import_module()
+        f = mod.inner
+        self.assertPreciseEqual(f(3, 2), 6)
+        self.check_pycache(4)  # same
+        self.assertPreciseEqual(f(3.5, 2), 6.5)
+        self.check_pycache(5)  # 2 index, 3 data
+
+    def test_no_caching(self):
+        mod = self.import_module()
+
+        f = mod.add_nocache_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+        self.check_pycache(0)
+
+    def test_looplifted(self):
+        # Loop-lifted functions can't be cached and raise a warning
+        mod = self.import_module()
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaWarning)
+
+            f = mod.looplifted
+            self.assertPreciseEqual(f(4), 6)
+            self.check_pycache(0)
+
+        self.assertEqual(len(w), 1)
+        self.assertIn('Cannot cache compiled function "looplifted" '
+                      'as it uses lifted code', str(w[0].message))
+
+    def test_big_array(self):
+        # Code references big array globals cannot be cached
+        mod = self.import_module()
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaWarning)
+
+            f = mod.use_big_array
+            np.testing.assert_equal(f(), mod.biggie)
+            self.check_pycache(0)
+
+        self.assertEqual(len(w), 1)
+        self.assertIn('Cannot cache compiled function "use_big_array" '
+                      'as it uses dynamic globals', str(w[0].message))
+
+    def test_ctypes(self):
+        # Functions using a ctypes pointer can't be cached and raise
+        # a warning.
+        mod = self.import_module()
+
+        for f in [mod.use_c_sin, mod.use_c_sin_nest1, mod.use_c_sin_nest2]:
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always', NumbaWarning)
+
+                self.assertPreciseEqual(f(0.0), 0.0)
+                self.check_pycache(0)
+
+            self.assertEqual(len(w), 1)
+            self.assertIn(
+                'Cannot cache compiled function "{}"'.format(f.__name__),
+                str(w[0].message),
+            )
+
+    def test_closure(self):
+        mod = self.import_module()
+
+        with warnings.catch_warnings():
+            warnings.simplefilter('error', NumbaWarning)
+
+            f = mod.closure1
+            self.assertPreciseEqual(f(3), 6) # 3 + 3 = 6
+            f = mod.closure2
+            self.assertPreciseEqual(f(3), 8) # 3 + 5 = 8
+            f = mod.closure3
+            self.assertPreciseEqual(f(3), 10) # 3 + 7 = 10
+            f = mod.closure4
+            self.assertPreciseEqual(f(3), 12) # 3 + 9 = 12
+            self.check_pycache(5) # 1 nbi, 4 nbc
+
+    def test_first_class_function(self):
+        mod = self.import_module()
+        f = mod.first_class_function_usecase
+        self.assertEqual(f(mod.first_class_function_mul, 1), 1)
+        self.assertEqual(f(mod.first_class_function_mul, 10), 100)
+        self.assertEqual(f(mod.first_class_function_add, 1), 2)
+        self.assertEqual(f(mod.first_class_function_add, 10), 20)
+        # 1 + 1 + 1 nbi, 1 + 1 + 2 nbc - a separate cache for each call to `f`
+        # with a different callback.
+        self.check_pycache(7)
+
+    def test_cache_reuse(self):
+        mod = self.import_module()
+        mod.add_usecase(2, 3)
+        mod.add_usecase(2.5, 3.5)
+        mod.add_objmode_usecase(2, 3)
+        mod.outer_uncached(2, 3)
+        mod.outer(2, 3)
+        mod.record_return(mod.packed_arr, 0)
+        mod.record_return(mod.aligned_arr, 1)
+        mod.generated_usecase(2, 3)
+        mtimes = self.get_cache_mtimes()
+        # Two signatures compiled
+        self.check_hits(mod.add_usecase, 0, 2)
+
+        mod2 = self.import_module()
+        self.assertIsNot(mod, mod2)
+        f = mod2.add_usecase
+        f(2, 3)
+        self.check_hits(f, 1, 0)
+        f(2.5, 3.5)
+        self.check_hits(f, 2, 0)
+        f = mod2.add_objmode_usecase
+        f(2, 3)
+        self.check_hits(f, 1, 0)
+
+        # The files haven't changed
+        self.assertEqual(self.get_cache_mtimes(), mtimes)
+
+        self.run_in_separate_process()
+        self.assertEqual(self.get_cache_mtimes(), mtimes)
+
+    def test_cache_invalidate(self):
+        mod = self.import_module()
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+
+        # This should change the functions' results
+        with open(self.modfile, "a") as f:
+            f.write("\nZ = 10\n")
+
+        mod = self.import_module()
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 15)
+        f = mod.add_objmode_usecase
+        self.assertPreciseEqual(f(2, 3), 15)
+
+    def test_recompile(self):
+        # Explicit call to recompile() should overwrite the cache
+        mod = self.import_module()
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+
+        mod = self.import_module()
+        f = mod.add_usecase
+        mod.Z = 10
+        self.assertPreciseEqual(f(2, 3), 6)
+        f.recompile()
+        self.assertPreciseEqual(f(2, 3), 15)
+
+        # Freshly recompiled version is re-used from other imports
+        mod = self.import_module()
+        f = mod.add_usecase
+        self.assertPreciseEqual(f(2, 3), 15)
+
+    def test_same_names(self):
+        # Function with the same names should still disambiguate
+        mod = self.import_module()
+        f = mod.renamed_function1
+        self.assertPreciseEqual(f(2), 4)
+        f = mod.renamed_function2
+        self.assertPreciseEqual(f(2), 8)
+
+    def test_frozen(self):
+        from .dummy_module import function
+        old_code = function.__code__
+        code_obj = compile('pass', 'tests/dummy_module.py', 'exec')
+        try:
+            function.__code__ = code_obj
+
+            source = inspect.getfile(function)
+            # doesn't return anything, since it cannot find the module
+            # fails unless the executable is frozen
+            locator = _UserWideCacheLocator.from_function(function, source)
+            self.assertIsNone(locator)
+
+            sys.frozen = True
+            # returns a cache locator object, only works when the executable
+            # is frozen
+            locator = _UserWideCacheLocator.from_function(function, source)
+            self.assertIsInstance(locator, _UserWideCacheLocator)
+
+        finally:
+            function.__code__ = old_code
+            del sys.frozen
+
+    def _test_pycache_fallback(self):
+        """
+        With a disabled __pycache__, test there is a working fallback
+        (e.g. on the user-wide cache dir)
+        """
+        mod = self.import_module()
+        f = mod.add_usecase
+        # Remove this function's cache files at the end, to avoid accumulation
+        # across test calls.
+        self.addCleanup(shutil.rmtree, f.stats.cache_path, ignore_errors=True)
+
+        self.assertPreciseEqual(f(2, 3), 6)
+        # It's a cache miss since the file was copied to a new temp location
+        self.check_hits(f, 0, 1)
+
+        # Test re-use
+        mod2 = self.import_module()
+        f = mod2.add_usecase
+        self.assertPreciseEqual(f(2, 3), 6)
+        self.check_hits(f, 1, 0)
+
+        # The __pycache__ is empty (otherwise the test's preconditions
+        # wouldn't be met)
+        self.check_pycache(0)
+
+    @skip_bad_access
+    @unittest.skipIf(os.name == "nt",
+                     "cannot easily make a directory read-only on Windows")
+    def test_non_creatable_pycache(self):
+        # Make it impossible to create the __pycache__ directory
+        old_perms = os.stat(self.tempdir).st_mode
+        os.chmod(self.tempdir, 0o500)
+        self.addCleanup(os.chmod, self.tempdir, old_perms)
+
+        self._test_pycache_fallback()
+
+    @skip_bad_access
+    @unittest.skipIf(os.name == "nt",
+                     "cannot easily make a directory read-only on Windows")
+    def test_non_writable_pycache(self):
+        # Make it impossible to write to the __pycache__ directory
+        pycache = os.path.join(self.tempdir, '__pycache__')
+        os.mkdir(pycache)
+        old_perms = os.stat(pycache).st_mode
+        os.chmod(pycache, 0o500)
+        self.addCleanup(os.chmod, pycache, old_perms)
+
+        self._test_pycache_fallback()
+
+    def test_ipython(self):
+        # Test caching in an IPython session
+        base_cmd = [sys.executable, '-m', 'IPython']
+        base_cmd += ['--quiet', '--quick', '--no-banner', '--colors=NoColor']
+        try:
+            ver = subprocess.check_output(base_cmd + ['--version'])
+        except subprocess.CalledProcessError as e:
+            self.skipTest("ipython not available: return code %d"
+                          % e.returncode)
+        ver = ver.strip().decode()
+        # Create test input
+        inputfn = os.path.join(self.tempdir, "ipython_cache_usecase.txt")
+        with open(inputfn, "w") as f:
+            f.write(r"""
+                import os
+                import sys
+
+                from numba import jit
+
+                # IPython 5 does not support multiline input if stdin isn't
+                # a tty (https://github.com/ipython/ipython/issues/9752)
+                f = jit(cache=True)(lambda: 42)
+
+                res = f()
+                # IPython writes on stdout, so use stderr instead
+                sys.stderr.write(u"cache hits = %d\n" % f.stats.cache_hits[()])
+
+                # IPython hijacks sys.exit(), bypass it
+                sys.stdout.flush()
+                sys.stderr.flush()
+                os._exit(res)
+                """)
+
+        def execute_with_input():
+            # Feed the test input as stdin, to execute it in REPL context
+            with open(inputfn, "rb") as stdin:
+                p = subprocess.Popen(base_cmd, stdin=stdin,
+                                     stdout=subprocess.PIPE,
+                                     stderr=subprocess.PIPE,
+                                     universal_newlines=True)
+                out, err = p.communicate()
+                if p.returncode != 42:
+                    self.fail("unexpected return code %d\n"
+                              "-- stdout:\n%s\n"
+                              "-- stderr:\n%s\n"
+                              % (p.returncode, out, err))
+                return err
+
+        execute_with_input()
+        # Run a second time and check caching
+        err = execute_with_input()
+        self.assertIn("cache hits = 1", err.strip())
+
+    @unittest.skipIf((ipykernel is None) or (ipykernel.version_info[0] < 6),
+                     "requires ipykernel >= 6")
+    def test_ipykernel(self):
+        # Test caching in an IPython session using ipykernel
+
+        base_cmd = [sys.executable, '-m', 'IPython']
+        base_cmd += ['--quiet', '--quick', '--no-banner', '--colors=NoColor']
+        try:
+            ver = subprocess.check_output(base_cmd + ['--version'])
+        except subprocess.CalledProcessError as e:
+            self.skipTest("ipython not available: return code %d"
+                          % e.returncode)
+        ver = ver.strip().decode()
+        # Create test input
+        from ipykernel import compiler
+        inputfn = compiler.get_tmp_directory()
+        with open(inputfn, "w") as f:
+            f.write(r"""
+                import os
+                import sys
+
+                from numba import jit
+
+                # IPython 5 does not support multiline input if stdin isn't
+                # a tty (https://github.com/ipython/ipython/issues/9752)
+                f = jit(cache=True)(lambda: 42)
+
+                res = f()
+                # IPython writes on stdout, so use stderr instead
+                sys.stderr.write(u"cache hits = %d\n" % f.stats.cache_hits[()])
+
+                # IPython hijacks sys.exit(), bypass it
+                sys.stdout.flush()
+                sys.stderr.flush()
+                os._exit(res)
+                """)
+
+        def execute_with_input():
+            # Feed the test input as stdin, to execute it in REPL context
+            with open(inputfn, "rb") as stdin:
+                p = subprocess.Popen(base_cmd, stdin=stdin,
+                                     stdout=subprocess.PIPE,
+                                     stderr=subprocess.PIPE,
+                                     universal_newlines=True)
+                out, err = p.communicate()
+                if p.returncode != 42:
+                    self.fail("unexpected return code %d\n"
+                              "-- stdout:\n%s\n"
+                              "-- stderr:\n%s\n"
+                              % (p.returncode, out, err))
+                return err
+
+        execute_with_input()
+        # Run a second time and check caching
+        err = execute_with_input()
+        self.assertIn("cache hits = 1", err.strip())
+
+
+@skip_parfors_unsupported
+class TestSequentialParForsCache(DispatcherCacheUsecasesTest):
+    def setUp(self):
+        super(TestSequentialParForsCache, self).setUp()
+        # Turn on sequential parfor lowering
+        parfor.sequential_parfor_lowering = True
+
+    def tearDown(self):
+        super(TestSequentialParForsCache, self).tearDown()
+        # Turn off sequential parfor lowering
+        parfor.sequential_parfor_lowering = False
+
+    def test_caching(self):
+        mod = self.import_module()
+        self.check_pycache(0)
+        f = mod.parfor_usecase
+        ary = np.ones(10)
+        self.assertPreciseEqual(f(ary), ary * ary + ary)
+        dynamic_globals = [cres.library.has_dynamic_globals
+                           for cres in f.overloads.values()]
+        self.assertEqual(dynamic_globals, [False])
+        self.check_pycache(2)  # 1 index, 1 data
+
+
+class TestCacheWithCpuSetting(DispatcherCacheUsecasesTest):
+    # Disable parallel testing due to envvars modification
+    _numba_parallel_test_ = False
+
+    def check_later_mtimes(self, mtimes_old):
+        match_count = 0
+        for k, v in self.get_cache_mtimes().items():
+            if k in mtimes_old:
+                self.assertGreaterEqual(v, mtimes_old[k])
+                match_count += 1
+        self.assertGreater(match_count, 0,
+                           msg='nothing to compare')
+
+    def test_user_set_cpu_name(self):
+        self.check_pycache(0)
+        mod = self.import_module()
+        mod.self_test()
+        cache_size = len(self.cache_contents())
+
+        mtimes = self.get_cache_mtimes()
+        # Change CPU name to generic
+        with override_env_config('NUMBA_CPU_NAME', 'generic'):
+            self.run_in_separate_process()
+
+        self.check_later_mtimes(mtimes)
+        self.assertGreater(len(self.cache_contents()), cache_size)
+        # Check cache index
+        cache = mod.add_usecase._cache
+        cache_file = cache._cache_file
+        cache_index = cache_file._load_index()
+        self.assertEqual(len(cache_index), 2)
+        [key_a, key_b] = cache_index.keys()
+        if key_a[1][1] == ll.get_host_cpu_name():
+            key_host, key_generic = key_a, key_b
+        else:
+            key_host, key_generic = key_b, key_a
+        self.assertEqual(key_host[1][1], ll.get_host_cpu_name())
+        self.assertEqual(key_host[1][2], codegen.get_host_cpu_features())
+        self.assertEqual(key_generic[1][1], 'generic')
+        self.assertEqual(key_generic[1][2], '')
+
+    def test_user_set_cpu_features(self):
+        self.check_pycache(0)
+        mod = self.import_module()
+        mod.self_test()
+        cache_size = len(self.cache_contents())
+
+        mtimes = self.get_cache_mtimes()
+        # Change CPU feature
+        my_cpu_features = '-sse;-avx'
+
+        system_features = codegen.get_host_cpu_features()
+
+        self.assertNotEqual(system_features, my_cpu_features)
+        with override_env_config('NUMBA_CPU_FEATURES', my_cpu_features):
+            self.run_in_separate_process()
+        self.check_later_mtimes(mtimes)
+        self.assertGreater(len(self.cache_contents()), cache_size)
+        # Check cache index
+        cache = mod.add_usecase._cache
+        cache_file = cache._cache_file
+        cache_index = cache_file._load_index()
+        self.assertEqual(len(cache_index), 2)
+        [key_a, key_b] = cache_index.keys()
+
+        if key_a[1][2] == system_features:
+            key_host, key_generic = key_a, key_b
+        else:
+            key_host, key_generic = key_b, key_a
+
+        self.assertEqual(key_host[1][1], ll.get_host_cpu_name())
+        self.assertEqual(key_host[1][2], system_features)
+        self.assertEqual(key_generic[1][1], ll.get_host_cpu_name())
+        self.assertEqual(key_generic[1][2], my_cpu_features)
+
+
+class TestMultiprocessCache(BaseCacheTest):
+
+    # Nested multiprocessing.Pool raises AssertionError:
+    # "daemonic processes are not allowed to have children"
+    _numba_parallel_test_ = False
+
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cache_usecases.py")
+    modname = "dispatcher_caching_test_fodder"
+
+    def test_multiprocessing(self):
+        # Check caching works from multiple processes at once (#2028)
+        mod = self.import_module()
+        # Calling a pure Python caller of the JIT-compiled function is
+        # necessary to reproduce the issue.
+        f = mod.simple_usecase_caller
+        n = 3
+        try:
+            ctx = multiprocessing.get_context('spawn')
+        except AttributeError:
+            ctx = multiprocessing
+        pool = ctx.Pool(n)
+        try:
+            res = sum(pool.imap(f, range(n)))
+        finally:
+            pool.close()
+        self.assertEqual(res, n * (n - 1) // 2)
+
+
+@skip_if_typeguard
+class TestCacheFileCollision(unittest.TestCase):
+    _numba_parallel_test_ = False
+
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cache_usecases.py")
+    modname = "caching_file_loc_fodder"
+    source_text_1 = """
+from numba import njit
+@njit(cache=True)
+def bar():
+    return 123
+"""
+    source_text_2 = """
+from numba import njit
+@njit(cache=True)
+def bar():
+    return 321
+"""
+
+    def setUp(self):
+        self.tempdir = temp_directory('test_cache_file_loc')
+        sys.path.insert(0, self.tempdir)
+        self.modname = 'module_name_that_is_unlikely'
+        self.assertNotIn(self.modname, sys.modules)
+        self.modname_bar1 = self.modname
+        self.modname_bar2 = '.'.join([self.modname, 'foo'])
+        foomod = os.path.join(self.tempdir, self.modname)
+        os.mkdir(foomod)
+        with open(os.path.join(foomod, '__init__.py'), 'w') as fout:
+            print(self.source_text_1, file=fout)
+        with open(os.path.join(foomod, 'foo.py'), 'w') as fout:
+            print(self.source_text_2, file=fout)
+
+    def tearDown(self):
+        sys.modules.pop(self.modname_bar1, None)
+        sys.modules.pop(self.modname_bar2, None)
+        sys.path.remove(self.tempdir)
+
+    def import_bar1(self):
+        return import_dynamic(self.modname_bar1).bar
+
+    def import_bar2(self):
+        return import_dynamic(self.modname_bar2).bar
+
+    def test_file_location(self):
+        bar1 = self.import_bar1()
+        bar2 = self.import_bar2()
+        # Check that the cache file is named correctly
+        idxname1 = bar1._cache._cache_file._index_name
+        idxname2 = bar2._cache._cache_file._index_name
+        self.assertNotEqual(idxname1, idxname2)
+        self.assertTrue(idxname1.startswith("__init__.bar-3.py"))
+        self.assertTrue(idxname2.startswith("foo.bar-3.py"))
+
+    @unittest.skipUnless(hasattr(multiprocessing, 'get_context'),
+                         'Test requires multiprocessing.get_context')
+    def test_no_collision(self):
+        bar1 = self.import_bar1()
+        bar2 = self.import_bar2()
+        with capture_cache_log() as buf:
+            res1 = bar1()
+        cachelog = buf.getvalue()
+        # bar1 should save new index and data
+        self.assertEqual(cachelog.count('index saved'), 1)
+        self.assertEqual(cachelog.count('data saved'), 1)
+        self.assertEqual(cachelog.count('index loaded'), 0)
+        self.assertEqual(cachelog.count('data loaded'), 0)
+        with capture_cache_log() as buf:
+            res2 = bar2()
+        cachelog = buf.getvalue()
+        # bar2 should save new index and data
+        self.assertEqual(cachelog.count('index saved'), 1)
+        self.assertEqual(cachelog.count('data saved'), 1)
+        self.assertEqual(cachelog.count('index loaded'), 0)
+        self.assertEqual(cachelog.count('data loaded'), 0)
+        self.assertNotEqual(res1, res2)
+
+        try:
+            # Make sure we can spawn new process without inheriting
+            # the parent context.
+            mp = multiprocessing.get_context('spawn')
+        except ValueError:
+            print("missing spawn context")
+
+        q = mp.Queue()
+        # Start new process that calls `cache_file_collision_tester`
+        proc = mp.Process(target=cache_file_collision_tester,
+                          args=(q, self.tempdir,
+                                self.modname_bar1,
+                                self.modname_bar2))
+        proc.start()
+        # Get results from the process
+        log1 = q.get()
+        got1 = q.get()
+        log2 = q.get()
+        got2 = q.get()
+        proc.join()
+
+        # The remote execution result of bar1() and bar2() should match
+        # the one executed locally.
+        self.assertEqual(got1, res1)
+        self.assertEqual(got2, res2)
+
+        # The remote should have loaded bar1 from cache
+        self.assertEqual(log1.count('index saved'), 0)
+        self.assertEqual(log1.count('data saved'), 0)
+        self.assertEqual(log1.count('index loaded'), 1)
+        self.assertEqual(log1.count('data loaded'), 1)
+
+        # The remote should have loaded bar2 from cache
+        self.assertEqual(log2.count('index saved'), 0)
+        self.assertEqual(log2.count('data saved'), 0)
+        self.assertEqual(log2.count('index loaded'), 1)
+        self.assertEqual(log2.count('data loaded'), 1)
+
+
+def cache_file_collision_tester(q, tempdir, modname_bar1, modname_bar2):
+    sys.path.insert(0, tempdir)
+    bar1 = import_dynamic(modname_bar1).bar
+    bar2 = import_dynamic(modname_bar2).bar
+    with capture_cache_log() as buf:
+        r1 = bar1()
+    q.put(buf.getvalue())
+    q.put(r1)
+    with capture_cache_log() as buf:
+        r2 = bar2()
+    q.put(buf.getvalue())
+    q.put(r2)
+
+
+class TestCacheMultipleFilesWithSignature(unittest.TestCase):
+    # Regression test for https://github.com/numba/numba/issues/3658
+
+    _numba_parallel_test_ = False
+
+    source_text_file1 = """
+from file2 import function2
+"""
+    source_text_file2 = """
+from numba import njit
+
+@njit('float64(float64)', cache=True)
+def function1(x):
+    return x
+
+@njit('float64(float64)', cache=True)
+def function2(x):
+    return x
+"""
+
+    def setUp(self):
+        self.tempdir = temp_directory('test_cache_file_loc')
+
+        self.file1 = os.path.join(self.tempdir, 'file1.py')
+        with open(self.file1, 'w') as fout:
+            print(self.source_text_file1, file=fout)
+
+        self.file2 = os.path.join(self.tempdir, 'file2.py')
+        with open(self.file2, 'w') as fout:
+            print(self.source_text_file2, file=fout)
+
+    def tearDown(self):
+        shutil.rmtree(self.tempdir)
+
+    def test_caching_mutliple_files_with_signature(self):
+        # Execute file1.py
+        popen = subprocess.Popen([sys.executable, self.file1],
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE)
+        out, err = popen.communicate()
+        msg = f"stdout:\n{out.decode()}\n\nstderr:\n{err.decode()}"
+        self.assertEqual(popen.returncode, 0, msg=msg)
+
+        # Execute file2.py
+        popen = subprocess.Popen([sys.executable, self.file2],
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE)
+        out, err = popen.communicate()
+        msg = f"stdout:\n{out.decode()}\n\nstderr:\n{err.decode()}"
+        self.assertEqual(popen.returncode, 0, msg)
+
+
+class TestCFuncCache(BaseCacheTest):
+
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "cfunc_cache_usecases.py")
+    modname = "cfunc_caching_test_fodder"
+
+    def run_in_separate_process(self):
+        # Cached functions can be run from a distinct process.
+        code = """if 1:
+            import sys
+
+            sys.path.insert(0, %(tempdir)r)
+            mod = __import__(%(modname)r)
+            mod.self_test()
+
+            f = mod.add_usecase
+            assert f.cache_hits == 1
+            f = mod.outer
+            assert f.cache_hits == 1
+            f = mod.div_usecase
+            assert f.cache_hits == 1
+            """ % dict(tempdir=self.tempdir, modname=self.modname)
+
+        popen = subprocess.Popen([sys.executable, "-c", code],
+                                 stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        out, err = popen.communicate()
+        if popen.returncode != 0:
+            raise AssertionError(f"process failed with code {popen.returncode}:"
+                                 f"stderr follows\n{err.decode()}\n")
+
+    def check_module(self, mod):
+        mod.self_test()
+
+    def test_caching(self):
+        self.check_pycache(0)
+        mod = self.import_module()
+        self.check_pycache(6)  # 3 index, 3 data
+
+        self.assertEqual(mod.add_usecase.cache_hits, 0)
+        self.assertEqual(mod.outer.cache_hits, 0)
+        self.assertEqual(mod.add_nocache_usecase.cache_hits, 0)
+        self.assertEqual(mod.div_usecase.cache_hits, 0)
+        self.check_module(mod)
+
+        # Reload module to hit the cache
+        mod = self.import_module()
+        self.check_pycache(6)  # 3 index, 3 data
+
+        self.assertEqual(mod.add_usecase.cache_hits, 1)
+        self.assertEqual(mod.outer.cache_hits, 1)
+        self.assertEqual(mod.add_nocache_usecase.cache_hits, 0)
+        self.assertEqual(mod.div_usecase.cache_hits, 1)
+        self.check_module(mod)
+
+        self.run_in_separate_process()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_casting.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_casting.py
new file mode 100644
index 000000000..75d164c66
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_casting.py
@@ -0,0 +1,140 @@
+import numpy as np
+from numba.core.compiler import compile_isolated
+from numba.core.errors import TypingError
+from numba import njit
+from numba.core import types
+import struct
+import unittest
+
+
+def float_to_int(x):
+    return types.int32(x)
+
+
+def int_to_float(x):
+    return types.float64(x) / 2
+
+
+def float_to_unsigned(x):
+    return types.uint32(x)
+
+
+def float_to_complex(x):
+    return types.complex128(x)
+
+
+def numpy_scalar_cast_error():
+    np.int32(np.zeros((4,)))
+
+class TestCasting(unittest.TestCase):
+    def test_float_to_int(self):
+        pyfunc = float_to_int
+        cr = compile_isolated(pyfunc, [types.float32])
+        cfunc = cr.entry_point
+
+        self.assertEqual(cr.signature.return_type, types.int32)
+        self.assertEqual(cfunc(12.3), pyfunc(12.3))
+        self.assertEqual(cfunc(12.3), int(12.3))
+        self.assertEqual(cfunc(-12.3), pyfunc(-12.3))
+        self.assertEqual(cfunc(-12.3), int(-12.3))
+
+    def test_int_to_float(self):
+        pyfunc = int_to_float
+        cr = compile_isolated(pyfunc, [types.int64])
+        cfunc = cr.entry_point
+
+        self.assertEqual(cr.signature.return_type, types.float64)
+        self.assertEqual(cfunc(321), pyfunc(321))
+        self.assertEqual(cfunc(321), 321. / 2)
+
+    def test_float_to_unsigned(self):
+        pyfunc = float_to_unsigned
+        cr = compile_isolated(pyfunc, [types.float32])
+        cfunc = cr.entry_point
+
+        self.assertEqual(cr.signature.return_type, types.uint32)
+        self.assertEqual(cfunc(3.21), pyfunc(3.21))
+        self.assertEqual(cfunc(3.21), struct.unpack('I', struct.pack('i',
+                                                                      3))[0])
+
+    def test_float_to_complex(self):
+        pyfunc = float_to_complex
+        cr = compile_isolated(pyfunc, [types.float64])
+        cfunc = cr.entry_point
+        self.assertEqual(cr.signature.return_type, types.complex128)
+        self.assertEqual(cfunc(-3.21), pyfunc(-3.21))
+        self.assertEqual(cfunc(-3.21), -3.21 + 0j)
+
+    def test_array_to_array(self):
+        """Make sure this compiles.
+
+        Cast C to A array
+        """
+        @njit("f8(f8[:])")
+        def inner(x):
+            return x[0]
+
+        inner.disable_compile()
+
+        @njit("f8(f8[::1])")
+        def driver(x):
+            return inner(x)
+
+        x = np.array([1234], dtype=np.float64)
+        self.assertEqual(driver(x), x[0])
+        self.assertEqual(len(inner.overloads), 1)
+
+    def test_0darrayT_to_T(self):
+        @njit
+        def inner(x):
+            return x.dtype.type(x)
+
+        inputs = [
+            (np.bool_, True),
+            (np.float32, 12.3),
+            (np.float64, 12.3),
+            (np.int64, 12),
+            (np.complex64, 2j+3),
+            (np.complex128, 2j+3),
+            (np.timedelta64, np.timedelta64(3, 'h')),
+            (np.datetime64, np.datetime64('2016-01-01')),
+            ('<U3', 'ABC'),
+        ]
+
+        for (T, inp) in inputs:
+            x = np.array(inp, dtype=T)
+            self.assertEqual(inner(x), x[()])
+
+    def test_array_to_scalar(self):
+        """
+        Ensure that a TypingError exception is raised if
+        user tries to convert numpy array to scalar
+        """
+
+        with self.assertRaises(TypingError) as raises:
+            compile_isolated(numpy_scalar_cast_error, ())
+
+        self.assertIn("Casting array(float64, 1d, C) to int32 directly is unsupported.",
+                      str(raises.exception))
+
+    def test_optional_to_optional(self):
+        """
+        Test error due mishandling of Optional to Optional casting
+
+        Related issue: https://github.com/numba/numba/issues/1718
+        """
+        # Attempt to cast optional(intp) to optional(float64)
+        opt_int = types.Optional(types.intp)
+        opt_flt = types.Optional(types.float64)
+        sig = opt_flt(opt_int)
+
+        @njit(sig)
+        def foo(a):
+            return a
+
+        self.assertEqual(foo(2), 2)
+        self.assertIsNone(foo(None))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cffi.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cffi.py
new file mode 100644
index 000000000..f9d97f49e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cffi.py
@@ -0,0 +1,194 @@
+import array
+import numpy as np
+
+from numba import jit
+import numba.core.typing.cffi_utils as cffi_support
+from numba.core import types, errors
+from numba.core.compiler import compile_isolated, Flags
+from numba.tests.support import TestCase, tag
+
+import numba.tests.cffi_usecases as mod
+import unittest
+
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+@unittest.skipUnless(cffi_support.SUPPORTED,
+                     "CFFI not supported -- please install the cffi module")
+class TestCFFI(TestCase):
+
+    # Need to run the tests serially because of race conditions in
+    # cffi's OOL mode.
+    _numba_parallel_test_ = False
+
+    def setUp(self):
+        mod.init()
+        mod.init_ool()
+
+    def test_type_map(self):
+        signature = cffi_support.map_type(mod.ffi.typeof(mod.cffi_sin))
+        self.assertEqual(len(signature.args), 1)
+        self.assertEqual(signature.args[0], types.double)
+
+    def _test_function(self, pyfunc, flags=enable_pyobj_flags):
+        cres = compile_isolated(pyfunc, [types.double], flags=flags)
+        cfunc = cres.entry_point
+
+        for x in [-1.2, -1, 0, 0.1, 3.14]:
+            self.assertPreciseEqual(pyfunc(x), cfunc(x))
+
+    def test_sin_function(self):
+        self._test_function(mod.use_cffi_sin)
+
+    def test_bool_function_ool(self):
+        pyfunc = mod.use_cffi_boolean_true
+        cres = compile_isolated(pyfunc, (), flags=no_pyobj_flags)
+        cfunc = cres.entry_point
+        self.assertEqual(pyfunc(), True)
+        self.assertEqual(cfunc(), True)
+
+    def test_sin_function_npm(self):
+        self._test_function(mod.use_cffi_sin, flags=no_pyobj_flags)
+
+    def test_sin_function_ool(self, flags=enable_pyobj_flags):
+        self._test_function(mod.use_cffi_sin_ool)
+
+    def test_sin_function_npm_ool(self):
+        self._test_function(mod.use_cffi_sin_ool, flags=no_pyobj_flags)
+
+    def test_two_funcs(self):
+        # Check that two constant functions don't get mixed up.
+        self._test_function(mod.use_two_funcs)
+
+    def test_two_funcs_ool(self):
+        self._test_function(mod.use_two_funcs_ool)
+
+    def test_function_pointer(self):
+        pyfunc = mod.use_func_pointer
+        cfunc = jit(nopython=True)(pyfunc)
+        for (fa, fb, x) in [
+            (mod.cffi_sin, mod.cffi_cos, 1.0),
+            (mod.cffi_sin, mod.cffi_cos, -1.0),
+            (mod.cffi_cos, mod.cffi_sin, 1.0),
+            (mod.cffi_cos, mod.cffi_sin, -1.0),
+            (mod.cffi_sin_ool, mod.cffi_cos_ool, 1.0),
+            (mod.cffi_sin_ool, mod.cffi_cos_ool, -1.0),
+            (mod.cffi_cos_ool, mod.cffi_sin_ool, 1.0),
+            (mod.cffi_cos_ool, mod.cffi_sin_ool, -1.0),
+            (mod.cffi_sin, mod.cffi_cos_ool, 1.0),
+            (mod.cffi_sin, mod.cffi_cos_ool, -1.0),
+            (mod.cffi_cos, mod.cffi_sin_ool, 1.0),
+            (mod.cffi_cos, mod.cffi_sin_ool, -1.0)]:
+            expected = pyfunc(fa, fb, x)
+            got = cfunc(fa, fb, x)
+            self.assertEqual(got, expected)
+        # A single specialization was compiled for all calls
+        self.assertEqual(len(cfunc.overloads), 1, cfunc.overloads)
+
+    def test_user_defined_symbols(self):
+        pyfunc = mod.use_user_defined_symbols
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertEqual(pyfunc(), cfunc())
+
+    def check_vector_sin(self, cfunc, x, y):
+        cfunc(x, y)
+        np.testing.assert_allclose(y, np.sin(x))
+
+    def _test_from_buffer_numpy_array(self, pyfunc, dtype):
+        x = np.arange(10).astype(dtype)
+        y = np.zeros_like(x)
+        cfunc = jit(nopython=True)(pyfunc)
+        self.check_vector_sin(cfunc, x, y)
+
+    def test_from_buffer_float32(self):
+        self._test_from_buffer_numpy_array(mod.vector_sin_float32, np.float32)
+
+    def test_from_buffer_float64(self):
+        self._test_from_buffer_numpy_array(mod.vector_sin_float64, np.float64)
+
+    def test_from_buffer_struct(self):
+        n = 10
+        x = np.arange(n) + np.arange(n * 2, n * 3) * 1j
+        y = np.zeros(n)
+        real_cfunc = jit(nopython=True)(mod.vector_extract_real)
+        real_cfunc(x, y)
+        np.testing.assert_equal(x.real, y)
+        imag_cfunc = jit(nopython=True)(mod.vector_extract_imag)
+        imag_cfunc(x, y)
+        np.testing.assert_equal(x.imag, y)
+
+    def test_from_buffer_pyarray(self):
+        pyfunc = mod.vector_sin_float32
+        cfunc = jit(nopython=True)(pyfunc)
+        x = array.array("f", range(10))
+        y = array.array("f", [0] * len(x))
+        self.check_vector_sin(cfunc, x, y)
+
+    def test_from_buffer_error(self):
+        pyfunc = mod.vector_sin_float32
+        cfunc = jit(nopython=True)(pyfunc)
+        # Non-contiguous array
+        x = np.arange(10).astype(np.float32)[::2]
+        y = np.zeros_like(x)
+        with self.assertRaises(errors.TypingError) as raises:
+            cfunc(x, y)
+        self.assertIn("from_buffer() unsupported on non-contiguous buffers",
+                      str(raises.exception))
+
+    def test_from_buffer_numpy_multi_array(self):
+        c1 = np.array([1, 2], order='C', dtype=np.float32)
+        c1_zeros = np.zeros_like(c1)
+        c2 = np.array([[1, 2], [3, 4]], order='C', dtype=np.float32)
+        c2_zeros = np.zeros_like(c2)
+        f1 = np.array([1, 2], order='F', dtype=np.float32)
+        f1_zeros = np.zeros_like(f1)
+        f2 = np.array([[1, 2], [3, 4]], order='F', dtype=np.float32)
+        f2_zeros = np.zeros_like(f2)
+        f2_copy = f2.copy('K')
+        pyfunc = mod.vector_sin_float32
+        cfunc = jit(nopython=True)(pyfunc)
+        # No exception because of C layout and single dimension
+        self.check_vector_sin(cfunc, c1, c1_zeros)
+        # No exception because of C layout
+        cfunc(c2, c2_zeros)
+        sin_c2 = np.sin(c2)
+        sin_c2[1] = [0, 0]  # Reset to zero, since cfunc only processes one row
+        np.testing.assert_allclose(c2_zeros, sin_c2)
+        # No exception because of single dimension
+        self.check_vector_sin(cfunc, f1, f1_zeros)
+        # Exception because multi-dimensional with F layout
+        with self.assertRaises(errors.TypingError) as raises:
+            cfunc(f2, f2_zeros)
+        np.testing.assert_allclose(f2, f2_copy)
+        self.assertIn("from_buffer() only supports multidimensional arrays with C layout",
+                      str(raises.exception))
+
+    def test_indirect_multiple_use(self):
+        """
+        Issue #2263
+
+        Linkage error due to multiple definition of global tracking symbol.
+        """
+        my_sin = mod.cffi_sin
+
+        # Use two jit functions that references `my_sin` to ensure multiple
+        # modules
+        @jit(nopython=True)
+        def inner(x):
+            return my_sin(x)
+
+        @jit(nopython=True)
+        def foo(x):
+            return inner(x) + my_sin(x + 1)
+
+        # Error occurs when foo is being compiled
+        x = 1.123
+        self.assertEqual(foo(x), my_sin(x) + my_sin(x + 1))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cfunc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cfunc.py
new file mode 100644
index 000000000..77ae941d4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cfunc.py
@@ -0,0 +1,412 @@
+"""
+Tests for @cfunc and friends.
+"""
+
+
+import ctypes
+import os
+import subprocess
+import sys
+from collections import namedtuple
+
+import numpy as np
+
+from numba import cfunc, carray, farray, njit
+from numba.core import types, typing, utils
+import numba.core.typing.cffi_utils as cffi_support
+from numba.tests.support import TestCase, tag, captured_stderr
+import unittest
+from numba.np import numpy_support
+
+skip_cffi_unsupported = unittest.skipUnless(
+    cffi_support.SUPPORTED,
+    "CFFI not supported -- please install the cffi module",
+)
+
+
+def add_usecase(a, b):
+    return a + b
+
+def div_usecase(a, b):
+    c = a / b
+    return c
+
+def square_usecase(a):
+    return a ** 2
+
+add_sig = "float64(float64, float64)"
+
+div_sig = "float64(int64, int64)"
+
+square_sig = "float64(float64)"
+
+def objmode_usecase(a, b):
+    object()
+    return a + b
+
+# Test functions for carray() and farray()
+
+CARRAY_USECASE_OUT_LEN = 8
+
+def make_cfarray_usecase(func):
+
+    def cfarray_usecase(in_ptr, out_ptr, m, n):
+        # Tuple shape
+        in_ = func(in_ptr, (m, n))
+        # Integer shape
+        out = func(out_ptr, CARRAY_USECASE_OUT_LEN)
+        out[0] = in_.ndim
+        out[1:3] = in_.shape
+        out[3:5] = in_.strides
+        out[5] = in_.flags.c_contiguous
+        out[6] = in_.flags.f_contiguous
+        s = 0
+        for i, j in np.ndindex(m, n):
+            s += in_[i, j] * (i - j)
+        out[7] = s
+
+    return cfarray_usecase
+
+carray_usecase = make_cfarray_usecase(carray)
+farray_usecase = make_cfarray_usecase(farray)
+
+
+def make_cfarray_dtype_usecase(func):
+    # Same as make_cfarray_usecase(), but with explicit dtype.
+
+    def cfarray_usecase(in_ptr, out_ptr, m, n):
+        # Tuple shape
+        in_ = func(in_ptr, (m, n), dtype=np.float32)
+        # Integer shape
+        out = func(out_ptr, CARRAY_USECASE_OUT_LEN, np.float32)
+        out[0] = in_.ndim
+        out[1:3] = in_.shape
+        out[3:5] = in_.strides
+        out[5] = in_.flags.c_contiguous
+        out[6] = in_.flags.f_contiguous
+        s = 0
+        for i, j in np.ndindex(m, n):
+            s += in_[i, j] * (i - j)
+        out[7] = s
+
+    return cfarray_usecase
+
+carray_dtype_usecase = make_cfarray_dtype_usecase(carray)
+farray_dtype_usecase = make_cfarray_dtype_usecase(farray)
+
+carray_float32_usecase_sig = types.void(types.CPointer(types.float32),
+                                        types.CPointer(types.float32),
+                                        types.intp, types.intp)
+
+carray_float64_usecase_sig = types.void(types.CPointer(types.float64),
+                                        types.CPointer(types.float64),
+                                        types.intp, types.intp)
+
+carray_voidptr_usecase_sig = types.void(types.voidptr, types.voidptr,
+                                        types.intp, types.intp)
+
+
+class TestCFunc(TestCase):
+
+    def test_basic(self):
+        """
+        Basic usage and properties of a cfunc.
+        """
+        f = cfunc(add_sig)(add_usecase)
+
+        self.assertEqual(f.__name__, "add_usecase")
+        self.assertEqual(f.__qualname__, "add_usecase")
+        self.assertIs(f.__wrapped__, add_usecase)
+
+        symbol = f.native_name
+        self.assertIsInstance(symbol, str)
+        self.assertIn("add_usecase", symbol)
+
+        addr = f.address
+        self.assertIsInstance(addr, int)
+
+        ct = f.ctypes
+        self.assertEqual(ctypes.cast(ct, ctypes.c_void_p).value, addr)
+
+        self.assertPreciseEqual(ct(2.0, 3.5), 5.5)
+
+    @skip_cffi_unsupported
+    def test_cffi(self):
+        from numba.tests import cffi_usecases
+        ffi, lib = cffi_usecases.load_inline_module()
+
+        f = cfunc(square_sig)(square_usecase)
+
+        res = lib._numba_test_funcptr(f.cffi)
+        self.assertPreciseEqual(res, 2.25)  # 1.5 ** 2
+
+    def test_locals(self):
+        # By forcing the intermediate result into an integer, we
+        # truncate the ultimate function result
+        f = cfunc(div_sig, locals={'c': types.int64})(div_usecase)
+        self.assertPreciseEqual(f.ctypes(8, 3), 2.0)
+
+    def test_errors(self):
+        f = cfunc(div_sig)(div_usecase)
+
+        with captured_stderr() as err:
+            self.assertPreciseEqual(f.ctypes(5, 2), 2.5)
+        self.assertEqual(err.getvalue(), "")
+
+        with captured_stderr() as err:
+            res = f.ctypes(5, 0)
+            # This is just a side effect of Numba zero-initializing
+            # stack variables, and could change in the future.
+            self.assertPreciseEqual(res, 0.0)
+        err = err.getvalue()
+        self.assertIn("ZeroDivisionError:", err)
+        self.assertIn("Exception ignored", err)
+
+    def test_llvm_ir(self):
+        f = cfunc(add_sig)(add_usecase)
+        ir = f.inspect_llvm()
+        self.assertIn(f.native_name, ir)
+        self.assertIn("fadd double", ir)
+
+    def test_object_mode(self):
+        """
+        Object mode is currently unsupported.
+        """
+        with self.assertRaises(NotImplementedError):
+            cfunc(add_sig, forceobj=True)(add_usecase)
+        with self.assertTypingError() as raises:
+            cfunc(add_sig)(objmode_usecase)
+        self.assertIn("Untyped global name 'object'", str(raises.exception))
+
+
+class TestCArray(TestCase):
+    """
+    Tests for carray() and farray().
+    """
+
+    def run_carray_usecase(self, pointer_factory, func):
+        a = np.arange(10, 16).reshape((2, 3)).astype(np.float32)
+        out = np.empty(CARRAY_USECASE_OUT_LEN, dtype=np.float32)
+        func(pointer_factory(a), pointer_factory(out), *a.shape)
+        return out
+
+    def check_carray_usecase(self, pointer_factory, pyfunc, cfunc):
+        expected = self.run_carray_usecase(pointer_factory, pyfunc)
+        got = self.run_carray_usecase(pointer_factory, cfunc)
+        self.assertPreciseEqual(expected, got)
+
+    def make_voidptr(self, arr):
+        return arr.ctypes.data_as(ctypes.c_void_p)
+
+    def make_float32_pointer(self, arr):
+        return arr.ctypes.data_as(ctypes.POINTER(ctypes.c_float))
+
+    def make_float64_pointer(self, arr):
+        return arr.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
+
+    def check_carray_farray(self, func, order):
+        def eq(got, expected):
+            # Same layout, dtype, shape, etc.
+            self.assertPreciseEqual(got, expected)
+            # Same underlying data
+            self.assertEqual(got.ctypes.data, expected.ctypes.data)
+
+        base = np.arange(6).reshape((2, 3)).astype(np.float32).copy(order=order)
+
+        # With typed pointer and implied dtype
+        a = func(self.make_float32_pointer(base), base.shape)
+        eq(a, base)
+        # Integer shape
+        a = func(self.make_float32_pointer(base), base.size)
+        eq(a, base.ravel('K'))
+
+        # With typed pointer and explicit dtype
+        a = func(self.make_float32_pointer(base), base.shape, base.dtype)
+        eq(a, base)
+        a = func(self.make_float32_pointer(base), base.shape, np.float32)
+        eq(a, base)
+
+        # With voidptr and explicit dtype
+        a = func(self.make_voidptr(base), base.shape, base.dtype)
+        eq(a, base)
+        a = func(self.make_voidptr(base), base.shape, np.int32)
+        eq(a, base.view(np.int32))
+
+        # voidptr without dtype
+        with self.assertRaises(TypeError):
+            func(self.make_voidptr(base), base.shape)
+        # Invalid pointer type
+        with self.assertRaises(TypeError):
+            func(base.ctypes.data, base.shape)
+        # Mismatching dtype
+        with self.assertRaises(TypeError) as raises:
+            func(self.make_float32_pointer(base), base.shape, np.int32)
+        self.assertIn("mismatching dtype 'int32' for pointer",
+                      str(raises.exception))
+
+    def test_carray(self):
+        """
+        Test pure Python carray().
+        """
+        self.check_carray_farray(carray, 'C')
+
+    def test_farray(self):
+        """
+        Test pure Python farray().
+        """
+        self.check_carray_farray(farray, 'F')
+
+    def make_carray_sigs(self, formal_sig):
+        """
+        Generate a bunch of concrete signatures by varying the width
+        and signedness of size arguments (see issue #1923).
+        """
+        for actual_size in (types.intp, types.int32, types.intc,
+                            types.uintp, types.uint32, types.uintc):
+            args = tuple(actual_size if a == types.intp else a
+                         for a in formal_sig.args)
+            yield formal_sig.return_type(*args)
+
+    def check_numba_carray_farray(self, usecase, dtype_usecase):
+        # With typed pointers and implicit dtype
+        pyfunc = usecase
+        for sig in self.make_carray_sigs(carray_float32_usecase_sig):
+            f = cfunc(sig)(pyfunc)
+            self.check_carray_usecase(self.make_float32_pointer, pyfunc, f.ctypes)
+
+        # With typed pointers and explicit (matching) dtype
+        pyfunc = dtype_usecase
+        for sig in self.make_carray_sigs(carray_float32_usecase_sig):
+            f = cfunc(sig)(pyfunc)
+            self.check_carray_usecase(self.make_float32_pointer, pyfunc, f.ctypes)
+        # With typed pointers and mismatching dtype
+        with self.assertTypingError() as raises:
+            f = cfunc(carray_float64_usecase_sig)(pyfunc)
+        self.assertIn("mismatching dtype 'float32' for pointer type 'float64*'",
+                      str(raises.exception))
+
+        # With voidptr
+        pyfunc = dtype_usecase
+        for sig in self.make_carray_sigs(carray_voidptr_usecase_sig):
+            f = cfunc(sig)(pyfunc)
+            self.check_carray_usecase(self.make_float32_pointer, pyfunc, f.ctypes)
+
+    def test_numba_carray(self):
+        """
+        Test Numba-compiled carray() against pure Python carray()
+        """
+        self.check_numba_carray_farray(carray_usecase, carray_dtype_usecase)
+
+    def test_numba_farray(self):
+        """
+        Test Numba-compiled farray() against pure Python farray()
+        """
+        self.check_numba_carray_farray(farray_usecase, farray_dtype_usecase)
+
+
+@skip_cffi_unsupported
+class TestCffiStruct(TestCase):
+    c_source = """
+typedef struct _big_struct {
+    int    i1;
+    float  f2;
+    double d3;
+    float  af4[9];
+} big_struct;
+
+typedef struct _error {
+    int bits:4;
+} error;
+
+typedef double (*myfunc)(big_struct*, size_t);
+"""
+
+    def get_ffi(self, src=c_source):
+        from cffi import FFI
+
+        ffi = FFI()
+        ffi.cdef(src)
+        return ffi
+
+    def test_type_parsing(self):
+        ffi = self.get_ffi()
+        # Check struct typedef
+        big_struct = ffi.typeof('big_struct')
+        nbtype = cffi_support.map_type(big_struct, use_record_dtype=True)
+        self.assertIsInstance(nbtype, types.Record)
+        self.assertEqual(len(nbtype), 4)
+        self.assertEqual(nbtype.typeof('i1'), types.int32)
+        self.assertEqual(nbtype.typeof('f2'), types.float32)
+        self.assertEqual(nbtype.typeof('d3'), types.float64)
+        self.assertEqual(
+            nbtype.typeof('af4'),
+            types.NestedArray(dtype=types.float32, shape=(9,)),
+        )
+
+        # Check function typedef
+        myfunc = ffi.typeof('myfunc')
+        sig = cffi_support.map_type(myfunc, use_record_dtype=True)
+        self.assertIsInstance(sig, typing.Signature)
+        self.assertEqual(sig.args[0], types.CPointer(nbtype))
+        self.assertEqual(sig.args[1], types.uintp)
+        self.assertEqual(sig.return_type, types.float64)
+
+    def test_cfunc_callback(self):
+        ffi = self.get_ffi()
+        big_struct = ffi.typeof('big_struct')
+        nb_big_struct = cffi_support.map_type(big_struct, use_record_dtype=True)
+        sig = cffi_support.map_type(ffi.typeof('myfunc'), use_record_dtype=True)
+
+        @njit
+        def calc(base):
+            tmp = 0
+            for i in range(base.size):
+                elem = base[i]
+                tmp += elem.i1 * elem.f2 / elem.d3
+                tmp += base[i].af4.sum()
+            return tmp
+
+        @cfunc(sig)
+        def foo(ptr, n):
+            base = carray(ptr, n)
+            return calc(base)
+
+        # Make data
+        mydata = ffi.new('big_struct[3]')
+        ptr = ffi.cast('big_struct*', mydata)
+        for i in range(3):
+            ptr[i].i1 = i * 123
+            ptr[i].f2 = i * 213
+            ptr[i].d3 = (1 + i) * 213
+            for j in range(9):
+                ptr[i].af4[j] = i * 10 + j
+
+        # Address of my data
+        addr = int(ffi.cast('size_t', ptr))
+        got = foo.ctypes(addr, 3)
+
+        # Make numpy array from the cffi buffer
+        array = np.ndarray(
+            buffer=ffi.buffer(mydata),
+            dtype=numpy_support.as_dtype(nb_big_struct),
+            shape=3,
+            )
+        expect = calc(array)
+        self.assertEqual(got, expect)
+
+    def test_unsupport_bitsize(self):
+        ffi = self.get_ffi()
+        with self.assertRaises(ValueError) as raises:
+            cffi_support.map_type(
+                ffi.typeof('error'),
+                use_record_dtype=True,
+            )
+        # When bitsize is provided, bitshift defaults to 0.
+        self.assertEqual(
+            "field 'bits' has bitshift, this is not supported",
+            str(raises.exception)
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cgutils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cgutils.py
new file mode 100644
index 000000000..e669d1c05
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cgutils.py
@@ -0,0 +1,152 @@
+import contextlib
+import ctypes
+import struct
+import sys
+
+import llvmlite.ir as ir
+import numpy as np
+
+import unittest
+from numba.core import types, typing, cgutils, cpu
+from numba.core.compiler_lock import global_compiler_lock
+from numba.tests.support import TestCase, run_in_subprocess
+
+
+machine_int = ir.IntType(types.intp.bitwidth)
+
+def machine_const(n):
+    return ir.Constant(machine_int, n)
+
+
+class StructureTestCase(TestCase):
+
+    def setUp(self):
+        typing_context = typing.Context()
+        self.context = cpu.CPUContext(typing_context)
+
+    @contextlib.contextmanager
+    def compile_function(self, nargs):
+        llvm_fnty = ir.FunctionType(machine_int, [machine_int] * nargs)
+        ctypes_fnty = ctypes.CFUNCTYPE(ctypes.c_size_t,
+                                    * (ctypes.c_size_t,) * nargs)
+        module = self.context.create_module("")
+
+        function = cgutils.get_or_insert_function(module, llvm_fnty, self.id())
+        assert function.is_declaration
+        entry_block = function.append_basic_block('entry')
+        builder = ir.IRBuilder(entry_block)
+
+        first = [True]
+
+        @global_compiler_lock
+        def call_func(*args):
+            codegen = self.context.codegen()
+            library = codegen.create_library("test_module.%s" % self.id())
+            library.add_ir_module(module)
+            cptr = library.get_pointer_to_function(function.name)
+            cfunc = ctypes_fnty(cptr)
+            return cfunc(*args)
+
+        yield self.context, builder, function.args, call_func
+
+
+    def get_bytearray_addr(self, ba):
+        assert isinstance(ba, bytearray)
+        ba_as_string = ctypes.pythonapi.PyByteArray_AsString
+        ba_as_string.argtypes = [ctypes.py_object]
+        ba_as_string.restype = ctypes.c_void_p
+        return ba_as_string(ba)
+
+    def test_compile_function(self):
+        # Simple self-test for compile_function()
+        with self.compile_function(2) as (context, builder, args, call):
+            res = builder.add(args[0], args[1])
+            builder.ret(res)
+        self.assertEqual(call(5, -2), 3)
+        self.assertEqual(call(4, 2), 6)
+
+    @contextlib.contextmanager
+    def run_struct_access(self, struct_class, buf, offset=0):
+        with self.compile_function(1) as (context, builder, args, call):
+            inst = struct_class(context, builder)
+            sptr = builder.add(args[0], machine_const(offset))
+            sptr = builder.inttoptr(sptr, ir.PointerType(inst._type))
+            inst = struct_class(context, builder, ref=sptr)
+
+            yield context, builder, args, inst
+
+            builder.ret(ir.Constant(machine_int, 0))
+        call(self.get_bytearray_addr(buf))
+
+    @contextlib.contextmanager
+    def run_simple_struct_test(self, struct_class, struct_fmt, struct_args):
+        # By using a too large buffer and a non-zero offset, we also check
+        # that surrounding memory isn't touched.
+        buf = bytearray(b'!') * 40
+        expected = buf[:]
+        offset = 8
+
+        with self.run_struct_access(struct_class, buf, offset) \
+            as (context, builder, args, inst):
+            yield context, builder, inst
+
+        self.assertNotEqual(buf, expected)
+        struct.pack_into(struct_fmt, expected, offset, *struct_args)
+        self.assertEqual(buf, expected)
+
+    def test_int_fields(self):
+        class S(cgutils.Structure):
+            _fields = [('a', types.int32),
+                       ('b', types.uint16)]
+
+        fmt = "=iH"
+        with self.run_simple_struct_test(S, fmt, (0x12345678, 0xABCD)) \
+            as (context, builder, inst):
+            inst.a = ir.Constant(ir.IntType(32), 0x12345678)
+            inst.b = ir.Constant(ir.IntType(16), 0xABCD)
+
+    def test_float_fields(self):
+        class S(cgutils.Structure):
+            _fields = [('a', types.float64),
+                       ('b', types.float32)]
+
+        fmt = "=df"
+        with self.run_simple_struct_test(S, fmt, (1.23, 4.56)) \
+            as (context, builder, inst):
+            inst.a = ir.Constant(ir.DoubleType(), 1.23)
+            inst.b = ir.Constant(ir.FloatType(), 4.56)
+
+
+class TestCGContext(TestCase):
+    """Tests for code generation context functionality"""
+
+    def test_printf(self):
+        # Tests the printf() method
+
+        value = 123456
+
+        code = f"""if 1:
+        from numba import njit, types
+        from numba.extending import intrinsic
+
+        @intrinsic
+        def printf(tyctx, int_arg):
+            sig = types.void(int_arg)
+            def codegen(cgctx, builder, sig, llargs):
+                cgctx.printf(builder, \"%d\\n\", *llargs)
+            return sig, codegen
+
+        @njit
+        def foo():
+            printf({value})
+
+        foo()
+        """
+
+        out, _ = run_in_subprocess(code)
+        self.assertIn(str(value), out.decode())
+
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_chained_assign.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_chained_assign.py
new file mode 100644
index 000000000..34aa091cc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_chained_assign.py
@@ -0,0 +1,145 @@
+from numba import jit
+import unittest
+import numpy as np
+import copy
+from numba.tests.support import MemoryLeakMixin
+
+
+@jit
+def inc(a):
+    for i in range(len(a)):
+        a[i] += 1
+    return a
+
+@jit
+def inc1(a):
+    a[0] += 1
+    return a[0]
+
+@jit
+def inc2(a):
+    a[0] += 1
+    return a[0], a[0] + 1
+
+
+def chain1(a):
+    x = y = z = inc(a)
+    return x + y + z
+
+
+def chain2(v):
+    a = np.zeros(2)
+    a[0] = x = a[1] = v
+    return a[0] + a[1] + (x / 2)
+
+
+def unpack1(x, y):
+    a, b = x, y
+    return a + b / 2
+
+
+def unpack2(x, y):
+    a, b = c, d = inc1(x), inc1(y)
+    return a + c / 2, b + d / 2
+
+
+def chain3(x, y):
+    a = (b, c) = (inc1(x), inc1(y))
+    (d, e) = f = (inc1(x), inc1(y))
+    return (a[0] + b / 2 + d + f[0]), (a[1] + c + e / 2 + f[1])
+
+
+def unpack3(x):
+    a, b = inc2(x)
+    return a + b / 2
+
+
+def unpack4(x):
+    a, b = c, d = inc2(x)
+    return a + c / 2, b + d / 2
+
+
+def unpack5(x):
+    a = b, c = inc2(x)
+    d, e = f = inc2(x)
+    return (a[0] + b / 2 + d + f[0]), (a[1] + c + e / 2 + f[1])
+
+
+def unpack6(x, y):
+    (a, b), (c, d) = (x, y), (y + 1, x + 1)
+    return a + c / 2, b / 2 + d
+
+
+class TestChainedAssign(MemoryLeakMixin, unittest.TestCase):
+    def test_chain1(self):
+        args = [
+            [np.arange(2)],
+            [np.arange(4, dtype=np.double)],
+        ]
+        self._test_template(chain1, args)
+
+    def test_chain2(self):
+        args = [
+            [3],
+            [3.0],
+        ]
+        self._test_template(chain2, args)
+
+    def test_unpack1(self):
+        args = [
+            [1, 3.0],
+            [1.0, 3],
+        ]
+        self._test_template(unpack1, args)
+
+    def test_unpack2(self):
+        args = [
+            [np.array([2]), np.array([4.0])],
+            [np.array([2.0]), np.array([4])],
+        ]
+        self._test_template(unpack2, args)
+
+    def test_chain3(self):
+        args = [
+            [np.array([0]), np.array([1.5])],
+            [np.array([0.5]), np.array([1])],
+        ]
+        self._test_template(chain3, args)
+
+    def test_unpack3(self):
+        args = [
+            [np.array([1])],
+            [np.array([1.0])],
+        ]
+        self._test_template(unpack3, args)
+
+    def test_unpack4(self):
+        args = [
+            [np.array([1])],
+            [np.array([1.0])],
+        ]
+        self._test_template(unpack4, args)
+
+    def test_unpack5(self):
+        args = [
+            [np.array([2])],
+            [np.array([2.0])],
+        ]
+        self._test_template(unpack5, args)
+
+    def test_unpack6(self):
+        args1 = 3.0, 2
+        args2 = 3.0, 2.0
+        self._test_template(unpack6, [args1, args2])
+
+    def _test_template(self, pyfunc, argcases):
+        cfunc = jit(pyfunc)
+        for args in argcases:
+            a1 = copy.deepcopy(args)
+            a2 = copy.deepcopy(args)
+            np.testing.assert_allclose(pyfunc(*a1), cfunc(*a2))
+
+
+if __name__ == '__main__':
+    unittest.main()
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_chrome_trace.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_chrome_trace.py
new file mode 100644
index 000000000..613a6d8d2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_chrome_trace.py
@@ -0,0 +1,51 @@
+import os
+import json
+import unittest
+from textwrap import dedent
+from tempfile import TemporaryDirectory
+
+from numba.tests.support import TestCase, run_in_subprocess
+
+
+class TestChromeTraceModule(TestCase):
+    """
+    Test chrome tracing generated file(s).
+    """
+
+    def test_trace_output(self):
+        code = """
+            from numba import njit
+            import numpy as np
+
+            x = np.arange(100).reshape(10, 10)
+
+            @njit
+            def go_fast(a):
+                trace = 0.0
+                for i in range(a.shape[0]):
+                    trace += np.tanh(a[i, i])
+                return a + trace
+
+            go_fast(x)
+        """
+
+        src = dedent(code)
+        with TemporaryDirectory() as tmpdir:
+            path = os.path.join(tmpdir, "test_trace.json")
+            env = os.environ.copy()
+            env['NUMBA_CHROME_TRACE'] = path
+            run_in_subprocess(src, env=env)
+            with open(path) as file:
+                events = json.load(file)
+                self.assertIsInstance(events, list)
+                for ev in events:
+                    self.assertIsInstance(ev, dict)
+                    # check that each record has the right fields.
+                    self.assertEqual(
+                        set(ev.keys()),
+                        {"cat", "pid", "tid", "ph", "name", "args", "ts"},
+                    )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cli.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cli.py
new file mode 100644
index 000000000..37b1dcefb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_cli.py
@@ -0,0 +1,281 @@
+# Tests for the CLI
+
+import os
+import subprocess
+import sys
+import threading
+import json
+from subprocess import CompletedProcess
+from tempfile import TemporaryDirectory
+from unittest import mock
+
+import unittest
+from numba.tests.support import TestCase, linux_only
+import numba.misc.numba_sysinfo as nsi
+from numba.tests.gdb_support import needs_gdb
+from numba.misc.numba_gdbinfo import collect_gdbinfo
+# Going to mock parts of this in testing
+from numba.misc.numba_gdbinfo import _GDBTestWrapper
+
+
+def run_cmd(cmdline, env=os.environ, timeout=60):
+    popen = subprocess.Popen(cmdline,
+                             stdout=subprocess.PIPE,
+                             stderr=subprocess.PIPE,
+                             env=env)
+
+    timeout_timer = threading.Timer(timeout, popen.kill)
+    try:
+        timeout_timer.start()
+        out, err = popen.communicate()
+        if popen.returncode != 0:
+            raise AssertionError(
+                "process failed with code %s: stderr follows\n%s\n" %
+                (popen.returncode, err.decode()))
+        return out.decode(), err.decode()
+    finally:
+        timeout_timer.cancel()
+    return None, None
+
+
+class TestCLI(TestCase):
+
+    def test_as_module_exit_code(self):
+        cmdline = [sys.executable, "-m", "numba"]
+        with self.assertRaises(AssertionError) as raises:
+            run_cmd(cmdline)
+
+        self.assertIn("process failed with code 1", str(raises.exception))
+
+    def test_sysinfo_from_module(self):
+        cmdline = [sys.executable, "-m", "numba", "-s"]
+        o, _ = run_cmd(cmdline)
+        self.assertIn("System info", o)
+
+    def test_json_sysinfo_from_module(self):
+        with TemporaryDirectory() as d:
+            path = os.path.join(d, "test_json_sysinfo.json")
+            cmdline = [sys.executable, "-m", "numba", "--sys-json", path]
+            run_cmd(cmdline)
+            with self.subTest(msg=f"{path} exists"):
+                self.assertTrue(os.path.exists(path))
+            with self.subTest(msg="json load"):
+                with open(path, 'r') as f:
+                    info = json.load(f)
+            safe_contents = {
+                int: (
+                    nsi._cpu_count,
+                ),
+                float: (
+                    nsi._runtime,
+                ),
+                str: (
+                    nsi._start,
+                    nsi._start_utc,
+                    nsi._machine,
+                    nsi._cpu_name,
+                    nsi._platform_name,
+                    nsi._os_name,
+                    nsi._os_version,
+                    nsi._python_comp,
+                    nsi._python_impl,
+                    nsi._python_version,
+                    nsi._llvm_version,
+                ),
+                bool: (
+                    nsi._cu_dev_init,
+                    nsi._svml_state,
+                    nsi._svml_loaded,
+                    nsi._svml_operational,
+                    nsi._llvm_svml_patched,
+                    nsi._tbb_thread,
+                    nsi._openmp_thread,
+                    nsi._wkq_thread,
+                ),
+                list: (
+                    nsi._errors,
+                    nsi._warnings,
+                ),
+                dict: (
+                    nsi._numba_env_vars,
+                ),
+            }
+            for t, keys in safe_contents.items():
+                for k in keys:
+                    with self.subTest(k=k):
+                        self.assertIsInstance(info[k], t)
+
+    @needs_gdb
+    def test_gdb_status_from_module(self):
+        # Check that the `python -m numba -g` works ok
+        cmdline = [sys.executable, "-m", "numba", "-g"]
+        o, _ = run_cmd(cmdline)
+        self.assertIn("GDB info", o)
+        # It's not known a priori whether the extension is supported, this just
+        # checks that the last logical item in the output is printed.
+        self.assertIn("Numba printing extension support", o)
+
+
+class TestGDBCLIInfo(TestCase):
+
+    def setUp(self):
+        # Mock the entire class, to report valid things,
+        # then override bits of it locally to check failures etc.
+
+        self._patches = []
+
+        mock_init = lambda self: None
+        self._patches.append(mock.patch.object(_GDBTestWrapper, '__init__',
+                                               mock_init))
+
+        bpath = 'numba.misc.numba_gdbinfo._GDBTestWrapper.gdb_binary'
+        self._patches.append(mock.patch(bpath, 'PATH_TO_GDB'))
+
+        def _patch(fnstr, func):
+            self._patches.append(mock.patch.object(_GDBTestWrapper, fnstr,
+                                                   func))
+
+        def mock_check_launch(self):
+            return CompletedProcess('COMMAND STRING', 0)
+
+        _patch('check_launch', mock_check_launch)
+
+        # NOTE: The Python and NumPy versions are set to something unsupported!
+        def mock_check_python(self):
+            return CompletedProcess('COMMAND STRING', 0,
+                                    stdout='(3, 2)',
+                                    stderr='')
+
+        _patch('check_python', mock_check_python)
+
+        def mock_check_numpy(self):
+            return CompletedProcess('COMMAND STRING', 0, stdout='True',
+                                    stderr='')
+
+        _patch('check_numpy', mock_check_numpy)
+
+        def mock_check_numpy_version(self):
+            return CompletedProcess('COMMAND STRING', 0, stdout='1.15',
+                                    stderr='')
+
+        _patch('check_numpy_version', mock_check_numpy_version)
+
+        # start the patching
+        for p in self._patches:
+            p.start()
+
+    def tearDown(self):
+        # stop the patching
+        for p in self._patches:
+            p.stop()
+
+    def test_valid(self):
+        collected = collect_gdbinfo()
+        self.assertEqual(collected.binary_loc, 'PATH_TO_GDB')
+        extp = os.path.exists(os.path.abspath(collected.extension_loc))
+        self.assertTrue(extp)
+        self.assertEqual(collected.py_ver, '3.2')
+        self.assertEqual(collected.np_ver, '1.15')
+        self.assertIn('Full', collected.supported)
+
+    def test_invalid_binary(self):
+
+        def mock_fn(self):
+            return CompletedProcess('INVALID_BINARY', 1)
+
+        with mock.patch.object(_GDBTestWrapper, 'check_launch', mock_fn):
+            info = collect_gdbinfo()
+            self.assertIn("Testing gdb binary failed.", info.binary_loc)
+            self.assertIn("gdb at 'PATH_TO_GDB' does not appear to work",
+                          info.binary_loc)
+
+    def test_no_python(self):
+        def mock_fn(self):
+            return CompletedProcess('NO PYTHON', 1)
+
+        with mock.patch.object(_GDBTestWrapper, 'check_python', mock_fn):
+            collected = collect_gdbinfo()
+            self.assertEqual(collected.py_ver, 'No Python support')
+            self.assertEqual(collected.supported, 'None')
+
+    def test_unparsable_python_version(self):
+        def mock_fn(self):
+            return CompletedProcess('NO PYTHON', 0, stdout='(NOT A VERSION)')
+
+        with mock.patch.object(_GDBTestWrapper, 'check_python', mock_fn):
+            collected = collect_gdbinfo()
+            self.assertEqual(collected.py_ver, 'No Python support')
+
+    def test_no_numpy(self):
+        def mock_fn(self):
+            return CompletedProcess('NO NUMPY', 1)
+
+        with mock.patch.object(_GDBTestWrapper, 'check_numpy', mock_fn):
+            collected = collect_gdbinfo()
+            self.assertEqual(collected.np_ver, 'No NumPy support')
+            self.assertEqual(collected.py_ver, '3.2')
+            self.assertIn('Partial', collected.supported)
+
+    def test_no_numpy_version(self):
+        def mock_fn(self):
+            return CompletedProcess('NO NUMPY VERSION', 1)
+
+        with mock.patch.object(_GDBTestWrapper, 'check_numpy_version', mock_fn):
+            collected = collect_gdbinfo()
+            self.assertEqual(collected.np_ver, 'Unknown')
+
+    def test_traceback_in_numpy_version(self):
+        def mock_fn(self):
+            return CompletedProcess('NO NUMPY VERSION', 0,
+                                    stdout='(NOT A VERSION)',
+                                    stderr='Traceback')
+
+        with mock.patch.object(_GDBTestWrapper, 'check_numpy_version', mock_fn):
+            collected = collect_gdbinfo()
+            self.assertEqual(collected.np_ver, 'Unknown')
+
+
+@linux_only
+class TestGDBCLIInfoBrokenGdbs(TestCase):
+    # In these tests, it's necessary to actually run the entry point in an env
+    # with the env var set as it's in part testing the processing of the
+    # handling of consumption of variables by numba.core.config.
+
+    def test_cannot_find_gdb_from_name(self):
+        # Tests that supplying an invalid gdb binary name is handled ok
+        env = os.environ.copy()
+        env['NUMBA_GDB_BINARY'] = 'THIS_IS_NOT_A_VALID_GDB_BINARY_NAME'
+        cmdline = [sys.executable, "-m", "numba", "-g"]
+        stdout, stderr = run_cmd(cmdline, env=env)
+        self.assertIn("Testing gdb binary failed", stdout)
+        self.assertIn("No such file or directory", stdout)
+        self.assertIn("'THIS_IS_NOT_A_VALID_GDB_BINARY_NAME'", stdout)
+
+    def test_cannot_find_gdb_from_path(self):
+        # Tests that an invalid path is handled ok
+        env = os.environ.copy()
+        with TemporaryDirectory() as d:
+            # This wont exist as a path...
+            path = os.path.join(d, 'CANNOT_EXIST')
+            env['NUMBA_GDB_BINARY'] = path
+            cmdline = [sys.executable, "-m", "numba", "-g"]
+            stdout, stderr = run_cmd(cmdline, env=env)
+            self.assertIn("Testing gdb binary failed", stdout)
+            self.assertIn("No such file or directory", stdout)
+            self.assertIn(path, stdout)
+
+    def test_nonsense_gdb_binary(self):
+        # Tests that a nonsense binary specified as gdb it picked up ok
+        env = os.environ.copy()
+        env['NUMBA_GDB_BINARY'] = 'python' # 'python' isn't gdb!
+        cmdline = [sys.executable, "-m", "numba", "-g"]
+        stdout, stderr = run_cmd(cmdline, env=env)
+        self.assertIn("Testing gdb binary failed", stdout)
+        # NOTE: should 'python' ever add support for the same flags as the gdb
+        # commands used in the information gathering code in `numba_gdbinfo`
+        # this test will fail, it's reasonably unlikely.
+        self.assertIn("Unknown option", stdout)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_closure.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_closure.py
new file mode 100644
index 000000000..81de272b8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_closure.py
@@ -0,0 +1,575 @@
+# import numpy in two ways, both uses needed
+import numpy as np
+import numpy
+
+import unittest
+from numba import njit, jit
+from numba.core.errors import TypingError, UnsupportedError
+from numba.core import ir
+from numba.tests.support import TestCase, IRPreservingTestPipeline
+
+
+class TestClosure(TestCase):
+
+    def run_jit_closure_variable(self, **jitargs):
+        Y = 10
+
+        def add_Y(x):
+            return x + Y
+
+        c_add_Y = jit('i4(i4)', **jitargs)(add_Y)
+        self.assertEqual(c_add_Y(1), 11)
+
+        # Like globals in Numba, the value of the closure is captured
+        # at time of JIT
+        Y = 12  # should not affect function
+        self.assertEqual(c_add_Y(1), 11)
+
+    def test_jit_closure_variable(self):
+        self.run_jit_closure_variable(forceobj=True)
+
+    def test_jit_closure_variable_npm(self):
+        self.run_jit_closure_variable(nopython=True)
+
+    def run_rejitting_closure(self, **jitargs):
+        Y = 10
+
+        def add_Y(x):
+            return x + Y
+
+        c_add_Y = jit('i4(i4)', **jitargs)(add_Y)
+        self.assertEqual(c_add_Y(1), 11)
+
+        # Redo the jit
+        Y = 12
+        c_add_Y_2 = jit('i4(i4)', **jitargs)(add_Y)
+        self.assertEqual(c_add_Y_2(1), 13)
+        Y = 13  # should not affect function
+        self.assertEqual(c_add_Y_2(1), 13)
+
+        self.assertEqual(c_add_Y(1), 11)  # Test first function again
+
+    def test_rejitting_closure(self):
+        self.run_rejitting_closure(forceobj=True)
+
+    def test_rejitting_closure_npm(self):
+        self.run_rejitting_closure(nopython=True)
+
+    def run_jit_multiple_closure_variables(self, **jitargs):
+        Y = 10
+        Z = 2
+
+        def add_Y_mult_Z(x):
+            return (x + Y) * Z
+
+        c_add_Y_mult_Z = jit('i4(i4)', **jitargs)(add_Y_mult_Z)
+        self.assertEqual(c_add_Y_mult_Z(1), 22)
+
+    def test_jit_multiple_closure_variables(self):
+        self.run_jit_multiple_closure_variables(forceobj=True)
+
+    def test_jit_multiple_closure_variables_npm(self):
+        self.run_jit_multiple_closure_variables(nopython=True)
+
+    def run_jit_inner_function(self, **jitargs):
+        def mult_10(a):
+            return a * 10
+
+        c_mult_10 = jit('intp(intp)', **jitargs)(mult_10)
+        c_mult_10.disable_compile()
+
+        def do_math(x):
+            return c_mult_10(x + 4)
+
+        c_do_math = jit('intp(intp)', **jitargs)(do_math)
+        c_do_math.disable_compile()
+
+        with self.assertRefCount(c_do_math, c_mult_10):
+            self.assertEqual(c_do_math(1), 50)
+
+    def test_jit_inner_function(self):
+        self.run_jit_inner_function(forceobj=True)
+
+    def test_jit_inner_function_npm(self):
+        self.run_jit_inner_function(nopython=True)
+
+
+class TestInlinedClosure(TestCase):
+    """
+    Tests for (partial) closure support in njit. The support is partial
+    because it only works for closures that can be successfully inlined
+    at compile time.
+    """
+
+    def test_inner_function(self):
+
+        def outer(x):
+
+            def inner(x):
+                return x * x
+
+            return inner(x) + inner(x)
+
+        cfunc = njit(outer)
+        self.assertEqual(cfunc(10), outer(10))
+
+    def test_inner_function_with_closure(self):
+
+        def outer(x):
+            y = x + 1
+
+            def inner(x):
+                return x * x + y
+
+            return inner(x) + inner(x)
+
+        cfunc = njit(outer)
+        self.assertEqual(cfunc(10), outer(10))
+
+    def test_inner_function_with_closure_2(self):
+
+        def outer(x):
+            y = x + 1
+
+            def inner(x):
+                return x * y
+
+            y = inner(x)
+            return y + inner(x)
+
+        cfunc = njit(outer)
+        self.assertEqual(cfunc(10), outer(10))
+
+    def test_inner_function_with_closure_3(self):
+
+        code = """
+            def outer(x):
+                y = x + 1
+                z = 0
+
+                def inner(x):
+                    nonlocal z
+                    z += x * x
+                    return z + y
+
+                return inner(x) + inner(x) + z
+        """
+        ns = {}
+        exec(code.strip(), ns)
+
+        cfunc = njit(ns['outer'])
+        self.assertEqual(cfunc(10), ns['outer'](10))
+
+    def test_inner_function_nested(self):
+
+        def outer(x):
+
+            def inner(y):
+
+                def innermost(z):
+                    return x + y + z
+
+                s = 0
+                for i in range(y):
+                    s += innermost(i)
+                return s
+
+            return inner(x * x)
+
+        cfunc = njit(outer)
+        self.assertEqual(cfunc(10), outer(10))
+
+    def test_bulk_use_cases(self):
+        """ Tests the large number of use cases defined below """
+
+        # jitted function used in some tests
+        @njit
+        def fib3(n):
+            if n < 2:
+                return n
+            return fib3(n - 1) + fib3(n - 2)
+
+        def outer1(x):
+            """ Test calling recursive function from inner """
+            def inner(x):
+                return fib3(x)
+            return inner(x)
+
+        def outer2(x):
+            """ Test calling recursive function from closure """
+            z = x + 1
+
+            def inner(x):
+                return x + fib3(z)
+            return inner(x)
+
+        def outer3(x):
+            """ Test recursive inner """
+            def inner(x):
+                if x < 2:
+                    return 10
+                else:
+                    inner(x - 1)
+            return inner(x)
+
+        def outer4(x):
+            """ Test recursive closure """
+            y = x + 1
+
+            def inner(x):
+                if x + y < 2:
+                    return 10
+                else:
+                    inner(x - 1)
+            return inner(x)
+
+        def outer5(x):
+            """ Test nested closure """
+            y = x + 1
+
+            def inner1(x):
+                z = y + x + 2
+
+                def inner2(x):
+                    return x + z
+
+                return inner2(x) + y
+
+            return inner1(x)
+
+        def outer6(x):
+            """ Test closure with list comprehension in body """
+            y = x + 1
+
+            def inner1(x):
+                z = y + x + 2
+                return [t for t in range(z)]
+            return inner1(x)
+
+        _OUTER_SCOPE_VAR = 9
+
+        def outer7(x):
+            """ Test use of outer scope var, no closure """
+            z = x + 1
+            return x + z + _OUTER_SCOPE_VAR
+
+        _OUTER_SCOPE_VAR = 9
+
+        def outer8(x):
+            """ Test use of outer scope var, with closure """
+            z = x + 1
+
+            def inner(x):
+                return x + z + _OUTER_SCOPE_VAR
+            return inner(x)
+
+        def outer9(x):
+            """ Test closure assignment"""
+            z = x + 1
+
+            def inner(x):
+                return x + z
+            f = inner
+            return f(x)
+
+        def outer10(x):
+            """ Test two inner, one calls other """
+            z = x + 1
+
+            def inner(x):
+                return x + z
+
+            def inner2(x):
+                return inner(x)
+
+            return inner2(x)
+
+        def outer11(x):
+            """ return the closure """
+            z = x + 1
+
+            def inner(x):
+                return x + z
+            return inner
+
+        def outer12(x):
+            """ closure with kwarg"""
+            z = x + 1
+
+            def inner(x, kw=7):
+                return x + z + kw
+            return inner(x)
+
+        def outer13(x, kw=7):
+            """ outer with kwarg no closure"""
+            z = x + 1 + kw
+            return z
+
+        def outer14(x, kw=7):
+            """ outer with kwarg used in closure"""
+            z = x + 1
+
+            def inner(x):
+                return x + z + kw
+            return inner(x)
+
+        def outer15(x, kw=7):
+            """ outer with kwarg as arg to closure"""
+            z = x + 1
+
+            def inner(x, kw):
+                return x + z + kw
+            return inner(x, kw)
+
+        def outer16(x):
+            """ closure is generator, consumed locally """
+            z = x + 1
+
+            def inner(x):
+                yield x + z
+
+            return list(inner(x))
+
+        def outer17(x):
+            """ closure is generator, returned """
+            z = x + 1
+
+            def inner(x):
+                yield x + z
+
+            return inner(x)
+
+        def outer18(x):
+            """ closure is generator, consumed in loop """
+            z = x + 1
+
+            def inner(x):
+                yield x + z
+
+            for i in inner(x):
+                t = i
+
+            return t
+
+        def outer19(x):
+            """ closure as arg to another closure """
+            z1 = x + 1
+            z2 = x + 2
+
+            def inner(x):
+                return x + z1
+
+            def inner2(f, x):
+                return f(x) + z2
+
+            return inner2(inner, x)
+
+        def outer20(x):
+            """ Test calling numpy in closure """
+            z = x + 1
+
+            def inner(x):
+                return x + numpy.cos(z)
+            return inner(x)
+
+        def outer21(x):
+            """ Test calling numpy import as in closure """
+            z = x + 1
+
+            def inner(x):
+                return x + np.cos(z)
+            return inner(x)
+
+        def outer22():
+            """Test to ensure that unsupported *args raises correctly"""
+            def bar(a, b):
+                pass
+            x = 1, 2
+            bar(*x)
+
+        # functions to test that are expected to pass
+        f = [outer1, outer2, outer5, outer6, outer7, outer8,
+             outer9, outer10, outer12, outer13, outer14,
+             outer15, outer19, outer20, outer21]
+        for ref in f:
+            cfunc = njit(ref)
+            var = 10
+            self.assertEqual(cfunc(var), ref(var))
+
+        # test functions that are expected to fail
+        with self.assertRaises(NotImplementedError) as raises:
+            cfunc = jit(nopython=True)(outer3)
+            cfunc(var)
+        msg = "Unsupported use of op_LOAD_CLOSURE encountered"
+        self.assertIn(msg, str(raises.exception))
+
+        with self.assertRaises(NotImplementedError) as raises:
+            cfunc = jit(nopython=True)(outer4)
+            cfunc(var)
+        msg = "Unsupported use of op_LOAD_CLOSURE encountered"
+        self.assertIn(msg, str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc = jit(nopython=True)(outer11)
+            cfunc(var)
+        msg = "Cannot capture the non-constant value"
+        self.assertIn(msg, str(raises.exception))
+
+        with self.assertRaises(UnsupportedError) as raises:
+            cfunc = jit(nopython=True)(outer16)
+            cfunc(var)
+        msg = "The use of yield in a closure is unsupported."
+        self.assertIn(msg, str(raises.exception))
+
+        with self.assertRaises(UnsupportedError) as raises:
+            cfunc = jit(nopython=True)(outer17)
+            cfunc(var)
+        msg = "The use of yield in a closure is unsupported."
+        self.assertIn(msg, str(raises.exception))
+
+        with self.assertRaises(UnsupportedError) as raises:
+            cfunc = jit(nopython=True)(outer18)
+            cfunc(var)
+        msg = "The use of yield in a closure is unsupported."
+        self.assertIn(msg, str(raises.exception))
+
+        with self.assertRaises(UnsupportedError) as raises:
+            cfunc = jit(nopython=True)(outer22)
+            cfunc()
+        msg = "Calling a closure with *args is unsupported."
+        self.assertIn(msg, str(raises.exception))
+
+    def test_closure_renaming_scheme(self):
+        # See #7380, this checks that inlined (from closure) variables have a
+        # name derived from the function they were defined in.
+
+        @njit(pipeline_class=IRPreservingTestPipeline)
+        def foo(a, b):
+            def bar(z):
+                x = 5
+                y = 10
+                return x + y + z
+            return bar(a), bar(b)
+
+        self.assertEqual(foo(10, 20), (25, 35))
+
+        # check IR. Look for the `x = 5`... there should be
+        # Two lots of `const(int, 5)`, one for each inline
+        # The LHS of the assignment will have a name like:
+        # closure__locals__bar_v2_x
+        # Ensure that this is the case!
+        func_ir = foo.overloads[foo.signatures[0]].metadata['preserved_ir']
+        store = []
+        for blk in func_ir.blocks.values():
+            for stmt in blk.body:
+                if isinstance(stmt, ir.Assign):
+                    if isinstance(stmt.value, ir.Const):
+                        if stmt.value.value == 5:
+                            store.append(stmt)
+
+        self.assertEqual(len(store), 2)
+        for i in store:
+            name = i.target.name
+            regex = r'closure__locals__bar_v[0-9]+.x'
+            self.assertRegex(name, regex)
+
+
+class TestObjmodeFallback(TestCase):
+    # These are all based on tests from real life issues where, predominantly,
+    # the object mode fallback compilation path would fail as a result of the IR
+    # being mutated by closure inlining in npm. Tests are named after issues,
+    # all of which failed to compile as of 0.44.
+
+    decorators = [jit, jit(forceobj=True)]
+
+    def test_issue2955(self):
+
+        def numbaFailure(scores, cooc):
+            rows, cols = scores.shape
+            for i in range(rows):
+                coxv = scores[i]
+                groups = sorted(set(coxv), reverse=True)
+                [set(np.argwhere(coxv == x).flatten()) for x in groups]
+
+        x = np.random.random((10, 10))
+        y = np.abs((np.random.randn(10, 10) * 1.732)).astype(int)
+        for d in self.decorators:
+            d(numbaFailure)(x, y)
+
+    def test_issue3239(self):
+
+        def fit(X, y):
+            if type(X) is not np.ndarray:
+                X = np.array(X)
+
+            if type(y) is not np.ndarray:
+                y = np.array(y)
+
+            m, _ = X.shape
+            X = np.hstack((
+                np.array([[1] for _ in range(m)]),
+                X
+            ))
+
+            res = np.dot(np.dot(X, X.T), y)
+            intercept = res[0]
+            coefs = res[1:]
+            return intercept, coefs
+
+        for d in self.decorators:
+            res = d(fit)(np.arange(10).reshape(1, 10),
+                         np.arange(10).reshape(1, 10))
+            exp = fit(np.arange(10).reshape(1, 10),
+                      np.arange(10).reshape(1, 10))
+            np.testing.assert_equal(res, exp)
+
+    def test_issue3289(self):
+        b = [(5, 124), (52, 5)]
+
+        def a():
+            [b[index] for index in [0, 1]]
+            for x in range(5):
+                pass
+        for d in self.decorators:
+            d(a)()
+
+    def test_issue3413(self):
+
+        def foo(data):
+            # commenting out this line prevents the crash:
+            t = max([len(m) for m in data['y']])
+
+            mask = data['x'] == 0
+            if any(mask):
+                z = 15
+            return t, z
+
+        data = {'x': np.arange(5), 'y': [[1], [2, 3]]}
+        for d in self.decorators:
+            res = d(foo)(data)
+            np.testing.assert_allclose(res, foo(data))
+
+    def test_issue3659(self):
+
+        def main():
+            a = np.array(((1, 2), (3, 4)))
+            return np.array([x for x in a])
+        for d in self.decorators:
+            res = d(main)()
+            np.testing.assert_allclose(res, main())
+
+    def test_issue3803(self):
+
+        def center(X):
+            np.array([np.float_(x) for x in X.T])
+            np.array([np.float_(1) for _ in X.T])
+            return X
+
+        X = np.zeros((10,))
+        for d in self.decorators:
+            res = d(center)(X)
+            np.testing.assert_allclose(res, center(X))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_codegen.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_codegen.py
new file mode 100644
index 000000000..834d9e676
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_codegen.py
@@ -0,0 +1,261 @@
+"""
+Tests for numba.core.codegen.
+"""
+
+
+import warnings
+import base64
+import ctypes
+import pickle
+import re
+import subprocess
+import sys
+import weakref
+
+import llvmlite.binding as ll
+
+import unittest
+from numba import njit
+from numba.core.codegen import JITCPUCodegen
+from numba.core.compiler_lock import global_compiler_lock
+from numba.tests.support import TestCase
+
+
+asm_sum = r"""
+    define i32 @sum(i32 %.1, i32 %.2) {
+      %.3 = add i32 %.1, %.2
+      ret i32 %.3
+    }
+    """
+
+# Note we're using a rather mangled function name to check that it
+# is compatible with object serialization.
+
+asm_sum_inner = """
+    define i32 @"__main__.ising_element_update$1.array(int8,_2d,_C).int64.int64"(i32 %.1, i32 %.2) {
+      %.3 = add i32 %.1, %.2
+      ret i32 %.3
+    }
+"""    # noqa: E501
+
+asm_sum_outer = """
+    declare i32 @"__main__.ising_element_update$1.array(int8,_2d,_C).int64.int64"(i32 %.1, i32 %.2)
+
+    define i32 @sum(i32 %.1, i32 %.2) {
+      %.3 = call i32 @"__main__.ising_element_update$1.array(int8,_2d,_C).int64.int64"(i32 %.1, i32 %.2)
+      ret i32 %.3
+    }
+"""    # noqa: E501
+
+
+ctypes_sum_ty = ctypes.CFUNCTYPE(ctypes.c_int, ctypes.c_int, ctypes.c_int)
+
+
+class JITCPUCodegenTestCase(TestCase):
+    """
+    Test the JIT code generation.
+    """
+
+    def setUp(self):
+        global_compiler_lock.acquire()
+        self.codegen = JITCPUCodegen('test_codegen')
+
+    def tearDown(self):
+        del self.codegen
+        global_compiler_lock.release()
+
+    def compile_module(self, asm, linking_asm=None):
+        library = self.codegen.create_library('compiled_module')
+        ll_module = ll.parse_assembly(asm)
+        ll_module.verify()
+        library.add_llvm_module(ll_module)
+        if linking_asm:
+            linking_library = self.codegen.create_library('linking_module')
+            ll_module = ll.parse_assembly(linking_asm)
+            ll_module.verify()
+            linking_library.add_llvm_module(ll_module)
+            library.add_linking_library(linking_library)
+        return library
+
+    @classmethod
+    def _check_unserialize_sum(cls, state):
+        codegen = JITCPUCodegen('other_codegen')
+        library = codegen.unserialize_library(state)
+        ptr = library.get_pointer_to_function("sum")
+        assert ptr, ptr
+        cfunc = ctypes_sum_ty(ptr)
+        res = cfunc(2, 3)
+        assert res == 5, res
+
+    def test_get_pointer_to_function(self):
+        library = self.compile_module(asm_sum)
+        ptr = library.get_pointer_to_function("sum")
+        self.assertIsInstance(ptr, int)
+        cfunc = ctypes_sum_ty(ptr)
+        self.assertEqual(cfunc(2, 3), 5)
+        # Note: With llvm3.9.1, deleting `library` will cause memory error in
+        #       the following code during running of optimization passes in
+        #       LLVM. The reason of the error is unclear. The error is known to
+        #       replicate on osx64 and linux64.
+
+        # Same, but with dependency on another library
+        library2 = self.compile_module(asm_sum_outer, asm_sum_inner)
+        ptr = library2.get_pointer_to_function("sum")
+        self.assertIsInstance(ptr, int)
+        cfunc = ctypes_sum_ty(ptr)
+        self.assertEqual(cfunc(2, 3), 5)
+
+    def test_magic_tuple(self):
+        tup = self.codegen.magic_tuple()
+        pickle.dumps(tup)
+        cg2 = JITCPUCodegen('xxx')
+        self.assertEqual(cg2.magic_tuple(), tup)
+
+    # Serialization tests.
+
+    def _check_serialize_unserialize(self, state):
+        self._check_unserialize_sum(state)
+
+    def _check_unserialize_other_process(self, state):
+        arg = base64.b64encode(pickle.dumps(state, -1))
+        code = """if 1:
+            import base64
+            import pickle
+            import sys
+            from numba.tests.test_codegen import %(test_class)s
+
+            state = pickle.loads(base64.b64decode(sys.argv[1]))
+            %(test_class)s._check_unserialize_sum(state)
+            """ % dict(test_class=self.__class__.__name__)
+        subprocess.check_call([sys.executable, '-c', code, arg.decode()])
+
+    def test_serialize_unserialize_bitcode(self):
+        library = self.compile_module(asm_sum_outer, asm_sum_inner)
+        state = library.serialize_using_bitcode()
+        self._check_serialize_unserialize(state)
+
+    def test_unserialize_other_process_bitcode(self):
+        library = self.compile_module(asm_sum_outer, asm_sum_inner)
+        state = library.serialize_using_bitcode()
+        self._check_unserialize_other_process(state)
+
+    def test_serialize_unserialize_object_code(self):
+        library = self.compile_module(asm_sum_outer, asm_sum_inner)
+        library.enable_object_caching()
+        state = library.serialize_using_object_code()
+        self._check_serialize_unserialize(state)
+
+    def test_unserialize_other_process_object_code(self):
+        library = self.compile_module(asm_sum_outer, asm_sum_inner)
+        library.enable_object_caching()
+        state = library.serialize_using_object_code()
+        self._check_unserialize_other_process(state)
+
+    def test_cache_disabled_inspection(self):
+        """
+        """
+        library = self.compile_module(asm_sum_outer, asm_sum_inner)
+        library.enable_object_caching()
+        state = library.serialize_using_object_code()
+
+        # exercise the valid behavior
+        with warnings.catch_warnings(record=True) as w:
+            old_llvm = library.get_llvm_str()
+            old_asm = library.get_asm_str()
+            library.get_function_cfg('sum')
+        self.assertEqual(len(w), 0)
+
+        # unserialize
+        codegen = JITCPUCodegen('other_codegen')
+        library = codegen.unserialize_library(state)
+
+        # the inspection methods would warn and give incorrect result
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            self.assertNotEqual(old_llvm, library.get_llvm_str())
+        self.assertEqual(len(w), 1)
+        self.assertIn("Inspection disabled", str(w[0].message))
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            self.assertNotEqual(library.get_asm_str(), old_asm)
+        self.assertEqual(len(w), 1)
+        self.assertIn("Inspection disabled", str(w[0].message))
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            with self.assertRaises(NameError) as raises:
+                library.get_function_cfg('sum')
+        self.assertEqual(len(w), 1)
+        self.assertIn("Inspection disabled", str(w[0].message))
+        self.assertIn("sum", str(raises.exception))
+
+    # Lifetime tests
+
+    @unittest.expectedFailure  # MCJIT removeModule leaks and it is disabled
+    def test_library_lifetime(self):
+        library = self.compile_module(asm_sum_outer, asm_sum_inner)
+        # Exercise code generation
+        library.enable_object_caching()
+        library.serialize_using_bitcode()
+        library.serialize_using_object_code()
+        u = weakref.ref(library)
+        v = weakref.ref(library._final_module)
+        del library
+        # Both the library and its backing LLVM module are collected
+        self.assertIs(u(), None)
+        self.assertIs(v(), None)
+
+
+class TestWrappers(TestCase):
+
+    def test_noinline_on_main_call(self):
+        # Checks that the cpython and cfunc wrapper produces a call with the
+        # "noinline" attr present for the decorated function.
+
+        @njit
+        def foo():
+            pass
+
+        foo()
+        sig = foo.signatures[0]
+
+        ol = foo.overloads[sig]
+        name = ol.fndesc.mangled_name.replace("$", r"\$")
+        p1 = r".*call.*{}".format(name)
+        p2 = r".*(#[0-9]+).*"
+        call_site = re.compile(p1 + p2)
+
+        lines = foo.inspect_llvm(sig).splitlines()
+        meta_data_idx = []
+        for l in lines:
+            matched = call_site.match(l)
+            if matched:
+                meta_data_idx.append(matched.groups()[0])
+
+        # should be 2 calls, one from cpython wrapper one from cfunc wrapper
+        self.assertEqual(len(meta_data_idx), 2)
+        # both calls should refer to the same metadata item
+        self.assertEqual(meta_data_idx[0], meta_data_idx[1])
+
+        p1 = r"^attributes\s+{}".format(meta_data_idx[0])
+        p2 = r"\s+=\s+{(.*)}.*$"
+        attr_site = re.compile(p1 + p2)
+
+        for l in reversed(lines):
+            matched = attr_site.match(l)
+            if matched:
+                meta_data = matched.groups()[0]
+                lmeta = meta_data.strip().split(' ')
+                for x in lmeta:
+                    if 'noinline' in x:
+                        break
+                else:
+                    continue
+                break
+        else:
+            return self.fail("Metadata did not match 'noinline'")
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compile_cache.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compile_cache.py
new file mode 100644
index 000000000..edeab592d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compile_cache.py
@@ -0,0 +1,153 @@
+import unittest
+from contextlib import contextmanager
+
+from llvmlite import ir
+
+from numba.core import types, typing, callconv, cpu, cgutils
+from numba.core.registry import cpu_target
+
+
+class TestCompileCache(unittest.TestCase):
+    '''
+    Tests that the caching in BaseContext.compile_internal() works correctly by
+    checking the state of the cache when it is used by the CPUContext.
+    '''
+
+    @contextmanager
+    def _context_builder_sig_args(self):
+        typing_context = cpu_target.typing_context
+        context = cpu_target.target_context
+        lib = context.codegen().create_library('testing')
+        with context.push_code_library(lib):
+            module = ir.Module("test_module")
+
+            sig = typing.signature(types.int32, types.int32)
+            llvm_fnty = context.call_conv.get_function_type(sig.return_type,
+                                                            sig.args)
+            function = cgutils.get_or_insert_function(module, llvm_fnty,
+                                                      'test_fn')
+            args = context.call_conv.get_arguments(function)
+            assert function.is_declaration
+            entry_block = function.append_basic_block('entry')
+            builder = ir.IRBuilder(entry_block)
+
+            yield context, builder, sig, args
+
+    def test_cache(self):
+        def times2(i):
+            return 2*i
+
+        def times3(i):
+            return i*3
+
+        with self._context_builder_sig_args() as (
+            context, builder, sig, args,
+        ):
+            initial_cache_size = len(context.cached_internal_func)
+            # Ensure the cache is empty to begin with
+            self.assertEqual(initial_cache_size + 0,
+                             len(context.cached_internal_func))
+
+            # After one compile, it should contain one entry
+            context.compile_internal(builder, times2, sig, args)
+            self.assertEqual(initial_cache_size + 1,
+                             len(context.cached_internal_func))
+
+            # After a second compilation of the same thing, it should still contain
+            # one entry
+            context.compile_internal(builder, times2, sig, args)
+            self.assertEqual(initial_cache_size + 1,
+                             len(context.cached_internal_func))
+
+            # After compilation of another function, the cache should have grown by
+            # one more.
+            context.compile_internal(builder, times3, sig, args)
+            self.assertEqual(initial_cache_size + 2,
+                             len(context.cached_internal_func))
+
+            sig2 = typing.signature(types.float64, types.float64)
+            llvm_fnty2 = context.call_conv.get_function_type(sig2.return_type,
+                                                            sig2.args)
+            function2 = cgutils.get_or_insert_function(builder.module,
+                                                       llvm_fnty2, 'test_fn_2')
+            args2 = context.call_conv.get_arguments(function2)
+            assert function2.is_declaration
+            entry_block2 = function2.append_basic_block('entry')
+            builder2 = ir.IRBuilder(entry_block2)
+
+            # Ensure that the same function with a different signature does not
+            # reuse an entry from the cache in error
+            context.compile_internal(builder2, times3, sig2, args2)
+            self.assertEqual(initial_cache_size + 3,
+                             len(context.cached_internal_func))
+
+    def test_closures(self):
+        """
+        Caching must not mix up closures reusing the same code object.
+        """
+        def make_closure(x, y):
+            def f(z):
+                return y + z
+            return f
+
+        with self._context_builder_sig_args() as (
+            context, builder, sig, args,
+        ):
+            # Closures with distinct cell contents must each be compiled.
+            clo11 = make_closure(1, 1)
+            clo12 = make_closure(1, 2)
+            clo22 = make_closure(2, 2)
+            initial_cache_size = len(context.cached_internal_func)
+            res1 = context.compile_internal(builder, clo11, sig, args)
+            self.assertEqual(initial_cache_size + 1,
+                             len(context.cached_internal_func))
+            res2 = context.compile_internal(builder, clo12, sig, args)
+            self.assertEqual(initial_cache_size + 2,
+                             len(context.cached_internal_func))
+            # Same cell contents as above (first parameter isn't captured)
+            res3 = context.compile_internal(builder, clo22, sig, args)
+            self.assertEqual(initial_cache_size + 2,
+                             len(context.cached_internal_func))
+
+    def test_error_model(self):
+        """
+        Caching must not mix up different error models.
+        """
+        def inv(x):
+            return 1.0 / x
+
+        inv_sig = typing.signature(types.float64, types.float64)
+
+        def compile_inv(context):
+            return context.compile_subroutine(builder, inv, inv_sig)
+
+        with self._context_builder_sig_args() as (
+            context, builder, sig, args,
+        ):
+            py_error_model = callconv.create_error_model('python', context)
+            np_error_model = callconv.create_error_model('numpy', context)
+
+            py_context1 = context.subtarget(error_model=py_error_model)
+            py_context2 = context.subtarget(error_model=py_error_model)
+            np_context = context.subtarget(error_model=np_error_model)
+
+            initial_cache_size = len(context.cached_internal_func)
+
+            # Note the parent context's cache is shared by subtargets
+            self.assertEqual(initial_cache_size + 0,
+                             len(context.cached_internal_func))
+            # Compiling with the same error model reuses the same cache slot
+            compile_inv(py_context1)
+            self.assertEqual(initial_cache_size + 1,
+                             len(context.cached_internal_func))
+            compile_inv(py_context2)
+            self.assertEqual(initial_cache_size + 1,
+                             len(context.cached_internal_func))
+            # Compiling with another error model creates a new cache slot
+            compile_inv(np_context)
+            self.assertEqual(initial_cache_size + 2,
+                             len(context.cached_internal_func))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compiler_flags.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compiler_flags.py
new file mode 100644
index 000000000..6bb809b1e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compiler_flags.py
@@ -0,0 +1,111 @@
+import re
+
+from numba import njit
+from numba.core.extending import overload
+from numba.core.targetconfig import ConfigStack
+from numba.core.compiler import Flags, DEFAULT_FLAGS
+from numba.core import types
+from numba.core.funcdesc import default_mangler
+
+from numba.tests.support import TestCase, unittest
+
+
+class TestCompilerFlagCachedOverload(TestCase):
+    def test_fastmath_in_overload(self):
+        def fastmath_status():
+            pass
+
+        @overload(fastmath_status)
+        def ov_fastmath_status():
+            flags = ConfigStack().top()
+            val = "Has fastmath" if flags.fastmath else "No fastmath"
+
+            def codegen():
+                return val
+
+            return codegen
+
+        @njit(fastmath=True)
+        def set_fastmath():
+            return fastmath_status()
+
+        @njit()
+        def foo():
+            a = fastmath_status()
+            b = set_fastmath()
+            return (a, b)
+
+        a, b = foo()
+        self.assertEqual(a, "No fastmath")
+        self.assertEqual(b, "Has fastmath")
+
+
+class TestFlagMangling(TestCase):
+
+    def test_demangle(self):
+
+        def check(flags):
+            mangled = flags.get_mangle_string()
+            out = flags.demangle(mangled)
+            # Demangle result MUST match summary()
+            self.assertEqual(out, flags.summary())
+
+        # test empty flags
+        flags = Flags()
+        check(flags)
+
+        # test default
+        check(DEFAULT_FLAGS)
+
+        # test other
+        flags = Flags()
+        flags.no_cpython_wrapper = True
+        flags.nrt = True
+        flags.fastmath = True
+        check(flags)
+
+    def test_mangled_flags_is_shorter(self):
+        # at least for these control cases
+        flags = Flags()
+        flags.nrt = True
+        flags.auto_parallel = True
+        self.assertLess(len(flags.get_mangle_string()), len(flags.summary()))
+
+    def test_mangled_flags_with_fastmath_parfors_inline(self):
+        # at least for these control cases
+        flags = Flags()
+        flags.nrt = True
+        flags.auto_parallel = True
+        flags.fastmath = True
+        flags.inline = "always"
+        self.assertLess(len(flags.get_mangle_string()), len(flags.summary()))
+        demangled = flags.demangle(flags.get_mangle_string())
+        # There should be no pointer value in the demangled string.
+        self.assertNotIn("0x", demangled)
+
+    def test_demangling_from_mangled_symbols(self):
+        """Test demangling of flags from mangled symbol"""
+        # Use default mangler to mangle the string
+        fname = 'foo'
+        argtypes = types.int32,
+        flags = Flags()
+        flags.nrt = True
+        flags.target_backend = "myhardware"
+        name = default_mangler(
+            fname, argtypes, abi_tags=[flags.get_mangle_string()],
+        )
+        # Find the ABI-tag. Starts with "B"
+        prefix = "_Z3fooB"
+        # Find the length of the ABI-tag
+        m = re.match("[0-9]+", name[len(prefix):])
+        size = m.group(0)
+        # Extract the ABI tag
+        base = len(prefix) + len(size)
+        abi_mangled = name[base:base + int(size)]
+        # Demangle and check
+        demangled = Flags.demangle(abi_mangled)
+        self.assertEqual(demangled, flags.summary())
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compiler_lock.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compiler_lock.py
new file mode 100644
index 000000000..7e25a8fd6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_compiler_lock.py
@@ -0,0 +1,23 @@
+import unittest
+from numba.core.compiler_lock import (
+    global_compiler_lock,
+    require_global_compiler_lock,
+)
+from numba.tests.support import TestCase
+
+
+class TestCompilerLock(TestCase):
+    def test_gcl_as_context_manager(self):
+        with global_compiler_lock:
+            require_global_compiler_lock()
+
+    def test_gcl_as_decorator(self):
+        @global_compiler_lock
+        def func():
+            require_global_compiler_lock()
+
+        func()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_complex.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_complex.py
new file mode 100644
index 000000000..528afbb30
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_complex.py
@@ -0,0 +1,315 @@
+import cmath
+import itertools
+import math
+import sys
+
+from numba.core.compiler import compile_isolated, Flags, utils
+from numba.core import types
+from numba.tests.support import TestCase, tag
+from .complex_usecases import *
+import unittest
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+class BaseComplexTest(object):
+
+    def basic_values(self):
+        reals = [-0.0, +0.0, 1, -1, +1.5, -3.5,
+                 float('-inf'), float('+inf')]
+        if sys.platform != 'win32':
+            reals += [float('nan')]
+        return [complex(x, y) for x, y in itertools.product(reals, reals)]
+
+    def more_values(self):
+        reals = [-0.0, +0.0, 1, -1, -math.pi, +math.pi,
+                 float('-inf'), float('+inf')]
+        if sys.platform != 'win32':
+            reals += [float('nan')]
+        return [complex(x, y) for x, y in itertools.product(reals, reals)]
+
+    def non_nan_values(self):
+        reals = [-0.0, +0.0, 1, -1, -math.pi, +math.pi,
+                 float('inf'), float('-inf')]
+        return [complex(x, y) for x, y in itertools.product(reals, reals)]
+
+    def run_unary(self, pyfunc, x_types, x_values, ulps=1, abs_tol=None,
+                  flags=enable_pyobj_flags):
+        for tx in x_types:
+            cr = compile_isolated(pyfunc, [tx], flags=flags)
+            cfunc = cr.entry_point
+            prec = 'single' if tx in (types.float32, types.complex64) else 'double'
+            for vx in x_values:
+                try:
+                    expected = pyfunc(vx)
+                except ValueError as e:
+                    self.assertIn("math domain error", str(e))
+                    continue
+                got = cfunc(vx)
+                msg = 'for input %r with prec %r' % (vx, prec)
+                self.assertPreciseEqual(got, expected, prec=prec,
+                                        ulps=ulps, abs_tol=abs_tol, msg=msg)
+
+    def run_binary(self, pyfunc, value_types, values, ulps=1,
+                   flags=enable_pyobj_flags):
+        for tx, ty in value_types:
+            cr = compile_isolated(pyfunc, [tx, ty], flags=flags)
+            cfunc = cr.entry_point
+            prec = ('single'
+                    if set([tx, ty]) & set([types.float32, types.complex64])
+                    else 'double')
+            for vx, vy in values:
+                try:
+                    expected = pyfunc(vx, vy)
+                except ValueError as e:
+                    self.assertIn("math domain error", str(e))
+                    continue
+                except ZeroDivisionError:
+                    continue
+                got = cfunc(vx, vy)
+                msg = 'for input %r with prec %r' % ((vx, vy), prec)
+                self.assertPreciseEqual(got, expected, prec=prec,
+                                        ulps=ulps, msg=msg)
+
+
+class TestComplex(BaseComplexTest, TestCase):
+
+    def test_real(self, flags=enable_pyobj_flags):
+        self.run_unary(real_usecase, [types.complex64, types.complex128],
+                       self.basic_values(), flags=flags)
+        self.run_unary(real_usecase, [types.int8, types.int64],
+                       [1, 0, -3], flags=flags)
+        self.run_unary(real_usecase, [types.float32, types.float64],
+                       [1.5, -0.5], flags=flags)
+
+    def test_real_npm(self):
+        self.test_real(flags=no_pyobj_flags)
+
+    def test_imag(self, flags=enable_pyobj_flags):
+        self.run_unary(imag_usecase, [types.complex64, types.complex128],
+                       self.basic_values(), flags=flags)
+        self.run_unary(imag_usecase, [types.int8, types.int64],
+                       [1, 0, -3], flags=flags)
+        self.run_unary(imag_usecase, [types.float32, types.float64],
+                       [1.5, -0.5], flags=flags)
+
+    def test_imag_npm(self):
+        self.test_imag(flags=no_pyobj_flags)
+
+    def test_conjugate(self, flags=enable_pyobj_flags):
+        self.run_unary(conjugate_usecase, [types.complex64, types.complex128],
+                       self.basic_values(), flags=flags)
+        self.run_unary(conjugate_usecase, [types.int8, types.int64],
+                       [1, 0, -3], flags=flags)
+        self.run_unary(conjugate_usecase, [types.float32, types.float64],
+                       [1.5, -0.5], flags=flags)
+
+    def test_conjugate_npm(self):
+        self.test_conjugate(flags=no_pyobj_flags)
+
+    def test_div(self, flags=enable_pyobj_flags):
+        """
+        Test complex.__div__ implementation with non-trivial values.
+        """
+        # XXX Fold into test_operator?
+        values = list(itertools.product(self.more_values(), self.more_values()))
+        value_types = [(types.complex128, types.complex128),
+                       (types.complex64, types.complex64)]
+        self.run_binary(div_usecase, value_types, values, flags=flags)
+
+    def test_div_npm(self):
+        self.test_div(flags=no_pyobj_flags)
+
+
+class TestCMath(BaseComplexTest, TestCase):
+    """
+    Tests for cmath module support.
+    """
+
+    def check_predicate_func(self, pyfunc, flags):
+        self.run_unary(pyfunc, [types.complex128, types.complex64],
+                       self.basic_values(), flags=flags)
+
+    def check_unary_func(self, pyfunc, flags, ulps=1, abs_tol=None,
+                         values=None):
+        self.run_unary(pyfunc, [types.complex128],
+                       values or self.more_values(), flags=flags, ulps=ulps,
+                       abs_tol=abs_tol)
+        # Avoid discontinuities around pi when in single precision.
+        self.run_unary(pyfunc, [types.complex64],
+                       values or self.basic_values(), flags=flags, ulps=ulps,
+                       abs_tol=abs_tol)
+
+    # Conversions
+
+    def test_phase(self):
+        self.check_unary_func(phase_usecase, enable_pyobj_flags)
+
+    def test_phase_npm(self):
+        self.check_unary_func(phase_usecase, no_pyobj_flags)
+
+    def test_polar(self):
+        self.check_unary_func(polar_usecase, enable_pyobj_flags)
+
+    def test_polar_npm(self):
+        self.check_unary_func(polar_usecase, no_pyobj_flags)
+
+    def test_rect(self, flags=enable_pyobj_flags):
+        def do_test(tp, seed_values):
+            values = [(z.real, z.imag) for z in seed_values
+                      if not math.isinf(z.imag) or z.real == 0]
+            self.run_binary(rect_usecase, [(tp, tp)], values, flags=flags)
+        do_test(types.float64, self.more_values())
+        # Avoid discontinuities around pi when in single precision.
+        do_test(types.float32, self.basic_values())
+
+    def test_rect_npm(self):
+        self.test_rect(flags=no_pyobj_flags)
+
+    # Classification
+
+    def test_isnan(self, flags=enable_pyobj_flags):
+        self.check_predicate_func(isnan_usecase, enable_pyobj_flags)
+
+    def test_isnan_npm(self):
+        self.check_predicate_func(isnan_usecase, no_pyobj_flags)
+
+    def test_isinf(self, flags=enable_pyobj_flags):
+        self.check_predicate_func(isinf_usecase, enable_pyobj_flags)
+
+    def test_isinf_npm(self):
+        self.check_predicate_func(isinf_usecase, no_pyobj_flags)
+
+    def test_isfinite(self, flags=enable_pyobj_flags):
+        self.check_predicate_func(isfinite_usecase, enable_pyobj_flags)
+
+    def test_isfinite_npm(self):
+        self.check_predicate_func(isfinite_usecase, no_pyobj_flags)
+
+    # Power and logarithms
+
+    def test_exp(self):
+        self.check_unary_func(exp_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_exp_npm(self):
+        # Aggressive optimization fixes the following subnormal float problem.
+        ## The two tests are failing due to subnormal float problems.
+        ## We are seeing (6.9532198665326e-310+2.1221202807e-314j) != 0j
+        self.check_unary_func(exp_usecase, no_pyobj_flags, ulps=2)
+
+    def test_log(self):
+        self.check_unary_func(log_usecase, enable_pyobj_flags)
+
+    def test_log_npm(self):
+        self.check_unary_func(log_usecase, no_pyobj_flags)
+
+    def test_log_base(self, flags=enable_pyobj_flags):
+        values = list(itertools.product(self.more_values(), self.more_values()))
+        value_types = [(types.complex128, types.complex128),
+                       (types.complex64, types.complex64)]
+        self.run_binary(log_base_usecase, value_types, values, flags=flags,
+                        ulps=3)
+
+    def test_log_base_npm(self):
+        self.test_log_base(flags=no_pyobj_flags)
+
+    def test_log10(self):
+        self.check_unary_func(log10_usecase, enable_pyobj_flags)
+
+    def test_log10_npm(self):
+        self.check_unary_func(log10_usecase, no_pyobj_flags)
+
+    def test_sqrt(self):
+        self.check_unary_func(sqrt_usecase, enable_pyobj_flags)
+
+    def test_sqrt_npm(self):
+        self.check_unary_func(sqrt_usecase, no_pyobj_flags)
+        # issue #3499, check large complex128 values, values cross the float32
+        # representation limit threshold
+        values = [-10 ** i for i in range(36, 41)]
+        self.run_unary(sqrt_usecase, [types.complex128],
+                       values, flags=no_pyobj_flags)
+
+    # Trigonometric functions
+
+    def test_acos(self):
+        self.check_unary_func(acos_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_acos_npm(self):
+        self.check_unary_func(acos_usecase, no_pyobj_flags, ulps=2)
+
+    def test_asin(self):
+        self.check_unary_func(asin_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_asin_npm(self):
+        self.check_unary_func(asin_usecase, no_pyobj_flags, ulps=2)
+
+    def test_atan(self):
+        self.check_unary_func(atan_usecase, enable_pyobj_flags, ulps=2,)
+
+    def test_atan_npm(self):
+        self.check_unary_func(atan_usecase, no_pyobj_flags, ulps=2,)
+
+    def test_cos(self):
+        self.check_unary_func(cos_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_cos_npm(self):
+        self.check_unary_func(cos_usecase, no_pyobj_flags, ulps=2)
+
+    def test_sin(self):
+        # See test_sinh.
+        self.check_unary_func(sin_usecase, enable_pyobj_flags, abs_tol='eps')
+
+    def test_sin_npm(self):
+        self.check_unary_func(sin_usecase, no_pyobj_flags, abs_tol='eps')
+
+    def test_tan(self):
+        self.check_unary_func(tan_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_tan_npm(self):
+        self.check_unary_func(tan_usecase, enable_pyobj_flags, ulps=2)
+
+    # Hyperbolic functions
+
+    def test_acosh(self):
+        self.check_unary_func(acosh_usecase, enable_pyobj_flags)
+
+    def test_acosh_npm(self):
+        self.check_unary_func(acosh_usecase, no_pyobj_flags)
+
+    def test_asinh(self):
+        self.check_unary_func(asinh_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_asinh_npm(self):
+        self.check_unary_func(asinh_usecase, no_pyobj_flags, ulps=2)
+
+    def test_atanh(self):
+        self.check_unary_func(atanh_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_atanh_npm(self):
+        self.check_unary_func(atanh_usecase, no_pyobj_flags, ulps=2)
+
+    def test_cosh(self):
+        self.check_unary_func(cosh_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_cosh_npm(self):
+        self.check_unary_func(cosh_usecase, no_pyobj_flags, ulps=2)
+
+    def test_sinh(self):
+        self.check_unary_func(sinh_usecase, enable_pyobj_flags, abs_tol='eps')
+
+    def test_sinh_npm(self):
+        self.check_unary_func(sinh_usecase, no_pyobj_flags, abs_tol='eps')
+
+    def test_tanh(self):
+        self.check_unary_func(tanh_usecase, enable_pyobj_flags, ulps=2)
+
+    def test_tanh_npm(self):
+        self.check_unary_func(tanh_usecase, enable_pyobj_flags, ulps=2)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_comprehension.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_comprehension.py
new file mode 100644
index 000000000..2cdd3dc25
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_comprehension.py
@@ -0,0 +1,527 @@
+import unittest
+from numba.tests.support import TestCase
+
+import sys
+import operator
+import numpy as np
+import numpy
+
+from numba.core.compiler import compile_isolated
+from numba import jit, typed
+from numba.core import types, utils
+from numba.core.errors import TypingError, LoweringError
+from numba.core.types.functions import _header_lead
+from numba.tests.support import tag, _32bit, captured_stdout
+
+
+# deliberately imported twice for different use cases
+
+
+PARALLEL_SUPPORTED = not _32bit
+
+def comp_list(n):
+    l = [i for i in range(n)]
+    s = 0
+    for i in l:
+        s += i
+    return s
+
+
+class TestListComprehension(TestCase):
+
+    def test_comp_list(self):
+        pyfunc = comp_list
+        cres = compile_isolated(pyfunc, [types.intp])
+        cfunc = cres.entry_point
+        self.assertEqual(cfunc(5), pyfunc(5))
+        self.assertEqual(cfunc(0), pyfunc(0))
+        self.assertEqual(cfunc(-1), pyfunc(-1))
+
+    def test_bulk_use_cases(self):
+        """ Tests the large number of use cases defined below """
+
+        # jitted function used in some tests
+        @jit(nopython=True)
+        def fib3(n):
+            if n < 2:
+                return n
+            return fib3(n - 1) + fib3(n - 2)
+
+        def list1(x):
+            """ Test basic list comprehension """
+            return [i for i in range(1, len(x) - 1)]
+
+        def list2(x):
+            """ Test conditional list comprehension """
+            return [y for y in x if y < 2]
+
+        def list3(x):
+            """ Test ternary list comprehension """
+            return [y if y < 2 else -1 for y in x]
+
+        def list4(x):
+            """ Test list comprehension to np.array ctor """
+            return np.array([1, 2, 3])
+
+        # expected fail, unsupported type in sequence
+        def list5(x):
+            """ Test nested list comprehension to np.array ctor """
+            return np.array([np.array([z for z in x]) for y in x])
+
+        def list6(x):
+            """ Test use of inner function in list comprehension """
+            def inner(x):
+                return x + 1
+            return [inner(z) for z in x]
+
+        def list7(x):
+            """ Test use of closure in list comprehension """
+            y = 3
+
+            def inner(x):
+                return x + y
+            return [inner(z) for z in x]
+
+        def list8(x):
+            """ Test use of list comprehension as arg to inner function """
+            l = [z + 1 for z in x]
+
+            def inner(x):
+                return x[0] + 1
+            q = inner(l)
+            return q
+
+        def list9(x):
+            """ Test use of list comprehension access in closure """
+            l = [z + 1 for z in x]
+
+            def inner(x):
+                return x[0] + l[1]
+            return inner(x)
+
+        def list10(x):
+            """ Test use of list comprehension access in closure and as arg """
+            l = [z + 1 for z in x]
+
+            def inner(x):
+                return [y + l[0] for y in x]
+            return inner(l)
+
+        def list11(x):
+            """ Test scalar array construction in list comprehension """
+            l = [np.array(z) for z in x]
+            return l
+
+        def list12(x):
+            """ Test scalar type conversion construction in list comprehension """
+            l = [np.float64(z) for z in x]
+            return l
+
+        def list13(x):
+            """ Test use of explicit numpy scalar ctor reference in list comprehension """
+            l = [numpy.float64(z) for z in x]
+            return l
+
+        def list14(x):
+            """ Test use of python scalar ctor reference in list comprehension """
+            l = [float(z) for z in x]
+            return l
+
+        def list15(x):
+            """ Test use of python scalar ctor reference in list comprehension followed by np array construction from the list"""
+            l = [float(z) for z in x]
+            return np.array(l)
+
+        def list16(x):
+            """ Test type unification from np array ctors consuming list comprehension """
+            l1 = [float(z) for z in x]
+            l2 = [z for z in x]
+            ze = np.array(l1)
+            oe = np.array(l2)
+            return ze + oe
+
+        def list17(x):
+            """ Test complex list comprehension including math calls """
+            return [(a, b, c)
+                    for a in x for b in x for c in x if np.sqrt(a**2 + b**2) == c]
+
+        _OUTER_SCOPE_VAR = 9
+
+        def list18(x):
+            """ Test loop list with outer scope var as conditional"""
+            z = []
+            for i in x:
+                if i < _OUTER_SCOPE_VAR:
+                    z.append(i)
+            return z
+
+        _OUTER_SCOPE_VAR = 9
+
+        def list19(x):
+            """ Test list comprehension with outer scope as conditional"""
+            return [i for i in x if i < _OUTER_SCOPE_VAR]
+
+        def list20(x):
+            """ Test return empty list """
+            return [i for i in x if i == -1000]
+
+        def list21(x):
+            """ Test call a jitted function in a list comprehension """
+            return [fib3(i) for i in x]
+
+        def list22(x):
+            """ Test create two lists comprehensions and a third walking the first two """
+            a = [y - 1 for y in x]
+            b = [y + 1 for y in x]
+            return [x for x in a for y in b if x == y]
+
+        def list23(x):
+            """ Test operation on comprehension generated list """
+            z = [y for y in x]
+            z.append(1)
+            return z
+
+        def list24(x):
+            """ Test type promotion """
+            z = [float(y) if y > 3 else y for y in x]
+            return z
+
+        def list25(x):
+            # See issue #6260. Old style inline_closure_call uses get_ir_of_code
+            # for the closure->IR transform, without SSA there's multiply
+            # defined labels, the unary negation is self referent and DCE runs
+            # eliminating the duplicated labels.
+            included = np.array([1, 2, 6, 8])
+            not_included = [i for i in range(10) if i not in list(included)]
+            return not_included
+
+        # functions to test that are expected to pass
+        f = [list1, list2, list3, list4,
+             list6, list7, list8, list9, list10, list11,
+             list12, list13, list14, list15,
+             list16, list17, list18, list19, list20,
+             list21, list22, list23, list24, list25]
+
+        var = [1, 2, 3, 4, 5]
+        for ref in f:
+            try:
+                cfunc = jit(nopython=True)(ref)
+                self.assertEqual(cfunc(var), ref(var))
+            except ValueError:  # likely np array returned
+                try:
+                    np.testing.assert_allclose(cfunc(var), ref(var))
+                except Exception:
+                    raise
+
+        # test functions that are expected to fail
+        with self.assertRaises(TypingError) as raises:
+            cfunc = jit(nopython=True)(list5)
+            cfunc(var)
+        # TODO: we can't really assert the error message for the above
+        # Also, test_nested_array is a similar case (but without list) that works.
+
+        if sys.maxsize > 2 ** 32:
+            bits = 64
+        else:
+            bits = 32
+
+    def test_objmode_inlining(self):
+        def objmode_func(y):
+            z = object()
+            inlined = [x for x in y]
+            return inlined
+
+        cfunc = jit(forceobj=True)(objmode_func)
+        t = [1, 2, 3]
+        expected = objmode_func(t)
+        got = cfunc(t)
+        self.assertPreciseEqual(expected, got)
+
+
+class TestArrayComprehension(unittest.TestCase):
+
+    _numba_parallel_test_ = False
+
+    def check(self, pyfunc, *args, **kwargs):
+        """A generic check function that run both pyfunc, and jitted pyfunc,
+        and compare results."""
+        run_parallel = kwargs.get('run_parallel', False)
+        assert_allocate_list = kwargs.get('assert_allocate_list', False)
+        assert_dtype = kwargs.get('assert_dtype', False)
+        cfunc = jit(nopython=True,parallel=run_parallel)(pyfunc)
+        pyres = pyfunc(*args)
+        cres = cfunc(*args)
+        np.testing.assert_array_equal(pyres, cres)
+        if assert_dtype:
+            self.assertEqual(cres[1].dtype, assert_dtype)
+        if assert_allocate_list:
+            self.assertIn('allocate list', cfunc.inspect_llvm(cfunc.signatures[0]))
+        else:
+            self.assertNotIn('allocate list', cfunc.inspect_llvm(cfunc.signatures[0]))
+        if run_parallel:
+            self.assertIn('@do_scheduling', cfunc.inspect_llvm(cfunc.signatures[0]))
+
+    def test_comp_with_array_1(self):
+        def comp_with_array_1(n):
+            m = n * 2
+            l = np.array([i + m for i in range(n)])
+            return l
+
+        self.check(comp_with_array_1, 5)
+        if PARALLEL_SUPPORTED:
+            self.check(comp_with_array_1, 5, run_parallel=True)
+
+    def test_comp_with_array_2(self):
+        def comp_with_array_2(n, threshold):
+            A = np.arange(-n, n)
+            return np.array([ x * x if x < threshold else x * 2 for x in A ])
+
+        self.check(comp_with_array_2, 5, 0)
+
+    def test_comp_with_array_noinline(self):
+        def comp_with_array_noinline(n):
+            m = n * 2
+            l = np.array([i + m for i in range(n)])
+            return l
+
+        import numba.core.inline_closurecall as ic
+        try:
+            ic.enable_inline_arraycall = False
+            self.check(comp_with_array_noinline, 5, assert_allocate_list=True)
+        finally:
+            ic.enable_inline_arraycall = True
+
+    def test_comp_with_array_noinline_issue_6053(self):
+        def comp_with_array_noinline(n):
+            lst = [0]
+            for i in range(n):
+                lst.append(i)
+            l = np.array(lst)
+            return l
+
+        self.check(comp_with_array_noinline, 5, assert_allocate_list=True)
+
+    def test_comp_nest_with_array(self):
+        def comp_nest_with_array(n):
+            l = np.array([[i * j for j in range(n)] for i in range(n)])
+            return l
+
+        self.check(comp_nest_with_array, 5)
+        if PARALLEL_SUPPORTED:
+            self.check(comp_nest_with_array, 5, run_parallel=True)
+
+    def test_comp_nest_with_array_3(self):
+        def comp_nest_with_array_3(n):
+            l = np.array([[[i * j * k for k in range(n)] for j in range(n)] for i in range(n)])
+            return l
+
+        self.check(comp_nest_with_array_3, 5)
+        if PARALLEL_SUPPORTED:
+            self.check(comp_nest_with_array_3, 5, run_parallel=True)
+
+    def test_comp_nest_with_array_noinline(self):
+        def comp_nest_with_array_noinline(n):
+            l = np.array([[i * j for j in range(n)] for i in range(n)])
+            return l
+
+        import numba.core.inline_closurecall as ic
+        try:
+            ic.enable_inline_arraycall = False
+            self.check(comp_nest_with_array_noinline, 5,
+                       assert_allocate_list=True)
+        finally:
+            ic.enable_inline_arraycall = True
+
+    def test_comp_with_array_range(self):
+        def comp_with_array_range(m, n):
+            l = np.array([i for i in range(m, n)])
+            return l
+
+        self.check(comp_with_array_range, 5, 10)
+
+    def test_comp_with_array_range_and_step(self):
+        def comp_with_array_range_and_step(m, n):
+            l = np.array([i for i in range(m, n, 2)])
+            return l
+
+        self.check(comp_with_array_range_and_step, 5, 10)
+
+    def test_comp_with_array_conditional(self):
+        def comp_with_array_conditional(n):
+            l = np.array([i for i in range(n) if i % 2 == 1])
+            return l
+        # arraycall inline would not happen when conditional is present
+        self.check(comp_with_array_conditional, 10, assert_allocate_list=True)
+
+    def test_comp_nest_with_array_conditional(self):
+        def comp_nest_with_array_conditional(n):
+            l = np.array([[i * j for j in range(n)] for i in range(n) if i % 2 == 1])
+            return l
+        self.check(comp_nest_with_array_conditional, 5,
+                   assert_allocate_list=True)
+
+    def test_comp_nest_with_dependency(self):
+        def comp_nest_with_dependency(n):
+            l = np.array([[i * j for j in range(i+1)] for i in range(n)])
+            return l
+        # test is expected to fail
+        with self.assertRaises(TypingError) as raises:
+            self.check(comp_nest_with_dependency, 5)
+        self.assertIn(_header_lead, str(raises.exception))
+        self.assertIn('array(undefined,', str(raises.exception))
+
+    def test_comp_unsupported_iter(self):
+        def comp_unsupported_iter():
+            val = zip([1, 2, 3], [4, 5, 6])
+            return np.array([a for a, b in val])
+        with self.assertRaises(TypingError) as raises:
+            self.check(comp_unsupported_iter)
+        self.assertIn(_header_lead, str(raises.exception))
+        self.assertIn('Unsupported iterator found in array comprehension',
+                      str(raises.exception))
+
+    def test_no_array_comp(self):
+        def no_array_comp1(n):
+            l = [1,2,3,4]
+            a = np.array(l)
+            return a
+        # const 1D array is actually inlined
+        self.check(no_array_comp1, 10, assert_allocate_list=False)
+        def no_array_comp2(n):
+            l = [1,2,3,4]
+            a = np.array(l)
+            l.append(5)
+            return a
+        self.check(no_array_comp2, 10, assert_allocate_list=True)
+
+    def test_nested_array(self):
+        def nested_array(n):
+            l = np.array([ np.array([x for x in range(n)]) for y in range(n)])
+            return l
+
+        self.check(nested_array, 10)
+
+    def test_nested_array_with_const(self):
+        def nested_array(n):
+            l = np.array([ np.array([x for x in range(3)]) for y in range(4)])
+            return l
+
+        self.check(nested_array, 0)
+
+    def test_array_comp_with_iter(self):
+        def array_comp(a):
+            l = np.array([ x * x for x in a ])
+            return l
+        # with list iterator
+        l = [1,2,3,4,5]
+        self.check(array_comp, l)
+        # with array iterator
+        self.check(array_comp, np.array(l))
+        # with tuple iterator (issue #7394)
+        self.check(array_comp, tuple(l))
+        # with typed.List iterator (issue #6550)
+        self.check(array_comp, typed.List(l))
+
+    def test_array_comp_with_dtype(self):
+        def array_comp(n):
+            l = np.array([i for i in range(n)], dtype=np.complex64)
+            return l
+
+        self.check(array_comp, 10, assert_dtype=np.complex64)
+
+    def test_array_comp_inferred_dtype(self):
+        def array_comp(n):
+            l = np.array([i * 1j for i in range(n)])
+            return l
+
+        self.check(array_comp, 10)
+
+    def test_array_comp_inferred_dtype_nested(self):
+        def array_comp(n):
+            l = np.array([[i * j for j in range(n)] for i in range(n)])
+            return l
+
+        self.check(array_comp, 10)
+
+    def test_array_comp_inferred_dtype_nested_sum(self):
+        def array_comp(n):
+            l = np.array([[i * j for j in range(n)] for i in range(n)])
+            # checks that operations on the inferred array
+            return l
+
+        self.check(array_comp, 10)
+
+    def test_array_comp_inferred_dtype_outside_setitem(self):
+        def array_comp(n, v):
+            arr = np.array([i for i in range(n)])
+            # the following should not change the dtype
+            arr[0] = v
+            return arr
+
+        # float to int cast is valid
+        v = 1.2
+        self.check(array_comp, 10, v, assert_dtype=np.intp)
+        # complex to int cast is invalid
+        with self.assertRaises(TypingError) as raises:
+            cfunc = jit(nopython=True)(array_comp)
+            cfunc(10, 2.3j)
+        self.assertIn(
+            _header_lead + " Function({})".format(operator.setitem),
+            str(raises.exception),
+        )
+        self.assertIn(
+            "(array({}, 1d, C), Literal[int](0), complex128)".format(types.intp),
+            str(raises.exception),
+        )
+
+    def test_array_comp_shuffle_sideeffect(self):
+        nelem = 100
+
+        @jit(nopython=True)
+        def foo():
+            numbers = np.array([i for i in range(nelem)])
+            np.random.shuffle(numbers)
+            print(numbers)
+
+        with captured_stdout() as gotbuf:
+            foo()
+        got = gotbuf.getvalue().strip()
+
+        with captured_stdout() as expectbuf:
+            print(np.array([i for i in range(nelem)]))
+        expect = expectbuf.getvalue().strip()
+
+        # For a large enough array, the chances of shuffle to not move any
+        # element is tiny enough.
+        self.assertNotEqual(got, expect)
+        self.assertRegexpMatches(got, r'\[(\s*\d+)+\]')
+
+    def test_empty_list_not_removed(self):
+        # see issue #3724
+        def f(x):
+            t = []
+            myList = np.array([1])
+            a = np.random.choice(myList, 1)
+            t.append(x + a)
+            return a
+        self.check(f, 5, assert_allocate_list=True)
+
+    def test_reuse_of_array_var(self):
+        """ Test issue 3742 """
+        # redefinition of z breaks array comp as there's multiple defn
+        def foo(n):
+            # doesn't matter where this is in the code, it's just to ensure a
+            # `make_function` opcode exists
+            [i for i in range(1)]
+            z = np.empty(n)
+            for i in range(n):
+                z = np.zeros(n)
+                z[i] = i # write is required to trip the bug
+
+            return z
+
+        self.check(foo, 10, assert_allocate_list=True)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_conditions_as_predicates.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_conditions_as_predicates.py
new file mode 100644
index 000000000..e0747de88
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_conditions_as_predicates.py
@@ -0,0 +1,193 @@
+from numba.tests.support import TestCase
+from numba import njit, types
+from numba.typed import List, Dict
+import numpy as np
+
+
+class TestConditionsAsPredicates(TestCase):
+
+    def test_scalars(self):
+        # checks that scalar types can be used as predicates
+        dts = [np.int8, np.uint16, np.int64, np.float32, np.float64,
+               np.complex128, int, float, complex, str, bool]
+        for dt in dts:
+            for c in 1, 0:
+                x = dt(c)
+
+                @njit
+                def foo():
+                    if x:
+                        return 10
+                    else:
+                        return 20
+                self.assertEqual(foo(), foo.py_func())
+                self.assertEqual(foo(), 10 if c == 1 or dt is str else 20)
+
+        # empty string
+        @njit
+        def foo(x):
+            if x:
+                return 10
+            else:
+                return 20
+        s = ""
+        self.assertEqual(foo(s), foo.py_func(s))
+
+    def test_typed_list(self):
+        @njit
+        def foo(x):
+            if x:
+                return 10
+            else:
+                return 20
+
+        # empty list
+        z = List.empty_list(types.int64)
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 20)
+
+        # non-empty list
+        z.append(1)
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 10)
+
+    def test_reflected_list(self):
+        # non-empty
+        @njit
+        def foo(x):
+            if x:
+                return 10
+            else:
+                return 20
+
+        z = [1]
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 10)
+
+        # non-empty local
+        @njit
+        def foo():
+            y = [1, 2]
+            if y:
+                return 10
+            else:
+                return 20
+
+        self.assertEqual(foo(), foo.py_func())
+        self.assertEqual(foo.py_func(), 10)
+
+        # empty local
+        @njit
+        def foo():
+            y = [1, 2]
+            y.pop()
+            y.pop()
+            assert len(y) == 0
+            if y:
+                return 10
+            else:
+                return 20
+
+        self.assertEqual(foo(), foo.py_func())
+        self.assertEqual(foo.py_func(), 20)
+
+    def test_reflected_set(self):
+        # non-empty
+        @njit
+        def foo(x):
+            if x:
+                return 10
+            else:
+                return 20
+
+        z = {1}
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 10)
+
+        # non-empty local
+        @njit
+        def foo():
+            y = {1, 2}
+            if y:
+                return 10
+            else:
+                return 20
+
+        self.assertEqual(foo(), foo.py_func())
+        self.assertEqual(foo.py_func(), 10)
+
+        # empty local
+        @njit
+        def foo():
+            y = {1, 2}
+            y.pop()
+            y.pop()
+            assert len(y) == 0
+            if y:
+                return 10
+            else:
+                return 20
+
+        self.assertEqual(foo(), foo.py_func())
+        self.assertEqual(foo.py_func(), 20)
+
+    def test_typed_dict(self):
+        @njit
+        def foo(x):
+            if x:
+                return 10
+            else:
+                return 20
+
+        # empty
+        z = Dict.empty(types.int64, types.int64)
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 20)
+
+        # non-empty
+        z[2] = 3
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 10)
+
+    def test_arrays(self):
+        @njit
+        def foo(x):
+            if x:
+                return 10
+            else:
+                return 20
+
+        # non-empty 0d, True
+        z = np.array(1)
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 10)
+
+        # non-empty 0d, False
+        z = np.array(0)
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 20)
+        # non-empty nd True
+        z = np.array([[[1]]])
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 10)
+
+        # non-empty nd False
+        z = np.array([[[0]]])
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 20)
+
+        # various problems:
+
+        # empty, NumPy warns
+        z = np.empty(0)
+        self.assertEqual(foo(z), foo.py_func(z))
+        self.assertEqual(foo.py_func(z), 20)
+
+        # nd, NumPy raises
+        z = np.array([1, 2])
+        with self.assertRaises(ValueError) as raises:
+            foo(z)
+
+        msg = ("The truth value of an array with more than one element is "
+               "ambiguous. Use a.any() or a.all()")
+        self.assertIn(msg, str(raises.exception))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_config.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_config.py
new file mode 100644
index 000000000..ef0d4efdc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_config.py
@@ -0,0 +1,123 @@
+import os
+import tempfile
+from textwrap import dedent
+import unittest
+from numba.tests.support import (TestCase, temp_directory, override_env_config,
+                                 run_in_subprocess)
+from numba.core import config
+
+try:
+    import yaml
+    _HAVE_YAML = True
+except ImportError:
+    _HAVE_YAML = False
+
+_skip_msg = "pyyaml needed for configuration file tests"
+needs_yaml = unittest.skipIf(not _HAVE_YAML, _skip_msg)
+
+
+@needs_yaml
+class TestConfig(TestCase):
+
+    # Disable parallel testing due to envvars modification
+    _numba_parallel_test_ = False
+
+    def setUp(self):
+        # use support.temp_directory, it can do the clean up
+        self.tmppath = temp_directory('config_tmp')
+        self.maxDiff = 2500
+        super(TestConfig, self).setUp()
+
+    def mock_cfg_location(self):
+        """
+        Creates a mock launch location.
+        Returns the location path.
+        """
+        return tempfile.mkdtemp(dir=self.tmppath)
+
+    def inject_mock_cfg(self, location, cfg):
+        """
+        Injects a mock configuration at 'location'
+        """
+        tmpcfg = os.path.join(location, config._config_fname)
+        with open(tmpcfg, 'wt') as f:
+            yaml.dump(cfg, f, default_flow_style=False)
+
+    def get_settings(self):
+        """
+        Gets the current numba config settings
+        """
+        store = dict()
+        for x in dir(config):
+            if x.isupper():
+                store[x] = getattr(config, x)
+        return store
+
+    def create_config_effect(self, cfg):
+        """
+        Returns a config "original" from a location with no config file
+        and then the impact of applying the supplied cfg dictionary as
+        a config file at a location in the returned "current".
+        """
+
+        # store original cwd
+        original_cwd = os.getcwd()
+
+        # create mock launch location
+        launch_dir = self.mock_cfg_location()
+
+        # switch cwd to the mock launch location, get and store settings
+        os.chdir(launch_dir)
+        # use override to ensure that the config is zero'd out with respect
+        # to any existing settings
+        with override_env_config('_', '_'):
+            original = self.get_settings()
+
+        # inject new config into a file in the mock launch location
+        self.inject_mock_cfg(launch_dir, cfg)
+
+        try:
+            # override something but don't change the value, this is to refresh
+            # the config and make sure the injected config file is read
+            with override_env_config('_', '_'):
+                current = self.get_settings()
+        finally:
+            # switch back to original dir with no new config
+            os.chdir(original_cwd)
+        return original, current
+
+    def test_config(self):
+        # ensure a non empty settings file does impact config and that the
+        # case of the key makes no difference
+        key = 'COLOR_SCHEME'
+        for case in [str.upper, str.lower]:
+            orig, curr = self.create_config_effect({case(key): 'light_bg'})
+            self.assertTrue(orig != curr)
+            self.assertTrue(orig[key] != curr[key])
+            self.assertEqual(curr[key], 'light_bg')
+            # check that just the color scheme is the cause of difference
+            orig.pop(key)
+            curr.pop(key)
+            self.assertEqual(orig, curr)
+
+    def test_empty_config(self):
+        # ensure an empty settings file does not impact config
+        orig, curr = self.create_config_effect({})
+        self.assertEqual(orig, curr)
+
+    def test_illegal_error_style_handling(self):
+        # ensure that illegal error styles are ignored
+        new_env = os.environ.copy()
+        new_env['NUMBA_CAPTURED_ERRORS'] = 'not_a_known_style'
+        source_compiled = "the source compiled"
+        code = ("from numba import njit\n@njit\ndef foo():\n\t"
+                f"print('{source_compiled}')\nfoo()")
+        out, err = run_in_subprocess(dedent(code), env=new_env)
+        expected = ("environ NUMBA_CAPTURED_ERRORS defined but failed to parse "
+                    "\'not_a_known_style\'")
+        self.assertIn(expected, err.decode('utf-8'))
+        self.assertIn(source_compiled, out.decode('utf-8'))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_conversion.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_conversion.py
new file mode 100644
index 000000000..918f91f0b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_conversion.py
@@ -0,0 +1,233 @@
+import array
+import gc
+import itertools
+import sys
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit
+from numba.core import types
+from numba.tests.support import TestCase
+from numba.np import numpy_support
+
+
+def identity(x):
+    return x
+
+def addition(x, y):
+    return x + y
+
+def equality(x, y):
+    return x == y
+
+def foobar(x, y, z):
+    return x
+
+
+class TestConversion(TestCase):
+    """
+    Testing Python to Native conversion
+    """
+
+    def test_complex_identity(self):
+        pyfunc = identity
+        cres = compile_isolated(pyfunc, [types.complex64],
+                                return_type=types.complex64)
+
+        xs = [1.0j, (1+1j), (-1-1j), (1+0j)]
+        for x in xs:
+            self.assertEqual(cres.entry_point(x), x)
+        for x in np.complex64(xs):
+            self.assertEqual(cres.entry_point(x), x)
+
+        cres = compile_isolated(pyfunc, [types.complex128],
+                                return_type=types.complex128)
+
+        xs = [1.0j, (1+1j), (-1-1j), (1+0j)]
+        for x in xs:
+            self.assertEqual(cres.entry_point(x), x)
+        for x in np.complex128(xs):
+            self.assertEqual(cres.entry_point(x), x)
+
+    def test_complex_addition(self):
+        pyfunc = addition
+        cres = compile_isolated(pyfunc, [types.complex64, types.complex64],
+                                return_type=types.complex64)
+
+        xs = [1.0j, (1+1j), (-1-1j), (1+0j)]
+        for x in xs:
+            y = x
+            self.assertEqual(cres.entry_point(x, y), x + y)
+        for x in np.complex64(xs):
+            y = x
+            self.assertEqual(cres.entry_point(x, y), x + y)
+
+
+        cres = compile_isolated(pyfunc, [types.complex128, types.complex128],
+                                return_type=types.complex128)
+
+        xs = [1.0j, (1+1j), (-1-1j), (1+0j)]
+        for x in xs:
+            y = x
+            self.assertEqual(cres.entry_point(x, y), x + y)
+        for x in np.complex128(xs):
+            y = x
+            self.assertEqual(cres.entry_point(x, y), x + y)
+
+    def test_boolean_as_int(self):
+        pyfunc = equality
+        cres = compile_isolated(pyfunc, [types.boolean, types.intp])
+        cfunc = cres.entry_point
+
+        xs = True, False
+        ys = -1, 0, 1
+
+        for xs, ys in itertools.product(xs, ys):
+            self.assertEqual(pyfunc(xs, ys), cfunc(xs, ys))
+
+    def test_boolean_as_float(self):
+        pyfunc = equality
+        cres = compile_isolated(pyfunc, [types.boolean, types.float64])
+        cfunc = cres.entry_point
+
+        xs = True, False
+        ys = -1, 0, 1
+
+        for xs, ys in itertools.product(xs, ys):
+            self.assertEqual(pyfunc(xs, ys), cfunc(xs, ys))
+
+    def test_boolean_eq_boolean(self):
+        pyfunc = equality
+        cres = compile_isolated(pyfunc, [types.boolean, types.boolean])
+        cfunc = cres.entry_point
+
+        xs = True, False
+        ys = True, False
+
+        for xs, ys in itertools.product(xs, ys):
+            self.assertEqual(pyfunc(xs, ys), cfunc(xs, ys))
+
+    # test when a function parameters are jitted as unsigned types
+    # the function is called with negative parameters the Python error 
+    # that it generates is correctly handled -- a Python error is returned to the user
+    # For more info, see the comment in Include/longobject.h for _PyArray_AsByteArray 
+    # which PyLong_AsUnsignedLongLong calls
+    def test_negative_to_unsigned(self):
+        def f(x):
+            return x
+        with self.assertRaises(OverflowError):
+            jit('uintp(uintp)', nopython=True)(f)(-5)
+
+    # test the switch logic in callwraper.py:build_wrapper() works for more than one argument
+    # and where the error occurs 
+    def test_multiple_args_negative_to_unsigned(self): 
+        pyfunc = foobar
+        cres = compile_isolated(pyfunc, [types.uint64, types.uint64, types.uint64],
+                                return_type=types.uint64)
+        cfunc = cres.entry_point
+        test_fail_args = ((-1, 0, 1), (0, -1, 1), (0, 1, -1))
+        with self.assertRaises(OverflowError):
+            for a, b, c in test_fail_args:
+                cfunc(a, b, c)
+
+    # test switch logic of callwraper.py:build_wrapper() with records as function parameters
+    def test_multiple_args_records(self): 
+        pyfunc = foobar
+
+        mystruct_dt = np.dtype([('p', np.float64),
+                           ('row', np.float64),
+                           ('col', np.float64)])
+        mystruct = numpy_support.from_dtype(mystruct_dt)
+
+        cres = compile_isolated(pyfunc, [mystruct[:], types.uint64, types.uint64],
+                                return_type=mystruct[:])
+        cfunc = cres.entry_point
+
+        st1 = np.recarray(3, dtype=mystruct_dt)
+
+        st1.p = np.arange(st1.size) + 1
+        st1.row = np.arange(st1.size) + 1
+        st1.col = np.arange(st1.size) + 1
+
+        with self.assertRefCount(st1):
+            test_fail_args = ((st1, -1, 1), (st1, 1, -1))
+
+            for a, b, c in test_fail_args:
+                with self.assertRaises(OverflowError):
+                    cfunc(a, b, c)
+
+            del test_fail_args, a, b, c
+            gc.collect()
+
+    # test switch logic of callwraper.py:build_wrapper() with no function parameters
+    def test_with_no_parameters(self):
+        def f():
+            pass 
+        self.assertEqual(f(), jit('()', nopython=True)(f)())
+
+    def check_argument_cleanup(self, typ, obj):
+        """
+        Check that argument cleanup doesn't leak references.
+        """
+        def f(x, y):
+            pass
+
+        def _objects(obj):
+            objs = [obj]
+            if isinstance(obj, tuple):
+                for v in obj:
+                    objs += _objects(v)
+            return objs
+
+        objects = _objects(obj)
+
+        cres = compile_isolated(f, (typ, types.uint32))
+        with self.assertRefCount(*objects):
+            cres.entry_point(obj, 1)
+        with self.assertRefCount(*objects):
+            with self.assertRaises(OverflowError):
+                cres.entry_point(obj, -1)
+
+        cres = compile_isolated(f, (types.uint32, typ))
+        with self.assertRefCount(*objects):
+            cres.entry_point(1, obj)
+        with self.assertRefCount(*objects):
+            with self.assertRaises(OverflowError):
+                cres.entry_point(-1, obj)
+
+    def test_cleanup_buffer(self):
+        mem = memoryview(bytearray(b"xyz"))
+        self.check_argument_cleanup(types.Buffer(types.intc, 1, 'C'), mem)
+
+    def test_cleanup_record(self):
+        dtype = np.dtype([('x', np.float64), ('y', np.float64)])
+        recarr = np.zeros(1, dtype=dtype)
+        self.check_argument_cleanup(numpy_support.from_dtype(dtype), recarr[0])
+
+    def test_cleanup_tuple(self):
+        mem = memoryview(bytearray(b"xyz"))
+        tp = types.UniTuple(types.Buffer(types.intc, 1, 'C'), 2)
+        self.check_argument_cleanup(tp, (mem, mem))
+
+    def test_cleanup_optional(self):
+        mem = memoryview(bytearray(b"xyz"))
+        tp = types.Optional(types.Buffer(types.intc, 1, 'C'))
+        self.check_argument_cleanup(tp, mem)
+
+    def test_stringliteral_to_unicode(self):
+        # See issue #6907, explicit signature on bar() takes a unicode_type but
+        # the call to bar() in foo() is with a StringLiteral
+
+        @jit(types.void(types.unicode_type), nopython=True)
+        def bar(string):
+            pass
+
+        @jit(types.void(), nopython=True)
+        def foo2():
+            bar("literal string")
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_copy_propagate.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_copy_propagate.py
new file mode 100644
index 000000000..cca0dd2b3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_copy_propagate.py
@@ -0,0 +1,171 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+from numba import jit, njit
+from numba.core import types, typing, ir, config, compiler, cpu
+from numba.core.registry import cpu_target
+from numba.core.annotations import type_annotations
+from numba.core.ir_utils import (copy_propagate, apply_copy_propagate,
+                            get_name_var_table)
+from numba.core.typed_passes import type_inference_stage
+from numba.tests.support import IRPreservingTestPipeline
+import numpy as np
+import unittest
+
+
+def test_will_propagate(b, z, w):
+    x = 3
+    x1 = x
+    if b > 0:
+        y = z + w
+    else:
+        y = 0
+    a = 2 * x1
+    return a < b
+
+
+def test_wont_propagate(b, z, w):
+    x = 3
+    if b > 0:
+        y = z + w
+        x = 1
+    else:
+        y = 0
+    a = 2 * x
+    return a < b
+
+
+def null_func(a,b,c,d):
+    False
+
+
+def inListVar(list_var, var):
+    for i in list_var:
+        if i.name == var:
+            return True
+    return False
+
+
+def findAssign(func_ir, var):
+    for label, block in func_ir.blocks.items():
+        for i, inst in enumerate(block.body):
+            if isinstance(inst, ir.Assign) and inst.target.name!=var:
+                all_var = inst.list_vars()
+                if inListVar(all_var, var):
+                    return True
+
+    return False
+
+
+class TestCopyPropagate(unittest.TestCase):
+    def test1(self):
+        typingctx = typing.Context()
+        targetctx = cpu.CPUContext(typingctx, 'cpu')
+        test_ir = compiler.run_frontend(test_will_propagate)
+        with cpu_target.nested_context(typingctx, targetctx):
+            typingctx.refresh()
+            targetctx.refresh()
+            args = (types.int64, types.int64, types.int64)
+            typemap, return_type, calltypes, _ = type_inference_stage(typingctx,
+                                                                      targetctx,
+                                                                      test_ir,
+                                                                      args,
+                                                                      None)
+            type_annotation = type_annotations.TypeAnnotation(
+                func_ir=test_ir,
+                typemap=typemap,
+                calltypes=calltypes,
+                lifted=(),
+                lifted_from=None,
+                args=args,
+                return_type=return_type,
+                html_output=config.HTML)
+            in_cps, out_cps = copy_propagate(test_ir.blocks, typemap)
+            apply_copy_propagate(test_ir.blocks, in_cps,
+                                 get_name_var_table(test_ir.blocks), typemap,
+                                 calltypes)
+
+            self.assertFalse(findAssign(test_ir, "x1"))
+
+    def test2(self):
+        typingctx = typing.Context()
+        targetctx = cpu.CPUContext(typingctx, 'cpu')
+        test_ir = compiler.run_frontend(test_wont_propagate)
+        with cpu_target.nested_context(typingctx, targetctx):
+            typingctx.refresh()
+            targetctx.refresh()
+            args = (types.int64, types.int64, types.int64)
+            typemap, return_type, calltypes, _ = type_inference_stage(typingctx,
+                                                                      targetctx,
+                                                                      test_ir,
+                                                                      args,
+                                                                      None)
+            type_annotation = type_annotations.TypeAnnotation(
+                func_ir=test_ir,
+                typemap=typemap,
+                calltypes=calltypes,
+                lifted=(),
+                lifted_from=None,
+                args=args,
+                return_type=return_type,
+                html_output=config.HTML)
+            in_cps, out_cps = copy_propagate(test_ir.blocks, typemap)
+            apply_copy_propagate(test_ir.blocks, in_cps, get_name_var_table(test_ir.blocks), typemap, calltypes)
+
+            self.assertTrue(findAssign(test_ir, "x"))
+
+    def test_input_ir_extra_copies(self):
+        """make sure Interpreter._remove_unused_temporaries() has removed extra copies
+        in the IR in simple cases so copy propagation is faster
+        """
+        def test_impl(a):
+            b = a + 3
+            return b
+
+        j_func = njit(pipeline_class=IRPreservingTestPipeline)(test_impl)
+        self.assertEqual(test_impl(5), j_func(5))
+
+        # make sure b is the target of the expression assignment, not a temporary
+        fir = j_func.overloads[j_func.signatures[0]].metadata['preserved_ir']
+        self.assertTrue(len(fir.blocks) == 1)
+        block = next(iter(fir.blocks.values()))
+        b_found = False
+        for stmt in block.body:
+            if isinstance(stmt, ir.Assign) and stmt.target.name == "b":
+                b_found = True
+                self.assertTrue(isinstance(stmt.value, ir.Expr)
+                    and stmt.value.op == "binop" and stmt.value.lhs.name == "a")
+
+        self.assertTrue(b_found)
+
+    def test_input_ir_copy_remove_transform(self):
+        """make sure Interpreter._remove_unused_temporaries() does not generate
+        invalid code for rare chained assignment cases
+        """
+        # regular chained assignment
+        def impl1(a):
+            b = c = a + 1
+            return (b, c)
+
+        # chained assignment with setitem
+        def impl2(A, i, a):
+            b = A[i] = a + 1
+            return b, A[i] + 2
+
+        # chained assignment with setattr
+        def impl3(A, a):
+            b = A.a = a + 1
+            return b, A.a + 2
+
+        class C:
+            pass
+
+        self.assertEqual(impl1(5), njit(impl1)(5))
+        self.assertEqual(impl2(np.ones(3), 0, 5), njit(impl2)(np.ones(3), 0, 5))
+        self.assertEqual(impl3(C(), 5), jit(forceobj=True)(impl3)(C(), 5))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ctypes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ctypes.py
new file mode 100644
index 000000000..2b93a65fd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ctypes.py
@@ -0,0 +1,261 @@
+from ctypes import *
+import sys
+import threading
+
+import numpy as np
+
+
+from numba.core.compiler import compile_isolated
+from numba import jit
+from numba.core import types, errors
+from numba.core.typing import ctypes_utils
+from numba.tests.support import MemoryLeakMixin, tag, TestCase
+from numba.tests.ctypes_usecases import *
+import unittest
+
+
+class TestCTypesTypes(TestCase):
+
+    def _conversion_tests(self, check):
+        check(c_double, types.float64)
+        check(c_int, types.intc)
+        check(c_uint16, types.uint16)
+        check(c_size_t, types.size_t)
+        check(c_ssize_t, types.ssize_t)
+
+        check(c_void_p, types.voidptr)
+        check(POINTER(c_float), types.CPointer(types.float32))
+        check(POINTER(POINTER(c_float)),
+              types.CPointer(types.CPointer(types.float32)))
+
+        check(None, types.void)
+
+    def test_from_ctypes(self):
+        """
+        Test converting a ctypes type to a Numba type.
+        """
+        def check(cty, ty):
+            got = ctypes_utils.from_ctypes(cty)
+            self.assertEqual(got, ty)
+
+        self._conversion_tests(check)
+
+        # An unsupported type
+        with self.assertRaises(TypeError) as raises:
+            ctypes_utils.from_ctypes(c_wchar_p)
+        self.assertIn("Unsupported ctypes type", str(raises.exception))
+
+    def test_to_ctypes(self):
+        """
+        Test converting a Numba type to a ctypes type.
+        """
+        def check(cty, ty):
+            got = ctypes_utils.to_ctypes(ty)
+            self.assertEqual(got, cty)
+
+        self._conversion_tests(check)
+
+        # An unsupported type
+        with self.assertRaises(TypeError) as raises:
+            ctypes_utils.to_ctypes(types.ellipsis)
+        self.assertIn("Cannot convert Numba type '...' to ctypes type",
+                      str(raises.exception))
+
+
+class TestCTypesUseCases(MemoryLeakMixin, TestCase):
+
+    def test_c_sin(self):
+        pyfunc = use_c_sin
+        cres = compile_isolated(pyfunc, [types.double])
+        cfunc = cres.entry_point
+        x = 3.14
+        self.assertEqual(pyfunc(x), cfunc(x))
+
+    def test_two_funcs(self):
+        # Check that two constant functions don't get mixed up.
+        pyfunc = use_two_funcs
+        cres = compile_isolated(pyfunc, [types.double])
+        cfunc = cres.entry_point
+        x = 3.14
+        self.assertEqual(pyfunc(x), cfunc(x))
+
+    @unittest.skipUnless(is_windows, "Windows-specific test")
+    def test_stdcall(self):
+        # Just check that it doesn't crash
+        cres = compile_isolated(use_c_sleep, [types.uintc])
+        cfunc = cres.entry_point
+        cfunc(1)
+
+    def test_ctype_wrapping(self):
+        pyfunc = use_ctype_wrapping
+        cres = compile_isolated(pyfunc, [types.double])
+        cfunc = cres.entry_point
+        x = 3.14
+        self.assertEqual(pyfunc(x), cfunc(x))
+
+    def test_ctype_voidptr(self):
+        pyfunc = use_c_pointer
+        # pyfunc will segfault if called
+        cres = compile_isolated(pyfunc, [types.int32])
+        cfunc = cres.entry_point
+        x = 123
+        self.assertEqual(cfunc(x), x + 1)
+
+    def test_function_pointer(self):
+        pyfunc = use_func_pointer
+        cfunc = jit(nopython=True)(pyfunc)
+        for (fa, fb, x) in [
+            (c_sin, c_cos, 1.0),
+            (c_sin, c_cos, -1.0),
+            (c_cos, c_sin, 1.0),
+            (c_cos, c_sin, -1.0)]:
+            expected = pyfunc(fa, fb, x)
+            got = cfunc(fa, fb, x)
+            self.assertEqual(got, expected)
+        # A single specialization was compiled for all calls
+        self.assertEqual(len(cfunc.overloads), 1, cfunc.overloads)
+
+    def test_untyped_function(self):
+        with self.assertRaises(TypeError) as raises:
+            compile_isolated(use_c_untyped, [types.double])
+        self.assertIn("ctypes function '_numba_test_exp' doesn't define its argument types",
+                      str(raises.exception))
+
+    def test_python_call_back(self):
+        mydct = {'what': 1232121}
+
+        def call_me_maybe(arr):
+            return mydct[arr[0].decode('ascii')]
+
+        # Create a callback into the python interpreter
+        py_call_back = CFUNCTYPE(c_int, py_object)(call_me_maybe)
+
+        def pyfunc(a):
+            what = py_call_back(a)
+            return what
+
+        cfunc = jit(nopython=True, nogil=True)(pyfunc)
+        arr = np.array(["what"], dtype='S10')
+        self.assertEqual(pyfunc(arr), cfunc(arr))
+
+    def test_python_call_back_threaded(self):
+        def pyfunc(a, repeat):
+            out = 0
+            for _ in range(repeat):
+                out += py_call_back(a)
+            return out
+
+        cfunc = jit(nopython=True, nogil=True)(pyfunc)
+
+        arr = np.array(["what"], dtype='S10')
+        repeat = 1000
+
+        expected = pyfunc(arr, repeat)
+        outputs = []
+
+        # Warm up
+        cfunc(arr, repeat)
+
+        # Test the function in multiple threads to exercise the
+        # GIL ensure/release code
+
+        def run(func, arr, repeat):
+            outputs.append(func(arr, repeat))
+
+        threads = [threading.Thread(target=run, args=(cfunc, arr, repeat))
+                   for _ in range(10)]
+
+        # Start threads
+        for th in threads:
+            th.start()
+
+        # End threads
+        for th in threads:
+            th.join()
+
+        # Check results
+        for got in outputs:
+            self.assertEqual(expected, got)
+
+    def test_passing_array_ctypes_data(self):
+        """
+        Test the ".ctypes.data" attribute of an array can be passed
+        as a "void *" parameter.
+        """
+        def pyfunc(arr):
+            return c_take_array_ptr(arr.ctypes.data)
+
+        cfunc = jit(nopython=True, nogil=True)(pyfunc)
+
+        arr = np.arange(5)
+
+        expected = pyfunc(arr)
+        got = cfunc(arr)
+
+        self.assertEqual(expected, got)
+
+    def check_array_ctypes(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        arr = np.linspace(0, 10, 5)
+        expected = arr ** 2.0
+        got = cfunc(arr)
+        self.assertPreciseEqual(expected, got)
+        return cfunc
+
+    def test_passing_array_ctypes_voidptr(self):
+        """
+        Test the ".ctypes" attribute of an array can be passed
+        as a "void *" parameter.
+        """
+        self.check_array_ctypes(use_c_vsquare)
+
+    def test_passing_array_ctypes_voidptr_pass_ptr(self):
+        """
+        Test the ".ctypes" attribute of an array can be passed
+        as a pointer parameter of the right type.
+        """
+        cfunc = self.check_array_ctypes(use_c_vcube)
+
+        # Non-compatible pointers are not accepted (here float32* vs. float64*)
+        with self.assertRaises(errors.TypingError) as raises:
+            cfunc(np.float32([0.0]))
+
+        self.assertIn("No implementation of function ExternalFunctionPointer",
+                      str(raises.exception))
+
+    def test_storing_voidptr_to_int_array(self):
+        # Make C callback that returns a void*
+        cproto = CFUNCTYPE(c_void_p)
+
+        @cproto
+        def get_voidstar():
+            return 0xdeadbeef
+
+        # Make python functions that use the C callback
+        def pyfunc(a):
+            ptr = get_voidstar()
+            a[0] = ptr
+            return ptr
+
+        # Compile it
+        cres = compile_isolated(pyfunc, [types.uintp[::1]])
+        cfunc = cres.entry_point
+
+        # Setup inputs
+        arr_got = np.zeros(1, dtype=np.uintp)
+        arr_expect = arr_got.copy()
+
+        # Run functions
+        ret_got = cfunc(arr_got)
+        ret_expect = pyfunc(arr_expect)
+
+        # Check
+        self.assertEqual(ret_expect, 0xdeadbeef)
+        self.assertPreciseEqual(ret_got, ret_expect)
+        self.assertPreciseEqual(arr_got, arr_expect)
+
+
+if __name__ == '__main__':
+    unittest.main()
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dataflow.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dataflow.py
new file mode 100644
index 000000000..17925c93a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dataflow.py
@@ -0,0 +1,192 @@
+import warnings
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types, errors
+from numba.tests.support import TestCase, CompilationCache
+
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+def assignments(a):
+    b = c = str(a)
+    return b + c
+
+
+def assignments2(a):
+    b = c = d = str(a)
+    return b + c + d
+
+
+# Use cases for issue #503
+
+def var_propagate1(a, b):
+    c = (a if a > b else b) + 5
+    return c
+
+
+def var_propagate2(a, b):
+    c = 5 + (a if a > b else b + 12) / 2.0
+    return c
+
+
+def var_propagate3(a, b):
+    c = 5 + (a > b and a or b)
+    return c
+
+
+def var_propagate4(a, b):
+    c = 5 + (a - 1 and b + 1) or (a + 1 and b - 1)
+    return c
+
+
+# Issue #480
+def chained_compare(a):
+    return 1 < a < 3
+
+
+# Issue #591
+def stack_effect_error(x):
+    i = 2
+    c = 1
+    if i == x:
+        for i in range(3):
+            c = i
+    return i + c
+
+# Some more issues with stack effect and blocks
+def for_break(n, x):
+    for i in range(n):
+        n = 0
+        if i == x:
+            break
+    else:
+        n = i
+    return i, n
+
+# Issue #571
+def var_swapping(a, b, c, d, e):
+    a, b = b, a
+    c, d, e = e, c, d
+    a, b, c, d = b, c, d, a
+    return a + b + c + d +e
+
+def unsupported_op_code():
+    # needs unsupported "SETUP_EXCEPT" opcode
+    try:
+        pass
+    except:
+        pass
+
+class TestDataFlow(TestCase):
+
+    def setUp(self):
+        self.cache = CompilationCache()
+
+    def test_assignments(self, flags=force_pyobj_flags):
+        pyfunc = assignments
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-1, 0, 1]:
+            self.assertPreciseEqual(pyfunc(x), cfunc(x))
+
+    def test_assignments2(self, flags=force_pyobj_flags):
+        pyfunc = assignments2
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [-1, 0, 1]:
+            self.assertPreciseEqual(pyfunc(x), cfunc(x))
+
+        if flags is force_pyobj_flags:
+            cfunc("a")
+
+    # The dataflow analysis must be good enough for native mode
+    # compilation to succeed, hence the no_pyobj_flags in the following tests.
+
+    def run_propagate_func(self, pyfunc, args):
+        cr = self.cache.compile(pyfunc, (types.int32, types.int32),
+                                flags=no_pyobj_flags)
+        cfunc = cr.entry_point
+        self.assertPreciseEqual(cfunc(*args), pyfunc(*args))
+
+    def test_var_propagate1(self):
+        self.run_propagate_func(var_propagate1, (2, 3))
+        self.run_propagate_func(var_propagate1, (3, 2))
+
+    def test_var_propagate2(self):
+        self.run_propagate_func(var_propagate2, (2, 3))
+        self.run_propagate_func(var_propagate2, (3, 2))
+
+    def test_var_propagate3(self):
+        self.run_propagate_func(var_propagate3, (2, 3))
+        self.run_propagate_func(var_propagate3, (3, 2))
+        self.run_propagate_func(var_propagate3, (2, 0))
+        self.run_propagate_func(var_propagate3, (-1, 0))
+        self.run_propagate_func(var_propagate3, (0, 2))
+        self.run_propagate_func(var_propagate3, (0, -1))
+
+    def test_var_propagate4(self):
+        self.run_propagate_func(var_propagate4, (1, 1))
+        self.run_propagate_func(var_propagate4, (1, 0))
+        self.run_propagate_func(var_propagate4, (1, -1))
+        self.run_propagate_func(var_propagate4, (0, 1))
+        self.run_propagate_func(var_propagate4, (0, 0))
+        self.run_propagate_func(var_propagate4, (0, -1))
+        self.run_propagate_func(var_propagate4, (-1, 1))
+        self.run_propagate_func(var_propagate4, (-1, 0))
+        self.run_propagate_func(var_propagate4, (-1, -1))
+
+    def test_chained_compare(self, flags=force_pyobj_flags):
+        pyfunc = chained_compare
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in [0, 1, 2, 3, 4]:
+            self.assertPreciseEqual(pyfunc(x), cfunc(x))
+
+    def test_chained_compare_npm(self):
+        self.test_chained_compare(no_pyobj_flags)
+
+    def test_stack_effect_error(self, flags=force_pyobj_flags):
+        # Issue #591: POP_BLOCK must undo all stack pushes done inside
+        # the block.
+        pyfunc = stack_effect_error
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cr.entry_point
+        for x in (0, 1, 2, 3):
+            self.assertPreciseEqual(pyfunc(x), cfunc(x))
+
+    def test_stack_effect_error_npm(self):
+        self.test_stack_effect_error(no_pyobj_flags)
+
+    def test_var_swapping(self, flags=force_pyobj_flags):
+        pyfunc = var_swapping
+        cr = compile_isolated(pyfunc, (types.int32,) * 5, flags=flags)
+        cfunc = cr.entry_point
+        args = tuple(range(0, 10, 2))
+        self.assertPreciseEqual(pyfunc(*args), cfunc(*args))
+
+    def test_var_swapping_npm(self):
+        self.test_var_swapping(no_pyobj_flags)
+
+    def test_for_break(self, flags=force_pyobj_flags):
+        # BREAK_LOOP must unwind the current inner syntax block.
+        pyfunc = for_break
+        cr = compile_isolated(pyfunc, (types.intp, types.intp), flags=flags)
+        cfunc = cr.entry_point
+        for (n, x) in [(4, 2), (4, 6)]:
+            self.assertPreciseEqual(pyfunc(n, x), cfunc(n, x))
+
+    def test_for_break_npm(self):
+        self.test_for_break(no_pyobj_flags)
+
+
+if __name__ == '__main__':
+    unittest.main()
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_datamodel.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_datamodel.py
new file mode 100644
index 000000000..e04ff3f19
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_datamodel.py
@@ -0,0 +1,219 @@
+from llvmlite import ir, binding as ll
+
+from numba.core import types, datamodel
+from numba.core.datamodel.testing import test_factory
+import unittest
+
+
+class TestBool(test_factory()):
+    fe_type = types.boolean
+
+
+class TestPyObject(test_factory()):
+    fe_type = types.pyobject
+
+
+class TestInt8(test_factory()):
+    fe_type = types.int8
+
+
+class TestInt16(test_factory()):
+    fe_type = types.int16
+
+
+class TestInt32(test_factory()):
+    fe_type = types.int32
+
+
+class TestInt64(test_factory()):
+    fe_type = types.int64
+
+
+class TestUInt8(test_factory()):
+    fe_type = types.uint8
+
+
+class TestUInt16(test_factory()):
+    fe_type = types.uint16
+
+
+class TestUInt32(test_factory()):
+    fe_type = types.uint32
+
+
+class TestUInt64(test_factory()):
+    fe_type = types.uint64
+
+
+class TestFloat(test_factory()):
+    fe_type = types.float32
+
+
+class TestDouble(test_factory()):
+    fe_type = types.float64
+
+
+class TestComplex(test_factory()):
+    fe_type = types.complex64
+
+
+class TestDoubleComplex(test_factory()):
+    fe_type = types.complex128
+
+
+class TestPointerOfInt32(test_factory()):
+    fe_type = types.CPointer(types.int32)
+
+
+class TestUniTupleOf2xInt32(test_factory()):
+    fe_type = types.UniTuple(types.int32, 2)
+
+
+class TestUniTupleEmpty(test_factory()):
+    fe_type = types.UniTuple(types.int32, 0)
+
+
+class TestTupleInt32Float32(test_factory()):
+    fe_type = types.Tuple([types.int32, types.float32])
+
+
+class TestTupleEmpty(test_factory()):
+    fe_type = types.Tuple([])
+
+
+class Test1DArrayOfInt32(test_factory()):
+    fe_type = types.Array(types.int32, 1, 'C')
+
+
+class Test2DArrayOfComplex128(test_factory()):
+    fe_type = types.Array(types.complex128, 2, 'C')
+
+
+class Test0DArrayOfInt32(test_factory()):
+    fe_type = types.Array(types.int32, 0, 'C')
+
+
+class TestArgInfo(unittest.TestCase):
+
+    def _test_as_arguments(self, fe_args):
+        """
+        Test round-tripping types *fe_args* through the default data model's
+        argument conversion and unpacking logic.
+        """
+        dmm = datamodel.default_manager
+        fi = datamodel.ArgPacker(dmm, fe_args)
+
+        module = ir.Module()
+        fnty = ir.FunctionType(ir.VoidType(), [])
+        function = ir.Function(module, fnty, name="test_arguments")
+        builder = ir.IRBuilder()
+        builder.position_at_end(function.append_basic_block())
+
+        args = [ir.Constant(dmm.lookup(t).get_value_type(), None)
+                for t in fe_args]
+
+        # Roundtrip
+        values = fi.as_arguments(builder, args)
+        asargs = fi.from_arguments(builder, values)
+
+        self.assertEqual(len(asargs), len(fe_args))
+        valtys = tuple([v.type for v in values])
+        self.assertEqual(valtys, fi.argument_types)
+
+        expect_types = [a.type for a in args]
+        got_types = [a.type for a in asargs]
+
+        self.assertEqual(expect_types, got_types)
+
+        # Assign names (check this doesn't raise)
+        fi.assign_names(values, ["arg%i" for i in range(len(fe_args))])
+
+        builder.ret_void()
+
+        ll.parse_assembly(str(module))
+
+    def test_int32_array_complex(self):
+        fe_args = [types.int32,
+                   types.Array(types.int32, 1, 'C'),
+                   types.complex64]
+        self._test_as_arguments(fe_args)
+
+    def test_two_arrays(self):
+        fe_args = [types.Array(types.int32, 1, 'C')] * 2
+        self._test_as_arguments(fe_args)
+
+    def test_two_0d_arrays(self):
+        fe_args = [types.Array(types.int32, 0, 'C')] * 2
+        self._test_as_arguments(fe_args)
+
+    def test_tuples(self):
+        fe_args = [types.UniTuple(types.int32, 2),
+                   types.UniTuple(types.int32, 3)]
+        self._test_as_arguments(fe_args)
+        # Tuple of struct-likes
+        arrty = types.Array(types.int32, 1, 'C')
+        fe_args = [types.UniTuple(arrty, 2),
+                   types.UniTuple(arrty, 3)]
+        self._test_as_arguments(fe_args)
+        # Nested tuple
+        fe_args = [types.UniTuple(types.UniTuple(types.int32, 2), 3)]
+        self._test_as_arguments(fe_args)
+
+    def test_empty_tuples(self):
+        # Empty tuple
+        fe_args = [types.UniTuple(types.int16, 0),
+                   types.Tuple(()),
+                   types.int32]
+        self._test_as_arguments(fe_args)
+
+    def test_nested_empty_tuples(self):
+        fe_args = [types.int32,
+                   types.UniTuple(types.Tuple(()), 2),
+                   types.int64]
+        self._test_as_arguments(fe_args)
+
+
+class TestMemInfo(unittest.TestCase):
+    def setUp(self):
+        self.dmm = datamodel.default_manager
+
+    def test_number(self):
+        ty = types.int32
+        dm = self.dmm[ty]
+        self.assertFalse(dm.contains_nrt_meminfo())
+
+    def test_array(self):
+        ty = types.int32[:]
+        dm = self.dmm[ty]
+        self.assertTrue(dm.contains_nrt_meminfo())
+
+    def test_tuple_of_number(self):
+        ty = types.UniTuple(dtype=types.int32, count=2)
+        dm = self.dmm[ty]
+        self.assertFalse(dm.contains_nrt_meminfo())
+
+    def test_tuple_of_array(self):
+        ty = types.UniTuple(dtype=types.int32[:], count=2)
+        dm = self.dmm[ty]
+        self.assertTrue(dm.contains_nrt_meminfo())
+
+
+class TestMisc(unittest.TestCase):
+
+    def test_issue2921(self):
+        import numpy as np
+        from numba import njit
+
+        @njit
+        def copy(a, b):
+            for i in range(a.shape[0]):
+                a[i] = b[i]
+
+        b = np.arange(5, dtype=np.uint8).view(np.bool_)
+        a = np.zeros_like(b)
+        copy(a, b)
+        np.testing.assert_equal(a, np.array((False,) + (True,) * 4))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_debug.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_debug.py
new file mode 100644
index 000000000..caeabe25b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_debug.py
@@ -0,0 +1,314 @@
+import os
+import platform
+import re
+import textwrap
+import warnings
+
+import numpy as np
+
+from numba.tests.support import (TestCase, override_config, override_env_config,
+                      captured_stdout, forbid_codegen, skip_parfors_unsupported,
+                      needs_blas)
+from numba import jit
+from numba.core import types, compiler
+from numba.core.compiler import compile_isolated, Flags
+from numba.core.cpu import ParallelOptions
+from numba.core.errors import NumbaPerformanceWarning
+from numba import prange
+from numba.experimental import jitclass
+import unittest
+
+
+def simple_nopython(somearg):
+    retval = somearg + 1
+    return retval
+
+def simple_gen(x, y):
+    yield x
+    yield y
+
+
+class SimpleClass(object):
+    def __init__(self):
+        self.h = 5
+
+simple_class_spec = [('h', types.int32)]
+
+def simple_class_user(obj):
+    return obj.h
+
+def unsupported_parfor(a, b):
+    return np.dot(a, b) # dot as gemm unsupported
+
+def supported_parfor(n):
+    a = np.ones(n)
+    for i in prange(n):
+        a[i] = a[i] + np.sin(i)
+    return a
+
+force_parallel_flags = Flags()
+force_parallel_flags.auto_parallel = ParallelOptions(True)
+force_parallel_flags.nrt = True
+
+class DebugTestBase(TestCase):
+
+    all_dumps = set(['bytecode', 'cfg', 'ir', 'typeinfer', 'llvm',
+                     'func_opt_llvm', 'optimized_llvm', 'assembly'])
+
+    def assert_fails(self, *args, **kwargs):
+        self.assertRaises(AssertionError, *args, **kwargs)
+
+    def check_debug_output(self, out, dump_names):
+        enabled_dumps = dict.fromkeys(self.all_dumps, False)
+        for name in dump_names:
+            assert name in enabled_dumps
+            enabled_dumps[name] = True
+        for name, enabled in sorted(enabled_dumps.items()):
+            check_meth = getattr(self, '_check_dump_%s' % name)
+            if enabled:
+                check_meth(out)
+            else:
+                self.assert_fails(check_meth, out)
+
+    def _check_dump_bytecode(self, out):
+        self.assertIn('BINARY_ADD', out)
+
+    def _check_dump_cfg(self, out):
+        self.assertIn('CFG dominators', out)
+
+    def _check_dump_ir(self, out):
+        self.assertIn('--IR DUMP: %s--' % self.func_name, out)
+
+    def _check_dump_typeinfer(self, out):
+        self.assertIn('--propagate--', out)
+
+    def _check_dump_llvm(self, out):
+        self.assertIn('--LLVM DUMP', out)
+        if compiler.Flags.options["auto_parallel"].default.enabled == False:
+            self.assertRegex(out, r'store i64 %\"\.\d", i64\* %"retptr"', out)
+
+    def _check_dump_func_opt_llvm(self, out):
+        self.assertIn('--FUNCTION OPTIMIZED DUMP %s' % self.func_name, out)
+        # allocas have been optimized away
+        self.assertIn('add nsw i64 %arg.somearg, 1', out)
+
+    def _check_dump_optimized_llvm(self, out):
+        self.assertIn('--OPTIMIZED DUMP %s' % self.func_name, out)
+        self.assertIn('add nsw i64 %arg.somearg, 1', out)
+
+    def _check_dump_assembly(self, out):
+        self.assertIn('--ASSEMBLY %s' % self.func_name, out)
+        if platform.machine() in ('x86_64', 'AMD64', 'i386', 'i686'):
+            self.assertIn('xorl', out)
+
+
+class FunctionDebugTestBase(DebugTestBase):
+
+    func_name = 'simple_nopython'
+
+    def compile_simple_nopython(self):
+        with captured_stdout() as out:
+            cres = compile_isolated(simple_nopython, (types.int64,))
+            # Sanity check compiled function
+            self.assertPreciseEqual(cres.entry_point(2), 3)
+        return out.getvalue()
+
+
+class TestFunctionDebugOutput(FunctionDebugTestBase):
+
+    def test_dump_bytecode(self):
+        with override_config('DUMP_BYTECODE', True):
+            out = self.compile_simple_nopython()
+        self.check_debug_output(out, ['bytecode'])
+
+    def test_dump_ir(self):
+        with override_config('DUMP_IR', True):
+            out = self.compile_simple_nopython()
+        self.check_debug_output(out, ['ir'])
+
+    def test_dump_cfg(self):
+        with override_config('DUMP_CFG', True):
+            out = self.compile_simple_nopython()
+        self.check_debug_output(out, ['cfg'])
+
+    def test_dump_llvm(self):
+        with override_config('DUMP_LLVM', True):
+            out = self.compile_simple_nopython()
+        self.check_debug_output(out, ['llvm'])
+
+    def test_dump_func_opt_llvm(self):
+        with override_config('DUMP_FUNC_OPT', True):
+            out = self.compile_simple_nopython()
+        self.check_debug_output(out, ['func_opt_llvm'])
+
+    def test_dump_optimized_llvm(self):
+        with override_config('DUMP_OPTIMIZED', True):
+            out = self.compile_simple_nopython()
+        self.check_debug_output(out, ['optimized_llvm'])
+
+    def test_dump_assembly(self):
+        with override_config('DUMP_ASSEMBLY', True):
+            out = self.compile_simple_nopython()
+        self.check_debug_output(out, ['assembly'])
+
+
+class TestGeneratorDebugOutput(DebugTestBase):
+
+    func_name = 'simple_gen'
+
+    def compile_simple_gen(self):
+        with captured_stdout() as out:
+            cres = compile_isolated(simple_gen, (types.int64, types.int64))
+            # Sanity check compiled function
+            self.assertPreciseEqual(list(cres.entry_point(2, 5)), [2, 5])
+        return out.getvalue()
+
+    def test_dump_ir_generator(self):
+        with override_config('DUMP_IR', True):
+            out = self.compile_simple_gen()
+        self.check_debug_output(out, ['ir'])
+        self.assertIn('--GENERATOR INFO: %s' % self.func_name, out)
+        expected_gen_info = textwrap.dedent("""
+            generator state variables: ['x', 'y']
+            yield point #1: live variables = ['y'], weak live variables = ['x']
+            yield point #2: live variables = [], weak live variables = ['y']
+            """)
+        self.assertIn(expected_gen_info, out)
+
+
+class TestDisableJIT(DebugTestBase):
+    """
+    Test the NUMBA_DISABLE_JIT environment variable.
+    """
+
+    def test_jit(self):
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                cfunc = jit(nopython=True)(simple_nopython)
+                self.assertPreciseEqual(cfunc(2), 3)
+
+    def test_jitclass(self):
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                SimpleJITClass = jitclass(simple_class_spec)(SimpleClass)
+
+                obj = SimpleJITClass()
+                self.assertPreciseEqual(obj.h, 5)
+
+                cfunc = jit(nopython=True)(simple_class_user)
+                self.assertPreciseEqual(cfunc(obj), 5)
+
+
+class TestEnvironmentOverride(FunctionDebugTestBase):
+    """
+    Test that environment variables are reloaded by Numba when modified.
+    """
+
+    # mutates env with os.environ so must be run serially
+    _numba_parallel_test_ = False
+
+    def test_debug(self):
+        out = self.compile_simple_nopython()
+        self.assertFalse(out)
+        with override_env_config('NUMBA_DEBUG', '1'):
+            out = self.compile_simple_nopython()
+            # Note that all variables dependent on NUMBA_DEBUG are
+            # updated too.
+            self.check_debug_output(out, ['ir', 'typeinfer',
+                                          'llvm', 'func_opt_llvm',
+                                          'optimized_llvm', 'assembly'])
+        out = self.compile_simple_nopython()
+        self.assertFalse(out)
+
+class TestParforsDebug(TestCase):
+    """
+    Tests debug options associated with parfors
+    """
+
+    # mutates env with os.environ so must be run serially
+    _numba_parallel_test_ = False
+
+    def check_parfors_warning(self, warn_list):
+        msg = ("'parallel=True' was specified but no transformation for "
+               "parallel execution was possible.")
+        warning_found = False
+        for w in warn_list:
+            if msg in str(w.message):
+                warning_found = True
+                break
+        self.assertTrue(warning_found, "Warning message should be found.")
+
+    @needs_blas
+    @skip_parfors_unsupported
+    def test_warns(self):
+        """
+        Test that using parallel=True on a function that does not have parallel
+        semantics warns.
+        """
+        arr_ty = types.Array(types.float64, 2, "C")
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always", NumbaPerformanceWarning)
+            cres = compile_isolated(unsupported_parfor, (arr_ty, arr_ty),
+                                    flags=force_parallel_flags)
+        self.check_parfors_warning(w)
+
+    @skip_parfors_unsupported
+    def test_array_debug_opt_stats(self):
+        """
+        Test that NUMBA_DEBUG_ARRAY_OPT_STATS produces valid output
+        """
+        # deliberately trigger a compilation loop to increment the
+        # Parfor class state, this is to ensure the test works based
+        # on indices computed based on this state and not hard coded
+        # indices.
+        cres = compile_isolated(supported_parfor, (types.int64,),
+                                flags=force_parallel_flags)
+
+        with override_env_config('NUMBA_DEBUG_ARRAY_OPT_STATS', '1'):
+            with captured_stdout() as out:
+                cres = compile_isolated(supported_parfor, (types.int64,),
+                                        flags=force_parallel_flags)
+
+            # grab the various parts out the output
+            output = out.getvalue().split('\n')
+            parallel_loop_output = \
+                [x for x in output if 'is produced from pattern' in x]
+            fuse_output = \
+                [x for x in output if 'is fused into' in x]
+            after_fusion_output = \
+                [x for x in output if 'After fusion, function' in x]
+
+            # Parfor's have a shared state index, grab the current value
+            # as it will be used as an offset for all loop messages
+            parfor_state = int(re.compile(r'#([0-9]+)').search(
+                parallel_loop_output[0]).group(1))
+            bounds = range(parfor_state,
+                           parfor_state + len(parallel_loop_output))
+
+            # Check the Parallel for-loop <index> is produced from <pattern>
+            # works first
+            pattern = ("('ones function', 'NumPy mapping')",
+                       ('prange', 'user', ''))
+            fmt = 'Parallel for-loop #{} is produced from pattern \'{}\' at'
+            for i, trials, lpattern in zip(bounds, parallel_loop_output,
+                                           pattern):
+                to_match = fmt.format(i, lpattern)
+                self.assertIn(to_match, trials)
+
+            # Check the fusion statements are correct
+            pattern = (parfor_state + 1, parfor_state + 0)
+            fmt = 'Parallel for-loop #{} is fused into for-loop #{}.'
+            for trials in fuse_output:
+                to_match = fmt.format(*pattern)
+                self.assertIn(to_match, trials)
+
+            # Check the post fusion statements are correct
+            pattern = (supported_parfor.__name__, 1, set([parfor_state]))
+            fmt = 'After fusion, function {} has {} parallel for-loop(s) #{}.'
+            for trials in after_fusion_output:
+                to_match = fmt.format(*pattern)
+                self.assertIn(to_match, trials)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_debuginfo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_debuginfo.py
new file mode 100644
index 000000000..138602105
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_debuginfo.py
@@ -0,0 +1,751 @@
+from collections import namedtuple
+import inspect
+import re
+import numpy as np
+import math
+from textwrap import dedent
+import unittest
+import warnings
+
+from numba.tests.support import (TestCase, override_config,
+                                 ignore_internal_warnings)
+from numba import jit, njit
+from numba.core import types
+from numba.core.datamodel import default_manager
+from numba.core.errors import NumbaDebugInfoWarning
+import llvmlite.binding as llvm
+
+#NOTE: These tests are potentially sensitive to changes in SSA or lowering
+# behaviour and may need updating should changes be made to the corresponding
+# algorithms.
+
+
+class TestDebugInfo(TestCase):
+    """
+    These tests only checks the compiled assembly for debuginfo.
+    """
+
+    def _getasm(self, fn, sig):
+        fn.compile(sig)
+        return fn.inspect_asm(sig)
+
+    def _check(self, fn, sig, expect):
+        asm = self._getasm(fn, sig=sig)
+        m = re.search(r"\.section.+debug", asm, re.I)
+        got = m is not None
+        self.assertEqual(expect, got, msg='debug info not found in:\n%s' % asm)
+
+    def test_no_debuginfo_in_asm(self):
+        @jit(nopython=True, debug=False)
+        def foo(x):
+            return x
+
+        self._check(foo, sig=(types.int32,), expect=False)
+
+    def test_debuginfo_in_asm(self):
+        @jit(nopython=True, debug=True)
+        def foo(x):
+            return x
+
+        self._check(foo, sig=(types.int32,), expect=True)
+
+    def test_environment_override(self):
+        with override_config('DEBUGINFO_DEFAULT', 1):
+            # Using default value
+            @jit(nopython=True)
+            def foo(x):
+                return x
+            self._check(foo, sig=(types.int32,), expect=True)
+
+            # User override default
+            @jit(nopython=True, debug=False)
+            def bar(x):
+                return x
+            self._check(bar, sig=(types.int32,), expect=False)
+
+    def test_llvm_inliner_flag_conflict(self):
+        # bar will be marked as 'alwaysinline', but when DEBUGINFO_DEFAULT is
+        # set functions are marked as 'noinline' this results in a conflict.
+        # baz will be marked as 'noinline' as a result of DEBUGINFO_DEFAULT
+
+        @njit(forceinline=True)
+        def bar(x):
+            return math.sin(x)
+
+        @njit(forceinline=False)
+        def baz(x):
+            return math.cos(x)
+
+        @njit
+        def foo(x):
+            a = bar(x)
+            b = baz(x)
+            return a, b
+
+        # check it compiles
+        with override_config('DEBUGINFO_DEFAULT', 1):
+            result = foo(np.pi)
+
+        self.assertPreciseEqual(result, foo.py_func(np.pi))
+
+        # check the LLVM IR has bar marked as 'alwaysinline' and baz as noinline
+        full_ir = foo.inspect_llvm(foo.signatures[0])
+        module = llvm.parse_assembly(full_ir)
+        name = foo.overloads[foo.signatures[0]].fndesc.mangled_name
+        funcs = [x for x in module.functions if x.name == name]
+        self.assertEqual(len(funcs), 1)
+        func = funcs[0]
+
+        # find the function calls and save the associated statements
+        f_names = []
+        for blk in func.blocks:
+            for stmt in blk.instructions:
+                if stmt.opcode == 'call':
+                    # stmt.function.name  This is the function being called
+                    f_names.append(str(stmt).strip())
+
+        # Need to check there's two specific things in the calls in the IR
+        # 1. a call to the llvm.sin.f64 intrinsic, this is from the inlined bar
+        # 2. a call to the baz function, this is from the noinline baz
+        found_sin = False
+        found_baz = False
+        baz_name = baz.overloads[baz.signatures[0]].fndesc.mangled_name
+        for x in f_names:
+            if not found_sin and re.match('.*llvm.sin.f64.*', x):
+                found_sin = True
+            if not found_baz and re.match(f'.*{baz_name}.*', x):
+                found_baz = True
+
+        self.assertTrue(found_sin)
+        self.assertTrue(found_baz)
+
+
+class TestDebugInfoEmission(TestCase):
+    """ Tests that debug info is emitted correctly.
+    """
+
+    _NUMBA_OPT_0_ENV = {'NUMBA_OPT': '0'}
+
+    def _get_llvmir(self, fn, sig):
+        with override_config('OPT', 0):
+            fn.compile(sig)
+            return fn.inspect_llvm(sig)
+
+    def _get_metadata(self, fn, sig):
+        ll = self._get_llvmir(fn, sig).splitlines()
+        meta_re = re.compile(r'![0-9]+ =.*')
+        metadata = []
+        for line in ll:
+            if meta_re.match(line):
+                metadata.append(line)
+        return metadata
+
+    def _get_metadata_map(self, metadata):
+        """Gets the map of DI label to md, e.g.
+        '!33' -> '!{!"branch_weights", i32 1, i32 99}'
+        """
+        metadata_definition_map = dict()
+        meta_definition_split = re.compile(r'(![0-9]+) = (.*)')
+        for line in metadata:
+            matched = meta_definition_split.match(line)
+            if matched:
+                dbg_val, info = matched.groups()
+                metadata_definition_map[dbg_val] = info
+        return metadata_definition_map
+
+    def _get_lines_from_debuginfo(self, metadata):
+        # Get the lines contained in the debug info
+        md_def_map = self._get_metadata_map(metadata)
+
+        lines = set()
+        for md in md_def_map.values():
+            m = re.match(r"!DILocation\(line: (\d+),", md)
+            if m:
+                ln = int(m.group(1))
+                lines.add(ln)
+        return lines
+
+    def test_DW_LANG(self):
+
+        @njit(debug=True)
+        def foo():
+            pass
+
+        metadata = self._get_metadata(foo, sig=())
+        DICompileUnit = metadata[0]
+        self.assertEqual('!0', DICompileUnit[:2])
+        self.assertIn('!DICompileUnit(language: DW_LANG_C_plus_plus',
+                      DICompileUnit)
+        self.assertIn('producer: "clang (Numba)"', DICompileUnit)
+
+    def test_DILocation(self):
+        """ Tests that DILocation information is reasonable.
+        """
+        @njit(debug=True, error_model='numpy')
+        def foo(a):
+            b = a + 1.23
+            c = a * 2.34
+            d = b / c
+            print(d)
+            return d
+
+        # the above produces LLVM like:
+        # define function() {
+        # entry:
+        #   alloca
+        #   store 0 to alloca
+        #   <arithmetic for doing the operations on b, c, d>
+        #   setup for print
+        #   branch
+        # other_labels:
+        # ... <elided>
+        # }
+        #
+        # The following checks that:
+        # * the alloca and store have no !dbg
+        # * the arithmetic occurs in the order defined and with !dbg
+        # * that the !dbg entries are monotonically increasing in value with
+        #   source line number
+
+        sig = (types.float64,)
+        metadata = self._get_metadata(foo, sig=sig)
+        full_ir = self._get_llvmir(foo, sig=sig)
+
+        module = llvm.parse_assembly(full_ir)
+
+        name = foo.overloads[foo.signatures[0]].fndesc.mangled_name
+        funcs = [x for x in module.functions if x.name == name]
+        self.assertEqual(len(funcs), 1)
+        func = funcs[0]
+        blocks = [x for x in func.blocks]
+        self.assertGreater(len(blocks), 1)
+        block = blocks[0]
+
+        # Find non-call instr and check the sequence is as expected
+        instrs = [x for x in block.instructions if x.opcode != 'call']
+        op_seq = [x.opcode for x in instrs]
+        op_expect = ('fadd', 'fmul', 'fdiv')
+        self.assertIn(''.join(op_expect), ''.join(op_seq))
+
+        # Parse out metadata from end of each line, check it monotonically
+        # ascends with LLVM source line. Also store all the dbg references,
+        # these will be checked later.
+        line2dbg = set()
+        re_dbg_ref = re.compile(r'.*!dbg (![0-9]+).*$')
+        found = -1
+        for instr in instrs:
+            inst_as_str = str(instr)
+            matched = re_dbg_ref.match(inst_as_str)
+            if not matched:
+                # if there's no match, ensure it is one of alloca or store,
+                # it's important that the zero init/alloca instructions have
+                # no dbg data
+                accepted = ('alloca ', 'store ')
+                self.assertTrue(any([x in inst_as_str for x in accepted]))
+                continue
+            groups = matched.groups()
+            self.assertEqual(len(groups), 1)
+            dbg_val = groups[0]
+            int_dbg_val = int(dbg_val[1:])
+            if found >= 0:
+                self.assertTrue(int_dbg_val >= found)
+            found = int_dbg_val
+            # some lines will alias dbg info, this is fine, it's only used to
+            # make sure that the line numbers are correct WRT python
+            line2dbg.add(dbg_val)
+
+        pysrc, pysrc_line_start = inspect.getsourcelines(foo)
+
+        # build a map of dbg reference to DI* information
+        metadata_definition_map = self._get_metadata_map(metadata)
+
+        # Pull out metadata entries referred to by the llvm line end !dbg
+        # check they match the python source, the +2 is for the @njit decorator
+        # and the function definition line.
+        offsets = [0,  # b = a + 1
+                   1,  # a * 2.34
+                   2,  # d = b / c
+                   3,  # print(d)
+                   ]
+        pyln_range = [pysrc_line_start + 2 + x for x in offsets]
+
+        # do the check
+        for (k, line_no) in zip(sorted(line2dbg, key=lambda x: int(x[1:])),
+                                pyln_range):
+            dilocation_info = metadata_definition_map[k]
+            self.assertIn(f'line: {line_no}', dilocation_info)
+
+        # Check that variable "a" is declared as on the same line as function
+        # definition.
+        expr = r'.*!DILocalVariable\(name: "a",.*line: ([0-9]+),.*'
+        match_local_var_a = re.compile(expr)
+        for entry in metadata_definition_map.values():
+            matched = match_local_var_a.match(entry)
+            if matched:
+                groups = matched.groups()
+                self.assertEqual(len(groups), 1)
+                dbg_line = int(groups[0])
+                # +1 for the decorator.
+                # Recall that Numba's DWARF refers to the "def" line, but
+                # `inspect` uses the decorator as the first line.
+                defline = pysrc_line_start + 1
+                self.assertEqual(dbg_line, defline)
+                break
+        else:
+            self.fail('Assertion on DILocalVariable not made')
+
+    @TestCase.run_test_in_subprocess(envvars=_NUMBA_OPT_0_ENV)
+    def test_DILocation_entry_blk(self):
+        # Needs a subprocess as jitting literally anything at any point in the
+        # lifetime of the process ends up with a codegen at opt 3. This is not
+        # amenable to this test!
+        # This test relies on the CFG not being simplified as it checks the jump
+        # from the entry block to the first basic block. Force OPT as 0, if set
+        # via the env var the targetmachine and various pass managers all end up
+        # at OPT 0 and the IR is minimally transformed prior to lowering to ELF.
+        #
+        # This tests that the unconditional jump emitted at the tail of
+        # the entry block has no debug metadata associated with it. In practice,
+        # if debug metadata is associated with it, it manifests as the
+        # prologue_end being associated with the end_sequence or similar (due to
+        # the way code gen works for the entry block).
+
+        @njit(debug=True)
+        def foo(a):
+            return a + 1
+        foo(123)
+
+        full_ir = foo.inspect_llvm(foo.signatures[0])
+        # The above produces LLVM like:
+        #
+        # define function() {
+        # entry:
+        #   alloca
+        #   store 0 to alloca
+        #   unconditional jump to body:
+        #
+        # body:
+        # ... <elided>
+        # }
+
+        module = llvm.parse_assembly(full_ir)
+        name = foo.overloads[foo.signatures[0]].fndesc.mangled_name
+        funcs = [x for x in module.functions if x.name == name]
+        self.assertEqual(len(funcs), 1)
+        func = funcs[0]
+        blocks = [x for x in func.blocks]
+        self.assertEqual(len(blocks), 2)
+        entry_block, body_block = blocks
+
+        # Assert that the tail of the entry block is an unconditional jump to
+        # the body block and that the jump has no associated debug info.
+        entry_instr = [x for x in entry_block.instructions]
+        ujmp = entry_instr[-1]
+        self.assertEqual(ujmp.opcode, 'br')
+        ujmp_operands = [x for x in ujmp.operands]
+        self.assertEqual(len(ujmp_operands), 1)
+        target_data = ujmp_operands[0]
+        target = str(target_data).split(':')[0].strip()
+        # check the unconditional jump target is to the body block
+        self.assertEqual(target, body_block.name)
+        # check the uncondition jump instr itself has no metadata
+        self.assertTrue(str(ujmp).endswith(target))
+
+    @TestCase.run_test_in_subprocess(envvars=_NUMBA_OPT_0_ENV)
+    def test_DILocation_decref(self):
+        """ This tests that decref's generated from `ir.Del`s as variables go
+        out of scope do not have debuginfo associated with them (the location of
+        `ir.Del` is an implementation detail).
+        """
+
+        @njit(debug=True)
+        def sink(*x):
+            pass
+
+        # This function has many decrefs!
+        @njit(debug=True)
+        def foo(a):
+            x = (a, a)
+            if a[0] == 0:
+                sink(x)
+                return 12
+            z = x[0][0]
+            return z
+
+        sig = (types.float64[::1],)
+        full_ir = self._get_llvmir(foo, sig=sig)
+
+        # make sure decref lines end with `meminfo.<number>)` without !dbg info.
+        count = 0
+        for line in full_ir.splitlines():
+            line_stripped = line.strip()
+            if line_stripped.startswith('call void @NRT_decref'):
+                self.assertRegex(line, r'.*meminfo\.[0-9]+\)$')
+                count += 1
+        self.assertGreater(count, 0) # make sure there were some decrefs!
+
+    def test_DILocation_undefined(self):
+        """ Tests that DILocation information for undefined vars is associated
+        with the line of the function definition (so it ends up in the prologue)
+        """
+        @njit(debug=True)
+        def foo(n):
+            if n:
+                if n > 0:
+                    c = 0
+                return c
+            else:
+                # variable c is not defined in this branch
+                c += 1
+                return c
+
+        sig = (types.intp,)
+        metadata = self._get_metadata(foo, sig=sig)
+        pysrc, pysrc_line_start = inspect.getsourcelines(foo)
+        # Looks for versions of variable "c" and captures the line number
+        expr = r'.*!DILocalVariable\(name: "c\$?[0-9]?",.*line: ([0-9]+),.*'
+        matcher = re.compile(expr)
+        associated_lines = set()
+        for md in metadata:
+            match = matcher.match(md)
+            if match:
+                groups = match.groups()
+                self.assertEqual(len(groups), 1)
+                associated_lines.add(int(groups[0]))
+        # 3 versions of 'c': `c = 0`, `return c`, `c+=1`
+        self.assertEqual(len(associated_lines), 3)
+        self.assertIn(pysrc_line_start, associated_lines)
+
+    def test_DILocation_versioned_variables(self):
+        """ Tests that DILocation information for versions of variables matches
+        up to their definition site."""
+        # Note: there's still something wrong in the DI/SSA naming, the ret c is
+        # associated with the logically first definition.
+
+        @njit(debug=True)
+        def foo(n):
+            if n:
+                c = 5
+            else:
+                c = 1
+            # prevents inline of return on py310
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return c
+
+        sig = (types.intp,)
+        metadata = self._get_metadata(foo, sig=sig)
+        pysrc, pysrc_line_start = inspect.getsourcelines(foo)
+
+        # Looks for SSA versioned names i.e. <basename>$<version id> of the
+        # variable 'c' and captures the line
+        expr = r'.*!DILocalVariable\(name: "c\$[0-9]?",.*line: ([0-9]+),.*'
+        matcher = re.compile(expr)
+        associated_lines = set()
+        for md in metadata:
+            match = matcher.match(md)
+            if match:
+                groups = match.groups()
+                self.assertEqual(len(groups), 1)
+                associated_lines.add(int(groups[0]))
+        self.assertEqual(len(associated_lines), 2) # 2 SSA versioned names 'c'
+
+        # Now find the `c = ` lines in the python source
+        py_lines = set()
+        for ix, pyln in enumerate(pysrc):
+            if 'c = ' in pyln:
+                py_lines.add(ix + pysrc_line_start)
+        self.assertEqual(len(py_lines), 2) # 2 assignments to c
+
+        # check that the DILocation from the DI for `c` matches the python src
+        self.assertEqual(associated_lines, py_lines)
+
+    def test_numeric_scalars(self):
+        """ Tests that dwarf info is correctly emitted for numeric scalars."""
+
+        DI = namedtuple('DI', 'name bits encoding')
+
+        type_infos = {np.float32: DI("float32", 32, "DW_ATE_float"),
+                      np.float64: DI("float64", 64, "DW_ATE_float"),
+                      np.int8: DI("int8", 8, "DW_ATE_signed"),
+                      np.int16: DI("int16", 16, "DW_ATE_signed"),
+                      np.int32: DI("int32", 32, "DW_ATE_signed"),
+                      np.int64: DI("int64", 64, "DW_ATE_signed"),
+                      np.uint8: DI("uint8", 8, "DW_ATE_unsigned"),
+                      np.uint16: DI("uint16", 16, "DW_ATE_unsigned"),
+                      np.uint32: DI("uint32", 32, "DW_ATE_unsigned"),
+                      np.uint64: DI("uint64", 64, "DW_ATE_unsigned"),
+                      np.complex64: DI("complex64", 64,
+                                       "DW_TAG_structure_type"),
+                      np.complex128: DI("complex128", 128,
+                                        "DW_TAG_structure_type"),}
+
+        for ty, dwarf_info in type_infos.items():
+
+            @njit(debug=True)
+            def foo():
+                a = ty(10)
+                return a
+
+            metadata = self._get_metadata(foo, sig=())
+            metadata_definition_map = self._get_metadata_map(metadata)
+
+            for k, v in metadata_definition_map.items():
+                if 'DILocalVariable(name: "a"' in v:
+                    lvar = metadata_definition_map[k]
+                    break
+            else:
+                assert 0, "missing DILocalVariable 'a'"
+
+            type_marker = re.match('.*type: (![0-9]+).*', lvar).groups()[0]
+            type_decl = metadata_definition_map[type_marker]
+
+            if 'DW_ATE' in dwarf_info.encoding:
+                expected = (f'!DIBasicType(name: "{dwarf_info.name}", '
+                            f'size: {dwarf_info.bits}, '
+                            f'encoding: {dwarf_info.encoding})')
+                self.assertEqual(type_decl, expected)
+            else: # numerical complex type
+                # Don't match the whole string, just the known parts
+                raw_flt = 'float' if dwarf_info.bits == 64 else 'double'
+                expected = (f'distinct !DICompositeType('
+                            f'tag: {dwarf_info.encoding}, '
+                            f'name: "{dwarf_info.name} '
+                            f'({{{raw_flt}, {raw_flt}}})", '
+                            f'size: {dwarf_info.bits}')
+                self.assertIn(expected, type_decl)
+
+    def test_arrays(self):
+
+        @njit(debug=True)
+        def foo():
+            a = np.ones((2, 3), dtype=np.float64)
+            return a
+
+        metadata = self._get_metadata(foo, sig=())
+        metadata_definition_map = self._get_metadata_map(metadata)
+
+        for k, v in metadata_definition_map.items():
+            if 'DILocalVariable(name: "a"' in v:
+                lvar = metadata_definition_map[k]
+                break
+        else:
+            assert 0, "missing DILocalVariable 'a'"
+
+        type_marker = re.match('.*type: (![0-9]+).*', lvar).groups()[0]
+        type_decl = metadata_definition_map[type_marker]
+
+        # check type
+        self.assertIn("!DICompositeType(tag: DW_TAG_structure_type", type_decl)
+        # check name encoding
+        self.assertIn(f'name: "{str(types.float64[:, ::1])}', type_decl)
+
+        # pop out the "elements" of the composite type
+        match_elements = re.compile(r'.*elements: (![0-9]+),.*')
+        elem_matches = match_elements.match(type_decl).groups()
+        self.assertEqual(len(elem_matches), 1)
+        elem_match = elem_matches[0]
+        # The match should be something like, it's the elements from an array
+        # data model.
+        # !{!35, !36, !37, !39, !40, !43, !45}'
+        struct_markers = metadata_definition_map[elem_match]
+        struct_pattern = '!{' + '(![0-9]+), ' * 6 + '(![0-9]+)}'
+        match_struct = re.compile(struct_pattern)
+        struct_member_matches = match_struct.match(struct_markers).groups()
+        self.assertIsNotNone(struct_member_matches is not None)
+        data_model = default_manager.lookup(types.float64[:, ::1])
+        self.assertEqual(len(struct_member_matches), len(data_model._fields))
+
+        ptr_size = types.intp.bitwidth
+        ptr_re = (r'!DIDerivedType\(tag: DW_TAG_pointer_type, '
+                  rf'baseType: ![0-9]+, size: {ptr_size}\)')
+        int_re = (rf'!DIBasicType\(name: "int{ptr_size}", size: {ptr_size}, '
+                  r'encoding: DW_ATE_signed\)')
+        utuple_re = (r'!DICompositeType\(tag: DW_TAG_array_type, '
+                     rf'name: "UniTuple\(int{ptr_size} x 2\) '
+                     rf'\(\[2 x i{ptr_size}\]\)", baseType: ![0-9]+, '
+                     rf'size: {2 * ptr_size}, elements: ![0-9]+, '
+                     rf'identifier: "\[2 x i{ptr_size}\]"\)')
+        expected = {'meminfo': ptr_re,
+                    'parent': ptr_re,
+                    'nitems': int_re,
+                    'itemsize': int_re,
+                    'data': ptr_re,
+                    'shape': utuple_re,
+                    'strides': utuple_re}
+
+        # look for `baseType: <>` for the type
+        base_type_pattern = r'!DIDerivedType\(.*, baseType: (![0-9]+),.*'
+        base_type_matcher = re.compile(base_type_pattern)
+
+        for ix, field in enumerate(data_model._fields):
+            derived_type = metadata_definition_map[struct_member_matches[ix]]
+            self.assertIn("DIDerivedType", derived_type)
+            self.assertIn(f'name: "{field}"', derived_type)
+            base_type_match = base_type_matcher.match(derived_type)
+            base_type_matches = base_type_match.groups()
+            self.assertEqual(len(base_type_matches), 1)
+            base_type_marker = base_type_matches[0]
+            data_type = metadata_definition_map[base_type_marker]
+            self.assertRegex(data_type, expected[field])
+
+    def test_debug_optnone(self):
+        def get_debug_lines(fn):
+            metadata = self._get_metadata(fn, fn.signatures[0])
+            lines = self._get_lines_from_debuginfo(metadata)
+            return lines
+
+        def get_func_attrs(fn):
+            cres = fn.overloads[fn.signatures[0]]
+            lib = cres.library
+            fn = lib._final_module.get_function(cres.fndesc.mangled_name)
+            attrs = set(b' '.join(fn.attributes).split())
+            return attrs
+
+        def foo():
+            n = 10
+            c = 0
+            for i in range(n):
+                c += i
+            return c
+
+        foo_debug = njit(debug=True)(foo)
+        foo_debug_optnone = njit(debug=True, _dbg_optnone=True)(foo)
+        foo_debug_optnone_inline = njit(debug=True, _dbg_optnone=True,
+                                        forceinline=True)(foo)
+
+        firstline = foo.__code__.co_firstlineno
+
+        expected_info = {}
+        expected_info[foo_debug] = dict(
+            # just the dummy line-0 and the line of the return statement
+            lines={0, firstline + 5},
+            must_have_attrs=set(),
+            must_not_have_attrs=set([b"optnone"]),
+        )
+        expected_info[foo_debug_optnone] = dict(
+            # all the lines should be included
+            lines=set(range(firstline + 1, firstline + 6)),
+            must_have_attrs=set([b"optnone"]),
+            must_not_have_attrs=set(),
+        )
+        expected_info[foo_debug_optnone_inline] = dict(
+            # optnone=True is overridden by forceinline, so this looks like the
+            # foo_debug version
+            lines={0, firstline + 5},
+            must_have_attrs=set([b"alwaysinline"]),
+            must_not_have_attrs=set([b"optnone"]),
+        )
+
+        expected_ret = foo()
+
+        for udt, expected in expected_info.items():
+            with self.subTest(udt.targetoptions):
+                got = udt()
+                self.assertEqual(got, expected_ret)
+
+                # Compare the line locations in the debug info.
+                self.assertEqual(get_debug_lines(udt), expected["lines"])
+
+                # Check for attributes on the LLVM function
+                attrs = get_func_attrs(udt)
+                must_have = expected["must_have_attrs"]
+                self.assertEqual(attrs & must_have, must_have)
+                must_not_have = expected["must_not_have_attrs"]
+                self.assertFalse(attrs & must_not_have)
+
+    def test_omitted_arg(self):
+        # See issue 7726
+        @njit(debug=True)
+        def foo(missing=None):
+            pass
+
+        # check that it will actually compile (verifies DI emission is ok)
+        with override_config('DEBUGINFO_DEFAULT', 1):
+            foo()
+
+        metadata = self._get_metadata(foo, sig=(types.Omitted(None),))
+        metadata_definition_map = self._get_metadata_map(metadata)
+
+        # Find DISubroutineType
+        tmp_disubr = []
+        for md in metadata:
+            if "DISubroutineType" in md:
+                tmp_disubr.append(md)
+        self.assertEqual(len(tmp_disubr), 1)
+        disubr = tmp_disubr.pop()
+
+        disubr_matched = re.match(r'.*!DISubroutineType\(types: ([!0-9]+)\)$',
+                                  disubr)
+        self.assertIsNotNone(disubr_matched)
+        disubr_groups = disubr_matched.groups()
+        self.assertEqual(len(disubr_groups), 1)
+        disubr_meta = disubr_groups[0]
+
+        # Find the types in the DISubroutineType arg list
+        disubr_types = metadata_definition_map[disubr_meta]
+        disubr_types_matched = re.match(r'!{(.*)}', disubr_types)
+        self.assertIsNotNone(disubr_matched)
+        disubr_types_groups = disubr_types_matched.groups()
+        self.assertEqual(len(disubr_types_groups), 1)
+
+        # fetch out and assert the last argument type, should be void *
+        md_fn_arg = [x.strip() for x in disubr_types_groups[0].split(',')][-1]
+        arg_ty = metadata_definition_map[md_fn_arg]
+        expected_arg_ty = (r'^.*!DICompositeType\(tag: DW_TAG_structure_type, '
+                           r'name: "Anonymous struct \({}\)", elements: '
+                           r'(![0-9]+), identifier: "{}"\)')
+        self.assertRegex(arg_ty, expected_arg_ty)
+        md_base_ty = re.match(expected_arg_ty, arg_ty).groups()[0]
+        base_ty = metadata_definition_map[md_base_ty]
+        # expect ir.LiteralStructType([])
+        self.assertEqual(base_ty, ('!{}'))
+
+    def test_missing_source(self):
+        strsrc = """
+        def foo():
+            return 1
+        """
+        l = dict()
+        exec(dedent(strsrc), {}, l)
+        foo = njit(debug=True)(l['foo'])
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaDebugInfoWarning)
+            ignore_internal_warnings()
+            foo()
+
+        self.assertEqual(len(w), 1)
+        found = w[0]
+        self.assertEqual(found.category, NumbaDebugInfoWarning)
+        msg = str(found.message)
+        # make sure the warning contains the right message
+        self.assertIn('Could not find source for function', msg)
+        # and refers to the offending function
+        self.assertIn(str(foo.py_func), msg)
+
+    def test_irregularly_indented_source(self):
+
+        @njit(debug=True)
+        def foo():
+# NOTE: THIS COMMENT MUST START AT COLUMN 0 FOR THIS SAMPLE CODE TO BE VALID # noqa: E115, E501
+            return 1
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaDebugInfoWarning)
+            ignore_internal_warnings()
+            foo()
+
+        # No warnings
+        self.assertEqual(len(w), 0)
+
+        metadata = self._get_metadata(foo, foo.signatures[0])
+        lines = self._get_lines_from_debuginfo(metadata)
+        # Only one line
+        self.assertEqual(len(lines), 1)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_deprecations.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_deprecations.py
new file mode 100644
index 000000000..70689e6c6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_deprecations.py
@@ -0,0 +1,59 @@
+import warnings
+from numba import jit
+from numba.core.errors import (NumbaDeprecationWarning,
+                          NumbaPendingDeprecationWarning, NumbaWarning)
+import unittest
+
+
+class TestDeprecation(unittest.TestCase):
+
+    def check_warning(self, warnings, expected_str, category):
+        self.assertEqual(len(warnings), 1)
+        self.assertEqual(warnings[0].category, category)
+        self.assertIn(expected_str, str(warnings[0].message))
+        self.assertIn("https://numba.readthedocs.io", str(warnings[0].message))
+
+    def test_jitfallback(self):
+        # tests that @jit falling back to object mode raises a
+        # NumbaDeprecationWarning
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("ignore", category=NumbaWarning)
+            warnings.simplefilter("always", category=NumbaDeprecationWarning)
+
+            def foo():
+                return []  # empty list cannot be typed
+            jit(foo)()
+
+            msg = ("Fall-back from the nopython compilation path to the object "
+                   "mode compilation path")
+            self.check_warning(w, msg, NumbaDeprecationWarning)
+
+    def test_reflection_of_mutable_container(self):
+        # tests that reflection in list/set warns
+        def foo_list(a):
+            return a.append(1)
+
+        def foo_set(a):
+            return a.add(1)
+
+        for f in [foo_list, foo_set]:
+            container = f.__name__.strip('foo_')
+            inp = eval(container)([10, ])
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter("ignore", category=NumbaWarning)
+                warnings.simplefilter("always",
+                                      category=NumbaPendingDeprecationWarning)
+                jit(nopython=True)(f)(inp)
+                self.assertEqual(len(w), 1)
+                self.assertEqual(w[0].category, NumbaPendingDeprecationWarning)
+                warn_msg = str(w[0].message)
+                msg = ("Encountered the use of a type that is scheduled for "
+                       "deprecation")
+                self.assertIn(msg, warn_msg)
+                msg = ("\'reflected %s\' found for argument" % container)
+                self.assertIn(msg, warn_msg)
+                self.assertIn("https://numba.readthedocs.io", warn_msg)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dictimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dictimpl.py
new file mode 100644
index 000000000..0bcbc542d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dictimpl.py
@@ -0,0 +1,651 @@
+"""
+Testing C implementation of the numba dictionary
+"""
+
+import ctypes
+import random
+
+from numba.tests.support import TestCase
+from numba import generated_jit, _helperlib, jit, typed, types
+from numba.core.config import IS_32BITS
+from numba.core.datamodel.models import UniTupleModel
+from numba.extending import register_model, typeof_impl, unbox
+
+
+DKIX_EMPTY = -1
+
+
+ALIGN = 4 if IS_32BITS else 8
+
+
+class Dict(object):
+    """A wrapper around the C-API to provide a minimal dictionary object for
+    testing.
+    """
+    def __init__(self, tc, keysize, valsize):
+        """
+        Parameters
+        ----------
+        tc : TestCase instance
+        keysize : int
+            byte size for the key
+        valsize : int
+            byte size for the value
+        """
+        self.tc = tc
+        self.keysize = keysize
+        self.valsize = valsize
+        self.dp = self.dict_new_minsize(keysize, valsize)
+
+    def __del__(self):
+        self.tc.numba_dict_free(self.dp)
+
+    def __len__(self):
+        return self.dict_length()
+
+    def __setitem__(self, k, v):
+        bk = bytes(k.encode())
+        bv = bytes(v.encode())
+        self.tc.assertEqual(len(bk), self.keysize)
+        self.tc.assertEqual(len(bv), self.valsize)
+        self.dict_insert(bk, bv)
+
+    def __getitem__(self, k):
+        bk = bytes(k.encode())
+        self.tc.assertEqual(len(bk), self.keysize)
+        ix, old = self.dict_lookup(bk)
+        if ix == DKIX_EMPTY:
+            raise KeyError
+        else:
+            return old.decode()
+
+    def __delitem__(self, k):
+        bk = bytes(k.encode())
+        self.tc.assertEqual(len(bk), self.keysize)
+        if not self.dict_delitem(bk):
+            raise KeyError(k)
+
+    def get(self, k):
+        try:
+            return self[k]
+        except KeyError:
+            return
+
+    def items(self):
+        return DictIter(self)
+
+    def popitem(self):
+        k, v = self.dict_popitem()
+        return k.decode(), v.decode()
+
+    #
+    # The methods below are higher-level wrappers for the C-API
+    #
+
+    def dict_new_minsize(self, key_size, val_size):
+        dp = ctypes.c_void_p()
+        status = self.tc.numba_dict_new_minsize(
+            ctypes.byref(dp), key_size, val_size,
+        )
+        self.tc.assertEqual(status, 0)
+        return dp
+
+    def dict_length(self):
+        return self.tc.numba_dict_length(self.dp)
+
+    def dict_insert(self, key_bytes, val_bytes):
+        hashval = hash(key_bytes)
+        status = self.tc.numba_dict_insert_ez(
+            self.dp, key_bytes, hashval, val_bytes,
+        )
+        self.tc.assertGreaterEqual(status, 0)
+
+    def dict_lookup(self, key_bytes):
+        hashval = hash(key_bytes)
+        oldval_bytes = ctypes.create_string_buffer(self.valsize)
+        ix = self.tc.numba_dict_lookup(
+            self.dp, key_bytes, hashval, oldval_bytes,
+        )
+        self.tc.assertGreaterEqual(ix, DKIX_EMPTY)
+        return ix, oldval_bytes.value
+
+    def dict_delitem(self, key_bytes):
+        ix, oldval = self.dict_lookup(key_bytes)
+        if ix == DKIX_EMPTY:
+            return False
+        hashval = hash(key_bytes)
+        status = self.tc.numba_dict_delitem(self.dp, hashval, ix)
+        self.tc.assertEqual(status, 0)
+        return True
+
+    def dict_popitem(self):
+        key_bytes = ctypes.create_string_buffer(self.keysize)
+        val_bytes = ctypes.create_string_buffer(self.valsize)
+        status = self.tc.numba_dict_popitem(self.dp, key_bytes, val_bytes)
+        if status != 0:
+            if status == -4:
+                raise KeyError('popitem(): dictionary is empty')
+            else:
+                self.tc._fail('Unknown')
+        return key_bytes.value, val_bytes.value
+
+    def dict_iter(self, itptr):
+        self.tc.numba_dict_iter(itptr, self.dp)
+
+    def dict_iter_next(self, itptr):
+        bk = ctypes.c_void_p(0)
+        bv = ctypes.c_void_p(0)
+        status = self.tc.numba_dict_iter_next(
+            itptr, ctypes.byref(bk), ctypes.byref(bv),
+        )
+        if status == -2:
+            raise ValueError('dictionary mutated')
+        elif status == -3:
+            return
+        else:
+            self.tc.assertGreaterEqual(status, 0)
+
+            # Check the alignment of the key-value in the entries.
+            # We know we are getting the pointers to data in the entries.
+
+            self.tc.assertEqual(bk.value % ALIGN, 0, msg='key not aligned')
+            self.tc.assertEqual(bv.value % ALIGN, 0, msg='val not aligned')
+
+            key = (ctypes.c_char * self.keysize).from_address(bk.value)
+            val = (ctypes.c_char * self.valsize).from_address(bv.value)
+            return key.value, val.value
+
+
+class DictIter(object):
+    """A iterator for the `Dict.items()`.
+
+    Only the `.items()` is needed.  `.keys` and `.values` can be trivially
+    implemented on the `.items` iterator.
+    """
+    def __init__(self, parent):
+        self.parent = parent
+        itsize = self.parent.tc.numba_dict_iter_sizeof()
+        self.it_state_buf = (ctypes.c_char_p * itsize)(0)
+        self.it = ctypes.cast(self.it_state_buf, ctypes.c_void_p)
+        self.parent.dict_iter(self.it)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        out = self.parent.dict_iter_next(self.it)
+        if out is None:
+            raise StopIteration
+        else:
+            k, v = out
+            return k.decode(), v.decode()
+
+    next = __next__    # needed for py2 only
+
+
+class Parametrized(tuple):
+    """supporting type for TestDictImpl.test_parametrized_types
+    needs to be global to be cacheable"""
+    def __init__(self, tup):
+        assert all(isinstance(v, str) for v in tup)
+
+
+class ParametrizedType(types.Type):
+    """this is essentially UniTuple(unicode_type, n)
+    BUT type name is the same for all n"""
+
+    def __init__(self, value):
+        super(ParametrizedType, self).__init__('ParametrizedType')
+        self.dtype = types.unicode_type
+        self.n = len(value)
+
+    @property
+    def key(self):
+        return self.n
+
+    def __len__(self):
+        return self.n
+
+
+class TestDictImpl(TestCase):
+    def setUp(self):
+        """Bind to the c_helper library and provide the ctypes wrapper.
+        """
+        dict_t = ctypes.c_void_p
+        iter_t = ctypes.c_void_p
+        hash_t = ctypes.c_ssize_t
+
+        def wrap(name, restype, argtypes=()):
+            proto = ctypes.CFUNCTYPE(restype, *argtypes)
+            return proto(_helperlib.c_helpers[name])
+
+        # numba_test_dict()
+        self.numba_test_dict = wrap(
+            'test_dict',
+            ctypes.c_int,
+        )
+        # numba_dict_new_minsize(
+        #    NB_Dict **out,
+        #    Py_ssize_t key_size,
+        #    Py_ssize_t val_size
+        # )
+        self.numba_dict_new_minsize = wrap(
+            'dict_new_minsize',
+            ctypes.c_int,
+            [
+                ctypes.POINTER(dict_t),  # out
+                ctypes.c_ssize_t,        # key_size
+                ctypes.c_ssize_t,        # val_size
+            ],
+        )
+        # numba_dict_free(NB_Dict *d)
+        self.numba_dict_free = wrap(
+            'dict_free',
+            None,
+            [dict_t],
+        )
+        # numba_dict_length(NB_Dict *d)
+        self.numba_dict_length = wrap(
+            'dict_length',
+            ctypes.c_ssize_t,
+            [dict_t],
+        )
+        # numba_dict_insert_ez(
+        #     NB_Dict    *d,
+        #     const char *key_bytes,
+        #     Py_hash_t   hash,
+        #     const char *val_bytes,
+        #     )
+        self.numba_dict_insert_ez = wrap(
+            'dict_insert_ez',
+            ctypes.c_int,
+            [
+                dict_t,             # d
+                ctypes.c_char_p,    # key_bytes
+                hash_t,             # hash
+                ctypes.c_char_p,    # val_bytes
+            ],
+        )
+        # numba_dict_lookup(
+        #       NB_Dict *d,
+        #       const char *key_bytes,
+        #       Py_hash_t hash,
+        #       char *oldval_bytes
+        # )
+        self.numba_dict_lookup = wrap(
+            'dict_lookup',
+            ctypes.c_ssize_t,
+            [
+                dict_t,             # d
+                ctypes.c_char_p,    # key_bytes
+                hash_t,             # hash
+                ctypes.c_char_p,    # oldval_bytes
+            ],
+        )
+        # numba_dict_delitem(
+        #     NB_Dict *d,
+        #     Py_hash_t hash,
+        #     Py_ssize_t ix
+        # )
+        self.numba_dict_delitem = wrap(
+            'dict_delitem',
+            ctypes.c_int,
+            [
+                dict_t,             # d
+                hash_t,             # hash
+                ctypes.c_ssize_t,   # ix
+            ],
+        )
+        # numba_dict_popitem(
+        #   NB_Dict *d,
+        #   char *key_bytes,
+        #   char *val_bytes
+        # )
+        self.numba_dict_popitem = wrap(
+            'dict_popitem',
+            ctypes.c_int,
+            [
+                dict_t,             # d
+                ctypes.c_char_p,    # key_bytes
+                ctypes.c_char_p,    # val_bytes
+            ],
+        )
+        # numba_dict_iter_sizeof()
+        self.numba_dict_iter_sizeof = wrap(
+            'dict_iter_sizeof',
+            ctypes.c_size_t,
+        )
+        # numba_dict_iter(
+        #     NB_DictIter *it,
+        #     NB_Dict     *d
+        # )
+        self.numba_dict_iter = wrap(
+            'dict_iter',
+            None,
+            [
+                iter_t,
+                dict_t,
+            ],
+        )
+        # numba_dict_iter_next(
+        #     NB_DictIter *it,
+        #     const char **key_ptr,
+        #     const char **val_ptr
+        # )
+        self.numba_dict_iter_next = wrap(
+            'dict_iter_next',
+            ctypes.c_int,
+            [
+                iter_t,                             # it
+                ctypes.POINTER(ctypes.c_void_p),    # key_ptr
+                ctypes.POINTER(ctypes.c_void_p),    # val_ptr
+            ],
+        )
+
+    def test_simple_c_test(self):
+        # Runs the basic test in C.
+        ret = self.numba_test_dict()
+        self.assertEqual(ret, 0)
+
+    def test_insertion_small(self):
+        # Tests insertion and lookup for a small dict.
+        d = Dict(self, 4, 8)
+        self.assertEqual(len(d), 0)
+        self.assertIsNone(d.get('abcd'))
+
+        # First key
+        d['abcd'] = 'beefcafe'
+        self.assertEqual(len(d), 1)
+        self.assertIsNotNone(d.get('abcd'))
+        self.assertEqual(d['abcd'], 'beefcafe')
+
+        # Duplicated key replaces
+        d['abcd'] = 'cafe0000'
+        self.assertEqual(len(d), 1)
+        self.assertEqual(d['abcd'], 'cafe0000')
+
+        # Second key
+        d['abce'] = 'cafe0001'
+        self.assertEqual(len(d), 2)
+        self.assertEqual(d['abcd'], 'cafe0000')
+        self.assertEqual(d['abce'], 'cafe0001')
+
+        # Third key
+        d['abcf'] = 'cafe0002'
+        self.assertEqual(len(d), 3)
+        self.assertEqual(d['abcd'], 'cafe0000')
+        self.assertEqual(d['abce'], 'cafe0001')
+        self.assertEqual(d['abcf'], 'cafe0002')
+
+    def check_insertion_many(self, nmax):
+        # Helper to test insertion/lookup/resize
+        d = Dict(self, 8, 8)
+
+        def make_key(v):
+            return "key_{:04}".format(v)
+
+        def make_val(v):
+            return "val_{:04}".format(v)
+
+        # Check insert
+        for i in range(nmax):
+            d[make_key(i)] = make_val(i)
+            self.assertEqual(len(d), i + 1)
+
+        # Check lookup
+        for i in range(nmax):
+            self.assertEqual(d[make_key(i)], make_val(i))
+
+    def test_insertion_many(self):
+        # Test insertion for differently sized dict
+        # Around minsize
+        self.check_insertion_many(nmax=7)
+        self.check_insertion_many(nmax=8)
+        self.check_insertion_many(nmax=9)
+        # Around nmax = 32
+        self.check_insertion_many(nmax=31)
+        self.check_insertion_many(nmax=32)
+        self.check_insertion_many(nmax=33)
+        # Around nmax = 1024
+        self.check_insertion_many(nmax=1023)
+        self.check_insertion_many(nmax=1024)
+        self.check_insertion_many(nmax=1025)
+        # Around nmax = 4096
+        self.check_insertion_many(nmax=4095)
+        self.check_insertion_many(nmax=4096)
+        self.check_insertion_many(nmax=4097)
+
+    def test_deletion_small(self):
+        # Test deletion
+        d = Dict(self, 4, 8)
+        self.assertEqual(len(d), 0)
+        self.assertIsNone(d.get('abcd'))
+
+        d['abcd'] = 'cafe0000'
+        d['abce'] = 'cafe0001'
+        d['abcf'] = 'cafe0002'
+
+        self.assertEqual(len(d), 3)
+        self.assertEqual(d['abcd'], 'cafe0000')
+        self.assertEqual(d['abce'], 'cafe0001')
+        self.assertEqual(d['abcf'], 'cafe0002')
+        self.assertEqual(len(d), 3)
+
+        # Delete first item
+        del d['abcd']
+        self.assertIsNone(d.get('abcd'))
+        self.assertEqual(d['abce'], 'cafe0001')
+        self.assertEqual(d['abcf'], 'cafe0002')
+        self.assertEqual(len(d), 2)
+
+        # Delete first item again
+        with self.assertRaises(KeyError):
+            del d['abcd']
+
+        # Delete third
+        del d['abcf']
+        self.assertIsNone(d.get('abcd'))
+        self.assertEqual(d['abce'], 'cafe0001')
+        self.assertIsNone(d.get('abcf'))
+        self.assertEqual(len(d), 1)
+
+        # Delete second
+        del d['abce']
+        self.assertIsNone(d.get('abcd'))
+        self.assertIsNone(d.get('abce'))
+        self.assertIsNone(d.get('abcf'))
+        self.assertEqual(len(d), 0)
+
+    def check_delete_randomly(self, nmax, ndrop, nrefill, seed=0):
+        # Helper to test deletion
+        random.seed(seed)
+
+        d = Dict(self, 8, 8)
+        keys = {}
+
+        def make_key(v):
+            return "k_{:06x}".format(v)
+
+        def make_val(v):
+            return "v_{:06x}".format(v)
+
+        for i in range(nmax):
+            d[make_key(i)] = make_val(i)
+
+        # Fill to nmax
+        for i in range(nmax):
+            k = make_key(i)
+            v = make_val(i)
+            keys[k] = v
+            self.assertEqual(d[k], v)
+
+        self.assertEqual(len(d), nmax)
+
+        # Randomly drop
+        droplist = random.sample(list(keys), ndrop)
+        remain = keys.copy()
+        for i, k in enumerate(droplist, start=1):
+            del d[k]
+            del remain[k]
+            self.assertEqual(len(d), nmax - i)
+        self.assertEqual(len(d), nmax - ndrop)
+
+        # Make sure everything dropped is gone
+        for k in droplist:
+            self.assertIsNone(d.get(k))
+
+        # Make sure everything else is still here
+        for k in remain:
+            self.assertEqual(d[k], remain[k])
+
+        # Refill
+        for i in range(nrefill):
+            k = make_key(nmax + i)
+            v = make_val(nmax + i)
+            remain[k] = v
+            d[k] = v
+
+        self.assertEqual(len(remain), len(d))
+
+        # Make sure everything is here
+        for k in remain:
+            self.assertEqual(d[k], remain[k])
+
+    def test_delete_randomly(self):
+        # Test deletion for differently sized dict
+        self.check_delete_randomly(nmax=8, ndrop=2, nrefill=2)
+        self.check_delete_randomly(nmax=13, ndrop=10, nrefill=31)
+        self.check_delete_randomly(nmax=100, ndrop=50, nrefill=200)
+        self.check_delete_randomly(nmax=100, ndrop=99, nrefill=100)
+        self.check_delete_randomly(nmax=100, ndrop=100, nrefill=100)
+        self.check_delete_randomly(nmax=1024, ndrop=999, nrefill=1)
+        self.check_delete_randomly(nmax=1024, ndrop=999, nrefill=2048)
+
+    def test_delete_randomly_large(self):
+        # Go beyond 2^16 to exercise large indices.
+        # Internally, size of index changes as the hashtable size changes.
+        # Size of index can be 8, 16, 32 or 64 bytes (on 64-bit).
+        # We are not inserting >2^32 elements because of limitation of time.
+        self.check_delete_randomly(nmax=2**17, ndrop=2**16, nrefill=2**10)
+
+    def test_popitem(self):
+        nmax = 10
+        d = Dict(self, 8, 8)
+
+        def make_key(v):
+            return "k_{:06x}".format(v)
+
+        def make_val(v):
+            return "v_{:06x}".format(v)
+
+        for i in range(nmax):
+            d[make_key(i)] = make_val(i)
+
+        self.assertEqual(len(d), nmax)
+        k, v = d.popitem()
+        self.assertEqual(len(d), nmax - 1)
+        self.assertEqual(k, make_key(len(d)))
+        self.assertEqual(v, make_val(len(d)))
+
+        while len(d):
+            n = len(d)
+            k, v = d.popitem()
+            self.assertEqual(len(d), n - 1)
+            self.assertEqual(k, make_key(len(d)))
+            self.assertEqual(v, make_val(len(d)))
+
+        self.assertEqual(len(d), 0)
+        with self.assertRaises(KeyError) as raises:
+            d.popitem()
+        self.assertIn(
+            'popitem(): dictionary is empty',
+            str(raises.exception),
+        )
+
+    def test_iter_items(self):
+        # Test .items iteration
+        d = Dict(self, 4, 4)
+        nmax = 1000
+
+        def make_key(v):
+            return "{:04}".format(v)
+
+        def make_val(v):
+            return "{:04}".format(v + nmax)
+
+        for i in range(nmax):
+            d[make_key(i)] = make_val(i)
+
+        # Check that the everything is ordered
+        for i, (k, v) in enumerate(d.items()):
+            self.assertEqual(make_key(i), k)
+            self.assertEqual(make_val(i), v)
+
+    def check_sizing(self, key_size, val_size, nmax):
+        # Helper to verify different key/value sizes.
+        d = Dict(self, key_size, val_size)
+
+        def make_key(v):
+            return "{:0{}}".format(v, key_size)[:key_size]
+
+        def make_val(v):
+            return "{:0{}}".format(nmax - v - 1, val_size)[:val_size]
+
+        for i in range(nmax):
+            d[make_key(i)] = make_val(i)
+
+        # Check that the everything is ordered
+        for i, (k, v) in enumerate(d.items()):
+            self.assertEqual(make_key(i), k)
+            self.assertEqual(make_val(i), v)
+
+    def test_sizing(self):
+        # Check different sizes of the key & value.
+        for i in range(1, 8):
+            self.check_sizing(key_size=i, val_size=i, nmax=2**i)
+
+    def test_parametrized_types(self):
+        """https://github.com/numba/numba/issues/6401"""
+
+        register_model(ParametrizedType)(UniTupleModel)
+
+        @typeof_impl.register(Parametrized)
+        def typeof_unit(val, c):
+            return ParametrizedType(val)
+
+        @unbox(ParametrizedType)
+        def unbox_parametrized(typ, obj, context):
+            return context.unbox(types.UniTuple(typ.dtype, len(typ)), obj)
+
+        @generated_jit
+        def dict_vs_cache_vs_parametrized(v):
+            typ = v
+
+            def objmode_vs_cache_vs_parametrized_impl(v):
+                # typed.List shows same behaviour after fix for #6397
+                d = typed.Dict.empty(types.unicode_type, typ)
+                d['data'] = v
+
+            return objmode_vs_cache_vs_parametrized_impl
+
+        @jit(nopython=True, cache=True)
+        def set_parametrized_data(x, y):
+            # Has had a tendency to segfault when the compiled function
+            # was loaded from cache in a different process than the one
+            # it was originally compiled in.
+            # The new process is simulated below by resetting the dispatchers
+            # and the target context
+            dict_vs_cache_vs_parametrized(x)
+            dict_vs_cache_vs_parametrized(y)
+
+        x, y = Parametrized(('a', 'b')), Parametrized(('a',))
+        set_parametrized_data(x, y)
+
+        # reset dispatchers and targetctx to force re-load from cache as if a
+        # new process would jit the function
+        set_parametrized_data._make_finalizer()()
+        set_parametrized_data._reset_overloads()
+        set_parametrized_data.targetctx.init()
+
+        for ii in range(50):  # <- sometimes works a few times
+            self.assertIsNone(set_parametrized_data(x, y))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dictobject.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dictobject.py
new file mode 100644
index 000000000..8ff6294fb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dictobject.py
@@ -0,0 +1,2429 @@
+"""
+Testing numba implementation of the numba dictionary.
+
+The tests here only check that the numba typing and codegen are working
+correctly.  Detailed testing of the underlying dictionary operations is done
+in test_dictimpl.py.
+"""
+
+import sys
+import warnings
+
+import numpy as np
+
+from numba import njit, literally
+from numba import int32, int64, float32, float64
+from numba import typeof
+from numba.typed import Dict, dictobject
+from numba.typed.typedobjectutils import _sentry_safe_cast
+from numba.core.errors import TypingError
+from numba.core import types
+from numba.tests.support import (TestCase, MemoryLeakMixin, unittest,
+                                 override_config, forbid_codegen)
+from numba.experimental import jitclass
+from numba.extending import overload
+
+
+class TestDictObject(MemoryLeakMixin, TestCase):
+    def test_dict_bool(self):
+        """
+        Exercise bool(dict)
+        """
+        @njit
+        def foo(n):
+            d = dictobject.new_dict(int32, float32)
+            for i in range(n):
+                d[i] = i + 1
+            return bool(d)
+
+        # Insert nothing
+        self.assertEqual(foo(n=0), False)
+        # Insert 1 entry
+        self.assertEqual(foo(n=1), True)
+        # Insert 2 entries
+        self.assertEqual(foo(n=2), True)
+        # Insert 100 entries
+        self.assertEqual(foo(n=100), True)
+
+    def test_dict_create(self):
+        """
+        Exercise dictionary creation, insertion and len
+        """
+        @njit
+        def foo(n):
+            d = dictobject.new_dict(int32, float32)
+            for i in range(n):
+                d[i] = i + 1
+            return len(d)
+
+        # Insert nothing
+        self.assertEqual(foo(n=0), 0)
+        # Insert 1 entry
+        self.assertEqual(foo(n=1), 1)
+        # Insert 2 entries
+        self.assertEqual(foo(n=2), 2)
+        # Insert 100 entries
+        self.assertEqual(foo(n=100), 100)
+
+    def test_dict_get(self):
+        """
+        Exercise dictionary creation, insertion and get
+        """
+        @njit
+        def foo(n, targets):
+            d = dictobject.new_dict(int32, float64)
+            # insertion loop
+            for i in range(n):
+                d[i] = i
+            # retrieval loop
+            output = []
+            for t in targets:
+                output.append(d.get(t))
+            return output
+
+        self.assertEqual(foo(5, [0, 1, 9]), [0, 1, None])
+        self.assertEqual(foo(10, [0, 1, 9]), [0, 1, 9])
+        self.assertEqual(foo(10, [-1, 9, 1]), [None, 9, 1])
+
+    def test_dict_get_with_default(self):
+        """
+        Exercise dict.get(k, d) where d is set
+        """
+        @njit
+        def foo(n, target, default):
+            d = dictobject.new_dict(int32, float64)
+            # insertion loop
+            for i in range(n):
+                d[i] = i
+            # retrieval loop
+            return d.get(target, default)
+
+        self.assertEqual(foo(5, 3, -1), 3)
+        self.assertEqual(foo(5, 5, -1), -1)
+
+    def test_dict_getitem(self):
+        """
+        Exercise dictionary __getitem__
+        """
+        @njit
+        def foo(keys, vals, target):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+
+            # lookup
+            return d[target]
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+        self.assertEqual(foo(keys, vals, 1), 0.1)
+        self.assertEqual(foo(keys, vals, 2), 0.2)
+        self.assertEqual(foo(keys, vals, 3), 0.3)
+        # check no leak so far
+        self.assert_no_memory_leak()
+        # disable leak check for exception test
+        self.disable_leak_check()
+        with self.assertRaises(KeyError):
+            foo(keys, vals, 0)
+        with self.assertRaises(KeyError):
+            foo(keys, vals, 4)
+
+    def test_dict_popitem(self):
+        """
+        Exercise dictionary .popitem
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+
+            # popitem
+            return d.popitem()
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+        for i in range(1, len(keys)):
+            self.assertEqual(
+                foo(keys[:i], vals[:i]),
+                (keys[i - 1], vals[i - 1]),
+            )
+
+    def test_dict_popitem_many(self):
+        """
+        Exercise dictionary .popitem
+        """
+
+        @njit
+        def core(d, npop):
+            # popitem
+            keysum, valsum = 0, 0
+            for _ in range(npop):
+                k, v = d.popitem()
+                keysum += k
+                valsum -= v
+            return keysum, valsum
+
+        @njit
+        def foo(keys, vals, npop):
+            d = dictobject.new_dict(int32, int32)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+
+            return core(d, npop)
+
+        keys = [1, 2, 3]
+        vals = [10, 20, 30]
+
+        for i in range(len(keys)):
+            self.assertEqual(
+                foo(keys, vals, npop=3),
+                core.py_func(dict(zip(keys, vals)), npop=3),
+            )
+
+        # check no leak so far
+        self.assert_no_memory_leak()
+        # disable leak check for exception test
+        self.disable_leak_check()
+
+        with self.assertRaises(KeyError):
+            foo(keys, vals, npop=4)
+
+    def test_dict_pop(self):
+        """
+        Exercise dictionary .pop
+        """
+        @njit
+        def foo(keys, vals, target):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+
+            # popitem
+            return d.pop(target, None), len(d)
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+
+        self.assertEqual(foo(keys, vals, 1), (0.1, 2))
+        self.assertEqual(foo(keys, vals, 2), (0.2, 2))
+        self.assertEqual(foo(keys, vals, 3), (0.3, 2))
+        self.assertEqual(foo(keys, vals, 0), (None, 3))
+
+        # check no leak so far
+        self.assert_no_memory_leak()
+        # disable leak check for exception test
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            # popitem
+            return d.pop(0)
+
+        with self.assertRaises(KeyError):
+            foo()
+
+    def test_dict_pop_many(self):
+        """
+        Exercise dictionary .pop
+        """
+
+        @njit
+        def core(d, pops):
+            total = 0
+            for k in pops:
+                total += k + d.pop(k, 0.123) + len(d)
+                total *= 2
+            return total
+
+        @njit
+        def foo(keys, vals, pops):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            # popitem
+            return core(d, pops)
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+        pops = [2, 3, 3, 1, 0, 2, 1, 0, -1]
+
+        self.assertEqual(
+            foo(keys, vals, pops),
+            core.py_func(dict(zip(keys, vals)), pops),
+        )
+
+    def test_dict_delitem(self):
+        @njit
+        def foo(keys, vals, target):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            del d[target]
+            return len(d), d.get(target)
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+        self.assertEqual(foo(keys, vals, 1), (2, None))
+        self.assertEqual(foo(keys, vals, 2), (2, None))
+        self.assertEqual(foo(keys, vals, 3), (2, None))
+        # check no leak so far
+        self.assert_no_memory_leak()
+        # disable leak check for exception test
+        self.disable_leak_check()
+        with self.assertRaises(KeyError):
+            foo(keys, vals, 0)
+
+    def test_dict_clear(self):
+        """
+        Exercise dict.clear
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            b4 = len(d)
+            # clear
+            d.clear()
+            return b4, len(d)
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+        self.assertEqual(foo(keys, vals), (3, 0))
+
+    def test_dict_items(self):
+        """
+        Exercise dict.items
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            out = []
+            for kv in d.items():
+                out.append(kv)
+            return out
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+
+        self.assertEqual(
+            foo(keys, vals),
+            list(zip(keys, vals)),
+        )
+
+        # Test .items() on empty dict
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            out = []
+            for kv in d.items():
+                out.append(kv)
+            return out
+
+        self.assertEqual(foo(), [])
+
+    def test_dict_keys(self):
+        """
+        Exercise dict.keys
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            out = []
+            for k in d.keys():
+                out.append(k)
+            return out
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+
+        self.assertEqual(
+            foo(keys, vals),
+            keys,
+        )
+
+    def test_dict_keys_len(self):
+        """
+        Exercise len(dict.keys())
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            return len(d.keys())
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+
+        self.assertEqual(
+            foo(keys, vals),
+            len(keys),
+        )
+
+    def test_dict_values(self):
+        """
+        Exercise dict.values
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            out = []
+            for v in d.values():
+                out.append(v)
+            return out
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+
+        self.assertEqual(
+            foo(keys, vals),
+            vals,
+        )
+
+    def test_dict_values_len(self):
+        """
+        Exercise len(dict.values())
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            return len(d.values())
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+
+        self.assertEqual(
+            foo(keys, vals),
+            len(vals),
+        )
+
+    def test_dict_items_len(self):
+        """
+        Exercise len(dict.items())
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            return len(d.items())
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+        self.assertPreciseEqual(
+            foo(keys, vals),
+            len(vals),
+        )
+
+    def test_dict_iter(self):
+        """
+        Exercise iter(dict)
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            out = []
+            for k in d:
+                out.append(k)
+            return out
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+
+        self.assertEqual(
+            foo(keys, vals),
+            [1, 2, 3]
+        )
+
+    def test_dict_contains(self):
+        """
+        Exercise operator.contains
+        """
+        @njit
+        def foo(keys, vals, checklist):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            out = []
+            for k in checklist:
+                out.append(k in d)
+            return out
+
+        keys = [1, 2, 3]
+        vals = [0.1, 0.2, 0.3]
+
+        self.assertEqual(
+            foo(keys, vals, [2, 3, 4, 1, 0]),
+            [True, True, False, True, False],
+        )
+
+    def test_dict_copy(self):
+        """
+        Exercise dict.copy
+        """
+        @njit
+        def foo(keys, vals):
+            d = dictobject.new_dict(int32, float64)
+            # insertion
+            for k, v in zip(keys, vals):
+                d[k] = v
+            return list(d.copy().items())
+
+        keys = list(range(20))
+        vals = [x + i / 100 for i, x in enumerate(keys)]
+        out = foo(keys, vals)
+        self.assertEqual(out, list(zip(keys, vals)))
+
+    def test_dict_setdefault(self):
+        """
+        Exercise dict.setdefault
+        """
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            d.setdefault(1, 1.2)  # used because key is not in
+            a = d.get(1)
+            d[1] = 2.3
+            b = d.get(1)
+            d[2] = 3.4
+            d.setdefault(2, 4.5)  # not used because key is in
+            c = d.get(2)
+            return a, b, c
+
+        self.assertEqual(foo(), (1.2, 2.3, 3.4))
+
+    def test_dict_equality(self):
+        """
+        Exercise dict.__eq__ and .__ne__
+        """
+        @njit
+        def foo(na, nb, fa, fb):
+            da = dictobject.new_dict(int32, float64)
+            db = dictobject.new_dict(int32, float64)
+            for i in range(na):
+                da[i] = i * fa
+            for i in range(nb):
+                db[i] = i * fb
+            return da == db, da != db
+
+        # Same keys and values
+        self.assertEqual(foo(10, 10, 3, 3), (True, False))
+        # Same keys and diff values
+        self.assertEqual(foo(10, 10, 3, 3.1), (False, True))
+        # LHS has more keys
+        self.assertEqual(foo(11, 10, 3, 3), (False, True))
+        # RHS has more keys
+        self.assertEqual(foo(10, 11, 3, 3), (False, True))
+
+    def test_dict_equality_more(self):
+        """
+        Exercise dict.__eq__
+        """
+        @njit
+        def foo(ak, av, bk, bv):
+            # The key-value types are different in the two dictionaries
+            da = dictobject.new_dict(int32, float64)
+            db = dictobject.new_dict(int64, float32)
+            for i in range(len(ak)):
+                da[ak[i]] = av[i]
+            for i in range(len(bk)):
+                db[bk[i]] = bv[i]
+            return da == db
+
+        # Simple equal case
+        ak = [1, 2, 3]
+        av = [2, 3, 4]
+        bk = [1, 2, 3]
+        bv = [2, 3, 4]
+        self.assertTrue(foo(ak, av, bk, bv))
+
+        # Equal with replacement
+        ak = [1, 2, 3]
+        av = [2, 3, 4]
+        bk = [1, 2, 2, 3]
+        bv = [2, 1, 3, 4]
+        self.assertTrue(foo(ak, av, bk, bv))
+
+        # Diff values
+        ak = [1, 2, 3]
+        av = [2, 3, 4]
+        bk = [1, 2, 3]
+        bv = [2, 1, 4]
+        self.assertFalse(foo(ak, av, bk, bv))
+
+        # Diff keys
+        ak = [0, 2, 3]
+        av = [2, 3, 4]
+        bk = [1, 2, 3]
+        bv = [2, 3, 4]
+        self.assertFalse(foo(ak, av, bk, bv))
+
+    def test_dict_equality_diff_type(self):
+        """
+        Exercise dict.__eq__
+        """
+        @njit
+        def foo(na, b):
+            da = dictobject.new_dict(int32, float64)
+            for i in range(na):
+                da[i] = i
+            return da == b
+
+        # dict != int
+        self.assertFalse(foo(10, 1))
+        # dict != tuple[int]
+        self.assertFalse(foo(10, (1,)))
+
+    def test_dict_to_from_meminfo(self):
+        """
+        Exercise dictobject.{_as_meminfo, _from_meminfo}
+        """
+        @njit
+        def make_content(nelem):
+            for i in range(nelem):
+                yield i, i + (i + 1) / 100
+
+        @njit
+        def boxer(nelem):
+            d = dictobject.new_dict(int32, float64)
+            for k, v in make_content(nelem):
+                d[k] = v
+            return dictobject._as_meminfo(d)
+
+        dcttype = types.DictType(int32, float64)
+
+        @njit
+        def unboxer(mi):
+            d = dictobject._from_meminfo(mi, dcttype)
+            return list(d.items())
+
+        mi = boxer(10)
+        self.assertEqual(mi.refcount, 1)
+
+        got = unboxer(mi)
+        expected = list(make_content.py_func(10))
+        self.assertEqual(got, expected)
+
+    def test_001_cannot_downcast_key(self):
+        @njit
+        def foo(n):
+            d = dictobject.new_dict(int32, float64)
+            for i in range(n):
+                d[i] = i + 1
+            # bad key type
+            z = d.get(1j)
+            return z
+
+        with self.assertRaises(TypingError) as raises:
+            foo(10)
+        self.assertIn(
+            'cannot safely cast complex128 to int32',
+            str(raises.exception),
+        )
+
+    def test_002_cannot_downcast_default(self):
+        @njit
+        def foo(n):
+            d = dictobject.new_dict(int32, float64)
+            for i in range(n):
+                d[i] = i + 1
+            # bad default type
+            z = d.get(2 * n, 1j)
+            return z
+
+        with self.assertRaises(TypingError) as raises:
+            foo(10)
+        self.assertIn(
+            'cannot safely cast complex128 to float64',
+            str(raises.exception),
+        )
+
+    def test_003_cannot_downcast_key(self):
+        @njit
+        def foo(n):
+            d = dictobject.new_dict(int32, float64)
+            for i in range(n):
+                d[i] = i + 1
+            # bad cast!?
+            z = d.get(2.4)
+            return z
+
+        # should raise
+        with self.assertRaises(TypingError) as raises:
+            foo(10)
+        self.assertIn(
+            'cannot safely cast float64 to int32',
+            str(raises.exception),
+        )
+
+    def test_004_cannot_downcast_key(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            # should raise TypingError
+            d[1j] = 7.
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            'cannot safely cast complex128 to int32',
+            str(raises.exception),
+        )
+
+    def test_005_cannot_downcast_value(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            # should raise TypingError
+            d[1] = 1j
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            'cannot safely cast complex128 to float64',
+            str(raises.exception),
+        )
+
+    def test_006_cannot_downcast_key(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            # raise TypingError
+            d[11.5]
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            'cannot safely cast float64 to int32',
+            str(raises.exception),
+        )
+
+    @unittest.skipUnless(sys.maxsize > 2 ** 32, "64 bit test only")
+    def test_007_collision_checks(self):
+        # this checks collisions in real life for 64bit systems
+        @njit
+        def foo(v1, v2):
+            d = dictobject.new_dict(int64, float64)
+            c1 = np.uint64(2 ** 61 - 1)
+            c2 = np.uint64(0)
+            assert hash(c1) == hash(c2)
+            d[c1] = v1
+            d[c2] = v2
+            return (d[c1], d[c2])
+
+        a, b = 10., 20.
+        x, y = foo(a, b)
+        self.assertEqual(x, a)
+        self.assertEqual(y, b)
+
+    def test_008_lifo_popitem(self):
+        # check that (keys, vals) are LIFO .popitem()
+        @njit
+        def foo(n):
+            d = dictobject.new_dict(int32, float64)
+            for i in range(n):
+                d[i] = i + 1
+            keys = []
+            vals = []
+            for i in range(n):
+                tmp = d.popitem()
+                keys.append(tmp[0])
+                vals.append(tmp[1])
+            return keys, vals
+
+        z = 10
+        gk, gv = foo(z)
+
+        self.assertEqual(gk, [x for x in reversed(range(z))])
+        self.assertEqual(gv, [x + 1 for x in reversed(range(z))])
+
+    def test_010_cannot_downcast_default(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            d[0] = 6.
+            d[1] = 7.
+            # pop'd default must have same type as value
+            d.pop(11, 12j)
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "cannot safely cast complex128 to float64",
+            str(raises.exception),
+        )
+
+    def test_011_cannot_downcast_key(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            d[0] = 6.
+            d[1] = 7.
+            # pop'd key must have same type as key
+            d.pop(11j)
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "cannot safely cast complex128 to int32",
+            str(raises.exception),
+        )
+
+    def test_012_cannot_downcast_key(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            d[0] = 6.
+            # invalid key type
+            return 1j in d
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "cannot safely cast complex128 to int32",
+            str(raises.exception),
+        )
+
+    def test_013_contains_empty_dict(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            # contains on empty dict
+            return 1 in d
+
+        self.assertFalse(foo())
+
+    def test_014_not_contains_empty_dict(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            # not contains empty dict
+            return 1 not in d
+
+        self.assertTrue(foo())
+
+    def test_015_dict_clear(self):
+        @njit
+        def foo(n):
+            d = dictobject.new_dict(int32, float64)
+            for i in range(n):
+                d[i] = i + 1
+            x = len(d)
+            d.clear()
+            y = len(d)
+            return x, y
+
+        m = 10
+        self.assertEqual(foo(m), (m, 0))
+
+    def test_016_cannot_downcast_key(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            # key is wrong type
+            d.setdefault(1j, 12.)
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "cannot safely cast complex128 to int32",
+            str(raises.exception),
+        )
+
+    def test_017_cannot_downcast_default(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            # default value is wrong type
+            d.setdefault(1, 12.j)
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "cannot safely cast complex128 to float64",
+            str(raises.exception),
+        )
+
+    def test_018_keys_iter_are_views(self):
+        # this is broken somewhere in llvmlite, intent of test is to check if
+        # keys behaves like a view or not
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            d[11] = 12.
+            k1 = d.keys()
+            d[22] = 9.
+            k2 = d.keys()
+            rk1 = [x for x in k1]
+            rk2 = [x for x in k2]
+            return rk1, rk2
+
+        a, b = foo()
+        self.assertEqual(a, b)
+        self.assertEqual(a, [11, 22])
+
+    # Not implemented yet
+    @unittest.expectedFailure
+    def test_019(self):
+        # should keys/vals be set-like?
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            d[11] = 12.
+            d[22] = 9.
+            k2 = d.keys() & {12, }
+            return k2
+
+        print(foo())
+
+    def test_020_string_key(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(types.unicode_type, float64)
+            d['a'] = 1.
+            d['b'] = 2.
+            d['c'] = 3.
+            d['d'] = 4.
+            out = []
+            for x in d.items():
+                out.append(x)
+            return out, d['a']
+
+        items, da = foo()
+        self.assertEqual(items, [('a', 1.), ('b', 2.), ('c', 3.), ('d', 4)])
+        self.assertEqual(da, 1.)
+
+    def test_021_long_str_key(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(types.unicode_type, float64)
+            tmp = []
+            for i in range(10000):
+                tmp.append('a')
+            s = ''.join(tmp)
+            d[s] = 1.
+            out = list(d.items())
+            return out
+        self.assertEqual(foo(), [('a' * 10000, 1)])
+
+    def test_022_references_juggle(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+            e = d
+            d[1] = 12.
+            e[2] = 14.
+            e = dictobject.new_dict(int32, float64)
+            e[1] = 100.
+            e[2] = 1000.
+            f = d
+            d = e
+
+            k1 = [x for x in d.items()]
+            k2 = [x for x in e.items()]
+            k3 = [x for x in f.items()]
+
+            return k1, k2, k3
+
+        k1, k2, k3 = foo()
+        self.assertEqual(k1, [(1, 100.0), (2, 1000.0)])
+        self.assertEqual(k2, [(1, 100.0), (2, 1000.0)])
+        self.assertEqual(k3, [(1, 12), (2, 14)])
+
+    def test_023_closure(self):
+        @njit
+        def foo():
+            d = dictobject.new_dict(int32, float64)
+
+            def bar():
+                d[1] = 12.
+                d[2] = 14.
+            bar()
+            return [x for x in d.keys()]
+
+        self.assertEqual(foo(), [1, 2])
+
+    def test_024_unicode_getitem_keys(self):
+        # See issue #6135
+        @njit
+        def foo():
+            s = 'a\u1234'
+            d = {s[0] : 1}
+            return d['a']
+
+        self.assertEqual(foo(), foo.py_func())
+
+        @njit
+        def foo():
+            s = 'abc\u1234'
+            d = {s[:1] : 1}
+            return d['a']
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_issue6570_alignment_padding(self):
+        # Create a key type that is 12-bytes long on a 8-byte aligned system
+        # so that the a 4-byte padding is needed.
+        # If the 4-byte padding is not zero-filled, it will have garbage data
+        # that affects key matching in the lookup.
+        keyty = types.Tuple([types.uint64, types.float32])
+
+        @njit
+        def foo():
+            d = dictobject.new_dict(keyty, float64)
+            t1 = np.array([3], dtype=np.uint64)
+            t2 = np.array([5.67], dtype=np.float32)
+            v1 = np.array([10.23], dtype=np.float32)
+            d[(t1[0], t2[0])] = v1[0]
+            return (t1[0], t2[0]) in d
+
+        self.assertTrue(foo())
+
+    def test_dict_update(self):
+        """
+        Tests dict.update works with various dictionaries.
+        """
+        n = 10
+
+        def f1(n):
+            """
+            Test update with a regular dictionary.
+            """
+            d1 = {i: i + 1 for i in range(n)}
+            d2 = {3 * i: i for i in range(n)}
+            d1.update(d2)
+            return d1
+
+        py_func = f1
+        cfunc = njit()(f1)
+        a = py_func(n)
+        b = cfunc(n)
+        self.assertEqual(a, b)
+
+        def f2(n):
+            """
+            Test update where one of the dictionaries
+            is created as a Python literal.
+            """
+            d1 = {
+                1: 2,
+                3: 4,
+                5: 6
+            }
+            d2 = {3 * i: i for i in range(n)}
+            d1.update(d2)
+            return d1
+
+        py_func = f2
+        cfunc = njit()(f2)
+        a = py_func(n)
+        b = cfunc(n)
+        self.assertEqual(a, b)
+
+
+class TestDictTypeCasting(TestCase):
+    def check_good(self, fromty, toty):
+        _sentry_safe_cast(fromty, toty)
+
+    def check_bad(self, fromty, toty):
+        with self.assertRaises(TypingError) as raises:
+            _sentry_safe_cast(fromty, toty)
+        self.assertIn(
+            'cannot safely cast {fromty} to {toty}'.format(**locals()),
+            str(raises.exception),
+        )
+
+    def test_cast_int_to(self):
+        self.check_good(types.int32, types.float32)
+        self.check_good(types.int32, types.float64)
+        self.check_good(types.int32, types.complex128)
+        self.check_good(types.int64, types.complex128)
+        self.check_bad(types.int32, types.complex64)
+        self.check_good(types.int8, types.complex64)
+
+    def test_cast_float_to(self):
+        self.check_good(types.float32, types.float64)
+        self.check_good(types.float32, types.complex64)
+        self.check_good(types.float64, types.complex128)
+
+    def test_cast_bool_to(self):
+        self.check_good(types.boolean, types.int32)
+        self.check_good(types.boolean, types.float64)
+        self.check_good(types.boolean, types.complex128)
+
+
+class TestTypedDict(MemoryLeakMixin, TestCase):
+    def test_basic(self):
+        d = Dict.empty(int32, float32)
+        # len
+        self.assertEqual(len(d), 0)
+        # setitems
+        d[1] = 1
+        d[2] = 2.3
+        d[3] = 3.4
+        self.assertEqual(len(d), 3)
+        # keys
+        self.assertEqual(list(d.keys()), [1, 2, 3])
+        # values
+        for x, y in zip(list(d.values()), [1, 2.3, 3.4]):
+            self.assertAlmostEqual(x, y, places=4)
+        # getitem
+        self.assertAlmostEqual(d[1], 1)
+        self.assertAlmostEqual(d[2], 2.3, places=4)
+        self.assertAlmostEqual(d[3], 3.4, places=4)
+        # deltiem
+        del d[2]
+        self.assertEqual(len(d), 2)
+        # get
+        self.assertIsNone(d.get(2))
+        # setdefault
+        d.setdefault(2, 100)
+        d.setdefault(3, 200)
+        self.assertEqual(d[2], 100)
+        self.assertAlmostEqual(d[3], 3.4, places=4)
+        # update
+        d.update({4: 5, 5: 6})
+        self.assertAlmostEqual(d[4], 5)
+        self.assertAlmostEqual(d[5], 6)
+        # contains
+        self.assertTrue(4 in d)
+        # items
+        pyd = dict(d.items())
+        self.assertEqual(len(pyd), len(d))
+        # pop
+        self.assertAlmostEqual(d.pop(4), 5)
+        # popitem
+        nelem = len(d)
+        k, v = d.popitem()
+        self.assertEqual(len(d), nelem - 1)
+        self.assertTrue(k not in d)
+        # __eq__ & copy
+        copied = d.copy()
+        self.assertEqual(copied, d)
+        self.assertEqual(list(copied.items()), list(d.items()))
+
+    def test_copy_from_dict(self):
+        expect = {k: float(v) for k, v in zip(range(10), range(10, 20))}
+        nbd = Dict.empty(int32, float64)
+        for k, v in expect.items():
+            nbd[k] = v
+        got = dict(nbd)
+        self.assertEqual(got, expect)
+
+    def test_compiled(self):
+        @njit
+        def producer():
+            d = Dict.empty(int32, float64)
+            d[1] = 1.23
+            return d
+
+        @njit
+        def consumer(d):
+            return d[1]
+
+        d = producer()
+        val = consumer(d)
+        self.assertEqual(val, 1.23)
+
+    def test_gh7908(self):
+        d = Dict.empty(
+            key_type=types.Tuple([types.uint32,
+                                  types.uint32]),
+            value_type=int64)
+
+        d[(1, 1)] = 12345
+        self.assertEqual(d[(1, 1)], d.get((1, 1)))
+
+    def check_stringify(self, strfn, prefix=False):
+        nbd = Dict.empty(int32, int32)
+        d = {}
+        nbd[1] = 2
+        d[1] = 2
+        checker = self.assertIn if prefix else self.assertEqual
+        checker(strfn(d), strfn(nbd))
+        nbd[2] = 3
+        d[2] = 3
+        checker(strfn(d), strfn(nbd))
+        for i in range(10, 20):
+            nbd[i] = i + 1
+            d[i] = i + 1
+        checker(strfn(d), strfn(nbd))
+        if prefix:
+            self.assertTrue(strfn(nbd).startswith('DictType'))
+
+    def test_repr(self):
+        self.check_stringify(repr, prefix=True)
+
+    def test_str(self):
+        self.check_stringify(str)
+
+
+class TestDictRefctTypes(MemoryLeakMixin, TestCase):
+
+    def test_str_key(self):
+        @njit
+        def foo():
+            d = Dict.empty(
+                key_type=types.unicode_type,
+                value_type=types.int32,
+            )
+            d["123"] = 123
+            d["321"] = 321
+            return d
+
+        d = foo()
+        self.assertEqual(d['123'], 123)
+        self.assertEqual(d['321'], 321)
+        expect = {'123': 123, '321': 321}
+        self.assertEqual(dict(d), expect)
+        # Test insert replacement
+        d['123'] = 231
+        expect['123'] = 231
+        self.assertEqual(d['123'], 231)
+        self.assertEqual(dict(d), expect)
+        # Test dictionary growth
+        nelem = 100
+        for i in range(nelem):
+            d[str(i)] = i
+            expect[str(i)] = i
+        for i in range(nelem):
+            self.assertEqual(d[str(i)], i)
+        self.assertEqual(dict(d), expect)
+
+    def test_str_val(self):
+        @njit
+        def foo():
+            d = Dict.empty(
+                key_type=types.int32,
+                value_type=types.unicode_type,
+            )
+            d[123] = "123"
+            d[321] = "321"
+            return d
+
+        d = foo()
+        self.assertEqual(d[123], '123')
+        self.assertEqual(d[321], '321')
+        expect = {123: '123', 321: '321'}
+        self.assertEqual(dict(d), expect)
+        # Test insert replacement
+        d[123] = "231"
+        expect[123] = "231"
+        self.assertEqual(dict(d), expect)
+        # Test dictionary growth
+        nelem = 1
+        for i in range(nelem):
+            d[i] = str(i)
+            expect[i] = str(i)
+        for i in range(nelem):
+            self.assertEqual(d[i], str(i))
+        self.assertEqual(dict(d), expect)
+
+    def test_str_key_array_value(self):
+        np.random.seed(123)
+        d = Dict.empty(
+            key_type=types.unicode_type,
+            value_type=types.float64[:],
+        )
+        expect = []
+        expect.append(np.random.random(10))
+        d['mass'] = expect[-1]
+        expect.append(np.random.random(20))
+        d['velocity'] = expect[-1]
+        for i in range(100):
+            expect.append(np.random.random(i))
+            d[str(i)] = expect[-1]
+        self.assertEqual(len(d), len(expect))
+        self.assertPreciseEqual(d['mass'], expect[0])
+        self.assertPreciseEqual(d['velocity'], expect[1])
+        # Ordering is kept
+        for got, exp in zip(d.values(), expect):
+            self.assertPreciseEqual(got, exp)
+
+        # Try deleting
+        self.assertTrue('mass' in d)
+        self.assertTrue('velocity' in d)
+        del d['mass']
+        self.assertFalse('mass' in d)
+        del d['velocity']
+        self.assertFalse('velocity' in d)
+        del expect[0:2]
+
+        for i in range(90):
+            k, v = d.popitem()
+            w = expect.pop()
+            self.assertPreciseEqual(v, w)
+
+        # Trigger a resize
+        expect.append(np.random.random(10))
+        d["last"] = expect[-1]
+
+        # Ordering is kept
+        for got, exp in zip(d.values(), expect):
+            self.assertPreciseEqual(got, exp)
+
+    def test_dict_of_dict_int_keyval(self):
+        def inner_numba_dict():
+            d = Dict.empty(
+                key_type=types.intp,
+                value_type=types.intp,
+            )
+            return d
+
+        d = Dict.empty(
+            key_type=types.intp,
+            value_type=types.DictType(types.intp, types.intp),
+        )
+
+        def usecase(d, make_inner_dict):
+            for i in range(100):
+                mid = make_inner_dict()
+                for j in range(i + 1):
+                    mid[j] = j * 10000
+                d[i] = mid
+            return d
+
+        got = usecase(d, inner_numba_dict)
+        expect = usecase({}, dict)
+
+        self.assertIsInstance(expect, dict)
+
+        self.assertEqual(dict(got), expect)
+
+        # Delete items
+        for where in [12, 3, 6, 8, 10]:
+            del got[where]
+            del expect[where]
+            self.assertEqual(dict(got), expect)
+
+    def test_dict_of_dict_npm(self):
+        inner_dict_ty = types.DictType(types.intp, types.intp)
+
+        @njit
+        def inner_numba_dict():
+            d = Dict.empty(
+                key_type=types.intp,
+                value_type=types.intp,
+            )
+            return d
+
+        @njit
+        def foo(count):
+            d = Dict.empty(
+                key_type=types.intp,
+                value_type=inner_dict_ty,
+            )
+            for i in range(count):
+                d[i] = inner_numba_dict()
+                for j in range(i + 1):
+                    d[i][j] = j
+
+            return d
+
+        d = foo(100)
+        ct = 0
+        for k, dd in d.items():
+            ct += 1
+            self.assertEqual(len(dd), k + 1)
+            for kk, vv in dd.items():
+                self.assertEqual(kk, vv)
+
+        self.assertEqual(ct, 100)
+
+    def test_delitem(self):
+        d = Dict.empty(types.int64, types.unicode_type)
+        d[1] = 'apple'
+
+        @njit
+        def foo(x, k):
+            del x[1]
+
+        foo(d, 1)
+        self.assertEqual(len(d), 0)
+        self.assertFalse(d)
+
+    def test_getitem_return_type(self):
+        # Dict.__getitem__ must return non-optional type.
+        d = Dict.empty(types.int64, types.int64[:])
+        d[1] = np.arange(10, dtype=np.int64)
+
+        @njit
+        def foo(d):
+            d[1] += 100
+            return d[1]
+
+        foo(d)
+        # Return type is an array, not optional
+        retty = foo.nopython_signatures[0].return_type
+        self.assertIsInstance(retty, types.Array)
+        self.assertNotIsInstance(retty, types.Optional)
+        # Value is correctly updated
+        self.assertPreciseEqual(d[1], np.arange(10, dtype=np.int64) + 100)
+
+    def test_storage_model_mismatch(self):
+        # https://github.com/numba/numba/issues/4520
+        # check for storage model mismatch in refcount ops generation
+        dct = Dict()
+        ref = [
+            ("a", True, "a"),
+            ("b", False, "b"),
+            ("c", False, "c"),
+        ]
+        # populate
+        for x in ref:
+            dct[x] = x
+        # test
+        for i, x in enumerate(ref):
+            self.assertEqual(dct[x], x)
+
+
+class TestDictForbiddenTypes(TestCase):
+    def assert_disallow(self, expect, callable):
+        with self.assertRaises(TypingError) as raises:
+            callable()
+        msg = str(raises.exception)
+        self.assertIn(expect, msg)
+
+    def assert_disallow_key(self, ty):
+        msg = '{} as key is forbidden'.format(ty)
+        self.assert_disallow(msg, lambda: Dict.empty(ty, types.intp))
+
+        @njit
+        def foo():
+            Dict.empty(ty, types.intp)
+        self.assert_disallow(msg, foo)
+
+    def assert_disallow_value(self, ty):
+        msg = '{} as value is forbidden'.format(ty)
+        self.assert_disallow(msg, lambda: Dict.empty(types.intp, ty))
+
+        @njit
+        def foo():
+            Dict.empty(types.intp, ty)
+        self.assert_disallow(msg, foo)
+
+    def test_disallow_list(self):
+        self.assert_disallow_key(types.List(types.intp))
+        self.assert_disallow_value(types.List(types.intp))
+
+    def test_disallow_set(self):
+        self.assert_disallow_key(types.Set(types.intp))
+        self.assert_disallow_value(types.Set(types.intp))
+
+
+class TestDictInferred(TestCase):
+    def test_simple_literal(self):
+        @njit
+        def foo():
+            d = Dict()
+            d[123] = 321
+            return d
+
+        k, v = 123, 321
+        d = foo()
+        self.assertEqual(dict(d), {k: v})
+        self.assertEqual(typeof(d).key_type, typeof(k))
+        self.assertEqual(typeof(d).value_type, typeof(v))
+
+    def test_simple_args(self):
+        @njit
+        def foo(k, v):
+            d = Dict()
+            d[k] = v
+            return d
+
+        k, v = 123, 321
+        d = foo(k, v)
+        self.assertEqual(dict(d), {k: v})
+        self.assertEqual(typeof(d).key_type, typeof(k))
+        self.assertEqual(typeof(d).value_type, typeof(v))
+
+    def test_simple_upcast(self):
+        @njit
+        def foo(k, v, w):
+            d = Dict()
+            d[k] = v
+            d[k] = w
+            return d
+
+        k, v, w = 123, 32.1, 321
+        d = foo(k, v, w)
+        self.assertEqual(dict(d), {k: w})
+        self.assertEqual(typeof(d).key_type, typeof(k))
+        self.assertEqual(typeof(d).value_type, typeof(v))
+
+    def test_conflicting_value_type(self):
+        @njit
+        def foo(k, v, w):
+            d = Dict()
+            d[k] = v
+            d[k] = w
+            return d
+
+        k, v, w = 123, 321, 32.1
+        with self.assertRaises(TypingError) as raises:
+            foo(k, v, w)
+        self.assertIn(
+            'cannot safely cast float64 to {}'.format(typeof(v)),
+            str(raises.exception),
+        )
+
+    def test_conflicting_key_type(self):
+        @njit
+        def foo(k, h, v):
+            d = Dict()
+            d[k] = v
+            d[h] = v
+            return d
+
+        k, h, v = 123, 123.1, 321
+        with self.assertRaises(TypingError) as raises:
+            foo(k, h, v)
+        self.assertIn(
+            'cannot safely cast float64 to {}'.format(typeof(v)),
+            str(raises.exception),
+        )
+
+    def test_conflict_key_type_non_number(self):
+        # Allow non-number types to cast unsafely
+        @njit
+        def foo(k1, v1, k2):
+            d = Dict()
+            d[k1] = v1
+            return d, d[k2]
+
+        # k2 will unsafely downcast typeof(k1)
+        k1 = (np.int8(1), np.int8(2))
+        k2 = (np.int32(1), np.int32(2))
+        v1 = np.intp(123)
+
+        with warnings.catch_warnings(record=True) as w:
+            d, dk2 = foo(k1, v1, k2)
+        self.assertEqual(len(w), 1)
+        # Make sure the warning is about unsafe cast
+        msg = 'unsafe cast from UniTuple(int32 x 2) to UniTuple(int8 x 2)'
+        self.assertIn(msg, str(w[0]))
+
+        keys = list(d.keys())
+        self.assertEqual(keys[0], (1, 2))
+        self.assertEqual(dk2, d[(np.int32(1), np.int32(2))])
+
+    def test_ifelse_filled_both_branches(self):
+        @njit
+        def foo(k, v):
+            d = Dict()
+            if k:
+                d[k] = v
+            else:
+                d[0xdead] = v + 1
+
+            return d
+
+        k, v = 123, 321
+        d = foo(k, v)
+        self.assertEqual(dict(d), {k: v})
+        k, v = 0, 0
+        d = foo(k, v)
+        self.assertEqual(dict(d), {0xdead: v + 1})
+
+    def test_ifelse_empty_one_branch(self):
+        @njit
+        def foo(k, v):
+            d = Dict()
+            if k:
+                d[k] = v
+
+            return d
+
+        k, v = 123, 321
+        d = foo(k, v)
+        self.assertEqual(dict(d), {k: v})
+        k, v = 0, 0
+        d = foo(k, v)
+        self.assertEqual(dict(d), {})
+        self.assertEqual(typeof(d).key_type, typeof(k))
+        self.assertEqual(typeof(d).value_type, typeof(v))
+
+    def test_loop(self):
+        @njit
+        def foo(ks, vs):
+            d = Dict()
+            for k, v in zip(ks, vs):
+                d[k] = v
+            return d
+
+        vs = list(range(4))
+        ks = list(map(lambda x : x + 100, vs))
+        d = foo(ks, vs)
+        self.assertEqual(dict(d), dict(zip(ks, vs)))
+
+    def test_unused(self):
+        @njit
+        def foo():
+            d = Dict()
+            return d
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "imprecise type",
+            str(raises.exception)
+        )
+
+    def test_define_after_use(self):
+        @njit
+        def foo(define):
+            d = Dict()
+            ct = len(d)
+            for k, v in d.items():
+                ct += v
+
+            if define:
+                # This will set the type
+                d[1] = 2
+            return ct, d, len(d)
+
+        ct, d, n = foo(True)
+        self.assertEqual(ct, 0)
+        self.assertEqual(n, 1)
+        self.assertEqual(dict(d), {1: 2})
+
+        ct, d, n = foo(False)
+        self.assertEqual(ct, 0)
+        self.assertEqual(dict(d), {})
+        self.assertEqual(n, 0)
+
+    def test_dict_of_dict(self):
+        @njit
+        def foo(k1, k2, v):
+            d = Dict()
+            z1 = Dict()
+            z1[k1 + 1] = v + k1
+            z2 = Dict()
+            z2[k2 + 2] = v + k2
+            d[k1] = z1
+            d[k2] = z2
+            return d
+
+        k1, k2, v = 100, 200, 321
+        d = foo(k1, k2, v)
+        self.assertEqual(
+            dict(d),
+            {
+                k1: {k1 + 1: k1 + v},
+                k2: {k2 + 2: k2 + v},
+            },
+        )
+
+    def test_comprehension_basic(self):
+        @njit
+        def foo():
+            return {i: 2 * i for i in range(10)}
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_comprehension_basic_mixed_type(self):
+        @njit
+        def foo():
+            return {i: float(j) for i, j in zip(range(10), range(10, 0, -1))}
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_comprehension_involved(self):
+        @njit
+        def foo():
+            a = {0: 'A', 1: 'B', 2: 'C'}
+            return {3 + i: a[i] for i in range(3)}
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_comprehension_fail_mixed_type(self):
+        @njit
+        def foo():
+            a = {0: 'A', 1: 'B', 2: 1j}
+            return {3 + i: a[i] for i in range(3)}
+
+        with self.assertRaises(TypingError) as e:
+            foo()
+
+        excstr = str(e.exception)
+        self.assertIn("Cannot cast complex128 to unicode_type", excstr)
+
+
+class TestNonCompiledInfer(TestCase):
+    def test_check_untyped_dict_ops(self):
+        # Check operation on untyped dictionary
+        d = Dict()
+        self.assertFalse(d._typed)
+        self.assertEqual(len(d), 0)
+        self.assertEqual(str(d), str({}))
+        self.assertEqual(list(iter(d)), [])
+        # Test __getitem__
+        with self.assertRaises(KeyError) as raises:
+            d[1]
+        self.assertEqual(str(raises.exception), str(KeyError(1)))
+        # Test __delitem__
+        with self.assertRaises(KeyError) as raises:
+            del d[1]
+        self.assertEqual(str(raises.exception), str(KeyError(1)))
+        # Test .pop
+        with self.assertRaises(KeyError):
+            d.pop(1)
+        self.assertEqual(str(raises.exception), str(KeyError(1)))
+        # Test .pop
+        self.assertIs(d.pop(1, None), None)
+        # Test .get
+        self.assertIs(d.get(1), None)
+        # Test .popitem
+        with self.assertRaises(KeyError) as raises:
+            d.popitem()
+        self.assertEqual(str(raises.exception),
+                         str(KeyError('dictionary is empty')))
+        # Test setdefault(k)
+        with self.assertRaises(TypeError) as raises:
+            d.setdefault(1)
+        self.assertEqual(
+            str(raises.exception),
+            str(TypeError('invalid operation on untyped dictionary')),
+        )
+        # Test __contains__
+        self.assertFalse(1 in d)
+        # It's untyped
+        self.assertFalse(d._typed)
+
+    def test_getitem(self):
+        # Test __getitem__
+        d = Dict()
+        d[1] = 2
+        # It's typed now
+        self.assertTrue(d._typed)
+        self.assertEqual(d[1], 2)
+
+    def test_setdefault(self):
+        # Test setdefault(k, d)
+        d = Dict()
+        d.setdefault(1, 2)
+        # It's typed now
+        self.assertTrue(d._typed)
+        self.assertEqual(d[1], 2)
+
+
+@jitclass(spec=[('a', types.intp)])
+class Bag(object):
+    def __init__(self, a):
+        self.a = a
+
+    def __hash__(self):
+        return hash(self.a)
+
+
+class TestDictWithJitclass(TestCase):
+    def test_jitclass_as_value(self):
+        @njit
+        def foo(x):
+            d = Dict()
+            d[0] = x
+            d[1] = Bag(101)
+            return d
+
+        d = foo(Bag(a=100))
+        self.assertEqual(d[0].a, 100)
+        self.assertEqual(d[1].a, 101)
+
+
+class TestNoJit(TestCase):
+    """Exercise dictionary creation with JIT disabled. """
+
+    def test_dict_create_no_jit_using_new_dict(self):
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                d = dictobject.new_dict(int32, float32)
+                self.assertEqual(type(d), dict)
+
+    def test_dict_create_no_jit_using_Dict(self):
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                d = Dict()
+                self.assertEqual(type(d), dict)
+
+    def test_dict_create_no_jit_using_empty(self):
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                d = Dict.empty(types.int32, types.float32)
+                self.assertEqual(type(d), dict)
+
+
+class TestDictIterator(TestCase):
+    def test_dict_iterator(self):
+        @njit
+        def fun1():
+            dd = Dict.empty(key_type=types.intp,
+                            value_type=types.intp)
+            dd[0] = 10
+            dd[1] = 20
+            dd[2] = 30
+
+            return list(dd.keys()), list(dd.values())
+
+        @njit
+        def fun2():
+            dd = Dict.empty(key_type=types.intp,
+                            value_type=types.intp)
+            dd[4] = 77
+            dd[5] = 88
+            dd[6] = 99
+
+            return list(dd.keys()), list(dd.values())
+        res1 = fun1()
+        res2 = fun2()
+
+        self.assertEqual([0,1,2], res1[0])
+        self.assertEqual([10,20,30], res1[1])
+        self.assertEqual([4,5,6], res2[0])
+        self.assertEqual([77,88,99], res2[1])
+
+
+class TestTypedDictInitialValues(MemoryLeakMixin, TestCase):
+    """Tests that typed dictionaries carry their initial value if present"""
+
+    def test_homogeneous_and_literal(self):
+        def bar(d):
+            ...
+
+        @overload(bar)
+        def ol_bar(d):
+            if d.initial_value is None:
+                return lambda d: literally(d)
+            self.assertTrue(isinstance(d, types.DictType))
+            self.assertEqual(d.initial_value, {'a': 1, 'b': 2, 'c': 3})
+            self.assertEqual(hasattr(d, 'literal_value'), False)
+            return lambda d: d
+
+        @njit
+        def foo():
+            # keys and values all have literal representation
+            x = {'a': 1, 'b': 2, 'c': 3}
+            bar(x)
+
+        foo()
+
+    def test_heterogeneous_but_castable_to_homogeneous(self):
+        def bar(d):
+            ...
+
+        @overload(bar)
+        def ol_bar(d):
+            self.assertTrue(isinstance(d, types.DictType))
+            self.assertEqual(d.initial_value, None)
+            self.assertEqual(hasattr(d, 'literal_value'), False)
+            return lambda d: d
+
+        @njit
+        def foo():
+            # This dictionary will be typed based on 1j, i.e. complex128
+            # as the values are not all literals, there's no "initial_value"
+            # available irrespective of whether it's possible to rip this
+            # information out of the bytecode.
+            x = {'a': 1j, 'b': 2, 'c': 3}
+            bar(x)
+
+        foo()
+
+    def test_heterogeneous_but_not_castable_to_homogeneous(self):
+        def bar(d):
+            ...
+
+        @overload(bar)
+        def ol_bar(d):
+            a = {'a': 1, 'b': 2j, 'c': 3}
+
+            def specific_ty(z):
+                return types.literal(z) if types.maybe_literal(z) else typeof(z)
+            expected = {types.literal(x): specific_ty(y) for x, y in a.items()}
+            self.assertTrue(isinstance(d, types.LiteralStrKeyDict))
+            self.assertEqual(d.literal_value, expected)
+            self.assertEqual(hasattr(d, 'initial_value'), False)
+            return lambda d: d
+
+        @njit
+        def foo():
+            # This dictionary will be typed based on 1, i.e. intp, as the values
+            # cannot all be cast to this type, but the keys are literal strings
+            # this is a LiteralStrKey[Dict], there's no initial_value but there
+            # is a literal_value.
+            x = {'a': 1, 'b': 2j, 'c': 3}
+            bar(x)
+
+        foo()
+
+    def test_mutation_not_carried(self):
+        def bar(d):
+            ...
+
+        @overload(bar)
+        def ol_bar(d):
+            if d.initial_value is None:
+                return lambda d: literally(d)
+            self.assertTrue(isinstance(d, types.DictType))
+            self.assertEqual(d.initial_value, {'a': 1, 'b': 2, 'c': 3})
+            return lambda d: d
+
+        @njit
+        def foo():
+            # This dictionary is mutated, check the initial_value carries
+            # correctly and is not mutated
+            x = {'a': 1, 'b': 2, 'c': 3}
+            x['d'] = 4
+            bar(x)
+
+        foo()
+
+    def test_mutation_not_carried_single_function(self):
+        # this is another pattern for using literally
+
+        @njit
+        def nop(*args):
+            pass
+
+        for fn, iv in (nop, None), (literally, {'a': 1, 'b': 2, 'c': 3}):
+            @njit
+            def baz(x):
+                pass
+
+            def bar(z):
+                pass
+
+            @overload(bar)
+            def ol_bar(z):
+                def impl(z):
+                    fn(z)
+                    baz(z)
+                return impl
+
+            @njit
+            def foo():
+                x = {'a': 1, 'b': 2, 'c': 3}
+                bar(x)
+                x['d'] = 4
+                return x
+
+            foo()
+            # baz should be specialised based on literally being invoked and
+            # the literal/unliteral arriving at the call site
+            larg = baz.signatures[0][0]
+            self.assertEqual(larg.initial_value, iv)
+
+    def test_unify_across_function_call(self):
+
+        @njit
+        def bar(x):
+            o = {1: 2}
+            if x:
+                o = {2: 3}
+            return o
+
+        @njit
+        def foo(x):
+            if x:
+                d = {3: 4}
+            else:
+                d = bar(x)
+            return d
+
+        e1 = Dict()
+        e1[3] = 4
+        e2 = Dict()
+        e2[1] = 2
+        self.assertEqual(foo(True), e1)
+        self.assertEqual(foo(False), e2)
+
+
+class TestLiteralStrKeyDict(MemoryLeakMixin, TestCase):
+    """ Tests for dictionaries with string keys that can map to anything!"""
+
+    def test_basic_const_lowering_boxing(self):
+        @njit
+        def foo():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            return (ld['a'], ld['b'], ld['c'])
+
+        self.assertEqual(foo(), (1, 2j, 'd'))
+
+    def test_basic_nonconst_in_scope(self):
+        @njit
+        def foo(x):
+            y = x + 5
+            e = True if y > 2 else False
+            ld = {'a': 1, 'b': 2j, 'c': 'd', 'non_const': e}
+            return ld['non_const']
+
+        # Recall that key non_const has a value of a known type, bool, and it's
+        # value is stuffed in at run time, this is permitted as the dictionary
+        # is immutable in type
+        self.assertTrue(foo(34))
+        self.assertFalse(foo(-100))
+
+    def test_basic_nonconst_freevar(self):
+        e = 5
+
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            self.assertEqual(x.literal_value,
+                             {types.literal('a'): types.literal(1),
+                              types.literal('b'): typeof(2j),
+                              types.literal('c'): types.literal('d'),
+                              types.literal('d'): types.literal(5)})
+
+            def impl(x):
+                pass
+            return impl
+
+        @njit
+        def foo():
+            ld = {'a': 1, 'b': 2j, 'c': 'd', 'd': e}
+            bar(ld)
+
+        foo()
+
+    def test_literal_value(self):
+
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            self.assertEqual(x.literal_value,
+                             {types.literal('a'): types.literal(1),
+                              types.literal('b'): typeof(2j),
+                              types.literal('c'): types.literal('d')})
+
+            def impl(x):
+                pass
+            return impl
+
+        @njit
+        def foo():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            bar(ld)
+
+        foo()
+
+    def test_list_and_array_as_value(self):
+
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            self.assertEqual(x.literal_value,
+                             {types.literal('a'): types.literal(1),
+                              types.literal('b'):
+                              types.List(types.intp, initial_value=[1,2,3]),
+                              types.literal('c'): typeof(np.zeros(5))})
+
+            def impl(x):
+                pass
+            return impl
+
+        @njit
+        def foo():
+            b = [1, 2, 3]
+            ld = {'a': 1, 'b': b, 'c': np.zeros(5)}
+            bar(ld)
+
+        foo()
+
+    def test_repeated_key_literal_value(self):
+
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            # order is important, 'a' was seen first, but updated later
+            self.assertEqual(x.literal_value,
+                             {types.literal('a'): types.literal('aaaa'),
+                              types.literal('b'): typeof(2j),
+                              types.literal('c'): types.literal('d')})
+
+            def impl(x):
+                pass
+            return impl
+
+        @njit
+        def foo():
+            ld = {'a': 1, 'a': 10, 'b': 2j, 'c': 'd', 'a': 'aaaa'} # noqa #F601
+            bar(ld)
+
+        foo()
+
+    def test_read_only(self):
+
+        def _len():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            return len(ld)
+
+        def static_getitem():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            return ld['b']
+
+        def contains():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            return 'b' in ld, 'f' in ld
+
+        def copy():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            new = ld.copy()
+            return ld == new
+
+        rdonlys = (_len, static_getitem, contains, copy)
+
+        for test in rdonlys:
+            with self.subTest(test.__name__):
+                self.assertPreciseEqual(njit(test)(), test())
+
+    def test_mutation_failure(self):
+
+        def setitem():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            ld['a'] = 12
+
+        def delitem():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            del ld['a']
+
+        def popitem():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            ld.popitem()
+
+        def pop():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            ld.pop()
+
+        def clear():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            ld.clear()
+
+        def setdefault():
+            ld = {'a': 1, 'b': 2j, 'c': 'd'}
+            ld.setdefault('f', 1)
+
+        illegals = (setitem, delitem, popitem, pop, clear, setdefault)
+
+        for test in illegals:
+            with self.subTest(test.__name__):
+                with self.assertRaises(TypingError) as raises:
+                    njit(test)()
+                expect = "Cannot mutate a literal dictionary"
+                self.assertIn(expect, str(raises.exception))
+
+    def test_get(self):
+
+        @njit
+        def get(x):
+            ld = {'a': 2j, 'c': 'd'}
+            return ld.get(x)
+
+        @njit
+        def getitem(x):
+            ld = {'a': 2j, 'c': 'd'}
+            return ld[x]
+
+        for test in (get, getitem):
+            with self.subTest(test.__name__):
+                with self.assertRaises(TypingError) as raises:
+                    test('a')
+                expect = "Cannot get{item}() on a literal dictionary"
+                self.assertIn(expect, str(raises.exception))
+
+    def test_dict_keys(self):
+
+        @njit
+        def foo():
+            ld = {'a': 2j, 'c': 'd'}
+            return [x for x in ld.keys()]
+
+        self.assertEqual(foo(), ['a', 'c'])
+
+    def test_dict_values(self):
+
+        @njit
+        def foo():
+            ld = {'a': 2j, 'c': 'd'}
+            return ld.values()
+
+        self.assertEqual(foo(), (2j, 'd'))
+
+    def test_dict_items(self):
+        @njit
+        def foo():
+            ld = {'a': 2j, 'c': 'd', 'f': np.zeros((5))}
+            return ld.items()
+
+        self.assertPreciseEqual(foo(),
+                                (('a', 2j), ('c', 'd'), ('f', np.zeros((5)))))
+
+    def test_dict_return(self):
+
+        @njit
+        def foo():
+            ld = {'a': 2j, 'c': 'd'}
+            return ld
+
+        # escaping heterogeneous dictionary is not supported
+        with self.assertRaises(TypeError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        self.assertIn("cannot convert native LiteralStrKey", excstr)
+
+    def test_dict_unify(self):
+        @njit
+        def foo(x):
+            if x + 7 > 4:
+                a = {'a': 2j, 'c': 'd', 'e': np.zeros(4)}
+            else:
+                # Note the use of a different literal str for key 'c'
+                a = {'a': 5j, 'c': 'CAT', 'e': np.zeros((5,))}
+            return a['c']
+
+        self.assertEqual(foo(100), 'd')
+        self.assertEqual(foo(-100), 'CAT')
+        self.assertEqual(foo(100), foo.py_func(100))
+        self.assertEqual(foo(-100), foo.py_func(-100))
+
+    def test_dict_not_unify(self):
+
+        @njit
+        def key_mismatch(x):
+            if x + 7 > 4:
+                a = {'BAD_KEY': 2j, 'c': 'd', 'e': np.zeros(4)}
+            else:
+                a = {'a': 5j, 'c': 'CAT', 'e': np.zeros((5,))}
+            # prevents inline of return on py310
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return a['a']
+
+        with self.assertRaises(TypingError) as raises:
+            key_mismatch(100)
+
+        self.assertIn("Cannot unify LiteralStrKey", str(raises.exception))
+
+        @njit
+        def value_type_mismatch(x):
+            if x + 7 > 4:
+                a = {'a': 2j, 'c': 'd', 'e': np.zeros((4, 3))}
+            else:
+                a = {'a': 5j, 'c': 'CAT', 'e': np.zeros((5,))}
+            # prevents inline of return on py310
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return a['a']
+
+        with self.assertRaises(TypingError) as raises:
+            value_type_mismatch(100)
+
+        self.assertIn("Cannot unify LiteralStrKey", str(raises.exception))
+
+    def test_dict_value_coercion(self):
+        # checks that things coerce or not!
+
+        p = {# safe and no conversion: TypedDict
+             (np.int32, np.int32): types.DictType,
+             # safe and convertible: TypedDict
+             (np.int32, np.int8): types.DictType,
+             # safe convertible: TypedDict
+             (np.complex128, np.int32): types.DictType,
+             # unsafe not convertible: LiteralStrKey
+             (np.int32, np.complex128): types.LiteralStrKeyDict,
+             # unsafe not convertible: LiteralStrKey
+             (np.int32, np.array): types.LiteralStrKeyDict,
+             # unsafe not convertible: LiteralStrKey
+             (np.array, np.int32): types.LiteralStrKeyDict,
+             # unsafe not convertible: LiteralStrKey
+             (np.int8, np.int32): types.LiteralStrKeyDict,
+             # unsafe not convertible: LiteralStrKey (issue #6420 case)
+             (np.int64, np.float64): types.LiteralStrKeyDict,}
+
+        def bar(x):
+            pass
+
+        for dts, container in p.items():
+            @overload(bar)
+            def ol_bar(x):
+                self.assertTrue(isinstance(x, container))
+
+                def impl(x):
+                    pass
+                return impl
+
+            ty1, ty2 = dts
+
+            @njit
+            def foo():
+                d = {'a': ty1(1), 'b': ty2(2)}
+                bar(d)
+
+            foo()
+
+    def test_build_map_op_code(self):
+        # tests building dictionaries via `build_map`, which, for statically
+        # determinable str key->things cases is just a single key:value
+        # any other build_map would either end up as being non-const str keys
+        # or keys of some non-string type and therefore not considered.
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            def impl(x):
+                pass
+            return impl
+
+        @njit
+        def foo():
+            a = {'a': {'b1': 10, 'b2': 'string'}}
+            bar(a)
+
+        foo()
+
+    def test_dict_as_arg(self):
+        @njit
+        def bar(fake_kwargs=None):
+            if fake_kwargs is not None:
+                # Add 10 to array in key 'd'
+                fake_kwargs['d'][:] += 10
+
+        @njit
+        def foo():
+            a = 1
+            b = 2j
+            c = 'string'
+            d = np.zeros(3)
+            e = {'a': a, 'b': b, 'c': c, 'd': d}
+            bar(fake_kwargs=e)
+            return e['d']
+
+        np.testing.assert_allclose(foo(), np.ones(3) * 10)
+
+    def test_dict_with_single_literallist_value(self):
+        #see issue #6094
+        @njit
+        def foo():
+            z = {"A": [lambda a: 2 * a, "B"]}
+            return z["A"][0](5)
+
+        self.assertPreciseEqual(foo(), foo.py_func())
+
+    def test_tuple_not_in_mro(self):
+        # Related to #6094, make sure that LiteralStrKey does not inherit from
+        # types.BaseTuple as this breaks isinstance checks.
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            self.assertFalse(isinstance(x, types.BaseTuple))
+            self.assertTrue(isinstance(x, types.LiteralStrKeyDict))
+            return lambda x: ...
+
+        @njit
+        def foo():
+            d = {'a': 1, 'b': 'c'}
+            bar(d)
+
+        foo()
+
+    def test_const_key_not_in_dict(self):
+
+        @njit
+        def foo():
+            a = {'not_a': 2j, 'c': 'd', 'e': np.zeros(4)}
+            return a['a']
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+
+        self.assertIn("Key 'a' is not in dict.", str(raises.exception))
+
+    def test_uncommon_identifiers(self):
+        # Tests uncommon identifiers like numerical values and operators in
+        # the key fields. See #6518 and #7416.
+
+        # Numerical values in keys
+        @njit
+        def foo():
+            d = {'0': np.ones(5), '1': 4}
+            return len(d)
+
+        self.assertPreciseEqual(foo(), foo.py_func())
+
+        # operators in keys
+        @njit
+        def bar():
+            d = {'+': np.ones(5), 'x--': 4}
+            return len(d)
+
+        self.assertPreciseEqual(bar(), bar.py_func())
+
+    def test_update_error(self):
+        # Tests that dict.update produces a reasonable
+        # error with a LiteralStrKeyDict input.
+        @njit
+        def foo():
+
+            d1 = {
+                'a': 2,
+                'b': 4,
+                'c': 'a'
+            }
+            d1.update({'x': 3})
+            return d1
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+
+        self.assertIn(
+            "Cannot mutate a literal dictionary",
+            str(raises.exception)
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dicts.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dicts.py
new file mode 100644
index 000000000..ae66407df
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dicts.py
@@ -0,0 +1,172 @@
+from numba import njit
+from numba.core.errors import TypingError
+import unittest
+from numba.tests.support import TestCase, force_pyobj_flags
+
+
+def build_map():
+    return {0: 1, 2: 3}
+
+def build_map_from_local_vars():
+    # There used to be a crash due to wrong IR generation for STORE_MAP
+    x = TestCase
+    return {0: x, x: 1}
+
+
+class DictTestCase(TestCase):
+
+    def test_build_map(self, flags=force_pyobj_flags):
+        self.run_nullary_func(build_map, flags=flags)
+
+    def test_build_map_from_local_vars(self, flags=force_pyobj_flags):
+        self.run_nullary_func(build_map_from_local_vars, flags=flags)
+
+
+class TestCompiledDict(TestCase):
+    """Testing `dict()` and `{}` usage that are redirected to
+    `numba.typed.Dict`.
+    """
+    def test_use_dict(self):
+        # Test dict()
+        @njit
+        def foo():
+            d = dict()
+            d[1] = 2
+            return d
+
+        d = foo()
+        self.assertEqual(d, {1: 2})
+
+    def test_unsupported_dict_usage(self):
+        # Test dict(dict())
+        from numba.core.typing.dictdecl import _message_dict_support
+
+        @njit
+        def foo():
+            d = dict()
+            d[1] = 2
+            return dict(d)
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+
+        self.assertIn(_message_dict_support, str(raises.exception))
+
+    def test_use_curlybraces(self):
+        # Test {} with empty args
+        @njit
+        def foo():
+            d = {}
+            d[1] = 2
+            return d
+
+        d = foo()
+        self.assertEqual(d, {1: 2})
+
+
+    def test_use_curlybraces_with_init1(self):
+        # Test {} with 1 item
+        @njit
+        def foo():
+            return {1: 2}
+
+        d = foo()
+        self.assertEqual(d, {1: 2})
+
+    def test_use_curlybraces_with_initmany(self):
+        # Test {} with many items
+        @njit
+        def foo():
+            return {1: 2.2, 3: 4.4, 5: 6.6}
+
+        d = foo()
+        self.assertEqual(d, {1: 2.2, 3: 4.4, 5: 6.6})
+
+    def test_curlybraces_init_with_coercion(self):
+        # Type coercion at dict init is tested
+        @njit
+        def foo():
+            return {1: 2.2, 3: 4, 5: 6}
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_use_curlybraces_with_manyvar(self):
+        # Test using variable in {}
+        @njit
+        def foo(x, y):
+            return {x: 1, y: x + y}
+
+        x, y = 10, 20
+        self.assertEqual(foo(x, y), foo.py_func(x, y))
+
+    def test_mixed_curlybraces_and_dict(self):
+        # Test mixed use of {} and dict()
+        @njit
+        def foo():
+            k = dict()
+            k[1] = {1: 3}
+            k[2] = {4: 2}
+            return k
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_dict_use_with_none_value(self):
+        # Test that NoneType cannot be used as value for Dict
+        @njit
+        def foo():
+            k = {1: None}
+            return k
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "Dict.value_type cannot be of type none",
+            str(raises.exception),
+        )
+
+
+    def test_dict_use_with_optional_value(self):
+        # Test that Optional cannot be used as value for Dict
+        @njit
+        def foo(choice):
+            optional = 2.5 if choice else None
+            k = {1: optional}
+            return k
+
+        with self.assertRaises(TypingError) as raises:
+            foo(True)
+        self.assertIn(
+            "Dict.value_type cannot be of type OptionalType(float64)",
+            str(raises.exception),
+        )
+
+    def test_dict_use_with_optional_key(self):
+        # Test that Optional cannot be used as a key for Dict
+        @njit
+        def foo(choice):
+            k = {2.5 if choice else None: 1}
+            return k
+
+        with self.assertRaises(TypingError) as raises:
+            foo(True)
+        self.assertIn(
+            "Dict.key_type cannot be of type OptionalType(float64)",
+            str(raises.exception),
+        )
+
+    def test_dict_use_with_none_key(self):
+        # Test that NoneType cannot be used as a key for Dict
+        @njit
+        def foo():
+            k = {None: 1}
+            return k
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "Dict.key_type cannot be of type none",
+            str(raises.exception),
+        )
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dispatcher.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dispatcher.py
new file mode 100644
index 000000000..4c66d1336
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dispatcher.py
@@ -0,0 +1,1227 @@
+import multiprocessing
+import platform
+import threading
+import pickle
+import weakref
+from itertools import chain
+from io import StringIO
+
+import numpy as np
+
+from numba import njit, jit, generated_jit, typeof, vectorize
+from numba.core import types, errors
+from numba import _dispatcher
+from numba.core.compiler import compile_isolated
+from numba.tests.support import TestCase, captured_stdout
+from numba.np.numpy_support import as_dtype
+from numba.core.dispatcher import Dispatcher
+from numba.tests.support import needs_lapack, SerialMixin
+from numba.testing.main import _TIMEOUT as _RUNNER_TIMEOUT
+import unittest
+
+
+_TEST_TIMEOUT = _RUNNER_TIMEOUT - 60.
+
+
+try:
+    import jinja2
+except ImportError:
+    jinja2 = None
+
+try:
+    import pygments
+except ImportError:
+    pygments = None
+
+_is_armv7l = platform.machine() == 'armv7l'
+
+
+def dummy(x):
+    return x
+
+
+def add(x, y):
+    return x + y
+
+
+def addsub(x, y, z):
+    return x - y + z
+
+
+def addsub_defaults(x, y=2, z=3):
+    return x - y + z
+
+
+def star_defaults(x, y=2, *z):
+    return x, y, z
+
+
+def generated_usecase(x, y=5):
+    if isinstance(x, types.Complex):
+        def impl(x, y):
+            return x + y
+    else:
+        def impl(x, y):
+            return x - y
+    return impl
+
+
+def bad_generated_usecase(x, y=5):
+    if isinstance(x, types.Complex):
+        def impl(x):
+            return x
+    else:
+        def impl(x, y=6):
+            return x - y
+    return impl
+
+
+def dtype_generated_usecase(a, b, dtype=None):
+    if isinstance(dtype, (types.misc.NoneType, types.misc.Omitted)):
+        out_dtype = np.result_type(*(np.dtype(ary.dtype.name)
+                                   for ary in (a, b)))
+    elif isinstance(dtype, (types.DType, types.NumberClass)):
+        out_dtype = as_dtype(dtype)
+    else:
+        raise TypeError("Unhandled Type %s" % type(dtype))
+
+    def _fn(a, b, dtype=None):
+        return np.ones(a.shape, dtype=out_dtype)
+
+    return _fn
+
+
+class BaseTest(TestCase):
+
+    jit_args = dict(nopython=True)
+
+    def compile_func(self, pyfunc):
+        def check(*args, **kwargs):
+            expected = pyfunc(*args, **kwargs)
+            result = f(*args, **kwargs)
+            self.assertPreciseEqual(result, expected)
+        f = jit(**self.jit_args)(pyfunc)
+        return f, check
+
+
+class TestDispatcher(BaseTest):
+
+    def test_equality(self):
+        @jit
+        def foo(x):
+            return x
+
+        @jit
+        def bar(x):
+            return x
+
+        # Written this way to verify `==` returns a bool (gh-5838). Using
+        # `assertTrue(foo == foo)` or `assertEqual(foo, foo)` would defeat the
+        # purpose of this test.
+        self.assertEqual(foo == foo, True)
+        self.assertEqual(foo == bar, False)
+        self.assertEqual(foo == None, False)  # noqa: E711
+
+    def test_dyn_pyfunc(self):
+        @jit
+        def foo(x):
+            return x
+
+        foo(1)
+        [cr] = foo.overloads.values()
+        # __module__ must be match that of foo
+        self.assertEqual(cr.entry_point.__module__, foo.py_func.__module__)
+
+    def test_no_argument(self):
+        @jit
+        def foo():
+            return 1
+
+        # Just make sure this doesn't crash
+        foo()
+
+    def test_coerce_input_types(self):
+        # Issue #486: do not allow unsafe conversions if we can still
+        # compile other specializations.
+        c_add = jit(nopython=True)(add)
+        self.assertPreciseEqual(c_add(123, 456), add(123, 456))
+        self.assertPreciseEqual(c_add(12.3, 45.6), add(12.3, 45.6))
+        self.assertPreciseEqual(c_add(12.3, 45.6j), add(12.3, 45.6j))
+        self.assertPreciseEqual(c_add(12300000000, 456), add(12300000000, 456))
+
+        # Now force compilation of only a single specialization
+        c_add = jit('(i4, i4)', nopython=True)(add)
+        self.assertPreciseEqual(c_add(123, 456), add(123, 456))
+        # Implicit (unsafe) conversion of float to int
+        self.assertPreciseEqual(c_add(12.3, 45.6), add(12, 45))
+        with self.assertRaises(TypeError):
+            # Implicit conversion of complex to int disallowed
+            c_add(12.3, 45.6j)
+
+    def test_ambiguous_new_version(self):
+        """Test compiling new version in an ambiguous case
+        """
+        @jit
+        def foo(a, b):
+            return a + b
+
+        INT = 1
+        FLT = 1.5
+        self.assertAlmostEqual(foo(INT, FLT), INT + FLT)
+        self.assertEqual(len(foo.overloads), 1)
+        self.assertAlmostEqual(foo(FLT, INT), FLT + INT)
+        self.assertEqual(len(foo.overloads), 2)
+        self.assertAlmostEqual(foo(FLT, FLT), FLT + FLT)
+        self.assertEqual(len(foo.overloads), 3)
+        # The following call is ambiguous because (int, int) can resolve
+        # to (float, int) or (int, float) with equal weight.
+        self.assertAlmostEqual(foo(1, 1), INT + INT)
+        self.assertEqual(len(foo.overloads), 4, "didn't compile a new "
+                                                "version")
+
+    def test_lock(self):
+        """
+        Test that (lazy) compiling from several threads at once doesn't
+        produce errors (see issue #908).
+        """
+        errors = []
+
+        @jit
+        def foo(x):
+            return x + 1
+
+        def wrapper():
+            try:
+                self.assertEqual(foo(1), 2)
+            except Exception as e:
+                errors.append(e)
+
+        threads = [threading.Thread(target=wrapper) for i in range(16)]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+        self.assertFalse(errors)
+
+    def test_explicit_signatures(self):
+        f = jit("(int64,int64)")(add)
+        # Approximate match (unsafe conversion)
+        self.assertPreciseEqual(f(1.5, 2.5), 3)
+        self.assertEqual(len(f.overloads), 1, f.overloads)
+        f = jit(["(int64,int64)", "(float64,float64)"])(add)
+        # Exact signature matches
+        self.assertPreciseEqual(f(1, 2), 3)
+        self.assertPreciseEqual(f(1.5, 2.5), 4.0)
+        # Approximate match (int32 -> float64 is a safe conversion)
+        self.assertPreciseEqual(f(np.int32(1), 2.5), 3.5)
+        # No conversion
+        with self.assertRaises(TypeError) as cm:
+            f(1j, 1j)
+        self.assertIn("No matching definition", str(cm.exception))
+        self.assertEqual(len(f.overloads), 2, f.overloads)
+        # A more interesting one...
+        f = jit(["(float32,float32)", "(float64,float64)"])(add)
+        self.assertPreciseEqual(f(np.float32(1), np.float32(2**-25)), 1.0)
+        self.assertPreciseEqual(f(1, 2**-25), 1.0000000298023224)
+        # Fail to resolve ambiguity between the two best overloads
+        f = jit(["(float32,float64)",
+                 "(float64,float32)",
+                 "(int64,int64)"])(add)
+        with self.assertRaises(TypeError) as cm:
+            f(1.0, 2.0)
+        # The two best matches are output in the error message, as well
+        # as the actual argument types.
+        self.assertRegexpMatches(
+            str(cm.exception),
+            r"Ambiguous overloading for <function add [^>]*> "
+            r"\(float64, float64\):\n"
+            r"\(float32, float64\) -> float64\n"
+            r"\(float64, float32\) -> float64"
+        )
+        # The integer signature is not part of the best matches
+        self.assertNotIn("int64", str(cm.exception))
+
+    def test_signature_mismatch(self):
+        tmpl = ("Signature mismatch: %d argument types given, but function "
+                "takes 2 arguments")
+        with self.assertRaises(TypeError) as cm:
+            jit("()")(add)
+        self.assertIn(tmpl % 0, str(cm.exception))
+        with self.assertRaises(TypeError) as cm:
+            jit("(intc,)")(add)
+        self.assertIn(tmpl % 1, str(cm.exception))
+        with self.assertRaises(TypeError) as cm:
+            jit("(intc,intc,intc)")(add)
+        self.assertIn(tmpl % 3, str(cm.exception))
+        # With forceobj=True, an empty tuple is accepted
+        jit("()", forceobj=True)(add)
+        with self.assertRaises(TypeError) as cm:
+            jit("(intc,)", forceobj=True)(add)
+        self.assertIn(tmpl % 1, str(cm.exception))
+
+    def test_matching_error_message(self):
+        f = jit("(intc,intc)")(add)
+        with self.assertRaises(TypeError) as cm:
+            f(1j, 1j)
+        self.assertEqual(str(cm.exception),
+                         "No matching definition for argument type(s) "
+                         "complex128, complex128")
+
+    def test_disabled_compilation(self):
+        @jit
+        def foo(a):
+            return a
+
+        foo.compile("(float32,)")
+        foo.disable_compile()
+        with self.assertRaises(RuntimeError) as raises:
+            foo.compile("(int32,)")
+        self.assertEqual(str(raises.exception), "compilation disabled")
+        self.assertEqual(len(foo.signatures), 1)
+
+    def test_disabled_compilation_through_list(self):
+        @jit(["(float32,)", "(int32,)"])
+        def foo(a):
+            return a
+
+        with self.assertRaises(RuntimeError) as raises:
+            foo.compile("(complex64,)")
+        self.assertEqual(str(raises.exception), "compilation disabled")
+        self.assertEqual(len(foo.signatures), 2)
+
+    def test_disabled_compilation_nested_call(self):
+        @jit(["(intp,)"])
+        def foo(a):
+            return a
+
+        @jit
+        def bar():
+            foo(1)
+            foo(np.ones(1))  # no matching definition
+
+        with self.assertRaises(TypeError) as raises:
+            bar()
+        m = "No matching definition for argument type(s) array(float64, 1d, C)"
+        self.assertEqual(str(raises.exception), m)
+
+    def test_fingerprint_failure(self):
+        """
+        Failure in computing the fingerprint cannot affect a nopython=False
+        function.  On the other hand, with nopython=True, a ValueError should
+        be raised to report the failure with fingerprint.
+        """
+        @jit
+        def foo(x):
+            return x
+
+        # Empty list will trigger failure in compile_fingerprint
+        errmsg = 'cannot compute fingerprint of empty list'
+        with self.assertRaises(ValueError) as raises:
+            _dispatcher.compute_fingerprint([])
+        self.assertIn(errmsg, str(raises.exception))
+        # It should work in fallback
+        self.assertEqual(foo([]), [])
+        # But, not in nopython=True
+        strict_foo = jit(nopython=True)(foo.py_func)
+        with self.assertRaises(ValueError) as raises:
+            strict_foo([])
+        self.assertIn(errmsg, str(raises.exception))
+
+        # Test in loop lifting context
+        @jit
+        def bar():
+            object()  # force looplifting
+            x = []
+            for i in range(10):
+                x = foo(x)
+            return x
+
+        self.assertEqual(bar(), [])
+        # Make sure it was looplifted
+        [cr] = bar.overloads.values()
+        self.assertEqual(len(cr.lifted), 1)
+
+    def test_serialization(self):
+        """
+        Test serialization of Dispatcher objects
+        """
+        @jit(nopython=True)
+        def foo(x):
+            return x + 1
+
+        self.assertEqual(foo(1), 2)
+
+        # get serialization memo
+        memo = Dispatcher._memo
+        Dispatcher._recent.clear()
+        memo_size = len(memo)
+
+        # pickle foo and check memo size
+        serialized_foo = pickle.dumps(foo)
+        # increases the memo size
+        self.assertEqual(memo_size + 1, len(memo))
+
+        # unpickle
+        foo_rebuilt = pickle.loads(serialized_foo)
+        self.assertEqual(memo_size + 1, len(memo))
+
+        self.assertIs(foo, foo_rebuilt)
+
+        # do we get the same object even if we delete all the explicit
+        # references?
+        id_orig = id(foo_rebuilt)
+        del foo
+        del foo_rebuilt
+        self.assertEqual(memo_size + 1, len(memo))
+        new_foo = pickle.loads(serialized_foo)
+        self.assertEqual(id_orig, id(new_foo))
+
+        # now clear the recent cache
+        ref = weakref.ref(new_foo)
+        del new_foo
+        Dispatcher._recent.clear()
+        self.assertEqual(memo_size, len(memo))
+
+        # show that deserializing creates a new object
+        pickle.loads(serialized_foo)
+        self.assertIs(ref(), None)
+
+    @needs_lapack
+    @unittest.skipIf(_is_armv7l, "Unaligned loads unsupported")
+    def test_misaligned_array_dispatch(self):
+        # for context see issue #2937
+        def foo(a):
+            return np.linalg.matrix_power(a, 1)
+
+        jitfoo = jit(nopython=True)(foo)
+
+        n = 64
+        r = int(np.sqrt(n))
+        dt = np.int8
+        count = np.complex128().itemsize // dt().itemsize
+
+        tmp = np.arange(n * count + 1, dtype=dt)
+
+        # create some arrays as Cartesian production of:
+        # [F/C] x [aligned/misaligned]
+        C_contig_aligned = tmp[:-1].view(np.complex128).reshape(r, r)
+        C_contig_misaligned = tmp[1:].view(np.complex128).reshape(r, r)
+        F_contig_aligned = C_contig_aligned.T
+        F_contig_misaligned = C_contig_misaligned.T
+
+        # checking routine
+        def check(name, a):
+            a[:, :] = np.arange(n, dtype=np.complex128).reshape(r, r)
+            expected = foo(a)
+            got = jitfoo(a)
+            np.testing.assert_allclose(expected, got)
+
+        # The checks must be run in this order to create the dispatch key
+        # sequence that causes invalid dispatch noted in #2937.
+        # The first two should hit the cache as they are aligned, supported
+        # order and under 5 dimensions. The second two should end up in the
+        # fallback path as they are misaligned.
+        check("C_contig_aligned", C_contig_aligned)
+        check("F_contig_aligned", F_contig_aligned)
+        check("C_contig_misaligned", C_contig_misaligned)
+        check("F_contig_misaligned", F_contig_misaligned)
+
+    @unittest.skipIf(_is_armv7l, "Unaligned loads unsupported")
+    def test_immutability_in_array_dispatch(self):
+
+        # RO operation in function
+        def foo(a):
+            return np.sum(a)
+
+        jitfoo = jit(nopython=True)(foo)
+
+        n = 64
+        r = int(np.sqrt(n))
+        dt = np.int8
+        count = np.complex128().itemsize // dt().itemsize
+
+        tmp = np.arange(n * count + 1, dtype=dt)
+
+        # create some arrays as Cartesian production of:
+        # [F/C] x [aligned/misaligned]
+        C_contig_aligned = tmp[:-1].view(np.complex128).reshape(r, r)
+        C_contig_misaligned = tmp[1:].view(np.complex128).reshape(r, r)
+        F_contig_aligned = C_contig_aligned.T
+        F_contig_misaligned = C_contig_misaligned.T
+
+        # checking routine
+        def check(name, a, disable_write_bit=False):
+            a[:, :] = np.arange(n, dtype=np.complex128).reshape(r, r)
+            if disable_write_bit:
+                a.flags.writeable = False
+            expected = foo(a)
+            got = jitfoo(a)
+            np.testing.assert_allclose(expected, got)
+
+        # all of these should end up in the fallback path as they have no write
+        # bit set
+        check("C_contig_aligned", C_contig_aligned, disable_write_bit=True)
+        check("F_contig_aligned", F_contig_aligned, disable_write_bit=True)
+        check("C_contig_misaligned", C_contig_misaligned,
+              disable_write_bit=True)
+        check("F_contig_misaligned", F_contig_misaligned,
+              disable_write_bit=True)
+
+    @needs_lapack
+    @unittest.skipIf(_is_armv7l, "Unaligned loads unsupported")
+    def test_misaligned_high_dimension_array_dispatch(self):
+
+        def foo(a):
+            return np.linalg.matrix_power(a[0, 0, 0, 0, :, :], 1)
+
+        jitfoo = jit(nopython=True)(foo)
+
+        def check_properties(arr, layout, aligned):
+            self.assertEqual(arr.flags.aligned, aligned)
+            if layout == "C":
+                self.assertEqual(arr.flags.c_contiguous, True)
+            if layout == "F":
+                self.assertEqual(arr.flags.f_contiguous, True)
+
+        n = 729
+        r = 3
+        dt = np.int8
+        count = np.complex128().itemsize // dt().itemsize
+
+        tmp = np.arange(n * count + 1, dtype=dt)
+
+        # create some arrays as Cartesian production of:
+        # [F/C] x [aligned/misaligned]
+        C_contig_aligned = tmp[:-1].view(np.complex128).\
+            reshape(r, r, r, r, r, r)
+        check_properties(C_contig_aligned, 'C', True)
+        C_contig_misaligned = tmp[1:].view(np.complex128).\
+            reshape(r, r, r, r, r, r)
+        check_properties(C_contig_misaligned, 'C', False)
+        F_contig_aligned = C_contig_aligned.T
+        check_properties(F_contig_aligned, 'F', True)
+        F_contig_misaligned = C_contig_misaligned.T
+        check_properties(F_contig_misaligned, 'F', False)
+
+        # checking routine
+        def check(name, a):
+            a[:, :] = np.arange(n, dtype=np.complex128).\
+                reshape(r, r, r, r, r, r)
+            expected = foo(a)
+            got = jitfoo(a)
+            np.testing.assert_allclose(expected, got)
+
+        # these should all hit the fallback path as the cache is only for up to
+        # 5 dimensions
+        check("F_contig_misaligned", F_contig_misaligned)
+        check("C_contig_aligned", C_contig_aligned)
+        check("F_contig_aligned", F_contig_aligned)
+        check("C_contig_misaligned", C_contig_misaligned)
+
+    def test_dispatch_recompiles_for_scalars(self):
+        # for context #3612, essentially, compiling a lambda x:x for a
+        # numerically wide type (everything can be converted to a complex128)
+        # and then calling again with e.g. an int32 would lead to the int32
+        # being converted to a complex128 whereas it ought to compile an int32
+        # specialization.
+        def foo(x):
+            return x
+
+        # jit and compile on dispatch for 3 scalar types, expect 3 signatures
+        jitfoo = jit(nopython=True)(foo)
+        jitfoo(np.complex128(1 + 2j))
+        jitfoo(np.int32(10))
+        jitfoo(np.bool_(False))
+        self.assertEqual(len(jitfoo.signatures), 3)
+        expected_sigs = [(types.complex128,), (types.int32,), (types.bool_,)]
+        self.assertEqual(jitfoo.signatures, expected_sigs)
+
+        # now jit with signatures so recompilation is forbidden
+        # expect 1 signature and type conversion
+        jitfoo = jit([(types.complex128,)], nopython=True)(foo)
+        jitfoo(np.complex128(1 + 2j))
+        jitfoo(np.int32(10))
+        jitfoo(np.bool_(False))
+        self.assertEqual(len(jitfoo.signatures), 1)
+        expected_sigs = [(types.complex128,)]
+        self.assertEqual(jitfoo.signatures, expected_sigs)
+
+    def test_dispatcher_raises_for_invalid_decoration(self):
+        # For context see https://github.com/numba/numba/issues/4750.
+
+        @jit(nopython=True)
+        def foo(x):
+            return x
+
+        with self.assertRaises(TypeError) as raises:
+            jit(foo)
+        err_msg = str(raises.exception)
+        self.assertIn(
+            "A jit decorator was called on an already jitted function", err_msg)
+        self.assertIn("foo", err_msg)
+        self.assertIn(".py_func", err_msg)
+
+        with self.assertRaises(TypeError) as raises:
+            jit(BaseTest)
+        err_msg = str(raises.exception)
+        self.assertIn("The decorated object is not a function", err_msg)
+        self.assertIn(f"{type(BaseTest)}", err_msg)
+
+
+class TestSignatureHandling(BaseTest):
+    """
+    Test support for various parameter passing styles.
+    """
+
+    def test_named_args(self):
+        """
+        Test passing named arguments to a dispatcher.
+        """
+        f, check = self.compile_func(addsub)
+        check(3, z=10, y=4)
+        check(3, 4, 10)
+        check(x=3, y=4, z=10)
+        # All calls above fall under the same specialization
+        self.assertEqual(len(f.overloads), 1)
+        # Errors
+        with self.assertRaises(TypeError) as cm:
+            f(3, 4, y=6, z=7)
+        self.assertIn("too many arguments: expected 3, got 4",
+                      str(cm.exception))
+        with self.assertRaises(TypeError) as cm:
+            f()
+        self.assertIn("not enough arguments: expected 3, got 0",
+                      str(cm.exception))
+        with self.assertRaises(TypeError) as cm:
+            f(3, 4, y=6)
+        self.assertIn("missing argument 'z'", str(cm.exception))
+
+    def test_default_args(self):
+        """
+        Test omitting arguments with a default value.
+        """
+        f, check = self.compile_func(addsub_defaults)
+        check(3, z=10, y=4)
+        check(3, 4, 10)
+        check(x=3, y=4, z=10)
+        # Now omitting some values
+        check(3, z=10)
+        check(3, 4)
+        check(x=3, y=4)
+        check(3)
+        check(x=3)
+        # Errors
+        with self.assertRaises(TypeError) as cm:
+            f(3, 4, y=6, z=7)
+        self.assertIn("too many arguments: expected 3, got 4",
+                      str(cm.exception))
+        with self.assertRaises(TypeError) as cm:
+            f()
+        self.assertIn("not enough arguments: expected at least 1, got 0",
+                      str(cm.exception))
+        with self.assertRaises(TypeError) as cm:
+            f(y=6, z=7)
+        self.assertIn("missing argument 'x'", str(cm.exception))
+
+    def test_star_args(self):
+        """
+        Test a compiled function with starargs in the signature.
+        """
+        f, check = self.compile_func(star_defaults)
+        check(4)
+        check(4, 5)
+        check(4, 5, 6)
+        check(4, 5, 6, 7)
+        check(4, 5, 6, 7, 8)
+        check(x=4)
+        check(x=4, y=5)
+        check(4, y=5)
+        with self.assertRaises(TypeError) as cm:
+            f(4, 5, y=6)
+        self.assertIn("some keyword arguments unexpected", str(cm.exception))
+        with self.assertRaises(TypeError) as cm:
+            f(4, 5, z=6)
+        self.assertIn("some keyword arguments unexpected", str(cm.exception))
+        with self.assertRaises(TypeError) as cm:
+            f(4, x=6)
+        self.assertIn("some keyword arguments unexpected", str(cm.exception))
+
+
+class TestSignatureHandlingObjectMode(TestSignatureHandling):
+    """
+    Sams as TestSignatureHandling, but in object mode.
+    """
+
+    jit_args = dict(forceobj=True)
+
+
+class TestGeneratedDispatcher(TestCase):
+    """
+    Tests for @generated_jit.
+    """
+
+    def test_generated(self):
+        f = generated_jit(nopython=True)(generated_usecase)
+        self.assertEqual(f(8), 8 - 5)
+        self.assertEqual(f(x=8), 8 - 5)
+        self.assertEqual(f(x=8, y=4), 8 - 4)
+        self.assertEqual(f(1j), 5 + 1j)
+        self.assertEqual(f(1j, 42), 42 + 1j)
+        self.assertEqual(f(x=1j, y=7), 7 + 1j)
+
+    def test_generated_dtype(self):
+        f = generated_jit(nopython=True)(dtype_generated_usecase)
+        a = np.ones((10,), dtype=np.float32)
+        b = np.ones((10,), dtype=np.float64)
+        self.assertEqual(f(a, b).dtype, np.float64)
+        self.assertEqual(f(a, b, dtype=np.dtype('int32')).dtype, np.int32)
+        self.assertEqual(f(a, b, dtype=np.int32).dtype, np.int32)
+
+    def test_signature_errors(self):
+        """
+        Check error reporting when implementation signature doesn't match
+        generating function signature.
+        """
+        f = generated_jit(nopython=True)(bad_generated_usecase)
+        # Mismatching # of arguments
+        with self.assertRaises(TypeError) as raises:
+            f(1j)
+        self.assertIn("should be compatible with signature '(x, y=5)', "
+                      "but has signature '(x)'",
+                      str(raises.exception))
+        # Mismatching defaults
+        with self.assertRaises(TypeError) as raises:
+            f(1)
+        self.assertIn("should be compatible with signature '(x, y=5)', "
+                      "but has signature '(x, y=6)'",
+                      str(raises.exception))
+
+
+class TestDispatcherMethods(TestCase):
+
+    def test_recompile(self):
+        closure = 1
+
+        @jit
+        def foo(x):
+            return x + closure
+        self.assertPreciseEqual(foo(1), 2)
+        self.assertPreciseEqual(foo(1.5), 2.5)
+        self.assertEqual(len(foo.signatures), 2)
+        closure = 2
+        self.assertPreciseEqual(foo(1), 2)
+        # Recompiling takes the new closure into account.
+        foo.recompile()
+        # Everything was recompiled
+        self.assertEqual(len(foo.signatures), 2)
+        self.assertPreciseEqual(foo(1), 3)
+        self.assertPreciseEqual(foo(1.5), 3.5)
+
+    def test_recompile_signatures(self):
+        # Same as above, but with an explicit signature on @jit.
+        closure = 1
+
+        @jit("int32(int32)")
+        def foo(x):
+            return x + closure
+        self.assertPreciseEqual(foo(1), 2)
+        self.assertPreciseEqual(foo(1.5), 2)
+        closure = 2
+        self.assertPreciseEqual(foo(1), 2)
+        # Recompiling takes the new closure into account.
+        foo.recompile()
+        self.assertPreciseEqual(foo(1), 3)
+        self.assertPreciseEqual(foo(1.5), 3)
+
+    def test_inspect_llvm(self):
+        # Create a jited function
+        @jit
+        def foo(explicit_arg1, explicit_arg2):
+            return explicit_arg1 + explicit_arg2
+
+        # Call it in a way to create 3 signatures
+        foo(1, 1)
+        foo(1.0, 1)
+        foo(1.0, 1.0)
+
+        # base call to get all llvm in a dict
+        llvms = foo.inspect_llvm()
+        self.assertEqual(len(llvms), 3)
+
+        # make sure the function name shows up in the llvm
+        for llvm_bc in llvms.values():
+            # Look for the function name
+            self.assertIn("foo", llvm_bc)
+
+            # Look for the argument names
+            self.assertIn("explicit_arg1", llvm_bc)
+            self.assertIn("explicit_arg2", llvm_bc)
+
+    def test_inspect_asm(self):
+        # Create a jited function
+        @jit
+        def foo(explicit_arg1, explicit_arg2):
+            return explicit_arg1 + explicit_arg2
+
+        # Call it in a way to create 3 signatures
+        foo(1, 1)
+        foo(1.0, 1)
+        foo(1.0, 1.0)
+
+        # base call to get all llvm in a dict
+        asms = foo.inspect_asm()
+        self.assertEqual(len(asms), 3)
+
+        # make sure the function name shows up in the llvm
+        for asm in asms.values():
+            # Look for the function name
+            self.assertTrue("foo" in asm)
+
+    def _check_cfg_display(self, cfg, wrapper=''):
+        # simple stringify test
+        if wrapper:
+            wrapper = "{}{}".format(len(wrapper), wrapper)
+        module_name = __name__.split('.', 1)[0]
+        module_len = len(module_name)
+        prefix = r'^digraph "CFG for \'_ZN{}{}{}'.format(wrapper,
+                                                         module_len,
+                                                         module_name)
+        self.assertRegexpMatches(str(cfg), prefix)
+        # .display() requires an optional dependency on `graphviz`.
+        # just test for the attribute without running it.
+        self.assertTrue(callable(cfg.display))
+
+    def test_inspect_cfg(self):
+        # Exercise the .inspect_cfg(). These are minimal tests and do not fully
+        # check the correctness of the function.
+        @jit
+        def foo(the_array):
+            return the_array.sum()
+
+        # Generate 3 overloads
+        a1 = np.ones(1)
+        a2 = np.ones((1, 1))
+        a3 = np.ones((1, 1, 1))
+        foo(a1)
+        foo(a2)
+        foo(a3)
+
+        # Call inspect_cfg() without arguments
+        cfgs = foo.inspect_cfg()
+
+        # Correct count of overloads
+        self.assertEqual(len(cfgs), 3)
+
+        # Makes sure all the signatures are correct
+        [s1, s2, s3] = cfgs.keys()
+        self.assertEqual(set([s1, s2, s3]),
+                         set(map(lambda x: (typeof(x),), [a1, a2, a3])))
+
+        for cfg in cfgs.values():
+            self._check_cfg_display(cfg)
+        self.assertEqual(len(list(cfgs.values())), 3)
+
+        # Call inspect_cfg(signature)
+        cfg = foo.inspect_cfg(signature=foo.signatures[0])
+        self._check_cfg_display(cfg)
+
+    def test_inspect_cfg_with_python_wrapper(self):
+        # Exercise the .inspect_cfg() including the python wrapper.
+        # These are minimal tests and do not fully check the correctness of
+        # the function.
+        @jit
+        def foo(the_array):
+            return the_array.sum()
+
+        # Generate 3 overloads
+        a1 = np.ones(1)
+        a2 = np.ones((1, 1))
+        a3 = np.ones((1, 1, 1))
+        foo(a1)
+        foo(a2)
+        foo(a3)
+
+        # Call inspect_cfg(signature, show_wrapper="python")
+        cfg = foo.inspect_cfg(signature=foo.signatures[0],
+                              show_wrapper="python")
+        self._check_cfg_display(cfg, wrapper='cpython')
+
+    def test_inspect_types(self):
+        @jit
+        def foo(a, b):
+            return a + b
+
+        foo(1, 2)
+        # Exercise the method
+        foo.inspect_types(StringIO())
+
+        # Test output
+        expected = str(foo.overloads[foo.signatures[0]].type_annotation)
+        with captured_stdout() as out:
+            foo.inspect_types()
+        assert expected in out.getvalue()
+
+    def test_inspect_types_with_signature(self):
+        @jit
+        def foo(a):
+            return a + 1
+
+        foo(1)
+        foo(1.0)
+        # Inspect all signatures
+        with captured_stdout() as total:
+            foo.inspect_types()
+        # Inspect first signature
+        with captured_stdout() as first:
+            foo.inspect_types(signature=foo.signatures[0])
+        # Inspect second signature
+        with captured_stdout() as second:
+            foo.inspect_types(signature=foo.signatures[1])
+
+        self.assertEqual(total.getvalue(), first.getvalue() + second.getvalue())
+
+    @unittest.skipIf(jinja2 is None, "please install the 'jinja2' package")
+    @unittest.skipIf(pygments is None, "please install the 'pygments' package")
+    def test_inspect_types_pretty(self):
+        @jit
+        def foo(a, b):
+            return a + b
+
+        foo(1, 2)
+
+        # Exercise the method, dump the output
+        with captured_stdout():
+            ann = foo.inspect_types(pretty=True)
+
+        # ensure HTML <span> is found in the annotation output
+        for k, v in ann.ann.items():
+            span_found = False
+            for line in v['pygments_lines']:
+                if 'span' in line[2]:
+                    span_found = True
+            self.assertTrue(span_found)
+
+        # check that file+pretty kwarg combo raises
+        with self.assertRaises(ValueError) as raises:
+            foo.inspect_types(file=StringIO(), pretty=True)
+
+        self.assertIn("`file` must be None if `pretty=True`",
+                      str(raises.exception))
+
+    def test_get_annotation_info(self):
+        @jit
+        def foo(a):
+            return a + 1
+
+        foo(1)
+        foo(1.3)
+
+        expected = dict(chain.from_iterable(foo.get_annotation_info(i).items()
+                                            for i in foo.signatures))
+        result = foo.get_annotation_info()
+        self.assertEqual(expected, result)
+
+    def test_issue_with_array_layout_conflict(self):
+        """
+        This test an issue with the dispatcher when an array that is both
+        C and F contiguous is supplied as the first signature.
+        The dispatcher checks for F contiguous first but the compiler checks
+        for C contiguous first. This results in an C contiguous code inserted
+        as F contiguous function.
+        """
+        def pyfunc(A, i, j):
+            return A[i, j]
+
+        cfunc = jit(pyfunc)
+
+        ary_c_and_f = np.array([[1.]])
+        ary_c = np.array([[0., 1.], [2., 3.]], order='C')
+        ary_f = np.array([[0., 1.], [2., 3.]], order='F')
+
+        exp_c = pyfunc(ary_c, 1, 0)
+        exp_f = pyfunc(ary_f, 1, 0)
+
+        self.assertEqual(1., cfunc(ary_c_and_f, 0, 0))
+        got_c = cfunc(ary_c, 1, 0)
+        got_f = cfunc(ary_f, 1, 0)
+
+        self.assertEqual(exp_c, got_c)
+        self.assertEqual(exp_f, got_f)
+
+
+class TestDispatcherFunctionBoundaries(TestCase):
+    def test_pass_dispatcher_as_arg(self):
+        # Test that a Dispatcher object can be pass as argument
+        @jit(nopython=True)
+        def add1(x):
+            return x + 1
+
+        @jit(nopython=True)
+        def bar(fn, x):
+            return fn(x)
+
+        @jit(nopython=True)
+        def foo(x):
+            return bar(add1, x)
+
+        # Check dispatcher as argument inside NPM
+        inputs = [1, 11.1, np.arange(10)]
+        expected_results = [x + 1 for x in inputs]
+
+        for arg, expect in zip(inputs, expected_results):
+            self.assertPreciseEqual(foo(arg), expect)
+
+        # Check dispatcher as argument from python
+        for arg, expect in zip(inputs, expected_results):
+            self.assertPreciseEqual(bar(add1, arg), expect)
+
+    def test_dispatcher_as_arg_usecase(self):
+        @jit(nopython=True)
+        def maximum(seq, cmpfn):
+            tmp = seq[0]
+            for each in seq[1:]:
+                cmpval = cmpfn(tmp, each)
+                if cmpval < 0:
+                    tmp = each
+            return tmp
+
+        got = maximum([1, 2, 3, 4], cmpfn=jit(lambda x, y: x - y))
+        self.assertEqual(got, 4)
+        got = maximum(list(zip(range(5), range(5)[::-1])),
+                      cmpfn=jit(lambda x, y: x[0] - y[0]))
+        self.assertEqual(got, (4, 0))
+        got = maximum(list(zip(range(5), range(5)[::-1])),
+                      cmpfn=jit(lambda x, y: x[1] - y[1]))
+        self.assertEqual(got, (0, 4))
+
+    def test_dispatcher_can_return_to_python(self):
+        @jit(nopython=True)
+        def foo(fn):
+            return fn
+
+        fn = jit(lambda x: x)
+
+        self.assertEqual(foo(fn), fn)
+
+    def test_dispatcher_in_sequence_arg(self):
+        @jit(nopython=True)
+        def one(x):
+            return x + 1
+
+        @jit(nopython=True)
+        def two(x):
+            return one(one(x))
+
+        @jit(nopython=True)
+        def three(x):
+            return one(one(one(x)))
+
+        @jit(nopython=True)
+        def choose(fns, x):
+            return fns[0](x), fns[1](x), fns[2](x)
+
+        # Tuple case
+        self.assertEqual(choose((one, two, three), 1), (2, 3, 4))
+        # List case
+        self.assertEqual(choose([one, one, one], 1), (2, 2, 2))
+
+
+class TestBoxingDefaultError(unittest.TestCase):
+    # Testing default error at boxing/unboxing
+    def test_unbox_runtime_error(self):
+        # Dummy type has no unbox support
+        def foo(x):
+            pass
+        cres = compile_isolated(foo, (types.Dummy("dummy_type"),))
+        with self.assertRaises(TypeError) as raises:
+            # Can pass in whatever and the unbox logic will always raise
+            # without checking the input value.
+            cres.entry_point(None)
+        self.assertEqual(str(raises.exception), "can't unbox dummy_type type")
+
+    def test_box_runtime_error(self):
+        def foo():
+            return unittest  # Module type has no boxing logic
+        cres = compile_isolated(foo, ())
+        with self.assertRaises(TypeError) as raises:
+            # Can pass in whatever and the unbox logic will always raise
+            # without checking the input value.
+            cres.entry_point()
+        pat = "cannot convert native Module.* to Python object"
+        self.assertRegexpMatches(str(raises.exception), pat)
+
+
+class TestNoRetryFailedSignature(unittest.TestCase):
+    """Test that failed-to-compile signatures are not recompiled.
+    """
+
+    def run_test(self, func):
+        fcom = func._compiler
+        self.assertEqual(len(fcom._failed_cache), 0)
+        # expected failure because `int` has no `__getitem__`
+        with self.assertRaises(errors.TypingError):
+            func(1)
+        self.assertEqual(len(fcom._failed_cache), 1)
+        # retry
+        with self.assertRaises(errors.TypingError):
+            func(1)
+        self.assertEqual(len(fcom._failed_cache), 1)
+        # retry with double
+        with self.assertRaises(errors.TypingError):
+            func(1.0)
+        self.assertEqual(len(fcom._failed_cache), 2)
+
+    def test_direct_call(self):
+        @jit(nopython=True)
+        def foo(x):
+            return x[0]
+
+        self.run_test(foo)
+
+    def test_nested_call(self):
+        @jit(nopython=True)
+        def bar(x):
+            return x[0]
+
+        @jit(nopython=True)
+        def foobar(x):
+            bar(x)
+
+        @jit(nopython=True)
+        def foo(x):
+            return bar(x) + foobar(x)
+
+        self.run_test(foo)
+
+    def test_error_count(self):
+        def check(field, would_fail):
+            # Slightly modified from the reproducer in issue #4117.
+            # Before the patch, the compilation time of the failing case is
+            # much longer than of the successful case. This can be detected
+            # by the number of times `trigger()` is visited.
+            k = 10
+            counter = {'c': 0}
+
+            @generated_jit
+            def trigger(x):
+                # Keep track of every visit
+                counter['c'] += 1
+                if would_fail:
+                    raise errors.TypingError("invoke_failed")
+                return lambda x: x
+
+            @jit(nopython=True)
+            def ident(out, x):
+                pass
+
+            def chain_assign(fs, inner=ident):
+                tab_head, tab_tail = fs[-1], fs[:-1]
+
+                @jit(nopython=True)
+                def assign(out, x):
+                    inner(out, x)
+                    out[0] += tab_head(x)
+
+                if tab_tail:
+                    return chain_assign(tab_tail, assign)
+                else:
+                    return assign
+
+            chain = chain_assign((trigger,) * k)
+            out = np.ones(2)
+            if would_fail:
+                with self.assertRaises(errors.TypingError) as raises:
+                    chain(out, 1)
+                self.assertIn('invoke_failed', str(raises.exception))
+            else:
+                chain(out, 1)
+
+            # Returns the visit counts
+            return counter['c']
+
+        ct_ok = check('a', False)
+        ct_bad = check('c', True)
+        # `trigger()` is visited exactly once for both successful and failed
+        # compilation.
+        self.assertEqual(ct_ok, 1)
+        self.assertEqual(ct_bad, 1)
+
+
+@njit
+def add_y1(x, y=1):
+    return x + y
+
+
+@njit
+def add_ynone(x, y=None):
+    return x + (1 if y else 2)
+
+
+@njit
+def mult(x, y):
+    return x * y
+
+
+@njit
+def add_func(x, func=mult):
+    return x + func(x, x)
+
+
+def _checker(f1, arg):
+    assert f1(arg) == f1.py_func(arg)
+
+
+class TestMultiprocessingDefaultParameters(SerialMixin, unittest.TestCase):
+    def run_fc_multiproc(self, fc):
+        try:
+            ctx = multiprocessing.get_context('spawn')
+        except AttributeError:
+            ctx = multiprocessing
+
+        # RE: issue #5973, this doesn't use multiprocessing.Pool.map as doing so
+        # causes the TBB library to segfault under certain conditions. It's not
+        # clear whether the cause is something in the complexity of the Pool
+        # itself, e.g. watcher threads etc, or if it's a problem synonymous with
+        # a "timing attack".
+        for a in [1, 2, 3]:
+            p = ctx.Process(target=_checker, args=(fc, a,))
+            p.start()
+            p.join(_TEST_TIMEOUT)
+            self.assertEqual(p.exitcode, 0)
+
+    def test_int_def_param(self):
+        """ Tests issue #4888"""
+
+        self.run_fc_multiproc(add_y1)
+
+    def test_none_def_param(self):
+        """ Tests None as a default parameter"""
+
+        self.run_fc_multiproc(add_func)
+
+    def test_function_def_param(self):
+        """ Tests a function as a default parameter"""
+
+        self.run_fc_multiproc(add_func)
+
+
+class TestVectorizeDifferentTargets(unittest.TestCase):
+    """Test that vectorize can be reapplied if the target is different
+    """
+
+    def test_cpu_vs_parallel(self):
+        @jit
+        def add(x, y):
+            return x + y
+
+        custom_vectorize = vectorize([], identity=None, target='cpu')
+
+        custom_vectorize(add)
+
+        custom_vectorize_2 = vectorize([], identity=None, target='parallel')
+
+        custom_vectorize_2(add)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dummyarray.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dummyarray.py
new file mode 100644
index 000000000..c5bda7d26
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dummyarray.py
@@ -0,0 +1,350 @@
+import unittest
+import itertools
+import numpy as np
+from numba.misc.dummyarray import Array
+
+
+class TestSlicing(unittest.TestCase):
+
+    def assertSameContig(self, arr, nparr):
+        attrs = 'C_CONTIGUOUS', 'F_CONTIGUOUS'
+        for attr in attrs:
+            if arr.flags[attr] != nparr.flags[attr]:
+                if arr.size == 0 and nparr.size == 0:
+                    # numpy <=1.7 bug that some empty array are contiguous and
+                    # some are not
+                    pass
+                else:
+                    self.fail("contiguous flag mismatch:\ngot=%s\nexpect=%s" %
+                              (arr.flags, nparr.flags))
+
+    #### 1D
+
+    def test_slice0_1d(self):
+        nparr = np.empty(4)
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        self.assertSameContig(arr, nparr)
+        xx = -2, -1, 0, 1, 2
+        for x in xx:
+            expect = nparr[x:]
+            got = arr[x:]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+    def test_slice1_1d(self):
+        nparr = np.empty(4)
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        xx = -2, -1, 0, 1, 2
+        for x in xx:
+            expect = nparr[:x]
+            got = arr[:x]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+    def test_slice2_1d(self):
+        nparr = np.empty(4)
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        xx = -2, -1, 0, 1, 2
+        for x, y in itertools.product(xx, xx):
+            expect = nparr[x:y]
+            got = arr[x:y]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+    #### 2D
+
+    def test_slice0_2d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        xx = -2, 0, 1, 2
+        for x in xx:
+            expect = nparr[x:]
+            got = arr[x:]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+        for x, y in itertools.product(xx, xx):
+            expect = nparr[x:, y:]
+            got = arr[x:, y:]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+    def test_slice1_2d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        xx = -2, 0, 2
+        for x in xx:
+            expect = nparr[:x]
+            got = arr[:x]
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+            self.assertSameContig(got, expect)
+
+        for x, y in itertools.product(xx, xx):
+            expect = nparr[:x, :y]
+            got = arr[:x, :y]
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+            self.assertSameContig(got, expect)
+
+    def test_slice2_2d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        xx = -2, 0, 2
+        for s, t, u, v in itertools.product(xx, xx, xx, xx):
+            expect = nparr[s:t, u:v]
+            got = arr[s:t, u:v]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+        for x, y in itertools.product(xx, xx):
+            expect = nparr[s:t, u:v]
+            got = arr[s:t, u:v]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+    #### Strided
+
+    def test_strided_1d(self):
+        nparr = np.empty(4)
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        xx = -2, -1, 1, 2
+        for x in xx:
+            expect = nparr[::x]
+            got = arr[::x]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+    def test_strided_2d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        xx = -2, -1, 1, 2
+        for a, b in itertools.product(xx, xx):
+            expect = nparr[::a, ::b]
+            got = arr[::a, ::b]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+    def test_strided_3d(self):
+        nparr = np.empty((4, 5, 6))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        xx = -2, -1, 1, 2
+        for a, b, c in itertools.product(xx, xx, xx):
+            expect = nparr[::a, ::b, ::c]
+            got = arr[::a, ::b, ::c]
+            self.assertSameContig(got, expect)
+            self.assertEqual(got.shape, expect.shape)
+            self.assertEqual(got.strides, expect.strides)
+
+    def test_issue_2766(self):
+        z = np.empty((1, 2, 3))
+        z = np.transpose(z, axes=(2, 0, 1))
+        arr = Array.from_desc(0, z.shape, z.strides, z.itemsize)
+        self.assertEqual(z.flags['C_CONTIGUOUS'], arr.flags['C_CONTIGUOUS'])
+        self.assertEqual(z.flags['F_CONTIGUOUS'], arr.flags['F_CONTIGUOUS'])
+
+
+class TestReshape(unittest.TestCase):
+    def test_reshape_2d2d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(5, 4)
+        got = arr.reshape(5, 4)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_2d1d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(5 * 4)
+        got = arr.reshape(5 * 4)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_3d3d(self):
+        nparr = np.empty((3, 4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(5, 3, 4)
+        got = arr.reshape(5, 3, 4)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_3d2d(self):
+        nparr = np.empty((3, 4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(3 * 4, 5)
+        got = arr.reshape(3 * 4, 5)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_3d1d(self):
+        nparr = np.empty((3, 4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(3 * 4 * 5)
+        got = arr.reshape(3 * 4 * 5)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_infer2d2d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(-1, 4)
+        got = arr.reshape(-1, 4)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_infer2d1d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(-1)
+        got = arr.reshape(-1)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_infer3d3d(self):
+        nparr = np.empty((3, 4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(5, -1, 4)
+        got = arr.reshape(5, -1, 4)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_infer3d2d(self):
+        nparr = np.empty((3, 4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(3, -1)
+        got = arr.reshape(3, -1)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_infer3d1d(self):
+        nparr = np.empty((3, 4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        expect = nparr.reshape(-1)
+        got = arr.reshape(-1)[0]
+        self.assertEqual(got.shape, expect.shape)
+        self.assertEqual(got.strides, expect.strides)
+
+    def test_reshape_infer_two_unknowns(self):
+        nparr = np.empty((3, 4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+
+        with self.assertRaises(ValueError) as raises:
+            arr.reshape(-1, -1, 3)
+        self.assertIn('can only specify one unknown dimension', str(raises.exception))
+
+    def test_reshape_infer_invalid_shape(self):
+        nparr = np.empty((3, 4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+
+        with self.assertRaises(ValueError) as raises:
+            arr.reshape(-1, 7)
+        self.assertIn('cannot infer valid shape for unknown dimension', str(raises.exception))
+
+
+class TestSqueeze(unittest.TestCase):
+    def test_squeeze(self):
+        nparr = np.empty((1, 2, 1, 4, 1, 3))
+        arr = Array.from_desc(
+            0, nparr.shape, nparr.strides, nparr.dtype.itemsize
+        )
+        def _assert_equal_shape_strides(arr1, arr2):
+            self.assertEqual(arr1.shape, arr2.shape)
+            self.assertEqual(arr1.strides, arr2.strides)
+        _assert_equal_shape_strides(arr, nparr)
+        _assert_equal_shape_strides(arr.squeeze()[0], nparr.squeeze())
+        for axis in (0, 2, 4, (0, 2), (0, 4), (2, 4), (0, 2, 4)):
+            _assert_equal_shape_strides(
+                arr.squeeze(axis=axis)[0], nparr.squeeze(axis=axis)
+            )
+
+    def test_squeeze_invalid_axis(self):
+        nparr = np.empty((1, 2, 1, 4, 1, 3))
+        arr = Array.from_desc(
+            0, nparr.shape, nparr.strides, nparr.dtype.itemsize
+        )
+        with self.assertRaises(ValueError):
+            arr.squeeze(axis=1)
+        with self.assertRaises(ValueError):
+            arr.squeeze(axis=(2, 3))
+
+
+class TestExtent(unittest.TestCase):
+    def test_extent_1d(self):
+        nparr = np.empty(4)
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        s, e = arr.extent
+        self.assertEqual(e - s, nparr.size * nparr.dtype.itemsize)
+
+    def test_extent_2d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        s, e = arr.extent
+        self.assertEqual(e - s, nparr.size * nparr.dtype.itemsize)
+
+    def test_extent_iter_1d(self):
+        nparr = np.empty(4)
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        [ext] = list(arr.iter_contiguous_extent())
+        self.assertEqual(ext, arr.extent)
+
+    def test_extent_iter_2d(self):
+        nparr = np.empty((4, 5))
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+        [ext] = list(arr.iter_contiguous_extent())
+        self.assertEqual(ext, arr.extent)
+
+        self.assertEqual(len(list(arr[::2].iter_contiguous_extent())), 2)
+
+
+class TestIterate(unittest.TestCase):
+    def test_for_loop(self):
+        # for #4201
+        N = 5
+        nparr = np.empty(N)
+        arr = Array.from_desc(0, nparr.shape, nparr.strides,
+                              nparr.dtype.itemsize)
+
+        x = 0  # just a placeholder
+        # this loop should not raise AssertionError
+        for val in arr:
+            x = val
+
+
+if __name__ == '__main__':
+    unittest.main()
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dyn_array.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dyn_array.py
new file mode 100644
index 000000000..41c429ca9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dyn_array.py
@@ -0,0 +1,1805 @@
+import contextlib
+import sys
+import numpy as np
+import random
+import re
+import threading
+import gc
+
+from numba.core.errors import TypingError
+from numba import njit
+from numba.core import types, utils, config
+from numba.tests.support import MemoryLeakMixin, TestCase, tag, skip_if_32bit
+import unittest
+
+
+nrtjit = njit(_nrt=True, nogil=True)
+
+
+def np_concatenate1(a, b, c):
+    return np.concatenate((a, b, c))
+
+def np_concatenate2(a, b, c, axis):
+    return np.concatenate((a, b, c), axis=axis)
+
+def np_stack1(a, b, c):
+    return np.stack((a, b, c))
+
+def np_stack2(a, b, c, axis):
+    return np.stack((a, b, c), axis=axis)
+
+def np_hstack(a, b, c):
+    return np.hstack((a, b, c))
+
+def np_vstack(a, b, c):
+    return np.vstack((a, b, c))
+
+def np_dstack(a, b, c):
+    return np.dstack((a, b, c))
+
+def np_column_stack(a, b, c):
+    return np.column_stack((a, b, c))
+
+
+class BaseTest(TestCase):
+
+    def check_outputs(self, pyfunc, argslist, exact=True):
+        cfunc = nrtjit(pyfunc)
+        for args in argslist:
+            expected = pyfunc(*args)
+            ret = cfunc(*args)
+            self.assertEqual(ret.size, expected.size)
+            self.assertEqual(ret.dtype, expected.dtype)
+            self.assertStridesEqual(ret, expected)
+            if exact:
+                np.testing.assert_equal(expected, ret)
+            else:
+                np.testing.assert_allclose(expected, ret)
+
+
+class NrtRefCtTest(MemoryLeakMixin):
+    def assert_array_nrt_refct(self, arr, expect):
+        self.assertEqual(arr.base.refcount, expect)
+
+
+class TestDynArray(NrtRefCtTest, TestCase):
+
+    def test_empty_0d(self):
+        @nrtjit
+        def foo():
+            arr = np.empty(())
+            arr[()] = 42
+            return arr
+
+        arr = foo()
+        self.assert_array_nrt_refct(arr, 1)
+        np.testing.assert_equal(42, arr)
+        self.assertEqual(arr.size, 1)
+        self.assertEqual(arr.shape, ())
+        self.assertEqual(arr.dtype, np.dtype(np.float64))
+        self.assertEqual(arr.strides, ())
+        arr.fill(123)  # test writability
+        np.testing.assert_equal(123, arr)
+        del arr
+
+    def test_empty_1d(self):
+        @nrtjit
+        def foo(n):
+            arr = np.empty(n)
+            for i in range(n):
+                arr[i] = i
+
+            return arr
+
+        n = 3
+        arr = foo(n)
+        self.assert_array_nrt_refct(arr, 1)
+        np.testing.assert_equal(np.arange(n), arr)
+        self.assertEqual(arr.size, n)
+        self.assertEqual(arr.shape, (n,))
+        self.assertEqual(arr.dtype, np.dtype(np.float64))
+        self.assertEqual(arr.strides, (np.dtype(np.float64).itemsize,))
+        arr.fill(123)  # test writability
+        np.testing.assert_equal(123, arr)
+        del arr
+
+    def test_empty_2d(self):
+        def pyfunc(m, n):
+            arr = np.empty((m, n), np.int32)
+            for i in range(m):
+                for j in range(n):
+                    arr[i, j] = i + j
+
+            return arr
+
+        cfunc = nrtjit(pyfunc)
+        m = 4
+        n = 3
+        expected_arr = pyfunc(m, n)
+        got_arr = cfunc(m, n)
+        self.assert_array_nrt_refct(got_arr, 1)
+        np.testing.assert_equal(expected_arr, got_arr)
+
+        self.assertEqual(expected_arr.size, got_arr.size)
+        self.assertEqual(expected_arr.shape, got_arr.shape)
+        self.assertEqual(expected_arr.strides, got_arr.strides)
+
+        del got_arr
+
+    def test_empty_3d(self):
+        def pyfunc(m, n, p):
+            arr = np.empty((m, n, p), np.int32)
+            for i in range(m):
+                for j in range(n):
+                    for k in range(p):
+                        arr[i, j, k] = i + j + k
+
+            return arr
+
+        cfunc = nrtjit(pyfunc)
+        m = 4
+        n = 3
+        p = 2
+        expected_arr = pyfunc(m, n, p)
+        got_arr = cfunc(m, n, p)
+        self.assert_array_nrt_refct(got_arr, 1)
+        np.testing.assert_equal(expected_arr, got_arr)
+
+        self.assertEqual(expected_arr.size, got_arr.size)
+        self.assertEqual(expected_arr.shape, got_arr.shape)
+        self.assertEqual(expected_arr.strides, got_arr.strides)
+
+        del got_arr
+
+    def test_empty_2d_sliced(self):
+        def pyfunc(m, n, p):
+            arr = np.empty((m, n), np.int32)
+            for i in range(m):
+                for j in range(n):
+                    arr[i, j] = i + j
+
+            return arr[p]
+
+        cfunc = nrtjit(pyfunc)
+        m = 4
+        n = 3
+        p = 2
+        expected_arr = pyfunc(m, n, p)
+        got_arr = cfunc(m, n, p)
+        self.assert_array_nrt_refct(got_arr, 1)
+        np.testing.assert_equal(expected_arr, got_arr)
+
+        self.assertEqual(expected_arr.size, got_arr.size)
+        self.assertEqual(expected_arr.shape, got_arr.shape)
+        self.assertEqual(expected_arr.strides, got_arr.strides)
+
+        del got_arr
+
+    def test_return_global_array(self):
+        y = np.ones(4, dtype=np.float32)
+        initrefct = sys.getrefcount(y)
+
+        def return_external_array():
+            return y
+
+        cfunc = nrtjit(return_external_array)
+        out = cfunc()
+
+        # out reference by cfunc
+        self.assertEqual(initrefct + 1, sys.getrefcount(y))
+
+        np.testing.assert_equal(y, out)
+        np.testing.assert_equal(y, np.ones(4, dtype=np.float32))
+        np.testing.assert_equal(out, np.ones(4, dtype=np.float32))
+
+        del out
+        gc.collect()
+        # out is only referenced by cfunc
+        self.assertEqual(initrefct + 1, sys.getrefcount(y))
+
+        del cfunc
+        gc.collect()
+        # y is no longer referenced by cfunc
+        self.assertEqual(initrefct, sys.getrefcount(y))
+
+    def test_return_global_array_sliced(self):
+        y = np.ones(4, dtype=np.float32)
+
+        def return_external_array():
+            return y[2:]
+
+        cfunc = nrtjit(return_external_array)
+        out = cfunc()
+        self.assertIsNone(out.base)
+
+        yy = y[2:]
+        np.testing.assert_equal(yy, out)
+        np.testing.assert_equal(yy, np.ones(2, dtype=np.float32))
+        np.testing.assert_equal(out, np.ones(2, dtype=np.float32))
+
+    def test_array_pass_through(self):
+        def pyfunc(y):
+            return y
+
+        arr = np.ones(4, dtype=np.float32)
+
+        cfunc = nrtjit(pyfunc)
+        expected = cfunc(arr)
+        got = pyfunc(arr)
+
+        np.testing.assert_equal(expected, arr)
+        np.testing.assert_equal(expected, got)
+        self.assertIs(expected, arr)
+        self.assertIs(expected, got)
+
+    def test_array_pass_through_sliced(self):
+        def pyfunc(y):
+            return y[y.size // 2:]
+
+        arr = np.ones(4, dtype=np.float32)
+
+        initrefct = sys.getrefcount(arr)
+
+        cfunc = nrtjit(pyfunc)
+        got = cfunc(arr)
+        self.assertEqual(initrefct + 1, sys.getrefcount(arr))
+        expected = pyfunc(arr)
+        self.assertEqual(initrefct + 2, sys.getrefcount(arr))
+
+        np.testing.assert_equal(expected, arr[arr.size // 2])
+        np.testing.assert_equal(expected, got)
+
+        del expected
+        self.assertEqual(initrefct + 1, sys.getrefcount(arr))
+        del got
+        self.assertEqual(initrefct, sys.getrefcount(arr))
+
+    def test_ufunc_with_allocated_output(self):
+
+        def pyfunc(a, b):
+            out = np.empty(a.shape)
+            np.add(a, b, out)
+            return out
+
+        cfunc = nrtjit(pyfunc)
+
+        # 1D case
+        arr_a = np.random.random(10)
+        arr_b = np.random.random(10)
+
+        np.testing.assert_equal(pyfunc(arr_a, arr_b),
+                                cfunc(arr_a, arr_b))
+
+        self.assert_array_nrt_refct(cfunc(arr_a, arr_b), 1)
+
+        # 2D case
+        arr_a = np.random.random(10).reshape(2, 5)
+        arr_b = np.random.random(10).reshape(2, 5)
+
+        np.testing.assert_equal(pyfunc(arr_a, arr_b),
+                                cfunc(arr_a, arr_b))
+
+        self.assert_array_nrt_refct(cfunc(arr_a, arr_b), 1)
+
+        # 3D case
+        arr_a = np.random.random(70).reshape(2, 5, 7)
+        arr_b = np.random.random(70).reshape(2, 5, 7)
+
+        np.testing.assert_equal(pyfunc(arr_a, arr_b),
+                                cfunc(arr_a, arr_b))
+
+        self.assert_array_nrt_refct(cfunc(arr_a, arr_b), 1)
+
+    def test_allocation_mt(self):
+        """
+        This test exercises the array allocation in multithreaded usecase.
+        This stress the freelist inside NRT.
+        """
+
+        def pyfunc(inp):
+            out = np.empty(inp.size)
+
+            # Zero fill
+            for i in range(out.size):
+                out[i] = 0
+
+            for i in range(inp[0]):
+                # Allocate inside a loop
+                tmp = np.empty(inp.size)
+                # Write to tmp
+                for j in range(tmp.size):
+                    tmp[j] = inp[j]
+                # out = tmp + i
+                for j in range(tmp.size):
+                    out[j] += tmp[j] + i
+
+            return out
+
+        cfunc = nrtjit(pyfunc)
+        size = 10  # small array size so that the computation is short
+        arr = np.random.randint(1, 10, size)
+        frozen_arr = arr.copy()
+
+        np.testing.assert_equal(pyfunc(arr), cfunc(arr))
+        # Ensure we did not modify the input
+        np.testing.assert_equal(frozen_arr, arr)
+
+        workers = []
+        inputs = []
+        outputs = []
+
+        # Make wrapper to store the output
+        def wrapped(inp, out):
+            out[:] = cfunc(inp)
+
+        # Create a lot of worker threads to create contention
+        for i in range(100):
+            arr = np.random.randint(1, 10, size)
+            out = np.empty_like(arr)
+            thread = threading.Thread(target=wrapped,
+                                      args=(arr, out),
+                                      name="worker{0}".format(i))
+            workers.append(thread)
+            inputs.append(arr)
+            outputs.append(out)
+
+        # Launch worker threads
+        for thread in workers:
+            thread.start()
+
+        # Join worker threads
+        for thread in workers:
+            thread.join()
+
+        # Check result
+        for inp, out in zip(inputs, outputs):
+            np.testing.assert_equal(pyfunc(inp), out)
+
+    def test_refct_mt(self):
+        """
+        This test exercises the refct in multithreaded code
+        """
+
+        def pyfunc(n, inp):
+            out = np.empty(inp.size)
+            for i in range(out.size):
+                out[i] = inp[i] + 1
+            # Use swap to trigger many refct ops
+            for i in range(n):
+                out, inp = inp, out
+            return out
+
+        cfunc = nrtjit(pyfunc)
+        size = 10
+        input = np.arange(size, dtype=float)
+        expected_refct = sys.getrefcount(input)
+        swapct = random.randrange(1000)
+        expected = pyfunc(swapct, input)
+        np.testing.assert_equal(expected, cfunc(swapct, input))
+        # The following checks can discover a reference count error
+        del expected
+        self.assertEqual(expected_refct, sys.getrefcount(input))
+
+        workers = []
+        outputs = []
+        swapcts = []
+
+        # Make wrapper to store the output
+        def wrapped(n, input, out):
+            out[:] = cfunc(n, input)
+
+        # Create worker threads
+        for i in range(100):
+            out = np.empty(size)
+            # All thread shares the same input
+            swapct = random.randrange(1000)
+            thread = threading.Thread(target=wrapped,
+                                      args=(swapct, input, out),
+                                      name="worker{0}".format(i))
+            workers.append(thread)
+            outputs.append(out)
+            swapcts.append(swapct)
+
+        # Launch worker threads
+        for thread in workers:
+            thread.start()
+
+        # Join worker threads
+        for thread in workers:
+            thread.join()
+
+        # Check result
+        for swapct, out in zip(swapcts, outputs):
+            np.testing.assert_equal(pyfunc(swapct, input), out)
+
+        del outputs, workers
+        # The following checks can discover a reference count error
+        self.assertEqual(expected_refct, sys.getrefcount(input))
+
+    @skip_if_32bit
+    def test_invalid_size_array(self):
+
+        @njit
+        def foo(x):
+            np.empty(x)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertRaises(MemoryError) as raises:
+            foo(types.size_t.maxval // 8 // 2)
+
+        self.assertIn("Allocation failed", str(raises.exception))
+
+    def test_swap(self):
+
+        def pyfunc(x, y, t):
+            """Swap array x and y for t number of times
+            """
+            for i in range(t):
+                x, y = y, x
+
+            return x, y
+
+
+        cfunc = nrtjit(pyfunc)
+
+        x = np.random.random(100)
+        y = np.random.random(100)
+
+        t = 100
+
+        initrefct = sys.getrefcount(x), sys.getrefcount(y)
+        expect, got = pyfunc(x, y, t), cfunc(x, y, t)
+        self.assertIsNone(got[0].base)
+        self.assertIsNone(got[1].base)
+        np.testing.assert_equal(expect, got)
+        del expect, got
+        self.assertEqual(initrefct, (sys.getrefcount(x), sys.getrefcount(y)))
+
+    def test_return_tuple_of_array(self):
+
+        def pyfunc(x):
+            y = np.empty(x.size)
+            for i in range(y.size):
+                y[i] = x[i] + 1
+            return x, y
+
+        cfunc = nrtjit(pyfunc)
+
+        x = np.random.random(5)
+        initrefct = sys.getrefcount(x)
+        expected_x, expected_y = pyfunc(x)
+        got_x, got_y = cfunc(x)
+        self.assertIs(x, expected_x)
+        self.assertIs(x, got_x)
+        np.testing.assert_equal(expected_x, got_x)
+        np.testing.assert_equal(expected_y, got_y)
+        del expected_x, got_x
+        self.assertEqual(initrefct, sys.getrefcount(x))
+
+        self.assertEqual(sys.getrefcount(expected_y), sys.getrefcount(got_y))
+
+    def test_return_tuple_of_array_created(self):
+
+        def pyfunc(x):
+            y = np.empty(x.size)
+            for i in range(y.size):
+                y[i] = x[i] + 1
+            out = y, y
+            return out
+
+        cfunc = nrtjit(pyfunc)
+
+        x = np.random.random(5)
+        expected_x, expected_y = pyfunc(x)
+        got_x, got_y = cfunc(x)
+        np.testing.assert_equal(expected_x, got_x)
+        np.testing.assert_equal(expected_y, got_y)
+        # getrefcount owns 1, got_y owns 1
+        self.assertEqual(2, sys.getrefcount(got_y))
+        # getrefcount owns 1, got_y owns 1
+        self.assertEqual(2, sys.getrefcount(got_y))
+
+    def test_issue_with_return_leak(self):
+        """
+        Dispatcher returns a new reference.
+        It need to workaround it for now.
+        """
+        @nrtjit
+        def inner(out):
+            return out
+
+        def pyfunc(x):
+            return inner(x)
+
+        cfunc = nrtjit(pyfunc)
+
+        arr = np.arange(10)
+        old_refct = sys.getrefcount(arr)
+
+        self.assertEqual(old_refct, sys.getrefcount(pyfunc(arr)))
+        self.assertEqual(old_refct, sys.getrefcount(cfunc(arr)))
+        self.assertEqual(old_refct, sys.getrefcount(arr))
+
+
+class ConstructorBaseTest(NrtRefCtTest):
+
+    def check_0d(self, pyfunc):
+        cfunc = nrtjit(pyfunc)
+        expected = pyfunc()
+        ret = cfunc()
+        self.assert_array_nrt_refct(ret, 1)
+        self.assertEqual(ret.size, expected.size)
+        self.assertEqual(ret.shape, expected.shape)
+        self.assertEqual(ret.dtype, expected.dtype)
+        self.assertEqual(ret.strides, expected.strides)
+        self.check_result_value(ret, expected)
+        # test writability
+        expected = np.empty_like(ret) # np.full_like was not added until Numpy 1.8
+        expected.fill(123)
+        ret.fill(123)
+        np.testing.assert_equal(ret, expected)
+
+    def check_1d(self, pyfunc):
+        cfunc = nrtjit(pyfunc)
+        n = 3
+        expected = pyfunc(n)
+        ret = cfunc(n)
+        self.assert_array_nrt_refct(ret, 1)
+        self.assertEqual(ret.size, expected.size)
+        self.assertEqual(ret.shape, expected.shape)
+        self.assertEqual(ret.dtype, expected.dtype)
+        self.assertEqual(ret.strides, expected.strides)
+        self.check_result_value(ret, expected)
+        # test writability
+        expected = np.empty_like(ret) # np.full_like was not added until Numpy 1.8
+        expected.fill(123)
+        ret.fill(123)
+        np.testing.assert_equal(ret, expected)
+        # errors
+        with self.assertRaises(ValueError) as cm:
+            cfunc(-1)
+        self.assertEqual(str(cm.exception), "negative dimensions not allowed")
+
+    def check_2d(self, pyfunc):
+        cfunc = nrtjit(pyfunc)
+        m, n = 2, 3
+        expected = pyfunc(m, n)
+        ret = cfunc(m, n)
+        self.assert_array_nrt_refct(ret, 1)
+        self.assertEqual(ret.size, expected.size)
+        self.assertEqual(ret.shape, expected.shape)
+        self.assertEqual(ret.dtype, expected.dtype)
+        self.assertEqual(ret.strides, expected.strides)
+        self.check_result_value(ret, expected)
+        # test writability
+        expected = np.empty_like(ret)  # np.full_like was not added until Numpy 1.8
+        expected.fill(123)
+        ret.fill(123)
+        np.testing.assert_equal(ret, expected)
+        # errors
+        with self.assertRaises(ValueError) as cm:
+            cfunc(2, -1)
+        self.assertEqual(str(cm.exception), "negative dimensions not allowed")
+
+    def check_alloc_size(self, pyfunc):
+        """Checks that pyfunc will error, not segfaulting due to array size."""
+        cfunc = nrtjit(pyfunc)
+        with self.assertRaises(ValueError) as e:
+            cfunc()
+        self.assertIn(
+            "array is too big",
+            str(e.exception)
+        )
+
+
+class TestNdZeros(ConstructorBaseTest, TestCase):
+
+    def setUp(self):
+        super(TestNdZeros, self).setUp()
+        self.pyfunc = np.zeros
+
+    def check_result_value(self, ret, expected):
+        np.testing.assert_equal(ret, expected)
+
+    def test_0d(self):
+        pyfunc = self.pyfunc
+        def func():
+            return pyfunc(())
+        self.check_0d(func)
+
+    def test_1d(self):
+        pyfunc = self.pyfunc
+        def func(n):
+            return pyfunc(n)
+        self.check_1d(func)
+
+    def test_1d_dtype(self):
+        pyfunc = self.pyfunc
+        def func(n):
+            return pyfunc(n, np.int32)
+        self.check_1d(func)
+
+    def test_1d_dtype_instance(self):
+        # dtype as numpy dtype, not as scalar class
+        pyfunc = self.pyfunc
+        _dtype = np.dtype('int32')
+        def func(n):
+            return pyfunc(n, _dtype)
+        self.check_1d(func)
+
+    def test_1d_dtype_str(self):
+        pyfunc = self.pyfunc
+        _dtype = 'int32'
+        def func(n):
+            return pyfunc(n, _dtype)
+        self.check_1d(func)
+
+        def func(n):
+            return pyfunc(n, 'complex128')
+        self.check_1d(func)
+
+    def test_1d_dtype_str_alternative_spelling(self):
+        # like test_1d_dtype_str but using the shorthand type spellings
+        pyfunc = self.pyfunc
+        _dtype = 'i4'
+        def func(n):
+            return pyfunc(n, _dtype)
+        self.check_1d(func)
+
+        def func(n):
+            return pyfunc(n, 'c8')
+        self.check_1d(func)
+
+    def test_1d_dtype_str_structured_dtype(self):
+        # test_1d_dtype_str but using a structured dtype
+        pyfunc = self.pyfunc
+        _dtype = "i4, (2,3)f8"
+        def func(n):
+            return pyfunc(n, _dtype)
+        self.check_1d(func)
+
+    def test_1d_dtype_non_const_str(self):
+        pyfunc = self.pyfunc
+
+        @njit
+        def func(n, dt):
+            return pyfunc(n, dt)
+
+        with self.assertRaises(TypingError) as raises:
+            func(5, 'int32')
+
+        excstr = str(raises.exception)
+        msg = (f"If np.{self.pyfunc.__name__} dtype is a string it must be a "
+               "string constant.")
+        self.assertIn(msg, excstr)
+
+    def test_1d_dtype_invalid_str(self):
+        pyfunc = self.pyfunc
+
+        @njit
+        def func(n):
+            return pyfunc(n, 'ABCDEF')
+
+        with self.assertRaises(TypingError) as raises:
+            func(5)
+
+        excstr = str(raises.exception)
+        self.assertIn("Invalid NumPy dtype specified: 'ABCDEF'", excstr)
+
+    def test_2d(self):
+        pyfunc = self.pyfunc
+        def func(m, n):
+            return pyfunc((m, n))
+        self.check_2d(func)
+
+    def test_2d_shape_dtypes(self):
+        # Test for issue #4575
+        pyfunc = self.pyfunc
+        def func1(m, n):
+            return pyfunc((np.int16(m), np.int32(n)))
+        self.check_2d(func1)
+        # Using a 64-bit value checks that 32 bit systems will downcast to intp
+        def func2(m, n):
+            return pyfunc((np.int64(m), np.int8(n)))
+        self.check_2d(func2)
+        # Make sure an error is thrown if we can't downcast safely
+        if config.IS_32BITS:
+            cfunc = nrtjit(lambda m, n: pyfunc((m, n)))
+            with self.assertRaises(ValueError):
+                cfunc(np.int64(1 << (32 - 1)), 1)
+
+    def test_2d_dtype_kwarg(self):
+        pyfunc = self.pyfunc
+        def func(m, n):
+            return pyfunc((m, n), dtype=np.complex64)
+        self.check_2d(func)
+
+    def test_2d_dtype_str_kwarg(self):
+        pyfunc = self.pyfunc
+        def func(m, n):
+            return pyfunc((m, n), dtype='complex64')
+        self.check_2d(func)
+
+    def test_2d_dtype_str_kwarg_alternative_spelling(self):
+        # as test_2d_dtype_str_kwarg but with the numpy shorthand type spelling
+        pyfunc = self.pyfunc
+        def func(m, n):
+            return pyfunc((m, n), dtype='c8')
+        self.check_2d(func)
+
+    def test_alloc_size(self):
+        pyfunc = self.pyfunc
+        width = types.intp.bitwidth
+        def gen_func(shape, dtype):
+            return lambda : pyfunc(shape, dtype)
+        # Under these values numba will segfault, but that's another issue
+        self.check_alloc_size(gen_func(1 << width - 2, np.intp))
+        self.check_alloc_size(gen_func((1 << width - 8, 64), np.intp))
+
+
+class TestNdOnes(TestNdZeros):
+
+    def setUp(self):
+        super(TestNdOnes, self).setUp()
+        self.pyfunc = np.ones
+
+    @unittest.expectedFailure
+    def test_1d_dtype_str_structured_dtype(self):
+        super().test_1d_dtype_str_structured_dtype()
+
+
+class TestNdFull(ConstructorBaseTest, TestCase):
+
+    def check_result_value(self, ret, expected):
+        np.testing.assert_equal(ret, expected)
+
+    def test_0d(self):
+        def func():
+            return np.full((), 4.5)
+        self.check_0d(func)
+
+    def test_1d(self):
+        def func(n):
+            return np.full(n, 4.5)
+        self.check_1d(func)
+
+    def test_1d_dtype(self):
+        def func(n):
+            return np.full(n, 4.5, np.bool_)
+        self.check_1d(func)
+
+    def test_1d_dtype_instance(self):
+        dtype = np.dtype('bool')
+        def func(n):
+            return np.full(n, 4.5, dtype)
+        self.check_1d(func)
+
+    def test_1d_dtype_str(self):
+        def func(n):
+            return np.full(n, 4.5, 'bool_')
+        self.check_1d(func)
+
+    def test_1d_dtype_str_alternative_spelling(self):
+        # like test_1d_dtype_str but using the shorthand type spelling
+        def func(n):
+            return np.full(n, 4.5, '?')
+        self.check_1d(func)
+
+    def test_1d_dtype_non_const_str(self):
+
+        @njit
+        def func(n, fv, dt):
+            return np.full(n, fv, dt)
+
+        with self.assertRaises(TypingError) as raises:
+            func((5,), 4.5, 'int32')
+
+        excstr = str(raises.exception)
+        self.assertIn('No match', excstr)
+        restr = r'\bfull\(UniTuple\(int.*? x 1\), float64, unicode_type\)\B'
+        regex = re.compile(restr)
+        self.assertRegex(excstr, regex)
+
+    def test_1d_dtype_invalid_str(self):
+
+        @njit
+        def func(n, fv):
+            return np.full(n, fv, 'ABCDEF')
+
+        with self.assertRaises(TypingError) as raises:
+            func(np.ones(4), 4.5)
+
+        excstr = str(raises.exception)
+        self.assertIn("Invalid NumPy dtype specified: 'ABCDEF'", excstr)
+
+    def test_2d(self):
+        def func(m, n):
+            return np.full((m, n), 4.5)
+        self.check_2d(func)
+
+    def test_2d_dtype_kwarg(self):
+        def func(m, n):
+            return np.full((m, n), 1 + 4.5j, dtype=np.complex64)
+        self.check_2d(func)
+
+    def test_2d_dtype_from_type(self):
+        # tests issue #2862
+        def func(m, n):
+            return np.full((m, n), np.int32(1))
+        self.check_2d(func)
+
+        # Complex uses `.real`, imaginary part dropped
+        def func(m, n):
+            return np.full((m, n), np.complex128(1))
+        self.check_2d(func)
+
+        # and that if a dtype is specified, this influences the return type
+        def func(m, n):
+            return np.full((m, n), 1, dtype=np.int8)
+        self.check_2d(func)
+
+    def test_2d_shape_dtypes(self):
+        # Test for issue #4575
+        def func1(m, n):
+            return np.full((np.int16(m), np.int32(n)), 4.5)
+        self.check_2d(func1)
+        # Using a 64-bit value checks that 32 bit systems will downcast to intp
+        def func2(m, n):
+            return np.full((np.int64(m), np.int8(n)), 4.5)
+        self.check_2d(func2)
+        # Make sure an error is thrown if we can't downcast safely
+        if config.IS_32BITS:
+            cfunc = nrtjit(lambda m, n: np.full((m, n), 4.5))
+            with self.assertRaises(ValueError):
+                cfunc(np.int64(1 << (32 - 1)), 1)
+
+    def test_alloc_size(self):
+        width = types.intp.bitwidth
+        def gen_func(shape, value):
+            return lambda : np.full(shape, value)
+        # Under these values numba will segfault, but that's another issue
+        self.check_alloc_size(gen_func(1 << width - 2, 1))
+        self.check_alloc_size(gen_func((1 << width - 8, 64), 1))
+
+
+class ConstructorLikeBaseTest(object):
+
+    def mutate_array(self, arr):
+        try:
+            arr.fill(42)
+        except (TypeError, ValueError):
+            # Try something else (e.g. Numpy 1.6 with structured dtypes)
+            fill_value = b'x' * arr.dtype.itemsize
+            arr.fill(fill_value)
+
+    def check_like(self, pyfunc, dtype):
+        def check_arr(arr):
+            expected = pyfunc(arr)
+            ret = cfunc(arr)
+            self.assertEqual(ret.size, expected.size)
+            self.assertEqual(ret.dtype, expected.dtype)
+            self.assertStridesEqual(ret, expected)
+            self.check_result_value(ret, expected)
+            # test writability
+            self.mutate_array(ret)
+            self.mutate_array(expected)
+            np.testing.assert_equal(ret, expected)
+
+        orig = np.linspace(0, 5, 6).astype(dtype)
+        cfunc = nrtjit(pyfunc)
+
+        for shape in (6, (2, 3), (1, 2, 3), (3, 1, 2), ()):
+            if shape == ():
+                arr = orig[-1:].reshape(())
+            else:
+                arr = orig.reshape(shape)
+            check_arr(arr)
+            # Non-contiguous array
+            if arr.ndim > 0:
+                check_arr(arr[::2])
+            # Check new array doesn't inherit readonly flag
+            arr.flags['WRITEABLE'] = False
+            # verify read-only
+            with self.assertRaises(ValueError):
+                arr[0] = 1
+            check_arr(arr)
+
+        # Scalar argument => should produce a 0-d array
+        check_arr(orig[0])
+
+
+class TestNdEmptyLike(ConstructorLikeBaseTest, TestCase):
+
+    def setUp(self):
+        super(TestNdEmptyLike, self).setUp()
+        self.pyfunc = np.empty_like
+
+    def check_result_value(self, ret, expected):
+        pass
+
+    def test_like(self):
+        pyfunc = self.pyfunc
+        def func(arr):
+            return pyfunc(arr)
+        self.check_like(func, np.float64)
+
+    def test_like_structured(self):
+        dtype = np.dtype([('a', np.int16), ('b', np.float32)])
+        pyfunc = self.pyfunc
+        def func(arr):
+            return pyfunc(arr)
+        self.check_like(func, dtype)
+
+    def test_like_dtype(self):
+        pyfunc = self.pyfunc
+        def func(arr):
+            return pyfunc(arr, np.int32)
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_instance(self):
+        dtype = np.dtype('int32')
+        pyfunc = self.pyfunc
+        def func(arr):
+            return pyfunc(arr, dtype)
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_structured(self):
+        dtype = np.dtype([('a', np.int16), ('b', np.float32)])
+        pyfunc = self.pyfunc
+        def func(arr):
+            return pyfunc(arr, dtype)
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_kwarg(self):
+        pyfunc = self.pyfunc
+        def func(arr):
+            return pyfunc(arr, dtype=np.int32)
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_str_kwarg(self):
+        pyfunc = self.pyfunc
+        def func(arr):
+            return pyfunc(arr, dtype='int32')
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_str_kwarg_alternative_spelling(self):
+        pyfunc = self.pyfunc
+        def func(arr):
+            return pyfunc(arr, dtype='i4')
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_non_const_str(self):
+        pyfunc = self.pyfunc
+
+        @njit
+        def func(n, dt):
+            return pyfunc(n, dt)
+
+        with self.assertRaises(TypingError) as raises:
+            func(np.ones(4), 'int32')
+
+        excstr = str(raises.exception)
+        msg = (f"If np.{self.pyfunc.__name__} dtype is a string it must be a "
+               "string constant.")
+        self.assertIn(msg, excstr)
+        self.assertIn(
+            '{}(array(float64, 1d, C), unicode_type)'.format(pyfunc.__name__),
+            excstr)
+
+    def test_like_dtype_invalid_str(self):
+        pyfunc = self.pyfunc
+
+        @njit
+        def func(n):
+            return pyfunc(n, 'ABCDEF')
+
+        with self.assertRaises(TypingError) as raises:
+            func(np.ones(4))
+
+        excstr = str(raises.exception)
+        self.assertIn("Invalid NumPy dtype specified: 'ABCDEF'", excstr)
+
+
+class TestNdZerosLike(TestNdEmptyLike):
+
+    def setUp(self):
+        super(TestNdZerosLike, self).setUp()
+        self.pyfunc = np.zeros_like
+
+    def check_result_value(self, ret, expected):
+        np.testing.assert_equal(ret, expected)
+
+    def test_like_structured(self):
+        super(TestNdZerosLike, self).test_like_structured()
+
+    def test_like_dtype_structured(self):
+        super(TestNdZerosLike, self).test_like_dtype_structured()
+
+
+class TestNdOnesLike(TestNdZerosLike):
+
+    def setUp(self):
+        super(TestNdOnesLike, self).setUp()
+        self.pyfunc = np.ones_like
+        self.expected_value = 1
+
+    # Not supported yet.
+
+    @unittest.expectedFailure
+    def test_like_structured(self):
+        super(TestNdOnesLike, self).test_like_structured()
+
+    @unittest.expectedFailure
+    def test_like_dtype_structured(self):
+        super(TestNdOnesLike, self).test_like_dtype_structured()
+
+
+class TestNdFullLike(ConstructorLikeBaseTest, TestCase):
+
+    def check_result_value(self, ret, expected):
+        np.testing.assert_equal(ret, expected)
+
+    def test_like(self):
+        def func(arr):
+            return np.full_like(arr, 3.5)
+        self.check_like(func, np.float64)
+
+    # Not supported yet.
+    @unittest.expectedFailure
+    def test_like_structured(self):
+        dtype = np.dtype([('a', np.int16), ('b', np.float32)])
+        def func(arr):
+            return np.full_like(arr, 4.5)
+        self.check_like(func, dtype)
+
+    def test_like_dtype(self):
+        def func(arr):
+            return np.full_like(arr, 4.5, np.bool_)
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_instance(self):
+        dtype = np.dtype('bool')
+        def func(arr):
+            return np.full_like(arr, 4.5, dtype)
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_kwarg(self):
+        def func(arr):
+            return np.full_like(arr, 4.5, dtype=np.bool_)
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_str_kwarg(self):
+        def func(arr):
+            return np.full_like(arr, 4.5, 'bool_')
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_str_kwarg_alternative_spelling(self):
+        def func(arr):
+            return np.full_like(arr, 4.5, dtype='?')
+        self.check_like(func, np.float64)
+
+    def test_like_dtype_non_const_str_kwarg(self):
+
+        @njit
+        def func(arr, fv, dt):
+            return np.full_like(arr, fv, dt)
+
+        with self.assertRaises(TypingError) as raises:
+            func(np.ones(3,), 4.5, 'int32')
+
+        excstr = str(raises.exception)
+        self.assertIn('No match', excstr)
+        self.assertIn('full_like(array(float64, 1d, C), float64, unicode_type)',
+                      excstr)
+
+    def test_like_dtype_invalid_str(self):
+
+        @njit
+        def func(arr, fv):
+            return np.full_like(arr, fv, "ABCDEF")
+
+        with self.assertRaises(TypingError) as raises:
+            func(np.ones(4), 3.4)
+
+        excstr = str(raises.exception)
+        self.assertIn("Invalid NumPy dtype specified: 'ABCDEF'", excstr)
+
+
+class TestNdIdentity(BaseTest):
+
+    def check_identity(self, pyfunc):
+        self.check_outputs(pyfunc, [(3,)])
+
+    def test_identity(self):
+        def func(n):
+            return np.identity(n)
+        self.check_identity(func)
+
+    def test_identity_dtype(self):
+        for dtype in (np.complex64, np.int16, np.bool_, np.dtype('bool'),
+                      'bool_'):
+            def func(n):
+                return np.identity(n, dtype)
+            self.check_identity(func)
+
+    def test_like_dtype_non_const_str_kwarg(self):
+
+        @njit
+        def func(n, dt):
+            return np.identity(n, dt)
+
+        with self.assertRaises(TypingError) as raises:
+            func(4, 'int32')
+
+        excstr = str(raises.exception)
+        self.assertIn('No match', excstr)
+        regex = re.compile(r'\bidentity\(int.*?, unicode_type\)\B')
+        self.assertRegex(excstr, regex)
+
+
+class TestNdEye(BaseTest):
+
+    def test_eye_n(self):
+        def func(n):
+            return np.eye(n)
+        self.check_outputs(func, [(1,), (3,)])
+
+    def test_eye_n_dtype(self):
+        # check None option, dtype class, instance of dtype class
+        for dt in (None, np.complex128, np.complex64(1)):
+            def func(n, dtype=dt):
+                return np.eye(n, dtype=dtype)
+            self.check_outputs(func, [(1,), (3,)])
+
+    def test_eye_n_m(self):
+        def func(n, m):
+            return np.eye(n, m)
+        self.check_outputs(func, [(1, 2), (3, 2), (0, 3)])
+
+    def check_eye_n_m_k(self, func):
+        self.check_outputs(func, [(1, 2, 0),
+                                  (3, 4, 1),
+                                  (3, 4, -1),
+                                  (4, 3, -2),
+                                  (4, 3, -5),
+                                  (4, 3, 5)])
+
+    def test_eye_n_m_k(self):
+        def func(n, m, k):
+            return np.eye(n, m, k)
+        self.check_eye_n_m_k(func)
+
+    def test_eye_n_m_k_dtype(self):
+        def func(n, m, k):
+            return np.eye(N=n, M=m, k=k, dtype=np.int16)
+        self.check_eye_n_m_k(func)
+
+    def test_eye_n_m_k_dtype_instance(self):
+        dtype = np.dtype('int16')
+        def func(n, m, k):
+            return np.eye(N=n, M=m, k=k, dtype=dtype)
+        self.check_eye_n_m_k(func)
+
+
+class TestNdDiag(TestCase):
+
+    def setUp(self):
+        v = np.array([1, 2, 3])
+        hv = np.array([[1, 2, 3]])
+        vv = np.transpose(hv)
+        self.vectors = [v, hv, vv]
+        a3x4 = np.arange(12).reshape(3, 4)
+        a4x3 = np.arange(12).reshape(4, 3)
+        self.matricies = [a3x4, a4x3]
+        def func(q):
+            return np.diag(q)
+        self.py = func
+        self.jit = nrtjit(func)
+
+        def func_kwarg(q, k=0):
+            return np.diag(q, k=k)
+        self.py_kw = func_kwarg
+        self.jit_kw = nrtjit(func_kwarg)
+
+    def check_diag(self, pyfunc, nrtfunc, *args, **kwargs):
+        expected = pyfunc(*args, **kwargs)
+        computed = nrtfunc(*args, **kwargs)
+        self.assertEqual(computed.size, expected.size)
+        self.assertEqual(computed.dtype, expected.dtype)
+        # NOTE: stride not tested as np returns a RO view, nb returns new data
+        np.testing.assert_equal(expected, computed)
+
+    # create a diag matrix from a vector
+    def test_diag_vect_create(self):
+        for d in self.vectors:
+            self.check_diag(self.py, self.jit, d)
+
+    # create a diag matrix from a vector at a given offset
+    def test_diag_vect_create_kwarg(self):
+        for k in range(-10, 10):
+            for d in self.vectors:
+                self.check_diag(self.py_kw, self.jit_kw, d, k=k)
+
+    # extract the diagonal
+    def test_diag_extract(self):
+        for d in self.matricies:
+            self.check_diag(self.py, self.jit, d)
+
+    # extract a diagonal at a given offset
+    def test_diag_extract_kwarg(self):
+        for k in range(-4, 4):
+            for d in self.matricies:
+                self.check_diag(self.py_kw, self.jit_kw, d, k=k)
+
+    # check error handling
+    def test_error_handling(self):
+        d = np.array([[[1.]]])
+        cfunc = nrtjit(self.py)
+
+        # missing arg
+        with self.assertRaises(TypeError):
+            cfunc()
+
+        # > 2d
+        with self.assertRaises(TypingError):
+            cfunc(d)
+        with self.assertRaises(TypingError):
+            dfunc = nrtjit(self.py_kw)
+            dfunc(d, k=3)
+
+class TestLinspace(BaseTest):
+
+    def test_linspace_2(self):
+        def pyfunc(n, m):
+            return np.linspace(n, m)
+        self.check_outputs(pyfunc,
+                           [(0, 4), (1, 100), (-3.5, 2.5), (-3j, 2+3j),
+                            (2, 1), (1+0.5j, 1.5j)])
+
+    def test_linspace_3(self):
+        def pyfunc(n, m, p):
+            return np.linspace(n, m, p)
+        self.check_outputs(pyfunc,
+                           [(0, 4, 9), (1, 4, 3), (-3.5, 2.5, 8),
+                            (-3j, 2+3j, 7), (2, 1, 0),
+                            (1+0.5j, 1.5j, 5), (1, 1e100, 1)])
+
+    def test_linspace_accuracy(self):
+        # Checking linspace reasonably replicates NumPy's algorithm
+        # see https://github.com/numba/numba/issues/6768
+        @nrtjit
+        def foo(n, m, p):
+            return np.linspace(n, m, p)
+
+        n, m, p = 0.0, 1.0, 100
+        self.assertPreciseEqual(foo(n, m, p), foo.py_func(n, m, p))
+
+
+class TestNpyEmptyKeyword(TestCase):
+    def _test_with_dtype_kw(self, dtype):
+        def pyfunc(shape):
+            return np.empty(shape, dtype=dtype)
+
+        shapes = [1, 5, 9]
+
+        cfunc = nrtjit(pyfunc)
+        for s in shapes:
+            expected = pyfunc(s)
+            got = cfunc(s)
+            self.assertEqual(expected.dtype, got.dtype)
+            self.assertEqual(expected.shape, got.shape)
+
+    def test_with_dtype_kws(self):
+        for dtype in [np.int32, np.float32, np.complex64, np.dtype('complex64')]:
+            self._test_with_dtype_kw(dtype)
+
+    def _test_with_shape_and_dtype_kw(self, dtype):
+        def pyfunc(shape):
+            return np.empty(shape=shape, dtype=dtype)
+
+        shapes = [1, 5, 9]
+
+        cfunc = nrtjit(pyfunc)
+        for s in shapes:
+            expected = pyfunc(s)
+            got = cfunc(s)
+            self.assertEqual(expected.dtype, got.dtype)
+            self.assertEqual(expected.shape, got.shape)
+
+    def test_with_shape_and_dtype_kws(self):
+        for dtype in [np.int32, np.float32, np.complex64, np.dtype('complex64')]:
+            self._test_with_shape_and_dtype_kw(dtype)
+
+    def test_empty_no_args(self):
+
+        def pyfunc():
+            return np.empty()
+
+        cfunc = nrtjit(pyfunc)
+
+        # Trigger the compilation
+        # That will cause a TypingError due to missing shape argument
+        with self.assertRaises(TypingError):
+            cfunc()
+
+
+class TestNpArray(MemoryLeakMixin, BaseTest):
+
+    def test_0d(self):
+        def pyfunc(arg):
+            return np.array(arg)
+
+        cfunc = nrtjit(pyfunc)
+        got = cfunc(42)
+        self.assertPreciseEqual(got, np.array(42, dtype=np.intp))
+        got = cfunc(2.5)
+        self.assertPreciseEqual(got, np.array(2.5))
+
+    def test_0d_with_dtype(self):
+        def pyfunc(arg):
+            return np.array(arg, dtype=np.int16)
+
+        self.check_outputs(pyfunc, [(42,), (3.5,)])
+
+    def test_1d(self):
+        def pyfunc(arg):
+            return np.array(arg)
+
+        cfunc = nrtjit(pyfunc)
+        # A list
+        got = cfunc([2, 3, 42])
+        self.assertPreciseEqual(got, np.intp([2, 3, 42]))
+        # A heterogeneous tuple
+        got = cfunc((1.0, 2.5j, 42))
+        self.assertPreciseEqual(got, np.array([1.0, 2.5j, 42]))
+        # An empty tuple
+        got = cfunc(())
+        self.assertPreciseEqual(got, np.float64(()))
+
+    def test_1d_with_dtype(self):
+        def pyfunc(arg):
+            return np.array(arg, dtype=np.float32)
+
+        self.check_outputs(pyfunc,
+                           [([2, 42],),
+                            ([3.5, 1.0],),
+                            ((1, 3.5, 42),),
+                            ((),),
+                            ])
+
+    def test_1d_with_str_dtype(self):
+        def pyfunc(arg):
+            return np.array(arg, dtype='float32')
+
+        self.check_outputs(pyfunc,
+                           [([2, 42],),
+                            ([3.5, 1.0],),
+                            ((1, 3.5, 42),),
+                            ((),),
+                            ])
+
+    def test_1d_with_non_const_str_dtype(self):
+
+        @njit
+        def func(arg, dt):
+            return np.array(arg, dtype=dt)
+
+        with self.assertRaises(TypingError) as raises:
+            func((5, 3), 'int32')
+
+        excstr = str(raises.exception)
+        self.assertIn('No match', excstr)
+        restr = r'\barray\(UniTuple\(int.*? x 2\), dtype=unicode_type\)\B'
+        regex = re.compile(restr)
+        self.assertRegex(excstr, regex)
+
+    def test_2d(self):
+        def pyfunc(arg):
+            return np.array(arg)
+
+        cfunc = nrtjit(pyfunc)
+        # A list of tuples
+        got = cfunc([(1, 2), (3, 4)])
+        self.assertPreciseEqual(got, np.intp([[1, 2], [3, 4]]))
+        got = cfunc([(1, 2.5), (3, 4.5)])
+        self.assertPreciseEqual(got, np.float64([[1, 2.5], [3, 4.5]]))
+        # A tuple of lists
+        got = cfunc(([1, 2], [3, 4]))
+        self.assertPreciseEqual(got, np.intp([[1, 2], [3, 4]]))
+        got = cfunc(([1, 2], [3.5, 4.5]))
+        self.assertPreciseEqual(got, np.float64([[1, 2], [3.5, 4.5]]))
+        # A tuple of tuples
+        got = cfunc(((1.5, 2), (3.5, 4.5)))
+        self.assertPreciseEqual(got, np.float64([[1.5, 2], [3.5, 4.5]]))
+        got = cfunc(((), ()))
+        self.assertPreciseEqual(got, np.float64(((), ())))
+
+    def test_2d_with_dtype(self):
+        def pyfunc(arg):
+            return np.array(arg, dtype=np.int32)
+
+        cfunc = nrtjit(pyfunc)
+        got = cfunc([(1, 2.5), (3, 4.5)])
+        self.assertPreciseEqual(got, np.int32([[1, 2], [3, 4]]))
+
+    def test_raises(self):
+
+        def pyfunc(arg):
+            return np.array(arg)
+
+        cfunc = nrtjit(pyfunc)
+
+        @contextlib.contextmanager
+        def check_raises(msg):
+            with self.assertRaises(TypingError) as raises:
+                yield
+            self.assertIn(msg, str(raises.exception))
+
+        with check_raises(('array(float64, 1d, C) not allowed in a '
+                           'homogeneous sequence')):
+            cfunc(np.array([1.]))
+
+        with check_raises(('type Tuple(int64, reflected list(int64)<iv=None>) '
+                          'does not have a regular shape')):
+            cfunc((np.int64(1), [np.int64(2)]))
+
+        with check_raises(
+                "cannot convert Tuple(int64, Record(a[type=int32;offset=0],"
+                "b[type=float32;offset=4];8;False)) to a homogeneous type",
+            ):
+            st = np.dtype([('a', 'i4'), ('b', 'f4')])
+            val = np.zeros(1, dtype=st)[0]
+            cfunc(((1, 2), (np.int64(1), val)))
+
+
+class TestNpConcatenate(MemoryLeakMixin, TestCase):
+    """
+    Tests for np.concatenate().
+    """
+
+    def _3d_arrays(self):
+        a = np.arange(24).reshape((4, 3, 2))
+        b = a + 10
+        c = (b + 10).copy(order='F')
+        d = (c + 10)[::-1]
+        e = (d + 10)[...,::-1]
+        return a, b, c, d, e
+
+    @contextlib.contextmanager
+    def assert_invalid_sizes_over_dim(self, axis):
+        with self.assertRaises(ValueError) as raises:
+            yield
+        self.assertIn("input sizes over dimension %d do not match" % axis,
+                      str(raises.exception))
+
+    def test_3d(self):
+        pyfunc = np_concatenate2
+        cfunc = nrtjit(pyfunc)
+
+        def check(a, b, c, axis):
+            for ax in (axis, -3 + axis):
+                expected = pyfunc(a, b, c, axis=ax)
+                got = cfunc(a, b, c, axis=ax)
+                self.assertPreciseEqual(got, expected)
+
+        def check_all_axes(a, b, c):
+            for axis in range(3):
+                check(a, b, c, axis)
+
+        a, b, c, d, e = self._3d_arrays()
+
+        # Inputs with equal sizes
+        # C, C, C
+        check_all_axes(a, b, b)
+        # C, C, F
+        check_all_axes(a, b, c)
+        # F, F, F
+        check_all_axes(a.T, b.T, a.T)
+        # F, F, C
+        check_all_axes(a.T, b.T, c.T)
+        # F, F, A
+        check_all_axes(a.T, b.T, d.T)
+        # A, A, A
+        # (note Numpy may select the layout differently for other inputs)
+        check_all_axes(d.T, e.T, d.T)
+
+        # Inputs with compatible sizes
+        check(a[1:], b, c[::-1], axis=0)
+        check(a, b[:,1:], c, axis=1)
+        check(a, b, c[:,:,1:], axis=2)
+
+        # Different but compatible dtypes
+        check_all_axes(a, b.astype(np.float64), b)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # Incompatible sizes
+        for axis in (1, 2, -2, -1):
+            with self.assert_invalid_sizes_over_dim(0):
+                cfunc(a[1:], b, b, axis)
+        for axis in (0, 2, -3, -1):
+            with self.assert_invalid_sizes_over_dim(1):
+                cfunc(a, b[:,1:], b, axis)
+
+    def test_3d_no_axis(self):
+        pyfunc = np_concatenate1
+        cfunc = nrtjit(pyfunc)
+
+        def check(a, b, c):
+            expected = pyfunc(a, b, c)
+            got = cfunc(a, b, c)
+            self.assertPreciseEqual(got, expected)
+
+        a, b, c, d, e = self._3d_arrays()
+
+        # Inputs with equal sizes
+        # C, C, C
+        check(a, b, b)
+        # C, C, F
+        check(a, b, c)
+        # F, F, F
+        check(a.T, b.T, a.T)
+        # F, F, C
+        check(a.T, b.T, c.T)
+        # F, F, A
+        check(a.T, b.T, d.T)
+        # A, A, A
+        # (note Numpy may select the layout differently for other inputs)
+        check(d.T, e.T, d.T)
+
+        # Inputs with compatible sizes
+        check(a[1:], b, c[::-1])
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # Incompatible sizes
+        with self.assert_invalid_sizes_over_dim(1):
+            cfunc(a, b[:,1:], b)
+
+    def test_typing_errors(self):
+        pyfunc = np_concatenate1
+        cfunc = nrtjit(pyfunc)
+
+        a = np.arange(15)
+        b = a.reshape((3, 5))
+        c = a.astype(np.dtype([('x', np.int8)]))
+        d = np.array(42)
+
+        # Different dimensionalities
+        with self.assertTypingError() as raises:
+            cfunc(a, b, b)
+        self.assertIn("all the input arrays must have same number of dimensions",
+                      str(raises.exception))
+
+        # Incompatible dtypes
+        with self.assertTypingError() as raises:
+            cfunc(a, c, c)
+        self.assertIn("input arrays must have compatible dtypes",
+                      str(raises.exception))
+
+        # 0-d arrays
+        with self.assertTypingError() as raises:
+            cfunc(d, d, d)
+        self.assertIn("zero-dimensional arrays cannot be concatenated",
+                      str(raises.exception))
+
+
+@unittest.skipUnless(hasattr(np, "stack"), "this Numpy doesn't have np.stack()")
+class TestNpStack(MemoryLeakMixin, TestCase):
+    """
+    Tests for np.stack().
+    """
+
+    def _3d_arrays(self):
+        a = np.arange(24).reshape((4, 3, 2))
+        b = a + 10
+        c = (b + 10).copy(order='F')
+        d = (c + 10)[::-1]
+        e = (d + 10)[...,::-1]
+        return a, b, c, d, e
+
+    @contextlib.contextmanager
+    def assert_invalid_sizes(self):
+        with self.assertRaises(ValueError) as raises:
+            yield
+        self.assertIn("all input arrays must have the same shape",
+                      str(raises.exception))
+
+    def check_stack(self, pyfunc, cfunc, args):
+        expected = pyfunc(*args)
+        got = cfunc(*args)
+        # Numba doesn't choose the same layout as Numpy.
+        # We would like to check the result is contiguous, but we can't
+        # rely on the "flags" attribute when there are 1-sized
+        # dimensions.
+        self.assertEqual(got.shape, expected.shape)
+        self.assertPreciseEqual(got.flatten(), expected.flatten())
+
+    def check_3d(self, pyfunc, cfunc, generate_starargs):
+        def check(a, b, c, args):
+            self.check_stack(pyfunc, cfunc, (a, b, c) + args)
+
+        def check_all_axes(a, b, c):
+            for args in generate_starargs():
+                check(a, b, c, args)
+
+        a, b, c, d, e = self._3d_arrays()
+
+        # C, C, C
+        check_all_axes(a, b, b)
+        # C, C, F
+        check_all_axes(a, b, c)
+        # F, F, F
+        check_all_axes(a.T, b.T, a.T)
+        # F, F, C
+        check_all_axes(a.T, b.T, c.T)
+        # F, F, A
+        check_all_axes(a.T, b.T, d.T)
+        # A, A, A
+        check_all_axes(d.T, e.T, d.T)
+
+        # Different but compatible dtypes
+        check_all_axes(a, b.astype(np.float64), b)
+
+    def check_runtime_errors(self, cfunc, generate_starargs):
+        # Exceptions leak references
+        self.assert_no_memory_leak()
+        self.disable_leak_check()
+
+        # Inputs have different shapes
+        a, b, c, d, e = self._3d_arrays()
+        with self.assert_invalid_sizes():
+            args = next(generate_starargs())
+            cfunc(a[:-1], b, c, *args)
+
+    def test_3d(self):
+        """
+        stack(3d arrays, axis)
+        """
+        pyfunc = np_stack2
+        cfunc = nrtjit(pyfunc)
+
+        def generate_starargs():
+            for axis in range(3):
+                yield (axis,)
+                yield (-3 + axis,)
+
+        self.check_3d(pyfunc, cfunc, generate_starargs)
+        self.check_runtime_errors(cfunc, generate_starargs)
+
+    def test_3d_no_axis(self):
+        """
+        stack(3d arrays)
+        """
+        pyfunc = np_stack1
+        cfunc = nrtjit(pyfunc)
+
+        def generate_starargs():
+            yield()
+
+        self.check_3d(pyfunc, cfunc, generate_starargs)
+        self.check_runtime_errors(cfunc, generate_starargs)
+
+    def test_0d(self):
+        """
+        stack(0d arrays)
+        """
+        pyfunc = np_stack1
+        cfunc = nrtjit(pyfunc)
+
+        a = np.array(42)
+        b = np.array(-5j)
+        c = np.array(True)
+
+        self.check_stack(pyfunc, cfunc, (a, b, c))
+
+    def check_xxstack(self, pyfunc, cfunc):
+        """
+        3d and 0d tests for hstack(), vstack(), dstack().
+        """
+        def generate_starargs():
+            yield()
+
+        self.check_3d(pyfunc, cfunc, generate_starargs)
+        # 0d
+        a = np.array(42)
+        b = np.array(-5j)
+        c = np.array(True)
+        self.check_stack(pyfunc, cfunc, (a, b, a))
+
+    def test_hstack(self):
+        pyfunc = np_hstack
+        cfunc = nrtjit(pyfunc)
+
+        self.check_xxstack(pyfunc, cfunc)
+        # 1d
+        a = np.arange(5)
+        b = np.arange(6) + 10
+        self.check_stack(pyfunc, cfunc, (a, b, b))
+        # 2d
+        a = np.arange(6).reshape((2, 3))
+        b = np.arange(8).reshape((2, 4)) + 100
+        self.check_stack(pyfunc, cfunc, (a, b, a))
+
+    def test_vstack(self):
+        pyfunc = np_vstack
+        cfunc = nrtjit(pyfunc)
+
+        self.check_xxstack(pyfunc, cfunc)
+        # 1d
+        a = np.arange(5)
+        b = a + 10
+        self.check_stack(pyfunc, cfunc, (a, b, b))
+        # 2d
+        a = np.arange(6).reshape((3, 2))
+        b = np.arange(8).reshape((4, 2)) + 100
+        self.check_stack(pyfunc, cfunc, (a, b, b))
+
+    def test_dstack(self):
+        pyfunc = np_dstack
+        cfunc = nrtjit(pyfunc)
+
+        self.check_xxstack(pyfunc, cfunc)
+        # 1d
+        a = np.arange(5)
+        b = a + 10
+        self.check_stack(pyfunc, cfunc, (a, b, b))
+        # 2d
+        a = np.arange(12).reshape((3, 4))
+        b = a + 100
+        self.check_stack(pyfunc, cfunc, (a, b, b))
+
+    def test_column_stack(self):
+        pyfunc = np_column_stack
+        cfunc = nrtjit(pyfunc)
+
+        a = np.arange(4)
+        b = a + 10
+        c = np.arange(12).reshape((4, 3))
+        self.check_stack(pyfunc, cfunc, (a, b, c))
+
+        # Exceptions leak references
+        self.assert_no_memory_leak()
+        self.disable_leak_check()
+
+        # Invalid dims
+        a = np.array(42)
+        with self.assertTypingError():
+            cfunc((a, a, a))
+        a = a.reshape((1, 1, 1))
+        with self.assertTypingError():
+            cfunc((a, a, a))
+
+
+def benchmark_refct_speed():
+    def pyfunc(x, y, t):
+        """Swap array x and y for t number of times
+        """
+        for i in range(t):
+            x, y = y, x
+        return x, y
+
+    cfunc = nrtjit(pyfunc)
+
+    x = np.random.random(100)
+    y = np.random.random(100)
+    t = 10000
+
+    def bench_pyfunc():
+        pyfunc(x, y, t)
+
+    def bench_cfunc():
+        cfunc(x, y, t)
+
+    python_time = utils.benchmark(bench_pyfunc)
+    numba_time = utils.benchmark(bench_cfunc)
+    print(python_time)
+    print(numba_time)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dyn_func.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dyn_func.py
new file mode 100644
index 000000000..df3b7aac1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_dyn_func.py
@@ -0,0 +1,43 @@
+import numpy as np
+
+import numba
+from numba.tests.support import TestCase
+
+
+class Issue455(object):
+    """
+    Test code from issue 455.
+    """
+
+    def __init__(self):
+        self.f = []
+
+    def create_f(self):
+        code = """
+        def f(x):
+            n = x.shape[0]
+            for i in range(n):
+                x[i] = 1.
+        """
+        d = {}
+        exec(code.strip(), d)
+        self.f.append(numba.jit("void(f8[:])", nopython=True)(d['f']))
+
+    def call_f(self):
+        a = np.zeros(10)
+        for f in self.f:
+            f(a)
+        return a
+
+
+class TestDynFunc(TestCase):
+
+    def test_issue_455(self):
+        inst = Issue455()
+        inst.create_f()
+        a = inst.call_f()
+        self.assertPreciseEqual(a, np.ones_like(a))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_entrypoints.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_entrypoints.py
new file mode 100644
index 000000000..226207767
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_entrypoints.py
@@ -0,0 +1,230 @@
+import sys
+from unittest import mock
+
+import types
+import warnings
+import unittest
+import os
+import subprocess
+import threading
+
+from numba import config, njit
+from numba.tests.support import TestCase
+from numba.testing.main import _TIMEOUT as _RUNNER_TIMEOUT
+
+if config.PYVERSION < (3, 9):
+    import importlib_metadata
+else:
+    from importlib import metadata as importlib_metadata
+
+_TEST_TIMEOUT = _RUNNER_TIMEOUT - 60.
+
+
+class _DummyClass(object):
+    def __init__(self, value):
+        self.value = value
+
+    def __repr__(self):
+        return '_DummyClass(%f, %f)' % self.value
+
+
+class TestEntrypoints(TestCase):
+    """
+    Test registration of init() functions from Numba extensions
+    """
+
+    def test_init_entrypoint(self):
+        # loosely based on Pandas test from:
+        #   https://github.com/pandas-dev/pandas/pull/27488
+
+        mod = mock.Mock(__name__='_test_numba_extension')
+
+        try:
+            # will remove this module at the end of the test
+            sys.modules[mod.__name__] = mod
+
+            my_entrypoint = importlib_metadata.EntryPoint(
+                'init', '_test_numba_extension:init_func', 'numba_extensions',
+            )
+
+            with mock.patch.object(
+                importlib_metadata,
+                'entry_points',
+                return_value={'numba_extensions': (my_entrypoint,)},
+            ):
+
+                from numba.core import entrypoints
+
+                # Allow reinitialization
+                entrypoints._already_initialized = False
+
+                entrypoints.init_all()
+
+                # was our init function called?
+                mod.init_func.assert_called_once()
+
+                # ensure we do not initialize twice
+                entrypoints.init_all()
+                mod.init_func.assert_called_once()
+        finally:
+            # remove fake module
+            if mod.__name__ in sys.modules:
+                del sys.modules[mod.__name__]
+
+    def test_entrypoint_tolerance(self):
+        # loosely based on Pandas test from:
+        #   https://github.com/pandas-dev/pandas/pull/27488
+
+        mod = mock.Mock(__name__='_test_numba_bad_extension')
+        mod.configure_mock(**{'init_func.side_effect': ValueError('broken')})
+
+        try:
+            # will remove this module at the end of the test
+            sys.modules[mod.__name__] = mod
+
+            my_entrypoint = importlib_metadata.EntryPoint(
+                'init',
+                '_test_numba_bad_extension:init_func',
+                'numba_extensions',
+            )
+
+            with mock.patch.object(
+                importlib_metadata,
+                'entry_points',
+                return_value={'numba_extensions': (my_entrypoint,)},
+            ):
+
+                from numba.core import entrypoints
+                # Allow reinitialization
+                entrypoints._already_initialized = False
+
+                with warnings.catch_warnings(record=True) as w:
+                    entrypoints.init_all()
+
+                bad_str = "Numba extension module '_test_numba_bad_extension'"
+                for x in w:
+                    if bad_str in str(x):
+                        break
+                else:
+                    raise ValueError("Expected warning message not found")
+
+                # was our init function called?
+                mod.init_func.assert_called_once()
+
+        finally:
+            # remove fake module
+            if mod.__name__ in sys.modules:
+                del sys.modules[mod.__name__]
+
+    _EP_MAGIC_TOKEN = 'RUN_ENTRY'
+
+    @unittest.skipIf(os.environ.get('_EP_MAGIC_TOKEN', None) != _EP_MAGIC_TOKEN,
+                     "needs token")
+    def test_entrypoint_handles_type_extensions(self):
+        # loosely based on Pandas test from:
+        #   https://github.com/pandas-dev/pandas/pull/27488
+        import numba
+
+        def init_function():
+            # This init function would normally just call a module init via
+            # import or similar, for the sake of testing, inline registration
+            # of how to handle the global "_DummyClass".
+            class DummyType(numba.types.Type):
+                def __init__(self):
+                    super(DummyType, self).__init__(name='DummyType')
+
+            @numba.extending.typeof_impl.register(_DummyClass)
+            def typer_DummyClass(val, c):
+                return DummyType()
+
+            @numba.extending.register_model(DummyType)
+            class DummyModel(numba.extending.models.StructModel):
+                def __init__(self, dmm, fe_type):
+                    members = [
+                        ('value', numba.types.float64), ]
+                    super(DummyModel, self).__init__(dmm, fe_type, members)
+
+            @numba.extending.unbox(DummyType)
+            def unbox_dummy(typ, obj, c):
+                value_obj = c.pyapi.object_getattr_string(obj, "value")
+                dummy_struct_proxy = numba.core.cgutils.create_struct_proxy(typ)
+                dummy_struct = dummy_struct_proxy(c.context, c.builder)
+                dummy_struct.value = c.pyapi.float_as_double(value_obj)
+                c.pyapi.decref(value_obj)
+                err_flag = c.pyapi.err_occurred()
+                is_error = numba.core.cgutils.is_not_null(c.builder, err_flag)
+                return numba.extending.NativeValue(dummy_struct._getvalue(),
+                                                   is_error=is_error)
+
+            @numba.extending.box(DummyType)
+            def box_dummy(typ, val, c):
+                dummy_struct_proxy = numba.core.cgutils.create_struct_proxy(typ)
+                dummy_struct = dummy_struct_proxy(c.context, c.builder)
+                value_obj = c.pyapi.float_from_double(dummy_struct.value)
+                serialized_clazz = c.pyapi.serialize_object(_DummyClass)
+                class_obj = c.pyapi.unserialize(serialized_clazz)
+                res = c.pyapi.call_function_objargs(class_obj, (value_obj,))
+                c.pyapi.decref(value_obj)
+                c.pyapi.decref(class_obj)
+                return res
+
+        mod = types.ModuleType("_test_numba_init_sequence")
+        mod.init_func = init_function
+
+        try:
+            # will remove this module at the end of the test
+            sys.modules[mod.__name__] = mod
+
+            my_entrypoint = importlib_metadata.EntryPoint(
+                'init',
+                '_test_numba_init_sequence:init_func',
+                'numba_extensions',
+            )
+
+            with mock.patch.object(
+                importlib_metadata,
+                'entry_points',
+                return_value={'numba_extensions': (my_entrypoint,)},
+            ):
+                @njit
+                def foo(x):
+                    return x
+
+                ival = _DummyClass(10)
+                foo(ival)
+        finally:
+            # remove fake module
+            if mod.__name__ in sys.modules:
+                del sys.modules[mod.__name__]
+
+    def run_cmd(self, cmdline, env):
+        popen = subprocess.Popen(cmdline,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 env=env)
+        # finish in _TEST_TIMEOUT seconds or kill it
+        timeout = threading.Timer(_TEST_TIMEOUT, popen.kill)
+        try:
+            timeout.start()
+            out, err = popen.communicate()
+            if popen.returncode != 0:
+                raise AssertionError(
+                    "process failed with code %s: stderr follows\n%s\n" %
+                    (popen.returncode, err.decode()))
+            return out.decode(), err.decode()
+        finally:
+            timeout.cancel()
+        return None, None
+
+    def test_entrypoint_extension_sequence(self):
+        env_copy = os.environ.copy()
+        env_copy['_EP_MAGIC_TOKEN'] = str(self._EP_MAGIC_TOKEN)
+        themod = self.__module__
+        thecls = type(self).__name__
+        methname = 'test_entrypoint_handles_type_extensions'
+        injected_method = '%s.%s.%s' % (themod, thecls, methname)
+        cmdline = [sys.executable, "-m", "numba.runtests", injected_method]
+        out, err = self.run_cmd(cmdline, env_copy)
+        _DEBUG = False
+        if _DEBUG:
+            print(out, err)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_enums.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_enums.py
new file mode 100644
index 000000000..535c33000
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_enums.py
@@ -0,0 +1,180 @@
+"""
+Tests for enum support.
+"""
+
+
+import numpy as np
+import unittest
+from numba import jit, vectorize, int8, int16, int32
+
+from numba.tests.support import TestCase
+from .enum_usecases import Color, Shape, Shake, Planet, RequestError, \
+                           IntEnumWithNegatives
+
+
+def compare_usecase(a, b):
+    return a == b, a != b, a is b, a is not b
+
+
+def getattr_usecase(a):
+    # Lookup of a enum member on its class
+    return a is Color.red
+
+
+def getitem_usecase(a):
+    """Lookup enum member by string name"""
+    return a is Color['red']
+
+
+def identity_usecase(a, b, c):
+    return (a is Shake.mint,
+            b is Shape.circle,
+            c is RequestError.internal_error,
+            )
+
+
+def make_constant_usecase(const):
+    def constant_usecase(a):
+        return a is const
+    return constant_usecase
+
+
+def return_usecase(a, b, pred):
+    return a if pred else b
+
+
+def int_coerce_usecase(x):
+    # Implicit coercion of intenums to ints
+    if x > RequestError.internal_error:
+        return x - RequestError.not_found
+    else:
+        return x + Shape.circle
+
+def int_cast_usecase(x):
+    # Explicit coercion of intenums to ints
+    if x > int16(RequestError.internal_error):
+        return x - int32(RequestError.not_found)
+    else:
+        return x + int8(Shape.circle)
+
+
+def vectorize_usecase(x):
+    if x != RequestError.not_found:
+        return RequestError['internal_error']
+    else:
+        return RequestError.dummy
+
+
+class BaseEnumTest(object):
+
+    def test_compare(self):
+        pyfunc = compare_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for args in self.pairs:
+            self.assertPreciseEqual(pyfunc(*args), cfunc(*args))
+
+    def test_return(self):
+        """
+        Passing and returning enum members.
+        """
+        pyfunc = return_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for pair in self.pairs:
+            for pred in (True, False):
+                args = pair + (pred,)
+                self.assertIs(pyfunc(*args), cfunc(*args))
+
+    def check_constant_usecase(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arg in self.values:
+            self.assertPreciseEqual(pyfunc(arg), cfunc(arg))
+
+    def test_constant(self):
+        self.check_constant_usecase(getattr_usecase)
+        self.check_constant_usecase(getitem_usecase)
+        self.check_constant_usecase(make_constant_usecase(self.values[0]))
+
+
+class TestEnum(BaseEnumTest, TestCase):
+    """
+    Tests for Enum classes and members.
+    """
+    values = [Color.red, Color.green]
+
+    pairs = [
+        (Color.red, Color.red),
+        (Color.red, Color.green),
+        (Shake.mint, Shake.vanilla),
+        (Planet.VENUS, Planet.MARS),
+        (Planet.EARTH, Planet.EARTH),
+        ]
+
+    def test_identity(self):
+        """
+        Enum with equal values should not compare identical
+        """
+        pyfunc = identity_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        args = (Color.blue, Color.green, Shape.square)
+        self.assertPreciseEqual(pyfunc(*args), cfunc(*args))
+
+
+class TestIntEnum(BaseEnumTest, TestCase):
+    """
+    Tests for IntEnum classes and members.
+    """
+    values = [Shape.circle, Shape.square]
+
+    pairs = [
+        (Shape.circle, Shape.circle),
+        (Shape.circle, Shape.square),
+        (RequestError.not_found, RequestError.not_found),
+        (RequestError.internal_error, RequestError.not_found),
+        ]
+
+    def test_int_coerce(self):
+        pyfunc = int_coerce_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arg in [300, 450, 550]:
+            self.assertPreciseEqual(pyfunc(arg), cfunc(arg))
+
+    def test_int_cast(self):
+        pyfunc = int_cast_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arg in [300, 450, 550]:
+            self.assertPreciseEqual(pyfunc(arg), cfunc(arg))
+
+    def test_vectorize(self):
+        cfunc = vectorize(nopython=True)(vectorize_usecase)
+        arg = np.array([2, 404, 500, 404])
+        sol = np.array([vectorize_usecase(i) for i in arg], dtype=arg.dtype)
+        self.assertPreciseEqual(sol, cfunc(arg))
+
+    def test_hash(self):
+        def pyfun(x):
+            return hash(x)
+        cfunc = jit(nopython=True)(pyfun)
+        for member in IntEnumWithNegatives:
+            self.assertPreciseEqual(pyfun(member), cfunc(member))
+
+    def test_int_shape_cast(self):
+        def pyfun_empty(x):
+            return np.empty((x, x), dtype='int64').fill(-1)
+        def pyfun_zeros(x):
+            return np.zeros((x, x), dtype='int64')
+        def pyfun_ones(x):
+            return np.ones((x, x), dtype='int64')
+        for pyfun in [pyfun_empty, pyfun_zeros, pyfun_ones]:
+            cfunc = jit(nopython=True)(pyfun)
+            for member in IntEnumWithNegatives:
+                if member >= 0:
+                    self.assertPreciseEqual(pyfun(member), cfunc(member))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_errorhandling.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_errorhandling.py
new file mode 100644
index 000000000..21b2543b4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_errorhandling.py
@@ -0,0 +1,496 @@
+"""
+Unspecified error handling tests
+"""
+
+import numpy as np
+import os
+
+from numba import jit, njit, typed, int64, types
+from numba.core import errors
+import numba.core.typing.cffi_utils as cffi_support
+from numba.experimental import structref
+from numba.extending import (overload, intrinsic, overload_method,
+                             overload_attribute)
+from numba.core.compiler import CompilerBase
+from numba.core.untyped_passes import (TranslateByteCode, FixupArgs,
+                                       IRProcessing,)
+from numba.core.typed_passes import (NopythonTypeInference, DeadCodeElimination,
+                                     NoPythonBackend, NativeLowering)
+from numba.core.compiler_machinery import PassManager
+from numba.core.types.functions import _err_reasons as error_reasons
+
+from numba.tests.support import (skip_parfors_unsupported, override_config,
+                                 SerialMixin, skip_unless_scipy)
+import unittest
+
+# used in TestMiscErrorHandling::test_handling_of_write_to_*_global
+_global_list = [1, 2, 3, 4]
+_global_dict = typed.Dict.empty(int64, int64)
+
+
+class TestErrorHandlingBeforeLowering(unittest.TestCase):
+
+    def test_unsupported_make_function_return_inner_func(self):
+        def func(x):
+            """ return the closure """
+            z = x + 1
+
+            def inner(x):
+                return x + z
+            return inner
+
+        for pipeline in jit, njit:
+            with self.assertRaises(errors.TypingError) as raises:
+                pipeline(func)(1)
+
+            expected = "Cannot capture the non-constant value"
+            self.assertIn(expected, str(raises.exception))
+
+
+class TestUnsupportedReporting(unittest.TestCase):
+
+    def test_unsupported_numpy_function(self):
+        # np.asanyarray(list) currently unsupported
+        @njit
+        def func():
+            np.asanyarray([1,2,3])
+
+        with self.assertRaises(errors.TypingError) as raises:
+            func()
+
+        expected = "Use of unsupported NumPy function 'numpy.asanyarray'"
+        self.assertIn(expected, str(raises.exception))
+
+
+class TestMiscErrorHandling(unittest.TestCase):
+
+    def test_use_of_exception_for_flow_control(self):
+        # constant inference uses exceptions with no Loc specified to determine
+        # flow control, this asserts that the construction of the lowering
+        # error context handler works in the case of an exception with no Loc
+        # specified. See issue #3135.
+        @njit
+        def fn(x):
+            return 10**x
+
+        a = np.array([1.0],dtype=np.float64)
+        fn(a) # should not raise
+
+    def test_commented_func_definition_is_not_a_definition(self):
+        # See issue #4056, the commented def should not be found as the
+        # definition for reporting purposes when creating the synthetic
+        # traceback because it is commented! Use of def in docstring would also
+        # cause this issue hence is tested.
+
+        def foo_commented():
+            #def commented_definition()
+            raise Exception('test_string')
+
+        def foo_docstring():
+            """ def docstring containing def might match function definition!"""
+            raise Exception('test_string')
+
+        for func in (foo_commented, foo_docstring):
+            with self.assertRaises(Exception) as raises:
+                func()
+
+            self.assertIn("test_string", str(raises.exception))
+
+    def test_use_of_ir_unknown_loc(self):
+        # for context see # 3390
+        class TestPipeline(CompilerBase):
+            def define_pipelines(self):
+                name = 'bad_DCE_pipeline'
+                pm = PassManager(name)
+                pm.add_pass(TranslateByteCode, "analyzing bytecode")
+                pm.add_pass(FixupArgs, "fix up args")
+                pm.add_pass(IRProcessing, "processing IR")
+                # remove dead before type inference so that the Arg node is
+                # removed and the location of the arg cannot be found
+                pm.add_pass(DeadCodeElimination, "DCE")
+                # typing
+                pm.add_pass(NopythonTypeInference, "nopython frontend")
+                pm.add_pass(NativeLowering, "native lowering")
+                pm.add_pass(NoPythonBackend, "nopython mode backend")
+                pm.finalize()
+                return [pm]
+
+        @njit(pipeline_class=TestPipeline)
+        def f(a):
+            return 0
+
+        with self.assertRaises(errors.TypingError) as raises:
+            f(iter([1,2]))  # use a type that Numba doesn't recognize
+
+        expected = 'File "unknown location", line 0:'
+        self.assertIn(expected, str(raises.exception))
+
+    def check_write_to_globals(self, func):
+        with self.assertRaises(errors.TypingError) as raises:
+            func()
+
+        expected = ["The use of a", "in globals, is not supported as globals"]
+        for ex in expected:
+            self.assertIn(ex, str(raises.exception))
+
+    def test_handling_of_write_to_reflected_global(self):
+        @njit
+        def foo():
+            _global_list[0] = 10
+
+        self.check_write_to_globals(foo)
+
+    def test_handling_of_write_to_typed_dict_global(self):
+        @njit
+        def foo():
+            _global_dict[0] = 10
+
+        self.check_write_to_globals(foo)
+
+    @skip_parfors_unsupported
+    def test_handling_forgotten_numba_internal_import(self):
+        @njit(parallel=True)
+        def foo():
+            for i in prange(10): # noqa: F821 prange is not imported
+                pass
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        expected = ("'prange' looks like a Numba internal function, "
+                    "has it been imported")
+        self.assertIn(expected, str(raises.exception))
+
+    def test_handling_unsupported_generator_expression(self):
+        def foo():
+            (x for x in range(10))
+
+        expected = "The use of yield in a closure is unsupported."
+
+        for dec in jit(forceobj=True), njit:
+            with self.assertRaises(errors.UnsupportedError) as raises:
+                dec(foo)()
+            self.assertIn(expected, str(raises.exception))
+
+    def test_handling_undefined_variable(self):
+        @njit
+        def foo():
+            return a # noqa: F821
+
+        expected = "NameError: name 'a' is not defined"
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+        self.assertIn(expected, str(raises.exception))
+
+
+class TestConstantInferenceErrorHandling(unittest.TestCase):
+
+    def test_basic_error(self):
+        # issue 3717
+        @njit
+        def problem(a,b):
+            if a == b:
+                raise Exception("Equal numbers: %i %i", a, b)
+            return a * b
+
+        with self.assertRaises(errors.ConstantInferenceError) as raises:
+            problem(1,2)
+
+        msg1 = "Constant inference not possible for: arg(0, name=a)"
+        msg2 = 'raise Exception("Equal numbers: %i %i", a, b)'
+        self.assertIn(msg1, str(raises.exception))
+        self.assertIn(msg2, str(raises.exception))
+
+
+class TestErrorMessages(unittest.TestCase):
+
+    def test_specific_error(self):
+
+        given_reason = "specific_reason"
+
+        def foo():
+            pass
+
+        @overload(foo)
+        def ol_foo():
+            raise errors.NumbaValueError(given_reason)
+
+        @njit
+        def call_foo():
+            foo()
+
+        with self.assertRaises(errors.TypingError) as raises:
+            call_foo()
+
+        excstr = str(raises.exception)
+        self.assertIn(error_reasons['specific_error'].splitlines()[0], excstr)
+        self.assertIn(given_reason, excstr)
+
+    def test_no_match_error(self):
+
+        def foo():
+            pass
+
+        @overload(foo)
+        def ol_foo():
+            return None # emulate no impl available for type
+
+        @njit
+        def call_foo():
+            foo()
+
+        with self.assertRaises(errors.TypingError) as raises:
+            call_foo()
+
+        excstr = str(raises.exception)
+        self.assertIn("No match", excstr)
+
+    @skip_unless_scipy
+    def test_error_function_source_is_correct(self):
+        """ Checks that the reported source location for an overload is the
+        overload implementation source, not the actual function source from the
+        target library."""
+
+        @njit
+        def foo():
+            np.linalg.svd("chars")
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        self.assertIn(error_reasons['specific_error'].splitlines()[0], excstr)
+        expected_file = os.path.join("numba", "np", "linalg.py")
+        expected = f"Overload in function 'svd_impl': File: {expected_file}:"
+        self.assertIn(expected.format(expected_file), excstr)
+
+    def test_concrete_template_source(self):
+        # hits ConcreteTemplate
+        @njit
+        def foo():
+            return 'a' + 1
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+
+        self.assertIn("Overload of function 'add'", excstr)
+        # there'll be numerous matched templates that don't work but as they
+        # are mostly "overload_glue"s they'll just appear as "No match".
+        self.assertIn("No match.", excstr)
+
+    def test_abstract_template_source(self):
+        # hits AbstractTemplate
+        @njit
+        def foo():
+            return len(1)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        self.assertIn("Overload of function 'len'", excstr)
+
+    def test_callable_template_source(self):
+        # hits CallableTemplate
+        @njit
+        def foo():
+            return np.angle(1)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        self.assertIn("No implementation of function Function(<function angle",
+                      excstr)
+
+    def test_overloadfunction_template_source(self):
+        # hits _OverloadFunctionTemplate
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            pass
+
+        @njit
+        def foo():
+            return bar(1)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        # there will not be "numerous" matched templates, there's just one,
+        # the one above, so assert it is reported
+        self.assertNotIn("<numerous>", excstr)
+        expected_file = os.path.join("numba", "tests",
+                                     "test_errorhandling.py")
+        expected_ol = f"Overload of function 'bar': File: {expected_file}:"
+        self.assertIn(expected_ol.format(expected_file), excstr)
+        self.assertIn("No match.", excstr)
+
+    def test_intrinsic_template_source(self):
+        # hits _IntrinsicTemplate
+        given_reason1 = "x must be literal"
+        given_reason2 = "array.ndim must be 1"
+
+        @intrinsic
+        def myintrin(typingctx, x, arr):
+            if not isinstance(x, types.IntegerLiteral):
+                raise errors.RequireLiteralValue(given_reason1)
+
+            if arr.ndim != 1:
+                raise errors.NumbaValueError(given_reason2)
+
+            sig = types.intp(x, arr)
+
+            def codegen(context, builder, signature, args):
+                pass
+            return sig, codegen
+
+        @njit
+        def call_intrin():
+            arr = np.zeros((2, 2))
+            myintrin(1, arr)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            call_intrin()
+
+        excstr = str(raises.exception)
+        self.assertIn(error_reasons['specific_error'].splitlines()[0], excstr)
+        self.assertIn(given_reason1, excstr)
+        self.assertIn(given_reason2, excstr)
+        self.assertIn("Intrinsic in function", excstr)
+
+    def test_overloadmethod_template_source(self):
+        # doesn't hit _OverloadMethodTemplate for source as it's a nested
+        # exception
+        @overload_method(types.UnicodeType, 'isnonsense')
+        def ol_unicode_isnonsense(self):
+            pass
+
+        @njit
+        def foo():
+            "abc".isnonsense()
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        self.assertIn("Overload of function 'ol_unicode_isnonsense'", excstr)
+
+    def test_overloadattribute_template_source(self):
+        # doesn't hit _OverloadMethodTemplate for source as it's a nested
+        # exception
+        @overload_attribute(types.UnicodeType, 'isnonsense')
+        def ol_unicode_isnonsense(self):
+            pass
+
+        @njit
+        def foo():
+            "abc".isnonsense
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        self.assertIn("Overload of function 'ol_unicode_isnonsense'", excstr)
+
+    def test_external_function_pointer_template_source(self):
+        from numba.tests.ctypes_usecases import c_cos
+
+        @njit
+        def foo():
+            c_cos('a')
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        self.assertIn("Type Restricted Function in function 'unknown'", excstr)
+
+    @unittest.skipUnless(cffi_support.SUPPORTED, "CFFI not supported")
+    def test_cffi_function_pointer_template_source(self):
+        from numba.tests import cffi_usecases as mod
+        mod.init()
+        func = mod.cffi_cos
+
+        @njit
+        def foo():
+            func('a')
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        self.assertIn("Type Restricted Function in function 'unknown'", excstr)
+
+    def test_missing_source(self):
+
+        @structref.register
+        class ParticleType(types.StructRef):
+            pass
+
+        class Particle(structref.StructRefProxy):
+            def __new__(cls, pos, mass):
+                return structref.StructRefProxy.__new__(cls, pos)
+                # didn't provide the required mass argument ----^
+
+        structref.define_proxy(Particle, ParticleType, ["pos", "mass"])
+
+        with self.assertRaises(errors.TypingError) as raises:
+            Particle(pos=1, mass=2)
+
+        excstr = str(raises.exception)
+        self.assertIn("missing a required argument: 'mass'", excstr)
+
+
+class TestDeveloperSpecificErrorMessages(SerialMixin, unittest.TestCase):
+
+    def test_bound_function_error_string(self):
+        # See PR #5952
+        def foo(x):
+            x.max(-1) # axis not supported
+
+        with override_config('DEVELOPER_MODE', 1):
+            with self.assertRaises(errors.TypingError) as raises:
+                njit("void(int64[:,:])")(foo)
+
+        excstr = str(raises.exception)
+        self.assertIn("args not supported", excstr)
+
+
+class TestCapturedErrorHandling(SerialMixin, unittest.TestCase):
+    """Checks that the way errors are captured changes depending on the env
+    var "NUMBA_CAPTURED_ERRORS".
+    """
+
+    def test_error_in_overload(self):
+
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            x.some_invalid_attr # doesn't exist!
+
+            def impl(x):
+                pass
+            return impl
+
+        for style, err_class in (('new_style', AttributeError),
+                                 ('old_style', errors.TypingError)):
+            with override_config('CAPTURED_ERRORS', style):
+                with self.assertRaises(err_class) as raises:
+                    @njit('void(int64)')
+                    def foo(x):
+                        bar(x)
+                expected = "object has no attribute 'some_invalid_attr'"
+                self.assertIn(expected, str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_errormodels.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_errormodels.py
new file mode 100644
index 000000000..9635da294
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_errormodels.py
@@ -0,0 +1,28 @@
+"""
+Test setting/overriding error models
+"""
+
+from numba import jit
+import unittest
+
+
+class TestErrorModel(unittest.TestCase):
+
+    def test_div_by_zero_python(self):
+        @jit   # python model is the default
+        def model_python(val):
+            return 1 / val
+
+        with self.assertRaises(ZeroDivisionError):
+            model_python(0)
+
+    def test_div_by_zero_numpy(self):
+        @jit(error_model='numpy')
+        def model_numpy(val):
+            return 1 / val
+
+        self.assertEqual(model_numpy(0), float('inf'))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_event.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_event.py
new file mode 100644
index 000000000..4d9e7a507
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_event.py
@@ -0,0 +1,217 @@
+import unittest
+import string
+
+import numpy as np
+
+from numba import njit, jit, literal_unroll
+from numba.core import event as ev
+from numba.tests.support import TestCase, override_config
+
+
+class TestEvent(TestCase):
+
+    def setUp(self):
+        # Trigger compilation to ensure all listeners are initialized
+        njit(lambda: None)()
+        self.__registered_listeners = len(ev._registered)
+
+    def tearDown(self):
+        # Check there is no lingering listeners
+        self.assertEqual(len(ev._registered), self.__registered_listeners)
+
+    def test_recording_listener(self):
+        @njit
+        def foo(x):
+            return x + x
+
+        with ev.install_recorder("numba:compile") as rec:
+            foo(1)
+
+        self.assertIsInstance(rec, ev.RecordingListener)
+        # Check there must be at least two events.
+        # Because there must be a START and END for the compilation of foo()
+        self.assertGreaterEqual(len(rec.buffer), 2)
+
+    def test_compiler_lock_event(self):
+        @njit
+        def foo(x):
+            return x + x
+
+        foo(1)
+        md = foo.get_metadata(foo.signatures[0])
+        lock_duration = md['timers']['compiler_lock']
+        self.assertIsInstance(lock_duration, float)
+        self.assertGreater(lock_duration, 0)
+
+    def test_llvm_lock_event(self):
+        @njit
+        def foo(x):
+            return x + x
+
+        foo(1)
+        md = foo.get_metadata(foo.signatures[0])
+        lock_duration = md['timers']['llvm_lock']
+        self.assertIsInstance(lock_duration, float)
+        self.assertGreater(lock_duration, 0)
+
+    def test_run_pass_event(self):
+        @njit
+        def foo(x):
+            return x + x
+
+        with ev.install_recorder("numba:run_pass") as recorder:
+            foo(2)
+
+        self.assertGreater(len(recorder.buffer), 0)
+        for _, event in recorder.buffer:
+            # Check that all fields are there
+            data = event.data
+            self.assertIsInstance(data['name'], str)
+            self.assertIsInstance(data['qualname'], str)
+            self.assertIsInstance(data['module'], str)
+            self.assertIsInstance(data['flags'], str)
+            self.assertIsInstance(data['args'], str)
+            self.assertIsInstance(data['return_type'], str)
+
+    def test_install_listener(self):
+        ut = self
+
+        class MyListener(ev.Listener):
+            def on_start(self, event):
+                ut.assertEqual(event.status, ev.EventStatus.START)
+                ut.assertEqual(event.kind, "numba:compile")
+                ut.assertIs(event.data["dispatcher"], foo)
+                dispatcher = event.data["dispatcher"]
+                ut.assertIs(dispatcher, foo)
+                # Check that the compiling signature is NOT in the overloads
+                ut.assertNotIn(event.data["args"], dispatcher.overloads)
+
+            def on_end(self, event):
+                ut.assertEqual(event.status, ev.EventStatus.END)
+                ut.assertEqual(event.kind, "numba:compile")
+                dispatcher = event.data["dispatcher"]
+                ut.assertIs(dispatcher, foo)
+                # Check that the compiling signature is in the overloads
+                ut.assertIn(event.data["args"], dispatcher.overloads)
+
+        @njit
+        def foo(x):
+            return x
+
+        listener = MyListener()
+        with ev.install_listener("numba:compile", listener) as yielded:
+            foo(1)
+
+        # Check that the yielded value is the same listener
+        self.assertIs(listener, yielded)
+
+    def test_global_register(self):
+        ut = self
+
+        class MyListener(ev.Listener):
+            def on_start(self, event):
+                ut.assertEqual(event.status, ev.EventStatus.START)
+                ut.assertEqual(event.kind, "numba:compile")
+                # Check it is the same dispatcher
+                dispatcher = event.data["dispatcher"]
+                ut.assertIs(dispatcher, foo)
+                # Check that the compiling signature is NOT in the overloads
+                ut.assertNotIn(event.data["args"], dispatcher.overloads)
+
+            def on_end(self, event):
+                ut.assertEqual(event.status, ev.EventStatus.END)
+                ut.assertEqual(event.kind, "numba:compile")
+                # Check it is the same dispatcher
+                dispatcher = event.data["dispatcher"]
+                ut.assertIs(dispatcher, foo)
+                # Check that the compiling signature is in the overloads
+                ut.assertIn(event.data["args"], dispatcher.overloads)
+
+        @njit
+        def foo(x):
+            return x
+
+        listener = MyListener()
+        ev.register("numba:compile", listener)
+        foo(1)
+        ev.unregister("numba:compile", listener)
+
+    def test_lifted_dispatcher(self):
+        @jit
+        def foo():
+            object()   # to trigger loop-lifting
+            c = 0
+            for i in range(10):
+                c += i
+            return c
+
+        with ev.install_recorder("numba:compile") as rec:
+            foo()
+
+        # Check that there are 4 events.
+        # Two for `foo()` and two for the lifted loop.
+        self.assertGreaterEqual(len(rec.buffer), 4)
+
+        cres = foo.overloads[foo.signatures[0]]
+        [ldisp] = cres.lifted
+
+        lifted_cres = ldisp.overloads[ldisp.signatures[0]]
+        self.assertIsInstance(
+            lifted_cres.metadata["timers"]["compiler_lock"],
+            float,
+        )
+        self.assertIsInstance(
+            lifted_cres.metadata["timers"]["llvm_lock"],
+            float,
+        )
+
+    def test_timing_properties(self):
+        a = tuple(string.ascii_lowercase)
+
+        @njit
+        def bar(x):
+            acc = 0
+            for i in literal_unroll(a):
+                if i in {'1': x}:
+                    acc += 1
+                else:
+                    acc += np.sqrt(x[0, 0])
+            return np.sin(x), acc
+
+        @njit
+        def foo(x):
+            return bar(np.zeros((x, x)))
+
+        with override_config('LLVM_PASS_TIMINGS', True):
+            foo(1)
+
+        def get_timers(fn, prop):
+            md = fn.get_metadata(fn.signatures[0])
+            return md[prop]
+
+        foo_timers = get_timers(foo, 'timers')
+        bar_timers = get_timers(bar, 'timers')
+        foo_llvm_timer = get_timers(foo, 'llvm_pass_timings')
+        bar_llvm_timer = get_timers(bar, 'llvm_pass_timings')
+
+        # Check: time spent in bar() must be longer than in foo()
+        self.assertLess(bar_timers['llvm_lock'],
+                        foo_timers['llvm_lock'])
+        self.assertLess(bar_timers['compiler_lock'],
+                        foo_timers['compiler_lock'])
+
+        # Check: time spent in LLVM itself must be less than in the LLVM lock
+        self.assertLess(foo_llvm_timer.get_total_time(),
+                        foo_timers['llvm_lock'])
+        self.assertLess(bar_llvm_timer.get_total_time(),
+                        bar_timers['llvm_lock'])
+
+        # Check: time spent in LLVM lock must be less than in compiler
+        self.assertLess(foo_timers['llvm_lock'],
+                        foo_timers['compiler_lock'])
+        self.assertLess(bar_timers['llvm_lock'],
+                        bar_timers['compiler_lock'])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_exceptions.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_exceptions.py
new file mode 100644
index 000000000..bd4a9c367
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_exceptions.py
@@ -0,0 +1,330 @@
+import numpy as np
+import sys
+import traceback
+
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit, njit
+from numba.core import types, errors
+from numba.tests.support import TestCase
+import unittest
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+class MyError(Exception):
+    pass
+
+
+class OtherError(Exception):
+    pass
+
+
+class UDEArgsToSuper(Exception):
+    def __init__(self, arg, value0):
+        super(UDEArgsToSuper, self).__init__(arg)
+        self.value0 = value0
+
+    def __eq__(self, other):
+        if not isinstance(other, self.__class__):
+            return False
+        same = True
+        same |= self.args == other.args
+        same |= self.value0 == other.value0
+        return same
+
+    def __hash__(self):
+        return hash((super(UDEArgsToSuper).__hash__(), self.value0))
+
+
+class UDENoArgSuper(Exception):
+    def __init__(self, arg, value0):
+        super(UDENoArgSuper, self).__init__()
+        self.deferarg = arg
+        self.value0 = value0
+
+    def __eq__(self, other):
+        if not isinstance(other, self.__class__):
+            return False
+        same = True
+        same |= self.args == other.args
+        same |= self.deferarg == other.deferarg
+        same |= self.value0 == other.value0
+        return same
+
+    def __hash__(self):
+        return hash((super(UDENoArgSuper).__hash__(), self.deferarg,
+                     self.value0))
+
+
+def raise_class(exc):
+    def raiser(i):
+        if i == 1:
+            raise exc
+        elif i == 2:
+            raise ValueError
+        elif i == 3:
+            # The exception type is looked up on a module (issue #1624)
+            raise np.linalg.LinAlgError
+        return i
+    return raiser
+
+
+def raise_instance(exc, arg):
+    def raiser(i):
+        if i == 1:
+            raise exc(arg, 1)
+        elif i == 2:
+            raise ValueError(arg, 2)
+        elif i == 3:
+            raise np.linalg.LinAlgError(arg, 3)
+        return i
+    return raiser
+
+
+def reraise():
+    raise
+
+
+def outer_function(inner):
+    def outer(i):
+        if i == 3:
+            raise OtherError("bar", 3)
+        return inner(i)
+    return outer
+
+
+def assert_usecase(i):
+    assert i == 1, "bar"
+
+
+def ude_bug_usecase():
+    raise UDEArgsToSuper()  # oops user forgot args to exception ctor
+
+
+class TestRaising(TestCase):
+
+    def test_unituple_index_error(self):
+        def pyfunc(a, i):
+            return a.shape[i]
+
+        cres = compile_isolated(pyfunc, (types.Array(types.int32, 1, 'A'),
+                                         types.int32))
+
+        cfunc = cres.entry_point
+        a = np.empty(2, dtype=np.int32)
+
+        self.assertEqual(cfunc(a, 0), pyfunc(a, 0))
+
+        with self.assertRaises(IndexError) as cm:
+            cfunc(a, 2)
+        self.assertEqual(str(cm.exception), "tuple index out of range")
+
+    def check_against_python(self, exec_mode, pyfunc, cfunc,
+                             expected_error_class, *args):
+
+        assert exec_mode in (force_pyobj_flags, no_pyobj_flags)
+
+        # invariant of mode, check the error class and args are the same
+        with self.assertRaises(expected_error_class) as pyerr:
+            pyfunc(*args)
+        with self.assertRaises(expected_error_class) as jiterr:
+            cfunc(*args)
+        self.assertEqual(pyerr.exception.args, jiterr.exception.args)
+
+        # special equality check for UDEs
+        if isinstance(pyerr.exception, (UDEArgsToSuper, UDENoArgSuper)):
+            self.assertTrue(pyerr.exception == jiterr.exception)
+
+        # in npm check bottom of traceback matches as frame injection with
+        # location info should ensure this
+        if exec_mode is no_pyobj_flags:
+
+            # we only care about the bottom two frames, the error and the
+            # location it was raised.
+            try:
+                pyfunc(*args)
+            except Exception:
+                py_frames = traceback.format_exception(*sys.exc_info())
+                expected_frames = py_frames[-2:]
+
+            try:
+                cfunc(*args)
+            except Exception:
+                c_frames = traceback.format_exception(*sys.exc_info())
+                got_frames = c_frames[-2:]
+
+            # check exception and the injected frame are the same
+            for expf, gotf in zip(expected_frames, got_frames):
+                self.assertEqual(expf, gotf)
+
+    def check_raise_class(self, flags):
+        pyfunc = raise_class(MyError)
+        cres = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cres.entry_point
+        self.assertEqual(cfunc(0), 0)
+        self.check_against_python(flags, pyfunc, cfunc, MyError, 1)
+        self.check_against_python(flags, pyfunc, cfunc, ValueError, 2)
+        self.check_against_python(flags, pyfunc, cfunc,
+                                  np.linalg.linalg.LinAlgError, 3)
+
+    def test_raise_class_nopython(self):
+        self.check_raise_class(flags=no_pyobj_flags)
+
+    def test_raise_class_objmode(self):
+        self.check_raise_class(flags=force_pyobj_flags)
+
+    def check_raise_instance(self, flags):
+        for clazz in [MyError, UDEArgsToSuper,
+                      UDENoArgSuper]:
+            pyfunc = raise_instance(clazz, "some message")
+            cres = compile_isolated(pyfunc, (types.int32,), flags=flags)
+            cfunc = cres.entry_point
+
+            self.assertEqual(cfunc(0), 0)
+            self.check_against_python(flags, pyfunc, cfunc, clazz, 1)
+            self.check_against_python(flags, pyfunc, cfunc, ValueError, 2)
+            self.check_against_python(flags, pyfunc, cfunc,
+                                      np.linalg.linalg.LinAlgError, 3)
+
+    def test_raise_instance_objmode(self):
+        self.check_raise_instance(flags=force_pyobj_flags)
+
+    def test_raise_instance_nopython(self):
+        self.check_raise_instance(flags=no_pyobj_flags)
+
+    def check_raise_nested(self, flags, **jit_args):
+        """
+        Check exception propagation from nested functions.
+        """
+        for clazz in [MyError, UDEArgsToSuper,
+                      UDENoArgSuper]:
+            inner_pyfunc = raise_instance(clazz, "some message")
+            pyfunc = outer_function(inner_pyfunc)
+            inner_cfunc = jit(**jit_args)(inner_pyfunc)
+            cfunc = jit(**jit_args)(outer_function(inner_cfunc))
+
+            self.check_against_python(flags, pyfunc, cfunc, clazz, 1)
+            self.check_against_python(flags, pyfunc, cfunc, ValueError, 2)
+            self.check_against_python(flags, pyfunc, cfunc, OtherError, 3)
+
+    def test_raise_nested_objmode(self):
+        self.check_raise_nested(force_pyobj_flags, forceobj=True)
+
+    def test_raise_nested_nopython(self):
+        self.check_raise_nested(no_pyobj_flags, nopython=True)
+
+    def check_reraise(self, flags):
+        def raise_exc(exc):
+            raise exc
+        pyfunc = reraise
+        cres = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cres.entry_point
+        for op, err in [(lambda : raise_exc(ZeroDivisionError),
+                         ZeroDivisionError),
+                        (lambda : raise_exc(UDEArgsToSuper("msg", 1)),
+                         UDEArgsToSuper),
+                        (lambda : raise_exc(UDENoArgSuper("msg", 1)),
+                         UDENoArgSuper)]:
+            def gen_impl(fn):
+                def impl():
+                    try:
+                        op()
+                    except err:
+                        fn()
+                return impl
+            pybased = gen_impl(pyfunc)
+            cbased = gen_impl(cfunc)
+            self.check_against_python(flags, pybased, cbased, err,)
+
+    def test_reraise_objmode(self):
+        self.check_reraise(flags=force_pyobj_flags)
+
+    def test_reraise_nopython(self):
+        self.check_reraise(flags=no_pyobj_flags)
+
+    def check_raise_invalid_class(self, cls, flags):
+        pyfunc = raise_class(cls)
+        cres = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cres.entry_point
+        with self.assertRaises(TypeError) as cm:
+            cfunc(1)
+        self.assertEqual(str(cm.exception),
+                         "exceptions must derive from BaseException")
+
+    def test_raise_invalid_class_objmode(self):
+        self.check_raise_invalid_class(int, flags=force_pyobj_flags)
+        self.check_raise_invalid_class(1, flags=force_pyobj_flags)
+
+    def test_raise_invalid_class_nopython(self):
+        msg = "Encountered unsupported constant type used for exception"
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            self.check_raise_invalid_class(int, flags=no_pyobj_flags)
+        self.assertIn(msg, str(raises.exception))
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            self.check_raise_invalid_class(1, flags=no_pyobj_flags)
+        self.assertIn(msg, str(raises.exception))
+
+    def test_raise_bare_string_nopython(self):
+        @njit
+        def foo():
+            raise "illegal"
+        msg = ("Directly raising a string constant as an exception is not "
+               "supported")
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+        self.assertIn(msg, str(raises.exception))
+
+    def check_assert_statement(self, flags):
+        pyfunc = assert_usecase
+        cres = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        cfunc = cres.entry_point
+        cfunc(1)
+        self.check_against_python(flags, pyfunc, cfunc, AssertionError, 2)
+
+    def test_assert_statement_objmode(self):
+        self.check_assert_statement(flags=force_pyobj_flags)
+
+    def test_assert_statement_nopython(self):
+        self.check_assert_statement(flags=no_pyobj_flags)
+
+    def check_raise_from_exec_string(self, flags):
+        # issue #3428
+        simple_raise = "def f(a):\n  raise exc('msg', 10)"
+        assert_raise = "def f(a):\n  assert a != 1"
+        for f_text, exc in [(assert_raise, AssertionError),
+                            (simple_raise, UDEArgsToSuper),
+                            (simple_raise, UDENoArgSuper)]:
+            loc = {}
+            exec(f_text, {'exc': exc}, loc)
+            pyfunc = loc['f']
+            cres = compile_isolated(pyfunc, (types.int32,), flags=flags)
+            cfunc = cres.entry_point
+            self.check_against_python(flags, pyfunc, cfunc, exc, 1)
+
+    def test_assert_from_exec_string_objmode(self):
+        self.check_raise_from_exec_string(flags=force_pyobj_flags)
+
+    def test_assert_from_exec_string_nopython(self):
+        self.check_raise_from_exec_string(flags=no_pyobj_flags)
+
+    def check_user_code_error_traceback(self, flags):
+        # this test checks that if a user tries to compile code that contains
+        # a bug in exception initialisation (e.g. missing arg) then this also
+        # has a frame injected with the location information.
+        pyfunc = ude_bug_usecase
+        cres = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cres.entry_point
+        self.check_against_python(flags, pyfunc, cfunc, TypeError)
+
+    def test_user_code_error_traceback_objmode(self):
+        self.check_user_code_error_traceback(flags=force_pyobj_flags)
+
+    def test_user_code_error_traceback_nopython(self):
+        self.check_user_code_error_traceback(flags=no_pyobj_flags)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extended_arg.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extended_arg.py
new file mode 100644
index 000000000..11f7a2499
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extended_arg.py
@@ -0,0 +1,42 @@
+import unittest
+
+import dis
+import struct
+import sys
+
+from numba import jit
+from numba.tests.support import TestCase, tweak_code
+
+
+class TestExtendedArg(TestCase):
+    """
+    Test support for the EXTENDED_ARG opcode.
+    """
+
+    def get_extended_arg_load_const(self):
+        """
+        Get a function with a EXTENDED_ARG opcode before a LOAD_CONST opcode.
+        """
+        def f():
+            x = 5
+            return x
+
+        b = bytearray(f.__code__.co_code)
+        consts = f.__code__.co_consts
+        bytecode_len = 0xff
+        bytecode_format = "<BB"
+        consts = consts + (None,) * bytecode_len + (42,)
+        b[:0] = struct.pack(bytecode_format, dis.EXTENDED_ARG, 1)
+        tweak_code(f, codestring=bytes(b), consts=consts)
+        return f
+
+    def test_extended_arg_load_const(self):
+        pyfunc = self.get_extended_arg_load_const()
+        self.assertPreciseEqual(pyfunc(), 42)
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertPreciseEqual(cfunc(), 42)
+
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extending.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extending.py
new file mode 100644
index 000000000..15430d89c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extending.py
@@ -0,0 +1,2077 @@
+import math
+import operator
+import sys
+import pickle
+import multiprocessing
+import ctypes
+import warnings
+from distutils.version import LooseVersion
+import re
+
+import numpy as np
+
+from numba import njit, jit, vectorize, guvectorize, objmode
+from numba.core import types, errors, typing, compiler, cgutils
+from numba.core.typed_passes import type_inference_stage
+from numba.core.registry import cpu_target
+from numba.core.compiler import compile_isolated
+from numba.core.imputils import lower_constant
+from numba.tests.support import (
+    TestCase,
+    captured_stdout,
+    temp_directory,
+    override_config,
+    run_in_new_process_in_cache_dir,
+    skip_if_typeguard,
+)
+from numba.core.errors import LoweringError
+import unittest
+
+from numba.extending import (
+    typeof_impl,
+    type_callable,
+    lower_builtin,
+    lower_cast,
+    overload,
+    overload_attribute,
+    overload_method,
+    models,
+    register_model,
+    box,
+    unbox,
+    NativeValue,
+    intrinsic,
+    _Intrinsic,
+    register_jitable,
+    get_cython_function_address,
+    is_jitted,
+    overload_classmethod,
+)
+from numba.core.typing.templates import (
+    ConcreteTemplate,
+    signature,
+    infer,
+    infer_global,
+    AbstractTemplate,
+)
+
+
+# Pandas-like API implementation
+from .pdlike_usecase import Index, Series
+
+
+try:
+    import scipy
+
+    if LooseVersion(scipy.__version__) < "0.19":
+        sc = None
+    else:
+        import scipy.special.cython_special as sc
+except ImportError:
+    sc = None
+
+
+# -----------------------------------------------------------------------
+# Define a custom type and an implicit cast on it
+
+
+class MyDummy(object):
+    pass
+
+
+class MyDummyType(types.Opaque):
+    def can_convert_to(self, context, toty):
+        if isinstance(toty, types.Number):
+            from numba.core.typeconv import Conversion
+
+            return Conversion.safe
+
+
+mydummy_type = MyDummyType("mydummy")
+mydummy = MyDummy()
+
+
+@typeof_impl.register(MyDummy)
+def typeof_mydummy(val, c):
+    return mydummy_type
+
+
+@lower_cast(MyDummyType, types.Number)
+def mydummy_to_number(context, builder, fromty, toty, val):
+    """
+    Implicit conversion from MyDummy to int.
+    """
+    return context.get_constant(toty, 42)
+
+
+def get_dummy():
+    return mydummy
+
+
+register_model(MyDummyType)(models.OpaqueModel)
+
+
+@unbox(MyDummyType)
+def unbox_index(typ, obj, c):
+    return NativeValue(c.context.get_dummy_value())
+
+
+# -----------------------------------------------------------------------
+# Define a second custom type but w/o implicit cast to Number
+
+
+def base_dummy_type_factory(name):
+    class DynType(object):
+        pass
+
+    class DynTypeType(types.Opaque):
+        pass
+
+    dyn_type_type = DynTypeType(name)
+
+    @typeof_impl.register(DynType)
+    def typeof_mydummy(val, c):
+        return dyn_type_type
+
+    register_model(DynTypeType)(models.OpaqueModel)
+    return DynTypeType, DynType, dyn_type_type
+
+
+MyDummyType2, MyDummy2, mydummy_type_2 = base_dummy_type_factory("mydummy2")
+
+
+@unbox(MyDummyType2)
+def unbox_index2(typ, obj, c):
+    return NativeValue(c.context.get_dummy_value())
+
+
+# -----------------------------------------------------------------------
+# Define a function's typing and implementation using the classical
+# two-step API
+
+
+def func1(x=None):
+    raise NotImplementedError
+
+
+def type_func1_(context):
+    def typer(x=None):
+        if x in (None, types.none):
+            # 0-arg or 1-arg with None
+            return types.int32
+        elif isinstance(x, types.Float):
+            # 1-arg with float
+            return x
+
+    return typer
+
+
+type_func1 = type_callable(func1)(type_func1_)
+
+
+@lower_builtin(func1)
+@lower_builtin(func1, types.none)
+def func1_nullary(context, builder, sig, args):
+    return context.get_constant(sig.return_type, 42)
+
+
+@lower_builtin(func1, types.Float)
+def func1_unary(context, builder, sig, args):
+    def func1_impl(x):
+        return math.sqrt(2 * x)
+
+    return context.compile_internal(builder, func1_impl, sig, args)
+
+
+# We can do the same for a known internal operation, here "print_item"
+# which we extend to support MyDummyType.
+
+
+@infer
+class PrintDummy(ConcreteTemplate):
+    key = "print_item"
+    cases = [signature(types.none, mydummy_type)]
+
+
+@lower_builtin("print_item", MyDummyType)
+def print_dummy(context, builder, sig, args):
+    [x] = args
+    pyapi = context.get_python_api(builder)
+    strobj = pyapi.unserialize(pyapi.serialize_object("hello!"))
+    pyapi.print_object(strobj)
+    pyapi.decref(strobj)
+    return context.get_dummy_value()
+
+
+# -----------------------------------------------------------------------
+# Define an overloaded function (combined API)
+
+
+def where(cond, x, y):
+    raise NotImplementedError
+
+
+def np_where(cond, x, y):
+    """
+    Wrap np.where() to allow for keyword arguments
+    """
+    return np.where(cond, x, y)
+
+
+def call_where(cond, x, y):
+    return where(cond, y=y, x=x)
+
+
+@overload(where)
+def overload_where_arrays(cond, x, y):
+    """
+    Implement where() for arrays.
+    """
+    # Choose implementation based on argument types.
+    if isinstance(cond, types.Array):
+        if x.dtype != y.dtype:
+            raise errors.TypingError("x and y should have the same dtype")
+
+        # Array where() => return an array of the same shape
+        if all(ty.layout == "C" for ty in (cond, x, y)):
+
+            def where_impl(cond, x, y):
+                """
+                Fast implementation for C-contiguous arrays
+                """
+                shape = cond.shape
+                if x.shape != shape or y.shape != shape:
+                    raise ValueError("all inputs should have the same shape")
+                res = np.empty_like(x)
+                cf = cond.flat
+                xf = x.flat
+                yf = y.flat
+                rf = res.flat
+                for i in range(cond.size):
+                    rf[i] = xf[i] if cf[i] else yf[i]
+                return res
+
+        else:
+
+            def where_impl(cond, x, y):
+                """
+                Generic implementation for other arrays
+                """
+                shape = cond.shape
+                if x.shape != shape or y.shape != shape:
+                    raise ValueError("all inputs should have the same shape")
+                res = np.empty_like(x)
+                for idx, c in np.ndenumerate(cond):
+                    res[idx] = x[idx] if c else y[idx]
+                return res
+
+        return where_impl
+
+
+# We can define another overload function for the same function, they
+# will be tried in turn until one succeeds.
+
+
+@overload(where)
+def overload_where_scalars(cond, x, y):
+    """
+    Implement where() for scalars.
+    """
+    if not isinstance(cond, types.Array):
+        if x != y:
+            raise errors.TypingError("x and y should have the same type")
+
+        def where_impl(cond, x, y):
+            """
+            Scalar where() => return a 0-dim array
+            """
+            scal = x if cond else y
+            # Can't use full_like() on Numpy < 1.8
+            arr = np.empty_like(scal)
+            arr[()] = scal
+            return arr
+
+        return where_impl
+
+
+# -----------------------------------------------------------------------
+# Overload an already defined built-in function, extending it for new types.
+
+
+@overload(len)
+def overload_len_dummy(arg):
+    if isinstance(arg, MyDummyType):
+
+        def len_impl(arg):
+            return 13
+
+        return len_impl
+
+
+@overload(operator.add)
+def overload_add_dummy(arg1, arg2):
+    if isinstance(arg1, (MyDummyType, MyDummyType2)) and isinstance(
+        arg2, (MyDummyType, MyDummyType2)
+    ):
+
+        def dummy_add_impl(arg1, arg2):
+            return 42
+
+        return dummy_add_impl
+
+
+@overload(operator.delitem)
+def overload_dummy_delitem(obj, idx):
+    if isinstance(obj, MyDummyType) and isinstance(idx, types.Integer):
+
+        def dummy_delitem_impl(obj, idx):
+            print("del", obj, idx)
+
+        return dummy_delitem_impl
+
+
+@overload(operator.getitem)
+def overload_dummy_getitem(obj, idx):
+    if isinstance(obj, MyDummyType) and isinstance(idx, types.Integer):
+
+        def dummy_getitem_impl(obj, idx):
+            return idx + 123
+
+        return dummy_getitem_impl
+
+
+@overload(operator.setitem)
+def overload_dummy_setitem(obj, idx, val):
+    if all(
+        [
+            isinstance(obj, MyDummyType),
+            isinstance(idx, types.Integer),
+            isinstance(val, types.Integer),
+        ]
+    ):
+
+        def dummy_setitem_impl(obj, idx, val):
+            print(idx, val)
+
+        return dummy_setitem_impl
+
+
+def call_add_operator(arg1, arg2):
+    return operator.add(arg1, arg2)
+
+
+def call_add_binop(arg1, arg2):
+    return arg1 + arg2
+
+
+@overload(operator.iadd)
+def overload_iadd_dummy(arg1, arg2):
+    if isinstance(arg1, (MyDummyType, MyDummyType2)) and isinstance(
+        arg2, (MyDummyType, MyDummyType2)
+    ):
+
+        def dummy_iadd_impl(arg1, arg2):
+            return 42
+
+        return dummy_iadd_impl
+
+
+def call_iadd_operator(arg1, arg2):
+    return operator.add(arg1, arg2)
+
+
+def call_iadd_binop(arg1, arg2):
+    arg1 += arg2
+
+    return arg1
+
+
+def call_delitem(obj, idx):
+    del obj[idx]
+
+
+def call_getitem(obj, idx):
+    return obj[idx]
+
+
+def call_setitem(obj, idx, val):
+    obj[idx] = val
+
+
+@overload_method(MyDummyType, "length")
+def overload_method_length(arg):
+    def imp(arg):
+        return len(arg)
+
+    return imp
+
+
+def cache_overload_method_usecase(x):
+    return x.length()
+
+
+def call_func1_nullary():
+    return func1()
+
+
+def call_func1_unary(x):
+    return func1(x)
+
+
+def len_usecase(x):
+    return len(x)
+
+
+def print_usecase(x):
+    print(x)
+
+
+def getitem_usecase(x, key):
+    return x[key]
+
+
+def npyufunc_usecase(x):
+    return np.cos(np.sin(x))
+
+
+def get_data_usecase(x):
+    return x._data
+
+
+def get_index_usecase(x):
+    return x._index
+
+
+def is_monotonic_usecase(x):
+    return x.is_monotonic_increasing
+
+
+def make_series_usecase(data, index):
+    return Series(data, index)
+
+
+def clip_usecase(x, lo, hi):
+    return x.clip(lo, hi)
+
+
+# -----------------------------------------------------------------------
+
+
+def return_non_boxable():
+    return np
+
+
+@overload(return_non_boxable)
+def overload_return_non_boxable():
+    def imp():
+        return np
+
+    return imp
+
+
+def non_boxable_ok_usecase(sz):
+    mod = return_non_boxable()
+    return mod.arange(sz)
+
+
+def non_boxable_bad_usecase():
+    return return_non_boxable()
+
+
+def mk_func_input(f):
+    pass
+
+
+@infer_global(mk_func_input)
+class MkFuncTyping(AbstractTemplate):
+    def generic(self, args, kws):
+        assert isinstance(args[0], types.MakeFunctionLiteral)
+        return signature(types.none, *args)
+
+
+def mk_func_test_impl():
+    mk_func_input(lambda a: a)
+
+
+# -----------------------------------------------------------------------
+# Define a types derived from types.Callable and overloads for them
+
+
+class MyClass(object):
+    pass
+
+
+class CallableTypeRef(types.Callable):
+
+    def __init__(self, instance_type):
+        self.instance_type = instance_type
+        self.sig_to_impl_key = {}
+        self.compiled_templates = []
+        super(CallableTypeRef, self).__init__('callable_type_ref'
+                                              '[{}]'.format(self.instance_type))
+
+    def get_call_type(self, context, args, kws):
+
+        res_sig = None
+        for template in context._functions[type(self)]:
+            try:
+                res_sig = template.apply(args, kws)
+            except Exception:
+                pass  # for simplicity assume args must match exactly
+            else:
+                compiled_ovlds = getattr(template, '_compiled_overloads', {})
+                if args in compiled_ovlds:
+                    self.sig_to_impl_key[res_sig] = compiled_ovlds[args]
+                    self.compiled_templates.append(template)
+                    break
+
+        return res_sig
+
+    def get_call_signatures(self):
+        sigs = list(self.sig_to_impl_key.keys())
+        return sigs, True
+
+    def get_impl_key(self, sig):
+        return self.sig_to_impl_key[sig]
+
+
+@register_model(CallableTypeRef)
+class CallableTypeModel(models.OpaqueModel):
+
+    def __init__(self, dmm, fe_type):
+
+        models.OpaqueModel.__init__(self, dmm, fe_type)
+
+
+infer_global(MyClass, CallableTypeRef(MyClass))
+
+
+@lower_constant(CallableTypeRef)
+def constant_callable_typeref(context, builder, ty, pyval):
+    return context.get_dummy_value()
+
+
+# -----------------------------------------------------------------------
+
+
+@overload(np.exp)
+def overload_np_exp(obj):
+    if isinstance(obj, MyDummyType):
+
+        def imp(obj):
+            # Returns a constant if a MyDummyType is seen
+            return 0xDEADBEEF
+
+        return imp
+
+
+class TestLowLevelExtending(TestCase):
+    """
+    Test the low-level two-tier extension API.
+    """
+
+    # We check with both @jit and compile_isolated(), to exercise the
+    # registration logic.
+
+    def test_func1(self):
+        pyfunc = call_func1_nullary
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertPreciseEqual(cfunc(), 42)
+        pyfunc = call_func1_unary
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertPreciseEqual(cfunc(None), 42)
+        self.assertPreciseEqual(cfunc(18.0), 6.0)
+
+    def test_func1_isolated(self):
+        pyfunc = call_func1_nullary
+        cr = compile_isolated(pyfunc, ())
+        self.assertPreciseEqual(cr.entry_point(), 42)
+        pyfunc = call_func1_unary
+        cr = compile_isolated(pyfunc, (types.float64,))
+        self.assertPreciseEqual(cr.entry_point(18.0), 6.0)
+
+    def test_type_callable_keeps_function(self):
+        self.assertIs(type_func1, type_func1_)
+        self.assertIsNotNone(type_func1)
+
+    def test_cast_mydummy(self):
+        pyfunc = get_dummy
+        cr = compile_isolated(pyfunc, (), types.float64)
+        self.assertPreciseEqual(cr.entry_point(), 42.0)
+
+    def test_mk_func_literal(self):
+        """make sure make_function is passed to typer class as a literal
+        """
+        test_ir = compiler.run_frontend(mk_func_test_impl)
+        typingctx = cpu_target.typing_context
+        targetctx = cpu_target.target_context
+        typingctx.refresh()
+        targetctx.refresh()
+        typing_res = type_inference_stage(typingctx, targetctx, test_ir, (),
+                                          None)
+        self.assertTrue(
+            any(
+                isinstance(a, types.MakeFunctionLiteral)
+                for a in typing_res.typemap.values()
+            )
+        )
+
+
+class TestPandasLike(TestCase):
+    """
+    Test implementing a pandas-like Index object.
+    Also stresses most of the high-level API.
+    """
+
+    def test_index_len(self):
+        i = Index(np.arange(3))
+        cfunc = jit(nopython=True)(len_usecase)
+        self.assertPreciseEqual(cfunc(i), 3)
+
+    def test_index_getitem(self):
+        i = Index(np.int32([42, 8, -5]))
+        cfunc = jit(nopython=True)(getitem_usecase)
+        self.assertPreciseEqual(cfunc(i, 1), 8)
+        ii = cfunc(i, slice(1, None))
+        self.assertIsInstance(ii, Index)
+        self.assertEqual(list(ii), [8, -5])
+
+    def test_index_ufunc(self):
+        """
+        Check Numpy ufunc on an Index object.
+        """
+        i = Index(np.int32([42, 8, -5]))
+        cfunc = jit(nopython=True)(npyufunc_usecase)
+        ii = cfunc(i)
+        self.assertIsInstance(ii, Index)
+        self.assertPreciseEqual(ii._data, np.cos(np.sin(i._data)))
+
+    def test_index_get_data(self):
+        # The _data attribute is exposed with make_attribute_wrapper()
+        i = Index(np.int32([42, 8, -5]))
+        cfunc = jit(nopython=True)(get_data_usecase)
+        data = cfunc(i)
+        self.assertIs(data, i._data)
+
+    def test_index_is_monotonic(self):
+        # The is_monotonic_increasing attribute is exposed with
+        # overload_attribute()
+        cfunc = jit(nopython=True)(is_monotonic_usecase)
+        for values, expected in [
+            ([8, 42, 5], False),
+            ([5, 8, 42], True),
+            ([], True),
+        ]:
+            i = Index(np.int32(values))
+            got = cfunc(i)
+            self.assertEqual(got, expected)
+
+    def test_series_len(self):
+        i = Index(np.int32([2, 4, 3]))
+        s = Series(np.float64([1.5, 4.0, 2.5]), i)
+        cfunc = jit(nopython=True)(len_usecase)
+        self.assertPreciseEqual(cfunc(s), 3)
+
+    def test_series_get_index(self):
+        i = Index(np.int32([2, 4, 3]))
+        s = Series(np.float64([1.5, 4.0, 2.5]), i)
+        cfunc = jit(nopython=True)(get_index_usecase)
+        got = cfunc(s)
+        self.assertIsInstance(got, Index)
+        self.assertIs(got._data, i._data)
+
+    def test_series_ufunc(self):
+        """
+        Check Numpy ufunc on an Series object.
+        """
+        i = Index(np.int32([42, 8, -5]))
+        s = Series(np.int64([1, 2, 3]), i)
+        cfunc = jit(nopython=True)(npyufunc_usecase)
+        ss = cfunc(s)
+        self.assertIsInstance(ss, Series)
+        self.assertIsInstance(ss._index, Index)
+        self.assertIs(ss._index._data, i._data)
+        self.assertPreciseEqual(ss._values, np.cos(np.sin(s._values)))
+
+    def test_series_constructor(self):
+        i = Index(np.int32([42, 8, -5]))
+        d = np.float64([1.5, 4.0, 2.5])
+        cfunc = jit(nopython=True)(make_series_usecase)
+        got = cfunc(d, i)
+        self.assertIsInstance(got, Series)
+        self.assertIsInstance(got._index, Index)
+        self.assertIs(got._index._data, i._data)
+        self.assertIs(got._values, d)
+
+    def test_series_clip(self):
+        i = Index(np.int32([42, 8, -5]))
+        s = Series(np.float64([1.5, 4.0, 2.5]), i)
+        cfunc = jit(nopython=True)(clip_usecase)
+        ss = cfunc(s, 1.6, 3.0)
+        self.assertIsInstance(ss, Series)
+        self.assertIsInstance(ss._index, Index)
+        self.assertIs(ss._index._data, i._data)
+        self.assertPreciseEqual(ss._values, np.float64([1.6, 3.0, 2.5]))
+
+
+class TestHighLevelExtending(TestCase):
+    """
+    Test the high-level combined API.
+    """
+
+    def test_where(self):
+        """
+        Test implementing a function with @overload.
+        """
+        pyfunc = call_where
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(*args, **kwargs):
+            expected = np_where(*args, **kwargs)
+            got = cfunc(*args, **kwargs)
+            self.assertPreciseEqual(expected, got)
+
+        check(x=3, cond=True, y=8)
+        check(True, 3, 8)
+        check(
+            np.bool_([True, False, True]),
+            np.int32([1, 2, 3]),
+            np.int32([4, 5, 5]),
+        )
+
+        # The typing error is propagated
+        with self.assertRaises(errors.TypingError) as raises:
+            cfunc(np.bool_([]), np.int32([]), np.int64([]))
+        self.assertIn(
+            "x and y should have the same dtype", str(raises.exception)
+        )
+
+    def test_len(self):
+        """
+        Test re-implementing len() for a custom type with @overload.
+        """
+        cfunc = jit(nopython=True)(len_usecase)
+        self.assertPreciseEqual(cfunc(MyDummy()), 13)
+        self.assertPreciseEqual(cfunc([4, 5]), 2)
+
+    def test_print(self):
+        """
+        Test re-implementing print() for a custom type with @overload.
+        """
+        cfunc = jit(nopython=True)(print_usecase)
+        with captured_stdout():
+            cfunc(MyDummy())
+            self.assertEqual(sys.stdout.getvalue(), "hello!\n")
+
+    def test_add_operator(self):
+        """
+        Test re-implementing operator.add() for a custom type with @overload.
+        """
+        pyfunc = call_add_operator
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.assertPreciseEqual(cfunc(1, 2), 3)
+        self.assertPreciseEqual(cfunc(MyDummy2(), MyDummy2()), 42)
+
+        # this will call add(Number, Number) as MyDummy implicitly casts to
+        # Number
+        self.assertPreciseEqual(cfunc(MyDummy(), MyDummy()), 84)
+
+    def test_add_binop(self):
+        """
+        Test re-implementing '+' for a custom type via @overload(operator.add).
+        """
+        pyfunc = call_add_binop
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.assertPreciseEqual(cfunc(1, 2), 3)
+        self.assertPreciseEqual(cfunc(MyDummy2(), MyDummy2()), 42)
+
+        # this will call add(Number, Number) as MyDummy implicitly casts to
+        # Number
+        self.assertPreciseEqual(cfunc(MyDummy(), MyDummy()), 84)
+
+    def test_iadd_operator(self):
+        """
+        Test re-implementing operator.add() for a custom type with @overload.
+        """
+        pyfunc = call_iadd_operator
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.assertPreciseEqual(cfunc(1, 2), 3)
+        self.assertPreciseEqual(cfunc(MyDummy2(), MyDummy2()), 42)
+
+        # this will call add(Number, Number) as MyDummy implicitly casts to
+        # Number
+        self.assertPreciseEqual(cfunc(MyDummy(), MyDummy()), 84)
+
+    def test_iadd_binop(self):
+        """
+        Test re-implementing '+' for a custom type via @overload(operator.add).
+        """
+        pyfunc = call_iadd_binop
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self.assertPreciseEqual(cfunc(1, 2), 3)
+        self.assertPreciseEqual(cfunc(MyDummy2(), MyDummy2()), 42)
+
+        # this will call add(Number, Number) as MyDummy implicitly casts to
+        # Number
+        self.assertPreciseEqual(cfunc(MyDummy(), MyDummy()), 84)
+
+    def test_delitem(self):
+        pyfunc = call_delitem
+        cfunc = jit(nopython=True)(pyfunc)
+        obj = MyDummy()
+        e = None
+
+        with captured_stdout() as out:
+            try:
+                cfunc(obj, 321)
+            except Exception as exc:
+                e = exc
+
+        if e is not None:
+            raise e
+        self.assertEqual(out.getvalue(), "del hello! 321\n")
+
+    def test_getitem(self):
+        pyfunc = call_getitem
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertPreciseEqual(cfunc(MyDummy(), 321), 321 + 123)
+
+    def test_setitem(self):
+        pyfunc = call_setitem
+        cfunc = jit(nopython=True)(pyfunc)
+        obj = MyDummy()
+        e = None
+
+        with captured_stdout() as out:
+            try:
+                cfunc(obj, 321, 123)
+            except Exception as exc:
+                e = exc
+
+        if e is not None:
+            raise e
+        self.assertEqual(out.getvalue(), "321 123\n")
+
+    def test_no_cpython_wrapper(self):
+        """
+        Test overloading whose return value cannot be represented in CPython.
+        """
+        # Test passing Module type from a @overload implementation to ensure
+        # that the *no_cpython_wrapper* flag works
+        ok_cfunc = jit(nopython=True)(non_boxable_ok_usecase)
+        n = 10
+        got = ok_cfunc(n)
+        expect = non_boxable_ok_usecase(n)
+        np.testing.assert_equal(expect, got)
+        # Verify that the Module type cannot be returned to CPython
+        bad_cfunc = jit(nopython=True)(non_boxable_bad_usecase)
+        with self.assertRaises(TypeError) as raises:
+            bad_cfunc()
+        errmsg = str(raises.exception)
+        expectmsg = "cannot convert native Module"
+        self.assertIn(expectmsg, errmsg)
+
+    def test_typing_vs_impl_signature_mismatch_handling(self):
+        """
+        Tests that an overload which has a differing typing and implementing
+        signature raises an exception.
+        """
+
+        def gen_ol(impl=None):
+            def myoverload(a, b, c, kw=None):
+                pass
+
+            @overload(myoverload)
+            def _myoverload_impl(a, b, c, kw=None):
+                return impl
+
+            @jit(nopython=True)
+            def foo(a, b, c, d):
+                myoverload(a, b, c, kw=d)
+
+            return foo
+
+        sentinel = "Typing and implementation arguments differ in"
+
+        # kwarg value is different
+        def impl1(a, b, c, kw=12):
+            if a > 10:
+                return 1
+            else:
+                return -1
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl1)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("keyword argument default values", msg)
+        self.assertIn('<Parameter "kw=12">', msg)
+        self.assertIn('<Parameter "kw=None">', msg)
+
+        # kwarg name is different
+        def impl2(a, b, c, kwarg=None):
+            if a > 10:
+                return 1
+            else:
+                return -1
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl2)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("keyword argument names", msg)
+        self.assertIn('<Parameter "kwarg=None">', msg)
+        self.assertIn('<Parameter "kw=None">', msg)
+
+        # arg name is different
+        def impl3(z, b, c, kw=None):
+            if a > 10:      # noqa: F821
+                return 1
+            else:
+                return -1
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl3)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("argument names", msg)
+        self.assertFalse("keyword" in msg)
+        self.assertIn('<Parameter "a">', msg)
+        self.assertIn('<Parameter "z">', msg)
+
+        from .overload_usecases import impl4, impl5
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl4)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("argument names", msg)
+        self.assertFalse("keyword" in msg)
+        self.assertIn("First difference: 'z'", msg)
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl5)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("argument names", msg)
+        self.assertFalse("keyword" in msg)
+        self.assertIn('<Parameter "a">', msg)
+        self.assertIn('<Parameter "z">', msg)
+
+        # too many args
+        def impl6(a, b, c, d, e, kw=None):
+            if a > 10:
+                return 1
+            else:
+                return -1
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl6)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("argument names", msg)
+        self.assertFalse("keyword" in msg)
+        self.assertIn('<Parameter "d">', msg)
+        self.assertIn('<Parameter "e">', msg)
+
+        # too few args
+        def impl7(a, b, kw=None):
+            if a > 10:
+                return 1
+            else:
+                return -1
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl7)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("argument names", msg)
+        self.assertFalse("keyword" in msg)
+        self.assertIn('<Parameter "c">', msg)
+
+        # too many kwargs
+        def impl8(a, b, c, kw=None, extra_kwarg=None):
+            if a > 10:
+                return 1
+            else:
+                return -1
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl8)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("keyword argument names", msg)
+        self.assertIn('<Parameter "extra_kwarg=None">', msg)
+
+        # too few kwargs
+        def impl9(a, b, c):
+            if a > 10:
+                return 1
+            else:
+                return -1
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(impl9)(1, 2, 3, 4)
+        msg = str(e.exception)
+        self.assertIn(sentinel, msg)
+        self.assertIn("keyword argument names", msg)
+        self.assertIn('<Parameter "kw=None">', msg)
+
+    def test_typing_vs_impl_signature_mismatch_handling_var_positional(self):
+        """
+        Tests that an overload which has a differing typing and implementing
+        signature raises an exception and uses VAR_POSITIONAL (*args) in typing
+        """
+
+        def myoverload(a, kw=None):
+            pass
+
+        from .overload_usecases import var_positional_impl
+
+        overload(myoverload)(var_positional_impl)
+
+        @jit(nopython=True)
+        def foo(a, b):
+            return myoverload(a, b, 9, kw=11)
+
+        with self.assertRaises(errors.TypingError) as e:
+            foo(1, 5)
+        msg = str(e.exception)
+        self.assertIn("VAR_POSITIONAL (e.g. *args) argument kind", msg)
+        self.assertIn("offending argument name is '*star_args_token'", msg)
+
+    def test_typing_vs_impl_signature_mismatch_handling_var_keyword(self):
+        """
+        Tests that an overload which uses **kwargs (VAR_KEYWORD)
+        """
+
+        def gen_ol(impl, strict=True):
+            def myoverload(a, kw=None):
+                pass
+
+            overload(myoverload, strict=strict)(impl)
+
+            @jit(nopython=True)
+            def foo(a, b):
+                return myoverload(a, kw=11)
+
+            return foo
+
+        # **kwargs in typing
+        def ol1(a, **kws):
+            def impl(a, kw=10):
+                return a
+
+            return impl
+
+        gen_ol(ol1, False)(1, 2)  # no error if strictness not enforced
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(ol1)(1, 2)
+        msg = str(e.exception)
+        self.assertIn("use of VAR_KEYWORD (e.g. **kwargs) is unsupported", msg)
+        self.assertIn("offending argument name is '**kws'", msg)
+
+        # **kwargs in implementation
+        def ol2(a, kw=0):
+            def impl(a, **kws):
+                return a
+
+            return impl
+
+        with self.assertRaises(errors.TypingError) as e:
+            gen_ol(ol2)(1, 2)
+        msg = str(e.exception)
+        self.assertIn("use of VAR_KEYWORD (e.g. **kwargs) is unsupported", msg)
+        self.assertIn("offending argument name is '**kws'", msg)
+
+    def test_overload_method_kwargs(self):
+        # Issue #3489
+        @overload_method(types.Array, "foo")
+        def fooimpl(arr, a_kwarg=10):
+            def impl(arr, a_kwarg=10):
+                return a_kwarg
+
+            return impl
+
+        @njit
+        def bar(A):
+            return A.foo(), A.foo(20), A.foo(a_kwarg=30)
+
+        Z = np.arange(5)
+
+        self.assertEqual(bar(Z), (10, 20, 30))
+
+    def test_overload_method_literal_unpack(self):
+        # Issue #3683
+        @overload_method(types.Array, "litfoo")
+        def litfoo(arr, val):
+            # Must be an integer
+            if isinstance(val, types.Integer):
+                # Must not be literal
+                if not isinstance(val, types.Literal):
+
+                    def impl(arr, val):
+                        return val
+
+                    return impl
+
+        @njit
+        def bar(A):
+            return A.litfoo(0xCAFE)
+
+        A = np.zeros(1)
+        bar(A)
+        self.assertEqual(bar(A), 0xCAFE)
+
+    def test_overload_ufunc(self):
+        # Issue #4133.
+        # Use an extended type (MyDummyType) to use with a customized
+        # ufunc (np.exp).
+        @njit
+        def test():
+            return np.exp(mydummy)
+
+        self.assertEqual(test(), 0xDEADBEEF)
+
+    def test_overload_method_stararg(self):
+        @overload_method(MyDummyType, "method_stararg")
+        def _ov_method_stararg(obj, val, val2, *args):
+            def get(obj, val, val2, *args):
+                return (val, val2, args)
+
+            return get
+
+        @njit
+        def foo(obj, *args):
+            # Test with expanding stararg
+            return obj.method_stararg(*args)
+
+        obj = MyDummy()
+        self.assertEqual(foo(obj, 1, 2), (1, 2, ()))
+        self.assertEqual(foo(obj, 1, 2, 3), (1, 2, (3,)))
+        self.assertEqual(foo(obj, 1, 2, 3, 4), (1, 2, (3, 4)))
+
+        @njit
+        def bar(obj):
+            # Test with explicit argument
+            return (
+                obj.method_stararg(1, 2),
+                obj.method_stararg(1, 2, 3),
+                obj.method_stararg(1, 2, 3, 4),
+            )
+
+        self.assertEqual(
+            bar(obj), ((1, 2, ()), (1, 2, (3,)), (1, 2, (3, 4))),
+        )
+
+        # Check cases that put tuple type into stararg
+        # NOTE: the expected result has an extra tuple because of stararg.
+        self.assertEqual(
+            foo(obj, 1, 2, (3,)), (1, 2, ((3,),)),
+        )
+        self.assertEqual(
+            foo(obj, 1, 2, (3, 4)), (1, 2, ((3, 4),)),
+        )
+        self.assertEqual(
+            foo(obj, 1, 2, (3, (4, 5))), (1, 2, ((3, (4, 5)),)),
+        )
+
+    def test_overload_classmethod(self):
+        # Add classmethod to a subclass of Array
+        class MyArray(types.Array):
+            pass
+
+        @overload_classmethod(MyArray, "array_alloc")
+        def ol_array_alloc(cls, nitems):
+            def impl(cls, nitems):
+                arr = np.arange(nitems)
+                return arr
+            return impl
+
+        @njit
+        def foo(nitems):
+            return MyArray.array_alloc(nitems)
+
+        nitems = 13
+        self.assertPreciseEqual(foo(nitems), np.arange(nitems))
+
+        # Check that the base type doesn't get the classmethod
+
+        @njit
+        def no_classmethod_in_base(nitems):
+            return types.Array.array_alloc(nitems)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            no_classmethod_in_base(nitems)
+        self.assertIn(
+            "Unknown attribute 'array_alloc' of",
+            str(raises.exception),
+        )
+
+    def test_overload_callable_typeref(self):
+
+        @overload(CallableTypeRef)
+        def callable_type_call_ovld1(x):
+            if isinstance(x, types.Integer):
+                def impl(x):
+                    return 42.5 + x
+                return impl
+
+        @overload(CallableTypeRef)
+        def callable_type_call_ovld2(x):
+            if isinstance(x, types.UnicodeType):
+                def impl(x):
+                    return '42.5' + x
+
+                return impl
+
+        @njit
+        def foo(a, b):
+            return MyClass(a), MyClass(b)
+
+        args = (4, '4')
+        expected = (42.5 + args[0], '42.5' + args[1])
+        self.assertPreciseEqual(foo(*args), expected)
+
+
+def _assert_cache_stats(cfunc, expect_hit, expect_misses):
+    hit = cfunc._cache_hits[cfunc.signatures[0]]
+    if hit != expect_hit:
+        raise AssertionError("cache not used")
+    miss = cfunc._cache_misses[cfunc.signatures[0]]
+    if miss != expect_misses:
+        raise AssertionError("cache not used")
+
+
+@skip_if_typeguard
+class TestOverloadMethodCaching(TestCase):
+    # Nested multiprocessing.Pool raises AssertionError:
+    # "daemonic processes are not allowed to have children"
+    _numba_parallel_test_ = False
+
+    def test_caching_overload_method(self):
+        self._cache_dir = temp_directory(self.__class__.__name__)
+        with override_config("CACHE_DIR", self._cache_dir):
+            self.run_caching_overload_method()
+
+    def run_caching_overload_method(self):
+        cfunc = jit(nopython=True, cache=True)(cache_overload_method_usecase)
+        self.assertPreciseEqual(cfunc(MyDummy()), 13)
+        _assert_cache_stats(cfunc, 0, 1)
+        llvmir = cfunc.inspect_llvm((mydummy_type,))
+        # Ensure the inner method is not a declaration
+        decls = [
+            ln
+            for ln in llvmir.splitlines()
+            if ln.startswith("declare") and "overload_method_length" in ln
+        ]
+        self.assertEqual(len(decls), 0)
+        # Test in a separate process
+        try:
+            ctx = multiprocessing.get_context("spawn")
+        except AttributeError:
+            ctx = multiprocessing
+        q = ctx.Queue()
+        p = ctx.Process(
+            target=run_caching_overload_method, args=(q, self._cache_dir)
+        )
+        p.start()
+        q.put(MyDummy())
+        p.join()
+        # Ensure subprocess exited normally
+        self.assertEqual(p.exitcode, 0)
+        res = q.get(timeout=1)
+        self.assertEqual(res, 13)
+
+
+def run_caching_overload_method(q, cache_dir):
+    """
+    Used by TestOverloadMethodCaching.test_caching_overload_method
+    """
+    with override_config("CACHE_DIR", cache_dir):
+        arg = q.get()
+        cfunc = jit(nopython=True, cache=True)(cache_overload_method_usecase)
+        res = cfunc(arg)
+        q.put(res)
+        # Check cache stat
+        _assert_cache_stats(cfunc, 1, 0)
+
+
+class TestIntrinsic(TestCase):
+    def test_void_return(self):
+        """
+        Verify that returning a None from codegen function is handled
+        automatically for void functions, otherwise raise exception.
+        """
+
+        @intrinsic
+        def void_func(typingctx, a):
+            sig = types.void(types.int32)
+
+            def codegen(context, builder, signature, args):
+                pass  # do nothing, return None, should be turned into
+                # dummy value
+
+            return sig, codegen
+
+        @intrinsic
+        def non_void_func(typingctx, a):
+            sig = types.int32(types.int32)
+
+            def codegen(context, builder, signature, args):
+                pass  # oops, should be returning a value here, raise exception
+
+            return sig, codegen
+
+        @jit(nopython=True)
+        def call_void_func():
+            void_func(1)
+            return 0
+
+        @jit(nopython=True)
+        def call_non_void_func():
+            non_void_func(1)
+            return 0
+
+        # void func should work
+        self.assertEqual(call_void_func(), 0)
+        # not void function should raise exception
+        with self.assertRaises(LoweringError) as e:
+            call_non_void_func()
+        self.assertIn("non-void function returns None", e.exception.msg)
+
+    def test_ll_pointer_cast(self):
+        """
+        Usecase test: custom reinterpret cast to turn int values to pointers
+        """
+        from ctypes import CFUNCTYPE, POINTER, c_float, c_int
+
+        # Use intrinsic to make a reinterpret_cast operation
+        def unsafe_caster(result_type):
+            assert isinstance(result_type, types.CPointer)
+
+            @intrinsic
+            def unsafe_cast(typingctx, src):
+                self.assertIsInstance(typingctx, typing.Context)
+                if isinstance(src, types.Integer):
+                    sig = result_type(types.uintp)
+
+                    # defines the custom code generation
+                    def codegen(context, builder, signature, args):
+                        [src] = args
+                        rtype = signature.return_type
+                        llrtype = context.get_value_type(rtype)
+                        return builder.inttoptr(src, llrtype)
+
+                    return sig, codegen
+
+            return unsafe_cast
+
+        # make a nopython function to use our cast op.
+        # this is not usable from cpython due to the returning of a pointer.
+        def unsafe_get_ctypes_pointer(src):
+            raise NotImplementedError("not callable from python")
+
+        @overload(unsafe_get_ctypes_pointer, strict=False)
+        def array_impl_unsafe_get_ctypes_pointer(arrtype):
+            if isinstance(arrtype, types.Array):
+                unsafe_cast = unsafe_caster(types.CPointer(arrtype.dtype))
+
+                def array_impl(arr):
+                    return unsafe_cast(src=arr.ctypes.data)
+
+                return array_impl
+
+        # the ctype wrapped function for use in nopython mode
+        def my_c_fun_raw(ptr, n):
+            for i in range(n):
+                print(ptr[i])
+
+        prototype = CFUNCTYPE(None, POINTER(c_float), c_int)
+        my_c_fun = prototype(my_c_fun_raw)
+
+        # Call our pointer-cast in a @jit compiled function and use
+        # the pointer in a ctypes function
+        @jit(nopython=True)
+        def foo(arr):
+            ptr = unsafe_get_ctypes_pointer(arr)
+            my_c_fun(ptr, arr.size)
+
+        # Test
+        arr = np.arange(10, dtype=np.float32)
+        with captured_stdout() as buf:
+            foo(arr)
+            got = buf.getvalue().splitlines()
+        buf.close()
+        expect = list(map(str, arr))
+        self.assertEqual(expect, got)
+
+    def test_serialization(self):
+        """
+        Test serialization of intrinsic objects
+        """
+        # define a intrinsic
+        @intrinsic
+        def identity(context, x):
+            def codegen(context, builder, signature, args):
+                return args[0]
+
+            sig = x(x)
+            return sig, codegen
+
+        # use in a jit function
+        @jit(nopython=True)
+        def foo(x):
+            return identity(x)
+
+        self.assertEqual(foo(1), 1)
+
+        # get serialization memo
+        memo = _Intrinsic._memo
+        memo_size = len(memo)
+
+        # pickle foo and check memo size
+        serialized_foo = pickle.dumps(foo)
+        # increases the memo size
+        memo_size += 1
+        self.assertEqual(memo_size, len(memo))
+        # unpickle
+        foo_rebuilt = pickle.loads(serialized_foo)
+        self.assertEqual(memo_size, len(memo))
+        # check rebuilt foo
+        self.assertEqual(foo(1), foo_rebuilt(1))
+
+        # pickle identity directly
+        serialized_identity = pickle.dumps(identity)
+        # memo size unchanged
+        self.assertEqual(memo_size, len(memo))
+        # unpickle
+        identity_rebuilt = pickle.loads(serialized_identity)
+        # must be the same object
+        self.assertIs(identity, identity_rebuilt)
+        # memo size unchanged
+        self.assertEqual(memo_size, len(memo))
+
+    def test_deserialization(self):
+        """
+        Test deserialization of intrinsic
+        """
+
+        def defn(context, x):
+            def codegen(context, builder, signature, args):
+                return args[0]
+
+            return x(x), codegen
+
+        memo = _Intrinsic._memo
+        memo_size = len(memo)
+        # invoke _Intrinsic indirectly to avoid registration which keeps an
+        # internal reference inside the compiler
+        original = _Intrinsic("foo", defn)
+        self.assertIs(original._defn, defn)
+        pickled = pickle.dumps(original)
+        # by pickling, a new memo entry is created
+        memo_size += 1
+        self.assertEqual(memo_size, len(memo))
+        del original  # remove original before unpickling
+
+        # by deleting, the memo entry is NOT removed due to recent
+        # function queue
+        self.assertEqual(memo_size, len(memo))
+
+        # Manually force clear of _recent queue
+        _Intrinsic._recent.clear()
+        memo_size -= 1
+        self.assertEqual(memo_size, len(memo))
+
+        rebuilt = pickle.loads(pickled)
+        # verify that the rebuilt object is different
+        self.assertIsNot(rebuilt._defn, defn)
+
+        # the second rebuilt object is the same as the first
+        second = pickle.loads(pickled)
+        self.assertIs(rebuilt._defn, second._defn)
+
+    def test_docstring(self):
+
+        @intrinsic
+        def void_func(typingctx, a: int):
+            """void_func docstring"""
+            sig = types.void(types.int32)
+
+            def codegen(context, builder, signature, args):
+                pass  # do nothing, return None, should be turned into
+                # dummy value
+
+            return sig, codegen
+
+        self.assertEqual("numba.tests.test_extending", void_func.__module__)
+        self.assertEqual("void_func", void_func.__name__)
+        self.assertEqual("TestIntrinsic.test_docstring.<locals>.void_func",
+                         void_func.__qualname__)
+        self.assertDictEqual({'a': int}, void_func.__annotations__)
+        self.assertEqual("void_func docstring", void_func.__doc__)
+
+
+class TestRegisterJitable(unittest.TestCase):
+    def test_no_flags(self):
+        @register_jitable
+        def foo(x, y):
+            return x + y
+
+        def bar(x, y):
+            return foo(x, y)
+
+        cbar = jit(nopython=True)(bar)
+
+        expect = bar(1, 2)
+        got = cbar(1, 2)
+        self.assertEqual(expect, got)
+
+    def test_flags_no_nrt(self):
+        @register_jitable(_nrt=False)
+        def foo(n):
+            return np.arange(n)
+
+        def bar(n):
+            return foo(n)
+
+        self.assertEqual(bar(3).tolist(), [0, 1, 2])
+
+        cbar = jit(nopython=True)(bar)
+        with self.assertRaises(errors.TypingError) as raises:
+            cbar(2)
+        msg = (
+            "Only accept returning of array passed into the function as "
+            "argument"
+        )
+        self.assertIn(msg, str(raises.exception))
+
+
+class TestImportCythonFunction(unittest.TestCase):
+    @unittest.skipIf(sc is None, "Only run if SciPy >= 0.19 is installed")
+    def test_getting_function(self):
+        addr = get_cython_function_address(
+            "scipy.special.cython_special", "j0"
+        )
+        functype = ctypes.CFUNCTYPE(ctypes.c_double, ctypes.c_double)
+        _j0 = functype(addr)
+        j0 = jit(nopython=True)(lambda x: _j0(x))
+        self.assertEqual(j0(0), 1)
+
+    def test_missing_module(self):
+        with self.assertRaises(ImportError) as raises:
+            get_cython_function_address("fakemodule", "fakefunction")
+        # The quotes are not there in Python 2
+        msg = "No module named '?fakemodule'?"
+        match = re.match(msg, str(raises.exception))
+        self.assertIsNotNone(match)
+
+    @unittest.skipIf(sc is None, "Only run if SciPy >= 0.19 is installed")
+    def test_missing_function(self):
+        with self.assertRaises(ValueError) as raises:
+            get_cython_function_address(
+                "scipy.special.cython_special", "foo"
+            )
+        msg = (
+            "No function 'foo' found in __pyx_capi__ of "
+            "'scipy.special.cython_special'"
+        )
+        self.assertEqual(msg, str(raises.exception))
+
+
+@overload_method(
+    MyDummyType, "method_jit_option_check_nrt", jit_options={"_nrt": True}
+)
+def ov_method_jit_option_check_nrt(obj):
+    def imp(obj):
+        return np.arange(10)
+
+    return imp
+
+
+@overload_method(
+    MyDummyType, "method_jit_option_check_no_nrt", jit_options={"_nrt": False}
+)
+def ov_method_jit_option_check_no_nrt(obj):
+    def imp(obj):
+        return np.arange(10)
+
+    return imp
+
+
+@overload_attribute(
+    MyDummyType, "attr_jit_option_check_nrt", jit_options={"_nrt": True}
+)
+def ov_attr_jit_option_check_nrt(obj):
+    def imp(obj):
+        return np.arange(10)
+
+    return imp
+
+
+@overload_attribute(
+    MyDummyType, "attr_jit_option_check_no_nrt", jit_options={"_nrt": False}
+)
+def ov_attr_jit_option_check_no_nrt(obj):
+    def imp(obj):
+        return np.arange(10)
+
+    return imp
+
+
+class TestJitOptionsNoNRT(TestCase):
+    # Test overload*(jit_options={...}) by turning off _nrt
+
+    def check_error_no_nrt(self, func, *args, **kwargs):
+        # Check that the compilation fails with a complaint about dynamic array
+        msg = (
+            "Only accept returning of array passed into "
+            "the function as argument"
+        )
+        with self.assertRaises(errors.TypingError) as raises:
+            func(*args, **kwargs)
+        self.assertIn(msg, str(raises.exception))
+
+    def no_nrt_overload_check(self, flag):
+        def dummy():
+            return np.arange(10)
+
+        @overload(dummy, jit_options={"_nrt": flag})
+        def ov_dummy():
+            def dummy():
+                return np.arange(10)
+
+            return dummy
+
+        @njit
+        def foo():
+            return dummy()
+
+        if flag:
+            self.assertPreciseEqual(foo(), np.arange(10))
+        else:
+            self.check_error_no_nrt(foo)
+
+    def test_overload_no_nrt(self):
+        self.no_nrt_overload_check(True)
+        self.no_nrt_overload_check(False)
+
+    def test_overload_method_no_nrt(self):
+        @njit
+        def udt(x):
+            return x.method_jit_option_check_nrt()
+
+        self.assertPreciseEqual(udt(mydummy), np.arange(10))
+
+        @njit
+        def udt(x):
+            return x.method_jit_option_check_no_nrt()
+
+        self.check_error_no_nrt(udt, mydummy)
+
+    def test_overload_attribute_no_nrt(self):
+        @njit
+        def udt(x):
+            return x.attr_jit_option_check_nrt
+
+        self.assertPreciseEqual(udt(mydummy), np.arange(10))
+
+        @njit
+        def udt(x):
+            return x.attr_jit_option_check_no_nrt
+
+        self.check_error_no_nrt(udt, mydummy)
+
+
+class TestBoxingCallingJIT(TestCase):
+    def setUp(self):
+        super().setUp()
+        many = base_dummy_type_factory("mydummy2")
+        self.DynTypeType, self.DynType, self.dyn_type_type = many
+        self.dyn_type = self.DynType()
+
+    def test_unboxer_basic(self):
+        # Implements an unboxer on DynType that calls an intrinsic into the
+        # unboxer code.
+        magic_token = 0xCAFE
+        magic_offset = 123
+
+        @intrinsic
+        def my_intrinsic(typingctx, val):
+            # An intrinsic that returns `val + magic_offset`
+            def impl(context, builder, sig, args):
+                [val] = args
+                return builder.add(val, val.type(magic_offset))
+
+            sig = signature(val, val)
+            return sig, impl
+
+        @unbox(self.DynTypeType)
+        def unboxer(typ, obj, c):
+            # The unboxer that calls some jitcode
+            def bridge(x):
+                # proof that this is a jit'ed context by calling jit only
+                # intrinsic
+                return my_intrinsic(x)
+
+            args = [c.context.get_constant(types.intp, magic_token)]
+            sig = signature(types.voidptr, types.intp)
+            is_error, res = c.pyapi.call_jit_code(bridge, sig, args)
+            return NativeValue(res, is_error=is_error)
+
+        @box(self.DynTypeType)
+        def boxer(typ, val, c):
+            # The boxer that returns an integer representation
+            res = c.builder.ptrtoint(val, cgutils.intp_t)
+            return c.pyapi.long_from_ssize_t(res)
+
+        @njit
+        def passthru(x):
+            return x
+
+        out = passthru(self.dyn_type)
+        self.assertEqual(out, magic_token + magic_offset)
+
+    def test_unboxer_raise(self):
+        # Testing exception raising in jitcode called from unboxing.
+        @unbox(self.DynTypeType)
+        def unboxer(typ, obj, c):
+            # The unboxer that calls some jitcode
+            def bridge(x):
+                if x > 0:
+                    raise ValueError("cannot be x > 0")
+                return x
+
+            args = [c.context.get_constant(types.intp, 1)]
+            sig = signature(types.voidptr, types.intp)
+            is_error, res = c.pyapi.call_jit_code(bridge, sig, args)
+            return NativeValue(res, is_error=is_error)
+
+        @box(self.DynTypeType)
+        def boxer(typ, val, c):
+            # The boxer that returns an integer representation
+            res = c.builder.ptrtoint(val, cgutils.intp_t)
+            return c.pyapi.long_from_ssize_t(res)
+
+        @njit
+        def passthru(x):
+            return x
+
+        with self.assertRaises(ValueError) as raises:
+            passthru(self.dyn_type)
+        self.assertIn(
+            "cannot be x > 0", str(raises.exception),
+        )
+
+    def test_boxer(self):
+        # Call jitcode inside the boxer
+        magic_token = 0xCAFE
+        magic_offset = 312
+
+        @intrinsic
+        def my_intrinsic(typingctx, val):
+            # An intrinsic that returns `val + magic_offset`
+            def impl(context, builder, sig, args):
+                [val] = args
+                return builder.add(val, val.type(magic_offset))
+
+            sig = signature(val, val)
+            return sig, impl
+
+        @unbox(self.DynTypeType)
+        def unboxer(typ, obj, c):
+            return NativeValue(c.context.get_dummy_value())
+
+        @box(self.DynTypeType)
+        def boxer(typ, val, c):
+            # Note: this doesn't do proper error handling
+            def bridge(x):
+                return my_intrinsic(x)
+
+            args = [c.context.get_constant(types.intp, magic_token)]
+            sig = signature(types.intp, types.intp)
+            is_error, res = c.pyapi.call_jit_code(bridge, sig, args)
+            return c.pyapi.long_from_ssize_t(res)
+
+        @njit
+        def passthru(x):
+            return x
+
+        r = passthru(self.dyn_type)
+        self.assertEqual(r, magic_token + magic_offset)
+
+    def test_boxer_raise(self):
+        # Call jitcode inside the boxer
+        @unbox(self.DynTypeType)
+        def unboxer(typ, obj, c):
+            return NativeValue(c.context.get_dummy_value())
+
+        @box(self.DynTypeType)
+        def boxer(typ, val, c):
+            def bridge(x):
+                if x > 0:
+                    raise ValueError("cannot do x > 0")
+                return x
+
+            args = [c.context.get_constant(types.intp, 1)]
+            sig = signature(types.intp, types.intp)
+            is_error, res = c.pyapi.call_jit_code(bridge, sig, args)
+            # The error handling
+            retval = cgutils.alloca_once(c.builder, c.pyapi.pyobj, zfill=True)
+            with c.builder.if_then(c.builder.not_(is_error)):
+                obj = c.pyapi.long_from_ssize_t(res)
+                c.builder.store(obj, retval)
+            return c.builder.load(retval)
+
+        @njit
+        def passthru(x):
+            return x
+
+        with self.assertRaises(ValueError) as raises:
+            passthru(self.dyn_type)
+        self.assertIn(
+            "cannot do x > 0", str(raises.exception),
+        )
+
+
+def with_objmode_cache_ov_example(x):
+    # This is the function stub for overloading inside
+    # TestCachingOverloadObjmode.test_caching_overload_objmode
+    pass
+
+
+@skip_if_typeguard
+class TestCachingOverloadObjmode(TestCase):
+    """Test caching of the use of overload implementations that use
+    `with objmode`
+    """
+    _numba_parallel_test_ = False
+
+    def setUp(self):
+        warnings.simplefilter("error", errors.NumbaWarning)
+
+    def tearDown(self):
+        warnings.resetwarnings()
+
+    def test_caching_overload_objmode(self):
+        cache_dir = temp_directory(self.__class__.__name__)
+        with override_config("CACHE_DIR", cache_dir):
+
+            def realwork(x):
+                # uses numpy code
+                arr = np.arange(x) / x
+                return np.linalg.norm(arr)
+
+            def python_code(x):
+                # create indirections
+                return realwork(x)
+
+            @overload(with_objmode_cache_ov_example)
+            def _ov_with_objmode_cache_ov_example(x):
+                def impl(x):
+                    with objmode(y="float64"):
+                        y = python_code(x)
+                    return y
+
+                return impl
+
+            @njit(cache=True)
+            def testcase(x):
+                return with_objmode_cache_ov_example(x)
+
+            expect = realwork(123)
+            got = testcase(123)
+            self.assertEqual(got, expect)
+
+            testcase_cached = njit(cache=True)(testcase.py_func)
+            got = testcase_cached(123)
+            self.assertEqual(got, expect)
+
+    @classmethod
+    def check_objmode_cache_ndarray(cls):
+        def do_this(a, b):
+            return np.sum(a + b)
+
+        def do_something(a, b):
+            return np.sum(a + b)
+
+        @overload(do_something)
+        def overload_do_something(a, b):
+            def _do_something_impl(a, b):
+                with objmode(y='float64'):
+                    y = do_this(a, b)
+                return y
+            return _do_something_impl
+
+        @njit(cache=True)
+        def test_caching():
+            a = np.arange(20)
+            b = np.arange(20)
+            return do_something(a, b)
+
+        got = test_caching()
+        expect = test_caching.py_func()
+
+        # Check result
+        if got != expect:
+            raise AssertionError("incorrect result")
+        return test_caching
+
+    @classmethod
+    def populate_objmode_cache_ndarray_check_cache(cls):
+        cls.check_objmode_cache_ndarray()
+
+    @classmethod
+    def check_objmode_cache_ndarray_check_cache(cls):
+        disp = cls.check_objmode_cache_ndarray()
+        if len(disp.stats.cache_misses) != 0:
+            raise AssertionError('unexpected cache miss')
+        if len(disp.stats.cache_hits) <= 0:
+            raise AssertionError("unexpected missing cache hit")
+
+    def test_check_objmode_cache_ndarray(self):
+        # See issue #6130.
+        # Env is missing after cache load.
+        cache_dir = temp_directory(self.__class__.__name__)
+        with override_config("CACHE_DIR", cache_dir):
+            # Run in new process to populate the cache
+            run_in_new_process_in_cache_dir(
+                self.populate_objmode_cache_ndarray_check_cache, cache_dir
+            )
+            # Run in new process to use the cache in a fresh process.
+            res = run_in_new_process_in_cache_dir(
+                self.check_objmode_cache_ndarray_check_cache, cache_dir
+            )
+        self.assertEqual(res['exitcode'], 0)
+
+
+class TestMisc(TestCase):
+    def test_is_jitted(self):
+        def foo(x):
+            pass
+
+        self.assertFalse(is_jitted(foo))
+        self.assertTrue(is_jitted(njit(foo)))
+        self.assertFalse(is_jitted(vectorize(foo)))
+        self.assertFalse(is_jitted(vectorize(parallel=True)(foo)))
+        self.assertFalse(
+            is_jitted(guvectorize("void(float64[:])", "(m)")(foo))
+        )
+
+    def test_overload_glue_arg_binding(self):
+        # See issue #7982, checks that calling a function with named args works
+        # correctly irrespective of the order in which the names are supplied.
+        @njit
+        def standard_order():
+            return np.full(shape=123, fill_value=456).shape
+
+        @njit
+        def reversed_order():
+            return np.full(fill_value=456, shape=123).shape
+
+        self.assertPreciseEqual(standard_order(), standard_order.py_func())
+        self.assertPreciseEqual(reversed_order(), reversed_order.py_func())
+
+
+class TestOverloadPreferLiteral(TestCase):
+    def test_overload(self):
+        def prefer_lit(x):
+            pass
+
+        def non_lit(x):
+            pass
+
+        def ov(x):
+            if isinstance(x, types.IntegerLiteral):
+                # With prefer_literal=False, this branch will not be reached.
+                if x.literal_value == 1:
+                    def impl(x):
+                        return 0xcafe
+                    return impl
+                else:
+                    raise errors.TypingError('literal value')
+            else:
+                def impl(x):
+                    return x * 100
+                return impl
+
+        overload(prefer_lit, prefer_literal=True)(ov)
+        overload(non_lit)(ov)
+
+        @njit
+        def check_prefer_lit(x):
+            return prefer_lit(1), prefer_lit(2), prefer_lit(x)
+
+        a, b, c = check_prefer_lit(3)
+        self.assertEqual(a, 0xcafe)
+        self.assertEqual(b, 200)
+        self.assertEqual(c, 300)
+
+        @njit
+        def check_non_lit(x):
+            return non_lit(1), non_lit(2), non_lit(x)
+
+        a, b, c = check_non_lit(3)
+        self.assertEqual(a, 100)
+        self.assertEqual(b, 200)
+        self.assertEqual(c, 300)
+
+    def test_overload_method(self):
+        def ov(self, x):
+            if isinstance(x, types.IntegerLiteral):
+                # With prefer_literal=False, this branch will not be reached.
+                if x.literal_value == 1:
+                    def impl(self, x):
+                        return 0xcafe
+                    return impl
+                else:
+                    raise errors.TypingError('literal value')
+            else:
+                def impl(self, x):
+                    return x * 100
+                return impl
+
+        overload_method(
+            MyDummyType, "method_prefer_literal",
+            prefer_literal=True,
+        )(ov)
+
+        overload_method(
+            MyDummyType, "method_non_literal",
+            prefer_literal=False,
+        )(ov)
+
+        @njit
+        def check_prefer_lit(dummy, x):
+            return (
+                dummy.method_prefer_literal(1),
+                dummy.method_prefer_literal(2),
+                dummy.method_prefer_literal(x),
+            )
+
+        a, b, c = check_prefer_lit(MyDummy(), 3)
+        self.assertEqual(a, 0xcafe)
+        self.assertEqual(b, 200)
+        self.assertEqual(c, 300)
+
+        @njit
+        def check_non_lit(dummy, x):
+            return (
+                dummy.method_non_literal(1),
+                dummy.method_non_literal(2),
+                dummy.method_non_literal(x),
+            )
+
+        a, b, c = check_non_lit(MyDummy(), 3)
+        self.assertEqual(a, 100)
+        self.assertEqual(b, 200)
+        self.assertEqual(c, 300)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extending_types.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extending_types.py
new file mode 100644
index 000000000..540472e37
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_extending_types.py
@@ -0,0 +1,170 @@
+"""
+Test extending types via the numba.extending.* API.
+"""
+import operator
+
+from numba import njit, literally
+from numba.core import types, cgutils
+from numba.core.errors import TypingError, NumbaTypeError
+from numba.core.extending import lower_builtin
+from numba.core.extending import models, register_model
+from numba.core.extending import make_attribute_wrapper
+from numba.core.extending import type_callable
+from numba.core.extending import overload
+from numba.core.extending import typeof_impl
+
+import unittest
+
+
+def gen_mock_float():
+    # Stub to overload, pretending to be `float`. The real `float` function is
+    # not used as multiple registrations can collide.
+    def mock_float(x):
+        pass
+    return mock_float
+
+
+class TestExtTypDummy(unittest.TestCase):
+
+    def setUp(self):
+        class Dummy(object):
+            def __init__(self, value):
+                self.value = value
+
+        class DummyType(types.Type):
+            def __init__(self):
+                super(DummyType, self).__init__(name='Dummy')
+
+        dummy_type = DummyType()
+
+        @register_model(DummyType)
+        class DummyModel(models.StructModel):
+            def __init__(self, dmm, fe_type):
+                members = [
+                    ('value', types.intp),
+                ]
+                models.StructModel.__init__(self, dmm, fe_type, members)
+
+        make_attribute_wrapper(DummyType, 'value', 'value')
+
+        @type_callable(Dummy)
+        def type_dummy(context):
+            def typer(value):
+                return dummy_type
+            return typer
+
+        @lower_builtin(Dummy, types.intp)
+        def impl_dummy(context, builder, sig, args):
+            typ = sig.return_type
+            [value] = args
+            dummy = cgutils.create_struct_proxy(typ)(context, builder)
+            dummy.value = value
+            return dummy._getvalue()
+
+        @typeof_impl.register(Dummy)
+        def typeof_dummy(val, c):
+            return DummyType()
+
+        # Store attributes
+        self.Dummy = Dummy
+        self.DummyType = DummyType
+
+    def _add_float_overload(self, mock_float_inst):
+        @overload(mock_float_inst)
+        def dummy_to_float(x):
+            if isinstance(x, self.DummyType):
+                def codegen(x):
+                    return float(x.value)
+                return codegen
+            else:
+                raise NumbaTypeError('cannot type float({})'.format(x))
+
+    def test_overload_float(self):
+        mock_float = gen_mock_float()
+        self._add_float_overload(mock_float)
+        Dummy = self.Dummy
+
+        @njit
+        def foo(x):
+            return mock_float(Dummy(x))
+
+        self.assertEqual(foo(123), float(123))
+
+    def test_overload_float_error_msg(self):
+        mock_float = gen_mock_float()
+        self._add_float_overload(mock_float)
+
+        @njit
+        def foo(x):
+            return mock_float(x)
+
+        with self.assertRaises(TypingError) as raises:
+            foo(1j)
+
+        self.assertIn("cannot type float(complex128)",
+                      str(raises.exception))
+
+    def test_unboxing(self):
+        """A test for the unboxing logic on unknown type
+        """
+        Dummy = self.Dummy
+
+        @njit
+        def foo(x):
+            # pass a dummy object into another function
+            bar(Dummy(x))
+
+        # make sure a cpython wrapper is created
+        @njit(no_cpython_wrapper=False)
+        def bar(dummy_obj):
+            pass
+
+        foo(123)
+        with self.assertRaises(TypeError) as raises:
+            bar(Dummy(123))
+        self.assertIn("can't unbox Dummy type", str(raises.exception))
+
+    def test_boxing(self):
+        """A test for the boxing logic on unknown type
+        """
+        Dummy = self.Dummy
+
+        @njit
+        def foo(x):
+            return Dummy(x)
+
+        with self.assertRaises(TypeError) as raises:
+            foo(123)
+        self.assertIn("cannot convert native Dummy to Python object",
+                      str(raises.exception))
+
+    def test_issue5565_literal_getitem(self):
+        # the following test is adapted from
+        # https://github.com/numba/numba/issues/5565
+        Dummy, DummyType = self.Dummy, self.DummyType
+
+        MAGIC_NUMBER = 12321
+
+        @overload(operator.getitem)
+        def dummy_getitem_ovld(self, idx):
+            if not isinstance(self, DummyType):
+                return None
+            # suppose we can only support idx as literal argument
+            if isinstance(idx, types.StringLiteral):
+                def dummy_getitem_impl(self, idx):
+                    return MAGIC_NUMBER
+                return dummy_getitem_impl
+
+            if isinstance(idx, types.UnicodeType):
+                def dummy_getitem_impl(self, idx):
+                    return literally(idx)
+                return dummy_getitem_impl
+
+            return None
+
+        @njit
+        def test_impl(x, y):
+            return Dummy(x)[y]
+
+        var = 'abc'
+        self.assertEqual(test_impl(1, var), MAGIC_NUMBER)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_fancy_indexing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_fancy_indexing.py
new file mode 100644
index 000000000..c9402a8e2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_fancy_indexing.py
@@ -0,0 +1,261 @@
+import itertools
+
+import numpy as np
+
+import unittest
+from numba import jit, typeof
+from numba.core import types
+from numba.core.errors import TypingError
+from numba.tests.support import MemoryLeakMixin, TestCase, tag
+
+
+def getitem_usecase(a, b):
+    return a[b]
+
+def setitem_usecase(a, idx, b):
+    a[idx] = b
+
+def np_take(A, indices):
+    return np.take(A, indices)
+
+def np_take_kws(A, indices, axis):
+    return np.take(A, indices, axis=axis)
+
+class TestFancyIndexing(MemoryLeakMixin, TestCase):
+
+    def generate_advanced_indices(self, N, many=True):
+        choices = [np.int16([0, N - 1, -2])]
+        if many:
+            choices += [np.uint16([0, 1, N - 1]),
+                        np.bool_([0, 1, 1, 0])]
+        return choices
+
+    def generate_basic_index_tuples(self, N, maxdim, many=True):
+        """
+        Generate basic index tuples with 0 to *maxdim* items.
+        """
+        # Note integers can be considered advanced indices in certain
+        # cases, so we avoid them here.
+        # See "Combining advanced and basic indexing"
+        # in http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+        if many:
+            choices = [slice(None, None, None),
+                       slice(1, N - 1, None),
+                       slice(0, None, 2),
+                       slice(N - 1, None, -2),
+                       slice(-N + 1, -1, None),
+                       slice(-1, -N, -2),
+                       ]
+        else:
+            choices = [slice(0, N - 1, None),
+                       slice(-1, -N, -2)]
+        for ndim in range(maxdim + 1):
+            for tup in itertools.product(choices, repeat=ndim):
+                yield tup
+
+    def generate_advanced_index_tuples(self, N, maxdim, many=True):
+        """
+        Generate advanced index tuples by generating basic index tuples
+        and adding a single advanced index item.
+        """
+        # (Note Numba doesn't support advanced indices with more than
+        #  one advanced index array at the moment)
+        choices = list(self.generate_advanced_indices(N, many=many))
+        for i in range(maxdim + 1):
+            for tup in self.generate_basic_index_tuples(N, maxdim - 1, many):
+                for adv in choices:
+                    yield tup[:i] + (adv,) + tup[i:]
+
+    def generate_advanced_index_tuples_with_ellipsis(self, N, maxdim, many=True):
+        """
+        Same as generate_advanced_index_tuples(), but also insert an
+        ellipsis at various points.
+        """
+        for tup in self.generate_advanced_index_tuples(N, maxdim, many):
+            for i in range(len(tup) + 1):
+                yield tup[:i] + (Ellipsis,) + tup[i:]
+
+    def check_getitem_indices(self, arr, indices):
+        pyfunc = getitem_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        orig = arr.copy()
+        orig_base = arr.base or arr
+
+        for index in indices:
+            expected = pyfunc(arr, index)
+            # Sanity check: if a copy wasn't made, this wasn't advanced
+            # but basic indexing, and shouldn't be tested here.
+            assert expected.base is not orig_base
+            got = cfunc(arr, index)
+            # Note Numba may not return the same array strides and
+            # contiguity as Numpy
+            self.assertEqual(got.shape, expected.shape)
+            self.assertEqual(got.dtype, expected.dtype)
+            np.testing.assert_equal(got, expected)
+            # Check a copy was *really* returned by Numba
+            if got.size:
+                got.fill(42)
+                np.testing.assert_equal(arr, orig)
+
+    def test_getitem_tuple(self):
+        # Test many variations of advanced indexing with a tuple index
+        N = 4
+        ndim = 3
+        arr = np.arange(N ** ndim).reshape((N,) * ndim).astype(np.int32)
+        indices = self.generate_advanced_index_tuples(N, ndim)
+
+        self.check_getitem_indices(arr, indices)
+
+    def test_getitem_tuple_and_ellipsis(self):
+        # Same, but also insert an ellipsis at a random point
+        N = 4
+        ndim = 3
+        arr = np.arange(N ** ndim).reshape((N,) * ndim).astype(np.int32)
+        indices = self.generate_advanced_index_tuples_with_ellipsis(N, ndim,
+                                                                    many=False)
+
+        self.check_getitem_indices(arr, indices)
+
+    def test_ellipsis_getsetitem(self):
+        # See https://github.com/numba/numba/issues/3225
+        @jit(nopython=True)
+        def foo(arr, v):
+            arr[..., 0] = arr[..., 1]
+
+        arr = np.arange(2)
+        foo(arr, 1)
+        self.assertEqual(arr[0], arr[1])
+
+    def test_getitem_array(self):
+        # Test advanced indexing with a single array index
+        N = 4
+        ndim = 3
+        arr = np.arange(N ** ndim).reshape((N,) * ndim).astype(np.int32)
+        indices = self.generate_advanced_indices(N)
+        self.check_getitem_indices(arr, indices)
+
+    def check_setitem_indices(self, arr, indices):
+        pyfunc = setitem_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for index in indices:
+            src = arr[index]
+            expected = np.zeros_like(arr)
+            got = np.zeros_like(arr)
+            pyfunc(expected, index, src)
+            cfunc(got, index, src)
+            # Note Numba may not return the same array strides and
+            # contiguity as Numpy
+            self.assertEqual(got.shape, expected.shape)
+            self.assertEqual(got.dtype, expected.dtype)
+            np.testing.assert_equal(got, expected)
+
+    def test_setitem_tuple(self):
+        # Test many variations of advanced indexing with a tuple index
+        N = 4
+        ndim = 3
+        arr = np.arange(N ** ndim).reshape((N,) * ndim).astype(np.int32)
+        indices = self.generate_advanced_index_tuples(N, ndim)
+        self.check_setitem_indices(arr, indices)
+
+    def test_setitem_tuple_and_ellipsis(self):
+        # Same, but also insert an ellipsis at a random point
+        N = 4
+        ndim = 3
+        arr = np.arange(N ** ndim).reshape((N,) * ndim).astype(np.int32)
+        indices = self.generate_advanced_index_tuples_with_ellipsis(N, ndim,
+                                                                    many=False)
+
+        self.check_setitem_indices(arr, indices)
+
+    def test_setitem_array(self):
+        # Test advanced indexing with a single array index
+        N = 4
+        ndim = 3
+        arr = np.arange(N ** ndim).reshape((N,) * ndim).astype(np.int32) + 10
+        indices = self.generate_advanced_indices(N)
+        self.check_setitem_indices(arr, indices)
+
+    def test_setitem_0d(self):
+        # Test setitem with a 0d-array
+        pyfunc = setitem_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        inps = [
+            (np.zeros(3), np.array(3.14)),
+            (np.zeros(2), np.array(2)),
+            (np.zeros(3, dtype=np.int64), np.array(3, dtype=np.int64)),
+            (np.zeros(3, dtype=np.float64), np.array(1, dtype=np.int64)),
+            (np.zeros(5, dtype='<U3'), np.array('abc')),
+            (np.zeros((3,), dtype='<U3'), np.array('a')),
+            (np.array(['abc','def','ghi'], dtype='<U3'),
+             np.array('WXYZ', dtype='<U4')),
+            (np.zeros(3, dtype=complex), np.array(2+3j, dtype=complex)),
+        ]
+
+        for x1, v in inps:
+            x2 = x1.copy()
+            pyfunc(x1, 0, v)
+            cfunc(x2, 0, v)
+            self.assertPreciseEqual(x1, x2)
+
+    def test_np_take(self):
+        # shorter version of array.take test in test_array_methods
+        pyfunc = np_take
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(arr, ind):
+            expected = pyfunc(arr, ind)
+            got = cfunc(arr, ind)
+            self.assertPreciseEqual(expected, got)
+            if hasattr(expected, 'order'):
+                self.assertEqual(expected.order == got.order)
+
+        # need to check:
+        # 1. scalar index
+        # 2. 1d array index
+        # 3. nd array index
+        # 4. reflected list
+        # 5. tuples
+
+        test_indices = []
+        test_indices.append(1)
+        test_indices.append(np.array([1, 5, 1, 11, 3]))
+        test_indices.append(np.array([[[1], [5]], [[1], [11]]]))
+        test_indices.append([1, 5, 1, 11, 3])
+        test_indices.append((1, 5, 1))
+        test_indices.append(((1, 5, 1), (11, 3, 2)))
+
+        for dt in [np.int64, np.complex128]:
+            A = np.arange(12, dtype=dt).reshape((4, 3))
+            for ind in test_indices:
+                check(A, ind)
+
+        #check illegal access raises
+        szA = A.size
+        illegal_indices = [szA, -szA - 1, np.array(szA), np.array(-szA - 1),
+                           [szA], [-szA - 1]]
+        for x in illegal_indices:
+            with self.assertRaises(IndexError):
+                cfunc(A, x) # oob raises
+
+        # check float indexing raises
+        with self.assertRaises(TypingError):
+            cfunc(A, [1.7])
+
+        # check unsupported arg raises
+        with self.assertRaises(TypingError):
+            take_kws = jit(nopython=True)(np_take_kws)
+            take_kws(A, 1, 1)
+
+        # check kwarg unsupported raises
+        with self.assertRaises(TypingError):
+            take_kws = jit(nopython=True)(np_take_kws)
+            take_kws(A, 1, axis=1)
+
+        #exceptions leak refs
+        self.disable_leak_check()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_fastmath.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_fastmath.py
new file mode 100644
index 000000000..528adf494
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_fastmath.py
@@ -0,0 +1,123 @@
+import math
+import numpy as np
+
+from numba.tests.support import captured_stdout, override_config
+from numba import njit, vectorize, guvectorize
+import unittest
+
+
+class TestFastMath(unittest.TestCase):
+    def test_jit(self):
+        def foo(x):
+            return x + math.sin(x)
+        fastfoo = njit(fastmath=True)(foo)
+        slowfoo = njit(foo)
+        self.assertEqual(fastfoo(0.5), slowfoo(0.5))
+        fastllvm = fastfoo.inspect_llvm(fastfoo.signatures[0])
+        slowllvm = slowfoo.inspect_llvm(slowfoo.signatures[0])
+        # Ensure fast attribute in fast version only
+        self.assertIn('fadd fast', fastllvm)
+        self.assertIn('call fast', fastllvm)
+        self.assertNotIn('fadd fast', slowllvm)
+        self.assertNotIn('call fast', slowllvm)
+
+    def test_jit_subset_behaviour(self):
+        def foo(x, y):
+            return (x - y) + y
+        fastfoo = njit(fastmath={'reassoc', 'nsz'})(foo)
+        slowfoo = njit(fastmath={'reassoc'})(foo)
+        self.assertEqual(fastfoo(0.5, np.inf), 0.5)
+        self.assertTrue(np.isnan(slowfoo(0.5, np.inf)))
+
+    def test_jit_subset_code(self):
+        def foo(x):
+            return x + math.sin(x)
+        fastfoo = njit(fastmath={'reassoc', 'nsz'})(foo)
+        slowfoo = njit()(foo)
+        self.assertEqual(fastfoo(0.5), slowfoo(0.5))
+        fastllvm = fastfoo.inspect_llvm(fastfoo.signatures[0])
+        slowllvm = slowfoo.inspect_llvm(slowfoo.signatures[0])
+        # Ensure fast attributes in fast version only
+        self.assertNotIn('fadd fast', slowllvm)
+        self.assertNotIn('call fast', slowllvm)
+        self.assertNotIn('fadd reassoc nsz', slowllvm)
+        self.assertNotIn('call reassoc nsz', slowllvm)
+        self.assertNotIn('fadd nsz reassoc', slowllvm)
+        self.assertNotIn('call nsz reassoc', slowllvm)
+        self.assertTrue(
+            ('fadd nsz reassoc' in fastllvm) or
+            ('fadd reassoc nsz' in fastllvm),
+            fastllvm
+        )
+        self.assertTrue(
+            ('call nsz reassoc' in fastllvm) or
+            ('call reassoc nsz' in fastllvm),
+            fastllvm
+        )
+
+    def test_jit_subset_errors(self):
+        with self.assertRaises(ValueError) as raises:
+            njit(fastmath={'spqr'})(lambda x: x + 1)(1)
+        self.assertIn(
+            "Unrecognized fastmath flags:",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(ValueError) as raises:
+            njit(fastmath={'spqr': False})(lambda x: x + 1)(1)
+        self.assertIn(
+            'Unrecognized fastmath flags:',
+            str(raises.exception),
+        )
+
+        with self.assertRaises(ValueError) as raises:
+            njit(fastmath=1337)(lambda x: x + 1)(1)
+        self.assertIn(
+            'Expected fastmath option(s) to be',
+            str(raises.exception),
+        )
+
+    def test_vectorize(self):
+        def foo(x):
+            return x + math.sin(x)
+        fastfoo = vectorize(fastmath=True)(foo)
+        slowfoo = vectorize(foo)
+        x = np.random.random(8).astype(np.float32)
+        # capture the optimized llvm to check for fast flag
+        with override_config('DUMP_OPTIMIZED', True):
+            with captured_stdout() as slow_cap:
+                expect = slowfoo(x)
+            slowllvm = slow_cap.getvalue()
+            with captured_stdout() as fast_cap:
+                got = fastfoo(x)
+            fastllvm = fast_cap.getvalue()
+        np.testing.assert_almost_equal(expect, got)
+        self.assertIn('fadd fast', fastllvm)
+        self.assertIn('call fast', fastllvm)
+        self.assertNotIn('fadd fast', slowllvm)
+        self.assertNotIn('call fast', slowllvm)
+
+    def test_guvectorize(self):
+        def foo(x, out):
+            out[0] = x + math.sin(x)
+        x = np.random.random(8).astype(np.float32)
+        with override_config('DUMP_OPTIMIZED', True):
+            types = ['(float32, float32[:])']
+            sig = '()->()'
+            with captured_stdout() as fast_cap:
+                fastfoo = guvectorize(types, sig, fastmath=True)(foo)
+            fastllvm = fast_cap.getvalue()
+            with captured_stdout() as slow_cap:
+                slowfoo = guvectorize(types, sig)(foo)
+            slowllvm = slow_cap.getvalue()
+        expect = slowfoo(x)
+        got = fastfoo(x)
+        np.testing.assert_almost_equal(expect, got)
+        self.assertIn('fadd fast', fastllvm)
+        self.assertIn('call fast', fastllvm)
+        self.assertNotIn('fadd fast', slowllvm)
+        self.assertNotIn('call fast', slowllvm)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_firstlinefinder.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_firstlinefinder.py
new file mode 100644
index 000000000..8f25c80b0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_firstlinefinder.py
@@ -0,0 +1,119 @@
+import unittest
+import inspect
+
+from numba import njit
+from numba.core.utils import PYVERSION
+from numba.tests.support import TestCase
+from numba.misc.firstlinefinder import get_func_body_first_lineno
+
+
+class TestFirstLineFinder(TestCase):
+    """
+    The following methods contains tests that are sensitive to the source
+    locations w.r.t. the beginning of each method.
+    """
+
+    def _get_grandparent_caller_code(self):
+        frame = inspect.currentframe()
+        caller_frame = inspect.getouterframes(frame)
+        return caller_frame[2].frame.f_code
+
+    def assert_line_location(self, expected, offset_from_caller):
+        grandparent_co = self._get_grandparent_caller_code()
+        lno = grandparent_co.co_firstlineno
+        self.assertEqual(expected, lno + offset_from_caller)
+
+    def test_decorated_odd_comment_indent(self):
+        @njit
+        def foo():
+# NOTE: THIS COMMENT MUST START AT COLUMN 0 FOR THIS SAMPLE CODE TO BE VALID # noqa: E115, E501
+            return 1
+
+        first_def_line = get_func_body_first_lineno(foo)
+        self.assert_line_location(first_def_line, 4)
+
+    def test_undecorated_odd_comment_indent(self):
+        def foo():
+# NOTE: THIS COMMENT MUST START AT COLUMN 0 FOR THIS SAMPLE CODE TO BE VALID # noqa: E115, E501
+            return 1
+
+        first_def_line = get_func_body_first_lineno(njit(foo))
+        self.assert_line_location(first_def_line, 3)
+
+    def test_unnamed_lambda(self):
+        foo = lambda: 1
+        first_def_line = get_func_body_first_lineno(njit(foo))
+        # Cannot determine first line of lambda function
+        self.assertIsNone(first_def_line)
+
+    def test_nested_function(self):
+        def foo():
+            @njit
+            def foo():
+                # This function is intentionally named the same as the parent
+                return 1
+
+            return foo
+
+        inner = foo()
+        first_def_line = get_func_body_first_lineno(inner)
+        self.assert_line_location(first_def_line, 5)
+
+    def test_pass_statement(self):
+        @njit
+        def foo():
+            pass
+
+        first_def_line = get_func_body_first_lineno(foo)
+        self.assert_line_location(first_def_line, 3)
+
+    def test_string_eval(self):
+        source = """def foo():
+            pass
+        """
+
+        globalns = {}
+        exec(source, globalns)
+        foo = globalns['foo']
+
+        first_def_line = get_func_body_first_lineno(foo)
+        # Cannot determine first line of string evaled functions
+        self.assertIsNone(first_def_line)
+
+    def test_single_line_function(self):
+        @njit
+        def foo(): pass   # noqa: E704
+
+        first_def_line = get_func_body_first_lineno(foo)
+        self.assert_line_location(first_def_line, 2)
+
+    def test_docstring(self):
+        @njit
+        def foo():
+            """Docstring
+            """
+            pass
+
+        first_def_line = get_func_body_first_lineno(foo)
+        self.assert_line_location(first_def_line, 5)
+
+    def test_docstring_2(self):
+        @njit
+        def foo():
+            """Docstring
+            """
+            """Not Docstring, but a bare string literal
+            """
+            pass
+        # Variation on test_docstring but with a "fake" docstring following
+        # the true docstring.
+        first_def_line = get_func_body_first_lineno(foo)
+        if PYVERSION == (3, 7):
+            # Python 3.7 oddly uses the last line for string literal AST nodes
+            self.assert_line_location(first_def_line, 6)
+        else:
+            self.assert_line_location(first_def_line, 5)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_flow_control.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_flow_control.py
new file mode 100644
index 000000000..e3fb514a1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_flow_control.py
@@ -0,0 +1,1311 @@
+import itertools
+
+import unittest
+from numba.core.controlflow import CFGraph, ControlFlowAnalysis
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types
+from numba.core.bytecode import FunctionIdentity, ByteCode
+from numba.core.utils import PYVERSION
+from numba.tests.support import TestCase
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+forceobj_flags = Flags()
+forceobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+no_pyobj_flags.nrt = True
+
+
+def for_loop_usecase1(x, y):
+    result = 0
+    for i in range(x):
+        result += i
+    return result
+
+
+def for_loop_usecase2(x, y):
+    result = 0
+    for i, j in enumerate(range(x, y, -1)):
+        result += i * j
+    return result
+
+
+def for_loop_usecase3(x, y):
+    result = 0
+    lst = [x, y]
+    for i in lst:
+        result += i
+    return result
+
+
+def for_loop_usecase4(x, y):
+    result = 0
+    for i in range(10):
+        for j in range(10):
+            result += 1
+    return result
+
+
+def for_loop_usecase5(x, y):
+    result = 0
+    for i in range(x):
+        result += 1
+        if result > y:
+            break
+    return result
+
+
+def for_loop_usecase6(x, y):
+    result = 0
+    for i in range(x):
+        if i > y:
+            continue
+        result += 1
+    return result
+
+
+def for_loop_usecase7(x, y):
+    for i in range(x):
+        x = 0
+        for j in range(x):
+            return 1
+        else:
+            pass
+    return 0
+
+
+def for_loop_usecase8(x, y):
+    result = 0
+    for i in range(x, y, y - x + 1):
+        result += 1
+    return result
+
+
+def for_loop_usecase9(x, y):
+    z = 0
+    for i in range(x):
+        x = 0
+        for j in range(x):
+            if j == x / 2:
+                z += j
+                break
+        else:
+            z += y
+
+    return z
+
+
+def for_loop_usecase10(x, y):
+    for i in range(x):
+        if i == y:
+            z = y
+            break
+    else:
+        z = i * 2
+    return z
+
+
+def while_loop_usecase1(x, y):
+    result = 0
+    i = 0
+    while i < x:
+        result += i
+        i += 1
+    return result
+
+
+def while_loop_usecase2(x, y):
+    result = 0
+    while result != x:
+        result += 1
+    return result
+
+
+def while_loop_usecase3(x, y):
+    result = 0
+    i = 0
+    j = 0
+    while i < x:
+        while j < y:
+            result += i + j
+            i += 1
+            j += 1
+    return result
+
+
+def while_loop_usecase4(x, y):
+    result = 0
+    while True:
+        result += 1
+        if result > x:
+            break
+    return result
+
+
+def while_loop_usecase5(x, y):
+    result = 0
+    while result < x:
+        if result > y:
+            result += 2
+            continue
+        result += 1
+    return result
+
+
+def ifelse_usecase1(x, y):
+    if x > 0:
+        pass
+    elif y > 0:
+        pass
+    else:
+        pass
+    return True
+
+
+def ifelse_usecase2(x, y):
+    if x > y:
+        return 1
+    elif x == 0 or y == 0:
+        return 2
+    else:
+        return 3
+
+
+def ifelse_usecase3(x, y):
+    if x > 0:
+        if y > 0:
+            return 1
+        elif y < 0:
+            return 1
+        else:
+            return 0
+    elif x < 0:
+        return 1
+    else:
+        return 0
+
+
+def ifelse_usecase4(x, y):
+    if x == y:
+        return 1
+
+
+def ternary_ifelse_usecase1(x, y):
+    return True if x > y else False
+
+
+def double_infinite_loop(x, y):
+    L = x
+    i = y
+
+    while True:
+        while True:
+            if i == L - 1:
+                break
+            i += 1
+        i += 1
+        if i >= L:
+            break
+
+    return i, L
+
+
+def try_except_usecase():
+    try:
+        pass
+    except Exception:
+        pass
+
+
+class TestFlowControl(TestCase):
+
+    def run_test(self, pyfunc, x_operands, y_operands,
+                 flags=enable_pyobj_flags):
+        cr = compile_isolated(pyfunc, (types.intp, types.intp), flags=flags)
+        cfunc = cr.entry_point
+        for x, y in itertools.product(x_operands, y_operands):
+            pyerr = None
+            cerr = None
+            try:
+                pyres = pyfunc(x, y)
+            except Exception as e:
+                pyerr = e
+
+            try:
+                cres = cfunc(x, y)
+            except Exception as e:
+                if pyerr is None:
+                    raise
+                cerr = e
+                self.assertEqual(type(pyerr), type(cerr))
+            else:
+                if pyerr is not None:
+                    self.fail("Invalid for pure-python but numba works\n" +
+                              pyerr)
+                self.assertEqual(pyres, cres)
+
+    def test_for_loop1(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase1, [-10, 0, 10], [0], flags=flags)
+
+    def test_for_loop1_npm(self):
+        self.test_for_loop1(flags=no_pyobj_flags)
+
+    def test_for_loop2(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase2, [-10, 0, 10], [-10, 0, 10],
+                      flags=flags)
+
+    def test_for_loop2_npm(self):
+        self.test_for_loop2(flags=no_pyobj_flags)
+
+    def test_for_loop3(self, flags=enable_pyobj_flags):
+        """
+        List requires pyobject
+        """
+        self.run_test(for_loop_usecase3, [1], [2],
+                      flags=flags)
+
+    def test_for_loop3_npm(self):
+        self.test_for_loop3(flags=no_pyobj_flags)
+
+    def test_for_loop4(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase4, [10], [10], flags=flags)
+
+    def test_for_loop4_npm(self):
+        self.test_for_loop4(flags=no_pyobj_flags)
+
+    def test_for_loop5(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase5, [100], [50], flags=flags)
+
+    def test_for_loop5_npm(self):
+        self.test_for_loop5(flags=no_pyobj_flags)
+
+    def test_for_loop6(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase6, [100], [50], flags=flags)
+
+    def test_for_loop6_npm(self):
+        self.test_for_loop6(flags=no_pyobj_flags)
+
+    def test_for_loop7(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase7, [5], [0], flags=flags)
+
+    def test_for_loop7_npm(self):
+        self.test_for_loop7(flags=no_pyobj_flags)
+
+    def test_for_loop8(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase8, [0, 1], [0, 2, 10], flags=flags)
+
+    def test_for_loop8_npm(self):
+        self.test_for_loop8(flags=no_pyobj_flags)
+
+    def test_for_loop9(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase9, [0, 1], [0, 2, 10], flags=flags)
+
+    def test_for_loop9_npm(self):
+        self.test_for_loop9(flags=no_pyobj_flags)
+
+    def test_for_loop10(self, flags=enable_pyobj_flags):
+        self.run_test(for_loop_usecase10, [5], [2, 7], flags=flags)
+
+    def test_for_loop10_npm(self):
+        self.test_for_loop10(flags=no_pyobj_flags)
+
+    def test_while_loop1(self, flags=enable_pyobj_flags):
+        self.run_test(while_loop_usecase1, [10], [0], flags=flags)
+
+    def test_while_loop1_npm(self):
+        self.test_while_loop1(flags=no_pyobj_flags)
+
+    def test_while_loop2(self, flags=enable_pyobj_flags):
+        self.run_test(while_loop_usecase2, [10], [0], flags=flags)
+
+    def test_while_loop2_npm(self):
+        self.test_while_loop2(flags=no_pyobj_flags)
+
+    def test_while_loop3(self, flags=enable_pyobj_flags):
+        self.run_test(while_loop_usecase3, [10], [10], flags=flags)
+
+    def test_while_loop3_npm(self):
+        self.test_while_loop3(flags=no_pyobj_flags)
+
+    def test_while_loop4(self, flags=enable_pyobj_flags):
+        self.run_test(while_loop_usecase4, [10], [0], flags=flags)
+
+    def test_while_loop4_npm(self):
+        self.test_while_loop4(flags=no_pyobj_flags)
+
+    def test_while_loop5(self, flags=enable_pyobj_flags):
+        self.run_test(while_loop_usecase5, [0, 5, 10], [0, 5, 10], flags=flags)
+
+    def test_while_loop5_npm(self):
+        self.test_while_loop5(flags=no_pyobj_flags)
+
+    def test_ifelse1(self, flags=enable_pyobj_flags):
+        self.run_test(ifelse_usecase1, [-1, 0, 1], [-1, 0, 1], flags=flags)
+
+    def test_ifelse1_npm(self):
+        self.test_ifelse1(flags=no_pyobj_flags)
+
+    def test_ifelse2(self, flags=enable_pyobj_flags):
+        self.run_test(ifelse_usecase2, [-1, 0, 1], [-1, 0, 1], flags=flags)
+
+    def test_ifelse2_npm(self):
+        self.test_ifelse2(flags=no_pyobj_flags)
+
+    def test_ifelse3(self, flags=enable_pyobj_flags):
+        self.run_test(ifelse_usecase3, [-1, 0, 1], [-1, 0, 1], flags=flags)
+
+    def test_ifelse3_npm(self):
+        self.test_ifelse3(flags=no_pyobj_flags)
+
+    def test_ifelse4(self, flags=enable_pyobj_flags):
+        self.run_test(ifelse_usecase4, [-1, 0, 1], [-1, 0, 1], flags=flags)
+
+    def test_ifelse4_npm(self):
+        self.test_ifelse4(flags=no_pyobj_flags)
+
+    def test_ternary_ifelse1(self, flags=enable_pyobj_flags):
+        self.run_test(ternary_ifelse_usecase1, [-1, 0, 1], [-1, 0, 1],
+                      flags=flags)
+
+    def test_ternary_ifelse1_npm(self):
+        self.test_ternary_ifelse1(flags=no_pyobj_flags)
+
+    def test_double_infinite_loop(self, flags=enable_pyobj_flags):
+        self.run_test(double_infinite_loop, [10], [0],
+                      flags=flags)
+
+    def test_double_infinite_loop_npm(self):
+        self.test_double_infinite_loop(flags=no_pyobj_flags)
+
+
+class TestCFGraph(TestCase):
+    """
+    Test the numba.controlflow.CFGraph class.
+    """
+
+    def from_adj_list(self, d, entry_point=0):
+        """
+        Build a CFGraph class from a dict of adjacency lists.
+        """
+        g = CFGraph()
+        # Need to add all nodes before adding edges
+        for node in d:
+            g.add_node(node)
+        for node, dests in d.items():
+            for dest in dests:
+                g.add_edge(node, dest)
+        return g
+
+    def loopless1(self):
+        """
+        A simple CFG corresponding to the following code structure:
+
+            c = (... if ... else ...) + ...
+            return b + c
+        """
+        g = self.from_adj_list({0: [18, 12], 12: [21], 18: [21], 21: []})
+        g.set_entry_point(0)
+        g.process()
+        return g
+
+    def loopless1_dead_nodes(self):
+        """
+        Same as loopless1(), but with added dead blocks (some of them
+        in a loop).
+        """
+        g = self.from_adj_list(
+            {0: [18, 12],
+             12: [21],
+             18: [21],
+             21: [],
+             91: [12, 0],
+             92: [91, 93],
+             93: [92],
+             94: [],
+             })
+        g.set_entry_point(0)
+        g.process()
+        return g
+
+    def loopless2(self):
+        """
+        A loopless CFG corresponding to the following code structure:
+
+            c = (... if ... else ...) + ...
+            if c:
+                return ...
+            else:
+                return ...
+
+        Note there are two exit points, and the entry point has been
+        changed to a non-zero value.
+        """
+        g = self.from_adj_list(
+            {99: [18, 12], 12: [21], 18: [21], 21: [42, 34], 34: [], 42: []})
+        g.set_entry_point(99)
+        g.process()
+        return g
+
+    def multiple_loops(self):
+        """
+        A CFG with multiple nested loops:
+
+            for y in b:
+                for x in a:
+                    # This loop has two back edges
+                    if b:
+                        continue
+                    else:
+                        continue
+            for z in c:
+                if z:
+                    return ...
+        """
+        g = self.from_adj_list({0: [7],
+                                7: [10, 60],
+                                10: [13],
+                                13: [20],
+                                20: [56, 23],
+                                23: [32, 44],
+                                32: [20],
+                                44: [20],
+                                56: [57],
+                                57: [7],
+                                60: [61],
+                                61: [68],
+                                68: [87, 71],
+                                71: [80, 68],
+                                80: [],
+                                87: [88],
+                                88: []}
+                               )
+        g.set_entry_point(0)
+        g.process()
+        return g
+
+    def multiple_exits(self):
+        """
+        A CFG with three loop exits, one of which is also a function
+        exit point, and another function exit point:
+
+            for x in a:
+                if a:
+                    return b
+                elif b:
+                    break
+            return c
+        """
+        g = self.from_adj_list(
+            {0: [7],
+             7: [10, 36],
+             10: [19, 23],
+             19: [],
+             23: [29, 7],
+             29: [37],
+             36: [37],
+             37: []
+             })
+        g.set_entry_point(0)
+        g.process()
+        return g
+
+    def infinite_loop1(self):
+        """
+        A CFG with a infinite loop and an alternate exit point:
+
+            if c:
+                return
+            while True:
+                if a:
+                    ...
+                else:
+                    ...
+        """
+        g = self.from_adj_list(
+            {0: [10, 6], 6: [], 10: [13], 13: [26, 19], 19: [13], 26: [13]})
+        g.set_entry_point(0)
+        g.process()
+        return g
+
+    def infinite_loop2(self):
+        """
+        A CFG with no exit point at all:
+
+            while True:
+                if a:
+                    ...
+                else:
+                    ...
+        """
+        g = self.from_adj_list({0: [3], 3: [16, 9], 9: [3], 16: [3]})
+        g.set_entry_point(0)
+        g.process()
+        return g
+
+    def test_simple_properties(self):
+        g = self.loopless1()
+        self.assertEqual(sorted(g.successors(0)), [(12, None), (18, None)])
+        self.assertEqual(sorted(g.successors(21)), [])
+        self.assertEqual(sorted(g.predecessors(0)), [])
+        self.assertEqual(sorted(g.predecessors(21)), [(12, None), (18, None)])
+
+    def test_exit_points(self):
+        g = self.loopless1()
+        self.assertEqual(sorted(g.exit_points()), [21])
+        g = self.loopless1_dead_nodes()
+        self.assertEqual(sorted(g.exit_points()), [21])
+        g = self.loopless2()
+        self.assertEqual(sorted(g.exit_points()), [34, 42])
+        g = self.multiple_loops()
+        self.assertEqual(sorted(g.exit_points()), [80, 88])
+        g = self.infinite_loop1()
+        self.assertEqual(sorted(g.exit_points()), [6])
+        g = self.infinite_loop2()
+        self.assertEqual(sorted(g.exit_points()), [])
+        g = self.multiple_exits()
+        self.assertEqual(sorted(g.exit_points()), [19, 37])
+
+    def test_dead_nodes(self):
+        g = self.loopless1()
+        self.assertEqual(len(g.dead_nodes()), 0)
+        self.assertEqual(sorted(g.nodes()),
+                         [0, 12, 18, 21])
+        g = self.loopless2()
+        self.assertEqual(len(g.dead_nodes()), 0)
+        self.assertEqual(sorted(g.nodes()),
+                         [12, 18, 21, 34, 42, 99])
+        g = self.multiple_loops()
+        self.assertEqual(len(g.dead_nodes()), 0)
+        g = self.infinite_loop1()
+        self.assertEqual(len(g.dead_nodes()), 0)
+        g = self.multiple_exits()
+        self.assertEqual(len(g.dead_nodes()), 0)
+        # Only this example has dead nodes
+        g = self.loopless1_dead_nodes()
+        self.assertEqual(sorted(g.dead_nodes()),
+                         [91, 92, 93, 94])
+        self.assertEqual(sorted(g.nodes()),
+                         [0, 12, 18, 21])
+
+    def test_descendents(self):
+        g = self.loopless2()
+        d = g.descendents(34)
+        self.assertEqual(sorted(d), [])
+        d = g.descendents(42)
+        self.assertEqual(sorted(d), [])
+        d = g.descendents(21)
+        self.assertEqual(sorted(d), [34, 42])
+        d = g.descendents(99)
+        self.assertEqual(sorted(d), [12, 18, 21, 34, 42])
+        g = self.infinite_loop1()
+        d = g.descendents(26)
+        self.assertEqual(sorted(d), [])
+        d = g.descendents(19)
+        self.assertEqual(sorted(d), [])
+        d = g.descendents(13)
+        self.assertEqual(sorted(d), [19, 26])
+        d = g.descendents(10)
+        self.assertEqual(sorted(d), [13, 19, 26])
+        d = g.descendents(6)
+        self.assertEqual(sorted(d), [])
+        d = g.descendents(0)
+        self.assertEqual(sorted(d), [6, 10, 13, 19, 26])
+
+    def test_topo_order(self):
+        g = self.loopless1()
+        self.assertIn(g.topo_order(),
+                      ([0, 12, 18, 21], [0, 18, 12, 21]))
+        g = self.loopless2()
+        self.assertIn(g.topo_order(),
+                      ([99, 18, 12, 21, 34, 42], [99, 12, 18, 21, 34, 42]))
+        g = self.infinite_loop2()
+        self.assertIn(g.topo_order(),
+                      ([0, 3, 9, 16], [0, 3, 16, 9]))
+        g = self.infinite_loop1()
+        self.assertIn(g.topo_order(),
+                      ([0, 6, 10, 13, 19, 26], [0, 6, 10, 13, 26, 19],
+                       [0, 10, 13, 19, 26, 6], [0, 10, 13, 26, 19, 6]))
+
+    def test_topo_sort(self):
+        def check_topo_sort(nodes, expected):
+            self.assertIn(list(g.topo_sort(nodes)), expected)
+            self.assertIn(list(g.topo_sort(nodes[::-1])), expected)
+            self.assertIn(list(g.topo_sort(nodes, reverse=True))[::-1],
+                          expected)
+            self.assertIn(list(g.topo_sort(nodes[::-1], reverse=True))[::-1],
+                          expected)
+            self.random.shuffle(nodes)
+            self.assertIn(list(g.topo_sort(nodes)), expected)
+            self.assertIn(list(g.topo_sort(nodes, reverse=True))[::-1],
+                          expected)
+
+        g = self.loopless2()
+        check_topo_sort([21, 99, 12, 34], ([99, 12, 21, 34],))
+        # NOTE: topo_sort() is not stable
+        check_topo_sort([18, 12, 42, 99],
+                        ([99, 12, 18, 42], [99, 18, 12, 42]))
+        g = self.multiple_exits()
+        check_topo_sort([19, 10, 7, 36],
+                        ([7, 10, 19, 36], [7, 10, 36, 19], [7, 36, 10, 19]))
+
+    def check_dominators(self, got, expected):
+        self.assertEqual(sorted(got), sorted(expected))
+        for node in sorted(got):
+            self.assertEqual(sorted(got[node]), sorted(expected[node]),
+                             "mismatch for %r" % (node,))
+
+    def test_dominators_loopless(self):
+        def eq_(d, l):
+            self.assertEqual(sorted(doms[d]), l)
+        for g in [self.loopless1(), self.loopless1_dead_nodes()]:
+            doms = g.dominators()
+            eq_(0, [0])
+            eq_(12, [0, 12])
+            eq_(18, [0, 18])
+            eq_(21, [0, 21])
+        g = self.loopless2()
+        doms = g.dominators()
+        eq_(99, [99])
+        eq_(12, [12, 99])
+        eq_(18, [18, 99])
+        eq_(21, [21, 99])
+        eq_(34, [21, 34, 99])
+        eq_(42, [21, 42, 99])
+
+    def test_dominators_loops(self):
+        g = self.multiple_exits()
+        doms = g.dominators()
+        self.check_dominators(doms,
+                              {0: [0],
+                               7: [0, 7],
+                               10: [0, 7, 10],
+                               19: [0, 7, 10, 19],
+                               23: [0, 7, 10, 23],
+                               29: [0, 7, 10, 23, 29],
+                               36: [0, 7, 36],
+                               37: [0, 7, 37],
+                               })
+        g = self.multiple_loops()
+        doms = g.dominators()
+        self.check_dominators(doms,
+                              {0: [0],
+                               7: [0, 7],
+                               10: [0, 10, 7],
+                               13: [0, 10, 13, 7],
+                               20: [0, 10, 20, 13, 7],
+                               23: [0, 20, 23, 7, 10, 13],
+                               32: [32, 0, 20, 23, 7, 10, 13],
+                               44: [0, 20, 23, 7, 10, 44, 13],
+                               56: [0, 20, 7, 56, 10, 13],
+                               57: [0, 20, 7, 56, 57, 10, 13],
+                               60: [0, 60, 7],
+                               61: [0, 60, 61, 7],
+                               68: [0, 68, 60, 61, 7],
+                               71: [0, 68, 71, 7, 60, 61],
+                               80: [80, 0, 68, 71, 7, 60, 61],
+                               87: [0, 68, 87, 7, 60, 61],
+                               88: [0, 68, 87, 88, 7, 60, 61]
+                               })
+        g = self.infinite_loop1()
+        doms = g.dominators()
+        self.check_dominators(doms,
+                              {0: [0],
+                               6: [0, 6],
+                               10: [0, 10],
+                               13: [0, 10, 13],
+                               19: [0, 10, 19, 13],
+                               26: [0, 10, 13, 26],
+                               })
+
+    def test_post_dominators_loopless(self):
+        def eq_(d, l):
+            self.assertEqual(sorted(doms[d]), l)
+        for g in [self.loopless1(), self.loopless1_dead_nodes()]:
+            doms = g.post_dominators()
+            eq_(0, [0, 21])
+            eq_(12, [12, 21])
+            eq_(18, [18, 21])
+            eq_(21, [21])
+        g = self.loopless2()
+        doms = g.post_dominators()
+        eq_(34, [34])
+        eq_(42, [42])
+        eq_(21, [21])
+        eq_(18, [18, 21])
+        eq_(12, [12, 21])
+        eq_(99, [21, 99])
+
+    def test_post_dominators_loops(self):
+        g = self.multiple_exits()
+        doms = g.post_dominators()
+        self.check_dominators(doms,
+                              {0: [0, 7],
+                               7: [7],
+                               10: [10],
+                               19: [19],
+                               23: [23],
+                               29: [29, 37],
+                               36: [36, 37],
+                               37: [37],
+                               })
+        g = self.multiple_loops()
+        doms = g.post_dominators()
+        self.check_dominators(doms,
+                              {0: [0, 60, 68, 61, 7],
+                               7: [60, 68, 61, 7],
+                               10: [68, 7, 10, 13, 20, 56, 57, 60, 61],
+                               13: [68, 7, 13, 20, 56, 57, 60, 61],
+                               20: [20, 68, 7, 56, 57, 60, 61],
+                               23: [68, 7, 20, 23, 56, 57, 60, 61],
+                               32: [32, 68, 7, 20, 56, 57, 60, 61],
+                               44: [68, 7, 44, 20, 56, 57, 60, 61],
+                               56: [68, 7, 56, 57, 60, 61],
+                               57: [57, 60, 68, 61, 7],
+                               60: [60, 68, 61],
+                               61: [68, 61],
+                               68: [68],
+                               71: [71],
+                               80: [80],
+                               87: [88, 87],
+                               88: [88]
+                               })
+
+    def test_post_dominators_infinite_loops(self):
+        # Post-dominators with infinite loops need special care
+        # (the ordinary algorithm won't work).
+        g = self.infinite_loop1()
+        doms = g.post_dominators()
+        self.check_dominators(doms,
+                              {0: [0],
+                               6: [6],
+                               10: [10, 13],
+                               13: [13],
+                               19: [19],
+                               26: [26],
+                               })
+        g = self.infinite_loop2()
+        doms = g.post_dominators()
+        self.check_dominators(doms,
+                              {0: [0, 3],
+                               3: [3],
+                               9: [9],
+                               16: [16],
+                               })
+
+    def test_dominator_tree(self):
+        def check(graph, expected):
+            domtree = graph.dominator_tree()
+            self.assertEqual(domtree, expected)
+
+        check(self.loopless1(),
+              {0: {12, 18, 21}, 12: set(), 18: set(), 21: set()})
+        check(self.loopless2(),
+              {12: set(), 18: set(), 21: {34, 42}, 34: set(), 42: set(),
+               99: {18, 12, 21}})
+        check(self.loopless1_dead_nodes(),
+              {0: {12, 18, 21}, 12: set(), 18: set(), 21: set()})
+        check(self.multiple_loops(),
+              {0: {7}, 7: {10, 60}, 60: {61}, 61: {68}, 68: {71, 87},
+               87: {88}, 88: set(), 71: {80}, 80: set(), 10: {13},
+               13: {20}, 20: {56, 23}, 23: {32, 44}, 44: set(),
+               32: set(), 56: {57}, 57: set()})
+        check(self.multiple_exits(),
+              {0: {7}, 7: {10, 36, 37}, 36: set(), 10: {19, 23},
+               23: {29}, 29: set(), 37: set(), 19: set()})
+        check(self.infinite_loop1(),
+              {0: {10, 6}, 6: set(), 10: {13}, 13: {26, 19}, 19: set(),
+               26: set()})
+        check(self.infinite_loop2(),
+              {0: {3}, 3: {16, 9}, 9: set(), 16: set()})
+
+    def test_immediate_dominators(self):
+        def check(graph, expected):
+            idoms = graph.immediate_dominators()
+            self.assertEqual(idoms, expected)
+
+        check(self.loopless1(),
+              {0: 0, 12: 0, 18: 0, 21: 0})
+        check(self.loopless2(),
+              {18: 99, 12: 99, 21: 99, 42: 21, 34: 21, 99: 99})
+        check(self.loopless1_dead_nodes(),
+              {0: 0, 12: 0, 18: 0, 21: 0})
+        check(self.multiple_loops(),
+              {0: 0, 7: 0, 10: 7, 13: 10, 20: 13, 23: 20,
+               32: 23, 44: 23, 56: 20, 57: 56, 60: 7, 61: 60,
+               68: 61, 71: 68, 80: 71, 87: 68, 88: 87})
+        check(self.multiple_exits(),
+              {0:0, 7: 0, 10: 7, 19: 10, 23: 10, 29: 23, 36: 7, 37: 7})
+        check(self.infinite_loop1(),
+              {0: 0, 6: 0, 10: 0, 13: 10, 19: 13, 26: 13})
+        check(self.infinite_loop2(),
+              {0: 0, 3: 0, 9: 3, 16: 3})
+
+    def test_dominance_frontier(self):
+        def check(graph, expected):
+            df = graph.dominance_frontier()
+            self.assertEqual(df, expected)
+
+        check(self.loopless1(),
+              {0: set(), 12: {21}, 18: {21}, 21: set()})
+        check(self.loopless2(),
+              {18: {21}, 12: {21}, 21: set(), 42: set(), 34: set(), 99: set()})
+        check(self.loopless1_dead_nodes(),
+              {0: set(), 12: {21}, 18: {21}, 21: set()})
+        check(self.multiple_loops(),
+              {0: set(), 7: {7}, 10: {7}, 13: {7}, 20: {20, 7}, 23: {20},
+               32: {20}, 44: {20}, 56: {7}, 57: {7}, 60: set(), 61: set(),
+               68: {68}, 71: {68}, 80: set(), 87: set(), 88: set()})
+        check(self.multiple_exits(),
+              {0: set(), 7: {7}, 10: {37, 7}, 19: set(),
+               23: {37, 7}, 29: {37}, 36: {37}, 37: set()})
+        check(self.infinite_loop1(),
+              {0: set(), 6: set(), 10: set(), 13: {13}, 19: {13}, 26: {13}})
+        check(self.infinite_loop2(),
+              {0: set(), 3: {3}, 9: {3}, 16: {3}})
+
+    def test_backbone_loopless(self):
+        for g in [self.loopless1(), self.loopless1_dead_nodes()]:
+            self.assertEqual(sorted(g.backbone()), [0, 21])
+        g = self.loopless2()
+        self.assertEqual(sorted(g.backbone()), [21, 99])
+
+    def test_backbone_loops(self):
+        g = self.multiple_loops()
+        self.assertEqual(sorted(g.backbone()), [0, 7, 60, 61, 68])
+        g = self.infinite_loop1()
+        self.assertEqual(sorted(g.backbone()), [0])
+        g = self.infinite_loop2()
+        self.assertEqual(sorted(g.backbone()), [0, 3])
+
+    def test_loops(self):
+        for g in [self.loopless1(), self.loopless1_dead_nodes(),
+                  self.loopless2()]:
+            self.assertEqual(len(g.loops()), 0)
+
+        g = self.multiple_loops()
+        # Loop headers
+        self.assertEqual(sorted(g.loops()), [7, 20, 68])
+        outer1 = g.loops()[7]
+        inner1 = g.loops()[20]
+        outer2 = g.loops()[68]
+        self.assertEqual(outer1.header, 7)
+        self.assertEqual(sorted(outer1.entries), [0])
+        self.assertEqual(sorted(outer1.exits), [60])
+        self.assertEqual(sorted(outer1.body),
+                         [7, 10, 13, 20, 23, 32, 44, 56, 57])
+        self.assertEqual(inner1.header, 20)
+        self.assertEqual(sorted(inner1.entries), [13])
+        self.assertEqual(sorted(inner1.exits), [56])
+        self.assertEqual(sorted(inner1.body), [20, 23, 32, 44])
+        self.assertEqual(outer2.header, 68)
+        self.assertEqual(sorted(outer2.entries), [61])
+        self.assertEqual(sorted(outer2.exits), [80, 87])
+        self.assertEqual(sorted(outer2.body), [68, 71])
+        for node in [0, 60, 61, 80, 87, 88]:
+            self.assertEqual(g.in_loops(node), [])
+        for node in [7, 10, 13, 56, 57]:
+            self.assertEqual(g.in_loops(node), [outer1])
+        for node in [20, 23, 32, 44]:
+            self.assertEqual(g.in_loops(node), [inner1, outer1])
+        for node in [68, 71]:
+            self.assertEqual(g.in_loops(node), [outer2])
+
+        g = self.infinite_loop1()
+        # Loop headers
+        self.assertEqual(sorted(g.loops()), [13])
+        loop = g.loops()[13]
+        self.assertEqual(loop.header, 13)
+        self.assertEqual(sorted(loop.entries), [10])
+        self.assertEqual(sorted(loop.exits), [])
+        self.assertEqual(sorted(loop.body), [13, 19, 26])
+        for node in [0, 6, 10]:
+            self.assertEqual(g.in_loops(node), [])
+        for node in [13, 19, 26]:
+            self.assertEqual(g.in_loops(node), [loop])
+
+        g = self.infinite_loop2()
+        # Loop headers
+        self.assertEqual(sorted(g.loops()), [3])
+        loop = g.loops()[3]
+        self.assertEqual(loop.header, 3)
+        self.assertEqual(sorted(loop.entries), [0])
+        self.assertEqual(sorted(loop.exits), [])
+        self.assertEqual(sorted(loop.body), [3, 9, 16])
+        for node in [0]:
+            self.assertEqual(g.in_loops(node), [])
+        for node in [3, 9, 16]:
+            self.assertEqual(g.in_loops(node), [loop])
+
+        g = self.multiple_exits()
+        # Loop headers
+        self.assertEqual(sorted(g.loops()), [7])
+        loop = g.loops()[7]
+        self.assertEqual(loop.header, 7)
+        self.assertEqual(sorted(loop.entries), [0])
+        self.assertEqual(sorted(loop.exits), [19, 29, 36])
+        self.assertEqual(sorted(loop.body), [7, 10, 23])
+        for node in [0, 19, 29, 36]:
+            self.assertEqual(g.in_loops(node), [])
+        for node in [7, 10, 23]:
+            self.assertEqual(g.in_loops(node), [loop])
+
+    def test_loop_dfs_pathological(self):
+        # The follow adjlist is an export from the reproducer in #6186
+        g = self.from_adj_list({
+            0: {38, 14},
+            14: {38, 22},
+            22: {38, 30},
+            30: {42, 38},
+            38: {42},
+            42: {64, 50},
+            50: {64, 58},
+            58: {128},
+            64: {72, 86},
+            72: {80, 86},
+            80: {128},
+            86: {108, 94},
+            94: {108, 102},
+            102: {128},
+            108: {128, 116},
+            116: {128, 124},
+            124: {128},
+            128: {178, 174},
+            174: {178},
+            178: {210, 206},
+            206: {210},
+            210: {248, 252},
+            248: {252},
+            252: {282, 286},
+            282: {286},
+            286: {296, 326},
+            296: {330},
+            326: {330},
+            330: {370, 340},
+            340: {374},
+            370: {374},
+            374: {380, 382},
+            380: {382},
+            382: {818, 390},
+            390: {456, 458},
+            456: {458},
+            458: {538, 566},
+            538: {548, 566},
+            548: set(),
+            566: {586, 572},
+            572: {586},
+            586: {708, 596},
+            596: {608},
+            608: {610},
+            610: {704, 620},
+            620: {666, 630},
+            630: {636, 646},
+            636: {666, 646},
+            646: {666},
+            666: {610},
+            704: {706},
+            706: {818},
+            708: {720},
+            720: {722},
+            722: {816, 732},
+            732: {778, 742},
+            742: {748, 758},
+            748: {778, 758},
+            758: {778},
+            778: {722},
+            816: {818},
+            818: set(),
+        })
+        g.set_entry_point(0)
+        g.process()
+        stats = {}
+        # Compute backedges and store the iteration count for testing
+        back_edges = g._find_back_edges(stats=stats)
+        self.assertEqual(back_edges, {(666, 610), (778, 722)})
+        self.assertEqual(stats['iteration_count'], 155)
+
+    def test_equals(self):
+
+        def get_new():
+            g = self.from_adj_list({0: [18, 12], 12: [21], 18: [21], 21: []})
+            g.set_entry_point(0)
+            g.process()
+            return g
+
+        x = get_new()
+        y = get_new()
+
+        # identical
+        self.assertEqual(x, y)
+
+        # identical but defined in a different order
+        g = self.from_adj_list({0: [12, 18], 18: [21], 21: [], 12: [21]})
+        g.set_entry_point(0)
+        g.process()
+        self.assertEqual(x, g)
+
+        # different entry point
+        z = get_new()
+        z.set_entry_point(18)
+        z.process()
+        self.assertNotEqual(x, z)
+
+        # extra node/edge, same entry point
+        z = self.from_adj_list({0: [18, 12], 12: [21], 18: [21], 21: [22],
+                                22: []})
+        z.set_entry_point(0)
+        z.process()
+        self.assertNotEqual(x, z)
+
+        # same nodes, different edges
+        a = self.from_adj_list({0: [18, 12], 12: [0], 18: []})
+        a.set_entry_point(0)
+        a.process()
+        z = self.from_adj_list({0: [18, 12], 12: [18], 18: []})
+        z.set_entry_point(0)
+        z.process()
+        self.assertNotEqual(a, z)
+
+
+class TestRealCodeDomFront(TestCase):
+    """Test IDOM and DOMFRONT computation on real python bytecode.
+    Note: there will be less testing on IDOM (esp in loop) because of
+    the extra blocks inserted by the interpreter.  But, testing on DOMFRONT
+    (which depends on IDOM) is easier.
+
+    Testing is done by associating names to basicblock by using globals of
+    the pattern "SET_BLOCK_<name>", which are scanned by
+    `.get_cfa_and_namedblocks` into *namedblocks* dictionary.  That way, we
+    can check that a block of a certain name is a IDOM or DOMFRONT of another
+    named block.
+    """
+    def cfa(self, bc):
+        cfa = ControlFlowAnalysis(bc)
+        cfa.run()
+        return cfa
+
+    def get_cfa_and_namedblocks(self, fn):
+        fid = FunctionIdentity.from_function(fn)
+        bc = ByteCode(func_id=fid)
+        cfa = self.cfa(bc)
+        namedblocks = self._scan_namedblocks(bc, cfa)
+
+        #### To debug, uncomment below
+        # print(bc.dump())
+        # print("IDOMS")
+        # for k, v in sorted(cfa.graph.immediate_dominators().items()):
+        #     print('{} -> {}'.format(k, v))
+        # print("DOMFRONT")
+        # for k, vs in sorted(cfa.graph.dominance_frontier().items()):
+        #     print('{} -> {}'.format(k, vs))
+        # print(namedblocks)
+        # cfa.graph.render_dot().view()
+
+        return cfa, namedblocks
+
+    def _scan_namedblocks(self, bc, cfa):
+        """Scan namedblocks as denoted by a LOAD_GLOBAL bytecode referring
+        to global variables with the pattern "SET_BLOCK_<name>", where "<name>"
+        would be the name for the current block.
+        """
+        namedblocks = {}
+        blocks = sorted([x.offset for x in cfa.iterblocks()])
+        prefix = 'SET_BLOCK_'
+
+        for inst in bc:
+            # Find LOAD_GLOBAL that refers to "SET_BLOCK_<name>"
+            if inst.opname == 'LOAD_GLOBAL':
+                gv = bc.co_names[inst.arg]
+                if gv.startswith(prefix):
+                    name = gv[len(prefix):]
+                    # Find the block where this instruction resides
+                    for s, e in zip(blocks, blocks[1:] + [blocks[-1] + 1]):
+                        if s <= inst.offset < e:
+                            break
+                    else:
+                        raise AssertionError('unreachable loop')
+                    blkno = s
+                    namedblocks[name] = blkno
+        return namedblocks
+
+    def test_loop(self):
+        def foo(n):
+            c = 0
+            SET_BLOCK_A                     # noqa: F821
+            i = 0
+            while SET_BLOCK_B0:             # noqa: F821
+                SET_BLOCK_B1                # noqa: F821
+                c += 1
+                i += 1
+            SET_BLOCK_C                     # noqa: F821
+            return c
+
+        cfa, blkpts = self.get_cfa_and_namedblocks(foo)
+        idoms = cfa.graph.immediate_dominators()
+
+        # Py3.10 turns while loop into if(...) { do {...} while(...) }.
+        # Also, `SET_BLOCK_B0` is duplicated. As a result, the second B0
+        # is picked up by `blkpts`.
+        if PYVERSION < (3, 10):
+            self.assertEqual(blkpts['B0'], idoms[blkpts['B1']])
+
+        domfront = cfa.graph.dominance_frontier()
+        self.assertFalse(domfront[blkpts['A']])
+        self.assertFalse(domfront[blkpts['C']])
+        if PYVERSION < (3, 10):
+            self.assertEqual({blkpts['B0']}, domfront[blkpts['B1']])
+
+    def test_loop_nested_and_break(self):
+        def foo(n):
+            SET_BLOCK_A                     # noqa: F821
+            while SET_BLOCK_B0:             # noqa: F821
+                SET_BLOCK_B1                # noqa: F821
+                while SET_BLOCK_C0:         # noqa: F821
+                    SET_BLOCK_C1            # noqa: F821
+                    if SET_BLOCK_D0:        # noqa: F821
+                        SET_BLOCK_D1        # noqa: F821
+                        break
+                    elif n:
+                        SET_BLOCK_D2        # noqa: F821
+                    SET_BLOCK_E             # noqa: F821
+                SET_BLOCK_F                 # noqa: F821
+            SET_BLOCK_G                     # noqa: F821
+
+        cfa, blkpts = self.get_cfa_and_namedblocks(foo)
+        idoms = cfa.graph.immediate_dominators()
+        self.assertEqual(blkpts['D0'], blkpts['C1'])
+
+        # Py3.10 changes while loop into if-do-while
+        if PYVERSION < (3, 10):
+            self.assertEqual(blkpts['C0'], idoms[blkpts['C1']])
+
+        domfront = cfa.graph.dominance_frontier()
+        self.assertFalse(domfront[blkpts['A']])
+        self.assertFalse(domfront[blkpts['G']])
+        if PYVERSION < (3, 10):
+            self.assertEqual({blkpts['B0']}, domfront[blkpts['B1']])
+        # 2 domfront members for C1
+        # C0 because of the loop; F because of the break.
+        if PYVERSION < (3, 10):
+            self.assertEqual({blkpts['C0'], blkpts['F']},
+                             domfront[blkpts['C1']])
+        self.assertEqual({blkpts['F']}, domfront[blkpts['D1']])
+        self.assertEqual({blkpts['E']}, domfront[blkpts['D2']])
+        if PYVERSION < (3, 10):
+            self.assertEqual({blkpts['C0']}, domfront[blkpts['E']])
+            self.assertEqual({blkpts['B0']}, domfront[blkpts['F']])
+            self.assertEqual({blkpts['B0']}, domfront[blkpts['B0']])
+
+    def test_if_else(self):
+        def foo(a, b):
+            c = 0
+            SET_BLOCK_A           # noqa: F821
+            if a < b:
+                SET_BLOCK_B       # noqa: F821
+                c = 1
+            elif SET_BLOCK_C0:    # noqa: F821
+                SET_BLOCK_C1      # noqa: F821
+                c = 2
+            else:
+                SET_BLOCK_D       # noqa: F821
+                c = 3
+
+            SET_BLOCK_E           # noqa: F821
+            if a % b == 0:
+                SET_BLOCK_F       # noqa: F821
+                c += 1
+            SET_BLOCK_G           # noqa: F821
+            return c
+
+        cfa, blkpts = self.get_cfa_and_namedblocks(foo)
+
+        idoms = cfa.graph.immediate_dominators()
+        self.assertEqual(blkpts['A'], idoms[blkpts['B']])
+        self.assertEqual(blkpts['A'], idoms[blkpts['C0']])
+        self.assertEqual(blkpts['C0'], idoms[blkpts['C1']])
+        self.assertEqual(blkpts['C0'], idoms[blkpts['D']])
+        self.assertEqual(blkpts['A'], idoms[blkpts['E']])
+        self.assertEqual(blkpts['E'], idoms[blkpts['F']])
+        self.assertEqual(blkpts['E'], idoms[blkpts['G']])
+
+        domfront = cfa.graph.dominance_frontier()
+        self.assertFalse(domfront[blkpts['A']])
+        self.assertFalse(domfront[blkpts['E']])
+        self.assertFalse(domfront[blkpts['G']])
+        self.assertEqual({blkpts['E']}, domfront[blkpts['B']])
+        self.assertEqual({blkpts['E']}, domfront[blkpts['C0']])
+        self.assertEqual({blkpts['E']}, domfront[blkpts['C1']])
+        self.assertEqual({blkpts['E']}, domfront[blkpts['D']])
+        self.assertEqual({blkpts['G']}, domfront[blkpts['F']])
+
+    def test_if_else_nested(self):
+        def foo():
+            if SET_BLOCK_A0:                # noqa: F821
+                SET_BLOCK_A1                # noqa: F821
+                if SET_BLOCK_B0:            # noqa: F821
+                    SET_BLOCK_B1            # noqa: F821
+                    a = 0
+                else:
+                    if SET_BLOCK_C0:        # noqa: F821
+                        SET_BLOCK_C1        # noqa: F821
+                        a = 1
+                    else:
+                        SET_BLOCK_C2        # noqa: F821
+                        a = 2
+                    SET_BLOCK_D             # noqa: F821
+                SET_BLOCK_E                 # noqa: F821
+            SET_BLOCK_F                     # noqa: F821
+            return a
+
+        cfa, blkpts = self.get_cfa_and_namedblocks(foo)
+
+        idoms = cfa.graph.immediate_dominators()
+        self.assertEqual(blkpts['A0'], idoms[blkpts['A1']])
+        self.assertEqual(blkpts['A1'], idoms[blkpts['B1']])
+        self.assertEqual(blkpts['A1'], idoms[blkpts['C0']])
+        self.assertEqual(blkpts['C0'], idoms[blkpts['D']])
+        self.assertEqual(blkpts['A1'], idoms[blkpts['E']])
+        self.assertEqual(blkpts['A0'], idoms[blkpts['F']])
+
+        domfront = cfa.graph.dominance_frontier()
+        self.assertFalse(domfront[blkpts['A0']])
+        self.assertFalse(domfront[blkpts['F']])
+        self.assertEqual({blkpts['E']}, domfront[blkpts['B1']])
+        self.assertEqual({blkpts['D']}, domfront[blkpts['C1']])
+        self.assertEqual({blkpts['E']}, domfront[blkpts['D']])
+        self.assertEqual({blkpts['F']}, domfront[blkpts['E']])
+
+    def test_infinite_loop(self):
+        def foo():
+            SET_BLOCK_A                     # noqa: F821
+            while True:  # infinite loop
+                if SET_BLOCK_B:             # noqa: F821
+                    SET_BLOCK_C             # noqa: F821
+                    return
+                SET_BLOCK_D                 # noqa: F821
+            SET_BLOCK_E                     # noqa: F821
+
+        cfa, blkpts = self.get_cfa_and_namedblocks(foo)
+
+        idoms = cfa.graph.immediate_dominators()
+        # Py3.10 optimizes away the infinite loop and removes SET_BLOCK_E from
+        # the bytecode.
+        if PYVERSION >= (3, 10):
+            self.assertNotIn('E', blkpts)
+        else:
+            self.assertNotIn(blkpts['E'], idoms)
+        self.assertEqual(blkpts['B'], idoms[blkpts['C']])
+        self.assertEqual(blkpts['B'], idoms[blkpts['D']])
+
+        domfront = cfa.graph.dominance_frontier()
+        if PYVERSION < (3, 10):
+            self.assertNotIn(blkpts['E'], domfront)
+        self.assertFalse(domfront[blkpts['A']])
+        self.assertFalse(domfront[blkpts['C']])
+        self.assertEqual({blkpts['B']}, domfront[blkpts['B']])
+        self.assertEqual({blkpts['B']}, domfront[blkpts['D']])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_func_interface.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_func_interface.py
new file mode 100644
index 000000000..546862858
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_func_interface.py
@@ -0,0 +1,43 @@
+import unittest
+from numba import jit
+
+
+class TestFuncInterface(unittest.TestCase):
+    def test_jit_function_docstring(self):
+
+        def add(x, y):
+            '''Return sum of two numbers'''
+            return x + y
+
+        c_add = jit(add)
+        self.assertEqual(c_add.__doc__, 'Return sum of two numbers')
+
+    def test_jit_function_name(self):
+
+        def add(x, y):
+            return x + y
+
+        c_add = jit(add)
+        self.assertEqual(c_add.__name__, 'add')
+
+    def test_jit_function_module(self):
+
+        def add(x, y):
+            return x + y
+
+        c_add = jit(add)
+        # Expected answer depends on how you run this test.
+        # Compare to python function instead.
+        self.assertEqual(c_add.__module__, add.__module__)
+
+    def test_jit_function_code_object(self):
+        def add(x, y):
+            return x + y
+
+        c_add = jit(add)
+        self.assertEqual(c_add.__code__, add.__code__)
+        self.assertEqual(c_add.func_code, add.__code__)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_func_lifetime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_func_lifetime.py
new file mode 100644
index 000000000..5624b5704
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_func_lifetime.py
@@ -0,0 +1,164 @@
+import gc
+import weakref
+
+from numba import jit
+from numba.core import types
+from numba.tests.support import TestCase
+import unittest
+
+
+class Dummy(object):
+
+    def __add__(self, other):
+        return other + 5
+
+
+def global_usecase1(x):
+    return x + 1
+
+def global_usecase2():
+    return global_obj + 1
+
+
+class TestFuncLifetime(TestCase):
+    """
+    Test the lifetime of compiled function objects and their dependencies.
+    """
+
+    def get_impl(self, dispatcher):
+        """
+        Get the single implementation (a C function object) of a dispatcher.
+        """
+        self.assertEqual(len(dispatcher.overloads), 1)
+        cres = list(dispatcher.overloads.values())[0]
+        return cres.entry_point
+
+    def check_local_func_lifetime(self, **jitargs):
+        def f(x):
+            return x + 1
+
+        c_f = jit('int32(int32)', **jitargs)(f)
+        self.assertPreciseEqual(c_f(1), 2)
+
+        cfunc = self.get_impl(c_f)
+
+        # Since we can't take a weakref to a C function object
+        # (see http://bugs.python.org/issue22116), ensure it's
+        # collected by taking a weakref to its __self__ instead
+        # (a _dynfunc._Closure object).
+        refs = [weakref.ref(obj) for obj in (f, c_f, cfunc.__self__)]
+        obj = f = c_f = cfunc = None
+        gc.collect()
+        self.assertEqual([wr() for wr in refs], [None] * len(refs))
+
+    def test_local_func_lifetime(self):
+        self.check_local_func_lifetime(forceobj=True)
+
+    def test_local_func_lifetime_npm(self):
+        self.check_local_func_lifetime(nopython=True)
+
+    def check_global_func_lifetime(self, **jitargs):
+        c_f = jit(**jitargs)(global_usecase1)
+        self.assertPreciseEqual(c_f(1), 2)
+
+        cfunc = self.get_impl(c_f)
+
+        wr = weakref.ref(c_f)
+        refs = [weakref.ref(obj) for obj in (c_f, cfunc.__self__)]
+        obj = c_f = cfunc = None
+        gc.collect()
+        self.assertEqual([wr() for wr in refs], [None] * len(refs))
+
+    def test_global_func_lifetime(self):
+        self.check_global_func_lifetime(forceobj=True)
+
+    def test_global_func_lifetime_npm(self):
+        self.check_global_func_lifetime(nopython=True)
+
+    def check_global_obj_lifetime(self, **jitargs):
+        # Since global objects can be recorded for typing purposes,
+        # check that they are not kept around after they are removed
+        # from the globals.
+        global global_obj
+        global_obj = Dummy()
+
+        c_f = jit(**jitargs)(global_usecase2)
+        self.assertPreciseEqual(c_f(), 6)
+
+        refs = [weakref.ref(obj) for obj in (c_f, global_obj)]
+        obj = c_f = global_obj = None
+        gc.collect()
+        self.assertEqual([wr() for wr in refs], [None] * len(refs))
+
+    def test_global_obj_lifetime(self):
+        self.check_global_obj_lifetime(forceobj=True)
+
+    def check_inner_function_lifetime(self, **jitargs):
+        """
+        When a jitted function calls into another jitted function, check
+        that everything is collected as desired.
+        """
+        def mult_10(a):
+            return a * 10
+
+        c_mult_10 = jit('intp(intp)', **jitargs)(mult_10)
+        c_mult_10.disable_compile()
+
+        def do_math(x):
+            return c_mult_10(x + 4)
+
+        c_do_math = jit('intp(intp)', **jitargs)(do_math)
+        c_do_math.disable_compile()
+
+        self.assertEqual(c_do_math(1), 50)
+
+        wrs = [weakref.ref(obj) for obj in
+               (mult_10, c_mult_10, do_math, c_do_math,
+                self.get_impl(c_mult_10).__self__,
+                self.get_impl(c_do_math).__self__,
+                )]
+        obj = mult_10 = c_mult_10 = do_math = c_do_math = None
+        gc.collect()
+        self.assertEqual([w() for w in wrs], [None] * len(wrs))
+
+    def test_inner_function_lifetime(self):
+        self.check_inner_function_lifetime(forceobj=True)
+
+    def test_inner_function_lifetime_npm(self):
+        self.check_inner_function_lifetime(nopython=True)
+
+
+class TestLifeTimeIssue(TestCase):
+    def test_double_free(self):
+        from numba import njit
+        import numpy as np
+
+        # This is the function that causes the crash
+
+        @njit
+        def is_point_in_polygons(point, polygons):
+            num_polygons = polygons.shape[0]
+            if num_polygons != 0:
+                # An extra decref is inserted in this block
+                intentionally_unused_variable = polygons[0]
+            return 0
+
+        # This function creates some NRT objects for the previous function
+        # to corrupt.
+
+        @njit
+        def dummy():
+            return np.empty(10, dtype=np.int64)
+
+        polygons = np.array([[[0, 1]]])
+        points = np.array([[-1.5, 0.5]])
+        a = dummy()
+        is_point_in_polygons(points[0], polygons)
+        b = dummy()
+        # Crash happens at second call
+        is_point_in_polygons(points[0], polygons)
+        c = dummy()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_funcdesc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_funcdesc.py
new file mode 100644
index 000000000..e5e899bfe
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_funcdesc.py
@@ -0,0 +1,61 @@
+
+import unittest
+
+from numba import njit
+from numba.core.funcdesc import PythonFunctionDescriptor, default_mangler
+from numba.core.compiler import run_frontend
+from numba.core.itanium_mangler import mangle_abi_tag
+
+
+class TestModule(unittest.TestCase):
+    def test_module_not_in_namespace(self):
+        """ Test of trying to run a compiled function
+        where the module from which the function is being compiled
+        doesn't exist in the namespace.
+        """
+        filename = 'test.py'
+        name = 'mypackage'
+        code = """
+def f(x):
+    return x
+"""
+
+        objs = dict(__file__=filename, __name__=name)
+        compiled = compile(code, filename, 'exec')
+        exec(compiled, objs)
+
+        compiled_f = njit(objs['f'])
+        self.assertEqual(compiled_f(3), 3)
+
+
+class TestFuncDescMangledName(unittest.TestCase):
+    def test_mangling_abi_tags(self):
+        """
+        This is a minimal test for the abi-tags support in the mangler.
+        """
+        def udt():
+            pass
+
+        # run minimal frontend to create a function descriptor
+        func_ir = run_frontend(udt)
+        typemap = {}
+        restype = None
+        calltypes = ()
+        mangler = default_mangler
+        inline = False
+        noalias = False
+
+        abi_tags = ("Shrubbery", "Herring")
+
+        fd = PythonFunctionDescriptor.from_specialized_function(
+            func_ir, typemap, restype, calltypes, mangler, inline, noalias,
+            abi_tags=abi_tags,
+        )
+
+        # mangled tag must exist in the mangled name
+        self.assertIn("".join([mangle_abi_tag(x) for x in abi_tags]),
+                      fd.mangled_name)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_function_type.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_function_type.py
new file mode 100644
index 000000000..d4b74d2ee
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_function_type.py
@@ -0,0 +1,1261 @@
+import unittest
+import types as pytypes
+from numba import jit, njit, cfunc, types, int64, float64, float32, errors
+from numba import literal_unroll
+from numba.core.config import IS_32BITS, IS_WIN32
+import ctypes
+import warnings
+
+from .support import TestCase
+
+
+def dump(foo):  # FOR DEBUGGING, TO BE REMOVED
+    from numba.core import function
+    foo_type = function.fromobject(foo)
+    foo_sig = foo_type.signature()
+    foo.compile(foo_sig)
+    print('{" LLVM IR OF "+foo.__name__+" ":*^70}')
+    print(foo.inspect_llvm(foo_sig.args))
+    print('{"":*^70}')
+
+
+# Decorators for transforming a Python function to different kinds of
+# functions:
+
+def mk_cfunc_func(sig):
+    def cfunc_func(func):
+        assert isinstance(func, pytypes.FunctionType), repr(func)
+        f = cfunc(sig)(func)
+        f.pyfunc = func
+        return f
+    return cfunc_func
+
+
+def njit_func(func):
+    assert isinstance(func, pytypes.FunctionType), repr(func)
+    f = jit(nopython=True)(func)
+    f.pyfunc = func
+    return f
+
+
+def mk_njit_with_sig_func(sig):
+    def njit_with_sig_func(func):
+        assert isinstance(func, pytypes.FunctionType), repr(func)
+        f = jit(sig, nopython=True)(func)
+        f.pyfunc = func
+        return f
+    return njit_with_sig_func
+
+
+def mk_ctypes_func(sig):
+    def ctypes_func(func, sig=int64(int64)):
+        assert isinstance(func, pytypes.FunctionType), repr(func)
+        cfunc = mk_cfunc_func(sig)(func)
+        addr = cfunc._wrapper_address
+        if sig == int64(int64):
+            f = ctypes.CFUNCTYPE(ctypes.c_int64)(addr)
+            f.pyfunc = func
+            return f
+        raise NotImplementedError(
+            f'ctypes decorator for {func} with signature {sig}')
+    return ctypes_func
+
+
+class WAP(types.WrapperAddressProtocol):
+    """An example implementation of wrapper address protocol.
+
+    """
+    def __init__(self, func, sig):
+        self.pyfunc = func
+        self.cfunc = cfunc(sig)(func)
+        self.sig = sig
+
+    def __wrapper_address__(self):
+        return self.cfunc._wrapper_address
+
+    def signature(self):
+        return self.sig
+
+    def __call__(self, *args, **kwargs):
+        return self.pyfunc(*args, **kwargs)
+
+
+def mk_wap_func(sig):
+    def wap_func(func):
+        return WAP(func, sig)
+    return wap_func
+
+
+class TestFunctionType(TestCase):
+    """Test first-class functions in the context of a Numba jit compiled
+    function.
+
+    """
+
+    def test_in__(self):
+        """Function is passed in as an argument.
+        """
+
+        def a(i):
+            return i + 1
+
+        def foo(f):
+            return 0
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig),
+                      njit_func,
+                      mk_njit_with_sig_func(sig),
+                      mk_ctypes_func(sig),
+                      mk_wap_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__, jit=jit_opts):
+                    a_ = decor(a)
+                    self.assertEqual(jit_(foo)(a_), foo(a))
+
+    def test_in_call__(self):
+        """Function is passed in as an argument and called.
+        Also test different return values.
+        """
+
+        def a_i64(i):
+            return i + 1234567
+
+        def a_f64(i):
+            return i + 1.5
+
+        def a_str(i):
+            return "abc"
+
+        def foo(f):
+            return f(123)
+
+        for f, sig in [(a_i64, int64(int64)), (a_f64, float64(int64))]:
+            for decor in [mk_cfunc_func(sig), njit_func,
+                          mk_njit_with_sig_func(sig),
+                          mk_wap_func(sig)]:
+                for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                    jit_ = jit(**jit_opts)
+                    with self.subTest(
+                            sig=sig, decor=decor.__name__, jit=jit_opts):
+                        f_ = decor(f)
+                        self.assertEqual(jit_(foo)(f_), foo(f))
+
+    def test_in_call_out(self):
+        """Function is passed in as an argument, called, and returned.
+        """
+
+        def a(i):
+            return i + 1
+
+        def foo(f):
+            f(123)
+            return f
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), njit_func,
+                      mk_njit_with_sig_func(sig), mk_wap_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    r1 = jit_(foo)(a_).pyfunc
+                    r2 = foo(a)
+                    self.assertEqual(r1, r2)
+
+    def test_in_seq_call(self):
+        """Functions are passed in as arguments, used as tuple items, and
+        called.
+
+        """
+        def a(i):
+            return i + 1
+
+        def b(i):
+            return i + 2
+
+        def foo(f, g):
+            r = 0
+            for f_ in (f, g):
+                r = r + f_(r)
+            return r
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), mk_wap_func(sig),
+                      mk_njit_with_sig_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    b_ = decor(b)
+                    self.assertEqual(jit_(foo)(a_, b_), foo(a, b))
+
+    def test_in_ns_seq_call(self):
+        """Functions are passed in as an argument and via namespace scoping
+        (mixed pathways), used as tuple items, and called.
+
+        """
+
+        def a(i):
+            return i + 1
+
+        def b(i):
+            return i + 2
+
+        def mkfoo(b_):
+            def foo(f):
+                r = 0
+                for f_ in (f, b_):
+                    r = r + f_(r)
+                return r
+            return foo
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig),
+                      mk_njit_with_sig_func(sig), mk_wap_func(sig),
+                      mk_ctypes_func(sig)][:-1]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    b_ = decor(b)
+                    self.assertEqual(jit_(mkfoo(b_))(a_), mkfoo(b)(a))
+
+    def test_ns_call(self):
+        """Function is passed in via namespace scoping and called.
+
+        """
+
+        def a(i):
+            return i + 1
+
+        def mkfoo(a_):
+            def foo():
+                return a_(123)
+            return foo
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), njit_func,
+                      mk_njit_with_sig_func(sig), mk_wap_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    self.assertEqual(jit_(mkfoo(a_))(), mkfoo(a)())
+
+    def test_ns_out(self):
+        """Function is passed in via namespace scoping and returned.
+
+        """
+        def a(i):
+            return i + 1
+
+        def mkfoo(a_):
+            def foo():
+                return a_
+            return foo
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), njit_func,
+                      mk_njit_with_sig_func(sig), mk_wap_func(sig),
+                      mk_ctypes_func(sig)][:-1]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    self.assertEqual(jit_(mkfoo(a_))().pyfunc, mkfoo(a)())
+
+    def test_ns_call_out(self):
+        """Function is passed in via namespace scoping, called, and then
+        returned.
+
+        """
+        def a(i):
+            return i + 1
+
+        def mkfoo(a_):
+            def foo():
+                a_(123)
+                return a_
+            return foo
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), njit_func,
+                      mk_njit_with_sig_func(sig), mk_wap_func(sig),
+                      mk_ctypes_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+            with self.subTest(decor=decor.__name__):
+                a_ = decor(a)
+                self.assertEqual(jit_(mkfoo(a_))().pyfunc, mkfoo(a)())
+
+    def test_in_overload(self):
+        """Function is passed in as an argument and called with different
+        argument types.
+
+        """
+        def a(i):
+            return i + 1
+
+        def foo(f):
+            r1 = f(123)
+            r2 = f(123.45)
+            return (r1, r2)
+
+        for decor in [njit_func]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    self.assertEqual(jit_(foo)(a_), foo(a))
+
+    def test_ns_overload(self):
+        """Function is passed in via namespace scoping and called with
+        different argument types.
+
+        """
+        def a(i):
+            return i + 1
+
+        def mkfoo(a_):
+            def foo():
+                r1 = a_(123)
+                r2 = a_(123.45)
+                return (r1, r2)
+            return foo
+
+        for decor in [njit_func]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    self.assertEqual(jit_(mkfoo(a_))(), mkfoo(a)())
+
+    def test_in_choose(self):
+        """Functions are passed in as arguments and called conditionally.
+
+        """
+        def a(i):
+            return i + 1
+
+        def b(i):
+            return i + 2
+
+        def foo(a, b, choose_left):
+            if choose_left:
+                r = a(1)
+            else:
+                r = b(2)
+            return r
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), njit_func,
+                      mk_njit_with_sig_func(sig), mk_wap_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    b_ = decor(b)
+                    self.assertEqual(jit_(foo)(a_, b_, True), foo(a, b, True))
+                    self.assertEqual(jit_(foo)(a_, b_, False),
+                                     foo(a, b, False))
+                    self.assertNotEqual(jit_(foo)(a_, b_, True),
+                                        foo(a, b, False))
+
+    def test_ns_choose(self):
+        """Functions are passed in via namespace scoping and called
+        conditionally.
+
+        """
+        def a(i):
+            return i + 1
+
+        def b(i):
+            return i + 2
+
+        def mkfoo(a_, b_):
+            def foo(choose_left):
+                if choose_left:
+                    r = a_(1)
+                else:
+                    r = b_(2)
+                return r
+            return foo
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), njit_func,
+                      mk_njit_with_sig_func(sig), mk_wap_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    b_ = decor(b)
+                    self.assertEqual(jit_(mkfoo(a_, b_))(True),
+                                     mkfoo(a, b)(True))
+                    self.assertEqual(jit_(mkfoo(a_, b_))(False),
+                                     mkfoo(a, b)(False))
+                    self.assertNotEqual(jit_(mkfoo(a_, b_))(True),
+                                        mkfoo(a, b)(False))
+
+    def test_in_choose_out(self):
+        """Functions are passed in as arguments and returned conditionally.
+
+        """
+        def a(i):
+            return i + 1
+
+        def b(i):
+            return i + 2
+
+        def foo(a, b, choose_left):
+            if choose_left:
+                return a
+            else:
+                return b
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), njit_func,
+                      mk_njit_with_sig_func(sig), mk_wap_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    b_ = decor(b)
+                    self.assertEqual(jit_(foo)(a_, b_, True).pyfunc,
+                                     foo(a, b, True))
+                    self.assertEqual(jit_(foo)(a_, b_, False).pyfunc,
+                                     foo(a, b, False))
+                    self.assertNotEqual(jit_(foo)(a_, b_, True).pyfunc,
+                                        foo(a, b, False))
+
+    def test_in_choose_func_value(self):
+        """Functions are passed in as arguments, selected conditionally and
+        called.
+
+        """
+        def a(i):
+            return i + 1
+
+        def b(i):
+            return i + 2
+
+        def foo(a, b, choose_left):
+            if choose_left:
+                f = a
+            else:
+                f = b
+            return f(1)
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), mk_wap_func(sig), njit_func,
+                      mk_njit_with_sig_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    b_ = decor(b)
+                    self.assertEqual(jit_(foo)(a_, b_, True), foo(a, b, True))
+                    self.assertEqual(jit_(foo)(a_, b_, False),
+                                     foo(a, b, False))
+                    self.assertNotEqual(jit_(foo)(a_, b_, True),
+                                        foo(a, b, False))
+
+    def test_in_pick_func_call(self):
+        """Functions are passed in as items of tuple argument, retrieved via
+        indexing, and called.
+
+        """
+        def a(i):
+            return i + 1
+
+        def b(i):
+            return i + 2
+
+        def foo(funcs, i):
+            f = funcs[i]
+            r = f(123)
+            return r
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), mk_wap_func(sig),
+                      mk_njit_with_sig_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    b_ = decor(b)
+                    self.assertEqual(jit_(foo)((a_, b_), 0), foo((a, b), 0))
+                    self.assertEqual(jit_(foo)((a_, b_), 1), foo((a, b), 1))
+                    self.assertNotEqual(jit_(foo)((a_, b_), 0), foo((a, b), 1))
+
+    def test_in_iter_func_call(self):
+        """Functions are passed in as items of tuple argument, retrieved via
+        indexing, and called within a variable for-loop.
+
+        """
+        def a(i):
+            return i + 1
+
+        def b(i):
+            return i + 2
+
+        def foo(funcs, n):
+            r = 0
+            for i in range(n):
+                f = funcs[i]
+                r = r + f(r)
+            return r
+
+        sig = int64(int64)
+
+        for decor in [mk_cfunc_func(sig), mk_wap_func(sig),
+                      mk_njit_with_sig_func(sig)]:
+            for jit_opts in [dict(nopython=True), dict(forceobj=True)]:
+                jit_ = jit(**jit_opts)
+                with self.subTest(decor=decor.__name__):
+                    a_ = decor(a)
+                    b_ = decor(b)
+                    self.assertEqual(jit_(foo)((a_, b_), 2), foo((a, b), 2))
+
+    def test_experimental_feature_warning(self):
+        @jit(nopython=True)
+        def more(x):
+            return x + 1
+
+        @jit(nopython=True)
+        def less(x):
+            return x - 1
+
+        @jit(nopython=True)
+        def foo(sel, x):
+            fn = more if sel else less
+            return fn(x)
+
+        with warnings.catch_warnings(record=True) as ws:
+            warnings.simplefilter("always")
+            res = foo(True, 10)
+
+        self.assertEqual(res, 11)
+        self.assertEqual(foo(False, 10), 9)
+
+        self.assertGreaterEqual(len(ws), 1)
+        pat = "First-class function type feature is experimental"
+        for w in ws:
+            if pat in str(w.message):
+                break
+        else:
+            self.fail("missing warning")
+
+
+class TestFunctionTypeExtensions(TestCase):
+    """Test calling external library functions within Numba jit compiled
+    functions.
+
+    """
+
+    def test_wrapper_address_protocol_libm(self):
+        """Call cos and sinf from standard math library.
+
+        """
+        import ctypes.util
+
+        class LibM(types.WrapperAddressProtocol):
+
+            def __init__(self, fname):
+                if IS_WIN32:
+                    lib = ctypes.cdll.msvcrt
+                else:
+                    libpath = ctypes.util.find_library('m')
+                    lib = ctypes.cdll.LoadLibrary(libpath)
+                self.lib = lib
+                self._name = fname
+                if fname == 'cos':
+                    # test for double-precision math function
+                    if IS_WIN32 and IS_32BITS:
+                        # 32-bit Windows math library does not provide
+                        # a double-precision cos function, so
+                        # disabling the function
+                        addr = None
+                        signature = None
+                    else:
+                        addr = ctypes.cast(self.lib.cos, ctypes.c_voidp).value
+                        signature = float64(float64)
+                elif fname == 'sinf':
+                    # test for single-precision math function
+                    if IS_WIN32 and IS_32BITS:
+                        # 32-bit Windows math library provides sin
+                        # (instead of sinf) that is a single-precision
+                        # sin function
+                        addr = ctypes.cast(self.lib.sin, ctypes.c_voidp).value
+                    else:
+                        # Other 32/64 bit platforms define sinf as the
+                        # single-precision sin function
+                        addr = ctypes.cast(self.lib.sinf, ctypes.c_voidp).value
+                    signature = float32(float32)
+                else:
+                    raise NotImplementedError(
+                        f'wrapper address of `{fname}`'
+                        f' with signature `{signature}`')
+                self._signature = signature
+                self._address = addr
+
+            def __repr__(self):
+                return f'{type(self).__name__}({self._name!r})'
+
+            def __wrapper_address__(self):
+                return self._address
+
+            def signature(self):
+                return self._signature
+
+        mycos = LibM('cos')
+        mysin = LibM('sinf')
+
+        def myeval(f, x):
+            return f(x)
+
+        # Not testing forceobj=True as it requires implementing
+        # LibM.__call__ using ctypes which would be out-of-scope here.
+        for jit_opts in [dict(nopython=True)]:
+            jit_ = jit(**jit_opts)
+            with self.subTest(jit=jit_opts):
+                if mycos.signature() is not None:
+                    self.assertEqual(jit_(myeval)(mycos, 0.0), 1.0)
+                if mysin.signature() is not None:
+                    self.assertEqual(jit_(myeval)(mysin, float32(0.0)), 0.0)
+
+    def test_compilation_results(self):
+        """Turn the existing compilation results of a dispatcher instance to
+        first-class functions with precise types.
+        """
+
+        @jit(nopython=True)
+        def add_template(x, y):
+            return x + y
+
+        # Trigger compilations
+        self.assertEqual(add_template(1, 2), 3)
+        self.assertEqual(add_template(1.2, 3.4), 4.6)
+
+        cres1, cres2 = add_template.overloads.values()
+
+        # Turn compilation results into first-class functions
+        iadd = types.CompileResultWAP(cres1)
+        fadd = types.CompileResultWAP(cres2)
+
+        @jit(nopython=True)
+        def foo(add, x, y):
+            return add(x, y)
+
+        @jit(forceobj=True)
+        def foo_obj(add, x, y):
+            return add(x, y)
+
+        self.assertEqual(foo(iadd, 3, 4), 7)
+        self.assertEqual(foo(fadd, 3.4, 4.5), 7.9)
+
+        self.assertEqual(foo_obj(iadd, 3, 4), 7)
+        self.assertEqual(foo_obj(fadd, 3.4, 4.5), 7.9)
+
+
+class TestMiscIssues(TestCase):
+    """Test issues of using first-class functions in the context of Numba
+    jit compiled functions.
+
+    """
+
+    def test_issue_3405_using_cfunc(self):
+
+        @cfunc('int64()')
+        def a():
+            return 2
+
+        @cfunc('int64()')
+        def b():
+            return 3
+
+        def g(arg):
+            if arg:
+                f = a
+            else:
+                f = b
+            return f()
+
+        self.assertEqual(jit(nopython=True)(g)(True), 2)
+        self.assertEqual(jit(nopython=True)(g)(False), 3)
+
+    def test_issue_3405_using_njit(self):
+
+        @jit(nopython=True)
+        def a():
+            return 2
+
+        @jit(nopython=True)
+        def b():
+            return 3
+
+        def g(arg):
+            if not arg:
+                f = b
+            else:
+                f = a
+            return f()
+
+        self.assertEqual(jit(nopython=True)(g)(True), 2)
+        self.assertEqual(jit(nopython=True)(g)(False), 3)
+
+    def test_pr4967_example(self):
+
+        @cfunc('int64(int64)')
+        def a(i):
+            return i + 1
+
+        @cfunc('int64(int64)')
+        def b(i):
+            return i + 2
+
+        @jit(nopython=True)
+        def foo(f, g):
+            i = f(2)
+            seq = (f, g)
+            for fun in seq:
+                i += fun(i)
+            return i
+
+        a_ = a._pyfunc
+        b_ = b._pyfunc
+        self.assertEqual(foo(a, b),
+                         a_(2) + a_(a_(2)) + b_(a_(2) + a_(a_(2))))
+
+    def test_pr4967_array(self):
+        import numpy as np
+
+        @cfunc("intp(intp[:], float64[:])")
+        def foo1(x, y):
+            return x[0] + y[0]
+
+        @cfunc("intp(intp[:], float64[:])")
+        def foo2(x, y):
+            return x[0] - y[0]
+
+        def bar(fx, fy, i):
+            a = np.array([10], dtype=np.intp)
+            b = np.array([12], dtype=np.float64)
+            if i == 0:
+                f = fx
+            elif i == 1:
+                f = fy
+            else:
+                return
+            return f(a, b)
+
+        r = jit(nopython=True, no_cfunc_wrapper=True)(bar)(foo1, foo2, 0)
+        self.assertEqual(r, bar(foo1, foo2, 0))
+        self.assertNotEqual(r, bar(foo1, foo2, 1))
+
+    def test_reference_example(self):
+        import numba
+
+        @numba.njit
+        def composition(funcs, x):
+            r = x
+            for f in funcs[::-1]:
+                r = f(r)
+            return r
+
+        @numba.cfunc("double(double)")
+        def a(x):
+            return x + 1.0
+
+        @numba.njit()
+        def b(x):
+            return x * x
+
+        r = composition((a, b, b, a), 0.5)
+        self.assertEqual(r, (0.5 + 1.0) ** 4 + 1.0)
+
+        r = composition((b, a, b, b, a), 0.5)
+        self.assertEqual(r, ((0.5 + 1.0) ** 4 + 1.0) ** 2)
+
+    def test_apply_function_in_function(self):
+
+        def foo(f, f_inner):
+            return f(f_inner)
+
+        @cfunc('int64(float64)')
+        def f_inner(i):
+            return int64(i * 3)
+
+        @cfunc(int64(types.FunctionType(f_inner._sig)))
+        def f(f_inner):
+            return f_inner(123.4)
+
+        self.assertEqual(jit(nopython=True)(foo)(f, f_inner),
+                         foo(f._pyfunc, f_inner._pyfunc))
+
+    def test_function_with_none_argument(self):
+
+        @cfunc(int64(types.none))
+        def a(i):
+            return 1
+
+        @jit(nopython=True)
+        def foo(f):
+            return f(None)
+
+        self.assertEqual(foo(a), 1)
+
+    def test_constant_functions(self):
+
+        @jit(nopython=True)
+        def a():
+            return 123
+
+        @jit(nopython=True)
+        def b():
+            return 456
+
+        @jit(nopython=True)
+        def foo():
+            return a() + b()
+
+        r = foo()
+        if r != 123 + 456:
+            print(foo.overloads[()].library.get_llvm_str())
+        self.assertEqual(r, 123 + 456)
+
+    def test_generators(self):
+
+        @jit(forceobj=True)
+        def gen(xs):
+            for x in xs:
+                x += 1
+                yield x
+
+        @jit(forceobj=True)
+        def con(gen_fn, xs):
+            return [it for it in gen_fn(xs)]
+
+        self.assertEqual(con(gen, (1, 2, 3)), [2, 3, 4])
+
+        @jit(nopython=True)
+        def gen_(xs):
+            for x in xs:
+                x += 1
+                yield x
+        self.assertEqual(con(gen_, (1, 2, 3)), [2, 3, 4])
+
+    def test_jit_support(self):
+
+        @jit(nopython=True)
+        def foo(f, x):
+            return f(x)
+
+        @jit()
+        def a(x):
+            return x + 1
+
+        @jit()
+        def a2(x):
+            return x - 1
+
+        @jit()
+        def b(x):
+            return x + 1.5
+
+        self.assertEqual(foo(a, 1), 2)
+        a2(5)  # pre-compile
+        self.assertEqual(foo(a2, 2), 1)
+        self.assertEqual(foo(a2, 3), 2)
+        self.assertEqual(foo(a, 2), 3)
+        self.assertEqual(foo(a, 1.5), 2.5)
+        self.assertEqual(foo(a2, 1), 0)
+        self.assertEqual(foo(a, 2.5), 3.5)
+        self.assertEqual(foo(b, 1.5), 3.0)
+        self.assertEqual(foo(b, 1), 2.5)
+
+    def test_signature_mismatch(self):
+        @jit(nopython=True)
+        def f1(x):
+            return x
+
+        @jit(nopython=True)
+        def f2(x):
+            return x
+
+        @jit(nopython=True)
+        def foo(disp1, disp2, sel):
+            if sel == 1:
+                fn = disp1
+            else:
+                fn = disp2
+            return fn([1]), fn(2)
+
+        with self.assertRaises(errors.UnsupportedError) as cm:
+            foo(f1, f2, sel=1)
+        self.assertRegex(
+            str(cm.exception), 'mismatch of function types:')
+
+        # this works because `sel == 1` condition is optimized away:
+        self.assertEqual(foo(f1, f1, sel=1), ([1], 2))
+
+    def test_unique_dispatcher(self):
+        # In general, the type of a dispatcher instance is imprecise
+        # and when used as an input to type-inference, the typing will
+        # likely fail. However, if a dispatcher instance contains
+        # exactly one overload and compilation is disabled for the dispatcher,
+        # then the type of dispatcher instance is interpreted as precise
+        # and is transformed to a FunctionType instance with the defined
+        # signature of the single overload.
+
+        def foo_template(funcs, x):
+            r = x
+            for f in funcs:
+                r = f(r)
+            return r
+
+        a = jit(nopython=True)(lambda x: x + 1)
+        b = jit(nopython=True)(lambda x: x + 2)
+        foo = jit(nopython=True)(foo_template)
+
+        # compiling and disabling compilation for `a` is sufficient,
+        # `b` will inherit its type from the container Tuple type
+        a(0)
+        a.disable_compile()
+
+        r = foo((a, b), 0)
+        self.assertEqual(r, 3)
+        # the Tuple type of foo's first argument is a precise FunctionType:
+        self.assertEqual(foo.signatures[0][0].dtype.is_precise(), True)
+
+    def test_zero_address(self):
+
+        sig = int64()
+
+        @cfunc(sig)
+        def test():
+            return 123
+
+        class Good(types.WrapperAddressProtocol):
+            """A first-class function type with valid address.
+            """
+
+            def __wrapper_address__(self):
+                return test.address
+
+            def signature(self):
+                return sig
+
+        class Bad(types.WrapperAddressProtocol):
+            """A first-class function type with invalid 0 address.
+            """
+
+            def __wrapper_address__(self):
+                return 0
+
+            def signature(self):
+                return sig
+
+        class BadToGood(types.WrapperAddressProtocol):
+            """A first-class function type with invalid address that is
+            recovered to a valid address.
+            """
+
+            counter = -1
+
+            def __wrapper_address__(self):
+                self.counter += 1
+                return test.address * min(1, self.counter)
+
+            def signature(self):
+                return sig
+
+        good = Good()
+        bad = Bad()
+        bad2good = BadToGood()
+
+        @jit(int64(sig.as_type()))
+        def foo(func):
+            return func()
+
+        @jit(int64())
+        def foo_good():
+            return good()
+
+        @jit(int64())
+        def foo_bad():
+            return bad()
+
+        @jit(int64())
+        def foo_bad2good():
+            return bad2good()
+
+        self.assertEqual(foo(good), 123)
+
+        self.assertEqual(foo_good(), 123)
+
+        with self.assertRaises(ValueError) as cm:
+            foo(bad)
+        self.assertRegex(
+            str(cm.exception),
+            'wrapper address of <.*> instance must be a positive')
+
+        with self.assertRaises(RuntimeError) as cm:
+            foo_bad()
+        self.assertRegex(
+            str(cm.exception), r'.* function address is null')
+
+        self.assertEqual(foo_bad2good(), 123)
+
+    def test_issue_5470(self):
+
+        @njit()
+        def foo1():
+            return 10
+
+        @njit()
+        def foo2():
+            return 20
+
+        formulae_foo = (foo1, foo1)
+
+        @njit()
+        def bar_scalar(f1, f2):
+            return f1() + f2()
+
+        @njit()
+        def bar():
+            return bar_scalar(*formulae_foo)
+
+        self.assertEqual(bar(), 20)
+
+        formulae_foo = (foo1, foo2)
+
+        @njit()
+        def bar():
+            return bar_scalar(*formulae_foo)
+
+        self.assertEqual(bar(), 30)
+
+    def test_issue_5540(self):
+
+        @njit(types.int64(types.int64))
+        def foo(x):
+            return x + 1
+
+        @njit
+        def bar_bad(foos):
+            f = foos[0]
+            return f(x=1)
+
+        @njit
+        def bar_good(foos):
+            f = foos[0]
+            return f(1)
+
+        self.assertEqual(bar_good((foo, )), 2)
+
+        # new/old style error handling
+        with self.assertRaises((errors.UnsupportedError,
+                                errors.TypingError)) as cm:
+            bar_bad((foo, ))
+
+        self.assertRegex(
+            str(cm.exception),
+            r'.*first-class function call cannot use keyword arguments')
+
+    def test_issue_5615(self):
+
+        @njit
+        def foo1(x):
+            return x + 1
+
+        @njit
+        def foo2(x):
+            return x + 2
+
+        @njit
+        def bar(fcs):
+            x = 0
+            a = 10
+            i, j = fcs[0]
+            x += i(j(a))
+            for t in literal_unroll(fcs):
+                i, j = t
+                x += i(j(a))
+            return x
+
+        tup = ((foo1, foo2), (foo2, foo1))
+
+        self.assertEqual(bar(tup), 39)
+
+    def test_issue_5685(self):
+
+        @njit
+        def foo1():
+            return 1
+
+        @njit
+        def foo2(x):
+            return x + 1
+
+        @njit
+        def foo3(x):
+            return x + 2
+
+        @njit
+        def bar(fcs):
+            r = 0
+            for pair in literal_unroll(fcs):
+                f1, f2 = pair
+                r += f1() + f2(2)
+            return r
+
+        self.assertEqual(bar(((foo1, foo2),)), 4)
+        self.assertEqual(bar(((foo1, foo2), (foo1, foo3))), 9)  # reproducer
+
+
+class TestBasicSubtyping(TestCase):
+    def test_basic(self):
+        """
+        Test that a dispatcher object *with* a pre-compiled overload
+        can be used as input to another function with locked-down signature
+        """
+        a = 1
+
+        @njit
+        def foo(x):
+            return x + 1
+
+        foo(a)
+        int_int_fc = types.FunctionType(types.int64(types.int64,))
+
+        @njit(types.int64(int_int_fc))
+        def bar(fc):
+            return fc(a)
+
+        self.assertEqual(bar(foo), foo(a))
+
+    def test_basic2(self):
+        """
+        Test that a dispatcher object *without* a pre-compiled overload
+        can be used as input to another function with locked-down signature
+        """
+        a = 1
+
+        @njit
+        def foo(x):
+            return x + 1
+
+        int_int_fc = types.FunctionType(types.int64(types.int64,))
+
+        @njit(types.int64(int_int_fc))
+        def bar(fc):
+            return fc(a)
+
+        self.assertEqual(bar(foo), foo(a))
+
+    def test_basic3(self):
+        """
+        Test that a dispatcher object *without* a pre-compiled overload
+        can be used as input to another function with locked-down signature and
+        that it behaves as a truly generic function (foo1 does not get locked)
+        """
+        a = 1
+
+        @njit
+        def foo1(x):
+            return x + 1
+
+        @njit
+        def foo2(x):
+            return x + 2
+
+        int_int_fc = types.FunctionType(types.int64(types.int64,))
+
+        @njit(types.int64(int_int_fc))
+        def bar(fc):
+            return fc(a)
+
+        self.assertEqual(bar(foo1) + 1, bar(foo2))
+
+    def test_basic4(self):
+        """
+        Test that a dispatcher object can be used as input to another
+         function with signature as part of a tuple
+        """
+        a = 1
+
+        @njit
+        def foo1(x):
+            return x + 1
+
+        @njit
+        def foo2(x):
+            return x + 2
+
+        tup = (foo1, foo2)
+        int_int_fc = types.FunctionType(types.int64(types.int64,))
+
+        @njit(types.int64(types.UniTuple(int_int_fc, 2)))
+        def bar(fcs):
+            x = 0
+            for i in range(2):
+                x += fcs[i](a)
+            return x
+        self.assertEqual(bar(tup), foo1(a) + foo2(a))
+
+    def test_basic5(self):
+        a = 1
+
+        @njit
+        def foo1(x):
+            return x + 1
+
+        @njit
+        def foo2(x):
+            return x + 2
+
+        @njit
+        def bar1(x):
+            return x / 10
+
+        @njit
+        def bar2(x):
+            return x / 1000
+
+        tup = (foo1, foo2)
+        tup_bar = (bar1, bar2)
+        int_int_fc = types.FunctionType(types.int64(types.int64,))
+
+        flt_flt_fc = types.FunctionType(types.float64(types.float64,))
+
+        @njit((types.UniTuple(int_int_fc, 2), types.UniTuple(flt_flt_fc, 2)))
+        def bar(fcs, ffs):
+            x = 0
+            for i in range(2):
+                x += fcs[i](a)
+            for fn in ffs:
+                x += fn(a)
+            return x
+
+        got = bar(tup, tup_bar)
+        expected = foo1(a) + foo2(a) + bar1(a) + bar2(a)
+        self.assertEqual(got, expected)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gdb_bindings.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gdb_bindings.py
new file mode 100644
index 000000000..cc87f6ac1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gdb_bindings.py
@@ -0,0 +1,271 @@
+"""
+Tests gdb bindings
+"""
+import os
+import platform
+import subprocess
+import sys
+import threading
+from itertools import permutations
+
+from numba import njit, gdb, gdb_init, gdb_breakpoint, prange
+from numba.core import errors
+from numba import jit
+
+from numba.tests.support import (TestCase, captured_stdout, tag,
+                                 skip_parfors_unsupported)
+from numba.tests.gdb_support import needs_gdb
+import unittest
+
+
+_platform = sys.platform
+
+_unix_like = (_platform.startswith('linux')
+              or _platform.startswith('darwin')
+              or ('bsd' in _platform))
+
+unix_only = unittest.skipUnless(_unix_like, "unix-like OS is required")
+not_unix = unittest.skipIf(_unix_like, "non unix-like OS is required")
+
+_arch_name = platform.machine()
+_is_arm = _arch_name in {'aarch64', 'armv7l'}
+not_arm = unittest.skipIf(_is_arm, "testing disabled on ARM")
+
+_gdb_cond = os.environ.get('GDB_TEST', None) == '1'
+needs_gdb_harness = unittest.skipUnless(_gdb_cond, "needs gdb harness")
+
+long_running = tag('long_running')
+
+_dbg_njit = njit(debug=True)
+_dbg_jit = jit(forceobj=True, debug=True)
+
+
+def impl_gdb_call(a):
+    gdb('-ex', 'set confirm off', '-ex', 'c', '-ex', 'q')
+    b = a + 1
+    c = a * 2.34
+    d = (a, b, c)
+    print(a, b, c, d)
+
+
+def impl_gdb_call_w_bp(a):
+    gdb_init('-ex', 'set confirm off', '-ex', 'c', '-ex', 'q')
+    b = a + 1
+    c = a * 2.34
+    d = (a, b, c)
+    gdb_breakpoint()
+    print(a, b, c, d)
+
+
+def impl_gdb_split_init_and_break_w_parallel(a):
+    gdb_init('-ex', 'set confirm off', '-ex', 'c', '-ex', 'q')
+    a += 3
+    for i in prange(4):
+        b = a + 1
+        c = a * 2.34
+        d = (a, b, c)
+        gdb_breakpoint()
+        print(a, b, c, d)
+
+
+@not_arm
+@unix_only
+class TestGdbBindImpls(TestCase):
+    """
+    Contains unit test implementations for gdb binding testing. Test must be
+    decorated with `@needs_gdb_harness` to prevent their running under normal
+    test conditions, the test methods must also end with `_impl` to be
+    considered for execution. The tests themselves are invoked by the
+    `TestGdbBinding` test class through the parsing of this class for test
+    methods and then running the discovered tests in a separate process. Test
+    names not including the word `quick` will be tagged as @tag('long_running')
+    """
+
+    @needs_gdb_harness
+    def test_gdb_cmd_lang_cpython_quick_impl(self):
+        with captured_stdout():
+            impl_gdb_call(10)
+
+    @needs_gdb_harness
+    def test_gdb_cmd_lang_nopython_quick_impl(self):
+        with captured_stdout():
+            _dbg_njit(impl_gdb_call)(10)
+
+    @needs_gdb_harness
+    def test_gdb_cmd_lang_objmode_quick_impl(self):
+        with captured_stdout():
+            _dbg_jit(impl_gdb_call)(10)
+
+    @needs_gdb_harness
+    def test_gdb_split_init_and_break_cpython_impl(self):
+        with captured_stdout():
+            impl_gdb_call_w_bp(10)
+
+    @needs_gdb_harness
+    def test_gdb_split_init_and_break_nopython_impl(self):
+        with captured_stdout():
+            _dbg_njit(impl_gdb_call_w_bp)(10)
+
+    @needs_gdb_harness
+    def test_gdb_split_init_and_break_objmode_impl(self):
+        with captured_stdout():
+            _dbg_jit(impl_gdb_call_w_bp)(10)
+
+    @skip_parfors_unsupported
+    @needs_gdb_harness
+    def test_gdb_split_init_and_break_w_parallel_cpython_impl(self):
+        with captured_stdout():
+            impl_gdb_split_init_and_break_w_parallel(10)
+
+    @skip_parfors_unsupported
+    @needs_gdb_harness
+    def test_gdb_split_init_and_break_w_parallel_nopython_impl(self):
+        with captured_stdout():
+            _dbg_njit(impl_gdb_split_init_and_break_w_parallel)(10)
+
+    @skip_parfors_unsupported
+    @needs_gdb_harness
+    def test_gdb_split_init_and_break_w_parallel_objmode_impl(self):
+        with captured_stdout():
+            _dbg_jit(impl_gdb_split_init_and_break_w_parallel)(10)
+
+
+@not_arm
+@unix_only
+@needs_gdb
+class TestGdbBinding(TestCase):
+    """
+    This test class is used to generate tests which will run the test cases
+    defined in TestGdbBindImpls in isolated subprocesses, this is for safety
+    in case something goes awry.
+    """
+
+    # test mutates env
+    _numba_parallel_test_ = False
+
+    _DEBUG = True
+
+    def run_cmd(self, cmdline, env, kill_is_ok=False):
+        popen = subprocess.Popen(cmdline,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 env=env,
+                                 shell=True)
+        # finish in 20s or kill it, there's no work being done
+
+        def kill():
+            popen.stdout.flush()
+            popen.stderr.flush()
+            popen.kill()
+        timeout = threading.Timer(20., kill)
+        try:
+            timeout.start()
+            out, err = popen.communicate()
+            retcode = popen.returncode
+            if retcode != 0:
+                raise AssertionError(
+                    "process failed with code %s: "
+                    "stderr follows\n%s\n"
+                    "stdout :%s" % (retcode, err.decode(), out.decode()))
+            return out.decode(), err.decode()
+        finally:
+            timeout.cancel()
+        return None, None
+
+    def run_test_in_separate_process(self, test, **kwargs):
+        env_copy = os.environ.copy()
+        env_copy['NUMBA_OPT'] = '1'
+        # Set GDB_TEST to permit the execution of tests decorated with
+        # @needs_gdb_harness
+        env_copy['GDB_TEST'] = '1'
+        cmdline = [sys.executable, "-m", "numba.runtests", test]
+        return self.run_cmd(' '.join(cmdline), env_copy, **kwargs)
+
+    @classmethod
+    def _inject(cls, name):
+        themod = TestGdbBindImpls.__module__
+        thecls = TestGdbBindImpls.__name__
+        # strip impl
+        assert name.endswith('_impl')
+        methname = name.replace('_impl', '')
+        injected_method = '%s.%s.%s' % (themod, thecls, name)
+
+        def test_template(self):
+            o, e = self.run_test_in_separate_process(injected_method)
+            dbgmsg = f'\nSTDOUT={o}\nSTDERR={e}\n'
+            self.assertIn('GNU gdb', o, msg=dbgmsg)
+            self.assertIn('OK', e, msg=dbgmsg)
+            self.assertNotIn('FAIL', e, msg=dbgmsg)
+            self.assertNotIn('ERROR', e, msg=dbgmsg)
+        if 'quick' in name:
+            setattr(cls, methname, test_template)
+        else:
+            setattr(cls, methname, long_running(test_template))
+
+    @classmethod
+    def generate(cls):
+        for name in dir(TestGdbBindImpls):
+            if name.startswith('test_gdb'):
+                cls._inject(name)
+
+
+TestGdbBinding.generate()
+
+
+@not_arm
+@unix_only
+@needs_gdb
+class TestGdbMisc(TestCase):
+
+    @long_running
+    def test_call_gdb_twice(self):
+        def gen(f1, f2):
+            @njit
+            def impl():
+                a = 1
+                f1()
+                b = 2
+                f2()
+                return a + b
+            return impl
+
+        msg_head = "Calling either numba.gdb() or numba.gdb_init() more than"
+
+        def check(func):
+            with self.assertRaises(errors.UnsupportedError) as raises:
+                func()
+            self.assertIn(msg_head, str(raises.exception))
+
+        for g1, g2 in permutations([gdb, gdb_init]):
+            func = gen(g1, g2)
+            check(func)
+
+        @njit
+        def use_globals():
+            a = 1
+            gdb()
+            b = 2
+            gdb_init()
+            return a + b
+
+        check(use_globals)
+
+
+@not_unix
+class TestGdbExceptions(TestCase):
+
+    def test_call_gdb(self):
+        def nop_compiler(x):
+            return x
+        for compiler in [nop_compiler, jit(forceobj=True), njit]:
+            for meth in [gdb, gdb_init]:
+                def python_func():
+                    meth()
+                with self.assertRaises(errors.TypingError) as raises:
+                    compiler(python_func)()
+                msg = "gdb support is only available on unix-like systems"
+                self.assertIn(msg, str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gdb_dwarf.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gdb_dwarf.py
new file mode 100644
index 000000000..07aecbf40
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gdb_dwarf.py
@@ -0,0 +1,261 @@
+"""Tests for gdb interacting with the DWARF numba generates"""
+from numba.tests.support import TestCase, linux_only
+from numba.tests.gdb_support import needs_gdb, skip_unless_pexpect, GdbMIDriver
+from unittest.mock import patch, Mock
+from numba.core import datamodel
+import numpy as np
+from numba import typeof
+import ctypes as ct
+import unittest
+
+
+@linux_only
+@needs_gdb
+@skip_unless_pexpect
+class TestGDBDwarf(TestCase):
+    # This runs the tests in numba.tests.gdb, each submodule must contain one
+    # test class called "Test" and it must contain one test called "test".
+    # Variation is provided by the module name. The reason this convention exits
+    # is because gdb tests tend to be line number sensitive (breakpoints etc
+    # care about this) and doing this prevents constant churn and permits the
+    # reuse of the existing subprocess_test_runner harness.
+    _NUMBA_OPT_0_ENV = {'NUMBA_OPT': '0'}
+
+    def _gdb_has_numpy(self):
+        """Returns True if gdb has NumPy support, False otherwise"""
+        driver = GdbMIDriver(__file__, debug=False,)
+        has_numpy = driver.supports_numpy()
+        driver.quit()
+        return has_numpy
+
+    def _subprocess_test_runner(self, test_mod):
+        themod = f'numba.tests.gdb.{test_mod}'
+        self.subprocess_test_runner(test_module=themod,
+                                    test_class='Test',
+                                    test_name='test',
+                                    envvars=self._NUMBA_OPT_0_ENV)
+
+    def test_basic(self):
+        self._subprocess_test_runner('test_basic')
+
+    def test_array_arg(self):
+        self._subprocess_test_runner('test_array_arg')
+
+    def test_conditional_breakpoint(self):
+        self._subprocess_test_runner('test_conditional_breakpoint')
+
+    def test_break_on_symbol(self):
+        self._subprocess_test_runner('test_break_on_symbol')
+
+    def test_break_on_symbol_version(self):
+        self._subprocess_test_runner('test_break_on_symbol_version')
+
+    def test_pretty_print(self):
+        if not self._gdb_has_numpy():
+            _msg = "Cannot find gdb with NumPy support"
+            self.skipTest(_msg)
+
+        self._subprocess_test_runner('test_pretty_print')
+
+
+class TestGDBPrettyPrinterLogic(TestCase):
+    # Tests the logic in numba.misc.gdb_print_extension.NumbaArrayPrinter
+    # it's quite involved and susceptible to changes to the string
+    # representation of Numba array and dtypes as it parses these
+    # representations and recreates NumPy array/dtypes based on them!
+
+    def setUp(self):
+        # Patch sys.modules with mock gdb modules such that the
+        # numba.misc.gdb_print_extension can import ok, the rest of the gdb
+        # classes etc are implemented later
+
+        mock_modules = {'gdb': Mock(),
+                        'gdb.printing': Mock()}
+        self.patched_sys = patch.dict('sys.modules', mock_modules)
+        self.patched_sys.start()
+
+        # Now sys.modules has a gdb in it, patch the gdb.selected_inferior.
+        # This function should return a process wrapping object that has a
+        # read_memory method that can read a memory region from a given address
+        # in the process' address space.
+
+        import gdb
+
+        class SelectedInferior():
+
+            def read_memory(self, data, extent):
+                buf = (ct.c_char * extent).from_address(data)
+                return buf.raw # this is bytes
+
+        si = SelectedInferior()
+        gdb.configure_mock(**{'selected_inferior': lambda :si})
+
+    def tearDown(self):
+        # drop the sys.modules patch
+        self.patched_sys.stop()
+
+    def get_gdb_repr(self, array):
+        # Returns the gdb repr of an array as reconstructed via the
+        # gdb_print_extension (should be the same as NumPy!).
+
+        # This is the module being tested, it uses gdb and gdb.printing, both
+        # of which are mocked in self.setUp()
+        from numba.misc import gdb_print_extension
+
+        # The following classes are ducks for the gdb classes (which are not
+        # easily/guaranteed importable from the test suite). They implement the
+        # absolute bare minimum necessary to test the gdb_print_extension.
+
+        class DISubrange():
+            def __init__(self, lo, hi):
+                self._lo = lo
+                self._hi = hi
+
+            @property
+            def type(self):
+                return self
+
+            def range(self):
+                return self._lo, self._hi
+
+        class DW_TAG_array_type():
+            def __init__(self, lo, hi):
+                self._lo, self._hi = lo, hi
+
+            def fields(self):
+                return [DISubrange(self._lo, self._hi),]
+
+        class DIDerivedType_tuple():
+            def __init__(self, the_tuple):
+                self._type = DW_TAG_array_type(0, len(the_tuple) - 1)
+                self._tuple = the_tuple
+
+            @property
+            def type(self):
+                return self._type
+
+            def __getitem__(self, item):
+                return self._tuple[item]
+
+        class DICompositeType_Array():
+            def __init__(self, arr, type_str):
+                self._arr = arr
+                self._type_str = type_str
+
+            def __getitem__(self, item):
+                return getattr(self, item)
+
+            @property
+            def data(self):
+                return self._arr.ctypes.data
+
+            @property
+            def itemsize(self):
+                return self._arr.itemsize
+
+            @property
+            def shape(self):
+                return DIDerivedType_tuple(self._arr.shape)
+
+            @property
+            def strides(self):
+                return DIDerivedType_tuple(self._arr.strides)
+
+            @property
+            def type(self):
+                return self._type_str
+
+        # The type string encoded into the DWARF is the string repr of the Numba
+        # type followed by the LLVM repr of the data model in brackets.
+        dmm = datamodel.default_manager
+        array_model = datamodel.models.ArrayModel(dmm, typeof(array))
+        data_type = array_model.get_data_type()
+        type_str = f"{typeof(array)} ({data_type.structure_repr()})"
+        fake_gdb_arr = DICompositeType_Array(array, type_str)
+
+        printer = gdb_print_extension.NumbaArrayPrinter(fake_gdb_arr)
+
+        return printer.to_string().strip() # strip, there's new lines
+
+    def check(self, array):
+        gdb_printed = self.get_gdb_repr(array)
+        self.assertEqual(str(gdb_printed), str(array))
+
+    def test_np_array_printer_simple_numeric_types(self):
+        # Tests printer over a selection of basic types
+        n = 4
+        m = 3
+
+        for dt in (np.int8, np.uint16, np.int64, np.float32, np.complex128):
+            arr = np.arange(m * n, dtype=dt).reshape(m, n)
+            self.check(arr)
+
+    def test_np_array_printer_simple_numeric_types_strided(self):
+        # Tests printer over randomized strided arrays
+        n_tests = 30
+        np.random.seed(0)
+
+        for _ in range(n_tests):
+
+            shape = np.random.randint(1, high=12, size=np.random.randint(1, 5))
+            tmp = np.arange(np.prod(shape)).reshape(shape)
+
+            slices = []
+            for x in shape:
+                start = np.random.randint(0, x)
+                # x + 3 is to ensure that sometimes the stop is beyond the
+                # end of the size in a given dimension
+                stop = np.random.randint(start + 1, max(start + 1, x + 3))
+                step = np.random.randint(1, 3) # step as 1, 2
+                strd = slice(start, stop, step)
+                slices.append(strd)
+
+            arr = tmp[tuple(slices)]
+            self.check(arr)
+
+    def test_np_array_printer_simple_structured_dtype(self):
+        # Tests printer over a selection of basic types
+        n = 4
+        m = 3
+
+        aligned = np.dtype([("x", np.int16), ("y", np.float64)], align=True)
+        unaligned = np.dtype([("x", np.int16), ("y", np.float64)], align=False)
+
+        for dt in (aligned, unaligned):
+            arr = np.empty(m * n, dtype=dt).reshape(m, n)
+            arr['x'] = np.arange(m * n, dtype=dt['x']).reshape(m, n)
+            arr['y'] = 100 * np.arange(m * n, dtype=dt['y']).reshape(m, n)
+            self.check(arr)
+
+    def test_np_array_printer_chr_array(self):
+        # Test unichr array
+        arr = np.array(['abcde'])
+        self.check(arr)
+
+    def test_np_array_printer_unichr_structured_dtype(self):
+        # Not supported yet
+        n = 4
+        m = 3
+
+        dt = np.dtype([("x", '<U5'), ("y", np.float64)], align=True)
+        arr = np.zeros(m * n, dtype=dt).reshape(m, n)
+        rep = self.get_gdb_repr(arr)
+        self.assertIn("array[Exception:", rep)
+        self.assertIn("Unsupported sub-type", rep)
+        self.assertIn("[unichr x 5]", rep)
+
+    def test_np_array_printer_nested_array_structured_dtype(self):
+        # Not supported yet
+        n = 4
+        m = 3
+
+        dt = np.dtype([("x", np.int16, (2,)), ("y", np.float64)], align=True)
+        arr = np.zeros(m * n, dtype=dt).reshape(m, n)
+        rep = self.get_gdb_repr(arr)
+        self.assertIn("array[Exception:", rep)
+        self.assertIn("Unsupported sub-type", rep)
+        self.assertIn("nestedarray(int16", rep)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_generators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_generators.py
new file mode 100644
index 000000000..305f63e5c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_generators.py
@@ -0,0 +1,682 @@
+import sys
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit, njit
+from numba.core import types
+from numba.tests.support import TestCase, MemoryLeakMixin
+from numba.core.datamodel.testing import test_factory
+
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+forceobj_flags = Flags()
+forceobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+def make_consumer(gen_func):
+    def consumer(x):
+        res = 0.0
+        for y in gen_func(x):
+            res += y
+        return res
+
+    return consumer
+
+
+def gen1(x):
+    for i in range(x):
+        yield i
+
+
+def gen2(x):
+    for i in range(x):
+        yield i
+        for j in range(1, 3):
+            yield i + j
+
+
+def gen3(x):
+    # Polymorphic yield types must be unified
+    yield x
+    yield x + 1.5
+    yield x + 1j
+
+
+def gen4(x, y, z):
+    for i in range(3):
+        yield z
+        yield y + z
+    return
+    yield x
+
+
+def gen5():
+    # The bytecode for this generator doesn't contain any YIELD_VALUE
+    # (it's optimized away).  We fail typing it, since the yield type
+    # is entirely undefined.
+    if 0:
+        yield 1
+
+
+def gen6(a, b):
+    # Infinite loop: exercise computation of state variables
+    x = a + 1
+    while True:
+        y = b + 2
+        yield x + y
+
+
+def gen7(arr):
+    # Array variable in generator state
+    for i in range(arr.size):
+        yield arr[i]
+
+
+# Optional arguments and boolean state members
+def gen8(x=1, y=2, b=False):
+    bb = not b
+    yield x
+    if bb:
+        yield y
+    if b:
+        yield x + y
+
+
+def genobj(x):
+    object()
+    yield x
+
+
+def return_generator_expr(x):
+    return (i * 2 for i in x)
+
+
+def gen_ndindex(shape):
+    for ind in np.ndindex(shape):
+        yield ind
+
+
+def gen_flat(arr):
+    for val in arr.flat:
+        yield val
+
+
+def gen_ndenumerate(arr):
+    for tup in np.ndenumerate(arr):
+        yield tup
+
+
+def gen_bool():
+    yield True
+
+
+def gen_unification_error():
+    yield None
+    yield 1j
+
+
+def gen_optional_and_type_unification_error():
+    # yields complex and optional(literalint)
+    i = 0
+    yield 1j
+    while True:
+        i = yield i
+
+
+def gen_changing_tuple_type():
+    # https://github.com/numba/numba/issues/7295
+    yield 1, 2
+    yield 3, 4
+
+
+def gen_changing_number_type():
+    # additional test for https://github.com/numba/numba/issues/7295
+    yield 1
+    yield 3.5
+    yield 67.8j
+
+
+class TestGenerators(MemoryLeakMixin, TestCase):
+    def check_generator(self, pygen, cgen):
+        self.assertEqual(next(cgen), next(pygen))
+        # Use list comprehensions to make sure we trash the generator's
+        # former C stack.
+        expected = [x for x in pygen]
+        got = [x for x in cgen]
+        self.assertEqual(expected, got)
+        with self.assertRaises(StopIteration):
+            next(cgen)
+
+    def check_gen1(self, flags=no_pyobj_flags):
+        pyfunc = gen1
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        pygen = pyfunc(8)
+        cgen = cr.entry_point(8)
+        self.check_generator(pygen, cgen)
+
+    def test_gen1(self):
+        self.check_gen1()
+
+    def test_gen1_objmode(self):
+        self.check_gen1(flags=forceobj_flags)
+
+    def check_gen2(self, flags=no_pyobj_flags):
+        pyfunc = gen2
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        pygen = pyfunc(8)
+        cgen = cr.entry_point(8)
+        self.check_generator(pygen, cgen)
+
+    def test_gen2(self):
+        self.check_gen2()
+
+    def test_gen2_objmode(self):
+        self.check_gen2(flags=forceobj_flags)
+
+    def check_gen3(self, flags=no_pyobj_flags):
+        pyfunc = gen3
+        cr = compile_isolated(pyfunc, (types.int32,), flags=flags)
+        pygen = pyfunc(8)
+        cgen = cr.entry_point(8)
+        self.check_generator(pygen, cgen)
+
+    def test_gen3(self):
+        self.check_gen3()
+
+    def test_gen3_objmode(self):
+        self.check_gen3(flags=forceobj_flags)
+
+    def check_gen4(self, flags=no_pyobj_flags):
+        pyfunc = gen4
+        cr = compile_isolated(pyfunc, (types.int32,) * 3, flags=flags)
+        pygen = pyfunc(5, 6, 7)
+        cgen = cr.entry_point(5, 6, 7)
+        self.check_generator(pygen, cgen)
+
+    def test_gen4(self):
+        self.check_gen4()
+
+    def test_gen4_objmode(self):
+        self.check_gen4(flags=forceobj_flags)
+
+    def test_gen5(self):
+        with self.assertTypingError() as cm:
+            compile_isolated(gen5, ())
+        self.assertIn("Cannot type generator: it does not yield any value",
+                      str(cm.exception))
+
+    def test_gen5_objmode(self):
+        cr = compile_isolated(gen5, (), flags=forceobj_flags)
+        cgen = cr.entry_point()
+        self.assertEqual(list(cgen), [])
+        with self.assertRaises(StopIteration):
+            next(cgen)
+
+    def check_gen6(self, flags=no_pyobj_flags):
+        pyfunc = gen6
+        cr = compile_isolated(pyfunc, (types.int32,) * 2, flags=flags)
+        cgen = cr.entry_point(5, 6)
+        l = []
+        for i in range(3):
+            l.append(next(cgen))
+        self.assertEqual(l, [14] * 3)
+
+    def test_gen6(self):
+        self.check_gen6()
+
+    def test_gen6_objmode(self):
+        self.check_gen6(flags=forceobj_flags)
+
+    def check_gen7(self, flags=no_pyobj_flags):
+        pyfunc = gen7
+        cr = compile_isolated(pyfunc, (types.Array(types.float64, 1, 'C'),),
+                              flags=flags)
+        arr = np.linspace(1, 10, 7)
+        pygen = pyfunc(arr.copy())
+        cgen = cr.entry_point(arr)
+        self.check_generator(pygen, cgen)
+
+    def test_gen7(self):
+        self.check_gen7()
+
+    def test_gen7_objmode(self):
+        self.check_gen7(flags=forceobj_flags)
+
+    def check_gen8(self, **jit_args):
+        pyfunc = gen8
+        cfunc = jit(**jit_args)(pyfunc)
+
+        def check(*args, **kwargs):
+            self.check_generator(pyfunc(*args, **kwargs),
+                                 cfunc(*args, **kwargs))
+
+        check(2, 3)
+        check(4)
+        check(y=5)
+        check(x=6, b=True)
+
+    def test_gen8(self):
+        self.check_gen8(nopython=True)
+
+    def test_gen8_objmode(self):
+        self.check_gen8(forceobj=True)
+
+    def check_gen9(self, flags=no_pyobj_flags):
+        pyfunc = gen_bool
+        cr = compile_isolated(pyfunc, (), flags=flags)
+        pygen = pyfunc()
+        cgen = cr.entry_point()
+        self.check_generator(pygen, cgen)
+
+    def test_gen9(self):
+        self.check_gen9(flags=no_pyobj_flags)
+
+    def test_gen9_objmode(self):
+        self.check_gen9(flags=forceobj_flags)
+
+    def check_consume_generator(self, gen_func):
+        cgen = jit(nopython=True)(gen_func)
+        cfunc = jit(nopython=True)(make_consumer(cgen))
+        pyfunc = make_consumer(gen_func)
+        expected = pyfunc(5)
+        got = cfunc(5)
+        self.assertPreciseEqual(got, expected)
+
+    def test_consume_gen1(self):
+        self.check_consume_generator(gen1)
+
+    def test_consume_gen2(self):
+        self.check_consume_generator(gen2)
+
+    def test_consume_gen3(self):
+        self.check_consume_generator(gen3)
+
+    # Check generator storage of some types
+
+    def check_ndindex(self, flags=no_pyobj_flags):
+        pyfunc = gen_ndindex
+        cr = compile_isolated(pyfunc, (types.UniTuple(types.intp, 2),),
+                              flags=flags)
+        shape = (2, 3)
+        pygen = pyfunc(shape)
+        cgen = cr.entry_point(shape)
+        self.check_generator(pygen, cgen)
+
+    def test_ndindex(self):
+        self.check_ndindex()
+
+    def test_ndindex_objmode(self):
+        self.check_ndindex(flags=forceobj_flags)
+
+    def check_np_flat(self, pyfunc, flags=no_pyobj_flags):
+        cr = compile_isolated(pyfunc, (types.Array(types.int32, 2, "C"),),
+                              flags=flags)
+        arr = np.arange(6, dtype=np.int32).reshape((2, 3))
+        self.check_generator(pyfunc(arr), cr.entry_point(arr))
+        cr = compile_isolated(pyfunc, (types.Array(types.int32, 2, "A"),),
+                              flags=flags)
+        arr = arr.T
+        self.check_generator(pyfunc(arr), cr.entry_point(arr))
+
+    def test_np_flat(self):
+        self.check_np_flat(gen_flat)
+
+    def test_np_flat_objmode(self):
+        self.check_np_flat(gen_flat, flags=forceobj_flags)
+
+    def test_ndenumerate(self):
+        self.check_np_flat(gen_ndenumerate)
+
+    def test_ndenumerate_objmode(self):
+        self.check_np_flat(gen_ndenumerate, flags=forceobj_flags)
+
+    def test_type_unification_error(self):
+        pyfunc = gen_unification_error
+        with self.assertTypingError() as e:
+            compile_isolated(pyfunc, (), flags=no_pyobj_flags)
+
+        msg = ("Can't unify yield type from the following types: complex128, "
+               "none")
+        self.assertIn(msg, str(e.exception))
+
+    def test_optional_expansion_type_unification_error(self):
+        pyfunc = gen_optional_and_type_unification_error
+        with self.assertTypingError() as e:
+            compile_isolated(pyfunc, (), flags=no_pyobj_flags)
+
+        msg = ("Can't unify yield type from the following types: complex128, "
+               "int%s, none")
+        self.assertIn(msg % types.intp.bitwidth, str(e.exception))
+
+    def test_changing_tuple_type(self):
+        # test https://github.com/numba/numba/issues/7295
+        pyfunc = gen_changing_tuple_type
+        expected = list(pyfunc())
+        got = list(njit(pyfunc)())
+        self.assertEqual(expected, got)
+
+    def test_changing_number_type(self):
+        # additional test for https://github.com/numba/numba/issues/7295
+        pyfunc = gen_changing_number_type
+        expected = list(pyfunc())
+        got = list(njit(pyfunc)())
+        self.assertEqual(expected, got)
+
+
+def nrt_gen0(ary):
+    for elem in ary:
+        yield elem
+
+
+def nrt_gen1(ary1, ary2):
+    for e1, e2 in zip(ary1, ary2):
+        yield e1
+        yield e2
+
+
+class TestNrtArrayGen(MemoryLeakMixin, TestCase):
+    def test_nrt_gen0(self):
+        pygen = nrt_gen0
+        cgen = jit(nopython=True)(pygen)
+
+        py_ary = np.arange(10)
+        c_ary = py_ary.copy()
+
+        py_res = list(pygen(py_ary))
+        c_res = list(cgen(c_ary))
+
+        np.testing.assert_equal(py_ary, c_ary)
+        self.assertEqual(py_res, c_res)
+        # Check reference count
+        self.assertEqual(sys.getrefcount(py_ary),
+                         sys.getrefcount(c_ary))
+
+    def test_nrt_gen1(self):
+        pygen = nrt_gen1
+        cgen = jit(nopython=True)(pygen)
+
+        py_ary1 = np.arange(10)
+        py_ary2 = py_ary1 + 100
+
+        c_ary1 = py_ary1.copy()
+        c_ary2 = py_ary2.copy()
+
+        py_res = list(pygen(py_ary1, py_ary2))
+        c_res = list(cgen(c_ary1, c_ary2))
+
+        np.testing.assert_equal(py_ary1, c_ary1)
+        np.testing.assert_equal(py_ary2, c_ary2)
+        self.assertEqual(py_res, c_res)
+        # Check reference count
+        self.assertEqual(sys.getrefcount(py_ary1),
+                         sys.getrefcount(c_ary1))
+        self.assertEqual(sys.getrefcount(py_ary2),
+                         sys.getrefcount(c_ary2))
+
+    def test_combine_gen0_gen1(self):
+        """
+        Issue #1163 is observed when two generator with NRT object arguments
+        is ran in sequence.  The first one does a invalid free and corrupts
+        the NRT memory subsystem.  The second generator is likely to segfault
+        due to corrupted NRT data structure (an invalid MemInfo).
+        """
+        self.test_nrt_gen0()
+        self.test_nrt_gen1()
+
+    def test_nrt_gen0_stop_iteration(self):
+        """
+        Test cleanup on StopIteration
+        """
+        pygen = nrt_gen0
+        cgen = jit(nopython=True)(pygen)
+
+        py_ary = np.arange(1)
+        c_ary = py_ary.copy()
+
+        py_iter = pygen(py_ary)
+        c_iter = cgen(c_ary)
+
+        py_res = next(py_iter)
+        c_res = next(c_iter)
+
+        with self.assertRaises(StopIteration):
+            py_res = next(py_iter)
+
+        with self.assertRaises(StopIteration):
+            c_res = next(c_iter)
+
+        del py_iter
+        del c_iter
+
+        np.testing.assert_equal(py_ary, c_ary)
+        self.assertEqual(py_res, c_res)
+        # Check reference count
+        self.assertEqual(sys.getrefcount(py_ary),
+                         sys.getrefcount(c_ary))
+
+    def test_nrt_gen0_no_iter(self):
+        """
+        Test cleanup for a initialized but never iterated (never call next())
+        generator.
+        """
+        pygen = nrt_gen0
+        cgen = jit(nopython=True)(pygen)
+
+        py_ary = np.arange(1)
+        c_ary = py_ary.copy()
+
+        py_iter = pygen(py_ary)
+        c_iter = cgen(c_ary)
+
+        del py_iter
+        del c_iter
+
+        np.testing.assert_equal(py_ary, c_ary)
+
+        # Check reference count
+        self.assertEqual(sys.getrefcount(py_ary),
+                         sys.getrefcount(c_ary))
+
+
+# TODO: fix nested generator and MemoryLeakMixin
+class TestNrtNestedGen(TestCase):
+    def test_nrt_nested_gen(self):
+
+        def gen0(arr):
+            for i in range(arr.size):
+                yield arr
+
+        def factory(gen0):
+            def gen1(arr):
+                out = np.zeros_like(arr)
+                for x in gen0(arr):
+                    out = out + x
+                return out, arr
+
+            return gen1
+
+        py_arr = np.arange(10)
+        c_arr = py_arr.copy()
+        py_res, py_old = factory(gen0)(py_arr)
+        c_gen = jit(nopython=True)(factory(jit(nopython=True)(gen0)))
+        c_res, c_old = c_gen(c_arr)
+
+        self.assertIsNot(py_arr, c_arr)
+        self.assertIs(py_old, py_arr)
+        self.assertIs(c_old, c_arr)
+
+        np.testing.assert_equal(py_res, c_res)
+
+        self.assertEqual(sys.getrefcount(py_res),
+                         sys.getrefcount(c_res))
+
+        # The below test will fail due to generator finalizer not invoked.
+        # This kept a reference of the c_old.
+        #
+        # self.assertEqual(sys.getrefcount(py_old),
+        #                  sys.getrefcount(c_old))
+
+    @unittest.expectedFailure
+    def test_nrt_nested_gen_refct(self):
+        def gen0(arr):
+            yield arr
+
+        def factory(gen0):
+            def gen1(arr):
+                for out in gen0(arr):
+                    return out
+
+            return gen1
+
+        py_arr = np.arange(10)
+        c_arr = py_arr.copy()
+        py_old = factory(gen0)(py_arr)
+        c_gen = jit(nopython=True)(factory(jit(nopython=True)(gen0)))
+        c_old = c_gen(c_arr)
+
+        self.assertIsNot(py_arr, c_arr)
+        self.assertIs(py_old, py_arr)
+        self.assertIs(c_old, c_arr)
+
+        self.assertEqual(sys.getrefcount(py_old),
+                         sys.getrefcount(c_old))
+
+    def test_nrt_nested_nopython_gen(self):
+        """
+        Test nesting three generators
+        """
+
+        def factory(decor=lambda x: x):
+            @decor
+            def foo(a, n):
+                for i in range(n):
+                    yield a[i]
+                    a[i] += i
+
+            @decor
+            def bar(n):
+                a = np.arange(n)
+                for i in foo(a, n):
+                    yield i * 2
+                for i in range(a.size):
+                    yield a[i]
+
+            @decor
+            def cat(n):
+                for i in bar(n):
+                    yield i + i
+
+            return cat
+
+        py_gen = factory()
+        c_gen = factory(jit(nopython=True))
+
+        py_res = list(py_gen(10))
+        c_res = list(c_gen(10))
+
+        self.assertEqual(py_res, c_res)
+
+
+class TestGeneratorWithNRT(MemoryLeakMixin, TestCase):
+    def test_issue_1254(self):
+        """
+        Missing environment for returning array
+        """
+
+        @jit(nopython=True)
+        def random_directions(n):
+            for i in range(n):
+                vec = np.empty(3)
+                vec[:] = 12
+                yield vec
+
+        outputs = list(random_directions(5))
+        self.assertEqual(len(outputs), 5)
+
+        expect = np.empty(3)
+        expect[:] = 12
+        for got in outputs:
+            np.testing.assert_equal(expect, got)
+
+    def test_issue_1265(self):
+        """
+        Double-free for locally allocated, non escaping NRT objects
+        """
+
+        def py_gen(rmin, rmax, nr):
+            a = np.linspace(rmin, rmax, nr)
+            yield a[0]
+            yield a[1]
+
+        c_gen = jit(nopython=True)(py_gen)
+
+        py_res = list(py_gen(-2, 2, 100))
+        c_res = list(c_gen(-2, 2, 100))
+
+        self.assertEqual(py_res, c_res)
+
+        def py_driver(args):
+            rmin, rmax, nr = args
+            points = np.empty(nr, dtype=np.complex128)
+            for i, c in enumerate(py_gen(rmin, rmax, nr)):
+                points[i] = c
+
+            return points
+
+        @jit(nopython=True)
+        def c_driver(args):
+            rmin, rmax, nr = args
+            points = np.empty(nr, dtype=np.complex128)
+            for i, c in enumerate(c_gen(rmin, rmax, nr)):
+                points[i] = c
+
+            return points
+
+        n = 2
+        patches = (-2, -1, n)
+
+        py_res = py_driver(patches)
+        # The error will cause a segfault here
+        c_res = c_driver(patches)
+
+        np.testing.assert_equal(py_res, c_res)
+
+    def test_issue_1808(self):
+        """
+        Incorrect return data model
+        """
+        magic = 0xdeadbeef
+
+        @njit
+        def generator():
+            yield magic
+
+        @njit
+        def get_generator():
+            return generator()
+
+        @njit
+        def main():
+            out = 0
+            for x in get_generator():
+                out += x
+
+            return out
+
+        self.assertEqual(main(), magic)
+
+
+class TestGeneratorModel(test_factory()):
+    fe_type = types.Generator(gen_func=None, yield_type=types.int32,
+                              arg_types=[types.int64, types.float32],
+                              state_types=[types.intp, types.intp[::1]],
+                              has_finalizer=False)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gil.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gil.py
new file mode 100644
index 000000000..7a60a7565
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_gil.py
@@ -0,0 +1,183 @@
+import ctypes
+import ctypes.util
+import os
+import sys
+import threading
+import warnings
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit
+from numba.core import errors
+from numba.tests.support import TestCase, tag
+
+
+# This CPython API function is a portable way to get the current thread id.
+PyThread_get_thread_ident = ctypes.pythonapi.PyThread_get_thread_ident
+PyThread_get_thread_ident.restype = ctypes.c_long
+PyThread_get_thread_ident.argtypes = []
+
+# A way of sleeping from nopython code
+if os.name == 'nt':
+    sleep = ctypes.windll.kernel32.Sleep
+    sleep.argtypes = [ctypes.c_uint]
+    sleep.restype = None
+    sleep_factor = 1  # milliseconds
+else:
+    sleep = ctypes.CDLL(ctypes.util.find_library("c")).usleep
+    sleep.argtypes = [ctypes.c_uint]
+    sleep.restype = ctypes.c_int
+    sleep_factor = 1000  # microseconds
+
+
+def f(a, indices):
+    # If run from one thread at a time, the function will always fill the
+    # array with identical values.
+    # If run from several threads at a time, the function will probably
+    # fill the array with differing values.
+    for idx in indices:
+        # Let another thread run
+        sleep(10 * sleep_factor)
+        a[idx] = PyThread_get_thread_ident()
+
+f_sig = "void(int64[:], intp[:])"
+
+def lifted_f(a, indices):
+    """
+    Same as f(), but inside a lifted loop
+    """
+    object()   # Force object mode
+    for idx in indices:
+        # Let another thread run
+        sleep(10 * sleep_factor)
+        a[idx] = PyThread_get_thread_ident()
+
+def object_f(a, indices):
+    """
+    Same as f(), but in object mode
+    """
+    for idx in indices:
+        # Let another thread run
+        sleep(10 * sleep_factor)
+        object()   # Force object mode
+        a[idx] = PyThread_get_thread_ident()
+
+
+class TestGILRelease(TestCase):
+
+    def make_test_array(self, n_members):
+        return np.arange(n_members, dtype=np.int64)
+
+    def run_in_threads(self, func, n_threads):
+        # Run the function in parallel over an array and collect results.
+        threads = []
+        # Warm up compilation, since we don't want that to interfere with
+        # the test proper.
+        func(self.make_test_array(1), np.arange(1, dtype=np.intp))
+        arr = self.make_test_array(50)
+        for i in range(n_threads):
+            # Ensure different threads write into the array in different
+            # orders.
+            indices = np.arange(arr.size, dtype=np.intp)
+            np.random.shuffle(indices)
+            t = threading.Thread(target=func, args=(arr, indices))
+            threads.append(t)
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+        return arr
+
+    def check_gil_held(self, func):
+        arr = self.run_in_threads(func, n_threads=4)
+        distinct = set(arr)
+        self.assertEqual(len(distinct), 1, distinct)
+
+    def check_gil_released(self, func):
+        for n_threads in (4, 12, 32):
+            # Try harder each time. On an empty machine 4 threads seems
+            # sufficient, but in some contexts (e.g. Travis CI) we need more.
+            arr = self.run_in_threads(func, n_threads)
+            distinct = set(arr)
+            try:
+                self.assertGreater(len(distinct), 1, distinct)
+            except AssertionError as e:
+                failure = e
+            else:
+                return
+        raise failure
+
+    def test_gil_held(self):
+        """
+        Test the GIL is held by default, by checking serialized runs
+        produce deterministic results.
+        """
+        cfunc = jit(f_sig, nopython=True)(f)
+        self.check_gil_held(cfunc)
+
+    def test_gil_released(self):
+        """
+        Test releasing the GIL, by checking parallel runs produce
+        unpredictable results.
+        """
+        cfunc = jit(f_sig, nopython=True, nogil=True)(f)
+        self.check_gil_released(cfunc)
+
+    def test_gil_released_inside_lifted_loop(self):
+        """
+        Test the GIL can by released by a lifted loop even though the
+        surrounding code uses object mode.
+        """
+        cfunc = jit(f_sig, nogil=True)(lifted_f)
+        self.check_gil_released(cfunc)
+
+    def test_gil_released_by_caller(self):
+        """
+        Releasing the GIL in the caller is sufficient to have it
+        released in a callee.
+        """
+        compiled_f = jit(f_sig, nopython=True)(f)
+        @jit(f_sig, nopython=True, nogil=True)
+        def caller(a, i):
+            compiled_f(a, i)
+        self.check_gil_released(caller)
+
+    def test_gil_released_by_caller_and_callee(self):
+        """
+        Same, but with both caller and callee asking to release the GIL.
+        """
+        compiled_f = jit(f_sig, nopython=True, nogil=True)(f)
+        @jit(f_sig, nopython=True, nogil=True)
+        def caller(a, i):
+            compiled_f(a, i)
+        self.check_gil_released(caller)
+
+    def test_gil_ignored_by_callee(self):
+        """
+        When only the callee asks to release the GIL, it gets ignored.
+        """
+        compiled_f = jit(f_sig, nopython=True, nogil=True)(f)
+        @jit(f_sig, nopython=True)
+        def caller(a, i):
+            compiled_f(a, i)
+        self.check_gil_held(caller)
+
+    def test_object_mode(self):
+        """
+        When the function is compiled in object mode, a warning is
+        printed out.
+        """
+        with warnings.catch_warnings(record=True) as wlist:
+            warnings.simplefilter('always', errors.NumbaWarning)
+            cfunc = jit(f_sig, nogil=True)(object_f)
+        self.assertTrue(any(w.category is errors.NumbaWarning
+                            and "Code running in object mode won't allow parallel execution" in str(w.message)
+                            for w in wlist), wlist)
+        # Just check it doesn't crash.
+        self.run_in_threads(cfunc, 2)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_globals.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_globals.py
new file mode 100644
index 000000000..6cdcb8605
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_globals.py
@@ -0,0 +1,266 @@
+import numpy as np
+from numba import jit, njit, errors
+from numba.extending import register_jitable
+from numba.tests import usecases
+import unittest
+
+X = np.arange(10)
+
+
+def global_ndarray_func(x):
+    y = x + X.shape[0]
+    return y
+
+
+# Create complex array with real and imaginary parts of distinct value
+cplx_X = np.arange(10, dtype=np.complex128)
+tmp = np.arange(10, dtype=np.complex128)
+cplx_X += (tmp+10)*1j
+
+
+def global_cplx_arr_copy(a):
+    for i in range(len(a)):
+        a[i] = cplx_X[i]
+
+
+# Create a recarray with fields of distinct value
+x_dt = np.dtype([('a', np.int32), ('b', np.float32)])
+rec_X = np.recarray(10, dtype=x_dt)
+for i in range(len(rec_X)):
+    rec_X[i].a = i
+    rec_X[i].b = i + 0.5
+
+
+def global_rec_arr_copy(a):
+    for i in range(len(a)):
+        a[i] = rec_X[i]
+
+
+def global_rec_arr_extract_fields(a, b):
+    for i in range(len(a)):
+        a[i] = rec_X[i].a
+        b[i] = rec_X[i].b
+
+
+# Create additional global recarray
+y_dt = np.dtype([('c', np.int16), ('d', np.float64)])
+rec_Y = np.recarray(10, dtype=y_dt)
+for i in range(len(rec_Y)):
+    rec_Y[i].c = i + 10
+    rec_Y[i].d = i + 10.5
+
+
+def global_two_rec_arrs(a, b, c, d):
+    for i in range(len(a)):
+        a[i] = rec_X[i].a
+        b[i] = rec_X[i].b
+        c[i] = rec_Y[i].c
+        d[i] = rec_Y[i].d
+
+
+# Test a global record
+record_only_X = np.recarray(1, dtype=x_dt)[0]
+record_only_X.a = 1
+record_only_X.b = 1.5
+
+@jit(nopython=True)
+def global_record_func(x):
+    return x.a == record_only_X.a
+
+
+@jit(nopython=True)
+def global_module_func(x, y):
+    return usecases.andornopython(x, y)
+
+# Test a global tuple
+tup_int = (1, 2)
+tup_str = ('a', 'b')
+tup_mixed = (1, 'a')
+tup_float = (1.2, 3.5)
+tup_npy_ints = (np.uint64(12), np.int8(3))
+tup_tup_array = ((np.ones(5),),)
+mixed_tup_tup_array = (('Z', np.ones(5),), 2j, 'A')
+
+def global_int_tuple():
+    return tup_int[0] + tup_int[1]
+
+
+def global_str_tuple():
+    return tup_str[0] + tup_str[1]
+
+
+def global_mixed_tuple():
+    idx = tup_mixed[0]
+    field = tup_mixed[1]
+    return rec_X[idx][field]
+
+
+def global_float_tuple():
+    return tup_float[0] + tup_float[1]
+
+
+def global_npy_int_tuple():
+    return tup_npy_ints[0] + tup_npy_ints[1]
+
+
+def global_write_to_arr_in_tuple():
+    tup_tup_array[0][0][0] = 10.
+
+
+def global_write_to_arr_in_mixed_tuple():
+    mixed_tup_tup_array[0][1][0] = 10.
+
+
+_glbl_np_bool_T = np.bool_(True)
+_glbl_np_bool_F = np.bool_(False)
+
+
+@register_jitable # consumer function
+def _sink(*args):
+    pass
+
+def global_npy_bool():
+    _sink(_glbl_np_bool_T, _glbl_np_bool_F)
+    return _glbl_np_bool_T, _glbl_np_bool_F
+
+
+class TestGlobals(unittest.TestCase):
+
+    def check_global_ndarray(self, **jitargs):
+        # (see github issue #448)
+        ctestfunc = jit(**jitargs)(global_ndarray_func)
+        self.assertEqual(ctestfunc(1), 11)
+
+    def test_global_ndarray(self):
+        # This also checks we can access an unhashable global value
+        # (see issue #697)
+        self.check_global_ndarray(forceobj=True)
+
+    def test_global_ndarray_npm(self):
+        self.check_global_ndarray(nopython=True)
+
+
+    def check_global_complex_arr(self, **jitargs):
+        # (see github issue #897)
+        ctestfunc = jit(**jitargs)(global_cplx_arr_copy)
+        arr = np.zeros(len(cplx_X), dtype=np.complex128)
+        ctestfunc(arr)
+        np.testing.assert_equal(arr, cplx_X)
+
+    def test_global_complex_arr(self):
+        self.check_global_complex_arr(forceobj=True)
+
+    def test_global_complex_arr_npm(self):
+        self.check_global_complex_arr(nopython=True)
+
+
+    def check_global_rec_arr(self, **jitargs):
+        # (see github issue #897)
+        ctestfunc = jit(**jitargs)(global_rec_arr_copy)
+        arr = np.zeros(rec_X.shape, dtype=x_dt)
+        ctestfunc(arr)
+        np.testing.assert_equal(arr, rec_X)
+
+    def test_global_rec_arr(self):
+        self.check_global_rec_arr(forceobj=True)
+
+    def test_global_rec_arr_npm(self):
+        self.check_global_rec_arr(nopython=True)
+
+
+    def check_global_rec_arr_extract(self, **jitargs):
+        # (see github issue #897)
+        ctestfunc = jit(**jitargs)(global_rec_arr_extract_fields)
+        arr1 = np.zeros(rec_X.shape, dtype=np.int32)
+        arr2 = np.zeros(rec_X.shape, dtype=np.float32)
+        ctestfunc(arr1, arr2)
+        np.testing.assert_equal(arr1, rec_X.a)
+        np.testing.assert_equal(arr2, rec_X.b)
+
+    def test_global_rec_arr_extract(self):
+        self.check_global_rec_arr_extract(forceobj=True)
+
+    def test_global_rec_arr_extract_npm(self):
+        self.check_global_rec_arr_extract(nopython=True)
+
+
+    def check_two_global_rec_arrs(self, **jitargs):
+        # (see github issue #897)
+        ctestfunc = jit(**jitargs)(global_two_rec_arrs)
+        arr1 = np.zeros(rec_X.shape, dtype=np.int32)
+        arr2 = np.zeros(rec_X.shape, dtype=np.float32)
+        arr3 = np.zeros(rec_Y.shape, dtype=np.int16)
+        arr4 = np.zeros(rec_Y.shape, dtype=np.float64)
+        ctestfunc(arr1, arr2, arr3, arr4)
+        np.testing.assert_equal(arr1, rec_X.a)
+        np.testing.assert_equal(arr2, rec_X.b)
+        np.testing.assert_equal(arr3, rec_Y.c)
+        np.testing.assert_equal(arr4, rec_Y.d)
+
+    def test_two_global_rec_arrs(self):
+        self.check_two_global_rec_arrs(forceobj=True)
+
+    def test_two_global_rec_arrs_npm(self):
+        self.check_two_global_rec_arrs(nopython=True)
+
+    def test_global_module(self):
+        # (see github issue #1059)
+        res = global_module_func(5, 6)
+        self.assertEqual(True, res)
+
+    def test_global_record(self):
+        # (see github issue #1081)
+        x = np.recarray(1, dtype=x_dt)[0]
+        x.a = 1
+        res = global_record_func(x)
+        self.assertEqual(True, res)
+        x.a = 2
+        res = global_record_func(x)
+        self.assertEqual(False, res)
+
+    def test_global_int_tuple(self):
+        pyfunc = global_int_tuple
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(), jitfunc())
+
+    def test_global_str_tuple(self):
+        pyfunc = global_str_tuple
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(), jitfunc())
+
+    def test_global_mixed_tuple(self):
+        pyfunc = global_mixed_tuple
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(), jitfunc())
+
+    def test_global_float_tuple(self):
+        pyfunc = global_float_tuple
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(), jitfunc())
+
+    def test_global_npy_int_tuple(self):
+        pyfunc = global_npy_int_tuple
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(), jitfunc())
+
+    def test_global_write_to_arr_in_tuple(self):
+        # Test writing to an array in a global tuple
+        # See issue https://github.com/numba/numba/issues/7120
+        for func in (global_write_to_arr_in_tuple,
+                     global_write_to_arr_in_mixed_tuple):
+            jitfunc = njit(func)
+            with self.assertRaises(errors.TypingError) as e:
+                jitfunc()
+            msg = "Cannot modify readonly array of type:"
+            self.assertIn(msg, str(e.exception))
+
+    def test_global_npy_bool(self):
+        # Test global NumPy bool
+        # See issue https://github.com/numba/numba/issues/6979
+        pyfunc = global_npy_bool
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(), jitfunc())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_hashing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_hashing.py
new file mode 100644
index 000000000..08443bb52
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_hashing.py
@@ -0,0 +1,383 @@
+# -*- coding: utf-8 -*-
+"""
+Test hashing of various supported types.
+"""
+
+import unittest
+
+import sys
+import subprocess
+from collections import defaultdict
+from textwrap import dedent
+
+import numpy as np
+
+from numba import jit
+from numba.core import types, utils
+import unittest
+from numba.tests.support import (TestCase, tag, CompilationCache,
+                                 skip_unless_py10_or_later)
+
+from numba.cpython.unicode import compile_time_get_string_data
+from numba.cpython import hashing
+
+
+def hash_usecase(x):
+    return hash(x)
+
+
+class TestHashingSetup(TestCase):
+
+    def test_warn_on_fnv(self):
+        # FNV hash alg variant is not supported, check Numba warns
+        work = """
+        import sys
+        import warnings
+        from collections import namedtuple
+
+        # hash_info is a StructSequence, mock as a named tuple
+        fields = ["width", "modulus", "inf", "nan", "imag", "algorithm",
+                  "hash_bits", "seed_bits", "cutoff"]
+
+        hinfo = sys.hash_info
+        FAKE_HASHINFO = namedtuple('FAKE_HASHINFO', fields)
+
+        fd = dict()
+        for f in fields:
+            fd[f] = getattr(hinfo, f)
+
+        fd['algorithm'] = 'fnv'
+
+        fake_hashinfo = FAKE_HASHINFO(**fd)
+
+        # replace the hashinfo with the fnv version
+        sys.hash_info = fake_hashinfo
+        with warnings.catch_warnings(record=True) as warns:
+            # Cause all warnings to always be triggered.
+            warnings.simplefilter("always")
+            from numba import njit
+            @njit
+            def foo():
+                hash(1)
+            foo()
+            assert len(warns) > 0
+            expect = "FNV hashing is not implemented in Numba. See PEP 456"
+            for w in warns:
+                if expect in str(w.message):
+                    break
+            else:
+                raise RuntimeError("Expected warning not found")
+        """
+        subprocess.check_call([sys.executable, '-c', dedent(work)])
+
+
+class BaseTest(TestCase):
+
+    def setUp(self):
+        self.cfunc = jit(nopython=True)(hash_usecase)
+
+    def check_hash_values(self, values):
+        cfunc = self.cfunc
+        for val in list(values):
+            nb_hash = cfunc(val)
+            self.assertIsInstance(nb_hash, int)
+            try:
+                self.assertEqual(nb_hash, hash(val))
+            except AssertionError as e:
+                print("val, nb_hash, hash(val)")
+                print(val, nb_hash, hash(val))
+                print("abs(val), hashing._PyHASH_MODULUS - 1")
+                print(abs(val), hashing._PyHASH_MODULUS - 1)
+                raise e
+
+    def int_samples(self, typ=np.int64):
+        for start in (0, -50, 60000, 1 << 32):
+            info = np.iinfo(typ)
+            if not info.min <= start <= info.max:
+                continue
+            n = 100
+            yield range(start, start + n)
+            yield range(start, start + 100 * n, 100)
+            yield range(start, start + 128 * n, 128)
+            yield [-1]
+
+    def float_samples(self, typ):
+        info = np.finfo(typ)
+
+        for start in (0, 10, info.max ** 0.5, info.max / 1000.0):
+            n = 100
+            min_step = max(info.tiny, start * info.resolution)
+            for step in (1.2, min_step ** 0.5, min_step):
+                if step < min_step:
+                    continue
+                a = np.linspace(start, start + n * step, n)
+                a = a.astype(typ)
+                yield a
+                yield -a
+                yield a + a.mean()
+
+        # Infs, nans, zeros, magic -1
+        a = [0.0, 0.5, -0.0, -1.0, float('inf'), -float('inf'),]
+
+        # Python 3.10 has a hash for nan based on the pointer to the PyObject
+        # containing the nan, skip this input and use explicit test instead.
+        if utils.PYVERSION < (3, 10):
+            a.append(float('nan'))
+
+        yield typ(a)
+
+    def complex_samples(self, typ, float_ty):
+        for real in self.float_samples(float_ty):
+            for imag in self.float_samples(float_ty):
+                # Ensure equal sizes
+                real = real[:len(imag)]
+                imag = imag[:len(real)]
+                a = real + typ(1j) * imag
+                # Python 3.10 has a hash for nan based on the pointer to the
+                # PyObject containing the nan, skip input that ends up as nan
+                if utils.PYVERSION >= (3, 10):
+                    if not np.any(np.isnan(a)):
+                        yield a
+                else:
+                    yield a
+
+
+class TestNumberHashing(BaseTest):
+    """
+    Test hashing of number types.
+    """
+
+    def check_floats(self, typ):
+        for a in self.float_samples(typ):
+            self.assertEqual(a.dtype, np.dtype(typ))
+            self.check_hash_values(a)
+
+    def check_complex(self, typ, float_ty):
+        for a in self.complex_samples(typ, float_ty):
+            self.assertEqual(a.dtype, np.dtype(typ))
+            self.check_hash_values(a)
+
+    def test_floats(self):
+        self.check_floats(np.float32)
+        self.check_floats(np.float64)
+
+    def test_complex(self):
+        self.check_complex(np.complex64, np.float32)
+        self.check_complex(np.complex128, np.float64)
+
+    def test_bool(self):
+        self.check_hash_values([False, True])
+
+    def test_ints(self):
+        minmax = []
+        for ty in [np.int8, np.uint8, np.int16, np.uint16,
+                   np.int32, np.uint32, np.int64, np.uint64]:
+            for a in self.int_samples(ty):
+                self.check_hash_values(a)
+            info = np.iinfo(ty)
+            # check hash(-1) = -2
+            # check hash(0) = 0
+            self.check_hash_values([ty(-1)])
+            self.check_hash_values([ty(0)])
+            signed = 'uint' not in str(ty)
+            # check bit shifting patterns from min through to max
+            sz = ty().itemsize
+            for x in [info.min, info.max]:
+                shifts = 8 * sz
+                # x is a python int, do shifts etc as a python int and init
+                # numpy type from that to avoid numpy type rules
+                y = x
+                for i in range(shifts):
+                    twiddle1 = 0xaaaaaaaaaaaaaaaa
+                    twiddle2 = 0x5555555555555555
+                    vals = [y]
+                    for tw in [twiddle1, twiddle2]:
+                        val = y & twiddle1
+                        if val < sys.maxsize:
+                            vals.append(val)
+                    for v in vals:
+                        self.check_hash_values([ty(v)])
+                    if signed:  # try the same with flipped signs
+                        # negated signed INT_MIN will overflow
+                        for v in vals:
+                            if v != info.min:
+                                self.check_hash_values([ty(-v)])
+                    if x == 0:  # unsigned min is 0, shift up
+                        y = (y | 1) << 1
+                    else:  # everything else shift down
+                        y = y >> 1
+
+        # these straddle the branch between returning the int as the hash and
+        # doing the PyLong hash alg
+        self.check_hash_values([np.int64(0x1ffffffffffffffe)])
+        self.check_hash_values([np.int64(0x1fffffffffffffff)])
+        self.check_hash_values([np.uint64(0x1ffffffffffffffe)])
+        self.check_hash_values([np.uint64(0x1fffffffffffffff)])
+
+        # check some values near sys int mins
+        self.check_hash_values([np.int64(-0x7fffffffffffffff)])
+        self.check_hash_values([np.int64(-0x7ffffffffffffff6)])
+        self.check_hash_values([np.int64(-0x7fffffffffffff9c)])
+        self.check_hash_values([np.int32(-0x7fffffff)])
+        self.check_hash_values([np.int32(-0x7ffffff6)])
+        self.check_hash_values([np.int32(-0x7fffff9c)])
+
+
+    @skip_unless_py10_or_later
+    def test_py310_nan_hash(self):
+        # On Python 3.10+ nan's hash to a value which is based on the pointer to
+        # the PyObject containing the nan. Numba cannot replicate as there's no
+        # object, it instead produces equivalent behaviour, i.e. hashes to
+        # something "unique".
+
+        # Run 10 hashes, make sure that the "uniqueness" is sufficient that
+        # there's more than one hash value. Not much more can be done!
+        x = [float('nan') for i in range(10)]
+        out = set([self.cfunc(z) for z in x])
+        self.assertGreater(len(out), 1)
+
+
+class TestTupleHashing(BaseTest):
+    """
+    Test hashing of tuples.
+    """
+
+    def check_tuples(self, value_generator, split):
+        for values in value_generator:
+            tuples = [split(a) for a in values]
+            self.check_hash_values(tuples)
+
+    def test_homogeneous_tuples(self):
+        typ = np.uint64
+
+        def split2(i):
+            """
+            Split i's bits into 2 integers.
+            """
+            i = typ(i)
+            return (i & typ(0x5555555555555555),
+                    i & typ(0xaaaaaaaaaaaaaaaa),
+                    )
+
+        def split3(i):
+            """
+            Split i's bits into 3 integers.
+            """
+            i = typ(i)
+            return (i & typ(0x2492492492492492),
+                    i & typ(0x4924924924924924),
+                    i & typ(0x9249249249249249),
+                    )
+
+        self.check_tuples(self.int_samples(), split2)
+        self.check_tuples(self.int_samples(), split3)
+
+        # Check exact. Sample values from:
+        # https://github.com/python/cpython/blob/b738237d6792acba85b1f6e6c8993a812c7fd815/Lib/test/test_tuple.py#L80-L93
+        # Untypable empty tuples are replaced with (7,).
+        self.check_hash_values([(7,), (0,), (0, 0), (0.5,),
+                                (0.5, (7,), (-2, 3, (4, 6)))])
+
+    def test_heterogeneous_tuples(self):
+        modulo = 2**63
+
+        def split(i):
+            a = i & 0x5555555555555555
+            b = (i & 0xaaaaaaaa) ^ ((i >> 32) & 0xaaaaaaaa)
+            return np.int64(a), np.float64(b * 0.0001)
+
+        self.check_tuples(self.int_samples(), split)
+
+
+class TestUnicodeHashing(BaseTest):
+
+    def test_basic_unicode(self):
+        kind1_string = "abcdefghijklmnopqrstuvwxyz"
+        for i in range(len(kind1_string)):
+            self.check_hash_values([kind1_string[:i]])
+
+        sep = "眼"
+        kind2_string = sep.join(list(kind1_string))
+        for i in range(len(kind2_string)):
+            self.check_hash_values([kind2_string[:i]])
+
+        sep = "🐍⚡"
+        kind4_string = sep.join(list(kind1_string))
+        for i in range(len(kind4_string)):
+            self.check_hash_values([kind4_string[:i]])
+
+        empty_string = ""
+        self.check_hash_values(empty_string)
+
+    def test_hash_passthrough(self):
+        # no `hash` call made, this just checks that `._hash` is correctly
+        # passed through from an already existing string
+        kind1_string = "abcdefghijklmnopqrstuvwxyz"
+
+        @jit(nopython=True)
+        def fn(x):
+            return x._hash
+
+        hash_value = compile_time_get_string_data(kind1_string)[-1]
+        self.assertTrue(hash_value != -1)
+        self.assertEqual(fn(kind1_string), hash_value)
+
+    def test_hash_passthrough_call(self):
+        # check `x._hash` and hash(x) are the same
+        kind1_string = "abcdefghijklmnopqrstuvwxyz"
+
+        @jit(nopython=True)
+        def fn(x):
+            return x._hash, hash(x)
+
+        hash_value = compile_time_get_string_data(kind1_string)[-1]
+        self.assertTrue(hash_value != -1)
+        self.assertEqual(fn(kind1_string), (hash_value, hash_value))
+
+    @unittest.skip("Needs hash computation at const unpickling time")
+    def test_hash_literal(self):
+        # a strconst always seem to have an associated hash value so the hash
+        # member of the returned value should contain the correct hash
+        @jit(nopython=True)
+        def fn():
+            x = "abcdefghijklmnopqrstuvwxyz"
+            return x
+        val = fn()
+        tmp = hash("abcdefghijklmnopqrstuvwxyz")
+        self.assertEqual(tmp, (compile_time_get_string_data(val)[-1]))
+
+    def test_hash_on_str_creation(self):
+        # In cPython some? new strings do not have a cached hash until hash() is
+        # called
+        def impl(do_hash):
+            const1 = "aaaa"
+            const2 = "眼眼眼眼"
+            new = const1 + const2
+            if do_hash:
+                hash(new)
+            return new
+
+        jitted = jit(nopython=True)(impl)
+
+        # do not compute the hash, cPython will have no cached hash, but Numba
+        # will
+        compute_hash = False
+        expected = impl(compute_hash)
+        got = jitted(compute_hash)
+        a = (compile_time_get_string_data(expected))
+        b = (compile_time_get_string_data(got))
+        self.assertEqual(a[:-1], b[:-1])
+        self.assertTrue(a[-1] != b[-1])
+
+        # now with compute hash enabled, cPython will have a cached hash as will
+        # Numba
+        compute_hash = True
+        expected = impl(compute_hash)
+        got = jitted(compute_hash)
+        a = (compile_time_get_string_data(expected))
+        b = (compile_time_get_string_data(got))
+        self.assertEqual(a, b)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_heapq.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_heapq.py
new file mode 100644
index 000000000..6d17300db
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_heapq.py
@@ -0,0 +1,489 @@
+import heapq as hq
+import itertools
+
+import numpy as np
+
+from numba import jit, typed
+from numba.core.compiler import Flags
+from numba.tests.support import TestCase, CompilationCache, MemoryLeakMixin
+
+no_pyobj_flags = Flags()
+no_pyobj_flags.nrt = True
+
+
+def heapify(x):
+    return hq.heapify(x)
+
+
+def heappop(heap):
+    return hq.heappop(heap)
+
+
+def heappush(heap, item):
+    return hq.heappush(heap, item)
+
+
+def heappushpop(heap, item):
+    return hq.heappushpop(heap, item)
+
+
+def heapreplace(heap, item):
+    return hq.heapreplace(heap, item)
+
+
+def nsmallest(n, iterable):
+    return hq.nsmallest(n, iterable)
+
+
+def nlargest(n, iterable):
+    return hq.nlargest(n, iterable)
+
+
+class _TestHeapq(MemoryLeakMixin):
+
+    def setUp(self):
+        super(_TestHeapq, self).setUp()
+        self.ccache = CompilationCache()
+        self.rnd = np.random.RandomState(42)
+
+    def test_heapify_basic_sanity(self):
+        pyfunc = heapify
+        cfunc = jit(nopython=True)(pyfunc)
+
+        a = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
+        b = self.listimpl(a)
+
+        pyfunc(a)
+        cfunc(b)
+        self.assertPreciseEqual(a, list(b))
+
+        # includes non-finite elements
+        element_pool = [3.142, -10.0, 5.5, np.nan, -np.inf, np.inf]
+
+        # list which may contain duplicate elements
+        for x in itertools.combinations_with_replacement(element_pool, 6):
+            a = list(x)
+            b = self.listimpl(a)
+
+            pyfunc(a)
+            cfunc(b)
+            self.assertPreciseEqual(a, list(b))
+
+        # single element list
+        for i in range(len(element_pool)):
+            a = [element_pool[i]]
+            b = self.listimpl(a)
+
+            pyfunc(a)
+            cfunc(b)
+            self.assertPreciseEqual(a, list(b))
+
+        # elements are tuples
+        a = [(3, 33), (1, 11), (2, 22)]
+        b = self.listimpl(a)
+        pyfunc(a)
+        cfunc(b)
+        self.assertPreciseEqual(a, list(b))
+
+    def check_invariant(self, heap):
+        for pos, item in enumerate(heap):
+            if pos:
+                parentpos = (pos - 1) >> 1
+                self.assertTrue(heap[parentpos] <= item)
+
+    def test_push_pop(self):
+        # inspired by
+        # https://github.com/python/cpython/blob/e42b7051/Lib/test/test_heapq.py
+        pyfunc_heappush = heappush
+        cfunc_heappush = jit(nopython=True)(pyfunc_heappush)
+
+        pyfunc_heappop = heappop
+        cfunc_heappop = jit(nopython=True)(pyfunc_heappop)
+
+        heap = self.listimpl([-1.0])
+        data = self.listimpl([-1.0])
+        self.check_invariant(heap)
+        for i in range(256):
+            item = self.rnd.randn(1).item(0)
+            data.append(item)
+            cfunc_heappush(heap, item)
+            self.check_invariant(heap)
+        results = []
+        while heap:
+            item = cfunc_heappop(heap)
+            self.check_invariant(heap)
+            results.append(item)
+        data_sorted = data[:]
+        data_sorted.sort()
+        self.assertPreciseEqual(list(data_sorted), results)
+        self.check_invariant(results)
+
+    def test_heapify(self):
+        # inspired by
+        # https://github.com/python/cpython/blob/e42b7051/Lib/test/test_heapq.py
+        pyfunc = heapify
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for size in list(range(1, 30)) + [20000]:
+            heap = self.listimpl(self.rnd.random_sample(size))
+            cfunc(heap)
+            self.check_invariant(heap)
+
+    def test_heapify_exceptions(self):
+        pyfunc = heapify
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertTypingError() as e:
+            cfunc((1, 5, 4))
+
+        msg = 'heap argument must be a list'
+        self.assertIn(msg, str(e.exception))
+
+        with self.assertTypingError() as e:
+            cfunc(self.listimpl([1 + 1j, 2 - 3j]))
+
+        msg = ("'<' not supported between instances "
+               "of 'complex' and 'complex'")
+        self.assertIn(msg, str(e.exception))
+
+    def test_heappop_basic_sanity(self):
+        pyfunc = heappop
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
+            yield [(3, 33), (1, 111), (2, 2222)]
+            yield np.full(5, fill_value=np.nan).tolist()
+            yield np.linspace(-10, -5, 100).tolist()
+
+        for a in a_variations():
+            heapify(a)
+            b = self.listimpl(a)
+
+            for i in range(len(a)):
+                val_py = pyfunc(a)
+                val_c = cfunc(b)
+                self.assertPreciseEqual(a, list(b))
+                self.assertPreciseEqual(val_py, val_c)
+
+    def test_heappop_exceptions(self):
+        pyfunc = heappop
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertTypingError() as e:
+            cfunc((1, 5, 4))
+
+        msg = 'heap argument must be a list'
+        self.assertIn(msg, str(e.exception))
+
+    def iterables(self):
+        yield self.listimpl([1, 3, 5, 7, 9, 2, 4, 6, 8, 0])
+        a = np.linspace(-10, 2, 23)
+        yield self.listimpl(a)
+        yield self.listimpl(a[::-1])
+        self.rnd.shuffle(a)
+        yield self.listimpl(a)
+
+    def test_heappush_basic(self):
+        pyfunc_push = heappush
+        cfunc_push = jit(nopython=True)(pyfunc_push)
+
+        pyfunc_pop = heappop
+        cfunc_pop = jit(nopython=True)(pyfunc_pop)
+
+        for iterable in self.iterables():
+            expected = sorted(iterable)
+            heap = self.listimpl([iterable.pop(0)])  # must initialise heap
+
+            for value in iterable:
+                cfunc_push(heap, value)
+
+            got = [cfunc_pop(heap) for _ in range(len(heap))]
+            self.assertPreciseEqual(expected, got)
+
+    def test_heappush_exceptions(self):
+        pyfunc = heappush
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertTypingError() as e:
+            cfunc((1, 5, 4), 6)
+
+        msg = 'heap argument must be a list'
+        self.assertIn(msg, str(e.exception))
+
+        with self.assertTypingError() as e:
+            cfunc(self.listimpl([1, 5, 4]), 6.0)
+
+        msg = 'heap type must be the same as item type'
+        self.assertIn(msg, str(e.exception))
+
+    def test_nsmallest_basic(self):
+        pyfunc = nsmallest
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for iterable in self.iterables():
+            for n in range(-5, len(iterable) + 3):
+                expected = pyfunc(1, iterable)
+                got = cfunc(1, iterable)
+                self.assertPreciseEqual(expected, got)
+
+        # n is boolean
+        out = cfunc(False, self.listimpl([3, 2, 1]))
+        self.assertPreciseEqual(out, [])
+
+        out = cfunc(True, self.listimpl([3, 2, 1]))
+        self.assertPreciseEqual(out, [1])
+
+        # iterable is not a list
+        out = cfunc(2, (6, 5, 4, 3, 2, 1))
+        self.assertPreciseEqual(out, [1, 2])
+
+        out = cfunc(3, np.arange(6))
+        self.assertPreciseEqual(out, [0, 1, 2])
+
+    def test_nlargest_basic(self):
+        pyfunc = nlargest
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for iterable in self.iterables():
+            for n in range(-5, len(iterable) + 3):
+                expected = pyfunc(1, iterable)
+                got = cfunc(1, iterable)
+                self.assertPreciseEqual(expected, got)
+
+        # n is boolean
+        out = cfunc(False, self.listimpl([3, 2, 1]))
+        self.assertPreciseEqual(out, [])
+
+        out = cfunc(True, self.listimpl([3, 2, 1]))
+        self.assertPreciseEqual(out, [3])
+
+        # iterable is not a list
+        out = cfunc(2, (6, 5, 4, 3, 2, 1))
+        self.assertPreciseEqual(out, [6, 5])
+
+        out = cfunc(3, np.arange(6))
+        self.assertPreciseEqual(out, [5, 4, 3])
+
+    def _assert_typing_error(self, cfunc):
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertTypingError() as e:
+            cfunc(2.2, self.listimpl([3, 2, 1]))
+
+        msg = "First argument 'n' must be an integer"
+        self.assertIn(msg, str(e.exception))
+
+        with self.assertTypingError() as e:
+            cfunc(2, 100)
+
+        msg = "Second argument 'iterable' must be iterable"
+        self.assertIn(msg, str(e.exception))
+
+    def test_nsmallest_exceptions(self):
+        pyfunc = nsmallest
+        cfunc = jit(nopython=True)(pyfunc)
+        self._assert_typing_error(cfunc)
+
+    def test_nlargest_exceptions(self):
+        pyfunc = nlargest
+        cfunc = jit(nopython=True)(pyfunc)
+        self._assert_typing_error(cfunc)
+
+    def test_heapreplace_basic(self):
+        pyfunc = heapreplace
+        cfunc = jit(nopython=True)(pyfunc)
+
+        a = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
+
+        heapify(a)
+        b = self.listimpl(a)
+
+        for item in [-4, 4, 14]:
+            pyfunc(a, item)
+            cfunc(b, item)
+            self.assertPreciseEqual(a, list(b))
+
+        a = np.linspace(-3, 13, 20)
+        a[4] = np.nan
+        a[-1] = np.inf
+        a = a.tolist()
+
+        heapify(a)
+        b = self.listimpl(a)
+
+        for item in [-4.0, 3.142, -np.inf, np.inf]:
+            pyfunc(a, item)
+            cfunc(b, item)
+            self.assertPreciseEqual(a, list(b))
+
+    def test_heapreplace_exceptions(self):
+        pyfunc = heapreplace
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertTypingError() as e:
+            cfunc((1, 5, 4), -1)
+
+        msg = 'heap argument must be a list'
+        self.assertIn(msg, str(e.exception))
+
+        with self.assertTypingError() as e:
+            cfunc(self.listimpl([1, 5, 4]), -1.0)
+
+        msg = 'heap type must be the same as item type'
+        self.assertIn(msg, str(e.exception))
+
+    def heapiter(self, heap):
+        try:
+            while 1:
+                yield heappop(heap)
+        except IndexError:
+            pass
+
+    def test_nbest(self):
+        # inspired by
+        # https://github.com/python/cpython/blob/e42b7051/Lib/test/test_heapq.py
+        cfunc_heapify = jit(nopython=True)(heapify)
+        cfunc_heapreplace = jit(nopython=True)(heapreplace)
+
+        data = self.rnd.choice(range(2000), 1000).tolist()
+        heap = self.listimpl(data[:10])
+        cfunc_heapify(heap)
+
+        for item in data[10:]:
+            if item > heap[0]:
+                cfunc_heapreplace(heap, item)
+
+        self.assertPreciseEqual(list(self.heapiter(list(heap))),
+                                sorted(data)[-10:])
+
+    def test_heapsort(self):
+        # inspired by
+        # https://github.com/python/cpython/blob/e42b7051/Lib/test/test_heapq.py
+        cfunc_heapify = jit(nopython=True)(heapify)
+        cfunc_heappush = jit(nopython=True)(heappush)
+        cfunc_heappop = jit(nopython=True)(heappop)
+
+        for trial in range(100):
+            # Ensure consistency of typing, use float64 as it's double
+            # everywhere
+            values = np.arange(5, dtype=np.float64)
+            data = self.listimpl(self.rnd.choice(values, 10))
+            if trial & 1:
+                heap = data[:]
+                cfunc_heapify(heap)
+            else:
+                heap = self.listimpl([data[0]])
+                for item in data[1:]:
+                    cfunc_heappush(heap, item)
+            heap_sorted = [cfunc_heappop(heap) for _ in range(10)]
+            self.assertPreciseEqual(heap_sorted, sorted(data))
+
+    def test_nsmallest(self):
+        # inspired by
+        # https://github.com/python/cpython/blob/e42b7051/Lib/test/test_heapq.py
+        pyfunc = nsmallest
+        cfunc = jit(nopython=True)(pyfunc)
+
+        data = self.listimpl(self.rnd.choice(range(2000), 1000))
+
+        for n in (0, 1, 2, 10, 100, 400, 999, 1000, 1100):
+            self.assertPreciseEqual(list(cfunc(n, data)), sorted(data)[:n])
+
+    def test_nlargest(self):
+        # inspired by
+        # https://github.com/python/cpython/blob/e42b7051/Lib/test/test_heapq.py
+        pyfunc = nlargest
+        cfunc = jit(nopython=True)(pyfunc)
+
+        data = self.listimpl(self.rnd.choice(range(2000), 1000))
+
+        for n in (0, 1, 2, 10, 100, 400, 999, 1000, 1100):
+            self.assertPreciseEqual(list(cfunc(n, data)),
+                                    sorted(data, reverse=True)[:n])
+
+    def test_nbest_with_pushpop(self):
+        # inspired by
+        # https://github.com/python/cpython/blob/e42b7051/Lib/test/test_heapq.py
+        pyfunc_heappushpop = heappushpop
+        cfunc_heappushpop = jit(nopython=True)(pyfunc_heappushpop)
+
+        pyfunc_heapify = heapify
+        cfunc_heapify = jit(nopython=True)(pyfunc_heapify)
+
+        # Ensure consistency of typing, use float64 as it's double everywhere
+        values = np.arange(2000, dtype=np.float64)
+        data = self.listimpl(self.rnd.choice(values, 1000))
+        heap = data[:10]
+        cfunc_heapify(heap)
+
+        for item in data[10:]:
+            cfunc_heappushpop(heap, item)
+
+        self.assertPreciseEqual(list(self.heapiter(list(heap))),
+                                sorted(data)[-10:])
+
+    def test_heappushpop(self):
+        # inspired by
+        # https://github.com/python/cpython/blob/e42b7051/Lib/test/test_heapq.py
+        pyfunc = heappushpop
+        cfunc = jit(nopython=True)(pyfunc)
+
+        h = self.listimpl([1.0])
+        x = cfunc(h, 10.0)
+        self.assertPreciseEqual((list(h), x), ([10.0], 1.0))
+        self.assertPreciseEqual(type(h[0]), float)
+        self.assertPreciseEqual(type(x), float)
+
+        h = self.listimpl([10])
+        x = cfunc(h, 9)
+        self.assertPreciseEqual((list(h), x), ([10], 9))
+
+        h = self.listimpl([10])
+        x = cfunc(h, 11)
+        self.assertPreciseEqual((list(h), x), ([11], 10))
+
+    def test_heappushpop_exceptions(self):
+        pyfunc = heappushpop
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertTypingError() as e:
+            cfunc((1, 5, 4), -1)
+
+        msg = 'heap argument must be a list'
+        self.assertIn(msg, str(e.exception))
+
+        with self.assertTypingError() as e:
+            cfunc(self.listimpl([1, 5, 4]), False)
+
+        msg = 'heap type must be the same as item type'
+        self.assertIn(msg, str(e.exception))
+
+
+class TestHeapqReflectedList(_TestHeapq, TestCase):
+    """Test heapq with reflected lists"""
+
+    listimpl = list
+
+
+class TestHeapqTypedList(_TestHeapq, TestCase):
+    """Test heapq with typed lists"""
+
+    listimpl = typed.List
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_help.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_help.py
new file mode 100644
index 000000000..8199d4c93
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_help.py
@@ -0,0 +1,92 @@
+import sys
+import subprocess
+import types as pytypes
+import os.path
+
+import numpy as np
+
+import builtins
+from numba.core import types
+from numba.tests.support import TestCase, temp_directory
+from numba.misc.help.inspector import inspect_function, inspect_module
+
+
+class TestInspector(TestCase):
+    def check_function_descriptor(self, info, must_be_defined=False):
+        self.assertIsInstance(info, dict)
+        self.assertIn('numba_type', info)
+        numba_type = info['numba_type']
+        if numba_type is None:
+            self.assertFalse(must_be_defined)
+        else:
+            self.assertIsInstance(numba_type, types.Type)
+            self.assertIn('explained', info)
+            self.assertIsInstance(info['explained'], str)
+            self.assertIn('source_infos', info)
+            self.assertIsInstance(info['source_infos'], dict)
+
+    def test_inspect_function_on_range(self):
+        info = inspect_function(range)
+        self.check_function_descriptor(info, must_be_defined=True)
+
+    def test_inspect_function_on_np_all(self):
+        info = inspect_function(np.all)
+        self.check_function_descriptor(info, must_be_defined=True)
+        source_infos = info['source_infos']
+        self.assertGreater(len(source_infos), 0)
+        c = 0
+        for srcinfo in source_infos.values():
+            self.assertIsInstance(srcinfo['kind'], str)
+            self.assertIsInstance(srcinfo['name'], str)
+            self.assertIsInstance(srcinfo['sig'], str)
+            self.assertIsInstance(srcinfo['filename'], str)
+            self.assertIsInstance(srcinfo['lines'], tuple)
+            self.assertIn('docstring', srcinfo)
+            c += 1
+        self.assertEqual(c, len(source_infos))
+
+    def test_inspect_module(self):
+        c = 0
+        for it in inspect_module(builtins):
+            self.assertIsInstance(it['module'], pytypes.ModuleType)
+            self.assertIsInstance(it['name'], str)
+            self.assertTrue(callable(it['obj']))
+            self.check_function_descriptor(it)
+            c += 1
+        self.assertGreater(c, 0)
+
+    def test_inspect_cli(self):
+        # Try CLI on math module
+        cmdbase = [sys.executable, '-m', 'numba.misc.help.inspector']
+
+        dirpath = temp_directory('{}.{}'.format(__name__,
+                                                self.__class__.__name__))
+        filename = os.path.join(dirpath, 'out')
+
+        # Try default format "html"
+        expected_file = filename + '.html'
+        cmds = cmdbase + ['--file', filename, 'math']
+        # File shouldn't exist yet
+        self.assertFalse(os.path.isfile(expected_file))
+        # Run CLI
+        subprocess.check_output(cmds)
+        # File should exist now
+        self.assertTrue(os.path.isfile(expected_file))
+
+        # Try changing the format to "rst"
+        cmds = cmdbase + ['--file', filename, '--format', 'rst', 'math']
+        expected_file = filename + '.rst'
+        # File shouldn't exist yet
+        self.assertFalse(os.path.isfile(expected_file))
+        # Run CLI
+        subprocess.check_output(cmds)
+        # File should exist now
+        self.assertTrue(os.path.isfile(expected_file))
+
+        # Try unsupported format
+        cmds = cmdbase + ['--file', filename, '--format', 'foo', 'math']
+        # Run CLI
+        with self.assertRaises(subprocess.CalledProcessError) as raises:
+            subprocess.check_output(cmds, stderr=subprocess.STDOUT)
+        self.assertIn("\'foo\' is not supported",
+                      raises.exception.stdout.decode())
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_import.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_import.py
new file mode 100644
index 000000000..6f1c931df
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_import.py
@@ -0,0 +1,112 @@
+import unittest
+from numba.tests.support import TestCase, run_in_subprocess
+
+
+class TestNumbaImport(TestCase):
+    """
+    Test behaviour of importing Numba.
+    """
+
+    def test_laziness(self):
+        """
+        Importing top-level numba features should not import too many modules.
+        """
+        # A heuristic set of modules that shouldn't be imported immediately
+        banlist = ['cffi',
+                   'distutils',
+                   'numba.cuda',
+                   'numba.cpython.mathimpl',
+                   'numba.cpython.randomimpl',
+                   'numba.tests',
+                   'numba.core.typing.collections',
+                   'numba.core.typing.listdecl',
+                   'numba.core.typing.npdatetime',
+                   ]
+        # Sanity check the modules still exist...
+        for mod in banlist:
+            if mod not in ('cffi',):
+                __import__(mod)
+
+        code = """if 1:
+            from numba import jit, vectorize
+            from numba.core import types
+            import sys
+            print(list(sys.modules))
+            """
+
+        out, _ = run_in_subprocess(code)
+        modlist = set(eval(out.strip()))
+        unexpected = set(banlist) & set(modlist)
+        self.assertFalse(unexpected, "some modules unexpectedly imported")
+
+    def test_no_impl_import(self):
+        """
+        Tests that importing jit does not trigger import of modules containing
+        lowering implementations that would likely install things in the
+        builtins registry and have side effects impacting other targets
+        """
+        # None of these modules should be imported through the process of
+        # doing 'import numba' or 'from numba import njit'
+        banlist = ['numba.cpython.slicing',
+                   'numba.cpython.tupleobj',
+                   'numba.cpython.enumimpl',
+                   'numba.cpython.hashing',
+                   'numba.cpython.heapq',
+                   'numba.cpython.iterators',
+                   'numba.cpython.numbers',
+                   'numba.cpython.rangeobj',
+                   'numba.cpython.cmathimpl',
+                   'numba.cpython.mathimpl',
+                   'numba.cpython.printimpl',
+                   'numba.cpython.randomimpl',
+                   'numba.core.optional',
+                   'numba.misc.gdb_hook',
+                   'numba.misc.literal',
+                   'numba.misc.cffiimpl',
+                   'numba.np.linalg',
+                   'numba.np.polynomial',
+                   'numba.np.arraymath',
+                   'numba.np.npdatetime',
+                   'numba.np.npyimpl',
+                   'numba.typed.typeddict',
+                   'numba.typed.typedlist',
+                   'numba.experimental.jitclass.base',]
+
+        code1 = """if 1:
+            import sys
+            import numba
+            print(list(sys.modules))
+            """
+
+        code2 = """if 1:
+            import sys
+            from numba import njit
+            @njit
+            def foo():
+                pass
+            print(list(sys.modules))
+            """
+
+        for code in (code1, code2):
+            out, _ = run_in_subprocess(code)
+            modlist = set(eval(out.strip()))
+            unexpected = set(banlist) & set(modlist)
+            self.assertFalse(unexpected, "some modules unexpectedly imported")
+
+    def test_no_accidental_warnings(self):
+        # checks that importing Numba isn't accidentally triggering warnings due
+        # to e.g. deprecated use of import locations from Python's stdlib
+        code = "import numba"
+        # See: https://github.com/numba/numba/issues/6831
+        # bug in setuptools/packaging causing a deprecation warning
+        flags = ["-Werror", "-Wignore::DeprecationWarning:packaging.version:"]
+        run_in_subprocess(code, flags)
+
+    def test_import_star(self):
+        # checks that "from numba import *" works.
+        code = "from numba import *"
+        run_in_subprocess(code)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_indexing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_indexing.py
new file mode 100644
index 000000000..4249b7ca7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_indexing.py
@@ -0,0 +1,1140 @@
+import decimal
+import itertools
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import njit, typeof
+from numba.core import utils, types, errors
+from numba.tests.support import TestCase, tag
+from numba.core.typing import arraydecl
+from numba.core.types import intp, ellipsis, slice2_type, slice3_type
+
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+Noflags = Flags()
+Noflags.nrt = True
+
+
+def slicing_1d_usecase(a, start, stop, step):
+    return a[start:stop:step]
+
+def slicing_1d_usecase2(a, start, stop, step):
+    b = a[start:stop:step]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def slicing_1d_usecase3(a, start, stop):
+    b = a[start:stop]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def slicing_1d_usecase4(a):
+    b = a[:]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def slicing_1d_usecase5(a, start):
+    b = a[start:]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def slicing_1d_usecase6(a, stop):
+    b = a[:stop]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def slicing_1d_usecase7(a, start):
+    # Omitted stop with negative step (issue #1690)
+    b = a[start::-2]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def slicing_1d_usecase8(a, start):
+    # Omitted start with negative step
+    b = a[::-2]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+
+def slicing_2d_usecase(a, start1, stop1, step1, start2, stop2, step2):
+    # The index is a homogeneous tuple of slices
+    return a[start1:stop1:step1, start2:stop2:step2]
+
+def slicing_2d_usecase3(a, start1, stop1, step1, index):
+    # The index is a heterogeneous tuple
+    return a[start1:stop1:step1, index]
+
+def slicing_3d_usecase(a, index0, start1, index2):
+    b = a[index0, start1:, index2]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def slicing_3d_usecase2(a, index0, stop1, index2):
+    b = a[index0, :stop1, index2]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def partial_1d_usecase(a, index):
+    b = a[index]
+    total = 0
+    for i in range(b.shape[0]):
+        total += b[i] * (i + 1)
+    return total
+
+def integer_indexing_1d_usecase(a, i):
+    return a[i]
+
+def integer_indexing_2d_usecase(a, i1, i2):
+    return a[i1,i2]
+
+def integer_indexing_2d_usecase2(a, i1, i2):
+    return a[i1][i2]
+
+def ellipsis_usecase1(a, i, j):
+    return a[i:j, ...]
+
+def ellipsis_usecase2(a, i, j):
+    return a[..., i:j]
+
+def ellipsis_usecase3(a, i, j):
+    return a[i, ..., j]
+
+def none_index_usecase(a):
+    return a[None]
+
+def empty_tuple_usecase(a):
+    return a[()]
+
+
+@njit
+def setitem_usecase(a, index, value):
+    a[index] = value
+
+
+@njit
+def setitem_broadcast_usecase(a, value):
+    a[:] = value
+
+
+def slicing_1d_usecase_set(a, b, start, stop, step):
+    a[start:stop:step] = b
+    return a
+
+def slicing_1d_usecase_add(a, b, start, stop):
+    # NOTE: uses the ROT_FOUR opcode on Python 2, only on the [start:stop]
+    # with inplace operator form.
+    a[start:stop] += b
+    return a
+
+def slicing_2d_usecase_set(a, b, start, stop, step, start2, stop2, step2):
+    a[start:stop:step,start2:stop2:step2] = b
+    return a
+
+
+class TestGetItem(TestCase):
+    """
+    Test basic indexed load from an array (returning a view or a scalar).
+    Note fancy indexing is tested in test_fancy_indexing.
+    """
+
+    def test_1d_slicing(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase
+        arraytype = types.Array(types.int32, 1, 'C')
+        argtys = (arraytype, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(10, dtype='i4')
+        for indices in [(0, 10, 1),
+                        (2, 3, 1),
+                        (10, 0, 1),
+                        (0, 10, -1),
+                        (0, 10, 2),
+                        (9, 0, -1),
+                        (-5, -2, 1),
+                        (0, -1, 1),
+                        ]:
+            expected = pyfunc(a, *indices)
+            self.assertPreciseEqual(cfunc(a, *indices), expected)
+
+    def test_1d_slicing_npm(self):
+        self.test_1d_slicing(flags=Noflags)
+
+    def test_1d_slicing2(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase2
+        arraytype = types.Array(types.int32, 1, 'C')
+        argtys = (arraytype, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(10, dtype='i4')
+
+        args = [(0, 10, 1),
+                (2, 3, 1),
+                (10, 0, 1),
+                (0, 10, -1),
+                (0, 10, 2)]
+
+        for arg in args:
+            self.assertEqual(pyfunc(a, *arg), cfunc(a, *arg))
+
+
+        # Any
+        arraytype = types.Array(types.int32, 1, 'A')
+        argtys = (arraytype, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(20, dtype='i4')[::2]
+        self.assertFalse(a.flags['C_CONTIGUOUS'])
+        self.assertFalse(a.flags['F_CONTIGUOUS'])
+
+        args = [(0, 10, 1),
+                (2, 3, 1),
+                (10, 0, 1),
+                (0, 10, -1),
+                (0, 10, 2)]
+
+        for arg in args:
+            self.assertEqual(pyfunc(a, *arg), cfunc(a, *arg))
+
+    def test_1d_slicing2_npm(self):
+        self.test_1d_slicing2(flags=Noflags)
+
+    def test_1d_slicing3(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase3
+        arraytype = types.Array(types.int32, 1, 'C')
+        argtys = (arraytype, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(10, dtype='i4')
+
+        args = [(3, 10),
+                (2, 3),
+                (10, 0),
+                (0, 10),
+                (5, 10)]
+
+        for arg in args:
+            self.assertEqual(pyfunc(a, *arg), cfunc(a, *arg))
+
+
+        # Any
+        arraytype = types.Array(types.int32, 1, 'A')
+        argtys = (arraytype, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(20, dtype='i4')[::2]
+        self.assertFalse(a.flags['C_CONTIGUOUS'])
+        self.assertFalse(a.flags['F_CONTIGUOUS'])
+
+        for arg in args:
+            self.assertEqual(pyfunc(a, *arg), cfunc(a, *arg))
+
+    def test_1d_slicing3_npm(self):
+        self.test_1d_slicing3(flags=Noflags)
+
+    def test_1d_slicing4(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase4
+        arraytype = types.Array(types.int32, 1, 'C')
+        argtys = (arraytype,)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(10, dtype='i4')
+        self.assertEqual(pyfunc(a), cfunc(a))
+
+        # Any
+        arraytype = types.Array(types.int32, 1, 'A')
+        argtys = (arraytype,)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(20, dtype='i4')[::2]
+        self.assertFalse(a.flags['C_CONTIGUOUS'])
+        self.assertFalse(a.flags['F_CONTIGUOUS'])
+        self.assertEqual(pyfunc(a), cfunc(a))
+
+    def test_1d_slicing4_npm(self):
+        self.test_1d_slicing4(flags=Noflags)
+
+    def check_1d_slicing_with_arg(self, pyfunc, flags):
+        args = list(range(-9, 10))
+
+        arraytype = types.Array(types.int32, 1, 'C')
+        argtys = (arraytype, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(10, dtype='i4')
+        for arg in args:
+            self.assertEqual(pyfunc(a, arg), cfunc(a, arg))
+
+        # Any
+        arraytype = types.Array(types.int32, 1, 'A')
+        argtys = (arraytype, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(20, dtype='i4')[::2]
+        self.assertFalse(a.flags['C_CONTIGUOUS'])
+        self.assertFalse(a.flags['F_CONTIGUOUS'])
+        for arg in args:
+            self.assertEqual(pyfunc(a, arg), cfunc(a, arg))
+
+    def test_1d_slicing5(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase5
+        self.check_1d_slicing_with_arg(pyfunc, flags)
+
+    def test_1d_slicing5_npm(self):
+        self.test_1d_slicing5(flags=Noflags)
+
+    def test_1d_slicing6(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase6
+        self.check_1d_slicing_with_arg(pyfunc, flags)
+
+    def test_1d_slicing6_npm(self):
+        self.test_1d_slicing6(flags=Noflags)
+
+    def test_1d_slicing7(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase7
+        self.check_1d_slicing_with_arg(pyfunc, flags)
+
+    def test_1d_slicing7_npm(self):
+        self.test_1d_slicing7(flags=Noflags)
+
+    def test_1d_slicing8(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase8
+        self.check_1d_slicing_with_arg(pyfunc, flags)
+
+    def test_1d_slicing8_npm(self):
+        self.test_1d_slicing8(flags=Noflags)
+
+    def test_2d_slicing(self, flags=enable_pyobj_flags):
+        """
+        arr_2d[a:b:c]
+        """
+        pyfunc = slicing_1d_usecase
+        arraytype = types.Array(types.int32, 2, 'C')
+        argtys = (arraytype, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(100, dtype='i4').reshape(10, 10)
+        for args in [(0, 10, 1), (2, 3, 1), (10, 0, 1),
+                     (0, 10, -1), (0, 10, 2)]:
+            self.assertPreciseEqual(pyfunc(a, *args), cfunc(a, *args),
+                                    msg="for args %s" % (args,))
+
+    def test_2d_slicing_npm(self):
+        self.test_2d_slicing(flags=Noflags)
+
+    def test_2d_slicing2(self, flags=enable_pyobj_flags):
+        """
+        arr_2d[a:b:c, d:e:f]
+        """
+        # C layout
+        pyfunc = slicing_2d_usecase
+        arraytype = types.Array(types.int32, 2, 'C')
+        argtys = (arraytype, types.int32, types.int32, types.int32,
+                  types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(100, dtype='i4').reshape(10, 10)
+
+        indices = [(0, 10, 1),
+                   (2, 3, 1),
+                   (10, 0, 1),
+                   (0, 10, -1),
+                   (0, 10, 2),
+                   (10, 0, -1),
+                   (9, 0, -2),
+                   (-5, -2, 1),
+                   (0, -1, 1),
+                   ]
+        args = [tup1 + tup2
+                for (tup1, tup2) in itertools.product(indices, indices)]
+        for arg in args:
+            expected = pyfunc(a, *arg)
+            self.assertPreciseEqual(cfunc(a, *arg), expected)
+
+        # Any layout
+        arraytype = types.Array(types.int32, 2, 'A')
+        argtys = (arraytype, types.int32, types.int32, types.int32,
+                  types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(400, dtype='i4').reshape(20, 20)[::2, ::2]
+
+        for arg in args:
+            expected = pyfunc(a, *arg)
+            self.assertPreciseEqual(cfunc(a, *arg), expected)
+
+    def test_2d_slicing2_npm(self):
+        self.test_2d_slicing2(flags=Noflags)
+
+    def test_2d_slicing3(self, flags=enable_pyobj_flags):
+        """
+        arr_2d[a:b:c, d]
+        """
+        # C layout
+        pyfunc = slicing_2d_usecase3
+        arraytype = types.Array(types.int32, 2, 'C')
+        argtys = (arraytype, types.int32, types.int32, types.int32,
+                  types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(100, dtype='i4').reshape(10, 10)
+
+        args = [
+            (0, 10, 1, 0),
+            (2, 3, 1, 1),
+            (10, 0, -1, 8),
+            (9, 0, -2, 4),
+            (0, 10, 2, 3),
+            (0, -1, 3, 1),
+        ]
+        for arg in args:
+            expected = pyfunc(a, *arg)
+            self.assertPreciseEqual(cfunc(a, *arg), expected)
+
+        # Any layout
+        arraytype = types.Array(types.int32, 2, 'A')
+        argtys = (arraytype, types.int32, types.int32, types.int32,
+                  types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(400, dtype='i4').reshape(20, 20)[::2, ::2]
+
+        for arg in args:
+            expected = pyfunc(a, *arg)
+            self.assertPreciseEqual(cfunc(a, *arg), expected)
+
+    def test_2d_slicing3_npm(self):
+        self.test_2d_slicing3(flags=Noflags)
+
+    def test_3d_slicing(self, flags=enable_pyobj_flags):
+        # C layout
+        pyfunc = slicing_3d_usecase
+        arraytype = types.Array(types.int32, 3, 'C')
+        argtys = (arraytype, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(1000, dtype='i4').reshape(10, 10, 10)
+
+        args = [
+            (0, 9, 1),
+            (2, 3, 1),
+            (9, 0, 1),
+            (0, 9, -1),
+            (0, 9, 2),
+        ]
+        for arg in args:
+            self.assertEqual(pyfunc(a, *arg), cfunc(a, *arg))
+
+        # Any layout
+        arraytype = types.Array(types.int32, 3, 'A')
+        argtys = (arraytype, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(2000, dtype='i4')[::2].reshape(10, 10, 10)
+
+        for arg in args:
+            self.assertEqual(pyfunc(a, *arg), cfunc(a, *arg))
+
+    def test_3d_slicing_npm(self):
+        self.test_3d_slicing(flags=Noflags)
+
+    def test_3d_slicing2(self, flags=enable_pyobj_flags):
+        # C layout
+        pyfunc = slicing_3d_usecase2
+        arraytype = types.Array(types.int32, 3, 'C')
+        argtys = (arraytype, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(1000, dtype='i4').reshape(10, 10, 10)
+
+        args = [
+            (0, 9, 1),
+            (2, 3, 1),
+            (9, 0, 1),
+            (0, 9, -1),
+            (0, 9, 2),
+        ]
+        for arg in args:
+            self.assertEqual(pyfunc(a, *arg), cfunc(a, *arg))
+
+        # Any layout
+        arraytype = types.Array(types.int32, 3, 'A')
+        argtys = (arraytype, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(2000, dtype='i4')[::2].reshape(10, 10, 10)
+
+        for arg in args:
+            self.assertEqual(pyfunc(a, *arg), cfunc(a, *arg))
+
+    def test_3d_slicing2_npm(self):
+        self.test_3d_slicing2(flags=Noflags)
+
+    def test_1d_integer_indexing(self, flags=enable_pyobj_flags):
+        # C layout
+        pyfunc = integer_indexing_1d_usecase
+        arraytype = types.Array(types.int32, 1, 'C')
+        cr = compile_isolated(pyfunc, (arraytype, types.int32), flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(10, dtype='i4')
+        self.assertEqual(pyfunc(a, 0), cfunc(a, 0))
+        self.assertEqual(pyfunc(a, 9), cfunc(a, 9))
+        self.assertEqual(pyfunc(a, -1), cfunc(a, -1))
+
+        # Any layout
+        arraytype = types.Array(types.int32, 1, 'A')
+        cr = compile_isolated(pyfunc, (arraytype, types.int32), flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(10, dtype='i4')[::2]
+        self.assertFalse(a.flags['C_CONTIGUOUS'])
+        self.assertFalse(a.flags['F_CONTIGUOUS'])
+        self.assertEqual(pyfunc(a, 0), cfunc(a, 0))
+        self.assertEqual(pyfunc(a, 2), cfunc(a, 2))
+        self.assertEqual(pyfunc(a, -1), cfunc(a, -1))
+
+        # Using a 0-d array as integer index
+        arraytype = types.Array(types.int32, 1, 'C')
+        indextype = types.Array(types.int16, 0, 'C')
+        cr = compile_isolated(pyfunc, (arraytype, indextype), flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(3, 13, dtype=np.int32)
+        for i in (0, 9, -2):
+            idx = np.array(i).astype(np.int16)
+            assert idx.ndim == 0
+            self.assertEqual(pyfunc(a, idx), cfunc(a, idx))
+
+    def test_1d_integer_indexing_npm(self):
+        self.test_1d_integer_indexing(flags=Noflags)
+
+    def test_integer_indexing_1d_for_2d(self, flags=enable_pyobj_flags):
+        # Test partial (1d) indexing of a 2d array
+        pyfunc = integer_indexing_1d_usecase
+        arraytype = types.Array(types.int32, 2, 'C')
+        cr = compile_isolated(pyfunc, (arraytype, types.int32), flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(100, dtype='i4').reshape(10, 10)
+        self.assertPreciseEqual(pyfunc(a, 0), cfunc(a, 0))
+        self.assertPreciseEqual(pyfunc(a, 9), cfunc(a, 9))
+        self.assertPreciseEqual(pyfunc(a, -1), cfunc(a, -1))
+
+        arraytype = types.Array(types.int32, 2, 'A')
+        cr = compile_isolated(pyfunc, (arraytype, types.int32), flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(20, dtype='i4').reshape(5, 4)[::2]
+        self.assertPreciseEqual(pyfunc(a, 0), cfunc(a, 0))
+
+    def test_integer_indexing_1d_for_2d_npm(self):
+        self.test_integer_indexing_1d_for_2d(flags=Noflags)
+
+    def test_2d_integer_indexing(self, flags=enable_pyobj_flags,
+                                 pyfunc=integer_indexing_2d_usecase):
+        # C layout
+        a = np.arange(100, dtype='i4').reshape(10, 10)
+        arraytype = types.Array(types.int32, 2, 'C')
+        cr = compile_isolated(pyfunc, (arraytype, types.int32, types.int32),
+                              flags=flags)
+        cfunc = cr.entry_point
+
+        self.assertEqual(pyfunc(a, 0, 3), cfunc(a, 0, 3))
+        self.assertEqual(pyfunc(a, 9, 9), cfunc(a, 9, 9))
+        self.assertEqual(pyfunc(a, -2, -1), cfunc(a, -2, -1))
+
+        # Any layout
+        a = np.arange(100, dtype='i4').reshape(10, 10)[::2, ::2]
+        self.assertFalse(a.flags['C_CONTIGUOUS'])
+        self.assertFalse(a.flags['F_CONTIGUOUS'])
+
+        arraytype = types.Array(types.int32, 2, 'A')
+        cr = compile_isolated(pyfunc, (arraytype, types.int32, types.int32),
+                              flags=flags)
+        cfunc = cr.entry_point
+
+        self.assertEqual(pyfunc(a, 0, 1), cfunc(a, 0, 1))
+        self.assertEqual(pyfunc(a, 2, 2), cfunc(a, 2, 2))
+        self.assertEqual(pyfunc(a, -2, -1), cfunc(a, -2, -1))
+
+        # With 0-d arrays as integer indices
+        a = np.arange(100, dtype='i4').reshape(10, 10)
+        arraytype = types.Array(types.int32, 2, 'C')
+        indextype = types.Array(types.int32, 0, 'C')
+        cr = compile_isolated(pyfunc, (arraytype, indextype, indextype),
+                              flags=flags)
+        cfunc = cr.entry_point
+
+        for i, j in [(0, 3), (8, 9), (-2, -1)]:
+            i = np.array(i).astype(np.int32)
+            j = np.array(j).astype(np.int32)
+            self.assertEqual(pyfunc(a, i, j), cfunc(a, i, j))
+
+    def test_2d_integer_indexing_npm(self):
+        self.test_2d_integer_indexing(flags=Noflags)
+
+    def test_2d_integer_indexing2(self):
+        self.test_2d_integer_indexing(pyfunc=integer_indexing_2d_usecase2)
+        self.test_2d_integer_indexing(flags=Noflags,
+                                      pyfunc=integer_indexing_2d_usecase2)
+
+    def test_2d_integer_indexing_via_call(self):
+        @njit
+        def index1(X, i0):
+            return X[i0]
+        @njit
+        def index2(X, i0, i1):
+            return index1(X[i0], i1)
+        a = np.arange(10).reshape(2, 5)
+        self.assertEqual(index2(a, 0, 0), a[0][0])
+        self.assertEqual(index2(a, 1, 1), a[1][1])
+        self.assertEqual(index2(a, -1, -1), a[-1][-1])
+
+    def test_2d_float_indexing(self, flags=enable_pyobj_flags):
+        a = np.arange(100, dtype='i4').reshape(10, 10)
+        pyfunc = integer_indexing_2d_usecase
+        arraytype = types.Array(types.int32, 2, 'C')
+        cr = compile_isolated(pyfunc, (arraytype, types.float32, types.int32),
+                              flags=flags)
+        cfunc = cr.entry_point
+
+        self.assertEqual(pyfunc(a, 0, 0), cfunc(a, 0, 0))
+        self.assertEqual(pyfunc(a, 9, 9), cfunc(a, 9, 9))
+        self.assertEqual(pyfunc(a, -1, -1), cfunc(a, -1, -1))
+
+    def test_partial_1d_indexing(self, flags=enable_pyobj_flags):
+        pyfunc = partial_1d_usecase
+
+        def check(arr, arraytype):
+            cr = compile_isolated(pyfunc, (arraytype, types.int32),
+                                  flags=flags)
+            cfunc = cr.entry_point
+            self.assertEqual(pyfunc(arr, 0), cfunc(arr, 0))
+            n = arr.shape[0] - 1
+            self.assertEqual(pyfunc(arr, n), cfunc(arr, n))
+            self.assertEqual(pyfunc(arr, -1), cfunc(arr, -1))
+
+        a = np.arange(12, dtype='i4').reshape((4, 3))
+        arraytype = types.Array(types.int32, 2, 'C')
+        check(a, arraytype)
+
+        a = np.arange(12, dtype='i4').reshape((3, 4)).T
+        arraytype = types.Array(types.int32, 2, 'F')
+        check(a, arraytype)
+
+        a = np.arange(12, dtype='i4').reshape((3, 4))[::2]
+        arraytype = types.Array(types.int32, 2, 'A')
+        check(a, arraytype)
+
+    def check_ellipsis(self, pyfunc, flags):
+        def compile_func(arr):
+            cr = compile_isolated(pyfunc, (typeof(arr), types.intp, types.intp),
+                                  flags=flags)
+            return cr.entry_point
+
+        def run(a):
+            bounds = (0, 1, 2, -1, -2)
+            cfunc = compile_func(a)
+            for i, j in itertools.product(bounds, bounds):
+                x = cfunc(a, i, j)
+                np.testing.assert_equal(pyfunc(a, i, j), cfunc(a, i, j))
+
+        run(np.arange(16, dtype='i4').reshape(4, 4))
+        run(np.arange(27, dtype='i4').reshape(3, 3, 3))
+
+    def test_ellipsis1(self, flags=enable_pyobj_flags):
+        self.check_ellipsis(ellipsis_usecase1, flags)
+
+    def test_ellipsis1_npm(self):
+        self.test_ellipsis1(flags=Noflags)
+
+    def test_ellipsis2(self, flags=enable_pyobj_flags):
+        self.check_ellipsis(ellipsis_usecase2, flags)
+
+    def test_ellipsis2_npm(self):
+        self.test_ellipsis2(flags=Noflags)
+
+    def test_ellipsis3(self, flags=enable_pyobj_flags):
+        self.check_ellipsis(ellipsis_usecase3, flags)
+
+    def test_ellipsis3_npm(self):
+        self.test_ellipsis3(flags=Noflags)
+
+    def test_ellipsis_issue1498(self):
+        # This is an issue due to incorrect layout inferred for when
+        # ellpsis is used and ndenumerate is specializing on the layout.
+        @njit
+        def udt(arr):
+            out = np.zeros_like(arr)
+            i = 0
+            for index, val in np.ndenumerate(arr[..., i]):
+                out[index][i] = val
+
+            return out
+
+        py_func = udt.py_func
+
+        outersize = 4
+        innersize = 4
+        arr = np.arange(outersize * innersize).reshape(outersize, innersize)
+        got = udt(arr)
+        expected = py_func(arr)
+        np.testing.assert_equal(got, expected)
+
+    def test_ellipsis_issue1499(self):
+        # This tests an issue when ndarray.__getitem__ recv a tuple of
+        # constants. The lowering is mishandling the constant value creation.
+        @njit
+        def udt(arr):
+            return arr[..., 0]
+
+        arr = np.arange(3)
+        got = udt(arr)
+        expected = udt.py_func(arr)
+        np.testing.assert_equal(got, expected)
+
+    def test_none_index(self, flags=enable_pyobj_flags):
+        pyfunc = none_index_usecase
+        arraytype = types.Array(types.int32, 2, 'C')
+        # TODO should be enable to handle this in NoPython mode
+        cr = compile_isolated(pyfunc, (arraytype,), flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(100, dtype='i4').reshape(10, 10)
+        self.assertPreciseEqual(pyfunc(a), cfunc(a))
+
+    def test_none_index_npm(self):
+        with self.assertTypingError():
+            self.test_none_index(flags=Noflags)
+
+    def test_empty_tuple_indexing(self, flags=enable_pyobj_flags):
+        pyfunc = empty_tuple_usecase
+        arraytype = types.Array(types.int32, 0, 'C')
+        cr = compile_isolated(pyfunc, (arraytype,), flags=flags)
+        cfunc = cr.entry_point
+
+        a = np.arange(1, dtype='i4').reshape(())
+        self.assertPreciseEqual(pyfunc(a), cfunc(a))
+
+    def test_empty_tuple_indexing_npm(self):
+        self.test_empty_tuple_indexing(flags=Noflags)
+
+
+class TestSetItem(TestCase):
+    """
+    Test basic indexed store into an array.
+    Note fancy indexing is tested in test_fancy_indexing.
+    """
+
+    def test_conversion_setitem(self, flags=enable_pyobj_flags):
+        """ this used to work, and was used in one of the tutorials """
+        from numba import jit
+
+        def pyfunc(array):
+            for index in range(len(array)):
+                array[index] = index % decimal.Decimal(100)
+
+        cfunc = jit("void(i8[:])")(pyfunc)
+
+        udt = np.arange(100, dtype='i1')
+        control = udt.copy()
+        pyfunc(control)
+        cfunc(udt)
+        self.assertPreciseEqual(udt, control)
+
+    def test_1d_slicing_set(self, flags=enable_pyobj_flags):
+        """
+        1d to 1d slice assignment
+        """
+        pyfunc = slicing_1d_usecase_set
+        # Note heterogeneous types for the source and destination arrays
+        # (int16[:] -> int32[:])
+        dest_type = types.Array(types.int32, 1, 'C')
+        src_type = types.Array(types.int16, 1, 'A')
+        argtys = (dest_type, src_type, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        N = 10
+        arg = np.arange(N, dtype='i2') + 40
+        bounds = [0, 2, N - 2, N, N + 1, N + 3,
+                  -2, -N + 2, -N, -N - 1, -N - 3]
+        def make_dest():
+            return np.zeros_like(arg, dtype='i4')
+        for start, stop in itertools.product(bounds, bounds):
+            for step in (1, 2, -1, -2):
+                args = start, stop, step
+                index = slice(*args)
+                pyleft = pyfunc(make_dest(), arg[index], *args)
+                cleft = cfunc(make_dest(), arg[index], *args)
+                self.assertPreciseEqual(pyleft, cleft)
+
+        # Mismatching input size and slice length
+        with self.assertRaises(ValueError):
+            cfunc(np.zeros_like(arg, dtype=np.int32), arg, 0, 0, 1)
+
+    def check_1d_slicing_set_sequence(self, flags, seqty, seq):
+        """
+        Generic sequence to 1d slice assignment
+        """
+        pyfunc = slicing_1d_usecase_set
+        dest_type = types.Array(types.int32, 1, 'C')
+        argtys = (dest_type, seqty, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        N = 10
+        k = len(seq)
+        arg = np.arange(N, dtype=np.int32)
+        args = (seq, 1, -N + k + 1, 1)
+        expected = pyfunc(arg.copy(), *args)
+        got = cfunc(arg.copy(), *args)
+        self.assertPreciseEqual(expected, got)
+
+        args = (seq, 1, -N + k, 1)
+        with self.assertRaises(ValueError) as raises:
+            cfunc(arg.copy(), *args)
+
+    def test_1d_slicing_set_tuple(self, flags=enable_pyobj_flags):
+        """
+        Tuple to 1d slice assignment
+        """
+        self.check_1d_slicing_set_sequence(
+            flags, types.UniTuple(types.int16, 2), (8, -42))
+
+    def test_1d_slicing_set_list(self, flags=enable_pyobj_flags):
+        """
+        List to 1d slice assignment
+        """
+        self.check_1d_slicing_set_sequence(
+            flags, types.List(types.int16), [8, -42])
+
+    def test_1d_slicing_broadcast(self, flags=enable_pyobj_flags):
+        """
+        scalar to 1d slice assignment
+        """
+        pyfunc = slicing_1d_usecase_set
+        arraytype = types.Array(types.int32, 1, 'C')
+        # Note heterogeneous types for the source scalar and the destination
+        # array (int16 -> int32[:])
+        argtys = (arraytype, types.int16, types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        N = 10
+        arg = np.arange(N, dtype='i4')
+        val = 42
+        bounds = [0, 2, N - 2, N, N + 1, N + 3,
+                  -2, -N + 2, -N, -N - 1, -N - 3]
+        for start, stop in itertools.product(bounds, bounds):
+            for step in (1, 2, -1, -2):
+                args = val, start, stop, step
+                pyleft = pyfunc(arg.copy(), *args)
+                cleft = cfunc(arg.copy(), *args)
+                self.assertPreciseEqual(pyleft, cleft)
+
+    def test_1d_slicing_add(self, flags=enable_pyobj_flags):
+        pyfunc = slicing_1d_usecase_add
+        arraytype = types.Array(types.int32, 1, 'C')
+        argtys = (arraytype, arraytype, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        arg = np.arange(10, dtype='i4')
+        for test in ((0, 10), (2, 5)):
+            pyleft = pyfunc(np.zeros_like(arg), arg[slice(*test)], *test)
+            cleft = cfunc(np.zeros_like(arg), arg[slice(*test)], *test)
+            self.assertPreciseEqual(pyleft, cleft)
+
+    def test_1d_slicing_set_npm(self):
+        self.test_1d_slicing_set(flags=Noflags)
+
+    def test_1d_slicing_set_list_npm(self):
+        self.test_1d_slicing_set_list(flags=Noflags)
+
+    def test_1d_slicing_set_tuple_npm(self):
+        self.test_1d_slicing_set_tuple(flags=Noflags)
+
+    def test_1d_slicing_broadcast_npm(self):
+        self.test_1d_slicing_broadcast(flags=Noflags)
+
+    def test_1d_slicing_add_npm(self):
+        self.test_1d_slicing_add(flags=Noflags)
+
+    def test_2d_slicing_set(self, flags=enable_pyobj_flags):
+        """
+        2d to 2d slice assignment
+        """
+        pyfunc = slicing_2d_usecase_set
+        arraytype = types.Array(types.int32, 2, 'A')
+        argtys = (arraytype, arraytype, types.int32, types.int32, types.int32,
+                  types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        arg = np.arange(10*10, dtype='i4').reshape(10,10)
+        tests = [
+            (0, 10, 1, 0, 10, 1),
+            (2, 3, 1, 2, 3, 1),
+            (10, 0, 1, 10, 0, 1),
+            (0, 10, -1, 0, 10, -1),
+            (0, 10, 2, 0, 10, 2),
+        ]
+        for test in tests:
+            pyleft = pyfunc(np.zeros_like(arg), arg[slice(*test[0:3]), slice(*test[3:6])], *test)
+            cleft = cfunc(np.zeros_like(arg), arg[slice(*test[0:3]), slice(*test[3:6])], *test)
+            self.assertPreciseEqual(cleft, pyleft)
+
+    def test_2d_slicing_broadcast(self, flags=enable_pyobj_flags):
+        """
+        scalar to 2d slice assignment
+        """
+        pyfunc = slicing_2d_usecase_set
+        arraytype = types.Array(types.int32, 2, 'C')
+        # Note heterogeneous types for the source scalar and the destination
+        # array (int16 -> int32[:])
+        argtys = (arraytype, types.int16, types.int32, types.int32, types.int32,
+                  types.int32, types.int32, types.int32)
+        cr = compile_isolated(pyfunc, argtys, flags=flags)
+        cfunc = cr.entry_point
+
+        arg = np.arange(10*10, dtype='i4').reshape(10,10)
+        val = 42
+        tests = [
+            (0, 10, 1, 0, 10, 1),
+            (2, 3, 1, 2, 3, 1),
+            (10, 0, 1, 10, 0, 1),
+            (0, 10, -1, 0, 10, -1),
+            (0, 10, 2, 0, 10, 2),
+        ]
+        for test in tests:
+            pyleft = pyfunc(arg.copy(), val, *test)
+            cleft = cfunc(arg.copy(), val, *test)
+            self.assertPreciseEqual(cleft, pyleft)
+
+    def test_2d_slicing_set_npm(self):
+        self.test_2d_slicing_set(flags=Noflags)
+
+    def test_2d_slicing_broadcast_npm(self):
+        self.test_2d_slicing_broadcast(flags=Noflags)
+
+    def test_setitem(self):
+        """
+        scalar indexed assignment
+        """
+        arr = np.arange(5)
+        setitem_usecase(arr, 1, 42)
+        self.assertEqual(arr.tolist(), [0, 42, 2, 3, 4])
+        # Using a 0-d array as scalar index
+        setitem_usecase(arr, np.array(3).astype(np.uint16), 8)
+        self.assertEqual(arr.tolist(), [0, 42, 2, 8, 4])
+        # Scalar Broadcasting
+        arr = np.arange(9).reshape(3, 3)
+        setitem_usecase(arr, 1, 42)
+        self.assertEqual(arr.tolist(), [[0, 1, 2], [42, 42, 42], [6, 7, 8]])
+
+    def test_setitem_broadcast(self):
+        """
+        broadcasted array assignment
+        """
+        # Scalar Broadcasting
+        dst = np.arange(5)
+        setitem_broadcast_usecase(dst, 42)
+        self.assertEqual(dst.tolist(), [42] * 5)
+        # 1D -> 2D Array Broadcasting
+        dst = np.arange(6).reshape(2, 3)
+        setitem_broadcast_usecase(dst, np.arange(1, 4))
+        self.assertEqual(dst.tolist(), [[1, 2, 3], [1, 2, 3]])
+        # 2D -> 2D Array Broadcasting
+        dst = np.arange(6).reshape(2, 3)
+        setitem_broadcast_usecase(dst, np.arange(1, 4).reshape(1, 3))
+        self.assertEqual(dst.tolist(), [[1, 2, 3], [1, 2, 3]])
+        # 2D -> 4D Array Broadcasting
+        dst = np.arange(12).reshape(2, 1, 2, 3)
+        setitem_broadcast_usecase(dst, np.arange(1, 4).reshape(1, 3))
+        inner2 = [[1, 2, 3], [1, 2, 3]]
+        self.assertEqual(dst.tolist(), [[inner2]] * 2)
+        # 2D -> 1D Array Broadcasting
+        dst = np.arange(5)
+        setitem_broadcast_usecase(dst, np.arange(1, 6).reshape(1, 5))
+        self.assertEqual(dst.tolist(), [1, 2, 3, 4, 5])
+        # 4D -> 2D Array Broadcasting
+        dst = np.arange(6).reshape(2, 3)
+        setitem_broadcast_usecase(dst, np.arange(1, 1 + dst.size).reshape(1, 1, 2, 3))
+        self.assertEqual(dst.tolist(), [[1, 2, 3], [4, 5, 6]])
+
+    def test_setitem_broadcast_error(self):
+        # higher dim assigned into lower dim
+        # 2D -> 1D
+        dst = np.arange(5)
+        src = np.arange(10).reshape(2, 5)
+        with self.assertRaises(ValueError) as raises:
+            setitem_broadcast_usecase(dst, src)
+        errmsg = str(raises.exception)
+        self.assertEqual('cannot broadcast source array for assignment',
+                         errmsg)
+        # 3D -> 2D
+        dst = np.arange(5).reshape(1, 5)
+        src = np.arange(10).reshape(1, 2, 5)
+        with self.assertRaises(ValueError) as raises:
+            setitem_broadcast_usecase(dst, src)
+        errmsg = str(raises.exception)
+        self.assertEqual('cannot assign slice from input of different size',
+                         errmsg)
+        # lower to higher
+        # 1D -> 2D
+        dst = np.arange(10).reshape(2, 5)
+        src = np.arange(4)
+        with self.assertRaises(ValueError) as raises:
+            setitem_broadcast_usecase(dst, src)
+        errmsg = str(raises.exception)
+        self.assertEqual('cannot assign slice from input of different size',
+                         errmsg)
+
+    def test_slicing_1d_broadcast(self):
+        # 1D -> 2D sliced (1)
+        dst = np.arange(6).reshape(3, 2)
+        src = np.arange(1, 3)
+        slicing_1d_usecase_set(dst, src, 0, 2, 1)
+        self.assertEqual(dst.tolist(), [[1, 2], [1, 2], [4, 5]])
+        # 1D -> 2D sliced (2)
+        dst = np.arange(6).reshape(3, 2)
+        src = np.arange(1, 3)
+        slicing_1d_usecase_set(dst, src, 0, None, 2)
+        self.assertEqual(dst.tolist(), [[1, 2], [2, 3], [1, 2]])
+        # 2D -> 2D sliced (3)
+        dst = np.arange(6).reshape(3, 2)
+        src = np.arange(1, 5).reshape(2, 2)
+        slicing_1d_usecase_set(dst, src, None, 2, 1)
+        self.assertEqual(dst.tolist(), [[1, 2], [3, 4], [4, 5]])
+
+    def test_setitem_readonly(self):
+        arr = np.arange(5)
+        arr.flags.writeable = False
+        with self.assertRaises((TypeError, errors.TypingError)) as raises:
+            setitem_usecase(arr, 1, 42)
+        self.assertIn("Cannot modify readonly array of type:",
+                      str(raises.exception))
+
+
+class TestTyping(TestCase):
+    """
+    Check typing of basic indexing operations
+    """
+
+    def test_layout(self):
+        """
+        Check an appropriate layout is inferred for the result of array
+        indexing.
+        """
+
+        func = arraydecl.get_array_index_type
+
+        cty = types.Array(types.float64, 3, 'C')
+        fty = types.Array(types.float64, 3, 'F')
+        aty = types.Array(types.float64, 3, 'A')
+
+        indices = [
+            # Tuples of (indexing arguments, keeps "C" layout, keeps "F" layout)
+            ((), True, True),
+            ((ellipsis,), True, True),
+
+            # Indexing from the left => can sometimes keep "C" layout
+            ((intp,), True, False),
+            ((slice2_type,), True, False),
+            ((intp, slice2_type), True, False),
+            ((slice2_type, intp), False, False),
+            ((slice2_type, slice2_type), False, False),
+            # Strided slices = > "A" layout
+            ((intp, slice3_type), False, False),
+            ((slice3_type,), False, False),
+
+            # Indexing from the right => can sometimes keep "F" layout
+            ((ellipsis, intp,), False, True),
+            ((ellipsis, slice2_type,), False, True),
+            ((ellipsis, intp, slice2_type,), False, False),
+            ((ellipsis, slice2_type, intp,), False, True),
+            ((ellipsis, slice2_type, slice2_type,), False, False),
+            # Strided slices = > "A" layout
+            ((ellipsis, slice3_type,), False, False),
+            ((ellipsis, slice3_type, intp,), False, False),
+
+            # Indexing from both sides => only if all dimensions are indexed
+            ((intp, ellipsis, intp,), False, False),
+            ((slice2_type, ellipsis, slice2_type,), False, False),
+            ((intp, intp, slice2_type,), True, False),
+            ((intp, ellipsis, intp, slice2_type,), True, False),
+            ((slice2_type, intp, intp,), False, True),
+            ((slice2_type, intp, ellipsis, intp,), False, True),
+            ((intp, slice2_type, intp,), False, False),
+            # Strided slices = > "A" layout
+            ((slice3_type, intp, intp,), False, False),
+            ((intp, intp, slice3_type,), False, False),
+            ]
+
+        for index_tuple, keep_c, _ in indices:
+            index = types.Tuple(index_tuple)
+            r = func(cty, index)
+            self.assertEqual(tuple(r.index), index_tuple)
+            self.assertEqual(r.result.layout, 'C' if keep_c else 'A',
+                             index_tuple)
+            self.assertFalse(r.advanced)
+
+        for index_tuple, _, keep_f in indices:
+            index = types.Tuple(index_tuple)
+            r = func(fty, index)
+            self.assertEqual(tuple(r.index), index_tuple)
+            self.assertEqual(r.result.layout, 'F' if keep_f else 'A',
+                             index_tuple)
+            self.assertFalse(r.advanced)
+
+        for index_tuple, _, _ in indices:
+            index = types.Tuple(index_tuple)
+            r = func(aty, index)
+            self.assertEqual(tuple(r.index), index_tuple)
+            self.assertEqual(r.result.layout, 'A')
+            self.assertFalse(r.advanced)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_init_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_init_utils.py
new file mode 100644
index 000000000..cff483c5e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_init_utils.py
@@ -0,0 +1,42 @@
+import unittest
+
+from numba.tests.support import TestCase
+
+from numba.misc.init_utils import version_info, generate_version_info
+
+
+class TestGenerateVersionInfo(TestCase):
+
+    def test_major_minor_patch(self):
+        expected = version_info(0, 1, 0,
+                                (0, 1), (0, 1, 0),
+                                "0.1.0", ('0', '1', '0'), None)
+        received = generate_version_info("0.1.0")
+        self.assertEqual(received, expected)
+
+    def test_unknown(self):
+        expected = version_info(None, None, None,
+                                (None, None), (None, None, None),
+                                '0+unknown', ('0+unknown',), None)
+        received = generate_version_info('0+unknown')
+        self.assertEqual(received, expected)
+
+    def test_dev(self):
+        expected = version_info(0, 1, None,
+                                (0, 1), (0, 1, None),
+                                '0.1.0dev0', ('0', '1', '0dev0'), None)
+        received = generate_version_info('0.1.0dev0')
+        self.assertEqual(received, expected)
+
+    def test_full_rev(self):
+        expected = version_info(0, 1, None,
+                                (0, 1), (0, 1, None),
+                                '0.1.0dev0+1.g0123456789abcdef',
+                                ('0', '1', '0dev0+1', 'g0123456789abcdef'),
+                                'g0123456789abcdef')
+        received = generate_version_info('0.1.0dev0+1.g0123456789abcdef')
+        self.assertEqual(received, expected)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_inlining.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_inlining.py
new file mode 100644
index 000000000..0541f7067
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_inlining.py
@@ -0,0 +1,286 @@
+import re
+import numpy as np
+
+from numba.tests.support import (TestCase, override_config, captured_stdout,
+                      skip_parfors_unsupported)
+from numba import jit, njit
+from numba.core import types, ir, postproc, compiler
+from numba.core.ir_utils import (guard, find_callname, find_const,
+                                 get_definition, simplify_CFG)
+from numba.core.registry import CPUDispatcher
+from numba.core.inline_closurecall import inline_closure_call
+
+from numba.core.untyped_passes import (ExtractByteCode, TranslateByteCode, FixupArgs,
+                             IRProcessing, DeadBranchPrune,
+                             RewriteSemanticConstants, GenericRewrites,
+                             WithLifting, PreserveIR, InlineClosureLikes)
+
+from numba.core.typed_passes import (NopythonTypeInference, AnnotateTypes,
+                           NopythonRewrites, PreParforPass, ParforPass,
+                           DumpParforDiagnostics, NativeLowering,
+                           IRLegalization, NoPythonBackend, NativeLowering)
+
+from numba.core.compiler_machinery import FunctionPass, PassManager, register_pass
+import unittest
+
+@jit((types.int32,), nopython=True)
+def inner(a):
+    return a + 1
+
+@jit((types.int32,), nopython=True)
+def more(a):
+    return inner(inner(a))
+
+def outer_simple(a):
+    return inner(a) * 2
+
+def outer_multiple(a):
+    return inner(a) * more(a)
+
+@njit
+def __dummy__():
+    return
+
+@register_pass(analysis_only=False, mutates_CFG=True)
+class InlineTestPass(FunctionPass):
+    _name = "inline_test_pass"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        # assuming the function has one block with one call inside
+        assert len(state.func_ir.blocks) == 1
+        block = list(state.func_ir.blocks.values())[0]
+        for i, stmt in enumerate(block.body):
+            if guard(find_callname,state.func_ir, stmt.value) is not None:
+                inline_closure_call(state.func_ir, {}, block, i, lambda: None,
+                                    state.typingctx, state.targetctx, (),
+                                    state.typemap, state.calltypes)
+                break
+        # also fix up the IR
+        post_proc = postproc.PostProcessor(state.func_ir)
+        post_proc.run()
+        post_proc.remove_dels()
+        return True
+
+
+def gen_pipeline(state, test_pass):
+        name = 'inline_test'
+        pm = PassManager(name)
+        pm.add_pass(TranslateByteCode, "analyzing bytecode")
+        pm.add_pass(FixupArgs, "fix up args")
+        pm.add_pass(IRProcessing, "processing IR")
+        pm.add_pass(WithLifting, "Handle with contexts")
+        # pre typing
+        if not state.flags.no_rewrites:
+            pm.add_pass(GenericRewrites, "nopython rewrites")
+            pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
+            pm.add_pass(DeadBranchPrune, "dead branch pruning")
+        pm.add_pass(InlineClosureLikes,
+                    "inline calls to locally defined closures")
+        # typing
+        pm.add_pass(NopythonTypeInference, "nopython frontend")
+
+        if state.flags.auto_parallel.enabled:
+            pm.add_pass(PreParforPass, "Preprocessing for parfors")
+        if not state.flags.no_rewrites:
+            pm.add_pass(NopythonRewrites, "nopython rewrites")
+        if state.flags.auto_parallel.enabled:
+            pm.add_pass(ParforPass, "convert to parfors")
+
+        pm.add_pass(test_pass, "inline test")
+
+        # legalise
+        pm.add_pass(IRLegalization, "ensure IR is legal prior to lowering")
+        pm.add_pass(AnnotateTypes, "annotate types")
+        pm.add_pass(PreserveIR, "preserve IR")
+
+        # lower
+        pm.add_pass(NativeLowering, "native lowering")
+        pm.add_pass(NoPythonBackend, "nopython mode backend")
+        pm.add_pass(DumpParforDiagnostics, "dump parfor diagnostics")
+        return pm
+
+class InlineTestPipeline(compiler.CompilerBase):
+    """compiler pipeline for testing inlining after optimization
+    """
+    def define_pipelines(self):
+        pm = gen_pipeline(self.state, InlineTestPass)
+        pm.finalize()
+        return [pm]
+
+class TestInlining(TestCase):
+    """
+    Check that jitted inner functions are inlined into outer functions,
+    in nopython mode.
+    Note that not all inner functions are guaranteed to be inlined.
+    We just trust LLVM's inlining heuristics.
+    """
+
+    def make_pattern(self, fullname):
+        """
+        Make regexpr to match mangled name
+        """
+        parts = fullname.split('.')
+        return r'_ZN?' + r''.join([r'\d+{}'.format(p) for p in parts])
+
+    def assert_has_pattern(self, fullname, text):
+        pat = self.make_pattern(fullname)
+        self.assertIsNotNone(re.search(pat, text),
+                             msg='expected {}'.format(pat))
+
+    def assert_not_has_pattern(self, fullname, text):
+        pat = self.make_pattern(fullname)
+        self.assertIsNone(re.search(pat, text),
+                          msg='unexpected {}'.format(pat))
+
+    def test_inner_function(self):
+        with override_config('DUMP_ASSEMBLY', True):
+            with captured_stdout() as out:
+                cfunc = jit((types.int32,), nopython=True)(outer_simple)
+        self.assertPreciseEqual(cfunc(1), 4)
+        # Check the inner function was elided from the output (which also
+        # guarantees it was inlined into the outer function).
+        asm = out.getvalue()
+        prefix = __name__
+        self.assert_has_pattern('%s.outer_simple' % prefix, asm)
+        self.assert_not_has_pattern('%s.inner' % prefix, asm)
+
+    def test_multiple_inner_functions(self):
+        # Same with multiple inner functions, and multiple calls to
+        # the same inner function (inner()).  This checks that linking in
+        # the same library/module twice doesn't produce linker errors.
+        with override_config('DUMP_ASSEMBLY', True):
+            with captured_stdout() as out:
+                cfunc = jit((types.int32,), nopython=True)(outer_multiple)
+        self.assertPreciseEqual(cfunc(1), 6)
+        asm = out.getvalue()
+        prefix = __name__
+        self.assert_has_pattern('%s.outer_multiple' % prefix, asm)
+        self.assert_not_has_pattern('%s.more' % prefix, asm)
+        self.assert_not_has_pattern('%s.inner' % prefix, asm)
+
+    @skip_parfors_unsupported
+    def test_inline_call_after_parfor(self):
+        # replace the call to make sure inlining doesn't cause label conflict
+        # with parfor body
+        def test_impl(A):
+            __dummy__()
+            return A.sum()
+        j_func = njit(parallel=True, pipeline_class=InlineTestPipeline)(
+                                                                    test_impl)
+        A = np.arange(10)
+        self.assertEqual(test_impl(A), j_func(A))
+
+    @skip_parfors_unsupported
+    def test_inline_update_target_def(self):
+
+        def test_impl(a):
+            if a == 1:
+                b = 2
+            else:
+                b = 3
+            return b
+
+        func_ir = compiler.run_frontend(test_impl)
+        blocks = list(func_ir.blocks.values())
+        for block in blocks:
+            for i, stmt in enumerate(block.body):
+                # match b = 2 and replace with lambda: 2
+                if (isinstance(stmt, ir.Assign) and isinstance(stmt.value, ir.Var)
+                        and guard(find_const, func_ir, stmt.value) == 2):
+                    # replace expr with a dummy call
+                    func_ir._definitions[stmt.target.name].remove(stmt.value)
+                    stmt.value = ir.Expr.call(ir.Var(block.scope, "myvar", loc=stmt.loc), (), (), stmt.loc)
+                    func_ir._definitions[stmt.target.name].append(stmt.value)
+                    #func = g.py_func#
+                    inline_closure_call(func_ir, {}, block, i, lambda: 2)
+                    break
+
+        self.assertEqual(len(func_ir._definitions['b']), 2)
+
+    @skip_parfors_unsupported
+    def test_inline_var_dict_ret(self):
+        # make sure inline_closure_call returns the variable replacement dict
+        # and it contains the original variable name used in locals
+        @njit(locals={'b': types.float64})
+        def g(a):
+            b = a + 1
+            return b
+
+        def test_impl():
+            return g(1)
+
+        func_ir = compiler.run_frontend(test_impl)
+        blocks = list(func_ir.blocks.values())
+        for block in blocks:
+            for i, stmt in enumerate(block.body):
+                if (isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Expr)
+                        and stmt.value.op == 'call'):
+                    func_def = guard(get_definition, func_ir, stmt.value.func)
+                    if (isinstance(func_def, (ir.Global, ir.FreeVar))
+                            and isinstance(func_def.value, CPUDispatcher)):
+                        py_func = func_def.value.py_func
+                        _, var_map = inline_closure_call(
+                            func_ir, py_func.__globals__, block, i, py_func)
+                        break
+
+        self.assertTrue('b' in var_map)
+
+    @skip_parfors_unsupported
+    def test_inline_call_branch_pruning(self):
+        # branch pruning pass should run properly in inlining to enable
+        # functions with type checks
+        @njit
+        def foo(A=None):
+            if A is None:
+                return 2
+            else:
+                return A
+
+        def test_impl(A=None):
+            return foo(A)
+
+        @register_pass(analysis_only=False, mutates_CFG=True)
+        class PruningInlineTestPass(FunctionPass):
+            _name = "pruning_inline_test_pass"
+
+            def __init__(self):
+                FunctionPass.__init__(self)
+
+            def run_pass(self, state):
+                # assuming the function has one block with one call inside
+                assert len(state.func_ir.blocks) == 1
+                block = list(state.func_ir.blocks.values())[0]
+                for i, stmt in enumerate(block.body):
+                    if (guard(find_callname, state.func_ir, stmt.value)
+                            is not None):
+                        inline_closure_call(state.func_ir, {}, block, i,
+                            foo.py_func, state.typingctx, state.targetctx,
+                            (state.typemap[stmt.value.args[0].name],),
+                             state.typemap, state.calltypes)
+                        break
+                return True
+
+        class InlineTestPipelinePrune(compiler.CompilerBase):
+
+            def define_pipelines(self):
+                pm = gen_pipeline(self.state, PruningInlineTestPass)
+                pm.finalize()
+                return [pm]
+
+        # make sure inline_closure_call runs in full pipeline
+        j_func = njit(pipeline_class=InlineTestPipelinePrune)(test_impl)
+        A = 3
+        self.assertEqual(test_impl(A), j_func(A))
+        self.assertEqual(test_impl(), j_func())
+
+        # make sure IR doesn't have branches
+        fir = j_func.overloads[(types.Omitted(None),)].metadata['preserved_ir']
+        fir.blocks = simplify_CFG(fir.blocks)
+        self.assertEqual(len(fir.blocks), 1)
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_interpreter.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_interpreter.py
new file mode 100644
index 000000000..f73986fe8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_interpreter.py
@@ -0,0 +1,1150 @@
+"""
+Test bytecode fixes provided in interpreter.py
+"""
+import unittest
+from numba import jit, njit, objmode, typeof, literally
+from numba.extending import overload
+from numba.core import types
+from numba.core.errors import UnsupportedError
+from numba.tests.support import TestCase, MemoryLeakMixin, skip_unless_py10
+
+
+@njit
+def sum_jit_func(
+    arg0=0,
+    arg1=0,
+    arg2=0,
+    arg3=0,
+    arg4=0,
+    arg5=0,
+    arg6=0,
+    arg7=0,
+    arg8=0,
+    arg9=0,
+    arg10=0,
+    arg11=0,
+    arg12=0,
+    arg13=0,
+    arg14=0,
+    arg15=0,
+    arg16=0,
+    arg17=0,
+    arg18=0,
+    arg19=0,
+    arg20=0,
+    arg21=0,
+    arg22=0,
+    arg23=0,
+    arg24=0,
+    arg25=0,
+    arg26=0,
+    arg27=0,
+    arg28=0,
+    arg29=0,
+    arg30=0,
+    arg31=0,
+    arg32=0,
+    arg33=0,
+    arg34=0,
+    arg35=0,
+    arg36=0,
+    arg37=0,
+    arg38=0,
+    arg39=0,
+    arg40=0,
+    arg41=0,
+    arg42=0,
+    arg43=0,
+    arg44=0,
+    arg45=0,
+    arg46=0,
+):
+    return (
+        arg0
+        + arg1
+        + arg2
+        + arg3
+        + arg4
+        + arg5
+        + arg6
+        + arg7
+        + arg8
+        + arg9
+        + arg10
+        + arg11
+        + arg12
+        + arg13
+        + arg14
+        + arg15
+        + arg16
+        + arg17
+        + arg18
+        + arg19
+        + arg20
+        + arg21
+        + arg22
+        + arg23
+        + arg24
+        + arg25
+        + arg26
+        + arg27
+        + arg28
+        + arg29
+        + arg30
+        + arg31
+        + arg32
+        + arg33
+        + arg34
+        + arg35
+        + arg36
+        + arg37
+        + arg38
+        + arg39
+        + arg40
+        + arg41
+        + arg42
+        + arg43
+        + arg44
+        + arg45
+        + arg46
+    )
+
+
+class TestCallFunctionExPeepHole(MemoryLeakMixin, TestCase):
+    """
+    gh #7812
+
+    Tests that check a peephole optimization for Function calls
+    in Python 3.10. The bytecode changes when
+    (n_args / 2) + n_kws > 15, which moves the arguments from
+    the stack into a tuple and dictionary.
+
+    This peephole optimization updates the IR to use the original format.
+    There are different paths when n_args > 30 and n_args <= 30 and when
+    n_kws > 15 and n_kws <= 15.
+    """
+    THRESHOLD_ARGS = 31
+    THRESHOLD_KWS = 16
+
+    def gen_func(self, n_args, n_kws):
+        """
+            Generates a function that calls sum_jit_func
+            with the desired number of args and kws.
+        """
+        param_list = [f"arg{i}" for i in range(n_args + n_kws)]
+        args_list = []
+        for i in range(n_args + n_kws):
+            # Call a function on every 5th argument to ensure
+            # we test function calls.
+            if i % 5 == 0:
+                arg_val = f"pow(arg{i}, 2)"
+            else:
+                arg_val = f"arg{i}"
+            args_list.append(arg_val)
+        total_params = ", ".join(param_list)
+        func_text = f"def impl({total_params}):\n"
+        func_text += "    return sum_jit_func(\n"
+        for i in range(n_args):
+            func_text += f"        {args_list[i]},\n"
+        for i in range(n_args, n_args + n_kws):
+            func_text += f"        {param_list[i]}={args_list[i]},\n"
+        func_text += "    )\n"
+        local_vars = {}
+        exec(func_text, {"sum_jit_func": sum_jit_func}, local_vars)
+        return local_vars["impl"]
+
+    @skip_unless_py10
+    def test_all_args(self):
+        """
+        Tests calling a function when n_args > 30 and
+        n_kws = 0. This shouldn't use the peephole, but
+        it should still succeed.
+        """
+        total_args = [i for i in range(self.THRESHOLD_ARGS)]
+        f = self.gen_func(self.THRESHOLD_ARGS, 0)
+        py_func = f
+        cfunc = njit()(f)
+        a = py_func(*total_args)
+        b = cfunc(*total_args)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_all_kws(self):
+        """
+        Tests calling a function when n_kws > 15 and
+        n_args = 0.
+        """
+        total_args = [i for i in range(self.THRESHOLD_KWS)]
+        f = self.gen_func(0, self.THRESHOLD_KWS)
+        py_func = f
+        cfunc = njit()(f)
+        a = py_func(*total_args)
+        b = cfunc(*total_args)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_small_args_small_kws(self):
+        """
+        Tests calling a function when (n_args / 2) + n_kws > 15,
+        but n_args <= 30 and n_kws <= 15
+        """
+        used_args = self.THRESHOLD_ARGS - 1
+        used_kws = self.THRESHOLD_KWS - 1
+        total_args = [i for i in range((used_args) + (used_kws))]
+        f = self.gen_func(used_args, used_kws)
+        py_func = f
+        cfunc = njit()(f)
+        a = py_func(*total_args)
+        b = cfunc(*total_args)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_small_args_large_kws(self):
+        """
+        Tests calling a function when (n_args / 2) + n_kws > 15,
+        but n_args <= 30 and n_kws > 15
+        """
+        used_args = self.THRESHOLD_ARGS - 1
+        used_kws = self.THRESHOLD_KWS
+        total_args = [i for i in range((used_args) + (used_kws))]
+        f = self.gen_func(used_args, used_kws)
+        py_func = f
+        cfunc = njit()(f)
+        a = py_func(*total_args)
+        b = cfunc(*total_args)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_large_args_small_kws(self):
+        """
+        Tests calling a function when (n_args / 2) + n_kws > 15,
+        but n_args > 30 and n_kws <= 15
+        """
+        used_args = self.THRESHOLD_ARGS
+        used_kws = self.THRESHOLD_KWS - 1
+        total_args = [i for i in range((used_args) + (used_kws))]
+        f = self.gen_func(used_args, used_kws)
+        py_func = f
+        cfunc = njit()(f)
+        a = py_func(*total_args)
+        b = cfunc(*total_args)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_large_args_large_kws(self):
+        """
+        Tests calling a function when (n_args / 2) + n_kws > 15,
+        but n_args > 30 and n_kws > 15
+        """
+        used_args = self.THRESHOLD_ARGS
+        used_kws = self.THRESHOLD_KWS
+        total_args = [i for i in range((used_args) + (used_kws))]
+        f = self.gen_func(used_args, used_kws)
+        py_func = f
+        cfunc = njit()(f)
+        a = py_func(*total_args)
+        b = cfunc(*total_args)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_large_kws_objmode(self):
+        """
+        Tests calling an objectmode function with > 15 return values.
+        """
+        def py_func():
+            return (
+                0,
+                1,
+                2,
+                3,
+                4,
+                5,
+                6,
+                7,
+                8,
+                9,
+                10,
+                11,
+                12,
+                13,
+                14,
+                15,
+            )
+
+        @njit
+        def objmode_func():
+            """
+            Wrapper to call py_func from objmode. This tests
+            large kws with objmode. If the definition for the
+            call is not properly updated this test will fail.
+            """
+            with objmode(
+                a='int64',
+                b='int64',
+                c='int64',
+                d='int64',
+                e='int64',
+                f='int64',
+                g='int64',
+                h='int64',
+                i='int64',
+                j='int64',
+                k='int64',
+                l='int64',
+                m='int64',
+                n='int64',
+                o='int64',
+                p='int64',
+            ):
+                (
+                    a,
+                    b,
+                    c,
+                    d,
+                    e,
+                    f,
+                    g,
+                    h,
+                    i,
+                    j,
+                    k,
+                    l,
+                    m,
+                    n,
+                    o,
+                    p
+                ) = py_func()
+            return (
+                a
+                + b
+                + c
+                + d
+                + e
+                + f
+                + g
+                + h
+                + i
+                + j
+                + k
+                + l
+                + m
+                + n
+                + o
+                + p
+            )
+
+        a = sum(list(py_func()))
+        b = objmode_func()
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_large_args_inline_controlflow(self):
+        """
+        Tests generating large args when one of the inputs
+        has inlined controlflow.
+        """
+        def inline_func(flag):
+            return sum_jit_func(
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1 if flag else 2,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                arg41=1,
+            )
+
+        with self.assertRaises(UnsupportedError) as raises:
+            njit()(inline_func)(False)
+        self.assertIn(
+            'You can resolve this issue by moving the control flow out',
+            str(raises.exception)
+        )
+
+    @skip_unless_py10
+    def test_large_args_noninlined_controlflow(self):
+        """
+        Tests generating large args when one of the inputs
+        has the change suggested in the error message
+        for inlined control flow.
+        """
+        def inline_func(flag):
+            a_val = 1 if flag else 2
+            return sum_jit_func(
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                a_val,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                arg41=1,
+            )
+
+        py_func = inline_func
+        cfunc = njit()(inline_func)
+        a = py_func(False)
+        b = cfunc(False)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_all_args_inline_controlflow(self):
+        """
+        Tests generating only large args when one of the inputs
+        has inlined controlflow. This requires a special check
+        inside peep_hole_call_function_ex_to_call_function_kw
+        because it usually only handles varkwargs.
+        """
+        def inline_func(flag):
+            return sum_jit_func(
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1 if flag else 2,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+            )
+
+        with self.assertRaises(UnsupportedError) as raises:
+            njit()(inline_func)(False)
+        self.assertIn(
+            'You can resolve this issue by moving the control flow out',
+            str(raises.exception)
+        )
+
+    @skip_unless_py10
+    def test_all_args_noninlined_controlflow(self):
+        """
+        Tests generating large args when one of the inputs
+        has the change suggested in the error message
+        for inlined control flow.
+        """
+        def inline_func(flag):
+            a_val = 1 if flag else 2
+            return sum_jit_func(
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                a_val,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+                1,
+            )
+
+        py_func = inline_func
+        cfunc = njit()(inline_func)
+        a = py_func(False)
+        b = cfunc(False)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_large_kws_inline_controlflow(self):
+        """
+        Tests generating large kws when one of the inputs
+        has inlined controlflow.
+        """
+        def inline_func(flag):
+            return sum_jit_func(
+                arg0=1,
+                arg1=1,
+                arg2=1,
+                arg3=1,
+                arg4=1,
+                arg5=1,
+                arg6=1,
+                arg7=1,
+                arg8=1,
+                arg9=1,
+                arg10=1,
+                arg11=1,
+                arg12=1,
+                arg13=1,
+                arg14=1,
+                arg15=1 if flag else 2,
+            )
+
+        with self.assertRaises(UnsupportedError) as raises:
+            njit()(inline_func)(False)
+        self.assertIn(
+            'You can resolve this issue by moving the control flow out',
+            str(raises.exception)
+        )
+
+    @skip_unless_py10
+    def test_large_kws_noninlined_controlflow(self):
+        """
+        Tests generating large kws when one of the inputs
+        has the change suggested in the error message
+        for inlined control flow.
+        """
+        def inline_func(flag):
+            a_val = 1 if flag else 2
+            return sum_jit_func(
+                arg0=1,
+                arg1=1,
+                arg2=1,
+                arg3=1,
+                arg4=1,
+                arg5=1,
+                arg6=1,
+                arg7=1,
+                arg8=1,
+                arg9=1,
+                arg10=1,
+                arg11=1,
+                arg12=1,
+                arg13=1,
+                arg14=1,
+                arg15=a_val,
+            )
+
+        py_func = inline_func
+        cfunc = njit()(inline_func)
+        a = py_func(False)
+        b = cfunc(False)
+        self.assertEqual(a, b)
+
+
+class TestLargeConstDict(TestCase, MemoryLeakMixin):
+    """
+    gh #7894
+
+    Tests that check a peephole optimization for constant
+    dictionaries in Python 3.10. The bytecode changes when
+    number of elements > 15, which splits the constant dictionary
+    into multiple dictionaries that are joined by a DICT_UPDATE
+    bytecode instruction.
+
+    This optimization modifies the IR to rejoin dictionaries
+    and remove the DICT_UPDATE generated code. This then allows
+    code that depends on literal dictionaries or literal keys
+    to succeed.
+    """
+
+    @skip_unless_py10
+    def test_large_heterogeneous_const_dict(self):
+        """
+        Tests that a function with a large heterogeneous constant
+        dictionary remains a constant.
+        """
+        def const_func():
+            # D is a heterogeneous dictionary
+            # so this code can only compile if
+            # d is constant.
+            d = {
+                "A": 1,
+                "B": 1,
+                "C": 1,
+                "D": 1,
+                "E": 1,
+                "F": 1,
+                "G": 1,
+                "H": 1,
+                "I": 1,
+                "J": 1,
+                "K": 1,
+                "L": 1,
+                "M": 1,
+                "N": 1,
+                "O": 1,
+                "P": 1,
+                "Q": 1,
+                "R": 1,
+                "S": 'a',
+            }
+            return d["S"]
+
+        py_func = const_func
+        cfunc = njit()(const_func)
+        a = py_func()
+        b = cfunc()
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_large_heterogeneous_LiteralStrKeyDict_literal_values(self):
+        """Check the literal values for a LiteralStrKeyDict requiring
+        optimizations because it is heterogeneous.
+        """
+
+        def bar(d):
+            ...
+
+        @overload(bar)
+        def ol_bar(d):
+            a = {
+                "A": 1,
+                "B": 1,
+                "C": 1,
+                "D": 1,
+                "E": 1,
+                "F": 1,
+                "G": 1,
+                "H": 1,
+                "I": 1,
+                "J": 1,
+                "K": 1,
+                "L": 1,
+                "M": 1,
+                "N": 1,
+                "O": 1,
+                "P": 1,
+                "Q": 1,
+                "R": 1,
+                "S": 'a',
+            }
+
+            def specific_ty(z):
+                return types.literal(z) if types.maybe_literal(z) else typeof(z)
+            expected = {types.literal(x): specific_ty(y) for x, y in a.items()}
+            self.assertTrue(isinstance(d, types.LiteralStrKeyDict))
+            self.assertEqual(d.literal_value, expected)
+            self.assertEqual(hasattr(d, 'initial_value'), False)
+            return lambda d: d
+
+        @njit
+        def foo():
+            # D is a heterogeneous dictionary
+            # so this code can only compile if
+            # d has the correct literal values.
+            d = {
+                "A": 1,
+                "B": 1,
+                "C": 1,
+                "D": 1,
+                "E": 1,
+                "F": 1,
+                "G": 1,
+                "H": 1,
+                "I": 1,
+                "J": 1,
+                "K": 1,
+                "L": 1,
+                "M": 1,
+                "N": 1,
+                "O": 1,
+                "P": 1,
+                "Q": 1,
+                "R": 1,
+                "S": 'a',
+            }
+            bar(d)
+
+        foo()
+
+    @skip_unless_py10
+    def test_large_heterogeneous_const_keys_dict(self):
+        """
+        Tests that a function with a large heterogeneous constant
+        dictionary remains a constant.
+        """
+        def const_keys_func(a):
+            # D is a heterogeneous dictionary
+            # so this code can only compile if
+            # d has constant keys.
+            d = {
+                "A": 1,
+                "B": 1,
+                "C": 1,
+                "D": 1,
+                "E": 1,
+                "F": 1,
+                "G": 1,
+                "H": 1,
+                "I": 1,
+                "J": 1,
+                "K": 1,
+                "L": 1,
+                "M": 1,
+                "N": 1,
+                "O": 1,
+                "P": 1,
+                "Q": 1,
+                "R": 1,
+                "S": a,
+            }
+            return d["S"]
+
+        py_func = const_keys_func
+        cfunc = njit()(const_keys_func)
+        # str to make the dictionary heterogeneous.
+        value = "a_string"
+        a = py_func(value)
+        b = cfunc(value)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_large_dict_mutation_not_carried(self):
+        """Checks that the optimization for large dictionaries
+        do not incorrectly update initial values due to other
+        mutations.
+        """
+        def bar(d):
+            ...
+
+        @overload(bar)
+        def ol_bar(d):
+            a = {
+                "A": 1,
+                "B": 1,
+                "C": 1,
+                "D": 1,
+                "E": 1,
+                "F": 1,
+                "G": 1,
+                "H": 1,
+                "I": 1,
+                "J": 1,
+                "K": 1,
+                "L": 1,
+                "M": 1,
+                "N": 1,
+                "O": 1,
+                "P": 1,
+                "Q": 1,
+                "R": 1,
+                "S": 7,
+            }
+            if d.initial_value is None:
+                return lambda d: literally(d)
+            self.assertTrue(isinstance(d, types.DictType))
+            self.assertEqual(d.initial_value, a)
+            return lambda d: d
+
+        @njit
+        def foo():
+            # This dictionary is mutated, check the initial_value carries
+            # correctly and is not mutated
+            d = {
+                "A": 1,
+                "B": 1,
+                "C": 1,
+                "D": 1,
+                "E": 1,
+                "F": 1,
+                "G": 1,
+                "H": 1,
+                "I": 1,
+                "J": 1,
+                "K": 1,
+                "L": 1,
+                "M": 1,
+                "N": 1,
+                "O": 1,
+                "P": 1,
+                "Q": 1,
+                "R": 1,
+                "S": 7,
+            }
+            d['X'] = 4
+            bar(d)
+
+        foo()
+
+    @skip_unless_py10
+    def test_usercode_update_use_d2(self):
+        """
+        Tests an example using a regular update is
+        not modified by the optimization.
+        """
+
+        def check_before(x):
+            pass
+
+        def check_after(x):
+            pass
+
+        checked_before = False
+        checked_after = False
+
+        @overload(check_before, prefer_literal=True)
+        def ol_check_before(d):
+            nonlocal checked_before
+            # Typing iteration from d1.update(d2)
+            # may reset the initial values to None,
+            # so we only check on the first iteration.
+            if not checked_before:
+                checked_before = True
+                a = {
+                    "a": 1,
+                    "b": 2,
+                    "c": 3,
+                }
+                self.assertTrue(isinstance(d, types.DictType))
+                self.assertEqual(d.initial_value, a)
+
+            return lambda d: None
+
+        @overload(check_after, prefer_literal=True)
+        def ol_check_after(d):
+            nonlocal checked_after
+            # Typing iteration from d1.update(d2)
+            # may reset the initial values to None,
+            # so we only check on the first iteration.
+            if not checked_after:
+                checked_after = True
+                self.assertTrue(isinstance(d, types.DictType))
+                self.assertTrue(d.initial_value is None)
+
+            return lambda d: None
+
+        def const_dict_func():
+            """
+            Dictionary update between two constant
+            dictionaries. This verifies d2 doesn't
+            get incorrectly removed.
+            """
+            d1 = {
+                "a": 1,
+                "b": 2,
+                "c": 3,
+            }
+            d2 = {
+                "d": 4,
+                "e": 4
+            }
+            check_before(d1)
+            d1.update(d2)
+            check_after(d1)
+            # Create a use of d2 in a new block.
+            if len(d1) > 4:
+                return d2
+            return d1
+
+        py_func = const_dict_func
+        cfunc = njit()(const_dict_func)
+        a = py_func()
+        b = cfunc()
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_large_const_dict_inline_controlflow(self):
+        """
+        Tests generating a large dictionary when one of
+        the inputs requires inline control flow
+        has the change suggested in the error message
+        for inlined control flow.
+        """
+        def inline_func(a, flag):
+            # D is a heterogeneous dictionary
+            # so this code can only compile if
+            # d has constant keys.
+            d = {
+                "A": 1,
+                "B": 1,
+                "C": 1,
+                "D": 1,
+                "E": 1,
+                "F": 1,
+                "G": 1,
+                "H": 1 if flag else 2,
+                "I": 1,
+                "J": 1,
+                "K": 1,
+                "L": 1,
+                "M": 1,
+                "N": 1,
+                "O": 1,
+                "P": 1,
+                "Q": 1,
+                "R": 1,
+                "S": a,
+            }
+            return d["S"]
+
+        with self.assertRaises(UnsupportedError) as raises:
+            njit()(inline_func)("a_string", False)
+        self.assertIn(
+            'You can resolve this issue by moving the control flow out',
+            str(raises.exception)
+        )
+
+    @skip_unless_py10
+    def test_large_const_dict_noninline_controlflow(self):
+        """
+        Tests generating large constant dict when one of the
+        inputs has the change suggested in the error message
+        for inlined control flow.
+        """
+        def non_inline_func(a, flag):
+            # D is a heterogeneous dictionary
+            # so this code can only compile if
+            # d has constant keys.
+            val = 1 if flag else 2
+            d = {
+                "A": 1,
+                "B": 1,
+                "C": 1,
+                "D": 1,
+                "E": 1,
+                "F": 1,
+                "G": 1,
+                "H": val,
+                "I": 1,
+                "J": 1,
+                "K": 1,
+                "L": 1,
+                "M": 1,
+                "N": 1,
+                "O": 1,
+                "P": 1,
+                "Q": 1,
+                "R": 1,
+                "S": a,
+            }
+            return d["S"]
+
+        py_func = non_inline_func
+        cfunc = njit()(non_inline_func)
+        value = "a_string"
+        a = py_func(value, False)
+        b = cfunc(value, False)
+        self.assertEqual(a, b)
+
+    @skip_unless_py10
+    def test_fuse_twice_literal_values(self):
+        """
+        Tests that the correct literal values are generated
+        for a dictionary that produces two DICT_UPDATE
+        bytecode entries for the same dictionary.
+        """
+        def bar(d):
+            ...
+
+        @overload(bar)
+        def ol_bar(d):
+            a = {
+                "a1" : 1,
+                "a2" : 2,
+                "a3" : 3,
+                "a4" : 4,
+                "a5" : 5,
+                "a6" : 6,
+                "a7" : 7,
+                "a8" : 8,
+                "a9" : 9,
+                "a10" : 10,
+                "a11" : 11,
+                "a12" : 12,
+                "a13" : 13,
+                "a14" : 14,
+                "a15" : 15,
+                "a16" : 16,
+                "a17" : 17,
+                "a18" : 18,
+                "a19" : 19,
+                "a20" : 20,
+                "a21" : 21,
+                "a22" : 22,
+                "a23" : 23,
+                "a24" : 24,
+                "a25" : 25,
+                "a26" : 26,
+                "a27" : 27,
+                "a28" : 28,
+                "a29" : 29,
+                "a30" : 30,
+                "a31" : 31,
+                "a32" : 32,
+                "a33" : 33,
+                "a34" : 34, # 34 items is the limit of
+                            # (LOAD_CONST + MAP_ADD)^n + DICT_UPDATE
+                "a35" : 35, # 35 Generates an additional BUILD_MAP + DICT_UPDATE
+            }
+            if d.initial_value is None:
+                return lambda d: literally(d)
+            self.assertTrue(isinstance(d, types.DictType))
+            self.assertEqual(d.initial_value, a)
+            return lambda d: d
+
+        @njit
+        def foo():
+            # This dictionary is mutated, check the initial_value carries
+            # correctly and is not mutated
+            d = {
+                "a1" : 1,
+                "a2" : 2,
+                "a3" : 3,
+                "a4" : 4,
+                "a5" : 5,
+                "a6" : 6,
+                "a7" : 7,
+                "a8" : 8,
+                "a9" : 9,
+                "a10" : 10,
+                "a11" : 11,
+                "a12" : 12,
+                "a13" : 13,
+                "a14" : 14,
+                "a15" : 15,
+                "a16" : 16,
+                "a17" : 17,
+                "a18" : 18,
+                "a19" : 19,
+                "a20" : 20,
+                "a21" : 21,
+                "a22" : 22,
+                "a23" : 23,
+                "a24" : 24,
+                "a25" : 25,
+                "a26" : 26,
+                "a27" : 27,
+                "a28" : 28,
+                "a29" : 29,
+                "a30" : 30,
+                "a31" : 31,
+                "a32" : 32,
+                "a33" : 33,
+                "a34" : 34, # 34 items is the limit of
+                            # (LOAD_CONST + MAP_ADD)^n + DICT_UPDATE
+                "a35" : 35, # 35 Generates an additional BUILD_MAP + DICT_UPDATE
+            }
+            bar(d)
+
+        foo()
+
+
+class TestListExtendInStarArgNonTupleIterable(MemoryLeakMixin, TestCase):
+    """Test `fn(pos_arg0, pos_arg1, *args)` where args is a non-tuple iterable.
+
+    Python 3.9+ will generate LIST_EXTEND bytecode to combine the positional
+    arguments with the `*args`.
+
+    See #8059
+
+    NOTE: At the moment, there are no meaningful tests for NoPython because the
+    lack of support for `tuple(iterable)` for most iterable types.
+    """
+    def test_list_extend_forceobj(self):
+        def consumer(*x):
+            return x
+
+        @jit(forceobj=True)
+        def foo(x):
+            return consumer(1, 2, *x)
+
+        got = foo("ijo")
+        expect = foo.py_func("ijo")
+        self.assertEqual(got, (1, 2, "i", "j", "o"))
+        self.assertEqual(got, expect)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_interproc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_interproc.py
new file mode 100644
index 000000000..56265dfbe
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_interproc.py
@@ -0,0 +1,47 @@
+import gc
+
+from numba import jit, int32
+import unittest
+
+
+def foo(a, b):
+    return a + b
+
+
+def bar(a, b):
+    return cfoo(a, b) + b
+
+@jit
+def inner(x, y):
+    return x + y
+
+@jit(nopython=True)
+def outer(x, y):
+    return inner(x, y)
+
+
+class TestInterProc(unittest.TestCase):
+
+    def test_bar_call_foo(self):
+        global cfoo
+        cfoo = jit((int32, int32), nopython=True)(foo)
+        cbar = jit((int32, int32), nopython=True)(bar)
+        self.assertEqual(cbar(1, 2), 1 + 2 + 2)
+
+    def test_bar_call_foo_compiled_twice(self):
+        # When a function is compiled twice, then called from another
+        # compiled function, check that the right target is called.
+        # (otherwise, LLVM would assert out or crash)
+        global cfoo
+        for i in range(2):
+            cfoo = jit((int32, int32), nopython=True)(foo)
+            gc.collect()
+        cbar = jit((int32, int32), nopython=True)(bar)
+        self.assertEqual(cbar(1, 2), 1 + 2 + 2)
+
+    def test_callsite_compilation(self):
+        self.assertEqual(outer(1, 2), 1 + 2)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_intwidth.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_intwidth.py
new file mode 100644
index 000000000..76c0effeb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_intwidth.py
@@ -0,0 +1,90 @@
+import unittest
+
+import math
+import sys
+
+from numba import jit
+from numba.core import utils
+from numba.tests.support import TestCase, tag
+
+
+max_uint64 = 18446744073709551615
+
+def usecase_uint64_global():
+    return max_uint64
+
+def usecase_uint64_constant():
+    return 18446744073709551615
+
+def usecase_uint64_func():
+    return max(18446744073709551614, 18446744073709551615)
+
+def usecase_int64_pos():
+    return 9223372036854775807
+
+def usecase_int64_neg():
+    return -9223372036854775808
+
+def usecase_int64_func():
+    return (max(9223372036854775807, -9223372036854775808)
+            + min(9223372036854775807, -9223372036854775808))
+
+
+class IntWidthTest(TestCase):
+
+    def check_nullary_func(self, pyfunc, **kwargs):
+        cfunc = jit(**kwargs)(pyfunc)
+        self.assertPreciseEqual(cfunc(), pyfunc())
+
+    def test_global_uint64(self, nopython=False):
+        pyfunc = usecase_uint64_global
+        self.check_nullary_func(pyfunc, nopython=nopython)
+
+    def test_global_uint64_npm(self):
+        self.test_global_uint64(nopython=True)
+
+    def test_constant_uint64(self, nopython=False):
+        pyfunc = usecase_uint64_constant
+        self.check_nullary_func(pyfunc, nopython=nopython)
+
+    def test_constant_uint64_npm(self):
+        self.test_constant_uint64(nopython=True)
+
+    def test_constant_uint64_function_call(self, nopython=False):
+        pyfunc = usecase_uint64_func
+        self.check_nullary_func(pyfunc, nopython=nopython)
+
+    def test_constant_uint64_function_call_npm(self):
+        self.test_constant_uint64_function_call(nopython=True)
+
+    def test_bit_length(self):
+        f = utils.bit_length
+        self.assertEqual(f(0x7f), 7)
+        self.assertEqual(f(-0x7f), 7)
+        self.assertEqual(f(0x80), 8)
+        self.assertEqual(f(-0x80), 7)
+        self.assertEqual(f(0xff), 8)
+        self.assertEqual(f(-0xff), 8)
+        self.assertEqual(f(0x100), 9)
+        self.assertEqual(f(-0x100), 8)
+        self.assertEqual(f(-0x101), 9)
+        self.assertEqual(f(0x7fffffff), 31)
+        self.assertEqual(f(-0x7fffffff), 31)
+        self.assertEqual(f(-0x80000000), 31)
+        self.assertEqual(f(0x80000000), 32)
+        self.assertEqual(f(0xffffffff), 32)
+        self.assertEqual(f(0xffffffffffffffff), 64)
+        self.assertEqual(f(0x10000000000000000), 65)
+
+    def test_constant_int64(self, nopython=False):
+        self.check_nullary_func(usecase_int64_pos, nopython=nopython)
+        self.check_nullary_func(usecase_int64_neg, nopython=nopython)
+        self.check_nullary_func(usecase_int64_func, nopython=nopython)
+
+    def test_constant_int64_npm(self):
+        self.test_constant_int64(nopython=True)
+
+
+if __name__ == '__main__':
+    unittest.main()
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir.py
new file mode 100644
index 000000000..8704c9028
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir.py
@@ -0,0 +1,560 @@
+import unittest
+from unittest.case import TestCase
+import warnings
+import numpy as np
+
+from numba import objmode
+from numba.core import ir, compiler
+from numba.core import errors
+from numba.core.compiler import (
+    CompilerBase,
+    ReconstructSSA,
+)
+from numba.core.compiler_machinery import (
+    FunctionPass,
+    PassManager,
+    register_pass,
+)
+from numba.core.untyped_passes import (
+    TranslateByteCode,
+    IRProcessing,
+)
+from numba import njit
+
+
+class TestIR(unittest.TestCase):
+
+    def test_IRScope(self):
+        filename = "<?>"
+        top = ir.Scope(parent=None, loc=ir.Loc(filename=filename, line=1))
+        local = ir.Scope(parent=top, loc=ir.Loc(filename=filename, line=2))
+
+        apple = local.define('apple', loc=ir.Loc(filename=filename, line=3))
+        self.assertIs(local.get('apple'), apple)
+        self.assertEqual(len(local.localvars), 1)
+
+        orange = top.define('orange', loc=ir.Loc(filename=filename, line=4))
+        self.assertEqual(len(local.localvars), 1)
+        self.assertEqual(len(top.localvars), 1)
+        self.assertIs(top.get('orange'), orange)
+        self.assertIs(local.get('orange'), orange)
+
+        more_orange = local.define('orange', loc=ir.Loc(filename=filename,
+                                                        line=5))
+        self.assertIs(top.get('orange'), orange)
+        self.assertIsNot(local.get('orange'), not orange)
+        self.assertIs(local.get('orange'), more_orange)
+
+        try:
+            local.define('orange', loc=ir.Loc(filename=filename, line=5))
+        except ir.RedefinedError:
+            pass
+        else:
+            self.fail("Expecting an %s" % ir.RedefinedError)
+
+
+class CheckEquality(unittest.TestCase):
+
+    var_a = ir.Var(None, 'a', ir.unknown_loc)
+    var_b = ir.Var(None, 'b', ir.unknown_loc)
+    var_c = ir.Var(None, 'c', ir.unknown_loc)
+    var_d = ir.Var(None, 'd', ir.unknown_loc)
+    var_e = ir.Var(None, 'e', ir.unknown_loc)
+    loc1 = ir.Loc('mock', 1, 0)
+    loc2 = ir.Loc('mock', 2, 0)
+    loc3 = ir.Loc('mock', 3, 0)
+
+    def check(self, base, same=[], different=[]):
+        for s in same:
+            self.assertTrue(base == s)
+        for d in different:
+            self.assertTrue(base != d)
+
+
+class TestIRMeta(CheckEquality):
+    """
+    Tests IR node meta, like Loc and Scope
+    """
+    def test_loc(self):
+        a = ir.Loc('file', 1, 0)
+        b = ir.Loc('file', 1, 0)
+        c = ir.Loc('pile', 1, 0)
+        d = ir.Loc('file', 2, 0)
+        e = ir.Loc('file', 1, 1)
+        self.check(a, same=[b,], different=[c, d, e])
+
+        f = ir.Loc('file', 1, 0, maybe_decorator=False)
+        g = ir.Loc('file', 1, 0, maybe_decorator=True)
+        self.check(a, same=[f, g])
+
+    def test_scope(self):
+        parent1 = ir.Scope(None, self.loc1)
+        parent2 = ir.Scope(None, self.loc1)
+        parent3 = ir.Scope(None, self.loc2)
+        self.check(parent1, same=[parent2, parent3,])
+
+        a = ir.Scope(parent1, self.loc1)
+        b = ir.Scope(parent1, self.loc1)
+        c = ir.Scope(parent1, self.loc2)
+        d = ir.Scope(parent3, self.loc1)
+        self.check(a, same=[b, c, d])
+
+        # parent1 and parent2 are equal, so children referring to either parent
+        # should be equal
+        e = ir.Scope(parent2, self.loc1)
+        self.check(a, same=[e,])
+
+
+class TestIRNodes(CheckEquality):
+    """
+    Tests IR nodes
+    """
+    def test_terminator(self):
+        # terminator base class inst should always be equal
+        t1 = ir.Terminator()
+        t2 = ir.Terminator()
+        self.check(t1, same=[t2])
+
+    def test_jump(self):
+        a = ir.Jump(1, self.loc1)
+        b = ir.Jump(1, self.loc1)
+        c = ir.Jump(1, self.loc2)
+        d = ir.Jump(2, self.loc1)
+        self.check(a, same=[b, c], different=[d])
+
+    def test_return(self):
+        a = ir.Return(self.var_a, self.loc1)
+        b = ir.Return(self.var_a, self.loc1)
+        c = ir.Return(self.var_a, self.loc2)
+        d = ir.Return(self.var_b, self.loc1)
+        self.check(a, same=[b, c], different=[d])
+
+    def test_raise(self):
+        a = ir.Raise(self.var_a, self.loc1)
+        b = ir.Raise(self.var_a, self.loc1)
+        c = ir.Raise(self.var_a, self.loc2)
+        d = ir.Raise(self.var_b, self.loc1)
+        self.check(a, same=[b, c], different=[d])
+
+    def test_staticraise(self):
+        a = ir.StaticRaise(AssertionError, None, self.loc1)
+        b = ir.StaticRaise(AssertionError, None, self.loc1)
+        c = ir.StaticRaise(AssertionError, None, self.loc2)
+        e = ir.StaticRaise(AssertionError, ("str",), self.loc1)
+        d = ir.StaticRaise(RuntimeError, None, self.loc1)
+        self.check(a, same=[b, c], different=[d, e])
+
+    def test_branch(self):
+        a = ir.Branch(self.var_a, 1, 2, self.loc1)
+        b = ir.Branch(self.var_a, 1, 2, self.loc1)
+        c = ir.Branch(self.var_a, 1, 2, self.loc2)
+        d = ir.Branch(self.var_b, 1, 2, self.loc1)
+        e = ir.Branch(self.var_a, 2, 2, self.loc1)
+        f = ir.Branch(self.var_a, 1, 3, self.loc1)
+        self.check(a, same=[b, c], different=[d, e, f])
+
+    def test_expr(self):
+        a = ir.Expr('some_op', self.loc1)
+        b = ir.Expr('some_op', self.loc1)
+        c = ir.Expr('some_op', self.loc2)
+        d = ir.Expr('some_other_op', self.loc1)
+        self.check(a, same=[b, c], different=[d])
+
+    def test_setitem(self):
+        a = ir.SetItem(self.var_a, self.var_b, self.var_c, self.loc1)
+        b = ir.SetItem(self.var_a, self.var_b, self.var_c, self.loc1)
+        c = ir.SetItem(self.var_a, self.var_b, self.var_c, self.loc2)
+        d = ir.SetItem(self.var_d, self.var_b, self.var_c, self.loc1)
+        e = ir.SetItem(self.var_a, self.var_d, self.var_c, self.loc1)
+        f = ir.SetItem(self.var_a, self.var_b, self.var_d, self.loc1)
+        self.check(a, same=[b, c], different=[d, e, f])
+
+    def test_staticsetitem(self):
+        a = ir.StaticSetItem(self.var_a, 1, self.var_b, self.var_c, self.loc1)
+        b = ir.StaticSetItem(self.var_a, 1, self.var_b, self.var_c, self.loc1)
+        c = ir.StaticSetItem(self.var_a, 1, self.var_b, self.var_c, self.loc2)
+        d = ir.StaticSetItem(self.var_d, 1, self.var_b, self.var_c, self.loc1)
+        e = ir.StaticSetItem(self.var_a, 2, self.var_b, self.var_c, self.loc1)
+        f = ir.StaticSetItem(self.var_a, 1, self.var_d, self.var_c, self.loc1)
+        g = ir.StaticSetItem(self.var_a, 1, self.var_b, self.var_d, self.loc1)
+        self.check(a, same=[b, c], different=[d, e, f, g])
+
+    def test_delitem(self):
+        a = ir.DelItem(self.var_a, self.var_b, self.loc1)
+        b = ir.DelItem(self.var_a, self.var_b, self.loc1)
+        c = ir.DelItem(self.var_a, self.var_b, self.loc2)
+        d = ir.DelItem(self.var_c, self.var_b, self.loc1)
+        e = ir.DelItem(self.var_a, self.var_c, self.loc1)
+        self.check(a, same=[b, c], different=[d, e])
+
+    def test_del(self):
+        a = ir.Del(self.var_a.name, self.loc1)
+        b = ir.Del(self.var_a.name, self.loc1)
+        c = ir.Del(self.var_a.name, self.loc2)
+        d = ir.Del(self.var_b.name, self.loc1)
+        self.check(a, same=[b, c], different=[d])
+
+    def test_setattr(self):
+        a = ir.SetAttr(self.var_a, 'foo', self.var_b, self.loc1)
+        b = ir.SetAttr(self.var_a, 'foo', self.var_b, self.loc1)
+        c = ir.SetAttr(self.var_a, 'foo', self.var_b, self.loc2)
+        d = ir.SetAttr(self.var_c, 'foo', self.var_b, self.loc1)
+        e = ir.SetAttr(self.var_a, 'bar', self.var_b, self.loc1)
+        f = ir.SetAttr(self.var_a, 'foo', self.var_c, self.loc1)
+        self.check(a, same=[b, c], different=[d, e, f])
+
+    def test_delattr(self):
+        a = ir.DelAttr(self.var_a, 'foo', self.loc1)
+        b = ir.DelAttr(self.var_a, 'foo', self.loc1)
+        c = ir.DelAttr(self.var_a, 'foo', self.loc2)
+        d = ir.DelAttr(self.var_c, 'foo', self.loc1)
+        e = ir.DelAttr(self.var_a, 'bar', self.loc1)
+        self.check(a, same=[b, c], different=[d, e])
+
+    def test_assign(self):
+        a = ir.Assign(self.var_a, self.var_b, self.loc1)
+        b = ir.Assign(self.var_a, self.var_b, self.loc1)
+        c = ir.Assign(self.var_a, self.var_b, self.loc2)
+        d = ir.Assign(self.var_c, self.var_b, self.loc1)
+        e = ir.Assign(self.var_a, self.var_c, self.loc1)
+        self.check(a, same=[b, c], different=[d, e])
+
+    def test_print(self):
+        a = ir.Print((self.var_a,), self.var_b, self.loc1)
+        b = ir.Print((self.var_a,), self.var_b, self.loc1)
+        c = ir.Print((self.var_a,), self.var_b, self.loc2)
+        d = ir.Print((self.var_c,), self.var_b, self.loc1)
+        e = ir.Print((self.var_a,), self.var_c, self.loc1)
+        self.check(a, same=[b, c], different=[d, e])
+
+    def test_storemap(self):
+        a = ir.StoreMap(self.var_a, self.var_b, self.var_c, self.loc1)
+        b = ir.StoreMap(self.var_a, self.var_b, self.var_c, self.loc1)
+        c = ir.StoreMap(self.var_a, self.var_b, self.var_c, self.loc2)
+        d = ir.StoreMap(self.var_d, self.var_b, self.var_c, self.loc1)
+        e = ir.StoreMap(self.var_a, self.var_d, self.var_c, self.loc1)
+        f = ir.StoreMap(self.var_a, self.var_b, self.var_d, self.loc1)
+        self.check(a, same=[b, c], different=[d, e, f])
+
+    def test_yield(self):
+        a = ir.Yield(self.var_a, self.loc1, 0)
+        b = ir.Yield(self.var_a, self.loc1, 0)
+        c = ir.Yield(self.var_a, self.loc2, 0)
+        d = ir.Yield(self.var_b, self.loc1, 0)
+        e = ir.Yield(self.var_a, self.loc1, 1)
+        self.check(a, same=[b, c], different=[d, e])
+
+    def test_enterwith(self):
+        a = ir.EnterWith(self.var_a, 0, 1, self.loc1)
+        b = ir.EnterWith(self.var_a, 0, 1, self.loc1)
+        c = ir.EnterWith(self.var_a, 0, 1, self.loc2)
+        d = ir.EnterWith(self.var_b, 0, 1, self.loc1)
+        e = ir.EnterWith(self.var_a, 1, 1, self.loc1)
+        f = ir.EnterWith(self.var_a, 0, 2, self.loc1)
+        self.check(a, same=[b, c], different=[d, e, f])
+
+    def test_arg(self):
+        a = ir.Arg('foo', 0, self.loc1)
+        b = ir.Arg('foo', 0, self.loc1)
+        c = ir.Arg('foo', 0, self.loc2)
+        d = ir.Arg('bar', 0, self.loc1)
+        e = ir.Arg('foo', 1, self.loc1)
+        self.check(a, same=[b, c], different=[d, e])
+
+    def test_const(self):
+        a = ir.Const(1, self.loc1)
+        b = ir.Const(1, self.loc1)
+        c = ir.Const(1, self.loc2)
+        d = ir.Const(2, self.loc1)
+        self.check(a, same=[b, c], different=[d])
+
+    def test_global(self):
+        a = ir.Global('foo', 0, self.loc1)
+        b = ir.Global('foo', 0, self.loc1)
+        c = ir.Global('foo', 0, self.loc2)
+        d = ir.Global('bar', 0, self.loc1)
+        e = ir.Global('foo', 1, self.loc1)
+        self.check(a, same=[b, c], different=[d, e])
+
+    def test_var(self):
+        a = ir.Var(None, 'foo', self.loc1)
+        b = ir.Var(None, 'foo', self.loc1)
+        c = ir.Var(None, 'foo', self.loc2)
+        d = ir.Var(ir.Scope(None, ir.unknown_loc), 'foo', self.loc1)
+        e = ir.Var(None, 'bar', self.loc1)
+        self.check(a, same=[b, c, d], different=[e])
+
+    def test_undefinedtype(self):
+        a = ir.UndefinedType()
+        b = ir.UndefinedType()
+        self.check(a, same=[b])
+
+    def test_loop(self):
+        a = ir.Loop(1, 3)
+        b = ir.Loop(1, 3)
+        c = ir.Loop(2, 3)
+        d = ir.Loop(1, 4)
+        self.check(a, same=[b], different=[c, d])
+
+    def test_with(self):
+        a = ir.With(1, 3)
+        b = ir.With(1, 3)
+        c = ir.With(2, 3)
+        d = ir.With(1, 4)
+        self.check(a, same=[b], different=[c, d])
+
+
+# used later
+_GLOBAL = 1234
+
+
+class TestIRCompounds(CheckEquality):
+    """
+    Tests IR concepts that have state
+    """
+    def test_varmap(self):
+        a = ir.VarMap()
+        a.define(self.var_a, 'foo')
+        a.define(self.var_b, 'bar')
+
+        b = ir.VarMap()
+        b.define(self.var_a, 'foo')
+        b.define(self.var_b, 'bar')
+
+        c = ir.VarMap()
+        c.define(self.var_a, 'foo')
+        c.define(self.var_c, 'bar')
+
+        self.check(a, same=[b], different=[c])
+
+    def test_block(self):
+        def gen_block():
+            parent = ir.Scope(None, self.loc1)
+            tmp = ir.Block(parent, self.loc2)
+            assign1 = ir.Assign(self.var_a, self.var_b, self.loc3)
+            assign2 = ir.Assign(self.var_a, self.var_c, self.loc3)
+            assign3 = ir.Assign(self.var_c, self.var_b, self.loc3)
+            tmp.append(assign1)
+            tmp.append(assign2)
+            tmp.append(assign3)
+            return tmp
+
+        a = gen_block()
+        b = gen_block()
+        c = gen_block().append(ir.Assign(self.var_a, self.var_b, self.loc3))
+
+        self.check(a, same=[b], different=[c])
+
+    def test_functionir(self):
+
+        def run_frontend(x):
+            return compiler.run_frontend(x, emit_dels=True)
+
+        # this creates a function full of all sorts of things to ensure the IR
+        # is pretty involved, it then compares two instances of the compiled
+        # function IR to check the IR is the same invariant of objects, and then
+        # a tiny mutation is made to the IR in the second function and detection
+        # of this change is checked.
+        def gen():
+            _FREEVAR = 0xCAFE
+
+            def foo(a, b, c=12, d=1j, e=None):
+                f = a + b
+                a += _FREEVAR
+                g = np.zeros(c, dtype=np.complex64)
+                h = f + g
+                i = 1j / d
+                if np.abs(i) > 0:
+                    k = h / i
+                    l = np.arange(1, c + 1)
+                    with objmode():
+                        print(e, k)
+                    m = np.sqrt(l - g)
+                    if np.abs(m[0]) < 1:
+                        n = 0
+                        for o in range(a):
+                            n += 0
+                            if np.abs(n) < 3:
+                                break
+                        n += m[2]
+                    p = g / l
+                    q = []
+                    for r in range(len(p)):
+                        q.append(p[r])
+                        if r > 4 + 1:
+                            with objmode(s='intp', t='complex128'):
+                                s = 123
+                                t = 5
+                            if s > 122:
+                                t += s
+                        t += q[0] + _GLOBAL
+
+                return f + o + r + t + r + a + n
+
+            return foo
+
+        x = gen()
+        y = gen()
+        x_ir = run_frontend(x)
+        y_ir = run_frontend(y)
+
+        self.assertTrue(x_ir.equal_ir(y_ir))
+
+        def check_diffstr(string, pointing_at=[]):
+            lines = string.splitlines()
+            for item in pointing_at:
+                for l in lines:
+                    if l.startswith('->'):
+                        if item in l:
+                            break
+                else:
+                    raise AssertionError("Could not find %s " % item)
+
+        self.assertIn("IR is considered equivalent", x_ir.diff_str(y_ir))
+
+        # minor mutation, simply switch branch targets on last branch
+        for label in reversed(list(y_ir.blocks.keys())):
+            blk = y_ir.blocks[label]
+            if isinstance(blk.body[-1], ir.Branch):
+                ref = blk.body[-1]
+                ref.truebr, ref.falsebr = ref.falsebr, ref.truebr
+                break
+
+        check_diffstr(x_ir.diff_str(y_ir), ['branch'])
+
+        z = gen()
+        self.assertFalse(x_ir.equal_ir(y_ir))
+
+        z_ir = run_frontend(z)
+
+        change_set = set()
+        for label in reversed(list(z_ir.blocks.keys())):
+            blk = z_ir.blocks[label]
+            ref = blk.body[:-1]
+            idx = None
+            for i in range(len(ref)):
+                # look for two adjacent Del
+                if (isinstance(ref[i], ir.Del) and
+                        isinstance(ref[i + 1], ir.Del)):
+                    idx = i
+                    break
+            if idx is not None:
+                b = blk.body
+                change_set.add(str(b[idx + 1]))
+                change_set.add(str(b[idx]))
+                b[idx], b[idx + 1] = b[idx + 1], b[idx]
+                break
+
+        # ensure that a mutation occurred.
+        self.assertTrue(change_set)
+
+        self.assertFalse(x_ir.equal_ir(z_ir))
+        self.assertEqual(len(change_set), 2)
+        for item in change_set:
+            self.assertTrue(item.startswith('del '))
+        check_diffstr(x_ir.diff_str(z_ir), change_set)
+
+        def foo(a, b):
+            c = a * 2
+            d = c + b
+            e = np.sqrt(d)
+            return e
+
+        def bar(a, b): # same as foo
+            c = a * 2
+            d = c + b
+            e = np.sqrt(d)
+            return e
+
+        def baz(a, b):
+            c = a * 2
+            d = b + c
+            e = np.sqrt(d + 1)
+            return e
+
+        foo_ir = run_frontend(foo)
+        bar_ir = run_frontend(bar)
+        self.assertTrue(foo_ir.equal_ir(bar_ir))
+        self.assertIn("IR is considered equivalent", foo_ir.diff_str(bar_ir))
+
+        baz_ir = run_frontend(baz)
+        self.assertFalse(foo_ir.equal_ir(baz_ir))
+        tmp = foo_ir.diff_str(baz_ir)
+        self.assertIn("Other block contains more statements", tmp)
+        check_diffstr(tmp, ["c + b", "b + c"])
+
+
+class TestIRPedanticChecks(TestCase):
+    def test_var_in_scope_assumption(self):
+        # Create a pass that clears ir.Scope in ir.Block
+        @register_pass(mutates_CFG=False, analysis_only=False)
+        class RemoveVarInScope(FunctionPass):
+            _name = "_remove_var_in_scope"
+
+            def __init__(self):
+                FunctionPass.__init__(self)
+
+            # implement method to do the work, "state" is the internal compiler
+            # state from the CompilerBase instance.
+            def run_pass(self, state):
+                func_ir = state.func_ir
+                # walk the blocks
+                for blk in func_ir.blocks.values():
+                    oldscope = blk.scope
+                    # put in an empty Scope
+                    blk.scope = ir.Scope(parent=oldscope.parent,
+                                         loc=oldscope.loc)
+                return True
+
+        # Create a pass that always fails, to stop the compiler
+        @register_pass(mutates_CFG=False, analysis_only=False)
+        class FailPass(FunctionPass):
+            _name = "_fail"
+
+            def __init__(self, *args, **kwargs):
+                FunctionPass.__init__(self)
+
+            def run_pass(self, state):
+                # This is unreachable. SSA pass should have raised before this
+                # pass when run with `error.NumbaPedanticWarning`s raised as
+                # errors.
+                raise AssertionError("unreachable")
+
+        class MyCompiler(CompilerBase):
+            def define_pipelines(self):
+                pm = PassManager("testing pm")
+                pm.add_pass(TranslateByteCode, "analyzing bytecode")
+                pm.add_pass(IRProcessing, "processing IR")
+                pm.add_pass(RemoveVarInScope, "_remove_var_in_scope")
+                pm.add_pass(ReconstructSSA, "ssa")
+                pm.add_pass(FailPass, "_fail")
+                pm.finalize()
+                return [pm]
+
+        @njit(pipeline_class=MyCompiler)
+        def dummy(x):
+            # To trigger SSA and the pedantic check, this function must have
+            # multiple assignments to the same variable in different blocks.
+            a = 1
+            b = 2
+            if a < b:
+                a = 2
+            else:
+                b = 3
+            return a, b
+
+        with warnings.catch_warnings():
+            # Make NumbaPedanticWarning an error
+            warnings.simplefilter("error", errors.NumbaPedanticWarning)
+            # Catch NumbaIRAssumptionWarning
+            with self.assertRaises(errors.NumbaIRAssumptionWarning) as raises:
+                dummy(1)
+            # Verify the error message
+            self.assertRegex(
+                str(raises.exception),
+                r"variable '[a-z]' is not in scope",
+            )
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir_inlining.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir_inlining.py
new file mode 100644
index 000000000..ce5909a73
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir_inlining.py
@@ -0,0 +1,1544 @@
+"""
+This tests the inline kwarg to @jit and @overload etc, it has nothing to do with
+LLVM or low level inlining.
+"""
+
+import operator
+import warnings
+from itertools import product
+import numpy as np
+
+from numba import njit, typeof, literally, prange
+from numba.core import types, ir, ir_utils, cgutils, errors
+from numba.core.extending import (
+    overload,
+    overload_method,
+    overload_attribute,
+    register_model,
+    typeof_impl,
+    unbox,
+    NativeValue,
+    models,
+    make_attribute_wrapper,
+    intrinsic,
+    register_jitable,
+)
+from numba.core.datamodel.models import OpaqueModel
+from numba.core.cpu import InlineOptions
+from numba.core.compiler import DefaultPassBuilder, CompilerBase
+from numba.core.typed_passes import InlineOverloads
+from numba.core.typing import signature
+from numba.tests.support import (TestCase, unittest, skip_py38_or_later,
+                                 MemoryLeakMixin, IRPreservingTestPipeline,
+                                 skip_parfors_unsupported,
+                                 ignore_internal_warnings)
+
+
+# this global has the same name as the global in inlining_usecases.py, it
+# is here to check that inlined functions bind to their own globals
+_GLOBAL1 = -50
+
+
+@njit(inline='always')
+def _global_func(x):
+    return x + 1
+
+
+# to be overloaded
+def _global_defn(x):
+    return x + 1
+
+
+@overload(_global_defn, inline='always')
+def _global_overload(x):
+    return _global_defn
+
+
+class InliningBase(TestCase):
+
+    _DEBUG = False
+
+    inline_opt_as_bool = {'always': True, 'never': False}
+
+    # --------------------------------------------------------------------------
+    # Example cost model
+
+    def sentinel_17_cost_model(self, func_ir):
+        # sentinel 17 cost model, this is a fake cost model that will return
+        # True (i.e. inline) if the ir.FreeVar(17) is found in the func_ir,
+        for blk in func_ir.blocks.values():
+            for stmt in blk.body:
+                if isinstance(stmt, ir.Assign):
+                    if isinstance(stmt.value, ir.FreeVar):
+                        if stmt.value.value == 17:
+                            return True
+        return False
+
+    # --------------------------------------------------------------------------
+
+    def check(self, test_impl, *args, **kwargs):
+        inline_expect = kwargs.pop('inline_expect', None)
+        assert inline_expect
+        block_count = kwargs.pop('block_count', 1)
+        assert not kwargs
+        for k, v in inline_expect.items():
+            assert isinstance(k, str)
+            assert isinstance(v, bool)
+
+        j_func = njit(pipeline_class=IRPreservingTestPipeline)(test_impl)
+
+        # check they produce the same answer first!
+        self.assertEqual(test_impl(*args), j_func(*args))
+
+        # make sure IR doesn't have branches
+        fir = j_func.overloads[j_func.signatures[0]].metadata['preserved_ir']
+        fir.blocks = ir_utils.simplify_CFG(fir.blocks)
+        if self._DEBUG:
+            print("FIR".center(80, "-"))
+            fir.dump()
+        if block_count != 'SKIP':
+            self.assertEqual(len(fir.blocks), block_count)
+        block = next(iter(fir.blocks.values()))
+
+        # if we don't expect the function to be inlined then make sure there is
+        # 'call' present still
+        exprs = [x for x in block.find_exprs()]
+        assert exprs
+        for k, v in inline_expect.items():
+            found = False
+            for expr in exprs:
+                if getattr(expr, 'op', False) == 'call':
+                    func_defn = fir.get_definition(expr.func)
+                    found |= func_defn.name == k
+                elif ir_utils.is_operator_or_getitem(expr):
+                    found |= expr.fn.__name__ == k
+            self.assertFalse(found == v)
+
+        return fir  # for use in further analysis
+
+    def make_dummy_type(self):
+        """ Use to generate a dummy type """
+
+        # Use test_id to make sure no collision is possible.
+        test_id = self.id()
+        DummyType = type('DummyTypeFor{}'.format(test_id), (types.Opaque,), {})
+
+        dummy_type = DummyType("my_dummy")
+        register_model(DummyType)(OpaqueModel)
+
+        class Dummy(object):
+            pass
+
+        @typeof_impl.register(Dummy)
+        def typeof_dummy(val, c):
+            return dummy_type
+
+        @unbox(DummyType)
+        def unbox_dummy(typ, obj, c):
+            return NativeValue(c.context.get_dummy_value())
+
+        return Dummy, DummyType
+
+
+# used in _gen_involved
+_GLOBAL = 1234
+
+
+def _gen_involved():
+    _FREEVAR = 0xCAFE
+
+    def foo(a, b, c=12, d=1j, e=None):
+        f = a + b
+        a += _FREEVAR
+        g = np.zeros(c, dtype=np.complex64)
+        h = f + g
+        i = 1j / d
+        # For SSA, zero init, n and t
+        n = 0
+        t = 0
+        if np.abs(i) > 0:
+            k = h / i
+            l = np.arange(1, c + 1)
+            m = np.sqrt(l - g) + e * k
+            if np.abs(m[0]) < 1:
+                for o in range(a):
+                    n += 0
+                    if np.abs(n) < 3:
+                        break
+                n += m[2]
+            p = g / l
+            q = []
+            for r in range(len(p)):
+                q.append(p[r])
+                if r > 4 + 1:
+                    s = 123
+                    t = 5
+                    if s > 122 - c:
+                        t += s
+                t += q[0] + _GLOBAL
+
+        return f + o + r + t + r + a + n
+
+    return foo
+
+
+class TestFunctionInlining(MemoryLeakMixin, InliningBase):
+
+    def test_basic_inline_never(self):
+        @njit(inline='never')
+        def foo():
+            return
+
+        def impl():
+            return foo()
+        self.check(impl, inline_expect={'foo': False})
+
+    def test_basic_inline_always(self):
+        @njit(inline='always')
+        def foo():
+            return
+
+        def impl():
+            return foo()
+        self.check(impl, inline_expect={'foo': True})
+
+    def test_basic_inline_combos(self):
+
+        def impl():
+            x = foo()
+            y = bar()
+            z = baz()
+            return x, y, z
+
+        opts = (('always'), ('never'))
+
+        for inline_foo, inline_bar, inline_baz in product(opts, opts, opts):
+
+            @njit(inline=inline_foo)
+            def foo():
+                return
+
+            @njit(inline=inline_bar)
+            def bar():
+                return
+
+            @njit(inline=inline_baz)
+            def baz():
+                return
+
+            inline_expect = {'foo': self.inline_opt_as_bool[inline_foo],
+                             'bar': self.inline_opt_as_bool[inline_bar],
+                             'baz': self.inline_opt_as_bool[inline_baz]}
+            self.check(impl, inline_expect=inline_expect)
+
+    @unittest.skip("Need to work out how to prevent this")
+    def test_recursive_inline(self):
+
+        @njit(inline='always')
+        def foo(x):
+            if x == 0:
+                return 12
+            else:
+                foo(x - 1)
+
+        a = 3
+
+        def impl():
+            b = 0
+            if a > 1:
+                b += 1
+            foo(5)
+            if b < a:
+                b -= 1
+
+        self.check(impl, inline_expect={'foo': True})
+
+    def test_freevar_bindings(self):
+
+        def factory(inline, x, y):
+            z = x + 12
+
+            @njit(inline=inline)
+            def func():
+                return (x, y + 3, z)
+            return func
+
+        def impl():
+            x = foo()
+            y = bar()
+            z = baz()
+            return x, y, z
+
+        opts = (('always'), ('never'))
+
+        for inline_foo, inline_bar, inline_baz in product(opts, opts, opts):
+
+            foo = factory(inline_foo, 10, 20)
+            bar = factory(inline_bar, 30, 40)
+            baz = factory(inline_baz, 50, 60)
+
+            inline_expect = {'foo': self.inline_opt_as_bool[inline_foo],
+                             'bar': self.inline_opt_as_bool[inline_bar],
+                             'baz': self.inline_opt_as_bool[inline_baz]}
+            self.check(impl, inline_expect=inline_expect)
+
+    def test_global_binding(self):
+
+        def impl():
+            x = 19
+            return _global_func(x)
+
+        self.check(impl, inline_expect={'_global_func': True})
+
+    def test_inline_from_another_module(self):
+
+        from .inlining_usecases import bar
+
+        def impl():
+            z = _GLOBAL1 + 2
+            return bar(), z
+
+        self.check(impl, inline_expect={'bar': True})
+
+    def test_inline_from_another_module_w_getattr(self):
+
+        import numba.tests.inlining_usecases as iuc
+
+        def impl():
+            z = _GLOBAL1 + 2
+            return iuc.bar(), z
+
+        self.check(impl, inline_expect={'bar': True})
+
+    def test_inline_from_another_module_w_2_getattr(self):
+
+        import numba.tests.inlining_usecases  # noqa forces registration
+        import numba.tests as nt
+
+        def impl():
+            z = _GLOBAL1 + 2
+            return nt.inlining_usecases.bar(), z
+
+        self.check(impl, inline_expect={'bar': True})
+
+    def test_inline_from_another_module_as_freevar(self):
+
+        def factory():
+            from .inlining_usecases import bar
+
+            @njit(inline='always')
+            def tmp():
+                return bar()
+            return tmp
+
+        baz = factory()
+
+        def impl():
+            z = _GLOBAL1 + 2
+            return baz(), z
+
+        self.check(impl, inline_expect={'bar': True})
+
+    def test_inline_w_freevar_from_another_module(self):
+
+        from .inlining_usecases import baz_factory
+
+        def gen(a, b):
+            bar = baz_factory(a)
+
+            def impl():
+                z = _GLOBAL1 + a * b
+                return bar(), z, a
+            return impl
+
+        impl = gen(10, 20)
+        self.check(impl, inline_expect={'bar': True})
+
+    def test_inlining_models(self):
+
+        def s17_caller_model(expr, caller_info, callee_info):
+            self.assertIsInstance(expr, ir.Expr)
+            self.assertEqual(expr.op, "call")
+            return self.sentinel_17_cost_model(caller_info)
+
+        def s17_callee_model(expr, caller_info, callee_info):
+            self.assertIsInstance(expr, ir.Expr)
+            self.assertEqual(expr.op, "call")
+            return self.sentinel_17_cost_model(callee_info)
+
+        # caller has sentinel
+        for caller, callee in ((11, 17), (17, 11)):
+
+            @njit(inline=s17_caller_model)
+            def foo():
+                return callee
+
+            def impl(z):
+                x = z + caller
+                y = foo()
+                return y + 3, x
+
+            self.check(impl, 10, inline_expect={'foo': caller == 17})
+
+        # callee has sentinel
+        for caller, callee in ((11, 17), (17, 11)):
+
+            @njit(inline=s17_callee_model)
+            def bar():
+                return callee
+
+            def impl(z):
+                x = z + caller
+                y = bar()
+                return y + 3, x
+
+            self.check(impl, 10, inline_expect={'bar': callee == 17})
+
+    def test_inline_inside_loop(self):
+        @njit(inline='always')
+        def foo():
+            return 12
+
+        def impl():
+            acc = 0.0
+            for i in range(5):
+                acc += foo()
+            return acc
+
+        self.check(impl, inline_expect={'foo': True}, block_count=4)
+
+    def test_inline_inside_closure_inside_loop(self):
+        @njit(inline='always')
+        def foo():
+            return 12
+
+        def impl():
+            acc = 0.0
+            for i in range(5):
+                def bar():
+                    return foo() + 7
+                acc += bar()
+            return acc
+
+        self.check(impl, inline_expect={'foo': True}, block_count=4)
+
+    def test_inline_closure_inside_inlinable_inside_closure(self):
+        @njit(inline='always')
+        def foo(a):
+            def baz():
+                return 12 + a
+            return baz() + 8
+
+        def impl():
+            z = 9
+
+            def bar(x):
+                return foo(z) + 7 + x
+            return bar(z + 2)
+
+        self.check(impl, inline_expect={'foo': True}, block_count=1)
+
+    @skip_py38_or_later
+    def test_inline_involved(self):
+
+        fortran = njit(inline='always')(_gen_involved())
+
+        @njit(inline='always')
+        def boz(j):
+            acc = 0
+
+            def biz(t):
+                return t + acc
+            for x in range(j):
+                acc += biz(8 + acc) + fortran(2., acc, 1, 12j, biz(acc))
+            return acc
+
+        @njit(inline='always')
+        def foo(a):
+            acc = 0
+            for p in range(12):
+                tmp = fortran(1, 1, 1, 1, 1)
+
+                def baz(x):
+                    return 12 + a + x + tmp
+                acc += baz(p) + 8 + boz(p) + tmp
+            return acc + baz(2)
+
+        def impl():
+            z = 9
+
+            def bar(x):
+                return foo(z) + 7 + x
+            return bar(z + 2)
+
+        self.check(impl, inline_expect={'foo': True, 'boz': True,
+                                        'fortran': True}, block_count=37)
+
+    def test_inline_renaming_scheme(self):
+        # See #7380, this checks that inlined variables have a name derived from
+        # the function they were defined in.
+
+        @njit(inline="always")
+        def bar(z):
+            x = 5
+            y = 10
+            return x + y + z
+
+        @njit(pipeline_class=IRPreservingTestPipeline)
+        def foo(a, b):
+            return bar(a), bar(b)
+
+        self.assertEqual(foo(10, 20), (25, 35))
+
+        # check IR. Look for the `x = 5`... there should be
+        # Two lots of `const(int, 5)`, one for each inline
+        # The LHS of the assignment will have a name like:
+        # TestFunctionInlining_test_inline_renaming_scheme__locals__bar_v2.x
+        # Ensure that this is the case!
+        func_ir = foo.overloads[foo.signatures[0]].metadata['preserved_ir']
+        store = []
+        for blk in func_ir.blocks.values():
+            for stmt in blk.body:
+                if isinstance(stmt, ir.Assign):
+                    if isinstance(stmt.value, ir.Const):
+                        if stmt.value.value == 5:
+                            store.append(stmt)
+
+        self.assertEqual(len(store), 2)
+        for i in store:
+            name = i.target.name
+            basename = self.id().lstrip(self.__module__)
+            regex = rf'{basename}__locals__bar_v[0-9]+.x'
+            self.assertRegex(name, regex)
+
+
+class TestRegisterJitableInlining(MemoryLeakMixin, InliningBase):
+
+    def test_register_jitable_inlines(self):
+
+        @register_jitable(inline='always')
+        def foo():
+            return 1
+
+        def impl():
+            foo()
+
+        self.check(impl, inline_expect={'foo': True})
+
+
+class TestOverloadInlining(MemoryLeakMixin, InliningBase):
+
+    def test_basic_inline_never(self):
+        def foo():
+            pass
+
+        @overload(foo, inline='never')
+        def foo_overload():
+            def foo_impl():
+                pass
+            return foo_impl
+
+        def impl():
+            return foo()
+
+        self.check(impl, inline_expect={'foo': False})
+
+    def test_basic_inline_always(self):
+
+        def foo():
+            pass
+
+        @overload(foo, inline='always')
+        def foo_overload():
+            def impl():
+                pass
+            return impl
+
+        def impl():
+            return foo()
+
+        self.check(impl, inline_expect={'foo': True})
+
+    def test_inline_always_kw_no_default(self):
+        # pass call arg by name that doesn't have default value
+        def foo(a, b):
+            return a + b
+
+        @overload(foo, inline='always')
+        def overload_foo(a, b):
+            return lambda a, b: a + b
+
+        def impl():
+            return foo(3, b=4)
+
+        self.check(impl, inline_expect={'foo': True})
+
+    def test_inline_operators_unary(self):
+
+        def impl_inline(x):
+            return -x
+
+        def impl_noinline(x):
+            return +x
+
+        dummy_unary_impl = lambda x: True
+        Dummy, DummyType = self.make_dummy_type()
+        setattr(Dummy, '__neg__', dummy_unary_impl)
+        setattr(Dummy, '__pos__', dummy_unary_impl)
+
+        @overload(operator.neg, inline='always')
+        def overload_dummy_neg(x):
+            if isinstance(x, DummyType):
+                return dummy_unary_impl
+
+        @overload(operator.pos, inline='never')
+        def overload_dummy_pos(x):
+            if isinstance(x, DummyType):
+                return dummy_unary_impl
+
+        self.check(impl_inline, Dummy(), inline_expect={'neg': True})
+        self.check(impl_noinline, Dummy(), inline_expect={'pos': False})
+
+    def test_inline_operators_binop(self):
+
+        def impl_inline(x):
+            return x == 1
+
+        def impl_noinline(x):
+            return x != 1
+
+        Dummy, DummyType = self.make_dummy_type()
+
+        dummy_binop_impl = lambda a, b: True
+        setattr(Dummy, '__eq__', dummy_binop_impl)
+        setattr(Dummy, '__ne__', dummy_binop_impl)
+
+        @overload(operator.eq, inline='always')
+        def overload_dummy_eq(a, b):
+            if isinstance(a, DummyType):
+                return dummy_binop_impl
+
+        @overload(operator.ne, inline='never')
+        def overload_dummy_ne(a, b):
+            if isinstance(a, DummyType):
+                return dummy_binop_impl
+
+        self.check(impl_inline, Dummy(), inline_expect={'eq': True})
+        self.check(impl_noinline, Dummy(), inline_expect={'ne': False})
+
+    def test_inline_operators_inplace_binop(self):
+
+        def impl_inline(x):
+            x += 1
+
+        def impl_noinline(x):
+            x -= 1
+
+        Dummy, DummyType = self.make_dummy_type()
+
+        dummy_inplace_binop_impl = lambda a, b: True
+        setattr(Dummy, '__iadd__', dummy_inplace_binop_impl)
+        setattr(Dummy, '__isub__', dummy_inplace_binop_impl)
+
+        @overload(operator.iadd, inline='always')
+        def overload_dummy_iadd(a, b):
+            if isinstance(a, DummyType):
+                return dummy_inplace_binop_impl
+
+        @overload(operator.isub, inline='never')
+        def overload_dummy_isub(a, b):
+            if isinstance(a, DummyType):
+                return dummy_inplace_binop_impl
+
+        # DummyType is not mutable, so lowering 'inplace_binop' Expr
+        # re-uses (requires) copying function definition
+        @overload(operator.add, inline='always')
+        def overload_dummy_add(a, b):
+            if isinstance(a, DummyType):
+                return dummy_inplace_binop_impl
+
+        @overload(operator.sub, inline='never')
+        def overload_dummy_sub(a, b):
+            if isinstance(a, DummyType):
+                return dummy_inplace_binop_impl
+
+        self.check(impl_inline, Dummy(), inline_expect={'iadd': True})
+        self.check(impl_noinline, Dummy(), inline_expect={'isub': False})
+
+    def test_inline_always_operators_getitem(self):
+
+        def impl(x, idx):
+            return x[idx]
+
+        def impl_static_getitem(x):
+            return x[1]
+
+        Dummy, DummyType = self.make_dummy_type()
+
+        dummy_getitem_impl = lambda obj, idx: None
+        setattr(Dummy, '__getitem__', dummy_getitem_impl)
+
+        @overload(operator.getitem, inline='always')
+        def overload_dummy_getitem(obj, idx):
+            if isinstance(obj, DummyType):
+                return dummy_getitem_impl
+
+        # note getitem and static_getitem Exprs refer to operator.getitem
+        # hence they are checked using the same expected key
+        self.check(impl, Dummy(), 1, inline_expect={'getitem': True})
+        self.check(impl_static_getitem, Dummy(),
+                   inline_expect={'getitem': True})
+
+    def test_inline_never_operators_getitem(self):
+
+        def impl(x, idx):
+            return x[idx]
+
+        def impl_static_getitem(x):
+            return x[1]
+
+        Dummy, DummyType = self.make_dummy_type()
+
+        dummy_getitem_impl = lambda obj, idx: None
+        setattr(Dummy, '__getitem__', dummy_getitem_impl)
+
+        @overload(operator.getitem, inline='never')
+        def overload_dummy_getitem(obj, idx):
+            if isinstance(obj, DummyType):
+                return dummy_getitem_impl
+
+        # both getitem and static_getitem Exprs refer to operator.getitem
+        # hence they are checked using the same expect key
+        self.check(impl, Dummy(), 1, inline_expect={'getitem': False})
+        self.check(impl_static_getitem, Dummy(),
+                   inline_expect={'getitem': False})
+
+    def test_inline_stararg_error(self):
+        def foo(a, *b):
+            return a + b[0]
+
+        @overload(foo, inline='always')
+        def overload_foo(a, *b):
+            return lambda a, *b: a + b[0]
+
+        def impl():
+            return foo(3, 3, 5)
+
+        with self.assertRaises(NotImplementedError) as e:
+            self.check(impl, inline_expect={'foo': True})
+
+        self.assertIn("Stararg not supported in inliner for arg 1 *b",
+                      str(e.exception))
+
+    def test_basic_inline_combos(self):
+
+        def impl():
+            x = foo()
+            y = bar()
+            z = baz()
+            return x, y, z
+
+        opts = (('always'), ('never'))
+
+        for inline_foo, inline_bar, inline_baz in product(opts, opts, opts):
+
+            def foo():
+                pass
+
+            def bar():
+                pass
+
+            def baz():
+                pass
+
+            @overload(foo, inline=inline_foo)
+            def foo_overload():
+                def impl():
+                    return
+                return impl
+
+            @overload(bar, inline=inline_bar)
+            def bar_overload():
+                def impl():
+                    return
+                return impl
+
+            @overload(baz, inline=inline_baz)
+            def baz_overload():
+                def impl():
+                    return
+                return impl
+
+            inline_expect = {'foo': self.inline_opt_as_bool[inline_foo],
+                             'bar': self.inline_opt_as_bool[inline_bar],
+                             'baz': self.inline_opt_as_bool[inline_baz]}
+            self.check(impl, inline_expect=inline_expect)
+
+    def test_freevar_bindings(self):
+
+        def impl():
+            x = foo()
+            y = bar()
+            z = baz()
+            return x, y, z
+
+        opts = (('always'), ('never'))
+
+        for inline_foo, inline_bar, inline_baz in product(opts, opts, opts):
+            # need to repeatedly clobber definitions of foo, bar, baz so
+            # @overload binds to the right instance WRT inlining
+
+            def foo():
+                x = 10
+                y = 20
+                z = x + 12
+                return (x, y + 3, z)
+
+            def bar():
+                x = 30
+                y = 40
+                z = x + 12
+                return (x, y + 3, z)
+
+            def baz():
+                x = 60
+                y = 80
+                z = x + 12
+                return (x, y + 3, z)
+
+            def factory(target, x, y, inline=None):
+                z = x + 12
+
+                @overload(target, inline=inline)
+                def func():
+                    def impl():
+                        return (x, y + 3, z)
+                    return impl
+
+            factory(foo, 10, 20, inline=inline_foo)
+            factory(bar, 30, 40, inline=inline_bar)
+            factory(baz, 60, 80, inline=inline_baz)
+
+            inline_expect = {'foo': self.inline_opt_as_bool[inline_foo],
+                             'bar': self.inline_opt_as_bool[inline_bar],
+                             'baz': self.inline_opt_as_bool[inline_baz]}
+
+            self.check(impl, inline_expect=inline_expect)
+
+    def test_global_overload_binding(self):
+
+        def impl():
+            z = 19
+            return _global_defn(z)
+
+        self.check(impl, inline_expect={'_global_defn': True})
+
+    def test_inline_from_another_module(self):
+
+        from .inlining_usecases import baz
+
+        def impl():
+            z = _GLOBAL1 + 2
+            return baz(), z
+
+        self.check(impl, inline_expect={'baz': True})
+
+    def test_inline_from_another_module_w_getattr(self):
+
+        import numba.tests.inlining_usecases as iuc
+
+        def impl():
+            z = _GLOBAL1 + 2
+            return iuc.baz(), z
+
+        self.check(impl, inline_expect={'baz': True})
+
+    def test_inline_from_another_module_w_2_getattr(self):
+
+        import numba.tests.inlining_usecases  # noqa forces registration
+        import numba.tests as nt
+
+        def impl():
+            z = _GLOBAL1 + 2
+            return nt.inlining_usecases.baz(), z
+
+        self.check(impl, inline_expect={'baz': True})
+
+    def test_inline_from_another_module_as_freevar(self):
+
+        def factory():
+            from .inlining_usecases import baz
+
+            @njit(inline='always')
+            def tmp():
+                return baz()
+            return tmp
+
+        bop = factory()
+
+        def impl():
+            z = _GLOBAL1 + 2
+            return bop(), z
+
+        self.check(impl, inline_expect={'baz': True})
+
+    def test_inline_w_freevar_from_another_module(self):
+
+        from .inlining_usecases import bop_factory
+
+        def gen(a, b):
+            bar = bop_factory(a)
+
+            def impl():
+                z = _GLOBAL1 + a * b
+                return bar(), z, a
+            return impl
+
+        impl = gen(10, 20)
+        self.check(impl, inline_expect={'bar': True})
+
+    def test_inlining_models(self):
+
+        def s17_caller_model(expr, caller_info, callee_info):
+            self.assertIsInstance(expr, ir.Expr)
+            self.assertEqual(expr.op, "call")
+            return self.sentinel_17_cost_model(caller_info.func_ir)
+
+        def s17_callee_model(expr, caller_info, callee_info):
+            self.assertIsInstance(expr, ir.Expr)
+            self.assertEqual(expr.op, "call")
+            return self.sentinel_17_cost_model(callee_info.func_ir)
+
+        # caller has sentinel
+        for caller, callee in ((10, 11), (17, 11)):
+
+            def foo():
+                return callee
+
+            @overload(foo, inline=s17_caller_model)
+            def foo_ol():
+                def impl():
+                    return callee
+                return impl
+
+            def impl(z):
+                x = z + caller
+                y = foo()
+                return y + 3, x
+
+            self.check(impl, 10, inline_expect={'foo': caller == 17})
+
+        # callee has sentinel
+        for caller, callee in ((11, 17), (11, 10)):
+
+            def bar():
+                return callee
+
+            @overload(bar, inline=s17_callee_model)
+            def bar_ol():
+                def impl():
+                    return callee
+                return impl
+
+            def impl(z):
+                x = z + caller
+                y = bar()
+                return y + 3, x
+
+            self.check(impl, 10, inline_expect={'bar': callee == 17})
+
+    def test_multiple_overloads_with_different_inline_characteristics(self):
+        # check that having different inlining options for different overloads
+        # of the same function works ok
+
+        # this is the Python equiv of the overloads below
+        def bar(x):
+            if isinstance(typeof(x), types.Float):
+                return x + 1234
+            else:
+                return x + 1
+
+        @overload(bar, inline='always')
+        def bar_int_ol(x):
+            if isinstance(x, types.Integer):
+                def impl(x):
+                    return x + 1
+                return impl
+
+        @overload(bar, inline='never')
+        def bar_float_ol(x):
+            if isinstance(x, types.Float):
+                def impl(x):
+                    return x + 1234
+                return impl
+
+        def always_inline_cost_model(*args):
+            return True
+
+        @overload(bar, inline=always_inline_cost_model)
+        def bar_complex_ol(x):
+            if isinstance(x, types.Complex):
+                def impl(x):
+                    return x + 1
+                return impl
+
+        def impl():
+            a = bar(1)  # integer literal, should inline
+            b = bar(2.3)  # float literal, should not inline
+            # complex literal, should inline by virtue of cost model
+            c = bar(3j)
+            return a + b + c
+
+        # there should still be a `bar` not inlined
+        fir = self.check(impl, inline_expect={'bar': False}, block_count=1)
+
+        # check there is one call left in the IR
+        block = next(iter(fir.blocks.items()))[1]
+        calls = [x for x in block.find_exprs(op='call')]
+        self.assertTrue(len(calls) == 1)
+
+        # check that the constant "1234" is not in the IR
+        consts = [x.value for x in block.find_insts(ir.Assign)
+                  if isinstance(getattr(x, 'value', None), ir.Const)]
+        for val in consts:
+            self.assertNotEqual(val.value, 1234)
+
+    def test_overload_inline_always_with_literally_in_inlinee(self):
+        # See issue #5887
+
+        def foo_ovld(dtype):
+
+            if not isinstance(dtype, types.StringLiteral):
+                def foo_noop(dtype):
+                    return literally(dtype)
+                return foo_noop
+
+            if dtype.literal_value == 'str':
+                def foo_as_str_impl(dtype):
+                    return 10
+                return foo_as_str_impl
+
+            if dtype.literal_value in ('int64', 'float64'):
+                def foo_as_num_impl(dtype):
+                    return 20
+                return foo_as_num_impl
+
+        # define foo for literal str 'str'
+        def foo(dtype):
+            return 10
+
+        overload(foo, inline='always')(foo_ovld)
+
+        def test_impl(dtype):
+            return foo(dtype)
+
+        # check literal dispatch on 'str'
+        dtype = 'str'
+        self.check(test_impl, dtype, inline_expect={'foo': True})
+
+        # redefine foo to be correct for literal str 'int64'
+        def foo(dtype):
+            return 20
+        overload(foo, inline='always')(foo_ovld)
+
+        # check literal dispatch on 'int64'
+        dtype = 'int64'
+        self.check(test_impl, dtype, inline_expect={'foo': True})
+
+    def test_inline_always_ssa(self):
+        # Make sure IR inlining uses SSA properly. Test for #6721.
+
+        dummy_true = True
+
+        def foo(A):
+            return True
+
+        @overload(foo, inline="always")
+        def foo_overload(A):
+
+            def impl(A):
+                s = dummy_true
+                for i in range(len(A)):
+                    dummy = dummy_true
+                    if A[i]:
+                        dummy = A[i]
+                    s *= dummy
+                return s
+            return impl
+
+        def impl():
+            return foo(np.array([True, False, True]))
+
+        self.check(impl, block_count='SKIP', inline_expect={'foo': True})
+
+    def test_inline_always_ssa_scope_validity(self):
+        # Make sure IR inlining correctly updates the scope(s). See #7802
+
+        def bar():
+            b = 5
+            while b > 1:
+                b //= 2
+
+            return 10
+
+        @overload(bar, inline="always")
+        def bar_impl():
+            return bar
+
+        @njit
+        def foo():
+            bar()
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', errors.NumbaIRAssumptionWarning)
+            ignore_internal_warnings()
+            self.assertEqual(foo(), foo.py_func())
+
+        # There should be no warnings as the IR scopes should be consistent with
+        # the IR involved.
+        self.assertEqual(len(w), 0)
+
+
+class TestOverloadMethsAttrsInlining(InliningBase):
+    def setUp(self):
+        self.make_dummy_type()
+        super(TestOverloadMethsAttrsInlining, self).setUp()
+
+    def check_method(self, test_impl, args, expected, block_count,
+                     expects_inlined=True):
+        j_func = njit(pipeline_class=IRPreservingTestPipeline)(test_impl)
+        # check they produce the same answer first!
+        self.assertEqual(j_func(*args), expected)
+
+        # make sure IR doesn't have branches
+        fir = j_func.overloads[j_func.signatures[0]].metadata['preserved_ir']
+        fir.blocks = fir.blocks
+        self.assertEqual(len(fir.blocks), block_count)
+        if expects_inlined:
+            # assert no calls
+            for block in fir.blocks.values():
+                calls = list(block.find_exprs('call'))
+                self.assertFalse(calls)
+        else:
+            # assert has call
+            allcalls = []
+            for block in fir.blocks.values():
+                allcalls += list(block.find_exprs('call'))
+            self.assertTrue(allcalls)
+
+    def check_getattr(self, test_impl, args, expected, block_count,
+                      expects_inlined=True):
+        j_func = njit(pipeline_class=IRPreservingTestPipeline)(test_impl)
+        # check they produce the same answer first!
+        self.assertEqual(j_func(*args), expected)
+
+        # make sure IR doesn't have branches
+        fir = j_func.overloads[j_func.signatures[0]].metadata['preserved_ir']
+        fir.blocks = fir.blocks
+        self.assertEqual(len(fir.blocks), block_count)
+        if expects_inlined:
+            # assert no getattr
+            for block in fir.blocks.values():
+                getattrs = list(block.find_exprs('getattr'))
+                self.assertFalse(getattrs)
+        else:
+            # assert has getattr
+            allgetattrs = []
+            for block in fir.blocks.values():
+                allgetattrs += list(block.find_exprs('getattr'))
+            self.assertTrue(allgetattrs)
+
+    def test_overload_method_default_args_always(self):
+        Dummy, DummyType = self.make_dummy_type()
+
+        @overload_method(DummyType, "inline_method", inline='always')
+        def _get_inlined_method(obj, val=None, val2=None):
+            def get(obj, val=None, val2=None):
+                return ("THIS IS INLINED", val, val2)
+            return get
+
+        def foo(obj):
+            return obj.inline_method(123), obj.inline_method(val2=321)
+
+        self.check_method(
+            test_impl=foo,
+            args=[Dummy()],
+            expected=(("THIS IS INLINED", 123, None),
+                      ("THIS IS INLINED", None, 321)),
+            block_count=1,
+        )
+
+    def make_overload_method_test(self, costmodel, should_inline):
+        def costmodel(*args):
+            return should_inline
+
+        Dummy, DummyType = self.make_dummy_type()
+
+        @overload_method(DummyType, "inline_method", inline=costmodel)
+        def _get_inlined_method(obj, val):
+            def get(obj, val):
+                return ("THIS IS INLINED!!!", val)
+            return get
+
+        def foo(obj):
+            return obj.inline_method(123)
+
+        self.check_method(
+            test_impl=foo,
+            args=[Dummy()],
+            expected=("THIS IS INLINED!!!", 123),
+            block_count=1,
+            expects_inlined=should_inline,
+        )
+
+    def test_overload_method_cost_driven_always(self):
+        self.make_overload_method_test(
+            costmodel='always',
+            should_inline=True,
+        )
+
+    def test_overload_method_cost_driven_never(self):
+        self.make_overload_method_test(
+            costmodel='never',
+            should_inline=False,
+        )
+
+    def test_overload_method_cost_driven_must_inline(self):
+        self.make_overload_method_test(
+            costmodel=lambda *args: True,
+            should_inline=True,
+        )
+
+    def test_overload_method_cost_driven_no_inline(self):
+        self.make_overload_method_test(
+            costmodel=lambda *args: False,
+            should_inline=False,
+        )
+
+    def make_overload_attribute_test(self, costmodel, should_inline):
+        Dummy, DummyType = self.make_dummy_type()
+
+        @overload_attribute(DummyType, "inlineme", inline=costmodel)
+        def _get_inlineme(obj):
+            def get(obj):
+                return "MY INLINED ATTRS"
+            return get
+
+        def foo(obj):
+            return obj.inlineme
+
+        self.check_getattr(
+            test_impl=foo,
+            args=[Dummy()],
+            expected="MY INLINED ATTRS",
+            block_count=1,
+            expects_inlined=should_inline,
+        )
+
+    def test_overload_attribute_always(self):
+        self.make_overload_attribute_test(
+            costmodel='always',
+            should_inline=True,
+        )
+
+    def test_overload_attribute_never(self):
+        self.make_overload_attribute_test(
+            costmodel='never',
+            should_inline=False,
+        )
+
+    def test_overload_attribute_costmodel_must_inline(self):
+        self.make_overload_attribute_test(
+            costmodel=lambda *args: True,
+            should_inline=True,
+        )
+
+    def test_overload_attribute_costmodel_no_inline(self):
+        self.make_overload_attribute_test(
+            costmodel=lambda *args: False,
+            should_inline=False,
+        )
+
+
+class TestGeneralInlining(MemoryLeakMixin, InliningBase):
+
+    def test_with_inlined_and_noninlined_variants(self):
+        # This test is contrived and was to demonstrate fixing a bug in the
+        # template walking logic where inlinable and non-inlinable definitions
+        # would not mix.
+
+        @overload(len, inline='always')
+        def overload_len(A):
+            if False:
+                return lambda A: 10
+
+        def impl():
+            return len([2, 3, 4])
+
+        # len(list) won't be inlined because the overload above doesn't apply
+        self.check(impl, inline_expect={'len': False})
+
+    def test_with_kwargs(self):
+
+        def foo(a, b=3, c=5):
+            return a + b + c
+
+        @overload(foo, inline='always')
+        def overload_foo(a, b=3, c=5):
+            def impl(a, b=3, c=5):
+                return a + b + c
+            return impl
+
+        def impl():
+            return foo(3, c=10)
+
+        self.check(impl, inline_expect={'foo': True})
+
+    def test_with_kwargs2(self):
+
+        @njit(inline='always')
+        def bar(a, b=12, c=9):
+            return a + b
+
+        def impl(a, b=7, c=5):
+            return bar(a + b, c=19)
+
+        self.check(impl, 3, 4, inline_expect={'bar': True})
+
+    def test_inlining_optional_constant(self):
+        # This testcase causes `b` to be a Optional(bool) constant once it is
+        # inlined into foo().
+        @njit(inline='always')
+        def bar(a=None, b=None):
+            if b is None:
+                b = 123     # this changes the type of `b` due to lack of SSA
+            return (a, b)
+
+        def impl():
+            return bar(), bar(123), bar(b=321)
+
+        self.check(impl, block_count='SKIP', inline_expect={'bar': True})
+
+
+class TestInlineOptions(TestCase):
+
+    def test_basic(self):
+        always = InlineOptions('always')
+        self.assertTrue(always.is_always_inline)
+        self.assertFalse(always.is_never_inline)
+        self.assertFalse(always.has_cost_model)
+        self.assertEqual(always.value, 'always')
+
+        never = InlineOptions('never')
+        self.assertFalse(never.is_always_inline)
+        self.assertTrue(never.is_never_inline)
+        self.assertFalse(never.has_cost_model)
+        self.assertEqual(never.value, 'never')
+
+        def cost_model(x):
+            return x
+        model = InlineOptions(cost_model)
+        self.assertFalse(model.is_always_inline)
+        self.assertFalse(model.is_never_inline)
+        self.assertTrue(model.has_cost_model)
+        self.assertIs(model.value, cost_model)
+
+
+class TestInlineMiscIssues(TestCase):
+
+    def test_issue4691(self):
+        def output_factory(array, dtype):
+            pass
+
+        @overload(output_factory, inline='always')
+        def ol_output_factory(array, dtype):
+            if isinstance(array, types.npytypes.Array):
+                def impl(array, dtype):
+                    shape = array.shape[3:]
+                    return np.zeros(shape, dtype=dtype)
+
+                return impl
+
+        @njit(nogil=True)
+        def fn(array):
+            out = output_factory(array, array.dtype)
+            return out
+
+        @njit(nogil=True)
+        def fn2(array):
+            return np.zeros(array.shape[3:], dtype=array.dtype)
+
+        fn(np.ones((10, 20, 30, 40, 50)))
+        fn2(np.ones((10, 20, 30, 40, 50)))
+
+    def test_issue4693(self):
+
+        @njit(inline='always')
+        def inlining(array):
+            if array.ndim != 1:
+                raise ValueError("Invalid number of dimensions")
+
+            return array
+
+        @njit
+        def fn(array):
+            return inlining(array)
+
+        fn(np.zeros(10))
+
+    def test_issue5476(self):
+        # Actual issue has the ValueError passed as an arg to `inlining` so is
+        # a constant inference error
+        @njit(inline='always')
+        def inlining():
+            msg = 'Something happened'
+            raise ValueError(msg)
+
+        @njit
+        def fn():
+            return inlining()
+
+        with self.assertRaises(ValueError) as raises:
+            fn()
+
+        self.assertIn("Something happened", str(raises.exception))
+
+    def test_issue5792(self):
+        # Issue is that overloads cache their IR and closure inliner was
+        # manipulating the cached IR in a way that broke repeated inlines.
+
+        class Dummy:
+            def __init__(self, data):
+                self.data = data
+
+            def div(self, other):
+                return data / other.data
+
+        class DummyType(types.Type):
+            def __init__(self, data):
+                self.data = data
+                super().__init__(name=f'Dummy({self.data})')
+
+        @register_model(DummyType)
+        class DummyTypeModel(models.StructModel):
+            def __init__(self, dmm, fe_type):
+                members = [
+                    ('data', fe_type.data),
+                ]
+                super().__init__(dmm, fe_type, members)
+
+        make_attribute_wrapper(DummyType, 'data', '_data')
+
+        @intrinsic
+        def init_dummy(typingctx, data):
+            def codegen(context, builder, sig, args):
+                typ = sig.return_type
+                data, = args
+                dummy = cgutils.create_struct_proxy(typ)(context, builder)
+                dummy.data = data
+
+                if context.enable_nrt:
+                    context.nrt.incref(builder, sig.args[0], data)
+
+                return dummy._getvalue()
+
+            ret_typ = DummyType(data)
+            sig = signature(ret_typ, data)
+
+            return sig, codegen
+
+        @overload(Dummy, inline='always')
+        def dummy_overload(data):
+            def ctor(data):
+                return init_dummy(data)
+
+            return ctor
+
+        @overload_method(DummyType, 'div', inline='always')
+        def div_overload(self, other):
+            def impl(self, other):
+                return self._data / other._data
+
+            return impl
+
+        @njit
+        def test_impl(data, other_data):
+            dummy = Dummy(data) # ctor inlined once
+            other = Dummy(other_data)  # ctor inlined again
+
+            return dummy.div(other)
+
+        data = 1.
+        other_data = 2.
+        res = test_impl(data, other_data)
+        self.assertEqual(res, data / other_data)
+
+    def test_issue5824(self):
+        """ Similar to the above test_issue5792, checks mutation of the inlinee
+        IR is local only"""
+
+        class CustomCompiler(CompilerBase):
+
+            def define_pipelines(self):
+                pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+                # Run the inliner twice!
+                pm.add_pass_after(InlineOverloads, InlineOverloads)
+                pm.finalize()
+                return [pm]
+
+        def bar(x):
+            ...
+
+        @overload(bar, inline='always')
+        def ol_bar(x):
+            if isinstance(x, types.Integer):
+                def impl(x):
+                    return x + 1.3
+                return impl
+
+        @njit(pipeline_class=CustomCompiler)
+        def foo(z):
+            return bar(z), bar(z)
+
+        self.assertEqual(foo(10), (11.3, 11.3))
+
+    @skip_parfors_unsupported
+    def test_issue7380(self):
+        # This checks that inlining a function containing a loop into another
+        # loop where the induction variable in both loops is the same doesn't
+        # end up with a name collision. Parfors can detect this so it is used.
+        # See: https://github.com/numba/numba/issues/7380
+
+        # Check Numba inlined function passes
+
+        @njit(inline="always")
+        def bar(x):
+            for i in range(x.size):
+                x[i] += 1
+
+        @njit(parallel=True)
+        def foo(a):
+            for i in prange(a.shape[0]):
+                bar(a[i])
+
+        a = np.ones((10, 10))
+        foo(a) # run
+        # check mutation of data is correct
+        self.assertPreciseEqual(a, 2 * np.ones_like(a))
+
+        # Check manually inlined equivalent function fails
+        @njit(parallel=True)
+        def foo_bad(a):
+            for i in prange(a.shape[0]):
+                x = a[i]
+                for i in range(x.size):
+                    x[i] += 1
+
+        with self.assertRaises(errors.UnsupportedRewriteError) as e:
+            foo_bad(a)
+
+        self.assertIn("Overwrite of parallel loop index", str(e.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir_utils.py
new file mode 100644
index 000000000..96deeaca0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ir_utils.py
@@ -0,0 +1,273 @@
+import numba
+from numba.tests.support import TestCase, unittest
+from numba.core.registry import cpu_target
+from numba.core.compiler import CompilerBase, Flags
+from numba.core.compiler_machinery import PassManager
+from numba.core import types, ir, bytecode, compiler, ir_utils, registry
+from numba.core.untyped_passes import (ExtractByteCode, TranslateByteCode,
+                                       FixupArgs, IRProcessing,)
+
+from numba.core.typed_passes import (NopythonTypeInference,
+                                     type_inference_stage, DeadCodeElimination)
+from numba.experimental import jitclass
+
+# global constant for testing find_const
+GLOBAL_B = 11
+
+
+@jitclass([('val', numba.core.types.List(numba.intp))])
+class Dummy(object):
+    def __init__(self, val):
+        self.val = val
+
+
+class TestIrUtils(TestCase):
+    """
+    Tests ir handling utility functions like find_callname.
+    """
+
+    def test_obj_func_match(self):
+        """Test matching of an object method (other than Array see #3449)
+        """
+
+        def test_func():
+            d = Dummy([1])
+            d.val.append(2)
+
+        test_ir = compiler.run_frontend(test_func)
+        typingctx = cpu_target.typing_context
+        targetctx = cpu_target.target_context
+        typing_res = type_inference_stage(
+            typingctx, targetctx, test_ir, (), None)
+        matched_call = ir_utils.find_callname(
+            test_ir, test_ir.blocks[0].body[7].value, typing_res.typemap)
+        self.assertTrue(isinstance(matched_call, tuple) and
+                        len(matched_call) == 2 and
+                        matched_call[0] == 'append')
+
+    def test_dead_code_elimination(self):
+
+        class Tester(CompilerBase):
+
+            @classmethod
+            def mk_pipeline(cls, args, return_type=None, flags=None, locals={},
+                            library=None, typing_context=None,
+                            target_context=None):
+                if not flags:
+                    flags = Flags()
+                flags.nrt = True
+                if typing_context is None:
+                    typing_context = registry.cpu_target.typing_context
+                if target_context is None:
+                    target_context = registry.cpu_target.target_context
+                return cls(typing_context, target_context, library, args,
+                           return_type, flags, locals)
+
+            def compile_to_ir(self, func, DCE=False):
+                """
+                Compile and return IR
+                """
+                func_id = bytecode.FunctionIdentity.from_function(func)
+                self.state.func_id = func_id
+                ExtractByteCode().run_pass(self.state)
+                state = self.state
+
+                name = "DCE_testing"
+                pm = PassManager(name)
+                pm.add_pass(TranslateByteCode, "analyzing bytecode")
+                pm.add_pass(FixupArgs, "fix up args")
+                pm.add_pass(IRProcessing, "processing IR")
+                pm.add_pass(NopythonTypeInference, "nopython frontend")
+                if DCE is True:
+                    pm.add_pass(DeadCodeElimination, "DCE after typing")
+                pm.finalize()
+                pm.run(state)
+                return state.func_ir
+
+        def check_initial_ir(the_ir):
+            # dead stuff:
+            # a const int value 0xdead
+            # an assign of above into to variable `dead`
+            # a const int above 0xdeaddead
+            # an assign of said int to variable `deaddead`
+            # this is 2 statements to remove
+
+            self.assertEqual(len(the_ir.blocks), 1)
+            block = the_ir.blocks[0]
+            deads = []
+            for x in block.find_insts(ir.Assign):
+                if isinstance(getattr(x, 'target', None), ir.Var):
+                    if 'dead' in getattr(x.target, 'name', ''):
+                        deads.append(x)
+
+            self.assertEqual(len(deads), 2)
+            for d in deads:
+                # check the ir.Const is the definition and the value is expected
+                const_val = the_ir.get_definition(d.value)
+                self.assertTrue(int('0x%s' % d.target.name, 16),
+                                const_val.value)
+
+            return deads
+
+        def check_dce_ir(the_ir):
+            self.assertEqual(len(the_ir.blocks), 1)
+            block = the_ir.blocks[0]
+            deads = []
+            consts = []
+            for x in block.find_insts(ir.Assign):
+                if isinstance(getattr(x, 'target', None), ir.Var):
+                    if 'dead' in getattr(x.target, 'name', ''):
+                        deads.append(x)
+                if isinstance(getattr(x, 'value', None), ir.Const):
+                    consts.append(x)
+            self.assertEqual(len(deads), 0)
+
+            # check the consts to make sure there's no reference to 0xdead or
+            # 0xdeaddead
+            for x in consts:
+                self.assertTrue(x.value.value not in [0xdead, 0xdeaddead])
+
+        def foo(x):
+            y = x + 1
+            dead = 0xdead  # noqa
+            z = y + 2
+            deaddead = 0xdeaddead  # noqa
+            ret = z * z
+            return ret
+
+        test_pipeline = Tester.mk_pipeline((types.intp,))
+        no_dce = test_pipeline.compile_to_ir(foo)
+        removed = check_initial_ir(no_dce)
+
+        test_pipeline = Tester.mk_pipeline((types.intp,))
+        w_dce = test_pipeline.compile_to_ir(foo, DCE=True)
+        check_dce_ir(w_dce)
+
+        # check that the count of initial - removed = dce
+        self.assertEqual(len(no_dce.blocks[0].body) - len(removed),
+                         len(w_dce.blocks[0].body))
+
+    def test_find_const_global(self):
+        """
+        Test find_const() for values in globals (ir.Global) and freevars
+        (ir.FreeVar) that are considered constants for compilation.
+        """
+        FREEVAR_C = 12
+
+        def foo(a):
+            b = GLOBAL_B
+            c = FREEVAR_C
+            return a + b + c
+
+        f_ir = compiler.run_frontend(foo)
+        block = f_ir.blocks[0]
+        const_b = None
+        const_c = None
+
+        for inst in block.body:
+            if isinstance(inst, ir.Assign) and inst.target.name == 'b':
+                const_b = ir_utils.guard(
+                    ir_utils.find_const, f_ir, inst.target)
+            if isinstance(inst, ir.Assign) and inst.target.name == 'c':
+                const_c = ir_utils.guard(
+                    ir_utils.find_const, f_ir, inst.target)
+
+        self.assertEqual(const_b, GLOBAL_B)
+        self.assertEqual(const_c, FREEVAR_C)
+
+    def test_flatten_labels(self):
+        """ tests flatten_labels """
+        def foo(a):
+            acc = 0
+            if a > 3:
+                acc += 1
+                if a > 19:
+                    return 53
+            elif a < 1000:
+                if a >= 12:
+                    acc += 1
+                for x in range(10):
+                    acc -= 1
+                    if acc < 2:
+                        break
+                else:
+                    acc += 7
+            else:
+                raise ValueError("some string")
+            # prevents inline of return on py310
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return acc
+
+        def bar(a):
+            acc = 0
+            z = 12
+            if a > 3:
+                acc += 1
+                z += 12
+                if a > 19:
+                    z += 12
+                    return 53
+            elif a < 1000:
+                if a >= 12:
+                    z += 12
+                    acc += 1
+                for x in range(10):
+                    z += 12
+                    acc -= 1
+                    if acc < 2:
+                        break
+                else:
+                    z += 12
+                    acc += 7
+            else:
+                raise ValueError("some string")
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return acc
+
+        def baz(a):
+            acc = 0
+            if a > 3:
+                acc += 1
+                if a > 19:
+                    return 53
+                else: # extra control flow in comparison to foo
+                    return 55
+            elif a < 1000:
+                if a >= 12:
+                    acc += 1
+                for x in range(10):
+                    acc -= 1
+                    if acc < 2:
+                        break
+                else:
+                    acc += 7
+            else:
+                raise ValueError("some string")
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return acc
+
+        def get_flat_cfg(func):
+            func_ir = ir_utils.compile_to_numba_ir(func, dict())
+            flat_blocks = ir_utils.flatten_labels(func_ir.blocks)
+            self.assertEqual(max(flat_blocks.keys()) + 1, len(func_ir.blocks))
+            return ir_utils.compute_cfg_from_blocks(flat_blocks)
+
+        foo_cfg = get_flat_cfg(foo)
+        bar_cfg = get_flat_cfg(bar)
+        baz_cfg = get_flat_cfg(baz)
+
+        self.assertEqual(foo_cfg, bar_cfg)
+        self.assertNotEqual(foo_cfg, baz_cfg)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_itanium_mangler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_itanium_mangler.py
new file mode 100644
index 000000000..99221e209
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_itanium_mangler.py
@@ -0,0 +1,80 @@
+# -*- coding: utf-8 -*-
+from numba import int32, int64, uint32, uint64, float32, float64
+from numba.core.types import range_iter32_type
+from numba.core import itanium_mangler
+import unittest
+
+
+class TestItaniumManager(unittest.TestCase):
+    def test_ident(self):
+        got = itanium_mangler.mangle_identifier("apple")
+        expect = "5apple"
+        self.assertEqual(expect, got)
+
+        got = itanium_mangler.mangle_identifier("ap_ple")
+        expect = "6ap_ple"
+        self.assertEqual(expect, got)
+
+        got = itanium_mangler.mangle_identifier("apple213")
+        expect = "8apple213"
+        self.assertEqual(expect, got)
+
+    def test_types(self):
+        got = itanium_mangler.mangle_type(int32)
+        expect = "i"
+        self.assertEqual(expect, got)
+
+        got = itanium_mangler.mangle_type(int64)
+        expect = "x"
+        self.assertEqual(expect, got)
+
+        got = itanium_mangler.mangle_type(uint32)
+        expect = "j"
+        self.assertEqual(expect, got)
+
+        got = itanium_mangler.mangle_type(uint64)
+        expect = "y"
+        self.assertEqual(expect, got)
+
+        got = itanium_mangler.mangle_type(float32)
+        expect = "f"
+        self.assertEqual(expect, got)
+
+        got = itanium_mangler.mangle_type(float64)
+        expect = "d"
+        self.assertEqual(expect, got)
+
+    def test_function(self):
+        got = itanium_mangler.mangle("what", [int32, float32])
+        expect = "_Z4whatif"
+        self.assertEqual(expect, got)
+
+        got = itanium_mangler.mangle("a_little_brown_fox", [uint64,
+                                                            uint32,
+                                                            float64])
+        expect = "_Z18a_little_brown_foxyjd"
+        self.assertEqual(expect, got)
+
+    def test_custom_type(self):
+        got = itanium_mangler.mangle_type(range_iter32_type)
+        name = str(range_iter32_type)
+        expect = "{n}{name}".format(n=len(name), name=name)
+        self.assertEqual(expect, got)
+
+    def test_mangle_literal(self):
+        # check int
+        got = itanium_mangler.mangle_value(123)
+        expect = "Li123E"
+        self.assertEqual(expect, got)
+        # check float (not handled using standard)
+        got = itanium_mangler.mangle_value(12.3)
+        self.assertRegexpMatches(got, r'^\d+_12_[0-9a-z][0-9a-z]3$')
+
+    def test_mangle_unicode(self):
+        name = u'f∂ƒ©z'
+        got = itanium_mangler.mangle_identifier(name)
+        self.assertRegexpMatches(got, r'^\d+f(_[a-z0-9][a-z0-9])+z$')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_iteration.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_iteration.py
new file mode 100644
index 000000000..231704aac
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_iteration.py
@@ -0,0 +1,254 @@
+import numpy as np
+
+from numba import njit
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types, errors
+from numba.tests.support import TestCase, MemoryLeakMixin, tag
+from numba.np import numpy_support
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+def int_tuple_iter_usecase():
+    res = 0
+    for i in (1, 2, 99, 3):
+        res += i
+    return res
+
+def float_tuple_iter_usecase():
+    res = 0.0
+    for i in (1.5, 2.0, 99.3, 3.4):
+        res += i
+    return res
+
+def tuple_tuple_iter_usecase():
+    # Recursively homogeneous tuple type
+    res = 0.0
+    for i in ((1.5, 2.0), (99.3, 3.4), (1.8, 2.5)):
+        for j in i:
+            res += j
+        res = res * 2
+    return res
+
+def enumerate_nested_tuple_usecase():
+    res = 0.0
+    for i, j in enumerate(((1.5, 2.0), (99.3, 3.4), (1.8, 2.5))):
+        for l in j:
+            res += i * l
+        res = res * 2
+    return res
+
+def nested_enumerate_usecase():
+    res = 0.0
+    for i, (j, k) in enumerate(enumerate(((1.5, 2.0), (99.3, 3.4), (1.8, 2.5)))):
+        for l in k:
+            res += i * j * l
+        res = res * 2
+    return res
+
+
+def enumerate_array_usecase():
+    res = 0
+    arrays = (np.ones(4), np.ones(5))
+    for i, v in enumerate(arrays):
+        res += v.sum()
+    return res
+
+
+def scalar_iter_usecase(iterable):
+    res = 0.0
+    for x in iterable:
+        res += x
+    return res
+
+def record_iter_usecase(iterable):
+    res = 0.0
+    for x in iterable:
+        res += x.a * x.b
+    return res
+
+def record_iter_mutate_usecase(iterable):
+    for x in iterable:
+        x.a = x.a + x.b
+
+
+record_dtype = np.dtype([('a', np.float64),
+                         ('b', np.int32),
+                         ])
+
+
+class IterationTest(MemoryLeakMixin, TestCase):
+
+    def run_nullary_func(self, pyfunc, flags):
+        cr = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cr.entry_point
+        expected = pyfunc()
+        self.assertPreciseEqual(cfunc(), expected)
+
+    def test_int_tuple_iter(self, flags=force_pyobj_flags):
+        self.run_nullary_func(int_tuple_iter_usecase, flags)
+
+    def test_int_tuple_iter_npm(self):
+        self.test_int_tuple_iter(flags=no_pyobj_flags)
+
+    # Type inference on tuples used to be hardcoded for ints, check
+    # that it works for other types.
+
+    def test_float_tuple_iter(self, flags=force_pyobj_flags):
+        self.run_nullary_func(float_tuple_iter_usecase, flags)
+
+    def test_float_tuple_iter_npm(self):
+        self.test_float_tuple_iter(flags=no_pyobj_flags)
+
+    def test_tuple_tuple_iter(self, flags=force_pyobj_flags):
+        self.run_nullary_func(tuple_tuple_iter_usecase, flags)
+
+    def test_tuple_tuple_iter_npm(self):
+        self.test_tuple_tuple_iter(flags=no_pyobj_flags)
+
+    def test_enumerate_nested_tuple(self, flags=force_pyobj_flags):
+        self.run_nullary_func(enumerate_nested_tuple_usecase, flags)
+
+    def test_enumerate_nested_tuple_npm(self):
+        self.test_enumerate_nested_tuple(flags=no_pyobj_flags)
+
+    def test_nested_enumerate(self, flags=force_pyobj_flags):
+        self.run_nullary_func(nested_enumerate_usecase, flags)
+
+    def test_nested_enumerate_npm(self):
+        self.test_nested_enumerate(flags=no_pyobj_flags)
+
+    def test_enumerate_refct(self):
+        # Test issue 3473
+        pyfunc = enumerate_array_usecase
+        cr = compile_isolated(pyfunc, ())
+        cfunc = cr.entry_point
+        expected = pyfunc()
+        self.assertPreciseEqual(cfunc(), expected)
+
+    def run_array_1d(self, item_type, arg, flags):
+        # Iteration over a 1d numpy array
+        pyfunc = scalar_iter_usecase
+        cr = compile_isolated(pyfunc, (types.Array(item_type, 1, 'A'),),
+                              item_type, flags=flags)
+        cfunc = cr.entry_point
+        self.assertPreciseEqual(cfunc(arg), pyfunc(arg))
+
+    def test_array_1d_float(self, flags=force_pyobj_flags):
+        self.run_array_1d(types.float64, np.arange(5.0), flags)
+
+    def test_array_1d_float_npm(self):
+        self.test_array_1d_float(no_pyobj_flags)
+
+    def test_array_1d_complex(self, flags=force_pyobj_flags):
+        self.run_array_1d(types.complex128, np.arange(5.0) * 1.0j, flags)
+
+    def test_array_1d_complex_npm(self):
+        self.test_array_1d_complex(no_pyobj_flags)
+
+    def test_array_1d_record(self, flags=force_pyobj_flags):
+        pyfunc = record_iter_usecase
+        item_type = numpy_support.from_dtype(record_dtype)
+        cr = compile_isolated(pyfunc, (types.Array(item_type, 1, 'A'),),
+                              flags=flags)
+        cfunc = cr.entry_point
+        arr = np.recarray(3, dtype=record_dtype)
+        for i in range(3):
+            arr[i].a = float(i * 2)
+            arr[i].b = i + 2
+        got = pyfunc(arr)
+        self.assertPreciseEqual(cfunc(arr), got)
+
+    def test_array_1d_record_npm(self):
+        self.test_array_1d_record(no_pyobj_flags)
+
+    def test_array_1d_record_mutate_npm(self, flags=no_pyobj_flags):
+        pyfunc = record_iter_mutate_usecase
+        item_type = numpy_support.from_dtype(record_dtype)
+        cr = compile_isolated(pyfunc, (types.Array(item_type, 1, 'A'),),
+                              flags=flags)
+        cfunc = cr.entry_point
+        arr = np.recarray(3, dtype=record_dtype)
+        for i in range(3):
+            arr[i].a = float(i * 2)
+            arr[i].b = i + 2
+        expected = arr.copy()
+        pyfunc(expected)
+        got = arr.copy()
+        cfunc(got)
+        self.assertPreciseEqual(expected, got)
+
+    def test_array_1d_record_mutate(self):
+        self.test_array_1d_record_mutate_npm(flags=force_pyobj_flags)
+
+    def test_array_0d_raises(self):
+
+        def foo(x):
+            for i in x:
+                pass
+
+        # 0d is typing error
+        with self.assertRaises(errors.TypingError) as raises:
+            aryty = types.Array(types.int32, 0, 'C')
+            compile_isolated(foo, (aryty,))
+
+        self.assertIn("0-d array", str(raises.exception))
+
+    def test_tuple_iter_issue1504(self):
+        # The issue is due to `row` being typed as heterogeneous tuple.
+        def bar(x, y):
+            total = 0
+            for row in zip(x, y):
+                total += row[0] + row[1]
+
+            return total
+
+        x = y = np.arange(3, dtype=np.int32)
+        aryty = types.Array(types.int32, 1, 'C')
+        cres = compile_isolated(bar, (aryty, aryty))
+
+        expect = bar(x, y)
+        got = cres.entry_point(x, y)
+        self.assertEqual(expect, got)
+
+    def test_tuple_of_arrays_iter(self):
+        # We used to leak a reference to each element of the tuple
+        def bar(arrs):
+            total = 0
+            for arr in arrs:
+                total += arr[0]
+
+            return total
+
+        x = y = np.arange(3, dtype=np.int32)
+        aryty = types.Array(types.int32, 1, 'C')
+        cres = compile_isolated(bar, (types.containers.UniTuple(aryty, 2),))
+
+        expect = bar((x, y))
+        got = cres.entry_point((x, y))
+        self.assertEqual(expect, got)
+
+
+
+class TestIterationRefct(MemoryLeakMixin, TestCase):
+    def test_zip_with_arrays(self):
+        @njit
+        def foo(sequence):
+            c = 0
+            for a, b in zip(range(len(sequence)), sequence):
+                c += (a + 1) * b.sum()
+            return
+
+        sequence = [np.arange(1 + i) for i in range(10)]
+        self.assertEqual(foo(sequence), foo.py_func(sequence))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jit_module.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jit_module.py
new file mode 100644
index 000000000..2eb1789bb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jit_module.py
@@ -0,0 +1,145 @@
+import os
+import sys
+import inspect
+import contextlib
+import numpy as np
+import logging
+from io import StringIO
+
+import unittest
+from numba.tests.support import SerialMixin, create_temp_module
+from numba.core import dispatcher
+
+
+@contextlib.contextmanager
+def captured_logs(l):
+    try:
+        buffer = StringIO()
+        handler = logging.StreamHandler(buffer)
+        l.addHandler(handler)
+        yield buffer
+    finally:
+        l.removeHandler(handler)
+
+
+class TestJitModule(SerialMixin, unittest.TestCase):
+
+    source_lines = """
+from numba import jit_module
+
+def inc(x):
+    return x + 1
+
+def add(x, y):
+    return x + y
+
+def inc_add(x):
+    y = inc(x)
+    return add(x, y)
+
+import numpy as np
+mean = np.mean
+
+class Foo(object):
+    pass
+
+jit_module({jit_options})
+"""
+
+    def test_create_temp_jitted_module(self):
+        sys_path_original = list(sys.path)
+        sys_modules_original = dict(sys.modules)
+        with create_temp_module(self.source_lines) as test_module:
+            temp_module_dir = os.path.dirname(test_module.__file__)
+            self.assertEqual(temp_module_dir, sys.path[0])
+            self.assertEqual(sys.path[1:], sys_path_original)
+            self.assertTrue(test_module.__name__ in sys.modules)
+        # Test that modifications to sys.path / sys.modules are reverted
+        self.assertEqual(sys.path, sys_path_original)
+        self.assertEqual(sys.modules, sys_modules_original)
+
+    def test_create_temp_jitted_module_with_exception(self):
+        try:
+            sys_path_original = list(sys.path)
+            sys_modules_original = dict(sys.modules)
+            with create_temp_module(self.source_lines):
+                raise ValueError("Something went wrong!")
+        except ValueError:
+            # Test that modifications to sys.path / sys.modules are reverted
+            self.assertEqual(sys.path, sys_path_original)
+            self.assertEqual(sys.modules, sys_modules_original)
+
+    def test_jit_module(self):
+        with create_temp_module(self.source_lines) as test_module:
+            self.assertIsInstance(test_module.inc, dispatcher.Dispatcher)
+            self.assertIsInstance(test_module.add, dispatcher.Dispatcher)
+            self.assertIsInstance(test_module.inc_add, dispatcher.Dispatcher)
+            self.assertTrue(test_module.mean is np.mean)
+            self.assertTrue(inspect.isclass(test_module.Foo))
+
+            # Test output of jitted functions is as expected
+            x, y = 1.7, 2.3
+            self.assertEqual(test_module.inc(x),
+                             test_module.inc.py_func(x))
+            self.assertEqual(test_module.add(x, y),
+                             test_module.add.py_func(x, y))
+            self.assertEqual(test_module.inc_add(x),
+                             test_module.inc_add.py_func(x))
+
+    def test_jit_module_jit_options(self):
+        jit_options = {"nopython": True,
+                       "nogil": False,
+                       "error_model": "numpy",
+                       "boundscheck": False,
+                       }
+        with create_temp_module(self.source_lines,
+                                **jit_options) as test_module:
+            self.assertEqual(test_module.inc.targetoptions, jit_options)
+
+    def test_jit_module_jit_options_override(self):
+        source_lines = """
+from numba import jit, jit_module
+
+@jit(nogil=True, forceobj=True)
+def inc(x):
+    return x + 1
+
+def add(x, y):
+    return x + y
+
+jit_module({jit_options})
+"""
+        jit_options = {"nopython": True,
+                       "error_model": "numpy",
+                       "boundscheck": False,
+                       }
+        with create_temp_module(source_lines=source_lines,
+                                **jit_options) as test_module:
+            self.assertEqual(test_module.add.targetoptions, jit_options)
+            # Test that manual jit-wrapping overrides jit_module options
+            self.assertEqual(test_module.inc.targetoptions,
+                             {'nogil': True, 'forceobj': True,
+                              'boundscheck': None})
+
+    def test_jit_module_logging_output(self):
+        logger = logging.getLogger('numba.core.decorators')
+        logger.setLevel(logging.DEBUG)
+        jit_options = {"nopython": True,
+                       "error_model": "numpy",
+                       }
+        with captured_logs(logger) as logs:
+            with create_temp_module(self.source_lines,
+                                    **jit_options) as test_module:
+                logs = logs.getvalue()
+                expected = ["Auto decorating function",
+                            "from module {}".format(test_module.__name__),
+                            "with jit and options: {}".format(jit_options)]
+                self.assertTrue(all(i in logs for i in expected))
+
+    def test_jit_module_logging_level(self):
+        logger = logging.getLogger('numba.core.decorators')
+        # Test there's no logging for INFO level
+        logger.setLevel(logging.INFO)
+        with captured_logs(logger) as logs:
+            with create_temp_module(self.source_lines):
+                self.assertEqual(logs.getvalue(), '')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jitclasses.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jitclasses.py
new file mode 100644
index 000000000..f9dc74dac
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jitclasses.py
@@ -0,0 +1,1878 @@
+import ctypes
+import itertools
+import pickle
+import random
+import typing as pt
+import unittest
+
+from collections import OrderedDict
+
+import numpy as np
+from numba import (boolean, deferred_type, float32, float64, int16, int32,
+                   njit, optional, typeof)
+from numba.core import errors, types
+from numba.core.dispatcher import Dispatcher
+from numba.core.errors import LoweringError, TypingError
+from numba.core.runtime.nrt import MemInfo
+from numba.core.utils import PYVERSION
+from numba.experimental import jitclass
+from numba.experimental.jitclass import _box
+from numba.experimental.jitclass.base import JitClassType
+from numba.tests.support import MemoryLeakMixin, TestCase, skip_if_typeguard
+
+
+class TestClass1(object):
+    def __init__(self, x, y, z=1, *, a=5):
+        self.x = x
+        self.y = y
+        self.z = z
+        self.a = a
+
+
+class TestClass2(object):
+    def __init__(self, x, y, z=1, *args, a=5):
+        self.x = x
+        self.y = y
+        self.z = z
+        self.args = args
+        self.a = a
+
+
+def _get_meminfo(box):
+    ptr = _box.box_get_meminfoptr(box)
+    mi = MemInfo(ptr)
+    mi.acquire()
+    return mi
+
+
+class TestJitClass(TestCase, MemoryLeakMixin):
+
+    def _check_spec(self, spec=None, test_cls=None, all_expected=None):
+        if test_cls is None:
+            @jitclass(spec)
+            class Test(object):
+
+                def __init__(self):
+                    pass
+            test_cls = Test
+
+        clsty = test_cls.class_type.instance_type
+        names = list(clsty.struct.keys())
+        values = list(clsty.struct.values())
+
+        if all_expected is None:
+            if isinstance(spec, OrderedDict):
+                all_expected = spec.items()
+            else:
+                all_expected = spec
+
+        assert all_expected is not None
+
+        self.assertEqual(len(names), len(all_expected))
+        for got, expected in zip(zip(names, values), all_expected):
+            self.assertEqual(got[0], expected[0])
+            self.assertEqual(got[1], expected[1])
+
+    def test_ordereddict_spec(self):
+        spec = OrderedDict()
+        spec["x"] = int32
+        spec["y"] = float32
+        self._check_spec(spec)
+
+    def test_list_spec(self):
+        spec = [("x", int32),
+                ("y", float32)]
+        self._check_spec(spec)
+
+    def test_type_annotations(self):
+        spec = [("x", int32)]
+
+        @jitclass(spec)
+        class Test1(object):
+            x: int
+            y: pt.List[float]
+
+            def __init__(self):
+                pass
+
+        self._check_spec(spec, Test1, spec + [("y", types.ListType(float64))])
+
+    def test_type_annotation_inheritance(self):
+
+        class Foo:
+            x: int
+
+        @jitclass
+        class Bar(Foo):
+            y: float
+
+            def __init__(self, value: float) -> None:
+                self.x = int(value)
+                self.y = value
+
+        self._check_spec(
+            test_cls=Bar, all_expected=[("x", typeof(0)), ("y", typeof(0.0))]
+        )
+
+    def test_spec_errors(self):
+        spec1 = [("x", int), ("y", float32[:])]
+        spec2 = [(1, int32), ("y", float32[:])]
+
+        class Test(object):
+
+            def __init__(self):
+                pass
+
+        with self.assertRaises(TypeError) as raises:
+            jitclass(Test, spec1)
+        self.assertIn("spec values should be Numba type instances",
+                      str(raises.exception))
+        with self.assertRaises(TypeError) as raises:
+            jitclass(Test, spec2)
+        self.assertEqual(str(raises.exception),
+                         "spec keys should be strings, got 1")
+
+    def test_init_errors(self):
+
+        @jitclass([])
+        class Test:
+            def __init__(self):
+                return 7
+
+        with self.assertRaises(errors.TypingError) as raises:
+            Test()
+
+        self.assertIn("__init__() should return None, not",
+                      str(raises.exception))
+
+    def _make_Float2AndArray(self):
+        spec = OrderedDict()
+        spec["x"] = float32
+        spec["y"] = float32
+        spec["arr"] = float32[:]
+
+        @jitclass(spec)
+        class Float2AndArray(object):
+
+            def __init__(self, x, y, arr):
+                self.x = x
+                self.y = y
+                self.arr = arr
+
+            def add(self, val):
+                self.x += val
+                self.y += val
+                return val
+
+        return Float2AndArray
+
+    def _make_Vector2(self):
+        spec = OrderedDict()
+        spec["x"] = int32
+        spec["y"] = int32
+
+        @jitclass(spec)
+        class Vector2(object):
+
+            def __init__(self, x, y):
+                self.x = x
+                self.y = y
+
+        return Vector2
+
+    def test_jit_class_1(self):
+        Float2AndArray = self._make_Float2AndArray()
+        Vector2 = self._make_Vector2()
+
+        @njit
+        def bar(obj):
+            return obj.x + obj.y
+
+        @njit
+        def foo(a):
+            obj = Float2AndArray(1, 2, a)
+            obj.add(123)
+
+            vec = Vector2(3, 4)
+            return bar(obj), bar(vec), obj.arr
+
+        inp = np.ones(10, dtype=np.float32)
+        a, b, c = foo(inp)
+        self.assertEqual(a, 123 + 1 + 123 + 2)
+        self.assertEqual(b, 3 + 4)
+        self.assertPreciseEqual(c, inp)
+
+    def test_jitclass_usage_from_python(self):
+        Float2AndArray = self._make_Float2AndArray()
+
+        @njit
+        def identity(obj):
+            return obj
+
+        @njit
+        def retrieve_attributes(obj):
+            return obj.x, obj.y, obj.arr
+
+        arr = np.arange(10, dtype=np.float32)
+        obj = Float2AndArray(1, 2, arr)
+        obj_meminfo = _get_meminfo(obj)
+        self.assertEqual(obj_meminfo.refcount, 2)
+        self.assertEqual(obj_meminfo.data, _box.box_get_dataptr(obj))
+        self.assertEqual(obj._numba_type_.class_type,
+                         Float2AndArray.class_type)
+        # Use jit class instance in numba
+        other = identity(obj)
+        other_meminfo = _get_meminfo(other)  # duplicates MemInfo object to obj
+        self.assertEqual(obj_meminfo.refcount, 4)
+        self.assertEqual(other_meminfo.refcount, 4)
+        self.assertEqual(other_meminfo.data, _box.box_get_dataptr(other))
+        self.assertEqual(other_meminfo.data, obj_meminfo.data)
+
+        # Check dtor
+        del other, other_meminfo
+        self.assertEqual(obj_meminfo.refcount, 2)
+
+        # Check attributes
+        out_x, out_y, out_arr = retrieve_attributes(obj)
+        self.assertEqual(out_x, 1)
+        self.assertEqual(out_y, 2)
+        self.assertIs(out_arr, arr)
+
+        # Access attributes from python
+        self.assertEqual(obj.x, 1)
+        self.assertEqual(obj.y, 2)
+        self.assertIs(obj.arr, arr)
+
+        # Access methods from python
+        self.assertEqual(obj.add(123), 123)
+        self.assertEqual(obj.x, 1 + 123)
+        self.assertEqual(obj.y, 2 + 123)
+
+        # Setter from python
+        obj.x = 333
+        obj.y = 444
+        obj.arr = newarr = np.arange(5, dtype=np.float32)
+        self.assertEqual(obj.x, 333)
+        self.assertEqual(obj.y, 444)
+        self.assertIs(obj.arr, newarr)
+
+    def test_jitclass_datalayout(self):
+        spec = OrderedDict()
+        # Boolean has different layout as value vs data
+        spec["val"] = boolean
+
+        @jitclass(spec)
+        class Foo(object):
+
+            def __init__(self, val):
+                self.val = val
+
+        self.assertTrue(Foo(True).val)
+        self.assertFalse(Foo(False).val)
+
+    def test_deferred_type(self):
+        node_type = deferred_type()
+
+        spec = OrderedDict()
+        spec["data"] = float32
+        spec["next"] = optional(node_type)
+
+        @njit
+        def get_data(node):
+            return node.data
+
+        @jitclass(spec)
+        class LinkedNode(object):
+
+            def __init__(self, data, next):
+                self.data = data
+                self.next = next
+
+            def get_next_data(self):
+                # use deferred type as argument
+                return get_data(self.next)
+
+            def append_to_tail(self, other):
+                cur = self
+                while cur.next is not None:
+                    cur = cur.next
+                cur.next = other
+
+        node_type.define(LinkedNode.class_type.instance_type)
+
+        first = LinkedNode(123, None)
+        self.assertEqual(first.data, 123)
+        self.assertIsNone(first.next)
+
+        second = LinkedNode(321, first)
+
+        first_meminfo = _get_meminfo(first)
+        second_meminfo = _get_meminfo(second)
+        self.assertEqual(first_meminfo.refcount, 3)
+        self.assertEqual(second.next.data, first.data)
+        self.assertEqual(first_meminfo.refcount, 3)
+        self.assertEqual(second_meminfo.refcount, 2)
+
+        # Test using deferred type as argument
+        first_val = second.get_next_data()
+        self.assertEqual(first_val, first.data)
+
+        # Check setattr (issue #2606)
+        self.assertIsNone(first.next)
+        second.append_to_tail(LinkedNode(567, None))
+        self.assertIsNotNone(first.next)
+        self.assertEqual(first.next.data, 567)
+        self.assertIsNone(first.next.next)
+        second.append_to_tail(LinkedNode(678, None))
+        self.assertIsNotNone(first.next.next)
+        self.assertEqual(first.next.next.data, 678)
+
+        # Check ownership
+        self.assertEqual(first_meminfo.refcount, 3)
+        del second, second_meminfo
+        self.assertEqual(first_meminfo.refcount, 2)
+
+    def test_c_structure(self):
+        spec = OrderedDict()
+        spec["a"] = int32
+        spec["b"] = int16
+        spec["c"] = float64
+
+        @jitclass(spec)
+        class Struct(object):
+
+            def __init__(self, a, b, c):
+                self.a = a
+                self.b = b
+                self.c = c
+
+        st = Struct(0xabcd, 0xef, 3.1415)
+
+        class CStruct(ctypes.Structure):
+            _fields_ = [
+                ("a", ctypes.c_int32),
+                ("b", ctypes.c_int16),
+                ("c", ctypes.c_double),
+            ]
+
+        ptr = ctypes.c_void_p(_box.box_get_dataptr(st))
+        cstruct = ctypes.cast(ptr, ctypes.POINTER(CStruct))[0]
+        self.assertEqual(cstruct.a, st.a)
+        self.assertEqual(cstruct.b, st.b)
+        self.assertEqual(cstruct.c, st.c)
+
+    def test_is(self):
+        Vector = self._make_Vector2()
+        vec_a = Vector(1, 2)
+
+        @njit
+        def do_is(a, b):
+            return a is b
+
+        with self.assertRaises(LoweringError) as raises:
+            # trigger compilation
+            do_is(vec_a, vec_a)
+        self.assertIn("no default `is` implementation", str(raises.exception))
+
+    def test_isinstance(self):
+        Vector2 = self._make_Vector2()
+        vec = Vector2(1, 2)
+        self.assertIsInstance(vec, Vector2)
+
+    def test_subclassing(self):
+        Vector2 = self._make_Vector2()
+        with self.assertRaises(TypeError) as raises:
+            class SubV(Vector2):
+                pass
+        self.assertEqual(str(raises.exception),
+                         "cannot subclass from a jitclass")
+
+    def test_base_class(self):
+        class Base(object):
+
+            def what(self):
+                return self.attr
+
+        @jitclass([("attr", int32)])
+        class Test(Base):
+
+            def __init__(self, attr):
+                self.attr = attr
+
+        obj = Test(123)
+        self.assertEqual(obj.what(), 123)
+
+    def test_globals(self):
+
+        class Mine(object):
+            constant = 123
+
+            def __init__(self):
+                pass
+
+        with self.assertRaises(TypeError) as raises:
+            jitclass(Mine)
+
+        self.assertEqual(str(raises.exception),
+                         "class members are not yet supported: constant")
+
+    def test_user_getter_setter(self):
+        @jitclass([("attr", int32)])
+        class Foo(object):
+
+            def __init__(self, attr):
+                self.attr = attr
+
+            @property
+            def value(self):
+                return self.attr + 1
+
+            @value.setter
+            def value(self, val):
+                self.attr = val - 1
+
+        foo = Foo(123)
+        self.assertEqual(foo.attr, 123)
+        # Getter
+        self.assertEqual(foo.value, 123 + 1)
+        # Setter
+        foo.value = 789
+        self.assertEqual(foo.attr, 789 - 1)
+        self.assertEqual(foo.value, 789)
+
+        # Test nopython mode usage of getter and setter
+        @njit
+        def bar(foo, val):
+            a = foo.value
+            foo.value = val
+            b = foo.value
+            c = foo.attr
+            return a, b, c
+
+        a, b, c = bar(foo, 567)
+        self.assertEqual(a, 789)
+        self.assertEqual(b, 567)
+        self.assertEqual(c, 567 - 1)
+
+    def test_user_deleter_error(self):
+        class Foo(object):
+
+            def __init__(self):
+                pass
+
+            @property
+            def value(self):
+                return 1
+
+            @value.deleter
+            def value(self):
+                pass
+
+        with self.assertRaises(TypeError) as raises:
+            jitclass(Foo)
+        self.assertEqual(str(raises.exception),
+                         "deleter is not supported: value")
+
+    def test_name_shadowing_error(self):
+        class Foo(object):
+
+            def __init__(self):
+                pass
+
+            @property
+            def my_property(self):
+                pass
+
+            def my_method(self):
+                pass
+
+        with self.assertRaises(NameError) as raises:
+            jitclass(Foo, [("my_property", int32)])
+        self.assertEqual(str(raises.exception), "name shadowing: my_property")
+
+        with self.assertRaises(NameError) as raises:
+            jitclass(Foo, [("my_method", int32)])
+        self.assertEqual(str(raises.exception), "name shadowing: my_method")
+
+    def test_distinct_classes(self):
+        # Different classes with the same names shouldn't confuse the compiler
+        @jitclass([("x", int32)])
+        class Foo(object):
+
+            def __init__(self, x):
+                self.x = x + 2
+
+            def run(self):
+                return self.x + 1
+
+        FirstFoo = Foo
+
+        @jitclass([("x", int32)])
+        class Foo(object):
+
+            def __init__(self, x):
+                self.x = x - 2
+
+            def run(self):
+                return self.x - 1
+
+        SecondFoo = Foo
+        foo = FirstFoo(5)
+        self.assertEqual(foo.x, 7)
+        self.assertEqual(foo.run(), 8)
+        foo = SecondFoo(5)
+        self.assertEqual(foo.x, 3)
+        self.assertEqual(foo.run(), 2)
+
+    def test_parameterized(self):
+        class MyClass(object):
+
+            def __init__(self, value):
+                self.value = value
+
+        def create_my_class(value):
+            cls = jitclass(MyClass, [("value", typeof(value))])
+            return cls(value)
+
+        a = create_my_class(123)
+        self.assertEqual(a.value, 123)
+
+        b = create_my_class(12.3)
+        self.assertEqual(b.value, 12.3)
+
+        c = create_my_class(np.array([123]))
+        np.testing.assert_equal(c.value, [123])
+
+        d = create_my_class(np.array([12.3]))
+        np.testing.assert_equal(d.value, [12.3])
+
+    def test_protected_attrs(self):
+        spec = {
+            "value": int32,
+            "_value": float32,
+            "__value": int32,
+            "__value__": int32,
+        }
+
+        @jitclass(spec)
+        class MyClass(object):
+
+            def __init__(self, value):
+                self.value = value
+                self._value = value / 2
+                self.__value = value * 2
+                self.__value__ = value - 1
+
+            @property
+            def private_value(self):
+                return self.__value
+
+            @property
+            def _inner_value(self):
+                return self._value
+
+            @_inner_value.setter
+            def _inner_value(self, v):
+                self._value = v
+
+            @property
+            def __private_value(self):
+                return self.__value
+
+            @__private_value.setter
+            def __private_value(self, v):
+                self.__value = v
+
+            def swap_private_value(self, new):
+                old = self.__private_value
+                self.__private_value = new
+                return old
+
+            def _protected_method(self, factor):
+                return self._value * factor
+
+            def __private_method(self, factor):
+                return self.__value * factor
+
+            def check_private_method(self, factor):
+                return self.__private_method(factor)
+
+        value = 123
+        inst = MyClass(value)
+        # test attributes
+        self.assertEqual(inst.value, value)
+        self.assertEqual(inst._value, value / 2)
+        self.assertEqual(inst.private_value, value * 2)
+        # test properties
+        self.assertEqual(inst._inner_value, inst._value)
+        freeze_inst_value = inst._value
+        inst._inner_value -= 1
+        self.assertEqual(inst._inner_value, freeze_inst_value - 1)
+
+        self.assertEqual(inst.swap_private_value(321), value * 2)
+        self.assertEqual(inst.swap_private_value(value * 2), 321)
+        # test methods
+        self.assertEqual(inst._protected_method(3), inst._value * 3)
+        self.assertEqual(inst.check_private_method(3), inst.private_value * 3)
+        # test special
+        self.assertEqual(inst.__value__, value - 1)
+        inst.__value__ -= 100
+        self.assertEqual(inst.__value__, value - 101)
+
+        # test errors
+        @njit
+        def access_dunder(inst):
+            return inst.__value
+
+        with self.assertRaises(errors.TypingError) as raises:
+            access_dunder(inst)
+        # It will appear as "_TestJitClass__value" because the `access_dunder`
+        # is under the scope of "TestJitClass".
+        self.assertIn("_TestJitClass__value", str(raises.exception))
+
+        with self.assertRaises(AttributeError) as raises:
+            access_dunder.py_func(inst)
+        self.assertIn("_TestJitClass__value", str(raises.exception))
+
+    @skip_if_typeguard
+    def test_annotations(self):
+        """
+        Methods with annotations should compile fine (issue #1911).
+        """
+        from .annotation_usecases import AnnotatedClass
+
+        spec = {"x": int32}
+        cls = jitclass(AnnotatedClass, spec)
+
+        obj = cls(5)
+        self.assertEqual(obj.x, 5)
+        self.assertEqual(obj.add(2), 7)
+
+    def test_docstring(self):
+
+        @jitclass
+        class Apple(object):
+            "Class docstring"
+
+            def __init__(self):
+                "init docstring"
+
+            def foo(self):
+                "foo method docstring"
+
+            @property
+            def aval(self):
+                "aval property docstring"
+
+        self.assertEqual(Apple.__doc__, "Class docstring")
+        self.assertEqual(Apple.__init__.__doc__, "init docstring")
+        self.assertEqual(Apple.foo.__doc__, "foo method docstring")
+        self.assertEqual(Apple.aval.__doc__, "aval property docstring")
+
+    def test_kwargs(self):
+        spec = [("a", int32),
+                ("b", float64)]
+
+        @jitclass(spec)
+        class TestClass(object):
+            def __init__(self, x, y, z):
+                self.a = x * y
+                self.b = z
+
+        x = 2
+        y = 2
+        z = 1.1
+        kwargs = {"y": y, "z": z}
+        tc = TestClass(x=2, **kwargs)
+        self.assertEqual(tc.a, x * y)
+        self.assertEqual(tc.b, z)
+
+    def test_default_args(self):
+        spec = [("x", int32),
+                ("y", int32),
+                ("z", int32)]
+
+        @jitclass(spec)
+        class TestClass(object):
+            def __init__(self, x, y, z=1):
+                self.x = x
+                self.y = y
+                self.z = z
+
+        tc = TestClass(1, 2, 3)
+        self.assertEqual(tc.x, 1)
+        self.assertEqual(tc.y, 2)
+        self.assertEqual(tc.z, 3)
+
+        tc = TestClass(1, 2)
+        self.assertEqual(tc.x, 1)
+        self.assertEqual(tc.y, 2)
+        self.assertEqual(tc.z, 1)
+
+        tc = TestClass(y=2, z=5, x=1)
+        self.assertEqual(tc.x, 1)
+        self.assertEqual(tc.y, 2)
+        self.assertEqual(tc.z, 5)
+
+    def test_default_args_keyonly(self):
+        spec = [("x", int32),
+                ("y", int32),
+                ("z", int32),
+                ("a", int32)]
+
+        TestClass = jitclass(TestClass1, spec)
+
+        tc = TestClass(2, 3)
+        self.assertEqual(tc.x, 2)
+        self.assertEqual(tc.y, 3)
+        self.assertEqual(tc.z, 1)
+        self.assertEqual(tc.a, 5)
+
+        tc = TestClass(y=4, x=2, a=42, z=100)
+        self.assertEqual(tc.x, 2)
+        self.assertEqual(tc.y, 4)
+        self.assertEqual(tc.z, 100)
+        self.assertEqual(tc.a, 42)
+
+        tc = TestClass(y=4, x=2, a=42)
+        self.assertEqual(tc.x, 2)
+        self.assertEqual(tc.y, 4)
+        self.assertEqual(tc.z, 1)
+        self.assertEqual(tc.a, 42)
+
+        tc = TestClass(y=4, x=2)
+        self.assertEqual(tc.x, 2)
+        self.assertEqual(tc.y, 4)
+        self.assertEqual(tc.z, 1)
+        self.assertEqual(tc.a, 5)
+
+    def test_default_args_starargs_and_keyonly(self):
+        spec = [("x", int32),
+                ("y", int32),
+                ("z", int32),
+                ("args", types.UniTuple(int32, 2)),
+                ("a", int32)]
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            jitclass(TestClass2, spec)
+
+        msg = "VAR_POSITIONAL argument type unsupported"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_generator_method(self):
+        spec = []
+
+        @jitclass(spec)
+        class TestClass(object):
+            def __init__(self):
+                pass
+
+            def gen(self, niter):
+                for i in range(niter):
+                    yield np.arange(i)
+
+        def expected_gen(niter):
+            for i in range(niter):
+                yield np.arange(i)
+
+        for niter in range(10):
+            for expect, got in zip(expected_gen(niter), TestClass().gen(niter)):
+                self.assertPreciseEqual(expect, got)
+
+    def test_getitem(self):
+        spec = [("data", int32[:])]
+
+        @jitclass(spec)
+        class TestClass(object):
+            def __init__(self):
+                self.data = np.zeros(10, dtype=np.int32)
+
+            def __setitem__(self, key, data):
+                self.data[key] = data
+
+            def __getitem__(self, key):
+                return self.data[key]
+
+        @njit
+        def create_and_set_indices():
+            t = TestClass()
+            t[1] = 1
+            t[2] = 2
+            t[3] = 3
+            return t
+
+        @njit
+        def get_index(t, n):
+            return t[n]
+
+        t = create_and_set_indices()
+        self.assertEqual(get_index(t, 1), 1)
+        self.assertEqual(get_index(t, 2), 2)
+        self.assertEqual(get_index(t, 3), 3)
+
+    def test_getitem_unbox(self):
+        spec = [("data", int32[:])]
+
+        @jitclass(spec)
+        class TestClass(object):
+            def __init__(self):
+                self.data = np.zeros(10, dtype=np.int32)
+
+            def __setitem__(self, key, data):
+                self.data[key] = data
+
+            def __getitem__(self, key):
+                return self.data[key]
+
+        t = TestClass()
+        t[1] = 10
+
+        @njit
+        def set2return1(t):
+            t[2] = 20
+            return t[1]
+
+        t_1 = set2return1(t)
+        self.assertEqual(t_1, 10)
+        self.assertEqual(t[2], 20)
+
+    def test_getitem_complex_key(self):
+        spec = [("data", int32[:, :])]
+
+        @jitclass(spec)
+        class TestClass(object):
+            def __init__(self):
+                self.data = np.zeros((10, 10), dtype=np.int32)
+
+            def __setitem__(self, key, data):
+                self.data[int(key.real), int(key.imag)] = data
+
+            def __getitem__(self, key):
+                return self.data[int(key.real), int(key.imag)]
+
+        t = TestClass()
+
+        t[complex(1, 1)] = 3
+
+        @njit
+        def get_key(t, real, imag):
+            return t[complex(real, imag)]
+
+        @njit
+        def set_key(t, real, imag, data):
+            t[complex(real, imag)] = data
+
+        self.assertEqual(get_key(t, 1, 1), 3)
+        set_key(t, 2, 2, 4)
+        self.assertEqual(t[complex(2, 2)], 4)
+
+    def test_getitem_tuple_key(self):
+        spec = [("data", int32[:, :])]
+
+        @jitclass(spec)
+        class TestClass(object):
+            def __init__(self):
+                self.data = np.zeros((10, 10), dtype=np.int32)
+
+            def __setitem__(self, key, data):
+                self.data[key[0], key[1]] = data
+
+            def __getitem__(self, key):
+                return self.data[key[0], key[1]]
+
+        t = TestClass()
+        t[1, 1] = 11
+
+        @njit
+        def get11(t):
+            return t[1, 1]
+
+        @njit
+        def set22(t, data):
+            t[2, 2] = data
+
+        self.assertEqual(get11(t), 11)
+        set22(t, 22)
+        self.assertEqual(t[2, 2], 22)
+
+    def test_getitem_slice_key(self):
+        spec = [("data", int32[:])]
+
+        @jitclass(spec)
+        class TestClass(object):
+            def __init__(self):
+                self.data = np.zeros(10, dtype=np.int32)
+
+            def __setitem__(self, slc, data):
+                self.data[slc.start] = data
+                self.data[slc.stop] = data + slc.step
+
+            def __getitem__(self, slc):
+                return self.data[slc.start]
+
+        t = TestClass()
+        # set t.data[1] = 1 and t.data[5] = 2
+        t[1:5:1] = 1
+
+        self.assertEqual(t[1:1:1], 1)
+        self.assertEqual(t[5:5:5], 2)
+
+        @njit
+        def get5(t):
+            return t[5:6:1]
+
+        self.assertEqual(get5(t), 2)
+
+        # sets t.data[2] = data, and t.data[6] = data + 1
+        @njit
+        def set26(t, data):
+            t[2:6:1] = data
+
+        set26(t, 2)
+        self.assertEqual(t[2:2:1], 2)
+        self.assertEqual(t[6:6:1], 3)
+
+    def test_jitclass_longlabel_not_truncated(self):
+        # See issue #3872, llvm 7 introduced a max label length of 1024 chars
+        # Numba ships patched llvm 7.1 (ppc64le) and patched llvm 8 to undo this
+        # change, this test is here to make sure long labels are ok:
+        alphabet = [chr(ord("a") + x) for x in range(26)]
+
+        spec = [(letter * 10, float64) for letter in alphabet]
+        spec.extend([(letter.upper() * 10, float64) for letter in alphabet])
+
+        @jitclass(spec)
+        class TruncatedLabel(object):
+            def __init__(self,):
+                self.aaaaaaaaaa = 10.
+
+            def meth1(self):
+                self.bbbbbbbbbb = random.gauss(self.aaaaaaaaaa, self.aaaaaaaaaa)
+
+            def meth2(self):
+                self.meth1()
+
+        # unpatched LLVMs will raise here...
+        TruncatedLabel().meth2()
+
+    def test_pickling(self):
+        @jitclass
+        class PickleTestSubject(object):
+            def __init__(self):
+                pass
+
+        inst = PickleTestSubject()
+        ty = typeof(inst)
+        self.assertIsInstance(ty, types.ClassInstanceType)
+        pickled = pickle.dumps(ty)
+        self.assertIs(pickle.loads(pickled), ty)
+
+    def test_static_methods(self):
+        @jitclass([("x", int32)])
+        class Test1:
+            def __init__(self, x):
+                self.x = x
+
+            def increase(self, y):
+                self.x = self.add(self.x, y)
+                return self.x
+
+            @staticmethod
+            def add(a, b):
+                return a + b
+
+            @staticmethod
+            def sub(a, b):
+                return a - b
+
+        @jitclass([("x", int32)])
+        class Test2:
+            def __init__(self, x):
+                self.x = x
+
+            def increase(self, y):
+                self.x = self.add(self.x, y)
+                return self.x
+
+            @staticmethod
+            def add(a, b):
+                return a - b
+
+        self.assertIsInstance(Test1.add, Dispatcher)
+        self.assertIsInstance(Test1.sub, Dispatcher)
+        self.assertIsInstance(Test2.add, Dispatcher)
+        self.assertNotEqual(Test1.add, Test2.add)
+
+        self.assertEqual(3, Test1.add(1, 2))
+        self.assertEqual(-1, Test2.add(1, 2))
+        self.assertEqual(4, Test1.sub(6, 2))
+
+        t1 = Test1(0)
+        t2 = Test2(0)
+        self.assertEqual(1, t1.increase(1))
+        self.assertEqual(-1, t2.increase(1))
+        self.assertEqual(2, t1.add(1, 1))
+        self.assertEqual(0, t1.sub(1, 1))
+        self.assertEqual(0, t2.add(1, 1))
+        self.assertEqual(2j, t1.add(1j, 1j))
+        self.assertEqual(1j, t1.sub(2j, 1j))
+        self.assertEqual("foobar", t1.add("foo", "bar"))
+
+        with self.assertRaises(AttributeError) as raises:
+            Test2.sub(3, 1)
+        self.assertIn("has no attribute 'sub'",
+                      str(raises.exception))
+
+        with self.assertRaises(TypeError) as raises:
+            Test1.add(3)
+        self.assertIn("not enough arguments: expected 2, got 1",
+                      str(raises.exception))
+
+        # Check error message for calling a static method as a class attr from
+        # another method (currently unsupported).
+
+        @jitclass([])
+        class Test3:
+            def __init__(self):
+                pass
+
+            @staticmethod
+            def a_static_method(a, b):
+                pass
+
+            def call_static(self):
+                return Test3.a_static_method(1, 2)
+
+        invalid = Test3()
+        with self.assertRaises(errors.TypingError) as raises:
+            invalid.call_static()
+
+        self.assertIn("Unknown attribute 'a_static_method'",
+                      str(raises.exception))
+
+    def test_jitclass_decorator_usecases(self):
+        spec = OrderedDict(x=float64)
+
+        @jitclass()
+        class Test1:
+            x: float
+
+            def __init__(self):
+                self.x = 0
+
+        self.assertIsInstance(Test1, JitClassType)
+        self.assertDictEqual(Test1.class_type.struct, spec)
+
+        @jitclass(spec=spec)
+        class Test2:
+
+            def __init__(self):
+                self.x = 0
+
+        self.assertIsInstance(Test2, JitClassType)
+        self.assertDictEqual(Test2.class_type.struct, spec)
+
+        @jitclass
+        class Test3:
+            x: float
+
+            def __init__(self):
+                self.x = 0
+
+        self.assertIsInstance(Test3, JitClassType)
+        self.assertDictEqual(Test3.class_type.struct, spec)
+
+        @jitclass(spec)
+        class Test4:
+
+            def __init__(self):
+                self.x = 0
+
+        self.assertIsInstance(Test4, JitClassType)
+        self.assertDictEqual(Test4.class_type.struct, spec)
+
+    def test_jitclass_function_usecases(self):
+        spec = OrderedDict(x=float64)
+
+        class AnnotatedTest:
+            x: float
+
+            def __init__(self):
+                self.x = 0
+
+        JitTest1 = jitclass(AnnotatedTest)
+        self.assertIsInstance(JitTest1, JitClassType)
+        self.assertDictEqual(JitTest1.class_type.struct, spec)
+
+        class UnannotatedTest:
+
+            def __init__(self):
+                self.x = 0
+
+        JitTest2 = jitclass(UnannotatedTest, spec)
+        self.assertIsInstance(JitTest2, JitClassType)
+        self.assertDictEqual(JitTest2.class_type.struct, spec)
+
+    def test_jitclass_isinstance(self):
+        spec = OrderedDict(value=int32)
+
+        @jitclass(spec)
+        class Foo(object):
+            def __init__(self, value):
+                self.value = value
+
+            def getValue(self):
+                return self.value
+
+            def getValueIncr(self):
+                return self.value + 1
+
+        @jitclass(spec)
+        class Bar(object):
+            def __init__(self, value):
+                self.value = value
+
+            def getValue(self):
+                return self.value
+
+        def test_jitclass_isinstance(obj):
+            if isinstance(obj, (Foo, Bar)):
+                # call something that both classes implements
+                x = obj.getValue()
+                if isinstance(obj, Foo):  # something that only Foo implements
+                    return obj.getValueIncr() + x, 'Foo'
+                else:
+                    return obj.getValue() + x, 'Bar'
+            else:
+                return 'no match'
+
+        pyfunc = test_jitclass_isinstance
+        cfunc = njit(test_jitclass_isinstance)
+
+        self.assertIsInstance(Foo, JitClassType)
+        self.assertEqual(pyfunc(Foo(3)), cfunc(Foo(3)))
+        self.assertEqual(pyfunc(Bar(123)), cfunc(Bar(123)))
+        self.assertEqual(pyfunc(0), cfunc(0))
+
+
+class TestJitClassOverloads(MemoryLeakMixin, TestCase):
+
+    class PyList:
+        def __init__(self):
+            self.x = [0]
+
+        def append(self, y):
+            self.x.append(y)
+
+        def clear(self):
+            self.x.clear()
+
+        def __abs__(self):
+            return len(self.x) * 7
+
+        def __bool__(self):
+            return len(self.x) % 3 != 0
+
+        def __complex__(self):
+            c = complex(2)
+            if self.x:
+                c += self.x[0]
+            return c
+
+        def __contains__(self, y):
+            return y in self.x
+
+        def __float__(self):
+            f = 3.1415
+            if self.x:
+                f += self.x[0]
+            return f
+
+        def __int__(self):
+            i = 5
+            if self.x:
+                i += self.x[0]
+            return i
+
+        def __len__(self):
+            return len(self.x) + 1
+
+        def __str__(self):
+            if len(self.x) == 0:
+                return "PyList empty"
+            else:
+                return "PyList non-empty"
+
+    @staticmethod
+    def get_int_wrapper():
+        @jitclass([("x", types.intp)])
+        class IntWrapper:
+            def __init__(self, value):
+                self.x = value
+
+            def __eq__(self, other):
+                return self.x == other.x
+
+            def __hash__(self):
+                return self.x
+
+            def __lshift__(self, other):
+                return IntWrapper(self.x << other.x)
+
+            def __rshift__(self, other):
+                return IntWrapper(self.x >> other.x)
+
+            def __and__(self, other):
+                return IntWrapper(self.x & other.x)
+
+            def __or__(self, other):
+                return IntWrapper(self.x | other.x)
+
+            def __xor__(self, other):
+                return IntWrapper(self.x ^ other.x)
+
+        return IntWrapper
+
+    @staticmethod
+    def get_float_wrapper():
+        @jitclass([("x", types.float64)])
+        class FloatWrapper:
+
+            def __init__(self, value):
+                self.x = value
+
+            def __eq__(self, other):
+                return self.x == other.x
+
+            def __hash__(self):
+                return self.x
+
+            def __ge__(self, other):
+                return self.x >= other.x
+
+            def __gt__(self, other):
+                return self.x > other.x
+
+            def __le__(self, other):
+                return self.x <= other.x
+
+            def __lt__(self, other):
+                return self.x < other.x
+
+            def __add__(self, other):
+                return FloatWrapper(self.x + other.x)
+
+            def __floordiv__(self, other):
+                return FloatWrapper(self.x // other.x)
+
+            def __mod__(self, other):
+                return FloatWrapper(self.x % other.x)
+
+            def __mul__(self, other):
+                return FloatWrapper(self.x * other.x)
+
+            def __neg__(self, other):
+                return FloatWrapper(-self.x)
+
+            def __pos__(self, other):
+                return FloatWrapper(+self.x)
+
+            def __pow__(self, other):
+                return FloatWrapper(self.x ** other.x)
+
+            def __sub__(self, other):
+                return FloatWrapper(self.x - other.x)
+
+            def __truediv__(self, other):
+                return FloatWrapper(self.x / other.x)
+
+        return FloatWrapper
+
+    def assertSame(self, first, second, msg=None):
+        self.assertEqual(type(first), type(second), msg=msg)
+        self.assertEqual(first, second, msg=msg)
+
+    def test_overloads(self):
+        # Check that the dunder methods are exposed on ClassInstanceType.
+
+        JitList = jitclass({"x": types.List(types.intp)})(self.PyList)
+
+        py_funcs = [
+            lambda x: abs(x),
+            lambda x: x.__abs__(),
+            lambda x: bool(x),
+            lambda x: x.__bool__(),
+            lambda x: complex(x),
+            lambda x: x.__complex__(),
+            lambda x: 0 in x,  # contains
+            lambda x: x.__contains__(0),
+            lambda x: float(x),
+            lambda x: x.__float__(),
+            lambda x: int(x),
+            lambda x: x.__int__(),
+            lambda x: len(x),
+            lambda x: x.__len__(),
+            lambda x: str(x),
+            lambda x: x.__str__(),
+            lambda x: 1 if x else 0,  # truth
+        ]
+        jit_funcs = [njit(f) for f in py_funcs]
+
+        py_list = self.PyList()
+        jit_list = JitList()
+        for py_f, jit_f in zip(py_funcs, jit_funcs):
+            self.assertSame(py_f(py_list), py_f(jit_list))
+            self.assertSame(py_f(py_list), jit_f(jit_list))
+
+        py_list.append(2)
+        jit_list.append(2)
+        for py_f, jit_f in zip(py_funcs, jit_funcs):
+            self.assertSame(py_f(py_list), py_f(jit_list))
+            self.assertSame(py_f(py_list), jit_f(jit_list))
+
+        py_list.append(-5)
+        jit_list.append(-5)
+        for py_f, jit_f in zip(py_funcs, jit_funcs):
+            self.assertSame(py_f(py_list), py_f(jit_list))
+            self.assertSame(py_f(py_list), jit_f(jit_list))
+
+        py_list.clear()
+        jit_list.clear()
+        for py_f, jit_f in zip(py_funcs, jit_funcs):
+            self.assertSame(py_f(py_list), py_f(jit_list))
+            self.assertSame(py_f(py_list), jit_f(jit_list))
+
+    def test_bool_fallback(self):
+
+        def py_b(x):
+            return bool(x)
+
+        jit_b = njit(py_b)
+
+        @jitclass([("x", types.List(types.intp))])
+        class LenClass:
+            def __init__(self, x):
+                self.x = x
+
+            def __len__(self):
+                return len(self.x) % 4
+
+            def append(self, y):
+                self.x.append(y)
+
+            def pop(self):
+                self.x.pop(0)
+
+        obj = LenClass([1, 2, 3])
+        self.assertTrue(py_b(obj))
+        self.assertTrue(jit_b(obj))
+
+        obj.append(4)
+        self.assertFalse(py_b(obj))
+        self.assertFalse(jit_b(obj))
+
+        obj.pop()
+        self.assertTrue(py_b(obj))
+        self.assertTrue(jit_b(obj))
+
+        @jitclass([("y", types.float64)])
+        class NormalClass:
+            def __init__(self, y):
+                self.y = y
+
+        obj = NormalClass(0)
+        self.assertTrue(py_b(obj))
+        self.assertTrue(jit_b(obj))
+
+    def test_numeric_fallback(self):
+        def py_c(x):
+            return complex(x)
+
+        def py_f(x):
+            return float(x)
+
+        def py_i(x):
+            return int(x)
+
+        jit_c = njit(py_c)
+        jit_f = njit(py_f)
+        jit_i = njit(py_i)
+
+        @jitclass([])
+        class FloatClass:
+            def __init__(self):
+                pass
+
+            def __float__(self):
+                return 3.1415
+
+        obj = FloatClass()
+        self.assertSame(py_c(obj), complex(3.1415))
+        self.assertSame(jit_c(obj), complex(3.1415))
+        self.assertSame(py_f(obj), 3.1415)
+        self.assertSame(jit_f(obj), 3.1415)
+
+        with self.assertRaises(TypeError) as e:
+            py_i(obj)
+        self.assertIn("int", str(e.exception))
+        with self.assertRaises(TypingError) as e:
+            jit_i(obj)
+        self.assertIn("int", str(e.exception))
+
+        @jitclass([])
+        class IntClass:
+            def __init__(self):
+                pass
+
+            def __int__(self):
+                return 7
+
+        obj = IntClass()
+        self.assertSame(py_i(obj), 7)
+        self.assertSame(jit_i(obj), 7)
+
+        with self.assertRaises(TypeError) as e:
+            py_c(obj)
+        self.assertIn("complex", str(e.exception))
+        with self.assertRaises(TypingError) as e:
+            jit_c(obj)
+        self.assertIn("complex", str(e.exception))
+        with self.assertRaises(TypeError) as e:
+            py_f(obj)
+        self.assertIn("float", str(e.exception))
+        with self.assertRaises(TypingError) as e:
+            jit_f(obj)
+        self.assertIn("float", str(e.exception))
+
+        @jitclass([])
+        class IndexClass:
+            def __init__(self):
+                pass
+
+            def __index__(self):
+                return 1
+
+        obj = IndexClass()
+
+        if PYVERSION >= (3, 8):
+            self.assertSame(py_c(obj), complex(1))
+            self.assertSame(jit_c(obj), complex(1))
+            self.assertSame(py_f(obj), 1.)
+            self.assertSame(jit_f(obj), 1.)
+            self.assertSame(py_i(obj), 1)
+            self.assertSame(jit_i(obj), 1)
+        else:
+            with self.assertRaises(TypeError) as e:
+                py_c(obj)
+            self.assertIn("complex", str(e.exception))
+            with self.assertRaises(TypingError) as e:
+                jit_c(obj)
+            self.assertIn("complex", str(e.exception))
+            with self.assertRaises(TypeError) as e:
+                py_f(obj)
+            self.assertIn("float", str(e.exception))
+            with self.assertRaises(TypingError) as e:
+                jit_f(obj)
+            self.assertIn("float", str(e.exception))
+            with self.assertRaises(TypeError) as e:
+                py_i(obj)
+            self.assertIn("int", str(e.exception))
+            with self.assertRaises(TypingError) as e:
+                jit_i(obj)
+            self.assertIn("int", str(e.exception))
+
+        @jitclass([])
+        class FloatIntIndexClass:
+            def __init__(self):
+                pass
+
+            def __float__(self):
+                return 3.1415
+
+            def __int__(self):
+                return 7
+
+            def __index__(self):
+                return 1
+
+        obj = FloatIntIndexClass()
+        self.assertSame(py_c(obj), complex(3.1415))
+        self.assertSame(jit_c(obj), complex(3.1415))
+        self.assertSame(py_f(obj), 3.1415)
+        self.assertSame(jit_f(obj), 3.1415)
+        self.assertSame(py_i(obj), 7)
+        self.assertSame(jit_i(obj), 7)
+
+    def test_arithmetic_logical(self):
+        IntWrapper = self.get_int_wrapper()
+        FloatWrapper = self.get_float_wrapper()
+
+        float_py_funcs = [
+            lambda x, y: x == y,
+            lambda x, y: x != y,
+            lambda x, y: x >= y,
+            lambda x, y: x > y,
+            lambda x, y: x <= y,
+            lambda x, y: x < y,
+            lambda x, y: x + y,
+            lambda x, y: x // y,
+            lambda x, y: x % y,
+            lambda x, y: x * y,
+            lambda x, y: x ** y,
+            lambda x, y: x - y,
+            lambda x, y: x / y,
+        ]
+        int_py_funcs = [
+            lambda x, y: x == y,
+            lambda x, y: x != y,
+            lambda x, y: x << y,
+            lambda x, y: x >> y,
+            lambda x, y: x & y,
+            lambda x, y: x | y,
+            lambda x, y: x ^ y,
+        ]
+
+        test_values = [
+            (0.0, 2.0),
+            (1.234, 3.1415),
+            (13.1, 1.01),
+        ]
+
+        def unwrap(value):
+            return getattr(value, "x", value)
+
+        for jit_f, (x, y) in itertools.product(
+                map(njit, float_py_funcs), test_values):
+
+            py_f = jit_f.py_func
+
+            expected = py_f(x, y)
+            jit_x = FloatWrapper(x)
+            jit_y = FloatWrapper(y)
+
+            check = (
+                self.assertEqual
+                if type(expected) is not float
+                else self.assertAlmostEqual
+            )
+            check(expected, jit_f(x, y))
+            check(expected, unwrap(py_f(jit_x, jit_y)))
+            check(expected, unwrap(jit_f(jit_x, jit_y)))
+
+        for jit_f, (x, y) in itertools.product(
+                map(njit, int_py_funcs), test_values):
+
+            py_f = jit_f.py_func
+            x, y = int(x), int(y)
+
+            expected = py_f(x, y)
+            jit_x = IntWrapper(x)
+            jit_y = IntWrapper(y)
+
+            self.assertEqual(expected, jit_f(x, y))
+            self.assertEqual(expected, unwrap(py_f(jit_x, jit_y)))
+            self.assertEqual(expected, unwrap(jit_f(jit_x, jit_y)))
+
+    def test_arithmetic_logical_inplace(self):
+
+        # If __i*__ methods are not defined, should fall back to normal methods.
+        JitIntWrapper = self.get_int_wrapper()
+        JitFloatWrapper = self.get_float_wrapper()
+
+        PyIntWrapper = JitIntWrapper.mro()[1]
+        PyFloatWrapper = JitFloatWrapper.mro()[1]
+
+        @jitclass([("x", types.intp)])
+        class JitIntUpdateWrapper(PyIntWrapper):
+            def __init__(self, value):
+                self.x = value
+
+            def __ilshift__(self, other):
+                return JitIntUpdateWrapper(self.x << other.x)
+
+            def __irshift__(self, other):
+                return JitIntUpdateWrapper(self.x >> other.x)
+
+            def __iand__(self, other):
+                return JitIntUpdateWrapper(self.x & other.x)
+
+            def __ior__(self, other):
+                return JitIntUpdateWrapper(self.x | other.x)
+
+            def __ixor__(self, other):
+                return JitIntUpdateWrapper(self.x ^ other.x)
+
+        @jitclass({"x": types.float64})
+        class JitFloatUpdateWrapper(PyFloatWrapper):
+
+            def __init__(self, value):
+                self.x = value
+
+            def __iadd__(self, other):
+                return JitFloatUpdateWrapper(self.x + 2.718 * other.x)
+
+            def __ifloordiv__(self, other):
+                return JitFloatUpdateWrapper(self.x * 2.718 // other.x)
+
+            def __imod__(self, other):
+                return JitFloatUpdateWrapper(self.x % (other.x + 1))
+
+            def __imul__(self, other):
+                return JitFloatUpdateWrapper(self.x * other.x + 1)
+
+            def __ipow__(self, other):
+                return JitFloatUpdateWrapper(self.x ** other.x + 1)
+
+            def __isub__(self, other):
+                return JitFloatUpdateWrapper(self.x - 3.1415 * other.x)
+
+            def __itruediv__(self, other):
+                return JitFloatUpdateWrapper((self.x + 1) / other.x)
+
+        PyIntUpdateWrapper = JitIntUpdateWrapper.mro()[1]
+        PyFloatUpdateWrapper = JitFloatUpdateWrapper.mro()[1]
+
+        def get_update_func(op):
+            template = f"""
+def f(x, y):
+    x {op}= y
+    return x
+"""
+            namespace = {}
+            exec(template, namespace)
+            return namespace["f"]
+
+        float_py_funcs = [get_update_func(op) for op in [
+            "+", "//", "%", "*", "**", "-", "/",
+        ]]
+        int_py_funcs = [get_update_func(op) for op in [
+            "<<", ">>", "&", "|", "^",
+        ]]
+
+        test_values = [
+            (0.0, 2.0),
+            (1.234, 3.1415),
+            (13.1, 1.01),
+        ]
+
+        for jit_f, (py_cls, jit_cls), (x, y) in itertools.product(
+                map(njit, float_py_funcs),
+                [
+                    (PyFloatWrapper, JitFloatWrapper),
+                    (PyFloatUpdateWrapper, JitFloatUpdateWrapper)
+                ],
+                test_values):
+            py_f = jit_f.py_func
+
+            expected = py_f(py_cls(x), py_cls(y)).x
+            self.assertAlmostEqual(expected, py_f(jit_cls(x), jit_cls(y)).x)
+            self.assertAlmostEqual(expected, jit_f(jit_cls(x), jit_cls(y)).x)
+
+        for jit_f, (py_cls, jit_cls), (x, y) in itertools.product(
+                map(njit, int_py_funcs),
+                [
+                    (PyIntWrapper, JitIntWrapper),
+                    (PyIntUpdateWrapper, JitIntUpdateWrapper)
+                ],
+                test_values):
+            x, y = int(x), int(y)
+            py_f = jit_f.py_func
+
+            expected = py_f(py_cls(x), py_cls(y)).x
+            self.assertEqual(expected, py_f(jit_cls(x), jit_cls(y)).x)
+            self.assertEqual(expected, jit_f(jit_cls(x), jit_cls(y)).x)
+
+    def test_hash_eq_ne(self):
+
+        class HashEqTest:
+            x: int
+
+            def __init__(self, x):
+                self.x = x
+
+            def __hash__(self):
+                return self.x % 10
+
+            def __eq__(self, o):
+                return (self.x - o.x) % 20 == 0
+
+        class HashEqNeTest(HashEqTest):
+            def __ne__(self, o):
+                return (self.x - o.x) % 20 > 1
+
+        def py_hash(x):
+            return hash(x)
+
+        def py_eq(x, y):
+            return x == y
+
+        def py_ne(x, y):
+            return x != y
+
+        def identity_decorator(f):
+            return f
+
+        comparisons = [
+            (0, 1),  # Will give different ne results.
+            (2, 22),
+            (7, 10),
+            (3, 3),
+        ]
+
+        for base_cls, use_jit in itertools.product(
+            [HashEqTest, HashEqNeTest], [False, True]
+        ):
+            decorator = njit if use_jit else identity_decorator
+            hash_func = decorator(py_hash)
+            eq_func = decorator(py_eq)
+            ne_func = decorator(py_ne)
+
+            jit_cls = jitclass(base_cls)
+
+            for v in [0, 2, 10, 24, -8]:
+                self.assertEqual(hash_func(jit_cls(v)), v % 10)
+
+            for x, y in comparisons:
+                self.assertEqual(
+                    eq_func(jit_cls(x), jit_cls(y)),
+                    base_cls(x) == base_cls(y),
+                )
+                self.assertEqual(
+                    ne_func(jit_cls(x), jit_cls(y)),
+                    base_cls(x) != base_cls(y),
+                )
+
+    def test_bool_fallback_len(self):
+        # Check that the fallback to using len(obj) to determine truth of an
+        # object is implemented correctly as per
+        # https://docs.python.org/3/library/stdtypes.html#truth-value-testing
+        #
+        # Relevant points:
+        #
+        # "By default, an object is considered true unless its class defines
+        # either a __bool__() method that returns False or a __len__() method
+        # that returns zero, when called with the object."
+        #
+        # and:
+        #
+        # "Operations and built-in functions that have a Boolean result always
+        # return 0 or False for false and 1 or True for true, unless otherwise
+        # stated."
+
+        class NoBoolHasLen:
+            def __init__(self, val):
+                self.val = val
+
+            def __len__(self):
+                return self.val
+
+            def get_bool(self):
+                return bool(self)
+
+        py_class = NoBoolHasLen
+        jitted_class = jitclass([('val', types.int64)])(py_class)
+
+        py_class_0_bool = py_class(0).get_bool()
+        py_class_2_bool = py_class(2).get_bool()
+        jitted_class_0_bool = jitted_class(0).get_bool()
+        jitted_class_2_bool = jitted_class(2).get_bool()
+
+        # Truth values from bool(obj) should be equal
+        self.assertEqual(py_class_0_bool, jitted_class_0_bool)
+        self.assertEqual(py_class_2_bool, jitted_class_2_bool)
+
+        # Truth values from bool(obj) should be the same type
+        self.assertEqual(type(py_class_0_bool), type(jitted_class_0_bool))
+        self.assertEqual(type(py_class_2_bool), type(jitted_class_2_bool))
+
+    def test_bool_fallback_default(self):
+        # Similar to test_bool_fallback, but checks the case where there is no
+        # __bool__() or __len__() defined, so the object should always be True.
+
+        class NoBoolNoLen:
+            def __init__(self):
+                pass
+
+            def get_bool(self):
+                return bool(self)
+
+        py_class = NoBoolNoLen
+        jitted_class = jitclass([])(py_class)
+
+        py_class_bool = py_class().get_bool()
+        jitted_class_bool = jitted_class().get_bool()
+
+        # Truth values from bool(obj) should be equal
+        self.assertEqual(py_class_bool, jitted_class_bool)
+
+        # Truth values from bool(obj) should be the same type
+        self.assertEqual(type(py_class_bool), type(jitted_class_bool))
+
+    def test_operator_reflection(self):
+        class OperatorsDefined:
+            def __init__(self, x):
+                self.x = x
+
+            def __eq__(self, other):
+                return self.x == other.x
+
+            def __le__(self, other):
+                return self.x <= other.x
+
+            def __lt__(self, other):
+                return self.x < other.x
+
+            def __ge__(self, other):
+                return self.x >= other.x
+
+            def __gt__(self, other):
+                return self.x > other.x
+
+        class NoOperatorsDefined:
+            def __init__(self, x):
+                self.x = x
+
+        spec = [('x', types.int32)]
+        JitOperatorsDefined = jitclass(spec)(OperatorsDefined)
+        JitNoOperatorsDefined = jitclass(spec)(NoOperatorsDefined)
+
+        py_ops_defined = OperatorsDefined(2)
+        py_ops_not_defined = NoOperatorsDefined(3)
+
+        jit_ops_defined = JitOperatorsDefined(2)
+        jit_ops_not_defined = JitNoOperatorsDefined(3)
+
+        self.assertEqual(py_ops_not_defined == py_ops_defined,
+                         jit_ops_not_defined == jit_ops_defined)
+
+        self.assertEqual(py_ops_not_defined <= py_ops_defined,
+                         jit_ops_not_defined <= jit_ops_defined)
+
+        self.assertEqual(py_ops_not_defined < py_ops_defined,
+                         jit_ops_not_defined < jit_ops_defined)
+
+        self.assertEqual(py_ops_not_defined >= py_ops_defined,
+                         jit_ops_not_defined >= jit_ops_defined)
+
+        self.assertEqual(py_ops_not_defined > py_ops_defined,
+                         jit_ops_not_defined > jit_ops_defined)
+
+    def test_implicit_hash_compiles(self):
+        # Ensure that classes with __hash__ implicitly defined as None due to
+        # the presence of __eq__ are correctly handled by ignoring the __hash__
+        # class member.
+        class ImplicitHash:
+            def __init__(self):
+                pass
+
+            def __eq__(self, other):
+                return False
+
+        jitted = jitclass([])(ImplicitHash)
+        instance = jitted()
+
+        self.assertFalse(instance == instance)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jitmethod.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jitmethod.py
new file mode 100644
index 000000000..192af60cb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_jitmethod.py
@@ -0,0 +1,72 @@
+import unittest
+
+import numpy as np
+
+from numba import jit
+from numba.core import types, config
+from numba.core.compiler import compile_isolated
+from numba.tests.support import override_config
+
+
+class TestJITMethod(unittest.TestCase):
+    def test_bound_jit_method_with_loop_lift(self):
+        class Something(object):
+            def __init__(self, x0):
+                self.x0 = x0
+
+            @jit
+            def method(self, x):
+                a = np.empty(shape=5, dtype=np.float32)
+                x0 = self.x0
+
+                for i in range(a.shape[0]):
+                    a[i] = x0 * x
+
+                return a
+
+        something = Something(3)
+        np.testing.assert_array_equal(something.method(5),
+            np.array([15, 15, 15, 15, 15], dtype=np.float32))
+
+        # Check that loop lifting in nopython mode was successful
+        [cres] = something.method.overloads.values()
+        jitloop = cres.lifted[0]
+        [loopcres] = jitloop.overloads.values()
+        self.assertTrue(loopcres.fndesc.native)
+
+    def test_unbound_jit_method(self):
+        class Something(object):
+            def __init__(self, x0):
+                self.x0 = x0
+
+            @jit
+            def method(self):
+                return self.x0
+
+        something = Something(3)
+        self.assertEquals(Something.method(something), 3)
+
+
+class TestDisabledJIT(unittest.TestCase):
+    def test_decorated_function(self):
+        with override_config('DISABLE_JIT', True):
+            def method(x):
+                return x
+            jitted = jit(method)
+
+        self.assertEqual(jitted, method)
+        self.assertEqual(10, method(10))
+        self.assertEqual(10, jitted(10))
+
+    def test_decorated_function_with_kwargs(self):
+        with override_config('DISABLE_JIT', True):
+            def method(x):
+                return x
+            jitted = jit(nopython=True)(method)
+
+        self.assertEqual(jitted, method)
+        self.assertEqual(10, method(10))
+        self.assertEqual(10, jitted(10))
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_linalg.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_linalg.py
new file mode 100644
index 000000000..db183059d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_linalg.py
@@ -0,0 +1,2691 @@
+import contextlib
+import gc
+from itertools import product, cycle
+import sys
+import warnings
+from numbers import Number, Integral
+import platform
+
+import numpy as np
+
+from numba import jit, njit, typeof
+from numba.core import errors
+from numba.tests.support import (TestCase, tag, needs_lapack, needs_blas,
+                                 _is_armv7l)
+from .matmul_usecase import matmul_usecase
+import unittest
+
+
+def dot2(a, b):
+    return np.dot(a, b)
+
+
+def dot3(a, b, out):
+    return np.dot(a, b, out=out)
+
+
+def vdot(a, b):
+    return np.vdot(a, b)
+
+
+class TestProduct(TestCase):
+    """
+    Tests for dot products.
+    """
+
+    dtypes = (np.float64, np.float32, np.complex128, np.complex64)
+
+    def setUp(self):
+        # Collect leftovers from previous test cases before checking for leaks
+        gc.collect()
+
+    def sample_vector(self, n, dtype):
+        # Be careful to generate only exactly representable float values,
+        # to avoid rounding discrepancies between Numpy and Numba
+        base = np.arange(n)
+        if issubclass(dtype, np.complexfloating):
+            return (base * (1 - 0.5j) + 2j).astype(dtype)
+        else:
+            return (base * 0.5 - 1).astype(dtype)
+
+    def sample_matrix(self, m, n, dtype):
+        return self.sample_vector(m * n, dtype).reshape((m, n))
+
+    @contextlib.contextmanager
+    def check_contiguity_warning(self, pyfunc):
+        """
+        Check performance warning(s) for non-contiguity.
+        """
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', errors.NumbaPerformanceWarning)
+            yield
+        self.assertGreaterEqual(len(w), 1)
+        self.assertIs(w[0].category, errors.NumbaPerformanceWarning)
+        self.assertIn("faster on contiguous arrays", str(w[0].message))
+        self.assertEqual(w[0].filename, pyfunc.__code__.co_filename)
+        # This works because our functions are one-liners
+        self.assertEqual(w[0].lineno, pyfunc.__code__.co_firstlineno + 1)
+
+    def check_func(self, pyfunc, cfunc, args):
+        with self.assertNoNRTLeak():
+            expected = pyfunc(*args)
+            got = cfunc(*args)
+            self.assertPreciseEqual(got, expected, ignore_sign_on_zero=True)
+            del got, expected
+
+
+    def _aligned_copy(self, arr):
+        # This exists for armv7l because NumPy wants aligned arrays for the
+        # `out` arg of functions, but np.empty/np.copy doesn't seem to always
+        # produce them, in particular for complex dtypes
+        size = (arr.size + 1) * arr.itemsize + 1
+        datasize = arr.size * arr.itemsize
+        tmp = np.empty(size, dtype=np.uint8)
+        for i in range(arr.itemsize + 1):
+            new = tmp[i : i + datasize].view(dtype=arr.dtype)
+            if new.flags.aligned:
+                break
+        else:
+            raise Exception("Could not obtain aligned array")
+        if arr.flags.c_contiguous:
+            new = np.reshape(new, arr.shape, order='C')
+        else:
+            new = np.reshape(new, arr.shape, order='F')
+        new[:] = arr[:]
+        assert new.flags.aligned
+        return new
+
+    def check_func_out(self, pyfunc, cfunc, args, out):
+        copier = self._aligned_copy if _is_armv7l else np.copy
+        with self.assertNoNRTLeak():
+            expected = copier(out)
+            got = copier(out)
+            self.assertIs(pyfunc(*args, out=expected), expected)
+            self.assertIs(cfunc(*args, out=got), got)
+            self.assertPreciseEqual(got, expected, ignore_sign_on_zero=True)
+            del got, expected
+
+    def assert_mismatching_sizes(self, cfunc, args, is_out=False):
+        with self.assertRaises(ValueError) as raises:
+            cfunc(*args)
+        msg = ("incompatible output array size" if is_out else
+               "incompatible array sizes")
+        self.assertIn(msg, str(raises.exception))
+
+    def assert_mismatching_dtypes(self, cfunc, args, func_name="np.dot()"):
+        with self.assertRaises(errors.TypingError) as raises:
+            cfunc(*args)
+        self.assertIn("%s arguments must all have the same dtype"
+                      % (func_name,),
+                      str(raises.exception))
+
+    def check_dot_vv(self, pyfunc, func_name):
+        n = 3
+        cfunc = jit(nopython=True)(pyfunc)
+        for dtype in self.dtypes:
+            a = self.sample_vector(n, dtype)
+            b = self.sample_vector(n, dtype)
+            self.check_func(pyfunc, cfunc, (a, b))
+            # Non-contiguous
+            self.check_func(pyfunc, cfunc, (a[::-1], b[::-1]))
+
+        # Mismatching sizes
+        a = self.sample_vector(n - 1, np.float64)
+        b = self.sample_vector(n, np.float64)
+        self.assert_mismatching_sizes(cfunc, (a, b))
+        # Mismatching dtypes
+        a = self.sample_vector(n, np.float32)
+        b = self.sample_vector(n, np.float64)
+        self.assert_mismatching_dtypes(cfunc, (a, b), func_name=func_name)
+
+    @needs_blas
+    def test_dot_vv(self):
+        """
+        Test vector * vector np.dot()
+        """
+        self.check_dot_vv(dot2, "np.dot()")
+
+    @needs_blas
+    def test_vdot(self):
+        """
+        Test np.vdot()
+        """
+        self.check_dot_vv(vdot, "np.vdot()")
+
+    def check_dot_vm(self, pyfunc2, pyfunc3, func_name):
+
+        def samples(m, n):
+            for order in 'CF':
+                a = self.sample_matrix(m, n, np.float64).copy(order=order)
+                b = self.sample_vector(n, np.float64)
+                yield a, b
+            for dtype in self.dtypes:
+                a = self.sample_matrix(m, n, dtype)
+                b = self.sample_vector(n, dtype)
+                yield a, b
+            # Non-contiguous
+            yield a[::-1], b[::-1]
+
+        cfunc2 = jit(nopython=True)(pyfunc2)
+        if pyfunc3 is not None:
+            cfunc3 = jit(nopython=True)(pyfunc3)
+
+        for m, n in [(2, 3),
+                     (3, 0),
+                     (0, 3)
+                     ]:
+            for a, b in samples(m, n):
+                self.check_func(pyfunc2, cfunc2, (a, b))
+                self.check_func(pyfunc2, cfunc2, (b, a.T))
+            if pyfunc3 is not None:
+                for a, b in samples(m, n):
+                    out = np.empty(m, dtype=a.dtype)
+                    self.check_func_out(pyfunc3, cfunc3, (a, b), out)
+                    self.check_func_out(pyfunc3, cfunc3, (b, a.T), out)
+
+        # Mismatching sizes
+        m, n = 2, 3
+        a = self.sample_matrix(m, n - 1, np.float64)
+        b = self.sample_vector(n, np.float64)
+        self.assert_mismatching_sizes(cfunc2, (a, b))
+        self.assert_mismatching_sizes(cfunc2, (b, a.T))
+        if pyfunc3 is not None:
+            out = np.empty(m, np.float64)
+            self.assert_mismatching_sizes(cfunc3, (a, b, out))
+            self.assert_mismatching_sizes(cfunc3, (b, a.T, out))
+            a = self.sample_matrix(m, m, np.float64)
+            b = self.sample_vector(m, np.float64)
+            out = np.empty(m - 1, np.float64)
+            self.assert_mismatching_sizes(cfunc3, (a, b, out), is_out=True)
+            self.assert_mismatching_sizes(cfunc3, (b, a.T, out), is_out=True)
+        # Mismatching dtypes
+        a = self.sample_matrix(m, n, np.float32)
+        b = self.sample_vector(n, np.float64)
+        self.assert_mismatching_dtypes(cfunc2, (a, b), func_name)
+        if pyfunc3 is not None:
+            a = self.sample_matrix(m, n, np.float64)
+            b = self.sample_vector(n, np.float64)
+            out = np.empty(m, np.float32)
+            self.assert_mismatching_dtypes(cfunc3, (a, b, out), func_name)
+
+    @needs_blas
+    def test_dot_vm(self):
+        """
+        Test vector * matrix and matrix * vector np.dot()
+        """
+        self.check_dot_vm(dot2, dot3, "np.dot()")
+
+    def check_dot_mm(self, pyfunc2, pyfunc3, func_name):
+
+        def samples(m, n, k):
+            for order_a, order_b in product('CF', 'CF'):
+                a = self.sample_matrix(m, k, np.float64).copy(order=order_a)
+                b = self.sample_matrix(k, n, np.float64).copy(order=order_b)
+                yield a, b
+            for dtype in self.dtypes:
+                a = self.sample_matrix(m, k, dtype)
+                b = self.sample_matrix(k, n, dtype)
+                yield a, b
+            # Non-contiguous
+            yield a[::-1], b[::-1]
+
+        cfunc2 = jit(nopython=True)(pyfunc2)
+        if pyfunc3 is not None:
+            cfunc3 = jit(nopython=True)(pyfunc3)
+
+        # Test generic matrix * matrix as well as "degenerate" cases
+        # where one of the outer dimensions is 1 (i.e. really represents
+        # a vector, which may select a different implementation),
+        # one of the matrices is empty, or both matrices are empty.
+        for m, n, k in [(2, 3, 4),  # Generic matrix * matrix
+                        (1, 3, 4),  # 2d vector * matrix
+                        (1, 1, 4),  # 2d vector * 2d vector
+                        (0, 3, 2),  # Empty matrix * matrix, empty output
+                        (3, 0, 2),  # Matrix * empty matrix, empty output
+                        (0, 0, 3),  # Both arguments empty, empty output
+                        (3, 2, 0),  # Both arguments empty, nonempty output
+                        ]:
+            for a, b in samples(m, n, k):
+                self.check_func(pyfunc2, cfunc2, (a, b))
+                self.check_func(pyfunc2, cfunc2, (b.T, a.T))
+            if pyfunc3 is not None:
+                for a, b in samples(m, n, k):
+                    out = np.empty((m, n), dtype=a.dtype)
+                    self.check_func_out(pyfunc3, cfunc3, (a, b), out)
+                    out = np.empty((n, m), dtype=a.dtype)
+                    self.check_func_out(pyfunc3, cfunc3, (b.T, a.T), out)
+
+        # Mismatching sizes
+        m, n, k = 2, 3, 4
+        a = self.sample_matrix(m, k - 1, np.float64)
+        b = self.sample_matrix(k, n, np.float64)
+        self.assert_mismatching_sizes(cfunc2, (a, b))
+        if pyfunc3 is not None:
+            out = np.empty((m, n), np.float64)
+            self.assert_mismatching_sizes(cfunc3, (a, b, out))
+            a = self.sample_matrix(m, k, np.float64)
+            b = self.sample_matrix(k, n, np.float64)
+            out = np.empty((m, n - 1), np.float64)
+            self.assert_mismatching_sizes(cfunc3, (a, b, out), is_out=True)
+        # Mismatching dtypes
+        a = self.sample_matrix(m, k, np.float32)
+        b = self.sample_matrix(k, n, np.float64)
+        self.assert_mismatching_dtypes(cfunc2, (a, b), func_name)
+        if pyfunc3 is not None:
+            a = self.sample_matrix(m, k, np.float64)
+            b = self.sample_matrix(k, n, np.float64)
+            out = np.empty((m, n), np.float32)
+            self.assert_mismatching_dtypes(cfunc3, (a, b, out), func_name)
+
+    @needs_blas
+    def test_dot_mm(self):
+        """
+        Test matrix * matrix np.dot()
+        """
+        self.check_dot_mm(dot2, dot3, "np.dot()")
+
+    @needs_blas
+    def test_matmul_vv(self):
+        """
+        Test vector @ vector
+        """
+        self.check_dot_vv(matmul_usecase, "'@'")
+
+    @needs_blas
+    def test_matmul_vm(self):
+        """
+        Test vector @ matrix and matrix @ vector
+        """
+        self.check_dot_vm(matmul_usecase, None, "'@'")
+
+    @needs_blas
+    def test_matmul_mm(self):
+        """
+        Test matrix @ matrix
+        """
+        self.check_dot_mm(matmul_usecase, None, "'@'")
+
+    @needs_blas
+    def test_contiguity_warnings(self):
+        m, k, n = 2, 3, 4
+        dtype = np.float64
+        a = self.sample_matrix(m, k, dtype)[::-1]
+        b = self.sample_matrix(k, n, dtype)[::-1]
+        out = np.empty((m, n), dtype)
+
+        cfunc = jit(nopython=True)(dot2)
+        with self.check_contiguity_warning(cfunc.py_func):
+            cfunc(a, b)
+        cfunc = jit(nopython=True)(dot3)
+        with self.check_contiguity_warning(cfunc.py_func):
+            cfunc(a, b, out)
+
+        a = self.sample_vector(n, dtype)[::-1]
+        b = self.sample_vector(n, dtype)[::-1]
+
+        cfunc = jit(nopython=True)(vdot)
+        with self.check_contiguity_warning(cfunc.py_func):
+            cfunc(a, b)
+
+
+# Implementation definitions for the purpose of jitting.
+
+def invert_matrix(a):
+    return np.linalg.inv(a)
+
+
+def cholesky_matrix(a):
+    return np.linalg.cholesky(a)
+
+
+def eig_matrix(a):
+    return np.linalg.eig(a)
+
+
+def eigvals_matrix(a):
+    return np.linalg.eigvals(a)
+
+
+def eigh_matrix(a):
+    return np.linalg.eigh(a)
+
+
+def eigvalsh_matrix(a):
+    return np.linalg.eigvalsh(a)
+
+
+def svd_matrix(a, full_matrices=1):
+    return np.linalg.svd(a, full_matrices)
+
+
+def qr_matrix(a):
+    return np.linalg.qr(a)
+
+
+def lstsq_system(A, B, rcond=-1):
+    return np.linalg.lstsq(A, B, rcond)
+
+
+def solve_system(A, B):
+    return np.linalg.solve(A, B)
+
+
+def pinv_matrix(A, rcond=1e-15):  # 1e-15 from numpy impl
+    return np.linalg.pinv(A)
+
+
+def slogdet_matrix(a):
+    return np.linalg.slogdet(a)
+
+
+def det_matrix(a):
+    return np.linalg.det(a)
+
+
+def norm_matrix(a, ord=None):
+    return np.linalg.norm(a, ord)
+
+
+def cond_matrix(a, p=None):
+    return np.linalg.cond(a, p)
+
+
+def matrix_rank_matrix(a, tol=None):
+    return np.linalg.matrix_rank(a, tol)
+
+
+def matrix_power_matrix(a, n):
+    return np.linalg.matrix_power(a, n)
+
+
+def trace_matrix(a, offset=0):
+    return np.trace(a, offset)
+
+
+def trace_matrix_no_offset(a):
+    return np.trace(a)
+
+
+def outer_matrix(a, b, out=None):
+    return np.outer(a, b, out=out)
+
+
+def kron_matrix(a, b):
+    return np.kron(a, b)
+
+
+class TestLinalgBase(TestCase):
+    """
+    Provides setUp and common data/error modes for testing np.linalg functions.
+    """
+
+    # supported dtypes
+    dtypes = (np.float64, np.float32, np.complex128, np.complex64)
+
+    def setUp(self):
+        # Collect leftovers from previous test cases before checking for leaks
+        gc.collect()
+
+    def sample_vector(self, n, dtype):
+        # Be careful to generate only exactly representable float values,
+        # to avoid rounding discrepancies between Numpy and Numba
+        base = np.arange(n)
+        if issubclass(dtype, np.complexfloating):
+            return (base * (1 - 0.5j) + 2j).astype(dtype)
+        else:
+            return (base * 0.5 + 1).astype(dtype)
+
+    def specific_sample_matrix(
+            self, size, dtype, order, rank=None, condition=None):
+        """
+        Provides a sample matrix with an optionally specified rank or condition
+        number.
+
+        size: (rows, columns), the dimensions of the returned matrix.
+        dtype: the dtype for the returned matrix.
+        order: the memory layout for the returned matrix, 'F' or 'C'.
+        rank: the rank of the matrix, an integer value, defaults to full rank.
+        condition: the condition number of the matrix (defaults to 1.)
+
+        NOTE: Only one of rank or condition may be set.
+        """
+
+        # default condition
+        d_cond = 1.
+
+        if len(size) != 2:
+            raise ValueError("size must be a length 2 tuple.")
+
+        if order not in ['F', 'C']:
+            raise ValueError("order must be one of 'F' or 'C'.")
+
+        if dtype not in [np.float32, np.float64, np.complex64, np.complex128]:
+            raise ValueError("dtype must be a numpy floating point type.")
+
+        if rank is not None and condition is not None:
+            raise ValueError("Only one of rank or condition can be specified.")
+
+        if condition is None:
+            condition = d_cond
+
+        if condition < 1:
+            raise ValueError("Condition number must be >=1.")
+
+        np.random.seed(0)  # repeatable seed
+        m, n = size
+
+        if m < 0 or n < 0:
+            raise ValueError("Negative dimensions given for matrix shape.")
+
+        minmn = min(m, n)
+        if rank is None:
+            rv = minmn
+        else:
+            if rank <= 0:
+                raise ValueError("Rank must be greater than zero.")
+            if not isinstance(rank, Integral):
+                raise ValueError("Rank must an integer.")
+            rv = rank
+            if rank > minmn:
+                raise ValueError("Rank given greater than full rank.")
+
+        if m == 1 or n == 1:
+            # vector, must be rank 1 (enforced above)
+            # condition of vector is also 1
+            if condition != d_cond:
+                raise ValueError(
+                    "Condition number was specified for a vector (always 1.).")
+            maxmn = max(m, n)
+            Q = self.sample_vector(maxmn, dtype).reshape(m, n)
+        else:
+            # Build a sample matrix via combining SVD like inputs.
+
+            # Create matrices of left and right singular vectors.
+            # This could use Modified Gram-Schmidt and perhaps be quicker,
+            # at present it uses QR decompositions to obtain orthonormal
+            # matrices.
+            tmp = self.sample_vector(m * m, dtype).reshape(m, m)
+            U, _ = np.linalg.qr(tmp)
+            # flip the second array, else for m==n the identity matrix appears
+            tmp = self.sample_vector(n * n, dtype)[::-1].reshape(n, n)
+            V, _ = np.linalg.qr(tmp)
+            # create singular values.
+            sv = np.linspace(d_cond, condition, rv)
+            S = np.zeros((m, n))
+            idx = np.nonzero(np.eye(m, n))
+            S[idx[0][:rv], idx[1][:rv]] = sv
+            Q = np.dot(np.dot(U, S), V.T)  # construct
+            Q = np.array(Q, dtype=dtype, order=order)  # sort out order/type
+
+        return Q
+
+    def assert_error(self, cfunc, args, msg, err=ValueError):
+        with self.assertRaises(err) as raises:
+            cfunc(*args)
+        self.assertIn(msg, str(raises.exception))
+
+    def assert_non_square(self, cfunc, args):
+        msg = "Last 2 dimensions of the array must be square."
+        self.assert_error(cfunc, args, msg, np.linalg.LinAlgError)
+
+    def assert_wrong_dtype(self, name, cfunc, args):
+        msg = "np.linalg.%s() only supported on float and complex arrays" % name
+        self.assert_error(cfunc, args, msg, errors.TypingError)
+
+    def assert_wrong_dimensions(self, name, cfunc, args, la_prefix=True):
+        prefix = "np.linalg" if la_prefix else "np"
+        msg = "%s.%s() only supported on 2-D arrays" % (prefix, name)
+        self.assert_error(cfunc, args, msg, errors.TypingError)
+
+    def assert_no_nan_or_inf(self, cfunc, args):
+        msg = "Array must not contain infs or NaNs."
+        self.assert_error(cfunc, args, msg, np.linalg.LinAlgError)
+
+    def assert_contig_sanity(self, got, expected_contig):
+        """
+        This checks that in a computed result from numba (array, possibly tuple
+        of arrays) all the arrays are contiguous in memory and that they are
+        all at least one of "C_CONTIGUOUS" or "F_CONTIGUOUS". The computed
+        result of the contiguousness is then compared against a hardcoded
+        expected result.
+
+        got: is the computed results from numba
+        expected_contig: is "C" or "F" and is the expected type of
+                        contiguousness across all input values
+                        (and therefore tests).
+        """
+
+        if isinstance(got, tuple):
+            # tuple present, check all results
+            for a in got:
+                self.assert_contig_sanity(a, expected_contig)
+        else:
+            if not isinstance(got, Number):
+                # else a single array is present
+                c_contig = got.flags.c_contiguous
+                f_contig = got.flags.f_contiguous
+
+                # check that the result (possible set of) is at least one of
+                # C or F contiguous.
+                msg = "Results are not at least one of all C or F contiguous."
+                self.assertTrue(c_contig | f_contig, msg)
+
+                msg = "Computed contiguousness does not match expected."
+                if expected_contig == "C":
+                    self.assertTrue(c_contig, msg)
+                elif expected_contig == "F":
+                    self.assertTrue(f_contig, msg)
+                else:
+                    raise ValueError("Unknown contig")
+
+    def assert_raise_on_singular(self, cfunc, args):
+        msg = "Matrix is singular to machine precision."
+        self.assert_error(cfunc, args, msg, err=np.linalg.LinAlgError)
+
+    def assert_is_identity_matrix(self, got, rtol=None, atol=None):
+        """
+        Checks if a matrix is equal to the identity matrix.
+        """
+        # check it is square
+        self.assertEqual(got.shape[-1], got.shape[-2])
+        # create identity matrix
+        eye = np.eye(got.shape[-1], dtype=got.dtype)
+        resolution = 5 * np.finfo(got.dtype).resolution
+        if rtol is None:
+            rtol = 10 * resolution
+        if atol is None:
+            atol = 100 * resolution  # zeros tend to be fuzzy
+        # check it matches
+        np.testing.assert_allclose(got, eye, rtol, atol)
+
+    def assert_invalid_norm_kind(self, cfunc, args):
+        """
+        For use in norm() and cond() tests.
+        """
+        msg = "Invalid norm order for matrices."
+        self.assert_error(cfunc, args, msg, ValueError)
+
+    def assert_raise_on_empty(self, cfunc, args):
+        msg = 'Arrays cannot be empty'
+        self.assert_error(cfunc, args, msg, np.linalg.LinAlgError)
+
+
+class TestTestLinalgBase(TestCase):
+    """
+    The sample matrix code TestLinalgBase.specific_sample_matrix()
+    is a bit involved, this class tests it works as intended.
+    """
+
+    def test_specific_sample_matrix(self):
+
+        # add a default test to the ctor, it never runs so doesn't matter
+        inst = TestLinalgBase('specific_sample_matrix')
+
+        sizes = [(7, 1), (11, 5), (5, 11), (3, 3), (1, 7)]
+
+        # test loop
+        for size, dtype, order in product(sizes, inst.dtypes, 'FC'):
+
+            m, n = size
+            minmn = min(m, n)
+
+            # test default full rank
+            A = inst.specific_sample_matrix(size, dtype, order)
+            self.assertEqual(A.shape, size)
+            self.assertEqual(np.linalg.matrix_rank(A), minmn)
+
+            # test reduced rank if a reduction is possible
+            if minmn > 1:
+                rank = minmn - 1
+                A = inst.specific_sample_matrix(size, dtype, order, rank=rank)
+                self.assertEqual(A.shape, size)
+                self.assertEqual(np.linalg.matrix_rank(A), rank)
+
+            resolution = 5 * np.finfo(dtype).resolution
+
+            # test default condition
+            A = inst.specific_sample_matrix(size, dtype, order)
+            self.assertEqual(A.shape, size)
+            np.testing.assert_allclose(np.linalg.cond(A),
+                                       1.,
+                                       rtol=resolution,
+                                       atol=resolution)
+
+            # test specified condition if matrix is > 1D
+            if minmn > 1:
+                condition = 10.
+                A = inst.specific_sample_matrix(
+                    size, dtype, order, condition=condition)
+                self.assertEqual(A.shape, size)
+                np.testing.assert_allclose(np.linalg.cond(A),
+                                           10.,
+                                           rtol=resolution,
+                                           atol=resolution)
+
+        # check errors are raised appropriately
+        def check_error(args, msg, err=ValueError):
+            with self.assertRaises(err) as raises:
+                inst.specific_sample_matrix(*args)
+            self.assertIn(msg, str(raises.exception))
+
+        # check the checker runs ok
+        with self.assertRaises(AssertionError) as raises:
+            msg = "blank"
+            check_error(((2, 3), np.float64, 'F'), msg, err=ValueError)
+
+        # check invalid inputs...
+
+        # bad size
+        msg = "size must be a length 2 tuple."
+        check_error(((1,), np.float64, 'F'), msg, err=ValueError)
+
+        # bad order
+        msg = "order must be one of 'F' or 'C'."
+        check_error(((2, 3), np.float64, 'z'), msg, err=ValueError)
+
+        # bad type
+        msg = "dtype must be a numpy floating point type."
+        check_error(((2, 3), np.int32, 'F'), msg, err=ValueError)
+
+        # specifying both rank and condition
+        msg = "Only one of rank or condition can be specified."
+        check_error(((2, 3), np.float64, 'F', 1, 1), msg, err=ValueError)
+
+        # specifying negative condition
+        msg = "Condition number must be >=1."
+        check_error(((2, 3), np.float64, 'F', None, -1), msg, err=ValueError)
+
+        # specifying negative matrix dimension
+        msg = "Negative dimensions given for matrix shape."
+        check_error(((2, -3), np.float64, 'F'), msg, err=ValueError)
+
+        # specifying negative rank
+        msg = "Rank must be greater than zero."
+        check_error(((2, 3), np.float64, 'F', -1), msg, err=ValueError)
+
+        # specifying a rank greater than maximum rank
+        msg = "Rank given greater than full rank."
+        check_error(((2, 3), np.float64, 'F', 4), msg, err=ValueError)
+
+        # specifying a condition number for a vector
+        msg = "Condition number was specified for a vector (always 1.)."
+        check_error(((1, 3), np.float64, 'F', None, 10), msg, err=ValueError)
+
+        # specifying a non integer rank
+        msg = "Rank must an integer."
+        check_error(((2, 3), np.float64, 'F', 1.5), msg, err=ValueError)
+
+
+class TestLinalgInv(TestLinalgBase):
+    """
+    Tests for np.linalg.inv.
+    """
+
+    @needs_lapack
+    def test_linalg_inv(self):
+        """
+        Test np.linalg.inv
+        """
+        n = 10
+        cfunc = jit(nopython=True)(invert_matrix)
+
+        def check(a, **kwargs):
+            expected = invert_matrix(a)
+            got = cfunc(a)
+            self.assert_contig_sanity(got, "F")
+
+            use_reconstruction = False
+
+            # try strict
+            try:
+                np.testing.assert_array_almost_equal_nulp(got, expected,
+                                                          nulp=10)
+            except AssertionError:
+                # fall back to reconstruction
+                use_reconstruction = True
+
+            if use_reconstruction:
+                rec = np.dot(got, a)
+                self.assert_is_identity_matrix(rec)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a)
+
+        for dtype, order in product(self.dtypes, 'CF'):
+            a = self.specific_sample_matrix((n, n), dtype, order)
+            check(a)
+
+        # 0 dimensioned matrix
+        check(np.empty((0, 0)))
+
+        # Non square matrix
+        self.assert_non_square(cfunc, (np.ones((2, 3)),))
+
+        # Wrong dtype
+        self.assert_wrong_dtype("inv", cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue
+        self.assert_wrong_dimensions("inv", cfunc, (np.ones(10),))
+
+        # Singular matrix
+        self.assert_raise_on_singular(cfunc, (np.zeros((2, 2)),))
+
+    @needs_lapack
+    def test_no_input_mutation(self):
+        X = np.array([[1., 3, 2, 7,],
+                      [-5, 4, 2, 3,],
+                      [9, -3, 1, 1,],
+                      [2, -2, 2, 8,]], order='F')
+
+        X_orig = np.copy(X)
+
+        @jit(nopython=True)
+        def ainv(X, test):
+            if test:
+                # not executed, but necessary to trigger A ordering in X
+                X = X[1:2, :]
+            return np.linalg.inv(X)
+
+        expected = ainv.py_func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        got = ainv(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        np.testing.assert_allclose(expected, got)
+
+
+class TestLinalgCholesky(TestLinalgBase):
+    """
+    Tests for np.linalg.cholesky.
+    """
+
+    def sample_matrix(self, m, dtype, order):
+        # pd. (positive definite) matrix has eigenvalues in Z+
+        np.random.seed(0)  # repeatable seed
+        A = np.random.rand(m, m)
+        # orthonormal q needed to form up q^{-1}*D*q
+        # no "orth()" in numpy
+        q, _ = np.linalg.qr(A)
+        L = np.arange(1, m + 1)  # some positive eigenvalues
+        Q = np.dot(np.dot(q.T, np.diag(L)), q)  # construct
+        Q = np.array(Q, dtype=dtype, order=order)  # sort out order/type
+        return Q
+
+    def assert_not_pd(self, cfunc, args):
+        msg = "Matrix is not positive definite."
+        self.assert_error(cfunc, args, msg, np.linalg.LinAlgError)
+
+    @needs_lapack
+    def test_linalg_cholesky(self):
+        """
+        Test np.linalg.cholesky
+        """
+        n = 10
+        cfunc = jit(nopython=True)(cholesky_matrix)
+
+        def check(a):
+            expected = cholesky_matrix(a)
+            got = cfunc(a)
+            use_reconstruction = False
+            # check that the computed results are contig and in the same way
+            self.assert_contig_sanity(got, "C")
+
+            # try strict
+            try:
+                np.testing.assert_array_almost_equal_nulp(got, expected,
+                                                          nulp=10)
+            except AssertionError:
+                # fall back to reconstruction
+                use_reconstruction = True
+
+            # try via reconstruction
+            if use_reconstruction:
+                rec = np.dot(got, np.conj(got.T))
+                resolution = 5 * np.finfo(a.dtype).resolution
+                np.testing.assert_allclose(
+                    a,
+                    rec,
+                    rtol=resolution,
+                    atol=resolution
+                )
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a)
+
+        for dtype, order in product(self.dtypes, 'FC'):
+            a = self.sample_matrix(n, dtype, order)
+            check(a)
+
+        # 0 dimensioned matrix
+        check(np.empty((0, 0)))
+
+        rn = "cholesky"
+        # Non square matrices
+        self.assert_non_square(cfunc, (np.ones((2, 3), dtype=np.float64),))
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue
+        self.assert_wrong_dimensions(rn, cfunc,
+                                     (np.ones(10, dtype=np.float64),))
+
+        # not pd
+        self.assert_not_pd(cfunc,
+                           (np.ones(4, dtype=np.float64).reshape(2, 2),))
+
+
+class TestLinalgEigenSystems(TestLinalgBase):
+    """
+    Tests for np.linalg.eig/eigvals.
+    """
+
+    def sample_matrix(self, m, dtype, order):
+        # This is a tridiag with the same but skewed values on the diagonals
+        v = self.sample_vector(m, dtype)
+        Q = np.diag(v)
+        idx = np.nonzero(np.eye(Q.shape[0], Q.shape[1], 1))
+        Q[idx] = v[1:]
+        idx = np.nonzero(np.eye(Q.shape[0], Q.shape[1], -1))
+        Q[idx] = v[:-1]
+        Q = np.array(Q, dtype=dtype, order=order)
+        return Q
+
+    def assert_no_domain_change(self, name, cfunc, args):
+        msg = name + "() argument must not cause a domain change."
+        self.assert_error(cfunc, args, msg)
+
+    def _check_worker(self, cfunc, name, expected_res_len,
+                      check_for_domain_change):
+        def check(*args):
+            expected = cfunc.py_func(*args)
+            got = cfunc(*args)
+            a = args[0]
+            # check that the returned tuple is same length
+            self.assertEqual(len(expected), len(got))
+            # and that dimension is correct
+            res_is_tuple = False
+            if isinstance(got, tuple):
+                res_is_tuple = True
+                self.assertEqual(len(got), expected_res_len)
+            else:  # its an array
+                self.assertEqual(got.ndim, expected_res_len)
+
+            # and that the computed results are contig and in the same way
+            self.assert_contig_sanity(got, "F")
+
+            use_reconstruction = False
+            # try plain match of each array to np first
+            for k in range(len(expected)):
+                try:
+                    np.testing.assert_array_almost_equal_nulp(
+                        got[k], expected[k], nulp=10)
+                except AssertionError:
+                    # plain match failed, test by reconstruction
+                    use_reconstruction = True
+
+            # If plain match fails then reconstruction is used.
+            # this checks that A*V ~== V*diag(W)
+            # i.e. eigensystem ties out
+            # this is required as numpy uses only double precision lapack
+            # routines and computation of eigenvectors is numerically
+            # sensitive, numba uses the type specific routines therefore
+            # sometimes comes out with a different (but entirely
+            # valid) answer (eigenvectors are not unique etc.).
+            # This is only applicable if eigenvectors are computed
+            # along with eigenvalues i.e. result is a tuple.
+            resolution = 5 * np.finfo(a.dtype).resolution
+            if use_reconstruction:
+                if res_is_tuple:
+                    w, v = got
+                    # modify 'a' if hermitian eigensystem functionality is
+                    # being tested. 'L' for use lower part is default and
+                    # the only thing used at present so we conjugate transpose
+                    # the lower part into the upper for use in the
+                    # reconstruction. By construction the sample matrix is
+                    # tridiag so this is just a question of copying the lower
+                    # diagonal into the upper and conjugating on the way.
+                    if name[-1] == 'h':
+                        idxl = np.nonzero(np.eye(a.shape[0], a.shape[1], -1))
+                        idxu = np.nonzero(np.eye(a.shape[0], a.shape[1], 1))
+                        cfunc(*args)
+                        # upper idx must match lower for default uplo="L"
+                        # if complex, conjugate
+                        a[idxu] = np.conj(a[idxl])
+                        # also, only the real part of the diagonals is
+                        # considered in the calculation so the imag is zeroed
+                        # out for the purposes of use in reconstruction.
+                        a[np.diag_indices(a.shape[0])] = np.real(np.diag(a))
+
+                    lhs = np.dot(a, v)
+                    rhs = np.dot(v, np.diag(w))
+
+                    np.testing.assert_allclose(
+                        lhs.real,
+                        rhs.real,
+                        rtol=resolution,
+                        atol=resolution
+                    )
+                    if np.iscomplexobj(v):
+                        np.testing.assert_allclose(
+                            lhs.imag,
+                            rhs.imag,
+                            rtol=resolution,
+                            atol=resolution
+                        )
+                else:
+                    # This isn't technically reconstruction but is here to
+                    # deal with that the order of the returned eigenvalues
+                    # may differ in the case of routines just returning
+                    # eigenvalues and there's no true reconstruction
+                    # available with which to perform a check.
+                    np.testing.assert_allclose(
+                        np.sort(expected),
+                        np.sort(got),
+                        rtol=resolution,
+                        atol=resolution
+                    )
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(*args)
+        return check
+
+    def checker_for_linalg_eig(
+            self, name, func, expected_res_len, check_for_domain_change=None):
+        """
+        Test np.linalg.eig
+        """
+        n = 10
+        cfunc = jit(nopython=True)(func)
+        check = self._check_worker(cfunc, name, expected_res_len,
+                                   check_for_domain_change)
+
+
+        # The main test loop
+        for dtype, order in product(self.dtypes, 'FC'):
+            a = self.sample_matrix(n, dtype, order)
+            check(a)
+
+        # Test both a real and complex type as the impls are different
+        for ty in [np.float32, np.complex64]:
+
+            # 0 dimensioned matrix
+            check(np.empty((0, 0), dtype=ty))
+
+            # Non square matrices
+            self.assert_non_square(cfunc, (np.ones((2, 3), dtype=ty),))
+
+            # Wrong dtype
+            self.assert_wrong_dtype(name, cfunc,
+                                    (np.ones((2, 2), dtype=np.int32),))
+
+            # Dimension issue
+            self.assert_wrong_dimensions(name, cfunc, (np.ones(10, dtype=ty),))
+
+            # no nans or infs
+            self.assert_no_nan_or_inf(cfunc,
+                                      (np.array([[1., 2., ], [np.inf, np.nan]],
+                                                dtype=ty),))
+
+        if check_for_domain_change:
+            # By design numba does not support dynamic return types, numpy does
+            # and uses this in the case of returning eigenvalues/vectors of
+            # a real matrix. The return type of np.linalg.eig(), when
+            # operating on a matrix in real space depends on the values present
+            # in the matrix itself (recalling that eigenvalues are the roots of the
+            # characteristic polynomial of the system matrix, which will by
+            # construction depend on the values present in the system matrix).
+            # This test asserts that if a domain change is required on the return
+            # type, i.e. complex eigenvalues from a real input, an error is raised.
+            # For complex types, regardless of the value of the imaginary part of
+            # the returned eigenvalues, a complex type will be returned, this
+            # follows numpy and fits in with numba.
+
+            # First check that the computation is valid (i.e. in complex space)
+            A = np.array([[1, -2], [2, 1]])
+            check(A.astype(np.complex128))
+            # and that the imaginary part is nonzero
+            l, _ = func(A)
+            self.assertTrue(np.any(l.imag))
+
+            # Now check that the computation fails in real space
+            for ty in [np.float32, np.float64]:
+                self.assert_no_domain_change(name, cfunc, (A.astype(ty),))
+
+    @needs_lapack
+    def test_linalg_eig(self):
+        self.checker_for_linalg_eig("eig", eig_matrix, 2, True)
+
+    @needs_lapack
+    def test_linalg_eigvals(self):
+        self.checker_for_linalg_eig("eigvals", eigvals_matrix, 1, True)
+
+    @needs_lapack
+    def test_linalg_eigh(self):
+        self.checker_for_linalg_eig("eigh", eigh_matrix, 2, False)
+
+    @needs_lapack
+    def test_linalg_eigvalsh(self):
+        self.checker_for_linalg_eig("eigvalsh", eigvalsh_matrix, 1, False)
+
+    @needs_lapack
+    def test_no_input_mutation(self):
+        # checks inputs are not mutated
+
+        for c in (('eig', 2, True),
+                  ('eigvals', 1, True),
+                  ('eigh', 2, False),
+                  ('eigvalsh', 1, False)):
+
+            m, nout, domain_change = c
+
+            meth = getattr(np.linalg, m)
+
+            @jit(nopython=True)
+            def func(X, test):
+                if test:
+                    # not executed, but necessary to trigger A ordering in X
+                    X = X[1:2, :]
+                return meth(X)
+
+            check = self._check_worker(func, m, nout, domain_change)
+
+            for dtype in (np.float64, np.complex128):
+                with self.subTest(meth=meth, dtype=dtype):
+                    # trivial system, doesn't matter, just checking if it gets
+                    # mutated
+                    X = np.array([[10., 1, 0, 1],
+                                [1, 9, 0, 0],
+                                [0, 0, 8, 0],
+                                [1, 0, 0, 7],
+                                ], order='F', dtype=dtype)
+
+                    X_orig = np.copy(X)
+
+                    expected = func.py_func(X, False)
+                    np.testing.assert_allclose(X, X_orig)
+
+                    got = func(X, False)
+                    np.testing.assert_allclose(X, X_orig)
+
+                    check(X, False)
+
+
+class TestLinalgSvd(TestLinalgBase):
+    """
+    Tests for np.linalg.svd.
+    """
+
+    @needs_lapack
+    def test_linalg_svd(self):
+        """
+        Test np.linalg.svd
+        """
+        cfunc = jit(nopython=True)(svd_matrix)
+
+        def check(a, **kwargs):
+            expected = svd_matrix(a, **kwargs)
+            got = cfunc(a, **kwargs)
+            # check that the returned tuple is same length
+            self.assertEqual(len(expected), len(got))
+            # and that length is 3
+            self.assertEqual(len(got), 3)
+            # and that the computed results are contig and in the same way
+            self.assert_contig_sanity(got, "F")
+
+            use_reconstruction = False
+            # try plain match of each array to np first
+            for k in range(len(expected)):
+
+                try:
+                    np.testing.assert_array_almost_equal_nulp(
+                        got[k], expected[k], nulp=10)
+                except AssertionError:
+                    # plain match failed, test by reconstruction
+                    use_reconstruction = True
+
+            # if plain match fails then reconstruction is used.
+            # this checks that A ~= U*S*V**H
+            # i.e. SV decomposition ties out
+            # this is required as numpy uses only double precision lapack
+            # routines and computation of svd is numerically
+            # sensitive, numba using the type specific routines therefore
+            # sometimes comes out with a different answer (orthonormal bases
+            # are not unique etc.).
+            if use_reconstruction:
+                u, sv, vt = got
+
+                # check they are dimensionally correct
+                for k in range(len(expected)):
+                    self.assertEqual(got[k].shape, expected[k].shape)
+
+                # regardless of full_matrices cols in u and rows in vt
+                # dictates the working size of s
+                s = np.zeros((u.shape[1], vt.shape[0]))
+                np.fill_diagonal(s, sv)
+
+                rec = np.dot(np.dot(u, s), vt)
+                resolution = np.finfo(a.dtype).resolution
+                np.testing.assert_allclose(
+                    a,
+                    rec,
+                    rtol=10 * resolution,
+                    atol=100 * resolution  # zeros tend to be fuzzy
+                )
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, **kwargs)
+
+        # test: column vector, tall, wide, square, row vector
+        # prime sizes
+        sizes = [(7, 1), (7, 5), (5, 7), (3, 3), (1, 7)]
+
+        # flip on reduced or full matrices
+        full_matrices = (True, False)
+
+        # test loop
+        for size, dtype, fmat, order in \
+                product(sizes, self.dtypes, full_matrices, 'FC'):
+
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a, full_matrices=fmat)
+
+        rn = "svd"
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue
+        self.assert_wrong_dimensions(rn, cfunc,
+                                     (np.ones(10, dtype=np.float64),))
+
+        # no nans or infs
+        self.assert_no_nan_or_inf(cfunc,
+                                  (np.array([[1., 2., ], [np.inf, np.nan]],
+                                            dtype=np.float64),))
+        # empty
+        for sz in [(0, 1), (1, 0), (0, 0)]:
+            args = (np.empty(sz), True)
+            self.assert_raise_on_empty(cfunc, args)
+
+    @needs_lapack
+    def test_no_input_mutation(self):
+        X = np.array([[1., 3, 2, 7,],
+                      [-5, 4, 2, 3,],
+                      [9, -3, 1, 1,],
+                      [2, -2, 2, 8,]], order='F')
+
+        X_orig = np.copy(X)
+
+        @jit(nopython=True)
+        def func(X, test):
+            if test:
+                # not executed, but necessary to trigger A ordering in X
+                X = X[1:2, :]
+            return np.linalg.svd(X)
+
+        expected = func.py_func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        got = func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        for e_a, g_a in zip(expected, got):
+            np.testing.assert_allclose(e_a, g_a)
+
+
+class TestLinalgQr(TestLinalgBase):
+    """
+    Tests for np.linalg.qr.
+    """
+
+    @needs_lapack
+    def test_linalg_qr(self):
+        """
+        Test np.linalg.qr
+        """
+        cfunc = jit(nopython=True)(qr_matrix)
+
+        def check(a, **kwargs):
+            expected = qr_matrix(a, **kwargs)
+            got = cfunc(a, **kwargs)
+
+            # check that the returned tuple is same length
+            self.assertEqual(len(expected), len(got))
+            # and that length is 2
+            self.assertEqual(len(got), 2)
+            # and that the computed results are contig and in the same way
+            self.assert_contig_sanity(got, "F")
+
+            use_reconstruction = False
+            # try plain match of each array to np first
+            for k in range(len(expected)):
+                try:
+                    np.testing.assert_array_almost_equal_nulp(
+                        got[k], expected[k], nulp=10)
+                except AssertionError:
+                    # plain match failed, test by reconstruction
+                    use_reconstruction = True
+
+            # if plain match fails then reconstruction is used.
+            # this checks that A ~= Q*R and that (Q^H)*Q = I
+            # i.e. QR decomposition ties out
+            # this is required as numpy uses only double precision lapack
+            # routines and computation of qr is numerically
+            # sensitive, numba using the type specific routines therefore
+            # sometimes comes out with a different answer (orthonormal bases
+            # are not unique etc.).
+            if use_reconstruction:
+                q, r = got
+
+                # check they are dimensionally correct
+                for k in range(len(expected)):
+                    self.assertEqual(got[k].shape, expected[k].shape)
+
+                # check A=q*r
+                rec = np.dot(q, r)
+                resolution = np.finfo(a.dtype).resolution
+                np.testing.assert_allclose(
+                    a,
+                    rec,
+                    rtol=10 * resolution,
+                    atol=100 * resolution  # zeros tend to be fuzzy
+                )
+
+                # check q is orthonormal
+                self.assert_is_identity_matrix(np.dot(np.conjugate(q.T), q))
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, **kwargs)
+
+        # test: column vector, tall, wide, square, row vector
+        # prime sizes
+        sizes = [(7, 1), (11, 5), (5, 11), (3, 3), (1, 7)]
+
+        # test loop
+        for size, dtype, order in \
+                product(sizes, self.dtypes, 'FC'):
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a)
+
+        rn = "qr"
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue
+        self.assert_wrong_dimensions(rn, cfunc,
+                                     (np.ones(10, dtype=np.float64),))
+
+        # no nans or infs
+        self.assert_no_nan_or_inf(cfunc,
+                                  (np.array([[1., 2., ], [np.inf, np.nan]],
+                                            dtype=np.float64),))
+
+        # empty
+        for sz in [(0, 1), (1, 0), (0, 0)]:
+            self.assert_raise_on_empty(cfunc, (np.empty(sz),))
+
+    @needs_lapack
+    def test_no_input_mutation(self):
+        X = np.array([[1., 3, 2, 7,],
+                      [-5, 4, 2, 3,],
+                      [9, -3, 1, 1,],
+                      [2, -2, 2, 8,]], order='F')
+
+        X_orig = np.copy(X)
+
+        @jit(nopython=True)
+        def func(X, test):
+            if test:
+                # not executed, but necessary to trigger A ordering in X
+                X = X[1:2, :]
+            return np.linalg.qr(X)
+
+        expected = func.py_func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        got = func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        for e_a, g_a in zip(expected, got):
+            np.testing.assert_allclose(e_a, g_a)
+
+
+class TestLinalgSystems(TestLinalgBase):
+    """
+    Base class for testing "system" solvers from np.linalg.
+    Namely np.linalg.solve() and np.linalg.lstsq().
+    """
+
+    # check for RHS with dimension > 2 raises
+    def assert_wrong_dimensions_1D(self, name, cfunc, args, la_prefix=True):
+        prefix = "np.linalg" if la_prefix else "np"
+        msg = "%s.%s() only supported on 1 and 2-D arrays" % (prefix, name)
+        self.assert_error(cfunc, args, msg, errors.TypingError)
+
+    # check that a dimensionally invalid system raises
+    def assert_dimensionally_invalid(self, cfunc, args):
+        msg = "Incompatible array sizes, system is not dimensionally valid."
+        self.assert_error(cfunc, args, msg, np.linalg.LinAlgError)
+
+    # check that args with differing dtypes raise
+    def assert_homogeneous_dtypes(self, name, cfunc, args):
+        msg = "np.linalg.%s() only supports inputs that have homogeneous dtypes." % name
+        self.assert_error(cfunc, args, msg, errors.TypingError)
+
+
+class TestLinalgLstsq(TestLinalgSystems):
+    """
+    Tests for np.linalg.lstsq.
+    """
+
+    # NOTE: The testing of this routine is hard as it has to handle numpy
+    # using double precision routines on single precision input, this has
+    # a knock on effect especially in rank deficient cases and cases where
+    # conditioning is generally poor. As a result computed ranks can differ
+    # and consequently the calculated residual can differ.
+    # The tests try and deal with this as best as they can through the use
+    # of reconstruction and measures like residual norms.
+    # Suggestions for improvements are welcomed!
+
+    @needs_lapack
+    def test_linalg_lstsq(self):
+        """
+        Test np.linalg.lstsq
+        """
+        cfunc = jit(nopython=True)(lstsq_system)
+
+        def check(A, B, **kwargs):
+            expected = lstsq_system(A, B, **kwargs)
+            got = cfunc(A, B, **kwargs)
+
+            # check that the returned tuple is same length
+            self.assertEqual(len(expected), len(got))
+            # and that length is 4
+            self.assertEqual(len(got), 4)
+            # and that the computed results are contig and in the same way
+            self.assert_contig_sanity(got, "C")
+
+            use_reconstruction = False
+
+            # check the ranks are the same and continue to a standard
+            # match if that is the case (if ranks differ, then output
+            # in e.g. residual array is of different size!).
+            try:
+                self.assertEqual(got[2], expected[2])
+                # try plain match of each array to np first
+                for k in range(len(expected)):
+                    try:
+                        np.testing.assert_array_almost_equal_nulp(
+                            got[k], expected[k], nulp=10)
+                    except AssertionError:
+                        # plain match failed, test by reconstruction
+                        use_reconstruction = True
+            except AssertionError:
+                use_reconstruction = True
+
+            if use_reconstruction:
+                x, res, rank, s = got
+
+                # indicies in the output which are ndarrays
+                out_array_idx = [0, 1, 3]
+
+                try:
+                    # check the ranks are the same
+                    self.assertEqual(rank, expected[2])
+                    # check they are dimensionally correct, skip [2] = rank.
+                    for k in out_array_idx:
+                        if isinstance(expected[k], np.ndarray):
+                            self.assertEqual(got[k].shape, expected[k].shape)
+                except AssertionError:
+                    # check the rank differs by 1. (numerical fuzz)
+                    self.assertTrue(abs(rank - expected[2]) < 2)
+
+                # check if A*X = B
+                resolution = np.finfo(A.dtype).resolution
+                try:
+                    # this will work so long as the conditioning is
+                    # ok and the rank is full
+                    rec = np.dot(A, x)
+                    np.testing.assert_allclose(
+                        B,
+                        rec,
+                        rtol=10 * resolution,
+                        atol=10 * resolution
+                    )
+                except AssertionError:
+                    # system is probably under/over determined and/or
+                    # poorly conditioned. Check slackened equality
+                    # and that the residual norm is the same.
+                    for k in out_array_idx:
+                        try:
+                            np.testing.assert_allclose(
+                                expected[k],
+                                got[k],
+                                rtol=100 * resolution,
+                                atol=100 * resolution
+                            )
+                        except AssertionError:
+                            # check the fail is likely due to bad conditioning
+                            c = np.linalg.cond(A)
+                            self.assertGreater(10 * c, (1. / resolution))
+
+                        # make sure the residual 2-norm is ok
+                        # if this fails its probably due to numpy using double
+                        # precision LAPACK routines for singles.
+                        res_expected = np.linalg.norm(
+                            B - np.dot(A, expected[0]))
+                        res_got = np.linalg.norm(B - np.dot(A, x))
+                        # rtol = 10. as all the systems are products of orthonormals
+                        # and on the small side (rows, cols) < 100.
+                        np.testing.assert_allclose(
+                            res_expected, res_got, rtol=10.)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(A, B, **kwargs)
+
+        # test: column vector, tall, wide, square, row vector
+        # prime sizes, the A's
+        sizes = [(7, 1), (11, 5), (5, 11), (3, 3), (1, 7)]
+        # compatible B's for Ax=B must have same number of rows and 1 or more
+        # columns
+
+        # This test takes ages! So combinations are trimmed via cycling
+
+        # gets a dtype
+        cycle_dt = cycle(self.dtypes)
+
+        orders = ['F', 'C']
+        # gets a memory order flag
+        cycle_order = cycle(orders)
+
+        # a specific condition number to use in the following tests
+        # there is nothing special about it other than it is not magic
+        specific_cond = 10.
+
+        # inner test loop, extracted as there's additional logic etc required
+        # that'd end up with this being repeated a lot
+        def inner_test_loop_fn(A, dt, **kwargs):
+            # test solve Ax=B for (column, matrix) B, same dtype as A
+            b_sizes = (1, 13)
+
+            for b_size in b_sizes:
+
+                # check 2D B
+                b_order = next(cycle_order)
+                B = self.specific_sample_matrix(
+                    (A.shape[0], b_size), dt, b_order)
+                check(A, B, **kwargs)
+
+                # check 1D B
+                b_order = next(cycle_order)
+                tmp = B[:, 0].copy(order=b_order)
+                check(A, tmp, **kwargs)
+
+        # test loop
+        for a_size in sizes:
+
+            dt = next(cycle_dt)
+            a_order = next(cycle_order)
+
+            # A full rank, well conditioned system
+            A = self.specific_sample_matrix(a_size, dt, a_order)
+
+            # run the test loop
+            inner_test_loop_fn(A, dt)
+
+            m, n = a_size
+            minmn = min(m, n)
+
+            # operations that only make sense with a 2D matrix system
+            if m != 1 and n != 1:
+
+                # Test a rank deficient system
+                r = minmn - 1
+                A = self.specific_sample_matrix(
+                    a_size, dt, a_order, rank=r)
+                # run the test loop
+                inner_test_loop_fn(A, dt)
+
+                # Test a system with a given condition number for use in
+                # testing the rcond parameter.
+                # This works because the singular values in the
+                # specific_sample_matrix code are linspace (1, cond, [0... if
+                # rank deficient])
+                A = self.specific_sample_matrix(
+                    a_size, dt, a_order, condition=specific_cond)
+                # run the test loop
+                rcond = 1. / specific_cond
+                approx_half_rank_rcond = minmn * rcond
+                inner_test_loop_fn(A, dt,
+                                   rcond=approx_half_rank_rcond)
+
+        # check empty arrays
+        empties = [
+        [(0, 1), (1,)], # empty A, valid b
+        [(1, 0), (1,)], # empty A, valid b
+        [(1, 1), (0,)], # valid A, empty 1D b
+        [(1, 1), (1, 0)],  # valid A, empty 2D b
+        ]
+
+        for A, b in empties:
+            args = (np.empty(A), np.empty(b))
+            self.assert_raise_on_empty(cfunc, args)
+
+        # Test input validation
+        ok = np.array([[1., 2.], [3., 4.]], dtype=np.float64)
+
+        # check ok input is ok
+        cfunc, (ok, ok)
+
+        # check bad inputs
+        rn = "lstsq"
+
+        # Wrong dtype
+        bad = np.array([[1, 2], [3, 4]], dtype=np.int32)
+        self.assert_wrong_dtype(rn, cfunc, (ok, bad))
+        self.assert_wrong_dtype(rn, cfunc, (bad, ok))
+
+        # different dtypes
+        bad = np.array([[1, 2], [3, 4]], dtype=np.float32)
+        self.assert_homogeneous_dtypes(rn, cfunc, (ok, bad))
+        self.assert_homogeneous_dtypes(rn, cfunc, (bad, ok))
+
+        # Dimension issue
+        bad = np.array([1, 2], dtype=np.float64)
+        self.assert_wrong_dimensions(rn, cfunc, (bad, ok))
+
+        # no nans or infs
+        bad = np.array([[1., 2., ], [np.inf, np.nan]], dtype=np.float64)
+        self.assert_no_nan_or_inf(cfunc, (ok, bad))
+        self.assert_no_nan_or_inf(cfunc, (bad, ok))
+
+        # check 1D is accepted for B (2D is done previously)
+        # and then that anything of higher dimension raises
+        oneD = np.array([1., 2.], dtype=np.float64)
+        cfunc, (ok, oneD)
+        bad = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float64)
+        self.assert_wrong_dimensions_1D(rn, cfunc, (ok, bad))
+
+        # check a dimensionally invalid system raises (1D and 2D cases
+        # checked)
+        bad1D = np.array([1.], dtype=np.float64)
+        bad2D = np.array([[1.], [2.], [3.]], dtype=np.float64)
+        self.assert_dimensionally_invalid(cfunc, (ok, bad1D))
+        self.assert_dimensionally_invalid(cfunc, (ok, bad2D))
+
+    @needs_lapack
+    def test_issue3368(self):
+        X = np.array([[1., 7.54, 6.52],
+                      [1., 2.70, 4.00],
+                      [1., 2.50, 3.80],
+                      [1., 1.15, 5.64],
+                      [1., 4.22, 3.27],
+                      [1., 1.41, 5.70],], order='F')
+
+        X_orig = np.copy(X)
+        y = np.array([1., 2., 3., 4., 5., 6.])
+
+        @jit(nopython=True)
+        def f2(X, y, test):
+            if test:
+                # never executed, but necessary to trigger the bug
+                X = X[1:2, :]
+            return np.linalg.lstsq(X, y)
+
+        f2(X, y, False)
+        np.testing.assert_allclose(X, X_orig)
+
+
+class TestLinalgSolve(TestLinalgSystems):
+    """
+    Tests for np.linalg.solve.
+    """
+
+    @needs_lapack
+    def test_linalg_solve(self):
+        """
+        Test np.linalg.solve
+        """
+        cfunc = jit(nopython=True)(solve_system)
+
+        def check(a, b, **kwargs):
+            expected = solve_system(a, b, **kwargs)
+            got = cfunc(a, b, **kwargs)
+
+            # check that the computed results are contig and in the same way
+            self.assert_contig_sanity(got, "F")
+
+            use_reconstruction = False
+            # try plain match of the result first
+            try:
+                np.testing.assert_array_almost_equal_nulp(
+                    got, expected, nulp=10)
+            except AssertionError:
+                # plain match failed, test by reconstruction
+                use_reconstruction = True
+
+            # If plain match fails then reconstruction is used,
+            # this checks that AX ~= B.
+            # Plain match can fail due to numerical fuzziness associated
+            # with system size and conditioning, or more simply from
+            # numpy using double precision routines for computation that
+            # could be done in single precision (which is what numba does).
+            # Therefore minor differences in results can appear due to
+            # e.g. numerical roundoff being different between two precisions.
+            if use_reconstruction:
+                # check they are dimensionally correct
+                self.assertEqual(got.shape, expected.shape)
+
+                # check AX=B
+                rec = np.dot(a, got)
+                resolution = np.finfo(a.dtype).resolution
+                np.testing.assert_allclose(
+                    b,
+                    rec,
+                    rtol=10 * resolution,
+                    atol=100 * resolution  # zeros tend to be fuzzy
+                )
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, b, **kwargs)
+
+        # test: prime size squares
+        sizes = [(1, 1), (3, 3), (7, 7)]
+
+        # test loop
+        for size, dtype, order in \
+                product(sizes, self.dtypes, 'FC'):
+            A = self.specific_sample_matrix(size, dtype, order)
+
+            b_sizes = (1, 13)
+
+            for b_size, b_order in product(b_sizes, 'FC'):
+                # check 2D B
+                B = self.specific_sample_matrix(
+                    (A.shape[0], b_size), dtype, b_order)
+                check(A, B)
+
+                # check 1D B
+                tmp = B[:, 0].copy(order=b_order)
+                check(A, tmp)
+
+        # check empty
+        cfunc(np.empty((0, 0)), np.empty((0,)))
+
+        # Test input validation
+        ok = np.array([[1., 0.], [0., 1.]], dtype=np.float64)
+
+        # check ok input is ok
+        cfunc(ok, ok)
+
+        # check bad inputs
+        rn = "solve"
+
+        # Wrong dtype
+        bad = np.array([[1, 0], [0, 1]], dtype=np.int32)
+        self.assert_wrong_dtype(rn, cfunc, (ok, bad))
+        self.assert_wrong_dtype(rn, cfunc, (bad, ok))
+
+        # different dtypes
+        bad = np.array([[1, 2], [3, 4]], dtype=np.float32)
+        self.assert_homogeneous_dtypes(rn, cfunc, (ok, bad))
+        self.assert_homogeneous_dtypes(rn, cfunc, (bad, ok))
+
+        # Dimension issue
+        bad = np.array([1, 0], dtype=np.float64)
+        self.assert_wrong_dimensions(rn, cfunc, (bad, ok))
+
+        # no nans or infs
+        bad = np.array([[1., 0., ], [np.inf, np.nan]], dtype=np.float64)
+        self.assert_no_nan_or_inf(cfunc, (ok, bad))
+        self.assert_no_nan_or_inf(cfunc, (bad, ok))
+
+        # check 1D is accepted for B (2D is done previously)
+        # and then that anything of higher dimension raises
+        ok_oneD = np.array([1., 2.], dtype=np.float64)
+        cfunc(ok, ok_oneD)
+        bad = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float64)
+        self.assert_wrong_dimensions_1D(rn, cfunc, (ok, bad))
+
+        # check an invalid system raises (1D and 2D cases checked)
+        bad1D = np.array([1.], dtype=np.float64)
+        bad2D = np.array([[1.], [2.], [3.]], dtype=np.float64)
+        self.assert_dimensionally_invalid(cfunc, (ok, bad1D))
+        self.assert_dimensionally_invalid(cfunc, (ok, bad2D))
+
+        # check that a singular system raises
+        bad2D = self.specific_sample_matrix((2, 2), np.float64, 'C', rank=1)
+        self.assert_raise_on_singular(cfunc, (bad2D, ok))
+
+    @needs_lapack
+    def test_no_input_mutation(self):
+        X = np.array([[1., 1, 1, 1],
+                      [0., 1, 1, 1],
+                      [0., 0, 1, 1],
+                      [1., 0, 0, 1],], order='F')
+
+        X_orig = np.copy(X)
+        y = np.array([1., 2., 3., 4])
+        y_orig = np.copy(y)
+
+        @jit(nopython=True)
+        def func(X, y, test):
+            if test:
+                # not executed, triggers A order in X
+                X = X[1:2, :]
+            return np.linalg.solve(X, y)
+
+        expected = func.py_func(X, y, False)
+        np.testing.assert_allclose(X, X_orig)
+        np.testing.assert_allclose(y, y_orig)
+
+        got = func(X, y, False)
+        np.testing.assert_allclose(X, X_orig)
+        np.testing.assert_allclose(y, y_orig)
+
+        np.testing.assert_allclose(expected, got)
+
+
+class TestLinalgPinv(TestLinalgBase):
+    """
+    Tests for np.linalg.pinv.
+    """
+
+    @needs_lapack
+    def test_linalg_pinv(self):
+        """
+        Test np.linalg.pinv
+        """
+        cfunc = jit(nopython=True)(pinv_matrix)
+
+        def check(a, **kwargs):
+            expected = pinv_matrix(a, **kwargs)
+            got = cfunc(a, **kwargs)
+
+            # check that the computed results are contig and in the same way
+            self.assert_contig_sanity(got, "F")
+
+            use_reconstruction = False
+            # try plain match of each array to np first
+
+            try:
+                np.testing.assert_array_almost_equal_nulp(
+                    got, expected, nulp=10)
+            except AssertionError:
+                # plain match failed, test by reconstruction
+                use_reconstruction = True
+
+            # If plain match fails then reconstruction is used.
+            # This can occur due to numpy using double precision
+            # LAPACK when single can be used, this creates round off
+            # problems. Also, if the matrix has machine precision level
+            # zeros in its singular values then the singular vectors are
+            # likely to vary depending on round off.
+            if use_reconstruction:
+
+                # check they are dimensionally correct
+                self.assertEqual(got.shape, expected.shape)
+
+                # check pinv(A)*A~=eye
+                # if the problem is numerical fuzz then this will probably
+                # work, if the problem is rank deficiency then it won't!
+                rec = np.dot(got, a)
+                try:
+                    self.assert_is_identity_matrix(rec)
+                except AssertionError:
+                    # check A=pinv(pinv(A))
+                    resolution = 5 * np.finfo(a.dtype).resolution
+                    rec = cfunc(got)
+                    np.testing.assert_allclose(
+                        rec,
+                        a,
+                        rtol=10 * resolution,
+                        atol=100 * resolution  # zeros tend to be fuzzy
+                    )
+                    if a.shape[0] >= a.shape[1]:
+                        # if it is overdetermined or fully determined
+                        # use numba lstsq function (which is type specific) to
+                        # compute the inverse and check against that.
+                        lstsq = jit(nopython=True)(lstsq_system)
+                        lstsq_pinv = lstsq(
+                            a, np.eye(
+                                a.shape[0]).astype(
+                                a.dtype), **kwargs)[0]
+                        np.testing.assert_allclose(
+                            got,
+                            lstsq_pinv,
+                            rtol=10 * resolution,
+                            atol=100 * resolution  # zeros tend to be fuzzy
+                        )
+                    # check the 2 norm of the difference is small
+                    self.assertLess(np.linalg.norm(got - expected), resolution)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, **kwargs)
+
+        # test: column vector, tall, wide, square, row vector
+        # prime sizes
+        sizes = [(7, 1), (11, 5), (5, 11), (3, 3), (1, 7)]
+
+        # When required, a specified condition number
+        specific_cond = 10.
+
+        # test loop
+        for size, dtype, order in \
+                product(sizes, self.dtypes, 'FC'):
+            # check a full rank matrix
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a)
+
+            m, n = size
+            if m != 1 and n != 1:
+                # check a rank deficient matrix
+                minmn = min(m, n)
+                a = self.specific_sample_matrix(size, dtype, order,
+                                                condition=specific_cond)
+                rcond = 1. / specific_cond
+                approx_half_rank_rcond = minmn * rcond
+                check(a, rcond=approx_half_rank_rcond)
+
+        # check empty
+        for sz in [(0, 1), (1, 0)]:
+            check(np.empty(sz))
+
+        rn = "pinv"
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue
+        self.assert_wrong_dimensions(rn, cfunc,
+                                     (np.ones(10, dtype=np.float64),))
+
+        # no nans or infs
+        self.assert_no_nan_or_inf(cfunc,
+                                  (np.array([[1., 2., ], [np.inf, np.nan]],
+                                            dtype=np.float64),))
+
+    @needs_lapack
+    def test_issue5870(self):
+        # testing for mutation of input matrix
+        @jit(nopython=True)
+        def some_fn(v):
+            return np.linalg.pinv(v[0])
+
+        v_data = np.array([[1., 3, 2, 7,],
+                           [-5, 4, 2, 3,],
+                           [9, -3, 1, 1,],
+                           [2, -2, 2, 8,]], order='F')
+
+        v_orig = np.copy(v_data)
+        reshaped_v = v_data.reshape((1, 4, 4))
+
+        expected = some_fn.py_func(reshaped_v)
+        np.testing.assert_allclose(v_data, v_orig)
+
+        got = some_fn(reshaped_v)
+        np.testing.assert_allclose(v_data, v_orig)
+
+        np.testing.assert_allclose(expected, got)
+
+
+class TestLinalgDetAndSlogdet(TestLinalgBase):
+    """
+    Tests for np.linalg.det. and np.linalg.slogdet.
+    Exactly the same inputs are used for both tests as
+    det() is a trivial function of slogdet(), the tests
+    are therefore combined.
+    """
+
+    def check_det(self, cfunc, a, **kwargs):
+        expected = det_matrix(a, **kwargs)
+        got = cfunc(a, **kwargs)
+
+        resolution = 5 * np.finfo(a.dtype).resolution
+
+        # check the determinants are the same
+        np.testing.assert_allclose(got, expected, rtol=resolution)
+
+        # Ensure proper resource management
+        with self.assertNoNRTLeak():
+            cfunc(a, **kwargs)
+
+    def check_slogdet(self, cfunc, a, **kwargs):
+        expected = slogdet_matrix(a, **kwargs)
+        got = cfunc(a, **kwargs)
+
+        # As numba returns python floats types and numpy returns
+        # numpy float types, some more adjustment and different
+        # types of comparison than those used with array based
+        # results is required.
+
+        # check that the returned tuple is same length
+        self.assertEqual(len(expected), len(got))
+        # and that length is 2
+        self.assertEqual(len(got), 2)
+
+        # check that the domain of the results match
+        for k in range(2):
+            self.assertEqual(
+                np.iscomplexobj(got[k]),
+                np.iscomplexobj(expected[k]))
+
+        # turn got[0] into the same dtype as `a`
+        # this is so checking with nulp will work
+        got_conv = a.dtype.type(got[0])
+        np.testing.assert_array_almost_equal_nulp(
+            got_conv, expected[0], nulp=10)
+        # compare log determinant magnitude with a more fuzzy value
+        # as numpy values come from higher precision lapack routines
+        resolution = 5 * np.finfo(a.dtype).resolution
+        np.testing.assert_allclose(
+            got[1], expected[1], rtol=resolution, atol=resolution)
+
+        # Ensure proper resource management
+        with self.assertNoNRTLeak():
+            cfunc(a, **kwargs)
+
+    def do_test(self, rn, check, cfunc):
+
+        # test: 1x1 as it is unusual, 4x4 as it is even and 7x7 as is it odd!
+        sizes = [(1, 1), (4, 4), (7, 7)]
+
+        # test loop
+        for size, dtype, order in \
+                product(sizes, self.dtypes, 'FC'):
+            # check a full rank matrix
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(cfunc, a)
+
+        # use a matrix of zeros to trip xgetrf U(i,i)=0 singular test
+        for dtype, order in product(self.dtypes, 'FC'):
+            a = np.zeros((3, 3), dtype=dtype)
+            check(cfunc, a)
+
+        # check empty
+        check(cfunc, np.empty((0, 0)))
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue
+        self.assert_wrong_dimensions(rn, cfunc,
+                                     (np.ones(10, dtype=np.float64),))
+
+        # no nans or infs
+        self.assert_no_nan_or_inf(cfunc,
+                                  (np.array([[1., 2., ], [np.inf, np.nan]],
+                                            dtype=np.float64),))
+
+    @needs_lapack
+    def test_linalg_det(self):
+        cfunc = jit(nopython=True)(det_matrix)
+        self.do_test("det", self.check_det, cfunc)
+
+    @needs_lapack
+    def test_linalg_slogdet(self):
+        cfunc = jit(nopython=True)(slogdet_matrix)
+        self.do_test("slogdet", self.check_slogdet, cfunc)
+
+    @needs_lapack
+    def test_no_input_mutation(self):
+        X = np.array([[1., 3, 2, 7,],
+                      [-5, 4, 2, 3,],
+                      [9, -3, 1, 1,],
+                      [2, -2, 2, 8,]], order='F')
+
+        X_orig = np.copy(X)
+
+        @jit(nopython=True)
+        def func(X, test):
+            if test:
+                # not executed, but necessary to trigger A ordering in X
+                X = X[1:2, :]
+            return np.linalg.slogdet(X)
+
+        expected = func.py_func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        got = func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        np.testing.assert_allclose(expected, got)
+
+# Use TestLinalgSystems as a base to get access to additional
+# testing for 1 and 2D inputs.
+
+
+class TestLinalgNorm(TestLinalgSystems):
+    """
+    Tests for np.linalg.norm.
+    """
+
+    @needs_lapack
+    def test_linalg_norm(self):
+        """
+        Test np.linalg.norm
+        """
+        cfunc = jit(nopython=True)(norm_matrix)
+
+        def check(a, **kwargs):
+            expected = norm_matrix(a, **kwargs)
+            got = cfunc(a, **kwargs)
+
+            # All results should be in the real domain
+            self.assertTrue(not np.iscomplexobj(got))
+
+            resolution = 5 * np.finfo(a.dtype).resolution
+
+            # check the norms are the same to the arg `a` precision
+            np.testing.assert_allclose(got, expected, rtol=resolution)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, **kwargs)
+
+        # Check 1D inputs
+        sizes = [1, 4, 7]
+        nrm_types = [None, np.inf, -np.inf, 0, 1, -1, 2, -2, 5, 6.7, -4.3]
+
+        # standard 1D input
+        for size, dtype, nrm_type in \
+                product(sizes, self.dtypes, nrm_types):
+            a = self.sample_vector(size, dtype)
+            check(a, ord=nrm_type)
+
+        # sliced 1D input
+        for dtype, nrm_type in \
+                product(self.dtypes, nrm_types):
+            a = self.sample_vector(10, dtype)[::3]
+            check(a, ord=nrm_type)
+
+        # Check 2D inputs:
+        # test: column vector, tall, wide, square, row vector
+        # prime sizes
+        sizes = [(7, 1), (11, 5), (5, 11), (3, 3), (1, 7)]
+        nrm_types = [None, np.inf, -np.inf, 1, -1, 2, -2]
+
+        # standard 2D input
+        for size, dtype, order, nrm_type in \
+                product(sizes, self.dtypes, 'FC', nrm_types):
+            # check a full rank matrix
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a, ord=nrm_type)
+
+        # check 2D slices work for the case where xnrm2 is called from
+        # BLAS (ord=None) to make sure it is working ok.
+        nrm_types = [None]
+        for dtype, nrm_type, order in \
+                product(self.dtypes, nrm_types, 'FC'):
+            a = self.specific_sample_matrix((17, 13), dtype, order)
+            # contig for C order
+            check(a[:3], ord=nrm_type)
+
+            # contig for Fortran order
+            check(a[:, 3:], ord=nrm_type)
+
+            # contig for neither order
+            check(a[1, 4::3], ord=nrm_type)
+
+        # check that numba returns zero for empty arrays. Numpy returns zero
+        # for most norm types and raises ValueError for +/-np.inf.
+        # there is not a great deal of consistency in Numpy's response so
+        # it is not being emulated in Numba
+        for dtype, nrm_type, order in \
+                product(self.dtypes, nrm_types, 'FC'):
+            a = np.empty((0,), dtype=dtype, order=order)
+            self.assertEqual(cfunc(a, nrm_type), 0.0)
+            a = np.empty((0, 0), dtype=dtype, order=order)
+            self.assertEqual(cfunc(a, nrm_type), 0.0)
+
+        rn = "norm"
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue, reuse the test from the TestLinalgSystems class
+        self.assert_wrong_dimensions_1D(
+            rn, cfunc, (np.ones(
+                12, dtype=np.float64).reshape(
+                2, 2, 3),))
+
+        # no nans or infs for 2d case when SVD is used (e.g 2-norm)
+        self.assert_no_nan_or_inf(cfunc,
+                                  (np.array([[1., 2.], [np.inf, np.nan]],
+                                            dtype=np.float64), 2))
+
+        # assert 2D input raises for an invalid norm kind kwarg
+        self.assert_invalid_norm_kind(cfunc, (np.array([[1., 2.], [3., 4.]],
+                                                       dtype=np.float64), 6))
+
+
+class TestLinalgCond(TestLinalgBase):
+    """
+    Tests for np.linalg.cond.
+    """
+
+    @needs_lapack
+    def test_linalg_cond(self):
+        """
+        Test np.linalg.cond
+        """
+
+        cfunc = jit(nopython=True)(cond_matrix)
+
+        def check(a, **kwargs):
+            expected = cond_matrix(a, **kwargs)
+            got = cfunc(a, **kwargs)
+
+            # All results should be in the real domain
+            self.assertTrue(not np.iscomplexobj(got))
+
+            resolution = 5 * np.finfo(a.dtype).resolution
+
+            # check the cond is the same to the arg `a` precision
+            np.testing.assert_allclose(got, expected, rtol=resolution)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, **kwargs)
+
+        # valid p values (used to indicate norm type)
+        ps = [None, np.inf, -np.inf, 1, -1, 2, -2]
+        sizes = [(3, 3), (7, 7)]
+
+        for size, dtype, order, p in \
+                product(sizes, self.dtypes, 'FC', ps):
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a, p=p)
+
+        # When p=None non-square matrices are accepted.
+        sizes = [(7, 1), (11, 5), (5, 11), (1, 7)]
+        for size, dtype, order in \
+                product(sizes, self.dtypes, 'FC'):
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a)
+
+        # empty
+        for sz in [(0, 1), (1, 0), (0, 0)]:
+            self.assert_raise_on_empty(cfunc, (np.empty(sz),))
+
+        # singular systems to trip divide-by-zero
+        x = np.array([[1, 0], [0, 0]], dtype=np.float64)
+        check(x)
+        check(x, p=2)
+        x = np.array([[0, 0], [0, 0]], dtype=np.float64)
+        check(x, p=-2)
+
+        # try an ill-conditioned system with 2-norm, make sure np raises an
+        # overflow warning as the result is `+inf` and that the result from
+        # numba matches.
+        with warnings.catch_warnings():
+            a = np.array([[1.e308, 0], [0, 0.1]], dtype=np.float64)
+            warnings.simplefilter("ignore", RuntimeWarning)
+            check(a)
+
+        rn = "cond"
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue
+        self.assert_wrong_dimensions(rn, cfunc,
+                                     (np.ones(10, dtype=np.float64),))
+
+        # no nans or infs when p="None" (default for kwarg).
+        self.assert_no_nan_or_inf(cfunc,
+                                  (np.array([[1., 2., ], [np.inf, np.nan]],
+                                            dtype=np.float64),))
+
+        # assert raises for an invalid norm kind kwarg
+        self.assert_invalid_norm_kind(cfunc, (np.array([[1., 2.], [3., 4.]],
+                                                       dtype=np.float64), 6))
+
+
+class TestLinalgMatrixRank(TestLinalgSystems):
+    """
+    Tests for np.linalg.matrix_rank.
+    """
+
+    @needs_lapack
+    def test_linalg_matrix_rank(self):
+        """
+        Test np.linalg.matrix_rank
+        """
+
+        cfunc = jit(nopython=True)(matrix_rank_matrix)
+
+        def check(a, **kwargs):
+            expected = matrix_rank_matrix(a, **kwargs)
+            got = cfunc(a, **kwargs)
+
+            # Ranks are integral so comparison should be trivial.
+            # check the rank is the same
+            np.testing.assert_allclose(got, expected)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, **kwargs)
+
+        sizes = [(7, 1), (11, 5), (5, 11), (3, 3), (1, 7)]
+
+        for size, dtype, order in \
+                product(sizes, self.dtypes, 'FC'):
+            # check full rank system
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a)
+
+            # If the system is a matrix, check rank deficiency is reported
+            # correctly. Check all ranks from 0 to (full rank - 1).
+            tol = 1e-13
+            # first check 1 to (full rank - 1)
+            for k in range(1, min(size) - 1):
+                # check rank k
+                a = self.specific_sample_matrix(size, dtype, order, rank=k)
+                self.assertEqual(cfunc(a), k)
+                check(a)
+                # check provision of a tolerance works as expected
+                # create a (m x n) diagonal matrix with a singular value
+                # guaranteed below the tolerance 1e-13
+                m, n = a.shape
+                a[:, :] = 0.  # reuse `a`'s memory
+                idx = np.nonzero(np.eye(m, n))
+                if np.iscomplexobj(a):
+                    b = 1. + np.random.rand(k) + 1.j +\
+                        1.j * np.random.rand(k)
+                    # min singular value is sqrt(2)*1e-14
+                    b[0] = 1e-14 + 1e-14j
+                else:
+                    b = 1. + np.random.rand(k)
+                    b[0] = 1e-14  # min singular value is 1e-14
+                a[idx[0][:k], idx[1][:k]] = b.astype(dtype)
+                # rank should be k-1 (as tol is present)
+                self.assertEqual(cfunc(a, tol), k - 1)
+                check(a, tol=tol)
+            # then check zero rank
+            a[:, :] = 0.
+            self.assertEqual(cfunc(a), 0)
+            check(a)
+            # add in a singular value that is small
+            if np.iscomplexobj(a):
+                a[-1, -1] = 1e-14 + 1e-14j
+            else:
+                a[-1, -1] = 1e-14
+            # check the system has zero rank to a given tolerance
+            self.assertEqual(cfunc(a, tol), 0)
+            check(a, tol=tol)
+
+        # check the zero vector returns rank 0 and a nonzero vector
+        # returns rank 1.
+        for dt in self.dtypes:
+            a = np.zeros((5), dtype=dt)
+            self.assertEqual(cfunc(a), 0)
+            check(a)
+            # make it a nonzero vector
+            a[0] = 1.
+            self.assertEqual(cfunc(a), 1)
+            check(a)
+
+        # empty
+        for sz in [(0, 1), (1, 0), (0, 0)]:
+            for tol in [None, 1e-13]:
+                self.assert_raise_on_empty(cfunc, (np.empty(sz), tol))
+
+        rn = "matrix_rank"
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32),))
+
+        # Dimension issue
+        self.assert_wrong_dimensions_1D(
+            rn, cfunc, (np.ones(
+                12, dtype=np.float64).reshape(
+                2, 2, 3),))
+
+        # no nans or infs for 2D case
+        self.assert_no_nan_or_inf(cfunc,
+                                  (np.array([[1., 2., ], [np.inf, np.nan]],
+                                            dtype=np.float64),))
+
+    @needs_lapack
+    def test_no_input_mutation(self):
+        # this is here to test no input mutation by
+        # numba.np.linalg._compute_singular_values
+        # which is the workhorse for norm with 2d input, rank and cond.
+
+        X = np.array([[1., 3, 2, 7,],
+                      [-5, 4, 2, 3,],
+                      [9, -3, 1, 1,],
+                      [2, -2, 2, 8,]], order='F')
+
+        X_orig = np.copy(X)
+
+        @jit(nopython=True)
+        def func(X, test):
+            if test:
+                # not executed, but necessary to trigger A ordering in X
+                X = X[1:2, :]
+            return np.linalg.matrix_rank(X)
+
+        expected = func.py_func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        got = func(X, False)
+        np.testing.assert_allclose(X, X_orig)
+
+        np.testing.assert_allclose(expected, got)
+
+
+class TestLinalgMatrixPower(TestLinalgBase):
+    """
+    Tests for np.linalg.matrix_power.
+    """
+
+    def assert_int_exponenent(self, cfunc, args):
+        # validate first arg is ok
+        cfunc(args[0], 1)
+        # pass in both args and assert fail
+        with self.assertRaises(errors.TypingError):
+            cfunc(*args)
+
+    @needs_lapack
+    def test_linalg_matrix_power(self):
+        cfunc = jit(nopython=True)(matrix_power_matrix)
+
+        def check(a, pwr):
+            expected = matrix_power_matrix(a, pwr)
+            got = cfunc(a, pwr)
+
+            # check that the computed results are contig and in the same way
+            self.assert_contig_sanity(got, "C")
+
+            res = 5 * np.finfo(a.dtype).resolution
+            np.testing.assert_allclose(got, expected, rtol=res, atol=res)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, pwr)
+
+        sizes = [(1, 1), (5, 5), (7, 7)]
+        powers = [-33, -17] + list(range(-10, 10)) + [17, 33]
+
+        for size, pwr, dtype, order in \
+                product(sizes, powers, self.dtypes, 'FC'):
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a, pwr)
+            a = np.empty((0, 0), dtype=dtype, order=order)
+            check(a, pwr)
+
+        rn = "matrix_power"
+
+        # Wrong dtype
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32), 1))
+
+        # not an int power
+        self.assert_wrong_dtype(rn, cfunc,
+                                (np.ones((2, 2), dtype=np.int32), 1))
+
+        # non square system
+        args = (np.ones((3, 5)), 1)
+        msg = 'input must be a square array'
+        self.assert_error(cfunc, args, msg)
+
+        # Dimension issue
+        self.assert_wrong_dimensions(rn, cfunc,
+                                     (np.ones(10, dtype=np.float64), 1))
+
+        # non-integer supplied as exponent
+        self.assert_int_exponenent(cfunc, (np.ones((2, 2)), 1.2))
+
+        # singular matrix is not invertible
+        self.assert_raise_on_singular(cfunc, (np.array([[0., 0], [1, 1]]), -1))
+
+
+class TestTrace(TestLinalgBase):
+    """
+    Tests for np.trace.
+    """
+
+    def setUp(self):
+        super(TestTrace, self).setUp()
+        # compile two versions, one with and one without the offset kwarg
+        self.cfunc_w_offset = jit(nopython=True)(trace_matrix)
+        self.cfunc_no_offset = jit(nopython=True)(trace_matrix_no_offset)
+
+    def assert_int_offset(self, cfunc, a, **kwargs):
+        # validate first arg is ok
+        cfunc(a)
+        # pass in kwarg and assert fail
+        with self.assertRaises(errors.TypingError):
+            cfunc(a, **kwargs)
+
+    def test_trace(self):
+
+        def check(a, **kwargs):
+            if 'offset' in kwargs:
+                expected = trace_matrix(a, **kwargs)
+                cfunc = self.cfunc_w_offset
+            else:
+                expected = trace_matrix_no_offset(a, **kwargs)
+                cfunc = self.cfunc_no_offset
+
+            got = cfunc(a, **kwargs)
+
+            res = 5 * np.finfo(a.dtype).resolution
+            np.testing.assert_allclose(got, expected, rtol=res, atol=res)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, **kwargs)
+
+        # test: column vector, tall, wide, square, row vector
+        # prime sizes
+        sizes = [(7, 1), (11, 5), (5, 11), (3, 3), (1, 7)]
+
+        # offsets to cover the range of the matrix sizes above
+        offsets = [-13, -12, -11] + list(range(-10, 10)) + [11, 12, 13]
+
+        for size, offset, dtype, order in \
+                product(sizes, offsets, self.dtypes, 'FC'):
+            a = self.specific_sample_matrix(size, dtype, order)
+            check(a, offset=offset)
+            if offset == 0:
+                check(a)
+            a = np.empty((0, 0), dtype=dtype, order=order)
+            check(a, offset=offset)
+            if offset == 0:
+                check(a)
+
+        rn = "trace"
+
+        # Dimension issue
+        self.assert_wrong_dimensions(rn, self.cfunc_w_offset,
+                                     (np.ones(10, dtype=np.float64), 1), False)
+        self.assert_wrong_dimensions(rn, self.cfunc_no_offset,
+                                     (np.ones(10, dtype=np.float64),), False)
+
+        # non-integer supplied as exponent
+        self.assert_int_offset(
+            self.cfunc_w_offset, np.ones(
+                (2, 2)), offset=1.2)
+
+    def test_trace_w_optional_input(self):
+        "Issue 2314"
+        @jit("(optional(float64[:,:]),)", nopython=True)
+        def tested(a):
+            return np.trace(a)
+
+        a = np.ones((5, 5), dtype=np.float64)
+        tested(a)
+
+        with self.assertRaises(TypeError) as raises:
+            tested(None)
+
+        errmsg = str(raises.exception)
+        self.assertEqual('expected array(float64, 2d, A), got None', errmsg)
+
+
+class TestBasics(TestLinalgSystems):  # TestLinalgSystems for 1d test
+
+    order1 = cycle(['F', 'C', 'C', 'F'])
+    order2 = cycle(['C', 'F', 'C', 'F'])
+
+    # test: column vector, matrix, row vector, 1d sizes
+    # (7, 1, 3) and two scalars
+    sizes = [(7, 1), (3, 3), (1, 7), (7,), (1,), (3,), 3., 5.]
+
+    def _assert_wrong_dim(self, rn, cfunc):
+        # Dimension issue
+        self.assert_wrong_dimensions_1D(
+            rn, cfunc, (np.array([[[1]]], dtype=np.float64), np.ones(1)), False)
+        self.assert_wrong_dimensions_1D(
+            rn, cfunc, (np.ones(1), np.array([[[1]]], dtype=np.float64)), False)
+
+    def _gen_input(self, size, dtype, order):
+        if not isinstance(size, tuple):
+            return size
+        else:
+            if len(size) == 1:
+                return self.sample_vector(size[0], dtype)
+            else:
+                return self.sample_vector(
+                    size[0] * size[1],
+                    dtype).reshape(
+                    size, order=order)
+
+    def _get_input(self, size1, size2, dtype):
+        a = self._gen_input(size1, dtype, next(self.order1))
+        b = self._gen_input(size2, dtype, next(self.order2))
+        # force domain consistency as underlying ufuncs require it
+        if np.iscomplexobj(a):
+            b = b + 1j
+        if np.iscomplexobj(b):
+            a = a + 1j
+        return (a, b)
+
+    def test_outer(self):
+        cfunc = jit(nopython=True)(outer_matrix)
+
+        def check(a, b, **kwargs):
+
+            # check without kwargs
+            expected = outer_matrix(a, b)
+            got = cfunc(a, b)
+
+            res = 5 * np.finfo(np.asarray(a).dtype).resolution
+            np.testing.assert_allclose(got, expected, rtol=res, atol=res)
+
+            # if kwargs present check with them too
+            if 'out' in kwargs:
+                got = cfunc(a, b, **kwargs)
+                np.testing.assert_allclose(got, expected, rtol=res,
+                                           atol=res)
+                np.testing.assert_allclose(kwargs['out'], expected,
+                                           rtol=res, atol=res)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, b, **kwargs)
+
+        dts = cycle(self.dtypes)
+        for size1, size2 in product(self.sizes, self.sizes):
+            dtype = next(dts)
+            (a, b) = self._get_input(size1, size2, dtype)
+            check(a, b)
+            c = np.empty((np.asarray(a).size, np.asarray(b).size),
+                            dtype=np.asarray(a).dtype)
+            check(a, b, out=c)
+
+        self._assert_wrong_dim("outer", cfunc)
+
+    def test_kron(self):
+        cfunc = jit(nopython=True)(kron_matrix)
+
+        def check(a, b, **kwargs):
+
+            expected = kron_matrix(a, b)
+            got = cfunc(a, b)
+
+            res = 5 * np.finfo(np.asarray(a).dtype).resolution
+            np.testing.assert_allclose(got, expected, rtol=res, atol=res)
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, b)
+
+        for size1, size2, dtype in \
+                product(self.sizes, self.sizes, self.dtypes):
+            (a, b) = self._get_input(size1, size2, dtype)
+            check(a, b)
+
+        self._assert_wrong_dim("kron", cfunc)
+
+        args = (np.empty(10)[::2], np.empty(10)[::2])
+        msg = "only supports 'C' or 'F' layout"
+        self.assert_error(cfunc, args, msg, err=errors.TypingError)
+
+
+class TestHelpers(TestCase):
+    def test_copy_to_fortran_order(self):
+        from numba.np.linalg import _copy_to_fortran_order
+
+        def check(udt, expectfn, shapes, dtypes, orders):
+            for shape, dtype, order in product(shapes, dtypes, orders):
+                a = np.arange(np.prod(shape)).reshape(shape, order=order)
+
+                r = udt(a)
+                # check correct operation
+                self.assertPreciseEqual(expectfn(a), r)
+                # check new copy has made
+                self.assertNotEqual(a.ctypes.data, r.ctypes.data)
+
+        @njit
+        def direct_call(a):
+            return _copy_to_fortran_order(a)
+
+        shapes = [(3, 4), (3, 2, 5)]
+        dtypes = [np.intp]
+        orders = ['C', 'F']
+        check(direct_call, np.asfortranarray, shapes, dtypes, orders)
+
+
+        @njit
+        def slice_to_any(a):
+            # make a 'any' layout slice
+            sliced = a[::2][0]
+            return _copy_to_fortran_order(sliced)
+
+        shapes = [(3, 3, 4), (3, 3, 2, 5)]
+        dtypes = [np.intp]
+        orders = ['C', 'F']
+
+        def expected_slice_to_any(a):
+            # make a 'any' layout slice
+            sliced = a[::2][0]
+            return np.asfortranarray(sliced)
+
+        check(slice_to_any, expected_slice_to_any, shapes, dtypes, orders)
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_listimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_listimpl.py
new file mode 100644
index 000000000..f55e2369a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_listimpl.py
@@ -0,0 +1,527 @@
+"""
+Testing C implementation of the numba typed-list
+"""
+
+import ctypes
+import struct
+
+from numba.tests.support import TestCase
+from numba import _helperlib
+
+
+LIST_OK = 0
+LIST_ERR_INDEX = -1
+LIST_ERR_NO_MEMORY = -2
+LIST_ERR_MUTATED = -3
+LIST_ERR_ITER_EXHAUSTED = -4
+LIST_ERR_IMMUTABLE = -5
+
+
+class List(object):
+    """A wrapper around the C-API to provide a minimal list object for
+    testing.
+    """
+    def __init__(self, tc, item_size, allocated):
+        """
+        Parameters
+        ----------
+        tc : TestCase instance
+        item_size : int
+            byte size for the items
+        allocated : int
+            number of items to allocate for
+        """
+        self.tc = tc
+        self.item_size = item_size
+        self.lp = self.list_new(item_size, allocated)
+
+    # The following methods implement part of the list API
+
+    def __del__(self):
+        self.tc.numba_list_free(self.lp)
+
+    def __len__(self):
+        return self.list_length()
+
+    def __setitem__(self, i, item):
+        return self.list_setitem(i, item)
+
+    def __getitem__(self, i):
+        return self.list_getitem(i)
+
+    def __iter__(self):
+        return ListIter(self)
+
+    def __delitem__(self, i):
+        self.list_delitem(i)
+
+    def handle_index(self, i):
+        # handling negative indices is done at the compiler level, so we only
+        # support -1 to be last element of the list here
+        if i < -1 or len(self) == 0:
+            IndexError("list index out of range")
+        elif i == -1:
+            i = len(self) - 1
+        return i
+
+    @property
+    def allocated(self):
+        return self.list_allocated()
+
+    @property
+    def is_mutable(self):
+        return self.list_is_mutable()
+
+    def set_mutable(self):
+        return self.list_set_is_mutable(1)
+
+    def set_immutable(self):
+        return self.list_set_is_mutable(0)
+
+    def append(self, item):
+        self.list_append(item)
+
+    def pop(self, i=-1):
+        return self.list_pop(i)
+
+    # The methods below are higher-level wrappers for the C-API wrappers
+
+    def list_new(self, item_size, allocated):
+        lp = ctypes.c_void_p()
+        status = self.tc.numba_list_new(
+            ctypes.byref(lp), item_size, allocated,
+        )
+        self.tc.assertEqual(status, LIST_OK)
+        return lp
+
+    def list_length(self):
+        return self.tc.numba_list_length(self.lp)
+
+    def list_allocated(self):
+        return self.tc.numba_list_allocated(self.lp)
+
+    def list_is_mutable(self):
+        return self.tc.numba_list_is_mutable(self.lp)
+
+    def list_set_is_mutable(self, is_mutable):
+        return self.tc.numba_list_set_is_mutable(self.lp, is_mutable)
+
+    def list_setitem(self, i, item):
+        status = self.tc.numba_list_setitem(self.lp, i, item)
+        if status == LIST_ERR_INDEX:
+            raise IndexError("list index out of range")
+        elif status == LIST_ERR_IMMUTABLE:
+            raise ValueError("list is immutable")
+        else:
+            self.tc.assertEqual(status, LIST_OK)
+
+    def list_getitem(self, i):
+        i = self.handle_index(i)
+        item_out_buffer = ctypes.create_string_buffer(self.item_size)
+        status = self.tc.numba_list_getitem(self.lp, i, item_out_buffer)
+        if status == LIST_ERR_INDEX:
+            raise IndexError("list index out of range")
+        else:
+            self.tc.assertEqual(status, LIST_OK)
+            return item_out_buffer.raw
+
+    def list_append(self, item):
+        status = self.tc.numba_list_append(self.lp, item)
+        if status == LIST_ERR_IMMUTABLE:
+            raise ValueError("list is immutable")
+        self.tc.assertEqual(status, LIST_OK)
+
+    def list_pop(self, i):
+        # pop is getitem and delitem
+        i = self.handle_index(i)
+        item = self.list_getitem(i)
+        self.list_delitem(i)
+        return item
+
+    def list_delitem(self, i):
+        # special case slice
+        if isinstance(i, slice):
+            status = self.tc.numba_list_delete_slice(self.lp,
+                                                     i.start,
+                                                     i.stop,
+                                                     i.step)
+            if status == LIST_ERR_IMMUTABLE:
+                raise ValueError("list is immutable")
+            self.tc.assertEqual(status, LIST_OK)
+        # must be an integer, defer to delitem
+        else:
+            i = self.handle_index(i)
+            status = self.tc.numba_list_delitem(self.lp, i)
+            if status == LIST_ERR_INDEX:
+                raise IndexError("list index out of range")
+            elif status == LIST_ERR_IMMUTABLE:
+                raise ValueError("list is immutable")
+            self.tc.assertEqual(status, LIST_OK)
+
+    def list_iter(self, itptr):
+        self.tc.numba_list_iter(itptr, self.lp)
+
+    def list_iter_next(self, itptr):
+        bi = ctypes.c_void_p(0)
+        status = self.tc.numba_list_iter_next(
+            itptr, ctypes.byref(bi),
+        )
+        if status == LIST_ERR_MUTATED:
+            raise ValueError('list mutated')
+        elif status == LIST_ERR_ITER_EXHAUSTED:
+            raise StopIteration
+        else:
+            self.tc.assertGreaterEqual(status, 0)
+            item = (ctypes.c_char * self.item_size).from_address(bi.value)
+            return item.value
+
+
+class ListIter(object):
+    """An iterator for the `List`.
+    """
+    def __init__(self, parent):
+        self.parent = parent
+        itsize = self.parent.tc.numba_list_iter_sizeof()
+        self.it_state_buf = (ctypes.c_char_p * itsize)(0)
+        self.it = ctypes.cast(self.it_state_buf, ctypes.c_void_p)
+        self.parent.list_iter(self.it)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        return self.parent.list_iter_next(self.it)
+
+    next = __next__    # needed for py2 only
+
+
+class TestListImpl(TestCase):
+    def setUp(self):
+        """Bind to the c_helper library and provide the ctypes wrapper.
+        """
+        list_t = ctypes.c_void_p
+        iter_t = ctypes.c_void_p
+
+        def wrap(name, restype, argtypes=()):
+            proto = ctypes.CFUNCTYPE(restype, *argtypes)
+            return proto(_helperlib.c_helpers[name])
+
+        # numba_test_list()
+        self.numba_test_list = wrap(
+            'test_list',
+            ctypes.c_int,
+        )
+
+        # numba_list_new(NB_List *l, Py_ssize_t item_size, Py_ssize_t allocated)
+        self.numba_list_new = wrap(
+            'list_new',
+            ctypes.c_int,
+            [ctypes.POINTER(list_t), ctypes.c_ssize_t, ctypes.c_ssize_t],
+        )
+        # numba_list_free(NB_List *l)
+        self.numba_list_free = wrap(
+            'list_free',
+            None,
+            [list_t],
+        )
+        # numba_list_length(NB_List *l)
+        self.numba_list_length = wrap(
+            'list_length',
+            ctypes.c_int,
+            [list_t],
+        )
+        # numba_list_allocated(NB_List *l)
+        self.numba_list_allocated = wrap(
+            'list_allocated',
+            ctypes.c_int,
+            [list_t],
+        )
+        # numba_list_is_mutable(NB_List *lp)
+        self.numba_list_is_mutable = wrap(
+            'list_is_mutable',
+            ctypes.c_int,
+            [list_t],
+        )
+        # numba_list_set_is_mutable(NB_List *lp, int is_mutable)
+        self.numba_list_set_is_mutable = wrap(
+            'list_set_is_mutable',
+            None,
+            [list_t, ctypes.c_int],
+        )
+        # numba_list_setitem(NB_List *l, Py_ssize_t i, const char *item)
+        self.numba_list_setitem = wrap(
+            'list_setitem',
+            ctypes.c_int,
+            [list_t, ctypes.c_ssize_t, ctypes.c_char_p],
+        )
+        # numba_list_append(NB_List *l, const char *item)
+        self.numba_list_append = wrap(
+            'list_append',
+            ctypes.c_int,
+            [list_t, ctypes.c_char_p],
+        )
+        # numba_list_getitem(NB_List *l,  Py_ssize_t i, char *out)
+        self.numba_list_getitem = wrap(
+            'list_getitem',
+            ctypes.c_int,
+            [list_t, ctypes.c_ssize_t, ctypes.c_char_p],
+        )
+        # numba_list_delitem(NB_List *l,  Py_ssize_t i)
+        self.numba_list_delitem = wrap(
+            'list_delitem',
+            ctypes.c_int,
+            [list_t, ctypes.c_ssize_t],
+        )
+        # numba_list_delete_slice(NB_List *l,
+        #                         Py_ssize_t start,
+        #                         Py_ssize_t stop,
+        #                         Py_ssize_t step)
+        self.numba_list_delete_slice = wrap(
+            'list_delete_slice',
+            ctypes.c_int,
+            [list_t, ctypes.c_ssize_t, ctypes.c_ssize_t, ctypes.c_ssize_t],
+        )
+        # numba_list_iter_sizeof()
+        self.numba_list_iter_sizeof = wrap(
+            'list_iter_sizeof',
+            ctypes.c_size_t,
+        )
+        # numba_list_iter(NB_ListIter *it, NB_List *l)
+        self.numba_list_iter = wrap(
+            'list_iter',
+            None,
+            [
+                iter_t,
+                list_t,
+            ],
+        )
+        # numba_list_iter_next(NB_ListIter *it, const char **item_ptr)
+        self.numba_list_iter_next = wrap(
+            'list_iter_next',
+            ctypes.c_int,
+            [
+                iter_t,                             # it
+                ctypes.POINTER(ctypes.c_void_p),    # item_ptr
+            ],
+        )
+
+    def test_simple_c_test(self):
+        # Runs the basic test in C.
+        ret = self.numba_test_list()
+        self.assertEqual(ret, 0)
+
+    def test_length(self):
+        l = List(self, 8, 0)
+        self.assertEqual(len(l), 0)
+
+    def test_allocation(self):
+        for i in range(16):
+            l = List(self, 8, i)
+            self.assertEqual(len(l), 0)
+            self.assertEqual(l.allocated, i)
+
+    def test_append_get_string(self):
+        l = List(self, 8, 1)
+        l.append(b"abcdefgh")
+        self.assertEqual(len(l), 1)
+        r = l[0]
+        self.assertEqual(r, b"abcdefgh")
+
+    def test_append_get_int(self):
+        l = List(self, 8, 1)
+        l.append(struct.pack("q", 1))
+        self.assertEqual(len(l), 1)
+        r = struct.unpack("q", l[0])[0]
+        self.assertEqual(r, 1)
+
+    def test_append_get_string_realloc(self):
+        l = List(self, 8, 1)
+        l.append(b"abcdefgh")
+        self.assertEqual(len(l), 1)
+        l.append(b"hijklmno")
+        self.assertEqual(len(l), 2)
+        r = l[1]
+        self.assertEqual(r, b"hijklmno")
+
+    def test_set_item_getitem_index_error(self):
+        l = List(self, 8, 0)
+        with self.assertRaises(IndexError):
+            l[0]
+        with self.assertRaises(IndexError):
+            l[0] = b"abcdefgh"
+
+    def test_iter(self):
+        l = List(self, 1, 0)
+        values = [b'a', b'b', b'c', b'd', b'e', b'f', b'g', b'h']
+        for i in values:
+            l.append(i)
+        received = []
+        for j in l:
+            received.append(j)
+        self.assertEqual(values, received)
+
+    def test_pop(self):
+        l = List(self, 1, 0)
+        values = [b'a', b'b', b'c', b'd', b'e', b'f', b'g', b'h']
+        for i in values:
+            l.append(i)
+        self.assertEqual(len(l), 8)
+
+        received = l.pop()
+        self.assertEqual(b'h', received)
+        self.assertEqual(len(l), 7)
+        received = [j for j in l]
+        self.assertEqual(received, values[:-1])
+
+        received = l.pop(0)
+        self.assertEqual(b'a', received)
+        self.assertEqual(len(l), 6)
+
+        received = l.pop(2)
+        self.assertEqual(b'd', received)
+        self.assertEqual(len(l), 5)
+
+        expected = [b'b', b'c', b'e', b'f', b'g']
+        received = [j for j in l]
+        self.assertEqual(received, expected)
+
+    def test_pop_index_error(self):
+        l = List(self, 8, 0)
+        with self.assertRaises(IndexError):
+            l.pop()
+
+    def test_pop_byte(self):
+        l = List(self, 4, 0)
+        values = [b'aaaa', b'bbbb', b'cccc', b'dddd',
+                  b'eeee', b'ffff', b'gggg', b'hhhhh']
+        for i in values:
+            l.append(i)
+        self.assertEqual(len(l), 8)
+
+        received = l.pop()
+        self.assertEqual(b'hhhh', received)
+        self.assertEqual(len(l), 7)
+        received = [j for j in l]
+        self.assertEqual(received, values[:-1])
+
+        received = l.pop(0)
+        self.assertEqual(b'aaaa', received)
+        self.assertEqual(len(l), 6)
+
+        received = l.pop(2)
+        self.assertEqual(b'dddd', received)
+        self.assertEqual(len(l), 5)
+
+        expected = [b'bbbb', b'cccc', b'eeee', b'ffff', b'gggg']
+        received = [j for j in l]
+        self.assertEqual(received, expected)
+
+    def test_delitem(self):
+        l = List(self, 1, 0)
+        values = [b'a', b'b', b'c', b'd', b'e', b'f', b'g', b'h']
+        for i in values:
+            l.append(i)
+        self.assertEqual(len(l), 8)
+
+        # delete first item
+        del l[0]
+        self.assertEqual(len(l), 7)
+        self.assertEqual(list(l), values[1:])
+        # delete last item
+        del l[-1]
+        self.assertEqual(len(l), 6)
+        self.assertEqual(list(l), values[1:-1])
+        # delete item from middle
+        del l[2]
+        self.assertEqual(len(l), 5)
+        self.assertEqual(list(l), [b'b', b'c', b'e', b'f', b'g'])
+
+    def test_delete_slice(self):
+        l = List(self, 1, 0)
+        values = [b'a', b'b', b'c', b'd', b'e', b'f', b'g', b'h']
+        for i in values:
+            l.append(i)
+        self.assertEqual(len(l), 8)
+
+        # delete every second item
+        # no slice default normalization here, be explicit about start anb stop
+        del l[0:8:2]
+        self.assertEqual(len(l), 4)
+        self.assertEqual(list(l), values[1:8:2])
+
+        # delete first item
+        del l[0:1:1]
+        self.assertEqual(len(l), 3)
+        self.assertEqual(list(l), [b'd', b'f', b'h'])
+
+        # delete last item
+        del l[2:3:1]
+        self.assertEqual(len(l), 2)
+        self.assertEqual(list(l), [b'd', b'f'])
+
+        # delete all left items
+        del l[0:2:1]
+        self.assertEqual(len(l), 0)
+        self.assertEqual(list(l), [])
+
+    def check_sizing(self, item_size, nmax):
+        # Helper to verify different item_sizes
+        l = List(self, item_size, 0)
+
+        def make_item(v):
+            tmp = "{:0{}}".format(nmax - v - 1, item_size).encode("latin-1")
+            return tmp[:item_size]
+
+        for i in range(nmax):
+            l.append(make_item(i))
+
+        self.assertEqual(len(l), nmax)
+
+        for i in range(nmax):
+            self.assertEqual(l[i], make_item(i))
+
+    def test_sizing(self):
+        # Check different sizes of the key & value.
+        for i in range(1, 16):
+            self.check_sizing(item_size=i, nmax=2**i)
+
+    def test_mutability(self):
+        # setup and populate a singleton
+        l = List(self, 8, 1)
+        one = struct.pack("q", 1)
+        l.append(one)
+        self.assertTrue(l.is_mutable)
+        self.assertEqual(len(l), 1)
+        r = struct.unpack("q", l[0])[0]
+        self.assertEqual(r, 1)
+
+        # set to immutable and test guards
+        l.set_immutable()
+        self.assertFalse(l.is_mutable)
+        # append
+        with self.assertRaises(ValueError) as raises:
+            l.append(one)
+        self.assertIn("list is immutable", str(raises.exception))
+        # setitem
+        with self.assertRaises(ValueError) as raises:
+            l[0] = one
+        self.assertIn("list is immutable", str(raises.exception))
+        # pop
+        with self.assertRaises(ValueError) as raises:
+            l.pop()
+        self.assertIn("list is immutable", str(raises.exception))
+        # delitem with index
+        with self.assertRaises(ValueError) as raises:
+            del l[0]
+        self.assertIn("list is immutable", str(raises.exception))
+        # delitem with slice
+        with self.assertRaises(ValueError) as raises:
+            del l[0:1:1]
+        self.assertIn("list is immutable", str(raises.exception))
+        l.set_mutable()
+
+        # check that nothing has changed
+        self.assertTrue(l.is_mutable)
+        self.assertEqual(len(l), 1)
+        r = struct.unpack("q", l[0])[0]
+        self.assertEqual(r, 1)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_listobject.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_listobject.py
new file mode 100644
index 000000000..b34e0f667
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_listobject.py
@@ -0,0 +1,1638 @@
+""" Tests for the compiler components of the Numba typed-list.
+
+The tests here should exercise everything within an `@njit` context.
+Importantly, the tests should not return a typed list from within such a
+context as this would require code from numba/typed/typedlist.py (this is
+tested separately).  Tests in this file build on each other in the order of
+writing. For example, the first test, tests the creation, append and len of the
+list. These are the barebones to do anything useful with a list. The subsequent
+test for getitem assumes makes use of these three operations and therefore
+assumes that they work.
+
+"""
+
+from textwrap import dedent
+
+from numba import njit
+from numba import int32
+from numba.extending import register_jitable
+from numba.core import types
+from numba.core.errors import TypingError
+from numba.tests.support import (TestCase, MemoryLeakMixin, override_config,
+                                 forbid_codegen)
+from numba.typed import listobject, List
+
+
+class TestCreateAppendLength(MemoryLeakMixin, TestCase):
+    """Test list creation, append and len. """
+
+    def test_list_create(self):
+        @njit
+        def foo(n):
+            l = listobject.new_list(int32)
+            for i in range(n):
+                l.append(i)
+            return len(l)
+
+        for i in (0, 1, 2, 100):
+            self.assertEqual(foo(i), i)
+
+    def test_list_create_no_jit(self):
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                l = listobject.new_list(int32)
+                self.assertEqual(type(l), list)
+
+    def test_nonempty_list_create_no_jit(self):
+        # See Issue #6001: https://github.com/numba/numba/issues/6001
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                l = List([1, 2, 3])
+                self.assertEqual(type(l), list)
+                self.assertEqual(l, [1, 2, 3])
+
+
+class TestBool(MemoryLeakMixin, TestCase):
+    """Test list bool."""
+
+    def test_list_bool(self):
+        @njit
+        def foo(n):
+            l = listobject.new_list(int32)
+            for i in range(n):
+                l.append(i)
+            return bool(l)
+
+        for i in (0, 1, 2, 100):
+            self.assertEqual(foo(i), i > 0)
+
+
+class TestAllocation(MemoryLeakMixin, TestCase):
+
+    def test_list_allocation(self):
+        @njit
+        def foo_kwarg(n):
+            l = listobject.new_list(int32, allocated=n)
+            return l._allocated()
+
+        for i in range(16):
+            self.assertEqual(foo_kwarg(i), i)
+
+        @njit
+        def foo_posarg(n):
+            l = listobject.new_list(int32, n)
+            return l._allocated()
+        for i in range(16):
+            self.assertEqual(foo_posarg(i), i)
+
+    def test_list_allocation_negative(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32, -1)
+            return l._allocated()
+
+        with self.assertRaises(RuntimeError) as raises:
+            self.assertEqual(foo(), -1)
+        self.assertIn(
+            "expecting *allocated* to be >= 0",
+            str(raises.exception),
+        )
+
+
+class TestToFromMeminfo(MemoryLeakMixin, TestCase):
+
+    def test_list_to_from_meminfo(self):
+        """
+        Exercise listobject.{_as_meminfo, _from_meminfo}
+        """
+
+        @njit
+        def boxer():
+            l = listobject.new_list(int32)
+            for i in range(10, 20):
+                l.append(i)
+            return listobject._as_meminfo(l)
+
+        lsttype = types.ListType(int32)
+
+        @njit
+        def unboxer(mi):
+            l = listobject._from_meminfo(mi, lsttype)
+            return l[0], l[1], l[2], l[3], l[4], l[5], l[6], l[7], l[8], l[9]
+
+        mi = boxer()
+        self.assertEqual(mi.refcount, 1)
+
+        received = list(unboxer(mi))
+        expected = list(range(10, 20))
+        self.assertEqual(received, expected)
+
+
+class TestGetitem(MemoryLeakMixin, TestCase):
+    """Test list getitem. """
+
+    def test_list_getitem_singleton(self):
+        @njit
+        def foo(n):
+            l = listobject.new_list(int32)
+            l.append(n)
+            return l[0]
+
+        self.assertEqual(foo(0), 0)
+
+    def test_list_getitem_singleton_negtive_index(self):
+        @njit
+        def foo(n):
+            l = listobject.new_list(int32)
+            l.append(n)
+            return l[-1]
+
+        self.assertEqual(foo(0), 0)
+
+    def test_list_getitem_multiple(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            return l[i]
+
+        for i,j in ((0, 10), (9, 19), (4, 14), (-5, 15), (-1, 19), (-10, 10)):
+            self.assertEqual(foo(i), j)
+
+    def test_list_getitem_empty_index_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            return l[i]
+
+        for i in (1, 0, -1):
+            with self.assertRaises(IndexError) as raises:
+                foo(i)
+            self.assertIn(
+                "list index out of range",
+                str(raises.exception),
+            )
+
+    def test_list_getitem_multiple_index_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            return l[i]
+
+        for i in (10, -11):
+            with self.assertRaises(IndexError) as raises:
+                foo(i)
+            self.assertIn(
+                "list index out of range",
+                str(raises.exception),
+            )
+
+    def test_list_getitem_empty_typing_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            return l[i]
+
+        for i in "xyz", 1.0, 1j:
+            with self.assertRaises(TypingError) as raises:
+                foo(i)
+            self.assertIn(
+                "list indices must be integers or slices",
+                str(raises.exception),
+            )
+
+    def test_list_getitem_integer_types_as_index(self):
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            return l[i]
+
+        # try all signed integers and make sure they are cast
+        for t in (types.signed_domain
+                  ):
+            self.assertEqual(foo((t(0))), 0)
+
+    def test_list_getitem_different_sized_int_index(self):
+        # Checks that the index type cast and ext/trunc to the
+        # type of the length is correct, both wraparound and
+        # direct index is tested via -1/0.
+
+        for ty in types.integer_domain:
+            @njit
+            def foo():
+                l = listobject.new_list(int32)
+                l.append(7)
+                return l[ty(0)], l[ty(-1)]
+
+            self.assertEqual(foo(), (7, 7))
+
+
+class TestGetitemSlice(MemoryLeakMixin, TestCase):
+    """Test list getitem when indexing with slices. """
+
+    def test_list_getitem_empty_slice_defaults(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            n = l[:]
+            return len(n)
+
+        self.assertEqual(foo(), 0)
+
+    def test_list_getitem_singleton_slice_defaults(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            n = l[:]
+            return len(n)
+
+        self.assertEqual(foo(), 1)
+
+    def test_list_getitem_multiple_slice_defaults(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[:]
+            return n[i]
+
+        for i,j in ((0, 10), (9, 19), (4, 14), (-5, 15), (-1, 19), (-10, 10)):
+            self.assertEqual(foo(i), j)
+
+    def test_list_getitem_multiple_slice_pos_start(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[5:]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (15, 16, 17, 18, 19))
+
+    def test_list_getitem_multiple_slice_pos_stop(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[:5]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (10, 11, 12, 13, 14))
+
+    def test_list_getitem_multiple_slice_pos_start_pos_stop(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[2:7]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (12, 13, 14, 15, 16))
+
+    def test_list_getitem_multiple_slice_pos_start_pos_stop_pos_step(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[1:9:2]
+            return len(n), (n[0], n[1], n[2], n[3])
+
+        length, items = foo()
+        self.assertEqual(length, 4)
+        self.assertEqual(items, (11, 13, 15, 17))
+
+    def test_list_getitem_multiple_slice_neg_start(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[-5:]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (15, 16, 17, 18, 19))
+
+    def test_list_getitem_multiple_slice_neg_stop(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[:-5]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (10, 11, 12, 13, 14))
+
+    def test_list_getitem_multiple_slice_neg_step(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[::-2]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (19, 17, 15, 13, 11))
+
+    def test_list_getitem_multiple_slice_pos_start_neg_step(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[4::-1]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (14, 13, 12, 11, 10))
+
+    def test_list_getitem_multiple_slice_neg_start_neg_step(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[-6::-1]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (14, 13, 12, 11, 10))
+
+    def test_list_getitem_multiple_slice_pos_stop_neg_step(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[:4:-1]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (19, 18, 17, 16, 15))
+
+    def test_list_getitem_multiple_slice_neg_stop_neg_step(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[:-6:-1]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (19, 18, 17, 16, 15))
+
+    def test_list_getitem_multiple_slice_pos_start_pos_stop_neg_step(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[8:3:-1]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (18, 17, 16, 15, 14))
+
+    def test_list_getitem_multiple_slice_neg_start_neg_stop_neg_step(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[-2:-7:-1]
+            return len(n), (n[0], n[1], n[2], n[3], n[4])
+
+        length, items = foo()
+        self.assertEqual(length, 5)
+        self.assertEqual(items, (18, 17, 16, 15, 14))
+
+    def test_list_getitem_multiple_slice_start_out_of_range(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[10:]
+            return len(n)
+
+        self.assertEqual(foo(), 0)
+
+    def test_list_getitem_multiple_slice_stop_zero(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            n = l[:0]
+            return len(n)
+
+        self.assertEqual(foo(), 0)
+
+    def test_list_getitem_multiple_slice_zero_step_index_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            l[::0]
+
+        with self.assertRaises(ValueError) as raises:
+            foo()
+        self.assertIn(
+            "slice step cannot be zero",
+            str(raises.exception),
+        )
+
+
+class TestSetitem(MemoryLeakMixin, TestCase):
+    """Test list setitem. """
+
+    def test_list_setitem_singleton(self):
+        @njit
+        def foo(n):
+            l = listobject.new_list(int32)
+            l.append(0)
+            l[0] = n
+            return l[0]
+
+        for i in (0, 1, 2, 100):
+            self.assertEqual(foo(i), i)
+
+    def test_list_setitem_singleton_negative_index(self):
+        @njit
+        def foo(n):
+            l = listobject.new_list(int32)
+            l.append(0)
+            l[0] = n
+            return l[-1]
+
+        for i in (0, 1, 2, 100):
+            self.assertEqual(foo(i), i)
+
+    def test_list_setitem_singleton_index_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            l[i] = 1
+
+        with self.assertRaises(IndexError):
+            foo(1)
+
+        with self.assertRaises(IndexError):
+            foo(-2)
+
+    def test_list_setitem_multiple(self):
+
+        @njit
+        def foo(i, n):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            l[i] = n
+            return l[i]
+
+        for i,n in zip(range(0,10), range(20,30)):
+            self.assertEqual(foo(i, n), n)
+
+    def test_list_setitem_multiple_index_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            l[i] = 0
+
+        with self.assertRaises(IndexError):
+            foo(10)
+
+        with self.assertRaises(IndexError):
+            foo(-11)
+
+    def test_list_setitem_singleton_typing_error_on_index(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            # slice with a non-{integer,slice}
+            l[i] = 1
+
+        for i in "xyz", 1.0, 1j:
+            with self.assertRaises(TypingError) as raises:
+                foo(i)
+            self.assertIn(
+                "list indices must be integers or slices",
+                str(raises.exception),
+            )
+
+    def test_list_setitem_singleton_typing_error_on_item(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            # assign a non-iterable to a slice
+            l[:] = 1
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "can only assign an iterable when using a slice "
+            "with assignment/setitem",
+            str(raises.exception),
+        )
+
+    def test_list_setitem_integer_types_as_index(self):
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            l[i] = 1
+            return l[i]
+
+        # try all signed integers and make sure they are cast
+        for t in (types.signed_domain
+                  ):
+            self.assertEqual(foo((t(0))), 1)
+
+
+class TestPop(MemoryLeakMixin, TestCase):
+    """Test list pop. """
+
+    def test_list_pop_singleton(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            return l.pop(), len(l)
+
+        self.assertEqual(foo(), (0, 0))
+
+    def test_list_pop_singleton_index(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            return l.pop(i), len(l)
+
+        self.assertEqual(foo(0), (0, 0))
+        self.assertEqual(foo(-1), (0, 0))
+
+    def test_list_pop_multiple(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in (10, 11, 12):
+                l.append(j)
+            return l.pop(), len(l)
+
+        self.assertEqual(foo(), (12, 2))
+
+    def test_list_pop_multiple_index(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in (10, 11, 12):
+                l.append(j)
+            return l.pop(i), len(l)
+
+        for i, n in ((0, 10), (1, 11), (2, 12)):
+            self.assertEqual(foo(i), (n, 2))
+
+        for i, n in ((-3, 10), (-2, 11), (-1, 12)):
+            self.assertEqual(foo(i), (n, 2))
+
+    def test_list_pop_integer_types_as_index(self):
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            return l.pop(i)
+
+        # try all signed integers and make sure they are cast
+        for t in (types.signed_domain
+                  ):
+            self.assertEqual(foo((t(0))), 0)
+
+    def test_list_pop_empty_index_error_no_index(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.pop()
+
+        with self.assertRaises(IndexError) as raises:
+            foo()
+        self.assertIn(
+            "pop from empty list",
+            str(raises.exception),
+        )
+
+    def test_list_pop_empty_index_error_with_index(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.pop(i)
+
+        with self.assertRaises(IndexError) as raises:
+            foo(-1)
+        self.assertIn(
+            "pop from empty list",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(IndexError) as raises:
+            foo(0)
+        self.assertIn(
+            "pop from empty list",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(IndexError) as raises:
+            foo(1)
+        self.assertIn(
+            "pop from empty list",
+            str(raises.exception),
+        )
+
+    def test_list_pop_mutiple_index_error_with_index(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in (10, 11, 12):
+                l.append(j)
+            l.pop(i)
+
+        with self.assertRaises(IndexError) as raises:
+            foo(-4)
+        self.assertIn(
+            "list index out of range",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(IndexError) as raises:
+            foo(3)
+        self.assertIn(
+            "list index out of range",
+            str(raises.exception),
+        )
+
+    def test_list_pop_singleton_typing_error_on_index(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            # slice with a non-{integer,slice}
+            return l.pop(i)
+
+        for i in "xyz", 1.0, 1j:
+            with self.assertRaises(TypingError) as raises:
+                foo(i)
+            self.assertIn(
+                "argument for pop must be an integer",
+                str(raises.exception),
+            )
+
+
+class TestListObjectDelitem(MemoryLeakMixin, TestCase):
+    """Test list delitem.
+    """
+
+    def test_list_singleton_delitem_index(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            del l[0]
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+    def test_list_singleton_delitem_slice_defaults(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            del l[:]
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+    def test_list_singleton_delitem_slice_start(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            del l[0:]
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+    def test_list_singleton_delitem_slice_stop(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            del l[:1]
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+    def test_list_singleton_delitem_slice_start_stop(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            del l[0:1]
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+    def test_list_singleton_delitem_slice_start_step(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            del l[0::1]
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+    def test_list_singleton_delitem_slice_start_stop_step(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            del l[0:1:1]
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+    def test_list_multiple_delitem(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in (10, 11, 12):
+                l.append(j)
+            del l[0]
+            return len(l), l[0], l[1]
+        self.assertEqual(foo(), (2, 11, 12))
+
+    def test_list_multiple_delitem_slice(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in (10, 11, 12):
+                l.append(j)
+            del l[:]
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+    def test_list_multiple_delitem_off_by_one(self):
+        # this was exposing a nasty off-by-one error, leaving it in to detect
+        # and regressions
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            k = listobject.new_list(int32)
+            for j in range(10, 20):
+                k.append(j)
+            # should be a no-op
+            del l[-9:-20]
+            return k == l
+        self.assertTrue(foo())
+
+
+class TestContains(MemoryLeakMixin, TestCase):
+    """Test list contains. """
+
+    def test_list_contains_empty(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            return i in l
+
+        self.assertFalse(foo(0))
+        self.assertFalse(foo(1))
+
+    def test_list_contains_singleton(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            return i in l
+
+        self.assertTrue(foo(0))
+        self.assertFalse(foo(1))
+
+    def test_list_contains_multiple(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            return i in l
+
+        for i in range(10, 20):
+            self.assertTrue(foo(i))
+
+        for i in range(20, 30):
+            self.assertFalse(foo(i))
+
+
+class TestCount(MemoryLeakMixin, TestCase):
+    """Test list count. """
+
+    def test_list_count_empty(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            return l.count(i)
+
+        self.assertEqual(foo(10), 0)
+
+    def test_list_count_singleton(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(10)
+            return l.count(i)
+
+        self.assertEqual(foo(1), 0)
+        self.assertEqual(foo(10), 1)
+
+    def test_list_count_mutiple(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in [11, 12, 12, 13, 13, 13]:
+                l.append(j)
+            return l.count(i)
+
+        self.assertEqual(foo(10), 0)
+        self.assertEqual(foo(11), 1)
+        self.assertEqual(foo(12), 2)
+        self.assertEqual(foo(13), 3)
+
+
+class TestExtend(MemoryLeakMixin, TestCase):
+    """Test list extend. """
+
+    def test_list_extend_empty(self):
+        @njit
+        def foo(items):
+            l = listobject.new_list(int32)
+            l.extend(items)
+            return len(l)
+
+        self.assertEqual(foo((1,)), 1)
+        self.assertEqual(foo((1,2)), 2)
+        self.assertEqual(foo((1,2,3)), 3)
+
+    def test_list_extend_typing_error_non_iterable(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.extend(1)
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "extend argument must be iterable",
+            str(raises.exception),
+        )
+
+
+class TestInsert(MemoryLeakMixin, TestCase):
+    """Test list insert. """
+
+    def test_list_insert_empty(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.insert(i, 1)
+            return len(l), l[0]
+
+        for i in (-10, -5, -1, 0, 1, 4, 9):
+            self.assertEqual(foo(i), (1, 1))
+
+    def test_list_insert_singleton(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            l.append(0)
+            l.insert(i, 1)
+            return len(l), l[0], l[1]
+
+        # insert before
+        for i in (-10, -3, -2, -1, 0):
+            self.assertEqual(foo(i), (2, 1, 0))
+
+        # insert after
+        for i in (1, 2, 3, 10):
+            self.assertEqual(foo(i), (2, 0, 1))
+
+    def test_list_insert_multiple(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10):
+                l.append(0)
+            l.insert(i, 1)
+            return len(l), l[i]
+
+        for i in (0, 4, 9):
+            self.assertEqual(foo(i), (11, 1))
+
+    def test_list_insert_multiple_before(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10):
+                l.append(0)
+            l.insert(i, 1)
+            return len(l), l[0]
+
+        for i in (-12, -11, -10, 0):
+            self.assertEqual(foo(i), (11, 1))
+
+    def test_list_insert_multiple_after(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10):
+                l.append(0)
+            l.insert(i, 1)
+            return len(l), l[10]
+
+        for i in (10, 11, 12):
+            self.assertEqual(foo(i), (11, 1))
+
+    def test_list_insert_typing_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.insert("a", 0)
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "list insert indices must be integers",
+            str(raises.exception),
+        )
+
+
+class TestRemove(MemoryLeakMixin, TestCase):
+    """Test list remove. """
+
+    def test_list_remove_empty(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.remove(0)
+
+        with self.assertRaises(ValueError):
+            foo()
+
+    def test_list_remove_singleton(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            l.remove(0)
+            return len(l)
+
+        self.assertEqual(foo(), 0)
+
+    def test_list_remove_singleton_value_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(1)
+            l.remove(0)
+
+        with self.assertRaises(ValueError):
+            foo()
+
+    def test_list_remove_multiple(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            l.remove(13)
+            l.remove(19)
+            return len(l)
+
+        self.assertEqual(foo(), 8)
+
+    def test_list_remove_multiple_value_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            l.remove(23)
+
+        with self.assertRaises(ValueError):
+            foo()
+
+
+class TestClear(MemoryLeakMixin, TestCase):
+    """Test list clear. """
+
+    def test_list_clear_empty(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.clear()
+            return len(l)
+
+        self.assertEqual(foo(), 0)
+
+    def test_list_clear_singleton(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            l.clear()
+            return len(l)
+
+        self.assertEqual(foo(), 0)
+
+    def test_list_clear_multiple(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10):
+                l.append(0)
+            l.clear()
+            return len(l)
+        self.assertEqual(foo(), 0)
+
+
+class TestReverse(MemoryLeakMixin, TestCase):
+    """Test list reverse. """
+
+    def test_list_reverse_empty(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.reverse()
+            return len(l)
+
+        self.assertEqual(foo(), 0)
+
+    def test_list_reverse_singleton(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            l.reverse()
+            return len(l), l[0]
+
+        self.assertEqual(foo(), (1, 0))
+
+    def test_list_reverse_multiple(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 13):
+                l.append(j)
+            l.reverse()
+            return len(l), l[0], l[1], l[2]
+        self.assertEqual(foo(), (3, 12, 11, 10))
+
+
+class TestCopy(MemoryLeakMixin, TestCase):
+    """Test list copy. """
+
+    def test_list_copy_empty(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            n = l.copy()
+            return len(l), len(n)
+
+        self.assertEqual(foo(), (0, 0))
+
+    def test_list_copy_singleton(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            n = l.copy()
+            return len(l), len(n), l[0], n[0]
+
+        self.assertEqual(foo(), (1, 1, 0, 0))
+
+    def test_list_copy_multiple(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 13):
+                l.append(j)
+            n = l.copy()
+            return len(l), len(n), l[0], l[1], l[2], l[0], l[1], l[2]
+
+        self.assertEqual(foo(), (3, 3, 10, 11, 12, 10, 11, 12))
+
+
+class TestIndex(MemoryLeakMixin, TestCase):
+
+    def test_index_singleton(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(1)
+            return l.index(1)
+
+        self.assertEqual(foo(), 0)
+
+    def test_index_multiple(self):
+        @njit
+        def foo(i):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            return l.index(i)
+
+        for i,v in zip(range(10), range(10,20)):
+            self.assertEqual(foo(v), i)
+
+    def test_index_duplicate(self):
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for _ in range(10, 20):
+                l.append(1)
+            return l.index(1)
+
+        self.assertEqual(foo(), 0)
+
+    def test_index_duplicate_with_start(self):
+        @njit
+        def foo(start):
+            l = listobject.new_list(int32)
+            for _ in range(10, 20):
+                l.append(1)
+            return l.index(1, start)
+
+        for i in range(10):
+            self.assertEqual(foo(i), i)
+
+    def test_index_singleton_value_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            return l.index(1)
+
+        with self.assertRaises(ValueError) as raises:
+            foo()
+        self.assertIn(
+            "item not in list",
+            str(raises.exception),
+        )
+
+    def test_index_multiple_value_error(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            return l.index(23)
+
+        with self.assertRaises(ValueError) as raises:
+            foo()
+        self.assertIn(
+            "item not in list",
+            str(raises.exception),
+        )
+
+    def test_index_multiple_value_error_start(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(start):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            return l.index(10, start)
+
+        self.assertEqual(foo(0), 0)
+        for i in range(1,10):
+            with self.assertRaises(ValueError) as raises:
+                foo(i)
+            self.assertIn(
+                "item not in list",
+                str(raises.exception),
+            )
+
+    def test_index_multiple_value_error_end(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(end):
+            l = listobject.new_list(int32)
+            for j in range(10, 20):
+                l.append(j)
+            return l.index(19, 0, end)
+
+        self.assertEqual(foo(10), 9)
+        for i in range(0,9):
+            with self.assertRaises(ValueError) as raises:
+                foo(i)
+            self.assertIn(
+                "item not in list",
+                str(raises.exception),
+            )
+
+    def test_index_typing_error_start(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            return l.index(0, start="a")
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "start argument for index must be an integer",
+            str(raises.exception),
+        )
+
+    def test_index_typing_error_end(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append(0)
+            return l.index(0, end="a")
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "end argument for index must be an integer",
+            str(raises.exception),
+        )
+
+
+class TestEqualNotEqual(MemoryLeakMixin, TestCase):
+    """Test list equal and not equal. """
+
+    def test_list_empty_equal(self):
+        @njit
+        def foo():
+            t = listobject.new_list(int32)
+            o = listobject.new_list(int32)
+            return t == o, t != o
+
+        self.assertEqual(foo(), (True, False))
+
+    def test_list_singleton_equal(self):
+        @njit
+        def foo():
+            t = listobject.new_list(int32)
+            t.append(0)
+            o = listobject.new_list(int32)
+            o.append(0)
+            return t == o, t != o
+
+        self.assertEqual(foo(), (True, False))
+
+    def test_list_singleton_not_equal(self):
+        @njit
+        def foo():
+            t = listobject.new_list(int32)
+            t.append(0)
+            o = listobject.new_list(int32)
+            o.append(1)
+            return t == o, t != o
+
+        self.assertEqual(foo(), (False, True))
+
+    def test_list_length_mismatch(self):
+        @njit
+        def foo():
+            t = listobject.new_list(int32)
+            t.append(0)
+            o = listobject.new_list(int32)
+            return t == o, t != o
+
+        self.assertEqual(foo(), (False, True))
+
+    def test_list_multiple_equal(self):
+        @njit
+        def foo():
+            t = listobject.new_list(int32)
+            o = listobject.new_list(int32)
+            for i in range(10):
+                t.append(i)
+                o.append(i)
+            return t == o, t != o
+
+        self.assertEqual(foo(), (True, False))
+
+    def test_list_multiple_not_equal(self):
+        @njit
+        def foo():
+            t = listobject.new_list(int32)
+            o = listobject.new_list(int32)
+            for i in range(10):
+                t.append(i)
+                o.append(i)
+            o[-1] = 42
+            return t == o, t != o
+
+        self.assertEqual(foo(), (False, True))
+
+
+class TestIter(MemoryLeakMixin, TestCase):
+    """Test list iter. """
+
+    def test_list_iter(self):
+        @njit
+        def foo(items):
+            l = listobject.new_list(int32)
+            l.extend(items)
+            # use a simple sum to check this w/o having to return a list
+            r = 0
+            for j in l:
+                r += j
+            return r
+
+        items = (1, 2, 3, 4)
+
+        self.assertEqual(
+            foo(items),
+            sum(items)
+        )
+
+    def test_list_iter_self_mutation(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.extend((1, 2, 3, 4))
+            for i in l:
+                l.append(i)
+
+        with self.assertRaises(RuntimeError) as raises:
+            foo()
+        self.assertIn(
+            'list was mutated during iteration'.format(**locals()),
+            str(raises.exception),
+        )
+
+
+class TestStringItem(MemoryLeakMixin, TestCase):
+    """Test list can take strings as items. """
+
+    def test_string_item(self):
+        @njit
+        def foo():
+            l = listobject.new_list(types.unicode_type)
+            l.append('a')
+            l.append('b')
+            l.append('c')
+            l.append('d')
+            return l[0], l[1], l[2], l[3]
+
+        items = foo()
+        self.assertEqual(['a', 'b', 'c', 'd'], list(items))
+
+
+class TestItemCasting(TestCase):
+
+    @njit
+    def foo(fromty, toty):
+        l = listobject.new_list(toty)
+        l.append(fromty(0))
+
+    def check_good(self, fromty, toty):
+        TestItemCasting.foo(fromty, toty)
+
+    def check_bad(self, fromty, toty):
+        with self.assertRaises(TypingError) as raises:
+            TestItemCasting.foo(fromty, toty)
+        self.assertIn(
+            'cannot safely cast {fromty} to {toty}'.format(**locals()),
+            str(raises.exception),
+        )
+
+    def test_cast_int_to(self):
+        self.check_good(types.int32, types.float32)
+        self.check_good(types.int32, types.float64)
+        self.check_good(types.int32, types.complex128)
+        self.check_good(types.int64, types.complex128)
+        self.check_bad(types.int32, types.complex64)
+        self.check_good(types.int8, types.complex64)
+
+    def test_cast_float_to(self):
+        self.check_good(types.float32, types.float64)
+        self.check_good(types.float32, types.complex64)
+        self.check_good(types.float64, types.complex128)
+
+    def test_cast_bool_to(self):
+        self.check_good(types.boolean, types.int32)
+        self.check_good(types.boolean, types.float64)
+        self.check_good(types.boolean, types.complex128)
+
+    def test_cast_fail_unicode_int(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(int32)
+            l.append("xyz")
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            'cannot safely cast unicode_type to int32',
+            str(raises.exception),
+        )
+
+    def test_cast_fail_int_unicode(self):
+
+        @njit
+        def foo():
+            l = listobject.new_list(types.unicode_type)
+            l.append(int32(0))
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            'Cannot cast int32 to unicode_type',
+            str(raises.exception),
+        )
+
+
+@register_jitable
+def make_test_list():
+    l = listobject.new_list(int32)
+    l.append(int32(1))
+    return l
+
+
+class TestImmutable(MemoryLeakMixin, TestCase):
+
+    def test_is_immutable(self):
+        @njit
+        def foo():
+            l = make_test_list()
+            return l._is_mutable()
+        self.assertTrue(foo())
+
+    def test_make_immutable_is_immutable(self):
+        @njit
+        def foo():
+            l = make_test_list()
+            l._make_immutable()
+            return l._is_mutable()
+        self.assertFalse(foo())
+
+    def test_length_still_works_when_immutable(self):
+        @njit
+        def foo():
+            l = make_test_list()
+            l._make_immutable()
+            return len(l),l._is_mutable()
+        length, mutable = foo()
+        self.assertEqual(length, 1)
+        self.assertFalse(mutable)
+
+    def test_getitem_still_works_when_immutable(self):
+        @njit
+        def foo():
+            l = make_test_list()
+            l._make_immutable()
+            return l[0], l._is_mutable()
+        test_item, mutable = foo()
+        self.assertEqual(test_item, 1)
+        self.assertFalse(mutable)
+
+    def test_append_fails(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = make_test_list()
+            l._make_immutable()
+            l.append(int32(1))
+        with self.assertRaises(ValueError) as raises:
+            foo()
+        self.assertIn(
+            'list is immutable',
+            str(raises.exception),
+        )
+
+    def test_mutation_fails(self):
+        """ Test that any attempt to mutate an immutable typed list fails. """
+        self.disable_leak_check()
+
+        def generate_function(line):
+            context = {}
+            exec(dedent("""
+                from numba.typed import listobject
+                from numba import int32
+                def bar():
+                    lst = listobject.new_list(int32)
+                    lst.append(int32(1))
+                    lst._make_immutable()
+                    zero = int32(0)
+                    {}
+                """.format(line)), context)
+            return njit(context["bar"])
+        for line in ("lst.append(zero)",
+                     "lst[0] = zero",
+                     "lst.pop()",
+                     "del lst[0]",
+                     "lst.extend((zero,))",
+                     "lst.insert(0, zero)",
+                     "lst.clear()",
+                     "lst.reverse()",
+                     "lst.sort()",
+                     ):
+            foo = generate_function(line)
+            with self.assertRaises(ValueError) as raises:
+                foo()
+            self.assertIn(
+                "list is immutable",
+                str(raises.exception),
+            )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_lists.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_lists.py
new file mode 100644
index 000000000..5cb1dc7ed
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_lists.py
@@ -0,0 +1,1832 @@
+from collections import namedtuple
+import contextlib
+import itertools
+import math
+import sys
+import ctypes as ct
+import numpy as np
+
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit, typeof, njit, literal_unroll, literally
+import unittest
+from numba import testing
+from numba.core import types, utils, errors
+from numba.tests.support import TestCase, MemoryLeakMixin, tag
+from numba.experimental import jitclass
+from numba.core.extending import overload
+
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+Point = namedtuple('Point', ('a', 'b'))
+
+
+def noop(x):
+    pass
+
+def unbox_usecase(x):
+    """
+    Expect a list of numbers
+    """
+    res = 0
+    for v in x:
+        res += v
+    return res
+
+def unbox_usecase2(x):
+    """
+    Expect a list of tuples
+    """
+    res = 0
+    for v in x:
+        res += len(v)
+    return res
+
+def unbox_usecase3(x):
+    """
+    Expect a (number, list of numbers) tuple.
+    """
+    a, b = x
+    res = a
+    for v in b:
+        res += v
+    return res
+
+def unbox_usecase4(x):
+    """
+    Expect a (number, list of tuples) tuple.
+    """
+    a, b = x
+    res = a
+    for v in b:
+        res += len(v)
+    return res
+
+
+def create_list(x, y, z):
+    return [x, y, z]
+
+def create_nested_list(x, y, z, a, b, c):
+    return [[x, y, z], [a, b, c]]
+
+def list_comprehension1():
+    return sum([x**2 for x in range(10)])
+
+def list_comprehension2():
+    return sum([x for x in range(10) if x % 2 == 0])
+
+def list_comprehension3():
+    return sum([math.pow(x, 2) for x in range(10)])
+
+def list_comprehension4():
+    return sum([x * y for x in range(10) for y in range(10)])
+
+def list_comprehension5():
+    return [x * 2 for x in range(10)]
+
+def list_comprehension6():
+    return [[x for x in range(y)] for y in range(3)]
+
+
+def list_constructor(n):
+    return list(range(n))
+
+def list_constructor_empty():
+    # cannot be typed, list is empty and no typing information is present to
+    # infer a type
+    return list()
+
+def list_constructor_empty_but_typeable(n):
+    # can be typed, list is empty but later append has typing info that allows
+    # for inference
+    y = list()
+    return y.append(n)
+
+def list_append(n):
+    l = []
+    l.append(42)
+    for i in range(n):
+        l.append(i)
+    return l
+
+def list_append_heterogeneous(n):
+    l = []
+    l.append(42.0)
+    for i in range(n):
+        l.append(i)
+    return l
+
+def list_extend(n):
+    l = []
+    # A non-list iterable and a list
+    l.extend(range(n))
+    l.extend(l[:-1])
+    l.extend(range(n, 0, -1))
+    return l
+
+def list_extend_heterogeneous(n):
+    l = []
+    # Extend with various iterables, including lists, with different types
+    l.extend(range(n))
+    l.extend(l[:-1])
+    l.extend((5, 42))
+    l.extend([123.0])
+    return l
+
+def list_pop0(n):
+    l = list(range(n))
+    res = 0
+    while len(l) > 0:
+        res += len(l) * l.pop()
+    return res
+
+def list_pop1(n, i):
+    l = list(range(n))
+    x = l.pop(i)
+    return x, l
+
+def list_len(n):
+    l = list(range(n))
+    return len(l)
+
+def list_getitem(n):
+    l = list(range(n))
+    res = 0
+    # Positive indices
+    for i in range(len(l)):
+        res += i * l[i]
+    # Negative indices
+    for i in range(-len(l), 0):
+        res -= i * l[i]
+    return res
+
+def list_setitem(n):
+    l = list(range(n))
+    res = 0
+    # Positive indices
+    for i in range(len(l)):
+        l[i] = i * l[i]
+    # Negative indices
+    for i in range(-len(l), 0):
+        l[i] = i * l[i]
+    for i in range(len(l)):
+        res += l[i]
+    return res
+
+def list_getslice2(n, start, stop):
+    l = list(range(n))
+    return l[start:stop]
+
+def list_getslice3(n, start, stop, step):
+    l = list(range(n))
+    return l[start:stop:step]
+
+def list_setslice2(n, n_source, start, stop):
+    # Generic setslice with size change
+    l = list(range(n))
+    v = list(range(100, 100 + n_source))
+    l[start:stop] = v
+    return l
+
+def list_setslice3(n, start, stop, step):
+    l = list(range(n))
+    v = l[start:stop:step]
+    for i in range(len(v)):
+        v[i] += 100
+    l[start:stop:step] = v
+    return l
+
+def list_setslice3_arbitrary(n, n_src, start, stop, step):
+    l = list(range(n))
+    l[start:stop:step] = list(range(100, 100 + n_src))
+    return l
+
+def list_delslice0(n):
+    l = list(range(n))
+    del l[:]
+    return l
+
+def list_delslice1(n, start, stop):
+    l = list(range(n))
+    del l[start:]
+    del l[:stop]
+    return l
+
+def list_delslice2(n, start, stop):
+    l = list(range(n))
+    del l[start:stop]
+    return l
+
+def list_clear(n):
+    l = list(range(n))
+    l.clear()
+    return l
+
+def list_copy(n):
+    l = list(range(n))
+    ll = l.copy()
+    l.append(42)
+    return l, ll
+
+def list_iteration(n):
+    l = list(range(n))
+    res = 0
+    for i, v in enumerate(l):
+        res += i * v
+    return res
+
+def list_contains(n):
+    l = list(range(n))
+    return (0 in l, 1 in l, n - 1 in l, n in l,
+            0 not in l, 1 not in l, n - 1 not in l, n not in l,
+            )
+
+def list_index1(n, v):
+    l = list(range(n, 0, -1))
+    return l.index(v)
+
+def list_index2(n, v, start):
+    l = list(range(n, 0, -1))
+    return l.index(v, start)
+
+def list_index3(n, v, start, stop):
+    l = list(range(n, 0, -1))
+    return l.index(v, start, stop)
+
+def list_remove(n, v):
+    l = list(range(n - 1, -1, -1))
+    l.remove(v)
+    return l
+
+def list_insert(n, pos, v):
+    l = list(range(0, n))
+    l.insert(pos, v)
+    return l
+
+def list_count(n, v):
+    l = []
+    for x in range(n):
+        l.append(x & 3)
+    return l.count(v)
+
+def list_reverse(n):
+    l = list(range(n))
+    l.reverse()
+    return l
+
+def list_add(m, n):
+    a = list(range(0, m))
+    b = list(range(100, 100 + n))
+    res = a + b
+    res.append(42)   # check result is a copy
+    return a, b, res
+
+def list_add_heterogeneous():
+    a = [1]
+    b = [2.0]
+    c = a + b
+    d = b + a
+    # check result is a copy
+    a.append(3)
+    b.append(4.0)
+    return a, b, c, d
+
+def list_add_inplace(m, n):
+    a = list(range(0, m))
+    b = list(range(100, 100 + n))
+    a += b
+    return a, b
+
+def list_add_inplace_heterogeneous():
+    a = [1]
+    b = [2.0]
+    a += b
+    b += a
+    return a, b
+
+def list_mul(n, v):
+    a = list(range(n))
+    return a * v
+
+def list_mul2(n, v):
+    a = list(range(n))
+    return v * a
+
+def list_mul_inplace(n, v):
+    a = list(range(n))
+    a *= v
+    return a
+
+def list_bool(n):
+    a = list(range(n))
+    return bool(a), (True if a else False)
+
+def eq_usecase(a, b):
+    return list(a) == list(b)
+
+def ne_usecase(a, b):
+    return list(a) != list(b)
+
+def gt_usecase(a, b):
+    return list(a) > list(b)
+
+def ge_usecase(a, b):
+    return list(a) >= list(b)
+
+def lt_usecase(a, b):
+    return list(a) < list(b)
+
+def le_usecase(a, b):
+    return list(a) <= list(b)
+
+def identity_usecase(n):
+    a = list(range(n))
+    b = a
+    c = a[:]
+    return (a is b), (a is not b), (a is c), (a is not c)
+
+def bool_list_usecase():
+    # Exercise getitem, setitem, iteration with bool values (issue #1373)
+    l = [False]
+    l[0] = True
+    x = False
+    for v in l:
+        x = x ^ v
+    return l, x
+
+def reflect_simple(l, ll):
+    x = l.pop()
+    y = l.pop()
+    l[0] = 42.
+    l.extend(ll)
+    return l, x, y
+
+def reflect_conditional(l, ll):
+    # `l` may or may not actually reflect a Python list
+    if ll[0]:
+        l = [11., 22., 33., 44.]
+    x = l.pop()
+    y = l.pop()
+    l[0] = 42.
+    l.extend(ll)
+    return l, x, y
+
+def reflect_exception(l):
+    l.append(42)
+    raise ZeroDivisionError
+
+def reflect_dual(l, ll):
+    l.append(ll.pop())
+    return l is ll
+
+
+class TestLists(MemoryLeakMixin, TestCase):
+
+    def test_create_list(self):
+        pyfunc = create_list
+        cr = compile_isolated(pyfunc, (types.int32, types.int32, types.int32))
+        cfunc = cr.entry_point
+        self.assertEqual(cfunc(1, 2, 3), pyfunc(1, 2, 3))
+
+    def test_create_nested_list(self):
+        pyfunc = create_nested_list
+        cr = compile_isolated(pyfunc, (types.int32, types.int32, types.int32,
+                                       types.int32, types.int32, types.int32))
+        cfunc = cr.entry_point
+        self.assertEqual(cfunc(1, 2, 3, 4, 5, 6), pyfunc(1, 2, 3, 4, 5, 6))
+
+    def check_unary_with_size(self, pyfunc, precise=True):
+        cfunc = jit(nopython=True)(pyfunc)
+        # Use various sizes, to stress the allocation algorithm
+        for n in [0, 3, 16, 70, 400]:
+            eq = self.assertPreciseEqual if precise else self.assertEqual
+            eq(cfunc(n), pyfunc(n))
+
+    def test_constructor(self):
+        self.check_unary_with_size(list_constructor)
+
+    def test_constructor_empty(self):
+        self.disable_leak_check()
+        cfunc = jit(nopython=True)(list_constructor_empty)
+        with self.assertRaises(errors.TypingError) as raises:
+            cfunc()
+        errmsg = str(raises.exception)
+        self.assertIn("Cannot infer the type of variable", errmsg)
+        self.assertIn("list(undefined)", errmsg)
+        # check error message went in
+        self.assertIn("For Numba to be able to compile a list", errmsg)
+
+    def test_constructor_empty_but_typeable(self):
+        args = [np.int32(1), 10., 1 + 3j, [7], [17., 14.], np.array([10])]
+        pyfunc = list_constructor_empty_but_typeable
+        for arg in args:
+            cfunc = jit(nopython=True)(pyfunc)
+            expected = pyfunc(arg)
+            got = cfunc(arg)
+            self.assertPreciseEqual(got, expected)
+
+    def test_append(self):
+        self.check_unary_with_size(list_append)
+
+    def test_append_heterogeneous(self):
+        self.check_unary_with_size(list_append_heterogeneous, precise=False)
+
+    def test_extend(self):
+        self.check_unary_with_size(list_extend)
+
+    def test_extend_heterogeneous(self):
+        self.check_unary_with_size(list_extend_heterogeneous, precise=False)
+
+    def test_pop0(self):
+        self.check_unary_with_size(list_pop0)
+
+    def test_pop1(self):
+        pyfunc = list_pop1
+        cfunc = jit(nopython=True)(pyfunc)
+        for n in [5, 40]:
+            for i in [0, 1, n - 2, n - 1, -1, -2, -n + 3, -n + 1]:
+                expected = pyfunc(n, i)
+                self.assertPreciseEqual(cfunc(n, i), expected)
+
+    def test_pop_errors(self):
+        # XXX References are leaked when an exception is raised
+        self.disable_leak_check()
+        cfunc = jit(nopython=True)(list_pop1)
+        with self.assertRaises(IndexError) as cm:
+            cfunc(0, 5)
+        self.assertEqual(str(cm.exception), "pop from empty list")
+        with self.assertRaises(IndexError) as cm:
+            cfunc(1, 5)
+        self.assertEqual(str(cm.exception), "pop index out of range")
+
+    def test_insert(self):
+        pyfunc = list_insert
+        cfunc = jit(nopython=True)(pyfunc)
+        for n in [5, 40]:
+            indices = [0, 1, n - 2, n - 1, n + 1, -1, -2, -n + 3, -n - 1]
+            for i in indices:
+                expected = pyfunc(n, i, 42)
+                self.assertPreciseEqual(cfunc(n, i, 42), expected)
+
+    def test_len(self):
+        self.check_unary_with_size(list_len)
+
+    def test_getitem(self):
+        self.check_unary_with_size(list_getitem)
+
+    def test_setitem(self):
+        self.check_unary_with_size(list_setitem)
+
+    def check_slicing2(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        sizes = [5, 40]
+        for n in sizes:
+            indices = [0, 1, n - 2, -1, -2, -n + 3, -n - 1, -n]
+            for start, stop in itertools.product(indices, indices):
+                expected = pyfunc(n, start, stop)
+                self.assertPreciseEqual(cfunc(n, start, stop), expected)
+
+    def test_getslice2(self):
+        self.check_slicing2(list_getslice2)
+
+    def test_setslice2(self):
+        pyfunc = list_setslice2
+        cfunc = jit(nopython=True)(pyfunc)
+        sizes = [5, 40]
+        for n, n_src in itertools.product(sizes, sizes):
+            indices = [0, 1, n - 2, -1, -2, -n + 3, -n - 1, -n]
+            for start, stop in itertools.product(indices, indices):
+                expected = pyfunc(n, n_src, start, stop)
+                self.assertPreciseEqual(cfunc(n, n_src, start, stop), expected)
+
+    def test_getslice3(self):
+        pyfunc = list_getslice3
+        cfunc = jit(nopython=True)(pyfunc)
+        for n in [10]:
+            indices = [0, 1, n - 2, -1, -2, -n + 3, -n - 1, -n]
+            steps = [4, 1, -1, 2, -3]
+            for start, stop, step in itertools.product(indices, indices, steps):
+                expected = pyfunc(n, start, stop, step)
+                self.assertPreciseEqual(cfunc(n, start, stop, step), expected)
+
+    def test_setslice3(self):
+        pyfunc = list_setslice3
+        cfunc = jit(nopython=True)(pyfunc)
+        for n in [10]:
+            indices = [0, 1, n - 2, -1, -2, -n + 3, -n - 1, -n]
+            steps = [4, 1, -1, 2, -3]
+            for start, stop, step in itertools.product(indices, indices, steps):
+                expected = pyfunc(n, start, stop, step)
+                self.assertPreciseEqual(cfunc(n, start, stop, step), expected)
+
+    def test_setslice3_resize(self):
+        # XXX References are leaked when an exception is raised
+        self.disable_leak_check()
+        pyfunc = list_setslice3_arbitrary
+        cfunc = jit(nopython=True)(pyfunc)
+        # step == 1 => can resize
+        cfunc(5, 10, 0, 2, 1)
+        # step != 1 => cannot resize
+        with self.assertRaises(ValueError) as cm:
+            cfunc(5, 100, 0, 3, 2)
+        self.assertIn("cannot resize", str(cm.exception))
+
+    def test_delslice0(self):
+        self.check_unary_with_size(list_delslice0)
+
+    def test_delslice1(self):
+        self.check_slicing2(list_delslice1)
+
+    def test_delslice2(self):
+        self.check_slicing2(list_delslice2)
+
+    def test_invalid_slice(self):
+        self.disable_leak_check()
+        pyfunc = list_getslice3
+        cfunc = jit(nopython=True)(pyfunc)
+        with self.assertRaises(ValueError) as cm:
+            cfunc(10, 1, 2, 0)
+        self.assertEqual(str(cm.exception), "slice step cannot be zero")
+
+    def test_iteration(self):
+        self.check_unary_with_size(list_iteration)
+
+    def test_reverse(self):
+        self.check_unary_with_size(list_reverse)
+
+    def test_contains(self):
+        self.check_unary_with_size(list_contains)
+
+    def check_index_result(self, pyfunc, cfunc, args):
+        try:
+            expected = pyfunc(*args)
+        except ValueError:
+            with self.assertRaises(ValueError):
+                cfunc(*args)
+        else:
+            self.assertPreciseEqual(cfunc(*args), expected)
+
+    def test_index1(self):
+        self.disable_leak_check()
+        pyfunc = list_index1
+        cfunc = jit(nopython=True)(pyfunc)
+        for v in (0, 1, 5, 10, 99999999):
+            self.check_index_result(pyfunc, cfunc, (16, v))
+
+    def test_index2(self):
+        self.disable_leak_check()
+        pyfunc = list_index2
+        cfunc = jit(nopython=True)(pyfunc)
+        n = 16
+        for v in (0, 1, 5, 10, 99999999):
+            indices = [0, 1, n - 2, n - 1, n + 1, -1, -2, -n + 3, -n - 1]
+            for start in indices:
+                self.check_index_result(pyfunc, cfunc, (16, v, start))
+
+    def test_index3(self):
+        self.disable_leak_check()
+        pyfunc = list_index3
+        cfunc = jit(nopython=True)(pyfunc)
+        n = 16
+        for v in (0, 1, 5, 10, 99999999):
+            indices = [0, 1, n - 2, n - 1, n + 1, -1, -2, -n + 3, -n - 1]
+            for start, stop in itertools.product(indices, indices):
+                self.check_index_result(pyfunc, cfunc, (16, v, start, stop))
+
+    def test_remove(self):
+        pyfunc = list_remove
+        cfunc = jit(nopython=True)(pyfunc)
+        n = 16
+        for v in (0, 1, 5, 15):
+            expected = pyfunc(n, v)
+            self.assertPreciseEqual(cfunc(n, v), expected)
+
+    def test_remove_error(self):
+        self.disable_leak_check()
+        pyfunc = list_remove
+        cfunc = jit(nopython=True)(pyfunc)
+        with self.assertRaises(ValueError) as cm:
+            cfunc(10, 42)
+        self.assertEqual(str(cm.exception), "list.remove(x): x not in list")
+
+    def test_count(self):
+        pyfunc = list_count
+        cfunc = jit(nopython=True)(pyfunc)
+        for v in range(5):
+            self.assertPreciseEqual(cfunc(18, v), pyfunc(18, v))
+
+    def test_clear(self):
+        self.check_unary_with_size(list_clear)
+
+    def test_copy(self):
+        self.check_unary_with_size(list_copy)
+
+    def check_add(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        sizes = [0, 3, 50, 300]
+        for m, n in itertools.product(sizes, sizes):
+            expected = pyfunc(m, n)
+            self.assertPreciseEqual(cfunc(m, n), expected)
+
+    def test_add(self):
+        self.check_add(list_add)
+
+    def test_add_heterogeneous(self):
+        pyfunc = list_add_heterogeneous
+        cfunc = jit(nopython=True)(pyfunc)
+        expected = pyfunc()
+        self.assertEqual(cfunc(), expected)
+
+    def test_add_inplace(self):
+        self.check_add(list_add_inplace)
+
+    def test_add_inplace_heterogeneous(self):
+        pyfunc = list_add_inplace_heterogeneous
+        cfunc = jit(nopython=True)(pyfunc)
+        expected = pyfunc()
+        self.assertEqual(cfunc(), expected)
+
+    def check_mul(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        for n in [0, 3, 50, 300]:
+            for v in [1, 2, 3, 0, -1, -42]:
+                expected = pyfunc(n, v)
+                self.assertPreciseEqual(cfunc(n, v), expected)
+
+    def test_mul(self):
+        self.check_mul(list_mul)
+
+    def test_mul2(self):
+        self.check_mul(list_mul2)
+
+    def test_mul_inplace(self):
+        self.check_mul(list_mul_inplace)
+
+    @unittest.skipUnless(sys.maxsize >= 2**32,
+                         "need a 64-bit system to test for MemoryError")
+    def test_mul_error(self):
+        self.disable_leak_check()
+        pyfunc = list_mul
+        cfunc = jit(nopython=True)(pyfunc)
+        # Fail in malloc()
+        with self.assertRaises(MemoryError):
+            cfunc(1, 2**58)
+        if sys.platform.startswith('darwin'):
+            libc = ct.CDLL('libc.dylib')
+            libc.printf("###Please ignore the above error message i.e. \
+can't allocate region. It is in fact the purpose of this test to \
+request more memory than can be provided###\n".encode("UTF-8"))
+        # Overflow size computation when multiplying by item size
+        with self.assertRaises(MemoryError):
+            cfunc(1, 2**62)
+
+    def test_bool(self):
+        pyfunc = list_bool
+        cfunc = jit(nopython=True)(pyfunc)
+        for n in [0, 1, 3]:
+            expected = pyfunc(n)
+            self.assertPreciseEqual(cfunc(n), expected)
+
+    def test_list_passing(self):
+        # Check one can pass a list from a Numba function to another
+        @jit(nopython=True)
+        def inner(lst):
+            return len(lst), lst[-1]
+
+        @jit(nopython=True)
+        def outer(n):
+            l = list(range(n))
+            return inner(l)
+
+        self.assertPreciseEqual(outer(5), (5, 4))
+
+    def _test_compare(self, pyfunc):
+        def eq(args):
+            self.assertIs(cfunc(*args), pyfunc(*args),
+                          "mismatch for arguments %s" % (args,))
+
+        cfunc = jit(nopython=True)(pyfunc)
+        eq(((1, 2), (1, 2)))
+        eq(((1, 2, 3), (1, 2)))
+        eq(((1, 2), (1, 2, 3)))
+        eq(((1, 2, 4), (1, 2, 3)))
+        eq(((1.0, 2.0, 3.0), (1, 2, 3)))
+        eq(((1.0, 2.0, 3.5), (1, 2, 3)))
+
+    def test_eq(self):
+        self._test_compare(eq_usecase)
+
+    def test_ne(self):
+        self._test_compare(ne_usecase)
+
+    def test_le(self):
+        self._test_compare(le_usecase)
+
+    def test_lt(self):
+        self._test_compare(lt_usecase)
+
+    def test_ge(self):
+        self._test_compare(ge_usecase)
+
+    def test_gt(self):
+        self._test_compare(gt_usecase)
+
+    def test_identity(self):
+        pyfunc = identity_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertPreciseEqual(cfunc(3), pyfunc(3))
+
+    def test_bool_list(self):
+        # Check lists of bools compile and run successfully
+        pyfunc = bool_list_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertPreciseEqual(cfunc(), pyfunc())
+
+
+class TestUnboxing(MemoryLeakMixin, TestCase):
+    """
+    Test unboxing of Python lists into native Numba lists.
+    """
+
+    @contextlib.contextmanager
+    def assert_type_error(self, msg):
+        with self.assertRaises(TypeError) as raises:
+            yield
+        if msg is not None:
+            self.assertRegexpMatches(str(raises.exception), msg)
+
+    def check_unary(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(arg):
+            expected = pyfunc(arg)
+            got = cfunc(arg)
+            self.assertPreciseEqual(got, expected)
+        return check
+
+    def test_numbers(self):
+        check = self.check_unary(unbox_usecase)
+        check([1, 2])
+        check([1j, 2.5j])
+
+    def test_tuples(self):
+        check = self.check_unary(unbox_usecase2)
+        check([(1, 2), (3, 4)])
+        check([(1, 2j), (3, 4j)])
+        check([(), (), ()])
+
+    def test_list_inside_tuple(self):
+        check = self.check_unary(unbox_usecase3)
+        check((1, [2, 3, 4]))
+
+    def test_list_of_tuples_inside_tuple(self):
+        check = self.check_unary(unbox_usecase4)
+        check((1, [(2,), (3,)]))
+
+    def test_errors(self):
+        # See #1545 and #1594: error checking should ensure the list is
+        # homogeneous
+        msg = "can't unbox heterogeneous list"
+        pyfunc = noop
+        cfunc = jit(nopython=True)(pyfunc)
+        lst = [1, 2.5]
+        with self.assert_type_error(msg):
+            cfunc(lst)
+        # The list hasn't been changed (bogus reflecting)
+        self.assertEqual(lst, [1, 2.5])
+        with self.assert_type_error(msg):
+            cfunc([1, 2j])
+        # Same when the list is nested in a tuple or namedtuple
+        with self.assert_type_error(msg):
+            cfunc((1, [1, 2j]))
+        with self.assert_type_error(msg):
+            cfunc(Point(1, [1, 2j]))
+        # Issue #1638: tuples of different size.
+        # Note the check is really on the tuple side.
+        lst = [(1,), (2, 3)]
+        with self.assertRaises(TypeError) as raises:
+            cfunc(lst)
+        msg = ("can't unbox heterogeneous list: "
+                "UniTuple({0} x 1) != UniTuple({0} x 2)")
+        self.assertEqual(str(raises.exception), msg.format(types.intp))
+
+
+
+class TestListReflection(MemoryLeakMixin, TestCase):
+    """
+    Test reflection of native Numba lists on Python list objects.
+    """
+
+    def check_reflection(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        samples = [([1., 2., 3., 4.], [0.]),
+                   ([1., 2., 3., 4.], [5., 6., 7., 8., 9.]),
+                   ]
+        for dest, src in samples:
+            expected = list(dest)
+            got = list(dest)
+            pyres = pyfunc(expected, src)
+            with self.assertRefCount(got, src):
+                cres = cfunc(got, src)
+                self.assertPreciseEqual(cres, pyres)
+                self.assertPreciseEqual(expected, got)
+                self.assertEqual(pyres[0] is expected, cres[0] is got)
+                del pyres, cres
+
+    def test_reflect_simple(self):
+        self.check_reflection(reflect_simple)
+
+    def test_reflect_conditional(self):
+        self.check_reflection(reflect_conditional)
+
+    def test_reflect_exception(self):
+        """
+        When the function exits with an exception, lists should still be
+        reflected.
+        """
+        pyfunc = reflect_exception
+        cfunc = jit(nopython=True)(pyfunc)
+        l = [1, 2, 3]
+        with self.assertRefCount(l):
+            with self.assertRaises(ZeroDivisionError):
+                cfunc(l)
+            self.assertPreciseEqual(l, [1, 2, 3, 42])
+
+    def test_reflect_same_list(self):
+        """
+        When the same list object is reflected twice, behaviour should
+        be consistent.
+        """
+        pyfunc = reflect_dual
+        cfunc = jit(nopython=True)(pyfunc)
+        pylist = [1, 2, 3]
+        clist = pylist[:]
+        expected = pyfunc(pylist, pylist)
+        got = cfunc(clist, clist)
+        self.assertPreciseEqual(expected, got)
+        self.assertPreciseEqual(pylist, clist)
+        self.assertPreciseEqual(sys.getrefcount(pylist), sys.getrefcount(clist))
+
+    def test_reflect_clean(self):
+        """
+        When the list wasn't mutated, no reflection should take place.
+        """
+        cfunc = jit(nopython=True)(noop)
+        # Use a complex, as Python integers can be cached
+        l = [12.5j]
+        ids = [id(x) for x in l]
+        cfunc(l)
+        self.assertEqual([id(x) for x in l], ids)
+
+
+class ManagedListTestCase(MemoryLeakMixin, TestCase):
+
+    def assert_list_element_precise_equal(self, expect, got):
+        self.assertEqual(len(expect), len(got))
+        for a, b in zip(expect, got):
+            self.assertPreciseEqual(a, b)
+
+
+class TestListManagedElements(ManagedListTestCase):
+    "Test list containing objects that need refct"
+
+    def _check_element_equal(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        con = [np.arange(3).astype(np.intp), np.arange(5).astype(np.intp)]
+        expect = list(con)
+        pyfunc(expect)
+        got = list(con)
+        cfunc(got)
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+
+    def test_reflect_passthru(self):
+        def pyfunc(con):
+            pass
+        self._check_element_equal(pyfunc)
+
+    def test_reflect_appended(self):
+        def pyfunc(con):
+            con.append(np.arange(10).astype(np.intp))
+
+        self._check_element_equal(pyfunc)
+
+    def test_reflect_setitem(self):
+        def pyfunc(con):
+            con[1] = np.arange(10)
+
+        self._check_element_equal(pyfunc)
+
+    def test_reflect_popped(self):
+        def pyfunc(con):
+            con.pop()
+
+        self._check_element_equal(pyfunc)
+
+    def test_reflect_insert(self):
+        """make sure list.insert() doesn't crash for refcounted objects (see #7553)
+        """
+
+        def pyfunc(con):
+            con.insert(1, np.arange(4).astype(np.intp))
+
+        self._check_element_equal(pyfunc)
+
+    def test_append(self):
+        def pyfunc():
+            con = []
+            for i in range(300):
+                con.append(np.arange(i, ).astype(np.intp))
+            return con
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+
+    def test_append_noret(self):
+        # This test make sure local dtor works
+        def pyfunc():
+            con = []
+            for i in range(300):
+                con.append(np.arange(i))
+            c = 0.0
+            for arr in con:
+                c += arr.sum() / (1 + arr.size)
+            return c
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assertEqual(expect, got)
+
+    def test_reassign_refct(self):
+        def pyfunc():
+            con = []
+            for i in range(5):
+                con.append(np.arange(2))
+            con[0] = np.arange(4)
+            return con
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+
+    def test_get_slice(self):
+        def pyfunc():
+            con = []
+            for i in range(5):
+                con.append(np.arange(2))
+            return con[2:4]
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+
+    def test_set_slice(self):
+        def pyfunc():
+            con = []
+            for i in range(5):
+                con.append(np.arange(2))
+            con[1:3] = con[2:4]
+            return con
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+
+    def test_pop(self):
+        def pyfunc():
+            con = []
+            for i in range(20):
+                con.append(np.arange(i + 1))
+            while len(con) > 2:
+                con.pop()
+            return con
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+
+    def test_pop_loc(self):
+        def pyfunc():
+            con = []
+            for i in range(1000):
+                con.append(np.arange(i + 1))
+            while len(con) > 2:
+                con.pop(1)
+            return con
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+
+    def test_del_range(self):
+        def pyfunc():
+            con = []
+            for i in range(20):
+                con.append(np.arange(i + 1))
+            del con[3:10]
+            return con
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+
+    def test_list_of_list(self):
+        def pyfunc():
+            con = []
+            for i in range(10):
+                con.append([0] * i)
+            return con
+
+        cfunc = jit(nopython=True)(pyfunc)
+        expect = pyfunc()
+        got = cfunc()
+
+        self.assertEqual(expect, got)
+
+
+
+def expect_reflection_failure(fn):
+    def wrapped(self, *args, **kwargs):
+        self.disable_leak_check()
+        with self.assertRaises(TypeError) as raises:
+            fn(self, *args, **kwargs)
+        expect_msg = 'cannot reflect element of reflected container'
+        self.assertIn(expect_msg, str(raises.exception))
+
+    return wrapped
+
+
+class TestListOfList(ManagedListTestCase):
+
+    def compile_and_test(self, pyfunc, *args):
+        from copy import deepcopy
+        expect_args = deepcopy(args)
+        expect = pyfunc(*expect_args)
+
+        njit_args = deepcopy(args)
+        cfunc = jit(nopython=True)(pyfunc)
+        got = cfunc(*njit_args)
+
+        self.assert_list_element_precise_equal(
+            expect=expect, got=got
+            )
+        # Check reflection
+        self.assert_list_element_precise_equal(
+            expect=expect_args, got=njit_args
+            )
+
+    def test_returning_list_of_list(self):
+        def pyfunc():
+            a = [[np.arange(i)] for i in range(4)]
+            return a
+
+        self.compile_and_test(pyfunc)
+
+    @expect_reflection_failure
+    def test_heterogeneous_list_error(self):
+        def pyfunc(x):
+            return x[1]
+
+        cfunc = jit(nopython=True)(pyfunc)
+        l2 = [[np.zeros(i) for i in range(5)],
+              [np.ones(i)+1j for i in range(5)]]
+        l3 = [[np.zeros(i) for i in range(5)], [(1,)]]
+        l4 = [[1], [{1}]]
+        l5 = [[1], [{'a': 1}]]
+
+        # TODO: this triggers a reflection error.
+        # Remove this line when nested reflection is supported
+        cfunc(l2)
+
+        # error_cases
+        with self.assertRaises(TypeError) as raises:
+            cfunc(l2)
+
+        self.assertIn(
+            ("reflected list(array(float64, 1d, C)) != "
+             "reflected list(array(complex128, 1d, C))"),
+            str(raises.exception)
+            )
+
+        with self.assertRaises(TypeError) as raises:
+            cfunc(l3)
+
+        self.assertIn(
+            ("reflected list(array(float64, 1d, C)) != "
+             "reflected list((int64 x 1))"),
+            str(raises.exception)
+            )
+
+        with self.assertRaises(TypeError) as raises:
+            cfunc(l4)
+        self.assertIn(
+            "reflected list(int64) != reflected list(reflected set(int64))",
+            str(raises.exception)
+            )
+
+        with self.assertRaises(ValueError) as raises:
+            cfunc(l5)
+        self.assertIn(
+            "Cannot type list element of <class 'dict'>",
+            str(raises.exception)
+            )
+
+    @expect_reflection_failure
+    def test_list_of_list_reflected(self):
+        def pyfunc(l1, l2):
+            l1.append(l2)
+            l1[-1].append(123)
+
+        cfunc = jit(nopython=True)(pyfunc)
+        l1 = [[0, 1], [2, 3]]
+        l2 = [4, 5]
+        expect = list(l1), list(l2)
+        got = list(l1), list(l2)
+        pyfunc(*expect)
+        cfunc(*got)
+        self.assertEqual(expect, got)
+
+    @expect_reflection_failure
+    def test_heterogeneous_list(self):
+        def pyfunc(x):
+            return x[1]
+
+        l1 = [[np.zeros(i) for i in range(5)], [np.ones(i) for i in range(5)]]
+
+        cfunc = jit(nopython=True)(pyfunc)
+        l1_got = cfunc(l1)
+        self.assertPreciseEqual(pyfunc(l1), l1_got)
+
+    @expect_reflection_failure
+    def test_c01(self):
+        def bar(x):
+            return x.pop()
+
+        r = [[np.zeros(0)], [np.zeros(10)*1j]]
+        # TODO: this triggers a reflection error.
+        # Remove this line when nested reflection is supported
+        self.compile_and_test(bar, r)
+
+        with self.assertRaises(TypeError) as raises:
+            self.compile_and_test(bar, r)
+        self.assertIn(
+            ("reflected list(array(float64, 1d, C)) != "
+             "reflected list(array(complex128, 1d, C))"),
+            str(raises.exception),
+            )
+
+    def test_c02(self):
+        def bar(x):
+            x.append(x)
+            return x
+
+        r = [[np.zeros(0)]]
+
+        with self.assertRaises(errors.TypingError) as raises:
+            self.compile_and_test(bar, r)
+        self.assertIn(
+            "Invalid use of BoundFunction(list.append",
+            str(raises.exception),
+            )
+
+    def test_c03(self):
+        def bar(x):
+            f = x
+            f[0] = 1
+            return f
+
+        r = [[np.arange(3)]]
+
+        with self.assertRaises(errors.TypingError) as raises:
+            self.compile_and_test(bar, r)
+        self.assertIn(
+            "invalid setitem with value of {} to element of {}".format(
+                typeof(1),
+                typeof(r[0]),
+                ),
+            str(raises.exception),
+        )
+
+    def test_c04(self):
+        def bar(x):
+            f = x
+            f[0][0] = 10
+            return f
+
+        r = [[np.arange(3)]]
+        with self.assertRaises(errors.TypingError) as raises:
+            self.compile_and_test(bar, r)
+        self.assertIn(
+            "invalid setitem with value of {} to element of {}".format(
+                typeof(10),
+                typeof(r[0][0]),
+                ),
+            str(raises.exception),
+            )
+
+    @expect_reflection_failure
+    def test_c05(self):
+        def bar(x):
+            f = x
+            f[0][0] = np.array([x for x in np.arange(10).astype(np.intp)])
+            return f
+
+        r = [[np.arange(3).astype(np.intp)]]
+        self.compile_and_test(bar, r)
+
+    def test_c06(self):
+        def bar(x):
+            f = x
+            f[0][0] = np.array([x + 1j for x in np.arange(10)])
+            return f
+
+        r = [[np.arange(3)]]
+        with self.assertRaises(errors.TypingError) as raises:
+            self.compile_and_test(bar, r)
+        self.assertIn("invalid setitem with value", str(raises.exception))
+
+    @expect_reflection_failure
+    def test_c07(self):
+        self.disable_leak_check()
+
+        def bar(x):
+            return x[-7]
+
+        r = [[np.arange(3)]]
+        cfunc = jit(nopython=True)(bar)
+        with self.assertRaises(IndexError) as raises:
+            cfunc(r)
+        self.assertIn("getitem out of range", str(raises.exception))
+
+    def test_c08(self):
+        self.disable_leak_check()
+
+        def bar(x):
+            x[5] = 7
+            return x
+
+        r = [1, 2, 3]
+        cfunc = jit(nopython=True)(bar)
+        with self.assertRaises(IndexError) as raises:
+            cfunc(r)
+        self.assertIn("setitem out of range", str(raises.exception))
+
+    def test_c09(self):
+        def bar(x):
+            x[-2] = 7j
+            return x
+
+        r = [1, 2, 3]
+        with self.assertRaises(errors.TypingError) as raises:
+            self.compile_and_test(bar, r)
+        self.assertIn("invalid setitem with value", str(raises.exception))
+
+    @expect_reflection_failure
+    def test_c10(self):
+        def bar(x):
+            x[0], x[1] = x[1], x[0]
+            return x
+
+        r = [[1, 2, 3], [4, 5, 6]]
+        self.compile_and_test(bar, r)
+
+    @expect_reflection_failure
+    def test_c11(self):
+        def bar(x):
+            x[:] = x[::-1]
+            return x
+
+        r = [[1, 2, 3], [4, 5, 6]]
+        self.compile_and_test(bar, r)
+
+    def test_c12(self):
+        def bar(x):
+            del x[-1]
+            return x
+
+        r = [x for x in range(10)]
+        self.compile_and_test(bar, r)
+
+
+class Item(object):
+    def __init__(self, many, scalar):
+        self.many = many
+        self.scalar = scalar
+
+
+class Container(object):
+    def __init__(self, n):
+        self.data = [[np.arange(i).astype(np.float64)] for i in range(n)]
+
+    def more(self, n):
+        for i in range(n):
+            self.data.append([np.arange(i).astype(np.float64)])
+
+
+class TestListAndJitClasses(ManagedListTestCase):
+    def make_jitclass_element(self):
+        spec = [
+            ('many', types.float64[:]),
+            ('scalar', types.float64),
+        ]
+        JCItem = jitclass(spec)(Item)
+        return JCItem
+
+    def make_jitclass_container(self):
+        spec = {
+            'data': types.List(dtype=types.List(types.float64[::1])),
+        }
+        JCContainer = jitclass(spec)(Container)
+        return JCContainer
+
+    def assert_list_element_with_tester(self, tester, expect, got):
+        for x, y in zip(expect, got):
+            tester(x, y)
+
+    def test_jitclass_instance_elements(self):
+        JCItem = self.make_jitclass_element()
+
+        def pyfunc(xs):
+            xs[1], xs[0] = xs[0], xs[1]
+            return xs
+
+        def eq(x, y):
+            self.assertPreciseEqual(x.many, y.many)
+            self.assertPreciseEqual(x.scalar, y.scalar)
+
+        cfunc = jit(nopython=True)(pyfunc)
+
+        arg = [JCItem(many=np.random.random(n + 1), scalar=n * 1.2)
+               for n in range(5)]
+
+        expect_arg = list(arg)
+        got_arg = list(arg)
+
+        expect_res = pyfunc(expect_arg)
+        got_res = cfunc(got_arg)
+
+        self.assert_list_element_with_tester(eq, expect_arg, got_arg)
+        self.assert_list_element_with_tester(eq, expect_res, got_res)
+
+    def test_jitclass_containing_list(self):
+        JCContainer = self.make_jitclass_container()
+
+        expect = Container(n=4)
+        got = JCContainer(n=4)
+        self.assert_list_element_precise_equal(got.data, expect.data)
+        expect.more(3)
+        got.more(3)
+        self.assert_list_element_precise_equal(got.data, expect.data)
+
+
+class TestListInitialValues(MemoryLeakMixin, TestCase):
+    """Tests that lists carry their initial value if present"""
+
+    def test_homogeneous_and_literal(self):
+        def bar(l):
+            ...
+
+        @overload(bar)
+        def ol_bar(l):
+            if l.initial_value is None:
+                return lambda l: literally(l)
+            self.assertTrue(isinstance(l, types.List))
+            self.assertEqual(l.initial_value, [1, 2, 3])
+            self.assertEqual(hasattr(l, 'literal_value'), False)
+            return lambda l: l
+
+        @njit
+        def foo():
+            # keys and values all have literal representation
+            x = [1, 2, 3]
+            bar(x)
+
+        foo()
+
+    def test_heterogeneous_but_castable_to_homogeneous(self):
+        def bar(l):
+            ...
+
+        @overload(bar)
+        def ol_bar(l):
+            self.assertTrue(isinstance(l, types.List))
+            self.assertEqual(l.initial_value, None)
+            self.assertEqual(hasattr(l, 'literal_value'), False)
+            return lambda l: l
+
+        @njit
+        def foo():
+            # This list will be typed based on 1j, i.e. complex128
+            # as the values are not all literals, there's no "initial_value"
+            # available irrespective of whether it's possible to rip this
+            # information out of the bytecode.
+            x = [1j, 2, 3]
+            bar(x)
+
+        foo()
+
+    def test_mutation_not_carried(self):
+        def bar(d):
+            ...
+
+        @overload(bar)
+        def ol_bar(d):
+            if d.initial_value is None:
+                return lambda d: literally(d)
+            self.assertTrue(isinstance(d, types.List))
+            self.assertEqual(d.initial_value, [1, 2, 3])
+            return lambda d: d
+
+        @njit
+        def foo():
+            # This list is mutated, check the initial_value carries
+            # correctly and is not mutated
+            x = [1, 2, 3]
+            x.append(4)
+            bar(x)
+
+        foo()
+
+    def test_mutation_not_carried_single_function(self):
+        # this is another pattern for using literally
+
+        @njit
+        def nop(*args):
+            pass
+
+        for fn, iv in (nop, None), (literally, [1, 2, 3]):
+            @njit
+            def baz(x):
+                pass
+
+            def bar(z):
+                pass
+
+            @overload(bar)
+            def ol_bar(z):
+                def impl(z):
+                    fn(z)
+                    baz(z)
+                return impl
+
+            @njit
+            def foo():
+                x = [1, 2, 3]
+                bar(x)
+                x.append(2)
+                return x
+
+            foo()
+            # baz should be specialised based on literally being invoked and
+            # the literal/unliteral arriving at the call site
+            larg = baz.signatures[0][0]
+            self.assertEqual(larg.initial_value, iv)
+
+    def test_list_of_list_ctor(self):
+        # see issue 6082
+        @njit
+        def bar(x):
+            pass
+
+        @njit
+        def foo():
+            x = [[1, 2, 3, 4, 5], [1, 2, 3, 4, 6]]
+            bar(x)
+
+        foo()
+        larg = bar.signatures[0][0]
+        self.assertEqual(larg.initial_value, None)
+        self.assertEqual(larg.dtype.initial_value, None)
+
+
+class TestLiteralLists(MemoryLeakMixin, TestCase):
+
+    def test_basic_compile(self):
+        @njit
+        def foo():
+            l = [1, 'a']
+
+        foo()
+
+    def test_literal_value_passthrough(self):
+
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            self.assertTrue(isinstance(x, types.LiteralList))
+            lv = x.literal_value
+            self.assertTrue(isinstance(lv, list))
+            self.assertEqual(lv[0], types.literal(1))
+            self.assertEqual(lv[1], types.literal('a'))
+            self.assertEqual(lv[2], types.Array(types.float64, 1, 'C'))
+            self.assertEqual(lv[3], types.List(types.intp, reflected=False,
+                                               initial_value=[1, 2, 3]))
+            self.assertTrue(isinstance(lv[4], types.LiteralList))
+            self.assertEqual(lv[4].literal_value[0], types.literal('cat'))
+            self.assertEqual(lv[4].literal_value[1], types.literal(10))
+            return lambda x: x
+
+        @njit
+        def foo():
+            otherhomogeneouslist = [1, 2, 3]
+            otherheterogeneouslist = ['cat', 10]
+            zeros = np.zeros(5)
+            l = [1, 'a', zeros, otherhomogeneouslist, otherheterogeneouslist]
+            bar(l)
+
+        foo()
+
+    def test_literal_value_involved_passthrough(self):
+
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            self.assertTrue(isinstance(x, types.LiteralStrKeyDict))
+            dlv = x.literal_value
+            inner_literal = {types.literal('g'): types.literal('h'),
+                             types.literal('i'):
+                                 types.Array(types.float64, 1, 'C')}
+            inner_dict = types.LiteralStrKeyDict(inner_literal)
+            outer_literal = {types.literal('a'):
+                            types.LiteralList([types.literal(1),
+                                               types.literal('a'),
+                                               types.DictType(
+                                                   types.unicode_type,
+                                                   types.intp,
+                                                   initial_value={'f': 1}),
+                                               inner_dict]),
+                        types.literal('b'): types.literal(2),
+                        types.literal('c'): types.List(types.complex128,
+                                                       reflected=False)}
+            def check_same(a, b):
+                if (isinstance(a, types.LiteralList) and
+                    isinstance(b, types.LiteralList)):
+                    for i, j in zip(a.literal_value, b.literal_value):
+                        check_same(a.literal_value, b.literal_value)
+                elif (isinstance(a, list) and
+                     isinstance(b, list)):
+                    for i, j in zip(a, b):
+                        check_same(i, j)
+                elif (isinstance(a, types.LiteralStrKeyDict) and
+                      isinstance(b, types.LiteralStrKeyDict)):
+                    for (ki, vi), (kj, vj) in zip(a.literal_value.items(),
+                                                  b.literal_value.items()):
+                        check_same(ki, kj)
+                        check_same(vi, vj)
+                elif (isinstance(a, dict) and
+                      isinstance(b, dict)):
+                    for (ki, vi), (kj, vj) in zip(a.items(), b.items()):
+                        check_same(ki, kj)
+                        check_same(vi, vj)
+                else:
+                    self.assertEqual(a, b)
+            check_same(dlv, outer_literal)
+            return lambda x: x
+
+        @njit
+        def foo():
+            # this stretches what's possible with LiteralStrKeyDict and
+            # LiteralList, it's nested and contains typed.Dict and np arrays
+            # as well as more constant type things.
+            l = {'a': [1, 'a', {'f': 1}, {'g': 'h', 'i': np.zeros(5)}], 'b': 2,
+                 'c': [1j, 2j, 3j]}
+            bar(l)
+
+        foo()
+
+    def test_mutation_failure(self):
+
+        def staticsetitem():
+            l = ['a', 1]
+            l[0] = 'b'
+
+        def delitem():
+            l = ['a', 1]
+            del l[0]
+
+        def append():
+            l = ['a', 1]
+            l.append(2j)
+
+        def extend():
+            l = ['a', 1]
+            l.extend([2j, 3j])
+
+        def insert():
+            l = ['a', 1]
+            l.insert(0, 2j)
+
+        def remove():
+            l = ['a', 1]
+            l.remove('a')
+
+        def pop():
+            l = ['a', 1]
+            l.pop()
+
+        def clear():
+            l = ['a', 1]
+            l.clear()
+
+        def sort():
+            l = ['a', 1]
+            l.sort()
+
+        def reverse():
+            l = ['a', 1]
+            l.reverse()
+
+        illegals = (staticsetitem, delitem, append, extend, insert, remove, pop,
+                    clear, sort, reverse)
+
+        for test in illegals:
+            with self.subTest(test.__name__):
+                with self.assertRaises(errors.TypingError) as raises:
+                    njit(test)()
+                expect = "Cannot mutate a literal list"
+                self.assertIn(expect, str(raises.exception))
+
+    def test_count(self):
+        @njit
+        def foo():
+            l = ['a', 1, 'a', 2, 'a', 3, 'b', 4, 'b', 5, 'c']
+            r = []
+            for x in 'abc':
+                r.append(l.count(x))
+            return r
+
+        self.assertEqual(foo.py_func(), foo())
+
+    def test_len(self):
+        @njit
+        def foo():
+            l = ['a', 1, 'a', 2, 'a', 3, 'b', 4, 'b', 5, 'c']
+            return len(l)
+
+        self.assertEqual(foo.py_func(), foo())
+
+    def test_contains(self):
+        @njit
+        def foo():
+            l = ['a', 1, 'a', 2, 'a', 3, 'b', 4, 'b', 5, 'c']
+            r = []
+            for x in literal_unroll(('a', 'd', 2, 6)):
+                r.append(x in l)
+            return r
+
+        self.assertEqual(foo.py_func(), foo())
+
+    def test_getitem(self):
+
+        @njit
+        def foo(x):
+            l = ['a', 1]
+            return l[x]
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo(0)
+        expect = "Cannot __getitem__ on a literal list"
+        self.assertIn(expect, str(raises.exception))
+
+    def test_staticgetitem(self):
+
+        @njit
+        def foo():
+            l = ['a', 1]
+            return l[0], l[1]
+
+        self.assertEqual(foo.py_func(), foo())
+
+    def test_staticgetitem_slice(self):
+        # this is forbidden by typing as there's no way to serialize a list of
+        # any kind as required by returning a (static) slice of a LiteralList
+        @njit
+        def foo():
+            l = ['a', 'b', 1]
+            return l[:2]
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+        expect = "Cannot __getitem__ on a literal list"
+        self.assertIn(expect, str(raises.exception))
+
+    def test_setitem(self):
+
+        @njit
+        def foo(x):
+            l = ['a', 1]
+            l[x] = 'b'
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo(0)
+        expect = "Cannot mutate a literal list"
+        self.assertIn(expect, str(raises.exception))
+
+    def test_unify(self):
+
+        @njit
+        def foo(x):
+            if x + 1 > 3:
+                l = ['a', 1]
+            else:
+                l = ['b', 2]
+            return l[0]
+
+        for x in (-100, 100):
+            self.assertEqual(foo.py_func(x), foo(x))
+
+    def test_not_unify(self):
+
+        @njit
+        def foo(x):
+            if x + 1 > 3:
+                l = ['a', 1, 2j]
+            else:
+                l = ['b', 2]
+            return l[0], l[1], l[0], l[1] # defeat py310 inliner
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo(100)
+        expect = "Cannot unify LiteralList"
+        self.assertIn(expect, str(raises.exception))
+
+    def test_index(self):
+        @njit
+        def foo():
+            l = ['a', 1]
+            l.index('a')
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+        expect = "list.index is unsupported for literal lists"
+        self.assertIn(expect, str(raises.exception))
+
+    def test_copy(self):
+        @njit
+        def foo():
+            l = ['a', 1].copy()
+            return l[0], l[1]
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_tuple_not_in_mro(self):
+        # Related to #6094, make sure that LiteralList does not inherit from
+        # types.BaseTuple as this breaks isinstance checks.
+        def bar(x):
+            pass
+
+        @overload(bar)
+        def ol_bar(x):
+            self.assertFalse(isinstance(x, types.BaseTuple))
+            self.assertTrue(isinstance(x, types.LiteralList))
+            return lambda x: ...
+
+        @njit
+        def foo():
+            l = ['a', 1]
+            bar(l)
+
+        foo()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_literal_dispatch.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_literal_dispatch.py
new file mode 100644
index 000000000..6d759f06e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_literal_dispatch.py
@@ -0,0 +1,400 @@
+import numpy as np
+
+import numba
+import unittest
+from numba.tests.support import TestCase
+from numba import njit
+from numba.core import types, errors, cgutils
+from numba.core.typing import signature
+from numba.core.datamodel import models
+from numba.core.extending import (
+    overload, SentryLiteralArgs, overload_method, register_model, intrinsic,
+)
+from numba.misc.special import literally
+
+
+class TestLiteralDispatch(TestCase):
+    def check_literal_basic(self, literal_args):
+        @njit
+        def foo(x):
+            return literally(x)
+
+        # Test with int
+        for lit in literal_args:
+            self.assertEqual(foo(lit), lit)
+
+        for lit, sig in zip(literal_args, foo.signatures):
+            self.assertEqual(sig[0].literal_value, lit)
+
+    def test_literal_basic(self):
+        self.check_literal_basic([123, 321])
+        self.check_literal_basic(["abc", "cb123"])
+
+    def test_literal_nested(self):
+        @njit
+        def foo(x):
+            return literally(x) * 2
+
+        @njit
+        def bar(y, x):
+            return foo(y) + x
+
+        y, x = 3, 7
+        self.assertEqual(bar(y, x), y * 2 + x)
+        [foo_sig] = foo.signatures
+        self.assertEqual(foo_sig[0], types.literal(y))
+        [bar_sig] = bar.signatures
+        self.assertEqual(bar_sig[0], types.literal(y))
+        self.assertNotIsInstance(bar_sig[1], types.Literal)
+
+    def test_literally_freevar(self):
+        # Try referring to numba.literally not in the globals
+        import numba
+
+        @njit
+        def foo(x):
+            return numba.literally(x)
+
+        self.assertEqual(foo(123), 123)
+        self.assertEqual(foo.signatures[0][0], types.literal(123))
+
+    def test_mutual_recursion_literal(self):
+        def get_functions(decor):
+            @decor
+            def outer_fac(n, value):
+                if n < 1:
+                    return value
+                return n * inner_fac(n - 1, value)
+
+            @decor
+            def inner_fac(n, value):
+                if n < 1:
+                    return literally(value)
+                return n * outer_fac(n - 1, value)
+
+            return outer_fac, inner_fac
+
+        ref_outer_fac, ref_inner_fac = get_functions(lambda x: x)
+        outer_fac, inner_fac = get_functions(njit)
+
+        self.assertEqual(outer_fac(10, 12), ref_outer_fac(10, 12))
+        self.assertEqual(outer_fac.signatures[0][1].literal_value, 12)
+        self.assertEqual(inner_fac.signatures[0][1].literal_value, 12)
+
+        self.assertEqual(inner_fac(11, 13), ref_inner_fac(11, 13))
+        self.assertEqual(outer_fac.signatures[1][1].literal_value, 13)
+        self.assertEqual(inner_fac.signatures[1][1].literal_value, 13)
+
+    def test_literal_nested_multi_arg(self):
+        @njit
+        def foo(a, b, c):
+            return inner(a, c)
+
+        @njit
+        def inner(x, y):
+            return x + literally(y)
+
+        kwargs = dict(a=1, b=2, c=3)
+        got = foo(**kwargs)
+        expect = (lambda a, b, c: a + c)(**kwargs)
+        self.assertEqual(got, expect)
+        [foo_sig] = foo.signatures
+        self.assertEqual(foo_sig[2], types.literal(3))
+
+    def test_unsupported_literal_type(self):
+        @njit
+        def foo(a, b, c):
+            return inner(a, c)
+
+        @njit
+        def inner(x, y):
+            return x + literally(y)
+
+        arr = np.arange(10)
+        with self.assertRaises(errors.LiteralTypingError) as raises:
+            foo(a=1, b=2, c=arr)
+        self.assertIn("numpy.ndarray", str(raises.exception))
+
+    def test_biliteral(self):
+        # Test usecase with more than one literal call
+        @njit
+        def foo(a, b, c):
+            return inner(a, b) + inner(b, c)
+
+        @njit
+        def inner(x, y):
+            return x + literally(y)
+
+        kwargs = dict(a=1, b=2, c=3)
+        got = foo(**kwargs)
+        expect = (lambda a, b, c: a + b + b + c)(**kwargs)
+        self.assertEqual(got, expect)
+        [(type_a, type_b, type_c)] = foo.signatures
+        self.assertNotIsInstance(type_a, types.Literal)
+        self.assertIsInstance(type_b, types.Literal)
+        self.assertEqual(type_b.literal_value, 2)
+        self.assertIsInstance(type_c, types.Literal)
+        self.assertEqual(type_c.literal_value, 3)
+
+    def test_literally_varargs(self):
+        @njit
+        def foo(a, *args):
+            return literally(args)
+
+        with self.assertRaises(errors.LiteralTypingError):
+            foo(1, 2, 3)
+
+        @njit
+        def bar(a, b):
+            foo(a, b)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            bar(1, 2)
+        self.assertIn(
+            "Cannot request literal type",
+            str(raises.exception),
+        )
+
+    @unittest.expectedFailure
+    def test_literally_defaults(self):
+        # Problem with OmittedArg
+        @njit
+        def foo(a, b=1):
+            return (a, literally(b))
+        foo(a=1)
+
+    @unittest.expectedFailure
+    def test_literally_defaults_inner(self):
+        # Problem with Omitted
+        @njit
+        def foo(a, b=1):
+            return (a, literally(b))
+
+        @njit
+        def bar(a):
+            return foo(a) + 1
+
+        bar(1)
+
+    def test_literally_from_module(self):
+        # Problem with Omitted
+        @njit
+        def foo(x):
+            return numba.literally(x)
+
+        got = foo(123)
+        self.assertEqual(got, foo.py_func(123))
+        self.assertIsInstance(foo.signatures[0][0], types.Literal)
+
+    def test_non_literal(self):
+        @njit
+        def foo(a, b):
+            return literally(1 + a)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo(1, 2)
+        self.assertIn(
+            "Invalid use of non-Literal type",
+            str(raises.exception),
+        )
+
+    def test_inlined_literal(self):
+        # Check that literally accepts inlined literal
+        @njit
+        def foo(a, b):
+            v = 1000
+            return a + literally(v) + literally(b)
+
+        got = foo(1, 2)
+        self.assertEqual(got, foo.py_func(1, 2))
+
+        @njit
+        def bar():
+            a = 100
+            b = 9
+            return foo(a=b, b=a)
+
+        got = bar()
+        self.assertEqual(got, bar.py_func())
+
+    def test_aliased_variable(self):
+        @njit
+        def foo(a, b, c):
+            def closure(d):
+                return literally(d) + 10 * inner(a, b)
+            # The inlining of the closure will create an alias to c
+            return closure(c)
+
+        @njit
+        def inner(x, y):
+            return x + literally(y)
+
+        kwargs = dict(a=1, b=2, c=3)
+        got = foo(**kwargs)
+        expect = (lambda a, b, c: c + 10 * (a + b))(**kwargs)
+        self.assertEqual(got, expect)
+        [(type_a, type_b, type_c)] = foo.signatures
+        self.assertNotIsInstance(type_a, types.Literal)
+        self.assertIsInstance(type_b, types.Literal)
+        self.assertEqual(type_b.literal_value, 2)
+        self.assertIsInstance(type_c, types.Literal)
+        self.assertEqual(type_c.literal_value, 3)
+
+    def test_overload_explicit(self):
+        # This test represents a more controlled usage with ensuring literal
+        # typing for an argument.
+        def do_this(x, y):
+            return x + y
+
+        @overload(do_this)
+        def ov_do_this(x, y):
+            SentryLiteralArgs(['x']).for_function(ov_do_this).bind(x, y)
+            return lambda x, y: x + y
+
+        @njit
+        def foo(a, b):
+            return do_this(a, b)
+
+        a = 123
+        b = 321
+        r = foo(a, b)
+        self.assertEqual(r, a + b)
+        [type_a, type_b] = foo.signatures[0]
+        self.assertIsInstance(type_a, types.Literal)
+        self.assertEqual(type_a.literal_value, a)
+        self.assertNotIsInstance(type_b, types.Literal)
+
+    def test_overload_implicit(self):
+        # This test represents the preferred usage style for using literally
+        # in overload. Here, literally() is used inside the "implementation"
+        # function of the overload.
+        def do_this(x, y):
+            return x + y
+
+        @njit
+        def hidden(x, y):
+            return literally(x) + y
+
+        @overload(do_this)
+        def ov_do_this(x, y):
+            if isinstance(x, types.Integer):
+                # At this point, `x` can be a literal or not
+                return lambda x, y: hidden(x, y)
+
+        @njit
+        def foo(a, b):
+            return do_this(a, b)
+
+        a = 123
+        b = 321
+        r = foo(a, b)
+        self.assertEqual(r, a + b)
+        [type_a, type_b] = foo.signatures[0]
+        self.assertIsInstance(type_a, types.Literal)
+        self.assertEqual(type_a.literal_value, a)
+        self.assertNotIsInstance(type_b, types.Literal)
+
+    def test_overload_error_loop(self):
+        # Test a case where a infinite compiling loop is caused because a
+        # literal type is requested but an error would raise for the
+        # literal-ized code path. This causes the overload resolution to
+        # retry by "de-literal-izing" the values.
+        def do_this(x, y):
+            return x + y
+
+        @njit
+        def hidden(x, y):
+            return literally(x) + y
+
+        @overload(do_this)
+        def ov_do_this(x, y):
+            if isinstance(y, types.IntegerLiteral):
+                # This error is however suppressed because a non-literal
+                # version is valid.
+                raise errors.NumbaValueError("oops")
+            else:
+                def impl(x, y):
+                    return hidden(x, y)
+                return impl
+
+        @njit
+        def foo(a, b):
+            return do_this(a, literally(b))
+
+        # Expect raising CompilerError to stop re-compiling with duplicated
+        # literal typing request.
+        with self.assertRaises(errors.CompilerError) as raises:
+            foo(a=123, b=321)
+        self.assertIn("Repeated literal typing request",
+                      str(raises.exception))
+
+
+class TestLiteralDispatchWithCustomType(TestCase):
+    def make_dummy_type(self):
+        class Dummy(object):
+            def lit(self, a):
+                return a
+
+        class DummyType(types.Type):
+            def __init__(self):
+                super(DummyType, self).__init__(name="dummy")
+
+        @register_model(DummyType)
+        class DummyTypeModel(models.StructModel):
+            def __init__(self, dmm, fe_type):
+                members = []
+                super(DummyTypeModel, self).__init__(dmm, fe_type, members)
+
+        @intrinsic
+        def init_dummy(typingctx):
+            def codegen(context, builder, signature, args):
+                dummy = cgutils.create_struct_proxy(
+                    signature.return_type)(context, builder)
+
+                return dummy._getvalue()
+
+            sig = signature(DummyType())
+            return sig, codegen
+
+        @overload(Dummy)
+        def dummy_overload():
+            def ctor():
+                return init_dummy()
+
+            return ctor
+
+        return (DummyType, Dummy)
+
+    def test_overload_method(self):
+        # from issue #5011
+        DummyType, Dummy = self.make_dummy_type()
+
+        @overload_method(DummyType, 'lit')
+        def lit_overload(self, a):
+            def impl(self, a):
+                return literally(a)  # <-- using literally here
+
+            return impl
+
+        @njit
+        def test_impl(a):
+            d = Dummy()
+
+            return d.lit(a)
+
+        # Successful case
+        self.assertEqual(test_impl(5), 5)
+
+        # Failing case
+        @njit
+        def inside(a):
+            return test_impl(a + 1)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            inside(4)
+
+        self.assertIn("Cannot request literal type.", str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_llvm_pass_timings.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_llvm_pass_timings.py
new file mode 100644
index 000000000..cdb128fb3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_llvm_pass_timings.py
@@ -0,0 +1,124 @@
+import unittest
+
+from numba import njit
+from numba.tests.support import TestCase, override_config
+from numba.misc import llvm_pass_timings as lpt
+
+
+timings_raw1 = """
+===-------------------------------------------------------------------------===
+                      ... Pass execution timing report ...
+===-------------------------------------------------------------------------===
+  Total Execution Time: 0.0001 seconds (0.0001 wall clock)
+
+   ---User Time---   --System Time--   --User+System--   ---Wall Time---  --- Name ---
+   0.0001 ( 90.1%)   0.0001 ( 90.1%)   0.0001 ( 90.1%)   0.0001 ( 90.1%)  A1
+   0.0000 (  9.9%)   0.0000 (  9.9%)   0.0000 (  9.9%)   0.0000 (  9.9%)  A2
+   0.0001 (100.0%)   0.0001 (100.0%)   0.0001 (100.0%)   0.0001 (100.0%)  Total
+
+"""    # noqa: E501
+
+timings_raw2 = """
+===-------------------------------------------------------------------------===
+                      ... Pass execution timing report ...
+===-------------------------------------------------------------------------===
+  Total Execution Time: 0.0001 seconds (0.0001 wall clock)
+
+   ---User Time---   --System Time--   --User+System--   ---Wall Time---  --- Name ---
+   0.0001 ( 90.1%)        -----        0.0001 ( 90.1%)   0.0001 ( 90.1%)  A1
+   0.0000 (  9.9%)        -----        0.0000 (  9.9%)   0.0000 (  9.9%)  A2
+   0.0001 (100.0%)        -----        0.0001 (100.0%)   0.0001 (100.0%)  Total
+
+"""    # noqa: E501
+
+
+class TestLLVMPassTimings(TestCase):
+
+    def test_usage(self):
+        @njit
+        def foo(n):
+            c = 0
+            for i in range(n):
+                c += i
+            return c
+
+        with override_config('LLVM_PASS_TIMINGS', True):
+            foo(10)
+
+        md = foo.get_metadata(foo.signatures[0])
+        timings = md['llvm_pass_timings']
+        # Check: timing is of correct type
+        self.assertIsInstance(timings, lpt.PassTimingsCollection)
+        # Check: basic for __str__
+        text = str(timings)
+        self.assertIn("Module passes (full optimization)", text)
+        # Check: there must be more than one record
+        self.assertGreater(len(timings), 0)
+        # Check: __getitem__
+        last = timings[-1]
+        self.assertIsInstance(last, lpt.NamedTimings)
+        # Check: NamedTimings
+        self.assertIsInstance(last.name, str)
+        self.assertIsInstance(last.timings, lpt.ProcessedPassTimings)
+
+    def test_analyze(self):
+        @njit
+        def foo(n):
+            c = 0
+            for i in range(n):
+                for j in range(i):
+                    c += j
+            return c
+
+        with override_config('LLVM_PASS_TIMINGS', True):
+            foo(10)
+
+        md = foo.get_metadata(foo.signatures[0])
+        timings_collection = md['llvm_pass_timings']
+        # Check: get_total_time()
+        self.assertIsInstance(timings_collection.get_total_time(), float)
+        # Check: summary()
+        self.assertIsInstance(timings_collection.summary(), str)
+        # Check: list_longest_first() ordering
+        longest_first = timings_collection.list_longest_first()
+        self.assertEqual(len(longest_first), len(timings_collection))
+        last = longest_first[0].timings.get_total_time()
+        for rec in longest_first[1:]:
+            cur = rec.timings.get_total_time()
+            self.assertGreaterEqual(last, cur)
+            cur = last
+
+    def test_parse_raw(self):
+        timings1 = lpt.ProcessedPassTimings(timings_raw1)
+        self.assertAlmostEqual(timings1.get_total_time(), 0.0001)
+        self.assertIsInstance(timings1.summary(), str)
+
+        timings2 = lpt.ProcessedPassTimings(timings_raw2)
+        self.assertAlmostEqual(timings2.get_total_time(), 0.0001)
+        self.assertIsInstance(timings2.summary(), str)
+
+
+class TestLLVMPassTimingsDisabled(TestCase):
+    def test_disabled_behavior(self):
+        @njit
+        def foo(n):
+            c = 0
+            for i in range(n):
+                c += i
+            return c
+
+        with override_config('LLVM_PASS_TIMINGS', False):
+            foo(10)
+
+        md = foo.get_metadata(foo.signatures[0])
+        timings = md['llvm_pass_timings']
+        # Check that the right message is returned
+        self.assertEqual(timings.summary(), "No pass timings were recorded")
+        # Check that None is returned
+        self.assertIsNone(timings.get_total_time())
+        # Check that empty list is returned
+        self.assertEqual(timings.list_longest_first(), [])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_llvm_version_check.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_llvm_version_check.py
new file mode 100644
index 000000000..0be6fa23e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_llvm_version_check.py
@@ -0,0 +1,42 @@
+import imp
+import sys
+
+import unittest
+
+
+class TestLlvmVersion(unittest.TestCase):
+
+    def test_llvmlite_version(self):
+        # test the system it's running on
+        import llvmlite
+        import numba
+        self.assertTrue(numba.__version__)
+
+        llvmlite_version = llvmlite.__version__
+        def cleanup():
+            llvmlite.__version__ = llvmlite_version
+        self.addCleanup(cleanup)
+
+        # explicitly test all 3 cases of version string
+        ver = numba._min_llvmlite_version
+        version_pass = '%d.%d.%d' % ver
+        git_version_pass = '%d.%d.%d-10-g92584ed' % ver
+        rc_version_pass = '%d.%d.%drc1' % (ver[0], ver[1], ver[2] + 1)
+        version_fail = '%d.%d.0' % (ver[0], ver[1] - 1)
+        git_version_fail = '%d.%d.9-10-g92584ed' % (ver[0], ver[1] - 1)
+
+        ver_pass = (version_pass, git_version_pass, rc_version_pass)
+        ver_fail = (version_fail, git_version_fail)
+        for v in ver_pass:
+            llvmlite.__version__ = v
+            imp.reload(numba)
+            self.assertTrue(numba.__version__)
+
+        for v in ver_fail:
+            with self.assertRaises(ImportError):
+                llvmlite.__version__ = v
+                imp.reload(numba)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_locals.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_locals.py
new file mode 100644
index 000000000..21f64a19b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_locals.py
@@ -0,0 +1,18 @@
+from numba import float32
+from numba.core import compiler
+import unittest
+
+def foo():
+    x = 123
+    return x
+
+
+class TestLocals(unittest.TestCase):
+
+    def test_seed_types(self):
+        cres = compiler.compile_isolated(foo, (), locals={'x': float32})
+        self.assertEqual(cres.signature.return_type, float32)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_looplifting.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_looplifting.py
new file mode 100644
index 000000000..23ffa9d23
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_looplifting.py
@@ -0,0 +1,604 @@
+from io import StringIO
+import numpy as np
+
+from numba.core import types
+from numba.core.compiler import compile_extra, Flags
+from numba.tests.support import TestCase, tag, MemoryLeakMixin
+import unittest
+
+
+looplift_flags = Flags()
+looplift_flags.enable_pyobject = True
+looplift_flags.enable_looplift = True
+
+pyobject_looplift_flags = looplift_flags.copy()
+pyobject_looplift_flags.enable_pyobject_looplift = True
+
+
+def compile_isolated(pyfunc, argtypes, **kwargs):
+    from numba.core.registry import cpu_target
+
+    kwargs.setdefault('return_type', None)
+    kwargs.setdefault('locals', {})
+    return compile_extra(
+        cpu_target.typing_context,
+        cpu_target.target_context,
+        pyfunc,
+        argtypes,
+        **kwargs,
+    )
+
+
+def lift1(x):
+    # Outer needs object mode because of np.empty()
+    a = np.empty(3)
+    for i in range(a.size):
+        # Inner is nopython-compliant
+        a[i] = x
+    return a
+
+
+def lift2(x):
+    # Outer needs object mode because of np.empty()
+    a = np.empty((3, 4))
+    for i in range(a.shape[0]):
+        for j in range(a.shape[1]):
+            # Inner is nopython-compliant
+            a[i, j] = x
+    return a
+
+
+def lift3(x):
+    # Output variable from the loop
+    _ = object()
+    a = np.arange(5, dtype=np.int64)
+    c = 0
+    for i in range(a.shape[0]):
+        c += a[i] * x
+    return c
+
+def lift4(x):
+    # Output two variables from the loop
+    _ = object()
+    a = np.arange(5, dtype=np.int64)
+    c = 0
+    d = 0
+    for i in range(a.shape[0]):
+        c += a[i] * x
+        d += c
+    return c + d
+
+def lift5(x):
+    _ = object()
+    a = np.arange(4)
+    for i in range(a.shape[0]):
+        # Inner has a break statement
+        if i > 2:
+            break
+    return a
+
+def lift_gen1(x):
+    # Outer needs object mode because of np.empty()
+    a = np.empty(3)
+    yield 0
+    for i in range(a.size):
+        # Inner is nopython-compliant
+        a[i] = x
+    yield np.sum(a)
+
+def lift_issue2561():
+    np.empty(1)   # This forces objectmode because no nrt
+    for i in range(10):
+        for j in range(10):
+            return 1
+    return 2
+
+def reject1(x):
+    a = np.arange(4)
+    for i in range(a.shape[0]):
+        # Inner returns a variable from outer "scope" => cannot loop-lift
+        return a
+    return a
+
+
+def reject_gen1(x):
+    _ = object()
+    a = np.arange(4)
+    for i in range(a.shape[0]):
+        # Inner is a generator => cannot loop-lift
+        yield a[i]
+
+def reject_gen2(x):
+    _ = object()
+    a = np.arange(3)
+    for i in range(a.size):
+        # Middle has a yield => cannot loop-lift
+        res = a[i] + x
+        for j in range(i):
+            # Inner is nopython-compliant, but the current algorithm isn't
+            # able to separate it.
+            res = res ** 2
+        yield res
+
+def reject_npm1(x):
+    a = np.empty(3, dtype=np.int32)
+    for i in range(a.size):
+        # Inner uses object() => cannot loop-lift
+        _ = object()
+        a[i] = np.arange(i + 1)[i]
+
+    return a
+
+
+class TestLoopLifting(MemoryLeakMixin, TestCase):
+
+    def try_lift(self, pyfunc, argtypes):
+        from numba.core.registry import cpu_target
+
+        cres = compile_extra(
+            cpu_target.typing_context,
+            cpu_target.target_context,
+            pyfunc, argtypes,
+            return_type=None, flags=looplift_flags, locals={},
+        )
+        # One lifted loop
+        self.assertEqual(len(cres.lifted), 1)
+        return cres
+
+    def assert_lifted_native(self, cres):
+        # Check if we have lifted in nopython mode
+        jitloop = cres.lifted[0]
+        [loopcres] = jitloop.overloads.values()
+        self.assertTrue(loopcres.fndesc.native)  # Lifted function is native
+
+    def check_lift_ok(self, pyfunc, argtypes, args):
+        """
+        Check that pyfunc can loop-lift even in nopython mode.
+        """
+        cres = self.try_lift(pyfunc, argtypes)
+        expected = pyfunc(*args)
+        got = cres.entry_point(*args)
+        self.assert_lifted_native(cres)
+        # Check return values
+        self.assertPreciseEqual(expected, got)
+
+    def check_lift_generator_ok(self, pyfunc, argtypes, args):
+        """
+        Check that pyfunc (a generator function) can loop-lift even in
+        nopython mode.
+        """
+        cres = self.try_lift(pyfunc, argtypes)
+        expected = list(pyfunc(*args))
+        got = list(cres.entry_point(*args))
+        self.assert_lifted_native(cres)
+        # Check return values
+        self.assertPreciseEqual(expected, got)
+
+    def check_no_lift(self, pyfunc, argtypes, args):
+        """
+        Check that pyfunc can't loop-lift.
+        """
+        cres = compile_isolated(pyfunc, argtypes,
+                                flags=looplift_flags)
+        self.assertFalse(cres.lifted)
+        expected = pyfunc(*args)
+        got = cres.entry_point(*args)
+        # Check return values
+        self.assertPreciseEqual(expected, got)
+
+    def check_no_lift_generator(self, pyfunc, argtypes, args):
+        """
+        Check that pyfunc (a generator function) can't loop-lift.
+        """
+        cres = compile_isolated(pyfunc, argtypes,
+                                flags=looplift_flags)
+        self.assertFalse(cres.lifted)
+        expected = list(pyfunc(*args))
+        got = list(cres.entry_point(*args))
+        self.assertPreciseEqual(expected, got)
+
+    def check_no_lift_nopython(self, pyfunc, argtypes, args):
+        """
+        Check that pyfunc will fail loop-lifting if pyobject mode
+        is disabled inside the loop, succeed otherwise.
+        """
+        cres = compile_isolated(pyfunc, argtypes,
+                                flags=looplift_flags)
+        self.assertTrue(cres.lifted)
+        with self.assertTypingError():
+            cres.entry_point(*args)
+        cres = compile_isolated(pyfunc, argtypes,
+                                flags=pyobject_looplift_flags)
+        self.assertTrue(cres.lifted)
+        expected = pyfunc(*args)
+        got = cres.entry_point(*args)
+        self.assertPreciseEqual(expected, got)
+
+    def test_lift1(self):
+        self.check_lift_ok(lift1, (types.intp,), (123,))
+
+    def test_lift2(self):
+        self.check_lift_ok(lift2, (types.intp,), (123,))
+
+    def test_lift3(self):
+        self.check_lift_ok(lift3, (types.intp,), (123,))
+
+    def test_lift4(self):
+        self.check_lift_ok(lift4, (types.intp,), (123,))
+
+    def test_lift5(self):
+        self.check_lift_ok(lift5, (types.intp,), (123,))
+
+    def test_lift_issue2561(self):
+        self.check_lift_ok(lift_issue2561, (), ())
+
+    def test_lift_gen1(self):
+        self.check_lift_generator_ok(lift_gen1, (types.intp,), (123,))
+
+    def test_reject1(self):
+        self.check_no_lift(reject1, (types.intp,), (123,))
+
+    def test_reject_gen1(self):
+        self.check_no_lift_generator(reject_gen1, (types.intp,), (123,))
+
+    def test_reject_gen2(self):
+        self.check_no_lift_generator(reject_gen2, (types.intp,), (123,))
+
+    def test_reject_npm1(self):
+        self.check_no_lift_nopython(reject_npm1, (types.intp,), (123,))
+
+
+class TestLoopLiftingAnnotate(TestCase):
+    def test_annotate_1(self):
+        """
+        Verify that annotation works as expected with one lifted loop
+        """
+        from numba import jit
+
+        # dummy function to force objmode
+        def bar():
+            pass
+
+        def foo(x):
+            bar()  # force obj
+            for i in range(x.size):
+                x[i] += 1
+
+            return x
+
+        cfoo = jit(foo)
+
+        x = np.arange(10)
+        xcopy = x.copy()
+        r = cfoo(x)
+        np.testing.assert_equal(r, xcopy + 1)
+
+        buf = StringIO()
+        cfoo.inspect_types(file=buf)
+        annotation = buf.getvalue()
+        buf.close()
+
+        self.assertIn("The function contains lifted loops", annotation)
+        line = foo.__code__.co_firstlineno + 2  # 2 lines down from func head
+        self.assertIn("Loop at line {line}".format(line=line), annotation)
+        self.assertIn("Has 1 overloads", annotation)
+
+    def test_annotate_2(self):
+        """
+        Verify that annotation works as expected with two lifted loops
+        """
+        from numba import jit
+
+        # dummy function to force objmode
+        def bar():
+            pass
+
+        def foo(x):
+            bar()  # force obj
+            # first lifted loop
+            for i in range(x.size):
+                x[i] += 1
+            # second lifted loop
+            for j in range(x.size):
+                x[j] *= 2
+            return x
+
+        cfoo = jit(foo)
+
+        x = np.arange(10)
+        xcopy = x.copy()
+        r = cfoo(x)
+        np.testing.assert_equal(r, (xcopy + 1) * 2)
+
+        buf = StringIO()
+        cfoo.inspect_types(file=buf)
+        annotation = buf.getvalue()
+        buf.close()
+
+        self.assertIn("The function contains lifted loops", annotation)
+        line1 = foo.__code__.co_firstlineno + 3  # 3 lines down from func head
+        line2 = foo.__code__.co_firstlineno + 6  # 6 lines down from func head
+        self.assertIn("Loop at line {line}".format(line=line1), annotation)
+        self.assertIn("Loop at line {line}".format(line=line2), annotation)
+
+
+class TestLoopLiftingInAction(MemoryLeakMixin, TestCase):
+    def assert_has_lifted(self, jitted, loopcount):
+        lifted = jitted.overloads[jitted.signatures[0]].lifted
+        self.assertEqual(len(lifted), loopcount)
+
+    def test_issue_734(self):
+        from numba import jit, void, int32, double
+
+        @jit(void(int32, double[:]), forceobj=True)
+        def forloop_with_if(u, a):
+            if u == 0:
+                for i in range(a.shape[0]):
+                    a[i] = a[i] * 2.0
+            else:
+                for i in range(a.shape[0]):
+                    a[i] = a[i] + 1.0
+
+        for u in (0, 1):
+            nb_a = np.arange(10, dtype='int32')
+            np_a = np.arange(10, dtype='int32')
+            forloop_with_if(u, nb_a)
+            forloop_with_if.py_func(u, np_a)
+            self.assertPreciseEqual(nb_a, np_a)
+
+    def test_issue_812(self):
+        from numba import jit
+
+        @jit('f8[:](f8[:])', forceobj=True)
+        def test(x):
+            res = np.zeros(len(x))
+            ind = 0
+            for ii in range(len(x)):
+                ind += 1
+                res[ind] = x[ind]
+                if x[ind] >= 10:
+                    break
+
+            # Invalid loopjitting will miss the usage of `ind` in the
+            # following loop.
+            for ii in range(ind + 1, len(x)):
+                res[ii] = 0
+            return res
+
+        x = np.array([1., 4, 2, -3, 5, 2, 10, 5, 2, 6])
+        np.testing.assert_equal(test.py_func(x), test(x))
+
+    def test_issue_2368(self):
+        from numba import jit
+
+        def lift_issue2368(a, b):
+            s = 0
+            for e in a:
+                s += e
+            h = b.__hash__()
+            return s, h
+
+        a = np.ones(10)
+        b = object()
+        jitted = jit(lift_issue2368)
+
+        expected = lift_issue2368(a, b)
+        got = jitted(a, b)
+
+        self.assertEqual(expected[0], got[0])
+        self.assertEqual(expected[1], got[1])
+
+        jitloop = jitted.overloads[jitted.signatures[0]].lifted[0]
+        [loopcres] = jitloop.overloads.values()
+        # assert lifted function is native
+        self.assertTrue(loopcres.fndesc.native)
+
+    def test_no_iteration_w_redef(self):
+        # redefinition of res in the loop with no use of res should not
+        # prevent lifting
+        from numba import jit
+
+        @jit(forceobj=True)
+        def test(n):
+            res = 0
+            for i in range(n):
+                res = i
+            return res
+
+        # loop count = 1, loop lift but loop body not execute
+        self.assertEqual(test.py_func(-1), test(-1))
+        self.assert_has_lifted(test, loopcount=1)
+        # loop count = 1, loop will lift and will execute
+        self.assertEqual(test.py_func(1), test(1))
+        self.assert_has_lifted(test, loopcount=1)
+
+    def test_no_iteration(self):
+        from numba import jit
+
+        @jit(forceobj=True)
+        def test(n):
+            res = 0
+            for i in range(n):
+                res += i
+            return res
+
+        # loop count = 1
+        self.assertEqual(test.py_func(-1), test(-1))
+        self.assert_has_lifted(test, loopcount=1)
+        # loop count = 1
+        self.assertEqual(test.py_func(1), test(1))
+        self.assert_has_lifted(test, loopcount=1)
+
+    def test_define_in_loop_body(self):
+        # tests a definition in a loop that leaves the loop is liftable
+        from numba import jit
+
+        @jit(forceobj=True)
+        def test(n):
+            for i in range(n):
+                res = i
+            return res
+
+        # loop count = 1
+        self.assertEqual(test.py_func(1), test(1))
+        self.assert_has_lifted(test, loopcount=1)
+
+    def test_invalid_argument(self):
+        """Test a problem caused by invalid discovery of loop argument
+        when a variable is used afterwards but not before.
+
+        Before the fix, this will result in::
+
+        numba.ir.NotDefinedError: 'i' is not defined
+        """
+        from numba import jit
+
+        @jit(forceobj=True)
+        def test(arg):
+            if type(arg) == np.ndarray: # force object mode
+                if arg.ndim == 1:
+                    result = 0.0
+                    j = 0
+                    for i in range(arg.shape[0]):
+                        pass
+                else:
+                    raise Exception
+            else:
+                result = 0.0
+                i, j = 0, 0
+                return result
+
+        arg = np.arange(10)
+        self.assertEqual(test.py_func(arg), test(arg))
+
+    def test_conditionally_defined_in_loop(self):
+        from numba import jit
+        @jit(forceobj=True)
+        def test():
+            x = 5
+            y = 0
+            for i in range(2):
+                if i > 0:
+                   x = 6
+                y += x
+            return y, x
+
+        self.assertEqual(test.py_func(), test())
+        self.assert_has_lifted(test, loopcount=1)
+
+    def test_stack_offset_error_when_has_no_return(self):
+        from numba import jit
+        import warnings
+
+        def pyfunc(a):
+            if a:
+                for i in range(10):
+                    pass
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+
+            cfunc = jit(forceobj=True)(pyfunc)
+            self.assertEqual(pyfunc(True), cfunc(True))
+
+    def test_variable_scope_bug(self):
+        """
+        https://github.com/numba/numba/issues/2179
+
+        Looplifting transformation is using the wrong version of variable `h`.
+        """
+        from numba import jit
+
+        def bar(x):
+            return x
+
+        def foo(x):
+            h = 0.
+            for k in range(x):
+                h = h + k
+            h = h - bar(x)
+            return h
+
+        cfoo = jit(foo)
+        self.assertEqual(foo(10), cfoo(10))
+
+    def test_recompilation_loop(self):
+        """
+        https://github.com/numba/numba/issues/2481
+        """
+        from numba import jit
+
+        def foo(x, y):
+            # slicing to make array `x` into different layout
+            # to cause a new compilation of the lifted loop
+            A = x[::y]
+            c = 1
+            for k in range(A.size):
+                object()  # to force objectmode and looplifting
+                c = c * A[::-1][k]   # the slice that is failing in static_getitem
+            return c
+
+        cfoo = jit(foo)
+        # First run just works
+        args = np.arange(10), 1
+        self.assertEqual(foo(*args), cfoo(*args))
+        # Exactly 1 lifted loop so far
+        self.assertEqual(len(cfoo.overloads[cfoo.signatures[0]].lifted), 1)
+        lifted = cfoo.overloads[cfoo.signatures[0]].lifted[0]
+        # The lifted loop has 1 signature
+        self.assertEqual(len(lifted.signatures), 1)
+        # Use different argument to trigger a new compilation of the lifted loop
+        args = np.arange(10), -1
+        self.assertEqual(foo(*args), cfoo(*args))
+        # Ensure that is really a new overload for the lifted loop
+        self.assertEqual(len(lifted.signatures), 2)
+
+    def test_lift_listcomp_block0(self):
+
+        def foo(X):
+            [y for y in (1,)]
+            for x in (1,):
+                pass
+            return X
+
+        # this is not nice, if you have 2+? liftable loops with one of them
+        # being list comp and in block 0 and force objmode compilation is set,
+        # in py27 this leads to a BUILD_LIST that is a lifting candidate with an
+        # entry of block 0, this is a problem as the loop lift prelude would be
+        # written to block -1 and havoc ensues. Therefore block 0 loop lifts
+        # are banned under this set of circumstances.
+
+        # check all compile and execute
+        from numba import jit
+        f = jit()(foo)
+        f(1)
+        self.assertEqual(f.overloads[f.signatures[0]].lifted, ())
+
+        f = jit(forceobj=True)(foo)
+        f(1)
+        self.assertEqual(len(f.overloads[f.signatures[0]].lifted), 1)
+
+    def test_lift_objectmode_issue_4223(self):
+        from numba import jit
+
+        @jit
+        def foo(a, b, c, d, x0, y0, n):
+            xs, ys = np.zeros(n), np.zeros(n)
+            xs[0], ys[0] = x0, y0
+            for i in np.arange(n-1):
+                xs[i+1] = np.sin(a * ys[i]) + c * np.cos(a * xs[i])
+                ys[i+1] = np.sin(b * xs[i]) + d * np.cos(b * ys[i])
+            object() # ensure object mode
+            return xs, ys
+
+        kwargs = dict(a=1.7, b=1.7, c=0.6, d=1.2, x0=0, y0=0, n=200)
+        got = foo(**kwargs)
+        expected = foo.py_func(**kwargs)
+        self.assertPreciseEqual(got[0], expected[0])
+        self .assertPreciseEqual(got[1], expected[1])
+        [lifted] = foo.overloads[foo.signatures[0]].lifted
+        self.assertEqual(len(lifted.nopython_signatures), 1)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_make_function_to_jit_function.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_make_function_to_jit_function.py
new file mode 100644
index 000000000..29161fff5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_make_function_to_jit_function.py
@@ -0,0 +1,284 @@
+from numba import njit
+from numba.core import errors
+from numba.core.extending import overload
+import numpy as np
+
+import unittest
+
+
+@njit
+def consumer(func, *args):
+    return func(*args)
+
+
+@njit
+def consumer2arg(func1, func2):
+    return func2(func1)
+
+
+_global = 123
+
+
+class TestMakeFunctionToJITFunction(unittest.TestCase):
+    """
+    This tests the pass that converts ir.Expr.op == make_function (i.e. closure)
+    into a JIT function.
+    """
+    # NOTE: testing this is a bit tricky. The function receiving a JIT'd closure
+    # must also be under JIT control so as to handle the JIT'd closure
+    # correctly, however, in the case of running the test implementations in the
+    # interpreter, the receiving function cannot be JIT'd else it will receive
+    # the Python closure and then complain about pyobjects as arguments.
+    # The way around this is to use a factory function to close over either the
+    # jitted or standard python function as the consumer depending on context.
+
+    def test_escape(self):
+
+        def impl_factory(consumer_func):
+            def impl():
+                def inner():
+                    return 10
+                return consumer_func(inner)
+            return impl
+
+        cfunc = njit(impl_factory(consumer))
+        impl = impl_factory(consumer.py_func)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_nested_escape(self):
+
+        def impl_factory(consumer_func):
+            def impl():
+                def inner():
+                    return 10
+
+                def innerinner(x):
+                    return x()
+                return consumer_func(inner, innerinner)
+            return impl
+
+        cfunc = njit(impl_factory(consumer2arg))
+        impl = impl_factory(consumer2arg.py_func)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_closure_in_escaper(self):
+
+        def impl_factory(consumer_func):
+            def impl():
+                def callinner():
+                    def inner():
+                        return 10
+                    return inner()
+                return consumer_func(callinner)
+            return impl
+
+        cfunc = njit(impl_factory(consumer))
+        impl = impl_factory(consumer.py_func)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_close_over_consts(self):
+
+        def impl_factory(consumer_func):
+            def impl():
+                y = 10
+
+                def callinner(z):
+                    return y + z + _global
+                return consumer_func(callinner, 6)
+            return impl
+
+        cfunc = njit(impl_factory(consumer))
+        impl = impl_factory(consumer.py_func)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_close_over_consts_w_args(self):
+
+        def impl_factory(consumer_func):
+            def impl(x):
+                y = 10
+
+                def callinner(z):
+                    return y + z + _global
+                return consumer_func(callinner, x)
+            return impl
+
+        cfunc = njit(impl_factory(consumer))
+        impl = impl_factory(consumer.py_func)
+
+        a = 5
+        self.assertEqual(impl(a), cfunc(a))
+
+    def test_with_overload(self):
+
+        def foo(func, *args):
+            nargs = len(args)
+            if nargs == 1:
+                return func(*args)
+            elif nargs == 2:
+                return func(func(*args))
+
+        @overload(foo)
+        def foo_ol(func, *args):
+            # specialise on the number of args, as per `foo`
+            nargs = len(args)
+            if nargs == 1:
+                def impl(func, *args):
+                    return func(*args)
+                return impl
+            elif nargs == 2:
+                def impl(func, *args):
+                    return func(func(*args))
+                return impl
+
+        def impl_factory(consumer_func):
+            def impl(x):
+                y = 10
+
+                def callinner(*z):
+                    return y + np.sum(np.asarray(z)) + _global
+                # run both specialisations, 1 arg, and 2 arg.
+                return foo(callinner, x), foo(callinner, x, x)
+            return impl
+
+        cfunc = njit(impl_factory(consumer))
+        impl = impl_factory(consumer.py_func)
+
+        a = 5
+        self.assertEqual(impl(a), cfunc(a))
+
+    def test_basic_apply_like_case(self):
+        def apply(array, func):
+            return func(array)
+
+        @overload(apply)
+        def ov_apply(array, func):
+            return lambda array, func: func(array)
+
+        def impl(array):
+            def mul10(x):
+                return x * 10
+            return apply(array, mul10)
+
+        cfunc = njit(impl)
+
+        a = np.arange(10)
+        np.testing.assert_allclose(impl(a), cfunc(a))
+
+    @unittest.skip("Needs option/flag inheritance to work")
+    def test_jit_option_inheritance(self):
+
+        def impl_factory(consumer_func):
+            def impl(x):
+                def inner(val):
+                    return 1 / val
+                return consumer_func(inner, x)
+            return impl
+
+        cfunc = njit(error_model='numpy')(impl_factory(consumer))
+        impl = impl_factory(consumer.py_func)
+
+        a = 0
+        self.assertEqual(impl(a), cfunc(a))
+
+    # this needs true SSA to be able to work correctly, check error for now
+    def test_multiply_defined_freevar(self):
+
+        @njit
+        def impl(c):
+            if c:
+                x = 3
+
+                def inner(y):
+                    return y + x
+
+                r = consumer(inner, 1)
+            else:
+                x = 6
+
+                def inner(y):
+                    return y + x
+
+                r = consumer(inner, 2)
+            return r
+
+        with self.assertRaises(errors.TypingError) as e:
+            impl(1)
+
+        self.assertIn("Cannot capture a constant value for variable",
+                      str(e.exception))
+
+    def test_non_const_in_escapee(self):
+
+        @njit
+        def impl(x):
+            z = np.arange(x)
+
+            def inner(val):
+                return 1 + z + val  # z is non-const freevar
+            return consumer(inner, x)
+
+        with self.assertRaises(errors.TypingError) as e:
+            impl(1)
+
+        self.assertIn("Cannot capture the non-constant value associated",
+                      str(e.exception))
+
+    def test_escape_with_kwargs(self):
+
+        def impl_factory(consumer_func):
+            def impl():
+                t = 12
+
+                def inner(a, b, c, mydefault1=123, mydefault2=456):
+                    z = 4
+                    return mydefault1 + mydefault2 + z + t + a + b + c
+                # this is awkward, top and tail closure inlining with a escapees
+                # in the middle that do/don't have defaults.
+                return (inner(1, 2, 5, 91, 53),
+                        consumer_func(inner, 1, 2, 3, 73),
+                        consumer_func(inner, 1, 2, 3,),
+                        inner(1, 2, 4))
+            return impl
+
+        cfunc = njit(impl_factory(consumer))
+        impl = impl_factory(consumer.py_func)
+
+        np.testing.assert_allclose(impl(), cfunc())
+
+    def test_escape_with_kwargs_override_kwargs(self):
+
+        @njit
+        def specialised_consumer(func, *args):
+            x, y, z = args  # unpack to avoid `CALL_FUNCTION_EX`
+            a = func(x, y, z, mydefault1=1000)
+            b = func(x, y, z, mydefault2=1000)
+            c = func(x, y, z, mydefault1=1000, mydefault2=1000)
+            return a + b + c
+
+        def impl_factory(consumer_func):
+            def impl():
+                t = 12
+
+                def inner(a, b, c, mydefault1=123, mydefault2=456):
+                    z = 4
+                    return mydefault1 + mydefault2 + z + t + a + b + c
+                # this is awkward, top and tail closure inlining with a escapees
+                # in the middle that get defaults specified in the consumer
+                return (inner(1, 2, 5, 91, 53),
+                        consumer_func(inner, 1, 2, 11),
+                        consumer_func(inner, 1, 2, 3,),
+                        inner(1, 2, 4))
+            return impl
+
+        cfunc = njit(impl_factory(specialised_consumer))
+        impl = impl_factory(specialised_consumer.py_func)
+
+        np.testing.assert_allclose(impl(), cfunc())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mandelbrot.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mandelbrot.py
new file mode 100644
index 000000000..b7c721a97
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mandelbrot.py
@@ -0,0 +1,35 @@
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types, utils
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+
+def is_in_mandelbrot(c):
+    i = 0
+    z = 0.0j
+    for i in range(100):
+        z = z ** 2 + c
+        if (z.real * z.real + z.imag * z.imag) >= 4:
+            return False
+    return True
+
+
+class TestMandelbrot(unittest.TestCase):
+
+    def test_mandelbrot(self):
+        pyfunc = is_in_mandelbrot
+        cr = compile_isolated(pyfunc, (types.complex64,))
+        cfunc = cr.entry_point
+
+        points = [0+0j, 1+0j, 0+1j, 1+1j, 0.1+0.1j]
+        for p in points:
+            self.assertEqual(cfunc(p), pyfunc(p))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mangling.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mangling.py
new file mode 100644
index 000000000..607c3051f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mangling.py
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+"""
+Test function name mangling.
+The mangling affects the ABI of numba compiled binaries.
+"""
+from numba.core import types
+from numba.core.funcdesc import default_mangler
+from numba.tests.support import unittest, TestCase
+
+
+class TestMangling(TestCase):
+    def test_one_args(self):
+        fname = 'foo'
+        argtypes = types.int32,
+        name = default_mangler(fname, argtypes)
+        self.assertEqual(name, '_Z3fooi')
+
+    def test_two_args(self):
+        fname = 'foo'
+        argtypes = types.int32, types.float32
+        name = default_mangler(fname, argtypes)
+        self.assertEqual(name, '_Z3fooif')
+
+    def test_unicode_fname(self):
+        fname = u'foಠ'
+        argtypes = types.int32, types.float32
+        name = default_mangler(fname, argtypes)
+        self.assertIsInstance(name, str)
+        # manually encode it
+        unichar = fname[2]
+        enc = ''.join('_{:02x}'.format(c)
+                      for c in unichar.encode('utf8'))
+        text = 'fo' + enc
+        expect = '_Z{}{}if'.format(len(text), text)
+        self.assertEqual(name, expect)
+        # ensure result chars are in the right charset
+        self.assertRegexpMatches(name, r'^_Z[a-zA-Z0-9_\$]+$')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_map_filter_reduce.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_map_filter_reduce.py
new file mode 100644
index 000000000..c6f46a156
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_map_filter_reduce.py
@@ -0,0 +1,91 @@
+from numba import njit
+
+from functools import reduce
+import unittest
+
+
+class TestMap(unittest.TestCase):
+
+    def test_basic_map_external_func(self):
+        func = njit(lambda x: x + 10)
+
+        def impl():
+            return [y for y in map(func, range(10))]
+
+        cfunc = njit(impl)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_basic_map_closure(self):
+        def impl():
+            return [y for y in map(lambda x: x + 10, range(10))]
+
+        cfunc = njit(impl)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_basic_map_closure_multiple_iterator(self):
+        def impl():
+            args = range(10), range(10, 20)
+            return [y for y in map(lambda a, b: (a + 10, b + 5), *args)]
+
+        cfunc = njit(impl)
+
+        self.assertEqual(impl(), cfunc())
+
+
+class TestFilter(unittest.TestCase):
+
+    def test_basic_filter_external_func(self):
+        func = njit(lambda x: x > 0)
+
+        def impl():
+            return [y for y in filter(func, range(-10, 10))]
+
+        cfunc = njit(impl)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_basic_filter_closure(self):
+        def impl():
+            return [y for y in filter(lambda x: x > 0, range(-10, 10))]
+
+        cfunc = njit(impl)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_basic_filter_none_func(self):
+        def impl():
+            return [y for y in filter(None, range(-10, 10))]
+
+        cfunc = njit(impl)
+
+        self.assertEqual(impl(), cfunc())
+
+
+class TestReduce(unittest.TestCase):
+
+    def test_basic_reduce_external_func(self):
+        func = njit(lambda x, y: x + y)
+
+        def impl():
+            return reduce(func, range(-10, 10))
+
+        cfunc = njit(impl)
+
+        self.assertEqual(impl(), cfunc())
+
+    def test_basic_reduce_closure(self):
+
+        def impl():
+            def func(x, y):
+                return x + y
+            return reduce(func, range(-10, 10), 100)
+
+        cfunc = njit(impl)
+
+        self.assertEqual(impl(), cfunc())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mathlib.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mathlib.py
new file mode 100644
index 000000000..a3f535316
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mathlib.py
@@ -0,0 +1,655 @@
+import itertools
+import math
+import sys
+import warnings
+
+import numpy as np
+
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import utils, types
+from numba.core.config import IS_WIN32, IS_32BITS
+from numba.tests.support import TestCase, CompilationCache, tag
+import unittest
+from numba.np import numpy_support
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+def sin(x):
+    return math.sin(x)
+
+
+def cos(x):
+    return math.cos(x)
+
+
+def tan(x):
+    return math.tan(x)
+
+
+def sinh(x):
+    return math.sinh(x)
+
+
+def cosh(x):
+    return math.cosh(x)
+
+
+def tanh(x):
+    return math.tanh(x)
+
+
+def asin(x):
+    return math.asin(x)
+
+
+def acos(x):
+    return math.acos(x)
+
+
+def atan(x):
+    return math.atan(x)
+
+
+def atan2(y, x):
+    return math.atan2(y, x)
+
+
+def asinh(x):
+    return math.asinh(x)
+
+
+def acosh(x):
+    return math.acosh(x)
+
+
+def atanh(x):
+    return math.atanh(x)
+
+
+def sqrt(x):
+    return math.sqrt(x)
+
+
+def npy_sqrt(x):
+    return np.sqrt(x)
+
+
+def exp(x):
+    return math.exp(x)
+
+
+def expm1(x):
+    return math.expm1(x)
+
+
+def log(x):
+    return math.log(x)
+
+
+def log1p(x):
+    return math.log1p(x)
+
+
+def log10(x):
+    return math.log10(x)
+
+
+def floor(x):
+    return math.floor(x)
+
+
+def ceil(x):
+    return math.ceil(x)
+
+
+def trunc(x):
+    return math.trunc(x)
+
+
+def isnan(x):
+    return math.isnan(x)
+
+
+def isinf(x):
+    return math.isinf(x)
+
+
+def isfinite(x):
+    return math.isfinite(x)
+
+
+def hypot(x, y):
+    return math.hypot(x, y)
+
+
+def degrees(x):
+    return math.degrees(x)
+
+
+def radians(x):
+    return math.radians(x)
+
+
+def erf(x):
+    return math.erf(x)
+
+
+def erfc(x):
+    return math.erfc(x)
+
+
+def gamma(x):
+    return math.gamma(x)
+
+
+def lgamma(x):
+    return math.lgamma(x)
+
+
+def pow(x, y):
+    return math.pow(x, y)
+
+def gcd(x, y):
+    return math.gcd(x, y)
+
+def copysign(x, y):
+    return math.copysign(x, y)
+
+
+def frexp(x):
+    return math.frexp(x)
+
+
+def ldexp(x, e):
+    return math.ldexp(x, e)
+
+
+def get_constants():
+    return math.pi, math.e
+
+
+class TestMathLib(TestCase):
+
+    def setUp(self):
+        self.ccache = CompilationCache()
+
+    def test_constants(self):
+        self.run_nullary_func(get_constants, no_pyobj_flags)
+
+    def run_unary(self, pyfunc, x_types, x_values, flags=enable_pyobj_flags,
+                  prec='exact', **kwargs):
+        for tx, vx in zip(x_types, x_values):
+            cr = self.ccache.compile(pyfunc, (tx,), flags=flags)
+            cfunc = cr.entry_point
+            got = cfunc(vx)
+            expected = pyfunc(vx)
+            actual_prec = 'single' if tx is types.float32 else prec
+            msg = 'for input %r' % (vx,)
+            self.assertPreciseEqual(got, expected, prec=actual_prec, msg=msg,
+                                    **kwargs)
+
+    def run_binary(self, pyfunc, x_types, x_values, y_values,
+                   flags=enable_pyobj_flags, prec='exact'):
+        for ty, x, y in zip(x_types, x_values, y_values):
+            cr = self.ccache.compile(pyfunc, (ty, ty), flags=flags)
+            cfunc = cr.entry_point
+            got = cfunc(x, y)
+            expected = pyfunc(x, y)
+            actual_prec = 'single' if ty is types.float32 else prec
+            msg = 'for inputs (%r, %r)' % (x, y)
+            self.assertPreciseEqual(got, expected, prec=actual_prec, msg=msg)
+
+    def check_predicate_func(self, pyfunc, flags=enable_pyobj_flags):
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float32, types.float32,
+                   types.float64, types.float64, types.float64]
+        x_values = [0, 0, 0, 0, 0, 0,
+                    float('inf'), 0.0, float('nan'),
+                    float('inf'), 0.0, float('nan')]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_sin(self, flags=enable_pyobj_flags):
+        pyfunc = sin
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [-2, -1, -2, 2, 1, 2, .1, .2]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_sin_npm(self):
+        self.test_sin(flags=no_pyobj_flags)
+
+    @unittest.skipIf(sys.platform == 'win32',
+                     "not exactly equal on win32 (issue #597)")
+    def test_cos(self, flags=enable_pyobj_flags):
+        pyfunc = cos
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [-2, -1, -2, 2, 1, 2, .1, .2]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_cos_npm(self):
+        self.test_cos(flags=no_pyobj_flags)
+
+    def test_tan(self, flags=enable_pyobj_flags):
+        pyfunc = tan
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [-2, -1, -2, 2, 1, 2, .1, .2]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_tan_npm(self):
+        self.test_tan(flags=no_pyobj_flags)
+
+    def test_sqrt(self, flags=enable_pyobj_flags):
+        pyfunc = sqrt
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [2, 1, 2, 2, 1, 2, .1, .2]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_sqrt_npm(self):
+        self.test_sqrt(flags=no_pyobj_flags)
+
+    def test_npy_sqrt(self, flags=enable_pyobj_flags):
+        pyfunc = npy_sqrt
+        x_values = [2, 1, 2, 2, 1, 2, .1, .2]
+        # XXX poor precision for int16 inputs
+        x_types = [types.int16, types.uint16]
+        self.run_unary(pyfunc, x_types, x_values, flags, prec='single')
+        x_types = [types.int32, types.int64,
+                   types.uint32, types.uint64,
+                   types.float32, types.float64]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_npy_sqrt_npm(self):
+        self.test_npy_sqrt(flags=no_pyobj_flags)
+
+    def test_exp(self, flags=enable_pyobj_flags):
+        pyfunc = exp
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [-2, -1, -2, 2, 1, 2, .1, .2]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_exp_npm(self):
+        self.test_exp(flags=no_pyobj_flags)
+
+    def test_expm1(self, flags=enable_pyobj_flags):
+        pyfunc = expm1
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [-2, -1, -2, 2, 1, 2, .1, .2]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_expm1_npm(self):
+        self.test_expm1(flags=no_pyobj_flags)
+
+    def test_log(self, flags=enable_pyobj_flags):
+        pyfunc = log
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 10, 100, 1000, 100000, 1000000, 0.1, 1.1]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_log_npm(self):
+        self.test_log(flags=no_pyobj_flags)
+
+    def test_log1p(self, flags=enable_pyobj_flags):
+        pyfunc = log1p
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 10, 100, 1000, 100000, 1000000, 0.1, 1.1]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_log1p_npm(self):
+        self.test_log1p(flags=no_pyobj_flags)
+
+    def test_log10(self, flags=enable_pyobj_flags):
+        pyfunc = log10
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 10, 100, 1000, 100000, 1000000, 0.1, 1.1]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_log10_npm(self):
+        self.test_log10(flags=no_pyobj_flags)
+
+    def test_asin(self, flags=enable_pyobj_flags):
+        pyfunc = asin
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 1, 1, 1, 1, 1, 1., 1.]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_asin_npm(self):
+        self.test_asin(flags=no_pyobj_flags)
+
+    def test_acos(self, flags=enable_pyobj_flags):
+        pyfunc = acos
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 1, 1, 1, 1, 1, 1., 1.]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_acos_npm(self):
+        self.test_acos(flags=no_pyobj_flags)
+
+    def test_atan(self, flags=enable_pyobj_flags):
+        pyfunc = atan
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [-2, -1, -2, 2, 1, 2, .1, .2]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_atan_npm(self):
+        self.test_atan(flags=no_pyobj_flags)
+
+    def test_atan2(self, flags=enable_pyobj_flags):
+        pyfunc = atan2
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [-2, -1, -2, 2, 1, 2, .1, .2]
+        y_values = [x * 2 for x in x_values]
+        self.run_binary(pyfunc, x_types, x_values, y_values, flags)
+
+    def test_atan2_npm(self):
+        self.test_atan2(flags=no_pyobj_flags)
+
+    def test_asinh(self, flags=enable_pyobj_flags):
+        pyfunc = asinh
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 1, 1, 1, 1, 1, 1., 1.]
+        self.run_unary(pyfunc, x_types, x_values, flags, prec='double')
+
+    def test_asinh_npm(self):
+        self.test_asinh(flags=no_pyobj_flags)
+
+    def test_acosh(self, flags=enable_pyobj_flags):
+        pyfunc = acosh
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 1, 1, 1, 1, 1, 1., 1.]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_acosh_npm(self):
+        self.test_acosh(flags=no_pyobj_flags)
+
+    def test_atanh(self, flags=enable_pyobj_flags):
+        pyfunc = atanh
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [0, 0, 0, 0, 0, 0, 0.1, 0.1]
+        self.run_unary(pyfunc, x_types, x_values, flags, prec='double')
+
+    def test_atanh_npm(self):
+        self.test_atanh(flags=no_pyobj_flags)
+
+    def test_sinh(self, flags=enable_pyobj_flags):
+        pyfunc = sinh
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 1, 1, 1, 1, 1, 1., 1.]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_sinh_npm(self):
+        self.test_sinh(flags=no_pyobj_flags)
+
+    def test_cosh(self, flags=enable_pyobj_flags):
+        pyfunc = cosh
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 1, 1, 1, 1, 1, 1., 1.]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_cosh_npm(self):
+        self.test_cosh(flags=no_pyobj_flags)
+
+    def test_tanh(self, flags=enable_pyobj_flags):
+        pyfunc = tanh
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [0, 0, 0, 0, 0, 0, 0.1, 0.1]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_tanh_npm(self):
+        self.test_tanh(flags=no_pyobj_flags)
+
+    def test_floor(self, flags=enable_pyobj_flags):
+        pyfunc = floor
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [0, 0, 0, 0, 0, 0, 0.1, 1.9]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_floor_npm(self):
+        self.test_floor(flags=no_pyobj_flags)
+
+    def test_ceil(self, flags=enable_pyobj_flags):
+        pyfunc = ceil
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [0, 0, 0, 0, 0, 0, 0.1, 1.9]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_ceil_npm(self):
+        self.test_ceil(flags=no_pyobj_flags)
+
+    def test_trunc(self, flags=enable_pyobj_flags):
+        pyfunc = trunc
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [0, 0, 0, 0, 0, 0, 0.1, 1.9]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_trunc_npm(self):
+        self.test_trunc(flags=no_pyobj_flags)
+
+    def test_isnan(self):
+        self.check_predicate_func(isnan, flags=enable_pyobj_flags)
+
+    def test_isnan_npm(self):
+        self.check_predicate_func(isnan, flags=no_pyobj_flags)
+
+    def test_isinf(self):
+        self.check_predicate_func(isinf, flags=enable_pyobj_flags)
+
+    def test_isinf_npm(self):
+        self.check_predicate_func(isinf, flags=no_pyobj_flags)
+
+    def test_isfinite(self):
+        self.check_predicate_func(isfinite, flags=enable_pyobj_flags)
+
+    def test_isfinite_npm(self):
+        self.check_predicate_func(isfinite, flags=no_pyobj_flags)
+
+    def test_hypot(self, flags=enable_pyobj_flags):
+        pyfunc = hypot
+        x_types = [types.int64, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 2, 3, 4, 5, 6, .21, .34]
+        y_values = [x + 2 for x in x_values]
+        # Issue #563: precision issues with math.hypot() under Windows.
+        prec = 'single'
+        self.run_binary(pyfunc, x_types, x_values, y_values, flags, prec)
+        # Check that values that overflow in naive implementations do not
+        # in the numba impl
+
+        def naive_hypot(x, y):
+            return math.sqrt(x * x + y * y)
+        for fltty in (types.float32, types.float64):
+            cr = self.ccache.compile(pyfunc, (fltty, fltty), flags=flags)
+            cfunc = cr.entry_point
+            dt = numpy_support.as_dtype(fltty).type
+            val = dt(np.finfo(dt).max / 30.)
+            nb_ans = cfunc(val, val)
+            self.assertPreciseEqual(nb_ans, pyfunc(val, val), prec='single')
+            self.assertTrue(np.isfinite(nb_ans))
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("error", RuntimeWarning)
+                self.assertRaisesRegexp(RuntimeWarning,
+                                        'overflow encountered in .*_scalars',
+                                        naive_hypot, val, val)
+
+    def test_hypot_npm(self):
+        self.test_hypot(flags=no_pyobj_flags)
+
+    def test_degrees(self, flags=enable_pyobj_flags):
+        pyfunc = degrees
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 1, 1, 1, 1, 1, 1., 1.]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_degrees_npm(self):
+        self.test_degrees(flags=no_pyobj_flags)
+
+    def test_radians(self, flags=enable_pyobj_flags):
+        pyfunc = radians
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [1, 1, 1, 1, 1, 1, 1., 1.]
+        self.run_unary(pyfunc, x_types, x_values, flags)
+
+    def test_radians_npm(self):
+        self.test_radians(flags=no_pyobj_flags)
+
+    def test_erf(self, flags=enable_pyobj_flags):
+        pyfunc = erf
+        x_values = [1., 1., -1., -0.0, 0.0, 0.5, 5, float('inf')]
+        x_types = [types.float32, types.float64] * (len(x_values) // 2)
+        self.run_unary(pyfunc, x_types, x_values, flags,
+                       prec='double', ulps=2)
+
+    def test_erf_npm(self):
+        self.test_erf(flags=no_pyobj_flags)
+
+    def test_erfc(self, flags=enable_pyobj_flags):
+        pyfunc = erfc
+        x_values = [1., 1., -1., -0.0, 0.0, 0.5, 5, float('inf')]
+        x_types = [types.float32, types.float64] * (len(x_values) // 2)
+        self.run_unary(pyfunc, x_types, x_values, flags,
+                       prec='double', ulps=4)
+
+    def test_erfc_npm(self):
+        self.test_erfc(flags=no_pyobj_flags)
+
+    def test_gamma(self, flags=enable_pyobj_flags):
+        pyfunc = gamma
+        x_values = [1., -0.9, -0.5, 0.5]
+        x_types = [types.float32, types.float64] * (len(x_values) // 2)
+        self.run_unary(pyfunc, x_types, x_values, flags, prec='double', ulps=3)
+        x_values = [-0.1, 0.1, 2.5, 10.1, 50., float('inf')]
+        x_types = [types.float64] * len(x_values)
+        self.run_unary(pyfunc, x_types, x_values, flags,
+                       prec='double', ulps=8)
+
+    def test_gamma_npm(self):
+        self.test_gamma(flags=no_pyobj_flags)
+
+    def test_lgamma(self, flags=enable_pyobj_flags):
+        pyfunc = lgamma
+        x_values = [1., -0.9, -0.1, 0.1, 200., 1e10, 1e30, float('inf')]
+        x_types = [types.float32, types.float64] * (len(x_values) // 2)
+        self.run_unary(pyfunc, x_types, x_values, flags, prec='double')
+
+    def test_lgamma_npm(self):
+        self.test_lgamma(flags=no_pyobj_flags)
+
+    def test_pow(self, flags=enable_pyobj_flags):
+        pyfunc = pow
+        x_types = [types.int16, types.int32, types.int64,
+                   types.uint16, types.uint32, types.uint64,
+                   types.float32, types.float64]
+        x_values = [-2, -1, -2, 2, 1, 2, .1, .2]
+        y_values = [x * 2 for x in x_values]
+        self.run_binary(pyfunc, x_types, x_values, y_values, flags)
+
+    def test_gcd(self, flags=enable_pyobj_flags):
+        from itertools import product, repeat, chain
+        pyfunc = gcd
+        signed_args = product(
+            sorted(types.signed_domain), *repeat((-2, -1, 0, 1, 2, 7, 10), 2)
+        )
+        unsigned_args = product(
+            sorted(types.unsigned_domain), *repeat((0, 1, 2, 7, 9, 16), 2)
+        )
+        x_types, x_values, y_values = zip(*chain(signed_args, unsigned_args))
+        self.run_binary(pyfunc, x_types, x_values, y_values, flags)
+
+    def test_gcd_npm(self):
+        self.test_gcd(flags=no_pyobj_flags)
+
+    def test_pow_npm(self):
+        self.test_pow(flags=no_pyobj_flags)
+
+    def test_copysign(self, flags=enable_pyobj_flags):
+        pyfunc = copysign
+        value_types = [types.float32, types.float64]
+        values = [-2, -1, -0.0, 0.0, 1, 2, float('-inf'), float('inf'),
+                  float('nan')]
+        x_types, x_values, y_values = list(zip(
+            *itertools.product(value_types, values, values)))
+        self.run_binary(pyfunc, x_types, x_values, y_values, flags)
+
+    def test_copysign_npm(self):
+        self.test_copysign(flags=no_pyobj_flags)
+
+    def test_frexp(self, flags=enable_pyobj_flags):
+        pyfunc = frexp
+        x_types = [types.float32, types.float64]
+        x_values = [-2.5, -0.0, 0.0, 3.5,
+                    float('-inf'), float('inf'), float('nan')]
+        self.run_unary(pyfunc, x_types, x_values, flags, prec='exact')
+
+    def test_frexp_npm(self):
+        self.test_frexp(flags=no_pyobj_flags)
+
+    def test_ldexp(self, flags=enable_pyobj_flags):
+        pyfunc = ldexp
+        for fltty in (types.float32, types.float64):
+            cr = self.ccache.compile(pyfunc, (fltty, types.int32), flags=flags)
+            cfunc = cr.entry_point
+            for args in [(2.5, -2), (2.5, 1), (0.0, 0), (0.0, 1),
+                         (-0.0, 0), (-0.0, 1),
+                         (float('inf'), 0), (float('-inf'), 0),
+                         (float('nan'), 0)]:
+                msg = 'for input %r' % (args,)
+                self.assertPreciseEqual(cfunc(*args), pyfunc(*args))
+
+    def test_ldexp_npm(self):
+        self.test_ldexp(flags=no_pyobj_flags)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_maxmin.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_maxmin.py
new file mode 100644
index 000000000..84da5a33a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_maxmin.py
@@ -0,0 +1,41 @@
+from numba.core.compiler import compile_isolated
+from numba.core import types
+import unittest
+
+
+def domax3(a, b, c):
+    return max(a, b, c)
+
+
+def domin3(a, b, c):
+    return min(a, b, c)
+
+
+class TestMaxMin(unittest.TestCase):
+    def test_max3(self):
+        pyfunc = domax3
+        argtys = (types.int32, types.float32, types.double)
+        cres = compile_isolated(pyfunc, argtys)
+        cfunc = cres.entry_point
+
+        a = 1
+        b = 2
+        c = 3
+
+        self.assertEqual(pyfunc(a, b, c), cfunc(a, b, c))
+
+    def test_min3(self):
+        pyfunc = domin3
+        argtys = (types.int32, types.float32, types.double)
+        cres = compile_isolated(pyfunc, argtys)
+        cfunc = cres.entry_point
+
+        a = 1
+        b = 2
+        c = 3
+
+        self.assertEqual(pyfunc(a, b, c), cfunc(a, b, c))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mixed_tuple_unroller.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mixed_tuple_unroller.py
new file mode 100644
index 000000000..7dd314a16
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_mixed_tuple_unroller.py
@@ -0,0 +1,1984 @@
+from collections import namedtuple
+import numpy as np
+
+from numba.tests.support import (TestCase, MemoryLeakMixin,
+                                 skip_parfors_unsupported, captured_stdout)
+from numba import njit, typed, literal_unroll, prange
+from numba.core import types, errors, ir
+from numba.testing import unittest
+from numba.core.extending import overload
+from numba.core.compiler_machinery import (PassManager, register_pass,
+                                           FunctionPass, AnalysisPass)
+from numba.core.compiler import CompilerBase
+from numba.core.untyped_passes import (FixupArgs, TranslateByteCode,
+                                       IRProcessing, InlineClosureLikes,
+                                       SimplifyCFG, IterLoopCanonicalization,
+                                       LiteralUnroll, PreserveIR)
+from numba.core.typed_passes import (NopythonTypeInference, IRLegalization,
+                                     NoPythonBackend, PartialTypeInference,
+                                     NativeLowering)
+from numba.core.ir_utils import (compute_cfg_from_blocks, flatten_labels)
+from numba.core.types.functions import _header_lead
+
+_X_GLOBAL = (10, 11)
+
+
+class TestLiteralTupleInterpretation(MemoryLeakMixin, TestCase):
+
+    def check(self, func, var):
+        cres = func.overloads[func.signatures[0]]
+        ty = cres.fndesc.typemap[var]
+        self.assertTrue(isinstance(ty, types.Tuple))
+        for subty in ty:
+            self.assertTrue(isinstance(subty, types.Literal), "non literal")
+
+    def test_homogeneous_literal(self):
+        @njit
+        def foo():
+            x = (1, 2, 3)
+            return x[1]
+
+        self.assertEqual(foo(), foo.py_func())
+        self.check(foo, 'x')
+
+    def test_heterogeneous_literal(self):
+        @njit
+        def foo():
+            x = (1, 2, 3, 'a')
+            return x[3]
+
+        self.assertEqual(foo(), foo.py_func())
+        self.check(foo, 'x')
+
+    def test_non_literal(self):
+        @njit
+        def foo():
+            x = (1, 2, 3, 'a', 1j)
+            return x[4]
+
+        self.assertEqual(foo(), foo.py_func())
+        with self.assertRaises(AssertionError) as e:
+            self.check(foo, 'x')
+
+        self.assertIn("non literal", str(e.exception))
+
+
+@register_pass(mutates_CFG=False, analysis_only=False)
+class ResetTypeInfo(FunctionPass):
+    _name = "reset_the_type_information"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        state.typemap = None
+        state.return_type = None
+        state.calltypes = None
+        return True
+
+
+class TestLoopCanonicalisation(MemoryLeakMixin, TestCase):
+
+    def get_pipeline(use_canonicaliser, use_partial_typing=False):
+        class NewCompiler(CompilerBase):
+
+            def define_pipelines(self):
+                pm = PassManager("custom_pipeline")
+
+                # untyped
+                pm.add_pass(TranslateByteCode, "analyzing bytecode")
+                pm.add_pass(IRProcessing, "processing IR")
+                pm.add_pass(InlineClosureLikes,
+                            "inline calls to locally defined closures")
+                if use_partial_typing:
+                    pm.add_pass(PartialTypeInference, "do partial typing")
+                if use_canonicaliser:
+                    pm.add_pass(IterLoopCanonicalization, "Canonicalise loops")
+                pm.add_pass(SimplifyCFG, "Simplify the CFG")
+
+                # typed
+                if use_partial_typing:
+                    pm.add_pass(ResetTypeInfo, "resets the type info state")
+
+                pm.add_pass(NopythonTypeInference, "nopython frontend")
+
+                # legalise
+                pm.add_pass(IRLegalization, "ensure IR is legal")
+
+                # preserve
+                pm.add_pass(PreserveIR, "save IR for later inspection")
+
+                # lower
+                pm.add_pass(NativeLowering, "native lowering")
+                pm.add_pass(NoPythonBackend, "nopython mode backend")
+
+                # finalise the contents
+                pm.finalize()
+
+                return [pm]
+        return NewCompiler
+
+    # generate variants
+    LoopIgnoringCompiler = get_pipeline(False)
+    LoopCanonicalisingCompiler = get_pipeline(True)
+    TypedLoopCanonicalisingCompiler = get_pipeline(True, True)
+
+    def test_simple_loop_in_depth(self):
+        """ This heavily checks a simple loop transform """
+
+        def get_info(pipeline):
+            @njit(pipeline_class=pipeline)
+            def foo(tup):
+                acc = 0
+                for i in tup:
+                    acc += i
+                return acc
+
+            x = (1, 2, 3)
+            self.assertEqual(foo(x), foo.py_func(x))
+            cres = foo.overloads[foo.signatures[0]]
+            func_ir = cres.metadata['preserved_ir']
+            return func_ir, cres.fndesc
+
+        ignore_loops_ir, ignore_loops_fndesc = \
+            get_info(self.LoopIgnoringCompiler)
+        canonicalise_loops_ir, canonicalise_loops_fndesc = \
+            get_info(self.LoopCanonicalisingCompiler)
+
+        # check CFG is the same
+        def compare_cfg(a, b):
+            a_cfg = compute_cfg_from_blocks(flatten_labels(a.blocks))
+            b_cfg = compute_cfg_from_blocks(flatten_labels(b.blocks))
+            self.assertEqual(a_cfg, b_cfg)
+
+        compare_cfg(ignore_loops_ir, canonicalise_loops_ir)
+
+        # check there's three more call types in the canonicalised one:
+        # len(tuple arg)
+        # range(of the len() above)
+        # getitem(tuple arg, index)
+        self.assertEqual(len(ignore_loops_fndesc.calltypes) + 3,
+                         len(canonicalise_loops_fndesc.calltypes))
+
+        def find_getX(fd, op):
+            return [x for x in fd.calltypes.keys()
+                    if isinstance(x, ir.Expr) and x.op == op]
+
+        il_getiters = find_getX(ignore_loops_fndesc, "getiter")
+        self.assertEqual(len(il_getiters), 1)  # tuple iterator
+
+        cl_getiters = find_getX(canonicalise_loops_fndesc, "getiter")
+        self.assertEqual(len(cl_getiters), 1)  # loop range iterator
+
+        cl_getitems = find_getX(canonicalise_loops_fndesc, "getitem")
+        self.assertEqual(len(cl_getitems), 1)  # tuple getitem induced by loop
+
+        # check the value of the untransformed IR getiter is now the value of
+        # the transformed getitem
+        self.assertEqual(il_getiters[0].value.name, cl_getitems[0].value.name)
+
+        # check the type of the transformed IR getiter is a range iter
+        range_inst = canonicalise_loops_fndesc.calltypes[cl_getiters[0]].args[0]
+        self.assertTrue(isinstance(range_inst, types.RangeType))
+
+    def test_transform_scope(self):
+        """ This checks the transform, when there's no typemap, will happily
+        transform a loop on something that's not tuple-like
+        """
+        def get_info(pipeline):
+            @njit(pipeline_class=pipeline)
+            def foo():
+                acc = 0
+                for i in [1, 2, 3]:
+                    acc += i
+                return acc
+
+            self.assertEqual(foo(), foo.py_func())
+            cres = foo.overloads[foo.signatures[0]]
+            func_ir = cres.metadata['preserved_ir']
+            return func_ir, cres.fndesc
+
+        ignore_loops_ir, ignore_loops_fndesc = \
+            get_info(self.LoopIgnoringCompiler)
+        canonicalise_loops_ir, canonicalise_loops_fndesc = \
+            get_info(self.LoopCanonicalisingCompiler)
+
+        # check CFG is the same
+        def compare_cfg(a, b):
+            a_cfg = compute_cfg_from_blocks(flatten_labels(a.blocks))
+            b_cfg = compute_cfg_from_blocks(flatten_labels(b.blocks))
+            self.assertEqual(a_cfg, b_cfg)
+
+        compare_cfg(ignore_loops_ir, canonicalise_loops_ir)
+
+        # check there's three more call types in the canonicalised one:
+        # len(literal list)
+        # range(of the len() above)
+        # getitem(literal list arg, index)
+        self.assertEqual(len(ignore_loops_fndesc.calltypes) + 3,
+                         len(canonicalise_loops_fndesc.calltypes))
+
+        def find_getX(fd, op):
+            return [x for x in fd.calltypes.keys()
+                    if isinstance(x, ir.Expr) and x.op == op]
+
+        il_getiters = find_getX(ignore_loops_fndesc, "getiter")
+        self.assertEqual(len(il_getiters), 1)  # list iterator
+
+        cl_getiters = find_getX(canonicalise_loops_fndesc, "getiter")
+        self.assertEqual(len(cl_getiters), 1)  # loop range iterator
+
+        cl_getitems = find_getX(canonicalise_loops_fndesc, "getitem")
+        self.assertEqual(len(cl_getitems), 1)  # list getitem induced by loop
+
+        # check the value of the untransformed IR getiter is now the value of
+        # the transformed getitem
+        self.assertEqual(il_getiters[0].value.name, cl_getitems[0].value.name)
+
+        # check the type of the transformed IR getiter is a range iter
+        range_inst = canonicalise_loops_fndesc.calltypes[cl_getiters[0]].args[0]
+        self.assertTrue(isinstance(range_inst, types.RangeType))
+
+    @unittest.skip("Waiting for pass to be enabled for all tuples")
+    def test_influence_of_typed_transform(self):
+        """ This heavily checks a typed transformation only impacts tuple
+        induced loops"""
+
+        def get_info(pipeline):
+            @njit(pipeline_class=pipeline)
+            def foo(tup):
+                acc = 0
+                for i in range(4):
+                    for y in tup:
+                        for j in range(3):
+                            acc += 1
+                return acc
+
+            x = (1, 2, 3)
+            self.assertEqual(foo(x), foo.py_func(x))
+            cres = foo.overloads[foo.signatures[0]]
+            func_ir = cres.metadata['func_ir']
+            return func_ir, cres.fndesc
+
+        ignore_loops_ir, ignore_loops_fndesc = \
+            get_info(self.LoopIgnoringCompiler)
+        canonicalise_loops_ir, canonicalise_loops_fndesc = \
+            get_info(self.TypedLoopCanonicalisingCompiler)
+
+        # check CFG is the same
+        def compare_cfg(a, b):
+            a_cfg = compute_cfg_from_blocks(flatten_labels(a.blocks))
+            b_cfg = compute_cfg_from_blocks(flatten_labels(b.blocks))
+            self.assertEqual(a_cfg, b_cfg)
+
+        compare_cfg(ignore_loops_ir, canonicalise_loops_ir)
+
+        # check there's three more call types in the canonicalised one:
+        # len(tuple arg)
+        # range(of the len() above)
+        # getitem(tuple arg, index)
+        self.assertEqual(len(ignore_loops_fndesc.calltypes) + 3,
+                         len(canonicalise_loops_fndesc.calltypes))
+
+        def find_getX(fd, op):
+            return [x for x in fd.calltypes.keys()
+                    if isinstance(x, ir.Expr) and x.op == op]
+
+        il_getiters = find_getX(ignore_loops_fndesc, "getiter")
+        self.assertEqual(len(il_getiters), 3)  # 1 * tuple + 2 * loop range
+
+        cl_getiters = find_getX(canonicalise_loops_fndesc, "getiter")
+        self.assertEqual(len(cl_getiters), 3)  # 3 * loop range iterator
+
+        cl_getitems = find_getX(canonicalise_loops_fndesc, "getitem")
+        self.assertEqual(len(cl_getitems), 1)  # tuple getitem induced by loop
+
+        # check the value of the untransformed IR getiter is now the value of
+        # the transformed getitem
+        self.assertEqual(il_getiters[1].value.name, cl_getitems[0].value.name)
+
+        # check the type of the transformed IR getiter's are all range iter
+        for x in cl_getiters:
+            range_inst = canonicalise_loops_fndesc.calltypes[x].args[0]
+            self.assertTrue(isinstance(range_inst, types.RangeType))
+
+    def test_influence_of_typed_transform_literal_unroll(self):
+        """ This heavily checks a typed transformation only impacts loops with
+        literal_unroll marker"""
+
+        def get_info(pipeline):
+            @njit(pipeline_class=pipeline)
+            def foo(tup):
+                acc = 0
+                for i in range(4):
+                    for y in literal_unroll(tup):
+                        for j in range(3):
+                            acc += 1
+                return acc
+
+            x = (1, 2, 3)
+            self.assertEqual(foo(x), foo.py_func(x))
+            cres = foo.overloads[foo.signatures[0]]
+            func_ir = cres.metadata['preserved_ir']
+            return func_ir, cres.fndesc
+
+        ignore_loops_ir, ignore_loops_fndesc = \
+            get_info(self.LoopIgnoringCompiler)
+        canonicalise_loops_ir, canonicalise_loops_fndesc = \
+            get_info(self.TypedLoopCanonicalisingCompiler)
+
+        # check CFG is the same
+        def compare_cfg(a, b):
+            a_cfg = compute_cfg_from_blocks(flatten_labels(a.blocks))
+            b_cfg = compute_cfg_from_blocks(flatten_labels(b.blocks))
+            self.assertEqual(a_cfg, b_cfg)
+
+        compare_cfg(ignore_loops_ir, canonicalise_loops_ir)
+
+        # check there's three more call types in the canonicalised one:
+        # len(tuple arg)
+        # range(of the len() above)
+        # getitem(tuple arg, index)
+        self.assertEqual(len(ignore_loops_fndesc.calltypes) + 3,
+                         len(canonicalise_loops_fndesc.calltypes))
+
+        def find_getX(fd, op):
+            return [x for x in fd.calltypes.keys()
+                    if isinstance(x, ir.Expr) and x.op == op]
+
+        il_getiters = find_getX(ignore_loops_fndesc, "getiter")
+        self.assertEqual(len(il_getiters), 3)  # 1 * tuple + 2 * loop range
+
+        cl_getiters = find_getX(canonicalise_loops_fndesc, "getiter")
+        self.assertEqual(len(cl_getiters), 3)  # 3 * loop range iterator
+
+        cl_getitems = find_getX(canonicalise_loops_fndesc, "getitem")
+        self.assertEqual(len(cl_getitems), 1)  # tuple getitem induced by loop
+
+        # check the value of the untransformed IR getiter is now the value of
+        # the transformed getitem
+        self.assertEqual(il_getiters[1].value.name, cl_getitems[0].value.name)
+
+        # check the type of the transformed IR getiter's are all range iter
+        for x in cl_getiters:
+            range_inst = canonicalise_loops_fndesc.calltypes[x].args[0]
+            self.assertTrue(isinstance(range_inst, types.RangeType))
+
+    @unittest.skip("Waiting for pass to be enabled for all tuples")
+    def test_lots_of_loops(self):
+        """ This heavily checks a simple loop transform """
+
+        def get_info(pipeline):
+            @njit(pipeline_class=pipeline)
+            def foo(tup):
+                acc = 0
+                for i in tup:
+                    acc += i
+                    for j in tup + (4, 5, 6):
+                        acc += 1 - j
+                        if j > 5:
+                            break
+                    else:
+                        acc -= 2
+                for i in tup:
+                    acc -= i % 2
+
+                return acc
+
+            x = (1, 2, 3)
+            self.assertEqual(foo(x), foo.py_func(x))
+            cres = foo.overloads[foo.signatures[0]]
+            func_ir = cres.metadata['preserved_ir']
+            return func_ir, cres.fndesc
+
+        ignore_loops_ir, ignore_loops_fndesc = \
+            get_info(self.LoopIgnoringCompiler)
+        canonicalise_loops_ir, canonicalise_loops_fndesc = \
+            get_info(self.LoopCanonicalisingCompiler)
+
+        # check CFG is the same
+        def compare_cfg(a, b):
+            a_cfg = compute_cfg_from_blocks(flatten_labels(a.blocks))
+            b_cfg = compute_cfg_from_blocks(flatten_labels(b.blocks))
+            self.assertEqual(a_cfg, b_cfg)
+
+        compare_cfg(ignore_loops_ir, canonicalise_loops_ir)
+
+        # check there's three * N more call types in the canonicalised one:
+        # len(tuple arg)
+        # range(of the len() above)
+        # getitem(tuple arg, index)
+        self.assertEqual(len(ignore_loops_fndesc.calltypes) + 3 * 3,
+                         len(canonicalise_loops_fndesc.calltypes))
+
+    def test_inlined_loops(self):
+        """ Checks a loop appearing from a closure """
+
+        def get_info(pipeline):
+            @njit(pipeline_class=pipeline)
+            def foo(tup):
+                def bar(n):
+                    acc = 0
+                    for i in range(n):
+                        acc += 1
+                    return acc
+
+                acc = 0
+                for i in tup:
+                    acc += i
+                    acc += bar(i)
+
+                return acc
+
+            x = (1, 2, 3)
+            self.assertEqual(foo(x), foo.py_func(x))
+            cres = foo.overloads[foo.signatures[0]]
+            func_ir = cres.metadata['preserved_ir']
+            return func_ir, cres.fndesc
+
+        ignore_loops_ir, ignore_loops_fndesc = \
+            get_info(self.LoopIgnoringCompiler)
+        canonicalise_loops_ir, canonicalise_loops_fndesc = \
+            get_info(self.LoopCanonicalisingCompiler)
+
+        # check CFG is the same
+        def compare_cfg(a, b):
+            a_cfg = compute_cfg_from_blocks(flatten_labels(a.blocks))
+            b_cfg = compute_cfg_from_blocks(flatten_labels(b.blocks))
+            self.assertEqual(a_cfg, b_cfg)
+
+        compare_cfg(ignore_loops_ir, canonicalise_loops_ir)
+
+        # check there's 2 * N - 1 more call types in the canonicalised one:
+        # The -1 comes from the closure being inlined and and the call removed.
+        # len(tuple arg)
+        # range(of the len() above)
+        # getitem(tuple arg, index)
+        self.assertEqual(len(ignore_loops_fndesc.calltypes) + 5,
+                         len(canonicalise_loops_fndesc.calltypes))
+
+
+class TestMixedTupleUnroll(MemoryLeakMixin, TestCase):
+
+    def test_01(self):
+        # test a case which is already in loop canonical form
+        @njit
+        def foo(idx, z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            acc = 0
+            for i in range(len(literal_unroll(a))):
+                acc += a[i]
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    break
+            return acc
+
+        f = 9
+        k = f
+
+        self.assertEqual(foo(2, k), foo.py_func(2, k))
+
+    def test_02(self):
+        # same as test_1 but without the explicit loop canonicalisation
+
+        @njit
+        def foo(idx, z):
+            x = (12, 12.7, 3j, 4, z, 2 * z)
+            acc = 0
+            for a in literal_unroll(x):
+                acc += a
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    break
+            return acc
+
+        f = 9
+        k = f
+
+        self.assertEqual(foo(2, k), foo.py_func(2, k))
+
+    def test_03(self):
+        # two unrolls
+        @njit
+        def foo(idx, z):
+            x = (12, 12.7, 3j, 4, z, 2 * z)
+            y = ('foo', z, 2 * z)
+            acc = 0
+            for a in literal_unroll(x):
+                acc += a
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    for t in literal_unroll(y):
+                        acc += t is False
+                    break
+            return acc
+
+        f = 9
+        k = f
+
+        self.assertEqual(foo(2, k), foo.py_func(2, k))
+
+    def test_04(self):
+        # mixed ref counted types
+        @njit
+        def foo(tup):
+            acc = 0
+            for a in literal_unroll(tup):
+                acc += a.sum()
+            return acc
+
+        n = 10
+        tup = (np.ones((n,)), np.ones((n, n)), np.ones((n, n, n)))
+        self.assertEqual(foo(tup), foo.py_func(tup))
+
+    def test_05(self):
+        # mix unroll and static_getitem
+        @njit
+        def foo(tup1, tup2):
+            acc = 0
+            for a in literal_unroll(tup1):
+                if a == 'a':
+                    acc += tup2[0].sum()
+                elif a == 'b':
+                    acc += tup2[1].sum()
+                elif a == 'c':
+                    acc += tup2[2].sum()
+                elif a == 12:
+                    acc += tup2[3].sum()
+                elif a == 3j:
+                    acc += tup2[4].sum()
+                else:
+                    raise RuntimeError("Unreachable")
+            return acc
+
+        n = 10
+        tup1 = ('a', 'b', 'c', 12, 3j,)
+        tup2 = (np.ones((n,)), np.ones((n, n)), np.ones((n, n, n)),
+                np.ones((n, n, n, n)), np.ones((n, n, n, n, n)))
+        self.assertEqual(foo(tup1, tup2), foo.py_func(tup1, tup2))
+
+    @unittest.skip("needs more clever branch prune")
+    def test_06(self):
+        # This wont work because both sides of the branch need typing as neither
+        # can be pruned by the current pruner
+        @njit
+        def foo(tup):
+            acc = 0
+            str_buf = typed.List.empty_list(types.unicode_type)
+            for a in literal_unroll(tup):
+                if a == 'a':
+                    str_buf.append(a)
+                else:
+                    acc += a
+            return acc
+
+        tup = ('a', 12)
+        self.assertEqual(foo(tup), foo.py_func(tup))
+
+    def test_07(self):
+        # A mix bag of stuff as an arg to a function that unifies as `intp`.
+        @njit
+        def foo(tup):
+            acc = 0
+            for a in literal_unroll(tup):
+                acc += len(a)
+            return acc
+
+        n = 10
+        tup = (np.ones((n,)), np.ones((n, n)), "ABCDEFGHJI", (1, 2, 3),
+               (1, 'foo', 2, 'bar'), {3, 4, 5, 6, 7})
+        self.assertEqual(foo(tup), foo.py_func(tup))
+
+    def test_08(self):
+        # dispatch to functions
+
+        @njit
+        def foo(tup1, tup2):
+            acc = 0
+            for a in literal_unroll(tup1):
+                if a == 'a':
+                    acc += tup2[0]()
+                elif a == 'b':
+                    acc += tup2[1]()
+                elif a == 'c':
+                    acc += tup2[2]()
+            return acc
+
+        def gen(x):
+            def impl():
+                return x
+            return njit(impl)
+
+        tup1 = ('a', 'b', 'c', 12, 3j, ('f',))
+        tup2 = (gen(1), gen(2), gen(3))
+        self.assertEqual(foo(tup1, tup2), foo.py_func(tup1, tup2))
+
+    def test_09(self):
+        # illegal RHS, has a mixed tuple being index dynamically
+
+        @njit
+        def foo(tup1, tup2):
+            acc = 0
+            idx = 0
+            for a in literal_unroll(tup1):
+                if a == 'a':
+                    acc += tup2[idx]
+                elif a == 'b':
+                    acc += tup2[idx]
+                elif a == 'c':
+                    acc += tup2[idx]
+                idx += 1
+            return idx, acc
+
+        @njit
+        def func1():
+            return 1
+
+        @njit
+        def func2():
+            return 2
+
+        @njit
+        def func3():
+            return 3
+
+        tup1 = ('a', 'b', 'c')
+        tup2 = (1j, 1, 2)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo(tup1, tup2)
+
+        self.assertIn(_header_lead, str(raises.exception))
+
+    def test_10(self):
+        # dispatch on literals triggering @overload resolution
+
+        def dt(value):
+            if value == "apple":
+                return 1
+            elif value == "orange":
+                return 2
+            elif value == "banana":
+                return 3
+            elif value == 0xca11ab1e:
+                return 0x5ca1ab1e + value
+
+        @overload(dt, inline='always')
+        def ol_dt(li):
+            if isinstance(li, types.StringLiteral):
+                value = li.literal_value
+                if value == "apple":
+                    def impl(li):
+                        return 1
+                elif value == "orange":
+                    def impl(li):
+                        return 2
+                elif value == "banana":
+                    def impl(li):
+                        return 3
+                return impl
+            elif isinstance(li, types.IntegerLiteral):
+                value = li.literal_value
+                if value == 0xca11ab1e:
+                    def impl(li):
+                        # close over the dispatcher :)
+                        return 0x5ca1ab1e + value
+                    return impl
+
+        @njit
+        def foo():
+            acc = 0
+            for t in literal_unroll(('apple', 'orange', 'banana', 3390155550)):
+                acc += dt(t)
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_11(self):
+
+        @njit
+        def foo():
+            x = []
+            z = ('apple', 'orange', 'banana')
+            for i in range(len(literal_unroll(z))):
+                t = z[i]
+                if t == "apple":
+                    x.append("0")
+                elif t == "orange":
+                    x.append(t)
+                elif t == "banana":
+                    x.append("2.0")
+            return x
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_11a(self):
+
+        @njit
+        def foo():
+            x = typed.List()
+            z = ('apple', 'orange', 'banana')
+            for i in range(len(literal_unroll(z))):
+                t = z[i]
+                if t == "apple":
+                    x.append("0")
+                elif t == "orange":
+                    x.append(t)
+                elif t == "banana":
+                    x.append("2.0")
+            return x
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_12(self):
+        # unroll the same target twice
+        @njit
+        def foo(idx, z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            acc = 0
+            for i in literal_unroll(a):
+                acc += i
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    for x in literal_unroll(a):
+                        acc += x
+                    break
+            if a[0] < 23:
+                acc += 2
+            return acc
+
+        f = 9
+        k = f
+
+        self.assertEqual(foo(2, k), foo.py_func(2, k))
+
+    def test_13(self):
+        # nesting unrolls is illegal
+        @njit
+        def foo(idx, z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            acc = 0
+            for i in literal_unroll(a):
+                acc += i
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    for x in literal_unroll(a):
+                        for j in literal_unroll(a):
+                            acc += j
+                        acc += x
+                for x in literal_unroll(a):
+                    acc += x
+            for x in literal_unroll(a):
+                acc += x
+            if a[0] < 23:
+                acc += 2
+            return acc
+
+        f = 9
+        k = f
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo(2, k)
+
+        self.assertIn("Nesting of literal_unroll is unsupported",
+                      str(raises.exception))
+
+    def test_14(self):
+        # unituple unroll can return derivative of the induction var
+
+        @njit
+        def foo():
+            x = (1, 2, 3, 4)
+            acc = 0
+            for a in literal_unroll(x):
+                acc += a
+            return a
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_15(self):
+        # mixed tuple unroll cannot return derivative of the induction var
+
+        @njit
+        def foo(x):
+            acc = 0
+            for a in literal_unroll(x):
+                acc += len(a)
+            return a
+
+        n = 5
+        tup = (np.ones((n,)), np.ones((n, n)), "ABCDEFGHJI", (1, 2, 3),
+               (1, 'foo', 2, 'bar'), {3, 4, 5, 6, 7})
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo(tup)
+
+        self.assertIn("Cannot unify", str(raises.exception))
+
+    def test_16(self):
+        # unituple slice and unroll is ok
+
+        def dt(value):
+            if value == 1000:
+                return "a"
+            elif value == 2000:
+                return "b"
+            elif value == 3000:
+                return "c"
+            elif value == 4000:
+                return "d"
+
+        @overload(dt, inline='always')
+        def ol_dt(li):
+            if isinstance(li, types.IntegerLiteral):
+                value = li.literal_value
+                if value == 1000:
+                    def impl(li):
+                        return "a"
+                elif value == 2000:
+                    def impl(li):
+                        return "b"
+                elif value == 3000:
+                    def impl(li):
+                        return "c"
+                elif value == 4000:
+                    def impl(li):
+                        return "d"
+                return impl
+
+        @njit
+        def foo():
+            x = (1000, 2000, 3000, 4000)
+            acc = ""
+            for a in literal_unroll(x[:2]):
+                acc += dt(a)
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_17(self):
+        # mixed tuple slice and unroll is ok
+
+        def dt(value):
+            if value == 1000:
+                return "a"
+            elif value == 2000:
+                return "b"
+            elif value == 3000:
+                return "c"
+            elif value == 4000:
+                return "d"
+            elif value == 'f':
+                return "EFF"
+
+        @overload(dt, inline='always')
+        def ol_dt(li):
+            if isinstance(li, types.IntegerLiteral):
+                value = li.literal_value
+                if value == 1000:
+                    def impl(li):
+                        return "a"
+                elif value == 2000:
+                    def impl(li):
+                        return "b"
+                elif value == 3000:
+                    def impl(li):
+                        return "c"
+                elif value == 4000:
+                    def impl(li):
+                        return "d"
+                return impl
+            elif isinstance(li, types.StringLiteral):
+                value = li.literal_value
+                if value == 'f':
+                    def impl(li):
+                        return "EFF"
+                    return impl
+
+        @njit
+        def foo():
+            x = (1000, 2000, 3000, 'f')
+            acc = ""
+            for a in literal_unroll(x[1:]):
+                acc += dt(a)
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_18(self):
+        # unituple backwards slice
+        @njit
+        def foo():
+            x = (1000, 2000, 3000, 4000, 5000, 6000)
+            count = 0
+            for a in literal_unroll(x[::-1]):
+                count += 1
+                if a < 3000:
+                    break
+            return count
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_19(self):
+        # mixed bag of refcounted
+        @njit
+        def foo():
+            acc = 0
+            l1 = [1, 2, 3, 4]
+            l2 = [10, 20]
+            tup = (l1, l2)
+            a1 = np.arange(20)
+            a2 = np.ones(5, dtype=np.complex128)
+            tup = (l1, a1, l2, a2)
+            for t in literal_unroll(tup):
+                acc += len(t)
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_20(self):
+        # testing partial type inference survives as the list append in the
+        # unrolled version is full inferable
+        @njit
+        def foo():
+            l = []
+            a1 = np.arange(20)
+            a2 = np.ones(5, dtype=np.complex128)
+            tup = (a1, a2)
+            for t in literal_unroll(tup):
+                l.append(t.sum())
+            return l
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_21(self):
+        # unroll in closure that gets inlined
+        @njit
+        def foo(z):
+            b = (23, 23.9, 6j, 8)
+
+            def bar():
+                acc = 0
+                for j in literal_unroll(b):
+                    acc += j
+                return acc
+            outer_acc = 0
+            for x in (1, 2, 3, 4):
+                outer_acc += bar() + x
+
+            return outer_acc
+
+        f = 9
+        k = f
+        self.assertEqual(foo(k), foo.py_func(k))
+
+    def test_22(self):
+        @njit
+        def foo(z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            b = (23, 23.9, 6j, 8)
+
+            def bar():
+                acc = 0
+                for j in literal_unroll(b):
+                    acc += j
+                return acc
+            acc = 0
+            # this loop is induced in `x` but `x` is not used, there is a nest
+            # here by virtue of inlining
+            for x in literal_unroll(a):
+                acc += bar()
+
+            return acc
+
+        f = 9
+        k = f
+        self.assertEqual(foo(k), foo.py_func(k))
+
+    def test_23(self):
+        # unroll from closure that ends up banned as it leads to nesting
+        @njit
+        def foo(z):
+            b = (23, 23.9, 6j, 8)
+
+            def bar():
+                acc = 0
+                for j in literal_unroll(b):
+                    acc += j
+                return acc
+            outer_acc = 0
+            # this drives an inlined literal_unroll loop but also has access to
+            # the induction variable, this is a nested literal_unroll so is
+            # banned
+            for x in literal_unroll(b):
+                outer_acc += bar() + x
+
+            return outer_acc
+
+        f = 9
+        k = f
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo(k)
+
+        self.assertIn("Nesting of literal_unroll is unsupported",
+                      str(raises.exception))
+
+    def test_24(self):
+        # unroll something unsupported
+        @njit
+        def foo():
+            for x in literal_unroll("ABCDE"):
+                print(x)
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+
+        msg = "argument should be a tuple or a list of constant values"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_25(self):
+        # use unroll by reference/alias
+        @njit
+        def foo():
+            val = literal_unroll(((1, 2, 3), (2j, 3j), [1, 2], "xyz"))
+            alias1 = val
+            alias2 = alias1
+            lens = []
+            for x in alias2:
+                lens.append(len(x))
+            return lens
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_26(self):
+        # var defined in unrolled body escapes
+        # untouched variable is untouched
+        # read only variable is only read
+        # mutated is muted correctly
+        @njit
+        def foo(z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            acc = 0
+            count = 0
+            untouched = 54
+            read_only = 17
+            mutated = np.empty((len(a),), dtype=np.complex128)
+            for x in literal_unroll(a):
+                acc += x
+                mutated[count] = x
+                count += 1
+                escape = count + read_only
+            return escape, acc, untouched, read_only, mutated
+
+        f = 9
+        k = f
+
+        self.assertPreciseEqual(foo(k), foo.py_func(k))
+
+    @skip_parfors_unsupported
+    def test_27(self):
+        # parfors loop in unrolled loop
+        @njit(parallel=True)
+        def foo(z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            acc = 0
+            for x in literal_unroll(a):
+                for k in prange(10):
+                    acc += 1
+            return acc
+
+        f = 9
+        k = f
+
+        self.assertEqual(foo(k), foo.py_func(k))
+
+    @skip_parfors_unsupported
+    def test_28(self):
+        # parfors reducing on the unrolled induction var
+        @njit(parallel=True)
+        def foo(z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            acc = 0
+            for x in literal_unroll(a):
+                for k in prange(10):
+                    acc += x
+            return acc
+
+        f = 9
+        k = f
+
+        # summation is unstable
+        np.testing.assert_allclose(foo(k), foo.py_func(k))
+
+    @skip_parfors_unsupported
+    def test_29(self):
+        # This "works" but parfors is not producing a parallel loop
+        # TODO: fix
+        @njit(parallel=True)
+        def foo(z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            acc = 0
+            for k in prange(10):
+                for x in literal_unroll(a):
+                    acc += x
+            return acc
+
+        f = 9
+        k = f
+
+        self.assertEqual(foo(k), foo.py_func(k))
+
+    def test_30(self):
+        # function escaping containing an unroll
+        @njit
+        def foo():
+            const = 1234
+
+            def bar(t):
+                acc = 0
+                a = (12, 12.7, 3j, 4)
+                for x in literal_unroll(a):
+                    acc += x + const
+                return acc, t
+            return [x for x in map(bar, (1, 2))]
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_31(self):
+        # this is testing that generators can survive partial typing
+        # invalid function escaping, map uses zip which can't handle the mixed
+        # tuple
+        @njit
+        def foo():
+            const = 1234
+
+            def bar(t):
+                acc = 0
+                a = (12, 12.7, 3j, 4)
+                for x in literal_unroll(a):
+                    acc += x + const
+                return acc, t
+            return [x for x in map(bar, (1, 2j))]
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        self.assertIn(_header_lead, str(raises.exception))
+        self.assertIn("zip", str(raises.exception))
+
+    def test_32(self):
+        # test yielding from an unroll
+        @njit
+        def gen(a):
+            for x in literal_unroll(a):
+                yield x
+
+        @njit
+        def foo():
+            return [x for x in gen((1, 2.3, 4j,))]
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_33(self):
+        # test yielding from unroll in escaping function that is consumed and
+        # yields
+
+        @njit
+        def consumer(func, arg):
+            yield func(arg)
+
+        def get(cons):
+            @njit
+            def foo():
+                def gen(a):
+                    for x in literal_unroll(a):
+                        yield x
+                return [next(x) for x in cons(gen, (1, 2.3, 4j,))]
+            return foo
+
+        cfunc = get(consumer)
+        pyfunc = get(consumer.py_func).py_func
+
+        self.assertEqual(cfunc(), pyfunc())
+
+    def test_34(self):
+        # mixed bag, redefinition of tuple
+        @njit
+        def foo():
+            acc = 0
+            l1 = [1, 2, 3, 4]
+            l2 = [10, 20]
+            if acc - 2 > 3:
+                tup = (l1, l2)
+            else:
+                a1 = np.arange(20)
+                a2 = np.ones(5, dtype=np.complex128)
+                tup = (l1, a1, l2, a2)
+            for t in literal_unroll(tup):
+                acc += len(t)
+            return acc
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+
+        self.assertIn("Invalid use of", str(raises.exception))
+        self.assertIn("found multiple definitions of variable",
+                      str(raises.exception))
+
+
+class TestConstListUnroll(MemoryLeakMixin, TestCase):
+
+    def test_01(self):
+
+        @njit
+        def foo():
+            a = [12, 12.7, 3j, 4]
+            acc = 0
+            for i in range(len(literal_unroll(a))):
+                acc += a[i]
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    break
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_02(self):
+        # same as test_1 but without the explicit loop canonicalisation
+
+        @njit
+        def foo():
+            x = [12, 12.7, 3j, 4]
+            acc = 0
+            for a in literal_unroll(x):
+                acc += a
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    break
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_03(self):
+        # two unrolls
+        @njit
+        def foo():
+            x = [12, 12.7, 3j, 4]
+            y = ['foo', 8]
+            acc = 0
+            for a in literal_unroll(x):
+                acc += a
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    for t in literal_unroll(y):
+                        acc += t is False
+                    break
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_04(self):
+        # two unrolls, one is a const list, one is a tuple
+        @njit
+        def foo():
+            x = [12, 12.7, 3j, 4]
+            y = ('foo', 8)
+            acc = 0
+            for a in literal_unroll(x):
+                acc += a
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    for t in literal_unroll(y):
+                        acc += t is False
+                    break
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_05(self):
+        # illegal, list has to be const
+        @njit
+        def foo(tup1, tup2):
+            acc = 0
+            for a in literal_unroll(tup1):
+                if a[0] > 1:
+                    acc += tup2[0].sum()
+            return acc
+
+        n = 10
+        tup1 = [np.zeros(10), np.zeros(10)]
+        tup2 = (np.ones((n,)), np.ones((n, n)), np.ones((n, n, n)),
+                np.ones((n, n, n, n)), np.ones((n, n, n, n, n)))
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo(tup1, tup2)
+
+        msg = "Invalid use of literal_unroll with a function argument"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_06(self):
+        # illegal: list containing non const
+        @njit
+        def foo():
+            n = 10
+            tup = [np.ones((n,)), np.ones((n, n)), "ABCDEFGHJI", (1, 2, 3),
+                   (1, 'foo', 2, 'bar'), {3, 4, 5, 6, 7}]
+            acc = 0
+            for a in literal_unroll(tup):
+                acc += len(a)
+            return acc
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+
+        self.assertIn("Found non-constant value at position 0",
+                      str(raises.exception))
+
+    def test_7(self):
+        # dispatch on literals triggering @overload resolution
+
+        def dt(value):
+            if value == "apple":
+                return 1
+            elif value == "orange":
+                return 2
+            elif value == "banana":
+                return 3
+            elif value == 0xca11ab1e:
+                return 0x5ca1ab1e + value
+
+        @overload(dt, inline='always')
+        def ol_dt(li):
+            if isinstance(li, types.StringLiteral):
+                value = li.literal_value
+                if value == "apple":
+                    def impl(li):
+                        return 1
+                elif value == "orange":
+                    def impl(li):
+                        return 2
+                elif value == "banana":
+                    def impl(li):
+                        return 3
+                return impl
+            elif isinstance(li, types.IntegerLiteral):
+                value = li.literal_value
+                if value == 0xca11ab1e:
+                    def impl(li):
+                        # close over the dispatcher :)
+                        return 0x5ca1ab1e + value
+                    return impl
+
+        @njit
+        def foo():
+            acc = 0
+            for t in literal_unroll(['apple', 'orange', 'banana', 3390155550]):
+                acc += dt(t)
+            return acc
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_8(self):
+
+        @njit
+        def foo():
+            x = []
+            z = ['apple', 'orange', 'banana']
+            for i in range(len(literal_unroll(z))):
+                t = z[i]
+                if t == "apple":
+                    x.append("0")
+                elif t == "orange":
+                    x.append(t)
+                elif t == "banana":
+                    x.append("2.0")
+            return x
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_9(self):
+        # unroll the same target twice
+        @njit
+        def foo(idx, z):
+            a = [12, 12.7, 3j, 4]
+            acc = 0
+            for i in literal_unroll(a):
+                acc += i
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    for x in literal_unroll(a):
+                        acc += x
+                    break
+            if a[0] < 23:
+                acc += 2
+            return acc
+
+        f = 9
+        k = f
+
+        self.assertEqual(foo(2, k), foo.py_func(2, k))
+
+    def test_10(self):
+        # nesting unrolls is illegal
+        @njit
+        def foo(idx, z):
+            a = (12, 12.7, 3j, 4, z, 2 * z)
+            b = [12, 12.7, 3j, 4]
+            acc = 0
+            for i in literal_unroll(a):
+                acc += i
+                if acc.real < 26:
+                    acc -= 1
+                else:
+                    for x in literal_unroll(a):
+                        for j in literal_unroll(b):
+                            acc += j
+                        acc += x
+                for x in literal_unroll(a):
+                    acc += x
+            for x in literal_unroll(a):
+                acc += x
+            if a[0] < 23:
+                acc += 2
+            return acc
+
+        f = 9
+        k = f
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo(2, k)
+
+        self.assertIn("Nesting of literal_unroll is unsupported",
+                      str(raises.exception))
+
+    def test_11(self):
+        # homogeneous const list unroll can return derivative of the induction
+        # var
+
+        @njit
+        def foo():
+            x = [1, 2, 3, 4]
+            acc = 0
+            for a in literal_unroll(x):
+                acc += a
+            return a
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_12(self):
+        # mixed unroll cannot return derivative of the induction var
+        @njit
+        def foo():
+            acc = 0
+            x = [1, 2, 'a']
+            for a in literal_unroll(x):
+                acc += bool(a)
+            return a
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        self.assertIn("Cannot unify", str(raises.exception))
+
+    def test_13(self):
+        # list slice is illegal
+
+        @njit
+        def foo():
+            x = [1000, 2000, 3000, 4000]
+            acc = 0
+            for a in literal_unroll(x[:2]):
+                acc += a
+            return acc
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+
+        self.assertIn("Invalid use of literal_unroll", str(raises.exception))
+
+    def test_14(self):
+        # list mutate is illegal
+
+        @njit
+        def foo():
+            x = [1000, 2000, 3000, 4000]
+            acc = 0
+            for a in literal_unroll(x):
+                acc += a
+            x.append(10)
+            return acc
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        self.assertIn("Unknown attribute 'append' of type Tuple",
+                      str(raises.exception))
+
+
+class TestMore(TestCase):
+    def test_invalid_use_of_unroller(self):
+        @njit
+        def foo():
+            x = (10, 20)
+            r = 0
+            for a in literal_unroll(x, x):
+                r += a
+            return r
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+        self.assertIn(
+            "literal_unroll takes one argument, found 2",
+            str(raises.exception),
+        )
+
+    def test_non_constant_list(self):
+
+        @njit
+        def foo(y):
+            x = [10, y]
+            r = 0
+            for a in literal_unroll(x):
+                r += a
+            return r
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo(10)
+        self.assertIn(
+            ("Found non-constant value at position 1 in a list argument to "
+             "literal_unroll"),
+            str(raises.exception)
+        )
+
+    @unittest.skip("numba.literally not supported yet")
+    def test_literally_constant_list(self):
+        # FAIL. May need to consider it in a future PR
+        from numba import literally
+
+        @njit
+        def foo(y):
+            x = [10, literally(y)]
+            r = 0
+            for a in literal_unroll(x):
+                r += a
+            return r
+
+        # Found non-constant value at position 1 in a list argument to
+        # literal_unroll
+        foo(12)
+
+        @njit
+        def bar():
+            return foo(12)
+
+        # Found non-constant value at position 1 in a list argument to
+        # literal_unroll
+        bar()
+
+    @unittest.skip("inlining of foo doesn't have const prop so y isn't const")
+    def test_inlined_unroll_list(self):
+        @njit(inline='always')
+        def foo(y):
+            x = [10, y]
+            r = 0
+            for a in literal_unroll(x):
+                r += a
+            return r
+
+        @njit
+        def bar():
+            return foo(12)
+
+        self.assertEqual(bar(), 10 + 12)
+
+    def test_unroll_tuple_arg(self):
+        @njit
+        def foo(y):
+            x = (10, y)
+            r = 0
+            for a in literal_unroll(x):
+                r += a
+            return r
+
+        self.assertEqual(foo(12), foo.py_func(12))
+        self.assertEqual(foo(1.2), foo.py_func(1.2))
+
+    def test_unroll_tuple_arg2(self):
+        @njit
+        def foo(x):
+            r = 0
+            for a in literal_unroll(x):
+                r += a
+            return r
+
+        self.assertEqual(foo((12, 1.2)), foo.py_func((12, 1.2)))
+        self.assertEqual(foo((12, 1.2)), foo.py_func((12, 1.2)))
+
+    def test_unroll_tuple_alias(self):
+        @njit
+        def foo():
+            x = (10, 1.2)
+            out = 0
+            for i in literal_unroll(x):
+                j = i
+                k = j
+                out += j + k + i
+            return out
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_unroll_tuple_nested(self):
+
+        @njit
+        def foo():
+            x = ((10, 1.2), (1j, 3.))
+            out = 0
+            for i in literal_unroll(x):
+                for j in (i):
+                    out += j
+            return out
+
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+
+        self.assertIn("getiter", str(raises.exception))
+        re = r".*Tuple\(int[0-9][0-9], float64\).*"
+        self.assertRegexpMatches(str(raises.exception), re)
+
+    def test_unroll_tuple_of_dict(self):
+
+        @njit
+        def foo():
+            x = {}
+            x["a"] = 1
+            x["b"] = 2
+            y = {}
+            y[3] = "c"
+            y[4] = "d"
+
+            for it in literal_unroll((x, y)):
+                for k, v in it.items():
+                    print(k, v)
+
+        with captured_stdout() as stdout:
+            foo()
+        lines = stdout.getvalue().splitlines()
+        self.assertEqual(
+            lines,
+            ['a 1', 'b 2', '3 c', '4 d'],
+        )
+
+    def test_unroll_named_tuple(self):
+        ABC = namedtuple('ABC', ['a', 'b', 'c'])
+
+        @njit
+        def foo():
+            abc = ABC(1, 2j, 3.4)
+            out = 0
+            for i in literal_unroll(abc):
+                out += i
+            return out
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_unroll_named_tuple_arg(self):
+        ABC = namedtuple('ABC', ['a', 'b', 'c'])
+
+        @njit
+        def foo(x):
+            out = 0
+            for i in literal_unroll(x):
+                out += i
+            return out
+
+        abc = ABC(1, 2j, 3.4)
+
+        self.assertEqual(foo(abc), foo.py_func(abc))
+
+    def test_unroll_named_unituple(self):
+        ABC = namedtuple('ABC', ['a', 'b', 'c'])
+
+        @njit
+        def foo():
+            abc = ABC(1, 2, 3)
+            out = 0
+            for i in literal_unroll(abc):
+                out += i
+            return out
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_unroll_named_unituple_arg(self):
+        ABC = namedtuple('ABC', ['a', 'b', 'c'])
+
+        @njit
+        def foo(x):
+            out = 0
+            for i in literal_unroll(x):
+                out += i
+            return out
+
+        abc = ABC(1, 2, 3)
+
+        self.assertEqual(foo(abc), foo.py_func(abc))
+
+    def test_unroll_global_tuple(self):
+
+        @njit
+        def foo():
+            out = 0
+            for i in literal_unroll(_X_GLOBAL):
+                out += i
+            return out
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_unroll_freevar_tuple(self):
+        x = (10, 11)
+
+        @njit
+        def foo():
+            out = 0
+            for i in literal_unroll(x):
+                out += i
+            return out
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_unroll_function_tuple(self):
+        @njit
+        def a():
+            return 1
+
+        @njit
+        def b():
+            return 2
+
+        x = (a, b)
+
+        @njit
+        def foo():
+            out = 0
+            for f in literal_unroll(x):
+                out += f()
+            return out
+
+        self.assertEqual(foo(), foo.py_func())
+
+    def test_unroll_indexing_list(self):
+        # See issue #5477
+        @njit
+        def foo(cont):
+            i = 0
+            acc = 0
+            normal_list = [a for a in cont]
+            heter_tuple = ('a', 25, 0.23, None)
+            for item in literal_unroll(heter_tuple):
+                acc += normal_list[i]
+                i += 1
+                print(item)
+            return i, acc
+
+        data = [j for j in range(4)]
+
+        # send stdout to nowhere, just check return values
+        with captured_stdout():
+            self.assertEqual(foo(data), foo.py_func(data))
+
+        # now capture stdout for jit function and check
+        with captured_stdout() as stdout:
+            foo(data)
+        lines = stdout.getvalue().splitlines()
+        self.assertEqual(
+            lines,
+            ['a', '25', '0.23', 'None'],
+        )
+
+    def test_unroller_as_freevar(self):
+        mixed = (np.ones((1,)), np.ones((1, 1)), np.ones((1, 1, 1)))
+        from numba import literal_unroll as freevar_unroll
+
+        @njit
+        def foo():
+            out = 0
+            for i in freevar_unroll(mixed):
+                out += i.ndim
+            return out
+
+        self.assertEqual(foo(), foo.py_func())
+
+
+def capture(real_pass):
+    """ Returns a compiler pass that captures the mutation state reported
+    by the pass used in the argument"""
+    @register_pass(mutates_CFG=False, analysis_only=True)
+    class ResultCapturer(AnalysisPass):
+        _name = "capture_%s" % real_pass._name
+        _real_pass = real_pass
+
+        def __init__(self):
+            FunctionPass.__init__(self)
+
+        def run_pass(self, state):
+            result = real_pass().run_pass(state)
+            mutation_results = state.metadata.setdefault('mutation_results', {})
+            mutation_results[real_pass] = result
+            return result
+
+    return ResultCapturer
+
+
+class CapturingCompiler(CompilerBase):
+    """ Simple pipeline that wraps passes with the ResultCapturer pass"""
+
+    def define_pipelines(self):
+        pm = PassManager("Capturing Compiler")
+
+        def add_pass(x, y):
+            return pm.add_pass(capture(x), y)
+
+        add_pass(TranslateByteCode, "analyzing bytecode")
+        add_pass(FixupArgs, "fix up args")
+        add_pass(IRProcessing, "processing IR")
+        add_pass(LiteralUnroll, "handles literal_unroll")
+
+        # typing
+        add_pass(NopythonTypeInference, "nopython frontend")
+
+        # legalise
+        add_pass(IRLegalization,
+                 "ensure IR is legal prior to lowering")
+
+        # lower
+        add_pass(NativeLowering, "native lowering")
+        add_pass(NoPythonBackend, "nopython mode backend")
+        pm.finalize()
+        return [pm]
+
+
+class TestLiteralUnrollPassTriggering(TestCase):
+
+    def test_literal_unroll_not_invoked(self):
+        @njit(pipeline_class=CapturingCompiler)
+        def foo():
+            acc = 0
+            for i in (1, 2, 3):
+                acc += i
+            return acc
+
+        foo()
+        cres = foo.overloads[foo.signatures[0]]
+        self.assertFalse(cres.metadata['mutation_results'][LiteralUnroll])
+
+    def test_literal_unroll_is_invoked(self):
+        @njit(pipeline_class=CapturingCompiler)
+        def foo():
+            acc = 0
+            for i in literal_unroll((1, 2, 3)):
+                acc += i
+            return acc
+
+        foo()
+        cres = foo.overloads[foo.signatures[0]]
+        self.assertTrue(cres.metadata['mutation_results'][LiteralUnroll])
+
+    def test_literal_unroll_is_invoked_via_alias(self):
+        alias = literal_unroll
+
+        @njit(pipeline_class=CapturingCompiler)
+        def foo():
+            acc = 0
+            for i in alias((1, 2, 3)):
+                acc += i
+            return acc
+
+        foo()
+        cres = foo.overloads[foo.signatures[0]]
+        self.assertTrue(cres.metadata['mutation_results'][LiteralUnroll])
+
+    def test_literal_unroll_assess_empty_function(self):
+        @njit(pipeline_class=CapturingCompiler)
+        def foo():
+            pass
+
+        foo()
+        cres = foo.overloads[foo.signatures[0]]
+        self.assertFalse(cres.metadata['mutation_results'][LiteralUnroll])
+
+    def test_literal_unroll_not_in_globals(self):
+        f = """def foo():\n\tpass"""
+        l = {}
+        exec(f, {}, l)
+        foo = njit(pipeline_class=CapturingCompiler)(l['foo'])
+        foo()
+        cres = foo.overloads[foo.signatures[0]]
+        self.assertFalse(cres.metadata['mutation_results'][LiteralUnroll])
+
+    def test_literal_unroll_globals_and_locals(self):
+        f = """def foo():\n\tfor x in literal_unroll((1,)):\n\t\tpass"""
+        l = {}
+        exec(f, {}, l)
+        foo = njit(pipeline_class=CapturingCompiler)(l['foo'])
+        with self.assertRaises(errors.TypingError) as raises:
+            foo()
+        self.assertIn("Untyped global name 'literal_unroll'",
+                      str(raises.exception))
+
+        # same as above but now add literal_unroll to globals
+        l = {}
+        exec(f, {'literal_unroll': literal_unroll}, l)
+        foo = njit(pipeline_class=CapturingCompiler)(l['foo'])
+        foo()
+        cres = foo.overloads[foo.signatures[0]]
+        self.assertTrue(cres.metadata['mutation_results'][LiteralUnroll])
+
+        # same as above, but now with import
+        from textwrap import dedent
+        f = """
+            def gen():
+                from numba import literal_unroll
+                def foo():
+                    for x in literal_unroll((1,)):
+                        pass
+                return foo
+            bar = gen()
+            """
+        l = {}
+        exec(dedent(f), {}, l)
+        foo = njit(pipeline_class=CapturingCompiler)(l['bar'])
+        foo()
+        cres = foo.overloads[foo.signatures[0]]
+        self.assertTrue(cres.metadata['mutation_results'][LiteralUnroll])
+
+        # same as above, but now with import as something else
+        from textwrap import dedent
+        f = """
+            def gen():
+                from numba import literal_unroll as something_else
+                def foo():
+                    for x in something_else((1,)):
+                        pass
+                return foo
+            bar = gen()
+            """
+        l = {}
+        exec(dedent(f), {}, l)
+        foo = njit(pipeline_class=CapturingCompiler)(l['bar'])
+        foo()
+        cres = foo.overloads[foo.signatures[0]]
+        self.assertTrue(cres.metadata['mutation_results'][LiteralUnroll])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_moved_modules.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_moved_modules.py
new file mode 100644
index 000000000..4ccaa06c9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_moved_modules.py
@@ -0,0 +1,30 @@
+"""Tests for moved modules and their redirection from old path
+"""
+from numba.tests.support import TestCase
+
+
+class TestMovedModule(TestCase):
+    """Testing moved modules in Q1 2020 but were decided to kept as public API
+    """
+    def tests_numba_types(self):
+        import numba.types
+        import numba.core.types as types
+        # The old module IS NOT the new module
+        self.assertIsNot(numba.types, types)
+        # Attribute access are there
+        self.assertIs(numba.types.intp, types.intp)
+        self.assertIs(numba.types.float64, types.float64)
+        self.assertIs(numba.types.Array, types.Array)
+        # Submodule access through old import path is possible
+        import numba.types.misc
+        self.assertIs(types.misc, numba.types.misc)
+        self.assertIs(types.misc.Optional, numba.types.misc.Optional)
+        # Import time code could be executed twice and causes the following to
+        # fail.
+        self.assertIs(types.StringLiteral, numba.types.misc.StringLiteral)
+        # Check numba.types.container
+        from numba.types import containers
+        self.assertIs(types.containers, containers)
+        self.assertIs(types.containers.Sequence, containers.Sequence)
+        from numba.types.containers import Sequence
+        self.assertIs(Sequence, containers.Sequence)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_multi3.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_multi3.py
new file mode 100644
index 000000000..018637922
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_multi3.py
@@ -0,0 +1,43 @@
+import random
+
+import numpy as np
+
+from numba import njit
+from numba.core import types
+import unittest
+
+class TestMulti3(unittest.TestCase):
+    """
+    This test is only relevant for 32-bit architectures.
+
+    Test __multi3 implementation in _helperlib.c.
+    The symbol defines a i128 multiplication.
+    It is necessary for working around an issue in LLVM (see issue #969).
+    The symbol does not exist in 32-bit platform, and should not be used by
+    LLVM.  However, optimization passes will create i65 multiplication that
+    is then lowered to __multi3.
+    """
+    def test_multi3(self):
+        @njit("(int64,)")
+        def func(x):
+            res = 0
+            for i in range(x):
+                res += i
+            return res
+
+        x_cases = [-1, 0, 1, 3, 4, 8,
+                   0xffffffff - 1, 0xffffffff, 0xffffffff + 1,
+                   0x123456789abcdef, -0x123456789abcdef]
+        for _ in range(500):
+            x_cases.append(random.randint(0, 0xffffffff))
+
+        def expected(x):
+            if x <= 0: return 0
+            return ((x * (x - 1)) // 2) & (2**64 - 1)
+
+        for x in x_cases:
+            self.assertEqual(expected(x), func(x))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nan.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nan.py
new file mode 100644
index 000000000..3bc81b36d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nan.py
@@ -0,0 +1,40 @@
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+def isnan(x):
+    return x != x
+
+def isequal(x):
+    return x == x
+
+
+class TestNaN(unittest.TestCase):
+
+    def test_nans(self, flags=enable_pyobj_flags):
+        pyfunc = isnan
+        cr = compile_isolated(pyfunc, (types.float64,), flags=flags)
+        cfunc = cr.entry_point
+
+        self.assertTrue(cfunc(float('nan')))
+        self.assertFalse(cfunc(1.0))
+
+        pyfunc = isequal
+        cr = compile_isolated(pyfunc, (types.float64,), flags=flags)
+        cfunc = cr.entry_point
+
+        self.assertFalse(cfunc(float('nan')))
+        self.assertTrue(cfunc(1.0))
+
+    def test_nans_npm(self):
+        self.test_nans(flags=no_pyobj_flags)
+
+if __name__ == '__main__':
+    unittest.main()
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ndarray_subclasses.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ndarray_subclasses.py
new file mode 100644
index 000000000..944d314e7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ndarray_subclasses.py
@@ -0,0 +1,346 @@
+"""
+Test NumPy Subclassing features
+"""
+
+import builtins
+import unittest
+from numbers import Number
+from functools import wraps
+
+import numpy as np
+from llvmlite import ir
+
+import numba
+from numba import njit, typeof, objmode
+from numba.core import cgutils, types, typing
+from numba.core.pythonapi import box
+from numba.core.errors import TypingError
+from numba.core.registry import cpu_target
+from numba.extending import (intrinsic, lower_builtin, overload_classmethod,
+                             register_model, type_callable, typeof_impl,
+                             register_jitable)
+from numba.np import numpy_support
+
+from numba.tests.support import TestCase, MemoryLeakMixin
+
+# A quick util to allow logging within jit code
+
+_logger = None
+
+
+def _do_log(*args):
+    if _logger is not None:
+        _logger.append(args)
+
+
+@register_jitable
+def log(*args):
+    with objmode():
+        _do_log(*args)
+
+
+def use_logger(fn):
+    @wraps(fn)
+    def core(*args, **kwargs):
+        global _logger
+        _logger = []
+        return fn(*args, **kwargs)
+    return core
+
+
+class MyArray(np.ndarray):
+    # Tell Numba to not seamlessly treat this type as a regular ndarray.
+    __numba_array_subtype_dispatch__ = True
+
+    # __array__ is not needed given that this is a ndarray subclass
+    #
+    # def __array__(self, dtype=None):
+    #     return self
+
+    # Interoperate with NumPy outside of Numba.
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        if method == "__call__":
+            N = None
+            scalars = []
+            for inp in inputs:
+                if isinstance(inp, Number):
+                    scalars.append(inp)
+                elif isinstance(inp, (type(self), np.ndarray)):
+                    if isinstance(inp, type(self)):
+                        scalars.append(np.ndarray(inp.shape, inp.dtype, inp))
+                    else:
+                        scalars.append(inp)
+                    if N is not None:
+                        if N != inp.shape:
+                            raise TypeError("inconsistent sizes")
+                    else:
+                        N = inp.shape
+                else:
+                    return NotImplemented
+            ret = ufunc(*scalars, **kwargs)
+            return self.__class__(ret.shape, ret.dtype, ret)
+        else:
+            return NotImplemented
+
+
+class MyArrayType(types.Array):
+    def __init__(self, dtype, ndim, layout, readonly=False, aligned=True):
+        name = f"MyArray({ndim}, {dtype}, {layout})"
+        super().__init__(dtype, ndim, layout, readonly=readonly,
+                         aligned=aligned, name=name)
+
+    def copy(self, *args, **kwargs):
+        # This is here to future-proof.
+        # The test here never uses this.
+        raise NotImplementedError
+
+    # Tell Numba typing how to combine MyArrayType with other ndarray types.
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        if method == "__call__":
+            for inp in inputs:
+                if not isinstance(inp, (types.Array, types.Number)):
+                    return NotImplemented
+            # Ban if all arguments are MyArrayType
+            if all(isinstance(inp, MyArrayType) for inp in inputs):
+                return NotImplemented
+            return MyArrayType
+        else:
+            return NotImplemented
+
+    @property
+    def box_type(self):
+        return MyArray
+
+
+@typeof_impl.register(MyArray)
+def typeof_ta_ndarray(val, c):
+    try:
+        dtype = numpy_support.from_dtype(val.dtype)
+    except NotImplementedError:
+        raise ValueError("Unsupported array dtype: %s" % (val.dtype,))
+    layout = numpy_support.map_layout(val)
+    readonly = not val.flags.writeable
+    return MyArrayType(dtype, val.ndim, layout, readonly=readonly)
+
+
+@register_model(MyArrayType)
+class MyArrayTypeModel(numba.core.datamodel.models.StructModel):
+    def __init__(self, dmm, fe_type):
+        ndim = fe_type.ndim
+        members = [
+            ('meminfo', types.MemInfoPointer(fe_type.dtype)),
+            ('parent', types.pyobject),
+            ('nitems', types.intp),
+            ('itemsize', types.intp),
+            ('data', types.CPointer(fe_type.dtype)),
+            ('shape', types.UniTuple(types.intp, ndim)),
+            ('strides', types.UniTuple(types.intp, ndim)),
+            ('extra_field', types.intp),
+        ]
+        super(MyArrayTypeModel, self).__init__(dmm, fe_type, members)
+
+
+@type_callable(MyArray)
+def type_myarray(context):
+    def typer(shape, dtype, buf):
+        out = MyArrayType(
+            dtype=buf.dtype, ndim=len(shape), layout=buf.layout
+        )
+        return out
+
+    return typer
+
+
+@lower_builtin(MyArray, types.UniTuple, types.DType, types.Array)
+def impl_myarray(context, builder, sig, args):
+    from numba.np.arrayobj import make_array, populate_array
+
+    srcaryty = sig.args[-1]
+    shape, dtype, buf = args
+
+    srcary = make_array(srcaryty)(context, builder, value=buf)
+    # Copy source array and remove the parent field to avoid boxer re-using
+    # the original ndarray instance.
+    retary = make_array(sig.return_type)(context, builder)
+    populate_array(retary,
+                   data=srcary.data,
+                   shape=srcary.shape,
+                   strides=srcary.strides,
+                   itemsize=srcary.itemsize,
+                   meminfo=srcary.meminfo)
+
+    ret = retary._getvalue()
+    context.nrt.incref(builder, sig.return_type, ret)
+    return ret
+
+
+@box(MyArrayType)
+def box_array(typ, val, c):
+    assert c.context.enable_nrt
+    np_dtype = numpy_support.as_dtype(typ.dtype)
+    dtypeptr = c.env_manager.read_const(c.env_manager.add_const(np_dtype))
+    newary = c.pyapi.nrt_adapt_ndarray_to_python(typ, val, dtypeptr)
+    # Steals NRT ref
+    c.context.nrt.decref(c.builder, typ, val)
+    return newary
+
+
+@overload_classmethod(MyArrayType, "_allocate")
+def _ol_array_allocate(cls, allocsize, align):
+    """Implements a Numba-only classmethod on the array type.
+    """
+    def impl(cls, allocsize, align):
+        log("LOG _ol_array_allocate", allocsize, align)
+        return allocator_MyArray(allocsize, align)
+
+    return impl
+
+
+@intrinsic
+def allocator_MyArray(typingctx, allocsize, align):
+    def impl(context, builder, sig, args):
+        context.nrt._require_nrt()
+        size, align = args
+
+        mod = builder.module
+        u32 = ir.IntType(32)
+        voidptr = cgutils.voidptr_t
+
+        get_alloc_fnty = ir.FunctionType(voidptr, ())
+        get_alloc_fn = cgutils.get_or_insert_function(
+            mod, get_alloc_fnty, name="_nrt_get_sample_external_allocator"
+        )
+        ext_alloc = builder.call(get_alloc_fn, ())
+
+        fnty = ir.FunctionType(voidptr, [cgutils.intp_t, u32, voidptr])
+        fn = cgutils.get_or_insert_function(
+            mod, fnty, name="NRT_MemInfo_alloc_safe_aligned_external"
+        )
+        fn.return_value.add_attribute("noalias")
+        if isinstance(align, builtins.int):
+            align = context.get_constant(types.uint32, align)
+        else:
+            assert align.type == u32, "align must be a uint32"
+        call = builder.call(fn, [size, align, ext_alloc])
+        call.name = "allocate_MyArray"
+        return call
+
+    mip = types.MemInfoPointer(types.voidptr)  # return untyped pointer
+    sig = typing.signature(mip, allocsize, align)
+    return sig, impl
+
+
+class TestNdarraySubclasses(MemoryLeakMixin, TestCase):
+
+    def test_myarray_return(self):
+        """This tests the path to `MyArrayType.box_type`
+        """
+        @njit
+        def foo(a):
+            return a + 1
+
+        buf = np.arange(4)
+        a = MyArray(buf.shape, buf.dtype, buf)
+        expected = foo.py_func(a)
+        got = foo(a)
+        self.assertIsInstance(got, MyArray)
+        self.assertIs(type(expected), type(got))
+        self.assertPreciseEqual(expected, got)
+
+    def test_myarray_passthru(self):
+        @njit
+        def foo(a):
+            return a
+
+        buf = np.arange(4)
+        a = MyArray(buf.shape, buf.dtype, buf)
+        expected = foo.py_func(a)
+        got = foo(a)
+        self.assertIsInstance(got, MyArray)
+        self.assertIs(type(expected), type(got))
+        self.assertPreciseEqual(expected, got)
+
+    def test_myarray_convert(self):
+        @njit
+        def foo(buf):
+            return MyArray(buf.shape, buf.dtype, buf)
+
+        buf = np.arange(4)
+        expected = foo.py_func(buf)
+        got = foo(buf)
+        self.assertIsInstance(got, MyArray)
+        self.assertIs(type(expected), type(got))
+        self.assertPreciseEqual(expected, got)
+
+    def test_myarray_asarray_non_jit(self):
+        def foo(buf):
+            converted = MyArray(buf.shape, buf.dtype, buf)
+            return np.asarray(converted) + buf
+
+        buf = np.arange(4)
+        got = foo(buf)
+        self.assertIs(type(got), np.ndarray)
+        self.assertPreciseEqual(got, buf + buf)
+
+    @unittest.expectedFailure
+    def test_myarray_asarray(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(buf):
+            converted = MyArray(buf.shape, buf.dtype, buf)
+            return np.asarray(converted)
+
+        buf = np.arange(4)
+        got = foo(buf)
+        # the following fails because our np.asarray is returning the source
+        # array type
+        self.assertIs(type(got), np.ndarray)
+
+    def test_myarray_ufunc_unsupported(self):
+        @njit
+        def foo(buf):
+            converted = MyArray(buf.shape, buf.dtype, buf)
+            return converted + converted
+
+        buf = np.arange(4, dtype=np.float32)
+        with self.assertRaises(TypingError) as raises:
+            foo(buf)
+
+        msg = ("No implementation of function",
+               "add(MyArray(1, float32, C), MyArray(1, float32, C))")
+        for m in msg:
+            self.assertIn(m, str(raises.exception))
+
+    @use_logger
+    def test_myarray_allocator_override(self):
+        """
+        Checks that our custom allocator is used
+        """
+        @njit
+        def foo(a):
+            b = a + np.arange(a.size, dtype=np.float64)
+            c = a + 1j
+            return b, c
+
+        buf = np.arange(4, dtype=np.float64)
+        a = MyArray(buf.shape, buf.dtype, buf)
+
+        expected = foo.py_func(a)
+        got = foo(a)
+
+        self.assertPreciseEqual(got, expected)
+
+        logged_lines = _logger
+
+        targetctx = cpu_target.target_context
+        nb_dtype = typeof(buf.dtype)
+        align = targetctx.get_preferred_array_alignment(nb_dtype)
+        self.assertEqual(logged_lines, [
+            ("LOG _ol_array_allocate", expected[0].nbytes, align),
+            ("LOG _ol_array_allocate", expected[1].nbytes, align),
+        ])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nested_calls.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nested_calls.py
new file mode 100644
index 000000000..6f18e6397
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nested_calls.py
@@ -0,0 +1,146 @@
+"""
+Test problems in nested calls.
+Usually due to invalid type conversion between function boundaries.
+"""
+
+
+from numba import int32, int64
+from numba import jit, generated_jit
+from numba.core import types
+from numba.tests.support import TestCase, tag
+import unittest
+
+
+@jit(nopython=True)
+def f_inner(a, b, c):
+    return a, b, c
+
+def f(x, y, z):
+    return f_inner(x, c=y, b=z)
+
+@jit(nopython=True)
+def g_inner(a, b=2, c=3):
+    return a, b, c
+
+def g(x, y, z):
+    return g_inner(x, b=y), g_inner(a=z, c=x)
+
+@jit(nopython=True)
+def star_inner(a=5, *b):
+    return a, b
+
+def star(x, y, z):
+    return star_inner(a=x), star_inner(x, y, z)
+
+def star_call(x, y, z):
+    return star_inner(x, *y), star_inner(*z)
+
+@jit(nopython=True)
+def argcast_inner(a, b):
+    if b:
+        # Here `a` is unified to int64 (from int32 originally)
+        a = int64(0)
+    return a
+
+def argcast(a, b):
+    return argcast_inner(int32(a), b)
+
+@generated_jit(nopython=True)
+def generated_inner(x, y=5, z=6):
+    if isinstance(x, types.Complex):
+        def impl(x, y, z):
+            return x + y, z
+    else:
+        def impl(x, y, z):
+            return x - y, z
+    return impl
+
+def call_generated(a, b):
+    return generated_inner(a, z=b)
+
+
+class TestNestedCall(TestCase):
+
+    def compile_func(self, pyfunc, objmode=False):
+        def check(*args, **kwargs):
+            expected = pyfunc(*args, **kwargs)
+            result = f(*args, **kwargs)
+            self.assertPreciseEqual(result, expected)
+        flags = dict(forceobj=True) if objmode else dict(nopython=True)
+        f = jit(**flags)(pyfunc)
+        return f, check
+
+    def test_boolean_return(self):
+        @jit(nopython=True)
+        def inner(x):
+            return not x
+
+        @jit(nopython=True)
+        def outer(x):
+            if inner(x):
+                return True
+            else:
+                return False
+
+        self.assertFalse(outer(True))
+        self.assertTrue(outer(False))
+
+    def test_named_args(self, objmode=False):
+        """
+        Test a nested function call with named (keyword) arguments.
+        """
+        cfunc, check = self.compile_func(f, objmode)
+        check(1, 2, 3)
+        check(1, y=2, z=3)
+
+    def test_named_args_objmode(self):
+        self.test_named_args(objmode=True)
+
+    def test_default_args(self, objmode=False):
+        """
+        Test a nested function call using default argument values.
+        """
+        cfunc, check = self.compile_func(g, objmode)
+        check(1, 2, 3)
+        check(1, y=2, z=3)
+
+    def test_default_args_objmode(self):
+        self.test_default_args(objmode=True)
+
+    def test_star_args(self):
+        """
+        Test a nested function call to a function with *args in its signature.
+        """
+        cfunc, check = self.compile_func(star)
+        check(1, 2, 3)
+
+    def test_star_call(self, objmode=False):
+        """
+        Test a function call with a *args.
+        """
+        cfunc, check = self.compile_func(star_call, objmode)
+        check(1, (2,), (3,))
+
+    def test_star_call_objmode(self):
+        self.test_star_call(objmode=True)
+
+    def test_argcast(self):
+        """
+        Issue #1488: implicitly casting an argument variable should not
+        break nested calls.
+        """
+        cfunc, check = self.compile_func(argcast)
+        check(1, 0)
+        check(1, 1)
+
+    def test_call_generated(self):
+        """
+        Test a nested function call to a generated jit function.
+        """
+        cfunc = jit(nopython=True)(call_generated)
+        self.assertPreciseEqual(cfunc(1, 2), (-4, 2))
+        self.assertPreciseEqual(cfunc(1j, 2), (1j + 5, 2))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_np_functions.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_np_functions.py
new file mode 100644
index 000000000..4cdaf548b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_np_functions.py
@@ -0,0 +1,4841 @@
+# Tests numpy methods of <class 'function'>
+
+import itertools
+import math
+import platform
+from functools import partial
+import warnings
+
+import numpy as np
+
+from numba.core.compiler import Flags
+from numba import jit, njit, typeof
+from numba.core import types
+from numba.typed import List, Dict
+from numba.np.numpy_support import numpy_version
+from numba.core.errors import TypingError, NumbaDeprecationWarning
+from numba.core.config import IS_WIN32, IS_32BITS
+from numba.core.utils import pysignature
+from numba.np.extensions import cross2d
+from numba.tests.support import (TestCase, CompilationCache, MemoryLeakMixin,
+                                 needs_blas)
+import unittest
+
+
+no_pyobj_flags = Flags()
+no_pyobj_flags.nrt = True
+
+
+def sinc(x):
+    return np.sinc(x)
+
+
+def angle1(x):
+    return np.angle(x)
+
+
+def angle2(x, deg):
+    return np.angle(x, deg)
+
+
+def array_equal(a, b):
+    return np.array_equal(a, b)
+
+
+def intersect1d(a, b):
+    return np.intersect1d(a, b)
+
+
+def append(arr, values, axis):
+    return np.append(arr, values, axis=axis)
+
+
+def count_nonzero(arr, axis):
+    return np.count_nonzero(arr, axis=axis)
+
+
+def delete(arr, obj):
+    return np.delete(arr, obj)
+
+
+def diff1(a):
+    return np.diff(a)
+
+
+def diff2(a, n):
+    return np.diff(a, n)
+
+
+def bincount1(a):
+    return np.bincount(a)
+
+
+def bincount2(a, w):
+    return np.bincount(a, weights=w)
+
+
+def bincount3(a, w=None, minlength=0):
+    return np.bincount(a, w, minlength)
+
+
+def searchsorted(a, v):
+    return np.searchsorted(a, v)
+
+
+def searchsorted_left(a, v):
+    return np.searchsorted(a, v, side='left')
+
+
+def searchsorted_right(a, v):
+    return np.searchsorted(a, v, side='right')
+
+
+def digitize(*args):
+    return np.digitize(*args)
+
+
+def histogram(*args):
+    return np.histogram(*args)
+
+
+def machar(*args):
+    return np.MachAr()
+
+
+def iscomplex(x):
+    return np.iscomplex(x)
+
+
+def iscomplexobj(x):
+    return np.iscomplexobj(x)
+
+
+def isscalar(x):
+    return np.isscalar(x)
+
+
+def isreal(x):
+    return np.isreal(x)
+
+
+def isrealobj(x):
+    return np.isrealobj(x)
+
+
+def isneginf(x, out=None):
+    return np.isneginf(x, out)
+
+
+def isposinf(x, out=None):
+    return np.isposinf(x, out)
+
+
+def isnat(x):
+    return np.isnat(x)
+
+
+def iinfo(*args):
+    return np.iinfo(*args)
+
+
+def finfo(*args):
+    return np.finfo(*args)
+
+
+def finfo_machar(*args):
+    return np.finfo(*args).machar
+
+
+def fliplr(a):
+    return np.fliplr(a)
+
+
+def flipud(a):
+    return np.flipud(a)
+
+
+def flip(a):
+    return np.flip(a)
+
+
+def logspace2(start, stop):
+    return np.logspace(start, stop)
+
+
+def logspace3(start, stop, num=50):
+    return np.logspace(start, stop, num=num)
+
+
+def rot90(a):
+    return np.rot90(a)
+
+
+def rot90_k(a, k=1):
+    return np.rot90(a, k)
+
+
+def array_split(a, indices, axis=0):
+    return np.array_split(a, indices, axis=axis)
+
+
+def split(a, indices, axis=0):
+    return np.split(a, indices, axis=axis)
+
+
+def correlate(a, v):
+    return np.correlate(a, v)
+
+
+def convolve(a, v):
+    return np.convolve(a, v)
+
+
+def tri_n(N):
+    return np.tri(N)
+
+
+def tri_n_m(N, M=None):
+    return np.tri(N, M)
+
+
+def tri_n_k(N, k=0):
+    return np.tri(N, k)
+
+
+def tri_n_m_k(N, M=None, k=0):
+    return np.tri(N, M, k)
+
+
+def tril_m(m):
+    return np.tril(m)
+
+
+def tril_m_k(m, k=0):
+    return np.tril(m, k)
+
+
+def tril_indices_n(n):
+    return np.tril_indices(n)
+
+
+def tril_indices_n_k(n, k=0):
+    return np.tril_indices(n, k)
+
+
+def tril_indices_n_m(n, m=None):
+    return np.tril_indices(n, m=m)
+
+
+def tril_indices_n_k_m(n, k=0, m=None):
+    return np.tril_indices(n, k, m)
+
+
+def tril_indices_from_arr(arr):
+    return np.tril_indices_from(arr)
+
+
+def tril_indices_from_arr_k(arr, k=0):
+    return np.tril_indices_from(arr, k)
+
+
+def triu_m(m):
+    return np.triu(m)
+
+
+def triu_m_k(m, k=0):
+    return np.triu(m, k)
+
+
+def triu_indices_n(n):
+    return np.triu_indices(n)
+
+
+def triu_indices_n_k(n, k=0):
+    return np.triu_indices(n, k)
+
+
+def triu_indices_n_m(n, m=None):
+    return np.triu_indices(n, m=m)
+
+
+def triu_indices_n_k_m(n, k=0, m=None):
+    return np.triu_indices(n, k, m)
+
+
+def triu_indices_from_arr(arr):
+    return np.triu_indices_from(arr)
+
+
+def triu_indices_from_arr_k(arr, k=0):
+    return np.triu_indices_from(arr, k)
+
+
+def vander(x, N=None, increasing=False):
+    return np.vander(x, N, increasing)
+
+
+def partition(a, kth):
+    return np.partition(a, kth)
+
+
+def cov(m, y=None, rowvar=True, bias=False, ddof=None):
+    return np.cov(m, y, rowvar, bias, ddof)
+
+
+def corrcoef(x, y=None, rowvar=True):
+    return np.corrcoef(x, y, rowvar)
+
+
+def ediff1d(ary, to_end=None, to_begin=None):
+    return np.ediff1d(ary, to_end, to_begin)
+
+
+def roll(a, shift):
+    return np.roll(a, shift)
+
+
+def asarray(a):
+    return np.asarray(a)
+
+
+def asarray_kws(a, dtype):
+    return np.asarray(a, dtype=dtype)
+
+
+def asfarray(a, dtype=np.float64):
+    return np.asfarray(a, dtype=dtype)
+
+
+def asfarray_default_kwarg(a):
+    return np.asfarray(a)
+
+
+def extract(condition, arr):
+    return np.extract(condition, arr)
+
+
+def np_trapz(y):
+    return np.trapz(y)
+
+
+def np_trapz_x(y, x):
+    return np.trapz(y, x)
+
+
+def np_trapz_dx(y, dx):
+    return np.trapz(y, dx=dx)
+
+
+def np_trapz_x_dx(y, x, dx):
+    return np.trapz(y, x, dx)
+
+
+def np_allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
+    return np.allclose(a, b, rtol, atol, equal_nan)
+
+
+def np_average(a, axis=None, weights=None):
+    return np.average(a, axis=axis, weights=weights)
+
+
+def interp(x, xp, fp):
+    return np.interp(x, xp, fp)
+
+
+def np_repeat(a, repeats):
+    return np.repeat(a, repeats)
+
+
+def array_repeat(a, repeats):
+    return np.asarray(a).repeat(repeats)
+
+
+def np_select(condlist, choicelist, default=0):
+    return np.select(condlist, choicelist, default=default)
+
+
+def np_select_defaults(condlist, choicelist):
+    return np.select(condlist, choicelist)
+
+
+def np_bartlett(M):
+    return np.bartlett(M)
+
+
+def np_blackman(M):
+    return np.blackman(M)
+
+
+def np_hamming(M):
+    return np.hamming(M)
+
+
+def np_hanning(M):
+    return np.hanning(M)
+
+
+def np_kaiser(M, beta):
+    return np.kaiser(M, beta)
+
+
+def np_cross(a, b):
+    return np.cross(a, b)
+
+
+def flip_lr(a):
+    return np.fliplr(a)
+
+
+def flip_ud(a):
+    return np.flipud(a)
+
+
+def np_asarray_chkfinite(a, dtype=None):
+    return np.asarray_chkfinite(a, dtype)
+
+
+def array_contains(a, key):
+    return key in a
+
+
+def swapaxes(a, a1, a2):
+    return np.swapaxes(a, a1, a2)
+
+
+class TestNPFunctions(MemoryLeakMixin, TestCase):
+    """
+    Tests for various Numpy functions.
+    """
+
+    def setUp(self):
+        super(TestNPFunctions, self).setUp()
+        self.ccache = CompilationCache()
+        self.rnd = np.random.RandomState(42)
+
+    def run_unary(self, pyfunc, x_types, x_values, flags=no_pyobj_flags,
+                  func_extra_types=None, func_extra_args=None,
+                  ignore_sign_on_zero=False, abs_tol=None, **kwargs):
+        """
+        Runs tests for a unary function operating in the numerical real space.
+
+        Parameters
+        ----------
+        pyfunc : a python function definition holding that calls the numpy
+                 functions to be tested.
+        x_types: the types of the values being tested, see numba.types
+        x_values: the numerical values of the values to be tested
+        flags: flags to pass to the CompilationCache::ccache::compile function
+        func_extra_types: the types of additional arguments to the numpy
+                          function
+        func_extra_args:  additional arguments to the numpy function
+        ignore_sign_on_zero: boolean as to whether to allow zero values
+        with incorrect signs to be considered equal
+        prec: the required precision match, see assertPreciseEqual
+
+        Notes:
+        ------
+        x_types and x_values must have the same length
+
+        """
+        for tx, vx in zip(x_types, x_values):
+            if func_extra_args is None:
+                func_extra_types = func_extra_args = [()]
+            for xtypes, xargs in zip(func_extra_types, func_extra_args):
+                cr = self.ccache.compile(pyfunc, (tx,) + xtypes,
+                                         flags=flags)
+                cfunc = cr.entry_point
+                got = cfunc(vx, *xargs)
+                expected = pyfunc(vx, *xargs)
+                try:
+                    scalty = tx.dtype
+                except AttributeError:
+                    scalty = tx
+                prec = ('single'
+                        if scalty in (types.float32, types.complex64)
+                        else 'double')
+                msg = 'for input %r with prec %r' % (vx, prec)
+                self.assertPreciseEqual(got, expected,
+                                        prec=prec,
+                                        msg=msg,
+                                        ignore_sign_on_zero=ignore_sign_on_zero,
+                                        abs_tol=abs_tol, **kwargs)
+
+    def test_sinc(self):
+        """
+        Tests the sinc() function.
+        This test is purely to assert numerical computations are correct.
+        """
+
+        # Ignore sign of zeros, this will need masking depending on numpy
+        # version once the fix to numpy complex division is in upstream
+        # See: https://github.com/numpy/numpy/pull/6699
+        isoz = True
+
+        # Testing sinc(1.) leads to sin(pi)/pi, which is below machine
+        # precision in practice on most machines. Small floating point
+        # differences in sin() etc. may lead to large differences in the result
+        # that are at a range that is inaccessible using standard width
+        # floating point representations.
+        # e.g. Assume float64 type.
+        # sin(pi) ~= 1e-16, but should be zero
+        # sin(pi)/pi ~= 1e-17, should be zero, error carried from above
+        # float64 has log10(2^53)~=15.9 digits of precision and the magnitude
+        # change in the alg is > 16  digits (1.0...0 -> 0.0...0),
+        # so comparison via ULP is invalid.
+        # We therefore opt to assume that values under machine precision are
+        # equal in this case.
+        tol = "eps"
+
+        pyfunc = sinc
+
+        def check(x_types, x_values, **kwargs):
+            self.run_unary(pyfunc, x_types, x_values,
+                           ignore_sign_on_zero=isoz, abs_tol=tol,
+                           **kwargs)
+
+        # real domain scalar context
+        x_values = [1., -1., 0.0, -0.0, 0.5, -0.5, 5, -5, 5e-21, -5e-21]
+        x_types = [types.float32, types.float64] * (len(x_values) // 2)
+        check(x_types, x_values)
+
+        # real domain vector context
+        x_values = [np.array(x_values, dtype=np.float64)]
+        x_types = [typeof(v) for v in x_values]
+        check(x_types, x_values)
+
+        # complex domain scalar context
+        x_values = [1.+0j, -1+0j, 0.0+0.0j, -0.0+0.0j, 0+1j, 0-1j, 0.5+0.0j, # noqa
+                    -0.5+0.0j, 0.5+0.5j, -0.5-0.5j, 5+5j, -5-5j,             # noqa
+                    # the following are to test sin(x)/x for small x
+                    5e-21+0j, -5e-21+0j, 5e-21j, +(0-5e-21j)                 # noqa
+                    ]
+        x_types = [types.complex64, types.complex128] * (len(x_values) // 2)
+        check(x_types, x_values, ulps=2)
+
+        # complex domain vector context
+        x_values = [np.array(x_values, dtype=np.complex128)]
+        x_types = [typeof(v) for v in x_values]
+        check(x_types, x_values, ulps=2)
+
+    def test_contains(self):
+        def arrs():
+            a_0 = np.arange(10, 50)
+            k_0 = 20
+
+            yield a_0, k_0
+
+            a_1 = np.arange(6)
+            k_1 = 10
+
+            yield a_1, k_1
+
+            single_val_a = np.asarray([20])
+            k_in = 20
+            k_out = 13
+
+            yield single_val_a, k_in
+            yield single_val_a, k_out
+
+            empty_arr = np.asarray([])
+            yield empty_arr, k_out
+
+            # np scalars
+
+            bool_arr = np.array([True, False])
+            yield bool_arr, True
+            yield bool_arr, k_0
+
+            np.random.seed(2)
+            float_arr = np.random.rand(10)
+            np.random.seed(2)
+            rand_k = np.random.rand()
+            present_k = float_arr[0]
+
+            yield float_arr, rand_k
+            yield float_arr, present_k
+
+            complx_arr = float_arr.view(np.complex128)
+            yield complx_arr, complx_arr[0]
+            yield complx_arr, rand_k
+
+            np.random.seed(2)
+            uint_arr = np.random.randint(10, size=15, dtype=np.uint8)
+            yield uint_arr, 5
+            yield uint_arr, 25
+
+        pyfunc = array_contains
+
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arr, key in arrs():
+            expected = pyfunc(arr, key)
+            received = cfunc(arr, key)
+
+            self.assertPreciseEqual(expected, received)
+
+    def test_angle(self, flags=no_pyobj_flags):
+        """
+        Tests the angle() function.
+        This test is purely to assert numerical computations are correct.
+        """
+        pyfunc1 = angle1
+        pyfunc2 = angle2
+
+        def check(x_types, x_values):
+            # angle(x)
+            self.run_unary(pyfunc1, x_types, x_values)
+            # angle(x, deg)
+            xtra_values = [(True,), (False,)]
+            xtra_types = [(types.bool_,)] * len(xtra_values)
+            self.run_unary(pyfunc2, x_types, x_values,
+                           func_extra_types=xtra_types,
+                           func_extra_args=xtra_values,)
+
+        # real domain scalar context
+        x_values = [1., -1., 0.0, -0.0, 0.5, -0.5, 5, -5]
+        x_types = [types.float32, types.float64] * (len(x_values) // 2 + 1)
+        check(x_types, x_values)
+
+        # real domain vector context
+        x_values = [np.array(x_values, dtype=np.float64)]
+        x_types = [typeof(v) for v in x_values]
+        check(x_types, x_values)
+
+        # complex domain scalar context
+        x_values = [1.+0j, -1+0j, 0.0+0.0j, -0.0+0.0j, 1j, -1j, 0.5+0.0j, # noqa
+                    -0.5+0.0j, 0.5+0.5j, -0.5-0.5j, 5+5j, -5-5j]          # noqa
+        x_types = [types.complex64, types.complex128] * (len(x_values) // 2 + 1)
+        check(x_types, x_values)
+
+        # complex domain vector context
+        x_values = np.array(x_values)
+        x_types = [types.complex64, types.complex128]
+        check(x_types, x_values)
+
+    def test_array_equal(self):
+        def arrays():
+            yield np.array([]), np.array([])
+            yield np.array([1, 2]), np.array([1, 2])
+            yield np.array([]), np.array([1])
+            x = np.arange(10).reshape(5, 2)
+            x[1][1] = 30
+            yield np.arange(10).reshape(5, 2), x
+            yield x, x
+            yield (1, 2, 3), (1, 2, 3)
+            yield 2, 2
+            yield 3, 2
+            yield True, True
+            yield True, False
+            yield True, 2
+            yield True, 1
+            yield False, 0
+
+        pyfunc = array_equal
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arr, obj in arrays():
+            expected = pyfunc(arr, obj)
+            got = cfunc(arr, obj)
+            self.assertPreciseEqual(expected, got)
+
+    def test_array_equal_exception(self):
+        pyfunc = array_equal
+        cfunc = jit(nopython=True)(pyfunc)
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(np.arange(3 * 4).reshape(3, 4), None)
+        self.assertIn(
+            'Both arguments to "array_equals" must be array-like',
+            str(raises.exception)
+        )
+
+    def test_intersect1d(self):
+
+        def arrays():
+            yield [], []  # two empty arrays
+            yield [1], []  # empty right
+            yield [], [1]  # empty left
+            yield [1], [2]  # singletons no intersection
+            yield [1], [1]  # singletons one intersection
+            yield [1, 2], [1]
+            yield [1, 2, 2], [2, 2]
+            yield [1, 2], [2, 1]
+            yield [1, 2, 3], [1, 2, 3]
+
+        pyfunc = intersect1d
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for a, b in arrays():
+            a = np.array(a)
+            b = np.array(b)
+            expected = pyfunc(a, b)
+            got = cfunc(a, b)
+            self.assertPreciseEqual(expected, got)
+
+    def test_count_nonzero(self):
+
+        def arrays():
+            yield np.array([]), None
+            yield np.zeros(10), None
+            yield np.arange(10), None
+            yield np.arange(3 * 4 * 5).reshape(3, 4, 5), None
+            yield np.arange(3 * 4).reshape(3, 4), 0
+            yield np.arange(3 * 4).reshape(3, 4), 1
+
+        pyfunc = count_nonzero
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arr, axis in arrays():
+            expected = pyfunc(arr, axis)
+            got = cfunc(arr, axis)
+            self.assertPreciseEqual(expected, got)
+
+    def test_np_append(self):
+        def arrays():
+            yield 2, 2, None
+            yield np.arange(10), 3, None
+            yield np.arange(10), np.arange(3), None
+            yield np.arange(10).reshape(5, 2), np.arange(3), None
+            yield np.array([[1, 2, 3], [4, 5, 6]]), np.array([[7, 8, 9]]), 0
+            arr = np.array([[1, 2, 3], [4, 5, 6]])
+            yield arr, arr, 1
+
+        pyfunc = append
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arr, obj, axis in arrays():
+            expected = pyfunc(arr, obj, axis)
+            got = cfunc(arr, obj, axis)
+            self.assertPreciseEqual(expected, got)
+
+    def test_np_append_exceptions(self):
+        pyfunc = append
+        cfunc = jit(nopython=True)(pyfunc)
+        arr = np.array([[1, 2, 3], [4, 5, 6]])
+        values = np.array([[7, 8, 9]])
+        axis = 0
+
+        # first argument must be array-like
+        with self.assertRaises(TypingError) as raises:
+            cfunc(None, values, axis)
+        self.assertIn(
+            'The first argument "arr" must be array-like',
+            str(raises.exception)
+        )
+
+        # second argument must also be array-like
+        with self.assertRaises(TypingError) as raises:
+            cfunc(arr, None, axis)
+        self.assertIn(
+            'The second argument "values" must be array-like',
+            str(raises.exception)
+        )
+
+        # third argument must be either nonelike or an integer
+        with self.assertRaises(TypingError) as raises:
+            cfunc(arr, values, axis=0.0)
+        self.assertIn(
+            'The third argument "axis" must be an integer',
+            str(raises.exception)
+        )
+
+    def test_delete(self):
+
+        def arrays():
+            # array, obj
+            #
+            # an array-like type
+            yield [1, 2, 3, 4, 5], 3
+            yield [1, 2, 3, 4, 5], [2, 3]
+            # 1d array, scalar
+            yield np.arange(10), 3
+            yield np.arange(10), -3 # Negative obj
+            # 1d array, list
+            yield np.arange(10), [3, 5, 6]
+            yield np.arange(10), [2, 3, 4, 5]
+            # 3d array, scalar
+            yield np.arange(3 * 4 * 5).reshape(3, 4, 5), 2
+            # 3d array, list
+            yield np.arange(3 * 4 * 5).reshape(3, 4, 5), [5, 30, 27, 8]
+            # slices
+            yield [1, 2, 3, 4], slice(1, 3, 1)
+            yield np.arange(10), slice(10)
+
+        pyfunc = delete
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arr, obj in arrays():
+            expected = pyfunc(arr, obj)
+            got = cfunc(arr, obj)
+            self.assertPreciseEqual(expected, got)
+
+    def test_delete_exceptions(self):
+        pyfunc = delete
+        cfunc = jit(nopython=True)(pyfunc)
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc([1, 2], 3.14)
+        self.assertIn(
+            'obj should be of Integer dtype',
+            str(raises.exception)
+        )
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(np.arange(10), [3.5, 5.6, 6.2])
+        self.assertIn(
+            'obj should be of Integer dtype',
+            str(raises.exception)
+        )
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(2, 3)
+        self.assertIn(
+            'arr must be either an Array or a Sequence',
+            str(raises.exception)
+        )
+
+        with self.assertRaises(IndexError) as raises:
+            cfunc([1, 2], 3)
+        self.assertIn(
+            'obj must be less than the len(arr)',
+            str(raises.exception),
+        )
+
+    def diff_arrays(self):
+        """
+        Some test arrays for np.diff()
+        """
+        a = np.arange(12) ** 3
+        yield a
+        b = a.reshape((3, 4))
+        yield b
+        c = np.arange(24).reshape((3, 2, 4)) ** 3
+        yield c
+
+    def test_diff1(self):
+        pyfunc = diff1
+        cfunc = jit(nopython=True)(pyfunc)
+        for arr in self.diff_arrays():
+            expected = pyfunc(arr)
+            got = cfunc(arr)
+            self.assertPreciseEqual(expected, got)
+
+        # 0-dim array
+        a = np.array(42)
+        with self.assertTypingError():
+            cfunc(a)
+
+    def test_diff2(self):
+        pyfunc = diff2
+        cfunc = jit(nopython=True)(pyfunc)
+        for arr in self.diff_arrays():
+            size = arr.shape[-1]
+            for n in (0, 1, 2, 3, size - 1, size, size + 1, 421):
+                expected = pyfunc(arr, n)
+                got = cfunc(arr, n)
+                self.assertPreciseEqual(expected, got)
+
+    def test_diff2_exceptions(self):
+        pyfunc = diff2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # 0-dim array
+        arr = np.array(42)
+        with self.assertTypingError():
+            cfunc(arr, 1)
+
+        # Invalid `n`
+        arr = np.arange(10)
+        for n in (-1, -2, -42):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(arr, n)
+            self.assertIn("order must be non-negative", str(raises.exception))
+
+    def test_isscalar(self):
+        def values():
+            yield 3
+            yield np.asarray([3])
+            yield (3,)
+            yield 3j
+            yield 'numba'
+            yield int(10)
+            yield np.int16(12345)
+            yield 4.234
+            yield True
+            yield None
+
+        pyfunc = isscalar
+        cfunc = jit(nopython=True)(pyfunc)
+        for x in values():
+            expected = pyfunc(x)
+            got = cfunc(x)
+            self.assertEqual(expected, got, x)
+
+    def test_isobj_functions(self):
+        def values():
+            yield 1
+            yield 1 + 0j
+            yield np.asarray([3, 1 + 0j, True])
+            yield "hello world"
+
+        @jit(nopython=True)
+        def optional_fn(x, cond, cfunc):
+            y = x if cond else None
+            return cfunc(y)
+
+        pyfuncs = [iscomplexobj, isrealobj]
+        for pyfunc in pyfuncs:
+            cfunc = jit(nopython=True)(pyfunc)
+            for x in values():
+                expected = pyfunc(x)
+                got = cfunc(x)
+                self.assertEqual(expected, got)
+
+                # optional type
+                expected_optional = optional_fn.py_func(x, True, pyfunc)
+                got_optional = optional_fn(x, True, cfunc)
+                self.assertEqual(expected_optional, got_optional)
+
+                # none type
+                expected_none = optional_fn.py_func(x, False, pyfunc)
+                got_none = optional_fn(x, False, cfunc)
+                self.assertEqual(expected_none, got_none)
+
+            self.assertEqual(len(cfunc.signatures), 8)
+
+    def test_is_real_or_complex(self):
+        def values():
+            yield np.array([1 + 1j, 1 + 0j, 4.5, 3, 2, 2j])
+            yield np.array([1, 2, 3])
+            yield 3
+            yield 12j
+            yield 1 + 4j
+            yield 10 + 0j
+            yield (1 + 4j, 2 + 0j)
+            yield np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
+
+        pyfuncs = [iscomplex, isreal]
+        for pyfunc in pyfuncs:
+            cfunc = jit(nopython=True)(pyfunc)
+            for x in values():
+                expected = pyfunc(x)
+                got = cfunc(x)
+                self.assertPreciseEqual(expected, got)
+
+    def test_isneg_or_ispos_inf(self):
+        def values():
+            yield np.NINF, None
+            yield np.inf, None
+            yield np.PINF, None
+            yield np.asarray([-np.inf, 0., np.inf]), None
+            yield np.NINF, np.zeros(1, dtype=np.bool)
+            yield np.inf, np.zeros(1, dtype=np.bool)
+            yield np.PINF, np.zeros(1, dtype=np.bool)
+            yield np.NINF, np.empty(12)
+            yield np.asarray([-np.inf, 0., np.inf]), np.zeros(3, dtype=np.bool)
+
+        pyfuncs = [isneginf, isposinf]
+        for pyfunc in pyfuncs:
+            cfunc = jit(nopython=True)(pyfunc)
+            for x, out in values():
+                expected = pyfunc(x, out)
+                got = cfunc(x, out)
+                self.assertPreciseEqual(expected, got)
+
+    def bincount_sequences(self):
+        """
+        Some test sequences for np.bincount()
+        """
+        a = [1, 2, 5, 2, 3, 20]
+        b = np.array([5, 8, 42, 5])
+        c = self.rnd.randint(0, 100, size=300).astype(np.int8)
+        return (a, b, c)
+
+    def test_bincount1(self):
+        pyfunc = bincount1
+        cfunc = jit(nopython=True)(pyfunc)
+        for seq in self.bincount_sequences():
+            expected = pyfunc(seq)
+            got = cfunc(seq)
+            self.assertPreciseEqual(expected, got)
+
+    def test_bincount1_exceptions(self):
+        pyfunc = bincount1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # Negative input
+        with self.assertRaises(ValueError) as raises:
+            cfunc([2, -1])
+        self.assertIn("first argument must be non-negative",
+                      str(raises.exception))
+
+    def test_bincount2(self):
+        pyfunc = bincount2
+        cfunc = jit(nopython=True)(pyfunc)
+        for seq in self.bincount_sequences():
+            w = [math.sqrt(x) - 2 for x in seq]
+            # weights as list, then array, mixed types, check upcast is ok
+            for weights in (w, np.array(w), seq, np.array(seq)):
+                expected = pyfunc(seq, weights)
+                got = cfunc(seq, weights)
+                self.assertPreciseEqual(expected, got)
+
+    def test_bincount2_exceptions(self):
+        pyfunc = bincount2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # Negative input
+        with self.assertRaises(ValueError) as raises:
+            cfunc([2, -1], [0, 0])
+        self.assertIn("first argument must be non-negative",
+                      str(raises.exception))
+
+        # Mismatching input sizes
+        with self.assertRaises(ValueError) as raises:
+            cfunc([2, -1], [0])
+        self.assertIn("weights and list don't have the same length",
+                      str(raises.exception))
+
+    def test_bincount3(self):
+        pyfunc = bincount3
+        cfunc = jit(nopython=True)(pyfunc)
+        for seq in self.bincount_sequences():
+            a_max = max(seq)
+            # Length should be a_max in the first case, minlength in the second
+            for minlength in (a_max, a_max + 2):
+                expected = pyfunc(seq, None, minlength)
+                got = cfunc(seq, None, minlength)
+                self.assertEqual(len(expected), len(got))
+                self.assertPreciseEqual(expected, got)
+
+    def test_bincount3_exceptions(self):
+        pyfunc = bincount3
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # Negative input
+        with self.assertRaises(ValueError) as raises:
+            cfunc([2, -1], [0, 0])
+        self.assertIn("first argument must be non-negative",
+                      str(raises.exception))
+
+        # Negative minlength
+        with self.assertRaises(ValueError) as raises:
+            cfunc([17, 38], None, -1)
+        self.assertIn("'minlength' must not be negative",
+                      str(raises.exception))
+
+    def test_searchsorted(self):
+        pyfunc = searchsorted
+        cfunc = jit(nopython=True)(pyfunc)
+
+        pyfunc_left = searchsorted_left
+        cfunc_left = jit(nopython=True)(pyfunc_left)
+
+        pyfunc_right = searchsorted_right
+        cfunc_right = jit(nopython=True)(pyfunc_right)
+
+        def check(a, v):
+            expected = pyfunc(a, v)
+            got = cfunc(a, v)
+            self.assertPreciseEqual(expected, got)
+
+            expected = pyfunc_left(a, v)
+            got = cfunc_left(a, v)
+            self.assertPreciseEqual(expected, got)
+
+            expected = pyfunc_right(a, v)
+            got = cfunc_right(a, v)
+            self.assertPreciseEqual(expected, got)
+
+        # First with integer values (no NaNs)
+        bins = np.arange(5) ** 2
+        values = np.arange(20) - 1
+
+        for a in (bins, list(bins)):
+            # Scalar values
+            for v in values:
+                check(a, v)
+            # Array values
+            for v in (values, values.reshape((4, 5))):
+                check(a, v)
+            # Sequence values
+            check(a, list(values))
+
+        # Second with float values (including NaNs)
+        bins = np.float64(list(bins) + [float('nan')] * 7) / 2.0
+        values = np.arange(20) - 0.5
+
+        for a in (bins, list(bins)):
+            # Scalar values
+            for v in values:
+                check(a, v)
+            # Array values
+            for v in (values, values.reshape((4, 5))):
+                check(a, v)
+            # Sequence values
+            check(a, list(values))
+
+        # nonsense value for 'side' raises TypingError
+        def bad_side(a, v):
+            return np.searchsorted(a, v, side='nonsense')
+        cfunc = jit(nopython=True)(bad_side)
+        with self.assertTypingError():
+            cfunc([1,2], 1)
+
+        # non-constant value for 'side' raises TypingError
+        def nonconst_side(a, v, side='left'):
+            return np.searchsorted(a, v, side=side)
+        cfunc = jit(nopython=True)(nonconst_side)
+        with self.assertTypingError():
+            cfunc([1,2], 1, side='right')
+
+        # Test unordered values
+        a = np.array([1, 2, 0])
+        v = np.array(
+            [
+                [5, 4],
+                [6, 7],
+                [2, 1],
+                [0, 3],
+            ]
+        )
+        check(a, v)
+
+        a = np.array([9, 1, 4, 2, 0, 3, 7, 6, 8])
+        v = np.array(
+            [
+                [5, 10],
+                [10, 5],
+                [-1, 5],
+            ]
+        )
+        check(a, v)
+
+    def test_digitize(self):
+        pyfunc = digitize
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(*args):
+            expected = pyfunc(*args)
+            got = cfunc(*args)
+            self.assertPreciseEqual(expected, got)
+
+        values = np.float64((0, 0.99, 1, 4.4, 4.5, 7, 8, 9, 9.5,
+                             float('inf'), float('-inf'), float('nan')))
+        assert len(values) == 12
+        self.rnd.shuffle(values)
+
+        bins1 = np.float64([1, 3, 4.5, 8])
+        bins2 = np.float64([1, 3, 4.5, 8, float('inf'), float('-inf')])
+        bins3 = np.float64([1, 3, 4.5, 8, float('inf'), float('-inf')]
+                           + [float('nan')] * 10)
+
+        all_bins = [bins1, bins2, bins3]
+        xs = [values, values.reshape((3, 4))]
+
+        # 2-ary digitize()
+        for bins in all_bins:
+            bins.sort()
+            for x in xs:
+                check(x, bins)
+                check(x, bins[::-1])
+
+        # 3-ary digitize()
+        for bins in all_bins:
+            bins.sort()
+            for right in (True, False):
+                check(values, bins, right)
+                check(values, bins[::-1], right)
+
+        # Sequence input
+        check(list(values), bins1)
+
+    def test_histogram(self):
+        pyfunc = histogram
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(*args):
+            pyhist, pybins = pyfunc(*args)
+            chist, cbins = cfunc(*args)
+            self.assertPreciseEqual(pyhist, chist)
+            # There can be a slight discrepancy in the linspace() result
+            # when `bins` is an integer...
+            self.assertPreciseEqual(pybins, cbins, prec='double', ulps=2)
+
+        def check_values(values):
+            # Explicit bins array
+            # (note Numpy seems to not support NaN bins)
+            bins = np.float64([1, 3, 4.5, 8])
+            check(values, bins)
+            check(values.reshape((3, 4)), bins)
+
+            # Explicit number of bins
+            check(values, 7)
+
+            # Explicit number of bins and bins range
+            check(values, 7, (1.0, 13.5))
+
+            # Implicit bins=10
+            check(values)
+
+        values = np.float64((0, 0.99, 1, 4.4, 4.5, 7, 8,
+                             9, 9.5, 42.5, -1.0, -0.0))
+        assert len(values) == 12
+        self.rnd.shuffle(values)
+
+        check_values(values)
+
+    def _test_correlate_convolve(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        # only 1d arrays are accepted, test varying lengths
+        # and varying dtype
+        lengths = (1, 2, 3, 7)
+        dts = [np.int8, np.int32, np.int64, np.float32, np.float64,
+               np.complex64, np.complex128]
+
+        for dt1, dt2, n, m in itertools.product(dts, dts, lengths, lengths):
+            a = np.arange(n, dtype=dt1)
+            v = np.arange(m, dtype=dt2)
+
+            if np.issubdtype(dt1, np.complexfloating):
+                a = (a + 1j * a).astype(dt1)
+            if np.issubdtype(dt2, np.complexfloating):
+                v = (v + 1j * v).astype(dt2)
+
+            expected = pyfunc(a, v)
+            got = cfunc(a, v)
+            self.assertPreciseEqual(expected, got)
+
+        _a = np.arange(12).reshape(4, 3)
+        _b = np.arange(12)
+        for x, y in [(_a, _b), (_b, _a)]:
+            with self.assertRaises(TypingError) as raises:
+                cfunc(x, y)
+            msg = 'only supported on 1D arrays'
+            self.assertIn(msg, str(raises.exception))
+
+    def test_correlate(self):
+        self._test_correlate_convolve(correlate)
+        if numpy_version < (1, 18):
+            # correlate supported 0 dimension arrays until 1.18
+            _a = np.ones(shape=(0,))
+            _b = np.arange(5)
+            cfunc = jit(nopython=True)(correlate)
+            for x, y in [(_a, _b), (_b, _a), (_a, _a)]:
+                expected = correlate(x, y)
+                got = cfunc(x, y)
+                self.assertPreciseEqual(expected, got)
+
+    def _test_correlate_convolve_exceptions(self, fn):
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # convolve raises if either array has a 0 dimension
+        _a = np.ones(shape=(0,))
+        _b = np.arange(5)
+        cfunc = jit(nopython=True)(fn)
+        for x, y in [(_a, _b), (_b, _a)]:
+            with self.assertRaises(ValueError) as raises:
+                cfunc(x, y)
+            if len(x) == 0:
+                self.assertIn("'a' cannot be empty", str(raises.exception))
+            else:
+                self.assertIn("'v' cannot be empty", str(raises.exception))
+
+    @unittest.skipIf(numpy_version < (1, 18), "NumPy > 1.17 required")
+    def test_correlate_exceptions(self):
+        # correlate supported 0 dimension arrays until 1.18
+        self._test_correlate_convolve_exceptions(correlate)
+
+    def test_convolve(self):
+        self._test_correlate_convolve(convolve)
+
+    def test_convolve_exceptions(self):
+        self._test_correlate_convolve_exceptions(convolve)
+
+    def _check_output(self, pyfunc, cfunc, params, abs_tol=None):
+        expected = pyfunc(**params)
+        got = cfunc(**params)
+        self.assertPreciseEqual(expected, got, abs_tol=abs_tol)
+
+    def test_vander_basic(self):
+        pyfunc = vander
+        cfunc = jit(nopython=True)(pyfunc)
+        _check_output = partial(self._check_output, pyfunc, cfunc)
+
+        def _check(x):
+            n_choices = [None, 0, 1, 2, 3, 4]
+            increasing_choices = [True, False]
+
+            # N and increasing defaulted
+            params = {'x': x}
+            _check_output(params)
+
+            # N provided and increasing defaulted
+            for n in n_choices:
+                params = {'x': x, 'N': n}
+                _check_output(params)
+
+            # increasing provided and N defaulted:
+            for increasing in increasing_choices:
+                params = {'x': x, 'increasing': increasing}
+                _check_output(params)
+
+            # both n and increasing supplied
+            for n in n_choices:
+                for increasing in increasing_choices:
+                    params = {'x': x, 'N': n, 'increasing': increasing}
+                    _check_output(params)
+
+        _check(np.array([1, 2, 3, 5]))
+        _check(np.arange(7) - 10.5)
+        _check(np.linspace(3, 10, 5))
+        _check(np.array([1.2, np.nan, np.inf, -np.inf]))
+        _check(np.array([]))
+        _check(np.arange(-5, 5) - 0.3)
+
+        # # boolean array
+        _check(np.array([True] * 5 + [False] * 4))
+
+        # cycle through dtypes to check type promotion a la numpy
+        for dtype in np.int32, np.int64, np.float32, np.float64:
+            _check(np.arange(10, dtype=dtype))
+
+        # non array inputs
+        _check([0, 1, 2, 3])
+        _check((4, 5, 6, 7))
+        _check((0.0, 1.0, 2.0))
+        _check(())
+
+        # edge cases
+        _check((3, 4.444, 3.142))
+        _check((True, False, 4))
+
+    def test_vander_exceptions(self):
+        pyfunc = vander
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        x = np.arange(5) - 0.5
+
+        def _check_n(N):
+            with self.assertTypingError() as raises:
+                cfunc(x, N=N)
+            self.assertIn("Second argument N must be None or an integer",
+                          str(raises.exception))
+
+        for N in 1.1, True, np.inf, [1, 2]:
+            _check_n(N)
+
+        with self.assertRaises(ValueError) as raises:
+            cfunc(x, N=-1)
+        self.assertIn("Negative dimensions are not allowed",
+                      str(raises.exception))
+
+        def _check_1d(x):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(x)
+            self.assertEqual("x must be a one-dimensional array or sequence.",
+                             str(raises.exception))
+
+        x = np.arange(27).reshape((3, 3, 3))
+        _check_1d(x)
+
+        x = ((2, 3), (4, 5))
+        _check_1d(x)
+
+    def test_tri_n_basic(self):
+        pyfunc = tri_n
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        def n_variations():
+            return np.arange(-4, 8)  # number of rows
+
+        # N supplied, M and k defaulted
+        for n in n_variations():
+            params = {'N': n}
+            _check(params)
+
+    def test_tri_n_m_basic(self):
+        pyfunc = tri_n_m
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        def n_variations():
+            return np.arange(-4, 8)  # number of rows
+
+        def m_variations():
+            # number of columns
+            return itertools.chain.from_iterable(([None], range(-5, 9)))
+
+        # N supplied, M and k defaulted
+        for n in n_variations():
+            params = {'N': n}
+            _check(params)
+
+        # N and M supplied, k defaulted
+        for n in n_variations():
+            for m in m_variations():
+                params = {'N': n, 'M': m}
+                _check(params)
+
+    def test_tri_n_k_basic(self):
+        pyfunc = tri_n_k
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        def n_variations():
+            return np.arange(-4, 8)  # number of rows
+
+        def k_variations():
+            return np.arange(-10, 10)  # offset
+
+        # N supplied, M and k defaulted
+        for n in n_variations():
+            params = {'N': n}
+            _check(params)
+
+        # N and k supplied, M defaulted
+        for n in n_variations():
+            for k in k_variations():
+                params = {'N': n, 'k': k}
+                _check(params)
+
+    def test_tri_n_m_k_basic(self):
+        pyfunc = tri_n_m_k
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        def n_variations():
+            return np.arange(-4, 8)  # number of rows
+
+        def m_variations():
+            # number of columns
+            return itertools.chain.from_iterable(([None], range(-5, 9)))
+
+        def k_variations():
+            return np.arange(-10, 10)  # offset
+
+        # N supplied, M and k defaulted
+        for n in n_variations():
+            params = {'N': n}
+            _check(params)
+
+        # N and M supplied, k defaulted
+        for n in n_variations():
+            for m in m_variations():
+                params = {'N': n, 'M': m}
+                _check(params)
+
+        # N and k supplied, M defaulted
+        for n in n_variations():
+            for k in k_variations():
+                params = {'N': n, 'k': k}
+                _check(params)
+
+        # N, M and k supplied
+        for n in n_variations():
+            for k in k_variations():
+                for m in m_variations():
+                    params = {'N': n, 'M': m, 'k': k}
+                    _check(params)
+
+    def test_tri_exceptions(self):
+        pyfunc = tri_n_m_k
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        def _check(k):
+            with self.assertTypingError() as raises:
+                cfunc(5, 6, k=k)
+            assert "k must be an integer" in str(raises.exception)
+
+        for k in 1.5, True, np.inf, [1, 2]:
+            _check(k)
+
+    def _triangular_matrix_tests_m(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def _check(arr):
+            expected = pyfunc(arr)
+            got = cfunc(arr)
+            # TODO: Contiguity of result not consistent with numpy
+            self.assertEqual(got.dtype, expected.dtype)
+            np.testing.assert_array_equal(got, expected)
+
+        return self._triangular_matrix_tests_inner(self, pyfunc, _check)
+
+    def _triangular_matrix_tests_m_k(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def _check(arr):
+            for k in itertools.chain.from_iterable(([None], range(-10, 10))):
+                if k is None:
+                    params = {}
+                else:
+                    params = {'k': k}
+                expected = pyfunc(arr, **params)
+                got = cfunc(arr, **params)
+                # TODO: Contiguity of result not consistent with numpy
+                self.assertEqual(got.dtype, expected.dtype)
+                np.testing.assert_array_equal(got, expected)
+
+        return self._triangular_matrix_tests_inner(self, pyfunc, _check)
+
+    @staticmethod
+    def _triangular_matrix_tests_inner(self, pyfunc, _check):
+
+        def check_odd(a):
+            _check(a)
+            a = a.reshape((9, 7))
+            _check(a)
+            a = a.reshape((7, 1, 3, 3))
+            _check(a)
+            _check(a.T)
+
+        def check_even(a):
+            _check(a)
+            a = a.reshape((4, 16))
+            _check(a)
+            a = a.reshape((4, 2, 2, 4))
+            _check(a)
+            _check(a.T)
+
+        check_odd(np.arange(63) + 10.5)
+        check_even(np.arange(64) - 10.5)
+
+        # edge cases
+        _check(np.arange(360).reshape(3, 4, 5, 6))
+        _check(np.array([]))
+        _check(np.arange(9).reshape((3, 3))[::-1])
+        _check(np.arange(9).reshape((3, 3), order='F'))
+
+        arr = (np.arange(64) - 10.5).reshape((4, 2, 2, 4))
+        _check(arr)
+        _check(np.asfortranarray(arr))
+
+    def _triangular_matrix_exceptions(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        a = np.ones((5, 6))
+        with self.assertTypingError() as raises:
+            cfunc(a, k=1.5)
+            self.assertIn("k must be an integer", str(raises.exception))
+
+    def _triangular_indices_tests_base(self, pyfunc, args):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for x in args:
+            expected = pyfunc(*x)
+            got = cfunc(*x)
+            self.assertEqual(type(expected), type(got))
+            self.assertEqual(len(expected), len(got))
+            for e, g in zip(expected, got):
+                np.testing.assert_array_equal(e, g)
+
+    def _triangular_indices_tests_n(self, pyfunc):
+        self._triangular_indices_tests_base(
+            pyfunc,
+            [[n] for n in range(10)]
+        )
+
+    def _triangular_indices_tests_n_k(self, pyfunc):
+        self._triangular_indices_tests_base(
+            pyfunc,
+            [[n, k] for n in range(10) for k in range(-n - 1, n + 2)]
+        )
+
+    def _triangular_indices_tests_n_m(self, pyfunc):
+        self._triangular_indices_tests_base(
+            pyfunc,
+            [[n, m] for n in range(10) for m in range(2 * n)]
+        )
+
+    def _triangular_indices_tests_n_k_m(self, pyfunc):
+        self._triangular_indices_tests_base(
+            pyfunc,
+            [[n, k, m] for n in range(10)
+             for k in range(-n - 1, n + 2)
+             for m in range(2 * n)]
+        )
+
+        # Check jitted version works with default values for kwargs
+        cfunc = jit(nopython=True)(pyfunc)
+        cfunc(1)
+
+    def _triangular_indices_from_tests_arr(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for dtype in [int, float, bool]:
+            for n,m in itertools.product(range(10), range(10)):
+                arr = np.ones((n, m), dtype)
+                expected = pyfunc(arr)
+                got = cfunc(arr)
+                self.assertEqual(type(expected), type(got))
+                self.assertEqual(len(expected), len(got))
+                for e, g in zip(expected, got):
+                    np.testing.assert_array_equal(e, g)
+
+    def _triangular_indices_from_tests_arr_k(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for dtype in [int, float, bool]:
+            for n,m in itertools.product(range(10), range(10)):
+                arr = np.ones((n, m), dtype)
+                for k in range(-10, 10):
+                    expected = pyfunc(arr)
+                    got = cfunc(arr)
+                    self.assertEqual(type(expected), type(got))
+                    self.assertEqual(len(expected), len(got))
+                    for e, g in zip(expected, got):
+                        np.testing.assert_array_equal(e, g)
+
+    def _triangular_indices_exceptions(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        parameters = pysignature(pyfunc).parameters
+
+        with self.assertTypingError() as raises:
+            cfunc(1.0)
+        self.assertIn("n must be an integer", str(raises.exception))
+
+        if 'k' in parameters:
+            with self.assertTypingError() as raises:
+                cfunc(1, k=1.0)
+            self.assertIn("k must be an integer", str(raises.exception))
+
+        if 'm' in parameters:
+            with self.assertTypingError() as raises:
+                cfunc(1, m=1.0)
+            self.assertIn("m must be an integer", str(raises.exception))
+
+    def _triangular_indices_from_exceptions(self, pyfunc, test_k=True):
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for ndims in [0, 1, 3]:
+            a = np.ones([5] * ndims)
+            with self.assertTypingError() as raises:
+                cfunc(a)
+            self.assertIn("input array must be 2-d", str(raises.exception))
+
+        if test_k:
+            a = np.ones([5, 5])
+            with self.assertTypingError() as raises:
+                cfunc(a, k=0.5)
+            self.assertIn("k must be an integer", str(raises.exception))
+
+    def test_tril_basic(self):
+        self._triangular_matrix_tests_m(tril_m)
+        self._triangular_matrix_tests_m_k(tril_m_k)
+
+    def test_tril_exceptions(self):
+        self._triangular_matrix_exceptions(tril_m_k)
+
+    def test_tril_indices(self):
+        self._triangular_indices_tests_n(tril_indices_n)
+        self._triangular_indices_tests_n_k(tril_indices_n_k)
+        self._triangular_indices_tests_n_m(tril_indices_n_m)
+        self._triangular_indices_tests_n_k_m(tril_indices_n_k_m)
+        self._triangular_indices_exceptions(tril_indices_n)
+        self._triangular_indices_exceptions(tril_indices_n_k)
+        self._triangular_indices_exceptions(tril_indices_n_m)
+        self._triangular_indices_exceptions(tril_indices_n_k_m)
+
+    def test_tril_indices_from(self):
+        self._triangular_indices_from_tests_arr(tril_indices_from_arr)
+        self._triangular_indices_from_tests_arr_k(tril_indices_from_arr_k)
+        self._triangular_indices_from_exceptions(tril_indices_from_arr, False)
+        self._triangular_indices_from_exceptions(tril_indices_from_arr_k, True)
+
+    def test_triu_basic(self):
+        self._triangular_matrix_tests_m(triu_m)
+        self._triangular_matrix_tests_m_k(triu_m_k)
+
+    def test_triu_exceptions(self):
+        self._triangular_matrix_exceptions(triu_m_k)
+
+    def test_triu_indices(self):
+        self._triangular_indices_tests_n(triu_indices_n)
+        self._triangular_indices_tests_n_k(triu_indices_n_k)
+        self._triangular_indices_tests_n_m(triu_indices_n_m)
+        self._triangular_indices_tests_n_k_m(triu_indices_n_k_m)
+        self._triangular_indices_exceptions(triu_indices_n)
+        self._triangular_indices_exceptions(triu_indices_n_k)
+        self._triangular_indices_exceptions(triu_indices_n_m)
+        self._triangular_indices_exceptions(triu_indices_n_k_m)
+
+    def test_triu_indices_from(self):
+        self._triangular_indices_from_tests_arr(triu_indices_from_arr)
+        self._triangular_indices_from_tests_arr_k(triu_indices_from_arr_k)
+        self._triangular_indices_from_exceptions(triu_indices_from_arr, False)
+        self._triangular_indices_from_exceptions(triu_indices_from_arr_k, True)
+
+    def partition_sanity_check(self, pyfunc, cfunc, a, kth):
+        # as NumPy uses a different algorithm, we do not expect to
+        # match outputs exactly...
+        expected = pyfunc(a, kth)
+        got = cfunc(a, kth)
+
+        # but we do expect the unordered collection of elements up to the
+        # kth to tie out
+        self.assertPreciseEqual(np.unique(expected[:kth]), np.unique(got[:kth]))
+
+        # likewise the unordered collection of elements from the kth onwards
+        self.assertPreciseEqual(np.unique(expected[kth:]), np.unique(got[kth:]))
+
+    def test_partition_fuzz(self):
+        # inspired by the test of the same name in:
+        # https://github.com/numpy/numpy/blob/043a840/numpy/core/tests/test_multiarray.py    # noqa: E501
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for j in range(10, 30):
+            for i in range(1, j - 2):
+                d = np.arange(j)
+                self.rnd.shuffle(d)
+                d = d % self.rnd.randint(2, 30)
+                idx = self.rnd.randint(d.size)
+                kth = [0, idx, i, i + 1, -idx, -i]  # include negative kth's
+                tgt = np.sort(d)[kth]
+                self.assertPreciseEqual(cfunc(d, kth)[kth],
+                                        tgt)  # a -> array
+                self.assertPreciseEqual(cfunc(d.tolist(), kth)[kth],
+                                        tgt)  # a -> list
+                self.assertPreciseEqual(cfunc(tuple(d.tolist()), kth)[kth],
+                                        tgt)  # a -> tuple
+
+                for k in kth:
+                    self.partition_sanity_check(pyfunc, cfunc, d, k)
+
+    def test_partition_exception_out_of_range(self):
+        # inspired by the test of the same name in:
+        # https://github.com/numpy/numpy/blob/043a840/numpy/core/tests/test_multiarray.py    # noqa: E501
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        # Test out of range values in kth raise an error
+        a = np.arange(10)
+
+        def _check(a, kth):
+            with self.assertRaises(ValueError) as e:
+                cfunc(a, kth)
+            assert str(e.exception) == "kth out of bounds"
+
+        _check(a, 10)
+        _check(a, -11)
+        _check(a, (3, 30))
+
+    def test_partition_exception_non_integer_kth(self):
+        # inspired by the test of the same name in:
+        # https://github.com/numpy/numpy/blob/043a840/numpy/core/tests/test_multiarray.py    # noqa: E501
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        def _check(a, kth):
+            with self.assertTypingError() as raises:
+                cfunc(a, kth)
+            self.assertIn("Partition index must be integer",
+                          str(raises.exception))
+
+        a = np.arange(10)
+        _check(a, 9.0)
+        _check(a, (3.3, 4.4))
+        _check(a, np.array((1, 2, np.nan)))
+
+    def test_partition_exception_a_not_array_like(self):
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        def _check(a, kth):
+            with self.assertTypingError() as raises:
+                cfunc(a, kth)
+            self.assertIn('The first argument must be an array-like',
+                          str(raises.exception))
+
+        _check(4, 0)
+        _check('Sausages', 0)
+
+    def test_partition_exception_a_zero_dim(self):
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        def _check(a, kth):
+            with self.assertTypingError() as raises:
+                cfunc(a, kth)
+            self.assertIn('The first argument must be at least 1-D (found 0-D)',
+                          str(raises.exception))
+
+        _check(np.array(1), 0)
+
+    def test_partition_exception_kth_multi_dimensional(self):
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        def _check(a, kth):
+            with self.assertRaises(ValueError) as raises:
+                cfunc(a, kth)
+            self.assertIn('kth must be scalar or 1-D', str(raises.exception))
+
+        _check(np.arange(10), kth=np.arange(6).reshape(3, 2))
+
+    def test_partition_empty_array(self):
+        # inspired by the test of the same name in:
+        # https://github.com/numpy/numpy/blob/043a840/numpy/core/tests/test_multiarray.py    # noqa: E501
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(a, kth=0):
+            expected = pyfunc(a, kth)
+            got = cfunc(a, kth)
+            self.assertPreciseEqual(expected, got)
+
+        # check axis handling for multidimensional empty arrays
+        a = np.array([])
+        a.shape = (3, 2, 1, 0)
+
+        # include this with some other empty data structures
+        for arr in a, (), np.array([]):
+            check(arr)
+
+    def test_partition_basic(self):
+        # inspired by the test of the same name in:
+        # https://github.com/numpy/numpy/blob/043a840/numpy/core/tests/test_multiarray.py    # noqa: E501
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        d = np.array([])
+        got = cfunc(d, 0)
+        self.assertPreciseEqual(d, got)
+
+        d = np.ones(1)
+        got = cfunc(d, 0)
+        self.assertPreciseEqual(d, got)
+
+        # kth not modified
+        kth = np.array([30, 15, 5])
+        okth = kth.copy()
+        cfunc(np.arange(40), kth)
+        self.assertPreciseEqual(kth, okth)
+
+        for r in ([2, 1], [1, 2], [1, 1]):
+            d = np.array(r)
+            tgt = np.sort(d)
+            for k in 0, 1:
+                self.assertPreciseEqual(cfunc(d, k)[k], tgt[k])
+                self.partition_sanity_check(pyfunc, cfunc, d, k)
+
+        for r in ([3, 2, 1], [1, 2, 3], [2, 1, 3], [2, 3, 1],
+                  [1, 1, 1], [1, 2, 2], [2, 2, 1], [1, 2, 1]):
+            d = np.array(r)
+            tgt = np.sort(d)
+            for k in 0, 1, 2:
+                self.assertPreciseEqual(cfunc(d, k)[k], tgt[k])
+                self.partition_sanity_check(pyfunc, cfunc, d, k)
+
+        d = np.ones(50)
+        self.assertPreciseEqual(cfunc(d, 0), d)
+
+        # sorted
+        d = np.arange(49)
+        for k in 5, 15:
+            self.assertEqual(cfunc(d, k)[k], k)
+            self.partition_sanity_check(pyfunc, cfunc, d, k)
+
+        # rsorted, with input flavours: array, list and tuple
+        d = np.arange(47)[::-1]
+        for a in d, d.tolist(), tuple(d.tolist()):
+            self.assertEqual(cfunc(a, 6)[6], 6)
+            self.assertEqual(cfunc(a, 16)[16], 16)
+            self.assertPreciseEqual(cfunc(a, -6), cfunc(a, 41))
+            self.assertPreciseEqual(cfunc(a, -16), cfunc(a, 31))
+            self.partition_sanity_check(pyfunc, cfunc, d, -16)
+
+        # median of 3 killer, O(n^2) on pure median 3 pivot quickselect
+        # exercises the median of median of 5 code used to keep O(n)
+        d = np.arange(1000000)
+        x = np.roll(d, d.size // 2)
+        mid = x.size // 2 + 1
+        self.assertEqual(cfunc(x, mid)[mid], mid)
+        d = np.arange(1000001)
+        x = np.roll(d, d.size // 2 + 1)
+        mid = x.size // 2 + 1
+        self.assertEqual(cfunc(x, mid)[mid], mid)
+
+        # max
+        d = np.ones(10)
+        d[1] = 4
+        self.assertEqual(cfunc(d, (2, -1))[-1], 4)
+        self.assertEqual(cfunc(d, (2, -1))[2], 1)
+        d[1] = np.nan
+        assert np.isnan(cfunc(d, (2, -1))[-1])
+
+        # equal elements
+        d = np.arange(47) % 7
+        tgt = np.sort(np.arange(47) % 7)
+        self.rnd.shuffle(d)
+        for i in range(d.size):
+            self.assertEqual(cfunc(d, i)[i], tgt[i])
+            self.partition_sanity_check(pyfunc, cfunc, d, i)
+
+        d = np.array([0, 1, 2, 3, 4, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
+                      7, 7, 7, 7, 7, 9])
+        kth = [0, 3, 19, 20]
+        self.assertEqual(tuple(cfunc(d, kth)[kth]), (0, 3, 7, 7))
+
+        td = [(dt, s) for dt in [np.int32, np.float32] for s in (9, 16)]
+        for dt, s in td:
+            d = np.arange(s, dtype=dt)
+            self.rnd.shuffle(d)
+            d1 = np.tile(np.arange(s, dtype=dt), (4, 1))
+            map(self.rnd.shuffle, d1)
+            for i in range(d.size):
+                p = cfunc(d, i)
+                self.assertEqual(p[i], i)
+                # all before are smaller
+                np.testing.assert_array_less(p[:i], p[i])
+                # all after are larger
+                np.testing.assert_array_less(p[i], p[i + 1:])
+                # sanity check
+                self.partition_sanity_check(pyfunc, cfunc, d, i)
+
+    def assert_partitioned(self, pyfunc, cfunc, d, kth):
+        prev = 0
+        for k in np.sort(kth):
+            np.testing.assert_array_less(d[prev:k], d[k],
+                                         err_msg='kth %d' % k)
+            self.assertTrue((d[k:] >= d[k]).all(),
+                            msg=("kth %d, %r not greater equal "
+                                 "%d" % (k, d[k:], d[k])))
+            prev = k + 1
+            self.partition_sanity_check(pyfunc, cfunc, d, k)
+
+    def test_partition_iterative(self):
+        # inspired by the test of the same name in:
+        # https://github.com/numpy/numpy/blob/043a840/numpy/core/tests/test_multiarray.py
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        assert_partitioned = partial(self.assert_partitioned, pyfunc, cfunc)
+
+        d = np.array([3, 4, 2, 1])
+        p = cfunc(d, (0, 3))
+        assert_partitioned(p, (0, 3))
+        assert_partitioned(d[np.argpartition(d, (0, 3))], (0, 3))
+
+        self.assertPreciseEqual(p, cfunc(d, (-3, -1)))
+
+        d = np.arange(17)
+        self.rnd.shuffle(d)
+        self.assertPreciseEqual(np.arange(17), cfunc(d, list(range(d.size))))
+
+        # test unsorted kth
+        d = np.arange(17)
+        self.rnd.shuffle(d)
+        keys = np.array([1, 3, 8, -2])
+        self.rnd.shuffle(d)
+        p = cfunc(d, keys)
+        assert_partitioned(p, keys)
+        self.rnd.shuffle(keys)
+        self.assertPreciseEqual(cfunc(d, keys), p)
+
+        # equal kth
+        d = np.arange(20)[::-1]
+        assert_partitioned(cfunc(d, [5] * 4), [5])
+        assert_partitioned(cfunc(d, [5] * 4 + [6, 13]), [5] * 4 + [6, 13])
+
+    def test_partition_multi_dim(self):
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(a, kth):
+            expected = pyfunc(a, kth)
+            got = cfunc(a, kth)
+            self.assertPreciseEqual(expected[:, :, kth], got[:, :, kth])
+
+            for s in np.ndindex(expected.shape[:-1]):
+                self.assertPreciseEqual(np.unique(expected[s][:kth]),
+                                        np.unique(got[s][:kth]))
+                self.assertPreciseEqual(np.unique(expected[s][kth:]),
+                                        np.unique(got[s][kth:]))
+
+        def a_variations(a):
+            yield a
+            yield a.T
+            yield np.asfortranarray(a)
+            yield np.full_like(a, fill_value=np.nan)
+            yield np.full_like(a, fill_value=np.inf)
+            # multi-dimensional tuple input
+            yield (((1.0, 3.142, -np.inf, 3),),)
+
+        a = np.linspace(1, 10, 48)
+        a[4:7] = np.nan
+        a[8] = -np.inf
+        a[9] = np.inf
+        a = a.reshape((4, 3, 4))
+
+        for arr in a_variations(a):
+            for k in range(-3, 3):
+                check(arr, k)
+
+    def test_partition_boolean_inputs(self):
+        pyfunc = partition
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for d in np.linspace(1, 10, 17), np.array((True, False, True)):
+            for kth in True, False, -1, 0, 1:
+                self.partition_sanity_check(pyfunc, cfunc, d, kth)
+
+    @needs_blas
+    def test_cov_invalid_ddof(self):
+        pyfunc = cov
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        m = np.array([[0, 2], [1, 1], [2, 0]]).T
+
+        for ddof in np.arange(4), 4j:
+            with self.assertTypingError() as raises:
+                cfunc(m, ddof=ddof)
+            self.assertIn('ddof must be a real numerical scalar type',
+                          str(raises.exception))
+
+        for ddof in np.nan, np.inf:
+            with self.assertRaises(ValueError) as raises:
+                cfunc(m, ddof=ddof)
+            self.assertIn('Cannot convert non-finite ddof to integer',
+                          str(raises.exception))
+
+        for ddof in 1.1, -0.7:
+            with self.assertRaises(ValueError) as raises:
+                cfunc(m, ddof=ddof)
+            self.assertIn('ddof must be integral value', str(raises.exception))
+
+    def corr_corrcoef_basic(self, pyfunc, first_arg_name):
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc, abs_tol=1e-14)
+
+        def input_variations():
+            # array inputs
+            yield np.array([[0, 2], [1, 1], [2, 0]]).T
+            yield self.rnd.randn(100).reshape(5, 20)
+            yield np.asfortranarray(np.array([[0, 2], [1, 1], [2, 0]]).T)
+            yield self.rnd.randn(100).reshape(5, 20)[:, ::2]
+            yield np.array([0.3942, 0.5969, 0.7730, 0.9918, 0.7964])
+            yield np.full((4, 5), fill_value=True)
+            yield np.array([np.nan, 0.5969, -np.inf, 0.9918, 0.7964])
+            yield np.linspace(-3, 3, 33).reshape(33, 1)
+
+            # non-array inputs
+            yield ((0.1, 0.2), (0.11, 0.19), (0.09, 0.21))  # UniTuple
+            yield ((0.1, 0.2), (0.11, 0.19), (0.09j, 0.21j))  # Tuple
+            yield (-2.1, -1, 4.3)
+            yield (1, 2, 3)
+            yield [4, 5, 6]
+            yield ((0.1, 0.2, 0.3), (0.1, 0.2, 0.3))
+            yield [(1, 2, 3), (1, 3, 2)]
+            yield 3.142
+            yield ((1.1, 2.2, 1.5),)
+
+            # empty data structures
+            yield np.array([])
+            yield np.array([]).reshape(0, 2)
+            yield np.array([]).reshape(2, 0)
+            yield ()
+
+        # all inputs other than the first are defaulted
+        for input_arr in input_variations():
+            _check({first_arg_name: input_arr})
+
+    @needs_blas
+    def test_corrcoef_basic(self):
+        pyfunc = corrcoef
+        self.corr_corrcoef_basic(pyfunc, first_arg_name='x')
+
+    @needs_blas
+    def test_cov_basic(self):
+        pyfunc = cov
+        self.corr_corrcoef_basic(pyfunc, first_arg_name='m')
+
+    @needs_blas
+    def test_cov_explicit_arguments(self):
+        pyfunc = cov
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc, abs_tol=1e-14)
+
+        m = self.rnd.randn(105).reshape(15, 7)
+        y_choices = None, m[::-1]
+        rowvar_choices = False, True
+        bias_choices = False, True
+        ddof_choice = None, -1, 0, 1, 3.0, True
+
+        products = itertools.product(y_choices, rowvar_choices,
+                                     bias_choices, ddof_choice)
+        for y, rowvar, bias, ddof in products:
+            params = {'m': m, 'y': y, 'ddof': ddof,
+                      'bias': bias, 'rowvar': rowvar}
+            _check(params)
+
+    @needs_blas
+    def test_corrcoef_explicit_arguments(self):
+        pyfunc = corrcoef
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc, abs_tol=1e-14)
+
+        x = self.rnd.randn(105).reshape(15, 7)
+        y_choices = None, x[::-1]
+        rowvar_choices = False, True
+
+        for y, rowvar in itertools.product(y_choices, rowvar_choices):
+            params = {'x': x, 'y': y, 'rowvar': rowvar}
+            _check(params)
+
+    def cov_corrcoef_edge_cases(self, pyfunc, first_arg_name):
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc, abs_tol=1e-14)
+
+        # some of these examples borrowed from numpy doc string examples:
+        # https://github.com/numpy/numpy/blob/v1.15.0/numpy/lib/function_base.py#L2199-L2231    # noqa: E501
+        # some borrowed from TestCov and TestCorrCoef:
+        # https://github.com/numpy/numpy/blob/80d3a7a/numpy/lib/tests/test_function_base.py    # noqa: E501
+        m = np.array([-2.1, -1, 4.3])
+        y = np.array([3, 1.1, 0.12])
+        params = {first_arg_name: m, 'y': y}
+        _check(params)
+
+        m = np.array([1, 2, 3])  # test case modified such that m is 1D
+        y = np.array([[1j, 2j, 3j]])
+        params = {first_arg_name: m, 'y': y}
+        _check(params)
+
+        m = np.array([1, 2, 3])
+        y = (1j, 2j, 3j)
+        params = {first_arg_name: m, 'y': y}
+        _check(params)
+        params = {first_arg_name: y, 'y': m}  # flip real and complex inputs
+        _check(params)
+
+        m = np.array([1, 2, 3])
+        y = (1j, 2j, 3)  # note last item is not complex
+        params = {first_arg_name: m, 'y': y}
+        _check(params)
+        params = {first_arg_name: y, 'y': m}  # flip real and complex inputs
+        _check(params)
+
+        m = np.array([])
+        y = np.array([])
+        params = {first_arg_name: m, 'y': y}
+        _check(params)
+
+        m = 1.1
+        y = 2.2
+        params = {first_arg_name: m, 'y': y}
+        _check(params)
+
+        m = self.rnd.randn(10, 3)
+        y = np.array([-2.1, -1, 4.3]).reshape(1, 3) / 10
+        params = {first_arg_name: m, 'y': y}
+        _check(params)
+
+        m = np.array([-2.1, -1, 4.3])
+        y = np.array([[3, 1.1, 0.12], [3, 1.1, 0.12]])
+        params = {first_arg_name: m, 'y': y}
+        _check(params)
+
+        for rowvar in False, True:
+            m = np.array([-2.1, -1, 4.3])
+            y = np.array([[3, 1.1, 0.12], [3, 1.1, 0.12], [4, 1.1, 0.12]])
+            params = {first_arg_name: m, 'y': y, 'rowvar': rowvar}
+            _check(params)
+            # swap m and y
+            params = {first_arg_name: y, 'y': m, 'rowvar': rowvar}
+            _check(params)
+
+    @needs_blas
+    def test_corrcoef_edge_cases(self):
+        pyfunc = corrcoef
+        self.cov_corrcoef_edge_cases(pyfunc, first_arg_name='x')
+
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc, abs_tol=1e-14)
+
+        for x in (np.nan, -np.inf, 3.142, 0):
+            params = {'x': x}
+            _check(params)
+
+    @needs_blas
+    def test_corrcoef_edge_case_extreme_values(self):
+        pyfunc = corrcoef
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc, abs_tol=1e-14)
+
+        # extreme values
+        x = ((1e-100, 1e100), (1e100, 1e-100))
+        params = {'x': x}
+        _check(params)
+
+    @needs_blas
+    def test_cov_edge_cases(self):
+        pyfunc = cov
+        self.cov_corrcoef_edge_cases(pyfunc, first_arg_name='m')
+
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc, abs_tol=1e-14)
+
+        # invalid ddof
+        m = np.array([[0, 2], [1, 1], [2, 0]]).T
+        params = {'m': m, 'ddof': 5}
+        _check(params)
+
+    @needs_blas
+    def test_cov_exceptions(self):
+        pyfunc = cov
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        def _check_m(m):
+            with self.assertTypingError() as raises:
+                cfunc(m)
+            self.assertIn('m has more than 2 dimensions', str(raises.exception))
+
+        m = np.ones((5, 6, 7))
+        _check_m(m)
+
+        m = ((((1, 2, 3), (2, 2, 2)),),)
+        _check_m(m)
+
+        m = [[[5, 6, 7]]]
+        _check_m(m)
+
+        def _check_y(m, y):
+            with self.assertTypingError() as raises:
+                cfunc(m, y=y)
+            self.assertIn('y has more than 2 dimensions', str(raises.exception))
+
+        m = np.ones((5, 6))
+        y = np.ones((5, 6, 7))
+        _check_y(m, y)
+
+        m = np.array((1.1, 2.2, 1.1))
+        y = (((1.2, 2.2, 2.3),),)
+        _check_y(m, y)
+
+        m = np.arange(3)
+        y = np.arange(4)
+        with self.assertRaises(ValueError) as raises:
+            cfunc(m, y=y)
+        self.assertIn('m and y have incompatible dimensions',
+                      str(raises.exception))
+        # Numpy raises ValueError: all the input array dimensions except for the
+        # concatenation axis must match exactly.
+
+        m = np.array([-2.1, -1, 4.3]).reshape(1, 3)
+        with self.assertRaises(RuntimeError) as raises:
+            cfunc(m)
+        self.assertIn('2D array containing a single row is unsupported',
+                      str(raises.exception))
+
+    def test_ediff1d_basic(self):
+        pyfunc = ediff1d
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        def to_variations(a):
+            yield None
+            yield a
+            yield a.astype(np.int16)
+
+        def ary_variations(a):
+            yield a
+            yield a.reshape(3, 2, 2)
+            yield a.astype(np.int32)
+
+        for ary in ary_variations(np.linspace(-2, 7, 12)):
+            params = {'ary': ary}
+            _check(params)
+
+            for a in to_variations(ary):
+                params = {'ary': ary, 'to_begin': a}
+                _check(params)
+
+                params = {'ary': ary, 'to_end': a}
+                _check(params)
+
+                for b in to_variations(ary):
+                    params = {'ary': ary, 'to_begin': a, 'to_end': b}
+                    _check(params)
+
+    @unittest.skipIf(numpy_version >= (1, 19), ("Unstable bevahiour, see:"
+                     "https://github.com/numpy/numpy/pull/17457"))
+    def test_ediff1d_edge_cases(self):
+        # NOTE: NumPy 1.16 has a variety of behaviours for type conversion, see
+        # https://github.com/numpy/numpy/issues/13103, as this is not resolved
+        # Numba replicates behaviours for <= 1.15 and conversion in 1.16.0 for
+        # finite inputs.
+        pyfunc = ediff1d
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        def _check_raises_type_error(params, arg):
+            with self.assertRaises(TypingError) as raises:
+                cfunc(**params)
+            msg = 'dtype of %s must be compatible with input ary' % arg
+            self.assertIn(msg, str(raises.exception))
+
+            with self.assertRaises(ValueError) as raises:
+                pyfunc(**params)
+            excstr = str(raises.exception)
+            self.assertIn("cannot convert", excstr)
+            self.assertIn("to array with dtype", excstr)
+            self.assertIn("as required for input ary", excstr)
+
+        def input_variations():
+            yield ((1, 2, 3), (4, 5, 6))
+            yield [4, 5, 6]
+            yield np.array([])
+            yield ()
+            if numpy_version < (1, 16):
+                yield np.array([np.nan, np.inf, 4, -np.inf, 3.142])
+                parts = np.array([np.nan, 2, np.nan, 4, 5, 6, 7, 8, 9])
+                a = parts + 1j * parts[::-1]
+                yield a.reshape(3, 3)
+
+        for i in input_variations():
+            params = {'ary': i, 'to_end': i, 'to_begin': i}
+            _check(params)
+
+        # to_end / to_begin are boolean
+        params = {'ary': [1], 'to_end': (False,), 'to_begin': (True, False)}
+        _check(params)
+
+        ## example of unsafe type casting (np.nan to np.int32)
+        ## fixed here: https://github.com/numpy/numpy/pull/12713 for np 1.16
+        to_begin = np.array([1, 2, 3.142, np.nan, 5, 6, 7, -8, np.nan])
+        params = {'ary': np.arange(-4, 6), 'to_begin': to_begin}
+        if numpy_version < (1, 16):
+            _check(params)
+        else:
+            # np 1.16 raises, cannot cast float64 array to intp array
+            _check_raises_type_error(params, 'to_begin')
+
+        # scalar inputs
+        params = {'ary': 3.142}
+        _check(params)
+
+        params = {'ary': 3, 'to_begin': 3.142}
+        if numpy_version < (1, 16):
+            _check(params)
+        else:
+            _check_raises_type_error(params, 'to_begin')
+            # now use 2 floats
+            params = {'ary': 3., 'to_begin': 3.142}
+            _check(params)
+
+        params = {'ary': np.arange(-4, 6), 'to_begin': -5, 'to_end': False}
+        if IS_WIN32 and not IS_32BITS and numpy_version >= (1, 16):
+            # XFAIL on 64-bits windows + numpy 1.16. See #3898
+            with self.assertRaises(TypingError) as raises:
+                _check(params)
+            expected_msg = "dtype of to_begin must be compatible with input ary"
+            self.assertIn(expected_msg, str(raises.exception))
+        else:
+            _check(params)
+
+        # the following would fail on one of the BITS32 builds (difference in
+        # overflow handling):
+        # params = {'ary': np.array([5, 6], dtype=np.int16), 'to_end': [1e100]}
+        # _check(params)
+
+    def test_ediff1d_exceptions(self):
+        pyfunc = ediff1d
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertTypingError() as e:
+            cfunc(np.array((True, True, False)))
+
+        msg = "Boolean dtype is unsupported (as per NumPy)"
+        assert msg in str(e.exception)
+
+    def test_fliplr_basic(self):
+        pyfunc = fliplr
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield np.arange(10).reshape(5, 2)
+            yield np.arange(20).reshape(5, 2, 2)
+            yield ((1, 2),)
+            yield ([1, 2], [3, 4],)
+
+        for a in a_variations():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc("abc")
+
+        self.assertIn("Cannot np.fliplr on %s type" % types.unicode_type,
+                      str(raises.exception))
+
+    def test_fliplr_exception(self):
+        pyfunc = fliplr
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(np.arange(3))
+
+        self.assertIn("cannot index array", str(raises.exception))
+        self.assertIn("with 2 indices", str(raises.exception))
+
+    def test_flipud_basic(self):
+        pyfunc = flipud
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield [1]
+            yield np.arange(10)
+            yield np.arange(10).reshape(5, 2)
+            yield np.arange(20).reshape(5, 2, 2)
+            yield ((1, 2),)
+            yield ([1, 2], [3, 4],)
+
+        for a in a_variations():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc("abc")
+
+        self.assertIn("Cannot np.flipud on %s type" % types.unicode_type,
+                      str(raises.exception))
+
+    def test_flipud_exception(self):
+        pyfunc = flipud
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(1)
+
+        self.assertIn("cannot index array", str(raises.exception))
+        self.assertIn("with 1 indices", str(raises.exception))
+
+    def test_flip_basic(self):
+        pyfunc = flip
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield np.array(1)
+            yield np.arange(10)
+            yield np.arange(10).reshape(5, 2)
+            yield np.arange(20).reshape(5, 2, 2)
+
+        for a in a_variations():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc((1, 2, 3))
+
+        self.assertIn("Cannot np.flip on UniTuple", str(raises.exception))
+
+    def test_logspace2_basic(self):
+
+        def inputs():
+            #start, stop
+            yield 1, 60
+            yield -1, 60
+            yield -60, -1
+            yield -1, -60
+            yield 60, -1
+            yield 1.0, 60.0
+            yield -60.0, -1.0
+            yield -1.0, 60.0
+            yield 0.0, np.e
+            yield 0.0, np.pi
+            yield np.complex64(1), np.complex64(2)
+            yield np.complex64(2j), np.complex64(4j)
+            yield np.complex64(2), np.complex64(4j)
+            yield np.complex64(1 + 2j), np.complex64(3 + 4j)
+            yield np.complex64(1 - 2j), np.complex64(3 - 4j)
+            yield np.complex64(-1 + 2j), np.complex64(3 + 4j)
+
+        pyfunc = logspace2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for start, stop in inputs():
+            np.testing.assert_allclose(pyfunc(start, stop), cfunc(start, stop))
+
+    def test_logspace2_exception(self):
+        cfunc = jit(nopython=True)(logspace2)
+
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc("abc", 5)
+        self.assertIn('The first argument "start" must be a number',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(5, "abc")
+        self.assertIn('The second argument "stop" must be a number',
+                      str(raises.exception))
+
+    def test_logspace3_basic(self):
+
+        def inputs():
+            #start, stop
+            yield 1, 60
+            yield -1, 60
+            yield -60, -1
+            yield -1, -60
+            yield 60, -1
+            yield 1.0, 60.0
+            yield -60.0, -1.0
+            yield -1.0, 60.0
+            yield 0.0, np.e
+            yield 0.0, np.pi
+            yield np.complex64(1), np.complex64(2)
+            yield np.complex64(2j), np.complex64(4j)
+            yield np.complex64(2), np.complex64(4j)
+            yield np.complex64(1 + 2j), np.complex64(3 + 4j)
+            yield np.complex64(1 - 2j), np.complex64(3 - 4j)
+            yield np.complex64(-1 + 2j), np.complex64(3 + 4j)
+
+        pyfunc = logspace3
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for start, stop in inputs():
+            np.testing.assert_allclose(pyfunc(start, stop), cfunc(start, stop))
+
+    def test_logspace3_with_num_basic(self):
+
+        def inputs():
+            #start, stop, num
+            yield 1, 60, 20
+            yield -1, 60, 30
+            yield -60, -1, 40
+            yield -1, -60, 50
+            yield 60, -1, 60
+            yield 1.0, 60.0, 70
+            yield -60.0, -1.0, 80
+            yield -1.0, 60.0, 90
+            yield 0.0, np.e, 20
+            yield 0.0, np.pi, 30
+            yield np.complex64(1), np.complex64(2), 40
+            yield np.complex64(2j), np.complex64(4j), 50
+            yield np.complex64(2), np.complex64(4j), 60
+            yield np.complex64(1 + 2j), np.complex64(3 + 4j), 70
+            yield np.complex64(1 - 2j), np.complex64(3 - 4j), 80
+            yield np.complex64(-1 + 2j), np.complex64(3 + 4j), 90
+
+        pyfunc = logspace3
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for start, stop, num in inputs():
+            np.testing.assert_allclose(pyfunc(start, stop, num),
+                                       cfunc(start, stop, num))
+
+    def test_logspace3_exception(self):
+        cfunc = jit(nopython=True)(logspace3)
+
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc("abc", 5)
+        self.assertIn('The first argument "start" must be a number',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(5, "abc")
+        self.assertIn('The second argument "stop" must be a number',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(0, 5, "abc")
+        self.assertIn('The third argument "num" must be an integer',
+                      str(raises.exception))
+
+    def test_rot90_basic(self):
+        pyfunc = rot90
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield np.arange(10).reshape(5, 2)
+            yield np.arange(20).reshape(5, 2, 2)
+            yield np.arange(64).reshape(2, 2, 2, 2, 2, 2)
+
+        for a in a_variations():
+            expected = pyfunc(a)
+            got = cfunc(a)
+            self.assertPreciseEqual(expected, got)
+
+    def test_rot90_with_k_basic(self):
+        pyfunc = rot90_k
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield np.arange(10).reshape(5, 2)
+            yield np.arange(20).reshape(5, 2, 2)
+            yield np.arange(64).reshape(2, 2, 2, 2, 2, 2)
+
+        for a in a_variations():
+            for k in range(-5, 6):
+                expected = pyfunc(a, k)
+                got = cfunc(a, k)
+                self.assertPreciseEqual(expected, got)
+
+    def test_rot90_exception(self):
+        pyfunc = rot90_k
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc("abc")
+
+        self.assertIn('The first argument "arr" must be an array',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(np.arange(4).reshape(2, 2), k="abc")
+
+        self.assertIn('The second argument "k" must be an integer',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(np.arange(3))
+
+        self.assertIn("Input must be >= 2-d.", str(raises.exception))
+
+    def _check_split(self, func):
+        # Since np.split and np.array_split are very similar
+        pyfunc = func
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def args_variations():
+            a = np.arange(100)
+            yield a, 2
+            yield a, 2, 0
+            yield a, [1, 4, 72]
+            yield list(a), [1, 4, 72]
+            yield tuple(a), [1, 4, 72]
+            yield a, [1, 4, 72], 0
+            yield list(a), [1, 4, 72], 0
+            yield tuple(a), [1, 4, 72], 0
+
+            a = np.arange(64).reshape(4, 4, 4)
+            yield a, 2
+            yield a, 2, 0
+            yield a, 2, 1
+            yield a, [2, 1, 5]
+            yield a, [2, 1, 5], 1
+            yield a, [2, 1, 5], 2
+            yield a, [1, 3]
+            yield a, [1, 3], 1
+            yield a, [1, 3], 2
+
+            a = np.arange(100).reshape(2, -1)
+            yield a, 1
+            yield a, 1, 0
+            yield a, [1], 0
+            yield a, 50, 1
+            yield a, np.arange(10, 50, 10), 1
+            yield a, (1,)
+            yield a, (np.int32(4), 10)
+
+            a = np.array([])
+            yield a, 1
+            yield a, 2
+            yield a, (2, 3), 0
+            yield a, 1, 0
+
+            a = np.array([[]])
+            yield a, 1
+            yield a, (2, 3), 1
+            yield a, 1, 0
+            yield a, 1, 1
+
+        for args in args_variations():
+            expected = pyfunc(*args)
+            got = cfunc(*args)
+
+            np.testing.assert_equal(expected, list(got))
+
+    def _check_array_split(self, func):
+        # array_split specific checks, mainly dealing with `int`s
+        pyfunc = func
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def args_variations():
+            yield np.arange(8), 3
+            yield list(np.arange(8)), 3
+            yield tuple(np.arange(8)), 3
+            yield np.arange(24).reshape(12, 2), 5
+
+        for args in args_variations():
+            expected = pyfunc(*args)
+            got = cfunc(*args)
+
+            np.testing.assert_equal(expected, list(got))
+
+    def test_array_split_basic(self):
+        self._check_split(array_split)
+        self._check_array_split(array_split)
+
+    def test_split_basic(self):
+        self._check_split(split)
+
+        self.disable_leak_check()  # The exception leaks
+        with self.assertRaises(ValueError) as raises:
+            njit(split)(np.ones(5), 2)
+        self.assertIn(
+            "array split does not result in an equal division",
+            str(raises.exception)
+        )
+
+    def test_roll_basic(self):
+        pyfunc = roll
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield np.arange(7)
+            yield np.arange(3 * 4 * 5).reshape(3, 4, 5)
+            yield [1.1, 2.2, 3.3]
+            yield (True, False, True)
+            yield False
+            yield 4
+            yield (9,)
+            yield np.asfortranarray(np.array([[1.1, np.nan], [np.inf, 7.8]]))
+            yield np.array([])
+            yield ()
+
+        def shift_variations():
+            return itertools.chain.from_iterable(((True, False),
+                                                  range(-10, 10)))
+
+        for a in a_variations():
+            for shift in shift_variations():
+                expected = pyfunc(a, shift)
+                got = cfunc(a, shift)
+                self.assertPreciseEqual(expected, got)
+
+    def test_roll_exceptions(self):
+        pyfunc = roll
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        for shift in 1.1, (1, 2):
+            with self.assertTypingError() as e:
+                cfunc(np.arange(10), shift)
+
+            msg = "shift must be an integer"
+            assert msg in str(e.exception)
+
+    def test_extract_basic(self):
+        pyfunc = extract
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        a = np.arange(10)
+        self.rnd.shuffle(a)
+        for threshold in range(-3, 13):
+            cond = a > threshold
+            _check({'condition': cond, 'arr': a})
+
+        a = np.arange(60).reshape(4, 5, 3)
+        cond = a > 11.2
+        _check({'condition': cond, 'arr': a})
+
+        a = ((1, 2, 3), (3, 4, 5), (4, 5, 6))
+        cond = np.eye(3).flatten()
+        _check({'condition': cond, 'arr': a})
+
+        a = [1.1, 2.2, 3.3, 4.4]
+        cond = [1, 1, 0, 1]
+        _check({'condition': cond, 'arr': a})
+
+        a = np.linspace(-2, 10, 6)
+        element_pool = (True, False, np.nan, -1, -1.0, -1.2, 1, 1.0, 1.5j)
+        for cond in itertools.combinations_with_replacement(element_pool, 4):
+            _check({'condition': cond, 'arr': a})
+            _check({'condition': np.array(cond).reshape(2, 2), 'arr': a})
+
+        a = np.array([1, 2, 3])
+        cond = np.array([])
+        _check({'condition': cond, 'arr': a})
+
+        a = np.array([1, 2, 3])
+        cond = np.array([1, 0, 1, 0])  # but [1, 0, 1, 0, 1] raises
+        _check({'condition': cond, 'arr': a})
+
+        a = np.array([[1, 2, 3], [4, 5, 6]])
+        cond = [1, 0, 1, 0, 1, 0]  # but [1, 0, 1, 0, 1, 0, 1] raises
+        _check({'condition': cond, 'arr': a})
+
+        a = np.array([[1, 2, 3], [4, 5, 6]])
+        cond = np.array([1, 0, 1, 0, 1, 0, 0, 0]).reshape(2, 2, 2)
+        _check({'condition': cond, 'arr': a})
+
+        a = np.asfortranarray(np.arange(60).reshape(3, 4, 5))
+        cond = np.repeat((0, 1), 30)
+        _check({'condition': cond, 'arr': a})
+        _check({'condition': cond, 'arr': a[::-1]})
+
+        a = np.array(4)
+        for cond in 0, 1:
+            _check({'condition': cond, 'arr': a})
+
+        a = 1
+        cond = 1
+        _check({'condition': cond, 'arr': a})
+
+        a = np.array(1)
+        cond = np.array([True, False])
+        _check({'condition': cond, 'arr': a})
+
+        a = np.arange(4)
+        cond = np.array([1, 0, 1, 0, 0, 0]).reshape(2, 3) * 1j
+        _check({'condition': cond, 'arr': a})
+
+    def test_extract_exceptions(self):
+        pyfunc = extract
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        a = np.array([])
+        cond = np.array([1, 2, 3])
+
+        with self.assertRaises(ValueError) as e:
+            cfunc(cond, a)
+        self.assertIn('Cannot extract from an empty array', str(e.exception))
+
+        def _check(cond, a):
+            msg = 'condition shape inconsistent with arr shape'
+            with self.assertRaises(ValueError) as e:
+                cfunc(cond, a)
+            self.assertIn(msg, str(e.exception))
+
+        a = np.array([[1, 2, 3], [1, 2, 3]])
+        cond = [1, 0, 1, 0, 1, 0, 1]
+        _check(cond, a)
+
+        a = np.array([1, 2, 3])
+        cond = np.array([1, 0, 1, 0, 1])
+        _check(cond, a)
+
+        a = np.array(60)  # note, this is 0D
+        cond = 0, 1
+        _check(cond, a)
+
+        a = np.arange(4)
+        cond = np.array([True, False, False, False, True])
+        _check(cond, a)
+
+        a = np.arange(4)
+        cond = np.array([True, False, True, False, False, True, False])
+        _check(cond, a)
+
+    def test_np_trapz_basic(self):
+        pyfunc = np_trapz
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        y = [1, 2, 3]
+        _check({'y': y})
+
+        y = (3, 1, 2, 2, 2)
+        _check({'y': y})
+
+        y = np.arange(15).reshape(3, 5)
+        _check({'y': y})
+
+        y = np.linspace(-10, 10, 60).reshape(4, 3, 5)
+        _check({'y': y}, abs_tol=1e-13)
+
+        self.rnd.shuffle(y)
+        _check({'y': y}, abs_tol=1e-13)
+
+        y = np.array([])
+        _check({'y': y})
+
+        y = np.array([3.142, np.nan, np.inf, -np.inf, 5])
+        _check({'y': y})
+
+        y = np.arange(20) + np.linspace(0, 10, 20) * 1j
+        _check({'y': y})
+
+        y = np.array([], dtype=np.complex128)
+        _check({'y': y})
+
+        y = (True, False, True)
+        _check({'y': y})
+
+    def test_np_trapz_x_basic(self):
+        pyfunc = np_trapz_x
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        y = [1, 2, 3]
+        x = [4, 6, 8]
+        _check({'y': y, 'x': x})
+
+        y = [1, 2, 3, 4, 5]
+        x = (4, 6)
+        _check({'y': y, 'x': x})
+
+        y = (1, 2, 3, 4, 5)
+        x = [4, 5, 6, 7, 8]
+        _check({'y': y, 'x': x})
+
+        y = np.array([1, 2, 3, 4, 5])
+        x = [4, 4]
+        _check({'y': y, 'x': x})
+
+        y = np.array([])
+        x = np.array([2, 3])
+        _check({'y': y, 'x': x})
+
+        y = (1, 2, 3, 4, 5)
+        x = None
+        _check({'y': y, 'x': x})
+
+        y = np.arange(20).reshape(5, 4)
+        x = np.array([4, 5])
+        _check({'y': y, 'x': x})
+
+        y = np.arange(20).reshape(5, 4)
+        x = np.array([4, 5, 6, 7])
+        _check({'y': y, 'x': x})
+
+        y = np.arange(60).reshape(5, 4, 3)
+        x = np.array([4, 5])
+        _check({'y': y, 'x': x})
+
+        y = np.arange(60).reshape(5, 4, 3)
+        x = np.array([4, 5, 7])
+        _check({'y': y, 'x': x})
+
+        y = np.arange(60).reshape(5, 4, 3)
+        self.rnd.shuffle(y)
+        x = y + 1.1
+        self.rnd.shuffle(x)
+        _check({'y': y, 'x': x})
+
+        y = np.arange(20)
+        x = y + np.linspace(0, 10, 20) * 1j
+        _check({'y': y, 'x': x})
+
+        y = np.array([1, 2, 3])
+        x = np.array([1 + 1j, 1 + 2j])
+        _check({'y': y, 'x': x})
+
+    @unittest.skip('NumPy behaviour questionable')
+    def test_trapz_numpy_questionable(self):
+        # https://github.com/numpy/numpy/issues/12858
+        pyfunc = np_trapz
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        # passes (NumPy and Numba return 2.0)
+        y = np.array([True, False, True, True]).astype(int)
+        _check({'y': y})
+
+        # fails (NumPy returns 1.5; Numba returns 2.0)
+        y = np.array([True, False, True, True])
+        _check({'y': y})
+
+    def test_np_trapz_dx_basic(self):
+        pyfunc = np_trapz_dx
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        y = [1, 2, 3]
+        dx = 2
+        _check({'y': y, 'dx': dx})
+
+        y = [1, 2, 3, 4, 5]
+        dx = [1, 4, 5, 6]
+        _check({'y': y, 'dx': dx})
+
+        y = [1, 2, 3, 4, 5]
+        dx = [1, 4, 5, 6]
+        _check({'y': y, 'dx': dx})
+
+        y = np.linspace(-2, 5, 10)
+        dx = np.nan
+        _check({'y': y, 'dx': dx})
+
+        y = np.linspace(-2, 5, 10)
+        dx = np.inf
+        _check({'y': y, 'dx': dx})
+
+        y = np.linspace(-2, 5, 10)
+        dx = np.linspace(-2, 5, 9)
+        _check({'y': y, 'dx': dx}, abs_tol=1e-13)
+
+        y = np.arange(60).reshape(4, 5, 3) * 1j
+        dx = np.arange(40).reshape(4, 5, 2)
+        _check({'y': y, 'dx': dx})
+
+        x = np.arange(-10, 10, .1)
+        r = cfunc(np.exp(-.5 * x ** 2) / np.sqrt(2 * np.pi), dx=0.1)
+        # check integral of normal equals 1
+        np.testing.assert_almost_equal(r, 1, 7)
+
+        y = np.arange(20)
+        dx = 1j
+        _check({'y': y, 'dx': dx})
+
+        y = np.arange(20)
+        dx = np.array([5])
+        _check({'y': y, 'dx': dx})
+
+    def test_np_trapz_x_dx_basic(self):
+        pyfunc = np_trapz_x_dx
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        # dx should be ignored
+        for dx in (None, 2, np.array([1, 2, 3, 4, 5])):
+            y = [1, 2, 3]
+            x = [4, 6, 8]
+            _check({'y': y, 'x': x, 'dx': dx})
+
+            y = [1, 2, 3, 4, 5]
+            x = [4, 6]
+            _check({'y': y, 'x': x, 'dx': dx})
+
+            y = [1, 2, 3, 4, 5]
+            x = [4, 5, 6, 7, 8]
+            _check({'y': y, 'x': x, 'dx': dx})
+
+            y = np.arange(60).reshape(4, 5, 3)
+            self.rnd.shuffle(y)
+            x = y * 1.1
+            x[2, 2, 2] = np.nan
+            _check({'y': y, 'x': x, 'dx': dx})
+
+    def test_np_trapz_x_dx_exceptions(self):
+        pyfunc = np_trapz_x_dx
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        def check_not_ok(params):
+            with self.assertRaises(ValueError) as e:
+                cfunc(*params)
+
+            self.assertIn('unable to broadcast', str(e.exception))
+
+        y = [1, 2, 3, 4, 5]
+        for x in [4, 5, 6, 7, 8, 9], [4, 5, 6]:
+            check_not_ok((y, x, 1.0))
+
+        y = np.arange(60).reshape(3, 4, 5)
+        x = np.arange(36).reshape(3, 4, 3)
+        check_not_ok((y, x, 1.0))
+
+        y = np.arange(60).reshape(3, 4, 5)
+        x = np.array([4, 5, 6, 7])
+        check_not_ok((y, x, 1.0))
+
+        y = [1, 2, 3, 4, 5]
+        dx = np.array([1.0, 2.0])
+        check_not_ok((y, None, dx))
+
+        y = np.arange(60).reshape(3, 4, 5)
+        dx = np.arange(60).reshape(3, 4, 5)
+        check_not_ok((y, None, dx))
+
+        with self.assertTypingError() as e:
+            y = np.array(4)
+            check_not_ok((y, None, 1.0))
+
+        self.assertIn('y cannot be 0D', str(e.exception))
+
+        for y in 5, False, np.nan:
+            with self.assertTypingError() as e:
+                cfunc(y, None, 1.0)
+
+            self.assertIn('y cannot be a scalar', str(e.exception))
+
+    def test_average(self):
+
+        #array of random numbers
+        N = 100
+        a = np.random.ranf(N) * 100
+        w = np.random.ranf(N) * 100
+        w0 = np.zeros(N)
+
+        #boolean array and weights
+        a_bool = np.random.ranf(N) > 0.5
+        w_bool = np.random.ranf(N) > 0.5
+
+        #array of random ints
+        a_int = np.random.randint(101, size=N)
+        w_int = np.random.randint(101, size=N)
+
+        #3D array of random numbers
+        d0 = 100
+        d1 = 50
+        d2 = 25
+        a_3d = np.random.rand(d0,d1,d2) * 100
+        w_3d = np.random.rand(d0,d1,d2) * 100
+
+        pyfunc = np_average
+        cfunc = jit(nopython=True)(pyfunc)
+
+        #test case for average with weights
+        #(number of elements in array and weight array are equal)
+        self.assertAlmostEqual( pyfunc(a,weights=w),
+                                cfunc(a,weights=w), places=10)
+        self.assertAlmostEqual( pyfunc(a_3d,weights=w_3d),
+                                cfunc(a_3d,weights=w_3d), places=10)
+
+        #test case for average with array and weights with
+        #int datatype (number of elements in array and weight array are equal)
+        self.assertAlmostEqual( pyfunc(a_int,weights=w_int),
+                                cfunc(a_int,weights=w_int), places=10)
+
+        #test case for average with boolean weights
+        self.assertAlmostEqual( pyfunc(a,weights=w_bool),
+                                cfunc(a,weights=w_bool), places=10)
+        self.assertAlmostEqual( pyfunc(a_bool,weights=w),
+                                cfunc(a_bool,weights=w), places=10)
+        self.assertAlmostEqual( pyfunc(a_bool, weights=w_bool),
+                                cfunc(a_bool, weights=w_bool), places=10)
+
+        #test case for average without weights
+        self.assertAlmostEqual(pyfunc(a), cfunc(a), places=10)
+        self.assertAlmostEqual(pyfunc(a_3d), cfunc(a_3d), places=10)
+
+        def test_weights_zero_sum(data, weights):
+            with self.assertRaises(ZeroDivisionError) as e:
+                cfunc(data, weights=weights)
+            err = e.exception
+            self.assertEqual(str(err),
+                             "Weights sum to zero, can't be normalized.")
+
+        #test case when sum of weights is zero
+        test_weights_zero_sum(a, weights=w0)
+
+        def test_1D_weights(data, weights):
+            with self.assertRaises(TypeError) as e:
+                cfunc(data, weights=weights)
+            err = e.exception
+            self.assertEqual(str(err),
+                             "Numba does not support average when shapes of "
+                             "a and weights differ.")
+
+        def test_1D_weights_axis(data, axis, weights):
+            with self.assertRaises(TypeError) as e:
+                cfunc(data,axis=axis, weights=weights)
+            err = e.exception
+            self.assertEqual(str(err),
+                             "Numba does not support average with axis.")
+
+        #small case to test exceptions for 2D array and 1D weights
+        data = np.arange(6).reshape((3,2,1))
+        w = np.asarray([1. / 4, 3. / 4])
+
+        #test without axis argument
+        test_1D_weights(data, weights=w)
+
+        #test with axis argument
+        test_1D_weights_axis(data, axis=1, weights=w)
+
+    def test_allclose(self):
+
+        pyfunc = np_allclose
+        cfunc = jit(nopython=True)(pyfunc)
+
+        min_int = np.iinfo(np.int_).min
+        a = np.array([min_int], dtype=np.int_)
+
+        simple_data = [
+            (np.asarray([1e10, 1e-7]), np.asarray([1.00001e10, 1e-8])),
+            (np.asarray([1e10, 1e-8]), np.asarray([1.00001e10, 1e-9])),
+            (np.asarray([1e10, 1e-8]), np.asarray([1.0001e10, 1e-9])),
+            (np.asarray([1e10]), np.asarray([1.0001e10, 1e-9])),
+            (1.0, 1.0),
+            (np.array([np.inf, 1]), np.array([0, np.inf])),
+            (a, a)
+        ]
+
+        for a, b in simple_data:
+            py_result = pyfunc(a, b)
+            c_result = cfunc(a, b)
+            self.assertEqual(py_result, c_result)
+
+        a = np.asarray([1.0, np.nan])
+        b = np.asarray([1.0, np.nan])
+        self.assertFalse(cfunc(a, b))
+        self.assertEquals(pyfunc(a, b, equal_nan=True),
+                          cfunc(a, b, equal_nan=True))
+
+        b = np.asarray([np.nan, 1.0])
+        self.assertEquals(pyfunc(a, b), cfunc(a, b))
+
+        noise_levels = [1.0, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 0.0]
+        zero_array = np.zeros((25, 4))
+        a = np.random.ranf((25, 4))
+        for noise in noise_levels:
+            for rtol in noise_levels:
+                for atol in noise_levels:
+                    py_result = pyfunc(zero_array, noise,
+                                       atol=atol, rtol=rtol)
+                    c_result = cfunc(zero_array, noise,
+                                     atol=atol, rtol=rtol)
+                    self.assertEqual(py_result, c_result)
+
+                    py_result = pyfunc(noise, zero_array,
+                                       atol=atol, rtol=rtol)
+                    c_result = cfunc(noise, zero_array,
+                                     atol=atol, rtol=rtol)
+                    self.assertEqual(py_result, c_result)
+
+                    py_result = pyfunc(np.asarray([noise]), zero_array,
+                                       atol=atol, rtol=rtol)
+                    c_result = cfunc(np.asarray([noise]), zero_array,
+                                     atol=atol, rtol=rtol)
+                    self.assertEqual(py_result, c_result)
+
+                    py_result = pyfunc(a, a + noise, atol=atol, rtol=rtol)
+                    c_result = cfunc(a, a + noise, atol=atol, rtol=rtol)
+                    self.assertEqual(py_result, c_result)
+
+                    py_result = pyfunc(a + noise, a, atol=atol, rtol=rtol)
+                    c_result = cfunc(a + noise, a, atol=atol, rtol=rtol)
+                    self.assertEqual(py_result, c_result)
+
+    def test_ip_allclose_numpy(self):
+        # https://github.com/numpy/numpy/blob/4adc87dff15a247e417d50f10cc4def8e1c17a03/numpy/core/tests/test_numeric.py#L2402-L2420    # noqa: E501
+        pyfunc = np_allclose
+        cfunc = jit(nopython=True)(pyfunc)
+
+        arr = np.array([100.0, 1000.0])
+        aran = np.arange(125).astype(dtype=np.float64).reshape((5, 5, 5))
+
+        atol = 1e-8
+        rtol = 1e-5
+
+        numpy_data = [
+            (np.asarray([1, 0]), np.asarray([1, 0])),
+            (np.asarray([atol]), np.asarray([0.0])),
+            (np.asarray([1.0]), np.asarray([1 + rtol + atol])),
+            (arr, arr + arr * rtol),
+            (arr, arr + arr * rtol + atol * 2),
+            (aran, aran + aran * rtol),
+            (np.inf, np.inf),
+            (np.inf, np.asarray([np.inf]))
+        ]
+
+        for (x, y) in numpy_data:
+            self.assertEquals(pyfunc(x, y), cfunc(x, y))
+
+    def test_ip_not_allclose_numpy(self):
+        # https://github.com/numpy/numpy/blob/4adc87dff15a247e417d50f10cc4def8e1c17a03/numpy/core/tests/test_numeric.py#L2422-L2441    # noqa: E501
+
+        pyfunc = np_allclose
+        cfunc = jit(nopython=True)(pyfunc)
+
+        aran = np.arange(125).astype(dtype=np.float64).reshape((5, 5, 5))
+
+        atol = 1e-8
+        rtol = 1e-5
+
+        numpy_data = [
+            (np.asarray([np.inf, 0]), np.asarray([1.0, np.inf])),
+            (np.asarray([np.inf, 0]), np.asarray([1.0, 0])),
+            (np.asarray([np.inf, np.inf]), np.asarray([1.0, np.inf])),
+            (np.asarray([np.inf, np.inf]), np.asarray([1.0, 0.0])),
+            (np.asarray([-np.inf, 0.0]), np.asarray([np.inf, 0.0])),
+            (np.asarray([np.nan, 0.0]), np.asarray([np.nan, 0.0])),
+            (np.asarray([atol * 2]), np.asarray([0.0])),
+            (np.asarray([1.0]), np.asarray([1 + rtol + atol * 2])),
+            (aran, aran + aran * atol + atol * 2),
+            (np.array([np.inf, 1.0]), np.array([0.0, np.inf]))
+        ]
+
+        for (x, y) in numpy_data:
+            self.assertEquals(pyfunc(x, y), cfunc(x, y))
+
+    def test_return_class_is_ndarray_numpy(self):
+        # https://github.com/numpy/numpy/blob/4adc87dff15a247e417d50f10cc4def8e1c17a03/numpy/core/tests/test_numeric.py#L2460-L2468    # noqa: E501
+
+        pyfunc = np_allclose
+        cfunc = jit(nopython=True)(pyfunc)
+
+        class Foo(np.ndarray):
+            def __new__(cls, *args, **kwargs):
+                return np.array(*args, **kwargs).view(cls)
+
+        a = Foo([1])
+        self.assertTrue(type(cfunc(a, a)) is bool)
+
+    def test_equalnan_numpy(self):
+        # https://github.com/numpy/numpy/blob/4adc87dff15a247e417d50f10cc4def8e1c17a03/numpy/core/tests/test_numeric.py#L2456-L2458    # noqa: E501
+        pyfunc = np_allclose
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.array([1.0, np.nan])
+
+        self.assertEquals(pyfunc(x, x, equal_nan=True),
+                          cfunc(x, x, equal_nan=True))
+
+    def test_no_parameter_modification_numpy(self):
+        # https://github.com/numpy/numpy/blob/4adc87dff15a247e417d50f10cc4def8e1c17a03/numpy/core/tests/test_numeric.py#L2443-L2448    # noqa: E501
+
+        pyfunc = np_allclose
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.array([np.inf, 1])
+        y = np.array([0, np.inf])
+
+        cfunc(x, y)
+        np.testing.assert_array_equal(x, np.array([np.inf, 1]))
+        np.testing.assert_array_equal(y, np.array([0, np.inf]))
+
+    def test_min_int_numpy(self):
+        # https://github.com/numpy/numpy/blob/4adc87dff15a247e417d50f10cc4def8e1c17a03/numpy/core/tests/test_numeric.py#L2450-L2454    # noqa: E501
+
+        pyfunc = np_allclose
+        cfunc = jit(nopython=True)(pyfunc)
+
+        min_int = np.iinfo(np.int_).min
+        a = np.array([min_int], dtype=np.int_)
+
+        self.assertEquals(pyfunc(a, a), cfunc(a, a))
+
+    def test_allclose_exception(self):
+        self.disable_leak_check()
+
+        pyfunc = np_allclose
+        cfunc = jit(nopython=True)(pyfunc)
+
+        a = np.asarray([1e10, 1e-9, np.nan])
+        b = np.asarray([1.0001e10, 1e-9])
+
+        with self.assertRaises(ValueError) as e:
+            cfunc(a, b)
+
+        self.assertIn(("shape mismatch: objects cannot be broadcast to "
+                       "a single shape"), str(e.exception))
+
+    def test_interp_basic(self):
+        pyfunc = interp
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc, abs_tol=1e-10)
+
+        x = np.linspace(-5, 5, 25)
+        xp = np.arange(-4, 8)
+        fp = xp + 1.5
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+        self.rnd.shuffle(x)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+        self.rnd.shuffle(fp)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        # alg changed in 1.16 and other things were found not-quite-right
+        # in inf/nan handling, skip for now
+        x[:5] = np.nan
+        x[-5:] = np.inf
+        self.rnd.shuffle(x)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+        fp[:5] = np.nan
+        fp[-5:] = -np.inf
+        self.rnd.shuffle(fp)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.arange(-4, 8)
+        xp = x + 1
+        fp = x + 2
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = (2.2, 3.3, -5.0)
+        xp = (2, 3, 4)
+        fp = (5, 6, 7)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = ((2.2, 3.3, -5.0), (1.2, 1.3, 4.0))
+        xp = np.linspace(-4, 4, 10)
+        fp = np.arange(-5, 5)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.array([1.4, np.nan, np.inf, -np.inf, 0.0, -9.1])
+        x = x.reshape(3, 2, order='F')
+        xp = np.linspace(-4, 4, 10)
+        fp = np.arange(-5, 5)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        for x in range(-2, 4):
+            xp = [0, 1, 2]
+            fp = (3, 4, 5)
+            _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.array([])
+        xp = [0, 1, 2]
+        fp = (3, 4, 5)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.linspace(0, 25, 60).reshape(3, 4, 5)
+        xp = np.arange(20)
+        fp = xp - 10
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.nan
+        xp = np.arange(5)
+        fp = np.full(5, np.nan)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.nan
+        xp = [3]
+        fp = [4]
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.arange(-4, 8)
+        xp = x
+        fp = x
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = [True, False]
+        xp = np.arange(-4, 8)
+        fp = xp
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = [-np.inf, -1.0, 0.0, 1.0, np.inf]
+        xp = np.arange(-4, 8)
+        fp = xp * 2.2
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.linspace(-10, 10, 10)
+        xp = np.array([-np.inf, -1.0, 0.0, 1.0, np.inf])
+        fp = xp * 2.2
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = self.rnd.randn(100)
+        xp = np.linspace(-3, 3, 100)
+        fp = np.full(100, fill_value=3.142)
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        for factor in 1, -1:
+            x = np.array([5, 6, 7]) * factor
+            xp = [1, 2]
+            fp = [3, 4]
+            _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = 1
+        xp = [1]
+        fp = [True]
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = np.linspace(0, 1, 50)
+        out = cfunc(x0, x, y)
+        np.testing.assert_almost_equal(out, x0)
+
+        x = np.array([1, 2, 3, 4])
+        xp = np.array([1, 2, 3, 4])
+        fp = np.array([1, 2, 3.01, 4])
+        _check(params={'x': x, 'xp': xp, 'fp': fp})
+
+        xp = [1]
+        fp = [np.inf]
+        _check(params={'x': 1, 'xp': xp, 'fp': fp})
+
+        # alg changed in 1.16 and other things were found not-quite-right
+        # in inf/nan handling, skip for now
+        x = np.array([1, 2, 2.5, 3, 4])
+        xp = np.array([1, 2, 3, 4])
+        fp = np.array([1, 2, np.nan, 4])
+        _check({'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.array([1, 1.5, 2, 2.5, 3, 4, 4.5, 5, 5.5])
+        xp = np.array([1, 2, 3, 4, 5])
+        fp = np.array([np.nan, 2, np.nan, 4, np.nan])
+        _check({'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.array([1, 2, 2.5, 3, 4])
+        xp = np.array([1, 2, 3, 4])
+        fp = np.array([1, 2, np.inf, 4])
+        _check({'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.array([1, 1.5, np.nan, 2.5, -np.inf, 4, 4.5, 5, np.inf, 0, 7])
+        xp = np.array([1, 2, 3, 4, 5, 6])
+        fp = np.array([1, 2, np.nan, 4, 3, np.inf])
+        _check({'x': x, 'xp': xp, 'fp': fp})
+
+        x = np.array([3.10034867, 3.0999066, 3.10001529])
+        xp = np.linspace(0, 10, 1 + 20000)
+        fp = np.sin(xp / 2.0)
+        _check({'x': x, 'xp': xp, 'fp': fp})
+
+        x = self.rnd.uniform(0, 2 * np.pi, (100,))
+        xp = np.linspace(0, 2 * np.pi, 1000)
+        fp = np.cos(xp)
+        exact = np.cos(x)
+        got = cfunc(x, xp, fp)
+        np.testing.assert_allclose(exact, got, atol=1e-5)
+
+        # very dense calibration
+        x = self.rnd.randn(10)
+        xp = np.linspace(-10, 10, 1000)
+        fp = np.ones_like(xp)
+        _check({'x': x, 'xp': xp, 'fp': fp})
+
+        # very sparse calibration
+        x = self.rnd.randn(1000)
+        xp = np.linspace(-10, 10, 10)
+        fp = np.ones_like(xp)
+        _check({'x': x, 'xp': xp, 'fp': fp})
+
+    def _make_some_values_non_finite(self, a):
+        p = a.size // 100
+        np.put(a, self.rnd.choice(range(a.size), p, replace=False), np.nan)
+        np.put(a, self.rnd.choice(range(a.size), p, replace=False), -np.inf)
+        np.put(a, self.rnd.choice(range(a.size), p, replace=False), np.inf)
+
+    def arrays(self, ndata):
+        # much_finer_grid
+        yield np.linspace(2.0, 7.0, 1 + ndata * 5)
+        # finer_grid
+        yield np.linspace(2.0, 7.0, 1 + ndata)
+        # similar_grid
+        yield np.linspace(2.1, 6.8, 1 + ndata // 2)
+        # coarser_grid
+        yield np.linspace(2.1, 7.5, 1 + ndata // 2)
+        # much_coarser_grid
+        yield np.linspace(1.1, 9.5, 1 + ndata // 5)
+        # finer_stretched_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata) * 1.09
+        # similar_stretched_grid
+        yield np.linspace(3.1, 8.3, 1 + ndata // 2) * 1.09
+        # finer_compressed_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata) * 0.91
+        # similar_compressed_grid
+        yield np.linspace(3.1, 8.3, 1 + ndata // 2) * 0.91
+        # warped_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata // 2) + 0.3 * np.sin(
+            np.arange(1 + ndata / 2) * np.pi / (1 + ndata / 2))
+        # very_low_noise_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata) + self.rnd.normal(
+            size=1 + ndata, scale=0.5 / ndata)
+        # low_noise_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata) + self.rnd.normal(
+            size=1 + ndata, scale=2.0 / ndata)
+        # med_noise_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata) + self.rnd.normal(
+            size=1 + ndata, scale=5.0 / ndata)
+        # high_noise_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata) + self.rnd.normal(
+            size=1 + ndata, scale=20.0 / ndata)
+        # very_high_noise_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata) + self.rnd.normal(
+            size=1 + ndata, scale=50.0 / ndata)
+        # extreme_noise_grid
+        yield np.linspace(3.1, 5.3, 1 + ndata) + self.rnd.normal(
+            size=1 + ndata, scale=200.0 / ndata)
+        # random_fine_grid
+        yield self.rnd.rand(1 + ndata) * 9.0 + 0.6
+        # random_grid
+        yield self.rnd.rand(1 + ndata * 2) * 4.0 + 1.3
+
+    def test_interp_stress_tests(self):
+        pyfunc = interp
+        cfunc = jit(nopython=True)(pyfunc)
+
+        ndata = 20000
+        xp = np.linspace(0, 10, 1 + ndata)
+        fp = np.sin(xp / 2.0)
+
+        for x in self.arrays(ndata):
+            atol = 1e-14  # using abs_tol as otherwise fails on 32bit builds
+
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            self.assertPreciseEqual(expected, got, abs_tol=atol)
+
+            # no longer require xp to be monotonically increasing
+            # (in keeping with numpy) even if the output might not
+            # be meaningful; shuffle all inputs
+            self.rnd.shuffle(x)
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            self.assertPreciseEqual(expected, got, abs_tol=atol)
+
+            self.rnd.shuffle(xp)
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            self.assertPreciseEqual(expected, got, abs_tol=atol)
+
+            self.rnd.shuffle(fp)
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            self.assertPreciseEqual(expected, got, abs_tol=atol)
+
+            # add some values non finite
+            self._make_some_values_non_finite(x)
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            self.assertPreciseEqual(expected, got, abs_tol=atol)
+
+            self._make_some_values_non_finite(xp)
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            self.assertPreciseEqual(expected, got, abs_tol=atol)
+
+            self._make_some_values_non_finite(fp)
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            self.assertPreciseEqual(expected, got, abs_tol=atol)
+
+    def test_interp_complex_stress_tests(self):
+        pyfunc = interp
+        cfunc = jit(nopython=True)(pyfunc)
+
+        ndata = 2000
+        xp = np.linspace(0, 10, 1 + ndata)
+
+        real = np.sin(xp / 2.0)
+        real[:200] = self.rnd.choice([np.inf, -np.inf, np.nan], 200)
+        self.rnd.shuffle(real)
+
+        imag = np.cos(xp / 2.0)
+        imag[:200] = self.rnd.choice([np.inf, -np.inf, np.nan], 200)
+        self.rnd.shuffle(imag)
+
+        fp = real + 1j * imag
+
+        for x in self.arrays(ndata):
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            np.testing.assert_allclose(expected, got, equal_nan=True)
+
+            self.rnd.shuffle(x)
+            self.rnd.shuffle(xp)
+            self.rnd.shuffle(fp)
+            np.testing.assert_allclose(expected, got, equal_nan=True)
+
+    def test_interp_exceptions(self):
+        pyfunc = interp
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        x = np.array([1, 2, 3])
+        xp = np.array([])
+        fp = np.array([])
+
+        with self.assertRaises(ValueError) as e:
+            cfunc(x, xp, fp)
+
+        msg = "array of sample points is empty"
+        self.assertIn(msg, str(e.exception))
+
+        x = 1
+        xp = np.array([1, 2, 3])
+        fp = np.array([1, 2])
+
+        with self.assertRaises(ValueError) as e:
+            cfunc(x, xp, fp)
+
+        msg = "fp and xp are not of the same size."
+        self.assertIn(msg, str(e.exception))
+
+        x = 1
+        xp = np.arange(6).reshape(3, 2)
+        fp = np.arange(6)
+
+        with self.assertTypingError() as e:
+            cfunc(x, xp, fp)
+
+        msg = "xp must be 1D"
+        self.assertIn(msg, str(e.exception))
+
+        x = 1
+        xp = np.arange(6)
+        fp = np.arange(6).reshape(3, 2)
+
+        with self.assertTypingError() as e:
+            cfunc(x, xp, fp)
+
+        msg = "fp must be 1D"
+        self.assertIn(msg, str(e.exception))
+
+        x = 1 + 1j
+        xp = np.arange(6)
+        fp = np.arange(6)
+
+        with self.assertTypingError() as e:
+            cfunc(x, xp, fp)
+
+        complex_dtype_msg = (
+            "Cannot cast array data from complex dtype "
+            "to float64 dtype"
+        )
+        self.assertIn(complex_dtype_msg, str(e.exception))
+
+        x = 1
+        xp = (np.arange(6) + 1j).astype(np.complex64)
+        fp = np.arange(6)
+
+        with self.assertTypingError() as e:
+            cfunc(x, xp, fp)
+
+        self.assertIn(complex_dtype_msg, str(e.exception))
+
+    def test_interp_non_finite_calibration(self):
+        # examples from
+        # https://github.com/numpy/numpy/issues/12951
+        pyfunc = interp
+        cfunc = jit(nopython=True)(pyfunc)
+        _check = partial(self._check_output, pyfunc, cfunc)
+
+        xp = np.array([0, 1, 9, 10])
+        fp = np.array([-np.inf, 0.1, 0.9, np.inf])
+        x = np.array([0.2, 9.5])
+        params = {'x': x, 'xp': xp, 'fp': fp}
+        _check(params)
+
+        xp = np.array([-np.inf, 1, 9, np.inf])
+        fp = np.array([0, 0.1, 0.9, 1])
+        x = np.array([0.2, 9.5])
+        params = {'x': x, 'xp': xp, 'fp': fp}
+        _check(params)
+
+    def test_interp_supplemental_tests(self):
+        # inspired by class TestInterp
+        # https://github.com/numpy/numpy/blob/f5b6850f231/numpy/lib/tests/test_function_base.py    # noqa: E501
+        pyfunc = interp
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for size in range(1, 10):
+            xp = np.arange(size, dtype=np.double)
+            yp = np.ones(size, dtype=np.double)
+            incpts = np.array([-1, 0, size - 1, size], dtype=np.double)
+            decpts = incpts[::-1]
+
+            incres = cfunc(incpts, xp, yp)
+            decres = cfunc(decpts, xp, yp)
+            inctgt = np.array([1, 1, 1, 1], dtype=float)
+            dectgt = inctgt[::-1]
+            np.testing.assert_almost_equal(incres, inctgt)
+            np.testing.assert_almost_equal(decres, dectgt)
+
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = 0
+        np.testing.assert_almost_equal(cfunc(x0, x, y), x0)
+        x0 = 0.3
+        np.testing.assert_almost_equal(cfunc(x0, x, y), x0)
+        x0 = np.float32(0.3)
+        np.testing.assert_almost_equal(cfunc(x0, x, y), x0)
+        x0 = np.float64(0.3)
+        np.testing.assert_almost_equal(cfunc(x0, x, y), x0)
+        x0 = np.nan
+        np.testing.assert_almost_equal(cfunc(x0, x, y), x0)
+
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = np.array(0.3)
+        np.testing.assert_almost_equal(cfunc(x0, x, y), x0)
+
+        xp = np.arange(0, 10, 0.0001)
+        fp = np.sin(xp)
+        np.testing.assert_almost_equal(cfunc(np.pi, xp, fp), 0.0)
+
+    def test_interp_supplemental_complex_tests(self):
+        # inspired by class TestInterp
+        # https://github.com/numpy/numpy/blob/f5b6850f231/numpy/lib/tests/test_function_base.py    # noqa: E501
+        pyfunc = interp
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5) + (1 + np.linspace(0, 1, 5)) * 1.0j
+        x0 = 0.3
+        y0 = x0 + (1 + x0) * 1.0j
+        np.testing.assert_almost_equal(cfunc(x0, x, y), y0)
+
+    def test_interp_float_precision_handled_per_numpy(self):
+        # test cases from https://github.com/numba/numba/issues/4890
+        pyfunc = interp
+        cfunc = jit(nopython=True)(pyfunc)
+        dtypes = [np.float32, np.float64, np.int32, np.int64]
+
+        for combo in itertools.combinations_with_replacement(dtypes, 3):
+            xp_dtype, fp_dtype, x_dtype = combo
+            xp = np.arange(10, dtype=xp_dtype)
+            fp = (xp ** 2).astype(fp_dtype)
+            x = np.linspace(2, 3, 10, dtype=x_dtype)
+
+            expected = pyfunc(x, xp, fp)
+            got = cfunc(x, xp, fp)
+            self.assertPreciseEqual(expected, got)
+
+    def test_isnat(self):
+        def values():
+            yield np.datetime64("2016-01-01")
+            yield np.datetime64("NaT")
+            yield np.datetime64('NaT', 'ms')
+            yield np.datetime64('NaT', 'ns')
+            yield np.datetime64('2038-01-19T03:14:07')
+
+            yield np.timedelta64('NaT', "ms")
+            yield np.timedelta64(34, "ms")
+
+            for unit in ['Y', 'M', 'W', 'D',
+                         'h', 'm', 's', 'ms', 'us',
+                         'ns', 'ps', 'fs', 'as']:
+                yield np.array([123, -321, "NaT"],
+                               dtype='<datetime64[%s]' % unit)
+                yield np.array([123, -321, "NaT"],
+                               dtype='<timedelta64[%s]' % unit)
+
+        pyfunc = isnat
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for x in values():
+            expected = pyfunc(x)
+            got = cfunc(x)
+            if isinstance(x, np.ndarray):
+                self.assertPreciseEqual(expected, got, (x,))
+            else:
+                self.assertEqual(expected, got, x)
+
+    def test_asarray(self):
+
+        def input_variations():
+            """
+            To quote from: https://docs.scipy.org/doc/numpy/reference/generated/numpy.asarray.html    # noqa: E501
+            Input data, in any form that can be converted to an array.
+            This includes:
+            * lists
+            * lists of tuples
+            * tuples
+            * tuples of tuples
+            * tuples of lists
+            * ndarrays
+            """
+            yield 1j
+            yield 1.2
+            yield False
+            yield 1
+            yield [1, 2, 3]
+            yield [(1, 2, 3), (1, 2, 3)]
+            yield (1, 2, 3)
+            yield ((1, 2, 3), (1, 2, 3))
+            yield ([1, 2, 3], [1, 2, 3])
+            yield np.array([])
+            yield np.arange(4)
+            yield np.arange(12).reshape(3, 4)
+            yield np.arange(12).reshape(3, 4).T
+
+            # Test cases for `numba.typed.List`
+            def make_list(values):
+                a = List()
+                for i in values:
+                    a.append(i)
+                return a
+            yield make_list((1, 2, 3))
+            yield make_list((1.0, 2.0, 3.0))
+            yield make_list((1j, 2j, 3j))
+            yield make_list((True, False, True))
+
+        # used to check that if the input is already an array and the dtype is
+        # the same as that of the input/omitted then the array itself is
+        # returned.
+        def check_pass_through(jitted, expect_same, params):
+            returned = jitted(**params)
+            if expect_same:
+                self.assertTrue(returned is params['a'])
+            else:
+                self.assertTrue(returned is not params['a'])
+                # should be numerically the same, just different dtype
+                np.testing.assert_allclose(returned, params['a'])
+                self.assertTrue(returned.dtype == params['dtype'])
+
+        for pyfunc in [asarray, asarray_kws]:
+            cfunc = jit(nopython=True)(pyfunc)
+            _check = partial(self._check_output, pyfunc, cfunc)
+
+            for x in input_variations():
+                params = {'a': x}
+                if 'kws' in pyfunc.__name__:
+                    for dt in [None, np.complex128]:
+                        params['dtype'] = dt
+                        _check(params)
+                else:
+                    _check(params)
+
+                # check the behaviour over a dtype change (or not!)
+                x = np.arange(10, dtype=np.float32)
+                params = {'a': x}
+                if 'kws' in pyfunc.__name__:
+                    params['dtype'] = None
+                    check_pass_through(cfunc, True, params)
+                    params['dtype'] = np.complex128
+                    check_pass_through(cfunc, False, params)
+                    params['dtype'] = np.float32
+                    check_pass_through(cfunc, True, params)
+                else:
+                    check_pass_through(cfunc, True, params)
+
+    def test_asarray_literal(self):
+
+        def case1():
+            return np.asarray("hello world")
+
+        def case2(): # kind1
+            s = "hello world"
+            return np.asarray(s)
+
+        def case3(): # kind2
+            s = '大处 着眼，小处着手。大大大处'
+            return np.asarray(s)
+
+        def case4():
+            s = ''
+            return np.asarray(s)
+
+        funcs = [case1, case2, case3, case4]
+
+        for pyfunc in funcs:
+            cfunc = jit(nopython=True)(pyfunc)
+            expected = pyfunc()
+            got = cfunc()
+            self.assertPreciseEqual(expected, got)
+
+    def test_asarray_rejects_List_with_illegal_dtype(self):
+        self.disable_leak_check()
+        cfunc = jit(nopython=True)(asarray)
+
+        def test_reject(alist):
+            with self.assertRaises(TypingError) as e:
+                cfunc(alist)
+            self.assertIn(
+                "asarray support for List is limited "
+                "to Boolean and Number types",
+                str(e.exception))
+
+        def make_none_typed_list():
+            l = List()
+            l.append(None)
+            return l
+
+        def make_nested_list():
+            l = List()
+            m = List()
+            m.append(1)
+            l.append(m)
+            return l
+
+        def make_nested_list_with_dict():
+            l = List()
+            d = Dict()
+            d[1] = "a"
+            l.append(d)
+            return l
+
+        def make_unicode_list():
+            l = List()
+            for i in ("a", "bc", "def"):
+                l.append(i)
+            return l
+
+        test_reject(make_none_typed_list())
+        test_reject(make_nested_list())
+        test_reject(make_nested_list_with_dict())
+        test_reject(make_unicode_list())
+
+    def test_asfarray(self):
+        def inputs():
+            yield np.array([1, 2, 3]), None
+            yield np.array([2, 3], dtype=np.float32), np.float32
+            yield np.array([2, 3], dtype=np.int8), np.int8
+            yield np.array([2, 3], dtype=np.int8), np.complex64
+            yield np.array([2, 3], dtype=np.int8), np.complex128
+
+        pyfunc = asfarray
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arr, dt in inputs():
+            if dt is None:
+                expected = pyfunc(arr)
+                got = cfunc(arr)
+            else:
+                expected = pyfunc(arr, dtype=dt)
+                got = cfunc(arr, dtype=dt)
+
+            self.assertPreciseEqual(expected, got)
+            self.assertTrue(np.issubdtype(got.dtype, np.inexact), got.dtype)
+
+        # test default kwarg variant
+        pyfunc = asfarray_default_kwarg
+        cfunc = jit(nopython=True)(pyfunc)
+        arr = np.array([1, 2, 3])
+        expected = pyfunc(arr)
+        got = cfunc(arr)
+        self.assertPreciseEqual(expected, got)
+        self.assertTrue(np.issubdtype(got.dtype, np.inexact), got.dtype)
+
+    def test_repeat(self):
+        # np.repeat(a, repeats)
+        np_pyfunc = np_repeat
+        np_nbfunc = njit(np_pyfunc)
+
+        # a.repeat(repeats)
+        array_pyfunc = array_repeat
+        array_nbfunc = njit(array_pyfunc)
+
+        for pyfunc, nbfunc in ((np_pyfunc, np_nbfunc),
+                               (array_pyfunc, array_nbfunc)):
+
+            def check(a, repeats):
+                self.assertPreciseEqual(pyfunc(a, repeats), nbfunc(a, repeats))
+
+            # test array arguments
+            target_numpy_values = [
+                np.ones(1),
+                np.arange(1000),
+                np.array([[0, 1], [2, 3]]),
+                np.array([]),
+                np.array([[], []]),
+            ]
+
+            target_numpy_types = [
+                np.uint32,
+                np.int32,
+                np.uint64,
+                np.int64,
+                np.float32,
+                np.float64,
+                np.complex64,
+                np.complex128,
+            ]
+
+            target_numpy_inputs = (np.array(a,dtype=t) for a,t in
+                                   itertools.product(target_numpy_values,
+                                                     target_numpy_types))
+
+            target_non_numpy_inputs = [
+                1,
+                1.0,
+                True,
+                1j,
+                [0, 1, 2],
+                (0, 1, 2),
+            ]
+            for i in itertools.chain(target_numpy_inputs,
+                                     target_non_numpy_inputs):
+                check(i, repeats=0)
+                check(i, repeats=1)
+                check(i, repeats=2)
+                check(i, repeats=3)
+                check(i, repeats=100)
+
+            # check broadcasting when repeats is an array/list
+            one = np.arange(1)
+            for i in ([0], [1], [2]):
+                check(one, repeats=i)
+                check(one, repeats=np.array(i))
+
+            two = np.arange(2)
+            for i in ([0, 0], [0, 1], [1, 0], [0, 1], [1, 2], [2, 1], [2, 2]):
+                check(two, repeats=i)
+                check(two, repeats=np.array(i))
+
+            check(two, repeats=np.array([2, 2], dtype=np.int32))
+            check(np.arange(10), repeats=np.arange(10))
+
+    def test_repeat_exception(self):
+        # np.repeat(a, repeats)
+        np_pyfunc = np_repeat
+        np_nbfunc = njit(np_pyfunc)
+
+        # a.repeat(repeats)
+        array_pyfunc = array_repeat
+        array_nbfunc = njit(array_pyfunc)
+
+        self.disable_leak_check()
+
+        for pyfunc, nbfunc in ((np_pyfunc, np_nbfunc),
+                               (array_pyfunc, array_nbfunc)):
+
+            # negative repeat argument
+            with self.assertRaises(ValueError) as e:
+                nbfunc(np.ones(1), -1)
+            self.assertIn("negative dimensions are not allowed",
+                          str(e.exception))
+
+            # float repeat argument has custom error message
+            with self.assertRaises(TypingError) as e:
+                nbfunc(np.ones(1), 1.0)
+            self.assertIn(
+                "The repeats argument must be an integer "
+                "or an array-like of integer dtype",
+                str(e.exception))
+
+            # negative repeat argument as array
+            with self.assertRaises(ValueError) as e:
+                nbfunc(np.ones(2), np.array([1, -1]))
+            self.assertIn("negative dimensions are not allowed",
+                          str(e.exception))
+
+            # broadcasting error, repeats too large
+            with self.assertRaises(ValueError) as e:
+                nbfunc(np.ones(2), np.array([1, 1, 1]))
+            self.assertIn("operands could not be broadcast together",
+                          str(e.exception))
+
+            # broadcasting error, repeats too small
+            with self.assertRaises(ValueError) as e:
+                nbfunc(np.ones(5), np.array([1, 1, 1, 1]))
+            self.assertIn("operands could not be broadcast together",
+                          str(e.exception))
+
+            # float repeat argument has custom error message
+            with self.assertRaises(TypingError) as e:
+                nbfunc(np.ones(2), [1.0, 1.0])
+            self.assertIn(
+                "The repeats argument must be an integer "
+                "or an array-like of integer dtype",
+                str(e.exception))
+
+            for rep in [True, "a", "1"]:
+                with self.assertRaises(TypingError):
+                    nbfunc(np.ones(1), rep)
+
+    def test_select(self):
+        np_pyfunc = np_select
+        np_nbfunc = njit(np_select)
+
+        test_cases = [
+            # Each test case below is one tuple.
+            # Each tuple is separated by a description of what's being tested
+
+            # test with arrays of length 3 instead of 2 and a different default
+            ([np.array([False, False, False]),
+              np.array([False, True, False]),
+              np.array([False, False, True])],
+             [np.array([1, 2, 3]),
+              np.array([4, 5, 6]),
+              np.array([7, 8, 9])], 15.3),
+            # test with arrays of length 1 instead of 2
+            ([np.array([True]),
+              np.array([False])], [np.array([1]), np.array([2])], 0),
+            # test with lists of length 100 of arrays of length 1
+            ([np.array([False])] * 100, [np.array([1])] * 100, 0),
+            # passing arrays with NaNs
+            ([np.isnan(np.array([1, 2, 3, np.nan, 5, 7]))] * 2,
+             [np.array([1, 2, 3, np.nan, 5, 7])] * 2, 0),
+            # passing lists with 2d arrays
+            ([np.isnan(np.array([[1, 2, 3, np.nan, 5, 7]]))] * 2,
+             [np.array([[1, 2, 3, np.nan, 5, 7]])] * 2, 0),
+            # passing arrays with complex numbers
+            ([np.isnan(np.array([1, 2, 3 + 2j, np.nan, 5, 7]))] * 2,
+             [np.array([1, 2, 3 + 2j, np.nan, 5, 7])] * 2, 0)
+        ]
+
+        for x in (np.arange(10), np.arange(10).reshape((5, 2))):
+            # test with two lists
+            test_cases.append(([x < 3, x > 5], [x, x ** 2], 0))
+            # test with two tuples
+            test_cases.append(((x < 3, x > 5), (x, x ** 2), 0))
+            # test with one list and one tuple
+            test_cases.append(([x < 3, x > 5], (x, x ** 2), 0))
+            # test with one tuple and one list
+            test_cases.append(((x < 3, x > 5), [x, x ** 2], 0))
+
+        for condlist,  choicelist, default in test_cases:
+            self.assertPreciseEqual(np_pyfunc(condlist, choicelist, default),
+                                    np_nbfunc(condlist, choicelist, default))
+
+        np_pyfunc_defaults = np_select_defaults
+        np_nbfunc_defaults = njit(np_select_defaults)
+        # check the defaults work, using whatever the last input was
+        self.assertPreciseEqual(np_pyfunc_defaults(condlist, choicelist),
+                                np_nbfunc_defaults(condlist, choicelist))
+
+    def test_select_exception(self):
+        np_nbfunc = njit(np_select)
+        x = np.arange(10)
+        self.disable_leak_check()
+        for condlist, choicelist, default, expected_error, expected_text in [
+            # Each test case below is one tuple.
+            # Each tuple is separated by the description of the intended error
+
+            # passing condlist of dim zero
+            ([np.array(True), np.array([False, True, False])],
+             [np.array(1), np.arange(12).reshape(4, 3)], 0,
+             TypingError, "condlist arrays must be of at least dimension 1"),
+            # condlist and choicelist with different dimensions
+            ([np.array(True), np.array(False)], [np.array([1]), np.array([2])],
+             0, TypingError, "condlist and choicelist elements must have the "
+                             "same number of dimensions"),
+            # condlist and choicelist with different dimensions
+            ([np.array([True]), np.array([False])],
+             [np.array([[1]]), np.array([[2]])], 0, TypingError,
+             "condlist and choicelist elements must have the "
+             "same number of dimensions"),
+            # passing choicelist of dim zero
+            ([np.array(True), np.array(False)], [np.array(1), np.array(2)], 0,
+             TypingError, "condlist arrays must be of at least dimension 1"),
+            # passing an array as condlist instead of a list or tuple
+            (np.isnan(np.array([1, 2, 3, np.nan, 5, 7])),
+             np.array([1, 2, 3, np.nan, 5, 7]), 0, TypingError,
+             "condlist must be a List or a Tuple"),
+            # default is a list
+            ([True], [0], [0], TypingError,
+             "default must be a scalar"),
+            # condlist with ints instead of booleans
+            ([(x < 3).astype(int), (x > 5).astype(int)], [x, x ** 2], 0,
+             TypingError, "condlist arrays must contain booleans"),
+            # condlist and choicelist of different length
+            ([x > 9, x > 8, x > 7, x > 6], [x, x**2, x], 0, ValueError,
+             "list of cases must be same length as list of conditions"),
+
+            # condlist contains tuples instead of arrays
+            # if in the future numba's np.where accepts tuples, the
+            # implementation of np.select should also accept them and
+            # the following two test cases should be normal tests
+            # instead of negative tests
+
+            # test with lists of length 100 of tuples of length 1 for condlist
+            ([(False,)] * 100, [np.array([1])] * 100, 0, TypingError,
+             'items of condlist must be arrays'),
+            # test with lists of length 100 of tuples of length 1 for choicelist
+            ([np.array([False])] * 100, [(1,)] * 100, 0, TypingError,
+             'items of choicelist must be arrays'),
+        ]:
+            with self.assertRaises(expected_error) as e:
+                np_nbfunc(condlist, choicelist, default)
+            self.assertIn(expected_text, str(e.exception))
+
+    def test_windowing(self):
+        def check_window(func):
+            np_pyfunc = func
+            np_nbfunc = njit(func)
+
+            for M in [0, 1, 5, 12]:
+                expected = np_pyfunc(M)
+                got = np_nbfunc(M)
+                self.assertPreciseEqual(expected, got, prec='double')
+
+            for M in ['a', 1.1, 1j]:
+                with self.assertRaises(TypingError) as raises:
+                    np_nbfunc(1.1)
+                self.assertIn("M must be an integer", str(raises.exception))
+
+        check_window(np_bartlett)
+        check_window(np_blackman)
+        check_window(np_hamming)
+        check_window(np_hanning)
+
+        # Test np.kaiser separately
+        np_pyfunc = np_kaiser
+        np_nbfunc = njit(np_kaiser)
+
+        for M in [0, 1, 5, 12]:
+            for beta in [0.0, 5.0, 14.0]:
+                expected = np_pyfunc(M, beta)
+                got = np_nbfunc(M, beta)
+
+                if IS_32BITS or platform.machine() in ['ppc64le', 'aarch64']:
+                    self.assertPreciseEqual(expected,
+                                            got, prec='double', ulps=2)
+                else:
+                    self.assertPreciseEqual(expected, got, prec='exact')
+
+        for M in ['a', 1.1, 1j]:
+            with self.assertRaises(TypingError) as raises:
+                np_nbfunc(M, 1.0)
+            self.assertIn("M must be an integer", str(raises.exception))
+
+        for beta in ['a', 1j]:
+            with self.assertRaises(TypingError) as raises:
+                np_nbfunc(5, beta)
+            self.assertIn("beta must be an integer or float",
+                          str(raises.exception))
+
+    def test_cross(self):
+        pyfunc = np_cross
+        cfunc = jit(nopython=True)(pyfunc)
+        pairs = [
+            # 3x3 (n-dims)
+            (
+                np.array([[1, 2, 3], [4, 5, 6]]),
+                np.array([[4, 5, 6], [1, 2, 3]])
+            ),
+            # 2x3 array-like (n-dims)
+            (
+                np.array([[1, 2, 3], [4, 5, 6]]),
+                ((4, 5), (1, 2))
+            ),
+            # 3x3 (1-dim) with type promotion
+            (
+                np.array([1, 2, 3], dtype=np.int64),
+                np.array([4, 5, 6], dtype=np.float64)
+            ),
+            # 3x3 array-like (1-dim)
+            (
+                (1, 2, 3),
+                (4, 5, 6)
+            ),
+            # 2x3 (1-dim)
+            (
+                np.array([1, 2]),
+                np.array([4, 5, 6])
+            ),
+            # 3x3 (with broadcasting 1d x 2d)
+            (
+                np.array([1, 2, 3]),
+                np.array([[4, 5, 6], [1, 2, 3]])
+            ),
+            # 3x3 (with broadcasting 2d x 1d)
+            (
+                np.array([[1, 2, 3], [4, 5, 6]]),
+                np.array([1, 2, 3])
+            ),
+            # 3x2 (with higher order broadcasting)
+            (
+                np.arange(36).reshape(6, 2, 3),
+                np.arange(4).reshape(2, 2)
+            )
+        ]
+
+        for x, y in pairs:
+            expected = pyfunc(x, y)
+            got = cfunc(x, y)
+            self.assertPreciseEqual(expected, got)
+
+    def test_cross_exceptions(self):
+        pyfunc = np_cross
+        cfunc = jit(nopython=True)(pyfunc)
+        self.disable_leak_check()
+
+        # test incompatible dimensions for ndim == 1
+        with self.assertRaises(ValueError) as raises:
+            cfunc(
+                np.arange(4),
+                np.arange(3)
+            )
+        self.assertIn(
+            'Incompatible dimensions for cross product',
+            str(raises.exception)
+        )
+
+        # test 2d cross product error for ndim == 1
+        with self.assertRaises(ValueError) as raises:
+            cfunc(
+                np.array((1, 2)),
+                np.array((3, 4))
+            )
+        self.assertIn(
+            'Dimensions for both inputs is 2.',
+            str(raises.exception)
+        )
+
+        self.assertIn(
+            '`cross2d(a, b)` from `numba.np.extensions`.',
+            str(raises.exception)
+        )
+
+        # test incompatible dimensions for ndim > 1
+        with self.assertRaises(ValueError) as raises:
+            cfunc(
+                np.arange(8).reshape((2, 4)),
+                np.arange(6)[::-1].reshape((2, 3))
+            )
+        self.assertIn(
+            'Incompatible dimensions for cross product',
+            str(raises.exception)
+        )
+
+        # test 2d cross product error for ndim == 1
+        with self.assertRaises(ValueError) as raises:
+            cfunc(
+                np.arange(8).reshape((4, 2)),
+                np.arange(8)[::-1].reshape((4, 2))
+            )
+        self.assertIn(
+            'Dimensions for both inputs is 2',
+            str(raises.exception)
+        )
+
+        # test non-array-like input
+        with self.assertRaises(TypingError) as raises:
+            cfunc(
+                set([1, 2, 3]),
+                set([4, 5, 6])
+            )
+        self.assertIn(
+            'Inputs must be array-like.',
+            str(raises.exception)
+        )
+
+    def test_cross2d(self):
+        pyfunc = np_cross
+        cfunc = cross2d
+        pairs = [
+            # 2x2 (n-dims)
+            (
+                np.array([[1, 2], [4, 5]]),
+                np.array([[4, 5], [1, 2]])
+            ),
+            # 2x2 array-like (n-dims)
+            (
+                np.array([[1, 2], [4, 5]]),
+                ((4, 5), (1, 2))
+            ),
+            # 2x2 (1-dim) with type promotion
+            (
+                np.array([1, 2], dtype=np.int64),
+                np.array([4, 5], dtype=np.float64)
+            ),
+            # 2x2 array-like (1-dim)
+            (
+                (1, 2),
+                (4, 5)
+            ),
+            # 2x2 (with broadcasting 1d x 2d)
+            (
+                np.array([1, 2]),
+                np.array([[4, 5], [1, 2]])
+            ),
+            # 2x2 (with broadcasting 2d x 1d)
+            (
+                np.array([[1, 2], [4, 5]]),
+                np.array([1, 2])
+            ),
+            # 2x2 (with higher order broadcasting)
+            (
+                np.arange(36).reshape(6, 3, 2),
+                np.arange(6).reshape(3, 2)
+            )
+        ]
+
+        for x, y in pairs:
+            expected = pyfunc(x, y)
+            got = cfunc(x, y)
+            self.assertPreciseEqual(expected, got)
+
+    def test_cross2d_exceptions(self):
+        cfunc = cross2d
+        self.disable_leak_check()
+
+        # test incompatible dimensions for ndim == 1
+        with self.assertRaises(ValueError) as raises:
+            cfunc(
+                np.array((1, 2, 3)),
+                np.array((4, 5, 6))
+            )
+        self.assertIn(
+            'Incompatible dimensions for 2D cross product',
+            str(raises.exception)
+        )
+
+        # test incompatible dimensions for ndim > 1
+        with self.assertRaises(ValueError) as raises:
+            cfunc(
+                np.arange(6).reshape((2, 3)),
+                np.arange(6)[::-1].reshape((2, 3))
+            )
+        self.assertIn(
+            'Incompatible dimensions for 2D cross product',
+            str(raises.exception)
+        )
+
+        # test non-array-like input
+        with self.assertRaises(TypingError) as raises:
+            cfunc(
+                set([1, 2]),
+                set([4, 5])
+            )
+        self.assertIn(
+            'Inputs must be array-like.',
+            str(raises.exception)
+        )
+
+    def test_asarray_chkfinite(self):
+        pyfunc = np_asarray_chkfinite
+        cfunc = jit(nopython=True)(pyfunc)
+        self.disable_leak_check()
+
+        pairs = [
+            #1D array with all args
+            (
+                np.array([1, 2, 3]),
+                np.float32,
+            ),
+            #1D array
+            (
+                np.array([1, 2, 3]),
+            ),
+            #1D array-like
+            (
+                [1, 2, 3, 4],
+            ),
+            # 2x2 (n-dims)
+            (
+                np.array([[1, 2], [3, 4]]),
+                np.float32,
+            ),
+            # 2x2 array-like (n-dims)
+            (
+                ((1, 2), (3, 4)),
+                np.int64
+            ),
+            # 2x2 (1-dim) with type promotion
+            (
+                np.array([1, 2], dtype=np.int64),
+            ),
+            # 3x2 (with higher order broadcasting)
+            (
+                np.arange(36).reshape(6, 2, 3),
+            ),
+        ]
+
+        for pair in pairs:
+            expected = pyfunc(*pair)
+            got = cfunc(*pair)
+            self.assertPreciseEqual(expected, got)
+
+    def test_asarray_chkfinite_exceptions(self):
+        cfunc = jit(nopython=True)(np_asarray_chkfinite)
+        self.disable_leak_check()
+
+        #test for single value
+        with self.assertRaises(TypingError) as e:
+            cfunc(2)
+        msg = "The argument to np.asarray_chkfinite must be array-like"
+        self.assertIn(msg, str(e.exception))
+
+        #test for NaNs
+        with self.assertRaises(ValueError) as e:
+            cfunc(np.array([2, 4, np.nan, 5]))
+        self.assertIn("array must not contain infs or NaNs", str(e.exception))
+
+        #test for infs
+        with self.assertRaises(ValueError) as e:
+            cfunc(np.array([1, 2, np.inf, 4]))
+        self.assertIn("array must not contain infs or NaNs", str(e.exception))
+
+        #test for dtype
+        with self.assertRaises(TypingError) as e:
+            cfunc(np.array([1, 2, 3, 4]), 'float32')
+        self.assertIn("dtype must be a valid Numpy dtype", str(e.exception))
+
+    def test_swapaxes_basic(self):
+        pyfunc = swapaxes
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def a_variations():
+            yield np.arange(10)
+            yield np.arange(10).reshape(2, 5)
+            yield np.arange(60).reshape(5, 4, 3)
+
+        for a in a_variations():
+            for a1 in range(-a.ndim, a.ndim):
+                for a2 in range(-a.ndim, a.ndim):
+                    expected = pyfunc(a, a1, a2)
+                    got = cfunc(a, a1, a2)
+                    self.assertPreciseEqual(expected, got)
+
+    def test_swapaxes_exception(self):
+        pyfunc = swapaxes
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Exceptions leak references
+        self.disable_leak_check()
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc('abc', 0, 0)
+
+        self.assertIn('The first argument "arr" must be an array',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(np.arange(4), 'abc', 0)
+
+        self.assertIn('The second argument "axis1" must be an integer',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(np.arange(4), 0, 'abc')
+
+        self.assertIn('The third argument "axis2" must be an integer',
+                      str(raises.exception))
+
+        with self.assertRaises(ValueError) as raises:
+            cfunc(np.arange(4), 1, 0)
+
+        self.assertIn('The second argument "axis1" is out of bounds for array'
+                      ' of given dimension', str(raises.exception))
+
+        with self.assertRaises(ValueError) as raises:
+            cfunc(np.arange(8).reshape(2, 4), 0, -3)
+
+        self.assertIn('The third argument "axis2" is out of bounds for array'
+                      ' of given dimension', str(raises.exception))
+
+    def test_take_along_axis(self):
+        a = np.arange(24).reshape((3, 1, 4, 2))
+
+        # For now axis must be literal, test explicitly defined implementations
+        @njit
+        def axis_none(a, i):
+            return np.take_along_axis(a, i, axis=None)
+
+        indices = np.array([1, 2], dtype=np.uint64)
+        self.assertPreciseEqual(axis_none(a, indices),
+                                axis_none.py_func(a, indices))
+
+        def gen(axis):
+            @njit
+            def impl(a, i):
+                return np.take_along_axis(a, i, axis)
+            return impl
+
+        for i in range(-1, a.ndim):
+            jfunc = gen(i)
+            ai = np.argsort(a, axis=i)
+            self.assertPreciseEqual(jfunc(a, ai), jfunc.py_func(a, ai))
+
+    def test_take_along_axis_broadcasting(self):
+        # Based on
+        # https://github.com/numpy/numpy/blob/v1.21.0/numpy/lib/tests/test_shape_base.py#L74-L79
+        # This demonstrates that arrays are broadcast before the algorithm is
+        # applied.
+        arr = np.ones((3, 4, 1))
+        ai = np.ones((1, 2, 5), dtype=np.intp)
+
+        def gen(axis):
+            @njit
+            def impl(a, i):
+                return np.take_along_axis(a, i, axis)
+            return impl
+
+        # Check same axis but expressed as positive/negative value
+        for i in (1, -2):
+            check = gen(i)
+            expected = check.py_func(arr, ai)
+            actual = check(arr, ai)
+            self.assertPreciseEqual(expected, actual)
+            self.assertEqual(actual.shape, (3, 2, 5))
+
+    def test_take_along_axis_exceptions(self):
+        arr2d = np.arange(8).reshape(2, 4)
+        # Valid indices when axis=None is passed to take_along_axis:
+        indices_none = np.array([0, 1], dtype=np.uint64)
+        indices = np.ones((2, 4), dtype=np.uint64)
+
+        # For now axis must be literal, so we need to construct functions with
+        # explicit axis:
+        def gen(axis):
+            @njit
+            def impl(a, i):
+                return np.take_along_axis(a, i, axis)
+            return impl
+
+        with self.assertRaises(TypingError) as raises:
+            gen("a")(arr2d, indices)
+        self.assertIn("axis must be an integer", str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            gen(-3)(arr2d, indices)
+        self.assertIn("axis is out of bounds", str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            gen(2)(arr2d, indices)
+        self.assertIn("axis is out of bounds", str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            gen(None)(12, indices_none)
+        self.assertIn('"arr" must be an array', str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            gen(None)(arr2d, 5)
+        self.assertIn('"indices" must be an array', str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            gen(None)(arr2d, np.array([0.0, 1.0]))
+        self.assertIn(
+            'indices array must contain integers',
+            str(raises.exception)
+        )
+
+        @njit
+        def not_literal_axis(a, i, axis):
+            return np.take_along_axis(a, i, axis)
+
+        with self.assertRaises(TypingError) as raises:
+            not_literal_axis(arr2d, indices, 0)
+        self.assertIn("axis must be a literal value", str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            gen(0)(arr2d, np.array([0, 1], dtype=np.uint64))
+        self.assertIn("must have the same number of dimensions",
+                      str(raises.exception))
+
+        # With axis None, array's ndim is implicitly 1.
+        with self.assertRaises(TypingError) as raises:
+            gen(None)(arr2d, arr2d)
+        self.assertIn("must have the same number of dimensions",
+                      str(raises.exception))
+
+        with self.assertRaises(ValueError) as raises:
+            gen(0)(arr2d, np.ones((2, 3), dtype=np.uint64))
+        self.assertIn("dimensions don't match", str(raises.exception))
+
+
+class TestNPMachineParameters(TestCase):
+    # tests np.finfo, np.iinfo, np.MachAr
+
+    template = '''
+def foo():
+    ty = np.%s
+    return np.%s(ty)
+'''
+
+    def check(self, func, attrs, *args):
+        pyfunc = func
+        cfunc = jit(nopython=True)(pyfunc)
+
+        expected = pyfunc(*args)
+        got = cfunc(*args)
+
+        # check result
+        for attr in attrs:
+            self.assertPreciseEqual(getattr(expected, attr),
+                                    getattr(got, attr))
+
+    def create_harcoded_variant(self, basefunc, ty):
+        #create an instance of using the function with a hardcoded type
+        #and eval it into existence, return the function for use
+        tystr = ty.__name__
+        basestr = basefunc.__name__
+        funcstr = self.template % (tystr, basestr)
+        eval(compile(funcstr, '<string>', 'exec'))
+        return locals()['foo']
+
+    def test_MachAr(self):
+        attrs = ('ibeta', 'it', 'machep', 'eps', 'negep', 'epsneg', 'iexp',
+                 'minexp', 'xmin', 'maxexp', 'xmax', 'irnd', 'ngrd',
+                 'epsilon', 'tiny', 'huge', 'precision', 'resolution',)
+        self.check(machar, attrs)
+
+    def test_finfo(self):
+        types = [np.float32, np.float64, np.complex64, np.complex128]
+        attrs = ('eps', 'epsneg', 'iexp', 'machep', 'max', 'maxexp', 'negep',
+                 'nexp', 'nmant', 'precision', 'resolution', 'tiny', 'bits',)
+        for ty in types:
+            self.check(finfo, attrs, ty(1))
+            hc_func = self.create_harcoded_variant(np.finfo, ty)
+            self.check(hc_func, attrs)
+
+        # check unsupported attr raises
+        with self.assertRaises(TypingError) as raises:
+            cfunc = jit(nopython=True)(finfo_machar)
+            cfunc(7.)
+        msg = "Unknown attribute 'machar' of type finfo"
+        self.assertIn(msg, str(raises.exception))
+
+        # check invalid type raises
+        with self.assertTypingError():
+            cfunc = jit(nopython=True)(finfo)
+            cfunc(np.int32(7))
+
+    def test_iinfo(self):
+        # check types and instances of types
+        types = [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16,
+                 np.uint32, np.uint64]
+        attrs = ('min', 'max', 'bits',)
+        for ty in types:
+            self.check(iinfo, attrs, ty(1))
+            hc_func = self.create_harcoded_variant(np.iinfo, ty)
+            self.check(hc_func, attrs)
+
+        # check invalid type raises
+        with self.assertTypingError():
+            cfunc = jit(nopython=True)(iinfo)
+            cfunc(np.float64(7))
+
+    @unittest.skipUnless(numpy_version >= (1, 22), "Needs NumPy >= 1.22")
+    @TestCase.run_test_in_subprocess
+    def test_np_MachAr_deprecation_np122(self):
+        # Tests that Numba is replaying the NumPy 1.22 deprecation warning
+        # raised on the getattr of 'MachAr' on the NumPy module.
+        # Needs to be run in a subprocess as the warning is generated from the
+        # typing part of the `np.MachAr` overload, which may already have been
+        # executed for the given types and so an empty in memory cache is
+        # needed.
+        msg = r'`np.MachAr` is deprecated \(NumPy 1.22\)'
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings("always", message=msg,
+                                    category=NumbaDeprecationWarning,)
+            f = njit(lambda : np.MachAr().eps)
+            f()
+
+        self.assertEqual(len(w), 1)
+        self.assertIn('`np.MachAr` is deprecated', str(w[0]))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_np_randomgen.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_np_randomgen.py
new file mode 100644
index 000000000..209b8582d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_np_randomgen.py
@@ -0,0 +1,153 @@
+import numba
+import numpy as np
+import sys
+
+from numba import types
+from numba.tests.support import TestCase, MemoryLeakMixin
+from numba.np.random.generator_methods import _get_proper_func
+from numba.np.random.generator_core import next_uint32, next_uint64, next_double
+from numpy.random import MT19937, Generator
+from numba.core.errors import TypingError
+from numba.tests.support import run_in_new_process_caching, SerialMixin
+
+
+class TestHelperFuncs(TestCase):
+    def test_proper_func_provider(self):
+        def test_32bit_func():
+            return 32
+
+        def test_64bit_func():
+            return 64
+
+        self.assertEqual(_get_proper_func(test_32bit_func, test_64bit_func,
+                         np.float64)[0](), 64)
+        self.assertEqual(_get_proper_func(test_32bit_func, test_64bit_func,
+                         np.float32)[0](), 32)
+        self.assertEqual(_get_proper_func(test_32bit_func, test_64bit_func,
+                         types.float64)[0](), 64)
+        self.assertEqual(_get_proper_func(test_32bit_func, test_64bit_func,
+                         types.float32)[0](), 32)
+
+        # With any other datatype it should return a TypeError
+        with self.assertRaises(TypingError) as raises:
+            _get_proper_func(test_32bit_func, test_64bit_func, np.int32)
+        self.assertIn(
+            'Unsupported dtype int32 for the given distribution',
+            str(raises.exception)
+        )
+
+
+def test_generator_caching():
+    nb_rng = np.random.default_rng(1)
+    np_rng = np.random.default_rng(1)
+    py_func = lambda x: x.random(10)
+    numba_func = numba.njit(cache=True)(py_func)
+    assert np.allclose(np_rng.random(10), numba_func(nb_rng))
+
+
+class TestRandomGenerators(MemoryLeakMixin, TestCase):
+    def check_numpy_parity(self, distribution_func,
+                           bitgen_type=None, seed=None,
+                           test_size=None, test_dtype=None):
+
+        distribution_func = numba.njit(distribution_func)
+        if seed is None:
+            seed = 1
+        if bitgen_type is None:
+            numba_rng_instance = np.random.default_rng(seed=seed)
+            numpy_rng_instance = np.random.default_rng(seed=seed)
+        else:
+            numba_rng_instance = Generator(bitgen_type(seed))
+            numpy_rng_instance = Generator(bitgen_type(seed))
+
+        # Check parity for different size cases
+        numba_res = distribution_func(numba_rng_instance,
+                                      test_size, test_dtype)
+        numpy_res = distribution_func.py_func(numpy_rng_instance,
+                                              test_size, test_dtype)
+
+        self.assertPreciseEqual(numba_res, numpy_res)
+
+        # Check if the end state of both BitGenerators is same
+        # after drawing the distributions
+        numba_gen_state = numba_rng_instance.__getstate__()['state']
+        numpy_gen_state = numpy_rng_instance.__getstate__()['state']
+
+        for _state_key in numpy_gen_state:
+            self.assertPreciseEqual(numba_gen_state[_state_key],
+                                    numpy_gen_state[_state_key])
+
+    def _test_bitgen_func_parity(self, func_name, bitgen_func, seed=1):
+        numba_rng_instance = np.random.default_rng(seed=seed)
+        numpy_rng_instance = np.random.default_rng(seed=seed)
+
+        numpy_func = getattr(numpy_rng_instance.bit_generator.ctypes, func_name)
+        numpy_res = numpy_func(numpy_rng_instance.bit_generator.ctypes.state)
+
+        numba_func = numba.njit(lambda x: bitgen_func(x.bit_generator))
+        numba_res = numba_func(numba_rng_instance)
+
+        self.assertPreciseEqual(numba_res, numpy_res)
+
+    def test_npgen_boxing_unboxing(self):
+        rng_instance = np.random.default_rng()
+        numba_func = numba.njit(lambda x: x)
+        self.assertEqual(rng_instance, numba_func(rng_instance))
+        self.assertEqual(id(rng_instance), id(numba_func(rng_instance)))
+
+    def test_npgen_boxing_refcount(self):
+        rng_instance = np.random.default_rng()
+        no_box = numba.njit(lambda x:x.random())
+        do_box = numba.njit(lambda x:x)
+
+        y = do_box(rng_instance)
+        ref_1 = sys.getrefcount(rng_instance)
+        del y
+        no_box(rng_instance)
+        ref_2 = sys.getrefcount(rng_instance)
+
+        self.assertEqual(ref_1, ref_2 + 1)
+
+    def test_bitgen_funcs(self):
+        func_names = ["next_uint32", "next_uint64", "next_double"]
+        funcs = [next_uint32, next_uint64, next_double]
+
+        for _func, _func_name in zip(funcs, func_names):
+            with self.subTest(_func=_func, _func_name=_func_name):
+                self._test_bitgen_func_parity(_func_name, _func)
+
+    def test_random(self):
+        test_sizes = [None, (), (100,), (10, 20, 30)]
+        test_dtypes = [np.float32, np.float64]
+        bitgen_types = [None, MT19937]
+
+        # Test with no arguments
+        dist_func = lambda x, size, dtype:x.random()
+        with self.subTest():
+            # Provide single values so this test would run exactly once
+            self.check_numpy_parity(dist_func, test_size=100,
+                                    test_dtype=np.float64)
+
+        dist_func = lambda x, size, dtype:x.random(size=size, dtype=dtype)
+
+        for _size in test_sizes:
+            for _dtype in test_dtypes:
+                for _bitgen in bitgen_types:
+                    with self.subTest(_size=_size, _dtype=_dtype,
+                                      _bitgen=_bitgen):
+                        self.check_numpy_parity(dist_func, _bitgen,
+                                                None, _size, _dtype)
+
+
+class TestGeneratorCaching(TestCase, SerialMixin):
+    def test_randomgen_caching(self):
+        nb_rng = np.random.default_rng(1)
+        np_rng = np.random.default_rng(1)
+
+        numba_func = numba.njit(lambda x: x.random(10), cache=True)
+        self.assertPreciseEqual(np_rng.random(10), numba_func(nb_rng))
+        # Run the function twice to make sure caching doesn't break anything.
+        self.assertPreciseEqual(np_rng.random(10), numba_func(nb_rng))
+        # Check that the function can be retrieved successfully from the cache.
+        res = run_in_new_process_caching(test_generator_caching)
+        self.assertEqual(res['exitcode'], 0)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_npdatetime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_npdatetime.py
new file mode 100644
index 000000000..713fb6c6c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_npdatetime.py
@@ -0,0 +1,1089 @@
+"""
+Test np.datetime64 and np.timedelta64 support.
+"""
+
+# NOTE: datetime64 and timedelta64 ufuncs are tested in test_ufuncs.
+
+
+import contextlib
+import itertools
+import re
+import unittest
+import warnings
+
+import numpy as np
+
+from numba import jit, vectorize, njit
+from numba.np.numpy_support import numpy_version
+from numba.core import types, config
+from numba.core.errors import TypingError
+from numba.tests.support import TestCase, tag, skip_parfors_unsupported
+from numba.np import npdatetime_helpers, numpy_support
+
+TIMEDELTA_M = np.dtype('timedelta64[M]')
+TIMEDELTA_Y = np.dtype('timedelta64[Y]')
+
+def value_unit(val):
+    ty = numpy_support.from_dtype(val.dtype)
+    return ty.unit
+
+
+date_units = ('Y', 'M')
+time_units = ('W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as')
+# All except generic ("")
+all_units = date_units + time_units
+
+
+def add_usecase(x, y):
+    return x + y
+
+def sub_usecase(x, y):
+    return x - y
+
+def mul_usecase(x, y):
+    return x * y
+
+def div_usecase(x, y):
+    return x / y
+
+def floordiv_usecase(x, y):
+    return x // y
+
+def eq_usecase(x, y):
+    return x == y
+
+def ne_usecase(x, y):
+    return x != y
+
+def lt_usecase(x, y):
+    return x < y
+
+def le_usecase(x, y):
+    return x <= y
+
+def gt_usecase(x, y):
+    return x > y
+
+def ge_usecase(x, y):
+    return x >= y
+
+def pos_usecase(x):
+    return +x
+
+def neg_usecase(x):
+    return -x
+
+def abs_usecase(x):
+    return abs(x)
+
+def hash_usecase(x):
+    return hash(x)
+
+def min_usecase(x, y):
+    return min(x, y)
+
+def max_usecase(x, y):
+    return max(x, y)
+
+def make_add_constant(const):
+    def add_constant(x):
+        return x + const
+    return add_constant
+
+
+class TestModuleHelpers(TestCase):
+    """
+    Test the various helpers in numba.npdatetime_helpers.
+    """
+
+    def test_can_cast_timedelta(self):
+        f = npdatetime_helpers.can_cast_timedelta_units
+        for a, b in itertools.product(date_units, time_units):
+            self.assertFalse(f(a, b), (a, b))
+            self.assertFalse(f(b, a), (a, b))
+        for unit in all_units:
+            self.assertFalse(f(unit, ''))
+            self.assertTrue(f('', unit))
+        for unit in all_units + ('',):
+            self.assertTrue(f(unit, unit))
+
+        def check_units_group(group):
+            for i, a in enumerate(group):
+                for b in group[:i]:
+                    # large into smaller is ok
+                    self.assertTrue(f(b, a))
+                    # small into larger is not
+                    self.assertFalse(f(a, b))
+
+        check_units_group(date_units)
+        check_units_group(time_units)
+
+    def test_timedelta_conversion(self):
+        f = npdatetime_helpers.get_timedelta_conversion_factor
+        for unit in all_units + ('',):
+            self.assertEqual(f(unit, unit), 1)
+        for unit in all_units:
+            self.assertEqual(f('', unit), 1)
+        for a, b in itertools.product(time_units, date_units):
+            self.assertIs(f(a, b), None)
+            self.assertIs(f(b, a), None)
+
+        def check_units_group(group):
+            for i, a in enumerate(group):
+                for b in group[:i]:
+                    self.assertGreater(f(b, a), 1, (b, a))
+                    self.assertIs(f(a, b), None)
+
+        check_units_group(date_units)
+        check_units_group(time_units)
+
+        # Check some hand-picked values
+        self.assertEqual(f('Y', 'M'), 12)
+        self.assertEqual(f('W', 'h'), 24 * 7)
+        self.assertEqual(f('W', 'm'), 24 * 7 * 60)
+        self.assertEqual(f('W', 'us'), 24 * 7 * 3600 * 1000 * 1000)
+
+    def test_datetime_timedelta_scaling(self):
+        f = npdatetime_helpers.get_datetime_timedelta_conversion
+        def check_error(dt_unit, td_unit):
+            with self.assertRaises(RuntimeError):
+                f(dt_unit, td_unit)
+        # Cannot combine a Y or M timedelta64 with a finer-grained datetime64
+        for dt_unit, td_unit in itertools.product(time_units, date_units):
+            check_error(dt_unit, td_unit)
+        # Sanity check that all other unit pairs can be converted, we'll
+        # check individual results below
+        for dt_unit, td_unit in itertools.product(time_units, time_units):
+            f(dt_unit, td_unit)
+        for dt_unit, td_unit in itertools.product(date_units, time_units):
+            f(dt_unit, td_unit)
+        for dt_unit, td_unit in itertools.product(date_units, date_units):
+            f(dt_unit, td_unit)
+        # No-op conversions
+        for unit in all_units:
+            self.assertEqual(f(unit, unit), (unit, 1, 1))
+            self.assertEqual(f(unit, ''), (unit, 1, 1))
+            self.assertEqual(f('', unit), ('', 1, 1))
+        self.assertEqual(f('', ''), ('', 1, 1))
+        # "Regular" values
+        self.assertEqual(f('Y', 'M'), ('M', 12, 1))
+        self.assertEqual(f('M', 'Y'), ('M', 1, 12))
+        self.assertEqual(f('W', 'D'), ('D', 7, 1))
+        self.assertEqual(f('D', 'W'), ('D', 1, 7))
+        self.assertEqual(f('W', 's'), ('s', 7 * 24 * 3600, 1))
+        self.assertEqual(f('s', 'W'), ('s', 1, 7 * 24 * 3600))
+        self.assertEqual(f('s', 'as'), ('as', 1000 ** 6, 1))
+        self.assertEqual(f('as', 's'), ('as', 1, 1000 ** 6))
+        # "Interesting" values
+        self.assertEqual(f('Y', 'D'), ('D', 97 + 400 * 365, 400))
+        self.assertEqual(f('Y', 'W'), ('W', 97 + 400 * 365, 400 * 7))
+        self.assertEqual(f('M', 'D'), ('D', 97 + 400 * 365, 400 * 12))
+        self.assertEqual(f('M', 'W'), ('W', 97 + 400 * 365, 400 * 12 * 7))
+        self.assertEqual(f('Y', 's'), ('s', (97 + 400 * 365) *  24 * 3600, 400))
+        self.assertEqual(f('M', 's'), ('s', (97 + 400 * 365) *  24 * 3600, 400 * 12))
+
+    def test_combine_datetime_timedelta_units(self):
+        f = npdatetime_helpers.combine_datetime_timedelta_units
+        for unit in all_units:
+            self.assertEqual(f(unit, unit), unit)
+            self.assertEqual(f('', unit), unit)
+            self.assertEqual(f(unit, ''), unit)
+        self.assertEqual(f('', ''), '')
+        for dt_unit, td_unit in itertools.product(time_units, date_units):
+            self.assertIs(f(dt_unit, td_unit), None)
+        for dt_unit, td_unit in itertools.product(date_units, time_units):
+            self.assertEqual(f(dt_unit, td_unit), td_unit)
+
+    def test_same_kind(self):
+        f = npdatetime_helpers.same_kind
+        for u in all_units:
+            self.assertTrue(f(u, u))
+        A = ('Y', 'M', 'W', 'D')
+        B = ('h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as')
+        for a, b in itertools.product(A, A):
+            self.assertTrue(f(a, b))
+        for a, b in itertools.product(B, B):
+            self.assertTrue(f(a, b))
+        for a, b in itertools.product(A, B):
+            self.assertFalse(f(a, b))
+            self.assertFalse(f(b, a))
+
+
+TD = np.timedelta64
+DT = np.datetime64
+
+
+class TestMiscCompiling(TestCase):
+
+    def test_jit_explicit_signature(self):
+        def _check_explicit_signature(sig):
+            f = jit(sig, nopython=True)(add_usecase)
+            # Just a sanity check
+            args = DT(1, 'ms'), TD(2, 'us')
+            expected = add_usecase(*args)
+            self.assertPreciseEqual(f(*args), expected)
+
+        # Test passing the signature in object form
+        sig = types.NPDatetime('us')(types.NPDatetime('ms'), types.NPTimedelta('us'))
+        _check_explicit_signature(sig)
+        # Same with the signature in string form
+        sig = "NPDatetime('us')(NPDatetime('ms'), NPTimedelta('us'))"
+        _check_explicit_signature(sig)
+
+    def test_vectorize_explicit_signature(self):
+        def _check_explicit_signature(sig):
+            f = vectorize([sig], nopython=True)(mul_usecase)
+            # This isn't really right but we can't do better than this,
+            # since Numpy's ufuncs don't store the metadata of return types.
+            # Related to https://github.com/numpy/numpy/issues/5429
+            self.assertPreciseEqual(f(TD(2), 3), TD(6))
+
+        # Test passing the signature in object form (issue #917)
+        sig = types.NPTimedelta('s')(types.NPTimedelta('s'), types.int64)
+        _check_explicit_signature(sig)
+        # Same with the signature in string form
+        sig = "NPTimedelta('s')(NPTimedelta('s'), int64)"
+        _check_explicit_signature(sig)
+
+    def test_constant_datetime(self):
+        def check(const):
+            pyfunc = make_add_constant(const)
+            f = jit(nopython=True)(pyfunc)
+            x = TD(4, 'D')
+            expected = pyfunc(x)
+            self.assertPreciseEqual(f(x), expected)
+        check(DT('2001-01-01'))
+        check(DT('NaT', 'D'))
+
+    def test_constant_timedelta(self):
+        def check(const):
+            pyfunc = make_add_constant(const)
+            f = jit(nopython=True)(pyfunc)
+            x = TD(4, 'D')
+            expected = pyfunc(x)
+            self.assertPreciseEqual(f(x), expected)
+        check(TD(4, 'D'))
+        check(TD(-4, 'D'))
+        check(TD('NaT', 'D'))
+
+
+class TestTimedeltaArithmetic(TestCase):
+
+    jitargs = dict(forceobj=True)
+
+    def jit(self, pyfunc):
+        return jit(**self.jitargs)(pyfunc)
+
+    def test_add(self):
+        f = self.jit(add_usecase)
+        def check(a, b, expected):
+            self.assertPreciseEqual(f(a, b), expected)
+            self.assertPreciseEqual(f(b, a), expected)
+
+        check(TD(1), TD(2), TD(3))
+        check(TD(1, 's'), TD(2, 's'), TD(3, 's'))
+        # Implicit unit promotion
+        check(TD(1, 's'), TD(2, 'us'), TD(1000002, 'us'))
+        check(TD(1, 'W'), TD(2, 'D'), TD(9, 'D'))
+        # NaTs
+        check(TD('NaT'), TD(1), TD('NaT'))
+        check(TD('NaT', 's'), TD(1, 'D'), TD('NaT', 's'))
+        check(TD('NaT', 's'), TD(1, 'ms'), TD('NaT', 'ms'))
+        # Cannot add days and months
+        with self.assertRaises((TypeError, TypingError)):
+            f(TD(1, 'M'), TD(1, 'D'))
+
+    def test_sub(self):
+        f = self.jit(sub_usecase)
+        def check(a, b, expected):
+            self.assertPreciseEqual(f(a, b), expected)
+            self.assertPreciseEqual(f(b, a), -expected)
+
+        check(TD(3), TD(2), TD(1))
+        check(TD(3, 's'), TD(2, 's'), TD(1, 's'))
+        # Implicit unit promotion
+        check(TD(3, 's'), TD(2, 'us'), TD(2999998, 'us'))
+        check(TD(1, 'W'), TD(2, 'D'), TD(5, 'D'))
+        # NaTs
+        check(TD('NaT'), TD(1), TD('NaT'))
+        check(TD('NaT', 's'), TD(1, 'D'), TD('NaT', 's'))
+        check(TD('NaT', 's'), TD(1, 'ms'), TD('NaT', 'ms'))
+        # Cannot sub days to months
+        with self.assertRaises((TypeError, TypingError)):
+            f(TD(1, 'M'), TD(1, 'D'))
+
+    def test_mul(self):
+        f = self.jit(mul_usecase)
+        def check(a, b, expected):
+            self.assertPreciseEqual(f(a, b), expected)
+            self.assertPreciseEqual(f(b, a), expected)
+
+        # non-int64 int * timedelta64
+        check(TD(3), np.uint32(2), TD(6))
+        # int * timedelta64
+        check(TD(3), 2, TD(6))
+        check(TD(3, 'ps'), 2, TD(6, 'ps'))
+        check(TD('NaT', 'ps'), 2, TD('NaT', 'ps'))
+        # float * timedelta64
+        check(TD(7), 1.5, TD(10))
+        check(TD(-7), 1.5, TD(-10))
+        check(TD(7, 'ps'), -1.5, TD(-10, 'ps'))
+        check(TD(-7), -1.5, TD(10))
+        check(TD('NaT', 'ps'), -1.5, TD('NaT', 'ps'))
+        check(TD(7, 'ps'), float('nan'), TD('NaT', 'ps'))
+        # wraparound on overflow
+        check(TD(2**62, 'ps'), 16, TD(0, 'ps'))
+
+    def test_div(self):
+        div = self.jit(div_usecase)
+        floordiv = self.jit(floordiv_usecase)
+        def check(a, b, expected):
+            self.assertPreciseEqual(div(a, b), expected)
+            self.assertPreciseEqual(floordiv(a, b), expected)
+
+        # timedelta64 / non-int64 int
+        check(TD(-3, 'ps'), np.uint32(2), TD(-1, 'ps'))
+        # timedelta64 / int
+        check(TD(3), 2, TD(1))
+        check(TD(-3, 'ps'), 2, TD(-1, 'ps'))
+        check(TD('NaT', 'ps'), 2, TD('NaT', 'ps'))
+        check(TD(3, 'ps'), 0, TD('NaT', 'ps'))
+        check(TD('NaT', 'ps'), 0, TD('NaT', 'ps'))
+        # timedelta64 / float
+        check(TD(7), 0.5, TD(14))
+        check(TD(-7, 'ps'), 1.5, TD(-4, 'ps'))
+        check(TD('NaT', 'ps'), 2.5, TD('NaT', 'ps'))
+        check(TD(3, 'ps'), 0.0, TD('NaT', 'ps'))
+        check(TD('NaT', 'ps'), 0.0, TD('NaT', 'ps'))
+        check(TD(3, 'ps'), float('nan'), TD('NaT', 'ps'))
+        check(TD('NaT', 'ps'), float('nan'), TD('NaT', 'ps'))
+
+    def test_homogeneous_div(self):
+        div = self.jit(div_usecase)
+        def check(a, b, expected):
+            self.assertPreciseEqual(div(a, b), expected)
+
+        # timedelta64 / timedelta64
+        check(TD(7), TD(3), 7. / 3.)
+        check(TD(7, 'us'), TD(3, 'ms'), 7. / 3000.)
+        check(TD(7, 'ms'), TD(3, 'us'), 7000. / 3.)
+        check(TD(7), TD(0), float('+inf'))
+        check(TD(-7), TD(0), float('-inf'))
+        check(TD(0), TD(0), float('nan'))
+        # NaTs
+        check(TD('nat'), TD(3), float('nan'))
+        check(TD(3), TD('nat'), float('nan'))
+        check(TD('nat'), TD(0), float('nan'))
+        # Cannot div months with days
+        with self.assertRaises((TypeError, TypingError)):
+            div(TD(1, 'M'), TD(1, 'D'))
+
+    def test_eq_ne(self):
+        eq = self.jit(eq_usecase)
+        ne = self.jit(ne_usecase)
+        def check(a, b, expected):
+            expected_val = expected
+            not_expected_val = not expected
+
+            if numpy_version >= (1, 16):
+                # since np 1.16 all NaT == comparisons are False, including
+                # NaT==NaT, conversely != is True
+                if np.isnat(a) or np.isnat(a):
+                    expected_val = False
+                    not_expected_val = True
+
+            self.assertPreciseEqual(eq(a, b), expected_val)
+            self.assertPreciseEqual(eq(b, a), expected_val)
+            self.assertPreciseEqual(ne(a, b), not_expected_val)
+            self.assertPreciseEqual(ne(b, a), not_expected_val)
+
+        check(TD(1), TD(2), False)
+        check(TD(1), TD(1), True)
+        check(TD(1, 's'), TD(2, 's'), False)
+        check(TD(1, 's'), TD(1, 's'), True)
+        check(TD(2000, 's'), TD(2, 's'), False)
+        check(TD(2000, 'ms'), TD(2, 's'), True)
+        check(TD(1, 'Y'), TD(12, 'M'), True)
+        # NaTs
+        check(TD('Nat'), TD('Nat'), True)
+        check(TD('Nat', 'ms'), TD('Nat', 's'), True)
+        check(TD('Nat'), TD(1), False)
+        # Incompatible units => timedeltas compare unequal
+        check(TD(1, 'Y'), TD(365, 'D'), False)
+        check(TD(1, 'Y'), TD(366, 'D'), False)
+        # ... except when both are NaT!
+        check(TD('NaT', 'W'), TD('NaT', 'D'), True)
+
+    def test_lt_ge(self):
+        lt = self.jit(lt_usecase)
+        ge = self.jit(ge_usecase)
+        def check(a, b, expected):
+            expected_val = expected
+            not_expected_val = not expected
+
+            if numpy_version >= (1, 16):
+                # since np 1.16 all NaT magnitude comparisons including equality
+                # are False (as NaT == NaT is now False)
+                if np.isnat(a) or np.isnat(a):
+                    expected_val = False
+                    not_expected_val = False
+
+            self.assertPreciseEqual(lt(a, b), expected_val)
+            self.assertPreciseEqual(ge(a, b), not_expected_val)
+
+        check(TD(1), TD(2), True)
+        check(TD(1), TD(1), False)
+        check(TD(2), TD(1), False)
+        check(TD(1, 's'), TD(2, 's'), True)
+        check(TD(1, 's'), TD(1, 's'), False)
+        check(TD(2, 's'), TD(1, 's'), False)
+        check(TD(1, 'm'), TD(61, 's'), True)
+        check(TD(1, 'm'), TD(60, 's'), False)
+        # NaTs
+        check(TD('Nat'), TD('Nat'), False)
+        check(TD('Nat', 'ms'), TD('Nat', 's'), False)
+        check(TD('Nat'), TD(-(2**63)+1), True)
+        # Incompatible units => exception raised
+        with self.assertRaises((TypeError, TypingError)):
+            lt(TD(1, 'Y'), TD(365, 'D'))
+        with self.assertRaises((TypeError, TypingError)):
+            ge(TD(1, 'Y'), TD(365, 'D'))
+        # ... even when both are NaT
+        with self.assertRaises((TypeError, TypingError)):
+            lt(TD('NaT', 'Y'), TD('NaT', 'D'))
+        with self.assertRaises((TypeError, TypingError)):
+            ge(TD('NaT', 'Y'), TD('NaT', 'D'))
+
+    def test_le_gt(self):
+        le = self.jit(le_usecase)
+        gt = self.jit(gt_usecase)
+        def check(a, b, expected):
+            expected_val = expected
+            not_expected_val = not expected
+
+            if numpy_version >= (1, 16):
+                # since np 1.16 all NaT magnitude comparisons including equality
+                # are False (as NaT == NaT is now False)
+                if np.isnat(a) or np.isnat(a):
+                    expected_val = False
+                    not_expected_val = False
+            self.assertPreciseEqual(le(a, b), expected_val)
+            self.assertPreciseEqual(gt(a, b), not_expected_val)
+
+        check(TD(1), TD(2), True)
+        check(TD(1), TD(1), True)
+        check(TD(2), TD(1), False)
+        check(TD(1, 's'), TD(2, 's'), True)
+        check(TD(1, 's'), TD(1, 's'), True)
+        check(TD(2, 's'), TD(1, 's'), False)
+        check(TD(1, 'm'), TD(61, 's'), True)
+        check(TD(1, 'm'), TD(60, 's'), True)
+        check(TD(1, 'm'), TD(59, 's'), False)
+        # NaTs
+        check(TD('Nat'), TD('Nat'), True)
+        check(TD('Nat', 'ms'), TD('Nat', 's'), True)
+        check(TD('Nat'), TD(-(2**63)+1), True)
+        # Incompatible units => exception raised
+        with self.assertRaises((TypeError, TypingError)):
+            le(TD(1, 'Y'), TD(365, 'D'))
+        with self.assertRaises((TypeError, TypingError)):
+            gt(TD(1, 'Y'), TD(365, 'D'))
+        # ... even when both are NaT
+        with self.assertRaises((TypeError, TypingError)):
+            le(TD('NaT', 'Y'), TD('NaT', 'D'))
+        with self.assertRaises((TypeError, TypingError)):
+            gt(TD('NaT', 'Y'), TD('NaT', 'D'))
+
+    def test_pos(self):
+        pos = self.jit(pos_usecase)
+        def check(a):
+            self.assertPreciseEqual(pos(a), +a)
+
+        check(TD(3))
+        check(TD(-4))
+        check(TD(3, 'ms'))
+        check(TD(-4, 'ms'))
+        check(TD('NaT'))
+        check(TD('NaT', 'ms'))
+
+    def test_neg(self):
+        neg = self.jit(neg_usecase)
+        def check(a):
+            self.assertPreciseEqual(neg(a), -a)
+
+        check(TD(3))
+        check(TD(-4))
+        check(TD(3, 'ms'))
+        check(TD(-4, 'ms'))
+        check(TD('NaT'))
+        check(TD('NaT', 'ms'))
+
+    def test_abs(self):
+        f = self.jit(abs_usecase)
+        def check(a):
+            self.assertPreciseEqual(f(a), abs(a))
+
+        check(TD(3))
+        check(TD(-4))
+        check(TD(3, 'ms'))
+        check(TD(-4, 'ms'))
+        check(TD('NaT'))
+        check(TD('NaT', 'ms'))
+
+    def test_hash(self):
+        f = self.jit(hash_usecase)
+        def check(a):
+            self.assertPreciseEqual(f(a), hash(a))
+
+        TD_CASES = ((3,), (-4,), (3, 'ms'), (-4, 'ms'), (27, 'D'),
+                    (2, 'D'), (2, 'W'), (2, 'Y'), (3, 'W'),
+                    (365, 'D'), (10000, 'D'), (-10000, 'D'),
+                    ('NaT',), ('NaT', 'ms'), ('NaT', 'D'), (-1,))
+        DT_CASES = (('2014',), ('2016',), ('2000',), ('2014-02',),
+                    ('2014-03',), ('2014-04',), ('2016-02',), ('2000-12-31',),
+                    ('2014-01-16',), ('2014-01-05',), ('2014-01-07',),
+                    ('2014-01-06',), ('2014-02-02',), ('2014-02-27',),
+                    ('2014-02-16',), ('2014-03-01',), ('2000-01-01T01:02:03.002Z',),
+                    ('2000-01-01T01:02:03Z',), ('NaT',))
+
+        for case, typ in zip(TD_CASES + DT_CASES,
+                             (TD,) * len(TD_CASES) + (DT,) * len(TD_CASES)):
+            check(typ(*case))
+
+    def _test_min_max(self, usecase):
+        f = self.jit(usecase)
+        def check(a, b):
+            self.assertPreciseEqual(f(a, b), usecase(a, b))
+
+        for cases in (
+            (TD(0), TD(1), TD(2), TD('NaT')),
+            (TD(0, 's'), TD(1, 's'), TD(2, 's'), TD('NaT', 's')),
+        ):
+            for a, b in itertools.product(cases, cases):
+                check(a, b)
+
+    def test_min(self):
+        self._test_min_max(min_usecase)
+
+    def test_max(self):
+        self._test_min_max(max_usecase)
+
+
+class TestTimedeltaArithmeticNoPython(TestTimedeltaArithmetic):
+
+    jitargs = dict(nopython=True)
+
+
+class TestDatetimeArithmetic(TestCase):
+
+    jitargs = dict(forceobj=True)
+
+    def jit(self, pyfunc):
+        return jit(**self.jitargs)(pyfunc)
+
+    @contextlib.contextmanager
+    def silence_numpy_warnings(self):
+        # Numpy can raise warnings when combining e.g. a generic timedelta64
+        # with a non-generic datetime64.
+        with warnings.catch_warnings():
+            warnings.filterwarnings('ignore',
+                                    message='Implicitly casting between incompatible kinds',
+                                    category=DeprecationWarning)
+            yield
+
+    def test_add_sub_timedelta(self):
+        """
+        Test `datetime64 + timedelta64` and `datetime64 - timedelta64`.
+        """
+        add = self.jit(add_usecase)
+        sub = self.jit(sub_usecase)
+        def check(a, b, expected):
+            with self.silence_numpy_warnings():
+                self.assertPreciseEqual(add(a, b), expected, (a, b))
+                self.assertPreciseEqual(add(b, a), expected, (a, b))
+                self.assertPreciseEqual(sub(a, -b), expected, (a, b))
+                # Did we get it right?
+                self.assertPreciseEqual(a + b, expected)
+
+        # Y + ...
+        check(DT('2014'), TD(2, 'Y'), DT('2016'))
+        check(DT('2014'), TD(2, 'M'), DT('2014-03'))
+        check(DT('2014'), TD(3, 'W'), DT('2014-01-16', 'W'))
+        check(DT('2014'), TD(4, 'D'), DT('2014-01-05'))
+        check(DT('2000'), TD(365, 'D'), DT('2000-12-31'))
+        # M + ...
+        check(DT('2014-02'), TD(2, 'Y'), DT('2016-02'))
+        check(DT('2014-02'), TD(2, 'M'), DT('2014-04'))
+        check(DT('2014-02'), TD(2, 'D'), DT('2014-02-03'))
+        # W + ...
+        check(DT('2014-01-07', 'W'), TD(2, 'W'), DT('2014-01-16', 'W'))
+        # D + ...
+        check(DT('2014-02-02'), TD(27, 'D'), DT('2014-03-01'))
+        check(DT('2012-02-02'), TD(27, 'D'), DT('2012-02-29'))
+        check(DT('2012-02-02'), TD(2, 'W'), DT('2012-02-16'))
+        # s + ...
+        check(DT('2000-01-01T01:02:03Z'), TD(2, 'h'), DT('2000-01-01T03:02:03Z'))
+        check(DT('2000-01-01T01:02:03Z'), TD(2, 'ms'), DT('2000-01-01T01:02:03.002Z'))
+        # More thorough checking with leap years and faraway years
+        for dt_str in ('600', '601', '604', '801',
+                       '1900', '1904', '2200', '2300', '2304',
+                       '2400', '6001'):
+            for dt_suffix in ('', '-01', '-12'):
+                dt = DT(dt_str + dt_suffix)
+                for td in [TD(2, 'D'), TD(2, 'W'),
+                           TD(100, 'D'), TD(10000, 'D'),
+                           TD(-100, 'D'), TD(-10000, 'D'),
+                           TD(100, 'W'), TD(10000, 'W'),
+                           TD(-100, 'W'), TD(-10000, 'W'),
+                           TD(100, 'M'), TD(10000, 'M'),
+                           TD(-100, 'M'), TD(-10000, 'M')]:
+                    self.assertEqual(add(dt, td), dt + td, (dt, td))
+                    self.assertEqual(add(td, dt), dt + td, (dt, td))
+                    self.assertEqual(sub(dt, -td), dt + td, (dt, td))
+
+        # NaTs
+        check(DT('NaT'), TD(2), DT('NaT'))
+        check(DT('NaT', 's'), TD(2, 'h'), DT('NaT', 's'))
+        check(DT('NaT', 's'), TD(2, 'ms'), DT('NaT', 'ms'))
+        check(DT('2014'), TD('NaT', 'W'), DT('NaT', 'W'))
+        check(DT('2014-01-01'), TD('NaT', 'W'), DT('NaT', 'D'))
+        check(DT('NaT', 's'), TD('NaT', 'ms'), DT('NaT', 'ms'))
+
+        # Cannot add datetime days and timedelta months or years
+        for f in (add, sub):
+            with self.assertRaises((TypeError, TypingError)):
+                f(DT(1, '2014-01-01'), TD(1, 'Y'))
+            with self.assertRaises((TypeError, TypingError)):
+                f(DT(1, '2014-01-01'), TD(1, 'M'))
+
+    def datetime_samples(self):
+        dt_years = ['600', '601', '604', '1968', '1969', '1973',
+                   '2000', '2004', '2005', '2100', '2400', '2401']
+        dt_suffixes = ['', '-01', '-12', '-02-28', '-12-31',
+                       '-01-05T12:30:56Z', '-01-05T12:30:56.008Z']
+        dts = [DT(a + b) for (a, b) in itertools.product(dt_years, dt_suffixes)]
+        dts += [DT(s, 'W') for s in dt_years]
+        return dts
+
+    def test_datetime_difference(self):
+        """
+        Test `datetime64 - datetime64`.
+        """
+        sub = self.jit(sub_usecase)
+        def check(a, b, expected=None):
+            with self.silence_numpy_warnings():
+                self.assertPreciseEqual(sub(a, b), a - b, (a, b))
+                self.assertPreciseEqual(sub(b, a), b - a, (a, b))
+                # Did we get it right?
+                self.assertPreciseEqual(a - b, expected)
+
+        check(DT('2014'), DT('2017'), TD(-3, 'Y'))
+        check(DT('2014-02'), DT('2017-01'), TD(-35, 'M'))
+        check(DT('2014-02-28'), DT('2015-03-01'), TD(-366, 'D'))
+        # NaTs
+        check(DT('NaT', 'M'), DT('2000'), TD('NaT', 'M'))
+        check(DT('NaT', 'M'), DT('2000-01-01'), TD('NaT', 'D'))
+        check(DT('NaT'), DT('NaT'), TD('NaT'))
+        # Test many more values
+        with self.silence_numpy_warnings():
+            dts = self.datetime_samples()
+            for a, b in itertools.product(dts, dts):
+                if (not npdatetime_helpers.same_kind(value_unit(a), value_unit(b))):
+                    continue
+                self.assertPreciseEqual(sub(a, b), a - b, (a, b))
+
+    def test_comparisons(self):
+        # Test all datetime comparisons all at once
+        eq = self.jit(eq_usecase)
+        ne = self.jit(ne_usecase)
+        lt = self.jit(lt_usecase)
+        le = self.jit(le_usecase)
+        gt = self.jit(gt_usecase)
+        ge = self.jit(ge_usecase)
+
+        def check_eq(a, b, expected):
+            expected_val = expected
+            not_expected_val = not expected
+
+            if numpy_version >= (1, 16):
+                # since np 1.16 all NaT comparisons bar != are False, including
+                # NaT==NaT
+                if np.isnat(a) or np.isnat(b):
+                    expected_val = False
+                    not_expected_val = True
+                    self.assertFalse(le(a, b), (a, b))
+                    self.assertFalse(ge(a, b), (a, b))
+                    self.assertFalse(le(b, a), (a, b))
+                    self.assertFalse(ge(b, a), (a, b))
+                    self.assertFalse(lt(a, b), (a, b))
+                    self.assertFalse(gt(a, b), (a, b))
+                    self.assertFalse(lt(b, a), (a, b))
+                    self.assertFalse(gt(b, a), (a, b))
+
+            with self.silence_numpy_warnings():
+                self.assertPreciseEqual(eq(a, b), expected_val, (a, b, expected))
+                self.assertPreciseEqual(eq(b, a), expected_val, (a, b, expected))
+                self.assertPreciseEqual(ne(a, b), not_expected_val, (a, b, expected))
+                self.assertPreciseEqual(ne(b, a), not_expected_val, (a, b, expected))
+                if expected_val:
+                    # If equal, then equal-ordered comparisons are true
+                    self.assertTrue(le(a, b), (a, b))
+                    self.assertTrue(ge(a, b), (a, b))
+                    self.assertTrue(le(b, a), (a, b))
+                    self.assertTrue(ge(b, a), (a, b))
+                    # and strictly ordered comparisons are false
+                    self.assertFalse(lt(a, b), (a, b))
+                    self.assertFalse(gt(a, b), (a, b))
+                    self.assertFalse(lt(b, a), (a, b))
+                    self.assertFalse(gt(b, a), (a, b))
+                # Did we get it right?
+                self.assertPreciseEqual(a == b, expected_val)
+
+        def check_lt(a, b, expected):
+            expected_val = expected
+            not_expected_val = not expected
+
+            if numpy_version >= (1, 16):
+                # since np 1.16 all NaT magnitude comparisons including equality
+                # are False (as NaT == NaT is now False)
+                if np.isnat(a) or np.isnat(b):
+                    expected_val = False
+                    not_expected_val = False
+
+            with self.silence_numpy_warnings():
+                lt = self.jit(lt_usecase)
+                self.assertPreciseEqual(lt(a, b), expected_val, (a, b, expected))
+                self.assertPreciseEqual(gt(b, a), expected_val, (a, b, expected))
+                self.assertPreciseEqual(ge(a, b), not_expected_val, (a, b, expected))
+                self.assertPreciseEqual(le(b, a), not_expected_val, (a, b, expected))
+                if expected_val:
+                    # If true, then values are not equal
+                    check_eq(a, b, False)
+                # Did we get it right?
+                self.assertPreciseEqual(a < b, expected_val)
+
+        check_eq(DT('2014'), DT('2017'), False)
+        check_eq(DT('2014'), DT('2014-01'), True)
+        check_eq(DT('2014'), DT('2014-01-01'), True)
+        check_eq(DT('2014'), DT('2014-01-01', 'W'), True)
+        check_eq(DT('2014-01'), DT('2014-01-01', 'W'), True)
+        # Yes, it's not transitive
+        check_eq(DT('2014-01-01'), DT('2014-01-01', 'W'), False)
+        check_eq(DT('2014-01-02'), DT('2014-01-06', 'W'), True)
+        # with times
+        check_eq(DT('2014-01-01T00:01:00Z', 's'),
+              DT('2014-01-01T00:01Z', 'm'), True)
+        check_eq(DT('2014-01-01T00:01:01Z', 's'),
+              DT('2014-01-01T00:01Z', 'm'), False)
+        # NaTs
+        check_lt(DT('NaT', 'Y'), DT('2017'), True)
+        check_eq(DT('NaT'), DT('NaT'), True)
+
+        # Check comparison between various units
+        dts = self.datetime_samples()
+        for a in dts:
+            # Take a number of smaller units
+            a_unit = a.dtype.str.split('[')[1][:-1]
+            i = all_units.index(a_unit)
+            units = all_units[i:i+6]
+            for unit in units:
+                # Force conversion
+                b = a.astype('M8[%s]' % unit)
+                if (not npdatetime_helpers.same_kind(value_unit(a),
+                                                     value_unit(b))):
+                    continue
+                check_eq(a, b, True)
+                check_lt(a, b + np.timedelta64(1, unit), True)
+                check_lt(b - np.timedelta64(1, unit), a, True)
+
+    def _test_min_max(self, usecase):
+        f = self.jit(usecase)
+        def check(a, b):
+            self.assertPreciseEqual(f(a, b), usecase(a, b))
+
+        for cases in (
+            (DT(0, 'ns'), DT(1, 'ns'), DT(2, 'ns'), DT('NaT', 'ns')),
+            (DT(0, 's'), DT(1, 's'), DT(2, 's'), DT('NaT', 's')),
+        ):
+            for a, b in itertools.product(cases, cases):
+                check(a, b)
+
+    def test_min(self):
+        self._test_min_max(min_usecase)
+
+    def test_max(self):
+        self._test_min_max(max_usecase)
+
+class TestDatetimeArithmeticNoPython(TestDatetimeArithmetic):
+
+    jitargs = dict(nopython=True)
+
+
+class TestMetadataScalingFactor(TestCase):
+    """
+    Tests than non-1 scaling factors are not supported in datetime64
+    and timedelta64 dtypes.
+    """
+
+    def test_datetime(self, **jitargs):
+        eq = jit(**jitargs)(eq_usecase)
+        self.assertTrue(eq(DT('2014', '10Y'), DT('2010')))
+
+    def test_datetime_npm(self):
+        with self.assertTypingError():
+            self.test_datetime(nopython=True)
+
+    def test_timedelta(self, **jitargs):
+        eq = jit(**jitargs)(eq_usecase)
+        self.assertTrue(eq(TD(2, '10Y'), TD(20, 'Y')))
+
+    def test_timedelta_npm(self):
+        with self.assertTypingError():
+            self.test_timedelta(nopython=True)
+
+
+class TestDatetimeDeltaOps(TestCase):
+    def test_div(self):
+        """
+        Test the division of a timedelta by numeric types
+        """
+        def arr_div(a, b):
+            return a / b
+
+        py_func = arr_div
+        cfunc = njit(arr_div)
+        test_cases = [
+            (np.ones(3, TIMEDELTA_M), np.ones(3, TIMEDELTA_M)),
+            (np.ones(3, TIMEDELTA_M), np.ones(3, TIMEDELTA_Y)),
+            (np.ones(3, TIMEDELTA_Y), np.ones(3, TIMEDELTA_M)),
+            (np.ones(3, TIMEDELTA_Y), np.ones(3, TIMEDELTA_Y)),
+            (np.ones(3, TIMEDELTA_M), 1),
+            (np.ones(3, TIMEDELTA_M), np.ones(3, np.int64)),
+            (np.ones(3, TIMEDELTA_M), np.ones(3, np.float64)),
+        ]
+        for a, b in test_cases:
+            self.assertTrue(np.array_equal(py_func(a, b), cfunc(a, b)))
+
+
+class TestDatetimeArrayOps(TestCase):
+
+    def _test_td_add_or_sub(self, operation, parallel):
+        """
+        Test the addition/subtraction of a datetime array with a timedelta type
+        """
+        def impl(a, b):
+            return operation(a, b)
+
+        arr_one = np.array([
+                        np.datetime64("2011-01-01"),
+                        np.datetime64("1971-02-02"),
+                        np.datetime64("2021-03-03"),
+                        np.datetime64("2004-12-07"),
+                    ], dtype="datetime64[ns]")
+        arr_two = np.array([
+                        np.datetime64("2011-01-01"),
+                        np.datetime64("1971-02-02"),
+                        np.datetime64("2021-03-03"),
+                        np.datetime64("2004-12-07"),
+                    ], dtype="datetime64[D]")
+        py_func = impl
+        cfunc = njit(parallel=parallel)(impl)
+        test_cases = [
+            (arr_one, np.timedelta64(1000)),
+            (arr_two, np.timedelta64(1000)),
+            (arr_one, np.timedelta64(-54557)),
+            (arr_two, np.timedelta64(-54557)),
+        ]
+        # np.add is commutative so test the reversed order
+        if operation is np.add:
+            test_cases.extend([
+                (np.timedelta64(1000), arr_one),
+                (np.timedelta64(1000), arr_two),
+                (np.timedelta64(-54557), arr_one),
+                (np.timedelta64(-54557), arr_two),
+            ])
+        for a, b in test_cases:
+            self.assertTrue(np.array_equal(py_func(a, b), cfunc(a, b)))
+
+    def test_add_td(self):
+        self._test_td_add_or_sub(np.add, False)
+
+    @skip_parfors_unsupported
+    def test_add_td_parallel(self):
+        self._test_td_add_or_sub(np.add, True)
+
+    def test_sub_td(self):
+        self._test_td_add_or_sub(np.subtract, False)
+
+    @skip_parfors_unsupported
+    def test_sub_td_parallel(self):
+        self._test_td_add_or_sub(np.subtract, True)
+
+    def _test_add_sub_td_no_match(self, operation):
+        """
+        Tests that attempting to add/sub a datetime64 and timedelta64
+        with types that cannot be cast raises a reasonable exception.
+        """
+        @njit
+        def impl(a, b):
+            return operation(a, b)
+
+        fname = operation.__name__
+        expected = re.escape((f"ufunc '{fname}' is not supported between "
+                              "datetime64[ns] and timedelta64[M]"))
+        with self.assertRaisesRegex((TypingError, TypeError), expected):
+            impl(
+                np.array([np.datetime64("2011-01-01"),],
+                         dtype="datetime64[ns]"),
+                np.timedelta64(1000,'M')
+            )
+
+    def test_add_td_no_match(self):
+        self._test_add_sub_td_no_match(np.add)
+
+    def test_sub_td_no_match(self):
+        self._test_add_sub_td_no_match(np.subtract)
+
+    def _test_min_max(self, operation, parallel, method):
+        if method:
+            if operation is np.min:
+                def impl(arr):
+                    return arr.min()
+            else:
+                def impl(arr):
+                    return arr.max()
+        else:
+            def impl(arr):
+                return operation(arr)
+
+        py_func = impl
+        cfunc = njit(parallel=parallel)(impl)
+
+        test_cases = [
+            np.array([
+                DT(0, "ns"),
+                DT(1, "ns"),
+                DT(2, "ns"),
+                DT(3, "ns"),
+            ]),
+            np.array([
+                DT("2011-01-01", "ns"),
+                DT("1971-02-02", "ns"),
+                DT("1900-01-01", "ns"),
+                DT("2021-03-03", "ns"),
+                DT("2004-12-07", "ns"),
+            ]),
+            np.array([
+                DT("2011-01-01", "D"),
+                DT("1971-02-02", "D"),
+                DT("1900-01-01", "D"),
+                DT("2021-03-03", "D"),
+                DT("2004-12-07", "D"),
+            ]),
+            np.array([
+                DT("2011-01-01", "ns"),
+                DT("1971-02-02", "ns"),
+                DT("1900-01-01", "ns"),
+                DT("2021-03-03", "ns"),
+                DT("2004-12-07", "ns"),
+                DT("NaT", "ns"),
+            ]),
+            np.array([
+                DT("NaT", "ns"),
+                DT("2011-01-01", "ns"),
+                DT("1971-02-02", "ns"),
+                DT("1900-01-01", "ns"),
+                DT("2021-03-03", "ns"),
+                DT("2004-12-07", "ns"),
+            ]),
+            np.array([
+                DT("1971-02-02", "ns"),
+                DT("NaT", "ns"),
+            ]),
+            np.array([
+                DT("NaT", "ns"),
+                DT("NaT", "ns"),
+                DT("NaT", "ns"),
+            ]),
+            np.array([
+                TD(1, "ns"),
+                TD(2, "ns"),
+                TD(3, "ns"),
+                TD(4, "ns"),
+            ]),
+            np.array([
+                TD(1, "D"),
+                TD(2, "D"),
+                TD(3, "D"),
+                TD(4, "D"),
+            ]),
+            np.array([
+                TD("NaT", "ns"),
+                TD(1, "ns"),
+                TD(2, "ns"),
+                TD(3, "ns"),
+                TD(4, "ns"),
+            ]),
+            np.array([
+                TD(1, "ns"),
+                TD(2, "ns"),
+                TD(3, "ns"),
+                TD(4, "ns"),
+                TD("NaT", "ns"),
+            ]),
+            np.array([
+                TD("NaT", "ns"),
+            ]),
+            np.array([
+                TD("NaT", "ns"),
+                TD("NaT", "ns"),
+                TD("NaT", "ns"),
+            ]),
+        ]
+
+        for arr in test_cases:
+            py_res = py_func(arr)
+            c_res = cfunc(arr)
+            if np.isnat(py_res) or np.isnat(c_res):
+                self.assertTrue(np.isnat(py_res))
+                self.assertTrue(np.isnat(c_res))
+            else:
+                self.assertEqual(py_res, c_res)
+
+    def test_min_func(self):
+        self._test_min_max(min, False, False)
+
+    def test_np_min_func(self):
+        self._test_min_max(np.min, False, False)
+
+    def test_min_method(self):
+        self._test_min_max(np.min, False, True)
+
+    def test_max_func(self):
+        self._test_min_max(max, False, False)
+
+    def test_np_max_func(self):
+        self._test_min_max(np.max, False, False)
+
+    def test_max_method(self):
+        self._test_min_max(np.max, False, True)
+
+    @skip_parfors_unsupported
+    def test_min_func_parallel(self):
+        self._test_min_max(np.min, True, False)
+
+    @skip_parfors_unsupported
+    def test_min_method_parallel(self):
+        self._test_min_max(np.min, True, True)
+
+    @skip_parfors_unsupported
+    def test_max_func_parallel(self):
+        self._test_min_max(np.max, True, False)
+
+    @skip_parfors_unsupported
+    def test_max_method_parallel(self):
+        self._test_min_max(np.max, True, True)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nrt.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nrt.py
new file mode 100644
index 000000000..f8f5bad94
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nrt.py
@@ -0,0 +1,696 @@
+import math
+import os
+import platform
+import sys
+import re
+
+import numpy as np
+
+from numba import njit
+from numba.core import types
+from numba.core.compiler import compile_isolated, Flags
+from numba.core.runtime import (
+    rtsys,
+    nrtopt,
+    _nrt_python,
+    nrt,
+)
+from numba.core.extending import intrinsic, include_path
+from numba.core.typing import signature
+from numba.core.imputils import impl_ret_untracked
+from llvmlite import ir
+import llvmlite.binding as llvm
+import numba.core.typing.cffi_utils as cffi_support
+from numba.core.unsafe.nrt import NRT_get_api
+
+from numba.tests.support import (MemoryLeakMixin, TestCase, temp_directory,
+                                 import_dynamic, skip_if_32bit)
+from numba.core.registry import cpu_target
+import unittest
+
+enable_nrt_flags = Flags()
+enable_nrt_flags.nrt = True
+
+linux_only = unittest.skipIf(not sys.platform.startswith('linux'),
+                             'linux only test')
+x86_only = unittest.skipIf(platform.machine() not in ('i386', 'x86_64'),
+                           'x86 only test')
+
+
+class Dummy(object):
+    alive = 0
+
+    def __init__(self):
+        type(self).alive += 1
+
+    def __del__(self):
+        type(self).alive -= 1
+
+
+class TestNrtMemInfoNotInitialized(unittest.TestCase):
+    """
+    Unit test for checking the use of the NRT fails if the
+    initialization sequence has not been run.
+    """
+    _numba_parallel_test_ = False
+
+    def test_init_fail(self):
+        methods = {'library': (),
+                   'meminfo_new': ((), ()),
+                   'meminfo_alloc': ((),),
+                   }
+
+        for meth, args in methods.items():
+            try:
+                with self.assertRaises(RuntimeError) as raises:
+                    rtsys._init = False
+                    fn = getattr(rtsys, meth)
+                    fn(*args)
+
+                msg = "Runtime must be initialized before use."
+                self.assertIn(msg, str(raises.exception))
+            finally:
+                rtsys._init = True
+
+
+class TestNrtMemInfo(unittest.TestCase):
+    """
+    Unit test for core MemInfo functionality
+    """
+
+    def setUp(self):
+        # Reset the Dummy class
+        Dummy.alive = 0
+        # initialize the NRT (in case the tests are run in isolation)
+        cpu_target.target_context
+
+    def test_meminfo_refct_1(self):
+        d = Dummy()
+        self.assertEqual(Dummy.alive, 1)
+        addr = 0xdeadcafe  # some made up location
+
+        mi = rtsys.meminfo_new(addr, d)
+        self.assertEqual(mi.refcount, 1)
+        del d
+        self.assertEqual(Dummy.alive, 1)
+        mi.acquire()
+        self.assertEqual(mi.refcount, 2)
+        self.assertEqual(Dummy.alive, 1)
+        mi.release()
+        self.assertEqual(mi.refcount, 1)
+        del mi
+        self.assertEqual(Dummy.alive, 0)
+
+    def test_meminfo_refct_2(self):
+        d = Dummy()
+        self.assertEqual(Dummy.alive, 1)
+        addr = 0xdeadcafe  # some made up location
+
+        mi = rtsys.meminfo_new(addr, d)
+        self.assertEqual(mi.refcount, 1)
+        del d
+        self.assertEqual(Dummy.alive, 1)
+        for ct in range(100):
+            mi.acquire()
+        self.assertEqual(mi.refcount, 1 + 100)
+        self.assertEqual(Dummy.alive, 1)
+        for _ in range(100):
+            mi.release()
+        self.assertEqual(mi.refcount, 1)
+        del mi
+        self.assertEqual(Dummy.alive, 0)
+
+    def test_fake_memoryview(self):
+        d = Dummy()
+        self.assertEqual(Dummy.alive, 1)
+        addr = 0xdeadcafe  # some made up location
+
+        mi = rtsys.meminfo_new(addr, d)
+        self.assertEqual(mi.refcount, 1)
+        mview = memoryview(mi)
+        self.assertEqual(mi.refcount, 1)
+        self.assertEqual(addr, mi.data)
+        self.assertFalse(mview.readonly)
+        self.assertIs(mi, mview.obj)
+        self.assertTrue(mview.c_contiguous)
+        self.assertEqual(mview.itemsize, 1)
+        self.assertEqual(mview.ndim, 1)
+        del d
+        del mi
+
+        self.assertEqual(Dummy.alive, 1)
+        del mview
+        self.assertEqual(Dummy.alive, 0)
+
+    def test_memoryview(self):
+        from ctypes import c_uint32, c_void_p, POINTER, cast
+
+        dtype = np.dtype(np.uint32)
+        bytesize = dtype.itemsize * 10
+        mi = rtsys.meminfo_alloc(bytesize, safe=True)
+        addr = mi.data
+        c_arr = cast(c_void_p(mi.data), POINTER(c_uint32 * 10))
+        # Check 0xCB-filling
+        for i in range(10):
+            self.assertEqual(c_arr.contents[i], 0xcbcbcbcb)
+
+        # Init array with ctypes
+        for i in range(10):
+            c_arr.contents[i] = i + 1
+        mview = memoryview(mi)
+        self.assertEqual(mview.nbytes, bytesize)
+        self.assertFalse(mview.readonly)
+        self.assertIs(mi, mview.obj)
+        self.assertTrue(mview.c_contiguous)
+        self.assertEqual(mview.itemsize, 1)
+        self.assertEqual(mview.ndim, 1)
+        del mi
+        arr = np.ndarray(dtype=dtype, shape=mview.nbytes // dtype.itemsize,
+                         buffer=mview)
+        del mview
+        # Modify array with NumPy
+        np.testing.assert_equal(np.arange(arr.size) + 1, arr)
+
+        arr += 1
+
+        # Check value reflected in ctypes
+        for i in range(10):
+            self.assertEqual(c_arr.contents[i], i + 2)
+
+        self.assertEqual(arr.ctypes.data, addr)
+        del arr
+        # At this point the memory is zero filled
+        # We can't check this deterministically because the memory could be
+        # consumed by another thread.
+
+    def test_buffer(self):
+        from ctypes import c_uint32, c_void_p, POINTER, cast
+
+        dtype = np.dtype(np.uint32)
+        bytesize = dtype.itemsize * 10
+        mi = rtsys.meminfo_alloc(bytesize, safe=True)
+        self.assertEqual(mi.refcount, 1)
+        addr = mi.data
+        c_arr = cast(c_void_p(addr), POINTER(c_uint32 * 10))
+        # Check 0xCB-filling
+        for i in range(10):
+            self.assertEqual(c_arr.contents[i], 0xcbcbcbcb)
+
+        # Init array with ctypes
+        for i in range(10):
+            c_arr.contents[i] = i + 1
+
+        arr = np.ndarray(dtype=dtype, shape=bytesize // dtype.itemsize,
+                         buffer=mi)
+        self.assertEqual(mi.refcount, 1)
+        del mi
+        # Modify array with NumPy
+        np.testing.assert_equal(np.arange(arr.size) + 1, arr)
+
+        arr += 1
+
+        # Check value reflected in ctypes
+        for i in range(10):
+            self.assertEqual(c_arr.contents[i], i + 2)
+
+        self.assertEqual(arr.ctypes.data, addr)
+        del arr
+        # At this point the memory is zero filled
+        # We can't check this deterministically because the memory could be
+        # consumed by another thread.
+
+    @skip_if_32bit
+    def test_allocate_invalid_size(self):
+        # Checks that attempting to allocate too big a region fails gracefully.
+        size = types.size_t.maxval // 8 // 2
+        for pred in (True, False):
+            with self.assertRaises(MemoryError) as raises:
+                rtsys.meminfo_alloc(size, safe=pred)
+            self.assertIn(f"Requested allocation of {size} bytes failed.",
+                          str(raises.exception))
+
+    def test_allocate_negative_size(self):
+        # Checks that attempting to allocate negative number of bytes fails
+        # gracefully.
+        size = -10
+        for pred in (True, False):
+            with self.assertRaises(ValueError) as raises:
+                rtsys.meminfo_alloc(size, safe=pred)
+            msg = f"Cannot allocate a negative number of bytes: {size}."
+            self.assertIn(msg, str(raises.exception))
+
+
+class TestTracemalloc(unittest.TestCase):
+    """
+    Test NRT-allocated memory can be tracked by tracemalloc.
+    """
+
+    def measure_memory_diff(self, func):
+        try:
+            import tracemalloc
+        except ImportError:
+            self.skipTest("tracemalloc not available")
+        tracemalloc.start()
+        try:
+            before = tracemalloc.take_snapshot()
+            # Keep the result and only delete it after taking a snapshot
+            res = func()
+            after = tracemalloc.take_snapshot()
+            del res
+            return after.compare_to(before, 'lineno')
+        finally:
+            tracemalloc.stop()
+
+    def test_snapshot(self):
+        N = 1000000
+        dtype = np.int8
+
+        @njit
+        def alloc_nrt_memory():
+            """
+            Allocate and return a large array.
+            """
+            return np.empty(N, dtype)
+
+        def keep_memory():
+            return alloc_nrt_memory()
+
+        def release_memory():
+            alloc_nrt_memory()
+
+        alloc_lineno = keep_memory.__code__.co_firstlineno + 1
+
+        # Warmup JIT
+        alloc_nrt_memory()
+
+        # The large NRT-allocated array should appear topmost in the diff
+        diff = self.measure_memory_diff(keep_memory)
+        stat = diff[0]
+        # There is a slight overhead, so the allocated size won't exactly be N
+        self.assertGreaterEqual(stat.size, N)
+        self.assertLess(stat.size, N * 1.015,
+                        msg=("Unexpected allocation overhead encountered. "
+                             "May be due to difference in CPython "
+                             "builds or running under coverage"))
+        frame = stat.traceback[0]
+        self.assertEqual(os.path.basename(frame.filename), "test_nrt.py")
+        self.assertEqual(frame.lineno, alloc_lineno)
+
+        # If NRT memory is released before taking a snapshot, it shouldn't
+        # appear.
+        diff = self.measure_memory_diff(release_memory)
+        stat = diff[0]
+        # Something else appears, but nothing the magnitude of N
+        self.assertLess(stat.size, N * 0.01)
+
+
+class TestNRTIssue(MemoryLeakMixin, TestCase):
+    def test_issue_with_refct_op_pruning(self):
+        """
+        GitHub Issue #1244 https://github.com/numba/numba/issues/1244
+        """
+        @njit
+        def calculate_2D_vector_mag(vector):
+            x, y = vector
+
+            return math.sqrt(x ** 2 + y ** 2)
+
+        @njit
+        def normalize_2D_vector(vector):
+            normalized_vector = np.empty(2, dtype=np.float64)
+
+            mag = calculate_2D_vector_mag(vector)
+            x, y = vector
+
+            normalized_vector[0] = x / mag
+            normalized_vector[1] = y / mag
+
+            return normalized_vector
+
+        @njit
+        def normalize_vectors(num_vectors, vectors):
+            normalized_vectors = np.empty((num_vectors, 2), dtype=np.float64)
+
+            for i in range(num_vectors):
+                vector = vectors[i]
+
+                normalized_vector = normalize_2D_vector(vector)
+
+                normalized_vectors[i, 0] = normalized_vector[0]
+                normalized_vectors[i, 1] = normalized_vector[1]
+
+            return normalized_vectors
+
+        num_vectors = 10
+        test_vectors = np.random.random((num_vectors, 2))
+        got = normalize_vectors(num_vectors, test_vectors)
+        expected = normalize_vectors.py_func(num_vectors, test_vectors)
+
+        np.testing.assert_almost_equal(expected, got)
+
+    def test_incref_after_cast(self):
+        # Issue #1427: when casting a value before returning it, the
+        # cast result should be incref'ed, not the original value.
+        def f():
+            return 0.0, np.zeros(1, dtype=np.int32)
+
+        # Note the return type isn't the same as the tuple type above:
+        # the first element is a complex rather than a float.
+        cres = compile_isolated(f, (),
+                                types.Tuple((types.complex128,
+                                             types.Array(types.int32, 1, 'C')
+                                             ))
+                                )
+        z, arr = cres.entry_point()
+        self.assertPreciseEqual(z, 0j)
+        self.assertPreciseEqual(arr, np.zeros(1, dtype=np.int32))
+
+    def test_refct_pruning_issue_1511(self):
+        @njit
+        def f():
+            a = np.ones(10, dtype=np.float64)
+            b = np.ones(10, dtype=np.float64)
+            return a, b[:]
+
+        a, b = f()
+        np.testing.assert_equal(a, b)
+        np.testing.assert_equal(a, np.ones(10, dtype=np.float64))
+
+    def test_refct_pruning_issue_1526(self):
+        @njit
+        def udt(image, x, y):
+            next_loc = np.where(image == 1)
+
+            if len(next_loc[0]) == 0:
+                y_offset = 1
+                x_offset = 1
+            else:
+                y_offset = next_loc[0][0]
+                x_offset = next_loc[1][0]
+
+            next_loc_x = (x - 1) + x_offset
+            next_loc_y = (y - 1) + y_offset
+
+            return next_loc_x, next_loc_y
+
+        a = np.array([[1, 0, 1, 0, 1, 0, 0, 1, 0, 0]])
+        expect = udt.py_func(a, 1, 6)
+        got = udt(a, 1, 6)
+
+        self.assertEqual(expect, got)
+
+
+class TestRefCtPruning(unittest.TestCase):
+
+    sample_llvm_ir = '''
+define i32 @"MyFunction"(i8** noalias nocapture %retptr, { i8*, i32 }** noalias nocapture %excinfo, i8* noalias nocapture readnone %env, double %arg.vt.0, double %arg.vt.1, double %arg.vt.2, double %arg.vt.3, double %arg.bounds.0, double %arg.bounds.1, double %arg.bounds.2, double %arg.bounds.3, i8* %arg.xs.0, i8* nocapture readnone %arg.xs.1, i64 %arg.xs.2, i64 %arg.xs.3, double* nocapture readonly %arg.xs.4, i64 %arg.xs.5.0, i64 %arg.xs.6.0, i8* %arg.ys.0, i8* nocapture readnone %arg.ys.1, i64 %arg.ys.2, i64 %arg.ys.3, double* nocapture readonly %arg.ys.4, i64 %arg.ys.5.0, i64 %arg.ys.6.0, i8* %arg.aggs_and_cols.0.0, i8* nocapture readnone %arg.aggs_and_cols.0.1, i64 %arg.aggs_and_cols.0.2, i64 %arg.aggs_and_cols.0.3, i32* nocapture %arg.aggs_and_cols.0.4, i64 %arg.aggs_and_cols.0.5.0, i64 %arg.aggs_and_cols.0.5.1, i64 %arg.aggs_and_cols.0.6.0, i64 %arg.aggs_and_cols.0.6.1) local_unnamed_addr {
+entry:
+tail call void @NRT_incref(i8* %arg.xs.0)
+tail call void @NRT_incref(i8* %arg.ys.0)
+tail call void @NRT_incref(i8* %arg.aggs_and_cols.0.0)
+%.251 = icmp sgt i64 %arg.xs.5.0, 0
+br i1 %.251, label %B42.preheader, label %B160
+
+B42.preheader:                                    ; preds = %entry
+%0 = add i64 %arg.xs.5.0, 1
+br label %B42
+
+B42:                                              ; preds = %B40.backedge, %B42.preheader
+%lsr.iv3 = phi i64 [ %lsr.iv.next, %B40.backedge ], [ %0, %B42.preheader ]
+%lsr.iv1 = phi double* [ %scevgep2, %B40.backedge ], [ %arg.xs.4, %B42.preheader ]
+%lsr.iv = phi double* [ %scevgep, %B40.backedge ], [ %arg.ys.4, %B42.preheader ]
+%.381 = load double, double* %lsr.iv1, align 8
+%.420 = load double, double* %lsr.iv, align 8
+%.458 = fcmp ole double %.381, %arg.bounds.1
+%not..432 = fcmp oge double %.381, %arg.bounds.0
+%"$phi82.1.1" = and i1 %.458, %not..432
+br i1 %"$phi82.1.1", label %B84, label %B40.backedge
+
+B84:                                              ; preds = %B42
+%.513 = fcmp ole double %.420, %arg.bounds.3
+%not..487 = fcmp oge double %.420, %arg.bounds.2
+%"$phi106.1.1" = and i1 %.513, %not..487
+br i1 %"$phi106.1.1", label %B108.endif.endif.endif, label %B40.backedge
+
+B160:                                             ; preds = %B40.backedge, %entry
+tail call void @NRT_decref(i8* %arg.ys.0)
+tail call void @NRT_decref(i8* %arg.xs.0)
+tail call void @NRT_decref(i8* %arg.aggs_and_cols.0.0)
+store i8* null, i8** %retptr, align 8
+ret i32 0
+
+B108.endif.endif.endif:                           ; preds = %B84
+%.575 = fmul double %.381, %arg.vt.0
+%.583 = fadd double %.575, %arg.vt.1
+%.590 = fptosi double %.583 to i64
+%.630 = fmul double %.420, %arg.vt.2
+%.638 = fadd double %.630, %arg.vt.3
+%.645 = fptosi double %.638 to i64
+tail call void @NRT_incref(i8* %arg.aggs_and_cols.0.0)              ; GONE 1
+tail call void @NRT_decref(i8* null)                                ; GONE 2
+tail call void @NRT_incref(i8* %arg.aggs_and_cols.0.0), !noalias !0 ; GONE 3
+%.62.i.i = icmp slt i64 %.645, 0
+%.63.i.i = select i1 %.62.i.i, i64 %arg.aggs_and_cols.0.5.0, i64 0
+%.64.i.i = add i64 %.63.i.i, %.645
+%.65.i.i = icmp slt i64 %.590, 0
+%.66.i.i = select i1 %.65.i.i, i64 %arg.aggs_and_cols.0.5.1, i64 0
+%.67.i.i = add i64 %.66.i.i, %.590
+%.84.i.i = mul i64 %.64.i.i, %arg.aggs_and_cols.0.5.1
+%.87.i.i = add i64 %.67.i.i, %.84.i.i
+%.88.i.i = getelementptr i32, i32* %arg.aggs_and_cols.0.4, i64 %.87.i.i
+%.89.i.i = load i32, i32* %.88.i.i, align 4, !noalias !3
+%.99.i.i = add i32 %.89.i.i, 1
+store i32 %.99.i.i, i32* %.88.i.i, align 4, !noalias !3
+tail call void @NRT_decref(i8* %arg.aggs_and_cols.0.0), !noalias !0 ; GONE 4
+tail call void @NRT_decref(i8* %arg.aggs_and_cols.0.0)              ; GONE 5
+br label %B40.backedge
+
+B40.backedge:                                     ; preds = %B108.endif.endif.endif, %B84, %B42
+%scevgep = getelementptr double, double* %lsr.iv, i64 1
+%scevgep2 = getelementptr double, double* %lsr.iv1, i64 1
+%lsr.iv.next = add i64 %lsr.iv3, -1
+%.294 = icmp sgt i64 %lsr.iv.next, 1
+br i1 %.294, label %B42, label %B160
+}
+    ''' # noqa
+
+    def test_refct_pruning_op_recognize(self):
+        input_ir = self.sample_llvm_ir
+        input_lines = list(input_ir.splitlines())
+        before_increfs = [ln for ln in input_lines if 'NRT_incref' in ln]
+        before_decrefs = [ln for ln in input_lines if 'NRT_decref' in ln]
+
+        # prune
+        output_ir = nrtopt._remove_redundant_nrt_refct(input_ir)
+        output_lines = list(output_ir.splitlines())
+        after_increfs = [ln for ln in output_lines if 'NRT_incref' in ln]
+        after_decrefs = [ln for ln in output_lines if 'NRT_decref' in ln]
+
+        # check
+        self.assertNotEqual(before_increfs, after_increfs)
+        self.assertNotEqual(before_decrefs, after_decrefs)
+
+        pruned_increfs = set(before_increfs) - set(after_increfs)
+        pruned_decrefs = set(before_decrefs) - set(after_decrefs)
+
+        # the symm difference == or-combined
+        combined = pruned_increfs | pruned_decrefs
+        self.assertEqual(combined, pruned_increfs ^ pruned_decrefs)
+        pruned_lines = '\n'.join(combined)
+
+        # all GONE lines are pruned
+        for i in [1, 2, 3, 4, 5]:
+            gone = '; GONE {}'.format(i)
+            self.assertIn(gone, pruned_lines)
+        # no other lines
+        self.assertEqual(len(list(pruned_lines.splitlines())), len(combined))
+
+    @unittest.skip("Pass removed as it was buggy. Re-enable when fixed.")
+    def test_refct_pruning_with_branches(self):
+        '''testcase from #2350'''
+        @njit
+        def _append_non_na(x, y, agg, field):
+            if not np.isnan(field):
+                agg[y, x] += 1
+
+        @njit
+        def _append(x, y, agg, field):
+            if not np.isnan(field):
+                if np.isnan(agg[y, x]):
+                    agg[y, x] = field
+                else:
+                    agg[y, x] += field
+
+        @njit
+        def append(x, y, agg, field):
+            _append_non_na(x, y, agg, field)
+            _append(x, y, agg, field)
+
+        # Disable python wrapper to avoid detecting necessary
+        # refcount inside it
+        @njit(no_cpython_wrapper=True)
+        def extend(arr, field):
+            for i in range(arr.shape[0]):
+                for j in range(arr.shape[1]):
+                    append(j, i, arr, field)
+
+        # Compile
+        extend.compile("(f4[:,::1], f4)")
+
+        # Test there are no reference count operations
+        llvmir = str(extend.inspect_llvm(extend.signatures[0]))
+        refops = list(re.finditer(r'(NRT_incref|NRT_decref)\([^\)]+\)', llvmir))
+        self.assertEqual(len(refops), 0)
+
+    @linux_only
+    @x86_only
+    def test_inline_asm(self):
+        """The InlineAsm class from llvmlite.ir has no 'name' attr the refcount
+        pruning pass should be tolerant to this"""
+        llvm.initialize()
+        llvm.initialize_native_target()
+        llvm.initialize_native_asmprinter()
+        llvm.initialize_native_asmparser()
+
+        @intrinsic
+        def bar(tyctx, x, y):
+            def codegen(cgctx, builder, sig, args):
+                (arg_0, arg_1) = args
+                fty = ir.FunctionType(ir.IntType(32), [ir.IntType(32),
+                                                       ir.IntType(32)])
+                mul = builder.asm(fty, "mov $2, $0; imul $1, $0", "=&r,r,r",
+                                  (arg_0, arg_1), name="asm_mul",
+                                  side_effect=False)
+                return impl_ret_untracked(cgctx, builder, sig.return_type, mul)
+            return signature(types.int32, types.int32, types.int32), codegen
+
+        @njit(['int32(int32)'])
+        def foo(x):
+            x += 1
+            z = bar(x, 2)
+            return z
+
+        self.assertEqual(foo(10), 22) # expect (10 + 1) * 2 = 22
+
+
+@unittest.skipUnless(cffi_support.SUPPORTED, "cffi required")
+class TestNrtExternalCFFI(MemoryLeakMixin, TestCase):
+    """Testing the use of externally compiled C code that use NRT
+    """
+    def setUp(self):
+        # initialize the NRT (in case the tests are run in isolation)
+        super(TestNrtExternalCFFI, self).setUp()
+        cpu_target.target_context
+
+    def compile_cffi_module(self, name, source, cdef):
+        from cffi import FFI
+
+        ffi = FFI()
+        ffi.set_source(name, source, include_dirs=[include_path()])
+        ffi.cdef(cdef)
+        tmpdir = temp_directory("cffi_test_{}".format(name))
+        ffi.compile(tmpdir=tmpdir)
+        sys.path.append(tmpdir)
+        try:
+            mod = import_dynamic(name)
+        finally:
+            sys.path.remove(tmpdir)
+
+        return ffi, mod
+
+    def get_nrt_api_table(self):
+        from cffi import FFI
+
+        ffi = FFI()
+        nrt_get_api = ffi.cast("void* (*)()", _nrt_python.c_helpers['get_api'])
+        table = nrt_get_api()
+        return table
+
+    def test_manage_memory(self):
+        name = "{}_test_manage_memory".format(self.__class__.__name__)
+        source = r"""
+#include <stdio.h>
+#include "numba/core/runtime/nrt_external.h"
+
+int status = 0;
+
+void my_dtor(void *ptr) {
+    free(ptr);
+    status = 0xdead;
+}
+
+NRT_MemInfo* test_nrt_api(NRT_api_functions *nrt) {
+    void * data = malloc(10);
+    NRT_MemInfo *mi = nrt->manage_memory(data, my_dtor);
+    nrt->acquire(mi);
+    nrt->release(mi);
+    status = 0xa110c;
+    return mi;
+}
+        """
+        cdef = """
+void* test_nrt_api(void *nrt);
+extern int status;
+        """
+
+        ffi, mod = self.compile_cffi_module(name, source, cdef)
+        # Init status is 0
+        self.assertEqual(mod.lib.status, 0)
+        table = self.get_nrt_api_table()
+        out = mod.lib.test_nrt_api(table)
+        # status is now 0xa110c
+        self.assertEqual(mod.lib.status, 0xa110c)
+        mi_addr = int(ffi.cast("size_t", out))
+        mi = nrt.MemInfo(mi_addr)
+        self.assertEqual(mi.refcount, 1)
+        del mi   # force deallocation on mi
+        # status is now 0xdead
+        self.assertEqual(mod.lib.status, 0xdead)
+
+    def test_allocate(self):
+        name = "{}_test_allocate".format(self.__class__.__name__)
+        source = r"""
+#include <stdio.h>
+#include "numba/core/runtime/nrt_external.h"
+
+NRT_MemInfo* test_nrt_api(NRT_api_functions *nrt, size_t n) {
+    size_t *data = NULL;
+    NRT_MemInfo *mi = nrt->allocate(n);
+    data = nrt->get_data(mi);
+    data[0] = 0xded;
+    data[1] = 0xabc;
+    data[2] = 0xdef;
+    return mi;
+}
+        """
+        cdef = "void* test_nrt_api(void *nrt, size_t n);"
+        ffi, mod = self.compile_cffi_module(name, source, cdef)
+
+        table = self.get_nrt_api_table()
+
+        numbytes = 3 * np.dtype(np.intp).itemsize
+        out = mod.lib.test_nrt_api(table, numbytes)
+
+        mi_addr = int(ffi.cast("size_t", out))
+        mi = nrt.MemInfo(mi_addr)
+        self.assertEqual(mi.refcount, 1)
+
+        buffer = ffi.buffer(ffi.cast("char [{}]".format(numbytes), mi.data))
+        arr = np.ndarray(shape=(3,), dtype=np.intp, buffer=buffer)
+        np.testing.assert_equal(arr, [0xded, 0xabc, 0xdef])
+
+    def test_get_api(self):
+        from cffi import FFI
+
+        @njit
+        def test_nrt_api():
+            return NRT_get_api()
+
+        ffi = FFI()
+        expect = int(ffi.cast('size_t', self.get_nrt_api_table()))
+        got = test_nrt_api()
+        self.assertEqual(expect, got)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nrt_refct.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nrt_refct.py
new file mode 100644
index 000000000..9b05f6ebe
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_nrt_refct.py
@@ -0,0 +1,114 @@
+"""
+Tests issues or edge cases for producing invalid NRT refct
+"""
+
+
+import gc
+
+import numpy as np
+
+import unittest
+from numba import njit
+from numba.core.runtime import rtsys
+from numba.tests.support import TestCase
+
+
+class TestNrtRefCt(TestCase):
+
+    def setUp(self):
+        # Clean up any NRT-backed objects hanging in a dead reference cycle
+        gc.collect()
+
+    def test_no_return(self):
+        """
+        Test issue #1291
+        """
+
+        @njit
+        def foo(n):
+            for i in range(n):
+                temp = np.zeros(2)
+            return 0
+
+        n = 10
+        init_stats = rtsys.get_allocation_stats()
+        foo(n)
+        cur_stats = rtsys.get_allocation_stats()
+        self.assertEqual(cur_stats.alloc - init_stats.alloc, n)
+        self.assertEqual(cur_stats.free - init_stats.free, n)
+
+    def test_escaping_var_init_in_loop(self):
+        """
+        Test issue #1297
+        """
+
+        @njit
+        def g(n):
+
+            x = np.zeros((n, 2))
+
+            for i in range(n):
+                y = x[i]
+
+            for i in range(n):
+                y = x[i]
+
+            return 0
+
+        init_stats = rtsys.get_allocation_stats()
+        g(10)
+        cur_stats = rtsys.get_allocation_stats()
+        self.assertEqual(cur_stats.alloc - init_stats.alloc, 1)
+        self.assertEqual(cur_stats.free - init_stats.free, 1)
+
+    def test_invalid_computation_of_lifetime(self):
+        """
+        Test issue #1573
+        """
+        @njit
+        def if_with_allocation_and_initialization(arr1, test1):
+            tmp_arr = np.zeros_like(arr1)
+
+            for i in range(tmp_arr.shape[0]):
+                pass
+
+            if test1:
+                np.zeros_like(arr1)
+
+            return tmp_arr
+
+        arr = np.random.random((5, 5))  # the values are not consumed
+
+        init_stats = rtsys.get_allocation_stats()
+        if_with_allocation_and_initialization(arr, False)
+        cur_stats = rtsys.get_allocation_stats()
+        self.assertEqual(cur_stats.alloc - init_stats.alloc,
+                         cur_stats.free - init_stats.free)
+
+    def test_del_at_beginning_of_loop(self):
+        """
+        Test issue #1734
+        """
+        @njit
+        def f(arr):
+            res = 0
+
+            for i in (0, 1):
+                # `del t` is issued here before defining t.  It must be
+                # correctly handled by the lowering phase.
+                t = arr[i]
+                if t[i] > 1:
+                    res += t[i]
+
+            return res
+
+        arr = np.ones((2, 2))
+        init_stats = rtsys.get_allocation_stats()
+        f(arr)
+        cur_stats = rtsys.get_allocation_stats()
+        self.assertEqual(cur_stats.alloc - init_stats.alloc,
+                         cur_stats.free - init_stats.free)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_num_threads.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_num_threads.py
new file mode 100644
index 000000000..d073eaa7c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_num_threads.py
@@ -0,0 +1,630 @@
+# -*- coding: utf-8 -*-
+from __future__ import print_function, absolute_import, division
+
+import sys
+import os
+import re
+import multiprocessing
+import unittest
+
+import numpy as np
+
+from numba import (njit, set_num_threads, get_num_threads, prange, config,
+                   threading_layer, guvectorize)
+from numba.np.ufunc.parallel import get_thread_id
+from numba.core.errors import TypingError
+from numba.tests.support import TestCase, skip_parfors_unsupported, tag
+from numba.tests.test_parallel_backend import TestInSubprocess
+
+
+class TestNumThreads(TestCase):
+    _numba_parallel_test_ = False
+
+    def setUp(self):
+        # Make sure the num_threads is set to the max. This also makes sure
+        # the threads are launched.
+        set_num_threads(config.NUMBA_NUM_THREADS)
+
+    def check_mask(self, expected, result):
+        # There's no guarantee that TBB will use a full mask worth of
+        # threads if it deems it inefficient to do so
+        if threading_layer() == 'tbb':
+            self.assertTrue(np.all(result <= expected))
+        elif threading_layer() in ('omp', 'workqueue'):
+            np.testing.assert_equal(expected, result)
+        else:
+            assert 0, 'unreachable'
+
+    @skip_parfors_unsupported
+    def test_set_num_threads_type(self):
+
+        @njit
+        def foo():
+            set_num_threads('wrong_type')
+
+        expected = "The number of threads specified must be an integer"
+        for fn, errty in ((foo, TypingError), (foo.py_func, TypeError)):
+            with self.assertRaises(errty) as raises:
+                fn()
+            self.assertIn(expected, str(raises.exception))
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_set_num_threads_basic(self):
+        max_threads = config.NUMBA_NUM_THREADS
+
+        self.assertEqual(get_num_threads(), max_threads)
+        set_num_threads(2)
+        self.assertEqual(get_num_threads(), 2)
+        set_num_threads(max_threads)
+        self.assertEqual(get_num_threads(), max_threads)
+
+        with self.assertRaises(ValueError):
+            set_num_threads(0)
+
+        with self.assertRaises(ValueError):
+            set_num_threads(max_threads + 1)
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_set_num_threads_basic_jit(self):
+        max_threads = config.NUMBA_NUM_THREADS
+
+        @njit
+        def get_n():
+            return get_num_threads()
+
+        self.assertEqual(get_n(), max_threads)
+        set_num_threads(2)
+        self.assertEqual(get_n(), 2)
+        set_num_threads(max_threads)
+        self.assertEqual(get_n(), max_threads)
+
+        @njit
+        def set_get_n(n):
+            set_num_threads(n)
+            return get_num_threads()
+
+        self.assertEqual(set_get_n(2), 2)
+        self.assertEqual(set_get_n(max_threads), max_threads)
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_set_num_threads_basic_guvectorize(self):
+        max_threads = config.NUMBA_NUM_THREADS
+
+        @guvectorize(['void(int64[:])'],
+                     '(n)',
+                     nopython=True,
+                     target='parallel')
+        def get_n(x):
+            x[:] = get_num_threads()
+
+        x = np.zeros((5000000,), dtype=np.int64)
+        get_n(x)
+        np.testing.assert_equal(x, max_threads)
+        set_num_threads(2)
+        x = np.zeros((5000000,), dtype=np.int64)
+        get_n(x)
+        np.testing.assert_equal(x, 2)
+        set_num_threads(max_threads)
+        x = np.zeros((5000000,), dtype=np.int64)
+        get_n(x)
+        np.testing.assert_equal(x, max_threads)
+
+        @guvectorize(['void(int64[:])'],
+                     '(n)',
+                     nopython=True,
+                     target='parallel')
+        def set_get_n(n):
+            set_num_threads(n[0])
+            n[:] = get_num_threads()
+
+        x = np.zeros((5000000,), dtype=np.int64)
+        x[0] = 2
+        set_get_n(x)
+        np.testing.assert_equal(x, 2)
+        x = np.zeros((5000000,), dtype=np.int64)
+        x[0] = max_threads
+        set_get_n(x)
+        np.testing.assert_equal(x, max_threads)
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_set_num_threads_outside_jit(self):
+
+        # Test set_num_threads outside a jitted function
+        set_num_threads(2)
+
+        @njit(parallel=True)
+        def test_func():
+            x = 5
+            buf = np.empty((x,))
+            for i in prange(x):
+                buf[i] = get_num_threads()
+            return buf
+
+        @guvectorize(['void(int64[:])'],
+                     '(n)',
+                     nopython=True,
+                     target='parallel')
+        def test_gufunc(x):
+            x[:] = get_num_threads()
+
+        out = test_func()
+        np.testing.assert_equal(out, 2)
+
+        x = np.zeros((5000000,), dtype=np.int64)
+        test_gufunc(x)
+        np.testing.assert_equal(x, 2)
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_set_num_threads_inside_jit(self):
+        # Test set_num_threads inside a jitted function
+        @njit(parallel=True)
+        def test_func(nthreads):
+            x = 5
+            buf = np.empty((x,))
+            set_num_threads(nthreads)
+            for i in prange(x):
+                buf[i] = get_num_threads()
+            return buf
+
+        mask = 2
+        out = test_func(mask)
+        np.testing.assert_equal(out, mask)
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_set_num_threads_inside_guvectorize(self):
+        # Test set_num_threads inside a jitted guvectorize function
+        @guvectorize(['void(int64[:])'],
+                     '(n)',
+                     nopython=True,
+                     target='parallel')
+        def test_func(x):
+            set_num_threads(x[0])
+            x[:] = get_num_threads()
+
+        x = np.zeros((5000000,), dtype=np.int64)
+        mask = 2
+        x[0] = mask
+        test_func(x)
+        np.testing.assert_equal(x, mask)
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_get_num_threads_truth_outside_jit(self):
+
+        for mask in range(2, min(6, config.NUMBA_NUM_THREADS + 1)):
+            set_num_threads(mask)
+
+            # a lot of work, hopefully will trigger "mask" count of threads to
+            # join the parallel region (for those backends with dynamic threads)
+            @njit(parallel=True)
+            def test_func():
+                x = 5000000
+                buf = np.empty((x,))
+                for i in prange(x):
+                    buf[i] = get_thread_id()
+                return len(np.unique(buf)), get_num_threads()
+
+            out = test_func()
+            self.check_mask((mask, mask), out)
+
+            @guvectorize(['void(int64[:], int64[:])'],
+                         '(n), (m)',
+                         nopython=True,
+                         target='parallel')
+            def test_gufunc(x, out):
+                x[:] = get_thread_id()
+                out[0] = get_num_threads()
+
+            # Reshape to force parallelism
+            x = np.full((5000000,), -1, dtype=np.int64).reshape((100, 50000))
+            out = np.zeros((1,), dtype=np.int64)
+            test_gufunc(x, out)
+            self.check_mask(mask, out)
+            self.check_mask(mask, len(np.unique(x)))
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_get_num_threads_truth_inside_jit(self):
+
+        for mask in range(2, min(6, config.NUMBA_NUM_THREADS + 1)):
+
+            # a lot of work, hopefully will trigger "mask" count of threads to
+            # join the parallel region (for those backends with dynamic threads)
+            @njit(parallel=True)
+            def test_func():
+                set_num_threads(mask)
+                x = 5000000
+                buf = np.empty((x,))
+                for i in prange(x):
+                    buf[i] = get_thread_id()
+                return len(np.unique(buf)), get_num_threads()
+
+            out = test_func()
+            self.check_mask((mask, mask), out)
+
+            @guvectorize(['void(int64[:], int64[:])'],
+                         '(n), (m)',
+                         nopython=True,
+                         target='parallel')
+            def test_gufunc(x, out):
+                set_num_threads(mask)
+                x[:] = get_thread_id()
+                out[0] = get_num_threads()
+
+            # Reshape to force parallelism
+            x = np.full((5000000,), -1, dtype=np.int64).reshape((100, 50000))
+            out = np.zeros((1,), dtype=np.int64)
+            test_gufunc(x, out)
+            self.check_mask(mask, out)
+            self.check_mask(mask, len(np.unique(x)))
+
+    # this test can only run on OpenMP (providing OMP_MAX_ACTIVE_LEVELS is not
+    # set or >= 2) and TBB backends
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_nested_parallelism_1(self):
+        if threading_layer() == 'workqueue':
+            self.skipTest("workqueue is not threadsafe")
+
+        # check that get_num_threads is ok in nesting
+        mask = config.NUMBA_NUM_THREADS - 1
+
+        N = config.NUMBA_NUM_THREADS
+        M = 2 * config.NUMBA_NUM_THREADS
+
+        @njit(parallel=True)
+        def child_func(buf, fid):
+            M, N = buf.shape
+            for i in prange(N):
+                buf[fid, i] = get_num_threads()
+
+        def get_test(test_type):
+            if test_type == 'njit':
+                def test_func(nthreads, py_func=False):
+                    @njit(parallel=True)
+                    def _test_func(nthreads):
+                        acc = 0
+                        buf = np.zeros((M, N))
+                        set_num_threads(nthreads)
+                        for i in prange(M):
+                            local_mask = 1 + i % mask
+                            # set threads in parent function
+                            set_num_threads(local_mask)
+                            if local_mask < N:
+                                child_func(buf, local_mask)
+                            acc += get_num_threads()
+                        return acc, buf
+                    if py_func:
+                        return _test_func.py_func(nthreads)
+                    else:
+                        return _test_func(nthreads)
+
+            elif test_type == 'guvectorize':
+                def test_func(nthreads, py_func=False):
+                    def _test_func(acc, buf, local_mask):
+                        set_num_threads(nthreads)
+                        # set threads in parent function
+                        set_num_threads(local_mask[0])
+                        if local_mask[0] < N:
+                            child_func(buf, local_mask[0])
+                        acc[0] += get_num_threads()
+
+                    buf = np.zeros((M, N), dtype=np.int64)
+                    acc = np.zeros((M, 1), dtype=np.int64)
+                    local_mask = (1 + np.arange(M) % mask).reshape((M, 1))
+                    sig = ['void(int64[:], int64[:, :], int64[:])']
+                    layout = '(p), (n, m), (p)'
+                    if not py_func:
+                        _test_func = guvectorize(sig, layout, nopython=True,
+                                                 target='parallel')(_test_func)
+                    else:
+                        _test_func = guvectorize(sig, layout,
+                                                 forceobj=True)(_test_func)
+                    _test_func(acc, buf, local_mask)
+                    return acc, buf
+
+            return test_func
+
+        for test_type in ['njit', 'guvectorize']:
+            test_func = get_test(test_type)
+            got_acc, got_arr = test_func(mask)
+            exp_acc, exp_arr = test_func(mask, py_func=True)
+            np.testing.assert_equal(exp_acc, got_acc)
+            np.testing.assert_equal(exp_arr, got_arr)
+
+            # check the maths reconciles, guvectorize does not reduce, njit does
+            math_acc_exp = 1 + np.arange(M) % mask
+            if test_type == 'guvectorize':
+                math_acc = math_acc_exp.reshape((M, 1))
+            else:
+                math_acc = np.sum(math_acc_exp)
+
+            np.testing.assert_equal(math_acc, got_acc)
+
+            math_arr = np.zeros((M, N))
+            for i in range(1, N):
+                # there's branches on 1, ..., num_threads - 1
+                math_arr[i, :] = i
+            np.testing.assert_equal(math_arr, got_arr)
+
+    # this test can only run on OpenMP (providing OMP_MAX_ACTIVE_LEVELS is not
+    # set or >= 2) and TBB backends
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    def _test_nested_parallelism_2(self):
+        if threading_layer() == 'workqueue':
+            self.skipTest("workqueue is not threadsafe")
+
+        # check that get_num_threads is ok in nesting
+
+        N = config.NUMBA_NUM_THREADS + 1
+        M = 4 * config.NUMBA_NUM_THREADS + 1
+
+        def get_impl(child_type, test_type):
+
+            if child_type == 'parallel':
+                child_dec = njit(parallel=True)
+            elif child_type == 'njit':
+                child_dec = njit(parallel=False)
+            elif child_type == 'none':
+                def child_dec(x):
+                    return x
+
+            @child_dec
+            def child(buf, fid):
+                M, N = buf.shape
+                set_num_threads(fid)  # set threads in child function
+                for i in prange(N):
+                    buf[fid, i] = get_num_threads()
+
+            if test_type in ['parallel', 'njit', 'none']:
+                if test_type == 'parallel':
+                    test_dec = njit(parallel=True)
+                elif test_type == 'njit':
+                    test_dec = njit(parallel=False)
+                elif test_type == 'none':
+                    def test_dec(x):
+                        return x
+
+                @test_dec
+                def test_func(nthreads):
+                    buf = np.zeros((M, N))
+                    set_num_threads(nthreads)
+                    for i in prange(M):
+                        local_mask = 1 + i % mask
+                        # when the threads exit the child functions they should
+                        # have a TLS slot value of the local mask as it was set
+                        # in child
+                        if local_mask < config.NUMBA_NUM_THREADS:
+                            child(buf, local_mask)
+                            assert get_num_threads() == local_mask
+                    return buf
+            else:
+                if test_type == 'guvectorize':
+                    test_dec = guvectorize(['int64[:,:], int64[:]'],
+                                           '(n, m), (k)', nopython=True,
+                                           target='parallel')
+                elif test_type == 'guvectorize-obj':
+                    test_dec = guvectorize(['int64[:,:], int64[:]'],
+                                           '(n, m), (k)', forceobj=True)
+
+                def test_func(nthreads):
+                    @test_dec
+                    def _test_func(buf, local_mask):
+                        set_num_threads(nthreads)
+                        # when the threads exit the child functions they should
+                        # have a TLS slot value of the local mask as it was set
+                        # in child
+                        if local_mask[0] < config.NUMBA_NUM_THREADS:
+                            child(buf, local_mask[0])
+                            assert get_num_threads() == local_mask[0]
+
+                    buf = np.zeros((M, N), dtype=np.int64)
+                    local_mask = (1 + np.arange(M) % mask).reshape((M, 1))
+                    _test_func(buf, local_mask)
+                    return buf
+
+            return test_func
+
+        mask = config.NUMBA_NUM_THREADS - 1
+
+        res_arrays = {}
+        for test_type in ['parallel', 'njit', 'none',
+                          'guvectorize', 'guvectorize-obj']:
+            for child_type in ['parallel', 'njit', 'none']:
+                if child_type == 'none' and test_type != 'none':
+                    continue
+                set_num_threads(mask)
+                res_arrays[test_type, child_type] = get_impl(
+                    child_type, test_type)(mask)
+
+        py_arr = res_arrays['none', 'none']
+        for arr in res_arrays.values():
+            np.testing.assert_equal(arr, py_arr)
+
+        # check the maths reconciles
+        math_arr = np.zeros((M, N))
+        # there's branches on modulo mask but only NUMBA_NUM_THREADS funcs
+        for i in range(1, config.NUMBA_NUM_THREADS):
+            math_arr[i, :] = i
+
+        np.testing.assert_equal(math_arr, py_arr)
+
+    # this test can only run on OpenMP (providing OMP_MAX_ACTIVE_LEVELS is not
+    # set or >= 2) and TBB backends
+    # This test needs at least 3 threads to run, N>=2 for the launch, M>=N+1 for
+    # the nested function
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 3, "Not enough CPU cores")
+    def _test_nested_parallelism_3(self):
+        if threading_layer() == 'workqueue':
+            self.skipTest("workqueue is not threadsafe")
+
+        # check that the right number of threads are present in nesting
+        # this relies on there being a load of cores present
+        BIG = 1000000
+
+        @njit(parallel=True)
+        def work(local_nt):  # arg is value 3
+            tid = np.zeros(BIG)
+            acc = 0
+            set_num_threads(local_nt)  # set to 3 threads
+            for i in prange(BIG):
+                acc += 1
+                tid[i] = get_thread_id()
+            return acc, np.unique(tid)
+
+        @njit(parallel=True)
+        def test_func_jit(nthreads):
+            set_num_threads(nthreads) # set to 2 threads
+            lens = np.zeros(nthreads)
+            total = 0
+            for i in prange(nthreads):
+                my_acc, tids = work(nthreads + 1)  # call with value 3
+                lens[i] = len(tids)
+                total += my_acc
+            return total, np.unique(lens)
+
+        NT = 2
+        expected_acc = BIG * NT
+        expected_thread_count = NT + 1
+
+        got_acc, got_tc = test_func_jit(NT)
+        self.assertEqual(expected_acc, got_acc)
+        self.check_mask(expected_thread_count, got_tc)
+
+        def test_guvectorize(nthreads):
+            @guvectorize(['int64[:], int64[:]'],
+                         '(n), (n)',
+                         nopython=True,
+                         target='parallel')
+            def test_func_guvectorize(total, lens):
+                my_acc, tids = work(nthreads + 1)
+                lens[0] = len(tids)
+                total[0] += my_acc
+
+            total = np.zeros((nthreads, 1), dtype=np.int64)
+            lens = np.zeros(nthreads, dtype=np.int64).reshape((nthreads, 1))
+
+            test_func_guvectorize(total, lens)
+            # vectorize does not reduce, so total is summed
+            return total.sum(), np.unique(lens)
+
+        got_acc, got_tc = test_guvectorize(NT)
+
+        self.assertEqual(expected_acc, got_acc)
+        self.check_mask(expected_thread_count, got_tc)
+
+    @skip_parfors_unsupported
+    @unittest.skipIf(config.NUMBA_NUM_THREADS < 2, "Not enough CPU cores")
+    @unittest.skipIf(not sys.platform.startswith('linux'), "Linux only")
+    def _test_threadmask_across_fork(self):
+        forkctx = multiprocessing.get_context('fork')
+
+        @njit
+        def foo():
+            return get_num_threads()
+
+        def wrap(queue):
+            queue.put(foo())
+
+        mask = 1
+        self.assertEqual(foo(), config.NUMBA_NUM_THREADS)
+        set_num_threads(mask)
+        self.assertEqual(foo(), mask)
+        shared_queue = forkctx.Queue()
+        # check TLS slot inheritance in fork
+        p = forkctx.Process(target=wrap, args=(shared_queue,))
+        p.start()
+        p.join()
+        self.assertEqual(shared_queue.get(), mask)
+
+    def tearDown(self):
+        set_num_threads(config.NUMBA_NUM_THREADS)
+
+    @skip_parfors_unsupported
+    def _test_get_thread_id_not_parallel(self):
+        python_get_thread_id = get_thread_id()
+        check_array_size = 8
+
+        @njit(parallel=False)
+        def par_false(size):
+            njit_par_false_tid = get_thread_id()
+            res = np.ones(size)
+            for i in prange(size):
+                res[i] = get_thread_id()
+            return njit_par_false_tid, res
+
+        @njit(parallel=True)
+        def par_true(size):
+            njit_par_true_tid = get_thread_id()
+            res = np.ones(size)
+            for i in range(size):
+                res[i] = get_thread_id()
+            return njit_par_true_tid, res
+
+        self.assertEqual(python_get_thread_id, 0)
+        njit_par_false_tid, njit_par_false_arr = par_false(check_array_size)
+        self.assertEqual(njit_par_false_tid, 0)
+        np.testing.assert_equal(njit_par_false_arr, 0)
+        njit_par_true_tid, njit_par_true_arr = par_true(check_array_size)
+        self.assertEqual(njit_par_true_tid, 0)
+        np.testing.assert_equal(njit_par_true_arr, 0)
+
+
+class TestNumThreadsBackends(TestInSubprocess, TestCase):
+    _class = TestNumThreads
+    _DEBUG = False
+
+    # 1 is mainly here to ensure tests skip correctly
+    num_threads = [i for i in [1, 2, 4, 8, 16] if i <= config.NUMBA_NUM_THREADS]
+
+    def run_test_in_separate_process(self, test, threading_layer, num_threads):
+        env_copy = os.environ.copy()
+        env_copy['NUMBA_THREADING_LAYER'] = str(threading_layer)
+        env_copy['NUMBA_NUM_THREADS'] = str(num_threads)
+        cmdline = [sys.executable, "-m", "numba.runtests", "-v", test]
+        return self.run_cmd(cmdline, env_copy)
+
+    @classmethod
+    def _inject(cls, name, backend, backend_guard, num_threads):
+        themod = cls.__module__
+        thecls = cls._class.__name__
+        injected_method = '%s.%s.%s' % (themod, thecls, name)
+
+        def test_template(self):
+            o, e = self.run_test_in_separate_process(injected_method, backend,
+                                                     num_threads)
+            if self._DEBUG:
+                print('stdout:\n "%s"\n stderr:\n "%s"' % (o, e))
+            self.assertIn('OK', e)
+            self.assertTrue('FAIL' not in e)
+            self.assertTrue('ERROR' not in e)
+            m = re.search(r"\.\.\. skipped '(.*?)'", e)
+            if m:
+                self.skipTest(m.group(1))
+
+        injected_test = "%s_%s_%s_threads" % (name[1:], backend, num_threads)
+        setattr(cls, injected_test,
+                tag('long_running')(backend_guard(test_template)))
+
+    @classmethod
+    def generate(cls):
+        for name in cls._class.__dict__.copy():
+            for backend, backend_guard in cls.backends.items():
+                for num_threads in cls.num_threads:
+                    if not name.startswith('_test_'):
+                        continue
+                    cls._inject(name, backend, backend_guard, num_threads)
+
+
+TestNumThreadsBackends.generate()
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numberctor.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numberctor.py
new file mode 100644
index 000000000..fdc83f21e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numberctor.py
@@ -0,0 +1,250 @@
+import numpy as np
+
+from numba.core.compiler import compile_isolated
+from numba import jit
+from numba.core import types
+
+from numba.tests.support import TestCase, tag
+import unittest
+
+
+def dobool(a):
+    return bool(a)
+
+
+def doint(a):
+    return int(a)
+
+
+def dofloat(a):
+    return float(a)
+
+
+def docomplex(a):
+    return complex(a)
+
+
+def docomplex2(a, b):
+    return complex(a, b)
+
+
+def complex_calc(a):
+    z = complex(a)
+    return z.real ** 2 + z.imag ** 2
+
+
+def complex_calc2(a, b):
+    z = complex(a, b)
+    return z.real ** 2 + z.imag ** 2
+
+
+def converter(tp):
+    def f(a):
+        return tp(a)
+    return f
+
+
+def real_np_types():
+    for tp_name in ('int8', 'int16', 'int32', 'int64',
+                    'uint8', 'uint16', 'uint32', 'uint64',
+                    'intc', 'uintc', 'intp', 'uintp',
+                    'float32', 'float64', 'bool_'):
+        yield tp_name
+
+def complex_np_types():
+    for tp_name in ('complex64', 'complex128'):
+        yield tp_name
+
+
+class TestScalarNumberCtor(TestCase):
+    """
+    Test <number class>(some scalar)
+    """
+
+    def check_int_constructor(self, pyfunc):
+        x_types = [
+            types.boolean, types.int32, types.int64, types.float32, types.float64
+        ]
+        x_values = [1, 0, 1000, 12.2, 23.4]
+
+        for ty, x in zip(x_types, x_values):
+            cres = compile_isolated(pyfunc, [ty])
+            cfunc = cres.entry_point
+            self.assertPreciseEqual(pyfunc(x), cfunc(x))
+
+    def test_bool(self):
+        self.check_int_constructor(dobool)
+
+    def test_int(self):
+        self.check_int_constructor(doint)
+
+    def test_float(self):
+        pyfunc = dofloat
+
+        x_types = [
+            types.int32, types.int64, types.float32, types.float64
+        ]
+        x_values = [1, 1000, 12.2, 23.4]
+
+        for ty, x in zip(x_types, x_values):
+            cres = compile_isolated(pyfunc, [ty])
+            cfunc = cres.entry_point
+            self.assertPreciseEqual(pyfunc(x), cfunc(x),
+                prec='single' if ty is types.float32 else 'exact')
+
+    def test_complex(self):
+        pyfunc = docomplex
+
+        x_types = [
+            types.int32, types.int64, types.float32, types.float64,
+            types.complex64, types.complex128,
+        ]
+        x_values = [1, 1000, 12.2, 23.4, 1.5-5j, 1-4.75j]
+
+        for ty, x in zip(x_types, x_values):
+            cres = compile_isolated(pyfunc, [ty])
+            cfunc = cres.entry_point
+            got = cfunc(x)
+            expected = pyfunc(x)
+            self.assertPreciseEqual(pyfunc(x), cfunc(x),
+                prec='single' if ty is types.float32 else 'exact')
+
+        # Check that complex(float32) really creates a complex64,
+        # by checking the accuracy of computations.
+        pyfunc = complex_calc
+        x = 1.0 + 2**-50
+        cres = compile_isolated(pyfunc, [types.float32])
+        cfunc = cres.entry_point
+        self.assertPreciseEqual(cfunc(x), 1.0)
+        # Control (complex128)
+        cres = compile_isolated(pyfunc, [types.float64])
+        cfunc = cres.entry_point
+        self.assertGreater(cfunc(x), 1.0)
+
+    def test_complex2(self):
+        pyfunc = docomplex2
+
+        x_types = [
+            types.int32, types.int64, types.float32, types.float64
+        ]
+        x_values = [1, 1000, 12.2, 23.4]
+        y_values = [x - 3 for x in x_values]
+
+        for ty, x, y in zip(x_types, x_values, y_values):
+            cres = compile_isolated(pyfunc, [ty, ty])
+            cfunc = cres.entry_point
+            self.assertPreciseEqual(pyfunc(x, y), cfunc(x, y),
+                prec='single' if ty is types.float32 else 'exact')
+
+        # Check that complex(float32, float32) really creates a complex64,
+        # by checking the accuracy of computations.
+        pyfunc = complex_calc2
+        x = 1.0 + 2**-50
+        cres = compile_isolated(pyfunc, [types.float32, types.float32])
+        cfunc = cres.entry_point
+        self.assertPreciseEqual(cfunc(x, x), 2.0)
+        # Control (complex128)
+        cres = compile_isolated(pyfunc, [types.float64, types.float32])
+        cfunc = cres.entry_point
+        self.assertGreater(cfunc(x, x), 2.0)
+
+    def check_type_converter(self, tp, np_type, values):
+        pyfunc = converter(tp)
+        cfunc = jit(nopython=True)(pyfunc)
+        if issubclass(np_type, np.integer):
+            # Converting from a Python int to a small Numpy int on 32-bit
+            # builds can raise "OverflowError: Python int too large to
+            # convert to C long".  Work around by going through a large
+            # Numpy int first.
+            np_converter = lambda x: np_type(np.int64(x))
+        else:
+            np_converter = np_type
+        dtype = np.dtype(np_type)
+        for val in values:
+            if dtype.kind == 'u' and isinstance(val, float) and val < 0.0:
+                # Converting negative float to unsigned int yields undefined
+                # behaviour (and concretely different on ARM vs. x86)
+                continue
+            expected = np_converter(val)
+            got = cfunc(val)
+            self.assertPreciseEqual(got, expected,
+                                    msg="for type %s with arg %s" % (np_type, val))
+
+    def check_number_types(self, tp_factory):
+        values = [0, 1, -1, 100003, 10000000000007, -100003, -10000000000007,
+                  1.5, -3.5]
+        for tp_name in real_np_types():
+            np_type = getattr(np, tp_name)
+            tp = tp_factory(tp_name)
+            self.check_type_converter(tp, np_type, values)
+        values.append(1.5+3j)
+        for tp_name in complex_np_types():
+            np_type = getattr(np, tp_name)
+            tp = tp_factory(tp_name)
+            self.check_type_converter(tp, np_type, values)
+
+    def test_numba_types(self):
+        """
+        Test explicit casting to Numba number types.
+        """
+        def tp_factory(tp_name):
+            return getattr(types, tp_name)
+        self.check_number_types(tp_factory)
+
+    def test_numpy_types(self):
+        """
+        Test explicit casting to Numpy number types.
+        """
+        def tp_factory(tp_name):
+            return getattr(np, tp_name)
+        self.check_number_types(tp_factory)
+
+
+class TestArrayNumberCtor(TestCase):
+    """
+    Test <number class>(some sequence)
+    """
+
+    def check_type_constructor(self, np_type, values):
+        pyfunc = converter(np_type)
+        cfunc = jit(nopython=True)(pyfunc)
+        for val in values:
+            expected = np_type(val)
+            got = cfunc(val)
+            self.assertPreciseEqual(got, expected)
+
+    def test_1d(self):
+        values = [
+            (1.0, 2.5),
+            (1, 2.5),
+            [1.0, 2.5],
+            (),
+            ]
+        for tp_name in real_np_types():
+            np_type = getattr(np, tp_name)
+            self.check_type_constructor(np_type, values)
+        values = [
+            (1j, 2.5),
+            [1.0, 2.5],
+            ]
+        for tp_name in complex_np_types():
+            np_type = getattr(np, tp_name)
+            self.check_type_constructor(np_type, values)
+
+    def test_2d(self):
+        values = [
+            ((1.0, 2.5), (3.5, 4)),
+            [(1.0, 2.5), (3.5, 4.0)],
+            ([1.0, 2.5], [3.5, 4.0]),
+            [(), ()],
+            ]
+        for tp_name in real_np_types():
+            np_type = getattr(np, tp_name)
+            self.check_type_constructor(np_type, values)
+        for tp_name in complex_np_types():
+            np_type = getattr(np, tp_name)
+            self.check_type_constructor(np_type, values)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numbers.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numbers.py
new file mode 100644
index 000000000..1f7c6d4c6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numbers.py
@@ -0,0 +1,90 @@
+# (Some) Tests for targets/numbers.py
+
+import numpy as np
+
+from numba import njit
+from numba.core import types
+from numba.core.errors import TypingError
+from numba.tests.support import TestCase
+
+
+def gen_view(a,b):
+    def impl(x):
+        return a(x).view(b)
+    return impl
+
+
+class TestViewIntFloat(TestCase):
+    """ This tests the 'view' method on NumPy scalars. """
+
+    def do_testing(self, inputs, dtypes):
+        for value, initial_type, expected in inputs:
+            for target_type, result in zip(dtypes, expected):
+                view = njit(gen_view(initial_type, target_type))
+                if not np.isnan(result):
+                    # check against predefined value
+                    self.assertEqual(view(value), target_type(result))
+                    # check against numpy
+                    self.assertEqual(view(value),
+                                     view.py_func(value))
+                else:
+                    # check that our implementation results in nan
+                    self.assertTrue(np.isnan(view(value)))
+                    # check that numpy results in nan
+                    self.assertTrue(np.isnan(view.py_func(value)))
+
+    def test_8_bits(self):
+        dtypes = (np.uint8, np.int8)
+        #        Value  Initial Type   Expected answers using dtypes
+        inputs = ((1,   np.uint8,    (1, 1)),
+                  (-1,  np.int8,     (255, -1)))
+        self.do_testing(inputs, dtypes)
+
+    def test_32_bits(self):
+        dtypes = (np.uint32, np.int32, np.float32)
+        #        Value  Initial Type   Expected answers using dtypes
+        inputs = ((1,   np.uint32,    (1, 1, 1.401298464324817e-45)),
+                  (-1,  np.int32,     (4294967295, -1, np.nan)),
+                  (1.0, np.float32,   (1065353216, 1065353216, 1.0)))
+        self.do_testing(inputs, dtypes)
+
+    def test_64_bits(self):
+        dtypes = (np.uint64, np.int64, np.float64)
+        #        Value  Initial Type   Expected answers using dtypes
+        inputs = ((1,   np.uint64,    (1, 1, 5e-324)),
+                  (-1,  np.int64,     (18446744073709551615, -1, np.nan)),
+                  (1.0, np.float64,   (4607182418800017408,
+                                       4607182418800017408,
+                                       1.0))
+                  )
+        self.do_testing(inputs, dtypes)
+
+    def test_python_scalar_exception(self):
+        intty = getattr(np, 'int{}'.format(types.intp.bitwidth))
+
+        @njit
+        def myview():
+            a = 1
+            a.view(intty)
+
+        with self.assertRaises(TypingError) as e:
+            myview()
+        self.assertIn("'view' can only be called on NumPy dtypes, "
+                      "try wrapping the variable 'a' with 'np.<dtype>()'",
+                      str(e.exception))
+
+    def do_testing_exceptions(self, pair):
+        with self.assertRaises(TypingError) as e:
+            view = njit(gen_view(pair[0], pair[1]))
+            view(1)
+        self.assertIn("Changing the dtype of a 0d array is only supported "
+                      "if the itemsize is unchanged",
+                      str(e.exception))
+
+    def test_exceptions32(self):
+        for pair in ((np.int32, np.int8), (np.int8, np.int32)):
+            self.do_testing_exceptions(pair)
+
+    def test_exceptions64(self):
+        for pair in ((np.int32, np.int64), (np.int64, np.int32)):
+            self.do_testing_exceptions(pair)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numconv.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numconv.py
new file mode 100644
index 000000000..ac0217ed8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numconv.py
@@ -0,0 +1,38 @@
+import itertools
+import unittest
+from numba.core.compiler import compile_isolated
+from numba.core import types
+
+
+def template(fromty, toty):
+    def closure(self):
+        def cast(x):
+            y = x
+            return y
+
+        cres = compile_isolated(cast, args=[fromty], return_type=toty)
+        self.assertAlmostEqual(cres.entry_point(1), 1)
+
+    return closure
+
+
+class TestNumberConversion(unittest.TestCase):
+    """
+    Test all int/float numeric conversion to ensure we have all the external
+    dependencies to perform these conversions.
+    """
+    # NOTE: more implicit tests are in test_numberctor
+
+    @classmethod
+    def automatic_populate(cls):
+        tys = types.integer_domain | types.real_domain
+        for fromty, toty in itertools.permutations(tys, r=2):
+            test_name = "test_{fromty}_to_{toty}".format(fromty=fromty,
+                                                         toty=toty)
+            setattr(cls, test_name, template(fromty, toty))
+
+
+TestNumberConversion.automatic_populate()
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numpy_support.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numpy_support.py
new file mode 100644
index 000000000..297827db6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numpy_support.py
@@ -0,0 +1,437 @@
+"""
+Test helper functions from numba.numpy_support.
+"""
+
+
+import sys
+from itertools import product
+
+import numpy as np
+
+import unittest
+from numba.core import types
+from numba.core.errors import NumbaNotImplementedError
+from numba.tests.support import TestCase
+from numba.tests.enum_usecases import Shake, RequestError
+from numba.np import numpy_support
+
+
+class TestFromDtype(TestCase):
+
+    def test_number_types(self):
+        """
+        Test from_dtype() and as_dtype() with the various scalar number types.
+        """
+        f = numpy_support.from_dtype
+
+        def check(typechar, numba_type):
+            # Only native ordering and alignment is supported
+            dtype = np.dtype(typechar)
+            self.assertIs(f(dtype), numba_type)
+            self.assertIs(f(np.dtype('=' + typechar)), numba_type)
+            self.assertEqual(dtype, numpy_support.as_dtype(numba_type))
+
+        check('?', types.bool_)
+        check('f', types.float32)
+        check('f4', types.float32)
+        check('d', types.float64)
+        check('f8', types.float64)
+
+        check('F', types.complex64)
+        check('c8', types.complex64)
+        check('D', types.complex128)
+        check('c16', types.complex128)
+
+        check('O', types.pyobject)
+
+        check('b', types.int8)
+        check('i1', types.int8)
+        check('B', types.uint8)
+        check('u1', types.uint8)
+
+        check('h', types.int16)
+        check('i2', types.int16)
+        check('H', types.uint16)
+        check('u2', types.uint16)
+
+        check('i', types.int32)
+        check('i4', types.int32)
+        check('I', types.uint32)
+        check('u4', types.uint32)
+
+        check('q', types.int64)
+        check('Q', types.uint64)
+        for name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
+                     'int64', 'uint64', 'intp', 'uintp'):
+            self.assertIs(f(np.dtype(name)), getattr(types, name))
+
+        # Non-native alignments are unsupported (except for 1-byte types)
+        foreign_align = '>' if sys.byteorder == 'little' else '<'
+        for letter in 'hHiIlLqQfdFD':
+            self.assertRaises(NumbaNotImplementedError, f,
+                              np.dtype(foreign_align + letter))
+
+    def test_string_types(self):
+        """
+        Test from_dtype() and as_dtype() with the character string types.
+        """
+        def check(typestring, numba_type):
+            # Only native ordering and alignment is supported
+            dtype = np.dtype(typestring)
+            self.assertEqual(numpy_support.from_dtype(dtype), numba_type)
+            self.assertEqual(dtype, numpy_support.as_dtype(numba_type))
+
+        check('S10', types.CharSeq(10))
+        check('a11', types.CharSeq(11))
+        check('U12', types.UnicodeCharSeq(12))
+
+    def check_datetime_types(self, letter, nb_class):
+        def check(dtype, numba_type, code):
+            tp = numpy_support.from_dtype(dtype)
+            self.assertEqual(tp, numba_type)
+            self.assertEqual(tp.unit_code, code)
+            self.assertEqual(numpy_support.as_dtype(numba_type), dtype)
+            self.assertEqual(numpy_support.as_dtype(tp), dtype)
+
+        # Unit-less ("generic") type
+        check(np.dtype(letter), nb_class(''), 14)
+
+    def test_datetime_types(self):
+        """
+        Test from_dtype() and as_dtype() with the datetime types.
+        """
+        self.check_datetime_types('M', types.NPDatetime)
+
+    def test_timedelta_types(self):
+        """
+        Test from_dtype() and as_dtype() with the timedelta types.
+        """
+        self.check_datetime_types('m', types.NPTimedelta)
+
+    def test_struct_types(self):
+        def check(dtype, fields, size, aligned):
+            tp = numpy_support.from_dtype(dtype)
+            self.assertIsInstance(tp, types.Record)
+            # Only check for dtype equality, as the Numba type may be interned
+            self.assertEqual(tp.dtype, dtype)
+            self.assertEqual(tp.fields, fields)
+            self.assertEqual(tp.size, size)
+            self.assertEqual(tp.aligned, aligned)
+
+        dtype = np.dtype([('a', np.int16), ('b', np.int32)])
+        check(dtype,
+              fields={'a': (types.int16, 0, None, None),
+                      'b': (types.int32, 2, None, None)},
+              size=6, aligned=False)
+
+        dtype = np.dtype([('a', np.int16), ('b', np.int32)], align=True)
+        check(dtype,
+              fields={'a': (types.int16, 0, None, None),
+                      'b': (types.int32, 4, None, None)},
+              size=8, aligned=True)
+
+        dtype = np.dtype([('m', np.int32), ('n', 'S5')])
+        check(dtype,
+              fields={'m': (types.int32, 0, None, None),
+                      'n': (types.CharSeq(5), 4, None, None)},
+              size=9, aligned=False)
+
+    def test_enum_type(self):
+
+        def check(base_inst, enum_def, type_class):
+            np_dt = np.dtype(base_inst)
+            nb_ty = numpy_support.from_dtype(np_dt)
+            inst = type_class(enum_def, nb_ty)
+            recovered = numpy_support.as_dtype(inst)
+            self.assertEqual(np_dt, recovered)
+
+        dts = [np.float64, np.int32, np.complex128, np.bool_]
+        enums = [Shake, RequestError]
+
+        for dt, enum in product(dts, enums):
+            check(dt, enum, types.EnumMember)
+
+        for dt, enum in product(dts, enums):
+            check(dt, enum, types.IntEnumMember)
+
+
+class ValueTypingTestBase(object):
+    """
+    Common tests for the typing of values.  Also used by test_special.
+    """
+
+    def check_number_values(self, func):
+        """
+        Test *func*() with scalar numeric values.
+        """
+        f = func
+        # Standard Python types get inferred by numpy
+        self.assertIn(f(1), (types.int32, types.int64))
+        self.assertIn(f(2**31 - 1), (types.int32, types.int64))
+        self.assertIn(f(-2**31), (types.int32, types.int64))
+        self.assertIs(f(1.0), types.float64)
+        self.assertIs(f(1.0j), types.complex128)
+        self.assertIs(f(True), types.bool_)
+        self.assertIs(f(False), types.bool_)
+        # Numpy scalar types get converted by from_dtype()
+        for name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
+                     'int64', 'uint64', 'intc', 'uintc', 'intp', 'uintp',
+                     'float32', 'float64', 'complex64', 'complex128',
+                     'bool_'):
+            val = getattr(np, name)()
+            self.assertIs(f(val), getattr(types, name))
+
+    def _base_check_datetime_values(self, func, np_type, nb_type):
+        f = func
+        for unit in [
+            '', 'Y', 'M', 'D', 'h', 'm', 's',
+            'ms', 'us', 'ns', 'ps', 'fs', 'as',
+        ]:
+            if unit:
+                t = np_type(3, unit)
+            else:
+                # "generic" datetime / timedelta
+                t = np_type('Nat')
+            tp = f(t)
+            # This ensures the unit hasn't been lost
+            self.assertEqual(tp, nb_type(unit))
+
+    def check_datetime_values(self, func):
+        """
+        Test *func*() with np.datetime64 values.
+        """
+        self._base_check_datetime_values(func, np.datetime64, types.NPDatetime)
+
+    def check_timedelta_values(self, func):
+        """
+        Test *func*() with np.timedelta64 values.
+        """
+        self._base_check_datetime_values(func, np.timedelta64,
+                                         types.NPTimedelta)
+
+
+class TestArrayScalars(ValueTypingTestBase, TestCase):
+
+    def test_number_values(self):
+        """
+        Test map_arrayscalar_type() with scalar number values.
+        """
+        self.check_number_values(numpy_support.map_arrayscalar_type)
+
+    def test_datetime_values(self):
+        """
+        Test map_arrayscalar_type() with np.datetime64 values.
+        """
+        f = numpy_support.map_arrayscalar_type
+        self.check_datetime_values(f)
+        # datetime64s with a non-one factor shouldn't be supported
+        t = np.datetime64('2014', '10Y')
+        with self.assertRaises(NotImplementedError):
+            f(t)
+
+    def test_timedelta_values(self):
+        """
+        Test map_arrayscalar_type() with np.timedelta64 values.
+        """
+        f = numpy_support.map_arrayscalar_type
+        self.check_timedelta_values(f)
+        # timedelta64s with a non-one factor shouldn't be supported
+        t = np.timedelta64(10, '10Y')
+        with self.assertRaises(NotImplementedError):
+            f(t)
+
+
+class FakeUFunc(object):
+    __slots__ = ('nin', 'nout', 'types', 'ntypes')
+    __name__ = "fake ufunc"
+
+    def __init__(self, types):
+        self.types = types
+        in_, out = self.types[0].split('->')
+        self.nin = len(in_)
+        self.nout = len(out)
+        self.ntypes = len(types)
+        for tp in types:
+            in_, out = self.types[0].split('->')
+            assert len(in_) == self.nin
+            assert len(out) == self.nout
+
+
+# Typical types for np.add, np.multiply, np.isnan
+_add_types = ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I',
+              'll->l', 'LL->L', 'qq->q', 'QQ->Q', 'ee->e', 'ff->f', 'dd->d',
+              'gg->g', 'FF->F', 'DD->D', 'GG->G', 'Mm->M', 'mm->m', 'mM->M',
+              'OO->O']
+
+_mul_types = ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I',
+              'll->l', 'LL->L', 'qq->q', 'QQ->Q', 'ee->e', 'ff->f', 'dd->d',
+              'gg->g', 'FF->F', 'DD->D', 'GG->G', 'mq->m', 'qm->m', 'md->m',
+              'dm->m', 'OO->O']
+
+# Those ones only have floating-point loops
+_isnan_types = ['e->?', 'f->?', 'd->?', 'g->?', 'F->?', 'D->?', 'G->?']
+_sqrt_types = ['e->e', 'f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
+
+
+class TestUFuncs(TestCase):
+    """
+    Test ufunc helpers.
+    """
+
+    def test_ufunc_find_matching_loop(self):
+        f = numpy_support.ufunc_find_matching_loop
+        np_add = FakeUFunc(_add_types)
+        np_mul = FakeUFunc(_mul_types)
+        np_isnan = FakeUFunc(_isnan_types)
+        np_sqrt = FakeUFunc(_sqrt_types)
+
+        def check(ufunc, input_types, sigs, output_types=()):
+            """
+            Check that ufunc_find_matching_loop() finds one of the given
+            *sigs* for *ufunc*, *input_types* and optional *output_types*.
+            """
+            loop = f(ufunc, input_types + output_types)
+            self.assertTrue(loop)
+            if isinstance(sigs, str):
+                sigs = (sigs,)
+            self.assertIn(loop.ufunc_sig, sigs,
+                          "inputs=%s and outputs=%s should have selected "
+                          "one of %s, got %s"
+                          % (input_types, output_types, sigs, loop.ufunc_sig))
+            self.assertEqual(len(loop.numpy_inputs), len(loop.inputs))
+            self.assertEqual(len(loop.numpy_outputs), len(loop.outputs))
+            if not output_types:
+                # Add explicit outputs and check the result is the same
+                loop_explicit = f(ufunc, list(input_types) + loop.outputs)
+                self.assertEqual(loop_explicit, loop)
+            else:
+                self.assertEqual(loop.outputs, list(output_types))
+            # Round-tripping inputs and outputs
+            loop_rt = f(ufunc, loop.inputs + loop.outputs)
+            self.assertEqual(loop_rt, loop)
+            return loop
+
+        def check_exact(ufunc, input_types, sigs, output_types=()):
+            """
+            Like check(), but also ensure no casting of inputs occurred.
+            """
+            loop = check(ufunc, input_types, sigs, output_types)
+            self.assertEqual(loop.inputs, list(input_types))
+
+        def check_no_match(ufunc, input_types):
+            loop = f(ufunc, input_types)
+            self.assertIs(loop, None)
+
+        # Exact matching for number types
+        check_exact(np_add, (types.bool_, types.bool_), '??->?')
+        check_exact(np_add, (types.int8, types.int8), 'bb->b')
+        check_exact(np_add, (types.uint8, types.uint8), 'BB->B')
+        check_exact(np_add, (types.int64, types.int64), ('ll->l', 'qq->q'))
+        check_exact(np_add, (types.uint64, types.uint64), ('LL->L', 'QQ->Q'))
+        check_exact(np_add, (types.float32, types.float32), 'ff->f')
+        check_exact(np_add, (types.float64, types.float64), 'dd->d')
+        check_exact(np_add, (types.complex64, types.complex64), 'FF->F')
+        check_exact(np_add, (types.complex128, types.complex128), 'DD->D')
+
+        # Exact matching for datetime64 and timedelta64 types
+        check_exact(np_add, (types.NPTimedelta('s'), types.NPTimedelta('s')),
+                    'mm->m', output_types=(types.NPTimedelta('s'),))
+        check_exact(np_add, (types.NPTimedelta('ms'), types.NPDatetime('s')),
+                    'mM->M', output_types=(types.NPDatetime('ms'),))
+        check_exact(np_add, (types.NPDatetime('s'), types.NPTimedelta('s')),
+                    'Mm->M', output_types=(types.NPDatetime('s'),))
+        check_exact(np_add, (types.NPDatetime('s'), types.NPTimedelta('')),
+                    'Mm->M', output_types=(types.NPDatetime('s'),))
+        check_exact(np_add, (types.NPDatetime('ns'), types.NPTimedelta('')),
+                    'Mm->M', output_types=(types.NPDatetime('ns'),))
+        check_exact(np_add, (types.NPTimedelta(''), types.NPDatetime('s')),
+                    'mM->M', output_types=(types.NPDatetime('s'),))
+        check_exact(np_add, (types.NPTimedelta(''), types.NPDatetime('ns')),
+                    'mM->M', output_types=(types.NPDatetime('ns'),))
+        check_exact(np_mul, (types.NPTimedelta('s'), types.int64),
+                    'mq->m', output_types=(types.NPTimedelta('s'),))
+        check_exact(np_mul, (types.float64, types.NPTimedelta('s')),
+                    'dm->m', output_types=(types.NPTimedelta('s'),))
+
+        # Mix and match number types, with casting
+        check(np_add, (types.bool_, types.int8), 'bb->b')
+        check(np_add, (types.uint8, types.bool_), 'BB->B')
+        check(np_add, (types.int16, types.uint16), 'ii->i')
+        check(np_add, (types.complex64, types.float64), 'DD->D')
+        check(np_add, (types.float64, types.complex64), 'DD->D')
+        # Integers, when used together with floating-point numbers,
+        # should cast to any real or complex (see #2006)
+        int_types = [types.int32, types.uint32, types.int64, types.uint64]
+        for intty in int_types:
+            check(np_add, (types.float32, intty), 'ff->f')
+            check(np_add, (types.float64, intty), 'dd->d')
+            check(np_add, (types.complex64, intty), 'FF->F')
+            check(np_add, (types.complex128, intty), 'DD->D')
+        # However, when used alone, they should cast only to
+        # floating-point types of sufficient precision
+        # (typical use case: np.sqrt(2) should give an accurate enough value)
+        for intty in int_types:
+            check(np_sqrt, (intty,), 'd->d')
+            check(np_isnan, (intty,), 'd->?')
+
+        # With some timedelta64 arguments as well
+        check(np_mul, (types.NPTimedelta('s'), types.int32),
+              'mq->m', output_types=(types.NPTimedelta('s'),))
+        check(np_mul, (types.NPTimedelta('s'), types.uint32),
+              'mq->m', output_types=(types.NPTimedelta('s'),))
+        check(np_mul, (types.NPTimedelta('s'), types.float32),
+              'md->m', output_types=(types.NPTimedelta('s'),))
+        check(np_mul, (types.float32, types.NPTimedelta('s')),
+              'dm->m', output_types=(types.NPTimedelta('s'),))
+
+        # No match
+        check_no_match(np_add, (types.NPDatetime('s'), types.NPDatetime('s')))
+        # No implicit casting from int64 to timedelta64 (Numpy would allow
+        # this).
+        check_no_match(np_add, (types.NPTimedelta('s'), types.int64))
+
+    def test_layout_checker(self):
+        def check_arr(arr):
+            dims = arr.shape
+            strides = arr.strides
+            itemsize = arr.dtype.itemsize
+            is_c = numpy_support.is_contiguous(dims, strides, itemsize)
+            is_f = numpy_support.is_fortran(dims, strides, itemsize)
+            expect_c = arr.flags['C_CONTIGUOUS']
+            expect_f = arr.flags['F_CONTIGUOUS']
+            self.assertEqual(is_c, expect_c)
+            self.assertEqual(is_f, expect_f)
+
+        arr = np.arange(24)
+        # 1D
+        check_arr(arr)
+        # 2D
+        check_arr(arr.reshape((3, 8)))
+        check_arr(arr.reshape((3, 8)).T)
+        check_arr(arr.reshape((3, 8))[::2])
+        # 3D
+        check_arr(arr.reshape((2, 3, 4)))
+        check_arr(arr.reshape((2, 3, 4)).T)
+        # middle axis is shape 1
+        check_arr(arr.reshape((2, 3, 4))[:, ::3])
+        check_arr(arr.reshape((2, 3, 4)).T[:, ::3])
+
+        # leading axis is shape 1
+        check_arr(arr.reshape((2, 3, 4))[::2])
+        check_arr(arr.reshape((2, 3, 4)).T[:, :, ::2])
+        # 2 leading axis are shape 1
+        check_arr(arr.reshape((2, 3, 4))[::2, ::3])
+        check_arr(arr.reshape((2, 3, 4)).T[:, ::3, ::2])
+        # single item slices for all axis
+        check_arr(arr.reshape((2, 3, 4))[::2, ::3, ::4])
+        check_arr(arr.reshape((2, 3, 4)).T[::4, ::3, ::2])
+        # 4D
+        check_arr(arr.reshape((2, 2, 3, 2))[::2, ::2, ::3])
+        check_arr(arr.reshape((2, 2, 3, 2)).T[:, ::3, ::2, ::2])
+        # outer zero dims
+        check_arr(arr.reshape((2, 2, 3, 2))[::5, ::2, ::3])
+        check_arr(arr.reshape((2, 2, 3, 2)).T[:, ::3, ::2, ::5])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numpyadapt.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numpyadapt.py
new file mode 100644
index 000000000..8df1f0288
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_numpyadapt.py
@@ -0,0 +1,43 @@
+from ctypes import *
+
+import numpy as np
+
+import unittest
+from numba import _helperlib
+
+
+class ArrayStruct3D(Structure):
+    # Mimic the structure defined in numba.targets.arrayobj's make_array()
+    _fields_ = [
+        ("meminfo", c_void_p),
+        ("parent", c_void_p),
+        ("nitems", c_ssize_t),
+        ("itemsize", c_ssize_t),
+        ("data", c_void_p),
+        ("shape", (c_ssize_t * 3)),
+        ("strides", (c_ssize_t * 3)),
+    ]
+
+
+class TestArrayAdaptor(unittest.TestCase):
+    def test_array_adaptor(self):
+        arystruct = ArrayStruct3D()
+
+        adaptorptr = _helperlib.c_helpers['adapt_ndarray']
+        adaptor = PYFUNCTYPE(c_int, py_object, c_void_p)(adaptorptr)
+
+        ary = np.arange(60).reshape(2, 3, 10)
+        status = adaptor(ary, byref(arystruct))
+        self.assertEqual(status, 0)
+        self.assertEqual(arystruct.data, ary.ctypes.data)
+        self.assertNotEqual(arystruct.meminfo, 0)
+        self.assertEqual(arystruct.parent, id(ary))
+        self.assertEqual(arystruct.nitems, 60)
+        self.assertEqual(arystruct.itemsize, ary.itemsize)
+        for i in range(3):
+            self.assertEqual(arystruct.shape[i], ary.ctypes.shape[i])
+            self.assertEqual(arystruct.strides[i], ary.ctypes.strides[i])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_obj_lifetime.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_obj_lifetime.py
new file mode 100644
index 000000000..d613c92ca
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_obj_lifetime.py
@@ -0,0 +1,503 @@
+import collections
+import sys
+import weakref
+import gc
+import operator
+from itertools import takewhile
+
+import unittest
+from numba import njit
+from numba.core.controlflow import CFGraph, Loop
+from numba.core.compiler import (compile_extra, compile_isolated, Flags,
+                                 CompilerBase, DefaultPassBuilder)
+from numba.core.untyped_passes import PreserveIR
+from numba.core.typed_passes import IRLegalization
+from numba.core import types, ir
+from numba.tests.support import TestCase, override_config, SerialMixin
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+forceobj_flags = Flags()
+forceobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+class _Dummy(object):
+
+    def __init__(self, recorder, name):
+        self.recorder = recorder
+        self.name = name
+        recorder._add_dummy(self)
+
+    def __add__(self, other):
+        assert isinstance(other, _Dummy)
+        return _Dummy(self.recorder, "%s + %s" % (self.name, other.name))
+
+    def __iter__(self):
+        return _DummyIterator(self.recorder, "iter(%s)" % self.name)
+
+
+class _DummyIterator(_Dummy):
+
+    count = 0
+
+    def __next__(self):
+        if self.count >= 3:
+            raise StopIteration
+        self.count += 1
+        return _Dummy(self.recorder, "%s#%s" % (self.name, self.count))
+
+    next = __next__
+
+
+class RefRecorder(object):
+    """
+    An object which records events when instances created through it
+    are deleted.  Custom events can also be recorded to aid in
+    diagnosis.
+    """
+
+    def __init__(self):
+        self._counts = collections.defaultdict(int)
+        self._events = []
+        self._wrs = {}
+
+    def make_dummy(self, name):
+        """
+        Make an object whose deletion will be recorded as *name*.
+        """
+        return _Dummy(self, name)
+
+    def _add_dummy(self, dummy):
+        wr = weakref.ref(dummy, self._on_disposal)
+        self._wrs[wr] = dummy.name
+
+    __call__ = make_dummy
+
+    def mark(self, event):
+        """
+        Manually append *event* to the recorded events.
+        *event* can be formatted using format().
+        """
+        count = self._counts[event] + 1
+        self._counts[event] = count
+        self._events.append(event.format(count=count))
+
+    def _on_disposal(self, wr):
+        name = self._wrs.pop(wr)
+        self._events.append(name)
+
+    @property
+    def alive(self):
+        """
+        A list of objects which haven't been deleted yet.
+        """
+        return [wr() for wr in self._wrs]
+
+    @property
+    def recorded(self):
+        """
+        A list of recorded events.
+        """
+        return self._events
+
+
+def simple_usecase1(rec):
+    a = rec('a')
+    b = rec('b')
+    c = rec('c')
+    a = b + c
+    rec.mark('--1--')
+    d = a + a   # b + c + b + c
+    rec.mark('--2--')
+    return d
+
+def simple_usecase2(rec):
+    a = rec('a')
+    b = rec('b')
+    rec.mark('--1--')
+    x = a
+    y = x
+    a = None
+    return y
+
+def looping_usecase1(rec):
+    a = rec('a')
+    b = rec('b')
+    c = rec('c')
+    x = b
+    for y in a:
+        x = x + y
+        rec.mark('--loop bottom--')
+    rec.mark('--loop exit--')
+    x = x + c
+    return x
+
+def looping_usecase2(rec):
+    a = rec('a')
+    b = rec('b')
+    cum = rec('cum')
+    for x in a:
+        rec.mark('--outer loop top--')
+        cum = cum + x
+        z = x + x
+        rec.mark('--inner loop entry #{count}--')
+        for y in b:
+            rec.mark('--inner loop top #{count}--')
+            cum = cum + y
+            rec.mark('--inner loop bottom #{count}--')
+        rec.mark('--inner loop exit #{count}--')
+        if cum:
+            cum = y + z
+        else:
+            # Never gets here, but let the Numba compiler see a `break` opcode
+            break
+        rec.mark('--outer loop bottom #{count}--')
+    else:
+        rec.mark('--outer loop else--')
+    rec.mark('--outer loop exit--')
+    return cum
+
+def generator_usecase1(rec):
+    a = rec('a')
+    b = rec('b')
+    yield a
+    yield b
+
+def generator_usecase2(rec):
+    a = rec('a')
+    b = rec('b')
+    for x in a:
+        yield x
+    yield b
+
+
+class MyError(RuntimeError):
+    pass
+
+def do_raise(x):
+    raise MyError(x)
+
+def raising_usecase1(rec):
+    a = rec('a')
+    b = rec('b')
+    d = rec('d')
+    if a:
+        do_raise("foo")
+        c = rec('c')
+        c + a
+    c + b
+
+def raising_usecase2(rec):
+    a = rec('a')
+    b = rec('b')
+    if a:
+        c = rec('c')
+        do_raise(b)
+    a + c
+
+def raising_usecase3(rec):
+    a = rec('a')
+    b = rec('b')
+    if a:
+        raise MyError(b)
+
+
+def del_before_definition(rec):
+    """
+    This test reveal a bug that there is a del on uninitialized variable
+    """
+    n = 5
+    for i in range(n):
+        rec.mark(str(i))
+        n = 0
+        for j in range(n):
+            return 0
+        else:
+            if i < 2:
+                continue
+            elif i == 2:
+                for j in range(i):
+                    return i
+                rec.mark('FAILED')
+            rec.mark('FAILED')
+        rec.mark('FAILED')
+    rec.mark('OK')
+    return -1
+
+
+def inf_loop_multiple_back_edge(rec):
+    """
+    test to reveal bug of invalid liveness when infinite loop has multiple
+    backedge.
+    """
+    while True:
+        rec.mark("yield")
+        yield
+        p = rec('p')
+        if p:
+            rec.mark('bra')
+            pass
+
+
+class TestObjLifetime(TestCase):
+    """
+    Test lifetime of Python objects inside jit-compiled functions.
+    """
+
+    def compile(self, pyfunc):
+        cr = compile_isolated(pyfunc, (types.pyobject,), flags=forceobj_flags)
+        return cr.entry_point
+
+    def compile_and_record(self, pyfunc, raises=None):
+        rec = RefRecorder()
+        cfunc = self.compile(pyfunc)
+        if raises is not None:
+            with self.assertRaises(raises):
+                cfunc(rec)
+        else:
+            cfunc(rec)
+        return rec
+
+    def assertRecordOrder(self, rec, expected):
+        """
+        Check that the *expected* markers occur in that order in *rec*'s
+        recorded events.
+        """
+        actual = []
+        recorded = rec.recorded
+        remaining = list(expected)
+        # Find out in which order, if any, the expected events were recorded
+        for d in recorded:
+            if d in remaining:
+                actual.append(d)
+                # User may or may not expect duplicates, handle them properly
+                remaining.remove(d)
+        self.assertEqual(actual, expected,
+                         "the full list of recorded events is: %r" % (recorded,))
+
+    def test_simple1(self):
+        rec = self.compile_and_record(simple_usecase1)
+        self.assertFalse(rec.alive)
+        self.assertRecordOrder(rec, ['a', 'b', '--1--'])
+        self.assertRecordOrder(rec, ['a', 'c', '--1--'])
+        self.assertRecordOrder(rec, ['--1--', 'b + c', '--2--'])
+
+    def test_simple2(self):
+        rec = self.compile_and_record(simple_usecase2)
+        self.assertFalse(rec.alive)
+        self.assertRecordOrder(rec, ['b', '--1--', 'a'])
+
+    def test_looping1(self):
+        rec = self.compile_and_record(looping_usecase1)
+        self.assertFalse(rec.alive)
+        # a and b are unneeded after the loop, check they were disposed of
+        self.assertRecordOrder(rec, ['a', 'b', '--loop exit--', 'c'])
+        # check disposal order of iterator items and iterator
+        self.assertRecordOrder(rec, ['iter(a)#1', '--loop bottom--',
+                                     'iter(a)#2', '--loop bottom--',
+                                     'iter(a)#3', '--loop bottom--',
+                                     'iter(a)', '--loop exit--',
+                                     ])
+
+    def test_looping2(self):
+        rec = self.compile_and_record(looping_usecase2)
+        self.assertFalse(rec.alive)
+        # `a` is disposed of after its iterator is taken
+        self.assertRecordOrder(rec, ['a', '--outer loop top--'])
+        # Check disposal of iterators
+        self.assertRecordOrder(rec, ['iter(a)', '--outer loop else--',
+                                     '--outer loop exit--'])
+        self.assertRecordOrder(rec, ['iter(b)', '--inner loop exit #1--',
+                                     'iter(b)', '--inner loop exit #2--',
+                                     'iter(b)', '--inner loop exit #3--',
+                                     ])
+        # Disposal of in-loop variable `x`
+        self.assertRecordOrder(rec, ['iter(a)#1', '--inner loop entry #1--',
+                                     'iter(a)#2', '--inner loop entry #2--',
+                                     'iter(a)#3', '--inner loop entry #3--',
+                                     ])
+        # Disposal of in-loop variable `z`
+        self.assertRecordOrder(rec, ['iter(a)#1 + iter(a)#1',
+                                     '--outer loop bottom #1--',
+                                     ])
+
+    def exercise_generator(self, genfunc):
+        cfunc = self.compile(genfunc)
+        # Exhaust the generator
+        rec = RefRecorder()
+        with self.assertRefCount(rec):
+            gen = cfunc(rec)
+            next(gen)
+            self.assertTrue(rec.alive)
+            list(gen)
+            self.assertFalse(rec.alive)
+        # Instantiate the generator but never iterate
+        rec = RefRecorder()
+        with self.assertRefCount(rec):
+            gen = cfunc(rec)
+            del gen
+            gc.collect()
+            self.assertFalse(rec.alive)
+        # Stop iterating before exhaustion
+        rec = RefRecorder()
+        with self.assertRefCount(rec):
+            gen = cfunc(rec)
+            next(gen)
+            self.assertTrue(rec.alive)
+            del gen
+            gc.collect()
+            self.assertFalse(rec.alive)
+
+    def test_generator1(self):
+        self.exercise_generator(generator_usecase1)
+
+    def test_generator2(self):
+        self.exercise_generator(generator_usecase2)
+
+    def test_del_before_definition(self):
+        rec = self.compile_and_record(del_before_definition)
+        self.assertEqual(rec.recorded, ['0', '1', '2'])
+
+    def test_raising1(self):
+        with self.assertRefCount(do_raise):
+            rec = self.compile_and_record(raising_usecase1, raises=MyError)
+            self.assertFalse(rec.alive)
+
+    def test_raising2(self):
+        with self.assertRefCount(do_raise):
+            rec = self.compile_and_record(raising_usecase2, raises=MyError)
+            self.assertFalse(rec.alive)
+
+    def test_raising3(self):
+        with self.assertRefCount(MyError):
+            rec = self.compile_and_record(raising_usecase3, raises=MyError)
+            self.assertFalse(rec.alive)
+
+    def test_inf_loop_multiple_back_edge(self):
+        cfunc = self.compile(inf_loop_multiple_back_edge)
+        rec = RefRecorder()
+        iterator = iter(cfunc(rec))
+        next(iterator)
+        self.assertEqual(rec.alive, [])
+        next(iterator)
+        self.assertEqual(rec.alive, [])
+        next(iterator)
+        self.assertEqual(rec.alive, [])
+        self.assertEqual(rec.recorded,
+                         ['yield', 'p', 'bra', 'yield', 'p', 'bra', 'yield'])
+
+
+class TestExtendingVariableLifetimes(SerialMixin, TestCase):
+    # Test for `numba.config.EXTEND_VARIABLE_LIFETIMES` which moves the ir.Del
+    # nodes to just before a block's terminator, i.e. their lifetime is extended
+    # beyond the point of last use.
+
+    def test_lifetime_basic(self):
+
+        def get_ir(extend_lifetimes):
+            class IRPreservingCompiler(CompilerBase):
+
+                def define_pipelines(self):
+                    pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+                    pm.add_pass_after(PreserveIR, IRLegalization)
+                    pm.finalize()
+                    return [pm]
+
+            @njit(pipeline_class=IRPreservingCompiler)
+            def foo():
+                a = 10
+                b = 20
+                c = a + b
+                # a and b are now unused, standard behaviour is ir.Del for them here
+                d = c / c
+                return d
+
+            with override_config('EXTEND_VARIABLE_LIFETIMES', extend_lifetimes):
+                foo()
+                cres = foo.overloads[foo.signatures[0]]
+                func_ir = cres.metadata['preserved_ir']
+
+            return func_ir
+
+
+        def check(func_ir, expect):
+            # assert single block
+            self.assertEqual(len(func_ir.blocks), 1)
+            blk = next(iter(func_ir.blocks.values()))
+
+            # check sequencing
+            for expect_class, got_stmt in zip(expect, blk.body):
+                self.assertIsInstance(got_stmt, expect_class)
+
+        del_after_use_ir = get_ir(False)
+        # should be 3 assigns (a, b, c), 2 del (a, b), assign (d), del (c)
+        # assign for cast d to return, del (d), return
+        expect = [*((ir.Assign,) * 3), ir.Del, ir.Del, ir.Assign, ir.Del,
+                  ir.Assign, ir.Del, ir.Return]
+        check(del_after_use_ir, expect)
+
+        del_at_block_end_ir = get_ir(True)
+        # should be 4 assigns (a, b, c, d), assign for cast d to return,
+        # 4 dels (a, b, c, d) then the return.
+        expect = [*((ir.Assign,) * 4), ir.Assign, *((ir.Del,) * 4), ir.Return]
+        check(del_at_block_end_ir, expect)
+
+    def test_dbg_extend_lifetimes(self):
+
+        def get_ir(**options):
+            class IRPreservingCompiler(CompilerBase):
+
+                def define_pipelines(self):
+                    pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+                    pm.add_pass_after(PreserveIR, IRLegalization)
+                    pm.finalize()
+                    return [pm]
+
+            @njit(pipeline_class=IRPreservingCompiler, **options)
+            def foo():
+                a = 10
+                b = 20
+                c = a + b
+                # a and b are now unused, standard behaviour is ir.Del for them here
+                d = c / c
+                return d
+
+            foo()
+            cres = foo.overloads[foo.signatures[0]]
+            func_ir = cres.metadata['preserved_ir']
+
+            return func_ir
+
+        # _dbg_extend_lifetimes is on when debug=True
+        ir_debug = get_ir(debug=True)
+        # explicitly turn on _dbg_extend_lifetimes
+        ir_debug_ext = get_ir(debug=True, _dbg_extend_lifetimes=True)
+        # explicitly turn off _dbg_extend_lifetimes
+        ir_debug_no_ext = get_ir(debug=True, _dbg_extend_lifetimes=False)
+
+        def is_del_grouped_at_the_end(fir):
+            [blk] = fir.blocks.values()
+            # Mark all statements that are ir.Del
+            inst_is_del = [isinstance(stmt, ir.Del) for stmt in blk.body]
+            # Get the leading segment that are not dels
+            not_dels = list(takewhile(operator.not_, inst_is_del))
+            # Compute the starting position of the dels
+            begin = len(not_dels)
+            # Get the remaining segment that are all dels
+            all_dels = list(takewhile(operator.truth, inst_is_del[begin:]))
+            # Compute the ending position of the dels
+            end = begin + len(all_dels)
+            # If the dels are all grouped at the end (before the terminator),
+            # the end position will be the last position of the list
+            return end == len(inst_is_del) - 1
+
+        self.assertTrue(is_del_grouped_at_the_end(ir_debug))
+        self.assertTrue(is_del_grouped_at_the_end(ir_debug_ext))
+        self.assertFalse(is_del_grouped_at_the_end(ir_debug_no_ext))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_object_mode.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_object_mode.py
new file mode 100644
index 000000000..e2dfedf82
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_object_mode.py
@@ -0,0 +1,192 @@
+"""
+Testing object mode specifics.
+
+"""
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit
+from numba.core import utils
+from numba.tests.support import TestCase
+
+
+def complex_constant(n):
+    tmp = n + 4
+    return tmp + 3j
+
+
+def long_constant(n):
+    return n + 100000000000000000000000000000000000000000000000
+
+
+def delitem_usecase(x):
+    del x[:]
+
+
+forceobj = Flags()
+forceobj.force_pyobject = True
+
+
+def loop_nest_3(x, y):
+    n = 0
+    for i in range(x):
+        for j in range(y):
+            for k in range(x + y):
+                n += i * j
+
+    return n
+
+
+def array_of_object(x):
+    return x
+
+
+class TestObjectMode(TestCase):
+
+    def test_complex_constant(self):
+        pyfunc = complex_constant
+        cres = compile_isolated(pyfunc, (), flags=forceobj)
+        cfunc = cres.entry_point
+        self.assertPreciseEqual(pyfunc(12), cfunc(12))
+
+    def test_long_constant(self):
+        pyfunc = long_constant
+        cres = compile_isolated(pyfunc, (), flags=forceobj)
+        cfunc = cres.entry_point
+        self.assertPreciseEqual(pyfunc(12), cfunc(12))
+
+    def test_loop_nest(self):
+        """
+        Test bug that decref the iterator early.
+        If the bug occurs, a segfault should occur
+        """
+        pyfunc = loop_nest_3
+        cres = compile_isolated(pyfunc, (), flags=forceobj)
+        cfunc = cres.entry_point
+        self.assertEqual(pyfunc(5, 5), cfunc(5, 5))
+
+        def bm_pyfunc():
+            pyfunc(5, 5)
+
+        def bm_cfunc():
+            cfunc(5, 5)
+
+        print(utils.benchmark(bm_pyfunc))
+        print(utils.benchmark(bm_cfunc))
+
+    def test_array_of_object(self):
+        cfunc = jit(array_of_object)
+        objarr = np.array([object()] * 10)
+        self.assertIs(cfunc(objarr), objarr)
+
+    def test_sequence_contains(self):
+        """
+        Test handling of the `in` comparison
+        """
+        @jit(forceobj=True)
+        def foo(x, y):
+            return x in y
+
+        self.assertTrue(foo(1, [0, 1]))
+        self.assertTrue(foo(0, [0, 1]))
+        self.assertFalse(foo(2, [0, 1]))
+
+        with self.assertRaises(TypeError) as raises:
+            foo(None, None)
+
+        self.assertIn("is not iterable", str(raises.exception))
+
+    def test_delitem(self):
+        pyfunc = delitem_usecase
+        cres = compile_isolated(pyfunc, (), flags=forceobj)
+        cfunc = cres.entry_point
+
+        l = [3, 4, 5]
+        cfunc(l)
+        self.assertPreciseEqual(l, [])
+        with self.assertRaises(TypeError):
+            cfunc(42)
+
+    def test_starargs_non_tuple(self):
+        def consumer(*x):
+            return x
+
+        @jit(forceobj=True)
+        def foo(x):
+            return consumer(*x)
+
+        arg = "ijo"
+        got = foo(arg)
+        expect = foo.py_func(arg)
+        self.assertEqual(got, tuple(arg))
+        self.assertEqual(got, expect)
+
+
+class TestObjectModeInvalidRewrite(TestCase):
+    """
+    Tests to ensure that rewrite passes didn't affect objmode lowering.
+    """
+
+    def _ensure_objmode(self, disp):
+        self.assertTrue(disp.signatures)
+        self.assertFalse(disp.nopython_signatures)
+        return disp
+
+    def test_static_raise_in_objmode_fallback(self):
+        """
+        Test code based on user submitted issue at
+        https://github.com/numba/numba/issues/2159
+        """
+        def test0(n):
+            return n
+
+        def test1(n):
+            if n == 0:
+                # static raise will fail in objmode if the IR is modified by
+                # rewrite pass
+                raise ValueError()
+            return test0(n)  # trigger objmode fallback
+
+        compiled = jit(test1)
+        self.assertEqual(test1(10), compiled(10))
+        self._ensure_objmode(compiled)
+
+    def test_static_setitem_in_objmode_fallback(self):
+        """
+        Test code based on user submitted issue at
+        https://github.com/numba/numba/issues/2169
+        """
+
+        def test0(n):
+            return n
+
+        def test(a1, a2):
+            a1 = np.asarray(a1)
+            # static setitem here will fail in objmode if the IR is modified by
+            # rewrite pass
+            a2[0] = 1
+            return test0(a1.sum() + a2.sum())   # trigger objmode fallback
+
+        compiled = jit(test)
+        args = np.array([3]), np.array([4])
+        self.assertEqual(test(*args), compiled(*args))
+        self._ensure_objmode(compiled)
+
+    def test_dynamic_func_objmode(self):
+        """
+        Test issue https://github.com/numba/numba/issues/3355
+        """
+        func_text = "def func():\n"
+        func_text += "    np.array([1,2,3])\n"
+        loc_vars = {}
+        custom_globals = {'np': np}
+        exec(func_text, custom_globals, loc_vars)
+        func = loc_vars['func']
+        jitted = jit(forceobj=True)(func)
+        jitted()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_objects.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_objects.py
new file mode 100644
index 000000000..565add230
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_objects.py
@@ -0,0 +1,69 @@
+"""
+Test generic manipulation of objects.
+"""
+
+
+import unittest
+from numba import njit
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types
+from numba.tests.support import TestCase
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+class C(object):
+    pass
+
+
+def setattr_usecase(o, v):
+    o.x = v
+
+def delattr_usecase(o):
+    del o.x
+
+
+class TestAttributes(TestCase):
+    def test_setattr(self, flags=enable_pyobj_flags):
+        pyfunc = setattr_usecase
+        cr = compile_isolated(pyfunc, (types.pyobject, types.int32), flags=flags)
+        cfunc = cr.entry_point
+        c = C()
+        cfunc(c, 123)
+        self.assertEqual(c.x, 123)
+
+    def test_setattr_attribute_error(self, flags=enable_pyobj_flags):
+        pyfunc = setattr_usecase
+        cr = compile_isolated(pyfunc, (types.pyobject, types.int32), flags=flags)
+        cfunc = cr.entry_point
+        # Can't set undeclared slot
+        with self.assertRaises(AttributeError):
+            cfunc(object(), 123)
+
+    def test_delattr(self, flags=enable_pyobj_flags):
+        pyfunc = delattr_usecase
+        cr = compile_isolated(pyfunc, (types.pyobject,), flags=flags)
+        cfunc = cr.entry_point
+        c = C()
+        c.x = 123
+        cfunc(c)
+        with self.assertRaises(AttributeError):
+            c.x
+
+    def test_delattr_attribute_error(self, flags=enable_pyobj_flags):
+        pyfunc = delattr_usecase
+        cr = compile_isolated(pyfunc, (types.pyobject,), flags=flags)
+        cfunc = cr.entry_point
+        # Can't delete non-existing attribute
+        with self.assertRaises(AttributeError):
+            cfunc(C())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_operators.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_operators.py
new file mode 100644
index 000000000..ac45cd945
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_operators.py
@@ -0,0 +1,1612 @@
+import copy
+import itertools
+import operator
+import sys
+import warnings
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit
+from numba.core import types, utils, errors, typeinfer
+from numba.core.types.functions import _header_lead
+from numba.tests.support import TestCase, tag, needs_blas
+from numba.tests.matmul_usecase import (matmul_usecase, imatmul_usecase,
+                                        DumbMatrix,)
+
+Noflags = Flags()
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+
+def make_static_power(exp):
+    def pow_usecase(x):
+        return x ** exp
+    return pow_usecase
+
+
+class LiteralOperatorImpl(object):
+
+    @staticmethod
+    def add_usecase(x, y):
+        return x + y
+
+    @staticmethod
+    def iadd_usecase(x, y):
+        x += y
+        return x
+
+    @staticmethod
+    def sub_usecase(x, y):
+        return x - y
+
+    @staticmethod
+    def isub_usecase(x, y):
+        x -= y
+        return x
+
+    @staticmethod
+    def mul_usecase(x, y):
+        return x * y
+
+    @staticmethod
+    def imul_usecase(x, y):
+        x *= y
+        return x
+
+    @staticmethod
+    def floordiv_usecase(x, y):
+        return x // y
+
+    @staticmethod
+    def ifloordiv_usecase(x, y):
+        x //= y
+        return x
+
+    @staticmethod
+    def truediv_usecase(x, y):
+        return x / y
+
+    @staticmethod
+    def itruediv_usecase(x, y):
+        x /= y
+        return x
+
+    if matmul_usecase:
+        matmul_usecase = staticmethod(matmul_usecase)
+        imatmul_usecase = staticmethod(imatmul_usecase)
+
+    @staticmethod
+    def mod_usecase(x, y):
+        return x % y
+
+    @staticmethod
+    def imod_usecase(x, y):
+        x %= y
+        return x
+
+    @staticmethod
+    def pow_usecase(x, y):
+        return x ** y
+
+    @staticmethod
+    def ipow_usecase(x, y):
+        x **= y
+        return x
+
+    @staticmethod
+    def bitshift_left_usecase(x, y):
+        return x << y
+
+    @staticmethod
+    def bitshift_ileft_usecase(x, y):
+        x <<= y
+        return x
+
+    @staticmethod
+    def bitshift_right_usecase(x, y):
+        return x >> y
+
+    @staticmethod
+    def bitshift_iright_usecase(x, y):
+        x >>= y
+        return x
+
+    @staticmethod
+    def bitwise_and_usecase(x, y):
+        return x & y
+
+    @staticmethod
+    def bitwise_iand_usecase(x, y):
+        x &= y
+        return x
+
+    @staticmethod
+    def bitwise_or_usecase(x, y):
+        return x | y
+
+    @staticmethod
+    def bitwise_ior_usecase(x, y):
+        x |= y
+        return x
+
+    @staticmethod
+    def bitwise_xor_usecase(x, y):
+        return x ^ y
+
+    @staticmethod
+    def bitwise_ixor_usecase(x, y):
+        x ^= y
+        return x
+
+    @staticmethod
+    def bitwise_not_usecase_binary(x, _unused):
+        return ~x
+
+    @staticmethod
+    def bitwise_not_usecase(x):
+        return ~x
+
+    @staticmethod
+    def not_usecase(x):
+        return not(x)
+
+    @staticmethod
+    def negate_usecase(x):
+        return -x
+
+    @staticmethod
+    def unary_positive_usecase(x):
+        return +x
+
+    @staticmethod
+    def lt_usecase(x, y):
+        return x < y
+
+    @staticmethod
+    def le_usecase(x, y):
+        return x <= y
+
+    @staticmethod
+    def gt_usecase(x, y):
+        return x > y
+
+    @staticmethod
+    def ge_usecase(x, y):
+        return x >= y
+
+    @staticmethod
+    def eq_usecase(x, y):
+        return x == y
+
+    @staticmethod
+    def ne_usecase(x, y):
+        return x != y
+
+    @staticmethod
+    def in_usecase(x, y):
+        return x in y
+
+    @staticmethod
+    def not_in_usecase(x, y):
+        return x not in y
+
+    @staticmethod
+    def is_usecase(x, y):
+        return x is y
+
+
+class FunctionalOperatorImpl(object):
+
+    @staticmethod
+    def add_usecase(x, y):
+        return operator.add(x, y)
+
+    @staticmethod
+    def iadd_usecase(x, y):
+        return operator.iadd(x, y)
+
+    @staticmethod
+    def sub_usecase(x, y):
+        return operator.sub(x, y)
+
+    @staticmethod
+    def isub_usecase(x, y):
+        return operator.isub(x, y)
+
+    @staticmethod
+    def mul_usecase(x, y):
+        return operator.mul(x, y)
+
+    @staticmethod
+    def imul_usecase(x, y):
+        return operator.imul(x, y)
+
+    @staticmethod
+    def floordiv_usecase(x, y):
+        return operator.floordiv(x, y)
+
+    @staticmethod
+    def ifloordiv_usecase(x, y):
+        return operator.ifloordiv(x, y)
+
+    @staticmethod
+    def truediv_usecase(x, y):
+        return operator.truediv(x, y)
+
+    @staticmethod
+    def itruediv_usecase(x, y):
+        return operator.itruediv(x, y)
+
+    @staticmethod
+    def mod_usecase(x, y):
+        return operator.mod(x, y)
+
+    @staticmethod
+    def imod_usecase(x, y):
+        return operator.imod(x, y)
+
+    @staticmethod
+    def pow_usecase(x, y):
+        return operator.pow(x, y)
+
+    @staticmethod
+    def ipow_usecase(x, y):
+        return operator.ipow(x, y)
+
+    @staticmethod
+    def matmul_usecase(x, y):
+        return operator.matmul(x, y)
+
+    @staticmethod
+    def imatmul_usecase(x, y):
+        return operator.imatmul(x, y)
+
+    @staticmethod
+    def bitshift_left_usecase(x, y):
+        return operator.lshift(x, y)
+
+    @staticmethod
+    def bitshift_ileft_usecase(x, y):
+        return operator.ilshift(x, y)
+
+    @staticmethod
+    def bitshift_right_usecase(x, y):
+        return operator.rshift(x, y)
+
+    @staticmethod
+    def bitshift_iright_usecase(x, y):
+        return operator.irshift(x, y)
+
+    @staticmethod
+    def bitwise_and_usecase(x, y):
+        return operator.and_(x, y)
+
+    @staticmethod
+    def bitwise_iand_usecase(x, y):
+        return operator.iand(x, y)
+
+    @staticmethod
+    def bitwise_or_usecase(x, y):
+        return operator.or_(x, y)
+
+    @staticmethod
+    def bitwise_ior_usecase(x, y):
+        return operator.ior(x, y)
+
+    @staticmethod
+    def bitwise_xor_usecase(x, y):
+        return operator.xor(x, y)
+
+    @staticmethod
+    def bitwise_ixor_usecase(x, y):
+        return operator.ixor(x, y)
+
+    @staticmethod
+    def bitwise_not_usecase_binary(x, _unused):
+        return operator.invert(x)
+
+    @staticmethod
+    def bitwise_not_usecase(x):
+        return operator.invert(x)
+
+    @staticmethod
+    def not_usecase(x):
+        return operator.not_(x)
+
+    @staticmethod
+    def negate_usecase(x):
+        return operator.neg(x)
+
+    @staticmethod
+    def unary_positive_usecase(x):
+        return operator.pos(x)
+
+    @staticmethod
+    def lt_usecase(x, y):
+        return operator.lt(x, y)
+
+    @staticmethod
+    def le_usecase(x, y):
+        return operator.le(x, y)
+
+    @staticmethod
+    def gt_usecase(x, y):
+        return operator.gt(x, y)
+
+    @staticmethod
+    def ge_usecase(x, y):
+        return operator.ge(x, y)
+
+    @staticmethod
+    def eq_usecase(x, y):
+        return operator.eq(x, y)
+
+    @staticmethod
+    def ne_usecase(x, y):
+        return operator.ne(x, y)
+
+    @staticmethod
+    def in_usecase(x, y):
+        return operator.contains(y, x)
+
+    @staticmethod
+    def not_in_usecase(x, y):
+        return not operator.contains(y, x)
+
+    @staticmethod
+    def is_usecase(x, y):
+        return operator.is_(x, y)
+
+
+class TestOperators(TestCase):
+    """
+    Test standard Python operators on scalars.
+
+    NOTE: operators on array are generally tested in test_ufuncs.
+    """
+
+    op = LiteralOperatorImpl
+
+    _bitwise_opnames = {
+        'bitshift_left_usecase': operator.lshift,
+        'bitshift_ileft_usecase': operator.ilshift,
+        'bitshift_right_usecase': operator.rshift,
+        'bitshift_iright_usecase': operator.irshift,
+        'bitwise_and_usecase': operator.and_,
+        'bitwise_iand_usecase': operator.iand,
+        'bitwise_or_usecase': operator.or_,
+        'bitwise_ior_usecase': operator.ior,
+        'bitwise_xor_usecase': operator.xor,
+        'bitwise_ixor_usecase': operator.ixor,
+        'bitwise_not_usecase_binary': operator.invert,
+    }
+
+    def run_test_ints(self, pyfunc, x_operands, y_operands, types_list,
+                      flags=force_pyobj_flags):
+        for arg_types in types_list:
+            cr = compile_isolated(pyfunc, arg_types, flags=flags)
+            cfunc = cr.entry_point
+            for x, y in itertools.product(x_operands, y_operands):
+                # For inplace ops, we check that the first operand
+                # was correctly mutated.
+                x_got = copy.copy(x)
+                x_expected = copy.copy(x)
+                got = cfunc(x_got, y)
+                expected = pyfunc(x_expected, y)
+                self.assertPreciseEqual(
+                    got, expected,
+                    msg="mismatch for (%r, %r) with types %s: %r != %r"
+                        % (x, y, arg_types, got, expected))
+                self.assertPreciseEqual(
+                    x_got, x_expected,
+                    msg="mismatch for (%r, %r) with types %s: %r != %r"
+                        % (x, y, arg_types, x_got, x_expected))
+
+    def run_test_floats(self, pyfunc, x_operands, y_operands, types_list,
+                        flags=force_pyobj_flags):
+        for arg_types in types_list:
+            cr = compile_isolated(pyfunc, arg_types, flags=flags)
+            cfunc = cr.entry_point
+            for x, y in itertools.product(x_operands, y_operands):
+                # For inplace ops, we check that the first operand
+                # was correctly mutated.
+                x_got = copy.copy(x)
+                x_expected = copy.copy(x)
+                got = cfunc(x_got, y)
+                expected = pyfunc(x_expected, y)
+                np.testing.assert_allclose(got, expected, rtol=1e-5)
+                np.testing.assert_allclose(x_got, x_expected, rtol=1e-5)
+
+    def coerce_operand(self, op, numba_type):
+        if hasattr(op, "dtype"):
+            return numba_type.cast_python_value(op)
+        elif numba_type in types.unsigned_domain:
+            return abs(int(op.real))
+        elif numba_type in types.integer_domain:
+            return int(op.real)
+        elif numba_type in types.real_domain:
+            return float(op.real)
+        else:
+            return op
+
+    def run_test_scalar_compare(self, pyfunc, flags=force_pyobj_flags,
+                                ordered=True):
+        ops = self.compare_scalar_operands
+        types_list = self.compare_types
+        if not ordered:
+            types_list = types_list + self.compare_unordered_types
+        for typ in types_list:
+            cr = compile_isolated(pyfunc, (typ, typ), flags=flags)
+            cfunc = cr.entry_point
+            for x, y in itertools.product(ops, ops):
+                x = self.coerce_operand(x, typ)
+                y = self.coerce_operand(y, typ)
+                expected = pyfunc(x, y)
+                got = cfunc(x, y)
+                # Scalar ops => scalar result
+                self.assertIs(type(got), type(expected))
+                self.assertEqual(got, expected,
+                                 "mismatch with %r (%r, %r)"
+                                 % (typ, x, y))
+
+
+    #
+    # Comparison operators
+    #
+
+    compare_scalar_operands = [-0.5, -1.0 + 1j, -1.0 + 2j, -0.5 + 1j, 1.5]
+    compare_types = [types.int32, types.int64,
+                     types.uint32, types.uint64,
+                     types.float32, types.float64]
+    compare_unordered_types = [types.complex64, types.complex128]
+
+    def test_lt_scalar(self, flags=force_pyobj_flags):
+        self.run_test_scalar_compare(self.op.lt_usecase, flags)
+
+    def test_lt_scalar_npm(self):
+        self.test_lt_scalar(flags=Noflags)
+
+    def test_le_scalar(self, flags=force_pyobj_flags):
+        self.run_test_scalar_compare(self.op.le_usecase, flags)
+
+    def test_le_scalar_npm(self):
+        self.test_le_scalar(flags=Noflags)
+
+    def test_gt_scalar(self, flags=force_pyobj_flags):
+        self.run_test_scalar_compare(self.op.gt_usecase, flags)
+
+    def test_gt_scalar_npm(self):
+        self.test_gt_scalar(flags=Noflags)
+
+    def test_ge_scalar(self, flags=force_pyobj_flags):
+        self.run_test_scalar_compare(self.op.ge_usecase, flags)
+
+    def test_ge_scalar_npm(self):
+        self.test_ge_scalar(flags=Noflags)
+
+    def test_eq_scalar(self, flags=force_pyobj_flags):
+        self.run_test_scalar_compare(self.op.eq_usecase, flags, ordered=False)
+
+    def test_eq_scalar_npm(self):
+        self.test_eq_scalar(flags=Noflags)
+
+    def test_ne_scalar(self, flags=force_pyobj_flags):
+        self.run_test_scalar_compare(self.op.ne_usecase, flags, ordered=False)
+
+    def test_ne_scalar_npm(self):
+        self.test_ne_scalar(flags=Noflags)
+
+    def test_is_ellipsis(self):
+        cres = compile_isolated(self.op.is_usecase, (types.ellipsis, types.ellipsis))
+        cfunc = cres.entry_point
+        self.assertTrue(cfunc(Ellipsis, Ellipsis))
+
+    def test_is_void_ptr(self):
+        # can't call this directly from python, as void cannot be unboxed
+        cfunc_void = jit(
+            (types.voidptr, types.voidptr), nopython=True
+        )(self.op.is_usecase)
+
+        # this wrapper performs the casts from int to voidptr for us
+        @jit(nopython=True)
+        def cfunc(x, y):
+            return cfunc_void(x, y)
+
+        self.assertTrue(cfunc(1, 1))
+        self.assertFalse(cfunc(1, 2))
+
+    #
+    # Arithmetic operators
+    #
+
+    def run_binop_bools(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = [False, False, True, True]
+        y_operands = [False, True, False, True]
+
+        types_list = [(types.boolean, types.boolean)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+    def run_binop_ints(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = [-5, 0, 1, 2]
+        y_operands = [-3, -1, 1, 3]
+
+        types_list = [(types.int32, types.int32),
+                      (types.int64, types.int64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = [2, 3]
+        y_operands = [1, 2]
+
+        types_list = [(types.byte, types.byte),
+                      (types.uint32, types.uint32),
+                      (types.uint64, types.uint64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+    def run_binop_floats(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = [-1.1, 0.0, 1.1]
+        y_operands = [-1.5, 0.8, 2.1]
+
+        types_list = [(types.float32, types.float32),
+                      (types.float64, types.float64)]
+
+        self.run_test_floats(pyfunc, x_operands, y_operands, types_list,
+                             flags=flags)
+
+    def run_binop_floats_floordiv(self, pyfunc, flags=force_pyobj_flags):
+        self.run_binop_floats(pyfunc, flags=flags)
+
+    def run_binop_complex(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = [-1.1 + 0.3j, 0.0 + 0.0j, 1.1j]
+        y_operands = [-1.5 - 0.7j, 0.8j, 2.1 - 2.0j]
+
+        types_list = [(types.complex64, types.complex64),
+                      (types.complex128, types.complex128)]
+
+        self.run_test_floats(pyfunc, x_operands, y_operands, types_list,
+                             flags=flags)
+
+    def generate_binop_tests(ns, usecases, tp_runners, npm_array=False):
+        for usecase in usecases:
+            for tp_name, runner_name in tp_runners.items():
+                for nopython in (False, True):
+                    test_name = "test_%s_%s" % (usecase, tp_name)
+                    if nopython:
+                        test_name += "_npm"
+                    flags = Noflags if nopython else force_pyobj_flags
+                    usecase_name = "%s_usecase" % usecase
+
+                    def inner(self, runner_name=runner_name,
+                              usecase_name=usecase_name, flags=flags):
+                        runner = getattr(self, runner_name)
+                        op_usecase = getattr(self.op, usecase_name)
+                        runner(op_usecase, flags)
+
+                    if nopython and 'array' in tp_name and not npm_array:
+                        def test_meth(self):
+                            with self.assertTypingError():
+                                inner()
+                    else:
+                        test_meth = inner
+
+                    test_meth.__name__ = test_name
+
+                    if nopython:
+                        test_meth = tag('important')(test_meth)
+
+                    ns[test_name] = test_meth
+
+
+    generate_binop_tests(locals(),
+                         ('add', 'iadd', 'sub', 'isub', 'mul', 'imul'),
+                         {'ints': 'run_binop_ints',
+                          'floats': 'run_binop_floats',
+                          'complex': 'run_binop_complex',
+                          })
+
+    generate_binop_tests(locals(),
+                         ('truediv', 'itruediv'),
+                         {'ints': 'run_binop_ints',
+                          'floats': 'run_binop_floats',
+                          'complex': 'run_binop_complex',
+                          })
+
+    # NOTE: floordiv and mod unsupported for complex numbers
+    generate_binop_tests(locals(),
+                         ('floordiv', 'ifloordiv', 'mod', 'imod'),
+                         {'ints': 'run_binop_ints',
+                          'floats': 'run_binop_floats_floordiv',
+                          })
+
+    def check_div_errors(self, usecase_name, msg, flags=force_pyobj_flags,
+                         allow_complex=False):
+        pyfunc = getattr(self.op, usecase_name)
+        # Signed and unsigned division can take different code paths,
+        # test them both.
+        arg_types = [types.int32, types.uint32, types.float64]
+        if allow_complex:
+            arg_types.append(types.complex128)
+        for tp in arg_types:
+            cr = compile_isolated(pyfunc, (tp, tp), flags=flags)
+            cfunc = cr.entry_point
+            with self.assertRaises(ZeroDivisionError) as cm:
+                cfunc(1, 0)
+            # Test exception message if not in object mode
+            if flags is not force_pyobj_flags:
+                self.assertIn(msg, str(cm.exception))
+
+    def test_truediv_errors(self, flags=force_pyobj_flags):
+        self.check_div_errors("truediv_usecase", "division by zero", flags=flags,
+                              allow_complex=True)
+
+    def test_truediv_errors_npm(self):
+        self.test_truediv_errors(flags=Noflags)
+
+    def test_floordiv_errors(self, flags=force_pyobj_flags):
+        self.check_div_errors("floordiv_usecase", "division by zero", flags=flags)
+
+    def test_floordiv_errors_npm(self):
+        self.test_floordiv_errors(flags=Noflags)
+
+    def test_mod_errors(self, flags=force_pyobj_flags):
+        self.check_div_errors("mod_usecase", "modulo by zero", flags=flags)
+
+    def test_mod_errors_npm(self):
+        self.test_mod_errors(flags=Noflags)
+
+    def run_pow_ints(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = [-2, -1, 0, 1, 2]
+        y_operands = [0, 1, 2]
+
+        types_list = [(types.int32, types.int32),
+                      (types.int64, types.int64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = [0, 1, 2]
+        y_operands = [0, 1, 2]
+
+        types_list = [(types.byte, types.byte),
+                      (types.uint32, types.uint32),
+                      (types.uint64, types.uint64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+    def run_pow_floats(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = [-222.222, -111.111, 111.111, 222.222]
+        y_operands = [-2, -1, 0, 1, 2]
+
+        types_list = [(types.float32, types.float32),
+                      (types.float64, types.float64)]
+
+        self.run_test_floats(pyfunc, x_operands, y_operands, types_list,
+                             flags=flags)
+
+        x_operands = [0.0]
+        y_operands = [0, 1, 2]  # TODO native handling of 0 ** negative power
+
+        types_list = [(types.float32, types.float32),
+                      (types.float64, types.float64)]
+
+        self.run_test_floats(pyfunc, x_operands, y_operands, types_list,
+                             flags=flags)
+
+    # XXX power operator is unsupported on complex numbers (see issue #488)
+    generate_binop_tests(locals(),
+                         ('pow', 'ipow'),
+                         {'ints': 'run_pow_ints',
+                          'floats': 'run_pow_floats',
+                          })
+
+    def test_add_complex(self, flags=force_pyobj_flags):
+        pyfunc = self.op.add_usecase
+
+        x_operands = [1+0j, 1j, -1-1j]
+        y_operands = x_operands
+
+        types_list = [(types.complex64, types.complex64),
+                      (types.complex128, types.complex128),]
+
+        self.run_test_floats(pyfunc, x_operands, y_operands, types_list,
+                             flags=flags)
+
+    def test_add_complex_npm(self):
+        self.test_add_complex(flags=Noflags)
+
+    def test_sub_complex(self, flags=force_pyobj_flags):
+        pyfunc = self.op.sub_usecase
+
+        x_operands = [1+0j, 1j, -1-1j]
+        y_operands = [1, 2, 3]
+
+        types_list = [(types.complex64, types.complex64),
+                      (types.complex128, types.complex128),]
+
+        self.run_test_floats(pyfunc, x_operands, y_operands, types_list,
+                             flags=flags)
+
+    def test_sub_complex_npm(self):
+        self.test_sub_complex(flags=Noflags)
+
+    def test_mul_complex(self, flags=force_pyobj_flags):
+        pyfunc = self.op.mul_usecase
+
+        x_operands = [1+0j, 1j, -1-1j]
+        y_operands = [1, 2, 3]
+
+        types_list = [(types.complex64, types.complex64),
+                      (types.complex128, types.complex128),]
+
+        self.run_test_floats(pyfunc, x_operands, y_operands, types_list,
+                             flags=flags)
+
+    def test_mul_complex_npm(self):
+        self.test_mul_complex(flags=Noflags)
+
+    def test_truediv_complex(self, flags=force_pyobj_flags):
+        pyfunc = self.op.truediv_usecase
+
+        x_operands = [1+0j, 1j, -1-1j]
+        y_operands = [1, 2, 3]
+
+        types_list = [(types.complex64, types.complex64),
+                      (types.complex128, types.complex128),]
+
+        self.run_test_floats(pyfunc, x_operands, y_operands, types_list,
+                             flags=flags)
+
+    def test_truediv_complex_npm(self):
+        self.test_truediv_complex(flags=Noflags)
+
+    def test_mod_complex(self, flags=force_pyobj_flags):
+        pyfunc = self.op.mod_usecase
+        with self.assertTypingError():
+            cres = compile_isolated(pyfunc, (types.complex64, types.complex64))
+
+    def test_mod_complex_npm(self):
+        self.test_mod_complex(flags=Noflags)
+
+    #
+    # Matrix multiplication
+    # (just check with simple values; computational tests are in test_linalg)
+    #
+
+    def check_matmul_objmode(self, pyfunc, inplace):
+        # Use dummy objects, to work with any NumPy / SciPy version
+        cres = compile_isolated(pyfunc, (), flags=force_pyobj_flags)
+        cfunc = cres.entry_point
+        a = DumbMatrix(3)
+        b = DumbMatrix(4)
+        got = cfunc(a, b)
+        self.assertEqual(got.value, 12)
+        if inplace:
+            self.assertIs(got, a)
+        else:
+            self.assertIsNot(got, a)
+            self.assertIsNot(got, b)
+
+    def test_matmul(self):
+        self.check_matmul_objmode(self.op.matmul_usecase, inplace=False)
+
+    def test_imatmul(self):
+        self.check_matmul_objmode(self.op.imatmul_usecase, inplace=True)
+
+    @needs_blas
+    def check_matmul_npm(self, pyfunc):
+        arrty = types.Array(types.float32, 1, 'C')
+        cres = compile_isolated(pyfunc, (arrty, arrty), flags=Noflags)
+        cfunc = cres.entry_point
+        a = np.float32([1, 2])
+        b = np.float32([3, 4])
+        got = cfunc(a, b)
+        self.assertPreciseEqual(got, np.dot(a, b))
+        # Never inplace
+        self.assertIsNot(got, a)
+        self.assertIsNot(got, b)
+
+    def test_matmul_npm(self):
+        self.check_matmul_npm(self.op.matmul_usecase)
+
+    def test_imatmul_npm(self):
+        with self.assertTypingError() as raises:
+            self.check_matmul_npm(self.op.imatmul_usecase)
+
+    #
+    # Bitwise operators
+    #
+
+    def run_bitshift_left(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = [0, 1]
+        y_operands = [0, 1, 2, 4, 8, 16, 31]
+
+        types_list = [(types.uint32, types.uint32)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = [0, 1]
+        y_operands = [0, 1, 2, 4, 8, 16, 32, 63]
+
+        types_list = [(types.uint64, types.uint64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = [0, -1]
+        y_operands = [0, 1, 2, 4, 8, 16, 31]
+
+        types_list = [(types.int32, types.int32)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = [0, -1]
+        y_operands = [0, 1, 2, 4, 8, 16, 32, 63]
+
+        types_list = [(types.int64, types.int64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+    generate_binop_tests(locals(),
+                         ('bitshift_left', 'bitshift_ileft'),
+                         {'ints': 'run_bitshift_left',
+                          })
+
+    def run_bitshift_right(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = [0, 1, 2**32 - 1]
+        y_operands = [0, 1, 2, 4, 8, 16, 31]
+
+        types_list = [(types.uint32, types.uint32)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = [0, 1, 2**64 - 1]
+        y_operands = [0, 1, 2, 4, 8, 16, 32, 63]
+
+        types_list = [(types.uint64, types.uint64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = [0, 1, -(2**31)]
+        y_operands = [0, 1, 2, 4, 8, 16, 31]
+
+        types_list = [(types.int32, types.int32)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = [0, -1, -(2**31)]
+        y_operands = [0, 1, 2, 4, 8, 16, 32, 63]
+
+        types_list = [(types.int64, types.int64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+    generate_binop_tests(locals(),
+                         ('bitshift_right', 'bitshift_iright'),
+                         {'ints': 'run_bitshift_right',
+                          })
+
+    def run_logical(self, pyfunc, flags=force_pyobj_flags):
+        x_operands = list(range(0, 8)) + [2**32 - 1]
+        y_operands = list(range(0, 8)) + [2**32 - 1]
+
+        types_list = [(types.uint32, types.uint32)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = list(range(0, 8)) + [2**64 - 1]
+        y_operands = list(range(0, 8)) + [2**64 - 1]
+
+        types_list = [(types.uint64, types.uint64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = list(range(-4, 4)) + [-(2**31), 2**31 - 1]
+        y_operands = list(range(-4, 4)) + [-(2**31), 2**31 - 1]
+
+        types_list = [(types.int32, types.int32)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = list(range(-4, 4)) + [-(2**63), 2**63 - 1]
+        y_operands = list(range(-4, 4)) + [-(2**63), 2**63 - 1]
+
+        types_list = [(types.int64, types.int64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+    generate_binop_tests(locals(),
+                         ('bitwise_and', 'bitwise_iand',
+                          'bitwise_or', 'bitwise_ior',
+                          'bitwise_xor', 'bitwise_ixor'),
+                         {'ints': 'run_logical',
+                          'bools': 'run_binop_bools',
+                          })
+
+    #
+    # Unary operators
+    #
+
+    def test_bitwise_not(self, flags=force_pyobj_flags):
+        pyfunc = self.op.bitwise_not_usecase_binary
+
+        x_operands = list(range(0, 8)) + [2**32 - 1]
+        x_operands = [np.uint32(x) for x in x_operands]
+        y_operands = [0]
+
+        types_list = [(types.uint32, types.uint32)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = list(range(-4, 4)) + [-(2**31), 2**31 - 1]
+        y_operands = [0]
+
+        types_list = [(types.int32, types.int32)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = list(range(0, 8)) + [2**64 - 1]
+        x_operands = [np.uint64(x) for x in x_operands]
+        y_operands = [0]
+
+        types_list = [(types.uint64, types.uint64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        x_operands = list(range(-4, 4)) + [-(2**63), 2**63 - 1]
+        y_operands = [0]
+
+        types_list = [(types.int64, types.int64)]
+
+        self.run_test_ints(pyfunc, x_operands, y_operands, types_list,
+                           flags=flags)
+
+        # For booleans, we follow Numpy semantics (i.e. ~True == False,
+        # not ~True == -2)
+        values = [False, False, True, True]
+        values = list(map(np.bool_, values))
+
+        pyfunc = self.op.bitwise_not_usecase
+        cres = compile_isolated(pyfunc, (types.boolean,), flags=flags)
+        cfunc = cres.entry_point
+        for val in values:
+            self.assertPreciseEqual(pyfunc(val), cfunc(val))
+
+    def test_bitwise_not_npm(self):
+        self.test_bitwise_not(flags=Noflags)
+
+    def test_bitwise_float(self):
+        """
+        Make sure that bitwise float operations are not allowed
+        """
+        def assert_reject_compile(pyfunc, argtypes, opname):
+            msg = 'expecting TypingError when compiling {}'.format(pyfunc)
+            with self.assertRaises(errors.TypingError, msg=msg) as raises:
+                compile_isolated(pyfunc, argtypes)
+            # check error message
+            fmt = _header_lead + ' {}'
+            expecting = fmt.format(opname
+                                   if isinstance(opname, str)
+                                   else 'Function({})'.format(opname))
+            self.assertIn(expecting, str(raises.exception))
+
+        methods = [
+            'bitshift_left_usecase',
+            'bitshift_ileft_usecase',
+            'bitshift_right_usecase',
+            'bitshift_iright_usecase',
+            'bitwise_and_usecase',
+            'bitwise_iand_usecase',
+            'bitwise_or_usecase',
+            'bitwise_ior_usecase',
+            'bitwise_xor_usecase',
+            'bitwise_ixor_usecase',
+            'bitwise_not_usecase_binary',
+        ]
+
+        for name in methods:
+            pyfunc = getattr(self.op, name)
+            assert_reject_compile(pyfunc, (types.float32, types.float32),
+                                  opname=self._bitwise_opnames[name])
+
+    def test_not(self):
+        pyfunc = self.op.not_usecase
+
+        values = [
+            1,
+            2,
+            3,
+            1.2,
+            3.4j,
+        ]
+
+        cres = compile_isolated(pyfunc, (), flags=force_pyobj_flags)
+        cfunc = cres.entry_point
+        for val in values:
+            self.assertEqual(pyfunc(val), cfunc(val))
+
+    def test_not_npm(self):
+        pyfunc = self.op.not_usecase
+        # test native mode
+        argtys = [
+            types.int8,
+            types.int32,
+            types.int64,
+            types.float32,
+            types.complex128,
+        ]
+        values = [
+            1,
+            2,
+            3,
+            1.2,
+            3.4j,
+        ]
+        for ty, val in zip(argtys, values):
+            cres = compile_isolated(pyfunc, [ty])
+            self.assertEqual(cres.signature.return_type, types.boolean)
+            cfunc = cres.entry_point
+            self.assertEqual(pyfunc(val), cfunc(val))
+
+    # XXX test_negate should check for negative and positive zeros and infinities
+
+    def test_negate_npm(self):
+        pyfunc = self.op.negate_usecase
+        # test native mode
+        argtys = [
+            types.int8,
+            types.int32,
+            types.int64,
+            types.float32,
+            types.float64,
+            types.complex128,
+            types.boolean,
+            types.boolean,
+        ]
+        values = [
+            1,
+            2,
+            3,
+            1.2,
+            2.4,
+            3.4j,
+            True,
+            False,
+        ]
+        for ty, val in zip(argtys, values):
+            cres = compile_isolated(pyfunc, [ty])
+            cfunc = cres.entry_point
+            self.assertAlmostEqual(pyfunc(val), cfunc(val))
+
+
+    def test_negate(self):
+        pyfunc = self.op.negate_usecase
+        values = [
+            1,
+            2,
+            3,
+            1.2,
+            3.4j,
+            True,
+            False,
+        ]
+        cres = compile_isolated(pyfunc, (), flags=force_pyobj_flags)
+        cfunc = cres.entry_point
+        for val in values:
+            self.assertEqual(pyfunc(val), cfunc(val))
+
+    def test_unary_positive_npm(self):
+        pyfunc = self.op.unary_positive_usecase
+        # test native mode
+        argtys = [
+            types.int8,
+            types.int32,
+            types.int64,
+            types.float32,
+            types.float64,
+            types.complex128,
+            types.boolean,
+            types.boolean,
+        ]
+        values = [
+            1,
+            2,
+            3,
+            1.2,
+            2.4,
+            3.4j,
+            True,
+            False
+        ]
+        for ty, val in zip(argtys, values):
+            cres = compile_isolated(pyfunc, [ty])
+            cfunc = cres.entry_point
+            self.assertAlmostEqual(pyfunc(val), cfunc(val))
+
+    def test_unary_positive(self):
+        pyfunc = self.op.unary_positive_usecase
+        values = [
+            1,
+            2,
+            3,
+            1.2,
+            3.4j,
+            True,
+            False,
+        ]
+        cres = compile_isolated(pyfunc, (), flags=force_pyobj_flags)
+        cfunc = cres.entry_point
+        for val in values:
+            self.assertEqual(pyfunc(val), cfunc(val))
+
+    def _check_in(self, pyfunc, flags):
+        dtype = types.int64
+        cres = compile_isolated(pyfunc, (dtype, types.UniTuple(dtype, 3)),
+                                flags=flags)
+        cfunc = cres.entry_point
+        for i in (3, 4, 5, 6, 42):
+            tup = (3, 42, 5)
+            self.assertPreciseEqual(pyfunc(i, tup), cfunc(i, tup))
+
+    def test_in(self, flags=force_pyobj_flags):
+        self._check_in(self.op.in_usecase, flags)
+
+    def test_in_npm(self):
+        self.test_in(flags=Noflags)
+
+    def test_not_in(self, flags=force_pyobj_flags):
+        self._check_in(self.op.not_in_usecase, flags)
+
+    def test_not_in_npm(self):
+        self.test_not_in(flags=Noflags)
+
+
+class TestOperatorModule(TestOperators):
+
+    op = FunctionalOperatorImpl
+
+    _bitwise_opnames = {
+        'bitshift_left_usecase': operator.lshift,
+        'bitshift_ileft_usecase': operator.ilshift,
+        'bitshift_right_usecase': operator.rshift,
+        'bitshift_iright_usecase': operator.irshift,
+        'bitwise_and_usecase': operator.and_,
+        'bitwise_iand_usecase': operator.iand,
+        'bitwise_or_usecase': operator.or_,
+        'bitwise_ior_usecase': operator.ior,
+        'bitwise_xor_usecase': operator.xor,
+        'bitwise_ixor_usecase': operator.ixor,
+        'bitwise_not_usecase_binary': operator.invert,
+    }
+
+
+class TestMixedInts(TestCase):
+    """
+    Tests for operator calls with mixed integer types.
+    """
+
+    op = LiteralOperatorImpl
+
+    int_samples = [0, 1, 3, 10, 42, 127, 10000, -1, -3, -10, -42, -127, -10000]
+
+    int_types = [types.int8, types.uint8, types.int64, types.uint64]
+    signed_types = [tp for tp in int_types if tp.signed]
+    unsigned_types = [tp for tp in int_types if not tp.signed]
+    type_pairs = list(itertools.product(int_types, int_types))
+    signed_pairs = [(u, v) for u, v in type_pairs
+                    if u.signed or v.signed]
+    unsigned_pairs = [(u, v) for u, v in type_pairs
+                      if not (u.signed or v.signed)]
+
+    def get_numpy_signed_upcast(self, *vals):
+        bitwidth = max(v.dtype.itemsize * 8 for v in vals)
+        bitwidth = max(bitwidth, types.intp.bitwidth)
+        return getattr(np, "int%d" % bitwidth)
+
+    def get_numpy_unsigned_upcast(self, *vals):
+        bitwidth = max(v.dtype.itemsize * 8 for v in vals)
+        bitwidth = max(bitwidth, types.intp.bitwidth)
+        return getattr(np, "uint%d" % bitwidth)
+
+    def get_typed_int(self, typ, val):
+        return getattr(np, typ.name)(val)
+
+    def get_control_signed(self, opname):
+        op = getattr(operator, opname)
+        def control_signed(a, b):
+            tp = self.get_numpy_signed_upcast(a, b)
+            return op(tp(a), tp(b))
+        return control_signed
+
+    def get_control_unsigned(self, opname):
+        op = getattr(operator, opname)
+        def control_unsigned(a, b):
+            tp = self.get_numpy_unsigned_upcast(a, b)
+            return op(tp(a), tp(b))
+        return control_unsigned
+
+    def run_binary(self, pyfunc, control_func, operands, types,
+                   expected_type=int, **assertPreciseEqualArgs):
+        for xt, yt in types:
+            cr = compile_isolated(pyfunc, (xt, yt), flags=Noflags)
+            cfunc = cr.entry_point
+            for x, y in itertools.product(operands, operands):
+                # Get Numpy typed scalars for the given types and values
+                x = self.get_typed_int(xt, x)
+                y = self.get_typed_int(yt, y)
+                expected = control_func(x, y)
+                got = cfunc(x, y)
+                self.assertIsInstance(got, expected_type)
+                msg = ("mismatch for (%r, %r) with types %s"
+                       % (x, y, (xt, yt)))
+                self.assertPreciseEqual(got, expected, msg=msg,
+                                        **assertPreciseEqualArgs)
+
+    def run_unary(self, pyfunc, control_func, operands, types,
+                  expected_type=int):
+        for xt in types:
+            cr = compile_isolated(pyfunc, (xt,), flags=Noflags)
+            cfunc = cr.entry_point
+            for x in operands:
+                x = self.get_typed_int(xt, x)
+                expected = control_func(x)
+                got = cfunc(x)
+                self.assertIsInstance(got, expected_type)
+                self.assertPreciseEqual(
+                    got, expected,
+                    msg="mismatch for %r with type %s: %r != %r"
+                        % (x, xt, got, expected))
+
+    def run_arith_binop(self, pyfunc, opname, samples,
+                        expected_type=int):
+        self.run_binary(pyfunc, self.get_control_signed(opname),
+                        samples, self.signed_pairs, expected_type)
+        self.run_binary(pyfunc, self.get_control_unsigned(opname),
+                        samples, self.unsigned_pairs, expected_type)
+
+    def test_add(self):
+        self.run_arith_binop(self.op.add_usecase, 'add', self.int_samples)
+
+    def test_sub(self):
+        self.run_arith_binop(self.op.sub_usecase, 'sub', self.int_samples)
+
+    def test_mul(self):
+        self.run_arith_binop(self.op.mul_usecase, 'mul', self.int_samples)
+
+    def test_floordiv(self):
+        samples = [x for x in self.int_samples if x != 0]
+        self.run_arith_binop(self.op.floordiv_usecase, 'floordiv', samples)
+
+    def test_mod(self):
+        samples = [x for x in self.int_samples if x != 0]
+        self.run_arith_binop(self.op.mod_usecase, 'mod', samples)
+
+    def test_pow(self):
+        pyfunc = self.op.pow_usecase
+        # Only test with positive values, as otherwise trying to write the
+        # control function in terms of Python or Numpy power turns out insane.
+        samples = [x for x in self.int_samples if x >= 0]
+        self.run_arith_binop(pyfunc, 'pow', samples)
+
+        # Now test all non-zero values, but only with signed types
+        def control_signed(a, b):
+            tp = self.get_numpy_signed_upcast(a, b)
+            if b >= 0:
+                return tp(a) ** tp(b)
+            else:
+                inv = tp(a) ** tp(-b)
+                if inv == 0:
+                    # Overflow
+                    return 0
+                return np.intp(1.0 / inv)
+        samples = [x for x in self.int_samples if x != 0]
+        signed_pairs = [(u, v) for u, v in self.type_pairs
+                        if u.signed and v.signed]
+        self.run_binary(pyfunc, control_signed,
+                        samples, signed_pairs)
+
+    def test_truediv(self):
+
+        def control(a, b):
+            return float(a) / float(b)
+        samples = [x for x in self.int_samples if x != 0]
+        pyfunc = self.op.truediv_usecase
+
+        # Note: there can be precision issues on x87
+        # e.g. for `1 / 18446744073709541616`
+        # -> 0x1.0000000000002p-64 vs. 0x1.0000000000003p-64.
+        self.run_binary(pyfunc, control, samples, self.signed_pairs,
+                        expected_type=float, prec='double')
+        self.run_binary(pyfunc, control, samples, self.unsigned_pairs,
+                        expected_type=float, prec='double')
+
+    def test_and(self):
+        self.run_arith_binop(self.op.bitwise_and_usecase, 'and_', self.int_samples)
+
+    def test_or(self):
+        self.run_arith_binop(self.op.bitwise_or_usecase, 'or_', self.int_samples)
+
+    def test_xor(self):
+        self.run_arith_binop(self.op.bitwise_xor_usecase, 'xor', self.int_samples)
+
+    def run_shift_binop(self, pyfunc, opname):
+        opfunc = getattr(operator, opname)
+        def control_signed(a, b):
+            tp = self.get_numpy_signed_upcast(a, b)
+            return opfunc(tp(a), tp(b))
+        def control_unsigned(a, b):
+            tp = self.get_numpy_unsigned_upcast(a, b)
+            return opfunc(tp(a), tp(b))
+
+        samples = self.int_samples
+
+        def check(xt, yt, control_func):
+            cr = compile_isolated(pyfunc, (xt, yt), flags=Noflags)
+            cfunc = cr.entry_point
+            for x in samples:
+                # Avoid shifting by more than the shiftand's bitwidth, as
+                # we would hit undefined behaviour.
+                maxshift = xt.bitwidth - 1
+                for y in (0, 1, 3, 5, maxshift - 1, maxshift):
+                    # Get Numpy typed scalars for the given types and values
+                    x = self.get_typed_int(xt, x)
+                    y = self.get_typed_int(yt, y)
+                    expected = control_func(x, y)
+                    got = cfunc(x, y)
+                    msg = ("mismatch for (%r, %r) with types %s"
+                           % (x, y, (xt, yt)))
+                    self.assertPreciseEqual(got, expected, msg=msg)
+
+        # For bitshifts, only the first operand's signedness matters
+        # to choose the operation's signedness.
+        signed_pairs = [(u, v) for u, v in self.type_pairs
+                        if u.signed]
+        unsigned_pairs = [(u, v) for u, v in self.type_pairs
+                          if not u.signed]
+
+        for xt, yt in signed_pairs:
+            check(xt, yt, control_signed)
+        for xt, yt in unsigned_pairs:
+            check(xt, yt, control_unsigned)
+
+    def test_lshift(self):
+        self.run_shift_binop(self.op.bitshift_left_usecase, 'lshift')
+
+    def test_rshift(self):
+        self.run_shift_binop(self.op.bitshift_right_usecase, 'rshift')
+
+    def test_unary_positive(self):
+        def control(a):
+            return a
+        samples = self.int_samples
+        pyfunc = self.op.unary_positive_usecase
+
+        self.run_unary(pyfunc, control, samples, self.int_types)
+
+    def test_unary_negative(self):
+        def control_signed(a):
+            tp = self.get_numpy_signed_upcast(a)
+            return tp(-a)
+        def control_unsigned(a):
+            tp = self.get_numpy_unsigned_upcast(a)
+            return tp(-a)
+        samples = self.int_samples
+        pyfunc = self.op.negate_usecase
+
+        self.run_unary(pyfunc, control_signed, samples, self.signed_types)
+        self.run_unary(pyfunc, control_unsigned, samples, self.unsigned_types)
+
+    def test_invert(self):
+        def control_signed(a):
+            tp = self.get_numpy_signed_upcast(a)
+            return tp(~a)
+        def control_unsigned(a):
+            tp = self.get_numpy_unsigned_upcast(a)
+            return tp(~a)
+        samples = self.int_samples
+        pyfunc = self.op.bitwise_not_usecase
+
+        self.run_unary(pyfunc, control_signed, samples, self.signed_types)
+        self.run_unary(pyfunc, control_unsigned, samples, self.unsigned_types)
+
+
+class TestMixedIntsOperatorModule(TestMixedInts):
+
+    op = FunctionalOperatorImpl
+
+
+class TestStaticPower(TestCase):
+    """
+    Test the ** operator with a static exponent, to exercise a
+    dedicated optimization.
+    """
+
+    def _check_pow(self, exponents, values):
+        for exp in exponents:
+            # test against non-static version of the @jit-ed function
+            regular_func = LiteralOperatorImpl.pow_usecase
+            static_func = make_static_power(exp)
+
+            static_cfunc = jit(nopython=True)(static_func)
+            regular_cfunc = jit(nopython=True)(regular_func)
+            for v in values:
+                try:
+                    expected = regular_cfunc(v, exp)
+                except ZeroDivisionError:
+                    with self.assertRaises(ZeroDivisionError):
+                        static_cfunc(v)
+                else:
+                    got = static_cfunc(v)
+                    self.assertPreciseEqual(expected, got, prec='double')
+
+    def test_int_values(self):
+        exponents = [1, 2, 3, 5, 17, 0, -1, -2, -3]
+        vals = [0, 1, 3, -1, -4, np.int8(-3), np.uint16(4)]
+
+        self._check_pow(exponents, vals)
+
+    def test_real_values(self):
+        exponents = [1, 2, 3, 5, 17, 0, -1, -2, -3, 0x111111, -0x111112]
+        vals = [1.5, 3.25, -1.25, np.float32(-2.0), float('inf'), float('nan')]
+
+        self._check_pow(exponents, vals)
+
+class TestStringConstComparison(TestCase):
+    """
+    Test comparison of string constants
+    """
+    def test_eq(self):
+        def test_impl1():
+            s = 'test'
+            return s == 'test'
+
+        def test_impl2():
+            s = 'test1'
+            return s == 'test'
+
+        cfunc1 = jit(nopython=True)(test_impl1)
+        cfunc2 = jit(nopython=True)(test_impl2)
+        self.assertEqual(test_impl1(), cfunc1())
+        self.assertEqual(test_impl2(), cfunc2())
+
+    def test_neq(self):
+        def test_impl1():
+            s = 'test'
+            return s != 'test'
+
+        def test_impl2():
+            s = 'test1'
+            return s != 'test'
+
+        cfunc1 = jit(nopython=True)(test_impl1)
+        cfunc2 = jit(nopython=True)(test_impl2)
+        self.assertEqual(test_impl1(), cfunc1())
+        self.assertEqual(test_impl2(), cfunc2())
+
+class TestBooleanLiteralOperators(TestCase):
+    """
+    Test operators with Boolean constants
+    """
+    def test_eq(self):
+
+        def test_impl1(b):
+            return a_val == b
+
+        def test_impl2(a):
+            return a == b_val
+
+        def test_impl3():
+            r1 = True == True
+            r2 = True == False
+            r3 = False == True
+            r4 = False == False
+            return (r1, r2, r3, r4)
+
+        for a_val, b in itertools.product([True, False], repeat=2):
+            cfunc1 = jit(nopython=True)(test_impl1)
+            self.assertEqual(test_impl1(b), cfunc1(b))
+
+        for a, b_val in itertools.product([True, False], repeat=2):
+            cfunc2 = jit(nopython=True)(test_impl2)
+            self.assertEqual(test_impl2(a), cfunc2(a))
+
+        cfunc3 = jit(nopython=True)(test_impl3)
+        self.assertEqual(test_impl3(), cfunc3())
+
+    def test_ne(self):
+
+        def test_impl1(b):
+            return a_val != b
+
+        def test_impl2(a):
+            return a != b_val
+
+        def test_impl3():
+            r1 = True != True
+            r2 = True != False
+            r3 = False != True
+            r4 = False != False
+            return (r1, r2, r3, r4)
+
+        for a_val, b in itertools.product([True, False], repeat=2):
+            cfunc1 = jit(nopython=True)(test_impl1)
+            self.assertEqual(test_impl1(b), cfunc1(b))
+
+        for a, b_val in itertools.product([True, False], repeat=2):
+            cfunc2 = jit(nopython=True)(test_impl2)
+            self.assertEqual(test_impl2(a), cfunc2(a))
+
+        cfunc3 = jit(nopython=True)(test_impl3)
+        self.assertEqual(test_impl3(), cfunc3())
+
+    def test_is(self):
+
+        def test_impl1(b):
+            return a_val is b
+
+        def test_impl2():
+            r1 = True is True
+            r2 = True is False
+            r3 = False is True
+            r4 = False is False
+            return (r1, r2, r3, r4)
+
+        for a_val, b in itertools.product([True, False], repeat=2):
+            cfunc1 = jit(nopython=True)(test_impl1)
+            self.assertEqual(test_impl1(b), cfunc1(b))
+
+        cfunc2 = jit(nopython=True)(test_impl2)
+        self.assertEqual(test_impl2(), cfunc2())
+
+    def test_not(self):
+
+        def test_impl():
+            a, b = False, True
+            return (not a, not b)
+
+        cfunc = jit(nopython=True)(test_impl)
+        self.assertEqual(test_impl(), cfunc())
+
+    def test_bool(self):
+
+        def test_impl():
+            a, b = False, True
+            return (bool(a), bool(b))
+
+        cfunc = jit(nopython=True)(test_impl)
+        self.assertEqual(test_impl(), cfunc())
+
+    def test_bool_to_str(self):
+
+        def test_impl():
+            a, b = False, True
+            return (str(a), str(b))
+
+        cfunc = jit(nopython=True)(test_impl)
+        self.assertEqual(test_impl(), cfunc())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_optional.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_optional.py
new file mode 100644
index 000000000..369acdeab
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_optional.py
@@ -0,0 +1,264 @@
+import itertools
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import typeof, njit
+from numba.core import types, lowering
+from numba.tests.support import TestCase
+
+
+def return_double_or_none(x):
+    if x:
+        ret = None
+    else:
+        ret = 1.2
+    return ret
+
+
+def return_different_statement(x):
+    if x:
+        return None
+    else:
+        return 1.2
+
+
+def return_bool_optional_or_none(x, y):
+    if y:
+        z = False
+    else:
+        z = None
+    if x == 2:
+        # A boolean
+        return True
+    elif x == 1:
+        # A runtime optional
+        return z
+    else:
+        # None
+        return None
+
+
+def is_this_a_none(x):
+    if x:
+        val_or_none = None
+    else:
+        val_or_none = x
+
+    if val_or_none is None:
+        return x - 1
+
+    if val_or_none is not None:
+        return x + 1
+
+
+def a_is_b(a, b):
+    """
+    Note in nopython mode, this operation does not make much sense.
+    Because we don't have objects anymore.
+    `a is b` is always False if not operating on None and Optional type
+    """
+    return a is b
+
+
+def a_is_not_b(a, b):
+    """
+    This is `not (a is b)`
+    """
+    return a is not b
+
+
+class TestOptional(TestCase):
+
+    _numba_parallel_test_ = False
+
+    def test_return_double_or_none(self):
+        pyfunc = return_double_or_none
+        cres = compile_isolated(pyfunc, [types.boolean])
+        cfunc = cres.entry_point
+
+        for v in [True, False]:
+            self.assertPreciseEqual(pyfunc(v), cfunc(v))
+
+    def test_return_different_statement(self):
+        pyfunc = return_different_statement
+        cres = compile_isolated(pyfunc, [types.boolean])
+        cfunc = cres.entry_point
+
+        for v in [True, False]:
+            self.assertPreciseEqual(pyfunc(v), cfunc(v))
+
+    def test_return_bool_optional_or_none(self):
+        pyfunc = return_bool_optional_or_none
+        cres = compile_isolated(pyfunc, [types.int32, types.int32])
+        cfunc = cres.entry_point
+
+        for x, y in itertools.product((0, 1, 2), (0, 1)):
+            self.assertPreciseEqual(pyfunc(x, y), cfunc(x, y))
+
+    def test_is_this_a_none(self):
+        pyfunc = is_this_a_none
+        cres = compile_isolated(pyfunc, [types.intp])
+        cfunc = cres.entry_point
+
+        for v in [-1, 0, 1, 2]:
+            self.assertPreciseEqual(pyfunc(v), cfunc(v))
+
+    def test_is_this_a_none_objmode(self):
+        pyfunc = is_this_a_none
+        flags = Flags()
+        flags.force_pyobject = True
+        cres = compile_isolated(pyfunc, [types.intp], flags=flags)
+        cfunc = cres.entry_point
+        self.assertTrue(cres.objectmode)
+        for v in [-1, 0, 1, 2]:
+            self.assertPreciseEqual(pyfunc(v), cfunc(v))
+
+    def test_a_is_b_intp(self):
+        pyfunc = a_is_b
+        cres = compile_isolated(pyfunc, [types.intp, types.intp])
+        cfunc = cres.entry_point
+        # integer identity relies on `==`
+        self.assertTrue(cfunc(1, 1))
+        self.assertFalse(cfunc(1, 2))
+
+    def test_a_is_not_b_intp(self):
+        pyfunc = a_is_not_b
+        cres = compile_isolated(pyfunc, [types.intp, types.intp])
+        cfunc = cres.entry_point
+        # integer identity relies on `==`
+        self.assertFalse(cfunc(1, 1))
+        self.assertTrue(cfunc(1, 2))
+
+    def test_optional_float(self):
+        def pyfunc(x, y):
+            if y is None:
+                return x
+            else:
+                return x + y
+
+        cfunc = njit("(float64, optional(float64))")(pyfunc)
+        self.assertAlmostEqual(pyfunc(1., 12.3), cfunc(1., 12.3))
+        self.assertAlmostEqual(pyfunc(1., None), cfunc(1., None))
+
+    def test_optional_array(self):
+        def pyfunc(x, y):
+            if y is None:
+                return x
+            else:
+                y[0] += x
+                return y[0]
+
+        cfunc = njit("(float32, optional(float32[:]))")(pyfunc)
+        cy = np.array([12.3], dtype=np.float32)
+        py = cy.copy()
+        self.assertAlmostEqual(pyfunc(1., py), cfunc(1., cy))
+        np.testing.assert_almost_equal(py, cy)
+        self.assertAlmostEqual(pyfunc(1., None), cfunc(1., None))
+
+    def test_optional_array_error(self):
+        def pyfunc(y):
+            return y[0]
+
+        cfunc = njit("(optional(int32[:]),)")(pyfunc)
+        with self.assertRaises(TypeError) as raised:
+            cfunc(None)
+        self.assertIn('expected array(int32, 1d, A), got None',
+                      str(raised.exception))
+
+        y = np.array([0xabcd], dtype=np.int32)
+        self.assertEqual(cfunc(y), pyfunc(y))
+
+    def test_optional_array_attribute(self):
+        """
+        Check that we can access attribute of an optional
+        """
+        def pyfunc(arr, do_it):
+            opt = None
+            if do_it:  # forces `opt` to be an optional of arr
+                opt = arr
+            return opt.shape[0]
+
+        cfunc = njit(pyfunc)
+        arr = np.arange(5)
+        self.assertEqual(pyfunc(arr, True), cfunc(arr, True))
+
+    def test_assign_to_optional(self):
+        """
+        Check that we can assign to a variable of optional type
+        """
+        @njit
+        def make_optional(val, get_none):
+            if get_none:
+                ret = None
+            else:
+                ret = val
+            return ret
+
+        @njit
+        def foo(val, run_second):
+            a = make_optional(val, True)
+            if run_second:
+                a = make_optional(val, False)
+            return a
+
+        self.assertIsNone(foo(123, False))
+        self.assertEqual(foo(231, True), 231)
+
+    def test_optional_thru_omitted_arg(self):
+        """
+        Issue 1868
+        """
+
+        def pyfunc(x=None):
+            if x is None:
+                x = 1
+            return x
+
+        cfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(), cfunc())
+        self.assertEqual(pyfunc(3), cfunc(3))
+
+    def test_optional_unpack(self):
+        """
+        Issue 2171
+        """
+        def pyfunc(x):
+            if x is None:
+                return
+            else:
+                a, b = x
+                return a, b
+
+        tup = types.Tuple([types.intp] * 2)
+        opt_tup = types.Optional(tup)
+        sig = (opt_tup,)
+        cfunc = njit(sig)(pyfunc)
+        self.assertEqual(pyfunc(None), cfunc(None))
+        self.assertEqual(pyfunc((1, 2)), cfunc((1, 2)))
+
+    def test_many_optional_none_returns(self):
+        """
+        Issue #4058
+        """
+        @njit
+        def foo(maybe):
+            lx = None
+            if maybe:
+                lx = 10
+            return 1, lx
+
+        def work():
+            tmp = []
+            for _ in range(20000):
+                maybe = False
+                _ = foo(maybe)
+
+        # this caused "Fatal Python error: deallocating None" as there was no
+        # incref being made on the returned None.
+        work()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_overlap.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_overlap.py
new file mode 100644
index 000000000..eecf77c09
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_overlap.py
@@ -0,0 +1,134 @@
+import numpy as np
+
+from numba import jit
+from numba.core import types
+from numba.tests.support import TestCase, tag
+import unittest
+
+
+# Array overlaps involving a displacement
+
+def array_overlap1(src, dest, k=1):
+    assert src.shape == dest.shape
+    dest[k:] = src[:-k]
+
+def array_overlap2(src, dest, k=1):
+    assert src.shape == dest.shape
+    dest[:-k] = src[k:]
+
+def array_overlap3(src, dest, k=1):
+    assert src.shape == dest.shape
+    dest[:,:-k] = src[:,k:]
+
+def array_overlap4(src, dest, k=1):
+    assert src.shape == dest.shape
+    dest[:,k:] = src[:,:-k]
+
+def array_overlap5(src, dest, k=1):
+    assert src.shape == dest.shape
+    dest[...,:-k] = src[...,k:]
+
+def array_overlap6(src, dest, k=1):
+    assert src.shape == dest.shape
+    dest[...,k:] = src[...,:-k]
+
+# Array overlaps involving an in-place reversal
+
+def array_overlap11(src, dest):
+    assert src.shape == dest.shape
+    dest[::-1] = src
+
+def array_overlap12(src, dest):
+    assert src.shape == dest.shape
+    dest[:] = src[::-1]
+
+def array_overlap13(src, dest):
+    assert src.shape == dest.shape
+    dest[:,::-1] = src
+
+def array_overlap14(src, dest):
+    assert src.shape == dest.shape
+    dest[:] = src[:,::-1]
+
+def array_overlap15(src, dest):
+    assert src.shape == dest.shape
+    dest[...,::-1] = src
+
+def array_overlap16(src, dest):
+    assert src.shape == dest.shape
+    dest[:] = src[...,::-1]
+
+
+class TestArrayOverlap(TestCase):
+
+    def check_overlap(self, pyfunc, min_ndim, have_k_argument=False):
+        N = 4
+
+        def vary_layouts(orig):
+            yield orig.copy(order='C')
+            yield orig.copy(order='F')
+            a = orig[::-1].copy()[::-1]
+            assert not a.flags.c_contiguous and not a.flags.f_contiguous
+            yield a
+
+        def check(pyfunc, cfunc, pydest, cdest, kwargs):
+            pyfunc(pydest, pydest, **kwargs)
+            cfunc(cdest, cdest, **kwargs)
+            self.assertPreciseEqual(pydest, cdest)
+
+        cfunc = jit(nopython=True)(pyfunc)
+        # Check for up to 3d arrays
+        for ndim in range(min_ndim, 4):
+            shape = (N,) * ndim
+            orig = np.arange(0, N**ndim).reshape(shape)
+            # Note we cannot copy a 'A' layout array exactly (bitwise),
+            # so instead we call vary_layouts() twice
+            for pydest, cdest in zip(vary_layouts(orig), vary_layouts(orig)):
+                if have_k_argument:
+                    for k in range(1, N):
+                        check(pyfunc, cfunc, pydest, cdest, dict(k=k))
+                else:
+                    check(pyfunc, cfunc, pydest, cdest, {})
+
+    def check_overlap_with_k(self, pyfunc, min_ndim):
+        self.check_overlap(pyfunc, min_ndim=min_ndim, have_k_argument=True)
+
+    def test_overlap1(self):
+        self.check_overlap_with_k(array_overlap1, min_ndim=1)
+
+    def test_overlap2(self):
+        self.check_overlap_with_k(array_overlap2, min_ndim=1)
+
+    def test_overlap3(self):
+        self.check_overlap_with_k(array_overlap3, min_ndim=2)
+
+    def test_overlap4(self):
+        self.check_overlap_with_k(array_overlap4, min_ndim=2)
+
+    def test_overlap5(self):
+        self.check_overlap_with_k(array_overlap5, min_ndim=1)
+
+    def test_overlap6(self):
+        self.check_overlap_with_k(array_overlap6, min_ndim=1)
+
+    def test_overlap11(self):
+        self.check_overlap(array_overlap11, min_ndim=1)
+
+    def test_overlap12(self):
+        self.check_overlap(array_overlap12, min_ndim=1)
+
+    def test_overlap13(self):
+        self.check_overlap(array_overlap13, min_ndim=2)
+
+    def test_overlap14(self):
+        self.check_overlap(array_overlap14, min_ndim=2)
+
+    def test_overlap15(self):
+        self.check_overlap(array_overlap15, min_ndim=1)
+
+    def test_overlap16(self):
+        self.check_overlap(array_overlap16, min_ndim=1)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parallel_backend.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parallel_backend.py
new file mode 100644
index 000000000..dcd84effd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parallel_backend.py
@@ -0,0 +1,1245 @@
+# -*- coding: utf-8 -*-
+
+"""
+Tests the parallel backend
+"""
+import faulthandler
+import itertools
+import multiprocessing
+import os
+import random
+import subprocess
+import sys
+import textwrap
+import threading
+import unittest
+
+import numpy as np
+
+from numba import jit, vectorize, guvectorize, set_num_threads
+from numba.tests.support import (temp_directory, override_config, TestCase, tag,
+                                 skip_parfors_unsupported, linux_only,
+                                 needs_external_compilers)
+
+import queue as t_queue
+from numba.testing.main import _TIMEOUT as _RUNNER_TIMEOUT
+from numba.core import config
+
+
+_TEST_TIMEOUT = _RUNNER_TIMEOUT - 60.
+
+
+# Check which backends are available
+# TODO: Put this in a subprocess so the address space is kept clean
+try:
+    # Check it's a compatible TBB before loading it
+    from numba.np.ufunc.parallel import _check_tbb_version_compatible
+    _check_tbb_version_compatible()
+    from numba.np.ufunc import tbbpool    # noqa: F401
+    _HAVE_TBB_POOL = True
+except ImportError:
+    _HAVE_TBB_POOL = False
+
+try:
+    from numba.np.ufunc import omppool
+    _HAVE_OMP_POOL = True
+except ImportError:
+    _HAVE_OMP_POOL = False
+
+try:
+    import scipy.linalg.cython_lapack    # noqa: F401
+    _HAVE_LAPACK = True
+except ImportError:
+    _HAVE_LAPACK = False
+
+# test skipping decorators
+skip_no_omp = unittest.skipUnless(_HAVE_OMP_POOL, "OpenMP threadpool required")
+skip_no_tbb = unittest.skipUnless(_HAVE_TBB_POOL, "TBB threadpool required")
+
+_gnuomp = _HAVE_OMP_POOL and omppool.openmp_vendor == "GNU"
+skip_unless_gnu_omp = unittest.skipUnless(_gnuomp, "GNU OpenMP only tests")
+
+_windows = sys.platform.startswith('win')
+_osx = sys.platform.startswith('darwin')
+_32bit = sys.maxsize <= 2 ** 32
+_parfors_unsupported = _32bit
+
+_HAVE_OS_FORK = not _windows
+
+
+# some functions to jit
+
+def foo(n, v):
+    return np.ones(n) + v
+
+
+if _HAVE_LAPACK:
+    def linalg(n, v):
+        x = np.dot(np.ones((n, n)), np.ones((n, n)))
+        return x + np.arange(n) + v
+else:
+    def linalg(n, v):
+        # no way to trigger MKL without the lapack bindings.
+        return np.arange(n) + v
+
+
+def ufunc_foo(a, b):
+    return a + b
+
+
+def gufunc_foo(a, b, out):
+    out[0] = a + b
+
+
+class runnable(object):
+    def __init__(self, **options):
+        self._options = options
+
+
+class jit_runner(runnable):
+
+    def __call__(self):
+        cfunc = jit(**self._options)(foo)
+        a = 4
+        b = 10
+        expected = foo(a, b)
+        got = cfunc(a, b)
+        np.testing.assert_allclose(expected, got)
+
+
+class mask_runner(object):
+    def __init__(self, runner, mask, **options):
+        self.runner = runner
+        self.mask = mask
+
+    def __call__(self):
+        if self.mask:
+            # Tests are all run in isolated subprocesses, so we
+            # don't have to worry about this affecting other tests
+            set_num_threads(self.mask)
+        self.runner()
+
+
+class linalg_runner(runnable):
+
+    def __call__(self):
+        cfunc = jit(**self._options)(linalg)
+        a = 4
+        b = 10
+        expected = linalg(a, b)
+        got = cfunc(a, b)
+        np.testing.assert_allclose(expected, got)
+
+
+class vectorize_runner(runnable):
+
+    def __call__(self):
+        cfunc = vectorize(['(f4, f4)'], **self._options)(ufunc_foo)
+        a = b = np.random.random(10).astype(np.float32)
+        expected = ufunc_foo(a, b)
+        got = cfunc(a, b)
+        np.testing.assert_allclose(expected, got)
+
+
+class guvectorize_runner(runnable):
+
+    def __call__(self):
+        sig = ['(f4, f4, f4[:])']
+        cfunc = guvectorize(sig, '(),()->()', **self._options)(gufunc_foo)
+        a = b = np.random.random(10).astype(np.float32)
+        expected = ufunc_foo(a, b)
+        got = cfunc(a, b)
+        np.testing.assert_allclose(expected, got)
+
+
+def chooser(fnlist, **kwargs):
+    q = kwargs.get('queue')
+    try:
+        faulthandler.enable()
+        for _ in range(int(len(fnlist) * 1.5)):
+            fn = random.choice(fnlist)
+            fn()
+    except Exception as e:
+        q.put(e)
+
+
+def compile_factory(parallel_class, queue_impl):
+    def run_compile(fnlist):
+        q = queue_impl()
+        kws = {'queue': q}
+        ths = [parallel_class(target=chooser, args=(fnlist,), kwargs=kws)
+               for i in range(4)]
+        for th in ths:
+            th.start()
+        for th in ths:
+            th.join()
+        if not q.empty():
+            errors = []
+            while not q.empty():
+                errors.append(q.get(False))
+            _msg = "Error(s) occurred in delegated runner:\n%s"
+            raise RuntimeError(_msg % '\n'.join([repr(x) for x in errors]))
+    return run_compile
+
+
+# workers
+_thread_class = threading.Thread
+
+
+class _proc_class_impl(object):
+
+    def __init__(self, method):
+        self._method = method
+
+    def __call__(self, *args, **kwargs):
+        ctx = multiprocessing.get_context(self._method)
+        return ctx.Process(*args, **kwargs)
+
+
+def _get_mp_classes(method):
+    if method == 'default':
+        method = None
+    ctx = multiprocessing.get_context(method)
+    proc = _proc_class_impl(method)
+    queue = ctx.Queue
+    return proc, queue
+
+
+thread_impl = compile_factory(_thread_class, t_queue.Queue)
+spawn_proc_impl = compile_factory(*_get_mp_classes('spawn'))
+if not _windows:
+    fork_proc_impl = compile_factory(*_get_mp_classes('fork'))
+    forkserver_proc_impl = compile_factory(*_get_mp_classes('forkserver'))
+
+# this is duplication as Py27, linux uses fork, windows uses spawn, it however
+# is kept like this so that when tests fail it's less confusing!
+default_proc_impl = compile_factory(*_get_mp_classes('default'))
+
+
+class TestParallelBackendBase(TestCase):
+    """
+    Base class for testing the parallel backends
+    """
+
+    all_impls = [
+        jit_runner(nopython=True),
+        jit_runner(nopython=True, cache=True),
+        jit_runner(nopython=True, nogil=True),
+        linalg_runner(nopython=True),
+        linalg_runner(nopython=True, nogil=True),
+        vectorize_runner(nopython=True),
+        vectorize_runner(nopython=True, target='parallel'),
+        vectorize_runner(nopython=True, target='parallel', cache=True),
+        guvectorize_runner(nopython=True),
+        guvectorize_runner(nopython=True, target='parallel'),
+        guvectorize_runner(nopython=True, target='parallel', cache=True),
+    ]
+
+    if not _parfors_unsupported:
+        parfor_impls = [
+            jit_runner(nopython=True, parallel=True),
+            jit_runner(nopython=True, parallel=True, cache=True),
+            linalg_runner(nopython=True, parallel=True),
+            linalg_runner(nopython=True, parallel=True, cache=True),
+        ]
+        all_impls.extend(parfor_impls)
+
+    if config.NUMBA_NUM_THREADS < 2:
+        # Not enough cores
+        masks = []
+    else:
+        masks = [1, 2]
+
+    mask_impls = []
+    for impl in all_impls:
+        for mask in masks:
+            mask_impls.append(mask_runner(impl, mask))
+
+    parallelism = ['threading', 'random']
+    parallelism.append('multiprocessing_spawn')
+    if _HAVE_OS_FORK:
+        parallelism.append('multiprocessing_fork')
+        parallelism.append('multiprocessing_forkserver')
+
+    runners = {
+        'concurrent_jit': [
+            jit_runner(nopython=True, parallel=(not _parfors_unsupported)),
+        ],
+        'concurrent_vectorize': [
+            vectorize_runner(nopython=True, target='parallel'),
+        ],
+        'concurrent_guvectorize': [
+            guvectorize_runner(nopython=True, target='parallel'),
+        ],
+        'concurrent_mix_use': all_impls,
+        'concurrent_mix_use_masks': mask_impls,
+    }
+
+    safe_backends = {'omp', 'tbb'}
+
+    def run_compile(self, fnlist, parallelism='threading'):
+        self._cache_dir = temp_directory(self.__class__.__name__)
+        with override_config('CACHE_DIR', self._cache_dir):
+            if parallelism == 'threading':
+                thread_impl(fnlist)
+            elif parallelism == 'multiprocessing_fork':
+                fork_proc_impl(fnlist)
+            elif parallelism == 'multiprocessing_forkserver':
+                forkserver_proc_impl(fnlist)
+            elif parallelism == 'multiprocessing_spawn':
+                spawn_proc_impl(fnlist)
+            elif parallelism == 'multiprocessing_default':
+                default_proc_impl(fnlist)
+            elif parallelism == 'random':
+                ps = [thread_impl, spawn_proc_impl]
+                if _HAVE_OS_FORK:
+                    ps.append(fork_proc_impl)
+                    ps.append(forkserver_proc_impl)
+
+                random.shuffle(ps)
+                for impl in ps:
+                    impl(fnlist)
+            else:
+                raise ValueError(
+                    'Unknown parallelism supplied %s' % parallelism)
+
+
+_specific_backends = config.THREADING_LAYER in ('omp', 'tbb', 'workqueue')
+
+
+@unittest.skipUnless(_specific_backends, "Threading layer not explicit")
+class TestParallelBackend(TestParallelBackendBase):
+    """ These are like the numba.tests.test_threadsafety tests but designed
+    instead to torture the parallel backend.
+    If a suitable backend is supplied via NUMBA_THREADING_LAYER these tests
+    can be run directly. This test class cannot be run using the multiprocessing
+    option to the test runner (i.e. `./runtests -m`) as daemon processes cannot
+    have children.
+    """
+
+    # NOTE: All tests are generated based on what a platform supports concurrent
+    # execution wise from Python, irrespective of whether the native libraries
+    # can actually handle the behaviour present.
+    @classmethod
+    def generate(cls):
+        for p in cls.parallelism:
+            for name, impl in cls.runners.items():
+                methname = "test_" + p + '_' + name
+
+                def methgen(impl, p):
+                    def test_method(self):
+                        selfproc = multiprocessing.current_process()
+                        # daemonized processes cannot have children
+                        if selfproc.daemon:
+                            _msg = 'daemonized processes cannot have children'
+                            self.skipTest(_msg)
+                        else:
+                            self.run_compile(impl, parallelism=p)
+                    return test_method
+                fn = methgen(impl, p)
+                fn.__name__ = methname
+                setattr(cls, methname, fn)
+
+
+TestParallelBackend.generate()
+
+
+class TestInSubprocess(object):
+    backends = {'tbb': skip_no_tbb,
+                'omp': skip_no_omp,
+                'workqueue': unittest.skipIf(False, '')}
+
+    def run_cmd(self, cmdline, env):
+        popen = subprocess.Popen(cmdline,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 env=env)
+        # finish in _TEST_TIMEOUT seconds or kill it
+        timeout = threading.Timer(_TEST_TIMEOUT, popen.kill)
+        try:
+            timeout.start()
+            out, err = popen.communicate()
+            if popen.returncode != 0:
+                raise AssertionError(
+                    "process failed with code %s: stderr follows\n%s\n" %
+                    (popen.returncode, err.decode()))
+            return out.decode(), err.decode()
+        finally:
+            timeout.cancel()
+        return None, None
+
+    def run_test_in_separate_process(self, test, threading_layer):
+        env_copy = os.environ.copy()
+        env_copy['NUMBA_THREADING_LAYER'] = str(threading_layer)
+        cmdline = [sys.executable, "-m", "numba.runtests", test]
+        return self.run_cmd(cmdline, env_copy)
+
+
+class TestSpecificBackend(TestInSubprocess, TestParallelBackendBase):
+    """
+    This is quite contrived, for each test in the TestParallelBackend tests it
+    generates a test that will run the TestParallelBackend test in a new python
+    process with an environment modified to ensure a specific threadsafe backend
+    is used. This is with view of testing the backends independently and in an
+    isolated manner such that if they hang/crash/have issues, it doesn't kill
+    the test suite.
+    """
+    _DEBUG = False
+
+    @classmethod
+    def _inject(cls, p, name, backend, backend_guard):
+        themod = cls.__module__
+        thecls = TestParallelBackend.__name__
+        methname = "test_" + p + '_' + name
+        injected_method = '%s.%s.%s' % (themod, thecls, methname)
+
+        def test_template(self):
+            o, e = self.run_test_in_separate_process(injected_method, backend)
+            if self._DEBUG:
+                print('stdout:\n "%s"\n stderr:\n "%s"' % (o, e))
+            self.assertIn('OK', e)
+            self.assertTrue('FAIL' not in e)
+            self.assertTrue('ERROR' not in e)
+        injected_test = "test_%s_%s_%s" % (p, name, backend)
+        # Mark as long_running
+        setattr(cls, injected_test,
+                tag('long_running')(backend_guard(test_template)))
+
+    @classmethod
+    def generate(cls):
+        for backend, backend_guard in cls.backends.items():
+            for p in cls.parallelism:
+                for name in cls.runners.keys():
+                    # handle known problem cases...
+
+                    # GNU OpenMP is not fork safe
+                    if (p in ('multiprocessing_fork', 'random') and
+                        backend == 'omp' and
+                            sys.platform.startswith('linux')):
+                        continue
+
+                    # workqueue is not thread safe
+                    if (p in ('threading', 'random') and
+                            backend == 'workqueue'):
+                        continue
+
+                    cls._inject(p, name, backend, backend_guard)
+
+
+TestSpecificBackend.generate()
+
+
+class ThreadLayerTestHelper(TestCase):
+    """
+    Helper class for running an isolated piece of code based on a template
+    """
+    # sys path injection and separate usecase module to make sure everything
+    # is importable by children of multiprocessing
+    _here = "%r" % os.path.dirname(__file__)
+
+    template = """if 1:
+    import sys
+    sys.path.insert(0, "%(here)r")
+    import multiprocessing
+    import numpy as np
+    from numba import njit
+    import numba
+    try:
+        import threading_backend_usecases
+    except ImportError as e:
+        print("DEBUG:", sys.path)
+        raise e
+    import os
+
+    sigterm_handler = threading_backend_usecases.sigterm_handler
+    busy_func = threading_backend_usecases.busy_func
+
+    def the_test():
+        %%s
+
+    if __name__ == "__main__":
+        the_test()
+    """ % {'here': _here}
+
+    def run_cmd(self, cmdline, env=None):
+        if env is None:
+            env = os.environ.copy()
+            env['NUMBA_THREADING_LAYER'] = str("omp")
+        popen = subprocess.Popen(cmdline,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 env=env)
+        # finish in _TEST_TIMEOUT seconds or kill it
+        timeout = threading.Timer(_TEST_TIMEOUT, popen.kill)
+        try:
+            timeout.start()
+            out, err = popen.communicate()
+            if popen.returncode != 0:
+                raise AssertionError(
+                    "process failed with code %s: stderr follows\n%s\n" %
+                    (popen.returncode, err.decode()))
+        finally:
+            timeout.cancel()
+        return out.decode(), err.decode()
+
+
+@skip_parfors_unsupported
+class TestThreadingLayerSelection(ThreadLayerTestHelper):
+    """
+    Checks that numba.threading_layer() reports correctly.
+    """
+    _DEBUG = False
+
+    backends = {'tbb': skip_no_tbb,
+                'omp': skip_no_omp,
+                'workqueue': unittest.skipIf(False, '')}
+
+    @classmethod
+    def _inject(cls, backend, backend_guard):
+
+        def test_template(self):
+            body = """if 1:
+                X = np.arange(1000000.)
+                Y = np.arange(1000000.)
+                Z = busy_func(X, Y)
+                assert numba.threading_layer() == '%s'
+            """
+            runme = self.template % (body % backend)
+            cmdline = [sys.executable, '-c', runme]
+            env = os.environ.copy()
+            env['NUMBA_THREADING_LAYER'] = str(backend)
+            out, err = self.run_cmd(cmdline, env=env)
+            if self._DEBUG:
+                print(out, err)
+        injected_test = "test_threading_layer_selector_%s" % backend
+        setattr(cls, injected_test,
+                tag("important")(backend_guard(test_template)))
+
+    @classmethod
+    def generate(cls):
+        for backend, backend_guard in cls.backends.items():
+            cls._inject(backend, backend_guard)
+
+
+TestThreadingLayerSelection.generate()
+
+
+@skip_parfors_unsupported
+class TestThreadingLayerPriority(ThreadLayerTestHelper):
+
+    def each_env_var(self, env_var: str):
+        """Test setting priority via env var NUMBA_THREADING_LAYER_PRIORITY.
+        """
+        env = os.environ.copy()
+        env['NUMBA_THREADING_LAYER'] = 'default'
+        env['NUMBA_THREADING_LAYER_PRIORITY'] = env_var
+
+        code = f"""
+                import numba
+
+                # trigger threading layer decision
+                # hence catching invalid THREADING_LAYER_PRIORITY
+                @numba.jit(
+                    'float64[::1](float64[::1], float64[::1])',
+                    nopython=True,
+                    parallel=True,
+                )
+                def plus(x, y):
+                    return x + y
+
+                captured_envvar = list("{env_var}".split())
+                assert numba.config.THREADING_LAYER_PRIORITY == \
+                    captured_envvar, "priority mismatch"
+                assert numba.threading_layer() == captured_envvar[0],\
+                    "selected backend mismatch"
+                """
+        cmd = [
+            sys.executable,
+            '-c',
+            textwrap.dedent(code),
+        ]
+        self.run_cmd(cmd, env=env)
+
+    @skip_no_omp
+    @skip_no_tbb
+    def test_valid_env_var(self):
+        default = ['tbb', 'omp', 'workqueue']
+        for p in itertools.permutations(default):
+            env_var = ' '.join(p)
+            self.each_env_var(env_var)
+
+    @skip_no_omp
+    @skip_no_tbb
+    def test_invalid_env_var(self):
+        env_var = 'tbb omp workqueue notvalidhere'
+        with self.assertRaises(AssertionError) as raises:
+            self.each_env_var(env_var)
+        for msg in (
+            "THREADING_LAYER_PRIORITY invalid:",
+            "It must be a permutation of"
+        ):
+            self.assertIn(f"{msg}", str(raises.exception))
+
+    @skip_no_omp
+    def test_omp(self):
+        for env_var in ("omp tbb workqueue", "omp workqueue tbb"):
+            self.each_env_var(env_var)
+
+    @skip_no_tbb
+    def test_tbb(self):
+        for env_var in ("tbb omp workqueue", "tbb workqueue omp"):
+            self.each_env_var(env_var)
+
+    def test_workqueue(self):
+        for env_var in ("workqueue tbb omp", "workqueue omp tbb"):
+            self.each_env_var(env_var)
+
+
+@skip_parfors_unsupported
+class TestMiscBackendIssues(ThreadLayerTestHelper):
+    """
+    Checks fixes for the issues with threading backends implementation
+    """
+    _DEBUG = False
+
+    @skip_no_omp
+    def test_omp_stack_overflow(self):
+        """
+        Tests that OMP does not overflow stack
+        """
+        runme = """if 1:
+            from numba import vectorize, threading_layer
+            import numpy as np
+
+            @vectorize(['f4(f4,f4,f4,f4,f4,f4,f4,f4)'], target='parallel')
+            def foo(a, b, c, d, e, f, g, h):
+                return a+b+c+d+e+f+g+h
+
+            x = np.ones(2**20, np.float32)
+            foo(*([x]*8))
+            assert threading_layer() == "omp", "omp not found"
+        """
+        cmdline = [sys.executable, '-c', runme]
+        env = os.environ.copy()
+        env['NUMBA_THREADING_LAYER'] = "omp"
+        env['OMP_STACKSIZE'] = "100K"
+        self.run_cmd(cmdline, env=env)
+
+    @skip_no_tbb
+    def test_single_thread_tbb(self):
+        """
+        Tests that TBB works well with single thread
+        https://github.com/numba/numba/issues/3440
+        """
+        runme = """if 1:
+            from numba import njit, prange, threading_layer
+
+            @njit(parallel=True)
+            def foo(n):
+                acc = 0
+                for i in prange(n):
+                    acc += i
+                return acc
+
+            foo(100)
+            assert threading_layer() == "tbb", "tbb not found"
+        """
+        cmdline = [sys.executable, '-c', runme]
+        env = os.environ.copy()
+        env['NUMBA_THREADING_LAYER'] = "tbb"
+        env['NUMBA_NUM_THREADS'] = "1"
+        self.run_cmd(cmdline, env=env)
+
+    def test_workqueue_aborts_on_nested_parallelism(self):
+        """
+        Tests workqueue raises sigabrt if a nested parallel call is performed
+        """
+        runme = """if 1:
+            from numba import njit, prange
+            import numpy as np
+
+            @njit(parallel=True)
+            def nested(x):
+                for i in prange(len(x)):
+                    x[i] += 1
+
+
+            @njit(parallel=True)
+            def main():
+                Z = np.zeros((5, 10))
+                for i in prange(Z.shape[0]):
+                    nested(Z[i])
+                return Z
+
+            main()
+        """
+        cmdline = [sys.executable, '-c', runme]
+        env = os.environ.copy()
+        env['NUMBA_THREADING_LAYER'] = "workqueue"
+        env['NUMBA_NUM_THREADS'] = "4"
+
+        try:
+            out, err = self.run_cmd(cmdline, env=env)
+        except AssertionError as e:
+            if self._DEBUG:
+                print(out, err)
+            e_msg = str(e)
+            self.assertIn("failed with code", e_msg)
+            # raised a SIGABRT, but the value is platform specific so just check
+            # the error message
+            self.assertIn("Terminating: Nested parallel kernel launch detected",
+                          e_msg)
+
+    @unittest.skipUnless(_HAVE_OS_FORK, "Test needs fork(2)")
+    def test_workqueue_handles_fork_from_non_main_thread(self):
+        # For context see #7872, but essentially the multiprocessing pool
+        # implementation has a number of Python threads for handling the worker
+        # processes, one of which calls fork(2), this results in a fork from a
+        # non-main thread.
+
+        runme = """if 1:
+            from numba import njit, prange, threading_layer
+            import numpy as np
+            import multiprocessing
+
+            if __name__ == "__main__":
+                # Need for force fork context (OSX default is "spawn")
+                multiprocessing.set_start_method('fork')
+
+                @njit(parallel=True)
+                def func(x):
+                    return 10. * x
+
+                arr = np.arange(2.)
+
+                # run in single process to start Numba's thread pool
+                np.testing.assert_allclose(func(arr), func.py_func(arr))
+
+                # now run in a multiprocessing pool to get a fork from a
+                # non-main thread
+                with multiprocessing.Pool(10) as p:
+                    result = p.map(func, [arr])
+                np.testing.assert_allclose(result,
+                                           func.py_func(np.expand_dims(arr, 0)))
+
+                assert threading_layer() == "workqueue"
+        """
+        cmdline = [sys.executable, '-c', runme]
+        env = os.environ.copy()
+        env['NUMBA_THREADING_LAYER'] = "workqueue"
+        env['NUMBA_NUM_THREADS'] = "4"
+
+        self.run_cmd(cmdline, env=env)
+
+
+# 32bit or windows py27 (not that this runs on windows)
+@skip_parfors_unsupported
+@skip_unless_gnu_omp
+class TestForkSafetyIssues(ThreadLayerTestHelper):
+    """
+    Checks Numba's behaviour in various situations involving GNU OpenMP and fork
+    """
+    _DEBUG = False
+
+    def test_check_threading_layer_is_gnu(self):
+        runme = """if 1:
+            from numba.np.ufunc import omppool
+            assert omppool.openmp_vendor == 'GNU'
+            """
+        cmdline = [sys.executable, '-c', runme]
+        out, err = self.run_cmd(cmdline)
+
+    def test_par_parent_os_fork_par_child(self):
+        """
+        Whilst normally valid, this actually isn't for Numba invariant of OpenMP
+        Checks SIGABRT is received.
+        """
+        body = """if 1:
+            X = np.arange(1000000.)
+            Y = np.arange(1000000.)
+            Z = busy_func(X, Y)
+            pid = os.fork()
+            if pid  == 0:
+                Z = busy_func(X, Y)
+            else:
+                os.wait()
+        """
+        runme = self.template % body
+        cmdline = [sys.executable, '-c', runme]
+        try:
+            out, err = self.run_cmd(cmdline)
+        except AssertionError as e:
+            self.assertIn("failed with code -6", str(e))
+
+    def test_par_parent_implicit_mp_fork_par_child(self):
+        """
+        Implicit use of multiprocessing fork context.
+        Does this:
+        1. Start with OpenMP
+        2. Fork to processes using OpenMP (this is invalid)
+        3. Joins fork
+        4. Check the exception pushed onto the queue that is a result of
+           catching SIGTERM coming from the C++ aborting on illegal fork
+           pattern for GNU OpenMP
+        """
+        body = """if 1:
+            mp = multiprocessing.get_context('fork')
+            X = np.arange(1000000.)
+            Y = np.arange(1000000.)
+            q = mp.Queue()
+
+            # Start OpenMP runtime on parent via parallel function
+            Z = busy_func(X, Y, q)
+
+            # fork() underneath with no exec, will abort
+            proc = mp.Process(target = busy_func, args=(X, Y, q))
+            proc.start()
+
+            err = q.get()
+            assert "Caught SIGTERM" in str(err)
+        """
+        runme = self.template % body
+        cmdline = [sys.executable, '-c', runme]
+        out, err = self.run_cmd(cmdline)
+        if self._DEBUG:
+            print(out, err)
+
+    @linux_only
+    def test_par_parent_explicit_mp_fork_par_child(self):
+        """
+        Explicit use of multiprocessing fork context.
+        Does this:
+        1. Start with OpenMP
+        2. Fork to processes using OpenMP (this is invalid)
+        3. Joins fork
+        4. Check the exception pushed onto the queue that is a result of
+           catching SIGTERM coming from the C++ aborting on illegal fork
+           pattern for GNU OpenMP
+        """
+        body = """if 1:
+            X = np.arange(1000000.)
+            Y = np.arange(1000000.)
+            q = multiprocessing.Queue()
+
+            # Start OpenMP runtime on parent via parallel function
+            Z = busy_func(X, Y, q)
+
+            # fork() underneath with no exec, will abort
+            ctx = multiprocessing.get_context('fork')
+            proc = ctx.Process(target = busy_func, args=(X, Y, q))
+            proc.start()
+            proc.join()
+
+            err = q.get()
+            assert "Caught SIGTERM" in str(err)
+        """
+        runme = self.template % body
+        cmdline = [sys.executable, '-c', runme]
+        out, err = self.run_cmd(cmdline)
+        if self._DEBUG:
+            print(out, err)
+
+    def test_par_parent_mp_spawn_par_child_par_parent(self):
+        """
+        Explicit use of multiprocessing spawn, this is safe.
+        Does this:
+        1. Start with OpenMP
+        2. Spawn to processes using OpenMP
+        3. Join spawns
+        4. Run some more OpenMP
+        """
+        body = """if 1:
+            X = np.arange(1000000.)
+            Y = np.arange(1000000.)
+            q = multiprocessing.Queue()
+
+            # Start OpenMP runtime and run on parent via parallel function
+            Z = busy_func(X, Y, q)
+            procs = []
+            ctx = multiprocessing.get_context('spawn')
+            for x in range(20): # start a lot to try and get overlap
+                ## fork() + exec() to run some OpenMP on children
+                proc = ctx.Process(target = busy_func, args=(X, Y, q))
+                procs.append(proc)
+                sys.stdout.flush()
+                sys.stderr.flush()
+                proc.start()
+
+            [p.join() for p in procs]
+
+            try:
+                q.get(False)
+            except multiprocessing.queues.Empty:
+                pass
+            else:
+                raise RuntimeError("Queue was not empty")
+
+            # Run some more OpenMP on parent
+            Z = busy_func(X, Y, q)
+        """
+        runme = self.template % body
+        cmdline = [sys.executable, '-c', runme]
+        out, err = self.run_cmd(cmdline)
+        if self._DEBUG:
+            print(out, err)
+
+    def test_serial_parent_implicit_mp_fork_par_child_then_par_parent(self):
+        """
+        Implicit use of multiprocessing (will be fork, but cannot declare that
+        in Py2.7 as there's no process launch context).
+        Does this:
+        1. Start with no OpenMP
+        2. Fork to processes using OpenMP
+        3. Join forks
+        4. Run some OpenMP
+        """
+        body = """if 1:
+            X = np.arange(1000000.)
+            Y = np.arange(1000000.)
+            q = multiprocessing.Queue()
+
+            # this is ok
+            procs = []
+            for x in range(10):
+                # fork() underneath with but no OpenMP in parent, this is ok
+                proc = multiprocessing.Process(target = busy_func,
+                                               args=(X, Y, q))
+                procs.append(proc)
+                proc.start()
+
+            [p.join() for p in procs]
+
+            # and this is still ok as the OpenMP happened in forks
+            Z = busy_func(X, Y, q)
+            try:
+                q.get(False)
+            except multiprocessing.queues.Empty:
+                pass
+            else:
+                raise RuntimeError("Queue was not empty")
+        """
+        runme = self.template % body
+        cmdline = [sys.executable, '-c', runme]
+        out, err = self.run_cmd(cmdline)
+        if self._DEBUG:
+            print(out, err)
+
+    @linux_only
+    def test_serial_parent_explicit_mp_fork_par_child_then_par_parent(self):
+        """
+        Explicit use of multiprocessing 'fork'.
+        Does this:
+        1. Start with no OpenMP
+        2. Fork to processes using OpenMP
+        3. Join forks
+        4. Run some OpenMP
+        """
+        body = """if 1:
+            X = np.arange(1000000.)
+            Y = np.arange(1000000.)
+            q = multiprocessing.Queue()
+
+            # this is ok
+            procs = []
+            ctx = multiprocessing.get_context('fork')
+            for x in range(10):
+                # fork() underneath with but no OpenMP in parent, this is ok
+                proc = ctx.Process(target = busy_func, args=(X, Y, q))
+                procs.append(proc)
+                proc.start()
+
+            [p.join() for p in procs]
+
+            # and this is still ok as the OpenMP happened in forks
+            Z = busy_func(X, Y, q)
+            try:
+                q.get(False)
+            except multiprocessing.queues.Empty:
+                pass
+            else:
+                raise RuntimeError("Queue was not empty")
+        """
+        runme = self.template % body
+        cmdline = [sys.executable, '-c', runme]
+        out, err = self.run_cmd(cmdline)
+        if self._DEBUG:
+            print(out, err)
+
+
+@skip_parfors_unsupported
+@skip_no_tbb
+class TestTBBSpecificIssues(ThreadLayerTestHelper):
+
+    _DEBUG = False
+
+    @linux_only # os.fork required.
+    def test_fork_from_non_main_thread(self):
+        # See issue #5973 and PR #6208 for original context.
+        # See issue #6963 for context on the following comments:
+        #
+        # Important things to note:
+        # 1. Compilation of code containing an objmode block will result in the
+        #    use of and `ObjModeLiftedWith` as the dispatcher. This inherits
+        #    from `LiftedCode` which handles the serialization. In that
+        #    serialization is a call to uuid.uuid1() which causes a fork_exec in
+        #    CPython internals.
+        # 2. The selected parallel backend thread pool is started during the
+        #    compilation of a function that has `parallel=True`.
+        # 3. The TBB backend can handle forks from the main thread, it will
+        #    safely reinitialise after so doing. If a fork occurs from a
+        #    non-main thread it will warn and the state is invalid in the child
+        #    process.
+        #
+        # Due to 1. and 2. the `obj_mode_func` function separated out and is
+        # `njit` decorated. This means during type inference of `work` it will
+        # trigger a standard compilation of the function and the thread pools
+        # won't have started yet as the parallelisation compiler passes for
+        # `work` won't yet have run. This mitigates the fork() call from 1.
+        # occurring after 2. The result of this is that 3. can be tested using
+        # the threading etc herein with the state being known as the above
+        # described, i.e. the TBB threading layer has not experienced a fork().
+
+        runme = """if 1:
+            import threading
+            import numba
+            numba.config.THREADING_LAYER='tbb'
+            from numba import njit, prange, objmode
+            from numba.core.serialize import PickleCallableByPath
+            import os
+
+            e_running = threading.Event()
+            e_proceed = threading.Event()
+
+            def indirect_core():
+                e_running.set()
+                # wait for forker() to have forked
+                while not e_proceed.isSet():
+                    pass
+
+            indirect = PickleCallableByPath(indirect_core)
+
+            @njit
+            def obj_mode_func():
+                with objmode():
+                    indirect()
+
+            @njit(parallel=True, nogil=True)
+            def work():
+                acc = 0
+                for x in prange(10):
+                    acc += x
+                obj_mode_func()
+                return acc
+
+            def runner():
+                work()
+
+            def forker():
+                # wait for the jit function to say it's running
+                while not e_running.isSet():
+                    pass
+                # then fork
+                os.fork()
+                # now fork is done signal the runner to proceed to exit
+                e_proceed.set()
+
+            numba_runner = threading.Thread(target=runner,)
+            fork_runner =  threading.Thread(target=forker,)
+
+            threads = (numba_runner, fork_runner)
+            for t in threads:
+                t.start()
+            for t in threads:
+                t.join()
+        """
+
+        cmdline = [sys.executable, '-c', runme]
+        out, err = self.run_cmd(cmdline)
+        # assert error message printed on stderr
+        msg_head = "Attempted to fork from a non-main thread, the TBB library"
+        self.assertIn(msg_head, err)
+
+        if self._DEBUG:
+            print("OUT:", out)
+            print("ERR:", err)
+
+    @needs_external_compilers
+    @linux_only # fork required.
+    def test_lifetime_of_task_scheduler_handle(self):
+        # See PR #7280 for context.
+        BROKEN_COMPILERS = 'SKIP: COMPILATION FAILED'
+        runme = """if 1:
+            import ctypes
+            import sys
+            import multiprocessing as mp
+            from tempfile import TemporaryDirectory, NamedTemporaryFile
+            from numba.pycc.platform import Toolchain, _external_compiler_ok
+            from numba import njit, prange, threading_layer
+            import faulthandler
+            faulthandler.enable()
+            if not _external_compiler_ok:
+                raise AssertionError('External compilers are not found.')
+            with TemporaryDirectory() as tmpdir:
+                with NamedTemporaryFile(dir=tmpdir) as tmpfile:
+                    try:
+                        src = \"\"\"
+                        #define TBB_PREVIEW_WAITING_FOR_WORKERS 1
+                        #include <tbb/tbb.h>
+                        static tbb::task_scheduler_handle tsh;
+                        extern "C"
+                        {
+                        void launch(void)
+                        {
+                            tsh = tbb::task_scheduler_handle::get();
+                        }
+                        }
+                        \"\"\"
+                        cxxfile = f"{tmpfile.name}.cxx"
+                        with open(cxxfile, 'wt') as f:
+                            f.write(src)
+                        tc = Toolchain()
+                        object_files = tc.compile_objects([cxxfile,],
+                                                           output_dir=tmpdir)
+                        dso_name = f"{tmpfile.name}.so"
+                        tc.link_shared(dso_name, object_files,
+                                       libraries=['tbb',],
+                                       export_symbols=['launch'])
+                        # Load into the process, it doesn't matter whether the
+                        # DSO exists on disk once it's loaded in.
+                        DLL = ctypes.CDLL(dso_name)
+                    except Exception as e:
+                        # Something is broken in compilation, could be one of
+                        # many things including, but not limited to: missing tbb
+                        # headers, incorrect permissions, compilers that don't
+                        # work for the above
+                        print(e)
+                        print('BROKEN_COMPILERS')
+                        sys.exit(0)
+
+                    # Do the test, launch this library and also execute a
+                    # function with the TBB threading layer.
+
+                    DLL.launch()
+
+                    @njit(parallel=True)
+                    def foo(n):
+                        acc = 0
+                        for i in prange(n):
+                            acc += i
+                        return acc
+
+                    foo(1)
+
+            # Check the threading layer used was TBB
+            assert threading_layer() == 'tbb'
+
+            # Use mp context for a controlled version of fork, this triggers the
+            # reported bug.
+
+            ctx = mp.get_context('fork')
+            def nowork():
+                pass
+            p = ctx.Process(target=nowork)
+            p.start()
+            p.join(10)
+            print("SUCCESS")
+            """.replace('BROKEN_COMPILERS', BROKEN_COMPILERS)
+
+        cmdline = [sys.executable, '-c', runme]
+        env = os.environ.copy()
+        env['NUMBA_THREADING_LAYER'] = 'tbb'
+        out, err = self.run_cmd(cmdline, env=env)
+
+        if BROKEN_COMPILERS in out:
+            self.skipTest("Compilation of DSO failed. Check output for details")
+        else:
+            self.assertIn("SUCCESS", out)
+
+        if self._DEBUG:
+            print("OUT:", out)
+            print("ERR:", err)
+
+
+@skip_parfors_unsupported
+class TestInitSafetyIssues(TestCase):
+
+    _DEBUG = False
+
+    def run_cmd(self, cmdline):
+        popen = subprocess.Popen(cmdline,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,)
+        # finish in _TEST_TIMEOUT seconds or kill it
+        timeout = threading.Timer(_TEST_TIMEOUT, popen.kill)
+        try:
+            timeout.start()
+            out, err = popen.communicate()
+            if popen.returncode != 0:
+                raise AssertionError(
+                    "process failed with code %s: stderr follows\n%s\n" %
+                    (popen.returncode, err.decode()))
+        finally:
+            timeout.cancel()
+        return out.decode(), err.decode()
+
+    @linux_only # only linux can leak semaphores
+    def test_orphaned_semaphore(self):
+        # sys path injection and separate usecase module to make sure everything
+        # is importable by children of multiprocessing
+
+        test_file = os.path.join(os.path.dirname(__file__),
+                                 "orphaned_semaphore_usecase.py")
+        cmdline = [sys.executable, test_file]
+        out, err = self.run_cmd(cmdline)
+
+        # assert no semaphore leaks reported on stderr
+        self.assertNotIn("leaked semaphore", err)
+
+        if self._DEBUG:
+            print("OUT:", out)
+            print("ERR:", err)
+
+    def test_lazy_lock_init(self):
+        # checks based on https://github.com/numba/numba/pull/5724
+        # looking for "lazy" process lock initialisation so as to avoid setting
+        # a multiprocessing context as part of import.
+        for meth in ('fork', 'spawn', 'forkserver'):
+            # if a context is available on the host check it can be set as the
+            # start method in a separate process
+            try:
+                multiprocessing.get_context(meth)
+            except ValueError:
+                continue
+            cmd = ("import numba; import multiprocessing;"
+                   "multiprocessing.set_start_method('{}');"
+                   "print(multiprocessing.get_context().get_start_method())")
+            cmdline = [sys.executable, "-c", cmd.format(meth)]
+            out, err = self.run_cmd(cmdline)
+            if self._DEBUG:
+                print("OUT:", out)
+                print("ERR:", err)
+            self.assertIn(meth, out)
+
+
+@skip_parfors_unsupported
+@skip_no_omp
+class TestOpenMPVendors(TestCase):
+
+    def test_vendors(self):
+        """
+        Checks the OpenMP vendor strings are correct
+        """
+        expected = dict()
+        expected['win32'] = "MS"
+        expected['darwin'] = "Intel"
+        expected['linux'] = "GNU"
+
+        # only check OS that are supported, custom toolchains may well work as
+        # may other OS
+        for k in expected.keys():
+            if sys.platform.startswith(k):
+                self.assertEqual(expected[k], omppool.openmp_vendor)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors.py
new file mode 100644
index 000000000..4ad00450f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors.py
@@ -0,0 +1,4623 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+
+from math import sqrt
+import re
+import dis
+import numbers
+import os
+import platform
+import sys
+import subprocess
+import types as pytypes
+import warnings
+from functools import reduce
+import numpy as np
+from numpy.random import randn
+import operator
+from collections import defaultdict, namedtuple
+import copy
+from itertools import cycle, chain
+import subprocess as subp
+
+import numba.parfors.parfor
+from numba import (njit, prange, parallel_chunksize,
+                   get_parallel_chunksize, set_parallel_chunksize,
+                   set_num_threads, get_num_threads, typeof)
+from numba.core import (types, utils, typing, errors, ir, rewrites,
+                        typed_passes, inline_closurecall, config, compiler, cpu)
+from numba.extending import (overload_method, register_model,
+                             typeof_impl, unbox, NativeValue, models)
+from numba.core.registry import cpu_target
+from numba.core.annotations import type_annotations
+from numba.core.ir_utils import (find_callname, guard, build_definitions,
+                            get_definition, is_getitem, is_setitem,
+                            index_var_of_get_setitem)
+from numba.np.unsafe.ndarray import empty_inferred as unsafe_empty
+from numba.core.bytecode import ByteCodeIter
+from numba.core.compiler import (compile_isolated, Flags, CompilerBase,
+                                 DefaultPassBuilder)
+from numba.core.compiler_machinery import register_pass, AnalysisPass
+from numba.core.typed_passes import IRLegalization
+from numba.tests.support import (TestCase, captured_stdout, MemoryLeakMixin,
+                      override_env_config, linux_only, tag,
+                      skip_parfors_unsupported, _32bit, needs_blas,
+                      needs_lapack, disabled_test, skip_unless_scipy,
+                      needs_subprocess)
+from numba.core.extending import register_jitable
+import cmath
+import unittest
+
+# NOTE: Each parfors test class is run in separate subprocess, this is to reduce
+# memory pressure in CI settings. The environment variable "SUBPROC_TEST" is
+# used to determine whether a test is skipped or not, such that if you want to
+# run any parfors test directly this environment variable can be set. The
+# subprocesses running the test classes set this environment variable as the new
+# process starts which enables the tests within the process. The decorator
+# @needs_subprocess is used to ensure the appropriate test skips are made.
+
+
+@skip_parfors_unsupported
+class TestParforsRunner(TestCase):
+
+    _numba_parallel_test_ = False
+
+    # Each test class can run for 30 minutes before time out. Extend this to an
+    # hour on aarch64 (some public CI systems were timing out).
+    _TIMEOUT = 1800 if platform.machine() != 'aarch64' else 3600
+
+    """This is the test runner for all the parfors tests, it runs them in
+    subprocesses as described above. The convention for the test method naming
+    is: `test_<TestClass>` where <TestClass> is the name of the test class in
+    this module.
+    """
+    def runner(self):
+        themod = self.__module__
+        test_clazz_name = self.id().split('.')[-1].split('_')[-1]
+        # don't specify a given test, it's an entire class that needs running
+        self.subprocess_test_runner(test_module=themod,
+                                    test_class=test_clazz_name,
+                                    timeout=self._TIMEOUT)
+
+    def test_TestParforBasic(self):
+        self.runner()
+
+    def test_TestParforNumericalMisc(self):
+        self.runner()
+
+    def test_TestParforNumPy(self):
+        self.runner()
+
+    def test_TestParfors(self):
+        self.runner()
+
+    def test_TestParforsBitMask(self):
+        self.runner()
+
+    def test_TestParforsDiagnostics(self):
+        self.runner()
+
+    def test_TestParforsLeaks(self):
+        self.runner()
+
+    def test_TestParforsMisc(self):
+        self.runner()
+
+    def test_TestParforsOptions(self):
+        self.runner()
+
+    def test_TestParforsSlice(self):
+        self.runner()
+
+    def test_TestParforsVectorizer(self):
+        self.runner()
+
+    def test_TestPrangeBasic(self):
+        self.runner()
+
+    def test_TestPrangeSpecific(self):
+        self.runner()
+
+
+x86_only = unittest.skipIf(platform.machine() not in ('i386', 'x86_64'), 'x86 only test')
+
+_GLOBAL_INT_FOR_TESTING1 = 17
+_GLOBAL_INT_FOR_TESTING2 = 5
+
+TestNamedTuple = namedtuple('TestNamedTuple', ('part0', 'part1'))
+
+def null_comparer(a, b):
+    """
+    Used with check_arq_equality to indicate that we do not care
+    whether the value of the parameter at the end of the function
+    has a particular value.
+    """
+    pass
+
+
+@needs_subprocess
+class TestParforsBase(TestCase):
+    """
+    Base class for testing parfors.
+    Provides functions for compilation and three way comparison between
+    python functions, njit'd functions and parfor njit'd functions.
+    """
+
+    _numba_parallel_test_ = False
+
+    def __init__(self, *args):
+        # flags for njit()
+        self.cflags = Flags()
+        self.cflags.nrt = True
+
+        # flags for njit(parallel=True)
+        self.pflags = Flags()
+        self.pflags.auto_parallel = cpu.ParallelOptions(True)
+        self.pflags.nrt = True
+
+        # flags for njit(parallel=True, fastmath=True)
+        self.fast_pflags = Flags()
+        self.fast_pflags.auto_parallel = cpu.ParallelOptions(True)
+        self.fast_pflags.nrt = True
+        self.fast_pflags.fastmath = cpu.FastMathOptions(True)
+        super(TestParforsBase, self).__init__(*args)
+
+    def _compile_this(self, func, sig, flags):
+        return compile_isolated(func, sig, flags=flags)
+
+    def compile_parallel(self, func, sig):
+        return self._compile_this(func, sig, flags=self.pflags)
+
+    def compile_parallel_fastmath(self, func, sig):
+        return self._compile_this(func, sig, flags=self.fast_pflags)
+
+    def compile_njit(self, func, sig):
+        return self._compile_this(func, sig, flags=self.cflags)
+
+    def compile_all(self, pyfunc, *args, **kwargs):
+        sig = tuple([numba.typeof(x) for x in args])
+
+        # compile the prange injected function
+        cpfunc = self.compile_parallel(pyfunc, sig)
+
+        # compile a standard njit of the original function
+        cfunc = self.compile_njit(pyfunc, sig)
+
+        return cfunc, cpfunc
+
+    def check_parfors_vs_others(self, pyfunc, cfunc, cpfunc, *args, **kwargs):
+        """
+        Checks python, njit and parfor impls produce the same result.
+
+        Arguments:
+            pyfunc - the python function to test
+            cfunc - CompilerResult from njit of pyfunc
+            cpfunc - CompilerResult from njit(parallel=True) of pyfunc
+            args - arguments for the function being tested
+        Keyword Arguments:
+            scheduler_type - 'signed', 'unsigned' or None, default is None.
+                           Supply in cases where the presence of a specific
+                           scheduler is to be asserted.
+            fastmath_pcres - a fastmath parallel compile result, if supplied
+                             will be run to make sure the result is correct
+            check_arg_equality - some functions need to check that a
+                                 parameter is modified rather than a certain
+                                 value returned.  If this keyword argument
+                                 is supplied, it should be a list of
+                                 comparison functions such that the i'th
+                                 function in the list is used to compare the
+                                 i'th parameter of the njit and parallel=True
+                                 functions against the i'th parameter of the
+                                 standard Python function, asserting if they
+                                 differ.  The length of this list must be equal
+                                 to the number of parameters to the function.
+                                 The null comparator is available for use
+                                 when you do not desire to test if some
+                                 particular parameter is changed.
+            Remaining kwargs are passed to np.testing.assert_almost_equal
+        """
+        scheduler_type = kwargs.pop('scheduler_type', None)
+        check_fastmath = kwargs.pop('check_fastmath', None)
+        fastmath_pcres = kwargs.pop('fastmath_pcres', None)
+        check_scheduling = kwargs.pop('check_scheduling', True)
+        check_args_for_equality = kwargs.pop('check_arg_equality', None)
+
+        def copy_args(*args):
+            if not args:
+                return tuple()
+            new_args = []
+            for x in args:
+                if isinstance(x, np.ndarray):
+                    new_args.append(x.copy('k'))
+                elif isinstance(x, np.number):
+                    new_args.append(x.copy())
+                elif isinstance(x, numbers.Number):
+                    new_args.append(x)
+                elif isinstance(x, tuple):
+                    new_args.append(copy.deepcopy(x))
+                elif isinstance(x, list):
+                    new_args.append(x[:])
+                else:
+                    raise ValueError('Unsupported argument type encountered')
+            return tuple(new_args)
+
+        # python result
+        py_args = copy_args(*args)
+        py_expected = pyfunc(*py_args)
+
+        # njit result
+        njit_args = copy_args(*args)
+        njit_output = cfunc.entry_point(*njit_args)
+
+        # parfor result
+        parfor_args = copy_args(*args)
+        parfor_output = cpfunc.entry_point(*parfor_args)
+
+        if check_args_for_equality is None:
+            np.testing.assert_almost_equal(njit_output, py_expected, **kwargs)
+            np.testing.assert_almost_equal(parfor_output, py_expected, **kwargs)
+            self.assertEqual(type(njit_output), type(parfor_output))
+        else:
+            assert(len(py_args) == len(check_args_for_equality))
+            for pyarg, njitarg, parforarg, argcomp in zip(
+                py_args, njit_args, parfor_args, check_args_for_equality):
+                argcomp(njitarg, pyarg, **kwargs)
+                argcomp(parforarg, pyarg, **kwargs)
+
+        if check_scheduling:
+            self.check_scheduling(cpfunc, scheduler_type)
+
+        # if requested check fastmath variant
+        if fastmath_pcres is not None:
+            parfor_fastmath_output = fastmath_pcres.entry_point(*copy_args(*args))
+            np.testing.assert_almost_equal(parfor_fastmath_output, py_expected,
+                                           **kwargs)
+
+    def check(self, pyfunc, *args, **kwargs):
+        """Checks that pyfunc compiles for *args under parallel=True and njit
+        and asserts that all version execute and produce the same result"""
+        cfunc, cpfunc = self.compile_all(pyfunc, *args)
+        self.check_parfors_vs_others(pyfunc, cfunc, cpfunc, *args, **kwargs)
+
+    def check_variants(self, impl, arg_gen, **kwargs):
+        """Run self.check(impl, ...) on array data generated from arg_gen.
+        """
+        for args in arg_gen():
+            with self.subTest(list(map(typeof, args))):
+                self.check(impl, *args, **kwargs)
+
+    def count_parfors_variants(self, impl, arg_gen, **kwargs):
+        """Run self.countParfors(impl, ...) on array types generated from
+        arg_gen.
+        """
+        for args in arg_gen():
+            with self.subTest(list(map(typeof, args))):
+                argtys = tuple(map(typeof, args))
+                # At least one parfors
+                self.assertGreaterEqual(countParfors(impl, argtys), 1)
+
+    def check_scheduling(self, cres, scheduler_type):
+        # make sure parfor set up scheduling
+        scheduler_str = '@do_scheduling'
+        if scheduler_type is not None:
+            if scheduler_type in ['signed', 'unsigned']:
+                scheduler_str += '_' + scheduler_type
+            else:
+                msg = "Unknown scheduler_type specified: %s"
+                raise ValueError(msg % scheduler_type)
+
+        self.assertIn(scheduler_str, cres.library.get_llvm_str())
+
+    def gen_linspace(self, n, ct):
+        """Make *ct* sample 1D arrays of length *n* using np.linspace().
+        """
+        def gen():
+            yield np.linspace(0, 1, n)
+            yield np.linspace(2, 1, n)
+            yield np.linspace(1, 2, n)
+
+        src = cycle(gen())
+        return [next(src) for i in range(ct)]
+
+    def gen_linspace_variants(self, ct):
+        """Make 1D, 2D, 3D variants of the data in C and F orders
+        """
+        # 1D
+        yield self.gen_linspace(10, ct=ct)
+
+        # 2D
+        arr2ds = [x.reshape((2, 3))
+                  for x in self.gen_linspace(n=2 * 3, ct=ct)]
+        yield arr2ds
+        # Fortran order
+        yield [np.asfortranarray(x) for x in arr2ds]
+
+        # 3D
+        arr3ds = [x.reshape((2, 3, 4))
+                  for x in self.gen_linspace(n=2 * 3 * 4, ct=ct)]
+        yield arr3ds
+        # Fortran order
+        yield [np.asfortranarray(x) for x in arr3ds]
+
+    def _filter_mod(self, mod, magicstr, checkstr=None):
+        """ helper function to filter out modules by name"""
+        filt = [x for x in mod if magicstr in x.name]
+        if checkstr is not None:
+            for x in filt:
+                assert checkstr in str(x)
+        return filt
+
+    def _get_gufunc_modules(self, cres, magicstr, checkstr=None):
+        """ gets the gufunc LLVM Modules"""
+        _modules = [x for x in cres.library._codegen._engine._ee._modules]
+        return self._filter_mod(_modules, magicstr, checkstr=checkstr)
+
+    def _get_gufunc_info(self, cres, fn):
+        """ helper for gufunc IR/asm generation"""
+        # get the gufunc modules
+        magicstr = '__numba_parfor_gufunc'
+        gufunc_mods = self._get_gufunc_modules(cres, magicstr)
+        x = dict()
+        for mod in gufunc_mods:
+            x[mod.name] = fn(mod)
+        return x
+
+    def _get_gufunc_ir(self, cres):
+        """
+        Returns the IR of the gufuncs used as parfor kernels
+        as a dict mapping the gufunc name to its IR.
+
+        Arguments:
+         cres - a CompileResult from `njit(parallel=True, ...)`
+        """
+        return self._get_gufunc_info(cres, str)
+
+    def _get_gufunc_asm(self, cres):
+        """
+        Returns the assembly of the gufuncs used as parfor kernels
+        as a dict mapping the gufunc name to its assembly.
+
+        Arguments:
+         cres - a CompileResult from `njit(parallel=True, ...)`
+        """
+        tm = cres.library._codegen._tm
+        def emit_asm(mod):
+            return str(tm.emit_assembly(mod))
+        return self._get_gufunc_info(cres, emit_asm)
+
+    def assert_fastmath(self, pyfunc, sig):
+        """
+        Asserts that the fastmath flag has some effect in that suitable
+        instructions are now labelled as `fast`. Whether LLVM can actually do
+        anything to optimise better now the derestrictions are supplied is
+        another matter!
+
+        Arguments:
+         pyfunc - a function that contains operations with parallel semantics
+         sig - the type signature of pyfunc
+        """
+
+        cres = self.compile_parallel_fastmath(pyfunc, sig)
+        _ir = self._get_gufunc_ir(cres)
+
+        def _get_fast_instructions(ir):
+            splitted = ir.splitlines()
+            fast_inst = []
+            for x in splitted:
+                m = re.search(r'\bfast\b', x)  # \b for wholeword
+                if m is not None:
+                    fast_inst.append(x)
+            return fast_inst
+
+        def _assert_fast(instrs):
+            ops = ('fadd', 'fsub', 'fmul', 'fdiv', 'frem', 'fcmp')
+            for inst in instrs:
+                count = 0
+                for op in ops:
+                    match = op + ' fast'
+                    if match in inst:
+                        count += 1
+                self.assertTrue(count > 0)
+
+        for name, guir in _ir.items():
+            inst = _get_fast_instructions(guir)
+            _assert_fast(inst)
+
+
+def blackscholes_impl(sptprice, strike, rate, volatility, timev):
+    # blackscholes example
+    logterm = np.log(sptprice / strike)
+    powterm = 0.5 * volatility * volatility
+    den = volatility * np.sqrt(timev)
+    d1 = (((rate + powterm) * timev) + logterm) / den
+    d2 = d1 - den
+    NofXd1 = 0.5 + 0.5 * 2.0 * d1
+    NofXd2 = 0.5 + 0.5 * 2.0 * d2
+    futureValue = strike * np.exp(- rate * timev)
+    c1 = futureValue * NofXd2
+    call = sptprice * NofXd1 - c1
+    put = call - futureValue + sptprice
+    return put
+
+
+def lr_impl(Y, X, w, iterations):
+    # logistic regression example
+    for i in range(iterations):
+        w -= np.dot(((1.0 / (1.0 + np.exp(-Y * np.dot(X, w))) - 1.0) * Y), X)
+    return w
+
+def example_kmeans_test(A, numCenter, numIter, init_centroids):
+    centroids = init_centroids
+    N, D = A.shape
+
+    for l in range(numIter):
+        dist = np.array([[sqrt(np.sum((A[i,:]-centroids[j,:])**2))
+                                for j in range(numCenter)] for i in range(N)])
+        labels = np.array([dist[i,:].argmin() for i in range(N)])
+
+        centroids = np.array([[np.sum(A[labels==i, j])/np.sum(labels==i)
+                                 for j in range(D)] for i in range(numCenter)])
+
+    return centroids
+
+def get_optimized_numba_ir(test_func, args, **kws):
+    typingctx = typing.Context()
+    targetctx = cpu.CPUContext(typingctx, 'cpu')
+    test_ir = compiler.run_frontend(test_func)
+    if kws:
+        options = cpu.ParallelOptions(kws)
+    else:
+        options = cpu.ParallelOptions(True)
+
+    tp = TestPipeline(typingctx, targetctx, args, test_ir)
+
+    with cpu_target.nested_context(typingctx, targetctx):
+        typingctx.refresh()
+        targetctx.refresh()
+
+        inline_pass = inline_closurecall.InlineClosureCallPass(tp.state.func_ir,
+                                                               options,
+                                                               typed=True)
+        inline_pass.run()
+
+        rewrites.rewrite_registry.apply('before-inference', tp.state)
+
+        tp.state.typemap, tp.state.return_type, tp.state.calltypes, _ = \
+        typed_passes.type_inference_stage(tp.state.typingctx,
+            tp.state.targetctx, tp.state.func_ir, tp.state.args, None)
+
+        type_annotations.TypeAnnotation(
+            func_ir=tp.state.func_ir,
+            typemap=tp.state.typemap,
+            calltypes=tp.state.calltypes,
+            lifted=(),
+            lifted_from=None,
+            args=tp.state.args,
+            return_type=tp.state.return_type,
+            html_output=config.HTML)
+
+        diagnostics = numba.parfors.parfor.ParforDiagnostics()
+
+        preparfor_pass = numba.parfors.parfor.PreParforPass(
+            tp.state.func_ir, tp.state.typemap, tp.state.calltypes,
+            tp.state.typingctx, tp.state.targetctx, options,
+            swapped=diagnostics.replaced_fns)
+        preparfor_pass.run()
+
+        rewrites.rewrite_registry.apply('after-inference', tp.state)
+
+        flags = compiler.Flags()
+        parfor_pass = numba.parfors.parfor.ParforPass(
+            tp.state.func_ir, tp.state.typemap, tp.state.calltypes,
+            tp.state.return_type, tp.state.typingctx, tp.state.targetctx,
+            options, flags, tp.state.metadata, diagnostics=diagnostics)
+        parfor_pass.run()
+        test_ir._definitions = build_definitions(test_ir.blocks)
+
+    return test_ir, tp
+
+def countParfors(test_func, args, **kws):
+    test_ir, tp = get_optimized_numba_ir(test_func, args, **kws)
+    ret_count = 0
+
+    for label, block in test_ir.blocks.items():
+        for i, inst in enumerate(block.body):
+            if isinstance(inst, numba.parfors.parfor.Parfor):
+                ret_count += 1
+
+    return ret_count
+
+
+def countArrays(test_func, args, **kws):
+    test_ir, tp = get_optimized_numba_ir(test_func, args, **kws)
+    return _count_arrays_inner(test_ir.blocks, tp.state.typemap)
+
+def get_init_block_size(test_func, args, **kws):
+    test_ir, tp = get_optimized_numba_ir(test_func, args, **kws)
+    blocks = test_ir.blocks
+
+    ret_count = 0
+
+    for label, block in blocks.items():
+        for i, inst in enumerate(block.body):
+            if isinstance(inst, numba.parfors.parfor.Parfor):
+                ret_count += len(inst.init_block.body)
+
+    return ret_count
+
+def _count_arrays_inner(blocks, typemap):
+    ret_count = 0
+    arr_set = set()
+
+    for label, block in blocks.items():
+        for i, inst in enumerate(block.body):
+            if isinstance(inst, numba.parfors.parfor.Parfor):
+                parfor_blocks = inst.loop_body.copy()
+                parfor_blocks[0] = inst.init_block
+                ret_count += _count_arrays_inner(parfor_blocks, typemap)
+            if (isinstance(inst, ir.Assign)
+                    and isinstance(typemap[inst.target.name],
+                                    types.ArrayCompatible)):
+                arr_set.add(inst.target.name)
+
+    ret_count += len(arr_set)
+    return ret_count
+
+def countArrayAllocs(test_func, args, **kws):
+    test_ir, tp = get_optimized_numba_ir(test_func, args, **kws)
+    ret_count = 0
+
+    for block in test_ir.blocks.values():
+        ret_count += _count_array_allocs_inner(test_ir, block)
+
+    return ret_count
+
+def _count_array_allocs_inner(func_ir, block):
+    ret_count = 0
+    for inst in block.body:
+        if isinstance(inst, numba.parfors.parfor.Parfor):
+            ret_count += _count_array_allocs_inner(func_ir, inst.init_block)
+            for b in inst.loop_body.values():
+                ret_count += _count_array_allocs_inner(func_ir, b)
+
+        if (isinstance(inst, ir.Assign) and isinstance(inst.value, ir.Expr)
+                and inst.value.op == 'call'
+                and (guard(find_callname, func_ir, inst.value) == ('empty', 'numpy')
+                or guard(find_callname, func_ir, inst.value)
+                    == ('empty_inferred', 'numba.np.unsafe.ndarray'))):
+            ret_count += 1
+
+    return ret_count
+
+def countNonParforArrayAccesses(test_func, args, **kws):
+    test_ir, tp = get_optimized_numba_ir(test_func, args, **kws)
+    return _count_non_parfor_array_accesses_inner(test_ir, test_ir.blocks,
+                                                  tp.state.typemap)
+
+def _count_non_parfor_array_accesses_inner(f_ir, blocks, typemap, parfor_indices=None):
+    ret_count = 0
+    if parfor_indices is None:
+        parfor_indices = set()
+
+    for label, block in blocks.items():
+        for stmt in block.body:
+            if isinstance(stmt, numba.parfors.parfor.Parfor):
+                parfor_indices.add(stmt.index_var.name)
+                parfor_blocks = stmt.loop_body.copy()
+                parfor_blocks[0] = stmt.init_block
+                ret_count += _count_non_parfor_array_accesses_inner(
+                    f_ir, parfor_blocks, typemap, parfor_indices)
+
+            # getitem
+            elif (is_getitem(stmt) and isinstance(typemap[stmt.value.value.name],
+                        types.ArrayCompatible) and not _uses_indices(
+                        f_ir, index_var_of_get_setitem(stmt), parfor_indices)):
+                ret_count += 1
+
+            # setitem
+            elif (is_setitem(stmt) and isinstance(typemap[stmt.target.name],
+                    types.ArrayCompatible) and not _uses_indices(
+                    f_ir, index_var_of_get_setitem(stmt), parfor_indices)):
+                ret_count += 1
+
+            # find parfor_index aliases
+            elif (isinstance(stmt, ir.Assign) and
+                  isinstance(stmt.value, ir.Var) and
+                  stmt.value.name in parfor_indices):
+                parfor_indices.add(stmt.target.name)
+
+    return ret_count
+
+def _uses_indices(f_ir, index, index_set):
+    if index.name in index_set:
+        return True
+
+    ind_def = guard(get_definition, f_ir, index)
+    if isinstance(ind_def, ir.Expr) and ind_def.op == 'build_tuple':
+        varnames = set(v.name for v in ind_def.items)
+        return len(varnames & index_set) != 0
+
+    return False
+
+
+class TestPipeline(object):
+    def __init__(self, typingctx, targetctx, args, test_ir):
+        self.state = compiler.StateDict()
+        self.state.typingctx = typingctx
+        self.state.targetctx = targetctx
+        self.state.args = args
+        self.state.func_ir = test_ir
+        self.state.typemap = None
+        self.state.return_type = None
+        self.state.calltypes = None
+        self.state.metadata = {}
+
+
+@skip_parfors_unsupported
+class TestParforBasic(TestParforsBase):
+    """Smoke tests for the parfors transforms. These tests check the most basic
+    functionality"""
+
+    def __init__(self, *args):
+        TestParforsBase.__init__(self, *args)
+        # these are used in the mass of simple tests
+        m = np.reshape(np.arange(12.), (3, 4))
+        self.simple_args = [np.arange(3.), np.arange(4.), m, m.T]
+
+    def test_simple01(self):
+        def test_impl():
+            return np.ones(())
+        with self.assertRaises(AssertionError) as raises:
+            self.check(test_impl)
+        self.assertIn("\'@do_scheduling\' not found", str(raises.exception))
+
+    def test_simple02(self):
+        def test_impl():
+            return np.ones((1,))
+        self.check(test_impl)
+
+    def test_simple03(self):
+        def test_impl():
+            return np.ones((1, 2))
+        self.check(test_impl)
+
+    def test_simple04(self):
+        def test_impl():
+            return np.ones(1)
+        self.check(test_impl)
+
+    def test_simple07(self):
+        def test_impl():
+            return np.ones((1, 2), dtype=np.complex128)
+        self.check(test_impl)
+
+    def test_simple08(self):
+        def test_impl():
+            return np.ones((1, 2)) + np.ones((1, 2))
+        self.check(test_impl)
+
+    def test_simple09(self):
+        def test_impl():
+            return np.ones((1, 1))
+        self.check(test_impl)
+
+    def test_simple10(self):
+        def test_impl():
+            return np.ones((0, 0))
+        self.check(test_impl)
+
+    def test_simple11(self):
+        def test_impl():
+            return np.ones((10, 10)) + 1.
+        self.check(test_impl)
+
+    def test_simple12(self):
+        def test_impl():
+            return np.ones((10, 10)) + np.complex128(1.)
+        self.check(test_impl)
+
+    def test_simple13(self):
+        def test_impl():
+            return np.complex128(1.)
+        with self.assertRaises(AssertionError) as raises:
+            self.check(test_impl)
+        self.assertIn("\'@do_scheduling\' not found", str(raises.exception))
+
+    def test_simple14(self):
+        def test_impl():
+            return np.ones((10, 10))[0::20]
+        self.check(test_impl)
+
+    def test_simple15(self):
+        def test_impl(v1, v2, m1, m2):
+            return v1 + v1
+        self.check(test_impl, *self.simple_args)
+
+    def test_simple16(self):
+        def test_impl(v1, v2, m1, m2):
+            return m1 + m1
+        self.check(test_impl, *self.simple_args)
+
+    def test_simple17(self):
+        def test_impl(v1, v2, m1, m2):
+            return m2 + v1
+        self.check(test_impl, *self.simple_args)
+
+    @needs_lapack
+    def test_simple18(self):
+        def test_impl(v1, v2, m1, m2):
+            return m1.T + np.linalg.svd(m2)[1]
+        self.check(test_impl, *self.simple_args)
+
+    @needs_blas
+    def test_simple19(self):
+        def test_impl(v1, v2, m1, m2):
+            return np.dot(m1, v2)
+        self.check(test_impl, *self.simple_args)
+
+    @needs_blas
+    def test_simple20(self):
+        def test_impl(v1, v2, m1, m2):
+            return np.dot(m1, m2)
+        # gemm is left to BLAS
+        with self.assertRaises(AssertionError) as raises:
+            self.check(test_impl, *self.simple_args)
+        self.assertIn("\'@do_scheduling\' not found", str(raises.exception))
+
+    @needs_blas
+    def test_simple21(self):
+        def test_impl(v1, v2, m1, m2):
+            return np.dot(v1, v1)
+        self.check(test_impl, *self.simple_args)
+
+    def test_simple22(self):
+        def test_impl(v1, v2, m1, m2):
+            return np.sum(v1 + v1)
+        self.check(test_impl, *self.simple_args)
+
+    def test_simple23(self):
+        def test_impl(v1, v2, m1, m2):
+            x = 2 * v1
+            y = 2 * v1
+            return 4 * np.sum(x**2 + y**2 < 1) / 10
+        self.check(test_impl, *self.simple_args)
+
+    def test_simple24(self):
+        def test_impl():
+            n = 20
+            A = np.ones((n, n))
+            b = np.arange(n)
+            return np.sum(A[:, b])
+        self.check(test_impl)
+
+    @disabled_test
+    def test_simple_operator_15(self):
+        """same as corresponding test_simple_<n> case but using operator.add"""
+        def test_impl(v1, v2, m1, m2):
+            return operator.add(v1, v1)
+
+        self.check(test_impl, *self.simple_args)
+
+    @disabled_test
+    def test_simple_operator_16(self):
+        def test_impl(v1, v2, m1, m2):
+            return operator.add(m1, m1)
+
+        self.check(test_impl, *self.simple_args)
+
+    @disabled_test
+    def test_simple_operator_17(self):
+        def test_impl(v1, v2, m1, m2):
+            return operator.add(m2, v1)
+
+        self.check(test_impl, *self.simple_args)
+
+    def test_inplace_alias(self):
+        # issue7201
+        def test_impl(a):
+            a += 1
+            a[:] = 3
+
+        def comparer(a, b):
+            np.testing.assert_equal(a, b)
+
+        x = np.ones(1)
+        self.check(test_impl, x, check_arg_equality=[comparer])
+
+
+@skip_parfors_unsupported
+class TestParforNumericalMisc(TestParforsBase):
+    """ Miscellaneous 'classical' numerical tests """
+
+    def test_pi(self):
+        def test_impl(n):
+            x = 2 * np.random.ranf(n) - 1
+            y = 2 * np.random.ranf(n) - 1
+            return 4 * np.sum(x**2 + y**2 < 1) / n
+
+        self.check(test_impl, 100000, decimal=1)
+        self.assertEqual(countParfors(test_impl, (types.int64, )), 1)
+        self.assertEqual(countArrays(test_impl, (types.intp,)), 0)
+
+    def test_blackscholes(self):
+        # blackscholes takes 5 1D float array args
+        args = (numba.float64[:], ) * 5
+        self.assertEqual(countParfors(blackscholes_impl, args), 1)
+
+    @needs_blas
+    def test_logistic_regression(self):
+        args = (numba.float64[:], numba.float64[:,:], numba.float64[:],
+                numba.int64)
+        self.assertEqual(countParfors(lr_impl, args), 2)
+        self.assertEqual(countArrayAllocs(lr_impl, args), 1)
+
+    def test_kmeans(self):
+        np.random.seed(0)
+        N = 1024
+        D = 10
+        centers = 3
+        A = np.random.ranf((N, D))
+        init_centroids = np.random.ranf((centers, D))
+        self.check(example_kmeans_test, A, centers, 3, init_centroids,
+                                                                    decimal=1)
+        # TODO: count parfors after k-means fusion is working
+        # requires recursive parfor counting
+        arg_typs = (types.Array(types.float64, 2, 'C'), types.intp, types.intp,
+                    types.Array(types.float64, 2, 'C'))
+        self.assertEqual(
+            countNonParforArrayAccesses(example_kmeans_test, arg_typs), 0)
+
+
+@skip_parfors_unsupported
+class TestParforNumPy(TestParforsBase):
+    """Tests NumPy functionality under parfors"""
+
+    @needs_blas
+    def test_mvdot(self):
+        def test_impl(a, v):
+            return np.dot(a, v)
+
+        A = np.linspace(0, 1, 20).reshape(2, 10)
+        v = np.linspace(2, 1, 10)
+
+        self.check(test_impl, A, v)
+
+    def test_fuse_argmin_argmax_max_min(self):
+        for op in [np.argmin, np.argmax, np.min, np.max]:
+            def test_impl(n):
+                A = np.ones(n)
+                C = op(A)
+                B = A.sum()
+                return B + C
+            self.check(test_impl, 256)
+            self.assertEqual(countParfors(test_impl, (types.int64, )), 1)
+            self.assertEqual(countArrays(test_impl, (types.intp,)), 0)
+
+    def test_np_random_func_direct_import(self):
+        def test_impl(n):
+            A = randn(n)
+            return A[0]
+        self.assertEqual(countParfors(test_impl, (types.int64, )), 1)
+
+    def test_arange(self):
+        # test with stop only
+        def test_impl1(n):
+            return np.arange(n)
+        # start and stop
+        def test_impl2(s, n):
+            return np.arange(n)
+        # start, step, stop
+        def test_impl3(s, n, t):
+            return np.arange(s, n, t)
+
+        for arg in [11, 128, 30.0, complex(4,5), complex(5,4)]:
+            self.check(test_impl1, arg)
+            self.check(test_impl2, 2, arg)
+            self.check(test_impl3, 2, arg, 2)
+
+    def test_linspace(self):
+        # without num
+        def test_impl1(start, stop):
+            return np.linspace(start, stop)
+        # with num
+        def test_impl2(start, stop, num):
+            return np.linspace(start, stop, num)
+
+        for arg in [11, 128, 30.0, complex(4,5), complex(5,4)]:
+            self.check(test_impl1, 2, arg)
+            self.check(test_impl2, 2, arg, 30)
+
+    def test_mean(self):
+        def test_impl(A):
+            return A.mean()
+        N = 100
+        A = np.random.ranf(N)
+        B = np.random.randint(10, size=(N, 3))
+        self.check(test_impl, A)
+        self.check(test_impl, B)
+        self.assertEqual(countParfors(test_impl, (types.Array(types.float64, 1, 'C'), )), 1)
+        self.assertEqual(countParfors(test_impl, (types.Array(types.float64, 2, 'C'), )), 1)
+
+        # Test variants
+        data_gen = lambda: self.gen_linspace_variants(1)
+        self.check_variants(test_impl, data_gen)
+        self.count_parfors_variants(test_impl, data_gen)
+
+    def test_var(self):
+        def test_impl(A):
+            return A.var()
+        N = 100
+        A = np.random.ranf(N)
+        B = np.random.randint(10, size=(N, 3))
+        C = A + 1j * A
+        self.check(test_impl, A)
+        self.check(test_impl, B)
+        self.check(test_impl, C)
+        self.assertEqual(countParfors(test_impl, (types.Array(types.float64, 1, 'C'), )), 2)
+        self.assertEqual(countParfors(test_impl, (types.Array(types.float64, 2, 'C'), )), 2)
+
+        # Test variants
+        data_gen = lambda: self.gen_linspace_variants(1)
+        self.check_variants(test_impl, data_gen)
+        self.count_parfors_variants(test_impl, data_gen)
+
+    def test_std(self):
+        def test_impl(A):
+            return A.std()
+        N = 100
+        A = np.random.ranf(N)
+        B = np.random.randint(10, size=(N, 3))
+        C = A + 1j * A
+        self.check(test_impl, A)
+        self.check(test_impl, B)
+        self.check(test_impl, C)
+        argty = (types.Array(types.float64, 1, 'C'),)
+        self.assertEqual(countParfors(test_impl, argty), 2)
+        self.assertEqual(countParfors(test_impl, argty), 2)
+
+        # Test variants
+        data_gen = lambda: self.gen_linspace_variants(1)
+        self.check_variants(test_impl, data_gen)
+        self.count_parfors_variants(test_impl, data_gen)
+
+    def test_random_parfor(self):
+        """
+        Test function with only a random call to make sure a random function
+        like ranf is actually translated to a parfor.
+        """
+        def test_impl(n):
+            A = np.random.ranf((n, n))
+            return A
+        self.assertEqual(countParfors(test_impl, (types.int64, )), 1)
+
+    def test_randoms(self):
+        def test_impl(n):
+            A = np.random.standard_normal(size=(n, n))
+            B = np.random.randn(n, n)
+            C = np.random.normal(0.0, 1.0, (n, n))
+            D = np.random.chisquare(1.0, (n, n))
+            E = np.random.randint(1, high=3, size=(n, n))
+            F = np.random.triangular(1, 2, 3, (n, n))
+            return np.sum(A+B+C+D+E+F)
+
+        n = 128
+        cpfunc = self.compile_parallel(test_impl, (numba.typeof(n),))
+        parfor_output = cpfunc.entry_point(n)
+        py_output = test_impl(n)
+        # check results within 5% since random numbers generated in parallel
+        np.testing.assert_allclose(parfor_output, py_output, rtol=0.05)
+        self.assertEqual(countParfors(test_impl, (types.int64, )), 1)
+
+    def test_dead_randoms(self):
+        def test_impl(n):
+            A = np.random.standard_normal(size=(n, n))
+            B = np.random.randn(n, n)
+            C = np.random.normal(0.0, 1.0, (n, n))
+            D = np.random.chisquare(1.0, (n, n))
+            E = np.random.randint(1, high=3, size=(n, n))
+            F = np.random.triangular(1, 2, 3, (n, n))
+            return 3
+
+        n = 128
+        cpfunc = self.compile_parallel(test_impl, (numba.typeof(n),))
+        parfor_output = cpfunc.entry_point(n)
+        py_output = test_impl(n)
+        self.assertEqual(parfor_output, py_output)
+        self.assertEqual(countParfors(test_impl, (types.int64, )), 0)
+
+    def test_min(self):
+        def test_impl1(A):
+            return A.min()
+
+        def test_impl2(A):
+            return np.min(A)
+
+        n = 211
+        A = np.random.ranf(n)
+        B = np.random.randint(10, size=n).astype(np.int32)
+        C = np.random.ranf((n, n))  # test multi-dimensional array
+        D = np.array([np.inf, np.inf])
+        self.check(test_impl1, A)
+        self.check(test_impl1, B)
+        self.check(test_impl1, C)
+        self.check(test_impl1, D)
+        self.check(test_impl2, A)
+        self.check(test_impl2, B)
+        self.check(test_impl2, C)
+        self.check(test_impl2, D)
+
+        # checks that 0d array input raises
+        msg = ("zero-size array to reduction operation "
+               "minimum which has no identity")
+        for impl in (test_impl1, test_impl2):
+            pcfunc = self.compile_parallel(impl, (types.int64[:],))
+            with self.assertRaises(ValueError) as e:
+                pcfunc.entry_point(np.array([], dtype=np.int64))
+            self.assertIn(msg, str(e.exception))
+
+        # Test variants
+        data_gen = lambda: self.gen_linspace_variants(1)
+        self.check_variants(test_impl1, data_gen)
+        self.count_parfors_variants(test_impl1, data_gen)
+        self.check_variants(test_impl2, data_gen)
+        self.count_parfors_variants(test_impl2, data_gen)
+
+    def test_max(self):
+        def test_impl1(A):
+            return A.max()
+
+        def test_impl2(A):
+            return np.max(A)
+
+        n = 211
+        A = np.random.ranf(n)
+        B = np.random.randint(10, size=n).astype(np.int32)
+        C = np.random.ranf((n, n))  # test multi-dimensional array
+        D = np.array([-np.inf, -np.inf])
+        self.check(test_impl1, A)
+        self.check(test_impl1, B)
+        self.check(test_impl1, C)
+        self.check(test_impl1, D)
+        self.check(test_impl2, A)
+        self.check(test_impl2, B)
+        self.check(test_impl2, C)
+        self.check(test_impl2, D)
+
+        # checks that 0d array input raises
+        msg = ("zero-size array to reduction operation "
+               "maximum which has no identity")
+        for impl in (test_impl1, test_impl2):
+            pcfunc = self.compile_parallel(impl, (types.int64[:],))
+            with self.assertRaises(ValueError) as e:
+                pcfunc.entry_point(np.array([], dtype=np.int64))
+            self.assertIn(msg, str(e.exception))
+
+        # Test variants
+        data_gen = lambda: self.gen_linspace_variants(1)
+        self.check_variants(test_impl1, data_gen)
+        self.count_parfors_variants(test_impl1, data_gen)
+        self.check_variants(test_impl2, data_gen)
+        self.count_parfors_variants(test_impl2, data_gen)
+
+    def test_argmax(self):
+        def test_impl1(A):
+            return A.argmax()
+
+        def test_impl2(A):
+            return np.argmax(A)
+
+        n = 211
+        A = np.array([1., 0., 3., 2., 3.])
+        B = np.random.randint(10, size=n).astype(np.int32)
+        C = np.random.ranf((n, n))  # test multi-dimensional array
+        D = np.array([1., 0., np.nan, 2., 3.])
+        self.check(test_impl1, A)
+        self.check(test_impl1, B)
+        self.check(test_impl1, C)
+        self.check(test_impl1, D)
+        self.check(test_impl2, A)
+        self.check(test_impl2, B)
+        self.check(test_impl2, C)
+        self.check(test_impl2, D)
+
+        # checks that 0d array input raises
+        msg = 'attempt to get argmax of an empty sequence'
+        for impl in (test_impl1, test_impl2):
+            pcfunc = self.compile_parallel(impl, (types.int64[:],))
+            with self.assertRaises(ValueError) as e:
+                pcfunc.entry_point(np.array([], dtype=np.int64))
+            self.assertIn(msg, str(e.exception))
+
+        # Test variants
+        data_gen = lambda: self.gen_linspace_variants(1)
+        self.check_variants(test_impl1, data_gen)
+        self.count_parfors_variants(test_impl1, data_gen)
+        self.check_variants(test_impl2, data_gen)
+        self.count_parfors_variants(test_impl2, data_gen)
+
+    def test_argmin(self):
+        def test_impl1(A):
+            return A.argmin()
+
+        def test_impl2(A):
+            return np.argmin(A)
+
+        n = 211
+        A = np.array([1., 0., 2., 0., 3.])
+        B = np.random.randint(10, size=n).astype(np.int32)
+        C = np.random.ranf((n, n))  # test multi-dimensional array
+        D = np.array([1., 0., np.nan, 0., 3.])
+        self.check(test_impl1, A)
+        self.check(test_impl1, B)
+        self.check(test_impl1, C)
+        self.check(test_impl1, D)
+        self.check(test_impl2, A)
+        self.check(test_impl2, B)
+        self.check(test_impl2, C)
+        self.check(test_impl2, D)
+
+        # checks that 0d array input raises
+        msg = 'attempt to get argmin of an empty sequence'
+        for impl in (test_impl1, test_impl2):
+            pcfunc = self.compile_parallel(impl, (types.int64[:],))
+            with self.assertRaises(ValueError) as e:
+                pcfunc.entry_point(np.array([], dtype=np.int64))
+            self.assertIn(msg, str(e.exception))
+
+        # Test variants
+        data_gen = lambda: self.gen_linspace_variants(1)
+        self.check_variants(test_impl1, data_gen)
+        self.count_parfors_variants(test_impl1, data_gen)
+        self.check_variants(test_impl2, data_gen)
+        self.count_parfors_variants(test_impl2, data_gen)
+
+    def test_ndarray_fill(self):
+        def test_impl(x):
+            x.fill(7.0)
+            return x
+        x = np.zeros(10)
+        self.check(test_impl, x)
+        argty = (types.Array(types.float64, 1, 'C'),)
+        self.assertEqual(countParfors(test_impl, argty), 1)
+
+    def test_ndarray_fill2d(self):
+        def test_impl(x):
+            x.fill(7.0)
+            return x
+        x = np.zeros((2,2))
+        self.check(test_impl, x)
+        argty = (types.Array(types.float64, 2, 'C'),)
+        self.assertEqual(countParfors(test_impl, argty), 1)
+
+    def test_reshape_with_neg_one(self):
+        # issue3314
+        def test_impl(a, b):
+            result_matrix = np.zeros((b, b, 1), dtype=np.float64)
+            sub_a = a[0:b]
+            a = sub_a.size
+            b = a / 1
+            z = sub_a.reshape(-1, 1)
+            result_data = sub_a / z
+            result_matrix[:,:,0] = result_data
+            return result_matrix
+
+        a = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
+                   7.0, 8.0, 9.0, 10.0, 11.0, 12.0])
+        b = 3
+
+        self.check(test_impl, a, b)
+
+    def test_reshape_with_large_neg(self):
+        # issue3314
+        def test_impl(a, b):
+            result_matrix = np.zeros((b, b, 1), dtype=np.float64)
+            sub_a = a[0:b]
+            a = sub_a.size
+            b = a / 1
+            z = sub_a.reshape(-1307, 1)
+            result_data = sub_a / z
+            result_matrix[:,:,0] = result_data
+            return result_matrix
+
+        a = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
+                   7.0, 8.0, 9.0, 10.0, 11.0, 12.0])
+        b = 3
+
+        self.check(test_impl, a, b)
+
+    def test_reshape_with_too_many_neg_one(self):
+        # issue3314
+        with self.assertRaises(errors.UnsupportedRewriteError) as raised:
+            @njit(parallel=True)
+            def test_impl(a, b):
+                rm = np.zeros((b, b, 1), dtype=np.float64)
+                sub_a = a[0:b]
+                a = sub_a.size
+                b = a / 1
+                z = sub_a.reshape(-1, -1)
+                result_data = sub_a / z
+                rm[:,:,0] = result_data
+                return rm
+
+            a = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
+                       7.0, 8.0, 9.0, 10.0, 11.0, 12.0])
+            b = 3
+            test_impl(a, b)
+
+        msg = ("The reshape API may only include one negative argument.")
+        self.assertIn(msg, str(raised.exception))
+
+    def test_0d_array(self):
+        def test_impl(n):
+            return np.sum(n) + np.prod(n) + np.min(n) + np.max(n) + np.var(n)
+        self.check(test_impl, np.array(7), check_scheduling=False)
+
+    def test_real_imag_attr(self):
+        # See issue 8012
+        def test_impl(z):
+            return np.sum(z.real ** 2 + z.imag ** 2)
+
+        z = np.arange(5) * (1 + 1j)
+        self.check(test_impl, z)
+        self.assertEqual(countParfors(test_impl, (types.complex128[::1],)), 1)
+
+
+class TestParforsUnsupported(TestCase):
+    """Tests for unsupported use of parfors"""
+    @unittest.skipIf(not _32bit, "Only impacts 32 bit hardware")
+    @needs_blas
+    def test_unsupported_combination_raises(self):
+        """
+        This test is in place until issues with the 'parallel'
+        target on 32 bit hardware are fixed.
+        """
+        with self.assertRaises(errors.UnsupportedParforsError) as raised:
+            @njit(parallel=True)
+            def ddot(a, v):
+                return np.dot(a, v)
+
+            A = np.linspace(0, 1, 20).reshape(2, 10)
+            v = np.linspace(2, 1, 10)
+            ddot(A, v)
+
+        msg = ("The 'parallel' target is not currently supported on 32 bit "
+               "hardware")
+        self.assertIn(msg, str(raised.exception))
+
+@skip_parfors_unsupported
+class TestParfors(TestParforsBase):
+    """ Tests cpython, reduction and various parfors features"""
+
+    def test_arraymap(self):
+        def test_impl(a, x, y):
+            return a * x + y
+
+        self.check_variants(test_impl, lambda: self.gen_linspace_variants(3))
+
+    def test_0d_broadcast(self):
+        def test_impl():
+            X = np.array(1)
+            Y = np.ones((10, 12))
+            return np.sum(X + Y)
+        self.check(test_impl)
+        self.assertEqual(countParfors(test_impl, ()), 1)
+
+    def test_2d_parfor(self):
+        def test_impl():
+            X = np.ones((10, 12))
+            Y = np.zeros((10, 12))
+            return np.sum(X + Y)
+        self.check(test_impl)
+        self.assertEqual(countParfors(test_impl, ()), 1)
+
+    def test_nd_parfor(self):
+        def case1():
+            X = np.ones((10, 12))
+            Y = np.zeros((10, 12))
+            yield (X, Y)
+
+        data_gen = lambda: chain(case1(), self.gen_linspace_variants(2))
+
+        def test_impl(X, Y):
+            return np.sum(X + Y)
+
+        self.check_variants(test_impl, data_gen)
+        self.count_parfors_variants(test_impl, data_gen)
+
+    def test_np_func_direct_import(self):
+        from numpy import ones  # import here becomes FreeVar
+        def test_impl(n):
+            A = ones(n)
+            return A[0]
+        n = 111
+        self.check(test_impl, n)
+
+    def test_size_assertion(self):
+        def test_impl(m, n):
+            A = np.ones(m)
+            B = np.ones(n)
+            return np.sum(A + B)
+
+        self.check(test_impl, 10, 10)
+        with self.assertRaises(AssertionError) as raises:
+            cfunc = njit(parallel=True)(test_impl)
+            cfunc(10, 9)
+        msg = "Sizes of A, B do not match"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_cfg(self):
+        # from issue #2477
+        def test_impl(x, is_positive, N):
+            for i in numba.prange(2):
+                for j in range( i*N//2, (i+1)*N//2 ):
+                    is_positive[j] = 0
+                    if x[j] > 0:
+                        is_positive[j] = 1
+
+            return is_positive
+
+        N = 100
+        x = np.random.rand(N)
+        is_positive = np.zeros(N)
+        self.check(test_impl, x, is_positive, N)
+
+    def test_reduce(self):
+        def test_impl(A):
+            init_val = 10
+            return reduce(lambda a,b: min(a, b), A, init_val)
+
+        n = 211
+        A = np.random.ranf(n)
+        self.check(test_impl, A)
+        A = np.random.randint(10, size=n).astype(np.int32)
+        self.check(test_impl, A)
+
+        # test checking the number of arguments for the reduce function
+        def test_impl():
+            g = lambda x: x ** 2
+            return reduce(g, np.array([1, 2, 3, 4, 5]), 2)
+        with self.assertTypingError():
+            self.check(test_impl)
+
+        # test checking reduction over bitarray masked arrays
+        n = 160
+        A = np.random.randint(10, size=n).astype(np.int32)
+        def test_impl(A):
+            return np.sum(A[A>=3])
+        self.check(test_impl, A)
+        # TODO: this should fuse
+        # self.assertTrue(countParfors(test_impl, (numba.float64[:],)) == 1)
+
+        def test_impl(A):
+            B = A[:,0]
+            return np.sum(A[B>=3,1])
+        self.check(test_impl, A.reshape((16,10)))
+        # TODO: this should also fuse
+        #self.assertTrue(countParfors(test_impl, (numba.float64[:,:],)) == 1)
+
+        def test_impl(A):
+            B = A[:,0]
+            return np.sum(A[B>=3,1:2])
+        self.check(test_impl, A.reshape((16,10)))
+        # this doesn't fuse due to mixed indices
+        self.assertEqual(countParfors(test_impl, (numba.float64[:,:],)), 2)
+
+    def test_use_of_reduction_var1(self):
+        def test_impl():
+            acc = 0
+            for i in prange(1):
+                acc = cmath.sqrt(acc)
+            return acc
+
+        # checks that invalid use of reduction variable is detected
+        msg = ("Use of reduction variable acc in an unsupported reduction function.")
+        with self.assertRaises(ValueError) as e:
+            pcfunc = self.compile_parallel(test_impl, ())
+        self.assertIn(msg, str(e.exception))
+
+    def test_unsupported_floordiv1(self):
+        def test_impl():
+            acc = 100
+            for i in prange(2):
+                acc //= 2
+            return acc
+
+        # checks that invalid use of ifloordiv reduction operator is detected
+        msg = ("Parallel floordiv reductions are not supported. "
+               "If all divisors are integers then a floordiv "
+               "reduction can in some cases be parallelized as "
+               "a multiply reduction followed by a floordiv of "
+               "the resulting product.")
+        with self.assertRaises(errors.NumbaValueError) as e:
+            pcfunc = self.compile_parallel(test_impl, ())
+        self.assertIn(msg, str(e.exception))
+
+    def test_unsupported_xor1(self):
+        def test_impl():
+            acc = 100
+            for i in prange(2):
+                acc ^= i + 2
+            return acc
+
+        msg = ("Use of reduction variable acc in an unsupported reduction function.")
+        with self.assertRaises(ValueError) as e:
+            pcfunc = self.compile_parallel(test_impl, ())
+        self.assertIn(msg, str(e.exception))
+
+    def test_parfor_array_access1(self):
+        # signed index of the prange generated by sum() should be replaced
+        # resulting in array A to be eliminated (see issue #2846)
+        def test_impl(n):
+            A = np.ones(n)
+            return A.sum()
+
+        n = 211
+        self.check(test_impl, n)
+        self.assertEqual(countArrays(test_impl, (types.intp,)), 0)
+
+    def test_parfor_array_access2(self):
+        # in this test, the prange index has the same name (i) in two loops
+        # thus, i has multiple definitions and is harder to replace
+        def test_impl(n):
+            A = np.ones(n)
+            m = 0
+            n = 0
+            for i in numba.prange(len(A)):
+                m += A[i]
+
+            for i in numba.prange(len(A)):
+                if m == n:  # access in another block
+                    n += A[i]
+
+            return m + n
+
+        n = 211
+        self.check(test_impl, n)
+        self.assertEqual(countNonParforArrayAccesses(test_impl, (types.intp,)), 0)
+
+    def test_parfor_array_access3(self):
+        def test_impl(n):
+            A = np.ones(n, np.int64)
+            m = 0
+            for i in numba.prange(len(A)):
+                m += A[i]
+                if m==2:
+                    i = m
+
+        n = 211
+        with self.assertRaises(errors.UnsupportedRewriteError) as raises:
+            self.check(test_impl, n)
+        self.assertIn("Overwrite of parallel loop index", str(raises.exception))
+
+    @needs_blas
+    def test_parfor_array_access4(self):
+        # in this test, one index of a multi-dim access should be replaced
+        # np.dot parallel implementation produces this case
+        def test_impl(A, b):
+            return np.dot(A, b)
+
+        n = 211
+        d = 4
+        A = np.random.ranf((n, d))
+        b = np.random.ranf(d)
+        self.check(test_impl, A, b)
+        # make sure the parfor index is replaced in build_tuple of access to A
+        test_ir, tp = get_optimized_numba_ir(
+            test_impl, (types.Array(types.float64, 2, 'C'),
+                        types.Array(types.float64, 1, 'C')))
+        # this code should have one basic block after optimization
+        self.assertTrue(len(test_ir.blocks) == 1 and 0 in test_ir.blocks)
+        block = test_ir.blocks[0]
+        parfor_found = False
+        parfor = None
+        for stmt in block.body:
+            if isinstance(stmt, numba.parfors.parfor.Parfor):
+                parfor_found = True
+                parfor = stmt
+
+        self.assertTrue(parfor_found)
+        build_tuple_found = False
+        # there should be only one build_tuple
+        for bl in parfor.loop_body.values():
+            for stmt in bl.body:
+                if (isinstance(stmt, ir.Assign)
+                        and isinstance(stmt.value, ir.Expr)
+                        and stmt.value.op == 'build_tuple'):
+                    build_tuple_found = True
+                    self.assertTrue(parfor.index_var in stmt.value.items)
+
+        self.assertTrue(build_tuple_found)
+
+    def test_parfor_dtype_type(self):
+        # test array type replacement creates proper type
+        def test_impl(a):
+            for i in numba.prange(len(a)):
+                a[i] = a.dtype.type(0)
+            return a[4]
+
+        a = np.ones(10)
+        self.check(test_impl, a)
+
+    def test_parfor_array_access5(self):
+        # one dim is slice in multi-dim access
+        def test_impl(n):
+            X = np.ones((n, 3))
+            y = 0
+            for i in numba.prange(n):
+                y += X[i,:].sum()
+            return y
+
+        n = 211
+        self.check(test_impl, n)
+        self.assertEqual(countNonParforArrayAccesses(test_impl, (types.intp,)), 0)
+
+    @disabled_test # Test itself is problematic, see #3155
+    def test_parfor_hoist_setitem(self):
+        # Make sure that read of out is not hoisted.
+        def test_impl(out):
+            for i in prange(10):
+                out[0] = 2 * out[0]
+            return out[0]
+
+        out = np.ones(1)
+        self.check(test_impl, out)
+
+    @needs_blas
+    def test_parfor_generate_fuse(self):
+        # issue #2857
+        def test_impl(N, D):
+            w = np.ones(D)
+            X = np.ones((N, D))
+            Y = np.ones(N)
+            for i in range(3):
+                B = (-Y * np.dot(X, w))
+
+            return B
+
+        n = 211
+        d = 3
+        self.check(test_impl, n, d)
+        self.assertEqual(countArrayAllocs(test_impl, (types.intp, types.intp)), 4)
+        self.assertEqual(countParfors(test_impl, (types.intp, types.intp)), 4)
+
+    def test_ufunc_expr(self):
+        # issue #2885
+        def test_impl(A, B):
+            return np.bitwise_and(A, B)
+
+        A = np.ones(3, np.uint8)
+        B = np.ones(3, np.uint8)
+        B[1] = 0
+        self.check(test_impl, A, B)
+
+    def test_find_callname_intrinsic(self):
+        def test_impl(n):
+            A = unsafe_empty((n,))
+            for i in range(n):
+                A[i] = i + 2.0
+            return A
+
+        # the unsafe allocation should be found even though it is imported
+        # as a different name
+        self.assertEqual(countArrayAllocs(test_impl, (types.intp,)), 1)
+
+    def test_reduction_var_reuse(self):
+        # issue #3139
+        def test_impl(n):
+            acc = 0
+            for i in prange(n):
+                acc += 1
+
+            for i in prange(n):
+                acc += 2
+
+            return acc
+        self.check(test_impl, 16)
+
+    def test_non_identity_initial(self):
+        # issue #7344
+        def test_impl(A, cond):
+            s = 1
+            for i in prange(A.shape[0]):
+                if cond[i]:
+                    s += 1
+            return s
+        self.check(test_impl, np.ones(10), np.ones(10).astype('bool'))
+
+    def test_if_not_else_reduction(self):
+        # issue #7344
+        def test_impl(A, cond):
+            s = 1
+            t = 10
+            for i in prange(A.shape[0]):
+                if cond[i]:
+                    s += 1
+                    t += 1
+                else:
+                    s += 2
+            return s + t
+        self.check(test_impl, np.ones(10), np.ones(10).astype('bool'))
+
+    def test_two_d_array_reduction_reuse(self):
+        def test_impl(n):
+            shp = (13, 17)
+            size = shp[0] * shp[1]
+            result1 = np.zeros(shp, np.int_)
+            tmp = np.arange(size).reshape(shp)
+
+            for i in numba.prange(n):
+                result1 += tmp
+
+            for i in numba.prange(n):
+                result1 += tmp
+
+            return result1
+
+        self.check(test_impl, 100)
+
+    def test_one_d_array_reduction(self):
+        def test_impl(n):
+            result = np.zeros(1, np.int_)
+
+            for i in numba.prange(n):
+                result += np.array([i], np.int_)
+
+            return result
+
+        self.check(test_impl, 100)
+
+    def test_two_d_array_reduction(self):
+        def test_impl(n):
+            shp = (13, 17)
+            size = shp[0] * shp[1]
+            result1 = np.zeros(shp, np.int_)
+            tmp = np.arange(size).reshape(shp)
+
+            for i in numba.prange(n):
+                result1 += tmp
+
+            return result1
+
+        self.check(test_impl, 100)
+
+    def test_two_d_array_reduction_with_float_sizes(self):
+        # result1 is float32 and tmp is float64.
+        # Tests reduction with differing dtypes.
+        def test_impl(n):
+            shp = (2, 3)
+            result1 = np.zeros(shp, np.float32)
+            tmp = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).reshape(shp)
+
+            for i in numba.prange(n):
+                result1 += tmp
+
+            return result1
+
+        self.check(test_impl, 100)
+
+    def test_two_d_array_reduction_prod(self):
+        def test_impl(n):
+            shp = (13, 17)
+            result1 = 2 * np.ones(shp, np.int_)
+            tmp = 2 * np.ones_like(result1)
+
+            for i in numba.prange(n):
+                result1 *= tmp
+
+            return result1
+
+        self.check(test_impl, 100)
+
+    def test_three_d_array_reduction(self):
+        def test_impl(n):
+            shp = (3, 2, 7)
+            result1 = np.zeros(shp, np.int_)
+
+            for i in numba.prange(n):
+                result1 += np.ones(shp, np.int_)
+
+            return result1
+
+        self.check(test_impl, 100)
+
+    def test_preparfor_canonicalize_kws(self):
+        # test canonicalize_array_math typing for calls with kw args
+        def test_impl(A):
+            return A.argsort() + 1
+
+        n = 211
+        A = np.arange(n)
+        self.check(test_impl, A)
+
+    def test_preparfor_datetime64(self):
+        # test array.dtype transformation for datetime64
+        def test_impl(A):
+            return A.dtype
+
+        A = np.empty(1, np.dtype('datetime64[ns]'))
+        cpfunc = self.compile_parallel(test_impl, (numba.typeof(A),))
+        self.assertEqual(cpfunc.entry_point(A), test_impl(A))
+
+    def test_no_hoisting_with_member_function_call(self):
+        def test_impl(X):
+            n = X.shape[0]
+            acc = 0
+            for i in prange(n):
+                R = {1, 2, 3}
+                R.add(i)
+                tmp = 0
+                for x in R:
+                    tmp += x
+                acc += tmp
+            return acc
+
+        self.check(test_impl, np.random.ranf(128))
+
+    def test_array_compare_scalar(self):
+        """ issue3671: X != 0 becomes an arrayexpr with operator.ne.
+            That is turned into a parfor by devectorizing.  Make sure
+            the return type of the devectorized operator.ne
+            on integer types works properly.
+        """
+        def test_impl():
+            X = np.zeros(10, dtype=np.int_)
+            return X != 0
+
+        self.check(test_impl)
+
+    def test_array_analysis_optional_def(self):
+        def test_impl(x, half):
+            size = len(x)
+            parr = x[0:size]
+
+            if half:
+                parr = x[0:size//2]
+
+            return parr.sum()
+        x = np.ones(20)
+        self.check(test_impl, x, True, check_scheduling=False)
+
+    def test_prange_side_effects(self):
+        def test_impl(a, b):
+            data = np.empty(len(a), dtype=np.float64)
+            size = len(data)
+            for i in numba.prange(size):
+                data[i] = a[i]
+            for i in numba.prange(size):
+                data[i] = data[i] + b[i]
+            return data
+
+        x = np.arange(10 ** 2, dtype=float)
+        y = np.arange(10 ** 2, dtype=float)
+
+        self.check(test_impl, x, y)
+        self.assertEqual(countParfors(test_impl,
+                                    (types.Array(types.float64, 1, 'C'),
+                                     types.Array(types.float64, 1, 'C'))), 1)
+
+    def test_tuple1(self):
+        def test_impl(a):
+            atup = (3, 4)
+            b = 7
+            for i in numba.prange(len(a)):
+                a[i] += atup[0] + atup[1] + b
+            return a
+
+        x = np.arange(10)
+        self.check(test_impl, x)
+
+    def test_tuple2(self):
+        def test_impl(a):
+            atup = a.shape
+            b = 7
+            for i in numba.prange(len(a)):
+                a[i] += atup[0] + b
+            return a
+
+        x = np.arange(10)
+        self.check(test_impl, x)
+
+    def test_tuple3(self):
+        def test_impl(a):
+            atup = (np.arange(10), 4)
+            b = 7
+            for i in numba.prange(len(a)):
+                a[i] += atup[0][5] + atup[1] + b
+            return a
+
+        x = np.arange(10)
+        self.check(test_impl, x)
+
+    def test_namedtuple1(self):
+        def test_impl(a):
+            antup = TestNamedTuple(part0=3, part1=4)
+            b = 7
+            for i in numba.prange(len(a)):
+                a[i] += antup.part0 + antup.part1 + b
+            return a
+
+        x = np.arange(10)
+        self.check(test_impl, x)
+
+    def test_namedtuple2(self):
+        TestNamedTuple2 = namedtuple('TestNamedTuple2', ('part0', 'part1'))
+        def test_impl(a):
+            antup = TestNamedTuple2(part0=3, part1=4)
+            b = 7
+            for i in numba.prange(len(a)):
+                a[i] += antup.part0 + antup.part1 + b
+            return a
+
+        x = np.arange(10)
+        self.check(test_impl, x)
+
+    def test_namedtuple3(self):
+        # issue5872: test that a.y[:] = 5 is not removed as
+        # deadcode.
+        TestNamedTuple3 = namedtuple(f'TestNamedTuple3',['y'])
+
+        def test_impl(a):
+            a.y[:] = 5
+
+        def comparer(a, b):
+            np.testing.assert_almost_equal(a.y, b.y)
+
+        x = TestNamedTuple3(y=np.zeros(10))
+        self.check(test_impl, x, check_arg_equality=[comparer])
+
+    def test_inplace_binop(self):
+        def test_impl(a, b):
+            b += a
+            return b
+
+        X = np.arange(10) + 10
+        Y = np.arange(10) + 100
+        self.check(test_impl, X, Y)
+        self.assertEqual(countParfors(test_impl,
+                                    (types.Array(types.float64, 1, 'C'),
+                                     types.Array(types.float64, 1, 'C'))), 1)
+
+    def test_tuple_concat(self):
+        # issue5383
+        def test_impl(a):
+            n = len(a)
+            array_shape = n, n
+            indices = np.zeros(((1,) + array_shape + (1,)), dtype=np.uint64)
+            k_list = indices[0, :]
+
+            for i, g in enumerate(a):
+                k_list[i, i] = i
+            return k_list
+
+        x = np.array([1, 1])
+        self.check(test_impl, x)
+
+    def test_tuple_concat_with_reverse_slice(self):
+        # issue5383
+        def test_impl(a):
+            n = len(a)
+            array_shape = n, n
+            indices = np.zeros(((1,) + array_shape + (1,))[:-1],
+                               dtype=np.uint64)
+            k_list = indices[0, :]
+
+            for i, g in enumerate(a):
+                k_list[i, i] = i
+            return k_list
+
+        x = np.array([1, 1])
+        self.check(test_impl, x)
+
+    def test_array_tuple_concat(self):
+        # issue6399
+        def test_impl(a):
+            S = (a,) + (a, a)
+            return S[0].sum()
+
+        x = np.ones((3,3))
+        self.check(test_impl, x)
+
+    def test_high_dimension1(self):
+        # issue6749
+        def test_impl(x):
+            return x * 5.0
+        x = np.ones((2, 2, 2, 2, 2, 15))
+        self.check(test_impl, x)
+
+    def test_tuple_arg(self):
+        def test_impl(x, sz):
+            for i in numba.pndindex(sz):
+                x[i] = 1
+            return x
+        sz = (10, 5)
+        self.check(test_impl, np.empty(sz), sz)
+
+    def test_tuple_arg_not_whole_array(self):
+        def test_impl(x, sz):
+            for i in numba.pndindex(sz):
+                x[i] = 1
+            return x
+        sz = (10, 5)
+        self.check(test_impl, np.zeros(sz), (10, 3))
+
+    def test_tuple_for_pndindex(self):
+        def test_impl(x):
+            sz = (10, 5)
+            for i in numba.pndindex(sz):
+                x[i] = 1
+            return x
+        sz = (10, 5)
+        self.check(test_impl, np.zeros(sz))
+
+    def test_tuple_arg_literal(self):
+        def test_impl(x, first):
+            sz = (first, 5)
+            for i in numba.pndindex(sz):
+                x[i] = 1
+            return x
+        sz = (10, 5)
+        self.check(test_impl, np.zeros(sz), 10)
+
+    def test_tuple_of_literal_nonliteral(self):
+        # This test has to be done manually as the self.check uses
+        # compile_isolated and one function cannot "see" the other
+
+        def test_impl(x, sz):
+            for i in numba.pndindex(sz):
+                x[i] = 1
+            return x
+
+        def call(x, fn):
+            return fn(x, (10, 3)) # Only want to iterate to the 3rd
+
+        get_input = lambda: np.zeros((10, 10))
+        expected = call(get_input(), test_impl)
+
+        def check(dec):
+            f1 = dec(test_impl)
+            f2 = njit(call) # no parallel semantics in the caller
+            got = f2(get_input(), f1)
+            self.assertPreciseEqual(expected, got)
+
+        for d in (njit, njit(parallel=True)):
+            check(d)
+
+    def test_tuple_arg_1d(self):
+        def test_impl(x, sz):
+            for i in numba.pndindex(sz):
+                x[i] = 1
+            return x
+        sz = (10,)
+        self.check(test_impl, np.zeros(sz), sz)
+
+    def test_tuple_arg_1d_literal(self):
+        def test_impl(x):
+            sz = (10,)
+            for i in numba.pndindex(sz):
+                x[i] = 1
+            return x
+        sz = (10,)
+        self.check(test_impl, np.zeros(sz))
+
+    def test_int_arg_pndindex(self):
+        def test_impl(x, sz):
+            for i in numba.pndindex(sz):
+                x[i] = 1
+            return x
+        self.check(test_impl, np.zeros((10, 10)), 3)
+
+    def test_prange_unknown_call1(self):
+        @register_jitable
+        def issue7854_proc(u, i, even, size):
+            for j in range((even + i + 1) % 2 + 1, size - 1, 2):
+                u[i, j] = u[i + 1, j] + 1
+
+        # issue7854
+        # Forbid fusion in unanalyzable call inside prange.
+        def test_impl(u, size):
+            for i in numba.prange(1, size - 1):
+                issue7854_proc(u, i, 0, size)
+            for i in numba.prange(1, size - 1):
+                issue7854_proc(u, i, 1, size)
+            return u
+
+        size = 4
+        u = np.zeros((size, size))
+        cptypes = (numba.float64[:, ::1], types.int64)
+        self.assertEqual(countParfors(test_impl, cptypes), 2)
+        self.check(test_impl, u, size)
+
+    def test_prange_index_calc1(self):
+        # Should forbid fusion due to cross-iteration dependency as
+        # detected by loop index calcuation (i+1) as array index.
+        def test_impl(u, size):
+            for i in numba.prange(1, size - 1):
+                for j in range((i + 1) % 2 + 1, size - 1, 2):
+                    u[i, j] = u[i + 1, j] + 1
+            for i in numba.prange(1, size - 1):
+                for j in range(i % 2 + 1, size - 1, 2):
+                    u[i, j] = u[i + 1, j] + 1
+            return u
+
+        size = 4
+        u = np.zeros((size, size))
+        cptypes = (numba.float64[:, ::1], types.int64)
+        self.assertEqual(countParfors(test_impl, cptypes), 2)
+        self.check(test_impl, u, size)
+
+    def test_prange_reverse_order1(self):
+        # Testing if reversed loop index usage as array index
+        # prevents fusion.
+        def test_impl(a, b, size):
+            for i in numba.prange(size):
+                for j in range(size):
+                    a[i, j] = b[i, j] + 1
+            for i in numba.prange(size):
+                for j in range(size):
+                    b[j, i] = 3
+            return a[0, 0] + b[0, 0]
+
+        size = 10
+        a = np.zeros((size, size))
+        b = np.zeros((size, size))
+        cptypes = (numba.float64[:, ::1], numba.float64[:, ::1], types.int64)
+        self.assertEqual(countParfors(test_impl, cptypes), 2)
+        self.check(test_impl, a, b, size)
+
+    def test_prange_parfor_index_then_not(self):
+        # Testing if accessing an array first with a parfor index then
+        # without will prevent fusion.
+        def test_impl(a, size):
+            b = 0
+            for i in numba.prange(size):
+                a[i] = i
+            for i in numba.prange(size):
+                b += a[5]
+            return b
+
+        size = 10
+        a = np.zeros(size)
+        cptypes = (numba.float64[:], types.int64)
+        self.assertEqual(countParfors(test_impl, cptypes), 2)
+        self.check(test_impl, a, size)
+
+    def test_prange_parfor_index_const_tuple_fusion(self):
+        # Testing if accessing a tuple with prange index
+        # and later with a constant will not prevent fusion.
+        def test_impl(a, tup, size):
+            acc = 0
+            for i in numba.prange(size):
+                a[i] = i + tup[i]
+            for i in numba.prange(size):
+                acc += a[i] + tup[1]
+            return acc
+
+        size = 10
+        a = np.zeros(size)
+        b = tuple(a)
+        cptypes = (numba.float64[:],
+                   types.containers.UniTuple(types.float64, size),
+                   types.intp)
+        self.assertEqual(countParfors(test_impl, cptypes), 1)
+        self.check(test_impl, a, b, size)
+
+    def test_prange_non_parfor_index_then_opposite(self):
+        # Testing if accessing an array first without a parfor index then
+        # with will prevent fusion.
+        def test_impl(a, b, size):
+            for i in numba.prange(size):
+                b[i] = a[5]
+            for i in numba.prange(size):
+                a[i] = i
+            # Need this to stop previous prange from being optimized away.
+            b[0] += a[0]
+            return b
+
+        size = 10
+        a = np.zeros(size)
+        b = np.zeros(size)
+        cptypes = (numba.float64[:], numba.float64[:], types.int64)
+        self.assertEqual(countParfors(test_impl, cptypes), 2)
+        self.check(test_impl, a, b, size)
+
+    def test_untraced_value_tuple(self):
+        # This is a test for issue #6478.
+        def test_impl():
+            a = (1.2, 1.3)
+            return a[0]
+
+        with self.assertRaises(AssertionError) as raises:
+            self.check(test_impl)
+        self.assertIn("\'@do_scheduling\' not found", str(raises.exception))
+
+    def test_recursive_untraced_value_tuple(self):
+        # This is a test for issue #6478.
+        def test_impl():
+            a = ((1.2, 1.3),)
+            return a[0][0]
+
+        with self.assertRaises(AssertionError) as raises:
+            self.check(test_impl)
+        self.assertIn("\'@do_scheduling\' not found", str(raises.exception))
+
+    def test_untraced_value_parfor(self):
+        # This is a test for issue #6478.
+        def test_impl(arr):
+            a = (1.2, 1.3)
+            n1 = len(arr)
+            arr2 = np.empty(n1, np.float64)
+            for i in prange(n1):
+                arr2[i] = arr[i] * a[0]
+            n2 = len(arr2)
+            arr3 = np.empty(n2, np.float64)
+            for j in prange(n2):
+                arr3[j] = arr2[j] - a[1]
+            total = 0.0
+            n3 = len(arr3)
+            for k in prange(n3):
+                total += arr3[k]
+            return total + a[0]
+
+        arg = (types.Array(types.int64, 1, 'C'), )
+        self.assertEqual(countParfors(test_impl, arg), 1)
+
+        arr = np.arange(10, dtype=np.int64)
+        self.check(test_impl, arr)
+
+    def test_setitem_2d_one_replaced(self):
+        # issue7843
+        def test_impl(x):
+            count = 0
+            for n in range(x.shape[0]):
+                # Useless "if" necessary to trigger bug.
+                if n:
+                    n
+                x[count, :] = 1
+                count += 1
+            return x
+
+        self.check(test_impl, np.zeros((3, 1)))
+
+@skip_parfors_unsupported
+class TestParforsLeaks(MemoryLeakMixin, TestParforsBase):
+    def check(self, pyfunc, *args, **kwargs):
+        cfunc, cpfunc = self.compile_all(pyfunc, *args)
+        self.check_parfors_vs_others(pyfunc, cfunc, cpfunc, *args, **kwargs)
+
+    def test_reduction(self):
+        # issue4299
+        @njit(parallel=True)
+        def test_impl(arr):
+            return arr.sum()
+
+        arr = np.arange(10).astype(np.float64)
+        self.check(test_impl, arr)
+
+    def test_multiple_reduction_vars(self):
+        @njit(parallel=True)
+        def test_impl(arr):
+            a = 0.
+            b = 1.
+            for i in prange(arr.size):
+                a += arr[i]
+                b += 1. / (arr[i] + 1)
+            return a * b
+        arr = np.arange(10).astype(np.float64)
+        self.check(test_impl, arr)
+
+
+@skip_parfors_unsupported
+class TestParforsSlice(TestParforsBase):
+
+    def test_parfor_slice1(self):
+        def test_impl(a):
+            (n,) = a.shape
+            b = a[0:n-2] + a[1:n-1]
+            return b
+
+        self.check(test_impl, np.ones(10))
+
+    def test_parfor_slice2(self):
+        def test_impl(a, m):
+            (n,) = a.shape
+            b = a[0:n-2] + a[1:m]
+            return b
+
+        # runtime assertion should succeed
+        self.check(test_impl, np.ones(10), 9)
+        # next we expect failure
+        with self.assertRaises(AssertionError) as raises:
+            njit(parallel=True)(test_impl)(np.ones(10),10)
+        self.assertIn("do not match", str(raises.exception))
+
+    def test_parfor_slice3(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a[0:m-1,0:n-1] + a[1:m,1:n]
+            return b
+
+        self.check(test_impl, np.ones((4,3)))
+
+    def test_parfor_slice4(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a[:,0:n-1] + a[:,1:n]
+            return b
+
+        self.check(test_impl, np.ones((4,3)))
+
+    def test_parfor_slice5(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a[0:m-1,:] + a[1:m,:]
+            return b
+
+        self.check(test_impl, np.ones((4,3)))
+
+    def test_parfor_slice6(self):
+        def test_impl(a):
+            b = a.transpose()
+            c = a[1,:] + b[:,1]
+            return c
+
+        self.check(test_impl, np.ones((4,3)))
+
+    def test_parfor_slice7(self):
+        def test_impl(a):
+            b = a.transpose()
+            c = a[1,:] + b[1,:]
+            return c
+
+        # runtime check should succeed
+        self.check(test_impl, np.ones((3,3)))
+        # next we expect failure
+        with self.assertRaises(AssertionError) as raises:
+            njit(parallel=True)(test_impl)(np.ones((3,4)))
+        self.assertIn("do not match", str(raises.exception))
+
+    @disabled_test
+    def test_parfor_slice8(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a.transpose()
+            b[1:m,1:n] = a[1:m,1:n]
+            return b
+
+        self.check(test_impl, np.arange(9).reshape((3,3)))
+
+    @disabled_test
+    def test_parfor_slice9(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a.transpose()
+            b[1:n,1:m] = a[:,1:m]
+            return b
+
+        self.check(test_impl, np.arange(12).reshape((3,4)))
+
+    @disabled_test
+    def test_parfor_slice10(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a.transpose()
+            b[2,1:m] = a[2,1:m]
+            return b
+
+        self.check(test_impl, np.arange(9).reshape((3,3)))
+
+    def test_parfor_slice11(self):
+        def test_impl(a):
+            (m,n,l) = a.shape
+            b = a.copy()
+            b[:,1,1:l] = a[:,2,1:l]
+            return b
+
+        self.check(test_impl, np.arange(27).reshape((3,3,3)))
+
+    def test_parfor_slice12(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a.copy()
+            b[1,1:-1] = a[0,:-2]
+            return b
+
+        self.check(test_impl, np.arange(12).reshape((3,4)))
+
+    def test_parfor_slice13(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a.copy()
+            c = -1
+            b[1,1:c] = a[0,-n:c-1]
+            return b
+
+        self.check(test_impl, np.arange(12).reshape((3,4)))
+
+    def test_parfor_slice14(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a.copy()
+            b[1,:-1] = a[0,-3:4]
+            return b
+
+        self.check(test_impl, np.arange(12).reshape((3,4)))
+
+    def test_parfor_slice15(self):
+        def test_impl(a):
+            (m,n) = a.shape
+            b = a.copy()
+            b[1,-(n-1):] = a[0,-3:4]
+            return b
+
+        self.check(test_impl, np.arange(12).reshape((3,4)))
+
+    @disabled_test
+    def test_parfor_slice16(self):
+        """ This test is disabled because if n is larger than the array size
+            then n and n-1 will both be the end of the array and thus the
+            slices will in fact be of different sizes and unable to fuse.
+        """
+        def test_impl(a, b, n):
+            assert(a.shape == b.shape)
+            a[1:n] = 10
+            b[0:(n-1)] = 10
+            return a * b
+
+        self.check(test_impl, np.ones(10), np.zeros(10), 8)
+        args = (numba.float64[:], numba.float64[:], numba.int64)
+        self.assertEqual(countParfors(test_impl, args), 2)
+
+    def test_parfor_slice17(self):
+        def test_impl(m, A):
+            B = np.zeros(m)
+            n = len(A)
+            B[-n:] = A
+            return B
+
+        self.check(test_impl, 10, np.ones(10))
+
+    def test_parfor_slice18(self):
+        # issue 3534
+        def test_impl():
+            a = np.zeros(10)
+            a[1:8] = np.arange(0, 7)
+            y = a[3]
+            return y
+
+        self.check(test_impl)
+
+    def test_parfor_slice19(self):
+        # issues #3561 and #3554, empty slice binop
+        def test_impl(X):
+            X[:0] += 1
+            return X
+
+        self.check(test_impl, np.ones(10))
+
+    def test_parfor_slice20(self):
+        # issue #4075, slice size
+        def test_impl():
+            a = np.ones(10)
+            c = a[1:]
+            s = len(c)
+            return s
+
+        self.check(test_impl, check_scheduling=False)
+
+    def test_parfor_slice21(self):
+        def test_impl(x1, x2):
+            x1 = x1.reshape(x1.size, 1)
+            x2 = x2.reshape(x2.size, 1)
+            return x1 >= x2[:-1, :]
+
+        x1 = np.random.rand(5)
+        x2 = np.random.rand(6)
+        self.check(test_impl, x1, x2)
+
+    def test_parfor_slice22(self):
+        def test_impl(x1, x2):
+            b = np.zeros((10,))
+            for i in prange(1):
+                b += x1[:, x2]
+            return b
+
+        x1 = np.zeros((10,7))
+        x2 = np.array(4)
+        self.check(test_impl, x1, x2)
+
+    def test_parfor_slice23(self):
+        # issue #4630
+        def test_impl(x):
+            x[:0] = 2
+            return x
+
+        self.check(test_impl, np.ones(10))
+
+    def test_parfor_slice24(self):
+        def test_impl(m, A, n):
+            B = np.zeros(m)
+            C = B[n:]
+            C = A[:len(C)]
+            return B
+
+        for i in range(-15, 15):
+            self.check(test_impl, 10, np.ones(10), i)
+
+    def test_parfor_slice25(self):
+        def test_impl(m, A, n):
+            B = np.zeros(m)
+            C = B[:n]
+            C = A[:len(C)]
+            return B
+
+        for i in range(-15, 15):
+            self.check(test_impl, 10, np.ones(10), i)
+
+    def test_parfor_slice26(self):
+        def test_impl(a):
+            (n,) = a.shape
+            b = a.copy()
+            b[-(n-1):] = a[-3:4]
+            return b
+
+        self.check(test_impl, np.arange(4))
+
+    def test_parfor_slice27(self):
+        # issue5601: tests array analysis of the slice with
+        # n_valid_vals of unknown size.
+        def test_impl(a):
+            n_valid_vals = 0
+
+            for i in prange(a.shape[0]):
+                if a[i] != 0:
+                    n_valid_vals += 1
+
+                if n_valid_vals:
+                    unused = a[:n_valid_vals]
+
+            return 0
+
+        self.check(test_impl, np.arange(3))
+
+    def test_parfor_array_access_lower_slice(self):
+        for ts in [slice(1, 3, None), slice(2, None, None), slice(None, 2, -1),
+                   slice(None, None, None), slice(None, None, -2)]:
+
+            def test_impl(n):
+                X = np.arange(n * 4).reshape((n, 4))
+                y = 0
+                for i in numba.prange(n):
+                    y += X[i, ts].sum()
+                return y
+
+            n = 10
+            self.check(test_impl, n)
+
+            X = np.arange(n * 4).reshape((n, 4))
+
+            def test_impl(X):
+                y = 0
+                for i in numba.prange(X.shape[0]):
+                    y += X[i, ts].sum()
+                return y
+
+            self.check(test_impl, X)
+
+
+@skip_parfors_unsupported
+class TestParforsOptions(TestParforsBase):
+
+    def test_parfor_options(self):
+        def test_impl(a):
+            n = a.shape[0]
+            b = np.ones(n)
+            c = np.array([ i for i in range(n) ])
+            b[:n] = a + b * c
+            for i in prange(n):
+                c[i] = b[i] * a[i]
+            return reduce(lambda x,y:x+y, c, 0)
+
+        self.check(test_impl, np.ones(10))
+        args = (numba.float64[:],)
+        # everything should fuse with default option
+        self.assertEqual(countParfors(test_impl, args), 1)
+        # with no fusion
+        self.assertEqual(countParfors(test_impl, args, fusion=False), 6)
+        # with no fusion, comprehension
+        self.assertEqual(countParfors(test_impl, args, fusion=False,
+                         comprehension=False), 5)
+        #with no fusion, comprehension, setitem
+        self.assertEqual(countParfors(test_impl, args, fusion=False,
+                         comprehension=False, setitem=False), 4)
+         # with no fusion, comprehension, prange
+        self.assertEqual(countParfors(test_impl, args, fusion=False,
+                         comprehension=False, setitem=False, prange=False), 3)
+         # with no fusion, comprehension, prange, reduction
+        self.assertEqual(countParfors(test_impl, args, fusion=False,
+                         comprehension=False, setitem=False, prange=False,
+                         reduction=False), 2)
+        # with no fusion, comprehension, prange, reduction, numpy
+        self.assertEqual(countParfors(test_impl, args, fusion=False,
+                         comprehension=False, setitem=False, prange=False,
+                         reduction=False, numpy=False), 0)
+
+
+@skip_parfors_unsupported
+class TestParforsBitMask(TestParforsBase):
+
+    def test_parfor_bitmask1(self):
+        def test_impl(a, n):
+            b = a > n
+            a[b] = 0
+            return a
+
+        self.check(test_impl, np.arange(10), 5)
+
+    def test_parfor_bitmask2(self):
+        def test_impl(a, b):
+            a[b] = 0
+            return a
+
+        a = np.arange(10)
+        b = a > 5
+        self.check(test_impl, a, b)
+
+    def test_parfor_bitmask3(self):
+        def test_impl(a, b):
+            a[b] = a[b]
+            return a
+
+        a = np.arange(10)
+        b = a > 5
+        self.check(test_impl, a, b)
+
+    def test_parfor_bitmask4(self):
+        def test_impl(a, b):
+            a[b] = (2 * a)[b]
+            return a
+
+        a = np.arange(10)
+        b = a > 5
+        self.check(test_impl, a, b)
+
+    def test_parfor_bitmask5(self):
+        def test_impl(a, b):
+            a[b] = a[b] * a[b]
+            return a
+
+        a = np.arange(10)
+        b = a > 5
+        self.check(test_impl, a, b)
+
+    def test_parfor_bitmask6(self):
+        def test_impl(a, b, c):
+            a[b] = c
+            return a
+
+        a = np.arange(10)
+        b = a > 5
+        c = np.zeros(sum(b))
+
+        # expect failure due to lack of parallelism
+        with self.assertRaises(AssertionError) as raises:
+            self.check(test_impl, a, b, c)
+        self.assertIn("\'@do_scheduling\' not found", str(raises.exception))
+
+
+@skip_parfors_unsupported
+class TestParforsMisc(TestParforsBase):
+    """
+    Tests miscellaneous parts of ParallelAccelerator use.
+    """
+    def test_no_warn_if_cache_set(self):
+
+        def pyfunc():
+            arr = np.ones(100)
+            for i in prange(arr.size):
+                arr[i] += i
+            return arr
+
+        cfunc = njit(parallel=True, cache=True)(pyfunc)
+
+        with warnings.catch_warnings(record=True) as raised_warnings:
+            warnings.simplefilter('always')
+            warnings.filterwarnings(action="ignore",
+                                    module="typeguard")
+            # Filter out warnings about TBB interface mismatch
+            warnings.filterwarnings(action='ignore',
+                                    message=r".*TBB_INTERFACE_VERSION.*",
+                                    category=numba.errors.NumbaWarning,
+                                    module=r'numba\.np\.ufunc\.parallel.*')
+            cfunc()
+
+        self.assertEqual(len(raised_warnings), 0)
+
+        # Make sure the dynamic globals flag is set
+        has_dynamic_globals = [cres.library.has_dynamic_globals
+                               for cres in cfunc.overloads.values()]
+        self.assertEqual(has_dynamic_globals, [False])
+
+    def test_statement_reordering_respects_aliasing(self):
+        def impl():
+            a = np.zeros(10)
+            a[1:8] = np.arange(0, 7)
+            print('a[3]:', a[3])
+            print('a[3]:', a[3])
+            return a
+
+        cres = self.compile_parallel(impl, ())
+        with captured_stdout() as stdout:
+            cres.entry_point()
+        for line in stdout.getvalue().splitlines():
+            self.assertEqual('a[3]: 2.0', line)
+
+    def test_parfor_ufunc_typing(self):
+        def test_impl(A):
+            return np.isinf(A)
+
+        A = np.array([np.inf, 0.0])
+        cfunc = njit(parallel=True)(test_impl)
+        # save global state
+        old_seq_flag = numba.parfors.parfor.sequential_parfor_lowering
+        try:
+            numba.parfors.parfor.sequential_parfor_lowering = True
+            np.testing.assert_array_equal(test_impl(A), cfunc(A))
+        finally:
+            # recover global state
+            numba.parfors.parfor.sequential_parfor_lowering = old_seq_flag
+
+    def test_init_block_dce(self):
+        # issue4690
+        def test_impl():
+            res = 0
+            arr = [1,2,3,4,5]
+            numba.parfors.parfor.init_prange()
+            dummy = arr
+            for i in numba.prange(5):
+                res += arr[i]
+            return res + dummy[2]
+
+        self.assertEqual(get_init_block_size(test_impl, ()), 0)
+
+    def test_alias_analysis_for_parfor1(self):
+        def test_impl():
+            acc = 0
+            for _ in range(4):
+                acc += 1
+
+            data = np.zeros((acc,))
+            return data
+
+        self.check(test_impl)
+
+    def test_no_state_change_in_gufunc_lowering_on_error(self):
+        # tests #5098, if there's an exception arising in gufunc lowering the
+        # sequential_parfor_lowering global variable should remain as False on
+        # stack unwind.
+
+        BROKEN_MSG = 'BROKEN_MSG'
+
+        @register_pass(mutates_CFG=True, analysis_only=False)
+        class BreakParfors(AnalysisPass):
+            _name = "break_parfors"
+
+            def __init__(self):
+                AnalysisPass.__init__(self)
+
+            def run_pass(self, state):
+                for blk in state.func_ir.blocks.values():
+                    for stmt in blk.body:
+                        if isinstance(stmt, numba.parfors.parfor.Parfor):
+                            # races should be a set(), that list is iterable
+                            # permits it to get through to the
+                            # _create_gufunc_for_parfor_body routine at which
+                            # point it needs to be a set so e.g. set.difference
+                            # can be computed, this therefore creates an error
+                            # in the right location.
+                            class Broken(list):
+
+                                def difference(self, other):
+                                    raise errors.LoweringError(BROKEN_MSG)
+
+                            stmt.races = Broken()
+                    return True
+
+
+        class BreakParforsCompiler(CompilerBase):
+
+            def define_pipelines(self):
+                pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+                pm.add_pass_after(BreakParfors, IRLegalization)
+                pm.finalize()
+                return [pm]
+
+
+        @njit(parallel=True, pipeline_class=BreakParforsCompiler)
+        def foo():
+            x = 1
+            for _ in prange(1):
+                x += 1
+            return x
+
+        # assert default state for global
+        self.assertFalse(numba.parfors.parfor.sequential_parfor_lowering)
+
+        with self.assertRaises(errors.LoweringError) as raises:
+            foo()
+
+        self.assertIn(BROKEN_MSG, str(raises.exception))
+
+        # assert state has not changed
+        self.assertFalse(numba.parfors.parfor.sequential_parfor_lowering)
+
+    def test_issue_5098(self):
+        class DummyType(types.Opaque):
+            pass
+
+        dummy_type = DummyType("my_dummy")
+        register_model(DummyType)(models.OpaqueModel)
+
+        class Dummy(object):
+            pass
+
+        @typeof_impl.register(Dummy)
+        def typeof_Dummy(val, c):
+            return dummy_type
+
+        @unbox(DummyType)
+        def unbox_index(typ, obj, c):
+            return NativeValue(c.context.get_dummy_value())
+
+        @overload_method(DummyType, "method1", jit_options={"parallel":True})
+        def _get_method1(obj, arr, func):
+            def _foo(obj, arr, func):
+                def baz(a, f):
+                    c = a.copy()
+                    c[np.isinf(a)] = np.nan
+                    return f(c)
+
+                length = len(arr)
+                output_arr = np.empty(length, dtype=np.float64)
+                for i in prange(length):
+                    output_arr[i] = baz(arr[i], func)
+                for i in prange(length - 1):
+                    output_arr[i] += baz(arr[i], func)
+                return output_arr
+            return _foo
+
+        @njit
+        def bar(v):
+            return v.mean()
+
+        @njit
+        def test1(d):
+            return d.method1(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), bar)
+
+        save_state = numba.parfors.parfor.sequential_parfor_lowering
+        self.assertFalse(save_state)
+        try:
+            test1(Dummy())
+            self.assertFalse(numba.parfors.parfor.sequential_parfor_lowering)
+        finally:
+            # always set the sequential_parfor_lowering state back to the
+            # original state
+            numba.parfors.parfor.sequential_parfor_lowering = save_state
+
+    def test_oversized_tuple_as_arg_to_kernel(self):
+
+        @njit(parallel=True)
+        def oversize_tuple(idx):
+            big_tup = (1,2,3,4)
+            z = 0
+            for x in prange(10):
+                z += big_tup[idx]
+            return z
+
+        with override_env_config('NUMBA_PARFOR_MAX_TUPLE_SIZE', '3'):
+            with self.assertRaises(errors.UnsupportedParforsError) as raises:
+                oversize_tuple(0)
+
+        errstr = str(raises.exception)
+        self.assertIn("Use of a tuple", errstr)
+        self.assertIn("in a parallel region", errstr)
+
+    def test_issue5167(self):
+
+        def ndvi_njit(img_nir, img_red):
+            fillvalue = 0
+            out_img = np.full(img_nir.shape, fillvalue, dtype=img_nir.dtype)
+            dims = img_nir.shape
+            for y in prange(dims[0]):
+                for x in prange(dims[1]):
+                    out_img[y, x] = ((img_nir[y, x] - img_red[y, x]) /
+                                     (img_nir[y, x] + img_red[y, x]))
+            return out_img
+
+        tile_shape = (4, 4)
+        array1 = np.random.uniform(low=1.0, high=10000.0, size=tile_shape)
+        array2 = np.random.uniform(low=1.0, high=10000.0, size=tile_shape)
+        self.check(ndvi_njit, array1, array2)
+
+    def test_issue5065(self):
+
+        def reproducer(a, dist, dist_args):
+            result = np.zeros((a.shape[0], a.shape[0]), dtype=np.float32)
+            for i in prange(a.shape[0]):
+                for j in range(i + 1, a.shape[0]):
+                    d = dist(a[i], a[j], *dist_args)
+                    result[i, j] = d
+                    result[j, i] = d
+            return result
+
+        @njit
+        def euclidean(x, y):
+            result = 0.0
+            for i in range(x.shape[0]):
+                result += (x[i] - y[i]) ** 2
+            return np.sqrt(result)
+
+        a = np.random.random(size=(5, 2))
+
+        got = njit(parallel=True)(reproducer)(a.copy(), euclidean,())
+        expected = reproducer(a.copy(), euclidean,())
+
+        np.testing.assert_allclose(got, expected)
+
+    def test_issue5001(self):
+
+        def test_numba_parallel(myarray):
+            result = [0] * len(myarray)
+            for i in prange(len(myarray)):
+                result[i] = len(myarray[i])
+            return result
+
+        myarray = (np.empty(100),np.empty(50))
+        self.check(test_numba_parallel, myarray)
+
+    def test_issue3169(self):
+
+        @njit
+        def foo(grids):
+            pass
+
+        @njit(parallel=True)
+        def bar(grids):
+            for x in prange(1):
+                foo(grids)
+
+        # returns nothing, just check it compiles
+        bar(([1],) * 2)
+
+    @disabled_test
+    def test_issue4846(self):
+
+        mytype = namedtuple("mytype", ("a", "b"))
+
+        def outer(mydata):
+            for k in prange(3):
+                inner(k, mydata)
+            return mydata.a
+
+        @njit(nogil=True)
+        def inner(k, mydata):
+            f = (k, mydata.a)
+            g = (k, mydata.b)
+
+        mydata = mytype(a="a", b="b")
+
+        self.check(outer, mydata)
+
+    def test_issue3748(self):
+
+        def test1b():
+            x = (1, 2, 3, 4, 5)
+            a = 0
+            for i in prange(len(x)):
+                a += x[i]
+            return a
+
+        self.check(test1b,)
+
+    def test_issue5277(self):
+
+        def parallel_test(size, arr):
+            for x in prange(size[0]):
+                for y in prange(size[1]):
+                    arr[y][x] = x * 4.5 + y
+            return arr
+
+        size = (10, 10)
+        arr = np.zeros(size, dtype=int)
+
+        self.check(parallel_test, size, arr)
+
+    def test_issue5570_ssa_races(self):
+        @njit(parallel=True)
+        def foo(src, method, out):
+            for i in prange(1):
+                for j in range(1):
+                    out[i, j] = 1
+            if method:
+                out += 1
+            return out
+
+        src = np.zeros((5,5))
+        method = 57
+        out = np.zeros((2, 2))
+
+        self.assertPreciseEqual(
+            foo(src, method, out),
+            foo.py_func(src, method, out)
+        )
+
+    def test_issue6095_numpy_max(self):
+        @njit(parallel=True)
+        def find_maxima_3D_jit(args):
+            package = args
+            for index in range(0, 10):
+                z_stack = package[index, :, :]
+            return np.max(z_stack)
+
+        np.random.seed(0)
+        args = np.random.random((10, 10, 10))
+        self.assertPreciseEqual(
+            find_maxima_3D_jit(args),
+            find_maxima_3D_jit.py_func(args),
+        )
+
+    def test_issue5942_1(self):
+        # issue5942: tests statement reordering of
+        # aliased arguments.
+        def test_impl(gg, gg_next):
+            gs = gg.shape
+            d = gs[0]
+            for i_gg in prange(d):
+                gg_next[i_gg, :]  = gg[i_gg, :]
+                gg_next[i_gg, 0] += 1
+
+            return gg_next
+
+        d = 4
+        k = 2
+
+        gg      = np.zeros((d, k), dtype = np.int32)
+        gg_next = np.zeros((d, k), dtype = np.int32)
+        self.check(test_impl, gg, gg_next)
+
+    def test_issue5942_2(self):
+        # issue5942: tests statement reordering
+        def test_impl(d, k):
+            gg      = np.zeros((d, k), dtype = np.int32)
+            gg_next = np.zeros((d, k), dtype = np.int32)
+
+            for i_gg in prange(d):
+                for n in range(k):
+                    gg[i_gg, n] = i_gg
+                gg_next[i_gg, :]  = gg[i_gg, :]
+                gg_next[i_gg, 0] += 1
+
+            return gg_next
+
+        d = 4
+        k = 2
+
+        self.check(test_impl, d, k)
+
+    @skip_unless_scipy
+    def test_issue6102(self):
+        # The problem is originally observed on Python3.8 because of the
+        # changes in how loops are represented in 3.8 bytecode.
+        @njit(parallel=True)
+        def f(r):
+            for ir in prange(r.shape[0]):
+                dist = np.inf
+                tr = np.array([0, 0, 0], dtype=np.float32)
+                for i in [1, 0, -1]:
+                    dist_t = np.linalg.norm(r[ir, :] + i)
+                    if dist_t < dist:
+                        dist = dist_t
+                        tr = np.array([i, i, i], dtype=np.float32)
+                r[ir, :] += tr
+            return r
+
+        r = np.array([[0., 0., 0.], [0., 0., 1.]])
+        self.assertPreciseEqual(f(r), f.py_func(r))
+
+    def test_issue6774(self):
+        @njit(parallel=True)
+        def test_impl():
+            n = 5
+            na_mask = np.ones((n,))
+            result = np.empty((n - 1,))
+            for i in prange(len(result)):
+                result[i] = np.sum(na_mask[i:i + 1])
+            return result
+
+        self.check(test_impl)
+
+    def test_issue4963_globals(self):
+        def test_impl():
+            buf = np.zeros((_GLOBAL_INT_FOR_TESTING1, _GLOBAL_INT_FOR_TESTING2))
+            return buf
+        self.check(test_impl)
+
+    def test_issue4963_freevars(self):
+        _FREEVAR_INT_FOR_TESTING1 = 17
+        _FREEVAR_INT_FOR_TESTING2 = 5
+        def test_impl():
+            buf = np.zeros((_FREEVAR_INT_FOR_TESTING1, _FREEVAR_INT_FOR_TESTING2))
+            return buf
+        self.check(test_impl)
+
+
+@skip_parfors_unsupported
+class TestParforsDiagnostics(TestParforsBase):
+
+    def check(self, pyfunc, *args, **kwargs):
+        cfunc, cpfunc = self.compile_all(pyfunc, *args)
+        self.check_parfors_vs_others(pyfunc, cfunc, cpfunc, *args, **kwargs)
+
+    def assert_fusion_equivalence(self, got, expected):
+        a = self._fusion_equivalent(got)
+        b = self._fusion_equivalent(expected)
+        self.assertEqual(a, b)
+
+    def _fusion_equivalent(self, thing):
+        # parfors indexes the Parfors class instance id's from wherever the
+        # internal state happens to be. To assert fusion equivalence we just
+        # check that the relative difference between fusion adjacency lists
+        # is the same. For example:
+        # {3: [2, 1]} is the same as {13: [12, 11]}
+        # this function strips the indexing etc out returning something suitable
+        # for checking equivalence
+        new = defaultdict(list)
+        min_key = min(thing.keys())
+        for k in sorted(thing.keys()):
+            new[k - min_key] = [x - min_key for x in thing[k]]
+        return new
+
+    def assert_diagnostics(self, diagnostics, parfors_count=None,
+                           fusion_info=None, nested_fusion_info=None,
+                           replaced_fns=None, hoisted_allocations=None):
+        if parfors_count is not None:
+            self.assertEqual(parfors_count, diagnostics.count_parfors())
+        if fusion_info is not None:
+            self.assert_fusion_equivalence(fusion_info, diagnostics.fusion_info)
+        if nested_fusion_info is not None:
+            self.assert_fusion_equivalence(nested_fusion_info,
+                                           diagnostics.nested_fusion_info)
+        if replaced_fns is not None:
+            repl = diagnostics.replaced_fns.values()
+            for x in replaced_fns:
+                for replaced in repl:
+                    if replaced[0] == x:
+                        break
+                else:
+                    msg = "Replacement for %s was not found. Had %s" % (x, repl)
+                    raise AssertionError(msg)
+
+        if hoisted_allocations is not None:
+            hoisted_allocs = diagnostics.hoisted_allocations()
+            self.assertEqual(hoisted_allocations, len(hoisted_allocs))
+
+        # just make sure that the dump() function doesn't have an issue!
+        with captured_stdout():
+            for x in range(1, 5):
+                diagnostics.dump(x)
+
+    def test_array_expr(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n)
+            b = np.zeros(n)
+            return a + b
+
+        self.check(test_impl,)
+        cpfunc = self.compile_parallel(test_impl, ())
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        self.assert_diagnostics(diagnostics, parfors_count=1,
+                                fusion_info = {3: [4, 5]})
+
+    def test_prange(self):
+        def test_impl():
+            n = 10
+            a = np.empty(n)
+            for i in prange(n):
+                a[i] = i * 10
+            return a
+
+        self.check(test_impl,)
+        cpfunc = self.compile_parallel(test_impl, ())
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        self.assert_diagnostics(diagnostics, parfors_count=1)
+
+    def test_user_varname(self):
+        """make sure original user variable name is used in fusion info
+        """
+        def test_impl():
+            n = 10
+            x = np.ones(n)
+            a = np.sin(x)
+            b = np.cos(a * a)
+            acc = 0
+            for i in prange(n - 2):
+                for j in prange(n - 1):
+                    acc += b[i] + b[j + 1]
+            return acc
+
+        self.check(test_impl,)
+        cpfunc = self.compile_parallel(test_impl, ())
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        # make sure original 'n' variable name is used in fusion report for loop
+        # dimension mismatch
+        self.assertTrue(
+            any("slice(0, n, 1)" in r.message for r in diagnostics.fusion_reports))
+
+    def test_nested_prange(self):
+        def test_impl():
+            n = 10
+            a = np.empty((n, n))
+            for i in prange(n):
+                for j in prange(n):
+                    a[i, j] = i * 10 + j
+            return a
+
+        self.check(test_impl,)
+        cpfunc = self.compile_parallel(test_impl, ())
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        self.assert_diagnostics(diagnostics, parfors_count=2,
+                                nested_fusion_info={2: [1]})
+
+    def test_function_replacement(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n)
+            b = np.argmin(a)
+            return b
+
+        self.check(test_impl,)
+        cpfunc = self.compile_parallel(test_impl, ())
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        self.assert_diagnostics(diagnostics, parfors_count=1,
+                                fusion_info={2: [3]},
+                                replaced_fns = [('argmin', 'numpy'),])
+
+    def test_reduction(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n + 1) # prevent fusion
+            acc = 0
+            for i in prange(n):
+                acc += a[i]
+            return acc
+
+        self.check(test_impl,)
+        cpfunc = self.compile_parallel(test_impl, ())
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        self.assert_diagnostics(diagnostics, parfors_count=2)
+
+    def test_setitem(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n)
+            a[:] = 7
+            return a
+
+        self.check(test_impl,)
+        cpfunc = self.compile_parallel(test_impl, ())
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        self.assert_diagnostics(diagnostics, parfors_count=1)
+
+    def test_allocation_hoisting(self):
+        def test_impl():
+            n = 10
+            m = 5
+            acc = 0
+            for i in prange(n):
+                temp = np.zeros((m,)) # the np.empty call should get hoisted
+                for j in range(m):
+                    temp[j] = i
+                acc += temp[-1]
+            return acc
+
+        self.check(test_impl,)
+        cpfunc = self.compile_parallel(test_impl, ())
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        self.assert_diagnostics(diagnostics, hoisted_allocations=1)
+
+
+class TestPrangeBase(TestParforsBase):
+
+    def generate_prange_func(self, pyfunc, patch_instance):
+        """
+        This function does the actual code augmentation to enable the explicit
+        testing of `prange` calls in place of `range`.
+        """
+        pyfunc_code = pyfunc.__code__
+
+        prange_names = list(pyfunc_code.co_names)
+
+        if patch_instance is None:
+            # patch all instances, cheat by just switching
+            # range for prange
+            assert 'range' in pyfunc_code.co_names
+            prange_names = tuple([x if x != 'range' else 'prange'
+                                  for x in pyfunc_code.co_names])
+            new_code = bytes(pyfunc_code.co_code)
+        else:
+            # patch specified instances...
+            # find where 'range' is in co_names
+            range_idx = pyfunc_code.co_names.index('range')
+            range_locations = []
+            # look for LOAD_GLOBALs that point to 'range'
+            for instr in dis.Bytecode(pyfunc_code):
+                if instr.opname == 'LOAD_GLOBAL':
+                    if instr.arg == range_idx:
+                        range_locations.append(instr.offset + 1)
+            # add in 'prange' ref
+            prange_names.append('prange')
+            prange_names = tuple(prange_names)
+            prange_idx = len(prange_names) - 1
+            new_code = bytearray(pyfunc_code.co_code)
+            assert len(patch_instance) <= len(range_locations)
+            # patch up the new byte code
+            for i in patch_instance:
+                idx = range_locations[i]
+                new_code[idx] = prange_idx
+            new_code = bytes(new_code)
+
+        # create new code parts
+        co_args = [pyfunc_code.co_argcount]
+
+        if utils.PYVERSION >= (3, 8):
+            co_args.append(pyfunc_code.co_posonlyargcount)
+        co_args.append(pyfunc_code.co_kwonlyargcount)
+        co_args.extend([pyfunc_code.co_nlocals,
+                        pyfunc_code.co_stacksize,
+                        pyfunc_code.co_flags,
+                        new_code,
+                        pyfunc_code.co_consts,
+                        prange_names,
+                        pyfunc_code.co_varnames,
+                        pyfunc_code.co_filename,
+                        pyfunc_code.co_name,
+                        pyfunc_code.co_firstlineno,
+                        pyfunc_code.co_lnotab,
+                        pyfunc_code.co_freevars,
+                        pyfunc_code.co_cellvars
+                        ])
+
+        # create code object with prange mutation
+        prange_code = pytypes.CodeType(*co_args)
+
+        # get function
+        pfunc = pytypes.FunctionType(prange_code, globals())
+
+        return pfunc
+
+    def prange_tester(self, pyfunc, *args, **kwargs):
+        """
+        The `prange` tester
+        This is a hack. It basically switches out range calls for prange.
+        It does this by copying the live code object of a function
+        containing 'range' then copying the .co_names and mutating it so
+        that 'range' is replaced with 'prange'. It then creates a new code
+        object containing the mutation and instantiates a function to contain
+        it. At this point three results are created:
+        1. The result of calling the original python function.
+        2. The result of calling a njit compiled version of the original
+            python function.
+        3. The result of calling a njit(parallel=True) version of the mutated
+           function containing `prange`.
+        The three results are then compared and the `prange` based function's
+        llvm_ir is inspected to ensure the scheduler code is present.
+
+        Arguments:
+         pyfunc - the python function to test
+         args - data arguments to pass to the pyfunc under test
+
+        Keyword Arguments:
+         patch_instance - iterable containing which instances of `range` to
+                          replace. If not present all instance of `range` are
+                          replaced.
+         scheduler_type - 'signed', 'unsigned' or None, default is None.
+                           Supply in cases where the presence of a specific
+                           scheduler is to be asserted.
+         check_fastmath - if True then a check will be performed to ensure the
+                          IR contains instructions labelled with 'fast'
+         check_fastmath_result - if True then a check will be performed to
+                                 ensure the result of running with fastmath
+                                 on matches that of the pyfunc
+         Remaining kwargs are passed to np.testing.assert_almost_equal
+
+
+        Example:
+            def foo():
+                acc = 0
+                for x in range(5):
+                    for y in range(10):
+                        acc +=1
+                return acc
+
+            # calling as
+            prange_tester(foo)
+            # will test code equivalent to
+            # def foo():
+            #     acc = 0
+            #     for x in prange(5): # <- changed
+            #         for y in prange(10): # <- changed
+            #             acc +=1
+            #     return acc
+
+            # calling as
+            prange_tester(foo, patch_instance=[1])
+            # will test code equivalent to
+            # def foo():
+            #     acc = 0
+            #     for x in range(5): # <- outer loop (0) unchanged
+            #         for y in prange(10): # <- inner loop (1) changed
+            #             acc +=1
+            #     return acc
+
+        """
+        patch_instance = kwargs.pop('patch_instance', None)
+        check_fastmath = kwargs.pop('check_fastmath', False)
+        check_fastmath_result = kwargs.pop('check_fastmath_result', False)
+
+        pfunc = self.generate_prange_func(pyfunc, patch_instance)
+
+        # Compile functions
+        # compile a standard njit of the original function
+        sig = tuple([numba.typeof(x) for x in args])
+        cfunc = self.compile_njit(pyfunc, sig)
+
+        # compile the prange injected function
+        with warnings.catch_warnings(record=True) as raised_warnings:
+            warnings.simplefilter('always')
+            cpfunc = self.compile_parallel(pfunc, sig)
+
+        # if check_fastmath is True then check fast instructions
+        if check_fastmath:
+            self.assert_fastmath(pfunc, sig)
+
+        # if check_fastmath_result is True then compile a function
+        # so that the parfors checker can assert the result is ok.
+        if check_fastmath_result:
+            fastcpfunc = self.compile_parallel_fastmath(pfunc, sig)
+            kwargs = dict({'fastmath_pcres': fastcpfunc}, **kwargs)
+
+        self.check_parfors_vs_others(pyfunc, cfunc, cpfunc, *args, **kwargs)
+        return raised_warnings
+
+
+@skip_parfors_unsupported
+class TestPrangeBasic(TestPrangeBase):
+    """ Tests Prange """
+
+    def test_prange01(self):
+        def test_impl():
+            n = 4
+            A = np.zeros(n)
+            for i in range(n):
+                A[i] = 2.0 * i
+            return A
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange02(self):
+        def test_impl():
+            n = 4
+            A = np.zeros(n - 1)
+            for i in range(1, n):
+                A[i - 1] = 2.0 * i
+            return A
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange03(self):
+        def test_impl():
+            s = 10
+            for i in range(10):
+                s += 2
+            return s
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange03mul(self):
+        def test_impl():
+            s = 3
+            for i in range(10):
+                s *= 2
+            return s
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange03sub(self):
+        def test_impl():
+            s = 100
+            for i in range(10):
+                s -= 2
+            return s
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange03div(self):
+        def test_impl():
+            s = 10
+            for i in range(10):
+                s /= 2
+            return s
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange04(self):
+        def test_impl():
+            a = 2
+            b = 3
+            A = np.empty(4)
+            for i in range(4):
+                if i == a:
+                    A[i] = b
+                else:
+                    A[i] = 0
+            return A
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange05(self):
+        def test_impl():
+            n = 4
+            A = np.ones((n), dtype=np.float64)
+            s = 0
+            for i in range(1, n - 1, 1):
+                s += A[i]
+            return s
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange06(self):
+        def test_impl():
+            n = 4
+            A = np.ones((n), dtype=np.float64)
+            s = 0
+            for i in range(1, 1, 1):
+                s += A[i]
+            return s
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange07(self):
+        def test_impl():
+            n = 4
+            A = np.ones((n), dtype=np.float64)
+            s = 0
+            for i in range(n, 1):
+                s += A[i]
+            return s
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange08(self):
+        def test_impl():
+            n = 4
+            A = np.ones((n))
+            acc = 0
+            for i in range(len(A)):
+                for j in range(len(A)):
+                    acc += A[i]
+            return acc
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange08_1(self):
+        def test_impl():
+            n = 4
+            A = np.ones((n))
+            acc = 0
+            for i in range(4):
+                for j in range(4):
+                    acc += A[i]
+            return acc
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange09(self):
+        def test_impl():
+            n = 4
+            acc = 0
+            for i in range(n):
+                for j in range(n):
+                    acc += 1
+            return acc
+        # patch inner loop to 'prange'
+        self.prange_tester(test_impl, patch_instance=[1],
+                           scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange10(self):
+        def test_impl():
+            n = 4
+            acc2 = 0
+            for j in range(n):
+                acc1 = 0
+                for i in range(n):
+                    acc1 += 1
+                acc2 += acc1
+            return acc2
+        # patch outer loop to 'prange'
+        self.prange_tester(test_impl, patch_instance=[0],
+                           scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    @unittest.skip("list append is not thread-safe yet (#2391, #2408)")
+    def test_prange11(self):
+        def test_impl():
+            n = 4
+            return [np.sin(j) for j in range(n)]
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange12(self):
+        def test_impl():
+            acc = 0
+            n = 4
+            X = np.ones(n)
+            for i in range(-len(X)):
+                acc += X[i]
+            return acc
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange13(self):
+        def test_impl(n):
+            acc = 0
+            for i in range(n):
+                acc += 1
+            return acc
+        self.prange_tester(test_impl, np.int32(4), scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange14(self):
+        def test_impl(A):
+            s = 3
+            for i in range(len(A)):
+                s += A[i]*2
+            return s
+        # this tests reduction detection well since the accumulated variable
+        # is initialized before the parfor and the value accessed from the array
+        # is updated before accumulation
+        self.prange_tester(test_impl, np.random.ranf(4),
+                           scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange15(self):
+        # from issue 2587
+        # test parfor type inference when there is multi-dimensional indexing
+        def test_impl(N):
+            acc = 0
+            for i in range(N):
+                x = np.ones((1, 1))
+                acc += x[0, 0]
+            return acc
+        self.prange_tester(test_impl, 1024, scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    # Tests for negative ranges
+    def test_prange16(self):
+        def test_impl(N):
+            acc = 0
+            for i in range(-N, N):
+                acc += 2
+            return acc
+        self.prange_tester(test_impl, 1024, scheduler_type='signed',
+                           check_fastmath=True)
+
+    def test_prange17(self):
+        def test_impl(N):
+            acc = 0
+            X = np.ones(N)
+            for i in range(-N, N):
+                acc += X[i]
+            return acc
+        self.prange_tester(test_impl, 9, scheduler_type='signed',
+                           check_fastmath=True)
+
+    def test_prange18(self):
+        def test_impl(N):
+            acc = 0
+            X = np.ones(N)
+            for i in range(-N, 5):
+                acc += X[i]
+                for j in range(-4, N):
+                    acc += X[j]
+            return acc
+        self.prange_tester(test_impl, 9, scheduler_type='signed',
+                           check_fastmath=True)
+
+    def test_prange19(self):
+        def test_impl(N):
+            acc = 0
+            M = N + 4
+            X = np.ones((N, M))
+            for i in range(-N, N):
+                for j in range(-M, M):
+                    acc += X[i, j]
+            return acc
+        self.prange_tester(test_impl, 9, scheduler_type='signed',
+                           check_fastmath=True)
+
+    def test_prange20(self):
+        def test_impl(N):
+            acc = 0
+            X = np.ones(N)
+            for i in range(-1, N):
+                acc += X[i]
+            return acc
+        self.prange_tester(test_impl, 9, scheduler_type='signed',
+                           check_fastmath=True)
+
+    def test_prange21(self):
+        def test_impl(N):
+            acc = 0
+            for i in range(-3, -1):
+                acc += 3
+            return acc
+        self.prange_tester(test_impl, 9, scheduler_type='signed',
+                           check_fastmath=True)
+
+    def test_prange22(self):
+        def test_impl():
+            a = 0
+            b = 3
+            A = np.empty(4)
+            for i in range(-2, 2):
+                if i == a:
+                    A[i] = b
+                elif i < 1:
+                    A[i] = -1
+                else:
+                    A[i] = 7
+            return A
+        self.prange_tester(test_impl, scheduler_type='signed',
+                           check_fastmath=True, check_fastmath_result=True)
+
+    def test_prange23(self):
+        # test non-contig input
+        def test_impl(A):
+            for i in range(len(A)):
+                A[i] = i
+            return A
+        A = np.zeros(32)[::2]
+        self.prange_tester(test_impl, A, scheduler_type='unsigned',
+                           check_fastmath=True, check_fastmath_result=True)
+
+    def test_prange24(self):
+        # test non-contig input, signed range
+        def test_impl(A):
+            for i in range(-len(A), 0):
+                A[i] = i
+            return A
+        A = np.zeros(32)[::2]
+        self.prange_tester(test_impl, A, scheduler_type='signed',
+                           check_fastmath=True, check_fastmath_result=True)
+
+    def test_prange25(self):
+        def test_impl(A):
+            n = len(A)
+            buf = [np.zeros_like(A) for _ in range(n)]
+            for i in range(n):
+                buf[i] = A + i
+            return buf
+        A = np.ones((10,))
+        self.prange_tester(test_impl, A,  patch_instance=[1],
+                           scheduler_type='unsigned', check_fastmath=True,
+                           check_fastmath_result=True)
+
+        cpfunc = self.compile_parallel(test_impl, (numba.typeof(A),))
+        diagnostics = cpfunc.metadata['parfor_diagnostics']
+        hoisted_allocs = diagnostics.hoisted_allocations()
+        self.assertEqual(len(hoisted_allocs), 0)
+
+    def test_prange26(self):
+        def test_impl(A):
+            B = A[::3]
+            for i in range(len(B)):
+                B[i] = i
+            return A
+        A = np.zeros(32)[::2]
+        self.prange_tester(test_impl, A, scheduler_type='unsigned',
+                           check_fastmath=True, check_fastmath_result=True)
+
+    def test_prange27(self):
+        # issue5597: usedef error in parfor
+        def test_impl(a, b, c):
+            for j in range(b[0]-1):
+                for k in range(2):
+                    z = np.abs(a[c-1:c+1])
+            return 0
+
+        # patch inner loop to 'prange'
+        self.prange_tester(test_impl,
+                           np.arange(20),
+                           np.asarray([4,4,4,4,4,4,4,4,4,4]),
+                           0,
+                           patch_instance=[1],
+                           scheduler_type='unsigned',
+                           check_fastmath=True)
+
+    def test_prange28(self):
+        # issue7105: label conflict in nested parfor
+        def test_impl(x, y):
+            out = np.zeros(len(y))
+            for idx in range(0, len(y)):
+                i0 = y[idx, 0]
+                i1 = y[idx, 1]
+                Pt1 = x[i0]
+                Pt2 = x[i1]
+                v = Pt1 - Pt2
+                vl2 = v[0] + v[1]
+                out[idx] = vl2
+            return out
+
+        X = np.array([[-1., -1.],
+                      [-1.,  1.],
+                      [ 0.,  0.],
+                      [ 1., -1.],
+                      [ 1.,  0.],
+                      [ 1.,  1.]])
+
+        Y = np.array([[0, 1],
+                      [1, 2],
+                      [2, 3],
+                      [3, 4],
+                      [4, 5]])
+
+        self.prange_tester(test_impl, X, Y, scheduler_type='unsigned',
+                           check_fastmath=True, check_fastmath_result=True)
+
+    def test_prange29(self):
+        # issue7630: SSA renaming in prange header
+        def test_impl(flag):
+            result = 0
+            if flag:
+                for i in range(1):
+                    result += 1
+            else:
+                for i in range(1):
+                    result -= 3
+            return result
+
+        self.prange_tester(test_impl, True)
+        self.prange_tester(test_impl, False)
+
+    def test_prange30(self):
+        # issue7675: broadcast setitem
+        def test_impl(x, par, numthreads):
+            n_par = par.shape[0]
+            n_x = len(x)
+            result = np.zeros((n_par, n_x), dtype=np.float64)
+            chunklen = (len(x) + numthreads - 1) // numthreads
+
+            for i in range(numthreads):
+                start = i * chunklen
+                stop = (i + 1) * chunklen
+                result[:, start:stop] = x[start:stop] * par[:]
+
+            return result
+
+        x = np.array(np.arange(0, 6, 1.0))
+        par = np.array([1.0, 2.0, 3.0])
+
+        self.prange_tester(test_impl, x, par, 2)
+
+
+@skip_parfors_unsupported
+class TestPrangeSpecific(TestPrangeBase):
+    """ Tests specific features/problems found under prange"""
+
+    def test_prange_two_instances_same_reduction_var(self):
+        # issue4922 - multiple uses of same reduction variable
+        def test_impl(n):
+            c = 0
+            for i in range(n):
+                c += 1
+                if i > 10:
+                    c += 1
+            return c
+        self.prange_tester(test_impl, 9)
+
+    def test_prange_conflicting_reduction_ops(self):
+        def test_impl(n):
+            c = 0
+            for i in range(n):
+                c += 1
+                if i > 10:
+                    c *= 1
+            return c
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            self.prange_tester(test_impl, 9)
+        msg = ('Reduction variable c has multiple conflicting reduction '
+               'operators.')
+        self.assertIn(msg, str(raises.exception))
+
+    def test_prange_two_conditional_reductions(self):
+        # issue6414
+        def test_impl():
+            A = B = 0
+            for k in range(1):
+                if k == 2:
+                    A += 1
+                else:
+                    x = np.zeros((1, 1))
+                    if x[0, 0]:
+                        B += 1
+            return A, B
+        self.prange_tester(test_impl)
+
+    def test_prange_nested_reduction1(self):
+        def test_impl():
+            A = 0
+            for k in range(1):
+                for i in range(1):
+                    if i == 0:
+                        A += 1
+            return A
+        self.prange_tester(test_impl)
+
+    @disabled_test
+    def test_check_error_model(self):
+        def test_impl():
+            n = 32
+            A = np.zeros(n)
+            for i in range(n):
+                A[i] = 1 / i # div-by-zero when i = 0
+            return A
+
+        with self.assertRaises(ZeroDivisionError) as raises:
+            test_impl()
+
+        # compile parallel functions
+        pfunc = self.generate_prange_func(test_impl, None)
+        pcres = self.compile_parallel(pfunc, ())
+        pfcres = self.compile_parallel_fastmath(pfunc, ())
+
+        # should raise
+        with self.assertRaises(ZeroDivisionError) as raises:
+            pcres.entry_point()
+
+        # should not raise
+        result = pfcres.entry_point()
+        self.assertEqual(result[0], np.inf)
+
+    def test_check_alias_analysis(self):
+        # check alias analysis reports ok
+        def test_impl(A):
+            for i in range(len(A)):
+                B = A[i]
+                B[:] = 1
+            return A
+        A = np.zeros(32).reshape(4, 8)
+        self.prange_tester(test_impl, A, scheduler_type='unsigned',
+                           check_fastmath=True, check_fastmath_result=True)
+        pfunc = self.generate_prange_func(test_impl, None)
+        sig = tuple([numba.typeof(A)])
+        cres = self.compile_parallel_fastmath(pfunc, sig)
+        _ir = self._get_gufunc_ir(cres)
+        for k, v in _ir.items():
+            for line in v.splitlines():
+                # get the fn definition line
+                if 'define' in line and k in line:
+                    # there should only be 2x noalias, one on each of the first
+                    # 2 args (retptr, excinfo).
+                    # Note: used to be 3x no noalias, but env arg is dropped.
+                    self.assertEqual(line.count('noalias'), 2)
+                    break
+
+    def test_prange_raises_invalid_step_size(self):
+        def test_impl(N):
+            acc = 0
+            for i in range(0, N, 2):
+                acc += 2
+            return acc
+
+        with self.assertRaises(errors.UnsupportedRewriteError) as raises:
+            self.prange_tester(test_impl, 1024)
+        msg = 'Only constant step size of 1 is supported for prange'
+        self.assertIn(msg, str(raises.exception))
+
+    def test_prange_fastmath_check_works(self):
+        # this function will benefit from `fastmath`, the div will
+        # get optimised to a multiply by reciprocal and the accumulator
+        # then becomes an fmadd: A = A + i * 0.5
+        def test_impl():
+            n = 128
+            A = 0
+            for i in range(n):
+                A += i / 2.0
+            return A
+        self.prange_tester(test_impl, scheduler_type='unsigned',
+                           check_fastmath=True)
+        pfunc = self.generate_prange_func(test_impl, None)
+        cres = self.compile_parallel_fastmath(pfunc, ())
+        ir = self._get_gufunc_ir(cres)
+        _id = '%[A-Z_0-9]?(.[0-9]+)+[.]?[i]?'
+        recipr_str = '\s+%s = fmul fast double %s, 5.000000e-01'
+        reciprocal_inst = re.compile(recipr_str % (_id, _id))
+        fadd_inst = re.compile('\s+%s = fadd fast double %s, %s'
+                               % (_id, _id, _id))
+        # check there is something like:
+        #  %.329 = fmul fast double %.325, 5.000000e-01
+        #  %.337 = fadd fast double %A.07, %.329
+        for name, kernel in ir.items():
+            splitted = kernel.splitlines()
+            for i, x in enumerate(splitted):
+                if reciprocal_inst.match(x):
+                    break
+            self.assertTrue(fadd_inst.match(splitted[i + 1]))
+
+    def test_parfor_alias1(self):
+        def test_impl(n):
+            b = np.zeros((n, n))
+            a = b[0]
+            for j in range(n):
+                a[j] = j + 1
+            return b.sum()
+        self.prange_tester(test_impl, 4)
+
+    def test_parfor_alias2(self):
+        def test_impl(n):
+            b = np.zeros((n, n))
+            for i in range(n):
+              a = b[i]
+              for j in range(n):
+                a[j] = i + j
+            return b.sum()
+        self.prange_tester(test_impl, 4)
+
+    def test_parfor_alias3(self):
+        def test_impl(n):
+            b = np.zeros((n, n, n))
+            for i in range(n):
+              a = b[i]
+              for j in range(n):
+                c = a[j]
+                for k in range(n):
+                  c[k] = i + j + k
+            return b.sum()
+        self.prange_tester(test_impl, 4)
+
+    def test_parfor_race_1(self):
+        def test_impl(x, y):
+            for j in range(y):
+                k = x
+            return k
+        raised_warnings = self.prange_tester(test_impl, 10, 20)
+        warning_obj = raised_warnings[0]
+        expected_msg = ("Variable k used in parallel loop may be written to "
+                        "simultaneously by multiple workers and may result "
+                        "in non-deterministic or unintended results.")
+        self.assertIn(expected_msg, str(warning_obj.message))
+
+    def test_nested_parfor_push_call_vars(self):
+        """ issue 3686: if a prange has something inside it that causes
+            a nested parfor to be generated and both the inner and outer
+            parfor use the same call variable defined outside the parfors
+            then ensure that when that call variable is pushed into the
+            parfor that the call variable isn't duplicated with the same
+            name resulting in a redundant type lock.
+        """
+        def test_impl():
+            B = 0
+            f = np.negative
+            for i in range(1):
+                this_matters = f(1.)
+                B += f(np.zeros(1,))[0]
+            for i in range(2):
+                this_matters = f(1.)
+                B += f(np.zeros(1,))[0]
+
+            return B
+        self.prange_tester(test_impl)
+
+    def test_copy_global_for_parfor(self):
+        """ issue4903: a global is copied next to a parfor so that
+            it can be inlined into the parfor and thus not have to be
+            passed to the parfor (i.e., an unsupported function type).
+            This global needs to be renamed in the block into which
+            it is copied.
+        """
+        def test_impl(zz, tc):
+            lh = np.zeros(len(tc))
+            lc = np.zeros(len(tc))
+            for i in range(1):
+                nt = tc[i]
+                for t in range(nt):
+                    lh += np.exp(zz[i, t])
+                for t in range(nt):
+                    lc += np.exp(zz[i, t])
+            return lh, lc
+
+        m = 2
+        zz = np.ones((m, m, m))
+        tc = np.ones(m, dtype=np.int_)
+        self.prange_tester(test_impl, zz, tc, patch_instance=[0])
+
+    def test_multiple_call_getattr_object(self):
+        def test_impl(n):
+            B = 0
+            f = np.negative
+            for i in range(1):
+                this_matters = f(1.0)
+                B += f(n)
+
+            return B
+        self.prange_tester(test_impl, 1.0)
+
+    def test_argument_alias_recarray_field(self):
+        # Test for issue4007.
+        def test_impl(n):
+            for i in range(len(n)):
+                n.x[i] = 7.0
+            return n
+        X1 = np.zeros(10, dtype=[('x', float), ('y', int), ])
+        X2 = np.zeros(10, dtype=[('x', float), ('y', int), ])
+        X3 = np.zeros(10, dtype=[('x', float), ('y', int), ])
+        v1 = X1.view(np.recarray)
+        v2 = X2.view(np.recarray)
+        v3 = X3.view(np.recarray)
+
+        # Numpy doesn't seem to support almost equal on recarray.
+        # So, we convert to list and use assertEqual instead.
+        python_res = list(test_impl(v1))
+        njit_res = list(njit(test_impl)(v2))
+        pa_func = njit(test_impl, parallel=True)
+        pa_res = list(pa_func(v3))
+        self.assertEqual(python_res, njit_res)
+        self.assertEqual(python_res, pa_res)
+
+    def test_mutable_list_param(self):
+        """ issue3699: test that mutable variable to call in loop
+            is not hoisted.  The call in test_impl forces a manual
+            check here rather than using prange_tester.
+        """
+        @njit
+        def list_check(X):
+            """ If the variable X is hoisted in the test_impl prange
+                then subsequent list_check calls would return increasing
+                values.
+            """
+            ret = X[-1]
+            a = X[-1] + 1
+            X.append(a)
+            return ret
+        def test_impl(n):
+            for i in prange(n):
+                X = [100]
+                a = list_check(X)
+            return a
+        python_res = test_impl(10)
+        njit_res = njit(test_impl)(10)
+        pa_func = njit(test_impl, parallel=True)
+        pa_res = pa_func(10)
+        self.assertEqual(python_res, njit_res)
+        self.assertEqual(python_res, pa_res)
+
+    def test_list_comprehension_prange(self):
+        # issue4569
+        def test_impl(x):
+            return np.array([len(x[i]) for i in range(len(x))])
+        x = [np.array([1,2,3], dtype=int),np.array([1,2], dtype=int)]
+        self.prange_tester(test_impl, x)
+
+    def test_ssa_false_reduction(self):
+        # issue5698
+        # SSA for h creates assignments to h that make it look like a
+        # reduction variable except that it lacks an associated
+        # reduction operator.  Test here that h is excluded as a
+        # reduction variable.
+        def test_impl(image, a, b):
+            empty = np.zeros(image.shape)
+            for i in range(image.shape[0]):
+                r = image[i][0] / 255.0
+                if a == 0:
+                    h = 0
+                if b == 0:
+                    h = 0
+                empty[i] = [h, h, h]
+            return empty
+
+        image = np.zeros((3, 3), dtype=np.int32)
+        self.prange_tester(test_impl, image, 0, 0)
+
+    def test_list_setitem_hoisting(self):
+        # issue5979
+        # Don't hoist list initialization if list item set.
+        def test_impl():
+            n = 5
+            a = np.empty(n, dtype=np.int64)
+            for k in range(5):
+                X = [0]
+                X[0] = 1
+                a[k] = X[0]
+            return a
+
+        self.prange_tester(test_impl)
+
+    def test_record_array_setitem(self):
+        # issue6704
+        state_dtype = np.dtype([('var', np.int32)])
+
+        def test_impl(states):
+            for i in range(1):
+                states[i]['var'] = 1
+
+        def comparer(a, b):
+            assert(a[0]['var'] == b[0]['var'])
+
+        self.prange_tester(test_impl,
+                           np.zeros(shape=1, dtype=state_dtype),
+                           check_arg_equality=[comparer])
+
+    def test_record_array_setitem_yield_array(self):
+        state_dtype = np.dtype([('x', np.intp)])
+
+        def test_impl(states):
+            n = states.size
+            for i in range(states.size):
+                states["x"][i] = 7 + i
+            return states
+
+        states = np.zeros(10, dtype=state_dtype)
+
+        def comparer(a, b):
+            np.testing.assert_equal(a, b)
+
+        self.prange_tester(test_impl,
+                           states,
+                           check_arg_equality=[comparer])
+
+    def test_issue7501(self):
+        def test_impl(size, case):
+            result = np.zeros((size,))
+            if case == 1:
+                for i in range(size):
+                    result[i] += 1
+            else:
+                for i in range(size):
+                    result[i] += 2
+            return result[0]
+
+        self.prange_tester(test_impl, 3, 1)
+
+    def test_kde_example(self):
+        def test_impl(X):
+            # KDE example
+            b = 0.5
+            points = np.array([-1.0, 2.0, 5.0])
+            N = points.shape[0]
+            n = X.shape[0]
+            exps = 0
+            for i in range(n):
+                p = X[i]
+                d = (-(p - points)**2) / (2 * b**2)
+                m = np.min(d)
+                exps += m - np.log(b * N) + np.log(np.sum(np.exp(d - m)))
+            return exps
+
+        n = 128
+        X = np.random.ranf(n)
+        self.prange_tester(test_impl, X)
+
+    @skip_parfors_unsupported
+    def test_issue_due_to_max_label(self):
+        # Run the actual test in a new process since it can only reproduce in
+        # a fresh state.
+        out = subp.check_output(
+            [sys.executable, '-m', 'numba.tests.parfors_max_label_error'],
+            timeout=30,
+            stderr=subp.STDOUT, # redirect stderr to stdout
+        )
+        self.assertIn("TEST PASSED", out.decode())
+
+
+@skip_parfors_unsupported
+class TestParforChunksizing(TestCase):
+    """
+    Tests chunksize handling in ParallelAccelerator.
+    """
+    _numba_parallel_test_ = False
+
+    def setUp(self):
+        set_parallel_chunksize(0)
+
+    def tearDown(self):
+        set_parallel_chunksize(0)
+
+    def test_python_parallel_chunksize_basic(self):
+        # Test basic chunksize operations outside njit.
+        self.assertEqual(get_parallel_chunksize(), 0)
+        set_parallel_chunksize(8)
+        self.assertEqual(get_parallel_chunksize(), 8)
+        set_parallel_chunksize(0)
+        self.assertEqual(get_parallel_chunksize(), 0)
+
+    def test_python_with_chunksize(self):
+        # Test "with parallel_chunksize" outside njit.
+        self.assertEqual(get_parallel_chunksize(), 0)
+        with parallel_chunksize(8):
+            self.assertEqual(get_parallel_chunksize(), 8)
+        self.assertEqual(get_parallel_chunksize(), 0)
+
+    def test_njit_parallel_chunksize_basic(self):
+        # Test basic chunksize operations inside njit.
+        @njit
+        def get_cs():
+            return get_parallel_chunksize()
+
+        @njit
+        def set_cs(x):
+            return set_parallel_chunksize(x)
+
+        self.assertEqual(get_cs(), 0)
+        set_cs(8)
+        self.assertEqual(get_cs(), 8)
+        set_cs(0)
+        self.assertEqual(get_cs(), 0)
+
+    def test_njit_with_chunksize(self):
+        # Test "with parallel_chunksize" inside njit.
+        @njit
+        def test_impl(x):
+            cs1 = get_parallel_chunksize()
+            with parallel_chunksize(8):
+                cs2 = get_parallel_chunksize()
+            cs3 = get_parallel_chunksize()
+            return cs1, cs2, cs3
+
+        cs1, cs2, cs3 = test_impl(8)
+
+        self.assertEqual(cs1, 0)
+        self.assertEqual(cs2, 8)
+        self.assertEqual(cs3, 0)
+
+    def test_all_iterations_reset_chunksize(self):
+        """ Test that all the iterations get run if you set the
+            chunksize.  Also check that the chunksize that each
+            worker thread sees has been reset to 0. """
+
+        @njit(parallel=True)
+        def test_impl(cs, n):
+            res = np.zeros(n)
+            inner_cs = np.full(n, -13)
+            with numba.parallel_chunksize(cs):
+                for i in numba.prange(n):
+                    inner_cs[i] = numba.get_parallel_chunksize()
+                    res[i] = 13
+            return res, inner_cs
+
+        # Test a variety of array and chunk sizes.
+        # 1000 is a round number, 997 is prime, 943 is product of two
+        # primes, 961 is square of a prime.
+        for j in [1000, 997, 943, 961]:
+            for i in range(15):
+                res, inner_cs = test_impl(i+1, j)
+                self.assertTrue(np.all(res == 13))
+                self.assertTrue(np.all(inner_cs == 0))
+
+    def test_njit_parallel_chunksize_negative(self):
+        # Test negative set_parallel_chunksize inside njit.
+        with self.assertRaises(ValueError) as raised:
+            @njit
+            def neg_test():
+                set_parallel_chunksize(-1)
+
+            neg_test()
+
+        msg = "chunksize must be greater than or equal to zero"
+        self.assertIn(msg, str(raised.exception))
+
+    def test_python_parallel_chunksize_negative(self):
+        # Test negative set_parallel_chunksize outside njit.
+        with self.assertRaises(ValueError) as raised:
+            set_parallel_chunksize(-1)
+
+        msg = "chunksize must be greater than or equal to zero"
+        self.assertIn(msg, str(raised.exception))
+
+    def test_njit_parallel_chunksize_invalid_type(self):
+        with self.assertRaises(errors.TypingError) as raised:
+            @njit
+            def impl():
+                set_parallel_chunksize('invalid_type')
+
+            impl()
+
+        msg = "The parallel chunksize must be an integer"
+        self.assertIn(msg, str(raised.exception))
+
+    def test_python_parallel_chunksize_invalid_type(self):
+        with self.assertRaises(TypeError) as raised:
+            set_parallel_chunksize('invalid_type')
+
+        msg = "The parallel chunksize must be an integer"
+        self.assertIn(msg, str(raised.exception))
+
+
+@skip_parfors_unsupported
+@x86_only
+class TestParforsVectorizer(TestPrangeBase):
+
+    # env mutating test
+    _numba_parallel_test_ = False
+
+    def get_gufunc_asm(self, func, schedule_type, *args, **kwargs):
+
+        fastmath = kwargs.pop('fastmath', False)
+        cpu_name = kwargs.pop('cpu_name', 'skylake-avx512')
+        assertions = kwargs.pop('assertions', True)
+        # force LLVM to use zmm registers for vectorization
+        # https://reviews.llvm.org/D67259
+        cpu_features = kwargs.pop('cpu_features', '-prefer-256-bit')
+
+        env_opts = {'NUMBA_CPU_NAME': cpu_name,
+                    'NUMBA_CPU_FEATURES': cpu_features,
+                    }
+
+        overrides = []
+        for k, v in env_opts.items():
+            overrides.append(override_env_config(k, v))
+
+        with overrides[0], overrides[1]:
+            sig = tuple([numba.typeof(x) for x in args])
+            pfunc_vectorizable = self.generate_prange_func(func, None)
+            if fastmath == True:
+                cres = self.compile_parallel_fastmath(pfunc_vectorizable, sig)
+            else:
+                cres = self.compile_parallel(pfunc_vectorizable, sig)
+
+            # get the gufunc asm
+            asm = self._get_gufunc_asm(cres)
+
+            if assertions:
+                schedty = re.compile('call\s+\w+\*\s+@do_scheduling_(\w+)\(')
+                matches = schedty.findall(cres.library.get_llvm_str())
+                self.assertGreaterEqual(len(matches), 1) # at least 1 parfor call
+                self.assertEqual(matches[0], schedule_type)
+                self.assertNotEqual(asm, {})
+
+            return asm
+
+    # this is a common match pattern for something like:
+    # \n\tvsqrtpd\t-192(%rbx,%rsi,8), %zmm0\n
+    # to check vsqrtpd operates on zmm
+    match_vsqrtpd_on_zmm = re.compile('\n\s+vsqrtpd\s+.*zmm.*\n')
+
+    @linux_only
+    def test_vectorizer_fastmath_asm(self):
+        """ This checks that if fastmath is set and the underlying hardware
+        is suitable, and the function supplied is amenable to fastmath based
+        vectorization, that the vectorizer actually runs.
+        """
+
+        # This function will benefit from `fastmath` if run on a suitable
+        # target. The vectorizer should unwind the loop and generate
+        # packed dtype=double add and sqrt instructions.
+        def will_vectorize(A):
+            n = len(A)
+            acc = 0
+            for i in range(n):
+                acc += np.sqrt(i)
+            return acc
+
+        arg = np.zeros(10)
+
+        fast_asm = self.get_gufunc_asm(will_vectorize, 'unsigned', arg,
+                                       fastmath=True)
+        slow_asm = self.get_gufunc_asm(will_vectorize, 'unsigned', arg,
+                                       fastmath=False)
+
+        for v in fast_asm.values():
+            # should unwind and call vector sqrt then vector add
+            # all on packed doubles using zmm's
+            self.assertTrue('vaddpd' in v)
+            self.assertTrue('vsqrtpd' in v)
+            self.assertTrue('zmm' in v)
+            # make sure vsqrtpd operates on zmm
+            self.assertTrue(len(self.match_vsqrtpd_on_zmm.findall(v)) > 1)
+
+        for v in slow_asm.values():
+            # vector variants should not be present
+            self.assertTrue('vaddpd' not in v)
+            self.assertTrue('vsqrtpd' not in v)
+            # check scalar variant is present
+            self.assertTrue('vsqrtsd' in v)
+            self.assertTrue('vaddsd' in v)
+            # check no zmm addressing is present
+            self.assertTrue('zmm' not in v)
+
+    @linux_only
+    def test_unsigned_refusal_to_vectorize(self):
+        """ This checks that if fastmath is set and the underlying hardware
+        is suitable, and the function supplied is amenable to fastmath based
+        vectorization, that the vectorizer actually runs.
+        """
+
+        def will_not_vectorize(A):
+            n = len(A)
+            for i in range(-n, 0):
+                A[i] = np.sqrt(A[i])
+            return A
+
+        def will_vectorize(A):
+            n = len(A)
+            for i in range(n):
+                A[i] = np.sqrt(A[i])
+            return A
+
+        arg = np.zeros(10)
+
+        # Boundschecking breaks vectorization
+        with override_env_config('NUMBA_BOUNDSCHECK', '0'):
+            novec_asm = self.get_gufunc_asm(will_not_vectorize, 'signed', arg,
+                                            fastmath=True)
+
+            vec_asm = self.get_gufunc_asm(will_vectorize, 'unsigned', arg,
+                                          fastmath=True)
+
+        for v in novec_asm.values():
+            # vector variant should not be present
+            self.assertTrue('vsqrtpd' not in v)
+            # check scalar variant is present
+            self.assertTrue('vsqrtsd' in v)
+            # check no zmm addressing is present
+            self.assertTrue('zmm' not in v)
+
+        for v in vec_asm.values():
+            # should unwind and call vector sqrt then vector mov
+            # all on packed doubles using zmm's
+            self.assertTrue('vsqrtpd' in v)
+            self.assertTrue('vmovupd' in v)
+            self.assertTrue('zmm' in v)
+            # make sure vsqrtpd operates on zmm
+            self.assertTrue(len(self.match_vsqrtpd_on_zmm.findall(v)) > 1)
+
+    @linux_only
+    # needed as 32bit doesn't have equivalent signed/unsigned instruction
+    # generation for this function
+    def test_signed_vs_unsigned_vec_asm(self):
+        """ This checks vectorization for signed vs unsigned variants of a
+        trivial accumulator, the only meaningful difference should be the
+        presence of signed vs. unsigned unpack instructions (for the
+        induction var).
+        """
+        def signed_variant():
+            n = 4096
+            A = 0.
+            for i in range(-n, 0):
+                A += i
+            return A
+
+        def unsigned_variant():
+            n = 4096
+            A = 0.
+            for i in range(n):
+                A += i
+            return A
+
+        # Boundschecking breaks the diff check below because of the pickled exception
+        with override_env_config('NUMBA_BOUNDSCHECK', '0'):
+            signed_asm = self.get_gufunc_asm(signed_variant, 'signed',
+                                             fastmath=True)
+            unsigned_asm = self.get_gufunc_asm(unsigned_variant, 'unsigned',
+                                               fastmath=True)
+
+        def strip_instrs(asm):
+            acc = []
+            for x in asm.splitlines():
+                spd = x.strip()
+                # filter out anything that isn't a trivial instruction
+                # and anything with the gufunc id as it contains an address
+                if spd != '' and not (spd.startswith('.')
+                                     or spd.startswith('_')
+                                     or spd.startswith('"')
+                                     or '__numba_parfor_gufunc' in spd):
+                        acc.append(re.sub('[\t]', '', spd))
+            return acc
+
+        for k, v in signed_asm.items():
+            signed_instr = strip_instrs(v)
+            break
+
+        for k, v in unsigned_asm.items():
+            unsigned_instr = strip_instrs(v)
+            break
+
+        from difflib import SequenceMatcher as sm
+        # make sure that the only difference in instruction (if there is a
+        # difference) is the char 'u'. For example:
+        # vcvtsi2sdq vs. vcvtusi2sdq
+        self.assertEqual(len(signed_instr), len(unsigned_instr))
+        for a, b in zip(signed_instr, unsigned_instr):
+            if a == b:
+                continue
+            else:
+                s = sm(lambda x: x == '\t', a, b)
+                ops = s.get_opcodes()
+                for op in ops:
+                    if op[0] == 'insert':
+                        self.assertEqual(b[op[-2]:op[-1]], 'u')
+
+
+@skip_parfors_unsupported
+class TestParforReductionSetNumThreads(TestCase):
+    """Test execution correctness on reductions with set_num_threads.
+    """
+    def test_add(self):
+        N = config.NUMBA_NUM_THREADS
+        M = 2 * N
+        mask = N - 1
+
+        @njit(parallel=True)
+        def udt(nthreads):
+            acc = 0
+            set_num_threads(nthreads)
+            for i in prange(M):
+                local_mask = 1 + i % mask
+                set_num_threads(local_mask)
+                gnt = get_num_threads()
+                acc += gnt
+            return acc
+
+        expect = udt.py_func(mask)
+        got = udt(mask)
+        self.assertPreciseEqual(expect, got)
+
+    def test_mul(self):
+        # This min will prevent larger thread counts from generating
+        # overflow in the loop below.
+        N = min(4, config.NUMBA_NUM_THREADS)
+        M = 2 * N
+        mask = N - 1
+
+        @njit(parallel=True)
+        def udt(nthreads):
+            acc = 1
+            set_num_threads(nthreads)
+            for i in prange(M):
+                local_mask = 1 + i % mask
+                set_num_threads(local_mask)
+                gnt = get_num_threads()
+                acc *= gnt
+            return acc
+
+        expect = udt.py_func(mask)
+        got = udt(mask)
+        self.assertPreciseEqual(expect, got)
+
+    def test_max(self):
+        N = config.NUMBA_NUM_THREADS
+        M = 2 * N
+        mask = N - 1
+
+        @njit(parallel=True)
+        def udt(nthreads):
+            acc = 1
+            set_num_threads(nthreads)
+            for i in prange(M):
+                local_mask = 1 + i % mask
+                set_num_threads(local_mask)
+                gnt = get_num_threads()
+                acc = max(acc, gnt)
+            return acc
+
+        expect = udt.py_func(mask)
+        got = udt(mask)
+        self.assertPreciseEqual(expect, got)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors_caching.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors_caching.py
new file mode 100644
index 000000000..87ee3c0e1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors_caching.py
@@ -0,0 +1,83 @@
+import os.path
+import subprocess
+import sys
+
+import numpy as np
+
+from numba.tests.support import skip_parfors_unsupported
+from .test_caching import DispatcherCacheUsecasesTest
+
+
+@skip_parfors_unsupported
+class TestParforsCache(DispatcherCacheUsecasesTest):
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "parfors_cache_usecases.py")
+    modname = "parfors_caching_test_fodder"
+
+    def run_test(self, fname, num_funcs=1):
+        mod = self.import_module()
+        self.check_pycache(0)
+        f = getattr(mod, fname)
+        ary = np.ones(10)
+        # The result of these functions is derived from e.g. out of order
+        # accumulation so allclose should be fine.
+        np.testing.assert_allclose(f(ary), f.py_func(ary))
+
+        dynamic_globals = [cres.library.has_dynamic_globals
+                           for cres in f.overloads.values()]
+        [cres] = f.overloads.values()
+        self.assertEqual(dynamic_globals, [False])
+        # For each cached func, there're 2 entries (index + data)
+        self.check_pycache(num_funcs * 2)
+
+        self.run_in_separate_process()
+
+    def test_arrayexprs(self):
+        f = 'arrayexprs_case'
+        self.run_test(f)
+
+    def test_prange(self):
+        f = 'prange_case'
+        self.run_test(f)
+
+    def test_caller(self):
+        f = 'caller_case'
+        # num_funcs=3 because, there's the `caller_case()` which calls
+        # the `prange_case()` and `arrayexprs_case()`
+        self.run_test(f, num_funcs=3)
+
+
+@skip_parfors_unsupported
+class TestParforsCacheChangingThreads(DispatcherCacheUsecasesTest):
+    # NOTE: This test is checking issue #7518, that thread counts are not
+    # baked into cached objects.
+
+    here = os.path.dirname(__file__)
+    usecases_file = os.path.join(here, "parfors_cache_usecases.py")
+    modname = "parfors_caching_test_fodder"
+
+    def run_in_separate_process(self, thread_count):
+        # Cached functions can be run from a distinct process.
+        code = """if 1:
+            import sys
+
+            sys.path.insert(0, %(tempdir)r)
+            mod = __import__(%(modname)r)
+            mod.self_run()
+            """ % dict(tempdir=self.tempdir, modname=self.modname)
+
+        new_env = {**os.environ, "NUMBA_NUM_THREADS" : str(thread_count)}
+        popen = subprocess.Popen([sys.executable, "-c", code],
+                                 stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+                                 env=new_env)
+        out, err = popen.communicate()
+        if popen.returncode != 0:
+            raise AssertionError(f"process failed with code {popen.returncode}:"
+                                 f"stderr follows\n{err.decode()}\n")
+
+    def test_caching(self):
+        self.check_pycache(0)
+        self.run_in_separate_process(1)
+        self.check_pycache(3 * 2) # ran 3 functions, 2 entries each
+        self.run_in_separate_process(2)
+        self.check_pycache(3 * 2) # ran 3 functions, 2 entries each
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors_passes.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors_passes.py
new file mode 100644
index 000000000..d2d69214f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_parfors_passes.py
@@ -0,0 +1,678 @@
+"""
+Tests for sub-components of parfors.
+These tests are aimed to produce a good-enough coverage of parfor passes
+so that refactoring on these passes are easier with faster testing turnaround.
+"""
+import unittest
+from functools import reduce
+
+import numpy as np
+
+from numba import njit, typeof, prange, pndindex
+import numba.parfors.parfor
+from numba.core import (
+    typing,
+    rewrites,
+    typed_passes,
+    untyped_passes,
+    inline_closurecall,
+    compiler,
+    cpu,
+    errors
+)
+from numba.core.registry import cpu_target
+from numba.tests.support import (TestCase, is_parfors_unsupported)
+
+
+class MyPipeline(object):
+    def __init__(self, typingctx, targetctx, args, test_ir):
+        self.state = compiler.StateDict()
+        self.state.typingctx = typingctx
+        self.state.targetctx = targetctx
+        self.state.args = args
+        self.state.func_ir = test_ir
+        self.state.typemap = None
+        self.state.return_type = None
+        self.state.calltypes = None
+        self.state.metadata = {}
+
+
+class BaseTest(TestCase):
+    @classmethod
+    def _run_parfor(cls, test_func, args, swap_map=None):
+        # TODO: refactor this with get_optimized_numba_ir() where this is
+        #       copied from
+        typingctx = typing.Context()
+        targetctx = cpu.CPUContext(typingctx)
+        test_ir = compiler.run_frontend(test_func)
+        options = cpu.ParallelOptions(True)
+
+        tp = MyPipeline(typingctx, targetctx, args, test_ir)
+
+        with cpu_target.nested_context(typingctx, targetctx):
+            typingctx.refresh()
+            targetctx.refresh()
+
+            inline_pass = inline_closurecall.InlineClosureCallPass(
+                tp.state.func_ir, options, typed=True
+            )
+            inline_pass.run()
+
+            rewrites.rewrite_registry.apply("before-inference", tp.state)
+
+            untyped_passes.ReconstructSSA().run_pass(tp.state)
+
+            (
+                tp.state.typemap,
+                tp.state.return_type,
+                tp.state.calltypes,
+                _
+            ) = typed_passes.type_inference_stage(
+                tp.state.typingctx, tp.state.targetctx, tp.state.func_ir,
+                tp.state.args, None
+            )
+
+            typed_passes.PreLowerStripPhis().run_pass(tp.state)
+
+            diagnostics = numba.parfors.parfor.ParforDiagnostics()
+
+            preparfor_pass = numba.parfors.parfor.PreParforPass(
+                tp.state.func_ir,
+                tp.state.typemap,
+                tp.state.calltypes,
+                tp.state.typingctx,
+                tp.state.targetctx,
+                options,
+                swapped=diagnostics.replaced_fns,
+                replace_functions_map=swap_map,
+            )
+            preparfor_pass.run()
+
+            rewrites.rewrite_registry.apply("after-inference", tp.state)
+            return tp, options, diagnostics, preparfor_pass
+
+    @classmethod
+    def run_parfor_sub_pass(cls, test_func, args):
+        tp, options, diagnostics, _ = cls._run_parfor(test_func, args)
+
+        flags = compiler.Flags()
+        parfor_pass = numba.parfors.parfor.ParforPass(
+            tp.state.func_ir,
+            tp.state.typemap,
+            tp.state.calltypes,
+            tp.state.return_type,
+            tp.state.typingctx,
+            tp.state.targetctx,
+            options,
+            flags,
+            tp.state.metadata,
+            diagnostics=diagnostics,
+        )
+        parfor_pass._pre_run()
+        # Run subpass
+        sub_pass = cls.sub_pass_class(parfor_pass)
+        sub_pass.run(parfor_pass.func_ir.blocks)
+
+        return sub_pass
+
+    @classmethod
+    def run_parfor_pre_pass(cls, test_func, args, swap_map=None):
+        tp, options, diagnostics, preparfor_pass = cls._run_parfor(
+            test_func, args, swap_map
+        )
+        return preparfor_pass
+
+    def _run_parallel(self, func, *args, **kwargs):
+        cfunc = njit(parallel=True)(func)
+        expect = func(*args, **kwargs)
+        got = cfunc(*args, **kwargs)
+        return expect, got
+
+    def run_parallel(self, func, *args, **kwargs):
+        if is_parfors_unsupported:
+            # Skip
+            return
+        expect, got = self._run_parallel(func, *args, **kwargs)
+        self.assertPreciseEqual(expect, got)
+
+    def run_parallel_check_output_array(self, func, *args, **kwargs):
+        if is_parfors_unsupported:
+            # Skip
+            return
+        expect, got = self._run_parallel(func, *args, **kwargs)
+        # Don't match the value, just the return type. must return array
+        self.assertIsInstance(expect, np.ndarray)
+        self.assertIsInstance(got, np.ndarray)
+        self.assertEqual(expect.shape, got.shape)
+
+    def check_records(self, records):
+        for rec in records:
+            self.assertIsInstance(rec["new"], numba.parfors.parfor.Parfor)
+
+
+class TestConvertSetItemPass(BaseTest):
+    sub_pass_class = numba.parfors.parfor.ConvertSetItemPass
+
+    def test_setitem_full_slice(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n)
+            a[:] = 7
+            return a
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "slice")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def test_setitem_slice_stop_bound(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n)
+            a[:5] = 7
+            return a
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "slice")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def test_setitem_slice_start_bound(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n)
+            a[4:] = 7
+            return a
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "slice")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def test_setitem_gather_if_scalar(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n)
+            b = np.ones_like(a, dtype=np.bool_)
+            a[b] = 7
+            return a
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "masked_assign_broadcast_scalar")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def test_setitem_gather_if_array(self):
+        def test_impl():
+            n = 10
+            a = np.ones(n)
+            b = np.ones_like(a, dtype=np.bool_)
+            c = np.ones_like(a)
+            a[b] = c[b]
+            return a
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "masked_assign_array")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+
+class TestConvertNumpyPass(BaseTest):
+    sub_pass_class = numba.parfors.parfor.ConvertNumpyPass
+
+    def check_numpy_allocators(self, fn):
+        def test_impl():
+            n = 10
+            a = fn(n)
+            return a
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "numpy_allocator")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def check_numpy_random(self, fn):
+        def test_impl():
+            n = 10
+            a = fn(n)
+            return a
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "numpy_allocator")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel_check_output_array(test_impl)
+
+    def test_numpy_allocators(self):
+        fns = [np.ones, np.zeros]
+        for fn in fns:
+            with self.subTest(fn.__name__):
+                self.check_numpy_allocators(fn)
+
+    def test_numpy_random(self):
+        fns = [np.random.random]
+        for fn in fns:
+            with self.subTest(fn.__name__):
+                self.check_numpy_random(fn)
+
+    def test_numpy_arrayexpr(self):
+        def test_impl(a, b):
+            return a + b
+
+        a = b = np.ones(10)
+
+        args = (a, b)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "arrayexpr")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl, *args)
+
+    def test_numpy_arrayexpr_ufunc(self):
+        def test_impl(a, b):
+            return np.sin(-a) + np.float64(1) / np.sqrt(b)
+
+        a = b = np.ones(10)
+
+        args = (a, b)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "arrayexpr")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl, *args)
+
+    def test_numpy_arrayexpr_boardcast(self):
+        def test_impl(a, b):
+            return a + b + np.array(1)
+
+        a = np.ones(10)
+        b = np.ones((3, 10))
+
+        args = (a, b)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "arrayexpr")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl, *args)
+
+    def test_numpy_arrayexpr_reshaped(self):
+        def test_impl(a, b):
+            a = a.reshape(1, a.size)  # shape[0] is now constant
+            return a + b
+
+        a = np.ones(10)
+        b = np.ones(10)
+
+        args = (a, b)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "arrayexpr")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl, *args)
+
+
+class TestConvertReducePass(BaseTest):
+    sub_pass_class = numba.parfors.parfor.ConvertReducePass
+
+    def test_reduce_max_basic(self):
+        def test_impl(arr):
+            return reduce(lambda x, y: max(x, y), arr, 0.0)
+
+        x = np.ones(10)
+        args = (x,)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "reduce")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl, *args)
+
+    def test_reduce_max_masked(self):
+        def test_impl(arr):
+            return reduce(lambda x, y: max(x, y), arr[arr > 5], 0.0)
+
+        x = np.ones(10)
+        args = (x,)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "reduce")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl, *args)
+
+
+class TestConvertLoopPass(BaseTest):
+    sub_pass_class = numba.parfors.parfor.ConvertLoopPass
+
+    def test_prange_reduce_simple(self):
+        def test_impl():
+            n = 20
+            c = 0
+            for i in prange(n):
+                c += i
+            return c
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def test_prange_map_simple(self):
+        def test_impl():
+            n = 20
+            arr = np.ones(n)
+            for i in prange(n):
+                arr[i] += i
+            return arr
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def test_prange_two_args(self):
+        def test_impl():
+            n = 20
+            arr = np.ones(n)
+            for i in prange(3, n):
+                arr[i] += i
+            return arr
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def test_prange_three_args(self):
+        def test_impl():
+            n = 20
+            arr = np.ones(n)
+            for i in prange(3, n, 2):
+                arr[i] += i
+            return arr
+
+        with self.assertRaises(errors.UnsupportedRewriteError) as raises:
+            self.run_parfor_sub_pass(test_impl, ())
+        self.assertIn(
+            "Only constant step size of 1 is supported for prange",
+            str(raises.exception),
+        )
+
+    def test_prange_map_inner_loop(self):
+        def test_impl():
+            n = 20
+            arr = np.ones((n, n))
+            for i in prange(n):
+                for j in range(i):
+                    arr[i, j] += i + j * n
+            return arr
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+        self.check_records(sub_pass.rewritten)
+
+        self.run_parallel(test_impl)
+
+    def test_prange_map_nested_prange(self):
+        def test_impl():
+            n = 20
+            arr = np.ones((n, n))
+            for i in prange(n):
+                for j in prange(i):
+                    arr[i, j] += i + j * n
+            return arr
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 2)
+        self.check_records(sub_pass.rewritten)
+        for record in sub_pass.rewritten:
+            self.assertEqual(record["reason"], "loop")
+
+        self.run_parallel(test_impl)
+
+    def test_prange_map_none_index(self):
+        def test_impl():
+            n = 20
+            arr = np.ones(n)
+            for i in prange(n):
+                inner = arr[i : i + 1]
+                inner[()] += 1
+            return arr
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        self.check_records(sub_pass.rewritten)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+
+        self.run_parallel(test_impl)
+
+    def test_prange_map_overwrite_index(self):
+        def test_impl():
+            n = 20
+            arr = np.ones(n)
+            for i in prange(n):
+                i += 1
+                arr[i - 1] = i
+            return arr
+
+        with self.assertRaises(errors.UnsupportedRewriteError) as raises:
+            self.run_parfor_sub_pass(test_impl, ())
+        self.assertIn(
+            "Overwrite of parallel loop index",
+            str(raises.exception),
+        )
+
+    def test_init_prange(self):
+        def test_impl():
+            n = 20
+            arr = np.ones(n)
+            numba.parfors.parfor.init_prange()
+            val = 0
+            for i in numba.parfors.parfor.internal_prange(len(arr)):
+                val += arr[i]
+            return val
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        self.check_records(sub_pass.rewritten)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+
+        self.run_parallel(test_impl)
+
+    def test_pndindex(self):
+        def test_impl():
+            n = 20
+            arr = np.ones((n, n))
+            val = 0
+            for idx in pndindex(arr.shape):
+                val += idx[0] * idx[1]
+            return val
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, ())
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        self.check_records(sub_pass.rewritten)
+
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+
+        self.run_parallel(test_impl)
+
+    def test_numpy_sum(self):
+        def test_impl(arr):
+            return np.sum(arr)
+
+        shape = 11, 13
+        arr = np.arange(np.prod(shape)).reshape(shape)
+        args = (arr,)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+        self.check_records(sub_pass.rewritten)
+        self.run_parallel(test_impl, *args)
+
+    def test_numpy_sum_bool_array_masked(self):
+        def test_impl(arr):
+            sliced = arr[:, 0]
+            return np.sum(arr[sliced >= 3, 1:2])
+
+        shape = 11, 13
+        arr = np.arange(np.prod(shape)).reshape(shape)
+        args = (arr,)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+        self.check_records(sub_pass.rewritten)
+        self.run_parallel(test_impl, *args)
+
+    def test_numpy_sum_int_array_masked(self):
+        def test_impl(arr):
+            sel = np.arange(arr.shape[1])
+            return np.sum(arr[:, sel])
+
+        shape = 11, 13
+        arr = np.arange(np.prod(shape)).reshape(shape)
+        args = (arr,)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        # 1 for arange; 1 for sum
+        self.assertEqual(len(sub_pass.rewritten), 2)
+        for record in sub_pass.rewritten:
+            self.assertEqual(record["reason"], "loop")
+        self.check_records(sub_pass.rewritten)
+        self.run_parallel(test_impl, *args)
+
+    def test_numpy_fill_method(self):
+        def test_impl(arr):
+            arr.fill(3)
+            return arr
+
+        shape = 11, 13
+        arr = np.arange(np.prod(shape)).reshape(shape)
+        args = (arr,)
+        argtypes = [typeof(x) for x in args]
+
+        sub_pass = self.run_parfor_sub_pass(test_impl, argtypes)
+        # 1 for arange; 1 for sum
+        self.assertEqual(len(sub_pass.rewritten), 1)
+        [record] = sub_pass.rewritten
+        self.assertEqual(record["reason"], "loop")
+        self.check_records(sub_pass.rewritten)
+        self.run_parallel(test_impl, *args)
+
+
+class TestPreParforPass(BaseTest):
+    class sub_pass_class:
+        def __init__(self, pass_states):
+            pass
+
+        def run(self, blocks):
+            pass
+
+    def test_dtype_conversion(self):
+        # array.dtype are converted to np.dtype(array) in the PreParforPass
+        def test_impl(a):
+            b = np.ones(20, dtype=a.dtype)
+            return b
+
+        arr = np.arange(10)
+        args = (arr,)
+        argtypes = [typeof(x) for x in args]
+
+        pre_pass = self.run_parfor_pre_pass(test_impl, argtypes)
+        self.assertEqual(pre_pass.stats["replaced_func"], 0)
+        self.assertEqual(pre_pass.stats["replaced_dtype"], 1)
+        self.run_parallel(test_impl, *args)
+
+    def test_sum_replacement(self):
+        def test_impl(a):
+            return np.sum(a)
+
+        arr = np.arange(10)
+        args = (arr,)
+        argtypes = [typeof(x) for x in args]
+
+        pre_pass = self.run_parfor_pre_pass(test_impl, argtypes)
+        self.assertEqual(pre_pass.stats["replaced_func"], 1)
+        self.assertEqual(pre_pass.stats["replaced_dtype"], 0)
+        self.run_parallel(test_impl, *args)
+
+    def test_replacement_map(self):
+        def test_impl(a):
+            return np.sum(a)
+
+        arr = np.arange(10)
+        args = (arr,)
+        argtypes = [typeof(x) for x in args]
+
+        swap_map = numba.parfors.parfor.swap_functions_map.copy()
+        swap_map.pop(("sum", "numpy"))
+        pre_pass = self.run_parfor_pre_pass(test_impl, argtypes, swap_map)
+        self.assertEqual(pre_pass.stats["replaced_func"], 0)
+        self.assertEqual(pre_pass.stats["replaced_dtype"], 0)
+        self.run_parallel(test_impl, *args)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_pipeline.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_pipeline.py
new file mode 100644
index 000000000..26f618bc7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_pipeline.py
@@ -0,0 +1,158 @@
+from numba.core.compiler import Compiler, DefaultPassBuilder
+from numba.core.compiler_machinery import (FunctionPass, AnalysisPass,
+                                           register_pass)
+from numba.core.untyped_passes import InlineInlinables
+from numba.core.typed_passes import IRLegalization
+from numba import jit, generated_jit, objmode, njit, cfunc
+from numba.core import types, postproc, errors
+from numba.core.ir import FunctionIR
+from numba.tests.support import TestCase
+
+
+class TestCustomPipeline(TestCase):
+    def setUp(self):
+        super(TestCustomPipeline, self).setUp()
+
+        # Define custom pipeline class
+        class CustomPipeline(Compiler):
+            custom_pipeline_cache = []
+
+            def compile_extra(self, func):
+                # Store the compiled function
+                self.custom_pipeline_cache.append(func)
+                return super(CustomPipeline, self).compile_extra(func)
+
+            def compile_ir(self, func_ir, *args, **kwargs):
+                # Store the compiled function
+                self.custom_pipeline_cache.append(func_ir)
+                return super(CustomPipeline, self).compile_ir(
+                    func_ir, *args, **kwargs)
+
+        self.pipeline_class = CustomPipeline
+
+    def test_jit_custom_pipeline(self):
+        self.assertListEqual(self.pipeline_class.custom_pipeline_cache, [])
+
+        @jit(pipeline_class=self.pipeline_class)
+        def foo(x):
+            return x
+
+        self.assertEqual(foo(4), 4)
+        self.assertListEqual(self.pipeline_class.custom_pipeline_cache,
+                             [foo.py_func])
+
+    def test_cfunc_custom_pipeline(self):
+        self.assertListEqual(self.pipeline_class.custom_pipeline_cache, [])
+
+        @cfunc(types.int64(types.int64), pipeline_class=self.pipeline_class)
+        def foo(x):
+            return x
+
+        self.assertEqual(foo(4), 4)
+        self.assertListEqual(self.pipeline_class.custom_pipeline_cache,
+                             [foo.__wrapped__])
+
+    def test_generated_jit_custom_pipeline(self):
+        self.assertListEqual(self.pipeline_class.custom_pipeline_cache, [])
+
+        def inner(x):
+            return x
+
+        @generated_jit(pipeline_class=self.pipeline_class)
+        def foo(x):
+            if isinstance(x, types.Integer):
+                return inner
+
+        self.assertEqual(foo(5), 5)
+        self.assertListEqual(self.pipeline_class.custom_pipeline_cache,
+                             [inner])
+
+    def test_objmode_custom_pipeline(self):
+        self.assertListEqual(self.pipeline_class.custom_pipeline_cache, [])
+
+        @jit(pipeline_class=self.pipeline_class)
+        def foo(x):
+            with objmode(x="intp"):
+                x += int(0x1)
+            return x
+
+        arg = 123
+        self.assertEqual(foo(arg), arg + 1)
+        # Two items in the list.
+        self.assertEqual(len(self.pipeline_class.custom_pipeline_cache), 2)
+        # First item is the `foo` function
+        first = self.pipeline_class.custom_pipeline_cache[0]
+        self.assertIs(first, foo.py_func)
+        # Second item is a FunctionIR of the obj-lifted function
+        second = self.pipeline_class.custom_pipeline_cache[1]
+        self.assertIsInstance(second, FunctionIR)
+
+
+class TestPassManagerFunctionality(TestCase):
+
+    def _create_pipeline_w_del(self, base=None, inject_after=None):
+        """
+        Creates a new compiler pipeline with the _InjectDelsPass injected after
+        the pass supplied in kwarg 'inject_after'.
+        """
+        self.assertTrue(inject_after is not None)
+        self.assertTrue(base is not None)
+
+        @register_pass(mutates_CFG=False, analysis_only=False)
+        class _InjectDelsPass(base):
+            """
+            This pass injects ir.Del nodes into the IR
+            """
+            _name = "inject_dels_%s" % str(base)
+
+            def __init__(self):
+                base.__init__(self)
+
+            def run_pass(self, state):
+                pp = postproc.PostProcessor(state.func_ir)
+                pp.run(emit_dels=True)
+                return True
+
+        class TestCompiler(Compiler):
+
+            def define_pipelines(self):
+                pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+                pm.add_pass_after(_InjectDelsPass, inject_after)
+                pm.finalize()
+                return [pm]
+
+        return TestCompiler
+
+    def test_compiler_error_on_ir_del_from_functionpass(self):
+        new_compiler = self._create_pipeline_w_del(FunctionPass,
+                                                   InlineInlinables)
+
+        @njit(pipeline_class=new_compiler)
+        def foo(x):
+            return x + 1
+
+        with self.assertRaises(errors.CompilerError) as raises:
+            foo(10)
+
+        errstr = str(raises.exception)
+
+        self.assertIn("Illegal IR, del found at:", errstr)
+        self.assertIn("del x", errstr)
+
+    def test_no_compiler_error_on_ir_del_after_legalization(self):
+        # Legalization should be the last FunctionPass to execute so it's fine
+        # for it to emit ir.Del nodes as no further FunctionPasses will run and
+        # therefore the checking routine in the PassManager won't execute.
+        # This test adds a new pass that is an AnalysisPass into the pipeline
+        # after legalisation, this pass will return with already existing dels
+        # in the IR but by virtue of it being an AnalysisPass the checking
+        # routine won't execute.
+
+        new_compiler = self._create_pipeline_w_del(AnalysisPass,
+                                                   IRLegalization)
+
+        @njit(pipeline_class=new_compiler)
+        def foo(x):
+            return x + 1
+
+        self.assertTrue(foo(10), foo.py_func(10))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_polynomial.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_polynomial.py
new file mode 100644
index 000000000..694492d0f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_polynomial.py
@@ -0,0 +1,116 @@
+import gc
+from itertools import product
+
+import numpy as np
+
+from numba import jit
+from numba.tests.support import TestCase, tag, needs_lapack
+import unittest
+
+
+def roots_fn(p):
+    return np.roots(p)
+
+
+class TestPolynomialBase(TestCase):
+    """
+    Provides setUp and common data/error modes for testing polynomial functions.
+    """
+
+    # supported dtypes
+    dtypes = (np.float64, np.float32, np.complex128, np.complex64)
+
+    def setUp(self):
+        # Collect leftovers from previous test cases before checking for leaks
+        gc.collect()
+
+    def assert_error(self, cfunc, args, msg, err=ValueError):
+        with self.assertRaises(err) as raises:
+            cfunc(*args)
+        self.assertIn(msg, str(raises.exception))
+
+    def assert_1d_input(self, cfunc, args):
+        msg = "Input must be a 1d array."
+        self.assert_error(cfunc, args, msg)
+
+
+class TestPoly1D(TestPolynomialBase):
+
+    def assert_no_domain_change(self, name, cfunc, args):
+        msg = name + "() argument must not cause a domain change."
+        self.assert_error(cfunc, args, msg)
+
+    @needs_lapack
+    def test_roots(self):
+
+        cfunc = jit(nopython=True)(roots_fn)
+
+        default_resolution = np.finfo(np.float64).resolution
+
+        def check(a, **kwargs):
+            expected = roots_fn(a, **kwargs)
+            got = cfunc(a, **kwargs)
+
+            # eigen decomposition used so type specific impl
+            # will be used in numba whereas a wide type impl
+            # will be used in numpy, so compare using a more
+            # fuzzy comparator
+
+            if a.dtype in self.dtypes:
+                resolution = np.finfo(a.dtype).resolution
+            else:
+                # this is for integer types when roots() will cast to float64
+                resolution = default_resolution
+
+            np.testing.assert_allclose(
+                expected,
+                got,
+                rtol=10 * resolution,
+                atol=100 * resolution  # zeros tend to be fuzzy
+            )
+
+            # Ensure proper resource management
+            with self.assertNoNRTLeak():
+                cfunc(a, **kwargs)
+
+        # test vectors in real space
+        # contrived examples to trip branches
+        r_vectors = (
+            np.array([1]),
+            np.array([1, 3, 2]),
+            np.array([0, 0, 0]),
+            np.array([1, 6, 11, 6]),
+            np.array([0, 0, 0, 1, 3, 2]),
+            np.array([1, 1, 0, 0, 0]),
+            np.array([0, 0, 1, 0, 0, 0])
+        )
+
+        # test loop real space
+        for v, dtype in \
+                product(r_vectors, [np.int32, np.int64] + list(self.dtypes)):
+            a = v.astype(dtype)
+            check(a)
+
+        c_vectors = (
+            np.array([1 + 1j]),
+            np.array([1, 3 + 1j, 2]),
+            np.array([0, 0 + 0j, 0]),
+            np.array([1, 6 + 1j, 11, 6]),
+            np.array([0, 0, 0, 1 + 1j, 3, 2]),
+            np.array([1 + 1j, 1, 0, 0, 0]),
+            np.array([0, 0, 1 + 1j, 0, 0, 0])
+        )
+
+        # test loop complex space
+        for v, dtype in product(c_vectors, self.dtypes[2:]):
+            a = v.astype(dtype)
+            check(a)
+
+        # check input with dimension > 1 raises
+        self.assert_1d_input(cfunc, (np.arange(4.).reshape(2, 2),))
+
+        # check real input with complex roots raises
+        x = np.array([7., 2., 0., 1.])
+        self.assert_no_domain_change("eigvals", cfunc, (x,))
+        # but works fine if type conv to complex first
+        cfunc(x.astype(np.complex128))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_practical_lowering_issues.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_practical_lowering_issues.py
new file mode 100644
index 000000000..a06959194
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_practical_lowering_issues.py
@@ -0,0 +1,212 @@
+"""
+Tests for practical lowering specific errors.
+"""
+
+import numpy as np
+from numba import njit
+from numba.core import types, ir
+from numba.core.compiler import CompilerBase, DefaultPassBuilder
+from numba.core.typed_passes import NopythonTypeInference
+from numba.core.compiler_machinery import register_pass, FunctionPass
+
+from numba.tests.support import MemoryLeakMixin, TestCase
+
+
+def issue7507_lround(a):
+    """Dummy function used in test"""
+    pass
+
+
+class TestLowering(MemoryLeakMixin, TestCase):
+    def test_issue4156_loop_vars_leak(self):
+        """Test issues with zero-filling of refct'ed variables inside loops.
+
+        Before the fix, the in-loop variables are always zero-filled at their
+        definition location. As a result, their state from the previous
+        iteration is erased. No decref is applied. To fix this, the
+        zero-filling must only happen once after the alloca at the function
+        entry block. The loop variables are technically defined once per
+        function (one alloca per definition per function), but semantically
+        defined once per assignment. Semantically, their lifetime stop only
+        when the variable is re-assigned or when the function ends.
+        """
+        @njit
+        def udt(N):
+            sum_vec = np.zeros(3)
+            for n in range(N):
+                if n >= 0:
+                    # `vec` would leak without the fix.
+                    vec = np.ones(1)
+                if n >= 0:
+                    sum_vec += vec[0]
+
+            return sum_vec
+
+        got = udt(4)
+        expect = udt.py_func(4)
+        self.assertPreciseEqual(got, expect)
+
+    def test_issue4156_loop_vars_leak_variant1(self):
+        """Variant of test_issue4156_loop_vars_leak.
+
+        Adding an outer loop.
+        """
+        @njit
+        def udt(N):
+            sum_vec = np.zeros(3)
+            for x in range(N):
+                for y in range(N):
+                    n = x + y
+                    if n >= 0:
+                        # `vec` would leak without the fix.
+                        vec = np.ones(1)
+                    if n >= 0:
+                        sum_vec += vec[0]
+
+            return sum_vec
+
+        got = udt(4)
+        expect = udt.py_func(4)
+        self.assertPreciseEqual(got, expect)
+
+    def test_issue4156_loop_vars_leak_variant2(self):
+        """Variant of test_issue4156_loop_vars_leak.
+
+        Adding deeper outer loop.
+        """
+        @njit
+        def udt(N):
+            sum_vec = np.zeros(3)
+            for z in range(N):
+                for x in range(N):
+                    for y in range(N):
+                        n = x + y + z
+                        if n >= 0:
+                            # `vec` would leak without the fix.
+                            vec = np.ones(1)
+                        if n >= 0:
+                            sum_vec += vec[0]
+
+            return sum_vec
+
+        got = udt(4)
+        expect = udt.py_func(4)
+        self.assertPreciseEqual(got, expect)
+
+    def test_issue4156_loop_vars_leak_variant3(self):
+        """Variant of test_issue4156_loop_vars_leak.
+
+        Adding inner loop around allocation
+        """
+        @njit
+        def udt(N):
+            sum_vec = np.zeros(3)
+            for z in range(N):
+                for x in range(N):
+                    n = x + z
+                    if n >= 0:
+                        for y in range(N):
+                            # `vec` would leak without the fix.
+                            vec = np.ones(y)
+                    if n >= 0:
+                        sum_vec += vec[0]
+
+            return sum_vec
+
+        got = udt(4)
+        expect = udt.py_func(4)
+        self.assertPreciseEqual(got, expect)
+
+    def test_issue4156_loop_vars_leak_variant4(self):
+        """Variant of test_issue4156_loop_vars_leak.
+
+        Interleaves loops and allocations
+        """
+        @njit
+        def udt(N):
+            sum_vec = 0
+
+            for n in range(N):
+                vec = np.zeros(7)
+                for n in range(N):
+                    z = np.zeros(7)
+                sum_vec += vec[0] + z[0]
+
+            return sum_vec
+
+        got = udt(4)
+        expect = udt.py_func(4)
+        self.assertPreciseEqual(got, expect)
+
+    def test_issue_with_literal_in_static_getitem(self):
+        """Test an issue with literal type used as index of static_getitem
+        """
+
+        @register_pass(mutates_CFG=False, analysis_only=False)
+        class ForceStaticGetitemLiteral(FunctionPass):
+
+            _name = "force_static_getitem_literal"
+
+            def __init__(self):
+                FunctionPass.__init__(self)
+
+            def run_pass(self, state):
+                repl = {}
+                # Force the static_getitem to have a literal type as
+                # index to replicate the problem.
+                for inst, sig in state.calltypes.items():
+                    if (isinstance(inst, ir.Expr) and
+                            inst.op == 'static_getitem'):
+                        [obj, idx] = sig.args
+                        new_sig = sig.replace(args=(obj,
+                                                    types.literal(inst.index)))
+                        repl[inst] = new_sig
+                state.calltypes.update(repl)
+                return True
+
+        class CustomPipeline(CompilerBase):
+            def define_pipelines(self):
+                pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+                pm.add_pass_after(ForceStaticGetitemLiteral,
+                                  NopythonTypeInference)
+                pm.finalize()
+                return [pm]
+
+        @njit(pipeline_class=CustomPipeline)
+        def foo(arr):
+            return arr[4]  # force static_getitem
+
+        arr = np.arange(10)
+        got = foo(arr)
+        expect = foo.py_func(arr)
+        self.assertEqual(got, expect)
+
+    def test_issue7507(self):
+        """
+        Test a problem with BaseContext.get_function() because of changes
+        related to the new style error handling.
+        """
+        from numba.core.typing.templates import AbstractTemplate, infer_global
+        from numba.core.imputils import lower_builtin
+
+        @infer_global(issue7507_lround)
+        class lroundTemplate(AbstractTemplate):
+            key = issue7507_lround
+
+            def generic(self, args, kws):
+                signature = types.int64(types.float64)
+
+                # insert a new builtin during the compilation process
+                @lower_builtin(issue7507_lround, types.float64)
+                def codegen(context, builder, sig, args):
+                    # Simply truncate with the cast to integer.
+                    return context.cast(builder, args[0], sig.args[0],
+                                        sig.return_type)
+
+                return signature
+
+        @njit('int64(float64)')
+        def foo(a):
+            return issue7507_lround(a)
+
+        self.assertEqual(foo(3.4), 3)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_print.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_print.py
new file mode 100644
index 000000000..a7e5c1ac2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_print.py
@@ -0,0 +1,197 @@
+import sys
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit
+from numba.core import types, errors, utils
+from numba.tests.support import captured_stdout, tag, TestCase
+
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+
+def print_value(x):
+    print(x)
+
+def print_array_item(arr, i):
+    print(arr[i].x)
+
+def print_values(a, b, c):
+    print(a, b, c)
+
+def print_empty():
+    print()
+
+def print_string(x):
+    print(x, "hop!", 3.5)
+
+def print_vararg(a, b, c):
+    print(a, b, *c)
+
+def print_string_vararg(a, b, c):
+    print(a, "hop!", b, *c)
+
+def make_print_closure(x):
+    def print_closure():
+        return x
+    return jit(nopython=True)(x)
+
+
+class TestPrint(TestCase):
+
+    def test_print_values(self):
+        """
+        Test printing a single argument value.
+        """
+        pyfunc = print_value
+
+        def check_values(typ, values):
+            cr = compile_isolated(pyfunc, (typ,))
+            cfunc = cr.entry_point
+            for val in values:
+                with captured_stdout():
+                    cfunc(val)
+                    self.assertEqual(sys.stdout.getvalue(), str(val) + '\n')
+
+        # Various scalars
+        check_values(types.int32, (1, -234))
+        check_values(types.int64, (1, -234,
+                                   123456789876543210, -123456789876543210))
+        check_values(types.uint64, (1, 234,
+                                   123456789876543210, 2**63 + 123))
+        check_values(types.boolean, (True, False))
+        check_values(types.float64, (1.5, 100.0**10.0, float('nan')))
+        check_values(types.complex64, (1+1j,))
+        check_values(types.NPTimedelta('ms'), (np.timedelta64(100, 'ms'),))
+
+        cr = compile_isolated(pyfunc, (types.float32,))
+        cfunc = cr.entry_point
+        with captured_stdout():
+            cfunc(1.1)
+            # Float32 will lose precision
+            got = sys.stdout.getvalue()
+            expect = '1.10000002384'
+            self.assertTrue(got.startswith(expect))
+            self.assertTrue(got.endswith('\n'))
+
+        # NRT-enabled type
+        with self.assertNoNRTLeak():
+            x = [1, 3, 5, 7]
+            with self.assertRefCount(x):
+                check_values(types.List(types.int32), (x,))
+
+        # Array will have to use object mode
+        arraytype = types.Array(types.int32, 1, 'C')
+        cr = compile_isolated(pyfunc, (arraytype,), flags=enable_pyobj_flags)
+        cfunc = cr.entry_point
+        with captured_stdout():
+            cfunc(np.arange(10, dtype=np.int32))
+            self.assertEqual(sys.stdout.getvalue(),
+                             '[0 1 2 3 4 5 6 7 8 9]\n')
+
+    def test_print_array_item(self):
+        """
+        Test printing a Numpy character sequence
+        """
+        dtype = np.dtype([('x', 'S4')])
+        arr = np.frombuffer(bytearray(range(1, 9)), dtype=dtype)
+
+        pyfunc = print_array_item
+        cfunc = jit(nopython=True)(pyfunc)
+        for i in range(len(arr)):
+            with captured_stdout():
+                cfunc(arr, i)
+                self.assertEqual(sys.stdout.getvalue(), str(arr[i]['x']) + '\n')
+
+    def test_print_multiple_values(self):
+        pyfunc = print_values
+        cr = compile_isolated(pyfunc, (types.int32,) * 3)
+        cfunc = cr.entry_point
+        with captured_stdout():
+            cfunc(1, 2, 3)
+            self.assertEqual(sys.stdout.getvalue(), '1 2 3\n')
+
+    def test_print_nogil(self):
+        pyfunc = print_values
+        cfunc = jit(nopython=True, nogil=True)(pyfunc)
+        with captured_stdout():
+            cfunc(1, 2, 3)
+            self.assertEqual(sys.stdout.getvalue(), '1 2 3\n')
+
+    def test_print_empty(self):
+        pyfunc = print_empty
+        cr = compile_isolated(pyfunc, ())
+        cfunc = cr.entry_point
+        with captured_stdout():
+            cfunc()
+            self.assertEqual(sys.stdout.getvalue(), '\n')
+
+    def test_print_strings(self):
+        pyfunc = print_string
+        cr = compile_isolated(pyfunc, (types.int32,))
+        cfunc = cr.entry_point
+        with captured_stdout():
+            cfunc(1)
+            self.assertEqual(sys.stdout.getvalue(), '1 hop! 3.5\n')
+
+    def test_print_vararg(self):
+        # Test *args support for print().  This is desired since
+        # print() can use a dedicated IR node.
+        pyfunc = print_vararg
+        cfunc = jit(nopython=True)(pyfunc)
+        with captured_stdout():
+            cfunc(1, (2, 3), (4, 5j))
+            self.assertEqual(sys.stdout.getvalue(), '1 (2, 3) 4 5j\n')
+
+        pyfunc = print_string_vararg
+        cfunc = jit(nopython=True)(pyfunc)
+        with captured_stdout():
+            cfunc(1, (2, 3), (4, 5j))
+            self.assertEqual(sys.stdout.getvalue(), '1 hop! (2, 3) 4 5j\n')
+
+    def test_inner_fn_print(self):
+        @jit(nopython=True)
+        def foo(x):
+            print(x)
+
+        @jit(nopython=True)
+        def bar(x):
+            foo(x)
+            foo('hello')
+
+        # Printing an array requires the Env.
+        # We need to make sure the inner function can obtain the Env.
+        x = np.arange(5)
+        with captured_stdout():
+            bar(x)
+            self.assertEqual(sys.stdout.getvalue(), '[0 1 2 3 4]\nhello\n')
+
+    def test_print_w_kwarg_raises(self):
+        @jit(nopython=True)
+        def print_kwarg():
+            print('x', flush=True)
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            print_kwarg()
+        expected = ("Numba's print() function implementation does not support "
+                    "keyword arguments.")
+        self.assertIn(raises.exception.msg, expected)
+
+    def test_print_no_truncation(self):
+        ''' See: https://github.com/numba/numba/issues/3811
+        '''
+        @jit(nopython=True)
+        def foo():
+            print(''.join(['a'] * 10000))
+        with captured_stdout():
+            foo()
+            self.assertEqual(sys.stdout.getvalue(), ''.join(['a'] * 10000) + '\n')
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_profiler.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_profiler.py
new file mode 100644
index 000000000..bf503d07b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_profiler.py
@@ -0,0 +1,98 @@
+import cProfile as profiler
+import os
+import pstats
+import subprocess
+import sys
+
+import numpy as np
+
+from numba import jit
+from numba.tests.support import needs_blas
+import unittest
+
+
+def dot(a, b):
+    sum = 0
+    for i in range(len(a)):
+        sum += a[i]*b[i]
+    return sum
+
+def np_dot(a, b):
+    return np.dot(a, b)
+
+
+class TestProfiler(unittest.TestCase):
+
+    def check_profiler_dot(self, pyfunc):
+        """
+        Make sure the jit-compiled function shows up in the profile stats
+        as a regular Python function.
+        """
+        a = np.arange(16, dtype=np.float32)
+        b = np.arange(16, dtype=np.float32)
+        cfunc = jit(nopython=True)(pyfunc)
+        # Warm up JIT
+        cfunc(a, b)
+        p = profiler.Profile()
+        p.enable()
+        try:
+            cfunc(a, b)
+        finally:
+            p.disable()
+        stats = pstats.Stats(p).strip_dirs()
+        code = pyfunc.__code__
+        expected_key = (os.path.basename(code.co_filename),
+                        code.co_firstlineno,
+                        code.co_name,
+                        )
+        self.assertIn(expected_key, stats.stats)
+
+    def test_profiler(self):
+        self.check_profiler_dot(dot)
+
+    @needs_blas
+    def test_profiler_np_dot(self):
+        # Issue #1786: initializing BLAS would crash when profiling
+        code = """if 1:
+            import cProfile as profiler
+
+            import numpy as np
+
+            from numba import jit
+            from numba.tests.test_profiler import np_dot
+
+            cfunc = jit(nopython=True)(np_dot)
+
+            a = np.arange(16, dtype=np.float32)
+            b = np.arange(16, dtype=np.float32)
+
+            p = profiler.Profile()
+            p.enable()
+            cfunc(a, b)
+            cfunc(a, b)
+            p.disable()
+            """
+        subprocess.check_call([sys.executable, "-c", code])
+
+    def test_issue_3229(self):
+        # Issue #3229: Seemingly random segfaults when profiling due to
+        # frame injection.
+        # numba.tests.npyufunc.test_dufunc.TestDUFunc.test_npm_call is the
+        # first test case crashing when profiling. Fingers crossed fixing
+        # this is sufficient proof for the general case.
+
+        code = """if 1:
+            import cProfile as profiler
+            p = profiler.Profile()
+            p.enable()
+
+            from numba.tests.npyufunc.test_dufunc import TestDUFunc
+            t = TestDUFunc('test_npm_call')
+            t.test_npm_call()
+
+            p.disable()
+            """
+        subprocess.check_call([sys.executable, "-c", code])
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_pycc.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_pycc.py
new file mode 100644
index 000000000..d5f01b996
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_pycc.py
@@ -0,0 +1,429 @@
+import contextlib
+import imp
+import os
+import shutil
+import subprocess
+import sys
+import tempfile
+from unittest import skip
+from ctypes import *
+
+import numpy as np
+
+import llvmlite.binding as ll
+
+from numba.core import utils
+from numba.pycc import main
+from numba.pycc.decorators import clear_export_registry
+from numba.pycc.platform import find_shared_ending, find_pyext_ending
+from numba.pycc.platform import _external_compiler_ok
+
+from numba.tests.support import (TestCase, tag, import_dynamic, temp_directory,
+                                 has_blas, needs_external_compilers)
+import unittest
+
+
+try:
+    import setuptools
+except ImportError:
+    setuptools = None
+
+_skip_reason = 'windows only'
+_windows_only = unittest.skipIf(not sys.platform.startswith('win'),
+                                _skip_reason)
+
+
+base_path = os.path.dirname(os.path.abspath(__file__))
+
+
+def unset_macosx_deployment_target():
+    """Unset MACOSX_DEPLOYMENT_TARGET because we are not building portable
+    libraries
+    """
+    if 'MACOSX_DEPLOYMENT_TARGET' in os.environ:
+        del os.environ['MACOSX_DEPLOYMENT_TARGET']
+
+class TestCompilerChecks(TestCase):
+
+    # NOTE: THIS TEST MUST ALWAYS RUN ON WINDOWS, DO NOT SKIP
+    @_windows_only
+    def test_windows_compiler_validity(self):
+        # When inside conda-build VSINSTALLDIR should be set and windows should
+        # have a valid compiler available, `_external_compiler_ok` should  agree
+        # with this. If this is not the case then error out to alert devs.
+        is_running_conda_build = os.environ.get('CONDA_BUILD', None) is not None
+        if is_running_conda_build:
+            if os.environ.get('VSINSTALLDIR', None) is not None:
+                self.assertTrue(_external_compiler_ok)
+
+
+class BasePYCCTest(TestCase):
+
+    def setUp(self):
+        unset_macosx_deployment_target()
+
+        self.tmpdir = temp_directory('test_pycc')
+        # Make sure temporary files and directories created by
+        # distutils don't clutter the top-level /tmp
+        tempfile.tempdir = self.tmpdir
+
+    def tearDown(self):
+        tempfile.tempdir = None
+        # Since we're executing the module-under-test several times
+        # from the same process, we must clear the exports registry
+        # between invocations.
+        clear_export_registry()
+
+    @contextlib.contextmanager
+    def check_c_ext(self, extdir, name):
+        sys.path.append(extdir)
+        try:
+            lib = import_dynamic(name)
+            yield lib
+        finally:
+            sys.path.remove(extdir)
+            sys.modules.pop(name, None)
+
+
+@needs_external_compilers
+class TestLegacyAPI(BasePYCCTest):
+
+    def test_pycc_ctypes_lib(self):
+        """
+        Test creating a C shared library object using pycc.
+        """
+        source = os.path.join(base_path, 'compile_with_pycc.py')
+        cdll_modulename = 'test_dll_legacy' + find_shared_ending()
+        cdll_path = os.path.join(self.tmpdir, cdll_modulename)
+        if os.path.exists(cdll_path):
+            os.unlink(cdll_path)
+
+        main(args=['--debug', '-o', cdll_path, source])
+        lib = CDLL(cdll_path)
+        lib.mult.argtypes = [POINTER(c_double), c_void_p,
+                             c_double, c_double]
+        lib.mult.restype = c_int
+
+        lib.multf.argtypes = [POINTER(c_float), c_void_p,
+                              c_float, c_float]
+        lib.multf.restype = c_int
+
+        res = c_double()
+        lib.mult(byref(res), None, 123, 321)
+        self.assertEqual(res.value, 123 * 321)
+
+        res = c_float()
+        lib.multf(byref(res), None, 987, 321)
+        self.assertEqual(res.value, 987 * 321)
+
+    def test_pycc_pymodule(self):
+        """
+        Test creating a CPython extension module using pycc.
+        """
+        self.skipTest("lack of environment can make the extension crash")
+
+        source = os.path.join(base_path, 'compile_with_pycc.py')
+        modulename = 'test_pyext_legacy'
+        out_modulename = os.path.join(self.tmpdir,
+                                      modulename + find_pyext_ending())
+        if os.path.exists(out_modulename):
+            os.unlink(out_modulename)
+
+        main(args=['--debug', '--python', '-o', out_modulename, source])
+
+        with self.check_c_ext(self.tmpdir, modulename) as lib:
+            res = lib.multi(123, 321)
+            self.assertPreciseEqual(res, 123 * 321)
+            res = lib.multf(987, 321)
+            self.assertPreciseEqual(res, 987.0 * 321.0)
+
+    def test_pycc_bitcode(self):
+        """
+        Test creating a LLVM bitcode file using pycc.
+        """
+        modulename = os.path.join(base_path, 'compile_with_pycc')
+        bitcode_modulename = os.path.join(self.tmpdir, 'test_bitcode_legacy.bc')
+        if os.path.exists(bitcode_modulename):
+            os.unlink(bitcode_modulename)
+
+        main(args=['--debug', '--llvm', '-o', bitcode_modulename,
+                   modulename + '.py'])
+
+        # Sanity check bitcode file contents
+        with open(bitcode_modulename, "rb") as f:
+            bc = f.read()
+
+        bitcode_wrapper_magic = b'\xde\xc0\x17\x0b'
+        bitcode_magic = b'BC\xc0\xde'
+        self.assertTrue(bc.startswith((bitcode_magic, bitcode_wrapper_magic)), bc)
+
+
+@needs_external_compilers
+class TestCC(BasePYCCTest):
+
+    def setUp(self):
+        super(TestCC, self).setUp()
+        from numba.tests import compile_with_pycc
+        self._test_module = compile_with_pycc
+        imp.reload(self._test_module)
+
+    @contextlib.contextmanager
+    def check_cc_compiled(self, cc):
+        #cc.verbose = True
+        cc.output_dir = self.tmpdir
+        cc.compile()
+
+        with self.check_c_ext(self.tmpdir, cc.name) as lib:
+            yield lib
+
+    def check_cc_compiled_in_subprocess(self, lib, code):
+        prolog = """if 1:
+            import sys
+            import types
+            # to disable numba package
+            sys.modules['numba'] = types.ModuleType('numba')
+            try:
+                from numba import njit
+            except ImportError:
+                pass
+            else:
+                raise RuntimeError('cannot disable numba package')
+
+            sys.path.insert(0, %(path)r)
+            import %(name)s as lib
+            """ % {'name': lib.__name__,
+                   'path': os.path.dirname(lib.__file__)}
+        code = prolog.strip(' ') + code
+        subprocess.check_call([sys.executable, '-c', code])
+
+    def test_cc_properties(self):
+        cc = self._test_module.cc
+        self.assertEqual(cc.name, 'pycc_test_simple')
+
+        # Inferred output directory
+        d = self._test_module.cc.output_dir
+        self.assertTrue(os.path.isdir(d), d)
+
+        # Inferred output filename
+        f = self._test_module.cc.output_file
+        self.assertFalse(os.path.exists(f), f)
+        self.assertTrue(os.path.basename(f).startswith('pycc_test_simple.'), f)
+        if sys.platform.startswith('linux'):
+            self.assertTrue(f.endswith('.so'), f)
+            self.assertIn(find_pyext_ending(), f)
+
+    def test_compile(self):
+        with self.check_cc_compiled(self._test_module.cc) as lib:
+            res = lib.multi(123, 321)
+            self.assertPreciseEqual(res, 123 * 321)
+            res = lib.multf(987, 321)
+            self.assertPreciseEqual(res, 987.0 * 321.0)
+            res = lib.square(5)
+            self.assertPreciseEqual(res, 25)
+            self.assertIs(lib.get_none(), None)
+            with self.assertRaises(ZeroDivisionError):
+                lib.div(1, 0)
+
+    def check_compile_for_cpu(self, cpu_name):
+        cc = self._test_module.cc
+        cc.target_cpu = cpu_name
+
+        with self.check_cc_compiled(cc) as lib:
+            res = lib.multi(123, 321)
+            self.assertPreciseEqual(res, 123 * 321)
+            self.assertEqual(lib.multi.__module__, 'pycc_test_simple')
+
+    def test_compile_for_cpu(self):
+        # Compiling for the host CPU should always succeed
+        self.check_compile_for_cpu(ll.get_host_cpu_name())
+
+    def test_compile_for_cpu_host(self):
+        # Compiling for the host CPU should always succeed
+        self.check_compile_for_cpu("host")
+
+    @unittest.skipIf(sys.platform == 'darwin' and
+                     utils.PYVERSION in ((3, 8), (3, 7)),
+                     'distutils incorrectly using gcc on python 3.8 builds')
+    def test_compile_helperlib(self):
+        with self.check_cc_compiled(self._test_module.cc_helperlib) as lib:
+            res = lib.power(2, 7)
+            self.assertPreciseEqual(res, 128)
+            for val in (-1, -1 + 0j, np.complex128(-1)):
+                res = lib.sqrt(val)
+                self.assertPreciseEqual(res, 1j)
+            for val in (4, 4.0, np.float64(4)):
+                res = lib.np_sqrt(val)
+                self.assertPreciseEqual(res, 2.0)
+            res = lib.spacing(1.0)
+            self.assertPreciseEqual(res, 2**-52)
+            # Implicit seeding at startup should guarantee a non-pathological
+            # start state.
+            self.assertNotEqual(lib.random(-1), lib.random(-1))
+            res = lib.random(42)
+            expected = np.random.RandomState(42).random_sample()
+            self.assertPreciseEqual(res, expected)
+            res = lib.size(np.float64([0] * 3))
+            self.assertPreciseEqual(res, 3)
+
+            code = """if 1:
+                from numpy.testing import assert_equal, assert_allclose
+                res = lib.power(2, 7)
+                assert res == 128
+                res = lib.random(42)
+                assert_allclose(res, %(expected)s)
+                res = lib.spacing(1.0)
+                assert_allclose(res, 2**-52)
+                """ % {'expected': expected}
+            self.check_cc_compiled_in_subprocess(lib, code)
+
+    def test_compile_nrt(self):
+        with self.check_cc_compiled(self._test_module.cc_nrt) as lib:
+            # Sanity check
+            self.assertPreciseEqual(lib.zero_scalar(1), 0.0)
+            res = lib.zeros(3)
+            self.assertEqual(list(res), [0, 0, 0])
+            if has_blas:
+                res = lib.vector_dot(4)
+                self.assertPreciseEqual(res, 30.0)
+            # test argsort
+            val = np.float64([2., 5., 1., 3., 4.])
+            res = lib.np_argsort(val)
+            expected = np.argsort(val)
+            self.assertPreciseEqual(res, expected)
+
+            code = """if 1:
+                from numpy.testing import assert_equal
+                from numpy import float64, argsort
+                res = lib.zero_scalar(1)
+                assert res == 0.0
+                res = lib.zeros(3)
+                assert list(res) == [0, 0, 0]
+                if %(has_blas)s:
+                    res = lib.vector_dot(4)
+                    assert res == 30.0
+                val = float64([2., 5., 1., 3., 4.])
+                res = lib.np_argsort(val)
+                expected = argsort(val)
+                assert_equal(res, expected)
+                """ % dict(has_blas=has_blas)
+            self.check_cc_compiled_in_subprocess(lib, code)
+
+    def test_hashing(self):
+        with self.check_cc_compiled(self._test_module.cc_nrt) as lib:
+            res = lib.hash_literal_str_A()
+            self.assertPreciseEqual(res, hash("A"))
+            res = lib.hash_str("A")
+            self.assertPreciseEqual(res, hash("A"))
+            
+            code = """if 1:
+                from numpy.testing import assert_equal
+                res = lib.hash_literal_str_A()
+                assert_equal(res, hash("A"))
+                res = lib.hash_str("A")
+                assert_equal(res, hash("A"))
+                """
+            self.check_cc_compiled_in_subprocess(lib, code)
+
+    def test_c_extension_usecase(self):
+        # Test C-extensions
+        with self.check_cc_compiled(self._test_module.cc_nrt) as lib:
+            arr = np.arange(128, dtype=np.intp)
+            got = lib.dict_usecase(arr)
+            expect = arr * arr
+            self.assertPreciseEqual(got, expect)
+
+
+@needs_external_compilers
+class TestDistutilsSupport(TestCase):
+
+    def setUp(self):
+        unset_macosx_deployment_target()
+
+        # Copy the test project into a temp directory to avoid
+        # keeping any build leftovers in the source tree
+        self.tmpdir = temp_directory('test_pycc_distutils')
+        source_dir = os.path.join(base_path, 'pycc_distutils_usecase')
+        self.usecase_dir = os.path.join(self.tmpdir, 'work')
+        shutil.copytree(source_dir, self.usecase_dir)
+
+    def check_setup_py(self, setup_py_file):
+        # Compute PYTHONPATH to ensure the child processes see this Numba
+        import numba
+        numba_path = os.path.abspath(os.path.dirname(
+                                     os.path.dirname(numba.__file__)))
+        env = dict(os.environ)
+        if env.get('PYTHONPATH', ''):
+            env['PYTHONPATH'] = numba_path + os.pathsep + env['PYTHONPATH']
+        else:
+            env['PYTHONPATH'] = numba_path
+
+        def run_python(args):
+            p = subprocess.Popen([sys.executable] + args,
+                                 cwd=self.usecase_dir,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.STDOUT,
+                                 env=env)
+            out, _ = p.communicate()
+            rc = p.wait()
+            if rc != 0:
+                self.fail("python failed with the following output:\n%s"
+                          % out.decode('utf-8', 'ignore'))
+
+        run_python([setup_py_file, "build_ext", "--inplace"])
+        code = """if 1:
+            import pycc_compiled_module as lib
+            assert lib.get_const() == 42
+            res = lib.ones(3)
+            assert list(res) == [1.0, 1.0, 1.0]
+            """
+        run_python(["-c", code])
+
+    def check_setup_nested_py(self, setup_py_file):
+        # Compute PYTHONPATH to ensure the child processes see this Numba
+        import numba
+        numba_path = os.path.abspath(os.path.dirname(
+                                     os.path.dirname(numba.__file__)))
+        env = dict(os.environ)
+        if env.get('PYTHONPATH', ''):
+            env['PYTHONPATH'] = numba_path + os.pathsep + env['PYTHONPATH']
+        else:
+            env['PYTHONPATH'] = numba_path
+
+        def run_python(args):
+            p = subprocess.Popen([sys.executable] + args,
+                                 cwd=self.usecase_dir,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.STDOUT,
+                                 env=env)
+            out, _ = p.communicate()
+            rc = p.wait()
+            if rc != 0:
+                self.fail("python failed with the following output:\n%s"
+                          % out.decode('utf-8', 'ignore'))
+
+        run_python([setup_py_file, "build_ext", "--inplace"])
+        code = """if 1:
+            import nested.pycc_compiled_module as lib
+            assert lib.get_const() == 42
+            res = lib.ones(3)
+            assert list(res) == [1.0, 1.0, 1.0]
+            """
+        run_python(["-c", code])
+
+    def test_setup_py_distutils(self):
+        self.check_setup_py("setup_distutils.py")
+
+    def test_setup_py_distutils_nested(self):
+        self.check_setup_nested_py("setup_distutils_nested.py")
+
+    @unittest.skipIf(setuptools is None, "test needs setuptools")
+    def test_setup_py_setuptools(self):
+        self.check_setup_py("setup_setuptools.py")
+
+    @unittest.skipIf(setuptools is None, "test needs setuptools")
+    def test_setup_py_setuptools_nested(self):
+        self.check_setup_nested_py("setup_setuptools_nested.py")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_python_int.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_python_int.py
new file mode 100644
index 000000000..e793182f4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_python_int.py
@@ -0,0 +1,54 @@
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types
+
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+
+
+def return_int(a, b):
+    return a + b
+
+
+class TestPythonInt(unittest.TestCase):
+
+    # Issue #474: ints should be returned rather than longs under Python 2,
+    # as much as possible.
+
+    def test_int_return_type(self, flags=force_pyobj_flags,
+                             int_type=types.int64, operands=(3, 4)):
+        pyfunc = return_int
+        cr = compile_isolated(pyfunc, (int_type, int_type), flags=flags)
+        cfunc = cr.entry_point
+        expected = pyfunc(*operands)
+        got = cfunc(*operands)
+        self.assertIs(type(got), type(expected))
+        self.assertEqual(got, expected)
+
+    def test_int_return_type_npm(self):
+        self.test_int_return_type(flags=no_pyobj_flags)
+
+    def test_unsigned_int_return_type(self, flags=force_pyobj_flags):
+        self.test_int_return_type(int_type=types.uint64, flags=flags)
+
+    def test_unsigned_int_return_type_npm(self):
+        self.test_unsigned_int_return_type(flags=no_pyobj_flags)
+
+    def test_long_int_return_type(self, flags=force_pyobj_flags):
+        # Same but returning a 64-bit integer.  The return type should be
+        # `int` on 64-bit builds, `long` on 32-bit ones (or Windows).
+        self.test_int_return_type(flags=flags, operands=(2**33, 2**40))
+
+    def test_long_int_return_type_npm(self):
+        self.test_long_int_return_type(flags=no_pyobj_flags)
+
+    def test_longer_int_return_type(self, flags=force_pyobj_flags):
+        # This won't be supported in nopython mode.
+        self.test_int_return_type(flags=flags, operands=(2**70, 2**75))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_random.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_random.py
new file mode 100644
index 000000000..d36eff869
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_random.py
@@ -0,0 +1,1677 @@
+import collections
+import functools
+import math
+import multiprocessing
+import os
+import random
+import subprocess
+import sys
+import threading
+import itertools
+
+import numpy as np
+
+import unittest
+from numba import jit, _helperlib
+from numba.core import types
+from numba.core.compiler import compile_isolated
+from numba.tests.support import TestCase, compile_function, tag
+from numba.core.errors import TypingError
+
+
+# State size of the Mersenne Twister
+N = 624
+
+
+def get_py_state_ptr():
+    return _helperlib.rnd_get_py_state_ptr()
+
+def get_np_state_ptr():
+    return _helperlib.rnd_get_np_state_ptr()
+
+
+def numpy_randint1(a):
+    return np.random.randint(a)
+
+def numpy_randint2(a, b):
+    return np.random.randint(a, b)
+
+def random_randint(a, b):
+    return random.randint(a, b)
+
+def random_randrange1(a):
+    return random.randrange(a)
+
+def random_randrange2(a, b):
+    return random.randrange(a, b)
+
+def random_randrange3(a, b, c):
+    return random.randrange(a, b, c)
+
+def numpy_choice1(a):
+    return np.random.choice(a)
+
+def numpy_choice2(a, size):
+    return np.random.choice(a, size=size)
+
+def numpy_choice3(a, size, replace):
+    return np.random.choice(a, size=size, replace=replace)
+
+def numpy_multinomial2(n, pvals):
+    return np.random.multinomial(n, pvals)
+
+def numpy_multinomial3(n, pvals, size):
+    return np.random.multinomial(n, pvals=pvals, size=size)
+
+def numpy_dirichlet(alpha, size):
+    return np.random.dirichlet(alpha, size=size)
+
+def numpy_dirichlet_default(alpha):
+    return np.random.dirichlet(alpha)
+
+def numpy_noncentral_chisquare(df, nonc, size):
+    return np.random.noncentral_chisquare(df, nonc, size=size)
+
+def numpy_noncentral_chisquare_default(df, nonc):
+    return np.random.noncentral_chisquare(df, nonc)
+
+def numpy_check_rand(seed, a, b):
+    np.random.seed(seed)
+    expected = np.random.random((a, b))
+    np.random.seed(seed)
+    got = np.random.rand(a, b)
+    return expected, got
+
+def numpy_check_randn(seed, a, b):
+    np.random.seed(seed)
+    expected = np.random.standard_normal((a, b))
+    np.random.seed(seed)
+    got = np.random.randn(a, b)
+    return expected, got
+
+def jit_with_args(name, argstring):
+    code = """def func(%(argstring)s):
+        return %(name)s(%(argstring)s)
+""" % locals()
+    pyfunc = compile_function("func", code, globals())
+    return jit(nopython=True)(pyfunc)
+
+def jit_nullary(name):
+    return jit_with_args(name, "")
+
+def jit_unary(name):
+    return jit_with_args(name, "a")
+
+def jit_binary(name):
+    return jit_with_args(name, "a, b")
+
+def jit_ternary(name):
+    return jit_with_args(name, "a, b, c")
+
+
+random_gauss = jit_binary("random.gauss")
+random_random = jit_nullary("random.random")
+random_seed = jit_unary("random.seed")
+
+numpy_normal = jit_binary("np.random.normal")
+numpy_random = jit_nullary("np.random.random")
+numpy_seed = jit_unary("np.random.seed")
+
+
+def _copy_py_state(r, ptr):
+    """
+    Copy state of Python random *r* to Numba state *ptr*.
+    """
+    mt = r.getstate()[1]
+    ints, index = mt[:-1], mt[-1]
+    _helperlib.rnd_set_state(ptr, (index, list(ints)))
+    return ints, index
+
+def _copy_np_state(r, ptr):
+    """
+    Copy state of Numpy random *r* to Numba state *ptr*.
+    """
+    ints, index = r.get_state()[1:3]
+    _helperlib.rnd_set_state(ptr, (index, [int(x) for x in ints]))
+    return ints, index
+
+def sync_to_numpy(r):
+    _ver, mt_st, _gauss_next = r.getstate()
+    mt_pos = mt_st[-1]
+    mt_ints = mt_st[:-1]
+    assert len(mt_ints) == 624
+
+    np_st = ('MT19937', np.array(mt_ints, dtype='uint32'), mt_pos)
+    if _gauss_next is None:
+        np_st += (0, 0.0)
+    else:
+        np_st += (1, _gauss_next)
+
+    np.random.set_state(np_st)
+
+# Pure Python equivalents of some of the Numpy distributions, using
+# Python's basic generators.
+
+def py_chisquare(r, df):
+    return 2.0 * r.gammavariate(df / 2.0, 1.0)
+
+def py_f(r, num, denom):
+    return ((py_chisquare(r, num) * denom) /
+            (py_chisquare(r, denom) * num))
+
+
+class BaseTest(TestCase):
+
+    def _follow_cpython(self, ptr, seed=2):
+        r = random.Random(seed)
+        _copy_py_state(r, ptr)
+        return r
+
+    def _follow_numpy(self, ptr, seed=2):
+        r = np.random.RandomState(seed)
+        _copy_np_state(r, ptr)
+        return r
+
+
+class TestInternals(BaseTest):
+    """
+    Test low-level internals of the implementation.
+    """
+
+    def _check_get_set_state(self, ptr):
+        state = _helperlib.rnd_get_state(ptr)
+        i, ints = state
+        self.assertIsInstance(i, int)
+        self.assertIsInstance(ints, list)
+        self.assertEqual(len(ints), N)
+        j = (i * 100007) % N
+        ints = [i * 3 for i in range(N)]
+        # Roundtrip
+        _helperlib.rnd_set_state(ptr, (j, ints))
+        self.assertEqual(_helperlib.rnd_get_state(ptr), (j, ints))
+
+    def _check_shuffle(self, ptr):
+        # We test shuffling against CPython
+        r = random.Random()
+        ints, index = _copy_py_state(r, ptr)
+        # Force shuffling in CPython generator
+        for i in range(index, N + 1, 2):
+            r.random()
+        _helperlib.rnd_shuffle(ptr)
+        # Check new integer keys
+        mt = r.getstate()[1]
+        ints, index = mt[:-1], mt[-1]
+        self.assertEqual(_helperlib.rnd_get_state(ptr)[1], list(ints))
+
+    def _check_init(self, ptr):
+        # We use the same integer seeding as Numpy
+        # (CPython is different: it treats the integer as a byte array)
+        r = np.random.RandomState()
+        for i in [0, 1, 125, 2**32 - 5]:
+            # Need to cast to a C-sized int (for Numpy <= 1.7)
+            r.seed(np.uint32(i))
+            st = r.get_state()
+            ints = list(st[1])
+            index = st[2]
+            assert index == N  # sanity check
+            _helperlib.rnd_seed(ptr, i)
+            self.assertEqual(_helperlib.rnd_get_state(ptr), (index, ints))
+
+    def _check_perturb(self, ptr):
+        states = []
+        for i in range(10):
+            # Initialize with known state
+            _helperlib.rnd_seed(ptr, 0)
+            # Perturb with entropy
+            _helperlib.rnd_seed(ptr, os.urandom(512))
+            states.append(tuple(_helperlib.rnd_get_state(ptr)[1]))
+        # No two identical states
+        self.assertEqual(len(set(states)), len(states))
+
+    def test_get_set_state(self):
+        self._check_get_set_state(get_py_state_ptr())
+
+    def test_shuffle(self):
+        self._check_shuffle(get_py_state_ptr())
+
+    def test_init(self):
+        self._check_init(get_py_state_ptr())
+
+    def test_perturb(self):
+        self._check_perturb(get_py_state_ptr())
+
+
+class TestRandom(BaseTest):
+
+    # NOTE: there may be cascading imprecision issues (e.g. between x87-using
+    # C code and SSE-using LLVM code), which is especially brutal for some
+    # iterative algorithms with sensitive exit conditions.
+    # Therefore we stick to hardcoded integers for seed values.
+
+    def _check_random_seed(self, seedfunc, randomfunc):
+        """
+        Check seed()- and random()-like functions.
+        """
+        # Our seed() mimics NumPy's.
+        r = np.random.RandomState()
+        for i in [0, 1, 125, 2**32 - 1]:
+            # Need to cast to a C-sized int (for Numpy <= 1.7)
+            r.seed(np.uint32(i))
+            seedfunc(i)
+            # Be sure to trigger a reshuffle
+            for j in range(N + 10):
+                self.assertPreciseEqual(randomfunc(), r.uniform(0.0, 1.0))
+
+    def test_random_random(self):
+        self._check_random_seed(random_seed, random_random)
+
+    def test_numpy_random(self):
+        self._check_random_seed(numpy_seed, numpy_random)
+        # Test aliases
+        self._check_random_seed(numpy_seed, jit_nullary("np.random.random_sample"))
+        self._check_random_seed(numpy_seed, jit_nullary("np.random.ranf"))
+        self._check_random_seed(numpy_seed, jit_nullary("np.random.sample"))
+        self._check_random_seed(numpy_seed, jit_nullary("np.random.rand"))
+
+    def _check_random_sized(self, seedfunc, randomfunc):
+        # Our seed() mimics NumPy's.
+        r = np.random.RandomState()
+        for i in [0, 1, 125, 2**32 - 1]:
+            # Need to cast to a C-sized int (for Numpy <= 1.7)
+            r.seed(np.uint32(i))
+            seedfunc(i)
+            for n in range(10):
+                self.assertPreciseEqual(randomfunc(n), r.uniform(0.0, 1.0, n))
+
+    def test_numpy_random_sized(self):
+        self._check_random_sized(numpy_seed, jit_unary("np.random.random_sample"))
+        self._check_random_sized(numpy_seed, jit_unary("np.random.ranf"))
+        self._check_random_sized(numpy_seed, jit_unary("np.random.sample"))
+        self._check_random_sized(numpy_seed, jit_unary("np.random.rand"))
+
+    def test_independent_generators(self):
+        # PRNGs for Numpy and Python are independent.
+        N = 10
+        random_seed(1)
+        py_numbers = [random_random() for i in range(N)]
+        numpy_seed(2)
+        np_numbers = [numpy_random() for i in range(N)]
+        random_seed(1)
+        numpy_seed(2)
+        pairs = [(random_random(), numpy_random()) for i in range(N)]
+        self.assertPreciseEqual([p[0] for p in pairs], py_numbers)
+        self.assertPreciseEqual([p[1] for p in pairs], np_numbers)
+
+    def _check_getrandbits(self, func, ptr):
+        """
+        Check a getrandbits()-like function.
+        """
+        # Our implementation follows CPython's for bits <= 64.
+        r = self._follow_cpython(ptr)
+        for nbits in range(1, 65):
+            expected = r.getrandbits(nbits)
+            got = func(nbits)
+            self.assertPreciseEqual(expected, got)
+        self.assertRaises(OverflowError, func, 65)
+        self.assertRaises(OverflowError, func, 9999999)
+        self.assertRaises(OverflowError, func, -1)
+
+    def test_random_getrandbits(self):
+        self._check_getrandbits(jit_unary("random.getrandbits"), get_py_state_ptr())
+
+    # Explanation for the large ulps value: on 32-bit platforms, our
+    # LLVM-compiled functions use SSE but they are compared against
+    # C functions which use x87.
+    # On some distributions, the errors seem to accumulate dramatically.
+
+    def _check_dist(self, func, pyfunc, argslist, niters=3,
+                    prec='double', ulps=12, pydtype=None):
+        assert len(argslist)
+        for args in argslist:
+            results = [func(*args) for i in range(niters)]
+            pyresults = [(pyfunc(*args, dtype=pydtype) if pydtype else pyfunc(*args))
+                         for i in range(niters)]
+            self.assertPreciseEqual(results, pyresults, prec=prec, ulps=ulps,
+                                    msg="for arguments %s" % (args,))
+
+    def _check_gauss(self, func2, func1, func0, ptr):
+        """
+        Check a gauss()-like function.
+        """
+        # Our implementation follows Numpy's.
+        r = self._follow_numpy(ptr)
+        if func2 is not None:
+            self._check_dist(func2, r.normal,
+                             [(1.0, 1.0), (2.0, 0.5), (-2.0, 0.5)],
+                             niters=N // 2 + 10)
+        if func1 is not None:
+            self._check_dist(func1, r.normal, [(0.5,)])
+        if func0 is not None:
+            self._check_dist(func0, r.normal, [()])
+
+    def test_random_gauss(self):
+        self._check_gauss(jit_binary("random.gauss"), None, None, get_py_state_ptr())
+
+    def test_random_normalvariate(self):
+        # normalvariate() is really an alias to gauss() in Numba
+        # (not in Python, though - they use different algorithms)
+        self._check_gauss(jit_binary("random.normalvariate"), None, None,
+                          get_py_state_ptr())
+
+    def test_numpy_normal(self):
+        self._check_gauss(jit_binary("np.random.normal"),
+                          jit_unary("np.random.normal"),
+                          jit_nullary("np.random.normal"),
+                          get_np_state_ptr())
+
+    def test_numpy_standard_normal(self):
+        self._check_gauss(None, None, jit_nullary("np.random.standard_normal"),
+                          get_np_state_ptr())
+
+    def test_numpy_randn(self):
+        self._check_gauss(None, None, jit_nullary("np.random.randn"),
+                          get_np_state_ptr())
+
+    def _check_lognormvariate(self, func2, func1, func0, ptr):
+        """
+        Check a lognormvariate()-like function.
+        """
+        # Our implementation follows Numpy's.
+        r = self._follow_numpy(ptr)
+        if func2 is not None:
+            self._check_dist(func2, r.lognormal,
+                             [(1.0, 1.0), (2.0, 0.5), (-2.0, 0.5)],
+                             niters=N // 2 + 10)
+        if func1 is not None:
+            self._check_dist(func1, r.lognormal, [(0.5,)])
+        if func0 is not None:
+            self._check_dist(func0, r.lognormal, [()])
+
+    def test_random_lognormvariate(self):
+        self._check_lognormvariate(jit_binary("random.lognormvariate"),
+                                   None, None, get_py_state_ptr())
+
+    def test_numpy_lognormal(self):
+        self._check_lognormvariate(jit_binary("np.random.lognormal"),
+                                   jit_unary("np.random.lognormal"),
+                                   jit_nullary("np.random.lognormal"),
+                                   get_np_state_ptr())
+
+    def _check_randrange(self, func1, func2, func3, ptr, max_width, is_numpy, tp=None):
+        """
+        Check a randrange()-like function.
+        """
+        # Sanity check
+        ints = []
+        for i in range(10):
+            ints.append(func1(500000000))
+            ints.append(func2(5, 500000000))
+            if func3 is not None:
+                ints.append(func3(5, 500000000, 3))
+        if is_numpy:
+            rr = self._follow_numpy(ptr).randint
+        else:
+            rr = self._follow_cpython(ptr).randrange
+        widths = [w for w in [1, 5, 8, 5000, 2**40, 2**62 + 2**61] if w < max_width]
+        pydtype = tp if is_numpy else None
+        for width in widths:
+            self._check_dist(func1, rr, [(width,)], niters=10,
+                            pydtype=pydtype)
+            self._check_dist(func2, rr, [(-2, 2 +width)], niters=10,
+                            pydtype=pydtype)
+            if func3 is not None:
+                self.assertPreciseEqual(func3(-2, 2 + width, 6),
+                                        rr(-2, 2 + width, 6))
+                self.assertPreciseEqual(func3(2 + width, 2, -3),
+                                        rr(2 + width, 2, -3))
+        # Empty ranges
+        self.assertRaises(ValueError, func1, 0)
+        self.assertRaises(ValueError, func1, -5)
+        self.assertRaises(ValueError, func2, 5, 5)
+        self.assertRaises(ValueError, func2, 5, 2)
+        if func3 is not None:
+            self.assertRaises(ValueError, func3, 5, 7, -1)
+            self.assertRaises(ValueError, func3, 7, 5, 1)
+
+    def test_random_randrange(self):
+        for tp, max_width in [(types.int64, 2**63), (types.int32, 2**31)]:
+            cr1 = compile_isolated(random_randrange1, (tp,))
+            cr2 = compile_isolated(random_randrange2, (tp, tp))
+            cr3 = compile_isolated(random_randrange3, (tp, tp, tp))
+            self._check_randrange(cr1.entry_point, cr2.entry_point,
+                                  cr3.entry_point, get_py_state_ptr(),
+                                  max_width, False)
+
+    def test_numpy_randint(self):
+        for tp, np_tp, max_width in [(types.int64, np.int64, 2**63),
+                                     (types.int32, np.int32, 2**31)]:
+            cr1 = compile_isolated(numpy_randint1, (tp,))
+            cr2 = compile_isolated(numpy_randint2, (tp, tp))
+            self._check_randrange(cr1.entry_point, cr2.entry_point,
+                                  None, get_np_state_ptr(), max_width, True, np_tp)
+
+    def _check_randint(self, func, ptr, max_width):
+        """
+        Check a randint()-like function.
+        """
+        # Sanity check
+        ints = []
+        for i in range(10):
+            ints.append(func(5, 500000000))
+        self.assertEqual(len(ints), len(set(ints)), ints)
+
+        r = self._follow_cpython(ptr)
+        for args in [(1, 5), (13, 5000), (20, 2**62 + 2**61)]:
+            if args[1] > max_width:
+                continue
+            self._check_dist(func, r.randint, [args], niters=10)
+        # Empty ranges
+        self.assertRaises(ValueError, func, 5, 4)
+        self.assertRaises(ValueError, func, 5, 2)
+
+    def test_random_randint(self):
+        for tp, max_width in [(types.int64, 2**63), (types.int32, 2**31)]:
+            cr = compile_isolated(random_randint, (tp, tp))
+            self._check_randint(cr.entry_point, get_py_state_ptr(), max_width)
+
+    def _check_uniform(self, func, ptr):
+        """
+        Check a uniform()-like function.
+        """
+        # Our implementation follows Python's.
+        r = self._follow_cpython(ptr)
+        self._check_dist(func, r.uniform,
+                         [(1.5, 1e6), (-2.5, 1e3), (1.5, -2.5)])
+
+    def test_random_uniform(self):
+        self._check_uniform(jit_binary("random.uniform"), get_py_state_ptr())
+
+    def test_numpy_uniform(self):
+        self._check_uniform(jit_binary("np.random.uniform"), get_np_state_ptr())
+
+    def _check_triangular(self, func2, func3, ptr):
+        """
+        Check a triangular()-like function.
+        """
+        # Our implementation follows Python's.
+        r = self._follow_cpython(ptr)
+        if func2 is not None:
+            self._check_dist(func2, r.triangular,
+                             [(1.5, 3.5), (-2.5, 1.5), (1.5, 1.5)])
+        self._check_dist(func3, r.triangular, [(1.5, 3.5, 2.2)])
+
+    def test_random_triangular(self):
+        self._check_triangular(jit_binary("random.triangular"),
+                               jit_ternary("random.triangular"),
+                               get_py_state_ptr())
+
+    def test_numpy_triangular(self):
+        triangular = jit_ternary("np.random.triangular")
+        fixed_triangular = lambda l, r, m: triangular(l, m, r)
+        self._check_triangular(None, fixed_triangular, get_np_state_ptr())
+
+    def _check_gammavariate(self, func2, func1, ptr):
+        """
+        Check a gammavariate()-like function.
+        """
+        # Our implementation follows Python's.
+        r = self._follow_cpython(ptr)
+        if func2 is not None:
+            self._check_dist(func2, r.gammavariate,
+                             [(0.5, 2.5), (1.0, 1.5), (1.5, 3.5)])
+        if func1 is not None:
+            self.assertPreciseEqual(func1(1.5), r.gammavariate(1.5, 1.0))
+        # Invalid inputs
+        if func2 is not None:
+            self.assertRaises(ValueError, func2, 0.0, 1.0)
+            self.assertRaises(ValueError, func2, 1.0, 0.0)
+            self.assertRaises(ValueError, func2, -0.5, 1.0)
+            self.assertRaises(ValueError, func2, 1.0, -0.5)
+        if func1 is not None:
+            self.assertRaises(ValueError, func1, 0.0)
+            self.assertRaises(ValueError, func1, -0.5)
+
+    def test_random_gammavariate(self):
+        self._check_gammavariate(jit_binary("random.gammavariate"), None,
+                                 get_py_state_ptr())
+
+    def test_numpy_gamma(self):
+        self._check_gammavariate(jit_binary("np.random.gamma"),
+                                 jit_unary("np.random.gamma"),
+                                 get_np_state_ptr())
+        self._check_gammavariate(None,
+                                 jit_unary("np.random.standard_gamma"),
+                                 get_np_state_ptr())
+
+    def _check_betavariate(self, func, ptr):
+        """
+        Check a betavariate()-like function.
+        """
+        # Our implementation follows Python's.
+        r = self._follow_cpython(ptr)
+        self._check_dist(func, r.betavariate, [(0.5, 2.5)])
+        # Invalid inputs
+        self.assertRaises(ValueError, func, 0.0, 1.0)
+        self.assertRaises(ValueError, func, 1.0, 0.0)
+        self.assertRaises(ValueError, func, -0.5, 1.0)
+        self.assertRaises(ValueError, func, 1.0, -0.5)
+
+    def test_random_betavariate(self):
+        self._check_betavariate(jit_binary("random.betavariate"), get_py_state_ptr())
+
+    def test_numpy_beta(self):
+        self._check_betavariate(jit_binary("np.random.beta"), get_np_state_ptr())
+
+    def _check_vonmisesvariate(self, func, ptr):
+        """
+        Check a vonmisesvariate()-like function.
+        """
+        r = self._follow_cpython(ptr)
+        self._check_dist(func, r.vonmisesvariate, [(0.5, 2.5)])
+
+    def test_random_vonmisesvariate(self):
+        self._check_vonmisesvariate(jit_binary("random.vonmisesvariate"),
+                                    get_py_state_ptr())
+
+    def test_numpy_vonmises(self):
+        self._check_vonmisesvariate(jit_binary("np.random.vonmises"),
+                                    get_np_state_ptr())
+
+    def _check_expovariate(self, func, ptr):
+        """
+        Check a expovariate()-like function.  Note the second argument
+        is inversed compared to np.random.exponential().
+        """
+        r = self._follow_numpy(ptr)
+        for lambd in (0.2, 0.5, 1.5):
+            for i in range(3):
+                self.assertPreciseEqual(func(lambd), r.exponential(1 / lambd),
+                                        prec='double')
+
+    def test_random_expovariate(self):
+        self._check_expovariate(jit_unary("random.expovariate"), get_py_state_ptr())
+
+    def _check_exponential(self, func1, func0, ptr):
+        """
+        Check a exponential()-like function.
+        """
+        r = self._follow_numpy(ptr)
+        if func1 is not None:
+            self._check_dist(func1, r.exponential, [(0.5,), (1.0,), (1.5,)])
+        if func0 is not None:
+            self._check_dist(func0, r.exponential, [()])
+
+    def test_numpy_exponential(self):
+        self._check_exponential(jit_unary("np.random.exponential"),
+                                jit_nullary("np.random.exponential"),
+                                get_np_state_ptr())
+
+    def test_numpy_standard_exponential(self):
+        self._check_exponential(None,
+                                jit_nullary("np.random.standard_exponential"),
+                                get_np_state_ptr())
+
+    def _check_paretovariate(self, func, ptr):
+        """
+        Check a paretovariate()-like function.
+        """
+        # Our implementation follows Python's.
+        r = self._follow_cpython(ptr)
+        self._check_dist(func, r.paretovariate, [(0.5,), (3.5,)])
+
+    def test_random_paretovariate(self):
+        self._check_paretovariate(jit_unary("random.paretovariate"), get_py_state_ptr())
+
+    def test_numpy_pareto(self):
+        pareto = jit_unary("np.random.pareto")
+        fixed_pareto = lambda a: pareto(a) + 1.0
+        self._check_paretovariate(fixed_pareto, get_np_state_ptr())
+
+    def _check_weibullvariate(self, func2, func1, ptr):
+        """
+        Check a weibullvariate()-like function.
+        """
+        # Our implementation follows Python's.
+        r = self._follow_cpython(ptr)
+        if func2 is not None:
+            self._check_dist(func2, r.weibullvariate, [(0.5, 2.5)])
+        if func1 is not None:
+            for i in range(3):
+                self.assertPreciseEqual(func1(2.5),
+                                        r.weibullvariate(1.0, 2.5))
+
+    def test_random_weibullvariate(self):
+        self._check_weibullvariate(jit_binary("random.weibullvariate"),
+                                   None, get_py_state_ptr())
+
+    def test_numpy_weibull(self):
+        self._check_weibullvariate(None, jit_unary("np.random.weibull"),
+                                   get_np_state_ptr())
+
+    def test_numpy_binomial(self):
+        # We follow Numpy's algorithm up to n*p == 30
+        binomial = jit_binary("np.random.binomial")
+        r = self._follow_numpy(get_np_state_ptr(), 0)
+        self._check_dist(binomial, r.binomial, [(18, 0.25)])
+        # Sanity check many values
+        for n in (100, 1000, 10000):
+            self.assertEqual(binomial(n, 0.0), 0)
+            self.assertEqual(binomial(n, 1.0), n)
+            for p in (0.0001, 0.1, 0.4, 0.49999, 0.5, 0.50001, 0.8, 0.9, 0.9999):
+                r = binomial(n, p)
+                if p > 0.5:
+                    r = n - r
+                    p = 1 - p
+                self.assertGreaterEqual(r, 0)
+                self.assertLessEqual(r, n)
+                expected = p * n
+                tol = 3 * n / math.sqrt(n)
+                self.assertGreaterEqual(r, expected - tol, (p, n, r))
+                self.assertLessEqual(r, expected + tol, (p, n, r))
+        # Invalid values
+        self.assertRaises(ValueError, binomial, -1, 0.5)
+        self.assertRaises(ValueError, binomial, 10, -0.1)
+        self.assertRaises(ValueError, binomial, 10, 1.1)
+
+    def test_numpy_chisquare(self):
+        chisquare = jit_unary("np.random.chisquare")
+        r = self._follow_cpython(get_np_state_ptr())
+        self._check_dist(chisquare,
+                         functools.partial(py_chisquare, r),
+                         [(1.5,), (2.5,)])
+
+    def test_numpy_f(self):
+        f = jit_binary("np.random.f")
+        r = self._follow_cpython(get_np_state_ptr())
+        self._check_dist(f, functools.partial(py_f, r),
+                         [(0.5, 1.5), (1.5, 0.8)])
+
+    def test_numpy_geometric(self):
+        geom = jit_unary("np.random.geometric")
+        # p out of domain
+        self.assertRaises(ValueError, geom, -1.0)
+        self.assertRaises(ValueError, geom, 0.0)
+        self.assertRaises(ValueError, geom, 1.001)
+        # Some basic checks
+        N = 200
+        r = [geom(1.0) for i in range(N)]
+        self.assertPreciseEqual(r, [1] * N)
+        r = [geom(0.9) for i in range(N)]
+        n = r.count(1)
+        self.assertGreaterEqual(n, N // 2)
+        self.assertLess(n, N)
+        self.assertFalse([i for i in r if i > 1000])  # unlikely
+        r = [geom(0.4) for i in range(N)]
+        self.assertTrue([i for i in r if i > 4])  # likely
+        r = [geom(0.01) for i in range(N)]
+        self.assertTrue([i for i in r if i > 50])  # likely
+        r = [geom(1e-15) for i in range(N)]
+        self.assertTrue([i for i in r if i > 2**32])  # likely
+
+    def test_numpy_gumbel(self):
+        gumbel = jit_binary("np.random.gumbel")
+        r = self._follow_numpy(get_np_state_ptr())
+        self._check_dist(gumbel, r.gumbel, [(0.0, 1.0), (-1.5, 3.5)])
+
+    def test_numpy_hypergeometric(self):
+        # Our implementation follows Numpy's up to nsamples = 10.
+        hg = jit_ternary("np.random.hypergeometric")
+        r = self._follow_numpy(get_np_state_ptr())
+        self._check_dist(hg, r.hypergeometric,
+                         [(1000, 5000, 10), (5000, 1000, 10)],
+                         niters=30)
+        # Sanity checks
+        r = [hg(1000, 1000, 100) for i in range(100)]
+        self.assertTrue(all(x >= 0 and x <= 100 for x in r), r)
+        self.assertGreaterEqual(np.mean(r), 40.0)
+        self.assertLessEqual(np.mean(r), 60.0)
+        r = [hg(1000, 100000, 100) for i in range(100)]
+        self.assertTrue(all(x >= 0 and x <= 100 for x in r), r)
+        self.assertLessEqual(np.mean(r), 10.0)
+        r = [hg(100000, 1000, 100) for i in range(100)]
+        self.assertTrue(all(x >= 0 and x <= 100 for x in r), r)
+        self.assertGreaterEqual(np.mean(r), 90.0)
+
+    def test_numpy_laplace(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        self._check_dist(jit_binary("np.random.laplace"), r.laplace,
+                         [(0.0, 1.0), (-1.5, 3.5)])
+        self._check_dist(jit_unary("np.random.laplace"), r.laplace,
+                         [(0.0,), (-1.5,)])
+        self._check_dist(jit_nullary("np.random.laplace"), r.laplace, [()])
+
+    def test_numpy_logistic(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        self._check_dist(jit_binary("np.random.logistic"), r.logistic,
+                         [(0.0, 1.0), (-1.5, 3.5)])
+        self._check_dist(jit_unary("np.random.logistic"), r.logistic,
+                         [(0.0,), (-1.5,)])
+        self._check_dist(jit_nullary("np.random.logistic"), r.logistic, [()])
+
+    def test_numpy_logseries(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        logseries = jit_unary("np.random.logseries")
+        self._check_dist(logseries, r.logseries,
+                         [(0.1,), (0.99,), (0.9999,)],
+                         niters=50)
+        # Numpy's logseries overflows on 32-bit builds, so instead
+        # hardcode Numpy's (correct) output on 64-bit builds.
+        r = self._follow_numpy(get_np_state_ptr(), seed=1)
+        self.assertEqual([logseries(0.9999999999999) for i in range(10)],
+                         [2022733531, 77296, 30, 52204, 9341294, 703057324,
+                          413147702918, 1870715907, 16009330, 738])
+        self.assertRaises(ValueError, logseries, 0.0)
+        self.assertRaises(ValueError, logseries, -0.1)
+        self.assertRaises(ValueError, logseries, 1.1)
+
+    def test_numpy_poisson(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        poisson = jit_unary("np.random.poisson")
+        # Our implementation follows Numpy's.
+        self._check_dist(poisson, r.poisson,
+                         [(0.0,), (0.5,), (2.0,), (10.0,), (900.5,)],
+                         niters=50)
+        self.assertRaises(ValueError, poisson, -0.1)
+
+    def test_numpy_negative_binomial(self):
+        self._follow_numpy(get_np_state_ptr(), 0)
+        negbin = jit_binary("np.random.negative_binomial")
+        self.assertEqual([negbin(10, 0.9) for i in range(10)],
+                         [2, 3, 1, 5, 2, 1, 0, 1, 0, 0])
+        self.assertEqual([negbin(10, 0.1) for i in range(10)],
+                         [55, 71, 56, 57, 56, 56, 34, 55, 101, 67])
+        self.assertEqual([negbin(1000, 0.1) for i in range(10)],
+                         [9203, 8640, 9081, 9292, 8938,
+                          9165, 9149, 8774, 8886, 9117])
+        m = np.mean([negbin(1000000000, 0.1)
+                     for i in range(50)])
+        self.assertGreater(m, 9e9 * 0.99)
+        self.assertLess(m, 9e9 * 1.01)
+        self.assertRaises(ValueError, negbin, 0, 0.5)
+        self.assertRaises(ValueError, negbin, -1, 0.5)
+        self.assertRaises(ValueError, negbin, 10, -0.1)
+        self.assertRaises(ValueError, negbin, 10, 1.1)
+
+    def test_numpy_power(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        power = jit_unary("np.random.power")
+        self._check_dist(power, r.power,
+                         [(0.1,), (0.5,), (0.9,), (6.0,)])
+        self.assertRaises(ValueError, power, 0.0)
+        self.assertRaises(ValueError, power, -0.1)
+
+    def test_numpy_rayleigh(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        rayleigh1 = jit_unary("np.random.rayleigh")
+        rayleigh0 = jit_nullary("np.random.rayleigh")
+        self._check_dist(rayleigh1, r.rayleigh,
+                         [(0.1,), (0.8,), (25.,), (1e3,)])
+        self._check_dist(rayleigh0, r.rayleigh, [()])
+        self.assertRaises(ValueError, rayleigh1, 0.0)
+        self.assertRaises(ValueError, rayleigh1, -0.1)
+
+    def test_numpy_standard_cauchy(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        cauchy = jit_nullary("np.random.standard_cauchy")
+        self._check_dist(cauchy, r.standard_cauchy, [()])
+
+    def test_numpy_standard_t(self):
+        # We use CPython's algorithm for the gamma dist and numpy's
+        # for the normal dist.  Standard T calls both so we can't check
+        # against either generator's output.
+        r = self._follow_cpython(get_np_state_ptr())
+        standard_t = jit_unary("np.random.standard_t")
+        avg = np.mean([standard_t(5) for i in range(5000)])
+        # Sanity check
+        self.assertLess(abs(avg), 0.5)
+
+    def test_numpy_wald(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        wald = jit_binary("np.random.wald")
+        self._check_dist(wald, r.wald, [(1.0, 1.0), (2.0, 5.0)])
+        self.assertRaises(ValueError, wald, 0.0, 1.0)
+        self.assertRaises(ValueError, wald, -0.1, 1.0)
+        self.assertRaises(ValueError, wald, 1.0, 0.0)
+        self.assertRaises(ValueError, wald, 1.0, -0.1)
+
+    def test_numpy_zipf(self):
+        r = self._follow_numpy(get_np_state_ptr())
+        zipf = jit_unary("np.random.zipf")
+        self._check_dist(zipf, r.zipf, [(1.5,), (2.5,)], niters=100)
+        for val in (1.0, 0.5, 0.0, -0.1):
+            self.assertRaises(ValueError, zipf, val)
+
+    def _check_shuffle(self, func, ptr, is_numpy):
+        """
+        Check a shuffle()-like function for arrays.
+        """
+        arrs = [np.arange(20), np.arange(32).reshape((8, 4))]
+        if is_numpy:
+            r = self._follow_numpy(ptr)
+        else:
+            r = self._follow_cpython(ptr)
+        for a in arrs:
+            for i in range(3):
+                got = a.copy()
+                expected = a.copy()
+                func(got)
+                if is_numpy or len(a.shape) == 1:
+                    r.shuffle(expected)
+                    self.assertPreciseEqual(got, expected)
+        # Test with an arbitrary buffer-providing object
+        a = arrs[0]
+        b = a.copy()
+        func(memoryview(b))
+        self.assertNotEqual(list(a), list(b))
+        self.assertEqual(sorted(a), sorted(b))
+        # Read-only object
+        with self.assertTypingError():
+            func(memoryview(b"xyz"))
+
+    def test_random_shuffle(self):
+        self._check_shuffle(jit_unary("random.shuffle"), get_py_state_ptr(), False)
+
+    def test_numpy_shuffle(self):
+        self._check_shuffle(jit_unary("np.random.shuffle"), get_np_state_ptr(), True)
+
+    def _check_startup_randomness(self, func_name, func_args):
+        """
+        Check that the state is properly randomized at startup.
+        """
+        code = """if 1:
+            from numba.tests import test_random
+            func = getattr(test_random, %(func_name)r)
+            print(func(*%(func_args)r))
+            """ % (locals())
+        numbers = set()
+        for i in range(3):
+            popen = subprocess.Popen([sys.executable, "-c", code],
+                                     stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            out, err = popen.communicate()
+            if popen.returncode != 0:
+                raise AssertionError("process failed with code %s: stderr follows\n%s\n"
+                                     % (popen.returncode, err.decode()))
+            numbers.add(float(out.strip()))
+        self.assertEqual(len(numbers), 3, numbers)
+
+    def test_random_random_startup(self):
+        self._check_startup_randomness("random_random", ())
+
+    def test_random_gauss_startup(self):
+        self._check_startup_randomness("random_gauss", (1.0, 1.0))
+
+    def test_numpy_random_startup(self):
+        self._check_startup_randomness("numpy_random", ())
+
+    def test_numpy_gauss_startup(self):
+        self._check_startup_randomness("numpy_normal", (1.0, 1.0))
+
+    def test_numpy_random_permutation(self):
+        func = jit_unary("np.random.permutation")
+        r = self._follow_numpy(get_np_state_ptr())
+        for s in [5, 10, 15, 20]:
+            a = np.arange(s)
+            b = a.copy()
+            # Test array version
+            self.assertPreciseEqual(func(a), r.permutation(a))
+            # Test int version
+            self.assertPreciseEqual(func(s), r.permutation(s))
+            # Permutation should not modify its argument
+            self.assertPreciseEqual(a, b)
+        # Check multi-dimensional arrays
+        arrs = [np.arange(10).reshape(2, 5),
+                np.arange(27).reshape(3, 3, 3),
+                np.arange(36).reshape(2, 3, 3, 2)]
+        for a in arrs:
+            b = a.copy()
+            self.assertPreciseEqual(func(a), r.permutation(a))
+            self.assertPreciseEqual(a, b)
+
+
+class TestRandomArrays(BaseTest):
+    """
+    Test array-producing variants of np.random.* functions.
+    """
+
+    def _compile_array_dist(self, funcname, nargs):
+        qualname = "np.random.%s" % (funcname,)
+        argstring = ', '.join('abcd'[:nargs])
+        return jit_with_args(qualname, argstring)
+
+    def _check_array_dist(self, funcname, scalar_args):
+        """
+        Check returning an array according to a given distribution.
+        """
+        cfunc = self._compile_array_dist(funcname, len(scalar_args) + 1)
+        r = self._follow_numpy(get_np_state_ptr())
+        pyfunc = getattr(r, funcname)
+        for size in (8, (2, 3)):
+            args = scalar_args + (size,)
+            expected = pyfunc(*args)
+            got = cfunc(*args)
+            # Numpy may return int32s where we return int64s, adjust
+            if (expected.dtype == np.dtype('int32')
+                and got.dtype == np.dtype('int64')):
+                expected = expected.astype(got.dtype)
+            self.assertPreciseEqual(expected, got, prec='double', ulps=5)
+
+    def test_numpy_randint(self):
+        cfunc = self._compile_array_dist("randint", 3)
+        low, high = 1000, 10000
+        size = (30, 30)
+        res = cfunc(low, high, size)
+        self.assertIsInstance(res, np.ndarray)
+        self.assertEqual(res.shape, size)
+        self.assertIn(res.dtype, (np.dtype('int32'), np.dtype('int64')))
+        self.assertTrue(np.all(res >= low))
+        self.assertTrue(np.all(res < high))
+        # Crude statistical tests
+        mean = (low + high) / 2
+        tol = (high - low) / 20
+        self.assertGreaterEqual(res.mean(), mean - tol)
+        self.assertLessEqual(res.mean(), mean + tol)
+
+    def test_numpy_random_random(self):
+        cfunc = self._compile_array_dist("random", 1)
+        size = (30, 30)
+        res = cfunc(size)
+        self.assertIsInstance(res, np.ndarray)
+        self.assertEqual(res.shape, size)
+        self.assertEqual(res.dtype, np.dtype('float64'))
+        # Results are within expected bounds
+        self.assertTrue(np.all(res >= 0.0))
+        self.assertTrue(np.all(res < 1.0))
+        # Crude statistical tests
+        self.assertTrue(np.any(res <= 0.1))
+        self.assertTrue(np.any(res >= 0.9))
+        mean = res.mean()
+        self.assertGreaterEqual(mean, 0.45)
+        self.assertLessEqual(mean, 0.55)
+
+    # Sanity-check various distributions.  For convenience, we only check
+    # those distributions that produce the exact same values as Numpy's.
+
+    def test_numpy_binomial(self):
+        self._check_array_dist("binomial", (20, 0.5))
+
+    def test_numpy_exponential(self):
+        self._check_array_dist("exponential", (1.5,))
+
+    def test_numpy_gumbel(self):
+        self._check_array_dist("gumbel", (1.5, 0.5))
+
+    def test_numpy_laplace(self):
+        self._check_array_dist("laplace", (1.5, 0.5))
+
+    def test_numpy_logistic(self):
+        self._check_array_dist("logistic", (1.5, 0.5))
+
+    def test_numpy_lognormal(self):
+        self._check_array_dist("lognormal", (1.5, 2.0))
+
+    def test_numpy_logseries(self):
+        self._check_array_dist("logseries", (0.8,))
+
+    def test_numpy_normal(self):
+        self._check_array_dist("normal", (0.5, 2.0))
+
+    def test_numpy_poisson(self):
+        self._check_array_dist("poisson", (0.8,))
+
+    def test_numpy_power(self):
+        self._check_array_dist("power", (0.8,))
+
+    def test_numpy_rand(self):
+        cfunc = jit(nopython=True)(numpy_check_rand)
+        expected, got = cfunc(42, 2, 3)
+        self.assertEqual(got.shape, (2, 3))
+        self.assertPreciseEqual(expected, got)
+
+    def test_numpy_randn(self):
+        cfunc = jit(nopython=True)(numpy_check_randn)
+        expected, got = cfunc(42, 2, 3)
+        self.assertEqual(got.shape, (2, 3))
+        self.assertPreciseEqual(expected, got)
+
+    def test_numpy_rayleigh(self):
+        self._check_array_dist("rayleigh", (0.8,))
+
+    def test_numpy_standard_cauchy(self):
+        self._check_array_dist("standard_cauchy", ())
+
+    def test_numpy_standard_exponential(self):
+        self._check_array_dist("standard_exponential", ())
+
+    def test_numpy_standard_normal(self):
+        self._check_array_dist("standard_normal", ())
+
+    def test_numpy_uniform(self):
+        self._check_array_dist("uniform", (0.1, 0.4))
+
+    def test_numpy_wald(self):
+        self._check_array_dist("wald", (0.1, 0.4))
+
+    def test_numpy_zipf(self):
+        self._check_array_dist("zipf", (2.5,))
+
+
+class TestRandomChoice(BaseTest):
+    """
+    Test np.random.choice.
+    """
+
+    def _check_results(self, pop, res, replace=True):
+        """
+        Check basic expectations about a batch of samples.
+        """
+        spop = set(pop)
+        sres = set(res)
+        # All results are in the population
+        self.assertLessEqual(sres, spop)
+        # Sorted results are unlikely
+        self.assertNotEqual(sorted(res), list(res))
+        if replace:
+            # Duplicates are likely
+            self.assertLess(len(sres), len(res), res)
+        else:
+            # No duplicates
+            self.assertEqual(len(sres), len(res), res)
+
+    def _check_dist(self, pop, samples):
+        """
+        Check distribution of some samples.
+        """
+        # Sanity check that we have enough samples
+        self.assertGreaterEqual(len(samples), len(pop) * 100)
+        # Check equidistribution of samples
+        expected_frequency = len(samples) / len(pop)
+        c = collections.Counter(samples)
+        for value in pop:
+            n = c[value]
+            self.assertGreaterEqual(n, expected_frequency * 0.5)
+            self.assertLessEqual(n, expected_frequency * 2.0)
+
+    def _accumulate_array_results(self, func, nresults):
+        """
+        Accumulate array results produced by *func* until they reach
+        *nresults* elements.
+        """
+        res = []
+        while len(res) < nresults:
+            res += list(func().flat)
+        return res[:nresults]
+
+    def _check_choice_1(self, a, pop):
+        """
+        Check choice(a) against pop.
+        """
+        cfunc = jit(nopython=True)(numpy_choice1)
+        n = len(pop)
+        res = [cfunc(a) for i in range(n)]
+        self._check_results(pop, res)
+        dist = [cfunc(a) for i in range(n * 100)]
+        self._check_dist(pop, dist)
+
+    def test_choice_scalar_1(self):
+        """
+        Test choice(int)
+        """
+        n = 50
+        pop = list(range(n))
+        self._check_choice_1(n, pop)
+
+    def test_choice_array_1(self):
+        """
+        Test choice(array)
+        """
+        pop = np.arange(50) * 2 + 100
+        self._check_choice_1(pop, pop)
+
+    def _check_array_results(self, func, pop, replace=True):
+        """
+        Check array results produced by *func* and their distribution.
+        """
+        n = len(pop)
+        res = list(func().flat)
+        self._check_results(pop, res, replace)
+        dist = self._accumulate_array_results(func, n * 100)
+        self._check_dist(pop, dist)
+
+    def _check_choice_2(self, a, pop):
+        """
+        Check choice(a, size) against pop.
+        """
+        cfunc = jit(nopython=True)(numpy_choice2)
+        n = len(pop)
+        # Final sizes should be large enough, so as to stress
+        # replacement
+        sizes = [n - 10, (3, (n - 1) // 3), n * 10]
+
+        for size in sizes:
+            # Check result shape
+            res = cfunc(a, size)
+            expected_shape = size if isinstance(size, tuple) else (size,)
+            self.assertEqual(res.shape, expected_shape)
+            # Check results and their distribution
+            self._check_array_results(lambda: cfunc(a, size), pop)
+
+    def test_choice_scalar_2(self):
+        """
+        Test choice(int, size)
+        """
+        n = 50
+        pop = np.arange(n)
+        self._check_choice_2(n, pop)
+
+    def test_choice_array_2(self):
+        """
+        Test choice(array, size)
+        """
+        pop = np.arange(50) * 2 + 100
+        self._check_choice_2(pop, pop)
+
+    def _check_choice_3(self, a, pop):
+        """
+        Check choice(a, size, replace) against pop.
+        """
+        cfunc = jit(nopython=True)(numpy_choice3)
+        n = len(pop)
+        # Final sizes should be close but slightly <= n, so as to stress
+        # replacement (or not)
+        sizes = [n - 10, (3, (n - 1) // 3)]
+        replaces = [True, False]
+
+        # Check result shapes
+        for size in sizes:
+            for replace in [True, False]:
+                res = cfunc(a, size, replace)
+                expected_shape = size if isinstance(size, tuple) else (size,)
+                self.assertEqual(res.shape, expected_shape)
+
+        # Check results for replace=True
+        for size in sizes:
+            self._check_array_results(lambda: cfunc(a, size, True), pop)
+        # Check results for replace=False
+        for size in sizes:
+            self._check_array_results(lambda: cfunc(a, size, False), pop, False)
+
+        # Can't ask for more samples than population size with replace=False
+        for size in [n + 1, (3, n // 3 + 1)]:
+            with self.assertRaises(ValueError):
+                cfunc(a, size, False)
+
+    def test_choice_scalar_3(self):
+        """
+        Test choice(int, size, replace)
+        """
+        n = 50
+        pop = np.arange(n)
+        self._check_choice_3(n, pop)
+
+    def test_choice_array_3(self):
+        """
+        Test choice(array, size, replace)
+        """
+        pop = np.arange(50) * 2 + 100
+        self._check_choice_3(pop, pop)
+
+    def test_choice_follows_seed(self):
+        # See issue #3888, np.random.choice must acknowledge the seed
+
+        @jit(nopython=True)
+        def numba_rands(n_to_return, choice_array):
+            np.random.seed(1337)
+            out = np.empty((n_to_return, 2), np.int32)
+            for i in range(n_to_return):
+                out[i] = np.random.choice(choice_array, 2, False)
+            return out
+
+        choice_array = np.random.randint(300, size=1000).astype(np.int32)
+        tmp_np = choice_array.copy()
+        expected = numba_rands.py_func(5, tmp_np)
+        tmp_nb = choice_array.copy()
+        got = numba_rands(5, tmp_nb)
+        np.testing.assert_allclose(expected, got)
+        # check no mutation
+        np.testing.assert_allclose(choice_array, tmp_np)
+        np.testing.assert_allclose(choice_array, tmp_nb)
+
+
+class TestRandomMultinomial(BaseTest):
+    """
+    Test np.random.multinomial.
+    """
+    # A biased dice
+    pvals = np.array([1, 1, 1, 2, 3, 1], dtype=np.float64)
+    pvals /= pvals.sum()
+
+    def _check_sample(self, n, pvals, sample):
+        """
+        Check distribution of some samples.
+        """
+        self.assertIsInstance(sample, np.ndarray)
+        self.assertEqual(sample.shape, (len(pvals),))
+        self.assertIn(sample.dtype, (np.dtype('int32'), np.dtype('int64')))
+        # Statistical properties
+        self.assertEqual(sample.sum(), n)
+        for p, nexp in zip(pvals, sample):
+            self.assertGreaterEqual(nexp, 0)
+            self.assertLessEqual(nexp, n)
+            pexp = float(nexp) / n
+            self.assertGreaterEqual(pexp, p * 0.5)
+            self.assertLessEqual(pexp, p * 2.0)
+
+    def test_multinomial_2(self):
+        """
+        Test multinomial(n, pvals)
+        """
+        cfunc = jit(nopython=True)(numpy_multinomial2)
+        n, pvals = 1000, self.pvals
+        res = cfunc(n, pvals)
+        self._check_sample(n, pvals, res)
+        # pvals as list
+        pvals = list(pvals)
+        res = cfunc(n, pvals)
+        self._check_sample(n, pvals, res)
+        # A case with extreme probabilities
+        n = 1000000
+        pvals = np.array([1, 0, n // 100, 1], dtype=np.float64)
+        pvals /= pvals.sum()
+        res = cfunc(n, pvals)
+        self._check_sample(n, pvals, res)
+
+    def test_multinomial_3_int(self):
+        """
+        Test multinomial(n, pvals, size: int)
+        """
+        cfunc = jit(nopython=True)(numpy_multinomial3)
+        n, pvals = 1000, self.pvals
+        k = 10
+        res = cfunc(n, pvals, k)
+        self.assertEqual(res.shape[0], k)
+        for sample in res:
+            self._check_sample(n, pvals, sample)
+
+    def test_multinomial_3_tuple(self):
+        """
+        Test multinomial(n, pvals, size: tuple)
+        """
+        cfunc = jit(nopython=True)(numpy_multinomial3)
+        n, pvals = 1000, self.pvals
+        k = (3, 4)
+        res = cfunc(n, pvals, k)
+        self.assertEqual(res.shape[:-1], k)
+        for sample in res.reshape((-1, res.shape[-1])):
+            self._check_sample(n, pvals, sample)
+
+
+class TestRandomDirichlet(BaseTest):
+    alpha = np.array([1, 1, 1, 2], dtype=np.float64)
+
+    def _check_sample(self, alpha, size, sample):
+
+        """Check output structure"""
+        self.assertIsInstance(sample, np.ndarray)
+        self.assertEqual(sample.dtype, np.float64)
+        if size is None:
+            self.assertEqual(sample.size, len(alpha))
+        elif type(size) is int:
+            self.assertEqual(sample.shape, (size, len(alpha)))
+        else:
+            self.assertEqual(sample.shape, size + (len(alpha),))
+
+        """Check statistical properties"""
+        for val in np.nditer(sample):
+            self.assertGreaterEqual(val, 0)
+            self.assertLessEqual(val, 1)
+        if size is None:
+            self.assertAlmostEqual(sample.sum(), 1, places=5)
+        else:
+            for totals in np.nditer(sample.sum(axis=-1)):
+                self.assertAlmostEqual(totals, 1, places=5)
+
+    def test_dirichlet_default(self):
+        """
+        Test dirichlet(alpha, size=None)
+        """
+        cfunc = jit(nopython=True)(numpy_dirichlet_default)
+        alphas = (
+            self.alpha,
+            tuple(self.alpha),
+            np.array([1, 1, 10000, 1], dtype=np.float64),
+            np.array([1, 1, 1.5, 1], dtype=np.float64),
+        )
+        for alpha in alphas:
+            res = cfunc(alpha)
+            self._check_sample(alpha, None, res)
+
+    def test_dirichlet(self):
+        """
+        Test dirichlet(alpha, size=None)
+        """
+        cfunc = jit(nopython=True)(numpy_dirichlet)
+        sizes = (None, (10,), (10, 10))
+        alphas = (
+            self.alpha,
+            tuple(self.alpha),
+            np.array([1, 1, 10000, 1], dtype=np.float64),
+            np.array([1, 1, 1.5, 1], dtype=np.float64),
+        )
+
+        for alpha, size in itertools.product(alphas, sizes):
+            res = cfunc(alpha, size)
+            self._check_sample(alpha, size, res)
+
+    def test_dirichlet_exceptions(self):
+        cfunc = jit(nopython=True)(numpy_dirichlet)
+        alpha = tuple((0, 1, 1))
+        with self.assertRaises(ValueError) as raises:
+            cfunc(alpha, 1)
+        self.assertIn("dirichlet: alpha must be > 0.0", str(raises.exception))
+        
+        alpha = self.alpha
+        sizes = (True, 3j, 1.5, (1.5, 1), (3j, 1), (3j, 3j), (np.int8(3), np.int64(7)))
+        for size in sizes:
+            with self.assertRaises(TypingError) as raises:
+                cfunc(alpha, size)
+            self.assertIn(
+                "np.random.dirichlet(): size should be int or "
+                "tuple of ints or None, got",
+                str(raises.exception),
+            )
+
+class TestRandomNoncentralChiSquare(BaseTest):
+
+    def _check_sample(self, size, sample):
+
+        # Check output structure
+        if size is not None:
+            self.assertIsInstance(sample, np.ndarray)
+            self.assertEqual(sample.dtype, np.float64)
+            
+            if isinstance(size, int):
+                self.assertEqual(sample.shape, (size,))
+            else:
+                self.assertEqual(sample.shape, size)
+        else:
+             self.assertIsInstance(sample, float)
+
+        # Check statistical properties
+        for val in np.nditer(sample):
+            self.assertGreaterEqual(val, 0)
+
+    def test_noncentral_chisquare_default(self):
+        """
+        Test noncentral_chisquare(df, nonc, size=None)
+        """
+        cfunc = jit(nopython=True)(numpy_noncentral_chisquare_default)
+        inputs = (
+            (0.5, 1), # test branch when df < 1
+            (1, 5),
+            (5, 1),
+            (100000, 1),
+            (1, 10000),
+        )
+        for df, nonc in inputs:
+            res = cfunc(df, nonc)
+            self._check_sample(None, res)
+            res = cfunc(df, np.nan) # test branch when nonc is nan
+            self.assertTrue(np.isnan(res))
+
+
+    def test_noncentral_chisquare(self):
+        """
+        Test noncentral_chisquare(df, nonc, size)
+        """
+        cfunc = jit(nopython=True)(numpy_noncentral_chisquare)
+        sizes = (None, 10, (10,), (10, 10))
+        inputs = (
+            (0.5, 1),
+            (1, 5),
+            (5, 1),
+            (100000, 1),
+            (1, 10000),
+        )
+
+        for (df, nonc), size in itertools.product(inputs, sizes):
+            res = cfunc(df, nonc, size)
+            self._check_sample(size, res)
+            res = cfunc(df, np.nan, size) # test branch when nonc is nan
+            self.assertTrue(np.isnan(res).all())
+
+    def test_noncentral_chisquare_exceptions(self):
+        cfunc = jit(nopython=True)(numpy_noncentral_chisquare)
+        df, nonc = 0, 1
+        with self.assertRaises(ValueError) as raises:
+            cfunc(df, nonc, 1)
+        self.assertIn("df <= 0", str(raises.exception))
+        
+        df, nonc = 1, -1
+        with self.assertRaises(ValueError) as raises:
+            cfunc(df, nonc, 1)
+        self.assertIn("nonc < 0", str(raises.exception))        
+
+        df, nonc = 1, 1
+        sizes = (True, 3j, 1.5, (1.5, 1), (3j, 1), (3j, 3j), (np.int8(3), np.int64(7)))
+        for size in sizes:
+            with self.assertRaises(TypingError) as raises:
+                cfunc(df, nonc, size)
+            self.assertIn(
+                "np.random.noncentral_chisquare(): size should be int or "
+                "tuple of ints or None, got",
+                str(raises.exception),
+            )
+
+@jit(nopython=True, nogil=True)
+def py_extract_randomness(seed, out):
+    if seed != 0:
+        random.seed(seed)
+    for i in range(out.size):
+        out[i] = random.getrandbits(32)
+
+_randint_limit = 1 << 32
+
+@jit(nopython=True, nogil=True)
+def np_extract_randomness(seed, out):
+    if seed != 0:
+        np.random.seed(seed)
+    s = 0
+    for i in range(out.size):
+        out[i] = np.random.randint(_randint_limit)
+
+
+
+class ConcurrencyBaseTest(TestCase):
+
+    # Enough iterations for:
+    # 1. Mersenne-Twister state shuffles to occur (once every 624)
+    # 2. Race conditions to be plausible
+    # 3. Nice statistical properties to emerge
+    _extract_iterations = 100000
+
+    def setUp(self):
+        # Warm up, to avoid compiling in the threads
+        args = (42, self._get_output(1))
+        py_extract_randomness(*args)
+        np_extract_randomness(*args)
+
+    def _get_output(self, size):
+        return np.zeros(size, dtype=np.uint32)
+
+    def check_output(self, out):
+        """
+        Check statistical properties of output.
+        """
+        # Output should follow a uniform distribution in [0, 1<<32)
+        expected_avg = 1 << 31
+        expected_std = (1 << 32) / np.sqrt(12)
+        rtol = 0.05  # given enough iterations
+        np.testing.assert_allclose(out.mean(), expected_avg, rtol=rtol)
+        np.testing.assert_allclose(out.std(), expected_std, rtol=rtol)
+
+    def check_several_outputs(self, results, same_expected):
+        # Outputs should have the expected statistical properties
+        # (an uninitialized PRNG or a PRNG whose internal state was
+        #  corrupted by a race condition could produce bogus randomness)
+        for out in results:
+            self.check_output(out)
+
+        # Check all threads gave either the same sequence or
+        # distinct sequences
+        if same_expected:
+            expected_distinct = 1
+        else:
+            expected_distinct = len(results)
+
+        heads = {tuple(out[:5]) for out in results}
+        tails = {tuple(out[-5:]) for out in results}
+        sums = {out.sum() for out in results}
+        self.assertEqual(len(heads), expected_distinct, heads)
+        self.assertEqual(len(tails), expected_distinct, tails)
+        self.assertEqual(len(sums), expected_distinct, sums)
+
+
+class TestThreads(ConcurrencyBaseTest):
+    """
+    Check the PRNG behaves well with threads.
+    """
+
+    def extract_in_threads(self, nthreads, extract_randomness, seed):
+        """
+        Run *nthreads* threads extracting randomness with the given *seed*
+        (no seeding if 0).
+        """
+        results = [self._get_output(self._extract_iterations)
+                   for i in range(nthreads + 1)]
+
+        def target(i):
+            # The PRNG will be seeded in thread
+            extract_randomness(seed=seed, out=results[i])
+
+        threads = [threading.Thread(target=target, args=(i,))
+                   for i in range(nthreads)]
+
+        for th in threads:
+            th.start()
+        # Exercise main thread as well
+        target(nthreads)
+        for th in threads:
+            th.join()
+
+        return results
+
+    def check_thread_safety(self, extract_randomness):
+        """
+        When initializing the PRNG the same way, each thread
+        should produce the same sequence of random numbers,
+        using independent states, regardless of parallel
+        execution.
+        """
+        # Note the seed value doesn't matter, as long as it's
+        # the same for all threads
+        results = self.extract_in_threads(15, extract_randomness, seed=42)
+
+        # All threads gave the same sequence
+        self.check_several_outputs(results, same_expected=True)
+
+    def check_implicit_initialization(self, extract_randomness):
+        """
+        The PRNG in new threads should be implicitly initialized with
+        system entropy, if seed() wasn't called.
+        """
+        results = self.extract_in_threads(4, extract_randomness, seed=0)
+
+        # All threads gave a different, valid random sequence
+        self.check_several_outputs(results, same_expected=False)
+
+    def test_py_thread_safety(self):
+        self.check_thread_safety(py_extract_randomness)
+
+    def test_np_thread_safety(self):
+        self.check_thread_safety(np_extract_randomness)
+
+    def test_py_implicit_initialization(self):
+        self.check_implicit_initialization(py_extract_randomness)
+
+    def test_np_implicit_initialization(self):
+        self.check_implicit_initialization(np_extract_randomness)
+
+
+@unittest.skipIf(os.name == 'nt', "Windows is not affected by fork() issues")
+class TestProcesses(ConcurrencyBaseTest):
+    """
+    Check the PRNG behaves well in child processes.
+    """
+
+    # Avoid nested multiprocessing AssertionError
+    # ("daemonic processes are not allowed to have children")
+    _numba_parallel_test_ = False
+
+
+    def extract_in_processes(self, nprocs, extract_randomness):
+        """
+        Run *nprocs* processes extracting randomness
+        without explicit seeding.
+        """
+        q = multiprocessing.Queue()
+        results = []
+
+        def target_inner():
+            out = self._get_output(self._extract_iterations)
+            extract_randomness(seed=0, out=out)
+            return out
+
+        def target():
+            try:
+                out = target_inner()
+                q.put(out)
+            except Exception as e:
+                # Ensure an exception in a child gets reported
+                # in the parent.
+                q.put(e)
+                raise
+
+        if hasattr(multiprocessing, 'get_context'):
+            # The test works only in fork context.
+            mpc = multiprocessing.get_context('fork')
+        else:
+            mpc = multiprocessing
+        procs = [mpc.Process(target=target)
+                 for i in range(nprocs)]
+        for p in procs:
+            p.start()
+        # Need to dequeue before joining, otherwise the large size of the
+        # enqueued objects will lead to deadlock.
+        for i in range(nprocs):
+            results.append(q.get(timeout=5))
+        for p in procs:
+            p.join()
+
+        # Exercise parent process as well; this will detect if the
+        # same state was reused for one of the children.
+        results.append(target_inner())
+        for res in results:
+            if isinstance(res, Exception):
+                self.fail("Exception in child: %s" % (res,))
+
+        return results
+
+    def check_implicit_initialization(self, extract_randomness):
+        """
+        The PRNG in new processes should be implicitly initialized
+        with system entropy, to avoid reproducing the same sequences.
+        """
+        results = self.extract_in_processes(2, extract_randomness)
+
+        # All processes gave a different, valid random sequence
+        self.check_several_outputs(results, same_expected=False)
+
+    def test_py_implicit_initialization(self):
+        self.check_implicit_initialization(py_extract_randomness)
+
+    def test_np_implicit_initialization(self):
+        self.check_implicit_initialization(np_extract_randomness)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_range.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_range.py
new file mode 100644
index 000000000..eb041c371
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_range.py
@@ -0,0 +1,196 @@
+import unittest
+
+import sys
+
+import numpy
+
+from numba.core.compiler import compile_isolated
+from numba import jit, njit
+from numba.core import types, utils
+from numba.tests.support import tag
+
+from numba.cpython.rangeobj import length_of_iterator
+def loop1(n):
+    s = 0
+    for i in range(n):
+        s += i
+    return s
+
+
+def loop2(a, b):
+    s = 0
+    for i in range(a, b):
+        s += i
+    return s
+
+
+def loop3(a, b, c):
+    s = 0
+    for i in range(a, b, c):
+        s += i
+    return s
+
+
+def range_len1(n):
+    return len(range(n))
+
+def range_len2(a, b):
+    return len(range(a, b))
+
+def range_len3(a, b, c):
+    return len(range(a, b, c))
+def range_iter_len1(a):
+    return length_of_iterator(iter(range(a)))
+
+def range_iter_len2(a):
+    return length_of_iterator(iter(a))
+
+def range_attrs(start, stop, step):
+    r1 = range(start)
+    r2 = range(start, stop)
+    r3 = range(start, stop, step)
+    tmp = []
+    for r in (r1, r2, r3):
+        tmp.append((r.start, r.stop, r.step))
+    return tmp
+
+def range_contains(val, start, stop, step):
+    r1 = range(start)
+    r2 = range(start, stop)
+    r3 = range(start, stop, step)
+    return [val in r for r in (r1, r2, r3)]
+
+
+class TestRange(unittest.TestCase):
+
+    def test_loop1_int16(self):
+        pyfunc = loop1
+        cres = compile_isolated(pyfunc, [types.int16])
+        cfunc = cres.entry_point
+        self.assertTrue(cfunc(5), pyfunc(5))
+
+    def test_loop2_int16(self):
+        pyfunc = loop2
+        cres = compile_isolated(pyfunc, [types.int16, types.int16])
+        cfunc = cres.entry_point
+        self.assertTrue(cfunc(1, 6), pyfunc(1, 6))
+
+    def test_loop3_int32(self):
+        pyfunc = loop3
+        cres = compile_isolated(pyfunc, [types.int32] * 3)
+        cfunc = cres.entry_point
+        arglist = [
+            (1, 2, 1),
+            (2, 8, 3),
+            (-10, -11, -10),
+            (-10, -10, -2),
+        ]
+        for args in arglist:
+            self.assertEqual(cfunc(*args), pyfunc(*args))
+
+    def test_range_len1(self):
+        pyfunc = range_len1
+        typelist = [types.int16, types.int32, types.int64]
+        arglist = [5, 0, -5]
+        for typ in typelist:
+            cres = compile_isolated(pyfunc, [typ])
+            cfunc = cres.entry_point
+            for arg in arglist:
+                self.assertEqual(cfunc(typ(arg)), pyfunc(typ(arg)))
+
+    def test_range_len2(self):
+        pyfunc = range_len2
+        typelist = [types.int16, types.int32, types.int64]
+        arglist = [(1,6), (6,1), (-5, -1)]
+        for typ in typelist:
+            cres = compile_isolated(pyfunc, [typ] * 2)
+            cfunc = cres.entry_point
+            for args in arglist:
+                args_ = tuple(typ(x) for x in args)
+                self.assertEqual(cfunc(*args_), pyfunc(*args_))
+
+    def test_range_len3(self):
+        pyfunc = range_len3
+        typelist = [types.int16, types.int32, types.int64]
+        arglist = [
+            (1, 2, 1),
+            (2, 8, 3),
+            (-10, -11, -10),
+            (-10, -10, -2),
+        ]
+        for typ in typelist:
+            cres = compile_isolated(pyfunc, [typ] * 3)
+            cfunc = cres.entry_point
+            for args in arglist:
+                args_ = tuple(typ(x) for x in args)
+                self.assertEqual(cfunc(*args_), pyfunc(*args_))
+
+    def test_range_iter_len1(self):
+        range_func = range_len1
+        range_iter_func = range_iter_len1
+        typelist = [types.int16, types.int32, types.int64]
+        arglist = [5, 0, -5]
+        for typ in typelist:
+            cres = compile_isolated(range_iter_func, [typ])
+            cfunc = cres.entry_point
+            for arg in arglist:
+                self.assertEqual(cfunc(typ(arg)), range_func(typ(arg)))
+
+    def test_range_iter_list(self):
+        range_iter_func = range_iter_len2
+        cres = compile_isolated(range_iter_func, [types.List(types.intp)])
+        cfunc = cres.entry_point
+        arglist = [1, 2, 3, 4, 5]
+        self.assertEqual(cfunc(arglist), len(arglist))
+
+    def test_range_attrs(self):
+        pyfunc = range_attrs
+        arglist = [(0, 0, 1),
+                   (0, -1, 1),
+                   (-1, 1, 1),
+                   (-1, 4, 1),
+                   (-1, 4, 10),
+                   (5, -5, -2),]
+
+        cres = compile_isolated(pyfunc, (types.int64,) * 3)
+        cfunc = cres.entry_point
+        for arg in arglist:
+            self.assertEqual(cfunc(*arg), pyfunc(*arg))
+
+    def test_range_contains(self):
+        pyfunc = range_contains
+        arglist = [(0, 0, 1),
+                   (-1, 0, 1),
+                   (1, 0, -1),
+                   (0, -1, 1),
+                   (0, 1, -1),
+                   (-1, 1, 1),
+                   (-1, 4, 1),
+                   (-1, 4, 10),
+                   (5, -5, -2),]
+
+        bool_vals = [True, False]
+        int_vals = [-10, -6, -5, -4, -2, -1, 0,
+                     1, 2, 4, 5, 6, 10]
+        float_vals = [-1.1, -1.0, 0.0, 1.0, 1.1]
+        complex_vals = [1 + 0j, 1 + 1j, 1.1 + 0j, 1.0 + 1.1j]
+
+        vallist = (bool_vals + int_vals + float_vals
+                + complex_vals)
+
+        cfunc = njit(pyfunc)
+        for arg in arglist:
+            for val in vallist:
+                self.assertEqual(cfunc(val, *arg), pyfunc(val, *arg))
+
+        non_numeric_vals = [{'a': 1}, [1, ], 'abc', (1,)]
+
+        cfunc_obj = jit(pyfunc, forceobj=True)
+        for arg in arglist:
+            for val in non_numeric_vals:
+                self.assertEqual(cfunc_obj(val, *arg), pyfunc(val, *arg))
+
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_recarray_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_recarray_usecases.py
new file mode 100644
index 000000000..1dba7bb9b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_recarray_usecases.py
@@ -0,0 +1,148 @@
+import sys
+
+import numpy as np
+
+from numba.core import types
+from numba.core.compiler import compile_isolated
+from numba.tests.support import captured_stdout, tag, TestCase
+import unittest
+from numba.np import numpy_support
+
+
+def usecase1(arr1, arr2):
+    """Base on https://github.com/numba/numba/issues/370
+
+    Modified to add test-able side effect.
+    """
+    n1 = arr1.size
+    n2 = arr2.size
+
+    for i1 in range(n1):
+        st1 = arr1[i1]
+        for i2 in range(n2):
+            st2 = arr2[i2]
+            st2.row += st1.p * st2.p + st1.row - st1.col
+
+        st1.p += st2.p
+        st1.col -= st2.col
+
+
+def usecase2(x, N):
+    """
+    Base on test1 of https://github.com/numba/numba/issues/381
+    """
+    for k in range(N):
+        y = x[k]
+        print(y.f1, y.s1, y.f2)
+
+
+def usecase3(x, N):
+    """
+    Base on test2 of https://github.com/numba/numba/issues/381
+    """
+    for k in range(N):
+        print(x.f1[k], x.s1[k], x.f2[k])
+
+
+def usecase4(x, N):
+    """
+    Base on test3 of https://github.com/numba/numba/issues/381
+    """
+    for k in range(N):
+        y = x[k]
+        print(y.f1, x.s1[k], y.f2)
+
+
+def usecase5(x, N):
+    """
+    Base on test4 of https://github.com/numba/numba/issues/381
+    """
+    for k in range(N):
+        print(x[k].f1, x.s1[k], x[k].f2)
+
+
+class TestRecordUsecase(TestCase):
+
+    def setUp(self):
+        fields = [('f1', '<f8'), ('s1', '|S3'), ('f2', '<f8')]
+        self.unaligned_dtype = np.dtype(fields)
+        self.aligned_dtype = np.dtype(fields, align=True)
+
+    def test_usecase1(self):
+        pyfunc = usecase1
+
+        # This is an unaligned dtype
+        mystruct_dt = np.dtype([('p', np.float64),
+                           ('row', np.float64),
+                           ('col', np.float64)])
+        mystruct = numpy_support.from_dtype(mystruct_dt)
+
+        cres = compile_isolated(pyfunc, (mystruct[:], mystruct[:]))
+        cfunc = cres.entry_point
+
+        st1 = np.recarray(3, dtype=mystruct_dt)
+        st2 = np.recarray(3, dtype=mystruct_dt)
+
+        st1.p = np.arange(st1.size) + 1
+        st1.row = np.arange(st1.size) + 1
+        st1.col = np.arange(st1.size) + 1
+
+        st2.p = np.arange(st2.size) + 1
+        st2.row = np.arange(st2.size) + 1
+        st2.col = np.arange(st2.size) + 1
+
+        expect1 = st1.copy()
+        expect2 = st2.copy()
+
+        got1 = expect1.copy()
+        got2 = expect2.copy()
+
+        pyfunc(expect1, expect2)
+        cfunc(got1, got2)
+
+        np.testing.assert_equal(expect1, got1)
+        np.testing.assert_equal(expect2, got2)
+
+    def _setup_usecase2to5(self, dtype):
+        N = 5
+        a = np.recarray(N, dtype=dtype)
+        a.f1 = np.arange(N)
+        a.f2 = np.arange(2, N + 2)
+        a.s1 = np.array(['abc'] * a.shape[0], dtype='|S3')
+        return a
+
+    def _test_usecase2to5(self, pyfunc, dtype):
+        array = self._setup_usecase2to5(dtype)
+        record_type = numpy_support.from_dtype(dtype)
+        cres = compile_isolated(pyfunc, (record_type[:], types.intp))
+        cfunc = cres.entry_point
+
+        with captured_stdout():
+            pyfunc(array, len(array))
+            expect = sys.stdout.getvalue()
+
+        with captured_stdout():
+            cfunc(array, len(array))
+            got = sys.stdout.getvalue()
+
+        self.assertEqual(expect, got)
+
+    def test_usecase2(self):
+        self._test_usecase2to5(usecase2, self.unaligned_dtype)
+        self._test_usecase2to5(usecase2, self.aligned_dtype)
+
+    def test_usecase3(self):
+        self._test_usecase2to5(usecase3, self.unaligned_dtype)
+        self._test_usecase2to5(usecase3, self.aligned_dtype)
+
+    def test_usecase4(self):
+        self._test_usecase2to5(usecase4, self.unaligned_dtype)
+        self._test_usecase2to5(usecase4, self.aligned_dtype)
+
+    def test_usecase5(self):
+        self._test_usecase2to5(usecase5, self.unaligned_dtype)
+        self._test_usecase2to5(usecase5, self.aligned_dtype)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_record_dtype.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_record_dtype.py
new file mode 100644
index 000000000..b76fdcacc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_record_dtype.py
@@ -0,0 +1,1737 @@
+import sys
+
+import numpy as np
+import ctypes
+from numba import jit, literal_unroll, njit, typeof
+from numba.core import types
+from numba.core.compiler import compile_isolated
+from numba.core.itanium_mangler import mangle_type
+from numba.core.config import IS_WIN32
+from numba.core.errors import TypingError
+from numba.np.numpy_support import numpy_version
+import unittest
+from numba.np import numpy_support
+from numba.tests.support import TestCase, skip_ppc64le_issue6465
+
+_FS = ('e', 'f')
+
+
+def get_a(ary, i):
+    return ary[i].a
+
+
+def get_b(ary, i):
+    return ary[i].b
+
+
+def get_c(ary, i):
+    return ary[i].c
+
+
+def make_getitem(item):
+    # This also exercises constant lookup from a closure variable
+    def get_xx(ary, i):
+        return ary[i][item]
+    return get_xx
+
+
+# Issue #1664: constant index lookup should fall back to regular getitem
+def get_zero_a(ary, _unused):
+    return ary[0].a
+
+
+getitem_a = make_getitem('a')
+getitem_b = make_getitem('b')
+getitem_c = make_getitem('c')
+getitem_0 = make_getitem(0)
+getitem_1 = make_getitem(1)
+getitem_2 = make_getitem(2)
+getitem_10 = make_getitem(10)  # OOB
+
+
+def get_a_subarray(ary, i):
+    return ary.a[i]
+
+
+def get_b_subarray(ary, i):
+    return ary.b[i]
+
+
+def get_c_subarray(ary, i):
+    return ary.c[i]
+
+
+def get_a_zero(ary, _unused):
+    return ary.a[0]
+
+
+def make_getitem_subarray(item):
+    # This also exercises constant lookup from a closure variable
+    def get_xx_subarray(ary, i):
+        return ary[item][i]
+    return get_xx_subarray
+
+
+getitem_a_subarray = make_getitem_subarray('a')
+getitem_b_subarray = make_getitem_subarray('b')
+getitem_c_subarray = make_getitem_subarray('c')
+
+
+def get_two_arrays_a(ary1, ary2, i):
+    return ary1[i].a + ary2[i].a
+
+
+def get_two_arrays_b(ary1, ary2, i):
+    return ary1[i].b + ary2[i].b
+
+
+def get_two_arrays_c(ary1, ary2, i):
+    return ary1[i].c + ary2[i].c
+
+
+def get_two_arrays_distinct(ary1, ary2, i):
+    return ary1[i].a + ary2[i].f
+
+
+def set_a(ary, i, v):
+    ary[i].a = v
+
+
+def set_b(ary, i, v):
+    ary[i].b = v
+
+
+def set_c(ary, i, v):
+    ary[i].c = v
+
+
+def make_setitem(item):
+    def set_xx(ary, i, v):
+        ary[i][item] = v
+    return set_xx
+
+
+setitem_a = make_setitem('a')
+setitem_b = make_setitem('b')
+setitem_c = make_setitem('c')
+setitem_0 = make_setitem(0)
+setitem_1 = make_setitem(1)
+setitem_2 = make_setitem(2)
+setitem_10 = make_setitem(10)  # OOB
+
+
+def set_a_subarray(ary, i, v):
+    ary.a[i] = v
+
+
+def set_b_subarray(ary, i, v):
+    ary.b[i] = v
+
+
+def set_c_subarray(ary, i, v):
+    ary.c[i] = v
+
+
+def make_setitem_subarray(item):
+    def set_xx_subarray(ary, i, v):
+        ary[item][i] = v
+    return set_xx_subarray
+
+
+setitem_a_subarray = make_setitem('a')
+setitem_b_subarray = make_setitem('b')
+setitem_c_subarray = make_setitem('c')
+
+
+def set_record(ary, i, j):
+    ary[i] = ary[j]
+
+
+def get_record_a(rec, val):
+    x = rec.a
+    rec.a = val
+    return x
+
+
+def get_record_b(rec, val):
+    x = rec.b
+    rec.b = val
+    return x
+
+
+def get_record_c(rec, val):
+    x = rec.c
+    rec.c = val
+    return x
+
+
+def get_record_rev_a(val, rec):
+    x = rec.a
+    rec.a = val
+    return x
+
+
+def get_record_rev_b(val, rec):
+    x = rec.b
+    rec.b = val
+    return x
+
+
+def get_record_rev_c(val, rec):
+    x = rec.c
+    rec.c = val
+    return x
+
+
+def get_two_records_a(rec1, rec2):
+    x = rec1.a + rec2.a
+    return x
+
+
+def get_two_records_b(rec1, rec2):
+    x = rec1.b + rec2.b
+    return x
+
+
+def get_two_records_c(rec1, rec2):
+    x = rec1.c + rec2.c
+    return x
+
+
+def get_two_records_distinct(rec1, rec2):
+    x = rec1.a + rec2.f
+    return x
+
+
+def record_return(ary, i):
+    return ary[i]
+
+
+def record_write_array(ary):
+    ary.g = 2
+    ary.h[0] = 3.0
+    ary.h[1] = 4.0
+
+
+def record_write_2d_array(ary):
+    ary.i = 3
+    ary.j[0, 0] = 5.0
+    ary.j[0, 1] = 6.0
+    ary.j[1, 0] = 7.0
+    ary.j[1, 1] = 8.0
+    ary.j[2, 0] = 9.0
+    ary.j[2, 1] = 10.0
+
+
+def record_write_full_array(rec):
+    rec.j[:, :] = np.ones((3, 2))
+
+
+def record_write_full_array_alt(rec):
+    rec['j'][:, :] = np.ones((3, 2))
+
+
+def recarray_set_record(ary, rec):
+    ary[0] = rec
+
+
+def recarray_write_array_of_nestedarray_broadcast(ary):
+    ary.j[:, :, :] = 1
+    return ary
+
+
+def record_setitem_array(rec_source, rec_dest):
+    rec_dest['j'] = rec_source['j']
+
+
+def recarray_write_array_of_nestedarray(ary):
+    ary.j[:, :, :] = np.ones((2, 3, 2), dtype=np.float64)
+    return ary
+
+
+def recarray_getitem_return(ary):
+    return ary[0]
+
+
+def recarray_getitem_field_return(ary):
+    return ary['h']
+
+
+def recarray_getitem_field_return2(ary):
+    return ary.h
+
+
+def recarray_getitem_field_return2_2d(ary):
+    return ary.j
+
+
+def rec_getitem_field_slice_2d(rec):
+    return rec.j[0]
+
+
+def recarray_getitem_field_slice_2d(ary):
+    return ary.j[0][0]
+
+
+def array_rec_getitem_field_slice_2d_0(rec):
+    return rec['j'][0]
+
+
+def array_getitem_field_slice_2d_0(ary):
+    return ary['j'][0][0]
+
+
+def array_rec_getitem_field_slice_2d_1(rec):
+    return rec['j'][1]
+
+
+def array_getitem_field_slice_2d_1(ary):
+    return ary['j'][1][0]
+
+
+def rec_getitem_range_slice_4d(rec):
+    return rec.p[0:2, 0, 1:4, 3:6]
+
+
+def recarray_getitem_range_slice_4d(ary):
+    return ary[0].p[0:2, 0, 1:4, 3:6]
+
+
+def record_read_array0(ary):
+    return ary.h[0]
+
+
+def record_read_array0_alt(ary):
+    return ary[0].h
+
+
+def record_read_array1(ary):
+    return ary.h[1]
+
+
+def record_read_whole_array(ary):
+    return ary.h
+
+
+def record_read_2d_array00(ary):
+    return ary.j[0,0]
+
+
+def record_read_2d_array10(ary):
+    return ary.j[1,0]
+
+
+def record_read_2d_array01(ary):
+    return ary.j[0,1]
+
+
+def record_read_first_arr(ary):
+    return ary.k[2, 2]
+
+
+def record_read_second_arr(ary):
+    return ary.l[2, 2]
+
+
+def get_shape(rec):
+    return np.shape(rec.j)
+
+
+def get_charseq(ary, i):
+    return ary[i].n
+
+
+def set_charseq(ary, i, cs):
+    ary[i].n = cs
+
+
+def get_charseq_tuple(ary, i):
+    return ary[i].m, ary[i].n
+
+
+def get_field1(rec):
+    fs = ('e', 'f')
+    f = fs[1]
+    return rec[f]
+
+
+def get_field2(rec):
+    fs = ('e', 'f')
+    out = 0
+    for f in literal_unroll(fs):
+        out += rec[f]
+    return out
+
+
+def get_field3(rec):
+    f = _FS[1]
+    return rec[f]
+
+
+def get_field4(rec):
+    out = 0
+    for f in literal_unroll(_FS):
+        out += rec[f]
+    return out
+
+
+def set_field1(rec):
+    fs = ('e', 'f')
+    f = fs[1]
+    rec[f] = 10
+    return rec
+
+
+def set_field2(rec):
+    fs = ('e', 'f')
+    for f in literal_unroll(fs):
+        rec[f] = 10
+    return rec
+
+
+def set_field3(rec):
+    f = _FS[1]
+    rec[f] = 10
+    return rec
+
+
+def set_field4(rec):
+    for f in literal_unroll(_FS):
+        rec[f] = 10
+    return rec
+
+
+def set_field_slice(arr):
+    arr['k'][:] = 0.0
+    return arr
+
+
+def assign_array_to_nested(dest):
+    tmp = (np.arange(3) + 1).astype(np.int16)
+    dest['array1'] = tmp
+
+
+def assign_array_to_nested_2d(dest):
+    tmp = (np.arange(6) + 1).astype(np.int16).reshape((3, 2))
+    dest['array2'] = tmp
+
+
+recordtype = np.dtype([('a', np.float64),
+                       ('b', np.int16),
+                       ('c', np.complex64),
+                       ('d', (np.str_, 5))])
+
+recordtype2 = np.dtype([('e', np.int32),
+                        ('f', np.float64)], align=True)
+
+recordtype3 = np.dtype([('first', np.float32),
+                        ('second', np.float64)])
+
+recordwitharray = np.dtype([('g', np.int32),
+                            ('h', np.float32, 2)])
+
+recordwith2darray = np.dtype([('i', np.int32),
+                              ('j', np.float32, (3, 2))])
+
+recordwith2arrays = np.dtype([('k', np.int32, (10, 20)),
+                              ('l', np.int32, (6, 12))])
+
+recordwithcharseq = np.dtype([('m', np.int32),
+                              ('n', 'S5')])
+
+recordwith4darray = np.dtype([('o', np.int64),
+                              ('p', np.float32, (3, 2, 5, 7)),
+                              ('q', 'U10'),])
+
+nested_array1_dtype = np.dtype([("array1", np.int16, (3,))], align=True)
+
+nested_array2_dtype = np.dtype([("array2", np.int16, (3, 2))], align=True)
+
+
+class TestRecordDtypeMakeCStruct(unittest.TestCase):
+    def test_two_scalars(self):
+
+        class Ref(ctypes.Structure):
+            _fields_ = [
+                ('apple', ctypes.c_int32),
+                ('orange', ctypes.c_float),
+            ]
+
+        ty = types.Record.make_c_struct([
+            ('apple', types.int32),
+            ('orange', types.float32),
+        ])
+        # Correct offsets
+        self.assertEqual(len(ty), 2)
+        self.assertEqual(ty.offset('apple'), Ref.apple.offset)
+        self.assertEqual(ty.offset('orange'), Ref.orange.offset)
+        # Correct size
+        self.assertEqual(ty.size, ctypes.sizeof(Ref))
+        # Is aligned
+        dtype = ty.dtype
+        self.assertTrue(dtype.isalignedstruct)
+
+    def test_three_scalars(self):
+
+        class Ref(ctypes.Structure):
+            _fields_ = [
+                ('apple', ctypes.c_int32),
+                ('mango', ctypes.c_int8),
+                ('orange', ctypes.c_float),
+            ]
+
+        ty = types.Record.make_c_struct([
+            ('apple', types.int32),
+            ('mango', types.int8),
+            ('orange', types.float32),
+        ])
+        # Correct offsets
+        self.assertEqual(len(ty), 3)
+        self.assertEqual(ty.offset('apple'), Ref.apple.offset)
+        self.assertEqual(ty.offset('mango'), Ref.mango.offset)
+        self.assertEqual(ty.offset('orange'), Ref.orange.offset)
+        # Correct size
+        self.assertEqual(ty.size, ctypes.sizeof(Ref))
+        # Is aligned
+        dtype = ty.dtype
+        self.assertTrue(dtype.isalignedstruct)
+
+    def test_complex_struct(self):
+        class Complex(ctypes.Structure):
+            _fields_ = [
+                ('real', ctypes.c_double),
+                ('imag', ctypes.c_double),
+            ]
+
+        class Ref(ctypes.Structure):
+            _fields_ = [
+                ('apple', ctypes.c_int32),
+                ('mango', Complex),
+            ]
+
+        ty = types.Record.make_c_struct([
+            ('apple', types.intc),
+            ('mango', types.complex128),
+        ])
+        # Correct offsets
+        self.assertEqual(len(ty), 2)
+        self.assertEqual(ty.offset('apple'), Ref.apple.offset)
+        self.assertEqual(ty.offset('mango'), Ref.mango.offset)
+        # Correct size
+        self.assertEqual(ty.size, ctypes.sizeof(Ref))
+        # Is aligned?
+        # NumPy version < 1.16 misalign complex-128 types to 16bytes.
+        # (it seems to align on windows?!)
+        if numpy_version >= (1, 16) or IS_WIN32:
+            dtype = ty.dtype
+            self.assertTrue(dtype.isalignedstruct)
+        else:
+            with self.assertRaises(ValueError) as raises:
+                dtype = ty.dtype
+            # get numpy alignment
+            npalign = np.dtype(np.complex128).alignment
+            # llvm should align to alignment of double.
+            llalign = np.dtype(np.double).alignment
+            self.assertIn(
+                ("NumPy is using a different alignment ({}) "
+                 "than Numba/LLVM ({}) for complex128. "
+                 "This is likely a NumPy bug.").format(npalign, llalign),
+                str(raises.exception),
+            )
+
+    def test_nestedarray_issue_8132(self):
+        # issue#8132 is caused by misrepresenting the NestedArray. Instead of
+        # using the storage layout for the contained data, it is represented
+        # as an array object structure.
+
+        # Make an array that is longer than the array object structure.
+        data = np.arange(27 * 2, dtype=np.float64).reshape(27, 2)
+        recty = types.Record.make_c_struct([
+            ('data', types.NestedArray(dtype=types.float64, shape=data.shape)),
+        ])
+        arr = np.array((data,), dtype=recty.dtype)
+        # unpack the nestedarray as a normal array
+        [extracted_array] = arr.tolist()
+        # check that is matches the original values
+        np.testing.assert_array_equal(extracted_array, data)
+
+
+class TestRecordDtype(unittest.TestCase):
+
+    def _createSampleArrays(self):
+        '''
+        Set up the data structures to be used with the Numpy and Numba
+        versions of functions.
+
+        In this case, both accept recarrays.
+        '''
+        self.refsample1d = np.recarray(3, dtype=recordtype)
+        self.refsample1d2 = np.recarray(3, dtype=recordtype2)
+        self.refsample1d3 = np.recarray(3, dtype=recordtype)
+
+        self.nbsample1d = np.recarray(3, dtype=recordtype)
+        self.nbsample1d2 = np.recarray(3, dtype=recordtype2)
+        self.nbsample1d3 = np.recarray(3, dtype=recordtype)
+
+    def setUp(self):
+
+        self._createSampleArrays()
+
+        for ary in (self.refsample1d, self.nbsample1d):
+            for i in range(ary.size):
+                x = i + 1
+                ary[i]['a'] = x / 2
+                ary[i]['b'] = x
+                ary[i]['c'] = x * 1j
+                ary[i]['d'] = "%d" % x
+
+        for ary2 in (self.refsample1d2, self.nbsample1d2):
+            for i in range(ary2.size):
+                x = i + 5
+                ary2[i]['e'] = x
+                ary2[i]['f'] = x / 2
+
+        for ary3 in (self.refsample1d3, self.nbsample1d3):
+            for i in range(ary3.size):
+                x = i + 10
+                ary3[i]['a'] = x / 2
+                ary3[i]['b'] = x
+                ary3[i]['c'] = x * 1j
+                ary3[i]['d'] = "%d" % x
+
+    def get_cfunc(self, pyfunc, argspec):
+        cres = compile_isolated(pyfunc, argspec)
+        return cres.entry_point
+
+    def test_from_dtype(self):
+        rec = numpy_support.from_dtype(recordtype)
+        self.assertEqual(rec.typeof('a'), types.float64)
+        self.assertEqual(rec.typeof('b'), types.int16)
+        self.assertEqual(rec.typeof('c'), types.complex64)
+        self.assertEqual(rec.typeof('d'), types.UnicodeCharSeq(5))
+        self.assertEqual(rec.offset('a'), recordtype.fields['a'][1])
+        self.assertEqual(rec.offset('b'), recordtype.fields['b'][1])
+        self.assertEqual(rec.offset('c'), recordtype.fields['c'][1])
+        self.assertEqual(rec.offset('d'), recordtype.fields['d'][1])
+        self.assertEqual(recordtype.itemsize, rec.size)
+
+    def _test_get_equal(self, pyfunc):
+        rec = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (rec[:], types.intp))
+        for i in range(self.refsample1d.size):
+            self.assertEqual(pyfunc(self.refsample1d, i),
+                             cfunc(self.nbsample1d, i))
+
+    def test_get_a(self):
+        self._test_get_equal(get_a)
+        self._test_get_equal(get_a_subarray)
+        self._test_get_equal(getitem_a)
+        self._test_get_equal(getitem_a_subarray)
+        self._test_get_equal(get_a_zero)
+        self._test_get_equal(get_zero_a)
+
+    def test_get_b(self):
+        self._test_get_equal(get_b)
+        self._test_get_equal(get_b_subarray)
+        self._test_get_equal(getitem_b)
+        self._test_get_equal(getitem_b_subarray)
+
+    def test_get_c(self):
+        self._test_get_equal(get_c)
+        self._test_get_equal(get_c_subarray)
+        self._test_get_equal(getitem_c)
+        self._test_get_equal(getitem_c_subarray)
+
+    def test_getitem_static_int_index(self):
+        self._test_get_equal(getitem_0)
+        self._test_get_equal(getitem_1)
+        self._test_get_equal(getitem_2)
+
+        # this exception on accessing OOB integer index
+        rec = numpy_support.from_dtype(recordtype)
+        with self.assertRaises(TypingError) as raises:
+            self.get_cfunc(getitem_10, (rec[:], types.intp))
+
+        msg = "Requested index 10 is out of range"
+        self.assertIn(msg, str(raises.exception))
+
+    def _test_get_two_equal(self, pyfunc):
+        '''
+        Test with two arrays of the same type
+        '''
+        rec = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (rec[:], rec[:], types.intp))
+        for i in range(self.refsample1d.size):
+            self.assertEqual(pyfunc(self.refsample1d, self.refsample1d3, i),
+                             cfunc(self.nbsample1d, self.nbsample1d3, i))
+
+    def test_two_distinct_arrays(self):
+        '''
+        Test with two arrays of distinct record types
+        '''
+        pyfunc = get_two_arrays_distinct
+        rec1 = numpy_support.from_dtype(recordtype)
+        rec2 = numpy_support.from_dtype(recordtype2)
+        cfunc = self.get_cfunc(pyfunc, (rec1[:], rec2[:], types.intp))
+        for i in range(self.refsample1d.size):
+            pres = pyfunc(self.refsample1d, self.refsample1d2, i)
+            cres = cfunc(self.nbsample1d, self.nbsample1d2, i)
+            self.assertEqual(pres,cres)
+
+    def test_get_two_a(self):
+        self._test_get_two_equal(get_two_arrays_a)
+
+    def test_get_two_b(self):
+        self._test_get_two_equal(get_two_arrays_b)
+
+    def test_get_two_c(self):
+        self._test_get_two_equal(get_two_arrays_c)
+
+    def _test_set_equal(self, pyfunc, value, valuetype):
+        rec = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (rec[:], types.intp, valuetype))
+
+        for i in range(self.refsample1d.size):
+            expect = self.refsample1d.copy()
+            pyfunc(expect, i, value)
+
+            got = self.nbsample1d.copy()
+            cfunc(got, i, value)
+
+            # Match the entire array to ensure no memory corruption
+            np.testing.assert_equal(expect, got)
+
+    def test_set_a(self):
+        def check(pyfunc):
+            self._test_set_equal(pyfunc, 3.1415, types.float64)
+            # Test again to check if coercion works
+            self._test_set_equal(pyfunc, 3., types.float32)
+        check(set_a)
+        check(set_a_subarray)
+        check(setitem_a)
+        check(setitem_a_subarray)
+
+    def test_set_b(self):
+        def check(pyfunc):
+            self._test_set_equal(pyfunc, 123, types.int32)
+            # Test again to check if coercion works
+            self._test_set_equal(pyfunc, 123, types.float64)
+        check(set_b)
+        check(set_b_subarray)
+        check(setitem_b)
+        check(setitem_b_subarray)
+
+    def test_set_c(self):
+        def check(pyfunc):
+            self._test_set_equal(pyfunc, 43j, types.complex64)
+            # Test again to check if coercion works
+            self._test_set_equal(pyfunc, 43j, types.complex128)
+        check(set_c)
+        check(set_c_subarray)
+        check(setitem_c)
+        check(setitem_c_subarray)
+
+    def test_setitem_static_int_index(self):
+        def check(pyfunc):
+            self._test_set_equal(pyfunc, 3.1415, types.float64)
+            # Test again to check if coercion works
+            self._test_set_equal(pyfunc, 3., types.float32)
+
+        check(setitem_0)
+        check(setitem_1)
+        check(setitem_2)
+
+        # this exception on accessing OOB integer index
+        rec = numpy_support.from_dtype(recordtype)
+        with self.assertRaises(TypingError) as raises:
+            self.get_cfunc(setitem_10, (rec[:], types.intp, types.float64))
+
+        msg = "Requested index 10 is out of range"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_set_record(self):
+        pyfunc = set_record
+        rec = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (rec[:], types.intp, types.intp))
+
+        test_indices = [(0, 1), (1, 2), (0, 2)]
+        for i, j in test_indices:
+            expect = self.refsample1d.copy()
+            pyfunc(expect, i, j)
+
+            got = self.nbsample1d.copy()
+            cfunc(got, i, j)
+
+            # Match the entire array to ensure no memory corruption
+            self.assertEqual(expect[i], expect[j])
+            self.assertEqual(got[i], got[j])
+            np.testing.assert_equal(expect, got)
+
+    def _test_record_args(self, revargs):
+        # Testing scalar record value as argument
+
+        npval = self.refsample1d.copy()[0]
+        nbval = self.nbsample1d.copy()[0]
+        attrs = 'abc'
+        valtypes = types.float64, types.int16, types.complex64
+        values = 1.23, 12345, 123 + 456j
+        old_refcnt = sys.getrefcount(nbval)
+
+        for attr, valtyp, val in zip(attrs, valtypes, values):
+            expected = getattr(npval, attr)
+            nbrecord = numpy_support.from_dtype(recordtype)
+
+            # Test with a record as either the first argument or the second
+            # argument (issue #870)
+            if revargs:
+                prefix = 'get_record_rev_'
+                argtypes = (valtyp, nbrecord)
+                args = (val, nbval)
+            else:
+                prefix = 'get_record_'
+                argtypes = (nbrecord, valtyp)
+                args = (nbval, val)
+
+            pyfunc = globals()[prefix + attr]
+            cfunc = self.get_cfunc(pyfunc, argtypes)
+
+            got = cfunc(*args)
+            try:
+                self.assertEqual(expected, got)
+            except AssertionError:
+                # On ARM, a LLVM misoptimization can produce buggy code,
+                # see https://llvm.org/bugs/show_bug.cgi?id=24669
+                import llvmlite.binding as ll
+                if attr != 'c':
+                    raise
+                if ll.get_default_triple() != 'armv7l-unknown-linux-gnueabihf':
+                    raise
+                self.assertEqual(val, got)
+            else:
+                self.assertEqual(nbval[attr], val)
+            del got, expected, args
+
+        # Check for potential leaks (issue #441)
+        self.assertEqual(sys.getrefcount(nbval), old_refcnt)
+
+    def test_record_args(self):
+        self._test_record_args(False)
+
+    def test_record_args_reverse(self):
+        self._test_record_args(True)
+
+    def test_two_records(self):
+        '''
+        Testing the use of two scalar records of the same type
+        '''
+        npval1 = self.refsample1d.copy()[0]
+        npval2 = self.refsample1d.copy()[1]
+        nbval1 = self.nbsample1d.copy()[0]
+        nbval2 = self.nbsample1d.copy()[1]
+        attrs = 'abc'
+        valtypes = types.float64, types.int32, types.complex64
+
+        for attr, valtyp in zip(attrs, valtypes):
+            expected = getattr(npval1, attr) + getattr(npval2, attr)
+
+            nbrecord = numpy_support.from_dtype(recordtype)
+            pyfunc = globals()['get_two_records_' + attr]
+            cfunc = self.get_cfunc(pyfunc, (nbrecord, nbrecord))
+
+            got = cfunc(nbval1, nbval2)
+            self.assertEqual(expected, got)
+
+    def test_two_distinct_records(self):
+        '''
+        Testing the use of two scalar records of differing type
+        '''
+        nbval1 = self.nbsample1d.copy()[0]
+        nbval2 = self.refsample1d2.copy()[0]
+        expected = nbval1['a'] + nbval2['f']
+
+        nbrecord1 = numpy_support.from_dtype(recordtype)
+        nbrecord2 = numpy_support.from_dtype(recordtype2)
+        cfunc = self.get_cfunc(get_two_records_distinct, (nbrecord1, nbrecord2))
+
+        got = cfunc(nbval1, nbval2)
+        self.assertEqual(expected, got)
+
+    def test_record_return(self):
+        # Testing scalar record value as return value.
+        # We can only return a copy of the record.
+        pyfunc = record_return
+        recty = numpy_support.from_dtype(recordtype)
+        cfunc = self.get_cfunc(pyfunc, (recty[:], types.intp))
+
+        attrs = 'abc'
+        indices = [0, 1, 2]
+        for index, attr in zip(indices, attrs):
+            nbary = self.nbsample1d.copy()
+            old_refcnt = sys.getrefcount(nbary)
+            res = cfunc(nbary, index)
+            self.assertEqual(nbary[index], res)
+            # Prove that this is a by-value copy
+            setattr(res, attr, 0)
+            self.assertNotEqual(nbary[index], res)
+            del res
+            # Check for potential leaks
+            self.assertEqual(sys.getrefcount(nbary), old_refcnt)
+
+    def test_record_arg_transform(self):
+        # Testing that transforming the name of a record type argument to a
+        # function does not result in the fields of the record being used to
+        # uniquely identify them, and that no other condition results in the
+        # transformed name being excessively long.
+
+        rec = numpy_support.from_dtype(recordtype3)
+        transformed = mangle_type(rec)
+        self.assertNotIn('first', transformed)
+        self.assertNotIn('second', transformed)
+        # len(transformed) is generally 10, but could be longer if a large
+        # number of typecodes are in use. Checking <20 should provide enough
+        # tolerance.
+        self.assertLess(len(transformed), 20)
+
+        struct_arr = types.Array(rec, 1, 'C')
+        transformed = mangle_type(struct_arr)
+        self.assertIn('Array', transformed)
+        self.assertNotIn('first', transformed)
+        self.assertNotIn('second', transformed)
+        # Length is usually 50 - 5 chars tolerance as above.
+        self.assertLess(len(transformed), 50)
+
+    def test_record_two_arrays(self):
+        # Tests that comparison of NestedArrays by key is working correctly. If
+        # the two NestedArrays in recordwith2arrays compare equal (same length
+        # and ndim but different shape) incorrect code will be generated for one
+        # of the functions.
+
+        nbrecord = numpy_support.from_dtype(recordwith2arrays)
+        rec = np.recarray(1, dtype=recordwith2arrays)[0]
+        rec.k[:] = np.arange(200).reshape(10,20)
+        rec.l[:] = np.arange(72).reshape(6,12)
+
+        pyfunc = record_read_first_arr
+        cfunc = self.get_cfunc(pyfunc, (nbrecord,))
+        self.assertEqual(cfunc(rec), pyfunc(rec))
+
+        pyfunc = record_read_second_arr
+        cfunc = self.get_cfunc(pyfunc, (nbrecord,))
+        self.assertEqual(cfunc(rec), pyfunc(rec))
+
+        pyfunc = set_field_slice
+        cfunc = self.get_cfunc(pyfunc, (nbrecord,))
+        np.testing.assert_array_equal(cfunc(rec), pyfunc(rec))
+
+    def test_structure_dtype_with_titles(self):
+        # the following is the definition of int4 vector type from pyopencl
+        vecint4 = np.dtype([(('x', 's0'), 'i4'), (('y', 's1'), 'i4'),
+                            (('z', 's2'), 'i4'), (('w', 's3'), 'i4')])
+        nbtype = numpy_support.from_dtype(vecint4)
+        self.assertEqual(len(nbtype.fields), len(vecint4.fields))
+
+        arr = np.zeros(10, dtype=vecint4)
+
+        def pyfunc(a):
+            for i in range(a.size):
+                j = i + 1
+                a[i]['s0'] = j * 2
+                a[i]['x'] += -1
+
+                a[i]['s1'] = j * 3
+                a[i]['y'] += -2
+
+                a[i]['s2'] = j * 4
+                a[i]['z'] += -3
+
+                a[i]['s3'] = j * 5
+                a[i]['w'] += -4
+
+            return a
+
+        expect = pyfunc(arr.copy())
+        cfunc = self.get_cfunc(pyfunc, (nbtype[:],))
+        got = cfunc(arr.copy())
+        np.testing.assert_equal(expect, got)
+
+    def test_record_dtype_with_titles_roundtrip(self):
+        recdtype = np.dtype([(("title a", 'a'), np.float_), ('b', np.float_)])
+        nbtype = numpy_support.from_dtype(recdtype)
+        self.assertTrue(nbtype.is_title('title a'))
+        self.assertFalse(nbtype.is_title('a'))
+        self.assertFalse(nbtype.is_title('b'))
+        got = numpy_support.as_dtype(nbtype)
+        self.assertTrue(got, recdtype)
+
+
+def _get_cfunc_nopython(pyfunc, argspec):
+    return jit(argspec, nopython=True)(pyfunc)
+
+
+class TestRecordDtypeWithDispatcher(TestRecordDtype):
+    '''
+    Same as TestRecordDtype, but stressing the Dispatcher's type dispatch
+    mechanism (issue #384). Note that this does not stress caching of ndarray
+    typecodes as the path that uses the cache is not taken with recarrays.
+    '''
+
+    def get_cfunc(self, pyfunc, argspec):
+        return _get_cfunc_nopython(pyfunc, argspec)
+
+
+class TestRecordDtypeWithStructArrays(TestRecordDtype):
+    '''
+    Same as TestRecordDtype, but using structured arrays instead of recarrays.
+    '''
+
+    def _createSampleArrays(self):
+        '''
+        Two different versions of the data structures are required because Numba
+        supports attribute access on structured arrays, whereas Numpy does not.
+
+        However, the semantics of recarrays and structured arrays are equivalent
+        for these tests so Numpy with recarrays can be used for comparison with
+        Numba using structured arrays.
+        '''
+
+        self.refsample1d = np.recarray(3, dtype=recordtype)
+        self.refsample1d2 = np.recarray(3, dtype=recordtype2)
+        self.refsample1d3 = np.recarray(3, dtype=recordtype)
+
+        self.nbsample1d = np.zeros(3, dtype=recordtype)
+        self.nbsample1d2 = np.zeros(3, dtype=recordtype2)
+        self.nbsample1d3 = np.zeros(3, dtype=recordtype)
+
+
+class TestRecordDtypeWithStructArraysAndDispatcher(TestRecordDtypeWithStructArrays):    # noqa: E501
+    '''
+    Same as TestRecordDtypeWithStructArrays, stressing the Dispatcher's type
+    dispatch mechanism (issue #384) and caching of ndarray typecodes for void
+    types (which occur in structured arrays).
+    '''
+
+    def get_cfunc(self, pyfunc, argspec):
+        return _get_cfunc_nopython(pyfunc, argspec)
+
+
+@skip_ppc64le_issue6465
+class TestRecordDtypeWithCharSeq(unittest.TestCase):
+
+    def _createSampleaArray(self):
+        self.refsample1d = np.recarray(3, dtype=recordwithcharseq)
+        self.nbsample1d = np.zeros(3, dtype=recordwithcharseq)
+
+    def _fillData(self, arr):
+        for i in range(arr.size):
+            arr[i]['m'] = i
+
+        arr[0]['n'] = 'abcde'  # no null-byte
+        arr[1]['n'] = 'xyz'  # null-byte
+        arr[2]['n'] = 'u\x00v\x00\x00'  # null-byte at the middle and at the end
+
+    def setUp(self):
+        self._createSampleaArray()
+        self._fillData(self.refsample1d)
+        self._fillData(self.nbsample1d)
+
+    def get_cfunc(self, pyfunc):
+        rectype = numpy_support.from_dtype(recordwithcharseq)
+        cres = compile_isolated(pyfunc, (rectype[:], types.intp))
+        return cres.entry_point
+
+    def test_return_charseq(self):
+        pyfunc = get_charseq
+        cfunc = self.get_cfunc(pyfunc)
+        for i in range(self.refsample1d.size):
+            expected = pyfunc(self.refsample1d, i)
+            got = cfunc(self.nbsample1d, i)
+            self.assertEqual(expected, got)
+
+    def test_npm_argument_charseq(self):
+        # Test CharSeq as NPM argument
+
+        def pyfunc(arr, i):
+            return arr[i].n
+
+        identity = jit(lambda x: x)   # an identity function
+
+        @jit(nopython=True)
+        def cfunc(arr, i):
+            return identity(arr[i].n)
+
+        for i in range(self.refsample1d.size):
+            expected = pyfunc(self.refsample1d, i)
+            got = cfunc(self.nbsample1d, i)
+            self.assertEqual(expected, got)
+
+    def test_py_argument_charseq(self):
+        # Test CharSeq as python wrapper argument
+
+        pyfunc = set_charseq
+
+        # compile
+        rectype = numpy_support.from_dtype(recordwithcharseq)
+        cres = compile_isolated(pyfunc, (rectype[:], types.intp,
+                                         rectype.typeof('n')))
+        cfunc = cres.entry_point
+
+        for i in range(self.refsample1d.size):
+            chars = "{0}".format(hex(i + 10))
+            pyfunc(self.refsample1d, i, chars)
+            cfunc(self.nbsample1d, i, chars)
+            np.testing.assert_equal(self.refsample1d, self.nbsample1d)
+
+    def test_py_argument_char_seq_near_overflow(self):
+        # Test strings that are as long as the charseq capacity
+
+        pyfunc = set_charseq
+        # compile
+        rectype = numpy_support.from_dtype(recordwithcharseq)
+        cres = compile_isolated(pyfunc, (rectype[:], types.intp,
+                                         rectype.typeof('n')))
+        cfunc = cres.entry_point
+
+        cs_near_overflow = "abcde"
+
+        self.assertEqual(len(cs_near_overflow),
+                         recordwithcharseq['n'].itemsize)
+
+        cfunc(self.nbsample1d, 0, cs_near_overflow)
+        self.assertEqual(self.nbsample1d[0]['n'].decode('ascii'),
+                         cs_near_overflow)
+        # Check that we didn't overwrite
+        np.testing.assert_equal(self.refsample1d[1:], self.nbsample1d[1:])
+
+    def test_py_argument_char_seq_truncate(self):
+        # NumPy silently truncates strings to fix inside charseq
+
+        pyfunc = set_charseq
+        # compile
+        rectype = numpy_support.from_dtype(recordwithcharseq)
+        cres = compile_isolated(pyfunc, (rectype[:], types.intp,
+                                         rectype.typeof('n')))
+        cfunc = cres.entry_point
+
+        cs_overflowed = "abcdef"
+
+        pyfunc(self.refsample1d, 1, cs_overflowed)
+        cfunc(self.nbsample1d, 1, cs_overflowed)
+        np.testing.assert_equal(self.refsample1d, self.nbsample1d)
+        self.assertEqual(self.refsample1d[1].n,
+                         cs_overflowed[:-1].encode("ascii"))
+
+    def test_return_charseq_tuple(self):
+        pyfunc = get_charseq_tuple
+        cfunc = self.get_cfunc(pyfunc)
+        for i in range(self.refsample1d.size):
+            expected = pyfunc(self.refsample1d, i)
+            got = cfunc(self.nbsample1d, i)
+            self.assertEqual(expected, got)
+
+
+class TestRecordArrayGetItem(unittest.TestCase):
+
+    # Test getitem when index is Literal[str]
+
+    def test_literal_variable(self):
+        arr = np.array([1, 2], dtype=recordtype2)
+        pyfunc = get_field1
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(arr[0]), jitfunc(arr[0]))
+
+    def test_literal_unroll(self):
+        arr = np.array([1, 2], dtype=recordtype2)
+        pyfunc = get_field2
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(arr[0]), jitfunc(arr[0]))
+
+    def test_literal_variable_global_tuple(self):
+        # This tests the getitem of record array when the indexes come from a
+        # global tuple. It tests getitem behaviour but also tests that a global
+        # tuple is being typed as a tuple of constants.
+
+        arr = np.array([1, 2], dtype=recordtype2)
+        pyfunc = get_field3
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(arr[0]), jitfunc(arr[0]))
+
+    def test_literal_unroll_global_tuple(self):
+        # This tests the getitem of record array when the indexes come from a
+        # global tuple and are being unrolled.
+        # It tests getitem behaviour but also tests that literal_unroll accepts
+        # a global tuple as argument
+
+        arr = np.array([1, 2], dtype=recordtype2)
+        pyfunc = get_field4
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(arr[0]), jitfunc(arr[0]))
+
+    def test_literal_unroll_free_var_tuple(self):
+        # This tests the getitem of record array when the indexes come from a
+        # free variable tuple (not local, not global) and are being unrolled.
+        # It tests getitem behaviour but also tests that literal_unroll accepts
+        # a free variable tuple as argument
+
+        fs = ('e', 'f')
+        arr = np.array([1, 2], dtype=recordtype2)
+
+        def get_field(rec):
+            out = 0
+            for f in literal_unroll(fs):
+                out += rec[f]
+            return out
+
+        jitfunc = njit(get_field)
+        self.assertEqual(get_field(arr[0]), jitfunc(arr[0]))
+
+    def test_error_w_invalid_field(self):
+        arr = np.array([1, 2], dtype=recordtype3)
+        jitfunc = njit(get_field1)
+        with self.assertRaises(TypingError) as raises:
+            jitfunc(arr[0])
+        self.assertIn("Field 'f' was not found in record with fields "
+                      "('first', 'second')", str(raises.exception))
+
+    def test_literal_unroll_dynamic_to_static_getitem_transform(self):
+        # See issue #6634
+        keys = ('a', 'b', 'c')
+        n = 5
+
+        def pyfunc(rec):
+            x = np.zeros((n,))
+            for o in literal_unroll(keys):
+                x += rec[o]
+            return x
+
+        dt = np.float64
+        ldd = [np.arange(dt(n)) for x in keys]
+        ldk = [(x, np.float64,) for x in keys]
+        rec = np.rec.fromarrays(ldd, dtype=ldk)
+
+        expected = pyfunc(rec)
+        got = njit(pyfunc)(rec)
+        np.testing.assert_allclose(expected, got)
+
+
+class TestRecordArraySetItem(unittest.TestCase):
+    """
+    Test setitem when index is Literal[str]
+    """
+    def test_literal_variable(self):
+        arr = np.array([1, 2], dtype=recordtype2)
+        pyfunc = set_field1
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(arr[0].copy()), jitfunc(arr[0].copy()))
+
+    def test_literal_unroll(self):
+        arr = np.array([1, 2], dtype=recordtype2)
+        pyfunc = set_field2
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(arr[0].copy()), jitfunc(arr[0].copy()))
+
+    def test_literal_variable_global_tuple(self):
+        # This tests the setitem of record array when the indexes come from a
+        # global tuple. It tests getitem behaviour but also tests that a global
+        # tuple is being typed as a tuple of constants.
+
+        arr = np.array([1, 2], dtype=recordtype2)
+        pyfunc = set_field3
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(arr[0].copy()), jitfunc(arr[0].copy()))
+
+    def test_literal_unroll_global_tuple(self):
+        # This tests the setitem of record array when the indexes come from a
+        # global tuple and are being unrolled.
+        # It tests setitem behaviour but also tests that literal_unroll accepts
+        # a global tuple as argument
+
+        arr = np.array([1, 2], dtype=recordtype2)
+        pyfunc = set_field4
+        jitfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(arr[0].copy()), jitfunc(arr[0].copy()))
+
+    def test_literal_unroll_free_var_tuple(self):
+
+        # This tests the setitem of record array when the indexes come from a
+        # free variable tuple (not local, not global) and are being unrolled.
+        # It tests setitem behaviour but also tests that literal_unroll accepts
+        # a free variable tuple as argument
+
+        arr = np.array([1, 2], dtype=recordtype2)
+        fs = arr.dtype.names
+
+        def set_field(rec):
+            for f in literal_unroll(fs):
+                rec[f] = 10
+            return rec
+
+        jitfunc = njit(set_field)
+        self.assertEqual(set_field(arr[0].copy()), jitfunc(arr[0].copy()))
+
+    def test_error_w_invalid_field(self):
+        arr = np.array([1, 2], dtype=recordtype3)
+        jitfunc = njit(set_field1)
+        with self.assertRaises(TypingError) as raises:
+            jitfunc(arr[0])
+        self.assertIn("Field 'f' was not found in record with fields "
+                      "('first', 'second')", str(raises.exception))
+
+
+class TestSubtyping(TestCase):
+    def setUp(self):
+        self.value = 2
+        a_dtype = np.dtype([('a', 'f8')])
+        ab_dtype = np.dtype([('a', 'f8'), ('b', 'f8')])
+        self.a_rec1 = np.array([1], dtype=a_dtype)[0]
+        self.a_rec2 = np.array([2], dtype=a_dtype)[0]
+        self.ab_rec1 = np.array([(self.value, 3)], dtype=ab_dtype)[0]
+        self.ab_rec2 = np.array([(self.value + 1, 3)], dtype=ab_dtype)[0]
+        self.func = lambda rec: rec['a']
+
+    def test_common_field(self):
+        # Test that subtypes do not require new compilations
+
+        njit_sig = njit(types.float64(typeof(self.a_rec1)))
+        functions = [
+            njit(self.func),  # jitted function with open njit
+            njit_sig(self.func)  # jitted fc with closed signature
+        ]
+
+        for fc in functions:
+            fc(self.a_rec1)
+            fc.disable_compile()
+            y = fc(self.ab_rec1)
+            self.assertEqual(self.value, y)
+
+    def test_tuple_of_records(self):
+
+        @njit
+        def foo(rec_tup):
+            x = 0
+            for i in range(len(rec_tup)):
+                x += rec_tup[i]['a']
+            return x
+
+        foo((self.a_rec1, self.a_rec2))
+        foo.disable_compile()
+        y = foo((self.ab_rec1, self.ab_rec2))
+        self.assertEqual(2 * self.value + 1, y)
+
+    def test_array_field(self):
+        # Tests subtyping with array fields
+
+        rec1 = np.empty(1, dtype=[('a', 'f8', (4,))])[0]
+        rec1['a'][0] = 1
+        rec2 = np.empty(1, dtype=[('a', 'f8', (4,)), ('b', 'f8')])[0]
+        rec2['a'][0] = self.value
+
+        @njit
+        def foo(rec):
+            return rec['a'][0]
+
+        foo(rec1)
+        foo.disable_compile()
+        y = foo(rec2)
+        self.assertEqual(self.value, y)
+
+    def test_no_subtyping1(self):
+        # test that conversion rules don't allow subtypes with different field
+        # names
+
+        c_dtype = np.dtype([('c', 'f8')])
+        c_rec1 = np.array([1], dtype=c_dtype)[0]
+
+        @njit
+        def foo(rec):
+            return rec['c']
+
+        foo(c_rec1)
+        foo.disable_compile()
+        with self.assertRaises(TypeError) as err:
+            foo(self.a_rec1)
+            self.assertIn("No matching definition for argument type(s) Record",
+                          str(err.exception))
+
+    def test_no_subtyping2(self):
+        # test that conversion rules don't allow smaller records as subtypes
+
+        jit_fc = njit(self.func)
+        jit_fc(self.ab_rec1)
+        jit_fc.disable_compile()
+        with self.assertRaises(TypeError) as err:
+            jit_fc(self.a_rec1)
+            self.assertIn("No matching definition for argument type(s) Record",
+                          str(err.exception))
+
+    def test_no_subtyping3(self):
+        # test that conversion rules don't allow records with fields with same
+        # name but incompatible type
+
+        other_a_rec = np.array(['a'], dtype=np.dtype([('a', 'U25')]))[0]
+        jit_fc = njit(self.func)
+        jit_fc(self.a_rec1)
+        jit_fc.disable_compile()
+        with self.assertRaises(TypeError) as err:
+            jit_fc(other_a_rec)
+            self.assertIn("No matching definition for argument type(s) Record",
+                          str(err.exception))
+
+    def test_branch_pruning(self):
+        # test subtyping behaviour in a case with a dead branch
+
+        @njit
+        def foo(rec, flag=None):
+            n = 0
+            n += rec['a']
+            if flag is not None:
+                # Dead branch pruning will hide this branch
+                n += rec['b']
+                rec['b'] += 20
+            return n
+
+        self.assertEqual(foo(self.a_rec1), self.a_rec1[0])
+
+        # storing value because it will be mutated
+        k = self.ab_rec1[1]
+        self.assertEqual(foo(self.ab_rec1, flag=1), self.ab_rec1[0] + k)
+        self.assertEqual(self.ab_rec1[1], k + 20)
+
+        foo.disable_compile()
+        self.assertEqual(len(foo.nopython_signatures), 2)
+        self.assertEqual(foo(self.a_rec1) + 1, foo(self.ab_rec1))
+        self.assertEqual(foo(self.ab_rec1, flag=1), self.ab_rec1[0] + k + 20)
+
+
+class TestNestedArrays(TestCase):
+
+    def get_cfunc(self, pyfunc, argspec):
+        cres = compile_isolated(pyfunc, argspec)
+        return cres.entry_point
+
+    def test_record_write_array(self):
+        # Testing writing to a 1D array within a structured type
+
+        nbval = np.recarray(1, dtype=recordwitharray)
+        nbrecord = numpy_support.from_dtype(recordwitharray)
+        cfunc = self.get_cfunc(record_write_array, (nbrecord,))
+        cfunc(nbval[0])
+
+        expected = np.recarray(1, dtype=recordwitharray)
+        expected[0].g = 2
+        expected[0].h[0] = 3.0
+        expected[0].h[1] = 4.0
+        np.testing.assert_equal(expected, nbval)
+
+    def test_record_write_2d_array(self):
+        # Test writing to a 2D array within a structured type
+
+        nbval = np.recarray(1, dtype=recordwith2darray)
+        nbrecord = numpy_support.from_dtype(recordwith2darray)
+        cfunc = self.get_cfunc(record_write_2d_array, (nbrecord,))
+        cfunc(nbval[0])
+
+        expected = np.recarray(1, dtype=recordwith2darray)
+        expected[0].i = 3
+        expected[0].j[:] = np.asarray([5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
+                                      np.float32).reshape(3, 2)
+        np.testing.assert_equal(expected, nbval)
+
+    def test_record_read_array(self):
+        # Test reading from a 1D array within a structured type
+
+        nbval = np.recarray(1, dtype=recordwitharray)
+        nbval[0].h[0] = 15.0
+        nbval[0].h[1] = 25.0
+        nbrecord = numpy_support.from_dtype(recordwitharray)
+        cfunc = self.get_cfunc(record_read_array0, (nbrecord,))
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].h[0])
+
+        cfunc = self.get_cfunc(record_read_array1, (nbrecord,))
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].h[1])
+
+    def test_record_read_arrays(self):
+        # Test reading the array of 1D array
+        #
+        # This array or arrays is created by getitem on the
+        # record array (as opposed to getitem on a record)
+
+        nbval = np.recarray(2, dtype=recordwitharray)
+        nbval[0].h[0] = 15.0
+        nbval[0].h[1] = 25.0
+        nbval[1].h[0] = 35.0
+        nbval[1].h[1] = 45.4
+        ty_arr = typeof(nbval)
+        cfunc = self.get_cfunc(record_read_whole_array, (ty_arr,))
+        res = cfunc(nbval)
+        np.testing.assert_equal(res, nbval.h)
+
+    def test_record_read_2d_array(self):
+        # Test reading from a 2D array within a structured type
+
+        nbval = np.recarray(1, dtype=recordwith2darray)
+        nbval[0].j = np.asarray([1.5, 2.5, 3.5, 4.5, 5.5, 6.5],
+                                np.float32).reshape(3, 2)
+        nbrecord = numpy_support.from_dtype(recordwith2darray)
+        cfunc = self.get_cfunc(record_read_2d_array00, (nbrecord,))
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].j[0, 0])
+
+        cfunc = self.get_cfunc(record_read_2d_array01, (nbrecord,))
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].j[0, 1])
+
+        cfunc = self.get_cfunc(record_read_2d_array10, (nbrecord,))
+        res = cfunc(nbval[0])
+        np.testing.assert_equal(res, nbval[0].j[1, 0])
+
+    def test_set_record(self):
+        # Test setting an entire record
+
+        rec = np.ones(2, dtype=recordwith2darray).view(np.recarray)[0]
+        nbarr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        arr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        ty_arr = typeof(nbarr)
+        ty_rec = typeof(rec)
+        pyfunc = recarray_set_record
+        pyfunc(arr, rec)
+        cfunc = self.get_cfunc(pyfunc, (ty_arr, ty_rec))
+        cfunc(nbarr, rec)
+        np.testing.assert_equal(nbarr, arr)
+
+    def test_set_array(self):
+        # Test setting an entire array within one record
+
+        arr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        rec = arr[0]
+        nbarr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        nbrec = nbarr[0]
+        ty = typeof(nbrec)
+        for pyfunc in (record_write_full_array, record_write_full_array_alt):
+            pyfunc(rec)
+            cfunc = self.get_cfunc(pyfunc, (ty,))
+            cfunc(nbrec)
+            np.testing.assert_equal(nbarr, arr)
+
+    def test_set_arrays(self):
+        # Test setting an entire array of arrays (multiple records)
+
+        arr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        nbarr = np.zeros(2, dtype=recordwith2darray).view(np.recarray)
+        ty = typeof(nbarr)
+        for pyfunc in (
+                recarray_write_array_of_nestedarray_broadcast,
+                recarray_write_array_of_nestedarray,
+        ):
+            arr_expected = pyfunc(arr)
+            cfunc = self.get_cfunc(pyfunc, (ty,))
+            arr_res = cfunc(nbarr)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+    def test_setitem(self):
+        def gen():
+            nbarr1 = np.recarray(1, dtype=recordwith2darray)
+            nbarr1[0] = np.array([(1, ((1, 2), (4, 5), (2, 3)))],
+                                 dtype=recordwith2darray)[0]
+            nbarr2 = np.recarray(1, dtype=recordwith2darray)
+            nbarr2[0] = np.array([(10, ((10, 20), (40, 50), (20, 30)))],
+                                 dtype=recordwith2darray)[0]
+            return nbarr1[0], nbarr2[0]
+        pyfunc = record_setitem_array
+        pyargs = gen()
+        pyfunc(*pyargs)
+
+        nbargs = gen()
+        cfunc = self.get_cfunc(pyfunc, tuple((typeof(arg) for arg in nbargs)))
+        cfunc(*nbargs)
+        np.testing.assert_equal(pyargs, nbargs)
+
+    def test_setitem_whole_array_error(self):
+        # Ensure we raise a suitable error when attempting to assign an
+        # array to a whole array's worth of nested arrays.
+        nbarr1 = np.recarray(1, dtype=recordwith2darray)
+        nbarr2 = np.recarray(1, dtype=recordwith2darray)
+        args = (nbarr1, nbarr2)
+        pyfunc = record_setitem_array
+        errmsg = "unsupported array index type"
+        with self.assertRaisesRegex(TypingError, errmsg):
+            self.get_cfunc(pyfunc, tuple((typeof(arg) for arg in args)))
+
+    def test_getitem_idx(self):
+        # Test __getitem__ with numerical index
+
+        # This test returning a record when passing an array and
+        # return the first item when passing a record
+
+        nbarr = np.recarray(2, dtype=recordwitharray)
+        nbarr[0] = np.array([(1, (2, 3))], dtype=recordwitharray)[0]
+        for arg in [nbarr, nbarr[0]]:
+            ty = typeof(arg)
+            pyfunc = recarray_getitem_return
+            arr_expected = pyfunc(arg)
+            cfunc = self.get_cfunc(pyfunc, (ty,))
+            arr_res = cfunc(arg)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+    def test_getitem_idx_2darray(self):
+        # Test __getitem__ with numerical index
+        #
+        # This test returning a record when passing an array and
+        # return the first item when passing a record
+
+        nbarr = np.recarray(2, dtype=recordwith2darray)
+        nbarr[0] = np.array([(1, ((1, 2), (4, 5), (2, 3)))],
+                            dtype=recordwith2darray)[0]
+        for arg in [nbarr, nbarr[0]]:
+            ty = typeof(arg)
+            pyfunc = recarray_getitem_field_return2_2d
+            arr_expected = pyfunc(arg)
+            cfunc = self.get_cfunc(pyfunc, (ty,))
+            arr_res = cfunc(arg)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+    def test_return_getattr_getitem_fieldname(self):
+        # Test __getitem__ with field name and getattr .field_name
+        #
+        # This tests returning a array of nestedarrays when passing an array and
+        # returning a nestedarray when passing a record
+
+        nbarr = np.recarray(2, dtype=recordwitharray)
+        nbarr[0] = np.array([(1, (2, 3))], dtype=recordwitharray)[0]
+        for arg in [nbarr, nbarr[0]]:
+            for pyfunc in [recarray_getitem_field_return,
+                           recarray_getitem_field_return2]:
+                ty = typeof(arg)
+                arr_expected = pyfunc(arg)
+                cfunc = self.get_cfunc(pyfunc, (ty,))
+                arr_res = cfunc(arg)
+                np.testing.assert_equal(arr_res, arr_expected)
+
+    def test_return_array(self):
+        # Test getitem record AND array within record and returning it
+
+        nbval = np.recarray(2, dtype=recordwitharray)
+        nbval[0] = np.array([(1, (2, 3))], dtype=recordwitharray)[0]
+        for pyfunc in [record_read_array0, record_read_array0_alt]:
+            arr_expected = pyfunc(nbval)
+            cfunc = self.get_cfunc(pyfunc, (typeof(nbval),))
+            arr_res = cfunc(nbval)
+            np.testing.assert_equal(arr_expected, arr_res)
+
+    def test_slice_2d_array(self):
+        # test slicing the nestedarray inside a record
+        nbarr = np.recarray(2, dtype=recordwith2darray)
+        nbarr[0] = np.array([(1, ((1, 2), (4, 5), (2, 3)))],
+                            dtype=recordwith2darray)[0]
+
+        funcs = rec_getitem_field_slice_2d, recarray_getitem_field_slice_2d
+        for arg, pyfunc in zip([nbarr[0], nbarr], funcs):
+            ty = typeof(arg)
+            arr_expected = pyfunc(arg)
+            cfunc = self.get_cfunc(pyfunc, (ty,))
+            arr_res = cfunc(arg)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+    def test_shape(self):
+        # test getting the shape of a nestedarray inside a record
+        nbarr = np.recarray(2, dtype=recordwith2darray)
+        nbarr[0] = np.array([(1, ((1, 2), (4, 5), (2, 3)))],
+                            dtype=recordwith2darray)[0]
+
+        arg = nbarr[0]
+        pyfunc = get_shape
+        ty = typeof(arg)
+        arr_expected = pyfunc(arg)
+        cfunc = self.get_cfunc(pyfunc, (ty,))
+        arr_res = cfunc(arg)
+        np.testing.assert_equal(arr_res, arr_expected)
+
+    def test_corner_slice(self):
+        # testing corner cases while slicing nested arrays
+        nbarr = np.recarray((1, 2, 3, 5, 7, 13, 17), dtype=recordwith4darray,
+                            order='F')
+        np.random.seed(1)
+        for index, _ in np.ndenumerate(nbarr):
+            nbarr[index].p = np.random.randint(0, 1000, (3, 2, 5, 7),
+                                               np.int64).astype(np.float32)
+
+        funcs = rec_getitem_range_slice_4d, recarray_getitem_range_slice_4d
+        for arg, pyfunc in zip([nbarr[0], nbarr], funcs):
+            ty = typeof(arg)
+            arr_expected = pyfunc(arg)
+            cfunc = self.get_cfunc(pyfunc, (ty,))
+            arr_res = cfunc(arg)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+    def test_broadcast_slice(self):
+        nbarr = np.recarray(2, dtype=recordwith2darray)
+        nbarr[0] = np.array([(1, ((1, 2), (4, 5), (2, 3)))],
+                            dtype=recordwith2darray)[0]
+        nbarr[1] = np.array([(10, ((10, 20), (40, 50), (20, 30)))],
+                            dtype=recordwith2darray)[0]
+        nbarr = np.broadcast_to(nbarr, (3, 2))
+
+        funcs = (
+            array_rec_getitem_field_slice_2d_0,
+            array_getitem_field_slice_2d_0,
+            array_rec_getitem_field_slice_2d_1,
+            array_getitem_field_slice_2d_1
+        )
+
+        for arg, pyfunc in zip([nbarr[0], nbarr, nbarr[1], nbarr], funcs):
+            ty = typeof(arg)
+            arr_expected = pyfunc(arg)
+            cfunc = self.get_cfunc(pyfunc, (ty,))
+            arr_res = cfunc(arg)
+            np.testing.assert_equal(arr_res, arr_expected)
+
+    def test_assign_array_to_nested(self):
+        got = np.zeros(2, dtype=nested_array1_dtype)
+        expected = np.zeros(2, dtype=nested_array1_dtype)
+
+        cfunc = njit(assign_array_to_nested)
+        cfunc(got[0])
+        assign_array_to_nested(expected[0])
+
+        np.testing.assert_array_equal(expected, got)
+
+    def test_assign_array_to_nested_2d(self):
+        got = np.zeros(2, dtype=nested_array2_dtype)
+        expected = np.zeros(2, dtype=nested_array2_dtype)
+
+        cfunc = njit(assign_array_to_nested_2d)
+        cfunc(got[0])
+        assign_array_to_nested_2d(expected[0])
+
+        np.testing.assert_array_equal(expected, got)
+
+    def test_issue_7693(self):
+        src_dtype = np.dtype([
+            ("user", np.float64),
+            ("array", np.int16, (3,))],
+            align=True)
+
+        dest_dtype = np.dtype([
+            ("user1", np.float64),
+            ("array1", np.int16, (3,))],
+            align=True)
+
+        @njit
+        def copy(index, src, dest):
+            dest['user1'] = src[index]['user']
+            dest['array1'] = src[index]['array']
+
+        source = np.zeros(2, dtype=src_dtype)
+        got = np.zeros(2, dtype=dest_dtype)
+        expected = np.zeros(2, dtype=dest_dtype)
+
+        source[0] = (1.2, [1, 2, 3])
+        copy(0, source, got[0])
+        copy.py_func(0, source, expected[0])
+
+        np.testing.assert_array_equal(expected, got)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_recursion.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_recursion.py
new file mode 100644
index 000000000..7a752c0ed
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_recursion.py
@@ -0,0 +1,124 @@
+import math
+import warnings
+
+from numba import jit
+from numba.core.errors import TypingError, NumbaWarning
+from numba.tests.support import TestCase
+import unittest
+
+
+class TestSelfRecursion(TestCase):
+
+    def setUp(self):
+        # Avoid importing this module at toplevel, as it triggers compilation
+        # and can therefore fail
+        from numba.tests import recursion_usecases
+        self.mod = recursion_usecases
+
+    def check_fib(self, cfunc):
+        self.assertPreciseEqual(cfunc(10), 55)
+
+    def test_global_explicit_sig(self):
+        self.check_fib(self.mod.fib1)
+
+    def test_inner_explicit_sig(self):
+        self.check_fib(self.mod.fib2)
+
+    def test_global_implicit_sig(self):
+        self.check_fib(self.mod.fib3)
+
+    def test_runaway(self):
+        with self.assertRaises(TypingError) as raises:
+            self.mod.runaway_self(123)
+        self.assertIn("cannot type infer runaway recursion",
+                      str(raises.exception))
+
+    def test_type_change(self):
+        pfunc = self.mod.make_type_change_self()
+        cfunc = self.mod.make_type_change_self(jit(nopython=True))
+        args = 13, 0.125
+        self.assertPreciseEqual(pfunc(*args), cfunc(*args))
+
+    def test_raise(self):
+        with self.assertRaises(ValueError) as raises:
+            self.mod.raise_self(3)
+
+        self.assertEqual(str(raises.exception), "raise_self")
+
+    def test_optional_return(self):
+        pfunc = self.mod.make_optional_return_case()
+        cfunc = self.mod.make_optional_return_case(jit(nopython=True))
+        for arg in (0, 5, 10, 15):
+            self.assertEqual(pfunc(arg), cfunc(arg))
+
+    def test_growing_return_tuple(self):
+        cfunc = self.mod.make_growing_tuple_case(jit(nopython=True))
+        with self.assertRaises(TypingError) as raises:
+            cfunc(100)
+        self.assertIn(
+            "Return type of recursive function does not converge",
+            str(raises.exception),
+        )
+
+
+class TestMutualRecursion(TestCase):
+
+    def setUp(self):
+        from numba.tests import recursion_usecases
+        self.mod = recursion_usecases
+
+    def test_mutual_1(self):
+        expect = math.factorial(10)
+        self.assertPreciseEqual(self.mod.outer_fac(10), expect)
+
+    def test_mutual_2(self):
+        pfoo, pbar = self.mod.make_mutual2()
+        cfoo, cbar = self.mod.make_mutual2(jit(nopython=True))
+        for x in [-1, 0, 1, 3]:
+            self.assertPreciseEqual(pfoo(x=x), cfoo(x=x))
+            self.assertPreciseEqual(pbar(y=x, z=1), cbar(y=x, z=1))
+
+    def test_runaway(self):
+        with self.assertRaises(TypingError) as raises:
+            self.mod.runaway_mutual(123)
+        self.assertIn("cannot type infer runaway recursion",
+                      str(raises.exception))
+
+    def test_type_change(self):
+        pfunc = self.mod.make_type_change_mutual()
+        cfunc = self.mod.make_type_change_mutual(jit(nopython=True))
+        args = 13, 0.125
+        self.assertPreciseEqual(pfunc(*args), cfunc(*args))
+
+    def test_four_level(self):
+        pfunc = self.mod.make_four_level()
+        cfunc = self.mod.make_four_level(jit(nopython=True))
+        arg = 7
+        self.assertPreciseEqual(pfunc(arg), cfunc(arg))
+
+    def test_inner_error(self):
+        # nopython mode
+        cfunc = self.mod.make_inner_error(jit(nopython=True))
+        with self.assertRaises(TypingError) as raises:
+            cfunc(2)
+        errmsg = 'Unknown attribute \'ndim\''
+        self.assertIn(errmsg, str(raises.exception))
+        # objectmode
+        # error is never trigger, function return normally
+        cfunc = self.mod.make_inner_error(jit)
+        pfunc = self.mod.make_inner_error()
+        with warnings.catch_warnings():
+            warnings.filterwarnings('ignore', category=NumbaWarning)
+            got = cfunc(6)
+        self.assertEqual(got, pfunc(6))
+
+    def test_raise(self):
+        cfunc = self.mod.make_raise_mutual()#jit(nopython=True))
+        with self.assertRaises(ValueError) as raises:
+            cfunc(2)
+
+        self.assertEqual(str(raises.exception), "raise_mutual")
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_refop_pruning.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_refop_pruning.py
new file mode 100644
index 000000000..04433bf80
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_refop_pruning.py
@@ -0,0 +1,160 @@
+import unittest
+import warnings
+from unittest import TestCase
+from contextlib import contextmanager
+
+import numpy as np
+
+import llvmlite.binding as llvm
+
+from numba import types
+from numba.core.errors import NumbaInvalidConfigWarning
+from numba.core.compiler import compile_isolated
+from numba.core.codegen import _parse_refprune_flags
+from numba.tests.support import override_config
+
+
+@contextmanager
+def set_refprune_flags(flags):
+    with override_config('LLVM_REFPRUNE_FLAGS', flags):
+        yield
+
+
+class TestRefOpPruning(TestCase):
+
+    _numba_parallel_test_ = False
+
+    def check(self, func, *argtys, **prune_types):
+        """
+        Asserts the the func compiled with argument types "argtys" reports
+        refop pruning statistics. The **prune_types** kwargs list each kind
+        of pruning and whether the stat should be zero (False) or >0 (True).
+
+        Note: The exact statistic varies across platform.
+        """
+
+        with override_config('LLVM_REFPRUNE_PASS', '1'):
+            cres = compile_isolated(func, (*argtys,))
+
+        pstats = cres.metadata.get('prune_stats', None)
+        self.assertIsNotNone(pstats)
+
+        for k, v in prune_types.items():
+            stat = getattr(pstats, k, None)
+            self.assertIsNotNone(stat)
+            msg = f'failed checking {k}'
+            if v:
+                self.assertGreater(stat, 0, msg=msg)
+            else:
+                self.assertEqual(stat, 0, msg=msg)
+
+    def test_basic_block_1(self):
+        # some nominally involved control flow and ops, there's only basic_block
+        # opportunities present here.
+        def func(n):
+            a = np.zeros(n)
+            acc = 0
+            if n > 4:
+                b = a[1:]
+                acc += b[1]
+            else:
+                c = a[:-1]
+                acc += c[0]
+            return acc
+
+        self.check(func, (types.intp), basicblock=True)
+
+    def test_diamond_1(self):
+        # most basic?! diamond
+        def func(n):
+            a = np.ones(n)
+            x = 0
+            if n > 2:
+                x = a.sum()
+            return x + 1
+
+        # disable fanout pruning
+        with set_refprune_flags('per_bb,diamond'):
+            self.check(func, (types.intp), basicblock=True, diamond=True,
+                       fanout=False, fanout_raise=False)
+
+    def test_diamond_2(self):
+        # more complex diamonds
+        def func(n):
+            con = []
+            for i in range(n):
+                con.append(np.arange(i))
+            c = 0.0
+            for arr in con:
+                c += arr.sum() / (1 + arr.size)
+            return c
+
+        # disable fanout pruning
+        with set_refprune_flags('per_bb,diamond'):
+            self.check(func, (types.intp), basicblock=True, diamond=True,
+                       fanout=False, fanout_raise=False)
+
+    def test_fanout_1(self):
+        # most basic?! fan-out
+        def func(n):
+            a = np.zeros(n)
+            b = np.zeros(n)
+            x = (a, b)
+            acc = 0.
+            for i in x:
+                acc += i[0]
+            return acc
+
+        self.check(func, (types.intp), basicblock=True, fanout=True)
+
+    def test_fanout_2(self):
+        # fanout with raise
+        def func(n):
+            a = np.zeros(n)
+            b = np.zeros(n)
+            x = (a, b)
+            for i in x:
+                if n:
+                    raise ValueError
+            return x
+
+        with set_refprune_flags('per_bb,fanout_raise'):
+            self.check(func, (types.intp), basicblock=True, diamond=False,
+                       fanout=False, fanout_raise=True)
+
+
+class TestRefPruneFlags(TestCase):
+    def setUp(self):
+        warnings.simplefilter('error', NumbaInvalidConfigWarning)
+
+    def tearDown(self):
+        warnings.resetwarnings()
+
+    def test_warn_invalid_flags(self):
+        with set_refprune_flags('abc,per_bb,cde'):
+            with self.assertWarns(NumbaInvalidConfigWarning) as cm:
+                optval = _parse_refprune_flags()
+            self.assertEqual(len(cm.warnings), 2)
+            self.assertIn('abc', str(cm.warnings[0].message))
+            self.assertIn('cde', str(cm.warnings[1].message))
+            self.assertEqual(optval, llvm.RefPruneSubpasses.PER_BB)
+
+    def test_valid_flag(self):
+        with set_refprune_flags('per_bb, diamond, fanout,fanout_raise'):
+            optval = _parse_refprune_flags()
+            self.assertEqual(optval, llvm.RefPruneSubpasses.ALL)
+
+    def test_the_all_flag(self):
+        with set_refprune_flags('all'):
+            optval = _parse_refprune_flags()
+            self.assertEqual(optval, llvm.RefPruneSubpasses.ALL)
+
+    def test_some_flags(self):
+        with set_refprune_flags('per_bb, fanout'):
+            optval = _parse_refprune_flags()
+            enumcls = llvm.RefPruneSubpasses
+            self.assertEqual(optval, enumcls.PER_BB | enumcls.FANOUT)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_remove_dead.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_remove_dead.py
new file mode 100644
index 000000000..36d842910
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_remove_dead.py
@@ -0,0 +1,304 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+import numba
+import numba.parfors.parfor
+from numba.core import ir_utils, cpu
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types, typing, ir, config, compiler
+from numba.core.registry import cpu_target
+from numba.core.annotations import type_annotations
+from numba.core.ir_utils import (copy_propagate, apply_copy_propagate,
+                            get_name_var_table, remove_dels, remove_dead,
+                            remove_call_handlers, alias_func_extensions)
+from numba.core.typed_passes import type_inference_stage
+from numba.core.compiler_machinery import FunctionPass, register_pass, PassManager
+from numba.core.untyped_passes import (ExtractByteCode, TranslateByteCode, FixupArgs,
+                             IRProcessing, DeadBranchPrune,
+                             RewriteSemanticConstants, GenericRewrites,
+                             WithLifting, PreserveIR, InlineClosureLikes)
+
+from numba.core.typed_passes import (NopythonTypeInference, AnnotateTypes,
+                           NopythonRewrites, PreParforPass, ParforPass,
+                           DumpParforDiagnostics, NativeLowering,
+                           IRLegalization, NoPythonBackend, NativeLowering)
+import numpy as np
+from numba.tests.support import skip_parfors_unsupported, needs_blas
+import unittest
+
+
+def test_will_propagate(b, z, w):
+    x1 = 3
+    x = x1
+    if b > 0:
+        y = z + w
+    else:
+        y = 0
+    a = 2 * x
+    return a < b
+
+def null_func(a,b,c,d):
+    False
+
+@numba.njit
+def dummy_aliased_func(A):
+    return A
+
+def alias_ext_dummy_func(lhs_name, args, alias_map, arg_aliases):
+    ir_utils._add_alias(lhs_name, args[0].name, alias_map, arg_aliases)
+
+def findLhsAssign(func_ir, var):
+    for label, block in func_ir.blocks.items():
+        for i, inst in enumerate(block.body):
+            if isinstance(inst, ir.Assign) and inst.target.name==var:
+                return True
+
+    return False
+
+class TestRemoveDead(unittest.TestCase):
+
+    _numba_parallel_test_ = False
+
+    def compile_parallel(self, func, arg_types):
+        fast_pflags = Flags()
+        fast_pflags.auto_parallel = cpu.ParallelOptions(True)
+        fast_pflags.nrt = True
+        fast_pflags.fastmath = cpu.FastMathOptions(True)
+        return compile_isolated(func, arg_types, flags=fast_pflags).entry_point
+
+    def test1(self):
+        typingctx = typing.Context()
+        targetctx = cpu.CPUContext(typingctx)
+        test_ir = compiler.run_frontend(test_will_propagate)
+        with cpu_target.nested_context(typingctx, targetctx):
+            typingctx.refresh()
+            targetctx.refresh()
+            args = (types.int64, types.int64, types.int64)
+            typemap, _, calltypes, _ = type_inference_stage(typingctx, targetctx, test_ir, args, None)
+            remove_dels(test_ir.blocks)
+            in_cps, out_cps = copy_propagate(test_ir.blocks, typemap)
+            apply_copy_propagate(test_ir.blocks, in_cps, get_name_var_table(test_ir.blocks), typemap, calltypes)
+
+            remove_dead(test_ir.blocks, test_ir.arg_names, test_ir)
+            self.assertFalse(findLhsAssign(test_ir, "x"))
+
+    def test2(self):
+        def call_np_random_seed():
+            np.random.seed(2)
+
+        def seed_call_exists(func_ir):
+            for inst in func_ir.blocks[0].body:
+                if (isinstance(inst, ir.Assign) and
+                    isinstance(inst.value, ir.Expr) and
+                    inst.value.op == 'call' and
+                    func_ir.get_definition(inst.value.func).attr == 'seed'):
+                    return True
+            return False
+
+        test_ir = compiler.run_frontend(call_np_random_seed)
+        remove_dead(test_ir.blocks, test_ir.arg_names, test_ir)
+        self.assertTrue(seed_call_exists(test_ir))
+
+    def run_array_index_test(self, func):
+        A1 = np.arange(6).reshape(2,3)
+        A2 = A1.copy()
+        i = 0
+        pfunc = self.compile_parallel(func, (numba.typeof(A1), numba.typeof(i)))
+
+        func(A1, i)
+        pfunc(A2, i)
+        np.testing.assert_array_equal(A1, A2)
+
+    def test_alias_ravel(self):
+        def func(A, i):
+            B = A.ravel()
+            B[i] = 3
+
+        self.run_array_index_test(func)
+
+    def test_alias_flat(self):
+        def func(A, i):
+            B = A.flat
+            B[i] = 3
+
+        self.run_array_index_test(func)
+
+    def test_alias_transpose1(self):
+        def func(A, i):
+            B = A.T
+            B[i,0] = 3
+
+        self.run_array_index_test(func)
+
+    def test_alias_transpose2(self):
+        def func(A, i):
+            B = A.transpose()
+            B[i,0] = 3
+
+        self.run_array_index_test(func)
+
+    def test_alias_transpose3(self):
+        def func(A, i):
+            B = np.transpose(A)
+            B[i,0] = 3
+
+        self.run_array_index_test(func)
+
+    @skip_parfors_unsupported
+    @needs_blas
+    def test_alias_ctypes(self):
+        # use xxnrm2 to test call a C function with ctypes
+        from numba.np.linalg import _BLAS
+        xxnrm2 = _BLAS().numba_xxnrm2(types.float64)
+
+        def remove_dead_xxnrm2(rhs, lives, call_list):
+            if call_list == [xxnrm2]:
+                return rhs.args[4].name not in lives
+            return False
+
+        # adding this handler has no-op effect since this function won't match
+        # anything else but it's a bit cleaner to save the state and recover
+        old_remove_handlers = remove_call_handlers[:]
+        remove_call_handlers.append(remove_dead_xxnrm2)
+
+        def func(ret):
+            a = np.ones(4)
+            xxnrm2(100, 4, a.ctypes, 1, ret.ctypes)
+
+        A1 = np.zeros(1)
+        A2 = A1.copy()
+
+        try:
+            pfunc = self.compile_parallel(func, (numba.typeof(A1),))
+            numba.njit(func)(A1)
+            pfunc(A2)
+        finally:
+            # recover global state
+            remove_call_handlers[:] = old_remove_handlers
+
+        self.assertEqual(A1[0], A2[0])
+
+    def test_alias_reshape1(self):
+        def func(A, i):
+            B = np.reshape(A, (3,2))
+            B[i,0] = 3
+
+        self.run_array_index_test(func)
+
+    def test_alias_reshape2(self):
+        def func(A, i):
+            B = A.reshape(3,2)
+            B[i,0] = 3
+
+        self.run_array_index_test(func)
+
+    def test_alias_func_ext(self):
+        def func(A, i):
+            B = dummy_aliased_func(A)
+            B[i, 0] = 3
+
+        # save global state
+        old_ext_handlers = alias_func_extensions.copy()
+        try:
+            alias_func_extensions[('dummy_aliased_func',
+                'numba.tests.test_remove_dead')] = alias_ext_dummy_func
+            self.run_array_index_test(func)
+        finally:
+            # recover global state
+            ir_utils.alias_func_extensions = old_ext_handlers
+
+    def test_rm_dead_rhs_vars(self):
+        """make sure lhs variable of assignment is considered live if used in
+        rhs (test for #6715).
+        """
+        def func():
+            for i in range(3):
+                a = (lambda j: j)(i)
+                a = np.array(a)
+            return a
+
+        self.assertEqual(func(), numba.njit(func)())
+
+    @skip_parfors_unsupported
+    def test_alias_parfor_extension(self):
+        """Make sure aliases are considered in remove dead extension for
+        parfors.
+        """
+        def func():
+            n = 11
+            numba.parfors.parfor.init_prange()
+            A = np.empty(n)
+            B = A  # create alias to A
+            for i in numba.prange(n):
+                A[i] = i
+
+            return B
+
+        @register_pass(analysis_only=False, mutates_CFG=True)
+        class LimitedParfor(FunctionPass):
+            _name = "limited_parfor"
+
+            def __init__(self):
+                FunctionPass.__init__(self)
+
+            def run_pass(self, state):
+                parfor_pass = numba.parfors.parfor.ParforPass(
+                    state.func_ir,
+                    state.typemap,
+                    state.calltypes,
+                    state.return_type,
+                    state.typingctx,
+                    state.flags.auto_parallel,
+                    state.flags,
+                    state.metadata,
+                    state.parfor_diagnostics
+                )
+                remove_dels(state.func_ir.blocks)
+                parfor_pass.array_analysis.run(state.func_ir.blocks)
+                parfor_pass._convert_loop(state.func_ir.blocks)
+                remove_dead(state.func_ir.blocks,
+                            state.func_ir.arg_names,
+                            state.func_ir,
+                            state.typemap)
+                numba.parfors.parfor.get_parfor_params(state.func_ir.blocks,
+                                                parfor_pass.options.fusion,
+                                                parfor_pass.nested_fusion_info)
+                return True
+
+        class TestPipeline(compiler.Compiler):
+            """Test pipeline that just converts prange() to parfor and calls
+            remove_dead(). Copy propagation can replace B in the example code
+            which this pipeline avoids.
+            """
+            def define_pipelines(self):
+                name = 'test parfor aliasing'
+                pm = PassManager(name)
+                pm.add_pass(TranslateByteCode, "analyzing bytecode")
+                pm.add_pass(FixupArgs, "fix up args")
+                pm.add_pass(IRProcessing, "processing IR")
+                pm.add_pass(WithLifting, "Handle with contexts")
+                # pre typing
+                if not self.state.flags.no_rewrites:
+                    pm.add_pass(GenericRewrites, "nopython rewrites")
+                    pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
+                    pm.add_pass(DeadBranchPrune, "dead branch pruning")
+                pm.add_pass(InlineClosureLikes,
+                            "inline calls to locally defined closures")
+                # typing
+                pm.add_pass(NopythonTypeInference, "nopython frontend")
+
+                # lower
+                pm.add_pass(NativeLowering, "native lowering")
+                pm.add_pass(NoPythonBackend, "nopython mode backend")
+                pm.finalize()
+                return [pm]
+
+        test_res = numba.jit(pipeline_class=TestPipeline)(func)()
+        py_res = func()
+        np.testing.assert_array_equal(test_res, py_res)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_retargeting.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_retargeting.py
new file mode 100644
index 000000000..ba872a5f8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_retargeting.py
@@ -0,0 +1,291 @@
+import unittest
+from contextlib import contextmanager
+
+from numba import njit
+from numba.core import errors, cpu, utils, typing
+from numba.core.descriptors import TargetDescriptor
+from numba.core.dispatcher import TargetConfigurationStack
+from numba.core.retarget import BasicRetarget
+from numba.core.extending import overload
+from numba.core.target_extension import (
+    dispatcher_registry,
+    CPUDispatcher,
+    CPU,
+    target_registry,
+    jit_registry,
+)
+
+
+# ------------ A custom target ------------
+
+CUSTOM_TARGET = ".".join([__name__, "CustomCPU"])
+
+
+class CustomCPU(CPU):
+    """Extend from the CPU target
+    """
+    pass
+
+
+# Nested contexts to help with isolatings bits of compilations
+class _NestedContext(object):
+    _typing_context = None
+    _target_context = None
+
+    @contextmanager
+    def nested(self, typing_context, target_context):
+        old_nested = self._typing_context, self._target_context
+        try:
+            self._typing_context = typing_context
+            self._target_context = target_context
+            yield
+        finally:
+            self._typing_context, self._target_context = old_nested
+
+
+# Implement a CustomCPU TargetDescriptor, this one borrows bits from the CPU
+class CustomTargetDescr(TargetDescriptor):
+    options = cpu.CPUTargetOptions
+    _nested = _NestedContext()
+
+    @utils.cached_property
+    def _toplevel_target_context(self):
+        # Lazily-initialized top-level target context, for all threads
+        return cpu.CPUContext(self.typing_context, self._target_name)
+
+    @utils.cached_property
+    def _toplevel_typing_context(self):
+        # Lazily-initialized top-level typing context, for all threads
+        return typing.Context()
+
+    @property
+    def target_context(self):
+        """
+        The target context for DPU targets.
+        """
+        nested = self._nested._target_context
+        if nested is not None:
+            return nested
+        else:
+            return self._toplevel_target_context
+
+    @property
+    def typing_context(self):
+        """
+        The typing context for CPU targets.
+        """
+        nested = self._nested._typing_context
+        if nested is not None:
+            return nested
+        else:
+            return self._toplevel_typing_context
+
+    def nested_context(self, typing_context, target_context):
+        """
+        A context manager temporarily replacing the contexts with the
+        given ones, for the current thread of execution.
+        """
+        return self._nested.nested(typing_context, target_context)
+
+
+custom_target = CustomTargetDescr(CUSTOM_TARGET)
+
+
+class CustomCPUDispatcher(CPUDispatcher):
+    targetdescr = custom_target
+
+
+target_registry[CUSTOM_TARGET] = CustomCPU
+dispatcher_registry[target_registry[CUSTOM_TARGET]] = CustomCPUDispatcher
+
+
+def custom_jit(*args, **kwargs):
+    assert 'target' not in kwargs
+    assert '_target' not in kwargs
+    return njit(*args, _target=CUSTOM_TARGET, **kwargs)
+
+
+jit_registry[target_registry[CUSTOM_TARGET]] = custom_jit
+
+# ------------ For switching target ------------
+
+
+class CustomCPURetarget(BasicRetarget):
+    @property
+    def output_target(self):
+        return CUSTOM_TARGET
+
+    def compile_retarget(self, cpu_disp):
+        kernel = njit(_target=CUSTOM_TARGET)(cpu_disp.py_func)
+        return kernel
+
+
+class TestRetargeting(unittest.TestCase):
+    def setUp(self):
+        # Generate fresh functions for each test method to avoid caching
+
+        @njit(_target="cpu")
+        def fixed_target(x):
+            """
+            This has a fixed target to "cpu".
+            Cannot be used in CUSTOM_TARGET target.
+            """
+            return x + 10
+
+        @njit
+        def flex_call_fixed(x):
+            """
+            This has a flexible target, but uses a fixed target function.
+            Cannot be used in CUSTOM_TARGET target.
+            """
+            return fixed_target(x) + 100
+
+        @njit
+        def flex_target(x):
+            """
+            This has a flexible target.
+            Can be used in CUSTOM_TARGET target.
+            """
+            return x + 1000
+
+        # Save these functions for use
+        self.functions = locals()
+        # Refresh the retarget function
+        self.retarget = CustomCPURetarget()
+
+    def switch_target(self):
+        return TargetConfigurationStack.switch_target(self.retarget)
+
+    @contextmanager
+    def check_retarget_error(self):
+        with self.assertRaises(errors.NumbaError) as raises:
+            yield
+        self.assertIn(f"{CUSTOM_TARGET} != cpu", str(raises.exception))
+
+    def check_non_empty_cache(self):
+        # Retargeting occurred. The cache must NOT be empty
+        stats = self.retarget.cache.stats()
+        # Because multiple function compilations are triggered, we don't know
+        # precisely how many cache hit/miss there are.
+        self.assertGreater(stats['hit'] + stats['miss'], 0)
+
+    def test_case0(self):
+        fixed_target = self.functions["fixed_target"]
+        flex_target = self.functions["flex_target"]
+
+        @njit
+        def foo(x):
+            x = fixed_target(x)
+            x = flex_target(x)
+            return x
+
+        r = foo(123)
+        self.assertEqual(r, 123 + 10 + 1000)
+        # No retargeting occurred. The cache must be empty
+        stats = self.retarget.cache.stats()
+        self.assertEqual(stats, dict(hit=0, miss=0))
+
+    def test_case1(self):
+        flex_target = self.functions["flex_target"]
+
+        @njit
+        def foo(x):
+            x = flex_target(x)
+            return x
+
+        with self.switch_target():
+            r = foo(123)
+        self.assertEqual(r, 123 + 1000)
+        self.check_non_empty_cache()
+
+    def test_case2(self):
+        """
+        The non-nested call into fixed_target should raise error.
+        """
+        fixed_target = self.functions["fixed_target"]
+        flex_target = self.functions["flex_target"]
+
+        @njit
+        def foo(x):
+            x = fixed_target(x)
+            x = flex_target(x)
+            return x
+
+        with self.check_retarget_error():
+            with self.switch_target():
+                foo(123)
+
+    def test_case3(self):
+        """
+        The nested call into fixed_target should raise error
+        """
+        flex_target = self.functions["flex_target"]
+        flex_call_fixed = self.functions["flex_call_fixed"]
+
+        @njit
+        def foo(x):
+            x = flex_call_fixed(x)  # calls fixed_target indirectly
+            x = flex_target(x)
+            return x
+
+        with self.check_retarget_error():
+            with self.switch_target():
+                foo(123)
+
+    def test_case4(self):
+        """
+        Same as case2 but flex_call_fixed() is invoked outside of CUSTOM_TARGET
+        target before the switch_target.
+        """
+        flex_target = self.functions["flex_target"]
+        flex_call_fixed = self.functions["flex_call_fixed"]
+
+        r = flex_call_fixed(123)
+        self.assertEqual(r, 123 + 100 + 10)
+
+        @njit
+        def foo(x):
+            x = flex_call_fixed(x)  # calls fixed_target indirectly
+            x = flex_target(x)
+            return x
+
+        with self.check_retarget_error():
+            with self.switch_target():
+                foo(123)
+
+    def test_case5(self):
+        """
+        Tests overload resolution with target switching
+        """
+
+        def overloaded_func(x):
+            pass
+
+        @overload(overloaded_func, target=CUSTOM_TARGET)
+        def ol_overloaded_func_custom_target(x):
+            def impl(x):
+                return 62830
+            return impl
+
+        @overload(overloaded_func, target='cpu')
+        def ol_overloaded_func_cpu(x):
+            def impl(x):
+                return 31415
+            return impl
+
+        @njit
+        def flex_resolve_overload(x):
+            return
+
+        @njit
+        def foo(x):
+            return x + overloaded_func(x)
+
+        r = foo(123)
+        self.assertEqual(r, 123 + 31415)
+
+        with self.switch_target():
+            r = foo(123)
+            self.assertEqual(r, 123 + 62830)
+
+        self.check_non_empty_cache()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_return_values.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_return_values.py
new file mode 100644
index 000000000..d8d32fdaf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_return_values.py
@@ -0,0 +1,78 @@
+"""
+Test return values
+"""
+
+
+import math
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types
+from numba.core.errors import TypingError, NumbaTypeError
+
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+no_pyobj_flags = Flags()
+
+
+def get_nopython_func():
+    return abs
+
+def get_pyobj_func():
+    return open
+
+def get_module_func():
+    return math.floor
+
+
+class TestReturnValues(unittest.TestCase):
+
+    def test_nopython_func(self, flags=enable_pyobj_flags):
+        # Test returning func that is supported in nopython mode
+        pyfunc = get_nopython_func
+        cr = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cr.entry_point
+        if flags == enable_pyobj_flags:
+            result = cfunc()
+            self.assertEqual(result, abs)
+        else:
+            result = cfunc()
+
+    def test_nopython_func_npm(self):
+        with self.assertRaises(NumbaTypeError):
+            self.test_nopython_func(flags=no_pyobj_flags)
+
+    def test_pyobj_func(self, flags=enable_pyobj_flags):
+        # Test returning func that is only supported in object mode
+        pyfunc = get_pyobj_func
+        cr = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cr.entry_point
+        if flags == enable_pyobj_flags:
+            result = cfunc()
+            self.assertEqual(result, open)
+        else:
+            result = cfunc()
+
+    def test_pyobj_func_npm(self):
+        with self.assertRaises(TypingError):
+            self.test_pyobj_func(flags=no_pyobj_flags)
+
+    def test_module_func(self, flags=enable_pyobj_flags):
+        # Test returning imported func that is only supported in object mode
+        pyfunc = get_module_func
+        cr = compile_isolated(pyfunc, (), flags=flags)
+        cfunc = cr.entry_point
+        if flags == enable_pyobj_flags:
+            result = cfunc()
+            self.assertEqual(result, math.floor)
+        else:
+            result = cfunc()
+
+    def test_module_func_npm(self):
+        with self.assertRaises(NumbaTypeError):
+            self.test_module_func(flags=no_pyobj_flags)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_runtests.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_runtests.py
new file mode 100644
index 000000000..6142a943b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_runtests.py
@@ -0,0 +1,205 @@
+import os
+import sys
+import subprocess
+
+from numba import cuda
+import unittest
+
+try:
+    import git  # noqa: F401 from gitpython package
+except ImportError:
+    has_gitpython = False
+else:
+    has_gitpython = True
+
+try:
+    import yaml  # from pyyaml package
+except ImportError:
+    has_pyyaml = False
+else:
+    has_pyyaml = True
+
+
+class TestCase(unittest.TestCase):
+    """These test cases are meant to test the Numba test infrastructure itself.
+    Therefore, the logic used here shouldn't use numba.testing, but only the
+    upstream unittest, and run the numba test suite only in a subprocess."""
+
+    def get_testsuite_listing(self, args, *, subp_kwargs=None):
+        """
+        Use `subp_kwargs` to pass extra argument to `subprocess.check_output`.
+        """
+        subp_kwargs = subp_kwargs or {}
+        cmd = [sys.executable, '-m', 'numba.runtests', '-l'] + list(args)
+        out_bytes = subprocess.check_output(cmd, **subp_kwargs)
+        lines = out_bytes.decode('UTF-8').splitlines()
+        lines = [line for line in lines if line.strip()]
+        return lines
+
+    def check_listing_prefix(self, prefix):
+        listing = self.get_testsuite_listing([prefix])
+        for ln in listing[:-1]:
+            errmsg = '{!r} not startswith {!r}'.format(ln, prefix)
+            self.assertTrue(ln.startswith(prefix), msg=errmsg)
+
+    def check_testsuite_size(self, args, minsize):
+        """
+        Check that the reported numbers of tests are at least *minsize*.
+        """
+        lines = self.get_testsuite_listing(args)
+        last_line = lines[-1]
+        self.assertTrue('tests found' in last_line)
+        number = int(last_line.split(' ')[0])
+        # There may be some "skipped" messages at the beginning,
+        # so do an approximate check.
+        self.assertIn(len(lines), range(number + 1, number + 20))
+        self.assertGreaterEqual(number, minsize)
+        return lines
+
+    def check_all(self, ids):
+        lines = self.check_testsuite_size(ids, 5000)
+        # CUDA should be included by default
+        self.assertTrue(any('numba.cuda.tests.' in line for line in lines))
+        # As well as subpackage
+        self.assertTrue(any('numba.tests.npyufunc.test_' in line
+                            for line in lines),)
+
+    def test_default(self):
+        self.check_all([])
+
+    def test_all(self):
+        self.check_all(['numba.tests'])
+
+    def test_cuda(self):
+        # Even without CUDA enabled, there is at least one test
+        # (in numba.cuda.tests.nocuda)
+        minsize = 100 if cuda.is_available() else 1
+        self.check_testsuite_size(['numba.cuda.tests'], minsize)
+
+    @unittest.skipIf(not cuda.is_available(), "NO CUDA")
+    def test_cuda_submodules(self):
+        self.check_listing_prefix('numba.cuda.tests.cudadrv')
+        self.check_listing_prefix('numba.cuda.tests.cudapy')
+        self.check_listing_prefix('numba.cuda.tests.nocuda')
+        self.check_listing_prefix('numba.cuda.tests.cudasim')
+
+    def test_module(self):
+        self.check_testsuite_size(['numba.tests.test_storeslice'], 2)
+        self.check_testsuite_size(['numba.tests.test_nested_calls'], 10)
+        # Several modules
+        self.check_testsuite_size(['numba.tests.test_nested_calls',
+                                   'numba.tests.test_storeslice'], 12)
+
+    def test_subpackage(self):
+        self.check_testsuite_size(['numba.tests.npyufunc'], 50)
+
+    def test_random(self):
+        self.check_testsuite_size(
+            ['--random', '0.1', 'numba.tests.npyufunc'], 5)
+
+    def test_include_exclude_tags(self):
+        def get_count(arg_list):
+            lines = self.get_testsuite_listing(arg_list)
+            self.assertIn('tests found', lines[-1])
+            count = int(lines[-1].split()[0])
+            self.assertTrue(count > 0)
+            return count
+
+        tags = ['long_running', 'long_running, important']
+
+        for tag in tags:
+            total = get_count(['numba.tests'])
+            included = get_count(['--tags', tag, 'numba.tests'])
+            excluded = get_count(['--exclude-tags', tag, 'numba.tests'])
+            self.assertEqual(total, included + excluded)
+
+            # check syntax with `=` sign in
+            total = get_count(['numba.tests'])
+            included = get_count(['--tags=%s' % tag, 'numba.tests'])
+            excluded = get_count(['--exclude-tags=%s' % tag, 'numba.tests'])
+            self.assertEqual(total, included + excluded)
+
+    def test_check_slice(self):
+        tmp = self.get_testsuite_listing(['-j','0,5,1'])
+        l = [x for x in tmp if x.startswith('numba.')]
+        self.assertEqual(len(l), 5)
+
+    def test_check_slicing_equivalent(self):
+        def filter_test(xs):
+            return [x for x in xs if x.startswith('numba.')]
+        full = filter_test(self.get_testsuite_listing([]))
+        sliced = []
+        for i in range(3):
+            subset = self.get_testsuite_listing(['-j', '{},None,3'.format(i)])
+            sliced.extend(filter_test(subset))
+        # The tests must be equivalent
+        self.assertEqual(sorted(full), sorted(sliced))
+
+    @unittest.skipUnless(has_gitpython, "Requires gitpython")
+    def test_gitdiff(self):
+        # Check for git
+        try:
+            subprocess.call("git",
+                            stdout=subprocess.DEVNULL,
+                            stderr=subprocess.DEVNULL)
+        except FileNotFoundError:
+            self.skipTest("no git available")
+
+        # default
+        outs = self.get_testsuite_listing(['-g'])
+        self.assertNotIn("Git diff by common ancestor", outs)
+        # using ancestor
+        outs = self.get_testsuite_listing(['-g=ancestor'])
+        self.assertIn("Git diff by common ancestor", outs)
+        # misspelled ancestor
+        subp_kwargs = dict(stderr=subprocess.DEVNULL)
+        with self.assertRaises(subprocess.CalledProcessError):
+            self.get_testsuite_listing(['-g=ancest'], subp_kwargs=subp_kwargs)
+
+    @unittest.skipUnless(has_pyyaml, "Requires pyyaml")
+    def test_azure_config(self):
+        from yaml import Loader
+        base_path = os.path.dirname(os.path.abspath(__file__))
+        azure_pipe = os.path.join(base_path, '..', '..', 'azure-pipelines.yml')
+        if not os.path.isfile(azure_pipe):
+            self.skipTest("'azure-pipelines.yml' is not available")
+        with open(os.path.abspath(azure_pipe), 'rt') as f:
+            data = f.read()
+        pipe_yml = yaml.load(data, Loader=Loader)
+
+        templates = pipe_yml['jobs']
+        # first look at the items in the first two templates, this is osx/linux
+        start_indexes = []
+        for tmplt in templates[:2]:
+            matrix = tmplt['parameters']['matrix']
+            for setup in matrix.values():
+                start_indexes.append(setup['TEST_START_INDEX'])
+
+        # next look at the items in the windows only template
+        winpath = ['..', '..', 'buildscripts', 'azure', 'azure-windows.yml']
+        azure_windows = os.path.join(base_path, *winpath)
+        if not os.path.isfile(azure_windows):
+            self.skipTest("'azure-windows.yml' is not available")
+        with open(os.path.abspath(azure_windows), 'rt') as f:
+            data = f.read()
+        windows_yml = yaml.load(data, Loader=Loader)
+
+        # There's only one template in windows and its keyed differently to the
+        # above, get its matrix.
+        matrix = windows_yml['jobs'][0]['strategy']['matrix']
+        for setup in matrix.values():
+            start_indexes.append(setup['TEST_START_INDEX'])
+
+        # sanity checks
+        # 1. That the TEST_START_INDEX is unique
+        self.assertEqual(len(start_indexes), len(set(start_indexes)))
+        # 2. That the TEST_START_INDEX is a complete range
+        lim_start_index = max(start_indexes) + 1
+        expected = [*range(lim_start_index)]
+        self.assertEqual(sorted(start_indexes), expected)
+        # 3. That the number of indexes matches the declared test count
+        self.assertEqual(lim_start_index, pipe_yml['variables']['TEST_COUNT'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_serialize.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_serialize.py
new file mode 100644
index 000000000..13cd98a26
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_serialize.py
@@ -0,0 +1,325 @@
+import contextlib
+import gc
+import pickle
+import runpy
+import subprocess
+import sys
+import unittest
+from multiprocessing import get_context
+
+import numba
+from numba.core.errors import TypingError
+from numba.tests.support import TestCase
+from numba.core.target_extension import resolve_dispatcher_from_str
+from numba.cloudpickle import dumps, loads
+from .serialize_usecases import *
+
+
+class TestDispatcherPickling(TestCase):
+
+    def run_with_protocols(self, meth, *args, **kwargs):
+        for proto in range(pickle.HIGHEST_PROTOCOL + 1):
+            meth(proto, *args, **kwargs)
+
+    @contextlib.contextmanager
+    def simulate_fresh_target(self):
+        hwstr = 'cpu'
+        dispatcher_cls = resolve_dispatcher_from_str(hwstr)
+        old_descr = dispatcher_cls.targetdescr
+        # Simulate fresh targetdescr
+        dispatcher_cls.targetdescr = type(dispatcher_cls.targetdescr)(hwstr)
+        try:
+            yield
+        finally:
+            # Be sure to reinstantiate old descriptor, otherwise other
+            # objects may be out of sync.
+            dispatcher_cls.targetdescr = old_descr
+
+    def check_call(self, proto, func, expected_result, args):
+        def check_result(func):
+            if (isinstance(expected_result, type)
+                and issubclass(expected_result, Exception)):
+                self.assertRaises(expected_result, func, *args)
+            else:
+                self.assertPreciseEqual(func(*args), expected_result)
+
+        # Control
+        check_result(func)
+        pickled = pickle.dumps(func, proto)
+        with self.simulate_fresh_target():
+            new_func = pickle.loads(pickled)
+            check_result(new_func)
+
+    def test_call_with_sig(self):
+        self.run_with_protocols(self.check_call, add_with_sig, 5, (1, 4))
+        # Compilation has been disabled => float inputs will be coerced to int
+        self.run_with_protocols(self.check_call, add_with_sig, 5, (1.2, 4.2))
+
+    def test_call_without_sig(self):
+        self.run_with_protocols(self.check_call, add_without_sig, 5, (1, 4))
+        self.run_with_protocols(self.check_call, add_without_sig, 5.5, (1.2, 4.3))
+        # Object mode is enabled
+        self.run_with_protocols(self.check_call, add_without_sig, "abc", ("a", "bc"))
+
+    def test_call_nopython(self):
+        self.run_with_protocols(self.check_call, add_nopython, 5.5, (1.2, 4.3))
+        # Object mode is disabled
+        self.run_with_protocols(self.check_call, add_nopython, TypingError, (object(), object()))
+
+    def test_call_nopython_fail(self):
+        # Compilation fails
+        self.run_with_protocols(self.check_call, add_nopython_fail, TypingError, (1, 2))
+
+    def test_call_objmode_with_global(self):
+        self.run_with_protocols(self.check_call, get_global_objmode, 7.5, (2.5,))
+
+    def test_call_closure(self):
+        inner = closure(1)
+        self.run_with_protocols(self.check_call, inner, 6, (2, 3))
+
+    def check_call_closure_with_globals(self, **jit_args):
+        inner = closure_with_globals(3.0, **jit_args)
+        self.run_with_protocols(self.check_call, inner, 7.0, (4.0,))
+
+    def test_call_closure_with_globals_nopython(self):
+        self.check_call_closure_with_globals(nopython=True)
+
+    def test_call_closure_with_globals_objmode(self):
+        self.check_call_closure_with_globals(forceobj=True)
+
+    def test_call_closure_calling_other_function(self):
+        inner = closure_calling_other_function(3.0)
+        self.run_with_protocols(self.check_call, inner, 11.0, (4.0, 6.0))
+
+    def test_call_closure_calling_other_closure(self):
+        inner = closure_calling_other_closure(3.0)
+        self.run_with_protocols(self.check_call, inner, 8.0, (4.0,))
+
+    def test_call_dyn_func(self):
+        # Check serializing a dynamically-created function
+        self.run_with_protocols(self.check_call, dyn_func, 36, (6,))
+
+    def test_call_dyn_func_objmode(self):
+        # Same with an object mode function
+        self.run_with_protocols(self.check_call, dyn_func_objmode, 36, (6,))
+
+    def test_renamed_module(self):
+        # Issue #1559: using a renamed module (e.g. `import numpy as np`)
+        # should not fail serializing
+        expected = get_renamed_module(0.0)
+        self.run_with_protocols(self.check_call, get_renamed_module,
+                                expected, (0.0,))
+
+    def test_call_generated(self):
+        self.run_with_protocols(self.check_call, generated_add,
+                                46, (1, 2))
+        self.run_with_protocols(self.check_call, generated_add,
+                                1j + 7, (1j, 2))
+
+    def test_other_process(self):
+        """
+        Check that reconstructing doesn't depend on resources already
+        instantiated in the original process.
+        """
+        func = closure_calling_other_closure(3.0)
+        pickled = pickle.dumps(func)
+        code = """if 1:
+            import pickle
+
+            data = {pickled!r}
+            func = pickle.loads(data)
+            res = func(4.0)
+            assert res == 8.0, res
+            """.format(**locals())
+        subprocess.check_call([sys.executable, "-c", code])
+
+    def test_reuse(self):
+        """
+        Check that deserializing the same function multiple times re-uses
+        the same dispatcher object.
+
+        Note that "same function" is intentionally under-specified.
+        """
+        func = closure(5)
+        pickled = pickle.dumps(func)
+        func2 = closure(6)
+        pickled2 = pickle.dumps(func2)
+
+        f = pickle.loads(pickled)
+        g = pickle.loads(pickled)
+        h = pickle.loads(pickled2)
+        self.assertIs(f, g)
+        self.assertEqual(f(2, 3), 10)
+        g.disable_compile()
+        self.assertEqual(g(2, 4), 11)
+
+        self.assertIsNot(f, h)
+        self.assertEqual(h(2, 3), 11)
+
+        # Now make sure the original object doesn't exist when deserializing
+        func = closure(7)
+        func(42, 43)
+        pickled = pickle.dumps(func)
+        del func
+        gc.collect()
+
+        f = pickle.loads(pickled)
+        g = pickle.loads(pickled)
+        self.assertIs(f, g)
+        self.assertEqual(f(2, 3), 12)
+        g.disable_compile()
+        self.assertEqual(g(2, 4), 13)
+
+    def test_imp_deprecation(self):
+        """
+        The imp module was deprecated in v3.4 in favour of importlib
+        """
+        code = """if 1:
+            import pickle
+            import warnings
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always', DeprecationWarning)
+                from numba import njit
+                @njit
+                def foo(x):
+                    return x + 1
+                foo(1)
+                serialized_foo = pickle.dumps(foo)
+            for x in w:
+                if 'serialize.py' in x.filename:
+                    assert "the imp module is deprecated" not in x.msg
+        """
+        subprocess.check_call([sys.executable, "-c", code])
+
+
+class TestSerializationMisc(TestCase):
+    def test_numba_unpickle(self):
+        # Test that _numba_unpickle is memorizing its output
+        from numba.core.serialize import _numba_unpickle
+
+        random_obj = object()
+        bytebuf = pickle.dumps(random_obj)
+        hashed = hash(random_obj)
+
+        got1 = _numba_unpickle(id(random_obj), bytebuf, hashed)
+        # not the original object
+        self.assertIsNot(got1, random_obj)
+        got2 = _numba_unpickle(id(random_obj), bytebuf, hashed)
+        # unpickled results are the same objects
+        self.assertIs(got1, got2)
+
+
+
+class TestCloudPickleIssues(TestCase):
+    """This test case includes issues specific to the cloudpickle implementation.
+    """
+    _numba_parallel_test_ = False
+
+    def test_dynamic_class_reset_on_unpickle(self):
+        # a dynamic class
+        class Klass:
+            classvar = None
+
+        def mutator():
+            Klass.classvar = 100
+
+        def check():
+            self.assertEqual(Klass.classvar, 100)
+
+        saved = dumps(Klass)
+        mutator()
+        check()
+        loads(saved)
+        # Without the patch, each `loads(saved)` will reset `Klass.classvar`
+        check()
+        loads(saved)
+        check()
+
+    @unittest.skipIf(__name__ == "__main__",
+                     "Test cannot run as when module is __main__")
+    def test_main_class_reset_on_unpickle(self):
+        mp = get_context('spawn')
+        proc = mp.Process(target=check_main_class_reset_on_unpickle)
+        proc.start()
+        proc.join(timeout=60)
+        self.assertEqual(proc.exitcode, 0)
+
+    def test_dynamic_class_reset_on_unpickle_new_proc(self):
+        # a dynamic class
+        class Klass:
+            classvar = None
+
+        # serialize Klass in this process
+        saved = dumps(Klass)
+
+        # Check the reset problem in a new process
+        mp = get_context('spawn')
+        proc = mp.Process(target=check_unpickle_dyn_class_new_proc, args=(saved,))
+        proc.start()
+        proc.join(timeout=60)
+        self.assertEqual(proc.exitcode, 0)
+
+    def test_dynamic_class_issue_7356(self):
+        cfunc = numba.njit(issue_7356)
+        self.assertEqual(cfunc(), (100, 100))
+
+
+class DynClass(object):
+    # For testing issue #7356
+    a = None
+
+
+def issue_7356():
+    with numba.objmode(before="intp"):
+        DynClass.a = 100
+        before = DynClass.a
+    with numba.objmode(after="intp"):
+        after = DynClass.a
+    return before, after
+
+
+def check_main_class_reset_on_unpickle():
+    # Load module and get its global dictionary
+    glbs = runpy.run_module(
+        "numba.tests.cloudpickle_main_class",
+        run_name="__main__",
+    )
+    # Get the Klass and check it is from __main__
+    Klass = glbs['Klass']
+    assert Klass.__module__ == "__main__"
+    assert Klass.classvar != 100
+    saved = dumps(Klass)
+    # mutate
+    Klass.classvar = 100
+    # check
+    _check_dyn_class(Klass, saved)
+
+
+def check_unpickle_dyn_class_new_proc(saved):
+    Klass = loads(saved)
+    assert Klass.classvar != 100
+    # mutate
+    Klass.classvar = 100
+    # check
+    _check_dyn_class(Klass, saved)
+
+
+def _check_dyn_class(Klass, saved):
+    def check():
+        if Klass.classvar != 100:
+            raise AssertionError("Check failed. Klass reset.")
+
+    check()
+    loaded = loads(saved)
+    if loaded is not Klass:
+        raise AssertionError("Expected reuse")
+    # Without the patch, each `loads(saved)` will reset `Klass.classvar`
+    check()
+    loaded = loads(saved)
+    if loaded is not Klass:
+        raise AssertionError("Expected reuse")
+    check()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sets.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sets.py
new file mode 100644
index 000000000..bba2651d1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sets.py
@@ -0,0 +1,785 @@
+import unittest
+
+from collections import namedtuple
+import contextlib
+import itertools
+import math
+import random
+import sys
+
+import numpy as np
+
+from numba.core.compiler import compile_isolated, Flags, errors
+from numba import jit
+from numba.core import types
+import unittest
+from numba.tests.support import (TestCase, enable_pyobj_flags, MemoryLeakMixin,
+                                 tag, compile_function)
+
+
+Point = namedtuple('Point', ('a', 'b'))
+
+
+def _build_set_literal_usecase(code, args):
+    code = code % {'initializer': ', '.join(repr(arg) for arg in args)}
+    return compile_function('build_set', code, globals())
+
+def set_literal_return_usecase(args):
+    code = """if 1:
+    def build_set():
+        return {%(initializer)s}
+    """
+    return _build_set_literal_usecase(code, args)
+
+def set_literal_convert_usecase(args):
+    code = """if 1:
+    def build_set():
+        my_set = {%(initializer)s}
+        return list(my_set)
+    """
+    return _build_set_literal_usecase(code, args)
+
+
+def empty_constructor_usecase():
+    s = set()
+    s.add(1)
+    return len(s)
+
+def constructor_usecase(arg):
+    s = set(arg)
+    return len(s)
+
+def iterator_usecase(arg):
+    s = set(arg)
+    l = []
+    for v in s:
+        l.append(v)
+    return l
+
+def update_usecase(a, b, c):
+    s = set()
+    s.update(a)
+    s.update(b)
+    s.update(c)
+    return list(s)
+
+def bool_usecase(arg):
+    # Remove one element to allow for empty sets.
+    s = set(arg[1:])
+    return bool(s)
+
+def remove_usecase(a, b):
+    s = set(a)
+    for v in b:
+        s.remove(v)
+    return list(s)
+
+def discard_usecase(a, b):
+    s = set(a)
+    for v in b:
+        s.discard(v)
+    return list(s)
+
+def add_discard_usecase(a, u, v):
+    s = set(a)
+    for i in range(1000):
+        s.add(u)
+        s.discard(v)
+    return list(s)
+
+def pop_usecase(a):
+    s = set(a)
+    l = []
+    while len(s) > 0:
+        l.append(s.pop())
+    return l
+
+def contains_usecase(a, b):
+    s = set(a)
+    l = []
+    for v in b:
+        l.append(v in s)
+    return l
+
+def difference_update_usecase(a, b):
+    s = set(a)
+    s.difference_update(set(b))
+    return list(s)
+
+def intersection_update_usecase(a, b):
+    s = set(a)
+    s.intersection_update(set(b))
+    return list(s)
+
+def symmetric_difference_update_usecase(a, b):
+    s = set(a)
+    s.symmetric_difference_update(set(b))
+    return list(s)
+
+def isdisjoint_usecase(a, b):
+    return set(a).isdisjoint(set(b))
+
+def issubset_usecase(a, b):
+    return set(a).issubset(set(b))
+
+def issuperset_usecase(a, b):
+    return set(a).issuperset(set(b))
+
+def clear_usecase(a):
+    s = set(a)
+    s.clear()
+    return len(s), list(s)
+
+def copy_usecase(a):
+    s = set(a)
+    ss = s.copy()
+    s.pop()
+    return len(ss), list(ss)
+
+def copy_usecase_empty(a):
+    s = set(a)
+    s.clear()
+    ss = s.copy()
+    s.add(a[0])
+    return len(ss), list(ss)
+
+def copy_usecase_deleted(a, b):
+    s = set(a)
+    s.remove(b)
+    ss = s.copy()
+    s.pop()
+    return len(ss), list(ss)
+
+def difference_usecase(a, b):
+    sa = set(a)
+    s = sa.difference(set(b))
+    return list(s)
+
+def intersection_usecase(a, b):
+    sa = set(a)
+    s = sa.intersection(set(b))
+    return list(s)
+
+def symmetric_difference_usecase(a, b):
+    sa = set(a)
+    s = sa.symmetric_difference(set(b))
+    return list(s)
+
+def union_usecase(a, b):
+    sa = set(a)
+    s = sa.union(set(b))
+    return list(s)
+
+def set_return_usecase(a):
+    s = set(a)
+    return s
+
+
+def make_operator_usecase(op):
+    code = """if 1:
+    def operator_usecase(a, b):
+        s = set(a) %(op)s set(b)
+        return list(s)
+    """ % dict(op=op)
+    return compile_function('operator_usecase', code, globals())
+
+def make_inplace_operator_usecase(op):
+    code = """if 1:
+    def inplace_operator_usecase(a, b):
+        sa = set(a)
+        sb = set(b)
+        sc = sa
+        sc %(op)s sb
+        return list(sc), list(sa)
+    """ % dict(op=op)
+    return compile_function('inplace_operator_usecase', code, globals())
+
+def make_comparison_usecase(op):
+    code = """if 1:
+    def comparison_usecase(a, b):
+        return set(a) %(op)s set(b)
+    """ % dict(op=op)
+    return compile_function('comparison_usecase', code, globals())
+
+
+def noop(x):
+    pass
+
+def unbox_usecase(x):
+    """
+    Expect a set of numbers
+    """
+    res = 0
+    for v in x:
+        res += v
+    return res
+
+def unbox_usecase2(x):
+    """
+    Expect a set of tuples
+    """
+    res = 0
+    for v in x:
+        res += len(v)
+    return res
+
+def unbox_usecase3(x):
+    """
+    Expect a (number, set of numbers) tuple.
+    """
+    a, b = x
+    res = a
+    for v in b:
+        res += v
+    return res
+
+def unbox_usecase4(x):
+    """
+    Expect a (number, set of tuples) tuple.
+    """
+    a, b = x
+    res = a
+    for v in b:
+        res += len(v)
+    return res
+
+
+def reflect_simple(sa, sb):
+    sa.add(42)
+    sa.update(sb)
+    return sa, len(sa), len(sb)
+
+def reflect_conditional(sa, sb):
+    # `sa` may or may not actually reflect a Python set
+    if len(sb) > 1:
+        sa = set((11., 22., 33., 44.))
+    sa.add(42.)
+    sa.update(sb)
+    # Combine with a non-reflected set (to check method typing)
+    sc = set((55., 66.))
+    sa.symmetric_difference_update(sc)
+    return sa, len(sa), len(sb)
+
+def reflect_exception(s):
+    s.add(42)
+    raise ZeroDivisionError
+
+def reflect_dual(sa, sb):
+    sa.add(sb.pop())
+    return sa is sb
+
+
+def unique_usecase(src):
+    seen = set()
+    res = []
+    for v in src:
+        if v not in seen:
+            seen.add(v)
+            res.append(v)
+    return res
+
+
+class BaseTest(MemoryLeakMixin, TestCase):
+
+    def setUp(self):
+        super(BaseTest, self).setUp()
+        self.rnd = random.Random(42)
+
+    def _range(self, stop):
+        return np.arange(int(stop))
+
+    def _random_choice(self, seq, n):
+        """
+        Choose *n* possibly duplicate items from sequence.
+        """
+        l = [self.rnd.choice(list(seq)) for i in range(n)]
+        if isinstance(seq, np.ndarray):
+            return np.array(l, dtype=seq.dtype)
+        else:
+            return l
+
+    def duplicates_array(self, n):
+        """
+        Get a 1d array with many duplicate values.
+        """
+        a = self._range(np.sqrt(n))
+        return self._random_choice(a, n)
+
+    def sparse_array(self, n):
+        """
+        Get a 1d array with values spread around.
+        """
+        # Note two calls to sparse_array() should generate reasonable overlap
+        a = self._range(n ** 1.3)
+        return self._random_choice(a, n)
+
+    def _assert_equal_unordered(self, a, b):
+        if isinstance(a, tuple):
+            self.assertIsInstance(b, tuple)
+            for u, v in zip(a, b):
+                self._assert_equal_unordered(u, v)
+        elif isinstance(a, list):
+            self.assertIsInstance(b, list)
+            self.assertPreciseEqual(sorted(a), sorted(b))
+        else:
+            self.assertPreciseEqual(a, b)
+
+    def unordered_checker(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(*args):
+            expected = pyfunc(*args)
+            got = cfunc(*args)
+            self._assert_equal_unordered(expected, got)
+        return check
+
+
+class TestSetLiterals(BaseTest):
+
+    def test_build_set(self, flags=enable_pyobj_flags):
+        pyfunc = set_literal_return_usecase((1, 2, 3, 2))
+        self.run_nullary_func(pyfunc, flags=flags)
+
+    def test_build_heterogeneous_set(self, flags=enable_pyobj_flags):
+        pyfunc = set_literal_return_usecase((1, 2.0, 3j, 2))
+        self.run_nullary_func(pyfunc, flags=flags)
+        pyfunc = set_literal_return_usecase((2.0, 2))
+        got, expected = self.run_nullary_func(pyfunc, flags=flags)
+
+        self.assertIs(type(got.pop()), type(expected.pop()))
+
+    def test_build_set_nopython(self):
+        arg = list(self.sparse_array(50))
+        pyfunc = set_literal_convert_usecase(arg)
+        cfunc = jit(nopython=True)(pyfunc)
+
+        expected = pyfunc()
+        got = cfunc()
+        self.assertPreciseEqual(sorted(expected), sorted(got))
+
+
+class TestSets(BaseTest):
+
+    def test_constructor(self):
+        pyfunc = empty_constructor_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertPreciseEqual(cfunc(), pyfunc())
+
+        pyfunc = constructor_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(arg):
+            self.assertPreciseEqual(pyfunc(arg), cfunc(arg))
+
+        check(self.duplicates_array(200))
+        check(self.sparse_array(200))
+
+    def test_set_return(self):
+        pyfunc = set_return_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        arg = self.duplicates_array(200)
+        self.assertEqual(cfunc(arg), set(arg))
+
+    def test_iterator(self):
+        pyfunc = iterator_usecase
+        check = self.unordered_checker(pyfunc)
+
+        check(self.duplicates_array(200))
+        check(self.sparse_array(200))
+
+    def test_update(self):
+        pyfunc = update_usecase
+        check = self.unordered_checker(pyfunc)
+
+        a = self.sparse_array(50)
+        b = self.duplicates_array(50)
+        c = self.sparse_array(50)
+        check(a, b, c)
+
+    def test_remove(self):
+        pyfunc = remove_usecase
+        check = self.unordered_checker(pyfunc)
+
+        a = self.sparse_array(50)
+        b = a[::10]
+        check(a, b)
+
+    def test_remove_error(self):
+        # References are leaked on exception
+        self.disable_leak_check()
+
+        pyfunc = remove_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # ensure that there will be a key error
+        items = tuple(set(self.sparse_array(3)))
+        a = items[1:]
+        b = (items[0],)
+        with self.assertRaises(KeyError):
+            cfunc(a, b)
+
+    def test_discard(self):
+        pyfunc = discard_usecase
+        check = self.unordered_checker(pyfunc)
+
+        a = self.sparse_array(50)
+        b = self.sparse_array(50)
+        check(a, b)
+
+    def test_add_discard(self):
+        """
+        Check that the insertion logic does not create an infinite lookup
+        chain with deleted entries (insertion should happen at the first
+        deleted entry, not at the free entry at the end of the chain).
+        See issue #1913.
+        """
+        pyfunc = add_discard_usecase
+        check = self.unordered_checker(pyfunc)
+
+        # ensure a and b are different
+        a = b = None
+        while a == b:
+            a, b = self.sparse_array(2)
+        check((a,), b, b)
+
+    def test_pop(self):
+        pyfunc = pop_usecase
+        check = self.unordered_checker(pyfunc)
+
+        check(self.sparse_array(50))
+
+    def test_contains(self):
+        pyfunc = contains_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(a, b):
+            self.assertPreciseEqual(pyfunc(a, b), cfunc(a, b))
+
+        a = self.sparse_array(50)
+        b = self.sparse_array(50)
+        check(a, b)
+
+    def _test_xxx_update(self, pyfunc):
+        check = self.unordered_checker(pyfunc)
+
+        sizes = (1, 50, 500)
+        for na, nb in itertools.product(sizes, sizes):
+            a = self.sparse_array(na)
+            b = self.sparse_array(nb)
+            check(a, b)
+
+    def test_difference_update(self):
+        self._test_xxx_update(difference_update_usecase)
+
+    def test_intersection_update(self):
+        self._test_xxx_update(intersection_update_usecase)
+
+    def test_symmetric_difference_update(self):
+        self._test_xxx_update(symmetric_difference_update_usecase)
+
+    def _test_comparator(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(a, b):
+            self.assertPreciseEqual(pyfunc(a, b), cfunc(a, b))
+
+        a, b = map(set, [self.sparse_array(10), self.sparse_array(15)])
+        args = [a & b, a - b, a | b, a ^ b]
+        args = [tuple(x) for x in args]
+        for a, b in itertools.product(args, args):
+            check(a, b)
+
+    def test_isdisjoint(self):
+        self._test_comparator(isdisjoint_usecase)
+
+    def test_issubset(self):
+        self._test_comparator(issubset_usecase)
+
+    def test_issuperset(self):
+        self._test_comparator(issuperset_usecase)
+
+    def test_clear(self):
+        pyfunc = clear_usecase
+        check = self.unordered_checker(pyfunc)
+
+        check(self.sparse_array(50))
+
+    def test_copy(self):
+        # Source set doesn't have any deleted entries
+        pyfunc = copy_usecase
+        check = self.unordered_checker(pyfunc)
+        check(self.sparse_array(50))
+
+        pyfunc = copy_usecase_empty
+        check = self.unordered_checker(pyfunc)
+        a = self.sparse_array(1)
+        check(a)
+
+        # Source set has deleted entries
+        pyfunc = copy_usecase_deleted
+        check = self.unordered_checker(pyfunc)
+        check((1, 2, 4, 11), 2)
+        a = self.sparse_array(50)
+        check(a, a[len(a) // 2])
+
+    def test_bool(self):
+        pyfunc = bool_usecase
+        check = self.unordered_checker(pyfunc)
+
+        check(self.sparse_array(1))
+        check(self.sparse_array(2))
+
+    def _test_set_operator(self, pyfunc):
+        check = self.unordered_checker(pyfunc)
+
+        a, b = (1, 2, 4, 11), (2, 3, 5, 11, 42)
+        check(a, b)
+
+        sizes = (1, 50, 500)
+        for na, nb in itertools.product(sizes, sizes):
+            a = self.sparse_array(na)
+            b = self.sparse_array(nb)
+            check(a, b)
+
+    def test_difference(self):
+        self._test_set_operator(difference_usecase)
+
+    def test_intersection(self):
+        self._test_set_operator(intersection_usecase)
+
+    def test_symmetric_difference(self):
+        self._test_set_operator(symmetric_difference_usecase)
+
+    def test_union(self):
+        self._test_set_operator(union_usecase)
+
+    def test_and(self):
+        self._test_set_operator(make_operator_usecase('&'))
+
+    def test_or(self):
+        self._test_set_operator(make_operator_usecase('|'))
+
+    def test_sub(self):
+        self._test_set_operator(make_operator_usecase('-'))
+
+    def test_xor(self):
+        self._test_set_operator(make_operator_usecase('^'))
+
+    def test_eq(self):
+        self._test_set_operator(make_comparison_usecase('=='))
+
+    def test_ne(self):
+        self._test_set_operator(make_comparison_usecase('!='))
+
+    def test_le(self):
+        self._test_set_operator(make_comparison_usecase('<='))
+
+    def test_lt(self):
+        self._test_set_operator(make_comparison_usecase('<'))
+
+    def test_ge(self):
+        self._test_set_operator(make_comparison_usecase('>='))
+
+    def test_gt(self):
+        self._test_set_operator(make_comparison_usecase('>'))
+
+    def test_iand(self):
+        self._test_set_operator(make_inplace_operator_usecase('&='))
+
+    def test_ior(self):
+        self._test_set_operator(make_inplace_operator_usecase('|='))
+
+    def test_isub(self):
+        self._test_set_operator(make_inplace_operator_usecase('-='))
+
+    def test_ixor(self):
+        self._test_set_operator(make_inplace_operator_usecase('^='))
+
+
+class TestFloatSets(TestSets):
+    """
+    Test sets with floating-point keys.
+    """
+    # Only a few basic tests here, as the sanity of most operations doesn't
+    # depend on the key type.
+
+    def _range(self, stop):
+        return np.arange(stop, dtype=np.float32) * np.float32(0.1)
+
+
+class TestTupleSets(TestSets):
+    """
+    Test sets with tuple keys.
+    """
+    def _range(self, stop):
+        a = np.arange(stop, dtype=np.int64)
+        b = a & 0x5555555555555555
+        c = (a & 0xaaaaaaaa).astype(np.int32)
+        d = ((a >> 32) & 1).astype(np.bool_)
+        return list(zip(b, c, d))
+
+
+class TestUnicodeSets(TestSets):
+    """
+    Test sets with unicode keys. For the purpose of testing refcounted sets.
+    """
+    def _range(self, stop):
+        return ['A{}'.format(i) for i in range(int(stop))]
+
+
+class TestUnboxing(BaseTest):
+    """
+    Test unboxing of Python sets into native Numba sets.
+    """
+
+    @contextlib.contextmanager
+    def assert_type_error(self, msg):
+        with self.assertRaises(TypeError) as raises:
+            yield
+        if msg is not None:
+            self.assertRegexpMatches(str(raises.exception), msg)
+
+    def check_unary(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        def check(arg):
+            expected = pyfunc(arg)
+            got = cfunc(arg)
+            self.assertPreciseEqual(got, expected)
+        return check
+
+    def test_numbers(self):
+        check = self.check_unary(unbox_usecase)
+        check(set([1, 2]))
+        check(set([1j, 2.5j]))
+        # Check allocation and sizing
+        check(set(range(100)))
+
+    def test_tuples(self):
+        check = self.check_unary(unbox_usecase2)
+        check(set([(1, 2), (3, 4)]))
+        check(set([(1, 2j), (3, 4j)]))
+
+    def test_set_inside_tuple(self):
+        check = self.check_unary(unbox_usecase3)
+        check((1, set([2, 3, 4])))
+
+    def test_set_of_tuples_inside_tuple(self):
+        check = self.check_unary(unbox_usecase4)
+        check((1, set([(2,), (3,)])))
+
+    def test_errors(self):
+        # Error checking should ensure the set is homogeneous
+        msg = "can't unbox heterogeneous set"
+        pyfunc = noop
+        cfunc = jit(nopython=True)(pyfunc)
+        val = set([1, 2.5])
+        with self.assert_type_error(msg):
+            cfunc(val)
+        # The set hasn't been changed (bogus reflecting)
+        self.assertEqual(val, set([1, 2.5]))
+        with self.assert_type_error(msg):
+            cfunc(set([1, 2j]))
+        # Same when the set is nested in a tuple or namedtuple
+        with self.assert_type_error(msg):
+            cfunc((1, set([1, 2j])))
+        with self.assert_type_error(msg):
+            cfunc(Point(1, set([1, 2j])))
+        # Tuples of different size.
+        # Note the check is really on the tuple side.
+        lst = set([(1,), (2, 3)])
+        # Depending on which tuple is examined first, we could get
+        # a IndexError or a ValueError.
+        with self.assertRaises((IndexError, ValueError)) as raises:
+            cfunc(lst)
+
+
+class TestSetReflection(BaseTest):
+    """
+    Test reflection of native Numba sets on Python set objects.
+    """
+
+    def check_reflection(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        samples = [(set([1., 2., 3., 4.]), set([0.])),
+                   (set([1., 2., 3., 4.]), set([5., 6., 7., 8., 9.])),
+                   ]
+        for dest, src in samples:
+            expected = set(dest)
+            got = set(dest)
+            pyres = pyfunc(expected, src)
+            with self.assertRefCount(got, src):
+                cres = cfunc(got, src)
+                self.assertPreciseEqual(cres, pyres)
+                self.assertPreciseEqual(expected, got)
+                self.assertEqual(pyres[0] is expected, cres[0] is got)
+                del pyres, cres
+
+    def test_reflect_simple(self):
+        self.check_reflection(reflect_simple)
+
+    def test_reflect_conditional(self):
+        self.check_reflection(reflect_conditional)
+
+    def test_reflect_exception(self):
+        """
+        When the function exits with an exception, sets should still be
+        reflected.
+        """
+        pyfunc = reflect_exception
+        cfunc = jit(nopython=True)(pyfunc)
+        s = set([1, 2, 3])
+        with self.assertRefCount(s):
+            with self.assertRaises(ZeroDivisionError):
+                cfunc(s)
+            self.assertPreciseEqual(s, set([1, 2, 3, 42]))
+
+    def test_reflect_same_set(self):
+        """
+        When the same set object is reflected twice, behaviour should
+        be consistent.
+        """
+        pyfunc = reflect_dual
+        cfunc = jit(nopython=True)(pyfunc)
+        pyset = set([1, 2, 3])
+        cset = pyset.copy()
+        expected = pyfunc(pyset, pyset)
+        got = cfunc(cset, cset)
+        self.assertPreciseEqual(expected, got)
+        self.assertPreciseEqual(pyset, cset)
+        self.assertPreciseEqual(sys.getrefcount(pyset), sys.getrefcount(cset))
+
+    def test_reflect_clean(self):
+        """
+        When the set wasn't mutated, no reflection should take place.
+        """
+        cfunc = jit(nopython=True)(noop)
+        # Use a complex, as Python integers can be cached
+        s = set([12.5j])
+        ids = [id(x) for x in s]
+        cfunc(s)
+        self.assertEqual([id(x) for x in s], ids)
+
+
+class TestExamples(BaseTest):
+    """
+    Examples of using sets.
+    """
+
+    def test_unique(self):
+        pyfunc = unique_usecase
+        check = self.unordered_checker(pyfunc)
+
+        check(self.duplicates_array(200))
+        check(self.sparse_array(200))
+
+    def test_type_coercion_from_update(self):
+        # see issue #6621
+        def impl():
+            i = np.uint64(1)
+            R = set()
+            R.update({1, 2, 3})
+            R.add(i)
+            return R
+        check = self.unordered_checker(impl)
+        check()
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_slices.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_slices.py
new file mode 100644
index 000000000..0759fd25b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_slices.py
@@ -0,0 +1,259 @@
+from functools import partial
+import itertools
+from itertools import chain, product, starmap
+import sys
+
+import numpy as np
+
+from numba import jit, literally, njit, typeof, TypingError
+from numba.core import utils, types
+from numba.tests.support import TestCase, MemoryLeakMixin
+from numba.core.types.functions import _header_lead
+import unittest
+
+
+def slice_passing(sl):
+    return sl.start, sl.stop, sl.step
+
+def slice_constructor(*args):
+    sl = slice(*args)
+    return sl.start, sl.stop, sl.step
+
+def slice_construct_and_use(args, l):
+    sl = slice(*args)
+    return l[sl]
+
+def slice_indices(s, *indargs):
+    return s.indices(*indargs)
+
+class TestSlices(MemoryLeakMixin, TestCase):
+
+    def test_slice_passing(self):
+        """
+        Check passing a slice object to a Numba function.
+        """
+        # NOTE this also checks slice attributes
+        def check(a, b, c, d, e, f):
+            sl = slice(a, b, c)
+            got = cfunc(sl)
+            self.assertPreciseEqual(got, (d, e, f))
+
+        maxposint = sys.maxsize
+        maxnegint = -maxposint - 1
+        cfunc = jit(nopython=True)(slice_passing)
+
+        # Positive steps
+        start_cases = [(None, 0), (42, 42), (-1, -1)]
+        stop_cases = [(None, maxposint), (9, 9), (-11, -11)]
+        step_cases = [(None, 1), (12, 12)]
+        for (a, d), (b, e), (c, f) in itertools.product(start_cases,
+                                                        stop_cases,
+                                                        step_cases):
+            check(a, b, c, d, e, f)
+
+        # Negative steps
+        start_cases = [(None, maxposint), (42, 42), (-1, -1)]
+        stop_cases = [(None, maxnegint), (9, 9), (-11, -11)]
+        step_cases = [(-1, -1), (-12, -12)]
+        for (a, d), (b, e), (c, f) in itertools.product(start_cases,
+                                                        stop_cases,
+                                                        step_cases):
+            check(a, b, c, d, e, f)
+
+        # Some member is neither integer nor None
+        with self.assertRaises(TypeError):
+            cfunc(slice(1.5, 1, 1))
+
+    def test_slice_constructor(self):
+        """
+        Test the 'happy path' for slice() constructor in nopython mode.
+        """
+        maxposint = sys.maxsize
+        maxnegint = -maxposint - 1
+        a = np.arange(10)
+        cfunc = jit(nopython=True)(slice_constructor)
+        cfunc_use = jit(nopython=True)(slice_construct_and_use)
+        for args, expected in [
+            ((None,), (0, maxposint, 1)),
+            ((5,), (0, 5, 1)),
+            ((None, None), (0, maxposint, 1)),
+            ((1, None), (1, maxposint, 1)),
+            ((None, 2), (0, 2, 1)),
+            ((1, 2), (1, 2, 1)),
+            ((None, None, 3), (0, maxposint, 3)),
+            ((None, 2, 3), (0, 2, 3)),
+            ((1, None, 3), (1, maxposint, 3)),
+            ((1, 2, 3), (1, 2, 3)),
+            ((None, None, -1), (maxposint, maxnegint, -1)),
+            ((10, None, -1), (10, maxnegint, -1)),
+            ((None, 5, -1), (maxposint, 5, -1)),
+            ((10, 5, -1), (10, 5, -1)),
+        ]:
+            got = cfunc(*args)
+            self.assertPreciseEqual(got, expected)
+            usage = slice_construct_and_use(args, a)
+            cusage = cfunc_use(args, a)
+            self.assertPreciseEqual(usage, cusage)
+
+    def test_slice_constructor_cases(self):
+        """
+        Test that slice constructor behaves same in python and compiled code.
+        """
+        options = (None, -1, 0, 1)
+        arg_cases = chain.from_iterable(
+            product(options, repeat=n) for n in range(5)
+        )
+        array = np.arange(10)
+
+        cfunc = jit(nopython=True)(slice_construct_and_use)
+
+        self.disable_leak_check()
+        for args in arg_cases:
+            try:
+                expected = slice_construct_and_use(args, array)
+            except TypeError as py_type_e:
+                # Catch cases of 0, or more than 3 arguments.
+                # This becomes a typing error in numba
+                n_args = len(args)
+                self.assertRegexpMatches(
+                    str(py_type_e),
+                    r"slice expected at (most|least) (3|1) arguments?, got {}"
+                    .format(n_args)
+                )
+                with self.assertRaises(TypingError) as numba_e:
+                    cfunc(args, array)
+                self.assertIn(
+                    _header_lead,
+                    str(numba_e.exception)
+                )
+                self.assertIn(
+                    ", ".join(str(typeof(arg)) for arg in args),
+                    str(numba_e.exception)
+                )
+            except Exception as py_e:
+                with self.assertRaises(type(py_e)) as numba_e:
+                    cfunc(args, array)
+                self.assertIn(
+                    str(py_e),
+                    str(numba_e.exception)
+                )
+            else:
+                self.assertPreciseEqual(expected, cfunc(args, array))
+
+    def test_slice_indices(self):
+        """Test that a numba slice returns same result for .indices as a python one."""
+        slices = starmap(
+            slice,
+            product(
+                chain(range(-5, 5), (None,)),
+                chain(range(-5, 5), (None,)),
+                chain(range(-5, 5), (None,))
+            )
+        )
+        lengths = range(-2, 3)
+
+        cfunc = jit(nopython=True)(slice_indices)
+
+        for s, l in product(slices, lengths):
+            try:
+                expected = slice_indices(s, l)
+            except Exception as py_e:
+                with self.assertRaises(type(py_e)) as numba_e:
+                    cfunc(s, l)
+                self.assertIn(
+                    str(py_e),
+                    str(numba_e.exception)
+                )
+            else:
+                self.assertPreciseEqual(expected, cfunc(s, l))
+
+    def test_slice_indices_examples(self):
+        """Tests for specific error cases."""
+        cslice_indices = jit(nopython=True)(slice_indices)
+
+        with self.assertRaises(TypingError) as e:
+            cslice_indices(slice(None), 1, 2, 3)
+        self.assertIn(
+             "indices() takes exactly one argument (3 given)",
+             str(e.exception)
+        )
+
+        with self.assertRaises(TypingError) as e:
+            cslice_indices(slice(None, None, 0), 1.2)
+        self.assertIn(
+            "'%s' object cannot be interpreted as an integer" % typeof(1.2),
+            str(e.exception)
+        )
+
+    def test_slice_from_constant(self):
+        test_tuple = (1, 2, 3, 4)
+
+        for ts in itertools.product(
+            [None, 1, 2, 3], [None, 1, 2, 3], [None, 1, 2, -1, -2]
+        ):
+            ts = slice(*ts)
+
+            @jit(nopython=True)
+            def test_fn():
+                return test_tuple[ts]
+
+            self.assertEqual(test_fn(), test_fn.py_func())
+
+    def test_literal_slice_distinct(self):
+        sl1 = types.misc.SliceLiteral(slice(1, None, None))
+        sl2 = types.misc.SliceLiteral(slice(None, None, None))
+        sl3 = types.misc.SliceLiteral(slice(1, None, None))
+
+        self.assertNotEqual(sl1, sl2)
+        self.assertEqual(sl1, sl3)
+
+    def test_literal_slice_boxing(self):
+        # Tests that a literal slice can be
+        # returned from a JIT function.
+        @njit
+        def f(x):
+            return literally(x)
+
+        slices = (
+            slice(1, 4, 2),
+            slice(1, 2),
+            slice(1),
+            slice(None, 1, 1),
+            slice(1, None, 1),
+            slice(None, None, 1),
+            slice(None),
+            slice(None, None, None)
+        )
+        for sl in slices:
+            self.assertEqual(sl, f(sl))
+
+
+    def test_literal_slice_freevar(self):
+        # Tests passing a literal slice as a freevar
+        # in a closure.
+        z = slice(1, 2, 3)
+        @njit
+        def foo():
+            return z
+
+        self.assertEqual(z, foo())
+
+    def test_literal_slice_maxint(self):
+        # Tests that passing a slice with an integer
+        # that exceeds the maxint size throws a reasonable
+        # error message.
+        @njit()
+        def foo(z):
+            return literally(z)
+
+        maxval = int(2**63)
+        with self.assertRaises(ValueError) as e:
+            foo(slice(None, None, -maxval-1))
+        self.assertIn(
+            "Int value is too large",
+            str(e.exception)
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sort.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sort.py
new file mode 100644
index 000000000..36788aaf2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sort.py
@@ -0,0 +1,1199 @@
+import copy
+import itertools
+import math
+import random
+import sys
+from typing import KeysView
+
+import numpy as np
+
+from numba.core.compiler import compile_isolated, Flags
+from numba import jit, njit
+from numba.core import types, utils, errors
+import unittest
+from numba import testing
+from numba.tests.support import TestCase, MemoryLeakMixin, tag
+
+from numba.misc.quicksort import make_py_quicksort, make_jit_quicksort
+from numba.misc.mergesort import make_jit_mergesort
+from numba.misc.timsort import make_py_timsort, make_jit_timsort, MergeRun
+
+
+def make_temp_list(keys, n):
+    return [keys[0]] * n
+
+def make_temp_array(keys, n):
+    return np.empty(n, keys.dtype)
+
+
+py_list_timsort = make_py_timsort(make_temp_list)
+
+py_array_timsort = make_py_timsort(make_temp_array)
+
+jit_list_timsort = make_jit_timsort(make_temp_list)
+
+jit_array_timsort = make_jit_timsort(make_temp_array)
+
+py_quicksort = make_py_quicksort()
+
+jit_quicksort = make_jit_quicksort()
+
+
+def sort_usecase(val):
+    val.sort()
+
+def argsort_usecase(val):
+    return val.argsort()
+
+def argsort_kind_usecase(val, is_stable=False):
+    if is_stable:
+        return val.argsort(kind='mergesort')
+    else:
+        return val.argsort(kind='quicksort')
+
+def sorted_usecase(val):
+    return sorted(val)
+
+def sorted_reverse_usecase(val, b):
+    return sorted(val, reverse=b)
+
+def np_sort_usecase(val):
+    return np.sort(val)
+
+def np_argsort_usecase(val):
+    return np.argsort(val)
+
+def np_argsort_kind_usecase(val, is_stable=False):
+    if is_stable:
+        return np.argsort(val, kind='mergesort')
+    else:
+        return np.argsort(val, kind='quicksort')
+
+def list_sort_usecase(n):
+    np.random.seed(42)
+    l = []
+    for i in range(n):
+        l.append(np.random.random())
+    ll = l[:]
+    ll.sort()
+    return l, ll
+
+def list_sort_reverse_usecase(n, b):
+    np.random.seed(42)
+    l = []
+    for i in range(n):
+        l.append(np.random.random())
+    ll = l[:]
+    ll.sort(reverse=b)
+    return l, ll
+
+
+class BaseSortingTest(object):
+
+    def random_list(self, n, offset=10):
+        random.seed(42)
+        l = list(range(offset, offset + n))
+        random.shuffle(l)
+        return l
+
+    def sorted_list(self, n, offset=10):
+        return list(range(offset, offset + n))
+
+    def revsorted_list(self, n, offset=10):
+        return list(range(offset, offset + n))[::-1]
+
+    def initially_sorted_list(self, n, m=None, offset=10):
+        if m is None:
+            m = n // 2
+        l = self.sorted_list(m, offset)
+        l += self.random_list(n - m, offset=l[-1] + offset)
+        return l
+
+    def duprandom_list(self, n, factor=None, offset=10):
+        random.seed(42)
+        if factor is None:
+            factor = int(math.sqrt(n))
+        l = (list(range(offset, offset + (n // factor) + 1)) * (factor + 1))[:n]
+        assert len(l) == n
+        random.shuffle(l)
+        return l
+
+    def dupsorted_list(self, n, factor=None, offset=10):
+        if factor is None:
+            factor = int(math.sqrt(n))
+        l = (list(range(offset, offset + (n // factor) + 1)) * (factor + 1))[:n]
+        assert len(l) == n, (len(l), n)
+        l.sort()
+        return l
+
+    def assertSorted(self, orig, result):
+        self.assertEqual(len(result), len(orig))
+        # sorted() returns a list, so make sure we compare to another list
+        self.assertEqual(list(result), sorted(orig))
+
+    def assertSortedValues(self, orig, orig_values, result, result_values):
+        self.assertEqual(len(result), len(orig))
+        self.assertEqual(list(result), sorted(orig))
+        zip_sorted = sorted(zip(orig, orig_values), key=lambda x: x[0])
+        zip_result = list(zip(result, result_values))
+        self.assertEqual(zip_sorted, zip_result)
+        # Check stability
+        for i in range(len(zip_result) - 1):
+            (k1, v1), (k2, v2) = zip_result[i], zip_result[i + 1]
+            if k1 == k2:
+                # Assuming values are unique, which is enforced by the tests
+                self.assertLess(orig_values.index(v1), orig_values.index(v2))
+
+    def fibo(self):
+        a = 1
+        b = 1
+        while True:
+            yield a
+            a, b = b, a + b
+
+    def make_sample_sorted_lists(self, n):
+        lists = []
+        for offset in (20, 120):
+            lists.append(self.sorted_list(n, offset))
+            lists.append(self.dupsorted_list(n, offset))
+        return lists
+
+    def make_sample_lists(self, n):
+        lists = []
+        for offset in (20, 120):
+            lists.append(self.sorted_list(n, offset))
+            lists.append(self.dupsorted_list(n, offset))
+            lists.append(self.revsorted_list(n, offset))
+            lists.append(self.duprandom_list(n, offset))
+        return lists
+
+
+class BaseTimsortTest(BaseSortingTest):
+
+    def merge_init(self, keys):
+        f = self.timsort.merge_init
+        return f(keys)
+
+    def test_binarysort(self):
+        n = 20
+        def check(l, n, start=0):
+            res = self.array_factory(l)
+            f(res, res, 0, n, start)
+            self.assertSorted(l, res)
+
+        f = self.timsort.binarysort
+        l = self.sorted_list(n)
+        check(l, n)
+        check(l, n, n//2)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.initially_sorted_list(n, n//2)
+        check(l, n)
+        check(l, n, n//2)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.random_list(n)
+        check(l, n)
+        l = self.duprandom_list(n)
+        check(l, n)
+
+    def test_binarysort_with_values(self):
+        n = 20
+        v = list(range(100, 100+n))
+
+        def check(l, n, start=0):
+            res = self.array_factory(l)
+            res_v = self.array_factory(v)
+            f(res, res_v, 0, n, start)
+            self.assertSortedValues(l, v, res, res_v)
+
+        f = self.timsort.binarysort
+        l = self.sorted_list(n)
+        check(l, n)
+        check(l, n, n//2)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.initially_sorted_list(n, n//2)
+        check(l, n)
+        check(l, n, n//2)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.random_list(n)
+        check(l, n)
+        l = self.duprandom_list(n)
+        check(l, n)
+
+    def test_count_run(self):
+        n = 16
+        f = self.timsort.count_run
+
+        def check(l, lo, hi):
+            n, desc = f(self.array_factory(l), lo, hi)
+            # Fully check invariants
+            if desc:
+                for k in range(lo, lo + n - 1):
+                    a, b = l[k], l[k + 1]
+                    self.assertGreater(a, b)
+                if lo + n < hi:
+                    self.assertLessEqual(l[lo + n - 1], l[lo + n])
+            else:
+                for k in range(lo, lo + n - 1):
+                    a, b = l[k], l[k + 1]
+                    self.assertLessEqual(a, b)
+                if lo + n < hi:
+                    self.assertGreater(l[lo + n - 1], l[lo + n], l)
+
+
+        l = self.sorted_list(n, offset=100)
+        check(l, 0, n)
+        check(l, 1, n - 1)
+        check(l, 1, 2)
+        l = self.revsorted_list(n, offset=100)
+        check(l, 0, n)
+        check(l, 1, n - 1)
+        check(l, 1, 2)
+        l = self.random_list(n, offset=100)
+        for i in range(len(l) - 1):
+            check(l, i, n)
+        l = self.duprandom_list(n, offset=100)
+        for i in range(len(l) - 1):
+            check(l, i, n)
+
+    def test_gallop_left(self):
+        n = 20
+        f = self.timsort.gallop_left
+
+        def check(l, key, start, stop, hint):
+            k = f(key, l, start, stop, hint)
+            # Fully check invariants
+            self.assertGreaterEqual(k, start)
+            self.assertLessEqual(k, stop)
+            if k > start:
+                self.assertLess(l[k - 1], key)
+            if k < stop:
+                self.assertGreaterEqual(l[k], key)
+
+        def check_all_hints(l, key, start, stop):
+            for hint in range(start, stop):
+                check(l, key, start, stop, hint)
+
+        def check_sorted_list(l):
+            l = self.array_factory(l)
+            for key in (l[5], l[15], l[0], -1000, l[-1], 1000):
+                check_all_hints(l, key, 0, n)
+                check_all_hints(l, key, 1, n - 1)
+                check_all_hints(l, key, 8, n - 8)
+
+        l = self.sorted_list(n, offset=100)
+        check_sorted_list(l)
+        l = self.dupsorted_list(n, offset=100)
+        check_sorted_list(l)
+
+    def test_gallop_right(self):
+        n = 20
+        f = self.timsort.gallop_right
+
+        def check(l, key, start, stop, hint):
+            k = f(key, l, start, stop, hint)
+            # Fully check invariants
+            self.assertGreaterEqual(k, start)
+            self.assertLessEqual(k, stop)
+            if k > start:
+                self.assertLessEqual(l[k - 1], key)
+            if k < stop:
+                self.assertGreater(l[k], key)
+
+        def check_all_hints(l, key, start, stop):
+            for hint in range(start, stop):
+                check(l, key, start, stop, hint)
+
+        def check_sorted_list(l):
+            l = self.array_factory(l)
+            for key in (l[5], l[15], l[0], -1000, l[-1], 1000):
+                check_all_hints(l, key, 0, n)
+                check_all_hints(l, key, 1, n - 1)
+                check_all_hints(l, key, 8, n - 8)
+
+        l = self.sorted_list(n, offset=100)
+        check_sorted_list(l)
+        l = self.dupsorted_list(n, offset=100)
+        check_sorted_list(l)
+
+    def test_merge_compute_minrun(self):
+        f = self.timsort.merge_compute_minrun
+
+        for i in range(0, 64):
+            self.assertEqual(f(i), i)
+        for i in range(6, 63):
+            if 2**i > sys.maxsize:
+                break
+            self.assertEqual(f(2**i), 32)
+        for i in self.fibo():
+            if i < 64:
+                continue
+            if i >= sys.maxsize:
+                break
+            k = f(i)
+            self.assertGreaterEqual(k, 32)
+            self.assertLessEqual(k, 64)
+            if i > 500:
+                # i/k is close to, but strictly less than, an exact power of 2
+                quot = i // k
+                p = 2 ** utils.bit_length(quot)
+                self.assertLess(quot, p)
+                self.assertGreaterEqual(quot, 0.9 * p)
+
+    def check_merge_lo_hi(self, func, a, b):
+        na = len(a)
+        nb = len(b)
+
+        # Add sentinels at start and end, to check they weren't moved
+        orig_keys = [42] + a + b + [-42]
+        keys = self.array_factory(orig_keys)
+        ms = self.merge_init(keys)
+        ssa = 1
+        ssb = ssa + na
+
+        #new_ms = func(ms, keys, [], ssa, na, ssb, nb)
+        new_ms = func(ms, keys, keys, ssa, na, ssb, nb)
+        self.assertEqual(keys[0], orig_keys[0])
+        self.assertEqual(keys[-1], orig_keys[-1])
+        self.assertSorted(orig_keys[1:-1], keys[1:-1])
+        # Check the MergeState result
+        self.assertGreaterEqual(len(new_ms.keys), len(ms.keys))
+        self.assertGreaterEqual(len(new_ms.values), len(ms.values))
+        self.assertIs(new_ms.pending, ms.pending)
+        self.assertGreaterEqual(new_ms.min_gallop, 1)
+
+    def test_merge_lo_hi(self):
+        f_lo = self.timsort.merge_lo
+        f_hi = self.timsort.merge_hi
+
+        # The larger sizes exercise galloping
+        for (na, nb) in [(12, 16), (40, 40), (100, 110), (1000, 1100)]:
+            for a, b in itertools.product(self.make_sample_sorted_lists(na),
+                                          self.make_sample_sorted_lists(nb)):
+                self.check_merge_lo_hi(f_lo, a, b)
+                self.check_merge_lo_hi(f_hi, b, a)
+
+    def check_merge_at(self, a, b):
+        f = self.timsort.merge_at
+        # Prepare the array to be sorted
+        na = len(a)
+        nb = len(b)
+        # Add sentinels at start and end, to check they weren't moved
+        orig_keys = [42] + a + b + [-42]
+        ssa = 1
+        ssb = ssa + na
+
+        stack_sentinel = MergeRun(-42, -42)
+
+        def run_merge_at(ms, keys, i):
+            new_ms = f(ms, keys, keys, i)
+            self.assertEqual(keys[0], orig_keys[0])
+            self.assertEqual(keys[-1], orig_keys[-1])
+            self.assertSorted(orig_keys[1:-1], keys[1:-1])
+            # Check stack state
+            self.assertIs(new_ms.pending, ms.pending)
+            self.assertEqual(ms.pending[i], (ssa, na + nb))
+            self.assertEqual(ms.pending[0], stack_sentinel)
+            return new_ms
+
+        # First check with i == len(stack) - 2
+        keys = self.array_factory(orig_keys)
+        ms = self.merge_init(keys)
+        # Push sentinel on stack, to check it wasn't touched
+        ms = self.timsort.merge_append(ms, stack_sentinel)
+        i = ms.n
+        ms = self.timsort.merge_append(ms, MergeRun(ssa, na))
+        ms = self.timsort.merge_append(ms, MergeRun(ssb, nb))
+        ms = run_merge_at(ms, keys, i)
+        self.assertEqual(ms.n, i + 1)
+
+        # Now check with i == len(stack) - 3
+        keys = self.array_factory(orig_keys)
+        ms = self.merge_init(keys)
+        # Push sentinel on stack, to check it wasn't touched
+        ms = self.timsort.merge_append(ms, stack_sentinel)
+        i = ms.n
+        ms = self.timsort.merge_append(ms, MergeRun(ssa, na))
+        ms = self.timsort.merge_append(ms, MergeRun(ssb, nb))
+        # A last run (trivial here)
+        last_run = MergeRun(ssb + nb, 1)
+        ms = self.timsort.merge_append(ms, last_run)
+        ms = run_merge_at(ms, keys, i)
+        self.assertEqual(ms.n, i + 2)
+        self.assertEqual(ms.pending[ms.n - 1], last_run)
+
+    def test_merge_at(self):
+        # The larger sizes exercise galloping
+        for (na, nb) in [(12, 16), (40, 40), (100, 110), (500, 510)]:
+            for a, b in itertools.product(self.make_sample_sorted_lists(na),
+                                          self.make_sample_sorted_lists(nb)):
+                self.check_merge_at(a, b)
+                self.check_merge_at(b, a)
+
+    def test_merge_force_collapse(self):
+        f = self.timsort.merge_force_collapse
+
+        # Test with runs of ascending sizes, then descending sizes
+        sizes_list = [(8, 10, 15, 20)]
+        sizes_list.append(sizes_list[0][::-1])
+
+        for sizes in sizes_list:
+            for chunks in itertools.product(*(self.make_sample_sorted_lists(n)
+                                              for n in sizes)):
+                # Create runs of the given sizes
+                orig_keys = sum(chunks, [])
+                keys = self.array_factory(orig_keys)
+                ms = self.merge_init(keys)
+                pos = 0
+                for c in chunks:
+                    ms = self.timsort.merge_append(ms, MergeRun(pos, len(c)))
+                    pos += len(c)
+                # Sanity check
+                self.assertEqual(sum(ms.pending[ms.n - 1]), len(keys))
+                # Now merge the runs
+                ms = f(ms, keys, keys)
+                # Remaining run is the whole list
+                self.assertEqual(ms.n, 1)
+                self.assertEqual(ms.pending[0], MergeRun(0, len(keys)))
+                # The list is now sorted
+                self.assertSorted(orig_keys, keys)
+
+    def test_run_timsort(self):
+        f = self.timsort.run_timsort
+
+        for size_factor in (1, 10):
+            # Make lists to be sorted from three chunks of different kinds.
+            sizes = (15, 30, 20)
+
+            all_lists = [self.make_sample_lists(n * size_factor) for n in sizes]
+            for chunks in itertools.product(*all_lists):
+                orig_keys = sum(chunks, [])
+                keys = self.array_factory(orig_keys)
+                f(keys)
+                # The list is now sorted
+                self.assertSorted(orig_keys, keys)
+
+    def test_run_timsort_with_values(self):
+        # Run timsort, but also with a values array
+        f = self.timsort.run_timsort_with_values
+
+        for size_factor in (1, 5):
+            chunk_size = 80 * size_factor
+            a = self.dupsorted_list(chunk_size)
+            b = self.duprandom_list(chunk_size)
+            c = self.revsorted_list(chunk_size)
+            orig_keys = a + b + c
+            orig_values = list(range(1000, 1000 + len(orig_keys)))
+
+            keys = self.array_factory(orig_keys)
+            values = self.array_factory(orig_values)
+            f(keys, values)
+            # This checks sort stability
+            self.assertSortedValues(orig_keys, orig_values, keys, values)
+
+
+class TestTimsortPurePython(BaseTimsortTest, TestCase):
+
+    timsort = py_list_timsort
+
+    # Much faster than a Numpy array in pure Python
+    array_factory = list
+
+
+class TestTimsortArraysPurePython(BaseTimsortTest, TestCase):
+
+    timsort = py_array_timsort
+
+    def array_factory(self, lst):
+        return np.array(lst, dtype=np.int32)
+
+
+class JITTimsortMixin(object):
+
+    timsort = jit_array_timsort
+
+    test_merge_at = None
+    test_merge_force_collapse = None
+
+    def wrap_with_mergestate(self, timsort, func, _cache={}):
+        """
+        Wrap *func* into another compiled function inserting a runtime-created
+        mergestate as the first function argument.
+        """
+        key = timsort, func
+        if key in _cache:
+            return _cache[key]
+
+        merge_init = timsort.merge_init
+
+        @timsort.compile
+        def wrapper(keys, values, *args):
+            ms = merge_init(keys)
+            res = func(ms, keys, values, *args)
+            return res
+
+        _cache[key] = wrapper
+        return wrapper
+
+
+class TestTimsortArrays(JITTimsortMixin, BaseTimsortTest, TestCase):
+
+    def array_factory(self, lst):
+        return np.array(lst, dtype=np.int32)
+
+    def check_merge_lo_hi(self, func, a, b):
+        na = len(a)
+        nb = len(b)
+
+        func = self.wrap_with_mergestate(self.timsort, func)
+
+        # Add sentinels at start and end, to check they weren't moved
+        orig_keys = [42] + a + b + [-42]
+        keys = self.array_factory(orig_keys)
+        ssa = 1
+        ssb = ssa + na
+
+        new_ms = func(keys, keys, ssa, na, ssb, nb)
+        self.assertEqual(keys[0], orig_keys[0])
+        self.assertEqual(keys[-1], orig_keys[-1])
+        self.assertSorted(orig_keys[1:-1], keys[1:-1])
+
+
+
+class BaseQuicksortTest(BaseSortingTest):
+
+    # NOTE these tests assume a non-argsort quicksort.
+
+    def test_insertion_sort(self):
+        n = 20
+        def check(l, n):
+            res = self.array_factory([9999] + l + [-9999])
+            f(res, res, 1, n)
+            self.assertEqual(res[0], 9999)
+            self.assertEqual(res[-1], -9999)
+            self.assertSorted(l, res[1:-1])
+
+        f = self.quicksort.insertion_sort
+        l = self.sorted_list(n)
+        check(l, n)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.initially_sorted_list(n, n//2)
+        check(l, n)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.random_list(n)
+        check(l, n)
+        l = self.duprandom_list(n)
+        check(l, n)
+
+    def test_partition(self):
+        n = 20
+        def check(l, n):
+            res = self.array_factory([9999] + l + [-9999])
+            index = f(res, res, 1, n)
+            self.assertEqual(res[0], 9999)
+            self.assertEqual(res[-1], -9999)
+            pivot = res[index]
+            for i in range(1, index):
+                self.assertLessEqual(res[i], pivot)
+            for i in range(index + 1, n):
+                self.assertGreaterEqual(res[i], pivot)
+
+        f = self.quicksort.partition
+        l = self.sorted_list(n)
+        check(l, n)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.initially_sorted_list(n, n//2)
+        check(l, n)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.random_list(n)
+        check(l, n)
+        l = self.duprandom_list(n)
+        check(l, n)
+
+    def test_partition3(self):
+        # Test the unused partition3() function
+        n = 20
+        def check(l, n):
+            res = self.array_factory([9999] + l + [-9999])
+            lt, gt = f(res, 1, n)
+            self.assertEqual(res[0], 9999)
+            self.assertEqual(res[-1], -9999)
+            pivot = res[lt]
+            for i in range(1, lt):
+                self.assertLessEqual(res[i], pivot)
+            for i in range(lt, gt + 1):
+                self.assertEqual(res[i], pivot)
+            for i in range(gt + 1, n):
+                self.assertGreater(res[i], pivot)
+
+        f = self.quicksort.partition3
+        l = self.sorted_list(n)
+        check(l, n)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.initially_sorted_list(n, n//2)
+        check(l, n)
+        l = self.revsorted_list(n)
+        check(l, n)
+        l = self.random_list(n)
+        check(l, n)
+        l = self.duprandom_list(n)
+        check(l, n)
+
+    def test_run_quicksort(self):
+        f = self.quicksort.run_quicksort
+
+        for size_factor in (1, 5):
+            # Make lists to be sorted from two chunks of different kinds.
+            sizes = (15, 20)
+
+            all_lists = [self.make_sample_lists(n * size_factor) for n in sizes]
+            for chunks in itertools.product(*all_lists):
+                orig_keys = sum(chunks, [])
+                keys = self.array_factory(orig_keys)
+                f(keys)
+                # The list is now sorted
+                self.assertSorted(orig_keys, keys)
+
+    def test_run_quicksort_lt(self):
+        def lt(a, b):
+            return a > b
+
+        f = self.make_quicksort(lt=lt).run_quicksort
+
+        for size_factor in (1, 5):
+            # Make lists to be sorted from two chunks of different kinds.
+            sizes = (15, 20)
+
+            all_lists = [self.make_sample_lists(n * size_factor) for n in sizes]
+            for chunks in itertools.product(*all_lists):
+                orig_keys = sum(chunks, [])
+                keys = self.array_factory(orig_keys)
+                f(keys)
+                # The list is now rev-sorted
+                self.assertSorted(orig_keys, keys[::-1])
+
+        # An imperfect comparison function, as LT(a, b) does not imply not LT(b, a).
+        # The sort should handle it gracefully.
+        def lt_floats(a, b):
+            return math.isnan(b) or a < b
+
+        f = self.make_quicksort(lt=lt_floats).run_quicksort
+
+        np.random.seed(42)
+        for size in (5, 20, 50, 500):
+            orig = np.random.random(size=size) * 100
+            orig[np.random.random(size=size) < 0.1] = float('nan')
+            orig_keys = list(orig)
+            keys = self.array_factory(orig_keys)
+            f(keys)
+            non_nans = orig[~np.isnan(orig)]
+            # Non-NaNs are sorted at the front
+            self.assertSorted(non_nans, keys[:len(non_nans)])
+
+
+class TestQuicksortPurePython(BaseQuicksortTest, TestCase):
+
+    quicksort = py_quicksort
+    make_quicksort = staticmethod(make_py_quicksort)
+
+    # Much faster than a Numpy array in pure Python
+    array_factory = list
+
+
+class TestQuicksortArrays(BaseQuicksortTest, TestCase):
+
+    quicksort = jit_quicksort
+    make_quicksort = staticmethod(make_jit_quicksort)
+
+    def array_factory(self, lst):
+        return np.array(lst, dtype=np.float64)
+
+class TestQuicksortMultidimensionalArrays(BaseSortingTest, TestCase):
+
+    quicksort = make_jit_quicksort(is_np_array=True)
+    make_quicksort = staticmethod(make_jit_quicksort)
+
+    def assertSorted(self, orig, result):
+        self.assertEqual(orig.shape, result.shape)
+        self.assertPreciseEqual(orig, result)
+
+    def array_factory(self, lst, shape=None):
+        array = np.array(lst, dtype=np.float64)
+        if shape is None:
+            return array.reshape(-1, array.shape[0])
+        else:
+            return array.reshape(shape)
+
+    def get_shapes(self, n):
+        shapes = []
+        if n == 1:
+            return shapes
+
+        for i in range(2, int(math.sqrt(n)) + 1):
+            if n % i == 0:
+                shapes.append((n // i, i))
+                shapes.append((i, n // i))
+                _shapes = self.get_shapes(n // i)
+                for _shape in _shapes:
+                    shapes.append((i,) + _shape)
+                    shapes.append(_shape + (i,))
+
+        return shapes
+
+    def test_run_quicksort(self):
+        f = self.quicksort.run_quicksort
+
+        for size_factor in (1, 5):
+            # Make lists to be sorted from two chunks of different kinds.
+            sizes = (15, 20)
+
+            all_lists = [self.make_sample_lists(n * size_factor) for n in sizes]
+            for chunks in itertools.product(*all_lists):
+                orig_keys = sum(chunks, [])
+                shape_list = self.get_shapes(len(orig_keys))
+                shape_list.append(None)
+                for shape in shape_list:
+                    keys = self.array_factory(orig_keys, shape=shape)
+                    keys_copy = self.array_factory(orig_keys, shape=shape)
+                    f(keys)
+                    keys_copy.sort()
+                    # The list is now sorted
+                    self.assertSorted(keys_copy, keys)
+
+    def test_run_quicksort_lt(self):
+        def lt(a, b):
+            return a > b
+
+        f = self.make_quicksort(lt=lt, is_np_array=True).run_quicksort
+
+        for size_factor in (1, 5):
+            # Make lists to be sorted from two chunks of different kinds.
+            sizes = (15, 20)
+
+            all_lists = [self.make_sample_lists(n * size_factor) for n in sizes]
+            for chunks in itertools.product(*all_lists):
+                orig_keys = sum(chunks, [])
+                shape_list = self.get_shapes(len(orig_keys))
+                shape_list.append(None)
+                for shape in shape_list:
+                    keys = self.array_factory(orig_keys, shape=shape)
+                    keys_copy = -self.array_factory(orig_keys, shape=shape)
+                    f(keys)
+                    # The list is now rev-sorted
+                    keys_copy.sort()
+                    keys_copy = -keys_copy
+                    self.assertSorted(keys_copy, keys)
+
+        # An imperfect comparison function, as LT(a, b) does not imply not LT(b, a).
+        # The sort should handle it gracefully.
+        def lt_floats(a, b):
+            return math.isnan(b) or a < b
+
+        f = self.make_quicksort(lt=lt_floats, is_np_array=True).run_quicksort
+
+        np.random.seed(42)
+        for size in (5, 20, 50, 500):
+            orig = np.random.random(size=size) * 100
+            orig[np.random.random(size=size) < 0.1] = float('nan')
+            orig_keys = list(orig)
+            shape_list = self.get_shapes(len(orig_keys))
+            shape_list.append(None)
+            for shape in shape_list:
+                keys = self.array_factory(orig_keys, shape=shape)
+                keys_copy = self.array_factory(orig_keys, shape=shape)
+                f(keys)
+                keys_copy.sort()
+                # Non-NaNs are sorted at the front
+                self.assertSorted(keys_copy, keys)
+
+class TestNumpySort(TestCase):
+
+    def setUp(self):
+        np.random.seed(42)
+
+    def int_arrays(self):
+        for size in (5, 20, 50, 500):
+            yield np.random.randint(99, size=size)
+
+    def float_arrays(self):
+        for size in (5, 20, 50, 500):
+            yield np.random.random(size=size) * 100
+        # Now with NaNs.  Numpy sorts them at the end.
+        for size in (5, 20, 50, 500):
+            orig = np.random.random(size=size) * 100
+            orig[np.random.random(size=size) < 0.1] = float('nan')
+            yield orig
+
+    def has_duplicates(self, arr):
+        """
+        Whether the array has duplicates.  Takes NaNs into account.
+        """
+        if np.count_nonzero(np.isnan(arr)) > 1:
+            return True
+        if np.unique(arr).size < arr.size:
+            return True
+        return False
+
+    def check_sort_inplace(self, pyfunc, cfunc, val):
+        expected = copy.copy(val)
+        got = copy.copy(val)
+        pyfunc(expected)
+        cfunc(got)
+        self.assertPreciseEqual(got, expected)
+
+    def check_sort_copy(self, pyfunc, cfunc, val):
+        orig = copy.copy(val)
+        expected = pyfunc(val)
+        got = cfunc(val)
+        self.assertPreciseEqual(got, expected)
+        # The original wasn't mutated
+        self.assertPreciseEqual(val, orig)
+
+    def check_argsort(self, pyfunc, cfunc, val, kwargs={}):
+        orig = copy.copy(val)
+        expected = pyfunc(val, **kwargs)
+        got = cfunc(val, **kwargs)
+        self.assertPreciseEqual(orig[got], np.sort(orig),
+                                msg="the array wasn't argsorted")
+        # Numba and Numpy results may differ if there are duplicates
+        # in the array
+        if not self.has_duplicates(orig):
+            self.assertPreciseEqual(got, expected)
+        # The original wasn't mutated
+        self.assertPreciseEqual(val, orig)
+
+    def test_array_sort_int(self):
+        pyfunc = sort_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for orig in self.int_arrays():
+            self.check_sort_inplace(pyfunc, cfunc, orig)
+
+    def test_array_sort_float(self):
+        pyfunc = sort_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for orig in self.float_arrays():
+            self.check_sort_inplace(pyfunc, cfunc, orig)
+
+    def test_np_sort_int(self):
+        pyfunc = np_sort_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for orig in self.int_arrays():
+            self.check_sort_copy(pyfunc, cfunc, orig)
+
+    def test_np_sort_float(self):
+        pyfunc = np_sort_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for size in (5, 20, 50, 500):
+            orig = np.random.random(size=size) * 100
+            orig[np.random.random(size=size) < 0.1] = float('nan')
+            self.check_sort_copy(pyfunc, cfunc, orig)
+
+    def test_argsort_int(self):
+        def check(pyfunc):
+            cfunc = jit(nopython=True)(pyfunc)
+            for orig in self.int_arrays():
+                self.check_argsort(pyfunc, cfunc, orig)
+
+        check(argsort_usecase)
+        check(np_argsort_usecase)
+
+    def test_argsort_kind_int(self):
+        def check(pyfunc, is_stable):
+            cfunc = jit(nopython=True)(pyfunc)
+            for orig in self.int_arrays():
+                self.check_argsort(pyfunc, cfunc, orig,
+                                   dict(is_stable=is_stable))
+
+        check(argsort_kind_usecase, is_stable=True)
+        check(np_argsort_kind_usecase, is_stable=True)
+        check(argsort_kind_usecase, is_stable=False)
+        check(np_argsort_kind_usecase, is_stable=False)
+
+    def test_argsort_float(self):
+        def check(pyfunc):
+            cfunc = jit(nopython=True)(pyfunc)
+            for orig in self.float_arrays():
+                self.check_argsort(pyfunc, cfunc, orig)
+
+        check(argsort_usecase)
+        check(np_argsort_usecase)
+
+    def test_argsort_float(self):
+        def check(pyfunc, is_stable):
+            cfunc = jit(nopython=True)(pyfunc)
+            for orig in self.float_arrays():
+                self.check_argsort(pyfunc, cfunc, orig,
+                                   dict(is_stable=is_stable))
+
+        check(argsort_kind_usecase, is_stable=True)
+        check(np_argsort_kind_usecase, is_stable=True)
+        check(argsort_kind_usecase, is_stable=False)
+        check(np_argsort_kind_usecase, is_stable=False)
+
+
+class TestPythonSort(TestCase):
+
+    def test_list_sort(self):
+        pyfunc = list_sort_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for size in (20, 50, 500):
+            orig, ret = cfunc(size)
+            self.assertEqual(sorted(orig), ret)
+            self.assertNotEqual(orig, ret)   # sanity check
+
+    def test_list_sort_reverse(self):
+        pyfunc = list_sort_reverse_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for size in (20, 50, 500):
+            for b in (False, True):
+                orig, ret = cfunc(size, b)
+                self.assertEqual(sorted(orig, reverse=b), ret)
+                self.assertNotEqual(orig, ret)   # sanity check
+
+    def test_sorted(self):
+        pyfunc = sorted_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for size in (20, 50, 500):
+            orig = np.random.random(size=size) * 100
+            expected = sorted(orig)
+            got = cfunc(orig)
+            self.assertPreciseEqual(got, expected)
+            self.assertNotEqual(list(orig), got)   # sanity check
+
+    def test_sorted_reverse(self):
+        pyfunc = sorted_reverse_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        size = 20
+
+        orig = np.random.random(size=size) * 100
+        for b in (False, True):
+            expected = sorted(orig, reverse=b)
+            got = cfunc(orig, b)
+            self.assertPreciseEqual(got, expected)
+            self.assertNotEqual(list(orig), got)   # sanity check
+
+
+class TestMergeSort(TestCase):
+    def setUp(self):
+        np.random.seed(321)
+
+    def check_argsort_stable(self, sorter, low, high, count):
+        # make data with high possibility of duplicated key
+        data = np.random.randint(low, high, count)
+        expect = np.argsort(data, kind='mergesort')
+        got = sorter(data)
+        np.testing.assert_equal(expect, got)
+
+    def test_argsort_stable(self):
+        arglist = [
+            (-2, 2, 5),
+            (-5, 5, 10),
+            (0, 10, 101),
+            (0, 100, 1003),
+        ]
+        imp = make_jit_mergesort(is_argsort=True)
+        toplevel = imp.run_mergesort
+        sorter = njit(lambda arr: toplevel(arr))
+        for args in arglist:
+            self.check_argsort_stable(sorter, *args)
+
+
+nop_compiler = lambda x:x
+
+
+class TestSortSlashSortedWithKey(MemoryLeakMixin, TestCase):
+
+    def test_01(self):
+
+        a = [3, 1, 4, 1, 5, 9]
+
+        @njit
+        def external_key(z):
+            return 1. / z
+
+        @njit
+        def foo(x, key=None):
+            new_x = x[:]
+            new_x.sort(key=key)
+            return sorted(x[:], key=key), new_x
+
+        self.assertPreciseEqual(foo(a[:]), foo.py_func(a[:]))
+        self.assertPreciseEqual(foo(a[:], external_key),
+                                foo.py_func(a[:], external_key))
+
+    def test_02(self):
+
+        a = [3, 1, 4, 1, 5, 9]
+
+        @njit
+        def foo(x):
+            def closure_key(z):
+                return 1. / z
+            new_x = x[:]
+            new_x.sort(key=closure_key)
+            return sorted(x[:], key=closure_key), new_x
+
+        self.assertPreciseEqual(foo(a[:]), foo.py_func(a[:]))
+
+    def test_03(self):
+
+        a = [3, 1, 4, 1, 5, 9]
+
+        def gen(compiler):
+
+            @compiler
+            def bar(x, func):
+                new_x = x[:]
+                new_x.sort(key=func)
+                return sorted(x[:], key=func), new_x
+
+            @compiler
+            def foo(x):
+                def closure_escapee_key(z):
+                    return 1. / z
+                return bar(x, closure_escapee_key)
+
+            return foo
+
+        self.assertPreciseEqual(gen(njit)(a[:]), gen(nop_compiler)(a[:]))
+
+    def test_04(self):
+
+        a = ['a','b','B','b','C','A']
+
+        @njit
+        def external_key(z):
+            return z.upper()
+
+        @njit
+        def foo(x, key=None):
+            new_x = x[:]
+            new_x.sort(key=key)
+            return sorted(x[:], key=key), new_x
+
+        self.assertPreciseEqual(foo(a[:]), foo.py_func(a[:]))
+        self.assertPreciseEqual(foo(a[:], external_key),
+                                foo.py_func(a[:], external_key))
+
+    def test_05(self):
+
+        a = ['a','b','B','b','C','A']
+
+        @njit
+        def external_key(z):
+            return z.upper()
+
+        @njit
+        def foo(x, key=None, reverse=False):
+            new_x = x[:]
+            new_x.sort(key=key, reverse=reverse)
+            return (sorted(x[:], key=key, reverse=reverse), new_x)
+
+        for key, rev in itertools.product((None, external_key),
+                                          (True, False, 1, -12, 0)):
+            self.assertPreciseEqual(foo(a[:], key, rev),
+                                    foo.py_func(a[:], key, rev))
+
+    def test_optional_on_key(self):
+        a = [3, 1, 4, 1, 5, 9]
+
+        @njit
+        def foo(x, predicate):
+            if predicate:
+                def closure_key(z):
+                    return 1. / z
+            else:
+                closure_key = None
+
+            new_x = x[:]
+            new_x.sort(key=closure_key)
+
+            return (sorted(x[:], key=closure_key), new_x)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            TF = True
+            foo(a[:], TF)
+
+        msg = "Key must concretely be None or a Numba JIT compiled function"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_exceptions_sorted(self):
+
+        @njit
+        def foo_sorted(x, key=None, reverse=False):
+            return sorted(x[:], key=key, reverse=reverse)
+
+        @njit
+        def foo_sort(x, key=None, reverse=False):
+            new_x = x[:]
+            new_x.sort(key=key, reverse=reverse)
+            return new_x
+
+        @njit
+        def external_key(z):
+            return 1. / z
+
+        a = [3, 1, 4, 1, 5, 9]
+
+        for impl in (foo_sort, foo_sorted):
+
+            # check illegal key
+            with self.assertRaises(errors.TypingError) as raises:
+                impl(a, key="illegal")
+
+            expect = "Key must be None or a Numba JIT compiled function"
+            self.assertIn(expect, str(raises.exception))
+
+            # check illegal reverse
+            with self.assertRaises(errors.TypingError) as raises:
+                impl(a, key=external_key, reverse="go backwards")
+
+            expect = "an integer is required for 'reverse'"
+            self.assertIn(expect, str(raises.exception))
+
+
+class TestArrayArgsort(MemoryLeakMixin, TestCase):
+    """Tests specific to array.argsort"""
+
+    def test_exceptions(self):
+
+        @njit
+        def nonliteral_kind(kind):
+            np.arange(5).argsort(kind=kind)
+
+        # check non-literal kind
+        with self.assertRaises(errors.TypingError) as raises:
+            # valid spelling but not literal
+            nonliteral_kind('quicksort')
+
+        expect = '"kind" must be a string literal'
+        self.assertIn(expect, str(raises.exception))
+
+        @njit
+        def unsupported_kwarg():
+            np.arange(5).argsort(foo='')
+
+        with self.assertRaises(errors.TypingError) as raises:
+            unsupported_kwarg()
+
+        expect = "Unsupported keywords: ['foo']"
+        self.assertIn(expect, str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ssa.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ssa.py
new file mode 100644
index 000000000..c25094ec1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ssa.py
@@ -0,0 +1,577 @@
+"""
+Tests for SSA reconstruction
+"""
+import sys
+import copy
+import logging
+
+import numpy as np
+
+from numba import njit, jit, types
+from numba.core import errors, ir
+from numba.core.compiler_machinery import FunctionPass, register_pass
+from numba.core.compiler import DefaultPassBuilder, CompilerBase
+from numba.core.untyped_passes import ReconstructSSA, PreserveIR
+from numba.core.typed_passes import NativeLowering
+from numba.extending import overload
+from numba.tests.support import MemoryLeakMixin, TestCase, override_config
+
+
+_DEBUG = False
+
+if _DEBUG:
+    # Enable debug logger on SSA reconstruction
+    ssa_logger = logging.getLogger("numba.core.ssa")
+    ssa_logger.setLevel(level=logging.DEBUG)
+    ssa_logger.addHandler(logging.StreamHandler(sys.stderr))
+
+
+class SSABaseTest(TestCase):
+
+    def check_func(self, func, *args):
+        got = func(*copy.deepcopy(args))
+        exp = func.py_func(*copy.deepcopy(args))
+        self.assertEqual(got, exp)
+
+
+class TestSSA(SSABaseTest):
+    """
+    Contains tests to help isolate problems in SSA
+    """
+
+    def test_argument_name_reused(self):
+        @njit
+        def foo(x):
+            x += 1
+            return x
+
+        self.check_func(foo, 123)
+
+    def test_if_else_redefine(self):
+        @njit
+        def foo(x, y):
+            z = x * y
+            if x < y:
+                z = x
+            else:
+                z = y
+            return z
+
+        self.check_func(foo, 3, 2)
+        self.check_func(foo, 2, 3)
+
+    def test_sum_loop(self):
+        @njit
+        def foo(n):
+            c = 0
+            for i in range(n):
+                c += i
+            return c
+
+        self.check_func(foo, 0)
+        self.check_func(foo, 10)
+
+    def test_sum_loop_2vars(self):
+        @njit
+        def foo(n):
+            c = 0
+            d = n
+            for i in range(n):
+                c += i
+                d += n
+            return c, d
+
+        self.check_func(foo, 0)
+        self.check_func(foo, 10)
+
+    def test_sum_2d_loop(self):
+        @njit
+        def foo(n):
+            c = 0
+            for i in range(n):
+                for j in range(n):
+                    c += j
+                c += i
+            return c
+
+        self.check_func(foo, 0)
+        self.check_func(foo, 10)
+
+    def check_undefined_var(self, should_warn):
+        @njit
+        def foo(n):
+            if n:
+                if n > 0:
+                    c = 0
+                return c
+            else:
+                # variable c is not defined in this branch
+                c += 1
+                return c
+
+        if should_warn:
+            with self.assertWarns(errors.NumbaWarning) as warns:
+                # n=1 so we won't actually run the branch with the uninitialized
+                self.check_func(foo, 1)
+            self.assertIn("Detected uninitialized variable c",
+                          str(warns.warning))
+        else:
+            self.check_func(foo, 1)
+
+        with self.assertRaises(UnboundLocalError):
+            foo.py_func(0)
+
+    def test_undefined_var(self):
+        with override_config('ALWAYS_WARN_UNINIT_VAR', 0):
+            self.check_undefined_var(should_warn=False)
+        with override_config('ALWAYS_WARN_UNINIT_VAR', 1):
+            self.check_undefined_var(should_warn=True)
+
+    def test_phi_propagation(self):
+        @njit
+        def foo(actions):
+            n = 1
+
+            i = 0
+            ct = 0
+            while n > 0 and i < len(actions):
+                n -= 1
+
+                while actions[i]:
+                    if actions[i]:
+                        if actions[i]:
+                            n += 10
+                        actions[i] -= 1
+                    else:
+                        if actions[i]:
+                            n += 20
+                        actions[i] += 1
+
+                    ct += n
+                ct += n
+            return ct, n
+
+        self.check_func(foo, np.array([1, 2]))
+
+    def test_unhandled_undefined(self):
+        def function1(arg1, arg2, arg3, arg4, arg5):
+            # This function is auto-generated.
+            if arg1:
+                var1 = arg2
+                var2 = arg3
+                var3 = var2
+                var4 = arg1
+                return
+            else:
+                if arg2:
+                    if arg4:
+                        var5 = arg4         # noqa: F841
+                        return
+                    else:
+                        var6 = var4
+                        return
+                    return var6
+                else:
+                    if arg5:
+                        if var1:
+                            if arg5:
+                                var1 = var6
+                                return
+                            else:
+                                var7 = arg2     # noqa: F841
+                                return arg2
+                            return
+                        else:
+                            if var2:
+                                arg5 = arg2
+                                return arg1
+                            else:
+                                var6 = var3
+                                return var4
+                            return
+                        return
+                    else:
+                        var8 = var1
+                        return
+                    return var8
+                var9 = var3         # noqa: F841
+                var10 = arg5        # noqa: F841
+                return var1
+
+        # The argument values is not critical for re-creating the bug
+        # because the bug is in compile-time.
+        expect = function1(2, 3, 6, 0, 7)
+        got = njit(function1)(2, 3, 6, 0, 7)
+        self.assertEqual(expect, got)
+
+
+class TestReportedSSAIssues(SSABaseTest):
+    # Tests from issues
+    # https://github.com/numba/numba/issues?q=is%3Aopen+is%3Aissue+label%3ASSA
+
+    def test_issue2194(self):
+
+        @njit
+        def foo():
+            V = np.empty(1)
+            s = np.uint32(1)
+
+            for i in range(s):
+                V[i] = 1
+            for i in range(s, 1):
+                pass
+
+        self.check_func(foo, )
+
+    def test_issue3094(self):
+
+        @njit
+        def doit(x):
+            return x
+
+        @njit
+        def foo(pred):
+            if pred:
+                x = True
+            else:
+                x = False
+            # do something with x
+            return doit(x)
+
+        self.check_func(foo, False)
+
+    def test_issue3931(self):
+
+        @njit
+        def foo(arr):
+            for i in range(1):
+                arr = arr.reshape(3 * 2)
+                arr = arr.reshape(3, 2)
+            return(arr)
+
+        np.testing.assert_allclose(foo(np.zeros((3, 2))),
+                                   foo.py_func(np.zeros((3, 2))))
+
+    def test_issue3976(self):
+
+        def overload_this(a):
+            return 'dummy'
+
+        @njit
+        def foo(a):
+            if a:
+                s = 5
+                s = overload_this(s)
+            else:
+                s = 'b'
+
+            return s
+
+        @overload(overload_this)
+        def ol(a):
+            return overload_this
+
+        self.check_func(foo, True)
+
+    def test_issue3979(self):
+
+        @njit
+        def foo(A, B):
+            x = A[0]
+            y = B[0]
+            for i in A:
+                x = i
+            for i in B:
+                y = i
+            return x, y
+
+        self.check_func(foo, (1, 2), ('A', 'B'))
+
+    def test_issue5219(self):
+
+        def overload_this(a, b=None):
+            if isinstance(b, tuple):
+                b = b[0]
+            return b
+
+        @overload(overload_this)
+        def ol(a, b=None):
+            b_is_tuple = isinstance(b, (types.Tuple, types.UniTuple))
+
+            def impl(a, b=None):
+                if b_is_tuple is True:
+                    b = b[0]
+                return b
+            return impl
+
+        @njit
+        def test_tuple(a, b):
+            overload_this(a, b)
+
+        self.check_func(test_tuple, 1, (2, ))
+
+    def test_issue5223(self):
+
+        @njit
+        def bar(x):
+            if len(x) == 5:
+                return x
+            x = x.copy()
+            for i in range(len(x)):
+                x[i] += 1
+            return x
+
+        a = np.ones(5)
+        a.flags.writeable = False
+
+        np.testing.assert_allclose(bar(a), bar.py_func(a))
+
+    def test_issue5243(self):
+
+        @njit
+        def foo(q):
+            lin = np.array((0.1, 0.6, 0.3))
+            stencil = np.zeros((3, 3))
+            stencil[0, 0] = q[0, 0]
+            return lin[0]
+
+        self.check_func(foo, np.zeros((2, 2)))
+
+    def test_issue5482_missing_variable_init(self):
+        # Test error that lowering fails because variable is missing
+        # a definition before use.
+        @njit("(intp, intp, intp)")
+        def foo(x, v, n):
+            for i in range(n):
+                if i == 0:
+                    if i == x:
+                        pass
+                    else:
+                        problematic = v
+                else:
+                    if i == x:
+                        pass
+                    else:
+                        problematic = problematic + v
+            return problematic
+
+    def test_issue5482_objmode_expr_null_lowering(self):
+        # Existing pipelines will not have the Expr.null in objmode.
+        # We have to create a custom pipeline to force a SSA reconstruction
+        # and stripping.
+        from numba.core.compiler import CompilerBase, DefaultPassBuilder
+        from numba.core.untyped_passes import ReconstructSSA, IRProcessing
+        from numba.core.typed_passes import PreLowerStripPhis
+
+        class CustomPipeline(CompilerBase):
+            def define_pipelines(self):
+                pm = DefaultPassBuilder.define_objectmode_pipeline(self.state)
+                # Force SSA reconstruction and stripping
+                pm.add_pass_after(ReconstructSSA, IRProcessing)
+                pm.add_pass_after(PreLowerStripPhis, ReconstructSSA)
+                pm.finalize()
+                return [pm]
+
+        @jit("(intp, intp, intp)", looplift=False,
+             pipeline_class=CustomPipeline)
+        def foo(x, v, n):
+            for i in range(n):
+                if i == n:
+                    if i == x:
+                        pass
+                    else:
+                        problematic = v
+                else:
+                    if i == x:
+                        pass
+                    else:
+                        problematic = problematic + v
+            return problematic
+
+    def test_issue5493_unneeded_phi(self):
+        # Test error that unneeded phi is inserted because variable does not
+        # have a dominance definition.
+        data = (np.ones(2), np.ones(2))
+        A = np.ones(1)
+        B = np.ones((1,1))
+
+        def foo(m, n, data):
+            if len(data) == 1:
+                v0 = data[0]
+            else:
+                v0 = data[0]
+                # Unneeded PHI node for `problematic` would be placed here
+                for _ in range(1, len(data)):
+                    v0 += A
+
+            for t in range(1, m):
+                for idx in range(n):
+                    t = B
+
+                    if idx == 0:
+                        if idx == n - 1:
+                            pass
+                        else:
+                            problematic = t
+                    else:
+                        if idx == n - 1:
+                            pass
+                        else:
+                            problematic = problematic + t
+            return problematic
+
+        expect = foo(10, 10, data)
+        res1 = njit(foo)(10, 10, data)
+        res2 = jit(forceobj=True, looplift=False)(foo)(10, 10, data)
+        np.testing.assert_array_equal(expect, res1)
+        np.testing.assert_array_equal(expect, res2)
+
+    def test_issue5623_equal_statements_in_same_bb(self):
+
+        def foo(pred, stack):
+            i = 0
+            c = 1
+
+            if pred is True:
+                stack[i] = c
+                i += 1
+                stack[i] = c
+                i += 1
+
+        python = np.array([0, 666])
+        foo(True, python)
+
+        nb = np.array([0, 666])
+        njit(foo)(True, nb)
+
+        expect = np.array([1, 1])
+
+        np.testing.assert_array_equal(python, expect)
+        np.testing.assert_array_equal(nb, expect)
+
+    def test_issue5678_non_minimal_phi(self):
+        # There should be only one phi for variable "i"
+
+        from numba.core.compiler import CompilerBase, DefaultPassBuilder
+        from numba.core.untyped_passes import (
+            ReconstructSSA, FunctionPass, register_pass,
+        )
+
+        phi_counter = []
+
+        @register_pass(mutates_CFG=False, analysis_only=True)
+        class CheckSSAMinimal(FunctionPass):
+            # A custom pass to count the number of phis
+
+            _name = self.__class__.__qualname__ + ".CheckSSAMinimal"
+
+            def __init__(self):
+                super().__init__(self)
+
+            def run_pass(self, state):
+                ct = 0
+                for blk in state.func_ir.blocks.values():
+                    ct += len(list(blk.find_exprs('phi')))
+                phi_counter.append(ct)
+                return True
+
+        class CustomPipeline(CompilerBase):
+            def define_pipelines(self):
+                pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+                pm.add_pass_after(CheckSSAMinimal, ReconstructSSA)
+                pm.finalize()
+                return [pm]
+
+        @njit(pipeline_class=CustomPipeline)
+        def while_for(n, max_iter=1):
+            a = np.empty((n,n))
+            i = 0
+            while i <= max_iter:
+                for j in range(len(a)):
+                    for k in range(len(a)):
+                        a[j,k] = j + k
+                i += 1
+            return a
+
+        # Runs fine?
+        self.assertPreciseEqual(while_for(10), while_for.py_func(10))
+        # One phi?
+        self.assertEqual(phi_counter, [1])
+
+
+class TestSROAIssues(MemoryLeakMixin, TestCase):
+    # This tests issues related to the SROA optimization done in lowering, which
+    # reduces time spent in the LLVM SROA pass. The optimization is related to
+    # SSA and tries to reduce the number of alloca statements for variables with
+    # only a single assignment.
+    def test_issue7258_multiple_assignment_post_SSA(self):
+        # This test adds a pass that will duplicate assignment statements to
+        # variables named "foobar".
+        # In the reported issue, the bug will cause a memory leak.
+        cloned = []
+
+        @register_pass(analysis_only=False, mutates_CFG=True)
+        class CloneFoobarAssignments(FunctionPass):
+            # A pass that clones variable assignments into "foobar"
+            _name = "clone_foobar_assignments_pass"
+
+            def __init__(self):
+                FunctionPass.__init__(self)
+
+            def run_pass(self, state):
+                mutated = False
+                for blk in state.func_ir.blocks.values():
+                    to_clone = []
+                    # find assignments to "foobar"
+                    for assign in blk.find_insts(ir.Assign):
+                        if assign.target.name == "foobar":
+                            to_clone.append(assign)
+                    # clone
+                    for assign in to_clone:
+                        clone = copy.deepcopy(assign)
+                        blk.insert_after(clone, assign)
+                        mutated = True
+                        # keep track of cloned statements
+                        cloned.append(clone)
+                return mutated
+
+        class CustomCompiler(CompilerBase):
+            def define_pipelines(self):
+                pm = DefaultPassBuilder.define_nopython_pipeline(
+                    self.state, "custom_pipeline",
+                )
+                pm._finalized = False
+                # Insert the cloning pass after SSA
+                pm.add_pass_after(CloneFoobarAssignments, ReconstructSSA)
+                # Capture IR post lowering
+                pm.add_pass_after(PreserveIR, NativeLowering)
+                pm.finalize()
+                return [pm]
+
+        @njit(pipeline_class=CustomCompiler)
+        def udt(arr):
+            foobar = arr + 1  # this assignment will be cloned
+            return foobar
+
+        arr = np.arange(10)
+        # Verify that the function works as expected
+        self.assertPreciseEqual(udt(arr), arr + 1)
+        # Verify that the expected statement is cloned
+        self.assertEqual(len(cloned), 1)
+        self.assertEqual(cloned[0].target.name, "foobar")
+        # Verify in the Numba IR that the expected statement is cloned
+        nir = udt.overloads[udt.signatures[0]].metadata['preserved_ir']
+        self.assertEqual(len(nir.blocks), 1,
+                         "only one block")
+        [blk] = nir.blocks.values()
+        assigns = blk.find_insts(ir.Assign)
+        foobar_assigns = [stmt for stmt in assigns
+                          if stmt.target.name == "foobar"]
+        self.assertEqual(
+            len(foobar_assigns), 2,
+            "expected two assignment statements into 'foobar'",
+        )
+        self.assertEqual(
+            foobar_assigns[0], foobar_assigns[1],
+            "expected the two assignment statements to be the same",
+        )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_stencils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_stencils.py
new file mode 100644
index 000000000..24739691a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_stencils.py
@@ -0,0 +1,2838 @@
+#
+# Copyright (c) 2017 Intel Corporation
+# SPDX-License-Identifier: BSD-2-Clause
+#
+
+
+import sys
+import numpy as np
+import ast
+import inspect
+import operator
+import types as pytypes
+from contextlib import contextmanager
+from copy import deepcopy
+
+import numba
+from numba import njit, stencil
+from numba.core.utils import PYVERSION
+from numba.core import types, registry
+from numba.core.compiler import compile_extra, Flags
+from numba.core.cpu import ParallelOptions
+from numba.tests.support import tag, skip_parfors_unsupported, _32bit
+from numba.core.errors import LoweringError, TypingError, NumbaValueError
+import unittest
+
+
+skip_unsupported = skip_parfors_unsupported
+
+
+@stencil
+def stencil1_kernel(a):
+    return 0.25 * (a[0, 1] + a[1, 0] + a[0, -1] + a[-1, 0])
+
+
+@stencil(neighborhood=((-5, 0), ))
+def stencil2_kernel(a):
+    cum = a[-5]
+    for i in range(-4, 1):
+        cum += a[i]
+    return 0.3 * cum
+
+
+@stencil(cval=1.0)
+def stencil3_kernel(a):
+    return 0.25 * a[-2, 2]
+
+
+@stencil
+def stencil_multiple_input_kernel(a, b):
+    return 0.25 * (a[0, 1] + a[1, 0] + a[0, -1] + a[-1, 0] +
+                   b[0, 1] + b[1, 0] + b[0, -1] + b[-1, 0])
+
+
+@stencil
+def stencil_multiple_input_kernel_var(a, b, w):
+    return w * (a[0, 1] + a[1, 0] + a[0, -1] + a[-1, 0] +
+                b[0, 1] + b[1, 0] + b[0, -1] + b[-1, 0])
+
+
+@stencil
+def stencil_multiple_input_mixed_types_2d(a, b, f):
+    return a[0, 0] if f[0, 0] else b[0, 0]
+
+
+@stencil(standard_indexing=("b",))
+def stencil_with_standard_indexing_1d(a, b):
+    return a[-1] * b[0] + a[0] * b[1]
+
+
+@stencil(standard_indexing=("b",))
+def stencil_with_standard_indexing_2d(a, b):
+    return (a[0, 1] * b[0, 1] + a[1, 0] * b[1, 0]
+            + a[0, -1] * b[0, -1] + a[-1, 0] * b[-1, 0])
+
+
+@njit
+def addone_njit(a):
+    return a + 1
+
+
+if not _32bit: # prevent compilation on unsupported 32bit targets
+    @njit(parallel=True)
+    def addone_pjit(a):
+        return a + 1
+
+
+@unittest.skipIf(PYVERSION != (3, 7), "Run under 3.7 only, AST unstable")
+class TestStencilBase(unittest.TestCase):
+
+    _numba_parallel_test_ = False
+
+    def __init__(self, *args):
+        # flags for njit()
+        self.cflags = Flags()
+        self.cflags.nrt = True
+
+        super(TestStencilBase, self).__init__(*args)
+
+    def _compile_this(self, func, sig, flags):
+        return compile_extra(registry.cpu_target.typing_context,
+                             registry.cpu_target.target_context, func, sig,
+                             None, flags, {})
+
+    def compile_parallel(self, func, sig, **kws):
+        flags = Flags()
+        flags.nrt = True
+        options = True if not kws else kws
+        flags.auto_parallel=ParallelOptions(options)
+        return self._compile_this(func, sig, flags)
+
+    def compile_njit(self, func, sig):
+        return self._compile_this(func, sig, flags=self.cflags)
+
+    def compile_all(self, pyfunc, *args, **kwargs):
+        sig = tuple([numba.typeof(x) for x in args])
+        # compile with parallel=True
+        cpfunc = self.compile_parallel(pyfunc, sig)
+        # compile a standard njit of the original function
+        cfunc = self.compile_njit(pyfunc, sig)
+        return cfunc, cpfunc
+
+    def check(self, no_stencil_func, pyfunc, *args):
+        cfunc, cpfunc = self.compile_all(pyfunc, *args)
+        # results without stencil macro
+        expected = no_stencil_func(*args)
+        # python result
+        py_output = pyfunc(*args)
+
+        # njit result
+        njit_output = cfunc.entry_point(*args)
+
+        # parfor result
+        parfor_output = cpfunc.entry_point(*args)
+
+        np.testing.assert_almost_equal(py_output, expected, decimal=3)
+        np.testing.assert_almost_equal(njit_output, expected, decimal=3)
+        np.testing.assert_almost_equal(parfor_output, expected, decimal=3)
+
+        # make sure parfor set up scheduling
+        self.assertIn('@do_scheduling', cpfunc.library.get_llvm_str())
+
+
+class TestStencil(TestStencilBase):
+
+    def __init__(self, *args, **kwargs):
+        super(TestStencil, self).__init__(*args, **kwargs)
+
+    @skip_unsupported
+    def test_stencil1(self):
+        """Tests whether the optional out argument to stencil calls works.
+        """
+        def test_with_out(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.zeros(n**2).reshape((n, n))
+            B = stencil1_kernel(A, out=B)
+            return B
+
+        def test_without_out(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = stencil1_kernel(A)
+            return B
+
+        def test_impl_seq(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.zeros(n**2).reshape((n, n))
+            for i in range(1, n - 1):
+                for j in range(1, n - 1):
+                    B[i, j] = 0.25 * (A[i, j + 1] +
+                                      A[i + 1, j] + A[i, j - 1] + A[i - 1, j])
+            return B
+
+        n = 100
+        self.check(test_impl_seq, test_with_out, n)
+        self.check(test_impl_seq, test_without_out, n)
+
+    @skip_unsupported
+    def test_stencil2(self):
+        """Tests whether the optional neighborhood argument to the stencil
+        decorate works.
+        """
+        def test_seq(n):
+            A = np.arange(n)
+            B = stencil2_kernel(A)
+            return B
+
+        def test_impl_seq(n):
+            A = np.arange(n)
+            B = np.zeros(n)
+            for i in range(5, len(A)):
+                B[i] = 0.3 * sum(A[i - 5:i + 1])
+            return B
+
+        n = 100
+        self.check(test_impl_seq, test_seq, n)
+        # variable length neighborhood in numba.stencil call
+        # only supported in parallel path
+
+        def test_seq(n, w):
+            A = np.arange(n)
+
+            def stencil2_kernel(a, w):
+                cum = a[-w]
+                for i in range(-w + 1, w + 1):
+                    cum += a[i]
+                return 0.3 * cum
+            B = numba.stencil(stencil2_kernel, neighborhood=((-w, w), ))(A, w)
+            return B
+
+        def test_impl_seq(n, w):
+            A = np.arange(n)
+            B = np.zeros(n)
+            for i in range(w, len(A) - w):
+                B[i] = 0.3 * sum(A[i - w:i + w + 1])
+            return B
+        n = 100
+        w = 5
+        cpfunc = self.compile_parallel(test_seq, (types.intp, types.intp))
+        expected = test_impl_seq(n, w)
+        # parfor result
+        parfor_output = cpfunc.entry_point(n, w)
+        np.testing.assert_almost_equal(parfor_output, expected, decimal=3)
+        self.assertIn('@do_scheduling', cpfunc.library.get_llvm_str())
+        # test index_offsets
+
+        def test_seq(n, w, offset):
+            A = np.arange(n)
+
+            def stencil2_kernel(a, w):
+                cum = a[-w + 1]
+                for i in range(-w + 1, w + 1):
+                    cum += a[i + 1]
+                return 0.3 * cum
+            B = numba.stencil(stencil2_kernel, neighborhood=((-w, w), ),
+                              index_offsets=(-offset, ))(A, w)
+            return B
+
+        offset = 1
+        cpfunc = self.compile_parallel(test_seq, (types.intp, types.intp,
+                                                  types.intp))
+        parfor_output = cpfunc.entry_point(n, w, offset)
+        np.testing.assert_almost_equal(parfor_output, expected, decimal=3)
+        self.assertIn('@do_scheduling', cpfunc.library.get_llvm_str())
+        # test slice in kernel
+
+        def test_seq(n, w, offset):
+            A = np.arange(n)
+
+            def stencil2_kernel(a, w):
+                return 0.3 * np.sum(a[-w + 1:w + 2])
+            B = numba.stencil(stencil2_kernel, neighborhood=((-w, w), ),
+                              index_offsets=(-offset, ))(A, w)
+            return B
+
+        offset = 1
+        cpfunc = self.compile_parallel(test_seq, (types.intp, types.intp,
+                                                  types.intp))
+        parfor_output = cpfunc.entry_point(n, w, offset)
+        np.testing.assert_almost_equal(parfor_output, expected, decimal=3)
+        self.assertIn('@do_scheduling', cpfunc.library.get_llvm_str())
+
+    @skip_unsupported
+    def test_stencil3(self):
+        """Tests whether a non-zero optional cval argument to the stencil
+        decorator works.  Also tests integer result type.
+        """
+        def test_seq(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = stencil3_kernel(A)
+            return B
+
+        test_njit = njit(test_seq)
+        test_par = njit(test_seq, parallel=True)
+
+        n = 5
+        seq_res = test_seq(n)
+        njit_res = test_njit(n)
+        par_res = test_par(n)
+
+        self.assertTrue(seq_res[0, 0] == 1.0 and seq_res[4, 4] == 1.0)
+        self.assertTrue(njit_res[0, 0] == 1.0 and njit_res[4, 4] == 1.0)
+        self.assertTrue(par_res[0, 0] == 1.0 and par_res[4, 4] == 1.0)
+
+    @skip_unsupported
+    def test_stencil_standard_indexing_1d(self):
+        """Tests standard indexing with a 1d array.
+        """
+        def test_seq(n):
+            A = np.arange(n)
+            B = [3.0, 7.0]
+            C = stencil_with_standard_indexing_1d(A, B)
+            return C
+
+        def test_impl_seq(n):
+            A = np.arange(n)
+            B = [3.0, 7.0]
+            C = np.zeros(n)
+
+            for i in range(1, n):
+                C[i] = A[i - 1] * B[0] + A[i] * B[1]
+            return C
+
+        n = 100
+        self.check(test_impl_seq, test_seq, n)
+
+    @skip_unsupported
+    def test_stencil_standard_indexing_2d(self):
+        """Tests standard indexing with a 2d array and multiple stencil calls.
+        """
+        def test_seq(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.ones((3, 3))
+            C = stencil_with_standard_indexing_2d(A, B)
+            D = stencil_with_standard_indexing_2d(C, B)
+            return D
+
+        def test_impl_seq(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.ones((3, 3))
+            C = np.zeros(n**2).reshape((n, n))
+            D = np.zeros(n**2).reshape((n, n))
+
+            for i in range(1, n - 1):
+                for j in range(1, n - 1):
+                    C[i, j] = (A[i, j + 1] * B[0, 1] + A[i + 1, j] * B[1, 0] +
+                               A[i, j - 1] * B[0, -1] + A[i - 1, j] * B[-1, 0])
+            for i in range(1, n - 1):
+                for j in range(1, n - 1):
+                    D[i, j] = (C[i, j + 1] * B[0, 1] + C[i + 1, j] * B[1, 0] +
+                               C[i, j - 1] * B[0, -1] + C[i - 1, j] * B[-1, 0])
+            return D
+
+        n = 5
+        self.check(test_impl_seq, test_seq, n)
+
+    @skip_unsupported
+    def test_stencil_multiple_inputs(self):
+        """Tests whether multiple inputs of the same size work.
+        """
+        def test_seq(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.arange(n**2).reshape((n, n))
+            C = stencil_multiple_input_kernel(A, B)
+            return C
+
+        def test_impl_seq(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.arange(n**2).reshape((n, n))
+            C = np.zeros(n**2).reshape((n, n))
+            for i in range(1, n - 1):
+                for j in range(1, n - 1):
+                    C[i, j] = 0.25 * \
+                        (A[i, j + 1] + A[i + 1, j]
+                         + A[i, j - 1] + A[i - 1, j]
+                         + B[i, j + 1] + B[i + 1, j]
+                         + B[i, j - 1] + B[i - 1, j])
+            return C
+
+        n = 3
+        self.check(test_impl_seq, test_seq, n)
+        # test stencil with a non-array input
+
+        def test_seq(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.arange(n**2).reshape((n, n))
+            w = 0.25
+            C = stencil_multiple_input_kernel_var(A, B, w)
+            return C
+        self.check(test_impl_seq, test_seq, n)
+
+    @skip_unsupported
+    def test_stencil_mixed_types(self):
+        def test_impl_seq(n):
+            A = np.arange(n ** 2).reshape((n, n))
+            B = n ** 2 - np.arange(n ** 2).reshape((n, n))
+            S = np.eye(n, dtype=np.bool_)
+            O = np.zeros((n, n), dtype=A.dtype)
+            for i in range(0, n):
+                for j in range(0, n):
+                    O[i, j] = A[i, j] if S[i, j] else B[i, j]
+            return O
+
+        def test_seq(n):
+            A = np.arange(n ** 2).reshape((n, n))
+            B = n ** 2 - np.arange(n ** 2).reshape((n, n))
+            S = np.eye(n, dtype=np.bool_)
+            O = stencil_multiple_input_mixed_types_2d(A, B, S)
+            return O
+
+        n = 3
+        self.check(test_impl_seq, test_seq, n)
+
+    @skip_unsupported
+    def test_stencil_call(self):
+        """Tests 2D numba.stencil calls.
+        """
+        def test_impl1(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.zeros(n**2).reshape((n, n))
+            numba.stencil(lambda a: 0.25 * (a[0, 1] + a[1, 0] + a[0, -1]
+                                            + a[-1, 0]))(A, out=B)
+            return B
+
+        def test_impl2(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.zeros(n**2).reshape((n, n))
+
+            def sf(a):
+                return 0.25 * (a[0, 1] + a[1, 0] + a[0, -1] + a[-1, 0])
+            B = numba.stencil(sf)(A)
+            return B
+
+        def test_impl_seq(n):
+            A = np.arange(n**2).reshape((n, n))
+            B = np.zeros(n**2).reshape((n, n))
+            for i in range(1, n - 1):
+                for j in range(1, n - 1):
+                    B[i, j] = 0.25 * (A[i, j + 1] + A[i + 1, j]
+                                      + A[i, j - 1] + A[i - 1, j])
+            return B
+
+        n = 100
+        self.check(test_impl_seq, test_impl1, n)
+        self.check(test_impl_seq, test_impl2, n)
+
+    @skip_unsupported
+    def test_stencil_call_1D(self):
+        """Tests 1D numba.stencil calls.
+        """
+        def test_impl(n):
+            A = np.arange(n)
+            B = np.zeros(n)
+            numba.stencil(lambda a: 0.3 * (a[-1] + a[0] + a[1]))(A, out=B)
+            return B
+
+        def test_impl_seq(n):
+            A = np.arange(n)
+            B = np.zeros(n)
+            for i in range(1, n - 1):
+                B[i] = 0.3 * (A[i - 1] + A[i] + A[i + 1])
+            return B
+
+        n = 100
+        self.check(test_impl_seq, test_impl, n)
+
+    @skip_unsupported
+    def test_stencil_call_const(self):
+        """Tests numba.stencil call that has an index that can be inferred as
+        constant from a unary expr. Otherwise, this would raise an error since
+        neighborhood length is not specified.
+        """
+        def test_impl1(n):
+            A = np.arange(n)
+            B = np.zeros(n)
+            c = 1
+            numba.stencil(lambda a,c : 0.3 * (a[-c] + a[0] + a[c]))(
+                                                                   A, c, out=B)
+            return B
+
+        def test_impl2(n):
+            A = np.arange(n)
+            B = np.zeros(n)
+            c = 2
+            numba.stencil(lambda a,c : 0.3 * (a[1-c] + a[0] + a[c-1]))(
+                                                                   A, c, out=B)
+            return B
+
+        # recursive expr case
+        def test_impl3(n):
+            A = np.arange(n)
+            B = np.zeros(n)
+            c = 2
+            numba.stencil(lambda a,c : 0.3 * (a[-c+1] + a[0] + a[c-1]))(
+                                                                   A, c, out=B)
+            return B
+
+        # multi-constant case
+        def test_impl4(n):
+            A = np.arange(n)
+            B = np.zeros(n)
+            d = 1
+            c = 2
+            numba.stencil(lambda a,c,d : 0.3 * (a[-c+d] + a[0] + a[c-d]))(
+                                                                A, c, d, out=B)
+            return B
+
+        def test_impl_seq(n):
+            A = np.arange(n)
+            B = np.zeros(n)
+            c = 1
+            for i in range(1, n - 1):
+                B[i] = 0.3 * (A[i - c] + A[i] + A[i + c])
+            return B
+
+        n = 100
+        # constant inference is only possible in parallel path
+        cpfunc1 = self.compile_parallel(test_impl1, (types.intp,))
+        cpfunc2 = self.compile_parallel(test_impl2, (types.intp,))
+        cpfunc3 = self.compile_parallel(test_impl3, (types.intp,))
+        cpfunc4 = self.compile_parallel(test_impl4, (types.intp,))
+        expected = test_impl_seq(n)
+        # parfor result
+        parfor_output1 = cpfunc1.entry_point(n)
+        parfor_output2 = cpfunc2.entry_point(n)
+        parfor_output3 = cpfunc3.entry_point(n)
+        parfor_output4 = cpfunc4.entry_point(n)
+        np.testing.assert_almost_equal(parfor_output1, expected, decimal=3)
+        np.testing.assert_almost_equal(parfor_output2, expected, decimal=3)
+        np.testing.assert_almost_equal(parfor_output3, expected, decimal=3)
+        np.testing.assert_almost_equal(parfor_output4, expected, decimal=3)
+
+        # check error in regular Python path
+        with self.assertRaises(NumbaValueError) as e:
+            test_impl4(4)
+
+        self.assertIn("stencil kernel index is not constant, "
+                      "'neighborhood' option required", str(e.exception))
+        # check error in njit path
+        # TODO: ValueError should be thrown instead of LoweringError
+        with self.assertRaises((LoweringError, NumbaValueError)) as e:
+            njit(test_impl4)(4)
+
+        self.assertIn("stencil kernel index is not constant, "
+                      "'neighborhood' option required", str(e.exception))
+
+    @skip_unsupported
+    def test_stencil_parallel_off(self):
+        """Tests 1D numba.stencil calls without parallel translation
+           turned off.
+        """
+        def test_impl(A):
+            return numba.stencil(lambda a: 0.3 * (a[-1] + a[0] + a[1]))(A)
+
+        cpfunc = self.compile_parallel(test_impl, (numba.float64[:],), stencil=False)
+        self.assertNotIn('@do_scheduling', cpfunc.library.get_llvm_str())
+
+    @skip_unsupported
+    def test_stencil_nested1(self):
+        """Tests whether nested stencil decorator works.
+        """
+        @njit(parallel=True)
+        def test_impl(n):
+            @stencil
+            def fun(a):
+                c = 2
+                return a[-c+1]
+            B = fun(n)
+            return B
+
+        def test_impl_seq(n):
+            B = np.zeros(len(n), dtype=int)
+            for i in range(1, len(n)):
+                B[i] = n[i-1]
+            return B
+
+        n = np.arange(10)
+        np.testing.assert_equal(test_impl(n), test_impl_seq(n))
+
+    @skip_unsupported
+    def test_out_kwarg_w_cval(self):
+        """ Issue #3518, out kwarg did not work with cval."""
+        # test const value that matches the arg dtype, and one that can be cast
+        const_vals = [7, 7.0]
+
+        def kernel(a):
+            return (a[0, 0] - a[1, 0])
+
+        for const_val in const_vals:
+            stencil_fn = numba.stencil(kernel, cval=const_val)
+
+            def wrapped():
+                A = np.arange(12).reshape((3, 4))
+                ret = np.ones_like(A)
+                stencil_fn(A, out=ret)
+                return ret
+
+            # stencil function case
+            A = np.arange(12).reshape((3, 4))
+            expected = np.full_like(A, -4)
+            expected[-1, :] = const_val
+            ret = np.ones_like(A)
+            stencil_fn(A, out=ret)
+            np.testing.assert_almost_equal(ret, expected)
+
+            # wrapped function case, check njit, then njit(parallel=True)
+            impls = self.compile_all(wrapped,)
+            for impl in impls:
+                got = impl.entry_point()
+                np.testing.assert_almost_equal(got, expected)
+
+        # now check exceptions for cval dtype mismatch with out kwarg dtype
+        stencil_fn = numba.stencil(kernel, cval=1j)
+
+        def wrapped():
+            A = np.arange(12).reshape((3, 4))
+            ret = np.ones_like(A)
+            stencil_fn(A, out=ret)
+            return ret
+
+        A = np.arange(12).reshape((3, 4))
+        ret = np.ones_like(A)
+        with self.assertRaises(NumbaValueError) as e:
+            stencil_fn(A, out=ret)
+        msg = "cval type does not match stencil return type."
+        self.assertIn(msg, str(e.exception))
+
+        for compiler in [self.compile_njit, self.compile_parallel]:
+            try:
+                compiler(wrapped,())
+            except(NumbaValueError, LoweringError) as e:
+                self.assertIn(msg, str(e))
+            else:
+                raise AssertionError("Expected error was not raised")
+
+    @skip_unsupported
+    def test_out_kwarg_w_cval_np_attr(self):
+        """ Test issue #7286 where the cval is a np attr/string-based numerical
+        constant"""
+        for cval in (np.nan, np.inf, -np.inf, float('inf'), -float('inf')):
+            def kernel(a):
+                return (a[0, 0] - a[1, 0])
+
+            stencil_fn = numba.stencil(kernel, cval=cval)
+
+            def wrapped():
+                A = np.arange(12.).reshape((3, 4))
+                ret = np.ones_like(A)
+                stencil_fn(A, out=ret)
+                return ret
+
+            # stencil function case
+            A = np.arange(12.).reshape((3, 4))
+            expected = np.full_like(A, -4)
+            expected[-1, :] = cval
+            ret = np.ones_like(A)
+            stencil_fn(A, out=ret)
+            np.testing.assert_almost_equal(ret, expected)
+
+            # wrapped function case, check njit, then njit(parallel=True)
+            impls = self.compile_all(wrapped,)
+            for impl in impls:
+                got = impl.entry_point()
+                np.testing.assert_almost_equal(got, expected)
+
+
+class pyStencilGenerator:
+    """
+    Holds the classes and methods needed to generate a python stencil
+    implementation from a kernel purely using AST transforms.
+    """
+
+    class Builder:
+        """
+        Provides code generation for the AST manipulation pipeline.
+        The class methods largely produce AST nodes/trees.
+        """
+
+        def __init__(self):
+            self.__state = 0
+
+        ids = [chr(ord(v) + x) for v in ['a', 'A'] for x in range(26)]
+
+        def varidx(self):
+            """
+            a monotonically increasing index for use in labelling variables.
+            """
+            tmp = self.__state
+            self.__state = self.__state + 1
+            return tmp
+
+        # builder functions
+        def gen_alloc_return(self, orig, var, dtype_var, init_val=0):
+            """
+            Generates an AST equivalent to:
+                `var = np.full(orig.shape, init_val, dtype = dtype_var)`
+            """
+            new = ast.Assign(
+                targets=[
+                    ast.Name(
+                        id=var,
+                        ctx=ast.Store())],
+                value=ast.Call(
+                    func=ast.Attribute(
+                        value=ast.Name(
+                            id='np',
+                            ctx=ast.Load()),
+                        attr='full',
+                        ctx=ast.Load()),
+                    args=[
+                        ast.Attribute(
+                            value=ast.Name(
+                                id=orig,
+                                ctx=ast.Load()),
+                            attr='shape',
+                            ctx=ast.Load()),
+                        self.gen_num(init_val)],
+                    keywords=[ast.keyword(arg='dtype',
+                                          value=self.gen_call('type', [dtype_var.id]).value)],
+                    starargs=None,
+                    kwargs=None),
+            )
+            return new
+
+        def gen_assign(self, var, value, index_names):
+            """
+            Generates an AST equivalent to:
+                `retvar[(*index_names,)] = value[<already present indexing>]`
+            """
+            elts_info = [ast.Name(id=x, ctx=ast.Load()) for x in index_names]
+            new = ast.Assign(
+                targets=[
+                    ast.Subscript(
+                        value=ast.Name(
+                            id=var,
+                            ctx=ast.Load()),
+                        slice=ast.Index(
+                            value=ast.Tuple(
+                                elts=elts_info,
+                                ctx=ast.Load())),
+                        ctx=ast.Store())],
+                value=value)
+            return new
+
+        def gen_loop(self, var, start=0, stop=0, body=None):
+            """
+            Generates an AST equivalent to a loop in `var` from
+            `start` to `stop` with body `body`.
+            """
+            if isinstance(start, int):
+                start_val = ast.Num(n=start)
+            else:
+                start_val = start
+            if isinstance(stop, int):
+                stop_val = ast.Num(n=stop)
+            else:
+                stop_val = stop
+            return ast.For(
+                target=ast.Name(id=var, ctx=ast.Store()),
+                iter=ast.Call(
+                    func=ast.Name(id='range', ctx=ast.Load()),
+                    args=[start_val, stop_val],
+                    keywords=[],
+                    starargs=None, kwargs=None),
+                body=body, orelse=[])
+
+        def gen_return(self, var):
+            """
+            Generates an AST equivalent to `return var`
+            """
+            return ast.Return(value=ast.Name(id=var, ctx=ast.Load()))
+
+        def gen_slice(self, value):
+            """Generates an Index with the given value"""
+            return ast.Index(value=ast.Num(n=value))
+
+        def gen_attr(self, name, attr):
+            """
+            Generates AST equivalent to `name.attr`
+            """
+            return ast.Attribute(
+                value=ast.Name(id=name, ctx=ast.Load()),
+                attr=attr, ctx=ast.Load())
+
+        def gen_subscript(self, name, attr, index, offset=None):
+            """
+            Generates an AST equivalent to a subscript, something like:
+                name.attr[slice(index) +/- offset]
+            """
+            attribute = self.gen_attr(name, attr)
+            slise = self.gen_slice(index)
+            ss = ast.Subscript(value=attribute, slice=slise, ctx=ast.Load())
+            if offset:
+                pm = ast.Add() if offset >= 0 else ast.Sub()
+                ss = ast.BinOp(left=ss, op=pm, right=ast.Num(n=abs(offset)))
+            return ss
+
+        def gen_num(self, value):
+            """
+            Generates an ast.Num of value `value`
+            """
+            # pretend bools are ints, ast has no boolean literal support
+            if isinstance(value, bool):
+                return ast.Num(int(value))
+            if abs(value) >= 0:
+                return ast.Num(value)
+            else:
+                return ast.UnaryOp(ast.USub(), ast.Num(-value))
+
+        def gen_call(self, call_name, args, kwargs=None):
+            """
+            Generates an AST equivalent to a call, something like:
+                `call_name(*args, **kwargs)
+            """
+            fixed_args = [ast.Name(id='%s' % x, ctx=ast.Load()) for x in args]
+            if kwargs is not None:
+                keywords = [ast.keyword(
+                            arg='%s' %
+                            x, value=ast.parse(str(x)).body[0].value)
+                            for x in kwargs]
+            else:
+                keywords = []
+            func = ast.Name(id=call_name, ctx=ast.Load())
+            return ast.Expr(value=ast.Call(
+                            func=func, args=fixed_args,
+                            keywords=keywords,
+                            starargs=None, kwargs=None), ctx=ast.Load())
+
+    # AST transformers
+    class FoldConst(ast.NodeTransformer, Builder):
+        """
+        Folds const expr, this is so const expressions in the relidx are
+        more easily handled
+        """
+
+        # just support a few for testing purposes
+        supported_ops = {
+            ast.Add: operator.add,
+            ast.Sub: operator.sub,
+            ast.Mult: operator.mul,
+        }
+
+        def visit_BinOp(self, node):
+            # does const expr folding
+            node = self.generic_visit(node)
+
+            op = self.supported_ops.get(node.op.__class__)
+            lhs = getattr(node, 'left', None)
+            rhs = getattr(node, 'right', None)
+
+            if not (lhs and rhs and op):
+                return node
+
+            if (isinstance(lhs, ast.Num) and
+                    isinstance(rhs, ast.Num)):
+                return ast.Num(op(node.left.n, node.right.n))
+            else:
+                return node
+
+    class FixRelIndex(ast.NodeTransformer, Builder):
+        """ Fixes the relative indexes to be written in as
+        induction index + relative index
+        """
+
+        def __init__(self, argnames, const_assigns,
+                     standard_indexing, neighborhood, *args, **kwargs):
+            ast.NodeTransformer.__init__(self, *args, **kwargs)
+            pyStencilGenerator.Builder.__init__(self, *args, **kwargs)
+            self._argnames = argnames
+            self._const_assigns = const_assigns
+            self._idx_len = -1
+            self._mins = None
+            self._maxes = None
+            self._imin = np.iinfo(int).min
+            self._imax = np.iinfo(int).max
+            self._standard_indexing = standard_indexing \
+                if standard_indexing else []
+            self._neighborhood = neighborhood
+            self._id_pat = '__%sn' if neighborhood else '__%s'
+
+        def get_val_from_num(self, node):
+            """
+            Gets the literal value from a Num or UnaryOp
+            """
+            if isinstance(node, ast.Num):
+                return node.n
+            elif isinstance(node, ast.UnaryOp):
+                return -node.operand.n
+            else:
+                raise ValueError(
+                    "get_val_from_num: Unknown indexing operation")
+
+        def visit_Subscript(self, node):
+            """
+            Transforms subscripts of the form `a[x]` and `a[x, y, z, ...]`
+            where `x, y, z` are relative indexes, to forms such as:
+            `a[x + i]` and `a[x + i, y + j, z + k]` for use in loop induced
+            indexing.
+            """
+
+            def handle2dindex(node):
+                idx = []
+                for x, val in enumerate(node.slice.value.elts):
+                    useval = self._const_assigns.get(val, val)
+                    idx.append(
+                        ast.BinOp(
+                            left=ast.Name(
+                                id=self._id_pat % self.ids[x],
+                                ctx=ast.Load()),
+                            op=ast.Add(),
+                            right=useval,
+                            ctx=ast.Load()))
+                if self._idx_len == -1:
+                    self._idx_len = len(idx)
+                else:
+                    if(self._idx_len != len(idx)):
+                        raise ValueError(
+                            "Relative indexing mismatch detected")
+                if isinstance(node.ctx, ast.Store):
+                    msg = ("Assignments to array passed to "
+                            "stencil kernels is not allowed")
+                    raise ValueError(msg)
+                context = ast.Load()
+                newnode = ast.Subscript(
+                    value=node.value,
+                    slice=ast.Index(
+                        value=ast.Tuple(
+                            elts=idx,
+                            ctx=ast.Load()),
+                        ctx=ast.Load()),
+                    ctx=context)
+                ast.copy_location(newnode, node)
+                ast.fix_missing_locations(newnode)
+
+                # now work out max/min for index ranges i.e. stencil size
+                if self._mins is None and self._maxes is None:
+                    # first pass
+                    self._mins = [self._imax] * self._idx_len
+                    self._maxes = [self._imin] * self._idx_len
+
+                if not self._neighborhood:
+                    for x, lnode in enumerate(node.slice.value.elts):
+                        if isinstance(lnode, ast.Num) or\
+                                isinstance(lnode, ast.UnaryOp):
+                            relvalue = self.get_val_from_num(lnode)
+                        elif (hasattr(lnode, 'id') and
+                                lnode.id in self._const_assigns):
+                            relvalue = self._const_assigns[lnode.id]
+                        else:
+                            raise ValueError(
+                                "Cannot interpret indexing value")
+                        if relvalue < self._mins[x]:
+                            self._mins[x] = relvalue
+                        if relvalue > self._maxes[x]:
+                            self._maxes[x] = relvalue
+                else:
+                    for x, lnode in enumerate(self._neighborhood):
+                        self._mins[x] = self._neighborhood[x][0]
+                        self._maxes[x] = self._neighborhood[x][1]
+
+                return newnode
+
+            def handle1dindex(node):
+                useval = self._const_assigns.get(
+                    node.slice.value, node.slice.value)
+                idx = ast.BinOp(left=ast.Name(
+                                id=self._id_pat % self.ids[0],
+                                ctx=ast.Load()),
+                                op=ast.Add(),
+                                right=useval,
+                                ctx=ast.Load())
+                if self._idx_len == -1:
+                    self._idx_len = 1
+                else:
+                    if(self._idx_len != 1):
+                        raise ValueError(
+                            "Relative indexing mismatch detected")
+                if isinstance(node.ctx, ast.Store):
+                    msg = ("Assignments to array passed to "
+                            "stencil kernels is not allowed")
+                    raise ValueError(msg)
+                context = ast.Load()
+                newnode = ast.Subscript(
+                    value=node.value,
+                    slice=ast.Index(
+                        value=idx,
+                        ctx=ast.Load()),
+                    ctx=context)
+                ast.copy_location(newnode, node)
+                ast.fix_missing_locations(newnode)
+
+                # now work out max/min for index ranges i.e. stencil size
+                if self._mins is None and self._maxes is None:
+                    # first pass
+                    self._mins = [self._imax, ]
+                    self._maxes = [self._imin, ]
+
+                if not self._neighborhood:
+                    if isinstance(node.slice.value, ast.Num) or\
+                            isinstance(node.slice.value, ast.UnaryOp):
+                        relvalue = self.get_val_from_num(node.slice.value)
+                    elif (hasattr(node.slice.value, 'id') and
+                            node.slice.value.id in self._const_assigns):
+                        relvalue = self._const_assigns[node.slice.value.id]
+                    else:
+                        raise ValueError("Cannot interpret indexing value")
+                    if relvalue < self._mins[0]:
+                        self._mins[0] = relvalue
+                    if relvalue > self._maxes[0]:
+                        self._maxes[0] = relvalue
+                else:
+                    self._mins[0] = self._neighborhood[0][0]
+                    self._maxes[0] = self._neighborhood[0][1]
+                ast.copy_location(newnode, node)
+                ast.fix_missing_locations(newnode)
+                return newnode
+
+            def computeSlice(i, node):
+                def gen_idx(val, x):
+                    useval = self._const_assigns.get(val, val)
+                    value = self.get_val_from_num(val)
+                    tmp = ast.BinOp(
+                            left=ast.Name(
+                                id=self._id_pat % self.ids[x],
+                                ctx=ast.Load()),
+                            op=ast.Add(),
+                            right=useval,
+                            ctx=ast.Load())
+                    ast.copy_location(tmp, node)
+                    ast.fix_missing_locations(tmp)
+                    return tmp
+                newnode = ast.Slice(gen_idx(node.lower, i),
+                                 gen_idx(node.upper, i),
+                                 node.step)
+                ast.copy_location(newnode, node)
+                ast.fix_missing_locations(newnode)
+                return newnode
+
+            def computeIndex(i, node):
+                useval = self._const_assigns.get(node.value, node.value)
+                idx = ast.BinOp(left=ast.Name(
+                                id=self._id_pat % self.ids[i],
+                                ctx=ast.Load()),
+                                op=ast.Add(),
+                                right=useval,
+                                ctx=ast.Load())
+                newnode = ast.Index(value=idx, ctx=ast.Load())
+                ast.copy_location(newnode, node)
+                ast.fix_missing_locations(newnode)
+                return newnode
+
+            def handleExtSlice(node):
+                idx = []
+                for i, val in enumerate(node.slice.dims):
+                    if isinstance(val, ast.Slice):
+                        idx.append(computeSlice(i, val))
+                    if isinstance(val, ast.Index):
+                        idx.append(computeIndex(i, val))
+                    # TODO: handle more node types
+                if self._idx_len == -1:
+                    self._idx_len = len(node.slice.dims)
+                else:
+                    if(self._idx_len != len(node.slice.dims)):
+                        raise ValueError(
+                            "Relative indexing mismatch detected")
+
+                if isinstance(node.ctx, ast.Store):
+                    msg = ("Assignments to array passed to "
+                            "stencil kernels is not allowed")
+                    raise ValueError(msg)
+                context = ast.Load()
+                newnode = ast.Subscript(
+                    value=node.value,
+                    slice=ast.ExtSlice(
+                        dims=idx,
+                        ctx=ast.Load()),
+                        ctx=context
+                        )
+
+                # now work out max/min for index ranges i.e. stencil size
+                if self._mins is None and self._maxes is None:
+                    # first pass
+                    self._mins = [self._imax] * self._idx_len
+                    self._maxes = [self._imin] * self._idx_len
+
+                if not self._neighborhood:
+                    for x, anode in enumerate(node.slice.dims):
+                        if isinstance(anode, ast.Slice):
+                            for lnode in [anode.lower, anode.upper]:
+                                if isinstance(lnode, ast.Num) or\
+                                        isinstance(lnode, ast.UnaryOp):
+                                    relvalue = self.get_val_from_num(lnode)
+                                elif (hasattr(lnode, 'id') and
+                                        lnode.id in self._const_assigns):
+                                    relvalue = self._const_assigns[lnode.id]
+                                else:
+                                    raise ValueError(
+                                        "Cannot interpret indexing value")
+                                if relvalue < self._mins[x]:
+                                    self._mins[x] = relvalue
+                                if relvalue > self._maxes[x]:
+                                    self._maxes[x] = relvalue
+                        else:
+                            val = anode.value
+                            if isinstance(val, ast.Num) or\
+                                    isinstance(val, ast.UnaryOp):
+                                relvalue = self.get_val_from_num(val)
+                            elif (hasattr(val, 'id') and
+                                    val.id in self._const_assigns):
+                                relvalue = self._const_assigns[val.id]
+                            else:
+                                raise ValueError(
+                                    "Cannot interpret indexing value")
+                            if relvalue < self._mins[x]:
+                                self._mins[x] = relvalue
+                            if relvalue > self._maxes[x]:
+                                self._maxes[x] = relvalue
+                else:
+                    for x, lnode in enumerate(self._neighborhood):
+                        self._mins[x] = self._neighborhood[x][0]
+                        self._maxes[x] = self._neighborhood[x][1]
+                ast.copy_location(newnode, node)
+                ast.fix_missing_locations(newnode)
+                return newnode
+
+
+            def handleSlice(node):
+                idx = computeSlice(0, node.slice)
+                idx.ctx=ast.Load()
+                if isinstance(node.ctx, ast.Store):
+                    msg = ("Assignments to array passed to "
+                            "stencil kernels is not allowed")
+                    raise ValueError(msg)
+                context = ast.Load()
+                newnode = ast.Subscript(
+                    value=node.value,
+                    slice=idx,
+                    ctx=context)
+                ast.copy_location(newnode, node)
+                ast.fix_missing_locations(newnode)
+
+                if self._idx_len == -1:
+                    self._idx_len = 1
+                else:
+                    if(self._idx_len != 1):
+                        raise ValueError(
+                            "Relative indexing mismatch detected")
+
+                # now work out max/min for index ranges i.e. stencil size
+                if self._mins is None and self._maxes is None:
+                    # first pass
+                    self._mins = [self._imax]
+                    self._maxes = [self._imin]
+
+                if not self._neighborhood:
+                    if isinstance(node.slice.value, ast.Num) or\
+                            isinstance(node.slice.value, ast.UnaryOp):
+                        relvalue = self.get_val_from_num(node.slice.value)
+                    elif (hasattr(node.slice.value, 'id') and
+                            node.slice.value.id in self._const_assigns):
+                        relvalue = self._const_assigns[node.slice.value.id]
+                    else:
+                        raise ValueError("Cannot interpret indexing value")
+                    if relvalue < self._mins[0]:
+                        self._mins[0] = relvalue
+                    if relvalue > self._maxes[0]:
+                        self._maxes[0] = relvalue
+                else:
+                    self._mins[0] = self._neighborhood[0][0]
+                    self._maxes[0] = self._neighborhood[0][1]
+
+                return newnode
+
+
+            node = self.generic_visit(node)
+            if (node.value.id in self._argnames) and (
+                    node.value.id not in self._standard_indexing):
+
+                # fancy slice
+                if isinstance(node.slice, ast.ExtSlice):
+                    return handleExtSlice(node)
+                # plain slice
+                if isinstance(node.slice, ast.Slice):
+                    return handleSlice(node)
+                # 2D index
+                if isinstance(node.slice.value, ast.Tuple):
+                    return handle2dindex(node)
+                # 1D index
+                elif isinstance(node.slice, ast.Index):
+                    return handle1dindex(node)
+                else:  # unknown
+                    raise ValueError("Unhandled subscript")
+            else:
+                return node
+
+        @property
+        def idx_len(self):
+            if self._idx_len == -1:
+                raise ValueError(
+                    'Transform has not been run/no indexes found')
+            else:
+                return self._idx_len
+
+        @property
+        def maxes(self):
+            return self._maxes
+
+        @property
+        def mins(self):
+            return self._mins
+
+        @property
+        def id_pattern(self):
+            return self._id_pat
+
+    class TransformReturns(ast.NodeTransformer, Builder):
+        """
+        Transforms return nodes into assignments.
+        """
+
+        def __init__(self, relidx_info, *args, **kwargs):
+            ast.NodeTransformer.__init__(self, *args, **kwargs)
+            pyStencilGenerator.Builder.__init__(self, *args, **kwargs)
+            self._relidx_info = relidx_info
+            self._ret_var_idx = self.varidx()
+            retvar = '__b%s' % self._ret_var_idx
+            self._retvarname = retvar
+
+        def visit_Return(self, node):
+            self.generic_visit(node)
+            nloops = self._relidx_info.idx_len
+            var_pattern = self._relidx_info.id_pattern
+            return self.gen_assign(
+                self._retvarname, node.value,
+                [var_pattern % self.ids[l] for l in range(nloops)])
+
+        @property
+        def ret_var_name(self):
+            return self._retvarname
+
+    class FixFunc(ast.NodeTransformer, Builder):
+        """ The main function rewriter, takes the body of the kernel and generates:
+         * checking function calls
+         * return value allocation
+         * loop nests
+         * return site
+         * Function definition as an entry point
+        """
+
+        def __init__(self, kprops, relidx_info, ret_info,
+                     cval, standard_indexing, neighborhood, *args, **kwargs):
+            ast.NodeTransformer.__init__(self, *args, **kwargs)
+            pyStencilGenerator.Builder.__init__(self, *args, **kwargs)
+            self._original_kernel = kprops.original_kernel
+            self._argnames = kprops.argnames
+            self._retty = kprops.retty
+            self._relidx_info = relidx_info
+            self._ret_info = ret_info
+            self._standard_indexing = standard_indexing \
+                if standard_indexing else []
+            self._neighborhood = neighborhood if neighborhood else tuple()
+            self._relidx_args = [
+                x for x in self._argnames if x not in self._standard_indexing]
+            # switch cval to python type
+            if hasattr(cval, 'dtype'):
+                self.cval = cval.tolist()
+            else:
+                self.cval = cval
+            self.stencil_arr = self._argnames[0]
+
+        def visit_FunctionDef(self, node):
+            """
+            Transforms the kernel function into a function that will perform
+            the stencil like behaviour on the kernel.
+            """
+            self.generic_visit(node)
+
+            # this function validates arguments and is injected into the top
+            # of the stencil call
+            def check_stencil_arrays(*args, **kwargs):
+                # the first has to be an array due to parfors requirements
+                neighborhood = kwargs.get('neighborhood')
+                init_shape = args[0].shape
+                if neighborhood is not None:
+                    if len(init_shape) != len(neighborhood):
+                        raise ValueError("Invalid neighborhood supplied")
+                for x in args[1:]:
+                    if hasattr(x, 'shape'):
+                        if init_shape != x.shape:
+                            raise ValueError(
+                                "Input stencil arrays do not commute")
+
+            checksrc = inspect.getsource(check_stencil_arrays)
+            check_impl = ast.parse(
+                checksrc.strip()).body[0]  # don't need module
+            ast.fix_missing_locations(check_impl)
+
+            checker_call = self.gen_call(
+                'check_stencil_arrays',
+                self._relidx_args,
+                kwargs=['neighborhood'])
+
+            nloops = self._relidx_info.idx_len
+
+            def computebound(mins, maxs):
+                minlim = 0 if mins >= 0 else -mins
+                maxlim = -maxs if maxs > 0 else 0
+                return (minlim, maxlim)
+
+            var_pattern = self._relidx_info.id_pattern
+
+            loop_body = node.body
+
+            # create loop nests
+            loop_count = 0
+            for l in range(nloops):
+                minlim, maxlim = computebound(
+                    self._relidx_info.mins[loop_count],
+                    self._relidx_info.maxes[loop_count])
+                minbound = minlim
+                maxbound = self.gen_subscript(
+                    self.stencil_arr, 'shape', loop_count, maxlim)
+                loops = self.gen_loop(
+                    var_pattern % self.ids[loop_count],
+                    minbound, maxbound, body=loop_body)
+                loop_body = [loops]
+                loop_count += 1
+
+            # patch loop location
+            ast.copy_location(loops, node)
+            _rettyname = self._retty.targets[0]
+
+            # allocate a return
+            retvar = self._ret_info.ret_var_name
+            allocate = self.gen_alloc_return(
+                self.stencil_arr, retvar, _rettyname, self.cval)
+            ast.copy_location(allocate, node)
+
+            # generate the return
+            returner = self.gen_return(retvar)
+            ast.copy_location(returner, node)
+
+            add_kwarg = [ast.arg('neighborhood', None)]
+            defaults = []
+
+            newargs = ast.arguments(
+                args=node.args.args +
+                add_kwarg,
+                defaults=defaults,
+                vararg=None,
+                kwarg=None,
+                kwonlyargs=[],
+                kw_defaults=[],
+                posonlyargs=[])
+            new = ast.FunctionDef(
+                name='__%s' %
+                node.name,
+                args=newargs,
+                body=[
+                    check_impl,
+                    checker_call,
+                    self._original_kernel,
+                    self._retty,
+                    allocate,
+                    loops,
+                    returner],
+                decorator_list=[])
+            ast.copy_location(new, node)
+            return new
+
+    class GetKernelProps(ast.NodeVisitor, Builder):
+        """ Gets the argument names and other properties
+        of the original kernel.
+        """
+
+        def __init__(self, *args, **kwargs):
+            ast.NodeVisitor.__init__(self, *args, **kwargs)
+            pyStencilGenerator.Builder.__init__(self, *args, **kwargs)
+            self._argnames = None
+            self._kwargnames = None
+            self._retty = None
+            self._original_kernel = None
+            self._const_assigns = {}
+
+        def visit_FunctionDef(self, node):
+            if self._argnames is not None or self._kwargnames is not None:
+                raise RuntimeError("multiple definition of function/args?")
+
+            attr = 'arg'
+            self._argnames = [getattr(x, attr) for x in node.args.args]
+            if node.args.kwarg:
+                self._kwargnames = [x.arg for x in node.args.kwarg]
+            compute_retdtype = self.gen_call(node.name, self._argnames)
+            self._retty = ast.Assign(targets=[ast.Name(
+                id='__retdtype',
+                ctx=ast.Store())], value=compute_retdtype.value)
+            self._original_kernel = ast.fix_missing_locations(deepcopy(node))
+            self.generic_visit(node)
+
+        def visit_Assign(self, node):
+            self.generic_visit(node)
+            tgt = node.targets
+            if len(tgt) == 1:
+                target = tgt[0]
+                if isinstance(target, ast.Name):
+                    if isinstance(node.value, ast.Num):
+                        self._const_assigns[target.id] = node.value.n
+                    elif isinstance(node.value, ast.UnaryOp):
+                        if isinstance(node.value, ast.UAdd):
+                            self._const_assigns[target.id] = node.value.n
+                        else:
+                            self._const_assigns[target.id] = -node.value.n
+
+        @property
+        def argnames(self):
+            """
+            The names of the arguments to the function
+            """
+            return self._argnames
+
+        @property
+        def const_assigns(self):
+            """
+            A map of variable name to constant for variables that are simple
+            constant assignments
+            """
+            return self._const_assigns
+
+        @property
+        def retty(self):
+            """
+            The return type
+            """
+            return self._retty
+
+        @property
+        def original_kernel(self):
+            """
+            The original unmutated kernel
+            """
+            return self._original_kernel
+
+    class FixCalls(ast.NodeTransformer):
+        """ Fixes call sites for astor (in case it is in use) """
+
+        def visit_Call(self, node):
+            self.generic_visit(node)
+            # Add in starargs and kwargs to calls
+            new = ast.Call(
+                func=node.func,
+                args=node.args,
+                keywords=node.keywords,
+                starargs=None,
+                kwargs=None)
+            return new
+
+    def generate_stencil_tree(
+            self, func, cval, standard_indexing, neighborhood):
+        """
+        Generates the AST tree for a stencil from:
+        func - a python stencil kernel
+        cval, standard_indexing and neighborhood as per the @stencil decorator
+        """
+        src = inspect.getsource(func)
+        tree = ast.parse(src.strip())
+
+        # Prints debugging information if True.
+        # If astor is installed the decompilation of the AST is also printed
+        DEBUG = False
+        if DEBUG:
+            print("ORIGINAL")
+            print(ast.dump(tree))
+
+        def pipeline(tree):
+            """ the pipeline of manipulations """
+
+            # get the arg names
+            kernel_props = self.GetKernelProps()
+            kernel_props.visit(tree)
+            argnm = kernel_props.argnames
+            const_asgn = kernel_props.const_assigns
+
+            if standard_indexing:
+                for x in standard_indexing:
+                    if x not in argnm:
+                        msg = ("Non-existent variable "
+                               "specified in standard_indexing")
+                        raise ValueError(msg)
+
+            # fold consts
+            fold_const = self.FoldConst()
+            fold_const.visit(tree)
+
+            # rewrite the relative indices as induced indices
+            relidx_fixer = self.FixRelIndex(
+                argnm, const_asgn, standard_indexing, neighborhood)
+            relidx_fixer.visit(tree)
+
+            # switch returns into assigns
+            return_transformer = self.TransformReturns(relidx_fixer)
+            return_transformer.visit(tree)
+
+            # generate the function body and loop nests and assemble
+            fixer = self.FixFunc(
+                kernel_props,
+                relidx_fixer,
+                return_transformer,
+                cval,
+                standard_indexing,
+                neighborhood)
+            fixer.visit(tree)
+
+            # fix up the call sites so they work better with astor
+            callFixer = self.FixCalls()
+            callFixer.visit(tree)
+            ast.fix_missing_locations(tree.body[0])
+
+        # run the pipeline of transforms on the tree
+        pipeline(tree)
+
+        if DEBUG:
+            print("\n\n\nNEW")
+            print(ast.dump(tree, include_attributes=True))
+            try:
+                import astor
+                print(astor.to_source(tree))
+            except ImportError:
+                pass
+
+        return tree
+
+
+def pyStencil(func_or_mode='constant', **options):
+    """
+    A pure python implementation of (a large subset of) stencil functionality,
+    equivalent to StencilFunc.
+    """
+
+    if not isinstance(func_or_mode, str):
+        mode = 'constant'  # default style
+        func = func_or_mode
+    else:
+        assert isinstance(func_or_mode, str), """stencil mode should be
+                                                        a string"""
+        mode = func_or_mode
+        func = None
+
+    for option in options:
+        if option not in ["cval", "standard_indexing", "neighborhood"]:
+            raise ValueError("Unknown stencil option " + option)
+
+    if mode != 'constant':
+        raise ValueError("Unsupported mode style " + mode)
+
+    cval = options.get('cval', 0)
+    standard_indexing = options.get('standard_indexing', None)
+    neighborhood = options.get('neighborhood', None)
+
+    # generate a new AST tree from the kernel func
+    gen = pyStencilGenerator()
+    tree = gen.generate_stencil_tree(func, cval, standard_indexing,
+                                     neighborhood)
+
+    # breathe life into the tree
+    mod_code = compile(tree, filename="<ast>", mode="exec")
+    func_code = mod_code.co_consts[0]
+    full_func = pytypes.FunctionType(func_code, globals())
+
+    return full_func
+
+
+@skip_unsupported
+class TestManyStencils(TestStencilBase):
+
+    def __init__(self, *args, **kwargs):
+        super(TestManyStencils, self).__init__(*args, **kwargs)
+
+    def check(self, pyfunc, *args, **kwargs):
+        """
+        For a given kernel:
+
+        The expected result is computed from a pyStencil version of the
+        stencil.
+
+        The following results are then computed:
+        * from a pure @stencil decoration of the kernel.
+        * from the njit of a trivial wrapper function around the pure @stencil
+          decorated function.
+        * from the njit(parallel=True) of a trivial wrapper function around
+           the pure @stencil decorated function.
+
+        The results are then compared.
+        """
+
+        options = kwargs.get('options', dict())
+        expected_exception = kwargs.get('expected_exception')
+
+        # DEBUG print output arrays
+        DEBUG_OUTPUT = False
+
+        # collect fails
+        should_fail = []
+        should_not_fail = []
+
+        # runner that handles fails
+        @contextmanager
+        def errorhandler(exty=None, usecase=None):
+            try:
+                yield
+            except Exception as e:
+                if exty is not None:
+                    lexty = exty if hasattr(exty, '__iter__') else [exty, ]
+                    found = False
+                    for ex in lexty:
+                        found |= isinstance(e, ex)
+                    if not found:
+                        raise
+                else:
+                    should_not_fail.append(
+                        (usecase, "%s: %s" %
+                         (type(e), str(e))))
+            else:
+                if exty is not None:
+                    should_fail.append(usecase)
+
+        if isinstance(expected_exception, dict):
+            pystencil_ex = expected_exception['pyStencil']
+            stencil_ex = expected_exception['stencil']
+            njit_ex = expected_exception['njit']
+            parfor_ex = expected_exception['parfor']
+        else:
+            pystencil_ex = expected_exception
+            stencil_ex = expected_exception
+            njit_ex = expected_exception
+            parfor_ex = expected_exception
+
+        stencil_args = {'func_or_mode': pyfunc}
+        stencil_args.update(options)
+
+        expected_present = True
+
+        try:
+            # ast impl
+            ast_impl = pyStencil(func_or_mode=pyfunc, **options)
+            expected = ast_impl(
+                *args, neighborhood=options.get('neighborhood'))
+            if DEBUG_OUTPUT:
+                print("\nExpected:\n", expected)
+        except Exception as ex:
+            # check exception is expected
+            with errorhandler(pystencil_ex, "pyStencil"):
+                raise ex
+            pyStencil_unhandled_ex = ex
+            expected_present = False
+        stencilfunc_output = None
+        with errorhandler(stencil_ex, "@stencil"):
+            stencil_func_impl = stencil(**stencil_args)
+            # stencil result
+            stencilfunc_output = stencil_func_impl(*args)
+
+        # wrapped stencil impl, could this be generated?
+        if len(args) == 1:
+            def wrap_stencil(arg0):
+                return stencil_func_impl(arg0)
+        elif len(args) == 2:
+            def wrap_stencil(arg0, arg1):
+                return stencil_func_impl(arg0, arg1)
+        elif len(args) == 3:
+            def wrap_stencil(arg0, arg1, arg2):
+                return stencil_func_impl(arg0, arg1, arg2)
+        else:
+            raise ValueError(
+                "Up to 3 arguments can be provided, found %s" %
+                len(args))
+
+        sig = tuple([numba.typeof(x) for x in args])
+
+        njit_output = None
+        with errorhandler(njit_ex, "njit"):
+            wrapped_cfunc = self.compile_njit(wrap_stencil, sig)
+            # njit result
+            njit_output = wrapped_cfunc.entry_point(*args)
+
+        parfor_output = None
+        with errorhandler(parfor_ex, "parfors"):
+            wrapped_cpfunc = self.compile_parallel(wrap_stencil, sig)
+            # parfor result
+            parfor_output = wrapped_cpfunc.entry_point(*args)
+
+        if DEBUG_OUTPUT:
+            print("\n@stencil_output:\n", stencilfunc_output)
+            print("\nnjit_output:\n", njit_output)
+            print("\nparfor_output:\n", parfor_output)
+
+        if expected_present:
+            try:
+                if not stencil_ex:
+                    np.testing.assert_almost_equal(
+                        stencilfunc_output, expected, decimal=1)
+                    self.assertEqual(expected.dtype, stencilfunc_output.dtype)
+            except Exception as e:
+                should_not_fail.append(
+                    ('@stencil', "%s: %s" %
+                     (type(e), str(e))))
+                print("@stencil failed: %s" % str(e))
+
+            try:
+                if not njit_ex:
+                    np.testing.assert_almost_equal(
+                        njit_output, expected, decimal=1)
+                    self.assertEqual(expected.dtype, njit_output.dtype)
+            except Exception as e:
+                should_not_fail.append(('njit', "%s: %s" % (type(e), str(e))))
+                print("@njit failed: %s" % str(e))
+
+            try:
+                if not parfor_ex:
+                    np.testing.assert_almost_equal(
+                        parfor_output, expected, decimal=1)
+                    self.assertEqual(expected.dtype, parfor_output.dtype)
+                    try:
+                        self.assertIn(
+                            '@do_scheduling',
+                            wrapped_cpfunc.library.get_llvm_str())
+                    except AssertionError:
+                        msg = 'Could not find `@do_scheduling` in LLVM IR'
+                        raise AssertionError(msg)
+            except Exception as e:
+                should_not_fail.append(
+                    ('parfors', "%s: %s" %
+                     (type(e), str(e))))
+                print("@njit(parallel=True) failed: %s" % str(e))
+
+        if DEBUG_OUTPUT:
+            print("\n\n")
+
+        if should_fail:
+            msg = ["%s" % x for x in should_fail]
+            raise RuntimeError(("The following implementations should have "
+                                "raised an exception but did not:\n%s") % msg)
+
+        if should_not_fail:
+            impls = ["%s" % x[0] for x in should_not_fail]
+            errs = ''.join(["%s: Message: %s\n\n" %
+                            x for x in should_not_fail])
+            str1 = ("The following implementations should not have raised an "
+                    "exception but did:\n%s\n" % impls)
+            str2 = "Errors were:\n\n%s" % errs
+            raise RuntimeError(str1 + str2)
+
+        if not expected_present:
+            if expected_exception is None:
+                raise RuntimeError(
+                    "pyStencil failed, was not caught/expected",
+                    pyStencil_unhandled_ex)
+
+    def exception_dict(self, **kwargs):
+        d = dict()
+        d['pyStencil'] = None
+        d['stencil'] = None
+        d['njit'] = None
+        d['parfor'] = None
+        for k, v in kwargs.items():
+            d[k] = v
+        return d
+
+    def test_basic00(self):
+        """rel index"""
+        def kernel(a):
+            return a[0, 0]
+        a = np.arange(12).reshape(3, 4)
+        self.check(kernel, a)
+
+    def test_basic01(self):
+        """rel index add const"""
+        def kernel(a):
+            return a[0, 1]
+        a = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a)
+
+    def test_basic02(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, -1]
+        self.check(kernel, a)
+
+    def test_basic03(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[1, 0]
+        self.check(kernel, a)
+
+    def test_basic04(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[-1, 0]
+        self.check(kernel, a)
+
+    def test_basic05(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[-1, 1]
+        self.check(kernel, a)
+
+    def test_basic06(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[1, -1]
+        self.check(kernel, a)
+
+    def test_basic07(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[1, 1]
+        self.check(kernel, a)
+
+    def test_basic08(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[-1, -1]
+        self.check(kernel, a)
+
+    def test_basic09(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[-2, 2]
+        self.check(kernel, a)
+
+    def test_basic10(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + a[1, 0]
+        self.check(kernel, a)
+
+    def test_basic11(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[-1, 0] + a[1, 0]
+        self.check(kernel, a)
+
+    def test_basic12(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[-1, 1] + a[1, -1]
+        self.check(kernel, a)
+
+    def test_basic13(self):
+        """rel index add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[-1, -1] + a[1, 1]
+        self.check(kernel, a)
+
+    def test_basic14(self):
+        """rel index add domain change const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + 1j
+        self.check(kernel, a)
+
+    def test_basic14b(self):
+        """rel index add domain change const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            t = 1.j
+            return a[0, 0] + t
+        self.check(kernel, a)
+
+    def test_basic15(self):
+        """two rel index, add const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + a[1, 0] + 1.
+        self.check(kernel, a)
+
+    def test_basic16(self):
+        """two rel index OOB, add const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + a[10, 0] + 1.
+
+        # only pyStencil bounds checks
+        ex = self.exception_dict(pyStencil=IndexError)
+        self.check(kernel, a, expected_exception=ex)
+
+    def test_basic17(self):
+        """two rel index boundary test, add const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + a[2, 0] + 1.
+        self.check(kernel, a)
+
+    def test_basic18(self):
+        """two rel index boundary test, add const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + a[-2, 0] + 1.
+        self.check(kernel, a)
+
+    def test_basic19(self):
+        """two rel index boundary test, add const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + a[0, 3] + 1.
+        self.check(kernel, a)
+
+    def test_basic20(self):
+        """two rel index boundary test, add const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + a[0, -3] + 1.
+        self.check(kernel, a)
+
+    def test_basic21(self):
+        """same rel, add const"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + a[0, 0] + 1.
+        self.check(kernel, a)
+
+    def test_basic22(self):
+        """rel idx const expr folding, add const"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[1 + 0, 0] + a[0, 0] + 1.
+        self.check(kernel, a)
+
+    def test_basic23(self):
+        """rel idx, work in body"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            x = np.sin(10 + a[2, 1])
+            return a[1 + 0, 0] + a[0, 0] + x
+        self.check(kernel, a)
+
+    def test_basic23a(self):
+        """rel idx, dead code should not impact rel idx"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            x = np.sin(10 + a[2, 1])
+            return a[1 + 0, 0] + a[0, 0]
+        self.check(kernel, a)
+
+    def test_basic24(self):
+        """1d idx on 2d arr"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return a[0] + 1.
+        self.check(kernel, a, expected_exception=[ValueError, TypingError])
+
+    def test_basic25(self):
+        """no idx on 2d arr"""
+        a = np.arange(12).reshape(3, 4)
+
+        def kernel(a):
+            return 1.
+        self.check(kernel, a, expected_exception=[ValueError, NumbaValueError])
+
+    def test_basic26(self):
+        """3d arr"""
+        a = np.arange(64).reshape(4, 8, 2)
+
+        def kernel(a):
+            return a[0, 0, 0] - a[0, 1, 0] + 1.
+        self.check(kernel, a)
+
+    def test_basic27(self):
+        """4d arr"""
+        a = np.arange(128).reshape(4, 8, 2, 2)
+
+        def kernel(a):
+            return a[0, 0, 0, 0] - a[0, 1, 0, -1] + 1.
+        self.check(kernel, a)
+
+    def test_basic28(self):
+        """type widen """
+        a = np.arange(12).reshape(3, 4).astype(np.float32)
+
+        def kernel(a):
+            return a[0, 0] + np.float64(10.)
+        self.check(kernel, a)
+
+    def test_basic29(self):
+        """const index from func """
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, int(np.cos(0))]
+        self.check(kernel, a, expected_exception=[ValueError, NumbaValueError,
+                                                  LoweringError])
+
+    def test_basic30(self):
+        """signed zeros"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[-0, -0]
+        self.check(kernel, a)
+
+    def test_basic31(self):
+        """does a const propagate? 2D"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            t = 1
+            return a[t, 0]
+        self.check(kernel, a)
+
+    @unittest.skip("constant folding not implemented")
+    def test_basic31b(self):
+        """does a const propagate?"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            s = 1
+            t = 1 - s
+            return a[t, 0]
+        self.check(kernel, a)
+
+    def test_basic31c(self):
+        """does a const propagate? 1D"""
+        a = np.arange(12.)
+
+        def kernel(a):
+            t = 1
+            return a[t]
+        self.check(kernel, a)
+
+    def test_basic32(self):
+        """typed int index"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[np.int8(1), 0]
+        self.check(kernel, a, expected_exception=[ValueError, NumbaValueError,
+                                                  LoweringError])
+
+    def test_basic33(self):
+        """add 0d array"""
+        a = np.arange(12.).reshape(3, 4)
+
+        def kernel(a):
+            return a[0, 0] + np.array(1)
+        self.check(kernel, a)
+
+    def test_basic34(self):
+        """More complex rel index with dependency on addition rel index"""
+        def kernel(a):
+            g = 4. + a[0, 1]
+            return g + (a[0, 1] + a[1, 0] + a[0, -1] + np.sin(a[-2, 0]))
+        a = np.arange(144).reshape(12, 12)
+        self.check(kernel, a)
+
+    def test_basic35(self):
+        """simple cval where cval is int but castable to dtype of float"""
+        def kernel(a):
+            return a[0, 1]
+        a = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, options={'cval': 5})
+
+    def test_basic36(self):
+        """more complex with cval"""
+        def kernel(a):
+            return a[0, 1] + a[0, -1] + a[1, -1] + a[1, -1]
+        a = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, options={'cval': 5.})
+
+    def test_basic37(self):
+        """cval is expr"""
+        def kernel(a):
+            return a[0, 1] + a[0, -1] + a[1, -1] + a[1, -1]
+        a = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, options={'cval': 5 + 63.})
+
+    def test_basic38(self):
+        """cval is complex"""
+        def kernel(a):
+            return a[0, 1] + a[0, -1] + a[1, -1] + a[1, -1]
+        a = np.arange(12.).reshape(3, 4)
+        ex = self.exception_dict(
+            stencil=NumbaValueError,
+            parfor=ValueError,
+            njit=NumbaValueError)
+        self.check(kernel, a, options={'cval': 1.j}, expected_exception=ex)
+
+    def test_basic39(self):
+        """cval is func expr"""
+        def kernel(a):
+            return a[0, 1] + a[0, -1] + a[1, -1] + a[1, -1]
+        a = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, options={'cval': np.sin(3.) + np.cos(2)})
+
+    def test_basic40(self):
+        """2 args!"""
+        def kernel(a, b):
+            return a[0, 1] + b[0, -2]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b)
+
+    def test_basic41(self):
+        """2 args! rel arrays wildly not same size!"""
+        def kernel(a, b):
+            return a[0, 1] + b[0, -2]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(1.).reshape(1, 1)
+        self.check(
+            kernel, a, b, expected_exception=[
+                ValueError, AssertionError])
+
+    def test_basic42(self):
+        """2 args! rel arrays very close in size"""
+        def kernel(a, b):
+            return a[0, 1] + b[0, -2]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(9.).reshape(3, 3)
+        self.check(
+            kernel, a, b, expected_exception=[
+                ValueError, AssertionError])
+
+    def test_basic43(self):
+        """2 args more complexity"""
+        def kernel(a, b):
+            return a[0, 1] + a[1, 2] + b[-2, 0] + b[0, -1]
+        a = np.arange(30.).reshape(5, 6)
+        b = np.arange(30.).reshape(5, 6)
+        self.check(kernel, a, b)
+
+    def test_basic44(self):
+        """2 args, has assignment before use"""
+        def kernel(a, b):
+            a[0, 1] = 12
+            return a[0, 1]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(
+            kernel, a, b, expected_exception=[
+                ValueError, LoweringError])
+
+    def test_basic45(self):
+        """2 args, has assignment and then cross dependency"""
+        def kernel(a, b):
+            a[0, 1] = 12
+            return a[0, 1] + a[1, 0]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(
+            kernel, a, b, expected_exception=[
+                ValueError, LoweringError])
+
+    def test_basic46(self):
+        """2 args, has cross relidx assignment"""
+        def kernel(a, b):
+            a[0, 1] = b[1, 2]
+            return a[0, 1] + a[1, 0]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(
+            kernel, a, b, expected_exception=[
+                ValueError, LoweringError])
+
+    def test_basic47(self):
+        """3 args"""
+        def kernel(a, b, c):
+            return a[0, 1] + b[1, 0] + c[-1, 0]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        c = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b, c)
+
+    # matches pyStencil, but all ought to fail
+    # probably hard to detect?
+    def test_basic48(self):
+        """2 args, has assignment before use via memory alias"""
+        def kernel(a):
+            c = a.T
+            c[:, :] = 10
+            return a[0, 1]
+        a = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a)
+
+    def test_basic49(self):
+        """2 args, standard_indexing on second"""
+        def kernel(a, b):
+            return a[0, 1] + b[0, 3]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b, options={'standard_indexing': 'b'})
+
+    @unittest.skip("dynamic range checking not implemented")
+    def test_basic50(self):
+        """2 args, standard_indexing OOB"""
+        def kernel(a, b):
+            return a[0, 1] + b[0, 15]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(
+            kernel,
+            a,
+            b,
+            options={
+                'standard_indexing': 'b'},
+            expected_exception=IndexError)
+
+    def test_basic51(self):
+        """2 args, standard_indexing, no relidx"""
+        def kernel(a, b):
+            return a[0, 1] + b[0, 2]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(
+            kernel, a, b, options={
+                'standard_indexing': [
+                    'a', 'b']}, expected_exception=[
+                ValueError, NumbaValueError])
+
+    def test_basic52(self):
+        """3 args, standard_indexing on middle arg """
+        def kernel(a, b, c):
+            return a[0, 1] + b[0, 1] + c[1, 2]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(4.).reshape(2, 2)
+        c = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b, c, options={'standard_indexing': 'b'})
+
+    def test_basic53(self):
+        """2 args, standard_indexing on variable that does not exist"""
+        def kernel(a, b):
+            return a[0, 1] + b[0, 2]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        ex = self.exception_dict(
+            pyStencil=ValueError,
+            stencil=Exception,
+            parfor=ValueError,
+            njit=Exception)
+        self.check(
+            kernel,
+            a,
+            b,
+            options={
+                'standard_indexing': 'c'},
+            expected_exception=ex)
+
+    def test_basic54(self):
+        """2 args, standard_indexing, index from var"""
+        def kernel(a, b):
+            t = 2
+            return a[0, 1] + b[0, t]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b, options={'standard_indexing': 'b'})
+
+    def test_basic55(self):
+        """2 args, standard_indexing, index from more complex var"""
+        def kernel(a, b):
+            s = 1
+            t = 2 - s
+            return a[0, 1] + b[0, t]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b, options={'standard_indexing': 'b'})
+
+    def test_basic56(self):
+        """2 args, standard_indexing, added complexity """
+        def kernel(a, b):
+            s = 1
+            acc = 0
+            for k in b[0, :]:
+                acc += k
+            t = 2 - s - 1
+            return a[0, 1] + b[0, t] + acc
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b, options={'standard_indexing': 'b'})
+
+    def test_basic57(self):
+        """2 args, standard_indexing, split index operation """
+        def kernel(a, b):
+            c = b[0]
+            return a[0, 1] + c[1]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b, options={'standard_indexing': 'b'})
+
+    def test_basic58(self):
+        """2 args, standard_indexing, split index with broadcast mutation """
+        def kernel(a, b):
+            c = b[0] + 1
+            return a[0, 1] + c[1]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(kernel, a, b, options={'standard_indexing': 'b'})
+
+    def test_basic59(self):
+        """3 args, mix of array, relative and standard indexing and const"""
+        def kernel(a, b, c):
+            return a[0, 1] + b[1, 1] + c
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        c = 10
+        self.check(kernel, a, b, c, options={'standard_indexing': ['b', 'c']})
+
+    def test_basic60(self):
+        """3 args, mix of array, relative and standard indexing,
+        tuple pass through"""
+        def kernel(a, b, c):
+            return a[0, 1] + b[1, 1] + c[0]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        c = (10,)
+        # parfors does not support tuple args for stencil kernels
+        ex = self.exception_dict(parfor=ValueError)
+        self.check(
+            kernel, a, b, c, options={
+                'standard_indexing': [
+                    'b', 'c']}, expected_exception=ex)
+
+    def test_basic61(self):
+        """2 args, standard_indexing on first"""
+        def kernel(a, b):
+            return a[0, 1] + b[1, 1]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(
+            kernel,
+            a,
+            b,
+            options={
+                'standard_indexing': 'a'},
+            expected_exception=Exception)
+
+    def test_basic62(self):
+        """2 args, standard_indexing and cval"""
+        def kernel(a, b):
+            return a[0, 1] + b[1, 1]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12.).reshape(3, 4)
+        self.check(
+            kernel,
+            a,
+            b,
+            options={
+                'standard_indexing': 'b',
+                'cval': 10.})
+
+    def test_basic63(self):
+        """2 args, standard_indexing applied to relative, should fail,
+        non-const idx"""
+        def kernel(a, b):
+            return a[0, b[0, 1]]
+        a = np.arange(12.).reshape(3, 4)
+        b = np.arange(12).reshape(3, 4)
+        ex = self.exception_dict(
+            pyStencil=ValueError,
+            stencil=NumbaValueError,
+            parfor=ValueError,
+            njit=NumbaValueError)
+        self.check(
+            kernel,
+            a,
+            b,
+            options={
+                'standard_indexing': 'b'},
+            expected_exception=ex)
+
+    # stencil, njit, parfors all fail. Does this make sense?
+    def test_basic64(self):
+        """1 arg that uses standard_indexing"""
+        def kernel(a):
+            return a[0, 0]
+        a = np.arange(12.).reshape(3, 4)
+        self.check(
+            kernel,
+            a,
+            options={
+                'standard_indexing': 'a'},
+            expected_exception=[
+                ValueError,
+                NumbaValueError])
+
+    def test_basic65(self):
+        """basic induced neighborhood test"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-29, 1):
+                cumul += a[i]
+            return cumul / 30
+        a = np.arange(60.)
+        self.check(kernel, a, options={'neighborhood': ((-29, 0),)})
+
+    # Should this work? a[0] is out of neighborhood?
+    def test_basic66(self):
+        """basic const neighborhood test"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-29, 1):
+                cumul += a[0]
+            return cumul / 30
+        a = np.arange(60.)
+        self.check(kernel, a, options={'neighborhood': ((-29, 0),)})
+
+    def test_basic67(self):
+        """basic 2d induced neighborhood test"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-5, 1):
+                for j in range(-10, 1):
+                    cumul += a[i, j]
+            return cumul / (10 * 5)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a, options={'neighborhood': ((-5, 0), (-10, 0),)})
+
+    def test_basic67b(self):
+        """basic 2d induced 1D neighborhood"""
+        def kernel(a):
+            cumul = 0
+            for j in range(-10, 1):
+                cumul += a[0, j]
+            return cumul / (10 * 5)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(
+            kernel,
+            a,
+            options={
+                'neighborhood': (
+                    (-10,
+                     0),
+                )},
+            expected_exception=[
+                TypingError,
+                ValueError])
+
+    # Should this work or is it UB? a[i, 0] is out of neighborhood?
+    def test_basic68(self):
+        """basic 2d one induced, one cost neighborhood test"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-5, 1):
+                for j in range(-10, 1):
+                    cumul += a[i, 0]
+            return cumul / (10 * 5)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a, options={'neighborhood': ((-5, 0), (-10, 0),)})
+
+    # Should this work or is it UB? a[0, 0] is out of neighborhood?
+    def test_basic69(self):
+        """basic 2d two cost neighborhood test"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-5, 1):
+                for j in range(-10, 1):
+                    cumul += a[0, 0]
+            return cumul / (10 * 5)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a, options={'neighborhood': ((-5, 0), (-10, 0),)})
+
+    def test_basic70(self):
+        """neighborhood adding complexity"""
+        def kernel(a):
+            cumul = 0
+            zz = 12.
+            for i in range(-5, 1):
+                t = zz + i
+                for j in range(-10, 1):
+                    cumul += a[i, j] + t
+            return cumul / (10 * 5)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a, options={'neighborhood': ((-5, 0), (-10, 0),)})
+
+    def test_basic71(self):
+        """neighborhood, type change"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-29, 1):
+                k = 0.
+                if i > -15:
+                    k = 1j
+                cumul += a[i] + k
+            return cumul / 30
+        a = np.arange(60.)
+        self.check(kernel, a, options={'neighborhood': ((-29, 0),)})
+
+    def test_basic72(self):
+        """neighborhood, narrower range than specified"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-19, -3):
+                cumul += a[i]
+            return cumul / 30
+        a = np.arange(60.)
+        self.check(kernel, a, options={'neighborhood': ((-29, 0),)})
+
+    def test_basic73(self):
+        """neighborhood, +ve range"""
+        def kernel(a):
+            cumul = 0
+            for i in range(5, 11):
+                cumul += a[i]
+            return cumul / 30
+        a = np.arange(60.)
+        self.check(kernel, a, options={'neighborhood': ((5, 10),)})
+
+    def test_basic73b(self):
+        """neighborhood, -ve range"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-10, -4):
+                cumul += a[i]
+            return cumul / 30
+        a = np.arange(60.)
+        self.check(kernel, a, options={'neighborhood': ((-10, -5),)})
+
+    def test_basic74(self):
+        """neighborhood, -ve->+ve range span"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-5, 11):
+                cumul += a[i]
+            return cumul / 30
+        a = np.arange(60.)
+        self.check(kernel, a, options={'neighborhood': ((-5, 10),)})
+
+    def test_basic75(self):
+        """neighborhood, -ve->-ve range span"""
+        def kernel(a):
+            cumul = 0
+            for i in range(-10, -1):
+                cumul += a[i]
+            return cumul / 30
+        a = np.arange(60.)
+        self.check(kernel, a, options={'neighborhood': ((-10, -2),)})
+
+    def test_basic76(self):
+        """neighborhood, mixed range span"""
+        def kernel(a):
+            cumul = 0
+            zz = 12.
+            for i in range(-3, 0):
+                t = zz + i
+                for j in range(-3, 4):
+                    cumul += a[i, j] + t
+            return cumul / (10 * 5)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a, options={'neighborhood': ((-3, -1), (-3, 3),)})
+
+    def test_basic77(self):
+        """ neighborhood, two args """
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-3, 1):
+                for j in range(-3, 1):
+                    cumul += a[i, j] + b[i, j]
+            return cumul / (9.)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        b = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a, b, options={'neighborhood': ((-3, 0), (-3, 0),)})
+
+    def test_basic78(self):
+        """ neighborhood, two args, -ve range, -ve range """
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-6, -2):
+                for j in range(-7, -1):
+                    cumul += a[i, j] + b[i, j]
+            return cumul / (9.)
+        a = np.arange(15. * 20.).reshape(15, 20)
+        b = np.arange(15. * 20.).reshape(15, 20)
+        self.check(
+            kernel, a, b, options={
+                'neighborhood': (
+                    (-6, -3), (-7, -2),)})
+
+    def test_basic78b(self):
+        """ neighborhood, two args, -ve range, +ve range """
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-6, -2):
+                for j in range(2, 10):
+                    cumul += a[i, j] + b[i, j]
+            return cumul / (9.)
+        a = np.arange(15. * 20.).reshape(15, 20)
+        b = np.arange(15. * 20.).reshape(15, 20)
+        self.check(kernel, a, b, options={'neighborhood': ((-6, -3), (2, 9),)})
+
+    def test_basic79(self):
+        """ neighborhood, two incompatible args """
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-3, 1):
+                for j in range(-3, 1):
+                    cumul += a[i, j] + b[i, j]
+            return cumul / (9.)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        b = np.arange(10. * 20.).reshape(10, 10, 2)
+        ex = self.exception_dict(
+            pyStencil=ValueError,
+            stencil=TypingError,
+            parfor=TypingError,
+            njit=TypingError)
+        self.check(
+            kernel, a, b, options={
+                'neighborhood': (
+                    (-3, 0), (-3, 0),)}, expected_exception=ex)
+
+    def test_basic80(self):
+        """ neighborhood, type change """
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-3, 1):
+                for j in range(-3, 1):
+                    cumul += a[i, j] + b
+            return cumul / (9.)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        b = 12.j
+        self.check(kernel, a, b, options={'neighborhood': ((-3, 0), (-3, 0))})
+
+    def test_basic81(self):
+        """ neighborhood, dimensionally incompatible arrays """
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-3, 1):
+                for j in range(-3, 1):
+                    cumul += a[i, j] + b[i]
+            return cumul / (9.)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        b = a[0].copy()
+        ex = self.exception_dict(
+            pyStencil=ValueError,
+            stencil=TypingError,
+            parfor=AssertionError,
+            njit=TypingError)
+        self.check(
+            kernel, a, b, options={
+                'neighborhood': (
+                    (-3, 0), (-3, 0))}, expected_exception=ex)
+
+    def test_basic82(self):
+        """ neighborhood, with standard_indexing"""
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-3, 1):
+                for j in range(-3, 1):
+                    cumul += a[i, j] + b[1, 3]
+            return cumul / (9.)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        b = a.copy()
+        self.check(
+            kernel, a, b, options={
+                'neighborhood': (
+                    (-3, 0), (-3, 0)), 'standard_indexing': 'b'})
+
+    def test_basic83(self):
+        """ neighborhood, with standard_indexing and cval"""
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-3, 1):
+                for j in range(-3, 1):
+                    cumul += a[i, j] + b[1, 3]
+            return cumul / (9.)
+        a = np.arange(10. * 20.).reshape(10, 20)
+        b = a.copy()
+        self.check(
+            kernel, a, b, options={
+                'neighborhood': (
+                    (-3, 0), (-3, 0)), 'standard_indexing': 'b', 'cval': 1.5})
+
+    def test_basic84(self):
+        """ kernel calls njit """
+        def kernel(a):
+            return a[0, 0] + addone_njit(a[0, 1])
+
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a)
+
+    def test_basic85(self):
+        """ kernel calls njit(parallel=True)"""
+        def kernel(a):
+            return a[0, 0] + addone_pjit(a[0, 1])
+
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a)
+
+    # njit/parfors fail correctly, but the error message isn't very informative
+    def test_basic86(self):
+        """ bad kwarg """
+        def kernel(a):
+            return a[0, 0]
+
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a, options={'bad': 10},
+                   expected_exception=[ValueError, TypingError])
+
+    def test_basic87(self):
+        """ reserved arg name in use """
+        def kernel(__sentinel__):
+            return __sentinel__[0, 0]
+
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a)
+
+    def test_basic88(self):
+        """ use of reserved word """
+        def kernel(a, out):
+            return out * a[0, 1]
+        a = np.arange(12.).reshape(3, 4)
+        ex = self.exception_dict(
+            pyStencil=ValueError,
+            stencil=NumbaValueError,
+            parfor=ValueError,
+            njit=NumbaValueError)
+        self.check(
+            kernel,
+            a,
+            1.0,
+            options={},
+            expected_exception=ex)
+
+    def test_basic89(self):
+        """ basic multiple return"""
+        def kernel(a):
+            if a[0, 1] > 10:
+                return 10.
+            elif a[0, 3] < 8:
+                return a[0, 0]
+            else:
+                return 7.
+
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a)
+
+    def test_basic90(self):
+        """ neighborhood, with standard_indexing and cval, multiple returns"""
+        def kernel(a, b):
+            cumul = 0
+            for i in range(-3, 1):
+                for j in range(-3, 1):
+                    cumul += a[i, j] + b[1, 3]
+            res = cumul / (9.)
+            if res > 200.0:
+                return res + 1.0
+            else:
+                return res
+        a = np.arange(10. * 20.).reshape(10, 20)
+        b = a.copy()
+        self.check(
+            kernel, a, b, options={
+                'neighborhood': (
+                    (-3, 0), (-3, 0)), 'standard_indexing': 'b', 'cval': 1.5})
+
+    def test_basic91(self):
+        """ Issue #3454, const(int) == const(int) evaluating incorrectly. """
+        def kernel(a):
+            b = 0
+            if(2 == 0):
+                b = 2
+            return a[0, 0] + b
+
+        a = np.arange(10. * 20.).reshape(10, 20)
+        self.check(kernel, a)
+
+    def test_basic92(self):
+        """ Issue #3497, bool return type evaluating incorrectly. """
+        def kernel(a):
+            return (a[-1, -1] ^ a[-1, 0] ^ a[-1, 1] ^
+                    a[0, -1]  ^ a[0, 0]  ^ a[0, 1]  ^
+                    a[1, -1]  ^ a[1, 0]  ^ a[1, 1])
+
+        A = np.array(np.arange(20) % 2).reshape(4, 5).astype(np.bool_)
+        self.check(kernel, A)
+
+    def test_basic93(self):
+        """ Issue #3497, bool return type evaluating incorrectly. """
+        def kernel(a):
+            return (a[-1, -1] ^ a[-1, 0] ^ a[-1, 1] ^
+                    a[0, -1]  ^ a[0, 0]  ^ a[0, 1]  ^
+                    a[1, -1]  ^ a[1, 0]  ^ a[1, 1])
+
+        A = np.array(np.arange(20) % 2).reshape(4, 5).astype(np.bool_)
+        self.check(kernel, A, options={'cval': True})
+
+    def test_basic94(self):
+        """ Issue #3528. Support for slices. """
+        def kernel(a):
+            return np.median(a[-1:2, -1:2])
+        a = np.arange(20, dtype=np.uint32).reshape(4, 5)
+        self.check(kernel, a, options={'neighborhood': ((-1, 1), (-1, 1),)})
+
+    @unittest.skip("not yet supported")
+    def test_basic95(self):
+        """ Slice, calculate neighborhood. """
+        def kernel(a):
+            return np.median(a[-1:2, -3:4])
+        a = np.arange(20, dtype=np.uint32).reshape(4, 5)
+        self.check(kernel, a)
+
+    def test_basic96(self):
+        """ 1D slice. """
+        def kernel(a):
+            return np.median(a[-1:2])
+        a = np.arange(20, dtype=np.uint32)
+        self.check(kernel, a, options={'neighborhood': ((-1, 1),)})
+
+    @unittest.skip("not yet supported")
+    def test_basic97(self):
+        """ 2D slice and index. """
+        def kernel(a):
+            return np.median(a[-1:2, 3])
+        a = np.arange(20, dtype=np.uint32).reshape(4, 5)
+        self.check(kernel, a)
+
+    def test_basic98(self):
+        """ Test issue #7286 where the cval is a np attr/string-based numerical
+        constant"""
+        for cval in (np.nan, np.inf, -np.inf, float('inf'), -float('inf')):
+            def kernel(a):
+                return a[0, 0]
+            a = np.arange(6.).reshape((2, 3))
+            self.check(kernel, a, options={'neighborhood': ((-1, 1), (-1, 1),),
+                       'cval':cval})
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_storeslice.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_storeslice.py
new file mode 100644
index 000000000..5443bcab7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_storeslice.py
@@ -0,0 +1,71 @@
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types, errors
+from numba.tests.support import TestCase
+
+
+def setitem_slice(a, start, stop, step, scalar):
+    a[start:stop:step] = scalar
+
+
+def usecase(obs, nPoints):
+    center = nPoints // 2
+    obs[0:center] = np.arange(center)
+    obs[center] = 321
+    obs[(center + 1):] = np.arange(nPoints - center - 1)
+
+
+class TestStoreSlice(TestCase):
+
+    def test_usecase(self):
+        n = 10
+        obs_got = np.zeros(n)
+        obs_expected = obs_got.copy()
+
+        flags = Flags()
+        flags.nrt = True
+        cres = compile_isolated(usecase, (types.float64[:], types.intp),
+                                flags=flags)
+        cres.entry_point(obs_got, n)
+        usecase(obs_expected, n)
+
+        self.assertPreciseEqual(obs_got, obs_expected)
+
+    def test_array_slice_setitem(self):
+        n = 10
+        argtys = (types.int64[:], types.int64, types.int64, types.int64,
+                  types.int64)
+        cres = compile_isolated(setitem_slice, argtys)
+        a = np.arange(n, dtype=np.int64)
+        # tuple is (start, stop, step, scalar)
+        tests = ((2, 6, 1, 7),
+                 (2, 6, -1, 7),
+                 (-2, len(a), 2, 77),
+                 (-2, 2 * len(a), 2, 77),
+                 (-2, -6, 3, 88),
+                 (-2, -6, -3, 9999),
+                 (-6, -2, 4, 88),
+                 (-6, -2, -4, 88),
+                 (16, 20, 2, 88),
+                 (16, 20, -2, 88),
+                 )
+
+        for start, stop, step, scalar in tests:
+            a = np.arange(n, dtype=np.int64)
+            b = np.arange(n, dtype=np.int64)
+            cres.entry_point(a, start, stop, step, scalar)
+            setitem_slice(b, start, stop, step, scalar)
+            self.assertPreciseEqual(a, b)
+
+        # test if step = 0
+        a = np.arange(n, dtype=np.int64)
+        with self.assertRaises(ValueError) as cm:
+            cres.entry_point(a, 3, 6, 0, 88)
+        self.assertEqual(str(cm.exception), "slice step cannot be zero")
+
+
+if __name__ == '__main__':
+    unittest.main()
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_struct_ref.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_struct_ref.py
new file mode 100644
index 000000000..13e52d19f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_struct_ref.py
@@ -0,0 +1,429 @@
+"""
+Test mutable struct, aka, structref
+"""
+import warnings
+
+import numpy as np
+
+from numba import typed, njit, errors
+from numba.core import types
+from numba.experimental import structref
+from numba.extending import overload_method, overload_attribute
+from numba.tests.support import (
+    MemoryLeakMixin, TestCase, temp_directory, override_config,
+)
+
+
+@structref.register
+class MySimplerStructType(types.StructRef):
+    """
+    Test associated with this type represent the lowest level uses of structref.
+    """
+    pass
+
+
+my_struct_ty = MySimplerStructType(
+    fields=[("values", types.intp[:]), ("counter", types.intp)]
+)
+
+structref.define_boxing(MySimplerStructType, structref.StructRefProxy)
+
+
+class MyStruct(structref.StructRefProxy):
+
+    def __new__(cls, values, counter):
+        # Define this method to customize the constructor.
+        # The default takes `*args`. Customizing allow the use of keyword-arg.
+        # The impl of the method calls `StructRefProxy.__new__`
+        return structref.StructRefProxy.__new__(cls, values, counter)
+
+    # The below defines wrappers for attributes and methods manually
+
+    @property
+    def values(self):
+        return get_values(self)
+
+    @values.setter
+    def values(self, val):
+        return set_values(self, val)
+
+    @property
+    def counter(self):
+        return get_counter(self)
+
+    def testme(self, arg):
+        return self.values * arg + self.counter
+
+    @property
+    def prop(self):
+        return self.values, self.counter
+
+
+@structref.register
+class MyStructType(types.StructRef):
+    """Test associated with this type represent the higher-level uses of
+    structef.
+    """
+    pass
+
+
+# Call to define_proxy is needed to register the use of `MyStruct` as a
+# PyObject proxy for creating a Numba-allocated structref.
+# The `MyStruct` class can then be used in both jit-code and interpreted-code.
+structref.define_proxy(
+    MyStruct,
+    MyStructType,
+    ['values', 'counter'],
+)
+
+
+@njit
+def my_struct(values, counter):
+    st = structref.new(my_struct_ty)
+    my_struct_init(st, values, counter)
+    return st
+
+
+@njit
+def my_struct_init(self, values, counter):
+    self.values = values
+    self.counter = counter
+
+
+@njit
+def ctor_by_intrinsic(vs, ctr):
+    st = my_struct(vs, counter=ctr)
+    st.values += st.values
+    st.counter *= ctr
+    return st
+
+
+@njit
+def ctor_by_class(vs, ctr):
+    return MyStruct(values=vs, counter=ctr)
+
+
+@njit
+def get_values(st):
+    return st.values
+
+
+@njit
+def set_values(st, val):
+    st.values = val
+
+
+@njit
+def get_counter(st):
+    return st.counter
+
+
+@njit
+def compute_fields(st):
+    return st.values + st.counter
+
+
+class TestStructRefBasic(MemoryLeakMixin, TestCase):
+    def test_structref_type(self):
+        sr = types.StructRef([('a', types.int64)])
+        self.assertEqual(sr.field_dict['a'], types.int64)
+        sr = types.StructRef([('a', types.int64), ('b', types.float64)])
+        self.assertEqual(sr.field_dict['a'], types.int64)
+        self.assertEqual(sr.field_dict['b'], types.float64)
+        # bad case
+        with self.assertRaisesRegex(ValueError,
+                                    "expecting a str for field name"):
+            types.StructRef([(1, types.int64)])
+        with self.assertRaisesRegex(ValueError,
+                                    "expecting a Numba Type for field type"):
+            types.StructRef([('a', 123)])
+
+    def test_invalid_uses(self):
+        with self.assertRaisesRegex(ValueError, "cannot register"):
+            structref.register(types.StructRef)
+        with self.assertRaisesRegex(ValueError, "cannot register"):
+            structref.define_boxing(types.StructRef, MyStruct)
+
+    def test_MySimplerStructType(self):
+        vs = np.arange(10, dtype=np.intp)
+        ctr = 13
+
+        first_expected = vs + vs
+        first_got = ctor_by_intrinsic(vs, ctr)
+        # the returned instance is a structref.StructRefProxy
+        # but not a MyStruct
+        self.assertNotIsInstance(first_got, MyStruct)
+        self.assertPreciseEqual(first_expected, get_values(first_got))
+
+        second_expected = first_expected + (ctr * ctr)
+        second_got = compute_fields(first_got)
+        self.assertPreciseEqual(second_expected, second_got)
+
+    def test_MySimplerStructType_wrapper_has_no_attrs(self):
+        vs = np.arange(10, dtype=np.intp)
+        ctr = 13
+        wrapper = ctor_by_intrinsic(vs, ctr)
+        self.assertIsInstance(wrapper, structref.StructRefProxy)
+        with self.assertRaisesRegex(AttributeError, 'values'):
+            wrapper.values
+        with self.assertRaisesRegex(AttributeError, 'counter'):
+            wrapper.counter
+
+    def test_MyStructType(self):
+        vs = np.arange(10, dtype=np.float64)
+        ctr = 11
+
+        first_expected_arr = vs.copy()
+        first_got = ctor_by_class(vs, ctr)
+        self.assertIsInstance(first_got, MyStruct)
+        self.assertPreciseEqual(first_expected_arr, first_got.values)
+
+        second_expected = first_expected_arr + ctr
+        second_got = compute_fields(first_got)
+        self.assertPreciseEqual(second_expected, second_got)
+        self.assertEqual(first_got.counter, ctr)
+
+    def test_MyStructType_mixed_types(self):
+        # structref constructor is generic
+        @njit
+        def mixed_type(x, y, m, n):
+            return MyStruct(x, y), MyStruct(m, n)
+
+        a, b = mixed_type(1, 2.3, 3.4j, (4,))
+        self.assertEqual(a.values, 1)
+        self.assertEqual(a.counter, 2.3)
+        self.assertEqual(b.values, 3.4j)
+        self.assertEqual(b.counter, (4,))
+
+    def test_MyStructType_in_dict(self):
+        td = typed.Dict()
+        td['a'] = MyStruct(1, 2.3)
+        self.assertEqual(td['a'].values, 1)
+        self.assertEqual(td['a'].counter, 2.3)
+        # overwrite
+        td['a'] = MyStruct(2, 3.3)
+        self.assertEqual(td['a'].values, 2)
+        self.assertEqual(td['a'].counter, 3.3)
+        # mutate
+        td['a'].values += 10
+        self.assertEqual(td['a'].values, 12)    # changed
+        self.assertEqual(td['a'].counter, 3.3)  # unchanged
+        # insert
+        td['b'] = MyStruct(4, 5.6)
+
+    def test_MyStructType_in_dict_mixed_type_error(self):
+        self.disable_leak_check()
+
+        td = typed.Dict()
+        td['a'] = MyStruct(1, 2.3)
+        self.assertEqual(td['a'].values, 1)
+        self.assertEqual(td['a'].counter, 2.3)
+
+        # ERROR: store different types
+        with self.assertRaisesRegex(errors.TypingError,
+                                    r"Cannot cast numba.MyStructType"):
+            # because first field is not a float;
+            # the second field is now an integer.
+            td['b'] = MyStruct(2.3, 1)
+
+
+@overload_method(MyStructType, "testme")
+def _ol_mystructtype_testme(self, arg):
+    def impl(self, arg):
+        return self.values * arg + self.counter
+    return impl
+
+
+@overload_attribute(MyStructType, "prop")
+def _ol_mystructtype_prop(self):
+    def get(self):
+        return self.values, self.counter
+    return get
+
+
+class TestStructRefExtending(MemoryLeakMixin, TestCase):
+    def test_overload_method(self):
+        @njit
+        def check(x):
+            vs = np.arange(10, dtype=np.float64)
+            ctr = 11
+            obj = MyStruct(vs, ctr)
+            return obj.testme(x)
+
+        x = 3
+        got = check(x)
+        expect = check.py_func(x)
+        self.assertPreciseEqual(got, expect)
+
+    def test_overload_attribute(self):
+        @njit
+        def check():
+            vs = np.arange(10, dtype=np.float64)
+            ctr = 11
+            obj = MyStruct(vs, ctr)
+            return obj.prop
+
+        got = check()
+        expect = check.py_func()
+        self.assertPreciseEqual(got, expect)
+
+
+def caching_test_make(x, y):
+    struct = MyStruct(values=x, counter=y)
+    return struct
+
+
+def caching_test_use(struct, z):
+    return struct.testme(z)
+
+
+class TestStructRefCaching(MemoryLeakMixin, TestCase):
+    def setUp(self):
+        self._cache_dir = temp_directory(TestStructRefCaching.__name__)
+        self._cache_override = override_config('CACHE_DIR', self._cache_dir)
+        self._cache_override.__enter__()
+        warnings.simplefilter("error")
+        warnings.filterwarnings(action="ignore", module="typeguard")
+
+    def tearDown(self):
+        self._cache_override.__exit__(None, None, None)
+        warnings.resetwarnings()
+
+    def test_structref_caching(self):
+        def assert_cached(stats):
+            self.assertEqual(len(stats.cache_hits), 1)
+            self.assertEqual(len(stats.cache_misses), 0)
+
+        def assert_not_cached(stats):
+            self.assertEqual(len(stats.cache_hits), 0)
+            self.assertEqual(len(stats.cache_misses), 1)
+
+        def check(cached):
+            check_make = njit(cache=True)(caching_test_make)
+            check_use = njit(cache=True)(caching_test_use)
+            vs = np.random.random(3)
+            ctr = 17
+            factor = 3
+            st = check_make(vs, ctr)
+            got = check_use(st, factor)
+            expect = vs * factor + ctr
+            self.assertPreciseEqual(got, expect)
+
+            if cached:
+                assert_cached(check_make.stats)
+                assert_cached(check_use.stats)
+            else:
+                assert_not_cached(check_make.stats)
+                assert_not_cached(check_use.stats)
+
+        check(cached=False)
+        check(cached=True)
+
+
+@structref.register
+class PolygonStructType(types.StructRef):
+
+    def preprocess_fields(self, fields):
+        # temp name to allow Optional instantiation
+        self.name = f"numba.PolygonStructType#{id(self)}"
+        fields = tuple([
+            ('value', types.Optional(types.int64)),
+            ('parent', types.Optional(self)),
+        ])
+
+        return fields
+
+
+polygon_struct_type = PolygonStructType(fields=(
+    ('value', types.Any),
+    ('parent', types.Any)
+))
+
+
+class PolygonStruct(structref.StructRefProxy):
+    def __new__(cls, value, parent):
+        return structref.StructRefProxy.__new__(cls, value, parent)
+
+    @property
+    def value(self):
+        return PolygonStruct_get_value(self)
+
+    @property
+    def parent(self):
+        return PolygonStruct_get_parent(self)
+
+
+@njit
+def PolygonStruct_get_value(self):
+    return self.value
+
+
+@njit
+def PolygonStruct_get_parent(self):
+    return self.parent
+
+
+structref.define_proxy(
+    PolygonStruct,
+    PolygonStructType,
+    ["value", "parent"]
+)
+
+
+@overload_method(PolygonStructType, "flip")
+def _ol_polygon_struct_flip(self):
+    def impl(self):
+        if self.value is not None:
+            self.value = -self.value
+    return impl
+
+
+@overload_attribute(PolygonStructType, "prop")
+def _ol_polygon_struct_prop(self):
+    def get(self):
+        return self.value, self.parent
+    return get
+
+
+class TestStructRefForwardTyping(MemoryLeakMixin, TestCase):
+    def test_same_type_assignment(self):
+        @njit
+        def check(x):
+            poly = PolygonStruct(None, None)
+            p_poly = PolygonStruct(None, None)
+            poly.value = x
+            poly.parent = p_poly
+            p_poly.value = x
+            return poly.parent.value
+
+        x = 11
+        got = check(x)
+        expect = x
+        self.assertPreciseEqual(got, expect)
+
+    def test_overload_method(self):
+        @njit
+        def check(x):
+            poly = PolygonStruct(None, None)
+            p_poly = PolygonStruct(None, None)
+            poly.value = x
+            poly.parent = p_poly
+            p_poly.value = x
+            poly.flip()
+            poly.parent.flip()
+            return poly.parent.value
+
+        x = 3
+        got = check(x)
+        expect = -x
+        self.assertPreciseEqual(got, expect)
+
+    def test_overload_attribute(self):
+        @njit
+        def check():
+            obj = PolygonStruct(5, None)
+            return obj.prop[0]
+
+        got = check()
+        expect = 5
+        self.assertPreciseEqual(got, expect)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_support.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_support.py
new file mode 100644
index 000000000..e35eda1ff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_support.py
@@ -0,0 +1,346 @@
+import itertools
+
+import numpy as np
+
+from numba import jit
+from numba.core import utils
+from numba.tests.support import TestCase, forbid_codegen
+from .enum_usecases import *
+import unittest
+
+DBL_EPSILON = 2**-52
+FLT_EPSILON = 2**-23
+
+INF = float('inf')
+NAN = float('nan')
+
+
+class TestAssertPreciseEqual(TestCase):
+    """
+    Tests for TestCase.assertPreciseEqual().
+    """
+
+    int_types = [int]
+    np_float_types = [np.float32, np.float64]
+    float_types = [float] + np_float_types
+    np_complex_types = [np.complex64, np.complex128]
+    complex_types = [complex] + np_complex_types
+    bool_types = [bool, np.bool_]
+
+    def eq(self, left, right, **kwargs):
+        def assert_succeed(left, right):
+            self.assertPreciseEqual(left, right, **kwargs)
+            self.assertPreciseEqual(right, left, **kwargs)
+        assert_succeed(left, right)
+        assert_succeed((left, left), (right, right))
+        assert_succeed([left, left], [right, right])
+
+    def ne(self, left, right, **kwargs):
+        def assert_fail(left, right):
+            try:
+                self.assertPreciseEqual(left, right, **kwargs)
+            except AssertionError:
+                pass
+            else:
+                self.fail("%s and %s unexpectedly considered equal" % (left, right))
+        assert_fail(left, right)
+        assert_fail(right, left)
+        assert_fail((left, left), (right, right))
+        assert_fail((right, right), (left, left))
+        assert_fail([left, left], [right, right])
+        assert_fail([right, right], [left, left])
+
+    def test_types(self):
+        # assertPreciseEqual() should test for type compatibility
+        # int-like, float-like, complex-like are not compatible
+        for i, f, c in itertools.product(self.int_types, self.float_types,
+                                         self.complex_types):
+            self.ne(i(1), f(1))
+            self.ne(f(1), c(1))
+            self.ne(i(1), c(1))
+        # int and long are compatible between each other
+        for u, v in itertools.product(self.int_types, self.int_types):
+            self.eq(u(1), v(1))
+        # int and bool are not compatible between each other
+        for u, v in itertools.product(self.int_types, self.bool_types):
+            self.ne(u(1), v(1))
+        # NumPy float types are not compatible between each other
+        for u, v in itertools.product(self.np_float_types, self.np_float_types):
+            if u is v:
+                self.eq(u(1), v(1))
+            else:
+                self.ne(u(1), v(1))
+        # NumPy complex types are not compatible between each other
+        for u, v in itertools.product(self.np_complex_types, self.np_complex_types):
+            if u is v:
+                self.eq(u(1), v(1))
+            else:
+                self.ne(u(1), v(1))
+
+    def test_int_values(self):
+        for tp in self.int_types:
+            for prec in ['exact', 'single', 'double']:
+                self.eq(tp(0), tp(0), prec=prec)
+                self.ne(tp(0), tp(1), prec=prec)
+                self.ne(tp(-1), tp(1), prec=prec)
+                self.ne(tp(2**80), tp(1+2**80), prec=prec)
+
+    def test_bool_values(self):
+        for tpa, tpb in itertools.product(self.bool_types, self.bool_types):
+            self.eq(tpa(True), tpb(True))
+            self.eq(tpa(False), tpb(False))
+            self.ne(tpa(True), tpb(False))
+
+    def test_abs_tol_parse(self):
+        # check invalid values in abs_tol kwarg raises
+        with self.assertRaises(ValueError):
+            self.eq(np.float64(1e-17), np.float64(1e-17), abs_tol="invalid")
+        with self.assertRaises(ValueError):
+            self.eq(np.float64(1), np.float64(2), abs_tol=int(7))
+
+    def test_float_values(self):
+        for tp in self.float_types:
+            for prec in ['exact', 'single', 'double']:
+                self.eq(tp(1.5), tp(1.5), prec=prec)
+                # Signed zeros
+                self.eq(tp(0.0), tp(0.0), prec=prec)
+                self.eq(tp(-0.0), tp(-0.0), prec=prec)
+                self.ne(tp(0.0), tp(-0.0), prec=prec)
+                self.eq(tp(0.0), tp(-0.0), prec=prec, ignore_sign_on_zero=True)
+                # Infinities
+                self.eq(tp(INF), tp(INF), prec=prec)
+                self.ne(tp(INF), tp(1e38), prec=prec)
+                self.eq(tp(-INF), tp(-INF), prec=prec)
+                self.ne(tp(INF), tp(-INF), prec=prec)
+                # NaNs
+                self.eq(tp(NAN), tp(NAN), prec=prec)
+                self.ne(tp(NAN), tp(0), prec=prec)
+                self.ne(tp(NAN), tp(INF), prec=prec)
+                self.ne(tp(NAN), tp(-INF), prec=prec)
+
+    def test_float64_values(self):
+        for tp in [float, np.float64]:
+            self.ne(tp(1.0 + DBL_EPSILON), tp(1.0))
+
+    def test_float32_values(self):
+        tp = np.float32
+        self.ne(tp(1.0 + FLT_EPSILON), tp(1.0))
+
+    def test_float64_values_inexact(self):
+        for tp in [float, np.float64]:
+            for scale in [1.0, -2**3, 2**-4, -2**-20]:
+                a = scale * 1.0
+                b = scale * (1.0 + DBL_EPSILON)
+                c = scale * (1.0 + DBL_EPSILON * 2)
+                d = scale * (1.0 + DBL_EPSILON * 4)
+                self.ne(tp(a), tp(b))
+                self.ne(tp(a), tp(b), prec='exact')
+                self.eq(tp(a), tp(b), prec='double')
+                self.eq(tp(a), tp(b), prec='double', ulps=1)
+                self.ne(tp(a), tp(c), prec='double')
+                self.eq(tp(a), tp(c), prec='double', ulps=2)
+                self.ne(tp(a), tp(d), prec='double', ulps=2)
+                self.eq(tp(a), tp(c), prec='double', ulps=3)
+                self.eq(tp(a), tp(d), prec='double', ulps=3)
+            # test absolute tolerance based on eps
+            self.eq(tp(1e-16), tp(3e-16), prec='double', abs_tol="eps")
+            self.ne(tp(1e-16), tp(4e-16), prec='double', abs_tol="eps")
+            # test absolute tolerance based on value
+            self.eq(tp(1e-17), tp(1e-18), prec='double', abs_tol=1e-17)
+            self.ne(tp(1e-17), tp(3e-17), prec='double', abs_tol=1e-17)
+
+    def test_float32_values_inexact(self):
+        tp = np.float32
+        for scale in [1.0, -2**3, 2**-4, -2**-20]:
+            # About the choice of 0.9: there seem to be issues when
+            # converting
+            a = scale * 1.0
+            b = scale * (1.0 + FLT_EPSILON)
+            c = scale * (1.0 + FLT_EPSILON * 2)
+            d = scale * (1.0 + FLT_EPSILON * 4)
+            self.ne(tp(a), tp(b))
+            self.ne(tp(a), tp(b), prec='exact')
+            self.ne(tp(a), tp(b), prec='double')
+            self.eq(tp(a), tp(b), prec='single')
+            self.ne(tp(a), tp(c), prec='single')
+            self.eq(tp(a), tp(c), prec='single', ulps=2)
+            self.ne(tp(a), tp(d), prec='single', ulps=2)
+            self.eq(tp(a), tp(c), prec='single', ulps=3)
+            self.eq(tp(a), tp(d), prec='single', ulps=3)
+        # test absolute tolerance based on eps
+        self.eq(tp(1e-7), tp(2e-7), prec='single', abs_tol="eps")
+        self.ne(tp(1e-7), tp(3e-7), prec='single', abs_tol="eps")
+        # test absolute tolerance based on value
+        self.eq(tp(1e-7), tp(1e-8), prec='single', abs_tol=1e-7)
+        self.ne(tp(1e-7), tp(3e-7), prec='single', abs_tol=1e-7)
+
+    def test_complex_values(self):
+        # Complex literals with signed zeros are confusing, better use
+        # the explicit constructor.
+        c_pp, c_pn, c_np, c_nn = [complex(0.0, 0.0), complex(0.0, -0.0),
+                                  complex(-0.0, 0.0), complex(-0.0, -0.0)]
+        for tp in self.complex_types:
+            for prec in ['exact', 'single', 'double']:
+                self.eq(tp(1 + 2j), tp(1 + 2j), prec=prec)
+                self.ne(tp(1 + 1j), tp(1 + 2j), prec=prec)
+                self.ne(tp(2 + 2j), tp(1 + 2j), prec=prec)
+                # Signed zeros
+                self.eq(tp(c_pp), tp(c_pp), prec=prec)
+                self.eq(tp(c_np), tp(c_np), prec=prec)
+                self.eq(tp(c_nn), tp(c_nn), prec=prec)
+                self.ne(tp(c_pp), tp(c_pn), prec=prec)
+                self.ne(tp(c_pn), tp(c_nn), prec=prec)
+                # Infinities
+                self.eq(tp(complex(INF, INF)), tp(complex(INF, INF)), prec=prec)
+                self.eq(tp(complex(INF, -INF)), tp(complex(INF, -INF)), prec=prec)
+                self.eq(tp(complex(-INF, -INF)), tp(complex(-INF, -INF)), prec=prec)
+                self.ne(tp(complex(INF, INF)), tp(complex(INF, -INF)), prec=prec)
+                self.ne(tp(complex(INF, INF)), tp(complex(-INF, INF)), prec=prec)
+                self.eq(tp(complex(INF, 0)), tp(complex(INF, 0)), prec=prec)
+                # NaNs
+                self.eq(tp(complex(NAN, 0)), tp(complex(NAN, 0)), prec=prec)
+                self.eq(tp(complex(0, NAN)), tp(complex(0, NAN)), prec=prec)
+                self.eq(tp(complex(NAN, NAN)), tp(complex(NAN, NAN)), prec=prec)
+                self.eq(tp(complex(INF, NAN)), tp(complex(INF, NAN)), prec=prec)
+                self.eq(tp(complex(NAN, -INF)), tp(complex(NAN, -INF)), prec=prec)
+                # FIXME
+                #self.ne(tp(complex(NAN, INF)), tp(complex(NAN, -INF)))
+                #self.ne(tp(complex(NAN, 0)), tp(complex(NAN, 1)))
+                #self.ne(tp(complex(INF, NAN)), tp(complex(-INF, NAN)))
+                #self.ne(tp(complex(0, NAN)), tp(complex(1, NAN)))
+                #self.ne(tp(complex(NAN, 0)), tp(complex(0, NAN)))
+            # XXX should work with other precisions as well?
+            self.ne(tp(complex(INF, 0)), tp(complex(INF, 1)), prec='exact')
+
+    def test_complex128_values_inexact(self):
+        for tp in [complex, np.complex128]:
+            for scale in [1.0, -2**3, 2**-4, -2**-20]:
+                a = scale * 1.0
+                b = scale * (1.0 + DBL_EPSILON)
+                c = scale * (1.0 + DBL_EPSILON * 2)
+                aa = tp(complex(a, a))
+                ab = tp(complex(a, b))
+                bb = tp(complex(b, b))
+                self.ne(tp(aa), tp(ab))
+                self.eq(tp(aa), tp(ab), prec='double')
+                self.eq(tp(ab), tp(bb), prec='double')
+                self.eq(tp(aa), tp(bb), prec='double')
+                ac = tp(complex(a, c))
+                cc = tp(complex(c, c))
+                self.ne(tp(aa), tp(ac), prec='double')
+                self.ne(tp(ac), tp(cc), prec='double')
+                self.eq(tp(aa), tp(ac), prec='double', ulps=2)
+                self.eq(tp(ac), tp(cc), prec='double', ulps=2)
+                self.eq(tp(aa), tp(cc), prec='double', ulps=2)
+                self.eq(tp(aa), tp(cc), prec='single')
+
+    def test_complex64_values_inexact(self):
+        tp = np.complex64
+        for scale in [1.0, -2**3, 2**-4, -2**-20]:
+            a = scale * 1.0
+            b = scale * (1.0 + FLT_EPSILON)
+            c = scale * (1.0 + FLT_EPSILON * 2)
+            aa = tp(complex(a, a))
+            ab = tp(complex(a, b))
+            bb = tp(complex(b, b))
+            self.ne(tp(aa), tp(ab))
+            self.ne(tp(aa), tp(ab), prec='double')
+            self.eq(tp(aa), tp(ab), prec='single')
+            self.eq(tp(ab), tp(bb), prec='single')
+            self.eq(tp(aa), tp(bb), prec='single')
+            ac = tp(complex(a, c))
+            cc = tp(complex(c, c))
+            self.ne(tp(aa), tp(ac), prec='single')
+            self.ne(tp(ac), tp(cc), prec='single')
+            self.eq(tp(aa), tp(ac), prec='single', ulps=2)
+            self.eq(tp(ac), tp(cc), prec='single', ulps=2)
+            self.eq(tp(aa), tp(cc), prec='single', ulps=2)
+
+    def test_enums(self):
+        values = [Color.red, Color.green, Color.blue, Shake.mint,
+                  Shape.circle, Shape.square, Planet.EARTH, Planet.MERCURY]
+        for val in values:
+            self.eq(val, val)
+            self.ne(val, val.value)
+        for a, b in itertools.combinations(values, 2):
+            self.ne(a, b)
+
+    def test_arrays(self):
+        a = np.arange(1, 7, dtype=np.int16).reshape((2, 3))
+        b = a.copy()
+        self.eq(a, b)
+        # Different values
+        self.ne(a, b + 1)
+        self.ne(a, b[:-1])
+        self.ne(a, b.T)
+        # Different dtypes
+        self.ne(a, b.astype(np.int32))
+        # Different layout
+        self.ne(a, b.T.copy().T)
+        # Different ndim
+        self.ne(a, b.flatten())
+        # Different writeability
+        b.flags.writeable = False
+        self.ne(a, b)
+        # Precision
+        a = np.arange(1, 3, dtype=np.float64)
+        b = a * (1.0 + DBL_EPSILON)
+        c = a * (1.0 + DBL_EPSILON * 2)
+        self.ne(a, b)
+        self.eq(a, b, prec='double')
+        self.ne(a, c, prec='double')
+
+    def test_npdatetime(self):
+        a = np.datetime64('1900', 'Y')
+        b = np.datetime64('1900', 'Y')
+        c = np.datetime64('1900-01-01', 'D')
+        d = np.datetime64('1901', 'Y')
+        self.eq(a, b)
+        # Different unit
+        self.ne(a, c)
+        # Different value
+        self.ne(a, d)
+
+    def test_nptimedelta(self):
+        a = np.timedelta64(1, 'h')
+        b = np.timedelta64(1, 'h')
+        c = np.timedelta64(60, 'm')
+        d = np.timedelta64(2, 'h')
+        self.eq(a, b)
+        # Different unit
+        self.ne(a, c)
+        # Different value
+        self.ne(a, d)
+
+
+class TestMisc(TestCase):
+
+    def test_assertRefCount(self):
+        # Use floats to avoid integer interning
+        x = 55.
+        y = 66.
+        l = []
+        with self.assertRefCount(x, y):
+            pass
+        with self.assertRaises(AssertionError) as cm:
+            # y gains a reference
+            with self.assertRefCount(x, y):
+                l.append(y)
+        self.assertIn("66", str(cm.exception))
+
+    def test_forbid_codegen(self):
+        """
+        Test that forbid_codegen() prevents code generation using the @jit
+        decorator.
+        """
+        def f():
+            return 1
+        with forbid_codegen():
+            with self.assertRaises(RuntimeError) as raises:
+                cfunc = jit(nopython=True)(f)
+                cfunc()
+        self.assertIn("codegen forbidden by test case", str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_svml.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_svml.py
new file mode 100644
index 000000000..1822b2e3f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_svml.py
@@ -0,0 +1,454 @@
+import math
+import numpy as np
+import subprocess
+import numbers
+import importlib
+import sys
+import re
+import traceback
+import multiprocessing as mp
+from itertools import chain, combinations
+
+import numba
+from numba.core import config, cpu
+from numba import prange, njit
+from numba.core.compiler import compile_isolated, Flags
+from numba.tests.support import TestCase, tag, override_env_config
+import unittest
+
+needs_svml = unittest.skipUnless(config.USING_SVML,
+                                 "SVML tests need SVML to be present")
+
+# a map of float64 vector lengths with corresponding CPU architecture
+vlen2cpu = {2: 'nehalem', 4: 'haswell', 8: 'skylake-avx512'}
+# force LLVM to use AVX512 registers for vectorization
+# https://reviews.llvm.org/D67259
+vlen2cpu_features = {2: '', 4: '', 8: '-prefer-256-bit'}
+
+# K: SVML functions, V: python functions which are expected to be SIMD-vectorized
+# using SVML, explicit references to Python functions here are mostly for sake of
+# instant import checks.
+# TODO: [] and comments below mean unused/untested SVML function, it's to be
+#       either enabled or to be replaced with the explanation why the function
+#       cannot be used in Numba
+# TODO: this test does not support functions with more than 1 arguments yet
+# The test logic should be modified if there is an SVML function being used under
+# different name or module from Python
+svml_funcs = {
+    "sin":     [np.sin, math.sin],
+    "cos":     [np.cos, math.cos],
+    "pow":        [],  # pow, math.pow],
+    "exp":     [np.exp, math.exp],
+    "log":     [np.log, math.log],
+    "acos":    [math.acos],
+    "acosh":   [math.acosh],
+    "asin":    [math.asin],
+    "asinh":   [math.asinh],
+    "atan2":      [],  # math.atan2],
+    "atan":    [math.atan],
+    "atanh":   [math.atanh],
+    "cbrt":       [],  # np.cbrt],
+    "cdfnorm":    [],
+    "cdfnorminv": [],
+    "ceil":       [],  # np.ceil, math.ceil],
+    "cosd":       [],
+    "cosh":    [np.cosh, math.cosh],
+    "erf":     [math.erf],  # np.erf is available in Intel Distribution
+    "erfc":    [math.erfc],
+    "erfcinv":    [],
+    "erfinv":     [],
+    "exp10":      [],
+    "exp2":       [],  # np.exp2],
+    "expm1":   [np.expm1, math.expm1],
+    "floor":      [],  # np.floor, math.floor],
+    "fmod":       [],  # np.fmod, math.fmod],
+    "hypot":      [],  # np.hypot, math.hypot],
+    "invsqrt":    [],  # available in Intel Distribution
+    "log10":   [np.log10, math.log10],
+    "log1p":   [np.log1p, math.log1p],
+    "log2":       [],  # np.log2],
+    "logb":       [],
+    "nearbyint":  [],
+    "rint":       [],  # np.rint],
+    "round":      [],  # round],
+    "sind":       [],
+    "sinh":    [np.sinh, math.sinh],
+    "sqrt":    [np.sqrt, math.sqrt],
+    "tan":     [np.tan, math.tan],
+    "tanh":    [np.tanh, math.tanh],
+    "trunc":      [],  # np.trunc, math.trunc],
+}
+# TODO: these functions are not vectorizable with complex types
+complex_funcs_exclude = ["sqrt", "tan", "log10", "expm1", "log1p", "tanh", "log"]
+
+# remove untested entries
+svml_funcs = {k: v for k, v in svml_funcs.items() if len(v) > 0}
+# lists for functions which belong to numpy and math modules correpondently
+numpy_funcs = [f for f, v in svml_funcs.items() if "<ufunc" in \
+                  [str(p).split(' ')[0] for p in v]]
+other_funcs = [f for f, v in svml_funcs.items() if "<built-in" in \
+                  [str(p).split(' ')[0] for p in v]]
+
+
+def func_patterns(func, args, res, dtype, mode, vlen, fastmath, pad=' '*8):
+    """
+    For a given function and its usage modes,
+    returns python code and assembly patterns it should and should not generate
+    """
+
+    # generate a function call according to the usecase
+    if mode == "scalar":
+        arg_list = ','.join([a+'[0]' for a in args])
+        body = '%s%s[0] += math.%s(%s)\n' % (pad, res, func, arg_list)
+    elif mode == "numpy":
+        body = '%s%s += np.%s(%s)' % (pad, res, func, ','.join(args))
+        body += '.astype(np.%s)\n' % dtype if dtype.startswith('int') else '\n'
+    else:
+        assert mode == "range" or mode == "prange"
+        arg_list = ','.join([a+'[i]' for a in args])
+        body = '{pad}for i in {mode}({res}.size):\n' \
+               '{pad}{pad}{res}[i] += math.{func}({arg_list})\n'. \
+               format(**locals())
+    # TODO: refactor so this for-loop goes into umbrella function,
+    #       'mode' can be 'numpy', '0', 'i' instead
+    # TODO: it will enable mixed usecases like prange + numpy
+
+    # type specialization
+    is_f32 = dtype == 'float32' or dtype == 'complex64'
+    f = func+'f' if is_f32 else func
+    v = vlen*2 if is_f32 else vlen
+    # general expectations
+    prec_suff = '' if fastmath else '_ha'
+    scalar_func = '$_'+f if config.IS_OSX else '$'+f
+    svml_func = '__svml_%s%d%s,' % (f, v, prec_suff)
+    if mode == "scalar":
+        contains = [scalar_func]
+        avoids = ['__svml_', svml_func]
+    else:            # will vectorize
+        contains = [svml_func]
+        avoids = []  # [scalar_func] - TODO: if possible, force LLVM to prevent
+                     #                     generating the failsafe scalar paths
+        if vlen != 8 and (is_f32 or dtype == 'int32'):  # Issue #3016
+            avoids += ['%zmm', '__svml_%s%d%s,' % (f, v*2, prec_suff)]
+    # special handling
+    if func == 'sqrt':
+        if mode == "scalar":
+            contains = ['sqrts']
+            avoids = [scalar_func, svml_func]  # LLVM uses CPU instruction
+        elif vlen == 8:
+            contains = ['vsqrtp']
+            avoids = [scalar_func, svml_func]  # LLVM uses CPU instruction
+        # else expect use of SVML for older architectures
+    return body, contains, avoids
+
+
+def usecase_name(dtype, mode, vlen, name):
+    """ Returns pretty name for given set of modes """
+
+    return f"{dtype}_{mode}{vlen}_{name}"
+
+
+def combo_svml_usecase(dtype, mode, vlen, fastmath, name):
+    """ Combine multiple function calls under single umbrella usecase """
+
+    name = usecase_name(dtype, mode, vlen, name)
+    body = """def {name}(n):
+        x   = np.empty(n*8, dtype=np.{dtype})
+        ret = np.empty_like(x)\n""".format(**locals())
+    funcs = set(numpy_funcs if mode == "numpy" else other_funcs)
+    if dtype.startswith('complex'):
+        funcs = funcs.difference(complex_funcs_exclude)
+    contains = set()
+    avoids = set()
+    # fill body and expectation patterns
+    for f in funcs:
+        b, c, a = func_patterns(f, ['x'], 'ret', dtype, mode, vlen, fastmath)
+        avoids.update(a)
+        body += b
+        contains.update(c)
+    body += " "*8 + "return ret"
+    # now compile and return it along with its body in __doc__  and patterns
+    ldict = {}
+    exec(body, globals(), ldict)
+    ldict[name].__doc__ = body
+    return ldict[name], contains, avoids
+
+
+@needs_svml
+class TestSVMLGeneration(TestCase):
+    """ Tests all SVML-generating functions produce desired calls """
+
+    # env mutating, must not run in parallel
+    _numba_parallel_test_ = False
+    # RE for a generic symbol reference and for each particular SVML function
+    asm_filter = re.compile('|'.join(['\$[a-z_]\w+,']+list(svml_funcs)))
+
+    @classmethod
+    def mp_runner(cls, testname, outqueue):
+        method = getattr(cls, testname)
+        try:
+            ok, msg = method()
+        except Exception:
+            msg = traceback.format_exc()
+            ok = False
+        outqueue.put({'status': ok, 'msg': msg})
+
+    @classmethod
+    def _inject_test(cls, dtype, mode, vlen, flags):
+        # unsupported combinations
+        if dtype.startswith('complex') and mode != 'numpy':
+            return
+        # TODO: address skipped tests below
+        skipped = dtype.startswith('int') and vlen == 2
+        sig = (numba.int64,)
+        # unit test body template
+        @staticmethod
+        def run_template():
+            fn, contains, avoids = combo_svml_usecase(dtype, mode, vlen,
+                                                      flags['fastmath'],
+                                                      flags['name'])
+            # look for specific patterns in the asm for a given target
+            with override_env_config('NUMBA_CPU_NAME', vlen2cpu[vlen]), \
+                 override_env_config('NUMBA_CPU_FEATURES', vlen2cpu_features[vlen]):
+                # recompile for overridden CPU
+                try:
+                    jitted_fn = njit(sig, fastmath=flags['fastmath'],
+                                     error_model=flags['error_model'],)(fn)
+                except:
+                    raise Exception("raised while compiling "+fn.__doc__)
+            asm = jitted_fn.inspect_asm(sig)
+            missed = [pattern for pattern in contains if not pattern in asm]
+            found = [pattern for pattern in avoids if pattern in asm]
+            ok = not missed and not found
+            detail = '\n'.join(
+                [line for line in asm.split('\n')
+                 if cls.asm_filter.search(line) and not '"' in line])
+            msg = (
+                f"While expecting {missed} and not {found},\n"
+                f"it contains:\n{detail}\n"
+                f"when compiling {fn.__doc__}"
+            )
+            return ok, msg
+        # inject it into the class
+        postfix = usecase_name(dtype, mode, vlen, flags['name'])
+        testname = f"run_{postfix}"
+        setattr(cls, testname, run_template)
+
+        @unittest.skipUnless(not skipped, "Not implemented")
+        def test_runner(self):
+            ctx = mp.get_context("spawn")
+            q = ctx.Queue()
+            p = ctx.Process(target=type(self).mp_runner, args=[testname, q])
+            p.start()
+            # timeout to avoid hanging and long enough to avoid bailing too
+            # early. Note: this was timeout=10 but that seemed to caused
+            # intermittent failures on heavily loaded machines.
+            term_or_timeout = p.join(timeout=30)
+            exitcode = p.exitcode
+            if term_or_timeout is None:
+                if exitcode is None:
+                    self.fail("Process timed out.")
+                elif exitcode < 0:
+                    self.fail(f"Process terminated with signal {-exitcode}.")
+            self.assertEqual(exitcode, 0, msg="process ended unexpectedly")
+            out = q.get()
+            status = out['status']
+            msg = out['msg']
+            self.assertTrue(status, msg=msg)
+
+        setattr(cls, f"test_{postfix}", test_runner)
+
+    @classmethod
+    def autogenerate(cls):
+        flag_list = [{'fastmath':False, 'error_model':'numpy',
+                     'name':'usecase'},
+                     {'fastmath':True, 'error_model':'numpy',
+                     'name':'fastmath_usecase'},]
+        # main loop covering all the modes and use-cases
+        for dtype in ('complex64', 'float64', 'float32', 'int32', ):
+            for vlen in vlen2cpu:
+                for flags in flag_list:
+                    for mode in "scalar", "range", "prange", "numpy":
+                        cls._inject_test(dtype, mode, vlen, dict(flags))
+        # mark important
+        for n in ( "test_int32_range4_usecase",  # issue #3016
+                    ):
+            setattr(cls, n, tag("important")(getattr(cls, n)))
+
+
+TestSVMLGeneration.autogenerate()
+
+
+def math_sin_scalar(x):
+    return math.sin(x)
+
+
+def math_sin_loop(n):
+    ret = np.empty(n, dtype=np.float64)
+    for x in range(n):
+        ret[x] = math.sin(np.float64(x))
+    return ret
+
+
+@needs_svml
+class TestSVML(TestCase):
+    """ Tests SVML behaves as expected """
+
+    # env mutating, must not run in parallel
+    _numba_parallel_test_ = False
+
+    def __init__(self, *args):
+        self.flags = Flags()
+        self.flags.nrt = True
+
+        # flags for njit(fastmath=True)
+        self.fastflags = Flags()
+        self.fastflags.nrt = True
+        self.fastflags.fastmath = cpu.FastMathOptions(True)
+        super(TestSVML, self).__init__(*args)
+
+    def compile(self, func, *args, **kwargs):
+        assert not kwargs
+        sig = tuple([numba.typeof(x) for x in args])
+
+        std = compile_isolated(func, sig, flags=self.flags)
+        fast = compile_isolated(func, sig, flags=self.fastflags)
+
+        return std, fast
+
+    def copy_args(self, *args):
+        if not args:
+            return tuple()
+        new_args = []
+        for x in args:
+            if isinstance(x, np.ndarray):
+                new_args.append(x.copy('k'))
+            elif isinstance(x, np.number):
+                new_args.append(x.copy())
+            elif isinstance(x, numbers.Number):
+                new_args.append(x)
+            else:
+                raise ValueError('Unsupported argument type encountered')
+        return tuple(new_args)
+
+    def check(self, pyfunc, *args, **kwargs):
+
+        jitstd, jitfast = self.compile(pyfunc, *args)
+
+        std_pattern = kwargs.pop('std_pattern', None)
+        fast_pattern = kwargs.pop('fast_pattern', None)
+        cpu_name = kwargs.pop('cpu_name', 'skylake-avx512')
+        # force LLVM to use AVX512 registers for vectorization
+        # https://reviews.llvm.org/D67259
+        cpu_features = kwargs.pop('cpu_features', '-prefer-256-bit')
+
+        # python result
+        py_expected = pyfunc(*self.copy_args(*args))
+
+        # jit result
+        jitstd_result = jitstd.entry_point(*self.copy_args(*args))
+
+        # fastmath result
+        jitfast_result = jitfast.entry_point(*self.copy_args(*args))
+
+        # assert numerical equality
+        np.testing.assert_almost_equal(jitstd_result, py_expected, **kwargs)
+        np.testing.assert_almost_equal(jitfast_result, py_expected, **kwargs)
+
+        # look for specific patterns in the asm for a given target
+        with override_env_config('NUMBA_CPU_NAME', cpu_name), \
+             override_env_config('NUMBA_CPU_FEATURES', cpu_features):
+            # recompile for overridden CPU
+            jitstd, jitfast = self.compile(pyfunc, *args)
+            if std_pattern:
+                self.check_svml_presence(jitstd, std_pattern)
+            if fast_pattern:
+                self.check_svml_presence(jitfast, fast_pattern)
+
+    def check_svml_presence(self, func, pattern):
+        asm = func.library.get_asm_str()
+        self.assertIn(pattern, asm)
+
+    def test_scalar_context(self):
+        # SVML will not be used.
+        pat = '$_sin' if config.IS_OSX else '$sin'
+        self.check(math_sin_scalar, 7., std_pattern=pat)
+        self.check(math_sin_scalar, 7., fast_pattern=pat)
+
+    def test_svml(self):
+        # loops both with and without fastmath should use SVML.
+        # The high accuracy routines are dropped if `fastmath` is set
+        std = "__svml_sin8_ha,"
+        fast = "__svml_sin8,"  # No `_ha`!
+        self.check(math_sin_loop, 10, std_pattern=std, fast_pattern=fast)
+
+    def test_svml_disabled(self):
+        code = """if 1:
+            import os
+            import numpy as np
+            import math
+
+            def math_sin_loop(n):
+                ret = np.empty(n, dtype=np.float64)
+                for x in range(n):
+                    ret[x] = math.sin(np.float64(x))
+                return ret
+
+            def check_no_svml():
+                try:
+                    # ban the use of SVML
+                    os.environ['NUMBA_DISABLE_INTEL_SVML'] = '1'
+
+                    # delay numba imports to account for env change as
+                    # numba.__init__ picks up SVML and it is too late by
+                    # then to override using `numba.config`
+                    import numba
+                    from numba import config
+                    from numba.core import cpu
+                    from numba.tests.support import override_env_config
+                    from numba.core.compiler import compile_isolated, Flags
+
+                    # compile for overridden CPU, with and without fastmath
+                    with override_env_config('NUMBA_CPU_NAME', 'skylake-avx512'), \
+                         override_env_config('NUMBA_CPU_FEATURES', ''):
+                        sig = (numba.int32,)
+                        f = Flags()
+                        f.nrt = True
+                        std = compile_isolated(math_sin_loop, sig, flags=f)
+                        f.fastmath = cpu.FastMathOptions(True)
+                        fast = compile_isolated(math_sin_loop, sig, flags=f)
+                        fns = std, fast
+
+                        # assert no SVML call is present in the asm
+                        for fn in fns:
+                            asm = fn.library.get_asm_str()
+                            assert '__svml_sin' not in asm
+                finally:
+                    # not really needed as process is separate
+                    os.environ['NUMBA_DISABLE_INTEL_SVML'] = '0'
+                    config.reload_config()
+            check_no_svml()
+            """
+        popen = subprocess.Popen(
+            [sys.executable, "-c", code],
+            stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        out, err = popen.communicate()
+        if popen.returncode != 0:
+            raise AssertionError(
+                "process failed with code %s: stderr follows\n%s\n" %
+                (popen.returncode, err.decode()))
+
+    def test_svml_working_in_non_isolated_context(self):
+        @njit(fastmath={'fast'}, error_model="numpy")
+        def impl(n):
+            x   = np.empty(n * 8, dtype=np.float64)
+            ret = np.empty_like(x)
+            for i in range(ret.size):
+                    ret[i] += math.cosh(x[i])
+            return ret
+        impl(1)
+        self.assertTrue('intel_svmlcc' in impl.inspect_llvm(impl.signatures[0]))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sys_stdin_assignment.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sys_stdin_assignment.py
new file mode 100644
index 000000000..f9d88e235
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sys_stdin_assignment.py
@@ -0,0 +1,67 @@
+import sys
+
+import unittest
+from numba import njit
+
+
+@njit
+def f0(a, b):
+    return a + b
+
+
+@njit
+def f1(begin1, end1, begin2, end2):
+    if begin1 > begin2: return f1(begin2, end2, begin1, end1)
+    return end1 + 1 >= begin2
+
+
+@njit
+def f0_2(a, b):
+    return a + b
+
+
+@njit
+def f1_2(begin1, end1, begin2, end2):
+    if begin1 > begin2: return f1_2(begin2, end2, begin1, end1)
+    return end1 + 1 >= begin2
+
+
+class TestSysStdinAssignment(unittest.TestCase):
+
+    def test_no_reassignment_of_stdout(self):
+        """
+        https://github.com/numba/numba/issues/3027
+        Older versions of colorama break stdout/err when recursive functions
+        are compiled.
+
+        This test should work irrespective of colorama version, or indeed its
+        presence. If the version is too low, it should be disabled and the test
+        should work anyway, if it is a sufficiently high version or it is not
+        present, it should work anyway.
+        """
+
+        originally = sys.stdout, sys.stderr
+
+        try:
+            sys.stdout = None
+            f0(0, 1)  # Not changed stdout?
+            self.assertEqual(sys.stdout, None)
+            f1(0, 1, 2, 3)  # Not changed stdout?
+            self.assertEqual(sys.stdout, None)
+
+            sys.stderr = None
+            f0_2(0, 1)  # Not changed stderr?
+            self.assertEqual(sys.stderr, None)
+            f1_2(0, 1, 2, 3)  # Not changed stderr?
+            self.assertEqual(sys.stderr, None)
+
+        finally:
+            sys.stdout, sys.stderr = originally
+
+        self.assertNotEqual(sys.stderr, None)
+        self.assertNotEqual(sys.stdout, None)
+
+
+if __name__ == '__main__':
+    unittest.main()
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sysinfo.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sysinfo.py
new file mode 100644
index 000000000..b63f601b9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_sysinfo.py
@@ -0,0 +1,189 @@
+import platform
+import unittest
+from unittest import skipUnless
+from unittest.mock import NonCallableMock
+from itertools import chain
+from datetime import datetime
+from contextlib import redirect_stdout
+from io import StringIO
+
+from numba.tests.support import TestCase
+import numba.misc.numba_sysinfo as nsi
+
+
+class TestSysInfo(TestCase):
+
+    def setUp(self):
+        super(TestSysInfo, self).setUp()
+        self.info = nsi.get_sysinfo()
+        self.safe_contents = {
+            int: (
+                nsi._cpu_count,
+            ),
+            float: (
+                nsi._runtime,
+            ),
+            str: (
+                nsi._machine,
+                nsi._cpu_name,
+                nsi._platform_name,
+                nsi._os_name,
+                nsi._os_version,
+                nsi._python_comp,
+                nsi._python_impl,
+                nsi._python_version,
+                nsi._llvm_version,
+                nsi._numpy_version,
+            ),
+            bool: (
+                nsi._cu_dev_init,
+                nsi._svml_state,
+                nsi._svml_loaded,
+                nsi._svml_operational,
+                nsi._llvm_svml_patched,
+                nsi._tbb_thread,
+                nsi._openmp_thread,
+                nsi._wkq_thread,
+                nsi._numpy_AVX512_SKX_detected,
+            ),
+            list: (
+                nsi._errors,
+                nsi._warnings,
+            ),
+            dict: (
+                nsi._numba_env_vars,
+            ),
+            datetime: (
+                nsi._start,
+                nsi._start_utc,
+            ),
+        }
+        self.safe_keys = chain(*self.safe_contents.values())
+
+    def tearDown(self):
+        super(TestSysInfo, self).tearDown()
+        # System info might contain long strings or lists so delete it.
+        del self.info
+
+    def test_has_safe_keys(self):
+        for k in self.safe_keys:
+            with self.subTest(k=k):
+                self.assertIn(k, self.info)
+
+    def test_safe_content_type(self):
+        for t, keys in self.safe_contents.items():
+            for k in keys:
+                with self.subTest(k=k):
+                    self.assertIsInstance(self.info[k], t)
+
+    def test_has_no_error(self):
+        self.assertFalse(self.info[nsi._errors])
+
+    def test_display_empty_info(self):
+        output = StringIO()
+        with redirect_stdout(output):
+            res = nsi.display_sysinfo({})
+        self.assertIsNone(res)
+        output.close()
+
+
+class TestSysInfoWithPsutil(TestCase):
+
+    mem_total = 2 * 1024 ** 2  # 2_097_152
+    mem_available = 1024 ** 2  # 1_048_576
+    cpus_list = [1, 2]
+
+    def setUp(self):
+        super(TestSysInfoWithPsutil, self).setUp()
+        self.psutil_orig_state = nsi._psutil_import
+        # Mocking psutil
+        nsi._psutil_import = True
+        nsi.psutil = NonCallableMock()
+        vm = nsi.psutil.virtual_memory.return_value
+        vm.total = self.mem_total
+        vm.available = self.mem_available
+        if platform.system() in ('Linux', 'Windows',):
+            # cpu_affiniy only available on Linux and Windows
+            proc = nsi.psutil.Process.return_value
+            proc.cpu_affinity.return_value = self.cpus_list
+        else:
+            nsi.psutil.Process.return_value = None
+
+        self.info = nsi.get_os_spec_info(platform.system())
+
+    def tearDown(self):
+        super(TestSysInfoWithPsutil, self).tearDown()
+        nsi._psutil_import = self.psutil_orig_state
+
+    def test_has_all_data(self):
+        keys = (nsi._mem_total, nsi._mem_available)
+        for k in keys:
+            with self.subTest(k=k):
+                self.assertIn(k, self.info.keys())
+                self.assertIsInstance(self.info[k], int)
+
+    def test_has_correct_values(self):
+        self.assertEqual(self.info[nsi._mem_total], self.mem_total)
+        self.assertEqual(self.info[nsi._mem_available], self.mem_available)
+
+    @skipUnless(platform.system() in ('Linux', 'Windows'),
+                "CPUs allowed info only available on Linux and Windows")
+    def test_cpus_list(self):
+        self.assertEqual(self.info[nsi._cpus_allowed], len(self.cpus_list))
+        self.assertEqual(self.info[nsi._cpus_list],
+                         ' '.join(str(n) for n in self.cpus_list))
+
+
+class TestSysInfoWithoutPsutil(TestCase):
+
+    def setUp(self):
+        super(TestSysInfoWithoutPsutil, self).setUp()
+        self.psutil_orig_state = nsi._psutil_import
+        nsi._psutil_import = False
+        self.info = nsi.get_os_spec_info(platform.system())
+
+    def tearDown(self):
+        super(TestSysInfoWithoutPsutil, self).tearDown()
+        nsi._psutil_import = self.psutil_orig_state
+
+    def test_has_all_data(self):
+        keys = (nsi._mem_total, nsi._mem_available)
+        for k in keys:
+            with self.subTest(k=k):
+                self.assertIn(k, self.info.keys())
+                self.assertIsInstance(self.info[k], int)
+
+
+class TestPlatformSpecificInfo(TestCase):
+
+    def setUp(self):
+        self.plat_spec_info = {
+            'Linux': {
+                str: (nsi._libc_version,),
+            },
+            'Windows': {
+                str: (nsi._os_spec_version,),
+            },
+            'Darwin': {
+                str: (nsi._os_spec_version,),
+            },
+        }
+        self.os_name = platform.system()
+        self.contents = self.plat_spec_info.get(self.os_name, {})
+        self.info = nsi.get_os_spec_info(self.os_name)
+
+    def test_has_all_data(self):
+        keys = chain(*self.contents.values())
+        for k in keys:
+            with self.subTest(k=k):
+                self.assertIn(k, self.info.keys())
+
+    def test_content_type(self):
+        for t, keys in self.contents.items():
+            for k in keys:
+                with self.subTest(k=k):
+                    self.assertIsInstance(self.info[k], t)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_target_extension.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_target_extension.py
new file mode 100644
index 000000000..a1b5c7ecd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_target_extension.py
@@ -0,0 +1,852 @@
+"""This tests the target extension API to ensure that rudimentary expected
+behaviours are present and correct. It uses a piece of fake hardware as a
+target, the Dummy Processing Unit (DPU), to do this. The DPU borrows a lot from
+the CPU but is part of the GPU class of target. The DPU target has deliberately
+strange implementations of fundamental operations so as to make it identifiable
+in testing."""
+
+import unittest
+from numba.tests.support import TestCase
+import contextlib
+import ctypes
+import operator
+import numpy as np
+from numba import njit, types
+from numba.extending import overload, intrinsic, overload_classmethod
+from numba.core.target_extension import (
+    JitDecorator,
+    target_registry,
+    dispatcher_registry,
+    jit_registry,
+    target_override,
+    GPU,
+    resolve_dispatcher_from_str,
+)
+from numba.core import utils, fastmathpass, errors
+from numba.core.dispatcher import Dispatcher
+from numba.core.descriptors import TargetDescriptor
+from numba.core import cpu, typing, cgutils
+from numba.core.base import BaseContext
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core.utils import cached_property
+from numba.core import callconv
+from numba.core.codegen import CPUCodegen, JITCodeLibrary
+from numba.core.callwrapper import PyCallWrapper
+from numba.core.imputils import RegistryLoader, Registry
+from numba import _dynfunc
+import llvmlite.binding as ll
+from llvmlite import ir as llir
+from numba.core.runtime import rtsys
+
+from numba.core import compiler
+from numba.core.compiler import CompilerBase, DefaultPassBuilder
+from numba.core.compiler_machinery import FunctionPass, register_pass
+from numba.core.typed_passes import PreLowerStripPhis
+
+# Define a new target, this target extends GPU, this places the DPU in the
+# target hierarchy as a type of GPU.
+
+
+class DPU(GPU):
+    ...
+
+
+# register the dpu target hierarchy token in the target registry, this
+# permits lookup and reference in userspace by the string "dpu"
+target_registry["dpu"] = DPU
+
+# Create a JIT DPU codegen for the DPU target
+
+
+class JITDPUCodegen(CPUCodegen):
+    # This largely rips off the CPU for ease
+
+    _library_class = JITCodeLibrary
+
+    def _customize_tm_options(self, options):
+        # Customize the target machine options.
+        options["cpu"] = self._get_host_cpu_name()
+        arch = ll.Target.from_default_triple().name
+        if arch.startswith("x86"):
+            reloc_model = "static"
+        elif arch.startswith("ppc"):
+            reloc_model = "pic"
+        else:
+            reloc_model = "default"
+        options["reloc"] = reloc_model
+        options["codemodel"] = "jitdefault"
+
+        # Set feature attributes (such as ISA extensions)
+        # This overrides default feature selection by CPU model above
+        options["features"] = self._tm_features
+
+        # Deal with optional argument to ll.Target.create_target_machine
+        sig = utils.pysignature(ll.Target.create_target_machine)
+        if "jit" in sig.parameters:
+            # Mark that this is making a JIT engine
+            options["jit"] = True
+
+    def _customize_tm_features(self):
+        # For JIT target, we will use LLVM to get the feature map
+        return self._get_host_cpu_features()
+
+    def _add_module(self, module):
+        self._engine.add_module(module)
+
+    def set_env(self, env_name, env):
+        """Set the environment address.
+
+        Update the GlobalVariable named *env_name* to the address of *env*.
+        """
+        gvaddr = self._engine.get_global_value_address(env_name)
+        envptr = (ctypes.c_void_p * 1).from_address(gvaddr)
+        envptr[0] = ctypes.c_void_p(id(env))
+
+
+# This is the function registry for the dpu, it just has one registry, this one!
+dpu_function_registry = Registry()
+
+# Implement a new context for the DPU target
+
+
+class DPUContext(BaseContext):
+    allow_dynamic_globals = True
+
+    # Overrides
+    def create_module(self, name):
+        return self._internal_codegen._create_empty_module(name)
+
+    @global_compiler_lock
+    def init(self):
+        self._internal_codegen = JITDPUCodegen("numba.exec")
+        # Initialize NRT runtime
+        rtsys.initialize(self)
+        self.refresh()
+
+    def refresh(self):
+        registry = dpu_function_registry
+        try:
+            loader = self._registries[registry]
+        except KeyError:
+            loader = RegistryLoader(registry)
+            self._registries[registry] = loader
+        self.install_registry(registry)
+        # Also refresh typing context, since @overload declarations can
+        # affect it.
+        self.typing_context.refresh()
+
+    @property
+    def target_data(self):
+        return self._internal_codegen.target_data
+
+    def codegen(self):
+        return self._internal_codegen
+
+    # Borrow the CPU call conv
+    @cached_property
+    def call_conv(self):
+        return callconv.CPUCallConv(self)
+
+    def get_env_body(self, builder, envptr):
+        """
+        From the given *envptr* (a pointer to a _dynfunc.Environment object),
+        get a EnvBody allowing structured access to environment fields.
+        """
+        body_ptr = cgutils.pointer_add(
+            builder, envptr, _dynfunc._impl_info["offsetof_env_body"]
+        )
+        return cpu.EnvBody(self, builder, ref=body_ptr, cast_ref=True)
+
+    def get_env_manager(self, builder):
+        envgv = self.declare_env_global(
+            builder.module, self.get_env_name(self.fndesc)
+        )
+        envarg = builder.load(envgv)
+        pyapi = self.get_python_api(builder)
+        pyapi.emit_environment_sentry(
+            envarg, debug_msg=self.fndesc.env_name,
+        )
+        env_body = self.get_env_body(builder, envarg)
+        return pyapi.get_env_manager(self.environment, env_body, envarg)
+
+    def get_generator_state(self, builder, genptr, return_type):
+        """
+        From the given *genptr* (a pointer to a _dynfunc.Generator object),
+        get a pointer to its state area.
+        """
+        return cgutils.pointer_add(
+            builder,
+            genptr,
+            _dynfunc._impl_info["offsetof_generator_state"],
+            return_type=return_type,
+        )
+
+    def post_lowering(self, mod, library):
+        if self.fastmath:
+            fastmathpass.rewrite_module(mod, self.fastmath)
+
+        library.add_linking_library(rtsys.library)
+
+    def create_cpython_wrapper(
+        self, library, fndesc, env, call_helper, release_gil=False
+    ):
+        wrapper_module = self.create_module("wrapper")
+        fnty = self.call_conv.get_function_type(fndesc.restype, fndesc.argtypes)
+        wrapper_callee = llir.Function(
+            wrapper_module, fnty, fndesc.llvm_func_name
+        )
+        builder = PyCallWrapper(
+            self,
+            wrapper_module,
+            wrapper_callee,
+            fndesc,
+            env,
+            call_helper=call_helper,
+            release_gil=release_gil,
+        )
+        builder.build()
+        library.add_ir_module(wrapper_module)
+
+    def create_cfunc_wrapper(self, library, fndesc, env, call_helper):
+        # There's no cfunc wrapper on the dpu
+        pass
+
+    def get_executable(self, library, fndesc, env):
+        """
+        Returns
+        -------
+        (cfunc, fnptr)
+
+        - cfunc
+            callable function (Can be None)
+        - fnptr
+            callable function address
+        - env
+            an execution environment (from _dynfunc)
+        """
+        # Code generation
+        fnptr = library.get_pointer_to_function(
+            fndesc.llvm_cpython_wrapper_name
+        )
+
+        # Note: we avoid reusing the original docstring to avoid encoding
+        # issues on Python 2, see issue #1908
+        doc = "compiled wrapper for %r" % (fndesc.qualname,)
+        cfunc = _dynfunc.make_function(
+            fndesc.lookup_module(),
+            fndesc.qualname.split(".")[-1],
+            doc,
+            fnptr,
+            env,
+            # objects to keepalive with the function
+            (library,),
+        )
+        library.codegen.set_env(self.get_env_name(fndesc), env)
+        return cfunc
+
+
+# Nested contexts to help with isolatings bits of compilations
+class _NestedContext(object):
+    _typing_context = None
+    _target_context = None
+
+    @contextlib.contextmanager
+    def nested(self, typing_context, target_context):
+        old_nested = self._typing_context, self._target_context
+        try:
+            self._typing_context = typing_context
+            self._target_context = target_context
+            yield
+        finally:
+            self._typing_context, self._target_context = old_nested
+
+
+# Implement a DPU TargetDescriptor, this one borrows bits from the CPU
+class DPUTarget(TargetDescriptor):
+    options = cpu.CPUTargetOptions
+    _nested = _NestedContext()
+
+    @utils.cached_property
+    def _toplevel_target_context(self):
+        # Lazily-initialized top-level target context, for all threads
+        return DPUContext(self.typing_context, self._target_name)
+
+    @utils.cached_property
+    def _toplevel_typing_context(self):
+        # Lazily-initialized top-level typing context, for all threads
+        return typing.Context()
+
+    @property
+    def target_context(self):
+        """
+        The target context for DPU targets.
+        """
+        nested = self._nested._target_context
+        if nested is not None:
+            return nested
+        else:
+            return self._toplevel_target_context
+
+    @property
+    def typing_context(self):
+        """
+        The typing context for CPU targets.
+        """
+        nested = self._nested._typing_context
+        if nested is not None:
+            return nested
+        else:
+            return self._toplevel_typing_context
+
+    def nested_context(self, typing_context, target_context):
+        """
+        A context manager temporarily replacing the contexts with the
+        given ones, for the current thread of execution.
+        """
+        return self._nested.nested(typing_context, target_context)
+
+
+# Create a DPU target instance
+dpu_target = DPUTarget("dpu")
+
+
+# Declare a dispatcher for the DPU target
+class DPUDispatcher(Dispatcher):
+    targetdescr = dpu_target
+
+
+# Register a dispatcher for the DPU target, a lot of the code uses this
+# internally to work out what to do RE compilation
+dispatcher_registry[target_registry["dpu"]] = DPUDispatcher
+
+# Implement a dispatcher for the DPU target
+
+
+class djit(JitDecorator):
+    def __init__(self, *args, **kwargs):
+        self._args = args
+        self._kwargs = kwargs
+
+    def __call__(self, *args):
+        assert len(args) < 2
+        if args:
+            func = args[0]
+        else:
+            func = self._args[0]
+        self.py_func = func
+        # wrap in dispatcher
+        return self.dispatcher_wrapper()
+
+    def get_dispatcher(self):
+        """
+        Returns the dispatcher
+        """
+        return dispatcher_registry[target_registry["dpu"]]
+
+    def dispatcher_wrapper(self):
+        disp = self.get_dispatcher()
+        # Parse self._kwargs here
+        topt = {}
+        if "nopython" in self._kwargs:
+            topt["nopython"] = True
+
+        # It would be easy to specialise the default compilation pipeline for
+        # this target here.
+        pipeline_class = compiler.Compiler
+        if "pipeline_class" in self._kwargs:
+            pipeline_class = self._kwargs["pipeline_class"]
+        return disp(
+            py_func=self.py_func,
+            targetoptions=topt,
+            pipeline_class=pipeline_class,
+        )
+
+
+# add it to the decorator registry, this is so e.g. @overload can look up a
+# JIT function to do the compilation work.
+jit_registry[target_registry["dpu"]] = djit
+
+# The DPU target "knows" nothing, add in some primitives for basic things...
+
+# need to register how to lower dummy for @intrinsic
+
+
+@dpu_function_registry.lower_constant(types.Dummy)
+def constant_dummy(context, builder, ty, pyval):
+    return context.get_dummy_value()
+
+
+# and how to deal with IntegerLiteral to Integer casts
+@dpu_function_registry.lower_cast(types.IntegerLiteral, types.Integer)
+def literal_int_to_number(context, builder, fromty, toty, val):
+    lit = context.get_constant_generic(
+        builder, fromty.literal_type, fromty.literal_value,
+    )
+    return context.cast(builder, lit, fromty.literal_type, toty)
+
+
+# and how to lower an Int constant
+@dpu_function_registry.lower_constant(types.Integer)
+def const_int(context, builder, ty, pyval):
+    lty = context.get_value_type(ty)
+    return lty(pyval)
+
+
+# and tell the DPU how to lower a float constant
+@dpu_function_registry.lower_constant(types.Float)
+def const_float(context, builder, ty, pyval):
+    lty = context.get_value_type(ty)
+    return lty(pyval)
+
+
+# The DPU actually subtracts when it's asked to 'add'!
+@intrinsic(target="dpu")
+def intrin_add(tyctx, x, y):
+    sig = x(x, y)
+
+    def codegen(cgctx, builder, tyargs, llargs):
+        return builder.sub(*llargs)
+
+    return sig, codegen
+
+
+# Use extending.overload API to register 'add', call the dpu specific intrinsic
+@overload(operator.add, target="dpu")
+def ol_add(x, y):
+    if isinstance(x, types.Integer) and isinstance(y, types.Integer):
+
+        def impl(x, y):
+            return intrin_add(x, y)
+
+        return impl
+
+
+class TestTargetHierarchySelection(TestCase):
+    """This tests that the target hierarchy is scanned in the right order,
+    that appropriate functions are selected based on what's available and that
+    the DPU target is distinctly different to the CPU"""
+
+    def test_0_dpu_registry(self):
+        """Checks that the DPU registry only contains the things added
+
+        This test must be first to execute among all tests in this file to
+        ensure the no lazily loaded entries are added yet.
+        """
+        self.assertFalse(dpu_function_registry.functions)
+        self.assertFalse(dpu_function_registry.getattrs)
+        # int literal -> int cast is registered
+        self.assertEqual(len(dpu_function_registry.casts), 1)
+        # int, float and dummy constants are registered
+        self.assertEqual(len(dpu_function_registry.constants), 3)
+
+    def test_specialise_gpu(self):
+        def my_func(x):
+            pass
+
+        # Can be used by both CPU and DPU
+        @overload(my_func, target="generic")
+        def ol_my_func1(x):
+            def impl(x):
+                return 1 + x
+
+            return impl
+
+        # Should be used by the DPU if there's no dpu specific one
+        @overload(my_func, target="gpu")
+        def ol_my_func2(x):
+            def impl(x):
+                return 10 + x
+
+            return impl
+
+        @djit()
+        def dpu_foo():
+            return my_func(7)
+
+        @njit()
+        def cpu_foo():
+            return my_func(7)
+
+        # DPU chooses the ol_my_func2 as it's most specific, and DPU subtracts
+        # for addition, so 10 + x -> 10 - 7 -> 3
+        self.assertPreciseEqual(dpu_foo(), 3)
+        # CPU uses the generic one function ol_my_func1 and adds
+        self.assertPreciseEqual(cpu_foo(), 8)
+
+    def test_specialise_dpu(self):
+        def my_func(x):
+            pass
+
+        # Can be used by both CPU and DPU
+        @overload(my_func, target="generic")
+        def ol_my_func1(x):
+            def impl(x):
+                return 1 + x
+
+            return impl
+
+        # Should be used by the DPU if there's no dpu specific one
+        @overload(my_func, target="gpu")
+        def ol_my_func2(x):
+            def impl(x):
+                return 10 + x
+
+            return impl
+
+        # Should be used by the DPU only
+        @overload(my_func, target="dpu")
+        def ol_my_func3(x):
+            def impl(x):
+                return 100 + x
+
+            return impl
+
+        @djit()
+        def dpu_foo():
+            return my_func(7)
+
+        @njit()
+        def cpu_foo():
+            return my_func(7)
+
+        # DPU chooses the ol_my_func3 as it's most specific, and DPU subtracts
+        # for addition, so 100 + x -> 100 - 7 -> 93
+        self.assertPreciseEqual(dpu_foo(), 93)
+        # CPU uses the generic one function ol_my_func1 and adds
+        self.assertPreciseEqual(cpu_foo(), 8)
+
+    def test_no_specialisation_found(self):
+
+        def my_func(x):
+            pass
+
+        # only create a cuda specialisation
+        @overload(my_func, target='cuda')
+        def ol_my_func_cuda(x):
+            return lambda x: None
+
+        @djit(nopython=True)
+        def dpu_foo():
+            my_func(1)
+
+        # new style errors raise UnsupportedError, old style ends up as
+        # TypingError
+        accept = (errors.UnsupportedError, errors.TypingError)
+        with self.assertRaises(accept) as raises:
+            dpu_foo()
+
+        msgs = ["Function resolution cannot find any matches for function",
+                "test_no_specialisation_found.<locals>.my_func",
+                "for the current target:",
+                "'numba.tests.test_target_extension.DPU'"]
+
+        for msg in msgs:
+            self.assertIn(msg, str(raises.exception))
+
+    def test_invalid_target_jit(self):
+
+        with self.assertRaises(errors.NumbaValueError) as raises:
+            @njit(_target='invalid_silicon')
+            def foo():
+                pass
+
+            foo()
+
+        msg = "No target is registered against 'invalid_silicon'"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_invalid_target_overload(self):
+
+        def bar():
+            pass
+
+        # This is a typing error at present as it fails during typing when the
+        # overloads are walked.
+        with self.assertRaises(errors.TypingError) as raises:
+            @overload(bar, target='invalid_silicon')
+            def ol_bar():
+                return lambda : None
+
+            @njit
+            def foo():
+                bar()
+
+            foo()
+
+        msg = "No target is registered against 'invalid_silicon'"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_intrinsic_selection(self):
+        """
+        Test to make sure that targets can share generic implementations and
+        cannot reach implementations that are not in their target hierarchy.
+        """
+
+        # NOTE: The actual operation performed by these functions is irrelevant
+        @intrinsic(target="generic")
+        def intrin_math_generic(tyctx, x, y):
+            sig = x(x, y)
+
+            def codegen(cgctx, builder, tyargs, llargs):
+                return builder.mul(*llargs)
+
+            return sig, codegen
+
+        @intrinsic(target="dpu")
+        def intrin_math_dpu(tyctx, x, y):
+            sig = x(x, y)
+
+            def codegen(cgctx, builder, tyargs, llargs):
+                return builder.sub(*llargs)
+
+            return sig, codegen
+
+        @intrinsic(target="cpu")
+        def intrin_math_cpu(tyctx, x, y):
+            sig = x(x, y)
+
+            def codegen(cgctx, builder, tyargs, llargs):
+                return builder.add(*llargs)
+
+            return sig, codegen
+
+        # CPU can use the CPU version
+        @njit
+        def cpu_foo_specific():
+            return intrin_math_cpu(3, 4)
+
+        self.assertEqual(cpu_foo_specific(), 7)
+
+        # CPU can use the 'generic' version
+        @njit
+        def cpu_foo_generic():
+            return intrin_math_generic(3, 4)
+
+        self.assertEqual(cpu_foo_generic(), 12)
+
+        # CPU cannot use the 'dpu' version
+        @njit
+        def cpu_foo_dpu():
+            return intrin_math_dpu(3, 4)
+
+        accept = (errors.UnsupportedError, errors.TypingError)
+        with self.assertRaises(accept) as raises:
+            cpu_foo_dpu()
+
+        msgs = ["Function resolution cannot find any matches for function",
+                "intrinsic intrin_math_dpu",
+                "for the current target",]
+        for msg in msgs:
+            self.assertIn(msg, str(raises.exception))
+
+        # DPU can use the DPU version
+        @djit(nopython=True)
+        def dpu_foo_specific():
+            return intrin_math_dpu(3, 4)
+
+        self.assertEqual(dpu_foo_specific(), -1)
+
+        # DPU can use the 'generic' version
+        @djit(nopython=True)
+        def dpu_foo_generic():
+            return intrin_math_generic(3, 4)
+
+        self.assertEqual(dpu_foo_generic(), 12)
+
+        # DPU cannot use the 'cpu' version
+        @djit(nopython=True)
+        def dpu_foo_cpu():
+            return intrin_math_cpu(3, 4)
+
+        accept = (errors.UnsupportedError, errors.TypingError)
+        with self.assertRaises(accept) as raises:
+            dpu_foo_cpu()
+
+        msgs = ["Function resolution cannot find any matches for function",
+                "intrinsic intrin_math_cpu",
+                "for the current target",]
+        for msg in msgs:
+            self.assertIn(msg, str(raises.exception))
+
+    def test_overload_allocation(self):
+        def cast_integer(context, builder, val, fromty, toty):
+            # XXX Shouldn't require this.
+            if toty.bitwidth == fromty.bitwidth:
+                # Just a change of signedness
+                return val
+            elif toty.bitwidth < fromty.bitwidth:
+                # Downcast
+                return builder.trunc(val, context.get_value_type(toty))
+            elif fromty.signed:
+                # Signed upcast
+                return builder.sext(val, context.get_value_type(toty))
+            else:
+                # Unsigned upcast
+                return builder.zext(val, context.get_value_type(toty))
+
+        @intrinsic(target='dpu')
+        def intrin_alloc(typingctx, allocsize, align):
+            """Intrinsic to call into the allocator for Array
+            """
+            def codegen(context, builder, signature, args):
+                [allocsize, align] = args
+
+                # XXX: error are being eaten.
+                #      example: replace the next line with `align_u32 = align`
+                align_u32 = cast_integer(context, builder, align,
+                                         signature.args[1], types.uint32)
+                meminfo = context.nrt.meminfo_alloc_aligned(builder, allocsize,
+                                                            align_u32)
+                return meminfo
+
+            from numba.core.typing import signature
+            mip = types.MemInfoPointer(types.voidptr)  # return untyped pointer
+            sig = signature(mip, allocsize, align)
+            return sig, codegen
+
+        @overload_classmethod(types.Array, '_allocate', target='dpu',
+                              jit_options={'nopython':True})
+        def _ol_arr_allocate_dpu(cls, allocsize, align):
+            def impl(cls, allocsize, align):
+                return intrin_alloc(allocsize, align)
+            return impl
+
+        @overload(np.empty, target='dpu', jit_options={'nopython':True})
+        def ol_empty_impl(n):
+            def impl(n):
+                return types.Array._allocate(n, 7)
+            return impl
+
+        def buffer_func():
+            pass
+
+        @overload(buffer_func, target='dpu', jit_options={'nopython':True})
+        def ol_buffer_func_impl():
+            def impl():
+                return np.empty(10)
+            return impl
+
+        from numba.core.target_extension import target_override
+
+        # XXX: this should probably go inside the dispatcher
+        with target_override('dpu'):
+            @djit(nopython=True)
+            def foo():
+                return buffer_func()
+            r = foo()
+        from numba.core.runtime import nrt
+        self.assertIsInstance(r, nrt.MemInfo)
+
+
+class TestTargetOffload(TestCase):
+    """In this use case the CPU compilation pipeline is extended with a new
+     compilation pass that runs just prior to lowering. The pass looks for
+     function calls and when it finds one it sees if there's a DPU function
+     available that is a valid overload for the function call. If there is one
+     then it swaps the CPU implementation out for a DPU implementation. This
+     producing an "offload" effect.
+    """
+
+    def test_basic_offload(self):
+
+        _DEBUG = False
+
+        # This is the DPU function for sin, it'll return a pi-like constant
+        @overload(np.sin, target="dpu")
+        def ol_np_sin_DPU(x):
+            def dpu_sin_impl(x):
+                return 314159.0
+
+            return dpu_sin_impl
+
+        # Check the DPU reports the correct overload value
+        @djit(nopython=True)
+        def foo(x):
+            return np.sin(x)
+
+        self.assertPreciseEqual(foo(5), 314159.0)
+
+        # Check the CPU call is correct
+
+        @njit
+        def foo(x):
+            return np.sin(x)
+
+        self.assertPreciseEqual(foo(5), np.sin(5))
+
+        @register_pass(mutates_CFG=False, analysis_only=False)
+        class DispatcherSwitcher(FunctionPass):
+            _name = "DispatcherSwitcher"
+
+            def __init__(self):
+                FunctionPass.__init__(self)
+
+            def run_pass(self, state):
+                func_ir = state.func_ir
+                mutated = False
+                for blk in func_ir.blocks.values():
+                    # find the assignment nodes in the block and walk them, if
+                    # there's a DPU version then swap out for a call to that
+                    for call in blk.find_exprs("call"):
+                        function = state.typemap[call.func.name]
+                        tname = "dpu"
+
+                        # Note: `target_override` context driven compilation can
+                        # be done here, the DPU target is in use.
+                        with target_override(tname):
+                            try:
+                                sig = function.get_call_type(
+                                    state.typingctx,
+                                    state.calltypes[call].args,
+                                    {},
+                                )
+                                disp = resolve_dispatcher_from_str(tname)
+                                # force compile check
+                                hw_ctx = disp.targetdescr.target_context
+                                hw_ctx.get_function(function, sig)
+                            except Exception as e:
+                                if _DEBUG:
+                                    msg = (
+                                        f"Failed to find and compile an "
+                                        f"overload for {function} for {tname} "
+                                        f"due to {e}"
+                                    )
+                                    print(msg)
+                                continue
+
+                            # This is a necessary hack at present so as to
+                            # generate code into the same library. I.e. the DPU
+                            # target is going to do code gen into the CPUs lib.
+                            hw_ctx._codelib_stack = (
+                                state.targetctx._codelib_stack
+                            )
+
+                            # All is good, so switch IR node for one targeting
+                            # this target. Should generate this, but for now
+                            # just mutate as:
+                            # ir.Expr.call(call.func, call.args, call.kws,
+                            #              call.loc, target='dpu')
+                            call.target = tname
+                            mutated = True
+                # return True if the IR was mutated, False if not.
+                return mutated
+
+        # DPU compiler pipeline, compiles with offload to the DPU target
+        class DPUOffloadCompiler(CompilerBase):
+            def define_pipelines(self):
+                pm = DefaultPassBuilder.define_nopython_pipeline(self.state)
+                pm.add_pass_after(DispatcherSwitcher, PreLowerStripPhis)
+                pm.finalize()
+                return [pm]
+
+        # Now compile for CPU, but with the DispatcherSwitcher pass in place
+        # that switches CPU calls for DPU calls
+        @njit(pipeline_class=DPUOffloadCompiler)
+        def foo(x):
+            return np.sin(x), np.cos(x)  # np.sin is DPU, np.cos is CPU
+
+        self.assertPreciseEqual(foo(5), (314159.0, np.cos(5)))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_target_overloadselector.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_target_overloadselector.py
new file mode 100644
index 000000000..8fa54bccc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_target_overloadselector.py
@@ -0,0 +1,148 @@
+from itertools import product, permutations
+from collections import defaultdict
+
+import unittest
+from numba.core.base import OverloadSelector
+from numba.core.registry import cpu_target
+from numba.core.imputils import builtin_registry, RegistryLoader
+from numba.core import types
+from numba.core.errors import NumbaNotImplementedError, NumbaTypeError
+
+
+class TestOverloadSelector(unittest.TestCase):
+    def test_select_and_sort_1(self):
+        os = OverloadSelector()
+        os.append(1, (types.Any, types.Boolean))
+        os.append(2, (types.Boolean, types.Integer))
+        os.append(3, (types.Boolean, types.Any))
+        os.append(4, (types.Boolean, types.Boolean))
+        compats = os._select_compatible((types.boolean, types.boolean))
+        self.assertEqual(len(compats), 3)
+        ordered, scoring = os._sort_signatures(compats)
+        self.assertEqual(len(ordered), 3)
+        self.assertEqual(len(scoring), 3)
+        self.assertEqual(ordered[0], (types.Boolean, types.Boolean))
+        self.assertEqual(scoring[types.Boolean, types.Boolean], 0)
+        self.assertEqual(scoring[types.Boolean, types.Any], 1)
+        self.assertEqual(scoring[types.Any, types.Boolean], 1)
+
+    def test_select_and_sort_2(self):
+        os = OverloadSelector()
+        os.append(1, (types.Container,))
+        os.append(2, (types.Sequence,))
+        os.append(3, (types.MutableSequence,))
+        os.append(4, (types.List,))
+        compats = os._select_compatible((types.List,))
+        self.assertEqual(len(compats), 4)
+        ordered, scoring = os._sort_signatures(compats)
+        self.assertEqual(len(ordered), 4)
+        self.assertEqual(len(scoring), 4)
+        self.assertEqual(ordered[0], (types.List,))
+        self.assertEqual(scoring[(types.List,)], 0)
+        self.assertEqual(scoring[(types.MutableSequence,)], 1)
+        self.assertEqual(scoring[(types.Sequence,)], 2)
+        self.assertEqual(scoring[(types.Container,)], 3)
+
+    def test_match(self):
+        os = OverloadSelector()
+        self.assertTrue(os._match(formal=types.Boolean, actual=types.boolean))
+        self.assertTrue(os._match(formal=types.Boolean, actual=types.Boolean))
+        # test subclass
+        self.assertTrue(issubclass(types.Sequence, types.Container))
+        self.assertTrue(os._match(formal=types.Container,
+                                  actual=types.Sequence))
+        self.assertFalse(os._match(formal=types.Sequence,
+                                   actual=types.Container))
+        # test any
+        self.assertTrue(os._match(formal=types.Any, actual=types.Any))
+        self.assertTrue(os._match(formal=types.Any, actual=types.Container))
+        self.assertFalse(os._match(formal=types.Container, actual=types.Any))
+
+    def test_ambiguous_detection(self):
+        os = OverloadSelector()
+        # unambiguous signatures
+        os.append(1, (types.Any, types.Boolean))
+        os.append(2, (types.Integer, types.Boolean))
+        self.assertEqual(os.find((types.boolean, types.boolean)), 1)
+        # not implemented
+        with self.assertRaises(NumbaNotImplementedError) as raises:
+            os.find((types.boolean, types.int32))
+        # generic
+        os.append(3, (types.Any, types.Any))
+        self.assertEqual(os.find((types.boolean, types.int32)), 3)
+        self.assertEqual(os.find((types.boolean, types.boolean)), 1)
+        # add ambiguous signature; can match (bool, any) and (any, bool)
+        os.append(4, (types.Boolean, types.Any))
+        with self.assertRaises(NumbaTypeError) as raises:
+            os.find((types.boolean, types.boolean))
+        self.assertIn('2 ambiguous signatures', str(raises.exception))
+        # disambiguous
+        os.append(5, (types.boolean, types.boolean))
+        self.assertEqual(os.find((types.boolean, types.boolean)), 5)
+
+    def test_subclass_specialization(self):
+        os = OverloadSelector()
+        self.assertTrue(issubclass(types.Sequence, types.Container))
+        os.append(1, (types.Container, types.Container,))
+        lstty = types.List(types.boolean)
+        self.assertEqual(os.find((lstty, lstty)), 1)
+        os.append(2, (types.Container, types.Sequence,))
+        self.assertEqual(os.find((lstty, lstty)), 2)
+
+    def test_cache(self):
+        os = OverloadSelector()
+        self.assertEqual(len(os._cache), 0)
+        os.append(1, (types.Any,))
+        self.assertEqual(os.find((types.int32,)), 1)
+        self.assertEqual(len(os._cache), 1)
+        os.append(2, (types.Integer,))
+        self.assertEqual(len(os._cache), 0)
+        self.assertEqual(os.find((types.int32,)), 2)
+        self.assertEqual(len(os._cache), 1)
+
+
+class TestAmbiguousOverloads(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        # ensure all impls are loaded
+        cpu_target.target_context.refresh()
+
+    def create_overload_selector(self, kind):
+        os = OverloadSelector()
+        loader = RegistryLoader(builtin_registry)
+        for impl, sig in loader.new_registrations(kind):
+            os.append(impl, sig)
+        return os
+
+    def test_ambiguous_casts(self):
+        os = self.create_overload_selector(kind='casts')
+        all_types = set(t for sig, impl in os.versions for t in sig)
+        # ensure there are no ambiguous cast overloads
+        # note: using permutations to avoid testing cast to the same type
+        for sig in permutations(all_types, r=2):
+            try:
+                os.find(sig)
+            except NumbaNotImplementedError:
+                pass   # ignore not implemented cast
+
+    def test_ambiguous_functions(self):
+        loader = RegistryLoader(builtin_registry)
+        selectors = defaultdict(OverloadSelector)
+        for impl, fn, sig in loader.new_registrations('functions'):
+            os = selectors[fn]
+            os.append(impl, sig)
+
+        for fn, os in selectors.items():
+            all_types = set(t for sig, impl in os.versions for t in sig)
+            # ensure there are no ambiguous overloads
+            for sig in product(all_types, all_types):
+                try:
+                    os.find(sig)
+                except NumbaNotImplementedError:
+                    pass   # ignore not implemented cast
+
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_threadsafety.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_threadsafety.py
new file mode 100644
index 000000000..19a974e24
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_threadsafety.py
@@ -0,0 +1,97 @@
+"""
+Test threadsafety for compiler.
+These tests will cause segfault if fail.
+"""
+import threading
+import random
+
+import numpy as np
+
+from numba import jit, vectorize, guvectorize
+
+from numba.tests.support import temp_directory, override_config
+from numba.core import config
+import unittest
+
+
+def foo(n, v):
+    return np.ones(n)
+
+
+def ufunc_foo(a, b):
+    return a + b
+
+
+def gufunc_foo(a, b, out):
+    out[0] = a + b
+
+
+
+class TestThreadSafety(unittest.TestCase):
+
+    def run_jit(self, **options):
+        def runner():
+            cfunc = jit(**options)(foo)
+
+            return cfunc(4, 10)
+        return runner
+
+    def run_compile(self, fnlist):
+        self._cache_dir = temp_directory(self.__class__.__name__)
+        with override_config('CACHE_DIR', self._cache_dir):
+            def chooser():
+                for _ in range(10):
+                    fn = random.choice(fnlist)
+                    fn()
+
+            ths = [threading.Thread(target=chooser)
+                   for i in range(4)]
+            for th in ths:
+                th.start()
+            for th in ths:
+                th.join()
+
+    def test_concurrent_jit(self):
+        self.run_compile([self.run_jit(nopython=True)])
+
+    def test_concurrent_jit_cache(self):
+        self.run_compile([self.run_jit(nopython=True, cache=True)])
+
+    def run_vectorize(self, **options):
+        def runner():
+            cfunc = vectorize(['(f4, f4)'], **options)(ufunc_foo)
+            a = b = np.random.random(10).astype(np.float32)
+            return cfunc(a, b)
+        return runner
+
+    def test_concurrent_vectorize(self):
+        self.run_compile([self.run_vectorize(nopython=True)])
+
+    def test_concurrent_vectorize_cache(self):
+        self.run_compile([self.run_vectorize(nopython=True, cache=True)])
+
+    def run_guvectorize(self, **options):
+        def runner():
+            sig = ['(f4, f4, f4[:])']
+            cfunc = guvectorize(sig, '(),()->()', **options)(gufunc_foo)
+            a = b = np.random.random(10).astype(np.float32)
+            return cfunc(a, b)
+        return runner
+
+    def test_concurrent_guvectorize(self):
+        self.run_compile([self.run_guvectorize(nopython=True)])
+
+    def test_concurrent_guvectorize_cache(self):
+        self.run_compile([self.run_guvectorize(nopython=True, cache=True)])
+
+    def test_concurrent_mix_use(self):
+        self.run_compile([self.run_jit(nopython=True, cache=True),
+                          self.run_jit(nopython=True),
+                          self.run_vectorize(nopython=True, cache=True),
+                          self.run_vectorize(nopython=True),
+                          self.run_guvectorize(nopython=True, cache=True),
+                          self.run_guvectorize(nopython=True)])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_tracing.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_tracing.py
new file mode 100644
index 000000000..0ab895efa
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_tracing.py
@@ -0,0 +1,182 @@
+from io import StringIO
+import logging
+
+import unittest
+from numba.core import tracing
+
+logger = logging.getLogger('trace')
+logger.setLevel(logging.INFO)
+
+# Make sure tracing is enabled
+orig_trace = tracing.trace
+tracing.trace = tracing.dotrace
+
+class CapturedTrace:
+    """Capture the trace temporarily for validation."""
+
+    def __init__(self):
+        self.buffer = StringIO()
+        self.handler = logging.StreamHandler(self.buffer)
+    def __enter__(self):
+        self._handlers = logger.handlers
+        self.buffer = StringIO()
+        logger.handlers = [logging.StreamHandler(self.buffer)]
+    def __exit__(self, type, value, traceback):
+        logger.handlers = self._handlers
+    def getvalue(self):
+
+        # Depending on how the tests are run, object names may be
+        # qualified by their containing module.
+        # Remove that to make the trace output independent from the testing mode.
+        log = self.buffer.getvalue()
+        log = log.replace(__name__ + '.','')
+        return log
+
+class Class(object):
+
+    @tracing.trace
+    @classmethod
+    def class_method(cls):
+        pass
+
+    @tracing.trace
+    @staticmethod
+    def static_method():
+        pass
+
+    __test = None
+
+    def _test_get(self):
+        return self.__test
+
+    def _test_set(self, value):
+        self.__test = value
+
+    test = tracing.trace(property(_test_get, _test_set))
+        
+    @tracing.trace
+    def method(self, some, other='value', *args, **kwds):
+        pass
+
+    def __repr__(self):
+        """Generate a deterministic string for testing."""
+        return '<Class instance>'
+
+class Class2(object):
+    @classmethod
+    def class_method(cls):
+        pass
+
+    @staticmethod
+    def static_method():
+        pass
+
+    __test = None
+    @property
+    def test(self):
+        return self.__test
+    @test.setter
+    def test(self, value):
+        self.__test = value
+
+    def method(self):
+        pass
+
+    def __str__(self):
+        return 'Test(' + str(self.test) + ')'
+
+    def __repr__(self):
+        """Generate a deterministic string for testing."""
+        return '<Class2 instance>'
+
+
+@tracing.trace
+def test(x, y, z = True):
+    a = x + y
+    b = x * y
+    if z: return a
+    else: return b
+
+class TestTracing(unittest.TestCase):
+
+    def __init__(self, *args):
+        super(TestTracing, self).__init__(*args)
+
+    def setUp(self):
+        self.capture = CapturedTrace()
+
+    def tearDown(self):
+        del self.capture
+        
+    def test_method(self):
+
+        with self.capture:
+            Class().method('foo', bar='baz')
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class.method(self=<Class instance>, some='foo', other='value', bar='baz')\n" +
+                         "<< Class.method\n")
+
+    def test_class_method(self):
+
+        with self.capture:
+            Class.class_method()
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class.class_method(cls=<class 'Class'>)\n" +
+                         "<< Class.class_method\n")
+
+    def test_static_method(self):
+
+        with self.capture:
+            Class.static_method()
+        self.assertEqual(self.capture.getvalue(),
+                         ">> static_method()\n" +
+                         "<< static_method\n")
+
+
+    def test_property(self):
+
+        with self.capture:
+            test = Class()
+            test.test = 1
+            assert 1 == test.test
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class._test_set(self=<Class instance>, value=1)\n" +
+                         "<< Class._test_set\n" +
+                         ">> Class._test_get(self=<Class instance>)\n" +
+                         "<< Class._test_get -> 1\n")
+
+    def test_function(self):
+
+        with self.capture:
+            test(5, 5)
+            test(5, 5, False)
+        self.assertEqual(self.capture.getvalue(),
+                         ">> test(x=5, y=5, z=True)\n" +
+                         "<< test -> 10\n" +
+                         ">> test(x=5, y=5, z=False)\n" +
+                         "<< test -> 25\n")
+
+    @unittest.skip("recursive decoration not yet implemented")
+    def test_injected(self):
+
+        with self.capture:
+            tracing.trace(Class2, recursive=True)
+            Class2.class_method()
+            Class2.static_method()
+            test = Class2()
+            test.test = 1
+            assert 1 == test.test
+            test.method()
+
+            self.assertEqual(self.capture.getvalue(),
+                         ">> Class2.class_method(cls=<type 'Class2'>)\n" +
+                         "<< Class2.class_method\n"
+                         ">> static_method()\n"
+                         "<< static_method\n")
+
+            
+# Reset tracing to its original value
+tracing.trace = orig_trace
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_try_except.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_try_except.py
new file mode 100644
index 000000000..5c0aa024b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_try_except.py
@@ -0,0 +1,851 @@
+import warnings
+import dis
+from itertools import product
+
+import numpy as np
+
+from numba import njit, typed, objmode, prange
+from numba.core.utils import PYVERSION
+from numba.core import ir_utils, ir
+from numba.core.errors import (
+    UnsupportedError, CompilerError, NumbaPerformanceWarning, TypingError,
+)
+from numba.tests.support import (
+    TestCase, unittest, captured_stdout, MemoryLeakMixin,
+    skip_parfors_unsupported, skip_unless_scipy,
+)
+
+
+class MyError(Exception):
+    pass
+
+
+class TestTryBareExcept(TestCase):
+    """Test the following pattern:
+
+        try:
+            <body>
+        except:
+            <handling>
+    """
+    def test_try_inner_raise(self):
+        @njit
+        def inner(x):
+            if x:
+                raise MyError
+
+        @njit
+        def udt(x):
+            try:
+                inner(x)
+                return "not raised"
+            except:             # noqa: E722
+                return "caught"
+
+        self.assertEqual(udt(False), "not raised")
+        self.assertEqual(udt(True), "caught")
+
+    def test_try_state_reset(self):
+        @njit
+        def inner(x):
+            if x == 1:
+                raise MyError("one")
+            elif x == 2:
+                raise MyError("two")
+
+        @njit
+        def udt(x):
+            try:
+                inner(x)
+                res = "not raised"
+            except:             # noqa: E722
+                res = "caught"
+            if x == 0:
+                inner(2)
+            return res
+
+        with self.assertRaises(MyError) as raises:
+            udt(0)
+        self.assertEqual(str(raises.exception), "two")
+        self.assertEqual(udt(1), "caught")
+        self.assertEqual(udt(-1), "not raised")
+
+    def _multi_inner(self):
+        @njit
+        def inner(x):
+            if x == 1:
+                print("call_one")
+                raise MyError("one")
+            elif x == 2:
+                print("call_two")
+                raise MyError("two")
+            elif x == 3:
+                print("call_three")
+                raise MyError("three")
+            else:
+                print("call_other")
+
+        return inner
+
+    def test_nested_try(self):
+        inner = self._multi_inner()
+
+        @njit
+        def udt(x, y, z):
+            try:
+                try:
+                    print("A")
+                    inner(x)
+                    print("B")
+                except:         # noqa: E722
+                    print("C")
+                    inner(y)
+                    print("D")
+            except:             # noqa: E722
+                print("E")
+                inner(z)
+                print("F")
+
+        # case 1
+        with self.assertRaises(MyError) as raises:
+            with captured_stdout() as stdout:
+                udt(1, 2, 3)
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "call_one", "C", "call_two", "E", "call_three"],
+        )
+        self.assertEqual(str(raises.exception), "three")
+
+        # case 2
+        with captured_stdout() as stdout:
+            udt(1, 0, 3)
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "call_one", "C", "call_other", "D"],
+        )
+
+        # case 3
+        with captured_stdout() as stdout:
+            udt(1, 2, 0)
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "call_one", "C", "call_two", "E", "call_other", "F"],
+        )
+
+    def test_loop_in_try(self):
+        inner = self._multi_inner()
+
+        @njit
+        def udt(x, n):
+            try:
+                print("A")
+                for i in range(n):
+                    print(i)
+                    if i == x:
+                        inner(i)
+            except:             # noqa: E722
+                print("B")
+            return i
+
+        # case 1
+        with captured_stdout() as stdout:
+            res = udt(3, 5)
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "0", "1", "2", "3", "call_three", "B"],
+        )
+        self.assertEqual(res, 3)
+
+        # case 2
+        with captured_stdout() as stdout:
+            res = udt(1, 3)
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "0", "1", "call_one", "B"],
+        )
+        self.assertEqual(res, 1)
+
+        # case 3
+        with captured_stdout() as stdout:
+            res = udt(0, 3)
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "0", "call_other", "1", "2"],
+        )
+        self.assertEqual(res, 2)
+
+    def test_raise_in_try(self):
+        @njit
+        def udt(x):
+            try:
+                print("A")
+                if x:
+                    raise MyError("my_error")
+                print("B")
+            except:             # noqa: E722
+                print("C")
+                return 321
+            return 123
+
+        # case 1
+        with captured_stdout() as stdout:
+            res = udt(True)
+
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "C"],
+        )
+        self.assertEqual(res, 321)
+
+        # case 2
+        with captured_stdout() as stdout:
+            res = udt(False)
+
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "B"],
+        )
+        self.assertEqual(res, 123)
+
+    def test_recursion(self):
+        @njit
+        def foo(x):
+            if x > 0:
+                try:
+                    foo(x - 1)
+                except:   # noqa: E722
+                    print("CAUGHT")
+                    return 12
+            if x == 1:
+                raise ValueError("exception")
+
+        with captured_stdout() as stdout:
+            res = foo(10)
+
+        self.assertIsNone(res)
+        self.assertEqual(stdout.getvalue().split(), ["CAUGHT",],)
+
+    def test_yield(self):
+        @njit
+        def foo(x):
+            if x > 0:
+                try:
+                    yield 7
+                    raise ValueError("exception")   # never hit
+                except Exception:
+                    print("CAUGHT")
+
+        @njit
+        def bar(z):
+            return next(foo(z))
+
+        with captured_stdout() as stdout:
+            res = bar(10)
+
+        self.assertEqual(res, 7)
+        self.assertEqual(stdout.getvalue().split(), [])
+
+    def test_closure2(self):
+        @njit
+        def foo(x):
+            def bar():
+                try:
+                    raise ValueError("exception")
+                except:  # noqa: E722
+                    print("CAUGHT")
+                    return 12
+            bar()
+
+        with captured_stdout() as stdout:
+            foo(10)
+
+        self.assertEqual(stdout.getvalue().split(), ["CAUGHT",],)
+
+    def test_closure3(self):
+        @njit
+        def foo(x):
+            def bar(z):
+                try:
+                    raise ValueError("exception")
+                except:  # noqa: E722
+                    print("CAUGHT")
+                    return z
+            return [x for x in map(bar, [1, 2, 3])]
+
+        with captured_stdout() as stdout:
+            res = foo(10)
+
+        self.assertEqual(res, [1, 2, 3])
+        self.assertEqual(stdout.getvalue().split(), ["CAUGHT",] * 3,)
+
+    def test_closure4(self):
+        @njit
+        def foo(x):
+            def bar(z):
+                if z < 0:
+                    raise ValueError("exception")
+                return z
+
+            try:
+                return [x for x in map(bar, [1, 2, 3, x])]
+            except:  # noqa: E722
+                print("CAUGHT")
+
+        with captured_stdout() as stdout:
+            res = foo(-1)
+
+        self.assertEqual(stdout.getvalue().strip(), "CAUGHT")
+        self.assertIsNone(res)
+
+        with captured_stdout() as stdout:
+            res = foo(4)
+        self.assertEqual(stdout.getvalue(), "")
+        self.assertEqual(res, [1, 2, 3, 4])
+
+    @skip_unless_scipy
+    def test_real_problem(self):
+        @njit
+        def foo():
+            a = np.zeros((4, 4))
+            try:
+                chol = np.linalg.cholesky(a)
+            except:  # noqa: E722
+                print("CAUGHT")
+                return chol
+
+        with captured_stdout() as stdout:
+            foo()
+
+        self.assertEqual(stdout.getvalue().split(), ["CAUGHT",])
+
+    def test_for_loop(self):
+        @njit
+        def foo(n):
+
+            for i in range(n):
+                try:
+                    if i > 5:
+                        raise ValueError
+                except:  # noqa: E722
+                    print("CAUGHT")
+            else:
+                try:
+                    try:
+                        try:
+                            if i > 5:
+                                raise ValueError
+                        except:  # noqa: E722
+                            print("CAUGHT1")
+                            raise ValueError
+                    except:  # noqa: E722
+                        print("CAUGHT2")
+                        raise ValueError
+                except:  # noqa: E722
+                    print("CAUGHT3")
+
+        with captured_stdout() as stdout:
+            foo(10)
+
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["CAUGHT",] * 4 + ["CAUGHT%s" % i for i in range(1, 4)],
+        )
+
+    def test_try_pass(self):
+        @njit
+        def foo(x):
+            try:
+                pass
+            except:     # noqa: E722
+                pass
+            return x
+
+        res = foo(123)
+        self.assertEqual(res, 123)
+
+    def test_try_except_reraise(self):
+        @njit
+        def udt():
+            try:
+                raise ValueError("ERROR")
+            except:    # noqa: E722
+                raise
+
+        with self.assertRaises(UnsupportedError) as raises:
+            udt()
+        self.assertIn(
+            "The re-raising of an exception is not yet supported.",
+            str(raises.exception),
+        )
+
+
+class TestTryExceptCaught(TestCase):
+    def test_catch_exception(self):
+        @njit
+        def udt(x):
+            try:
+                print("A")
+                if x:
+                    raise ZeroDivisionError("321")
+                print("B")
+            except Exception:
+                print("C")
+            print("D")
+
+        # case 1
+        with captured_stdout() as stdout:
+            udt(True)
+
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "C", "D"],
+        )
+
+        # case 2
+        with captured_stdout() as stdout:
+            udt(False)
+
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "B", "D"],
+        )
+
+    def test_return_in_catch(self):
+        @njit
+        def udt(x):
+            try:
+                print("A")
+                if x:
+                    raise ZeroDivisionError
+                print("B")
+                r = 123
+            except Exception:
+                print("C")
+                r = 321
+                return r
+            print("D")
+            return r
+
+        # case 1
+        with captured_stdout() as stdout:
+            res = udt(True)
+
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "C"],
+        )
+        self.assertEqual(res, 321)
+
+        # case 2
+        with captured_stdout() as stdout:
+            res = udt(False)
+
+        self.assertEqual(
+            stdout.getvalue().split(),
+            ["A", "B", "D"],
+        )
+        self.assertEqual(res, 123)
+
+    def test_save_caught(self):
+        @njit
+        def udt(x):
+            try:
+                if x:
+                    raise ZeroDivisionError
+                r = 123
+            except Exception as e:  # noqa: F841
+                r = 321
+                return r
+            return r
+
+        with self.assertRaises(UnsupportedError) as raises:
+            udt(True)
+        self.assertIn(
+            "Exception object cannot be stored into variable (e)",
+            str(raises.exception)
+        )
+
+    def test_try_except_reraise(self):
+        @njit
+        def udt():
+            try:
+                raise ValueError("ERROR")
+            except Exception:
+                raise
+
+        with self.assertRaises(UnsupportedError) as raises:
+            udt()
+        self.assertIn(
+            "The re-raising of an exception is not yet supported.",
+            str(raises.exception),
+        )
+
+    def test_try_except_reraise_chain(self):
+        @njit
+        def udt():
+            try:
+                raise ValueError("ERROR")
+            except Exception:
+                try:
+                    raise
+                except Exception:
+                    raise
+
+        with self.assertRaises(UnsupportedError) as raises:
+            udt()
+        self.assertIn(
+            "The re-raising of an exception is not yet supported.",
+            str(raises.exception),
+        )
+
+    def test_division_operator(self):
+        # This test that old-style implementation propagate exception
+        # to the exception handler properly.
+        @njit
+        def udt(y):
+            try:
+                1 / y
+            except Exception:
+                return 0xdead
+            else:
+                return 1 / y
+
+        self.assertEqual(udt(0), 0xdead)
+        self.assertEqual(udt(2), 0.5)
+
+
+class TestTryExceptNested(TestCase):
+    "Tests for complicated nesting"
+
+    def check_compare(self, cfunc, pyfunc, *args, **kwargs):
+        with captured_stdout() as stdout:
+            pyfunc(*args, **kwargs)
+        expect = stdout.getvalue()
+
+        with captured_stdout() as stdout:
+            cfunc(*args, **kwargs)
+        got = stdout.getvalue()
+        self.assertEqual(
+            expect, got,
+            msg="args={} kwargs={}".format(args, kwargs)
+        )
+
+    def test_try_except_else(self):
+        @njit
+        def udt(x, y, z, p):
+            print('A')
+            if x:
+                print('B')
+                try:
+                    print('C')
+                    if y:
+                        print('D')
+                        raise MyError("y")
+                    print('E')
+                except Exception: # noqa: F722
+                    print('F')
+                    try:
+                        print('H')
+                        try:
+                            print('I')
+                            if z:
+                                print('J')
+                                raise MyError('z')
+                            print('K')
+                        except Exception:
+                            print('L')
+                        else:
+                            print('M')
+                    except Exception:
+                        print('N')
+                    else:
+                        print('O')
+                    print('P')
+                else:
+                    print('G')
+                print('Q')
+            print('R')
+
+        cases = list(product([True, False], repeat=4))
+        self.assertTrue(cases)
+        for x, y, z, p in cases:
+            self.check_compare(
+                udt, udt.py_func,
+                x=x, y=y, z=z, p=p,
+            )
+
+    def test_try_except_finally(self):
+        @njit
+        def udt(p, q):
+            try:
+                print('A')
+                if p:
+                    print('B')
+                    raise MyError
+                print('C')
+            except:             # noqa: E722
+                print('D')
+            finally:
+                try:
+                    print('E')
+                    if q:
+                        print('F')
+                        raise MyError
+                except Exception:
+                    print('G')
+                else:
+                    print('H')
+                finally:
+                    print('I')
+
+        cases = list(product([True, False], repeat=2))
+        self.assertTrue(cases)
+        for p, q in cases:
+            self.check_compare(
+                udt, udt.py_func,
+                p=p, q=q,
+            )
+
+
+class TestTryExceptRefct(MemoryLeakMixin, TestCase):
+    def test_list_direct_raise(self):
+        @njit
+        def udt(n, raise_at):
+            lst = typed.List()
+            try:
+                for i in range(n):
+                    if i == raise_at:
+                        raise IndexError
+                    lst.append(i)
+            except Exception:
+                return lst
+            else:
+                return lst
+
+        out = udt(10, raise_at=5)
+        self.assertEqual(list(out), list(range(5)))
+        out = udt(10, raise_at=10)
+        self.assertEqual(list(out), list(range(10)))
+
+    def test_list_indirect_raise(self):
+        @njit
+        def appender(lst, n, raise_at):
+            for i in range(n):
+                if i == raise_at:
+                    raise IndexError
+                lst.append(i)
+            return lst
+
+        @njit
+        def udt(n, raise_at):
+            lst = typed.List()
+            lst.append(0xbe11)
+            try:
+                appender(lst, n, raise_at)
+            except Exception:
+                return lst
+            else:
+                return lst
+
+        out = udt(10, raise_at=5)
+        self.assertEqual(list(out), [0xbe11] + list(range(5)))
+        out = udt(10, raise_at=10)
+        self.assertEqual(list(out), [0xbe11] + list(range(10)))
+
+    def test_incompatible_refinement(self):
+        @njit
+        def udt():
+            try:
+                lst = typed.List()
+                print("A")
+                lst.append(0)
+                print("B")
+                lst.append("fda") # invalid type will cause typing error
+                print("C")
+                return lst
+            except Exception:
+                print("D")
+
+        with self.assertRaises(TypingError) as raises:
+            udt()
+        self.assertRegexpMatches(
+            str(raises.exception),
+            r"Cannot refine type|cannot safely cast unicode_type to int(32|64)"
+        )
+
+
+class TestTryExceptOtherControlFlow(TestCase):
+    def test_yield(self):
+        @njit
+        def udt(n, x):
+            for i in range(n):
+                try:
+                    if i == x:
+                        raise ValueError
+                    yield i
+                except Exception:
+                    return
+
+        self.assertEqual(list(udt(10, 5)), list(range(5)))
+        self.assertEqual(list(udt(10, 10)), list(range(10)))
+
+    def test_objmode(self):
+        @njit
+        def udt():
+            try:
+                with objmode():
+                    print(object())
+            except Exception:
+                return
+
+        with self.assertRaises(CompilerError) as raises:
+            udt()
+        if PYVERSION <= (3,7):
+            msg = ("unsupported control flow: due to return statements inside "
+                   "with block")
+        else:
+            msg = ("unsupported control flow: with-context contains branches "
+                   "(i.e. break/return/raise) that can leave the block ")
+        self.assertIn(
+            msg,
+            str(raises.exception),
+        )
+
+    def test_objmode_output_type(self):
+        def bar(x):
+            return np.asarray(list(reversed(x.tolist())))
+
+        @njit
+        def test_objmode():
+            x = np.arange(5)
+            y = np.zeros_like(x)
+            try:
+                with objmode(y='intp[:]'):  # annotate return type
+                    # this region is executed by object-mode.
+                    y += bar(x)
+            except Exception:
+                pass
+            return y
+
+        with self.assertRaises(CompilerError) as raises:
+            test_objmode()
+        if PYVERSION == (3,7):
+            msg = ("unsupported control flow: due to return statements inside "
+                   "with block")
+        else:
+            msg = ("unsupported control flow: with-context contains branches "
+                   "(i.e. break/return/raise) that can leave the block ")
+        self.assertIn(
+            msg,
+            str(raises.exception),
+        )
+
+    @unittest.skipIf(PYVERSION < (3, 9), "Python 3.9+ only")
+    def test_reraise_opcode_unreachable(self):
+        # The opcode RERAISE was added in python 3.9, there should be no
+        # supported way to actually reach it. This test just checks that an
+        # exception is present to deal with if it is reached in a case known
+        # to produce this opcode.
+        def pyfunc():
+            try:
+                raise Exception
+            except Exception:
+                raise ValueError("ERROR")
+        for inst in dis.get_instructions(pyfunc):
+            if inst.opname == 'RERAISE':
+                break
+        else:
+            self.fail("expected RERAISE opcode not found")
+        func_ir = ir_utils.get_ir_of_code({}, pyfunc.__code__)
+        found = False
+        for lbl, blk in func_ir.blocks.items():
+            for stmt in blk.find_insts(ir.StaticRaise):
+                # don't worry about guarding this strongly, if the exec_args[0]
+                # is a string it'll either be "ERROR" or the guard message
+                # saying unreachable has been reached
+                msg = "Unreachable condition reached (op code RERAISE executed)"
+                if stmt.exc_args and msg in stmt.exc_args[0]:
+                    found = True
+        if not found:
+            self.fail("expected RERAISE unreachable message not found")
+
+
+@skip_parfors_unsupported
+class TestTryExceptParfors(TestCase):
+
+    def test_try_in_prange_reduction(self):
+        # The try-except is transformed basically into chains of if-else
+        def udt(n):
+            c = 0
+            for i in prange(n):
+                try:
+                    c += 1
+                except Exception:
+                    c += 1
+            return c
+
+        args = [10]
+        expect = udt(*args)
+        self.assertEqual(njit(parallel=False)(udt)(*args), expect)
+        self.assertEqual(njit(parallel=True)(udt)(*args), expect)
+
+    def test_try_outside_prange_reduction(self):
+        # The try-except is transformed basically into chains of if-else
+        def udt(n):
+            c = 0
+            try:
+                for i in prange(n):
+                    c += 1
+            except Exception:
+                return 0xdead
+            else:
+                return c
+
+        args = [10]
+        expect = udt(*args)
+        self.assertEqual(njit(parallel=False)(udt)(*args), expect)
+        # Parfors transformation didn't happen
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaPerformanceWarning)
+            self.assertEqual(njit(parallel=True)(udt)(*args), expect)
+        self.assertEqual(len(w), 1)
+        self.assertIn("no transformation for parallel execution was possible",
+                      str(w[0]))
+
+    def test_try_in_prange_map(self):
+        def udt(arr, x):
+            out = arr.copy()
+            for i in prange(arr.size):
+                try:
+                    if i == x:
+                        raise ValueError
+                    out[i] = arr[i] + i
+                except Exception:
+                    out[i] = -1
+            return out
+
+        args = [np.arange(10), 6]
+        expect = udt(*args)
+        self.assertPreciseEqual(njit(parallel=False)(udt)(*args), expect)
+        self.assertPreciseEqual(njit(parallel=True)(udt)(*args), expect)
+
+    def test_try_outside_prange_map(self):
+        def udt(arr, x):
+            out = arr.copy()
+            try:
+                for i in prange(arr.size):
+                    if i == x:
+                        raise ValueError
+                    out[i] = arr[i] + i
+            except Exception:
+                out[i] = -1
+            return out
+
+        args = [np.arange(10), 6]
+        expect = udt(*args)
+        self.assertPreciseEqual(njit(parallel=False)(udt)(*args), expect)
+        self.assertPreciseEqual(njit(parallel=True)(udt)(*args), expect)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_tuples.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_tuples.py
new file mode 100644
index 000000000..f1c49d25c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_tuples.py
@@ -0,0 +1,777 @@
+import collections
+import itertools
+
+import numpy as np
+
+from numba.core.compiler import compile_isolated
+from numba import njit, jit, typeof, literally
+from numba.core import types, errors, utils
+from numba.tests.support import TestCase, MemoryLeakMixin, tag
+import unittest
+
+
+Rect = collections.namedtuple('Rect', ('width', 'height'))
+
+Point = collections.namedtuple('Point', ('x', 'y', 'z'))
+
+Point2 = collections.namedtuple('Point2', ('x', 'y', 'z'))
+
+Empty = collections.namedtuple('Empty', ())
+
+def tuple_return_usecase(a, b):
+    return a, b
+
+def tuple_first(tup):
+    a, b = tup
+    return a
+
+def tuple_second(tup):
+    a, b = tup
+    return b
+
+def tuple_index(tup, idx):
+    return tup[idx]
+
+def tuple_index_static(tup):
+    # Note the negative index
+    return tup[-2]
+
+def tuple_slice2(tup):
+    return tup[1:-1]
+
+def tuple_slice3(tup):
+    return tup[1::2]
+
+def len_usecase(tup):
+    return len(tup)
+
+def add_usecase(a, b):
+    return a + b
+
+def eq_usecase(a, b):
+    return a == b
+
+def ne_usecase(a, b):
+    return a != b
+
+def gt_usecase(a, b):
+    return a > b
+
+def ge_usecase(a, b):
+    return a >= b
+
+def lt_usecase(a, b):
+    return a < b
+
+def le_usecase(a, b):
+    return a <= b
+
+def in_usecase(a, b):
+    return a in b
+
+def bool_usecase(tup):
+    return bool(tup), (3 if tup else 2)
+
+def getattr_usecase(tup):
+    return tup.z, tup.y, tup.x
+
+def make_point(a, b, c):
+    return Point(a, b, c)
+
+def make_point_kws(a, b, c):
+    return Point(z=c, y=b, x=a)
+
+def make_point_nrt(n):
+    r = Rect(list(range(n)), np.zeros(n + 1))
+    # This also exercises attribute access
+    p = Point(r, len(r.width), len(r.height))
+    return p
+
+def type_usecase(tup, *args):
+    return type(tup)(*args)
+
+def identity(tup):
+    return tup
+
+def index_method_usecase(tup, value):
+    return tup.index(value)
+
+def tuple_unpack_static_getitem_err():
+    # see issue3895, `c` is imprecise
+    a, b, c, d = [], [], [], 0.0
+    a.append(1)
+    b.append(1)
+    return
+
+
+class TestTupleLengthError(unittest.TestCase):
+
+    def test_tuple_length_error(self):
+        # issue 2195
+        # raise an error on tuples greater than 1000 in length
+        @njit
+        def eattuple(tup):
+            return len(tup)
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            tup = tuple(range(1001))
+            eattuple(tup)
+
+        expected = "Tuple 'tup' length must be smaller than 1000"
+        self.assertIn(expected, str(raises.exception))
+
+class TestTupleTypeNotIterable(unittest.TestCase):
+    '''
+    issue 4369
+    raise an error if 'type' is not iterable
+    '''
+    def test_namedtuple_types_exception(self):
+        with self.assertRaises(errors.TypingError) as raises:
+            types.NamedTuple(types.uint32, 'p')
+        self.assertIn(
+            "Argument 'types' is not iterable",
+            str(raises.exception)
+        )
+
+    def test_tuple_types_exception(self):
+        with self.assertRaises(errors.TypingError) as raises:
+            types.Tuple((types.uint32))
+        self.assertIn(
+            "Argument 'types' is not iterable",
+            str(raises.exception)
+        )
+
+
+class TestTupleReturn(TestCase):
+
+    def test_array_tuple(self):
+        aryty = types.Array(types.float64, 1, 'C')
+        cres = compile_isolated(tuple_return_usecase, (aryty, aryty))
+        a = b = np.arange(5, dtype='float64')
+        ra, rb = cres.entry_point(a, b)
+        self.assertPreciseEqual(ra, a)
+        self.assertPreciseEqual(rb, b)
+        del a, b
+        self.assertPreciseEqual(ra, rb)
+
+    def test_scalar_tuple(self):
+        scalarty = types.float32
+        cres = compile_isolated(tuple_return_usecase, (scalarty, scalarty))
+        a = b = 1
+        ra, rb = cres.entry_point(a, b)
+        self.assertEqual(ra, a)
+        self.assertEqual(rb, b)
+
+    def test_hetero_tuple(self):
+        alltypes = []
+        allvalues = []
+
+        alltypes.append((types.int32, types.int64))
+        allvalues.append((1, 2))
+
+        alltypes.append((types.float32, types.float64))
+        allvalues.append((1.125, .25))
+
+        alltypes.append((types.int32, types.float64))
+        allvalues.append((1231, .5))
+
+        for (ta, tb), (a, b) in zip(alltypes, allvalues):
+            cres = compile_isolated(tuple_return_usecase, (ta, tb))
+            ra, rb = cres.entry_point(a, b)
+            self.assertPreciseEqual((ra, rb), (a, b))
+
+
+class TestTuplePassing(TestCase):
+
+    def test_unituple(self):
+        tuple_type = types.UniTuple(types.int32, 2)
+        cr_first = compile_isolated(tuple_first, (tuple_type,))
+        cr_second = compile_isolated(tuple_second, (tuple_type,))
+        self.assertPreciseEqual(cr_first.entry_point((4, 5)), 4)
+        self.assertPreciseEqual(cr_second.entry_point((4, 5)), 5)
+
+    def test_hetero_tuple(self):
+        tuple_type = types.Tuple((types.int64, types.float32))
+        cr_first = compile_isolated(tuple_first, (tuple_type,))
+        cr_second = compile_isolated(tuple_second, (tuple_type,))
+        self.assertPreciseEqual(cr_first.entry_point((2**61, 1.5)), 2**61)
+        self.assertPreciseEqual(cr_second.entry_point((2**61, 1.5)), 1.5)
+
+    def test_size_mismatch(self):
+        # Issue #1638: tuple size should be checked when unboxing
+        tuple_type = types.UniTuple(types.int32, 2)
+        cr = compile_isolated(tuple_first, (tuple_type,))
+        with self.assertRaises(ValueError) as raises:
+            cr.entry_point((4, 5, 6))
+        self.assertEqual(str(raises.exception),
+                         "size mismatch for tuple, expected 2 element(s) but got 3")
+
+
+class TestOperations(TestCase):
+
+    def test_len(self):
+        pyfunc = len_usecase
+        cr = compile_isolated(pyfunc,
+                              [types.Tuple((types.int64, types.float32))])
+        self.assertPreciseEqual(cr.entry_point((4, 5)), 2)
+        cr = compile_isolated(pyfunc,
+                              [types.UniTuple(types.int64, 3)])
+        self.assertPreciseEqual(cr.entry_point((4, 5, 6)), 3)
+
+    def test_index_literal(self):
+        # issue #6023, test non-static getitem with IntegerLiteral index
+        def pyfunc(tup, idx):
+            idx = literally(idx)
+            return tup[idx]
+        cfunc = njit(pyfunc)
+
+        tup = (4, 3.1, 'sss')
+        for i in range(len(tup)):
+            self.assertPreciseEqual(cfunc(tup, i), tup[i])
+
+    def test_index(self):
+        pyfunc = tuple_index
+        cr = compile_isolated(pyfunc,
+                              [types.UniTuple(types.int64, 3), types.int64])
+        tup = (4, 3, 6)
+        for i in range(len(tup)):
+            self.assertPreciseEqual(cr.entry_point(tup, i), tup[i])
+
+        # test negative indexing
+        for i in range(len(tup) + 1):
+            self.assertPreciseEqual(cr.entry_point(tup, -i), tup[-i])
+
+        # oob indexes, +ve then -ve
+        with self.assertRaises(IndexError) as raises:
+            cr.entry_point(tup, len(tup))
+        self.assertEqual("tuple index out of range", str(raises.exception))
+        with self.assertRaises(IndexError) as raises:
+            cr.entry_point(tup, -(len(tup) + 1))
+        self.assertEqual("tuple index out of range", str(raises.exception))
+
+        # Test empty tuple
+        cr = compile_isolated(pyfunc,
+                              [types.UniTuple(types.int64, 0), types.int64])
+        with self.assertRaises(IndexError) as raises:
+            cr.entry_point((), 0)
+        self.assertEqual("tuple index out of range", str(raises.exception))
+
+        # test uintp indexing (because, e.g., parfor generates unsigned prange)
+        cr = compile_isolated(pyfunc,
+                              [types.UniTuple(types.int64, 3), types.uintp])
+        for i in range(len(tup)):
+            self.assertPreciseEqual(cr.entry_point(tup, types.uintp(i)), tup[i])
+
+        # With a compile-time static index (the code generation path is different)
+        pyfunc = tuple_index_static
+        for typ in (types.UniTuple(types.int64, 4),
+                    types.Tuple((types.int64, types.int32, types.int64, types.int32))):
+            cr = compile_isolated(pyfunc, (typ,))
+            tup = (4, 3, 42, 6)
+            self.assertPreciseEqual(cr.entry_point(tup), pyfunc(tup))
+
+        typ = types.UniTuple(types.int64, 1)
+        with self.assertTypingError():
+            cr = compile_isolated(pyfunc, (typ,))
+
+        # test unpack, staticgetitem with imprecise type (issue #3895)
+        pyfunc = tuple_unpack_static_getitem_err
+        with self.assertTypingError() as raises:
+            cr = compile_isolated(pyfunc, ())
+        msg = ("Cannot infer the type of variable 'c', have imprecise type: "
+               "list(undefined)<iv=None>.")
+        self.assertIn(msg, str(raises.exception))
+
+    def test_in(self):
+        pyfunc = in_usecase
+        cr = compile_isolated(pyfunc,
+                              [types.int64, types.UniTuple(types.int64, 3)])
+        tup = (4, 1, 5)
+        for i in range(5):
+            self.assertPreciseEqual(cr.entry_point(i, tup), pyfunc(i, tup))
+
+        # Test the empty case
+        cr = compile_isolated(pyfunc, [types.int64, types.Tuple([])])
+        self.assertPreciseEqual(cr.entry_point(1, ()), pyfunc(1, ()))
+
+    def check_slice(self, pyfunc):
+        tup = (4, 5, 6, 7)
+        cr = compile_isolated(pyfunc,
+                              [types.UniTuple(types.int64, 4)])
+        self.assertPreciseEqual(cr.entry_point(tup), pyfunc(tup))
+        cr = compile_isolated(
+            pyfunc,
+            [types.Tuple((types.int64, types.int32, types.int64, types.int32))])
+        self.assertPreciseEqual(cr.entry_point(tup), pyfunc(tup))
+
+    def test_slice2(self):
+        self.check_slice(tuple_slice2)
+
+    def test_slice3(self):
+        self.check_slice(tuple_slice3)
+
+    def test_bool(self):
+        pyfunc = bool_usecase
+        cr = compile_isolated(pyfunc,
+                              [types.Tuple((types.int64, types.int32))])
+        args = ((4, 5),)
+        self.assertPreciseEqual(cr.entry_point(*args), pyfunc(*args))
+        cr = compile_isolated(pyfunc,
+                              [types.UniTuple(types.int64, 3)])
+        args = ((4, 5, 6),)
+        self.assertPreciseEqual(cr.entry_point(*args), pyfunc(*args))
+        cr = compile_isolated(pyfunc,
+                              [types.Tuple(())])
+        self.assertPreciseEqual(cr.entry_point(()), pyfunc(()))
+
+    def test_add(self):
+        pyfunc = add_usecase
+        samples = [(types.Tuple(()), ()),
+                   (types.UniTuple(types.int32, 0), ()),
+                   (types.UniTuple(types.int32, 1), (42,)),
+                   (types.Tuple((types.int64, types.float32)), (3, 4.5)),
+                   ]
+        for (ta, a), (tb, b) in itertools.product(samples, samples):
+            cr = compile_isolated(pyfunc, (ta, tb))
+            expected = pyfunc(a, b)
+            got = cr.entry_point(a, b)
+            self.assertPreciseEqual(got, expected, msg=(ta, tb))
+
+    def _test_compare(self, pyfunc):
+        def eq(pyfunc, cfunc, args):
+            self.assertIs(cfunc(*args), pyfunc(*args),
+                          "mismatch for arguments %s" % (args,))
+
+        # Same-sized tuples
+        argtypes = [types.Tuple((types.int64, types.float32)),
+                    types.UniTuple(types.int32, 2)]
+        for ta, tb in itertools.product(argtypes, argtypes):
+            cr = compile_isolated(pyfunc, (ta, tb))
+            cfunc = cr.entry_point
+            for args in [((4, 5), (4, 5)),
+                         ((4, 5), (4, 6)),
+                         ((4, 6), (4, 5)),
+                         ((4, 5), (5, 4))]:
+                eq(pyfunc, cfunc, args)
+        # Different-sized tuples
+        argtypes = [types.Tuple((types.int64, types.float32)),
+                    types.UniTuple(types.int32, 3)]
+        cr = compile_isolated(pyfunc, tuple(argtypes))
+        cfunc = cr.entry_point
+        for args in [((4, 5), (4, 5, 6)),
+                     ((4, 5), (4, 4, 6)),
+                     ((4, 5), (4, 6, 7))]:
+            eq(pyfunc, cfunc, args)
+
+    def test_eq(self):
+        self._test_compare(eq_usecase)
+
+    def test_ne(self):
+        self._test_compare(ne_usecase)
+
+    def test_gt(self):
+        self._test_compare(gt_usecase)
+
+    def test_ge(self):
+        self._test_compare(ge_usecase)
+
+    def test_lt(self):
+        self._test_compare(lt_usecase)
+
+    def test_le(self):
+        self._test_compare(le_usecase)
+
+
+class TestNamedTuple(TestCase, MemoryLeakMixin):
+
+    def test_unpack(self):
+        def check(p):
+            for pyfunc in tuple_first, tuple_second:
+                cfunc = jit(nopython=True)(pyfunc)
+                self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check(Rect(4, 5))
+        # Heterogeneous
+        check(Rect(4, 5.5))
+
+    def test_len(self):
+        def check(p):
+            pyfunc = len_usecase
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check(Rect(4, 5))
+        check(Point(4, 5, 6))
+        # Heterogeneous
+        check(Rect(4, 5.5))
+        check(Point(4, 5.5, 6j))
+
+    def test_index(self):
+        pyfunc = tuple_index
+        cfunc = jit(nopython=True)(pyfunc)
+
+        p = Point(4, 5, 6)
+        for i in range(len(p)):
+            self.assertPreciseEqual(cfunc(p, i), pyfunc(p, i))
+
+        # test uintp indexing (because, e.g., parfor generates unsigned prange)
+        for i in range(len(p)):
+            self.assertPreciseEqual(cfunc(p, types.uintp(i)), pyfunc(p, i))
+
+    def test_bool(self):
+        def check(p):
+            pyfunc = bool_usecase
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check(Rect(4, 5))
+        # Heterogeneous
+        check(Rect(4, 5.5))
+        check(Empty())
+
+    def _test_compare(self, pyfunc):
+        def eq(pyfunc, cfunc, args):
+            self.assertIs(cfunc(*args), pyfunc(*args),
+                          "mismatch for arguments %s" % (args,))
+
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Same-sized named tuples
+        for a, b in [((4, 5), (4, 5)),
+                     ((4, 5), (4, 6)),
+                     ((4, 6), (4, 5)),
+                     ((4, 5), (5, 4))]:
+            eq(pyfunc, cfunc, (Rect(*a), Rect(*b)))
+
+        # Different-sized named tuples
+        for a, b in [((4, 5), (4, 5, 6)),
+                     ((4, 5), (4, 4, 6)),
+                     ((4, 5), (4, 6, 7))]:
+            eq(pyfunc, cfunc, (Rect(*a), Point(*b)))
+
+    def test_eq(self):
+        self._test_compare(eq_usecase)
+
+    def test_ne(self):
+        self._test_compare(ne_usecase)
+
+    def test_gt(self):
+        self._test_compare(gt_usecase)
+
+    def test_ge(self):
+        self._test_compare(ge_usecase)
+
+    def test_lt(self):
+        self._test_compare(lt_usecase)
+
+    def test_le(self):
+        self._test_compare(le_usecase)
+
+    def test_getattr(self):
+        pyfunc = getattr_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for args in (4, 5, 6), (4, 5.5, 6j):
+            p = Point(*args)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+    def test_construct(self):
+        def check(pyfunc):
+            cfunc = jit(nopython=True)(pyfunc)
+            for args in (4, 5, 6), (4, 5.5, 6j):
+                expected = pyfunc(*args)
+                got = cfunc(*args)
+                self.assertIs(type(got), type(expected))
+                self.assertPreciseEqual(got, expected)
+
+        check(make_point)
+        check(make_point_kws)
+
+    def test_type(self):
+        # Test the type() built-in on named tuples
+        pyfunc = type_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        arg_tuples = [(4, 5, 6), (4, 5.5, 6j)]
+        for tup_args, args in itertools.product(arg_tuples, arg_tuples):
+            tup = Point(*tup_args)
+            expected = pyfunc(tup, *args)
+            got = cfunc(tup, *args)
+            self.assertIs(type(got), type(expected))
+            self.assertPreciseEqual(got, expected)
+
+    def test_literal_unification(self):
+        # Test for #3565.
+        @jit(nopython=True)
+        def Data1(value):
+            return Rect(value, -321)
+
+        @jit(nopython=True)
+        def call(i, j):
+            if j == 0:
+                # In the error, `result` is typed to `Rect(int, LiteralInt)`
+                # because of the `-321` literal.  This doesn't match the
+                # `result` type in the other branch.
+                result = Data1(i)
+            else:
+                # `result` is typed to be `Rect(int, int)`
+                result = Rect(i, j)
+            return result
+
+        r = call(123, 1321)
+        self.assertEqual(r, Rect(width=123, height=1321))
+        r = call(123, 0)
+        self.assertEqual(r, Rect(width=123, height=-321))
+
+    def test_string_literal_in_ctor(self):
+        # Test for issue #3813
+
+        @jit(nopython=True)
+        def foo():
+            return Rect(10, 'somestring')
+
+        r = foo()
+        self.assertEqual(r, Rect(width=10, height='somestring'))
+
+    def test_dispatcher_mistreat(self):
+        # Test for issue #5215 that mistreat namedtuple as tuples
+        @jit(nopython=True)
+        def foo(x):
+            return x
+
+        in1 = (1, 2,  3)
+        out1 = foo(in1)
+        self.assertEqual(in1, out1)
+
+        in2 = Point(1, 2, 3)
+        out2 = foo(in2)
+        self.assertEqual(in2, out2)
+
+        # Check the signatures
+        self.assertEqual(len(foo.nopython_signatures), 2)
+        self.assertEqual(foo.nopython_signatures[0].args[0], typeof(in1))
+        self.assertEqual(foo.nopython_signatures[1].args[0], typeof(in2))
+
+        # Differently named
+        in3 = Point2(1, 2, 3)
+        out3 = foo(in3)
+        self.assertEqual(in3, out3)
+        self.assertEqual(len(foo.nopython_signatures), 3)
+        self.assertEqual(foo.nopython_signatures[2].args[0], typeof(in3))
+
+
+class TestTupleNRT(TestCase, MemoryLeakMixin):
+    def test_tuple_add(self):
+        def pyfunc(x):
+            a = np.arange(3)
+            return (a,) + (x,)
+
+        cfunc = jit(nopython=True)(pyfunc)
+        x = 123
+        expect_a, expect_x = pyfunc(x)
+        got_a, got_x = cfunc(x)
+        np.testing.assert_equal(got_a, expect_a)
+        self.assertEqual(got_x, expect_x)
+
+
+class TestNamedTupleNRT(TestCase, MemoryLeakMixin):
+
+    def test_return(self):
+        # Check returning a namedtuple with a list inside it
+        pyfunc = make_point_nrt
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arg in (3, 0):
+            expected = pyfunc(arg)
+            got = cfunc(arg)
+            self.assertIs(type(got), type(expected))
+            self.assertPreciseEqual(got, expected)
+
+
+class TestConversions(TestCase):
+    """
+    Test implicit conversions between tuple types.
+    """
+
+    def check_conversion(self, fromty, toty, val):
+        pyfunc = identity
+        cr = compile_isolated(pyfunc, (fromty,), toty)
+        cfunc = cr.entry_point
+        res = cfunc(val)
+        self.assertEqual(res, val)
+
+    def test_conversions(self):
+        check = self.check_conversion
+        fromty = types.UniTuple(types.int32, 2)
+        check(fromty, types.UniTuple(types.float32, 2), (4, 5))
+        check(fromty, types.Tuple((types.float32, types.int16)), (4, 5))
+        aty = types.UniTuple(types.int32, 0)
+        bty = types.Tuple(())
+        check(aty, bty, ())
+        check(bty, aty, ())
+
+        with self.assertRaises(errors.TypingError) as raises:
+            check(fromty, types.Tuple((types.float32,)), (4, 5))
+        msg = "No conversion from UniTuple(int32 x 2) to UniTuple(float32 x 1)"
+        self.assertIn(msg, str(raises.exception))
+
+
+class TestMethods(TestCase):
+
+    def test_index(self):
+        pyfunc = index_method_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertEqual(cfunc((1, 2, 3), 2), 1)
+
+        with self.assertRaises(ValueError) as raises:
+            cfunc((1, 2, 3), 4)
+        msg = 'tuple.index(x): x not in tuple'
+        self.assertEqual(msg, str(raises.exception))
+
+
+class TestTupleBuild(TestCase):
+
+    def test_build_unpack(self):
+        def check(p):
+            pyfunc = lambda a: (1, *a)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_build_unpack_assign_like(self):
+        # see #6534
+        def check(p):
+            pyfunc = lambda a: (*a,)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_build_unpack_fail_on_list_assign_like(self):
+        # see #6534
+        def check(p):
+            pyfunc = lambda a: (*a,)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        with self.assertRaises(errors.TypingError) as raises:
+            check([4, 5])
+
+        if utils.PYVERSION > (3, 8):
+            # Python 3.9 has a peephole rewrite due to large changes in tuple
+            # unpacking. It results in a tuple + list situation from the above
+            # so the error message reflects that. Catching this specific and
+            # seemingly rare sequence in the peephole rewrite is prohibitively
+            # hard. Should it be reported numerous times, revisit then.
+            msg1 = "No implementation of function"
+            self.assertIn(msg1, str(raises.exception))
+            msg2 = "tuple(reflected list(" # ignore the rest of reflected list
+                                           # part, it's repr is quite volatile.
+            self.assertIn(msg2, str(raises.exception))
+        else:
+            msg = "Only tuples are supported when unpacking a single item"
+            self.assertIn(msg, str(raises.exception))
+
+    def test_build_unpack_more(self):
+        def check(p):
+            pyfunc = lambda a: (1, *a, (1, 2), *a)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_build_unpack_call(self):
+        def check(p):
+            @jit
+            def inner(*args):
+                return args
+            pyfunc = lambda a: inner(1, *a)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_build_unpack_call_more(self):
+        def check(p):
+            @jit
+            def inner(*args):
+                return args
+            pyfunc = lambda a: inner(1, *a, *(1, 2), *a)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_tuple_constructor(self):
+        def check(pyfunc, arg):
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(arg), pyfunc(arg))
+
+        # empty
+        check(lambda _: tuple(), ())
+        # Homogeneous
+        check(lambda a: tuple(a), (4, 5))
+        # Heterogeneous
+        check(lambda a: tuple(a), (4, 5.5))
+
+    @unittest.skipIf(utils.PYVERSION < (3, 9), "needs Python 3.9+")
+    def test_unpack_with_predicate_fails(self):
+        # this fails as the list_to_tuple/list_extend peephole bytecode
+        # rewriting needed for Python 3.9+ cannot yet traverse the CFG.
+        @njit
+        def foo():
+            a = (1,)
+            b = (3,2,  4)
+            return (*(b if a[0] else (5, 6)),)
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+        msg = "op_LIST_EXTEND at the start of a block"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_build_unpack_with_calls_in_unpack(self):
+        def check(p):
+            def pyfunc(a):
+                z = [1, 2]
+                return (*a, z.append(3), z.extend(a), np.ones(3)), z
+
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        check((4, 5))
+
+    def test_build_unpack_complicated(self):
+        def check(p):
+            def pyfunc(a):
+                z = [1, 2]
+                return (*a, *(*a, a), *(a, (*(a, (1, 2), *(3,), *a),
+                        (a, 1, (2, 3), *a, 1), (1,))),
+                        *(z.append(4), z.extend(a))), z
+
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        check((10, 20))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeconv.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeconv.py
new file mode 100644
index 000000000..4042542b6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeconv.py
@@ -0,0 +1,295 @@
+import itertools
+
+from numba.core import types
+from numba.core.typeconv.typeconv import TypeManager, TypeCastingRules
+from numba.core.typeconv import rules
+from numba.core.typeconv import castgraph, Conversion
+import unittest
+
+
+class CompatibilityTestMixin(unittest.TestCase):
+
+    def check_number_compatibility(self, check_compatible):
+        b = types.boolean
+        i8 = types.int8
+        i16 = types.int16
+        i32 = types.int32
+        i64 = types.int64
+        u8 = types.uint8
+        u16 = types.uint16
+        u32 = types.uint32
+        u64 = types.uint64
+        f16 = types.float16
+        f32 = types.float32
+        f64 = types.float64
+        c64 = types.complex64
+        c128 = types.complex128
+
+        self.assertEqual(check_compatible(i32, i32), Conversion.exact)
+
+        self.assertEqual(check_compatible(b, i8), Conversion.safe)
+        self.assertEqual(check_compatible(b, u8), Conversion.safe)
+        self.assertEqual(check_compatible(i8, b), Conversion.unsafe)
+        self.assertEqual(check_compatible(u8, b), Conversion.unsafe)
+
+        self.assertEqual(check_compatible(i32, i64), Conversion.promote)
+        self.assertEqual(check_compatible(i32, u32), Conversion.unsafe)
+        self.assertEqual(check_compatible(u32, i32), Conversion.unsafe)
+        self.assertEqual(check_compatible(u32, i64), Conversion.safe)
+
+        self.assertEqual(check_compatible(i16, f16), Conversion.unsafe)
+        self.assertEqual(check_compatible(i32, f32), Conversion.unsafe)
+        self.assertEqual(check_compatible(u32, f32), Conversion.unsafe)
+        self.assertEqual(check_compatible(i32, f64), Conversion.safe)
+        self.assertEqual(check_compatible(u32, f64), Conversion.safe)
+        # Note this is inconsistent with i32 -> f32...
+        self.assertEqual(check_compatible(i64, f64), Conversion.safe)
+        self.assertEqual(check_compatible(u64, f64), Conversion.safe)
+
+        self.assertEqual(check_compatible(f32, c64), Conversion.safe)
+        self.assertEqual(check_compatible(f64, c128), Conversion.safe)
+        self.assertEqual(check_compatible(f64, c64), Conversion.unsafe)
+
+        # Propagated compatibility relationships
+        self.assertEqual(check_compatible(i16, f64), Conversion.safe)
+        self.assertEqual(check_compatible(i16, i64), Conversion.promote)
+        self.assertEqual(check_compatible(i32, c64), Conversion.unsafe)
+        self.assertEqual(check_compatible(i32, c128), Conversion.safe)
+        self.assertEqual(check_compatible(i32, u64), Conversion.unsafe)
+
+        for ta, tb in itertools.product(types.number_domain,
+                                        types.number_domain):
+            if ta in types.complex_domain and tb not in types.complex_domain:
+                continue
+            self.assertTrue(check_compatible(ta, tb) is not None,
+                            msg="No cast from %s to %s" % (ta, tb))
+
+
+class TestTypeConv(CompatibilityTestMixin, unittest.TestCase):
+
+    def test_typeconv(self):
+        tm = TypeManager()
+
+        i32 = types.int32
+        i64 = types.int64
+        f32 = types.float32
+
+        tm.set_promote(i32, i64)
+        tm.set_unsafe_convert(i32, f32)
+
+        sig = (i32, f32)
+        ovs = [
+            (i32, i32),
+            (f32, f32),
+            (i64, i64),
+        ]
+
+        # allow_unsafe = True => a conversion from i32 to f32 is chosen
+        sel = tm.select_overload(sig, ovs, True, False)
+        self.assertEqual(sel, 1)
+        # allow_unsafe = False => no overload available
+        with self.assertRaises(TypeError):
+            sel = tm.select_overload(sig, ovs, False, False)
+
+    def test_default_rules(self):
+        tm = rules.default_type_manager
+        self.check_number_compatibility(tm.check_compatible)
+
+    def test_overload1(self):
+        tm = rules.default_type_manager
+
+        i32 = types.int32
+        i64 = types.int64
+
+        sig = (i64, i32, i32)
+        ovs = [
+            (i32, i32, i32),
+            (i64, i64, i64),
+        ]
+        # The first overload is unsafe, the second is safe => the second
+        # is always chosen, regardless of allow_unsafe.
+        self.assertEqual(tm.select_overload(sig, ovs, True, False), 1)
+        self.assertEqual(tm.select_overload(sig, ovs, False, False), 1)
+
+    def test_overload2(self):
+        tm = rules.default_type_manager
+
+        i16 = types.int16
+        i32 = types.int32
+        i64 = types.int64
+
+        sig = (i32, i16, i32)
+        ovs = [
+            # Three promotes
+            (i64, i64, i64),
+            # One promotes, two exact types
+            (i32, i32, i32),
+            # Two unsafe converts, one exact type
+            (i16, i16, i16),
+        ]
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=False,
+                                            exact_match_required=False), 1)
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=True,
+                                            exact_match_required=False), 1)
+
+        # The same in reverse order
+        ovs.reverse()
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=False,
+                                            exact_match_required=False), 1)
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=True,
+                                            exact_match_required=False), 1)
+
+    def test_overload3(self):
+        # Promotes should be preferred over safe converts
+        tm = rules.default_type_manager
+
+        i32 = types.int32
+        i64 = types.int64
+        f64 = types.float64
+
+        sig = (i32, i32)
+        ovs = [
+            # Two promotes
+            (i64, i64),
+            # Two safe converts
+            (f64, f64),
+        ]
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=False,
+                                            exact_match_required=False), 0)
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=True,
+                                            exact_match_required=False), 0)
+
+        # The same in reverse order
+        ovs.reverse()
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=False,
+                                            exact_match_required=False), 1)
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=True,
+                                            exact_match_required=False), 1)
+
+    def test_overload4(self):
+        tm = rules.default_type_manager
+
+        i16 = types.int16
+        i32 = types.int32
+        i64 = types.int64
+        f16 = types.float16
+        f32 = types.float32
+
+        sig = (i16, f16, f16)
+        ovs = [
+            # One unsafe, one promote, one exact
+            (f16, f32, f16),
+            # Two unsafe, one exact types
+            (f32, i32, f16),
+        ]
+
+        self.assertEqual(tm.select_overload(sig, ovs, allow_unsafe=True,
+                                            exact_match_required=False), 0)
+
+    def test_type_casting_rules(self):
+        tm = TypeManager()
+        tcr = TypeCastingRules(tm)
+
+        i16 = types.int16
+        i32 = types.int32
+        i64 = types.int64
+        f64 = types.float64
+        f32 = types.float32
+        f16 = types.float16
+        made_up = types.Dummy("made_up")
+
+        tcr.promote_unsafe(i32, i64)
+        tcr.safe_unsafe(i32, f64)
+        tcr.promote_unsafe(f32, f64)
+        tcr.promote_unsafe(f16, f32)
+        tcr.unsafe_unsafe(i16, f16)
+
+        def base_test():
+            # As declared
+            self.assertEqual(tm.check_compatible(i32, i64), Conversion.promote)
+            self.assertEqual(tm.check_compatible(i32, f64), Conversion.safe)
+            self.assertEqual(tm.check_compatible(f16, f32), Conversion.promote)
+            self.assertEqual(tm.check_compatible(f32, f64), Conversion.promote)
+            self.assertEqual(tm.check_compatible(i64, i32), Conversion.unsafe)
+            self.assertEqual(tm.check_compatible(f64, i32), Conversion.unsafe)
+            self.assertEqual(tm.check_compatible(f64, f32), Conversion.unsafe)
+
+            # Propagated
+            self.assertEqual(tm.check_compatible(i64, f64), Conversion.unsafe)
+            self.assertEqual(tm.check_compatible(f64, i64), Conversion.unsafe)
+            self.assertEqual(tm.check_compatible(i64, f32), Conversion.unsafe)
+            self.assertEqual(tm.check_compatible(i32, f32), Conversion.unsafe)
+            self.assertEqual(tm.check_compatible(f32, i32), Conversion.unsafe)
+            self.assertEqual(tm.check_compatible(i16, f16), Conversion.unsafe)
+            self.assertEqual(tm.check_compatible(f16, i16), Conversion.unsafe)
+
+        # Test base graph
+        base_test()
+
+        self.assertIsNone(tm.check_compatible(i64, made_up))
+        self.assertIsNone(tm.check_compatible(i32, made_up))
+        self.assertIsNone(tm.check_compatible(f32, made_up))
+        self.assertIsNone(tm.check_compatible(made_up, f64))
+        self.assertIsNone(tm.check_compatible(made_up, i64))
+
+        # Add new test
+        tcr.promote(f64, made_up)
+        tcr.unsafe(made_up, i32)
+
+        # Ensure the graph did not change by adding the new type
+        base_test()
+
+        # To "made up" type
+        self.assertEqual(tm.check_compatible(i64, made_up), Conversion.unsafe)
+        self.assertEqual(tm.check_compatible(i32, made_up), Conversion.safe)
+        self.assertEqual(tm.check_compatible(f32, made_up), Conversion.promote)
+        self.assertEqual(tm.check_compatible(made_up, f64), Conversion.unsafe)
+        self.assertEqual(tm.check_compatible(made_up, i64), Conversion.unsafe)
+
+    def test_castgraph_propagate(self):
+        saved = []
+
+        def callback(src, dst, rel):
+            saved.append((src, dst, rel))
+
+        tg = castgraph.TypeGraph(callback)
+
+        i32 = types.int32
+        i64 = types.int64
+        f64 = types.float64
+        f32 = types.float32
+
+        tg.insert_rule(i32, i64, Conversion.promote)
+        tg.insert_rule(i64, i32, Conversion.unsafe)
+
+        saved.append(None)
+
+        tg.insert_rule(i32, f64, Conversion.safe)
+        tg.insert_rule(f64, i32, Conversion.unsafe)
+
+        saved.append(None)
+
+        tg.insert_rule(f32, f64, Conversion.promote)
+        tg.insert_rule(f64, f32, Conversion.unsafe)
+
+        self.assertIn((i32, i64, Conversion.promote), saved[0:2])
+        self.assertIn((i64, i32, Conversion.unsafe), saved[0:2])
+        self.assertIs(saved[2], None)
+
+        self.assertIn((i32, f64, Conversion.safe), saved[3:7])
+        self.assertIn((f64, i32, Conversion.unsafe), saved[3:7])
+        self.assertIn((i64, f64, Conversion.unsafe), saved[3:7])
+        self.assertIn((i64, f64, Conversion.unsafe), saved[3:7])
+        self.assertIs(saved[7], None)
+
+        self.assertIn((f32, f64, Conversion.promote), saved[8:14])
+        self.assertIn((f64, f32, Conversion.unsafe), saved[8:14])
+        self.assertIn((f32, i32, Conversion.unsafe), saved[8:14])
+        self.assertIn((i32, f32, Conversion.unsafe), saved[8:14])
+        self.assertIn((f32, i64, Conversion.unsafe), saved[8:14])
+        self.assertIn((i64, f32, Conversion.unsafe), saved[8:14])
+        self.assertEqual(len(saved[14:]), 0)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typedlist.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typedlist.py
new file mode 100644
index 000000000..6a78a3bb8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typedlist.py
@@ -0,0 +1,1660 @@
+import sys
+import subprocess
+from itertools import product
+from textwrap import dedent
+
+import numpy as np
+
+from numba import config
+from numba import njit
+from numba import int32, float32, prange
+from numba.core import types
+from numba import typeof
+from numba.typed import List, Dict
+from numba.core.errors import TypingError
+from numba.tests.support import (TestCase, MemoryLeakMixin, override_config,
+                                 forbid_codegen, skip_parfors_unsupported)
+from numba.core.unsafe.refcount import get_refcount
+from numba.experimental import jitclass
+
+
+# global typed-list for testing purposes
+global_typed_list = List.empty_list(int32)
+for i in (1, 2, 3):
+    global_typed_list.append(int32(i))
+
+
+def to_tl(l):
+    """ Convert cpython list to typed-list. """
+    tl = List.empty_list(int32)
+    for k in l:
+        tl.append(k)
+    return tl
+
+
+class TestTypedList(MemoryLeakMixin, TestCase):
+    def test_basic(self):
+        l = List.empty_list(int32)
+        # len
+        self.assertEqual(len(l), 0)
+        # append
+        l.append(0)
+        # len
+        self.assertEqual(len(l), 1)
+        # setitem
+        l.append(0)
+        l.append(0)
+        l[0] = 10
+        l[1] = 11
+        l[2] = 12
+        # getitem
+        self.assertEqual(l[0], 10)
+        self.assertEqual(l[1], 11)
+        self.assertEqual(l[2], 12)
+        self.assertEqual(l[-3], 10)
+        self.assertEqual(l[-2], 11)
+        self.assertEqual(l[-1], 12)
+        # __iter__
+        # the default __iter__ from MutableSequence will raise an IndexError
+        # via __getitem__ and thus leak an exception, so this shouldn't
+        for i in l:
+            pass
+        # contains
+        self.assertTrue(10 in l)
+        self.assertFalse(0 in l)
+        # count
+        l.append(12)
+        self.assertEqual(l.count(0), 0)
+        self.assertEqual(l.count(10), 1)
+        self.assertEqual(l.count(12), 2)
+        # pop
+        self.assertEqual(len(l), 4)
+        self.assertEqual(l.pop(), 12)
+        self.assertEqual(len(l), 3)
+        self.assertEqual(l.pop(1), 11)
+        self.assertEqual(len(l), 2)
+        # extend
+        l.extend((100, 200, 300))
+        self.assertEqual(len(l), 5)
+        self.assertEqual(list(l), [10, 12, 100, 200, 300])
+        # insert
+        l.insert(0, 0)
+        self.assertEqual(list(l), [0, 10, 12, 100, 200, 300])
+        l.insert(3, 13)
+        self.assertEqual(list(l), [0, 10, 12, 13, 100, 200, 300])
+        l.insert(100, 400)
+        self.assertEqual(list(l), [0, 10, 12, 13, 100, 200, 300, 400])
+        # remove
+        l.remove(0)
+        l.remove(400)
+        l.remove(13)
+        self.assertEqual(list(l), [10, 12, 100, 200, 300])
+        # clear
+        l.clear()
+        self.assertEqual(len(l), 0)
+        self.assertEqual(list(l), [])
+        # reverse
+        l.extend(tuple(range(10, 20)))
+        l.reverse()
+        self.assertEqual(list(l), list(range(10, 20))[::-1])
+        # copy
+        new = l.copy()
+        self.assertEqual(list(new), list(range(10, 20))[::-1])
+        # equal
+        self.assertEqual(l, new)
+        # not equal
+        new[-1] = 42
+        self.assertNotEqual(l, new)
+        # index
+        self.assertEqual(l.index(15), 4)
+
+    def test_list_extend_refines_on_unicode_type(self):
+        @njit
+        def foo(string):
+            l = List()
+            l.extend(string)
+            return l
+
+        for func in (foo, foo.py_func):
+            for string in ("a", "abc", "\nabc\t"):
+                self.assertEqual(list(func(string)), list(string))
+
+    def test_unsigned_access(self):
+        L = List.empty_list(int32)
+        ui32_0 = types.uint32(0)
+        ui32_1 = types.uint32(1)
+        ui32_2 = types.uint32(2)
+
+        # insert
+        L.append(types.uint32(10))
+        L.append(types.uint32(11))
+        L.append(types.uint32(12))
+        self.assertEqual(len(L), 3)
+
+        # getitem
+        self.assertEqual(L[ui32_0], 10)
+        self.assertEqual(L[ui32_1], 11)
+        self.assertEqual(L[ui32_2], 12)
+
+        # setitem
+        L[ui32_0] = 123
+        L[ui32_1] = 456
+        L[ui32_2] = 789
+        self.assertEqual(L[ui32_0], 123)
+        self.assertEqual(L[ui32_1], 456)
+        self.assertEqual(L[ui32_2], 789)
+
+        # index
+        ui32_123 = types.uint32(123)
+        ui32_456 = types.uint32(456)
+        ui32_789 = types.uint32(789)
+        self.assertEqual(L.index(ui32_123), 0)
+        self.assertEqual(L.index(ui32_456), 1)
+        self.assertEqual(L.index(ui32_789), 2)
+
+        # delitem
+        L.__delitem__(ui32_2)
+        del L[ui32_1]
+        self.assertEqual(len(L), 1)
+        self.assertEqual(L[ui32_0], 123)
+
+        # pop
+        L.append(2)
+        L.append(3)
+        L.append(4)
+        self.assertEqual(len(L), 4)
+        self.assertEqual(L.pop(), 4)
+        self.assertEqual(L.pop(ui32_2), 3)
+        self.assertEqual(L.pop(ui32_1), 2)
+        self.assertEqual(L.pop(ui32_0), 123)
+
+    def test_dtype(self):
+
+        L = List.empty_list(int32)
+        self.assertEqual(L._dtype, int32)
+
+        L = List.empty_list(float32)
+        self.assertEqual(L._dtype, float32)
+
+        @njit
+        def foo():
+            li, lf = List(), List()
+            li.append(int32(1))
+            lf.append(float32(1.0))
+            return li._dtype, lf._dtype
+
+        self.assertEqual(foo(), (np.dtype('int32'), np.dtype('float32')))
+        self.assertEqual(foo.py_func(), (int32, float32))
+
+    def test_dtype_raises_exception_on_untyped_list(self):
+
+        with self.assertRaises(RuntimeError) as raises:
+            L = List()
+            L._dtype
+        self.assertIn(
+            "invalid operation on untyped list",
+            str(raises.exception),
+        )
+
+    @skip_parfors_unsupported
+    def test_unsigned_prange(self):
+        @njit(parallel=True)
+        def foo(a):
+            r = types.uint64(3)
+            s = types.uint64(0)
+            for i in prange(r):
+                s = s + a[i]
+            return s
+
+        a = List.empty_list(types.uint64)
+        a.append(types.uint64(12))
+        a.append(types.uint64(1))
+        a.append(types.uint64(7))
+        self.assertEqual(foo(a), 20)
+
+    def test_compiled(self):
+        @njit
+        def producer():
+            l = List.empty_list(int32)
+            l.append(23)
+            return l
+
+        @njit
+        def consumer(l):
+            return l[0]
+
+        l = producer()
+        val = consumer(l)
+        self.assertEqual(val, 23)
+
+    def test_getitem_slice(self):
+        """ Test getitem using a slice.
+
+        This tests suffers from combinatorial explosion, so we parametrize it
+        and compare results against the regular list in a quasi fuzzing
+        approach.
+
+        """
+        # initialize regular list
+        rl = list(range(10, 20))
+        # initialize typed list
+        tl = List.empty_list(int32)
+        for i in range(10, 20):
+            tl.append(i)
+        # define the ranges
+        start_range = list(range(-20, 30))
+        stop_range = list(range(-20, 30))
+        step_range = [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5]
+
+        # check that they are the same initially
+        self.assertEqual(rl, list(tl))
+        # check that copy by slice works, no start, no stop, no step
+        self.assertEqual(rl[:], list(tl[:]))
+
+        # start only
+        for sa in start_range:
+            self.assertEqual(rl[sa:], list(tl[sa:]))
+        # stop only
+        for so in stop_range:
+            self.assertEqual(rl[:so], list(tl[:so]))
+        # step only
+        for se in step_range:
+            self.assertEqual(rl[::se], list(tl[::se]))
+
+        # start and stop
+        for sa, so in product(start_range, stop_range):
+            self.assertEqual(rl[sa:so], list(tl[sa:so]))
+        # start and step
+        for sa, se in product(start_range, step_range):
+            self.assertEqual(rl[sa::se], list(tl[sa::se]))
+        # stop and step
+        for so, se in product(stop_range, step_range):
+            self.assertEqual(rl[:so:se], list(tl[:so:se]))
+
+        # start, stop and step
+        for sa, so, se in product(start_range, stop_range, step_range):
+            self.assertEqual(rl[sa:so:se], list(tl[sa:so:se]))
+
+    def test_setitem_slice(self):
+        """ Test setitem using a slice.
+
+        This tests suffers from combinatorial explosion, so we parametrize it
+        and compare results against the regular list in a quasi fuzzing
+        approach.
+
+        """
+
+        def setup(start=10, stop=20):
+            # initialize regular list
+            rl_ = list(range(start, stop))
+            # initialize typed list
+            tl_ = List.empty_list(int32)
+            # populate typed list
+            for i in range(start, stop):
+                tl_.append(i)
+            # check they are the same
+            self.assertEqual(rl_, list(tl_))
+            return rl_, tl_
+
+        ### Simple slicing ###
+
+        # assign to itself
+        rl, tl = setup()
+        rl[:], tl[:] = rl, tl
+        self.assertEqual(rl, list(tl))
+
+        # extend self
+        rl, tl = setup()
+        rl[len(rl):], tl[len(tl):] = rl, tl
+        self.assertEqual(rl, list(tl))
+        # prepend self
+        rl, tl = setup()
+        rl[:0], tl[:0] = rl, tl
+        self.assertEqual(rl, list(tl))
+        # partial assign to self, with equal length
+        rl, tl = setup()
+        rl[3:5], tl[3:5] = rl[6:8], tl[6:8]
+        self.assertEqual(rl, list(tl))
+        # partial assign to self, with larger slice
+        rl, tl = setup()
+        rl[3:5], tl[3:5] = rl[6:9], tl[6:9]
+        self.assertEqual(rl, list(tl))
+        # partial assign to self, with smaller slice
+        rl, tl = setup()
+        rl[3:5], tl[3:5] = rl[6:7], tl[6:7]
+        self.assertEqual(rl, list(tl))
+
+        # extend
+        rl, tl = setup()
+        rl[len(rl):] = list(range(110, 120))
+        tl[len(tl):] = to_tl(range(110,120))
+        self.assertEqual(rl, list(tl))
+        # extend empty
+        rl, tl = setup(0, 0)
+        rl[len(rl):] = list(range(110, 120))
+        tl[len(tl):] = to_tl(range(110,120))
+        self.assertEqual(rl, list(tl))
+        # extend singleton
+        rl, tl = setup(0, 1)
+        rl[len(rl):] = list(range(110, 120))
+        tl[len(tl):] = to_tl(range(110,120))
+        self.assertEqual(rl, list(tl))
+
+        # prepend
+        rl, tl = setup()
+        rl[:0], tl[:0] = list(range(110, 120)), to_tl(range(110,120))
+        self.assertEqual(rl, list(tl))
+        # prepend empty
+        rl, tl = setup(0,0)
+        rl[:0], tl[:0] = list(range(110, 120)), to_tl(range(110,120))
+        self.assertEqual(rl, list(tl))
+        # prepend singleton
+        rl, tl = setup(0,1)
+        rl[:0], tl[:0] = list(range(110, 120)), to_tl(range(110,120))
+        self.assertEqual(rl, list(tl))
+
+        # simple equal length assignment, just replace
+        rl, tl = setup()
+        rl[1:3], tl[1:3] = [100, 200], to_tl([100, 200])
+        self.assertEqual(rl, list(tl))
+
+        # slice for assignment is larger, need to replace and insert
+        rl, tl = setup()
+        rl[1:3], tl[1:3] = [100, 200, 300, 400], to_tl([100, 200, 300, 400])
+        self.assertEqual(rl, list(tl))
+
+        # slice for assignment is smaller, need to replace and delete
+        rl, tl = setup()
+        rl[1:3], tl[1:3] = [100], to_tl([100])
+        self.assertEqual(rl, list(tl))
+
+        # slice for assignment is smaller and item is empty, need to delete
+        rl, tl = setup()
+        rl[1:3], tl[1:3] = [], to_tl([])
+        self.assertEqual(rl, list(tl))
+
+        # Synonym for clear
+        rl, tl = setup()
+        rl[:], tl[:] = [], to_tl([])
+        self.assertEqual(rl, list(tl))
+
+        ### Extended slicing ###
+
+        # replace every second element
+        rl, tl = setup()
+        rl[::2], tl[::2] = [100,200,300,400,500], to_tl([100,200,300,400,500])
+        self.assertEqual(rl, list(tl))
+        # replace every second element, backwards
+        rl, tl = setup()
+        rl[::-2], tl[::-2] = [100,200,300,400,500], to_tl([100,200,300,400,500])
+        self.assertEqual(rl, list(tl))
+
+        # reverse assign to itself
+        rl, tl = setup()
+        rl[::-1], tl[::-1] = rl, tl
+        self.assertEqual(rl, list(tl))
+
+    def test_setitem_slice_value_error(self):
+        self.disable_leak_check()
+
+        tl = List.empty_list(int32)
+        for i in range(10,20):
+            tl.append(i)
+
+        assignment = List.empty_list(int32)
+        for i in range(1, 4):
+            assignment.append(i)
+
+        with self.assertRaises(ValueError) as raises:
+            tl[8:3:-1] = assignment
+        self.assertIn(
+            "length mismatch for extended slice and sequence",
+            str(raises.exception),
+        )
+
+    def test_delitem_slice(self):
+        """ Test delitem using a slice.
+
+        This tests suffers from combinatorial explosion, so we parametrize it
+        and compare results against the regular list in a quasi fuzzing
+        approach.
+
+        """
+
+        def setup(start=10, stop=20):
+            # initialize regular list
+            rl_ = list(range(start, stop))
+            # initialize typed list
+            tl_ = List.empty_list(int32)
+            # populate typed list
+            for i in range(start, stop):
+                tl_.append(i)
+            # check they are the same
+            self.assertEqual(rl_, list(tl_))
+            return rl_, tl_
+
+        # define the ranges
+        start_range = list(range(-20, 30))
+        stop_range = list(range(-20, 30))
+        step_range = [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5]
+
+        rl, tl = setup()
+        # check that they are the same initially
+        self.assertEqual(rl, list(tl))
+        # check that deletion of the whole list by slice works
+        del rl[:]
+        del tl[:]
+        self.assertEqual(rl, list(tl))
+
+        # start only
+        for sa in start_range:
+            rl, tl = setup()
+            del rl[sa:]
+            del tl[sa:]
+            self.assertEqual(rl, list(tl))
+        # stop only
+        for so in stop_range:
+            rl, tl = setup()
+            del rl[:so]
+            del tl[:so]
+            self.assertEqual(rl, list(tl))
+        # step only
+        for se in step_range:
+            rl, tl = setup()
+            del rl[::se]
+            del tl[::se]
+            self.assertEqual(rl, list(tl))
+
+        # start and stop
+        for sa, so in product(start_range, stop_range):
+            rl, tl = setup()
+            del rl[sa:so]
+            del tl[sa:so]
+            self.assertEqual(rl, list(tl))
+        # start and step
+        for sa, se in product(start_range, step_range):
+            rl, tl = setup()
+            del rl[sa::se]
+            del tl[sa::se]
+            self.assertEqual(rl, list(tl))
+        # stop and step
+        for so, se in product(stop_range, step_range):
+            rl, tl = setup()
+            del rl[:so:se]
+            del tl[:so:se]
+            self.assertEqual(rl, list(tl))
+
+        # start, stop and step
+        for sa, so, se in product(start_range, stop_range, step_range):
+            rl, tl = setup()
+            del rl[sa:so:se]
+            del tl[sa:so:se]
+            self.assertEqual(rl, list(tl))
+
+    def test_list_create_no_jit_using_empty_list(self):
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                l = List.empty_list(types.int32)
+                self.assertEqual(type(l), list)
+
+    def test_list_create_no_jit_using_List(self):
+        with override_config('DISABLE_JIT', True):
+            with forbid_codegen():
+                l = List()
+                self.assertEqual(type(l), list)
+
+    def test_catch_global_typed_list(self):
+        @njit()
+        def foo():
+            x = List()
+            for i in global_typed_list:
+                x.append(i)
+
+        expected_message = ("The use of a ListType[int32] type, assigned to "
+                            "variable 'global_typed_list' in globals, is not "
+                            "supported as globals are considered compile-time "
+                            "constants and there is no known way to compile "
+                            "a ListType[int32] type as a constant.")
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            expected_message,
+            str(raises.exception),
+        )
+
+    def test_repr(self):
+        l = List()
+        expected = "ListType[Undefined]([])"
+        self.assertEqual(expected, repr(l))
+
+        l = List([int32(i) for i in (1, 2, 3)])
+        expected = "ListType[int32]([1, 2, 3])"
+        self.assertEqual(expected, repr(l))
+
+    def test_repr_long_list_ipython(self):
+        # Test repr of long typed Lists in an IPython session
+        args = ["-m", "IPython", "--quiet", "--quick", "--no-banner",
+                "--colors=NoColor", "-c"]
+        base_cmd = [sys.executable] + args
+        try:
+            subprocess.check_output(base_cmd + ["--version"])
+        except subprocess.CalledProcessError as e:
+            self.skipTest("ipython not found: return code %d" % e.returncode)
+        repr_cmd = [" ".join(
+            [
+                "import sys;",
+                "from numba.typed import List;",
+                "res = repr(List(range(1005)));",
+                "sys.stderr.write(res);"
+            ]
+        )]
+        cmd = base_cmd + repr_cmd
+        p = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            universal_newlines=True,
+        )
+        out, err = p.communicate()
+
+        l = List(range(1005))
+        # Assert that the long list is truncated
+        expected = f"{typeof(l)}([{', '.join(map(str, l[:1000]))}, ...])"
+        self.assertEqual(expected, err)
+
+    def test_iter_mutates_self(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo(x):
+            count = 0
+            for i in x:
+                if count > 1:
+                    x.append(2.)
+                count += 1
+
+        l = List()
+        l.append(1.)
+        l.append(1.)
+        l.append(1.)
+        with self.assertRaises(RuntimeError) as raises:
+            foo(l)
+
+        msg = "list was mutated during iteration"
+        self.assertIn(msg, str(raises.exception))
+
+
+class TestNoneType(MemoryLeakMixin, TestCase):
+
+    def test_append_none(self):
+        @njit
+        def impl():
+            l = List()
+            l.append(None)
+            return l
+
+        self.assertEqual(impl.py_func(), impl())
+
+    def test_len_none(self):
+        @njit
+        def impl():
+            l = List()
+            l.append(None)
+            return len(l)
+
+        self.assertEqual(impl.py_func(), impl())
+
+    def test_getitem_none(self):
+        @njit
+        def impl():
+            l = List()
+            l.append(None)
+            return l[0]
+
+        self.assertEqual(impl.py_func(), impl())
+
+    def test_setitem_none(self):
+        @njit
+        def impl():
+            l = List()
+            l.append(None)
+            l[0] = None
+            return l
+
+        self.assertEqual(impl.py_func(), impl())
+
+    def test_equals_none(self):
+        @njit
+        def impl():
+            l = List()
+            l.append(None)
+            m = List()
+            m.append(None)
+            return l == m, l != m, l < m, l <= m, l > m, l >= m
+
+        self.assertEqual(impl.py_func(), impl())
+
+    def test_not_equals_none(self):
+        @njit
+        def impl():
+            l = List()
+            l.append(None)
+            m = List()
+            m.append(1)
+            return l == m, l != m, l < m, l <= m, l > m, l >= m
+
+        self.assertEqual(impl.py_func(), impl())
+
+    def test_iter_none(self):
+        @njit
+        def impl():
+            l = List()
+            l.append(None)
+            l.append(None)
+            l.append(None)
+            count = 0
+            for i in l:
+                count += 1
+            return count
+
+        self.assertEqual(impl.py_func(), impl())
+
+    def test_none_typed_method_fails(self):
+        """ Test that unsupported operations on List[None] raise. """
+        def generate_function(line1, line2):
+            context = {}
+            exec(dedent("""
+                from numba.typed import List
+                def bar():
+                    lst = List()
+                    {}
+                    {}
+                """.format(line1, line2)), context)
+            return njit(context["bar"])
+        for line1, line2 in (
+                ("lst.append(None)", "lst.pop()"),
+                ("lst.append(None)", "del lst[0]"),
+                ("lst.append(None)", "lst.count(None)"),
+                ("lst.append(None)", "lst.index(None)"),
+                ("lst.append(None)", "lst.insert(0, None)"),
+                (""                , "lst.insert(0, None)"),
+                ("lst.append(None)", "lst.clear()"),
+                ("lst.append(None)", "lst.copy()"),
+                ("lst.append(None)", "lst.extend([None])"),
+                ("",                 "lst.extend([None])"),
+                ("lst.append(None)", "lst.remove(None)"),
+                ("lst.append(None)", "lst.reverse()"),
+                ("lst.append(None)", "None in lst"),
+        ):
+            with self.assertRaises(TypingError) as raises:
+                foo = generate_function(line1, line2)
+                foo()
+            self.assertIn(
+                "method support for List[None] is limited",
+                str(raises.exception),
+            )
+
+
+class TestAllocation(MemoryLeakMixin, TestCase):
+
+    def test_allocation(self):
+        # kwarg version
+        for i in range(16):
+            tl = List.empty_list(types.int32, allocated=i)
+            self.assertEqual(tl._allocated(), i)
+
+        # posarg version
+        for i in range(16):
+            tl = List.empty_list(types.int32, i)
+            self.assertEqual(tl._allocated(), i)
+
+    def test_allocation_njit(self):
+        # kwarg version
+        @njit
+        def foo(i):
+            tl = List.empty_list(types.int32, allocated=i)
+            return tl._allocated()
+
+        for j in range(16):
+            self.assertEqual(foo(j), j)
+
+        # posarg version
+        @njit
+        def foo(i):
+            tl = List.empty_list(types.int32, i)
+            return tl._allocated()
+
+        for j in range(16):
+            self.assertEqual(foo(j), j)
+
+    def test_growth_and_shrinkage(self):
+        tl = List.empty_list(types.int32)
+        growth_before = {0: 0, 4:4, 8:8, 16:16}
+        growth_after = {0: 4, 4:8, 8:16, 16:25}
+        for i in range(17):
+            if i in growth_before:
+                self.assertEqual(growth_before[i], tl._allocated())
+            tl.append(i)
+            if i in growth_after:
+                self.assertEqual(growth_after[i], tl._allocated())
+
+        shrink_before = {17: 25, 12:25, 9:18, 6:12, 4:8, 3:6, 2:5, 1:4}
+        shrink_after = {17: 25, 12:18, 9:12, 6:8, 4:6, 3:5, 2:4, 1:0}
+        for i in range(17, 0, -1):
+            if i in shrink_before:
+                self.assertEqual(shrink_before[i], tl._allocated())
+            tl.pop()
+            if i in shrink_after:
+                self.assertEqual(shrink_after[i], tl._allocated())
+
+
+class TestExtend(MemoryLeakMixin, TestCase):
+
+    def test_extend_other(self):
+        @njit
+        def impl(other):
+            l = List.empty_list(types.int32)
+            for x in range(10):
+                l.append(x)
+            l.extend(other)
+            return l
+
+        other = List.empty_list(types.int32)
+        for x in range(10):
+            other.append(x)
+
+        expected = impl.py_func(other)
+        got = impl(other)
+        self.assertEqual(expected, got)
+
+    def test_extend_self(self):
+        @njit
+        def impl():
+            l = List.empty_list(types.int32)
+            for x in range(10):
+                l.append(x)
+            l.extend(l)
+            return l
+
+        expected = impl.py_func()
+        got = impl()
+        self.assertEqual(expected, got)
+
+    def test_extend_tuple(self):
+        @njit
+        def impl():
+            l = List.empty_list(types.int32)
+            for x in range(10):
+                l.append(x)
+            l.extend((100,200,300))
+            return l
+
+        expected = impl.py_func()
+        got = impl()
+        self.assertEqual(expected, got)
+
+    def test_extend_single_value_container(self):
+        @njit
+        def impl():
+            l = List()
+            l.extend((100,))
+            return l
+
+        expected = impl.py_func()
+        got = impl()
+        self.assertEqual(expected, got)
+
+    def test_extend_empty_unrefined(self):
+        # Extending an unrefined list with an empty iterable doesn't work in a
+        # jit compiled function as the list remains untyped.
+        l = List()
+        ret = l.extend(tuple())
+        self.assertIsNone(ret)
+        self.assertEqual(len(l), 0)
+        self.assertFalse(l._typed)
+
+    def test_extend_empty_refiend(self):
+        # Extending a refined list with an empty iterable doesn't work in a
+        # jit compiled function as the (empty) argument can't be typed
+        l = List((1,))
+        l.extend(tuple())
+        self.assertEqual(len(l), 1)
+        self.assertTrue(l._typed)
+
+
+@njit
+def cmp(a, b):
+    return a < b, a <= b, a == b, a != b, a >= b, a > b
+
+
+class TestComparisons(MemoryLeakMixin, TestCase):
+
+    def _cmp_dance(self, expected, pa, pb, na, nb):
+        # interpreter with regular list
+        self.assertEqual(cmp.py_func(pa, pb), expected)
+
+        # interpreter with typed-list
+        py_got = cmp.py_func(na, nb)
+        self.assertEqual(py_got, expected)
+
+        # compiled with typed-list
+        jit_got = cmp(na, nb)
+        self.assertEqual(jit_got, expected)
+
+    def test_empty_vs_empty(self):
+        pa, pb = [], []
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = False, True, True, False, True, False
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_empty_vs_singleton(self):
+        pa, pb = [], [0]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = True, True, False, True, False, False
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_singleton_vs_empty(self):
+        pa, pb = [0], []
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = False, False, False, True, True, True
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_singleton_vs_singleton_equal(self):
+        pa, pb = [0], [0]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = False, True, True, False, True, False
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_singleton_vs_singleton_less_than(self):
+        pa, pb = [0], [1]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = True, True, False, True, False, False
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_singleton_vs_singleton_greater_than(self):
+        pa, pb = [1], [0]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = False, False, False, True, True, True
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_equal(self):
+        pa, pb = [1, 2, 3], [1, 2, 3]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = False, True, True, False, True, False
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_first_shorter(self):
+        pa, pb = [1, 2], [1, 2, 3]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = True, True, False, True, False, False
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_second_shorter(self):
+        pa, pb = [1, 2, 3], [1, 2]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = False, False, False, True, True, True
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_first_less_than(self):
+        pa, pb = [1, 2, 2], [1, 2, 3]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = True, True, False, True, False, False
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_first_greater_than(self):
+        pa, pb = [1, 2, 3], [1, 2, 2]
+        na, nb = to_tl(pa), to_tl(pb)
+        expected = False, False, False, True, True, True
+        self._cmp_dance(expected, pa, pb, na, nb)
+
+    def test_equals_non_list(self):
+        l = to_tl([1, 2, 3])
+        self.assertFalse(any(cmp.py_func(l, 1)))
+        self.assertFalse(any(cmp(l, 1)))
+
+
+class TestListInferred(TestCase):
+
+    def test_simple_refine_append(self):
+        @njit
+        def foo():
+            l = List()
+            l.append(1)
+            return l
+
+        expected = foo.py_func()
+        got = foo()
+        self.assertEqual(expected, got)
+        self.assertEqual(list(got), [1])
+        self.assertEqual(typeof(got).item_type, typeof(1))
+
+    def test_simple_refine_insert(self):
+        @njit
+        def foo():
+            l = List()
+            l.insert(0, 1)
+            return l
+
+        expected = foo.py_func()
+        got = foo()
+        self.assertEqual(expected, got)
+        self.assertEqual(list(got), [1])
+        self.assertEqual(typeof(got).item_type, typeof(1))
+
+    def test_refine_extend_list(self):
+        @njit
+        def foo():
+            a = List()
+            b = List()
+            for i in range(3):
+                b.append(i)
+            a.extend(b)
+            return a
+
+        expected = foo.py_func()
+        got = foo()
+        self.assertEqual(expected, got)
+        self.assertEqual(list(got), [0, 1, 2])
+        self.assertEqual(typeof(got).item_type, typeof(1))
+
+    def test_refine_extend_set(self):
+        @njit
+        def foo():
+            l = List()
+            l.extend((0, 1, 2))
+            return l
+
+        expected = foo.py_func()
+        got = foo()
+        self.assertEqual(expected, got)
+        self.assertEqual(list(got), [0, 1, 2])
+        self.assertEqual(typeof(got).item_type, typeof(1))
+
+    def test_refine_list_extend_iter(self):
+        @njit
+        def foo():
+            l = List()
+            d = Dict()
+            d[0] = 0
+            # d.keys() provides a DictKeysIterableType
+            l.extend(d.keys())
+            return l
+
+        got = foo()
+        self.assertEqual(0, got[0])
+
+
+class TestListRefctTypes(MemoryLeakMixin, TestCase):
+
+    def test_str_item(self):
+        @njit
+        def foo():
+            l = List.empty_list(types.unicode_type)
+            for s in ("a", "ab", "abc", "abcd"):
+                l.append(s)
+            return l
+
+        l = foo()
+        expected = ["a", "ab", "abc", "abcd"]
+        for i, s in enumerate(expected):
+            self.assertEqual(l[i], s)
+        self.assertEqual(list(l), expected)
+        # Test insert replacement
+        l[3] = 'uxyz'
+        self.assertEqual(l[3], 'uxyz')
+        # Test list growth
+        nelem = 100
+        for i in range(4, nelem):
+            l.append(str(i))
+            self.assertEqual(l[i], str(i))
+
+    def test_str_item_refcount_replace(self):
+        @njit
+        def foo():
+            # use some tricks to make ref-counted unicode
+            i, j = 'ab', 'c'
+            a = i + j
+            m, n = 'zy', 'x'
+            z = m + n
+            l = List.empty_list(types.unicode_type)
+            l.append(a)
+            # This *should* dec' a and inc' z thus tests that items that are
+            # replaced are also dec'ed.
+            l[0] = z
+            ra, rz = get_refcount(a), get_refcount(z)
+            return l, ra, rz
+
+        l, ra, rz = foo()
+        self.assertEqual(l[0], "zyx")
+        self.assertEqual(ra, 1)
+        self.assertEqual(rz, 2)
+
+    def test_dict_as_item_in_list(self):
+        @njit
+        def foo():
+            l = List.empty_list(Dict.empty(int32, int32))
+            d = Dict.empty(int32, int32)
+            d[0] = 1
+            # This increments the refcount for d
+            l.append(d)
+            return get_refcount(d)
+
+        c = foo()
+        if config.LLVM_REFPRUNE_PASS:
+            # Because the pruner cleared all other increfs
+            self.assertEqual(1, c)
+        else:
+            self.assertEqual(2, c)
+
+    def test_dict_as_item_in_list_multi_refcount(self):
+        @njit
+        def foo():
+            l = List.empty_list(Dict.empty(int32, int32))
+            d = Dict.empty(int32, int32)
+            d[0] = 1
+            # This increments the refcount for d, twice
+            l.append(d)
+            l.append(d)
+            return get_refcount(d)
+
+        c = foo()
+        if config.LLVM_REFPRUNE_PASS:
+            # Because the pruner cleared all other increfs
+            self.assertEqual(1, c)
+        else:
+            self.assertEqual(3, c)
+
+    def test_list_as_value_in_dict(self):
+        @njit
+        def foo():
+            d = Dict.empty(int32, List.empty_list(int32))
+            l = List.empty_list(int32)
+            l.append(0)
+            # This increments the refcount for l
+            d[0] = l
+            return get_refcount(l)
+
+        c = foo()
+        if config.LLVM_REFPRUNE_PASS:
+            # Because the pruner cleared all other increfs
+            self.assertEqual(1, c)
+        else:
+            self.assertEqual(2, c)
+
+    def test_list_as_item_in_list(self):
+        nested_type = types.ListType(types.int32)
+
+        @njit
+        def foo():
+            la = List.empty_list(nested_type)
+            lb = List.empty_list(types.int32)
+            lb.append(1)
+            la.append(lb)
+            return la
+
+        expected = foo.py_func()
+        got = foo()
+        self.assertEqual(expected, got)
+
+    def test_array_as_item_in_list(self):
+        nested_type = types.Array(types.float64, 1, 'C')
+
+        @njit
+        def foo():
+            l = List.empty_list(nested_type)
+            a = np.zeros((1,))
+            l.append(a)
+            return l
+
+        expected = foo.py_func()
+        got = foo()
+        # Need to compare the nested arrays
+        self.assertTrue(np.all(expected[0] == got[0]))
+
+    def test_array_pop_from_single_value_list(self):
+        @njit
+        def foo():
+            l = List((np.zeros((1,)),))
+            l.pop()
+            return l
+
+        expected, got = foo.py_func(), foo()
+        # Need to compare the nested arrays
+        self.assertEqual(len(expected), 0)
+        self.assertEqual(len(got), 0)
+        # FIXME comparison of empty array-typed lists fails
+        # self.assertEqual(expected, got)
+
+    def test_5264(self):
+        # Test the reproducer from #5264 and make sure it doesn't segfault
+        float_array = types.float64[:]
+        l = List.empty_list(float_array)
+        l.append(np.ones(3,dtype=np.float64))
+        l.pop()
+        self.assertEqual(0, len(l))
+
+    def test_jitclass_as_item_in_list(self):
+
+        spec = [
+            ('value', int32),               # a simple scalar field
+            ('array', float32[:]),          # an array field
+        ]
+
+        @jitclass(spec)
+        class Bag(object):
+            def __init__(self, value):
+                self.value = value
+                self.array = np.zeros(value, dtype=np.float32)
+
+            @property
+            def size(self):
+                return self.array.size
+
+            def increment(self, val):
+                for i in range(self.size):
+                    self.array[i] += val
+                return self.array
+
+        @njit
+        def foo():
+            l = List()
+            l.append(Bag(21))
+            l.append(Bag(22))
+            l.append(Bag(23))
+            return l
+
+        expected = foo.py_func()
+        got = foo()
+
+        def bag_equal(one, two):
+            # jitclasses couldn't override __eq__ at time of writing
+            self.assertEqual(one.value, two.value)
+            np.testing.assert_allclose(one.array, two.array)
+
+        [bag_equal(a, b) for a, b in zip(expected, got)]
+
+    def test_4960(self):
+        # Test the reproducer from #4960 and make sure it doesn't segfault
+        @jitclass([('value', int32)])
+        class Simple(object):
+            def __init__(self, value):
+                self.value = value
+
+        @njit
+        def foo():
+            l = List((Simple(23),Simple(24)))
+            l.pop()
+            return l
+
+        l = foo()
+        self.assertEqual(1, len(l))
+
+    def test_storage_model_mismatch(self):
+        # https://github.com/numba/numba/issues/4520
+        # check for storage model mismatch in refcount ops generation
+        lst = List()
+        ref = [
+            ("a", True, "a"),
+            ("b", False, "b"),
+            ("c", False, "c"),
+        ]
+        # populate
+        for x in ref:
+            lst.append(x)
+        # test
+        for i, x in enumerate(ref):
+            self.assertEqual(lst[i], ref[i])
+
+    def test_equals_on_list_with_dict_for_equal_lists(self):
+        # https://github.com/numba/numba/issues/4879
+        a, b = List(), Dict()
+        b["a"] = 1
+        a.append(b)
+
+        c, d = List(), Dict()
+        d["a"] = 1
+        c.append(d)
+
+        self.assertEqual(a, c)
+
+    def test_equals_on_list_with_dict_for_unequal_dicts(self):
+        # https://github.com/numba/numba/issues/4879
+        a, b = List(), Dict()
+        b["a"] = 1
+        a.append(b)
+
+        c, d = List(), Dict()
+        d["a"] = 2
+        c.append(d)
+
+        self.assertNotEqual(a, c)
+
+    def test_equals_on_list_with_dict_for_unequal_lists(self):
+        # https://github.com/numba/numba/issues/4879
+        a, b = List(), Dict()
+        b["a"] = 1
+        a.append(b)
+
+        c, d, e = List(), Dict(), Dict()
+        d["a"] = 1
+        e["b"] = 2
+        c.append(d)
+        c.append(e)
+
+        self.assertNotEqual(a, c)
+
+
+class TestListSort(MemoryLeakMixin, TestCase):
+    def setUp(self):
+        super(TestListSort, self).setUp()
+        np.random.seed(0)
+
+    def make(self, ctor, data):
+        lst = ctor()
+        lst.extend(data)
+        return lst
+
+    def make_both(self, data):
+        return {
+            'py': self.make(list, data),
+            'nb': self.make(List, data),
+        }
+
+    def test_sort_no_args(self):
+        def udt(lst):
+            lst.sort()
+            return lst
+
+        for nelem in [13, 29, 127]:
+            my_lists = self.make_both(np.random.randint(0, nelem, nelem))
+            self.assertEqual(list(udt(my_lists['nb'])), udt(my_lists['py']))
+
+    def test_sort_all_args(self):
+        def udt(lst, key, reverse):
+            lst.sort(key=key, reverse=reverse)
+            return lst
+
+        possible_keys = [
+            lambda x: -x,           # negative
+            lambda x: 1 / (1 + x),  # make float
+            lambda x: (x, -x),      # tuple
+            lambda x: x,            # identity
+        ]
+        possible_reverse = [True, False]
+        for key, reverse in product(possible_keys, possible_reverse):
+            my_lists = self.make_both(np.random.randint(0, 100, 23))
+            msg = "case for key={} reverse={}".format(key, reverse)
+            self.assertEqual(
+                list(udt(my_lists['nb'], key=key, reverse=reverse)),
+                udt(my_lists['py'], key=key, reverse=reverse),
+                msg=msg,
+            )
+
+    def test_sort_dispatcher_key(self):
+        def udt(lst, key):
+            lst.sort(key=key)
+            return lst
+
+        my_lists = self.make_both(np.random.randint(0, 100, 31))
+        py_key = lambda x: x + 1
+        nb_key = njit(lambda x: x + 1)
+        # test typedlist with jitted function
+        self.assertEqual(
+            list(udt(my_lists['nb'], key=nb_key)),
+            udt(my_lists['py'], key=py_key),
+        )
+        # test typedlist with and without jitted function
+        self.assertEqual(
+            list(udt(my_lists['nb'], key=nb_key)),
+            list(udt(my_lists['nb'], key=py_key)),
+        )
+
+    def test_sort_in_jit_w_lambda_key(self):
+        @njit
+        def udt(lst):
+            lst.sort(key=lambda x: -x)
+            return lst
+
+        lst = self.make(List, np.random.randint(0, 100, 31))
+        self.assertEqual(udt(lst), udt.py_func(lst))
+
+    def test_sort_in_jit_w_global_key(self):
+        @njit
+        def keyfn(x):
+            return -x
+
+        @njit
+        def udt(lst):
+            lst.sort(key=keyfn)
+            return lst
+
+        lst = self.make(List, np.random.randint(0, 100, 31))
+        self.assertEqual(udt(lst), udt.py_func(lst))
+
+    def test_sort_on_arrays(self):
+        @njit
+        def foo(lst):
+            lst.sort(key=lambda arr: np.sum(arr))
+            return lst
+
+        arrays = [np.random.random(3) for _ in range(10)]
+        my_lists = self.make_both(arrays)
+        self.assertEqual(
+            list(foo(my_lists['nb'])),
+            foo.py_func(my_lists['py']),
+        )
+
+
+class TestImmutable(MemoryLeakMixin, TestCase):
+
+    def test_is_immutable(self):
+        @njit
+        def foo():
+            l = List()
+            l.append(1)
+            return l._is_mutable()
+        self.assertTrue(foo())
+        self.assertTrue(foo.py_func())
+
+    def test_make_immutable_is_immutable(self):
+        @njit
+        def foo():
+            l = List()
+            l.append(1)
+            l._make_immutable()
+            return l._is_mutable()
+        self.assertFalse(foo())
+        self.assertFalse(foo.py_func())
+
+    def test_length_still_works_when_immutable(self):
+        @njit
+        def foo():
+            l = List()
+            l.append(1)
+            l._make_immutable()
+            return len(l),l._is_mutable()
+        length, mutable = foo()
+        self.assertEqual(length, 1)
+        self.assertFalse(mutable)
+
+    def test_getitem_still_works_when_immutable(self):
+        @njit
+        def foo():
+            l = List()
+            l.append(1)
+            l._make_immutable()
+            return l[0], l._is_mutable()
+        test_item, mutable = foo()
+        self.assertEqual(test_item, 1)
+        self.assertFalse(mutable)
+
+    def test_append_fails(self):
+        self.disable_leak_check()
+
+        @njit
+        def foo():
+            l = List()
+            l.append(1)
+            l._make_immutable()
+            l.append(1)
+
+        for func in (foo, foo.py_func):
+            with self.assertRaises(ValueError) as raises:
+                func()
+            self.assertIn(
+                'list is immutable',
+                str(raises.exception),
+            )
+
+    def test_mutation_fails(self):
+        """ Test that any attempt to mutate an immutable typed list fails. """
+        self.disable_leak_check()
+
+        def generate_function(line):
+            context = {}
+            exec(dedent("""
+                from numba.typed import List
+                def bar():
+                    lst = List()
+                    lst.append(1)
+                    lst._make_immutable()
+                    {}
+                """.format(line)), context)
+            return njit(context["bar"])
+        for line in ("lst.append(0)",
+                     "lst[0] = 0",
+                     "lst.pop()",
+                     "del lst[0]",
+                     "lst.extend((0,))",
+                     "lst.insert(0, 0)",
+                     "lst.clear()",
+                     "lst.reverse()",
+                     "lst.sort()",
+                     ):
+            foo = generate_function(line)
+            for func in (foo, foo.py_func):
+                with self.assertRaises(ValueError) as raises:
+                    func()
+                self.assertIn(
+                    "list is immutable",
+                    str(raises.exception),
+                )
+
+
+class TestListFromIter(MemoryLeakMixin, TestCase):
+
+    def test_simple_iterable_types(self):
+        """Test all simple iterables that a List can be constructed from."""
+
+        def generate_function(line):
+            context = {}
+            code = dedent("""
+                from numba.typed import List
+                def bar():
+                    {}
+                    return l
+                """).format(line)
+            exec(code, context)
+            return njit(context["bar"])
+        for line in ("l = List([0, 1, 2])",
+                     "l = List(range(3))",
+                     "l = List(List([0, 1, 2]))",
+                     "l = List((0, 1, 2))",
+                     "l = List(set([0, 1, 2]))",
+                     ):
+            foo = generate_function(line)
+            cf_received, py_received = foo(), foo.py_func()
+            for result in (cf_received, py_received):
+                for i in range(3):
+                    self.assertEqual(i, result[i])
+
+    def test_unicode(self):
+        """Test that a List can be created from a unicode string."""
+        @njit
+        def foo():
+            l = List("abc")
+            return l
+        expected = List()
+        for i in ("a", "b", "c"):
+            expected.append(i)
+        self.assertEqual(foo.py_func(), expected)
+        self.assertEqual(foo(), expected)
+
+    def test_dict_iters(self):
+        """Test that a List can be created from Dict iterators."""
+
+        def generate_function(line):
+            context = {}
+            code = dedent("""
+                from numba.typed import List, Dict
+                def bar():
+                    d = Dict()
+                    d[0], d[1], d[2] = "a", "b", "c"
+                    {}
+                    return l
+                """).format(line)
+            exec(code, context)
+            return njit(context["bar"])
+
+        def generate_expected(values):
+            expected = List()
+            for i in values:
+                expected.append(i)
+            return expected
+
+        for line, values in (
+                ("l = List(d)", (0, 1, 2)),
+                ("l = List(d.keys())", (0, 1, 2)),
+                ("l = List(d.values())", ("a", "b", "c")),
+                ("l = List(d.items())", ((0, "a"), (1, "b"), (2, "c"))),
+        ):
+            foo, expected = generate_function(line), generate_expected(values)
+            for func in (foo, foo.py_func):
+                self.assertEqual(func(), expected)
+
+    def test_ndarray_scalar(self):
+
+        @njit
+        def foo():
+            return List(np.ones(3))
+
+        expected = List()
+        for i in range(3):
+            expected.append(1)
+
+        self.assertEqual(expected, foo())
+        self.assertEqual(expected, foo.py_func())
+
+    def test_ndarray_oned(self):
+
+        @njit
+        def foo():
+            return List(np.array(1))
+
+        expected = List()
+        expected.append(1)
+
+        self.assertEqual(expected, foo())
+        self.assertEqual(expected, foo.py_func())
+
+    def test_ndarray_twod(self):
+
+        @njit
+        def foo(x):
+            return List(x)
+
+        carr = np.array([[1, 2], [3, 4]])
+        farr = np.asfortranarray(carr)
+        aarr = np.arange(8).reshape((2, 4))[:, ::2]
+
+        for layout, arr in zip('CFA', (carr, farr, aarr)):
+            self.assertEqual(typeof(arr).layout, layout)
+            expected = List()
+            expected.append(arr[0, :])
+            expected.append(arr[1, :])
+            received = foo(arr)
+
+            np.testing.assert_equal(expected[0], received[0])
+            np.testing.assert_equal(expected[1], received[1])
+
+            pyreceived = foo.py_func(arr)
+
+            np.testing.assert_equal(expected[0], pyreceived[0])
+            np.testing.assert_equal(expected[1], pyreceived[1])
+
+    def test_exception_on_plain_int(self):
+        @njit
+        def foo():
+            l = List(23)
+            return l
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "List() argument must be iterable",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(TypeError) as raises:
+            List(23)
+        self.assertIn(
+            "List() argument must be iterable",
+            str(raises.exception),
+        )
+
+    def test_exception_on_inhomogeneous_tuple(self):
+        @njit
+        def foo():
+            l = List((1, 1.0))
+            return l
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "List() argument must be iterable",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(TypingError) as raises:
+            List((1, 1.0))
+        # FIXME this bails with a length casting error when we attempt to
+        # append 1.0 to an int typed list.
+
+    def test_exception_on_too_many_args(self):
+        @njit
+        def foo():
+            l = List((0, 1, 2), (3, 4, 5))
+            return l
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "List() expected at most 1 argument, got 2",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(TypeError) as raises:
+            List((0, 1, 2), (3, 4, 5))
+        self.assertIn(
+            "List() expected at most 1 argument, got 2",
+            str(raises.exception),
+        )
+
+        @njit
+        def foo():
+            l = List((0, 1, 2), (3, 4, 5), (6, 7, 8))
+            return l
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "List() expected at most 1 argument, got 3",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(TypeError) as raises:
+            List((0, 1, 2), (3, 4, 5), (6, 7, 8))
+        self.assertIn(
+            "List() expected at most 1 argument, got 3",
+            str(raises.exception),
+        )
+
+    def test_exception_on_kwargs(self):
+        @njit
+        def foo():
+            l = List(iterable=(0, 1, 2))
+            return l
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        self.assertIn(
+            "List() takes no keyword arguments",
+            str(raises.exception),
+        )
+
+        with self.assertRaises(TypeError) as raises:
+            List(iterable=(0, 1, 2))
+        self.assertIn(
+            "List() takes no keyword arguments",
+            str(raises.exception),
+        )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typedobjectutils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typedobjectutils.py
new file mode 100644
index 000000000..f93d6252d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typedobjectutils.py
@@ -0,0 +1,68 @@
+"""
+Unit-tests for `typedobjectutils.py`
+"""
+
+import warnings
+
+
+from numba.core import types
+from numba.tests.support import TestCase
+from numba.typed.typedobjectutils import _sentry_safe_cast
+
+
+class TestTypedObjectUtils(TestCase):
+    def test_sentry_safe_cast_warnings(self):
+        warn_cases = []
+        warn_cases += [
+            # integer cases
+            (types.int32, types.int16),
+            (types.int32, types.uint32),
+            (types.int64, types.uint32),
+            # float cases
+            (types.float64, types.float32),
+            # complex cases
+            (types.complex128, types.complex64),
+            # int to float cases
+            (types.int32, types.float32),
+            (types.int64, types.float32),
+            # tuple-of-ints to tuple-of-floats,
+            (types.Tuple([types.int32]), types.Tuple([types.float32])),
+        ]
+
+        for fromty, toty in warn_cases:
+            with self.subTest(fromty=fromty, toty=toty):
+                with warnings.catch_warnings(record=True) as w:
+                    _sentry_safe_cast(fromty, toty)
+                self.assertEqual(len(w), 1)
+                # Make sure the warning is about unsafe cast
+                self.assertIn(
+                    "unsafe cast from {} to {}".format(fromty, toty),
+                    str(w[0]),
+                )
+
+    def test_sentry_safe_cast_no_warn(self):
+        ok_cases = []
+        ok_cases += [
+            # integer cases
+            (types.int32, types.int64),
+            (types.uint8, types.int32),
+            # float cases
+            (types.float32, types.float64),
+            # complex cases
+            (types.complex64, types.complex128),
+            # int to float cases
+            (types.int32, types.float64),
+            (types.uint8, types.float32),
+            # float to complex cases
+            (types.float32, types.complex128),
+            (types.float64, types.complex128),
+            # tuple-of-ints to tuple-of-ints,
+            (types.Tuple([types.int32]), types.Tuple([types.int64])),
+        ]
+
+        for fromty, toty in ok_cases:
+            with self.subTest(fromty=fromty, toty=toty):
+                with warnings.catch_warnings(record=True) as w:
+                    _sentry_safe_cast(fromty, toty)
+                # Expect no warnings
+                self.assertEqual(len(w), 0)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeguard.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeguard.py
new file mode 100644
index 000000000..83c466846
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeguard.py
@@ -0,0 +1,35 @@
+"""
+Tests to ensure that typeguard is working as expected.
+This mostly contains negative tests as proof that typeguard can catch errors.
+"""
+import unittest
+from numba.tests.support import TestCase, skip_unless_typeguard
+
+
+def guard_args(val: int):
+    return
+
+
+def guard_ret(val) -> int:
+    return val
+
+
+@skip_unless_typeguard
+class TestTypeGuard(TestCase):
+    def test_check_args(self):
+        with self.assertRaises(TypeError):
+            guard_args(float(1.2))
+
+    def test_check_ret(self):
+        with self.assertRaises(TypeError):
+            guard_ret(float(1.2))
+
+    def test_check_does_not_work_with_inner_func(self):
+        def guard(val: int) -> int:
+            return
+
+        guard(float(1.2))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeinfer.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeinfer.py
new file mode 100644
index 000000000..f9d257533
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeinfer.py
@@ -0,0 +1,837 @@
+import os, sys, subprocess
+import itertools
+
+import numpy as np
+
+import numba
+from numba.core.compiler import compile_isolated
+from numba import jit
+from numba.core import errors, ir, types, typing, typeinfer, utils
+from numba.core.typeconv import Conversion
+
+from numba.tests.support import TestCase, tag
+from numba.tests.test_typeconv import CompatibilityTestMixin
+from numba.core.untyped_passes import TranslateByteCode, IRProcessing
+from numba.core.typed_passes import PartialTypeInference
+from numba.core.compiler_machinery import FunctionPass, register_pass
+import unittest
+
+
+i8 = types.int8
+i16 = types.int16
+i32 = types.int32
+i64 = types.int64
+u8 = types.uint8
+u16 = types.uint16
+u32 = types.uint32
+u64 = types.uint64
+f32 = types.float32
+f64 = types.float64
+c64 = types.complex64
+c128 = types.complex128
+
+
+class TestArgRetCasting(unittest.TestCase):
+    def test_arg_ret_casting(self):
+        def foo(x):
+            return x
+
+        args = (i32,)
+        return_type = f32
+        cres = compile_isolated(foo, args, return_type)
+        self.assertTrue(isinstance(cres.entry_point(123), float))
+        self.assertEqual(cres.signature.args, args)
+        self.assertEqual(cres.signature.return_type, return_type)
+
+    def test_arg_ret_mismatch(self):
+        def foo(x):
+            return x
+
+        args = (types.Array(i32, 1, 'C'),)
+        return_type = f32
+        try:
+            cres = compile_isolated(foo, args, return_type)
+        except errors.TypingError as e:
+            pass
+        else:
+            self.fail("Should complain about array casting to float32")
+
+    def test_invalid_arg_type_forcing(self):
+        def foo(iters):
+            a = range(iters)
+            return iters
+
+        args = (u32,)
+        return_type = u8
+        cres = compile_isolated(foo, args, return_type)
+        typemap = cres.type_annotation.typemap
+        # Argument "iters" must be uint32
+        self.assertEqual(typemap['iters'], u32)
+
+
+class TestUnify(unittest.TestCase):
+    """
+    Tests for type unification with a typing context.
+    """
+
+    int_unify = {
+        ('uint8', 'uint8'): 'uint8',
+        ('int8', 'int8'): 'int8',
+        ('uint16', 'uint16'): 'uint16',
+        ('int16', 'int16'): 'int16',
+        ('uint32', 'uint32'): 'uint32',
+        ('int32', 'int32'): 'int32',
+        ('uint64', 'uint64'): 'uint64',
+        ('int64', 'int64'): 'int64',
+
+        ('int8', 'uint8'): 'int16',
+        ('int8', 'uint16'): 'int32',
+        ('int8', 'uint32'): 'int64',
+
+        ('uint8', 'int32'): 'int32',
+        ('uint8', 'uint64'): 'uint64',
+
+        ('int16', 'int8'): 'int16',
+        ('int16', 'uint8'): 'int16',
+        ('int16', 'uint16'): 'int32',
+        ('int16', 'uint32'): 'int64',
+        ('int16', 'int64'): 'int64',
+        ('int16', 'uint64'): 'float64',
+
+        ('uint16', 'uint8'): 'uint16',
+        ('uint16', 'uint32'): 'uint32',
+        ('uint16', 'int32'): 'int32',
+        ('uint16', 'uint64'): 'uint64',
+
+        ('int32', 'int8'): 'int32',
+        ('int32', 'int16'): 'int32',
+        ('int32', 'uint32'): 'int64',
+        ('int32', 'int64'): 'int64',
+
+        ('uint32', 'uint8'): 'uint32',
+        ('uint32', 'int64'): 'int64',
+        ('uint32', 'uint64'): 'uint64',
+
+        ('int64', 'int8'): 'int64',
+        ('int64', 'uint8'): 'int64',
+        ('int64', 'uint16'): 'int64',
+
+        ('uint64', 'int8'): 'float64',
+        ('uint64', 'int32'): 'float64',
+        ('uint64', 'int64'): 'float64',
+    }
+
+    def assert_unify(self, aty, bty, expected):
+        ctx = typing.Context()
+        template = "{0}, {1} -> {2} != {3}"
+        for unify_func in ctx.unify_types, ctx.unify_pairs:
+            unified = unify_func(aty, bty)
+            self.assertEqual(unified, expected,
+                             msg=template.format(aty, bty, unified, expected))
+            unified = unify_func(bty, aty)
+            self.assertEqual(unified, expected,
+                             msg=template.format(bty, aty, unified, expected))
+
+    def assert_unify_failure(self, aty, bty):
+        self.assert_unify(aty, bty, None)
+
+    def test_integer(self):
+        ctx = typing.Context()
+        for aty, bty in itertools.product(types.integer_domain,
+                                          types.integer_domain):
+            key = (str(aty), str(bty))
+            try:
+                expected = self.int_unify[key]
+            except KeyError:
+                expected = self.int_unify[key[::-1]]
+            self.assert_unify(aty, bty, getattr(types, expected))
+
+    def test_bool(self):
+        aty = types.boolean
+        for bty in types.integer_domain:
+            self.assert_unify(aty, bty, bty)
+        # Not sure about this one, but it respects transitivity
+        for cty in types.real_domain:
+            self.assert_unify(aty, cty, cty)
+
+    def unify_number_pair_test(self, n):
+        """
+        Test all permutations of N-combinations of numeric types and ensure
+        that the order of types in the sequence is irrelevant.
+        """
+        ctx = typing.Context()
+        for tys in itertools.combinations(types.number_domain, n):
+            res = [ctx.unify_types(*comb)
+                   for comb in itertools.permutations(tys)]
+            first_result = res[0]
+            # Sanity check
+            self.assertIsInstance(first_result, types.Number)
+            # All results must be equal
+            for other in res[1:]:
+                self.assertEqual(first_result, other)
+
+    def test_unify_number_pair(self):
+        self.unify_number_pair_test(2)
+        self.unify_number_pair_test(3)
+
+    def test_none_to_optional(self):
+        """
+        Test unification of `none` and multiple number types to optional type
+        """
+        ctx = typing.Context()
+        for tys in itertools.combinations(types.number_domain, 2):
+            # First unify without none, to provide the control value
+            tys = list(tys)
+            expected = types.Optional(ctx.unify_types(*tys))
+            results = [ctx.unify_types(*comb)
+                       for comb in itertools.permutations(tys  + [types.none])]
+            # All results must be equal
+            for res in results:
+                self.assertEqual(res, expected)
+
+    def test_none(self):
+        aty = types.none
+        bty = types.none
+        self.assert_unify(aty, bty, types.none)
+
+    def test_optional(self):
+        aty = types.Optional(i32)
+        bty = types.none
+        self.assert_unify(aty, bty, aty)
+        aty = types.Optional(i32)
+        bty = types.Optional(i64)
+        self.assert_unify(aty, bty, bty)
+        aty = types.Optional(i32)
+        bty = i64
+        self.assert_unify(aty, bty, types.Optional(i64))
+        # Failure
+        aty = types.Optional(i32)
+        bty = types.Optional(types.slice3_type)
+        self.assert_unify_failure(aty, bty)
+
+    def test_tuple(self):
+        aty = types.UniTuple(i32, 3)
+        bty = types.UniTuple(i64, 3)
+        self.assert_unify(aty, bty, types.UniTuple(i64, 3))
+        # (Tuple, UniTuple) -> Tuple
+        aty = types.UniTuple(i32, 2)
+        bty = types.Tuple((i16, i64))
+        self.assert_unify(aty, bty, types.Tuple((i32, i64)))
+        aty = types.UniTuple(i64, 0)
+        bty = types.Tuple(())
+        self.assert_unify(aty, bty, bty)
+        # (Tuple, Tuple) -> Tuple
+        aty = types.Tuple((i8, i16, i32))
+        bty = types.Tuple((i32, i16, i8))
+        self.assert_unify(aty, bty, types.Tuple((i32, i16, i32)))
+        aty = types.Tuple((i8, i32))
+        bty = types.Tuple((i32, i8))
+        self.assert_unify(aty, bty, types.Tuple((i32, i32)))
+        aty = types.Tuple((i8, i16))
+        bty = types.Tuple((i16, i8))
+        self.assert_unify(aty, bty, types.Tuple((i16, i16)))
+        # Different number kinds
+        aty = types.UniTuple(f64, 3)
+        bty = types.UniTuple(c64, 3)
+        self.assert_unify(aty, bty, types.UniTuple(c128, 3))
+        # Tuples of tuples
+        aty = types.UniTuple(types.Tuple((u32, f32)), 2)
+        bty = types.UniTuple(types.Tuple((i16, f32)), 2)
+        self.assert_unify(aty, bty,
+                          types.UniTuple(types.Tuple((i64, f32)), 2))
+        # Failures
+        aty = types.UniTuple(i32, 1)
+        bty = types.UniTuple(types.slice3_type, 1)
+        self.assert_unify_failure(aty, bty)
+        aty = types.UniTuple(i32, 1)
+        bty = types.UniTuple(i32, 2)
+        self.assert_unify_failure(aty, bty)
+        aty = types.Tuple((i8, types.slice3_type))
+        bty = types.Tuple((i32, i8))
+        self.assert_unify_failure(aty, bty)
+
+    def test_optional_tuple(self):
+        # Unify to optional tuple
+        aty = types.none
+        bty = types.UniTuple(i32, 2)
+        self.assert_unify(aty, bty, types.Optional(types.UniTuple(i32, 2)))
+        aty = types.Optional(types.UniTuple(i16, 2))
+        bty = types.UniTuple(i32, 2)
+        self.assert_unify(aty, bty, types.Optional(types.UniTuple(i32, 2)))
+        # Unify to tuple of optionals
+        aty = types.Tuple((types.none, i32))
+        bty = types.Tuple((i16, types.none))
+        self.assert_unify(aty, bty, types.Tuple((types.Optional(i16),
+                                                 types.Optional(i32))))
+        aty = types.Tuple((types.Optional(i32), i64))
+        bty = types.Tuple((i16, types.Optional(i8)))
+        self.assert_unify(aty, bty, types.Tuple((types.Optional(i32),
+                                                 types.Optional(i64))))
+
+    def test_arrays(self):
+        aty = types.Array(i32, 3, "C")
+        bty = types.Array(i32, 3, "A")
+        self.assert_unify(aty, bty, bty)
+        aty = types.Array(i32, 3, "C")
+        bty = types.Array(i32, 3, "F")
+        self.assert_unify(aty, bty, types.Array(i32, 3, "A"))
+        aty = types.Array(i32, 3, "C")
+        bty = types.Array(i32, 3, "C", readonly=True)
+        self.assert_unify(aty, bty, bty)
+        aty = types.Array(i32, 3, "A")
+        bty = types.Array(i32, 3, "C", readonly=True)
+        self.assert_unify(aty, bty,
+                          types.Array(i32, 3, "A", readonly=True))
+        # Failures
+        aty = types.Array(i32, 2, "C")
+        bty = types.Array(i32, 3, "C")
+        self.assert_unify_failure(aty, bty)
+        aty = types.Array(i32, 2, "C")
+        bty = types.Array(u32, 2, "C")
+        self.assert_unify_failure(aty, bty)
+
+    def test_list(self):
+        aty = types.List(types.undefined)
+        bty = types.List(i32)
+        self.assert_unify(aty, bty, bty)
+        aty = types.List(i16)
+        bty = types.List(i32)
+        self.assert_unify(aty, bty, bty)
+        aty = types.List(types.Tuple([i32, i16]))
+        bty = types.List(types.Tuple([i16, i64]))
+        cty = types.List(types.Tuple([i32, i64]))
+        self.assert_unify(aty, bty, cty)
+        # Different reflections
+        aty = types.List(i16, reflected=True)
+        bty = types.List(i32)
+        cty = types.List(i32, reflected=True)
+        self.assert_unify(aty, bty, cty)
+        # Incompatible dtypes
+        aty = types.List(i16)
+        bty = types.List(types.Tuple([i16]))
+        self.assert_unify_failure(aty, bty)
+
+    def test_set(self):
+        # Different reflections
+        aty = types.Set(i16, reflected=True)
+        bty = types.Set(i32)
+        cty = types.Set(i32, reflected=True)
+        self.assert_unify(aty, bty, cty)
+        # Incompatible dtypes
+        aty = types.Set(i16)
+        bty = types.Set(types.Tuple([i16]))
+        self.assert_unify_failure(aty, bty)
+
+    def test_range(self):
+        aty = types.range_state32_type
+        bty = types.range_state64_type
+        self.assert_unify(aty, bty, bty)
+
+
+class TestTypeConversion(CompatibilityTestMixin, unittest.TestCase):
+    """
+    Test for conversion between types with a typing context.
+    """
+
+    def assert_can_convert(self, aty, bty, expected):
+        ctx = typing.Context()
+        got = ctx.can_convert(aty, bty)
+        self.assertEqual(got, expected)
+
+    def assert_cannot_convert(self, aty, bty):
+        ctx = typing.Context()
+        got = ctx.can_convert(aty, bty)
+        self.assertIsNone(got)
+
+    def test_convert_number_types(self):
+        # Check that Context.can_convert() is compatible with the default
+        # number conversion rules registered in the typeconv module
+        # (which is used internally by the C _Dispatcher object).
+        ctx = typing.Context()
+        self.check_number_compatibility(ctx.can_convert)
+
+    def test_tuple(self):
+        # UniTuple -> UniTuple
+        aty = types.UniTuple(i32, 3)
+        bty = types.UniTuple(i64, 3)
+        self.assert_can_convert(aty, aty, Conversion.exact)
+        self.assert_can_convert(aty, bty, Conversion.promote)
+        aty = types.UniTuple(i32, 3)
+        bty = types.UniTuple(f64, 3)
+        self.assert_can_convert(aty, bty, Conversion.safe)
+        # Tuple -> Tuple
+        aty = types.Tuple((i32, i32))
+        bty = types.Tuple((i32, i64))
+        self.assert_can_convert(aty, bty, Conversion.promote)
+        # UniTuple <-> Tuple
+        aty = types.UniTuple(i32, 2)
+        bty = types.Tuple((i32, i64))
+        self.assert_can_convert(aty, bty, Conversion.promote)
+        self.assert_can_convert(bty, aty, Conversion.unsafe)
+        # Empty tuples
+        aty = types.UniTuple(i64, 0)
+        bty = types.UniTuple(i32, 0)
+        cty = types.Tuple(())
+        self.assert_can_convert(aty, bty, Conversion.safe)
+        self.assert_can_convert(bty, aty, Conversion.safe)
+        self.assert_can_convert(aty, cty, Conversion.safe)
+        self.assert_can_convert(cty, aty, Conversion.safe)
+        # Failures
+        aty = types.UniTuple(i64, 3)
+        bty = types.UniTuple(types.none, 3)
+        self.assert_cannot_convert(aty, bty)
+        aty = types.UniTuple(i64, 2)
+        bty = types.UniTuple(i64, 3)
+
+    def test_arrays(self):
+        # Different layouts
+        aty = types.Array(i32, 3, "C")
+        bty = types.Array(i32, 3, "A")
+        self.assert_can_convert(aty, bty, Conversion.safe)
+        aty = types.Array(i32, 2, "C")
+        bty = types.Array(i32, 2, "F")
+        self.assert_cannot_convert(aty, bty)
+        # Different mutabilities
+        aty = types.Array(i32, 3, "C")
+        bty = types.Array(i32, 3, "C", readonly=True)
+        self.assert_can_convert(aty, aty, Conversion.exact)
+        self.assert_can_convert(bty, bty, Conversion.exact)
+        self.assert_can_convert(aty, bty, Conversion.safe)
+        self.assert_cannot_convert(bty, aty)
+        # Various failures
+        aty = types.Array(i32, 2, "C")
+        bty = types.Array(i32, 3, "C")
+        self.assert_cannot_convert(aty, bty)
+        aty = types.Array(i32, 2, "C")
+        bty = types.Array(i64, 2, "C")
+        self.assert_cannot_convert(aty, bty)
+
+    def test_optional(self):
+        aty = types.int32
+        bty = types.Optional(i32)
+        self.assert_can_convert(types.none, bty, Conversion.promote)
+        self.assert_can_convert(aty, bty, Conversion.promote)
+        self.assert_cannot_convert(bty, types.none)
+        self.assert_can_convert(bty, aty, Conversion.safe)  # XXX ???
+        # Optional array
+        aty = types.Array(i32, 2, "C")
+        bty = types.Optional(aty)
+        self.assert_can_convert(types.none, bty, Conversion.promote)
+        self.assert_can_convert(aty, bty, Conversion.promote)
+        self.assert_can_convert(bty, aty, Conversion.safe)
+        aty = types.Array(i32, 2, "C")
+        bty = types.Optional(aty.copy(layout="A"))
+        self.assert_can_convert(aty, bty, Conversion.safe)  # C -> A
+        self.assert_cannot_convert(bty, aty)                # A -> C
+        aty = types.Array(i32, 2, "C")
+        bty = types.Optional(aty.copy(layout="F"))
+        self.assert_cannot_convert(aty, bty)
+        self.assert_cannot_convert(bty, aty)
+
+
+class TestResolveOverload(unittest.TestCase):
+    """
+    Tests for typing.Context.resolve_overload().
+    """
+
+    def assert_resolve_overload(self, cases, args, expected):
+        ctx = typing.Context()
+        got = ctx.resolve_overload("foo", cases, args, {})
+        self.assertEqual(got, expected)
+
+    def test_non_ambiguous_match(self):
+        def check(args, expected):
+            self.assert_resolve_overload(cases, args, expected)
+            # Order shouldn't matter here
+            self.assert_resolve_overload(cases[::-1], args, expected)
+
+        cases = [i8(i8, i8), i32(i32, i32), f64(f64, f64)]
+        # Exact match
+        check((i8, i8), cases[0])
+        check((i32, i32), cases[1])
+        check((f64, f64), cases[2])
+        # "Promote" conversion
+        check((i8, i16), cases[1])
+        check((i32, i8), cases[1])
+        check((i32, i8), cases[1])
+        check((f32, f32), cases[2])
+        # "Safe" conversion
+        check((u32, u32), cases[2])
+        # "Unsafe" conversion
+        check((i64, i64), cases[2])
+
+    def test_ambiguous_match(self):
+        # When the best match is ambiguous (there is a tie), the first
+        # best case in original sequence order should be returned.
+        def check(args, expected, expected_reverse):
+            self.assert_resolve_overload(cases, args, expected)
+            self.assert_resolve_overload(cases[::-1], args, expected_reverse)
+
+        cases = [i16(i16, i16), i32(i32, i32), f64(f64, f64)]
+        # Two "promote" conversions
+        check((i8, i8), cases[0], cases[1])
+        # Two "safe" conversions
+        check((u16, u16), cases[1], cases[2])
+
+        cases = [i32(i32, i32), f32(f32, f32)]
+        # Two "unsafe" conversions
+        check((u32, u32), cases[0], cases[1])
+
+    def test_ambiguous_error(self):
+        ctx = typing.Context()
+        cases = [i16(i16, i16), i32(i32, i32)]
+        with self.assertRaises(TypeError) as raises:
+            ctx.resolve_overload("foo", cases, (i8, i8), {},
+                                 allow_ambiguous=False)
+        self.assertEqual(str(raises.exception).splitlines(),
+                         ["Ambiguous overloading for foo (int8, int8):",
+                          "(int16, int16) -> int16",
+                          "(int32, int32) -> int32",
+                          ])
+
+
+class TestUnifyUseCases(unittest.TestCase):
+    """
+    Concrete cases where unification would fail.
+    """
+
+    @staticmethod
+    def _actually_test_complex_unify():
+        def pyfunc(a):
+            res = 0.0
+            for i in range(len(a)):
+                res += a[i]
+            return res
+
+        argtys = [types.Array(c128, 1, 'C')]
+        cres = compile_isolated(pyfunc, argtys)
+        return (pyfunc, cres)
+
+    def test_complex_unify_issue599(self):
+        pyfunc, cres = self._actually_test_complex_unify()
+        arg = np.array([1.0j])
+        cfunc = cres.entry_point
+        self.assertEqual(cfunc(arg), pyfunc(arg))
+
+    def test_complex_unify_issue599_multihash(self):
+        """
+        Test issue #599 for multiple values of PYTHONHASHSEED.
+        """
+        env = os.environ.copy()
+        for seedval in (1, 2, 1024):
+            env['PYTHONHASHSEED'] = str(seedval)
+            subproc = subprocess.Popen(
+                [sys.executable, '-c',
+                 'import numba.tests.test_typeinfer as test_mod\n' +
+                 'test_mod.TestUnifyUseCases._actually_test_complex_unify()'],
+                env=env)
+            subproc.wait()
+            self.assertEqual(subproc.returncode, 0, 'Child process failed.')
+
+    def test_int_tuple_unify(self):
+        """
+        Test issue #493
+        """
+        def foo(an_int32, an_int64):
+            a = an_int32, an_int32
+            while True:  # infinite loop
+                a = an_int32, an_int64
+            return a
+
+        args = (i32, i64)
+        # Check if compilation is successful
+        cres = compile_isolated(foo, args)
+
+
+def issue_797(x0, y0, x1, y1, grid):
+    nrows, ncols = grid.shape
+
+    dx = abs(x1 - x0)
+    dy = abs(y1 - y0)
+
+    sx = 0
+    if x0 < x1:
+        sx = 1
+    else:
+        sx = -1
+    sy = 0
+    if y0 < y1:
+        sy = 1
+    else:
+        sy = -1
+
+    err = dx - dy
+
+    while True:
+        if x0 == x1 and y0 == y1:
+            break
+
+        if 0 <= x0 < nrows and 0 <= y0 < ncols:
+            grid[x0, y0] += 1
+
+        e2 = 2 * err
+        if e2 > -dy:
+            err -= dy
+            x0 += sx
+        if e2 < dx:
+            err += dx
+            y0 += sy
+
+
+def issue_1080(a, b):
+    if not a:
+        return True
+    return b
+
+
+def list_unify_usecase1(n):
+    res = 0
+    x = []
+    if n < 10:
+        x.append(np.int32(n))
+    else:
+        for i in range(n):
+            x.append(np.int64(i))
+    x.append(5.0)
+
+    # Note `i` and `j` may have different types (int64 vs. int32)
+    for j in range(len(x)):
+        res += j * x[j]
+    for val in x:
+        res += int(val) & len(x)
+    while len(x) > 0:
+        res += x.pop()
+    return res
+
+def list_unify_usecase2(n):
+    res = []
+    for i in range(n):
+        if i & 1:
+            res.append((i, 1.0))
+        else:
+            res.append((2.0, i))
+    res.append((123j, 42))
+    return res
+
+def range_unify_usecase(v):
+    if v:
+        r = range(np.int32(3))
+    else:
+        r = range(np.int64(5))
+    for x in r:
+        return x
+
+def issue_1394(a):
+    if a:
+        for i in range(a):
+            a += i
+        i = 1.2
+    else:
+        i = 3
+    return a, i
+
+
+class TestMiscIssues(TestCase):
+
+    def test_issue_797(self):
+        """https://github.com/numba/numba/issues/797#issuecomment-58592401
+
+        Undeterministic triggering of tuple coercion error
+        """
+        foo = jit(nopython=True)(issue_797)
+        g = np.zeros(shape=(10, 10), dtype=np.int32)
+        foo(np.int32(0), np.int32(0), np.int32(1), np.int32(1), g)
+
+    def test_issue_1080(self):
+        """https://github.com/numba/numba/issues/1080
+
+        Erroneous promotion of boolean args to int64
+        """
+        foo = jit(nopython=True)(issue_1080)
+        foo(True, False)
+
+    def test_list_unify1(self):
+        """
+        Exercise back-propagation of refined list type.
+        """
+        pyfunc = list_unify_usecase1
+        cfunc = jit(nopython=True)(pyfunc)
+        for n in [5, 100]:
+            res = cfunc(n)
+            self.assertPreciseEqual(res, pyfunc(n))
+
+    def test_list_unify2(self):
+        pyfunc = list_unify_usecase2
+        cfunc = jit(nopython=True)(pyfunc)
+        res = cfunc(3)
+        # NOTE: the types will differ (Numba returns a homogeneous list with
+        # converted values).
+        self.assertEqual(res, pyfunc(3))
+
+    def test_range_unify(self):
+        pyfunc = range_unify_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        for v in (0, 1):
+            res = cfunc(v)
+            self.assertPreciseEqual(res, pyfunc(v))
+
+    def test_issue_1394(self):
+        pyfunc = issue_1394
+        cfunc = jit(nopython=True)(pyfunc)
+        for v in (0, 1, 2):
+            res = cfunc(v)
+            self.assertEqual(res, pyfunc(v))
+
+    def test_issue_6293(self):
+        """https://github.com/numba/numba/issues/6293
+
+        Typer does not propagate return type to all return variables
+        """
+        @jit(nopython=True)
+        def confuse_typer(x):
+            if x == x:
+                return int(x)
+            else:
+                return x
+
+        confuse_typer.compile((types.float64,))
+        cres = confuse_typer.overloads[(types.float64,)]
+        typemap = cres.type_annotation.typemap
+        return_vars = {}
+
+        for block in cres.type_annotation.blocks.values():
+            for inst in block.body:
+                if isinstance(inst, ir.Return):
+                    varname = inst.value.name
+                    return_vars[varname] = typemap[varname]
+
+        self.assertTrue(all(vt == types.float64 for vt in return_vars.values()))
+
+
+class TestFoldArguments(unittest.TestCase):
+    def check_fold_arguments_list_inputs(self, func, args, kws):
+        def make_tuple(*args):
+            return args
+
+        unused_handler = None
+
+        pysig = utils.pysignature(func)
+        names = list(pysig.parameters)
+
+        with self.subTest(kind='dict'):
+            folded_dict = typing.fold_arguments(
+                pysig, args, kws, make_tuple, unused_handler, unused_handler,
+            )
+            # correct ordering
+            for i, (j, k) in enumerate(zip(folded_dict, names)):
+                (got_index, got_param, got_name) = j
+                self.assertEqual(got_index, i)
+                self.assertEqual(got_name, f'arg.{k}')
+
+        kws = list(kws.items())
+        with self.subTest(kind='list'):
+            folded_list = typing.fold_arguments(
+                pysig, args, kws, make_tuple, unused_handler, unused_handler,
+            )
+            self.assertEqual(folded_list, folded_dict)
+
+    def test_fold_arguments_list_inputs(self):
+        cases = [
+            dict(
+                func=lambda a, b, c, d: None,
+                args=['arg.a', 'arg.b'],
+                kws=dict(c='arg.c', d='arg.d')
+            ),
+            dict(
+                func=lambda: None,
+                args=[],
+                kws=dict(),
+            ),
+            dict(
+                func=lambda a: None,
+                args=['arg.a'],
+                kws={},
+            ),
+            dict(
+                func=lambda a: None,
+                args=[],
+                kws=dict(a='arg.a'),
+            ),
+        ]
+        for case in cases:
+            with self.subTest(**case):
+                self.check_fold_arguments_list_inputs(**case)
+
+
+@register_pass(mutates_CFG=False, analysis_only=True)
+class DummyCR(FunctionPass):
+    """Dummy pass to add "cr" to compiler state to avoid errors in TyperCompiler since
+    it doesn't have lowering.
+    """
+
+    _name = "dummy_cr"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        state.cr = 1  # arbitrary non-None value
+        return True
+
+
+class TyperCompiler(numba.core.compiler.CompilerBase):
+    """A compiler pipeline that skips passes after typing (provides partial typing info
+    but not lowering).
+    """
+
+    def define_pipelines(self):
+        pm = numba.core.compiler_machinery.PassManager("custom_pipeline")
+        pm.add_pass(TranslateByteCode, "analyzing bytecode")
+        pm.add_pass(IRProcessing, "processing IR")
+        pm.add_pass(PartialTypeInference, "do partial typing")
+        pm.add_pass_after(DummyCR, PartialTypeInference)
+        pm.finalize()
+        return [pm]
+
+
+def get_func_typing_errs(func, arg_types):
+    """
+    Get typing errors for function 'func'. It creates a pipeline that runs untyped
+    passes as well as type inference.
+    """
+    typingctx = numba.core.registry.cpu_target.typing_context
+    targetctx = numba.core.registry.cpu_target.target_context
+    library = None
+    return_type = None
+    _locals = {}
+    flags = numba.core.compiler.Flags()
+    flags.nrt = True
+
+    pipeline = TyperCompiler(
+        typingctx, targetctx, library, arg_types, return_type, flags, _locals
+    )
+    pipeline.compile_extra(func)
+    return pipeline.state.typing_errors
+
+
+class TestPartialTypingErrors(unittest.TestCase):
+    """
+    Make sure partial typing stores type errors in compiler state properly
+    """
+    def test_partial_typing_error(self):
+        # example with type unification error
+        def impl(flag):
+            if flag:
+                a = 1
+            else:
+                a = str(1)
+            return a
+
+        typing_errs = get_func_typing_errs(impl, (types.bool_,))
+        self.assertTrue(isinstance(typing_errs, list) and len(typing_errs) == 1)
+        self.assertTrue(isinstance(typing_errs[0], errors.TypingError) and
+                        "Cannot unify" in typing_errs[0].msg)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typenames.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typenames.py
new file mode 100644
index 000000000..531a89297
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typenames.py
@@ -0,0 +1,17 @@
+import numpy as np
+
+from numba.core import types
+import unittest
+
+
+class TestTypeNames(unittest.TestCase):
+    def test_numpy_integers(self):
+        expect = getattr(types, "int%d" % (np.dtype("int").itemsize * 8))
+        self.assertEqual(types.int_, expect)
+
+        expect = getattr(types, "uint%d" % (np.dtype("uint").itemsize * 8))
+        self.assertEqual(types.uint, expect)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeof.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeof.py
new file mode 100644
index 000000000..7845bdb27
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typeof.py
@@ -0,0 +1,569 @@
+"""
+Tests for the typeof() machinery.
+"""
+import array
+from collections import namedtuple
+import enum
+import mmap
+import typing as py_typing
+
+import numpy as np
+
+import unittest
+import numba.core.typing.cffi_utils as cffi_support
+from numba.core import types
+from numba.core.errors import NumbaValueError
+from numba.misc.special import typeof
+from numba.core.dispatcher import OmittedArg
+from numba._dispatcher import compute_fingerprint
+
+from numba.tests.support import TestCase, tag
+from numba.tests.test_numpy_support import ValueTypingTestBase
+from numba.tests.ctypes_usecases import *
+from numba.tests.enum_usecases import *
+from numba.np import numpy_support
+
+
+recordtype = np.dtype([('a', np.float64),
+                       ('b', np.int32),
+                       ('c', np.complex64),
+                       ('d', (np.str_, 5))])
+
+recordtype2 = np.dtype([('e', np.int8),
+                        ('f', np.float64)])
+
+recordtype3 = np.dtype([('e', np.int8),
+                        ('f', np.float64)], align=True)
+
+Point = namedtuple('Point', ('x', 'y'))
+
+Rect = namedtuple('Rect', ('width', 'height'))
+
+
+class Custom(object):
+
+    @property
+    def _numba_type_(self):
+        """
+        Magic attribute expected by Numba to get the numba type that
+        represents this object.
+        """
+        return types.UniTuple(types.boolean, 42)
+
+
+class TestTypeof(ValueTypingTestBase, TestCase):
+    """
+    Test typeof() and, implicitly, typing.Context.get_argument_type().
+    """
+
+    def test_number_values(self):
+        """
+        Test special.typeof() with scalar number values.
+        """
+        self.check_number_values(typeof)
+        # These values mirror Dispatcher semantics
+        self.assertEqual(typeof(1), types.intp)
+        self.assertEqual(typeof(-1), types.intp)
+        self.assertEqual(typeof(2**40), types.int64)
+        self.assertEqual(typeof(2**63), types.uint64)
+        self.assertEqual(typeof(2**63 - 1), types.int64)
+        self.assertEqual(typeof(-2**63), types.int64)
+
+    def test_datetime_values(self):
+        """
+        Test special.typeof() with np.timedelta64 values.
+        """
+        self.check_datetime_values(typeof)
+
+    def test_timedelta_values(self):
+        """
+        Test special.typeof() with np.timedelta64 values.
+        """
+        self.check_timedelta_values(typeof)
+
+    def test_array_values(self):
+        """
+        Test special.typeof() with ndarray values.
+        """
+        def check(arr, ndim, layout, mutable, aligned):
+            ty = typeof(arr)
+            self.assertIsInstance(ty, types.Array)
+            self.assertEqual(ty.ndim, ndim)
+            self.assertEqual(ty.layout, layout)
+            self.assertEqual(ty.mutable, mutable)
+            self.assertEqual(ty.aligned, aligned)
+
+        a1 = np.arange(10)
+        check(a1, 1, 'C', True, True)
+        a2 = np.arange(10).reshape(2, 5)
+        check(a2, 2, 'C', True, True)
+        check(a2.T, 2, 'F', True, True)
+        a3 = (np.arange(60))[::2].reshape((2, 5, 3))
+        check(a3, 3, 'A', True, True)
+        a4 = np.arange(1).reshape(())
+        check(a4, 0, 'C', True, True)
+        a4.flags.writeable = False
+        check(a4, 0, 'C', False, True)
+
+        # Unsupported dtype
+        a5 = a1.astype(a1.dtype.newbyteorder())
+        with self.assertRaises(NumbaValueError) as raises:
+            typeof(a5)
+        self.assertIn("Unsupported array dtype: %s" % (a5.dtype,),
+                      str(raises.exception))
+
+    def test_structured_arrays(self):
+        def check(arr, dtype, ndim, layout, aligned):
+            ty = typeof(arr)
+            self.assertIsInstance(ty, types.Array)
+            self.assertEqual(ty.dtype, dtype)
+            self.assertEqual(ty.ndim, ndim)
+            self.assertEqual(ty.layout, layout)
+            self.assertEqual(ty.aligned, aligned)
+
+        dtype = np.dtype([('m', np.int32), ('n', 'S5')])
+        rec_ty = numpy_support.from_struct_dtype(dtype)
+
+        arr = np.empty(4, dtype=dtype)
+        check(arr, rec_ty, 1, "C", False)
+        arr = np.recarray(4, dtype=dtype)
+        check(arr, rec_ty, 1, "C", False)
+
+        dtype = np.dtype([('m', np.int32), ('n', 'S5')], align=True)
+        rec_ty = numpy_support.from_struct_dtype(dtype)
+
+        arr = np.empty(4, dtype=dtype)
+        check(arr, rec_ty, 1, "C", True)
+        arr = np.recarray(4, dtype=dtype)
+        check(arr, rec_ty, 1, "C", True)
+
+    def test_buffers(self):
+        b = b"xx"
+        ty = typeof(b)
+        self.assertEqual(ty, types.Bytes(types.uint8, 1, "C"))
+        self.assertFalse(ty.mutable)
+        ty = typeof(memoryview(b))
+        self.assertEqual(ty, types.MemoryView(types.uint8, 1, "C",
+                                                readonly=True))
+        self.assertFalse(ty.mutable)
+        ty = typeof(array.array('i', [0, 1, 2]))
+        self.assertEqual(ty, types.PyArray(types.int32, 1, "C"))
+        self.assertTrue(ty.mutable)
+
+        b = bytearray(10)
+        ty = typeof(b)
+        self.assertEqual(ty, types.ByteArray(types.uint8, 1, "C"))
+        self.assertTrue(ty.mutable)
+
+    def test_none(self):
+        ty = typeof(None)
+        self.assertEqual(ty, types.none)
+
+    def test_ellipsis(self):
+        ty = typeof(Ellipsis)
+        self.assertEqual(ty, types.ellipsis)
+
+    def test_str(self):
+        ty = typeof("abc")
+        self.assertEqual(ty, types.string)
+
+    def test_slices(self):
+        for args in [(1,), (1, 2), (1, 2, 1), (1, 2, None)]:
+            v = slice(*args)
+            self.assertIs(typeof(v), types.slice2_type)
+        for args in [(1, 2, 2), (1, 2, -1), (None, None, -2)]:
+            v = slice(*args)
+            self.assertIs(typeof(v), types.slice3_type)
+
+    def test_tuples(self):
+        v = (1, 2)
+        self.assertEqual(typeof(v), types.UniTuple(types.intp, 2))
+        v = (1, (2.0, 3))
+        self.assertEqual(typeof(v),
+                         types.Tuple((types.intp,
+                                      types.Tuple((types.float64, types.intp))))
+                         )
+
+    def test_lists(self):
+        v = [1.0] * 100
+        self.assertEqual(typeof(v), types.List(types.float64, reflected=True))
+
+        bad_v = [{1: 3}]
+        with self.assertRaises(ValueError) as raises:
+            typeof(bad_v)
+        self.assertIn("Cannot type list element type", str(raises.exception))
+
+    def test_sets(self):
+        v = set([1.0, 2.0, 3.0])
+        self.assertEqual(typeof(v), types.Set(types.float64, reflected=True))
+        v = frozenset(v)
+        with self.assertRaises(ValueError) as raises:
+            typeof(v)
+        self.assertIn("Cannot determine Numba type of", str(raises.exception))
+
+    def test_namedtuple(self):
+        v = Point(1, 2)
+        tp_point = typeof(v)
+        self.assertEqual(tp_point,
+                         types.NamedUniTuple(types.intp, 2, Point))
+        v = Point(1, 2.0)
+        self.assertEqual(typeof(v),
+                         types.NamedTuple([types.intp, types.float64], Point))
+        w = Rect(3, 4)
+        tp_rect = typeof(w)
+        self.assertEqual(tp_rect,
+                         types.NamedUniTuple(types.intp, 2, Rect))
+        self.assertNotEqual(tp_rect, tp_point)
+        self.assertNotEqual(tp_rect, types.UniTuple(tp_rect.dtype, tp_rect.count))
+
+    def test_enum(self):
+        tp_red = typeof(Color.red)
+        self.assertEqual(tp_red, types.EnumMember(Color, types.intp))
+        self.assertEqual(tp_red, typeof(Color.blue))
+        tp_choc = typeof(Shake.chocolate)
+        self.assertEqual(tp_choc, types.EnumMember(Shake, types.intp))
+        self.assertEqual(tp_choc, typeof(Shake.mint))
+        self.assertNotEqual(tp_choc, tp_red)
+        tp_404 = typeof(RequestError.not_found)
+        self.assertEqual(tp_404, types.IntEnumMember(RequestError, types.intp))
+        self.assertEqual(tp_404, typeof(RequestError.internal_error))
+
+        with self.assertRaises(ValueError) as raises:
+            typeof(HeterogeneousEnum.red)
+        self.assertEqual(str(raises.exception),
+                         "Cannot type heterogeneous enum: got value types complex128, float64")
+
+    def test_enum_class(self):
+        tp_color = typeof(Color)
+        self.assertEqual(tp_color, types.EnumClass(Color, types.intp))
+        tp_shake = typeof(Shake)
+        self.assertEqual(tp_shake, types.EnumClass(Shake, types.intp))
+        self.assertNotEqual(tp_shake, tp_color)
+        tp_shape = typeof(Shape)
+        self.assertEqual(tp_shape, types.IntEnumClass(Shape, types.intp))
+        tp_error = typeof(RequestError)
+        self.assertEqual(tp_error,
+                         types.IntEnumClass(RequestError, types.intp))
+        self.assertNotEqual(tp_error, tp_shape)
+
+        with self.assertRaises(ValueError) as raises:
+            typeof(HeterogeneousEnum)
+        self.assertEqual(str(raises.exception),
+                         "Cannot type heterogeneous enum: got value types complex128, float64")
+
+    def test_dtype(self):
+        dtype = np.dtype('int64')
+        self.assertEqual(typeof(dtype), types.DType(types.int64))
+
+        dtype = np.dtype([('m', np.int32), ('n', 'S5')])
+        rec_ty = numpy_support.from_struct_dtype(dtype)
+        self.assertEqual(typeof(dtype), types.DType(rec_ty))
+
+    def test_dtype_values(self):
+        self.assertEqual(typeof(np.int64), types.NumberClass(types.int64))
+        self.assertEqual(typeof(np.float64), types.NumberClass(types.float64))
+        self.assertEqual(typeof(np.int32), types.NumberClass(types.int32))
+        self.assertEqual(typeof(np.int8), types.NumberClass(types.int8))
+
+    def test_ctypes(self):
+        ty_cos = typeof(c_cos)
+        ty_sin = typeof(c_sin)
+        self.assertIsInstance(ty_cos, types.ExternalFunctionPointer)
+        self.assertEqual(ty_cos.sig.args, (types.float64,))
+        self.assertEqual(ty_cos.sig.return_type, types.float64)
+        self.assertEqual(ty_cos, ty_sin)
+        self.assertNotEqual(ty_cos.get_pointer(c_cos),
+                            ty_sin.get_pointer(c_sin))
+
+    @unittest.skipUnless(cffi_support.SUPPORTED, "CFFI not supported")
+    def test_cffi(self):
+        from numba.tests import cffi_usecases as mod
+        mod.init()
+        ty_cffi_cos = typeof(mod.cffi_cos)
+        ty_cffi_sin = typeof(mod.cffi_sin)
+        ty_cffi_boolean = typeof(mod.cffi_bool)
+        self.assertIsInstance(ty_cffi_cos, types.ExternalFunctionPointer)
+        self.assertEqual(ty_cffi_boolean.sig.return_type, types.boolean)
+        self.assertEqual(ty_cffi_cos.sig.args, (types.float64,))
+        self.assertEqual(ty_cffi_cos.sig.return_type, types.float64)
+        self.assertEqual(ty_cffi_cos, ty_cffi_sin)
+        ty_ctypes_cos = typeof(c_cos)
+        self.assertNotEqual(ty_cffi_cos, ty_ctypes_cos)
+        self.assertNotEqual(ty_cffi_cos.get_pointer(mod.cffi_cos),
+                            ty_cffi_sin.get_pointer(mod.cffi_sin))
+        self.assertEqual(ty_cffi_cos.get_pointer(mod.cffi_cos),
+                         ty_ctypes_cos.get_pointer(c_cos))
+
+    def test_custom(self):
+        ty = typeof(Custom())
+        self.assertEqual(ty, types.UniTuple(types.boolean, 42))
+
+    def test_omitted_args(self):
+        ty0 = typeof(OmittedArg(0.0))
+        ty1 = typeof(OmittedArg(1))
+        ty2 = typeof(OmittedArg(1.0))
+        ty3 = typeof(OmittedArg(1.0))
+        self.assertEqual(ty0, types.Omitted(0.0))
+        self.assertEqual(ty1, types.Omitted(1))
+        self.assertEqual(ty2, types.Omitted(1.0))
+        self.assertEqual(len({ty0, ty1, ty2}), 3)
+        self.assertEqual(ty3, ty2)
+
+    def test_np_random(self):
+        rng = np.random.default_rng()
+        ty_rng = typeof(rng)
+        ty_bitgen = typeof(rng.bit_generator)
+
+        self.assertEqual(ty_rng, types.npy_rng)
+        self.assertEqual(ty_bitgen, types.npy_bitgen)
+
+
+class DistinctChecker(object):
+
+    def __init__(self):
+        self._distinct = set()
+
+    def add(self, obj):
+        if obj in self._distinct:
+            raise AssertionError("%r already in %r" % (obj, self._distinct))
+        self._distinct.add(obj)
+
+
+class TestFingerprint(TestCase):
+    """
+    Tests for _dispatcher.compute_fingerprint()
+
+    Each fingerprint must denote values of only one Numba type (this is
+    the condition for correctness), but values of a Numba type may be
+    denoted by several distinct fingerprints (it only makes the cache
+    less efficient).
+    """
+
+    def test_floats(self):
+        s = compute_fingerprint(1.0)
+        self.assertEqual(compute_fingerprint(2.0), s)
+        s = compute_fingerprint(np.float32())
+        self.assertEqual(compute_fingerprint(np.float32(2.0)), s)
+        self.assertNotEqual(compute_fingerprint(np.float64()), s)
+
+    def test_ints(self):
+        s = compute_fingerprint(1)
+        for v in (-1, 2**60):
+            self.assertEqual(compute_fingerprint(v), s)
+        # Different int widths resolve to different fingerprints
+        distinct = set()
+        for tp in ('int8', 'int16', 'int32', 'int64',
+                   'uint8', 'uint16', 'uint32', 'uint64'):
+            tp = getattr(np, tp)
+            distinct.add(compute_fingerprint(tp()))
+        self.assertEqual(len(distinct), 8, distinct)
+
+    def test_bool(self):
+        s = compute_fingerprint(True)
+        self.assertEqual(compute_fingerprint(False), s)
+        self.assertNotEqual(compute_fingerprint(1), s)
+
+    def test_complex(self):
+        s = compute_fingerprint(1j)
+        self.assertEqual(s, compute_fingerprint(1+0j))
+        s = compute_fingerprint(np.complex64())
+        self.assertEqual(compute_fingerprint(np.complex64(2.0)), s)
+        self.assertNotEqual(compute_fingerprint(np.complex128()), s)
+
+    def test_none(self):
+        compute_fingerprint(None)
+
+    def test_enums(self):
+        # Enums should fail fingerprinting, even IntEnums
+        with self.assertRaises(NotImplementedError):
+            compute_fingerprint(Color.red)
+        with self.assertRaises(NotImplementedError):
+            compute_fingerprint(RequestError.not_found)
+
+    def test_records(self):
+        d1 = np.dtype([('m', np.int32), ('n', np.int64)])
+        d2 = np.dtype([('m', np.int32), ('n', np.int16)])
+        v1 = np.empty(1, dtype=d1)[0]
+        v2 = np.empty(1, dtype=d2)[0]
+        self.assertNotEqual(compute_fingerprint(v1),
+                            compute_fingerprint(v2))
+
+    def test_datetime(self):
+        a = np.datetime64(1, 'Y')
+        b = np.datetime64(2, 'Y')
+        c = np.datetime64(2, 's')
+        d = np.timedelta64(2, 's')
+        self.assertEqual(compute_fingerprint(a),
+                         compute_fingerprint(b))
+        distinct = set(compute_fingerprint(x) for x in (a, c, d))
+        self.assertEqual(len(distinct), 3, distinct)
+
+    def test_arrays(self):
+        distinct = DistinctChecker()
+
+        # 1D
+        arr = np.empty(4, dtype=np.float64)
+        s = compute_fingerprint(arr)
+        distinct.add(s)
+        self.assertEqual(compute_fingerprint(arr[:1]), s)
+        # Non-contiguous
+        distinct.add(compute_fingerprint(arr[::2]))
+        # Other type
+        distinct.add(compute_fingerprint(arr.astype(np.complex64)))
+        # Readonly
+        arr.setflags(write=False)
+        distinct.add(compute_fingerprint(arr))
+
+        # 2D
+        arr = np.empty((4, 4), dtype=np.float64)
+        distinct.add(compute_fingerprint(arr))
+        # F-contiguous
+        distinct.add(compute_fingerprint(arr.T))
+        # Non-contiguous
+        distinct.add(compute_fingerprint(arr[::2]))
+
+        # 0D
+        arr = np.empty((), dtype=np.float64)
+        distinct.add(compute_fingerprint(arr))
+
+        # Structured arrays
+        arr = np.empty(5, dtype=recordtype)
+        s = compute_fingerprint(arr)
+        distinct.add(s)
+        self.assertEqual(compute_fingerprint(arr[:1]), s)
+        arr = np.empty(5, dtype=recordtype2)
+        distinct.add(compute_fingerprint(arr))
+        arr = np.empty(5, dtype=recordtype3)
+        distinct.add(compute_fingerprint(arr))
+
+        # np.recarray() is peculiar: it creates a new dtype instance in
+        # its constructor; check that the fingerprint remains efficient
+        a = np.recarray(1, dtype=recordtype)
+        b = np.recarray(1, dtype=recordtype)
+        self.assertEqual(compute_fingerprint(a),
+                         compute_fingerprint(b))
+
+    def test_buffers(self):
+        distinct = DistinctChecker()
+
+        s = compute_fingerprint(b'')
+        self.assertEqual(compute_fingerprint(b'xx'), s)
+        distinct.add(s)
+        distinct.add(compute_fingerprint(bytearray()))
+        distinct.add(compute_fingerprint(memoryview(b'')))
+        m_uint8_1d = compute_fingerprint(memoryview(bytearray()))
+        distinct.add(m_uint8_1d)
+
+        arr = array.array('B', [42])
+        distinct.add(compute_fingerprint(arr))
+        self.assertEqual(compute_fingerprint(memoryview(arr)), m_uint8_1d)
+        for array_code in 'bi':
+            arr = array.array(array_code, [0, 1, 2])
+            distinct.add(compute_fingerprint(arr))
+            distinct.add(compute_fingerprint(memoryview(arr)))
+
+        arr = np.empty(16, dtype=np.uint8)
+        distinct.add(compute_fingerprint(arr))
+        self.assertEqual(compute_fingerprint(memoryview(arr)), m_uint8_1d)
+        arr = arr.reshape((4, 4))
+        distinct.add(compute_fingerprint(arr))
+        distinct.add(compute_fingerprint(memoryview(arr)))
+        arr = arr.T
+        distinct.add(compute_fingerprint(arr))
+        distinct.add(compute_fingerprint(memoryview(arr)))
+        arr = arr[::2]
+        distinct.add(compute_fingerprint(arr))
+        distinct.add(compute_fingerprint(memoryview(arr)))
+
+        m = mmap.mmap(-1, 16384)
+        distinct.add(compute_fingerprint(m))
+        self.assertEqual(compute_fingerprint(memoryview(m)), m_uint8_1d)
+
+    def test_dtype(self):
+        distinct = DistinctChecker()
+
+        s = compute_fingerprint(np.dtype('int64'))
+        self.assertEqual(compute_fingerprint(np.dtype('int64')), s)
+        distinct.add(s)
+
+        for descr in ('int32', 'm8[s]', 'm8[W]', 'M8[s]'):
+            distinct.add(np.dtype(descr))
+
+        distinct.add(recordtype)
+        distinct.add(recordtype2)
+
+        # np.recarray() is peculiar: it creates a new dtype instance in
+        # its constructor; check that the fingerprint remains efficient
+        a = np.recarray(1, dtype=recordtype)
+        b = np.recarray(1, dtype=recordtype)
+        self.assertEqual(compute_fingerprint(a.dtype),
+                         compute_fingerprint(b.dtype))
+
+    def test_tuples(self):
+        distinct = DistinctChecker()
+
+        s = compute_fingerprint((1,))
+        self.assertEqual(compute_fingerprint((2,)), s)
+        distinct.add(s)
+
+        distinct.add(compute_fingerprint(()))
+        distinct.add(compute_fingerprint((1, 2, 3)))
+        distinct.add(compute_fingerprint((1j, 2, 3)))
+        distinct.add(compute_fingerprint((1, (), np.empty(5))))
+        distinct.add(compute_fingerprint((1, (), np.empty((5, 1)))))
+
+    def test_lists(self):
+        distinct = DistinctChecker()
+
+        s = compute_fingerprint([1])
+        self.assertEqual(compute_fingerprint([2, 3]), s)
+        distinct.add(s)
+
+        distinct.add(compute_fingerprint([1j]))
+        distinct.add(compute_fingerprint([4.5, 6.7]))
+        distinct.add(compute_fingerprint([(1,)]))
+
+        with self.assertRaises(ValueError):
+            compute_fingerprint([])
+
+    def test_sets(self):
+        distinct = DistinctChecker()
+
+        s = compute_fingerprint(set([1]))
+        self.assertEqual(compute_fingerprint(set([2, 3])), s)
+        distinct.add(s)
+
+        distinct.add(compute_fingerprint([1]))
+        distinct.add(compute_fingerprint(set([1j])))
+        distinct.add(compute_fingerprint(set([4.5, 6.7])))
+        distinct.add(compute_fingerprint(set([(1,)])))
+
+        with self.assertRaises(ValueError):
+            compute_fingerprint(set())
+        with self.assertRaises(NotImplementedError):
+            compute_fingerprint(frozenset([2, 3]))
+
+    def test_omitted_args(self):
+        distinct = DistinctChecker()
+
+        v0 = OmittedArg(0.0)
+        v1 = OmittedArg(1.0)
+        v2 = OmittedArg(1)
+
+        s = compute_fingerprint(v0)
+        self.assertEqual(compute_fingerprint(v1), s)
+        distinct.add(s)
+        distinct.add(compute_fingerprint(v2))
+        distinct.add(compute_fingerprint(0.0))
+        distinct.add(compute_fingerprint(1))
+
+    def test_complicated_type(self):
+        # Generating a large fingerprint
+        t = None
+        for i in range(1000):
+            t = (t,)
+        s = compute_fingerprint(t)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_types.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_types.py
new file mode 100644
index 000000000..ce05fab65
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_types.py
@@ -0,0 +1,879 @@
+"""
+Tests for numba.types.
+"""
+
+
+from collections import namedtuple
+import gc
+import os
+import operator
+import sys
+import weakref
+
+import numpy as np
+
+from numba.core import types, typing, errors, sigutils
+from numba.core.types.abstract import _typecache
+from numba.core.types.functions import _header_lead
+from numba.core.typing.templates import make_overload_template
+from numba import jit, njit, typeof
+from numba.core.extending import (overload, register_model, models, unbox,
+                                  NativeValue, typeof_impl)
+from numba.tests.support import TestCase, create_temp_module
+from numba.tests.enum_usecases import Color, Shake, Shape
+import unittest
+from numba.np import numpy_support
+
+
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+
+
+Point = namedtuple('Point', ('x', 'y'))
+
+Rect = namedtuple('Rect', ('width', 'height'))
+
+
+def gen(x):
+    yield x + 1
+
+
+class Dummy(object):
+    pass
+
+
+class TestTypes(TestCase):
+
+    def test_equality(self):
+        self.assertEqual(types.int32, types.int32)
+        self.assertEqual(types.uint32, types.uint32)
+        self.assertEqual(types.complex64, types.complex64)
+        self.assertEqual(types.float32, types.float32)
+        # Different signedness
+        self.assertNotEqual(types.int32, types.uint32)
+        # Different width
+        self.assertNotEqual(types.int64, types.int32)
+        self.assertNotEqual(types.float64, types.float32)
+        self.assertNotEqual(types.complex64, types.complex128)
+        # Different domain
+        self.assertNotEqual(types.int64, types.float64)
+        self.assertNotEqual(types.uint64, types.float64)
+        self.assertNotEqual(types.complex64, types.float64)
+        # Same arguments but different return types
+        get_pointer = None
+        sig_a = typing.signature(types.intp, types.intp)
+        sig_b = typing.signature(types.voidptr, types.intp)
+        a = types.ExternalFunctionPointer(sig=sig_a, get_pointer=get_pointer)
+        b = types.ExternalFunctionPointer(sig=sig_b, get_pointer=get_pointer)
+        self.assertNotEqual(a, b)
+        # Different call convention
+        a = types.ExternalFunctionPointer(sig=sig_a, get_pointer=get_pointer)
+        b = types.ExternalFunctionPointer(sig=sig_a, get_pointer=get_pointer,
+                                          cconv='stdcall')
+        self.assertNotEqual(a, b)
+        # Different get_pointer
+        a = types.ExternalFunctionPointer(sig=sig_a, get_pointer=get_pointer)
+        b = types.ExternalFunctionPointer(sig=sig_a, get_pointer=object())
+        self.assertNotEqual(a, b)
+
+        # Different template classes bearing the same name
+        class DummyTemplate(object):
+            key = "foo"
+        a = types.BoundFunction(DummyTemplate, types.int32)
+
+        class DummyTemplate(object):
+            key = "bar"
+        b = types.BoundFunction(DummyTemplate, types.int32)
+        self.assertNotEqual(a, b)
+
+        # Different dtypes
+        self.assertNotEqual(types.DType(types.int32), types.DType(types.int64))
+
+        # CPointer with same addrspace
+        self.assertEqual(types.CPointer(types.float32),
+                         types.CPointer(types.float32))
+
+        # CPointer with different addrspace
+        self.assertNotEqual(types.CPointer(types.float32, 0),
+                            types.CPointer(types.float32, 1))
+
+    def test_weaktype(self):
+        d = Dummy()
+        e = Dummy()
+        a = types.Dispatcher(d)
+        b = types.Dispatcher(d)
+        c = types.Dispatcher(e)
+        self.assertIs(a.dispatcher, d)
+        self.assertIs(b.dispatcher, d)
+        self.assertIs(c.dispatcher, e)
+        # Equality of alive references
+        self.assertTrue(a == b)
+        self.assertFalse(a != b)
+        self.assertTrue(a != c)
+        self.assertFalse(a == c)
+        z = types.int8
+        self.assertFalse(a == z)
+        self.assertFalse(b == z)
+        self.assertFalse(c == z)
+        self.assertTrue(a != z)
+        self.assertTrue(b != z)
+        self.assertTrue(c != z)
+        # Hashing and mappings
+        s = set([a, b, c])
+        self.assertEqual(len(s), 2)
+        self.assertIn(a, s)
+        self.assertIn(b, s)
+        self.assertIn(c, s)
+        # Kill the references
+        d = e = None
+        gc.collect()
+        with self.assertRaises(ReferenceError):
+            a.dispatcher
+        with self.assertRaises(ReferenceError):
+            b.dispatcher
+        with self.assertRaises(ReferenceError):
+            c.dispatcher
+        # Dead references are always unequal
+        self.assertFalse(a == b)
+        self.assertFalse(a == c)
+        self.assertFalse(b == c)
+        self.assertFalse(a == z)
+        self.assertTrue(a != b)
+        self.assertTrue(a != c)
+        self.assertTrue(b != c)
+        self.assertTrue(a != z)
+
+    def test_interning(self):
+        # Test interning and lifetime of dynamic types.
+        a = types.Dummy('xyzzyx')
+        code = a._code
+        b = types.Dummy('xyzzyx')
+        self.assertIs(b, a)
+        wr = weakref.ref(a)
+        del a
+        gc.collect()
+        c = types.Dummy('xyzzyx')
+        self.assertIs(c, b)
+        # The code is always the same
+        self.assertEqual(c._code, code)
+        del b, c
+        gc.collect()
+        self.assertIs(wr(), None)
+        d = types.Dummy('xyzzyx')
+        # The original code wasn't reused.
+        self.assertNotEqual(d._code, code)
+
+    def test_cache_trimming(self):
+        # Test that the cache doesn't grow in size when types are
+        # created and disposed of.
+        cache = _typecache
+        gc.collect()
+        # Keep strong references to existing types, to avoid spurious failures
+        existing_types = [wr() for wr in cache]  # noqa: F841
+        cache_len = len(cache)
+        a = types.Dummy('xyzzyx')
+        b = types.Dummy('foox')
+        self.assertEqual(len(cache), cache_len + 2)
+        del a, b
+        gc.collect()
+        self.assertEqual(len(cache), cache_len)
+
+    def test_array_notation(self):
+        def check(arrty, scalar, ndim, layout):
+            self.assertIs(arrty.dtype, scalar)
+            self.assertEqual(arrty.ndim, ndim)
+            self.assertEqual(arrty.layout, layout)
+
+        def check_index_error(callable):
+            with self.assertRaises(KeyError) as raises:
+                callable()
+            self.assertIn(
+                "Can only index numba types with slices with no start or "
+                "stop, got", str(raises.exception))
+
+        scalar = types.int32
+        check(scalar[:], scalar, 1, 'A')
+        check(scalar[::1], scalar, 1, 'C')
+        check(scalar[:, :], scalar, 2, 'A')
+        check(scalar[:, ::1], scalar, 2, 'C')
+        check(scalar[::1, :], scalar, 2, 'F')
+
+        check_index_error(lambda: scalar[0])
+        check_index_error(lambda: scalar[:, 4])
+        check_index_error(lambda: scalar[::1, 1:])
+        check_index_error(lambda: scalar[:2])
+        check_index_error(lambda: list(scalar))
+
+    def test_array_notation_for_dtype(self):
+        def check(arrty, scalar, ndim, layout):
+            self.assertIs(arrty.dtype, scalar)
+            self.assertEqual(arrty.ndim, ndim)
+            self.assertEqual(arrty.layout, layout)
+        scalar = types.int32
+        dtyped = types.DType(scalar)
+        check(dtyped[:], scalar, 1, 'A')
+        check(dtyped[::1], scalar, 1, 'C')
+        check(dtyped[:, :], scalar, 2, 'A')
+        check(dtyped[:, ::1], scalar, 2, 'C')
+        check(dtyped[::1, :], scalar, 2, 'F')
+
+    def test_call_notation(self):
+        # Function call signature
+        i = types.int32
+        d = types.double
+        self.assertEqual(i(), typing.signature(i))
+        self.assertEqual(i(d), typing.signature(i, d))
+        self.assertEqual(i(d, d), typing.signature(i, d, d))
+        # Value cast
+        self.assertPreciseEqual(i(42.5), 42)
+        self.assertPreciseEqual(d(-5), -5.0)
+        ty = types.NPDatetime('Y')
+        self.assertPreciseEqual(ty('1900'), np.datetime64('1900', 'Y'))
+        self.assertPreciseEqual(ty('NaT'), np.datetime64('NaT', 'Y'))
+        ty = types.NPTimedelta('s')
+        self.assertPreciseEqual(ty(5), np.timedelta64(5, 's'))
+        self.assertPreciseEqual(ty('NaT'), np.timedelta64('NaT', 's'))
+        ty = types.NPTimedelta('')
+        self.assertPreciseEqual(ty(5), np.timedelta64(5))
+        self.assertPreciseEqual(ty('NaT'), np.timedelta64('NaT'))
+
+    def test_list_type_getitem(self):
+        for listty in (types.int64, types.Array(types.float64, 1, 'C')):
+            l_int = types.List(listty)
+            self.assertTrue(isinstance(l_int, types.List))
+            self.assertTrue(isinstance(l_int[0], type(listty)))
+
+    def test_function_incompatible_templates(self):
+        # issue 4345
+        def func_stub():
+            pass
+
+        def func_stub2():
+            pass
+
+        def ol():
+            pass
+
+        template1 = make_overload_template(func_stub, ol, {}, True, 'never')
+        template2 = make_overload_template(func_stub2, ol, {}, True, 'never')
+
+        with self.assertRaises(ValueError) as raises:
+            types.Function((template1, template2))
+        self.assertIn("incompatible templates:", str(raises.exception))
+
+
+class TestNumbers(TestCase):
+    """
+    Tests for number types.
+    """
+
+    def test_bitwidth(self):
+        """
+        All numeric types have bitwidth attribute
+        """
+        for ty in types.number_domain:
+            self.assertTrue(hasattr(ty, "bitwidth"))
+
+    def test_minval_maxval(self):
+        self.assertEqual(types.int8.maxval, 127)
+        self.assertEqual(types.int8.minval, -128)
+        self.assertEqual(types.uint8.maxval, 255)
+        self.assertEqual(types.uint8.minval, 0)
+        self.assertEqual(types.int64.maxval, (1 << 63) - 1)
+        self.assertEqual(types.int64.minval, -(1 << 63))
+        self.assertEqual(types.uint64.maxval, (1 << 64) - 1)
+        self.assertEqual(types.uint64.minval, 0)
+
+    def test_from_bidwidth(self):
+        f = types.Integer.from_bitwidth
+        self.assertIs(f(32), types.int32)
+        self.assertIs(f(8, signed=False), types.uint8)
+
+    def test_ordering(self):
+        def check_order(values):
+            for i in range(len(values)):
+                self.assertLessEqual(values[i], values[i])
+                self.assertGreaterEqual(values[i], values[i])
+                self.assertFalse(values[i] < values[i])
+                self.assertFalse(values[i] > values[i])
+                for j in range(i):
+                    self.assertLess(values[j], values[i])
+                    self.assertLessEqual(values[j], values[i])
+                    self.assertGreater(values[i], values[j])
+                    self.assertGreaterEqual(values[i], values[j])
+                    self.assertFalse(values[i] < values[j])
+                    self.assertFalse(values[i] <= values[j])
+                    self.assertFalse(values[j] > values[i])
+                    self.assertFalse(values[j] >= values[i])
+
+        check_order([types.int8, types.int16, types.int32, types.int64])
+        check_order([types.uint8, types.uint16, types.uint32, types.uint64])
+        check_order([types.float32, types.float64])
+        check_order([types.complex64, types.complex128])
+
+        with self.assertRaises(TypeError):
+            types.int8 <= types.uint32
+        with self.assertRaises(TypeError):
+            types.int8 <= types.float32
+        with self.assertRaises(TypeError):
+            types.float64 <= types.complex128
+
+
+class TestNdIter(TestCase):
+
+    def test_properties(self):
+        def check(ty, dtypes, ndim, layout, indexers=None):
+            self.assertEqual(ty.ndim, ndim)
+            self.assertEqual(ty.layout, layout)
+            self.assertEqual(ty.dtypes, dtypes)
+            views = [types.Array(dtype, 0, "C") for dtype in dtypes]
+            if len(views) > 1:
+                self.assertEqual(
+                    ty.yield_type,
+                    types.BaseTuple.from_types(views))
+            else:
+                self.assertEqual(ty.yield_type, views[0])
+            if indexers is not None:
+                self.assertEqual(ty.indexers, indexers)
+
+        f32 = types.float32
+        c64 = types.complex64
+        i16 = types.int16
+        a = types.Array(f32, 1, "C")
+        b = types.Array(f32, 2, "C")
+        c = types.Array(c64, 2, "F")
+        d = types.Array(i16, 2, "A")
+        e = types.Array(i16, 0, "C")
+        f = types.Array(f32, 1, "A")
+        g = types.Array(f32, 0, "C")
+
+        # 0-dim iterator
+        ty = types.NumpyNdIterType((e,))
+        check(ty, (i16,), 0, "C", [('0d', 0, 0, [0])])
+        self.assertFalse(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((e, g))
+        check(ty, (i16, f32), 0, "C", [('0d', 0, 0, [0, 1])])
+        self.assertFalse(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((e, c64))
+        check(ty, (i16, c64), 0, "C",
+              [('0d', 0, 0, [0]), ('scalar', 0, 0, [1])])
+        self.assertFalse(ty.need_shaped_indexing)
+
+        # 1-dim iterator
+        ty = types.NumpyNdIterType((a,))
+        check(ty, (f32,), 1, "C",
+              [('flat', 0, 1, [0])])
+        self.assertFalse(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((a, a))
+        check(ty, (f32, f32), 1, "C",
+              [('flat', 0, 1, [0, 1])])
+        self.assertFalse(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((a, e, e, c64))
+        check(ty, (f32, i16, i16, c64), 1, "C",
+              [('flat', 0, 1, [0]),  # a
+               ('0d', 0, 0, [1, 2]),  # e, e
+               ('scalar', 0, 0, [3]),  # c64
+               ])
+        self.assertFalse(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((a, f))
+        check(ty, (f32, f32), 1, "C",
+              [('flat', 0, 1, [0]), ('indexed', 0, 1, [1])])
+        self.assertTrue(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((f,))
+        check(ty, (f32,), 1, "C", [('indexed', 0, 1, [0])])
+        self.assertTrue(ty.need_shaped_indexing)
+
+        # 2-dim C-order iterator
+        ty = types.NumpyNdIterType((b,))
+        check(ty, (f32,), 2, "C", [('flat', 0, 2, [0])])
+        self.assertFalse(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((b, c))
+        check(
+            ty, (f32, c64), 2, "C", [
+                ('flat', 0, 2, [0]), ('indexed', 0, 2, [1])])
+        self.assertTrue(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((d,))
+        check(ty, (i16,), 2, "C", [('indexed', 0, 2, [0])])
+        self.assertTrue(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((b, c, d, d, e))
+        check(ty, (f32, c64, i16, i16, i16), 2, "C",
+              [('flat', 0, 2, [0]),  # b
+               ('indexed', 0, 2, [1, 2, 3]),  # c, d, d
+               ('0d', 0, 0, [4]),  # e
+               ])
+        self.assertTrue(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((a, b, c, d, d, f))
+        check(ty, (f32, f32, c64, i16, i16, f32), 2, "C",
+              [('flat', 1, 2, [0]),  # a
+               ('flat', 0, 2, [1]),  # b
+               ('indexed', 0, 2, [2, 3, 4]),  # c, d, d
+               ('indexed', 1, 2, [5]),  # f
+               ])
+        self.assertTrue(ty.need_shaped_indexing)
+
+        # 2-dim F-order iterator
+        ty = types.NumpyNdIterType((c,))
+        check(ty, (c64,), 2, "F", [('flat', 0, 2, [0])])
+        self.assertFalse(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((c, b, c, f))
+        check(ty, (c64, f32, c64, f32), 2, "F",
+              [('flat', 0, 2, [0, 2]),  # c, c
+               ('indexed', 0, 2, [1]),  # b
+               ('indexed', 0, 1, [3]),  # f
+               ])
+        self.assertTrue(ty.need_shaped_indexing)
+        ty = types.NumpyNdIterType((b, c, c, d, d, a, e))
+        check(ty, (f32, c64, c64, i16, i16, f32, i16), 2, "F",
+              [('indexed', 0, 2, [0, 3, 4]),  # b, d, d
+               ('flat', 0, 2, [1, 2]),  # c, c
+               ('flat', 0, 1, [5]),  # a
+               ('0d', 0, 0, [6]),  # e
+               ])
+        self.assertTrue(ty.need_shaped_indexing)
+
+
+class TestPickling(TestCase):
+    """
+    Pickling and unpickling should preserve type identity (singleton-ness)
+    and the _code attribute.  This is only a requirement for types that
+    can be part of function signatures.
+    """
+
+    def predefined_types(self):
+        """
+        Yield all predefined type instances
+        """
+        for ty in types.__dict__.values():
+            if isinstance(ty, types.Type):
+                yield ty
+
+    def check_pickling(self, orig):
+        pickled = pickle.dumps(orig, protocol=-1)
+        ty = pickle.loads(pickled)
+        self.assertIs(ty, orig)
+        self.assertGreaterEqual(ty._code, 0)
+
+    def test_predefined_types(self):
+        tys = list(self.predefined_types())
+        self.assertIn(types.int16, tys)
+        for ty in tys:
+            self.check_pickling(ty)
+
+    def test_atomic_types(self):
+        for unit in ('M', 'ms'):
+            ty = types.NPDatetime(unit)
+            self.check_pickling(ty)
+            ty = types.NPTimedelta(unit)
+            self.check_pickling(ty)
+
+    def test_arrays(self):
+        for ndim in (0, 1, 2):
+            for layout in ('A', 'C', 'F'):
+                ty = types.Array(types.int16, ndim, layout)
+                self.check_pickling(ty)
+
+    def test_records(self):
+        recordtype = np.dtype([('a', np.float64),
+                               ('b', np.int32),
+                               ('c', np.complex64),
+                               ('d', (np.str_, 5))])
+        ty = numpy_support.from_dtype(recordtype)
+        self.check_pickling(ty)
+        self.check_pickling(types.Array(ty, 1, 'A'))
+
+    def test_optional(self):
+        ty = types.Optional(types.int32)
+        self.check_pickling(ty)
+
+    def test_tuples(self):
+        ty1 = types.UniTuple(types.int32, 3)
+        self.check_pickling(ty1)
+        ty2 = types.Tuple((types.int32, ty1))
+        self.check_pickling(ty2)
+
+    def test_namedtuples(self):
+        ty1 = types.NamedUniTuple(types.intp, 2, Point)
+        self.check_pickling(ty1)
+        ty2 = types.NamedTuple((types.intp, types.float64), Point)
+        self.check_pickling(ty2)
+
+    def test_enums(self):
+        ty1 = types.EnumMember(Color, types.int32)
+        self.check_pickling(ty1)
+        ty2 = types.EnumMember(Shake, types.int64)
+        self.check_pickling(ty2)
+        ty3 = types.IntEnumMember(Shape, types.int64)
+        self.check_pickling(ty3)
+
+    def test_lists(self):
+        ty = types.List(types.int32)
+        self.check_pickling(ty)
+
+    def test_generator(self):
+        cfunc = jit("(int32,)", nopython=True)(gen)
+        sigs = list(cfunc.nopython_signatures)
+        ty = sigs[0].return_type
+        self.assertIsInstance(ty, types.Generator)
+        self.check_pickling(ty)
+
+    # call templates are not picklable
+    @unittest.expectedFailure
+    def test_external_function_pointers(self):
+        from numba.core.typing import ctypes_utils
+        from numba.tests.ctypes_usecases import c_sin, c_cos
+        for fnptr in (c_sin, c_cos):
+            ty = ctypes_utils.make_function_type(fnptr)
+            self.assertIsInstance(ty, types.ExternalFunctionPointer)
+            self.check_pickling(ty)
+
+
+class TestSignatures(TestCase):
+
+    def test_normalize_signature(self):
+        f = sigutils.normalize_signature
+
+        def check(sig, args, return_type):
+            self.assertEqual(f(sig), (args, return_type))
+
+        def check_error(sig, msg):
+            with self.assertRaises(TypeError) as raises:
+                f(sig)
+            self.assertIn(msg, str(raises.exception))
+
+        f32 = types.float32
+        c64 = types.complex64
+        i16 = types.int16
+        a = types.Array(f32, 1, "C")
+
+        check((c64,), (c64,), None)
+        check((f32, i16), (f32, i16), None)
+        check(a(i16), (i16,), a)
+        check("int16(complex64)", (c64,), i16)
+        check("(complex64, int16)", (c64, i16), None)
+        check(typing.signature(i16, c64), (c64,), i16)
+
+        msg = "invalid type in signature: expected a type instance"
+        check_error((types.Integer,), msg)
+        check_error((None,), msg)
+        check_error([], "invalid signature")
+
+
+class TestRecordDtype(unittest.TestCase):
+    def test_record_type_equiv(self):
+        rec_dt = np.dtype([('a', np.int32), ('b', np.float32)])
+        rec_ty = typeof(rec_dt)
+        art1 = rec_ty[::1]
+        arr = np.zeros(5, dtype=rec_dt)
+        art2 = typeof(arr)
+        self.assertEqual(art2.dtype.dtype, rec_ty)
+        self.assertEqual(art1, art2)
+
+    def test_user_specified(self):
+        rec_dt = np.dtype([('a', np.int32), ('b', np.float32)])
+        rec_type = typeof(rec_dt)
+
+        @jit((rec_type[:],), nopython=True)
+        def foo(x):
+            return x['a'], x['b']
+
+        arr = np.zeros(1, dtype=rec_dt)
+        arr[0]['a'] = 123
+        arr[0]['b'] = 32.1
+
+        a, b = foo(arr)
+
+        self.assertEqual(a, arr[0]['a'])
+        self.assertEqual(b, arr[0]['b'])
+
+
+class TestDType(TestCase):
+    def test_type_attr(self):
+        # Test .type attribute of dtype
+        def conv(arr, val):
+            return arr.dtype.type(val)
+
+        jit_conv = jit(nopython=True)(conv)
+
+        def assert_matches(arr, val, exact):
+            expect = conv(arr, val)
+            got = jit_conv(arr, val)
+            self.assertPreciseEqual(expect, exact)
+            self.assertPreciseEqual(typeof(expect), typeof(got))
+            self.assertPreciseEqual(expect, got)
+
+        arr = np.zeros(5)
+        assert_matches(arr.astype(np.intp), 1.2, 1)
+        assert_matches(arr.astype(np.float64), 1.2, 1.2)
+        assert_matches(arr.astype(np.complex128), 1.2, (1.2 + 0j))
+        assert_matches(arr.astype(np.complex128), 1.2j, 1.2j)
+
+    def test_kind(self):
+        def tkind(A):
+            return A.dtype.kind == 'f'
+        jit_tkind = jit(nopython=True)(tkind)
+        self.assertEqual(tkind(np.ones(3)), jit_tkind(np.ones(3)))
+        self.assertEqual(tkind(np.ones(3, dtype=np.intp)),
+                         jit_tkind(np.ones(3, dtype=np.intp)))
+
+    def test_dtype_with_type(self):
+        def impl():
+            a = np.dtype(np.float64)
+            return a.type(0)
+        jit_impl = jit(nopython=True)(impl)
+        self.assertEqual(impl(), jit_impl())
+
+    def test_dtype_with_string(self):
+        def impl():
+            a = np.dtype('float64')
+            return a.type(0)
+        jit_impl = jit(nopython=True)(impl)
+        self.assertEqual(impl(), jit_impl())
+
+
+class TestIsInternalTypeMarker(TestCase):
+    """Tests the use of the Type metaclass init correctly setting the flag on
+    the `is_internal` attr of a concrete Type class
+    """
+    source_lines = """
+from numba.core import types
+
+class FooType(types.Type):
+    def __init__(self):
+        super(FooType, self).__init__(name='Foo')
+"""
+
+    def test_create_temp_module(self):
+        sys_path_original = list(sys.path)
+        sys_modules_original = dict(sys.modules)
+        with create_temp_module(self.source_lines) as test_module:
+            temp_module_dir = os.path.dirname(test_module.__file__)
+            self.assertEqual(temp_module_dir, sys.path[0])
+            self.assertEqual(sys.path[1:], sys_path_original)
+            self.assertTrue(test_module.__name__ in sys.modules)
+        # Test that modifications to sys.path / sys.modules are reverted
+        self.assertEqual(sys.path, sys_path_original)
+        self.assertEqual(sys.modules, sys_modules_original)
+
+    def test_create_temp_module_with_exception(self):
+        try:
+            sys_path_original = list(sys.path)
+            sys_modules_original = dict(sys.modules)
+            with create_temp_module(self.source_lines):
+                raise ValueError("Something went wrong!")
+        except ValueError:
+            # Test that modifications to sys.path / sys.modules are reverted
+            self.assertEqual(sys.path, sys_path_original)
+            self.assertEqual(sys.modules, sys_modules_original)
+
+    def test_externally_defined_type_is_external(self):
+
+        with create_temp_module(self.source_lines) as test_module:
+            FooType = test_module.FooType
+            self.assertFalse(FooType().is_internal)
+
+            # set up an extension type
+            class Foo(object):
+                pass
+
+            register_model(FooType)(models.OpaqueModel)
+
+            @typeof_impl.register(Foo)
+            def _typ_foo(val, c):
+                return FooType()
+
+            @unbox(FooType)
+            def unbox_foo(typ, obj, c):
+                return NativeValue(c.context.get_dummy_value())
+
+            # function to overload
+            def false_if_not_array(a):
+                pass
+
+            # Set up an overload which will accept all types irrespective of
+            # whether they are from Numba's closed type system
+            @overload(false_if_not_array)
+            def ol_false_if_not_array(a):
+                if isinstance(a, types.Array):
+                    return lambda a : True
+                else:
+                    return lambda a : False
+
+            @njit
+            def call_false_if_not_array(a):
+                return false_if_not_array(a)
+
+            self.assertTrue(call_false_if_not_array(np.zeros(10)))
+            self.assertFalse(call_false_if_not_array(10))
+
+            # The extension type was accepted
+            self.assertFalse(call_false_if_not_array(Foo()))
+
+            # Now do the same sort of overload but put in a guard based on the
+            # use of internal types
+
+            def false_if_not_array_closed_system(a):
+                pass
+
+            @overload(false_if_not_array_closed_system)
+            def ol_false_if_not_array_closed_system(a):
+                if a.is_internal:  # guard
+                    if isinstance(a, types.Array):
+                        return lambda a : True
+                    else:
+                        return lambda a : False
+
+            @njit
+            def call_false_if_not_array_closed_system(a):
+                return false_if_not_array_closed_system(a)
+
+            self.assertTrue(call_false_if_not_array_closed_system(np.zeros(10)))
+            self.assertFalse(call_false_if_not_array_closed_system(10))
+
+            with self.assertRaises(errors.TypingError) as raises:
+                call_false_if_not_array_closed_system(Foo())
+            estr = str(raises.exception)
+            self.assertIn(_header_lead, estr)
+            self.assertIn("false_if_not_array_closed_system", estr)
+            self.assertIn("(Foo)", estr)
+
+    def test_mixin_against_real_example(self):
+        # See issue #4970, this checks that unicode eq/ne now ignores extension
+        # types.
+
+        with create_temp_module(self.source_lines) as test_module:
+            FooType = test_module.FooType
+            self.assertFalse(FooType().is_internal)
+
+            # set up an extension type
+            class Foo(object):
+                pass
+
+            register_model(FooType)(models.OpaqueModel)
+
+            @typeof_impl.register(Foo)
+            def _typ_foo(val, c):
+                return FooType()
+
+            @unbox(FooType)
+            def unbox_foo(typ, obj, c):
+                return NativeValue(c.context.get_dummy_value())
+
+            @overload(operator.eq)
+            def foo_eq(a, b):
+                if a == FooType():
+                    return lambda a, b: "RAN CUSTOM EQ OVERLOAD"
+
+            @overload(operator.ne)
+            def foo_ne(a, b):
+                if a == FooType():
+                    return lambda a, b: "RAN CUSTOM NE OVERLOAD"
+
+            @njit
+            def f(a):
+                return a == "A", a != "A"
+
+            self.assertEqual(("RAN CUSTOM EQ OVERLOAD",
+                              "RAN CUSTOM NE OVERLOAD"),
+                             f(Foo()))
+
+
+class TestIssues(TestCase):
+    def test_omitted_type(self):
+        # issue https://github.com/numba/numba/issues/5471
+        def inner(a):
+            pass
+
+        @overload(inner)
+        def inner_overload(a):
+            if not isinstance(a, types.Literal):
+                return
+            return lambda a: a
+
+        @njit
+        def my_func(a='a'):
+            return inner(a)
+
+        @njit
+        def f():
+            return my_func()
+
+        @njit
+        def g():
+            return my_func('b')
+
+        self.assertEqual(f(), 'a')
+        self.assertEqual(g(), 'b')
+
+    def test_type_of_literal(self):
+        # type(val) where val is a literal should not give a literal type.
+        def inner(a):
+            pass
+
+        @overload(inner)
+        def inner_overload(a):
+            if not isinstance(a, types.Literal):
+                return
+            self.assertIsInstance(a, types.Literal)
+            # NOTE: using 1.23 to ensure that the result is indeed an int.
+            return lambda a: type(a)(a + 1.23)
+
+        @njit
+        def my_func(a=1):
+            return inner(a)
+
+        @njit
+        def f():
+            return my_func()
+
+        @njit
+        def g():
+            return my_func(100)
+
+        self.assertEqual(f(), 2)
+        self.assertEqual(g(), 101)
+
+    def test_issue_typeref_key(self):
+        # issue https://github.com/numba/numba/issues/6336
+        class NoUniqueNameType(types.Dummy):
+            def __init__(self, param):
+                super(NoUniqueNameType, self).__init__('NoUniqueNameType')
+                self.param = param
+
+            @property
+            def key(self):
+                return self.param
+
+        no_unique_name_type_1 = NoUniqueNameType(1)
+        no_unique_name_type_2 = NoUniqueNameType(2)
+
+        for ty1 in (no_unique_name_type_1, no_unique_name_type_2):
+            for ty2 in (no_unique_name_type_1, no_unique_name_type_2):
+                self.assertIs(
+                    types.TypeRef(ty1) == types.TypeRef(ty2), ty1 == ty2)
+
+    def test_issue_list_type_key(self):
+        # https://github.com/numba/numba/issues/6397
+        class NoUniqueNameType(types.Dummy):
+            def __init__(self, param):
+                super(NoUniqueNameType, self).__init__('NoUniqueNameType')
+                self.param = param
+
+            @property
+            def key(self):
+                return self.param
+
+        no_unique_name_type_1 = NoUniqueNameType(1)
+        no_unique_name_type_2 = NoUniqueNameType(2)
+
+        for ty1 in (no_unique_name_type_1, no_unique_name_type_2):
+            for ty2 in (no_unique_name_type_1, no_unique_name_type_2):
+                self.assertIs(
+                    types.ListType(ty1) == types.ListType(ty2),  # noqa: E721
+                    ty1 == ty2
+                )
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typingerror.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typingerror.py
new file mode 100644
index 000000000..157beeb24
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_typingerror.py
@@ -0,0 +1,237 @@
+import math
+import re
+import textwrap
+import operator
+
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated
+from numba import jit
+from numba.core import types
+from numba.core.errors import TypingError
+from numba.core.types.functions import _header_lead
+from numba.tests.support import TestCase
+
+
+def what():
+    pass
+
+def foo():
+    return what()
+
+def bar(x):
+    return x.a
+
+def issue_868(a):
+    return a.shape * 2
+
+def impossible_return_type(x):
+    if x > 0:
+        return ()
+    else:
+        return 1j
+
+def bad_hypot_usage():
+    return math.hypot(1)
+
+def imprecise_list():
+    l = []
+    return len(l)
+
+def using_imprecise_list():
+    a = np.array([])
+    return a.astype(np.int32)
+
+def unknown_module():
+    return numpyz.int32(0)
+
+def nop(x, y, z):
+    pass
+
+def array_setitem_invalid_cast():
+    arr = np.empty(1, dtype=np.float64)
+    arr[0] = 1j  # invalid cast from complex to float
+    return arr
+
+
+class Foo(object):
+    def __repr__(self):
+        return "<Foo instance>"
+
+
+class TestTypingError(unittest.TestCase):
+
+    def test_unknown_function(self):
+        try:
+            compile_isolated(foo, ())
+        except TypingError as e:
+            self.assertIn("Untyped global name 'what'", str(e))
+        else:
+            self.fail("Should raise error")
+
+    def test_unknown_attrs(self):
+        try:
+            compile_isolated(bar, (types.int32,))
+        except TypingError as e:
+            self.assertIn("Unknown attribute 'a' of type int32", str(e))
+        else:
+            self.fail("Should raise error")
+
+    def test_unknown_module(self):
+        # This used to print "'object' object has no attribute 'int32'"
+        with self.assertRaises(TypingError) as raises:
+            compile_isolated(unknown_module, ())
+        self.assertIn("name 'numpyz' is not defined", str(raises.exception))
+
+    def test_issue_868(self):
+        '''
+        Summary: multiplying a scalar by a non-scalar would cause a crash in
+        type inference because TimeDeltaMixOp always assumed at least one of
+        its operands was an NPTimeDelta in its generic() method.
+        '''
+        with self.assertRaises(TypingError) as raises:
+            compile_isolated(issue_868, (types.Array(types.int32, 1, 'C'),))
+
+        expected = ((_header_lead + " Function(<built-in function mul>) found "
+                     "for signature:\n \n >>> mul(UniTuple({} x 1), {})")
+            .format(str(types.intp), types.IntegerLiteral(2)))
+        self.assertIn(expected, str(raises.exception))
+        self.assertIn("During: typing of", str(raises.exception))
+
+    def test_return_type_unification(self):
+        with self.assertRaises(TypingError) as raises:
+            compile_isolated(impossible_return_type, (types.int32,))
+        msg = ("Can't unify return type from the following types: Tuple(), "
+               "complex128")
+        self.assertIn(msg, str(raises.exception))
+
+    def test_bad_hypot_usage(self):
+        with self.assertRaises(TypingError) as raises:
+            compile_isolated(bad_hypot_usage, ())
+
+        errmsg = str(raises.exception)
+        # Make sure it listed the known signatures.
+        # This is sensitive to the formatting of the error message.
+        self.assertIn(" * (float64, float64) -> float64", errmsg)
+
+        # find the context lines
+        ctx_lines = [x for x in errmsg.splitlines() if "During:" in x ]
+
+        # Check contextual msg
+        self.assertTrue(re.search(r'.*During: resolving callee type: Function.*hypot', ctx_lines[0]))
+        self.assertTrue(re.search(r'.*During: typing of call .*test_typingerror.py', ctx_lines[1]))
+
+
+    def test_imprecise_list(self):
+        """
+        Type inference should catch that a list type's remain imprecise,
+        instead of letting lowering fail.
+        """
+        with self.assertRaises(TypingError) as raises:
+            compile_isolated(imprecise_list, ())
+
+        errmsg = str(raises.exception)
+        msg = ("Cannot infer the type of variable 'l', have imprecise type: "
+               "list(undefined)")
+        self.assertIn(msg, errmsg)
+        # check help message has gone in
+        self.assertIn("For Numba to be able to compile a list", errmsg)
+
+    def test_using_imprecise_list(self):
+        """
+        Type inference should report informative error about untyped list.
+        TODO: #2931
+        """
+        with self.assertRaises(TypingError) as raises:
+            compile_isolated(using_imprecise_list, ())
+
+        errmsg = str(raises.exception)
+        self.assertIn("Undecided type", errmsg)
+
+    def test_array_setitem_invalid_cast(self):
+        with self.assertRaises(TypingError) as raises:
+            compile_isolated(array_setitem_invalid_cast, ())
+
+        errmsg = str(raises.exception)
+        self.assertIn(
+            _header_lead + " Function({})".format(operator.setitem),
+            errmsg,
+        )
+        self.assertIn(
+            "(array(float64, 1d, C), Literal[int](0), complex128)",
+            errmsg,
+        )
+
+    def test_template_rejection_error_message_cascade(self):
+        from numba import njit
+        @njit
+        def foo():
+            z = 1
+            for a, b in enumerate(z):
+                pass
+            return z
+
+        with self.assertRaises(TypingError) as raises:
+            foo()
+        errmsg = str(raises.exception)
+        expected = "No match."
+        self.assertIn(expected, errmsg)
+
+        ctx_lines = [x for x in errmsg.splitlines() if "During:" in x ]
+        search = [r'.*During: resolving callee type: Function.*enumerate',
+                  r'.*During: typing of call .*test_typingerror.py']
+        for i, x in enumerate(search):
+            self.assertTrue(re.search(x, ctx_lines[i]))
+
+
+class TestArgumentTypingError(unittest.TestCase):
+    """
+    Test diagnostics of typing errors caused by argument inference failure.
+    """
+
+    def test_unsupported_array_dtype(self):
+        # See issue #1943
+        cfunc = jit(nopython=True)(nop)
+        a = np.ones(3)
+        a = a.astype(a.dtype.newbyteorder())
+        with self.assertRaises(TypingError) as raises:
+            cfunc(1, a, a)
+        expected = f"Unsupported array dtype: {a.dtype}"
+        self.assertIn(expected, str(raises.exception))
+
+    def test_unsupported_type(self):
+        cfunc = jit(nopython=True)(nop)
+        foo = Foo()
+        with self.assertRaises(TypingError) as raises:
+            cfunc(1, foo, 1)
+
+        expected=re.compile(("This error may have been caused by the following "
+                             "argument\(s\):\\n- argument 1:.*Cannot determine "
+                             "Numba type of "
+                             "<class \'numba.tests.test_typingerror.Foo\'>"))
+        self.assertTrue(expected.search(str(raises.exception)) is not None)
+
+
+class TestCallError(unittest.TestCase):
+    def test_readonly_array(self):
+        @jit("(f8[:],)", nopython=True)
+        def inner(x):
+            return x
+
+        @jit(nopython=True)
+        def outer():
+            return inner(gvalues)
+
+        gvalues = np.ones(10, dtype=np.float64)
+
+        with self.assertRaises(TypingError) as raises:
+            outer()
+
+        got = str(raises.exception)
+        pat = r"Invalid use of.*readonly array\(float64, 1d, C\)"
+        self.assertIsNotNone(re.search(pat, got))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ufuncs.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ufuncs.py
new file mode 100644
index 000000000..c4a363799
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_ufuncs.py
@@ -0,0 +1,1831 @@
+import functools
+import itertools
+import re
+import sys
+import warnings
+import threading
+import operator
+
+import numpy as np
+
+import unittest
+from numba import typeof, njit, guvectorize
+from numba.core import types, typing, utils
+from numba.core.compiler import compile_isolated, Flags, DEFAULT_FLAGS
+from numba.np.numpy_support import from_dtype
+from numba import jit, vectorize
+from numba.core.errors import LoweringError, TypingError, NumbaTypeError
+from numba.tests.support import TestCase, CompilationCache, MemoryLeakMixin, tag
+from numba.core.typing.npydecl import supported_ufuncs, all_ufuncs
+from numba.np import numpy_support
+from numba.core.registry import cpu_target
+from numba.core.base import BaseContext
+from numba.np import ufunc_db
+
+is32bits = tuple.__itemsize__ == 4
+iswindows = sys.platform.startswith('win32')
+
+# NOTE: to test the implementation of Numpy ufuncs, we disable rewriting
+# of array expressions.
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+enable_pyobj_flags.no_rewrites = True
+
+no_pyobj_flags = Flags()
+no_pyobj_flags.no_rewrites = True
+
+enable_nrt_flags = Flags()
+enable_nrt_flags.nrt = True
+enable_nrt_flags.no_rewrites = True
+
+
+def _unimplemented(func):
+    """An 'expectedFailure' like decorator that only expects compilation errors
+    caused by unimplemented functions that fail in no-python mode"""
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        try:
+            func(*args, **kwargs)
+        except TypingError:
+            raise unittest._ExpectedFailure(sys.exc_info())
+        raise unittest._UnexpectedSuccess
+
+def _make_ufunc_usecase(ufunc):
+    ldict = {}
+    arg_str = ','.join(['a{0}'.format(i) for i in range(ufunc.nargs)])
+    func_str = 'def fn({0}):\n    np.{1}({0})'.format(arg_str, ufunc.__name__)
+    exec(func_str, globals(), ldict)
+    fn = ldict['fn']
+    fn.__name__ = '{0}_usecase'.format(ufunc.__name__)
+    return fn
+
+def _make_unary_ufunc_op_usecase(ufunc_op):
+    ldict = {}
+    exec("def fn(x):\n    return {0}(x)".format(ufunc_op), globals(), ldict)
+    fn = ldict["fn"]
+    fn.__name__ = "usecase_{0}".format(hash(ufunc_op))
+    return fn
+
+def _make_binary_ufunc_op_usecase(ufunc_op):
+    ldict = {}
+    exec("def fn(x,y):\n    return x{0}y".format(ufunc_op), globals(), ldict)
+    fn = ldict["fn"]
+    fn.__name__ = "usecase_{0}".format(hash(ufunc_op))
+    return fn
+
+
+def _make_inplace_ufunc_op_usecase(ufunc_op):
+    """Generates a function to be compiled that performs an inplace operation
+
+    ufunc_op can be a string like '+=' or a function like operator.iadd
+    """
+    if isinstance(ufunc_op, str):
+        ldict = {}
+        exec("def fn(x,y):\n    x{0}y".format(ufunc_op), globals(), ldict)
+        fn = ldict["fn"]
+        fn.__name__ = "usecase_{0}".format(hash(ufunc_op))
+    else:
+        def inplace_op(x, y):
+            ufunc_op(x, y)
+        fn = inplace_op
+    return fn
+
+
+def _as_dtype_value(tyargs, args):
+    """Convert python values into numpy scalar objects.
+    """
+    return [np.dtype(str(ty)).type(val) for ty, val in zip(tyargs, args)]
+
+
+
+class BaseUFuncTest(MemoryLeakMixin):
+
+    def setUp(self):
+        super(BaseUFuncTest, self).setUp()
+        self.inputs = [
+            (np.uint32(0), types.uint32),
+            (np.uint32(1), types.uint32),
+            (np.int32(-1), types.int32),
+            (np.int32(0), types.int32),
+            (np.int32(1), types.int32),
+            (np.uint64(0), types.uint64),
+            (np.uint64(1), types.uint64),
+            (np.int64(-1), types.int64),
+            (np.int64(0), types.int64),
+            (np.int64(1), types.int64),
+
+            (np.float32(-0.5), types.float32),
+            (np.float32(0.0), types.float32),
+            (np.float32(0.5), types.float32),
+
+            (np.float64(-0.5), types.float64),
+            (np.float64(0.0), types.float64),
+            (np.float64(0.5), types.float64),
+
+            (np.array([0,1], dtype='u4'), types.Array(types.uint32, 1, 'C')),
+            (np.array([0,1], dtype='u8'), types.Array(types.uint64, 1, 'C')),
+            (np.array([-1,0,1], dtype='i4'), types.Array(types.int32, 1, 'C')),
+            (np.array([-1,0,1], dtype='i8'), types.Array(types.int64, 1, 'C')),
+            (np.array([-0.5, 0.0, 0.5], dtype='f4'), types.Array(types.float32, 1, 'C')),
+            (np.array([-0.5, 0.0, 0.5], dtype='f8'), types.Array(types.float64, 1, 'C')),
+
+            (np.array([0,1], dtype=np.int8), types.Array(types.int8, 1, 'C')),
+            (np.array([0,1], dtype=np.int16), types.Array(types.int16, 1, 'C')),
+            (np.array([0,1], dtype=np.uint8), types.Array(types.uint8, 1, 'C')),
+            (np.array([0,1], dtype=np.uint16), types.Array(types.uint16, 1, 'C')),
+            ]
+        self.cache = CompilationCache()
+
+    def _determine_output_type(self, input_type, int_output_type=None,
+                               float_output_type=None):
+        ty = input_type
+        if isinstance(ty, types.Array):
+            ty = ty.dtype
+
+        if ty in types.signed_domain:
+            if int_output_type:
+                output_type = types.Array(int_output_type, 1, 'C')
+            else:
+                output_type = types.Array(ty, 1, 'C')
+        elif ty in types.unsigned_domain:
+            if int_output_type:
+                output_type = types.Array(int_output_type, 1, 'C')
+            else:
+                output_type = types.Array(ty, 1, 'C')
+        else:
+            if float_output_type:
+                output_type = types.Array(float_output_type, 1, 'C')
+            else:
+                output_type = types.Array(ty, 1, 'C')
+        return output_type
+
+
+class TestUFuncs(BaseUFuncTest, TestCase):
+
+    def basic_ufunc_test(self, ufunc, flags=no_pyobj_flags,
+                         skip_inputs=[], additional_inputs=[],
+                         int_output_type=None, float_output_type=None,
+                         kinds='ifc', positive_only=False):
+
+        # Necessary to avoid some Numpy warnings being silenced, despite
+        # the simplefilter() call below.
+        self.reset_module_warnings(__name__)
+
+        pyfunc = _make_ufunc_usecase(ufunc)
+
+        inputs = list(self.inputs) + additional_inputs
+
+        for input_tuple in inputs:
+            input_operand = input_tuple[0]
+            input_type = input_tuple[1]
+
+            is_tuple = isinstance(input_operand, tuple)
+            if is_tuple:
+                args = input_operand
+            else:
+                args = (input_operand,) * ufunc.nin
+
+            if input_type in skip_inputs:
+                continue
+            if positive_only and np.any(args[0] < 0):
+                continue
+
+            # Some ufuncs don't allow all kinds of arguments
+            if (args[0].dtype.kind not in kinds):
+                continue
+
+            output_type = self._determine_output_type(
+                input_type, int_output_type, float_output_type)
+
+            input_types = (input_type,) * ufunc.nin
+            output_types = (output_type,) * ufunc.nout
+            cr = self.cache.compile(pyfunc, input_types + output_types,
+                                    flags=flags)
+            cfunc = cr.entry_point
+
+            if isinstance(args[0], np.ndarray):
+                results = [
+                    np.zeros(args[0].size,
+                             dtype=out_ty.dtype.name)
+                    for out_ty in output_types
+                ]
+                expected = [
+                    np.zeros(args[0].size,
+                                    dtype=out_ty.dtype.name)
+                    for out_ty in output_types
+                ]
+            else:
+                results = [
+                    np.zeros(1, dtype=out_ty.dtype.name)
+                    for out_ty in output_types
+                ]
+                expected = [
+                    np.zeros(1, dtype=out_ty.dtype.name)
+                    for out_ty in output_types
+                ]
+
+            invalid_flag = False
+            with warnings.catch_warnings(record=True) as warnlist:
+                warnings.simplefilter('always')
+                pyfunc(*args, *expected)
+
+                warnmsg = "invalid value encountered"
+                for thiswarn in warnlist:
+
+                    if (issubclass(thiswarn.category, RuntimeWarning)
+                        and str(thiswarn.message).startswith(warnmsg)):
+                        invalid_flag = True
+
+            cfunc(*args, *results)
+
+            for expected_i, result_i in zip(expected, results):
+                msg = '\n'.join(["ufunc '{0}' failed",
+                                 "inputs ({1}):", "{2}",
+                                 "got({3})", "{4}",
+                                 "expected ({5}):", "{6}"
+                                 ]).format(ufunc.__name__,
+                                           input_type, input_operand,
+                                           output_type, result_i,
+                                           expected_i.dtype, expected_i)
+                try:
+                    np.testing.assert_array_almost_equal(
+                        expected_i, result_i,
+                        decimal=5,
+                        err_msg=msg)
+                except AssertionError:
+                    if invalid_flag:
+                        # Allow output to mismatch for invalid input
+                        print("Output mismatch for invalid input",
+                              input_tuple, result_i, expected_i)
+                    else:
+                        raise
+
+    def basic_int_ufunc_test(self, name=None, flags=no_pyobj_flags):
+        self.basic_ufunc_test(name, flags=flags,
+            skip_inputs=[types.float32, types.float64,
+                types.Array(types.float32, 1, 'C'),
+                types.Array(types.float64, 1, 'C')])
+
+    ############################################################################
+    # Math operations
+
+    def test_add_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.add, flags=flags)
+
+    def test_subtract_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.subtract, flags=flags)
+
+    def test_multiply_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.multiply, flags=flags)
+
+    def test_divide_ufunc(self, flags=no_pyobj_flags):
+        # Bear in mind that in python3 divide IS true_divide
+        # so the out type for int types will be a double
+        int_out_type = None
+        int_out_type = types.float64
+
+        self.basic_ufunc_test(np.divide, flags=flags, int_output_type=int_out_type)
+
+    def test_logaddexp_ufunc(self):
+        self.basic_ufunc_test(np.logaddexp, kinds='f')
+
+    def test_logaddexp2_ufunc(self):
+        self.basic_ufunc_test(np.logaddexp2, kinds='f')
+
+    def test_true_divide_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.true_divide, flags=flags, int_output_type=types.float64)
+
+    def test_floor_divide_ufunc(self):
+        self.basic_ufunc_test(np.floor_divide)
+
+    def test_negative_ufunc(self, flags=no_pyobj_flags):
+        # NumPy ufunc has bug with uint32 as input and int64 as output,
+        # so skip uint32 input.
+        self.basic_ufunc_test(np.negative, int_output_type=types.int64,
+                              skip_inputs=[types.Array(types.uint32, 1, 'C'), types.uint32],
+                              flags=flags)
+
+    def test_positive_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.positive, flags=flags)
+
+    def test_power_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.power, flags=flags,
+                               positive_only=True)
+
+    def test_float_power_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.float_power, flags=flags, kinds="fc")
+
+    def test_gcd_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.gcd, flags=flags, kinds="iu")
+
+    def test_lcm_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.lcm, flags=flags, kinds="iu")
+
+    def test_remainder_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.remainder, flags=flags)
+
+    def test_mod_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.mod, flags=flags, kinds='ifcu',
+            additional_inputs = [
+                ((np.uint64(np.iinfo(np.uint64).max), np.uint64(16)), types.uint64)
+            ])
+
+    def test_fmod_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.fmod, flags=flags)
+
+    def test_abs_ufunc(self, flags=no_pyobj_flags, ufunc=np.abs):
+        self.basic_ufunc_test(ufunc, flags=flags,
+            additional_inputs = [
+                (np.uint32(np.iinfo(np.uint32).max), types.uint32),
+                (np.uint64(np.iinfo(np.uint64).max), types.uint64),
+                (np.float32(np.finfo(np.float32).min), types.float32),
+                (np.float64(np.finfo(np.float64).min), types.float64)
+                ])
+
+    def test_absolute_ufunc(self, flags=no_pyobj_flags):
+        self.test_abs_ufunc(flags=flags, ufunc=np.absolute)
+
+    def test_fabs_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.fabs, flags=flags, kinds='f')
+
+    def test_rint_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.rint, flags=flags, kinds='cf')
+
+    def test_sign_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.sign, flags=flags)
+
+    def test_conj_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.conj, flags=flags)
+
+    def test_exp_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.exp, flags=flags, kinds='cf')
+
+    def test_exp2_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.exp2, flags=flags, kinds='cf')
+
+    def test_log_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.log, flags=flags, kinds='cf')
+
+    def test_log2_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.log2, flags=flags, kinds='cf')
+
+    def test_log10_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.log10, flags=flags, kinds='cf')
+
+    def test_expm1_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.expm1, flags=flags, kinds='cf')
+
+    def test_log1p_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.log1p, flags=flags, kinds='cf')
+
+    def test_sqrt_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.sqrt, flags=flags, kinds='cf')
+
+    def test_square_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.square, flags=flags)
+
+    def test_cbrt_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.cbrt, flags=flags, kinds='f')
+
+    def test_reciprocal_ufunc(self, flags=no_pyobj_flags):
+        # reciprocal for integers doesn't make much sense and is problematic
+        # in the case of division by zero, as an inf will overflow float to
+        # int conversions, which is undefined behavior.
+        to_skip = [types.Array(types.uint32, 1, 'C'), types.uint32,
+                   types.Array(types.int32, 1, 'C'), types.int32,
+                   types.Array(types.uint64, 1, 'C'), types.uint64,
+                   types.Array(types.int64, 1, 'C'), types.int64]
+        self.basic_ufunc_test(np.reciprocal, skip_inputs=to_skip, flags=flags)
+
+    def test_conjugate_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.conjugate, flags=flags)
+
+
+    ############################################################################
+    # Trigonometric Functions
+
+    def test_sin_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.sin, flags=flags, kinds='cf')
+
+    def test_cos_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.cos, flags=flags, kinds='cf')
+
+    def test_tan_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.tan, flags=flags, kinds='cf')
+
+    def test_arcsin_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.arcsin, flags=flags, kinds='cf')
+
+    def test_arccos_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.arccos, flags=flags, kinds='cf')
+
+    def test_arctan_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.arctan, flags=flags, kinds='cf')
+
+    def test_arctan2_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.arctan2, flags=flags, kinds='cf')
+
+    def test_hypot_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.hypot, kinds='f')
+
+    def test_sinh_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.sinh, flags=flags, kinds='cf')
+
+    def test_cosh_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.cosh, flags=flags, kinds='cf')
+
+    def test_tanh_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.tanh, flags=flags, kinds='cf')
+
+    def test_arcsinh_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.arcsinh, flags=flags, kinds='cf')
+
+    def test_arccosh_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.arccosh, flags=flags, kinds='cf')
+
+    def test_arctanh_ufunc(self, flags=no_pyobj_flags):
+        # arctanh is only valid is only finite in the range ]-1, 1[
+        # This means that for any of the integer types it will produce
+        # conversion from infinity/-infinity to integer. That's undefined
+        # behavior in C, so the results may vary from implementation to
+        # implementation. This means that the result from the compiler
+        # used to compile NumPy may differ from the result generated by
+        # llvm. Skipping the integer types in this test avoids failed
+        # tests because of this.
+        to_skip = [types.Array(types.uint32, 1, 'C'), types.uint32,
+                   types.Array(types.int32, 1, 'C'), types.int32,
+                   types.Array(types.uint64, 1, 'C'), types.uint64,
+                   types.Array(types.int64, 1, 'C'), types.int64]
+
+        self.basic_ufunc_test(np.arctanh, skip_inputs=to_skip, flags=flags,
+                              kinds='cf')
+
+    def test_deg2rad_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.deg2rad, flags=flags, kinds='f')
+
+    def test_rad2deg_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.rad2deg, flags=flags, kinds='f')
+
+    def test_degrees_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.degrees, flags=flags, kinds='f')
+
+    def test_radians_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.radians, flags=flags, kinds='f')
+
+    ############################################################################
+    # Bit-twiddling Functions
+
+    def test_bitwise_and_ufunc(self, flags=no_pyobj_flags):
+        self.basic_int_ufunc_test(np.bitwise_and, flags=flags)
+
+    def test_bitwise_or_ufunc(self, flags=no_pyobj_flags):
+        self.basic_int_ufunc_test(np.bitwise_or, flags=flags)
+
+    def test_bitwise_xor_ufunc(self, flags=no_pyobj_flags):
+        self.basic_int_ufunc_test(np.bitwise_xor, flags=flags)
+
+    def test_invert_ufunc(self, flags=no_pyobj_flags):
+        self.basic_int_ufunc_test(np.invert, flags=flags)
+
+    def test_bitwise_not_ufunc(self, flags=no_pyobj_flags):
+        self.basic_int_ufunc_test(np.bitwise_not, flags=flags)
+
+    # Note: there is no entry for left_shift and right_shift as this harness
+    #       is not valid for them. This is so because left_shift and right
+    #       shift implementation in NumPy has undefined behavior (in C-parlance)
+    #       when the second argument is a negative (or bigger than the number
+    #       of bits) value.
+    #       Also, right_shift for negative first arguments also relies on
+    #       implementation defined behavior, although numba warantees "sane"
+    #       behavior (arithmetic shifts on signed integers, logic shifts on
+    #       unsigned integers).
+
+    ############################################################################
+    # Comparison functions
+    def test_greater_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.greater, flags=flags)
+
+    def test_greater_equal_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.greater_equal, flags=flags)
+
+    def test_less_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.less, flags=flags)
+
+    def test_less_equal_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.less_equal, flags=flags)
+
+    def test_not_equal_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.not_equal, flags=flags)
+
+    def test_equal_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.equal, flags=flags)
+
+    def test_logical_and_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.logical_and, flags=flags)
+
+    def test_logical_or_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.logical_or, flags=flags)
+
+    def test_logical_xor_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.logical_xor, flags=flags)
+
+    def test_logical_not_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.logical_not, flags=flags)
+
+    def test_maximum_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.maximum, flags=flags)
+
+    def test_minimum_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.minimum, flags=flags)
+
+    def test_fmax_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.fmax, flags=flags)
+
+    def test_fmin_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.fmin, flags=flags)
+
+
+    ############################################################################
+    # Floating functions
+
+    def bool_additional_inputs(self):
+        return [
+            (np.array([True, False], dtype=np.bool_),
+             types.Array(types.bool_, 1, 'C')),
+            ]
+
+    def test_isfinite_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(
+            np.isfinite, flags=flags, kinds='ifcb',
+            additional_inputs=self.bool_additional_inputs(),
+        )
+
+    def test_isinf_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(
+            np.isinf, flags=flags, kinds='ifcb',
+            additional_inputs=self.bool_additional_inputs(),
+        )
+
+    def test_isnan_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(
+            np.isnan, flags=flags, kinds='ifcb',
+            additional_inputs=self.bool_additional_inputs(),
+        )
+
+    def test_signbit_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.signbit, flags=flags)
+
+    def test_copysign_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.copysign, flags=flags, kinds='f')
+
+    def test_nextafter_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.nextafter, flags=flags, kinds='f')
+
+    @_unimplemented
+    def test_modf_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.modf, flags=flags, kinds='f')
+
+    # Note: there is no entry for ldexp as this harness isn't valid for this
+    #       ufunc. this is so because ldexp requires heterogeneous inputs.
+    #       However, this ufunc is tested by the TestLoopTypes test classes.
+
+    @_unimplemented
+    def test_frexp_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.frexp, flags=flags, kinds='f')
+
+    def test_floor_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.floor, flags=flags, kinds='f')
+
+    def test_ceil_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.ceil, flags=flags, kinds='f')
+
+    def test_trunc_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.trunc, flags=flags, kinds='f')
+
+    def test_spacing_ufunc(self, flags=no_pyobj_flags):
+        self.basic_ufunc_test(np.spacing, flags=flags, kinds='f')
+
+    ############################################################################
+    # Other tests
+
+    def binary_ufunc_mixed_types_test(self, ufunc, flags=no_pyobj_flags):
+        ufunc_name = ufunc.__name__
+        ufunc = _make_ufunc_usecase(ufunc)
+        inputs1 = [
+            (1, types.uint64),
+            (-1, types.int64),
+            (0.5, types.float64),
+
+            (np.array([0, 1], dtype='u8'), types.Array(types.uint64, 1, 'C')),
+            (np.array([-1, 1], dtype='i8'), types.Array(types.int64, 1, 'C')),
+            (np.array([-0.5, 0.5], dtype='f8'), types.Array(types.float64, 1, 'C'))]
+
+        inputs2 = inputs1
+
+        output_types = [types.Array(types.int64, 1, 'C'),
+                        types.Array(types.float64, 1, 'C')]
+
+        pyfunc = ufunc
+
+        for input1, input2, output_type in itertools.product(inputs1, inputs2, output_types):
+
+            input1_operand = input1[0]
+            input1_type = input1[1]
+
+            input2_operand = input2[0]
+            input2_type = input2[1]
+
+            # Skip division by unsigned int because of NumPy bugs
+            if ufunc_name == 'divide' and (input2_type == types.Array(types.uint32, 1, 'C') or
+                    input2_type == types.Array(types.uint64, 1, 'C')):
+                continue
+
+            # Skip some subtraction tests because of NumPy bugs
+            if ufunc_name == 'subtract' and input1_type == types.Array(types.uint32, 1, 'C') and \
+                    input2_type == types.uint32 and types.Array(types.int64, 1, 'C'):
+                continue
+            if ufunc_name == 'subtract' and input1_type == types.Array(types.uint32, 1, 'C') and \
+                    input2_type == types.uint64 and types.Array(types.int64, 1, 'C'):
+                continue
+
+            if ((isinstance(input1_type, types.Array) or
+                    isinstance(input2_type, types.Array)) and
+                    not isinstance(output_type, types.Array)):
+                continue
+
+            cr = self.cache.compile(pyfunc,
+                                    (input1_type, input2_type, output_type),
+                                    flags=flags)
+            cfunc = cr.entry_point
+
+            if isinstance(input1_operand, np.ndarray):
+                result = np.zeros(input1_operand.size,
+                                  dtype=output_type.dtype.name)
+                expected = np.zeros(input1_operand.size,
+                                    dtype=output_type.dtype.name)
+            elif isinstance(input2_operand, np.ndarray):
+                result = np.zeros(input2_operand.size,
+                                  dtype=output_type.dtype.name)
+                expected = np.zeros(input2_operand.size,
+                                    dtype=output_type.dtype.name)
+            else:
+                result = np.zeros(1, dtype=output_type.dtype.name)
+                expected = np.zeros(1, dtype=output_type.dtype.name)
+
+            cfunc(input1_operand, input2_operand, result)
+            pyfunc(input1_operand, input2_operand, expected)
+
+            scalar_type = getattr(output_type, 'dtype', output_type)
+            prec = ('single'
+                    if scalar_type in (types.float32, types.complex64)
+                    else 'double')
+            self.assertPreciseEqual(expected, result, prec=prec)
+
+    def test_broadcasting(self):
+
+        # Test unary ufunc
+        pyfunc = _make_ufunc_usecase(np.negative)
+
+        input_operands = [
+            np.arange(3, dtype='i8'),
+            np.arange(3, dtype='i8').reshape(3,1),
+            np.arange(3, dtype='i8').reshape(1,3),
+            np.arange(3, dtype='i8').reshape(3,1),
+            np.arange(3, dtype='i8').reshape(1,3),
+            np.arange(3*3, dtype='i8').reshape(3,3)]
+
+        output_operands = [
+            np.zeros(3*3, dtype='i8').reshape(3,3),
+            np.zeros(3*3, dtype='i8').reshape(3,3),
+            np.zeros(3*3, dtype='i8').reshape(3,3),
+            np.zeros(3*3*3, dtype='i8').reshape(3,3,3),
+            np.zeros(3*3*3, dtype='i8').reshape(3,3,3),
+            np.zeros(3*3*3, dtype='i8').reshape(3,3,3)]
+
+        for x, result in zip(input_operands, output_operands):
+
+            input_type = types.Array(types.uint64, x.ndim, 'C')
+            output_type = types.Array(types.int64, result.ndim, 'C')
+
+            cr = self.cache.compile(pyfunc, (input_type, output_type),
+                                    flags=no_pyobj_flags)
+            cfunc = cr.entry_point
+
+            expected = np.zeros(result.shape, dtype=result.dtype)
+            np.negative(x, expected)
+
+            cfunc(x, result)
+            self.assertPreciseEqual(result, expected)
+
+        # Test binary ufunc
+        pyfunc = _make_ufunc_usecase(np.add)
+
+        input1_operands = [
+            np.arange(3, dtype='u8'),
+            np.arange(3*3, dtype='u8').reshape(3,3),
+            np.arange(3*3*3, dtype='u8').reshape(3,3,3),
+            np.arange(3, dtype='u8').reshape(3,1),
+            np.arange(3, dtype='u8').reshape(1,3),
+            np.arange(3, dtype='u8').reshape(3,1,1),
+            np.arange(3*3, dtype='u8').reshape(3,3,1),
+            np.arange(3*3, dtype='u8').reshape(3,1,3),
+            np.arange(3*3, dtype='u8').reshape(1,3,3)]
+
+        input2_operands = input1_operands
+
+        for x, y in itertools.product(input1_operands, input2_operands):
+
+            input1_type = types.Array(types.uint64, x.ndim, 'C')
+            input2_type = types.Array(types.uint64, y.ndim, 'C')
+            output_type = types.Array(types.uint64, max(x.ndim, y.ndim), 'C')
+
+            cr = self.cache.compile(pyfunc, (input1_type, input2_type, output_type),
+                                    flags=no_pyobj_flags)
+            cfunc = cr.entry_point
+
+            expected = np.add(x, y)
+            result = np.zeros(expected.shape, dtype='u8')
+
+            cfunc(x, y, result)
+            self.assertPreciseEqual(result, expected)
+
+    def test_implicit_output_npm(self):
+        with self.assertRaises(NumbaTypeError):
+            def myadd(a0, a1):
+                return np.add(a0, a1)
+            arr_ty = types.Array(types.uint64, 1, 'C')
+            cr = compile_isolated(myadd, (arr_ty, arr_ty),
+                                  flags=no_pyobj_flags)
+
+    def test_broadcast_implicit_output_npm_nrt(self):
+        def pyfunc(a0, a1):
+            return np.add(a0, a1)
+
+        input1_operands = [
+            np.arange(3, dtype='u8'),
+            np.arange(3*3, dtype='u8').reshape(3,3),
+            np.arange(3*3*3, dtype='u8').reshape(3,3,3),
+            np.arange(3, dtype='u8').reshape(3,1),
+            np.arange(3, dtype='u8').reshape(1,3),
+            np.arange(3, dtype='u8').reshape(3,1,1),
+            np.arange(3*3, dtype='u8').reshape(3,3,1),
+            np.arange(3*3, dtype='u8').reshape(3,1,3),
+            np.arange(3*3, dtype='u8').reshape(1,3,3)]
+
+        input2_operands = input1_operands
+
+        for x, y in itertools.product(input1_operands, input2_operands):
+
+            input1_type = types.Array(types.uint64, x.ndim, 'C')
+            input2_type = types.Array(types.uint64, y.ndim, 'C')
+
+            cr = self.cache.compile(pyfunc, (input1_type, input2_type),
+                                    flags=enable_nrt_flags)
+            cfunc = cr.entry_point
+
+            expected = np.add(x, y)
+            result = cfunc(x, y)
+            np.testing.assert_array_equal(expected, result)
+
+    def test_implicit_output_layout_binary(self):
+        def pyfunc(a0, a1):
+            return np.add(a0, a1)
+
+        # C layout
+        X = np.linspace(0, 1, 20).reshape(4, 5)
+        # F layout
+        Y = np.array(X, order='F')
+        # A layout
+        Z = X.reshape(5, 4).T[0]
+
+        Xty = typeof(X)
+        assert X.flags.c_contiguous and Xty.layout == 'C'
+        Yty = typeof(Y)
+        assert Y.flags.f_contiguous and Yty.layout == 'F'
+        Zty = typeof(Z)
+        assert Zty.layout == 'A'
+        assert not Z.flags.c_contiguous
+        assert not Z.flags.f_contiguous
+
+        testcases = list(itertools.permutations([X, Y, Z], 2))
+        testcases += [(X, X)]
+        testcases += [(Y, Y)]
+        testcases += [(Z, Z)]
+
+        for arg0, arg1 in testcases:
+            cr = self.cache.compile(pyfunc, (typeof(arg0), typeof(arg1)),
+                                    flags=enable_nrt_flags)
+            expected = pyfunc(arg0, arg1)
+            result = cr.entry_point(arg0, arg1)
+
+            self.assertEqual(expected.flags.c_contiguous,
+                             result.flags.c_contiguous)
+            self.assertEqual(expected.flags.f_contiguous,
+                             result.flags.f_contiguous)
+            np.testing.assert_array_equal(expected, result)
+
+    def test_implicit_output_layout_unary(self):
+        def pyfunc(a0):
+            return np.sqrt(a0)
+
+        # C layout
+        X = np.linspace(0, 1, 20).reshape(4, 5)
+        # F layout
+        Y = np.array(X, order='F')
+        # A layout
+        Z = X.reshape(5, 4).T[0]
+
+        Xty = typeof(X)
+        assert X.flags.c_contiguous and Xty.layout == 'C'
+        Yty = typeof(Y)
+        assert Y.flags.f_contiguous and Yty.layout == 'F'
+        Zty = typeof(Z)
+        assert Zty.layout == 'A'
+        assert not Z.flags.c_contiguous
+        assert not Z.flags.f_contiguous
+
+        for arg0 in [X, Y, Z]:
+            cr = self.cache.compile(pyfunc, (typeof(arg0),),
+                                    flags=enable_nrt_flags)
+            expected = pyfunc(arg0)
+            result = cr.entry_point(arg0)
+
+            self.assertEqual(expected.flags.c_contiguous,
+                             result.flags.c_contiguous)
+            self.assertEqual(expected.flags.f_contiguous,
+                             result.flags.f_contiguous)
+            np.testing.assert_array_equal(expected, result)
+
+
+
+class TestArrayOperators(BaseUFuncTest, TestCase):
+
+    def _check_results(self, expected, got):
+        self.assertEqual(expected.dtype.kind, got.dtype.kind)
+        np.testing.assert_array_almost_equal(expected, got)
+
+    def unary_op_test(self, operator, flags=enable_nrt_flags,
+                      skip_inputs=[], additional_inputs=[],
+                      int_output_type=None, float_output_type=None):
+        operator_func = _make_unary_ufunc_op_usecase(operator)
+        inputs = list(self.inputs)
+        inputs.extend(additional_inputs)
+        pyfunc = operator_func
+        for input_tuple in inputs:
+            input_operand, input_type = input_tuple
+
+            if ((input_type in skip_inputs) or
+                (not isinstance(input_type, types.Array))):
+                continue
+
+            cr = self.cache.compile(pyfunc, (input_type,),
+                                    flags=flags)
+            cfunc = cr.entry_point
+            expected = pyfunc(input_operand)
+            got = cfunc(input_operand)
+            self._check_results(expected, got)
+
+    def binary_op_test(self, operator, flags=enable_nrt_flags,
+                       skip_inputs=[], additional_inputs=[],
+                       int_output_type=None, float_output_type=None,
+                       positive_rhs=False):
+        operator_func = _make_binary_ufunc_op_usecase(operator)
+        inputs = list(self.inputs)
+        inputs.extend(additional_inputs)
+        pyfunc = operator_func
+        # when generating arbitrary sequences, we use a fixed seed
+        # for deterministic testing
+        random_state = np.random.RandomState(1)
+        for input_tuple in inputs:
+            input_operand1, input_type = input_tuple
+            input_dtype = numpy_support.as_dtype(
+                getattr(input_type, "dtype", input_type))
+            input_type1 = input_type
+
+            if input_type in skip_inputs:
+                continue
+
+            if positive_rhs:
+                zero = np.zeros(1, dtype=input_dtype)[0]
+            # If we only use two scalars, the code generator will not
+            # select the ufunctionalized operator, so we mix it up.
+            if isinstance(input_type, types.Array):
+                input_operand0 = input_operand1
+                input_type0 = input_type
+                if positive_rhs and np.any(input_operand1 < zero):
+                    continue
+            else:
+                input_operand0 = (random_state.uniform(0, 100, 10)).astype(
+                    input_dtype)
+                input_type0 = typeof(input_operand0)
+                if positive_rhs and input_operand1 < zero:
+                    continue
+
+            cr = self.cache.compile(pyfunc, (input_type0, input_type1),
+                                    flags=flags)
+            cfunc = cr.entry_point
+            expected = pyfunc(input_operand0, input_operand1)
+            got = cfunc(input_operand0, input_operand1)
+            self._check_results(expected, got)
+
+    def bitwise_additional_inputs(self):
+        # For bitwise operators, we want to check the results for boolean
+        # arrays (see #1813).
+        return [
+            (True, types.boolean),
+            (False, types.boolean),
+            (np.array([True, False]), types.Array(types.boolean, 1, 'C')),
+            ]
+
+    def binary_int_op_test(self, *args, **kws):
+        skip_inputs = kws.setdefault('skip_inputs', [])
+        skip_inputs += [
+            types.float32, types.float64,
+            types.Array(types.float32, 1, 'C'),
+            types.Array(types.float64, 1, 'C'),
+            ]
+        return self.binary_op_test(*args, **kws)
+
+    def binary_bitwise_op_test(self, *args, **kws):
+        additional_inputs = kws.setdefault('additional_inputs', [])
+        additional_inputs += self.bitwise_additional_inputs()
+        return self.binary_int_op_test(*args, **kws)
+
+    def inplace_op_test(self, operator, lhs_values, rhs_values,
+                        lhs_dtypes, rhs_dtypes):
+        operator_func = _make_inplace_ufunc_op_usecase(operator)
+        pyfunc = operator_func
+
+        # The left operand can only be an array, while the right operand
+        # can be either an array or a scalar
+        lhs_inputs = [np.array(lhs_values, dtype=dtype)
+                      for dtype in lhs_dtypes]
+
+        rhs_arrays = [np.array(rhs_values, dtype=dtype)
+                      for dtype in rhs_dtypes]
+        rhs_scalars = [dtype(v) for v in rhs_values for dtype in rhs_dtypes]
+        rhs_inputs = rhs_arrays + rhs_scalars
+
+        for lhs, rhs in itertools.product(lhs_inputs, rhs_inputs):
+            lhs_type = typeof(lhs)
+            rhs_type = typeof(rhs)
+            cr = self.cache.compile(pyfunc, (lhs_type, rhs_type),
+                                    flags=no_pyobj_flags)
+            cfunc = cr.entry_point
+            expected = lhs.copy()
+            pyfunc(expected, rhs)
+            got = lhs.copy()
+            cfunc(got, rhs)
+            self.assertPreciseEqual(got, expected)
+
+    def inplace_float_op_test(self, operator, lhs_values, rhs_values):
+        # Also accept integer inputs for the right operand (they should
+        # be converted to float).
+        return self.inplace_op_test(operator, lhs_values, rhs_values,
+                                    (np.float32, np.float64),
+                                    (np.float32, np.float64, np.int64))
+
+    def inplace_int_op_test(self, operator, lhs_values, rhs_values):
+        self.inplace_op_test(operator, lhs_values, rhs_values,
+                             (np.int16, np.int32, np.int64),
+                             (np.int16, np.uint32))
+
+    def inplace_bitwise_op_test(self, operator, lhs_values, rhs_values):
+        self.inplace_int_op_test(operator, lhs_values, rhs_values)
+        self.inplace_op_test(operator, lhs_values, rhs_values,
+                             (np.bool_,), (np.bool_, np.bool_))
+
+    # ____________________________________________________________
+    # Unary operators
+
+    def test_unary_positive_array_op(self):
+        self.unary_op_test('+')
+
+    def test_unary_negative_array_op(self):
+        self.unary_op_test('-')
+
+    def test_unary_invert_array_op(self):
+        self.unary_op_test('~',
+                           skip_inputs=[types.float32, types.float64,
+                                        types.Array(types.float32, 1, 'C'),
+                                        types.Array(types.float64, 1, 'C')],
+                           additional_inputs=self.bitwise_additional_inputs())
+
+    # ____________________________________________________________
+    # Inplace operators
+
+    def test_inplace_add(self):
+        self.inplace_float_op_test('+=', [-1, 1.5, 3], [-5, 0, 2.5])
+        self.inplace_float_op_test(operator.iadd, [-1, 1.5, 3], [-5, 0, 2.5])
+
+    def test_inplace_sub(self):
+        self.inplace_float_op_test('-=', [-1, 1.5, 3], [-5, 0, 2.5])
+        self.inplace_float_op_test(operator.isub, [-1, 1.5, 3], [-5, 0, 2.5])
+
+    def test_inplace_mul(self):
+        self.inplace_float_op_test('*=', [-1, 1.5, 3], [-5, 0, 2.5])
+        self.inplace_float_op_test(operator.imul, [-1, 1.5, 3], [-5, 0, 2.5])
+
+    def test_inplace_floordiv(self):
+        self.inplace_float_op_test('//=', [-1, 1.5, 3], [-5, 1.25, 2.5])
+        self.inplace_float_op_test(operator.ifloordiv, [-1, 1.5, 3], [-5, 1.25, 2.5])
+
+    def test_inplace_div(self):
+        self.inplace_float_op_test('/=', [-1, 1.5, 3], [-5, 0, 2.5])
+        self.inplace_float_op_test(operator.itruediv, [-1, 1.5, 3], [-5, 1.25, 2.5])
+
+    def test_inplace_remainder(self):
+        self.inplace_float_op_test('%=', [-1, 1.5, 3], [-5, 2, 2.5])
+        self.inplace_float_op_test(operator.imod, [-1, 1.5, 3], [-5, 2, 2.5])
+
+    def test_inplace_pow(self):
+        self.inplace_float_op_test('**=', [-1, 1.5, 3], [-5, 2, 2.5])
+        self.inplace_float_op_test(operator.ipow, [-1, 1.5, 3], [-5, 2, 2.5])
+
+    def test_inplace_and(self):
+        self.inplace_bitwise_op_test('&=', [0, 1, 2, 3, 51], [0, 13, 16, 42, 255])
+        self.inplace_bitwise_op_test(operator.iand, [0, 1, 2, 3, 51], [0, 13, 16, 42, 255])
+
+    def test_inplace_or(self):
+        self.inplace_bitwise_op_test('|=', [0, 1, 2, 3, 51], [0, 13, 16, 42, 255])
+        self.inplace_bitwise_op_test(operator.ior, [0, 1, 2, 3, 51], [0, 13, 16, 42, 255])
+
+    def test_inplace_xor(self):
+        self.inplace_bitwise_op_test('^=', [0, 1, 2, 3, 51], [0, 13, 16, 42, 255])
+        self.inplace_bitwise_op_test(operator.ixor, [0, 1, 2, 3, 51], [0, 13, 16, 42, 255])
+
+    def test_inplace_lshift(self):
+        self.inplace_int_op_test('<<=', [0, 5, -10, -51], [0, 1, 4, 14])
+        self.inplace_int_op_test(operator.ilshift, [0, 5, -10, -51], [0, 1, 4, 14])
+
+    def test_inplace_rshift(self):
+        self.inplace_int_op_test('>>=', [0, 5, -10, -51], [0, 1, 4, 14])
+        self.inplace_int_op_test(operator.irshift, [0, 5, -10, -51], [0, 1, 4, 14])
+
+    def test_unary_positive_array_op(self):
+        '''
+        Verify that the unary positive operator copies values, and doesn't
+        just alias to the input array (mirrors normal Numpy/Python
+        interaction behavior).
+        '''
+        # Test originally from @gmarkall
+        def f(a1):
+            a2 = +a1
+            a1[0] = 3
+            a2[1] = 4
+            return a2
+
+        a1 = np.zeros(10)
+        a2 = f(a1)
+        self.assertTrue(a1[0] != a2[0] and a1[1] != a2[1])
+        a3 = np.zeros(10)
+        a4 = njit(f)(a3)
+        self.assertTrue(a3[0] != a4[0] and a3[1] != a4[1])
+        np.testing.assert_array_equal(a1, a3)
+        np.testing.assert_array_equal(a2, a4)
+
+    # ____________________________________________________________
+    # Binary operators
+
+    def test_add_array_op(self):
+        self.binary_op_test('+')
+
+    def test_subtract_array_op(self):
+        self.binary_op_test('-')
+
+    def test_multiply_array_op(self):
+        self.binary_op_test('*')
+
+    def test_divide_array_op(self):
+        int_out_type = None
+        int_out_type = types.float64
+        self.binary_op_test('/', int_output_type=int_out_type)
+
+    def test_floor_divide_array_op(self):
+        # Avoid floating-point zeros as x // 0.0 can have varying results
+        # depending on the algorithm (which changed across Numpy versions)
+        self.inputs = [
+            (np.uint32(1), types.uint32),
+            (np.int32(-2), types.int32),
+            (np.int32(0), types.int32),
+            (np.uint64(4), types.uint64),
+            (np.int64(-5), types.int64),
+            (np.int64(0), types.int64),
+
+            (np.float32(-0.5), types.float32),
+            (np.float32(1.5), types.float32),
+
+            (np.float64(-2.5), types.float64),
+            (np.float64(3.5), types.float64),
+
+            (np.array([1,2], dtype='u4'), types.Array(types.uint32, 1, 'C')),
+            (np.array([3,4], dtype='u8'), types.Array(types.uint64, 1, 'C')),
+            (np.array([-1,1,5], dtype='i4'), types.Array(types.int32, 1, 'C')),
+            (np.array([-1,1,6], dtype='i8'), types.Array(types.int64, 1, 'C')),
+            (np.array([-0.5, 1.5], dtype='f4'), types.Array(types.float32, 1, 'C')),
+            (np.array([-2.5, 3.5], dtype='f8'), types.Array(types.float64, 1, 'C')),
+            ]
+        self.binary_op_test('//')
+
+    def test_remainder_array_op(self):
+        self.binary_op_test('%')
+
+    def test_power_array_op(self):
+        self.binary_op_test('**', positive_rhs=True)
+
+    def test_left_shift_array_op(self):
+        self.binary_int_op_test('<<', positive_rhs=True)
+
+    def test_right_shift_array_op(self):
+        self.binary_int_op_test('>>', positive_rhs=True)
+
+    def test_bitwise_and_array_op(self):
+        self.binary_bitwise_op_test('&')
+
+    def test_bitwise_or_array_op(self):
+        self.binary_bitwise_op_test('|')
+
+    def test_bitwise_xor_array_op(self):
+        self.binary_bitwise_op_test('^')
+
+    def test_equal_array_op(self):
+        self.binary_op_test('==')
+
+    def test_greater_array_op(self):
+        self.binary_op_test('>')
+
+    def test_greater_equal_array_op(self):
+        self.binary_op_test('>=')
+
+    def test_less_array_op(self):
+        self.binary_op_test('<')
+
+    def test_less_equal_array_op(self):
+        self.binary_op_test('<=')
+
+    def test_not_equal_array_op(self):
+        self.binary_op_test('!=')
+
+
+class TestScalarUFuncs(TestCase):
+    """check the machinery of ufuncs works when the result is an scalar.
+    These are not exhaustive because:
+    - the machinery to support this case is the same for all the functions of a
+      given arity.
+    - the result of the inner function itself is already tested in TestUFuncs
+
+    This class tests regular uses. A subclass tests the no python backend.
+    """
+
+    _compile_flags = enable_pyobj_flags
+
+    def run_ufunc(self, pyfunc, arg_types, arg_values):
+        for tyargs, args in zip(arg_types, arg_values):
+            cr = compile_isolated(pyfunc, tyargs, flags=self._compile_flags)
+            cfunc = cr.entry_point
+            got = cfunc(*args)
+            expected = pyfunc(*_as_dtype_value(tyargs, args))
+
+            msg = 'for args {0} typed {1}'.format(args, tyargs)
+
+            # note: due to semantics of ufuncs, thing like adding a int32 to a
+            # uint64 results in doubles (as neither int32 can be cast safely
+            # to uint64 nor vice-versa, falling back to using the float version.
+            # Modify in those cases the expected value (the numpy version does
+            # not use typed integers as inputs so its result is an integer)
+            special = set([(types.int32, types.uint64), (types.uint64, types.int32),
+                           (types.int64, types.uint64), (types.uint64, types.int64)])
+            if tyargs in special:
+                expected = float(expected)
+            else:
+                # The numba version of scalar ufuncs return an actual value that
+                # gets converted to a Python type, instead of using NumPy scalars.
+                # although in python 2 NumPy scalars are considered and instance of
+                # the appropriate python type, in python 3 that is no longer the case.
+                # This is why the expected result is casted to the appropriate Python
+                # type (which is actually the expected behavior of the ufunc translation)
+                if np.issubdtype(expected.dtype, np.inexact):
+                    expected = float(expected)
+                elif np.issubdtype(expected.dtype, np.integer):
+                    expected = int(expected)
+                elif np.issubdtype(expected.dtype, np.bool_):
+                    expected = bool(expected)
+
+            alltypes = cr.signature.args + (cr.signature.return_type,)
+
+            # select the appropriate precision for comparison: note that an argument
+            # typed at a lower precision can introduce precision problems. For this
+            # reason the argument types must be taken into account.
+            if any([t==types.float32 for t in alltypes]):
+                prec='single'
+            elif any([t==types.float64 for t in alltypes]):
+                prec='double'
+            else:
+                prec='exact'
+
+            self.assertPreciseEqual(got, expected, msg=msg, prec=prec)
+
+
+    def test_scalar_unary_ufunc(self):
+        def _func(x):
+            return np.sqrt(x)
+
+        vals = [(2,), (2,), (1,), (2,), (.1,), (.2,)]
+        tys = [(types.int32,), (types.uint32,),
+               (types.int64,), (types.uint64,),
+               (types.float32,), (types.float64,)]
+        self.run_ufunc(_func, tys, vals)
+
+
+    def test_scalar_binary_uniform_ufunc(self):
+        def _func(x,y):
+            return np.add(x,y)
+
+        vals = [2, 2, 1, 2, .1, .2]
+        tys = [types.int32, types.uint32,
+               types.int64, types.uint64, types.float32, types.float64]
+        self.run_ufunc(_func, zip(tys, tys), zip(vals, vals))
+
+
+    def test_scalar_binary_mixed_ufunc(self, flags=enable_pyobj_flags):
+        def _func(x,y):
+            return np.add(x,y)
+
+        vals = [2, 2, 1, 2, .1, .2]
+        tys = [types.int32, types.uint32,
+               types.int64, types.uint64,
+               types.float32, types.float64]
+        self.run_ufunc(_func, itertools.product(tys, tys),
+                       itertools.product(vals, vals))
+
+
+class TestScalarUFuncsNoPython(TestScalarUFuncs):
+    """Same tests as TestScalarUFuncs, but forcing no python mode"""
+    _compile_flags = no_pyobj_flags
+
+
+class TestUfuncIssues(TestCase):
+
+    def test_issue_651(self):
+        # Exercise the code path to make sure this does not fail
+        @vectorize(["(float64,float64)"])
+        def foo(x1, x2):
+            return np.add(x1, x2) + np.add(x1, x2)
+
+        a = np.arange(10, dtype='f8')
+        b = np.arange(10, dtype='f8')
+        self.assertPreciseEqual(foo(a, b), (a + b) + (a + b))
+
+    @unittest.skipIf(numpy_support.numpy_version >= (1, 22),
+                     "Complex floor division support removed in NumPy 1.22")
+    def test_issue_713(self):
+        def foo(x,y):
+            return np.floor_divide(x,y)
+
+        cr = compile_isolated(foo, [types.complex128, types.complex128])
+        self.assertEqual(foo(1j, 1j), cr.entry_point(1j, 1j))
+
+    def test_issue_2006(self):
+        """
+        <float32 ** int> should return float32, not float64.
+        """
+        def foo(x, y):
+            return np.power(x, y)
+        pyfunc = foo
+        cfunc = jit(nopython=True)(pyfunc)
+
+        def check(x, y):
+            got = cfunc(x, y)
+            np.testing.assert_array_almost_equal(got, pyfunc(x, y))
+            # Check the power operation conserved the input's dtype
+            # (this is different from Numpy, whose behaviour depends on
+            #  the *values* of the arguments -- see PyArray_CanCastArrayTo).
+            self.assertEqual(got.dtype, x.dtype)
+
+        xs = [np.float32([1, 2, 3]), np.complex64([1j, 2, 3-3j])]
+        for x in xs:
+            check(x, 3)
+            check(x, np.uint64(3))
+            check(x, np.int64([2, 2, 3]))
+
+
+class _LoopTypesTester(TestCase):
+    """Test code generation for the different loop types defined by ufunc.
+
+    This test relies on class variables to configure the test. Subclasses
+    of this class can just override some of these variables to check other
+    ufuncs in a different compilation context. The variables supported are:
+
+    _funcs: the ufuncs to test
+    _compile_flags: compilation flags to use (to force nopython mode)
+    _skip_types: letter types that force skipping the loop when testing
+                 if present in the NumPy ufunc signature.
+    _supported_types: only test loops where all the types in the loop
+                      signature are in this collection. If unset, all.
+
+    Note that both, _skip_types and _supported_types must be met for a loop
+    to be tested.
+
+    The NumPy ufunc signature has a form like 'ff->f' (for a binary ufunc
+    loop taking 2 floats and resulting in a float). In a NumPy ufunc object
+    you can get a list of supported signatures by accessing the attribute
+    'types'.
+    """
+    _skip_types = 'OegG'
+
+    # Allowed deviation between Numpy and Numba results
+    _ulps = {('arccos', 'F'): 2,
+             ('arcsin', 'D'): 4,
+             ('arcsin', 'F'): 4,
+             ('log10', 'D'): 5,
+             ('tanh', 'F'): 2,
+             ('cbrt', 'd'): 2,
+             }
+
+    def _arg_for_type(self, a_letter_type, index=0):
+        """return a suitable array argument for testing the letter type"""
+        # Note all possible arrays must have the same size, since they
+        # may be used as inputs to the same func.
+        if a_letter_type in 'bhilq':
+            # an integral
+            return np.array([1, 4, 0, -2], dtype=a_letter_type)
+        if a_letter_type in 'BHILQ':
+            return np.array([1, 2, 4, 0], dtype=a_letter_type)
+        elif a_letter_type in '?':
+            # a boolean
+            return np.array([True, False, False, True], dtype=a_letter_type)
+        elif a_letter_type[0] == 'm':
+            # timedelta64
+            if len(a_letter_type) == 1:
+                a_letter_type = 'm8[D]'
+            return np.array([2, -3, 'NaT', 0], dtype=a_letter_type)
+        elif a_letter_type[0] == 'M':
+            # datetime64
+            if len(a_letter_type) == 1:
+                a_letter_type = 'M8[D]'
+            return np.array(['Nat', 1, 25, 0], dtype=a_letter_type)
+        elif a_letter_type in 'fd':
+            # floating point
+            return np.array([1.5, -3.5, 0.0, float('nan')],
+                            dtype=a_letter_type)
+        elif a_letter_type in 'FD':
+            # complex
+            # Note `-1j` is different on 2.x and 3.x, hence the explicit spelling
+            if sys.platform != 'win32':
+                # Other platforms have better handling of negative zeros,
+                # test them
+                negzero = -(0.0 + 1.0j)
+            else:
+                negzero = 0.0 - 1.0j
+            return np.array([negzero, 1.5 + 1.5j, 1j * float('nan'), 0j],
+                            dtype=a_letter_type)
+        else:
+            raise RuntimeError("type %r not understood" % (a_letter_type,))
+
+    def _check_loop(self, fn, ufunc, loop):
+        # the letter types for the args
+        letter_types = loop[:ufunc.nin] + loop[-ufunc.nout:]
+
+        # ignore the loops containing an object argument. They will always
+        # fail in no python mode. Usually the last loop in ufuncs is an all
+        # object fallback
+        supported_types = getattr(self, '_supported_types', [])
+        if (supported_types and
+            any(l not in supported_types for l in letter_types)):
+            return
+        skip_types = getattr(self, '_skip_types', [])
+        if any(l in skip_types for l in letter_types):
+            return
+        # if the test case requires some types to be present, skip loops
+        # not involving any of those types.
+        required_types = getattr(self, '_required_types', [])
+        if required_types and not any(l in letter_types
+                                      for l in required_types):
+            return
+
+        self._check_ufunc_with_dtypes(fn, ufunc, letter_types)
+
+    def _check_ufunc_with_dtypes(self, fn, ufunc, dtypes):
+        arg_dty = [np.dtype(t) for t in dtypes]
+        arg_nbty = [types.Array(from_dtype(t), 1, 'C') for t in arg_dty]
+        cr = compile_isolated(fn, arg_nbty, flags=self._compile_flags)
+
+        # Ensure a good mix of input values
+        c_args = [self._arg_for_type(t, index=index).repeat(2)
+                  for index, t in enumerate(dtypes)]
+        for arr in c_args:
+            self.random.shuffle(arr)
+        py_args = [a.copy() for a in c_args]
+
+        cr.entry_point(*c_args)
+        fn(*py_args)
+
+        # Check each array (including inputs, to ensure they weren't
+        # mutated).
+        for dtype, py_arg, c_arg in zip(arg_dty, py_args, c_args):
+            py_arg, c_arg = self._fixup_results(dtype, py_arg, c_arg)
+            typechar = c_arg.dtype.char
+            ulps = self._ulps.get((ufunc.__name__, typechar), 1)
+            prec = 'single' if typechar in 'fF' else 'exact'
+            prec = 'double' if typechar in 'dD' else prec
+            msg = '\n'.join(["ufunc '{0}' arrays differ ({1}):",
+                             "args: {2}", "expected {3}", "got {4}"])
+            msg = msg.format(ufunc.__name__, c_args, prec, py_arg, c_arg)
+            self.assertPreciseEqual(py_arg, c_arg, prec=prec, msg=msg,
+                                    ulps=ulps)
+
+    def _fixup_results(self, dtype, py_arg, c_arg):
+        return py_arg, c_arg
+
+    @classmethod
+    def _check_ufunc_loops(cls, ufunc):
+        for loop in ufunc.types:
+            cls._inject_test(ufunc, loop)
+
+    @classmethod
+    def _inject_test(cls, ufunc, loop):
+        def test_template(self):
+            fn = _make_ufunc_usecase(ufunc)
+            self._check_loop(fn, ufunc, loop)
+        setattr(cls, "test_{0}_{1}".format(ufunc.__name__,
+                                           loop.replace('->', '_')),
+                test_template)
+
+    @classmethod
+    def autogenerate(cls):
+        for ufunc in cls._ufuncs:
+            cls._check_ufunc_loops(ufunc)
+
+
+class TestLoopTypesIntNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = supported_ufuncs[:]
+    # reciprocal and power need a special test due to issue #757
+    _ufuncs.remove(np.power)
+    _ufuncs.remove(np.reciprocal)
+    _ufuncs.remove(np.left_shift) # has its own test class
+    _ufuncs.remove(np.right_shift) # has its own test class
+    # special test for bool subtract/negative
+    _ufuncs.remove(np.subtract)
+    _ufuncs.remove(np.negative)
+    _required_types = '?bBhHiIlLqQ'
+    _skip_types = 'fdFDmMO' + _LoopTypesTester._skip_types
+
+TestLoopTypesIntNoPython.autogenerate()
+
+
+class TestLoopTypesSubtractAndNegativeNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = [np.subtract, np.negative]
+    _required_types = '?bBhHiIlLqQfdFD'
+    _skip_types = 'mMO' + _LoopTypesTester._skip_types + '?'
+
+TestLoopTypesSubtractAndNegativeNoPython.autogenerate()
+
+
+class TestLoopTypesReciprocalNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = [np.reciprocal] # issue #757
+    _required_types = 'bBhHiIlLqQfdFD'
+    _skip_types = 'mMO' + _LoopTypesTester._skip_types
+
+    def _arg_for_type(self, a_letter_type, index=0):
+        res = super(self.__class__, self)._arg_for_type(a_letter_type,
+                                                        index=index)
+        if a_letter_type in 'bBhHiIlLqQ':
+            # For integer reciprocal, avoid 0 as argument, as it triggers
+            # undefined behavior that may differ in results from Numba
+            # to the compiler used to compile NumPy.
+            res[res == 0] = 42
+        return res
+
+TestLoopTypesReciprocalNoPython.autogenerate()
+
+
+class TestLoopTypesPowerNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = [np.power] # issue #757
+    _required_types = 'bBhHiIlLqQfdFD'
+    _skip_types = 'mMO' + _LoopTypesTester._skip_types
+
+    def _arg_for_type(self, a_letter_type, index=0):
+        res = super(self.__class__, self)._arg_for_type(a_letter_type,
+                                                        index=index)
+        if a_letter_type in 'bBhHiIlLqQ' and index == 1:
+            # For integer power, avoid a negative exponent, as it triggers
+            # undefined behavior that may differ in results from Numba
+            # to the compiler used to compile NumPy
+            res[res < 0] = 3
+        return res
+
+TestLoopTypesPowerNoPython.autogenerate()
+
+
+class TestLoopTypesIntLeftShiftNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = [np.left_shift]
+    _required_types = 'bBhHiIlLqQ'
+    _skip_types = 'fdFDmMO' + _LoopTypesTester._skip_types
+
+    def _arg_for_type(self, a_letter_type, index=0):
+        res = super(self.__class__, self)._arg_for_type(a_letter_type,
+                                                        index=index)
+        # Shifting by a negative amount (argument with index 1) is undefined
+        # behavior in C. It is also undefined behavior in numba. In the same
+        # sense, it is also undefined behavior when the shift amount is larger
+        # than the number of bits in the shifted integer.
+        # To avoid problems in the test, the values are clamped (clipped) so
+        # that 0 <= shift_amount < bitcount(shifted_integer)
+        if index == 1:
+            bit_count = res.dtype.itemsize * 8
+            res = np.clip(res, 0, bit_count-1)
+        return res
+
+TestLoopTypesIntLeftShiftNoPython.autogenerate()
+
+
+class TestLoopTypesIntRightShiftNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = [np.right_shift]
+    _required_types = 'bBhHiIlLqQ'
+    _skip_types = 'fdFDmMO' + _LoopTypesTester._skip_types
+
+    def _arg_for_type(self, a_letter_type, index=0):
+        res = super(self.__class__, self)._arg_for_type(a_letter_type,
+                                                        index=index)
+        # Shifting by a negative amount (argument with index 1) is undefined
+        # behavior in C. It is also undefined behavior in numba. In the same
+        # sense, it is also undefined behavior when the shift amount is larger
+        # than the number of bits in the shifted integer.
+        # To avoid problems in the test, the values are clamped (clipped) so
+        # that 0 <= shift_amount < bitcount(shifted_integer)
+        if index == 1:
+            bit_count = res.dtype.itemsize * 8
+            res = np.clip(res, 0, bit_count-1)
+
+        # Right shift has "implementation defined behavior" when the number
+        # shifted is negative (in C). In numba, right shift for signed integers
+        # is "arithmetic" while for unsigned integers is "logical".
+        # This test compares against the NumPy implementation, that relies
+        # on "implementation defined behavior", so the test could be a false
+        # failure if the compiler used to compile NumPy doesn't follow the same
+        # policy.
+        # Hint: do not rely on right shifting negative numbers in NumPy.
+        if index == 0:
+            res = np.abs(res)
+        return res
+
+TestLoopTypesIntRightShiftNoPython.autogenerate()
+
+
+class TestLoopTypesFloorDivideNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = [np.floor_divide, np.remainder, np.divmod]
+    _required_types = 'bBhHiIlLqQfdFD'
+    _skip_types = 'mMO' + _LoopTypesTester._skip_types
+
+    def _fixup_results(self, dtype, py_arg, c_arg):
+        if dtype.kind == 'f':
+            # Discrepancies on floating-point floor division and remainder:
+            # Numpy may return nan where Numba returns inf, e.g. 1. // 0.
+            pred = (np.isinf(c_arg) & np.isnan(py_arg))
+            # Numpy and Numba may differ in signed zeros, e.g. -0. // -1.
+            pred |= (py_arg == 0.0) & (c_arg == 0.0)
+            c_arg[pred] = py_arg[pred]
+        return py_arg, c_arg
+
+TestLoopTypesFloorDivideNoPython.autogenerate()
+
+
+class TestLoopTypesFloatNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = supported_ufuncs[:]
+    if iswindows:
+        _ufuncs.remove(np.signbit) # TODO: fix issue #758
+    _ufuncs.remove(np.floor_divide) # has its own test class
+    _ufuncs.remove(np.remainder) # has its own test class
+    _ufuncs.remove(np.divmod) # has its own test class
+    _ufuncs.remove(np.mod) # same as np.remainder
+    _required_types = 'fd'
+    _skip_types = 'FDmMO' + _LoopTypesTester._skip_types
+
+TestLoopTypesFloatNoPython.autogenerate()
+
+
+class TestLoopTypesComplexNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = supported_ufuncs[:]
+
+    # Test complex types
+    # Every loop containing a complex argument must be tested
+    _required_types = 'FD'
+    _skip_types = 'mMO' + _LoopTypesTester._skip_types
+
+TestLoopTypesComplexNoPython.autogenerate()
+
+
+class TestLoopTypesDatetimeNoPython(_LoopTypesTester):
+    _compile_flags = no_pyobj_flags
+    _ufuncs = supported_ufuncs[:]
+
+    _ufuncs.remove(np.divmod)  # not implemented yet
+
+    # NOTE: the full list of ufuncs supporting datetime64 and timedelta64
+    # types in Numpy is:
+    # ['absolute', 'add', 'divide', 'equal', 'floor_divide', 'fmax', 'fmin',
+    #  'greater', 'greater_equal', 'less', 'less_equal', 'maximum',
+    #  'minimum', 'multiply', 'negative', 'not_equal', 'sign', 'subtract',
+    #  'true_divide']
+
+    # Test datetime64 and timedelta64 types.
+    _required_types = 'mM'
+
+    # Test various units combinations (TestLoopTypes is only able to test
+    # homogeneous units).
+
+    def test_add(self):
+        ufunc = np.add
+        fn = _make_ufunc_usecase(ufunc)
+        # heterogeneous inputs
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[s]', 'm8[m]', 'm8[s]'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'm8[s]', 'm8[s]'])
+        # heterogeneous inputs, scaled output
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[s]', 'm8[m]', 'm8[ms]'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'm8[s]', 'm8[ms]'])
+        # Cannot upscale result (Numpy would accept this)
+        with self.assertRaises(LoweringError):
+            self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'm8[s]', 'm8[m]'])
+
+    def test_subtract(self):
+        ufunc = np.subtract
+        fn = _make_ufunc_usecase(ufunc)
+        # heterogeneous inputs
+        self._check_ufunc_with_dtypes(fn, ufunc, ['M8[s]', 'M8[m]', 'm8[s]'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['M8[m]', 'M8[s]', 'm8[s]'])
+        # heterogeneous inputs, scaled output
+        self._check_ufunc_with_dtypes(fn, ufunc, ['M8[s]', 'M8[m]', 'm8[ms]'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['M8[m]', 'M8[s]', 'm8[ms]'])
+        # Cannot upscale result (Numpy would accept this)
+        with self.assertRaises(LoweringError):
+            self._check_ufunc_with_dtypes(fn, ufunc, ['M8[m]', 'M8[s]', 'm8[m]'])
+
+    def test_multiply(self):
+        ufunc = np.multiply
+        fn = _make_ufunc_usecase(ufunc)
+        # scaled output
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[s]', 'q', 'm8[us]'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['q', 'm8[s]', 'm8[us]'])
+        # Cannot upscale result (Numpy would accept this)
+        with self.assertRaises(LoweringError):
+            self._check_ufunc_with_dtypes(fn, ufunc, ['m8[s]', 'q', 'm8[m]'])
+
+    def test_true_divide(self):
+        ufunc = np.true_divide
+        fn = _make_ufunc_usecase(ufunc)
+        # heterogeneous inputs
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'm8[s]', 'd'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[s]', 'm8[m]', 'd'])
+        # scaled output
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'q', 'm8[s]'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'd', 'm8[s]'])
+        # Cannot upscale result (Numpy would accept this)
+        with self.assertRaises(LoweringError):
+            self._check_ufunc_with_dtypes(fn, ufunc, ['m8[s]', 'q', 'm8[m]'])
+
+    def test_floor_divide(self):
+        ufunc = np.floor_divide
+        fn = _make_ufunc_usecase(ufunc)
+        # scaled output
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'q', 'm8[s]'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'd', 'm8[s]'])
+        # Cannot upscale result (Numpy would accept this)
+        with self.assertRaises(LoweringError):
+            self._check_ufunc_with_dtypes(fn, ufunc, ['m8[s]', 'q', 'm8[m]'])
+
+    def _check_comparison(self, ufunc):
+        fn = _make_ufunc_usecase(ufunc)
+        # timedelta
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[m]', 'm8[s]', '?'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['m8[s]', 'm8[m]', '?'])
+        # datetime
+        self._check_ufunc_with_dtypes(fn, ufunc, ['M8[m]', 'M8[s]', '?'])
+        self._check_ufunc_with_dtypes(fn, ufunc, ['M8[s]', 'M8[m]', '?'])
+
+    def test_comparisons(self):
+        for ufunc in [np.equal, np.not_equal, np.less, np.less_equal,
+                      np.greater, np.greater_equal]:
+            self._check_comparison(ufunc)
+
+TestLoopTypesDatetimeNoPython.autogenerate()
+
+
+class TestUFuncBadArgsNoPython(TestCase):
+    _compile_flags = no_pyobj_flags
+
+    def test_missing_args(self):
+        def func(x):
+            """error: np.add requires two args"""
+            result = np.add(x)
+            return result
+
+        self.assertRaises(TypingError, compile_isolated, func, [types.float64],
+                          return_type=types.float64, flags=self._compile_flags)
+
+
+    def test_too_many_args(self):
+        def func(x, out, out2):
+            """error: too many args"""
+            result = np.add(x, x, out, out2)
+            return result
+
+        array_type = types.Array(types.float64, 1, 'C')
+        self.assertRaises(TypingError, compile_isolated, func, [array_type] *3,
+                          return_type=array_type, flags=self._compile_flags)
+
+    def test_no_scalar_result_by_reference(self):
+        def func(x):
+            """error: scalar as a return value is not supported"""
+            y = 0
+            np.add(x, x, y)
+        self.assertRaises(TypingError, compile_isolated, func, [types.float64],
+                          return_type=types.float64, flags=self._compile_flags)
+
+class TestUFuncCompilationThreadSafety(TestCase):
+
+    def test_lock(self):
+        """
+        Test that (lazy) compiling from several threads at once doesn't
+        produce errors (see issue #2403).
+        """
+        errors = []
+
+        @vectorize
+        def foo(x):
+            return x + 1
+
+        def wrapper():
+            try:
+                a = np.ones((10,), dtype = np.float64)
+                expected = np.ones((10,), dtype = np.float64) + 1.
+                np.testing.assert_array_equal(foo(a), expected)
+            except Exception as e:
+                errors.append(e)
+
+        threads = [threading.Thread(target=wrapper) for i in range(16)]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+        self.assertFalse(errors)
+
+
+class TestUfuncOnContext(TestCase):
+    def test_cpu_get_ufunc_info(self):
+        # The CPU context defines get_ufunc_info that is the same as
+        # ufunc_db.get_ufunc_info.
+        targetctx = cpu_target.target_context
+        # Check: get_ufunc_info returns a dict
+        add_info = targetctx.get_ufunc_info(np.add)
+        self.assertIsInstance(add_info, dict)
+        # Check: it is the same as ufunc_db.get_ufunc_info
+        expected = ufunc_db.get_ufunc_info(np.add)
+        self.assertEqual(add_info, expected)
+        # Check: KeyError raised on bad key
+        badkey = object()
+        with self.assertRaises(KeyError) as raises:
+            ufunc_db.get_ufunc_info(badkey)
+        self.assertEqual(raises.exception.args, (badkey,))
+
+    def test_base_get_ufunc_info(self):
+        # The BaseContext always raises NotImplementedError
+        targetctx = BaseContext(cpu_target.typing_context, 'cpu')
+        with self.assertRaises(NotImplementedError) as raises:
+            targetctx.get_ufunc_info(np.add)
+        self.assertRegex(
+            str(raises.exception),
+            r"<numba\..*\.BaseContext object at .*> does not support ufunc",
+        )
+
+
+class TestUfuncWriteInput(TestCase):
+    def test_write_input_arg(self):
+        @guvectorize(["void(float64[:], uint8[:])"], "(n)->(n)", nopython=True)
+        def func(x, out):
+
+            for i in range(x.size):
+                # set every fourth element to 1
+                if i % 4 == 0:
+                    out[i] = 1
+
+        x = np.random.rand(10, 5)
+        out = np.zeros_like(x, dtype=np.int8)
+
+        func(x, out)
+        np.testing.assert_array_equal(
+            np.array([True, False, False, False, True], dtype=np.bool_),
+            out.any(axis=0))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode.py
new file mode 100644
index 000000000..5eedc9a38
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode.py
@@ -0,0 +1,2615 @@
+# -*- coding: utf-8 -*-
+from itertools import product
+from itertools import permutations
+
+from numba import njit
+from numba.core import types, utils
+import unittest
+from numba.tests.support import (TestCase, no_pyobj_flags, MemoryLeakMixin)
+from numba.core.errors import TypingError, UnsupportedError
+from numba.cpython.unicode import _MAX_UNICODE
+from numba.core.types.functions import _header_lead
+from numba.extending import overload
+
+
+_py37_or_later = utils.PYVERSION >= (3, 7)
+
+
+def isascii(s):
+    return all(ord(c) < 128 for c in s)
+
+
+def literal_usecase():
+    return '大处着眼，小处着手。'
+
+
+def passthrough_usecase(x):
+    return x
+
+
+def eq_usecase(x, y):
+    return x == y
+
+
+def len_usecase(x):
+    return len(x)
+
+
+def bool_usecase(x):
+    return bool(x)
+
+
+def getitem_usecase(x, i):
+    return x[i]
+
+
+def getitem_check_kind_usecase(x, i):
+    return hash(x[i])
+
+
+def zfill_usecase(x, y):
+    return x.zfill(y)
+
+
+def concat_usecase(x, y):
+    return x + y
+
+
+def repeat_usecase(x, y):
+    return x * y
+
+
+def inplace_concat_usecase(x, y):
+    x += y
+    return x
+
+
+def in_usecase(x, y):
+    return x in y
+
+
+def lt_usecase(x, y):
+    return x < y
+
+
+def le_usecase(x, y):
+    return x <= y
+
+
+def gt_usecase(x, y):
+    return x > y
+
+
+def ge_usecase(x, y):
+    return x >= y
+
+
+def partition_usecase(s, sep):
+    return s.partition(sep)
+
+
+def find_usecase(x, y):
+    return x.find(y)
+
+
+def find_with_start_only_usecase(x, y, start):
+    return x.find(y, start)
+
+
+def find_with_start_end_usecase(x, y, start, end):
+    return x.find(y, start, end)
+
+
+def rpartition_usecase(s, sep):
+    return s.rpartition(sep)
+
+
+def count_usecase(x, y):
+    return x.count(y)
+
+
+def count_with_start_usecase(x, y, start):
+    return x.count(y, start)
+
+
+def count_with_start_end_usecase(x, y, start, end):
+    return x.count(y, start, end)
+
+
+def rfind_usecase(x, y):
+    return x.rfind(y)
+
+
+def rfind_with_start_only_usecase(x, y, start):
+    return x.rfind(y, start)
+
+
+def rfind_with_start_end_usecase(x, y, start, end):
+    return x.rfind(y, start, end)
+
+
+def replace_usecase(s, x, y):
+    return s.replace(x, y)
+
+
+def replace_with_count_usecase(s, x, y, count):
+    return s.replace(x, y, count)
+
+
+def rindex_usecase(x, y):
+    return x.rindex(y)
+
+
+def rindex_with_start_only_usecase(x, y, start):
+    return x.rindex(y, start)
+
+
+def rindex_with_start_end_usecase(x, y, start, end):
+    return x.rindex(y, start, end)
+
+
+def index_usecase(x, y):
+    return x.index(y)
+
+
+def index_with_start_only_usecase(x, y, start):
+    return x.index(y, start)
+
+
+def index_with_start_end_usecase(x, y, start, end):
+    return x.index(y, start, end)
+
+
+def startswith_usecase(x, y):
+    return x.startswith(y)
+
+
+def endswith_usecase(x, y):
+    return x.endswith(y)
+
+
+def expandtabs_usecase(s):
+    return s.expandtabs()
+
+
+def expandtabs_with_tabsize_usecase(s, tabsize):
+    return s.expandtabs(tabsize)
+
+
+def expandtabs_with_tabsize_kwarg_usecase(s, tabsize):
+    return s.expandtabs(tabsize=tabsize)
+
+
+def endswith_with_start_only_usecase(x, y, start):
+    return x.endswith(y, start)
+
+
+def endswith_with_start_end_usecase(x, y, start, end):
+    return x.endswith(y, start, end)
+
+
+def split_usecase(x, y):
+    return x.split(y)
+
+
+def split_with_maxsplit_usecase(x, y, maxsplit):
+    return x.split(y, maxsplit)
+
+
+def split_with_maxsplit_kwarg_usecase(x, y, maxsplit):
+    return x.split(y, maxsplit=maxsplit)
+
+
+def split_whitespace_usecase(x):
+    return x.split()
+
+
+def splitlines_usecase(s):
+    return s.splitlines()
+
+
+def splitlines_with_keepends_usecase(s, keepends):
+    return s.splitlines(keepends)
+
+
+def splitlines_with_keepends_kwarg_usecase(s, keepends):
+    return s.splitlines(keepends=keepends)
+
+
+def rsplit_usecase(s, sep):
+    return s.rsplit(sep)
+
+
+def rsplit_with_maxsplit_usecase(s, sep, maxsplit):
+    return s.rsplit(sep, maxsplit)
+
+
+def rsplit_with_maxsplit_kwarg_usecase(s, sep, maxsplit):
+    return s.rsplit(sep, maxsplit=maxsplit)
+
+
+def rsplit_whitespace_usecase(s):
+    return s.rsplit()
+
+
+def lstrip_usecase(x):
+    return x.lstrip()
+
+
+def lstrip_usecase_chars(x, chars):
+    return x.lstrip(chars)
+
+
+def rstrip_usecase(x):
+    return x.rstrip()
+
+
+def rstrip_usecase_chars(x, chars):
+    return x.rstrip(chars)
+
+
+def strip_usecase(x):
+    return x.strip()
+
+
+def strip_usecase_chars(x, chars):
+    return x.strip(chars)
+
+
+def join_usecase(x, y):
+    return x.join(y)
+
+
+def join_empty_usecase(x):
+    # hack to make empty typed list
+    l = ['']
+    l.pop()
+    return x.join(l)
+
+
+def center_usecase(x, y):
+    return x.center(y)
+
+
+def center_usecase_fillchar(x, y, fillchar):
+    return x.center(y, fillchar)
+
+
+def ljust_usecase(x, y):
+    return x.ljust(y)
+
+
+def ljust_usecase_fillchar(x, y, fillchar):
+    return x.ljust(y, fillchar)
+
+
+def rjust_usecase(x, y):
+    return x.rjust(y)
+
+
+def rjust_usecase_fillchar(x, y, fillchar):
+    return x.rjust(y, fillchar)
+
+
+def istitle_usecase(x):
+    return x.istitle()
+
+
+def iter_usecase(x):
+    l = []
+    for i in x:
+        l.append(i)
+    return l
+
+
+def title(x):
+    return x.title()
+
+
+def literal_iter_usecase():
+    l = []
+    for i in '大处着眼，小处着手。':
+        l.append(i)
+    return l
+
+
+def enumerated_iter_usecase(x):
+    buf = ""
+    scan = 0
+    for i, s in enumerate(x):
+        buf += s
+        scan += 1
+    return buf, scan
+
+
+def iter_stopiteration_usecase(x):
+    n = len(x)
+    i = iter(x)
+    for _ in range(n + 1):
+        next(i)
+
+
+def literal_iter_stopiteration_usecase():
+    s = '大处着眼，小处着手。'
+    i = iter(s)
+    n = len(s)
+    for _ in range(n + 1):
+        next(i)
+
+
+def islower_usecase(x):
+    return x.islower()
+
+
+def lower_usecase(x):
+    return x.lower()
+
+
+def ord_usecase(x):
+    return ord(x)
+
+
+def chr_usecase(x):
+    return chr(x)
+
+
+class BaseTest(MemoryLeakMixin, TestCase):
+    def setUp(self):
+        super(BaseTest, self).setUp()
+
+
+UNICODE_EXAMPLES = [
+    '',
+    'ascii',
+    '12345',
+    '1234567890',
+    '¡Y tú quién te crees?',
+    '🐍⚡',
+    '大处着眼，小处着手。',
+]
+
+UNICODE_ORDERING_EXAMPLES = [
+    '',
+    'a'
+    'aa',
+    'aaa',
+    'b',
+    'aab',
+    'ab',
+    'asc',
+    'ascih',
+    'ascii',
+    'ascij',
+    '大处着眼，小处着手',
+    '大处着眼，小处着手。',
+    '大处着眼，小处着手。🐍⚡',
+]
+
+UNICODE_COUNT_EXAMPLES = [
+    ('', ''),
+    ('', 'ascii'),
+    ('ascii', ''),
+    ('asc ii', ' '),
+    ('ascii', 'ci'),
+    ('ascii', 'ascii'),
+    ('ascii', 'Ă'),
+    ('ascii', '大处'),
+    ('ascii', 'étú?'),
+    ('', '大处 着眼，小处着手。大大大处'),
+    ('大处 着眼，小处着手。大大大处', ''),
+    ('大处 着眼，小处着手。大大大处', ' '),
+    ('大处 着眼，小处着手。大大大处', 'ci'),
+    ('大处 着眼，小处着手。大大大处', '大处大处'),
+    ('大处 着眼，小处着手。大大大处', '大处 着眼，小处着手。大大大处'),
+    ('大处 着眼，小处着手。大大大处', 'Ă'),
+    ('大处 着眼，小处着手。大大大处', '大处'),
+    ('大处 着眼，小处着手。大大大处', 'étú?'),
+    ('', 'tú quién te crees?'),
+    ('tú quién te crees?', ''),
+    ('tú quién te crees?', ' '),
+    ('tú quién te crees?', 'ci'),
+    ('tú quién te crees?', 'tú quién te crees?'),
+    ('tú quién te crees?', 'Ă'),
+    ('tú quién te crees?', '大处'),
+    ('tú quién te crees?', 'étú?'),
+    ('abababab', 'a'),
+    ('abababab', 'ab'),
+    ('abababab', 'aba'),
+    ('aaaaaaaaaa', 'aaa'),
+    ('aaaaaaaaaa', 'aĂ'),
+    ('aabbaaaabbaa', 'aa')
+]
+
+
+class TestUnicode(BaseTest):
+
+    def test_literal(self, flags=no_pyobj_flags):
+        pyfunc = literal_usecase
+        self.run_nullary_func(pyfunc, flags=flags)
+
+    def test_passthrough(self, flags=no_pyobj_flags):
+        pyfunc = passthrough_usecase
+        cfunc = njit(pyfunc)
+        for s in UNICODE_EXAMPLES:
+            self.assertEqual(pyfunc(s), cfunc(s))
+
+    def test_eq(self, flags=no_pyobj_flags):
+        pyfunc = eq_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            for b in reversed(UNICODE_EXAMPLES):
+                self.assertEqual(pyfunc(a, b),
+                                 cfunc(a, b), '%s, %s' % (a, b))
+                # comparing against something that's not unicode
+                self.assertEqual(pyfunc(a, 1),
+                                 cfunc(a, 1), '%s, %s' % (a, 1))
+                self.assertEqual(pyfunc(1, b),
+                                 cfunc(1, b), '%s, %s' % (1, b))
+
+    def test_eq_optional(self):
+        # See issue #7474
+        @njit
+        def foo(pred1, pred2):
+            if pred1 > 0:
+                resolved1 = 'concrete'
+            else:
+                resolved1 = None
+            if pred2 < 0:
+                resolved2 = 'concrete'
+            else:
+                resolved2 = None
+
+            # resolved* are Optionals
+            if resolved1 == resolved2:
+                return 10
+            else:
+                return 20
+
+        for (p1, p2) in product(*((-1, 1),) * 2):
+            self.assertEqual(foo(p1, p2), foo.py_func(p1, p2))
+
+    def _check_ordering_op(self, usecase):
+        pyfunc = usecase
+        cfunc = njit(pyfunc)
+
+        # Check comparison to self
+        for a in UNICODE_ORDERING_EXAMPLES:
+            self.assertEqual(
+                pyfunc(a, a),
+                cfunc(a, a),
+                '%s: "%s", "%s"' % (usecase.__name__, a, a),
+            )
+
+        # Check comparison to adjacent
+        for a, b in permutations(UNICODE_ORDERING_EXAMPLES, r=2):
+            self.assertEqual(
+                pyfunc(a, b),
+                cfunc(a, b),
+                '%s: "%s", "%s"' % (usecase.__name__, a, b),
+            )
+            # and reversed
+            self.assertEqual(
+                pyfunc(b, a),
+                cfunc(b, a),
+                '%s: "%s", "%s"' % (usecase.__name__, b, a),
+            )
+
+    def test_lt(self, flags=no_pyobj_flags):
+        self._check_ordering_op(lt_usecase)
+
+    def test_le(self, flags=no_pyobj_flags):
+        self._check_ordering_op(le_usecase)
+
+    def test_gt(self, flags=no_pyobj_flags):
+        self._check_ordering_op(gt_usecase)
+
+    def test_ge(self, flags=no_pyobj_flags):
+        self._check_ordering_op(ge_usecase)
+
+    def test_len(self, flags=no_pyobj_flags):
+        pyfunc = len_usecase
+        cfunc = njit(pyfunc)
+        for s in UNICODE_EXAMPLES:
+            self.assertEqual(pyfunc(s), cfunc(s))
+
+    def test_bool(self, flags=no_pyobj_flags):
+        pyfunc = bool_usecase
+        cfunc = njit(pyfunc)
+        for s in UNICODE_EXAMPLES:
+            self.assertEqual(pyfunc(s), cfunc(s))
+
+    def test_expandtabs(self):
+        pyfunc = expandtabs_usecase
+        cfunc = njit(pyfunc)
+
+        cases = ['', '\t', 't\tt\t', 'a\t', '\t⚡', 'a\tbc\nab\tc',
+                 '🐍\t⚡', '🐍⚡\n\t\t🐍\t', 'ab\rab\t\t\tab\r\n\ta']
+
+        msg = 'Results of "{}".expandtabs() must be equal'
+        for s in cases:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_expandtabs_with_tabsize(self):
+        fns = [njit(expandtabs_with_tabsize_usecase),
+               njit(expandtabs_with_tabsize_kwarg_usecase)]
+        messages = ['Results of "{}".expandtabs({}) must be equal',
+                    'Results of "{}".expandtabs(tabsize={}) must be equal']
+
+        cases = ['', '\t', 't\tt\t', 'a\t', '\t⚡', 'a\tbc\nab\tc',
+                 '🐍\t⚡', '🐍⚡\n\t\t🐍\t', 'ab\rab\t\t\tab\r\n\ta']
+
+        for s in cases:
+            for tabsize in range(-1, 10):
+                for fn, msg in zip(fns, messages):
+                    self.assertEqual(fn.py_func(s, tabsize), fn(s, tabsize),
+                                     msg=msg.format(s, tabsize))
+
+    def test_expandtabs_exception_noninteger_tabsize(self):
+        pyfunc = expandtabs_with_tabsize_usecase
+        cfunc = njit(pyfunc)
+
+        accepted_types = (types.Integer, int)
+        with self.assertRaises(TypingError) as raises:
+            cfunc('\t', 2.4)
+        msg = '"tabsize" must be {}, not float'.format(accepted_types)
+        self.assertIn(msg, str(raises.exception))
+
+    def test_startswith(self, flags=no_pyobj_flags):
+        pyfunc = startswith_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            for b in ['', 'x', a[:-2], a[3:], a, a + a]:
+                self.assertEqual(pyfunc(a, b),
+                                 cfunc(a, b),
+                                 '%s, %s' % (a, b))
+
+    def test_endswith(self, flags=no_pyobj_flags):
+        pyfunc = endswith_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            for b in ['', 'x', a[:-2], a[3:], a, a + a]:
+                self.assertEqual(pyfunc(a, b),
+                                 cfunc(a, b),
+                                 '%s, %s' % (a, b))
+
+    def test_endswith_default(self):
+        pyfunc = endswith_usecase
+        cfunc = njit(pyfunc)
+
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/865c3b257fe38154a4320c7ee6afb416f665b9c2/Lib/test/string_tests.py#L1049-L1099    # noqa: E501
+        cpython_str = ['hello', 'helloworld', '']
+        cpython_subs = [
+            'he', 'hello', 'helloworld', 'ello',
+            '', 'lowo', 'lo', 'he', 'lo', 'o',
+        ]
+        extra_subs = ['hellohellohello', ' ']
+        for s in cpython_str + UNICODE_EXAMPLES:
+            default_subs = ['', 'x', s[:-2], s[3:], s, s + s]
+            for sub_str in cpython_subs + default_subs + extra_subs:
+                msg = 'Results "{}".endswith("{}") must be equal'
+                self.assertEqual(pyfunc(s, sub_str), cfunc(s, sub_str),
+                                 msg=msg.format(s, sub_str))
+
+    def test_endswith_with_start(self):
+        pyfunc = endswith_with_start_only_usecase
+        cfunc = njit(pyfunc)
+
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/865c3b257fe38154a4320c7ee6afb416f665b9c2/Lib/test/string_tests.py#L1049-L1099    # noqa: E501
+        cpython_str = ['hello', 'helloworld', '']
+        cpython_subs = [
+            'he', 'hello', 'helloworld', 'ello',
+            '', 'lowo', 'lo', 'he', 'lo', 'o',
+        ]
+        extra_subs = ['hellohellohello', ' ']
+        for s in cpython_str + UNICODE_EXAMPLES:
+            default_subs = ['', 'x', s[:-2], s[3:], s, s + s]
+            for sub_str in cpython_subs + default_subs + extra_subs:
+                for start in list(range(-20, 20)) + [None]:
+                    msg = 'Results "{}".endswith("{}", {}) must be equal'
+                    self.assertEqual(pyfunc(s, sub_str, start),
+                                     cfunc(s, sub_str, start),
+                                     msg=msg.format(s, sub_str, start))
+
+    def test_endswith_with_start_end(self):
+        pyfunc = endswith_with_start_end_usecase
+        cfunc = njit(pyfunc)
+
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/865c3b257fe38154a4320c7ee6afb416f665b9c2/Lib/test/string_tests.py#LL1049-L1099    # noqa: E501
+        cpython_str = ['hello', 'helloworld', '']
+        cpython_subs = [
+            'he', 'hello', 'helloworld', 'ello',
+            '', 'lowo', 'lo', 'he', 'lo', 'o',
+        ]
+        extra_subs = ['hellohellohello', ' ']
+        for s in cpython_str + UNICODE_EXAMPLES:
+            default_subs = ['', 'x', s[:-2], s[3:], s, s + s]
+            for sub_str in cpython_subs + default_subs + extra_subs:
+                for start in list(range(-20, 20)) + [None]:
+                    for end in list(range(-20, 20)) + [None]:
+                        msg = 'Results "{}".endswith("{}", {}, {})\
+                               must be equal'
+                        self.assertEqual(pyfunc(s, sub_str, start, end),
+                                         cfunc(s, sub_str, start, end),
+                                         msg=msg.format(s, sub_str, start, end))
+
+    def test_endswith_tuple(self):
+        pyfunc = endswith_usecase
+        cfunc = njit(pyfunc)
+
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/865c3b257fe38154a4320c7ee6afb416f665b9c2/Lib/test/string_tests.py#L1049-L1099    # noqa: E501
+        cpython_str = ['hello', 'helloworld', '']
+        cpython_subs = [
+            'he', 'hello', 'helloworld', 'ello',
+            '', 'lowo', 'lo', 'he', 'lo', 'o',
+        ]
+        extra_subs = ['hellohellohello', ' ']
+        for s in cpython_str + UNICODE_EXAMPLES:
+            default_subs = ['', 'x', s[:-2], s[3:], s, s + s]
+            for sub_str in cpython_subs + default_subs + extra_subs:
+                msg = 'Results "{}".endswith({}) must be equal'
+                tuple_subs = (sub_str, 'lo')
+                self.assertEqual(pyfunc(s, tuple_subs),
+                                 cfunc(s, tuple_subs),
+                                 msg=msg.format(s, tuple_subs))
+
+    def test_endswith_tuple_args(self):
+        pyfunc = endswith_with_start_end_usecase
+        cfunc = njit(pyfunc)
+
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/865c3b257fe38154a4320c7ee6afb416f665b9c2/Lib/test/string_tests.py#L1049-L1099    # noqa: E501
+        cpython_str = ['hello', 'helloworld', '']
+        cpython_subs = [
+            'he', 'hello', 'helloworld', 'ello',
+            '', 'lowo', 'lo', 'he', 'lo', 'o',
+        ]
+        extra_subs = ['hellohellohello', ' ']
+        for s in cpython_str + UNICODE_EXAMPLES:
+            default_subs = ['', 'x', s[:-2], s[3:], s, s + s]
+            for sub_str in cpython_subs + default_subs + extra_subs:
+                for start in list(range(-20, 20)) + [None]:
+                    for end in list(range(-20, 20)) + [None]:
+                        msg = 'Results "{}".endswith("{}", {}, {})\
+                               must be equal'
+                        tuple_subs = (sub_str, 'lo')
+                        self.assertEqual(pyfunc(s, tuple_subs, start, end),
+                                         cfunc(s, tuple_subs, start, end),
+                                         msg=msg.format(s, tuple_subs,
+                                                        start, end))
+
+    def test_in(self, flags=no_pyobj_flags):
+        pyfunc = in_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            extras = ['', 'xx', a[::-1], a[:-2], a[3:], a, a + a]
+            for substr in extras:
+                self.assertEqual(pyfunc(substr, a),
+                                 cfunc(substr, a),
+                                 "'%s' in '%s'?" % (substr, a))
+
+    def test_partition_exception_invalid_sep(self):
+        self.disable_leak_check()
+
+        pyfunc = partition_usecase
+        cfunc = njit(pyfunc)
+
+        # Handle empty separator exception
+        for func in [pyfunc, cfunc]:
+            with self.assertRaises(ValueError) as raises:
+                func('a', '')
+            self.assertIn('empty separator', str(raises.exception))
+
+        accepted_types = (types.UnicodeType, types.UnicodeCharSeq)
+        with self.assertRaises(TypingError) as raises:
+            cfunc('a', None)
+        msg = '"sep" must be {}, not none'.format(accepted_types)
+        self.assertIn(msg, str(raises.exception))
+
+    def test_partition(self):
+        pyfunc = partition_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            ('', '⚡'),
+            ('abcabc', '⚡'),
+            ('🐍⚡', '⚡'),
+            ('🐍⚡🐍', '⚡'),
+            ('abababa', 'a'),
+            ('abababa', 'b'),
+            ('abababa', 'c'),
+            ('abababa', 'ab'),
+            ('abababa', 'aba'),
+        ]
+        msg = 'Results of "{}".partition("{}") must be equal'
+        for s, sep in CASES:
+            self.assertEqual(pyfunc(s, sep), cfunc(s, sep),
+                             msg=msg.format(s, sep))
+
+    def test_find(self, flags=no_pyobj_flags):
+        pyfunc = find_usecase
+        cfunc = njit(pyfunc)
+
+        default_subs = [
+            (s, ['', 'xx', s[:-2], s[3:], s]) for s in UNICODE_EXAMPLES
+        ]
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L202-L231    # noqa: E501
+        cpython_subs = [
+            ('a' * 100 + '\u0102', ['\u0102', '\u0201', '\u0120', '\u0220']),
+            ('a' * 100 + '\U00100304', ['\U00100304', '\U00100204',
+                                        '\U00102004']),
+            ('\u0102' * 100 + 'a', ['a']),
+            ('\U00100304' * 100 + 'a', ['a']),
+            ('\U00100304' * 100 + '\u0102', ['\u0102']),
+            ('a' * 100, ['\u0102', '\U00100304', 'a\u0102', 'a\U00100304']),
+            ('\u0102' * 100, ['\U00100304', '\u0102\U00100304']),
+            ('\u0102' * 100 + 'a_', ['a_']),
+            ('\U00100304' * 100 + 'a_', ['a_']),
+            ('\U00100304' * 100 + '\u0102_', ['\u0102_']),
+        ]
+        for s, subs in default_subs + cpython_subs:
+            for sub_str in subs:
+                msg = 'Results "{}".find("{}") must be equal'
+                self.assertEqual(pyfunc(s, sub_str), cfunc(s, sub_str),
+                                 msg=msg.format(s, sub_str))
+
+    def test_find_with_start_only(self):
+        pyfunc = find_with_start_only_usecase
+        cfunc = njit(pyfunc)
+
+        for s in UNICODE_EXAMPLES:
+            for sub_str in ['', 'xx', s[:-2], s[3:], s]:
+                for start in list(range(-20, 20)) + [None]:
+                    msg = 'Results "{}".find("{}", {}) must be equal'
+                    self.assertEqual(pyfunc(s, sub_str, start),
+                                     cfunc(s, sub_str, start),
+                                     msg=msg.format(s, sub_str, start))
+
+    def test_find_with_start_end(self):
+        pyfunc = find_with_start_end_usecase
+        cfunc = njit(pyfunc)
+
+        starts = ends = list(range(-20, 20)) + [None]
+        for s in UNICODE_EXAMPLES:
+            for sub_str in ['', 'xx', s[:-2], s[3:], s]:
+                for start, end in product(starts, ends):
+                    msg = 'Results of "{}".find("{}", {}, {}) must be equal'
+                    self.assertEqual(pyfunc(s, sub_str, start, end),
+                                     cfunc(s, sub_str, start, end),
+                                     msg=msg.format(s, sub_str, start, end))
+
+    def test_find_exception_noninteger_start_end(self):
+        pyfunc = find_with_start_end_usecase
+        cfunc = njit(pyfunc)
+
+        accepted = (types.Integer, types.NoneType)
+        for start, end, name in [(0.1, 5, 'start'), (0, 0.5, 'end')]:
+            with self.assertRaises(TypingError) as raises:
+                cfunc('ascii', 'sci', start, end)
+            msg = '"{}" must be {}, not float'.format(name, accepted)
+            self.assertIn(msg, str(raises.exception))
+
+    def test_rpartition_exception_invalid_sep(self):
+        self.disable_leak_check()
+
+        pyfunc = rpartition_usecase
+        cfunc = njit(pyfunc)
+
+        # Handle empty separator exception
+        for func in [pyfunc, cfunc]:
+            with self.assertRaises(ValueError) as raises:
+                func('a', '')
+            self.assertIn('empty separator', str(raises.exception))
+
+        accepted_types = (types.UnicodeType, types.UnicodeCharSeq)
+        with self.assertRaises(TypingError) as raises:
+            cfunc('a', None)
+        msg = '"sep" must be {}, not none'.format(accepted_types)
+        self.assertIn(msg, str(raises.exception))
+
+    def test_rpartition(self):
+        pyfunc = rpartition_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            ('', '⚡'),
+            ('abcabc', '⚡'),
+            ('🐍⚡', '⚡'),
+            ('🐍⚡🐍', '⚡'),
+            ('abababa', 'a'),
+            ('abababa', 'b'),
+            ('abababa', 'c'),
+            ('abababa', 'ab'),
+            ('abababa', 'aba'),
+        ]
+        msg = 'Results of "{}".rpartition("{}") must be equal'
+        for s, sep in CASES:
+            self.assertEqual(pyfunc(s, sep), cfunc(s, sep),
+                             msg=msg.format(s, sep))
+
+    def test_count(self):
+        pyfunc = count_usecase
+        cfunc = njit(pyfunc)
+        error_msg = "'{0}'.py_count('{1}') = {2}\n'{0}'.c_count('{1}') = {3}"
+
+        for s, sub in UNICODE_COUNT_EXAMPLES:
+            py_result = pyfunc(s, sub)
+            c_result = cfunc(s, sub)
+            self.assertEqual(py_result, c_result,
+                             error_msg.format(s, sub, py_result, c_result))
+
+    def test_count_with_start(self):
+        pyfunc = count_with_start_usecase
+        cfunc = njit(pyfunc)
+        error_msg = "%s\n%s" % ("'{0}'.py_count('{1}', {2}) = {3}",
+                                "'{0}'.c_count('{1}', {2}) = {4}")
+
+        for s, sub in UNICODE_COUNT_EXAMPLES:
+            for i in range(-18, 18):
+                py_result = pyfunc(s, sub, i)
+                c_result = cfunc(s, sub, i)
+                self.assertEqual(py_result, c_result,
+                                 error_msg.format(s, sub, i, py_result,
+                                                  c_result))
+
+            py_result = pyfunc(s, sub, None)
+            c_result = cfunc(s, sub, None)
+            self.assertEqual(py_result, c_result,
+                             error_msg.format(s, sub, None, py_result,
+                                              c_result))
+
+    def test_count_with_start_end(self):
+        pyfunc = count_with_start_end_usecase
+        cfunc = njit(pyfunc)
+        error_msg = "%s\n%s" % ("'{0}'.py_count('{1}', {2}, {3}) = {4}",
+                                "'{0}'.c_count('{1}', {2}, {3}) = {5}")
+
+        for s, sub in UNICODE_COUNT_EXAMPLES:
+            for i, j in product(range(-18, 18), (-18, 18)):
+                py_result = pyfunc(s, sub, i, j)
+                c_result = cfunc(s, sub, i, j)
+                self.assertEqual(py_result, c_result,
+                                 error_msg.format(s, sub, i, j, py_result,
+                                                  c_result))
+
+            for j in range(-18, 18):
+                py_result = pyfunc(s, sub, None, j)
+                c_result = cfunc(s, sub, None, j)
+                self.assertEqual(py_result, c_result,
+                                 error_msg.format(s, sub, None, j, py_result,
+                                                  c_result))
+
+            py_result = pyfunc(s, sub, None, None)
+            c_result = cfunc(s, sub, None, None)
+            self.assertEqual(py_result, c_result,
+                             error_msg.format(s, sub, None, None, py_result,
+                                              c_result))
+
+    def test_count_arg_type_check(self):
+        cfunc = njit(count_with_start_end_usecase)
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc('ascii', 'c', 1, 0.5)
+        self.assertIn('The slice indices must be an Integer or None',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc('ascii', 'c', 1.2, 7)
+        self.assertIn('The slice indices must be an Integer or None',
+                      str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc('ascii', 12, 1, 7)
+        self.assertIn('The substring must be a UnicodeType, not',
+                      str(raises.exception))
+
+    def test_count_optional_arg_type_check(self):
+        pyfunc = count_with_start_end_usecase
+
+        def try_compile_bad_optional(*args):
+            bad_sig = types.int64(types.unicode_type,
+                                  types.unicode_type,
+                                  types.Optional(types.float64),
+                                  types.Optional(types.float64))
+            njit([bad_sig])(pyfunc)
+
+        with self.assertRaises(TypingError) as raises:
+            try_compile_bad_optional('tú quis?', 'tú', 1.1, 1.1)
+        self.assertIn('The slice indices must be an Integer or None',
+                      str(raises.exception))
+
+        error_msg = "%s\n%s" % ("'{0}'.py_count('{1}', {2}, {3}) = {4}",
+                                "'{0}'.c_count_op('{1}', {2}, {3}) = {5}")
+        sig_optional = types.int64(types.unicode_type,
+                                   types.unicode_type,
+                                   types.Optional(types.int64),
+                                   types.Optional(types.int64))
+        cfunc_optional = njit([sig_optional])(pyfunc)
+
+        py_result = pyfunc('tú quis?', 'tú', 0, 8)
+        c_result = cfunc_optional('tú quis?', 'tú', 0, 8)
+        self.assertEqual(py_result, c_result,
+                         error_msg.format('tú quis?', 'tú', 0, 8, py_result,
+                                          c_result))
+
+    def test_rfind(self):
+        pyfunc = rfind_usecase
+        cfunc = njit(pyfunc)
+
+        default_subs = [
+            (s, ['', 'xx', s[:-2], s[3:], s]) for s in UNICODE_EXAMPLES
+        ]
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L233-L259    # noqa: E501
+        cpython_subs = [
+            ('\u0102' + 'a' * 100, ['\u0102', '\u0201', '\u0120', '\u0220']),
+            ('\U00100304' + 'a' * 100, ['\U00100304', '\U00100204',
+                                        '\U00102004']),
+            ('abcdefghiabc', ['abc', '']),
+            ('a' + '\u0102' * 100, ['a']),
+            ('a' + '\U00100304' * 100, ['a']),
+            ('\u0102' + '\U00100304' * 100, ['\u0102']),
+            ('a' * 100, ['\u0102', '\U00100304', '\u0102a', '\U00100304a']),
+            ('\u0102' * 100, ['\U00100304', '\U00100304\u0102']),
+            ('_a' + '\u0102' * 100, ['_a']),
+            ('_a' + '\U00100304' * 100, ['_a']),
+            ('_\u0102' + '\U00100304' * 100, ['_\u0102']),
+        ]
+        for s, subs in default_subs + cpython_subs:
+            for sub_str in subs:
+                msg = 'Results "{}".rfind("{}") must be equal'
+                self.assertEqual(pyfunc(s, sub_str), cfunc(s, sub_str),
+                                 msg=msg.format(s, sub_str))
+
+    def test_rfind_with_start_only(self):
+        pyfunc = rfind_with_start_only_usecase
+        cfunc = njit(pyfunc)
+
+        for s in UNICODE_EXAMPLES:
+            for sub_str in ['', 'xx', s[:-2], s[3:], s]:
+                for start in list(range(-20, 20)) + [None]:
+                    msg = 'Results "{}".rfind("{}", {}) must be equal'
+                    self.assertEqual(pyfunc(s, sub_str, start),
+                                     cfunc(s, sub_str, start),
+                                     msg=msg.format(s, sub_str, start))
+
+    def test_rfind_with_start_end(self):
+        pyfunc = rfind_with_start_end_usecase
+        cfunc = njit(pyfunc)
+
+        starts = list(range(-20, 20)) + [None]
+        ends = list(range(-20, 20)) + [None]
+        for s in UNICODE_EXAMPLES:
+            for sub_str in ['', 'xx', s[:-2], s[3:], s]:
+                for start, end in product(starts, ends):
+                    msg = 'Results of "{}".rfind("{}", {}, {}) must be equal'
+                    self.assertEqual(pyfunc(s, sub_str, start, end),
+                                     cfunc(s, sub_str, start, end),
+                                     msg=msg.format(s, sub_str, start, end))
+
+    def test_rfind_wrong_substr(self):
+        cfunc = njit(rfind_usecase)
+
+        for s in UNICODE_EXAMPLES:
+            for sub_str in [None, 1, False]:
+                with self.assertRaises(TypingError) as raises:
+                    cfunc(s, sub_str)
+                msg = 'must be {}'.format(types.UnicodeType)
+                self.assertIn(msg, str(raises.exception))
+
+    def test_rfind_wrong_start_end(self):
+        cfunc = njit(rfind_with_start_end_usecase)
+
+        accepted_types = (types.Integer, types.NoneType)
+        for s in UNICODE_EXAMPLES:
+            for sub_str in ['', 'xx', s[:-2], s[3:], s]:
+                # test wrong start
+                for start, end in product([0.1, False], [-1, 1]):
+                    with self.assertRaises(TypingError) as raises:
+                        cfunc(s, sub_str, start, end)
+                    msg = '"start" must be {}'.format(accepted_types)
+                    self.assertIn(msg, str(raises.exception))
+
+                # test wrong end
+                for start, end in product([-1, 1], [-0.1, True]):
+                    with self.assertRaises(TypingError) as raises:
+                        cfunc(s, sub_str, start, end)
+                    msg = '"end" must be {}'.format(accepted_types)
+                    self.assertIn(msg, str(raises.exception))
+
+    def test_rfind_wrong_start_end_optional(self):
+        s = UNICODE_EXAMPLES[0]
+        sub_str = s[1:-1]
+        accepted_types = (types.Integer, types.NoneType)
+        msg = 'must be {}'.format(accepted_types)
+
+        def try_compile_wrong_start_optional(*args):
+            wrong_sig_optional = types.int64(types.unicode_type,
+                                             types.unicode_type,
+                                             types.Optional(types.float64),
+                                             types.Optional(types.intp))
+            njit([wrong_sig_optional])(rfind_with_start_end_usecase)
+
+        with self.assertRaises(TypingError) as raises:
+            try_compile_wrong_start_optional(s, sub_str, 0.1, 1)
+        self.assertIn(msg, str(raises.exception))
+
+        def try_compile_wrong_end_optional(*args):
+            wrong_sig_optional = types.int64(types.unicode_type,
+                                             types.unicode_type,
+                                             types.Optional(types.intp),
+                                             types.Optional(types.float64))
+            njit([wrong_sig_optional])(rfind_with_start_end_usecase)
+
+        with self.assertRaises(TypingError) as raises:
+            try_compile_wrong_end_optional(s, sub_str, 1, 0.1)
+        self.assertIn(msg, str(raises.exception))
+
+    def test_rindex(self):
+        pyfunc = rindex_usecase
+        cfunc = njit(pyfunc)
+
+        default_subs = [
+            (s, ['', s[:-2], s[3:], s]) for s in UNICODE_EXAMPLES
+        ]
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L284-L308    # noqa: E501
+        cpython_subs = [
+            ('abcdefghiabc', ['', 'def', 'abc']),
+            ('a' + '\u0102' * 100, ['a']),
+            ('a' + '\U00100304' * 100, ['a']),
+            ('\u0102' + '\U00100304' * 100, ['\u0102']),
+            ('_a' + '\u0102' * 100, ['_a']),
+            ('_a' + '\U00100304' * 100, ['_a']),
+            ('_\u0102' + '\U00100304' * 100, ['_\u0102'])
+        ]
+        for s, subs in default_subs + cpython_subs:
+            for sub_str in subs:
+                msg = 'Results "{}".rindex("{}") must be equal'
+                self.assertEqual(pyfunc(s, sub_str), cfunc(s, sub_str),
+                                 msg=msg.format(s, sub_str))
+
+    def test_index(self):
+        pyfunc = index_usecase
+        cfunc = njit(pyfunc)
+
+        default_subs = [
+            (s, ['', s[:-2], s[3:], s]) for s in UNICODE_EXAMPLES
+        ]
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L260-L282    # noqa: E501
+        cpython_subs = [
+            ('abcdefghiabc', ['', 'def', 'abc']),
+            ('\u0102' * 100 + 'a', ['a']),
+            ('\U00100304' * 100 + 'a', ['a']),
+            ('\U00100304' * 100 + '\u0102', ['\u0102']),
+            ('\u0102' * 100 + 'a_', ['a_']),
+            ('\U00100304' * 100 + 'a_', ['a_']),
+            ('\U00100304' * 100 + '\u0102_', ['\u0102_'])
+        ]
+        for s, subs in default_subs + cpython_subs:
+            for sub_str in subs:
+                msg = 'Results "{}".index("{}") must be equal'
+                self.assertEqual(pyfunc(s, sub_str), cfunc(s, sub_str),
+                                 msg=msg.format(s, sub_str))
+
+    def test_index_rindex_with_start_only(self):
+        pyfuncs = [index_with_start_only_usecase,
+                   rindex_with_start_only_usecase]
+        messages = ['Results "{}".index("{}", {}) must be equal',
+                    'Results "{}".rindex("{}", {}) must be equal']
+        unicode_examples = [
+            'ascii',
+            '12345',
+            '1234567890',
+            '¡Y tú quién te crees?',
+            '大处着眼，小处着手。',
+        ]
+        for pyfunc, msg in zip(pyfuncs, messages):
+            cfunc = njit(pyfunc)
+            for s in unicode_examples:
+                l = len(s)
+                cases = [
+                    ('', list(range(-10, l + 1))),
+                    (s[:-2], [0] + list(range(-10, 1 - l))),
+                    (s[3:], list(range(4)) + list(range(-10, 4 - l))),
+                    (s, [0] + list(range(-10, 1 - l))),
+                ]
+                for sub_str, starts in cases:
+                    for start in starts + [None]:
+                        self.assertEqual(pyfunc(s, sub_str, start),
+                                         cfunc(s, sub_str, start),
+                                         msg=msg.format(s, sub_str, start))
+
+    def test_index_rindex_with_start_end(self):
+        pyfuncs = [index_with_start_end_usecase, rindex_with_start_end_usecase]
+        messages = ['Results of "{}".index("{}", {}, {}) must be equal',
+                    'Results of "{}".rindex("{}", {}, {}) must be equal']
+        unicode_examples = [
+            'ascii',
+            '12345',
+            '1234567890',
+            '¡Y tú quién te crees?',
+            '大处着眼，小处着手。',
+        ]
+        for pyfunc, msg in zip(pyfuncs, messages):
+            cfunc = njit(pyfunc)
+            for s in unicode_examples:
+                l = len(s)
+                cases = [
+                    ('', list(range(-10, l + 1)), list(range(l, 10))),
+                    (s[:-2], [0] + list(range(-10, 1 - l)),
+                     [-2, -1] + list(range(l - 2, 10))),
+                    (s[3:], list(range(4)) + list(range(-10, -1)),
+                     list(range(l, 10))),
+                    (s, [0] + list(range(-10, 1 - l)), list(range(l, 10))),
+                ]
+                for sub_str, starts, ends in cases:
+                    for start, end in product(starts + [None], ends):
+                        self.assertEqual(pyfunc(s, sub_str, start, end),
+                                         cfunc(s, sub_str, start, end),
+                                         msg=msg.format(s, sub_str, start, end))
+
+    def test_index_rindex_exception_substring_not_found(self):
+        self.disable_leak_check()
+
+        unicode_examples = [
+            'ascii',
+            '12345',
+            '1234567890',
+            '¡Y tú quién te crees?',
+            '大处着眼，小处着手。',
+        ]
+        pyfuncs = [index_with_start_end_usecase, rindex_with_start_end_usecase]
+        for pyfunc in pyfuncs:
+            cfunc = njit(pyfunc)
+            for s in unicode_examples:
+                l = len(s)
+                cases = [
+                    ('', list(range(l + 1, 10)), [l]),
+                    (s[:-2], [0], list(range(l - 2))),
+                    (s[3:], list(range(4, 10)), [l]),
+                    (s, [None], list(range(l))),
+                ]
+                for sub_str, starts, ends in cases:
+                    for start, end in product(starts, ends):
+                        for func in [pyfunc, cfunc]:
+                            with self.assertRaises(ValueError) as raises:
+                                func(s, sub_str, start, end)
+                            msg = 'substring not found'
+                            self.assertIn(msg, str(raises.exception))
+
+    def test_index_rindex_exception_noninteger_start_end(self):
+        accepted = (types.Integer, types.NoneType)
+        pyfuncs = [index_with_start_end_usecase, rindex_with_start_end_usecase]
+        for pyfunc in pyfuncs:
+            cfunc = njit(pyfunc)
+            for start, end, name in [(0.1, 5, 'start'), (0, 0.5, 'end')]:
+                with self.assertRaises(TypingError) as raises:
+                    cfunc('ascii', 'sci', start, end)
+                msg = '"{}" must be {}, not float'.format(name, accepted)
+                self.assertIn(msg, str(raises.exception))
+
+    def test_getitem(self):
+        pyfunc = getitem_usecase
+        cfunc = njit(pyfunc)
+
+        for s in UNICODE_EXAMPLES:
+            for i in range(-len(s), len(s)):
+                self.assertEqual(pyfunc(s, i),
+                                 cfunc(s, i),
+                                 "'%s'[%d]?" % (s, i))
+
+    def test_getitem_scalar_kind(self):
+        # See issue #6135, make sure that getitem returns a char of the minimal
+        # kind required to represent the "got" item, this is done via the use
+        # of `hash` in the test function as it is sensitive to kind.
+        pyfunc = getitem_check_kind_usecase
+        cfunc = njit(pyfunc)
+        samples = ['a\u1234', '¡着']
+        for s in samples:
+            for i in range(-len(s), len(s)):
+                self.assertEqual(pyfunc(s, i),
+                                 cfunc(s, i),
+                                 "'%s'[%d]?" % (s, i))
+
+    def test_getitem_error(self):
+        self.disable_leak_check()
+
+        pyfunc = getitem_usecase
+        cfunc = njit(pyfunc)
+
+        for s in UNICODE_EXAMPLES:
+            with self.assertRaises(IndexError) as raises:
+                pyfunc(s, len(s))
+            self.assertIn('string index out of range', str(raises.exception))
+
+            with self.assertRaises(IndexError) as raises:
+                cfunc(s, len(s))
+            self.assertIn('string index out of range', str(raises.exception))
+
+    def test_slice2(self):
+        pyfunc = getitem_usecase
+        cfunc = njit(pyfunc)
+
+        for s in UNICODE_EXAMPLES:
+            for i in list(range(-len(s), len(s))):
+                for j in list(range(-len(s), len(s))):
+                    sl = slice(i, j)
+                    self.assertEqual(pyfunc(s, sl),
+                                     cfunc(s, sl),
+                                     "'%s'[%d:%d]?" % (s, i, j))
+
+    def test_slice2_error(self):
+        pyfunc = getitem_usecase
+        cfunc = njit(pyfunc)
+
+        for s in UNICODE_EXAMPLES:
+            for i in [-2, -1, len(s), len(s) + 1]:
+                for j in [-2, -1, len(s), len(s) + 1]:
+                    sl = slice(i, j)
+                    self.assertEqual(pyfunc(s, sl),
+                                     cfunc(s, sl),
+                                     "'%s'[%d:%d]?" % (s, i, j))
+
+    def test_getitem_slice2_kind(self):
+        # See issue #6135. Also see note in test_getitem_scalar_kind regarding
+        # testing.
+        pyfunc = getitem_check_kind_usecase
+        cfunc = njit(pyfunc)
+        samples = ['abc\u1234\u1234', '¡¡¡着着着']
+        for s in samples:
+            for i in [-2, -1, 0, 1, 2, len(s), len(s) + 1]:
+                for j in [-2, -1, 0, 1, 2, len(s), len(s) + 1]:
+                    sl = slice(i, j)
+                    self.assertEqual(pyfunc(s, sl),
+                                     cfunc(s, sl),
+                                     "'%s'[%d:%d]?" % (s, i, j))
+
+    def test_slice3(self):
+        pyfunc = getitem_usecase
+        cfunc = njit(pyfunc)
+
+        for s in UNICODE_EXAMPLES:
+            for i in range(-len(s), len(s)):
+                for j in range(-len(s), len(s)):
+                    for k in [-2, -1, 1, 2]:
+                        sl = slice(i, j, k)
+                        self.assertEqual(pyfunc(s, sl),
+                                         cfunc(s, sl),
+                                         "'%s'[%d:%d:%d]?" % (s, i, j, k))
+
+    def test_getitem_slice3_kind(self):
+        # See issue #6135. Also see note in test_getitem_scalar_kind regarding
+        # testing.
+        pyfunc = getitem_check_kind_usecase
+        cfunc = njit(pyfunc)
+        samples = ['abc\u1234\u1234',
+                   'a\u1234b\u1234c'
+                   '¡¡¡着着着',
+                   '¡着¡着¡着',
+                   '着a着b着c',
+                   '¡着a¡着b¡着c',
+                   '¡着a着¡c',]
+        for s in samples:
+            for i in range(-len(s), len(s)):
+                for j in range(-len(s), len(s)):
+                    for k in [-2, -1, 1, 2]:
+                        sl = slice(i, j, k)
+                        self.assertEqual(pyfunc(s, sl),
+                                         cfunc(s, sl),
+                                         "'%s'[%d:%d:%d]?" % (s, i, j, k))
+
+    def test_slice3_error(self):
+        pyfunc = getitem_usecase
+        cfunc = njit(pyfunc)
+
+        for s in UNICODE_EXAMPLES:
+            for i in [-2, -1, len(s), len(s) + 1]:
+                for j in [-2, -1, len(s), len(s) + 1]:
+                    for k in [-2, -1, 1, 2]:
+                        sl = slice(i, j, k)
+                        self.assertEqual(pyfunc(s, sl),
+                                         cfunc(s, sl),
+                                         "'%s'[%d:%d:%d]?" % (s, i, j, k))
+
+    def test_slice_ascii_flag(self):
+        """
+        Make sure ascii flag is False when ascii and non-ascii characters are
+        mixed in output of Unicode slicing.
+        """
+        @njit
+        def f(s):
+            return s[::2]._is_ascii, s[1::2]._is_ascii
+
+        s = "¿abc¡Y tú, quién te cre\t\tes?"
+        self.assertEqual(f(s), (0, 1))
+
+    def test_zfill(self):
+        pyfunc = zfill_usecase
+        cfunc = njit(pyfunc)
+
+        ZFILL_INPUTS = [
+            'ascii',
+            '+ascii',
+            '-ascii',
+            '-asc ii-',
+            '12345',
+            '-12345',
+            '+12345',
+            '',
+            '¡Y tú crs?',
+            '🐍⚡',
+            '+🐍⚡',
+            '-🐍⚡',
+            '大眼，小手。',
+            '+大眼，小手。',
+            '-大眼，小手。',
+        ]
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc(ZFILL_INPUTS[0], 1.1)
+        self.assertIn('<width> must be an Integer', str(raises.exception))
+
+        for s in ZFILL_INPUTS:
+            for width in range(-3, 20):
+                self.assertEqual(pyfunc(s, width),
+                                 cfunc(s, width))
+
+    def test_concat(self, flags=no_pyobj_flags):
+        pyfunc = concat_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            for b in UNICODE_EXAMPLES[::-1]:
+                self.assertEqual(pyfunc(a, b),
+                                 cfunc(a, b),
+                                 "'%s' + '%s'?" % (a, b))
+
+    def test_repeat(self, flags=no_pyobj_flags):
+        pyfunc = repeat_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            for b in (-1, 0, 1, 2, 3, 4, 5, 7, 8, 15, 70):
+                self.assertEqual(pyfunc(a, b),
+                                 cfunc(a, b))
+                self.assertEqual(pyfunc(b, a),
+                                 cfunc(b, a))
+
+    def test_repeat_exception_float(self):
+        self.disable_leak_check()
+        cfunc = njit(repeat_usecase)
+        with self.assertRaises(TypingError) as raises:
+            cfunc('hi', 2.5)
+        self.assertIn(_header_lead + ' Function(<built-in function mul>)',
+                      str(raises.exception))
+
+    def test_split_exception_empty_sep(self):
+        self.disable_leak_check()
+
+        pyfunc = split_usecase
+        cfunc = njit(pyfunc)
+
+        # Handle empty separator exception
+        for func in [pyfunc, cfunc]:
+            with self.assertRaises(ValueError) as raises:
+                func('a', '')
+            self.assertIn('empty separator', str(raises.exception))
+
+    def test_split_exception_noninteger_maxsplit(self):
+        pyfunc = split_with_maxsplit_usecase
+        cfunc = njit(pyfunc)
+
+        # Handle non-integer maxsplit exception
+        for sep in [' ', None]:
+            with self.assertRaises(TypingError) as raises:
+                cfunc('a', sep, 2.4)
+            self.assertIn('float64', str(raises.exception),
+                          'non-integer maxsplit with sep = %s' % sep)
+
+    def test_split(self):
+        pyfunc = split_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            (' a ', None),
+            ('', '⚡'),
+            ('abcabc', '⚡'),
+            ('🐍⚡', '⚡'),
+            ('🐍⚡🐍', '⚡'),
+            ('abababa', 'a'),
+            ('abababa', 'b'),
+            ('abababa', 'c'),
+            ('abababa', 'ab'),
+            ('abababa', 'aba'),
+        ]
+
+        for test_str, splitter in CASES:
+            self.assertEqual(pyfunc(test_str, splitter),
+                             cfunc(test_str, splitter),
+                             "'%s'.split('%s')?" % (test_str, splitter))
+
+    def test_split_with_maxsplit(self):
+        CASES = [
+            (' a ', None, 1),
+            ('', '⚡', 1),
+            ('abcabc', '⚡', 1),
+            ('🐍⚡', '⚡', 1),
+            ('🐍⚡🐍', '⚡', 1),
+            ('abababa', 'a', 2),
+            ('abababa', 'b', 1),
+            ('abababa', 'c', 2),
+            ('abababa', 'ab', 1),
+            ('abababa', 'aba', 5),
+        ]
+
+        for pyfunc, fmt_str in [(split_with_maxsplit_usecase,
+                                 "'%s'.split('%s', %d)?"),
+                                (split_with_maxsplit_kwarg_usecase,
+                                 "'%s'.split('%s', maxsplit=%d)?")]:
+
+            cfunc = njit(pyfunc)
+            for test_str, splitter, maxsplit in CASES:
+                self.assertEqual(pyfunc(test_str, splitter, maxsplit),
+                                 cfunc(test_str, splitter, maxsplit),
+                                 fmt_str % (test_str, splitter, maxsplit))
+
+    def test_split_whitespace(self):
+        # explicit sep=None cases covered in test_split and
+        # test_split_with_maxsplit
+        pyfunc = split_whitespace_usecase
+        cfunc = njit(pyfunc)
+
+        # list copied from
+        # https://github.com/python/cpython/blob/master/Objects/unicodetype_db.h
+        all_whitespace = ''.join(map(chr, [
+            0x0009, 0x000A, 0x000B, 0x000C, 0x000D, 0x001C, 0x001D, 0x001E,
+            0x001F, 0x0020, 0x0085, 0x00A0, 0x1680, 0x2000, 0x2001, 0x2002,
+            0x2003, 0x2004, 0x2005, 0x2006, 0x2007, 0x2008, 0x2009, 0x200A,
+            0x2028, 0x2029, 0x202F, 0x205F, 0x3000
+        ]))
+
+        CASES = [
+            '',
+            'abcabc',
+            '🐍 ⚡',
+            '🐍 ⚡ 🐍',
+            '🐍   ⚡ 🐍  ',
+            '  🐍   ⚡ 🐍',
+            ' 🐍' + all_whitespace + '⚡ 🐍  ',
+        ]
+        for test_str in CASES:
+            self.assertEqual(pyfunc(test_str),
+                             cfunc(test_str),
+                             "'%s'.split()?" % (test_str,))
+
+    def test_split_exception_invalid_keepends(self):
+        pyfunc = splitlines_with_keepends_usecase
+        cfunc = njit(pyfunc)
+
+        accepted_types = (types.Integer, int, types.Boolean, bool)
+        for ty, keepends in (('none', None), ('unicode_type', 'None')):
+            with self.assertRaises(TypingError) as raises:
+                cfunc('\n', keepends)
+            msg = '"keepends" must be {}, not {}'.format(accepted_types, ty)
+            self.assertIn(msg, str(raises.exception))
+
+    def test_splitlines(self):
+        pyfunc = splitlines_usecase
+        cfunc = njit(pyfunc)
+
+        cases = ['', '\n', 'abc\r\rabc\r\n', '🐍⚡\v', '\f🐍⚡\f\v\v🐍\x85',
+                 '\u2028aba\u2029baba', '\n\r\na\v\fb\x0b\x0cc\x1c\x1d\x1e']
+
+        msg = 'Results of "{}".splitlines() must be equal'
+        for s in cases:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_splitlines_with_keepends(self):
+        pyfuncs = [
+            splitlines_with_keepends_usecase,
+            splitlines_with_keepends_kwarg_usecase
+        ]
+        messages = [
+            'Results of "{}".splitlines({}) must be equal',
+            'Results of "{}".splitlines(keepends={}) must be equal'
+        ]
+        cases = ['', '\n', 'abc\r\rabc\r\n', '🐍⚡\v', '\f🐍⚡\f\v\v🐍\x85',
+                 '\u2028aba\u2029baba', '\n\r\na\v\fb\x0b\x0cc\x1c\x1d\x1e']
+        all_keepends = [True, False, 0, 1, -1, 100]
+
+        for pyfunc, msg in zip(pyfuncs, messages):
+            cfunc = njit(pyfunc)
+            for s, keepends in product(cases, all_keepends):
+                self.assertEqual(pyfunc(s, keepends), cfunc(s, keepends),
+                                 msg=msg.format(s, keepends))
+
+    def test_rsplit_exception_empty_sep(self):
+        self.disable_leak_check()
+
+        pyfunc = rsplit_usecase
+        cfunc = njit(pyfunc)
+
+        # Handle empty separator exception
+        for func in [pyfunc, cfunc]:
+            with self.assertRaises(ValueError) as raises:
+                func('a', '')
+            self.assertIn('empty separator', str(raises.exception))
+
+    def test_rsplit_exception_noninteger_maxsplit(self):
+        pyfunc = rsplit_with_maxsplit_usecase
+        cfunc = njit(pyfunc)
+
+        accepted_types = (types.Integer, int)
+        for sep in [' ', None]:
+            with self.assertRaises(TypingError) as raises:
+                cfunc('a', sep, 2.4)
+            msg = '"maxsplit" must be {}, not float'.format(accepted_types)
+            self.assertIn(msg, str(raises.exception))
+
+    def test_rsplit(self):
+        pyfunc = rsplit_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            (' a ', None),
+            ('', '⚡'),
+            ('abcabc', '⚡'),
+            ('🐍⚡', '⚡'),
+            ('🐍⚡🐍', '⚡'),
+            ('abababa', 'a'),
+            ('abababa', 'b'),
+            ('abababa', 'c'),
+            ('abababa', 'ab'),
+            ('abababa', 'aba'),
+        ]
+        msg = 'Results of "{}".rsplit("{}") must be equal'
+        for s, sep in CASES:
+            self.assertEqual(pyfunc(s, sep), cfunc(s, sep),
+                             msg=msg.format(s, sep))
+
+    def test_rsplit_with_maxsplit(self):
+        pyfuncs = [rsplit_with_maxsplit_usecase,
+                   rsplit_with_maxsplit_kwarg_usecase]
+        CASES = [
+            (' a ', None, 1),
+            ('', '⚡', 1),
+            ('abcabc', '⚡', 1),
+            ('🐍⚡', '⚡', 1),
+            ('🐍⚡🐍', '⚡', 1),
+            ('abababa', 'a', 2),
+            ('abababa', 'b', 1),
+            ('abababa', 'c', 2),
+            ('abababa', 'ab', 1),
+            ('abababa', 'aba', 5),
+        ]
+        messages = [
+            'Results of "{}".rsplit("{}", {}) must be equal',
+            'Results of "{}".rsplit("{}", maxsplit={}) must be equal'
+        ]
+
+        for pyfunc, msg in zip(pyfuncs, messages):
+            cfunc = njit(pyfunc)
+            for test_str, sep, maxsplit in CASES:
+                self.assertEqual(pyfunc(test_str, sep, maxsplit),
+                                 cfunc(test_str, sep, maxsplit),
+                                 msg=msg.format(test_str, sep, maxsplit))
+
+    def test_rsplit_whitespace(self):
+        pyfunc = rsplit_whitespace_usecase
+        cfunc = njit(pyfunc)
+
+        # list copied from
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodetype_db.h#L5996-L6031    # noqa: E501
+        all_whitespace = ''.join(map(chr, [
+            0x0009, 0x000A, 0x000B, 0x000C, 0x000D, 0x001C, 0x001D, 0x001E,
+            0x001F, 0x0020, 0x0085, 0x00A0, 0x1680, 0x2000, 0x2001, 0x2002,
+            0x2003, 0x2004, 0x2005, 0x2006, 0x2007, 0x2008, 0x2009, 0x200A,
+            0x2028, 0x2029, 0x202F, 0x205F, 0x3000
+        ]))
+
+        CASES = [
+            '',
+            'abcabc',
+            '🐍 ⚡',
+            '🐍 ⚡ 🐍',
+            '🐍   ⚡ 🐍  ',
+            '  🐍   ⚡ 🐍',
+            ' 🐍' + all_whitespace + '⚡ 🐍  ',
+        ]
+        msg = 'Results of "{}".rsplit() must be equal'
+        for s in CASES:
+            self.assertEqual(pyfunc(s), cfunc(s), msg.format(s))
+
+    def test_join_empty(self):
+        # Can't pass empty list to nopython mode, so we have to make a
+        # separate test case
+        pyfunc = join_empty_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            '',
+            '🐍🐍🐍',
+        ]
+
+        for sep in CASES:
+            self.assertEqual(pyfunc(sep),
+                             cfunc(sep),
+                             "'%s'.join([])?" % (sep,))
+
+    def test_join_non_string_exception(self):
+        # Verify that join of list of integers raises typing exception
+        pyfunc = join_usecase
+        cfunc = njit(pyfunc)
+
+        # Handle empty separator exception
+        with self.assertRaises(TypingError) as raises:
+            cfunc('', [1, 2, 3])
+        # This error message is obscure, but indicates the error was trapped
+        # in the typing of str.join()
+        # Feel free to change this as we update error messages.
+        exc_message = str(raises.exception)
+        self.assertIn(
+            "During: resolving callee type: BoundFunction",
+            exc_message,
+        )
+        # could be int32 or int64
+        self.assertIn("reflected list(int", exc_message)
+
+    def test_join(self):
+        pyfunc = join_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            ('', ['', '', '']),
+            ('a', ['', '', '']),
+            ('', ['a', 'bbbb', 'c']),
+            ('🐍🐍🐍', ['⚡⚡'] * 5),
+        ]
+
+        for sep, parts in CASES:
+            self.assertEqual(pyfunc(sep, parts),
+                             cfunc(sep, parts),
+                             "'%s'.join('%s')?" % (sep, parts))
+
+    def test_join_interleave_str(self):
+        # can pass a string as the parts iterable
+        pyfunc = join_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            ('abc', '123'),
+            ('🐍🐍🐍', '⚡⚡'),
+        ]
+
+        for sep, parts in CASES:
+            self.assertEqual(pyfunc(sep, parts),
+                             cfunc(sep, parts),
+                             "'%s'.join('%s')?" % (sep, parts))
+
+    def test_justification(self):
+        for pyfunc, case_name in [(center_usecase, 'center'),
+                                  (ljust_usecase, 'ljust'),
+                                  (rjust_usecase, 'rjust')]:
+            cfunc = njit(pyfunc)
+
+            with self.assertRaises(TypingError) as raises:
+                cfunc(UNICODE_EXAMPLES[0], 1.1)
+            self.assertIn('The width must be an Integer', str(raises.exception))
+
+            for s in UNICODE_EXAMPLES:
+                for width in range(-3, 20):
+                    self.assertEqual(pyfunc(s, width),
+                                     cfunc(s, width),
+                                     "'%s'.%s(%d)?" % (s, case_name, width))
+
+    def test_justification_fillchar(self):
+        for pyfunc, case_name in [(center_usecase_fillchar, 'center'),
+                                  (ljust_usecase_fillchar, 'ljust'),
+                                  (rjust_usecase_fillchar, 'rjust')]:
+            cfunc = njit(pyfunc)
+
+            # allowed fillchar cases
+            for fillchar in [' ', '+', 'ú', '处']:
+                with self.assertRaises(TypingError) as raises:
+                    cfunc(UNICODE_EXAMPLES[0], 1.1, fillchar)
+                self.assertIn('The width must be an Integer',
+                              str(raises.exception))
+
+                for s in UNICODE_EXAMPLES:
+                    for width in range(-3, 20):
+                        self.assertEqual(pyfunc(s, width, fillchar),
+                                         cfunc(s, width, fillchar),
+                                         "'%s'.%s(%d, '%s')?" % (s, case_name,
+                                                                 width,
+                                                                 fillchar))
+
+    def test_justification_fillchar_exception(self):
+        self.disable_leak_check()
+
+        for pyfunc in [center_usecase_fillchar,
+                       ljust_usecase_fillchar,
+                       rjust_usecase_fillchar]:
+            cfunc = njit(pyfunc)
+
+            # disallowed fillchar cases
+            for fillchar in ['', '+0', 'quién', '处着']:
+                with self.assertRaises(ValueError) as raises:
+                    cfunc(UNICODE_EXAMPLES[0], 20, fillchar)
+                self.assertIn('The fill character must be exactly one',
+                              str(raises.exception))
+
+            # forbid fillchar cases with different types
+            for fillchar in [1, 1.1]:
+                with self.assertRaises(TypingError) as raises:
+                    cfunc(UNICODE_EXAMPLES[0], 20, fillchar)
+                self.assertIn('The fillchar must be a UnicodeType',
+                              str(raises.exception))
+
+    def test_inplace_concat(self, flags=no_pyobj_flags):
+        pyfunc = inplace_concat_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            for b in UNICODE_EXAMPLES[::-1]:
+                self.assertEqual(pyfunc(a, b),
+                                 cfunc(a, b),
+                                 "'%s' + '%s'?" % (a, b))
+
+    def test_isidentifier(self):
+        def pyfunc(s):
+            return s.isidentifier()
+
+        cfunc = njit(pyfunc)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L695-L708    # noqa: E501
+        cpython = ['a', 'Z', '_', 'b0', 'bc', 'b_', 'µ',
+                   '𝔘𝔫𝔦𝔠𝔬𝔡𝔢', ' ', '[', '©', '0']
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L742-L749    # noqa: E501
+        cpython_extras = ['\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF',
+                          'a\uD800b\uDFFF', 'a\uDFFFb\uD800',
+                          'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a']
+
+        msg = 'Results of "{}".isidentifier() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython + cpython_extras:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_strip(self):
+
+        STRIP_CASES = [
+            ('ass cii', 'ai'),
+            ('ass cii', None),
+            ('asscii', 'ai '),
+            ('asscii ', 'ai '),
+            (' asscii  ', 'ai '),
+            (' asscii  ', 'asci '),
+            (' asscii  ', 's'),
+            ('      ', ' '),
+            ('', ' '),
+            ('', ''),
+            ('', None),
+            (' ', None),
+            ('  asscii  ', 'ai '),
+            ('  asscii  ', ''),
+            ('  asscii  ', None),
+            ('tú quién te crees?', 'étú? '),
+            ('  tú quién te crees?   ', 'étú? '),
+            ('  tú qrees?   ', ''),
+            ('  tú quién te crees?   ', None),
+            ('大处 着眼，小处着手。大大大处', '大处'),
+            (' 大处大处  ', ''),
+            ('\t\nabcd\t', '\ta'),
+            (' 大处大处  ', None),
+            ('\t abcd \t', None),
+            ('\n abcd \n', None),
+            ('\r abcd \r', None),
+            ('\x0b abcd \x0b', None),
+            ('\x0c abcd \x0c', None),
+            ('\u2029abcd\u205F', None),
+            ('\u0085abcd\u2009', None)
+        ]
+
+        # form with no parameter
+        for pyfunc, case_name in [(strip_usecase, 'strip'),
+                                  (lstrip_usecase, 'lstrip'),
+                                  (rstrip_usecase, 'rstrip')]:
+            cfunc = njit(pyfunc)
+
+            for string, chars in STRIP_CASES:
+                self.assertEqual(pyfunc(string),
+                                 cfunc(string),
+                                 "'%s'.%s()?" % (string, case_name))
+        # parametrized form
+        for pyfunc, case_name in [(strip_usecase_chars, 'strip'),
+                                  (lstrip_usecase_chars, 'lstrip'),
+                                  (rstrip_usecase_chars, 'rstrip')]:
+            cfunc = njit(pyfunc)
+
+            sig1 = types.unicode_type(types.unicode_type,
+                                      types.Optional(types.unicode_type))
+            cfunc_optional = njit([sig1])(pyfunc)
+
+            def try_compile_bad_optional(*args):
+                bad = types.unicode_type(types.unicode_type,
+                                         types.Optional(types.float64))
+                njit([bad])(pyfunc)
+
+            for fn in cfunc, try_compile_bad_optional:
+                with self.assertRaises(TypingError) as raises:
+                    fn('tú quis?', 1.1)
+                self.assertIn('The arg must be a UnicodeType or None',
+                              str(raises.exception))
+
+            for fn in cfunc, cfunc_optional:
+
+                for string, chars in STRIP_CASES:
+                    self.assertEqual(pyfunc(string, chars),
+                                     fn(string, chars),
+                                     "'%s'.%s('%s')?" % (string, case_name,
+                                                         chars))
+
+    def test_isspace(self):
+        def pyfunc(s):
+            return s.isspace()
+
+        cfunc = njit(pyfunc)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L613-L621    # noqa: E501
+        cpython = ['\u2000', '\u200a', '\u2014', '\U00010401', '\U00010427',
+                   '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F']
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L742-L749    # noqa: E501
+        cpython_extras = ['\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF',
+                          'a\uD800b\uDFFF', 'a\uDFFFb\uD800',
+                          'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a']
+
+        msg = 'Results of "{}".isspace() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython + cpython_extras:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_istitle(self):
+        pyfunc = istitle_usecase
+        cfunc = njit(pyfunc)
+        error_msg = "'{0}'.py_istitle() = {1}\n'{0}'.c_istitle() = {2}"
+
+        unicode_title = [x.title() for x in UNICODE_EXAMPLES]
+        special = [
+            '',
+            '    ',
+            '  AA  ',
+            '  Ab  ',
+            '1',
+            'A123',
+            'A12Bcd',
+            '+abA',
+            '12Abc',
+            'A12abc',
+            '%^Abc 5 $% Def'
+            '𐐁𐐩',
+            '𐐧𐑎',
+            '𐐩',
+            '𐑎',
+            '🐍 Is',
+            '🐍 NOT',
+            '👯Is',
+            'ῼ',
+            'Greek ῼitlecases ...'
+        ]
+        ISTITLE_EXAMPLES = UNICODE_EXAMPLES + unicode_title + special
+
+        for s in ISTITLE_EXAMPLES:
+            py_result = pyfunc(s)
+            c_result = cfunc(s)
+            self.assertEqual(py_result, c_result,
+                             error_msg.format(s, py_result, c_result))
+
+    def test_isprintable(self):
+        def pyfunc(s):
+            return s.isprintable()
+
+        cfunc = njit(pyfunc)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L710-L723    # noqa: E501
+        cpython = ['', ' ', 'abcdefg', 'abcdefg\n', '\u0374', '\u0378',
+                   '\ud800', '\U0001F46F', '\U000E0020']
+
+        msg = 'Results of "{}".isprintable() must be equal'
+        for s in UNICODE_EXAMPLES + cpython:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_pointless_slice(self, flags=no_pyobj_flags):
+        def pyfunc(a):
+            return a[:]
+        cfunc = njit(pyfunc)
+        args = ['a']
+        self.assertEqual(pyfunc(*args), cfunc(*args))
+
+    def test_walk_backwards(self, flags=no_pyobj_flags):
+        def pyfunc(a):
+            return a[::-1]
+        cfunc = njit(pyfunc)
+        args = ['a']
+        self.assertEqual(pyfunc(*args), cfunc(*args))
+
+    def test_stride_slice(self, flags=no_pyobj_flags):
+        def pyfunc(a):
+            return a[::2]
+        cfunc = njit(pyfunc)
+        args = ['a']
+        self.assertEqual(pyfunc(*args), cfunc(*args))
+
+    def test_basic_lt(self, flags=no_pyobj_flags):
+        def pyfunc(a, b):
+            return a < b
+        cfunc = njit(pyfunc)
+        args = ['ab', 'b']
+        self.assertEqual(pyfunc(*args), cfunc(*args))
+
+    def test_basic_gt(self, flags=no_pyobj_flags):
+        def pyfunc(a, b):
+            return a > b
+        cfunc = njit(pyfunc)
+        args = ['ab', 'b']
+        self.assertEqual(pyfunc(*args), cfunc(*args))
+
+    def test_comparison(self):
+        def pyfunc(option, x, y):
+            if option == '==':
+                return x == y
+            elif option == '!=':
+                return x != y
+            elif option == '<':
+                return x < y
+            elif option == '>':
+                return x > y
+            elif option == '<=':
+                return x <= y
+            elif option == '>=':
+                return x >= y
+            else:
+                return None
+
+        cfunc = njit(pyfunc)
+
+        for x, y in permutations(UNICODE_ORDERING_EXAMPLES, r=2):
+            for cmpop in ['==', '!=', '<', '>', '<=', '>=', '']:
+                args = [cmpop, x, y]
+                self.assertEqual(pyfunc(*args), cfunc(*args),
+                                 msg='failed on {}'.format(args))
+
+    def test_literal_concat(self):
+        def pyfunc(x):
+            abc = 'abc'
+            if len(x):
+                return abc + 'b123' + x + 'IO'
+            else:
+                return x + abc + '123' + x
+
+        cfunc = njit(pyfunc)
+        args = ['x']
+        self.assertEqual(pyfunc(*args), cfunc(*args))
+        args = ['']
+        self.assertEqual(pyfunc(*args), cfunc(*args))
+
+    def test_literal_comparison(self):
+        def pyfunc(option):
+            x = 'a123'
+            y = 'aa12'
+            if option == '==':
+                return x == y
+            elif option == '!=':
+                return x != y
+            elif option == '<':
+                return x < y
+            elif option == '>':
+                return x > y
+            elif option == '<=':
+                return x <= y
+            elif option == '>=':
+                return x >= y
+            else:
+                return None
+
+        cfunc = njit(pyfunc)
+        for cmpop in ['==', '!=', '<', '>', '<=', '>=', '']:
+            args = [cmpop]
+            self.assertEqual(pyfunc(*args), cfunc(*args),
+                             msg='failed on {}'.format(args))
+
+    def test_literal_len(self):
+        def pyfunc():
+            return len('abc')
+        cfunc = njit(pyfunc)
+        self.assertEqual(pyfunc(), cfunc())
+
+    def test_literal_getitem(self):
+        def pyfunc(which):
+            return 'abc'[which]
+        cfunc = njit(pyfunc)
+        for a in [-1, 0, 1, slice(1, None), slice(None, -1)]:
+            args = [a]
+            self.assertEqual(pyfunc(*args), cfunc(*args),
+                             msg='failed on {}'.format(args))
+
+    def test_literal_in(self):
+        def pyfunc(x):
+            return x in '9876zabiuh'
+
+        cfunc = njit(pyfunc)
+        for a in ['a', '9', '1', '', '8uha', '987']:
+            args = [a]
+            self.assertEqual(pyfunc(*args), cfunc(*args),
+                             msg='failed on {}'.format(args))
+
+    def test_literal_xyzwith(self):
+        def pyfunc(x, y):
+            return 'abc'.startswith(x), 'cde'.endswith(y)
+
+        cfunc = njit(pyfunc)
+        for args in permutations('abcdefg', r=2):
+            self.assertEqual(pyfunc(*args), cfunc(*args),
+                             msg='failed on {}'.format(args))
+
+    def test_literal_find(self):
+        def pyfunc(x):
+            return 'abc'.find(x), x.find('a')
+
+        cfunc = njit(pyfunc)
+        for a in ['ab']:
+            args = [a]
+            self.assertEqual(pyfunc(*args), cfunc(*args),
+                             msg='failed on {}'.format(args))
+
+    def test_not(self):
+        def pyfunc(x):
+            return not x
+
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            args = [a]
+            self.assertEqual(pyfunc(*args), cfunc(*args),
+                             msg='failed on {}'.format(args))
+
+    def test_capitalize(self):
+        def pyfunc(x):
+            return x.capitalize()
+
+        cfunc = njit(pyfunc)
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L800-L815    # noqa: E501
+        cpython = ['\U0001044F', '\U0001044F\U0001044F', '\U00010427\U0001044F',
+                   '\U0001044F\U00010427', 'X\U00010427x\U0001044F', 'h\u0130',
+                   '\u1fd2\u0130', 'finnish', 'A\u0345\u03a3']
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L926    # noqa: E501
+        cpython_extras = ['\U00010000\U00100000']
+
+        msg = 'Results of "{}".capitalize() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython + cpython_extras:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_isupper(self):
+        def pyfunc(x):
+            return x.isupper()
+
+        cfunc = njit(pyfunc)
+        uppers = [x.upper() for x in UNICODE_EXAMPLES]
+        extras = ["AA12A", "aa12a", "大AA12A", "大aa12a", "AAADŽA", "A 1 1 大"]
+
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L585-L599    # noqa: E501
+        cpython = ['\u2167', '\u2177', '\U00010401', '\U00010427', '\U00010429',
+                   '\U0001044E', '\U0001F40D', '\U0001F46F']
+        fourxcpy = [x * 4 for x in cpython]
+
+        for a in UNICODE_EXAMPLES + uppers + extras + cpython + fourxcpy:
+            args = [a]
+            self.assertEqual(pyfunc(*args), cfunc(*args),
+                             msg='failed on {}'.format(args))
+
+    def test_upper(self):
+        def pyfunc(x):
+            return x.upper()
+
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            args = [a]
+            self.assertEqual(pyfunc(*args), cfunc(*args),
+                             msg='failed on {}'.format(args))
+
+    def test_casefold(self):
+        def pyfunc(x):
+            return x.casefold()
+
+        cfunc = njit(pyfunc)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L774-L781    # noqa: E501
+        cpython = ['hello', 'hELlo', 'ß', 'fi', '\u03a3',
+                   'A\u0345\u03a3', '\u00b5']
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L924    # noqa: E501
+        cpython_extras = ['\U00010000\U00100000']
+
+        msg = 'Results of "{}".casefold() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython + cpython_extras:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_isalpha(self):
+        def pyfunc(x):
+            return x.isalpha()
+
+        cfunc = njit(pyfunc)
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L630-L640    # noqa: E501
+        cpython = ['\u1FFc', '\U00010401', '\U00010427', '\U00010429',
+                   '\U0001044E', '\U0001F40D', '\U0001F46F']
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L738-L745    # noqa: E501
+        extras = ['\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF',
+                  'a\uD800b\uDFFF', 'a\uDFFFb\uD800',
+                  'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a']
+
+        msg = 'Results of "{}".isalpha() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + extras + cpython:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    @unittest.skipUnless(_py37_or_later,
+                         'isascii method requires Python 3.7 or later')
+    def test_isascii(self):
+        def pyfunc(x):
+            return x.isascii()
+
+        cfunc = njit(pyfunc)
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/865c3b257fe38154a4320c7ee6afb416f665b9c2/Lib/test/string_tests.py#L913-L926     # noqa: E501
+        cpython = ['', '\x00', '\x7f', '\x00\x7f', '\x80', '\xe9', ' ']
+
+        msg = 'Results of "{}".isascii() must be equal'
+        for s in UNICODE_EXAMPLES + cpython:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_title(self):
+        pyfunc = title
+        cfunc = njit(pyfunc)
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L813-L828    # noqa: E501
+        cpython = ['\U0001044F', '\U0001044F\U0001044F',
+                   '\U0001044F\U0001044F \U0001044F\U0001044F',
+                   '\U00010427\U0001044F \U00010427\U0001044F',
+                   '\U0001044F\U00010427 \U0001044F\U00010427',
+                   'X\U00010427x\U0001044F X\U00010427x\U0001044F',
+                   'fiNNISH', 'A\u03a3 \u1fa1xy', 'A\u03a3A']
+
+        msg = 'Results of "{}".title() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_swapcase(self):
+        def pyfunc(x):
+            return x.swapcase()
+
+        cfunc = njit(pyfunc)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L834-L858    # noqa: E501
+        cpython = ['\U0001044F', '\U00010427', '\U0001044F\U0001044F',
+                   '\U00010427\U0001044F', '\U0001044F\U00010427',
+                   'X\U00010427x\U0001044F', 'fi', '\u0130', '\u03a3',
+                   '\u0345\u03a3', 'A\u0345\u03a3', 'A\u0345\u03a3a',
+                   'A\u0345\u03a3', 'A\u03a3\u0345', '\u03a3\u0345 ',
+                   '\u03a3', 'ß', '\u1fd2']
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L928    # noqa: E501
+        cpython_extras = ['\U00010000\U00100000']
+
+        msg = 'Results of "{}".swapcase() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython + cpython_extras:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_islower(self):
+        pyfunc = islower_usecase
+        cfunc = njit(pyfunc)
+        lowers = [x.lower() for x in UNICODE_EXAMPLES]
+        extras = ['AA12A', 'aa12a', '大AA12A', '大aa12a', 'AAADŽA', 'A 1 1 大']
+
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L586-L600    # noqa: E501
+        cpython = ['\u2167', '\u2177', '\U00010401', '\U00010427',
+                   '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F']
+        cpython += [x * 4 for x in cpython]
+
+        msg = 'Results of "{}".islower() must be equal'
+        for s in UNICODE_EXAMPLES + lowers + [''] + extras + cpython:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_isalnum(self):
+        def pyfunc(x):
+            return x.isalnum()
+
+        cfunc = njit(pyfunc)
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L624-L628    # noqa: E501
+        cpython = ['\U00010401', '\U00010427', '\U00010429', '\U0001044E',
+                   '\U0001D7F6', '\U00011066', '\U000104A0', '\U0001F107']
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L738-L745    # noqa: E501
+        extras = ['\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF',
+                  'a\uD800b\uDFFF', 'a\uDFFFb\uD800',
+                  'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a']
+
+        msg = 'Results of "{}".isalnum() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + extras + cpython:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_lower(self):
+        pyfunc = lower_usecase
+        cfunc = njit(pyfunc)
+        extras = ['AA12A', 'aa12a', '大AA12A', '大aa12a', 'AAADŽA', 'A 1 1 大']
+
+        # Samples taken from CPython testing:
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L748-L758    # noqa: E501
+        cpython = ['\U00010401', '\U00010427', '\U0001044E', '\U0001F46F',
+                   '\U00010427\U00010427', '\U00010427\U0001044F',
+                   'X\U00010427x\U0001044F', '\u0130']
+
+        # special cases for sigma from CPython testing:
+        # https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Lib/test/test_unicode.py#L759-L768    # noqa: E501
+        sigma = ['\u03a3', '\u0345\u03a3', 'A\u0345\u03a3', 'A\u0345\u03a3a',
+                 '\u03a3\u0345 ', '\U0008fffe', '\u2177']
+
+        extra_sigma = 'A\u03a3\u03a2'
+        sigma.append(extra_sigma)
+
+        msg = 'Results of "{}".lower() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + extras + cpython + sigma:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_isnumeric(self):
+        def pyfunc(x):
+            return x.isnumeric()
+
+        cfunc = njit(pyfunc)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L676-L693    # noqa: E501
+        cpython = ['', 'a', '0', '\u2460', '\xbc', '\u0660', '0123456789',
+                   '0123456789a', '\U00010401', '\U00010427', '\U00010429',
+                   '\U0001044E', '\U0001F40D', '\U0001F46F', '\U00011065',
+                   '\U0001D7F6', '\U00011066', '\U000104A0', '\U0001F107']
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L742-L749    # noqa: E501
+        cpython_extras = ['\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF',
+                          'a\uD800b\uDFFF', 'a\uDFFFb\uD800', 'a\uD800b\uDFFFa',
+                          'a\uDFFFb\uD800a']
+
+        msg = 'Results of "{}".isnumeric() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython + cpython_extras:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_isdigit(self):
+        def pyfunc(x):
+            return x.isdigit()
+
+        cfunc = njit(pyfunc)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L664-L674    # noqa: E501
+        cpython = ['\u2460', '\xbc', '\u0660', '\U00010401', '\U00010427',
+                   '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F',
+                   '\U00011065', '\U0001D7F6', '\U00011066', '\U000104A0',
+                   '\U0001F107']
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L742-L749    # noqa: E501
+        cpython_extras = ['\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF',
+                          'a\uD800b\uDFFF', 'a\uDFFFb\uD800',
+                          'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a']
+
+        msg = 'Results of "{}".isdigit() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython + cpython_extras:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_isdecimal(self):
+        def pyfunc(x):
+            return x.isdecimal()
+
+        cfunc = njit(pyfunc)
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L646-L662    # noqa: E501
+        cpython = ['', 'a', '0', '\u2460', '\xbc', '\u0660', '0123456789',
+                   '0123456789a', '\U00010401', '\U00010427', '\U00010429',
+                   '\U0001044E', '\U0001F40D', '\U0001F46F', '\U00011065',
+                   '\U0001F107', '\U0001D7F6', '\U00011066', '\U000104A0']
+        # https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Lib/test/test_unicode.py#L742-L749    # noqa: E501
+        cpython_extras = ['\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF',
+                          'a\uD800b\uDFFF', 'a\uDFFFb\uD800', 'a\uD800b\uDFFFa',
+                          'a\uDFFFb\uD800a']
+
+        msg = 'Results of "{}".isdecimal() must be equal'
+        for s in UNICODE_EXAMPLES + [''] + cpython + cpython_extras:
+            self.assertEqual(pyfunc(s), cfunc(s), msg=msg.format(s))
+
+    def test_replace(self):
+        pyfunc = replace_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            ('abc', '', 'A'),
+            ('', '⚡', 'A'),
+            ('abcabc', '⚡', 'A'),
+            ('🐍⚡', '⚡', 'A'),
+            ('🐍⚡🐍', '⚡', 'A'),
+            ('abababa', 'a', 'A'),
+            ('abababa', 'b', 'A'),
+            ('abababa', 'c', 'A'),
+            ('abababa', 'ab', 'A'),
+            ('abababa', 'aba', 'A'),
+        ]
+
+        for test_str, old_str, new_str in CASES:
+            self.assertEqual(pyfunc(test_str, old_str, new_str),
+                             cfunc(test_str, old_str, new_str),
+                             "'%s'.replace('%s', '%s')?" %
+                             (test_str, old_str, new_str))
+
+    def test_replace_with_count(self):
+        pyfunc = replace_with_count_usecase
+        cfunc = njit(pyfunc)
+
+        CASES = [
+            ('abc', '', 'A'),
+            ('', '⚡', 'A'),
+            ('abcabc', '⚡', 'A'),
+            ('🐍⚡', '⚡', 'A'),
+            ('🐍⚡🐍', '⚡', 'A'),
+            ('abababa', 'a', 'A'),
+            ('abababa', 'b', 'A'),
+            ('abababa', 'c', 'A'),
+            ('abababa', 'ab', 'A'),
+            ('abababa', 'aba', 'A'),
+        ]
+
+        count_test = [-1, 1, 0, 5]
+
+        for test_str, old_str, new_str in CASES:
+            for count in count_test:
+                self.assertEqual(pyfunc(test_str, old_str, new_str, count),
+                                 cfunc(test_str, old_str, new_str, count),
+                                 "'%s'.replace('%s', '%s', '%s')?" %
+                                 (test_str, old_str, new_str, count))
+
+    def test_replace_unsupported(self):
+        def pyfunc(s, x, y, count):
+            return s.replace(x, y, count)
+
+        cfunc = njit(pyfunc)
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc('ababababab', 'ba', 'qqq', 3.5)
+        msg = 'Unsupported parameters. The parameters must be Integer.'
+        self.assertIn(msg, str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc('ababababab', 0, 'qqq', 3)
+        msg = 'The object must be a UnicodeType.'
+        self.assertIn(msg, str(raises.exception))
+
+        with self.assertRaises(TypingError) as raises:
+            cfunc('ababababab', 'ba', 0, 3)
+        msg = 'The object must be a UnicodeType.'
+        self.assertIn(msg, str(raises.exception))
+
+
+class TestUnicodeInTuple(BaseTest):
+
+    def test_const_unicode_in_tuple(self):
+        # Issue 3673
+        @njit
+        def f():
+            return ('aa',) < ('bb',)
+
+        self.assertEqual(f.py_func(), f())
+
+        @njit
+        def f():
+            return ('cc',) < ('bb',)
+
+        self.assertEqual(f.py_func(), f())
+
+    def test_const_unicode_in_hetero_tuple(self):
+        @njit
+        def f():
+            return ('aa', 1) < ('bb', 1)
+
+        self.assertEqual(f.py_func(), f())
+
+        @njit
+        def f():
+            return ('aa', 1) < ('aa', 2)
+
+        self.assertEqual(f.py_func(), f())
+
+    def test_ascii_flag_unbox(self):
+        @njit
+        def f(s):
+            return s._is_ascii
+
+        for s in UNICODE_EXAMPLES:
+            self.assertEqual(f(s), isascii(s))
+
+    def test_ascii_flag_join(self):
+        @njit
+        def f():
+            s1 = 'abc'
+            s2 = '123'
+            s3 = '🐍⚡'
+            s4 = '大处着眼，小处着手。'
+            return (",".join([s1, s2])._is_ascii,
+                    "🐍⚡".join([s1, s2])._is_ascii,
+                    ",".join([s1, s3])._is_ascii,
+                    ",".join([s3, s4])._is_ascii)
+
+        self.assertEqual(f(), (1, 0, 0, 0))
+
+    def test_ascii_flag_getitem(self):
+        @njit
+        def f():
+            s1 = 'abc123'
+            s2 = '🐍⚡🐍⚡🐍⚡'
+            return (s1[0]._is_ascii, s1[2:]._is_ascii, s2[0]._is_ascii,
+                    s2[2:]._is_ascii)
+
+        self.assertEqual(f(), (1, 1, 0, 0))
+
+    def test_ascii_flag_add_mul(self):
+        @njit
+        def f():
+            s1 = 'abc'
+            s2 = '123'
+            s3 = '🐍⚡'
+            s4 = '大处着眼，小处着手。'
+            return ((s1 + s2)._is_ascii,
+                    (s1 + s3)._is_ascii,
+                    (s3 + s4)._is_ascii,
+                    (s1 * 2)._is_ascii,
+                    (s3 * 2)._is_ascii)
+
+        self.assertEqual(f(), (1, 0, 0, 1, 0))
+
+
+class TestUnicodeIteration(BaseTest):
+
+    def test_unicode_iter(self):
+        pyfunc = iter_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            self.assertPreciseEqual(pyfunc(a), cfunc(a))
+
+    def test_unicode_literal_iter(self):
+        pyfunc = literal_iter_usecase
+        cfunc = njit(pyfunc)
+        self.assertPreciseEqual(pyfunc(), cfunc())
+
+    def test_unicode_enumerate_iter(self):
+        pyfunc = enumerated_iter_usecase
+        cfunc = njit(pyfunc)
+        for a in UNICODE_EXAMPLES:
+            self.assertPreciseEqual(pyfunc(a), cfunc(a))
+
+    def test_unicode_stopiteration_iter(self):
+        self.disable_leak_check()
+        pyfunc = iter_stopiteration_usecase
+        cfunc = njit(pyfunc)
+        for f in (pyfunc, cfunc):
+            for a in UNICODE_EXAMPLES:
+                with self.assertRaises(StopIteration):
+                    f(a)
+
+    def test_unicode_literal_stopiteration_iter(self):
+        pyfunc = literal_iter_stopiteration_usecase
+        cfunc = njit(pyfunc)
+        for f in (pyfunc, cfunc):
+            with self.assertRaises(StopIteration):
+                f()
+
+
+class TestUnicodeAuxillary(BaseTest):
+
+    def test_ord(self):
+        pyfunc = ord_usecase
+        cfunc = njit(pyfunc)
+        for ex in UNICODE_EXAMPLES:
+            for a in ex:
+                self.assertPreciseEqual(pyfunc(a), cfunc(a))
+
+    def test_ord_invalid(self):
+        self.disable_leak_check()
+
+        pyfunc = ord_usecase
+        cfunc = njit(pyfunc)
+
+        # wrong number of chars
+        for func in (pyfunc, cfunc):
+            for ch in ('', 'abc'):
+                with self.assertRaises(TypeError) as raises:
+                    func(ch)
+                self.assertIn('ord() expected a character',
+                              str(raises.exception))
+
+        # wrong type
+        with self.assertRaises(TypingError) as raises:
+            cfunc(1.23)
+        self.assertIn(_header_lead, str(raises.exception))
+
+    def test_chr(self):
+        pyfunc = chr_usecase
+        cfunc = njit(pyfunc)
+        for ex in UNICODE_EXAMPLES:
+            for x in ex:
+                a = ord(x)
+                self.assertPreciseEqual(pyfunc(a), cfunc(a))
+        # test upper/lower bounds
+        for a in (0x0, _MAX_UNICODE):
+            self.assertPreciseEqual(pyfunc(a), cfunc(a))
+
+    def test_chr_invalid(self):
+        pyfunc = chr_usecase
+        cfunc = njit(pyfunc)
+
+        # value negative/>_MAX_UNICODE
+        for func in (pyfunc, cfunc):
+            for v in (-2, _MAX_UNICODE + 1):
+                with self.assertRaises(ValueError) as raises:
+                    func(v)
+                self.assertIn("chr() arg not in range", str(raises.exception))
+
+        # wrong type
+        with self.assertRaises(TypingError) as raises:
+            cfunc('abc')
+        self.assertIn(_header_lead, str(raises.exception))
+
+    def test_unicode_type_mro(self):
+        # see issue #5635
+        def bar(x):
+            return True
+
+        @overload(bar)
+        def ol_bar(x):
+            ok = False
+            if isinstance(x, types.UnicodeType):
+                if isinstance(x, types.Hashable):
+                    ok = True
+            return lambda x: ok
+
+        @njit
+        def foo(strinst):
+            return bar(strinst)
+
+        inst = "abc"
+        self.assertEqual(foo.py_func(inst), foo(inst))
+        self.assertIn(types.Hashable, types.unicode_type.__class__.__mro__)
+
+    def test_f_strings(self):
+        """test f-string support, which requires bytecode handling
+        """
+        # requires formatting (FORMAT_VALUE) and concatenation (BUILD_STRINGS)
+        def impl1(a):
+            return f"AA_{a+3}_B"
+
+        # does not require concatenation
+        def impl2(a):
+            return f"{a+2}"
+
+        # no expression
+        def impl3(a):
+            return f"ABC_{a}"
+
+        # format spec not allowed
+        def impl4(a):
+            return f"ABC_{a:0}"
+
+        # corner case: empty string
+        def impl5():
+            return f""  # noqa: F541
+
+        self.assertEqual(impl1(3), njit(impl1)(3))
+        self.assertEqual(impl2(2), njit(impl2)(2))
+        # string input
+        self.assertEqual(impl3("DE"), njit(impl3)("DE"))
+        # check error when input type doesn't have str() implementation
+        with self.assertRaises(TypingError) as raises:
+            njit(impl3)(["A", "B"])
+        msg = "No implementation of function Function(<class 'str'>)"
+        self.assertIn(msg, str(raises.exception))
+        # check error when format spec provided
+        with self.assertRaises(UnsupportedError) as raises:
+            njit(impl4)(["A", "B"])
+        msg = "format spec in f-strings not supported yet"
+        self.assertIn(msg, str(raises.exception))
+        self.assertEqual(impl5(), njit(impl5)())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode_array.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode_array.py
new file mode 100644
index 000000000..5e11b711d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode_array.py
@@ -0,0 +1,892 @@
+import numpy as np
+
+import unittest
+from numba import jit, from_dtype
+from numba.core import types, utils
+from numba.typed import Dict
+from numba.tests.support import (TestCase, skip_ppc64le_issue4563)
+
+require_py37 = unittest.skipIf(utils.PYVERSION < (3, 7), "requires Python 3.7+")
+
+
+def getitem(x, i):
+    return x[i]
+
+
+def getitem2(x, i, j):
+    return x[i][j]
+
+
+def setitem(x, i, v):
+    x[i] = v
+    return x
+
+
+def setitem2(x, i, y, j):
+    x[i] = y[j]
+    return x
+
+
+def setitem_literal(x, i):
+    x[i] = '123'
+    return x
+
+
+def getitem_key(x, y, j):
+    x[y[j]] = 123
+
+
+def return_len(x, i):
+    return len(x[i])
+
+
+def return_bool(x, i):
+    return bool(x[i])
+
+
+def equal_getitem(x, i, j):
+    return x[i] == x[j]
+
+
+def notequal_getitem(x, i, j):
+    return x[i] != x[j]
+
+
+def lessthan_getitem(x, i, j):
+    return x[i] < x[j]
+
+
+def greaterthan_getitem(x, i, j):
+    return x[i] > x[j]
+
+
+def lessequal_getitem(x, i, j):
+    return x[i] <= x[j]
+
+
+def greaterequal_getitem(x, i, j):
+    return x[i] >= x[j]
+
+
+def contains_getitem2(x, i, y, j):
+    return x[i] in y[j]
+
+
+def equal_getitem_value(x, i, v):
+    r1 = x[i] == v
+    r2 = v == x[i]
+    if r1 == r2:
+        return r1
+    raise ValueError('x[i] == v and v == x[i] are unequal')
+
+
+def notequal_getitem_value(x, i, v):
+    r1 = x[i] != v
+    r2 = v != x[i]
+    if r1 == r2:
+        return r1
+    raise ValueError('x[i] != v and v != x[i] are unequal')
+
+
+def return_isascii(x, i):
+    return x[i].isascii()
+
+
+def return_isupper(x, i):
+    return x[i].isupper()
+
+
+def return_upper(x, i):
+    return x[i].upper()
+
+
+def return_str(x, i):
+    return str(x[i])
+
+
+def return_bytes(x, i):
+    return bytes(x[i])
+
+
+def return_hash(x, i):
+    return hash(x[i])
+
+
+def return_find(x, i, y, j):
+    return x[i].find(y[j])
+
+
+def return_rfind(x, i, y, j):
+    return x[i].rfind(y[j])
+
+
+def return_startswith(x, i, y, j):
+    return x[i].startswith(y[j])
+
+
+def return_endswith(x, i, y, j):
+    return x[i].endswith(y[j])
+
+
+def return_split1(x, i):
+    return x[i].split()
+
+
+def return_split2(x, i, y, j):
+    return x[i].split(y[j])
+
+
+def return_split3(x, i, y, j, maxsplit):
+    return x[i].split(sep=y[j], maxsplit=maxsplit)
+
+
+# NOT IMPLEMENTED: tests for rsplit
+
+
+def return_center1(x, i, w):
+    return x[i].center(w)
+
+
+def return_center2(x, i, w, y, j):
+    return x[i].center(w, y[j])
+
+
+def return_ljust1(x, i, w):
+    return x[i].ljust(w)
+
+
+def return_ljust2(x, i, w, y, j):
+    return x[i].ljust(w, y[j])
+
+
+def return_rjust1(x, i, w):
+    return x[i].rjust(w)
+
+
+def return_rjust2(x, i, w, y, j):
+    return x[i].rjust(w, y[j])
+
+
+def return_join(x, i, y, j, z, k):
+    return x[i].join([y[j], z[k]])
+
+
+def return_zfill(x, i, w):
+    return x[i].zfill(w)
+
+
+def return_lstrip1(x, i):
+    return x[i].lstrip()
+
+
+def return_lstrip2(x, i, y, j):
+    return x[i].lstrip(y[j])
+
+
+def return_rstrip1(x, i):
+    return x[i].rstrip()
+
+
+def return_rstrip2(x, i, y, j):
+    return x[i].rstrip(y[j])
+
+
+def return_strip1(x, i):
+    return x[i].strip()
+
+
+def return_strip2(x, i, y, j):
+    return x[i].strip(y[j])
+
+
+def return_add(x, i, y, j):
+    return x[i] + y[j]
+
+
+def return_iadd(x, i, y, j):
+    x[i] += y[j]
+    return x[i]
+
+
+def return_mul(x, i, y, j):
+    return x[i] * y[j]  # one of the operants must be integer
+
+
+def return_not(x, i):
+    return not x[i]
+
+
+def join_string_array(str_arr):
+    return ",".join(str_arr)
+
+
+@skip_ppc64le_issue4563
+class TestUnicodeArray(TestCase):
+
+    def _test(self, pyfunc, cfunc, *args, **kwargs):
+        expected = pyfunc(*args, **kwargs)
+        self.assertPreciseEqual(cfunc(*args, **kwargs), expected)
+
+    def test_getitem2(self):
+        cgetitem2 = jit(nopython=True)(getitem2)
+
+        arr = np.array(b'12')
+        self.assertPreciseEqual(cgetitem2(arr, (), 0), getitem2(arr, (), 0))
+        with self.assertRaisesRegex(IndexError, 'index out of range'):
+            cgetitem2(arr, (), 2)
+
+        arr = np.array('12')
+        self.assertPreciseEqual(cgetitem2(arr, (), 0), getitem2(arr, (), 0))
+        with self.assertRaisesRegex(IndexError, 'index out of range'):
+            cgetitem2(arr, (), 2)
+
+        arr = np.array([b'12', b'3'])
+        self.assertPreciseEqual(cgetitem2(arr, 0, 0), getitem2(arr, 0, 0))
+        self.assertPreciseEqual(cgetitem2(arr, 0, 1), getitem2(arr, 0, 1))
+        self.assertPreciseEqual(cgetitem2(arr, 1, 0), getitem2(arr, 1, 0))
+        with self.assertRaisesRegex(IndexError, 'index out of range'):
+            cgetitem2(arr, 1, 1)
+
+        arr = np.array(['12', '3'])
+        self.assertPreciseEqual(cgetitem2(arr, 0, 0), getitem2(arr, 0, 0))
+        self.assertPreciseEqual(cgetitem2(arr, 0, 1), getitem2(arr, 0, 1))
+        self.assertPreciseEqual(cgetitem2(arr, 1, 0), getitem2(arr, 1, 0))
+        with self.assertRaisesRegex(IndexError, 'index out of range'):
+            cgetitem2(arr, 1, 1)
+
+    def test_getitem(self):
+        pyfunc = getitem
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, b'12', 1)
+        self._test(pyfunc, cfunc, np.array(b'12'), ())
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 0)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 1)
+
+        self._test(pyfunc, cfunc, '12', 1)
+        self._test(pyfunc, cfunc, np.array('12'), ())
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 0)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 1)
+
+    def test_getitem_key(self):
+        pyfunc = getitem_key
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for x, i in [
+                (np.array('123'), ()),
+                (np.array(['123']), 0),
+                (np.array(b'123'), ()),
+                (np.array([b'123']), 0)
+        ]:
+            d1 = {}
+            d2 = Dict.empty(from_dtype(x.dtype), types.int64)
+            pyfunc(d1, x, i)
+            cfunc(d2, x, i)
+            self.assertEqual(d1, d2)
+            # check for charseq to str conversion:
+            str(d2)
+
+    def test_setitem(self):
+        pyfunc = setitem
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.array(12)
+        self._test(pyfunc, cfunc, x, (), 34)
+
+        x1 = np.array(b'123')
+        x2 = np.array(b'123')
+        y1 = pyfunc(x1, (), b'34')
+        y2 = cfunc(x2, (), b'34')
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array(['123'])
+        x2 = np.array(['123'])
+        y1 = pyfunc(x1, 0, '34')
+        y2 = cfunc(x2, 0, '34')
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+    def test_setitem2(self):
+        pyfunc = setitem2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x1 = np.array(['123', 'ABC'])
+        x2 = np.array(['123', 'ABC'])
+        y1 = pyfunc(x1, 0, x1, 1)
+        y2 = cfunc(x2, 0, x2, 1)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array([b'123', b'ABC'])
+        x2 = np.array([b'123', b'ABC'])
+        y1 = pyfunc(x1, 0, x1, 1)
+        y2 = cfunc(x2, 0, x2, 1)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = np.array('ABC')
+        z2 = np.array('ABC')
+        y1 = pyfunc(x1, (), z1, ())
+        y2 = cfunc(x2, (), z2, ())
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array(123)
+        x2 = np.array(123)
+        z1 = 456,
+        z2 = 456,
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # bytes
+        x1 = np.array(b'123')
+        x2 = np.array(b'123')
+        z1 = b'ABC',
+        z2 = b'ABC',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-8
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'ABC',
+        z2 = 'ABC',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-16
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'AB\u01e9',
+        z2 = 'AB\u01e9',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-32
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'AB\U00108a0e',
+        z2 = 'AB\U00108a0e',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-8, assign longer value (truncates as in numpy)
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'ABCD',
+        z2 = 'ABCD',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # UTF-8, assign shorter value
+        x1 = np.array('123')
+        x2 = np.array('123')
+        z1 = 'AB',
+        z2 = 'AB',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # bytes, assign longer value (truncates as in numpy)
+        x1 = np.array(b'123')
+        x2 = np.array(b'123')
+        z1 = b'ABCD',
+        z2 = b'ABCD',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        # bytes, assign shorter value
+        x1 = np.array(b'123')
+        x2 = np.array(b'123')
+        z1 = b'AB',
+        z2 = b'AB',
+        y1 = pyfunc(x1, (), z1, 0)
+        y2 = cfunc(x2, (), z2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+    def test_setitem_literal(self):
+        pyfunc = setitem_literal
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x1 = np.array('ABC')
+        x2 = np.array('ABC')
+        y1 = pyfunc(x1, ())
+        y2 = cfunc(x2, ())
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array(['ABC', '5678'])
+        x2 = np.array(['ABC', '5678'])
+        y1 = pyfunc(x1, 0)
+        y2 = cfunc(x2, 0)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+        x1 = np.array(['ABC', '5678'])
+        x2 = np.array(['ABC', '5678'])
+        y1 = pyfunc(x1, 1)
+        y2 = cfunc(x2, 1)
+        self.assertPreciseEqual(x1, x2)
+        self.assertPreciseEqual(y1, y2)
+
+    def test_return_len(self):
+        pyfunc = return_len
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array(''), ())
+        self._test(pyfunc, cfunc, np.array(b''), ())
+        self._test(pyfunc, cfunc, np.array(b'12'), ())
+        self._test(pyfunc, cfunc, np.array('12'), ())
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 0)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 0)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 1)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 1)
+
+    def test_return_bool(self):
+        pyfunc = return_bool
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array(''), ())
+        self._test(pyfunc, cfunc, np.array(b''), ())
+        self._test(pyfunc, cfunc, np.array(b'12'), ())
+        self._test(pyfunc, cfunc, np.array('12'), ())
+        self._test(pyfunc, cfunc, np.array([b'12', b'']), 0)
+        self._test(pyfunc, cfunc, np.array(['12', '']), 0)
+        self._test(pyfunc, cfunc, np.array([b'12', b'']), 1)
+        self._test(pyfunc, cfunc, np.array(['12', '']), 1)
+
+    def _test_op_getitem(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array([1, 2]), 0, 1)
+        self._test(pyfunc, cfunc, '12', 0, 1)
+        self._test(pyfunc, cfunc, b'12', 0, 1)
+        self._test(pyfunc, cfunc, np.array(b'12'), (), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ())
+
+        self._test(pyfunc, cfunc, np.array([b'1', b'2']), 0, 0)
+        self._test(pyfunc, cfunc, np.array([b'1', b'2']), 0, 1)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 0, 0)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 1, 1)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 0, 1)
+        self._test(pyfunc, cfunc, np.array([b'12', b'3']), 1, 0)
+
+        self._test(pyfunc, cfunc, np.array(['1', '2']), 0, 0)
+        self._test(pyfunc, cfunc, np.array(['1', '2']), 0, 1)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 0, 0)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 1, 1)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 0, 1)
+        self._test(pyfunc, cfunc, np.array(['12', '3']), 1, 0)
+
+    def test_equal_getitem(self):
+        self._test_op_getitem(equal_getitem)
+
+    def test_notequal_getitem(self):
+        self._test_op_getitem(notequal_getitem)
+
+    def test_lessthan_getitem(self):
+        self._test_op_getitem(lessthan_getitem)
+
+    def test_greaterthan_getitem(self):
+        self._test_op_getitem(greaterthan_getitem)
+
+    def test_lessequal_getitem(self):
+        self._test_op_getitem(lessequal_getitem)
+
+    def test_greaterequal_getitem(self):
+        self._test_op_getitem(greaterequal_getitem)
+
+    def _test_op_getitem_value(self, pyfunc):
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array([1, 2]), 0, 1)
+        self._test(pyfunc, cfunc, '12', 0, '1')
+        self._test(pyfunc, cfunc, '12', 1, '3')
+        self._test(pyfunc, cfunc, np.array('1234'), (), '1234')
+        self._test(pyfunc, cfunc, np.array(['1234']), 0, '1234')
+        self._test(pyfunc, cfunc, np.array(['1234']), 0, 'abc')
+        # fails: No conversion from array(bool, 1d, C) to bool
+        #self._test(pyfunc, cfunc, b'12', 0, b'1')
+        self._test(pyfunc, cfunc, np.array(b'12'), (), b'12')
+        self._test(pyfunc, cfunc, np.array([b'12']), 0, b'12')
+        self._test(pyfunc, cfunc, np.array([b'12']), 0, b'a')
+
+    def test_equal_getitem_value(self):
+        self._test_op_getitem_value(equal_getitem_value)
+
+    def test_notequal_getitem_value(self):
+        self._test_op_getitem_value(notequal_getitem_value)
+
+    def test_contains_getitem2(self):
+        pyfunc = contains_getitem2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        x = np.array('123')
+        y = np.array('12345')
+        self._test(pyfunc, cfunc, x, (), y, ())
+        self._test(pyfunc, cfunc, y, (), x, ())
+
+        x = np.array(b'123')
+        y = np.array(b'12345')
+        self._test(pyfunc, cfunc, x, (), y, ())
+        self._test(pyfunc, cfunc, y, (), x, ())
+
+        x = ('123',)
+        y = np.array('12345')
+        self._test(pyfunc, cfunc, x, 0, y, ())
+        self._test(pyfunc, cfunc, y, (), x, 0)
+
+        x = (b'123',)
+        y = np.array(b'12345')
+        self._test(pyfunc, cfunc, x, 0, y, ())
+        self._test(pyfunc, cfunc, y, (), x, 0)
+
+    @require_py37
+    def test_return_isascii(self):
+        pyfunc = return_isascii
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('1234'), ())
+        self._test(pyfunc, cfunc, np.array(['1234']), 0)
+        self._test(pyfunc, cfunc, np.array('1234\u00e9'), ())
+        self._test(pyfunc, cfunc, np.array(['1234\u00e9']), 0)
+
+    def test_return_isupper(self):
+        pyfunc = return_isupper
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('abc'), ())
+        self._test(pyfunc, cfunc, np.array(['abc']), 0)
+
+        self._test(pyfunc, cfunc, np.array(b'abc'), ())
+        self._test(pyfunc, cfunc, np.array([b'abc']), 0)
+
+    def test_return_str(self):
+        pyfunc = return_str
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('1234'), ())
+        self._test(pyfunc, cfunc, np.array(['1234']), 0)
+
+    def test_return_bytes(self):
+        pyfunc = return_bytes
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array(b'1234'), ())
+        self._test(pyfunc, cfunc, np.array([b'1234']), 0)
+
+    def test_return_upper(self):
+        pyfunc = return_upper
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('abc'), ())
+        self._test(pyfunc, cfunc, np.array(['abc']), 0)
+
+        self._test(pyfunc, cfunc, np.array(b'abc'), ())
+        self._test(pyfunc, cfunc, np.array([b'abc']), 0)
+
+    def test_hash(self):
+        pyfunc = return_hash
+        cfunc = jit(nopython=True)(pyfunc)
+
+        hash1 = pyfunc(np.array('123'), ())
+        hash2 = hash('123')
+        hash3 = hash(np.array('123')[()])
+        self.assertTrue(hash1 == hash2 == hash3)
+
+        self._test(pyfunc, cfunc, np.array('1234'), ())
+        self._test(pyfunc, cfunc, np.array(['1234']), 0)
+
+        self._test(pyfunc, cfunc, np.array('1234\u00e9'), ())
+        self._test(pyfunc, cfunc, np.array(['1234u00e9']), 0)
+
+        self._test(pyfunc, cfunc, np.array('1234\U00108a0e'), ())
+        self._test(pyfunc, cfunc, np.array(['1234\U00108a0e']), 0)
+
+        self._test(pyfunc, cfunc, np.array(b'1234'), ())
+        self._test(pyfunc, cfunc, np.array([b'1234']), 0)
+
+    def test_return_find(self):
+        pyfunc = return_find
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('1234'), (), np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ('23',), 0)
+        self._test(pyfunc, cfunc, ('1234',), 0, np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), np.array(b'23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), (b'23',), 0)
+        self._test(pyfunc, cfunc, (b'1234',), 0, np.array(b'23'), ())
+
+    def test_return_rfind(self):
+        pyfunc = return_rfind
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array('1234'), (), np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ('23',), 0)
+        self._test(pyfunc, cfunc, ('1234',), 0, np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), np.array(b'23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), (b'23',), 0)
+        self._test(pyfunc, cfunc, (b'1234',), 0, np.array(b'23'), ())
+
+    def test_return_startswith(self):
+        pyfunc = return_startswith
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1234'), (), np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ('23',), 0)
+        self._test(pyfunc, cfunc, ('1234',), 0, np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), np.array(b'23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), (b'23',), 0)
+        self._test(pyfunc, cfunc, (b'1234',), 0, np.array(b'23'), ())
+
+    def test_return_endswith(self):
+        pyfunc = return_endswith
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1234'), (), np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array('1234'), (), ('23',), 0)
+        self._test(pyfunc, cfunc, ('1234',), 0, np.array('23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), np.array(b'23'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), (), (b'23',), 0)
+        self._test(pyfunc, cfunc, (b'1234',), 0, np.array(b'23'), ())
+
+    def test_return_split1(self):
+        pyfunc = return_split1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('12 34'), ())
+        self._test(pyfunc, cfunc, np.array(b'1234'), ())
+
+    def test_return_split2(self):
+        pyfunc = return_split2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('12 34'), (), np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array('12 34'), (), (' ',), 0)
+        self._test(pyfunc, cfunc, ('12 34',), 0, np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array(b'12 34'), (), np.array(b' '), ())
+        self._test(pyfunc, cfunc, np.array(b'12 34'), (), (b' ',), 0)
+        self._test(pyfunc, cfunc, (b'12 34',), 0, np.array(b' '), ())
+
+    def test_return_split3(self):
+        pyfunc = return_split3
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (),
+                   np.array(' '), (), 2)
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), (' ',), 0, 2)
+        self._test(pyfunc, cfunc, ('1 2 3 4',), 0, np.array(' '), (), 2)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (),
+                   np.array(b' '), (), 2)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), (b' ',), 0, 2)
+        self._test(pyfunc, cfunc, (b'1 2 3 4',), 0, np.array(b' '), (), 2)
+
+    def test_return_ljust1(self):
+        pyfunc = return_ljust1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40)
+
+    def test_return_ljust2(self):
+        pyfunc = return_ljust2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40,
+                   np.array('='), ())
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40, ('=',), 0)
+        self._test(pyfunc, cfunc, ('1 2 3 4',), 0, 40, np.array('='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40,
+                   np.array(b'='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40, (b'=',), 0)
+        self._test(pyfunc, cfunc, (b'1 2 3 4',), 0, 40, np.array(b'='), ())
+
+    def test_return_rjust1(self):
+        pyfunc = return_rjust1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40)
+
+    def test_return_rjust2(self):
+        pyfunc = return_rjust2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40,
+                   np.array('='), ())
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40, ('=',), 0)
+        self._test(pyfunc, cfunc, ('1 2 3 4',), 0, 40, np.array('='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40,
+                   np.array(b'='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40, (b'=',), 0)
+        self._test(pyfunc, cfunc, (b'1 2 3 4',), 0, 40, np.array(b'='), ())
+
+    def test_return_center1(self):
+        pyfunc = return_center1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40)
+
+    def test_return_center2(self):
+        pyfunc = return_center2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40,
+                   np.array('='), ())
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40, ('=',), 0)
+        self._test(pyfunc, cfunc, ('1 2 3 4',), 0, 40, np.array('='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40,
+                   np.array(b'='), ())
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40, (b'=',), 0)
+        self._test(pyfunc, cfunc, (b'1 2 3 4',), 0, 40, np.array(b'='), ())
+
+    def test_return_join(self):
+        pyfunc = return_join
+        cfunc = jit(nopython=True)(pyfunc)
+        self._test(pyfunc, cfunc, np.array(','), (), np.array('abc'), (),
+                   np.array('123'), ())
+        self._test(pyfunc, cfunc, np.array(','), (), np.array('abc'), (),
+                   ('123',), 0)
+        self._test(pyfunc, cfunc, (',',), 0, np.array('abc'), (),
+                   np.array('123'), ())
+        self._test(pyfunc, cfunc, (',',), 0, np.array('abc'), (),
+                   ('123',), 0)
+        self._test(pyfunc, cfunc, np.array(b','), (), np.array(b'abc'), (),
+                   np.array(b'123'), ())
+        self._test(pyfunc, cfunc, np.array(b','), (), np.array(b'abc'), (),
+                   (b'123',), 0)
+        self._test(pyfunc, cfunc, (b',',), 0, np.array(b'abc'), (),
+                   np.array(b'123'), ())
+        self._test(pyfunc, cfunc, (b',',), 0, np.array(b'abc'), (),
+                   (b'123',), 0)
+
+    def test_return_zfill(self):
+        pyfunc = return_zfill
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('1 2 3 4'), (), 40)
+        self._test(pyfunc, cfunc, np.array(b'1 2 3 4'), (), 40)
+
+    def test_return_lstrip1(self):
+        pyfunc = return_lstrip1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), ())
+
+    def test_return_lstrip2(self):
+        pyfunc = return_lstrip2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), (), np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array('  123  '), (), (' ',), 0)
+        self._test(pyfunc, cfunc, ('  123  ',), 0, np.array(' '), ())
+
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), np.array(b' '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), (b' ',), 0)
+        self._test(pyfunc, cfunc, (b'  123  ',), 0, np.array(b' '), ())
+
+    def test_return_rstrip1(self):
+        pyfunc = return_rstrip1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), ())
+
+    def test_return_rstrip2(self):
+        pyfunc = return_rstrip2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), (), np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array('  123  '), (), (' ',), 0)
+        self._test(pyfunc, cfunc, ('  123  ',), 0, np.array(' '), ())
+
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), np.array(b' '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), (b' ',), 0)
+        self._test(pyfunc, cfunc, (b'  123  ',), 0, np.array(b' '), ())
+
+    def test_return_strip1(self):
+        pyfunc = return_strip1
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), ())
+
+    def test_return_strip2(self):
+        pyfunc = return_strip2
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('  123  '), (), np.array(' '), ())
+        self._test(pyfunc, cfunc, np.array('  123  '), (), (' ',), 0)
+        self._test(pyfunc, cfunc, ('  123  ',), 0, np.array(' '), ())
+
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), np.array(b' '), ())
+        self._test(pyfunc, cfunc, np.array(b'  123  '), (), (b' ',), 0)
+        self._test(pyfunc, cfunc, (b'  123  ',), 0, np.array(b' '), ())
+
+    def test_return_add(self):
+        pyfunc = return_add
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('ab'), (), np.array('cd'), ())
+        self._test(pyfunc, cfunc, np.array('ab'), (), ('cd',), 0)
+        self._test(pyfunc, cfunc, ('ab',), 0, np.array('cd'), ())
+
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), np.array(b'cd'), ())
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), (b'cd',), 0)
+        self._test(pyfunc, cfunc, (b'ab',), 0, np.array(b'cd'), ())
+
+    def test_return_iadd(self):
+        pyfunc = return_iadd
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('ab'), (), np.array('cd'), ())
+        self._test(pyfunc, cfunc, np.array('ab'), (), ('cd',), 0)
+        expected = pyfunc(['ab'], 0, np.array('cd'), ())
+        result = pyfunc(['ab'], 0, np.array('cd'), ())
+        self.assertPreciseEqual(result, expected)
+
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), np.array(b'cd'), ())
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), (b'cd',), 0)
+        expected = pyfunc([b'ab'], 0, np.array(b'cd'), ())
+        result = pyfunc([b'ab'], 0, np.array(b'cd'), ())
+        self.assertPreciseEqual(result, expected)
+
+    def test_return_mul(self):
+        pyfunc = return_mul
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('ab'), (), (5,), 0)
+        self._test(pyfunc, cfunc, (5,), 0, np.array('ab'), ())
+        self._test(pyfunc, cfunc, np.array(b'ab'), (), (5,), 0)
+        self._test(pyfunc, cfunc, (5,), 0, np.array(b'ab'), ())
+
+    def test_return_not(self):
+        pyfunc = return_not
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array('ab'), ())
+        self._test(pyfunc, cfunc, np.array(b'ab'), ())
+        self._test(pyfunc, cfunc, (b'ab',), 0)
+
+        self._test(pyfunc, cfunc, np.array(''), ())
+        self._test(pyfunc, cfunc, np.array(b''), ())
+        self._test(pyfunc, cfunc, (b'',), 0)
+
+    def test_join(self):
+        pyfunc = join_string_array
+        cfunc = jit(nopython=True)(pyfunc)
+
+        self._test(pyfunc, cfunc, np.array(["hi", "there"]))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode_names.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode_names.py
new file mode 100644
index 000000000..0ed8fb756
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unicode_names.py
@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+
+
+from numba import njit, cfunc
+from numba.tests.support import TestCase, unittest
+from numba.core import cgutils
+
+unicode_name1 = u"""
+def unicode_name1(ಠ_ರೃ, ಠਊಠ):
+    return (ಠ_ರೃ) + (ಠਊಠ)
+"""
+
+unicode_name2 = u"""
+def Ծ_Ծ(ಠ_ರೃ, ಠਊಠ):
+    return (ಠ_ರೃ) + (ಠਊಠ)
+"""
+
+
+class TestUnicodeNames(TestCase):
+    def make_testcase(self, src, fname):
+        glb = {}
+        exec(src, glb)
+        fn = glb[fname]
+        return fn
+
+    def test_unicode_name1(self):
+        fn = self.make_testcase(unicode_name1, 'unicode_name1')
+        cfn = njit(fn)
+        self.assertEqual(cfn(1, 2), 3)
+
+    def test_unicode_name2(self):
+        fn = self.make_testcase(unicode_name2, 'Ծ_Ծ')
+        cfn = njit(fn)
+        self.assertEqual(cfn(1, 2), 3)
+
+    def test_cfunc(self):
+        fn = self.make_testcase(unicode_name2, 'Ծ_Ծ')
+        cfn = cfunc("int32(int32, int32)")(fn)
+        self.assertEqual(cfn.ctypes(1, 2), 3)
+
+
+class TestUnicodeUtils(TestCase):
+    def test_normalize_ir_text(self):
+        # non-unicode input
+        out = cgutils.normalize_ir_text('abc')
+        # str returned
+        self.assertIsInstance(out, str)
+        # try encoding to latin
+        out.encode('latin1')
+
+    def test_normalize_ir_text_unicode(self):
+        # unicode input
+        out = cgutils.normalize_ir_text(unicode_name2)
+        # str returned
+        self.assertIsInstance(out, str)
+        # try encoding to latin
+        out.encode('latin1')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unpack_sequence.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unpack_sequence.py
new file mode 100644
index 000000000..1f7a433fb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unpack_sequence.py
@@ -0,0 +1,237 @@
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import errors, types
+from numba import typeof
+from numba.tests.support import TestCase, MemoryLeakMixin, tag
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+no_pyobj_flags = Flags()
+no_pyobj_flags.nrt = True
+
+
+def unpack_list(l):
+    a, b, c = l
+    return (a, b, c)
+
+
+def unpack_shape(a):
+    x, y, z = a.shape
+    return x + y + z
+
+
+def unpack_range():
+    a, b, c = range(3)
+    return a + b + c
+
+
+def unpack_range_too_small():
+    a, b, c = range(2)
+    return a + b + c
+
+
+def unpack_range_too_large():
+    a, b, c = range(4)
+    return a + b + c
+
+
+def unpack_tuple():
+    a, b, c = (1, 2, 3)
+    return a + b + c
+
+
+def unpack_tuple_too_small():
+    a, b, c = (1, 2)
+    return a + b + c
+
+
+def unpack_tuple_too_large():
+    a, b, c = (1, 2, 3, 4)
+    return a + b + c
+
+
+def unpack_heterogeneous_tuple_too_small():
+    a, b, c = (1, 2.5j)
+    return a + b + c
+
+
+def unpack_heterogeneous_tuple_too_large():
+    a, b, c = (1, 2.5, 3j, 4)
+    return a + b + c
+
+
+def unpack_heterogeneous_tuple():
+    a, b, c = (1, 2.5, 3j)
+    return a + b + c
+
+
+def unpack_nested_heterogeneous_tuple():
+    a, (b, c) = (1, (2.5, 3j))
+    return a + b + c
+
+
+def unpack_arbitrary(seq):
+    a, b = seq
+    return b, a
+
+
+def unpack_nrt():
+    a = np.zeros(1)
+    b = np.zeros(2)
+    tup = b, a
+    alpha, beta = tup
+    return alpha, beta
+
+
+def chained_unpack_assign1(x, y):
+    # Used to fail in object mode (issue #580)
+    a = (b, c) = (x, y)
+    (d, e) = a
+    return d + e + b + c
+
+
+def conditional_swap(x, y):
+    # Used to produce invalid code (issue #977)
+    if x > 0:
+        x, y = y, x
+    return x, y
+
+
+class TestUnpack(MemoryLeakMixin, TestCase):
+
+    def test_unpack_list(self):
+        pyfunc = unpack_list
+        cr = compile_isolated(pyfunc, (), flags=force_pyobj_flags)
+        cfunc = cr.entry_point
+        l = [1, 2, 3]
+        self.assertEqual(cfunc(l), pyfunc(l))
+
+    def test_unpack_shape(self, flags=force_pyobj_flags):
+        pyfunc = unpack_shape
+        cr = compile_isolated(pyfunc, [types.Array(dtype=types.int32,
+                                                        ndim=3,
+                                                        layout='C')],
+                              flags=flags)
+        cfunc = cr.entry_point
+        a = np.zeros(shape=(1, 2, 3)).astype(np.int32)
+        self.assertPreciseEqual(cfunc(a), pyfunc(a))
+
+    def test_unpack_shape_npm(self):
+        self.test_unpack_shape(flags=no_pyobj_flags)
+
+    def test_unpack_range(self, flags=force_pyobj_flags):
+        self.run_nullary_func(unpack_range, flags)
+
+    def test_unpack_range_npm(self):
+        self.test_unpack_range(flags=no_pyobj_flags)
+
+    def test_unpack_tuple(self, flags=force_pyobj_flags):
+        self.run_nullary_func(unpack_tuple, flags)
+
+    def test_unpack_tuple_npm(self):
+        self.test_unpack_tuple(flags=no_pyobj_flags)
+
+    def test_unpack_heterogeneous_tuple(self, flags=force_pyobj_flags):
+        self.run_nullary_func(unpack_heterogeneous_tuple, flags)
+
+    def test_unpack_heterogeneous_tuple_npm(self):
+        self.test_unpack_heterogeneous_tuple(flags=no_pyobj_flags)
+
+    def test_unpack_nested_heterogeneous_tuple(self, flags=force_pyobj_flags):
+        self.run_nullary_func(unpack_nested_heterogeneous_tuple, flags)
+
+    def test_unpack_nested_heterogeneous_tuple_npm(self):
+        self.test_unpack_nested_heterogeneous_tuple(flags=no_pyobj_flags)
+
+    def test_chained_unpack_assign(self, flags=force_pyobj_flags):
+        pyfunc = chained_unpack_assign1
+        cr = compile_isolated(pyfunc, [types.int32, types.int32],
+                              flags=flags)
+        cfunc = cr.entry_point
+        args = (4, 5)
+        self.assertPreciseEqual(cfunc(*args), pyfunc(*args))
+
+    def test_chained_unpack_assign_npm(self):
+        self.test_chained_unpack_assign(flags=no_pyobj_flags)
+
+    def check_unpack_error(self, pyfunc, flags=force_pyobj_flags, exc=ValueError):
+        with self.assertRaises(exc):
+            cr = compile_isolated(pyfunc, (), flags=flags)
+            cfunc = cr.entry_point
+            cfunc()
+
+    def test_unpack_tuple_too_small(self):
+        self.check_unpack_error(unpack_tuple_too_small)
+        self.check_unpack_error(unpack_heterogeneous_tuple_too_small)
+
+    def test_unpack_tuple_too_small_npm(self):
+        self.check_unpack_error(unpack_tuple_too_small, no_pyobj_flags,
+                                errors.TypingError)
+        self.check_unpack_error(unpack_heterogeneous_tuple_too_small,
+                                no_pyobj_flags, errors.TypingError)
+
+    def test_unpack_tuple_too_large(self):
+        self.check_unpack_error(unpack_tuple_too_large)
+        self.check_unpack_error(unpack_heterogeneous_tuple_too_large)
+
+    def test_unpack_tuple_too_large_npm(self):
+        self.check_unpack_error(unpack_tuple_too_large, no_pyobj_flags,
+                                errors.TypingError)
+        self.check_unpack_error(unpack_heterogeneous_tuple_too_large,
+                                no_pyobj_flags, errors.TypingError)
+
+    def test_unpack_range_too_small(self):
+        self.check_unpack_error(unpack_range_too_small)
+
+    def test_unpack_range_too_small_npm(self):
+        self.check_unpack_error(unpack_range_too_small, no_pyobj_flags)
+
+    def test_unpack_range_too_large(self):
+        self.check_unpack_error(unpack_range_too_large)
+
+    def test_unpack_range_too_large_npm(self):
+        self.check_unpack_error(unpack_range_too_large, no_pyobj_flags)
+
+    def check_conditional_swap(self, flags=force_pyobj_flags):
+        cr = compile_isolated(conditional_swap, (types.int32, types.int32),
+                              flags=flags)
+        cfunc = cr.entry_point
+        self.assertPreciseEqual(cfunc(4, 5), (5, 4))
+        self.assertPreciseEqual(cfunc(0, 5), (0, 5))
+
+    def test_conditional_swap(self):
+        self.check_conditional_swap()
+
+    def test_conditional_swap_npm(self):
+        self.check_conditional_swap(no_pyobj_flags)
+
+    def test_unpack_tuple_of_arrays(self):
+        tup = tuple(np.zeros(i + 1) for i in range(2))
+        tupty = typeof(tup)
+        pyfunc = unpack_arbitrary
+        cr = compile_isolated(pyfunc, (tupty,),
+                              flags=no_pyobj_flags)
+        cfunc = cr.entry_point
+        self.assertPreciseEqual(cfunc(tup), pyfunc(tup))
+
+    def test_unpack_nrt(self):
+        pyfunc = unpack_nrt
+        cr = compile_isolated(pyfunc, (), flags=no_pyobj_flags)
+        cfunc = cr.entry_point
+        self.assertPreciseEqual(cfunc(), pyfunc())
+
+    def test_invalid_unpack(self):
+        pyfunc = unpack_arbitrary
+        with self.assertRaises(errors.TypingError) as raises:
+            compile_isolated(pyfunc, (types.int32,), flags=no_pyobj_flags)
+        self.assertIn("failed to unpack int32", str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unpickle_without_module.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unpickle_without_module.py
new file mode 100644
index 000000000..ddbfbd751
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unpickle_without_module.py
@@ -0,0 +1,49 @@
+import unittest
+import pickle
+import sys
+import tempfile
+from pathlib import Path
+
+
+class TestUnpickleDeletedModule(unittest.TestCase):
+    def test_loading_pickle_with_no_module(self):
+        """Create a module that uses Numba, import a function from it.
+        Then delete the module and pickle the function. The function
+        should load from the pickle without a problem.
+
+        Note - This is a simplified version of how Numba might be used
+        on a distributed system using e.g. dask distributed. With the
+        pickle being sent to the worker but not the original module.
+        """
+
+        # Source code for temporary module we will make
+        source = "\n".join(
+            [
+                "from numba import vectorize",
+                "@vectorize(['float64(float64)'])",
+                "def inc1(x):",
+                "    return x + 1",
+            ]
+        )
+
+        # Create a temporary directory and add it to path.
+        modname = "tmp_module"
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            sys.path.append(tmp_dir)
+
+            # Create tmp_module.py in there with our source code above.
+            filename = Path(f"{tmp_dir}/{modname}.py")
+            f = open(filename, "a")
+            f.write(source)
+            f.close()
+
+            # Import the temporary module before file is deleted
+            from tmp_module import inc1
+
+        # Remove from imported libraries
+        del sys.modules[modname]
+
+        # Pickle function and assert that it loads correctly
+        pkl = pickle.dumps(inc1)
+        f = pickle.loads(pkl)
+        self.assertEqual(f(2), 3)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unsafe_intrinsics.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unsafe_intrinsics.py
new file mode 100644
index 000000000..d53e0a652
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_unsafe_intrinsics.py
@@ -0,0 +1,230 @@
+import random
+import numpy as np
+
+from numba.tests.support import TestCase, captured_stdout
+from numba import njit, literally
+from numba.core import types
+from numba.cpython.unsafe.tuple import tuple_setitem, build_full_slice_tuple
+from numba.np.unsafe.ndarray import to_fixed_tuple, empty_inferred
+from numba.core.unsafe.bytes import memcpy_region
+from numba.core.unsafe.refcount import dump_refcount
+from numba.cpython.unsafe.numbers import trailing_zeros, leading_zeros
+from numba.core.errors import TypingError
+
+
+class TestTupleIntrinsic(TestCase):
+    """Tests for numba.unsafe.tuple
+    """
+    def test_tuple_setitem(self):
+        @njit
+        def foo(tup, idxs, vals):
+            out_tup = tup
+            for i, v in zip(idxs, vals):
+                out_tup = tuple_setitem(out_tup, i, v)
+            return tup, out_tup
+
+        random.seed(123)
+        for _ in range(20):
+            # Random data
+            n = random.randint(1, 10)
+            tup = tuple([random.randint(0, n) for i in range(n)])
+            vals = tuple([random.randint(10, 20) for i in range(n)])
+            idxs = list(range(len(vals)))
+            random.shuffle(idxs)
+            idxs = tuple(idxs)
+            # Expect
+            expect_tup = tuple(tup)
+            expect_out = np.asarray(expect_tup)
+            expect_out[np.asarray(idxs)] = vals
+            # Got
+            got_tup, got_out = foo(tup, idxs, vals)
+            # Check
+            self.assertEqual(got_tup, expect_tup)
+            self.assertEqual(got_out, tuple(expect_out))
+
+    def test_slice_tuple(self):
+        @njit
+        def full_slice_array(a, n):
+            # Since numba slices can't be boxed at the moment
+            return a[build_full_slice_tuple(literally(n))]
+
+        for n in range(1, 3):
+            a = np.random.random(np.arange(n) + 1)
+            for i in range(1, n + 1):
+                np.testing.assert_array_equal(a, full_slice_array(a, i))
+            with self.assertRaises(TypingError):
+                # numpy would throw an IndexError here
+                full_slice_array(a, n + 1)
+
+
+class TestNdarrayIntrinsic(TestCase):
+    """Tests for numba.unsafe.ndarray
+    """
+    def test_to_fixed_tuple(self):
+        const = 3
+
+        @njit
+        def foo(array):
+            a = to_fixed_tuple(array, length=1)
+            b = to_fixed_tuple(array, 2)
+            c = to_fixed_tuple(array, const)
+            d = to_fixed_tuple(array, 0)
+            return a, b, c, d
+
+        np.random.seed(123)
+        for _ in range(10):
+            # Random data
+            arr = np.random.random(3)
+            # Run
+            a, b, c, d = foo(arr)
+            # Check
+            self.assertEqual(a, tuple(arr[:1]))
+            self.assertEqual(b, tuple(arr[:2]))
+            self.assertEqual(c, tuple(arr[:3]))
+            self.assertEqual(d, ())
+
+        # Check error with ndim!=1
+        with self.assertRaises(TypingError) as raises:
+            foo(np.random.random((1, 2)))
+        self.assertIn("Not supported on array.ndim=2",
+                      str(raises.exception))
+
+        # Check error with non-constant length
+        @njit
+        def tuple_with_length(array, length):
+            return to_fixed_tuple(array, length)
+
+        with self.assertRaises(TypingError) as raises:
+            tuple_with_length(np.random.random(3), 1)
+        expectmsg = "*length* argument must be a constant"
+        self.assertIn(expectmsg, str(raises.exception))
+
+    def test_issue_3586_variant1(self):
+        @njit
+        def func():
+            S = empty_inferred((10,))
+            a = 1.1
+            for i in range(len(S)):
+                S[i] = a + 2
+            return S
+
+        got = func()
+        expect = np.asarray([3.1] * 10)
+        np.testing.assert_array_equal(got, expect)
+
+    def test_issue_3586_variant2(self):
+        @njit
+        def func():
+            S = empty_inferred((10,))
+            a = 1.1
+            for i in range(S.size):
+                S[i] = a + 2
+            return S
+
+        got = func()
+        expect = np.asarray([3.1] * 10)
+        np.testing.assert_array_equal(got, expect)
+
+
+class TestBytesIntrinsic(TestCase):
+    """Tests for numba.unsafe.bytes
+    """
+    def test_memcpy_region(self):
+        @njit
+        def foo(dst, dst_index, src, src_index, nbytes):
+            # last arg is assume 1 byte alignment
+            memcpy_region(dst.ctypes.data, dst_index,
+                          src.ctypes.data, src_index, nbytes, 1)
+
+        d = np.zeros(10, dtype=np.int8)
+        s = np.arange(10, dtype=np.int8)
+
+        # copy s[1:6] to d[4:9]
+        foo(d, 4, s, 1, 5)
+
+        expected = [0, 0, 0, 0, 1, 2, 3, 4, 5, 0]
+        np.testing.assert_array_equal(d, expected)
+
+
+class TestRefCount(TestCase):
+    def test_dump_refcount(self):
+        @njit
+        def use_dump_refcount():
+            a = np.ones(10)
+            b = (a, a)
+            dump_refcount(a)
+            dump_refcount(b)
+
+        # Capture output to sys.stdout
+        with captured_stdout() as stream:
+            use_dump_refcount()
+
+        output = stream.getvalue()
+        # Check that it printed
+        pat = "dump refct of {}"
+        aryty = types.float64[::1]
+        tupty = types.Tuple.from_types([aryty] * 2)
+        self.assertIn(pat.format(aryty), output)
+        self.assertIn(pat.format(tupty), output)
+
+
+class TestZeroCounts(TestCase):
+    def test_zero_count(self):
+        lz = njit(lambda x: leading_zeros(x))
+        tz = njit(lambda x: trailing_zeros(x))
+
+        evens = [2, 42, 126, 128]
+
+        for T in types.unsigned_domain:
+            self.assertTrue(tz(T(0)) == lz(T(0)) == T.bitwidth)
+            for i in range(T.bitwidth):
+                val = T(2 ** i)
+                self.assertEqual(lz(val) + tz(val) + 1, T.bitwidth)
+            for n in evens:
+                self.assertGreater(tz(T(n)), 0)
+                self.assertEqual(tz(T(n + 1)), 0)
+
+        for T in types.signed_domain:
+            self.assertTrue(tz(T(0)) == lz(T(0)) == T.bitwidth)
+            for i in range(T.bitwidth - 1):
+                val = T(2 ** i)
+                self.assertEqual(lz(val) + tz(val) + 1, T.bitwidth)
+                self.assertEqual(lz(-val), 0)
+                self.assertEqual(tz(val), tz(-val))
+            for n in evens:
+                self.assertGreater(tz(T(n)), 0)
+                self.assertEqual(tz(T(n + 1)), 0)
+
+    def check_error_msg(self, func):
+        cfunc = njit(lambda *x: func(*x))
+        func_name = func._name
+
+        unsupported_types = filter(
+            lambda x: not isinstance(x, types.Integer), types.number_domain
+        )
+        for typ in sorted(unsupported_types, key=str):
+            with self.assertRaises(TypingError) as e:
+                cfunc(typ(2))
+            self.assertIn(
+                "{} is only defined for integers, but value passed was '{}'."
+                .format(func_name, typ),
+                str(e.exception),
+            )
+
+        # Testing w/ too many/few arguments
+        def check(args, string):
+            with self.assertRaises((TypingError, TypeError)) as e:
+                cfunc(*args)
+            self.assertIn(
+                "{}() ".format(func_name),
+                str(e.exception)
+            )
+
+        check((1, 2), "takes 2 positional arguments but 3 were given")
+        check((), "missing 1 required positional argument")
+
+    def test_trailing_zeros_error(self):
+        self.check_error_msg(trailing_zeros)
+
+    def test_leading_zeros_error(self):
+        self.check_error_msg(leading_zeros)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_usecases.py
new file mode 100644
index 000000000..08cc674ac
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_usecases.py
@@ -0,0 +1,225 @@
+import itertools
+import numpy as np
+
+import unittest
+from numba.core.compiler import compile_isolated, Flags
+from numba.core import types, utils
+from numba.tests import usecases
+from numba.tests.support import TestCase, tag
+
+enable_pyobj_flags = Flags()
+enable_pyobj_flags.enable_pyobject = True
+
+force_pyobj_flags = Flags()
+force_pyobj_flags.force_pyobject = True
+
+
+class TestUsecases(TestCase):
+
+    def test_andor(self):
+        pyfunc = usecases.andor
+        cr = compile_isolated(pyfunc, (types.int32, types.int32))
+        cfunc = cr.entry_point
+
+        # Argument boundaries
+        xs = -1, 0, 1, 9, 10, 11
+        ys = -1, 0, 1, 9, 10, 11
+
+        for args in itertools.product(xs, ys):
+            self.assertEqual(pyfunc(*args), cfunc(*args), "args %s" % (args,))
+
+    def test_sum1d(self):
+        pyfunc = usecases.sum1d
+        cr = compile_isolated(pyfunc, (types.int32, types.int32))
+        cfunc = cr.entry_point
+
+        ss = -1, 0, 1, 100, 200
+        es = -1, 0, 1, 100, 200
+
+        for args in itertools.product(ss, es):
+            self.assertEqual(pyfunc(*args), cfunc(*args), args)
+
+    def test_sum1d_pyobj(self):
+        pyfunc = usecases.sum1d
+        cr = compile_isolated(pyfunc, (types.int32, types.int32),
+                              flags=force_pyobj_flags)
+        cfunc = cr.entry_point
+
+        ss = -1, 0, 1, 100, 200
+        es = -1, 0, 1, 100, 200
+
+        for args in itertools.product(ss, es):
+            self.assertEqual(pyfunc(*args), cfunc(*args), args)
+
+        args = 0, 500
+
+        def bm_python():
+            pyfunc(*args)
+
+        def bm_numba():
+            cfunc(*args)
+
+        print(utils.benchmark(bm_python, maxsec=.1))
+        print(utils.benchmark(bm_numba, maxsec=.1))
+
+    def test_sum2d(self):
+        pyfunc = usecases.sum2d
+        cr = compile_isolated(pyfunc, (types.int32, types.int32))
+        cfunc = cr.entry_point
+
+        ss = -1, 0, 1, 100, 200
+        es = -1, 0, 1, 100, 200
+
+        for args in itertools.product(ss, es):
+            self.assertEqual(pyfunc(*args), cfunc(*args), args)
+
+    def test_while_count(self):
+        pyfunc = usecases.while_count
+        cr = compile_isolated(pyfunc, (types.int32, types.int32))
+        cfunc = cr.entry_point
+
+        ss = -1, 0, 1, 100, 200
+        es = -1, 0, 1, 100, 200
+
+        for args in itertools.product(ss, es):
+            self.assertEqual(pyfunc(*args), cfunc(*args), args)
+
+    def test_copy_arrays(self):
+        pyfunc = usecases.copy_arrays
+        arraytype = types.Array(types.int32, 1, 'A')
+        cr = compile_isolated(pyfunc, (arraytype, arraytype))
+        cfunc = cr.entry_point
+
+        nda = 0, 1, 10, 100
+
+        for nd in nda:
+            a = np.arange(nd, dtype='int32')
+            b = np.empty_like(a)
+            args = a, b
+
+            cfunc(*args)
+            self.assertPreciseEqual(a, b, msg=str(args))
+
+    def test_copy_arrays2d(self):
+        pyfunc = usecases.copy_arrays2d
+        arraytype = types.Array(types.int32, 2, 'A')
+        cr = compile_isolated(pyfunc, (arraytype, arraytype))
+        cfunc = cr.entry_point
+
+        nda = (0, 0), (1, 1), (2, 5), (4, 25)
+
+        for nd in nda:
+            d1, d2 = nd
+            a = np.arange(d1 * d2, dtype='int32').reshape(d1, d2)
+            b = np.empty_like(a)
+            args = a, b
+
+            cfunc(*args)
+            self.assertPreciseEqual(a, b, msg=str(args))
+
+    def run_ifelse(self, pyfunc):
+        cr = compile_isolated(pyfunc, (types.int32, types.int32))
+        cfunc = cr.entry_point
+
+        xs = -1, 0, 1
+        ys = -1, 0, 1
+
+        for x, y in itertools.product(xs, ys):
+            args = x, y
+            self.assertEqual(pyfunc(*args), cfunc(*args), args)
+
+    def test_string_concat(self):
+        pyfunc = usecases.string_concat
+        cr = compile_isolated(pyfunc, (types.int32, types.int32),
+                              flags=enable_pyobj_flags)
+        cfunc = cr.entry_point
+
+        xs = -1, 0, 1
+        ys = -1, 0, 1
+
+        for x, y in itertools.product(xs, ys):
+            args = x, y
+            self.assertEqual(pyfunc(*args), cfunc(*args), args)
+
+    def test_string_len(self):
+        pyfunc = usecases.string_len
+        cr = compile_isolated(pyfunc, (types.pyobject,),
+                              flags=enable_pyobj_flags)
+        cfunc = cr.entry_point
+
+        test_str = '123456'
+        self.assertEqual(pyfunc(test_str), cfunc(test_str))
+        test_str = '1'
+        self.assertEqual(pyfunc(test_str), cfunc(test_str))
+        test_str = ''
+        self.assertEqual(pyfunc(test_str), cfunc(test_str))
+
+    def test_string_slicing(self):
+        pyfunc = usecases.string_slicing
+        cr = compile_isolated(pyfunc, (types.pyobject,) * 3,
+                              flags=enable_pyobj_flags)
+        cfunc = cr.entry_point
+
+        test_str = '123456'
+        self.assertEqual(pyfunc(test_str, 0, 3), cfunc(test_str, 0, 3))
+        self.assertEqual(pyfunc(test_str, 1, 5), cfunc(test_str, 1, 5))
+        self.assertEqual(pyfunc(test_str, 2, 3), cfunc(test_str, 2, 3))
+
+    def test_string_conversion(self):
+        pyfunc = usecases.string_conversion
+
+        cr = compile_isolated(pyfunc, (types.int32,),
+                              flags=enable_pyobj_flags)
+        cfunc = cr.entry_point
+        self.assertEqual(pyfunc(1), cfunc(1))
+
+        cr = compile_isolated(pyfunc, (types.float32,),
+                              flags=enable_pyobj_flags)
+        cfunc = cr.entry_point
+        self.assertEqual(pyfunc(1.1), cfunc(1.1))
+
+    def test_string_comparisons(self):
+        import operator
+        pyfunc = usecases.string_comparison
+        cr = compile_isolated(pyfunc, (types.pyobject, types.pyobject, types.pyobject),
+                              flags=enable_pyobj_flags)
+        cfunc = cr.entry_point
+
+        test_str1 = '123'
+        test_str2 = '123'
+        op = operator.eq
+        self.assertEqual(pyfunc(test_str1, test_str2, op),
+            cfunc(test_str1, test_str2, op))
+
+        test_str1 = '123'
+        test_str2 = '456'
+        op = operator.eq
+        self.assertEqual(pyfunc(test_str1, test_str2, op),
+            cfunc(test_str1, test_str2, op))
+
+        test_str1 = '123'
+        test_str2 = '123'
+        op = operator.ne
+        self.assertEqual(pyfunc(test_str1, test_str2, op),
+            cfunc(test_str1, test_str2, op))
+
+        test_str1 = '123'
+        test_str2 = '456'
+        op = operator.ne
+        self.assertEqual(pyfunc(test_str1, test_str2, op),
+            cfunc(test_str1, test_str2, op))
+
+    def test_blackscholes_cnd(self):
+        pyfunc = usecases.blackscholes_cnd
+        cr = compile_isolated(pyfunc, (types.float32,))
+        cfunc = cr.entry_point
+
+        ds = -0.5, 0, 0.5
+
+        for d in ds:
+            args = (d,)
+            self.assertEqual(pyfunc(*args), cfunc(*args), args)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_vectorization.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_vectorization.py
new file mode 100644
index 000000000..80fe5c364
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_vectorization.py
@@ -0,0 +1,73 @@
+import platform
+import numpy as np
+from numba import types
+from unittest import TestCase, skipIf
+from numba.tests.support import override_env_config
+from numba.core.compiler import compile_isolated, Flags
+from numba.core.cpu_options import FastMathOptions
+
+_DEBUG = False
+if _DEBUG:
+    from llvmlite import binding as llvm
+    # Prints debug info from the LLVMs vectorizer
+    llvm.set_option("", "--debug-only=loop-vectorize")
+
+
+@skipIf(platform.machine() != 'x86_64', 'x86_64 only test')
+class TestVectorization(TestCase):
+    """
+    Tests to assert that code which should vectorize does indeed vectorize
+    """
+    def gen_ir(self, func, args_tuple, fastmath=False):
+        with override_env_config(
+            "NUMBA_CPU_NAME", "skylake-avx512"
+        ), override_env_config("NUMBA_CPU_FEATURES", ""):
+            _flags = Flags()
+            _flags.fastmath = FastMathOptions(fastmath)
+            _flags.nrt = True
+            jitted = compile_isolated(func, args_tuple, flags=_flags)
+            return jitted.library.get_llvm_str()
+
+    def test_nditer_loop(self):
+        # see https://github.com/numba/numba/issues/5033
+        def do_sum(x):
+            acc = 0
+            for v in np.nditer(x):
+                acc += v.item()
+            return acc
+
+        llvm_ir = self.gen_ir(do_sum, (types.float64[::1],), fastmath=True)
+        self.assertIn("vector.body", llvm_ir)
+        self.assertIn("llvm.loop.isvectorized", llvm_ir)
+
+    def test_slp(self):
+        # Sample translated from:
+        # https://www.llvm.org/docs/Vectorizers.html#the-slp-vectorizer
+
+        def foo(a1, a2, b1, b2, A):
+            A[0] = a1 * (a1 + b1)
+            A[1] = a2 * (a2 + b2)
+            A[2] = a1 * (a1 + b1)
+            A[3] = a2 * (a2 + b2)
+
+        ty = types.float64
+        llvm_ir = self.gen_ir(foo, ((ty,) * 4 + (ty[::1],)), fastmath=True)
+        self.assertIn("2 x double", llvm_ir)
+
+    def test_instcombine_effect(self):
+        # Without instcombine running ahead of refprune, the IR has refops that
+        # are trivially prunable (same BB) but the arguments are obfuscated
+        # through aliases etc. The follow case triggers this situation as the
+        # typed.List has a structproxy call for computing `len` and getting the
+        # base pointer for use in iteration.
+
+        def sum_sqrt_list(lst):
+            acc = 0.0
+            for item in lst:
+                acc += np.sqrt(item)
+            return acc
+
+        llvm_ir = self.gen_ir(sum_sqrt_list, (types.ListType(types.float64),),
+                              fastmath=True)
+        self.assertIn("vector.body", llvm_ir)
+        self.assertIn("llvm.loop.isvectorized", llvm_ir)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_vectorization_type_inference.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_vectorization_type_inference.py
new file mode 100644
index 000000000..0cb3adbeb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_vectorization_type_inference.py
@@ -0,0 +1,43 @@
+from numba import vectorize, jit, bool_, double, int_, float_, typeof, int8
+import unittest
+import numpy as np
+
+
+def add(a, b):
+    return a + b
+
+
+def func(dtypeA, dtypeB):
+    A = np.arange(10, dtype=dtypeA)
+    B = np.arange(10, dtype=dtypeB)
+    return typeof(vector_add(A, B))
+
+
+class TestVectTypeInfer(unittest.TestCase):
+
+    def test_type_inference(self):
+        """This is testing numpy ufunc dispatch machinery
+        """
+        global vector_add
+        vector_add = vectorize([
+            bool_(double, int_),
+            double(double, double),
+            float_(double, float_),
+        ])(add)
+
+        cfunc = jit(func)
+
+        def numba_type_equal(a, b):
+            self.assertEqual(a.dtype, b.dtype)
+            self.assertEqual(a.ndim, b.ndim)
+
+        numba_type_equal(cfunc(np.dtype(np.float64), np.dtype('i')), bool_[:])
+        numba_type_equal(cfunc(np.dtype(np.float64), np.dtype(np.float64)),
+                         double[:])
+        # This is because the double(double, double) matches first
+        numba_type_equal(cfunc(np.dtype(np.float64), np.dtype(np.float32)),
+                         double[:])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_warnings.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_warnings.py
new file mode 100644
index 000000000..b1f974f0f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_warnings.py
@@ -0,0 +1,222 @@
+import os
+import subprocess
+import sys
+import warnings
+import numpy as np
+
+import unittest
+from numba import jit
+from numba.core.errors import NumbaWarning, deprecated, NumbaDeprecationWarning
+from numba.core import errors
+from numba.tests.support import ignore_internal_warnings
+
+
+class TestBuiltins(unittest.TestCase):
+
+    def check_objmode_deprecation_warning(self, w):
+        # Object mode fall-back is slated for deprecation, check the warning
+        msg = ("Fall-back from the nopython compilation path to the object "
+               "mode compilation path has been detected")
+        self.assertEqual(w.category, NumbaDeprecationWarning)
+        self.assertIn(msg, str(w.message))
+
+    def test_type_infer_warning(self):
+        def add(x, y):
+            a = {} # noqa dead
+            return x + y
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaWarning)
+            ignore_internal_warnings()
+
+            cfunc = jit(add)
+            cfunc(1, 2)
+
+            self.assertEqual(len(w), 3)
+            # Type inference failure
+            self.assertEqual(w[0].category, NumbaWarning)
+            self.assertIn('type inference', str(w[0].message))
+
+            # Object mode
+            self.assertEqual(w[1].category, NumbaWarning)
+            self.assertIn('object mode', str(w[1].message))
+
+            # check objmode deprecation warning
+            self.check_objmode_deprecation_warning(w[2])
+
+    def test_return_type_warning(self):
+        y = np.ones(4, dtype=np.float32)
+
+        def return_external_array():
+            return y
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaWarning)
+            ignore_internal_warnings()
+
+            cfunc = jit(_nrt=False)(return_external_array)
+            cfunc()
+
+            self.assertEqual(len(w), 3)
+
+            # Legal return value failure
+            self.assertEqual(w[0].category, NumbaWarning)
+            self.assertIn('return type', str(w[0].message))
+
+            # Object mode fall-back
+            self.assertEqual(w[1].category, NumbaWarning)
+            self.assertIn('object mode without forceobj=True',
+                          str(w[1].message))
+
+            # check objmode deprecation warning
+            self.check_objmode_deprecation_warning(w[2])
+
+    def test_return_type_warning_with_nrt(self):
+        """
+        Rerun test_return_type_warning with nrt
+        """
+        y = np.ones(4, dtype=np.float32)
+
+        def return_external_array():
+            return y
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaWarning)
+            ignore_internal_warnings()
+
+            cfunc = jit(return_external_array)
+            cfunc()
+            # No more warning
+            self.assertEqual(len(w), 0)
+
+    def test_no_warning_with_forceobj(self):
+        def add(x, y):
+            a = [] # noqa dead
+            return x + y
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaWarning)
+            ignore_internal_warnings()
+
+            cfunc = jit(add, forceobj=True)
+            cfunc(1, 2)
+
+            self.assertEqual(len(w), 0)
+
+    def test_loop_lift_warn(self):
+        def do_loop(x):
+            a = {} # noqa dead
+            for i in range(x.shape[0]):
+                x[i] *= 2
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', NumbaWarning)
+            ignore_internal_warnings()
+
+            x = np.ones(4, dtype=np.float32)
+            cfunc = jit(do_loop)
+            cfunc(x)
+
+            msg = '\n'.join(f"----------\n{x.message}" for x in w)
+
+            self.assertEqual(len(w), 4, msg=msg)
+
+            # Type inference failure (1st pass, in npm, fall-back to objmode
+            # with looplift)
+            self.assertEqual(w[0].category, NumbaWarning)
+            self.assertIn('type inference', str(w[0].message))
+            self.assertIn('WITH looplifting', str(w[0].message))
+
+            # Type inference failure (2nd pass, objmode with lifted loops,
+            # loop found but still failed, fall back to objmode no looplift)
+            self.assertEqual(w[1].category, NumbaWarning)
+            self.assertIn('type inference', str(w[1].message))
+            self.assertIn('WITHOUT looplifting', str(w[1].message))
+
+            # States compilation outcome
+            self.assertEqual(w[2].category, NumbaWarning)
+            self.assertIn('compiled in object mode without forceobj=True',
+                          str(w[2].message))
+            self.assertIn('but has lifted loops', str(w[2].message))
+
+            # check objmode deprecation warning
+            self.check_objmode_deprecation_warning(w[3])
+
+    def test_deprecated(self):
+        @deprecated('foo')
+        def bar():
+            pass
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            ignore_internal_warnings()
+            bar()
+
+            self.assertEqual(len(w), 1)
+            self.assertEqual(w[0].category, DeprecationWarning)
+            self.assertIn('bar', str(w[0].message))
+            self.assertIn('foo', str(w[0].message))
+
+    def test_warnings_fixer(self):
+        # For some context, see #4083
+
+        wfix = errors.WarningsFixer(errors.NumbaWarning)
+        with wfix.catch_warnings('foo', 10):
+            warnings.warn(errors.NumbaWarning('same'))
+            warnings.warn(errors.NumbaDeprecationWarning('same'))
+            ignore_internal_warnings()
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            ignore_internal_warnings()
+            wfix.flush()
+
+            self.assertEqual(len(w), 2)
+            # the order of these will be backwards to the above, the
+            # WarningsFixer flush method sorts with a key based on str
+            # comparison
+            self.assertEqual(w[0].category, NumbaDeprecationWarning)
+            self.assertEqual(w[1].category, NumbaWarning)
+            self.assertIn('same', str(w[0].message))
+            self.assertIn('same', str(w[1].message))
+
+    def test_disable_performance_warnings(self):
+
+        not_found_ret_code = 55
+        found_ret_code = 99
+        expected = "'parallel=True' was specified but no transformation"
+
+        # NOTE: the error_usecases is needed as the NumbaPerformanceWarning's
+        # for parallel=True failing to parallelise do not appear for functions
+        # defined by string eval/exec etc.
+        parallel_code = """if 1:
+            import warnings
+            from numba.tests.error_usecases import foo
+            import numba
+            from numba.tests.support import ignore_internal_warnings
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always')
+                ignore_internal_warnings()
+                foo()
+            for x in w:
+                if x.category == numba.errors.NumbaPerformanceWarning:
+                    if "%s" in str(x.message):
+                        exit(%s)
+            exit(%s)
+        """ % (expected, found_ret_code, not_found_ret_code)
+
+        # run in the standard env, warning should raise
+        popen = subprocess.Popen([sys.executable, "-c", parallel_code])
+        out, err = popen.communicate()
+        self.assertEqual(popen.returncode, found_ret_code)
+
+        # run in an env with performance warnings disabled, should not warn
+        env = dict(os.environ)
+        env['NUMBA_DISABLE_PERFORMANCE_WARNINGS'] = "1"
+        popen = subprocess.Popen([sys.executable, "-c", parallel_code], env=env)
+        out, err = popen.communicate()
+        self.assertEqual(popen.returncode, not_found_ret_code)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_withlifting.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_withlifting.py
new file mode 100644
index 000000000..8c5bf44f0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_withlifting.py
@@ -0,0 +1,1256 @@
+import copy
+import os
+import signal
+import subprocess
+import sys
+import tempfile
+import threading
+import warnings
+import numpy as np
+
+import numba
+from numba.core.transforms import find_setupwiths, with_lifting
+from numba.core.withcontexts import bypass_context, call_context, objmode_context
+from numba.core.bytecode import FunctionIdentity, ByteCode
+from numba.core.interpreter import Interpreter
+from numba.core import typing, errors, cpu
+from numba.core.registry import cpu_target
+from numba.core.compiler import compile_ir, DEFAULT_FLAGS
+from numba import njit, typeof, objmode, types
+from numba.core.extending import overload
+from numba.tests.support import (MemoryLeak, TestCase, captured_stdout,
+                                 skip_unless_scipy, linux_only,
+                                 strace_supported, strace)
+from numba.core.utils import PYVERSION
+from numba.experimental import jitclass
+import unittest
+
+
+def get_func_ir(func):
+    func_id = FunctionIdentity.from_function(func)
+    bc = ByteCode(func_id=func_id)
+    interp = Interpreter(func_id)
+    func_ir = interp.interpret(bc)
+    return func_ir
+
+
+def lift1():
+    print("A")
+    with bypass_context:
+        print("B")
+        b()
+    print("C")
+
+
+def lift2():
+    x = 1
+    print("A", x)
+    x = 1
+    with bypass_context:
+        print("B", x)
+        x += 100
+        b()
+    x += 1
+    with bypass_context:
+        print("C", x)
+        b()
+        x += 10
+    x += 1
+    print("D", x)
+
+
+def lift3():
+    x = 1
+    y = 100
+    print("A", x, y)
+    with bypass_context:
+        print("B")
+        b()
+        x += 100
+        with bypass_context:
+            print("C")
+            y += 100000
+            b()
+    x += 1
+    y += 1
+    print("D", x, y)
+
+
+def lift4():
+    x = 0
+    print("A", x)
+    x += 10
+    with bypass_context:
+        print("B")
+        b()
+        x += 1
+        for i in range(10):
+            with bypass_context:
+                print("C")
+                b()
+                x += i
+    with bypass_context:
+        print("D")
+        b()
+        if x:
+            x *= 10
+    x += 1
+    print("E", x)
+
+
+def lift5():
+    print("A")
+
+
+def liftcall1():
+    x = 1
+    print("A", x)
+    with call_context:
+        x += 1
+    print("B", x)
+    return x
+
+
+def liftcall2():
+    x = 1
+    print("A", x)
+    with call_context:
+        x += 1
+    print("B", x)
+    with call_context:
+        x += 10
+    print("C", x)
+    return x
+
+
+def liftcall3():
+    x = 1
+    print("A", x)
+    with call_context:
+        if x > 0:
+            x += 1
+    print("B", x)
+    with call_context:
+        for i in range(10):
+            x += i
+    print("C", x)
+    return x
+
+
+def liftcall4():
+    with call_context:
+        with call_context:
+            pass
+
+
+def liftcall5():
+    for i in range(10):
+        with call_context:
+            print(i)
+            if i == 5:
+                print("A")
+                break
+    return i
+
+
+def lift_undefiend():
+    with undefined_global_var:
+        pass
+
+
+bogus_contextmanager = object()
+
+
+def lift_invalid():
+    with bogus_contextmanager:
+        pass
+
+
+gv_type = types.intp
+
+
+class TestWithFinding(TestCase):
+    def check_num_of_with(self, func, expect_count):
+        the_ir = get_func_ir(func)
+        ct = len(find_setupwiths(the_ir)[0])
+        self.assertEqual(ct, expect_count)
+
+    def test_lift1(self):
+        self.check_num_of_with(lift1, expect_count=1)
+
+    def test_lift2(self):
+        self.check_num_of_with(lift2, expect_count=2)
+
+    def test_lift3(self):
+        self.check_num_of_with(lift3, expect_count=1)
+
+    def test_lift4(self):
+        self.check_num_of_with(lift4, expect_count=2)
+
+    def test_lift5(self):
+        self.check_num_of_with(lift5, expect_count=0)
+
+
+class BaseTestWithLifting(TestCase):
+    def setUp(self):
+        super(BaseTestWithLifting, self).setUp()
+        self.typingctx = typing.Context()
+        self.targetctx = cpu.CPUContext(self.typingctx)
+        self.flags = DEFAULT_FLAGS
+
+    def check_extracted_with(self, func, expect_count, expected_stdout):
+        the_ir = get_func_ir(func)
+        new_ir, extracted = with_lifting(
+            the_ir, self.typingctx, self.targetctx, self.flags,
+            locals={},
+        )
+        self.assertEqual(len(extracted), expect_count)
+        cres = self.compile_ir(new_ir)
+
+        with captured_stdout() as out:
+            cres.entry_point()
+
+        self.assertEqual(out.getvalue(), expected_stdout)
+
+    def compile_ir(self, the_ir, args=(), return_type=None):
+        typingctx = self.typingctx
+        targetctx = self.targetctx
+        flags = self.flags
+        # Register the contexts in case for nested @jit or @overload calls
+        with cpu_target.nested_context(typingctx, targetctx):
+            return compile_ir(typingctx, targetctx, the_ir, args,
+                              return_type, flags, locals={})
+
+
+class TestLiftByPass(BaseTestWithLifting):
+
+    def test_lift1(self):
+        self.check_extracted_with(lift1, expect_count=1,
+                                  expected_stdout="A\nC\n")
+
+    def test_lift2(self):
+        self.check_extracted_with(lift2, expect_count=2,
+                                  expected_stdout="A 1\nD 3\n")
+
+    def test_lift3(self):
+        self.check_extracted_with(lift3, expect_count=1,
+                                  expected_stdout="A 1 100\nD 2 101\n")
+
+    def test_lift4(self):
+        self.check_extracted_with(lift4, expect_count=2,
+                                  expected_stdout="A 0\nE 11\n")
+
+    def test_lift5(self):
+        self.check_extracted_with(lift5, expect_count=0,
+                                  expected_stdout="A\n")
+
+
+class TestLiftCall(BaseTestWithLifting):
+
+    def check_same_semantic(self, func):
+        """Ensure same semantic with non-jitted code
+        """
+        jitted = njit(func)
+        with captured_stdout() as got:
+            jitted()
+
+        with captured_stdout() as expect:
+            func()
+
+        self.assertEqual(got.getvalue(), expect.getvalue())
+
+    def test_liftcall1(self):
+        self.check_extracted_with(liftcall1, expect_count=1,
+                                  expected_stdout="A 1\nB 2\n")
+        self.check_same_semantic(liftcall1)
+
+    def test_liftcall2(self):
+        self.check_extracted_with(liftcall2, expect_count=2,
+                                  expected_stdout="A 1\nB 2\nC 12\n")
+        self.check_same_semantic(liftcall2)
+
+    def test_liftcall3(self):
+        self.check_extracted_with(liftcall3, expect_count=2,
+                                  expected_stdout="A 1\nB 2\nC 47\n")
+        self.check_same_semantic(liftcall3)
+
+    def test_liftcall4(self):
+        accept = (errors.TypingError, errors.NumbaRuntimeError,
+                  errors.NumbaValueError, errors.CompilerError)
+        with self.assertRaises(accept) as raises:
+            njit(liftcall4)()
+        # Known error.  We only support one context manager per function
+        # for body that are lifted.
+        msg = ("compiler re-entrant to the same function signature")
+        self.assertIn(msg, str(raises.exception))
+
+    # 3.7 fails to interpret the bytecode for this example
+    @unittest.skipIf(PYVERSION <= (3, 8),
+                     "unsupported on py3.8 and before")
+    def test_liftcall5(self):
+        self.check_extracted_with(liftcall5, expect_count=1,
+                                  expected_stdout="0\n1\n2\n3\n4\n5\nA\n")
+        self.check_same_semantic(liftcall5)
+
+
+def expected_failure_for_list_arg(fn):
+    def core(self, *args, **kwargs):
+        with self.assertRaises(errors.TypingError) as raises:
+            fn(self, *args, **kwargs)
+        self.assertIn('Does not support list type',
+                      str(raises.exception))
+    return core
+
+
+def expected_failure_for_function_arg(fn):
+    def core(self, *args, **kwargs):
+        with self.assertRaises(errors.TypingError) as raises:
+            fn(self, *args, **kwargs)
+        self.assertIn('Does not support function type',
+                      str(raises.exception))
+    return core
+
+
+class TestLiftObj(MemoryLeak, TestCase):
+
+    def setUp(self):
+        warnings.simplefilter("error", errors.NumbaWarning)
+
+    def tearDown(self):
+        warnings.resetwarnings()
+
+    def assert_equal_return_and_stdout(self, pyfunc, *args):
+        py_args = copy.deepcopy(args)
+        c_args = copy.deepcopy(args)
+        cfunc = njit(pyfunc)
+
+        with captured_stdout() as stream:
+            expect_res = pyfunc(*py_args)
+            expect_out = stream.getvalue()
+
+        # avoid compiling during stdout-capturing for easier print-debugging
+        cfunc.compile(tuple(map(typeof, c_args)))
+        with captured_stdout() as stream:
+            got_res = cfunc(*c_args)
+            got_out = stream.getvalue()
+
+        self.assertEqual(expect_out, got_out)
+        self.assertPreciseEqual(expect_res, got_res)
+
+    def test_lift_objmode_basic(self):
+        def bar(ival):
+            print("ival =", {'ival': ival // 2})
+
+        def foo(ival):
+            ival += 1
+            with objmode_context:
+                bar(ival)
+            return ival + 1
+
+        def foo_nonglobal(ival):
+            ival += 1
+            with numba.objmode:
+                bar(ival)
+            return ival + 1
+
+        self.assert_equal_return_and_stdout(foo, 123)
+        self.assert_equal_return_and_stdout(foo_nonglobal, 123)
+
+    def test_lift_objmode_array_in(self):
+        def bar(arr):
+            print({'arr': arr // 2})
+            # arr is modified. the effect is visible outside.
+            arr *= 2
+
+        def foo(nelem):
+            arr = np.arange(nelem).astype(np.int64)
+            with objmode_context:
+                # arr is modified inplace inside bar()
+                bar(arr)
+            return arr + 1
+
+        nelem = 10
+        self.assert_equal_return_and_stdout(foo, nelem)
+
+    def test_lift_objmode_define_new_unused(self):
+        def bar(y):
+            print(y)
+
+        def foo(x):
+            with objmode_context():
+                y = 2 + x           # defined but unused outside
+                a = np.arange(y)    # defined but unused outside
+                bar(a)
+            return x
+
+        arg = 123
+        self.assert_equal_return_and_stdout(foo, arg)
+
+    def test_lift_objmode_return_simple(self):
+        def inverse(x):
+            print(x)
+            return 1 / x
+
+        def foo(x):
+            with objmode_context(y="float64"):
+                y = inverse(x)
+            return x, y
+
+        def foo_nonglobal(x):
+            with numba.objmode(y="float64"):
+                y = inverse(x)
+            return x, y
+
+        arg = 123
+        self.assert_equal_return_and_stdout(foo, arg)
+        self.assert_equal_return_and_stdout(foo_nonglobal, arg)
+
+    def test_lift_objmode_return_array(self):
+        def inverse(x):
+            print(x)
+            return 1 / x
+
+        def foo(x):
+            with objmode_context(y="float64[:]", z="int64"):
+                y = inverse(x)
+                z = int(y[0])
+            return x, y, z
+
+        arg = np.arange(1, 10, dtype=np.float64)
+        self.assert_equal_return_and_stdout(foo, arg)
+
+    @expected_failure_for_list_arg
+    def test_lift_objmode_using_list(self):
+        def foo(x):
+            with objmode_context(y="float64[:]"):
+                print(x)
+                x[0] = 4
+                print(x)
+                y = [1, 2, 3] + x
+                y = np.asarray([1 / i for i in y])
+            return x, y
+
+        arg = [1, 2, 3]
+        self.assert_equal_return_and_stdout(foo, arg)
+
+    def test_lift_objmode_var_redef(self):
+        def foo(x):
+            for x in range(x):
+                pass
+            if x:
+                x += 1
+            with objmode_context(x="intp"):
+                print(x)
+                x -= 1
+                print(x)
+                for i in range(x):
+                    x += i
+                    print(x)
+            return x
+
+        arg = 123
+        self.assert_equal_return_and_stdout(foo, arg)
+
+    @expected_failure_for_list_arg
+    def test_case01_mutate_list_ahead_of_ctx(self):
+        def foo(x, z):
+            x[2] = z
+
+            with objmode_context():
+                # should print [1, 2, 15] but prints [1, 2, 3]
+                print(x)
+
+            with objmode_context():
+                x[2] = 2 * z
+                # should print [1, 2, 30] but prints [1, 2, 15]
+                print(x)
+
+            return x
+
+        self.assert_equal_return_and_stdout(foo, [1, 2, 3], 15)
+
+    def test_case02_mutate_array_ahead_of_ctx(self):
+        def foo(x, z):
+            x[2] = z
+
+            with objmode_context():
+                # should print [1, 2, 15]
+                print(x)
+
+            with objmode_context():
+                x[2] = 2 * z
+                # should print [1, 2, 30]
+                print(x)
+
+            return x
+
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x, 15)
+
+    @expected_failure_for_list_arg
+    def test_case03_create_and_mutate(self):
+        def foo(x):
+            with objmode_context(y='List(int64)'):
+                y = [1, 2, 3]
+            with objmode_context():
+                y[2] = 10
+            return y
+        self.assert_equal_return_and_stdout(foo, 1)
+
+    def test_case04_bogus_variable_type_info(self):
+
+        def foo(x):
+            # should specifying nonsense type info be considered valid?
+            with objmode_context(k="float64[:]"):
+                print(x)
+            return x
+
+        x = np.array([1, 2, 3])
+        cfoo = njit(foo)
+        with self.assertRaises(errors.TypingError) as raises:
+            cfoo(x)
+        self.assertIn(
+            "Invalid type annotation on non-outgoing variables",
+            str(raises.exception),
+            )
+
+    def test_case05_bogus_type_info(self):
+        def foo(x):
+            # should specifying the wrong type info be considered valid?
+            # z is complex.
+            # Note: for now, we will coerce for scalar and raise for array
+            with objmode_context(z="float64[:]"):
+                z = x + 1.j
+            return z
+
+        x = np.array([1, 2, 3])
+        cfoo = njit(foo)
+        with self.assertRaises(TypeError) as raises:
+            got = cfoo(x)
+        self.assertIn(
+            ("can't unbox array from PyObject into native value."
+             "  The object maybe of a different type"),
+            str(raises.exception),
+        )
+
+    def test_case06_double_objmode(self):
+        def foo(x):
+            # would nested ctx in the same scope ever make sense? Is this
+            # pattern useful?
+            with objmode_context():
+                #with npmmode_context(): not implemented yet
+                    with objmode_context():
+                        print(x)
+            return x
+
+        with self.assertRaises(errors.TypingError) as raises:
+            njit(foo)(123)
+        # Check that an error occurred in with-lifting in objmode
+        pat = ("During: resolving callee type: "
+               "type\(ObjModeLiftedWith\(<.*>\)\)")
+        self.assertRegex(str(raises.exception), pat)
+
+    def test_case07_mystery_key_error(self):
+        # this raises a key error
+        def foo(x):
+            with objmode_context():
+                t = {'a': x}
+                u = 3
+            return x, t, u
+        x = np.array([1, 2, 3])
+        cfoo = njit(foo)
+
+        with self.assertRaises(errors.TypingError) as raises:
+            cfoo(x)
+
+        exstr = str(raises.exception)
+        self.assertIn("Missing type annotation on outgoing variable(s): "
+                      "['t', 'u']",
+                      exstr)
+        self.assertIn("Example code: with objmode"
+                      "(t='<add_type_as_string_here>')",
+                      exstr)
+
+    def test_case08_raise_from_external(self):
+        # this segfaults, expect its because the dict needs to raise as '2' is
+        # not in the keys until a later loop (looking for `d['0']` works fine).
+        d = dict()
+
+        def foo(x):
+            for i in range(len(x)):
+                with objmode_context():
+                    k = str(i)
+                    v = x[i]
+                    d[k] = v
+                    print(d['2'])
+            return x
+
+        x = np.array([1, 2, 3])
+        cfoo = njit(foo)
+        with self.assertRaises(KeyError) as raises:
+            cfoo(x)
+        self.assertEqual(str(raises.exception), "'2'")
+
+    def test_case09_explicit_raise(self):
+        def foo(x):
+            with objmode_context():
+                raise ValueError()
+            return x
+
+        x = np.array([1, 2, 3])
+        cfoo = njit(foo)
+        with self.assertRaises(errors.CompilerError) as raises:
+            cfoo(x)
+        self.assertIn(
+            ('unsupported control flow due to raise statements inside '
+             'with block'),
+            str(raises.exception),
+        )
+
+    @expected_failure_for_list_arg
+    def test_case10_mutate_across_contexts(self):
+        # This shouldn't work due to using List as input.
+        def foo(x):
+            with objmode_context(y='List(int64)'):
+                y = [1, 2, 3]
+            with objmode_context():
+                y[2] = 10
+            return y
+
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x)
+
+    def test_case10_mutate_array_across_contexts(self):
+        # Sub-case of case-10.
+        def foo(x):
+            with objmode_context(y='int64[:]'):
+                y = np.asarray([1, 2, 3], dtype='int64')
+            with objmode_context():
+                # Note: `y` is not an output.
+                y[2] = 10
+            return y
+
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x)
+
+    def test_case11_define_function_in_context(self):
+        # should this work? no, global name 'bar' is not defined
+        def foo(x):
+            with objmode_context():
+                def bar(y):
+                    return y + 1
+            return x
+
+        x = np.array([1, 2, 3])
+        cfoo = njit(foo)
+        with self.assertRaises(NameError) as raises:
+            cfoo(x)
+        self.assertIn(
+            "global name 'bar' is not defined",
+            str(raises.exception),
+        )
+
+    def test_case12_njit_inside_a_objmode_ctx(self):
+        # TODO: is this still the cases?
+        # this works locally but not inside this test, probably due to the way
+        # compilation is being done
+        def bar(y):
+            return y + 1
+
+        def foo(x):
+            with objmode_context(y='int64[:]'):
+                y = njit(bar)(x).astype('int64')
+            return x + y
+
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x)
+
+    def test_case13_branch_to_objmode_ctx(self):
+        # Checks for warning in dataflow.py due to mishandled stack offset
+        # dataflow.py:57: RuntimeWarning: inconsistent stack offset ...
+        def foo(x, wobj):
+            if wobj:
+                with objmode_context(y='int64[:]'):
+                    y = (x + 1).astype('int64')
+            else:
+                y = x + 2
+
+            return x + y
+
+        x = np.array([1, 2, 3], dtype='int64')
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always", RuntimeWarning)
+            self.assert_equal_return_and_stdout(foo, x, True)
+        # Assert no warnings from dataflow.py
+        for each in w:
+            self.assertFalse(each.filename.endswith('dataflow.py'),
+                             msg='there were warnings in dataflow.py')
+
+    def test_case14_return_direct_from_objmode_ctx(self):
+        def foo(x):
+            with objmode_context(x='int64[:]'):
+                x += 1
+                return x
+
+        if PYVERSION <= (3,8):
+            # 3.8 and below don't support return inside with
+            with self.assertRaises(errors.CompilerError) as raises:
+                cfoo = njit(foo)
+                cfoo(np.array([1, 2, 3]))
+            msg = "unsupported control flow: due to return statements inside with block"
+            self.assertIn(msg, str(raises.exception))
+        else:
+            result = foo(np.array([1, 2, 3]))
+            np.testing.assert_array_equal(np.array([2, 3, 4]), result)
+
+    # No easy way to handle this yet.
+    @unittest.expectedFailure
+    def test_case15_close_over_objmode_ctx(self):
+        # Fails with Unsupported constraint encountered: enter_with $phi8.1
+        def foo(x):
+            j = 10
+
+            def bar(x):
+                with objmode_context(x='int64[:]'):
+                    print(x)
+                    return x + j
+            return bar(x) + 2
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x)
+
+    @skip_unless_scipy
+    def test_case16_scipy_call_in_objmode_ctx(self):
+        from scipy import sparse as sp
+
+        def foo(x):
+            with objmode_context(k='int64'):
+                print(x)
+                spx = sp.csr_matrix(x)
+                # the np.int64 call is pointless, works around:
+                # https://github.com/scipy/scipy/issues/10206
+                # which hit the SciPy 1.3 release.
+                k = np.int64(spx[0, 0])
+            return k
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x)
+
+    def test_case17_print_own_bytecode(self):
+        import dis
+
+        def foo(x):
+            with objmode_context():
+                dis.dis(foo)
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x)
+
+    @expected_failure_for_function_arg
+    def test_case18_njitfunc_passed_to_objmode_ctx(self):
+        def foo(func, x):
+            with objmode_context():
+                func(x[0])
+
+        x = np.array([1, 2, 3])
+        fn = njit(lambda z: z + 5)
+        self.assert_equal_return_and_stdout(foo, fn, x)
+
+    def test_case19_recursion(self):
+        def foo(x):
+            with objmode_context():
+                if x == 0:
+                    return 7
+            ret = foo(x - 1)
+            return ret
+        with self.assertRaises((errors.TypingError, errors.CompilerError)) as raises:
+            cfoo = njit(foo)
+            cfoo(np.array([1, 2, 3]))
+        if PYVERSION <= (3, 7):
+            # 3.7 and below can't handle the return
+            msg = "unsupported control flow: due to return statements inside with block"
+        else:
+            # above can't handle the recursion
+            msg = "Untyped global name 'foo'"
+        self.assertIn(msg, str(raises.exception))
+
+    @unittest.expectedFailure
+    def test_case20_rng_works_ok(self):
+        def foo(x):
+            np.random.seed(0)
+            y = np.random.rand()
+            with objmode_context(z="float64"):
+                # It's known that the random state does not sync
+                z = np.random.rand()
+            return x + z + y
+
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x)
+
+    def test_case21_rng_seed_works_ok(self):
+        def foo(x):
+            np.random.seed(0)
+            y = np.random.rand()
+            with objmode_context(z="float64"):
+                # Similar to test_case20_rng_works_ok but call seed
+                np.random.seed(0)
+                z = np.random.rand()
+            return x + z + y
+
+        x = np.array([1, 2, 3])
+        self.assert_equal_return_and_stdout(foo, x)
+
+    def test_example01(self):
+        # Example from _ObjModeContextType.__doc__
+        def bar(x):
+            return np.asarray(list(reversed(x.tolist())))
+
+        @njit
+        def foo():
+            x = np.arange(5)
+            with objmode(y='intp[:]'):  # annotate return type
+                # this region is executed by object-mode.
+                y = x + bar(x)
+            return y
+
+        self.assertPreciseEqual(foo(), foo.py_func())
+        self.assertIs(objmode, objmode_context)
+
+    def test_objmode_in_overload(self):
+        def foo(s):
+            pass
+
+        @overload(foo)
+        def foo_overload(s):
+            def impl(s):
+                with objmode(out='intp'):
+                    out = s + 3
+                return out
+            return impl
+
+        @numba.njit
+        def f():
+            return foo(1)
+
+        self.assertEqual(f(), 1 + 3)
+
+    def test_objmode_gv_variable(self):
+        @njit
+        def global_var():
+            with objmode(val=gv_type):
+                val = 12.3
+            return val
+
+        ret = global_var()
+        # the result is truncated because of the intp return-type
+        self.assertIsInstance(ret, int)
+        self.assertEqual(ret, 12)
+
+    def test_objmode_gv_variable_error(self):
+        @njit
+        def global_var():
+            with objmode(val=gv_type2):
+                val = 123
+            return val
+
+        with self.assertRaisesRegex(
+            errors.CompilerError,
+            ("Error handling objmode argument 'val'. "
+             "Global 'gv_type2' is not defined\.")
+        ):
+            global_var()
+
+    def test_objmode_gv_mod_attr(self):
+        @njit
+        def modattr1():
+            with objmode(val=types.intp):
+                val = 12.3
+            return val
+
+        @njit
+        def modattr2():
+            with objmode(val=numba.types.intp):
+                val = 12.3
+            return val
+
+        for fn in (modattr1, modattr2):
+            with self.subTest(fn=str(fn)):
+                ret = fn()
+                # the result is truncated because of the intp return-type
+                self.assertIsInstance(ret, int)
+                self.assertEqual(ret, 12)
+
+    def test_objmode_gv_mod_attr_error(self):
+        @njit
+        def moderror():
+            with objmode(val=types.THIS_DOES_NOT_EXIST):
+                val = 12.3
+            return val
+        with self.assertRaisesRegex(
+            errors.CompilerError,
+            ("Error handling objmode argument 'val'. "
+             "Getattr cannot be resolved at compile-time"),
+        ):
+            moderror()
+
+    def test_objmode_gv_mod_attr_error_multiple(self):
+        @njit
+        def moderror():
+            with objmode(v1=types.intp, v2=types.THIS_DOES_NOT_EXIST,
+                         v3=types.float32):
+                v1 = 12.3
+                v2 = 12.3
+                v3 = 12.3
+            return val
+        with self.assertRaisesRegex(
+            errors.CompilerError,
+            ("Error handling objmode argument 'v2'. "
+             "Getattr cannot be resolved at compile-time"),
+        ):
+            moderror()
+
+    def test_objmode_closure_type_in_overload(self):
+        def foo():
+            pass
+
+        @overload(foo)
+        def foo_overload():
+            shrubbery = types.float64[:]
+            def impl():
+                with objmode(out=shrubbery):
+                    out = np.arange(10).astype(np.float64)
+                return out
+            return impl
+
+        @njit
+        def bar():
+            return foo()
+
+        self.assertPreciseEqual(bar(), np.arange(10).astype(np.float64))
+
+    def test_objmode_closure_type_in_overload_error(self):
+        def foo():
+            pass
+
+        @overload(foo)
+        def foo_overload():
+            shrubbery = types.float64[:]
+            def impl():
+                with objmode(out=shrubbery):
+                    out = np.arange(10).astype(np.float64)
+                return out
+            # Remove closure var.
+            # Otherwise, it will "shrubbery" will be a global
+            del shrubbery
+            return impl
+
+        @njit
+        def bar():
+            return foo()
+
+        with self.assertRaisesRegex(
+            errors.TypingError,
+            ("Error handling objmode argument 'out'. "
+             "Freevar 'shrubbery' is not defined"),
+        ):
+            bar()
+
+    def test_objmode_invalid_use(self):
+        @njit
+        def moderror():
+            with objmode(bad=1 + 1):
+                out = 1
+            return val
+        with self.assertRaisesRegex(
+            errors.CompilerError,
+            ("Error handling objmode argument 'bad'. "
+             "The value must be a compile-time constant either as "
+             "a non-local variable or a getattr expression that "
+             "refers to a Numba type."),
+        ):
+            moderror()
+
+    def test_objmode_multi_type_args(self):
+        array_ty = types.int32[:]
+        @njit
+        def foo():
+            # t1 is a string
+            # t2 is a global type
+            # t3 is a non-local/freevar
+            with objmode(t1="float64", t2=gv_type, t3=array_ty):
+                t1 = 793856.5
+                t2 = t1         # to observe truncation
+                t3 = np.arange(5).astype(np.int32)
+            return t1, t2, t3
+
+        t1, t2, t3 = foo()
+        self.assertPreciseEqual(t1, 793856.5)
+        self.assertPreciseEqual(t2, 793856)
+        self.assertPreciseEqual(t3, np.arange(5).astype(np.int32))
+
+    def test_objmode_jitclass(self):
+        spec = [
+            ('value', types.int32),               # a simple scalar field
+            ('array', types.float32[:]),          # an array field
+        ]
+
+        @jitclass(spec)
+        class Bag(object):
+            def __init__(self, value):
+                self.value = value
+                self.array = np.zeros(value, dtype=np.float32)
+
+            @property
+            def size(self):
+                return self.array.size
+
+            def increment(self, val):
+                for i in range(self.size):
+                    self.array[i] += val
+                return self.array
+
+            @staticmethod
+            def add(x, y):
+                return x + y
+
+        n = 21
+        mybag = Bag(n)
+
+        def foo():
+            pass
+
+        @overload(foo)
+        def foo_overload():
+            shrubbery = mybag._numba_type_
+            def impl():
+                with objmode(out=shrubbery):
+                    out = Bag(123)
+                    out.increment(3)
+                return out
+            return impl
+
+        @njit
+        def bar():
+            return foo()
+
+        z = bar()
+        self.assertIsInstance(z, Bag)
+        self.assertEqual(z.add(2, 3), 2 + 3)
+        exp_array = np.zeros(123, dtype=np.float32) + 3
+        self.assertPreciseEqual(z.array, exp_array)
+
+
+    @staticmethod
+    def case_objmode_cache(x):
+        with objmode(output='float64'):
+            output = x / 10
+        return output
+
+    def test_objmode_reflected_list(self):
+        ret_type = typeof([1, 2, 3, 4, 5])
+        @njit
+        def test2():
+            with objmode(out=ret_type):
+                out = [1, 2, 3, 4, 5]
+            return out
+
+        with self.assertRaises(errors.CompilerError) as raises:
+            test2()
+        self.assertRegex(
+            str(raises.exception),
+            (r"Objmode context failed. "
+             r"Argument 'out' is declared as an unsupported type: "
+             r"reflected list\(int(32|64)\)<iv=None>. "
+             r"Reflected types are not supported."),
+        )
+
+    def test_objmode_reflected_set(self):
+        ret_type = typeof({1, 2, 3, 4, 5})
+        @njit
+        def test2():
+            with objmode(result=ret_type):
+                result = {1, 2, 3, 4, 5}
+            return result
+
+        with self.assertRaises(errors.CompilerError) as raises:
+            test2()
+        self.assertRegex(
+            str(raises.exception),
+            (r"Objmode context failed. "
+             r"Argument 'result' is declared as an unsupported type: "
+             r"reflected set\(int(32|64)\). "
+             r"Reflected types are not supported."),
+        )
+
+    def test_objmode_typed_dict(self):
+        ret_type = types.DictType(types.unicode_type, types.int64)
+        @njit
+        def test4():
+            with objmode(res=ret_type):
+                res = {'A': 1, 'B': 2}
+            return res
+
+        with self.assertRaises(TypeError) as raises:
+            test4()
+        self.assertIn(
+            ("can't unbox a <class 'dict'> "
+             "as a <class 'numba.typed.typeddict.Dict'>"),
+            str(raises.exception),
+        )
+
+    def test_objmode_typed_list(self):
+        ret_type = types.ListType(types.int64)
+        @njit
+        def test4():
+            with objmode(res=ret_type):
+                res = [1, 2]
+            return res
+
+        with self.assertRaises(TypeError) as raises:
+            test4()
+        # Note: in python3.6, the Generic[T] on typedlist is causing it to
+        #       format differently.
+        self.assertRegex(
+            str(raises.exception),
+            (r"can't unbox a <class 'list'> "
+             r"as a (<class ')?numba.typed.typedlist.List('>)?"),
+        )
+
+    def test_objmode_use_of_view(self):
+        # See issue #7158, npm functionality should only be validated if in
+        # npm.
+        @njit
+        def foo(x):
+            with numba.objmode(y="int64[::1]"):
+                y = x.view("int64")
+            return y
+
+        a = np.ones(1, np.int64).view('float64')
+        expected = foo.py_func(a)
+        got = foo(a)
+        self.assertPreciseEqual(expected, got)
+
+
+def case_inner_pyfunc(x):
+    return x / 10
+
+
+def case_objmode_cache(x):
+    with objmode(output='float64'):
+        output = case_inner_pyfunc(x)
+    return output
+
+
+class TestLiftObjCaching(MemoryLeak, TestCase):
+    # Warnings in this test class are converted to errors
+
+    def setUp(self):
+        warnings.simplefilter("error", errors.NumbaWarning)
+
+    def tearDown(self):
+        warnings.resetwarnings()
+
+    def check(self, py_func):
+        first = njit(cache=True)(py_func)
+        self.assertEqual(first(123), 12.3)
+
+        second = njit(cache=True)(py_func)
+        self.assertFalse(second._cache_hits)
+        self.assertEqual(second(123), 12.3)
+        self.assertTrue(second._cache_hits)
+
+    def test_objmode_caching_basic(self):
+        def pyfunc(x):
+            with objmode(output='float64'):
+                output = x / 10
+            return output
+
+        self.check(pyfunc)
+
+    def test_objmode_caching_call_closure_bad(self):
+        def other_pyfunc(x):
+            return x / 10
+
+        def pyfunc(x):
+            with objmode(output='float64'):
+                output = other_pyfunc(x)
+            return output
+
+        self.check(pyfunc)
+
+    def test_objmode_caching_call_closure_good(self):
+        self.check(case_objmode_cache)
+
+
+class TestBogusContext(BaseTestWithLifting):
+    def test_undefined_global(self):
+        the_ir = get_func_ir(lift_undefiend)
+
+        with self.assertRaises(errors.CompilerError) as raises:
+            with_lifting(
+                the_ir, self.typingctx, self.targetctx, self.flags, locals={},
+            )
+        self.assertIn(
+            "Undefined variable used as context manager",
+            str(raises.exception),
+            )
+
+    def test_invalid(self):
+        the_ir = get_func_ir(lift_invalid)
+
+        with self.assertRaises(errors.CompilerError) as raises:
+            with_lifting(
+                the_ir, self.typingctx, self.targetctx, self.flags, locals={},
+            )
+        self.assertIn(
+            "Unsupported context manager in use",
+            str(raises.exception),
+            )
+
+    def test_with_as_fails_gracefully(self):
+        @njit
+        def foo():
+            with open('') as f:
+                pass
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            foo()
+
+        excstr = str(raises.exception)
+        msg = ("The 'with (context manager) as (variable):' construct is not "
+               "supported.")
+        self.assertIn(msg, excstr)
+
+
+class TestMisc(TestCase):
+    # Tests for miscellaneous objmode issues. Run serially.
+
+    _numba_parallel_test_ = False
+
+    @linux_only
+    @TestCase.run_test_in_subprocess
+    def test_no_fork_in_compilation(self):
+        # Checks that there is no fork/clone/execve during compilation, see
+        # issue #7881. This needs running in a subprocess as the offending fork
+        # call that triggered #7881 occurs on the first call to uuid1 as it's
+        # part if the initialisation process for that function (gets hardware
+        # address of machine).
+
+        if not strace_supported():
+            # Needs strace support.
+            self.skipTest("strace support missing")
+
+        def force_compile():
+            @njit('void()') # force compilation
+            def f():
+                with numba.objmode():
+                    pass
+
+        # capture these syscalls:
+        syscalls = ['fork', 'clone', 'execve']
+
+        # check that compilation does not trigger fork, clone or execve
+        strace_data = strace(force_compile, syscalls)
+        self.assertFalse(strace_data)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_wrapper.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_wrapper.py
new file mode 100644
index 000000000..b26a804ac
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/test_wrapper.py
@@ -0,0 +1,104 @@
+import numpy as np
+
+import unittest
+from numba.core import types, utils, compiler, registry
+
+
+def overhead(x):
+    return x
+
+
+def array_overhead(x):
+    x[0] = 1
+    x[1] = 2
+
+
+def add(x):
+    return x + x + x + x + x
+
+
+class TestWrapper(unittest.TestCase):
+    def test_overhead(self):
+        """
+        This will show higher overhead due to unboxing in the native version.
+        """
+        cr = compiler.compile_isolated(overhead, [types.int32])
+        cfunc = cr.entry_point
+        disp = registry.CPUDispatcher(overhead)
+        disp.add_overload(cr)
+
+        x = 321
+
+        def python():
+            overhead(x)
+
+        def pycfunc():
+            cfunc(x)
+
+        def overloaded():
+            disp(x)
+
+        print(overhead)
+        print(utils.benchmark(python, maxsec=.5))
+        print(utils.benchmark(pycfunc, maxsec=.5))
+        print(utils.benchmark(overloaded, maxsec=.5))
+
+    def test_array_overhead(self):
+        """
+        The time to set two array element seems to be more expensive than
+        the overhead of the overloaded call.
+        """
+        cr = compiler.compile_isolated(array_overhead, [types.int32[::1]])
+        cfunc = cr.entry_point
+        disp = registry.CPUDispatcher(array_overhead)
+        disp.add_overload(cr)
+
+        self.assertEqual(cr.signature.args[0].layout, 'C')
+
+        x = np.zeros(shape=2, dtype='int32')
+
+        def python():
+            array_overhead(x)
+
+        def pycfunc():
+            cfunc(x)
+
+        def overloaded():
+            disp(x)
+
+        print(array_overhead)
+        print(utils.benchmark(python, maxsec=.5))
+        print(utils.benchmark(pycfunc, maxsec=.5))
+        print(utils.benchmark(overloaded, maxsec=.5))
+
+
+    def test_add(self):
+        """
+        This seems to be about the amount of work to balance out the overhead
+        by the overloaded one
+        """
+        cr = compiler.compile_isolated(add, [types.int32])
+        cfunc = cr.entry_point
+        disp = registry.CPUDispatcher(add)
+        disp.add_overload(cr)
+
+        x = 321
+
+        def python():
+            add(x)
+
+        def pycfunc():
+            cfunc(x)
+
+        def overloaded():
+            disp(x)
+
+        print(add)
+        print(utils.benchmark(python, maxsec=.5))
+        print(utils.benchmark(pycfunc, maxsec=.5))
+        print(utils.benchmark(overloaded, maxsec=.5))
+
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/threading_backend_usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/threading_backend_usecases.py
new file mode 100644
index 000000000..3188cfb9b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/threading_backend_usecases.py
@@ -0,0 +1,29 @@
+import signal
+import sys
+from numba import njit
+import numpy as np
+
+
+def sigterm_handler(signum, frame):
+    raise RuntimeError("Caught SIGTERM")
+
+
+@njit(parallel=True)
+def busy_func_inner(a, b):
+    c = a + b * np.sqrt(a) + np.sqrt(b)
+    d = np.sqrt(a + b * np.sqrt(a) + np.sqrt(b))
+    return c + d
+
+
+def busy_func(a, b, q=None):
+    sys.stdout.flush()
+    sys.stderr.flush()
+    signal.signal(signal.SIGTERM, sigterm_handler)
+    try:
+        z = busy_func_inner(a, b)
+        sys.stdout.flush()
+        sys.stderr.flush()
+        return z
+    except Exception as e:
+        if q is not None:
+            q.put(e)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/usecases.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/usecases.py
new file mode 100644
index 000000000..740fcd2fb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/tests/usecases.py
@@ -0,0 +1,91 @@
+import math
+import numpy as np
+from numba import jit
+
+_GLOBAL_STR = "abc"
+
+def sum1d(s, e):
+    c = 0
+    for i in range(s, e):
+        c += i
+    return c
+
+
+def sum2d(s, e):
+    c = 0
+    for i in range(s, e):
+        for j in range(s, e):
+            c += i * j
+    return c
+
+
+def while_count(s, e):
+    i = s
+    c = 0
+    while i < e:
+        c += i
+        i += 1
+    return c
+
+
+def copy_arrays(a, b):
+    for i in range(a.shape[0]):
+        b[i] = a[i]
+
+
+def copy_arrays2d(a, b):
+    for i in range(a.shape[0]):
+        for j in range(a.shape[1]):
+            b[i, j] = a[i, j]
+
+
+def redefine1():
+    x = 0
+    for i in range(5):
+        x += 1
+    x = 0. + x
+    for i in range(5):
+        x += 1
+    return x
+
+
+def andor(x, y):
+    return (x > 0 and x < 10) or (y > 0 and y < 10)
+
+andornopython = jit(nopython=True)(andor)
+
+
+def string_concat(x, y):
+    a = "whatzup"
+    return a + str(x + y)
+
+
+def string_len(s):
+    return len(s)
+
+
+def string_slicing(s, start, stop):
+    return s[start:stop]
+
+
+def string_conversion(x):
+    return str(x)
+
+
+def string_comparison(s1, s2, op):
+    return op(s1, s2)
+
+
+def blackscholes_cnd(d):
+    A1 = 0.31938153
+    A2 = -0.356563782
+    A3 = 1.781477937
+    A4 = -1.821255978
+    A5 = 1.330274429
+    RSQRT2PI = 0.39894228040143267793994605993438
+    K = 1.0 / (1.0 + 0.2316419 * math.fabs(d))
+    ret_val = (RSQRT2PI * math.exp(-0.5 * d * d) *
+               (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+    if d > 0:
+        ret_val = 1.0 - ret_val
+    return ret_val
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/__init__.py
new file mode 100644
index 000000000..62004deb0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/__init__.py
@@ -0,0 +1,20 @@
+import importlib
+
+
+_delayed_symbols = {
+    "Dict": ".typeddict",
+    "List": ".typedlist",
+}
+
+
+def __getattr__(name):
+    # Uses PEP-562 but requires python>3.6
+    if name in _delayed_symbols:
+        modpath = _delayed_symbols[name]
+        mod = importlib.import_module(modpath, __name__)
+        return getattr(mod, name)
+    else:
+        try:
+            return importlib.import_module(f".{name}", __name__)
+        except ModuleNotFoundError:
+            raise AttributeError
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/dictimpl.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/dictimpl.py
new file mode 100644
index 000000000..d18043921
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/dictimpl.py
@@ -0,0 +1,20 @@
+"""
+This file implements the lowering for `dict()`
+"""
+from numba.core.imputils import lower_builtin
+
+
+@lower_builtin(dict)
+def impl_dict(context, builder, sig, args):
+    """
+    The `dict()` implementation simply forwards the work to `Dict.empty()`.
+    """
+    from numba.typed import Dict
+
+    dicttype = sig.return_type
+    kt, vt = dicttype.key_type, dicttype.value_type
+
+    def call_ctor():
+        return Dict.empty(kt, vt)
+
+    return context.compile_internal(builder, call_ctor, sig, args)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/dictobject.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/dictobject.py
new file mode 100644
index 000000000..e131cb21a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/dictobject.py
@@ -0,0 +1,1349 @@
+"""
+Compiler-side implementation of the dictionary.
+"""
+import ctypes
+import operator
+from enum import IntEnum
+
+from llvmlite import ir
+
+from numba import _helperlib
+
+from numba.core.extending import (
+    overload,
+    overload_method,
+    intrinsic,
+    register_model,
+    models,
+    lower_builtin,
+    lower_cast,
+    make_attribute_wrapper,
+)
+from numba.core.imputils import iternext_impl, impl_ret_untracked
+from numba.core import types, cgutils
+from numba.core.types import (
+    DictType,
+    DictItemsIterableType,
+    DictKeysIterableType,
+    DictValuesIterableType,
+    DictIteratorType,
+    Type,
+)
+from numba.core.imputils import impl_ret_borrowed, RefType
+from numba.core.errors import TypingError, LoweringError
+from numba.core import typing
+from numba.typed.typedobjectutils import (_as_bytes, _cast, _nonoptional,
+                                          _sentry_safe_cast_default,
+                                          _get_incref_decref,
+                                          _get_equal, _container_get_data,)
+
+ll_dict_type = cgutils.voidptr_t
+ll_dictiter_type = cgutils.voidptr_t
+ll_voidptr_type = cgutils.voidptr_t
+ll_status = cgutils.int32_t
+ll_ssize_t = cgutils.intp_t
+ll_hash = ll_ssize_t
+ll_bytes = cgutils.voidptr_t
+
+
+_meminfo_dictptr = types.MemInfoPointer(types.voidptr)
+
+
+# The following enums must match _dictobject.c
+
+class DKIX(IntEnum):
+    """Special return value of dict lookup.
+    """
+    EMPTY = -1
+
+
+class Status(IntEnum):
+    """Status code for other dict operations.
+    """
+    OK = 0
+    OK_REPLACED = 1
+    ERR_NO_MEMORY = -1
+    ERR_DICT_MUTATED = -2
+    ERR_ITER_EXHAUSTED = -3
+    ERR_DICT_EMPTY = -4
+    ERR_CMP_FAILED = -5
+
+
+def new_dict(key, value):
+    """Construct a new dict.
+
+    Parameters
+    ----------
+    key, value : TypeRef
+        Key type and value type of the new dict.
+    """
+    # With JIT disabled, ignore all arguments and return a Python dict.
+    return dict()
+
+
+@register_model(DictType)
+class DictModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('meminfo', _meminfo_dictptr),
+            ('data', types.voidptr),   # ptr to the C dict
+        ]
+        super(DictModel, self).__init__(dmm, fe_type, members)
+
+
+@register_model(DictItemsIterableType)
+@register_model(DictKeysIterableType)
+@register_model(DictValuesIterableType)
+@register_model(DictIteratorType)
+class DictIterModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('parent', fe_type.parent),  # reference to the dict
+            ('state', types.voidptr),    # iterator state in C code
+        ]
+        super(DictIterModel, self).__init__(dmm, fe_type, members)
+
+
+# Make _parent available to make len simple
+make_attribute_wrapper(DictItemsIterableType, "parent", "_parent")
+make_attribute_wrapper(DictKeysIterableType, "parent", "_parent")
+make_attribute_wrapper(DictValuesIterableType, "parent", "_parent")
+
+
+def _raise_if_error(context, builder, status, msg):
+    """Raise an internal error depending on the value of *status*
+    """
+    ok_status = status.type(int(Status.OK))
+    with builder.if_then(builder.icmp_signed('!=', status, ok_status)):
+        context.call_conv.return_user_exc(builder, RuntimeError, (msg,))
+
+
+@intrinsic
+def _as_meminfo(typingctx, dctobj):
+    """Returns the MemInfoPointer of a dictionary.
+    """
+    if not isinstance(dctobj, types.DictType):
+        raise TypingError('expected *dctobj* to be a DictType')
+
+    def codegen(context, builder, sig, args):
+        [td] = sig.args
+        [d] = args
+        # Incref
+        context.nrt.incref(builder, td, d)
+        ctor = cgutils.create_struct_proxy(td)
+        dstruct = ctor(context, builder, value=d)
+        # Returns the plain MemInfo
+        return dstruct.meminfo
+
+    sig = _meminfo_dictptr(dctobj)
+    return sig, codegen
+
+
+@intrinsic
+def _from_meminfo(typingctx, mi, dicttyperef):
+    """Recreate a dictionary from a MemInfoPointer
+    """
+    if mi != _meminfo_dictptr:
+        raise TypingError('expected a MemInfoPointer for dict.')
+    dicttype = dicttyperef.instance_type
+    if not isinstance(dicttype, DictType):
+        raise TypingError('expected a {}'.format(DictType))
+
+    def codegen(context, builder, sig, args):
+        [tmi, tdref] = sig.args
+        td = tdref.instance_type
+        [mi, _] = args
+
+        ctor = cgutils.create_struct_proxy(td)
+        dstruct = ctor(context, builder)
+
+        data_pointer = context.nrt.meminfo_data(builder, mi)
+        data_pointer = builder.bitcast(data_pointer, ll_dict_type.as_pointer())
+
+        dstruct.data = builder.load(data_pointer)
+        dstruct.meminfo = mi
+
+        return impl_ret_borrowed(
+            context,
+            builder,
+            dicttype,
+            dstruct._getvalue(),
+        )
+
+    sig = dicttype(mi, dicttyperef)
+    return sig, codegen
+
+
+def _call_dict_free(context, builder, ptr):
+    """Call numba_dict_free(ptr)
+    """
+    fnty = ir.FunctionType(
+        ir.VoidType(),
+        [ll_dict_type],
+    )
+    free = cgutils.get_or_insert_function(builder.module, fnty,
+                                          'numba_dict_free')
+    builder.call(free, [ptr])
+
+
+def _imp_dtor(context, module):
+    """Define the dtor for dictionary
+    """
+    llvoidptr = context.get_value_type(types.voidptr)
+    llsize = context.get_value_type(types.uintp)
+    fnty = ir.FunctionType(
+        ir.VoidType(),
+        [llvoidptr, llsize, llvoidptr],
+    )
+    fname = '_numba_dict_dtor'
+    fn = cgutils.get_or_insert_function(module, fnty, fname)
+
+    if fn.is_declaration:
+        # Set linkage
+        fn.linkage = 'linkonce_odr'
+        # Define
+        builder = ir.IRBuilder(fn.append_basic_block())
+        dp = builder.bitcast(fn.args[0], ll_dict_type.as_pointer())
+        d = builder.load(dp)
+        _call_dict_free(context, builder, d)
+        builder.ret_void()
+
+    return fn
+
+
+@intrinsic
+def _dict_new_minsize(typingctx, keyty, valty):
+    """Wrap numba_dict_new_minsize.
+
+    Allocate a new dictionary object with the minimum capacity.
+
+    Parameters
+    ----------
+    keyty, valty: Type
+        Type of the key and value, respectively.
+
+    """
+    resty = types.voidptr
+    sig = resty(keyty, valty)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_dict_type.as_pointer(), ll_ssize_t, ll_ssize_t],
+        )
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_dict_new_minsize')
+        # Determine sizeof key and value types
+        ll_key = context.get_data_type(keyty.instance_type)
+        ll_val = context.get_data_type(valty.instance_type)
+        sz_key = context.get_abi_sizeof(ll_key)
+        sz_val = context.get_abi_sizeof(ll_val)
+        refdp = cgutils.alloca_once(builder, ll_dict_type, zfill=True)
+        status = builder.call(
+            fn,
+            [refdp, ll_ssize_t(sz_key), ll_ssize_t(sz_val)],
+        )
+        _raise_if_error(
+            context, builder, status,
+            msg="Failed to allocate dictionary",
+        )
+        dp = builder.load(refdp)
+        return dp
+
+    return sig, codegen
+
+
+@intrinsic
+def _dict_set_method_table(typingctx, dp, keyty, valty):
+    """Wrap numba_dict_set_method_table
+    """
+    resty = types.void
+    sig = resty(dp, keyty, valty)
+
+    def codegen(context, builder, sig, args):
+        vtablety = ir.LiteralStructType([
+            ll_voidptr_type,  # equal
+            ll_voidptr_type,  # key incref
+            ll_voidptr_type,  # key decref
+            ll_voidptr_type,  # val incref
+            ll_voidptr_type,  # val decref
+        ])
+        setmethod_fnty = ir.FunctionType(
+            ir.VoidType(),
+            [ll_dict_type, vtablety.as_pointer()]
+        )
+        setmethod_fn = ir.Function(
+            builder.module,
+            setmethod_fnty,
+            name='numba_dict_set_method_table',
+        )
+        dp = args[0]
+        vtable = cgutils.alloca_once(builder, vtablety, zfill=True)
+
+        # install key incref/decref
+        key_equal_ptr = cgutils.gep_inbounds(builder, vtable, 0, 0)
+        key_incref_ptr = cgutils.gep_inbounds(builder, vtable, 0, 1)
+        key_decref_ptr = cgutils.gep_inbounds(builder, vtable, 0, 2)
+        val_incref_ptr = cgutils.gep_inbounds(builder, vtable, 0, 3)
+        val_decref_ptr = cgutils.gep_inbounds(builder, vtable, 0, 4)
+
+        dm_key = context.data_model_manager[keyty.instance_type]
+        if dm_key.contains_nrt_meminfo():
+            equal = _get_equal(context, builder.module, dm_key, 'dict_key')
+            key_incref, key_decref = _get_incref_decref(
+                context, builder.module, dm_key, 'dict_key'
+            )
+            builder.store(
+                builder.bitcast(equal, key_equal_ptr.type.pointee),
+                key_equal_ptr,
+            )
+            builder.store(
+                builder.bitcast(key_incref, key_incref_ptr.type.pointee),
+                key_incref_ptr,
+            )
+            builder.store(
+                builder.bitcast(key_decref, key_decref_ptr.type.pointee),
+                key_decref_ptr,
+            )
+
+        dm_val = context.data_model_manager[valty.instance_type]
+        if dm_val.contains_nrt_meminfo():
+            val_incref, val_decref = _get_incref_decref(
+                context, builder.module, dm_val, 'dict_value'
+            )
+            builder.store(
+                builder.bitcast(val_incref, val_incref_ptr.type.pointee),
+                val_incref_ptr,
+            )
+            builder.store(
+                builder.bitcast(val_decref, val_decref_ptr.type.pointee),
+                val_decref_ptr,
+            )
+
+        builder.call(setmethod_fn, [dp, vtable])
+
+    return sig, codegen
+
+
+@intrinsic
+def _dict_insert(typingctx, d, key, hashval, val):
+    """Wrap numba_dict_insert
+    """
+    resty = types.int32
+    sig = resty(d, d.key_type, types.intp, d.value_type)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_dict_type, ll_bytes, ll_hash, ll_bytes, ll_bytes],
+        )
+        [d, key, hashval, val] = args
+        [td, tkey, thashval, tval] = sig.args
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_dict_insert')
+
+        dm_key = context.data_model_manager[tkey]
+        dm_val = context.data_model_manager[tval]
+
+        data_key = dm_key.as_data(builder, key)
+        data_val = dm_val.as_data(builder, val)
+
+        ptr_key = cgutils.alloca_once_value(builder, data_key)
+        cgutils.memset_padding(builder, ptr_key)
+
+        ptr_val = cgutils.alloca_once_value(builder, data_val)
+        # TODO: the ptr_oldval is not used.  needed for refct
+        ptr_oldval = cgutils.alloca_once(builder, data_val.type)
+
+        dp = _container_get_data(context, builder, td, d)
+        status = builder.call(
+            fn,
+            [
+                dp,
+                _as_bytes(builder, ptr_key),
+                hashval,
+                _as_bytes(builder, ptr_val),
+                _as_bytes(builder, ptr_oldval),
+            ],
+        )
+        return status
+
+    return sig, codegen
+
+
+@intrinsic
+def _dict_length(typingctx, d):
+    """Wrap numba_dict_length
+
+    Returns the length of the dictionary.
+    """
+    resty = types.intp
+    sig = resty(d)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_ssize_t,
+            [ll_dict_type],
+        )
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_dict_length')
+        [d] = args
+        [td] = sig.args
+        dp = _container_get_data(context, builder, td, d)
+        n = builder.call(fn, [dp])
+        return n
+
+    return sig, codegen
+
+
+@intrinsic
+def _dict_dump(typingctx, d):
+    """Dump the dictionary keys and values.
+    Wraps numba_dict_dump for debugging.
+    """
+    resty = types.void
+    sig = resty(d)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ir.VoidType(),
+            [ll_dict_type],
+        )
+        [td] = sig.args
+        [d] = args
+        dp = _container_get_data(context, builder, td, d)
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_dict_dump')
+
+        builder.call(fn, [dp])
+
+    return sig, codegen
+
+
+@intrinsic
+def _dict_lookup(typingctx, d, key, hashval):
+    """Wrap numba_dict_lookup
+
+    Returns 2-tuple of (intp, ?value_type)
+    """
+    resty = types.Tuple([types.intp, types.Optional(d.value_type)])
+    sig = resty(d, key, hashval)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_ssize_t,
+            [ll_dict_type, ll_bytes, ll_hash, ll_bytes],
+        )
+        [td, tkey, thashval] = sig.args
+        [d, key, hashval] = args
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_dict_lookup')
+
+        dm_key = context.data_model_manager[tkey]
+        dm_val = context.data_model_manager[td.value_type]
+
+        data_key = dm_key.as_data(builder, key)
+        ptr_key = cgutils.alloca_once_value(builder, data_key)
+        cgutils.memset_padding(builder, ptr_key)
+
+        ll_val = context.get_data_type(td.value_type)
+        ptr_val = cgutils.alloca_once(builder, ll_val)
+
+        dp = _container_get_data(context, builder, td, d)
+        ix = builder.call(
+            fn,
+            [
+                dp,
+                _as_bytes(builder, ptr_key),
+                hashval,
+                _as_bytes(builder, ptr_val),
+            ],
+        )
+        # Load value if output is available
+        found = builder.icmp_signed('>', ix, ix.type(int(DKIX.EMPTY)))
+
+        out = context.make_optional_none(builder, td.value_type)
+        pout = cgutils.alloca_once_value(builder, out)
+
+        with builder.if_then(found):
+            val = dm_val.load_from_data_pointer(builder, ptr_val)
+            context.nrt.incref(builder, td.value_type, val)
+            loaded = context.make_optional_value(builder, td.value_type, val)
+            builder.store(loaded, pout)
+
+        out = builder.load(pout)
+        return context.make_tuple(builder, resty, [ix, out])
+
+    return sig, codegen
+
+
+@intrinsic
+def _dict_popitem(typingctx, d):
+    """Wrap numba_dict_popitem
+    """
+
+    keyvalty = types.Tuple([d.key_type, d.value_type])
+    resty = types.Tuple([types.int32, types.Optional(keyvalty)])
+    sig = resty(d)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_dict_type, ll_bytes, ll_bytes],
+        )
+        [d] = args
+        [td] = sig.args
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_dict_popitem')
+
+        dm_key = context.data_model_manager[td.key_type]
+        dm_val = context.data_model_manager[td.value_type]
+
+        ptr_key = cgutils.alloca_once(builder, dm_key.get_data_type())
+        ptr_val = cgutils.alloca_once(builder, dm_val.get_data_type())
+
+        dp = _container_get_data(context, builder, td, d)
+        status = builder.call(
+            fn,
+            [
+                dp,
+                _as_bytes(builder, ptr_key),
+                _as_bytes(builder, ptr_val),
+            ],
+        )
+        out = context.make_optional_none(builder, keyvalty)
+        pout = cgutils.alloca_once_value(builder, out)
+
+        cond = builder.icmp_signed('==', status, status.type(int(Status.OK)))
+        with builder.if_then(cond):
+            key = dm_key.load_from_data_pointer(builder, ptr_key)
+            val = dm_val.load_from_data_pointer(builder, ptr_val)
+            keyval = context.make_tuple(builder, keyvalty, [key, val])
+            optkeyval = context.make_optional_value(builder, keyvalty, keyval)
+            builder.store(optkeyval, pout)
+
+        out = builder.load(pout)
+        return cgutils.pack_struct(builder, [status, out])
+
+    return sig, codegen
+
+
+@intrinsic
+def _dict_delitem(typingctx, d, hk, ix):
+    """Wrap numba_dict_delitem
+    """
+    resty = types.int32
+    sig = resty(d, hk, types.intp)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_dict_type, ll_hash, ll_ssize_t],
+        )
+        [d, hk, ix] = args
+        [td, thk, tix] = sig.args
+
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_dict_delitem')
+
+        dp = _container_get_data(context, builder, td, d)
+        status = builder.call(fn, [dp, hk, ix])
+        return status
+
+    return sig, codegen
+
+
+def _iterator_codegen(resty):
+    """The common codegen for iterator intrinsics.
+
+    Populates the iterator struct and increfs.
+    """
+
+    def codegen(context, builder, sig, args):
+        [d] = args
+        [td] = sig.args
+        iterhelper = context.make_helper(builder, resty)
+        iterhelper.parent = d
+        iterhelper.state = iterhelper.state.type(None)
+        return impl_ret_borrowed(
+            context,
+            builder,
+            resty,
+            iterhelper._getvalue(),
+        )
+
+    return codegen
+
+
+@intrinsic
+def _dict_items(typingctx, d):
+    """Get dictionary iterator for .items()"""
+    resty = types.DictItemsIterableType(d)
+    sig = resty(d)
+    codegen = _iterator_codegen(resty)
+    return sig, codegen
+
+
+@intrinsic
+def _dict_keys(typingctx, d):
+    """Get dictionary iterator for .keys()"""
+    resty = types.DictKeysIterableType(d)
+    sig = resty(d)
+    codegen = _iterator_codegen(resty)
+    return sig, codegen
+
+
+@intrinsic
+def _dict_values(typingctx, d):
+    """Get dictionary iterator for .values()"""
+    resty = types.DictValuesIterableType(d)
+    sig = resty(d)
+    codegen = _iterator_codegen(resty)
+    return sig, codegen
+
+
+@intrinsic
+def _make_dict(typingctx, keyty, valty, ptr):
+    """Make a dictionary struct with the given *ptr*
+
+    Parameters
+    ----------
+    keyty, valty: Type
+        Type of the key and value, respectively.
+    ptr : llvm pointer value
+        Points to the dictionary object.
+    """
+    dict_ty = types.DictType(keyty.instance_type, valty.instance_type)
+
+    def codegen(context, builder, signature, args):
+        [_, _, ptr] = args
+        ctor = cgutils.create_struct_proxy(dict_ty)
+        dstruct = ctor(context, builder)
+        dstruct.data = ptr
+
+        alloc_size = context.get_abi_sizeof(
+            context.get_value_type(types.voidptr),
+        )
+        dtor = _imp_dtor(context, builder.module)
+        meminfo = context.nrt.meminfo_alloc_dtor(
+            builder,
+            context.get_constant(types.uintp, alloc_size),
+            dtor,
+        )
+
+        data_pointer = context.nrt.meminfo_data(builder, meminfo)
+        data_pointer = builder.bitcast(data_pointer, ll_dict_type.as_pointer())
+        builder.store(ptr, data_pointer)
+
+        dstruct.meminfo = meminfo
+
+        return dstruct._getvalue()
+
+    sig = dict_ty(keyty, valty, ptr)
+    return sig, codegen
+
+
+@overload(new_dict)
+def impl_new_dict(key, value):
+    """Creates a new dictionary with *key* and *value* as the type
+    of the dictionary key and value, respectively.
+    """
+    if any([
+        not isinstance(key, Type),
+        not isinstance(value, Type),
+    ]):
+        raise TypeError("expecting *key* and *value* to be a numba Type")
+
+    keyty, valty = key, value
+
+    def imp(key, value):
+        dp = _dict_new_minsize(keyty, valty)
+        _dict_set_method_table(dp, keyty, valty)
+        d = _make_dict(keyty, valty, dp)
+        return d
+
+    return imp
+
+
+@overload(len)
+def impl_len(d):
+    """len(dict)
+    """
+    if not isinstance(d, types.DictType):
+        return
+
+    def impl(d):
+        return _dict_length(d)
+
+    return impl
+
+
+@overload(len)
+def impl_len_iters(d):
+    """len(dict.keys()), len(dict.values()), len(dict.items())
+    """
+    if not isinstance(d, (DictKeysIterableType,
+                      DictValuesIterableType, DictItemsIterableType)):
+        return
+
+    def impl(d):
+        return _dict_length(d._parent)
+
+    return impl
+
+
+@overload_method(types.DictType, '__setitem__')
+@overload(operator.setitem)
+def impl_setitem(d, key, value):
+    if not isinstance(d, types.DictType):
+        return
+
+    keyty, valty = d.key_type, d.value_type
+
+    def impl(d, key, value):
+        castedkey = _cast(key, keyty)
+        castedval = _cast(value, valty)
+        status = _dict_insert(d, castedkey, hash(castedkey), castedval)
+        if status == Status.OK:
+            return
+        elif status == Status.OK_REPLACED:
+            # replaced
+            # XXX handle refcount
+            return
+        elif status == Status.ERR_CMP_FAILED:
+            raise ValueError('key comparison failed')
+        else:
+            raise RuntimeError('dict.__setitem__ failed unexpectedly')
+
+    if d.is_precise():
+        # Handle the precise case.
+        return impl
+    else:
+        # Handle the imprecise case.
+        d = d.refine(key, value)
+        # Re-bind the key type and value type to match the arguments.
+        keyty, valty = d.key_type, d.value_type
+        # Create the signature that we wanted this impl to have.
+        sig = typing.signature(types.void, d, keyty, valty)
+        return sig, impl
+
+
+@overload_method(types.DictType, 'get')
+def impl_get(dct, key, default=None):
+    if not isinstance(dct, types.DictType):
+        return
+    keyty = dct.key_type
+    valty = dct.value_type
+    _sentry_safe_cast_default(default, valty)
+
+    def impl(dct, key, default=None):
+        castedkey = _cast(key, keyty)
+        ix, val = _dict_lookup(dct, castedkey, hash(castedkey))
+        if ix > DKIX.EMPTY:
+            return val
+        return default
+
+    return impl
+
+
+@overload(operator.getitem)
+def impl_getitem(d, key):
+    if not isinstance(d, types.DictType):
+        return
+
+    keyty = d.key_type
+
+    def impl(d, key):
+        castedkey = _cast(key, keyty)
+        ix, val = _dict_lookup(d, castedkey, hash(castedkey))
+        if ix == DKIX.EMPTY:
+            raise KeyError()
+        elif ix < DKIX.EMPTY:
+            raise AssertionError("internal dict error during lookup")
+        else:
+            return _nonoptional(val)
+
+    return impl
+
+
+@overload_method(types.DictType, 'popitem')
+def impl_popitem(d):
+    if not isinstance(d, types.DictType):
+        return
+
+    def impl(d):
+        status, keyval = _dict_popitem(d)
+        if status == Status.OK:
+            return _nonoptional(keyval)
+        elif status == Status.ERR_DICT_EMPTY:
+            raise KeyError()
+        else:
+            raise AssertionError('internal dict error during popitem')
+
+    return impl
+
+
+@overload_method(types.DictType, 'pop')
+def impl_pop(dct, key, default=None):
+    if not isinstance(dct, types.DictType):
+        return
+
+    keyty = dct.key_type
+    valty = dct.value_type
+    should_raise = isinstance(default, types.Omitted)
+    _sentry_safe_cast_default(default, valty)
+
+    def impl(dct, key, default=None):
+        castedkey = _cast(key, keyty)
+        hashed = hash(castedkey)
+        ix, val = _dict_lookup(dct, castedkey, hashed)
+        if ix == DKIX.EMPTY:
+            if should_raise:
+                raise KeyError()
+            else:
+                return default
+        elif ix < DKIX.EMPTY:
+            raise AssertionError("internal dict error during lookup")
+        else:
+            status = _dict_delitem(dct,hashed, ix)
+            if status != Status.OK:
+                raise AssertionError("internal dict error during delitem")
+            return val
+
+    return impl
+
+
+@overload(operator.delitem)
+def impl_delitem(d, k):
+    if not isinstance(d, types.DictType):
+        return
+
+    def impl(d, k):
+        d.pop(k)
+    return impl
+
+
+@overload(operator.contains)
+def impl_contains(d, k):
+    if not isinstance(d, types.DictType):
+        return
+
+    keyty = d.key_type
+
+    def impl(d, k):
+        k = _cast(k, keyty)
+        ix, val = _dict_lookup(d, k, hash(k))
+        return ix > DKIX.EMPTY
+    return impl
+
+
+@overload_method(types.DictType, 'clear')
+def impl_clear(d):
+    if not isinstance(d, types.DictType):
+        return
+
+    def impl(d):
+        while len(d):
+            d.popitem()
+
+    return impl
+
+
+@overload_method(types.DictType, 'copy')
+def impl_copy(d):
+    if not isinstance(d, types.DictType):
+        return
+
+    key_type, val_type = d.key_type, d.value_type
+
+    def impl(d):
+        newd = new_dict(key_type, val_type)
+        for k, v in d.items():
+            newd[k] = v
+        return newd
+
+    return impl
+
+
+@overload_method(types.DictType, 'setdefault')
+def impl_setdefault(dct, key, default=None):
+    if not isinstance(dct, types.DictType):
+        return
+
+    def impl(dct, key, default=None):
+        if key not in dct:
+            dct[key] = default
+        return dct[key]
+
+    return impl
+
+
+@overload_method(types.DictType, 'items')
+def impl_items(d):
+    if not isinstance(d, types.DictType):
+        return
+
+    def impl(d):
+        it = _dict_items(d)
+        return it
+
+    return impl
+
+
+@overload_method(types.DictType, 'keys')
+def impl_keys(d):
+    if not isinstance(d, types.DictType):
+        return
+
+    def impl(d):
+        return _dict_keys(d)
+
+    return impl
+
+
+@overload_method(types.DictType, 'values')
+def impl_values(d):
+    if not isinstance(d, types.DictType):
+        return
+
+    def impl(d):
+        return _dict_values(d)
+
+    return impl
+
+
+@overload_method(types.DictType, 'update')
+def ol_dict_update(d, other):
+    if not isinstance(d, types.DictType):
+        return
+    if not isinstance(other, types.DictType):
+        return
+
+    def impl(d, other):
+        for k, v in other.items():
+            d[k] = v
+    return impl
+
+
+@overload(operator.eq)
+def impl_equal(da, db):
+    if not isinstance(da, types.DictType):
+        return
+    if not isinstance(db, types.DictType):
+        # If RHS is not a dictionary, always returns False
+        def impl_type_mismatch(da, db):
+            return False
+        return impl_type_mismatch
+
+    otherkeyty = db.key_type
+
+    def impl_type_matched(da, db):
+        if len(da) != len(db):
+            return False
+        for ka, va in da.items():
+            # Cast key from LHS to the key-type of RHS
+            kb = _cast(ka, otherkeyty)
+            ix, vb = _dict_lookup(db, kb, hash(kb))
+            if ix <= DKIX.EMPTY:
+                # Quit early if the key is not found
+                return False
+            if va != vb:
+                # Quit early if the values do not match
+                return False
+        return True
+
+    return impl_type_matched
+
+
+@overload(operator.ne)
+def impl_not_equal(da, db):
+    if not isinstance(da, types.DictType):
+        return
+
+    def impl(da, db):
+        return not (da == db)
+
+    return impl
+
+
+@lower_builtin('getiter', types.DictItemsIterableType)
+@lower_builtin('getiter', types.DictKeysIterableType)
+@lower_builtin('getiter', types.DictValuesIterableType)
+def impl_iterable_getiter(context, builder, sig, args):
+    """Implement iter() for .keys(), .values(), .items()
+    """
+    iterablety = sig.args[0]
+    it = context.make_helper(builder, iterablety.iterator_type, args[0])
+
+    fnty = ir.FunctionType(
+        ir.VoidType(),
+        [ll_dictiter_type, ll_dict_type],
+    )
+
+    fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                        'numba_dict_iter')
+
+    proto = ctypes.CFUNCTYPE(ctypes.c_size_t)
+    dictiter_sizeof = proto(_helperlib.c_helpers['dict_iter_sizeof'])
+    state_type = ir.ArrayType(ir.IntType(8), dictiter_sizeof())
+
+    pstate = cgutils.alloca_once(builder, state_type, zfill=True)
+    it.state = _as_bytes(builder, pstate)
+
+    dp = _container_get_data(context, builder, iterablety.parent, it.parent)
+    builder.call(fn, [it.state, dp])
+    return impl_ret_borrowed(
+        context,
+        builder,
+        sig.return_type,
+        it._getvalue(),
+    )
+
+
+@lower_builtin('getiter', types.DictType)
+def impl_dict_getiter(context, builder, sig, args):
+    """Implement iter(Dict).  Semantically equivalent to dict.keys()
+    """
+    [td] = sig.args
+    [d] = args
+    iterablety = types.DictKeysIterableType(td)
+    it = context.make_helper(builder, iterablety.iterator_type)
+
+    fnty = ir.FunctionType(
+        ir.VoidType(),
+        [ll_dictiter_type, ll_dict_type],
+    )
+
+    fn = cgutils.get_or_insert_function(builder.module, fnty, 'numba_dict_iter')
+
+    proto = ctypes.CFUNCTYPE(ctypes.c_size_t)
+    dictiter_sizeof = proto(_helperlib.c_helpers['dict_iter_sizeof'])
+    state_type = ir.ArrayType(ir.IntType(8), dictiter_sizeof())
+
+    pstate = cgutils.alloca_once(builder, state_type, zfill=True)
+    it.state = _as_bytes(builder, pstate)
+    it.parent = d
+
+    dp = _container_get_data(context, builder, iterablety.parent, args[0])
+    builder.call(fn, [it.state, dp])
+    return impl_ret_borrowed(
+        context,
+        builder,
+        sig.return_type,
+        it._getvalue(),
+    )
+
+
+@lower_builtin('iternext', types.DictIteratorType)
+@iternext_impl(RefType.BORROWED)
+def impl_iterator_iternext(context, builder, sig, args, result):
+    iter_type = sig.args[0]
+    it = context.make_helper(builder, iter_type, args[0])
+
+    p2p_bytes = ll_bytes.as_pointer()
+
+    iternext_fnty = ir.FunctionType(
+        ll_status,
+        [ll_bytes, p2p_bytes, p2p_bytes]
+    )
+    iternext = cgutils.get_or_insert_function(
+        builder.module, iternext_fnty, 'numba_dict_iter_next',
+    )
+    key_raw_ptr = cgutils.alloca_once(builder, ll_bytes)
+    val_raw_ptr = cgutils.alloca_once(builder, ll_bytes)
+
+    status = builder.call(iternext, (it.state, key_raw_ptr, val_raw_ptr))
+    # TODO: no handling of error state i.e. mutated dictionary
+    #       all errors are treated as exhausted iterator
+    is_valid = builder.icmp_unsigned('==', status, status.type(0))
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        yield_type = iter_type.yield_type
+        key_ty, val_ty = iter_type.parent.keyvalue_type
+
+        dm_key = context.data_model_manager[key_ty]
+        dm_val = context.data_model_manager[val_ty]
+
+        key_ptr = builder.bitcast(
+            builder.load(key_raw_ptr),
+            dm_key.get_data_type().as_pointer(),
+        )
+        val_ptr = builder.bitcast(
+            builder.load(val_raw_ptr),
+            dm_val.get_data_type().as_pointer(),
+        )
+
+        key = dm_key.load_from_data_pointer(builder, key_ptr)
+        val = dm_val.load_from_data_pointer(builder, val_ptr)
+
+        # All dict iterators use this common implementation.
+        # Their differences are resolved here.
+        if isinstance(iter_type.iterable, DictItemsIterableType):
+            # .items()
+            tup = context.make_tuple(builder, yield_type, [key, val])
+            result.yield_(tup)
+        elif isinstance(iter_type.iterable, DictKeysIterableType):
+            # .keys()
+            result.yield_(key)
+        elif isinstance(iter_type.iterable, DictValuesIterableType):
+            # .values()
+            result.yield_(val)
+        else:
+            # unreachable
+            raise AssertionError('unknown type: {}'.format(iter_type.iterable))
+
+
+def build_map(context, builder, dict_type, item_types, items):
+
+    if isinstance(dict_type, types.LiteralStrKeyDict):
+        unliteral_tys = [x for x in
+                         dict_type.literal_value.values()]
+        nbty = types.NamedTuple(unliteral_tys,
+                                dict_type.tuple_ty)
+        values = [x[1] for x in items]
+        # replace with make_tuple call?
+        tup = context.get_constant_undef(nbty)
+        literal_tys = [x for x in dict_type.literal_value.values()]
+
+        # this is to deal with repeated keys
+        value_index = dict_type.value_index
+        if value_index is None:
+            # 1:1 map keys:values
+            value_indexer = range(len(values))
+        else:
+            # 1:>1 map keys:values, e.g. {'a':1, 'a': 'foo'}
+            value_indexer = value_index.values()
+
+        for i, ix in enumerate(value_indexer):
+            val = values[ix]
+            casted = context.cast(builder, val, literal_tys[i],
+                                  unliteral_tys[i])
+            tup = builder.insert_value(tup, casted, i)
+        d = tup
+        context.nrt.incref(builder, nbty, d)
+
+    else:
+        from numba.typed import Dict
+
+        dt = types.DictType(dict_type.key_type, dict_type.value_type)
+        kt, vt = dict_type.key_type, dict_type.value_type
+        sig = typing.signature(dt)
+
+        def make_dict():
+            return Dict.empty(kt, vt)
+
+        d = context.compile_internal(builder, make_dict, sig, ())
+
+        if items:
+            for (kt, vt), (k, v) in zip(item_types, items):
+                sig = typing.signature(types.void, dt, kt, vt)
+                args = d, k, v
+
+                def put(d, k, v):
+                    d[k] = v
+
+                context.compile_internal(builder, put, sig, args)
+
+    return d
+
+
+# ------------------------------------------------------------------------------
+# Literal dictionaries
+# ------------------------------------------------------------------------------
+
+@intrinsic
+def _mixed_values_to_tuple(tyctx, d):
+    keys = [x for x in d.literal_value.keys()]
+    literal_tys = [x for x in d.literal_value.values()]
+
+    def impl(cgctx, builder, sig, args):
+        lld, = args
+        impl = cgctx.get_function('static_getitem',
+                                  types.none(d, types.literal('dummy')))
+        items = []
+        for k in range(len(keys)):
+            item = impl(builder, (lld, k),)
+            casted = cgctx.cast(builder, item, literal_tys[k], d.types[k])
+            items.append(casted)
+            cgctx.nrt.incref(builder, d.types[k], item)
+        ret = cgctx.make_tuple(builder, sig.return_type, items)
+        return ret
+    sig = types.Tuple(d.types)(d)
+    return sig, impl
+
+
+@overload_method(types.LiteralStrKeyDict, 'values')
+def literalstrkeydict_impl_values(d):
+    # This requires faking a values() iterator simply as a tuple, creating a
+    # type specialising iterator would be possible but horrendous and end up
+    # down the "versioned" loop body route.
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+
+    def impl(d):
+        return _mixed_values_to_tuple(d)
+    return impl
+
+
+@overload_method(types.LiteralStrKeyDict, 'keys')
+def literalstrkeydict_impl_keys(d):
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+    # create a key iterator by specialising a DictType instance with the
+    # literal keys and returning that
+    t = tuple([x.literal_value for x in d.literal_value.keys()])
+
+    def impl(d):
+        d = dict()
+        for x in t:
+            d[x] = 0
+        return d.keys()
+    return impl
+
+
+# have to lower_builtin as this inherits from tuple and literal, both of which
+# provide a match, hence ambiguous before proper resolution gets a chance
+@lower_builtin(operator.eq, types.LiteralStrKeyDict, types.LiteralStrKeyDict)
+def literalstrkeydict_impl_equals(context, builder, sig, args):
+    tu, tv = sig.args
+    u, v = args
+    pred = tu.literal_value == tv.literal_value
+    res = context.get_constant(types.boolean, pred)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@overload(operator.getitem)
+@overload_method(types.LiteralStrKeyDict, 'get')
+def literalstrkeydict_impl_get(dct, *args):
+    if not isinstance(dct, types.LiteralStrKeyDict):
+        return
+    msg = ("Cannot get{item}() on a literal dictionary, return type cannot be "
+           "statically determined")
+    raise TypingError(msg)
+
+
+@overload_method(types.LiteralStrKeyDict, 'copy')
+def literalstrkeydict_impl_copy(d):
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+
+    def impl(d):
+        return d
+    return impl
+
+
+@intrinsic
+def _str_items_mixed_values_to_tuple(tyctx, d):
+    keys = [x for x in d.literal_value.keys()]
+    literal_tys = [x for x in d.literal_value.values()]
+
+    def impl(cgctx, builder, sig, args):
+
+        lld, = args
+        impl = cgctx.get_function('static_getitem',
+                                  types.none(d, types.literal('dummy')))
+        items = []
+        from numba.cpython.unicode import make_string_from_constant
+        for k in range(len(keys)):
+            item = impl(builder, (lld, k),)
+            casted = cgctx.cast(builder, item, literal_tys[k], d.types[k])
+            cgctx.nrt.incref(builder, d.types[k], item)
+            keydata = make_string_from_constant(cgctx, builder,
+                                                types.unicode_type,
+                                                keys[k].literal_value)
+            pair = cgctx.make_tuple(builder,
+                                    types.Tuple([types.unicode_type,
+                                                d.types[k]]), (keydata, casted))
+            items.append(pair)
+        ret = cgctx.make_tuple(builder, sig.return_type, items)
+        return ret
+    kvs = [types.Tuple((types.unicode_type, x)) for x in d.types]
+    sig = types.Tuple(kvs)(d)
+    return sig, impl
+
+
+@overload_method(types.LiteralStrKeyDict, 'items')
+def literalstrkeydict_impl_items(d):
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+
+    def impl(d):
+        return _str_items_mixed_values_to_tuple(d)
+    return impl
+
+
+@overload(operator.contains)
+def literalstrkeydict_impl_contains(d, k):
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+
+    def impl(d, k):
+        for key in d.keys():
+            if k == key:
+                return True
+        return False
+    return impl
+
+
+@overload(len)
+def literalstrkeydict_impl_len(d):
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+    l = d.count
+    return lambda d: l
+
+
+@overload(operator.setitem)
+def literalstrkeydict_banned_impl_setitem(d, key, value):
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+    raise TypingError("Cannot mutate a literal dictionary")
+
+
+@overload(operator.delitem)
+def literalstrkeydict_banned_impl_delitem(d, k):
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+    raise TypingError("Cannot mutate a literal dictionary")
+
+
+@overload_method(types.LiteralStrKeyDict, 'popitem')
+@overload_method(types.LiteralStrKeyDict, 'pop')
+@overload_method(types.LiteralStrKeyDict, 'clear')
+@overload_method(types.LiteralStrKeyDict, 'setdefault')
+@overload_method(types.LiteralStrKeyDict, 'update')
+def literalstrkeydict_banned_impl_mutators(d, *args):
+    if not isinstance(d, types.LiteralStrKeyDict):
+        return
+    raise TypingError("Cannot mutate a literal dictionary")
+
+
+@lower_cast(types.LiteralStrKeyDict, types.LiteralStrKeyDict)
+def cast_LiteralStrKeyDict_LiteralStrKeyDict(context, builder, fromty, toty,
+                                             val):
+    # should have been picked up by typing
+    for (k1, v1), (k2, v2) in zip(fromty.literal_value.items(),
+                                  toty.literal_value.items()):
+        # these checks are just guards, typing should have picked up any
+        # problems
+        if k1 != k2: # keys must be same
+            msg = "LiteralDictionary keys are not the same {} != {}"
+            raise LoweringError(msg.format(k1, k2))
+        # values must be same ty
+        if context.typing_context.unify_pairs(v1, v2) is None:
+            msg = "LiteralDictionary values cannot by unified, have {} and {}"
+            raise LoweringError(msg.format(v1, v2))
+    else:
+        fromty = types.Tuple(fromty.types)
+        toty = types.Tuple(toty.types)
+        olditems = cgutils.unpack_tuple(builder, val, len(fromty))
+        items = [context.cast(builder, v, f, t)
+                 for v, f, t in zip(olditems, fromty, toty)]
+        return context.make_tuple(builder, toty, items)
+
+
+@lower_cast(types.DictType, types.DictType)
+def cast_DictType_DictType(context, builder, fromty, toty, val):
+    # should have been picked up by typing
+    return val
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/listobject.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/listobject.py
new file mode 100644
index 000000000..77021cb69
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/listobject.py
@@ -0,0 +1,1531 @@
+"""
+Compiler-side implementation of the Numba  typed-list.
+"""
+import operator
+from enum import IntEnum
+
+from llvmlite import ir
+
+from numba.core.extending import (
+    overload,
+    overload_method,
+    overload_attribute,
+    register_jitable,
+    intrinsic,
+    register_model,
+    models,
+    lower_builtin,
+)
+from numba.core.imputils import iternext_impl
+from numba.core import types, cgutils
+from numba.core.types import (
+    ListType,
+    ListTypeIterableType,
+    ListTypeIteratorType,
+    Type,
+    NoneType,
+)
+from numba.core.imputils import impl_ret_borrowed, RefType
+from numba.core.errors import TypingError
+from numba.core import typing
+from numba.typed.typedobjectutils import (_as_bytes, _cast, _nonoptional,
+                                          _get_incref_decref,
+                                          _container_get_data,
+                                          _container_get_meminfo,)
+from numba.cpython import listobj
+
+ll_list_type = cgutils.voidptr_t
+ll_listiter_type = cgutils.voidptr_t
+ll_voidptr_type = cgutils.voidptr_t
+ll_status = cgutils.int32_t
+ll_ssize_t = cgutils.intp_t
+ll_bytes = cgutils.voidptr_t
+
+
+_meminfo_listptr = types.MemInfoPointer(types.voidptr)
+
+INDEXTY = types.intp
+
+index_types = types.integer_domain
+
+DEFAULT_ALLOCATED = 0
+
+
+@register_model(ListType)
+class ListModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('meminfo', _meminfo_listptr),
+            ('data', types.voidptr),   # ptr to the C list
+        ]
+        super(ListModel, self).__init__(dmm, fe_type, members)
+
+
+@register_model(ListTypeIterableType)
+@register_model(ListTypeIteratorType)
+class ListIterModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ('size', types.intp), # the size of the iteration space
+            ('parent', fe_type.parent), # the parent list
+            ('index', types.EphemeralPointer(types.intp)), # current index
+        ]
+        super(ListIterModel, self).__init__(dmm, fe_type, members)
+
+
+class ListStatus(IntEnum):
+    """Status code for other list operations.
+    """
+    LIST_OK = 0,
+    LIST_ERR_INDEX = -1
+    LIST_ERR_NO_MEMORY = -2
+    LIST_ERR_MUTATED = -3
+    LIST_ERR_ITER_EXHAUSTED = -4
+    LIST_ERR_IMMUTABLE = -5
+
+
+class ErrorHandler(object):
+    """ErrorHandler for calling codegen functions from this file.
+
+    Stores the state needed to raise an exception from nopython mode.
+    """
+
+    def __init__(self, context):
+        self.context = context
+
+    def __call__(self, builder, status, msg):
+        ok_status = status.type(int(ListStatus.LIST_OK))
+        with builder.if_then(builder.icmp_signed('!=', status, ok_status),
+                             likely=True):
+            self.context.call_conv.return_user_exc(
+                builder, RuntimeError, (msg,))
+
+
+def _check_for_none_typed(lst, method):
+    if isinstance(lst.dtype, NoneType):
+        raise TypingError("method support for List[None] is limited, "
+                          "not supported: '{}'.".format(method))
+
+
+@intrinsic
+def _as_meminfo(typingctx, lstobj):
+    """Returns the MemInfoPointer of a list.
+    """
+    if not isinstance(lstobj, types.ListType):
+        raise TypingError('expected *lstobj* to be a ListType')
+
+    def codegen(context, builder, sig, args):
+        [tl] = sig.args
+        [l] = args
+        # Incref
+        context.nrt.incref(builder, tl, l)
+        ctor = cgutils.create_struct_proxy(tl)
+        lstruct = ctor(context, builder, value=l)
+        # Returns the plain MemInfo
+        return lstruct.meminfo
+
+    sig = _meminfo_listptr(lstobj)
+    return sig, codegen
+
+
+@intrinsic
+def _from_meminfo(typingctx, mi, listtyperef):
+    """Recreate a list from a MemInfoPointer
+    """
+    if mi != _meminfo_listptr:
+        raise TypingError('expected a MemInfoPointer for list.')
+    listtype = listtyperef.instance_type
+    if not isinstance(listtype, ListType):
+        raise TypingError('expected a {}'.format(ListType))
+
+    def codegen(context, builder, sig, args):
+        [tmi, tdref] = sig.args
+        td = tdref.instance_type
+        [mi, _] = args
+
+        ctor = cgutils.create_struct_proxy(td)
+        dstruct = ctor(context, builder)
+
+        data_pointer = context.nrt.meminfo_data(builder, mi)
+        data_pointer = builder.bitcast(data_pointer, ll_list_type.as_pointer())
+
+        dstruct.data = builder.load(data_pointer)
+        dstruct.meminfo = mi
+
+        return impl_ret_borrowed(
+            context,
+            builder,
+            listtype,
+            dstruct._getvalue(),
+        )
+
+    sig = listtype(mi, listtyperef)
+    return sig, codegen
+
+
+def _list_codegen_set_method_table(context, builder, lp, itemty):
+    vtablety = ir.LiteralStructType([
+        ll_voidptr_type,  # item incref
+        ll_voidptr_type,  # item decref
+    ])
+    setmethod_fnty = ir.FunctionType(
+        ir.VoidType(),
+        [ll_list_type, vtablety.as_pointer()]
+    )
+
+    setmethod_fn = cgutils.get_or_insert_function(
+        builder.module,
+        setmethod_fnty,
+        'numba_list_set_method_table')
+    vtable = cgutils.alloca_once(builder, vtablety, zfill=True)
+
+    # install item incref/decref
+    item_incref_ptr = cgutils.gep_inbounds(builder, vtable, 0, 0)
+    item_decref_ptr = cgutils.gep_inbounds(builder, vtable, 0, 1)
+
+    dm_item = context.data_model_manager[itemty]
+    if dm_item.contains_nrt_meminfo():
+        item_incref, item_decref = _get_incref_decref(
+            context, builder.module, dm_item, "list"
+        )
+        builder.store(
+            builder.bitcast(item_incref, item_incref_ptr.type.pointee),
+            item_incref_ptr,
+        )
+        builder.store(
+            builder.bitcast(item_decref, item_decref_ptr.type.pointee),
+            item_decref_ptr,
+        )
+
+    builder.call(setmethod_fn, [lp, vtable])
+
+
+@intrinsic
+def _list_set_method_table(typingctx, lp, itemty):
+    """Wrap numba_list_set_method_table
+    """
+    resty = types.void
+    sig = resty(lp, itemty)
+
+    def codegen(context, builder, sig, args):
+        _list_codegen_set_method_table(
+            context, builder, args[0], itemty.instance_type)
+
+    return sig, codegen
+
+
+@lower_builtin(operator.is_, types.ListType, types.ListType)
+def list_is(context, builder, sig, args):
+    a_meminfo = _container_get_meminfo(context, builder, sig.args[0], args[0])
+    b_meminfo = _container_get_meminfo(context, builder, sig.args[1], args[1])
+    ma = builder.ptrtoint(a_meminfo, cgutils.intp_t)
+    mb = builder.ptrtoint(b_meminfo, cgutils.intp_t)
+    return builder.icmp_signed('==', ma, mb)
+
+
+def _call_list_free(context, builder, ptr):
+    """Call numba_list_free(ptr)
+    """
+    fnty = ir.FunctionType(
+        ir.VoidType(),
+        [ll_list_type],
+    )
+    free = cgutils.get_or_insert_function(builder.module, fnty,
+                                          'numba_list_free')
+    builder.call(free, [ptr])
+
+
+# FIXME: this needs a careful review
+def _imp_dtor(context, module):
+    """Define the dtor for list
+    """
+    llvoidptr = context.get_value_type(types.voidptr)
+    llsize = context.get_value_type(types.uintp)
+    fnty = ir.FunctionType(
+        ir.VoidType(),
+        [llvoidptr, llsize, llvoidptr],
+    )
+    fname = '_numba_list_dtor'
+    fn = cgutils.get_or_insert_function(module, fnty, fname)
+
+    if fn.is_declaration:
+        # Set linkage
+        fn.linkage = 'linkonce_odr'
+        # Define
+        builder = ir.IRBuilder(fn.append_basic_block())
+        lp = builder.bitcast(fn.args[0], ll_list_type.as_pointer())
+        l = builder.load(lp)
+        _call_list_free(context, builder, l)
+        builder.ret_void()
+
+    return fn
+
+
+def new_list(item, allocated=DEFAULT_ALLOCATED):
+    """Construct a new list. (Not implemented in the interpreter yet)
+
+    Parameters
+    ----------
+    item: TypeRef
+        Item type of the new list.
+    allocated: int
+        number of items to pre-allocate
+
+    """
+    # With JIT disabled, ignore all arguments and return a Python list.
+    return list()
+
+
+def _add_meminfo(context, builder, lstruct):
+    alloc_size = context.get_abi_sizeof(
+        context.get_value_type(types.voidptr),
+    )
+    dtor = _imp_dtor(context, builder.module)
+    meminfo = context.nrt.meminfo_alloc_dtor(
+        builder,
+        context.get_constant(types.uintp, alloc_size),
+        dtor,
+    )
+
+    data_pointer = context.nrt.meminfo_data(builder, meminfo)
+    data_pointer = builder.bitcast(data_pointer, ll_list_type.as_pointer())
+    builder.store(lstruct.data, data_pointer)
+    lstruct.meminfo = meminfo
+
+
+@intrinsic
+def _make_list(typingctx, itemty, ptr):
+    """Make a list struct with the given *ptr*
+
+    Parameters
+    ----------
+    itemty: Type
+        Type of the item.
+    ptr : llvm pointer value
+        Points to the list object.
+    """
+    list_ty = types.ListType(itemty.instance_type)
+
+    def codegen(context, builder, signature, args):
+        ptr = args[1]
+        ctor = cgutils.create_struct_proxy(list_ty)
+        lstruct = ctor(context, builder)
+        lstruct.data = ptr
+        _add_meminfo(context, builder, lstruct)
+        return lstruct._getvalue()
+
+    sig = list_ty(itemty, ptr)
+    return sig, codegen
+
+
+def _list_new_codegen(context, builder, itemty, new_size, error_handler):
+    fnty = ir.FunctionType(
+        ll_status,
+        [ll_list_type.as_pointer(), ll_ssize_t, ll_ssize_t],
+    )
+    fn = cgutils.get_or_insert_function(builder.module, fnty, 'numba_list_new')
+    # Determine sizeof item types
+    ll_item = context.get_data_type(itemty)
+    sz_item = context.get_abi_sizeof(ll_item)
+    reflp = cgutils.alloca_once(builder, ll_list_type, zfill=True)
+    status = builder.call(
+        fn,
+        [reflp, ll_ssize_t(sz_item), new_size],
+    )
+    msg = "Failed to allocate list"
+    error_handler(
+        builder,
+        status,
+        msg,
+    )
+    lp = builder.load(reflp)
+    return lp
+
+
+@intrinsic
+def _list_new(typingctx, itemty, allocated):
+    """Wrap numba_list_new.
+
+    Allocate a new list object with zero capacity.
+
+    Parameters
+    ----------
+    itemty: Type
+        Type of the items
+    allocated: int
+        number of items to pre-allocate
+
+    """
+    resty = types.voidptr
+    sig = resty(itemty, allocated)
+
+    def codegen(context, builder, sig, args):
+        error_handler = ErrorHandler(context)
+        return _list_new_codegen(context,
+                                 builder,
+                                 itemty.instance_type,
+                                 args[1],
+                                 error_handler,
+                                 )
+
+    return sig, codegen
+
+
+@overload(new_list)
+def impl_new_list(item, allocated=DEFAULT_ALLOCATED):
+    """Creates a new list.
+
+    Parameters
+    ----------
+    item: Numba type
+        type of the list item.
+    allocated: int
+        number of items to pre-allocate
+
+    """
+    if not isinstance(item, Type):
+        raise TypeError("expecting *item* to be a numba Type")
+
+    itemty = item
+
+    def imp(item, allocated=DEFAULT_ALLOCATED):
+        if allocated < 0:
+            raise RuntimeError("expecting *allocated* to be >= 0")
+        lp = _list_new(itemty, allocated)
+        _list_set_method_table(lp, itemty)
+        l = _make_list(itemty, lp)
+        return l
+
+    return imp
+
+
+@overload(len)
+def impl_len(l):
+    """len(list)
+    """
+    if isinstance(l, types.ListType):
+        def impl(l):
+            return _list_length(l)
+
+        return impl
+
+
+@intrinsic
+def _list_length(typingctx, l):
+    """Wrap numba_list_length
+
+    Returns the length of the list.
+    """
+    sig = types.intp(l)
+
+    def codegen(context, builder, sig, args):
+        [tl] = sig.args
+        [l] = args
+        fnty = ir.FunctionType(
+            ll_ssize_t,
+            [ll_list_type],
+        )
+        fname = 'numba_list_size_address'
+        fn = cgutils.get_or_insert_function(builder.module, fnty, fname)
+        fn.attributes.add('alwaysinline')
+        fn.attributes.add('readonly')
+        fn.attributes.add('nounwind')
+        lp = _container_get_data(context, builder, tl, l)
+        len_addr = builder.call(fn, [lp,],)
+        ptr = builder.inttoptr(len_addr, cgutils.intp_t.as_pointer())
+        return builder.load(ptr)
+
+    return sig, codegen
+
+
+@overload_method(types.ListType, "_allocated")
+def impl_allocated(l):
+    """list._allocated()
+    """
+    if isinstance(l, types.ListType):
+        def impl(l):
+            return _list_allocated(l)
+
+        return impl
+
+
+@intrinsic
+def _list_allocated(typingctx, l):
+    """Wrap numba_list_allocated
+
+    Returns the allocation of the list.
+    """
+    resty = types.intp
+    sig = resty(l)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_ssize_t,
+            [ll_list_type],
+        )
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_list_allocated')
+        [l] = args
+        [tl] = sig.args
+        lp = _container_get_data(context, builder, tl, l)
+        n = builder.call(fn, [lp])
+        return n
+
+    return sig, codegen
+
+
+@overload_method(types.ListType, "_is_mutable")
+def impl_is_mutable(l):
+    """list._is_mutable()"""
+    if isinstance(l, types.ListType):
+        def impl(l):
+            return bool(_list_is_mutable(l))
+
+        return impl
+
+
+@intrinsic
+def _list_is_mutable(typingctx, l):
+    """Wrap numba_list_is_mutable
+
+    Returns the state of the is_mutable member
+    """
+    resty = types.int32
+    sig = resty(l)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_list_type],
+        )
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_list_is_mutable')
+        [l] = args
+        [tl] = sig.args
+        lp = _container_get_data(context, builder, tl, l)
+        n = builder.call(fn, [lp])
+        return n
+
+    return sig, codegen
+
+
+@overload_method(types.ListType, "_make_mutable")
+def impl_make_mutable(l):
+    """list._make_mutable()"""
+    if isinstance(l, types.ListType):
+        def impl(l):
+            _list_set_is_mutable(l, 1)
+
+        return impl
+
+
+@overload_method(types.ListType, "_make_immutable")
+def impl_make_immutable(l):
+    """list._make_immutable()"""
+    if isinstance(l, types.ListType):
+        def impl(l):
+            _list_set_is_mutable(l, 0)
+
+        return impl
+
+
+@intrinsic
+def _list_set_is_mutable(typingctx, l, is_mutable):
+    """Wrap numba_list_set_mutable
+
+    Sets the state of the is_mutable member.
+    """
+    resty = types.void
+    sig = resty(l, is_mutable)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ir.VoidType(),
+            [ll_list_type, cgutils.intp_t],
+        )
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_list_set_is_mutable')
+        [l, i] = args
+        [tl, ti] = sig.args
+        lp = _container_get_data(context, builder, tl, l)
+        builder.call(fn, [lp, i])
+
+    return sig, codegen
+
+
+@intrinsic
+def _list_append(typingctx, l, item):
+    """Wrap numba_list_append
+    """
+    resty = types.int32
+    sig = resty(l, l.item_type)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_list_type, ll_bytes],
+        )
+        [l, item] = args
+        [tl, titem] = sig.args
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_list_append')
+
+        dm_item = context.data_model_manager[titem]
+
+        data_item = dm_item.as_data(builder, item)
+
+        ptr_item = cgutils.alloca_once_value(builder, data_item)
+
+        lp = _container_get_data(context, builder, tl, l)
+        status = builder.call(
+            fn,
+            [
+                lp,
+                _as_bytes(builder, ptr_item),
+            ],
+        )
+        return status
+
+    return sig, codegen
+
+
+@overload_method(types.ListType, 'append')
+def impl_append(l, item):
+    if not isinstance(l, types.ListType):
+        return
+
+    itemty = l.item_type
+
+    def impl(l, item):
+        casteditem = _cast(item, itemty)
+        status = _list_append(l, casteditem)
+        if status == ListStatus.LIST_OK:
+            return
+        elif status == ListStatus.LIST_ERR_IMMUTABLE:
+            raise ValueError('list is immutable')
+        elif status == ListStatus.LIST_ERR_NO_MEMORY:
+            raise MemoryError('Unable to allocate memory to append item')
+        else:
+            raise RuntimeError('list.append failed unexpectedly')
+
+    if l.is_precise():
+        # Handle the precise case.
+        return impl
+    else:
+        # Handle the imprecise case.
+        l = l.refine(item)
+        # Re-bind the item type to match the arguments.
+        itemty = l.item_type
+        # Create the signature that we wanted this impl to have.
+        sig = typing.signature(types.void, l, itemty)
+        return sig, impl
+
+
+@intrinsic
+def fix_index(tyctx, list_ty, index_ty):
+    sig = types.intp(list_ty, index_ty)
+
+    def codegen(context, builder, sig, args):
+        [list_ty, index_ty] = sig.args
+        [ll_list, ll_idx] = args
+        is_negative = builder.icmp_signed('<', ll_idx,
+                                          ir.Constant(ll_idx.type, 0))
+        fast_len_sig, length_fn = _list_length._defn(context.typing_context,
+                                                     list_ty)
+        length = length_fn(context, builder, fast_len_sig, (ll_list,))
+        # length is an intp
+        # index can be any sort of int
+        # indexing in general is done with a ssize_t which correlates to an
+        # intp. In llvmlite sext and trunc are guarded to return the value
+        # itself if the types are the same, so there's no need to handle the
+        # "equal widths" case separately. This sexts/truncs the index to the
+        # length type such that `add` works for the wraparound case.
+        st = 'sext' if ll_idx.type.width < length.type.width else 'trunc'
+        op = getattr(builder, st)
+        fixedup_idx = op(ll_idx, length.type)
+        wrapped_index = builder.add(fixedup_idx, length)
+        return builder.select(is_negative, wrapped_index, fixedup_idx)
+    return sig, codegen
+
+
+@register_jitable
+def handle_index(l, index):
+    """Handle index.
+
+    If the index is negative, convert it. If the index is out of range, raise
+    an IndexError.
+    """
+    # convert negative indices to positive ones
+    index = fix_index(l, index)
+    # check that the index is in range
+    if index < 0 or index >= len(l):
+        raise IndexError("list index out of range")
+    return index
+
+
+@register_jitable
+def handle_slice(l, s):
+    """Handle slice.
+
+    Convert a slice object for a given list into a range object that can be
+    used to index the list. Many subtle caveats here, especially if the step is
+    negative.
+    """
+    if len(l) == 0:  # ignore slice for empty list
+        return range(0)
+    ll, sa, so, se = len(l), s.start, s.stop, s.step
+    if se > 0:
+        start = max(ll + sa,  0) if s.start < 0 else min(ll, sa)
+        stop = max(ll + so, 0) if so < 0 else min(ll, so)
+    elif se < 0:
+        start = max(ll + sa,  -1) if s.start < 0 else min(ll - 1, sa)
+        stop = max(ll + so, -1) if so < 0 else min(ll, so)
+    else:
+        # should be caught earlier, but isn't, so we raise here
+        raise ValueError("slice step cannot be zero")
+    return range(start, stop, s.step)
+
+
+def _gen_getitem(borrowed):
+
+    @intrinsic
+    def impl(typingctx, l_ty, index_ty):
+
+        is_none = isinstance(l_ty.item_type, types.NoneType)
+        if is_none:
+            resty = types.Tuple([types.int32, l_ty.item_type])
+        else:
+            resty = types.Tuple([types.int32, types.Optional(l_ty.item_type)])
+        sig = resty(l_ty, index_ty)
+
+        def codegen(context, builder, sig, args):
+            [tl, tindex] = sig.args
+            [l, index] = args
+            fnty = ir.FunctionType(
+                ll_voidptr_type,
+                [ll_list_type],
+            )
+            fname = 'numba_list_base_ptr'
+            fn = cgutils.get_or_insert_function(builder.module, fnty, fname)
+            fn.attributes.add('alwaysinline')
+            fn.attributes.add('nounwind')
+            fn.attributes.add('readonly')
+
+            lp = _container_get_data(context, builder, tl, l)
+
+            base_ptr = builder.call(
+                fn,
+                [lp,],
+            )
+
+            llty = context.get_data_type(tl.item_type)
+            casted_base_ptr = builder.bitcast(base_ptr, llty.as_pointer())
+
+            item_ptr = cgutils.gep(builder, casted_base_ptr, index)
+
+            if is_none:
+                out = builder.load(item_ptr)
+            else:
+                out = context.make_optional_none(builder, tl.item_type)
+                pout = cgutils.alloca_once_value(builder, out)
+
+                dm_item = context.data_model_manager[tl.item_type]
+                item = dm_item.load_from_data_pointer(builder, item_ptr)
+                if not borrowed:
+                    context.nrt.incref(builder, tl.item_type, item)
+
+                if is_none:
+                    loaded = item
+                else:
+                    loaded = context.make_optional_value(builder, tl.item_type,
+                                                         item)
+                builder.store(loaded, pout)
+
+                out = builder.load(pout)
+            return context.make_tuple(builder, resty, [ll_status(0), out])
+
+        return sig, codegen
+    return impl
+
+
+_list_getitem = _gen_getitem(False)
+_list_getitem_borrowed = _gen_getitem(True)
+
+
+@overload(operator.getitem)
+def impl_getitem(l, index):
+    if not isinstance(l, types.ListType):
+        return
+
+    indexty = INDEXTY
+    itemty = l.item_type
+    IS_NOT_NONE = not isinstance(l.item_type, types.NoneType)
+
+    if index in index_types:
+        if IS_NOT_NONE:
+            def integer_non_none_impl(l, index):
+                index = handle_index(l, index)
+                castedindex = _cast(index, indexty)
+                status, item = _list_getitem(l, castedindex)
+                if status == ListStatus.LIST_OK:
+                    return _nonoptional(item)
+                else:
+                    raise AssertionError("internal list error during getitem")
+            return integer_non_none_impl
+        else:
+            def integer_none_impl(l, index):
+                index = handle_index(l, index)
+                return None
+            return integer_none_impl
+
+    elif isinstance(index, types.SliceType):
+        def slice_impl(l, index):
+            newl = new_list(itemty)
+            for i in handle_slice(l, index):
+                newl.append(l[i])
+            return newl
+
+        return slice_impl
+
+    else:
+        raise TypingError("list indices must be integers or slices")
+
+
+@intrinsic
+def _list_setitem(typingctx, l, index, item):
+    """Wrap numba_list_setitem
+    """
+    resty = types.int32
+    sig = resty(l, index, item)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_list_type, ll_ssize_t, ll_bytes],
+        )
+        [l, index, item] = args
+        [tl, tindex, titem] = sig.args
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_list_setitem')
+
+        dm_item = context.data_model_manager[titem]
+        data_item = dm_item.as_data(builder, item)
+        ptr_item = cgutils.alloca_once_value(builder, data_item)
+
+        lp = _container_get_data(context, builder, tl, l)
+        status = builder.call(
+            fn,
+            [
+                lp,
+                index,
+                _as_bytes(builder, ptr_item),
+            ],
+        )
+        return status
+
+    return sig, codegen
+
+
+@overload(operator.setitem)
+def impl_setitem(l, index, item):
+    if not isinstance(l, types.ListType):
+        return
+
+    indexty = INDEXTY
+    itemty = l.item_type
+
+    if index in index_types:
+        def impl_integer(l, index, item):
+            index = handle_index(l, index)
+            castedindex = _cast(index, indexty)
+            casteditem = _cast(item, itemty)
+            status = _list_setitem(l, castedindex, casteditem)
+            if status == ListStatus.LIST_OK:
+                return
+            elif status == ListStatus.LIST_ERR_IMMUTABLE:
+                raise ValueError("list is immutable")
+            else:
+                raise AssertionError("internal list error during settitem")
+
+        return impl_integer
+
+    elif isinstance(index, types.SliceType):
+        if not isinstance(item, types.IterableType):
+            raise TypingError("can only assign an iterable when using a slice "
+                              "with assignment/setitem")
+
+        def impl_slice(l, index, item):
+            if not l._is_mutable():
+                raise ValueError("list is immutable")
+            # special case "a[i:j] = a", need to copy first
+            if l is item:
+                item = item.copy()
+            slice_range = handle_slice(l, index)
+            # non-extended (simple) slices
+            if slice_range.step == 1:
+                # replace
+                if len(item) == len(slice_range):
+                    for i, j in zip(slice_range, item):
+                        l[i] = j
+                # replace and insert
+                if len(item) > len(slice_range):
+                    # do the replaces we can
+                    for i, j in zip(slice_range, item[:len(slice_range)]):
+                        l[i] = j
+                    # insert the remaining ones
+                    insert_range = range(slice_range.stop,
+                                         slice_range.stop +
+                                         len(item) - len(slice_range))
+                    for i, k in zip(insert_range, item[len(slice_range):]):
+                        # FIXME: This may be slow.  Each insert can incur a
+                        # memory copy of one or more items.
+                        l.insert(i, k)
+                # replace and delete
+                if len(item) < len(slice_range):
+                    # do the replaces we can
+                    replace_range = range(slice_range.start,
+                                          slice_range.start + len(item))
+                    for i,j in zip(replace_range, item):
+                        l[i] = j
+                    # delete remaining ones
+                    del l[slice_range.start + len(item):slice_range.stop]
+            # Extended slices
+            else:
+                if len(slice_range) != len(item):
+                    raise ValueError("length mismatch for extended slice "
+                                     "and sequence")
+                # extended slice can only replace
+                for i, j in zip(slice_range, item):
+                    l[i] = j
+
+        return impl_slice
+
+    else:
+        raise TypingError("list indices must be integers or slices")
+
+
+@overload_method(types.ListType, 'pop')
+def impl_pop(l, index=-1):
+    if not isinstance(l, types.ListType):
+        return
+
+    _check_for_none_typed(l, 'pop')
+
+    indexty = INDEXTY
+
+    # FIXME: this type check works, but it isn't clear why and if it optimal
+    if (isinstance(index, int)
+            or index in index_types
+            or isinstance(index, types.Omitted)):
+        def impl(l, index=-1):
+            if len(l) == 0:
+                raise IndexError("pop from empty list")
+            cindex = _cast(handle_index(l, index), indexty)
+            item = l[cindex]
+            del l[cindex]
+            return item
+        return impl
+
+    else:
+        raise TypingError("argument for pop must be an integer")
+
+
+@intrinsic
+def _list_delitem(typingctx, l, index):
+    resty = types.int32
+    sig = resty(l, index)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_list_type, ll_ssize_t],
+        )
+        [tl, tindex] = sig.args
+        [l, index] = args
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_list_delitem')
+
+        lp = _container_get_data(context, builder, tl, l)
+        status = builder.call(fn, [lp, index])
+        return status
+
+    return sig, codegen
+
+
+@intrinsic
+def _list_delete_slice(typingctx, l, start, stop, step):
+    """Wrap numba_list_delete_slice
+    """
+    resty = types.int32
+    sig = resty(l, start, stop, step)
+
+    def codegen(context, builder, sig, args):
+        fnty = ir.FunctionType(
+            ll_status,
+            [ll_list_type, ll_ssize_t, ll_ssize_t, ll_ssize_t],
+        )
+        [l, start, stop, step] = args
+        [tl, tstart, tstop, tstep] = sig.args
+        fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                            'numba_list_delete_slice')
+
+        lp = _container_get_data(context, builder, tl, l)
+        status = builder.call(
+            fn,
+            [
+                lp,
+                start,
+                stop,
+                step,
+            ],
+        )
+        return status
+
+    return sig, codegen
+
+
+@overload(operator.delitem)
+def impl_delitem(l, index):
+    if not isinstance(l, types.ListType):
+        return
+
+    _check_for_none_typed(l, 'delitem')
+
+    if index in index_types:
+        def integer_impl(l, index):
+            cindex = _cast(handle_index(l, index), INDEXTY)
+            status = _list_delitem(l, cindex)
+            if status == ListStatus.LIST_OK:
+                return
+            elif status == ListStatus.LIST_ERR_IMMUTABLE:
+                raise ValueError("list is immutable")
+            else:
+                raise AssertionError("internal list error during delitem")
+        return integer_impl
+
+    elif isinstance(index, types.SliceType):
+        def slice_impl(l, index):
+            slice_range = handle_slice(l, index)
+            status = _list_delete_slice(
+                l,
+                slice_range.start,
+                slice_range.stop,
+                slice_range.step)
+            if status == ListStatus.LIST_ERR_MUTATED:
+                raise ValueError("list is immutable")
+        return slice_impl
+
+    else:
+        raise TypingError("list indices must be integers or slices")
+
+
+@overload(operator.contains)
+def impl_contains(l, item):
+    if not isinstance(l, types.ListType):
+        return
+
+    itemty = l.item_type
+    _check_for_none_typed(l, "__contains__")
+
+    def impl(l, item):
+        casteditem = _cast(item, itemty)
+        for i in l:
+            if i == casteditem:
+                return True
+        else:
+            return False
+    return impl
+
+
+@overload_method(types.ListType, 'count')
+def impl_count(l, item):
+    if not isinstance(l, types.ListType):
+        return
+
+    _check_for_none_typed(l, 'count')
+
+    itemty = l.item_type
+
+    def impl(l, item):
+        casteditem = _cast(item, itemty)
+        total = 0
+        for i in l:
+            if i == casteditem:
+                total += 1
+        return total
+
+    return impl
+
+
+@overload_method(types.ListType, 'extend')
+def impl_extend(l, iterable):
+    if not isinstance(l, types.ListType):
+        return
+    if not isinstance(iterable, types.IterableType):
+        raise TypingError("extend argument must be iterable")
+
+    _check_for_none_typed(l, 'extend')
+
+    def select_impl():
+        if isinstance(iterable, types.ListType):
+            def impl(l, iterable):
+                if not l._is_mutable():
+                    raise ValueError("list is immutable")
+                # guard against l.extend(l)
+                if l is iterable:
+                    iterable = iterable.copy()
+                for i in iterable:
+                    l.append(i)
+
+            return impl
+        else:
+            def impl(l, iterable):
+                for i in iterable:
+                    l.append(i)
+
+            return impl
+
+    if l.is_precise():
+        # Handle the precise case.
+        return select_impl()
+    else:
+        # Handle the imprecise case, try to 'guess' the underlying type of the
+        # values in the iterable.
+        if hasattr(iterable, "dtype"):  # tuples and arrays
+            ty = iterable.dtype
+        elif hasattr(iterable, "item_type"):  # lists
+            ty = iterable.item_type
+        elif hasattr(iterable, "yield_type"):  # iterators and generators
+            ty = iterable.yield_type
+        elif isinstance(iterable, types.UnicodeType):
+            ty = iterable
+        else:
+            raise TypingError("unable to extend list, iterable is missing "
+                              "either *dtype*, *item_type* or *yield_type*.")
+        l = l.refine(ty)
+        # Create the signature that we wanted this impl to have
+        sig = typing.signature(types.void, l, iterable)
+        return sig, select_impl()
+
+
+@overload_method(types.ListType, 'insert')
+def impl_insert(l, index, item):
+    if not isinstance(l, types.ListType):
+        return
+
+    _check_for_none_typed(l, 'insert')
+    # insert can refine
+    if isinstance(item, NoneType):
+        raise TypingError("method support for List[None] is limited")
+
+    if index in index_types:
+        def impl(l, index, item):
+            # If the index is larger than the size of the list or if the list is
+            # empty, just append.
+            if index >= len(l) or len(l) == 0:
+                l.append(item)
+            # Else, do the insert dance
+            else:
+                # convert negative indices
+                if index < 0:
+                    # if the index is still negative after conversion, use 0
+                    index = max(len(l) + index, 0)
+                # grow the list by one, make room for item to insert
+                l.append(l[0])
+                # reverse iterate over the list and shift all elements
+                i = len(l) - 1
+                while(i > index):
+                    l[i] = l[i - 1]
+                    i -= 1
+                # finally, insert the item
+                l[index] = item
+
+        if l.is_precise():
+            # Handle the precise case.
+            return impl
+        else:
+            # Handle the imprecise case
+            l = l.refine(item)
+            # Re-bind the item type to match the arguments.
+            itemty = l.item_type
+            # Create the signature that we wanted this impl to have.
+            sig = typing.signature(types.void, l, INDEXTY, itemty)
+            return sig, impl
+    else:
+        raise TypingError("list insert indices must be integers")
+
+
+@overload_method(types.ListType, 'remove')
+def impl_remove(l, item):
+    if not isinstance(l, types.ListType):
+        return
+
+    _check_for_none_typed(l, 'remove')
+
+    itemty = l.item_type
+
+    def impl(l, item):
+        casteditem = _cast(item, itemty)
+        for i, n in enumerate(l):
+            if casteditem == n:
+                del l[i]
+                return
+        else:
+            raise ValueError("list.remove(x): x not in list")
+
+    return impl
+
+
+@overload_method(types.ListType, 'clear')
+def impl_clear(l):
+    if not isinstance(l, types.ListType):
+        return
+
+    def impl(l):
+        while len(l):
+            del l[-1]
+
+    return impl
+
+
+@overload_method(types.ListType, 'reverse')
+def impl_reverse(l):
+    if not isinstance(l, types.ListType):
+        return
+
+    _check_for_none_typed(l, 'reverse')
+
+    def impl(l):
+        if not l._is_mutable():
+            raise ValueError("list is immutable")
+        front = 0
+        back = len(l) - 1
+        while front < back:
+            l[front], l[back] = l[back], l[front]
+            front += 1
+            back -= 1
+
+    return impl
+
+
+@overload_method(types.ListType, 'copy')
+def impl_copy(l):
+
+    _check_for_none_typed(l, 'copy')
+
+    itemty = l.item_type
+
+    if isinstance(l, types.ListType):
+        def impl(l):
+            newl = new_list(itemty, len(l))
+            for i in l:
+                newl.append(i)
+            return newl
+
+        return impl
+
+
+@overload_method(types.ListType, 'index')
+def impl_index(l, item, start=None, end=None):
+    if not isinstance(l, types.ListType):
+        return
+
+    _check_for_none_typed(l, 'index')
+
+    itemty = l.item_type
+
+    def check_arg(arg, name):
+        if not (arg is None
+                or arg in index_types
+                or isinstance(arg, (types.Omitted, types.NoneType))):
+            raise TypingError("{} argument for index must be an integer"
+                              .format(name))
+    check_arg(start, "start")
+    check_arg(end, "end")
+
+    def impl(l, item, start=None, end=None):
+        casteditem = _cast(item, itemty)
+        for i in handle_slice(l, slice(start, end, 1)):
+            if l[i] == casteditem:
+                return i
+        else:
+            raise ValueError("item not in list")
+
+    return impl
+
+
+@overload_method(types.ListType, "sort")
+def ol_list_sort(lst, key=None, reverse=False):
+    # The following is mostly borrowed from listobj.ol_list_sort
+    from numba.typed import List
+
+    listobj._sort_check_key(key)
+    listobj._sort_check_reverse(reverse)
+
+    if cgutils.is_nonelike(key):
+        KEY = False
+        sort_f = listobj.sort_forwards
+        sort_b = listobj.sort_backwards
+    elif isinstance(key, types.Dispatcher):
+        KEY = True
+        sort_f = listobj.arg_sort_forwards
+        sort_b = listobj.arg_sort_backwards
+
+    def impl(lst, key=None, reverse=False):
+        if not lst._is_mutable():
+            raise ValueError("list is immutable")
+        if KEY is True:
+            # There's an unknown refct problem in reflected list.
+            # Using an explicit loop with typedlist somehow "fixed" it.
+            _lst = List()
+            for x in lst:
+                _lst.append(key(x))
+        else:
+            _lst = lst
+        if reverse is False or reverse == 0:
+            tmp = sort_f(_lst)
+        else:
+            tmp = sort_b(_lst)
+        if KEY is True:
+            # There's an unknown refct problem in reflected list.
+            # Using an explicit loop with typedlist somehow "fixed" it.
+            ordered = List()
+            for i in tmp:
+                ordered.append(lst[i])
+            lst[:] = ordered
+    return impl
+
+
+@overload_method(types.ListType, "getitem_unchecked")
+def ol_getitem_unchecked(lst, index):
+    if not isinstance(index, types.Integer):
+        return
+
+    def impl(lst, index):
+        index = fix_index(lst, index)
+        castedindex = _cast(index, types.intp)
+        _, item = _list_getitem(lst, castedindex)
+        return _nonoptional(item)
+    return impl
+
+
+@overload_attribute(types.ListType, '_dtype')
+def impl_dtype(l):
+    if not isinstance(l, types.ListType):
+        return
+    dt = l.dtype
+
+    def impl(l):
+        return dt
+
+    return impl
+
+
+def _equals_helper(this, other, OP):
+    if not isinstance(this, types.ListType):
+        return
+    if not isinstance(other, types.ListType):
+        return lambda this, other: False
+
+    this_is_none = isinstance(this.dtype, types.NoneType)
+    other_is_none = isinstance(other.dtype, types.NoneType)
+
+    if this_is_none or other_is_none:
+        def impl_some_none(this, other):
+            def equals(this, other):
+                # Equal if both none-typed and have equal length
+                return bool(this_is_none == other_is_none
+                            and len(this) == len(other))
+            return OP(equals(this, other))
+        return impl_some_none
+    else:
+        def impl_not_none(this, other):
+            def equals(this, other):
+                if len(this) != len(other):
+                    return False
+                for i in range(len(this)):
+                    if this[i] != other[i]:
+                        return False
+                else:
+                    return True
+            return OP(equals(this, other))
+        return impl_not_none
+
+
+@overload(operator.eq)
+def impl_equals(this, other):
+    return _equals_helper(this, other, operator.truth)
+
+
+@overload(operator.ne)
+def impl_not_equals(this, other):
+    return _equals_helper(this, other, operator.not_)
+
+
+@register_jitable
+def compare_not_none(this, other):
+    """Oldschool (python 2.x) cmp.
+
+       if this < other return -1
+       if this = other return 0
+       if this > other return 1
+    """
+    if len(this) != len(other):
+        return -1 if len(this) < len(other) else 1
+    for i in range(len(this)):
+        this_item, other_item = this[i], other[i]
+        if this_item != other_item:
+            return -1 if this_item < other_item else 1
+    else:
+        return 0
+
+
+@register_jitable
+def compare_some_none(this, other, this_is_none, other_is_none):
+    """Oldschool (python 2.x) cmp for None typed lists.
+
+       if this < other return -1
+       if this = other return 0
+       if this > other return 1
+    """
+    if len(this) != len(other):
+        return -1 if len(this) < len(other) else 1
+    if this_is_none and other_is_none: # both none
+        return 0
+    # to get here there is precisely one none, and if the first is none, by
+    # induction, the second cannot be
+    return -1 if this_is_none else 1
+
+
+def compare_helper(this, other, accepted):
+    if not isinstance(this, types.ListType):
+        return
+    if not isinstance(other, types.ListType):
+        return lambda this, other: False
+
+    this_is_none = isinstance(this.dtype, types.NoneType)
+    other_is_none = isinstance(other.dtype, types.NoneType)
+
+    if this_is_none or other_is_none:
+        def impl(this, other):
+            return compare_some_none(
+                this, other, this_is_none, other_is_none) in accepted
+    else:
+        def impl(this, other):
+            return compare_not_none(this, other) in accepted
+    return impl
+
+
+@overload(operator.lt)
+def impl_less_than(this, other):
+    return compare_helper(this, other, (-1, ))
+
+
+@overload(operator.le)
+def impl_less_than_or_equal(this, other):
+    return compare_helper(this, other, (-1, 0))
+
+
+@overload(operator.gt)
+def impl_greater_than(this, other):
+    return compare_helper(this, other, (1,))
+
+
+@overload(operator.ge)
+def impl_greater_than_or_equal(this, other):
+    return compare_helper(this, other, (0, 1))
+
+
+class ListIterInstance(object):
+
+    def __init__(self, context, builder, iter_type, iter_val):
+        self._context = context
+        self._builder = builder
+        self._iter_ty = iter_type
+        self._list_ty = self._iter_ty.parent
+        self._iter = context.make_helper(builder, iter_type, iter_val)
+
+    @classmethod
+    def from_list(cls, context, builder, iter_type, list_val):
+        self = cls(context, builder, iter_type, None)
+        index = context.get_constant(types.intp, 0)
+        self._iter.index = cgutils.alloca_once_value(builder, index)
+        self._iter.parent = list_val
+        self._iter.size = cls._size_of_list(context, builder, self._list_ty,
+                                            self._iter.parent)
+        return self
+
+    @classmethod
+    def _size_of_list(cls, context, builder, list_ty, ll_list):
+        tyctx = context.typing_context
+        fnty = tyctx.resolve_value_type(len)
+        sig = fnty.get_call_type(tyctx, (list_ty,), {})
+        impl = context.get_function(fnty, sig)
+        return impl(builder, (ll_list,))
+
+    @property
+    def size(self):
+        tyctx = self._context.typing_context
+        fnty = tyctx.resolve_value_type(len)
+        ty = self._list_ty
+        sig = fnty.get_call_type(tyctx, (ty,), {})
+        impl = self._context.get_function(fnty, sig)
+        return impl(self._builder, (self._iter.parent,))
+
+    @property
+    def value(self):
+        return self._iter._getvalue()
+
+    def getitem(self, index):
+        tyctx = self._context.typing_context
+        ty = self._list_ty
+        sig, fn = _list_getitem_borrowed._defn(tyctx, ty, types.intp)
+
+        statnitem = fn(self._context, self._builder, sig, (self._iter.parent,
+                                                           index))
+        _, item = cgutils.unpack_tuple(self._builder, statnitem)
+        retty = sig.return_type[1]
+        if isinstance(self._list_ty.dtype, types.NoneType):
+            raw_ty = self._list_ty.dtype
+        else:
+            raw_ty = retty.type
+        raw_item = self._context.cast(self._builder, item, retty, raw_ty)
+        return raw_item
+
+    @property
+    def index(self):
+        return self._builder.load(self._iter.index)
+
+    @index.setter
+    def index(self, value):
+        self._builder.store(value, self._iter.index)
+
+
+@lower_builtin('getiter', types.ListType)
+def getiter_list(context, builder, sig, args):
+    inst = ListIterInstance.from_list(context, builder, sig.return_type,
+                                      args[0])
+    return impl_ret_borrowed(context, builder, sig.return_type, inst.value)
+
+
+@lower_builtin('iternext', types.ListTypeIteratorType)
+@iternext_impl(RefType.BORROWED)
+def iternext_listiter(context, builder, sig, args, result):
+    inst = ListIterInstance(context, builder, sig.args[0], args[0])
+    index = inst.index
+
+    nitems = inst.size # this is current size
+    init_size = inst._iter.size # this is initial size
+
+    # if the current count is different to the initial count, bail, list is
+    # being mutated whilst iterated.
+    is_mutated = builder.icmp_signed('!=', init_size, nitems)
+    with builder.if_then(is_mutated, likely=False):
+        context.call_conv.return_user_exc(
+            builder, RuntimeError, ("list was mutated during iteration",))
+
+    is_valid = builder.icmp_signed('<', index, nitems)
+    result.set_valid(is_valid)
+    with builder.if_then(is_valid):
+        result.yield_(inst.getitem(index))
+        inst.index = builder.add(index, context.get_constant(types.intp, 1))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/py.typed b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/py.typed
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typeddict.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typeddict.py
new file mode 100644
index 000000000..fcf3b2d75
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typeddict.py
@@ -0,0 +1,355 @@
+"""
+Python wrapper that connects CPython interpreter to the numba dictobject.
+"""
+from collections.abc import MutableMapping
+from numba.core.types import DictType
+from numba.core.imputils import numba_typeref_ctor
+from numba import njit, typeof
+from numba.core import types, errors, config, cgutils
+from numba.core.extending import (
+    overload,
+    box,
+    unbox,
+    NativeValue,
+    type_callable,
+    overload_classmethod,
+)
+from numba.typed import dictobject
+from numba.core.typing import signature
+
+
+@njit
+def _make_dict(keyty, valty):
+    return dictobject._as_meminfo(dictobject.new_dict(keyty, valty))
+
+
+@njit
+def _length(d):
+    return len(d)
+
+
+@njit
+def _setitem(d, key, value):
+    d[key] = value
+
+
+@njit
+def _getitem(d, key):
+    return d[key]
+
+
+@njit
+def _delitem(d, key):
+    del d[key]
+
+
+@njit
+def _contains(d, key):
+    return key in d
+
+
+@njit
+def _get(d, key, default):
+    return d.get(key, default)
+
+
+@njit
+def _setdefault(d, key, default):
+    return d.setdefault(key, default)
+
+
+@njit
+def _iter(d):
+    return list(d.keys())
+
+
+@njit
+def _popitem(d):
+    return d.popitem()
+
+
+@njit
+def _copy(d):
+    return d.copy()
+
+
+def _from_meminfo_ptr(ptr, dicttype):
+    d = Dict(meminfo=ptr, dcttype=dicttype)
+    return d
+
+
+class Dict(MutableMapping):
+    """A typed-dictionary usable in Numba compiled functions.
+
+    Implements the MutableMapping interface.
+    """
+
+    def __new__(cls, dcttype=None, meminfo=None):
+        if config.DISABLE_JIT:
+            return dict.__new__(dict)
+        else:
+            return object.__new__(cls)
+
+    @classmethod
+    def empty(cls, key_type, value_type):
+        """Create a new empty Dict with *key_type* and *value_type*
+        as the types for the keys and values of the dictionary respectively.
+        """
+        if config.DISABLE_JIT:
+            return dict()
+        else:
+            return cls(dcttype=DictType(key_type, value_type))
+
+    def __init__(self, **kwargs):
+        """
+        For users, the constructor does not take any parameters.
+        The keyword arguments are for internal use only.
+
+        Parameters
+        ----------
+        dcttype : numba.core.types.DictType; keyword-only
+            Used internally for the dictionary type.
+        meminfo : MemInfo; keyword-only
+            Used internally to pass the MemInfo object when boxing.
+        """
+        if kwargs:
+            self._dict_type, self._opaque = self._parse_arg(**kwargs)
+        else:
+            self._dict_type = None
+
+    def _parse_arg(self, dcttype, meminfo=None):
+        if not isinstance(dcttype, DictType):
+            raise TypeError('*dcttype* must be a DictType')
+
+        if meminfo is not None:
+            opaque = meminfo
+        else:
+            opaque = _make_dict(dcttype.key_type, dcttype.value_type)
+        return dcttype, opaque
+
+    @property
+    def _numba_type_(self):
+        if self._dict_type is None:
+            raise TypeError("invalid operation on untyped dictionary")
+        return self._dict_type
+
+    @property
+    def _typed(self):
+        """Returns True if the dictionary is typed.
+        """
+        return self._dict_type is not None
+
+    def _initialise_dict(self, key, value):
+        dcttype = types.DictType(typeof(key), typeof(value))
+        self._dict_type, self._opaque = self._parse_arg(dcttype)
+
+    def __getitem__(self, key):
+        if not self._typed:
+            raise KeyError(key)
+        else:
+            return _getitem(self, key)
+
+    def __setitem__(self, key, value):
+        if not self._typed:
+            self._initialise_dict(key, value)
+        return _setitem(self, key, value)
+
+    def __delitem__(self, key):
+        if not self._typed:
+            raise KeyError(key)
+        _delitem(self, key)
+
+    def __iter__(self):
+        if not self._typed:
+            return iter(())
+        else:
+            return iter(_iter(self))
+
+    def __len__(self):
+        if not self._typed:
+            return 0
+        else:
+            return _length(self)
+
+    def __contains__(self, key):
+        if len(self) == 0:
+            return False
+        else:
+            return _contains(self, key)
+
+    def __str__(self):
+        buf = []
+        for k, v in self.items():
+            buf.append("{}: {}".format(k, v))
+        return '{{{0}}}'.format(', '.join(buf))
+
+    def __repr__(self):
+        body = str(self)
+        prefix = str(self._dict_type)
+        return "{prefix}({body})".format(prefix=prefix, body=body)
+
+    def get(self, key, default=None):
+        if not self._typed:
+            return default
+        return _get(self, key, default)
+
+    def setdefault(self, key, default=None):
+        if not self._typed:
+            if default is not None:
+                self._initialise_dict(key, default)
+        return _setdefault(self, key, default)
+
+    def popitem(self):
+        if len(self) == 0:
+            raise KeyError('dictionary is empty')
+        return _popitem(self)
+
+    def copy(self):
+        return _copy(self)
+
+
+@overload_classmethod(types.DictType, 'empty')
+def typeddict_empty(cls, key_type, value_type):
+    if cls.instance_type is not DictType:
+        return
+
+    def impl(cls, key_type, value_type):
+        return dictobject.new_dict(key_type, value_type)
+
+    return impl
+
+
+@box(types.DictType)
+def box_dicttype(typ, val, c):
+    context = c.context
+    builder = c.builder
+
+    # XXX deduplicate
+    ctor = cgutils.create_struct_proxy(typ)
+    dstruct = ctor(context, builder, value=val)
+    # Returns the plain MemInfo
+    boxed_meminfo = c.box(
+        types.MemInfoPointer(types.voidptr),
+        dstruct.meminfo,
+    )
+
+    modname = c.context.insert_const_string(
+        c.builder.module, 'numba.typed.typeddict',
+    )
+    typeddict_mod = c.pyapi.import_module_noblock(modname)
+    fmp_fn = c.pyapi.object_getattr_string(typeddict_mod, '_from_meminfo_ptr')
+
+    dicttype_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ))
+
+    result_var = builder.alloca(c.pyapi.pyobj)
+    builder.store(cgutils.get_null_value(c.pyapi.pyobj), result_var)
+    with builder.if_then(cgutils.is_not_null(builder, dicttype_obj)):
+        res = c.pyapi.call_function_objargs(
+            fmp_fn, (boxed_meminfo, dicttype_obj),
+        )
+        c.pyapi.decref(fmp_fn)
+        c.pyapi.decref(typeddict_mod)
+        c.pyapi.decref(boxed_meminfo)
+        builder.store(res, result_var)
+    return builder.load(result_var)
+
+
+@unbox(types.DictType)
+def unbox_dicttype(typ, val, c):
+    context = c.context
+
+    # Check that `type(val) is Dict`
+    dict_type = c.pyapi.unserialize(c.pyapi.serialize_object(Dict))
+    valtype = c.pyapi.object_type(val)
+    same_type = c.builder.icmp_unsigned("==", valtype, dict_type)
+
+    with c.builder.if_else(same_type) as (then, orelse):
+        with then:
+            miptr = c.pyapi.object_getattr_string(val, '_opaque')
+
+            mip_type = types.MemInfoPointer(types.voidptr)
+            native = c.unbox(mip_type, miptr)
+
+            mi = native.value
+
+            argtypes = mip_type, typeof(typ)
+
+            def convert(mi, typ):
+                return dictobject._from_meminfo(mi, typ)
+
+            sig = signature(typ, *argtypes)
+            nil_typeref = context.get_constant_null(argtypes[1])
+            args = (mi, nil_typeref)
+            is_error, dctobj = c.pyapi.call_jit_code(convert , sig, args)
+            # decref here because we are stealing a reference.
+            c.context.nrt.decref(c.builder, typ, dctobj)
+
+            c.pyapi.decref(miptr)
+            bb_unboxed = c.builder.basic_block
+
+        with orelse:
+            # Raise error on incorrect type
+            c.pyapi.err_format(
+                "PyExc_TypeError",
+                "can't unbox a %S as a %S",
+                valtype, dict_type,
+            )
+            bb_else = c.builder.basic_block
+
+    # Phi nodes to gather the output
+    dctobj_res = c.builder.phi(dctobj.type)
+    is_error_res = c.builder.phi(is_error.type)
+
+    dctobj_res.add_incoming(dctobj, bb_unboxed)
+    dctobj_res.add_incoming(dctobj.type(None), bb_else)
+
+    is_error_res.add_incoming(is_error, bb_unboxed)
+    is_error_res.add_incoming(cgutils.true_bit, bb_else)
+
+    # cleanup
+    c.pyapi.decref(dict_type)
+    c.pyapi.decref(valtype)
+
+    return NativeValue(dctobj_res, is_error=is_error_res)
+
+
+@type_callable(DictType)
+def typeddict_call(context):
+    """
+    Defines typing logic for ``Dict()``.
+    Produces Dict[undefined, undefined]
+    """
+    def typer():
+        return types.DictType(types.undefined, types.undefined)
+    return typer
+
+
+@overload(numba_typeref_ctor)
+def impl_numba_typeref_ctor(cls):
+    """
+    Defines ``Dict()``, the type-inferred version of the dictionary ctor.
+
+    Parameters
+    ----------
+    cls : TypeRef
+        Expecting a TypeRef of a precise DictType.
+
+    See also: `redirect_type_ctor` in numba/cpython/bulitins.py
+    """
+    dict_ty = cls.instance_type
+    if not isinstance(dict_ty, types.DictType):
+        msg = "expecting a DictType but got {}".format(dict_ty)
+        return  # reject
+    # Ensure the dictionary is precisely typed.
+    if not dict_ty.is_precise():
+        msg = "expecting a precise DictType but got {}".format(dict_ty)
+        raise errors.LoweringError(msg)
+
+    key_type = types.TypeRef(dict_ty.key_type)
+    value_type = types.TypeRef(dict_ty.value_type)
+
+    def impl(cls):
+        # Simply call .empty() with the key/value types from *cls*
+        return Dict.empty(key_type, value_type)
+
+    return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typedlist.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typedlist.py
new file mode 100644
index 000000000..9626afc6e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typedlist.py
@@ -0,0 +1,687 @@
+"""
+Python wrapper that connects CPython interpreter to the Numba typed-list.
+
+This is the code that is used when creating typed lists outside of a `@jit`
+context and when returning a typed-list from a `@jit` decorated function. It
+basically a Python class that has a Numba allocated typed-list under the hood
+and uses `@jit` functions to access it. Since it inherits from MutableSequence
+it should really quack like the CPython `list`.
+
+"""
+from collections.abc import MutableSequence
+
+from numba.core.types import ListType
+from numba.core.imputils import numba_typeref_ctor
+from numba.core.dispatcher import Dispatcher
+from numba.core import types, config, cgutils
+from numba import njit, typeof
+from numba.core.extending import (
+    overload,
+    box,
+    unbox,
+    NativeValue,
+    type_callable,
+    overload_classmethod,
+)
+from numba.typed import listobject
+from numba.core.errors import TypingError, LoweringError
+from numba.core.typing.templates import Signature
+import typing as pt
+import sys
+
+
+Int_or_Slice = pt.Union["pt.SupportsIndex", slice]
+
+
+if sys.version_info >= (3, 8):
+    T_co = pt.TypeVar('T_co', covariant=True)
+
+    class _Sequence(pt.Protocol[T_co]):
+        def __getitem__(self, i: int) -> T_co:
+            ...
+
+        def __len__(self) -> int:
+            ...
+
+
+DEFAULT_ALLOCATED = listobject.DEFAULT_ALLOCATED
+
+
+@njit
+def _make_list(itemty, allocated=DEFAULT_ALLOCATED):
+    return listobject._as_meminfo(listobject.new_list(itemty,
+                                                      allocated=allocated))
+
+
+@njit
+def _length(l):
+    return len(l)
+
+
+@njit
+def _allocated(l):
+    return l._allocated()
+
+
+@njit
+def _is_mutable(l):
+    return l._is_mutable()
+
+
+@njit
+def _make_mutable(l):
+    return l._make_mutable()
+
+
+@njit
+def _make_immutable(l):
+    return l._make_immutable()
+
+
+@njit
+def _append(l, item):
+    l.append(item)
+
+
+@njit
+def _setitem(l, i, item):
+    l[i] = item
+
+
+@njit
+def _getitem(l, i):
+    return l[i]
+
+
+@njit
+def _contains(l, item):
+    return item in l
+
+
+@njit
+def _count(l, item):
+    return l.count(item)
+
+
+@njit
+def _pop(l, i):
+    return l.pop(i)
+
+
+@njit
+def _delitem(l, i):
+    del l[i]
+
+
+@njit
+def _extend(l, iterable):
+    return l.extend(iterable)
+
+
+@njit
+def _insert(l, i, item):
+    l.insert(i, item)
+
+
+@njit
+def _remove(l, item):
+    l.remove(item)
+
+
+@njit
+def _clear(l):
+    l.clear()
+
+
+@njit
+def _reverse(l):
+    l.reverse()
+
+
+@njit
+def _copy(l):
+    return l.copy()
+
+
+@njit
+def _eq(t, o):
+    return t == o
+
+
+@njit
+def _ne(t, o):
+    return t != o
+
+
+@njit
+def _lt(t, o):
+    return t < o
+
+
+@njit
+def _le(t, o):
+    return t <= o
+
+
+@njit
+def _gt(t, o):
+    return t > o
+
+
+@njit
+def _ge(t, o):
+    return t >= o
+
+
+@njit
+def _index(l, item, start, end):
+    return l.index(item, start, end)
+
+
+@njit
+def _sort(l, key, reverse):
+    return l.sort(key, reverse)
+
+
+def _from_meminfo_ptr(ptr, listtype):
+    return List(meminfo=ptr, lsttype=listtype)
+
+
+T = pt.TypeVar('T')
+T_or_ListT = pt.Union[T, 'List[T]']
+
+
+class List(MutableSequence, pt.Generic[T]):
+    """A typed-list usable in Numba compiled functions.
+
+    Implements the MutableSequence interface.
+    """
+
+    _legal_kwargs = ["lsttype", "meminfo", "allocated"]
+
+    def __new__(cls,
+                *args,
+                lsttype=None,
+                meminfo=None,
+                allocated=DEFAULT_ALLOCATED,
+                **kwargs):
+        if config.DISABLE_JIT:
+            return list(*args, **kwargs)
+        else:
+            return object.__new__(cls)
+
+    @classmethod
+    def empty_list(cls, item_type, allocated=DEFAULT_ALLOCATED):
+        """Create a new empty List.
+
+        Parameters
+        ----------
+        item_type: Numba type
+            type of the list item.
+        allocated: int
+            number of items to pre-allocate
+        """
+        if config.DISABLE_JIT:
+            return list()
+        else:
+            return cls(lsttype=ListType(item_type), allocated=allocated)
+
+    def __init__(self, *args, **kwargs):
+        """
+        For users, the constructor does not take any parameters.
+        The keyword arguments are for internal use only.
+
+        Parameters
+        ----------
+        args: iterable
+            The iterable to initialize the list from
+        lsttype : numba.core.types.ListType; keyword-only
+            Used internally for the list type.
+        meminfo : MemInfo; keyword-only
+            Used internally to pass the MemInfo object when boxing.
+        allocated: int; keyword-only
+            Used internally to pre-allocate space for items
+        """
+        illegal_kwargs = any((kw not in self._legal_kwargs for kw in kwargs))
+        if illegal_kwargs or args and kwargs:
+            raise TypeError("List() takes no keyword arguments")
+        if kwargs:
+            self._list_type, self._opaque = self._parse_arg(**kwargs)
+        else:
+            self._list_type = None
+            if args:
+                if not 0 <= len(args) <= 1:
+                    raise TypeError(
+                        "List() expected at most 1 argument, got {}"
+                        .format(len(args))
+                    )
+                iterable = args[0]
+                # Special case Numpy scalars or anything that quacks like a
+                # NumPy Array.
+                if hasattr(iterable, "ndim") and iterable.ndim == 0:
+                    self.append(iterable.item())
+                else:
+                    try:
+                        iter(iterable)
+                    except TypeError:
+                        raise TypeError("List() argument must be iterable")
+                    for i in args[0]:
+                        self.append(i)
+
+    def _parse_arg(self, lsttype, meminfo=None, allocated=DEFAULT_ALLOCATED):
+        if not isinstance(lsttype, ListType):
+            raise TypeError('*lsttype* must be a ListType')
+
+        if meminfo is not None:
+            opaque = meminfo
+        else:
+            opaque = _make_list(lsttype.item_type, allocated=allocated)
+        return lsttype, opaque
+
+    @property
+    def _numba_type_(self):
+        if self._list_type is None:
+            raise TypeError("invalid operation on untyped list")
+        return self._list_type
+
+    @property
+    def _typed(self):
+        """Returns True if the list is typed.
+        """
+        return self._list_type is not None
+
+    @property
+    def _dtype(self):
+        if not self._typed:
+            raise RuntimeError("invalid operation on untyped list")
+        return self._list_type.dtype
+
+    def _initialise_list(self, item):
+        lsttype = types.ListType(typeof(item))
+        self._list_type, self._opaque = self._parse_arg(lsttype)
+
+    def __len__(self) -> int:
+        if not self._typed:
+            return 0
+        else:
+            return _length(self)
+
+    def _allocated(self):
+        if not self._typed:
+            return DEFAULT_ALLOCATED
+        else:
+            return _allocated(self)
+
+    def _is_mutable(self):
+        return _is_mutable(self)
+
+    def _make_mutable(self):
+        return _make_mutable(self)
+
+    def _make_immutable(self):
+        return _make_immutable(self)
+
+    def __eq__(self, other):
+        return _eq(self, other)
+
+    def __ne__(self, other):
+        return _ne(self, other)
+
+    def __lt__(self, other):
+        return _lt(self, other)
+
+    def __le__(self, other):
+        return _le(self, other)
+
+    def __gt__(self, other):
+        return _gt(self, other)
+
+    def __ge__(self, other):
+        return _ge(self, other)
+
+    def append(self, item: T) -> None:
+        if not self._typed:
+            self._initialise_list(item)
+        _append(self, item)
+
+    # noqa F811 comments required due to github.com/PyCQA/pyflakes/issues/592
+    # noqa E704 required to follow overload style of using ... in the same line
+    @pt.overload  # type: ignore[override]
+    def __setitem__(self, i: int, o: T) -> None: ...  # noqa: F811, E704
+    @pt.overload
+    def __setitem__(self, s: slice, o: 'List[T]') -> None: ...  # noqa: F811, E704, E501
+
+    def __setitem__(self, i: Int_or_Slice, item: T_or_ListT) -> None:  # noqa: F811, E501
+        if not self._typed:
+            self._initialise_list(item)
+        _setitem(self, i, item)
+
+    # noqa F811 comments required due to github.com/PyCQA/pyflakes/issues/592
+    # noqa E704 required to follow overload style of using ... in the same line
+    @pt.overload
+    def __getitem__(self, i: int) -> T: ...  # noqa: F811, E704
+    @pt.overload
+    def __getitem__(self, i: slice) -> 'List[T]': ...  # noqa: F811, E704
+
+    def __getitem__(self, i: Int_or_Slice) -> T_or_ListT:  # noqa: F811
+        if not self._typed:
+            raise IndexError
+        else:
+            return _getitem(self, i)
+
+    def __iter__(self) -> pt.Iterator[T]:
+        for i in range(len(self)):
+            yield self[i]
+
+    def __contains__(self, item: T) -> bool:  # type: ignore[override]
+        return _contains(self, item)
+
+    def __delitem__(self, i: Int_or_Slice) -> None:
+        _delitem(self, i)
+
+    def insert(self, i: int, item: T) -> None:
+        if not self._typed:
+            self._initialise_list(item)
+        _insert(self, i, item)
+
+    def count(self, item: T) -> int:
+        return _count(self, item)
+
+    def pop(self, i: "pt.SupportsIndex" = -1) -> T:
+        return _pop(self, i)
+
+    def extend(self, iterable: "_Sequence[T]") -> None: #type: ignore[override]
+        # Empty iterable, do nothing
+        if len(iterable) == 0:
+            return None
+        if not self._typed:
+            # Need to get the first element of the iterable to initialise the
+            # type of the list. FIXME: this may be a problem if the iterable
+            # can not be sliced.
+            self._initialise_list(iterable[0])
+        return _extend(self, iterable)
+
+    def remove(self, item: T) -> None:
+        return _remove(self, item)
+
+    def clear(self):
+        return _clear(self)
+
+    def reverse(self):
+        return _reverse(self)
+
+    def copy(self):
+        return _copy(self)
+
+    def index(self, item: T, start: pt.Optional[int] = None,
+              stop: pt.Optional[int] = None) -> int:
+        return _index(self, item, start, stop)
+
+    def sort(self, key=None, reverse=False):
+        """Sort the list inplace.
+
+        See also ``list.sort()``
+        """
+        # If key is not already a dispatcher object, make it so
+        if callable(key) and not isinstance(key, Dispatcher):
+            key = njit(key)
+        return _sort(self, key, reverse)
+
+    def __str__(self):
+        buf = []
+        for x in self:
+            buf.append("{}".format(x))
+        # Check whether the code was invoked from IPython shell
+        try:
+            get_ipython
+            return '[{0}, ...]'.format(', '.join(buf[:1000]))
+        except (NameError, IndexError):
+            return '[{0}]'.format(', '.join(buf))
+
+    def __repr__(self):
+        body = str(self)
+        prefix = str(self._list_type) if self._typed else "ListType[Undefined]"
+        return "{prefix}({body})".format(prefix=prefix, body=body)
+
+
+@overload_classmethod(ListType, 'empty_list')
+def typedlist_empty(cls, item_type, allocated=DEFAULT_ALLOCATED):
+    if cls.instance_type is not ListType:
+        return
+
+    def impl(cls, item_type, allocated=DEFAULT_ALLOCATED):
+        return listobject.new_list(item_type, allocated=allocated)
+
+    return impl
+
+
+@box(types.ListType)
+def box_lsttype(typ, val, c):
+    context = c.context
+    builder = c.builder
+
+    # XXX deduplicate
+    ctor = cgutils.create_struct_proxy(typ)
+    lstruct = ctor(context, builder, value=val)
+    # Returns the plain MemInfo
+    boxed_meminfo = c.box(
+        types.MemInfoPointer(types.voidptr),
+        lstruct.meminfo,
+    )
+
+    modname = c.context.insert_const_string(
+        c.builder.module, 'numba.typed.typedlist',
+    )
+    typedlist_mod = c.pyapi.import_module_noblock(modname)
+    fmp_fn = c.pyapi.object_getattr_string(typedlist_mod, '_from_meminfo_ptr')
+
+    lsttype_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ))
+
+    result_var = builder.alloca(c.pyapi.pyobj)
+    builder.store(cgutils.get_null_value(c.pyapi.pyobj), result_var)
+
+    with builder.if_then(cgutils.is_not_null(builder, lsttype_obj)):
+        res = c.pyapi.call_function_objargs(
+            fmp_fn, (boxed_meminfo, lsttype_obj),
+        )
+        c.pyapi.decref(fmp_fn)
+        c.pyapi.decref(typedlist_mod)
+        c.pyapi.decref(boxed_meminfo)
+        builder.store(res, result_var)
+    return builder.load(result_var)
+
+
+@unbox(types.ListType)
+def unbox_listtype(typ, val, c):
+    context = c.context
+    builder = c.builder
+
+    # Check that `type(val) is Dict`
+    list_type = c.pyapi.unserialize(c.pyapi.serialize_object(List))
+    valtype = c.pyapi.object_type(val)
+    same_type = builder.icmp_unsigned("==", valtype, list_type)
+
+    with c.builder.if_else(same_type) as (then, orelse):
+        with then:
+            miptr = c.pyapi.object_getattr_string(val, '_opaque')
+
+            native = c.unbox(types.MemInfoPointer(types.voidptr), miptr)
+
+            mi = native.value
+            ctor = cgutils.create_struct_proxy(typ)
+            lstruct = ctor(context, builder)
+
+            data_pointer = context.nrt.meminfo_data(builder, mi)
+            data_pointer = builder.bitcast(
+                data_pointer,
+                listobject.ll_list_type.as_pointer(),
+            )
+
+            lstruct.data = builder.load(data_pointer)
+            lstruct.meminfo = mi
+
+            lstobj = lstruct._getvalue()
+            c.pyapi.decref(miptr)
+            bb_unboxed = c.builder.basic_block
+
+        with orelse:
+            # Raise error on incorrect type
+            c.pyapi.err_format(
+                "PyExc_TypeError",
+                "can't unbox a %S as a %S",
+                valtype, list_type,
+            )
+            bb_else = c.builder.basic_block
+
+    # Phi nodes to gather the output
+    lstobj_res = c.builder.phi(lstobj.type)
+    is_error_res = c.builder.phi(cgutils.bool_t)
+
+    lstobj_res.add_incoming(lstobj, bb_unboxed)
+    lstobj_res.add_incoming(lstobj.type(None), bb_else)
+
+    is_error_res.add_incoming(cgutils.false_bit, bb_unboxed)
+    is_error_res.add_incoming(cgutils.true_bit, bb_else)
+
+    # cleanup
+    c.pyapi.decref(list_type)
+    c.pyapi.decref(valtype)
+
+    return NativeValue(lstobj_res, is_error=is_error_res)
+
+
+#
+# The following contains the logic for the type-inferred constructor
+#
+
+def _guess_dtype(iterable):
+    """Guess the correct dtype of the iterable type. """
+    if not isinstance(iterable, types.IterableType):
+        raise TypingError(
+            "List() argument must be iterable")
+    # Special case for nested NumPy arrays.
+    elif isinstance(iterable, types.Array) and iterable.ndim > 1:
+        return iterable.copy(ndim=iterable.ndim - 1, layout='A')
+    elif hasattr(iterable, "dtype"):
+        return iterable.dtype
+    elif hasattr(iterable, "yield_type"):
+        return iterable.yield_type
+    elif isinstance(iterable, types.UnicodeType):
+        return iterable
+    elif isinstance(iterable, types.DictType):
+        return iterable.key_type
+    else:
+        # This should never happen, since the 'dtype' of any iterable
+        # should have determined above.
+        raise TypingError(
+            "List() argument does not have a suitable dtype")
+
+
+@type_callable(ListType)
+def typedlist_call(context):
+    """Defines typing logic for ``List()`` and ``List(iterable)``.
+
+    If no argument is given, the returned typer types a new typed-list with an
+    undefined item type. If a single argument is given it must be iterable with
+    a guessable 'dtype'. In this case, the typer types a new typed-list with
+    the type set to the 'dtype' of the iterable arg.
+
+    Parameters
+    ----------
+    arg : single iterable (optional)
+        The single optional argument.
+
+    Returns
+    -------
+    typer : function
+        A typer suitable to type constructor calls.
+
+    Raises
+    ------
+    The returned typer raises a TypingError in case of unsuitable arguments.
+
+    """
+
+    class Typer(object):
+
+        def attach_sig(self):
+            from inspect import signature as mypysig
+
+            def mytyper(iterable):
+                pass
+            self.pysig = mypysig(mytyper)
+
+        def __call__(self, *args, **kwargs):
+            if kwargs:
+                raise TypingError(
+                    "List() takes no keyword arguments"
+                )
+            elif args:
+                if not 0 <= len(args) <= 1:
+                    raise TypingError(
+                        "List() expected at most 1 argument, got {}"
+                        .format(len(args))
+                    )
+                rt = types.ListType(_guess_dtype(args[0]))
+                self.attach_sig()
+                return Signature(rt, args, None, pysig=self.pysig)
+            else:
+                item_type = types.undefined
+                return types.ListType(item_type)
+
+    return Typer()
+
+
+@overload(numba_typeref_ctor)
+def impl_numba_typeref_ctor(cls, *args):
+    """Defines lowering for ``List()`` and ``List(iterable)``.
+
+    This defines the lowering logic to instantiate either an empty typed-list
+    or a typed-list initialised with values from a single iterable argument.
+
+    Parameters
+    ----------
+    cls : TypeRef
+        Expecting a TypeRef of a precise ListType.
+    args: tuple
+        A tuple that contains a single iterable (optional)
+
+    Returns
+    -------
+    impl : function
+        An implementation suitable for lowering the constructor call.
+
+    See also: `redirect_type_ctor` in numba/cpython/bulitins.py
+    """
+    list_ty = cls.instance_type
+    if not isinstance(list_ty, types.ListType):
+        return  # reject
+    # Ensure the list is precisely typed.
+    if not list_ty.is_precise():
+        msg = "expecting a precise ListType but got {}".format(list_ty)
+        raise LoweringError(msg)
+
+    item_type = types.TypeRef(list_ty.item_type)
+    if args:
+        # special case 0d Numpy arrays
+        if isinstance(args[0], types.Array) and args[0].ndim == 0:
+            def impl(cls, *args):
+                # Instantiate an empty list and populate it with the single
+                # value from the array.
+                r = List.empty_list(item_type)
+                r.append(args[0].item())
+                return r
+        else:
+            def impl(cls, *args):
+                # Instantiate an empty list and populate it with values from
+                # the iterable.
+                r = List.empty_list(item_type)
+                for i in args[0]:
+                    r.append(i)
+                return r
+    else:
+        def impl(cls, *args):
+            # Simply call .empty_list with the item type from *cls*
+            return List.empty_list(item_type)
+
+    return impl
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typedobjectutils.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typedobjectutils.py
new file mode 100644
index 000000000..8c961eba8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/typed/typedobjectutils.py
@@ -0,0 +1,199 @@
+""" Common compiler level utilities for typed dict and list. """
+
+import operator
+import warnings
+
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core import typing
+from numba.core.registry import cpu_target
+from numba.core.typeconv import Conversion
+from numba.core.extending import intrinsic
+from numba.core.errors import TypingError, NumbaTypeSafetyWarning
+
+
+def _as_bytes(builder, ptr):
+    """Helper to do (void*)ptr
+    """
+    return builder.bitcast(ptr, cgutils.voidptr_t)
+
+
+@intrinsic
+def _cast(typingctx, val, typ):
+    """Cast *val* to *typ*
+    """
+    def codegen(context, builder, signature, args):
+        [val, typ] = args
+        context.nrt.incref(builder, signature.return_type, val)
+        return val
+    # Using implicit casting in argument types
+    casted = typ.instance_type
+    _sentry_safe_cast(val, casted)
+    sig = casted(casted, typ)
+    return sig, codegen
+
+
+def _sentry_safe_cast(fromty, toty):
+    """Check and raise TypingError if *fromty* cannot be safely cast to *toty*
+    """
+    tyctxt = cpu_target.typing_context
+    fromty, toty = map(types.unliteral, (fromty, toty))
+    by = tyctxt.can_convert(fromty, toty)
+
+    def warn():
+        m = 'unsafe cast from {} to {}. Precision may be lost.'
+        warnings.warn(m.format(fromty, toty),
+                      category=NumbaTypeSafetyWarning)
+
+    isint = lambda x: isinstance(x, types.Integer)
+    isflt = lambda x: isinstance(x, types.Float)
+    iscmplx = lambda x: isinstance(x, types.Complex)
+    isdict = lambda x: isinstance(x, types.DictType)
+    # Only check against numeric types.
+    if by is None or by > Conversion.safe:
+        if isint(fromty) and isint(toty):
+            # Accept if both types are ints
+            warn()
+        elif isint(fromty) and isflt(toty):
+            # Accept if ints to floats
+            warn()
+        elif isflt(fromty) and isflt(toty):
+            # Accept if floats to floats
+            warn()
+        elif iscmplx(fromty) and iscmplx(toty):
+            # Accept if complex to complex
+            warn()
+        elif isdict(fromty) and isdict(toty):
+            pass # it's complaining about initial values being different
+        elif not isinstance(toty, types.Number):
+            # Non-numbers
+            warn()
+        else:
+            # Make it a hard error for numeric type that changes domain.
+            m = 'cannot safely cast {} to {}. Please cast explicitly.'
+            raise TypingError(m.format(fromty, toty))
+
+
+def _sentry_safe_cast_default(default, valty):
+    """Similar to _sentry_safe_cast but handle default value.
+    """
+    # Handle default values
+    # TODO: simplify default values; too many possible way to spell None
+    if default is None:
+        return
+    if isinstance(default, (types.Omitted, types.NoneType)):
+        return
+    return _sentry_safe_cast(default, valty)
+
+
+@intrinsic
+def _nonoptional(typingctx, val):
+    """Typing trick to cast Optional[T] to T
+    """
+    if not isinstance(val, types.Optional):
+        raise TypeError('expected an optional')
+
+    def codegen(context, builder, sig, args):
+        context.nrt.incref(builder, sig.return_type, args[0])
+        return args[0]
+
+    casted = val.type
+    sig = casted(casted)
+    return sig, codegen
+
+
+def _container_get_data(context, builder, container_ty, c):
+    """Helper to get the C list pointer in a numba containers.
+    """
+    ctor = cgutils.create_struct_proxy(container_ty)
+    conatainer_struct = ctor(context, builder, value=c)
+    return conatainer_struct.data
+
+
+def _container_get_meminfo(context, builder, container_ty, c):
+    """Helper to get the meminfo for a container
+    """
+    ctor = cgutils.create_struct_proxy(container_ty)
+    conatainer_struct = ctor(context, builder, value=c)
+    return conatainer_struct.meminfo
+
+
+def _get_incref_decref(context, module, datamodel, container_element_type):
+    assert datamodel.contains_nrt_meminfo()
+
+    fe_type = datamodel.fe_type
+    data_ptr_ty = datamodel.get_data_type().as_pointer()
+    refct_fnty = ir.FunctionType(ir.VoidType(), [data_ptr_ty])
+    incref_fn = cgutils.get_or_insert_function(
+        module, refct_fnty, '.numba_{}.{}_incref'.format(
+            context.fndesc.mangled_name, container_element_type),)
+
+    builder = ir.IRBuilder(incref_fn.append_basic_block())
+    context.nrt.incref(
+        builder, fe_type,
+        datamodel.load_from_data_pointer(builder, incref_fn.args[0]),
+    )
+    builder.ret_void()
+
+    decref_fn = cgutils.get_or_insert_function(
+        module, refct_fnty, name='.numba_{}.{}_decref'.format(
+            context.fndesc.mangled_name, container_element_type),)
+    builder = ir.IRBuilder(decref_fn.append_basic_block())
+    context.nrt.decref(
+        builder, fe_type,
+        datamodel.load_from_data_pointer(builder, decref_fn.args[0]),
+    )
+    builder.ret_void()
+
+    return incref_fn, decref_fn
+
+
+def _get_equal(context, module, datamodel, container_element_type):
+    assert datamodel.contains_nrt_meminfo()
+
+    fe_type = datamodel.fe_type
+    data_ptr_ty = datamodel.get_data_type().as_pointer()
+
+    wrapfnty = context.call_conv.get_function_type(types.int32,
+                                                   [fe_type, fe_type])
+    argtypes = [fe_type, fe_type]
+
+    def build_wrapper(fn):
+        builder = ir.IRBuilder(fn.append_basic_block())
+        args = context.call_conv.decode_arguments(builder, argtypes, fn)
+
+        sig = typing.signature(types.boolean, fe_type, fe_type)
+        op = operator.eq
+        fnop = context.typing_context.resolve_value_type(op)
+        fnop.get_call_type(context.typing_context, sig.args, {})
+        eqfn = context.get_function(fnop, sig)
+        res = eqfn(builder, args)
+        intres = context.cast(builder, res, types.boolean, types.int32)
+        context.call_conv.return_value(builder, intres)
+
+    wrapfn = cgutils.get_or_insert_function(
+        module, wrapfnty, name='.numba_{}.{}_equal.wrap'.format(
+            context.fndesc.mangled_name, container_element_type))
+    build_wrapper(wrapfn)
+
+    equal_fnty = ir.FunctionType(ir.IntType(32), [data_ptr_ty, data_ptr_ty])
+    equal_fn = cgutils.get_or_insert_function(
+        module, equal_fnty, name='.numba_{}.{}_equal'.format(
+            context.fndesc.mangled_name, container_element_type),)
+    builder = ir.IRBuilder(equal_fn.append_basic_block())
+    lhs = datamodel.load_from_data_pointer(builder, equal_fn.args[0])
+    rhs = datamodel.load_from_data_pointer(builder, equal_fn.args[1])
+
+    status, retval = context.call_conv.call_function(
+        builder, wrapfn, types.boolean, argtypes, [lhs, rhs],
+    )
+    with builder.if_then(status.is_ok, likely=True):
+        with builder.if_then(status.is_none):
+            builder.ret(context.get_constant(types.int32, 0))
+        retval = context.cast(builder, retval, types.boolean, types.int32)
+        builder.ret(retval)
+    # Error out
+    builder.ret(context.get_constant(types.int32, -1))
+
+    return equal_fn
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/types/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/types/__init__.py
new file mode 100644
index 000000000..825cc2440
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/numba/types/__init__.py
@@ -0,0 +1,3 @@
+import sys
+from numba.core.utils import _RedirectSubpackage
+sys.modules[__name__] = _RedirectSubpackage(locals(), "numba.core.types")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/requirements.txt b/cv/3d_detection/pointrcnn-iou/pytorch/numba/requirements.txt
new file mode 100644
index 000000000..d6e1198b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/requirements.txt
@@ -0,0 +1 @@
+-e .
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/runtests.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/runtests.py
new file mode 100644
index 000000000..9c37cc997
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/runtests.py
@@ -0,0 +1,16 @@
+#!/usr/bin/env python
+import runpy
+import os
+
+if bool(os.environ.get('NUMBA_USE_TYPEGUARD')):
+    # The typeguard import hook must be installed prior to importing numba.
+    # Therefore, this cannot be part of the numba package.
+    from typeguard.importhook import install_import_hook
+    install_import_hook(packages=['numba'])
+
+# ensure full tracebacks are available and no help messages appear in test mode
+os.environ['NUMBA_DEVELOPER_MODE'] = '1'
+
+
+if __name__ == "__main__":
+    runpy.run_module('numba.runtests', run_name='__main__')
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/setup.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/setup.py
new file mode 100644
index 000000000..e88ed1263
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/setup.py
@@ -0,0 +1,439 @@
+import os
+import platform
+import sys
+from distutils import sysconfig
+from distutils.command import build
+from distutils.command.build_ext import build_ext
+from distutils.spawn import spawn
+
+from setuptools import Extension, find_packages, setup
+import versioneer
+
+_version_module = None
+try:
+    from packaging import version as _version_module
+except ImportError:
+    try:
+        from setuptools._vendor.packaging import version as _version_module
+    except ImportError:
+        pass
+
+
+min_python_version = "3.7"
+max_python_version = "3.11"  # exclusive
+min_numpy_build_version = "1.11"
+min_numpy_run_version = "1.18"
+max_numpy_run_version = "1.24"
+min_llvmlite_version = "0.39.1"
+max_llvmlite_version = "0.40"
+
+if sys.platform.startswith('linux'):
+    # Patch for #2555 to make wheels without libpython
+    sysconfig.get_config_vars()['Py_ENABLE_SHARED'] = 0
+
+
+def _guard_py_ver():
+    if _version_module is None:
+        return
+
+    parse = _version_module.parse
+
+    min_py = parse(min_python_version)
+    max_py = parse(max_python_version)
+    cur_py = parse('.'.join(map(str, sys.version_info[:3])))
+
+    if not min_py <= cur_py < max_py:
+        msg = ('Cannot install on Python version {}; only versions >={},<{} '
+               'are supported.')
+        raise RuntimeError(msg.format(cur_py, min_py, max_py))
+
+
+_guard_py_ver()
+
+
+class build_doc(build.build):
+    description = "build documentation"
+
+    def run(self):
+        spawn(['make', '-C', 'docs', 'html'])
+
+
+versioneer.VCS = 'git'
+versioneer.versionfile_source = 'numba/_version.py'
+versioneer.versionfile_build = 'numba/_version.py'
+versioneer.tag_prefix = ''
+versioneer.parentdir_prefix = 'numba-'
+
+cmdclass = versioneer.get_cmdclass()
+cmdclass['build_doc'] = build_doc
+
+extra_link_args = []
+install_name_tool_fixer = []
+if sys.platform == 'darwin':
+    install_name_tool_fixer += ['-headerpad_max_install_names']
+if platform.machine() == 'ppc64le':
+    extra_link_args += ['-pthread']
+
+build_ext = cmdclass.get('build_ext', build_ext)
+
+numba_be_user_options = [
+    ('werror', None, 'Build extensions with -Werror'),
+    ('wall', None, 'Build extensions with -Wall'),
+    ('noopt', None, 'Build extensions without optimization'),
+]
+
+
+class NumbaBuildExt(build_ext):
+
+    user_options = build_ext.user_options + numba_be_user_options
+    boolean_options = build_ext.boolean_options + ['werror', 'wall', 'noopt']
+
+    def initialize_options(self):
+        super().initialize_options()
+        self.werror = 0
+        self.wall = 0
+        self.noopt = 0
+
+    def run(self):
+        extra_compile_args = []
+        if self.noopt:
+            if sys.platform == 'win32':
+                extra_compile_args.append('/Od')
+            else:
+                extra_compile_args.append('-O0')
+        if self.werror:
+            extra_compile_args.append('-Werror')
+        if self.wall:
+            extra_compile_args.append('-Wall')
+        for ext in self.extensions:
+            ext.extra_compile_args.extend(extra_compile_args)
+
+        super().run()
+
+
+cmdclass['build_ext'] = NumbaBuildExt
+
+
+def is_building():
+    """
+    Parse the setup.py command and return whether a build is requested.
+    If False is returned, only an informational command is run.
+    If True is returned, information about C extensions will have to
+    be passed to the setup() function.
+    """
+    if len(sys.argv) < 2:
+        # User forgot to give an argument probably, let setuptools handle that.
+        return True
+
+    build_commands = ['build', 'build_py', 'build_ext', 'build_clib'
+                      'build_scripts', 'install', 'install_lib',
+                      'install_headers', 'install_scripts', 'install_data',
+                      'sdist', 'bdist', 'bdist_dumb', 'bdist_rpm',
+                      'bdist_wininst', 'check', 'build_doc', 'bdist_wheel',
+                      'bdist_egg', 'develop', 'easy_install', 'test']
+    return any(bc in sys.argv[1:] for bc in build_commands)
+
+
+def get_ext_modules():
+    """
+    Return a list of Extension instances for the setup() call.
+    """
+    # Note we don't import Numpy at the toplevel, since setup.py
+    # should be able to run without Numpy for pip to discover the
+    # build dependencies
+    import numpy.distutils.misc_util as np_misc
+
+    # Inject required options for extensions compiled against the Numpy
+    # C API (include dirs, library dirs etc.)
+    np_compile_args = np_misc.get_info('npymath')
+
+    ext_devicearray = Extension(name='numba._devicearray',
+                                sources=['numba/_devicearray.cpp'],
+                                depends=['numba/_pymodule.h',
+                                         'numba/_devicearray.h'],
+                                include_dirs=['numba'],
+                                extra_compile_args=['-std=c++11'],
+                                )
+
+    ext_dynfunc = Extension(name='numba._dynfunc',
+                            sources=['numba/_dynfuncmod.c'],
+                            depends=['numba/_pymodule.h',
+                                     'numba/_dynfunc.c'])
+
+    ext_dispatcher = Extension(name="numba._dispatcher",
+                               sources=['numba/_dispatcher.cpp',
+                                        'numba/_typeof.c',
+                                        'numba/_hashtable.c',
+                                        'numba/core/typeconv/typeconv.cpp'],
+                               depends=["numba/_pymodule.h",
+                                        "numba/_typeof.h",
+                                        "numba/_hashtable.h"],
+                               **np_compile_args)
+
+    ext_helperlib = Extension(name="numba._helperlib",
+                              sources=["numba/_helpermod.c",
+                                       "numba/cext/utils.c",
+                                       "numba/cext/dictobject.c",
+                                       "numba/cext/listobject.c",
+                                       ],
+                              # numba/_random.c needs pthreads
+                              extra_link_args=install_name_tool_fixer +
+                              extra_link_args,
+                              depends=["numba/_pymodule.h",
+                                       "numba/_helperlib.c",
+                                       "numba/_lapack.c",
+                                       "numba/_npymath_exports.c",
+                                       "numba/_random.c",
+                                       "numba/mathnames.inc",
+                                       ],
+                              **np_compile_args)
+
+    ext_typeconv = Extension(name="numba.core.typeconv._typeconv",
+                             sources=["numba/core/typeconv/typeconv.cpp",
+                                      "numba/core/typeconv/_typeconv.cpp"],
+                             depends=["numba/_pymodule.h"],
+                             )
+
+    ext_np_ufunc = Extension(name="numba.np.ufunc._internal",
+                             sources=["numba/np/ufunc/_internal.c"],
+                             depends=["numba/np/ufunc/_ufunc.c",
+                                      "numba/np/ufunc/_internal.h",
+                                      "numba/_pymodule.h"],
+                             **np_compile_args)
+
+    ext_npyufunc_num_threads = Extension(name="numba.np.ufunc._num_threads",
+                                         sources=[
+                                             "numba/np/ufunc/_num_threads.c"],
+                                         depends=["numba/_pymodule.h"],
+                                         )
+
+    ext_np_ufunc_backends = []
+
+    def check_file_at_path(path2file):
+        """
+        Takes a list as a path, a single glob (*) is permitted as an entry which
+        indicates that expansion at this location is required (i.e. version
+        might not be known).
+        """
+        found = None
+        path2check = [os.path.split(os.path.split(sys.executable)[0])[0]]
+        path2check += [os.getenv(n, '') for n in ['CONDA_PREFIX', 'PREFIX']]
+        if sys.platform.startswith('win'):
+            path2check += [os.path.join(p, 'Library') for p in path2check]
+        for p in path2check:
+            if p:
+                if '*' in path2file:
+                    globloc = path2file.index('*')
+                    searchroot = os.path.join(*path2file[:globloc])
+                    try:
+                        potential_locs = os.listdir(os.path.join(p, searchroot))
+                    except BaseException:
+                        continue
+                    searchfor = path2file[globloc + 1:]
+                    for x in potential_locs:
+                        potpath = os.path.join(p, searchroot, x, *searchfor)
+                        if os.path.isfile(potpath):
+                            found = p  # the latest is used
+                elif os.path.isfile(os.path.join(p, *path2file)):
+                    found = p  # the latest is used
+        return found
+
+    # Set various flags for use in TBB and openmp. On OSX, also find OpenMP!
+    have_openmp = True
+    if sys.platform.startswith('win'):
+        if 'MSC' in sys.version:
+            cpp11flags = []
+            ompcompileflags = ['-openmp']
+            omplinkflags = []
+        else:
+            # For non-MSVC toolchain e.g. gcc and clang with mingw
+            cpp11flags = ['-std=c++11']
+            ompcompileflags = ['-fopenmp']
+            omplinkflags = ['-fopenmp']
+    elif sys.platform.startswith('darwin'):
+        cpp11flags = ['-std=c++11']
+        # This is a bit unusual but necessary...
+        # llvm (clang) OpenMP is used for headers etc at compile time
+        # Intel OpenMP (libiomp5) provides the link library.
+        # They are binary compatible and may not safely coexist in a process, as
+        # libiomp5 is more prevalent and often linked in for NumPy it is used
+        # here!
+        ompcompileflags = ['-fopenmp']
+        omplinkflags = ['-fopenmp=libiomp5']
+        omppath = ['lib', 'clang', '*', 'include', 'omp.h']
+        have_openmp = check_file_at_path(omppath)
+    else:
+        cpp11flags = ['-std=c++11']
+        ompcompileflags = ['-fopenmp']
+        if platform.machine() == 'ppc64le':
+            omplinkflags = ['-fopenmp']
+        else:
+            omplinkflags = ['-fopenmp']
+
+    # Disable tbb if forced by user with NUMBA_DISABLE_TBB=1
+    if os.getenv("NUMBA_DISABLE_TBB"):
+        print("TBB disabled")
+    else:
+        # Search for Intel TBB, first check env var TBBROOT then conda locations
+        tbb_root = os.getenv('TBBROOT')
+        if not tbb_root:
+            tbb_root = check_file_at_path(['include', 'tbb', 'tbb.h'])
+
+        if tbb_root:
+            print("Using Intel TBB from:", tbb_root)
+            ext_np_ufunc_tbb_backend = Extension(
+                name='numba.np.ufunc.tbbpool',
+                sources=[
+                    'numba/np/ufunc/tbbpool.cpp',
+                    'numba/np/ufunc/gufunc_scheduler.cpp',
+                ],
+                depends=['numba/np/ufunc/workqueue.h'],
+                include_dirs=[os.path.join(tbb_root, 'include')],
+                extra_compile_args=cpp11flags,
+                extra_link_args=extra_link_args,
+                libraries=['tbb'],  # TODO: if --debug or -g, use 'tbb_debug'
+                library_dirs=[
+                    # for Linux
+                    os.path.join(tbb_root, 'lib', 'intel64', 'gcc4.4'),
+                    # for MacOS
+                    os.path.join(tbb_root, 'lib'),
+                    # for Windows
+                    os.path.join(tbb_root, 'lib', 'intel64', 'vc_mt'),
+                ],
+            )
+            ext_np_ufunc_backends.append(ext_np_ufunc_tbb_backend)
+        else:
+            print("TBB not found")
+
+    # Disable OpenMP if forced by user with NUMBA_DISABLE_OPENMP=1
+    if os.getenv('NUMBA_DISABLE_OPENMP'):
+        print("OpenMP disabled")
+    elif have_openmp:
+        print("Using OpenMP from:", have_openmp)
+        # OpenMP backed work queue
+        ext_np_ufunc_omppool_backend = Extension(
+            name='numba.np.ufunc.omppool',
+            sources=[
+                'numba/np/ufunc/omppool.cpp',
+                'numba/np/ufunc/gufunc_scheduler.cpp',
+            ],
+            depends=['numba/np/ufunc/workqueue.h'],
+            extra_compile_args=ompcompileflags + cpp11flags,
+            extra_link_args=omplinkflags,
+        )
+
+        ext_np_ufunc_backends.append(ext_np_ufunc_omppool_backend)
+    else:
+        print("OpenMP not found")
+
+    # Build the Numba workqueue implementation irrespective of whether the TBB
+    # version is built. Users can select a backend via env vars.
+    ext_np_ufunc_workqueue_backend = Extension(
+        name='numba.np.ufunc.workqueue',
+        sources=['numba/np/ufunc/workqueue.c',
+                 'numba/np/ufunc/gufunc_scheduler.cpp'],
+        depends=['numba/np/ufunc/workqueue.h'],
+        extra_link_args=extra_link_args)
+    ext_np_ufunc_backends.append(ext_np_ufunc_workqueue_backend)
+
+    ext_mviewbuf = Extension(name='numba.mviewbuf',
+                             extra_link_args=install_name_tool_fixer,
+                             sources=['numba/mviewbuf.c'])
+
+    ext_nrt_python = Extension(name='numba.core.runtime._nrt_python',
+                               sources=['numba/core/runtime/_nrt_pythonmod.c',
+                                        'numba/core/runtime/nrt.c'],
+                               depends=['numba/core/runtime/nrt.h',
+                                        'numba/_pymodule.h',
+                                        'numba/core/runtime/_nrt_python.c'],
+                               **np_compile_args)
+
+    ext_jitclass_box = Extension(name='numba.experimental.jitclass._box',
+                                 sources=['numba/experimental/jitclass/_box.c'],
+                                 depends=['numba/experimental/_pymodule.h'],
+                                 )
+
+    ext_cuda_extras = Extension(name='numba.cuda.cudadrv._extras',
+                                sources=['numba/cuda/cudadrv/_extras.c'],
+                                depends=['numba/_pymodule.h'],
+                                include_dirs=["numba"])
+
+    ext_modules = [ext_dynfunc, ext_dispatcher, ext_helperlib,
+                   ext_typeconv, ext_np_ufunc, ext_npyufunc_num_threads,
+                   ext_mviewbuf, ext_nrt_python, ext_jitclass_box,
+                   ext_cuda_extras, ext_devicearray]
+
+    ext_modules += ext_np_ufunc_backends
+
+    return ext_modules
+
+
+packages = find_packages(include=["numba", "numba.*"])
+
+build_requires = ['numpy >={},<{}'.format(min_numpy_build_version,
+                                          max_numpy_run_version)]
+install_requires = [
+    'llvmlite >={},<{}'.format(min_llvmlite_version, max_llvmlite_version),
+    'numpy >={},<{}'.format(min_numpy_run_version, max_numpy_run_version),
+    'setuptools',
+    'importlib_metadata; python_version < "3.9"',
+]
+
+def read(fname):
+    return open(os.path.join(os.path.dirname(__file__), fname)).read()
+
+numba_version = read('version.txt').replace("\n", "")
+numba_local_identifier = os.environ.get('NUMBA_LOCAL_IDENTIFIER', '')
+if numba_local_identifier != '':
+    numba_version += '+' + numba_local_identifier
+
+metadata = dict(
+    name='numba',
+    description="compiling Python code using LLVM",
+    version=numba_version,
+    classifiers=[
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Developers",
+        "License :: OSI Approved :: BSD License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Topic :: Software Development :: Compilers",
+    ],
+    package_data={
+        # HTML templates for type annotations
+        "numba.core.annotations": ["*.html"],
+        # Various test data
+        "numba.cuda.tests.cudadrv.data": ["*.ptx", "*.cu"],
+        "numba.cuda.tests.doc_examples.ffi": ["*.cu"],
+        "numba.tests": ["pycc_distutils_usecase/*.py"],
+        # Some C files are needed by pycc
+        "numba": ["*.c", "*.h"],
+        "numba.pycc": ["*.c", "*.h"],
+        "numba.core.runtime": ["*.cpp", "*.c", "*.h"],
+        "numba.cext": ["*.c", "*.h"],
+        # numba gdb hook init command language file
+        "numba.misc": ["cmdlang.gdb"],
+        "numba.typed": ["py.typed"],
+    },
+    scripts=["numba/pycc/pycc", "bin/numba"],
+    url="https://numba.pydata.org",
+    packages=packages,
+    setup_requires=build_requires,
+    install_requires=install_requires,
+    python_requires=">={}".format(min_python_version),
+    license="BSD",
+    cmdclass=cmdclass,
+)
+
+with open('README.rst') as f:
+    metadata['long_description'] = f.read()
+
+if is_building():
+    metadata['ext_modules'] = get_ext_modules()
+
+setup(**metadata)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/version.txt b/cv/3d_detection/pointrcnn-iou/pytorch/numba/version.txt
new file mode 100644
index 000000000..2c432f4cb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/version.txt
@@ -0,0 +1 @@
+v0.56.4
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/numba/versioneer.py b/cv/3d_detection/pointrcnn-iou/pytorch/numba/versioneer.py
new file mode 100644
index 000000000..c00770fe4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/numba/versioneer.py
@@ -0,0 +1,1046 @@
+
+# Version: 0.14
+
+"""
+The Versioneer
+==============
+
+* like a rocketeer, but for versions!
+* https://github.com/warner/python-versioneer
+* Brian Warner
+* License: Public Domain
+* Compatible With: python2.6, 2.7, 3.2, 3.3, 3.4, and pypy
+* [![Latest Version]
+(https://pypip.in/version/versioneer/badge.svg?style=flat)
+](https://pypi.python.org/pypi/versioneer/)
+* [![Build Status]
+(https://travis-ci.org/warner/python-versioneer.png?branch=master)
+](https://travis-ci.org/warner/python-versioneer)
+
+This is a tool for managing a recorded version number in distutils-based
+python projects. The goal is to remove the tedious and error-prone "update
+the embedded version string" step from your release process. Making a new
+release should be as easy as recording a new tag in your version-control
+system, and maybe making new tarballs.
+
+
+## Quick Install
+
+* `pip install versioneer` to somewhere to your $PATH
+* run `versioneer-installer` in your source tree: this installs `versioneer.py`
+* follow the instructions below (also in the `versioneer.py` docstring)
+
+## Version Identifiers
+
+Source trees come from a variety of places:
+
+* a version-control system checkout (mostly used by developers)
+* a nightly tarball, produced by build automation
+* a snapshot tarball, produced by a web-based VCS browser, like github's
+  "tarball from tag" feature
+* a release tarball, produced by "setup.py sdist", distributed through PyPI
+
+Within each source tree, the version identifier (either a string or a number,
+this tool is format-agnostic) can come from a variety of places:
+
+* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows
+  about recent "tags" and an absolute revision-id
+* the name of the directory into which the tarball was unpacked
+* an expanded VCS keyword ($Id$, etc)
+* a `_version.py` created by some earlier build step
+
+For released software, the version identifier is closely related to a VCS
+tag. Some projects use tag names that include more than just the version
+string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool
+needs to strip the tag prefix to extract the version identifier. For
+unreleased software (between tags), the version identifier should provide
+enough information to help developers recreate the same tree, while also
+giving them an idea of roughly how old the tree is (after version 1.2, before
+version 1.3). Many VCS systems can report a description that captures this,
+for example 'git describe --tags --dirty --always' reports things like
+"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
+0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
+uncommitted changes.
+
+The version identifier is used for multiple purposes:
+
+* to allow the module to self-identify its version: `myproject.__version__`
+* to choose a name and prefix for a 'setup.py sdist' tarball
+
+## Theory of Operation
+
+Versioneer works by adding a special `_version.py` file into your source
+tree, where your `__init__.py` can import it. This `_version.py` knows how to
+dynamically ask the VCS tool for version information at import time. However,
+when you use "setup.py build" or "setup.py sdist", `_version.py` in the new
+copy is replaced by a small static file that contains just the generated
+version data.
+
+`_version.py` also contains `$Revision$` markers, and the installation
+process marks `_version.py` to have this marker rewritten with a tag name
+during the "git archive" command. As a result, generated tarballs will
+contain enough information to get the proper version.
+
+
+## Installation
+
+First, decide on values for the following configuration variables:
+
+* `VCS`: the version control system you use. Currently accepts "git".
+
+* `versionfile_source`:
+
+  A project-relative pathname into which the generated version strings should
+  be written. This is usually a `_version.py` next to your project's main
+  `__init__.py` file, so it can be imported at runtime. If your project uses
+  `src/myproject/__init__.py`, this should be `src/myproject/_version.py`.
+  This file should be checked in to your VCS as usual: the copy created below
+  by `setup.py versioneer` will include code that parses expanded VCS
+  keywords in generated tarballs. The 'build' and 'sdist' commands will
+  replace it with a copy that has just the calculated version string.
+
+  This must be set even if your project does not have any modules (and will
+  therefore never import `_version.py`), since "setup.py sdist" -based trees
+  still need somewhere to record the pre-calculated version strings. Anywhere
+  in the source tree should do. If there is a `__init__.py` next to your
+  `_version.py`, the `setup.py versioneer` command (described below) will
+  append some `__version__`-setting assignments, if they aren't already
+  present.
+
+* `versionfile_build`:
+
+  Like `versionfile_source`, but relative to the build directory instead of
+  the source directory. These will differ when your setup.py uses
+  'package_dir='. If you have `package_dir={'myproject': 'src/myproject'}`,
+  then you will probably have `versionfile_build='myproject/_version.py'` and
+  `versionfile_source='src/myproject/_version.py'`.
+
+  If this is set to None, then `setup.py build` will not attempt to rewrite
+  any `_version.py` in the built tree. If your project does not have any
+  libraries (e.g. if it only builds a script), then you should use
+  `versionfile_build = None` and override `distutils.command.build_scripts`
+  to explicitly insert a copy of `versioneer.get_version()` into your
+  generated script.
+
+* `tag_prefix`:
+
+  a string, like 'PROJECTNAME-', which appears at the start of all VCS tags.
+  If your tags look like 'myproject-1.2.0', then you should use
+  tag_prefix='myproject-'. If you use unprefixed tags like '1.2.0', this
+  should be an empty string.
+
+* `parentdir_prefix`:
+
+  a string, frequently the same as tag_prefix, which appears at the start of
+  all unpacked tarball filenames. If your tarball unpacks into
+  'myproject-1.2.0', this should be 'myproject-'.
+
+This tool provides one script, named `versioneer-installer`. That script does
+one thing: write a copy of `versioneer.py` into the current directory.
+
+To versioneer-enable your project:
+
+* 1: Run `versioneer-installer` to copy `versioneer.py` into the top of your
+  source tree.
+
+* 2: add the following lines to the top of your `setup.py`, with the
+  configuration values you decided earlier:
+
+  ````
+  import versioneer
+  versioneer.VCS = 'git'
+  versioneer.versionfile_source = 'src/myproject/_version.py'
+  versioneer.versionfile_build = 'myproject/_version.py'
+  versioneer.tag_prefix = '' # tags are like 1.2.0
+  versioneer.parentdir_prefix = 'myproject-' # dirname like 'myproject-1.2.0'
+  ````
+
+* 3: add the following arguments to the setup() call in your setup.py:
+
+        version=versioneer.get_version(),
+        cmdclass=versioneer.get_cmdclass(),
+
+* 4: now run `setup.py versioneer`, which will create `_version.py`, and will
+  modify your `__init__.py` (if one exists next to `_version.py`) to define
+  `__version__` (by calling a function from `_version.py`). It will also
+  modify your `MANIFEST.in` to include both `versioneer.py` and the generated
+  `_version.py` in sdist tarballs.
+
+* 5: commit these changes to your VCS. To make sure you won't forget,
+  `setup.py versioneer` will mark everything it touched for addition.
+
+## Post-Installation Usage
+
+Once established, all uses of your tree from a VCS checkout should get the
+current version string. All generated tarballs should include an embedded
+version string (so users who unpack them will not need a VCS tool installed).
+
+If you distribute your project through PyPI, then the release process should
+boil down to two steps:
+
+* 1: git tag 1.0
+* 2: python setup.py register sdist upload
+
+If you distribute it through github (i.e. users use github to generate
+tarballs with `git archive`), the process is:
+
+* 1: git tag 1.0
+* 2: git push; git push --tags
+
+Currently, all version strings must be based upon a tag. Versioneer will
+report "unknown" until your tree has at least one tag in its history. This
+restriction will be fixed eventually (see issue #12).
+
+## Version-String Flavors
+
+Code which uses Versioneer can learn about its version string at runtime by
+importing `_version` from your main `__init__.py` file and running the
+`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can
+import the top-level `versioneer.py` and run `get_versions()`.
+
+Both functions return a dictionary with different keys for different flavors
+of the version string:
+
+* `['version']`: A condensed PEP440-compliant string, equal to the
+  un-prefixed tag name for actual releases, and containing an additional
+  "local version" section with more detail for in-between builds. For Git,
+  this is TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe
+  --tags --dirty --always`. For example "0.11+2.g1076c97.dirty" indicates
+  that the tree is like the "1076c97" commit but has uncommitted changes
+  (".dirty"), and that this commit is two revisions ("+2") beyond the "0.11"
+  tag. For released software (exactly equal to a known tag), the identifier
+  will only contain the stripped tag, e.g. "0.11".
+
+* `['full']`: detailed revision identifier. For Git, this is the full SHA1
+  commit id, followed by ".dirty" if the tree contains uncommitted changes,
+  e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac.dirty".
+
+Some variants are more useful than others. Including `full` in a bug report
+should allow developers to reconstruct the exact code being tested (or
+indicate the presence of local changes that should be shared with the
+developers). `version` is suitable for display in an "about" box or a CLI
+`--version` output: it can be easily compared against release notes and lists
+of bugs fixed in various releases.
+
+The `setup.py versioneer` command adds the following text to your
+`__init__.py` to place a basic version in `YOURPROJECT.__version__`:
+
+    from ._version import get_versions
+    __version__ = get_versions()['version']
+    del get_versions
+
+## Updating Versioneer
+
+To upgrade your project to a new release of Versioneer, do the following:
+
+* install the new Versioneer (`pip install -U versioneer` or equivalent)
+* re-run `versioneer-installer` in your source tree to replace your copy of
+  `versioneer.py`
+* edit `setup.py`, if necessary, to include any new configuration settings
+  indicated by the release notes
+* re-run `setup.py versioneer` to replace `SRC/_version.py`
+* commit any changed files
+
+### Upgrading from 0.10 to 0.11
+
+You must add a `versioneer.VCS = "git"` to your `setup.py` before re-running
+`setup.py versioneer`. This will enable the use of additional version-control
+systems (SVN, etc) in the future.
+
+### Upgrading from 0.11 to 0.12
+
+Nothing special.
+
+## Upgrading to 0.14
+
+0.14 changes the format of the version string. 0.13 and earlier used
+hyphen-separated strings like "0.11-2-g1076c97-dirty". 0.14 and beyond use a
+plus-separated "local version" section strings, with dot-separated
+components, like "0.11+2.g1076c97". PEP440-strict tools did not like the old
+format, but should be ok with the new one.
+
+## Future Directions
+
+This tool is designed to make it easily extended to other version-control
+systems: all VCS-specific components are in separate directories like
+src/git/ . The top-level `versioneer.py` script is assembled from these
+components by running make-versioneer.py . In the future, make-versioneer.py
+will take a VCS name as an argument, and will construct a version of
+`versioneer.py` that is specific to the given VCS. It might also take the
+configuration arguments that are currently provided manually during
+installation by editing setup.py . Alternatively, it might go the other
+direction and include code from all supported VCS systems, reducing the
+number of intermediate scripts.
+
+
+## License
+
+To make Versioneer easier to embed, all its code is hereby released into the
+public domain. The `_version.py` that it creates is also in the public
+domain.
+
+"""
+
+import errno
+import os
+import re
+import subprocess
+import sys
+from distutils.command.build import build as _build
+from distutils.command.sdist import sdist as _sdist
+from distutils.core import Command
+
+# these configuration settings will be overridden by setup.py after it
+# imports us
+versionfile_source = None
+versionfile_build = None
+tag_prefix = None
+parentdir_prefix = None
+VCS = None
+
+# these dictionaries contain VCS-specific tools
+LONG_VERSION_PY = {}
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
+    assert isinstance(commands, list)
+    p = None
+    for c in commands:
+        try:
+            # remember shell=False, so use git.cmd on windows, not just git
+            p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,
+                                 stderr=(subprocess.PIPE if hide_stderr
+                                         else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % args[0])
+                print(e)
+            return None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None
+    stdout = p.communicate()[0].strip()
+    if sys.version_info[0] >= 3:
+        stdout = stdout.decode()
+    if p.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % args[0])
+        return None
+    return stdout
+LONG_VERSION_PY['git'] = '''
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain. Generated by
+# versioneer-0.14 (https://github.com/warner/python-versioneer)
+
+import errno
+import os
+import re
+import subprocess
+import sys
+
+# these strings will be replaced by git during git-archive
+git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
+git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
+
+# these strings are filled in when 'setup.py versioneer' creates _version.py
+tag_prefix = "%(TAG_PREFIX)s"
+parentdir_prefix = "%(PARENTDIR_PREFIX)s"
+versionfile_source = "%(VERSIONFILE_SOURCE)s"
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
+    assert isinstance(commands, list)
+    p = None
+    for c in commands:
+        try:
+            # remember shell=False, so use git.cmd on windows, not just git
+            p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,
+                                 stderr=(subprocess.PIPE if hide_stderr
+                                         else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %%s" %% args[0])
+                print(e)
+            return None
+    else:
+        if verbose:
+            print("unable to find command, tried %%s" %% (commands,))
+        return None
+    stdout = p.communicate()[0].strip()
+    if sys.version_info[0] >= 3:
+        stdout = stdout.decode()
+    if p.returncode != 0:
+        if verbose:
+            print("unable to run %%s (error)" %% args[0])
+        return None
+    return stdout
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose=False):
+    # Source tarballs conventionally unpack into a directory that includes
+    # both the project name and a version string.
+    dirname = os.path.basename(root)
+    if not dirname.startswith(parentdir_prefix):
+        if verbose:
+            print("guessing rootdir is '%%s', but '%%s' doesn't start with "
+                  "prefix '%%s'" %% (root, dirname, parentdir_prefix))
+        return None
+    return {"version": dirname[len(parentdir_prefix):], "full": ""}
+
+
+def git_get_keywords(versionfile_abs):
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        f = open(versionfile_abs, "r")
+        for line in f.readlines():
+            if line.strip().startswith("git_refnames ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["refnames"] = mo.group(1)
+            if line.strip().startswith("git_full ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["full"] = mo.group(1)
+        f.close()
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+def git_versions_from_keywords(keywords, tag_prefix, verbose=False):
+    if not keywords:
+        return {}  # keyword-finding function failed to find keywords
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        return {}  # unexpanded, so not in an unpacked git-archive tarball
+    refs = set([r.strip() for r in refnames.strip("()").split(",")])
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %%d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = set([r for r in refs if re.search(r'\d', r)])
+        if verbose:
+            print("discarding '%%s', no digits" %% ",".join(refs-tags))
+    if verbose:
+        print("likely tags: %%s" %% ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            if verbose:
+                print("picking %%s" %% r)
+            return {"version": r,
+                    "full": keywords["full"].strip()}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full": keywords["full"].strip()}
+
+
+def git_parse_vcs_describe(git_describe, tag_prefix, verbose=False):
+    # TAG-NUM-gHEX[-dirty] or HEX[-dirty] . TAG might have hyphens.
+
+    # dirty
+    dirty = git_describe.endswith("-dirty")
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+    dirty_suffix = ".dirty" if dirty else ""
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" not in git_describe:  # just HEX
+        return "0+untagged.g"+git_describe+dirty_suffix, dirty
+
+    # just TAG-NUM-gHEX
+    mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+    if not mo:
+        # unparseable. Maybe git-describe is misbehaving?
+        return "0+unparseable"+dirty_suffix, dirty
+
+    # tag
+    full_tag = mo.group(1)
+    if not full_tag.startswith(tag_prefix):
+        if verbose:
+            fmt = "tag '%%s' doesn't start with prefix '%%s'"
+            print(fmt %% (full_tag, tag_prefix))
+        return None, dirty
+    tag = full_tag[len(tag_prefix):]
+
+    # distance: number of commits since tag
+    distance = int(mo.group(2))
+
+    # commit: short hex revision ID
+    commit = mo.group(3)
+
+    # now build up version string, with post-release "local version
+    # identifier". Our goal: TAG[+NUM.gHEX[.dirty]] . Note that if you get a
+    # tagged build and then dirty it, you'll get TAG+0.gHEX.dirty . So you
+    # can always test version.endswith(".dirty").
+    version = tag
+    if distance or dirty:
+        version += "+%%d.g%%s" %% (distance, commit) + dirty_suffix
+
+    return version, dirty
+
+
+def git_versions_from_vcs(tag_prefix, root, verbose=False):
+    # this runs 'git' from the root of the source tree. This only gets called
+    # if the git-archive 'subst' keywords were *not* expanded, and
+    # _version.py hasn't already been rewritten with a short version string,
+    # meaning we're inside a checked out source tree.
+
+    if not os.path.exists(os.path.join(root, ".git")):
+        if verbose:
+            print("no .git in %%s" %% root)
+        return {}  # get_versions() will try next method
+
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+    # if there is a tag, this yields TAG-NUM-gHEX[-dirty]
+    # if there are no tags, this yields HEX[-dirty] (no NUM)
+    stdout = run_command(GITS, ["describe", "--tags", "--dirty",
+                                "--always", "--long"],
+                         cwd=root)
+    # --long was added in git-1.5.5
+    if stdout is None:
+        return {}  # try next method
+    version, dirty = git_parse_vcs_describe(stdout, tag_prefix, verbose)
+
+    # build "full", which is FULLHEX[.dirty]
+    stdout = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if stdout is None:
+        return {}
+    full = stdout.strip()
+    if dirty:
+        full += ".dirty"
+
+    return {"version": version, "full": full}
+
+
+def get_versions(default={"version": "0+unknown", "full": ""}, verbose=False):
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    keywords = {"refnames": git_refnames, "full": git_full}
+    ver = git_versions_from_keywords(keywords, tag_prefix, verbose)
+    if ver:
+        return ver
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for i in versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return default
+
+    return (git_versions_from_vcs(tag_prefix, root, verbose)
+            or versions_from_parentdir(parentdir_prefix, root, verbose)
+            or default)
+'''
+
+
+def git_get_keywords(versionfile_abs):
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        f = open(versionfile_abs, "r")
+        for line in f.readlines():
+            if line.strip().startswith("git_refnames ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["refnames"] = mo.group(1)
+            if line.strip().startswith("git_full ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["full"] = mo.group(1)
+        f.close()
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+def git_versions_from_keywords(keywords, tag_prefix, verbose=False):
+    if not keywords:
+        return {}  # keyword-finding function failed to find keywords
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        return {}  # unexpanded, so not in an unpacked git-archive tarball
+    refs = set([r.strip() for r in refnames.strip("()").split(",")])
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = set([r for r in refs if re.search(r'\d', r)])
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs-tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full": keywords["full"].strip()}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full": keywords["full"].strip()}
+
+
+def git_parse_vcs_describe(git_describe, tag_prefix, verbose=False):
+    # TAG-NUM-gHEX[-dirty] or HEX[-dirty] . TAG might have hyphens.
+
+    # dirty
+    dirty = git_describe.endswith("-dirty")
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+    dirty_suffix = ".dirty" if dirty else ""
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" not in git_describe:  # just HEX
+        return "0+untagged.g"+git_describe+dirty_suffix, dirty
+
+    # just TAG-NUM-gHEX
+    mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+    if not mo:
+        # unparseable. Maybe git-describe is misbehaving?
+        return "0+unparseable"+dirty_suffix, dirty
+
+    # tag
+    full_tag = mo.group(1)
+    if not full_tag.startswith(tag_prefix):
+        if verbose:
+            fmt = "tag '%s' doesn't start with prefix '%s'"
+            print(fmt % (full_tag, tag_prefix))
+        return None, dirty
+    tag = full_tag[len(tag_prefix):]
+
+    # distance: number of commits since tag
+    distance = int(mo.group(2))
+
+    # commit: short hex revision ID
+    commit = mo.group(3)
+
+    # now build up version string, with post-release "local version
+    # identifier". Our goal: TAG[+NUM.gHEX[.dirty]] . Note that if you get a
+    # tagged build and then dirty it, you'll get TAG+0.gHEX.dirty . So you
+    # can always test version.endswith(".dirty").
+    version = tag
+    if distance or dirty:
+        version += "+%d.g%s" % (distance, commit) + dirty_suffix
+
+    return version, dirty
+
+
+def git_versions_from_vcs(tag_prefix, root, verbose=False):
+    # this runs 'git' from the root of the source tree. This only gets called
+    # if the git-archive 'subst' keywords were *not* expanded, and
+    # _version.py hasn't already been rewritten with a short version string,
+    # meaning we're inside a checked out source tree.
+
+    if not os.path.exists(os.path.join(root, ".git")):
+        if verbose:
+            print("no .git in %s" % root)
+        return {}  # get_versions() will try next method
+
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+    # if there is a tag, this yields TAG-NUM-gHEX[-dirty]
+    # if there are no tags, this yields HEX[-dirty] (no NUM)
+    stdout = run_command(GITS, ["describe", "--tags", "--dirty",
+                                "--always", "--long"],
+                         cwd=root)
+    # --long was added in git-1.5.5
+    if stdout is None:
+        return {}  # try next method
+    version, dirty = git_parse_vcs_describe(stdout, tag_prefix, verbose)
+
+    # build "full", which is FULLHEX[.dirty]
+    stdout = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if stdout is None:
+        return {}
+    full = stdout.strip()
+    if dirty:
+        full += ".dirty"
+
+    return {"version": version, "full": full}
+
+
+def do_vcs_install(manifest_in, versionfile_source, ipy):
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+    files = [manifest_in, versionfile_source]
+    if ipy:
+        files.append(ipy)
+    try:
+        me = __file__
+        if me.endswith(".pyc") or me.endswith(".pyo"):
+            me = os.path.splitext(me)[0] + ".py"
+        versioneer_file = os.path.relpath(me)
+    except NameError:
+        versioneer_file = "versioneer.py"
+    files.append(versioneer_file)
+    present = False
+    try:
+        f = open(".gitattributes", "r")
+        for line in f.readlines():
+            if line.strip().startswith(versionfile_source):
+                if "export-subst" in line.strip().split()[1:]:
+                    present = True
+        f.close()
+    except EnvironmentError:
+        pass
+    if not present:
+        f = open(".gitattributes", "a+")
+        f.write("%s export-subst\n" % versionfile_source)
+        f.close()
+        files.append(".gitattributes")
+    run_command(GITS, ["add", "--"] + files)
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose=False):
+    # Source tarballs conventionally unpack into a directory that includes
+    # both the project name and a version string.
+    dirname = os.path.basename(root)
+    if not dirname.startswith(parentdir_prefix):
+        if verbose:
+            print("guessing rootdir is '%s', but '%s' doesn't start with "
+                  "prefix '%s'" % (root, dirname, parentdir_prefix))
+        return None
+    return {"version": dirname[len(parentdir_prefix):], "full": ""}
+
+SHORT_VERSION_PY = """
+# This file was generated by 'versioneer.py' (0.14) from
+# revision-control system data, or from the parent directory name of an
+# unpacked source archive. Distribution tarballs contain a pre-generated copy
+# of this file.
+
+version_version = '%(version)s'
+version_full = '%(full)s'
+def get_versions(default={}, verbose=False):
+    return {'version': version_version, 'full': version_full}
+
+"""
+
+DEFAULT = {"version": "0+unknown", "full": "unknown"}
+
+
+def versions_from_file(filename):
+    versions = {}
+    try:
+        with open(filename) as f:
+            for line in f.readlines():
+                mo = re.match("version_version = '([^']+)'", line)
+                if mo:
+                    versions["version"] = mo.group(1)
+                mo = re.match("version_full = '([^']+)'", line)
+                if mo:
+                    versions["full"] = mo.group(1)
+    except EnvironmentError:
+        return {}
+
+    return versions
+
+
+def write_to_version_file(filename, versions):
+    with open(filename, "w") as f:
+        f.write(SHORT_VERSION_PY % versions)
+
+    print("set %s to '%s'" % (filename, versions["version"]))
+
+
+def get_root():
+    try:
+        return os.path.dirname(os.path.abspath(__file__))
+    except NameError:
+        return os.path.dirname(os.path.abspath(sys.argv[0]))
+
+
+def vcs_function(vcs, suffix):
+    return getattr(sys.modules[__name__], '%s_%s' % (vcs, suffix), None)
+
+
+def get_versions(default=DEFAULT, verbose=False):
+    # returns dict with two keys: 'version' and 'full'
+    assert versionfile_source is not None, \
+        "please set versioneer.versionfile_source"
+    assert tag_prefix is not None, "please set versioneer.tag_prefix"
+    assert parentdir_prefix is not None, \
+        "please set versioneer.parentdir_prefix"
+    assert VCS is not None, "please set versioneer.VCS"
+
+    # I am in versioneer.py, which must live at the top of the source tree,
+    # which we use to compute the root directory. py2exe/bbfreeze/non-CPython
+    # don't have __file__, in which case we fall back to sys.argv[0] (which
+    # ought to be the setup.py script). We prefer __file__ since that's more
+    # robust in cases where setup.py was invoked in some weird way (e.g. pip)
+    root = get_root()
+    versionfile_abs = os.path.join(root, versionfile_source)
+
+    # extract version from first of _version.py, VCS command (e.g. 'git
+    # describe'), parentdir. This is meant to work for developers using a
+    # source checkout, for users of a tarball created by 'setup.py sdist',
+    # and for users of a tarball/zipball created by 'git archive' or github's
+    # download-from-tag feature or the equivalent in other VCSes.
+
+    get_keywords_f = vcs_function(VCS, "get_keywords")
+    versions_from_keywords_f = vcs_function(VCS, "versions_from_keywords")
+    if get_keywords_f and versions_from_keywords_f:
+        vcs_keywords = get_keywords_f(versionfile_abs)
+        ver = versions_from_keywords_f(vcs_keywords, tag_prefix)
+        if ver:
+            if verbose:
+                print("got version from expanded keyword %s" % ver)
+            return ver
+
+    ver = versions_from_file(versionfile_abs)
+    if ver:
+        if verbose:
+            print("got version from file %s %s" % (versionfile_abs, ver))
+        return ver
+
+    versions_from_vcs_f = vcs_function(VCS, "versions_from_vcs")
+    if versions_from_vcs_f:
+        ver = versions_from_vcs_f(tag_prefix, root, verbose)
+        if ver:
+            if verbose:
+                print("got version from VCS %s" % ver)
+            return ver
+
+    ver = versions_from_parentdir(parentdir_prefix, root, verbose)
+    if ver:
+        if verbose:
+            print("got version from parentdir %s" % ver)
+        return ver
+
+    if verbose:
+        print("got version from default %s" % default)
+    return default
+
+
+def get_version(verbose=False):
+    return get_versions(verbose=verbose)["version"]
+
+
+class cmd_version(Command):
+    description = "report generated version string"
+    user_options = []
+    boolean_options = []
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+    def run(self):
+        ver = get_version(verbose=True)
+        print("Version is currently: %s" % ver)
+
+
+class cmd_build(_build):
+    def run(self):
+        versions = get_versions(verbose=True)
+        _build.run(self)
+        # now locate _version.py in the new build/ directory and replace it
+        # with an updated value
+        if versionfile_build:
+            target_versionfile = os.path.join(self.build_lib,
+                                              versionfile_build)
+            print("UPDATING %s" % target_versionfile)
+            os.unlink(target_versionfile)
+            with open(target_versionfile, "w") as f:
+                f.write(SHORT_VERSION_PY % versions)
+
+if 'cx_Freeze' in sys.modules:  # cx_freeze enabled?
+    from cx_Freeze.dist import build_exe as _build_exe
+
+    class cmd_build_exe(_build_exe):
+        def run(self):
+            versions = get_versions(verbose=True)
+            target_versionfile = versionfile_source
+            print("UPDATING %s" % target_versionfile)
+            os.unlink(target_versionfile)
+            with open(target_versionfile, "w") as f:
+                f.write(SHORT_VERSION_PY % versions)
+
+            _build_exe.run(self)
+            os.unlink(target_versionfile)
+            with open(versionfile_source, "w") as f:
+                assert VCS is not None, "please set versioneer.VCS"
+                LONG = LONG_VERSION_PY[VCS]
+                f.write(LONG % {"DOLLAR": "$",
+                                "TAG_PREFIX": tag_prefix,
+                                "PARENTDIR_PREFIX": parentdir_prefix,
+                                "VERSIONFILE_SOURCE": versionfile_source,
+                                })
+
+
+class cmd_sdist(_sdist):
+    def run(self):
+        versions = get_versions(verbose=True)
+        self._versioneer_generated_versions = versions
+        # unless we update this, the command will keep using the old version
+        self.distribution.metadata.version = versions["version"]
+        return _sdist.run(self)
+
+    def make_release_tree(self, base_dir, files):
+        _sdist.make_release_tree(self, base_dir, files)
+        # now locate _version.py in the new base_dir directory (remembering
+        # that it may be a hardlink) and replace it with an updated value
+        target_versionfile = os.path.join(base_dir, versionfile_source)
+        print("UPDATING %s" % target_versionfile)
+        os.unlink(target_versionfile)
+        with open(target_versionfile, "w") as f:
+            f.write(SHORT_VERSION_PY % self._versioneer_generated_versions)
+
+INIT_PY_SNIPPET = """
+from ._version import get_versions
+__version__ = get_versions()['version']
+del get_versions
+"""
+
+
+class cmd_update_files(Command):
+    description = ("install/upgrade Versioneer files: "
+                   "__init__.py SRC/_version.py")
+    user_options = []
+    boolean_options = []
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+    def run(self):
+        print(" creating %s" % versionfile_source)
+        with open(versionfile_source, "w") as f:
+            assert VCS is not None, "please set versioneer.VCS"
+            LONG = LONG_VERSION_PY[VCS]
+            f.write(LONG % {"DOLLAR": "$",
+                            "TAG_PREFIX": tag_prefix,
+                            "PARENTDIR_PREFIX": parentdir_prefix,
+                            "VERSIONFILE_SOURCE": versionfile_source,
+                            })
+
+        ipy = os.path.join(os.path.dirname(versionfile_source), "__init__.py")
+        if os.path.exists(ipy):
+            try:
+                with open(ipy, "r") as f:
+                    old = f.read()
+            except EnvironmentError:
+                old = ""
+            if INIT_PY_SNIPPET not in old:
+                print(" appending to %s" % ipy)
+                with open(ipy, "a") as f:
+                    f.write(INIT_PY_SNIPPET)
+            else:
+                print(" %s unmodified" % ipy)
+        else:
+            print(" %s doesn't exist, ok" % ipy)
+            ipy = None
+
+        # Make sure both the top-level "versioneer.py" and versionfile_source
+        # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so
+        # they'll be copied into source distributions. Pip won't be able to
+        # install the package without this.
+        manifest_in = os.path.join(get_root(), "MANIFEST.in")
+        simple_includes = set()
+        try:
+            with open(manifest_in, "r") as f:
+                for line in f:
+                    if line.startswith("include "):
+                        for include in line.split()[1:]:
+                            simple_includes.add(include)
+        except EnvironmentError:
+            pass
+        # That doesn't cover everything MANIFEST.in can do
+        # (http://docs.python.org/2/distutils/sourcedist.html#commands), so
+        # it might give some false negatives. Appending redundant 'include'
+        # lines is safe, though.
+        if "versioneer.py" not in simple_includes:
+            print(" appending 'versioneer.py' to MANIFEST.in")
+            with open(manifest_in, "a") as f:
+                f.write("include versioneer.py\n")
+        else:
+            print(" 'versioneer.py' already in MANIFEST.in")
+        if versionfile_source not in simple_includes:
+            print(" appending versionfile_source ('%s') to MANIFEST.in" %
+                  versionfile_source)
+            with open(manifest_in, "a") as f:
+                f.write("include %s\n" % versionfile_source)
+        else:
+            print(" versionfile_source already in MANIFEST.in")
+
+        # Make VCS-specific changes. For git, this means creating/changing
+        # .gitattributes to mark _version.py for export-time keyword
+        # substitution.
+        do_vcs_install(manifest_in, versionfile_source, ipy)
+
+
+def get_cmdclass():
+    cmds = {'version': cmd_version,
+            'versioneer': cmd_update_files,
+            'build': cmd_build,
+            'sdist': cmd_sdist,
+            }
+    if 'cx_Freeze' in sys.modules:  # cx_freeze enabled?
+        cmds['build_exe'] = cmd_build_exe
+        del cmds['build']
+
+    return cmds
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/.github/workflows/close_stale_issues.yml b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/.github/workflows/close_stale_issues.yml
new file mode 100644
index 000000000..c1e59d4b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/.github/workflows/close_stale_issues.yml
@@ -0,0 +1,27 @@
+# https://github.com/actions/stale
+# https://github.com/marketplace/actions/close-stale-issues
+# https://docs.github.com/en/actions/managing-issues-and-pull-requests/closing-inactive-issues
+
+name: close stale issues
+on:
+  schedule:
+    - cron: "30 1 * * *"
+
+jobs:
+  close-issues:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/stale@v4.1.0
+        with:
+          days-before-issue-stale: 30
+          days-before-issue-close: 14
+          stale-issue-label: "stale"
+          stale-issue-message: "This issue is stale because it has been open for 30 days with no activity."
+          close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale."
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          operations-per-run: 1000
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/.gitignore b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/.gitignore
new file mode 100644
index 000000000..c74eff803
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/.gitignore
@@ -0,0 +1,31 @@
+**__pycache__**
+**egg-info**
+**dist**
+data/
+*.pyc
+venv/
+*.idea/
+*.so
+*.yaml
+*.pth
+*.pkl
+*.zip
+*.bin
+output
+version.py
+build/
+build_pip/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/LICENSE b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/LICENSE
new file mode 100644
index 000000000..261eeb9e9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/README-ILUVATAR.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/README-ILUVATAR.md
new file mode 100644
index 000000000..f70f5f2de
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/README-ILUVATAR.md
@@ -0,0 +1,6 @@
+# install
+```
+pip3 install -r requirements.txt
+bash build_openpcdet.sh
+bash install_openpcdet.sh
+```
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/README.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/README.md
new file mode 100644
index 000000000..779571acb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/README.md
@@ -0,0 +1,291 @@
+<img src="docs/open_mmlab.png" align="right" width="30%">
+
+# OpenPCDet
+
+`OpenPCDet` is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. 
+
+It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A2-Net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192), [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712), [`[PV-RCNN++]`](https://arxiv.org/abs/2102.00463) and [`[MPPNet]`](https://arxiv.org/abs/2205.05979). 
+
+**Highlights**: 
+* `OpenPCDet` has been updated to `v0.6.0` (Sep. 2022).
+* The codes of PV-RCNN++ has been supported.
+* The codes of MPPNet has been supported. 
+* The multi-modal 3D detection approaches on Nuscenes have been supported. 
+
+## Overview
+- [Changelog](#changelog)
+- [Design Pattern](#openpcdet-design-pattern)
+- [Model Zoo](#model-zoo)
+- [Installation](docs/INSTALL.md)
+- [Quick Demo](docs/DEMO.md)
+- [Getting Started](docs/GETTING_STARTED.md)
+- [Citation](#citation)
+
+
+## Changelog
+[2023-06-30] **NEW:** Added support for [`DSVT`](https://arxiv.org/abs/2301.06051), which achieves state-of-the-art performance on large-scale Waymo Open Dataset with real-time inference speed (27HZ with TensorRT).
+
+[2023-05-13] **NEW:** Added support for the multi-modal 3D object detection models on Nuscenes dataset.  
+* Support multi-modal Nuscenes detection (See the [GETTING_STARTED.md](docs/GETTING_STARTED.md) to process data).
+* Support [TransFusion-Lidar](https://arxiv.org/abs/2203.11496) head, which ahcieves 69.43% NDS on Nuscenes validation dataset.
+* Support [`BEVFusion`](https://arxiv.org/abs/2205.13542), which fuses multi-modal information on BEV space and reaches 70.98% NDS on Nuscenes validation dataset. (see the [guideline](docs/guidelines_of_approaches/bevfusion.md) on how to train/test with BEVFusion).
+
+[2023-04-02] Added support for [`VoxelNeXt`](https://arxiv.org/abs/2303.11301) on Nuscenes, Waymo, and Argoverse2 datasets. It is a fully sparse 3D object detection network, which is a clean sparse CNNs network and predicts 3D objects directly upon voxels.
+
+[2022-09-02] **NEW:** Update `OpenPCDet` to v0.6.0:
+* Official code release of [`MPPNet`](https://arxiv.org/abs/2205.05979) for temporal 3D object detection, which supports long-term multi-frame 3D object detection and ranks 1st place on [3D detection learderboard](https://waymo.com/open/challenges/2020/3d-detection) of Waymo Open Dataset on Sept. 2th, 2022. For validation dataset, MPPNet achieves 74.96%, 75.06% and 74.52% for vehicle, pedestrian and cyclist classes in terms of mAPH@Level_2. (see the [guideline](docs/guidelines_of_approaches/mppnet.md) on how to train/test with MPPNet).
+* Support multi-frame training/testing on Waymo Open Dataset (see the [change log](docs/changelog.md) for more details on how to process data).
+* Support to save changing training details (e.g., loss, iter, epoch) to file (previous tqdm progress bar is still supported by using `--use_tqdm_to_record`). Please use `pip install gpustat` if you also want to log the GPU related information.
+* Support to save latest model every 5 mintues, so you can restore the model training from latest status instead of previous epoch.   
+
+[2022-08-22] Added support for [custom dataset tutorial and template](docs/CUSTOM_DATASET_TUTORIAL.md) 
+
+[2022-07-05] Added support for the 3D object detection backbone network [`Focals Conv`](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Focal_Sparse_Convolutional_Networks_for_3D_Object_Detection_CVPR_2022_paper.pdf).
+
+[2022-02-12] Added support for using docker. Please refer to the guidance in [./docker](./docker).
+
+[2022-02-07] Added support for Centerpoint models on Nuscenes Dataset.
+
+[2022-01-14] Added support for dynamic pillar voxelization, following the implementation proposed in [`H^23D R-CNN`](https://arxiv.org/abs/2107.14391) with unique operation and [`torch_scatter`](https://github.com/rusty1s/pytorch_scatter) package.
+
+[2022-01-05] **NEW:** Update `OpenPCDet` to v0.5.2:
+* The code of [`PV-RCNN++`](https://arxiv.org/abs/2102.00463) has been released to this repo, with higher performance, faster training/inference speed and less memory consumption than PV-RCNN.
+* Add performance of several models trained with full training set of [Waymo Open Dataset](#waymo-open-dataset-baselines).
+* Support Lyft dataset, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/720).
+
+
+[2021-12-09] **NEW:**  Update `OpenPCDet` to v0.5.1:
+* Add PointPillar related baseline configs/results on [Waymo Open Dataset](#waymo-open-dataset-baselines).
+* Support Pandaset dataloader, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/396).
+* Support a set of new augmentations, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/653).
+
+[2021-12-01] **NEW:** `OpenPCDet` v0.5.0 is released with the following features:
+* Improve the performance of all models on [Waymo Open Dataset](#waymo-open-dataset-baselines). Note that you need to re-prepare the training/validation data and ground-truth database of Waymo Open Dataset (see [GETTING_STARTED.md](docs/GETTING_STARTED.md)). 
+* Support anchor-free [CenterHead](pcdet/models/dense_heads/center_head.py), add configs of `CenterPoint` and `PV-RCNN with CenterHead`.
+* Support lastest **PyTorch 1.1~1.10** and **spconv 1.0~2.x**, where **spconv 2.x** should be easy to install with pip and faster than previous version (see the official update of spconv [here](https://github.com/traveller59/spconv)).  
+* Support config [`USE_SHARED_MEMORY`](tools/cfgs/dataset_configs/waymo_dataset.yaml) to use shared memory to potentially speed up the training process in case you suffer from an IO problem.  
+* Support better and faster [visualization script](tools/visual_utils/open3d_vis_utils.py), and you need to install [Open3D](https://github.com/isl-org/Open3D) firstly. 
+
+[2021-06-08] Added support for the voxel-based 3D object detection model [`Voxel R-CNN`](#KITTI-3D-Object-Detection-Baselines).
+
+[2021-05-14] Added support for the monocular 3D object detection model [`CaDDN`](#KITTI-3D-Object-Detection-Baselines).
+
+[2020-11-27] Bugfixed: Please re-prepare the validation infos of Waymo dataset (version 1.2) if you would like to 
+use our provided Waymo evaluation tool (see [PR](https://github.com/open-mmlab/OpenPCDet/pull/383)). 
+Note that you do not need to re-prepare the training data and ground-truth database. 
+
+[2020-11-10] The [Waymo Open Dataset](#waymo-open-dataset-baselines) has been supported with state-of-the-art results. Currently we provide the 
+configs and results of `SECOND`, `PartA2` and `PV-RCNN` on the Waymo Open Dataset, and more models could be easily supported by modifying their dataset configs. 
+
+[2020-08-10] Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.
+
+[2020-07-30] `OpenPCDet` v0.3.0 is released with the following features:
+   * The Point-based and Anchor-Free models ([`PointRCNN`](#KITTI-3D-Object-Detection-Baselines), [`PartA2-Free`](#KITTI-3D-Object-Detection-Baselines)) are supported now.
+   * The NuScenes dataset is supported with strong baseline results ([`SECOND-MultiHead (CBGS)`](#NuScenes-3D-Object-Detection-Baselines) and [`PointPillar-MultiHead`](#NuScenes-3D-Object-Detection-Baselines)).
+   * High efficiency than last version, support **PyTorch 1.1~1.7** and **spconv 1.0~1.2** simultaneously.
+   
+[2020-07-17]  Add simple visualization codes and a quick demo to test with custom data. 
+
+[2020-06-24] `OpenPCDet` v0.2.0 is released with pretty new structures to support more models and datasets. 
+
+[2020-03-16] `OpenPCDet` v0.1.0 is released. 
+
+
+## Introduction
+
+
+### What does `OpenPCDet` toolbox do?
+
+Note that we have upgrated `PCDet` from `v0.1` to `v0.2` with pretty new structures to support various datasets and models.
+
+`OpenPCDet` is a general PyTorch-based codebase for 3D object detection from point cloud. 
+It currently supports multiple state-of-the-art 3D object detection methods with highly refactored codes for both one-stage and two-stage 3D detection frameworks.
+
+Based on `OpenPCDet` toolbox, we win the Waymo Open Dataset challenge in [3D Detection](https://waymo.com/open/challenges/3d-detection/), 
+[3D Tracking](https://waymo.com/open/challenges/3d-tracking/), [Domain Adaptation](https://waymo.com/open/challenges/domain-adaptation/) 
+three tracks among all LiDAR-only methods, and the Waymo related models will be released to `OpenPCDet` soon.    
+
+We are actively updating this repo currently, and more datasets and models will be supported soon. 
+Contributions are also welcomed. 
+
+### `OpenPCDet` design pattern
+
+* Data-Model separation with unified point cloud coordinate for easily extending to custom datasets:
+<p align="center">
+  <img src="docs/dataset_vs_model.png" width="95%" height="320">
+</p>
+
+* Unified 3D box definition: (x, y, z, dx, dy, dz, heading).
+
+* Flexible and clear model structure to easily support various 3D detection models: 
+<p align="center">
+  <img src="docs/model_framework.png" width="95%">
+</p>
+
+* Support various models within one framework as: 
+<p align="center">
+  <img src="docs/multiple_models_demo.png" width="95%">
+</p>
+
+
+### Currently Supported Features
+
+- [x] Support both one-stage and two-stage 3D object detection frameworks
+- [x] Support distributed training & testing with multiple GPUs and multiple machines
+- [x] Support multiple heads on different scales to detect different classes
+- [x] Support stacked version set abstraction to encode various number of points in different scenes
+- [x] Support Adaptive Training Sample Selection (ATSS) for target assignment
+- [x] Support RoI-aware point cloud pooling & RoI-grid point cloud pooling
+- [x] Support GPU version 3D IoU calculation and rotated NMS 
+
+
+## Model Zoo
+
+### KITTI 3D Object Detection Baselines
+Selected supported methods are shown in the below table. The results are the 3D detection performance of moderate difficulty on the *val* set of KITTI dataset.
+* All LiDAR-based models are trained with 8 GTX 1080Ti GPUs and are available for download. 
+* The training time is measured with 8 TITAN XP GPUs and PyTorch 1.5.
+
+|                                             | training time | Car@R11 | Pedestrian@R11 | Cyclist@R11  | download | 
+|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:---------:|
+| [PointPillar](tools/cfgs/kitti_models/pointpillar.yaml) |~1.2 hours| 77.28 | 52.29 | 62.68 | [model-18M](https://drive.google.com/file/d/1wMxWTpU1qUoY3DsCH31WJmvJxcjFXKlm/view?usp=sharing) | 
+| [SECOND](tools/cfgs/kitti_models/second.yaml)       |  ~1.7 hours  | 78.62 | 52.98 | 67.15 | [model-20M](https://drive.google.com/file/d/1-01zsPOsqanZQqIIyy7FpNXStL3y4jdR/view?usp=sharing) |
+| [SECOND-IoU](tools/cfgs/kitti_models/second_iou.yaml)       | -  | 79.09 | 55.74 | 71.31 | [model-46M](https://drive.google.com/file/d/1AQkeNs4bxhvhDQ-5sEo_yvQUlfo73lsW/view?usp=sharing) |
+| [PointRCNN](tools/cfgs/kitti_models/pointrcnn.yaml) | ~3 hours | 78.70 | 54.41 | 72.11 | [model-16M](https://drive.google.com/file/d/1BCX9wMn-GYAfSOPpyxf6Iv6fc0qKLSiU/view?usp=sharing)| 
+| [PointRCNN-IoU](tools/cfgs/kitti_models/pointrcnn_iou.yaml) | ~3 hours | 78.75 | 58.32 | 71.34 | [model-16M](https://drive.google.com/file/d/1V0vNZ3lAHpEEt0MlT80eL2f41K2tHm_D/view?usp=sharing)|
+| [Part-A2-Free](tools/cfgs/kitti_models/PartA2_free.yaml)   | ~3.8 hours| 78.72 | 65.99 | 74.29 | [model-226M](https://drive.google.com/file/d/1lcUUxF8mJgZ_e-tZhP1XNQtTBuC-R0zr/view?usp=sharing) |
+| [Part-A2-Anchor](tools/cfgs/kitti_models/PartA2.yaml)    | ~4.3 hours| 79.40 | 60.05 | 69.90 | [model-244M](https://drive.google.com/file/d/10GK1aCkLqxGNeX3lVu8cLZyE0G8002hY/view?usp=sharing) |
+| [PV-RCNN](tools/cfgs/kitti_models/pv_rcnn.yaml) | ~5 hours| 83.61 | 57.90 | 70.47 | [model-50M](https://drive.google.com/file/d/1lIOq4Hxr0W3qsX83ilQv0nk1Cls6KAr-/view?usp=sharing) |
+| [Voxel R-CNN (Car)](tools/cfgs/kitti_models/voxel_rcnn_car.yaml) | ~2.2 hours| 84.54 | - | - | [model-28M](https://drive.google.com/file/d/19_jiAeGLz7V0wNjSJw4cKmMjdm5EW5By/view?usp=sharing) |
+| [Focals Conv - F](tools/cfgs/kitti_models/voxel_rcnn_car_focal_multimodal.yaml) | ~4 hours| 85.66 | - | - | [model-30M](https://drive.google.com/file/d/1u2Vcg7gZPOI-EqrHy7_6fqaibvRt2IjQ/view?usp=sharing) |
+||
+| [CaDDN (Mono)](tools/cfgs/kitti_models/CaDDN.yaml) |~15 hours| 21.38 | 13.02 | 9.76 | [model-774M](https://drive.google.com/file/d/1OQTO2PtXT8GGr35W9m2GZGuqgb6fyU1V/view?usp=sharing) |
+
+### Waymo Open Dataset Baselines
+We provide the setting of [`DATA_CONFIG.SAMPLED_INTERVAL`](tools/cfgs/dataset_configs/waymo_dataset.yaml) on the Waymo Open Dataset (WOD) to subsample partial samples for training and evaluation, 
+so you could also play with WOD by setting a smaller `DATA_CONFIG.SAMPLED_INTERVAL` even if you only have limited GPU resources. 
+
+By default, all models are trained with **a single frame** of **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).    
+
+|    Performance@(train with 20\% Data)            | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
+|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
+| [SECOND](tools/cfgs/waymo_models/second.yaml) | 70.96/70.34|62.58/62.02|65.23/54.24	|57.22/47.49|	57.13/55.62 |	54.97/53.53 | 
+| [PointPillar](tools/cfgs/waymo_models/pointpillar_1x.yaml) | 70.43/69.83 |	62.18/61.64 | 66.21/46.32|58.18/40.64|55.26/51.75|53.18/49.80 |
+[CenterPoint-Pillar](tools/cfgs/waymo_models/centerpoint_pillar_1x.yaml)| 70.50/69.96|62.18/61.69|73.11/61.97|65.06/55.00|65.44/63.85|62.98/61.46| 
+[CenterPoint-Dynamic-Pillar](tools/cfgs/waymo_models/centerpoint_dyn_pillar_1x.yaml)| 70.46/69.93|62.06/61.58|73.92/63.35|65.91/56.33|66.24/64.69|63.73/62.24| 
+[CenterPoint](tools/cfgs/waymo_models/centerpoint_without_resnet.yaml)| 71.33/70.76|63.16/62.65|	72.09/65.49	|64.27/58.23|	68.68/67.39	|66.11/64.87|
+| [CenterPoint (ResNet)](tools/cfgs/waymo_models/centerpoint.yaml)|72.76/72.23|64.91/64.42	|74.19/67.96	|66.03/60.34|	71.04/69.79	|68.49/67.28 |
+| [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 74.66/74.12	|65.82/65.32	|71.71/62.24	|62.46/54.06	|66.53/65.18	|64.05/62.75 |
+| [PV-RCNN (AnchorHead)](tools/cfgs/waymo_models/pv_rcnn.yaml) | 75.41/74.74	|67.44/66.80	|71.98/61.24	|63.70/53.95	|65.88/64.25	|63.39/61.82 | 
+| [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 75.95/75.43	|68.02/67.54	|75.94/69.40	|67.66/61.62	|70.18/68.98	|67.73/66.57|
+| [Voxel R-CNN (CenterHead)-Dynamic-Voxel](tools/cfgs/waymo_models/voxel_rcnn_with_centerhead_dyn_voxel.yaml) | 76.13/75.66	|68.18/67.74	|78.20/71.98	|69.29/63.59	| 70.75/69.68	|68.25/67.21|
+| [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 77.82/77.32|	69.07/68.62|	77.99/71.36|	69.92/63.74|	71.80/70.71|	69.31/68.26|
+| [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) |77.61/77.14|	69.18/68.75|	79.42/73.31|	70.88/65.21|	72.50/71.39|	69.84/68.77|
+
+Here we also provide the performance of several models trained on the full training set (refer to the paper of [PV-RCNN++](https://arxiv.org/abs/2102.00463)):
+
+| Performance@(train with 100\% Data)                                                       | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
+|-------------------------------------------------------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
+| [SECOND](tools/cfgs/waymo_models/second.yaml)                                             | 72.27/71.69 | 63.85/63.33 | 68.70/58.18 | 60.72/51.31 | 60.62/59.28 | 58.34/57.05 | 
+| [CenterPoint-Pillar](tools/cfgs/waymo_models/centerpoint_pillar_1x.yaml)                  | 73.37/72.86 | 65.09/64.62 | 75.35/65.11 | 67.61/58.25 | 67.76/66.22 | 65.25/63.77 | 
+| [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml)                                     | 77.05/76.51 | 68.47/67.97 | 75.24/66.87 | 66.18/58.62 | 68.60/67.36 | 66.13/64.93 |
+| [VoxelNeXt-2D](tools/cfgs/waymo_models/voxelnext2d_ioubranch.yaml)                        | 77.94/77.47	|69.68/69.25	|80.24/73.47	|72.23/65.88	|73.33/72.20	|70.66/69.56 | 
+| [VoxelNeXt](tools/cfgs/waymo_models/voxelnext_ioubranch_large.yaml)                       | 78.16/77.70	|69.86/69.42	|81.47/76.30	|73.48/68.63	|76.06/74.90	|73.29/72.18 |
+| [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml)          | 78.00/77.50 | 69.43/68.98 | 79.21/73.03 | 70.42/64.72 | 71.46/70.27 | 68.95/67.79 |
+| [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml)                                | 79.10/78.63 | 70.34/69.91 | 80.62/74.62 | 71.86/66.30 | 73.49/72.38 | 70.70/69.62 |
+| [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml)                | 79.25/78.78 | 70.61/70.18 | 81.83/76.28 | 73.17/68.00 | 73.72/72.66 | 71.21/70.19 |
+| [DSVT-Pillar](tools/cfgs/waymo_models/dsvt_pillar.yaml)                             | 79.44/78.97 | 71.24/70.81 | 83.00/77.22 | 75.45/69.95 | 76.70/75.70 | 73.83/72.86 |
+| [DSVT-Voxel](tools/cfgs/waymo_models/dsvt_voxel.yaml)                             | 79.77/79.31 | 71.67/71.25 | 83.75/78.92 | 76.21/71.57 | 77.57/76.58 | 74.70/73.73 |
+| [PV-RCNN++ (ResNet, 2 frames)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet_2frames.yaml) | 80.17/79.70 | 72.14/71.70 | 83.48/80.42 | 75.54/72.61 | 74.63/73.75 | 72.35/71.50 |
+| [MPPNet (4 frames)](docs/guidelines_of_approaches/mppnet.md)                              | 81.54/81.06 | 74.07/73.61 | 84.56/81.94 | 77.20/74.67 | 77.15/76.50 | 75.01/74.38 |
+| [MPPNet (16 frames)](docs/guidelines_of_approaches/mppnet.md)                             | 82.74/82.28 | 75.41/74.96 | 84.69/82.25 | 77.43/75.06 | 77.28/76.66 | 75.13/74.52 |
+
+
+
+
+
+
+
+We could not provide the above pretrained models due to [Waymo Dataset License Agreement](https://waymo.com/open/terms/), 
+but you could easily achieve similar performance by training with the default configs.
+
+### NuScenes 3D Object Detection Baselines
+All models are trained with 8 GPUs and are available for download. For training BEVFusion, please refer to the [guideline](docs/guidelines_of_approaches/bevfusion.md).
+
+|                                                                                                    |   mATE |  mASE  |  mAOE  | mAVE  | mAAE  |  mAP  |  NDS   |                                              download                                              | 
+|----------------------------------------------------------------------------------------------------|-------:|:------:|:------:|:-----:|:-----:|:-----:|:------:|:--------------------------------------------------------------------------------------------------:|
+| [PointPillar-MultiHead](tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml)                         | 33.87	 | 26.00  | 32.07	 | 28.74 | 20.15 | 44.63 | 58.23	 |  [model-23M](https://drive.google.com/file/d/1p-501mTWsq0G9RzroTWSXreIMyTUUpBM/view?usp=sharing)   | 
+| [SECOND-MultiHead (CBGS)](tools/cfgs/nuscenes_models/cbgs_second_multihead.yaml)                   |  31.15 | 	25.51 | 	26.64 | 26.26 | 20.46 | 50.59 | 62.29  |  [model-35M](https://drive.google.com/file/d/1bNzcOnE3u9iooBFMk2xK7HqhdeQ_nwTq/view?usp=sharing)   |
+| [CenterPoint-PointPillar](tools/cfgs/nuscenes_models/cbgs_dyn_pp_centerpoint.yaml)                 |  31.13 | 	26.04 | 	42.92 | 23.90 | 19.14 | 50.03 | 60.70  |  [model-23M](https://drive.google.com/file/d/1UvGm6mROMyJzeSRu7OD1leU_YWoAZG7v/view?usp=sharing)   |
+| [CenterPoint (voxel_size=0.1)](tools/cfgs/nuscenes_models/cbgs_voxel01_res3d_centerpoint.yaml)     |  30.11 | 	25.55 | 	38.28 | 21.94 | 18.87 | 56.03 | 64.54  |  [model-34M](https://drive.google.com/file/d/1Cz-J1c3dw7JAWc25KRG1XQj8yCaOlexQ/view?usp=sharing)   |
+| [CenterPoint (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_res3d_centerpoint.yaml) |  28.80 | 	25.43 | 	37.27 | 21.55 | 18.24 | 59.22 | 66.48  |  [model-34M](https://drive.google.com/file/d/1XOHAWm1MPkCKr1gqmc3TWi5AYZgPsgxU/view?usp=sharing)   |
+| [VoxelNeXt (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_voxelnext.yaml)   |  30.11 | 	25.23 | 	40.57 | 21.69 | 18.56 | 60.53 | 66.65  | [model-31M](https://drive.google.com/file/d/1IV7e7G9X-61KXSjMGtQo579pzDNbhwvf/view?usp=share_link) |
+| [TransFusion-L*](tools/cfgs/nuscenes_models/transfusion_lidar.yaml)   |  27.96 | 	25.37 | 	29.35 | 27.31 | 18.55 | 64.58 | 69.43  | [model-32M](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link) |
+| [BEVFusion](tools/cfgs/nuscenes_models/bevfusion.yaml)   |  28.03 | 	25.43 | 	30.19 | 26.76 | 18.48 | 67.75 | 70.98  | [model-157M](https://drive.google.com/file/d/1X50b-8immqlqD8VPAUkSKI0Ls-4k37g9/view?usp=share_link) |
+
+*: Use the fade strategy, which disables data augmentations in the last several epochs during training.
+
+### ONCE 3D Object Detection Baselines
+All models are trained with 8 GPUs.
+
+|                                                        | Vehicle | Pedestrian | Cyclist | mAP    |
+| ------------------------------------------------------ | :-----: | :--------: | :-----: | :----: |
+| [PointRCNN](tools/cfgs/once_models/pointrcnn.yaml)     | 52.09   | 4.28       | 29.84   | 28.74  |
+| [PointPillar](tools/cfgs/once_models/pointpillar.yaml) | 68.57   | 17.63      | 46.81   | 44.34  |
+| [SECOND](tools/cfgs/once_models/second.yaml)           | 71.19   | 26.44      | 58.04   | 51.89  |
+| [PV-RCNN](tools/cfgs/once_models/pv_rcnn.yaml)         | 77.77   | 23.50      | 59.37   | 53.55  |
+| [CenterPoint](tools/cfgs/once_models/centerpoint.yaml) | 78.02   | 49.74      | 67.22   | 64.99  |
+
+### Argoverse2 3D Object Detection Baselines
+All models are trained with 4 GPUs.
+
+|                                                         | mAP  |                                              download                                              | 
+|---------------------------------------------------------|:----:|:--------------------------------------------------------------------------------------------------:|
+| [VoxelNeXt](tools/cfgs/argo2_models/cbgs_voxel01_voxelnext.yaml)        | 30.5 | [model-32M](https://drive.google.com/file/d/1YP2UOz-yO-cWfYQkIqILEu6bodvCBVrR/view?usp=share_link) |
+
+### Other datasets
+Welcome to support other datasets by submitting pull request. 
+
+## Installation
+
+Please refer to [INSTALL.md](docs/INSTALL.md) for the installation of `OpenPCDet`.
+
+
+## Quick Demo
+Please refer to [DEMO.md](docs/DEMO.md) for a quick demo to test with a pretrained model and 
+visualize the predicted results on your custom data or the original KITTI data.
+
+## Getting Started
+
+Please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) to learn more usage about this project.
+
+
+## License
+
+`OpenPCDet` is released under the [Apache 2.0 license](LICENSE).
+
+## Acknowledgement
+`OpenPCDet` is an open source project for LiDAR-based 3D scene perception that supports multiple
+LiDAR-based perception models as shown above. Some parts of `PCDet` are learned from the official released codes of the above supported methods. 
+We would like to thank for their proposed methods and the official implementation.   
+
+We hope that this repo could serve as a strong and flexible codebase to benefit the research community by speeding up the process of reimplementing previous works and/or developing new methods.
+
+
+## Citation 
+If you find this project useful in your research, please consider cite:
+
+
+```
+@misc{openpcdet2020,
+    title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds},
+    author={OpenPCDet Development Team},
+    howpublished = {\url{https://github.com/open-mmlab/OpenPCDet}},
+    year={2020}
+}
+```
+
+## Contribution
+Welcome to be a member of the OpenPCDet development team by contributing to this repo, and feel free to contact us for any potential contributions. 
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/build_openpcdet.sh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/build_openpcdet.sh
new file mode 100644
index 000000000..473ebbb17
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/build_openpcdet.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+COREX_VERSION=${COREX_VERSION:-latest}
+MAX_JOBS=${MAX_JOBS:-$(nproc --all)}
+PYTHON_PATH=$(which python3)
+${PYTHON_PATH} -m pip list | grep "^torch .*+corex" || {
+  echo "ERROR: building mmcv requries the corex torch has been installed."
+  exit 1
+}
+
+export MAX_JOBS=${MAX_JOBS}
+
+${PYTHON_PATH} setup.py bdist_wheel -d build_pip || exit
+# Return 0 status if all finished
+exit 0
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/clean_openpcdet.sh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/clean_openpcdet.sh
new file mode 100644
index 000000000..feda20c48
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/clean_openpcdet.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+PYTHON_PATH=$(which python3)
+
+rm -rf build
+${PYTHON_PATH} setup.py clean || true
+rm -rf build_pip
+
+# Return 0 status if all finished
+exit 0
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/Dockerfile b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/Dockerfile
new file mode 100644
index 000000000..3f0114f70
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/Dockerfile
@@ -0,0 +1,55 @@
+FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
+
+RUN echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections
+
+# Install basics
+RUN apt-get update -y \
+    && apt-get install build-essential \
+    && apt-get install -y apt-utils git curl ca-certificates bzip2 tree htop wget \
+    && apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev bmon iotop g++ python3.7 python3.7-dev python3.7-distutils
+
+# Install cmake v3.13.2
+RUN apt-get purge -y cmake && \
+    mkdir /root/temp && \
+    cd /root/temp && \
+    wget https://github.com/Kitware/CMake/releases/download/v3.13.2/cmake-3.13.2.tar.gz && \
+    tar -xzvf cmake-3.13.2.tar.gz && \
+    cd cmake-3.13.2 && \
+    bash ./bootstrap && \
+    make && \
+    make install && \
+    cmake --version && \
+    rm -rf /root/temp
+
+# Install python
+RUN ln -sv /usr/bin/python3.7 /usr/bin/python
+RUN wget https://bootstrap.pypa.io/get-pip.py && \
+	python get-pip.py && \
+	rm get-pip.py
+
+# Install python packages
+RUN PIP_INSTALL="python -m pip --no-cache-dir install" && \
+    $PIP_INSTALL numpy==1.19.3 llvmlite numba 
+
+# Install torch and torchvision
+# See https://pytorch.org/ for other options if you use a different version of CUDA
+RUN pip install --user torch==1.6 torchvision==0.7.0 -f https://download.pytorch.org/whl/cu102/torch_stable.html
+
+# Install python packages
+RUN PIP_INSTALL="python -m pip --no-cache-dir install" && \
+    $PIP_INSTALL tensorboardX easydict pyyaml scikit-image tqdm SharedArray six
+
+WORKDIR /root
+
+# Install Boost geometry
+RUN wget https://jaist.dl.sourceforge.net/project/boost/boost/1.68.0/boost_1_68_0.tar.gz && \
+    tar xzvf boost_1_68_0.tar.gz && \
+    cp -r ./boost_1_68_0/boost /usr/include && \
+    rm -rf ./boost_1_68_0 && \
+    rm -rf ./boost_1_68_0.tar.gz 
+
+# A weired problem that hasn't been solved yet
+RUN pip uninstall -y SharedArray && \
+    pip install SharedArray
+
+RUN pip install spconv-cu102
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/README.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/README.md
new file mode 100644
index 000000000..5e258fbed
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/README.md
@@ -0,0 +1,25 @@
+# Guidance to use OpenPCDet with docker
+
+You can either build the docker image through Dockerfile or pull the docker image from dockerhub. Please make sure nvidia-docker is corretly installed.
+
+## Build Through Dockerfile
+Build docker image that support OpenPCDet through:
+```shell script
+docker build ./ -t openpcdet-docker
+```
+Note that if you would like to use dynamic voxelization, you need further install [`torch_scatter`](https://github.com/rusty1s/pytorch_scatter) package. 
+
+From this Dockerfile, the installed version of spconv is 2.x, if you would like to use spconv 1.2.1, please follow these steps:
+```shell script
+git clone -b v1.2.1 https://github.com/djiajunustc/spconv spconv --recursive
+cd spconv
+python setup.py bdist_wheel
+cd ./dist
+pip install *.whl
+```
+
+## Pull From Dockerhub
+Run the following script to pull the docker image:
+```shell script
+docker pull djiajun1206/pcdet:pytorch1.6
+```
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/cu116.Dockerfile b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/cu116.Dockerfile
new file mode 100644
index 000000000..f68e4bdf7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docker/cu116.Dockerfile
@@ -0,0 +1,83 @@
+FROM nvidia/cuda:11.6.2-devel-ubuntu20.04
+
+# Set environment variables
+ENV NVENCODE_CFLAGS "-I/usr/local/cuda/include"
+ENV CV_VERSION=4.2.0
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Get all dependencies
+RUN apt-get update && apt-get install -y \
+    git zip unzip libssl-dev libcairo2-dev lsb-release libgoogle-glog-dev libgflags-dev libatlas-base-dev libeigen3-dev software-properties-common \
+    build-essential cmake pkg-config libapr1-dev autoconf automake libtool curl libc6 libboost-all-dev debconf libomp5 libstdc++6 \
+    libqt5core5a libqt5xml5 libqt5gui5 libqt5widgets5 libqt5concurrent5 libqt5opengl5 libcap2 libusb-1.0-0 libatk-adaptor neovim \
+    python3-pip python3-tornado python3-dev python3-numpy python3-virtualenv libpcl-dev libgoogle-glog-dev libgflags-dev libatlas-base-dev \
+    libsuitesparse-dev python3-pcl pcl-tools libgtk2.0-dev libavcodec-dev libavformat-dev libswscale-dev libtbb2 libtbb-dev libjpeg-dev \
+    libpng-dev libtiff-dev libdc1394-22-dev xfce4-terminal &&\
+    rm -rf /var/lib/apt/lists/*
+
+# OpenCV with CUDA support
+WORKDIR /opencv
+RUN git clone https://github.com/opencv/opencv.git -b $CV_VERSION &&\
+    git clone https://github.com/opencv/opencv_contrib.git -b $CV_VERSION
+
+# While using OpenCV 4.2.0 we have to apply some fixes to ensure that CUDA is fully supported, thanks @https://github.com/gismo07 for this fix
+RUN mkdir opencvfix && cd opencvfix &&\
+    git clone https://github.com/opencv/opencv.git -b 4.5.2 &&\
+    cd opencv/cmake &&\
+    cp -r FindCUDA /opencv/opencv/cmake/ &&\
+    cp FindCUDA.cmake /opencv/opencv/cmake/ &&\
+    cp FindCUDNN.cmake /opencv/opencv/cmake/ &&\
+    cp OpenCVDetectCUDA.cmake /opencv/opencv/cmake/
+ 
+WORKDIR /opencv/opencv/build
+
+RUN cmake -D CMAKE_BUILD_TYPE=RELEASE \
+-D CMAKE_INSTALL_PREFIX=/usr/local \
+-D OPENCV_GENERATE_PKGCONFIG=ON \
+-D BUILD_EXAMPLES=OFF \
+-D INSTALL_PYTHON_EXAMPLES=OFF \
+-D INSTALL_C_EXAMPLES=OFF \
+-D PYTHON_EXECUTABLE=$(which python2) \
+-D PYTHON3_EXECUTABLE=$(which python3) \
+-D PYTHON3_INCLUDE_DIR=$(python3 -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())") \
+-D PYTHON3_PACKAGES_PATH=$(python3 -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") \
+-D BUILD_opencv_python2=ON \
+-D BUILD_opencv_python3=ON \
+-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules/ \
+-D WITH_GSTREAMER=ON \
+-D WITH_CUDA=ON \
+-D ENABLE_PRECOMPILED_HEADERS=OFF \
+.. &&\
+make -j$(nproc) &&\
+make install &&\
+ldconfig &&\
+rm -rf /opencv
+
+WORKDIR /
+ENV OpenCV_DIR=/usr/share/OpenCV
+
+
+# PyTorch for CUDA 11.6
+RUN pip3 install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
+ENV TORCH_CUDA_ARCH_LIST="3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX"
+    
+# OpenPCDet
+RUN pip3 install numpy==1.23.0 llvmlite numba tensorboardX easydict pyyaml scikit-image tqdm SharedArray open3d mayavi av2 kornia pyquaternion
+RUN pip3 install spconv-cu116
+
+RUN git clone https://github.com/open-mmlab/OpenPCDet.git
+
+WORKDIR OpenPCDet
+
+RUN python3 setup.py develop
+    
+WORKDIR /
+
+ENV NVIDIA_VISIBLE_DEVICES="all" \
+    OpenCV_DIR=/usr/share/OpenCV \
+    NVIDIA_DRIVER_CAPABILITIES="video,compute,utility,graphics" \
+    LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/lib:/usr/lib:/usr/local/lib \
+    QT_GRAPHICSSYSTEM="native"
+
+# Build instructions: docker build -f minimal.Dockerfile -t openpcdet:cuda11 .
+# Start instructions: xhost local:root && docker run -it --rm -e SDL_VIDEODRIVER=x11 -e DISPLAY=$DISPLAY --env='DISPLAY' --gpus all --ipc host --privileged --network host -p 8080:8081 -v /tmp/.X11-unix:/tmp/.X11-unix:rw -v file_locations:/storage -v /weights:/weights openpcdet:cuda11 xfce4-terminal --title=openPCDet
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/CUSTOM_DATASET_TUTORIAL.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/CUSTOM_DATASET_TUTORIAL.md
new file mode 100644
index 000000000..edaee47ce
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/CUSTOM_DATASET_TUTORIAL.md
@@ -0,0 +1,108 @@
+# Custom Dataset Tutorial
+For the custom dataset template, we only consider the basic scenario: raw point clouds and 
+their corresponding annotations. Point clouds are supposed to be stored in `.npy` format.
+
+## Label format
+We only consider the most basic information -- category and bounding box in the label template.
+Annotations are stored in the `.txt`. Each line represents a box in a given scene as below:
+```
+# format: [x y z dx dy dz heading_angle category_name]
+1.50 1.46 0.10 5.12 1.85 4.13 1.56 Vehicle
+5.54 0.57 0.41 1.08 0.74 1.95 1.57 Pedestrian
+```
+The box should in the unified 3D box definition (see [README](../README.md))
+
+## Files structure
+Files should be placed as the following folder structure:
+```
+OpenPCDet
+├── data
+│   ├── custom
+│   │   │── ImageSets
+│   │   │   │── train.txt
+│   │   │   │── val.txt
+│   │   │── points
+│   │   │   │── 000000.npy
+│   │   │   │── 999999.npy
+│   │   │── labels
+│   │   │   │── 000000.txt
+│   │   │   │── 999999.txt
+├── pcdet
+├── tools
+```
+Dataset splits need to be pre-defined and placed in `ImageSets`
+
+## Hyper-parameters Configurations
+
+### Point cloud features
+Modify following configurations in `custom_dataset.yaml` to 
+suit your own point clouds.
+```yaml
+POINT_FEATURE_ENCODING: {
+    encoding_type: absolute_coordinates_encoding,
+    used_feature_list: ['x', 'y', 'z', 'intensity'],
+    src_feature_list: ['x', 'y', 'z', 'intensity'],
+}
+...
+# In gt_sampling data augmentation
+NUM_POINT_FEATURES: 4
+
+```
+
+#### Point cloud range and voxel sizes
+For voxel based detectors such as SECOND, PV-RCNN and CenterPoint, the point cloud range and voxel size should follow:
+1. Point cloud range along z-axis / voxel_size is 40
+2. Point cloud range along x&y-axis / voxel_size is the multiple of 16.
+
+Notice that the second rule also suit pillar based detectors such as PointPillar and CenterPoint-Pillar.
+
+### Category names and anchor sizes
+Category names and anchor size are need to be adapted to custom datasets.
+ ```yaml
+CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']  
+...
+MAP_CLASS_TO_KITTI: {
+    'Vehicle': 'Car',
+    'Pedestrian': 'Pedestrian',
+    'Cyclist': 'Cyclist',
+}
+...
+'anchor_sizes': [[3.9, 1.6, 1.56]],
+...
+# In gt sampling data augmentation
+PREPARE: {
+ filter_by_min_points: ['Vehicle:5', 'Pedestrian:5', 'Cyclist:5'],
+ filter_by_difficulty: [-1],
+}
+SAMPLE_GROUPS: ['Vehicle:20','Pedestrian:15', 'Cyclist:15']
+...
+ ```
+In addition, please also modify the default category names for creating infos in `custom_dataset.py`
+```
+create_custom_infos(
+    dataset_cfg=dataset_cfg,
+    class_names=['Vehicle', 'Pedestrian', 'Cyclist'],
+    data_path=ROOT_DIR / 'data' / 'custom',
+    save_path=ROOT_DIR / 'data' / 'custom',
+)
+```
+
+
+## Create data info
+Generate the data infos by running the following command:
+```shell
+python -m pcdet.datasets.custom.custom_dataset create_custom_infos tools/cfgs/dataset_configs/custom_dataset.yaml
+```
+
+
+## Evaluation
+Here, we only provide an implementation for KITTI stype evaluation.
+The category mapping between custom dataset and KITTI need to be defined 
+in the `custom_dataset.yaml`
+```yaml
+MAP_CLASS_TO_KITTI: {
+    'Vehicle': 'Car',
+    'Pedestrian': 'Pedestrian',
+    'Cyclist': 'Cyclist',
+}
+```
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/DEMO.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/DEMO.md
new file mode 100644
index 000000000..c7554302d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/DEMO.md
@@ -0,0 +1,51 @@
+# Quick Demo
+
+Here we provide a quick demo to test a pretrained model on the custom point cloud data and visualize the predicted results. 
+
+We suppose you already followed the [INSTALL.md](INSTALL.md) to install the `OpenPCDet` repo successfully. 
+
+1. Download the provided pretrained models as shown in the [README.md](../README.md). 
+
+2. Make sure you have already installed the [`Open3D`](https://github.com/isl-org/Open3D) (faster) or `mayavi` visualization tools. 
+If not, you could install it as follows:
+   ```
+   pip install open3d
+   # or 
+   pip install mayavi
+   ```
+
+3. Prepare your custom point cloud data (skip this step if you use the original KITTI data). 
+   * You need to transform the coordinate of your custom point cloud to 
+the unified normative coordinate of `OpenPCDet`, that is, x-axis points towards to front direction, 
+y-axis points towards to the left direction, and z-axis points towards to the top direction.
+   * (Optional) the z-axis origin of your point cloud coordinate should be about 1.6m above the ground surface, 
+   since currently the provided models are trained on the KITTI dataset. 
+   * Set the intensity information, and save your transformed custom data to `numpy file`: 
+   ```python
+   # Transform your point cloud data
+   ...
+   
+   # Save it to the file. 
+   # The shape of points should be (num_points, 4), that is [x, y, z, intensity] (Only for KITTI dataset).  
+   # If you doesn't have the intensity information, just set them to zeros. 
+   # If you have the intensity information, you should normalize them to [0, 1].
+   points[:, 3] = 0 
+   np.save(`my_data.npy`, points) 
+   ```      
+  
+4. Run the demo with a pretrained model (e.g. PV-RCNN) and your custom point cloud data as follows:
+```shell
+python demo.py --cfg_file cfgs/kitti_models/pv_rcnn.yaml \
+    --ckpt pv_rcnn_8369.pth \
+    --data_path ${POINT_CLOUD_DATA}
+```
+Here `${POINT_CLOUD_DATA}` could be in any of the following format: 
+* Your transformed custom data with a single numpy file like `my_data.npy`.  
+* Your transformed custom data with a directory to test with multiple point cloud data.
+* The original KITTI `.bin` data within `data/kitti`, like `data/kitti/training/velodyne/000008.bin`.   
+
+Then you could see the predicted results with visualized point cloud as follows:
+
+<p align="center">
+  <img src="demo.png" width="99%">
+</p>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/GETTING_STARTED.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/GETTING_STARTED.md
new file mode 100644
index 000000000..9bc558da9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/GETTING_STARTED.md
@@ -0,0 +1,273 @@
+# Getting Started
+The dataset configs are located within [tools/cfgs/dataset_configs](../tools/cfgs/dataset_configs), 
+and the model configs are located within [tools/cfgs](../tools/cfgs) for different datasets. 
+
+
+## Dataset Preparation
+
+Currently we provide the dataloader of KITTI, NuScenes, Waymo, Lyft and Pandaset. If you want to use a custom dataset, Please refer to our [custom dataset template](CUSTOM_DATASET_TUTORIAL.md).
+
+### KITTI Dataset
+* Please download the official [KITTI 3D object detection](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) dataset and organize the downloaded files as follows (the road planes could be downloaded from [[road plane]](https://drive.google.com/file/d/1d5mq0RXRnvHPVeKx6Q612z0YRO1t2wAp/view?usp=sharing), which are optional for data augmentation in the training):
+* If you would like to train [CaDDN](../tools/cfgs/kitti_models/CaDDN.yaml), download the precomputed [depth maps](https://drive.google.com/file/d/1qFZux7KC_gJ0UHEg-qGJKqteE9Ivojin/view?usp=sharing) for the KITTI training set
+* NOTE: if you already have the data infos from `pcdet v0.1`, you can choose to use the old infos and set the DATABASE_WITH_FAKELIDAR option in tools/cfgs/dataset_configs/kitti_dataset.yaml as True. The second choice is that you can create the infos and gt database again and leave the config unchanged.
+
+```
+OpenPCDet
+├── data
+│   ├── kitti
+│   │   │── ImageSets
+│   │   │── training
+│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
+│   │   │── testing
+│   │   │   ├──calib & velodyne & image_2
+├── pcdet
+├── tools
+```
+
+* Generate the data infos by running the following command: 
+```python 
+python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
+```
+
+### NuScenes Dataset
+* Please download the official [NuScenes 3D object detection dataset](https://www.nuscenes.org/download) and 
+organize the downloaded files as follows: 
+```
+OpenPCDet
+├── data
+│   ├── nuscenes
+│   │   │── v1.0-trainval (or v1.0-mini if you use mini)
+│   │   │   │── samples
+│   │   │   │── sweeps
+│   │   │   │── maps
+│   │   │   │── v1.0-trainval  
+├── pcdet
+├── tools
+```
+
+* Install the `nuscenes-devkit` with version `1.0.5` by running the following command: 
+```shell script
+pip install nuscenes-devkit==1.0.5
+```
+
+* Generate the data infos by running the following command (it may take several hours): 
+```python 
+# for lidar-only setting
+python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos \
+    --cfg_file tools/cfgs/dataset_configs/nuscenes_dataset.yaml \
+    --version v1.0-trainval
+
+# for multi-modal setting
+python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos \
+    --cfg_file tools/cfgs/dataset_configs/nuscenes_dataset.yaml \
+    --version v1.0-trainval \
+    --with_cam
+```
+
+### Waymo Open Dataset
+* Please download the official [Waymo Open Dataset](https://waymo.com/open/download/), 
+including the training data `training_0000.tar~training_0031.tar` and the validation 
+data `validation_0000.tar~validation_0007.tar`.
+* Unzip all the above `xxxx.tar` files to the directory of `data/waymo/raw_data` as follows (You could get 798 *train* tfrecord and 202 *val* tfrecord ):  
+```
+OpenPCDet
+├── data
+│   ├── waymo
+│   │   │── ImageSets
+│   │   │── raw_data
+│   │   │   │── segment-xxxxxxxx.tfrecord
+|   |   |   |── ...
+|   |   |── waymo_processed_data_v0_5_0
+│   │   │   │── segment-xxxxxxxx/
+|   |   |   |── ...
+│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1/  (old, for single-frame)
+│   │   │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl  (old, for single-frame)
+│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy (optional, old, for single-frame)
+│   │   │── waymo_processed_data_v0_5_0_infos_train.pkl (optional)
+│   │   │── waymo_processed_data_v0_5_0_infos_val.pkl (optional)
+|   |   |── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_multiframe_-4_to_0 (new, for single/multi-frame)
+│   │   │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1_multiframe_-4_to_0.pkl (new, for single/multi-frame)
+│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_multiframe_-4_to_0_global.np  (new, for single/multi-frame)
+ 
+├── pcdet
+├── tools
+```
+* Install the official `waymo-open-dataset` by running the following command: 
+```shell script
+pip3 install --upgrade pip
+# tf 2.0.0
+pip3 install waymo-open-dataset-tf-2-5-0 --user
+```
+
+* Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours, 
+and you could refer to `data/waymo/waymo_processed_data_v0_5_0` to see how many records that have been processed): 
+```python 
+# only for single-frame setting
+python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
+    --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml
+
+# for single-frame or multi-frame setting
+python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
+    --cfg_file tools/cfgs/dataset_configs/waymo_dataset_multiframe.yaml
+# Ignore 'CUDA_ERROR_NO_DEVICE' error as this process does not require GPU.
+```
+
+Note that you do not need to install `waymo-open-dataset` if you have already processed the data before and do not need to evaluate with official Waymo Metrics. 
+
+### Argoverse2 Dataset
+* Download the **Argoverse 2 Sensor Dataset** from the [official website](https://www.argoverse.org/av2.html#download-link), and then extract them.
+* Install the official API of Argoverse 2
+```shell
+pip install av2==0.2.0
+```
+* Generate info files for `train` and `val`. 
+```python
+python -m pcdet.datasets.argo2.argo2_dataset --root_path data/argo2/sensor --output_dir data/argo2
+```
+- Note that this [issue](https://github.com/argoverse/av2-api/issues/102) from the argo2 api might be noticed. 
+- If the CPU memory of your machine is limited, you can set `--workers=0` in the training script.
+- The organized files are as follows:
+```
+OpenPCDet
+├── data
+│   ├── argo2
+│   │   │── ImageSets
+│   │   │   ├──train.txt & val.txt
+│   │   │── training
+│   │   │   ├──velodyne
+│   │   │── sensor
+│   │   │   ├──val
+│   │   │── argo2_infos_train.pkl
+│   │   │── argo2_infos_val.pkl
+│   │   │── val_anno.feather
+
+├── pcdet
+├── tools
+```
+
+
+### ONCE Dataset
+* Please download train/val/test of the official [ONCE Dataset](https://once-for-auto-driving.github.io/download.html#downloads) and 
+organize the downloaded files as follows:
+* Note that the whole dataset is large (2TB) and most scenes are unlabeled, so if you only need ONCE for supervised 3D object detection and model development, you can just download the training/validation/testing split. If you use ONCE for semi-supervised/self-supervised 3D object detection, you can choose to download the respective unlabeled splits (unlabeled small split: 100k unlabeled scenes; unlabeled medium split: 500k unlabeled scenes; unlabeled large split: 1M unlabeled scenes).
+```
+ONCE_Benchmark
+├── data
+│   ├── once
+│   │   │── ImageSets
+|   |   |   ├──train.txt
+|   |   |   ├──val.txt
+|   |   |   ├──test.txt
+|   |   |   ├──raw_small.txt (100k unlabeled)
+|   |   |   ├──raw_medium.txt (500k unlabeled)
+|   |   |   ├──raw_large.txt (1M unlabeled)
+│   │   │── data
+│   │   │   ├──000000
+|   |   |   |   |──000000.json (infos)
+|   |   |   |   |──lidar_roof (point clouds)
+|   |   |   |   |   |──frame_timestamp_1.bin
+|   |   |   |   |  ...
+|   |   |   |   |──cam0[1-9] (images)
+|   |   |   |   |   |──frame_timestamp_1.jpg
+|   |   |   |   |  ...
+|   |   |   |  ...
+├── pcdet
+├── tools
+```
+
+* Generate the data infos by running the following command: 
+```python 
+python -m pcdet.datasets.once.once_dataset --func create_once_infos --cfg_file tools/cfgs/dataset_configs/once_dataset.yaml
+```
+
+
+### Lyft Dataset
+* Please download the official [Lyft Level5 perception dataset](https://level-5.global/data/perception) and 
+organize the downloaded files as follows: 
+```
+OpenPCDet
+├── data
+│   ├── lyft
+│   │   │── ImageSets
+│   │   │── trainval
+│   │   │   │── data & maps(train_maps) & images(train_images) & lidar(train_lidar) & train_lidar
+│   │   │── test
+│   │   │   │── data & maps(test_maps) & test_images & test_lidar
+├── pcdet
+├── tools
+```
+
+* Install the `lyft-dataset-sdk` with version `0.0.8` by running the following command: 
+```shell script
+pip install -U lyft_dataset_sdk==0.0.8
+```
+
+* Generate the training & validation data infos by running the following command (it may take several hours): 
+```python 
+python -m pcdet.datasets.lyft.lyft_dataset --func create_lyft_infos \
+    --cfg_file tools/cfgs/dataset_configs/lyft_dataset.yaml
+```
+* Generate the test data infos by running the following command: 
+```python 
+python -m pcdet.datasets.lyft.lyft_dataset --func create_lyft_infos \
+    --cfg_file tools/cfgs/dataset_configs/lyft_dataset.yaml --version test
+```
+
+* You need to check carefully since we don't provide a benchmark for it.
+
+
+## Pretrained Models
+If you would like to train [CaDDN](../tools/cfgs/kitti_models/CaDDN.yaml), download the pretrained [DeepLabV3 model](https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth) and place within the `checkpoints` directory. Please make sure the [kornia](https://github.com/kornia/kornia) is installed since it is needed for `CaDDN`.
+```
+OpenPCDet
+├── checkpoints
+│   ├── deeplabv3_resnet101_coco-586e9e4e.pth
+├── data
+├── pcdet
+├── tools
+```
+
+## Training & Testing
+
+
+### Test and evaluate the pretrained models
+* Test with a pretrained model: 
+```shell script
+python test.py --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE} --ckpt ${CKPT}
+```
+
+* To test all the saved checkpoints of a specific training setting and draw the performance curve on the Tensorboard, add the `--eval_all` argument: 
+```shell script
+python test.py --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE} --eval_all
+```
+
+* To test with multiple GPUs:
+```shell script
+sh scripts/dist_test.sh ${NUM_GPUS} \
+    --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE}
+
+# or
+
+sh scripts/slurm_test_mgpu.sh ${PARTITION} ${NUM_GPUS} \
+    --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE}
+```
+
+
+### Train a model
+You could optionally add extra command line parameters `--batch_size ${BATCH_SIZE}` and `--epochs ${EPOCHS}` to specify your preferred parameters. 
+  
+
+* Train with multiple GPUs or multiple machines
+```shell script
+sh scripts/dist_train.sh ${NUM_GPUS} --cfg_file ${CONFIG_FILE}
+
+# or 
+
+sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} ${NUM_GPUS} --cfg_file ${CONFIG_FILE}
+```
+
+* Train with a single GPU:
+```shell script
+python train.py --cfg_file ${CONFIG_FILE}
+```
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/INSTALL.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/INSTALL.md
new file mode 100644
index 000000000..21055903c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/INSTALL.md
@@ -0,0 +1,38 @@
+# Installation
+
+### Requirements
+All the codes are tested in the following environment:
+* Linux (tested on Ubuntu 14.04/16.04/18.04/20.04/21.04)
+* Python 3.6+
+* PyTorch 1.1 or higher (tested on PyTorch 1.1, 1,3, 1,5~1.10)
+* CUDA 9.0 or higher (PyTorch 1.3+ needs CUDA 9.2+)
+* [`spconv v1.0 (commit 8da6f96)`](https://github.com/traveller59/spconv/tree/8da6f967fb9a054d8870c3515b1b44eca2103634) or [`spconv v1.2`](https://github.com/traveller59/spconv) or [`spconv v2.x`](https://github.com/traveller59/spconv)
+
+
+### Install `pcdet v0.5`
+NOTE: Please re-install `pcdet v0.5` by running `python setup.py develop` even if you have already installed previous version.
+
+a. Clone this repository.
+```shell
+git clone https://github.com/open-mmlab/OpenPCDet.git
+```
+
+b. Install the dependent libraries as follows:
+
+[comment]: <> (* Install the dependent python libraries: )
+
+[comment]: <> (```)
+
+[comment]: <> (pip install -r requirements.txt )
+
+[comment]: <> (```)
+
+* Install the SparseConv library, we use the implementation from [`[spconv]`](https://github.com/traveller59/spconv). 
+    * If you use PyTorch 1.1, then make sure you install the `spconv v1.0` with ([commit 8da6f96](https://github.com/traveller59/spconv/tree/8da6f967fb9a054d8870c3515b1b44eca2103634)) instead of the latest one.
+    * If you use PyTorch 1.3+, then you need to install the `spconv v1.2`. As mentioned by the author of [`spconv`](https://github.com/traveller59/spconv), you need to use their docker if you use PyTorch 1.4+. 
+    * You could also install latest `spconv v2.x` with pip, see the official documents of [spconv](https://github.com/traveller59/spconv).
+  
+c. Install this `pcdet` library and its dependent libraries by running the following command:
+```shell
+python setup.py develop
+```
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/changelog.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/changelog.md
new file mode 100644
index 000000000..b23b14364
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/changelog.md
@@ -0,0 +1,40 @@
+# Changelog and Guidelines
+
+### [2022-09-02] Update to v0.6.0:
+
+* How to process data to support multi-frame training/testing on Waymo Open Dataset?
+   * If you never use the OpenPCDet, you can directly follow the [GETTING_STARTED.md](GETTING_STARTED.md)
+   * If you have been using previous OpenPCDet (`v0.5`), then you need to follow the following steps to update your data:
+       * Update your waymo infos  (the `*.pkl` files for each sequence) by adding argument `--update_info_only`:
+        ```
+        python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml --update_info_only
+        ```   
+       * Generate multi-frame GT database for copy-paste augmentation of multi-frame training. There is also a faster version with parallel data generation by adding `--use_parallel`, but you need to read the codes and rename the file after getting the results.
+        ```
+        python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_gt_database --cfg_file tools/cfgs/dataset_configs/waymo_dataset_multiframe.yaml 
+        ```
+        This will generate the new files like the following (the last three lines under `data/waymo`): 
+
+```
+OpenPCDet
+├── data
+│   ├── waymo
+│   │   │── ImageSets
+│   │   │── raw_data
+│   │   │   │── segment-xxxxxxxx.tfrecord
+|   |   |   |── ...
+|   |   |── waymo_processed_data_v0_5_0
+│   │   │   │── segment-xxxxxxxx/
+|   |   |   |── ...
+│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1/
+│   │   │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl
+│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy (optional)
+│   │   │── waymo_processed_data_v0_5_0_infos_train.pkl (optional)
+│   │   │── waymo_processed_data_v0_5_0_infos_val.pkl (optional)
+|   |   |── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_multiframe_-4_to_0 (new)
+│   │   │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1_multiframe_-4_to_0.pkl (new)
+│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_multiframe_-4_to_0_global.np  (new, optional)
+ 
+├── pcdet
+├── tools
+```
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/dataset_vs_model.png b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/dataset_vs_model.png
new file mode 100644
index 0000000000000000000000000000000000000000..7add8edfc97bc6b1e4050a988b7d2840faa37840
GIT binary patch
literal 126450
zcmeFZWmr|+`Zubggc8!V2uYRhZYe1R0qO4UMN4-}mxLf)(%sS}-7HeN8_wW)p8f8<
z|L6U1KArR7yz`pZx|n0lHRc$1{O;d<k1+}QAS;gYoZ$JRM~_e>CEm$Ddi3nuqeoBJ
z5aB@!X`x0H_<SV!PUsW#@lGp3gYvW1`@<owW`}d)mNm_q9hBf7-;7r%Y`z(?6We8*
z<S2zp%WS_Hqm`*B{G}*tY4DmE@`cp3-9Wu2wEn#5yn2x9e&gcCvoR7uw88lC;`yxg
z9fCJ0;(wh7wTb`TK#C|FpYZ>v2|C?9Xc!W?`>!6`&|i~lFEJrM6?*w_E{AL|y&~7f
zfEkbweGK@v_8+(P7%eeD{(ajTO!}XTzkdlnGo=3i(9{6r41r~iAX2JvVD9X!#^CvT
z4Uv$(#FbMOqsrUD$IA)Q!TU!SZDj>Tm9a&l$y3U@x@2(hXsxhA+t+71Gm;iEC`?|0
zUxY;bmyfS6ooHt31M+*Ht50*)p1j>@$IWOej+^IkT6z>DDTz$X4H0_jOCE#^KK0by
z9oPhF*Pr*Ee?3UVgMVYP1ra!79jrTG9!#)j;!jU!h=FmMt5=2wH`a5VN7FDO!~LV6
zi=(0oYbIq)m&ez=gS%ZJR)YphOBrrTyA~F6NAvroE|<)@<>huzyJcn<9IS)=vs`iF
z^~>D*;O)#3jaHI07u1OEzGe2~%J|D^i|Um>Jldo$l&snqA8aQaiwan1+bbK0DgohV
zMpy3hMSE-xS^i`qHTFY;ytlJ`K2CETVeu*Lb!#E5Hh<X~n9V(;l5Lz;>*#TSu2YZf
zM2V&!FgWXXEa;%iv)Gg*u@Vost#K>T#w(O;<z}zd^DS}N2;OLgF-GJVyuRRaoi%xn
zo86E%3k_ZjNqV!qr@!F(6RudJtP%e;XS7w${!}&}W7amKWlmJ9A-7ax*5JP$a!dx4
zv}3k1*)+f=P!|$m1EK9BBMAGT@NqC;f6$=or;+iGqAo?ns`zWmx?u5K*<9D5Crz(W
zUp{wS;Y9t%9}m*a`f|c{=9<F1MEq?S2V`Vup+>|HqCg*d2WB~?>?TZxufnCTzZMx4
zD<Y<Zia%Ye@3bv+p4N!xyu#V!wBdf^@y8xMj59nT70BvH!Luz`>|y@k^wo>lI$S!x
zwizFvC^bJsaKquLvife^?xOCm^Wd3~vImFr6)|w(MJ8rTswcU8k;qls(Qw<#dKQOe
z$5>@BGBfv1!vTAI?fY*<<<W(SUfjHjtkkYL)qI-XA9=L7c^RmE!4HCXI0M96NS3k{
z<k}5p+3glmybM&_2)b<&c+B9Jt?|ZK@ws&=0^Z^w%PT9$h^o8+nzR2|eeBFxC1WP_
zLJ@oI_rxd6=kS*eknW7Uyr}$O+x)k^CYGihRTf~7;f|UQLme_(I{2u54Xby)C|0v!
z`#oL?T~Nb9#p}Oc=KK`5YtX}ERbjY=@F{jL1B_FGGjwu;oQp2C?XeH%X<><N+Xah+
zgftlyLD0ddf@ik56OyQzGJ3^&I~w#Nw^|nuo;t+^9+OJOP;1wM&Rn(9yYtvcLH(y!
zRh*%BYAT+;kel$6d6zTYj)WL4*iFys>Dvb-J)CtdUn;0&b=O8IpYrfbm)pScgj6$W
z55MG>6mcgap@rfqySY8tNb!7?JW&GEhIHrXi!-rkxhG;v#eYy%6Zyd+K}s*EDhA&j
z!4)JK?-tf75&%1Oxnyk}1ZJ%<@YCq7Ye9pOm5puX_;NFCGNwe6HL*m~9w}W>&FqB~
z4&Bl&zE!E-wbG2Lnhhm&?y%JoDU~LaDL2IfK1j0XLDyjqz=N0`uY;Fze@f$gG<iNU
zF&b*Ndqk;jb31Tx^H?`C3el>WJM0bGJ;yk<fm`%xUp%ATOht<0!n2zbC%Ndf#4s8R
z)Z@Lv$*G?%&#q@Q8e-==)ok$nTYV*CY`DVP_jc8wH@Hq^Zp%EMM_wb8JG86f^rsE>
z$qDojq45u1*bR#RBQE~ts4Tf}-<)2}R%4BP(CQ8d!C`!mR?kGHj5)*GHnx?E5v;TA
z?p7ep#H)U+w2NJl-;?9Eq*Aza`hUheVY8E(-DsY+42MWa5D7gIIJDavOU=|X-i!}{
z?iSNOqx|;!jRp(x@%(#oZ6E=uowFBf^G@YUsm@?v*J|~q&&ITzqmVXaSB=nkrT6*W
zfyZ#p{1pN<(9<J-9qM&sR@6gjPAl0uIK$ghCbd>4tLG&gMDgiMhQ!~ia$ox0Zl*1U
zbgW|W5~I=2R;e($!ZXM^Kbfrp@&IN+2BZ#c<kxNBCjoy^P*GwAhEV6^l&7R?-qZ2B
zIiV`C2J7HaP*6pUWl;loB4s1T^dt>W$S-faxvC=E+A;pOsQL?OWw~yV)vGLy&q6t@
zApvk;O0^Y+FHr*ypuEyaZur^<Cc+snBkr!6FE8aRvzm%=;wQH$xw!m@`Hm$eW!g86
zVWyU*t8x*%{c@gA%vSBl+sZ0dyYmQ-mY+sxWLVFagF`~dX?Ow(cOWY(D`gfTP8<E}
zsnv_CV^~(RZKsP3DWIV%Sj>i!f@+kng!&d9m5hrLdyF;6xBUSh3GcSIzsB#00tR5L
z_kaiXip{UugG?;VzhxDLCHT(}<nZ3SpzUjm9pj6*zCU2Cn20-=DJL^5oEs$UtG4YM
z@)S`O!(p;&<c-a_;#RjoLgGE??`x>DXUR>o<7e7XcjGFdr(3C9Q>(cfytbaM8j$k5
zCI=z(7`=U|+{|({{e=>X*?_F3N_o)q_W8lZiwZpVUgf~Nl04l#GlswL37Efz-QXoI
z24ctZOoI+l{d-4iQin=5T9ukd%I{6z8l~9LBe1x3ZQooTiU&$c$!#b{+<A#^SC_g;
zAxW`Y!phzZ4TH%umE0XW-|??Wn$H?)Sv4IJST)@}XMzhrHkj$>qFq#N3ofdM9uCx|
zSRZ|cbhp|-_$8(6Sek3L9jmP9+5p(5cpiT5S1FHPQ+xlp)CKc6zGUqWp{F1zJ?do6
z>Gs9+u0V&)162yAdWed`%4_yR?Tj+&sk;`wB0{=Kv~B^wOlpwZmo_}z{OuTOy)S&<
z@$2Gc8yFsjZCkCcUlUX0<Yb1H&+`OH%1To5(F6#pLzsX}_4N&6Kp0VXds2Lr^cH6*
zU8WznZtbnt;#k(Vm+g$!mp2|_>9*X>7h@HxxM$>syXBOh;~s+Q!|CQBA|9H(|HZ3D
zH7Qwz(~};d+-9EOeQ-65+p@n0>twGz*W^b5tVf)rtIR^ZcHZ^iPs;h_8QWw`YD|a@
zUbd0G?Z{;D(m2=ts8fre?%mNv*6&Y>7zzp+oeG{VkZv1m6hdc+Rlin;Butj_WneoA
zqIFvj47wMan8J4G#y@a<ykK~Ey}!&X6SHku7uziB9>jkOubo6yoObza?tW?i;&A?4
z2M@mZ+3^O5cVzz|!e=WhOvrG`tQZ-Mm3|59UGVV8L_(%6@a^591|11Dkc8GLqz!F6
zcqR+F0Z6{KI0F7Om;JVipqnmPyw<flkhLJQT^*D0s!fOe&&m}}_P->8=zVZ$pl`@@
z7s_G-Oj2!3YeUd*2<jt25jwfO<}F1J(`L6)@17V)4yxV0(_%K>{D7LRtfOUl+r!uK
zTS=MeewQQY%|e|Y(QNhd(dF&U2`*Y>bW-n~{MhJ4&yc6+ds6}gT{RpAzI!KHnqae~
zVVyabNj^xRNs_wF+Bh;0SFtr`e*uH^U!JG?x94#}o^-$Jf*roJn;z$2G+4iGxLP*!
z5OCnW6Z`^KW|5rZ?{eeC`2hC;`9}NvyzCb6V+(4Xzz>=JbSPX<>=w~+qTXYKiM9O0
zf?n2brW`Yn0*zGa>=K;q(d%d_#$p$KK6+fdGpF8_c7}~=IK6^q4IxyPw|yQ%e@mc!
zAUT+7|LPV2h01Vk6s@fAbuk)lQv`O{O7GyH&YX$XatzklR`+I%?7HAvn%-%Ra-Rew
zqjd52@870Vu+Su9cBcvV2UC&&v$JMB{CiriW-qE>OGj639h4Lpu+z%}@RzU1E7O9v
zfN15E7030C@_jX@(Vgvb_!PTKiS--<#)%rx%P6ygfe{4r<O&xNu|RQPi)Eb{mseBl
zSs<F7YI26F52=<{){qQ^asF-Oh{BU7c)Y$|A_3((1E$`oL=7xP?2~f`aJmzPpT(vZ
z_-bT1ogvPA$Cs?(un*-~2?;vn;^-vlExAZfS2hK#Uv{chFXhuRWK~qeWM}I;?!SFy
z2-J3Q<H0Yf?#3(PAGUPsY&WH)DEdq$45b6hLVd{Rd~qOmy3`Vd$<;CTWtiB3i#r$#
z1m&-QtpOM$E(Af@O&0^G&mGL-G7V#0&eQLFF-cazoo?5n$iAu?JxuJ9>}TcA()D&f
z>GU&X*Wl`ri5Y2eHNxc&FT4L2UuKY1b%BQsafYCnDe29!SL8MivT@veN#Lr;!9)!>
zymXUpux9qxIWQrMLZlmz={VV(j`2B!@}B%+Ak8T%ik0B>wwPu8J&7JOTf~D9qx_-8
z>t}T}`g8w4XU{wJg>-@Vq24Nsq~xSu{k>ujv0w?<rO;-+Oo8&t7=Zl0Pmpy1XfDsP
zKzHtV@@VJVy*j8Vq7!GsZrVI(wJuNr<9r~Syl#_p;F5m%L*%V<gXSnp#nwc{7vQz;
zrGo#(ZQHvUkq7zipWK~w==rw4Qex10Zy?Gw@4EU?<>O#5TKrOPAH$MwOxKYL;BBEM
zNsv$gK{+{P0<-0TzU?<5twNW#k5MTwP%$erJ1*QjKpYPs!$JnOX8rTvFN+hkP<+-Y
zN*L6`up?zEM#Z!p=iVaXv>>ggf%3otEF;pePPXJ0Tiahl7!%U`+_$3sA_Rkk6EUR*
zht8F(Y)9AEmK#3KR_HNliK6G;3nxUZq5MAn!v*_~T(C}ZTs;_w>-SN?q%H$}{sCDC
zmHQQ%{;xpvLbb|h35vEk4kPRF(?z4p712-1iYweUb9wed+dZw&Mol+<0jAW{R0y!$
zpOuvuF)1nDi~HT%$5AQK!qTttR<%vd^0^`fNxuwjw+D+IB0QP%*dM}Yb3qOuqv1gS
zY&y#iq=ZkGk8L5zTSEde6p$5i|JE9oq4w?5<v+$&u*5|HiHFGoVq)fTqd<SP&Ze*b
zfRfkwB}G(_=jbp__5jnvhH9wi88XNz7<KzU??Df2u26}CyI}@eK${bz(xI@ae_X3=
zIVvfxYs)BYpiYs-X28{qYO(H5eZR9ERg^Ve`p{5E2$dclJc3>l6@Fr32bR@*TPvVz
zlsv%?VXSlG9M-!vk(!V?TkQk;qH`em_WijnX;4p3M`t%WvLgmk00`Me57;s`RTXBT
z0(Iwq2mtJVWtJP2c4p`$yw^s$wsogVt0gHi(hBZGY8=Jn>pLOMU5Xrwl7_PM{yL@2
z7KD>!46{{SpJ;FzL-b~ARxmM~wR>6NkqLvqoIEXx2EOi3i-zt_asC@I00r)NK@B@}
zv7yr{Pso%Z{M-aZFK+X=#j}|+<}ZlqP8aMS+?}1Ocg0Q5sr>FY-WP{4RxhXJ#!pfl
z)g1<ptPawJk4km%a-*Yj{eS~QB!+IDFqfBG16DnXhNC;no(?F1rmN1|YV~s>kGq&z
z==zYH_UuC#5`TgDpv4d1*5X~Yn){prll&6$^_Ba&xD;Z(pdc|a5V0^=tS??mM+Cqj
zJ>yAaI>kT34)UD_xd8ADX;zHZWmpm+VPH@Hu9)mOFMm(c_mDUAGlJlo$G}d26ahPy
zb~)ebKKR%Td;ZLa*Lq)rm7RTw`||hLuic5%A=CaT8$f1P%4tL1$lVar5UdHB&#wD5
z3&?=<hrXSW^V~ZGR1zkGazm+EcwQ)ryYhJ6&!)H9a7K2%STHy}GJr@Z$<mAXM^G5<
zC9|khLDAE*%Ns%k?)F|8HeE5x-EP@vdLE`H<OG4-c%<zi?YiI$xK&a53UagF^Bvuh
zIS%IwY7V<y<!*%rXFet$@v!wHGpjaM8Qm33%$}_@@&3{CbzmgZT0bgcj6Cj1_nS}Q
zub(^Gmol_79$emb?98dYqrlSDYP>>wpie19gcG);1@W}2Bd(s(6c8#ZtewAU*@HZm
z4<w}DuM{3Pz3&IP%geBL2LL<)6A#FgyZ4gjbRuKAhyE$K*%}Ez3J~6*!3a7S+DKa|
zTjUk&4}^)c=ucVeK6k;jU9Z*ZT;oJlNP~u&4DtC{sO^}w&y3%!;|jD_S)g_>A~(4Y
zH~@%T0`9o6M{@f`W_z-{Id0w1!)9`Khjt;n4|ZsMio)^gf5piivp+ErM=M_iN(BI+
zf*{P6m(qr~uL0ea+#T_Pa?cJ8C0*)c5pnr4Cyru+{h^J$?dBdzWleEQP5#z!)G7=0
z?qr5r9~m>H(q{h|)3f-fSSjg|nOJq_z0WSI=PNZa-F9mO)hvK2KrgL4LCFJDG;FV9
z<E?ii_6-kyU;;@zq~fhR_zgVUY%^+VQ_6Z8bX6AQ|DZrX07yXvF6D{c58|idMEk#G
zwBxbb;{}ktM6{lrKd-Re!pNtgDWeNny#2G`R%85lL(>}l0oS#Z`>u~SnD2{J04Jq+
z#T2D=w`UU_tq^#4eLH8nF;A;<!N2z1wJh_a$5lH?ss8elCff-vAiYwPTW-ypZkFGZ
z6GtYp+-s~&Hsh7N$E%n%j^?wys}*t^z55F-Krb<o2@+d-^Ebz}xzkENgA$nZx(~-g
z7#{R(yz&srrsA=~bVak=aP9;l)SheR0PUD;kB_FcNa@qI_ZlKl3gSjSsD;5eY293@
zq!yC{hBkmA+cI9pVRtghh^29c*IYp37~F6tXADAWGqX|wbJq<g;YiDq;}13v1$mvu
zZ17ZNQJ}UFf>!;RnCxBebc*X1cEjoDagFKQK4(y-0O^aD&dV@gq=2?BA}&hoMw-@d
zO87XQb(cK^)HI@5JWe;Pf9R<-6zhaufAx^M)N##Lw@Z#Q<RnxYRk7L22f9--bd<HP
zA);vTtjeMS+UoisYT^>yzVcGKkc9pnb7JTTl2{mV`<)B+*>aSCKxagg6H4rw!Bkvl
zzp~a!5r<|ct4!v|>u}i8@#U{R25nG+L+#i&KFq4$`(9a_IH{QsY%y!^Xn!y%z#BZd
z(f-%nRR-vGa|xK2?PEijB<WusytxB-^D>@M0=$2#5Qz2|7%{)LMq-w!Q~IkdZ_wo$
zYa_L8Po067FDY)}QPx^>HMRRMkU*W32J#%#q~05e^Pr;o!6Dck4|xob4>b=yTdWCA
z2rGzFP*ZGe3MX;mR-SjEHc3?3DQK!n0(b!kVa=rx+R^U*eZuchX>}#m_u{UEL>5JW
zPRft5BwSQl9f1V0<}UntQ)LPv-eQ(Efe-HAG|GoXCI+va@sZChpb!r{m&wEP%Q1Zo
zXq!qm!Rr7OU7Z6ae;w;T_(seY>$B?6tu>0=?3HqhWKfc_P^lSZVM(;_F_2E?PnN{h
z3N}gF8Dd0!$nn$Mow+wOtkG=_4{Ja<5hG}c6P0Ve&SsYH+-~>W>QxsjN{hDE7JlN*
zkyFHBpZHx1@E%4Pv|?ilVNR^JDq3~tXU~8y$R#qL8n~>U>U-vxV1Rh~5GDxPFG+4Z
z3AeMJ(me!!JxNxAjGy`lQPv4^dI?mTCGmplyghj)6yLM8@u<=j0o_)cD9QMlop8ig
zUt0JY6dsWw{K5Qi0g;JgDD8}!JI1--=S+=PsH87RhMGV+5&PYBs>+0DAPgn@yHWIH
zL1fcIdHT`@pfn#(RfS!j;d`guZ)Ht!tG`*FKfh)H+0@DF*BvB?Ads!qOMxD{W0TT^
z01$!kwxPe_XzkDgwCLcwiItfFurBqStqhQUUfg<|O^(jJ+e(|S;U#8pzea1>IIjSx
zNcH}Fr)%x>YIDr<5r29F9Fm{#&YS6;x^bFm5P6H}2C|I|<%bI*Gswk1PF0ftr45mR
zdgM<VzU0Xgy`f?ZUQM*d>yxz(o7~ZYQU;46I#AQ-jEzeZib%V}^KZQarODzdf1=r{
za*u(xe@x9vEs{az2`cU)_d+6@Dj&X>xNt^x`MbRBeQyIY&MgjYQZSSIo(8JbF4sV&
zl>65E>N5WK!?u6NRABaqUz4?0nEtu+W$>Exor9t=J?QJd&SA>`)AYAU{*NU7Wf{Yi
znoUomtOG|=JD>Wb0s;`qjO-J-qYi))JzR1`!gn-Umz!tip|XhqXr5y=vA)B$*8xdU
zRZddyXd`DhNIAN{o%BmTcfr<8pz5w040i<aFxWr^^o1N85|R7hYi=HQsSR#O>&<s$
z3^L9s+TuJQHO)e9CvDz;<Z-gyz4h@!pmqQB$Ddw{3pV7SdIE+AK|*fVKCq#-y^pyU
zyofma#jp=+_uB{QIQY(|?(`QdFMffPjV@JhzHm}=y+_=`=kJHU2b?NCg10rnCiiH6
zbxo?&a*NEHX!l_}c94M4)Y-f#4dDoHVP8F2GX*&CFpyS*vJ7Ar2kEpJs2{ZyL>0Fz
zeea<Lu4e!_Rls6KXXh)#Hl}RzePXbjfE3V<_~6pzw9yLkk}^^v00t#2WZ1wG7N{;C
z?XRy%X;0%YHTgbD<0JuN6%igF;*0~~oHIPkT6%lj>LnWQ@$<i2+7ZiQDb1|O-}+tc
zxmN(b`w=IQvwtYb0?G1y&E}ESL8F@0L-)VXO0H20;@y9FK4H)*|F|d+@;E-#7f3!*
zMVVKqZ%_3vcKt5^$ch8}_P;!z)IR9{{U+RHYvUqI3WCt3LubiBnf8MOx9GDvoVHyd
z^_*^sHylI}Ln1#aW5WHF)O%{TpfESia1k{J%#jbiB5SAD2VIwlRrc0Dq$4EdExC!)
z5xUDw^2}SR%x{8hC=9>99GjjJEB)>R2MTiStcUOoPopuxPcGUxzqCg6J$_gY{P($x
zxGu^G$??2r_*z6u<@$km@nVArbYe})rq-8m=+pe6y75Un>+73dYkH3rSE$>exr^SL
z<Gs}2?PP`pp%NRPAb9cy8(8`qtwtY_Se^#l#x7Ld&abtQ{OpuZO^0nn`ltIsMhZMb
z8W%L&e^l{UO^|W<4*8@bg3Nj_D?VTTc0=trNq|1##~6+IMUc?_O;Qh!)rR%1gA<hy
zfjfD2`r;1;yeve<RFg^^t2?8pDls#W2H$ohh{7QEp^EVDQ=E#SZTTgNXsIxI;Rh^G
z#m}QDUOL^rmzb}zMQ;B#RJmJK`LDeIzUb0dyW>xzVKyr0FKDn@4MZ(;Q-1hiR!*f`
zOuKbFYG-A-;N*dHmYJvg=)5zZecat@kU-_%+CBC0MYki!8bw&~x}J-cwtcjQ=oXgx
zVFxcUOJlY#k)vvhXTIZZe@ZxO{5mfax~CdE2<gcdFMjogBdk5cPz|5ODp7cTg84{}
zK=5Ho(T#o8#}h<D80Mx~`}pk=y|NE9_}^!^w5BhENPDrVX4BC*>(@`q1dNAJD!wa?
zm9dx$)`c(eW1hBec2SCUtOhkab1Rx+L)$Dv>rbD!Aw52ejZAwdtn_?7V!Qor;^tQ4
zjCR4hd*7hVT{-e=a#xd^jfeaZ^F^Us&|}%!Q{mAUc9(&>FOmGW%#E|$22dUQwd7dw
z47+bC`x^|N$Me~}06MA5w^YMd+;&<<x!}Z5EM~~?aODc(%KNtxfI`}$20{)*&88Fw
zewOgPvoj$o)`?GTb4JsYRDb^j0Z^evIifZ%TIiAi_qLkkFKNSrOFcx2ZadOipX2Gp
zJv8<TqeC3&h@LNY5!8*O$;Ag(?k9YDPv^x?*6*z$@0D>_VC&7JKkNN9NX!ifj`!`3
zP%!@~TKyxr<=4xi7i8m>3>&cp#96gJQ73;`r4FhH+*8@SL4vR#``j?WC0q<UL2s`F
zFif}fI~UiPuXr#IIuWMg^>|q0ClxBbe+|D<DzwJZ(*1Ix$z@Nn@WLvEB%Y|!cuqkC
zwxFsGYLi;(UM5M|XZ;lPY*BdKHpY3z-*SF@J-fmw{P0vu_{&dk^E#CMFo9Ps_sfC`
zCUk7Gy~ZV$T?YviYuTd~P=;vR(F+PCiub4MjlcH`$wrAg(jjtZUwCcd&pQ}pbPFZE
z`fH4{Dzh$?ljBs*(ggZvX5;F^<bp;1Z5&{_;k3X$-aCwFF4Qbb5;!V(J<clOjzKzi
zOBJ-`?S{a)gb_-&*0f>`sff7Bu&kL$ZSq+%GKJNfeLnd`__d+h>w_7!5=F2cPXaNf
zRYQ7c(>&Mq#^SfhDWRX^RgAY?0t(fQka+BH=Ib*frba5G`#-ax4Q{-}>*X#u0_;A!
zb`vj6cUzu|+n6J?>#c+w$1Ly!u<YG(yAR=o>)_Q07p2ldHx=2N%R;TysFgUM!$WWQ
zF_>fUPY1@hF1t!r<d92Cj>dVQ`;#RfDm`$c247Yg=(+G-#H_r=;|Z+TtM?A&pSNb)
zErP}F^NLA{@)c_<@UVCu;5Ixa!R58#yB&i3Yx(4FBZR0(`!Wcxb<Ou*+-d!oRYLV|
z4Vp~tOBAU0evrsptCsGTV=-7c8O~aP)5|f<+5Quf8$X#6+KjA>u&)ise+jGyPO@K;
zat<fk^p<{oo+mcE4oA8~()JvAg(do7{ZZHQZ603*B1I?ckFl}AM8sWJUEYh$A6CH2
zQ@zW49)`YZx#i2_{mE*f@8&vZM1J8*-*a&lAoD_(bt3LRY7%t6a?USat){cS)M`kN
zJ93HRW%r4d8`9JIPE&RKhs{%aXoZX{RfqY?F?VTIDEoZy)$f^}Gp+*X!`X;Y&Bfq8
z*l}cZyrjA*8GXB6<Q!e<?=+PQ7~KEx#O@I58pYzp&g*6mza+ZvH5e7q_@1-+AW%TI
znXesT<l!P_2INf1np*GIl2Y+<S`U>|d!~{Hsb8_+gQ!wux{|7O6SLyH{bOGK{Z{ZU
zatibbF7KH}RYR@!bhSXp)-mH@M!_#4RY6iN;>oIN<%;w6B5i`uiz{f?{e><@g*;eJ
zt@=ZS!+Y{v&9L~JjuB;jJQmZ@Hq4~NEW^0V7SgS=NeScJZlVbsl4uK``BcoXTr``O
zk&zu7#=x-dTQEcx36W#P(m9h$%c{&r_q64OdAc#R)8k8#QhE(`u}{dVwUi4C<ba+@
z_?bc~JRG0p@v+qE_sTp-8t3-RVMQJ@@np(DA8Q>WJ_#hm{}D4F&eFc^dEXn-rk(i3
zfg^VOoUAJSlX%HlHBd&+&xbT-w(`f^oII!Q$JdKceOrGV#yx}>iGjWj+>P#ZJaNNC
zfVG!Tc=^%7cZWZz2IvrU<Wi%@M}O&Ke6SRW{`akf2g~^j4e=MD6wKES8^1|3B~(3d
znxF!AzK`(zYEy1*j}NK4mZwk$zB|-#gP>7h+QCTb^!ahm{OBRP+XW`v2?0j}W-(v#
zO$_|-_V3nr-a6_$xaQeaKd>e@_obIQwg)L|)wn@-dCrNU_sgQlwHFlXX7`8u1K6i{
zIg-L+%wDUU>i*pnugHTs3bA<k8UADbfcUK*l0jrMCS-x9LSeVawVM=G04qxKQqj_(
zF%2WPn|3oF7?Hyj{Y}2;EZLd|B(Qj%Imp&6^CFXecZH|#E)TNzN2+uf)tr3d?*(l9
zl2*3fow-|!>+x*4B+aj`wJG+fLB@<K(p|QJj(6=J)$g*QPu?IsO`cJ?>gGVIM!>fo
zw8>sG!i<mmm0&ig+h1t}YSi@Ij#HJ^EF?_G@LJYn02QKS`3+mRx+|7D(GaT<^5#`#
zRXzT-M)<c%t?j`~EEk{xxpTIU^{lwlQ(7+@u0wDG?R^vM&IK+eq@1E>SVHs^Yv=-r
z%B(xTQ@xByh!u8o!LVSH!5UDN9hhKFI{Q5=KOzWX)_{S-(gAw+GK=Id2`YZNsIwWI
zs;0`|q@wSvyTuvbGre*~xmA1rSepk#aH1A}-P=M=<&9(W^o$tvXU7p>PDLY07d33&
zJK-!wyB#DDEh+Pn_$BeA9gsAF^H0a1<6WNgcG!7&v$c3&f`9be|BU%hmp5UeoGHAI
zNnlpV{ZoY0x-NVv)~708Y3JVcxDpyqee9`kb_tS<aKrV~{i65L;o}<;CelhH93~~s
z-e_vWvi1G_5vBQep*l$knlXa5Pk?%@A^bEn7zUSLYYSaWiRrLWI`DG5woaGig30G_
zI?9N2c|8~~_Zm}xp-%AK^An+1OnnTQDKhwNNvC)FBIMWRWa_bpJEdPP%CqiQb2t<^
zF2}?zOM=oR2+C%P<o|6_Cl4kCtUI($mp5n9r;qT!=5D`u*~(}^s<bSOZY(~=$xT4E
zVGjaUeLw4p(>c1A;>UMyf1f>A=%Dp*qmm*{4kD@ql(mO&KHjq3HHz;C^)#F-2CxtX
z%)M8m`0x<^5Nw6@vV#oevbK-B9wdC>`y%J)jXeavV^W0pfXddVOYXb}{gtdQ;sxb-
zwQ7&$Wow-=>Tz((u6@Yf<CteQ)ja90G69m@p1h4Jk*g1DfL%pKruovmVo9vHzgD}5
zfnBoVH~z0e!e5lkH!A{nIV3JAzQ-9&qo+UlE|{qDV#&!Wl7}^YAs2c1vwWou8s)x4
zOKfDSFi^R5r4mQkRjpH57Ea!?Y4x*{0eccnMEHC8CPV6~-3Rm1p4nFYe7fUSX#F+Y
za!dW{_rPlUFbVSAcT*Vgjs#XbjI#!Ol~c~GN<+BGX2}RQ$RGaLhy)@0ffDjV&Eb<Z
zRfJ1xQ&abgsqcL~vQGzFu}xpdGdjKmaInKLik|5r^+u8_kHSgnU;ADDwzP15s<wEY
zO+RX;opKwHy@Eb2&t<Xz6zYi%#4)cg9P_4zS>WaL9Ns&?FL06+#|67&wjvg{jRlOj
zzk7w})?dX{PzKyt={H#ypev9*s+^*_APHAQ<<=g!9+EK<WPZbL*KH&xU+d{OKeNuU
zW2k8xU&B#q_m732@t#b`%vbiX)qNQXesX-}AAE@ZLW7ZR>PoNMk~|v%ET(t*Y#>Vf
z*Qg5A_esrZXA10cTC%{N-8vq3JN_2|@+FTCIxt|AjzMzLlAZX*tIn1z<$Rx^90cDE
zK`Hzp3Q+xP(Tvb^J`qT849}zK%-zkfkN48Me)f@Mx(&#x&qvU#K7<*nH$2!8Lp}vn
zKfX@ni|GQfqm<C<tbMHoi9F3HI#n0pez$=*-5LF?m>EdLw0(U;DH?3})g3yq0b|6}
zQ)5qF_}o=4(CDW(T|-9nO8t@=wIzJUcZS>fJf2}xbzVX`o24VJT4{88%72Tg2Y(84
zGE$N{db$~*czmd-ug;U6lpuJiJPW7uc%u9y!U_XL^nki+#HtNZx536keYuL%pullh
zY0MWp{C^}q#0+imlpw)i*{}EB;^37mt@WW}i5t`VMAD`1$}V1KtrqEg&Kt4CX2XTb
zSrvvItSw5b?r$=zXYV=axk><1i9pG%G@*qAkDY|-@L&hZhJ&!h(d*x@w39v+PD_sa
z&o6O;46X2k?|M1+&JVw>)5w(M@U2sAaXOs4bvC6N3wr8##wg5^zrs|x^Od}5_1B{z
z8seOxt#(Ft&9{?rA8L!A&Q=|ET?WFFv&N6>iPGA_Z?Ifs#!Q|3!EmIT>0e~H!JZe#
zS_wH<P*xWjSa0NvlR1Z74d180tS>R1NY|Wi14fsQ#9qy^zmQj$TK5HT)vX^E8M^bb
z!gQ(78%?UYa|aN0110y;!nc?}SW39B$a6c8ATye?<7%gh_#cTCAkOWrj9^*yF`io{
ze=eHQ$+%G&5IZ`G=;xyVmDSYY2x9UVOvrbgjGUb9Ee3e-V6OwRF0zjgGcsKKL;bn+
z-WCE<_A>()=^IHK3aZYS37X`Lm~Q5S2AE{8q@I?4_iEvFlNWYyIbcP<v0~*FJ_Yxk
zxcnj*_EE5GjHI(F*559~{!~np@x46i=M61jWO~1=<&18BioW9(2InWMIUiWdmXpT&
zA4M@5A?ymxjow$g*fq#w6lQ{GvUhjN(~bHs2(_-hBxTBj;1xMt!eg}H^o$d=bH6al
z#0f>F-YoEt^694Qx&#%F0ziU}69HE&Oix!fDG=Z5R&3u+?q=G~(*tve9B)R@D;9`H
zk52_#=Q>V!GX&tM!*piV3J@zDF-&)}U5+*VWr#yaf8Fb$@h@c+qjldm^QYJMY0&o=
z?57Go!QVk){`BKZp!W-EQ;pB(e{3f{Qp?gRV&Nh_fBsfVaGFq<)GJcxjMxc3J~@5p
z{;=$Rn_&?PyI{g}aftfKe;>`XE>As>o1J%_L)dvd9#cuTK6b-Yp=P;?+sc;tC3N`O
z*gI2rSYMXt{kwEd)-pKx3wjo<TJ-99zc*8%t>&xOc+TxyCjw4SqiNCcu-mGco+Z~>
zk61u8h@WJ7M>#O_w-<9IH^m^>y)BpRVi@+-iWCqUd3EtD;era?9hPu=RrG0>Psq-O
zPD6%Wy45hVA|{aVtE^zQUyYCna^oLq{nSt{+LvMANjnJZk%!3{pJ|eZueX8*C559Y
z+T=59mnxk<?b762zC%exQ3@5hduKq2E<lmm@<Us`>&&RdPcO=z+3VvgFb$D}!xfa@
zByV!Jplnr1IJ~I1puGcG;uRGKzTP(hz8>zxxoJHK{Mq!Gh8R|&k~W<RJybL@R<@*z
z)~(MgZJPrE6kkl_1}M+l%6?A%)Mg|MB1#DU@;{Fjh6rLW5|N#jPqOhnx>U~6o-P#~
z{yIS@Fndbm?Y?H3207YgcU>+bZlAR5kBKSoKdU0^m@wVof$3k#62DvQ@!^ae{`y*2
zA#-zpVfbA-3xf6AE>h2{6s|A)MyN}lyfMiTWdpx)qzZ}OMB1+eWFNaf?evXymVKSL
zfxK*g82}&6WWe~}YJRwCHfR37H$Joo1cv_4N&I!)f<{J8`oB94e10MRZ;u<&|1Tdw
z{1_ZwBvMB8VH4|O_S}3Q%MS5wZZ`AEiv9Hh&_+i;%V|gY`F^z*CMUD0*SJWA3k(f{
z<z_=0e@}i|KCD4PMIq;8g*^1~ABPu-53yGoRz$o;Nl~lPcqkWdU|IEGi#Gdc@`~bA
zwJ{tWrGqhsxM4Jp8M4Gp$X=CfliQI9t7O)7lwGUw9j=2~{nYWf!&LI@C2q9uf^iGp
zsvJ-`Qu~#5&<hcx;bT1oUd=iS$0u#xf-AV)Fnd@P&mkJhyQSvQu5psX9g=+tR<dZ^
zxI(H%Eq4BZ^S#v!wRN!Z>v4WCmDzzcGCW*2DvAE8Zv}L3KH~DtbYan)V~_nGv;3Mr
zPUqI`?r3BOKF)l^aW}iqOlfE?a53<1LKEPg1WLWvX*n3_p!~jM#CoWA?9ymIHB;*A
z`>Kyw;Cp$dFu(c<K`w=tY)SVbL{Qp|%ttJo@>M08SPYWa{=oL(@Q5WPkd<Wg@Tb^V
zutXiQc?xf+;SQIFfBW+9gA+xw1UI=dEOMApUu8YF%4UAUT(#PN_`uq-Zr75wwe`V*
zZf3U7H;ee2Q}hbXGmpnu4n_}ssl-Q;>t}n;aFxd9N4;`@x8f_n>meWIR`>FWLfl+g
zk`7Kj-znE}*K=Ks$QELUx#4DYN>B7hN!K{xc#XWe`OHmFXzqE3IkP=eV?9!|iR|LR
z^vRgcN_)G4B<pyLN$>6y@kaTmX1*o`RUH)}ICBYR%k8|J;hI-OD;`ZD{n+bQxwO10
z36P;R7ZGPFs)uQQ=8`R!4Hr?(JpEG-EX|St1|`pq!*+yAdJhk=E}6tZI!}+#j<5ES
z6duT_>@@ofZc**7jz<@5=ZIl}tGS;vG`oVUac#NnUHC9_(z-P~ox^`jL7&Pd7ghRk
zq`9X2Ig(F<x+C4KKVn6xR4n}5@G`5X;|EJ_WYP%Q-?r$rKWtfXbRMszy;w{{`?<mQ
z;FpAlDZ<&#`aA~?MttP2*va`oN=`g*x0e<V-xIzb7a~c$D?9U3%?5h3%79)(nefc&
zx*OX%HSU8v0aZyOA8fry0lvKa_iKA(tkJYl-28-(PwqUPQj0BRK^tngQ|+rAzd;)~
zXLlhk=Td);WNoL*uNean4n>?(EGOoUsX7T!quAfXYY~Xor;b<CXS--Iap8uGE;TEe
z3uBN|r^$S<Mtm$JbOFR1PQo8(I8_tGdo{!JG{Aq^mJ<rA?Pes9(MKWnZ(&GlT@qKf
zeO@jbAa>|d=H#GbX3@H44@5qFGyOh%jcCd5dx7M@=1_#r@>oRaPMC7fFXBf(Zm9i4
z#V{c_T>^P;Evu8ys+4AmETDYE?4EVes0FdP3+iIuDDk}ZS5KkPtMlr^&U`d{W<%aw
zs#mWLXh7rC$@Ketxwz=a=&r9X=$E@!PF+T}?Ef-L2A8w40O#XM<zKx-fx*3=z8#Yk
zlEQ+5f=ix$#9j-dRI5u0mFG{VS;W0M1b_&)a@vihGahqVIL>LHzIDV!U!aalYp_Hc
zzYZRGr%$CFgeLywD@%Fp$$b6h!1TyK&?&<7(aC#YydWTLz?@&yxJ}>Qe8|sVad5u)
zE|2}Jb604od10z&43Ec|pwVHr{=lWtxJxEY7OKxm;Qq~Pme}RFmsZ%x9alq@E!<-v
z92Z+IBFJ2AtTOR63w8MVM`evxpX&a#H3Kt5q!$|jwp^(<>g%^#3@IE&^o@D>ZFk@u
zho$ubV+&L5#i{}G*#j;Gh6ZG>{p=_7xiJc0#TT)pXzmL|-^l>MmzI+7+1-_j?^YGp
z1q^r|T@*R$)<^=M)XZo~20Z=$<Uhxu)$|4-qvf6~cdW;AX3fsC4<B==W7Ud^mki^<
zBJZEMJau^?7uFU1QpnYTCCpEev%ZcA{t5=bE^kJ`H|M^V{Pp5T2{jcs>S~C=aLVB?
zZhR8u03ZLXSYr(T(^fof;Uz+RpPQakzPN-Geg2vwXY*K@uU2oqofME~%k_&2E?sYz
zsyujb?qp2D3_@1VJGtOVDd!%fIeKdJ5hh>p&Toe<ykChK1QiuWnJb2|)^~Huf8xdW
zNkTud){2N@5MztTRaGeguOHq%1v{t6dzfRtce4Igg6UaUWNb<hi+Ort$dCRg$<HpR
z@cFi+y%H_IH2`x|u-!qV4hbI_pZ4Nn4E!B>r@xN;Zer_#%0Uqu!sGU3B1g>ITLh2M
zjnzYp{E5H|fvlj9F*%WJP|jmRne}%<{M6yAWp^;Nn$<eB3$|#A#APGtl|k|JwPm%E
z*6qVmJ(%iXA|HZe^vjFtXR6-#+>T^Vp5%60?yn3ImgtPtu}LTL!##k6*L{L-wmwF5
zG&(@w3$0A?B`pbzJR^M@=$2BF-0$J}2|uP`lJsIK!c+0L!`UfOeuLliJXPROo?+Xw
ze=h6rTUN09fgI89N3|AePLUcKQBje+!f^C@+CHJH?JZ<NmLgSJP@ge~oTVp~&3`k6
zf<m$lW?gX}cKS1pZ3$Oj_ozMol_W-E<=2U_?Qu4V@{T1MYV>h|s4z5!R+u%*pY=R@
zwrS3T!T8@B=^fi=7;zW_U!=ODSj>=v#h0gYU4PwMBNTmi!0V?1JP^-uK!pN%Tgoaw
z^FwWYptqu{F>PUHW6rZ?K}@FFo>g_mq@O{xzg&nGlb@m|9!lTb`V}Uf6XfSdH=7fJ
zS(9(;yQQSgV|hl2_vEy*;wUYbI+AVaKOysxgd|Gt6*9fspa7#)ybj?y*^6f-E)X93
z{JAs9b&-tpZ30s>4{l6nXqSPLeBBmfC*koA0adT8q8E~JjN+dqAPez3FT^l8I8h{{
z4-V^iQ>Lr~_0sn;YO_(r)u_d!9*w93>B|znPp?4GVZ0ns6ZeVDq5(a<Au%(+%+J)8
z4E#2v0*43Pf<7VlIefEVpWq$KV^Ux&tk8J=G=07@@Y~dfu6ZWRRH1+tj+t5$KF`K|
z9AHDxt0FYunT&kXGpd)cQ#-I<e0_A=YOU*<M0jlG_ph*xbiZ}4OHZTD*%w?c)+B>_
zmY)=btQcS7pm)yA?i)>vbC616B$Z6KwY@-_7nk__bf~cc<NnK|y_y|f?m~-;hWi<}
zI0l#4)sv<hhY{wqOBm7W{%mKd4W_f<q4<gY&Vi7o>&oMB$W&PSLYVSlW{g8ZzhQX*
z^2T0w8Z}#%0H1?{ed$GK^U)T<{;-Zk`>sva*cJdFqrtFh*kKL&#q?)k;o9>O2Ae*=
zW&rW!Cgaa@wCpLpa9;3RR);jIaa0)Wz6H_3x2WjZr`V-Z#Bz8vy`%mRJl-p0eU+T5
z@|0@L3mOC`3sD7Sjh<t)Ig!uru<R@w!V-KbSi#;O&_kKSd@iJqM^H_QB|rZqgg~tM
zu?XjN!!$C9Cr&x-u^u72<r5IK>-dNZ>11kBWKUf9`f%yK75<1Ui^<J=``l{KGkWmM
zk=Yi(cI&j78Ty-(HFcMr-M!A6&P-Y1qsx={F-d)C2su?s=g$`m)NdLY`1AA24KH0+
zcc+EUBGHlY*rV61n#2~;i0qvlFo@EB{RoOl^f<(R*G)Z|ocS7yh^u3Li2s%6zHAbK
zDIgV3pQ=jirw?@Gu_Q7&PyvP&HVM<Y%zXA08=Kkj=+h_B<$+7N(dh+Qu{}Zh#ychW
z2!eq8{1jfB(1G-#)ullT)(IupW|bZ)GFh!0ypeR`U?PhlCZ4{Kf;~mVS_qCc?>%YK
z(F%;${ocY?T-2_!q29Q<>S(WED2oHHW^*Q4vadfg7r@kn+X*0;JKr)_pVH_00Evpp
zB(<G{l#nu0p^}tLwnsNNN*I_hVdf4{ykbj@SaeRkoo#NLnDM4$4Q<WpV$vH_XSPFl
zbr1q5HFzg7EC207dT}2Db}Tq=B~ni{x)UZP<-aFxy0;|D%&fVKn+5Tf_r4?RM@y>b
z1xpG4Ijfd?Sy~I}3!_w)u*C&iLY-xFP9oGQ^O+W0GXdDI43VFeRltOo(W8)wnW7VM
zHQP#~vLY$)(^1k()<Y`OF>#0o>73>RC^>ycF-2+Jww#|lfveqZ5-4{##x4AO#c$9_
z{Q=AG>rZ~^dTpEyEhv(g5YAI@c$l0Zro2Czx`U`R5xJUC(|vaU(_d03+vX$=AXF(K
zH&ACUr?uN`EGboD9g$ZJprl6*%g_I`OD~+ofiFQE8zko{^TKRTt-r_O88p?>rCgC+
z!S&nMwg@M?1saZMZu%F~-?AF3VjIqzLFQ^}6hY}I`20Q~-xfN}ezb7^y!jDkmL~^M
z$(YO?!mPCe*9UCB(Sb{V%ZFu(>>q2dIW=T<f5viH11#r7<=~g;&VRkA*;44&Om6at
zia*?uM~m+gU0ivuLuWTLcRiCJcmhIto>`W1+VUOG=jO7BLXylv7_~JBeJVdGZ%)Iv
zd#h{6{N0%5SX<IHj-9RAW6j*;H1pVj-^8!KAHe2Q&U?eM!}~HX-_c!?n78LvwDvB4
zS3{Bw)xM~VVN&V0XP^0eeck!hk~_3}Ifq(Jk;NgP_w|K7+E{vhT<9C05$HTI`>lb0
zSbzP$_5$D|EG~e@uBV%-ns6(19uA$y@R_kH+`F*3WIaw#^7&p^yH7{(qToa0bN;G)
z!+b|v8~Zi-LDxfgU4-m!d3`)RBN2`(dPU}B`?n>CLxFlbMH}7^2zgYaVejx00L_O6
zO&0~lXRgZB8brH*b-H0GW)x%*4Zxfh--*|U4Idyzk6w*VjJ~sTr$eb6Z#bGt-{i1H
zoc0OGvZ+@GeZV%1Itrj8gvcG#xjHXQb55X-lfbV)z8EAaA(lHfVzHlDkd<obipz%$
znd?rsY)F~uwFQ}`<d!>9L~urBZB%eH*_q{Zhla9xi#PUmkc_k#%$^f^IkH&c(xV3D
zB?f&-?!`zdiz85nJioXei&#2@zPwObLnV?-FI0byw!5K!$dvTQ4DsjZVK5?=doS0i
zQd2I}`qZ>P`7~T<o{?b%0xM?$>AGFtmB-Yzy7SK$J966%4Q_kU5K&QJ92E?dRIiY7
zvyyR1>LP~Xo5iaeL(={f(7fS}?#_`@wmv2yS<5uDT<<pA-x2(X#roI_2jq|de0K>a
zORcsId!>S3)BoyTnD%;j_pVT=9go)(qG&+LdwV_0S!(x!xSBBCKk4mr)V+sC)*MF}
z;oT?O2}J{gz2#|IIX2jrw>BmTN%KtUnlb&P2yj|a=x!m^T=f<mt?2|lvDWACV}m9?
z^wwa(`BFtyElE)E($kdFc?C~x(!KW?!kP6poZN~L>&juxbdwFrb(LjKLw4Ybx7$uC
zk+JF9-d_Z)p<d)&rIwu-IJ|d{WKJu8<iD3&>+GH`v!T1La_=EY)A+(g8krwM=~f5+
z6F`wnm|N40M&`;2*M0A{abbbZ__dWTYa$+p0XNu7d$BY3GaQg+OrVZKm&S|VN^y3^
zlk5|;p78?w*936l;iAzL4a8^<gIjLo$tx+&E-&=0%eQqSFfDjP|I29hYRGwWO%xfQ
z<*)h!$iCwI>r~_DJupc?_A3|1KuJLx`hq^qN3+DJXGPTbk7@o?oe6nx<~I`)OWld@
z@#K;iXbY|gX3C!4S@p?jV9z+Go3%iC@#jIkp~j9-72IVz<9q%%dd+w0!en+<4bP;k
z5!uESX;m5mzqAN~+oyhd(9!2&ruQDj?95d8-PSuEt-dH(v0{Iy-W*5)9{35=Co)+G
zKJ3WmpQVwy>(Uc+!q!$Np0F~($s;17k$HS32^BKjq1#QFQ<C_DKWz*krqbEKSQjB@
zg@hnb?C`C*rwtorGAbZdqk|B^!NQTJV(?>{lU*a&Jb81@q2YB(XACofD+f)RVesV)
zcmu7~93Y9raw`u3Y@I@-Ew|vi55O~H3^Bu(ageZz3c~aov??8Bc9x4?$qp3B+{4e<
z0rBRo<6GL>EHpMw^@y^F)UFeF{W?Nl-5q?(86t*NtPCd2%p?e6gT%nQWW0^uKKtfk
zhfpM`2+H%7vUg`;S>J!AiR^p&RW?M4?0W<;{p#<yHUbI%v3nw|p{{LAnIhZR$I18A
zm6!q*=$!?nx1Hl(&ACGhV<dU(9&uHOVdCLSyJ`@MEAM36ZZLsORMXAIa6#?Y(X7ld
zJ)X7RK2}zl-?PIH_{)&%9hvVkyb`?OfYr~J6fJio!I4T)MD*#lbixpWQ8Thy-!JF$
zeYN6yubdU@Ze>nt;l}O~L$bybUUpx^@L^7KbcjG=&rc62uyj~nb;zrlW5m8N*c+Q?
zi^uupZ4QbE*f7y+<kTcIc4&G+C2*C-lhAMHPu7EBpcXD)YD+ESr&xFo<553an7;^k
z8VTxsE-_xWAx+;JbNr^f@{k+MpRAV(>VwKvx_ZDs@9Hsw;hx1gIzFa2D2{#i^bEod
z!5tl+ZcSR-kK%)J=7Qv>eawnJE2Ao@ZwTUV2<RHqZYIs})+wG0m2Bs(?a>XK(?>Ox
zaNt<A)r&(XQpFUnIeiBm3C#pl+X#pF#biYBL$q9iUDKeCeOe>j!Ap6Lo$}yaG%gV_
z>!BEi$Wdy{fUIN&e$BzQT;kTXpRW!oq8+#al@VXgxSg6&HOVia1OzfazZBr4Th$H`
zmgp;|DG9^=L@7{xnwY?;D!%?|LCBxmSQPu+(>1EXNL@nLg~a3jH;vofvMXe|Ir&!t
zC;jE_K4M5H8U34Qii#@Lx^HL}ory$0g7+C;v(P6?x!JxYnqaT;Eb|9cZfx2TcKeKh
z@ivo&5dr>C@1tVuP+Vik?Ap7S=n*uPTsV$<d*~1<V4e5hG7_W>0Vv~fI1}tnu%}%g
zYwS#yqo-;P{X#N2PM)?yL@u{BK4<6GI-_S>GiW&3B$4RZ6qi@ka(VlP_epfe0Sgt=
zbv_|t>F24MKjafdpM`Cf(bH<K4K=T!<m+DqS)E-S57uv*M=dQV>EHMr(>KTUr+C$9
z(4+3p_vDf^$hfM}q%*k2<Hu{thz$z5a5oqsl+%uR4u6oQVv#vz3r*$?8`Tq6Iq2{K
zuZ6xO#vbVJE1@*nl)&ngYHf9QYueEu*fyOiw`3b*X!3qYKlz||=lqvsG>dmU-@9K(
zNJzp~wk=;#(54Irz6Q~PD>jqdnb&!U7oZ%|)isOr%w=d2E1={=gp)^WNpi0l=vSg<
zi(!70>Cto4K51v>(}4@x+P|}VLit?Z&TD9xYuw{M3pjhILmY{|z+==!c4-84aZw{z
zo_h8Yy=z>vA4Rm?oBKmcQ7}%|bw9Uc5LH2SQ-42B1|;d<+9&)qtw?#_`nP5cX;1Xy
zQ&3WRQA9aulb9(LW#_L@BxUOP@t$9h`-;C~-1xK2m?UisnV*MD)rS-%bJ*@|q8Anw
zZJxN0Wo6>?8`8$)rb*eF5=2wH`U?_cW^x(i8ZO?_#5Ht#@zn>0hr#Nct1-~AMRjeT
zJFtykxSe*w3%9pz#i#lV%!rBIn`6W%d}h4IS8}gGR#e32ccG2fbVp$-vti|6D_@U^
z1^2tCCM7{(<G5VcDO(rG18(uf;9p)8-}(MX_)K4OAum6AEgzZQZA2r`HZ##%H=fSu
zI=Gg?v}OJm`;>fOpAx!HGxA1QaX!g=)<7cqd=}&)8Y^S!8e&L=W;!6OaS0#Dt~(XV
zvQqQsMQe6sZ~Dbjb{z?xza6HC*o`nB&L~r2|5$#=_~R|8Ju{gt(<RUvx?QF7q)^(W
zWf*EPfuajwZHSVgq;;NTOmGT6_&&TJNrDS)zJ7A^MEc_QM*Nst1N`(ia-#|3$O{2`
zF}@g*#uTt55_!sv6@lBiyVjycXyd?*7>cTMMp%szlk80)xjIPPeq^Tk&1DGb&r$CO
zMQq7v<JUh{e+BS-;ML45k4d=fm3e1GdBlc&mCBQ1pv)_7X2OTCI4ym4nHAV0vwj_Y
z%H6+=)089ZBt_k7vkaDZUQa{uKE-B_+$K~W;7H@J@ZSy*4MuYfeW)mTs6%iQ{$D(O
z1yt1C7Ox_b(j^U&A|YMUB_QBXij+u-gmi<1(ycTODJ{|h0)nJ;mw>c%cfHN`-hFqm
z*8SF!nEB5+`}}I}V;zvw28vpy<Dk(CrBw{Tr0rU<edPNVR{R_(&t34fw1O!=_Ph_Z
zX^~*WM_vvF?#)(k+1qy_UWB|U87?RMOe%8{Va#g7bvY}Qax{R0D*L=IE25X#Op>Rw
zI4QEAEP3^UX+K%uPWNRC<!f<-e_=ei!(?^XXF+QJ2HGU=!9ar4*sc!}b;{kBgjnL!
zEq&GI1hL6a-bxF#J=;o$61-h+cPH6{6y=IL__bH{d~{B5Sg;HcAx$H&@a3No9Kh_<
z9T)ItWaCWP(~X3vrj8T**#iO;(g230{Pv~R6>>hNOLAD&>s}O`32yAg3-6feG8LSX
zlCkFRoN@hBLCY~a_hvbIge&>rfkawu0g>`!)+&izVjcev>D=gOb~{dcxtv_aCmGKW
zS|q+mEm=n@Lk+FMqHYTjD}j+>y$>0z0C9KMCm(=5-DEznzvs65*{$$o+Y3Nu9;=vC
z!%hE0{v))Ih$TCdD;w!w3<=!JY|;!FeL!h<PfA@7#P5M0Ue^4(^@=EVCsAm_6UC1m
zbnB?44=<D=$)ZH9h-U&TvJ)rru`U(0faE%k6&4y+hYc&wNF#{Z>5al9%+cx;W15>^
zFYhg%U~?xu;TzH<ij8WnAyFQ$HDxGe=RIiZ{JzH3OJIR1&wH6dzJN>F5_{B@nO~R?
zIqexPO5^omT0TM|G|<2;HYoQlpTB%q3nAg}+G25q{{CN9dIto{`T}I#r=Mv1psKP8
zeLO?DC-$3Pq9HLgdKsX~E?;?)0ke;w`J;SYQ)3u|g9m$-d-uoy9a`k(F*DN#w{VHc
z8C&x+`|wtY?Gs-sq{tnOo@})%y-WMH1EF>2>-wCls1c;{WY(|XtNH&eUUY1nzKPHD
zLK}qN+3rpIEyFYkP+{H<Uf?;*@(UXAon#8H{TR<W=ro(vz|+vMi!rtR(WD<KrluCd
z4g-g({~T^>fJM{)r1qcvX0^LO+a4Ywp$d7riUs;3!Exh6QnKrM59Rp5qWdEoL2-b{
zO9f5^cFryiY#cZTKeo5MtaPd<0;g1MlO=RJmX)U$7Uj13xI_alYGHQFiTU<@E)Jv1
zSReWdHN&2UW1cW(BIuHTwDXhpczr(d-|Bi+6@P(*S%<FeL!a5?I}{?<o|vy;(st0}
zuQi+USh&k}FMBzg817XchZF<{g-L5X(+H33H<`<eH#GiTPCjO(4X<lM-~rO-2MV0N
z@u{5|c-r`+G`_t<dz^nOXAHWb06sO)R+4igzbLgAqNYzzXZN%v|D_Nv1*(CL@GaMU
zyraJH@y2JAf^S>1%KlR{nW71VpuRjNkAMHAqL9X;3`b?H!+nB{qMv*}E39+wO^obn
z09904MIr7RIfkS!MEu}jWGGT2{TUT4_T_Kg;!2i0EbI^a;l{=sn)o@)mznUxq13#c
z+qTG*#W_mYK9U<nWwHY;AMfK_CL7y(<Yvp-AwE9&lhdr5`=q>dpS(2!@2xpmnAAm-
z2MZsY=s!%l2DggR*EX@8EE>f#1;L&O8@_Bqnnt5CfXedIuDBotp2wojzUB_AyLe<$
z7gjc<y3_jTk~)UnsQh?-etlg&Q;_1W>o{tudAQj^w+3+QeUm$U(GSb<DBuhjU0JIA
zcfP-1z!$MTZhTkFmg17$cYlHGC8Lq6A)i4NI>@ylA+?>pt;78)<0F}ly0uV*Ri4=J
zEOUKNRGzL)PJf0BmOo%4_AaH=LIn4ynvL<B8*d-|QZVo1U0>x5UXOp5pHFpW#br}V
z<cplzwj&A-3Q5oT+xHOk?LNoJjq@?w^)bJ;4fz;GX4TZ-%t_V2Zl(XeLh8!=dA~eS
znEy6;U(DcLQPFz<4Hl<3cc1k1Mej3q@C<U+qd;Xsk*Jjny{#D6@95;129i&T)3ntW
zLwlxYGw!tM1c-eTU}JoiR1__cO`Gm;iFOiUR!#1xjyGt5@}~&!Pwa#|{0_Mn8igDJ
z+@W2sy0hP9UCI23Ze`58wj(AspT*u&ntyHq{j+6Pu6XX-Bv5!g$Bpj&PU<JNpt_IF
zoZaw^dwIuOo=|-de!algON_`i84&(Qr%x16I2w=JOS=*Wxn4FOmp#?`l$ty;IPynU
zO(ga6o6Tb}0tEN*-HVGvrvn_2@X}oCV0U35Z!*X`G-{1HT#}6Pm~^3DmydK~mNdcR
z^p#bM96^c9=(R1ccaq8v(Igl!^H%=hpq=jb0`s;tTa}CFwOe&Nr;U*v`D=uFJ1`B#
zBrVSjkJ}iJE*+%hFWez-3=?+XPC5rA;Jo_PbSmRfhvc>Q@~x6@Fc!3}0-HKA2T3|{
zgdOIS6lZY?g{o9gEK$85R!z2?qi-2ae(2qHe`JD3-qY>l`jK+WC&ByHho_$V4}m7H
zs33?*#jb=X#SK18t}wccvD$cTE7G{mZvAn*`jut2ui&t5As%{Ff1|kVS6_5y^-4r-
zaJZUeY)o9w#?e*q8=I8Cju~1x#PMWk@80a$R^QZA_q#@!lV6#Ud*?Hxb~7@OF`q<l
z=)|y!dxyzk8???l|3^gM2wZlRCGuFKx%>M<CCM<J?_0z9Z-mfihueFbKLWpPW4P}g
z3G%+$f6hl!g&tU!JjlN{CwzyNAB|Qq=D0g-F9=@8;B&*sYp;nboW|EShL0`wmaTQO
z_V5!A>j!b)-~O(ok~?6#D|0KDHYP$1_$Wk8{#kCO?YQzIE{O8RoT$Pfg0dqsumgSv
z_v~lvJ`k#Eiu7HnIKee8CmvOI$PFv)KJipR03Rq;B-G)RFFjZx`M$x4B{8n@JHZ<q
zhsq=~HWwP1!R10<zz+X7GHlu9p`ZX)J(fLCmW(lV=eeEOpUcKd!<BS;9-Eer+8PiI
zuT-NWMdagwk1Kk7o7AspEKRw^nb{TRdDx9I#=+ReO=4teJ2hgkg|~(D9QmHVzW+6Q
z=_$Es^4>{OMn*o{4AVoN1m4Z0;MTRnxo=LhcHdVlz743WD_9$ln;KF}WwNEQvBfY(
zKl&npPHNk<w!1xUT}+gk{G@5SK1-#GiJ2vMf%^i0((&^*FaBAUr$aP`q&9+Kr}$h3
zrO$DwVs_Pjjtq=8bCTGo0I>cT*Bp<@!!3wgIwOlp3sFJu?DE;B31jwgcMzsN){OfU
zCH(T?VGk~!;tKgcM|by7H6?nwcE6$`fE=F&XC<|W`IyVdRV1U{Kd`N)3?E&~{$<v(
z=-4;$=UPUFQ?10Mli8EFxXI;uov(ip?Sp9`H6o&KMW~+ZCgrj}NdMPPe`r0~C6UW&
zz%>&OzgSoIftX@UK;fVfE(RkXPl(Pl{a(4zKVhoHIf9uTPwd^%#KvFf=H{`=Fm#B7
zVB>12N2%GONl5TfZ)m?rqQJn3Y-ZuSpSa%mYlVY{IU;P`7hs1*g3Ar$3YlRCXx*|}
z#5EiG<pNbe4>M0q?lZMT*uQKe{bNH$Q9P<_D<7M<uz$xrS;(3hWN=w6Eh=YGYYvq$
zQPDhc9qE-FY21xz`PvGS@C?@zmrrE0ZT;2?y!LY@?DXmTW_%~SvnhM7%b(2i3fOa|
zCPJ7PQIUVYy#)xh!tscWB0PhY)c_Ag;yF-a&yrOczD#D7*>q?zWaps(Licrcr`GmW
zTWiI>wys-D@yJ)uK|X|$_YxIzK^Ef<a<BNDLoh`!_;=~6!>wsWWo2If!0<F%GYQ;{
zNo9Q%{zRSeKW9G~j$v5U?nxVMIxCipeAq&fDH5NCJh+!+boyrs`SQ~!q{6hX*Y$85
zWJ198Ml04b(-sh^`}`RQ(ec#OQ6>!187tII!9`aIKln&O7VGoxhOU6MBplo!bBm6U
zo#=TdwNFZH(zvh{*}CEUe<@d{!r$a(9MxZU7b5Gj{UKomK`lf@Er!Zm?vp#0@}A)M
zVFcZ29Uhl#>6thA%=@uM#?&H4;Yh}ouP=6PN4@$8)osf*UA%!xR|R{M6a9+OPDzTJ
zK1+p4vOnnpbetzHx-2)N8#OnDeJe^a{l_0)kuwO9T=hv_zGs{z!Z(^VY$vQ6*A?kI
z4!r&94xb%h0CEkB8(%rmdwOiZs-KA;-AFI!_&v(FjfdT&u;ZHd$HVW(QPoqVVMNiF
z+b;3`J~7AB#KqFxjq12{M@$uNEU%%@Xn5r>lJQcm^<(3m8qa&6mA9<zKK1zaal$`)
zUH^N;&wuzaZKmAk6CKTXDiAIKjV(7M<jdj9`wzZ)1=|jezihE~xwjpwu2mlkF3L0`
zBSc8p^iwyEQ`kk&Vw&^*;domlQLern+W<9y4-PR^riVhPnH{vgNPS~{e(}b1qhr<(
z>G2&DZFYmlmzPsR&z=p8+sJ5X@x?IictJKnt^-rt)8XdHnKJ^U*ud_P{Co>W#ks%)
zo;5=Iz=&oAX=&SsFTp%wrjM@IxrKpIx~=kxFt(5K&i>GFe?(a=r3ygcBSR0&nJ*xI
zS1vKTheW7>!~L(jw>J{x0=|(YGh#WZI8(mx3VzSi#>w@GAD@##cmSq8CB7ZbsF<7o
zTQFGh3H+J2Y}VZ>h597{MPQ}r8^|jgi-Bq96LsSg?Qb({Igq1Ef;&886PiIYY+XA5
zIV3X&f-;PX8G?1wKSwo5NPKA@gZfvf4<4D+$jbNGX>`ocuvV6P%yDa*rwr)MR*n7E
zIV_QFzsAaP)ODQwgaxNn-&))igQy6%NtY{IXUIzBB+x8RGWee3GG-0?IiY7)hs;e3
z3@i89ANiFKu^^(5frI_wP{6JOAEWF{9Mp<2|GL)>t_i$H=w(m(g-71kLa=kFug|6W
zJ7^LhGpwkLtVEfTLy`P1K{D`<;FvjX>|xVc^DEnxG|0xhCUSVqmM;}!I?!r97<*sn
z-S@`V<*8O_hfnWsRB-T-4AXTG<`I>yC~9dsb3FewlJjiN$)oRaOhAtX-93iS$Mmnq
z@{{jReN?k8HBbQU@cxT3WY4L<Zx^b}t1tp$&mY)Y2uzi5W<a7jAod<>6Xqzy^gVw3
zu30eJggl&4`EgA1(c-H8z}rF!74Y$W2}t?!GfLkyQdEl95f)GS1Hf~XzePa4(~PJ1
zOMB|c$`?=7c)*6we*|AU_-ngq(Ttd>{gST8=W}Q|Mz$+|n<sAVXF+W1BJoXo61SgU
zqzj@{82J_LL4N+g_{;2<EvicO?ei%t#rGgHnU<S-?(gG@-?%gDNvRkY<-~&`fu*H|
z(0o4))iq?avnrbLavrE=KO}@4&KT4P2uT~Of+N(LUvA56nM)3WUYYk@UlphY07{0l
zqclt`Tzc?2;E{VCoUlA{T;mt2t?h>=HvN(g49d$}fqqzT#62Sib4t%TasK#BF#%(P
zZIM1o8KkV5J@b=3;rv49U^@A{Rz6H3bZ@C^amExAH(d~3)d{-8OS+B~a?b~fO@Ga^
zSn8(VF)E&8D@xB@u|Vbr7`%P2RvD0N8PM-z*oml-kL3?<44f@*U;3RuOiZ<fkg6$f
zNK5PORRx*Dqbqz0#Tdz$prD9s+rt~DW{bNW9=5Q&7XzcBwxgMXZ8Ks`86|Inl_W-f
z9)@wgkP=~|N+lM?EG*_Pt|Po3m(-7h_X8rMv%AttYzOl56(!it8LU`EDma=SQ~tJJ
zPEk!)5nUf4Ax$odsw(rvT%11@w6rv}va9o=!40LFsEVgH^a_?oXcB8l{kOEc2&trP
z!MuL8;&DD3p$q3OREDNr3&tWfLo{4+`Kqa&Rg%sVaYh~3yVpt3?Ke=zC$m)B(=B^3
zUv#eK{c)o5a1aGL)l5LX9|vi$CN<~5VQ6gipx@x<_@{!V-fGsbF&@WYg>1koo|J)g
z2R3Kw5T>x~ez2T*U*`DnKlG0_zZDh@4^nwB90G2`uWK7!XMvO~4y05}x~bkb&O{<(
zxcdTHjn<k>U0&hhWe8SHDY_`5Q$T=CL_%!l*+KoQ7s{)Bj1ldU%Q*@fm5UgY%f(Iq
zk+-gYGb$`j(zpa(uYe|62#BBF%ue1#2#aZXsBSM3bwYe2K^o<**IIQJv<jQd2swJY
z^G$xf3LtuecZ01i8+0KjPl1qU=_TJnN;wo*UwA}K_%lH$&U5E4Ax{sv^jmdCh&E$i
z3t=A{^n8D+sm7S{L8j2I_Fqw5(e0eUMxW%Y{d$X<T9FR0uFAXEuhA2gM2rGNxuUu4
z9G%A=qN8Tyu?zd==Rb~-RqMt%aCC72eVk3yKoXFEh5D%pAvsatm(s!nmaWu4D9AuA
zu2py_aWP5#w6P83-K*K5I(xGox$r;F_|^#un)-tm08Ie-a5kK%GN<tR!yi5eO~LBa
zk*Q$Xg9tXwxgdx?jC8CR-9HJ8-RxF!QgB<=T5^iAG39np&>&EjS}q`%ff*Vk9b*cc
zF7l5v_3)x80KFdZt9w1k4*cZfw=i+B6*LUgn+M)=`BFaBQVkrPpRJad>exA#0ZR3+
ztY;Ry#*CSFjRA7<nRx|YO*TTZUxGG&;&OtHdL2e-J$+HH2(lLVVS{TW0Ri{6X_t3g
zzg1SG1&4$yTpu$3Y?=A)g=na2g+wgutf1S*HrSF4vnXG4ra&NT8OFESJ<Pd1T&$N(
z6fr?`xXv7)7>Xt#+kPF*KozH>dl;p=Srf_In<zii<$X7tnJ<*|M>8z!{@njcwe29o
zwl##zS|HPlm}%{gbjidF!DE4%JN?RD<6)rgze;EI@0NH;X=4xX@e=*ZgT)PGIaR9H
z_Vf3cLUIPl7|0l>#T4hULEVe(Nn<fTynD#0rvS@R)k;YAS#0>b$Jk!IH0^2}Y!8Bo
z)^G<9qw@15Y><-tx3O?2{fJXA>D&JE`eKM?=_1eWBL9T8(Sy+NyNYbCQWTc$`BLzM
zjwEgr#^BEs*lZ_B`M?IQ7<cg4t`u6n&VLu722x@!;Yse#DR%4nd$8DmedOkcw9US{
zno}8~VPj$z{M5hqg?`Nhe6ZjS6|=F5SRIe6R}t(p5d{U5Z?t$37laRAoZC&N!gv3I
z<U5wY;N-}%I0!Ai+v41J8f>s&llw#+qm%d!n(Yc^+KJS>hH;fuGPRY1ugi94m^pYU
zQw<_J7flu?iyhiB)iOsZw}C<pE0bP(*ij~lLAw;w|H}f1(zqp!JJ>Svx?qqnv9NYc
zs$JIW-=X6E@EMMFIb99N$x#-Mc6R!l5UDB%CIt`nvakxFc1L^FmQ+;SQzRUn*}DP5
zkho$P`!y723F^Q2<+JX8L1DY<?e{;kcw-~&u(-83`@w@26jpaO&aO5E1o0&$Dw+;P
zXwa81>E9t(uL}$4*#tgMYjf#$O>-m%si`jdJ@8T1w0b0do%$0TB8vYgtp>%Fm~1I?
z1yTY@yD|6je=GHci92B7bzO;qtRR;9$nH*LTev*pM@j??q7A{0OzY^PZjLSHYFx1W
zl2h~BzD8uCKiA;>mCPKiX1#{x%q8GC0CKy|w#y*k8u28rC?bfRt*75HL&gS@Sg4ot
znsSY6&Y5u3Vg^s|i%>%*7(xx7jz}N%Qud0eR@Gr^l7u(*MjkI{o|@pK)#v+Vq957U
zXlgA&5O}xQ-v`o(x3skSL2!uC7y#`}M#K1)hl`EEe-&S5@ocQ>4LWQSk<^G8iCrNF
z+k`+y#eGfJeGCc`5;??mc>CD!3siYoa~310wRJV`H{)>mUK5o<J4fC7oEH2TV#V&8
zlv=f+eOaHQ0axs2xsvdeB5?t0M=`pyy>NNA>eXUFT<kZe^^XJ01YFK(Sp|Jc^y!J9
z{M=^TCk#BipOG&n0;ph*gcfvme7%h%f|iYU7}(;t$*CKA&(gowL1-JAA(~fqg{D5*
za+#c+YYi=@%EmtA@vk?>X;#=_voMjM1`C!BUV8cFjq98(tHJG-f@_GPQ!F#TYX9!H
zM4kZ`g--D=DpCw6tJ#K28ufMf;rXmU%k%TqcjagnC$aOyp34K4!U(}^QP|2+u;sh{
z1sYKju&`Wy<KxzyF;$=|3;(cs@T)l4uP}BePyA_91ex}{liOo*WOw^ouN1^YV+`cy
z*B$BJPP`(F!6-)s=4d>=RvabjNNlHg-}RAAWzkhim7zBf$WEVRyV#t5+n@N5pIC&f
z1b+O-row{awgp>QMC+4>Og=tK2!KQB1kqv1Jb*hBocm3!X12+x1s-sTdrIKdAB)`g
zI!hm~a1+~VI7ESrz3-I}WV0ZQuETew=xk#U2wO4K!K}BjZS5c}+9VMWI^gFVSS7oQ
z=Cqk$2t-bXM_!u>ohv`*o#$ab`}TE_|8J+Xn_q<B^<9^ajkyLtMsAzme7KtvN-#as
zwqc*oMf~)RyFab?Vv8KR`=;G$KHKA!<PN#f=_hJahn&-12f~}Doet1*(Fbc>yM9mP
zV0gG)ggW+1Y9s`^cdic$+lPG`Fz9Tn{+!()5<Pr!an3O}1a_~5pS1MO9`AsL%QG@%
z()ysf0;2HF#byPReKZNasVV(y0f3qDk)ISpTj`rU_iI&P1P96Owj>95PhN{;#QRtr
z6om|Op;vc|2oS6x@LX8y8h>83(RkjFM?A!;^)@#<vvJ>|$3&Ih;&*&aidvG!DxfJY
zdWaSD9G{qkM3Y$RzPjvF*{JC^bo;pUSyA=l^`fFj$gAm;<k(vwZD`+niKr+(<Y)XK
zZZJeEA6kNAN;^(tbfn_JiT$pw(1t)0n2oM&b>%AqsUP>z64<5ghA}hOJqo#~t;$S$
zd%x0px?OZ$-ngKWkb<<QuXZb2InrRrwhv$VDZYGWu!{k0_hC1s6he~zopP1aAX*4E
z)-f$=p9LTERzO~^z1`~Z)29!8NLscO@Z~OFhs;FMh3Mf@;flWTHt314PA<{bA|xP}
zi{BJm87aWKsn^%&RSf~;ptPu$B2F6-jV~9JvLGZ?J!SL5fGb|<@Z@Q5jI66HqaWSV
z$&v6iojOl)wwA57<voI*5?>0-1EwacN^R9}Tjbum{;`2sbBaoe*~ZR->Z{rG7$`os
z<q%IX{UmX8;!b93Z>LGdl5#vQMs^rVA&4t&rynDvZW166jw{o#J|P*b7=Du0rrAtZ
zdbI$9EoSnt5!@xHZ?y*Te-?8qpc15hk{upbC)Sb59F6%?LO_<$|3-0hEXtuaVm|)j
z@e7>$sg#IkR`VvZip^_p%Vwuqf?^)Ol}6pPWNCZ0VLCaq=(^6SDISlG9F)<wQ_%?S
zQOclOW<1=}!iaf1FGY>dPv}$8EI^)hWYajyG}$rZ9d`T3OR|>03sV&%rzSVX8p)ms
zKagKYIY|`z>52Y(57phN_Q<^1qZ7Nyl;?ZOC8QtcORAr~|02u#vPmq6BAB8iJeGio
zp!iq!2NyC1rF)H-1m6o%sSGRPtu^B3OKP8<=+Stp;;~5)_b1}yVjX>cHGJ$AkS#m7
z9>>|trz$`6_*jodK-Iu+B#VVl7%Q^7rNilLT;PC3?~}7;X<Ojviu{|t!R?yLlxf$f
z-#-XR#f}L!Xx9}Ut22tn#}gGC+P~b<*0kdoz=*(87+#5fBV1z1HR51e=;1;C$y}-Z
zM<tF(tZ#-_&bZKthe;%!VsZ9xs#u@Som|FLv?8g%)vi|mLhpH9X~KIJZVE24;j%Z#
zKEZI`J?GT1{Z2Kiu{wWrv_r-sBhpXVOew;4#N#Z+e2I1<>w2pDXtx*)?{+HV{gKj=
z6Q&LKNu*uwzE>f|40m5pb&Usizj&{mGoQZPMo3K=nQ!f^_LbIM5W60H&8bOp;qQ3i
zFUng@s~lVnadCC|{piwIv2LC-?W!UF9<b2LW)5fUo|#e!P%dNlB0M+QM}A{zrq4!)
zQ3wV@yu;Fj&(cr30OtBWboebQ+tw2Moa<IqXXEFh<ODG%o|fAuxhCU*brVWBkdexF
z$Whzrpe*i7Q^a<kc}qkdo|HDcvg7`z>tr8X%b3TVT}|_LWwy)OW@@Qj)x0c-``+)N
zq;Zm61pes(K;1r7@_wt~#P9bm58AXh9{ucWim>;`VsX#FjPpl)6dV_}_5SS%&(BMd
z6LCF>ko#KU{iltJczG}&$DVk>l-Bo6N7;SqEm3~J5Fb>x5}ksva(i-Y^Cy}5qXrm$
zKl6+O6T}X@4wu){K2((YDHD)WDwtSN$u0Mk`Ya2WsU|+Iy1doDTAm{2mg0u=X|Akz
zAcc6U^WZ@qeb7BKW&Hek^rM{}%HpV)>37~TAMCkZ#2<xtiB`QT)8^iJ&m!b-&T3s&
z>yI)yQv9l?91n7@pf%c1?_f|Akw)q=ziarUn!{3JAR??QXn(lC{kw<k8uM5WYmuag
zIi=K87M_wPS6~%sx8;t`WFO(k;LAl%sM3mtDeXJ}#YsP;$*0WdfJ^gZe0sly+sc7p
zwdvRt5E$#W2rBt(R;-W={GUu`t^=qeMK2oPV`Ky|D%}l3yO(MF-KG!!gT%v_e+kbo
z*-Lxd;=<RS^SKE0{y3$u+rd01TCcRMKXc|}iJ&ps*Cjg1ar<M_T6fGo<9$J56CX+e
zu8d9HDgMvfUV>Yc;umjuR&XeIJUY~xTgj^lZ{J4G6mi&Pw2gF2WbW2|JC#O%9tSbz
z^kGZr{&<pjEC7+>tS{NdRpVnnj5v^9$23gKp$NA+1Nf^b$CJX;xJ%thjbUmM%vW8n
z^*G5YpvIXv-Vr<72OA%mY2fa4`2;FrSHdB8N8Z6*k#hq}IO|~iHK%58!yy_2ZSO)c
zU8lHs;-hH(2XTX?)*dyETOBiCx~L+BdOU3FdbZK!2Ws@uEvbYZ-?eSlV#6U-Ufht~
z#{a*HNQkOl%eOQZLiO|~F@daJnI<Q5wgW?@nr_7UK{_^1RG)}{&433#OSUp;&Im1?
z?VeFRwK%!gHv(j5Xl7T4c>7~{WL(Ph+Z)AGDn^^H314JuB+~`+Rp$}Cug*c3NThcl
z7+u;Iw&*Mw-6Ls2Y9~7KoX&)64=|RUQzgwupn7e4n4%)+GSwcq|8T^9kr`35zFZ*m
z%>tXu7=t_Z4|nWWHN1N@M)xYRk1SP{;@MS;BqXPoetC*|@|?b85RWHQNF?!!iVhR!
z5U!iOVNjdg^k<N<TuEU->H6>X#cZ_G&`#!XDh#}Syb&X(lp}AlZ`0F{bYLCx@;h;9
zh~yp$7+q}9(GDLeWSLJQU9c9v4jDs%&Py$`aCacvt)!X#do(EoFZs4GOTX@&b_4Rr
z?%)ho2!`}~bUj5y$dbenszk=y8Vx51kA*xK=k~hou~aCNIXL&#dG4sgB1}X?!uwX@
z#S0CYH+?aE%>KN|c(WzdW9OIZd}tiuw(IZco1feu%NC??`**@Ev@USCG4}h!&$EYY
zAu}W_*m&$$=JT`pSBF-UFamD~AJvz_kItSRiWwnlH2mkb4<;SPIoVBLiVV7^GWe4C
zsG1pc>bl9CQ*I}9YA3IDlc_|bI!pZFcuoja!bgo6q<nkkA8B{dHyacapj0rp^VcDh
zJE@yJmDj&?PtHnjhk?z%zd5aT)4X$)gm@tphSH-P4~`dV6~3s63hk#1=X*UIaVB3U
zTcD~>0Iy~xJgM($N%XnM@`GJfs`+XUTS%c&6_3i8>fC>k`dL-%%%0Ai1q$}U=9u)B
zuVAL-M2(ny8F|iBPvneu_wIX#U^T^Tjo$h@HGAGmJ|}R)402&HaFdbo5ejlt+0^ZR
zOjf!~q&U@hRba@#-uifD@LC}5vi)_ZiH`wQTXOHCuEcqT=her$BV2|d{Z_38)-7v0
zP=CK)R?hyh-}1TPJ8_4Z2=?-pnO9G5=#t+J)mmz;i^4CDBxV`Hn0eXCn1YP1$Xf!7
z>m*Ix$hYcF?-)f#V_;~;#m^pme?82d9Fu|4^Z8Lv|1A%wtbMDF=M?aWHt@t7Db*sI
z&5B-4Od1~_hDJs+;%F^q!ycumu<#$9UmEk(>!Fg6c2ne@MQ{gYJccKBVl@2(BEEYK
z$Ee}j%JbSA-xIV`Yj%)iZ96LgCGZz_PnzwzKA+OC1$IMnMpm|Mw|NZHgx^->#ubKQ
zCVg$M6ML#CQDArLdS0xX)&8V~lZYm?nsgpvw~N|^)llJ5viWz&N-K%UBC;xr-e|vt
zhtH-H2lJkDQKe)v`_t@&)`yn9O+H=0K~o<Xh-M6)sI+?C7ST<K=<p`jiNpLG^WXmy
z$VLC{dkTqz>|>H226(E0vA{_4*&dv1rwWc&zP`2P$rtXva&W@So+OxD(kt<viS;hH
zxDt(v_B>0~InN(1zj24_Di&W}O^dPXJjAQ&l(Tp)q@$8duS8fh=|uM~bocC{y)?}5
z&^P04w&m1U2IjKcfm5x1<7LM-(^N6)tI-K5UB$2azi^D=)&y@Y_s;iS>dF5R59s^$
z79=9m>})9U?uQ$_Jw>{?mD(j9Z36B`wQ)Bt1xE6f47?5%>#v+SfwMr0LrIZIoEuXy
zGd({eCAKXBp|02bLx*>)><!C2v+5VXnW$2KBiSBs7%k|_j8|A=t2I3OFhNXgq9eZ;
zZ+fXz%FfWq1J77Z$Zj9Q<&V%i2lKt}ClR7~P-)?N;!KHK)NVWz!?}4Lw}6|N<@md4
z`4E3lC^7B(Lr!L>UF3Di#t-uDJ>rOqYQ|XQd5>;4ReMx2A8II6%}iD`9Bg=Ke1|_a
zX)0mlmj_`qL7Q5yXK+y#mM-EpS<HYicIx=M$yGZ3TJFXjf*~eLra9g0StoZ4m9Q*M
znV{eR0rK1@vr<DOG5D&|$#=@psAIHc!lME(Y|j7DA99Q}r!!5KkUYER;dxhv1GVKt
z|H4uaUwu0D=}RJhK6ehgZgp;j+Zyrj-iZ&%KGnOMuGpXD{94v3mKgrgDY41_`{vZy
z*iH*qie7B!<Z3h58TW@I#crD?YqMg(?LqT7=Jn0m>hFtk{V2L^*OU%Ulp-!t{MQdN
z_*b_@{@j)B+V@$r$)IGibCkoiDY(_3)_T{HIa@>K#q_v;LZOMb_iIwElk73&!rXn*
zTeqg`lRrk_bm)J>G_S@f{`Gz&7Z7XALPWTQ*bkQIgZaFoj<kq?(NLqKE`H*5ao^er
z%n(0ewuHU~i-~C;9H`k*jue-nbbYhwm#$PcJywr`)_2nRSL}3B;iaH%7w{xknlzEk
zl-iC8L~PE44jO?&`<`?|H1<UUy>*$#t0Gnw0q#T!V0E6mQ%X<iD!iGUf@7M4%neN2
zr|^tQpXc@$UEySn*am6mFwirFQviAX|KG{a7#^2n;1j=^uO!{nl&3<>RCCba`^8%H
zEu&!IeBhyRQ6U|qUYm#bpgzbz*2GB%8tt~n`}fHmADlVcWN)vNcg4{qmA_Vt)!wL{
zJ-j7kB>>5o<4j22_fdxb+ef?XMnz;v%`aJ0ytrh!>F^XE<_=u<dgW_8ODeYv>^J$s
z36&|cxL!(0{+9Qp4NYaf5->0`<%i+AmGh^QfrCG+S-#ZsCmEwV<mDHp$p_MSxgNKS
z>Qw%vq^h(m7ZDRLCM#0ymFbP7zt53MyVR3alaEzA>fiAy-KZ>1&t*;jnsTme)i?U4
zY>X9Z>4o$KJx;289m&Qc5;vp#k#T^x?5*&6mY`)*OZj?2cXKi6x8t>y_2&&}Z?tZg
zt}}>N)Aa9P-uZDsQ=`XwBbF(s2Jo@#{%}>o*$VmsQapHOiv=Dm?o_eS_BH`yRu*FN
zr|2d<%q4iw`vsx?{9WgwWWiwca0Y*!?T^HIe;eZyzwyH7kMLrJgW$p6GoF9k+tsxA
zEp|E=lWf5HC(hk&vn4Nq_kLwLKq;UI%WG@6>xl+=3*wj_Egg7@UNGE|=QUjpg{B&*
z;pJ9c)^^eE8f1OjhJ6Mcm9;6T?*sbFRnW^tsOgPnhX9y1x`fl!A&5x54(=-)ZZsNI
z(PN>MV0Q4?&LqM=rFqYrVfejMB4*+#P5OJ(nZ@5VC*723Y%%fWR$SUk_<T{0e@!@T
z{6~Lkt%*V#PEd+e)voBPGW#vZOHa+~J+G|K;p!n%J}W=E5O~C0XI5B8k1ihpDG;J|
zcD6R?9THTSw;w}Pr{F%K5o8OO_iwb2tuSwMu1`6HG3c24>yyZ?!q%XSpkFrgp=w|7
zX$TZ$O^Bh{x+qy-d-J$NQ(H65zV;7TN0+#`wbTuUt%Sr~!*rbV(ZRG{N}5mD+2jz=
z1U_c1U1>AXo<cr&|51D|(01~RuGk+}<Ie)*WVaO-ms8VAGf<SDKD;7Ej&^p;w?Pg_
zj}xnR(YPpII)*RIeMM{Y$CebT6krmR@2gI0Q-RBE^*tHFhQ?*%eJhT~o(AwVUR15U
zW4p%_Qo|lc*ZPA$_!Ot?d8I4;NIn~^TPb?_@|C{ebe9?BJ#A~(i^XLjw|!QU?Cc?^
z>FG|jxUy}n8G|c`_aEN(*Lb0WrIl`EG{gi^8kJ;d(!z`kYUb(;iUGpO*vVrX;T<gJ
zP~W(96#Fse_eJC*)h+K)LP{i<ZO;qXguhF~=f$#=cn>Q@Q2psGixh9(7sl@Bvh1Q=
z{5IJk>O`m$m#17jy0?JMmC1^=A!@$&j6g29v1(d%PrFF*vFa@HDZ-$&y39@QF<e2V
z_UMX-BK$*n7#ix^VWN*${#U*AJhYrTv%LbF{k7u_#@P9lYKF@G^c$Er<PdM4iQ0X9
zHh*-&{UPC@!uz**xEJ63TNosy@c&lYerB#vCcQ!}?)1O#Bmu;mDi?X52Q^&vmm!=#
ze}+}57Ke|$yu;|3yAbqxSXJG@qbpLj+-dy(r2bxIvwpYcq%s|DH=zB1&%2cS5>C3R
z<39#K{q=@%Wy1sRvtz&^VwlY;#J2IB&5AX|k9)|EekhDJST8Jt)ipoWb;{t56f+>z
zXMWjLUO@+rnE5#~pQR^lM)7}Y8Lf;C3fD*k)(hfgde1NY!;$ASYpeP6#B-AXRzLS;
z=n2%^6lY2j(`?s2oa|Ec0SXEXA%qPri*NtF9ea2;>~1$Hs#MyCL8F|~LgK2<;Bmqy
zu8wzN811sZJ=4u&ebBgkKuAm>|M<BOMR?rAd1=>o7TUjUZe@>)W}<W&tYjq9vcBH{
z(1H`mD8xvN>KnTAeF{L-+&3-gH-#j$T*2U;f?FL;{_oCj*T`*OwzWAOI#G#nlX2n)
zeE}@K<O7ANK@M@Q`SFvwzE&`65NhCiB0^Ht8}aMMjd}((M`lf#uZ~St+%K)#a2Iy}
z)Kx|KoHFL8MLlE;mjzWqBo#MxTGC023C@r0em^9FWO12$wMU9d;ya!cKDe+@yRecg
zLgpOI`zgctECy2ig++JHOuzIbb-wfUhjg<`YxvSHmuXo9u_kD9I39SubPwka>DR}r
zZ)wSD@*(=UA?3Q;n*zb!sNr(qv!kWId+v$HJLZ3Qtb}|jQsG9wgZ<Zc?g}N~j!$9W
zVlIx}sZFOfS51AnYi`(IBg4W;c8yw`8Vcz+GH>pA>q$X(7nwbs3*J@r87^4Ae)5^K
z2a2>0*Xz-bGp9WqsA5|qnYe}D+s0o=+gplV+EiLT_!W4^WbzBne%&V8mtOs53G2ob
zok!erG=4JDi3d}7<u;?fH@pYGlOc^J(6^%d>0+xT3h4yIeLLlx2TyZJizXgxI^KzI
zn*9a|PVT$m3D+O|Uv*e-Q#P4D%84V}RNg*jjYjkbWwaYMuDpF{)TJDtAaDOGYVxs?
ziX1VMcejUam}!4XF}d@V%%kIDjdRLEo$8d@z&qf(teOkV)u}b4xfKY2{HH%W!q)oe
zh#1?f7nJ6&w4S`NU%VqaOqL>{`-9Kl-6cEs+q-4?$oYPAgTFHtZE-4hYQ9kB3oFOR
zPGt(-JTLlumpertp6b9c=6<aaz7I<{2Df}L1qI4+4wQEKu>kt9=B-5sw=rH1>e5MN
z^)s|)0hL8$2LC^pd@{GijDhmuB@uiz#81DHwdLNGz(6^rV}P%Qc=_TMLZT2R=8wwE
z7zs`6@TJElh2Mvs+(#sNBmP$WVB=xMgYhz_TO1-LgI962?G}IT!1B7`6Vz|jXi>TC
zLH>jp$jo(aoI9yYFRx3gF~Q&Oq|OoT7UuoedTgl%YL0p+J_1GB3eY$Xo-Yhh3%woN
z(zHd7ac?%dAD$`EPt!a&q<#o`VDd6IbXOiU{IwT~^1{Nre{k2nSB7|Tx#X~|1(=6A
z17M*NjrxVq4%-s*HTgfBWO65NV9LI5T5a9H6H~ot?RGu<Dg~Ep#Uz&{-@Jv+fPun5
z<CX&Wc<kE}nF%sXyC6H`M;M4AJDV(nCvWF;L6*e=D&Rd$?kGYHMbx3UUBP1f3kM!f
zZTMV<^x;;=Y@E@rlY)-YuFI6Eh4nFc?O3xrH5<4hFo>UtHK718Z^q%e7>@bKzW1Tk
z@Nie^36sAKb;vlOEKb9Wn$)EZTxr|^YV=rs0XPk5a7H+;PNYHz)Kc@Au1<p`6T1CT
zM(L#W`T4J-=2n(I?pjJ%b-BcH>EHfRQI4E9`HFdbdr^RagPkdt?0hXMx93`L!rr{T
zbFb2kL3pwXp{IGhm~kjf`|Dh2L1h9`uE7#f<}@#&)I*l}ySLMLr^*2X3p4#q@3cjQ
zh$iHYG6QkHIZSFt&<cl5X{*5g)2}(o(kU2$ePCGt?8WkgN7L(T-Dj=N_!92}Hugp-
z$E~UOi18^nD2KG7eBxl2ml=1OX^JD`aSD89*H<(c8FTt7Wmv8?A$tes67wTfRT{C7
z-esoyOrO5ieN9Kin*BR)w*+tebofDDer}rFTBYV>K3uy>s#@!>Z9NH=Akad_?2$}(
zN<-S#MX!c;O><WTtz_XD=Wci+=;4kJxsQxBhczGi`W|Z1L<VZKMV2_tyGPK9NyEsI
z7j<wq52}cXOQz{sC3Npu5~p;UpMAke;2@oFT5XC9B`bxOoMCLd;&KtrXFE)R_!wnM
z>z1Q-{dVDZ_!%C?4wsXU;?ekJu<EHme;IRZ;*2srDqsQlx}?F%W@<U#9mFd<x4U15
zuGgkgH6v%`i4wy|a03P-_2&GDWbd>ne_`OYM*X7Prn3bC&fB}kRUO^q-RT$Ag$6rB
zXGaTtUp2@yLCr(PaiYnkdmz0(5|fmi2W|;_?V`$OMt2o5@2`#`;f(-4t^gM4Y&{t=
zxFT6$Yl|D&kSe41>fVN^>3qCt9D5R8i1c7w5qJ;5b4O*bFJ371k_~3LDZe5jd|qXF
zTUXHPr?xdv&EPj^dJ3^i96+vP_F4+hSI`d^*U{wY8OrD{zBWX|<P-o!AX`of*k=9H
z6OZoihjoUn)zh<)BJAd;++S&I4x?4_5c<}OKc~g4W`0EboqK}$0{D@K!ek&@L2ZDC
zBCX#7Fq+l)w>ax$fv3h7tA?x(?$a0OI8g9)Wq_pu_Z89y6H=Gf5|U8M;K(6ZIoV&H
zErnBwyS{63I6(~z=qopUP;>ZC1@Io&GBP`7<Ivojy&U@bPvLF5;9#q%@1IrPi~^{U
z2jadV>XL5k>;kz=jK8$$^zR<L?_D%H8hrx`C}rF=D7L_XK-Tw5m186Rt9$QmpkVw(
z&e>Iu)jKl@Cy)03kP#T2@#Z2APX>&ON%!Bs`>795K0N)k@-d#+q5j(q1aYuhULa@)
z!?|{`xdl+4!jOmjpBA$}l9Jr(6u+18DkwrNtF>3xnE~@$5QNnym;1`FCGEzn13t*T
ztbG1-*mf-FNa7+UB*Z=ht7rDWJvc0cnf`v_!Z%Ee|78Ivlt--QkbmP%54XB!|JK);
z2-q}5iGGNkDieg0w?Jj|^JrA!aDe_~Wt7V)BTEEzwod0r?zuOl|Lb4PeeWkGBT=7R
z_LI~eH2ZPbV>02=Bky#$|N67r@y$Xltz5R>zwe%%58e%vha*z9%3u^O64!a;!+EU`
zLru(8!AFJ9kH=>2%yFeZbOKPmu0Wyb`ktD@TSHE#Y*W6!!-6yE&l+wPzPMyC{_GJ#
z0mW*&z0O6QD^px~)QXw!N!!WXy)RGPlWonre`@QiSku<;k&2z}n|4iZsdI_>Q^4lh
zfgi|lKh85xJiM$u_7vBIbMV#X012PX2q^8g)6GP9f5Lm0WdPPD9-rRTys-HmT}dsH
zE&>;FY=u#Ft0|Mt+tuc6)KwWQpEe5e!pjW^_>f^QLLKIF;)Y8*)?4H<)m`)H0V^B3
zB)>=NGVFXv=u(%}@)%zdc9CN9j})E0SJ<p08m{oSdG&i=2Q(_nYnf=&&>c5+yeUb)
zI^e?xqYCYv)(2eQEH(upDz{3A>RUgGvoMu6h>43SRbkvIPg8xQ`U-9Yny$sW*5e_y
zbLxHh$a~^@YT93XGcWP*qiHGyH@FqO`@~WNQ?UbeJ2Ib@%lLBCaAyy1X3*#FOSBn*
zu(4xTS;X8-VPRH)%GyYp<zzb1HFno+YGU}loF3>lyL0ujYW&#rga=BNUw&a>fWjvE
zht<Y51@5cl#E<cD>bjolgqbrb?Euz5@8lA{jB43iRyQ7>lVjKY5k%v4+V71JP>qlM
z@8{l+PXC=n^v><LC-8Z*i&k2_YG#Ox^%00f)?Y<Th$r(TF1Ll{D2B#8x*B^NK_l|m
z8mZ!j#A`^9tBpuSbd*L$GS_t{k#xQuuM~qLLi%7hhqmnH;iz6n2hyt}D%~YR<eM#*
zG14QXh;nrP=q&F6^hPTn-9Dq!coEiiw+56He={Fdwe;Mbmeo|NKgLq46H5Apezeyv
z_6uURo&p}?;4dgJb@E^@dv<?Rv~}1WL#!%2QJozYWEt2xrNo!R{M5E)G;mz9K2{nm
zx>=2MI5F3$-8HvNzT81~?AmN?j+9^!uAsSjzQKq3Hha6;YyER|(g4G9ZuAg+Y~k7@
z0GXSpZ-s#kuIn{~OeK#DWXK5kZ;>myYxBw?7_WCf-8*co$PjvHxm9Kg`G2rv&jX;_
zb5~T@+e1u2_ht#Aq2JbC7*s;4iTKVnu16~JW(*&uy=cYGa3o{+^f&-O)SVufk5+Pk
zFXXV=%$-zW1(`(lls%kY@m;!*jF)_g|3=m*pp*)GzLc1(vYM0JG@$uDPwM@=>V4Kn
zP3d31{=vbtK3yL^uYhO#kYXiaD*Rgz1}wU#7vC(z)xv+!7PkSp&Qd=5oRyXBaK3F+
zX{Faz!Vp+}RM}WKRvJ1~e#dfsx+FZ>Wl9y6A}~HUR|kKn==P*`8!tvG3%i}O_CDIB
z<6YunhU)KCOxu2_LYd3T#lpt?;V`xAy4xjZ5TT{hsKKyh-W{@-`)Z!(-=88;w9IH&
zuJ%g{nA#kM^0U<rT?q{d$IXnZrxpWnP)WuxLeqK~!g$rx<So78{rh^N78p6IR17Xh
zoaHY#Hbl%}JdNtwA@x%dGDwed_(PW`J_gF`14=jz&4F1bk9<wf{Yw?P<SnCUM_}k(
zd)L4JvGtx=k0g^4Aqt%mp;s|w`<(6a+x<RzEbQG5i$45MGh60^HN!(=wQpDibot?P
z$^}G7Ir>$8ILi$iuAoDNd+g{ec^}*k$%xY93jZqRK|o9;J)IX@Xi!3x^Q&<;JM{Vq
zpUI1(_(_J(HKBn0<x7VFp^6OFYmf%pnzSpiWsOTLbaJEBs-&;z8Ew<Ortxwc4yc2`
zSGaEKkrsaX)OEIs9p_c`?ox2Nx!dIq3o|E;6#lW`M1?V>I1*1QkW-&Ku}d!m9xrBO
zSXS2e1P;oPd!0NnjEKqdqZybPEMn%8>9uUNtBN!^8Q8in<Ppb0`#nA=msP}ZQz<zj
zH8(%sWbd>BfYmh^GSfDOmDQbnJQo`Y@CCumZF2Na4nC6gu^RHgsh+GI<35XWScB1t
z!wIK2lqC>kFjxKj_s{qv<~s2YD-L9@FtL_i@lXn;I~;w!RwqD-*+>@57+Aw$WX^#`
z8uq`z=$o^B^?DGVQ3;GFw;a9Mz+Ps)jBD>~lQZ$6g(a3HW-60x1&60&(+LMHGZ^-M
zrsZS_FSnf?pXHyAhP=iTZ9gt|9bh6MG+1tkIMMTq`4cJNmYL4G@9y<%Z%<Y2b#w{u
z*V@owpj3G6EiUK6Np3Nd{Q$Q%9I_Wi*A}HW2hv+8ag?P_?S_vi&P~1heC{~h;j#Bc
z5nw!lZKm2FxY_)_<K2EP-qhLlMdxPsn#FkDgIilvz98JayK(u-Pg2SKR#N4Lq_h%6
z1}h;H13funx!JK<{~w3v8eONJ6u&loqG(3<=PjY_BTNZFtfZ{;G?k0<)qYtf7{{>4
zff_3pRBj+nF1+K|&*R9Dt_h_Bgwr%<SQ_;Sa-PzJTdJwxZ$=nHm6Da0<Gq~1Q&cSq
z{H<-h*up{!<_|s*SsOaCH%nu56A0VP)^a@3`{j_#kp|pzd>n81H#V`X-=Z8oU*-r(
z71+1TWr@#P)n1Ny;lcCCrzPWB*dcJy0GnWRP{Oq7<LmO*Gtsj$Xr<PJXfG<PdxsJo
z@^(%X26c`)u{7(>3_Anv4+cd9%Pn4y-^ptOgw2e8v|;+Yxr+R1I`Ep2s@3r&q0>@c
z^B%x)fO4^4Pt`LNO}dy|t|vY^`C0ee&(CeALh$u;K0iNm!-2@PvCV&eiSFjTPKgRe
zGn=qsvj$%H%YzW)*f%qh8P5~)qX^HBH6kusjp#_P_x&cX+Tyx{V%mF^$uzWINZa(-
z@KEr{nihODTOQ#UYVQ^OwGzvMfgLI7#CgSz?r?N3=-$Ow+Mw0_j(PUag6MLH_RaBR
zwfZC(i|2RwaW^{`ztoF?t+F#1vvut}KQZI2AP<u_NES-e-MrgVFIEf3bUJXmNap(R
z$qi-!@z0UwU0?KbICE2QMI)yqQWljPAPI@`-AYrp$WwGr+55Q=5-8&JJ+4rZPpX7H
zPKo(TlLY3~vxf>4fc!JHv;b+#`wEdI3VD4zi32J%@hcv8|C`YNx0plg=Gi<2Ivx2@
z6Yu$VZ%P-5ZCdvO>Yr)T`uEw`Uv3vw?fv8BdYfl5t`J#&abnUc2#*v|$7{n!`^42B
zyer1=4{b=V=(f)u1V!3`qiaV04!p)|>>#hW1#p=dbWAed-JKlrljFf5(}K#~zSOF8
z042|+6C`UR^q+DV)+ETB+4o!UJv#iO9OgbtVhQQ@p(B=^!?9<%tPhN#c=K8Jhb$XV
zw?%Ly-*r?G)XX&Se7{apcfx<-W_}F^3?c)8W-xp23QxJ~`~TiL+5@1nx8Q&D3O=ST
z2etk$GAz*Ekyikglz7q(PCs`^?ln@~q`!6j9=m@p5|b_vks%-7@Okjx21TP5^xUSq
zn5m~k5cdD8fUwXFl<%dUg2ohY;y4%bS+;#J^fs`1Qs$oB#}ZB+DX~4f7PA0>H^i}q
z{5feKymQve3a80{1sur|UuyDWFnhxmlrrKa|NhoS7(|S&&5ovscz@jC66;*&63>L2
zEeoVzfxvK!PM%MTi@9;^34s;)gu|ip(^;m;&e!tz8v4wW%}ssJ(^(Jj{UXU)BUk{1
z8<sitqX1q<h@CVQK(VKm!haf^@p$WU)UH<sZ*Mwz{`dqancX^TVmO7vLf=i?&B@Sw
zfWm+5wlF{<Yz6d{DEJ1gw$eH~KcDc8770u?qX#l9=g`o)bwoso{0XmtQG_{;LOwRQ
z-?r}8Cbh0G9C~k_75!aL9AwNH$*ioT7TxJ1QZssZH*e+}flol&{8DXonG~Ykaw3ey
z(DM@v+O(9FYujyIxWB(Klmv_BIvi3p?S_YO9={!gVc_kB!fm&c>wZA_Zpwz?9$V7+
z58gt<ikQZ#JGZ6Ma<{1z@Mz&?R*<!=hP4HXbY(gPHl8kSPImxmhoOr92I^SyLFIT+
zZmnQ+sY63^03#WUhDOqi7$#gco46z{g5Y5y*E7FfZ<PJ79{8xyan=**FAgv60icj^
zI`YRG1U74VSH-MV#S>XXDR`|pFRpjTBV}?%9r&Y76T#oyc|+w)iM`zQ>SUm0zj_nG
zeb^_0kk~^oF!F^dXZKrRV&cdw+Kc3U&A-RT7dBCv#BaVpqy~!Vb;>SM{Nnpg++@PZ
zxF*O74(I=jppYy?g048*0)2ag#81NON2h*1LwZZY<?$-;v(3h88N;hjUss5Hz-@@6
z))j!*p}G!2Ua5}H8xPL?gyqk#rt&}K%0Jpgb@?NTf*?jf_5}!gsC=f;@dX|tnGOTg
z%OK$MYIX>E7$kdcd$B!*l3%2id&#@1Q**ekf`cGl@tBg_blWx(PdO%q4fXu2v-b|U
zJ4*%RaQ1d7iJ$;y%LD1IUO6=K<a>=GQ1EGYKz(kG2=lLPHW1d}tWQzp%)m^4Yk|x=
z!$tH{tMPS}-F;63F4fxb8z{$XiZQ5Q72G@*2Ew<yC#R>#hk}9fjIZ|Eh(9N7VAs&n
z@$Fq(v;_VLJ!sj(E5N@&p^AW11q2oC0D-P5^>)cF9-Rq$?>_`aq8DZuH(u?;3kqLu
z{O86zqPE-zbyBx9y&I783c!SNxnaFzWc;8Cm;V(mhzG}{B@wFceZ#J%<vm!W50kpa
ztcUg5$9Z$^v$0AJ=e=<Op)=#_->9c(Q|_Wft@YBdieO<}!|@7!+bL3LTtm+Oz0|X+
zwA38Mp^ygG$Ek&habS6ZGU2NG3GUWRV7GQS6KfC!pf^5?p`eK_GwjsbgO}5V<W%ib
zGOu4_f*59HP(?bv%#bN1Ge9w9-h!um?TOpOW2YLlts;G-KPAP<n`skiT8lcGE%`CI
z*3rOvzkW|4yF6ICd&2f_CE(+E+96I({QgQF-(s?a$PW@Jpl-lSI+*0JVy9xb8WOIS
zW<KM64c6n!N@EE5Uf?_<YM6<Hic<xm@Z@6FLkU`FF>eTF4H?>g;O&hE8PMR2Ao<Mn
zuf8%c5S%e!f{y~{R^XrCUA<qRH)&GPnh{>{We^O(;h{7|=z?egXNQ*A{1;U8z^A&v
zEHYRD1*+!6&itC++3v5kg=>%4{}bIn3zjJuRyS_`73nY<6u5nMfqMIP=I)6nF&SwT
z(&Ymz>DjkJPfvarTvj>TOoCX|+`{s<``sD{e-pBZg&qRDo!RE9ryGB2-qTm%WL82%
zA&Kvel)mEqyj+upu+cS!cMYi@{v$pO{oM>S*;>n+kV!rEUdmxVId$+>vGkYM<8@_)
zIL}IQG5&u9iV0@K&oAPczhw#*zzRZnRt~Df_GxG@bo*2%z?(eP_hA^tsZY#Nm~Mj=
zQa$kLQ{3Fq*|irod*CW$s22dxYLkWs9~NIHG!)Jc?aVu_4F`rsU+~DizYU7#b=(1B
z9yOlgwEU{6LJ_N;b_BqhcyxopB9^2<*smu3v`7Ljq2>V(54=@u#iIBKMn!0x{@vM`
z`+%PViuf^a3Kx5pd|lY-HOzsI@T553-;4I@uvM;?nO(3VB#Kv8fy0L`bu(`b-G^wJ
z>f^_(x&mKihO0gBV${`jPUi8b{=_T8fDR0lv%`h7)v2o&5Ig~92rTHl%=~%vI`857
zMB@q(OKu_SFC&+y`qpv~DZYQM=3e*tIDm1LjgwW9go;Ax@$<X?hp4xJs&ebThYdtP
zM7pFKq?MEqq&ozqL8PU-6{H(!5Trpw8bu@}rMtV4&O->_I`==`?>C0S>pdLKd7fwQ
zy<)Dp<|2wsNoZQJ>3;eVv?UfT7o7x24!iNT<|<Od2L2vbTF%9!+BAGLEuQ2&u5$&M
z&_~yV5YZ2V2VYXFNmBxbiwcRNDcpR)BerZ9s;u%-Tn$nO@OSXgE>-`lP)JzA3lJy*
zwy+ud1efZw8a{h6Ygaem5JR)RL=$V-CQ~j7N!<7Anx0t$1F~VR@7uPH*6|@XoIvDa
z6@L5HW`vBoA>I|;^fIFUvi^ggRZL7*M%U}se>@l|cME2b32E!HeRSYC>Uq??zm`|M
z5pd(mg%p`S-?)+j@m64--qa;<^0-X|(VVQs@y63-tVx$`MJSg&9laKZG#tAzUJ6@+
zwTW6+)=z;ZwvnLcO*IvjN+K1SL~F~SXQT5p=Q8gi9xjdqj%wTw(&e4m5ILl%by{c^
z0(!3tBvd5^WzhU&^kV-jGOsuYobiO-qWFF<o+(s@6kRp0Kz`6XI3!GZ#id3?fZXMU
z_9sIBrbbHY3#5rOd6M#jIJ+}AXMhR_G~m`c3W%>*MKTobgXDbcD?irZXp`hgh<IwE
zHMa$^NeC1dYP?+D&WNj73PUA8EPJ3oQ~o9BVF3t^pWd~$9Aexdg2a!F&@u8Hzo!qw
z<0BaoSzS7vMJBU=5DjiPNk~b5jG2he^7A@4JothANlDqxoXJ^$4C@6fnk*?h-4mg-
zu21y*Nnl35F=!VPMo2A+W1`ZsO;wrgS^qYl{dpfc?|ri?!_SAuD;)2R-T#R$hJD7y
z7)hC1{)<fGjT0yh1pEY>$1YJcLSyj+NKEb9t5Mu3;g_TlISYNjGVV%bH(mU}#s5iZ
zaFfq)uss6@?oCf4p5WIBM6XBxTwfJy40GzRLiu{&tBH`nQ`dPXegv&o`2*m|LhCj=
zGKesJj8%KT!9YqC-}12zt8VnO8`rG&VM&qJVJ|<LE~KUM{HT6kbMkckxr`g#|9;XA
z2hmBFtnOT|tI9txI!+C$kUvu5664Du8GHfzcUZpfS<wg%33&tygi9ra>`+(SvjW3>
z28!TFA~g#Q3zQL9>FA!s8Q_r;MzXMr;KX-rv}o$}1dMO<vUGKoq1=JgzPWYXNl-#n
zTti>`Pb(m0i^pydu=MxaZQ@G47Y%DN|BT$lL|~j-0|H<x(IJBkFqh(scpuFHWDZz6
znE2sB(4+I6L39@r&N`rwI20tZuO#Rs@2riN8<B(4`938nr!A<cQ<>8SWsKy#$w!b+
zd<c;4FS@=l>Aa=j@lPS^^=rU-7bm1CX+$|5sHh-zQEo!W_^mY9?gfZdCJuE5Kz5GP
zXwqc(WH8HF$L8lhrIha$K?CN13u+bdwzRaW>%MCIl99Ndq1uOyL(*!iRpmtzPp|@y
zvZVetn7*H1HR5$;Al?B<QVq8^biQ%#8t06l9*SI0<cCATiEtIo5zB5H2LFFH|LOZH
z{xz#NS`5WLx6gGwZ}(_d(a`fmKrIPSU;hiI_6@twOqV4jm{KoBG#^1_<sKY2Sfor0
z3@q69ph#LsjooM>_Z)eMVZ|@0W2K&pTM7c{B_2!o0vDG;_J2R&@}t5ge<)pYHRiA#
z@mxl(IEV^O*BZ?G72jh4;3t0>-4={a(|(|eFRP#=rUA(zBwOH_hT{R_wtD%Eia{hD
zCTI9W&{@b7CL)_o$SNkprThbjHQd-U2t?Vbp^Up)Jo#BpMnOD(3^Zo>y*E(+L{%R{
zlJ(yHxd0SnZ*@5;I5aXZu=$!pkQZVb1$bC@PuCXhYX>%thx3>)JY9C7ist9%kA{AK
zr6evTK`g@C)5US9-v~+Mh7#Go8c!8T390|e^L|wQMFzf_*T2H?q@zmhcKAT*0j>xN
zcAqWB3h&7ib{j8kYQRn?0Lt@wDE>tRI5uQXT1sY*AftDPCW&QZ3l>~z1zdR_#4o;l
zwh)_>X9CInib?J=5Lfg)P^*|jn`8X%LOH{Plhk7H7yb8HXAZV0=Vv8jcsIlnsLto?
zXdmZ!ckj~%oq3$+3khv%J<(<(a@^XZHuXItfp{lrod4&^W={Z_NNE(Z_!&x5wX(XE
zC*|R<u&MUrl1$hsI#Eja4^Tpob*amweZY-c1LD#+Ok1Pm_s+cN#^jf}&cbB-%%54;
z2-MmMk{95&_W9zVSTbQW6v8D=@!953U9-}+N=isbsgBjB>2;Jp(17A0tau}V*s_7l
ziCY<Af-xIYyQ<9+lGpJyVCB`lKl?R8m&unRB%ZPaXSI=z<{K_2y;F^4@t*v?>#W~c
zo`xDM%(d)(1X|#0_*HF{$g0m|R$>C9ehy#nth^x7Fyts~*4viYe{`9P4*@ersn-J8
zteYjB!LL(Pr66)75O{SI<-SxH)1AT{;;RiT2oUj>r?z-PmuJFx8A9Z4@(1%AtHwWi
zPv*+fMTh56eQNupNtivbsrLF*ke_*=rmfo%EAV){-iP3(?f{Ay6}Mo@)GkSrXGV|V
zGF~45i1)#6O}@}6Lcr7aLiDCWpoxr0mS%APF{DDPw3(t%#RnZ-FK%}lDDO=I7zwHV
zN1_a`4;QwzA+&c-)t?WVtDOBqx*n+_mu(zL93U!HK44g|8!v>xGJBoNA-|q%a_vwG
zeh{v8`7Ea(OV1*R3f_9Uq`uLnD33SjcdzT=s@-{uJ)Hj!0YS8AG|<uy<6MS=Ln5+`
zAP9c?-Ny9yqy{u8SW9D(8%~_gWy*D#KZDeVJwA&G1jV5<8U}{QLtLZ;REig(Uf<ur
zfFM;6XGh}eJ;;*8P#K<>Sd%XDoZ^D{vp2da%9<AS#qpG4czw9)DFjs&j!&hdpnsmT
zzs}Xb@(~-m6ajF$x_GZ2c<$Y#(0I@J_CjDyp+_NBb~ip6+hbQU6#EMRU$0bNyioZX
z{AaRuyfu*EMd`a+axv_6t}@~tyC&RqX}f|K28VTLhek7cuHLRYwb0_a08H0*Eb{rS
zPCTY*3O*pc>Ksu%`Ox^;@U7;fM<Q-Sp!8-%)SGTPHp+(Bx25_#w6QVcOBGdzVBui(
z%(!|Ui0pn?+Le%9jP#_SqG2Izohtd&@+WGx-Lz^jfwgOS#niFSnom_**N;g~A=O)W
zd(zB~83c*NG)(RkkBp4&fn3aIG)#=wjq^s(Oel_h2VW`Q|C(+@Bc4^8GinQ1H0D*)
zK7IcblDWb%hF;sW*;HkjXAHa_cG<Ro5g8ReJujc|@n+f8=ptaBL=A1sk71JX!uTL_
z8_VUPmE8p=LGvEds)JrDc~u~rl*S}3wHpM6UW?~SvmcB^lq{4Bglr~tXi&~dswkj|
z=9^#NsZYKT)K3gz-esfmR(NnllJba@AF(6F{+3@sU6!plge4>xYu_nVAkOCH1F?q%
zYYk$?vTO$39x<y29+S8zH)V^(jr(!&iKG_aVL8=$pc!qy#+G>ROE|HVD_9$TfOmG3
z#1q~?3YrBIWkTH=5l(62M50$qWK-W$ZD0C4yHL_gcKFA(ej5{xz=_RNOTBgK`g9S;
zSZ_n3?_KEhd{3V0ela1P?72&e`S59o!Q;IH`|xzZd&bbhN2+DI&A3~!l}sO=WJeDg
z)2&gW@gR?sGf&fgvTZu}RREq6rO%~VsZQ~)>&1`btc0`wY#we4IyFZ%<hX2*{5n~X
z%gQoF%zZ8_dtYVX^Pc$$e!y0InYMNTE63V=eFjkS(10QhozgQxQCI$f^N&rN36LfA
z-xsH4=R;APsHj}0kx#iuZ<lGaeSLMsZl&oRnDsor7n4koEaL{?dwSMvb0z-*Gif;(
z>5GMFrJ{1L=QFpa>OSt)YbzR-hD;I7N~@Hj*Ut1NJ`y!u3EIt;=~{2>$4ShVQM3Xy
zw!(d>)l9%=nNxm8rz5L9osN@}xM-xgt52vigf<8;DOIf{u7D4(zfbgARxv4&i~iZ!
z7KQ#rMGsSk3frFnfB_D@<~{s=LAdDM)b+0EH+cN%duv)wYu3XfqaYpdF~6?`ANP+f
zh;{V-JG5=xu<I|HyFr1N!n-6@#RtUUsr&2we;{^bzq{?|z8LUwy}$4=X!zUhQXs3C
z(>8pTRVm3ZHA$+dbpskWb4=burzG@ddV9h)1%}8uCh-U5J`rzpPGT-TzRX~GYw5Ke
zVRN=BZ^7E{wz=Q(QYh<net&&vBVIhP=pZOi>dFVV8a%WiVN{{L?;(6isONF?y1%q*
z{Kt+?we@AX8Kq~^om1gp(5m`T`mWX{`FYqxIHj*301eis>@-}=cjC1pC=>DVi2Abo
z>N{@VTOAmDy}EyQ*SIfoTi1K$zSHi+!zw9xu<SbZ2JP*;b&Bv$KPpbc4i%faLz@>Y
z1c_=dqWczWZPo_X5^zb#6^*@xIYiEwq#x`+%jmbd4b5AasQ8c$$f{+I*I7RTeJG@f
zAFgz|`jn=0H;k}*siWy16N9chfiZ&u4F}&{tptlloSeZR&E}^-4KbDdc?%cT&cBeI
zx(jgJnqdLTbG|`Qv+nmw#=@EhU$-$f8aA5Ej2YnKl1h1r(?FZ2?w=&|m(kPg5x>&j
z)}L?TDKOyi&GNz)!>OoA9y4$YES(ln{2v#f=izAPI-fZjz#olK4VVPi0lz3XC~EIT
z%}Ab=37s@ly&q84^@q0C(5?N&>YBZu!cbkgA|Va6LY%ja6$t8m5XVMAfezZ+`ca8r
z9!tJ;ce^8b!OpocQe*Ca3o)m3;$}ak2i>Ch5X_`Z?X(QMZPgukHW3q+hVUXv6y**3
zHiYWmDB8VrV7LCS=d$uhC-$z{pz$c@m7J~%W37R1SImRze&;r9L8YxQN?#H`CaO^I
z{0F;gX^CAkW4a-%p8qZVz`pF|#6fvq*<x`nv7g_M)f3Nt^D4xC{=VDv*MCj|UFlll
zYx@6^_?JAQ=`Qz{M4#(AungJ${KS|3s(UbKceS{L7rOj>E>|F|-fNK6v%cTQs@2Ug
zuyNG=$(PYpWDlFAij)!*sI&XnOjcXCl2TK(P7dzDQO2@I4;daAR#Ddrpb_E@O`JOe
zBFDb0oRVbmz<lYH`%am(+kU6CyTyb<o}MFHl*lRWm;!~gwKESTyQlw1hL3{RYnq7T
z;};s5NSklEG1Ub}Km{5WkNNtR^0#&1P-7c>y96>z0|T`{z-Fie>E>Uk!h@+;%bVJj
z$Q;>_k&%;9HPGq$Dr*cT=4@lxUCK1VxcArDuZ#08e1c`irK3k7kr9$bBkH<IfG<J)
zFR%MYpcdbB%PyRr36Lnj3c3w<XX`xEbI4!m``}@NPFH8PbQ1SDH!ypUjMBJxK#sDQ
z9NO!`h<PoaGz3Xyuav813sl-Zz%VwXn(;htVic~xQymz9XQOhoHIxW}zILjhBd>w?
z33EaMVY-JKPSHy*1!(UBF6{Ek8weHwNk(!`b@UX0Jep~=0NJC|Z?Il8d>6NfDmY3n
zd&UqBt&G6fQ`3F*ZfZpw(gG*Jy(MM(!WYn1xYS;fBIM2R^W6=KUx&PwMMV>pS311K
z0B7LG&=IgF;!&#NC-XZCMhSn9Lx%T}RH1KY5W=1=HjEI@e+hM9Wn+W<CgY}mlJB)Y
zB1ya%1S>vQ{s=Y>!m`sCMdRjvTDH9>d-W+4<KWEVf)_en%1lShzG@wlh_1oSyI&uc
zB=aA$IB=w8|16KSVu;FyGC5>~>}kT!trdCN8lNdpXFlN_R!_<Z8hK6KXh_Ati<Qk4
zoj~{)JV0BDMh2Jn$^BoKy$<ZHhEEQfAMV<9Z50(36|No=WvpboCgKp_J%<ERQ~q8k
z6a~}Wj}<YcXnihCf)VxyhcnApZhoR+TgT-~$sC1(dMJL+#2cKx>DIq<_cWOQHe}1~
z`d2+%*MaAKSte7tFMs#dcRr8~sWyAcz?RycV{({O`IZ7tzc!gm*+9C%T?_j8vPwsi
zClHO&Y1dbr*O)f7AE$fT?W)HSj~r60b*Et%PfrM?FLa~5Zldig-Ae6Y^;l56(u0{_
z<y$MF$*i94CY>N=HxkX`Oh}^lbE!S!&$dIE=MboMr|$iZx)uWSX)#I`JIKvnZpJw0
zeS%ZYx)eltO_9ZP;COWNCS7iI_T1+tR^3aqr;+KaP3`FnR8;S(f1YmsPGNgWlFUO`
zrR`r+lYGyt4|(9UvyG++ULLgmR78>#M*|~Bdm;{Zo@EbY?YRlzudZbwV@Ct)>9%p%
zj{}xfue7`0WIUCJC@WU3wV-TC>)-Dv*r-vXkUgIn9+Q5n**67?{gv?Egjrv|ih=UG
zrbg>~yGuO}UShxH&IFHGOX2mQ{`TFap~2UA<yjC={qTj_q27te_R}|TJP1ro@K+65
z{vO9pihRm9aQ9!Eb9?AN5Ddxzh~U_kT!=@hLYbf|jR)|5_#Y;W$~?^?_a%P<R-Z)@
z;9MXRV0=OqeQg*$4!7iQlHt|s9VHY<CyOjmVN2j=(H+R6>ha!!4@HI#kP|Irs}&M+
z9&zJmF>!lb(oJp-)N+(?T*p<0V*yH8-uGz*Y)EHUC$(?|Ek|8lT3`Le%>bA$*S^d5
z8CwD;fC>#Fz}%c%*ZCoyhe=K&BhXiql1kdy*VDA*^4O!9ZpX_I(rc<<u)FiHRPN;r
zx=rWg7fHUmG+D;4RpCo?*%>ipV~Ir=!dx}Ku2!9j(g1p5;LtAofYWZ>1QLJ)N5|62
zN_d7`u2*_q2R(v*et!GF3}iI$h=_C9-Y+aF&djXG62rzuxf$^P?iK$I-0MIBRVX|r
z@i>V|TuF44E2UV8E74n7Oj*%%H~~y@RC}rDS?{ROrn#?ecH9xFeu~sUqk?f+?dhMU
zEZ&;WAK2p|4uXoOJ;r93Fb>%ruDUJo#hMuOJubY%-@ix1#S;Slh<yj=zb^)>y#c<k
zi1+O({2mlJ!H)e%^1fM=dk5^>P)S|-{o0cu5n4P-c)R4y047=8P^M5=7XYd*5krN0
zzb(otVB}xOc6un+pOyw~Zyy5syN)V!NAhOpjIP;yBzs&R?%>9gD2rcX*8g14U>#9R
zJ2R#9Uei~4_6qx&O+1!%gWH748hF3ap5K=9U)Hbm*QVM}q+KG`&2lg|g4xI4L6mw7
z7Rxe739(-U;+MVqw?%OvEQ|YVD}^r!p6$Bt@A2OG(u4A)pxt%8w{)kXhoqJfsG?U^
zD9PeKEPI6#M7;Pi>Z{@g)W0z_8n!$Tz4D^`s_hR&2Q|>fSCzn*L}nZ)KC-53-@JZU
zvN>1r=yrFuy|sbig0U^j_&}O~m8*ur!voPXvO~gIX+>uORR5Uuael8!vo{C5p(9`b
zVpAR8ujVl{H#f^F$WpTk-I7(aX9nO+=wefc)#uUz_GNBneR>kYO>HXIh!d42l%`lx
zvc2^D0?A}Dvay6Ml(M&nhR+fo%KZ475fBi-kEv?E_`urL!bl&JmJ%qH4b$tjyS9dt
z!>Ua2Nvi_oW#z2flVeGrN#W9KrwUm!EHNreK6_R?(4PQBwpqPXhu>`9KPILsnz##C
zMx+1*6*pE*rD&wml#}?(XUykUIgzZ|zC5&Sb`>_uy?H~YnpF%tG+sKx6FmUDom!hh
zQVU4*GO@8|<rHAweE%U1KNdd03gFM~R_(_a@j2pLSD}TvUwLS$K98Cs1VUsxf9*Hr
zzj&z`otWwm<x&V{WjmSQHU0`Xr9q=1g>|_^^u<NTb%W1a{C1Zz?@N|n?4mfhrIwa9
zq#`IY9SU5N4jmnX{qFCm*i`TI^2OH*)YLg9PE_OgeoUd2BNeU&)mx&V?aizd<PSv=
z@HntTY!u;o<RZXf9<-kKz#&E64WKnAX+Phwb``v^oFsknmVd45?@f{jsW9<ewX(~@
zl6^N}=$NA6DHb-9v%HZ!Cyq}K>l|83;THVRrsh*tvk*(yALv|o<Fq9KwIxvPDYW9V
zNpHZ$2H6xi35J);g_z{-0tC7AGO?ksBVYjQKh*ow-}6Y2@2E_eD7@FzfC!yTA!Ro;
z_}u!IfrdLa?qmbyZYpFbqagBN@O2JKMUuGm>?Q}5$>AA71UGM1N%=$APH??~iPQAT
zGNqu4*=$VSisbb2K3Z0zt<kr)jicsb#Y2X2;m@$|Xgh+Y7LjH)nr!H^;<2pKVV))P
z>MR-ZQIfjO#_Q+U!G9+rD^+I%&d%7kBG}-ac-VvzItf2bzx3i87g~tFkVTOS!X9D9
zvHqMczwhyDc<rUyNA<w$i9qdq20EUUg2)gV%aGA&ETZE(8faP1UeywVn&}GTfdxzP
z^Ak5NspQ~|1i#rg^J}1VZ0Yc)psT3Gq%0e&A9s{Jumfu!Ew*Eq9_`l8`5S?=p7cE?
z$7u$x*pBYPjGEhd*Tu#bT_%q3eNx8u_u0e9#c&5w!zD}^u<?mPsnD3-Mi6cuu-IvD
zF>Uo|e&LAA8vKF|w}~z31((AkVDf};Wd3a##Ic}70@--d;%^`%SA*Oc0pmT3Zw2je
z9bB8}vXOK&!dSA|i)*heQwxRkKIB%T2XJvGC4FOFe;!_GT4<fekWcn~CZt*0Ebm;L
z*|IJ$I!ES84jPUbv9?XRCP&hQeLkG0ST@tpSR_s)Kl+yZ{JY3)9Qu&ax0?4veNG83
zzT4(eT?nnNN;T7DZ}8X=8|U|AdAEOqZK*>Mm0AZw1D7srJNrcBtu^kvZ=!bE97yF-
zH|;muvqMpBS!;PO{Pkkdn8tXKM8F<nx8aCxEs~O&l_jN2-HdCyZ7TW&3+t+Bb+N(k
zQD+17v_yZPW;ty)@f;F5Faw6lk=P*FJMob&uX-)8mN0X?)){YV)K}3#8+9qK&lE0y
z>vG{Ki(F{*u;rxlddr8R5)<=L2|8Ixt8!NOw63T*@7963+5%R#Hk%4;Lo^t^7txd$
zWpcD!gpPp@g35cXwIz;=^CDiFb=M4{)@SMOX)B0%Hdws2cppBkH|QGOtp`1vTQ0Tn
zpatcDMcj<AsOVfDBu9Dg8$Z%D*SzAyZHRaVEuS^`CGF1_THneYa10Jke`v<Wmr$#9
z!kx+mAVCDk{aMpwKvP^8-~y)lpr)dvKu^T|^;@@sWo5+iCAA%Eb#?ij)BX(70KSF;
z%7&X0cjKW?NS3-B6FR+N8Vk$=3aBA^7MN4%^s7AakTFtCRqX?fph!&Qj3=<rRbT07
z^E<y0bsmXFA-*lEV680Y!2l6HGa!k-?wK(&{|N4}Y%;{8V`lS@J5oS~Em{f8k60Rw
zDsXV{<PGgObMKrjr$3Eooet+>N;>)U6E`s_90DAOADDU@f^%ELn_t^7>^5tGhK+p)
zvkVIX+*KlsZ2J>>P*}($T|9vOF-gIr{hRr*hxXx*8GQY2shDB*PvwiZdNcS#rLyVk
ztXvmAs+|eHA=xY{Dqv#I`St}D@-FEY353R6sayd;c%&4vy~!W(^}3t3U2q9W6obAA
z15G2l81^Trr4=U>X+YMr@b?91R5J<*2C~<k1IJ^}UXToaB`+%{cr_0d^SxM3<?gfB
zwN6l&SxZWN*eT-dH~9tQ8O*~;IqIE3;|{Q6F5k-i^&>cJO=)O)_?=V{EiZNQQLo;>
z=Fw$Z${z;|hh7+vd8a&_nKjKN`4e>AEvX@&zQ7tY2YXIoCIz&&zR(rge(EeqX*j?3
zs=Ej7H4o<DrjGbAKS@JAI*^<MKvz>;Rmsu#c2j01vA>_dPg^3zV`pe?9w?Nigzj62
zp1m&L%z7PA%iTL1uiAgwrtf^;2eH34Wd7vxu`CNg%m9~!RQsL|C`|$H0VhUPH<V5m
zvL`4hweC`f(&R;}oZlyHw$0SEl2MQYk1qygu-Ms5+6{8S11kt+@`n!P;l=zRduC**
z(8DK$3T^%NEuiDx1(0BV3_4AG?ulU!fc^s+xEHzH)v@*Co0WYXIo?tX*64okl=Aa=
zWMiZ0gc~4`gQ^l99+9$6Y)(7G8Hey=eadGJmQQ2Vr}m@3^Ez!!$47puJt#uC>HX|R
zp~XgJ?$CyXJoqff|E_G`5sI(*Ljwug*<wA{5FCC|YNv#%b6zyI0(ITSsQ+~kU12mG
z0etZ@(CQK+_qR~QmxLBrQR&zhz+%ECApdO(aye)8|J}?b;=Ag;!MAqj-vSL+*v<kg
zY5z<qEogtna#a&GNS%00T%3FKew$I2kP>=X^5ZUefYC3#@uiz7i7E>2zBifupJu68
z@W@yagq9n-^A}@1xv34%=6n6IC@Sv96wlo_sbym-S1Seik7gN^yviQRJL7?Hr5L{I
z#*+^@65NQLM;&ByL8F_8k0$DlbPin8ng`zg2D$!F8q)0aBnoCs1EGj<8)mTdmh5o{
zXW>Mue}}_$JZL=)^rVk#aMLvJc5t(NLRD8o`Mr}N9<=XmUbOj^t5zQo80#;<ul*K0
zj)biE!ve;7TfuCKRM}USPkcY{swijFWOq(D;YSTA?j+zHH8``1=zkA7oTFTAoO)={
zQPONtwd4#utDCaf;%40a?th-4&UxSY`T0g~@zai8v-)PE>P#l#w64%c84PsHv06n$
znfI_7|L(_LEZLvb?F%~-b=hl^!Iz`lq^rWvD}9fJcfom7&y4bc^z11~=!2Egf1Mh`
zZxR7j-uqb>%z{&_s_ZoOCXg*f&a32!w&TPnva>zuNbmAb316hZlVQXw`Ev<=xH>Ff
zci#q&AYs3pT}-B5e<x@nkjCuL9^*8P7Suxj)_W&4V5E*Tx0f_DzNBQ$&aFYufccQ+
z${B`ZZO+p_?6@A=*5Lujx43vV^y{d9CQZcQM&eM2GFZ}y{KyLTZ5Eq)Tl9~nJz}wR
zUNErB-F_bijh)}-4ZD+(66$ckk!Erc$fIlDquA}cZ1KX9C)6+P@a?N(W>7r1tTee@
zW2T&0@QvyDi3ecQJx_Rs_or_kPFyTWK|&;AB6){wrZy~i{ng}whcMar8SO>2b(UjE
z{j}W}4ZeU`WQEa4K3z^;nbnX-FS)2+`%+u}uYz|V@X38TE1l%sUI(DGIBd#lqx&Ty
z=Qnr=OrXlHOs)}!ue&_|t$1s0pEj!D3<q-Yz9><<#d&?N$ldFZr3+u04*cc<*_W9G
zg<t0b?h=*SPBG{zF^W{h6D+3-bb@zU9hHpYhBO?yPcw?C)`ad>+lq0y9l3#pDTd@D
zIs2~rZYv{LK{EL;66i=4&mA?y#LOC-?|N0bOaiKlDlqlTeytlFy5nu!=WP{I9Um6E
z3sZ==t&o8DV&IX|jNATF2^uHrqT^MI>a52(PVXmvzZuJ>rTx!CccWutItw1<?e|FU
z!VGw~%}?=88#7TL=xI~yfX}@*eW2lW(py9}ca6tebSFTB>Ya`E-7-Y3$qRg<gXYCr
zCbo{>M&Gm5ia1e|0nih)`L#xU3sXw_sqX>PuFxs5SrmEm<RCmxDYZgQ)H_3-{U+1<
z9YZbjx${d7{BGwvIyG(!m=(6QL4iL>d42u~tma991SFu-RO(DiCG`mcuEN38oYBIf
zSB(G-w!P-f=J#D>I?RuNNuMz<MJ^&sJIwK6dB1w>I>VSeQY-m;E~x~nanM_mW9qcr
zR!cPGi!cYvrK+deX8kf!9CgPH!7So&Eb!%zJdlkFexRhgpFZZUJzeZucByguw|`dv
z2FztMDz`7~mmb%alqU6bloLfLNjvG%RM<A8+7FpQlD~g;;Qt-5`tfufA}Y{fxZFEl
zg9QL<cUvlawdR3lHh;A(ALXwDOo4o5!wPLXflmd)mf>Oy=UC*uY!8IwfQ()&u;nu9
zy^mwyi{Onn$pxh)e9#1f3;L#GCu*Fh3kJO{Kd(G3O5Zz@{Nd=}^l&f?=);#hLZ!7J
zueCe$mR}b}aHEJdwlCQqE_C&k`;4?d85x6qW)<)Z3YiPx^$voLgzRnD0OreHNzQt0
zQp2u9ha4IX9uov5H(MAO9J~Jc8m}jzgGgD_kdfI~-tkwrLwZn&E!AQJ?ZaVhw!D73
z^1NQd6YEn$jC;}x_O)m8EBVNi@Vnh0B)2AHCY5jAL9qW0F|nvO@2ThoX=wkR3^LTZ
zBG*B<wBFU_J;8u$_CYKJQU33*_J*X;YX?x{!mKglez>C_IrQ4>LcJ(tcL+>3c7qJ6
zSs1yF_q`K_dob!Rp?#%IbIz}M$zcgE1vh|M09WVvy|bsv6)v+nP8rT51E28FL+?vM
z=>57jA9qSPz8bQCR6^h8<dV|`(2)-!Mpuq{j{TXTHvG$w<X3||ij*7V+w6aD-5S>W
zIzRPvHgECGO(<dGQz{w8p?g-upZ}7>a7oV;DT%e9<e>ZMhzs)F{>8K5diaQ)6W3Cn
zO2c3@8YXs3c}2X@b3)7B#Dk^m47_w4WjAe15rTXSEziHHkgHJ3(0~&{`EciTG2f8N
zE5)5v<KZ<8&c9ufDD46J(h_f4a3;87Lh;Vx$m@dJSESU6RMPY-qSzi3H-eStS`Xe#
z3Ko18mOUY-s6@gRA3^5jiYI1oboA*dsp2~<cuwrKcyM8I*=@uYG*m)pV)!a=uI)bT
z?p|PfX5XpOfBwc)h}0h|XhO=`Mu2T3g6yC9!!{W_|J@XnTf-|Fr8Z5qPY5-#;fr=+
z!?p99)P^LyedzDh_8NXOH*8xweJvOK44&WkIf5wdRT*AXS48g{s;uWi<uCIx3sM3v
z<*J3saq~ySu65dJf^SVe6As*?($&065k*u5PGeBw*()kv`ni&;IG8ON4r0{2%mI%~
zd#JOm9)Mi1*8Zs4pRjw#y<`ERnE!&J@?fu<?bd!8lEwlA0$x}>c$8;^hPjBinz`H?
zF&oy@+#6MYfJchcS^8C$=}k%E8WEczI0fKhn10uPqsQ}c{KFfEzE7qzYm};5+TCAH
zXOU5()*&H$p2#uJ$ne-GHeQAd1eb95NgzuAHv%aQel1_Ibb`sx6OzUcBx3-8l)3(W
z@b>IPK4tSSq7D)QHSK8W+V0-)H2yp4uTU5t>=;k|EU_Qo{;GdPsP(dkhJ}|x(%y&N
zR;Vm#^FR$wp04|h?$6_gDCON}AKPDDw=%{N5!u<TX#z;p2G_3ZyLS6X$l=`s19!sK
z__0|D)6>};*Ui9Y)2h8Tya(l0OM=)a9+<?<kPrRFC+w|R3eq}lZ6AISci8I~6*F7-
zFnPvvzmL8Gz+AAFZJ!OH%|}v)NQr!@Db-X(T`N+qn4!k|?k#`C@nXWzLykd}m%po>
z4YEfz5K~rNI4Czk4o&?zxl0j2P>+-jx1lxH&<lg0nnD~9=BlyV<d?Upja<Cc1&Wji
zeFT*{2vtj_iQ30OB4RI)wtnjfIGFg5J+z&}bi_G#JP;WJ2^QScBn3qife7cStvR59
z?j}p`OsEf!Y2u|qS50nKd1X@Iq$Q+jsGZwSIgQ$!5orP*!9h0ZvL;&Su{ks%^zrl2
zoSRlf5T(DFI=AiU6KSuVv!R9YE&;4vNvWh3UrLNy%X2HA@MI2H%F0*-{io9L;pe8D
zi0_3)))FofQ3Ko>E@i=q->z1#fQJ-vUNrP5i8wlZccR{dmQQ^43&ZyKAVZMjycJ$k
z7&7Q5q(na3XMDJ3*QYMunZk{SaucK<jJ?T2$0&WH6IL1UL8=!rISB<;UtX<*vZPeF
zjHMusm#?cTu?;bEAcjE8l9@ewjK8Wsze0by08OkeOV)_-uY|CB#=m>x+2jLfETGw@
zB6ggeTq|LsddDigZWpV&Cjzby)RZ_;P}Oxb#GKukbw+(;cP1b@>f~$5uYA6+@8$+u
z=T+UMA|#c8jZ}k>u@n!)W_U+J=p%I9=mjhlt4(As_%kb<aVM>7A)`7Q1U~2mMR2Ub
zk{2(-()de*q<4S3Z=b!4u756_1q5;Ao~5imV1?WtrWj@|;zrl$Be%GgX0w@XL+I4D
z(ZUkPXLAdi#pDZ{rSbZif)Uc#$$UIwGQX-;1)mcEhraa1vO9bSlV9#TZO>2!0HI-N
z%)oF(hr{^0*1%n28QDIG;sF)?7cy{5j}q&|2U^J}^v|*KO6~BW)B-uw<qsDg&7wOK
ze<h77Dx;_CZ;P;0rDSU1AysJ1A}O-IWQ8wRVqWh3ejxh4zoLA(2(GtLI*6yj{ASFM
zfYIh5%f#9ASh%M>7O<2{KHtMKocBY&w40M2-|Sho5mA~wQAcSH!b>5Q6rXciyvV^z
zAdTQ+Ar`~8_}ko;ou*eo3H%1#k|$EEH7OmD=xAv8mre<kJJ;CYcb;ba3(dJnj;%rS
z)Bq6}`tgnoDUa|s5~(?z3Qj4MCG#@Jld0R6M}yiaa+VVplY$I&F84NXu!)qvY#Z$t
zPu%d|@SWnvTzC(l&ctyusRJK*cFotwtq5s!1J0WP?JK5TvGL#_GO_vxZD(Ko>?4(W
zR)C}9AMmlrwPH8oKDZ^rxzvZm|61PCRkiHqa+#xtqGv9m{IFK~dd!9*m0ZfB&Hv*q
zEmlPYXu7otd6I|x8&v&U$<NAr;F@kUloe&4WSw@gL#3C0KkGMQw&hr11>9Wb<tvJP
z@4=*_w&GwG^a$0U-*1;7>nP*X=9EBvyqf%1hGZJ2N<VDqb-vxM0Ix6teN^x<5ieQZ
zTGelKw0q0&*pU8W@Jr1>-BX$M)JVA^xZURWuDlM2e{92gtc<Q*=;zZ;0-^Ur$rf7v
zd~(yZS@g)~T3pzWaF&xu$lTQB6201f{6&eVY`5w-Cu#$k6$zJ+EB)zL`dV|QxXG^B
z@1yWQioJ$Zws^55E3-O~L-bW3(sghp=g;bkt?7I31A60s=VjFI*EXiG@A}O;(5k=w
zi+wEpfHs&0&s4Xsx!s_V7(e{=_;?6}vKinbKG&4JSDteVL7o7pf-LsyyLer*L11Ck
z&zbP+Tm<IsFa??_8qRlcv8F%$2$+b7`;ugvLb8~I6KioR1Ks|SXU4~g;=5HE@j!Z6
zHsEiEz`Yq}^cXi{r|X`e$CT|OKY)%!BqmDVJwcSFLPt5>%kvI;p#{^oB%ZtK=EOy_
zzWxAXC{fNT4-*WjY|Z?Pf$ss3)m7*K|D~=Yt;it%QQ!Z#0Jo)0V+L}cf*HmNt4YSY
zd&>Q~86P!*6sggVJiMV-zoj30UB)Aj2i*E^J%I11dLohSvxsQ0)}erq<*qem3f0du
ze0lT-u^d6<eqI+3D6lW#_pYA(+ny>&Zv*hiDD=r^G#rwGumKpO9p4k4z{q5Xx3Qs(
zG~i>q+eS<+b9YT6@OuiFm}y`UME}@S9PC^5sitLwNfjB7{;Ey#_+3nV+aHD^FBj)S
z78_X%q#z$9u<6G6y^E7)r?`YTlB)Vx%5#p7B#9jPOO5Zb#HUOViuSH|zN9UDu{Bj4
z0;U!l?vt|NA{71cx91BEg6=*8UMyX5Xq==q3*RKeKY84L&wvM>1?DHEY3$Z1fgRHP
zRTVcy>4U2pXI@;BnLZpI*==xui7a$F2u@VlliP}(@@;88N*()314a@xocQ?#q(Y0m
zHDpr_sogtuZkIh#{hP;7i2=<9R^JWg-VC9@9JLr8X9mX|kLc0@^RK3lb;f*qf=Yzs
z!6EF{@1c<YJfQ=>s%-`1>-!7Jba~7uc;X2_N(#Ez5cDue^c5Q$0%#e4y<*h%NI6Ri
z0Nb7t;1mE>FEzpXL9#0J>xkUr0ZAkk8rej}$J_^Vidw2S!A>ePbg?sX@kETmMT4<l
zo!#douRUGuE_;sIl_KFRNg_{KbJx+4RxA%2@;p`chpu;S20pW7>4J6y0$!hEk~;!x
z?(Us+ta@dHV%U&=5Lt<MQvyG(7VFciz3*ll!xaxKs{#)hqxL&~S;0J#<)yLo{JB!q
zq~>O=nk^Ho>q>IfIAR`mQpa8QU{IX?s(H`zyUp?^Gs@iRFMh}@1fWV|OpLY_yHdz}
z_S~j1iMawj({<%Q;YWX>tJTrW4VKNJH*NWX<E7owOc*+Vb(?!Wy=20{Ici+C`%B>&
zA`hP%a`F}hd=m9)4-4CEfm8f%jdG|^+LlhXbwK#(+2yCP(y6a4jNgr6`t@MlE)~RR
zt?o6y&W4{Ovrx}!uf9aeU#&f-*Ab(vY{<rlnzMR(@Rv(RG5(lj$&yyyK@k!Q4gv!1
zb{w4+B>$InAVD%og|}qf*oIFQAr-k4qwy(lCPkg&2`Q~4extRz{AFf&oBuxdH8+V?
zT@qrlr{zdSGFKf29>82@8rvym7m*7d1KoeO9_0(SuhM6zrm9D7#anD6{*b$+**G!W
z*6Pz!9(;$j?tg#X@Af+9H=;Tl;sB5z#vki{r7gdbOoQR_=_bi!#QL9L`o|#61S~up
zVw0@vVy&ZOPXRxs*xD78TxpI$O*bjBrSyjP1(E2)w2BhW3d`kpOK3P5P$nL!+n3+S
z$ofabB$^VGbyz8esn3tDF3wNtA32ZUM4fZKqnCGI%HHFc0)VYETDo|QPo3|giZky>
zdW#X*<Uh1lxglIaDb|*hzOE?sLXao0l<KDfxy(Vd5)&(rBEQa7!7(XRb_$-b-~+g2
zoD=e{{~1u%%W?=IA=9(FF=L9EyVOz-wTS{J%UjL2HuwuEx20U38(DE0+g8(fngCdq
z)sM0%yOsE!o#{rrrlO$}+CQ648WVHI*dQly<KYFz^|~H-T$nLU;oeuMv8bk5_jo_^
zk;qUab>@uOijjYA1)s8T%C~gvHbCq|OzVfEfr}9#7RELBL8PU_p!MvdbRiR)6xY?J
z)dr9bi=B5y62CG(UVrLD*SY=JoQtn)t$jDfgu@6Yrdta^1;McwY^`8)b(pitiUf_8
z(*#0*reXa1J6I~%U5<nzGH+jVLvKy&L>1%E+U)zR!urVDPpkV!e{5823}^FYG2xKl
zNdxhZ*3%Wrp%S@jkC#@@B-+}RAfo<pa^i^|5xf|cB;qOp79RnLK7)ZLo`~~^1|lcN
z_04LrYb=j7VIqtnuby<>5{GUW46t?vKy3wia?q#E8rb}?w2%+b_B;@J>rnODf<x3*
z0kHb`rCjYQ82~J^C`zfA=#)Us{Ol@-Ng;27qs95=$c@3kkrr%<xw8jw)Q-q`WA}$l
zv+c&;4r^-cMRnBLCHxlMe#Yh0jbh_HubTCGi3<gFpkDnYVZ#ycerMq&)cZ)LkH;Nz
zOk9uyCU*XzaytxaATT>q8jtT$AiXY3pkwI$^`5=%A{&Y@&D#!{{6DN7P2?+o{&6sw
zn(RF#EYaGEXUGY`e|OI*htYCQk&4%0XYGr`!~xBB108-1!oTOc+Hr8m<>48_!|iRv
z(P9GAUQkjfl|6z(ND?4bB$+v|i3X$W@JS<?LUn~)Q~YbIv(`slslw8I{aakNxrlFv
z1c#Wk_1sHx|EmZ3iSDX5;n8IlLj=3yxvBx4v1@sPkNY@K=Zwd|XOHkO--OFo8~LBI
z`AvJW*^1k(8`*bX@mtaS-%s`jiuw*cVK7|r1ED|BTzLI=iTgKh6MLQUud;e!L&$Gp
zfi7x2(U_}{<}M7>)+nUU1vJonuIyfF{h8m-O&0tywjUWqtKGCPz{Xewn|UL7`d>@f
z5$O|5i1WXDKXza1`#gTsLsa>M566(xVJ*9l%kB+f6SWbPcs6*<iDBmj%N6H+`x%=R
zHW5d#QUR4XXqi<K_N-X18IzUkdS=TB?z1%~nGb1BRSQSb1b<~f$u6sOf;EnCH5<lB
zJlbd81~&$A2@<%QaGjPn{hogGP!QKDj6~92A4@6dc7&$%n8<Bp9+W+TONRRgu`$N!
zu=oNJOmPKqT*z-G{;t&ai8N2Xma^q6-`gi57O~-`Ucf;?O<f145%ueU0%};$ELrV!
zK@XuqCC^2qXALA<sFB@^0AJ#QbiP$t2r~+C1d|fQB=oYk{{NBt(i)KX4q5}*zG^ol
z1ML+V)L&5%_%Mmb(vVb}v&_w0VNoQevdL@W9&Z08rtv#_iA^7KG$1Uql_<9V{@(Jo
zkmA7XVdV>+Mb4?cAJfAt3F?VDN&uzkUbXPN3Eu|J3=F81J2A_aAsJ2X=yub_lZb&T
z03G+^_l-%|ey_#(YtAuG7#^YZ?}z%#$J<wrTd-Nb;lpZZ01R7dB$-An&?&2`8{J#=
zuDJypKRc#zTnO-_0mu-M^z2=-uMxvu7pf7}PrRGo_1r2M@51hKXq%tS<x5{8Hi#qd
zGQ9g7yBAZZkFQFOm$W}o2Qfdod{|REpjG7pWqevV8Z;Os?O|h&4y~GpUMF2`%)mRK
zBHsM}-eLbl2L}i=V@oh_n=1Gg;4>Ij){p0=4;X~a9xzr*vz%kozUQLWtvi5vLUa4<
zo7_t`ico~eHW&SerMG26+A0c6Wy0#rlaA?s9!vh%R(gU;?&}PtMz8{4765PT%oW<Z
z{EOxnoRw9X^yNSeKY7Lj2bN3+vaYT+Wz!)(14Ax8b-*~<cj`KYT}dUq99ZBzNWxEe
z3V-|<r4A51-eib0eIVf$$zqC4m<XtLP|9fhdyGr+P}yT`r_uxyBWtcS=Itf)jy{Nx
zlDCwSp2P?XZ*E_4SbS{QVh+()1Zx-M#G961N~@EY**X|SFUS}lZ0COai2?V%F!rk3
z6vL6@Gtj#Hwhr-Ebw9Ix?+R$%uv1ahmH@+X%?dF?9>5Lz%!R>O5>|QroasKhk2k+O
zLPrm~A@&SV$+9WyS3Ras=)=%Tu&>d*q=m5K13(%uV}M|68Um03ZHS%$u34@lN}pdl
zB6T9kD@D5sdiL7V6F-)Y%WE1`&>E$4)o@?}FnqJ&cP*}hTLP6<k1)iri65_6LaYp=
zj#2i9e94scx*<TA;J6?g*bk{BBq5ic%cQkXjD6v>KIy;xaCF{90D4O<Qydv1k;P=G
zqLnWKhhSaIL2;{1u{Q8H!&d^Lz93iIr94lHtST_PIOK_qyQ(ksB#mr%6JLMVX726E
ztqNzKITNONz1Z>7lF<4-L|Y$*UGSH6=lj1|>pr%nwvI^SyaOaWWK+R%Qyr5yElu*1
zzo^LKFi`Em*furk>A>s<Fo6D0caGedipY8n)`5&at{R!%z!30E991Mv4tYMHOPR!6
zMi^O4M%EUY|6LN4a5c2h*OV!YyW>dAt33P^Gl5Q2>eA|fGGD&`F+^Fel^DM}sdI=#
z*j+G-3HmlE0_X~1fuVRKo;PcEuMYJ-Eex=`;UCjbS4}3H+nf)!4(hhq9b1zhZ_>8)
zYwD_DXsMeWG<Ef<XlTm8y@S;C<%9QKB}suyvX?;W3s$y~hNAND*b9j3K`{0I;yGkF
zH7C~$cyK<K{x1;wuD9O2InEjyexIgYm3kgVu;p|1R7#X9%$FVlmknc&SdlzNio~GF
zG;S6p!sv#-n0IRXUfY^y7Um=<hKbVC34}ttRXO}*!R3d_hul`GLXAoSV(YA>oO%Sn
zwtlSH<EN7zk>0-|s;8Cs*i-S4AyqMYmW+xQv9&BIL9BKFREYKqdy3|F-y|1DdB4{#
z$z)5M2~Y-5)%OD&@QJsr(nR?0#B((O#+Foh%T!gfl4jr=e10hhID!RBQ8sTrSz>GN
zT<1I1Lo2$7(CB3uzt_(TyNakh54j+qGiUjhTlh8d=d=Tv=|f+}Ltp1)-smBNe;;!5
zk}tROJLY^aybEHIb_(h4|Dp4CdLXWz6*G(*dfv?P%Z4L#@VS7O`^=+C`rFPm1WYMi
zn|4oi%`pY9HaD%ls1o1uhx&{ovmezWz&K@7Uzwn^gb)CQPB0=M-=KKk+0BHbC*qh1
zQXQ2d9$mR;W_$?c*?J<L@bN&W<sOL1!l1(e5P>5IZ8XF?zi~SemjL#(2_`Q%zrlnY
zwIzsnig`vy$}M_WT&_wJ2AjQL<_yp*FF#Si9qR~eX+lJg?~)oHWRu*=0la`0iSY-0
z!>@G0l%tvBswHG;1liy36%d0E24bNo9#{jJP^J4*t`>Ue2ow2MuVYQFa7-l6n4km&
zm;WQqg<KE{x}uB>)Fi6-7K%kf+1T_U5YkKznm<8wyldai%%P$Lbn#c|y3#NyoAtz3
zHbmU=T5|`h_PwY`vEqTjgjqw0h2uYNCdmfsn!4|38nKb^F*`jB(C+VGf-*@#;g`mZ
z(jjdyQb1(g<vRaUB-^td48-o0i;)b6ofg$e6BpBit%s_G!w9;(+(S>g?|siG4L=u0
zm!gR`(=g57BA^ebB!Cc#kU-H2<N!89x$)5pkwH&Q9FP%c3SU4S1`u<LuO+|1MdM^l
zV*!Ifr-?c<Jc;v82H><nObM-elu%){<T2?`s|Q7(ET+zeVDX}{$1CTlXX_(tTztHH
zoew$69VWWya;o#?cYTn#+pK>O9vLo@&2(<S8wy;B4gts5t)X`J)!8X1puKV0QTp!V
zPn^ILf}~fNJ2R2~{aad@1Qx>u8b9oqA=!q<3zAKM!D>H7MbbNZuZ<qC-}w9W!N2Q<
zhCTsC#+}i;!dmrAKl#k{^+5GE9d-|Ix4onO?Y)kAJMektGiH1Y=gl2O`oSl%OFPo(
z*WQSk!v-yzu<2)+M~5EXi9bHjksVj@AhdP%1Es0ZfQk!$nw2{uuop2L9fu8isdoP=
z4ijpYu13@xiI@_%jbA4Dy{8)ICH7w_N3(a=&AGoRH~IM+rsyf!mjk)8gI>Ogs}<7C
zu%hw3CYr%34_-x$;|~(~E&JY2#&~em5;SfE)^R6fR+q+BhnoZkM=f4h7FyHgxIL%v
zG-mbBt}ckZ4jVmZu*CRd|D$<YBDW1kEk&bo%X5z2wu#}Eh$wX%H*sP>*I(-X#igKv
z4rbGny4_e|PxAMg|NW?|nS7^dZY#TZ_-1n2)#m-~v#DDAZ<Au2hC?s3lzY>Tm%Fw4
z{DMT^jMoG?c9WYeM1*|K{T34)cpymnXmk9bF$w7(OZ4+wm!v5EVq^(j&E>51VK*^R
zgSZGijl-LW{eQ2x7@K#!Gb&;2nzz$p=M2PFenq^1gHuU_iHZocuHXaW=cNk!_lI33
z``+<8?90{iy{5mvnDDd0XX)lR$z}I7W*(6|iBq2Q{uDa+x}I;U4QKU!e&1(muZRBB
z&{aXH4g=MxfzPrftEfTWH|_b*z~_jfM=|09G4gVtaoYkq+uBuh^a6fwpTL0i&UEN2
zUqnAF=L2QYtq86_&XUC4*HgR^$5kKR>et5Bm9gHFmNmM$-Ad0;#@I&e6P%DIMV4O5
z36$muysmN?1*KLTG!($3GmDSd_0BQ_$%WF5?nW`vVWX8Awc_%Y=t-`1+b9TLt2O^x
zxHO=r%P(iNi3(aDcO$#+?2K~zCTn6?G$IH8*8)zZ$zA`->dN`xFy)q4gr1AXfk;ve
z05yt=iqJWNy?57+E;n!Adw6UFH#Vd0lb*oS@rPaIOw7Qv(jrW}+;<T&(id?%rhJhQ
zkBx@XF_kVkd%~Ir7}^F&^5sTTgX6e8qft0DJ6FhEeUH>NO2nfu)1J?{g&}!?sH5yU
zEzoxAw!I-u?0U36ebpi=vmN)oZ%mGAzC9*7se2aYdLf1K9F1ueBkotmPcq-`T3M_G
zX8EGI<=e*HQ|kZ!1awFAYbt09wTrsl+%?#v6U)fZa;@u0dc=5Oz4f^#9g(VI!9k41
zz2e8nr2X;v=DbbX=?)ulS1BX<jC!)&u6vx#tfZot0R2PP#bCKe>YT#7_q((#%!Jaf
zocM$OS>W#LsUvs?nC&k)0(aSvue*8SOV8O8;)D!%r6RiVuQ)8jSeScQT4H#AJlx@Y
z5SilVAoBB4;7>V*U5hDQ3|BcH^S{S38yT`~V!~SI&6o)d{CK5jBAcN_2*e+v@jBkS
z(oP<?^rVg%a(#)^?!Fncc;z};FPssetdPxSjG3H$VYk5Os#aeYyjAnJ^TOltnf&)b
zwJDj;Bkh~MeR-K*6CUf=M|QNl5=T`c?Rxi7`p`O@>qEQbzEyv&r!sQkAyz44Yz;WW
zUpD=iK0sis<<{J67e_!;&Zn_r0$%GiYZ&6gn{Z%hNiG$9Vfwv$D28$^|J<9cYD9w{
zhW<em>6O?L_k`@&xeDH1&X`+!?~M7*t()mJr@XvhwF9{V2ZAVn3;6y>xO)D8hME3;
zc;?q1!4pIIi>217ySM~+^5PB{k=^Na?#G9cTUG;QWgni5jt%Gons|D>@37jF6bRh4
z7^Cd=0jowsj}uCX2I}9P+TT-Zzty8XOch2)^}pn(R+M*qyq%ll+O}|51%<qOpSaCS
zzU2juCYaJpt8RsukO=;HMOcO&!oT=2w&rRlC?qs=VG1eedV(29>EE^8v~YR>y{2!J
z-%de^^;S^(5;e30q<nD@*JbBxc*3TrxD_(DmR(;>Dh<-R30;yp?1=$fgw`a+b~Q8n
zqz^Ys{dqkXPpi}nbi!yEcoPsU63{>x`7?#3KI%iu8#1F=JN92YrqDCk{2c}t)3<Ez
z+wDhux1WvYD>v=DmB5koJG5l26cki%`G~d#)c)8WbadOQDhz*m&QF#s>~T0RZ>wHi
z8*OrER&n`P;H&nNsPY*}{)F!2&iiZWe?&#oBf1=nNbX5L*JJY>dSxID9FNGPA8wNl
zHG1zBYM(qAUI-2jZr(omKlcEKLw{}w>ve}#P?^ymaTXW+vgL_BxR2|mcV(E7@A{(1
zD=kXC{YW`sO$;b0jBg7S{PUeqtv)+AuAXEof{)>r?OPP<)*Qw~tOvj6rsaBixwk$?
zlwn%)#El&+tn}f0BJA69jG`(t6C~dIj*y|8j-Uv-pH=s+PgzUz=4_Ho*3i;MaEty1
zzn5b1o5&4$xjD?q!|w>DnewoXoL8?07ZI6$S2v0bGQvtSNsKZ7DU(We1U?{Cudce$
zUJ%A;hjeVLf5OUG#W&@x5P?0X<tsx3Tl$Iq#_JP7_S1DA_uD=gvxY^V50aAc@{Yf|
zugG)%#aiNGL<CKZvRUkflj#wC`-0V5qp6oxNuG1~miHcwSJ8z{&JfU@$ozhV`qX?W
z0we!AB%8BskMeE7w3c7PgtUT(kWERdf&Xx?9@=5x(D3-P(en{Z3U|)!y8SIYFT`XR
zFx?l*#zg6uc|O4$!0N9$iW=@D=$*`!CR4m&X1?!fxw%8*dOz9M2p?_nMLTy9RXj4g
zTkGJ$X^4=S)DgP4C;szfr|WdUY+j|Fb5})ESHfkKLC|$uNymk=<9tOP!K@Lt#b7mK
zNjBqgx>7bKLIr&e)p8JJ6@O{L!9<;)O}#dNv7LpVe$F#>cNx9h!Q%Ej%B!n+EV3F_
zxEq~55I@9Vpb%Am8&O7=mj@29+WO0*E(=?5aNXCh1tb02$lIQCG)RN2O@hgLF4$!0
zvcc9<{NW}>oc!84DPS`(RkZZQJ!d?Qn+9O7E%fSo9Fafs1m)b8YHJ@k_QWvu84s|=
z=7z)6(y<9KvYz1imfy}Oyp|*FEZn$!F(~NVG%(!%Ojh+KD8uH|ZT@li!t%$yg`N)N
zAI@?gb#$33TYg~yv9rbj=88viK~qcATmtcLKT~nsZRiVPWDP&TIB?jz(6*<`%ZZ6j
zlY8-&RfE7}Yc^h)6T8u&#=NMeEN1G;4l72Li_CL#w_|JKp)uyohNy0eI)Q_nA#;xT
z(~q^nVVt)7%od;QP;T9n3&WVGdlZ$K-OhDVute;*46li{-l=sv%{4>J#}n;URdaft
zzi1=(CR4nBAAkD~EiqcZ**vzA$i*!sAxG}|QP7Ul?^So56CeFKvNiteJ*b8D3sgLW
zDW@t}?h`s?)^G*eD`vKg#Aw*7$G8s@8?PmuCGSLze?V#erH=yTDdZnSh!)y5i823b
z<l~Ut6u)ei?<h}+mqkgY7mWB}o10sk=M_II%9uWTIPsAp=)o=H0**q{1@(-6wYiqr
zW%h9i6D9OWh?`)wWOh>D)0#C&Sz<gwJm7tsQI+uN9$$sU=4&!=Z~GD+5Fvg2|JZxW
zs4Cm7fAmpQL=mJLL<B@Sq{9MKN<jpqOF%lLLmH$(I+lQffOL0v2}qZ)=<bgHT)LmV
zpMB04=hHjRIUn9J_`q0kuj{_<YtH$bzgbR(!T`Efjblr_cw}6BTqqpS{SXP#;&=8t
zYb=y6y>Wz3P(F0|aIR5*#BkkCyBDp(YG?JklimdpkLuy(C7$`*weicmx5KnaPDVF9
zUg@k~A*(|s5!>%++G^isix7SK*p3;D()86U?n4h_O^MMWYH=_x8Dwyj6B}Li3)C3m
z6TeAG<O@Pfnz7WX9_Lw)H3F!Ow9U|Vxx?EIS#TWfaE|{7R1goHTWwfVkInQ|^4bT%
z15Jd2IGT5Mp4R7T5Afg1^E1dUEppwb6REA1Df71Ja{e?|MpvbY*+fiU`=RqJW&M{X
z2<C;+w@ZesvSsBTiau^_M?wAlrHjt?H;5$%6P$=0W^#F-U%PI*>`9Mt<zhkMyB^26
zF0ek85(-Dy6t22T<ieZy<eAK!6<a^uAx-AT>zB@yrsv3YUO-g-B5!X`HV-A!L0cEU
z$$4bvJmPa%Ufj*mqUggOn0Tw!EyJKcI#d)u1D#f6q9Ti8$jI#W8}+_rq%}<$8dEpm
z&Cz~ItcobY5?XtrV#3oYB_(LNZVWK&$7}P4!sqrY!gJ0?IO>v4WTsCpiKIZOQIa;&
z96GUUk3Jk}>mClIz?`-y<y}3Ifa2OFi_#imIH<c!@%sdmImU37v9fu{{cB&Aa7Jpd
zJy8By?l)AVNwK<1AsFZJ1wSS>ChBP21$X1vAfw>h2UcNQKST_>ml{+K0;1F!QO?E%
zY}sC9EbMqnn-$09Y8gac`&~x(bF#ng)YU6!Lqi^zg8KLq8)i_67ri&fIAk5XSCw$c
z;3;5f=1=Uxd+Z)BP?TrA_-I7AN`vrFph(+avjYe3Kp5aACb=-K__HaNxvqn#J<u<Q
z8@ogpB<yEq{_Hy|0)I8n6=n=o*$J&?s&EDAZol2^Z<Yg7eWy_F3K(<{)0E2HYs{UU
z1;r+Zx3h${pRfp;Fv(2p(v#<>%FI?Ii(8!XrRKnU<W0n5CfLxRd*&qL?CJAVOu#>d
zyl3{io+=i-Q-ZeJ@=*8mD~IX2sVy%}6IIu6r0V0RU{4w1Ia(G5@S2<C+lNcu@vZj1
zmft!&Hl2DZz5HZQ&FLT!FhfPjRdJ(7w-eY{|9D?+w@w<EJcN9u1LhY5y6Y$?n-YlI
zgU5<F0|(0JB2KrjzgdbgEBMKsI~|@q#IH^3z_G!j{cI)VN>OJ-P~_Ic4E@m7qDA_4
zyMCH!inR@aXGXr#!B4KLvSsFz&deuwn%Fi9*ggD9wQfg@iW)z>yiIlXp)B+rd6BVP
z0ZU|~3nGvv%kUaWy+bHp8TNS7jhbBJxp#yj#|)XfogVm^?Ydn~>pd#tACmWT>lb~5
z@w~$La~J~)O<6T|$t*YT7f<K7KincT)jXr2nl?15wNT#Se|LBF{;Qua3+TL)?(^Yi
zwkB%yV{H+dyd8H?=bTNVkA6R>d_byf=2^-&(<JA+8u#?6QR*e~%z#cX$$k`=xU%Kt
zq1`6-(O$4j&^fga{_@r(8PhZ&ClIifE6o4Y)MjrqN|z#c5z7Ul;D&h^SKK^)hQH$6
zE*ea0W30v5xqS5q4X|s-5KX{<7hzGCxzNVnxd2YNAEl^RX&10>$`%y{pL|s|e{$yu
zPHcjY_Sk)T?NX*CYlW?TRnwOMl!F1sRF%zB+$$pLc{3>)MC0`<Ah1TWR8`T0C@_z?
z{M&@Qw4A)qvENT=lDPMa_e+E*Es9O%2K>6U2I%F63*KI~)N}0UB#M_7HDCwj9%tVr
z>S0$IXFI1;w!bLOM*M3$GGX>CHcJOJ<4n0K%0|sGrab#p2$cXP{={sJU=WXlAV9k#
zq?tqOWQv#2`M&!oE2871TST+I?W^P2+348GF=xwo(dFjoNHx)S_Ig!82@rJJ+_7Yj
zXdIdKD?P=Y&()Z-8h~~sWZl9|G%i(yqeX`dv~;P#6SR2Eds54Gn>`LKT_X)yV-!r$
zoMEHcQ5|uMa$tf`wU5*&j9sxClxn-!Q=O~RB}tA9if*B9PWWqxDe91cN8O75di9Ba
zr=bD|SiH58X=v{@@nP1&eC$j9!q2Z{B6P*gj}fl!<|r#S*CaObT&lYUfQM4GBgTbl
zMDWPcb2aDb3$K}DylIFrdq;ZxY3|WQ?D$KPOUyerZe6xSDU2eiiM>ogi{(PD``ka&
zvM;#v9(|geRup#O#y&rMV^OU4QdOz;h(O|CxxYH~u1Jv6)IykxM6sHadBu6#c1P3}
zT_!U#FX%OixsI8_RI0Dg=mcD5c5!kEam5JHs6oHvTRgFDV@5EB4{Xm48Aseh8my*V
z{k{2$y^dsvHX*_PXtaF0)h34xbG&^dB&RogQ#a&tnEd)!t3t~WcAnc=6*$H8{(+zZ
z=hDheG!;y^sSP6>I~iq!Jto&_m}$Zu)5o>!Yx+-laCXrMa^Q{@*=>e6C~~_H!s!;5
z(?(mbI$+usKEEW6sI6ZRnYl$?UA|xe!)LzTW1XeKo_q`jg!-}_ALOaUA2<nz_6cwr
zbx`jt@O+?=Ik*uB*l@cJ%|=#1PU>mbK&Rk#@hC)f>3;279&FMhi3O)MP-qOY&=A;@
z?eM5IMtL1GGnryeFB5!iB-UQIuW>W*A$mYcTB0lS@@vl7*?FMHXuiNeO};*nx~UsM
zxAMhJRal7V<KGKY$`BXI%b%P=GnR=w-{AC*9B0nmh}K~^g$wn8bV4*?RPPkJumF^q
z!(NhWWBy0aja_x<JNF3Dks)n9vLVtK<zBrZ_#V@e$`%2-?sX-H-wCY}c)Ks-)x{bE
z#}!$ww<B*aT>tU<(f+Hz(q;0uV<-1+eC?lMZA~)k$4Zi&-T-J)aG;-M{N-yA4uEjq
z9gCLT)E@@=*pIFxSg?^zs6Sn+2r!B(ivmUT<;J4e52DW+v(`XYnR}K-n{}7kCRGq?
z^X@dsF$@=km5s|YIZkWmz5RaYy@rBg0Q1V5P*DIS5u=UYB<O;{F{i*GT%2tYL3A}M
zR6VeiLd#!sj@^|b3?1WfPf54G7Gav4mQESj+^NYEFJ>f$yE@A{Q714j_}qu00OR2F
zAofkJJ9l8tH#0GHYeAYWk&i|8<ib(m*<C4Li$Y5Gr8MWKnL_Qq$|<ObP7KDTO(;DF
z_Mpm(5G;WH_wO`m3iK*!*deXvyy+Gaw%Wa?Pv{yg)*Om^jU8QX<7(@tf7hNuQpuI@
z$Aa6%^{GEtzy+^gKmzdF=KaDSBR?eJUKZWU*|(CkTcWF7=hCkTmoMrLRPY6PP#SOz
za=j?vkBxD=rlM&grece!*)zrKbz&kIo61aUdPbM0mz~U%@nI)%93#jV#ox^;UpKcR
zop66iMCAugV9s4u9E_K|*A#TOj|}#4!f~5v)yL}3rW-iBblc11J5DxK0rl3ZXP13|
z<{2@hWSfOWs9t=cgX49eP(CDE4pNSt{OS|5YVTiZ8?N24IqOuQlBr$Ygl+}iU=%3G
zn(Y)OS~jbNVs93Y)>Fuwgq3F<3Xv(W>b1s2e^=oRf)>u;Uc*M0j8oFZS<^+HZCV3W
zwO*gc#8SG+-DXX+wc8=1w7IJS`cN{8C(i4L-=7_N5%{@P7&+eq$GfX?`bv?Binx^S
z<?Pc_oRrGLd(ho$Wa%E1{Vnj!+yV)R9r*qk)J1R7MOgtGYHGglDt9kv9B-djY;@b9
zovw+BGlX4S`WDB%7)I0b$ljz!c>~#RQ40h=+^6L(+_=860TlFE&OA#sUzD=Z@Skd)
z*|SBt5wBSfW1tu9OLYYam<vaJ>V-+4tc-olW^Su>G+S0BSms=l=392A=I-?68Iy50
zaBVbh`2f{WqCteEjY*Giy3jrDf(`<GvYUQ=kw=FYyeri8fDBkDv4yaNLHn$Q<(}?f
z9zYyW)GY-(>Y7uLoIXAulTz~B%AoAELJjWpf;;<(4ytYE(%x~&@yMi{oP29?v1)fg
zv09??x-(F9IhwPw(2``s!_&N9eT@c2A_bb23f)P4U-$A8qPb8n;i;o6M^N=9jzN66
z*>I=L)*vJ67vOS;s7fRG5YA85FSs)>Fx@gdeFqWDO)qcD2?8EAjiIN`*P4jE0x6q+
z^dJv%QCwz*vkm!UF-l2GIXu+Hr&svHsIRW-r68--;n?*IXi28B)jWFG>~4el;Z+yj
zAZMVC_%iYAM)QVpb!n;w2t#H2N@iVCb5vN!ld%P2l4TMa{25I1YzK%`Dpzffu*qCU
zSKp3r(c%-$Aoo1X7pcx4+0RSDxdEN-dS9ZNPQ*cAy=sT4eXcucGI$Cm?hIrrN*cVR
zhTZk87Vv)QP3cLKX-VX4IYEmuIAF+qmb>_UUC{$>^K&P-jZqlAsk~U-EuPw85@*|F
z#~XOQI_!8>J33=BTDROS+y-s(ppclY_Q8km?+sgq%P^W7CgUH^XN{Yao7bf=#q#*E
zrc`x$g-ev~t>BTc8QlTOSqCbFv*K34{kcNP_3?UkC5g7uP0X#w-<5NTQ9dsiH^0C6
zo8u3$XrUJ{;fOeBm#UIm_nF>RDU9DT7Ujh}r@6-$fm|0rTn=)6YOHy=ae4c$8p1ml
z(^sgE#A$ew$yVoH0Kt1=7}nnO!=}#cwt7{BsEkainLM{t_<M*G{D=?`^#9~c;$J#_
z=?KAftz8!f6-eksMzS4G*bK2d2%#Wbt~L++I)+d$vCXdUj)3MJaJ5Ki?9Jh0Ilpp)
z?hcoPh29Wg7|sa`9_sSN6xhGoq-uA2jg4m#_;zrGNwjN-!9g?L6|}gDM^zQv2upBO
z#u3fPhY{qRJpR;GhS!o=_yR+SC?QO7;Kfdi8Tgs?0bn!Z(t07lcgTQ%FYOCem;%c$
zII!3J7(1ktRhxI`gUO_I5B=0%sH=3&1x4Hhx<}{Nm)so@11`k)0V`<TmV|uQjvl96
z6+#+Tey<Z<<+O2mX=6>=_oDKkUw<5aRwH-bz7rvMOj77cpyQMm$8<(zDtYNtnzXTI
z(tpZNq*_{AlzBLRuxGi2+DfQ|6_2VS7{$msI^fu;o8E?C3$H(WgVpn2pIgN_miC)*
zr}yl6XImwrEhq7<JKl%`UN5{%-~nc8hkOu$ToXV<71%yfq2x(siGDCV0CZM*8uPc$
zS>HnEM#Sp0X@;pFHn5#~jF|bH>%Db|ZYn3M_`uNc`m?k}Xi8?$MU|!dGf{kc&lU>T
ziQg}T2p-NgJ02WKpwuVGzgz9Cw0MpMP#q|3f*v4*3HVJJnvYF~C`{@dm1l46b}WMY
zPwyW-m3)S32oz^_R7%di`Kkg3#{))VYaB$uyiq@T4t_5o=#GI5q(5u*-=Cb$Fp&Fp
zmzkCrGk7?ehdzNXdLc>?<%~rO9sk<%n9A`N1zZP^BYE&ct%p_HO4A0rcW`jrM;<}9
zJ;*_*Z>GFfB$djdia}!8kSlnQZDTu+kdX_Hm3r(%0o_%F-MuUh8swr5$}PcHWi}Pu
z=hakW)uk<ewmNpXJ+EZ`aL{(jRe+(RyZ$fOih?38JZP|(edZ3tDfYokyi5K@I|=l6
z`vv#)RGJn>Bjd@41kuRwhDRM-ayDur#)>z)%_?X`v|HG3q*VU#lW#o6v-XymCI>2H
zboF>s7saR|*RmyH=MlL$w2wu(ga%=(bLv5pGJwXIPL?>kN)DcrSM#m!8;uquWLHN?
zLyV+-uCoq@5~mf1{>%&JOl>d8VQcy)W|~wUZxzq0&wp)R*B;Mn1WywvwC6AvPdv`-
z9?7`<#=vzM2%fM+l6g<L5Nr1&u!E2=jc_*IZrBjPLfBs85jn7#u-#+d%D2gJZo~z3
z<yNmqsOqpc)gh5RH<Qm=qewW(_F;;wiFmI8LR`dbUZ#o>jbFA|b%!gm@3yojTG>$R
zHi~{)Ww{NC4U5#_u8qj{HPrnp4`x#XpO`n0*)(NQx-gQ#I)5_7FU`N~2&NHa9Kv-c
zy;jTPLBP^00YUlJiG#p9mx-fnUuP?v$D)mo$d%oLSQ{mx_2*hjg!5|7LM)N7`|5TH
z({8HY71_LlLBU@M5c<S;?ucFAME=NU6gd?mu-1$>r`xrYhQHSF)l83t+JGcFZrj_C
z?{MWhO0lEp&8$@Qxzgix4P^SPj-K&t0Yf}$c+})JBhf03NMrD)rh$J~*^R938G6xN
z$10Kx*8$D}17q8sgyS94;F!5ceEA-VL?kN~+9L;3O0PVSrPCKfVDu*6Ef^T`Kw&#v
zg-LS}I9h>#VX~@^H6g5VFkNrMz&g=6*oeTzg@$`kG=a0&#V6q`LiRXX{<WI^p6n~~
zw{B|m*V*wNVNr_Ni_!fJh*!{>&R9>1ZxA@gpmB>7RjWGSkSQ?LvLDIN_w4VmL-~FB
z(r_hrq{gkMBf%yOUmCZKpBJwr73JV!T9J;#fBVEclHM}W+1027Y+93|*O!U-Mdy|W
zS_1UJ)mDtIgd*|cJGVL5WA=bH|76OVq$4L>C`!Qf`X~6XaJ4Y*5=Ov-IMMj+69>wU
z_?exFpxECx%6o`<hK;3ik|f)Q{kZQbWbplaGQ819wyn>UT~9{#4B&W~|B94Wx0S!{
zRRciZ8jaTe>|d}~rC(Vv84o{<O}anKC4c{BG`Mk7LJ(dl`B*1E{K=#>HLc9m$3q{t
zEVW11iVC+Z<wrzzu4i5U@IkE83F{T0>1?%We4qNz(Ft8>*Ujaz{*hwU&T{Sb>SudB
zhKDY}B`nW$;D=Ka$8(Ka)I&u-_samwZs1-%1s>YgswjTe*h5QWnw~WC9e(?}IGa5{
zB*M!pZ*l|DD`YQbI>XlV>b|1lzp)yai&-9FTtE2p`K#wl@m{;PzEl;&#7h**hN+9G
zn%__GVZ(Ju34^d(Yo&lD(_;tVAsWeSWC2`{)LGQr9Hfo@bf&t%k<Me1?Y##fa@yFf
z;Ywj5^~cwH3^sR!oG-x@oTdA^oxMgXILKY>50u2H*j?9qM@Ak&CM1wh&_DpX;P}s4
zY#J{a8h*=Sq9?}0#UqnZ(ZY?;-Bbmj2(DlKydm_g^*U(K8KcCe=pTB4imkeSKzPi&
zLPc{UPn@a17yH@&3W;%@hMhJ5AaIq$tzW5CMTFv3l1wbcv3viy5VQjyI(Q<esp%L?
zuw6Z!tDK#<Uz0d?-gbQQqE;jLdJt9fz^);$Nw3n_?oRcFhaE<)acpRv>i0JlA5{6E
z>(n=TKvrnpL0UB%ArM5((_MWB;Q(JBf31dtdJJ$cwUFt$-_=V$wdHYuWRP%aan(1I
zKZ3CKurfDFHa_Ddo0pDVI&p?YU4`l0MaEl}MRH27AKkini<-f6&FP49ck=ufc+k}B
zwBdRau@4as9~&4OXwKG^0uO+2ef)HF%(DpcU5E2b2qI>+78Y_*o$g;l&=_zswAXC~
z!|}_a)F9)_w+{gs5h3w~zF^m&8PY;;GSOr409v4EN3LCQH&QJ9bTmRL+bhs#v%xmP
z$kIYDV92RC!FDBpHwuIs=rL5VWjTU134((jGx$xEc1#$pX&zuD^33~XAz1Sms`J=4
zeXZ8}P_clJfzs<u#E$?zE0&rGYoM+Q7vHwrE-czgz3$u|TB^>m^UGXtch8Ej`zQ`Z
zd})3FO>h3Aq#h_kG;K`dS>ws)Q(fe@lafd{wlp9vzV{Z~XzuSS&nE&iAz&I37a=`a
zTMxU6&z`%v;@dsgwimEGob!&M@$zAbkS1y&wKUB?3iYV`*$WiZ71~q!+fn-jEsLOY
zMsQCdI+|C|7rkoN5*3z_>sKzKKxC2BG6PFXYHng6aJxrXBwfF7l6@tfCRM_&4P>i4
zci!WMcn5n^a70M!uMqS;mxYHVDkdY>FP-R^4mSWOWf+T=U?aX$DM9BhMlLz|uzthK
z8xOJ(1h`E<gBj_`HOC~mPuQL}6b6Y!U4oFM1+CUC=h5O^cBQ}&P+J?Rb*~a39zwi_
zRZ_j?><)8poi6WZN#Xam1uXOJmc9-W^I1c0J@N-wWWDIk-uXnKp)pOaYppO+4W?@A
zY<*QQJKW`=zFK!-#;cu0r@yyx%=w%~Qt&<Qap<U9!Brya|3@Wz)y?d$Xy-l7pL5|;
z2YvP397jGFSJC7CuL7d($i7RlR=S1@o$L+=Q@0mK^07s{SH4RLnm1GVe)QGd>gQp%
z`l&c_?b5}Q*<Q;i0i-sE!`>W%s+$gkrp~#3aHI{va=<Jx=iK_ky?HZxaT{lkhMTJD
zjFqzN@LgzMubt)IHC(yggT((AwuBI6f^yV(k7l01u|YuN&9_p=e*rbfXIoDL2taef
z-=N~GBl+iR1)%Fd4M3WFRn!p{I6-wi4mBFE&YPDssSLhlXg7W0F@szY4%|?=KXED4
z8|E~q%!*@Ah?+%CSfSgfZ#xJ(Z|epMtV)*cEHqX{jPSYRAhGB38B9?=dFesJV?O;A
zO~@!{883@2Y$w9oIDRI*+;Dkt%wJatmKucO8<!QBQ1aVVwnFNk`+xhf0Bz}nhEP8B
zjaGqdc3`kdH%)`I{2QzA3Fl_Z$|>vM0mLm*p|Vg;R#pr8;Vw*^H`Xyp*-0PX+zUzH
z;!ty$dkJc`Sa^pE=5qWiHN-6kyA%iFZ62rI>?+R7LeQfg7p6TtR;7TWy<&**jA86}
zth#jJNLeX=#_;3yEdv7sItJPR>f|O4s~ts%_gp=uEpD!-GAZ?&RJG#Ue8zC~H~^xU
zUIBa_eO=Wan?Qd2+f|DqLq+z6vZ2sndPIQgFSI7b6BY19C(y3+!Gb|6Y5PAMS|`ya
z2HjfcoGLzfVQlM1AF(!D)8E&q6P^=8?aiJ*Q{KT>c6hz6yCaY3-%-P8#lP?D?{7Tv
z{@<7WK(hbYohZTaz%s}(_*|NT<4?_hAFFuqvGN530+I|?9za2y+3heb{p`dFLUrxy
zO}aSoq(SXS=uY^r=q~CXk5eDAhYP{FtX(>*%G>7N4!nz-ekg=Da&Po?OF_ojLrW=|
z%0QB~)*$sq4(bz!bcYf}xFI(izqqJLL5{>fuLY+n=<fxY*wC(-VOPz)9=Tp{l*X8q
zR9fbm(q_N+B{wOeY&;yW;3X#*vGLEf40WTv?7JK)dGuqgq>}RXbdo+R)4jNz+Hs&?
z@8s%{I)wf#=aiDa@uWd~%6I4Xe;u+q=ca&LRxLw}(i$rS^f^2XPMlGKyWCZs4d^Tn
z{`oU-%TZ8*@HasI^N*F!|1VyObuwB$ZojJ-468v8ZVDy6G<D7}5?y~fsOk?w#;T<K
zSg6i<-jkk+mK!y))B|mNrCUUIIVAvPV}`4AIjiwa_AY>vR4CA%Hm(FEi|F^XZ(==#
ztYBN3+xkY|^iH=#;g(OPNgk`na@4V^X<2X_+VL5US3nz2tOw?s7$vGEmtYhtTSVhp
z-9>96LipVaP0Kgv0ooyAW1T)bOkOZjD~x+k69gB}Iher*MU8{?^wcZx*HN|5Ge{;z
zsbw^6rm}sC4;y~_%>rw5hDb3Z0Uxs(hI>QM&A+PQB_17z#fq&5n(RDFG~`*_Vq08k
zwdz`VUb8L^H}wK=WpY!%{QTRM^E#K&7gMQx8r#~2u=?G)QphW(fh9o()%ej)>Lk?T
zY5#t_Q}n@v22&jHmR5Vy?wHx4r;78b)X834>+eUL6I9Cl)XxlO`Yn}@|Hru>NSXgS
zd~k^a`Mm!+Cn)K_`nL`E`_upX(*M9t{IT7?Mew9IQ?)SkFu(dzEL8&;WtV|ooT&Xg
z=HC6$B8U*S|9K=+fBpJxaDFR{gRja}2ZofD``ITi*mHCJd2KNl<2i>S+n!6<?&W&x
z8yU^?=$%97HMl<dUp)U7^g32m6-PnU^8L@1>224R-Wi%h4{RW59xC^+vp;$AB!?A-
zN0_ZP&gbi&sW?0AmHs|AW*^Lc1C4(zHr{%!l-y;@RDazwm2$qcnW|}XrFv55*RR9^
zUC_Y%UQP@*uf9-F@G~C$^Yy>}>t5|D)gbkI6{fM=1V>C9n$G`Oo(TFs*6I4C;`=8h
zgiG6?<y*N|iXU{D%63;;+3{p%e6#~;`02cg+-m<)<}rOcr`I=Bp()fT3PW$idMg!7
z1BK5nVew7hO-f2KXHw4#W)+F;0x2dZ8_1g7JYJ`!We%`pEyFJ!4fwq~)v_l4R>2-<
zjb85!OID9E6szCf2c|e!*ROgG$*^uV<AJo>NSlAQ{1e05c(fyG4`+YEnp5<0xxV34
z1)roH^1k#zr@`2H17`#Mojlt!!L!YuNDrgYJYQpF?w-{1zq;ZasZEN1!;YKIU&ns$
z%b%y?);m3}!hXnPJvaC32KM|9?V$M|$w7&QUp?*AJ9QdEMIG-W<sd0TaDOQl=%cK7
zJHdm&>KCta{7i)GITpWLE)3)o!4y%)jjmXu;hY46_3GvvoaB;~7&2m;AsiL!)f<k2
zv#M>+KQ8vU{6&BA78knYLFnesIU=z9$CDko?EWypW$Ps@MWa+j#im`o<fM!Y13Re3
z{OoJw_7BzK?IVfvU-?188e6oH<}Fj=xdx|iK3==-<KsV5MV~PB4Wk)vYc#(b*&v3V
zSNeA@z+>zb7<nqXJ#TSF8_78rT))6$cC(!bLKR=4643*GMF^}zJ9(5Q|L`9Rv|%{-
z^Jhx|_C!7V)~{R@`~LSH-r_fL$;g^-6i-Nty`=+Jx>(&Q`azA1)cgC+2yFF?pp{G|
zaNsmYxdT7_<f1${wiG<8t1elKsQ!hq{d=?PE_D2P>ZlX9VHk;seXR5hva@!qigb-r
zDhCD#`E{)l%?4SvEUbU+qc6aQ<(ixSe5GJHNm0zP>b}pr5~a;I9fc%jKhmI+6vXiV
zqX=sMc~u43irC<8;g~i(DyiAgnfW<L;vr$T$E>cNCQWde#(AR*7}kQ%@HNZ)qBqqo
z7HU@v`h*U<>T3U&+D)2&uIPdG2crJb>VsYD+jyZ-pCq+paVH^@2w3Hk%_0F3mxB=0
zQ)fyLiL6sS1kYVco`9-i_X?-*S+MeYRnT>o%8NnG(A*Sm7ya%(q$^wX?f#mFiv8|;
z6|2L3$4H-+r{oZ4+4QDrtwmxdW)(%O7%NAM95WJjBHGh+CG?>ac8|k(TS<MO28j8;
zl!67=pz~%*mA~}mZ#hn$#ar$RHFC*u$@X6L`TXJ2wU1ia8AHGBEu||Oel{$(_RA`@
zrY`c4?TxPsuE+0lnm?S#pA<Om;`GZn?#Q}*k}dHs%Y$Vdtu$=wBn&eOh6|i2)DYOk
zqf&Apjgfl7Qj1l%{5Fei&r;)CPfV-cF7979FCRadidnovN6V?Paq|i$WgTr_)|@Z;
zIudsGb4pnZNMPUThT6$FrcuP7^W}XF?{5WuX-Z>$_lVf>C!3MF6L+lVq_exOsDX|W
zVLKt~^chL<R)hQg7N`vkB|=2^nNKrSiMK+6i`TB<>%Lbi(2kSex`7STNG%&XRx^f|
z9LorjuK(<g4;#vz`SXVb<8NaP&#VL}gCf0->{Ha)Jr&e#12dJmb7^eIs4BPV24!NO
zJE>AY6x5P6;bZ%(R>vlC^Z0ayk!5;dIZLaH2$5&#z#?EXv$d;-*l0l8$Mt>E($bb<
z7*wBIs=^|uhFwH^ooHigTV}g)CCKN=A6#g5)ABz6k^I^b7IDkZjO^LrX38WDQ%aX3
zBo}U2sCnu5CwwA2EbooO6UovgQm-IVcjE;gZNAg}FuT*nd8JB65+cNVQU(Kqp*;cu
z+-sgNwQ06(+4MGJ>J4s0x6FRMLoR4MFg+!QON<-G+KRcxJOHBp>_f1{f0l6ay70N=
z9Yz84Sg12fJ=siAUKl}|ogH<A&79?UKDhPZF8&vJ+1nKS6it`qSoOv{e4|o)JCU0f
zr^YUGIa%X&r`g?dioqc81QC}vF*0EiFnXk9c??c+)3h0H4vAMSRaUe#48Qdw>Yyj}
z1%m1>BQq_F@P*$G0!!9w*JZ;clkrC1&3{9hDW1FIrqgUUJrK1Y#TZ&9C0eVrB>V1U
zzBde&>owygKZ$z5_Vn2^b{MM-Ni5Ibkwy>moe)E51uNs%s;t0<8J&|e;<GUBO!ano
zqWI>o3TpKXdhvfmu9__sV^J7AAnx~0{FXv*(t57ejxK6W0dGY6taVsOw3OIeZV_)y
z;ybjd4>*1sLh@^p|2{eY`X^EJe_#3|O#ibx@sB4{cflB@E3dSSKbP}#*uLrwG2Y6H
zsqLb2=H{FJ#17=YAI*u|e|>`)|Jqi|IOkQ*M_l{6sriRTeZyQFnC`7|EP9uke;2*f
zv9>^O|9|TNeIff@%^yFs5BOwzd-*mCIDhMiDWbRkk&&zDTtS2Fi`5AKzQt*EcB!z<
zFlidv`!L20F5|5E@1O=aJ2lG_`8@7kl%3U~2_dbU^isdwS`T&CKe+GDVcHN>W7UhZ
z5ynalJB6#6(<ewScl@C#4hW+Db295p>!vD?+09yB?9tOO#+uVh|6aI<y6-=(c0vc7
zW|#1}mZZ0P3TC}V$3a+2hsnQAJMMp!>cfN1h4J&NM-=QnDIEEh!~&XsFy6_>f1Kkf
zo_aP+XPc?7CF3SWUYcxgPoC+_7W}?X5A~3L{%Xn&mDQcfqZ>8Q*~t$n&42mNX6XK7
zGd5zih_C3Wn_)nMHzd9EZ5qaZVJ+Q%2>pSKa_NS$nytna;A&w5eK@4T641QaW>d<2
zWF-IW-npAqwQv2e;H`~u&Vw0u8(*>HtKQ`8c~`w3@y1C@_C4Xf^Io8WeqZEJkUiHN
zwSU#T{}S_)i&ba8q@G{f#<?_gv)I1J#%Q!8!dd)=`0o=iC89YS+IWeS^d^=;h*{Lt
zI_h_D;Gkj)2agQ;HoI+PSZ-avdWjT14Ep;?9rd4Mb0!BRZr1HW*uD8OB9|WWdc--8
zJ%yK0tL1&bd+52#yRJVHC`=BnEA@6t^+PFVzTZ#&>N#^OLz<4x{BOkr?(Vy|le&{M
z^E=k>0Ihi6>|6cE$%Jcti~i4H_;Z0re*ZWh|NGLv_VHi4^uL?)_kR3;u@k%~#ffz(
z<f%tp#W;lSZxSIM$i5@3ic<WyvPG%O>v31B86rdv*PLdUN1xA5$fjR!%f0>bgkkfJ
zP1mh_8Fc6os7-vOE+{XF(8~Zs83f&Sb`$+zCR4#6e%z}ZaZUytHP;9v83#=`3zf$5
z+{ExU=Z(iC<4k}_;xANRc46q9By6Wh%~z*EEUaCc#C`SS=$htG)F$enp!@Hu*+&#7
zxVY_nCpWR%vFqi?yW29_7_KM%SNn7u$-d>!Z%BRLr+4bwzWObW6$)hH;^lg}J?s)e
z>cFt>Rt!9P_R|4@o6ZrsldNC9<$O40$3#o*gN9H{N_@upeOW?S-tK5n2|?`TYNnCH
zG?1y(m_;$_>nEeJun_`)6tx{wHM36+q;i-(LT$)KZqRU{K4)Pgf#OTQ6v<TgsGqiF
zkn=*V;7>2iNiyYf_5P({K?f2C1!KJq|9tj%tV>goa<Z>lx{Q^tdb&NijYBH`DSYDx
z^B`2oJ&O4l;79MWFa6x_`mn;Q8W-i9MA!JyB8`mnyC|i{`}tEie!k@>p<CjuM2(xu
zIn*^>nX$R@@}GKeVGg*UV|Tox@bY7|;|;twv~*g2Pm)(g650dDjPYmIMGuB4T{hIJ
zj*lev+S%Bo@FLr#R8+`eNZ8z#Vb;74O;8<|l=BUOS>1}$k#>)IO_uT_eh;)qKl5yN
zJ@xFC!y^}d9I^?=R^xkjqtxpy@{+i#=w<qM+OXBX&n%Ble%4Z8=eF^Wd~GbZNq11|
zR+dM4&zeY1^+ngm>aIkMPAobhm&=<<tyf)$Fgy?EvWk@sw|ELDdI^A!@lb!AF{6_=
zQbaGrh~78#`K9IrC8S*jX71Net2mA`L+lP$PFAVf0z(ZE^cq4Da1`p!XDrHF-i=bm
zif|`HDCW*Ms5xxfLDmi7d5{6A&fLsR-zQI>Nj*Loz8!Kk<msh8E?=3aHCz;2c}#w0
zypk{r4Cd(I6A<Q@kH4XGI-~W%qH1N$EKC2m7^&VCiRhZM31fZ2`x%}P>icHIC>zPU
z!H0uob(mpsxN;QvjXA4f%DUIiNaTtdN5rQ<5?j2Jp{3^;!{ruaelP&OlCdb)X4ZOc
zY?m7k5$C2!eDw*<%jYgF^te~4uT~4zv+=IFrTNENaYH5vjDaETA9w|YLuO?vk}-}9
zXhFh+vARlQ+b3l3ET(ba{Dwd0a`tbrWU7w&A}*uyfu7vKz0jV-2i^Q=y3fL2+<juC
zB3M_=3~=L^(#5M_$BI-bLX;Oj<(5cLw2W|5ujuu=_gdz2kJnQas?Qpl={F?a>3^fQ
zS$<uYc(d+8BO9g|6;A@pLX?&5Gb{Uz%<5b_ea&tee)~3Cemt34RD#>LsPW?yl6S65
z&a5P!iy(I5BTMs*Tc;aE6*fJ1_}?eGQi2FMSAy`KEq-zZ$`(go|JIKeL5pTL@$mdK
zW4Lsc2>Zhch8)@1;gK4F*&c~`hgp9MM!)HEJ-2(t4(^_Y>)?8-yu~%xl@virsyoC{
zSm<7wpe9fwsnV0kW9vcw_Fl-p4D4I1Bf@(usUp~;j}I8{><JCdy>^MT%rt6J45WpF
zkA%J`yy>O6q5Zqk?)xo#Oh_Sv46w%_!!K|H#M08z8wf$u@#3a9P70HfE}8Mh!IU4U
z!S;Z)5@2#c4li_7saxKD`C3*ICJ?G+2YvZz6~{spkkoUxpI{c43>++Pv(hmLuicFN
zajl;1fW7Cb$HzQepaOG}2+vA*al_<>sp`3W157<TXDxC?5?}b__2MoqFF_ej^`uNj
z!yN#W-0dG_<y2ul-3{*cyl+|GzreI>7fDQTUx!L;;rKzW*s1T6+c1z&R#{Gpjkspk
z0&g;%U~?0L*z2KOt;UD45&k%7dSEU+9#OYF--BFSbgzd-M&!>J7(i)Z5rU)4GF}!I
z5-w#~d5rz}{4)*T>A_22=zZ@qD&!cqzXfP{_2FOjY{Wd8w~=&`zoCpX8Ay2ExI|Ho
zVGo^=5*k1IPCA}>N&HFFYrZt{`^Fd7RS$?Yf{!~%W%+e~yt;#*$W{Ddbuu&6$bq^?
zBdVn14nMgiZ>B?0{j6o*tDGWNW6J@J3#v^!U$ee@WvXYV*!EH0u4TjEOS2*!_A4;q
z<5j_m@w-%yZ#q)HXGivJtP-1%lvY^4GmyqUbfWSGR&iL$yFK~5<Ks?VL6#AU{&;3Y
zw#I%^=YiZbuaTq($FX&dRTKzW`j*6SrR{Z5vi%<~UivMv`971nrT0TqXMfp;ej+H7
zBm0x8ACH{stH8{%oVWlg*80d_spCJ^c!gL<FQ+dL=NmDWS5Hc=Ry|h{=1Cj3Sm3Ro
z#~5xUPVqJSac~e2d3ewn`Lw%Z>gbq?p6`BqO4}a6X7eP%e|6f>RC8egnQN?69CK_M
zC1&*K;^4$C8&w5In4P+#BviRY5YW;Jxj$_7pB`h(qDgfUH-e;@iT3WzMs(BAgy>G=
zpvs!Hu}ne7<-$*^j7@}?m+Y3ZL~vq*A3*(H>AR-WF~>zdn9s<zH^a&6&?e6%4+E<W
zWrQ1&OshWMGUciDx}t?dF8(M)$LkV|WT!{CHZLcqy<lr0f^Lc52i0|sBLYytaYR(Z
zheR6Q-4m25CBOG^Z*8v0udAtU`skQ)-d#a2Prr)@7Ep)W4H-_M(56f1`m)VJoh5%e
zK-g*iy|C?jIw*}zo&WH#!*PM-<Cb+;RA^JnW~y1IDz;|EUFNCrYT^1~gwvCWu2rbN
zpAPc<YMjdA&o_%nmtaXP?1I@zK(sQO87;%Hp09*Iw@ytO7~GRW5!O}i@xV#w_nThV
zyK3sRmTqto3u<Aa#BF)-`}9{mgx02;9X=5uZeh}wuf-|rIPOsvvs8=2sZ_dOX~!IQ
zXTPgf;ws-Io=M4IrKh8dV9c)e#Yt{9@BHaERIbOPEge-8J~$~Ucl^=Mc5{*bU@xP)
zbu>RV{o^$)<TTX&FyhGwg!OetO9CPYs5+U^PH;YW$p3z|O*B#pnGK_R-At<!OTOLg
z<7(|PsE^vD=h#g*d?x*D_62Hmt~q^t$3SQ7x6HH@HWyYY(7qE1s!XaSAE>xZlfLUv
z!F+p6V#hmZL4qGWUi)(CDG|b|nc{dNJC_ILR1}{|8evih*k}S<WH4HV1_MAxBLbF{
ziVhso?=j}gnvAwJuU9_?((<u4Rl5u*1V`>=G^Y0HZ`xunROMTV^7V3^mS00IKzzBi
z?#~iD{rG5K%_y#qPi`}vtdZSgytmL_EK*o_zhb|5@LP69@;VM03CO$o=H}3&#G4{U
zBQ<Th5<^A0*LQc{>*(lOcH|~6PUI7T);mp0%X+)}QI|=LEhwq(;+w+fU*saQ$b~HM
zq2WbT$}_nL-Ir0-k#?5VZ~Mv$K`n*>yL$f6okVa)AD~E2oUYQR$nad-S{oyk;)pso
z#}j7};QFQ)LXr&z6Vaqo{Zi_t*c_}^b<TLy^MS;J!P<Aee(5cr{ObzE{ZBKWUSZb%
zT4(ZmZ}>Xb(t%|Y0|S%7nF+93rGs_;yJ6hvX*^>1slAnn)<J|*UDuc~amurK7F80y
zHQSK_;~;|=E~0B!h6^->COZ#9E5^%V8s(0F)=hCLftXOIMx)^p*Vn$!Fv-lBP=49Q
zy7_JL9Hya@R*R&>t1Jj_>d2sG3gm2Rfk+Da<E*#`?*@%VuolJST$k+UL{4xR)ngum
zdP^Y`Ji$>7T<?w&=%N<p^c4%Y`&!|$i8s_VVaYNnA{?83!V&Xkr$rpI!iv0E65+AP
zX7EZ~tv*g*Qs>mc?<$Xn{<{hH^}gkTME@$JjV<z&>z-m|{64GmMbivo`#6g`jA|z_
zVuq7%YAz$jy+jxS8@nC5LiI#atG>s$Xg!FfDd#ww@_Y6!vT4t5`EX__nroz(At)$C
zSoD$fwJ^QO=PJuja+t0Su1{~rs!^LRzK+>WrDI!(f#i0*OIVR;Q=eZH*zm-@&i)J)
zR4;Bluyj4L2N>p>34sN%et7~Tl}hsB=JxktGTMVh%;3J0W6}3K{RA$9<Ia)a%1~K!
zb;SuK#<SL;YkMnNeM<zd!Xv>xre$W**=%1~T7H+S^!k%qg=*9x7DyY0cl}9@^Np{K
zo;&1hQ7L;ylTAC@GBof>%=(&HYvi8ow~XIaBd8j~UB*cE(*$vF6^1|;bdTAPnF{ao
z0tim+^XywqNl6dQ7Pj}kFF^#%kJ&eUNbo@}%?_DVEHJ@R>V97h!~4;U$(!!JSe_is
zehw3d6H!=1F2VeKuEFj#_|&u?_QaQ>vTLJNb&k(JUe5`~Corq`)Pt%MEhFhjvf^fm
zTR&>NP$jbVe2he$$b@LqUsh%MuX6N=iq)(s4I^0U*f626Ii;_5BED46;@C>I_f4P5
z=c<H&j7V)p*74?Z?M;{?Z_(Ee9)OC!K@1Qz<XGsJ9kyFWEB!88&Tn*NnD$o5=~)z?
zJ*b_i9AawM8Vp>G@_MGE#1NwYNosj84@ATJ{rW?>Se~r;I!^jrV6nY4e_50RYY#)g
z^R!yEUNbAVos?SIo_Hz%v0VRJ<yA{ec>8drCAwc#tOQ&ZC@1oiXLs?nj*Bt{w_DWj
zc1*UT$p{B^&m&*_+O*9j@p1)mxpw7Z1;@5h!a=(e(AhnoG>LasMuSGz9eil6VArx?
zk}@mtB9>F7X!pIDxrp${+JP$W`k9ec51FA|%h_8U$u5aKZl#urY&KZq_IuMA>k3<P
z?+gv2wvIV-RUI4UJM8z3VTiB6($s*<T#*CSOv5vj?^twn#J^Uu$<J-7*sT=Cs<9TS
z{vxWHXq1r?kqF@YdWxF#%ZC?&gv@x>E6(l#gy-6A5whV}Yw#U&o_wsM^R}lYj|oK|
zODU+=ex#k$Rw9(rEjDQ18C{#1>Fb6^WYnYo$!5|v{MfYG6Esl_FpQLyM;r6rl-f0)
zX$;==Z+%s9X~gF?R>Y1Fm2-B%BvN&z?Ms>TfR2`gSl+f%Y0Y*!Rpn{v-#?dP#q)C_
zUA;+!-!TD3Eo4%&Ut^fCpQYuuTIz_L%x;+4&NZ7*IkR2#ELa}$^-V>z`pF1M%j?og
z3E5AML&o1@Nx@Y~%=*tSRaKd7#vRdI92PG>O1u@I=6Hm0v?JnHFpzn;6&AEOl1#78
zss!N{k=vxcH+{tpZFu9QG3y`P#4?fgK$CY8y#ZcHZsQl)V5cW~&R8<vgK&5f#4JNz
zcx0O#lVzOmF)RL(_?V1rQ7bv6v}T@=TpV!ZcB$~!koxe9leHOdIk}Y8#PPDSY%`{8
zxz5WN<p+xc7a#3Dj1b<xOcA*FY?N9x*VA27xNKUf$f=N2Sp~`jgBD#iV^-7(qPOBj
zzuRe3hP;Tvz*hIKU;UEi_3_Q?ii7)7x;GY{NIt(Pp?(m$zUG3>h}#%RERp)M5)j$v
zvd?ju>X~$ka@}j9u}WJVdj93}D!8L5J>XGoMHqjr`cA4YOU{QTfH%sIlR6k+Jc)f+
zA#{iX4ZPSf-FMVhTPT0{#w1;{b}UXPa|(BKYF~zXvq!LFKK!8Z*;RGa`BzT8gXLyn
zaqw`8&?t8%a`^adaJ2(EnF6!+VudNO&?eU&q@vN-t|gl)R$AfgOI3q<tC#1b{0x5Q
zfi}{tRAog5V84wjr{`EF85^eird~ob3DK!5cmYsw*Pg%M35ol<j#~qanEZ2!Vu2!;
zNB1qOXI7(SaIRfx?J#JbEgtKfIz<ny)}x0WSppeEtdKY~1<__bqTy8mi~3WU-esTK
zmEo!YaXppp-t{@Q^L82AdCyed^K>%(^rQmtTS>SaxrC0fb)bC-xy#ae&bYuS_IK4i
zA>epV4t$}YKqNG}FG96c*={qjT#4TlYto6^FKE&>+IL>XN0azT@=G<_1(N|LRS(T5
zOla>xq_#~TDl3cnB#<y@$Je$GORY^NyW)AIQoocCBPP2)F=lH{FY1N<xEC_KUfqs3
zw-TDw{W@m1ZKdXHL)cxlM~LV=3U)9pCy5;GeB52}Akt*K^<7^Cm7KC%{ZGqot-(DN
zqp&Vv@772au{X;DMvCQL=<5{$Hw*_i`cv~uO`Dj<Hfw0O<ken(G>E~Z5{kIa8Y}k3
zp5O2_lU~z}L0M(x`gtms2HL)}r`f`%yhEP#)K!Rc=AxkzUJj1@T+c_;lqcJ(g1Z}N
zL!iWoEq2Tx0H9R-?lPdnx2tUgDpmt`)SO(nE01r~ypBS2Q>Ci3ti=oX1o&w;M49Up
z8%vmLQmZ=(`o%2WTSJ&L#+O?mOYme;9J1B?i8%wm`VpcWt=F+dJ4<~*+^eJ8>-J}x
z;yJrQTDQb0gxuUt1chUFsT(~ntJw9}-AVnzVRi5{#sT@2$e?Y}3eu3wVPvlP;pO>_
z)06caT^(I^+XZ$ISi;WZ2tX{`t?277gCm>OCl7-G+87BGn`&<v(1#!*y)*R?&O2nh
zC?B!v%!=Et+{V29ejo4{vjCNHnZQIjyT`Y_68k$Bz%WJ4pCAB+c9C#f;6p48g=A*^
zs`XA|<W+3v?g+;hM}3duhCm~c`8XAeW_#0(gHva(AWDhRKney%hAGHB_?(^>+PqdB
zyGJ2lKC<4`xQBW|=Yh%?TwKB7VfDF4>05Lx5xW_$bM+^RjV6o2W3#H_ctp-Zi&h0T
z<gDjRkRc9d{`FIz6(-MgK+~qrN}vLN2Cc%BCCJx-#DX3x+{UO!cILRfTd{S*IV9WH
zN5ZXz5AOfrWS1tuN`PFP%u&Y#e$~fbF3lwK>W@aT;%|Dm$Kp*rkXKg&aCJdh>)6UM
zQ%m2Ms!Fl7%!bJAvT@K-(=!=qYGE1-<vaLu86>7TTiKRFo8$c4U2mRG<_BrIh>clX
zac3p*u3v5VD5i99K<{WXS?;lP@w<x)j<L5@W6{yB>F`<S8zWVE%$b8@I;2csJ>hrb
zUwve1Z%;?BLvuhaEQA$)yx3k6A9Pn88tvApnR$tCJQUD}OsYzXkk9UF{P^VAT3oA=
zsfu_;96qW1T=>Sw7l*r(N*!H?WZzisrjEZHZ&?})v7r;G+cO;u=_#iOd#mPJKifcv
z;T7$$Kl*AmeA=v>*cq9InP5TgWuU=05!Z|^htQgA5@K>RB3$*}y(P?v@$iFx9Jk=^
zL}Wwp^M-*^aT!@<J+{-6KHxBP8tY6+P|B=%J}6PUxSnBL!K0H+%7Wi2$udZ72;%-j
z2-->K&Nk8QoOp`~S`~+V8dPkZVKH;DHy>L8;dC3SRA&|)OpxDR!oV|oJ7l!@Rj!|S
zgZDzp_09s;oUUjX0(&d2b`A~m$Xf?)7^1kapP(UaS2|@{Oa?C~&r?>GAX_7&t>-XM
zpw3kIoL5hk|3-GVjGVi;|5Vnz8a*9%pcte=>rTw)=R(U`UtsQh2j`n3;s#<&SS#xb
z!(^b2%hY!5On}(D?#Ub4uQ|p@{y7&uGUfB+z&Su$K;GoU+wL8H(S6GacMc#AId6O3
zIXIkeNRQ<mCNln>YF<?^Tr7;W(my8V9ZIfa2QqcRAwg*vIIS9-Qeo1nWxbVprbahz
z+*s-MUax2ce=SF<YKOz5dYzRwp2a9yiEn3(=fh;Y6?yDHHnKmun`1WJu(hKtC5<LX
z*h^2{!7nOG(Fh`swX^1ytGbau5_R`_ZZGzyQlaN3xj9mn?z0efBfK-U=UHY741<h!
zx4sJQH_#06Qw(^fsyl^2o{`q|EAABsJ4iv>dE-yvpUk%V71#{d9!d7;4DVlO7>f1o
zUY{QxoS2K!7VpzXmK92p3b-w$KNq$=z7a@ab1^k;Q@|^LxG@^poTrPl52SD)w-GtH
zbgn!vbBM;%RYC^jQG~Af((okP?igp<UDo-o*evY<p6i>om0UbC3sZvp+q&R_Nf5H!
zN;OtaIB7p+QsSi=1$x|kus$d(G{!5lD8&nuO9MSsRWo>ns4Tcr^CndwBPpxqyJzNS
zr_A_EkGw?KjgO=ttHAhdj89T%o#|d%Thw8LHYOSSN2f;5G5b|{#h$w)O2dMuZDc!C
zyu|fvK4LVE2VKfzC(Vp^#}zs%InNr9oa(hjZ)Yv6O}ZPAyITt#k$h_nVFp~QWn_HL
zL$|<jjoO_UmE@h}st~3^rHwpzj7A|q)#PnS7##KKNpdQcUGVYSvATqK0i}vx0wYE1
zHQhLf;2I|x!S(~+FtFQCc{`}OM7(8$9fQUNY}n36`QqNMT^@SPqS`U3W`AMyL#_DM
zN``4jLWr;NV4zBhDpxdtL>1(XB7ky?tE;p-5dbj)jf4P=)c-<W{2Pl8GaYThX&Q5y
zR$sFOMn8JZG!=vq2?zN@DCwzu7ul4y2Lx}s-egb1@Q*R)-XwX(Wl<7G?yyZV!+hvk
zS16#gyQESUAac>P0+BL8J{<m#D4=o5T8mRMOjwVqvnhyBu0oS%R>04oU}@LXS1jpH
zkpyx`xvK_2E=~h9J91qpD1Mv!g`8s77EyK5YH(Ibg-Og3ZypgEE4jO7jLzQT2NYt0
zuQgjYh-&MHhdq+R7-p~{?e~V1-aD_T;BRIf)A?r=v)zhX#pO{=?PHFGUUTA<hsgz5
z!~P|kT*4(g&)O{ghPH++0!z#=-TtCaxF3B0#6^Fa1FLK=Cv?62l2Z2Z{EI$Un_ISU
z?OWn2M5>mY;FhO^%J%Y3`CCFa8Cp-f@}c-+v@{YWBgI1U(clnm-tXe3F<Z?|<^xa#
z(_G;9TbuZcY^!jL79^*A`N>5WcR#v%%<=yH@bK@dWj?OW{z++g-NFbIO|BHtc*3fu
z&57y0omE1JQZNvul8zVj)ZMO1EGP*!CL83`STN(xQhO+BfWcDiE!T~38$~9jxz>yI
z8rFm`#q)S0a08x19rqIr@l#q8<4N-c%t`YhjnXsjwYGGmIHgLTlmiCeo_7vl=4J_G
zP*8L4TTDtT*cpKAu5cC3+1tRDOgisg)|06UPH}dbqHSns=>7RpJg8>XT8;`!PF1zD
z_Lhh#cBJ}-1Dx0ZNUdzVd>Nk*|Am6GyRH)CVr4}+(>!Z!1!y+mu>BtB_oC5tmncvS
zy)CQOoQJ*bij`U<`I9b@D4$PjQ43{7>)a}V-Gxj6=>GsG4ICNh^z<tx*7WQ5f=|My
z=o&F}og^pg@0WVR+p{Y#fC}ISJ{O=-7w^7fg3}H)cb0PdT)^_;xanU~v~;xFspV@l
zG@RE*qwwf%Uh}kF?;M2S{7ivy^9|0m+qCxs#DGs8+g=F!u2ST2QX%BHyV$J4wrk<$
z`j-S@SMz{r&4a9xy(^M^UwVE_(eRnnJrQ<3rvMfm>`%vco5{qyB=XS!&b6fTnpy~_
zb#hU}SuHJoS1ojlmJw|GZZk36G`fBpGIyqv9$ma~15zk~6^ACU^=vtX#*-n-CYA0<
zCyw@etKPuiKB>c>nbo}RT@QU+WB7MhCgs##i#a-81T{Q0A7i!0=a1mUfN%1}NoQ3|
zy`6K~CXf#ghwzslGid<F@^~?L|GebVYxWBmy__5dKZ~5iO)8KqU9ncUh4=dbf8g<3
zFN)nEf9oRI=jz$GKk5AVb0Vek8!<YAOir9Xa<3b81^iR9$3CZNEa|^c0TJL`PM<CJ
zc$py4alfS}>ujqTu`_IfAhs0|^{TwtC%-nh_QhJAc2~C_#pZ-_F0jR^5lPuiJ065h
z=Xjz$G`inN041(<5y4^4x$qN#IPd(rum4y|lP}zk*5e(SncevBZ(;U*N&6&C634%N
zt*(~Mq}p5ThWz_ivym^$GCw$ptTetAO#(eD^3_(3@<w>!P_IMLR|v;-qJ9WhJ1w12
zcQT^#>P6T9l!WnRcYC+5HWB)I9Ll6;XV99?Tcc>`LGiWAm@(~>m1xSa59@0b9*-VA
zs{N~|-hQuf(V|BMiuh21iVq#t@cXz7vY?B_P+bU+b|!ki*iWFvC^bzgvy|v<oFda<
zkIp=%3A-r+NA_GU1vOd8WTDYw4v<Rg`LhqYN)5CHNmAZjTzdTEJCwX0U$^Hr<kHjc
zF)*;y4P-s04-J*mXMuA;rZ?7k{`zyGtB`<=*0PnNf^461zKDAesh2?vNREZekejX&
z*Vd6^L03mg>Ck~KW<7u^Z(d?(2AZ!k<#-nE-@(LEy>>+`kkUI76cq@Q<}ySvy#jb6
z<|@>v^x|zUpoC0=ttEz_omXq)CcpKXTk|sQj~_LKk*akqxm0Y9JH+<;6c5S+<4ZR3
z42ZpIyKw@<u3oyRNO<0`X!Xn5LH`}()uFg(2JiaX)>K3zKxz+NB_^ek4-=<Xe!PNW
z2-ULP&uQ_cqj%v3xgB>DVaWYcOW$X$aaJQFf~OnqD<iq_PM)5xUdsoA3t$iv=QuYE
z>>7%hP+oKXuxt|T3P5Rj7aDhKL1xd7#BI%$u>=WA@As=-%(%We@7v!(R*?Q+6%9!2
z&w-z2?S*|&+l}-nR|d6)+&;}Xn@yllKc}&bW9#))n4;*QuqLee1K(6Oogp)<^}a|*
z@imDvCxUR9WpqkwuqF+d%@lC9%*us^F5b9c)Gsd&DO|XtQ%*+!pjcy;`%^_KkM@wq
zhm%mnC<9s3*ue8>x&V)hs5(dWhf;?AntEzx48E<cSL<rKea${^H8ua*D^Fu}kD0c}
z`OE#hcF{cKr*Obg12L)6OTQ&Ij=CGvIZQ@{U;90l+=reQ&2En{?B|Hoj$AR?oxT4v
zaxio#@SIv$AbL)+uVKncBcJKI@_Wt8J!Y4bCwi%DcW26n5f9!|6#XBf-U2G>?)x5A
z0RidmZt0K?=^P~lq`SMMyStPYq)S@5q`N^tx<Q(u^SwOZ-+#R<)>2?*K6USjefHTW
zt@BNQ%gg(L`@lWNIIy*xLM&F#Jc~e9JN1+1K%?o~<RKhTUc>l_f?o8K3iA#0xM-La
z4rCvX;U7N>LmAU*e$ro<yqg>Rf=z+2>dYrABUixJ#Rl$3d(Cz1o6rEzx;(Xeh?lvp
z2T*wIt;3Fu3$QGzXbSVHM2gEe0jc1>E(6}Ld3B?PzM%C5h@l1ryet_UGJi|K9uBH{
zY1KJO&!VK~r|GOvmCK{$@Uj+XGVY4mP!aB!2icMS^_`;f5^8*8L#ywp1HR(anE7>e
zueIbf`BA5geGLsa!akZyO1cxw%%}n)WiALw4Gj$~M!6u*=g7-@ZDfegwkrb``IA2W
z^}P=$=>qVd3?>wrhEo7Ow9B6vv=k`mhLm~teE_+@C0!vzK#-Kx|2<5PWbj-FF=s~A
z#>dS>wB=mTH*mG=79G3n9+|gk{MY568~6+R#c;Z*I&XNN!HhAHjWOS*Oc`o1P+Gj)
z8A#SGEROju0HC}PldWGJYxbZHPycpK-szcz+@hQImU&*99=r2Twu%{Gy!nDzNB{{1
zR()_to`HEj)I`#uxz*Tot5O7j{DAzgUs_t04+<`z7@pz#R|0wJM<Za6=8`ln@NUmd
zoRe{-ASQm;S(?=Y?k3>*B<IOmqz#ohY!>Rx4z95X1bhJh)uk7B%l7sYOx%(5p`>V{
zxkjPnLQ)Suyeyvpu<kvs$bL5<ViE9d^I~e&uI~WOt&E&(Hed2Wv_aZjjvBVskL*m4
zkz4cGEC5>E$j!YEa3UD6*>E}W2$9=9Jpo|ed36pD()%EE_3H1JU0D48x8P}d*2Ax0
z@;_~VQ;t^`0~sQi@sQkG?AwaTXgERtiOl%VFSGe(FQVw2Y&JEnh!HkqCZ@|ioQ)3f
z%?6=dLl|*4^t#6i^u<IC0>W+WoG8zKC)w!&-_9wYF(M0yKft$j<3EPOiFh?8@VT~0
z&R>fCl)+A4V>_kmi~lQ>u5aU4Z3B0&9Up@3YvyO*7u+Mlj_ftyFb7_>wrZ&H`MySx
zzQ5fR#1HM%Q)|BuPYoj>GWzSsESrVS|0b{)Ee(L%d;jB^^}9xvGl6i2h5R}!NF4Zd
zP+Vk49t!T-%VX$bJGs4|yRWJO(kjQNZr=BdtugpfBZrszkrGrAod%a<;J&)GJm;N&
zdj9mf>&29_KGJKL&z}!IQe#q}oB!U1-T_=s3S_`3uR{ZXnjWd!86QVY^$mR2Z$NB}
z!l?y3zBw-+5s}_K$F&Q0L|8(E(5^YK#9c+ss2|`_O)ue(t6iY9ZwH4MSl;F}6vY7?
z=j1k$^9Cv&%VFJ3uQ0XQ_eov7iqn+{FrBu+o-k*jWyyR1&HcPOETGQrAM)ZeE+juM
ze@dGgv!j~HTDU!n3=0b@(1JfqDmRGA;GgAwB_A>BeHHsX_D}>SGHfNVl`9(e&ukh^
z4=X@dcbNm*sD2sx0@y~QKh1VkCR0*_^76p9<>WPl;mG4c|00b!EwuU?Ogqr6+Vk!%
z))I}iK5|#Ioe)E}y`I@d7DW|wc2_KCUyj!!=WFoUJ($4fksk_tSuX^q2^cO<!#^4U
zUxJsocl*>0unX8uZoEJU(++6%R!OXDP_z{qVpO9v+P*Bu*Fv}FeQe0EYvZdDyo&A{
z51YtGa;LpQ)41vh0<O#tV<fUF1yiEh9+Z&af3H9VJnf98XYIVHs+Q<api=$!f%eO%
zDtNpXn%^Q{m9N;9i4-=6XKN8kV8Fm(2bG1<=Y&O5uJ__fDphufVM}7ivFu63jb<A(
zr5xQlm3hv#xsIlGiT3bDoC(E}7wO`+fAcj)k0@D%(?mJw{g!LHy@UA5Q0zJjRt#Y@
z*_9&@g>@{OEB8gsl3bUS=G1CQk_1<559`|$zg_(=-c4ybWaSb&>(|nu`fM7^+E0Uu
z|6D8v$JjMSWbQc;{5$q9ax8R5)it%d^LuTtXaqS31Lv*R!cP+wv4f$F86Vyb%2nQ7
z8(y8#Fz+Ep<QVo3$nA)oRdY_bkRT!=h7Un>ztG|(q^HIdSQM4WnHyubOQ@<E{^RM^
z=SXdQD#ioT$=1EV$lS>s$^&8HId5<FAW2Chz}NWkD=*B0Yt5@sgFrhfuG0P<<`rAU
z(5ToH7q(<JOKjgra+Xg}k0AhOj(#1_1?!2wOGQ9_(`nDbh!xuxIY0cAkd-bE+HOL|
zEA4~OwPRpkNh+a7boMBJf~)BX$Zn{N%Tm@+m6eo)3t5d*w1k6$gZ?g}BjE6YEtU<Z
zCKmjSmd@iSxW+c0N{~lO$6-4y{&TS@vMA-VkDS*AEi&mB*0chLHd;u!L)+P;GE3gi
zU$BK;{uI1i*g-#7B$arxdMrhiu#rL2*ZtSdfLATLXc0N+*x0Z5pVm$ro?${UF!l&A
z%&xC*4Qe$Jkz#*8Yv2tB4^_=u-)Df6wuFO)bvzD?b-Ww*l!1I$-(3s5uXKm$iy@}u
z6#5?Z>ys`%XM`zpJ?2USgBSuKkIiUl;z};yC4KGYJox(vBj7vNIj=$oc8Tdmn(Aj8
zZsAlucuA$}+_?i`Fgh&mTl@!ITU0zSVTQhcOj=BAokvd4B>)F$d%dVwl>UuS`TocZ
z&tg~(y^M^uu!4;CpyuUqXBC`)y(-@vrwz)fdOc;H61Ir+tP59ca+6ortIF5c@4sNo
zNN&OYLF!vK+VUds98*k4`>5Zl81OInR12;<NWWUcezu{ArG&h}fSR^66-r|#B&RCM
z*MZXMFT*0W-b?k>6q_7dG?wme%E;OGQ%!i2+!WF^nhrRMkF0$cLQu)7OXDfOP!Lb-
zhF7^9yxAWzv3KbRcDRAvA^|;?J&LLtqM*abWv^*;!n?U~I%U{&eVxdNo`FgKDTdj3
ze<r=jS;uT9!0t1b6F-<8DtNmHtLXwp2fD%XrS1{WwSAimMM8XolABb%e=t}1=D#&O
z5li%E{O(Uj5iM3Nm<p~n%hab|_1Ao<jk8^X{tZeRSC^u$W@n}iQG-c8SZnt@-${tn
zpf_4rCuqoq*H{}31s{Dif9;3$SmsveWC&?-kJqX2{Bz{DoV)5WWeFKD&Jp#Z8qE+C
zolI59qo$e6({TF7?{o%B)+Q7lamLXbo%Q}MMh9K&gp7YTt#_<y;oz6~QpiKIYeG^+
zcRb8pL$w*}zcUXZuXeYWwE(H{>z0I`p3-o<jp$aPlNI!i0Eqm7a9hUlZoIg;E^A2%
zCJss9+H|;4hG3fCyy04sX#Et(5kLstErcehAf@>>QO`YIbqF^x@%za96de$?kbDvF
z6g%p>Ki<el!tc6e_?^$?6=Vw7fTkD{K83R0ku)cEAT3?_xeO4h9M8;Ofkwibc?F%O
za;Ni?m(DtL<ZK8cBECP>h`uzVp-Q93nvL00XmOXDOkU%KM@6iE8#u;LZ+{sAh!A3(
zonfWwSx^(xyee-fZH}QK0t!-BoZBsRUo7npD?%9f71?ygzf;>0my0edGNUGDr<Xy=
z8g;7zXHZ8zmwSAm#_+s<!HEOL2iBp03oRu_#F6@y;W_7+llhA7qtHK!$%I7Sc>5r5
zI51V^CKqf%OcK1l_`o|pcP@W$&w;fmW@P0Fo!*q7rl$4@{CUomH2PiA-2zbzK~rxc
zUh6fQq|zs9Y8KWvn90=*AEGVXt<;0izvgXK-JkAd$jeAV-T1P~_del#t$Her-u&L5
z$0Dg3W<iGb>dgl;2g@yCXPv_Gv;?_7!UB=sjN0^PRP>Zn^?L2{Q$FP^^cnTfxupFf
z`<S!O@1ly6RI`4%=OuFM^hfv!Wi*12Masm7;dM<#^lSB6iFfHmrEI6f1kE3f#@~_X
zOtB-tim9@>E$NQmu=+b>UrWQ~TJgiT&nuXS5<CT#JALUgP!oBekte%NfvMr9IE_We
zAc@Q=`t&vFr0XwEgANOF+N}I~I4tf+MsNOZh!*_DKAGQIF5*~XYFbeFK0X&@<9woP
z_;J%a;Ufv%#Q_5$mrYEM;nwYAJw;IGKv3Y%6alQl+}DBS`*|6B1NW@LQewckIR0`O
z;kdM32m=-0)eDashYiBIvIkP}h0&$3NW0;lhNEp!SXeltYmY(W4|K%9%p*6IgF|-W
z9k+ZKOij$-`;u~P%FO=jXgv5fIz7jVaA?P?X9A`fIGVt|L(};KRz4>||HyZ%mlSi>
z+>X^XH<o{;KdF6EECu~_V<NYHgAtBiUfz2yT@ZY*3|~W_jw}L5$y4D47TRGsCFf7G
z`kz#LHcn4Kr5)JD7;vee1sF^xFkM@;V*{dLjwWMP@eSr*$jJCzTQS8&eMnbE(!jf?
zL5A74n5t)3Ie+Nf+(Z{rujiS{juySz8eWm`Jyn#M0v2VR$U!HoZF-W6Bv27MC`aGE
zS^HkiN}Y*{Q|Mb0h^=nE^qS#&c|YtrjMQpJ&CPjm+5RQ&ie2D#vOl(cUs$fv7hB~s
zBZthaSx?K)oiu)8|L<KCu!*>%na>G}3*Yw-YD^g)EtM%X2nc|}&v>>IPY^obLKJ-T
zMk}0>)dAD5VpqNoJLQs@bg%kn@P5JW3O!aZJ?~#@f!;Fa6;Lb5BZyjS3)${^a+Z`B
zj&nsXI_=;?#}Rb#p}d^JsS&dq*-7)(*+bvdCt6IbkokY?^ZQaq_H;sc(B3OyM?}6!
z<$F*8(@UNWr2O_64b3SNCMIUPvV*}(`E4(V&nO@A#Rd`Q^>X~@GH$SML};m5OlZ@C
zb;p)JEDs<%#uhjoDRMZ$w@X5OzMKCogrSJeQH0RSaU#hq`DE1Wqp2h$M2gY}#L==N
zsCJBBWD8T<2}zmQB5JLC4$I#1E2obTQ&r+4>K)5IvF1h)O(a^tnkLU!*(fP|o#0K5
zXeNoWuX~?FM7IdZ)wMy7_`%ei@@~u<;-Ulxb~H*8W)`{#i<EgTJ^E_7qrGE~E~VB?
zpD`!cJ2!<K8qncckZu^BGzq%tkRpp=q}Z4u-_UW$%=={gZeZ(7lfas!k6=7%3L#t3
z`=GSWrxtY^)s}cnMb3ilEVJYgFoLzS;m}Y8e>bsxSsHgOz5Az>M>IPd2#rEVq>mGl
z+(K~Gov8(wgGU&Xl{K})&|m4yHh$%@nu=LC@($yh5+*<0RFYa+4)v*Dfjsm=2IS;T
zM{*iBO-=MUv>KotbO@*@efC$mMssaGG^q@-UXP<z8FIW5JEfjVDam~$a4QMEEJXh~
zv-qLC&3x$)hoQJNF5CIEZmhn~?aspAwoJh8gZ4XNHGIZkcUZjItWofE5=<=~h-x(>
z_qJ`0q-EApiA(PKE9oTrkv|eWz<22mo&AEy5KKSSzutH<oopw|CBGsbTNFrn6hEe6
zX=6w9bged+7X!gHUg@!)Ka<u8kf8l(bTrH=E6WcCr9A*g_EV=Z3jEdxpZEHMW}R&$
zh@W67U;#<@^og{q^OA(Xam-uW(};{EPb;>R!2S+hjVW#HaY=xHyOIpXmP`!H)uF>g
ztuZ`(H8?a_sae)?-D_;kO@xUDLFiaD=(+)1L<Sb7%?BPj{i`a@)jEol5mVqM+@0>B
z#DClIC}3Ofmc$-jpW@pd%x0DC&n6~(?bpK6T@Rk6#ULes+s(_T=5+c?haVs&X8yMY
z?sd(4)Vy_XrQOws%Y#MH&u+YswA$6#{y~|Y{Hk2{ht}fm?mxLXIT;LzbY~lN%=kwQ
zJGx?BosIZnN)5-^(tKYw(er6&T6ixJb$#vLes6N_v)`UZ>=oun0OskF>Q-HL*9jUz
zw+MNhCz)5!=|*o{W?JW5s5FPgKRWP$)|~lM`3{Y^%XFgA12Siq-Wz}7^0p(9=wn&p
zZLmXwnvQgfu*HQ+MR)X##isqUH1)mVi`*IQ9sg;<Sl)K&fy{H>nmPj<`?vVfyi?Yb
z!A)1-+<W4HpT`3V%NwWY==c&)wX*Qi6+douXL3fW?=FnVoEwS7i_qU)Da^QB6}?TW
zjB_<k0wLGE5JV)To!CFFzu%jBvquL7!l!V!Q|9+bp0^*Oj=QKa>Hc=DX%m}udG?^e
zAO=YqzDYaZlt2b^l^6NW3|_qojEid!OpR-p2B9I_W_p!1zDV*E<JB5d#rAFh39(LP
zAmFCfIBe}cp1ZY(lxXyz&o>D#k?wWX$wn3rvzlzB5Ljn6ym;y-k5F-PQ0HUscCqPI
zHL{gN=s2GE;py}jZNKM{fQTM4WOmw*b{dJ?iMuH+t4W$kh!@h^TJdA3f0-N>FL&bN
z5FrXFm#HQWhLb1$*TeE#rT@nT;Pa2WmMMv3>=#NyZHoEm_TpX7H(o|7`k}YfO*-P(
zHFEk7_Q>yOzA@on3Y+RNqS?78u7<=rx3%*WFT^I+vc^vz(p|UABEj-RNH}>iK*pqJ
zok3K~D7_-4T}Uf)PbJYaW47Csn|gXg#A{ii0k-0P@@U@=`ZrHaSE6+lI<f}3sL7bB
z?@oVDu_~v4{nxV+pZtB`XY6tTOHEH_9K4jnO5&Izqu|>Ro`Nxh``oSp8Cm0@l6-58
z+OJRO0Ws>jBk`>Q6I>|&?S9!vM*ajhL4Qe3k=*jtaeG}iJ3<=)*PU7X+i@&lPO1+U
z2!K`MJcgnb%PV2uF8bi*>bWDSsusIU{h;`Yi^E}a<_5+Fr9AB2O>?9w{5t*<806jD
zfDgjDidN3x>PcnPqoonhi8J45!{~CcClZIJn-w);+c(}|;l%HJG?@M1IWRCvNhc85
zziaMs(H4)yo;uQUF~Oz%fo@-og^ur(NoB<&u<8CD#_jY%7P-xe7qeY6@Vd9<@#xd@
zMmekI$n;)UTK2IK?C8igE+_<>g6-G-c)U7ot9+S%;IVN}4&1@tw&-I#R%2+>yJ@>Q
z1PBy_%Ai~I_sN{f6YX1C+`uf~7eG82KVdA>z&>jqWRBYsYA@UW)Io?EsywCF|Ggs@
zVGWHqb3|tT`^#i;s3N=MEuPiyL-DzGGdw5L(ba?E#6_VWtRU7*QK03Rni}Uq=^DS2
za$FcUm~OJ!UHYk28$ebM23?fk0G(HVFi2ZT`;&};k8o4@RDm~qZ|gnb>*l4=AlGHH
zU1sm1l9J60S6WW1TdwtgvxSvE`~q6Ektwnb6jFt;B$e0}orhj{TpV>3fT<>x9y9`V
zwgsYIM(A-#U&BD_2HSePQa6ufz<}}ksjtY;^Bo==S&L52GP{*l35zb3R#Kwa;ot;L
zJ+6`s^wbk5m~y5SoJyaeA#B#xi|BWgz%Vc~81&MSacm@_6`NFju3GReDlV2%0t!Qz
z?O+^n^+B^8pEuLXz=|vp*BH03q7+`>!}VyXA5mZBh&R~W(kFyyk4(S`$o&24*SD4f
zVqF9)Qh8{)z?6G8%5@05W7|>eB~_&^zCfYR$K`MZ(BuY)99{Ys^-euYtqqpvFTA>7
zifFJ!nX}(K^1O`rO%75ETemG1vGm?bv%iE9DraEiV>V?WY@pCw>?oC|(^ipI<@G#x
zOYjpqcri4XJHl|aFt+;U;>MUvCWnhTYx0*D@?%xDv-b&Y+QnXf#sZy6yvp$247|C6
zlNo>ngtpZjV+s<gpj@wAza^eI^}zB(b@MR!3AB5sk*KJ}vH1#&R$E4lX6C9Hr@ubO
zU&=~?X`;mT?-uC|qsz)Qiq-w8#g#nWJq2<qc9iu-i}CN;?hlaeKe2^5KfmJIET}M+
z8yHDNxuYh|D`<ORjhb<2;qzKk!!_=h|7jw5O#!Q}>x=b8PY*bks`<m|2%GUR45Q30
zV{WY3N{rfJQzUURQi!S~-(j<kq>ix9W6R|p(`yvpxRDiBTA@9rcQdN6p}{Kc4l1`O
zq*94VHoSU6jQku{4=jI}(uO*|FM!S@DmMBc+u7y*_7IWZvt(`2eTM}a`;2kmt3eyt
z)0*iEncg4f$C!XcCs^3v@j54-TeQ~X%6{{I|J+nrrQ8I8&)Fcu?+#93R?AEk{Prjw
zTH|m-neZ{|M|Dor?^VTh!fvN28*z)ND#jfZ2<?%_rfid*ClzDcUE1apojvH~nXP26
zM{On(d3wR^-Jat2L;qDk06T1Oh{Kc0SnfVQM5{ix9JrFt4IWfh>MUkMx9^P2Omo2W
zT1cAX)Cf$&Wv?&*j>-T=)0R=6VrS|lrRm6FS{fXq(**Y9mGC*eWN`4W<LOjjG45{+
zkHmZy>w@1gCM6`e;f8jJx=Fy2G&!4$$2EtCN9UHKesAXRScG2R-hpY59XB8mh>f}7
z0nst3P{r1<0sw5pFvwxslh6ZzA)$Ut8zQ@*x7zRjfQ^H#_({<ZOzBKmIBp81%X0Vk
z`%P|na|3@eFwEz5eng!#oGuSS@xmCg9`c|cK3#6Tf>)S<$D}bOCHUBqYdt))iQm5s
z3>YEe0~HE@xKM!sY>^kgizcY)GEMHbA>f~m)qOD6o<iK+Vcqx@OkfdB(0c@bQ6(If
zZexzqTLBew&^MChdo66iFaYk6VyP??u;VvBhaflOB@QT*ffH&=X^sP-2Z!Asg}01@
zd;mjuT{_ZxC?0w`jkFOS6;q%VI=b|^X%8L{Y9*g<6i9VHkcfnISaUmxucQ;)tuy|z
z^@OVhsfgQC4T0BfhX7N?14=$kPlm8Vl%~T45u4=!=i)m<YT6nC@z7D7RCZSim1U1W
zFt<!OB}>}Chx9dA?6FqmTdAiN6U7VPrj@aA0&_~hKX8HQ0D|Mq%0UQ1$NwamUp~6z
zS!voQT+s3^9yDYs$0bLO9EsnJekV9Rp*QtDB>=fsr)3QV1;r_n8f)fQwvYhG^}yxW
zbRFf&Mydf=YB}ifY5mFdXMbQ<DO5V0*8{fi>bQ5@WRaPRXb~lRMFHyOpf;`OQ=U@o
z)Z=EEaUFDx^+|-dy`TJ3Bq9Z@h%9{Ul%$0z1&zgxo3`z%jZTDevKZ!Z_-y*=d@KGV
zOS;#IBT<IRd|7>KVo-CCcUae-i0ty)hDNSWdku0RjlcU2!vBDOp++c`Q~>rHDP1de
z@PCmw5|Hy&J8WSshp99!;g?qn3))L|3IJL47}t=$aE(UNnNm`C1Ck$2-Q>{f14?2O
z)$0(rRWclKtm1qt!qGSIH)hpEgRDL4R#ZizurDVlQfzS1W$rx~Lwyw7HD87}*!hYj
z>++nbEVAH9UmmQUjM@m^cS<}mr4_7QxUm(LMC6p*>+Gh5%<fChzQdN3R8j@F6tT|i
zM2jxhNTedR0xc&n?U>1l)0}jT(6mc@+7DTfEZ?Q@*$vStt{_T9RV*<CA6A`NFywr<
zDMS(bld2>_upUUebt~|6W|dTNBvl(f#|ytHS@k{fXmxAVh5eDHKp)Zh5j-OZ^wld>
z@aN}~@fdd@kyHY&r-1bh@w7dZ_pszpG4m2HJQ8a7#A|7J;vs|}*<pJniqK)V<;XuU
zI!OJF0i$qA^pgT|G(grw$k8R08qh0d<uxo20T#PyY(@p!-93}5G_?)#d~j7C`DbQk
zGKL0ono1IYTrVYME`WK*7+6_zRTbz%P7{~c9;M0X_J%DuCY4Xo5b`XbxIrP(KT6&{
zC4PXoprmd07;_la&R;V$m8+=eKr9_8HBt%a#n83dUp}9JWi4gUsu&j}H>V{ku`fnh
z`;&y5F)a8C6)!cc>N^J@nUxN>t00gl$Z5%_JVE%!6Gg`)_$qOJIM_T*O2dL-UIU!9
zN%kHg5M$u7Sbmj05`!K9ZPa0T1Ee`d`^iOk@Y?-e5WcgK`}7IfG%~m;;{!ZtJyACO
z_4T#kiQ6JC2`c)l<|TmL^6CLJsHxn5TMGbf_#lsQXx5RC?D0cy(Eh+x5rY1zrbhw1
zqRVc7EZf_2Z|NA<lv%SQzuau006)q0F&+r2Q&T?*3IN{rUGU2DN=Y3#C-cHnU$MuE
z$RVe2aA#*gw@@!O5nDf&VlSQxe5i!K!vHaNdm32OVxOn*JJWhk<bx=I=e_0w3pk4!
z*0lLszuHBZwQ8(hPSG1&5u)~P>l*Yw)FG9$?Al@TeS^V8xL6dY@%Px-^mDJgi&kn0
zFn!-9PYuK`Eod~KXeE50Mkgg1%92&W#*Pebr^=>J!3JnPg1;q4wjn@{0CptuDG~9T
zND}(<J*}X@hD*#jkp?RYEHQjBgikZw75Vf)$w_p*eeU4PBRy{EH{egg>=uI}^Gn`>
z7%C_zWCCzgd{Te+^(E7eq(MDDGpT9Twg#x#RIKhGg0ce$wP1zY+J4#2X8t++>r{m;
zNx?>+!svOqZkRe^`dk<ekKQn3h!C@vLFk~M_+>Gh8EH`WIV`+PTESY#Cw#^dEEA>V
z0ps3Hv+W%KCOWp=0CH$lY#}B-@o=wDXeEfJ_7C=8s56Cwvo)S=m&|<-$oQVeUCnNE
zfW?Rie`)WwVk>CNv&)Y+S>%rN8kHpG8GFBXN_}?M*GP@Ts4)lg9ZkVCvizxjQ)}=4
zyE*^6Luh{*G&toVA^{u1@Qji5bR5elEGE2X&)M<(M3lDmj1p&S>-zS_VF}_L4?YW#
zG!`BRRxuZLiCV5}_=Ni^qBoUOost!Yo6R-4V5Jpg)4x&I@udPj*r9EcvvBUA3zrMh
zWy~?r{6r2^spA`r|L>$g&cf%Qk5;-fMtSP?ilq<+WEubW75+q}uDcWo?xNvU3|Y#>
zO`)}RO~UUV`hui9eDm{Pza{BceI|HZrE)%WzyY>Mq{Faw{HH3BPnib_5k=7Zb&<~8
z9ESc83QlG?aajinsw_7`JrQ3^)uiMa=OZS^Y7X{a$A7>3{UE1iv7iMxCiSDlJ5^QU
zxvJoc$Zh+7{Vx|;=$?%nCZ=!sYNdUM$f=z(31Pi?bWl*YT^{|L_zCtO`{1rnILIO)
zqHMHUa9*Dr^4u6erQV#K^(%Z7SHcE>p~XxciTE+mz_{nJv?}EJ!NHP|iI9?9QDU}7
zf-K_UxFOQ6&{slL5^irm_P)U#hG)rxNDP4sVsw#kvuSE(5Ec=gyYfSza{fR@dr|>>
zg3Nt5uI~k651c!ITYWy5xbyAfj{Jv`wj+Y8;Du=Y>Wy>0N09A{J^6^CkAc9#3N!>{
z?oEJOPN{bk+T{l$<YctZ2)lXKAI93Kfo(f;bb4mO7*=5t6Ut3(Gb5lPN<MF$vB^A1
zMvvw3&}hbFV2tM*O{hjR>+=mZ3aE+ah918!{O3|fnFB*HNpYgPX~u%US9caYt3YC;
zlCI|P{hkU~F#jz;9PAF0!h$Bk@b)P50AN=rPwOdzepo~%aoJ2@f@dDyo`4=;WqTm6
z=}1`^OQWcvH2OCm>1Gy`2Ynuixgg>Qq7~`46FDO|GWX0W|9vD@?crqL@YfnJIy!rw
zUxbE<Bdsb?SmJ*RdtDu&mVsVdxA)1Dt8u6L|D6U=^}Kh()@3%hvr3xU5|--(rV8$%
zfJuN1(W+hh+g1-Y6Q(cH|1Ajb*DH|3KkAhZdxC$MGv&chT|C1oO^d~9=Krk>%yzN7
z*nqbUKmS7|pBEAJW3S;p&yTU+3mWH#>cFwdMMkn+!@+X6UCUT=3t;*Zz4>oRRj*be
zga`_T#Nyg*UdG2_p<@R;-@E_+tJ|bR<*L+twu;kb$!mcmNKyjaF<@jtJcD<Ngs>SC
zDg`}S{D$mvf+*HW^?#T5zk3a$^I=d_R{{~0P9G}C?+pj4(|W__+p^9|9Yjvg6}2EO
zJn91Ty2fS(v#v~Ya7zm6Av;$OrdJVnQs(T=Z4e@-6;yc+d4ML@Fsr#RwL<}@`lJgA
z1@?cJNd5RM>M&q7rq7*jfxdz;WBmNlPwzoJF!<)Cd#2)}z9QT6RqDouLQ2)*zQ=#i
zDU2mCnXMZ+Z+WB*${|0k*B4tCt45=%ISRI$5Y%OnU@uNN#C>JdRWw^h5M5y>t#3`A
z{Ay&wWGEd;79gNSUr-uv4)2sk-pXoW&hcAdsQC&!EH}lTY&+bOl7>buVx>ydmWK|Q
zb|~s7cPvsI`;|Lg%=!On$w%^)ZYntKYL=H>bPEbgx@fpL#7Ob}nRhHa5_0OqcK3M?
z+H0S)@NV<!gm-C^bd0>(gxHjfF<C%T2^5#N>+Yl-Anl?Vf-SK_;b7)_dhCkHab2|Y
zkvj>Ar*>e+#+TEz(y@fpv~Ffs)I!y}(R&<c$i_i<?(l>48>%c1|Bw;Q=Z6L!+t}Gd
z0$j`KBvjk`@S+8J>J0o6Vz`Ku+`0zHs4s|mRZ4;Ui9w+4OC7TVB-W|Ir}vMx9GsJG
zyGH`=Lz8Jg6zp8IHsUYv*cpSeS3}!Jk>&eUBkL~$;7%tu8iiig`0k+XHpMLUC6|M8
z)m?!LDEIXqCr?y5sQl~Wky^Lw2<i^z9V{6T$@HH+TuUa_t4<uJWnbiVz>oH*1tFB4
z?5wlg<XOK!Qp~(9oKByj$|up~Dral|wuZMP_oTsQV^xRWX}%WhaBePC!AgYcpYRK9
zl0do?RTX<zzLkqxc)WD)n^gLQ1DRsul`DPzoSw0enw+$iT;6xFWXn2okKxEi84p~d
z7hUn!pad2c7EVP;8#5FcvwZ<NW<YppxyKJS4Je(Ysc%#`8Xteq_1Gi@Yz_R&IhAMI
z2drPs=61$LYdBWXPkOWdlRHp10l)NNDNJnC8(SUvD6*bC@sWLfO=;SZpM+S%5kVZl
zCce_|W@f+-_~Jl253ls&lh)1IRP5&}^Or&u&Hkd|N6#ym14titHQ~tH^Am$|K<vJU
z)0CgDlBTjm=VL3VOIA9rxi0cpBKthP1lChQ=02f_aXs8zHTEfI9~8`p^Hs4kMsx19
zF@nu%G@jP2Lq^@x;-r+cBZh_5?v2DM;jVvZj(}!dBv*TnEn%^A36<oWx3JwoKVWe6
zh@QAin}hvQ(7|gJ)R5Zy25>xitBmpdFFJw4wV<s@r9g|y0bFVT^siSae+9XcE-G&6
zGb#txkT0&ztozl4I`BFhb=j^{`JQON5aBoS&3m!AQ_@IEf>6HO7Z&#Q>269P!PRw_
z*Zs1V^TFTmw<sIHax@@=Xz~xUmV}QjYKX^abYtDv2moteFFNoHn0-*v(ETttKV`jA
znIs%&`|QM?-OznPU7+Xq;aZ!vjFKD+W8W~j)rv+$q7!eGA*)ay>ZUUGN-6UdlMiY3
zTd+`0Ja<W1@eI*U&F0qeUXZkhkw03jH*p)yt?yt&h`o1aYY*L?02gXfQsTHH{_%es
zhT_zBbNV!wDU}Bgq;36uQbx6Z8;D8dd51Gt+kelF#j=mJOX%yPW45QymS$TKVm=K)
z>KEnq#|j<Xo^)}CGkY46>BxgXptH_VW7ek@L&k^D6ayfrhesFqHlIg)P%3V`(G&m7
zv10`moQUoG`>ui!^O<rO%w#Ss%ywx|&MEiYL&aTuAT57O2TG*5LVPbIRZn=e-Gm5B
zp0gqV?T#<%f@fy^QW!x}Q66f^%V>Er-7xNg(qrBE&#Vt*d`?`kc+&3gNDGN5V8Mef
z=>c$vjp;pQAjYdtHG{z*bJWIp=cY{>gq~uJ`}6S&wra<Z|FTEYWca}Hy@nEE@C_2U
zAj*?iWHI<*Zce-$_m+l|3^!sH5Vc#XRoyPI&ogC=Z8&eZcmfMaTS^c+6<yOpSg`=g
zg-mVSOg_(vj#^q@WNypr*s0+6`lq#qmsO4w;FazD`av&dtk=armI-RZf-F2@2nuSP
zz`RDt_B@{lA)RhA(f@xQ*0|5-x6l9lT2wsnk&3Fr+(>G^M4&>UZILDgmgSw#oW%OT
z#;2rYOi~j0$U2hUMJPYOV|lX{4k`CG*<;8Sr8U<Hh~Ldf^mb1k{w1<lt`p*JM#Iao
zAe`T0ApTn$_^Vq5=qh1hl8WAyt51|$p#jvVnDMk*NqIDbnu9iG?sfF%8gtatV%ctP
z>+XvOyIj!?Q+$xk4iv`JSPglMn&cVOE?BHwyyd^#9RP_`Z~4f6Q3-vat{0Z3%WhQp
zeuki<-_L1dLn7`Ri_$t`k%rh-5$hZ_R`}*vva-LfId?8Rn1mQTU~2=F1jn~)<kDBi
z1>`*N$y5FndM~22>IxR#@u2Ipzl_LPcb>M_@8d>2oc9-nf*8qx*&^J`e4$YQ)VGv}
z4!^`Fxuvyg@x;fc1@#?LQt~I+wt7shN5sr|d;QOCH~a!7uPyVD0T7uOEA}L7lFEvP
zlvD!yHb6`ZqTJ%*;_RFj<k*F1OOCajo!Hn4RxyNsj*Z$4K(3D#i@TWw)MzC&7{DN}
z#DO3^KM`T!w&{}ck{m$yub7Pl=>;qK3s7tg27TthqXY8>yu`e9$LtDP^HS&v70n)#
zQ+uYUy$n5C6d?rcsP=E=`=8WRl~i=1hqlC&a6n1y@#V{+lJG3>h(OyZNGU-)Z&|yx
ze;hhvxlssyE-5VqTA~QQEo;`u(8B<f?4_#uZ8r@#qnN(K>|c5KBqva4-`Gl4lQs(-
zNGHIlBde97JpFz@Y|4o@v{nRK$uV_i*#R37G#9a@j{wHeXrTq+H=HeSCb|2BxRE&E
z#Z0(JE{#jJfcpf>%(e5+8l@nntmzR`v`3&#4A%rnIH2%=Bo?G7Gsc~>vv?rp2aEsf
zzkhE_N=xkdbm$`jW=wm=L|)X)506c7MFR-SvZNHmv5_-!@Ac75Yf_;%w>Ru3F#>!o
zzN{;(8vT`bWYo0fq|aV9{ve8ryG+x+?@)^V&620i$0_irN{41zd3`p?>9Ar}i;4|K
z5`w`bAq-61m~NV(pzdRw2|?7FrHInO@X|^DzP(;!_Ns@6E#m`WNaUl=_`B$(tnv;G
zHHf_ztPa_pbf0HA9Lb<l#N6GDM?xL7Vv<tW>|qD#fwc3T8-MIBWMlttPRgfI^{*L2
znPY$AZo3@bI?5|5yu~Q{H{m@!vYR$~qwFg<5ObAea9Z!QVdaXo%#@_rR1n{`&joL4
zv%Q#YT=Hg6k(m~d{4Bi01)78=ll^wlBb1$<tJK&unO%yRI^Ab|!d>8)$B3>N=p>y1
zo+2+u%DwJ!)2{X<n000uhTa+<pSyq%3Zh~95|~Ee?8ok3b`phmcI0hlM4xIRL}#5Z
zz3P(kAkyZ}-U5Vr3rRXzKPj#BC}(<bfR*b^?-{o>|H!}S%?!+8g=#|P*z(wHe9IR7
z)=mUQ1bK@)@sjj~_!QS@vI}`UGZvnHwbt&#KOKo$i}%brQv-{>Nr5%oiM1l}gDuLS
ziuT?a;k5OyJ;3xqAugXQ*YDlfNY04@Zb(T0hg#GlvDOb(o~ZW3l*?%Quj)>#`;ZLX
z)~v}Wv5c)55DLQbg#9>0Iov(_4Y`(n?a&CK5H;qMk)30OALKj4E+B8;kI`{Eec0o&
zok-KX*{M$DGr_dN^L?Ic?J|;ai`#mOs(;XZ6&_J0tpik2m{Lv=!2ssC$#u$qX=PsT
z)4{v{)w)_t6@$DExfIuHsAWtf$Gg5%5&N5XbG?cckOEN9s(uMT4e1J@zKBLn2Y%vd
z&9e*VCLK>I6B@~ku|r38lY3qcmBd}&oz|AgHF;-<(dq1zMyo#e>?KiHkF62(O8&${
z@SaCBcbhr&WIzEB#`=9HTu_g1;?eXxV^k1B!BT_5g#>Zjej7M%cjC>L3|4SAAtqo5
z#|Ihp)Jmq`TF;e;(YO$rei0MvMkyL?6)5I`y{A0OL)O~$&KsWX%le%}&mZyUL_%zR
zGCuKp&y_r`VMF%V$lRgu^qHGri@+%ksNZ${pm3e2h6o6IxME79kpXTaww|7dX01)%
z2|Q`RlZ`7NZx+?sk22woeTVEE3E>M`^OCjS;WpRV<u_C!w7>9u=8^&D4R|Q(&oHxv
z%JSB3cHs2%CoskwGPdu@d3lo@9$qR97Nab$S6u3p@b>tl&}OCpfI}#FslTe(EU-Iw
z!<-YbA*ia}FwT5ZuW?>;1(8JPbRKmC5>n+~YoIDNkvr=mDCofi_%#$kKLBOV*dz~n
zasF|>$$)uYBh_t@lrlHBA;$=h)MK52-+3=0hkp9ZLy&l}J#<fHh3@p6VbsLL-!G-X
zLwjl8gVgI@7NBqdMiW<6rHPHjm$Hy$j}>`fB2ud`-b#=(Z;~plQ-7|R@6F{C0ruzl
zxM*9(CB>cVF{KDAo;aw*FIBXK^kouCgA#fpKAD2YcQS4pP$|Le;?eW!z5T|x>n?8h
z_^@6&t*W9jqCO>Tc-q33N1fSo_yvoURP-vd)$ME^4-gG#YXA7ON2O+YE7ahYHDn);
zv<R)4C?{Y7n&^))KmV4Sjy`-~Wk0id;xginJ%gLzr;`h4p+zJva?=J&$kG$JT6%O7
zsHs}1m+3G6WR>N23IR1mCS1mNc6R4|hj&zDM3B<DLv%6wHegx{AnwUqNIQv12|=?P
zG?Z^6N-J73>3W4e0<LSu@nC5M1s7As`B&^eWttuR(pmq7WYz8?asaOaB_zXtGljE|
z2XwJ)7FAUOwoTi!Q4<v%RY_ST3QylfsdiCUa)$v9ic?R(Wdk7&`&^(m`vQ|WDJuxH
z2mWTsxG3@UFt|^@74(crTYrPxNH?i*JKl#~xM_eM@%uT4IV*sjbj{c-se);|E!M&i
zQ)Iky<3~D97jsv`c%6*^2&e}zvOn=J2-HLbc1l_3sjPe{>)z{uxNN%CuJ?6aOXG`*
zDHt(w*#8}qK`<RMSpa*aasR6P4g3x&?S?}Wn_GtMSxmmsY5fLMMF-Y8EiiT=5s4b8
z?f;Jp(1Q6-ZqYtyX2VqZn_h${Jy<59eBnf>iPeYr>d}qgN_!V0EY&7lzOR4PuD+yG
z*7?+t`B8nN#F;$k_cN*J$o@@Z-=uxgto=+P%=NoBqsBG6k7x`ZMmxx{{VC`uf^lL$
z_R)1`L1(bl6KX{;BC}AWHE>)=(ptYkD!fCR2N7Qdp@R#Ml;y*w*|Pt#L?QLl15W*0
zU`7`M?_eoApeCKgngL5&#84&%wfu?y02jWG=3h>Q{SskO74<BpFws@;`{sx=LEE{c
zwZE|Thd(TtLz`w&00^$UxgIZ;W+mjZ?M>Z~=KW<Q(1&`k6$~HB;dJ-`gnslu<^7w|
zm4o@!7i1-ei#>oaD3pZ52-G{UP(_!OCMRYkj=<L>V-Vx|ckJ1pobIz$8vohBzlRCj
z`@8S)Do~8i$DAyjF4pnjw{E7dJ-x~{Q=vxs<+KY(5Rt{`5hV3@=gnTE%?|yN-O#w6
z;Q|~HCBW7tf<UY7?o7YfX^h!b*&Mz>7H;$T)p{!jf~G#S!(1>&`YR{hjq$p?SG75F
zEyq-X`$hQOz{uclWA3TP5?wC4Dbo5O+0M453?ZMO>UGLCx4U;umo21ZdLrfjmhjpi
z{4)f-UPvlEBPOo`#f$M)@B<eP4gZynRXZ*-0op1xQvhk}9iX>wjUSQZWe9itnHd7|
z0ETDD(31<a|3R?pla(Gf${;RdPaQrdVT)@5_`z^77V%tj`q>%Bq>2iN*ATK_@B<9*
zV5u>X3jS)tnXeI=i7`DWBfs@~c?lbLv*yc|<D^OghS<xOCNwk$bJgLwp=caO`G_ri
zkM6|pP=hGn&khXAm@WUTkohQUdK6I$h)l?cp{VvxB!Qrs2=H=Xwg4w_f!juZp#Y`j
z_K&9^+do-Qd93(hL3sSpBbtEhV-H~S)4zEw4SD9@#yMXir_YudJeFK^^VrIj1>Rkr
zY{Fb6#3w~O6W0Ne5O6D?UE9$LH(s>f@W|aj$mn`?WcK<z(Df8lZ79&i^kpD&pj;CV
zge*?h)K|u#e~H)t=`e<P@gqptE84G_0GEE6-{YEGHx|C@OcLsTTHIIsvj~(m!P$N>
zeI41JJ`ys+c;mQi=%MYz+)fQl@&{6Z2tdB9=H%o}{wAx?dYf@Pb+wXRRQW<@7DEhV
zE_^%H&urhzqs5Lh$O7;pp%j2k0^|85(d!Z!)Q}7FiV(zYNBNX7#|-J28BEV%uBw3S
zfwPV@IBWnw-BAx0hCqE}zSf&D%S#9z)MG%+>zTqdP|*^$@EjSwvgaqsqoIoj{Oaus
zt82?gWuR9#db8~Qp!x<1WkaACclmxWjQpIU`nNV~G<aaePfSeD{E}o$t1}-sA`z*y
z?r@C)0wvh00ES)Xa5&D*ZN!hCsA@UC{6zi8jZN)?rZY9q=}j_vW{JGUyib8Gssl_Q
zf@=26k0WRA1~iAIw5b+7IuPo8es?Jk=no)7Bspr$46XZMc-~Qre0%Q@ut9aoyL9ki
zjY8l#`kcD{^s;=tr6CDEWU7Bzu;B(my3C!*D2?`h*16=(P~&F}_=Gtw15({W_$)==
z+cp>0+{>1+T!EErXdn+IwkY5&{%JpF#$aA=u-kIeMEe&*LC*4(q9)7qx*9Lsb~Y|6
z0ko6MY_#RPZO5K5%(*K{cejTUMg4`6J}kGPWmUcaDX4(ReddO|t3w+wf*`*<47uDq
z>KWtox{-JG{7F{Q6SO@s_cSA=t%}N$v_10~y2u1D2lSFo9M`LL|7a|C<2x;F&Li%i
z9K}@i!Ja|Bw7~+zm9=t5L$tH}E2}Oy>)Gb#11@DXz-1#0)<k}W9?SyX#%g9h>-gi5
z72CBGtmECRQNz{U>Gb~O*F0uvS#4Kz=kFh`uD0Vz>`sq4vrd2ahI6&&BZb^!5fG<d
z!6YXFsltM`TPA>Zo)qCPKYbxk*)Cr#o?Y2W47PILY(&JL#2l-1m={BiH1i%^YZWov
z_1@QSo3BhqBpch@D%NAOTQKlIYk{GFr9(Y5J2F!N*+>^4V0;$dVMK;#)^4SvG^Yw0
zk&d<BA#%<ht*)8%8dYgB^z-#it7l1qC>f8Gq^mG;_Do!keG0c?r$E#-&TE@GvMntn
zdkcpowb2wFOE`64f2wV5=$puRiMW|hsnm!TyVGH;SBw5TaJKrF1+82*OQ_<VZ8~|G
zLXu_QpluX9j4(AlgAKAOsda8bMzk#>;J-ItHb{?roPo{V+%a}q?X!V}4SkLa7ol@g
z_zI>-&oA3#-I>@cv0^{?+#cZ~-zGlm!hh8gGoOBSIvA>VHoS?J+UmdxxGB*C#)d1G
zBi6S*?9X5^NUvUl5V%c8#L#@D5eXw+>Hcd?;fEaeR>AA*M5d+6Z#Shp_E%x>Y^e$$
z;WYIYj!sJ>98FG2P*C$A6hCT=%_pj>_L}-)+v<%FQk{U=8%q=j4zYNxe&aI~t8lk1
zkj8BnzBc~Ydf#A!RJ-hJ^a}3AU81k|Y$AEZo}Mp~s(9!6e7_N7Z~E8zlM;Yvaa~F*
z;23tZGTgm?@hEc80q=)xl6kVWqNTbdB*eF4(9gFM6+mC)r!m}6uHm4g1NGQ$`A5k1
z6A{Z44*R8~<0xO3M92W2i>s=)y65cG$Y_9HVPWT!JHEgpQ=7)G0A}|droRo}WoMWD
zs6==|eOu07C7G;&Qp|eW0&QYqEg9hPuK(Llt+8gdy4pPJMyfyu^Q2r<Y0y@ncxR5)
zV4h~|if1TZrVsHHa$H8*;EJw$U{CrNHmiZR<t;~2i?%$H!SIb+C5-(;0|ku_o?o!A
zi26)?TaqH_bb5YMbnOb*vjI(&t)l@RMNqxA))w^jK48GLTkPn(SZFw&C<McGff`YO
zfNfh$$)H@!W=CPxM(vddtsv#Az8Ri}@Cv-L)yz<`+(sZ7Fv+2La=lV`@-T?v#rB{T
z62(i<=ij3Jtf!F1$jX?T^E1jP@U_!JSv}{$rjkO-y&N&xqB{{%TY9unchQ8`{fO(&
z<!>W}E2Bo;42A0{LQee0tvCF2HXHqa>yvqOeO|V&jQud6y8d?;Y(2nedQ&H~=(g}3
z$P~y$M7V7=7J}&y+Yv};lhrO-kC%f)!&<fRt|D2o?f!ud2SCqnOxP=V8;lpH`bZQ>
z#Y&g{aCb?LjsP35A_UVB|Mf^n=FjBeJ=#J+b`xODzTx(5`U<mu1t!p946r+k_#%W(
zUZEA*5Bh2cneKjmi8V5=A>cv^&tsO$75#EH)?`y?CzlIFIvEj0=3hp~ugI)yj=~h0
zF$i7jM=j_2j!btVhQ*BN+~aTHGhpztn~{m>R=i0mCbEZpuZ?0taM(12w}`BV#9WCo
zpuHo;sQ<#0-}DvT(JE_w>bWFfoELHxYHNC}+*D6Z$@e;xu^Xa1iz`vky>4DqvZ45=
zjWhOdjvSY`+4KX}p6g68J!Eay>4qMY-}Gb;XG{rbTK?nNo-|M(TU?{yn%S?8kMh|y
zM6O4Y*39BgX?tO{d>?aLZTW`VG@h@4N}9@QfE)GaQef^KoH)T<Hx522UW|cnrXLSW
z0G}&l%e>6Xh5NBdiYvLJA-Oz%i9tw7ZjJY56k5axv{$4f=j-i}Tuf%DT}P-n-l2xB
zIm9pK!%Z+_{tjt**X$6~E6s6VSA!rQ))~L}E|EP}N9r_2&@z&-xSwH7qT1_``0db;
zy+VsAhj(bEM4T;thG)+^ue*78pn@NlUYpz-;?eeSAZ|TG$y|fOvv|kG?tlpE-qqQ#
zUyL{3VhR_0)5w$teTS#(jpcwS3*H!-;{O7ujx3OQW}YXNAv!h-X*)iYS?koN(X5OZ
z-u1d1%r(S}w%$%ooA4}u$fBUj{+e^Ox;9rgVs_jTvT`iW&=Y+nBqt|DE4o+q*S<?A
z_M|Yf4-A&&ReL^snSe7bJzp6do{m6jDD4x*6tZGmvUus`cl#7mznCRYjO3KzVl$oh
zDsl{?4JVGmo_DH*M1)23jBDmz4-?JTghb);3fmvigdt*%Z$(3fdQh$eJ40~f=i3~Z
z^;(~3b9=wbyBTA@@_$c`e&1wiSu)>XixLBL4viYLi9uuJ;(2m&!$o;Jz~*Aw9^jQU
zGzNI|e57<gaC^EQ=UH84a=C)3WYAqF*7eh%RT+7^j<X&l(2m>f@U$_;c6!DLP%vHZ
zJ75`xwsxYx1ZV%ng9l!qMMU(cti$0i0T%CmK%}DF8ouw*4Hq}J{T+%r76yUr<)b&q
zp-+wKY?k|xK8nN;AfSIuP7lllst)ao&L{>3Yp^u5h4y_9XjnKTBS4ein+WKCJa@u$
zmAQ_Wnb{G?$14e*PdhzJ(Jcwo0r?OyZyjcXHH_i(2NW$H+xJsk<*8nbt|BM&Y2_OY
zZj-9|3yXI<1DjjMHD0G;AB2pE?OM(SW5W9B3q~@;7cNP_D%`#9UXg*w?@Y8WLqMs=
zuJxAVO5_q7671G0cs>lxa9IwX^RsxZD0h0cl9^Li7lK}q3<RVSl?uPdfc^H4=PA67
zgx`Ma#w@<p745i1I5^$584EJSn}M5J#s)(hVzAoZKb+?|woELiE%xfzi~sxOcmCY3
zIo^e%zs%<&puJI>tNvbp%5*!j&q;X0cnZ8ffGJx3?_WS>=j%+cR=jTb>TEdIzpA+3
z9A1S3mPP#646!R+?}pn+sK)h@wyyUh!Dxy-YVjTcxKO3f_AfrlAk2Pv<n+Ep>%<@+
z==uJ1%I4{MXuQ?}hGfntDd|c9Q3nVOH4U^QO5<0*b#Sgu--o=se9(r052)4}^&U8(
zlf_m%Tsnm<KcfKCZlXX39u};olV($L-UJjfCVgmMq{Scy^H0BUn|WSsT^{jAuFUH%
z7Q};oR=q!fp=uiX{pRyQX7fwy^zjCS*Sb8f^DVe=R=B@?FuOGB`Kp0^+@E{)o`Ibi
zbN;T2mbknU{Z@f6_=p2x(m9$G57`8$^%V^=uE}`0L>sfthvbg*ljdDnL@Gy+C(lBj
ztUge2u>>@5GtfgEeiFX58qldllJ>g$)TGCToX_wW8W81kxN9$^$KBHIclHpBeLPH6
z_E9U1(0&OYxjvG7$z<Hr*!54#4N0BJ)MuT(iNC=56tMa+PK#&B7V5ic!!kf}7aGMs
z-~6lpWQ^lLt@hh^3@zQ>81+a7&|H<|F!GvFZXbXcM{Hz6U2%ihB5~5IE}PXN)-uzH
z8odd#N&5ALf}B0VweSh;#~C5>rkJ7qZ3!@g52#jlts$Orcy!*-=bOLfpiS}}{_Ho2
zO|h(aR&%AIs~lvsI#8I9G@rIjnQO#%n1SSe)nnE_wk&P2CW@RK30g2$L7jk62p&1R
z5#5e{gQY0|Sfre`x)2>Lo{2_vy@0RE=LaJA`Piau0EQ^j3#4jQC2G1Lk#YL&ab93l
zpJ}a8A7NzBpnXor8?~*h^=mWBn#k9PN_2MVhFpEg11)C!1c6vskBSB!iN1ftbYv0^
zD1&t&!}2B!o+)1*F2+#N3csu$f{ZRNan07LsMpzP%WF7b;F!<^NlQ@x=lH6lq7)3v
z11-qJHh*2gmPDoN-rpLuvXhwN3J|2AKqhl}1J*Rb%l53MznW=NT+vWgFp`fh=tqB%
z41oNk!6rOK4384n@#2C|(YH#<12uF$H}WhHFAz7C15JvU#v0Sf(v=q<2)*BenQA|<
zH^y514CrDhyNTog{Z`r$(AoJo3pcLrEJly7YRs3fxMp%ptZ1LMNON*>Z`eVrXg)Sj
z3{r4EvaoGAMCEtg_Xk<nluLmRKZ$f0PY`H^28!IFd^I3i0+KYeI!-WY<yd)N4bKVw
z3oJk*0Z7k!A6q@=fNWh%xVpA%`WF&KISga`*E_mi1X(`UqyU;UI^yAMc&aKiFY)dh
zjpDaqlABWWD`>O;gGfUgYaEb;EU3jl5(a}sosTUp9hC<LI)L`1028Dgr;F(NRTPx1
z9X8A1y<avfFTwK!V*s2F7J~!f&p<QxmLf}oH72-tUgTt?zJTNHaWJ7lIbz?oF?u)U
zbJ`meq>X<>wgzjiP7F-+`d|)lUV#}H{RX-+^o*z~T2Gkbm6XnRm+*k40L1*^ud`sV
zUXWXBP;l_nB7s?!yoVGmB;Twq57PYQD>ja@nr3G<HEljR35hshMe^}!Ai(yaO(9nr
zPI!f62ok=mvq?;^jr}Rqj4$lL6tI!zzbeDj9=^q6A!qSsX@gRbA+0-R@4V^USTk!H
zC!ZmWv&A-Na>Kl&EhnS|#)S7CMOdxnj5$=f;T?QvdwG0X;Fd&wk5b(+dCV>@Vi29=
zl(x(#`~e-#;%A#+8Np~#^s_5|vIZxlPtu9t;cxsz1&N3%M2QAvtCm;U;}<3M+ppVS
z3(Wa`aS{oCes2U3QiUIs+3$KfkqE)eM3;10E@BH{n(9<a)sy?PUiP^_(ka^k!x?`n
zbaDyaUZS8EGZk0f>4*)jxb?4MylQv+bduVXqHZ8nu@x=&?MS|}mF%F3x+52V)Ls`*
zZtK-MHd+#Z&Ix<durnu`^R*zQ2J$CNw$d!8*ngl-GEr934x{3xz_9yFKf;h7786NV
zl=CylZzYy0*G22AGO--MdCk*8R(5i$TcV2Q0LSgEgYhrR$N~%E=NN*e9{&HM>pS47
z?%V(GB2-AoUfIgtTXtlGN<#Ju*?Zmg-m*s$Z8ADGAr6@#vK?FY-u$m~KhN{K>wcd9
z>!nxgobUIuuIqYV<8!_1i{eYBfLbOhhKW}&x!}C76tHy(1H&>8E@@|QPzYg4Qe$z%
z9hH_@*S^dVqJSnWn-TifSyz+va|2uk9h(PEL**tnv9}!Gyn9nnhzO|~rU<Z!dH<p;
zspU|VN~hY@er)KKJu6Um2*3T>{`v-2V6rV*mi;FO?>~4DV5=)UMM!u6^`skLTAKUx
zoSkv;frnQm5!BQRCDl8x*i;8E!lmi><ZtK8k*Zo?^IWSP9wrAvMy4gpNph`5Fc`z5
za@1k#6C)$jH!$UD6fQR;j&@$jzT$*_Oy8hYi%~~KDZCN$+RS{h(aICOr$Vv{nNfq3
z%6E+&FI|7krPy12cI#1@O*4pHs>IgKWR6F-9w}VLiWLsO&Y)JHxK;;rmqhopb(_G0
zpaNs~(yZ)x<(HSHs~-CfNt(ivN9z(cBP~3p`$|wI>jbjnygHnvKT;lF>PH(L9nJqN
zHB&{-lvvelWlR=eMa<|jd$!NAbb2X!<kw9{ssoQoz%o;4sqBI3PUoiQY~>^97<^1i
z3s$;QrJgCmM%8Jg+@=v>OT{O7l-j&i|Edy_g#o)OL(zG*p)t;j-y-p#`Mq%)eW9?O
zcpIA)E{y{rr~swCai80&?YG#PbIaCxE~@rfex;mZ5*H^_G?B#!C8QuxuzW}cECW!s
z#y~(NRkFKm-h!l!8>-@1O#2`l52Q1t+o5X@dF?p$J$Iz-NBIb-l)y;lwGRbaD)aF>
zmtR&7O8K~d8|+n6_}4H+D+<+vB_GV6KEmDtc<g?Ql)h1Dse7Sp(_T>b9xGIc8F#G5
zO3jTth)K?o|EKa>oQT^osFcZ~J)^_(!t|QP!kHf{&`-M+nA7spGyPI@tlT0+C!2m=
zWkB>%+D=M2kI%XEI}1r_p*%jXIo>p0CA=1a60}t7BfhHijVih-;YI+xmht|CGWb1j
zV)eU9>bsxQSEp9>J^}8XHiPQW`$e71q742tDN)bYUTT0d!E$~CVfDTUiNeE^?Q@R#
z!mA3*C}1}6k(3l61&eUhtgt@XQjYLC1i8zKP^Gj-k4!hLS%2YIO^*nRXk6LtNrFCO
zg8kF>)nj0KySViLSK7a?$u)w%sX17hH3y4tq+~p-w4~YjBp{rm2_@TcaR~|lu$NM}
zfL196V0}{wJ5_pNF1ctZAqlzCLsQ1|f~z=~|CST&$Bz{5y1QdaF<)m(ll|r#O*@T;
z2Yfu~TC7y4t5+?Rr!7Yv=WF4HIQL#g8l=e*QnH{~iD0RP8Ve_9wwiu51nty&^L4Kz
zX&$F?j(KvM{S4(7nBI!5bm+|oqTlr|69&v242cDxF_aV{RcO2qMY28DeO+zFRLtZ>
zAn$E6QWNiH;P<RQ3b!T4>)fDP-LuyVWIuoYeEsfSb9`oc3yL)#=k43~bgXOw(Xx;@
zfHsX(!1|!2AN-UI)65hh$0n}@*?P>CQN%O2BC%<LPt%H=x1O0N1o-Zx%$8KJ&oWX_
z8TOj>g^7t(VNF@mb1SWhsoRbvyQtf@Ri^w|a!}vA){L8m1z>_2`uCexvqlb}$o00G
zs>$}Eg=3$sTS984jMLTl_+!fayXjL%2;V}?AqFnlVFpYGPRyG$Wa{kuNc!&YT60z&
zVma&2%Rv{M>T+8nxX0TzRE`a_gO{%;{_Bw6QxLbR9a-0^tf5Ko!^7+Bu}2#VUmIHA
zD9wn4C`_vK-06?U%m~{pJ7U&+VTwWmA!&2E&s@@0my#OcEK|iSUNKA)#0OBR1sf|Y
zS~Wt)D2m%h`dmcalofw0f0`SE1Yx$dMy$*h6LNKmxph0^xQI=@C{nS<@it3I%8Frk
z4IZ&=d;ZT`<lx>$xXtmLp<>^3u+5bza!t!Uw-Vh@ViSIQ<0}uwIsAOk?$GzKks;5%
zQo>TXNkij7BMPckjMC2m(%}`wf@%g_p_qtXLi|^Vf{4RmiX=@4E%hd8Ub@SlPl<(!
z*MLX0LkH=(^XH^KeZPO405E0k%4pjQ5x!!7SN$qJ=j}!3<$=f~k~Cm5>V>AZOnHaK
z1A`cXiiFgZp<;}fiuFbem9zdV2M!T4+qh^@Pq)7V^$`KblC)AS+|It2#(Ff;Ta4IZ
z9)XjfUblZK#lVMp3&Q4`@|RnOt5X1lLEZO5aX*^Gu8hw&wWMB7JiksAt{LH%ODwHy
z1*>eX%(K~F?7TRh_`N-a*RBgX`Rm?+LNTbPGhG>Ly3;MLG*DM7e$Yq^91+}l!eGo4
zW&2#vBM9yLaaTPKIyTJj5zXM?<yXeV5d?T{FDiCR`W)~mTLlHVA5?-QJ*<QpC{D0^
z?zO@-<9EA+kMtTFn+|xu<6(Tg_Gd%Q!3HV1Nu>v{sXYCz3~{g5Z{ITtiAB0S&Vb}h
zwfpY(^4ltE1#GH)`)tsLu1tlBvz*d@azC!tPDrfW=1Z~J2?hR)dE&Pc0hWZ#UP6+N
zwB~fP$T!ewkrnP*G&pqMTDQgt2kk=?!@$lHQLL`#uNg6S9?$o9Cz|Z~(<lN`gw@q#
zvhchD;M*eollwGy@iHV98@`2s{#~R%SZO@dZprP}?k`!bJ&<FRPZW7cq+#Vib=mDT
zHp7?TJo@sWrZ=K@LuRi$Cl|f`&FJ_Z`N;_1#>vP>+l)EATpB8r5|1z_%XOjs_01yo
z_7{hSB8m(*B=9?XiqA9OKgc)4i10(bvo_tKbh@Y>QPBJu|BETJqj15*7j3OrA<@ii
zQoL8lFGl^u5AN3R<%~_(8g9M1WBK~E*-F0$Zl#$xDqMqV4UQ#5Oky^BjlnMj-16$p
zt<T*{>JqJAgSRY4-rPh7GoHqV0-A8O9SQ>lIqTS$WIJM`TaPt@2oH;3W~Wd17<Suu
zil}w0KGa97y!WoQ;%!UTPTPr(`0XWV(k&KdC&s7cp)=bgHjipEYfii%+r=n&J-*<{
zyLW7@?WmM{aT%Q@9;I;V)g+GAXLNJoGLxmX-lcRPc~z+pW)8On&%h;#4{No&wB^Kh
zlaro}1H;aetn-6GU23L}*X_1eMm7OzbSMPJ*XN>=66UjK&(?L#M+AW&r+=WoCr_L9
zaGUn_^h&hp+DLl=-qQYZpqoyWy(IRPnXQHqyPkIjez;A|XP<zJ8OXh!reyGSOUCkb
ziKc*UcU}9@1%0DE)}jYlZ-}?PFOr<!d{>k|@%cL7BoySyz+}Kx9=1OI1MlG@zz;o8
zX$ki`aK3hSy>P2P$3MSyfc_MiH3oimDg${DDdtia$|Ow)B|Xn=s$7d_xy6?L?oA<B
zpqkuV2weI`K0;VUHmx`~E$Q#Y4^`oH>J%u+dx&Jm5!B>9rYf+~g#NU)Ls2wd%z4Oc
zC5Zf1yV+PAB*=U3?`D{;O|(xPY(1<qy-w6s9hW2P<0G--vI7KX30HA^&%EmtNPL7s
zZ_)k9Fu7N1zsk;hih2Zd-$1)jF4LlQ85<X{x3=I2g2$4ZYlIWyMG;^S*uKQfD?y_u
z|KREc%Dui!+mZL)K7zE5_lXVkjtJ#PoWK?)(R^@#Cg}<FzO<d+lkYhgHu=Vcv@cz!
z7a&pW+Faou=<m}TNchm<H)CQxdQ1UdI$+7S{~>6&l}E2>a`8@7ql4kt;~S0<R$bc{
ze(+f1;oGSQ$G>qnzIQ!7_vN;w{FCm1m%BEqfB(i<rrs1kP%WUWb&oVDvE2Mhr@*~|
z`06L5icRYkVJfjr@#W@1F0%ssDe!Mwv?oDni3TiwV~<ajHVkY4NrWz5;od7++du`Y
z^Q^_k0-yn9%!usql+#x8SuplAkR%M96?$`nby^PXck`<vaWVIee@d+%`S9OQ%|SNW
zS;UpPk%DyeM<tsgmcmSSrVeZ#eWJ3-C;(w;of0vaw(NUzGn~x|D)oEw-TbI7R1SG>
zEgk`9y%t0yJC4)OWyk!d7QpgGOxS{D)%Id{JQ*h`P`zAJH1i<ulX53&=f7BDn{og0
ze6hudxvvtsvBvpxRMb^F^lZRu28Lq7dz<E5J#Fa38p~}V+|$VDi8Y4XgDzFutD%|^
zwPJ|PlkM)0wvh!l+Lb*v3o|^ny9YP-SA_09&<sKg4Q72^Yb&cv4U{W4Hy<b4J}OUs
z$0eC_S)H~MSOOf}#c77!es*|}>VLht2CU2+)QS!M-p7(s^&zA=9?(EdByq&9CjISO
z<s?mX1&!bO-KVujMaQ$J*Z@Fo(S7!>5r@2Y>3wS|>`g~>z^ik1)^uyx<ch-{hY+vr
z&k^Uv0gR^E#*LLIF7Q(MQ!+l~?7Y)Pp^45F8K5D3#XcrgI|&E$Zws2EprEL5+T79e
z+)W!5IKYBBJrTnEef3MBW+TT2mSY9f=>-}(8Vyqy(1&;~nM?nR{4~;jHrRs4`Y=U!
z+;2;OQ_4+z@UdikZ-OxuWR$j7rQ#^hys`JBPKh7eu@+b*K*e`=KDbbaRgb8k|0}GI
z-;medN2hj$^x>MPc-W3k!r4OzRUM_PIW;Guv^A%^7d$G_dkP^0LW+pVidWb7KRJt7
z{{haciHn4k_~u2I{O_1U$*QYA$KIbJ^)3mnzOpg69?w?%oH(mxN6~5V;kiUe3%ml1
zAzfFc@SsL813Y9Zye5+#D?L9dk9TgRK65uz>Xq(-3;gbMgBl;e3rDDPi&2HXFxVsh
zdjrN7R6g3`H(#47Xi6Pt&4<{U+2Uosjqid4rWaHMz01NzXT|IpUAyS&^e@%FbbYnc
zLQN82`-t6Z!y0m)4Rbl?!9}k_vtMb(JQ#Gs%Hx-BXkkZq7XwzJdkt><HLbd46#icC
z6L(Uo5M(ez&tzU*4arTz7fCPbYLdY#7y{=QTW}SLun9OHeGYthdsfw}@noIh5IDcO
zO)E)=6w0YVh8CS>eJ#=ax%32}UWUbL?K5b`4;Zk&l<SLqrLbM^CwG~ed)i4`Gx|cM
z#;85$;i?55UxC2QinXeC;A~S6!fna+yJ&PZ9*~IB)T~@{i%~kW7ENuePEL)w>V2l2
zPoI`2J_RDr92u(1pUPNKuw+T1sbFGSK5BK-mD*xct<@g08~CiFNiO@p_kT296{^{8
zl}IgU<6;xxz<`KJj7vL7vC|e71tsOWfZ*Ln_gde@o?%b;?p}w))9ZI{uD4QBM1Th6
zJKC}WQy2n(4)|Jph%UChfzW;G<_`ntsiF_|&GL28^er`#*y97y-L+u=5V^0i)t*Eq
zqaE*C>q}cfL2=pN&Lej}WL(5NfBZlm*<1pJAFeAuT1JX$RY2)WBW$%GoNH@~7qcIP
z2?0WrXG0|d^C~q#4)d9c59KhW<mm_up(O<SA<Ap6Gdmc%v_iAXh5(Eoi0EGagoH$g
zC~`l+@g8odCh<5(V*!57>#b{he6~MziD%|fuGQo&!w)t4_!gurabkw}3bw}Hr-6D2
zfEH37i+1Xo_uL5}(mnm&N#xPIzBjot8=AFAx2RT5-<Ct#w?2nU>NJiV$lMOU-z>JJ
zgtb$ga;(-!Z`9Cxh=vQmR;$F4BbvPbMUF}<+b12GI~(heNHaCdi~F9{+KiR?3$&;7
zLF^9Pu2S=6-hmH#T`9DFVURh1KADiHyqF4V8A#^Vo!Z9BQ=kEjj7-f-^D+RnK3hlL
z?5Do?Fo*&`mcW~(N;f(0KC>lI@?=rwvG?)yfx?Fo<T%fhUWlmnt$WH|rP{-Vi}->p
zF0W1&eSYlDUMeB6o6Xv|(zlgxRqhp1tBfOMhnD?0;x*xzNe?9UCZjJR<9j5Zji<z#
zuINrAmOmlw<Md5mCB<(dy83msa_NVJ8;0T?D+*aV^6okH<h$CM&E;-2vRx7S5J?Uz
zLd19GaH2j})w1fF^r%`)ZdN%2eF;Immb}}tongR`D@zghR5%dA=lJ=^FDRQcPl4c!
z(xLU!^U8a~Jq~()qee6^LbIdo`>CgYjQ_w185cb_W@ea1QlC^Y(6(K<Nt|52jfX9?
zYBAib-naDv<!-|81ETG&-nHo_JnL1(HV5A1*M{W1&pwCF6ph?@XQa>Va}MEMmo)mU
z+J3u!mtV%D(ZL;l^(Wus*-9Q%TF#U{vu^1znX*T8KU9)6<H*t9IWNIQp{ALla*P+f
zeDIV(%;H1Cw<mJi=pcaxYktMSDH84gV$3o%*BrOjdUMa}7!whSq^8^fN+zgi+?~}%
z`z*q0<bE_64~ixu-3(Z)2k}pxzX=m+_Cl{_jLM-O$q*tf<*bb;p4XGRI})Q`u~>(6
zKU{5t?B4q1%)2nv+l=D85doPt$G)`6Y@SyrT0XfQV=Lag!JJ&3?HxbDDHWRjRvB2(
zqH8uf7@(y6(v=wu!4MQZ!TQ#naMjy0FZ)>5XRGbd_IfbO=Mr_*?ky_{FN7`dMnr|Z
zwghyIsEWv}tp;gFlg}#KNrwp~fhQ;F;dCjdMkH8j)Kh41Y=_QP%w=|J>8a6I6Tkej
z1-jVNgGUA8uCxx}Z8K&o`E>{q#Wm@sRQ?0}B+Y+|_~qCz@Sk#*hdw1nTMtg|9l(?)
zUUVdXAa~tl>p1WfEw%8L2j_meOEL$91pMqR)QH_(oyRe-Bwh75DecY0KY=)ttA^*<
zS#<Gq*W8Ja!K<>0R4=>QaF=%vdfX-rscxolnh*DvL+fGmJ%UQzD^!7@(T2<WrTbHP
zSN0FvyM-Ux|5?hJdRtD8aci!_cwn0g^5RA;IgN2wS%@e|T5?vaVn5x~?)mZKv#R#v
zmP%56nR;niXl*^U#3g06vuP$=wY$(PpLqKDTh-#O*ufkSa&~r21;3)Pm`?wm={koA
zcX%HHq&D!~1|10{xt+9jsZnq_?lwLB3vOo&Wc8VQu)Y}L8*oKFty)p@Wn;6l8r`24
zbKlPvYi%3uhBV`Jz@4J!*0I@1$w}+qs_jZ`w-l&|y6WSg!^R|;<R&qM2IV2cD8BL8
z2UrY)ud_sd0j^%x*-Mh1dmNwvJ`9)TpFPh#)_YK-7h^jDjz@2;wjJd1%iq^lqi-RH
zmi*=PWhz7t*otI>mJ=+}RoxwzfP4x;EDI)NC_+L!N&KWTLJmCx_*GdL`HBDK2lB}1
zMfpw|Q`~qmB;?0u=5=Ww)evj>b-jw*vEBXfF;7H+TBRR7H2b$XF&1ea*-G8}44IJl
zT)Bc~jT(uRJXAFPbmpMl%)2Fn+=7n*s<a&FlsHZ=ZdP_mlwoV<G|m%&vdbu?Z)j(&
zLL{S+Lj)9v+=zyRa8lR|Sy#HF1m9ENtN^^DR-L-Q%vmwWEOEr}s6UpTdmeLaK<0Zi
z1Q4JE6&Ref!ZsNiD#hHoUKMVa^0h%#hblVm^<Cx~XAXn0{Y%e8m~2kG$#ta+6ykb`
z^}DD$!bZ9kAW0l`x&m>-SG?sxQ`8>lblN9vYb-s!kgIMv-7j8AzCwHq$nWl+G+@2G
zI27T~ZO|EBpr5i@b38=#*mvgf1J2{@h!Rcc-0Sd#iJ8x8lR8suks`pkX>uSmyFh>b
zY0ci|1lmDtJk|?%^l9x2lhEkb#d(7WBQnUunH<Y&$0cMp+*Cit9vLXgg&IsKtfT_m
z992J~%vpK*ps|qwr<Wfuw3k0=D((<hB0NPZqjQ3`B2x8TiiYOHKbxj^9Cyw9Ucxsk
z4Sb_|ogqmE)eb$*(SDjxtB9{U_=mica^EdB;!CQ17ppeL6*t^v4}R7Pfh{*biC_{Q
z%_dNYmWriZ^$oLvF5wn-^<NQq)}X~?BL!DlZN_3U*8DP4XzD`In9xJ#2nX|+EF>~~
zP=%R^A+bE$D?ndY@a_XWImna6thcf<>uq9l)-Sq(Tl}xsVY&36i$b(W7R1sRp{OTw
z`NVmlKX~FT%i~lT4V1Ltd!6Qz;L8sY_UBX=LmF20BtEzHwQO3W2?ei+AF%I@dlEsS
zyyoO*JNg&@Gw=%4>yJetTrUhnvU}p&WX-|YIuNpXm%;_&n$Q0oP&kd(DHHUru*ank
z3?QBGO&KXXVgP;sm5f7@zJfaWIL(NLCiST_wlg|$@qoN*)I{xf?CQH`q#jG}*447_
z`*L2c%ekWk?@&oQysq!LJ!2`SAkX92vFN>ddJo`4+R+Z~?%Gi2r^K@kuEi3m$BK{D
z@X_gUK-P~vPRY~7d>V(u50@jsgP=<LFueX_i;M3dR{JireuiK^v?;7p)oOcVCBA#y
zZfc5{neX}0^BHi213>aW_k-j>a=D<or?Ikh<H(yTqNq4Nc*3V$URyubh>VI0coMol
z=Wde=Cl#1`AD@fH!;_O!Fw$ZEBp?9oMTF3zt3GrVeuk*zzZGb;<>a+Lr(~m3OQl#p
z(*69z17rWg%@Maam$fd4GKgjm#gbr%+OxCw8TYY7vJEHSxu<X*qfjKe=_3E(&{mUz
zYuBSY@@hQ(3R0+x@m~;1xnQut0hP6in0x<c+xUfyq22G707(BKFB8h|X~2^JIJzX|
zDNDh9LZ3pi-~wx6o3F+Ye{t{_+paTagg_AgeXV<KKp=$nSiwE0Xp#xDeI_?wik%?x
z97B1~@`m3Eji7H?YWkG&Xf91vwood`iP*>WH{Fo3>>|QS7%%5gJ$OR-L(=J**PQK(
zx6g4$O2TeOTz9gxsES%sf9OrjdfJAtMW`AmoeoPt)RYeZfn|uA7KD!8ySoUc7c4k@
zvnLcSaDrLWLvasa{V2$#|43G~86iH@lT0{-{>&#I!y(G*sZJ4&a@Bw^(#{&!=XEl4
zqR;pdbMHM{rE3|I`Z8T5)PNvRn^oaf&*WZhoLGT2OE3J^-_WV^{><?DckFhUw==~=
zPK?Qsv96%JnBx$)?NvY~(BK?`?eSK{Wg?YgS;!sawV$Xx)44CCZ@%7WaX3ok`eygV
z()R`*!Sk03bVFXQy$=o4pF}=*X^vq0LKy+^ILP-5{<yVzT1(Z@F3l$I=*bKXO`Gj2
z?<zY8L;g9?Lu6)kA`<*z9;Rp#+hK^p$g@SGvug$9x7dQ)SESjRQJCj_H%oUDQ);1r
z)@rPn4+6Rm3&O3ipaEOTh-_R@@wpMiHy5;rdUgGXTa_rEA7rj`C^@)$Fbv{hd5fab
z5MtMPtUP||1HoR1q^mCBXF+cy={^_m&durmY+BiPo)prAU<avz7Fx&^*TkfBX$X_4
z9{5P&&&|iBe7XW@Ted}x+qT~lpyNwY0XoqhV4$b@I;2oIS5MRzcj#zu^H{2&*J9Ez
z1`QaRyj*}tDJ<j6QONChM>?qh^^rXh^o#jkjBT0CSTYn4-f{xYLbG8pJE%yoZ+oT<
zIQu4*RL4oO{8k+UwA7c<b0dN{J?4e;=OjJ+ZlLels2;`Wi7(y&fBiKuHM}XRm(j5T
zf^yN2t9fcnuj>lIMW{E1G7~Cx;ae{E1t7`nyf7N<#95PCDB+oFMN!W@f<PmK#Jvfl
z`CVILQkpLa8^VMHGG1dw@LEzKTaj*U`PyQ2kS}ek6L-_8)Zt~hW!uO2JTkZ4gP@S$
zZxCH1rfQv^cAGFi`FTI-*)sydDJF(eCX<$&<}i)Cix)Qt`vImzs!Kty9f2mr81oq`
z6Ol{^)zuwtGoW*ZM$+eEfUvP@XBiMnAw8$0zX>Ejnvd*G0s(><rk{IMZr$BkJ`ruE
zD}J;P2!XhuEtcMQc7Ev)X5o@bYbkKCzI|JHu0?9X-=sM~z9akK8g%y4bJ%hD!o=vv
znru~KG)7V2cgO?FoByvKK~U$&eLA{DE`DqZiivFm!3M!XmNj*=rIsCH8u8n7lPr=v
z;irKi_FZ4@+*VOmU<=MGdzlK<7Uw@!PWIkh82x~)I)vy%uGKche8^C;`<5UzE}T{X
zJi(3E=A&Qb=K9y4O;e+jKFB8z%Uy-*Qp=&O1|yu2?ih{?_xV}i!|*P%8KG(7{!#05
zGplOvC5-HNnH|pR;HW3dhhdjV_hd+f7M-l{09Z=V^I_2Oax1o9Er&drBa%U#kmhRe
zWQDP1+|2ijn!A2e^|qXNJ%+=_qcn9Mgt`y7kl)sJ`7Hg$|G+$J;pI@tp&cpthD$wW
z_ME>t9%>)^NI&O#=~-5_sGG}vo=0AKXjuhyi>#{C_BMTuyc|gdJ-AtUW{0Zmet%H*
z;8Bm+u3ML!XD*QXLvZ0KMrY4Kn+?uB20SO9IArA3B1Nc)F3E$>GFF#5d#t`9Y@iN_
zoLl41Io=+9(RxKbmb%;bAMnJC<amesGAJ<o%gB<(5x>p7sm7WTLo2&`S}T4Q`v%NI
zs)>;fiJ#ZD^y%qLUYXT||Ke6M0X0#RT^2dc<lYev2a*1uL$w6l2eu2h9l<>IJw7M(
z!>~dRed~a6a&LO;$R2{SKx~c#j~XN%hLPSl>j+gUQrPv~p;D7blw&0~E~zq`@1+q0
zxbcP~z`4=Oe_8}7OZdE1I^3*npH+Z%^HK2p^XEYMAZ$%4a7Q_XSev^SPwS7v%O87$
zXZsv(G;Hh0$s_IGhqzdWPDgs&fsC6B-M$M9YTV<%qhxp0{L32?a?zjsbtjud&>pV+
zH6OG6W-6Z!_dF%KTO9JKM8$sWAdC&k(9Y{|Qu*q~Cl@}6L%his?+WwEUWaLCdl0Mk
zRjj?KHzDK1L|&5(WGqsP^Peo2=@Z~D3N^_m7fsLID!mIOQj@6HtopKp0x{24d%wY=
zUrW!;BNvsrd?94ykMf3Zchf^w2ZV|9*__M$R|Wst0CX+lGBl~_{WQ2WaFeUpZtrO_
zM|$@nQ5ULQO$ZmzV7Urf=RygC6FXG3G|f&~vgNP4%Y1I??gH{}$La$S)_kDzJ*I%P
z1ml<XVyG~5tVI8VCO!$(Bv3&Er8Q^mPl^{WOd!@@5BD`<dIburW?gx8%c1BUT@_=}
z5#(ewQF-(7oVRS(X)QEh@}K@z1=QNrCSnjXiV`(}M)ILw(CXnQGMjn?b}nE#JLIo8
zC^}b0kog>lfDW2=gkBKWh|FGqrgxA5uK)ON<TieS?f7$X8s0upV`QX~>5TVb7@-Iy
zQ1X2)!>-F6!9YVdvNGomRSNCPTvAQ^7`W6T>HXUz!ax$xqr?z?=eC@^OwWJT3v@iu
z50~;$1*rCWSn7BIQpHGwEmT->F?yXzN_rpR0f4Jm89xe-j#Y$^hxNC&ct%&RQbj|7
zV=tesLrpP9<UwhD!TXnCN!z$sIsAZcag^2Wyqfrk!coJa_-jUgd7}2`Bg3Mm#uam(
zYo!#j>(<0UX%_lDrfmVVVemic8E0Sqyxs(JMfwLE>m_$(KygYGjUPTV2I4m(<Ami3
zoeVNWhl~IHP?id5Sw-%XP$t;3>QS_{Y7I_Nof?hgY*^U^)D5L2Ze4>vT9=C?L49R&
zN_c+zWI=oDTc!~i3=Qk=q0zw+fXCKzqq&<f{BPN3{y<m(6*RDvyE@HXAcf-`K$QN=
z7X5n&RDnRY2^k9}D4w+Gw}K$ibzzj%|BdfIFKEyJwcb3@T|g(5QV}GlkR@`?{}2BE
z%;G<v@}Fd#HD56~2%v=%VbU!^TO>sMzdr^MzJt@H#r}bNeRX%VtM<tMytQudzn*9F
zxI8^eR}l1S+necb@BlCa&R+agK=)U!>OUh;ME5m19x@=}R>i`V3Xe+ua24lN3h%ei
zphvlX6=7RyI!*<6AwD6wD=z-kizLtvXaD2XiZd{k?^D5+Rb5IZU38+%B>s~I|6^s0
zpc;9t{xkyCQPg!sZEyU90OvI8f2?#1w22)mHY0+w_m>2z<D74(@B8~K{CgDxWFg>b
zv73@ZxPC+~yll$*2%$hH%>UnW5e}lQ%Tj{NoJwqOa499QRN-0ps<OYFu75o-olVsJ
z&tNBWl<>;?t+1#Fc$)ZsKg|+yXXd`5c=Z#Cz;L@bnn!xRA@+Zajv4XZplLUHX(K3z
zd+z-faHip4N~!(#TTgW%spe$i?Qf=oOOBRaT+9H5|M9#t2vN3^cI44<rTo~(REVJl
z2lfB+$$QY~Jb`u&*cVK?L}Q`+5Hlbz@4wDMQ1@VKjSiZz^Mxw1Yny@_$A5C+zxS=!
zrw<FJ5-G$V)z>Xat1m==6Z5MI`<Dm^7vI-@z`d414yEoVA^5N4mk=P_3ja0T%pb{J
zXW2E&Ctp~Y_#8XZ;6wktCYcqRC3~gb4dP%3;esRqId$MI`{n;SdJW-ikR*FLmjcBh
zy>)klJh!PqI{L`|DU<;@+7KS}7}5b@YPB3nPycxXnA#OTuOZj=jIdNhU6-WImk{g*
z!dJjw$Ggk`b)Qg&)n<h*11hjOPJn`g&MuCOi;-c5{p3Xd+Q{htk{DrE-<;TpP0g>$
z%9b{-U+J#-f+eL|dcbbh2lO?`%{i-HVZ90Tb*H8j>4nfa7V<ab)UuwD$sJjz7e0Lw
zfVd>e@mrx5>8bseZO7ph(PqhRVqAiZ@*^x5iYYXgFI+mNdSd++tq=CSqtS<@W55;?
zt)igD(1&>nXqjl&HE6K1>+9FV*7&16WpTpk!*6)<kbQiE6?o1>&z@?;flzE{TUF4E
zt6ZYGvfXcMsP^n>Nt<X!w3gs%wDCu)f*;P@wPBCHSM%qkc}(m*&D2u6UeP0cC~+l#
z4(G2Q@3Py%D&jN@Y)Ktcq<-xiC9?bjgMFj@x1T<i#0bSlns_L+E@`w*Ud@9#coq?k
z=%g~|y5o7j;6)xD)hi)Z2?^;D$ddh0p*mS{8kW&v87C74e?vlmDP%ppy>j0Rw`_FI
z1litLd7l)WX7jPA+t(};FkxWDXAEY+qu@+ve$cJVjBv7m*L)6o;|3X-0Q&=WOduos
z`)A*zaH)5+zZEEiL_T`4KwsN@#qM$T{TZS%egUI&8mw3{d80M@O;2LD7s`!0uFY#m
z&}ZJAmlrVZQ;~Yy_*kz);xY-Z?XGxwM!MzuqP)sCSN-TdFA^c$Ep{#qup7ucP~((1
zB6b=aJ!<Xf0<0hzvsr54?zVFYIPAWTIl(_l6BI@T|9jQ`wRSl5(^Z_Pfd`+P9rUtK
z4%9^)4Qc&fG6Gd&Tj!Em<4LS91u$`1r9&`Gnl4oieq5gl4UdWVFrVBBwcoTP$KG3=
z>u8$>e!n4qpQM$E_ArO(wyu?s?)iCRDx}fSJFec|(T+cJo5)0;;0D;w{=O=??U9I2
z8u~h2g&#6RK3b5DUB2Y8uBEOy>PMFkRJd+EhK^OUK<#$%^wyu0&GT38{-_RX9G6!~
z6%D*y@$43Nc(k=P5P9gnH`rlrKEdX79K{TYp$Z(F&&!kbaw~P47d-a=!>!xv4?nsv
zIz5rge{TnD%|e3SV6%(9z5eO+*_mR@Giu<g-MsA8`>ulPBCPzhIdBCxyd=2j*6kP9
zi^}^^e4y|8bo_SnfzvkUVx$zomlF2#{&c_)-^_JNGU!4sWOd_0trr81cIfYG>?{(E
zw`23DJjCAm@*bym($pyi8x40@ZeB}Viou^oWTO$6yht@*W;$E6FH4T8-n>~no?E2V
za5$yXvgph$EC_*pyxrYlJS?%rt$HQE-sPd5#jI}ZRl7(A92rtVTTA)B4jSswqmuz%
zeA>}i^cH)UZ8}-}*x$W@upPanE37i%pZ~>#$Gk%q-snsMq;Ua{eLk)T%PPj2?NCO#
zpOQ-DjiK3!935{2*zdp_UJcSrZVzMp`<w*O>9MshF&TY}?7j{!1>GNFM2AD$=>mzq
zn@Zgs<|ki;;C>DU2G$jm#{TLB{oX6>OZlan?(7QDl<H97eLRIc>uAd}+rGhR^u@Lu
zmMNv#B`!WI5(X<pka%z7^?zys7}-Rw{k2g+?`Hc9_Ia1Ii@p@w!hQuD;<Gbxa8OI{
z+s|64mm0TOXNtNev7Bw|s?>Vno2;d(>;+^)=b$RAK<F%MzdLggb{|$8s9>)Ky=$Km
ztyl|}0tU8kcXgwCm_EtVC$0Fz_D89BlI+PYB;fX$PKNPpQ;XyN`{ic6+i5E4l0@jg
z?wfR~M%R>@Gp!Xq_P0fni#(;zogq%p(rkM{sjs9ZEAcM5F2)vF>9fi2d2LUgpinY$
z>A7MNz2w<gz2ZX2jqWUzmw`vX)oGTEG^wLIzvu;m!e^Gcn1jI~RgVVhDwAILVx+ou
z*KYsRidx+0`Cd`jUa(^=?6WDXQ#qE(YmJXyb?uzr57_ADvU?^GGG={ulWBWlD1&^9
zWPPZ}H!<5MV&l-;`To3G&div=yY*C>f>DW3U@gQ%4S)PqHxUHsJ_3tm$7Rrnh^AaW
zI40hq{L?C(c#rnBHc2e<VX5~;JeN<~H>n$6I}i~BStDhB`}R`}&ce%fHOJy!t~O>1
zuSZe^y*S4<FBANzKav11MNN}6t$J=WvU^RDppg1^8LXI<8yWsl@l+tMMb)r3N++sf
z#T5&e9N2Sl$`EBA9XG7u|0`^#7$Xkx<Om8m{_*Ynq#UuPQmHdG3pFaFiD#;?E!MWo
zzTddc2?<P^v09f#Yc)F%Aa~;@v+MtNlF@%C#}uQP&nnk0S)L_be@JpgVuLsjIpM#K
zeWNSKZ0zeSqe}h!W8RXIt3lS?|Cp^Oq``&pp^xOt=Wet#?#FyHbF+Zb8`mle+e@?!
zUgp0EKPMZ)<J@nH)3{^w`)h@`2J@FEeL-C})&%Fw_#>qPXLks3Ft47r*6EA=sJCVT
z%j4FWrcOr&)4lSK>9RqLWH*}9efk+GY!Wb5Zo(>a-WVQeq2!WM7bh`kOj_J#zX58V
z_8;F|ggDPzA(D;V&HR}i*2y|WnpYp)t~ScUkVC%PY54Bq>WEa1ikJvOn<(iD-`}+~
zs(>(3fk!0~jwlvBc}pg?l(fPF!A9)2h3^eJH<_ToeOqKpsk;?M%=M4=THU*!RJxr%
zpDOCb=|t;|*~h<rfPq8HN)uUZyzve!pN6<00Ygj7U;K}Uz*qW<h7QVTY%@`GKg{vF
zHRNC0|KL6$4%P<x+do?yd}_+g7--s!R|9iC&u?AF8T_Y6*dy_2XVPfTxKv_8)qPOb
z+tmDID27k`LdC~B+E5(hkXX=Ls?HN&Z}RZBbJ2)5D=Zkgx7NJvzMp94n^JR3gG1Y%
zjJG80vW)Y_YH?KJ>>MVlrT8}i2%?-@Kdl8>jq<phD(r&Q_T~)^?VW>4zV|*$c+eS?
zZ>=^5I9&hxdrRFeL!WFfbDbPYwXKY2z3@FzQd5=Re>k;4_AZ(8MR8pW7z=U<lp1`s
zgdphM+gwD*@Y_qGZ$G@HqlxZ)sNKFHd$icfuX^b)X|nj&U0a~n8#F@}xIq}iAU^(C
zGe)c*2@v<L7gLXxW=M!3uBs?ctsYzf>aeRpl<hJEg>MgspPV8Gb3oM&i^RxQ<fqv#
zJt%fts&>97`h?2haEbPxFX+k<>FiC(Vc=s)3C=mC8uKEi_qOTmRDJI`?PB~*utg<J
zEvN1T9uERk_%^%wgq4CjHEXSKRz|HX`e9kLYk`&H{PMy6@ws1ieS!Yx6YJKlm6i@K
z(jKtH9tRh2QcSmozeB_P%;iyW=FU{8^1x|~wxGw+er810;kB&<e5<tiXFc(_<a{*0
zoS&+&Krh#gQoqx4$glW3yD@Atv!rx-320Dc(W#7!yINt*JXGm{)7aSnC%4MKNl^=C
zN((%^bCQRQlJ&H|KI>1odA~VTbc%s=02HZMVH#sn`ZHR-e{kS#zuzCAy>1aar2*LY
zoxGq;B4?i;#F+M_dk=v^R8G(@h)e&Li4S$X$D!gEV+We!{=^y5UAYR=(A`y|?%fZV
z0n!B%PF#RF0_?ZALG!$D?th3CL)O&n&K~JSdj!>w^nV{#8B+W0o^wB~tvwdPS=fG#
z(4rEWUucZG&t7=(O;8~w<CaGKIhb2BEdIql==h6b5ndk`r2oL--2du~PGLd>@0a<B
zRz75JsqAAR=W!Je_K9fyx)=glGi5nT;HI$KAu+kmkV=*SX)H33Zd4D<M>-p|L`<)S
z$zto$mc!a~4ph4Qx`$t;iplO6c*gi0HS7sLa1Dcf!)>}%VG8$eY7P!>RundY!e~R|
z(WjEIW}Q%cv>ZyqLZ)1xzw!BcR3<n1uS4TO7VFgiR2|nZ4Y+E8?0feb6ICLNfTNe4
z$3a+N`k784rj}Z|LL?2RQ9!_O-x4d9Mlqn9)^N8er?I5w?*$9!311oclz1)DAkSct
zV9sxf2Sl^cOvKS%Y7NSJXLop)>aaJk17OkG;6MP}F`L7PCB$XY(iBZXm8<_^27=T$
zNuo7>RA1SW+7O1VZZxy9wK5vA=7P)d<*J!-MN@>Bs5vtSYAUgcloa1n6N)EquU5T$
z$gV6-GP@jZay&*#3y?#l&$Q=tamQKz{6*&Azcq?zzP<HLme^++qBe{Gvk8^e6e`#Y
zmp<-feL@2s&?M#^wO9JK>2QR&B9{y|{7y1gmb4k1J@7t#=kI^O;H-nZLTk9b;P9J3
z<n}pGg_ch|`da-?d$U!`z;H4Sg_H8(?TgG$b{tLIxU1=2v~aBmX5Tp@3J!)W0p@iQ
zTZZ1@|M*!r0X=Lh6Q2_CZce;}RX<GHR;;kuco+pWOqvF|vPK#_0KuZ@MvvJGOC?J~
z5`_wIqzS6x_D*dtpnfv{-}wkqdspx_LB9Kp)QUU}Xs~3?H_$bji8)%rE{ums5tt4f
zW3L~2!#KL9MB6!6q`xhr;MKv)N8ddgzpMVepIHxN^*IWxc<oSmrt@>x^$-Ke<_|bM
zvJeOy^d3NygR4Qhfxkl~J%ou_=)(Qc6uaXcDYWt{Y#neLbgEhAUQE*&71)zR^AHah
z{vu{D`;^&j*s_r;b;!5*MbkS6+$uz<lKaUy7AqNdfDZai>0H9I#7MePQI-FjB>E4i
z1o@F+_XDBsxTMUEg$*KLTt}6N#58?nD$+2}y#u<DOv0Bwz}}kYce8hAN)(?GiiNf}
z@;a*4q<Kpm2?|>qpopW|iPGONZqaF#i9kh$;3)_5-#Y^;#$E~J9)E2@*5S-QDvkSM
z6E^{;E+e6N^9$|ppr$A?vFwD?KtOu2zpse54T2hppr-NIy!^#jgS}m*f<9xQaBjP8
z+H`h?*Q$kGJUN(d)bk%xGjY&Qq0SgbrTHBhfLl>g;&ip}wyn!GfRL98+q3x0<NH2{
z!PNd?(PQ)BxS<7*gNm6<1zuISv^n=e+wzIcbM*<|C<>u-FzO)P#PDBKCb%*$-Njpe
znnhY!Jux2exk7*&3=6Ws5c&iB4Zr{`H5V9z6JW1X{`oZq>?<F=M$8q)CH+qWL$B+_
zTb4yLafwHoj6~J>(98Q^0}d4Y9kJ}+u8oLD+gs1WB@-t`FJaD7$xfC_rtl*LR;FM;
z)@Un;8ZiG2);XIME+rU}VbPj)$9y0wXtO0+{lYJSqx}+5kPfqDGMcO1OU#ZBryD)|
zZ5@LOzangXPr0OKzFYV;W4|f-VD#<sS##C3_HcweET}k*#cb~&+zWg4T$}c34nH0)
zhIGYF+TS7o2SERsRXRXr`=39qQjN?R4;1OCY58Edjk^f>8xo)*sKmVEV51r~J=2bQ
z7@!sbGuMgxxARtYqlvq<^(*t}u=1Tde57y1{bD|>6<U0bGuoyE%oI_E;0IUh-Io^{
z3Zt%KOefg%I9Z{;t#r>bv)P#*l8b%4qhEp{AOr@|qPIh5-`h8+eZI<OVjmy$5nSyG
zTy&Z<No5Y`@%SBc>Fb!gasqp7asjjGrE-Mks2yF-(V-ho{!4nj_^%OL?+q%cn*J0k
zm{3(K7l-lxE-zIjQSWU4FsVl4IhQRzBXvimNHx*oOl`O?uu?speK;fy)~Hbt!r-9i
z@~TP#8YShUs+}$c^c<xAzLGkuBINZ_xAax7t-zJ0p&DPj5F@D9T+tguA6tvFX%_Jl
zb*aha#Y7z1#ny%sGzLWr<X#6$DzOZK=<lFA_vU5h;M<piA)ZO!;6gR<;bF*9l}hCi
zjd-$rz_c*cXm#XZ_KAQh9-c~;L@apY1d{i3b%W67Wpwy!>P5F+sA7THmi%oPzz1H|
zBTYF2v@995R8_`@8`#CkMogbd`~p;J5cB~-*p1#=41-p0<AFgB*}tZn-FaAR!vnD^
z@s0eVZ}rZsjf%rnM${~^$?@4~&HXw~8}8ybGV`Xt9_OAm+eh^wc<;vPb4?A|w&1ea
z6-Ud7r^$8OR58=shOPE<;_KXl%8XRZgio0y_#!FU2vq2kGF5z_OxJdL!@UBW;E-tY
z(8Z0ry86wL^@k#Wr_I*aHPFGXmu99Q&sFJ99&q?jk2s|An~;ocdePxzrTnUvWLQ!_
ze|)-sUyk009>wjZlA#3AEz(l6rboc*>~EE>lFpN1o%<<2je~N)!LZv)e51L@A&)#n
zTEP|n+I+XheI2!z0v(yylKw`Y!Lf3m73wB<YKzJ~BIR`$#`TtzYD8|%EpheTHBkRc
z51*>RN|*k&(leeU&*Cz2y5E>6f?%Q+axp+5I_9STRF0trP6hCQAO`3lARy3RYTGAP
zQ|);H&&5Uj;MD|d$CP>8v#dL1AEIG5rzf-Z#W!Y)v=DYz(1d)jVxigY5p@`McvOi{
zCL*r;e^{tze_q;hgvcEZ6AJ@QR>tpZF~8$U?l7{FDd~%gba1y@VpfQ)$LQl<Tv#8M
z;1}pmyvzGBX}g2HFa#VlR~z3H)un;ES~~tqRZ%&-S@51p9T+)G-)IlfxsM_JpngNt
z>}Ze2eZ~@Y@*>3gAn}AU5p#Hn3sEdDq^a`Z6*&S(cK!mwf;bI7Pa4(Kdb8o}xMYfW
zWPaV6hiK<5u|Wr@Eo6w9p4>Sk6ZB*cF|ucZ>h>R%9t^sI@AKati^ojI^>EZ<g9}94
z#9g8D(!vaU<qdbL>ije!#jh1db=Ya#JDd<mYh-fLPl0ShL05L@><%cI-&{Hw(zwLI
ztZ}B3AD?XbI8;^9;!{I&p-jwg6&IF|?s}2px!~H!3Oxu^K)b(qj-`o$l4J40>7K6X
z4Q|sJvbQ&HzAk(}X|ADrKYVch6$fF<bTPyWnB`Bf?t=T<$YcdU^0B1qwd-Q#lmR^g
zzo{tabAU69msg<QAl?XK2-3=KYW0+T)7OpGx>mpsCsYoNWdtjq@2DS2ogq{LzU{qd
zIY<|MPSX*=n<+IdVDlYHBakNVfn@~#9%4V_nX+xNOsxe=FTFtVtLH{g6$aC6ZCg@1
z7~+HYMtfQ!a#81AfbB?4xd%hx5~0c^4N-5#6y$R+>FYj+yRRzW#1ABs*z1e(;s)MT
zzVx{0O0L$?jFtf-0WLj`{%^rZ8PfRl<gPy(npma0LJ+ruYS(OES!i5jiAjbateOnK
zR>!T)N~#c}ID?+VV0H0k>OPAS-`R}iaYV3v#WlSYOxQz{VHGr11lyAp{;M63A?3B{
zcnHqeWQp|%3AmqYF(1B6I}nD%l7|k?Pb<%oZkwWr)Hj~3oeE5^rbczs(lt0xJ>b%9
zOiN?|(OXdY%{W%Y!TxUF0LG!hf^<HX*iwsmSHfPt(gh()9BnP=eT9zM`0WZ!SR5xU
zf)hDRZ}fP7wi{xa=#d5gKC-DjY%)P}s*4-$ncwZ$Um!qR0wPFA&+hsZ*T)fiMC%h^
zce=8d#_fW2|Jv;P#|W}P7qF5GxW5l6_1s@yI#<{Z$@AM#%&alZY?#hG7-5tO5Y%CU
zA$B9W{tz2fmcadIU|gKBe#2<`DvuN(aE_fgDU5&dAXsAYTdB}spp|FqVlonf`w7Va
z5pZNIeuJMq&0w?@qOu@tTSm$2hzka1scqXGNW%Wd^Y2O*jYBJpI71zhCOgVdaN%Gr
zQkvy{QEa)FpFr{;zcM^%>Re#!*EV$QxB@y*N0Zn^79T9m_Gi7G)J#&L?D}&kuW}rp
zV?u2xA}gq*k8Ce^2(wRil(l$3(AbtIfj7Zw{)_Dczq1SUcpTel{6W1QCV%6KAgLK)
z{1L&YbTpCi+~&vB?&2gcl18&qf~Wi}=1%TIu3b;1zUJe+WZ^U1wJa<z9f?^Vw;M<p
z7fXqcRlS}{Gvx`|;46m3!@VZ$+nwE}a}!buVqzo~YmsX`dP9F+Th|b^V~0on`{xhv
zLmskYRZ#sXjRetWzAhjU*Z^(2;Q#fmUPhT4drFv+xFNamB$b~$X4b!7QJ5xgaNu@h
zH`}!T)hmbVP5eA6L;&~x55Naac+9Und;jqG1{M}arz#^^NL!ZMSakQiB6_{(Ng6vD
z-!bNNbDrtG|C1DjQHcm7Dz7BFIXQiLc6?(4L1rcKi`@>IxEP#ZW&5-sZ|WRI<yK)e
zdZ7wagXx@agc#lBe*1rKg+Q~chr}f|a^bM_ugU>RN`OM6-jvQ8={$zd1l=^hKKj$O
zzQUh~a(DU;zT*+8MdAQM<YfWv{;jTh&VmnYD&J<V<JPUY5-vZ?%zNkNv>v^Zv?d~5
zfw-uLW6qo?^&++RV0uBqfgr?c-RNz5*T!mWvZ1GV)KMdQ$<bV&20NkIbZm;I-`j`)
zo)fZz1yj){Z=TM4p5mbL^1AX}v8!+3M+DL$zE>h~d>WhgI5Dwx;n|(W(`H@akCvCC
z=btDEBV4&6>vs9AE)1z04xEQ!+@J8uzAbxMJ~5Pgziwpl^{)Gc5kF`gA;I!S?6E(J
zr|rg~+F7+C@4~nPoG<5l2uD(E5<BL1FU4eO(_8Db1s&F-ZTT|`Tgt>|aM=xiG=^{2
zdl47R{%xHqtacWLB45?*+D>f<7g$Y!ho*{-+N0inBzWq<x$Lv34-ih*>N*2WT1Rlq
zhq3luiwFLfo3%pYdj^Qj5B!d<!5gpBg!gJ_c~Cz)Qg^a%dr)w)(-gHoV=AdKUHQmW
zs+=Zv(7318q-`}rUC~uHs;S-WZru^4D}Pt3B1>PP@tGBg&(6e~VLu9_>%!{|6+Dab
z<l?nYs^Xi+<*mz!$RTP)k&l*qD?elI-*tN*Fe)94UY^W~&QoonUO3QPBA+LZp0YHA
zk1X%+PzWAxE%9sj_h~e}xuN!c1X-so#CsH)!&LoJYz5A&;hxh{+d^NoY`g2-({UcE
zkzD<zMf3;Tzu0wH9H-11{^G9%Wh5Apx%)`gNTOAC<9&<kqbjg1b6-8#>r<|n9I_|$
zCN})}7>a5iq^gc>T*7A`Zu}VRX6&gl4AJ4!K_S!^o07x1zAc1rMNE5)L6Ol~``V-^
z=Y{eevwem{rJJoUJ|V(o+Ywp_Z|wLG5`_2(bN&_*b@@WYaAs;}#@Wf>y=9SbkXX+%
z+wBYyLO<oWs>El@zP=I`<<z;Src7`g(yjasxbyd7m_%5jJEim$oa7~frw{pN8p0XP
z^4I<Kgg=_ze0RF5V~k?;-zI315=jgA##A=IU%d7`I&;`LOe+Jwc)Q!TOBU+F8@36k
zzf%`IKb2z=!MIGuy(cK6!<ISjn2J$^-sIw)`I}^ilX99{e1GdJv~(3BWh&D6Gv7`P
zsUI_h7_A%eMs8J{Ss1iTZzZJXDCH;x!hw}OSS7kgD|u)2v&`^!nw#^>yLpVns^l_`
zk457TNp|ZI7Y7}b*0S0IyN}s&-`^YF$q`A&pE$hW_4voF=^=_IF`2K9K1%A@&hKST
zj_xur;(A!p^VRRMKKOj6+LupZy(6<e59KUifk?V~MX`<cvD~9rc2}wT?70X}M{E-A
z&2MeqsrEj68NQoX#rDKECrujq3<|ob^T`UN8$n}xY!&%7)uoKjE+ZJ0eOwq!Bqg#g
z&*Ck0`bZtnr^$OT;`*5qHbfC4PM1|5-S{a|yMtbJ2=)9eO$1dmM4&Jgr~9cy)Ln)|
zKuc)oyT<%1ak`h1l~=V)_1$d#HK&;3Sh%uY=pK1U!*h>vl<Fo?q4nHCx`gw+-2pE-
zibosF2ju*Fg3tnC+mlt_UaW~G?MqNm7gN$RWyRzPMbr;pMqz|(*{5jhh8#lE<(v-V
z9-nf)xl%Wy3pLJ3NzY`;d$SqkbVux|LfbW*-e*Qgur&PHd(@)AZ^a#A)x*hlGkC+G
zDs*O)A7sd1KBF>~N4<-#lr`3srl`3US<q8hS#s`|Nr5d-XAjCy(tL0!Ps)k3U>4QH
zIA37oDN(j0`BJDd(9_XXm;8v-)_i&1-1rfV9@1!HDZ`#RbhYU)PV8eGrk`V+gCB1s
zCd*TpC^JcTujcn#o!=b0)+8I+c8)CPML7p$TqnE>y!WIDjpJE4zDIv~G-4^NE87{r
zl{w@aR4NveFe!>(p_d%t^UB@^yKgD?Od_u`bvePi3U5_@>*v_LRjFvAavqK=E8>!o
znWq=@Sd$VJ#8^ns2IudeDrT@|gkM$vlJW~RdYAm35Mr#7cMjQ_^(SRbAFmQ^lj*1&
zjBDLnky4d88_HXMg=sY}ia+b6UOb)hPPp_0C)&c=()v<h`x+V6psYCI^t!It&{i+j
zTl}p2%9<E=#+Eg5z2bp%W<r%dMh>aO$c=sWgzEgnNxA7Vf#8egjD4JOB#oq-PS;~+
zbF-Fy50&XB1^4J|*IElx`qd;2q$}WaO{m4jH!mBMrMdGfaNi7uGv3qXqQA>E+&EQq
z66MZ#Da7B3!W~1h_sE&S?;))o(rj;Qsggnu38{I`nIHIe-t(l(9!402)5fV-Ay$O9
zuf&hl|4G9TAzVAlJ?~02s|@+4Biz#~fme)Enb-JmRoZ103(Vlpf1?95OwbF0k(q9<
zDin=i+Z%3>zz90BChBr(i)=7S4@X5(`)PE_-ibWwDp?bURcW5y<K0-=)8w@!8}g~1
z#yeDUUn{pBIyv?npRe*}4cx1&5O&D8+??mIj36T&-dGY?_CQhVA(_B#mHU}-N+5R`
zYApl{kbnz34&KmiNT#&U-NBEW9OmqNhUa{9HWt1<w<$^}E_vBcnc$ZlhTWEAzMNaS
z?k<7rYj_?z*(P^Gk6PDvms!=q`Zk&4nY;MoNLLF)^3^wXQ02kw<8^Mjo*x_c^j347
zR!z5y19i%k>0<Wr`3gA--O9U8W)kp1y@iFh`V}#OLT%jvuj*AmxSUOqHn8Q_8(gSA
z&&m|GEB<^ioQNs*)X8S$al|WJl+4bncW=bqLxMUIR{w*t{sp(}FRBD}y+pn?E;-uy
z9Mke}FSaNy?tX6;Xk_OZQT#PNynr+|!*%Q~8?nZQd|f4b{g2ZQk}BK`K`0&vn^1;#
zuHQ7o2~FmU1_`VzQ8I<oafTOs+!K$TVYk;#TseG;=db;z7Qh90<Mu}r!NTbTLw6OG
zSS6V<whqNM&P6rXdYZ|q<>Q?vFo)bcIJ5&fMb9_%@IW{C4Y2+yF#y*v0j|A7-Md!g
zv%x|}lNd|*Rn6?Dfp`!X@pFCFo6i!eL$rJr*o%_|2@gAI<mou%6e}?<!?9ns%yc_5
z<7un3GQH-;GBDiI7L7f$xz=taYM>ZkGEycGa=^_t#@9wUoUtVq>oEDLGpIz1En76o
z(kZo;tNzZAu2{ottTXKZdeTINw&tCC28PS?Ys_pnpC!M=!e<}<+BNaM!9|~NIK2_n
zVZiL()P|RmaLE(Wcc-Twy`H8#hkbpOsV|rBwTO!Z&YrEL$Yn5yAFWIAqSWW(K`$ZZ
zaJ(LSh6YBK|9N@n#EW~||KdM43$69an9w_MAgt+S>M&nUGI@E!E5wT1{ZtqsdBW_<
zA7kY~R%0j*Yo`HrErdO@wealG9l!#QJvJ3FdlO*?P*vvaIr44#O(NANIH+NBH9HBF
z#bXEWIKxMTc{Dj+Hv=ugLi21++jw1%9R<`zTVh(;erCiXEi@|Eh^eF9oiBlM%`I4_
z0;Gl7ukvvZ^fmr)l|K4IkYn#6p9gp7#1FNXg=t?gUM)H)vW+uJUC6s5d^XNQzr$g3
zJjnF$92Wy^Mq@L`dQT{`re9SAhkCsdAhYDN1Fj7w?BYC!sd!eh9$R~^)l=Ho#8Idc
z`CDniap3e1w$CG{NvyeE%fk_QCmuc!JJ=Ll5>d@JlN%1Ilcn@E<xuljWt`B0$*=)C
zV>M%rPmvAdXior9G*QRx0(%>&5GijK357kgm9srwK#4@Q5j~u>ZbFxkyDS1yM1cK$
zLK@<*SqN`mE?!A{)}QmfF}!&r`2SV+ol#A$TbOoKngY^6rK6D&gH%zfbQDmcQlx`G
z=)E1O(nLC;3J5uhA_0*q)er;Hl`0@zq(~9zFgu=m&s@))HEYe9`7<uo5>4{u``&lI
zyFB~Zdmn&F_sRIIEYfJ1`<OOO+|VnEdGI~F7O}7JGyE+QW#a<&(Iz5iUW$ZuiTbfD
zeKnWwe-><;s%kA^s2OO&tg${I!%iIik>4mG%I5H1`)T$m@Sr$z)vRCI^A&)>8-5Z)
z0!7v^xQXAv-h}v#hZEB{UZT>hmTkW27%C(2;70?yl-l|C@C*U+lFvMYWQEMVFEc!v
zr4@R5>Nn^Q;N<wx-P7B~KKI8I?UD=T$)pt+)fFc)mA2OL{Q>@{c(k=G+e<OczE0-d
zgfrQ5dQ{yL&i$fk`OA0atl0z9=zjgzm&Wo(>QC_@j)s)XCp5JAAfC%DFI0ams3F5h
z%wm7H?6V;OUTa111GMd-n09p?OJvRUeWR(jw-;->JaZy~9d^H5i^94x{nFtE2Z!P`
zWRCnxpF47(`CKWzt*y?wy4CPpfgk-y;_TVCB4O$#DM#vmm<P^~430O7EaLb^r@K-(
z<3BF2{Nqd{{to<--QrWfgUTQ=!RJl?6BRw<%iQZko|slq5&QHy(q@L`P`o_(y0upS
z`8LNOj=mMn+Ksi-@rz$@nda*$c;A##hno7kk;aD))@*0gKJ}5)E_~OV_o~FQpRTK<
zczs)ZH~r&l*MSTv|Ags)rHjBoIox}gYLk{z-p~HRYN=Qh>y_BUF}@MD&wvmRuvt}m
zhA|qlUi@UN4q`&$IQu|tn2{21aGb8nL!#;SqC<^3P?E-R8q)xHavA6YBDyTRw<yjd
zv$-KG>iFIf_(EB`-Fp+CpT$#gN~UW2POW@afiM>aLZ)<?aEnmUkFNcONnwhB$W>k%
zLI#zug)0vpEqn9$-ryzu=NIVCejWe<SDhP-se6N38E^<7Y+S^B6@X4|&zGGWUS4>u
z)L>xy#*E&^o|o00o6%mO-1O$;wAXyah~wt^jhe;zJuLyrkV8Z_xF&#<X9pPN$`{}U
zfh~6oqWVL<ZsD)r&|&PeFR)K3J8^_TXXN4uf)$B0+XnsG7d<tCM}7xx$GW<@XN<0^
z-1k1h8KYD+)ugPIC8H&u<$thzFFvbb*&B}#j$U>1^fN*tOHN`WfgRv<Z%qrLZC7(v
zKCg|-Dq5;%FL2Y3=0i$Ha%kC+JuPa^4wF#MVYRYZM(*DPyrPEFzfnNGj$zUyuqI~Y
zZXBxF<^!drr6$C2$ZOlvf9I(!Vw&gNm}4s9V9RO`b8La!StP#Nh;y$&--(?fYb3@|
zmrOyD*=2!(zS&i$#_}(fLJbuE0U&_)%3}we>G1ohf<RQ*iP=!GpESh>{zgiIqNO_x
z@cAAY^w#6;9ryPV-0kk9k}0C&9#jUzyzD~87>ko?TJ`RB?1N2C>_SaWcEB32!PxO|
z#*D9esj9c1fwne{BI{=$g@XF{>16bq(UmPy8)?!Rl0J9trixzX1g2X!aU3`}T?1W#
zCRcTwk7N5KfkaG!pO=VUU44xbKN+pOwBh?S&nhlG6)KxuKQe}&I96ur_4ZJp>PNks
z9bF%Pu}8}=o)?4xL+0VZ<pZ(PP3$I0>{8%t9D^F)`7$=nn6O|^8D%#M-q9XLL<^Us
zSTxYpfMIJ2IUHaQKrV_th5#f;;DZ9<4O{lwg*_}OUm5Na{EX!MQ17@nc^+OIq;{bp
zN3Roj@WGG|j_tWc7pJSc<cScPs1yQ0?^}{c>Qx81djwdWTj?h<h8tK<=DjX%>=+bU
z-whr}W#t;ZMMV{u%>$q#JemPyTu|a-UGN~bwv=$EP-O?NEPBfLo)~=84Yir-F7%z&
z3|Z~h?s)S71&BFR?iz4@Klc4GD*;FIp>i&Cb{QXwOUjh<wq)cG``FtFACh4toIV<t
znASo8vOs*}Btug>U5WbG8w=A5fP(=Yt-C&6kNH(Qg)DYCdL+JFA{pob<i$|pl6VtM
zEI!JMdz^_4vAF#@QYjZc31ltRhlCv1$<fsf6)mlL5zPUDnJ6UYfTREdu)Vtv_#8YT
z@~-Y=Q10)a3`N35$@Vw?y};gCi&5MaA`}h*ANUGLMfoEI9cLu4i!b`M`Fb<0<qb`(
zI;<=V7N}^pf5Ep0VDm$dWNx)*0oPX}aJ;X2V?e!nXG#ayw99p7S)=`X>&KuWF%^ec
zXg?)m*9|v|!v(|Wcd-YzKuY;JV@6jtfDK~Omx9+wzZ3-8l>;*8ebu}6Z>md@Qk#zt
zaGl(KaAxDO57MoN-M~PIQ14fDd$Y@hoS}B>F(9{13Fi)j^zebkGrVGnW4l2qk`DVN
zy@qDqyBVHqX$PiGucXt{sHxB+0|Q)u6&E`@ld~0|A^D-%copP3%^IJV3CtNP0)R@+
za=<emQaB4U<Lh@~Wb3v4*Zt@i{Wk^4Gjf2c40Rp{dwGA<4hF5PpPQBL9M2kAN#99N
zpJS6gbo9{CsuxNpP92K))b+fy+xza|K-+5%qwbo8j4kh0V~?a>iEimO*E!3VF9&*F
zT0331gFllNeBunHs){^gu`vHMkJXiFFHBY&JAb@fkxr>X?RSr)ti$R_#ar=NTy&h%
zd2h8ldp@do`ckD0$!TFYZN*3GLNAD$lns&qkY1VoCb7L)@Wa&&Da*)}sF8!BhBmJL
zH$>AgpWBK**><>QbXfnv@4tR|vr^bJoM<YuGaiQTpa1MVjl)*mI~n7*b~h>}1YNqz
z{keSQR_eF5<>eCXTpQLZStq7bi7i+=ghX>Yd|dfjsGzX{$=<{7h`Llp{;sQ-LrqOq
z%|fIi_eqiZ@l~Y7Ce<Z}LDh{h=!{i;cg8hhvl}O|+4Sn`+dd^8b|?WQPMdv;!hU`|
zYxV(=2EDqX@Yf$d=MIx_+di7zd81+6>?=&!OlNOyW-7KA6pFxUC<!V7F_h}Z>hfmG
zmbHUqyQc`G$QwspV~x@8yKdT|YbTYfYY<3C2FLhMXB~@)acO)M;|x6DHt~;Urg6sT
zck>=2jnyHM50!GOHwuh(T*jCmjGm<nKLk~A{VP)?_lK1E)Nwj8D}A&x-)HiDop{TJ
zR#^~Bis|3|SH2g0_FuEIdHyO7ly7I<(HXttP$5nFXk#~oPm9QF!3wY*a~WGadtZy)
z%(Ey9jZ%|JY_KegpKQ0Z>^Hy+tct4PTI-w(7f`}G+k%1Ijp$0D*GnB&b}mbLZTN1g
z2ZRc@#Mm&q8dy(8>l-~?k*6FtGh?1X!K8+qIGrB*;pXMp9up6V@>~7C*B+HC^UymT
z@%HIJMPI935vHQ0`Qj)ARh>LzPE87DS=&B}$Vk*B?~VzRNG(Jp<8jfBK?N>Oq6$v5
z%LddR-Z~))l&(|;A8R;RJcXcxRI6(z*{MZt1UQJt_|EQ$_Kk$;9FJ)aBTjLjjOp%m
ziLLQtnz`>VesRr@x~lO3Z-&-NvrD_<L|wsC9YJ<2{@pqDxMw`}t(rg9T;tXFHVhTH
zP2m2H9#Q_fV9n1!HRf8e=;;S^lfVc2pb+A$4Cfg6Qf0(Q0yMOpwe=c{{eF^_1Bx0y
z8wvY+(7+vL7HWA{33m+-G~gEA(!LU=Z1EI$f7OB3tp3J*?VGU=iTGe*Qyoe)3U%=9
zW%SS83E~eo<9C)Nqf4IQM6+P!gL)jY4`a2uKMgAfIM70i^fID4g4N{7t1$have6@1
zjS;1@BniJU<|_f)*WTrvgel^juV^)URe0lqiNvF2(MuEDBmCzF+d+DO^P|F}!F7_K
zn>G3g(M{(WL%xkj-v|-yn1KHxkks|8`&dl$NzT*bAD6iG^n|R6AH89`)AlJh!Sve?
zELx*`;!A;WNx+CvT=*gPs)NQ!r$xd;8&TpPJBPh9r5851S_<#ND1h_Oq{jGr0Jhi2
z^O2&=qgD)Yb|o7{Snp$=Zp+Lsd70SmH!qxWxs1JK@ykB#y>*J$s7v<_z{uMGEum-T
zfpr}xZU%*wJJw*UWCKo@Ot%qDQ~6p~OJ$bcuN{qk1hX+Pub673!$3^6_4(fpG1)lS
zkLs=shxI0h5L9q1bKk9G7$y>}viO*fJ{Coy9uAK4CaNv15KoT+8w!eip_KJ|J726y
zhhu&yJRsji&G^`QIoM_aEHav^QRQOCV9+F%$Z2BkxDTe1wM_D1HBLk-!yScZ$^1C8
zxAJONn2@K72Pw_m>NiW5@;}swa;h3K-zeE1Z_Oir&+}p3;)w^NCbD~<UO^$UDqoyp
z!}!x~8-%iyCJ%elsIv*{5$FdQFvZX=^HZTIP!45CULqSQyh<?KP^I)B3+q?2!IwTT
zT+BB1_rkeA+b9!eI4IX|)jVeN7*@l`o#hTXI5$$344*u*<^4?Bu(gQ|Go_=JuQgud
zuzgy{{q;(pM=jJ}&8@F<e0YTpY@DeZb#)dbDF)&Kwnc(={6JmJiaS^gO}&IifUllj
zP0qyaeh_TSxZHX-EP#ByrY=2?Br^)y$7*>3naD&#p9Ry+ImxK7!0O%Q6=1lsccZ+7
z!POy|$2H$9=yx4=FD@>bsq&09mf4U1O?8K@z0)f@aak!59hkd6)BlHwd~R;voI5Dm
z`ervaC!gvcZXWo6k&@SCsBW}RDh%{--WpmHxq+gS>$QJ@uFAY^u`*NL(eT`j4f`jq
zB#6aj${FmqfKUcm3Ti#aL=QA7q|0pbsR`bCal5_grTDmO#h=n{amH`UW6Y8DOX4H<
z2m`BVQ@nuiyk7(w6?la|Im2hc+@D|y2S2-WA3yM}d$)}F64jjNzU;Sbzk5~7h;waw
zCQH}vhe+)<q1{O$<MZmqmcTizycR|%IqvTDy3Zk}(w})pf`I_PrQ866gf0S8@0<WU
z&WdkoXtqG#VP)&9br6QN-p(<7Gg28=L3v(8Grp(kh|%^&{Go%~GJ8L?1@9J}2ymy-
z@9Ol<>^1hzRFu)uGLjv<@+NHKRAi_9CD0T2@1(2~a4<t~SzpuxmXcxVjQoEJwq+GS
z)9}etk^OJB+>b}7GgCfVo_h7uW%;W~=;l`6+PiVz1zTfcR)&Hjs;)amR>RtrA#JE_
z%3c~>f#Jr)B-bnm{rv8e^fSql)mEE)v@x|?oP3l=4osY4C|=ln$*i3EpvEY_ErmqN
zb#UG!Kia*fbfmlgHVxP{ZV}Y`0%4hJX7en=k)sd%^EI;SqouDjCCpr8raUaU{_Xt7
zj6y!fXy@%O``t&DY!Y<R3of_LJSWE&ppN>S2KfaM7YHb-&B`iIoA2G^%?ByznQ}oG
zmWC2qZo#xYR<d4$7cTm_vt+H$y_Lw&s=n<XdStV2?V~me<q;4ayP=9Si-O)UN2!?w
zyJ><|<`Y~&d?SUC*>D5l!`<?S|9yt8yK4z$ZGG!?@{vJDsHrVkS}X+x>{5Wy5dxz9
zQfOKQER&Ypjbo!rP6#zYXXh2cbAp684W9A@@KY)rYmT*~#@Jb|ad_m;UP2fdGiFt;
z$#Qxv?gwqT@@=KP*n5XBU5M!#ZH*QeC$u?QCi&FV%~h=6*GpW=w<wLzN+9J2H~5AN
zK&sb2PS-{?&b-q>8$@(5#02cgfqI?4FfC}T7oNBIHSJJnSP|J!r+JLPT${)%(qUs=
z&t!ho?ffvB8=L49?=SXas@OQJp@+AV%-Wt~a+JuZYenaDim-qWBJ1|uk0DW`9$W}T
zZGDQZwRr-v#7@X#J4X0knRF3DVnSDEk9;GRHI!l#Xr9ny0JPi^{zPg%elp4Mh0)$g
z<Yx?RneQAF`(5gA%Z|?8_17Ws2$iL62XbvoR-mEezqaThn0Z9FVmit0lJxl=yDu7_
z?gF>+o9*-4AZ09#K|8R2X3x--QHX#`N#gz+TqMN<$t|rJSzBBdD@<{C=OJZh%Htbt
zSGK9^>yCyQZShJw?D42|m#2fu0o7a7KR>teVfxQM4YZmnXUpiAS;tnBvv37(qw9BN
zb)NA=ms;UJq+d+-sac+uFcd=6Ag+)_C8h3e*zgm0SLj8t7elW@zU4a5F>^K)yAd&%
zVLxd#K?K<Q2zA_Y$HBF3AADr|wGs1{pN11tOd_$}lVfkHuC(Ojolp7N@yb8k<mr;t
zwBs66jl7AIAS0YskCClYHCwH<xm6_@BZ<W6@xt~I;zyPeSW~U+qNl-dvOq{HI=VD|
zIa9g;`A4|zzqFG^%{+FF<>}($Z`?AfFVy}_P}p<567CtbJB&eSB}X=l>44xc;#!n$
zs2;1tH0t=x9c?_g11h@g^Uq_KFmJI@phOuMk{2_!_CmqVP#AeV=y#bmD;cclvHrba
z*C6PYeP8h4xJgbRnICh!=$)ykf0jn~(@{I>+B}B49&^gQ_8Jq;KIkeN%A}?14{I_b
z!Dsk--<E*%sOPe56Efq{?C!7jW2!7%F?u=l`fyk`N%7*;2f~ai*#k$@g-cw}oG$XL
zHYyx>b1;vA-eP(R?*u0z+%r9?=?LH5w<h#7Fl-iSwR_<B@ks2St^=;fTZKaphZfE#
z^x88dC77!6(Sy^1aG6S6N!(l2-zFvRMtzzU7sVbkpPn#P&5Hw7;dAp=%^!hdrU)_q
zN3mKslC_Q9Jy&yPxS8Ucws;;oC$g7}t+hT@(mW;F|DoUF16Kc0Uhb+4SfdtITsP`d
z1_5bhB<aGWClad@u`2%gU0ciu30J$0o0ypF1g&e$6V+-tzM1AwKjSP4Z<%$dJPh!L
zO)BUsO|Fx;ycu@JnBJDvLH8#Kra4d*a0xRM$_p-KS6`huK{sF2L+&trNMP;{SND6*
z4BR*lhY&O-mNxnv8>6e4Rv3kH;KBKpy-QM%W_(HD6NQIQCJp{v%zL%xuuwi7nV^Af
zGB?ViP17XCsqt0Bxt>$f9lDLE@H+frxG0>oOc{YLb{0#jui*@=-`C5<_ILM#U)9P1
zS2Y^HsDXFpoS*}08;NbTBwCi4fJa%Y{meWr`+=-~3u=Q9ACqe%6(2&BH>di!YFg5-
z!bgu*!)adYcH>KvwY^k{;RbKGs(6rN-NhOEVa62#ut<}qWG@Gt-K031Zl{XR2F)*u
zD+KZ&TpO+5&sW?(KQT4sB6(Pe7g`o5(%>SawsW}nN(MR1Q?}V~Ev+9UU9gf?Lfi^L
z2G%yF=GKq1cV)rP`g*_iDvECEN=mgpm>}Nsh2s!5JgLZjbloxGbG2{RGX^mSjz%4{
zPV?NxBpRF=Rh%xmQc7qsKCGrOzyc}L7sD$eqj(PcZq#*ItBEL2=6@^Y={icJYb2#5
zxA$aJk(j^E62_J`z&~it11&eUY9Y#b{3J6g7b^`t)uEyUjWp=)GNogg*s3HL^2?(V
z!CsN3z;zQ86?Xh!w30>h53GQuSuxgP#Zwp#PX{U^9J)0*O5vG9_w&^zrs-eB810|Y
zi&$uLKeDyS%*lGMs5W`HPzY?9N}dkcTUKk5mxH@Q!(W-(oeBoc_l#+ita+L}MHKzz
zPWH0?V;@f=!@8A}xj3iQ;9N20F}Q~Xk>DF!n@y2<>(qoT$eE3GGPA&Z0Rz(ejx!K@
zfip`;P9)^5Z}&q7A0HEsKG>seCbj($;Zd*Ukl4Dh_O{Us2}6tlCvB%=3L4R%2vUX{
zY@LK=n?gibJq-1a)gmzatg~%#4~Y;~LFyD{!@$ko7tBMKVh&QQ)xttgd?1ze`L57Z
zR!M!W=nJ>l_*U9CPlM=G3684kIP)M|PKX~RrFJfH%$aM{Nu%-ISUYKQ2oPQLoI3^B
z3iVFc3{d?$mxrEq3j)=JWPX5b$2=gMa`KW<Y&0>&AB~>`(Y@{I8%1S&i!nJaBO_Ah
zICN(>T;KK=Qp!&GfYNMB_l+bQkDZ~Rr(J?kuBlX-&>K)hb2wU&OYU4^16_=gq^?_f
zOE@tHv{*UgKZ4Em$6nP&NQll2R2@-&5JnwyW-syS{-F~=8`s4^lS~;oIBU`JCgW>m
zbPHC@gA-;#shOzq&>2LSJcWQ}P5Hr?;f>=c%|=)!FMil}bD0N(N#~2P`7_W?m0h!U
z!;hJRWvcVLZ{%=f1{!Q5h+UjM`dxy}#S}PS4%^GT-|lOfrW%ws#OYYJrfJ{R<#xkq
z74L4cNqGE3#V$F4%Y6HKMHE%?pr-Ohj@pzyVyZZyVaDA_5{4k;F@WM|tID2{Vr&!v
z0W*lONd=S>0XAa65&E`}UzJwH>F|snLkK0OkzCWFN$fQIETD?jRoP%)Cp_|C{`0NX
zC2pCuZh*oYzE*0PI02haiGeX0={AJ`2IWR)(4Ha>`_xd8^jx3aB?N>y6`1i=gQUQm
zE<uAxyWj1L?|Uep=y2uW&d8rO<$tDkJgv1U5A-$RCncFpNfQ=y<<O<H3^>xDfcnc<
zEe$ZV@x1ZJ<Y9<o3<boo_dHJ+mYJ|{vVYN0zNQRXWi1V&#rdUs5QI>4AIeKXh#(cu
zB`OMr^iEGVI<ME+LX==~+h>Q`uD^y7j4%xj8czX2qj#>pR<z;{d|9IHr49g@rbBLR
z`uM<5r6#0Hn}M1dF7C#4y(qFo37)%PvGOxRATI>~A+>WEP2;O$u9fBKb8`lBG!)>(
zfKAP}$pJNQjwA_ZdXjw>%Ja#i?QKnfPqNU42nd|BELQPMSMg+z92_UiIFJ7HETW?R
z5o8mnqru3`!j-6&lh81`vVs9-Wt=D=7<o}tw9Ebx%3SQ8GhR8dBAl;chCDvEHt?!D
z`MmA3b4R=#G6ao7Kvi%-Or1O!M3`SH^4g+F=t@Db9;1&|bo8k%|BNYA`j5sy<Te$j
zk*|a!;{<i_uhR=~xPl;$E-*&`EhLQzc02mK-98Jr3hWy?C-!J54kV9{9(qiNH&Sf0
ziSH#jjO_J7=pGlZf7M9tY!aAj2%t1>>AS?~AqiWpGtRn53)Uo1*mu&{u!ux8vLnK1
z*o(bn%Xm5obRJCSm~9ygy(_B*LZp>U3G%sw7lmlk4E)IM341j`B*@&*bFhZs*61Ng
zw!*SR2>794^jxKzb){Bl%luhsaawg<zYDn!O!y*+W-_}SWg83N1jYn>mox1YNQNGL
zhY19<)WDsiRJ5YE@49dk{dc5Z$?hQ(4(@1VNm6L`hAVNWzmz2t>w6r3qOt+Sz*<;9
zB&L?H(TMywa?RF-J3dcG^?3|OJ#=yDwrm|65<mzNvND45b~UI+mqhu1d|bk>z39)%
z!D8u)2+n)qyT3r(j_bTM%mdeI3R!6d3y27fow7fkK-TJq;zz(nPB5J^Vfi{cDzAPq
z9+*VxK3QW`d+TK~+roP)0ovldTnA3?2<+QOeOOgdPVXNPJS`Y#vzg~&<bBcJ`3g8!
zj|k`SSPh<sO59P8r~rwR%k-Vt0*T1CLEL?Nq-v=Cbc97g&*RDVrez2&oJW1>Lrv}h
znDet|h23-G@Bsyds7FyEem(5at!g9H%{sO1p0P05F;tO=0$>VEr{m%$eGyGKUvNp|
z5*qSAn;@o4uRsXyU?ailko5W?Qi^+C1U?zk%$?;lRL@5OY(mFQ4K!gESXY4lR3Sb>
z0C?DqA{7nJqX9k&@_P%mmx{YJ(bMlX;?5g7bw5opymfG3j_K3~<2rLTO!=`q8>$~a
z7Y8V7&oHp_a&tyHIqtQ9z*<>9$*SHyz^+B1?>yAh3JxypDZBUlDX<{Ozy~Ldd`2Y^
z!!HnkqKeoq$V5ay)j|C8+%Smg>9{laa#!TfQy6(?>LJ4i$MZ~QKr@@cqeIg+6Xxp0
z5A*=Gg1{$%+>vs_o?X5fl2`OJfu*FyQ`~i^X}NUvhFa&R;il1EV_wck6Icug>;9DT
zeYh6iJt^PohJd&a4k}B&4TMJx50bO^GqlW9`RL3A@;dA<As|Q@H#<v5=4}2e(t~Ci
z$!oH$SB-^PKpmx)J#zlGelnDT;<{R_{`I=j_rEu1st?l;BwPbr8~cqOT*eNFJx*U)
z-v=(t(ng62UmV~n0Pz5%sV1280<AgOJiD#{aKn3WY`k`#Wg$VQ#MJEU15{h^Tt>s!
z_abg$p8!h$jg9&2bsoaeBr!H|U430mAN7mm6XQ2z_yJq{7?jq)w{zf)$O=H%RSZ$r
z^GEDCB`-rx%Q#-4OhOQ*+w-e(Gp080fCFNb6$2=bVz0d~dg>-(3>*Nk=Q2CFVd%F@
z-e4+NI9Z<nzddrEsi@x$(9?$TaR77c!eB&DZUPrpW$|-6XYP|fQ)V58<u=B64M@$U
zY!YCeU8SP)d^_oi!O656rxth0|0cmCJ>WIl!RG4m`l7=q7Z?eqJE_)~KO5upufK;?
zf-z$7X*V`9zW%Z`n%#frCa4+RTQ&Q-gI%n&yNCK=bnagUK?vejuPrTr<#GX4UGJx@
zRacsHR>QU51Lrfe8+Y{ZH5hKOnpTE{!-DufScK}k;&yui(||~K^tQY*+CI!-f8j%l
zVUzW};LX(gAhN^SP?61`ja7C;UA;Ez<-gofcDvYAtWpyPfM*H+2p1g<<RU^%POx$S
z-jR?GXdii>b~7=YyU7l58EGvXYrYSbmDJId_o;7Qqv`DV{9b`}`!eh?sa5z%RY_3j
zMI502aL(+8-_VD36aGu6PVY+|Y@M#%bR&bvuP=!`en^~|`F4*hj{$)AQ?}wow(S`b
z=`ep*DRgW|HzFW6MRDm8A>g{kllRxv@Fp#q!OGVp-51@s#>e$IBo#veIqxNIUJx&a
z6-CO(p7!uxP?uTx+6caUekqi0<SQdm3w80XlC9KkJoLx>0~nG`Aqrh>(}p3sQ}0AU
z{3V>fh#_HI?UV9gGGu5n={%gz;Gk;|IOfcdjDz2{({9dQk=OO(0MZs9x8sv<wPPV6
zgsplK2#E=@u!C1;V=qO7{l{xyW#c6$DQ#?}u34k5TBGWI+;$N#etn2A*;;^B#Q5(A
z4bsO!ll0OEP-s%RkT{?+EiyY_X)F4MmGynt4Cn35!SFcEFf70epy`$|66{*COW5|6
z54p76!#(m~VeDI@%_Y)>dK`wE^V2YL-6+KQUi&iuAcN*bGP38vgF&985lsC2YVk~y
zJ?sXhVJ{v4In3LYZW!Sq=@bRQ(Ia-xXe!NKK>;C?-pndAx+1+ya5%sG^@};X-H8_C
zanfmj$T<nI+O~I~eU3!7T3RhF0|lnA2}LEOD*9k7BEgi)u(!WrnkJAo282!>FgAGe
zJs6Da?9=Dma&6{qpDN%s-jz~Eqp0HbuRqMUd0|lY01h}PuAz*1NTi)$r-o`S$Rv%(
zox?sYoZ{gQ3L>8#WI}!{)Q4Rt^NUkzx#jp$XUbvm`~5K8cU^q74gou#eEuwu3bj}j
zcSi=avqHDQ)3Hno+xqhJLFgK%y`UF0yL^8&!K<c?xXfB953b1W;6>`7!bg;-Pgxe8
z_x`|NKat1KUs1upkXU5B0VCbjD-s|_25K=Ir9|jDHNnJyjJ07+e_6-{7(L{`pu;WR
z^>rpL_vI;&VaWCY){1UYkkfkW27`kcFBr;!j{<%#Sp|TqjeFLeVvJ8AK{#S9p{ro*
z!GwqO^@uh=GwMmNay*;$gvh};;QOG~nW>|`EO}$g98aF_!94x;{w%<2{|d<q&7YKo
zHC2}MakwWTjHBDXTX8k2kTYSR+QC(@Y^!$Wvy~osS5X3Kg<0N2jj{Gti=NM3kQ*T;
zPg>=)y<r@<d)KjINUyMx1^_L^8P`;93Zq6H0Oy}wv_y7-YPKpUC_+?WE&$ZT_s!Zi
zJGb<3SY!Qj>NHTBXew^aof+0UX{^jFtncEC^*(Gz5CEc=m&%x0uRn;+Z%=KV^O`h=
zEKs`ltUzPX!M=I-DIo(zhz_(kB^9B}cHqNpQ0;%H+dRLb9-vLj`6{_NvA{{fusYtH
ztG;63B6KP_!OSVYpNtTuiG}5ze+6mB<tjCvv2<yl)FhYHL(0mI66^q4nV3u>E!(gy
z;H^^Y_<ZAm)1uf!T(aB{z)EEhTJ&$|=|Jmdb({aqdH`<mDOQ1p?wI8VL9<)^GQ+9}
z)=@jRQ})wofK10hKCwP74;F9w-DnUH;&LX>zZVv0Kv8Mwb66o5asV|8QBN`f#D-mR
zku|A!IYc8vG(H^M$n6Sm$ofKk1iHrZa%#Q_3+3TwaL$GcwdCvfX_bXNgK95x!np{w
zd`JBF0m$D$Y%|&X$=qW49(WCq8TqEIPahq;WMSbl)HF*vFYwOvY4__=RHvR#QvI$`
z9EqpGYa>A;LwWMK7#X5X>O%>*&zcLYh_8E;HVT^B#E+EK?tZ)c@g_%O@g|!zw$_Ro
z8fC`3r}Wr8pZ!w~F;c!Kcgr|{{x4)^n1aAbZTNSeK;M0KtPE9d$p7lQ2i@k%&;7%x
z|8)g}Q@{I+e_ggP;&(UVuV!`nO8?ax5W0F6{4ZKo9;m<B`FjHP*T23z{|9dPE6Q`c
z`TyDN+WFSq`FXS3WBCAiXH;Cz1ph+E{>I9_q*{(2d~)6VP}|x`i4hp4ww{~!kJT`6
zLCNmZwMxz=_r6@bCUjmpIeq)UzdL^Z>q@=sY^VRw_8+zVAdsdlAQA`{;)clD;*@$y
zm9^mCU-#?F18pB61A+qdrPH~RE<uylf4#a#eaZ*r8uIV^Tijn{bA7Id^dJ3Y|Gv!s
zi_Kx9_q021_&=p&+hd~Rs{ZkRH(va*gR|Y{Z{DW<_wD`t<yX&mw*OyCLGvC(6^|0t
U5jPI~eeg$BNlUR%{$Ak!01v`zHvj+t

literal 0
HcmV?d00001

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/demo.png b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/demo.png
new file mode 100644
index 0000000000000000000000000000000000000000..6c6e63531c58c483dd73097f97cc0403344d6654
GIT binary patch
literal 620088
zcmb5WWmuJ4_dU$sZeXJzrP8oz6s5aES{eySY3c4(Lg|n$X#@n3ZWT%C?v#}7{;$nB
z&-4C1KJSNfU5IDjd#$<V8gq;>=k0qX1u3k%#CK6pP_SgApR1stU~HqH+~I(uf#0-m
zBOFmsFs0sziz~^9izAdAY)#)-nV_IZzyGL#uBqDjC|z4c#@rt+jnVj?s!<x_lLSuG
zlX18V;!|rP&hx)rSpk*=xMiP;s6Ko_#o<3LXE?qt%gib^{2tVm@<4O!YSwd(e~$a)
zn%A+{S9WEV|Bjymr{a?y31>)0>~i-awV0-n%8+IbJqm;{6T-)`y7x!LMMsCjZ_xLq
z-m3;u@=3>8+2P9Zy7@!82*c_fzgE#y%BX__#4I|B-tUC2eF)-Qr12=us}L{p?YIaU
z{yWXjty&g3#jQjZI-kmA!d68Z?x3)Rx5t`5GG|~%#ZaF|%L7fQUyGh2;!!figLtBt
z7cmeK@7$#IW@%`HTv{lX(9?^X?M*T5_T9AZXzGT_c6>6d<w)Q@y0L1%iEq%6@Wtfk
zF{jcpZYVb$uh!;`&5BDzvrnL*rh8=Q9U}Z5^@(SQ@@Hw>q?{@#`7?+6e~Apl{yv!x
zloYkPYhOddP4_Chuj4_mBV`aH?Ae1=M|{qvd++wkU|43ARw`ar!o6|!<HU0w=`S8?
zyzy0uj%^*?ox{VZy}H~9eg6LOMk=d?KMyaOp>;^oe(F1hN-R@Vn#I<`sg4JGU6<B~
zV|-aEvEj?{cQ3Tu8rt;Y>32#Av=MlYMjSP4`f-GX3lra03-`GLYGfE*(;pqc{}^Rm
zSmMIPe-2j~B?QLM$Tw=%a|qMuEnTQ5ljMJGFZ?kL&z-$%sOl~n&_;m$JNTYTPh`9F
z!4lqx`y$aqL$v*d*RB{277yY<nIAL)lLf>0j=iOZ5y53{ItID`;*ORGT!$msC-T8a
z7)%de1osr5iZTlOG_b%lFcW2?8OwY`gCMFf`I(aYvR^*Vsy{>2JzqvOy%>M<&UuXC
zkQ>}IY{#BJao!U}G7^g{PGSjRX1V;i7(z)*T1mX0?;~n3$U`oc26l-tgGsjEUL2cU
zBnU1%{uI6&VwcvhP$Q@;pmD4!_>|>t#ZUM`ax%M5F;Vr~;&eASIm#I&cW<k_-@R=J
z_18TF;S|0FZ}RVtBR9XVs)>Ep>e?a}WknyqW%&LeI76^9twIo8Gfwd8tN4ZvJKA{{
zQQ{e|&}y*4y?Mb6GdU|r6#@TSvi9EB!DW&yKOzj}$0h!j;b9kN(SLq9Oz}+bqvHK#
z+#70gMSRr>nOe&<%lu7^Bb}x)r86H`s?1;^n|(Y(#;+h-AIeb11#Ge(4vx-_s3K=%
z%FTJRtS{%#-??8i&orE&z=n^Hj>s<M2u2_+vtuYqJ2$!C`hrUD;G&pm>^&@pR^Hdp
zgZtfen#VW6{O!+a=XX>CVV=hh5G5qUFchVdM#VQskgyZ%Zh62$_8UT*iTM<@NQ71s
zo6FzYfPVZA@pno)(o{5p785(N>A<4=I}Naz`8#V8IPc!|<DwacX(FmZ*cXFN5&qJI
zc;T(oh0=U6fzmjrY-CBnag^QBvc}XZp_dV)A`#T9QC!Ph`#9^*XQ}j+Fmpel6%bhj
z-xv2*;vx>D%UT+E>csZy-J|T#fx8uGGyYte?F0ALoGZw@-ya*G4E;z<q+JO(Ga!BX
zNbV;aD-jny<FBr-#Kq*6K{p34i^(>B2rWHN_5Wr^P!mACN0S<<*k-mEx-08NGV7Po
zTDpk6XLHH#ufRgfgMSIv`9Uc5==I%C@01kA#l{tRP&4xG|3IgT?x2~(Z(JBkl>JSo
zK+Qz;g?5{YoVp5^0^d`bCWf^$>4C@x+CAyB9K1o|!TW>CgAY~E*YUMNtwdd@gJXv}
z@b+~ac-{HW;AeNwh-wqE<i6&fDjPnAcBysYb(zYt|G~?BJ194jJ*j%9;Qg@G2B`&V
zWxB@)kH~WcIk|4xZVk(dcumVH^}tb^l5elDD}{y*-zae0w<eVr)qXK0%P(0k?<ti(
zw6*sAki$dBo3%0CC*a!eS|;U_pjt}Nv;Op4-Q1Z>Z;6YK_a)lZgG<;&^QH53%u0J{
z)0lKWd`V-HO33F|t(HCU+m@dp7Yv%UYt(EMXq51nI_g~)9HU>KS`S{ocNluay`ItU
z`@4m36!ZSuzQp$N-#ow8SP;bHBz9QkSY4Pd#1+I&BpW0igmfg2h}DV6xvb0#U+~i<
zBz5cjnEu)D{gSQ23db|buK$PV(c3SW=jI&a4CB(~Y8DmUYjpzzM)CHE_7Qt*OOi_$
zbT#i|JI!Ms(`~*`NOHpBBK#|yC|u*ToBrMPg|aIBklWCkyp$Kr$;XLFDH4e?J)?<f
zESqd*$>=F&DWx33hO=+Jm%%G=w2LcvEcOCSIt;%#Xgw^BEw+(U?Ut4c{3F?|-mS;z
zSwvG*t6pEct*u((UCdsh@v2(WbxLzQtUSp^)y%Oy-f+{1v0T^Mv+X!Ltx@;Gj90y6
z{pzvgvCak61>c3|eWzeVaOZu_`(vc<BZMM&BP_|7`9CS7D^TS>$(QRb_?GZ(+`Ytc
z3g-vT9Ayk8r?^dBzs@)lDw8u4b}(D8Bz78qh5cI5Yp$PzLC;8BOvha{6vuSNdcL>@
zZ(>vMdTsBnEZEMZPM_Mmv>BUT-(J}MHk~p(=f>j+7aVn;-ZyrO*iYS=SUl_Lo|0R6
zxihkEy_B}CyX8I9-a_AN7Tu?%A7C4@^K?$UDdVc)a{uc1wDH(=uWxT|`^M|t6?`G2
zhngH+6LlBLg;s%H4jsC~4XZ-eLfZ?l2p~t*M!y$O4HGuvFaI{dKVB-PBW_H2Aui-_
z$-~amF7~xQq<^q~l;x@{r>v_CeL;OeYr%P81~(mdh$=z?k1CrgL1K0}+5T+$mCcFt
zPBgn@dRUeO?emwCf*)Ky#7l5Tib;i&RvHdB5>64yyfAti@~7=fNF#+A1vl^Zy!ql<
z&A`j-j5h4Z402yO|7aH0*PcIuTccWO-@C?=#ypUD*2&ko6k{1HocAv8sp1<7cB|iE
ztOP3ZD!&Tl-{e#*S3G+oX!Nt~3-J_ducJ<94aw40J3)jn_jhBl?RP)%o$2=Jw&m$_
z@^fqzZ@NPNjO==_)ewyk&3N7S<-Ey7XqKt;KgPsiA`t4v?9fR+6JSJGN~q^ENUJez
z;5oK_T;!gJp4j{*ywbX2)wA9$-#PP9s#mU$A2E=yEUloxmZ8Q|D8KRaS#*`+aY}=Q
zL-kYHDv5}W_U@E@tofj;tRJF#p^L9Y4Mbml&wt#hdi-ePq3m5|1?S{CyNz#IyIFI2
z3qz+vuky4NGT5&cFptB+*`t$)*tJcy`kthZu<{!oSMd(>@;Y88Zt}UFO`ja7tZ)*f
zJnUogxs+>VcBY-AEsse#xLPgM_hZIkHrHKivg`S=lN&I2VDiP}kBL6P9zoiT^J(eB
z=Hq9*I@5KnmYsFEt+_F|-0`T-rmA#o-3RvD86`{1H9KEv)$Lz>eqYr4(KD`8UtYIF
zb4CwWcc7xpv}cf0pHbrDSdrLgmzOE(%*EF=>Sf1Wb@R?gn?0imB{$`w4GW`fA>mk@
zE}VZ-LQ?K>3C^T8oc*>HY96s+olvRzGo{`+dThNnr7-F-HfqB?)0A_+M0MDCo3MIm
zzqdb4{)p*_#Ff?c;}%-|!+QP8^y`aoL1oWZ$LD`b=FI9#J%o*X-`p8wsr*#0JUl)d
zH#zKLu<Rdidb4tc9!x&%(Xp>I^7`jbxd^4m2Ndi4ube7Y6Av}V_KKGxwdty+1zA^G
zD`T@2tI|L4IUhTnuH&1Hn?<B!@p!JK&eh&o_3q`?+pX)j8#q|J78bY}pW7IjwClW3
z88Vsc?y;txjL^ZTbGWov8yOm_8Dr9ktethCUT-T&S+c2`!<+W-;XgS};YW4#*y(MO
zs?qS!Kk+%cGlb50<8V3l#ijK4C$>0+o>$!N+P2a4Qi9^LVw1}Q;auTozNUM{$F)-j
z;%N(M3Ho8)1A8jx<<&*YijBfW`n(O3Cw!+zzqVOt$ZU;m&u5uD%r0#<JtI$^3EB%{
z3oU!EU1^?oo(@&@31#$o$(-#MyLM>|j(xC=m`iS!@@^hW7#pMi)9LoP1O*K}ISHd>
zssyE96=I+hbP@b@!!cbJ=V(GLu*LVs#<xt_29$ze?msSRj+lNkcH^kx+Y=}Z)f?}>
zqd2=#5E4h5)zuf)n(wxXpAL_m^kSl@u%pZwdwVxpUYs{s2X)S(&5s)&ovA#2?#4Af
z|LM6#V&J#$iu<-x5`L<qS|d0>Szy>nYXNETh!*)DL`H>X7X?KGMdrDvnj7SA8hYvj
ziP4sfmU6jXUNaafE(BEqf*%88^$#gPEGBKM9UlJn3N{mA{ZxRUa`S^b+=T|vh_b~j
zMSokaxp%t7SlT{eV&-Z}?|hmdoSMG9@%iL>WJr5q#FOAMJ^39B0vADn;`pJ!5Geoa
zk0LMN%ONt))S&<C2ma5){V4qw$g!dSKfdkX4<mwLEYqQsR-Xg@&(r?L9}Q~S+5hL=
z{eRC4p@d+b7{XIN|L>vs?}uy4#q7SL5*UB{#Wl85zax~yY7}HOUiO`fjS>z^6*HJ^
zaNpl3D(&*UKA^}CA$8@gcUq_FPGIJ~S!BY5y2ZDAHEPfCINB)pJhACx+f80{o%61K
zs#zwYUaGfsccilP>|&?6UW_2l&u<DtV7KMY{!eV-j+LI2nJ-VyMNyz!wEfk#`tCPZ
zClVJeHu3}81iWw3nTxnaefF4}FR3iYzVtj{9HRtLc_)!+K9Duo=;=ztVj!aV=lRA;
zDz9VL#5XhVUs`iVWTiK~Y5c48Z4lV>^A?ZMnH*Kqy34B_-vsViH}RO*SoZEVPGk<@
z()U+dYG{=kzb}4O`DD>V6#)*}-vF-T*uxnU6@^gEdx1e$Z+!sc?^}Hm={fnvbqJPY
zoxucir1dAUsS%}9O#dr8vsgI%9-n?uOVHWVPvqB!hbB9-jiPV5<~lN2q91p`^xXeE
zbl)h<U*4*oiw`FE=pfu$ZwuZ0QqR=IsVeZFtWuzl;&-)?k&&^lBkrJpKwW3u^PlS0
zJ;-i^m*v3WA_yUv(=%$@*@mXBBU>Fq!>qG(dPVZDUpDjeF3%1wMuim8_)L1&Z$7^)
zq9ft9;h5uh`OAP$_hMIf2?c6HUFN*0{#d`MVQN^60=e@l>dYmZB5Xtc1spgOXBXz5
z;d3tI6gJ%8?x?u7N&K_%cx$TrN8mm0w`z(N<g#Lj0N8yBq2IkF2=`ZQij!}8J}-Gp
zm?WTTJ()m62}05DlXf`JJCA&RX|lKYt4%~ULw}QKl=v$08$HBT6u|-AX}b7=NyPR%
z-S^s^h|j5@_AK3VJG$0!HAo$&7I~z|8@)^FUp?y~$1xdTGCt3X!tj?m^PbH0Y@QbZ
z5Aw3(ph7`>@YZ{3!%4W~-wfrQ<&0q2FgKlMoe3GX!oP{+&eAf~F5@{WH0l-YrPCb=
zyr$)xEYhhhPnsj*cPXxTGt^esN)OYK5LspE{>Vr_Rc-6~QIFvc`1Y0vm}EFHt%i<$
z-~_@z>MRNaEC&s8gC%D(Hoh0+8UJ0hGln*N0M}-I=DH(`0=B3qqMNMeeKbP%LPy;u
z>suYUuXR*Q3Czj$xZI>W%mai!3R~#3<=v0bRgIe&hwf)tXlmc<)|?sXxz|mO-v2F#
zZMNE=beL=M&ib^?=d@<f3KH~Yb0f;t#R}gzK*R;U7C=ksQ7#_g335^zm&fO_X0P1g
z{BT|Rp$zYRnoOf4>#3^Fu8)jl41!eRC{P9J%r=LUS<h0h!vQJWnahHdF{b8A2i3fY
zBz^Crm+mV`W;9X}q*>_)d5RfKJ;|K05}}Wh1b&4QBxFlPqP3d!>^yj?sU-XH$wL+j
z0>r!;bd-Ht?JSxEK&{^pe9beVM@7Msb2-qj9)5Yc6sc0@^ybZQL3I6tZV?hGlI?Xr
z^&9gw6m<eGZtKZ7{|~81)WdOynsmkC>T($F@$Fk(VR^AJ>K^3wrCfWOp41;D*WDk@
zA!pF@+~RaBzL)Yd7&}2x=&aRi?50q8E|Jy9YmV1W2(pDQpT;*5Pj0TprR(OlUAyX)
z*?e>Dxr8z38gK_3-7)NNCuqCsnNCgFmWhN2_)Q~B=;~0l_Ud@*+o8|49&~o}#m}5I
z9)ZF1(gyj5Ta+mNzd>{>E6q!crljeg4K?fdw!=mR5^j#dHpZgroi~PA6DF&y8JQb)
z#Uv+q(EnRB0_ZYRcAUliY@Q$hVBv%i_|N%V^th4sePq;Vcpf6t7~wOt;a6!hQ>Rj(
zM5$F~0Eu;BbvG`6L1*a<<P+Cvl9>pjs1!x!wutds4bz%}p1l0r;Rm_%J11|&da|O>
zd@TxmHaBVQg>QyNlVUPQXMxAc?<5Y=k+j_!`z0^_T5hXx1iR^Pc*g`|LuyX`QA)?t
z7bn=lm-E_6Whpk=)iy`&{%X45Gt`!&#S26g+TxVhFgD6eavess0>s(=pKcn01Y~h|
zp5PY*8&0c=$CYrh%y#0(Rq87p({)YTtE%=obKrj;^b{Ps9LCysrPtX$-G(H`OEqRZ
z0zJ1+r610ZHbW%|%U2qQSrk=6A3gIjGEXDv4?!e9$q(xCzB(G~`Py4}v_90H(3iUG
z``<#1fI%nG2aj^%pJ{*KW23wcAUedGpm415Ri%Z@)@0=prP%Kcr;3GimAcc#&_DCv
z-#RRJL??=7Q^9MZ{hCgtweb5fz5$3!cI^dq<6DzI=>%1o@6EN}Nug+R(@9Cr>@vq;
zo`(p{`@@|%VJtLn)2mpY6X8|gzZsE67r6aZpS8Z_{hh8=b=jJ9PWL+}AA9d{6+Zr~
z(2980?8iF{)4i6P+5_RLl<5wD3aQ|rSg-X&di%Z;ApvTDp2g?Vt#&2ZGD{zqbNqu}
zP@J0h$6UFDNm-_M{wT0%IT7xyS?`k>Hn8jvq9290dMRAiUj4Fh43yp%#~%TvUi8j5
z_6e$#>Ioj`mcB{Uk-s`WI&g1Po<LR!M;`#7c`t6xblWD;ux{S|TbU6guwtKg9{gtH
zEF>tks37g&kWJm36gLq1CpO1*tL8riXWqM<?!Nx!M;0BQj{|p01i{*_+hqC{I{cF`
zK;>#sj`^c_@cqv1nfkX~ih2F;HJT;rNP(EPn%~XukTBBC+-ybn;+awA7v1`<@vlbI
zT<<sZDcW($@F7^rh2*8foKEM)&IUsn)jwT*CoUtm5F~6MBA;;kQN{R);lX*RNEFY<
zPvo?Wudp2JUAI8^!eJcGV-@<#{h46q#(^lXA9R_aBk{pw(%~itt9`a%kY@|S^|R@M
zp39YEdIvqug4d@@Y|a~MMmb}j#L<<pkNME^l<A+kE2IlN?n&WRWG9H%lk^oADiRX<
zPsQ*PDT6tY_iuQ@Jh9JivpU8v;*Du<>YJ5!PT?G^tgJD=rt+Qwbb9kMmB(JJ%4))p
zBohvW(Bt<<v5($Ypx?x`o^T#ylDaR|EHw0DLn1|^;7@*rF{V@V%0XX|Z_QORdR^oJ
zjvX81h-H0+j8?&h#5mYb1757x`09yzF#`q$hMqE!AXrceo@SYW|DL3k>K+c`57_WQ
zg-7oB(U`uqV0w7j<O2XAt<iqH>~GAELD`d3yBkhyf3a1a;+i!23C)98@O$#ue#X@P
ziQSU#tbS%#FSpIK=9k9fS1X;DqV|~oB9KT2>=BcWxtQ8#2nDE6ej)<!H8-ht*`Yjn
z{7P&_of<oV))0@hh)Mw&S=kuXd<CLX4dwjjs5rYQ5bp)&o!PV~cG0TJeFwwAz=%5M
z%}Jld?JmYrOyW`x{gIkuB6&45T{scI$3#544StTir~*tV7fojK3?Mkk5+c?2qpU;7
zqCq`Efi<0PFs=i}<yVyIf)6>W()g_Q7|EoRL%(LEyduy=80bSMPhQ%(3SaJut6>Hm
z>8{)e9SGk~kd3C2$T9AWS$OpG>Oa^3681RIv@hLS;QA4RQp-;oB{Goa+z`uR4)1d(
zqt(nRLmRmW&gpt^_g;kLnRLh3mdN*`L+vRI8g~~}YTrJ2spwfZ_421O4{M?5bSQI^
zx#vK8I0?aFPJzb<uk-bM>rzz?)ME9L<fTRt$yjWYsbIfKw@hD0r#;Tb1;t$1k2;$x
zlR_naG&mhXkfP>-+2E3;zX@$=$QAyeFSDNFdaPEUbc(7RV2xq!yZ5>1ON@L9S7$P(
zr4=K60Kl?s>dXV*(LDD2Bkz%MyVej~p|hDWKJ)*G6_E-I%_|CBf)QyE8YGtNfdbAR
z-t?*JzD*d~vgXf5cC%h-Sy@?u46KbH6qtZ?rt{{g!{2dMtp>NR)Ri|YDYna_+P0Nf
zt!Y74RcNSM{@074%*%j9eQi|GTF31@0@YmR`tt1O;{Z=e9DHb_$5D*$<q}2S7r@su
z900WE7P=1R$g~~#B*%Jz1GJJsj5iNeJs|%CF3uPZA&*_Az|pYkmjQH%Ui!@0W2fwx
zAPf3il&1CPvYzZ%8^|`&$}MH;Hc(PrGjx~i6pqvzgOl_L-!zyfPD_!yw@!;?2vR$w
z{uj6&KsU^@!Men1rj-h!3BZwZKrF4YjVdfv(i&>F(&F|Cm<JzF>!fd2$iH|I2RP{C
zAGE5ZND}@VqhP@9{QcM8J7udcHzQL7bGy^kY=$3e;%x*jc0^NuH>C$8OmZGTM{F>K
z&-k$KPUA_J=T4*9Bg*2FjX(lDhUV*Y%jxB8LqMK+pc~#a{ZK<Y+NgWTYqJbb<#T@G
z5U3ne0$=0FfBiFv&E>DAc4r_9D0<v*4(rKbf-L??;?2ZyVggHDzIIN77L?k<K{@$l
zY~9o+uPO=@F=r0*+=q*<#`Jxydnp9mlt7x!HhP)>0npSyilno*0@dFRzDD;MsG{0l
zX*yE6>3aYMBK?|TlR4fPw#n?pQtfl{585fB=RSQPas4{;`R!sGd;^xAGa=LDJ5E1p
zFrfCRu^$D%(&XU{2fh4*CEDD|%+mkl5nU$9o$Kq9=5}q&jYz1!POXDXI5DU3#qpLh
zLmCwlD2){myu7>zn6LFl!YzR|h`YYJAfXn}Jvg13yEe^|H6K`Nk34C-3Bn?wqgP1X
z9pQ1?HE{IWUzR~Kb>7)p$9%7o8P{_61Jfif>sQ3$B6H#YXM<Kah`WYdnfCVp+v9}3
zMZ?+;Psvi33A@+(aOwQV7lmhaatTb&jM~C5>Yv^%<A%(b6yQIifsY6JZC8vOE=3AY
zO3*ALV>R!FR6S<NsPJ~icYuDwA)I-;CwCWdE0yt~IAxR&U6GCi<_s)a@f<t2c}Hvn
z8UF{9R6GSOWKcyA{ILDy&e*i6fF`r{2(mWnt19a$uag&vETT`}iZV_{9oHT;_?9>P
zEgYLT94q$vU8GHO`tCm^RtGxCJcxDYHl!|yOcDVQ2nIoqLl%;xX_jOAxb|C$0!Z_n
z`;uF`Q3&x<n3ID`cl<NLt-#0_JgOIn$B#kosvvV0a)Q6h{dey~(<1E#Xg<Qj`Wsde
zaRNNMp9bTBJpr;fyEG7Qibg=y2a7bA6*qPybDfn7JWsaSEQT2d`Xg@_@s+N>-qqH9
zv!puf)mAjM)H2Y{34gj1E;h8PZPNA<SNWqoqSAb-IcG;5E+enH{}BG0a1c05ZXPK$
z!3Xs5@bCf}1HF)`m?bO1EmnA?>Cz^r{P1Z?Ab*z~Gb^j|-3JVae8r63yt?TX7}Tsc
zH5wFTK2bH}LqbS(!|B$v6_GrhsZ+0ha2TflWvL^mM6C&aP5k^$Ylu7O_v0K_dpnIH
z1?eJ%uWKy%hW2vhws1lyAe^6-uqpnHW*P7U3SU)z#-%OVdrP(~5%e2ltwCv`+~m*h
z!p|hL<tNDd&Vbt=A`D^>uCgA@JbGiBLB7dz32rgVr{$!+FacZHe~-#7+%KXB5UTG%
zD5abN=VC1@FhED?BL((DdU7!p1lkF=KR#BKe4_EiN{1XRpSU(vR)pY)UNQYQ#SITv
z=8dr;>8~=~5x#+3a!Q#2>dY{D`Q+uP%Iuv05&STqD<94L$=(SA_Ca^7q7V^_?IBH`
zF-R^#XpwqJjBWGPsBxO0XVsc89>`ZKsJ_oxE}%OKPL2<e<=Br5?sYVxG1%no*#>hl
z4`VSo-PFH%QrdKGk(g`8kKE^9e7g_aNino?A3T!#YrhqhHof9#_`(^PM}B7xFvq{*
z^9r|G`1V3biHt2uBM<NI^Lc>2St(zpqezWvG|CJ_zfGyTD#td6wj>+x22~vos!UiG
zd@}s?VM4BqM^^Q#p~QevZ72bAMD~N(f>&4`#d9~sTHl~gyysgWT6Io^wO_ve9s8n7
z!oo}d7IliNWVCy<Io3T~s45|7%%oGJ^ysOkpTEy#^1Eb899$?8HTax2q`&CYQmK#!
zBGC;V>=!}7F>C;+hdis5<h@|i%k2@D&8h0rOR(pdZ)SaMiZ~X?QYJrNn4cb_q38Y!
z7ZXrm`bx_&uJR49S4eK`ut^nvO^qxv$PELXi_;&DwLzAKESoI~uwme)D+*iFN0-xv
zQe&PBo0p0O-rYaw6E>5Ru>ty0j6RdH@Yzyf|BlNX*~Xr#VAmRxy2>GZO)T6)y^QaM
zPnp<!!1`wp7Ms(Wk`bK4c2;1ihf!&EP3A=>C<E%S%bWPqovs7njF&_@!642u04kHN
zvjkJn3K8=%P`*crLf&;8R^w5wvEmshTNE2Xyyx%lTZ^;?`N_f8E;EiD)1nT&-w>@9
zem~e-YEMIOv5Eb=nRyW?bK_x*z`<{J$k<=f0z9NhXF^29#8A2KFWWcJMX*dW>NV8t
zt?X`=Ht)B$>_w8^<?;7pa=YBJ^~FCjmpp&#*L3K(@*5+90l&lCpDx?++Ry;LtoYXG
zpA-@K3t#R{)2@hoj+EL4=+L?|DXbGdm%rxd_ED51Fg>q5iGg1Tkg6#DZg?FQ<WJJe
zmAL^-YeAc@j#HMq6Z!}@O}S1=H))9_sJsUqiL7b*ii;0Tb^-hH^NWQWA2K<`Tz1@r
zz-watdg-s0C~oX399B;_tn(t6&<Yo30sRRx#!R&y6$x3Ag<kd!a#;eGu;+^}R=e!~
zF+ES;`b7+U)+kM15JL?^()8B1eoQf60EO;~dyF5sQOJ%7CHBx2yotU2%h(oh!pt|9
ztHQs0W6cHre6j&Ia33~I9crSfQLM?jRH!mWIbxaXtFrV!m!N;R*3pbe*r$PawBcNF
zDDJW9a$brJTYK^t+aka7MjUVZ4f3fq;eIyrm9zdfLs;J6&#QP!Q}H(!%{TaDeY~6#
zvv#Fwg(`!^TI@*z?&YOJn`+}7SP(c*Y|AeK{in3DaZzUns|Vx`rubBxAe^^!9G5Qh
z9$vO&cnp(n9m{Ki*HuW4Z=BK68B5=jwQ+SamvJRl!@dG@TFAGZYiiS^TluI;iTtoI
z)S@Oks8vD1B2Eol<!mv3D5_VGl)@;+1SO6P6rWxp4yao*En}pg25cv~IdJ)Nb?aSL
zG#l>-1CF**^Rh^Nf5vq#X$kxQdIf)a?8U?a&i)bG(~_A$5=U@_*=u`dV#5%-^pKfE
z`@^*X%to4<r0yoEDz53FUkEbu?WyWMoYs4Z-V`)6Z~am*%sXf|uZ+CwwDg@Eg&|mw
zPeFS$@aM52z-^3srp=Z7Qi%&Bj}%lrLs}3+v)%qd@Mk2yIE5E{5A3D>YOo=0cyR~o
z&0wj%@KpDEKafK87&NrBouGV?&Q&*VRZSVU_(Af%0^986`apJYJ<0to1P&CU>(~Hz
zy)m%1v}P+`|3oVUn@SG9^tfT~6QDky8Z=^DxBt`i(PXO1di3$F_N$2(ad0TNbQWg?
zFi{IToH@L{*y#q!dR3a%%DP6Q+*sz#R8<Kkh0t`g9WF7{29e3neue(YrXPb0G4Bj5
z(XNjFBp&QRcIR9wNd$0~=dP@`xp&LkM4n=h@;OPjg%ME$XT|PmQSKKjJO*GdX)TZ1
zIo(z@K;OwL(6ESU6!gMId8Yvf*4YM@`O=%k0VxX3P>tVPZ3mrMHvG69y?N|6o&mg7
zu}vMw<^zHlMp!TL_K69AiD*P-io5I9BrV9T$mjN~&wd_%Gu%i|2jB-dqQ?`Z9Ubsx
zErKv7O4aZaX`B!`fA@nG8DJS?YoVF|H2D`aF)_^DFpp9d%7r1Eac8?fvHwihIvx`|
z!T_hEfv@yd@Q8G0dD&KN`9|+Zc*poE3VFS6;r0}Nyxb%-j!}c@IsgGd;)D}SA(e-c
zS}NkfOv`eGvGz~FqkU%}9y<AV8kRWnI_^O<a2Wt<kqY}vzvK(ddXdKGoMYOP%;s^p
zres6S{GZDKJY@)$_32pi^@!Wy?z<lh7~nPqv;cBp41AWhr6_9HaY58WM)f#w*tnzP
z*;|p5z-{nnGwJFWEzxE@uTsrM+=FF3Z<)K3dt-mT!B+oBVfOH3{}!5t;9kcQW^OgE
zud*qbVFJ%ZF6=-x!6RGZeLMl!bfYJby1KxL(WU?TTpeB$gKSN-cGt6wsNS*3&^<HH
zegN?D4)oKDfJqUZ=O7kPoQ^d9>Rr$KD8WOZFigYEVZjaV2g&E|Qol-wpz1n>Dzr%B
z$M|%2AtojU59yF1aV(%k@MM}A@aF^|wtr>uZGdMjN{fu~0TgBcQh<Gve5THs{on@4
zB~@r?XhPEORnRTYl~zP>?vS}oFNEsk>^wjsG=v+U$9+F1Wkj4CocVWH1i7F^o6ECT
z6;Z%X=99$NCf+X8KF6b#4e^P&{pwi1APonP#4xx0Y=Dm_&?Mac_dU4Ok{$BgGjUp6
zUqDgiqQ@~8{kdXDO%B+K0~F~=1H~>Ee4h-;)KEw2UoujEv0eWs#kT2dWf&Zwms_x3
z@6Dgi*mnNA*^glY3iF`f6Awx*(Oqz3xJU*jE8ZA2t#*BsJJiX{S(-!kK!`ca4bBsZ
zd5aCc2Mm^*$jy1SK4DZMN(I#vG4BMed!KMy%fG-ljJH9PW;tHl=4(;NYNrYRPeJbi
zV4rU7gDaCp|CIiAS1}^B4u77D|A`>sW{MwCAwWh(1GmhDqDbYsJw9gRwL1XBQ4J&t
zpyMe;s&7nu_?IgjHs;h@fp(N_YgYk<A-#JeO}7MrF3mjxCK{UD1Y$B>N&_S4xx%Zj
zhJncBZBwBl259yeI!W*K_iOtLIH%zDU~VW;pUd|2gxV*)oI@Q`Zz-%s^$K(4*k<60
zs{n&zeWWPi?_BfTv@0OylCnBoKu04C<e<V=hp`-Q23zCHDM4nTKxpljJ8&n;UcVhK
zQ2vQ4Vr5p7uXpTJ*5J6RAcgW=8XRyV49UDDK0Js9EV@QRh69M^MM=arjFuXVhk+lQ
z>Gy%MWdyi#={{={_Z?hl6TsJ$3zC4O^Vh`fX9*^@2)W(44Xh&aQyXCd;G*tVIQ5+y
zsP4trr5<0A#sQ9A=e?4M2%Iuuf6#F1CB#2aDEHN-q=XsDAfn9NxB4*2_|$wZ_oVO*
zPd;ksMFS3a@%)}s2J;<sBtqzj7=Qweq_YI(0e1nOYh#cCrCVh)^8^@j+)rLPs<@1d
zsRw!TIxMp=<sk)k{k|9wt}Ws)K%s+A2(c@<KZ_yMVHM5U>4A+L(fMfo;G*(je@!4v
z%@?3Y%u_)bHNM1w-b<}CwHk=#w3tXE{nk{q;>K91ty=j7O`3L%Sx#{ISkv4?4V~9v
z$4;y4|5EK#A@5f};QU40MOu-ph|H^KWPfj1HJ#k|`fN2OF7AO-&$XjgoevH}I4Lh%
zKW#4(GwlQ*x=UqvOID$<46vmcxYb;fF47nzZ9h9%_)C}Hb+D>Ef;RYuUPG`Pv@a2=
z+{0Wr4{^b<V&m{Qu7sJN+T2nqX?*D$8~cNKb!XMcCmYe-Jsy(hM4trUJmG-D-QLmh
zXg}g@V6XO6^H@p|H6mgaqPT#q2TXW+BtM=?Cm<#xqcm`Yj-IN8A72t=STk`gf_saL
zVDY{7;`KNzc>D%f`N|UwrLSpZkS8)NE7I-?v~)BOw;o-h{@PRcjfi=5XfQSf7o{WT
zViBDsrn!{2wMmTz@R*a>%_0_zT|=e#x#-xybEB!FK-2KCc2#i!I?+FS!0Qjci^L`g
zz&JFJmazu5&DDj|b$hh2tQJ7RON{wknrmx682GsjHf(1Lw6z1!IA&lxPnq68J`8}h
z#_`_?YC=ybGTw*aAVhKc_uc@tWDJ;Kx$Dj>VjJK%Vjc}BY$8sJIOO$tI_m>0IV6tS
zk8n()-JYj5hPki>5QT$fuf?lBhv<Q$k%JeMw1YC8Bl;GtN-wnX<APqb&5ZGMjXje)
zOz93dN;YJ@NVJpPz-W18Y*#MSaUTL7kM;8)b)WY~|NEF!1*t6H{w=`j%yIbBML8|1
zj)cmu)ONsG<8|B31RXCHmyas3k$y}YI_`fymIL}R&`j`;a-~!%dBtg@l$x4qMGzeY
zzAuCgawDI6-LCv4ei6xhY9jn>7Q=%UzHN$p!U15S0d0?@NFZW2m0lmr4d0T|3GB>q
zm;#FQ$xB&q@QLpWbZ(ns{7_;#jYh*ahA0&L+ruzODXtPWi-xZAL$n`ymTrfEe)lji
zNw{8koNRB2^(cYx*xiAo*-lnkCREtWOk!>DsG!e~<_EF5O*tALP;$U@q*^9(iZTFl
zI;8n9AW`8TZeS=1Y>7@aqoWc-WqN1iFX11N^pqC#0aYr@2b65XwS2((1dqp!*v$Jg
z6CW!`AhV2(I`fl5B)MR+)+1yY!3iORV7{XciU|M&fd{W2!g70Jx)=rYC}C}jLwrau
zAjc+ns)<7ZDR>CTg$N&%56yP$OWK+z=+nxtf7!qNtrvzS8q53l@2!r*ZB><k9*)8Q
z)HL_?chBR$@9)ID;IRh}E<^X1iKRnYnbw=meUAXRIIcXv(iQ`eOO+5oj~cbP`%%ZY
zn=jGH1E~DeX}h0TY+k2!`9%Sj|FD|>ZXXdPYXGW6)@%e|j`t!7@ytPsZLO`QP&NO&
zVY0o<<909uCrad#Sl}{{z6~i*yhSL?P`rgrB~;$Jw?HC1^x|-xdZ+nDh(s}Uz*rgs
zqKMb;Is$UiYU_JfEY{mWu-711Z@M5OF!ifSo`pWcJOQ38@A_D&eh>;TAU)#=w=)62
zDiVU&)~(Yev6+0>saM6W`1U5S)A{p#q|Erwamo9kCTQa43jvy_1_i>8H8i8-Rps3y
z^?kEgKZOtbCzL6>HCW(0_#jvbNYS?_p7|n)tsIO-BznGakZpc0_ye>#sD#4BOOUY$
zpvl}((^aKKo}nGozqI-K6$sf=0$r!*t=901jzgp{2L|Shive;Bpd+~jzzUMhHHB>$
zRsesyX=j1_0fUlA1Sv0`4eK;EWD37#zl+gi;YVOq0zQNihsnRm`vPgZ65ReZd^}F%
z=K4y)clDneM3<Sqm{x9JX%_LlW3)h-`Y|BUYdVnI^htv|{T){xE!LuDR4*=z)dcid
z5qG@Nv%0{@Pm`XLMXS~!7btI*(U+<ew=4k9z-z0^RN^tbCf=|2-lY8<jcKft4-X#y
z_<}nobj+~g0^Rty4J;)gPE{3^WgwZFcKq)Tx-Jg-3fs;5)gGPhEje`3CcvK1S**Uq
z=6-h>PK>yFKSku+OfUS{X)qB6sm4DAjqN|V33{^Grss3w$)QN2oK!+YJp2vM^IMk2
zkDmwxB1X79n0}bhdePpk-t6OD2>J?PB;37wb(u)kBLwBU|BFBDV)9`)$XMUEeAuPy
z=bNPofP1AZnPqsZqunPpk2tEf=pm7C@JDuw%gqO<RD(S#Rd-x&1BU}oLsC}b-TCkP
zU`4?`?5@Zh3Ay7Bw3IB+D>>>Vuj~s1?LOLrrU-Bn|A0{%%%@};NVbI6FIpQkTsa-}
zs(u=j8nZ)da1qbcezt%EMeS)JZr#MXZWkqpN>*-!06cjmbhaFOOWmP(yS|S`KKKI0
zYU)tqv_2@r>}j!<N&Zv@$By6sEjo4=IP9=w+lIX$C(ugzg;&kZckUkHcRcrxN%cZx
zPPI<?p7_#QRZPU&-c(-v8CeD-Ul$>StyKp>BDE=TUPJ4?ic!ay-+a<<_Sx^Cj!YzO
ziy&jvt#e{+2Wk9}vnJH3D}}qXU;zV}B`C0RsTN<Xr=GNSNGj+*j(2)_(3cAuK=ymr
zq|!q*gxgxoy8s!D3UCTcc(5S}K<|W;@w=Q>c!5cQ<-_%%l?y;Sy;hG}Jj_lrDtfgt
zJmj%ZoDF;rdbrJ|aA+tgMS-0d-4>ha+ej)7?%lc0HTa4Z-cgwavvl{i2cKr20BZF|
zDU@0mrXxN-cXM5x<2zR`$@{?HFBoGj3;i~mXE0u52<hH^_=IMpNTa&GAR<oD^_jlM
zdM=ykZ$hcA$o^Y3ycV<|=y}`$?7!&}hDlL`0^n{RXUw_yA}wgsPR8K0NJBA6qeMA7
z2*ch|H?r02tKqLK;wSRXKy$g#JkP~MVnr2g=BD20`4{WJ=PbxSufxrkr7G`zmHQC7
zknYk1&PS3+(8DPRjn~EhEhz}U6MW6@2y{tsjv8`gnvpwS-D=aohFgp;2pecHd)qdh
z<?W>E)q~!GgrJ*+3Uw54LX(&NUMdP^`~gIczuEyC2E{8&H}cj)lDvc883OJqO;^X-
z*Z0wK+EBUi`%kU|Q12N7r|lALdb)pY1$bv|mP2{r^v+Um03YOq-6!LtLB0DRDoY}i
zPs$u!COIyy)szRlk|0a0#S)32BrnCuO81`Y!*l3aj5}~@7lKFzGZYSVf6l6Z(c$f>
zJ=DJ(gIz`rt=?O`0Fy~WgrhHkC`(=Jt=GUJ)cB&~KI^W4yd-w`5Z%p@Gm#&E>Iz>D
zphCClGA-l4!q`)u%f>%_bI-)crY0YUqtbjp?9sDVG&MQZza0H=a6%sY8=y~~`$$tf
z(FjjF)IsW6#S9_IZNSevK*ywK?_|!G8R(0?c*>CbIi)p=ca78tRS{|{7SMCooix{3
zne6Nv+m;(1dAcT%mS3>T49^=Qf}$`BoLx)^*%Bw{DY2htF#vqffr9=wcaWz`Ql=wQ
zr!ei9#3|@lj)#qvV9ELF{Q*df<Xr_)sAO;P9<g6r`@^<rG7mDrir7*<vJWW<R^9+m
zNHTeU&iU~r*3t3y%w`cIe>A1VXz^p<1s!}`@Xb_!0?G2Rr1I{K0}n7^5eBZ%$@U+c
z=`-3G$VPdnUnLW$SPJ;j;unE?3as4Is&Kyd+?JzHI{|5IILh|{hlU0_Y}{RrRXi7%
z(fahfGB&^cnpy5B?*w=)DX5Q*qB3O~PjXLmOdIXQl@$dqIW2~zA2I8t=JhIdAxb^r
zr^2q+?Vl_X)67_h`8u%wDRX7_KrGf5VgHl>4D%bk_INNs5f9kegqE6AXWqift7@BP
zz<}T|vS<^Fun>9|=tmDPVD|?NQXIN5{F3mPJp|6*IUmmS5&(iatMlwJl~xnwl--A=
zzE^S<BcDG$-VWR7Iz(eaI<a<y5Hh1pNv2!y&d2FL84bauUXI}3jsjzI?Ye(!8bpDs
z?_y*g3)rYD3LLPy@wY8Ndu_6>G+v)?4#7YA7!)xN0_^U#ENy(WFTyAPeZ9$Cu(Pvs
z#p;}I$mCB^DBNjtw8uOa*eHPag}yiQowBY@A{*cE&IaVassDGhns$cK?Fv9t!(mPd
z&;H%M=29bY!)3n*ph`40Mg5@844Sfl)p{IRs7C$bX;7&}&R&TagLVl;FN~@?ZV-WU
zAHsi3AQmmN>IyEc=@J}4djGcl)v^xbc$MtI@(g%0LoY><$43n-7MlC;knwj=j9U?z
zBaqv#OZA)ehD8F93{eDQ4do<=#0F<;{LSD@X%g+GM6yt1x<+LB#8IN%@n0>vok8)V
z7T4~d*~CNztL-^CH11%qEPUnR6SXK%@VTiTfT<mmt^o`~@3yN9qp(w|fJ8BnTK^=@
z&Xn)Xh5pj7F0@4ia&*?;4_1LK<hrd>2(V2JTxbI!CYY3n;JE8rzbM^{M4SOEm+vDA
zKs%y9weXe1t<x?hfbJYk>V$wNBH+;LgL+Y8dY~jgvl@>F6m%l}q651B9Bb`&5DrP?
z1xV0wkkMKFW@uA01{!F9i-+}zv_B3HN(GTTgui2J=>a`N;Pw8hd0L`Y7}Lj?9K<tt
z2nvxjplITc)b)Q8k4m||i)xU_1btV@*PNu*{h9uxf*xwX>^8mTVSZI14O$@&qJ@Vy
zY&2B2r)&QJxB7(4k|9vZ=gx}c^}o|%Wsm)ly|E}t@#Rm!<mH9$LQug*=CPsb6gusG
zb?(7sLJNXKSR`C=THj2Q#GY0yeC(J(7Cb7sOua^rwx6Fp)NY5&%HYNopZKyJ)qVaM
zdqEgM>qHj;T_w_Aeu@kS310sh<JJl2`pVY1Y!Qzg0Xt^9a1b4voP&SLYMcpa6Y%)?
zF`*&nARK-@)aYpOzZ2eSfpb~ieAPpG@^?bD35;7saSrrLBS&_T)*9tpH`Xz|67W8j
z<dqwnUQsG@VA`0*vRsGCU$;+;RDl4lsB_{JYktk*9qmIB<a!B*Tbc%rKOTmMkDpZV
z&7>Ov+>j_-e{jRpiMef_0;{iQjcv6Yk%<-w<j%*ndSBouB}IHKXZX;K+<TVn{O=@o
zd)<)tpB)|mEoYqK*L#6noGE_rvE?BTp5yn4M<FCg?Zp%4*CbwYRQ-E&mh>~ZA+onZ
zYjAWyr;ut+deJJ^gNy<F(@J##(D`XN%+-UphWojw3=cheEC2;i!a;tN_ae#g$1cMd
za84%P@nGc}dq3WUk$0wn(Ve$Ut<QAp)Hp6n=&-`^cIK8rZ#)O2`}VsCb>up4(;SH|
zaR23H<6vnEVb+l$3(#{n0X=9*9?Ks?J~33d0P3Bg=S?3@1fs^KP_7!KTITG2-i%X4
z5AbfjdMbRd+__Y=4H!z3(f!*8BHW<h5wSo`g-X^M>JSQWI{*kf1}?F(ZS9}<8Vr2)
zpN;6`ZY{0oqX}FXP(2!@tmha%$|OH6P>-AqIXH2{!Rmo=!fZj4|2n=Jcp7oTW!*1Y
zYrZuO+`VN}i;8|eFE6K1Fu;wR$0%4m0+^=WmB37KDZk7CvjO^qh}VIJ1G${>WD%fM
zo@cZCPT}qwb)X`JO-qWbP79x}Q9sr!%Vb?76!NN3R>t~Mar3aS_R6lNaL)itvr2?}
za)N0{CV6`oR;Cx!5A<)J71grvGq~qG#iQbOqQs8MA2^lLo1-O5R35*J)y8`sB^>9f
zkl>Ct`!=hU>di>vO{m6!Mo13s(?@rVyoCi%=id%C``&OkAN&%8727d@c6$>Br63(r
z+8_@E44PUH-h4-=2=UPQ=PT|)u#P+cj^&3~lJII*TF`=aXjQlX82$oyLJb8)hbu^&
z*xMi6j@EcT;@__h%7`CI#P*K#Cb8331;7fPHSWy=$z<X188X-ZN!;796JoQ<aeMZs
zxc&B(CUCOQBi-iDyvWux4wrx6J%TP^l@qw|PCQ1?1$n0NyT*f&md<yyzayfi??AY5
z`XfFN??kiF892`~H6E(OiiWQPw~^?Z|5p!vqiDLND*JA&O(Mrop*1Avft{7HLuSIF
zg?)YaLqn{P`y9j}<S>saIH>`x-dx_kgcjI#<B1;9^S@&@e;2v76rBF3HJ(RhGDe7?
zeE6EzUnBy7!3as)qo#ZrtTW_f?AIx;k}nHhmwWA!ZJ^S2fKl7utpCg#`p?6Q^_yqi
zW-P(1(C<;#x3OV5$S)26<4tc*rX3?YNxPdY)UZyTN&tdk!nYD1=xit50kF;Oz2CN#
zHV|!~(cD&0Qomj2w2sFC-Sp1pKBYj{M%l?Xkm2Rd5HbSRmk{%`C<kX(?>9nucLrpU
zLq?Dhy5SrtkNY)CUJzh44VXAC67h$&-FZRN<>*O-M5f<~tm0f&<MS>r2e85A<AGqk
z)vI^0MmGO`a*BGvc_RIK+f6xyEz;z6wt5wa$pLDE;ins8hUT>8=Z}yfb)m`(nmoTo
z0#{nA*zK=G@ioCn#U&v9$SJ8`!zjEO{dlI-*)DaZbsJB#s;r1+kITPDY9r=3p`fVE
zc|R$lO!L^l<XXRw{Q1>-qMUa5J<p7@+&-GWhQ9YpFk1SM-Q=kD5;TNzfaGE=iv2Jx
zFA{FxW>#AtZuBWF=qb>q!3F+M4Cnw=6uX%Me#GCZDI5GFs~1Rj2+7p?o`5nw4ZLxr
zv!UGgDW?UtF<Qb1SnR!2e@0}uti(hvvMdt4!PIAp^gBYF-A9l#ijkgk;x|$k8Y$ei
z@nA5VA1$Cu5&6j5yKc{w;{JA3LID7@56mg{bMsiE<{^5vX-z(}$;(UtZf&P4psE~G
zlfQmHS#2BHeFbNCma{+C=5bn!dhqNOM_zpG_g&RHQGj(NLB8Ejc8=yGmzg8l@X-^u
zL{B*+pK1I=*7EKhtPnJM*O*?(p9_xXU~<H11IKVF&$p~RhWP?^=su7p#69Qr4fx|P
zgTDz;9!(6+H8NeJvMVVQ9qr@>g?7Z!M<(?TUc@u$KB;w(*ix)g)Vu#zUL+$Bh%0HN
zQ}g2<v(ujl{VOcsMfVe46hNx4UE08`-|$ZRY>653`fyQ+O|kMUXkCW-9s2^;-10oh
zqLo~B?&&KwWhP`a!1VW=#MQ+(c=_X6(iH8<(d1ifAqJ#wdQ@A3oV9ErhdD(AveX`%
z-^<x$g6Q&p8-*kPTFSp)$nfbSOEOf{*T^DdEG9BmZdi$y@SgD2*^9D<)(M?~gO@GU
zYg`7iO}#F>pQZ^p8Bw-6K-gXa0rD6OLgXSL?uhvfXMg{D6g=1^#+ohV<Hs_y9-GhH
zK`-L>*PA0ZicQCQ-y5HAn}@)+u;<hnib(Cku~MNf2IJGou05+6zD)XFdx7qK47MYT
zi8*&{y`|(+d2nr6zN0w0kz3nx9*3EngGmev%#w=j;uxXobAivw@h<TDq&$d8-u^k%
zAWy9U#~WwPszKik+AT2i`{l33dhaHXI^F0LUIq<D+Y{f~*FHDjQCSAz>O&5#ei4xk
z0C#AT9)QDpq%|&bUjb>Umw|zw?V8K|ZWCbVh(<DQ<x4oxJ^?l}QPK12N3zIP5RQ;P
zp!hNeD?KK_)L0;p!~l1k82X*K;*Ag%uPN<O6T#ocQNg{_Va^^5e7Z#UNw;Ce+v&?x
z*?CZ*al=ouHmiY`==hl!X_>jD`tkjF0&Iid49|P)6gm6fD;#C!qM>itcl5{zN_(CC
zNw87Jo%uo2|3#NSNyytHG57iEF@^6XD<7&<t=f^mOE5@qwh&D5!*HtNjcO9FqiI}j
zt{WV1QmlKv7hBv&@BEBlT6cN>u0N2Q-QYf%fPVa!m+rus+y?_z#BcY3kyxkR=f=45
zPG;YYJ?nZGV~r+F$nw_0QJ~D+dCJM<1HAI}P;~PO`V23XS+4_m0V?$QpGZlp2jeIY
zIhNUrHz{=yB{svaL5{qGd7v0zR-@&HTwgn-l>u|lv_y%UiU0e(l|1>B-;x!XaWwe1
zB`Ak7lVs0XocUHssA8;nTmga4;Ct4szYIppLhFc?*{4;-1&`1UzywVv5b@bx;15Mw
zwqYPUn=)NuIL-T8{%&ae#GJvc<K^_)0zSTV_N;aF3>_G0C=V1sGSf!SlP)l8`PMKc
z8oVeq7O(Gn#hERa`c?!?aEUE|=9Ja-Yc+hw%ZLIc0<u(=+9ch?b0k5?o@h+F5JR;5
zcnL4-NVbNar;MIF`e&hVO<xKEAiIHBWFev3y}U5nM9zKhujglc*yg(}=XOwtcQF3v
z9*k#!VG2}bhQIFCF2~4;;nlkBEea{Nfo3`O`mCn0y3r2-&@i1y%-q4|<e|AG0wQZL
zCxs}aya9R$`9>61z%Q}L0-|kQ->ai6eifF<PziRGU{$zgC`wW%aD}zmF}?(O&eXdm
z0K09n%WemB!M+9p7v&rvirMwLJ&P6l;+L=t@b*j?ll^@tEB*Wp=><i*4$hN`XAtkX
zTKE2LN82eW?C*%EXHeo<Fu^vXk|T`^dJ6_sUGsFvlJk`=)4K#IPmqIZ$dMr1Xx>qW
z@vlOhKD?gC#bgrVxBuHJE`wHij?>1l46p~?W;u~&#saRij0{F03TN22n47Q8Oz9bu
zCyobn6K>_Xpy%6?0t7lA(CoFKagzh)Y+p;fa3mJp^3(&q6W+$0O#b@&2Ao|yNZP&n
zo9l}|V505)=SgMOY1PuZ7TyInz_k<Eg;lU;2iYed(q`(NXQd5GT!B&V7t%irHo$8Z
z>Q#+XsH#3??w8^GmyvH9=eqK36l8EhxFAQPUwy95%{9c&nCLUF7}KV6eE;kRNeaSi
zLi{`o#^f2i&kv&kl+;VJt0B93FItH8Z9t!mBl#UMBKA4C&*^I-He>02M|1_Vc)%7l
zY!mH0E)c@7a;nVq@<UgvTu?|h`wueCT@_X{WkBD;PlzA&-lI7Bbhm)oLZt;0>gH>O
zoAa?|(<KEP@C^t9Z0I@pP%{})R|vvBlt0+0bq39PQznkCm&yVFfQ+DEd@=z*TmQS>
zaP<-22t8^|anOwX-AcDWPYwT=B_P`J3ZZ0T^e|$zq<S_6T<&B$?(bs93GCyZf0HBM
zKdG+@t6zTA<W-ANjZ-ui!Nfw&W7ndKGtlOCw|{rNNTW0e4S6-&ag_Q{2}$|$YH=Qb
zF;?nbfHGak7c8y|(UqC=!7G5$O&8nzyx@hd<(`zWBZk8rb@0kyuHC|qiR0}Z4`~KC
zBn7|!tf{s2IkE>R0BF}`{LPpiA2SlPjNXN#zf2Ks<$9p#s{#6c@y$0^J6v&09#mj|
zeTV*88scrV!obsQGg@08%4hFw(L!b+7k&TdmyAp9HlU%%mGEE>Nz0Ap5tH8KAtc*{
zQ>sU^oZ#)k;!0|C5G&W)N!)3ZkOUOI2^_{doo?&WjeE42<bzIB9DsV8iyqeg{yU*&
zo60wkoP;|MPC9%{p=3CQ(j3xZt_`g-DDJy$>mkVpl5catoK$)@ub9ac|AOAG4H!Rr
z(T@1~CVG50YpL4zYE$3Thn<U41vx|f271n~p1lM}0{fr@e^UeSdON{u6wIJUWZ%HV
zFi#2v?X(y$kC5E-983jCaOd$+zNz^MUbYhf@4M|Mh8B4%9X%r6l2}Y+lney6s~LRl
zLXP`z<KpSJqo;i+qRQare1&m5X%8b}&oS#uXLH+~FIdC12L{RBXXSsI6EhfV+btl&
zCv;ydJF-A#0$){GX@M@cbpI<bNCo~yh;_Pk(31~3DR_Me@bwueOx?S6=@ktIYfKwA
zU>ut+Orqttj-Ou-oF%GS>n-9w><4&{;!RudYq4b)5D*)fQ^V=sZ+%@iS93S+E}s@a
zO0XM6DrZanDj_RC4)cl>z=p>Ih1Sl7D{S?eeXb6e?a-jgbi3f|WPn2-u6zj=kP9k=
z@)uUNU(9+9<AhuPkEyqg%PQ;sKmk!gX(gql5tQz3DFNwHKtPm6l#q~6N*d{qZV;qH
zP>_&L>5`I=4uQK4^ZxF=f6Zr{5uWFqz1LprTl>DwEwYqom~dVNH~$&`oCTgAMAO#)
zANa&Xq%JR7J6&=Ou&WLN9fl0zmnsQ7_kF=&BRKD?L4!UoDS=_Gy(9jm=h$m4?0%@7
ziX$#+^0(H76sKQZ?<2v-JLPAAC3^sZ)=u3&7`5`DC$O4`R>}qK{O^bWojL@+Y_Q(Y
z&)WzSx&h9oTw44-&w>Bue_xOuIy874&H#Hx)cpY-+ev|kt<IO|4HIGCww3WdZa9oN
zg3c@WDaJ`hvvl0(BFYlW_K0+R?Uqk;thdfZN>)~Ll`c`=j5#a-kCf-bR-6TuvASI!
zA9?<JSx)!O1zF}~07d7>5z<{!UP7q*qz%u8S!));gflXfWN;3LYSk|yXl9Agex>)7
zV#9Rj7E82`Xj_P+)}}glUM>;i+78p-zu#Y!pB|@z`DeO8xXV@E?IG`EJ^AESCGu<h
zWB6FlKIU_oNB?8`@$Un7XO|$R%v*_*WmVT1fDDGTvL3td>5XOz@7ycrV?Lrrzx_bs
z;!ED&|5ySdtopxg6azVG)J#ECvE5M6cv6~&7piRYFQFcLY#%t%6rg3b0N+~}H?AYL
z*`l8WGddTS+vlFFR0`}YL*d^I@FP@S83=a<C?k^qyfpqrgROu6*Duj2&O$TS_E-&i
zWjeh=7Pcn=?r?Y$-?CpD$|E3&M5D!5L!0rsXaMa8k0yb$H_*mPQSxR67&cl_*6D^J
ztUh29>q?V+o1u^4FWuaO?p*2&gHgPF|Ba+SlNy^bDyelK)R(sf0P@o!xEDio(Dx@j
zKv@j--23bkr3)zk%K})2Q&;EO;il(}zHWMS9%Zs?)l}Bchx~xwwra`*sFq`h-6_Vl
zdhS~P544jHm*=)3x^tnmES+g%tK!|jLSwqL2>Z?YUDeT_ha&&BTS3&Zn3XM~gS_f^
zLPWflOXAMA_cZ8zFzj?`%-k!SE)oM7ve#MQ6qd;f`#6_EzHti}%%M(b3##zd--?`}
z=07xkTGv#lNb~bkTLJk(g1H?Uv?KT%_ac!I4{)?dpDP&Tl_Z?T>`{y=JLG74l>|PZ
z?mfLR1Etn0QT;h^VX;w6YS|Hyp|sZa+gH)T0J?RLwLIuP84A<9;RCZlFFvxpGXVok
z>8}F2)6+Mc;oyt+mO#o+1Uk6T{h2BB_$kkJ3dthqchIR`FA`{4fxudZp0k=|)h-`K
z$F}RiiF;Bc$d?^wNZXP%0-sZUG_uL+zT01-Z3YDD6X{^z?F4ZQ!NVH5C)GOH%495H
zPyrl`DWl~fDf+xhGuIDI!G9mFbi|1DmOgGpvsm&-f=-nR5F}c4aPcmTEAK!0K>Hw;
z{eFIlex(u+z%hA`%XLW8E&pJxG<yO@SN`VA?ct{shlMt>)`tF1@?F3~4q)y<Ey_CX
zfe+ZMhrVa`VN=Ei#2M<I&n&G2E8u;QU|KcRfB?W)eR$;+5T;~X?J$}ig!C{Yl41}A
z-*rLtzi&RWk+(YrK5jZEDZNj0^5|GYB_=b`*%W+R)eE>fqF$?`0d(&|Gmw)+Akur^
zc*wq{utTUE3xu8cm=dN7ZJ})uapa8htg_$i)vgYBk&C@e){l5b-sy0~2QZ{+NgSOu
z?=CkTJ~pMRG-+4vbiIv#$rx~hEb{kOC9gNOy>JetRI|0`y^Cdz0{jFhZu!#ssP{o<
z6@qD%`U+|-Kp{~f98S1UmA&aPJs_cgU`EA8{yQMXY_+B3Pk)jN2L)bkMlJ?e2jpw+
zpdGdTEy~r<(MqyF{AJVkLb6KiY=Q<9+@pdod3->1#~J$X+)3J+0~Qamiw&hsVXn}C
z_x~<1#u<m^CPF;ROYqm`7a`7AdPes#_`y^<3x~zJWi6Pp^1@GENK10iATALSt2wb-
z9JMQS{b6)iXtqpg$pL^McOcG6XscDZt$)tg99A}dSw*H?a@)%Qjb>YP&`u%z=u4vO
zuaQTKu3`!Wq8Kl(K+YYSf1bIJFLpCjJQSCL_R{MJ|3dm<lNE25D!wYn_S}8`J=2pm
zmOKsB)uRmyjnhr7lABEzYF~zW^<874DFO}IH_oRd2)mYeM)m784Qx;5@p_-f+kAb5
zDf7X5kne}w2XqExcEtSPZTtICYlDJYHntkwCc;(0tO!_|5~3tOz58!KriE{^0Pq?Z
znvJyX36)1!5q828%TP%J!kF=nS;cy~hdbf+z)U_gO#RDKO3L~McP3BA!6mgo`yGq?
z<bwDLrLZKP`Tr~=63rB#M=Lk)Aj|^-z~&Z*W_HNRwJ9KttF`5Cf(~y|CC#ioY-cIX
zq}HJ0Ud1l8d%I&8vq>m=%})HU^7GT=a=ZNqah{`8%Z7l4g08EA9O0D<u0K|dESCb*
zI3ur6gmuKmyZ>|uSbMq{!}-aHY`R+!abc;Yi0nWo)83uS>ttOGTR@_j@=`mZCp+a3
zm~921-z$Ibp5(l@bpu%s<Yksh!Sb+QU#nTgxc5YGEco5QP2^~g^mcRa?-RdcN7&c&
zD9-3*JX4AfSm(QjbxcAZH^@`DBzxO|(8Wwdf7PxGlyw>a5_6Ne{rygo=>J5tB*oZq
zxo0CHR$2w~6&MUYIsa2YFe32#|B@#INcXS(A1z9*s5-EHXI#8FnhWeMcL2?aw|5bi
zA<Px!aGn};k*dw@*^L9Vh&Va#-}<EPe}~0W@v+{0tDMEVu?A}gtn0hA{QG%XTxF*v
z8im@Z9TCnx?36nudIy^?^#otV4XtYnnh9!(?%%4uS2ZbAQ|6p9roj4z<)8d((k+Yb
zu4|TOp>lT}xP&Z?f|qmLHvfSVb)E&y1z9rol&GP&F_Dq;LJjIjdIcY9vNQ<J^kqU0
z%NraslGk8@CYNl4jDQfd`ZMJWQS{r=5=OC902kfgp|f(vL`-3zj37yFd7+!H+I>w4
zmRl?!s#j~fUt<vbC`{hSZ@bP}PXp4J1_HajG)avdRZ1t3r`;cUdM%*&_>+(32n~iL
zVP8iL90P7G_F@Wm%wgr+R-geU8uU0E57pRhAJpF0;n(_iFkxq_d1*SpBj`KiVR1r(
zd6Pf!H&^ei@d75i>>u$_mMZD8`B~=;HKl1Kz7683;c{v^s{21&)18y2wG#Fl)NK<7
zzL73jD;V+i3~rrY2&pNF9=d4ed8jDn4dh2V9~G8qJ=6JSd1Hkw>B<CtpI`yN+X+7n
zYtxMzNGDvV9<&$3ArW?6xQ?8=?dr7{pcRVg?t$G50vsN8^W#-x({;ij1F0ZRRuFm;
z0b;<w7xi%)5zhg23n57bg-I_;xee3A%c4LDXiW{V5Uc18w0H}Vg2hGnO`W`d2H}L1
zm6nJ&r`hh4#*6<t@acmXHX-J>Xm3@yX~4y^RYh$_3gfo~8q%jhu<^H<WN;*%3sWYg
z8B9SIpcWbUB##GUiyEB?Dg#2@zC%UbZ{9(K{hxcq*kEwhxbZAR<<{EkJkW=6J^A)-
zOiSS|Qadvw(m?IVW!mh`9Q~)5ZXE~^80~}OHEM!>hjU!iC=u@bj^_AW9PYcctJE_o
zm}wZ55lO#%)p4_XOC!UUmR@c3T3(y$Y9#rxMf3r?@EyCj3uBH81DBeAe-k20v<9fh
zht`c}&kD$HeXk)Ucm>+Rkg}9w1|Z&r<tl{=$St^E@0j-=>*KH~UT!5~v^DiCNT?~E
zL|=~Olfr|3-yMDLESutSYbagpi&oJMi2;Xh;{^xMwBU=Q)tW)`$jHRhu7>$6jKT*o
zVnOZUfTJ=j@y5%7YEa6M1!V?kkuF)j3y311o4xS3D)c2EyQ<YDrj-ePgb5lsYutTv
zj|uNzaB6L1Rlg9)lXK^va*hhQEvTWIDn=<rqXZ9%2A>HHMy4phb=vY?E^8rs{3BLk
z-E-B|hClj>?9rxc?4ygl@9<tfxRb)vUK%^7DZ0uVl}T6QIXYb+sOXq!@?9zUvyzyj
zw#Gq9)PlQOd_6y|6;1nLQsiBtcNOz*EkC~n3Cnk;h>NnV%uw9&1d^X?ZUXta4;KKw
z3UO_*@0>G6+L@R1X*MY5>nJay@GcJ@^SEY6dw@mDkrDCivoNEI_#&1Paq<vU_+*dm
zuNaa`O#i`N63w{iJU#URtMnEsrl33B0oc9^%!eOAvIIXDSq{iyhOpm&|1eN{?nQ|F
z{YJFYMqyViwN57+WLmKt6f1*;`6^LDs)f4k@pQH+=Y~@z-+S_n4Z~LwaurEzSpLJ5
z#@^BN!=!f0ddH=kLp%X)9d*4|tLHup2G2~g9zS4GjFPue(wGy+Ip=DC))VM-ek)|M
zt*~26wFRJf^;cy9!d5&REq-z0GqSHh@i(tOh4JoxVt5sFXQxKDvj2E03RD-;n8pjz
z{4hbO2Fcy^2fQYY9^*AJ5c>kmRE+n_8)SQJm00uoH31!>t{l5qP1V?>?VX^Ue^CSb
z^5^`gO}7&ufGnU*bBKPIKczhTlYizWj?rf7KvN>vOK!cCA8qmogOzba^aHv=<16zs
zHx%H9%XkA3jA0HXU;sE7%xK~!I%kc);4){ZF#ng}odACWlc4YK5?~j%(+8Lt2he*M
zQ=?|o&N!a=k!+jf_orlYpAP=Ul8YUbKet2=7fO}&l@LI8-uMjEQ1&}=-e*4mL_*ZN
zPo11Vxw{A|mwWB!E-NByYr_RupnE&d5kwAr#CC$XHLPbE8?ad7HwjHH8Qv6d3tj2H
zZSWH3Nb2Cy^;mQKJMpJ)<HBk>{H<9QG*B*w_lli{+^b1><`Rt)+iYOhU{Oy1Z~jpu
z=#&ud#<uINzkFjj7Gj*vNblmc*8KuhWEap$OlzxUP&b}F(USf`DeA_vSYCq)ST?5*
zIPVp~l9dVn<Z_soImHe#N7j`uD9mq$Dx|Xvq-gu*iapT-0W+O8@l|Bhd`E)n8J8!o
z6{&(HGoFImZqE0@H899!rH_{W7`S7=FFEe->Hs}dDd-A5ryG^|CQXfYDQvAblK$&T
zq$Nb}<jgeVkt4gqmKML~w!sgoLqu4L=q;cfsqv?Fga1TDs}ACc@dkJPHU+&3{^Z~6
zC^g&VUycF3Rbj?FOA>Z11_fw~e?ms25$1|hHq|~)9>L7~_u!vp0hZH^o^^u8<>pw$
zyEs@NR5--%E6a3iy8AH0&pld-2zJe9bdY+|T^#p%Wde2(cH1nUb|0K<oNHL|$aIE$
zQ$`=`pFt0Du;OGtV|NBfd;=A*D2{t}>d5awiQV7YVz=njQ%tWKg3ZFvxnH*X{nhIs
z9{=CAWCUVGW%-wL&)uP$k#i~fnA4!&MOv)@eY|7jt$$1QuB{!#e~}3ov-vl9YR@~o
zHY434^g2e;5)(;_9%yr_H`?BMcZz_Q)9hPLJ9&y8$yd<Ed%I)7%q%_R{ofe5ioR#`
z1q4nex;#w#-=X8V`$6>JV?8K(RPC-UB8&tLr2F{RgPJw`2cwUDy_ET*rM$bAORd3g
z?u~zR>kJ|u-B6tc=q?u00W4UuZ39%5mAko2(r!GfaD|MU#2=CZ>R2-Gci~rSVp&yB
zx^LFV14Xr{bu0W*wv&zST&%J@>AZP28onvG=$A9$CuMMCB=gXcVdL+>5w}caf3h_d
zX@b!`^7mNZ?Nr%~1KbciR1VQfCpIu+9`LbO1bVAr<)Z$Eaa<<!$)9uK%4s&%&lhFl
z`s?)<g0QJkmx=mRZjQ)dzgh0FQ{(aW9>rRDl5Z<z1<_2O>Nmm66R1FL?DeXj8jXW4
z&4yU}VnFuUcja4b>{b=5-SMZsR<}HjYp{-;l3l}yz||{(jG&<g;G8s1N&sDx%Fgef
zY`@|Lz^V9^C%9awZP>;~YV8_Zuq{3dkd$^k>|>`t5Y0<77W!D<x}Ua`92|GI(S%G$
z*RJKI;T$?|y8{{1g(t2noVndYq-y5CiJl}Qfy0K_Q<>f;$!W)2!1jm$T%Y3bz?#GD
zPcZqOZiiQyA(_j@uSZZREIIIQ3#h;RGxj)a{}*ENn_&52o+}%!hB@}Zd!M38Y=dcz
znsC)!mPxeIF0Y-8L9`^@iHMMptF~JZK36*Wknk`lS2Lk1p?Y25KEf%3$A(9U*sYKl
zm*Se=<ewTYNV|#!ut_-B@kF-0Z;;+=E~cZql$@qvtuPwZf58Z4YYj=~V7AbGr=t3W
z>Q$cBhE_M@08Ib|=8EC5n)ulr*rYK~S`yDSQ^h<N5g*`MDCvDEKn_<mdHZh1aY~0{
zAb2e#ZasKU$lzQ5_L9<1l9m|dpxbYB4jWN`nE+_?o|^rQ3I0qKQZIdbb4ev8YKV2Z
z3**;~JYV*pB`&CZ_-M}z=N5-RAO+M`ZDwYsfL`eHE@PPGSI5det#B+tgY^Jf;+}QV
z_19A!Ie7p>STkOO(24np1`kNecxr%adn47Gi1<qDXe42|v_xtsjEwO!$KXMaOQw6`
zrnbK}EIXr;ut!-G6>+l3qWz~=@c=jx3OwKYw?aChWako&)wz~neVBq=wcHWuIb@GN
zk(Ch5w=xfceUQZ878G+MedrTWAOh1%)%e#V|HtzOv+<TG1XCW6YehEEX(j&^hR%h=
z<<Fly=6*QyEg1?_IdOu_L485ch~y!C6inkvR9^oakj8Kk=GS}2NH7<`GM&ypwfMiT
z+i^r|0pHT^CG_S$@`wU;s;^*6V5D|(q<jTJoWSA;Rj+rm<-#Lv0hS}85d#ft0SgA(
ziiCh5p3g2;?pK?N$XLqYu+R~Qg;0LE2caLM6O)o|mp>Tzh)MiO38+6N*GY%KydpTV
zmMe3<{1rd{ix<!LBBA5c^dTZD4cphRd%3ar?LE=`$k=CKpYVDM-5UppF}a0>ctx&Y
zp(!;ho^gAYtoZ(g953aCyss&ZPJ2^kWcW5z$tXDd^_XgayPHBUXrJ!MeTj?m<0C4!
zzwa_AvXa7we*DL3-9@^ZrPbrcKVLq%VGZJ^0@n!XCbeYKN&tYTS^bzq3^6r&`%ep<
z9BYrYmdgv_c&B&LO_M-H(mY&*a@BC?^>WqI({>^+8M!FXS!m3>hm$l7Z#gqS8VoWt
z=4jM!j?$67NpglHdd?WhYWQVS5(A)5_Ufx^M%T$`XYZqt$37CKKG_16J1pS<X|xvM
zK|NbCxGLO1`Ym$&g4^r-UpY|NYFmv)6;=_5oZ(s*j8u0a<IOzbu*QpG-Kij06{&c~
zXC=o@O9F4ufw^K}0gUUuM|nunJQEJDG>tRb`PMDZ<w;nXzmq?0r2v+hfOfX)XM6?G
z#J^-rYjXeZc@gw|e?;jdL_0Z~llo?jac>0~mXo|~F=_qfBu?jrTs?t#?z6qa4-fkH
zAacfaLPiEBvfmN9SffDuuC&~;jmw)Z{gQZbuSP*HFbTb0Ne`6D$a%=hb5r2*c}-yI
zI-S=ZUur0uH_&4_O{gk@B*`|db2N;4Y|4#uB}XOXFkgjxyWJf`O*l8^F;wM=R4vy6
z=H_?0$*@ymjI*P%MXAVVz2%LAysc6I(}Hm$8&ZaZ6D?M5t$0$c)Q^sjD_(#$`3LmO
zi{)J$Q2<cO$?!~3<_k*z`^JkpEm+2=>WTkO9Cbf4Pmbe~Z?>gPo8xr;9PNhppC(G;
zIi^;=mf}c>iCPnv%w)({X>_@lm`4PqZ^Uc)b|E{6yu=;ol3=W?g8m5GhJ9`D1%0S7
z`!iPdTXT7V*N$0n*@IW4#P(a&4OHox)XUBLN0r8PbMn5)%okyzMFc33HwB7Kk}W1{
zE~Qc6{!6R`CNf%6%xLC^bZeBjHsPdfi`oJ`pl3VH*K*x9`0_jg$i<VM@xKxzfl#0S
zFza`lm%pU38S8+P@q3{$YY;o?lyx4hqsu(wfTC+~K<z2_0GB$rSD`Tguws*E0z6QE
zx7Tq(`w3z22kUtAa2XTwUZD0!OGrP0HC7b~TQ?CqkAWSnDfQSSx1U*HrYD~C_;q<{
z^wQH&B84WtPwCE;oViY0@*k*pPi7#S@%Jv!+yn5qdHU_j(K2@r*M8Q3p*JOrF8UTz
zF<=foK#wX?@L1sw)b-c=q7y`V)<P_E!HiW1yjXc61|I=4#hU`>JNp%5uPXxktZCOC
z1CWeOg)6uql?Dn=1a3m?Z}H6Ug7LM;`A~71(we@*ZwxTfK0$+)kiG=MzD@=`kk$n0
z&HcSOp3S;&vRbhKiTIAl2fCmiu)jhrqXpheR3cwkL{j23BB6qF)enCu#VVR6nq9jf
zExMM~>zP6h4Oieti-pCha<6>?C!Nl}+_cjwFEM2TB;3pz_p+Y4Oo=x&PVjIAxgl-K
zaz^?4i6$|mnZW!O>3y)osFHApQpow9O?O#gVlDD<#$qI51KigfkmkxyE={-WiP#7C
zHQIHkc<HxHTPJ~h=G6d!I%}wNZKCqUIEx<9t61eVRk1U{^#mO;@MBt9PuJHx9QBH9
zUN=k{IR^-Y<m_%wB0uLu2F5cdPmX`7q0e=iLy<3r{6eJF+-64Iu#s;S{vz*?0m%a>
zJ~yydCS^ecWdVX|M^IYc*tH*cgB~>UgHuPun0v=J>zBi3ii7fqS5;T^)Ji&eq%Mc$
z;(EFarnv-7pNP#hJ!zq=FxtXUTflo%o$M;slr^y~^@%bO-WNhc@!X`7@&vrH@V;^B
z!n5Y4DeUU!Xk74bfd<nagjT6E<i&Ft!5llDRTGk>R~n#0ChK1x$}{7?6(Vx(W|gDX
zdWb}VKGpdJMLxvlwE;F2eR{>NP0i|IiH05yG%*Oue=&G_BDxvS5+vYo96LfCoSJ$S
zRs?OY`Cb@&BkZNn-Mt|Jo`^Bsxk_Sr6J+7L3C`5I6c_@XGObQ}_wqfxmw@=c?%No&
zoeM)Kv|8?xJ>q5yjMTv`^ZzbHj+T^9%<>AJ)|CVHHjRJh4jZ}@efS&7d1$m_VJ`F2
zRv@k!zWxZFqe?MQ>Ma7QeLv)-18zLF<-h*Y5N5I@=~=LaN5EJiEN^-T()(!eWzqD=
zH;*Z3WFO)N1$f(F<vt#Q^*djjdf`1_LK>rn;Q?y*eR0a|G2B}7J4tK(O<rD*5SLT)
z56<pRB*K^BK`c+Y?7q+{9@=W^Ro=-{7~X$=-y3CrN|xNak`$Awc1kc-?eJ;q@Ui%6
zI3`+NzoW*?;9!ng9H4Rdx1rO<z_b!qX>H;Mul1E*8FbJJ-9r17+V2H06&EY_(;cO*
zPY58whBo5<Xr_{U$<N#iO`e~jtG;TaO{>qz@*_qV%eKO!gPhQ1YKH6!syw$AcKI3c
z4`L^|NNizr1#n^9UvNH;@l*jNe_9_pp<m)g{-1&FI;sm#KX5Nqxo@eEW!i$Q5o6sf
zCvNzSu4o7ni%@EV$-1EW%!BY=p3U+AR&QQC%PD@hJu4(m`M4I;Ed8a8kQ$glWi**I
z;PgXDhjP(%bwCzz4Mw@Z;zkT#U3aS{3AY9N-GN>;d$pzl?HkTV{(_U56i>ADFO^;@
zYejYtwEK#8H?XB_1GE0%mrXegI};uL2s*cjrJL>r!hho978U}ijS{!9*bTlYgYwI?
zMPN<f(lC#hfz!5z5&19V?%f4K-e3_sJ2RI+z<tkE@9)-&his|7Mlv*&6IvCIBc0=2
z`~hIR;}U3@Z_CF!Js4^Q+FkB#gbwQ}HuCa&7N%vxZdlz{_~IxIjtIfy*|&KA)Gl)i
zoByKoJfr-tM$6orTww9`?gDnb5J0@ELmD_f&gC|sdPdzH+wirh&x~?`DiYcBZ;4Y*
zW-2-T%G31|kmOjuOy2<Cuq~^M7S&SPoOhA=s-0(**zh~e2Eu3h4Q`c)M!D8Jtf=GK
zOouZD;vTU*-robO$AHn24LhclLDP22buOph$`C~L>`VR-mjZq%J*ubc>I_@G=tP+d
z<o)*3rZt@c4kDLhuXMTInb2w<%aLe<<%gOHE0-97!0WJpd>iaG%g)TKHs~j{A+Zv>
zPx$1{;$t#a!rPR3a*P44Mo;-;47{xVoVs=$hA#e2R{^x~$-wzwo5mLlR0Hy@r5_Qk
z-B~3@bt6@{1gl@b+zJ?-<G1H2qxdzzz;%=cS)JQ*7lub6OAK<wfHWZBh?qxeqCk{)
z-ITiEXO(brGy;aSYp)^KH-=2+sd;Hse~mhN-`B_07JeG3zlTqTFgFmyAqCB4do7jy
zHDol<z;q&N=bGSUTy=Y0#wQz~h~Z^L&IViZFk5()zbkmhs;(WzI#{M14DG^zkW%sk
zAd*qnZpr6Z&=W`lyw0$e2UY@pg3{?J+W{;isK?z{)l+PazkfM_yVsr@l`_@H<L`Hk
z8b^!q`q!TmAIU<J1sL<`O@HdG=E#lF$cA~#E#$(Sv4|bwsQ64EOKvBDL*a~*SG^%g
zbie(OSx4os4`!~9v7EcVW^X0+lmzypZnmaV-iKj^Bno*a{@fgQ_fs-kupHMvKIQt-
zWjM~aYTtG|@2s6F8cLc@vA{d<kVgZCFZ20t?-}ouj2r?Gos}oY?~Dg->LG{!q#aRM
z^$-jUh?g3f$g9$*VMtnrPx+Xr*`D*c-%S)$OMJ``nwA9gE~Ns1M-`zAECHU@&1$1n
zybNheJQWGGp`%`89Yq|m4fi5)Y7aLiM(y8#XJU|a^VkWF`p^~e3#Zl8;Julmw(HK*
zwLO;4F|8&%cRHAUn>>G(d8s*uO}U5pe6srCv!PsCNS}-ZTcAt{ozf-!Wril~x)}|b
zfgKQl_HNENWL%RHDxEQ!5I<OtF%V^<omswnWoXgUPTpdc@O~Yk^!&JdT9X^p96E-h
zGQap6^foD+azZXP-fF!qoni1=&2+9gCf7vEYkNr|s;*t^aU!KC_OeZ9zvgq(gZDyL
zJ!j7Frt?G|Ixb(dQkZrAb<$W}dou9YYC*hZAv-os*o&jXXx2-vR#J@qd`W)y)oioR
zLl<Hs4Ob9`=<ZbUncTnNf=N^}iqZq%2izgDpi7^&%2yaimhlrQiRCMQwW*1QkO2O%
zB$NPmK0BIkZzU=)k0|Vpsn_r5!lQ+tXqNZ@Hm$eS(EENHvRA{|F9rcN5wSK#!AWnV
zD19r}lxN=PRLVaWYOwV@<Oi9CAHm4>m-CMKC9g2EW&CYBn`MR2%>Q}_pAf9)zbZON
zH-}41OdzQ}$$_y4p%v_zec+r@1l2<(D6Sj<q6>WHGlu+~KeA~G5p;~$K1(lt8V?9Q
zAiof70V_kB%+Y5{jiNFxrM#SBHiP4?;Eo!U*BUv63_6=hT*We8tCi_lcYX`Ah?7vT
zd=JVA(5#FkP8GV9<nd3f_5M0xD#R|7xN)r3%<2oetn}IsKTFd|drtLG&24qCuOO#m
z?q`n1MBA4Bv$u>}LQEZwBj%jy!e2=@ijJQKK2IsrvGu6)5uC9;TH@N-W*-+DH#R=x
zp;O`+1ksgj>r%EFL=}buVUsur`}l!Z{1)<6ZYz4fNv2Q4Nk=eTWNEiZu;8MBCiR*G
zq+3ToU=48B2FfdXICG!gwty&39iF-QjY9w{SRh_H{Ilf+sf@wNYKt^5DV_mNBga;6
zVj)*=v+3fES+(Ub(T{LN5_v>REc9_-qjrD9AhZ^;iDKHRy$sn~MU%(C1dxqLfQ(yw
zivN2bhzUtTsWbJu7%n~u0n)>mD@ugY4q;|MNp@V#it7X*p}aou#^wJW&Wq~V1g9VO
zddT9BNbjD!wfa1_ZET*V1PfZ)r@8J1SMwY+<9QAY^!)<Ve`>P3s%Irf-6VSXh`WTm
zPLFZ2OzY0DwLyXF`j0Hpf&{M3ME4?($$KuECp&|ZF^Z6Yob;fQ>A{b<u7A5tyUU}a
zKYDxZ!f#(a=jqclXy6XixXU`6%O%liz#F%BzJG08$R<^}+Vj2PKhqj<r|fC3uhgo?
z=OW8K^q!-uO?!%71+{|z(dsJb_4tB6RW|%NV2e9rnpTrg7Dr>F%Hyv<Csb%ur;UV0
zA(5%M!H1wQR05F3V+W6n>zn1QYriZBna-ca;5>}7k7NCk&w%$=nfgk8ecGX&vdt-2
zr?r?8(pSJp^@qDq>rF}hPLQGdM&<d>_WS>D%lwWFbwB)e@VU8ROr|4XUry+ShY7Z?
z1`m_S<1=S&EDJI-Bl>n4Wi)bHU~U;eyO412!Tif$Z`-}f^QU^t8OQP5uDBhVJZ4uK
z9^cLBN=g0Dbroxwg6(QF%WzksVqSpPa!=-YFQ>)g@y{);!0|ibx*R$GET%#m>vTe=
z8E0IK7)kDAeKF|V+Hqf-zWDqsm&9C&JynxR-|5qzk6sezQ)?e^mSfYWT^mT0;=6)6
zDQAosQma04xG&$vE7x+h7!0+Ae7ugnE2(unvJ~(g3eb&T&0T#B6*dc)Fk@NoX=H_<
zw~A7sVZMs?CU4D?=TmW;1cYx_VdWS=zT=+WRB@A`D1$;x=%-|*o54Ulu0`4Hrvag1
zi=E%udY<rt6Va?9=f$(+U>9MH{~6byI4G#if(N24m*MPn<0aL6#Rtl5C$wWv_#Ul(
z4EYZPA7@IF>+{p-5^D<C6!8=~pjsvNtbdq&X5k_?|Iw)_6?b~2t0{c-Lvh69Rl>|^
zBd;EdQ<G%@DfEHyM<X?rzDZ({behzLw;ZoxF*yC8Pt@;9u?RH(79*bO>b3a~YsW10
zQDl{FhI8t)lvu-){M)+G<c_0e9Xm2vTjEprNbo)|8}X1*tA%*5^9-tr+ux;uvtgDG
zt+oT_L#M6Hqdm}%NHNSxG7YBF(TSr^DmKr&>be*+nb=!icwC_ak^m%#qSva|q_I29
zg(lJXynCnaZvy!$10!Sh!=LNQ1I@F+P%er|Yj*A-!<e^3Jtkst-&yYEgdEi1ILx8T
z*wuV=pl;u$D{5hB3S741LOI{-wO<v1`)QMBj(@P1B0xvT)jgtlE6*5@Vp8W(@tcC!
zhZny1bb8Cm|5~xPpF8T?y<Zhfd2qZ?>-K!_N%@<cXB9cth>#i*m&U6d=;hpvJ!tyK
zz~JW{=oZJv{tV&XiMF4r@mq-?qK<hL#CWCwBX;<=YM^;6`ZHufuyhi>>16O5pvWJP
zYB8Bjd?NF)8A+78a_z=D`#dD95BXl0p(w$n>3`_}6#{7~6h?lEez4GO-A<2y!5a~9
zB9d%AyJ>c41gqlBdkE#FaZG^O5KG6S68YE`-s?)+g0b9V-WzP}_dTr|ldz9(*o&?4
z7wae6c>2%_4e1nRTQ-~v;-vQ0Ol-lWxzHku<(4Zu7N523+*d`8kAqL9jJnMCM<<q2
z$nIvniV``G6LCP)bbLnc8Ceer;p%|2$Bqysz+fjW;SLtki}5;afr228H!GXCtDE7Q
z_?ePY>gwvs66k+y7d6pu-Mn>2=B9dPG{<UqByJW(rhCoIzury5%|_3f)A`0K%Y(y%
zo@59z5_dA|5p`0{kdwH1NVvcKVEHQQGG?=eL2fHV7^n~f)@jk!@V+^-+~XgEkmDjI
z2p>BA3SrW(c=h+mt|6T-3O=cjGqdDtHD4MUOiVOi@Bi-~=+{uI1)_MLnU>zlhd&P&
z!1vzQc=3YOVQ=iB!LOc#gST>rzGu#efwVV=d%d{dukFFV85WEnmcbL#&7bMTNhbl#
z>9=yM&GTxs&gXo!%8kSlj*r}G-<%Wko}`I9qEc_Zd%vi{)nmbH&uoJ<OVqVCc}?^C
zAKS#$u@k2uo7Y52@xl(CBg%0clyg+flKA-O=spT^D8}g?XT-NVUMb9=qe{~Jr41MP
z>!Rnz4xc^+-@ExKEIO^qXOC(<j>~iA>$wMKq*4HLaeqGg2-RzMKA*BKr`71*%f;1F
zxFX?KnOUG%%qN;=Ox+mstAvuxxR|pGK9lVm_Xu0w(%_4rr}g=Q>*4aL$grXzazRKe
z9)N~otVl2OK=XtFd1ewc1f{KV3layMeHE0W<0DSYdoN}hMbp?b6-drlI=6Q!#f{1q
zqwnOoCU)5wzRMt{XDvq~lSEa1M8xKWN~x+8!6#@Wep6Pw%0SY{aww}H$F46<aJg*2
zc#!rM^V-v!8;S0b#gx@5azjSO1H>w-o-e1VUs(52WQl5pK3o&_n#Eo!ij66cRd~Rp
znr}zE8GW|N$8uj%;_`{zla%1I_Un+qN`$Wge<I_+NmGjbP?E-jpbwh}AfhxtV>Lh_
zhh<BY<PU*)12E=G!Joa1-FTE>i|1eDz>IrB?(C^M*}8cHtBk;#Fq=Q<W0_4y{N-Bt
zDvLsmxBU+!f;b3xR2}-j2KhyV=)ZfCAAkTd3-a#zK*uK4@|K77e~-(1$s0t^1S9!`
z@CuQS=^;%6J<ZKqw`4)FxmT1CeL#*{?ZT;D_;GZJNaLPok5+V}47O7I*M084Bg4Xb
zd$CW&c%4?qcxyfW31#(0C9E$MN5^w)iYNw<vhHHZmt1EX$7$KHd3e)h0oS6I=`KT8
zxE~*mu#939Q~K{#)&lDO!s&JEQNJidKIzqHkz)DLlq|)4ZTY(PeeGja7PIs7ZQ{;H
z_qq3V$HD`i9w%=a{{6zm&w25`&-*+Cr4b7v=#n7VPY(3wmG^U65-uNykE8^T0ovOF
z^;_sIXsAUi*PgoE=9C9*`KNX&_mJsV3pfKK)uu&7FhZ4`xXiU&Jpp9pN!GEO?*RmA
zF}cAlL%Jx-g_L&IzS>ZuR{>wz=^r)d%S^ib@q99-Kf-At*M)(?k&zt>S$z23SM#A;
zFVlEHvvOVK@^Sw0ZU$9g9`Fo)E_)qO{nVu!&2iiRZSW}qtAR``19qGWS%JERaQyR%
zNu5EfkwUVcaNwqv52TopaS!%JXy7d}{S)zj>y@G%`>S5PTLaq&o?TYx%w|rx(PsL5
z(Qc$ed)gPX{Qgm!<^CnnDiL0WV=Rf;qk#uJ>mB<;Z#-~>8g}W5PPa`?)}!7=i@0#r
z=BU1W#$B60{=vohWiP{8Wd_z`!yF!?^~3?Hqu_Y#K%JEI=L}rxKG}LJ%^`IuPYL_3
z=3OosZ%BGXFI8Ls+tVk|OMF0Fb`<t^*e?qfz9F)vt6#yolTg67XL#k4F#)0oYiOk_
z?B}GSs-USYWAH|9c~Nfdd4R54ehQRuE)a6;Jkn%3dp|p==pdLi3+#9)w+@|!sq(A4
zAwKyYv~FEm0?vBsT>tmd-=n0ksMEyZze1mVBT>gx*+z6?h$pf<Aa#e%`%puan!e0y
zfa&rDq!q@26kaKcG1Qy(zp^@e1&!Zs`erY=*z8gv^K>Qn?cV^zNN@0EYodx11Oo3s
zZ`wL?;~|EHFPW;Xpc+$EN=|gAKgZ3j!VM<1sF3W+=~+=>#rISvpZT(W#zt~bH7b$C
z?DP8xPcxsto(YO}_CYD6G#2*?-kDB}iL?-tc(@$H<bQaPUb%-i{HBSt@~p8|FDmB1
zo;Y<iJDP_?q|&Hnu0c9EXC&JiuO$6Uv-e{Kz32dV?UYukq7>Q{PMF}M&xN9lbe3v!
z{#Sm#z4O)|P`E6_vT5M#-GiGaz7cu|9Ne<Cf4n4v;Zca(a_}UcPVN^FIGwUIGCnm=
zY)Y}U3s<Mxz<TB~=wD#lK2aOCRLVvV!(v#6BgcQw2VV+(`*#0V@=@zK=b?Y=^sfR3
z5^;r$s9kDvKGp4LpCp9w{v>ymZBM|}IV{b;&X5OI#D{<r2;e>-1`FZ){b|#poQc>^
zH29e4pc|(Ra`!>ewt`#fyo>O4Yn@r@uJ(r0zAZW;e0updOWDw1%wO6cfgG?uolnNL
zhvC|+&Io#pUBpr5JKPXnZU5onn9!%f&l?}zK0ZAhQ|1<Wkr|)NA1`haeW0<ZtRy-z
zlvVhBVDRX~Yao5;y?g}Q%_vUAkfVj3jgLNB%S^FG*{Q$&DPCQNt1lJ`)H^5d`wu-H
zPCQ+iobF;Q$a4S9w;(3pmr2NZce5%g=8)>BEW7tkOAT5fe;qbmor8Z!=5^TK0<`w0
z2QPpyr6`GSeQ!7S2r25QUNrc-*EaApq@>|r{{*1i9CKWL*+AOnYYRY61%!`$Nx(Qy
zW|`9)Oe4i$^E}tT;oAc`jGGT$-)j_*BA5f^q%)Xcjv~X|d?jS)W=qz2>Aps&UcHJc
zA`kIm%6AREEG~z*sOm46Ub9G7h!yiZN!$MYg8o(8-k<S74^`h@K%o18&TH?KgM8C8
z&<M44dVv|81%eIty19ExH3SR0qy(`ZgYweD&%F-@U8e@HvwSW%fheZ|V1X4NI5Qm*
zh3<J)7s$IqSWz|z<L$`{XIci>Z*I>ziUE?QjIn{DGj%O&7OtbJ*aIy-p&tzl7IOz>
zQ4hW9eF#ylXqw62R*EMvU!eP-(4mZ49^Ff1(ac8qLYH|JIax3%(O&Vj{_+HD`RI(s
zIZ2`G@+|kmLxb2_^QCftZ2a&B`R0der>X9lqFB4ODr^nq5q(W~-1k<aUZ`UJ#!~*r
z_}ZRA`HgI?EOW9=+k;*IR385*&73@5W}`4Ip7ailT9*^ttZo|BY>%&fqvcgSGwD-<
zAIj@~d8lx+ehvuoC2!=mDe14}Jk%&7e$DKaY*mA{gZ-sPN-sDk2j|m)lV(v<LtLkL
z6ld-D(GB;I#@T$em-j=lJ<TRL+5@$DNFMKc0Y&HV%3nDbv`*RVtg5Jr$Rd!W3BmWS
zYykLiyKU#*69e5_$R9RyV!oSCF)_VK=j$y>Fji$pU6)pgZC#i&Pv3|;QmyLbx>s4x
zrhlGs<pd7-Y=EWvffF|5Wm@%>$dE$mtOF7C%{i3}a+lMW#7b7+mo>ZQuvMb>nQ<${
z>EJf}!L2OS<X_s!K2qRx3dQVvAwUOgp)b6>Zw|~gQy}h`<h)H_`W2<qQC-)sg=#Xv
zD)^fE4`~TEaOw%*GKe#y!R<cr(4eaUfb+!d2fJqWjTm;l$nCj&Af*f&L2PDt4T4Js
zJHxCndf!aR0k&Kta?jtwANag7$2;u%MJ7!t)328)zdZ4onkcOe_C8;EDIj@{g@!7F
zvg-n{NHnL3n6?~_siu@ZvR{0{Y13w2r_~3ZCTsu3Ba<E|y*x}Gd!yX!T6{xx;nVUN
zgQ9Ow-%xX)_l_r3h*|kfS8>^mvolha^~TuPhR?~^yq$`a*^>_x2ak(i$22`9?lfVe
zc=2zOy0%(W4(o4mwJ_<FlJg50BOa^GT?3XPjo88q?eUfKcGE_$a|fZSEv`qaMt+gm
zDlJOxM%lyZcZqQOB9~tdMDkblr*6H-ITbHajyYkgc~8Y=5@;M_vb!m+Tyks~le>2P
z>efKeEP>BX(Sj@0k2e;(2IzRT&MNo{ewe0Fr|=31ANOV<%Q}38Ks}hJN)pv?)bbeX
z8W)B+F8T{xW()sUm08gQH?rzUN@^WbqfR~U^q^+_GXr2vvG_e^jTajO`zHV@AM>Vk
zx+SKwZ+xHEW7#I_EKj-*2H1rNd8#>)Xh}W8KqXAFg8Hdqm8=j;5MR?0e^5g05uxU{
zO-e7^ieH}I*(#oaGM)0jGHn}b<2Le%cZR$wHlkXD|H^#~#4svdV|Q?Tf`!g01p(;Y
zsPR{PK3+MgIRTJ~LP>IF^{l5TyazAdXC<yLdR_Lncejs3H>$;c(x^5li(K8BZpb7?
zD13$`8}JLIjdX2Uo%tl5%Z}^Qex_1eFuwtXix~UUFzc`c>rj&7Dz0A_+QU{N85QY;
zRq?MkkCfw?^7@@LMHk9m;DjhIX~w(!I@9o<kPx;GX5=57>W>uJx;H9d!JHg<fAh$_
zvhJuY#9*+b%ig$vnr(+*xi&>in@rbxckPYVWc6W|Z~t+uLrEVK^|FrJW{Q9dFOI0%
zy`Ce7TK@n&4rL49&M4mq2wWdMb6wG4F4?ee*FQSkI3cM=FOld)^%!*9s2n{yXFBAI
zxARG{lQQ|Od+)b*RYrZ?MAA(iq$_T|z-%V}fZmIF6XLjv&e9eESrgH*e<;=Pqvl7%
zqA3kZmj-yxZpdj41dw1RiF>6?MRmH&UDeh2W(R*$3g+i{s=`(rGOjzsFS0&L!{udJ
zKp4bCqupL+VGv6x5O~O0R@OPOVGf>^%$vAo8;$`5`sju>rB6|!&Ks|}o{j7k@f2B)
zRI>?<INMArX|@*~k5u=>!3oQZZjaNEbp2+nln!$IczZXtQ$jCXbkylyODQM&3Mo!?
zbZIvanBioe*P}<h;<wxln9O)B?~hr*y>ZLbh+oQaDEWU2+;(+uzBoNLb+%~uqw{2P
z(586c(1rNn9HU^~T9oXnPyC_VbDGx&nL}oeJ(ZFf7|teNG8i45UaPa&*L{eMj_dRB
zzR_`$sPn5~GS<OkD*f}XxcW!kVFE8~|NX)LNl_jDtU*1J+*3V;6^|LeDByRnu6TCC
zxSFl$$-ekqG8KANn~1pCKW7h{%;b#ivgDCnG=^X8xP%Qpz-V&5=geF!-}sIOOeAR{
z=xKu7-+Q1b99{`VA%19o%J7vN&3+goi_`B2`$MUO|8*R!ukt9x)N>gR^?p9_G2Ir#
zY$A^FXV)NZ^KGfdwDU##tQI<Zj51nqvZ85`ND`1Z1}ZY{I9>Brqe5cV`6E!tf40%K
zA)l;Y6XM%S=}7s!-l><MN9#vW`5@7t-4kOMmlh7cKanI3)imDMP0>Yy_jf-MEvC}o
z%b*vVG;tL7`M#^-K}OPaX_Q0~h%YCt*q2ywW4#NF1dL=}=-*Uq|CJ5$CKo-Nv(?o5
z+%N0vrkuj4_vawZBP+VH=gcLUvGQM(mu%v4Hplg6-#9D<AH=d??dy+wYgi~rE|9Zl
z6<kb)Y5yAR^o}hj#1TU~&bQ2F_aAg>^(SAxV`1ITLS!I+JKdzNDEs&=pC*j2xt(dG
zOQO-*<@9!~Jq}FO%#*v0+l~2EDr4EV7q`NbSJ%lMdG8uri5jVX@sU{WvNzi%@PX#M
zAFi>>1td^xBzhh`Jq@NGKUzjL$@Bu#<OC+F2a0^3{I{}q&|>3cNaE;bT8rVgXK;u~
z4!%<}GFg`}bw1v)Fe!*%O5gK2QAde}A>m#-@TqbDX=TFv9_hG5W_Ycoq7uxSeQ@Vf
zYqa}g;dIPQ)#L=2@Y_bNfT1By-SJCqNh;aJzg@N4U5Wa+xgSh*+xPp4lkHpl-el{i
z=8OnL&lQS?H#?D7I2)S1by9Ok{%}%ioSQmVi=#rcEEHCrn=>)Q6OKooqnbQl4(8*u
zv(rPTdazA;^tLI+aTviYrkDhZ){;S(ZMqz$uBHAEmxMtL!QI(E5(N)Sq8KDxo?!GS
z{x{G5;0kiL{D;x-Nh1xoR<-@(6OeB`Z_|5KZss{XRG`yqtbM~Q`cSpFT1uY8IMc3x
zBh44bQN7Bda?_XcQ%DSt3gKEq83EfyXmR7mV(O5fPZb#cVpy~iG*3fzhJVPd?q)u{
zwwafl99eygcHCQHY26hkZ}m0UAh%-{d+4>X3+ogcztIJ!o7wr#uBr@SLg|S|C2wr2
zd^Ivv<bJkC2Y;zuPOoPZdZ`vz<qtYBKZws%0CJk5YnVn8?iLXQMP*C$=kW&Wmek8*
z7+{;{6xbgBVF3!tnANXes4Cx6FuCgEM~`wf1VIW6HCsZZnXeeeP>Feb)Uz5ZrG)`n
z$oLQRl8n<MTl2$dTG+zqrj!IuZvg)(tS5O%iAmd9ZP~mL?Dy?`Gn|sI0)=meL39bl
z0gVK~U+`u<p24%EY;LDxrf<nQ1@5Ir0n(C{t2Rq%j$o+igAm$0fU1+4o{w{V;dvJY
zNJd#2YG*Abv|ttt!pz?vQ_;Z9Es3*SoS^mh5SJxqzcv5%9AbeSI%BcL&yUQ))Z)4P
zA-$^OFIe%^Ayv@2H%0Wf=Mf0Gvxh|ru5vSPTZ^Tw6uWXrS>tJY;ZK7b0C9L(Lko}e
zx{e9|h|qoCdr!r@e*50P0@>FQq0M0aJbI1Wwe)|;$rE~%?>B$wa2i(Q3rqLR`=d;k
z!Cg?Q(6)Vo78ify{TFOvo7eX4r;o;8M>lEntu@xFR-mPJs%0ExiC5NP!7^a23ZHe0
zdz;14ljCBb#>AKUp(pC%t0YR0<FngJBGFO#hj&%k#HSTah?AvrTn#gg*~m3ICpX2V
zZO{MRyZh?#^;^%Pqx8L;IAJUe()KJ5)(<OGI-E>p(I@LwQN!&O>>VLz+hdlw%cNCp
zF#XV{*UdIm_|pc<j9xNI&YVyRw<WH-vK1g^T}0`V^$?@m;?CgvNRhw!59aI3X&Nm6
zW$`z(_2wszCf7;3rF7xcc^)89@%~=oeTF|UqrO9K9KfXR1UXG7^-@1Zy}Cq%<n{S<
z^}r9u(E4Lrk)&7Xz=cY-SBU%cL8eo30E$mV;46c5jO^G@>DMuSfrRnfgTDkk6_4ob
zCSo12<WY8|4ZhbnEXcKBG16gw{2b=1(Y{WrZol$9tnt8i*f#BiNY%OMP(bwjWF?Q9
zl&Nld+5SxW>s`XuuWvC{r`@-fmLEfZTpbKeYZlC!4!1j<t7B=@-vtGo+arlKgF(;1
z_Nv!s54W^&;B^sH+0FbGn8c=+NH2ygOy`3?dT`SzW6sx~_oCe^dG(7b0=uD5B_4hN
z=jn%Axi&+&lHg*c9=)rP`7ZA!riH|xPPx))*YNDEiCKf);@9iL1tdQ|-hVsuVz%f1
z5APmPqgLmTJU}~df5Z&)vBVL*w>y1d;Dc>y<mR^wBBqR~ppy^JOqq?w-Gdw1#R5+@
zLSwBDe`%!7KIMPZc5~0dr{<>1-{LPRkFx!bHn?3^PL8dDU--<a=C(;n;K)aWjpt5P
zuT>lbH&m=`oE3^#%8*6V+K~s<zHzOe9|~0*6PdqvCOU^1<9>&Fia(RiNbI!{iw)z$
zk<;0qKR%;vy?5zPRCIb2;&27$yYTSy{+{u?vm^4o_Rn4fb;GhkzMVD~29H?P`Gq~>
z-Kog#yQQgZ*eUaKOlLU6#1(uH?q8~|4?UM`^!u50ueJ{aOtJSR8TTM@st=e0GkR73
zTbGM!J`lxxNnH!VAGsxQ{}0~q_C77*c#n#@`iW+i34NQ+2X9_iz}kZJw#{&@%?R~~
z^L&BKa+nGxgK5J93=a<sam><@E3)24=zFqWe?F*r+O46`S6}}bdzXS_6#M%0(6B|R
z!A(>pTteTs-YIq;srAVi1n$Z|Wy3Evb<J#aMbD43H!GZwG*A=yi|z?~AJvXu{R~UK
z_HRvldaAv}l`QtU+AHlI*l~C$jXe}+%KlOmei@jh(K#`TX=!*%Ld;vz_lZz@jf!pt
z3RPRKE$7{0qLimfnGi@3^GvW#I|pT|%5@?uc>`XViC_BED{uLS29;dX3bvSPaqz#N
z`r>NyS3^_RUIw=t@wgv6w~73A)yJDYob?f_69-29eQODHil(;z%L2F@rSR!d)|sEo
z@qK$s^AqVSHt?IDGW|Gd`#^0cJlztX4hLMK&4~Kp!ofc2-MqR7LjvqyaW+Z(m-|X5
zz0?l|$XG0D7kNZ>ldQ5tABYxu2HqfRiCx$i(k=0{GYXpiMjTju=Lo~+qXYAp&~=x8
zdBiVBD^t%s9`mz(Pxry%jU8NC7|pwgAddd_7Ar#8LPI0f^JnfRTbABqJZvw_Hp*Tl
zFJ=;#jx+}x;$1`QT_=X9z_X^5bWZG^^xGEP8u9HhOz7d7O}O0o12B^J{dlsQx7>Ep
zJ!3XO-Z$pfwGvlsvl4;hqC%c#mxD6i3zXzh9bRJrO?Q%D6cYX<T*gxWAMR*}PD3Af
z+t7<YjGs1-8G*4RNNJ%<e9WWU^6VAeHiQi)hDg7+g*sCvmXsKe^-hBM@5-kSjNUC%
z`TC^}{0!S=IVo<PT5_fHOE0?YQ+?8I5!V+nEzN)MZu)KTPw-t~g1y5r*xt}HHIY%7
zpP6QJtOp?al?l5h9ECcmAwjh1$%Uk;KyVgx9}3j;3bqf&X;~g+G!{7PRMfew%R$!N
z#S;r@f-fL+EF0MC09!4yCgak)>{U^`v@~>`+R`sqst#VI=;W&VxDtXwC<eI{7A|qf
z8t;KC%~Zh@!86!gIRWTcSSv5KA;-n>a;l#V)nh<cCv!lA@t45%jU+YkQQ8V*ecn_=
zZt43Aom+XLq3*9%ju+^<+c*_VMz#ttoD-wJ0S)Q7dckrz!-I^=o=)aAn)-R*RUhC7
zevOnne)Hj=B!uB%f<9d2wPTAumJKo1xvVM7|5}Y6uP+EgF)B~&VzRJ3cV26S)H?74
zo&0&xb~p29M9@j{bGhW-+8>H7A42JEIJAP>TO9;mpDR32ijWrb`HtGAhE>HelU?{o
zQ!~!m#cJ^IVgTmv>8!%GcJ-cXjU3hC#mwuk-*QxP?WPD1CY_{M5wDNh_&dHWWM|F(
znbOG~_%3gejBU8%y+)gG!wG9>LDNSi(eTtwYJ-#=z8(n``Pg)WoZ$wU7v|`ml;t-S
z)#Act(sHj~%j$|xh~l1#iZ!}tBI=mP^;@kV_}59Uh2E>2p+uDzKTGc+FsrY39H!}~
zn#T6mda^+P>qEOfg86D}nQ+9D==W9B3A!}bi`QK=#;PL2$d%+x-(ImU7pky%4z*(p
zl9+fzh>r=gZT0e0G(f->dc;jhW8c}`+H#i^(?&(8>!m4Ivnbix&NIT26+u{M?zr5W
z`eC>SuulO|{Ed^~i*GU*#TpnlNSkfdU7GgC?gX4BZ5C|0r-i-hIRjtfUXSn=K0EG;
zx3j0++fzNubm+@P#7aZ1_zUZPlSb}yADf?rfCWSSnlsRYsvLen?tvYBOZOY1^4sb%
zL8Sgb^{tC>b+#ywkkCqnz8>0iB!O#?2aZ6_s!K)^Blm&oC-H@SkxhXIEY&4fj49$@
zlGXNeXvk&cw*x6mO?^<lizR(}vo_Q(Wz!Sx<tq_R(EOPdu1GKJBVWwU61=SHQoblD
z$FJ~)Y}ch*{Ql#cyhHJuPex-N*;AB}#TJ^X1cxrY#AF$Gb<V>(D|P*4#HnKEcyzY9
z<67PF8`enyCYO(|InPF3z8%MTaW<`7Dko#t8x&x&!0>A^D{^DEe}o#huO+9dJ-g`U
zs!(r%>eaQSw9L+>yOTY*^+S<MH#;<$f{nD3DUIA?8HY8?`-l3;;{zN^ChT=t*0!Zp
z5*%F^D3kV0l7xB)i%ryj`LbpzrzYJ!%<4(nvAxf5*gto=)3q`eQ^{gkqb$-MZu|7D
zf1Rw$Ed$CqRuZm4xX9_g$=ArdacuO*a!`-y$Wedf#j)#2ffzdnl=*VbCRt9GTMOb#
z3h2RAhmS55h7ygHW{NV0ZD=4=%lN(J2KBM~-pU+NiJK(GiCx1!aiUgx;3-T+1Hc-4
zX55--u@X*4<<ZElU~eH(QeP96MG=txia<esa~0O2z3;fmG*gehHKVVN^@2QNT_vBe
z4S^Klf$-f~8Wew4T?^@4lP0mR^W>8`46)v?F)g^6tF5yJB=#wT%Js>5eqHlJ-9(Fi
z&j0+~xj-ox?OX63L)ZRq((v<BoYeJYw$xlNfs4QG)cbj>J6O~!mGifw&((1`>@0sK
ze7D%&J%{9*B|IJQ6)!n^d`l4b5^H~mf&B)vG$qiRXG8lL<d-A`9HM7_nqdwUh(@2o
zCfRc~sV;35kB&L@yTa^9u8zK4rv2Mcf|ht59+fFK`gS8OTOC~1QATFj5_UP00IM1M
z%|YC6-vR4feilqq=S<{kK`GORsp4Kb)_5w0!uE-2%^271K9txhy~xqZjyK}_{@Eq(
zHy(N<vw!{F1-!m#@vwdXrP?383?BK)ov0Mcsvk_afGI0xiHhwmG+9IW!i8H=gT&uO
zvg_|aPa@BkO*-GJO~O?-5~f~!X5fFh*Dh2bfI4#V#J`UHo6?s-EW<@&4JCJodrF6s
zB^7MRD&VYQb=O+~|3b3vE#u4K`5ix6I3D8zVx4i}Y4og&%P+%+$OE2>2mnQCDvKhF
zkv=rnxfc1Q)U0S>RazMR&R7vjfn=#|hS9xe1>)NJK4I?j!;enE?fCSN%zmGS_8z8g
zd)v6kb>#x7VH3=izq@zv_jTN*n9ebqe+9iX@>n0a3#rxm|K9sstB9-{iZ1^fW-ufw
z7DMxdv~k-+A2iB0C$&<Kzs<eWZ+9pXG`0&{HXQ2^cU>y1)?MgX6L=u)bO|x39pY$X
zz<}OOEDXWN^L_@3gy?92D17F#V*jK6zxg)9yLf%26;dE0bZgnqZX%F3;QaT|##Z8i
zwMgrvI#3alZpyg`*7r0TezA3$T9*ogX<Lw6*wtPDINEr1l||!twL702Teaq%l^Ip!
zV{;BNCrC=Kk|(<saxA}*OPl|H*gET|D%bW~lL8_s-5>}^3DO<XNU4OR(%lUTNVlXk
zNGd5vhky#WkPsA*Zcw^YJonP?cgFe7Ib;9f*kkN5+$`SpKKC>4Ij>m|iY?XnE>v6w
zi|uECi62q+G$Gfd@7e6lWJVLJGyZJt3Ar6vk*z`UW5V)%kDN!DPaRvb@CRF@m9=La
zp8qTzA?Kd)_|Rv=`^V9!<sRlEi(;&2WvaNwQ?i*-Quh7D;X2=n{lC`^TZufiYH0s(
zXD}a?++gT%zrp%$&RT!s+gsxsNK`dvE##<3%%nhv&2+Km@w_d?XcvffN3gn!ON=u?
zmL1#YZ1QDWBbnph&*0xm;4K4Er?Mz4%6%M*LWZB~zZcmjIkzY_b(BxD04VjP_)S>R
zxaccdL3e08_<rP&2vYT{pNagBe~yGtAM2B1i}T)x_C?R1$6#`M`TO{R;$EAa^dM_Y
zKMc@vFEw6T5qCi<kKQ!@e#^&e1eKwx41sn8Wb<gtl0Pch3TCS>sn!Dh9?3pW2`lrK
zoy5Ky5MbgZZ?O7d!VF{(>_NTO`>;X`jztknA>lTBX`})7Y>DxHRlmRQtq_bLU<hb~
zp;mUd{y%SUhJ;4KjtL%E0x|yNFMrpB`&(VGAo*va*u#mDL-ne5mFt;21zY=6Lv0%5
zMWxhQ7%Wu>KZi#uCL2nB1*E(_|0SASVoPQuANTZbN5rse3NykvC-HGWeNyWCnlTSt
z+g?Rd<GY$Oe)B4JR2j=&&2I7|5e8h!Dz6G{9<n?%cX^+kEU9bzOSIN_DwvmD^IdIN
z;pdd#RM(VDCI?s1gY(lCH=O$#LS+j{zbb2V7Eh{U=FNUnL|NYM4m3@<H$M>;jwPl)
zZhjN}ga-l|7(Y-2Q7jS3k*5AAvoHBzbgMus0R#CEyw~m_=R6HodjB2N?-d4VSFTY=
zgUVY7PAJR-IYus+6n14$j`=Q_|Bj#u6oe7=_p&K`Zv~KGbWxzLcl8+wxi^75;O#<A
zOHJ?d1WRxW@Pkn<&hHle+6CZ6UwVn14&!^uKMp4e-g?h-SX6F@5olGW^Hq!tIWGcp
z%h^cJ)jk}B<k<P1GPV?$5BcoGFMJoMdpx%}a?(W`qt^X<3hG+Ta$4N%By?SjW;3cr
z4u}RngJRa>qb_TW)9|I*7`Ne;?={H%D#9|R0Q{0euyJ(QjmtY{>giQPar1#*#RqM^
z9tb4h$V<=n-}HP9t=6(X@z^UJ51s^AXDDTh3Um^;gmf_FjXe!=_@wwtrS*K{#W|Zr
z7|DUn1Vx#>qY{mYYb0q+u~c)a?E#O>LlZWo*?3A-he!3xkCfpFr#>J~_3#<wI(xGr
zj!-)--P+b@)SZyKubFzEiIi=dQF85l3)!Cb$~^^kH&%gXpZS>csjA5GgGwHK?HH*T
z$8`F9bgC9smD4Vh7H_gTTVyuIMp=}{<2Dd|NNwQc;_g#XSYl@|SmBcGR8SyB8E<)=
zdIsXDpH&)1>-J#<t^%Bn(N9bZ*s4CTr(0@=K9Zao^GF>8csn>seuMDGIotIaB0Bvw
zI~gwE7~^*P^^IL;0=<6xHhPgnZ(ksJ@=<GN7S3lW<ZF%!?fiJV1k!l5tBrXAxA6W=
zicA>hr^HNiMfx%f3D`(i3ew^mAB|U*&`sslZ2%#*ES7UFyn}rHxoBlqw#3I@uS5qC
zqdo5c%~2J|lmtBf^3GHgG3fH7E!P+W3G?Oh2+5k~!=(MIkUnrNaE<A`#%uBHOX28o
z*%;!|DW~YcCB5~r>pe8V?BN?L+V8}K;$CO_#<XU23nN5RJ=h=)hVZmp8<3!w+2&eo
zu~J=S2r4yA@d%cyYbk81ld&G<MPB~$9IC9JDmzD)g!&#C<c0!txe;U-mHC!t9tyCK
zUqFn_d`3#V?S`}bDJ%8~4ywd#wa5o@jxSGbjeGxH!?ZDgA##2AsL18t1Q>}zqG5XK
zcX6`2@`yS4;;!%HL|ZZKSvs=^v9Is5>nbU{B6xk5Sd=qVV_FDPzh|QNYIF27Vx?|r
zJRie<m0tF2nfo<MO$Z8;v&A+S#O~3!oDT5>{<Mo*+{>7Lkm@>N|HV6NIzTzLO6K(*
zYe81si1r_JuF^mGsVh!%KXe|fE)Tm~uF8ux9P|Ad*lI2;zcur?W>)9XT@!M{-uGIe
zWLkfo7cB6n@fp<!+sr4UG~sb}``B5$fo&@PlaJW7(K|2i0HtzrR`IdLtU+IHLJD%G
zp6Y>H6>+;hLx-&~fT>GCQG)h2rmZ{WtJ%UX!Hb$ZBRa>8!Dl5F_`(ecn)08`<utGY
zXE?x?eu{6G@lP&{a7S8Yh86YB(n^VjqVYHb!KJ%+hfHrt8Wjaq=M;^y_+Dmr)KzQr
zXhA1gz|81tp^XMIpAZDWrBfTC=PwyZl=0~$H>4J1+1G%{DLq7;dIb|TlL4j7Rnz%H
zUEn!2Mjq_ytXM><v5Um8Q3!V~T>P)hpS})#?!9F_wg)%HBA{o7u6KSH8-SBX-WCMm
z5|K;De9r21;{QBLzq-5+MJ=^`#~}Dsd+;{0grnt^dA?)m2U)FFtPPH&EJP|-U;n*q
z(Uefo4hNPbe|qMjJZ$xL;YMHgZI%KhuMp~-hZ0#-)jqX>>mfPk*`*J6**1L#N+T@J
zgCkLrH4bU6S+)$P{vD6<@6N#&0oP)Gxc=|IGs>f6`qaIOYQ?(pl&x4J*RgW{pt!1|
z!M6dktqGrO)Yr*I=`|m`1+IQ664MPBYLwF|YHhH)P(CXdH2LPPgsFqVv8$=BlBz8k
zFfA{3`>mJ?>h@6`n@VN%)^8o2Ux#aETh!WShXJ7$7+#+Te~$#y8BM&5tB_J;75r^=
znK#}rSH#6%zmUscyrF%WKM>}0bY~L{?@5c)z<qJuDZz(gAal^KXuGF)|AETu#4#1?
z94d%PjFoskU#}DweEDP%EBOb*dT@+txaPFtV-Zqs)BA5yb!F(V7Ew-S3g6$?U)ImC
z((O51PjPiz?B-mj0(1(L;JVR-CoTYZQeT<CqavWb+vsbGJA9|ArO+NZIz3YlmHYbz
zBL+OMj|(-4PwO;7<931wVYgcz5_AZ<b^zT6Sk6*H)_lLo*nf)>;hXa#ZbIU5_w-lo
z)X*3pSo=L3rKSBG9T0B<A)9>9UpSSI{rmmyl%#Ye<R|-DWPLEtN}05kYvM`Sjq33R
zSIB@SHxGr_^3xH`tDn2!fOIIGZq6#k$Pb$c>T|JpJq#($u=`ox6*2EjKb-nrXT`Ps
zCZ^W?@4++(2h$X7c8XW(6)N!P^C4T+k7j9&K1csnBNbnW?*W*y4O@Qumlp4-WAm9$
z&t`BugR^N@0u30m>G?K?6z63s1=7*^uxM5T&)DqPFbNieu;>zw-!lNVqw+WOjS}-u
z^+~3WdGA`<6=?tQiz&28qL2_869V<XvamOWzf(Nf?^e~<-DBHs+%W?eYU>_)ciPpm
ztl75kX}4!LyPF5FK6ziztft;6zs*fipd+QwXOWenbv4~<RZgw-l9i>g;t7GJP$F~f
zl9=J0pQ?qob0mj8dRzN&$$@oCwI@Go#4~`iK%9;u;q9Ay>0x__xwQIZzKnd5>DE_8
zhRak^7dz-_0ZT6wW5$gAVi#=}Uyt#Z3k%2PF8Y93Q!gjR3Cs35YeSJ>^_~`G;BEMZ
zcWqw6%`2?tmknDyepX4VIEBiO0q^-x@)&4haL%Zk4(YD&f3!HT7VLB&$1Xv2;&>&!
zY+~b=S58(PmrZ4rL8n5sivSjM`aMCWF*WI|mP8MCDVML_S58~Mw<7Jl3Ttlh4QXd;
z^SNL#=ir7phX#>3oBTzdrx2*Kg!6HxjGU)GiEZojkMGy<Gdsi3xMw{|@$r&3F-)-4
zl8aQUDw{&y-SGH*2kWlOpp<afi2pm|Rnf}$weYz00(Cd>v4ErGty=&tIOX&PUta}V
ze3Ucu+F%SS`V|U<#iOCg?_;qv*Js;wyA8Xi@ZCE8S`Iwd3U(OAGLV*pI5!k}vsqC3
zo^IDx-lOUJ?9;Ux$Kh{ObWZJ70lnmY>IGj{i}Z*%+&)=jGH5u+=$9$=*ei4j>MvYe
zt?PfXWRu+4`+&RXtC^9J))emWnSfs`e>wAU$A({QvQHAF^4eChqV6Kjr?%CByBvO>
zw1?b!++y~Ow~b=G?UM>!XVvR%Z5j>BA{~>_r|VWNa`sY9zf~BFRoUs7kR0=OLzDgO
zyz-JkBt2>oi5wpEXf7*oW$jb3@6}D+z1ABm&<2->bd|{KH`Q(<+XLA&2#PzgmhmDd
zwdnleq$cmSxgPZ+Zu92S)5SNAuryK25p`cPg+jn-=}ERKPGUZS@sE|Q78J0*;Dbb$
znY7I*aCZ5sU4~vLifEkdE8Hfz_FDi8Twj(*XMN>;p4@wvT}ErY0-SCgQf?gvS~=sm
zx_8u(rt&FyX)g1voYHYXTPw4F`=o;VG4hN}6&QQFO3lCwfcipF?h;4$Vn4Zf(xO0J
zY_`6bLeC7JhdNg==}}oO7+9_`HDW21QYS&&dPI&ug5*M7DLObXm&n=QQ6DC+>2k1~
zjGcN4zoA7St#6i?j#A&YY~nt|_Col(a~I~W>P$ily!dbGkJTrEx-{M77M_eLBN-lT
zh|$>e@ai5-IL92wV$L*<3UfCb09L&bh8gL6j7w!rF0*M}2-gwfocGqM$e-r#$nk&t
zajAe}N8Wo%c<^=B0lUo_BhU%LhB#ZC6^wd~cI5S#zgF|Ugceu~EVq#6Ef~2aXs@`P
zK0lv)!$a~aPU`zB?)RgS`|B5~w7(>)P$in)Qi6^7$418U(n+<n2zo-zT%v*UHic-D
zKbcX2efLP|+Y|$)`St3{IhmUrI+A<tnjAUyxmv0<IY`kijkw0IvbgSh+^KVIi*a7u
zbCA5UK!1jkR)J`dW9U7<d)*scbl=KrVQQFY@#4vacf*H`dL8z~^s%<oQ)%PE5wJe?
zEzb|@u-70*z7rZgR*M&Li-<S$fYwUZgej|b<CTsN_Pls+cE@v(`YaVrv#7`ntSKj&
zzVvi%Cg<8opxHcCU}K@vrV}WII}5TTtTJCv;y!drh*Xw05PQp0p8%38c4YohBV?Bj
zfId3{`*tOtMFtC+U}O>|_td4cm>KDOL?P5ny{GJBi;H5x$o}_(4<J-}Wt=e8PIZ2^
zXP#S$!x}8Tpnx(~8QwR^A`QC162T!5#`B>SZvP2q`#VEH;=ya2H}jQB4Nnp$4&{N0
z80jUo_*wSj!`guqqzw^%vuc>}5$pCZz1W~n*L`<EUb}_i=legGH!6}BUmJ|_9zD?6
z+l)jB(V+0Fi~gQ-?Me{GffQVzEmbw8?~l&ud1Es&VH6=SvDnk73k{ui_SZolx0<I;
zPRtJ!1frmh=6x|+<;_->Wxh1^EMXb=8&~X9n))qdXfb@+LqLHg&PgXcLhk7>12t=&
z*HqNzod*-J5PFlbX$I!Ghg)%@4R?M3u*Ye7xD`FdE6v{MsHlY|(>D4*r{#q-nBU|O
zhvFP7GN<@mqks@~%l#H-tqzG}k$Gbz{6uF>PHYZqa1;imgc^4lwmUFl>U^UXaV@On
zR?1)?BmpnaVsrOY^52JA`iTs~ke$<ueU{X8W)N4puuzwcUnI9E8uyuwB!qO_*qQd4
z{d!$PoaiJh>s`X;4F5MzH35Y2+1A;NGH;r4ndA?7_Tige=}!GhIX5Y}d|a^Pja<)h
zzC4m&*p+6m`~K~qBff5J;s^DCTi<v7ydK<l_S4#r-aEG4w?!<_O}DydN)8H+*|hiF
z9jDfm`?3ANO(k0dGa%8mzVFp(hGk*+WvNDH`=2y2$+l?=5iv{Y*Ny<*;B43pld^I(
zDK8STAMdE)>n)eO*pZ_W_r8rs#UFtuVEUYG$qF{`1xU8jF}g)Qcj~L`!zq2StrIsL
z!0vcJIRxbya9w|bOKD5V#Te8F>^-dm%qX7(KN$u5CUO!|4`+M15`*{w@7fhrLr_&W
zLQ%}8;9bGedk{}A<;b%VLj89<5@(pJ%hDiqr+y%&_r3S;fOOLhW$Po5cysc0Z<(~W
z<|khvzI$WnI2LkE)lK`-4c9*(d`J6Evk33l3C(DaeeuNe?3ZW7$^J2Letm8X`}})y
zdWuB1$-F~C^tLv_FHGBYQ8))v>3NyQVSbOMSj?$71op$U!xp^(7=Ep8ag+_{(Lr`-
z%ikRh(YeJ5!`BIlO1oX^GRN()d}c0(QnkbG43SMmcgW+MxIZ*GmdCk{_b#%4Sh}0t
zsZFCLs>Sw(<j+yT<R=X8aoHYM{^VYUv70|Rff+w{*GBc1x72&$J32_n@}wO*ui1@&
zG&`jg>7@bG23b)lbHr5SRMQPJ4m0XXH))3ld`K#wv6bE!^ZW#T%YFqfRE5Oj)$MXL
zuD4Kll~DE-^5=|?^m;pM;T249ghDqh>F!C{p}JPVMeS*DUloBXYD>Id$4NBh$h9JE
z3XKg%b9WP?9;I~FO1lSk=gujUe>Q#_#~0<)W+;S8o{r-=ZXQQ97v_^3H>!<jb*4NP
z-h1P7v|LJZ90Qk_=8a5_M+m>?`6LrhzCUmM{Lsg)S*$&Kxk>eY>f4xx8Q<v1hh&>E
z$XkPjCpcl3-ho0v<?I)EG|wa<8Bn)Zx#%2(EA(DiS0yS;`>GJKic1np15};)Ms6NO
zAE!hSLSr{+pXa>EV-!ts%hPv}F2?Fi^qHu=*2#>*9Ao57j6ocXyYB$p=xeUSRjH=x
z5$B@Ea8<r~Z~k?_OZhM#bDUl1Rb(5#^&JjtZ>wp*i7slW*-j9nHs)^4zJJf<bG;Y?
z)r9B-R&<S54V~6}+C0{E>ThB-Y{5Qc-d|kRRGt8J7iv$Pkqra>cfmhC`Q-#IcfxVc
zsD&K)7u)ZG^_rwim~*ZPx0z?0wtRA`)#uKBv|la$%tsIXXEYvu5Kg_i?Rg2dv>$sp
zTe-xod+L_Y-m&mwpgIwD(AY!X$AG%y&b|LW6{VLLQP$_H6GHOFm|u*ko*yWg(Ig(*
zaeAeEe-|z0IQ*q&^lRN8$FH9@g#yr7yMJXkr4+h%5JpgrsJ1wMje8yb`bf2i&FS!q
zyQ#vvYu%ZiF2Cg@gXcKoM9Px=aC8<xWKuLxq$`>ndsX_q)Q?^NjrqHaWr-R7>(zXt
zPE>QGj=GNpwvRSDntpnWQhMJX68`1i<CeYk)0!6R0Eg^i?(h}dQ1~BrVVr@8jzs5X
z#W`KoYmtKb^o>ubKPSwv+vgcf=)ZC=`A#e^16*o5GDYvS&i4Bg@+QEdvPc{Z$=Unh
zRyx4|qkPhxe~v4{c+#zfWk+K(>QdY7;Uj{~9JCR3c)Z!BJz7v}B>HR0mcqVU74ch6
zwG;*j<l-ti3g6jI2sZAXDG&=0H}+l=w^HOZMCyGonDd1`$=A!%J4eZZwYml@CS1B}
z(vcl2p0N>6Rd(=DIc7>D<++mO=U(kXK&YK4X}BJ|hwg1n^yBR}y7CxIWl(SrK)}x!
z6cHCI?*zlmI^QV2o#ATuo;CSX2Gh`W)DSR5)i96@5;Rr>Gw1~~5OQM7aZ^<m&Z9m0
ze}1Kb9tyxlTZ4p5T<gG(cZ!b$u|p}#tDs}4Va}DRV9mqzQ;Ky_Tg!sO_DKX6+D>I$
z@>NbWUb7mH1sT!$c^U!l7ETMcO`i_|#mVXVIM)z5&d;yvRz}E)lzVN@(CF%nzXr%P
zh8%iv&FhRiV(sxW)%tGIk!$UzqVX3I*ybXLOuQ;18L~_@ImTJ78CL8qW}*X+0ao5W
zSw$AT2|=I|eY_y!(1}Sd!34f_8pSyzA79kO_0;8;Vm&894h2x{;!1V)nsj+502S2&
zg7e)f0m8Fy&9Ls}h^JQv9m3O~NSwRACrmHq$;r+`7U6ph>8|0xdo>3pik~q1T--zF
zb7oUx!uuT}X0&qzE79BcmKJlYKB1Vo&juX7J)hHzA-{7I84&~W&t1Vafd|@}a8PuS
zpu#m^B<OlQCMqXibshy=wx0)`08&)Vbc##x0L=mn0<EeQ&{W)t;;Gxiys?Ts0dg?&
zb~oBjMLM4l;D2q`lP>Qikh}RZuGCkXcgv!9b0iHTa~95LAhJ?ANw7X4O}(BAK$NhI
z5>3NN$spEH%ph-u1d5fqc4+Ocmkw$apk2it7=Gopb=i>mcDTiWN%TsQqXVhtnDRAm
zu?2?JBWV<!tLB$K%s4}E|DI(3*(zK>0*D%lgo-vN8_qM|4Y>uO%v98&;YF!s3iiu?
zM!9K_vR%H5BFJ8d9uugY_56F={Yh@k0oQak2f@M2s><2cfiUcjACQ#So5$US6D>Ux
zXWnQY0%`|9rQIo(U8;3c^msY-7&Et?!lYAeF@*5qxqxWfR~OjfLnM&YoqNtj)-R>Q
zLlUawA)P`4vOpYQ9>1*H?!0cJfV$k5q?-^a_}nVXz6JDxCA=)9z~StpFZ^cR*N!@n
zZ7W2A<Ba}L<Ao6_90Dx`!SUnPxR|1PopLSM0A{mXZbT~M6?=@EfT=EkYFGxu@r{aW
zy*Q4Sj~zR_F>`1&2}AZ6Vx&9Rg7CMNuzER+TZO6PrF~56;r3$mFb=s!G{bPNxJ5qx
z;MLujuKHLX($T-4UjzZtwMzf3Fz#=Lt=AjqgeUTt8>YMww0F<YTg1q;8sNhX`9$F<
z1XD+pSNUUB131OMyqOu|!#!u`gYMH{4oR<0+oS1e*)O!QA76%(KVnnJXt~bCfE`D6
zdw~)&?DyGW0kR7@DfrJD8AdE_W^=>e;jcAo=+S1x3(65-;@*lC;%{Xy_<4FBVMig>
z_n4MkEOd7xY_G1?j@riACE7|fr(kUxMB5(D&RaY)M4C4tezUUm6KKmrAjl4&5o}n$
zhv?mF5bz9FK?Y%HWN;ZxeSgk=fPw0TZK}Q=kGh3W&iSt^lJp5<pa=da2XSz82mD^t
zICRQQi)b3@Qy8$BDS{|4zgReJ7mkQ=YnObkW4CZT&ZT!6^2p$`xcY2R*mL9MPa{-F
zW{ng0d;uwMEf;bWh?)G^K{wx~O3YXLa^@Zc{#~xir~6+oge?TIBReV}5>`G3g(9l;
zme_{*(q4e4T$ux=iICBW>{|j<G7Uz#;KOF9@#=n%7s7g_?e+ELIs69LmhcChxsNpG
z=@iQRhG%OSnE0B>Fs(nn)e}?&>zC@_DY)tiKLgC=JAfg^$+3@ojpedxJWV6iCr!Rv
zr#%nGydM=#Y!=TPo7we|4+AV|^rq}gjUN1am%fMyJQrG3+IV{a@IMBO9ho+;k-qg^
z+I)p+Ir6pz8&xa|PUvo2v6qTtsPFQ=kSksPs7KPdK>wxm#wA$S)>g9V+?IM}#<MZk
zIf%t?BtX|)H!cfNmEKz>TuIY*6SBgb-TgWYPu^nHw1F|(r#$g*L($?|7#6qjX+PKA
zKm#8xb8TpLr>5YCJ6Fl{V80{-yBD!Kn~^ZgaAf!jn3I}qzK420ptxS4^#cWc2oy+a
zc*nt~6AB^MZhkty4PG5j^j&0oS61Dklejx|<=P7D5J5WV_~A^h4MoFo<eYj50GsV|
zIk+Ai1&8OXJ3HDK-^toTzyNTg2i-!e^vVvif(ZEHQ_Ku#wqkt;{?t!wJ7z+jIpRKz
z?cjKojYQ#8N#imYohm0*F)noYKVL-{sDGGHnr@INGh+~-`X3m#`-=dPEy1Fu(@y`9
z$~EjM@u%TWtsAqrY2Pbo*xSq6T=moWQulPQy1T-=DbXyhqD2WuB7*K^(Ndl!-L;^-
z9FCld3oH(N`5vgtsU@Z@-v;DC9Mo1KW{o@0%N=AT!)`-2o20A^flc+(tMZ62+YfB*
zwD4UoJ({`$y`J^W$Iw?>O?r2UfZ}%GIvTpYwc<H87+dCqyEoxvwx=XeajkwC&hL#H
z?3(*N*yE|*V%)(CtL1)PlL3(DJ1zm;`SQ02DgNKM+71!wq3p6Co2&P9O0Fq`TZg5a
zG#B53#LdxW5V+#T&VdOV3qg?X7A{5KxYfALTXwRJlW&Y#>hnE+P=a~G%V6Mb_DA1J
zlH1oTUP+Sa#JyzIvA>vh5?lBc!&>ytd`@)F>rbe$>1>L#(}O5$A~DC&CfisuV|Gk|
ztp?&>yISn&lq!V1E=5C@YB}ORG`WSZEx-Dp=_q*3!eLlb#i?7Br|qK!(y1)R{>$Y8
zWb-@8e3|)%eiSM`u_MWLgg4-BOrt9niFYwnsckOgo;fq%q&Q>r{eDYA6Q1rV0+GY(
zvhY#Q;#*IO-s)T}kO(f+K2BLfJ$HKi*<2BzOtQk7mE`;ozUlcPZ)paT*^}}~UR|>Y
z9sKy=MG0igC}1czkAd)*)01UA^#OE00;TekUv?_`G5pD|UW@cx=b#&QY8o)dXO0t@
z(@|OtS4&rnG^6`0+$S8~N7He}W#}EtED43#H|)eRKcC&Od795d!2RZNt9NbPi)QH>
z;W6Ri^V~Kmoo3^6k%c_Yg12g2ywkopMcTI<%SN(nZ}vm1&-KxWmPJ3m9j#bTZorYg
zoWsNc?ksSe@>_Q(66Dx<c-8j72=4!Y+AJ1yB?`=|By(>5=)qZdd$;R8CyjK3Pb_`j
z)ikrrz7faAw{t~5e_{yHddu!<*u=OkTl90w*-HR+3;))<!W4sS69YNkq$t2AK1pQG
zA0JfJxaB{=@oxSY<eiX_lK3mA>QT8+n+I-+bntQWad*B%AN{1sFnM2R&YLk*5V^4N
z%nu1Q2ZMW~TJ;I8`RsFfgX%=TupB$h9VF9ka$PMgr66GCULnWU<4T!)d=;Mva0GU0
zrp^n;k$(rJh7y5BlPf1&f*C=`C%Y3-P=ntSwW6sWnxLisZE;U{EXs!s{gZ%&QQUeS
z*N`hTobAt!owJgl<zttuvo5VwIxM$`r0gQ+G$dB2`y)1*ifM7~>yA}OYJW+pY3xHY
z*m>CCXtW4$S%GhkwsM=zf~R4oWGc_{cEKS=rKP1ME(uEI6Z!Wc2g<)DR@;fy&GM(D
zvD|Mc3qNLTMF=pvG3>4WlkP3><u7Rxc~g1uOGI`6;(ThcNunNrPg*s4$g)(nHQJfK
zpfwSjb*`JZWPYHhJ4WGNm-6D4iT$!f*@7{!vtwY=X=x>pwo&VgGVG6MW{*rc$eEyT
z+r7TLQP^nJc4T<inL%grNy1J{6#o=|$u1#V<^|rSX@OK6Tfws3=n*>YSnE$u8W3h{
z3-+2HOXG)$FQ<-a(uu@CMI{yD!<9Z%#(g=uGtB0JvnVGm#3!D#=NE*oa#jw?kCvw5
z%{gyaak>~S`a-mz)v)20F{_&aa?wKfS6OA-`W$=Daf1&_RuZdvU%5%-Ac}yqImh=G
zdldqK&LK{x$EV&&&5F%)P`47T$vkd`QFM!lp(lqtQpSZK%4$M3%eA00%KN3?qtgx8
z+4ygVr!)fD!j)!pp&Dib+g0EdvNgQzhDK>U=MqD2=XB_PAJue}5XS#>M=#-irBFX+
zyKDdBTji%_m4o1l<+?n5p2|VBRcYe1Pt*4}jW;CQdOucZX0GloBpyzuNa+*l&P7SZ
z(ZYLxRcHx5L4}oqaszYxUK%A5_>qE){rf)e-*O)0=+voB5t*B$2~0TvA%-=|uf=Gk
z8+moDt2Xi`mh^9N?82#{vs<s0HHxBnD}gQTvfXs2KbA(>jgnP8XQxks!Qt!Dl+GB-
z*Bq?9X>zJ)zqp!InEcPC-k;psdhCd-%kGAu^NWM%9&ve-nZp=WL;W9^Z}!UU>ukU3
z5Hqg!WeAihmNQI_X1YC2p&uk<o3#m*u%oVA&@??53p3DlQ1V@x88f$+ld9jjR?zmj
z*E{5?xcN2s2j4pfeFm|en3sq4%|eq$hkKtHna;)tGQE-ar7p?!yP`@H!}vY0xkwKY
zYKk!`vsZqFlUE6cZVeV=fN8J5XsDWsNUX_`S8-wQe21ni&2Igo=Bff5C)nn99?KiB
zZxK1`gArD5cay8ud-hvZ;b>#a*-Ub6>f3GsBP*NU`(0AauFFx}D~Df6C8}`7^YYB-
zlWRz3pFwY@1Nv4-=*OrIpwL@G^n1@xsr<V185cG)AI5>0R$g<gRU5|NvlieHU@yLh
zcvwlRr_3+T#zGEQu$z79#P>>y3K-?{R#(oF3)B>-QdbF@1CH?b6Zr#(soX0`jfA~Z
zy2(wHg}&uV$u=*rul?BU|Ct-gYV$es*~ns9=5X=$pQ2C=r(-OiDjUNV&se1M#nq_y
zf%BHCW|SXt2Gj1w_kV4f`fw{$*}=642K6qreXEaJj)gWHeG!F%J;ZS;ufvd6VpvTV
zAP14b6jp5QE$^RD_Q|wN8JvtpYxMqU_O@aN&TdF6R?C9wKC2@9P!ZQKM7_4u5R=zt
zSD6YFhmrRi*oC(SyjQ0BDXoOOvN5<4Y8Fo`>)$6XYU`!c{uzmjadxz+iom<MD*F}x
zD()pV?3K-BBBiV`6T@msZz3pzFvi2)Rq#}fHj3%Av0HJO#o9Fw4CnFVvV1<lusT?f
zV@>v;InOh+8$l-!Csav=Q6?}9pH{_NP`2@3-T>)b`{&x?BP5hxn2RsaNyK^DH~i$4
zy!0gA+m{5qm5RB{8Wn<EJ#3o*5023(*8}BZnY))20=pK)K2cNcWEB)I#UE<(y(-9g
zgTYLXuo;hu%}Y5-CTG;K-LI3ezZJ@q#e@116Ea4w%gE$N52nf;3VMspIDNSbSPre6
z0+vA@k;1YHX@0Ll@y7`@SCllz6#~)T)+kutB0^_X^dTWXn5X{*g;bG0*~q^bUeBB-
zs+Q4>$7F@98kkZHFpAZ)RY2KB4v`@a^dbL@7+FX!{D9^Qe&l~6f~MChf*bj;bai|i
zYK*j@n<Gnx*@h^v1E|gVc|ny2+q1cFB~HyVCWXLjbr}(3zAWKQ;<;z@9cF=ygnNv4
zQDh~S`EG>JV3gRbyM1~{ljXiP6a=tnAxm4hKo?FCV};Au$p8C5|MR&b|6n2uf~Ldp
zog_k045-oR=VMy;z3o1w)b}E)qY=^fb7M=Df9|fTO6rfM?<QY5A(MF@U^x18XCDZp
zhTuY^s1!a%yP5v+x`I&L-LJY9E80Q6BBjR@#W_(}oU^yazGV*L(}^pA*mBf-?|>5Z
zs+DGh8@Xw~FKD#69j!NsMLSm-&sOLYoJO83;eT>_EVP7-ZMDw+2ZX^hyKOX4koVI5
z3%F)D(-^;yK8B@)f1Ge|or9H2wk7TTor3i{L_&X>y|x_@rILRx+;VuN{#@LVIGKBH
zSOym!HPxn>WjFf)`9L)0@-vYuOCR=f|9y}DbA6YM4P@I2^~ER*kYSJ|s^+dQcd_#_
zDgqyC4suD$W!rtYFrXGO6mdgv?HDF~B;0dq5f+G_CJmp4$dYMI2QR#t!Hl@!X`>E-
zHfX51UiB27Chp>jPM0{wLH&%nI0%hMGtJpwil~l=3$NHbrrE1-F`CI((bG-)s;EnL
zob|o7SbuWp9kJ`XVNcTI6iH8??H{$_p|3%n&jX^cb1TR|{Lsy%C#y3Q=6t{9m*Z3;
z@Bf!dg^h+I!*J^s;%74#BY#n)6E@PuxGbF@(zPFiAp_S>wt&j*KAr+3M5*I`MRLbY
zM-Jz*X6QZ~I03iSYv6^yHBSafEgv#}Jr!%5EDAwi^mW*V4L_$%?YaW@7G`X68IgfZ
zl=b_X^&_q~*h+umy#{bt33w9U(yRgG_pxgRYj36&)=y#h91wn*Xd$bZg-HJdKR7j$
z>V+<(1;dW&ilo_Af>_zEb{PqEfqeA(A%_6{1K=^_j$OHWQ$4GfTCI~>u*Iz1eRCoO
z*p^@3wVr~Jn=h#W6lY>XT-CpChW~j%e8U#@xrZ;HI@}IF;$1W}!n12|@d2Drtgs47
z^D{Q|+Mmurc6dk@2TYuLNy7LRH?7197D!?OF-+0-cgMK|Vew<^F9n4F?=1Srwx(hA
zv<7M_(a`;$!425bn!zT}$k1BxB=z|)-b##sX<VlVnr9=@6N)VPNE>@oXDqu&SiUyr
z#KL+{H*P)>Wn~GTRUCQ(LHluOXS*PN>>Y*&AN*x#T3o$0#ygP~zs2dcaU)z+A3{zf
z)Sc!TNONnD{w@M6Z5l&_+$AhL5Gv()EwHskfFI6by7~K}ViW+Qs`vPRiCihnkA)?o
z3-vf9e$Z<vG;29i_yX<djj;~1s=b_hOWVlpat+9tc_8+=z^ya$v_1Gb_PwtJR;Z5m
z=DIMvZM=8}5X!LRQo`85UqdjtF-($9{56^W?=|kFV#h7&9Vw6eH9WMQczP>&s^srv
z4Xv+PEih1X2_uLK3`IP$%Zk2abE<pv2EHre&wjnk)2IvFzT~{t)q9OK$7Hc@gl9Qz
ziR3bDI%BNm1$ygR>l)$yDwrB6Qw(zzT~*2oza@wcH)T!<=tz*}K<eALpkCEHo_p<6
zvyivmtRnXmmaF}KSI*ELjc$vILLAN7#Lvkaiu-K9zsV`|{DKlS>Lr*XpHQ(8M2ECv
zSY7xUyoV4*IYs1N!u)(@H1aEt&4a;EGCC^vA=+(T=kI$^2)mS=ra|W6r?R>W4D@vL
zNN@a7dA-)|_mHVofMDd*=tHrx7`RZnrh^7WiQsP<*#nO-|F<o6Ij*bOE8+VAh^Q)U
zHQw_QPiiV8p!<z(m1AkPlclfl@k8(U+5HEDxL<HsFm{E>lW+HGswjs&YNb#iLRk0|
z9hgI3!dGM;x)$^<r!8ObI*$5W!flxX1nz~S<UZV-#Y^iOi8gb_uQ?sBxW{Y+<`Pfs
z-a-BSF!=6P6)`{6OQ{paxb=Kc471W0<*vxF=TjnHG3RnT&dujcbfs~lax-Z16!^w%
zp>8)`ou5yFCQ_OF5wC7?%zCn(%Y<X=EZ&OH+wI|%u@@7aUrrHq(fP>)MyVe1A09T>
zNZUPtt_XYyrwOg77?)o^J<G=m<Ir&rJG(U<bz*=(!duTqphoYz#wf?W)l^vW6T$oR
z*r&7qjw9kxVLryCKLKH@_#z7hjshQ6mlu8`>F_Dz{S}3i0X#}E$`++`bi~!9uMJKn
z#*6YQxm^|XzIYJ=wJSy?KixzS5<O0C49fhe=-l<Ujaz?YtkT2{(mC1=ybpPoQmN;9
z!{O_i$*<tgF-!#Ji=IOY{SDD4MX@0};mu&I6M{A37AcSI^*8K)9#X%>Tjm}s^HrH2
zTyD7D8~9yeT+iLPe~}JFgnF-&bpo`~EM+V{{vr9CnvYh}m0^v0OC1*RkZ+%LV~Ey4
zbi7?F$V#=jQTZ&teN5U|7<|GjicK>D->#S@3H2AyY{#XUGU|;x;IH}hBE3>GEgRhh
zJ2%E+kv5r8k+}*sf>E)lD;!#peauNBgl8goCMv|DWICb%jc5i0?T!tqS&>bw!B4Sb
zjxrN<a<|f?8;baHo4tFHSat@U@p3=!>Ps$O%81Br9z>0|PXusoAZC&EU$>Zj7Kmht
z3^fQdkR^rctBH$;SQ!Ju;&scnW+^Lxr1oWPxCoacFNMb>=d`Pdt=RWzJ@b`M5>{^A
z`WZQHaXzDN8o@Gl`u7IEe|<DcHMU&gpr10Bo$}qIRpOCNW0aq2U#4p#w~61TfKGFd
zoARc&m+zmQu+-#Te<NIlcC!Q6dhOTVtKO#->r}f9Kbz!#d_sKWT~^$^dY|>=f6;V^
z85JIJO`RRWa`faO8HwX>@~6-JayOgb-Q@UY@j5vwzWvr#5l2o}ueL~C(AkPcww%o1
z?s0EevqFW}D<K!{n1wvr?#Ww|E}!Bbx(q&~ER_4K-lyvl=L-Qg<MMNk<edblToZLm
zVvoUKz@u)5L(;*t_KoYqFE%ip=W|X|BjbH1hCY_2ly?y8xexJ+M-IMbUjD4P6z>Ye
z*y-qg#Kwk<!}&Xn1Yi#K*4Eo>Dwdwq@7>sKaZ{@%<ZMkxhbg5o2yc5PIfaj}1bHRH
zUZ&a6Nlr85Wk({vy0B3_Tt*;tj(u|wZlg3#12R}!$8%(l=et7ber`-PjhIo|p$?77
z^n6ESiz|~dy5a-p&$kknIOl$>q`enZ+{Ufe%|*fiXZ$TsW4^VkywAE}+9p}<VnkKx
zIBzMf-}wr>4fnw`^Mj>y;zw!pOT|Wv?_(oS9sl8H9RHz*jgB^vmCdNX$wD6H7`lqI
zEI@>qEF=;^Au;jnK|&mF7GW^+TOdeGdR2a4D|Gz0a7!&_bpBGCvq<H9ZPdR<+-lXi
zPXtb8-^<h0etRo^*nDT005a^`mYJtS3()+5!&acr(@tdPSGllTv{^obG~%D=qZ_(c
z88#&vO@2t(0y(FDHKi^KCLL1-hDz7xK{fCGLw5UToG>M7^jIfr5lZrE=~&o^6kWRe
z>v==TlSCeD2(vQZl@c<P%U*q0GPAyAmXSJ$^>#w(#T|AFp3JzdsrK`L`R-7IZvjuy
zRgwfO%4>v0st-t5l;2QkGTQ7hyFc0Yd31KcEvc*G_w|<5S1ZOpPAQuw2sJnltik2B
zQCSbmGxvp$INDJ|$UZ4gk#p#g<y9`brssfRB~+I%_zP*<c%l8|#?<qaPZH`nIds^r
zk9wcIJ6FyEFvFyawP;e4%|!drAfl{zepAb*9RfF)=DskP;_#IbNL?h*1srHm0Vq_!
zW;jG4hN82^W_dRffT}coo7Hvn?R%SC@!IO}xIkK`o!p>mv7?ST`5%A_A5HoBNX$DX
zFz{U7m2OIosOyd3aaax;GK^>{2*UivQPsLo8KLW=ha$1pd*eZ?_pb2rG*?`;-h)bf
zpLvRsCm75qCY&@q0LQ~!f+2Yb<f99|ZXy(oS3l7zQR~DKl0SVCzUuz+288Kv)Zr!~
zZ<Q+Lw&aY1<W?wcfYz|Aab;0~Rd|ft2<O~tgloREZ8+=whhj812n}J&&xxU_5TApA
zkaxA7v;5-P)0asf#K({zx-;v3Sm?I56mZ9{S3LW3iP4!|v!a5~rejxidt1ql&9>Vr
zKXhpLwO&#~=DOPqcc-t^kT^R>J#sHcSg9+XmZD0cBl+!g>kJoD{vSm2f6pC%r;v|W
za1FYO#E&e?+4*<uyU}{p=t&8&EMWv$FN?4orBC>0M=I=n`^={C;o5_|hnD+kC0bW(
z&y+>}+Qn*0^H2Srd<bxNoVA#^vre&@9dI1J|GIzlYjUyODb}(yfLfS}TbiF)w#-un
z5!0cWKh%71f6Mk>k;M98`C`H*n+9tu(fwme8_fwfGMeRgqi**`^d-D(;`UhBu*m>P
z_zP&{P=ICT;{nz%99Kt-opp!6C&rLnXcE5mfGwUI;}O6wS(bl9yn6MDf|aB(;9L-y
zX_!e&b%mdh@Cr5*YQ`hGioXkO-Cm2U{+shaiCoB0LzC$!#unzb<<C^ob$oE${so?U
zb<5S!Aj7(%$(Ze~YlpNpx{yXjt{w_$_uc*C6gj7o`y_)?xSiI8hpYFSNKKw`Mr=;$
z&i6~8fyK|9<zbJuZ=C7nhlv$%itGPagNvo%JHO+tw<Mtet20kjO1VV}68WIGSBzd2
zyWNzLNRRlsNQmMi>lz5J)e~M_H$(azt4yZp?Ia>`(O!~+)LaOUTg}`S03%n7K1yF{
zu{b1J&Le~?f-bjJc0=WYq5f~VY0u3Gm)~$>+U=qnB>(oo<M{=Q@HO&CrbM-c>Q0cm
zE~*C%mm1K5V)f}gvg!2paYbw6o3fDr_Of+vKPVA!$$E7r8v2BX(`=#1Fkcq4mgX$_
z{0hk?tcm(Z`Cde3Pa~%$mmpwM@eqTg#1z<1L)iXbcLW#!H84OI_>_8_f0q&Y@+PK4
z<9(H~nMB~$bKgq6Q|Ey0qJsM;J;~8kq0jP27uDtEZr<tSoqdXXHnka+a48<4Z#@6d
zG&1)nX7Qc3Fth9ryuBN2#dX$SyOPD460cSseEc64fWC;TSL#aT_Q2A^I2#R#Ly{zK
zXh-ld<23h1{N{Edl=njl0&vB3O@~iwe?^OCf9dlF8`!q4#MTMR7%83^_I`d3Q|H?>
za&wuZ(Af2fDC_r6f9olP^M^qFZ>@Nr!GxhQ#KwaI9f&&W;KlGH_)yFLe$v19?OldE
z<E*JG-<4Yz{<+YDMe-zzscn0t$IVP&;4Coh3|2q?TK!l~PXCr`M#;7Wz_R(0<(V5S
z-1S8ezeNpK10UJHbep0&E|#Jew7;Fq6sJ$`L1QDFWAHPTO{+_jtf8^J#?gkYGg7(l
zk;bMuf3gMNok5ikyp9#CFIiXHO-d>r)$t`WrRa`_-)ToM735F3J4=mKnK;c|#$Vb`
z*2l&pVmaucj>M5owMYRZt5ZYv97@~#0hhWDqiiPa;yu}qc^xMB(Fb*{e3ZaD*|H1j
zw~OS2R<LxMh0)g?K_mUcJIOnIrUz}yA0FW!qqX|6k>3FK7eCT=B*%X34m?XI@b|jy
zHzHMaO)_bRPc3!y#K*rSuj9XD$9<sy$F#*vsHqV#{=S@d%$IdZ-ng>A_V(*HiifS5
zq<U$p2WgNy>4jb%bAQ=8l1vRacw2n41ujm4%~`t({i6zP@zK<H;c0x&?!D(WBe70z
zw7^3oMZN&r3PzP*#aN@jmM~WKGJBu*74sKtxIYrK+wXk4qV6UkY1)CZZ!8JztK34M
zTFE)o-?=<~|A#W6gdI01tC9pi7UnokooCXun)$L>VIJ)hdQ=><FBGj-OR+?I?~@&U
z+1401TP$xc{4AllB7HI#1pbp5%^VB=_Gi80;eW>aC=e83Bgkn53zx}JPT1p-cS<z>
z@Ja_L%wIk!3LiU+S3mBC?Y~32I6JOnSk0@RRm}uV%dh?7R=*#Jy^BdRdHTbF(&~`;
z-WE>eWBe023RIaW*XxL}ad3k;v6Wf9trWtx3S0(wC7xk?B&u%9Auli6mi}rD{LEyc
z1S$c*saifA&yzuy(@$6y4he$U$6Zvz!$>h>U?6~fy}Vt>2Js}@1qv!kT7B<J5n3d-
zkdk>nvf=Pzx<cYrVMCl|Wna3|0d_NHH!YH5<op$}_HS?XMRWRnk&%yAf(U~f3Uz6Q
zfL(VKEg$^`FMXcr^_aS<m<EaNNykOa-yTAlHHpl|Ux?qpYN9y*+bf~>ZGSG$6+o#u
zl{kaBecsN*Cw9)w;w?gRM*EtDK>Xlidl?_^d2f)-VnITpL_B%Zw0jQrN3S>b;jzos
z*Qq5^SR^as?=`?8M979cUhSen$*K7hm5I;noWIj*X<hs{$-09CdzyC@2jzJ$jbE$5
zll!f#i?{hMUx-}tRq|EN^I^4;F0<&;CL+JCf_J64B(WXWx+L=GZL!F_&|N&-o}>k<
z5XBqK-m>$jYr)oS0vOhw-as5?@^>atmxT~o{p&eQ7^sH#bAoFUd$trcvPD11f0qq2
zB7}b2xM_k`FeBW=VWDACOcQW^jhBXwtS{?RzDu|xgf`k_A1hX0un!d9jxD2Idt`_3
zA3|M3$%R{axSP?KevVlNcP3~p00t+YZghD*{X#fZwO7O%>A9_vn>LnRV|mBDLk<4X
zq_2baH1l2Hk1{34m|IZzzpPOwjCsjNNY;cqprv-qB)f!1*V|y>prAKcC^jF`(M<4%
zMdg*S=gkTCR5JB!T*j&do<opSpv~a(ERsF*VM=doc&yTBk$+uc4z@Aw;8{?I;gp?z
zSHnS+rMH_wqnn5kGT)r6=TJhA!|e)_kQ{vNg;(>%w|Eg}bh!oUpyLV0olC8;vB*d&
ziqxfNEw*3OB8J@m_`l|{fH92Wf~e!S8O%q8t#vQ<zI@<*`{BLrFgi#EN?)*<?|sQm
zYsW?NROo0vslJn&<6V0s*8}1Deigr*Urh8Wezrp7(1@lxL(*WB4%+QR9P4AYT?P~x
zoZtz4WyE30k6l5Bi6J0`d|Ykh&L0n_?@DIBkK`-Uga^uRK3Y2ab6FN*1E&@PL!eK1
zXajiZhj|?(#s<lfYr8A<seE?v;AYD@)d$ZbwFxoZy8h{ro^T*$x&K<D<dtguzp>Z<
zmTib)P)5D5^$esR%gL@0b?CveIvoiFb)&85VLzC{!HT?!sx^e$>&&r@-^xpU)dP!w
z&OEdQIm*6!m9y90H6|(O#!;ma(mw;9=@L*<c%=~gSR8-mmq4n+ke5pSig=cgG%xw8
zmEGkOi8<K?t(@Pym+%b0&1<kiGC!RGx!M{mA)ID8yk=K8NPeBi_GVECLQD}ty3*3K
zQb_uKd*8?BF%SNE00og?R);WI3go|9Vb3jdWO?1wPf^V})ELGHl4i^^q$lJ|WZal;
zNlV#wHvnh`F37~%xx^Sk6TghZ@oBB06Dcd4Ru$l0caV=d_7yZ>(-Bw<=44|ABOYE6
zdhLa_c!MA$gizG?DgqOm-HSRW%I8aX^>wl5%a3-jQ%E5F7K}oIVEOMk6mYl+o9sfC
z@1WrDzB-ZQlp=Qt99x1FR$PXSnv%YXTbinaFidDhWeV7l$)w|_ZYF0ML`6ltf?|yt
z(pDJDZ!;x$%(utfo5J4dfZhh*I5|NqTpetr&I*j(A*9f^Cp`2AW<wkVA6R)r(kKP(
zDd=hc*nFZYG<d(OH$uDK+w|8x)Xso{oA>DTc$%;FKNYKmk-<b_Ly)N}o?#9Zb;C0F
zkRD?rVc4?!asC>7gR}4({93%WSG*hld<fP${yGrwwdG?+fxy<9>$iab!kLXAX;?%0
zjJe4+1--g%C}8#F<%^^3S^B=;Tp>3I$Md*8@><mypcUH9D}(TFrvFQoO+(v+aOX-8
z{L1$ojrF2F;v0p4A#aDM(t?NwI^lBHpN*bZsy|<tLE0g<L}5`>meeMRjC4=-9BM)_
z$3&A`Nl)_th?Sp<F#&CI5AYnTMqgq25o`+CsRzuh|I>>Tmv$5D=xj^INNgmyb}P%p
z;r|}!=rJIEBZq}gV=5oNGcQ^r@*5^mJ;4y;rvQt3F|GeIn;{{BaLCojosv!$2`+34
zC#G+I*nu4OJAk871Rk4OdkV6{ydA6`Wag&Mb?a&a7Da<0tru3)A*(T!Y37A5|9<Lk
z2`Rw%SO+F`BkvwW!gY8#!0jIo^)PW+u=KJ+@1KAunEv-Y`wh#Lj-W`%v{XJ$L=g1*
zCGQtZ^58rFtVS?qr^kMSO65nFX6gs~Npg-$s0`)3eY4{n#&HF(>O1s#H4a27ElrAs
z4@nhqnlJTdj+Hen>gg`JogLUz<|2i@O-ufPT&clT0o!l4!KrCEtYBa}R+L|!k#3U_
zMzeXiRbcpl0?Lh(TwJ}$%HpNMT+uhl(tzYhp<iR}V$l7=zvnRJj+J_4NqGsCcl|Hd
z%1Vi)GTL6GiS<j3lAEsa$h%1`_Dd?2-7(D;vRFtaQNt^CUJ@|uy&e0M7q>7a3n2LX
z_=i->JlDmE=OA)7W@cm-oa=OgV;r3J1=VlxKW6unNxusVe{+8(P|50sz-;CO3ki*x
zd79C8o)k`Xrv}}h!WN%|H!+HbtfV8ce!g7PN1ibUZR3-BTZpDt%P);Vh&1a8!BK!C
zBz92pJZ2lToalVP#6AhdEfFNf_rwDqS#rK7p|g@~vYOnonzt0X@|!Ko<ya?~mF)N%
z8$32$IH+aIoG;mQ$;Gz)gF_`8EC_T?L2RZ}pLRu*J=!3eV(zNRGeY0_14-Lb08k!`
zo5jD-9yim04@G67uE5BLyj?aLSsiW-UxQ)~v3|U%$tSGLEAR0~g@+GBR%YRSH(pW7
z9gVyuog9GnzY$jq0WSY`K)6r|J2Uj`5;$}^bipR<3qL&4`3lm5PP+bj`}5sL&=>9U
zFnQ!Lsk#9ax(qSiXEzyv7wN$3>H{oQH%L(wL4U-jCdZ10DY8T`RwFV#E<;-BF0v;r
zWozr>ga+K#XrHYhqt2ix^$ykmk-Yjm8Hx5Mor0cb*ZTm8x-q5e%mV`l#&y)P=xoz^
z4u`+__`eqhBsB9R3}y5xvhf0D1)@<);*D@=IC3_7lAW31o&8k4gSsj9J5!G1R7UY)
zUY%JFzIHbmwyQ{QIYXa<XsbB`!94sw-|X#$G?N@08>{?{<KJrp)s^O?%39U-u@Xn!
ztyXdDb>1McX#IqNilV*S|K7B~aw<(@Z;<g&=YN0+I=o;ux-IIHxPHK(TX!FN*!3f+
z_aUJLF#b!Zzn7iHQZ$4&ttA@^XOc$Gh2}oT=H$M}6%HL0Z-)VDt3{0HBsgu~jqm|l
zP#}47LyzB-6xV%aFxQFgap*1_PA@$w_J^swMk@<{I1MlhM*9Jp9m~MLsMVWya2YcB
zJBFif;_$`nhHyakfYY;nljY*+wQsMJeq1R(=hCb02wYaNqaIzy8Os(Jq}m>Tdt;T|
z!b$R%r2e`=-;JkWjHZUfgNyRWTYrXuiU4hHw;#W@mp&)O0-1oi`WIz{U}*90Eg4Vh
zB$!n6R&wr*v3h25bj43;bh-S~%J0zd66GvEN);iTamxc;^9rX|pYVho&2)vlMfrw+
z4j)~lop`W=3&9Loau9aA;s?<g7%Hy$+fng8!6*rUZIS|{;Dy^^b5sC(IeQt>W5NI@
zpkUr!WmQf6!lqZv(1R}GRB_C+bKZeY;GC(VpU@_kw+S(*>TvA3bfLKmjG8K0F&Cph
zd^5Y$PX4M}V^z67SrnXO=;MMj=We<&+(x=>j9mp#{w6lmh&0JXUx(s&fFJUk;(>6R
z^iyH1jRZ}^z52D_N7pjQLIc;4)(Wt36ebHRu`7RC;GJ@b|4u1_LxEP^i)|M>+7#Md
zzB-<c9j&WkDu*Dw1EqGkWZZ^IaNAw=3Ae-GM{Whl=!p~qA98N+X-CQW$2}YMdA&GV
z|0S512*W-Q#dCJ1Js=Zlb#~JobO>mD`dhR=M`Cj6!Jy5Z&gh7e(<;M(B<-gHRm#h*
z8OhNE&}d$f9%eao1QwS8GEObJ`E0M5`I_qC@WA?HT%$NZPUa*tBtgbqUF!G2^}ory
z?B5>-EH>2eEdniS(~Ny1k@cld;;iVmlslAy83?apB=3o`hg!bh`bHoxRrs%O<Ugxs
zVsy0X;1Gd?kUzCvbf!Ajjp(HgKz_cLF!RE=Fp5p-#T$mFWOwD|?W>mMF;P3wBCbYY
zw=)Gt1T*QaG0<u?A8<PaO<O?K5ti%cwyEj17C+U*^W4=R_xO}82QDlVq0n8V=MlyZ
zH#I3K%Z^}fmY1zvUD$8}3J&vKl(7@YhZe!JAi8(;VEAM!5#kau{GnirULS#VT=Aji
zkVu(wohZSqOkRPU?QDA+Ls#_MHt_RizjvCIY>SDGPJ)hO#j5jt9^Pp~e~UZ`@G^dN
z2sY+&m{`wk2HpA1p_?#)!zULwFXF2xR+3O~ghNoa^by$l>vOcj%!1D`lo*I{O0m)~
zlAd(^z&7vTHJ#_Z?YW_+cogqz<A#(`zJrurT}Qz8ay#hOMX4QhesGh9=?#kFme^Z1
zHEzYsvKOg2<I#$KlUVt(NRZR~AVNJ&`)=c{2c>h8-D`f{OTk!DX4I+{jb70`2zUjz
zJSa6x)7EQz;H?$E*uUyJ?Q3=`QG6@Cr@iW}^^BA8v4{N=A6PyXtA<wH8?G}wlD0~o
z1^8$hU-|dtgY_|uL6sb2iI{#jPuoG_5mSWQ^)o!7v7}*7D%YD{udfkK*QR~oQgxl|
z*e~}oQMJj1rc*xUF-Mj4#nS<5xsCfruOIeE2dgxrY`n8=VJELrjzhJIL-lreqg`}0
zcarmsflfa64<WKm>Y3}=J{kulCeP<x2o4u~h1^BE>*5{Ers;nqy2WT&`L6=ncYL#n
zr`^ndS?)O2nzH?fm&dh={wQGS=r^>$8t%0A@Dete1qKYZH~ga5(M?HvBoG*hq0~`~
z@l7?J?FLPzN^bS|%h*XUfa^nnX&2TNm#XF$ztZ)?#`q4I*g(j<PD-ZhCmdqGhJKUy
z_I0JLmzq4JuezT7dZl@#JNE^+L%3ezM6!s-reBW>0I1!;(<Nl<=yqIW<cmL`UC^14
z7@xqRxvXuM(j&cZK@>+XD9~r)Jh7*BwsmV=Z~rjDV_z_iN3S}sBPoY3gLG=~xAKm3
z_>X2N2G6+6?h;``pW%J+K#34pBTggXdq7t8w)78NyvdV;LS~(v(*GA%Zy8qA`h5+<
zW-B2Ag3{e3sdPz~fT(~pk_yt@B_&9g2#SD!G^lh-mtfE(-6b8qbD#SCpZB`<2M*^P
zV6Anp8DowyCMW_K*^%L=q?E}zq7JG&-vG)mg9Ugs5%xWVy{_dpUe~rS#6KqgPDny3
z`v3Ex4??S>CEJpW9enKdX%`N~0b$Be9J~~1XvJ<axu*K3BLY9;@AJ^}&Pd>we2{+-
z0;np=c}Mp7_Zv5KwTFd)$&A|e>#cPUR^0Q79UhC(W~eSVM2XA^2ahsJA+E`yiMY;D
zlicaExUb}oq?Gj!@xohG6=2%M5;~`2d`)9EOKs?J_HFYQ$j|aIN%R}=$!{q_LfR7e
zXzxiQJM}Pem)KMlxa4xM;Vmw^1sX=iZlYufO!Fq5Jb+I<Yr)S1wJYw)j}M99ct4S)
zzr{OJ?PU4pbm!MDq{Z-89*lrN#|uum%g`)6yEX5?C#cx8h_v@%<<39xY!SHtxFaj7
zbnq5wC!3we5LDWX&u!WDt4i@4^k5y>T`U(Vx^QHWL~xbQ@j~+c?OcAlv%Lnx*p2j1
z9;T!7GUXO=L`WE{q;UvUD~qs6U?|5vvbLcZO&IYcq(%SVM={0!Q1nTQf)h7(jQ=Ed
z%5+YY%d)Srza{5j`=7HV$z9L|EiZmxmDTmjd3Set15)%#SE&$<iPJUuVb&{|ZkaNH
z#kDYCJ~;xlp$clSQoni5UEMy(Lma$^$yl)-Qam2hQ7d7K3NP_!uXV(r$J;y&<k#!c
ziiK~z^7rIb;mL3RR>VqXk86(`Y>u0k+#2)qiePuQhVpPeE{{Ga+<vS<S^m9;P9~&k
z#y0g(?t+U^;4;w1X4%32IIbgNcR5eymqkxf-#!HB>hCW?2KW*XEtU;@+k2^YooO(p
zJtWm`&zET3B=2y1Qsh9bl<9~eevmMjS^!qDCu16yON${4*4jr4u8zX^%xkh!#cIXp
z{1QiYk()Sn*YyY=C}*91<lk4Qkj`@tp)UO?PJi9B>8XntFqgO%G9ieR{1}t+aHn#B
zi#Q_3z-C`Tlz!7r@14_`C!Hb1`boMxTEXIiPdv==M$}DB@;*!&(g%JLZxt9D<Z=${
zHljD<BwGJ=a(4dM@H2C`+I{DpA&l$Ft-*1M>o)I?PteRpdj(Ybc6*Jp3KH~WPlSSX
z+|ti<R1;bZXnnQo`pB$i{OqSQcF(IaE4vwsyh#|m@{dA3Z6;5hXHuG0_11W9olksr
z{Zr;QBB@4&aq(f7M}@w5{(+PY3AgV{bn9sJ8~xvMEKDw)H<NR}g6`q#ev_1Xwf68@
zcR{M*2cGI;u(I*$yw;n1|D}p`w2IL=28MWrCLPn<+-B{T>)kgJlRvT)@RMH+=5?e?
zE|B4XgQHqi<lIhRpf@MDE#>wA-y&lTaLU28ss>y++{h#2TgwGLy%p^#s^PO4uL4%J
zbmesY$cKXlH-WiU8#NGPsV~~@dqz##h%HZ@{EH8Fq#4mTz#fc!^?lyi$1ipvukKo7
z3%kE5ZMS1O4LF}k;Ej13$Au=E-V(ksb9;c-_?#DQ66T4wkLoOf+%W6**zk4NIVgVL
z*z)<=c$o7sI`B!=QV~l~;l(0Tt^p5T@Frz^mxDwSyK+=V{d;hv=UV0}`_uCLip0WB
z@i=D%Hv`q4@0L^b#+h}P4`sDaA^pse3;rx=_yliDgXMInDeh50w`rdK4^dSJG(Q=5
zK<|W#MS<)od=g)|CjK1F;Tzyj)ND;u-&aoNx(Q0V^!`(LgmP@;lbH^&`L?B=G=GGX
z(eyW<mKUA6Opj{|CCmN=HW70e-v>vUw78v2F<m7XEy$9+=!W$ByJ|1D4%n1oeAM4f
zI32{=KWj#I!i<6mV%Iwhs;9^Hlr5X|`D8yF=R2>}U%+~{h+WqF;p2fvNKo5nbu@t%
z==G0MS>1TH`^=NJd;3`lu8vFTZr$LmJ7LKoqjhIQj{%_PI|-N)rh7?DEjIJLse4J@
zxt88wmN``K<u<80p`FknF6{X`?^{~m!K2q`z6Ee}A3LzIQDZ|j^Y=%V3;njR)^=~Z
znGeEUU5k3Ph?|v(5uH%+8hS-WFKg$t*ql2j9QK0NZ;uV`Mzl$X=01q;*!Q8C){Y_2
zJwz>zLn((gVC%<2XuPwAqx@8oxm?y3A6zGIY^YB$Ke+L9xNbTxR0X2j5)q8(odc)$
zU#t7ygkMs68+{)w-dHngFXg=H+3pPLjcg-r>J5_`ju&{QOL7tE=&;BC6QfLawB3fw
zNnCz(QEacK?2XErzTS3KP6E!(OMLg%it=QdoYF9D*O#yZxFY+voTJ#L$>*o~`mg6Y
z5Sz7D1*t48uhd@)4KfVWNmj2x<LAf*CQ`|1`X16AQ!qTzM@juUKZnNgukHS!j#{Ip
z<LT`6voMp>qQp2L{X1sF&oxoc`JaFBEk7moPtJiuP|L1W*tv8&&D&@c>^@GrnSIO;
zPcatMj|90=s1MIfN|@1WvnpX}QOk41o7>%^dG{Eg9{P5Oy&M*~d^4=u-*Hw6Kl7=>
z1!ybns^6rD&yh`QMCM33?xX~FTs@A#?`YcR-G)VHQ;vV=C=6*RF(@%+DN*5J*+G4u
zkO(rvJ{HnceiL#mvchLJ02j3u3;kJ@rhT}k;wm4kw&hNtvtV?8QscZ5H^m8S4#D9|
zvYm_TrXpGfNlyYBQHdEen91Oj#WZz3JC%tm(|QxJR*8mg{&DF5_X)Z~AafuN`b}k%
z<$D)-_p;`}vgfe#%9qO)=+m%hIDoa=)f_Biv{8Iv(Q3BR3T)(@$KdFf5t08TGOVis
zka)X*eX#)O9M0Y6g-mV8nL8?EgV|M-#8L=GV;aPfMZhv=*u*>By^k|G<RYJ7Qmq1E
zJqJ?2F2D#8uhxsB^9}238an1O?tx`wItdi!U>!o}UiZPM@#5woQH5pQ+Qs#RPc#e^
zPh8Iw5R3#uh@;EBvIuzDT^XfX#YGRu&6&|diKN0bZgbF&7Tw}VMJ;ND_w&2?k75Ho
zR7K<-E*tUk<<Fk^vs1WF=CHY(I``cf@WNKa>Pijk-0zjN8sp=?;2Dw8tmr|`a-<fj
zpeYBXq>$pHgQqbq@Fa#junHm@jhPpCRoh=ut=($_<2vUs<zVrm`?)VWBuHOKcLfIX
zRH-teA=s)1MxWERq|0=LIYXn{1Fug`$v}dB7SFnb*FhoIx3e?^Q`Yg6@l-Cp>$c>_
zagB0d*BfwXJ1ulC%n(;R(PBZHn+JxYHjuray4+)<`9H5TC3>Bty=JC(O+CQlws)S$
z&dE35AjG0TSJk+Z*Z_`ast79Qmz2U2TR(ZeQt+9DU4&%P_jf`9VEe0t%qRnF6`u=x
zlAmyF=^mCKss^O>M<y((-x;xGMm^k8D)jyB1Y^ys?^N#b{q@?ekh%kUe_pI!|K&u?
z6$m56uqV7~rr)66|3r+L=}^!-c&R&Gw9;rnD_^s1t_p;?wUd3hvX?U`&Zb%Rjx;Z`
z7sF)2>$mya(<XhI76Mk8`_U%=nJ;v|7OoiB0c&z|w>*n2UJ@(eM~2%S8G>{3!QJ~@
z41}Kn{>|SyUHts2gu|(!Y|~nDm;9F144?ERy1IB_*-b#zmQDZ?6DwBF63gZ6)9+og
zG6G73ekFJt-K2|1A9DT7@cXc8o==-5h>R<Fi|u6t3CrQwjK@KB^zmu|GSL}*WTilD
zan!4~^v{T&76k@Y{0QvtOWvJnpMF9iR*>Iix6VTGJN`$pQ#aA;X=-n$D&W8ye6@QX
zR91PxLdjR!KNES=k!e_w90QDywRAQ!W51+4TM&P4+VqEari4A3`tW<+V|uu+#OSSA
z>fW09B#MVzE*0@GyHut2n7`%$eFD;OFvbv;8;;hv6yPa&!RDF6od8{+9K8jT8F>=R
zqz>(4C|<vfcF!mSS@v!HZL*AGZ>oX1Z6b}!j)5^Lq(<^6JAwreL$zB3_8he%EmUns
z%jLY<^}r^Lbx!>lUhqq^im+03tr&mljgW)%OKNfUMIL`Zy}r>U^ZL&C42U^(Zu?(q
zj^s^#8#6)1i|m~nth04P1FS0%DE>dP$hPAwgX#TXO24p@`{w6xV)f+IT2;S;T|1{K
z108<G&8q_+Lh)&c6D+fV|8!0qozgCm%h_xU(Bu%LHa*h*3~{|_{MntzaAb=Tnqef7
zmo09tcVXK9=|p0sAclV=alU<xoiNR=R^Sg9{ODTj)s}CN-w9A^qC<kVnlX&iL^c4+
zrJ}cHTVuHI<gYWVfehTaE2L3gMu|GaVbN$;4}USVdK1zHlwRYZP(LC>aL0hX@Acc(
z(pw<=tS;0klcvyRi&hlDw;cz!o0YZS{!4c<0Pa=+nkpeD#Vl%27C*x~L1u`6A}o2U
z;Z%(x+Z!mgUBs775hj=FS`}M5%|Qtx;=KaU!KZ6e0AMYE?1;~0^?{p@kdeplI7}LE
z4g3BV+zqTxKj(N!YQ0xYjVt7})%x;kSR7|5gaNDRtvPI9A6v$mtxjaotaQjDA`grC
z3MgTN!u?*;6FA55;NY=(UiIF7<2JZ21)=XYUmA`VMi$>&hU8-29C*nQcEFyCnkL|9
zje(2ab57@QI`zEdqh~=T-ayC}tD0?iK7EUs5K9nUKOXB@l$rjU*`)A&Xb%1<6r{3=
znrfvXk<e2GJsUGB(!DKCM!wa7s-|X{2&ky~PA%PQ{Xe0)34i}g4ri(RBS^TdSoBh@
z^;oMm0*T0VDXHf}F(@hl@x3d=oOw|!h8_zy>H+pMEr<Ol^>}mPfa~?Z89*9#wCv2f
z0+qoL;)ve7fTffJaBq4`J1vuf)+!058fi15eUBGNo^c9V_P=J_Pdu!W5uOUU2@{{{
z-V|OLNa@Vr`uJSG+EK3I;>23~1!4OgMfNWhzgK^#KzUbm^+7BqzNe_`dJH(nYw<Wy
zbqT54G0xIlFGJz|VBh0K`in6Wi~6YvV$mV2L5t4r<427%53k$`d;T-RtL@J6rw>N7
zJYc)pgekfFi<C_LEcvV8{PThf-*DfX%(?Z*3x;SXHHXDI&u0?=c&@Ifmo#&?tHpP2
z!f7WY{dG|Vh>!;mB9M)3irZCguxS_bW8B?RQS`I^3$VgJsr=S8oHcnGzWd)Jki`oC
znfx*<Mwmd889pLf;v~<@@fM@N+Ku%=gt8JU)&2bu#BLJCY^dOcHZI0L9qX(8fLguW
zBc-NhByP@EDz^zbut5EBvk&%^0?MLU!hlOIorp?rcCAt%X=wbNuZpuDs@I9PC9~ZW
zjGT+*d{n;dV)RG)<{NIZ6t{SkY-<D03zuYUt8VY5mp)TYiTyehyxA~rs@VqR;p$FA
zX<taLT=3ZQJJ-aX(=GI+X&buFUaB{IuS&KuT9FIhvHGtB?YLkZn+yo^gDpVTGtNMZ
zN_gW&PHW$?>Nm-`6JB$sDjXTigf=s71P{FmrwQd_RnymWWQH^jTVGOCS9O#MB?{I%
zQrhigK?0Bi#JSC{sYe_B0h~iXQke&`Yyxb(P=*aW`Fl+m8ArcV)Ru^iYGb3zkP<wP
z+w0dL0i7HcK`Vis-tNOlpoyqzzvf9jbu0Bh!~?!)QAgX-h1We9>ZDL3Qso)J0BEXL
zOnDtR*lKthIc5U>yX}kw^oX#p@~!m*%@()eyXXutM<g@;58DFa=g=pjNu05~|J{56
z{(x4pKuG$bLYk4lp{-T|VYcPjS4%GLGDn%_3BAjmuR*`6@t{v6PN_*Ogy0vciB+G+
zl+UW&Z4C{tP){zs&xHc>l#sM-CTkv2To(taMU5hBg##X;bo?1GUw&j9%=F<b(e>uX
zf*OqzEC!=ss`y1lYdy3sTKqt#&agF-uT)la)&tjXQ6MdPw&oK`-J<Rl6tt5dneTnP
zd&2XZyN=`4eaDW^)wmqRDC8(mGWcF-C??}an#)t|HG>Bro~SCh&!A3|eIX7`OS{nC
zlcjCu96D@L;TiFcMNQ8+%(Y`nd-KQo|G+YShl#uO|G%yAAKtDRmy@8&N(NMV7;Uv4
zJjKpo`WFESb4GkMXFyzo8W#<FB*OoMHW``(hn;9#q02&-HAOCiGJ6YT+7AK`i5ANG
z0pf1ddwH*&q;fvzL56Yqmda0{BM%GgV!n9su>1C5G|rn9&C5yXYZOkS@?L!&VAJWC
zpDH1-W(_!y3Rz)ILS>`g!EoWDZx5^NO_FyfmNcoK+m70Oe_o#GC^mK8?w~)m<qM#=
z7fE}Vq>m-FQ~OTCZlsTz?yK=9hOk>xyFu6vB8RVoK=R7G3qOFkV^#tH4!N?)unily
zO}2UAO~-RO)U$GT6vG~kLBevOW8bFFqg%%TJ{HpwDjG^8{^^ty2UHa6q6g_eI?#Um
zx~sz|6Z1`<%|Y>73Hw^eXxXQIXVg~vyb8zeM$TiT?%U0n2Ecw>6#s5<sF52b^EvcT
zt2P>4Y&g%#*1LGNKA|y}5+jKe)i98<YoDL+KtMpgkpdF59N89=&b1aHfFjS6Out{<
zND?csIEr%tpCYXrg1_da|DekH%2Uug;^bM%8u{4uTZ4A~*3<8bUg-QmDEuRmlvf(8
zN#DtAuwTmesAO$bU-_CDWK3zYSaa><`yB13fO6BvkLxO`H_#ADE82T0{I51S21b^$
zbE?x9U7RTTj=>lxDUyu;X>{U|Phjr%FTT0G=5j3}?sR87BTW=^V%-hDhg9NjTWj4R
z-R@20S3q3;JopRw>(?*YyBA@Hhl9U<-52{`pJ9)SZeE&Op9UaKKku`Mgb<K^-X-SL
zd7EbKYxgqYpd$Uh7qy?xf9Vwz!ON_0n;g^{%b6HwoDsJsoILR>d>D<<5j?WeqyvBy
zPCou<MvQ;?x*9I89)vC@{%UFqgptpF;{3oHBLaFsw#)Yd2*=&;DW(Y&B|I>3ggFNn
zNTckHDB69q8b}P??=+f$pQ&zwws`)%tdO#v`Ue$bmo^(wufiD%57@@so8sKZ-dh?V
z2_84wW%MV|#!Aetvk2R5poMX19K?Z6h&?XuTR@ZW%xJnvh=maVY|l<OLUa)Stw^6*
zCjNI>@Wtf-AO-QM7NfVmSJ~>y7I-Ef*CtRoUY9W+=#Ka|llwDaQHr^1fETt2_9Ip#
z>f1*${zk{=nO{ud57U%wvI#RYJmgA<xMoK`fp-f%l2@z{f)xE%xwTz@bQddko<Q|1
z`+UK<XLfC@6TA}+utI`Rdsm-LfX#{Kp>TUQWZ;jOoTh2e^aliE&lqatYch@2x|K8(
z+lw0l?`Srj-hK;+c3c2lCZbeJ2)zO3@o=<A)vT<n4Mslm|3U#Ry27=l;1)<a2I_hC
z4_fK7c}chp6JY<7b@cCRLuSzy(k#Qd>NMvU);|aCX>SCCQ~YyFj42TIgcwU?if7`L
zcPsB{5QbU7Q+~0$X*s9Vq>G$sNC`&<Dm4{)Uy&E*`BLz(AjwFMM#5VMhnN#&Us!VC
zr3s4I_4>4weUPY#IA4eYCT&Z|Gkv9ZO)z?f9BTr)ue0s;%zyLlKfgAH6<syE$w*&c
z|6Ru}n)S6N#?O!2B9vgy`zK@Ul{bY<9>zJSfio_Z$O96bfjynv2HMidN96xJpWp~I
z=A&PSl!G%zUexTXg`v=TF(Hf7$aAxi87in0k50P{-Ap%F5+gp$1icH#H$z{hB%T#S
zXBCcL-C@ae5CIJyRX>Sg2?4Vklp(%7aU%iPKhj33C)A-cPDQ#;hxPhasuYm%O>v3K
z3BzT<bB7Q38J!589z1REK4xTRS9V<NWdk(yhsx<^=Hob6Klo@n^cHv{@iM2F#%;m^
zW|l^%t#+?0rT%l}QV2R0|LJ{rSwrg#wG>f*YZskVv{zSVyA%9)gc@3fBLsyw1FlHy
zNHg!M=R@|u7SpV-xNAz1jK^fW>37Qk@O3`$>4>Q$=0=1(<}T|74I3vgQ@*z603Yca
zP7`={bU}j82FfxYIA0##<^{UcG29SBj-irsh`C8n1*Xau`40ZN1AhQ*DFC$1^%#u>
z;M|!2$CnTnTPLCk*aN?}P_7U+nMj_~{hM}Z0~G0JiqgWfKaW=z@r=ntm}QA?U9@IT
zjEv}{PXfues6u8w5=I|hG~Lhsi#;z0XDagLL_;1!d_7`>>y;?uiHE250J%Mbl<~(U
z;|nvfPkvo<uU&MjjsZACNlNj|T`&_jNA)~KdV;X9Q_(SEURZ$6I5HDh<tngM*TrfS
zg{Yx)Ei1&YMO=V8Va<YqenUclCw3#4#RJTeKbBvoK8Qq-`Al!O^#GXB$q+3T_sdZS
zDnF@tf+q1fD6VUo0mAsv_$l_A=jZQS$nDzbuREx+{l8Bp6N*dpWU8(@!=H5Q)gwJx
ztn9mKTCg&d0o>;X+f2%dogb<WW0Mj&zqro!OCDhX_uj?M>G{7MS(7Jndy>@euODiR
z3GY~i4EH^GLVF%`v0MAggE9m-x0t98{jC5V6#0npQz43+2^z}yxsGVl`z)<ZJtK=l
zWJsP6SYpZ6mk{SZk`=uwpSk4{$RaP6LZRXmU%yvZ+1GK|iYnqOTssO&%7Hsr?sMw;
z%nU8svt6g&em1c9YP;vf%SV3EGR?4dYS>Rnm_@43k{7O0fYIOUlEeh9i4VY-u<*1|
z5!L`r9^Rx7ctI~3gU0OtrL9f4)ZB*f-w26@t&@f(8KGP2M;MPc$N<kf_Ybb=t<^}v
z_aRC$SoN4$4X`>fJhMmTh;#G5K!wGG?tbF}7O%|%4J8)b+(OHr{**)AW+mn(jcO(N
zb|TbEaQ7`#SmnS)xZdaOFZ~1t`8AY<Kr%QX4nnXJg0m4VFvxM7iF6E8u$xFXCu13y
z;&B#qPNuwa#$s5q-8)_mM;*|nt)7bJ-c9~^ysK2uMWaOSK7Y^Skn`-0itoVT3tMW6
zFQ1KZ!L+#+r1IC@Zend6f=iNh5UJZ@)D6llphrOKU9VN%?&b}@%naCto0Atw8xjE=
z@Yh~bYVt1O`6+okPt@aeJF2rkVq3n|9-sPk>^5iTUx?a2Cx?I>;eJ^LiO%dr=s!q(
zLJ&(AX;ybiHm*O#=<gPVO6^(oP-OSOiMHFEhxq2tgngY;=WCqfJyu8yGerLUS4|&F
zZ(EyQMg7DWZ#X$B5~3N<$`H^Zwm)Jr-BXTxlnJ@$!XDD0uBWLIw$+u47ZM?Zi{uNJ
zR=XSjbYHqbe!SjL@3=E^*X{+uDR@shKJ=(57|Sim?n-!6X?FN-EySzV??{62W~=H1
zoSYu`yKH&cd7{VXv4RMyuoKalPMF(J171jd(rAbJ6RkX^vEj%<Su|?%o!4;E>ydu=
zMU4FB|BDjnwM72BvI@5y2xD*6x!Akf=`nWEP*L4P=|gBv23IU5DV)b<LXZp%z!NyW
zhroyb6;21~2CXL&Dq5*|!WsUj^9SO3Y6w^8kHtmzi6P&G5;hg~8;c9wN#%ne+m$O;
zM2FH9vEg;CeYGvUTT#~$p=a_wQVH*-At#G=QJi0bWgGw6umOJTi==#?v?(f<#LHr|
z`H8W44r=TR>6mPvJOl7u_{^|m>$mxSuNIUr3pp)=z>E(lfo~101FSH6P(YqUfYelN
z;k2%@6$=9Ox`&(r82&kaBq8zN*V`XybC;HjOU;W~hT*8#c)GYy_tOXB44;%U2~NYh
zD%e<fpCm@q&6W+QKD$@uP}u;T!GAg*I1%?*sCnLTh9TGG&<_3m5?iAoa*wIlD?=V5
za6npBkvJIiUyk?xgoWya{LG@FqOQI&1~{V@&xGJ3j*h(|)=YB^w04#3fr<Ar)EZj4
zJr{0h8M=S=6W061WpH@?;YULspQo`DJu$Z!u@al&r@^_KG38E*&O1T@UTIXmgOg>f
zjo5ZX$B_67x%_9!iaLIH@1Xm01e|w9yPZ5x*%&bAnlp&e_W?d6_+Il`_XzyDa)5hQ
z;{n#F$*6X+BpIZj8Aq?jR&0`suryd~3pU@CG7|70U@8<31~yuk@u?|G`5qCVVhJ)x
zYRve0Hy<j&HW?v+p_%~KCTeB4J(FGp+lVeATIV=b(L%hIdw`ebp`0JNH^d$tamo5e
zoVDTQx7)>TW^FjtuCsyjcHb3!ecemV3Y^obY`wo5@y{9<{Cu@DC)V?E_9yUxZc+5o
zqToZ+#RVub`Y%biXPW=>&wqUvOCkUm$34)BFrkE#8Ie}kuX!&ve=vd7OmS)}UjeS>
z7_YQTe#rjn(*0emne=%G7{h+7Am7JxgjF$Ju?(SrGKPc{ceQp`DALLy0g$9l6?H3C
zAgj_ku3ZQSreK`WL4=)zY#;oc2+R>@A50deGHn<M(2?)5vVvBxMsph-(8wO4!B&R5
z!eG63%Wmq7xbh%B!7cryH6xb#3bAsDoTTR&U5RqzMI4tO`pWS+J=;*tC)}y>heNCj
zd_&?!y)_N5Z|iYyoZv@<KvT56&{|BTSp9bmgOEm3u7X+_n*K)!P3kNE=anT4dxMry
zPk@dVeo}cP{Kx_Sx8maOHWDMOcbQ-x{s`uz4SLRY?PXI-WXBM|j2noXtU)iu2Ss|o
ztOg|H9Mg`MA~s8X{8jRBcvTT$n*5+VF_43HiU|(>5NNe7Y%dXs^f05pxK;%N7fKOl
z#Xop@s<$F!P^Zuq)nR1K;K2KzhmFdwklL5EcAwYy{<M6CfT`**0+_u@#N3AK#6Jts
zWGsYpGz$NG|K{lzxm)OV@%2u%{e0<#sNc5#N3z3WF^8kD?4H+;D7I#s3gupe4@q|`
z<fyFdNw7cEgnKiP7G6z-Wq{E}`&S2@9V`V9;H3(pRK=!lq*{g;8<=}kh;27vXc5Pk
zqi~|XKmHroQp4cS=kloD^V7ySVfa;lYV1t?%rv>xsrp(9jL5!U-}veNK(n6+OU`Se
zmz2+txG#-QAgT5MMH<Df96L2Uvvk0RtE9z-%`wubzO_VD-|y(BaquTRGjYxr@GjIK
zdAi(y{y7BHi!~gjE{MJK8Gyyo`(hEGVFyPyft-XbWzgdenI3PfzhD5oIm?B4d8}Qz
z(zET!nz_|v`#$?4ya<N6E~bbzNz%o0ak~utTF-}%9|{*!@Hb-?|B)HW8+D0|F}A6I
z_gB%1eH_YB0$BvO(FYx@lYL>=xrL1kMw9mDJ(H8tC@1)@m?I2JMq}Rp`1FS|0F24*
z^%ZSid~P~{Qu&%W6F$Q*MSs?dFqLQ>;gq1uv;;Q^Ywz0Dw5=6I9;I?K&RF@?MorzP
z;`dw5LpPj{H7~IHjBzj;EHgahVF;FE?gbN)Ao6f#B{X*)Y@r0SO}nZC7$ma5LgY9Y
z$y>FW&!bQ~Sv>URE6`*U3SYHQfs<{cF!TOX(1vVo6PzPg7W;=}_f0~$h_H|7{V`X*
zXgb_5d^gwerV3xh$Mk)vs7w)ORms}^-P;`QO$U-p>c=>XlP`@Vy^l(-vuZT$Y?xud
zj|@~nqw9%^zS>+Ap7S5V`1dK$Nk^0LCYQ(eRxkCmn`Eas*+h-~Mq(eyeIVtxh>WBX
z{mC$07qBOBG=t%k^y#Moy?O0YqVy-n7i%BJw)xxI^yBIG_Z0JqkL`rB?9(T9CuJy@
z78NIMp2oeJ^(i9Fe1ESWFmUtjnP#P%aUJm6kM1;`j<vFNwu<)+OGrk{)4^p5F!<XI
zhUA(Uo(imqMW`UINdp<^+R&g4GwQJFr28MXIyyT$zcV3oP<y9TA577;U1e!AZXygq
zFjS)neYeOxhh|Qgi0aOwJ?F3>cuaij20{3{E2rp-J9dWnwZ?Y{npQThAox_Scb$eY
z)dWZ%ft_XwK=Ye7TBAR>WBvP9zyo1K7qfO*{(1*S{zS1GZqivk_0#DRKDw9jD3@(B
zO5EIxma5gU29;l}=V$i?k25i^uHL-eZdk*98#6K1IW=D5-r4#EYwECDRQ5oI8drMN
zyB@a58vleR_ca`ox>;FX%1&w;Df2avP#;E`xzF-{_Sq3UaDYN6MhJb5yv2_Aoy7je
zxb|Xxrx;B1ZY+<_`fNQXF;uLhrM`n;oWVkX-%KKyO5!l*@=GT&)FedFqIX<-JDV|A
zlW>(DeT|66@YT${t81vqqsGPTY&F!NBHoSwoDy9ka@u+Pu7ebFLfkK4z9_~$wS7nH
z5ej=Dq@Y+!7BaSAy$6zfP5|3>*2Ji<OCYd#D}WEn29_HrlnCX!{sa_oM@SsazIGp)
zHq*$4y?)WLQfEMuYUKAWXv3Qx!-=C0ZUTXuIs8gg%L8Mz68h{Pe?QH?&t33Kv`AW5
zH+d6=16T$*g={m(-mTfMgDr0_T~38<Br&jC=I+V<8p*cEeO=?4vwU#p-p^L77uzBJ
z<U2$?+W5OFTWRZ$S;m7Wnq8@rBn!W5dTJ)lqRLrIe5xm0s(#t3;ka79w{QD8P1Yu*
z9+&{^qf~)suTXR`yE#vOvAxt&)vb9+$e`}nGvs=8?qw4mOX<5nYE=d*fj4mTQB)0K
zzl%DkVwk6#s)2~v<becD^6!1f=04<`!X;j#^n~_EbolZe#V8hGDG7Srtyxoc!vvnR
zc(!uvlteXnN=ZPFctQ&;kP3ln@vBeS;}ag`Dp6S&J$}hacJ&*l?xdj~c#}Wb)7?vY
z*AQK1zX_(sXK{FxeZBVS&F%qRV@Sx_3<kA^_)B#_ANPQ1Q_)R(zMNL~b#QHeP-18R
zGhuerc*xks5B_Dj%_txFFk?K2Is-akRU+D^pnFmiCinkS4mA-~dG3D@{jM{kVi}%E
z@4vJN81+h55RcU{v^35?P(Z*`zBH49=4w|Ae^lt;RR1M~`O4u+m7jrO5_DraTVL%g
znKt!zuWsN}b}}DwKV|n*oyrp1Fdw8ojH5SIj+P@uc~$8sf56TNYe1*+ge8s6lH$NR
zqi#!c`9LlDYjj%x@x_D`b5_w}v~O)T=DCYVGD>9WJcrVu)(2A$DWV3pG7IE9Rg3{x
zbpH0$?l1qYaySUL{l;Q%<m94CkIzgR*^1YmeKhGM{rF6V)x8{BiY*9v>6%634K971
zpT!0RmW{{vS^L#XTAJjIgt&A6j9vS~_Ten#4;9;`g5ekspUvbvqK-MqpY2{v*Z418
zEp8ubzgu3kY;myYatq6pk_}kt<&F!=;Xv4g!f?&E7Wz-<2-_w@PoyaR)PmQ+0ysQ>
z+gt(zp|??=AlW)i(vv0{KA`3=n>G$Cy#Y{0g~JOr5$K5?V$B3ymyqE5UlxYKF$2it
zVzelKj+rYK;dCrKfanw@2l=`}A)+hk0QONuxqCk?gY|`IZjcLDgbu$>Vj8eb+D*q%
z$=Ds8pLyrxXtg7JY}wq{#XnZdJ5VV#S2$+1RZ<;lwlXI=Kh$MKqlpyc5E0P@W_TV;
zZ8RCby!Pe#L4a=dQCd#6<t#95@*<r)UM(q9wyObqkghbr5|{+ZdTj=2w*k1n;*_c4
zz1Js*s*A?#E1)62WBvM(gsQ&WBv4?#)$kVb$4c>Fq5hN2#~{ki%F3wX|Bln1;bHUY
z&>{V=SI4+2RFcu~w13U$GxP{*5aC_7?sTg|@HZC2H3{dzRgG0>BE@j$%@A^U2YgNa
zf%)fyagA)I;c>7&CVXIhjIC$dU4{<^)-(AzdX+y<4?pm5l?jTSFu4cbb!)-G(dRXd
zQ7-xF{Ej!{9tEykOw&{fRg_Ec(=X?E-S5DIXxY?Q<8dLy(lV7`zu?b}I#Bmy3;cw^
zU<#)}%>xQ+3i${tk`X%9ZFq)RY2gkq*25MROf843r4x*nVE^Qsp&OT7gSwuqSOi4k
ze^Hgs%>Ei5+y_j{CDGlR1PO85AyHV_=<1eSC%VBxF)hwo&~b6oK%#_u!E@26=cZuf
zwynic+57eO-5&#-K7O{$qK)G8)B97|>dNt4-fsrpvOMocZt<g{JRl%XQX62?HPs2x
z6k#lC44Myk{ALMgzpR6TgD~+|eV|GC>4PI9DJv-z=zG~j`f8SxJ7epYA#i!^dVhC#
zya@lnAI)7Za|x2QuhAxiEY6ld3ur7xOi|S{sHY6@&(FjmHNo&pLDtuEIWyGx9@ZgV
ziC!3%G?l0F%C0wIDLy*s%ehKG(~|0d8SpG20ZAVsbu=YLf0>s;85%BTf09h7jh>ch
zx(i!Ih#^QZP)ZV1lXr2pr+)4)1}re8h*@f}jT^=HwO`tf(2}@a0H2bdlue5*+dF63
zY01TP%x3gH2W$1vzg_^@&btp?t9I*dJQ}$G>fT485I%1Ga^X*g{7x0IQy_9-h57L?
ze=@KOxKOlIQ;ONUF_8{0Nn~0%&=X{M^lvAxAZ?*ZPe2VR-_OYO1KU~>z_+m8G{J#M
zEg3osP6r;LUf>^aWTiI+JcDiuiyPg0q(vbUW=$H`NT!O7H~xFZc#Kj5?Re`Ceq=*P
zpL$$n{v+vq&CbKM!`WK1zF-5#!{45}BZC5(*)FcP)A&|2mp)|NIr;Nb+Vzk9gGIMV
zTCNgS&q0zpzBog<@&TSTH4Q9hPM+mccoHtDs0pn+)`-=&_fZF8Q-zQC>+y$I!Fr5I
zK;Y-IBTQ3?;F;%{)P%UNAQ5Es_}`%+Y)8CXcxy{4O4g+pYQEead9Fk5Aw&{;=Yv+!
zhaO;Fm*O2?*?LKlAVGrdNVkjB1e4o&aHF3&O?%($7r^MhGYRxGgKXZZx1yoVYq0Qx
zy7Pk$F~p}f?3pxNX7>5+#GYSqnlN#J{?dN(>K(?<ub(l)4WbAT-K>&<!iWLPb4}s{
zZiFVGT^F^GyAPrRCRmAJ%kVy1F<xH$!La9%iI1}H{+$NzttrjV2AK5*@09LW7hYF>
z^6*$b;@+&#6?W4>arSwI^)8|~It9FXj?EChZlQiW6@G<nS@%fDf;f@JecsnrgV{=v
zxCcB!U?LtOpxD~W8SHh*tF?lt-4#Ys6YbVQTZ!6+gDpp^Bu<T&w0_MbT9on(`yWW|
z_3{Jy;ob!!3y)+k@yyS!4o!%K>J+5Q7PIGHq{WYQe2(!?#F}y2`I;#+1bX{JOm>bg
zg-6hz{PSt*M-Jtx+ga|g)#4+LVxru%oXUGd*X>#_l5VYNDsBAX;HW1Sh#k=7jGyZ{
z9~QPONHDxy{OsnbuLRe$^F?c<)R09DwVK^_y8vHV_v70hLO)F#ZwTN~3e6CkLM!R%
z10vE*F(Zzz=Cj-lNCp-e7N_xNl`CZd*uz-$dSHGYpwI|Bn}^1<BQW#`D5i<mx^{8^
zjqLYWDX4BCX+Ktd`kd=#ChHY26FlUh{M2*wBARZmNaihoQI4Pn#Sh@&0^z=0w3sav
zUb2_b86Mc0eM~As(J^nJ?`Pg>&L$5MRz73Ec!~Zpz&|Pg@Vi>*sL4uq-J{Dl#dX;p
zO`I1dNEE|JX0krYmw)T9lE&#r;Q~1^#ggZVa-`v7DnE6Dmj@#PyJhRgr;ggIoR6F*
z?{?G|L>G+s`K#Q;*pKNjB9r!coeI;RbbVmb3^^7M6cz_wx-$0eYNiEo-U-UgWp(UR
zfd%d+j0l|AFvRMMS~#j;#qa!LiN%fDVT^~8-S;BY&Ph7??(1p+?e_P+sIh;|%Y?|)
z)+lR8lbZ)=9X6V4nkGj2Pk*M>7G`JIEzJE;bx-DVbT%6=>`gC!bSO-h;*(XkQT+gm
z+0f2AbQb8RrIVzJe@$bX>kiA;JJ0&e8x^z~A^Pue?KgjOJ=A*nEx059mH#q|mA0vE
zp0}V$J<K`U@u)k;Nu220;E#=t8tUB~Wxsl#bPMg_)4lIq^JS6^7XuI2izFI4%1?Hv
zr}slePEyt@bD|GZj=J468~Db)ZG<*-h#3OXyrdbg^U07EDvT4-+nM~MHTd3w8nKV)
zlRibP&axZSe9RhuLzr$tTb%!8Ro%vq*36g;`ww$0{xQ-E_m=I%NKV~%krP7-n-I=7
z!)zV?e@vT<9!_1&{e^S$FC-v13{8KrfaEQ~kM^f6N>b;fN;|*W_H9&y2~iZqP@$1G
z-<{<=W>4G(Y?Tt3qx3bYf4kn)XSF=sNc`SCN3wf(@jIUK=-h3z(K&y&!A+T@@m}Aq
zGaJc>VxxrcgLf}!;$PmENP4g`v?5mU2ZN<o^`Y_u6y?JLCl&TSGh-<{Rl|DGoFIm%
z;`Z2UwVB1Fc9$Z_!&R4KV>F-dj1~FafikH8f}+Fp0Kq0hOjq||7dY@rSDxnQW(?g$
zNo2sNx#kh$(wO`mD<=T*U084AY+q$mQ%SgF7Z~CuuYx5@rwCk^qq>!ue)Dhty}B*(
zXj2EI?1DB&L0Y_Z`Lk4%nVPf0SE)a&C+PDI=NNo7jbA^x$P+7lm&1x7+4X?$!v8*5
zM0RY)g)N7|&-B>QKJAY232W{6;@Zec9*Qj<vEO&5gLWK&xid)~T>JcxZ}~ZaF9G(f
z`)nJpFm{c=ZKYenxx17vh@{*tg5S51p1{hsh2q*xoYXI%A0FJ_9IqUp`t%+`y(iVK
z)UY+94X_vtVK?SuGgRj{!-J^m&(Ctx$t@RVoIykEG*%0}b+CakF0g)SBN+`hqewi~
zloZ1eN@#TaCL>Nz6uMPukS@+N3stY|Uij?7!k69c&l`^3d&H@JhX$HD&kdLNTOW^B
zxR;&nej#uh+_3DA8{e!|7x6s{ryT#N)NebLeb~G_V2f?yfEq-=qJ{`<XI8CTpi=?A
z?ByZpW|b0Jh`Wa3T$T)!ur5MvDEbb?s}`ve)KCKkG6D3MmuXRMF&C|*0u(DbW@6I`
zi;KE=H~NKvKKD3SD8O6GarWiG@Ppah*ru<qHM?7c{9r~o1WZ{y-1~hlqt7OL`)tea
z8xoc>pnEH}U^05^w<|FIKfOu<5zo}9rXit9DR}@;2(`<>`{s689Opn??Vp(97puyE
zJuH-^KQs64qG4QAe61k7vYVSkcRJR!bJNn?A}f*lwCb86qN)SAD}mwj6nJJRvTx-r
z+!}%9iS5l4NxutZE9SH#AAg+c3>b;Jy!i%VuR~8(E$H<o;-fOZ0TlXPKqI8MUoqu%
zJ+xqCqOOJCiw(v*7vuiT6#htQX@BZN>A=)P2(zTf2PsmIK0QrDQE@td4Fw>LOG8s@
zy6YPS+&vc(wI{ec0yG-TG(zqw<|H&5<_bG4bjC65elv|$PFMIo9Y!)C`;B#ugJI!j
zjM2JfF$;o$>qelo{YKu`DtRYwuHhm*g)H$mX0{ekBxr+C$2<Nc_L4~U@1cf9)s8s=
z?HsVFZK~aY{dd9X7i03(YL4nMph@wk@|Xlp1)^u-3{z<2<hc1LP2zpI{aPbk<5m7w
zv6p;fGG0M_H|gF_C?H}Ljp1d3J55~;mM9Em$2R=`!$**>o4|6N`N-6fIRI`=H;@)`
zz-?#Dy{)@QHC-TJK(`yuQf>SbpUAn})0VAA9*_M*m5i1bcUE?Dk}^Zbdb&22<}1c}
z64(4VDig|c5}IJREf#t3wnCteb2hyUdMlBZ@Y#DjIs3vJ-m1YtcS@RZ2aS2E$*9+R
z<?URp1m~YqyMutOl5}HMv>j#u*-;1UUx~W`^o!v`{H9Ll^A!UEDXkFFwxJA}>zxEe
zsuY46IU_NxoRK9KJSQK4HIp-m^$c745a<}mP=pzU;8t&<bL+@+4BNU7R&zKp=NA|@
zm3%10wP$%jBUE8oZ$x}E33%h#c~P@M$G}rhf-OpngCA!5hIph^e$QYDqsiM<O-pr|
zO}P0U&0-2WFUNowK$_q($rNpD-6(9kd;lmaF|lTqE9?x;TWjABvcHO@O~l5ga~PpG
z+u;i0x(P;q%lP>atC~kR!~cac_(IB`MmSv*&-g2161(n?3{4;wQcIwl%~!#&+RcP&
zn(8s~SmUzCn;(yg>sQ5h)V-e7VXAw{;8!N!7wzx)NZx29IL80-(16^jRT1s7DbMvQ
zvV3(){jYATpzzuk0QMv_xks;PAuhYQcLi;J1kmZ^Ei~VEQFJ<nlqQu-DwMbOM+Lcw
zX;|H6cax9VCT%|&CGHf}CBSPct71Vlv_c>(>!B4<fFWo4sMO~OX!??nr0l~<lk%BL
zO%2Gx!rx`w53Lo!3)&@M7o*()Wi&~+k4M6q&%xH#Tpfft&v9eucR5b3z1;JI6p^P1
zuf0~EjQVZ7KPY<T=-{{5-sACh6;Do>XN;eN@K*s5U7m?|JI{APY*^d<u;I^z7b^D{
z`+x(EW{IJAk@x2w%urs__RgP0dY_is(r(WE%ewd*g8C1Ls^dr(_Y!cvQT{%L&_4jj
z8K1|VH%_vKb#BC-)0ErbZk28x(xfi8Zgv0BH$~&R(CZW~58rE@M#+1<3i<TMgnljQ
zrrvy3w8T)nHTa-1+vCK%Mz(UQ&ey|8Hr}a*VF2hyk>J3hP4Nr7kJNeCC@(*o`3?nL
zH3~Z@>!AKifmlgHG#5!wo<i^}8EGo+{y;Y2hO9}mU?lX~O0bK`Wu9C%#1kAkT%2Yc
zuDACbeGcFa!n%oqK>A_it?TG_|Fu$Z5sZO=)$2KN#SOpImoSVLfuaKxcfu-1vO7ht
zzV~;AypFa_CM!Xf{o`tg;{c`Zt&!{c4i1jDnRT_}d9}I7-dbRAJ&+IXmhLgQjr+;=
z)lPK<#^+t|dVGQ*T6e~B+k@ic<MB=m1;2!xlm*f*c`qR647YoL4Nj?O?>Z=i%%M#g
zv&TOVLT0$~j`6q0PR`$+S6dmXx~!?Irrh7*@_Y?H;xQ`5pI-AP?8iAIJdMrz@?WRL
zIHP0?=0~W~eL&S#I0Wst63<x0NkxTH-xAF!MMM+#W}U6K)8lbrr8KrEGtLb8zz>YS
z)kUZi(a<C1NTZsq@UcU*Ae#}&4l6#Del#Gq7wHY?ew@lm6v*FLP>aM_--8;BdOWri
zf(_rsL%czy7~{M{K)%=ZVQOrBvFIhu>T<3dhITGPYVY01GgkiO8}qY|!<jOeWa6G5
z6>k1H9`MTsAHX3<733QpF|(Jkg`&~w!lms}UQgdx8Z>?ZuWg|eU>sV@m%3rhE{#_4
zStzJkIQ?q!G0#XVXC2VJmP5!)6l8vM8(bmj4#cF1xjZ1op&m)WeLI1kS^oKTs>AQ`
zL&{_kp4p7nhhqH)BRqz`W1keZDz1MXw~YrML3c`Po7aF#mu!xGQsL$|@6Ns&=zG4e
zT@T)WWrn`M81%$8%|rpI97?E>AHLYO|Gk8Pg~sl0Z@<Mik6E!;$X{I4;yimtShs@0
z=J;TD;GybQn7wT5QdY3fo=g;X)lRxTQ_x&!zwQ61xJs)f(SB0v!viIPi!_u<+7}6Q
zP<$VnfT8qoq_kYqS?yJ((%9Z(PznC?JdH^Z?zw7dNzGpCLq=VRL|e1l#bH*L6t%p3
z!=q}LEf3|G@;ye`-^b^wIma-p4Rro~)mZSgdTD-Va)Z(G2kyz1r%e0<MtnSUc(@EE
zlr5b&mme}cOwq~)YS4-~G`7F_GX3Nq@l3S_uK|^NEsU3R>B$f)gIXer7Y@n+<vh}k
z4cM(#m4P%Ydo1TuRH9d@2D>ZQ940B&Uw?InH7M_iKWyQkKoNln-Z9K`30$!v*De0}
zIY{rc_DFE%$a>?k9Tol;ZJ<zU<~{K!&XC4sIKT=-O+D9)JlCh&LONu7+v`CP;0zO|
zQKF=fHhm%PdmYg$HTWFb7kWQPYu}5l-Q;{AtCoG2MN;e}jIzpPBvBK+&y>XU7$~>N
zq12v=6r-6oq`WOw+!7^jSZ!!bZg5uj3EPpt^#P-b_f~S~xL+aul;XqhDJCXS&H}5F
zt}o$4Z7Js|L#{gK-u#v_TC9V>BjD~60ScER;8%S%KvmuSLZrUj+RqVmw^U}Cdfn3T
za)2%Sbywv`uD-dhx=n5>chN5+<Bfiejk()Ga9vlf`78JXyKw5)-DYWe5#8&1KErjk
zwjYJ3ZRRZu1!?R;UBp;^na<o-7tawtZ&AU@!04liSpSNL%E5oZVwVGe2G<1>bs6$U
z&3S1(Ux_;7S)wx(b#v9Idc+%LX<o(;j2pgbN~KMuKo@e<>Y>HAtG<s{EXKr7VR%?0
zn6>FBT(|fUS9e;MT4zwRJV~2+!kEF8-%24+&dG8an{@g*>=NT{4_-!z=ubpj)9e~n
zlBL|Y+(8fN()pd`jkhN3I4r5(zEhb7{y+IzMX%oWD&AdmyRlb8<o<T6)_LV!)GfgN
z^3s9@=xsKEDxc{$4xR|Ki5%Llf+VNoAD=J)Tx7rG0Mw_ZROGTvd+K?&R;)i2HjwOJ
z*2||VHCZ4d@f~>@X4m!8Tr%(-K!qTeekCXg+)#plkn%rkR|R19@#xXc_397VEi^;V
zM|8sP_soIrVc~wR^Hkz>&+OJKq86^?8?ZT{VSGHTxal{($s-`a*>qp?;Y@LXJ?I<~
zM!=>=$)UZ^_lI}Es%g~}5I?Rrz9;#pEf1bv03?iM(IseZi!FXJo1lsO9IywiB}#cJ
za>W#P!q9Vtson<!Aw^a|lRo@?0G2x~BA2gLSL5QL>l3xtx92r`^&WK#;!5yj82TQY
z2cXmbM3GkMqtQ0WT(7Q}E={$ZQydz2FCT%%=T|-ETnb9Vvb}?Qj<joq4F%DLe?ZWh
z4MWe*U;MF2tOSREc7vQvN%^OSX(~oSE6)fvDf9e^2BJLSj&C$QZ;AB)U5F*zGGuXE
zwVH6}3=2bS|Kv@jEi>82;!o>;7J_?iKaa^&ag&$CrC)j3nfW8ve4E+)^?fFvckNzf
z$;BQC%~4+odDfh_Uk(GBb^g-CJycbde9oKHzPVK&7#cYR5kC<FK4I)Z^LCHIf3%(d
zr1#%JVBGaiUWLJlz;{`*tIL}vuS0oe2gDCwAGBK~)dA#+kg-6erX|8ALF>80=ISD|
z9=MEk)Iid8g{@l&A;9d9NmG>>ssJ6(!DtoJbt)vvg3`kuYbmHmh0K|b>fzK{<aIAL
z<zmC?cY@!{6uI-iw+aqnLLMJHJKp!yyhx1-o_R#T&lg)794fx&o25^>NvB%ihTvEa
zq8kF2>a2l!@8j|VYRiP6HAaab#W~U~$FlUV5=I@N90Pi4JOQD1SXE%X11G?n-%aeI
zQq_2DWCM8_`s?4F5Yse~+|-@7brR|@t`d^htzUGQDQM&sh{1o(QAgQ)gfYuCbL0AK
z-Yiuh#4}nxY$;*8vjftePR46vS`>%R-0!xTg$54+t#U=cMkbH2HJ%^$cw<?PWX`7I
zl~_5f1g3JuHcVWpzMXlk$>;Bv9n^{V?Z7Q7uSrSZmO=uYa}zbfLm36U$-;Qxy#1ou
z<YRK<Jz}!bx}5dnofPjM7DWS|OVhP?7AHPwty~}I3@Xr5Dkdi@0g8ha&D7yjJWjU!
zildR)3TF$T3~|@TNbb)H`#@cQ^3^SpWQU3>Bm3MAhsEpkzF;&q4f-<H_$TY!tobG#
zSVsRYke~<aYGhZ#RSJOwK!>d`Tx670v3zF&Fp=@AsKfnQ1#PemAno^!O2U<iHe}Ff
z8=%B9e^F$?17has8H`6zt2@#vgMaCTOO2lF&u4mV#ZE%9w!KY3n(r8GsQJO1gRR*4
z$@8I{WZltn9MwbpBE5o@&mQ}{O2?Sy8jyYFV-=_e+dVB{ck^5kXCR?f93LUbSp>Uz
zy>R&FZ!L)fN~sC!mrRqr!uP<>*N!dc12+bC=V*zMWZ(4)s%s)gz*|Mgh?CxIY5E1K
z8MjbMYUn=|C?+YMwi#RW&(-i_8&evKorBo7CID1vB*8KPnM)`OZ0ajm$=<v3N<O$P
z%&k1cAzs_IE7oEn@x*#SR5WHrEBVB8!UcofQ$v%ff&Cc*QbF6-4O_n?BB%F{Hk&(*
zpn>Jo`>aY)ee=P9GmmF$1-{tn3|X^n*O|ApU9{_(c=ulnWyYDiZ=sw*w0fF4rXTv+
zibgj9k?5nKnOJV>8*9#K&5`eH>Ry-9%o2TjUiB1jMR!ls7OOWzsUKJ;8}QXmJ{2y%
zi2QwAmj8f8Q`KvA?}kX_Y{d;GnTECapgE4{;{!Uh=;`)gx>LowO82GF1`P-XFP%$(
zq~GmcivAw}LzkbaBHG>18ak?X?4KhkRnP{6f!(D226s;xR9aNyY66iE!6kr!X8ES!
zT{fe!H6kTFjmtnpuaZtPIC`t~!;SGb5~lK9uQ-wus>m6RADey*TM%)MdH3}F#El#s
z*Nt(8vWkY7k#D*4+&OikUi&qn5`N=>WbRO94pHwK)0$-mUr!!MCXgY0Pp8U-i|%0K
zyKsqs{|MDEiU;4}^)Iytp<x0<K}7&KKb6m*NHw5;T_clP`Bp$cEkUS%s>hb;1AuNK
zjN(+l3_AzD))fox>&ZLeN?sKWru4r@99cv_jZgO|MX`wlJu(-lE-MB+%bm&GZ<^A@
z4U1!qzv=?<{*IO0dgx9Ez^fC(4E=(d(!+TUxTtchpVnVvZ*XwX5sa=xv+f8yeSX|v
zZ73+%{Mzn9=rLsV0T&2-e|-YJ5;mQRG0WU3>5xSvG1<3Tm1ykQ3|T7ij~;JaOP%%!
z3Zof(R$}1XrJX+987p~__;c$)yjO!2?I_1IR(b*XYl-o`uXV-Ng8Mj0=69NZG5Nbi
z!?sraHP)!0!gn-$^Zpq<DNWBhJI!aXQbwh<#lcFyC{!VsZh0nC)EJtWFkD241kMSI
zZ0BE0w*Y&hx3h*-UjF;>Qb+=S6TFrXa>E$<w_TsD-(^zz&$t_JjFe>w<dr{_(aZ^v
zS2HVcG?hG<Q_q&KKhdl=$y)n0#nYd9%eK&Fs#bj}MdTr#?WC*Ebkv_G{D$~Dzm(_A
zDNQRs?S8L>I-g9~F?$@XGmD3Y!We7#lt*n(O`~Z7bj3CL>p|D_zP-QuX{l5`s<;xJ
z-}}RZe0o5X;-R%)r3Ytoj%|Hxk_*t~*2)q8Hi6xUoct8zAz@Y|xJ0}%joG`-W~6sj
zQ+V!#VxX4S?DzWY9qlc)JtN$fL!0^yV?)+l^*2A2wJGnH8sB;HvoKX+!e%>QZO|){
zt^{ZVV+2}nn*88VzGU|ILX-Z?7c0ezo#N-!yOx;{;PTH*S;`Tcw9&xbZ8v9J(Y9J_
z-~`DNe&ZU|*Tc_>%vtqQFVecF_waiH8z|atY&4!ZEa~rVVEA<&Mn8+WqQIr{=FKbR
zgYjbZR|X=FU%VEz?;>VpHT#7m$VZ=Miwt(SfX#<pV4Xvy6(rn=E>jL;$SY=MIAQ@W
zNVvNTN*X?hTZ7GqvJ*Ho2UFv3f5%U628d`Lr~p`NI#}L4kLZCbCXM7X>jb4}Fq#AC
z*rk@S@-SPF@o`8yp?}5@X3+uQb6O2z*5vg)4}6?HaUBnNC${KkHE$Af*^~Lo%WdA6
z7wB~DcgAKF8**q@50S9uEz~`mp&c$A5jsEIZk8!-xv5vPuyxU6Cn9Nmj3ZLAC%Cuo
z+Wh|RWC!D|{)CpXlDS^<LWA0$Ibs1!?inSHKYAPW`gxbw?7X=a@;8%AdtERePbS+e
zIwl4rB%znHse2Yf;Z&2rB31vz!EM4J)Eesi%Fa}s>P3+%-}5m{qqA6rvqR~<D!!65
z)aIMMoVwrD!WUq$%VoZvTbUS}FFNgwH#}#lCSYco%lo!&q-L{jPt9;iz~fE$mV|9C
zuVtqvOn<6TfL=!iPWAjA8D8U^MN9EHeJ+MWiL}2gRfIp&yV;a&jUFmdVYuTzjK?xy
zcSL)WWwT3}_+8?_r7l8f7RBB(KNWN9i!8Q$EK(!VTez51rcq%dvQ_-&hkZ_eoHJEM
zs!R<c-@s>ha@NyI`^vC^5&R2fj$*IIlB}Kq9kHldwLM||%8v8f4Tq6n&`^ug`)GTg
z%KT^qD8eZpdxFyRPQ$dcQS{cshLay>h6hj?)_kA$eA96(lI(F%g<tw+M19H>Z5ng4
zW{vC64FR4viz%p)i{c8(W=kR3E8-T+D5UJMjX%eEu3+0Taf-JgLtvQOJ))fa?auQw
zU<_JE^x$ftlU$6^RfxL8wYaP**DA%k++L|LchauZ@^BX2-5b!9exB49sDImf@b#kG
z3g_U`fYKI|id^+CzOCKrV&4Zoiyg`N*ToU|9*d<VShFkX-ig)+*DYY_h!>C*J_$;g
z8aXCooxgbWeJUR`tz%bc&4QLBgBKuP7m9xWG>c9^hm7H2cmmpFK46=5Wr)|RzPhEf
z1S~0iFEr#8r`;bGd%nf-QjS<D9f=;3`ae<9ZXk-dS;Arq*>a1%50x-ba*Ebq+YG9b
z{)@i_%NM_H_z&wbuMzrJ8oG<N`_##X6TaO3|EPKkpsM$%T^Ke<BPorv2ucegUD8N{
zA}vY?NGcslNVkAUHxeS<je>NGq=a;L`K|4F-+S*jb7nXW#^xWtwbrwq2tI>34fJI;
z_YO`{=3&5P>86(obg#1AR^$EeT$X1}yX9iFt%?^z{iK0_;MmCPG`nta#*OwkoVx@`
zFzY_-VXd(;i=7cSJT5<;_EB-*V7C+!*?!S}v*}jZWJzq<d1jW>AI2faCunS1O!3Di
zQ<V&;WsO*8N-M%qX(X+cuHJ$l1Kl~A($P~zfa(MSxbVJtPw#ivk@>OYOMX%jxH4zR
zBic>?)#X7im^_5PU)uQa)fvtu9aae?s?W!SOH8PNggai?4)SF*gQ3>la~vS(9{Q}y
zMN8Jq8P*+nS(|RCJo1pkq0r&Yt1L36+1O}ynoYAClbM#80mOg!L{f4k6YXv3KQxkx
zf{*^d!v#GxzVibiJgglJpNNX?kQBMm6_U0*ETtuU^}t-o6=b@~^uolVHNE~eWQ-o<
z=i!>jMu9gIEBBO)lnaW7pi#L+)UWTVuEZk_^yr*J;4=k%;oXe-L`&(1w~u<q>$u3y
zWUiQe35lB%w8|`zD*CgJaONf#8@Nu#fcFqCo=)kb%oM`cIG-7jzv~sb22jYQAK&@R
zOVWIKL)H~L8>|f#&W^WB2%r}Z7QS$m`t#V8&dVKG;&);9IodDFJ-6=_(8hB@+bYAl
z@MN&tCd2x_4m#=~iY&8-TyUY-{VKAUX|eN-vM<zpiO+dU%)duH(u*iAZkycFbJ1#C
zUvIPEN-Suah#6m8>GXDXez>+2rR-W9*vmiO@9a)e4S4<Z*^^8bupP`T?apq)o)g6r
zdU<iU@l*3Utx(qAG_Jt;8F;0iLu-U=Zv$hD_n{w_K<T@a7H@)Nk^9;a%TxDtKe-RD
zrx{^0ZsgBrkIl*_&x+dd#14Ld#t2qT$-Hg{7VEg1(yd@=i(5EQrFts=_4Y#Lt&$5l
zx@!a;Hg!&e>(g<%)pofM3ua05#gCUB$eELw(@+X-19lY)qBuoBCJuWP4}O52yc~f{
zCEFW&`l<_%RCYiu@e}NqAVmz_TOM-8v+8#{srSHLc2&qJ=S&wE<qcLn5*--zYb+lu
z3gamlwg~u9_Y?FhtrJBc^5rW8@Yvi%ePaCGoT58GPhbpb5+j7Lx3^FK9Ln!XcpJut
zi^6(_HZ`F}KlidIJTB+XO4|74*_->dH5|(xaBu2U*Z;dU%!Jnw0g`AlF)NpBG@`~~
zLPQGlfVqqj-nz;5b+MoRsn}mRGI2uH5yB;plJ$(*w+l{@B`)2&eT}aVKQ0!<R$u)%
z7qR^nC;<+)ivW-68LxEkFx04~iXB;^u&5BB)(3(^vY}jvD*$$2yDeH<4}&FA_oS&M
zpbDgmFLb`ZUHT4LfK1?LPwX=H?7zE22py4NUfClg%6qZ`CM>Q%<aS1{n7patiIj`|
zi2?EYPd5zbq^)Cx?FG3d@$SrIlk&iuc8TJBpX2SbURqVURFmAi!e6L@I*trwxmGr@
zK9ZmLkeb`r2Qr}_Q4h3`&<z1S0_pDY0d#Q<v@71+cpl(PRZio}nY4rye8Vd2{6lFt
z_s1Qtp!Jx4AelV<4my$p&3YHT(d@(jVnWP>76@(&5Tyj()vq4d3wY{lEBu3`#SeGo
zbK~hU^(B#Tf-)Y{VC;x`qcj8CSB|QzNA54}*mOTBImG|?Sok|WVv2$K)@FPHpZ(B!
zd;n}xw#?$%VhR;dI^AU7qUwTc8)Wg)$&$qrNuWgC2g{$r!_9zrugsqbPDj96Usq`Q
z518y@@yXLj7)fnGT$lKyl*j=@N%>G<q!Tl>U&2jiV{DG3VrwNYk4bg;jh(NS{`O#N
z`suidIl;Xj%f0d0s^To!fp~{=qg$5**@p?G-EaF@C>;$6q|cAfoOy+WWQ`l+C0!SB
zo!_bxwhmF*R;A<J58Vx20wHg@^oW!I#Q~+#GXwLiIl53wxloFEBCqS0um12vB4|YT
z5!e*tA8S^T3IUVUURQnA{{$U0sQY}XbBfM}3Y}#c`+A*Z|22CPVG;ngvp{s^R}+oR
z&f!OUS1<-@03osl7>ffPoqWHM1|;c$H=`lMC8%mQR2*T}6KA+XZgiG6rb_I5Pe;p4
z`ESfu)Oxo^{)6?uqq7~o<7TZ?<8D7#`g<xeq=Rmp*G=k*4)TTDC;M@b7|IDGj-iwj
zaQtJa=K8nxNkU0c*nv3#uG~D*0K_iJynz|2=1$tX0V?G&dOP6KDnaI07C1abi+g*$
z#EO$*Gvzdj#$9tgTf<j<(o}yDy;!#?kUy1~I~BuJcTwD#HdGO$<ez%U{q*|tFRa8(
zLR|i>%nokwQ+eDp@#E-x)U(x+`k>!KL(!ZGxbqrwqY?1n&D}!%9Qe0}jSaayi&mod
zJUf^Sk#gw&<H}5kg9yM}RvYOMf^Q-Jqh~-2h|Wc@?O_@JUWsCo6QN4cFo;#BkkWv4
zk*@oJ^u1u@aSl|lti#wYp2=TF9jK@2f5{Y*M~@$O5Qf)bWv4Mn4#m@M*{~iL=P~$O
z8KshbIv{olxPg7bmvi}`GuS3mS%HZgCY!{2meCNcsYWGWeS4?<KG5L|u%vGD3CRt)
z>{$?hxq~XxRGZVt@Q)QLQH?dVCN82M{lySU{x>3sPOVKzM`n_JtLQDp2amxxq{`e~
zR~Ve$43g#<j<xyG1Yb3r4INaBJBhW<b5ac$9dSHiR4T49>zfN)oCpwS)?ORg)1IB-
z&DK$=ao8#pd;KCwXObyZ&R25k@fTxXPFlY4^hg<tKA`xmlD~zr`2+A%M&!r}{<rJ(
ze^T5Zakp*%z1seZj50tv_mK9VwJ5j3L8XpL*Y5RVu#d&~%I>-08{3edyu^0beZi^#
zsmsZND3Cr&(x<-#5ipX!(NCJ7|8K@TCu%lR^}`mioAbH!%WX$xu|xV|)w}L%6o&Bn
zeH3yo7_$!po+{EcCV9Hv)$tTuy27NR%R}qjF44~UpMUzZ&UKy7Q`5K{(}c5*Ge})E
z+Haxd!z~nZwCZnh98brjA5qveW21_OyBXUbbOL^kPI77I{r0#Y4%6yixS~S%u2X{0
zkcHpc0Z<i(CRMOwJ}6T-Bn)~NVj$B?3xDWkM7=}!9jLHmXftgIg_3jN>4V$^$x+%E
zXqp&0#@=N}++U7qf*tZ^*^3}0%54Mn09&lJ-SLbL5GmSEqhiLFM3y}j_)(ES(cq~?
z+3U{>db>xufiF!rCQ98pXFzvVvVXKSeFt<*AAnid4MJh(8IREvq%aL>O^EgC0u~dP
z%KEjJ*C`aq%rp^QJoRnPj^AkCtgOe6N2&;?n9AKn^>As~ctX+3_dhe>KY>rXDhd(G
zNh66kUG8>`%EQu$GV=)AW#l0wZpxb7OCerbS}LaMiyC@LfZ4&;kYdpJwBTN&==rJj
zT|N%gH0Vet&zV}K{(O&;{_!%@Ncqe(7!Og+I-latvL*MJiVfWDO)n^2^<i->7!MI`
z%zu)69fQ6+hjSnuT-X}ahF%yj6Plp!WVD&7AYQA#Kb7WsL^*G8dTivmQYt>Cbddhe
z&GXcIHd0ggDpvTLY9wN`V6EOMk#C))b(t}{J5rKM`mjHtl$@5fDC{7O;tU)b4}Ubn
zmS-t9)himvuifA>S0KG~1q&3nF&@%vyFt45Kes$~v9w>6-TX|_?70KYL^R~>k!@j^
zI3r_}4wu!%;0dHdx0~KS9l@k{@qe$F4;T`VeagDe%OCdHi|_;v(roBmh9EN@oPV?F
z%=N|Fxt&^1e82G;VUt+$GG~NHKfqosF7C)4@K51tx#)4<*shEg=<ijv$}e-F+{-t}
z=QHS6$|lU}o~sepmVHhb+1cOi@^}E7o>2i*_f8fTGD5z-WSVBgKHo&As_eX51(E&=
z7^Ps4Bza;7*EUMwebFu7?+nf6bKh5AR>q<i%FZo64H#w46oIQ8PEVMc(Mc5cjOngq
zn*4OFMun;ohZ5DH!*sUuoqXM<#`4}cO^1S?(MzC0uznc4b{Vfq6CZqerj#S%`h3%G
zH)E!jV-UlL3r)>QN<C=6OO2R0tNb9B=wUEv6kKJ^(=CF#H%WNV(-Qdmvkr6TcY?8>
zO~x9=T#a-f%_9(iv}G$aETi2)Re}=wKiCoKOOlo8x+-fht8`EQTA3BgIe|{dfviJb
zx{hFt<$`|{Dh1+jCOYqn(wUwQ97$$93F_`bZH~3`l(15r-f~_L+27k(wn%jQ*1ues
zR+6jeIgG;fEG4PaK=tphmdq`WL*5@~)F0RCf1Qy>e^_|6KQVh5=%!Ka`Wk4Cet{@X
zLjM@SG*OSf^ePz6uj1Yr`KMX~y*wkVKvK$We(8W-TG$fyq*_IK+7bAn1hR2orBu~1
zh_N_*q3d5YkC_{59RekbeDi(Z^2V~ne^4hIQzV4%78Uw_^+&o%q^T86zHN%}wv9KK
z_2wGAGkc4ifHqo6an(V?zqNO=F!7gNNigruOMa{JkJBGzz1+|8je~DQe(lBZTs~YR
z4X1nubzZeEUnCHnMgc{!3hb!_e%dIl@+WQtyCRX2uh{g99x#cpfbkWg&EWs#MWL!9
zcG&&YqLi8cc^;&SV{ecDESt58#CTUP<&V<^tmks`G9-)k>YtC?lPcR}2sf9cMrxvE
zVI(PxP%Z2$Pqd}#XL)T+e`F-+q>%N<)?nnXO`T@vqW<l1PPTq;!@j@z-PFb#OY1}s
z3%QZ7k>J{6aOo*N8dI^o9o?qXSGQkHEQT0a^^0g2wd%ZF2RQbC@FgUKZP~00&vTG|
zqcmxs7Op|gy%gZNgw&aDUYLU0zzDTaj`>eQB)h!P|5t9wZ8os}kpQ2^gC2ChvvxEB
zO*?EzvF5M7@%YPuRsiD@{|4#cpMN7rugsg2HE<;SK6$>`GfVv0l!_C79zPSYQ(g2e
zbX3+IQ@2np{Z4UR{kSmg1%d6wHsB9GK_Zs&;QRVuOLA`xOOOP2Z28Q1{PF8(^u-o<
zk5E~B*6jhad)vhVn>##<pLYQ-{;x}bMS%oLIs$wwR<yrkCsU>gEUHD_1z?Kx{qIP%
zJ+G#KT~-lolc64@n<7-txGqIib7qkw0<il4OMNx~h@$}uQUJEnbKu2vGJMeETq#uu
zf2B?09i8^O_{dIub;pu_I(+1bW>-gMJj?QLZKaBkR^X=GKL15MOV)F@JD#gVdI9Ve
zwHhyvo}R)Ge&`wOCdS879OF}lMRAO9G0Mi&F+b#ZDvfxJG~oE3H5C&fD7sS8UyAHZ
z6k=BrANSs$jfH*DgyM26j{9j(lf&vFcGoAaCK+1sTce8c(L>X<-DAh%JGal;k8Cvb
zYbxa^445uUv~lg9Fe?``Xny&i_O*3dJQh#B{^q-fAD5z=57&`l@ko~O1Lw8a#u;!H
z$PDwR(0Iq>hu*5xd~zwBr|%N|Z>Id;(_x3%N5LCu2LS_~>(-o3J8|BVvYeh|VO`m9
z8a4=mI!Z#T*96Zx6+17`NglYusOXgXN+hX{FS;7h|60h^wO+}T$BQ<l!b3<QnKRDp
z`-eWy`a5T`9fkoOU;gqem$dDt%zKu!v5r3P|DA~x#=N1_XFz?0sOH>h>;^#_*l|aJ
zRUCF>1s?c`yU&8DXe0cZ#Y3v}1q{ED#LZ>g_1BE5?OHNtfJD9f>mw!+k~@w55hetL
zO6(F;3=0BxwE-9L2|^V+tU3R$@W)%&rdqSF>g?Zn44-?gHk`0N*jb3+-StI{E|!q&
z(x?s9*9q_#&&Vi0Xd-ae!_g^WVEHKETy@K4Yl_(Yx1am3t~*$A8g;L2JPmTRc-G7w
z>Aa#gchbuZ1aEUj?)RdgTL@G`TBwn%HSSHmEa`%`*lecW)$K2AQL_PEz{{PN454Nw
z6hl;}?j3V`Ts#5Fp2P|Ez`DiBxesxq&kgM6+YA81Xn-C2gEw2BuH#xfe88r;g4OI}
z0Rz1TSgmflg2TQdwwT<ruX;e}pFYb)wjp-c>&Kd7n2xS7)|fuzjjy*AjOTeNn;%&h
zEng`UnylZwU~rn*U9c6<lAQWhCN(6EGj%-v6H8fj|9jCWJ;m8%-Qv#nu53@n8;aVo
zFP{{5$1&!zcu$)Cu8n<!bFYv6w0kM~Al^~_^iv$OXXin5iI!o$8gFhE4jx98h`xdt
z>i7-*o6!Qy2lM$uIni48Hw9z3d{&k9nZUGZ^w?OR5AtILXv!60-y4X6To0LQ1FU^I
zq(NWfYzxzEFY?|?Uw2x@DWoNqU`#X4jiU8`tt{vs#$@XTn0}Y!pEb&>dh~5L0&A^M
ziazelB5E#^O`P;;_UGG}*b{*g07hm*L$n7OxIg7As1-j#63_#1Zk1s}<L^Ol!}ZF_
z&QQcYIcVL^4tBFM^w?bP|2ag||D)6X8B2wROFNsaD0G61hii4a^-uGB^tETceEPI=
zj=#VdN_5Me!~&ejnnPBho3Yvs5R^FsneY!J3}Q1`zH2cE%?>+&g3;hUUA=El`;o4~
zezsGN+cD+bxELNxKR=Y>d3bfY!8Ar%gYFJe2k)CjjCJHF1x)#9cH#&aBJBiGJ^W;D
zYf&&6^J-vdwj%=XC-;T~qI9A=)p6JVg4U1}<Z>T44cM`AH||K9^VrQvhz8+fcn@f|
z5|S##LTCU<BG_|DgdS@csBpyJN&P_FU%j};5N~#s8QY$%Ip%?#rDdPbqMiE=n}+Cb
zF_O!ZPx~huu*PTxccBMUe0BP(O-e&qx`rp6y0Ig!4^4RuqE=p?Gas9eF3F@wM>qU^
z@Sx$A4i0C7fyQ(42l}2NSr6hL;%snnj1RSU7*QTGr}}8%8r5Z8xw=Y*mPqK9%cr>W
zDV|MPwm%<Y`}U?jieNj0ys}e%Wcf2H{|w(&o1LHC?(c^-%%&q<&n>X%+m8WMIlND%
zJn7Hy9KZsBk(SEz1tiwt0l2|EI|7xeFJ#164^XEfpatN7csD0^$%iwr2bSi<y#tB~
zI}B9sLl02)%X*jHU^Zq$>}+X+3^ufUpeTI}y<SEN8)JdMW%8W{KQW2nhtY_H*xGHe
zRbIyr8egYA86+w41aqm}R@XNTrwM;m#k9vG%N=tHe><vrun;0xg&K7Usc12S;wfO`
z>)6pLiq$n6XiJ0ZPls0o5n+BRQ2%|oiK;=*0i}imcJX7}3e8r?CWYP$q2OBizA5(P
zn4+J1m##$=>ROaMGL&fYu?ZkMQ4=1OKQY0T1dP}o!S3mPKG=E~f7O%1PPactLY9;w
zE?*$=?uqf;D`2;FBMqOx1yOb7fO+%BIDOf{--?IPeLwFx8%5)ltGS#Nu9~gBw{ci9
z{&aAjS-RLH8W`GDq4w~!`hhF#K^33ni<Gf=gqG|l#PcHJ&d6h{6Ozv6{S-qQ)9Ts0
zbX13)E4ZnBU1ECkz2NVLLf(nl<~qPag+Pgay*-m$xbLdbjcvSl04#+3%<&5t{myP;
zMYtoyA>`q=sdu`OaKoHFf~)D`Yk0xJ6xrARrjEIi6s}d(>~!mE>qv|Y?pXgaubOer
zODH)kK!}>ejgO+U`~jkZ?)QIE<2$>*SZ=(%wzX`e$=CkW`yxndeS9|AywF3x?!!XZ
z(SVMNkxaD}1s2cGB%dY%rz6{J(d@t<7qACX1k%l=EsDriOUN}?WBeaag@6C^I0(fF
zD*+wsb1;0Neqmij8Y`Q*B=H_4#ZQ=1Y??H{wtP~U?gK>kj?21()OxU!n|nveQZeRg
z`LXEPx2CV|gX#)NJXZr5`NP>CW&<vhGpsbTPrZ+d5q$+2A*lwn)T<b)ThX>^QL=<Y
z57mk4#BUHjyoP<_A!(@U!&ZV=j2q}#v1*D6Y}urddZvx1{k-w6uZ*@DJ+~X*^lxl$
zCWrrp#LM434}#*4fAh7UKA(D)(?BXo>-1YMf?W4<_TV&FGW+tdOnl`a-I0`h`-w6g
z^RB_kIq?#EvcJ`oc6ayr(CJGJlBm0W7l~@$)>I0zwFRGQ-0%elqTBiI#`;%)7v=#2
z%n3|SiJ7+~-`)=GD*y9RUwJgxVL23qDY5{|0|tIYJ#Nl&iT60vvOYw{=&imed|Uo#
z&pF=_*-;=8zGo@L_n8!3_jEjLB?uLU4Py={QGr6UpYL^fhRi0f1yp1Ox570X!UDC{
zq=4Ni2Uh;H;I?8oO3~w%^W<oUTkmShku&-Wy+7A{iFmIC&&xn6Vu}}p!9O{d1Ad~Q
z(uhfns&WU+b{P-+t`3!0V`x?$%n#)F(}Ts0fX~?sUj_puAc>B2cm{x>%#uC*1ubcb
zZKX8?ci`(|zBc9ahU1yfpP<1j8<Mz|S9NLn>U2%SQ}o#DoV>`@GNR-*4Otu3JsQK0
zRHE9F6+0JQ=z2#7WZio7nX$zyb%U$Cx#<-pTj`(wcskCEy4Irm?(T04h6GEN>-{Cx
zy86&sZoZo8w3mz9h&w-!Cxx;w0Olp1*8nMbaY!TPQT<Io;>HP>N3H_VWEbp|;X@Nn
zV@XWzK)j6w0PH>0<kJRS73Sy}IH)hCLJGEick?2S<WQ=j!EO5?zz&t(&ut8)t>42q
zuaKWb0hwzi?{Jr)6n%B=)y#DRy%yt?lkXW>0?^X(Qgerj6$^=r=a<+-K3rbEDI!1N
zWZniBDL(|oc5>}k<ub!NOVw#AyDj8R)<-US=f2|-c6Edd^Zf7i43)5E@@Wt$d<Odn
z(qH&XW7qsqj#wC+aWKch>rczQ0ld|5*a>80#b6r9fJN{iBqC-F!DRS!(-9QR36Qao
z&bf+_yjy{8$?Fnd9zP}Wz~F#BE%8WgLAhr5b8w0G^HjH-gW29PD~*~y|CO(?U+!0Q
zRmB$B-9juFw&>O5Y5kyPUYNOxC2Lu=@k8lkr#SX(0GuUttLd*75_}q}^>rcC_Z4*!
zKwi%}EaR=V0C++veC-(R402oqm6pCzpvuy}AZoVBgifpgKRLn1jGZ4ZMCuJ6_|zd$
zIz6luA?I$<2_oBp5dh3qcIMmssOuh+y0>-{8B>Ja<Kf-}OTj9lkRs$i!H(W@jOY5%
z)_QV2hht=UEv!*3?#C%SgAyx5Ct#lt+fDt#i9odS6VtzE68=atmJA)z!%6d;!c+SX
zRW30Cib6IF)luLFP{3K=9QLD}ds*+XEBekhxC7(>n)jFooXAwbUr$Zh-TR8tV3LRP
z@1|UA(x_skAFl5mY~9tEvDx7vRsqf2V=K+Dzz_p2q1D&thun0Pzm`YUmEHMji!!)u
z4Hf=c*I4jiF#cQ$QN|RT3t6MzSXW8l_63(oN#jZ2VUf=Z2l1$FpGu}R!@fvcIEL?*
zEWd=lt86m^mKh{ka$O2#mDD7L`WO%zcZ${>ppU;JdQXJTX;3QzoWL2|kf>Y!MU;<E
z=8xDChMO;Vz<X!1+MekW(0!B7?`Uy&$?Jgmn*o_LgV3R7792szx>a-dB2Xx>>^c?Q
z0Fqjm4|Y#h!;sj0O>qe;#Tt3LoZ!7+o#AhIVBYvHk~l5#IqXvkIyVO5dLF>jDPhI1
z14hDXfT*(pc`{!c{m+m8dkTa~jL@TWN!G`c^H1OS5PTC{jV)1Kfy7}6^;!#9tslj`
z#hkZiruC>XHHq{)JC-ODPEK5$CCLkJ17GtI7!u;$`uzUeW=;9cj_2~X?mupSNtblv
zy(+a_oEuqY*uwRJA=WCj&x6{lAAa)ZpABSaEM{6eMoy1PAKB-R-5<cj|1i{8<2@X;
zO;RzJS8qxpWgAD0fJ?x_P16r1j7YQij%v(XV#8oqnhMIG_(Gn+p1@6bRE$;8!DU<u
z;P6uOrD`4I^A>}}y<n*P`p*4eBB0s;k2pJ;MJY$VLDJ0X=BWlh(|549l7*_NS@q}`
za!pTz{Yx>Z(73-z9Ejj$;kPO$3G~(ueoi#k@_Bcac!KP?(O)o5d3EB8Qy4F4S<}d1
zOpRzsNHjg&dLljf2Z*O~tg0zm+f-U7p;Y|cOQP@EeW>`Y25hRPb8Khe9UtxPeA=jk
zUWt6-Wr&(l`Mr>L3|STaeX-+30L>;7&`}aj#!?gXdkEvlrzZ;$tH4EJ(I>Q-uH|ny
z`5jXJqL+%6WzHOYN3hvnw_ITMGP+$4zp%4>K&AYt<y5;+K`i*$W_{&aW%Y?*!BB2>
z!EopOziBKHHaTCDan#rE7pO0__!B1{6Rw5$ZtFcxrK3{Y%{g6ZpbE)m*RT1r#?qVZ
z7*E2pL!3hK{ck1_>c*e>B&<z2r0p{Qi<+t>c<%1OX7tUg*L?7UgwlU`vu(%@jYnvC
zKvD~`miR!4dnnCiPfO{0Taq5W6P$>(c0Iv_q52V8n>LIY`QT8pcI%GqCofo=@I+)P
zur{M4y{>jmii}K~6oXJLT(n%?Y|GGrE7<O-mzs90KtV)TpN8IuNgiK{CU3Rg<8(4d
z-EQ$)DJ7~Llh2!Vs0`}FcU0)-BHrNil)8+kVcgVst^!8zzimYY)<FX#BY5x@{Vl9I
zs<}@REhqZmgC0qsXgq(+@k#XT?Mvhb|99YUBci0~<-%#KIGlj|@?HC_QYAma`|>c}
z)D#tsZsZrnOFPL^@xP}L8X>|jdMNHZ;9QmZXyl;$(P>_Ej96o2QrfjsgUXbHuWPl$
zF0002x3+t`cl)MV=$ktMud<IwUJf;L+*eObacxkVB`5eX^}k*KX#2^rJ#WrG%4$^`
z-gL|TEhW^z&rmuUIq|2ZoXQ|9*ty3wnQcIt<|z0P`T?ciOpM$e@MM-D{Az3^+v+tB
z<gARcDE*i4IALM=!u7pI8o~6h96|D6Yh5e$!>|Vg@XaqA2*HaH%rrOOXTQ29^Gt!m
zng(n<k}Tnx*}3=6X-ZEXp3~b3!I@hi2YEd?3C{pVv*u}N=f#-$J}^ZgaSo|q*%U{?
z<Xn#cLS^8-_f}Gen$<MxSD&gZJt@}Q4m}KXPSBz}`ROZpbalR6l@Pew*Ra!y8{AS0
zBL}-7R9g#ZC6J-Xt&guSPd>G+@dpIJbg7LD`2krG$N*=_=v&K1|Nqy69Z_A_9vJ;-
z^Kx!7tbyIJiG5%MhI%IXTye+c&=5U+?t@EEr24*CAl@FR%C&46NHw^S+O&JMUKHm(
zkl&W8&7<4zI=_tL%p%xi#z4>9PfM{q7CxnP$LQxH*KYI7r{ejl9l~9g=1$3MEZbDW
zhJO#k@{N1-@0Io-gzA3$?N~W@GOY38f-**1{1GWFmT?oUhFs~Z0WeCl0$+n@F)G@M
zKYG0IQrsp)Mu%9g45X)j58nUxem8R=daD1BGlnDEEnh|yZA`s0wKU65Gz3*2sqfn=
z-=DI37d>zQ&=-kTTJ0;m4tCkc7a+^f<mp`OVS9C3(Q&<~idf*_ldX>mL?^_Ja2kUq
z9M@_f4HYSCDF*|iZv*d#SABlbiJ{X!KxU!`?_SS-y*2fdon|}p0jKoZ^Kap_+9&Y(
zuC@_~Yeh@Vx~+4tL~Vo5gxcD>$WCSR=iiZc;JqNi<^Hayzf%wJD`o4&^#7kT${3x|
zw>j+oOkP&NGXrQB?BvqGzT*HO19k9~v;h0pwYMB&-_+cHfhE%M%Dl4?i;&}vhy0Iw
z<J(^{i$LdJQ6c_id*o)Vg96#z-lq8eJWroU9Und=BE;7^;;^n*R?o5euYsM5ALXdU
zC!|GR+|OXNai~`8ido${$;X~n<6sTr5=-U0us3i>oyA$@y_8+V8Vr_zS@FiZUC0e0
zUDyR=A+v%_{}uc!@)!uUMA$iPnPn|9|9ijDBP1s0V(f6>o5twTj3@yOu<aumVf})Q
zhsx~@Cg5+u$o~<<xNGCxoFHxOXT#7ahMa1pYVe5`LI50tv`-v_Asd_6H^p_C@<!hM
z>&Y@q<LV~VOp!uZa}8_Y8s1&6Q>3S8^WZwm7fAv(k4V2@>PqhSxctj{aW!7#E!E*)
z00tim&TXStAf<L!OK(9oUK_qXUgGIjdP*Uca=q1L|8pAs`{GA{TKHz?SsRv%lriXK
z4&eUf;Z_3ZnhnaQuoT$fgMEBzZTT{ris0pMsk_mLmwuq#^h1VVgYaHm1$Ed|s+B)3
ze@L!`DGeX(9Hkm10Td&jf4a@~?HMag5Ppvp7M-rCVN)quE~wSg_sMvck$r5FSxz~u
zw*Bg3o{;2M!*~KKP3SAJhW4Aw<L#KEc=)X2mfu=O_~e#>{W0`3+DX0JO`CNwcfMmW
z<Dy<}s_K|NYDb={5`<WOF0LdpT{Lm<bDH9y=Iy{3+-U05%;+EX2K@^gY}(a3BCq9$
zhQ-I+@`>cd8g;S|=i4;My<v>V5w(8%_9jx{5h6zTL*bqS?TtzvWG_B*hg)O4l)~y2
z!!GeH@2iXQ=Re3kBbJ3NCW%^{l3vS~_~_vyTU%psbeCt-UY@P|rvDXMWRJVU;Nu)(
zi0NOV#mS~e=tK3q=mr|OVyefUg0}O?JVN>IN8}ua%o*n2ga<_D>G5ATpS=(Av9*q8
zP8r?$8}lv3y#yF^j$F;%ejFy!D0nW7J*~pt>6?561-Fm4E#x|qmX(NxXGZVz)p%^<
zjz1<i2X}BF<tY2{5Y6_0@lf@>8hDh-VG-u_*Fen#lZj9mtNk0_9hHrPTUCBUU+yDB
zCMW8=t6wAAGM}dbkL#OvZu$SQ9Zq_n)>R34Mt7oPcDfUIGOdo`Z+=9`-`{xyHE$NU
zsGz&Mr=T$PEC8YFG0}mQMTP(kkXn-6cRH&clP^Sp+mo>QildHx-l8AEDzMS)7nXEb
z{ru(37u~3m%Qj)WAW!gg7HIg1ERq6}DEF6!vz5$aChhqD-*yFy4gm}B9N>Eo0C-2$
zC{>e{_izq8+|uCFq|CVfp{)f8S4Ji66?^!ljr?M#-c4}|nu#{UdIxo((!a0b@7_aO
z01`b`d@NA}!HEI=*W`sHv80vbQ@Pt>MH{m(zhcS|PTCJb=kh9NiOV*Q_<T(*zu47!
zT((%vg6Ml9A8WF>x8Ww*ZyLF#<J#EK&$^nx%LPIU+;hTbSjN}==>D6zHK<`KRI>4V
z;QjBVXGRE;c5dEHK*PMQ29n>{Y0JMLqstLM@;%bC_=JisuO@}TB@>+6l5g}XC@1S=
z{%pnaqeLq6<I|R9m53#Fjd`K6HLU{AhaJxgFsf`2RA~VD32WO5W;n}>lilF1*I@tf
zKDvEJ_!Bzukukcpe=|7o<->8Qm8Zt=bTs4+a;E&>w_L)C$)^IRL|21)X`+jcieYc*
zhn%rm+w)-yzG}Zew#{aNO0GGB@oqC-=Qh~_<R-@5mXqcAl)ay6&oO{{S%!1`x~2c6
z>2)kzgu#oEw6C-{Lr<5;hJ3ZZt?v2Xxwm1AshD&><u{+v!s5OrNyl)0;??5g?#?Gz
z#v;4-as+X&*>W(=-9>ll+kk+=F<wNJRCCqFiu`EskLU2V<VmC4CuZ<`ucCo&hl@)s
zj%40Ytq`U{s_45k!wm2gRuqbp{zz-bmvqlEgTe=ypKeDL{vCe6bV(EqwU+u^eA{o*
zn_=g-reIFQ+Fk?q1=8W0S-YN4C;hfl8-q&r=U9>R9s%OwN><g#UWM}ilpW+u;~+RD
zU`^pGLDXDJsgav8C=mu*g&G6q=W(~y-vXqo7%7(D3l?K(Q*ho58ft-i-H)w)q>!e-
zut5lzfiZ3eE0R^yuc;-`aF6hNe!RWfo8K9cOk2nuUFl519hU2D%-%oe947A_&wINy
zH5GePY)bOyx8)=`-AAY9oti!F^7G;a-SK}PiX)5-#lTN%l263zqWal2J{fOS2V4r@
zIik6SwmF^!Qsna|g6MfpBJ$$XTR8Bh2z}Un09YA@5#bNnZ);XEJ7X;H7A{2m24plG
z4V~IhjReH>YD$;PBSWPMGAK#!pi=T{7e0=H&b9pu@(4vI9C)@5WhV|DHLrtBxA;@`
z6)mpgRKYuOib#_K6aPB5P%N#~5Hq-2q>Rl~FEp{}S<V0Nc?Wlp_#4Rmq)BcmtHnpU
zh><!QP#obrGq`}Hsm~6FR1Y@db~Mte*ODA@4tqiD9X$ejYHek=T2U7ut|f8qrtw@O
zKW}!@Wp-E3H{|EOrUTF(>xFTO=g||E!M_#bhK^lHv<xy5(ao#Uf7ry5V?V~Tgcql%
zD3aKS7fRi5C(Ty<#hIJ=x!-_GU$HMq&*qx1E0@7?`N8+n@PijEr%tEsqwDrl*7m#h
zPFrTbuU?P6)v#j9+GgPx;<zE4NsELT)nFUcfK!4JV+*_+tkBkf6K^G<AgtzU7yFe@
zN=HkksAQXMvCu1rzC10e5knsTchKoA38I6VN1A1eN0(#?@U&8-7>64@&trcn{DnXn
zWwj7tFcqf)#SxeiRK!^86Zr&3!S~tw0UfTx_!<KDur(N^(|YP{U`sXV=wDAZdJ2Dz
zz<J4KXA&xLLgZs*-Ka(AHB}???|}JtO1`H-Of(I@q<b+LqAxC^*_DRm*J=X8+NMgj
z@1r;q%*Rt9hkp6uRO9n!*e^scj5{OfRPRqa#eDtys*k4oiD2DV%rgS_c4L<jW%ugl
z^`Ic`feXPDbM&;WqXvy1w_o~(Izg;!anvHV#riS3`slBaVR8enG^W|Eki%OwKJ$-i
zTk7xEY*ab@uHrk`iMk$J`k1O)f3j>U2JgYpAlEic2LAR|&g+GjYEc3Y^naM?_AvOo
zL7JLyF3{b(JI~uFVEmpAl;E%TVvtoE%NJP@`T9jjq&a@)z$58HO9a4%@Da{<5Yc;H
zBz;bv5<+~wg8bFF^nIIbKg<s+Pzzo^>B)W+bkuXhCJ<dV&j93G5gk1i{gblAI?d)V
z(uJxn*7_*DzK!_UGHETQHNO_-b|PIc^DIL2h3x#B(QEjxxe=wl%}`_tnC0F*iO2uH
z=n#A(TmlScV%yVzDbwkuS>wqdYYf&WPQZH)6daE=*R4_Iu{w}Sj5T-Sz|{XT8gHGx
z_ZsjLQaU=-ilp(Z1$SaDmXHwNAYHy=ipWa<u<`-9F35tiw&Qii-=ldE8T#g<>;CV2
zCHA_L9{bq37qV7YFI)?}%oRQk+T5Uh?7|*p+23%EcN`cOKU`XryV7E?x!PznsS&_v
z8LhLecTrAB5&<XV9%nL_rRh<SEPBtI8t5ZOAbp`5ikK%*VhbKjn12F)27C%`da9&v
z&M)A_6~~fr!GAfki$;uzoU?=BVmFXVf2ZU|^vR+yHp9$NNOr4Fma+8kar->ViTB@g
zIupK;e2nNtuZvx|kgg8>DWMxAWz$>A(xDMS*MCv^j>w^GPk|ee3`}5qdK6>}!0l$r
zduLQ?dTstivX}1ktVv0t9TIh$phBf-mJGvZwvyPpi7cY;@zGC&X{j&27a^Oc5lMP5
zTNr`#po&)=3(wb#X+mn=>d1ytNJ5rN7Jw|18A0}z#UUz6g6<EPouz~!QZCbL`8=IP
zE2lrYdi-gjbg>M^7kb5qUdCAMR|vd>lQf+&Snvk(aKA4UEFeHHUt2s=jnAnYE6<ob
zj!S>i;GN~^$TG|#-)TIw|Dm^>N_+rW+WN!1hcAUEH5$(n;|?0s1{13seu@hbHeby_
zxaSJMa_AWP^}pPj2p2M6^o50w*>ONsp(jM&35VTA5{OWU<(<O+m0^jmfYLRXwR8r-
z+bh9@a-#;2M(27hen-AP0Gj3k29x~CSe|h(L`Q||k++od&eK;79o2%#gw>ao5-3)7
z3_N40k~H!}4=-Tyx>I`MX3h7_%lp($5P3NXkeAc>#?0oAcxlH8^ClgKuupGMykq2P
zy&%Ok>d)jO1~*$|`D4cQ_47l2`DtYRquKNJAetZvU{u(&RUh{^Iy!lruSOodJAIV!
z+Ti;b7r{WB-{+BMI+<yd$2}L^uh2dK`jmxKEgM_~82!Dv)K(WM8Ges>65HdVhhqxc
zti0t+4V;CTCCl@kclVBq56jKRf_xH>oPvz*8A+$|KP}4I#(zBc)~(-0`Pxgx0F|~Z
zmDKOwv9%klKfRLxm>pZ=xV)vVX!fVoXaSMd@>dgh6egtRz$aVL_y<t8NuJ^_j3Ez(
zA|&GZ)EQGZfatv#`WNm&#<#ur?^j+bbX$d1iXRL}$8s5oxvgf$YkZ*04S#~3rtxu9
z@tgIrLt6$Gs&!t&+1h8P-+oi|htIA~|Ih)WG(qEUGRISa3QFBtCrhMc%6V>y75DmU
zdW75Xn6FAi?Jdxh87ll5-v62R5=A$Gs5$^;j7e~V!>F-7lV`rBGP5p~x*xg`FG8P>
zC9o72g#Fe<`=YUI^9|G<K0xVYzz#MWKopHc-gHfsbv+FqPPPY|+hOD@=j7h_+>%%s
zUA*z)wK3)FW3h{Q#{;Fd*1sx?Dyn8+_^$;I6xR|4EW7(E(@K`S?V6`o_Xlz;-Pz=J
zdq#dQu^;r#;UB1^s+8}?G}ZHMN+MDTn6k#ERo99#Qg3bN#MJ%G?o`#a3%WeNXbZks
zaKIsH>2TK7!@?q*L5aFSBsDw)2Br!Sk97NRby%*U%q2D5-x646WK2f)s$$lWU3&39
z$QcITKWkl_|B8^*d*q^tP`NLd$c_;a`6wE+psWH06+cB1N_0zZn(TE35TPJ$*;vSc
z197gvoQVIb;03aKfKwuAab~8?7Y(yk3#G@c!Tngr3vzMpG+gZcSc^np8d`q}@?o8{
zKSm34-k6Aji}u2I@#Z%iW2JUNiPX8)4p<hDmXTa9`w3WJs07$*u|ZK8obn?brR_nl
zM(tm)2{vAAM|MeVSjPPYu)jS3>y_Qdlx>$ZYdNdi?91H%++~1EpPXUEI=^!+<yU`u
z)ib?B``z?s14DQoD|{2LYL>_f4C*SC$gM66M1Gnud@<Y6kxF$+(FhE`xH~$ph{j*_
zEBDH>W4GX@?vbTG>b1PiwvN2cFBvzzM+3;x-+fjc=rS-byx>e$jr~%!{$t_b{DMhq
ze@i2_P9zokF-k{uHV}W-sDmrWH>#&_GiS}~%W_0J=jWDr5aCiN@!lpUzc%u0|8(*q
zOr(8OZ?$6Zlec5F-$YbD(E@JzB*ay3@b{^P93b#QzQEHq1UF}NlU=+^a6%HxM<DG8
zLH&Dc?fBsLw4|M#CMD`G2A^ZXKpilo$blu)<HNSzpI|pL;DDs2@}PRai_lH5@E1(Q
zMpV6V3%^?jS)zmPO0mLfGy`-#$i^ZcG~vL0W2l`EptgXcU14$t+}&A5OToAb*nk2;
zo@e*qw+BPGv80-M=Ph7`(=@x+gFB2HL?2j;7aJTL&jtmGMwQTx1B!SZbzl!_FqwV^
zMOd%nCdKAsTQWGI{Lqm)$N~?SLOkqG;os5A-zq6Y@(x$qKU@1%RT3?5!!Zm~e1?Bn
z#K?w37w!Gx9W-$h<50hr6k$%X>vUpMiY@MhWTfL97<A9ivH9b?i-(@%AH}fTHdvO?
zt{i8oo8(MxQ0|q}u|LFXc3$Z9x9A@dUtLrszg-l2gz0$bur~A%WmHj}G^eO}p8B3e
zWN>Kdz_bMoc=5>sXMl?XR2QN+b;x60k;o?}966VPoX--}#_E$e#RACFm=eapGRJMZ
zX$sUNCKNUqz=v|cNH$+umDT1fy(n8TE(84e2Y?)_$|M`A)<Y+-&0B1kK;!~wbv|ee
zAfD*(X`)Fng8Uo2dSZkL5G4loC86CPIF!IwDjV)0v29Q1w{M!T+#}^1g9kYuwhv=(
z`Yy&2U(<EisfE&`VALZ^3dm_ccfQWc0*f0Vuw(ay)CRG59n-R-aN#XAuZa=K6IlOC
zA!8{g=N^EQuad`W@Y27$xj;z$424>jUXN&?T)$ZVfsn%@R=;>=1yjw;zlk!75mEi6
z{Uf6W(Hun|ReU7xiynEXB$`8>r7tQgup_biv+<+1*>w-`wEkRujislo6-k8M7@OzV
z4o$wV<QxpP1ysmpRPS9low#8Qzhh*skkE|k(X&da#Jtn@-KdhK4kNG6j=5YDhmBDV
zKJ0XNeAX&h!q-ZFN^~f4I_&jIJ+o6B)Mx0mLp>JCPg~y5GH;o9+k-hHm^uo^wE%1L
zarTQHH3uBo<}oY@!EYd%l7}arn{Vr9ZEm$gsKfyyVT!iw!S6PH<QzkXHD{-(6gr6c
zP%Pt{GGP{%$u2)^A+q&~fS}JaTq!}V0eWI*dI}2<){b|iIgmau05|`x`spJp@hbiU
zEI~^Td{MVX$B{MLH?Ul_EdxIiLlz$#7x^E+&8vz-J$`r1{~1Tq<x!I9BpS*@WCy`A
zu%~oX_fr+PA{Eo|DXb<Y$WhB?H=ZgLQ!>p_`@%Ff3_?hGWc1MXdzx-|7~ta1-9}Ga
zCXIhp!E7(bpF*9)h~E5t9|_or1mIC-a8}R%PD<`sdRd@UB&4uh<klabA@aOzX!Ony
zk&kbCxs<bmW@GTr%g&THv#O{HfA*82BCufnKt3<Ujkh6wc|Zb%Sczx$QL@meoMIrR
z0!ID9FV*3<>EfD4jESB{npA8Yug>K(vTY-etD39dJmKAK0f+v);?s-S_zYqf?u@yw
z3`b|B0Y2?=FJ|_q<4#hLCTW&Nt&{6u<*1=?0hxcr#E2$f7e7wqP9Y<;HbKw0=?j@(
zc|64U^KXRL>SCzjCAyB<6zVQmZT7Ew@)1S|PPCu+bYUwOM%*}~Cl7ftC509v2Utj)
zR?g#B!e`{0<DU5E|6nJZ`)!2dma@YbJ1sVkt+S8@FbX<vg)9X^B^-!QM12G9(LOLK
zRN&NMQ;7#Aa%*Q&Cq-zL@#OGU>~jAqyxGk&SjP4KEy59{68)|l1}}?gndX9hQevc|
z?=D}zz<2BjH2C)t2-2c@P_ye6D+2@IwGipl6+O{_Bc2FyfpLVYTcmfxj5D7^RHd`s
zvuX`ZQ6uWi^|qMJZ!!La0`@5#<`pS|B4Gu(s*4m}vtuu7vQ&YTqh9P1bmtA}HVJ7T
zfyfPh^FA8qRkd^f^C0J#8FGBNpN-v0C&#=hP0rb7^*xfCoug@pQHQ}CY)mP=B0;tf
z1U+6kPwrWh943rLy7gE~q&n-XU<Z~Yv{Y6}?_Gcp@HXsZ%VC{{V48$}YC+?bQFWb+
z(|femyJA2pW*PVmSzpZZWwd-Rl%vO_l}Hjkoz^9xQvte^L57;wYzR7+p+;W7Li21e
zbw9z>wh<)p>C@x$dI!I!WdChbcKlER2=Zii_(EM2shqtmGHpw2%Sa}RgmD1#bj9-e
z(nioJs_ZJXLPxef(cDacanMq5L?eSTrS;!z>zg2fGHZXS5)p)9&qNdGlVc>x>P&;e
zGcq?a6ZniOh{1FohC@k4q`^Ca2^G#LHT0d{f%8=8qMJ;&*wi!7r`^_z)`r%gC?a~<
zwSRY*hh^$4u&Y;Gu|zL6&9$Viu9W27<`lDU;#5GtA8~Te{-FPA-^r`QoWI6)kZG$>
z`!m@k$RzH8#>j0gKX-&>e^IM@<5|{mdC4hUAT>C33pW@Pyg+^uj4`*G5_p40hK$c!
zJ}zLf1wwSA_l(Vs#k@*MlEo7xUfp=CoT!+SjrqDV^YTZ;V0_W|txknyZv$JP=3))q
zIzL>O<ELYj<uEEz+z+^r`ISSX{@~4JjzFBPrq4`K?fbqBEW^DN&RocJf*W{i63o(g
zUw-;(2HMXYu*OgW5d_v+VvCbbngpsFFoiTeluB!$@nN~SfKiK9$Zj7Hzr>y9BrGW9
zX%VdVV~`s<LKL6fGvG3p+tAjS!lP`=g?`}HZB%r@yJhkf`ZnZ3i3Amo8(de_t4a(#
z54DjnFeQ!S>%2!E%ZcWB;9Y7ZmGdj)q4Qnp@@?2C7X(#%>H^3v1e-F4^$2CaQ7mD1
z7JL&Kr}o3n#hTjU8SMBKpYOo{kv5`@lJqziKf?Hn`FA28b_eUTxv_Qd?2#ty=tKVN
zjPFo7Ur%LpPGSwcVMxcH6}=R__tO6rN+Ut!YkwD)k3HKj^eca1q8@NvuctSe1N{7g
zqe^w$PUV8$=M=#?G&go%OF$)&_hFc-S9FImcjr^ln+u|2FNwK%P4VCHP<l~B_>zn{
zP}KQSw{puSEeK@Un&SJpl3y=oGC82SThT4+4!vclPZ?pei-5%d{YQYjlSL>E6-@f1
zgan=n1C@eZBkR5C@+p9{*zPTGmSsVe$ahxFdYUYtOY@v7&et!Um|SD`k*%02qsz?#
z`TMLZdkp@spb4_NdMi3f{i=S@{ALQpesjVhII|;F(3WuUDkjPEv2{@DJ!ID@l!ub9
z#U9<F7I0C@3E)Sy=^BFX7Z}-$dUOw;O_)CyK{rjw*ymZt>5@fy#pAp~m+D!Ht?8_O
zGrx`g(WT9_TStr*;<z|ApX`zMu2s)SV_!9VA*cdmzoR{10(k6MAaWQ4BHwK>N16(c
zGDgYng@fWds}Zh$)vB&J=b8PWw$O-sc(C2KNJRTZE(sX~aYS=5&U<KT+$4_NVH-eS
z;dTajAMgm4_BDSzt@)Fpc@XKw|33Uvx*-xXNHrZpQ+X@fGUo~d1v5VPs*nP`J6u}{
zEe4e3WzbL$`(cx06wK&9L81UO2shjHDh%EXRVF?k8L=xK3}rNBG|qeU1mb@Bc{L{;
zdyg5&SEL?&rx`Y!j^BHc97#=*0i5ESzc|U0rEJGs@rGU7&opO5NTZ`h2PxX!=)hqL
z)M8|`LQckKzh7|=OZZtz@Ro#thva9kX7?HhUrqI7%)a>^5PF<0c_w5^GM_E;Hww;Q
z&dDJlW3CPjxx*P6*svRkStO<CObFXClD)`5`vZ5N?2o?avS5X#)4Jks0JW`}yTKl}
z%Be!v-QU1F{@Mx<t1}M-)BR|0*)?V0QoenEOk$6F5(oEKc+N3~6Ww>-mLT4Io5%0`
z8|5WaqES08S3O~=ec0;V+SR+TAV<uO!kdM=frjjmJj1d`d2{lyN;IcuIl7G5n1>^q
zg}Jy`^J<8e0~3D5vTG~e1viDmM6+@d;4zayZgqkS7$=R0XcP>QMKh@S$Fph5E-@q?
zXr*S-EMiwZ$*JF=M{f2QC~NgEoN2NKkBn3Dh5^^mnnb3KfNn%v{uW#ROa87u$1YN=
zyUkGo>|T{Pt8wR*uFlcg0&78TXlNUOucoSb6vQf0@1}R>34Zaxi3xS@PCo2(QuvKA
zxdo&Y>@NDmjRW5gF;gyS90Qo#QRF7pPx}`uRdd@YayPG1(!2+-XZY*I1*Pp(_h<iJ
z=?<3EiPvSG*L}bIvvJk-X$9O;w71>4pJ5D#m8}Mx&;GomThX~`n|E?gS-$TU^OpCP
z_p5vKEZ^RP)Re8uwEgLY#o_iyeoSQah-qVT%9%ObvJ8;TO3f5Hnw^W9AjBPcZ6_N#
z%)M{QX-c6PUqn^ex-wXl_3p$TSl-QY9-0W0zMk(T-|!AzNKIAOeSOG>nf_B%3bcNN
z@?RJ?N#4`pUT*mb7~3XIwXv|ywb_JTeCnj}uAmKkXv`^a{w+D&S)VVQRot&_7LRX#
z=nB?;K^D`s723;iWmhx;k)bSIb$-1z`H%V)k*siVSjGFnF}jVN&!|%aU=~x>0ofg8
z#S%h(tC25L8U{&1#<HF*%3-W)Kf$K$IhRghLZnsa$b7DrdiI4{qt@l~rTK8`&X=LT
zzJG>mP9z$6P@_QpR-!!0I}~-;XWzmYc10Ik`zPeYR47bz%Qx$dEc)$b_y|m9FoaX8
zo$zjD0t4m_Rr58oLQVT!w5C`OlIHenIgb{3pqXZ1xfk3ocCI`H(Inlfim0R-MQNtD
z+g;y|EN5I~<ET5Ynt|j#INuH^Loh&+ja{gfWJXoJxIJ<hYvoYnN`1jgrkY9d=d95B
z_7HifD$ha+CuV}l^?Wc{__He0*?@yaOnK~9Vv*L}qb8^lT`d&W#F{{oeYRkhdmJww
zh<VkhSj3tEu_+9zxC3HF*YRlFb2K~I^(@lB=lD0c!w_@{5^>lpyX~EK-HY^~!g9J3
zL3!-b(nm*@<C|uQQuRLT_S}0#2CQ*RM4*_U7LKlK6sH_rXZlTJJsBgp+DEVN8Wltk
zg6#v>v$h5Ef?*WfUwujURi~WC4e|{?(9ZG~YwnSw<%iKG)E&yYS;gPzx+qnTz;`mh
z+!wZ-bQVmRx#$(Nu41*3Zq-Q@^K`jtn|-sMth!Za6^=HuG1H05C-yrb)X@%5%_m5n
z*Uj}4_|#7|7VJ*H$5I8hsd+XxT8*;b5zPvIR_u#54MW+`-Zwpoc(fp|<tg&$RUEK0
z?0A^0j6DJ_1G>)`)*4|$c7p&@=M05>Q^FN^iRF8dRugyGn2cjFp3(lEAFs4|>OA*(
znj+q_NVD0==4NGLLaS|URPL(4W^KKlINJ1pl5M?RRp5`ZKldZfJDyx3@>`7)=DptN
zG&y=ZdIu(9aL=}RknBr|>iz(~mX9x2FVQ^J5w<;bgy9dEmR~*gPI6RybvSP6?4lna
z*dh3)f1BfOXT#G!V~=R|vt-0*xwKT@bc!zi^7eeD9e8jpn)P`k<+0SPYRgfTA*K(d
z_VBAT*K#mzKw2`ciClAZ%6}xd=tOQ|AZWN~8FWs6bdEw!(IZN{wLh`#xb<vnA$C9_
z?flK|S3=~{y%`JL-$!3LhBQnSsP_cy2KQvIpbo7-LI-_~T}bB|rh)nCE`7d0FywhC
z?!BfofDG*wkx!Ax%t~e!Pr>y3sZJ~=`uo8aK<!u~pD(c64fVlAhLztisCOCc;3A_1
z%3%&{6Q1s)c(7LRY)%rWZf?icy!&}%QT5C=<N@b$EfT3Zo%#4WETAq`;Z6?ig3nZY
zHUPJyR3wp@7py8DW%e8*yNPYP?#i9awDo-C{}IfU%{Ksz+zOzK%q$&)NNVRNfWU^}
z*KrNd<T?G|s2Tu^3kAiMY*=Cr!3J9^A@;G{cWLUyuzKUHEa6QRi0jRTJZ1<3{3Bm*
zTzS#!&{M<39SK`ehq(`dT0ks&a))D2?$=HdxUTQXL8b30?bKvr^JgZhc3_Ht74aaD
zGz({%K*%suY*1JD1>V}hAQDzBvG*bNKgWfZVB}=!3blyyceBV`&z^Rv4P57r?fBOj
z`aYJBC?LrNB)(lrvl^dBY7V5|I<r5(sqis>^M!<Be#I2bblkH_^73lP$4A)ntUMzb
z(04YUtSr$(KWayAzh~jWr_^J!%b$j*GlYt6c-eH5vBukD%M}mF^)_YlZsMo~MttrL
zO1W{z?8C{uplgN=4x;;a;)(zKBu%Y9efH@+t7`+Zne$@kTRD`2_s+^d4)Q4G@A<T}
z#8hG1B82l&oQ>@h^8#HLuG))xmB{@D9;5LVQ@ingwE{dnkgs_n!-z<8j0%+~XYzUH
z=BjyrGKom*GWi~Mn+e_ZtqB)d6igQlQ)~qVmNApGKM5N@u(D`SX}$oj!Vf%A`wu=i
z2tEiTWbm=yDXN;u07%X6ww$~9yQA8Ew0a)Lge&=d@biq0rFy=N;jpPs*eppxea>Lq
z_%w%>pMN@s*5npn@_pf<6(^ThM(&&Z(d=y_?&2n`=r;n&A?3NM6OsQS6c!%o*D38E
zziYFm5q#_Vs6a__&<Wa(A<S3^8Pz6#Hs?LpjOv_*E!+d!#6pjjySez+Iqie6Fcykl
ze2-bHyOBwA0nliEx5N`cnrmpZ6f?#*JrC4{Tz1mFW_sp{@A*Yp^^B1GwUdXi<n4Uv
zVE|1Eju@|Cm#pn&A<W6UT2pfFva(gl`+>|a{cadn>(oM<&6(x06zw?Pu$Rk6*Vdx2
z)rfMtB(JL}Av%$$E96i?amP6!G4*Hxxo_Y`yq0bDyj$UDP-A-}gEA}a@fWGZY>Y@_
z%MW`C|BiV-j$;wcI}{6b{rWzMzyPC$P8sr40q1b>abV89oVf_A^-v2`ocZ{RX^bG%
zw&dbaQ?6T6m_#&XAV{xCH>mrlPmW_YKkx0;W_`>f(qGaBP5CO<lFWw(PbpuZXD!+}
zyRr|M+MmjApBfkWSK#&a_3-15X=uEy?%soUk6HUM=10+Q&;HLM8x@lwQix9%6ZN~w
zbM#LEC<2yQYmCk=3+0u<=?yDJG{viE;;iKlCImiG&*X?scnFFy5n>_Ic=D}e9zJ)&
zMdt}8xsEOo^2k`PnZ+OJ)<>(Xpe^DAIt|TY{Tj$=m*iBwb!)j9W2gj4f2=1mQX1l2
z_&I8O^x3lNrKe`5p>e|NxA(e?OZiqA2<!T0L)W|d&2psm`5G<1)$v0+8C_5|a()aZ
zS9u%%A5Ui)RaM(YYeGsIq+3e5yGuGmKvHQ5>245^?gk0z76c?VDJ3beDe3O+_B{Lj
z&N;(>42A<1Yp-=b^PcmXC$hxtDf)OA2w>AB?Xl!lBFx!!AAYULGtFl=r}ftX@IU}u
zFHtE@l$r+nc4IVq-t;EP##~#dH0ozJ;(BnZCF4|3SuH#e#)>6~6*BL^ODV;u{26Ue
zFbSW{3#mDBj59ehUYSMMuG9psxZ~f$wnRC*@eUcNwInEpN=sh>`+W)BrD96Gj^J4~
z&?#Yz{*>S=BpaNWXa`E+jHjTjFi%V2x(_DUeKD=Bz8h!<^#<iSv@0b;2ripIgfP2w
z0Ox5|wD52M1tbG@!yp2z-B56%lEr23JMc&@vL2<!6ZvoDUBiU{7Uql(v%vKMo<TfC
zlw#hm27Y?m<45`e^C@*Rs<D46m&|L@1U04P)GN>1wD&D<7n{)fp)M0cCExMVFPDZB
zUJcxh9L&(`7pPfJ^pAJ(F<K3*R3$Tctlhyq>20MHEubh*9H^0%9?tG1Lv)De0zd!t
zl!d!T0O#$W)1j2KUZ*`s-+?cs`?=jjA)3@@L91uBQQ*fiFaZxe;-DQ_I3n(EQm`HI
z_+PkcKA0Yi2h)Qs8Xb#>;qPAtb~gabat0yC`%e^!vyDFu>x^;7P>i~Y*KhuNt%3Ec
zG~9BOxZfQPF}>-O@hG&~NMuTsa~;+VYq#)u7ogZcnIZwmGRt`Zb;1f5%-i$lnK2%p
z@0tK)T4VZ$Pug9aT9)8)K0~JRJHh+%(#|NP557Lg11?&%#sic$a#eRN)qPYi!a{Xt
zv%@-8UvBr)HYE3S-Jgze9E);$wgxl3=>PmwM!W-idjbXR%@b<yq_B}1IMXvZEVVu_
z03Th0VTZ$cMx`3yLJfY77|i;s0sh<w?AufDReU`;OsMILL8f=`J;>t{S);sa1Sx_@
zAtDtQKu}(4({9Uqk7Jh8w+>P_&$OMa7_DCO%u4a$f$hBKD^zz*6@WhI>ZLNV`VMir
zKJOF!*azLr@E86-QM`Dhe(m_dJ|ttcnBukt!Y4+J;Tjn1KZ%Q?lP&D_<Bt4s0*uH*
zm?D!pwCw&26rC~tLGTni++8YV^m^177|B4X57s6blLBDb`Xrco2V7=97c?%zcweET
zxN-g_vl}a4TA5x(Vxckk-)$#^Q2BCZi{b_SRJR8?(JM#E7_UWu0FeT2<Cw%b7d|rz
zc!iSDWG)^Hj>L!@wH>t~qADz(q(A?%Up0&Q9eK8R>%DhR#_-M=PI)Gk_J^^YJuZpa
z17Gcf=J=@FU3N9UQ1O96E-ACW*i_h~C@rV$G;7gM65B*$jOp;E6{Owm;x0M%66+f7
z`9JQjsR?mA{o|WI;78eQ@WT=p>A3M(IpQ&uSni3p{luJ@oS3gj<Iyorf$qa2#wiB-
z0w-oj5b{Qq%6P4ryu2A32~&vZD-T{|_v|R}x71Vk@-zLIO#Nbe0Q=GWF3TNq^{vEv
z5sbdlOE_`2{+E;PiyvVj!OpKUZ^=t;6t~{K`6i7N8f0FMca|sB3i=nMx%rqnzy<<s
zV5RgxQ!X<;41bV)s-pZ_lKb*`*@X5j!7B|(g-gkN$JJk^Kn5CA=U#{aF!ooFWJ@NP
z!N&rnJ@Hgkp-*x%6>l@d{Gjd^PW8Rwbto8CQParEbo3qE3!T(U&arx-!}IlFFCBQW
zSDsZi<R&vPZoosFmtCQ%2!8q97meE7KlS_*Yc;83VresotHe|Hazo6W>weixmeSCj
zXFF$YQnQ^+>MHkHu*4(>GQzO*3yBGuz$VrId>>d`iGd$wGj%UA)cm1-0oJV{3l4#7
z*I-&=g>~jMyJcTODL#5MR_DgX-6>d#r7QyvbBMVTj%H+hN=Kh^zsDc-`>?&;|IYW{
z${Dx2eTq~NuS(#3Q3`wXZAI6`>jHBBHpCV7=<&%zLDfk;Bb^K6a-`9=t@-`vw_9GJ
z^$;y7mOBS4*7~;nnTF68ooUQw#!2IwT3+<_qjsAT#%B1N0vnPnxM>Wv3mBnE)htnN
zzsuJ6a^stp5VH9xy$UY&S)HeTzp~HKd%IEU>&HIm`c>F(2lJiSs2BS6)^U#7dj@=6
zd7Ad*SoRJ2mr**cn_W+<hqirLr)p@JztKyi-=AzW$CIyXex@z4c=#%b7-YY>di6tn
z{aH;@jOZ&)%x7wdx`D00asNQGHGm3*1OFt6tvVy3eKheBP1E5EP}~1#mM8Q|($WWU
zErtdiA!HCWJ18J0ep!Dez8v{N$Fd{yN@0pob8{^*5=wGiIH$`6nkx9VlnAO!!P8{=
z#+F+w!_{=oZQ1LL2b$&H!?&JgBHUr+wdB~1i4bkm(his(M4Fjd&S?NrWQr#vNKEGj
zImHa2+EEWcSxwcnf5so$6&Td&+HNTXJAB9E9ah$Whogbl0qBql9ak;i)3zWN^&E@j
zm_m*MQ?S1MCrNeCfYMz1#FN1ScJ*wXz7gK(o1I`J_fNapZP_dF>Dc?Z@95zHdaWyk
zX<{SL7q%8U@$D_W$#a5&w9=472D7-#z6{BsG0D9Ftu?<QQCb)FtIic-O8%&};5;jS
zoh_t(AF$pW_w}#5rWk}Tl?zia30Zpl_-;PBxib(Ru?OH?rg|g6Q2(5>BP<_dG<cV%
zh&M0e61WAm#?6<!GhVnDhQ%3iz^A;@)HMH-NXyxhq7v|zrHJV7V2R6Z5XtwE<U!_t
z<=8K9f#giEi{2L{C6dV^QBQ9Wdcn+=j<y=B%(#6~1dTMD;t97$-)HuSF_DuiNW&+A
z2w(0|+cZ?L|5!?AuW1<&eH%$qaveuAO}Q1CBgxq<jI;j}hVmL&aC*ZQ?@Hbu5Wx95
zI$_ZY?*0|%F4^|@+Mx)O>cp+7$Erjv7tEy36cya>QJwu>N^k|!Av5iXY`D^D8<&YT
zY#?9o5en&hqg?xK+~?c}-UnJ!yIH}74HNI545zT94c(AdMEl&NN@|j&6W72aE2??Z
zeggUe!g2@@ww{yM*tT>B-GZyc+IY47ac_)%Ju{pIs|+DKF0i`G5b*$14zDITg`PGD
zEGjjWm{?5Il8Nnf>rD=2E!w-ODf*oprj)x+K`J`>g(Y0<@(*t)Gi+ZxQpk|9g}#W!
z6D%FSW!10$ov70UTB?FZj40f<hDmH&J6~fxoIZ*GLnZ+X8VzE(1WK6{g*bcl-&@Zj
zm4#0FZ_JdIc<wby&aZi)O+1wuCdEeEWn5!Nl8!Ql(YeI(Zo>75?qK%O{`t@)i}FQ|
z;uuISsT6+JYsl7tnsT)Su`+eo;y0bn!CY_3>wYZ!hQ7^m)m!yOZuyqB;JIppl4f@7
zKye+%bBD70^4jU^{St{E)t<lmKXIkY1Qi44B^;ZtLLD1sd!tS7nzY?}WoOQ^d(iXf
z(NpklnK$rq9WoeNBxd9`DpRF4%8~~5IXtSO^R;F~nXx&Sk<O!rh#Bv`JFs@j&icJE
zl4CSJ*uS;&v-tC#tBu&Zva0l43oYtPW;83u(fqRh0_1zqZzG<9%|E;ezL^S4?(wN8
zs`y*IxsqqTz-`tGg6|(MY1nnG2e!cDv+ha)aUg14w)!85PoFprY2a2c$WiDY`%qg8
zVnGRE@)Y&hBScU(`PtgkZ@G*aPAu30Vw;9q;88)z5Y?-jo(EacP2Lr72oi@`=&(DJ
zA7P_A_OQ(RoFty)te+vvz@m_if!LUhiz<Y0HkJ!U?fgE@)(`2XUDz&V!12Ua?d7dB
z<x&sK2*7qt;Rm{G=!ksjrv8?BXmKBv)xIgG!|?JeYQc$2lED4-4bH6b?`tD#u_MDa
z@01fCkc_bcY#W&A7q$uz0}dm)skH?2Ph*NV1E{kI@B@ZQz#vo%YTSYglUZ`z5+??B
zz^6H+IZ>-Y0SsaVpx^6Wr90?Kb|vFu&NnbV%EWxUsx-NnQ3@bBY3o^_LSEMJ{ddtQ
z9q?}TOP9bFD4*E7-`n*mcsyB@6AwpUuV78)(0-hNs!LS0_i7HZC^87m0V0o5l;b3#
zTQY{}AUn3bf|5DyKz#Aq$~_ylWhrn|(oNxN0!PNqsK~LFN!Jr7T}H4NP9Hbpt{;M4
zVLpt%UP0jrc=EMbZWmKT6D-d;aZxX+l}@)7sO52z5jBqVbrrJwM~%HGp!-L$SVY<&
zkSr(M0GP|c5(-^$%hy~UC<)s8N-LO%81&sVVke!5gKSgliOC1@FfNs|$ZoDrKxoV1
z&PSMHgL9Aif=M$3nBb)nO{fPvsILtEu&Jm%qk~eya<3Z}M2QpLZ|h!-X5pd|2h}&P
z?W}ePPt@9#SnFRBXysY!>^0Vt9O~aNviaDci*Y?D4$jdBVU14*p3C+ap~q@uUO1l*
z^E$J;-7Z)Re&A!=e3hE<v~VNvc)yLryu?dYG?bXyx0NgZ?ujebi0g7f%{-;4RdM)K
zU-wUz&-js@k5%{a{&3<frPMRf%_;-db1%QD-*TUw;b@m>hHiw?HlQ3>vvZJKPkJ_k
zXc^2Uwk+zG(r*HOH)E=2<?p_$X>v3Afp)2@vKKQUV2$-DEU=_Gs9$L?Ts%f4go*wi
zF=8=%F}xgNSrNlK%B#|cOZ;`LgW|0I{ITd>SdKY<Sn5ITTx5h72Z1ymC6_Q3Oqc{P
zmxLw>BgsUvL}7oIF>6xTzd?0MPnKQpGirn8gfP*upLDG-wf=VuIYDYB-gTtMAbpF!
zkwFT6<SBusJ=o+I6>d_9`_9rY!-{8pLAeV;Ingo*P!KR+pNE<wXf$4o`wvjBBr?2k
zTN9N&Uvw0X^$d_IcyfeOT@Ulj{P|e4>Ucj<>LrtN&vfw^LN);84y|MKX(D|#j&=gz
zjMaGpo)kh*Cfv9rreHdiI6dsZe20Q}a)SApf_WZf#H@>w!q}v_BUrA1<$)d6kYe!<
ztXSp1$s@%mAo!#`YuVj}E&mX8#yvT#-WyD{8REKgT<3gHrD93=%c|nspXwlUB6>Ob
zR)@In8*mG}5@_!)F4xUQcEO#c`K!*&Xkh&Yt5Atrg_|l8xL@^5VEsPILK+qG<=QL+
zSPfS#<k$RGjT{RaIXOf-!optUm;XuRF<cTd5<j_$qsy}YQ^}`^?Mu9!jPn19!CI<H
zjYM31&{Xip)$!eTx^GyW^0$9@oBr;a&UdYNng6H4C`n-e^4R}6|FOP2|NMe9|D0$F
zX>zAMhf~Eh`>nj|g99HI!z$PK?)e@wh7)NVGt1h}U3>1|QDK2eqQ|tx7v1>Ck$5Rl
zWvA|)#l__+3jcW4kxl#G8-vryXEh>)C340ak6}Zo$zl1-yPTR3-b*kx)9pflZC}@x
zPT{dym+Q@4L5b)r$Bnq@FfotFm?TK;rMM$iy&1)FTN6HkbnzNXzyxt@fg@kiBg1<)
z_G`3jMLKKh7i0mz4S);dMEyuVonm*XHJ|e<TIHPaK*Ep%8;Yu5MoI!0&jU(K26(rW
zMAY^JJ{%$;&H;h7lz5Cc34XFNXaFwk>L!Ev+L0QIFYEW)ib$7$mb9>uyaPS_scyb1
zm!uty#-!3aN!-)l9TYu?@^c{@9{*OS*S_H&PJUTaHM74Erj!(;Af|Q<j|<%aHl`F(
z5%Mu*G=;r*3Ji4pP&UFcM_Yo=8g}3&e;qBNFIMf7h3J&E`3!HF-Knz`?UK~JOucm9
z3Fw`@g@k*)LLEEAVO<KL*QDj*eCvse_v~X1zBWcWwfN!;ggFPWoft82;q&>!QX$ex
zC>r*h3A>2%z<fA#*SG2u*eZz)JYL@;S?Hg_!Bi84N3RqPs}8<hH7L>kmdUJHl*~s{
zR8uaf$z1_!n*Oma@~ohvOrN6SsW@|)GP&v;gjhkrOr$$!NEemc*znJ;izvoQ*E=BM
zMglia#RIsmcJLT(vq1Y{ecc0g#!RBF>3x`oo!11zkD7bADLQ{Ra00+ID6Mo}|ErT2
z0JaG?zbGw%RKKoC@!ki{9Yt$@W|ehtWFs546biSkCJD^eK>>Wu_JWMFVQ1H@n~Vpi
zg>42SRD@=S#AShK3Ns>AupUD-Sa;=}%G$R%z8?OHvIyuk(n>=OTNDc(S4Sd@7uf07
zc@tN=E{nZ|MeE6aE8hho2pzO>K(gXxi^Q^=)^CG>P|Fh48(s4_<%Qs^+tp7jtZxl1
zxk_imQs*f;?vK4CFYXKLJg-kG)}|xav_wn$<Zb~rL*y7?x})hNv>{#uvkAfinxP7L
z-D&7+qJXIvz0wYrhwIo~I5+V)7{k6U;<>|9X#oq4Z4aX(e6A55u>H;XzXRoZPzZ`<
zaZsSOWI3BA3hw}g=j9ewy2Qn5Vn?fEJm4&|Ys)ja>#lSTeIUhBzcYznn+?@M+;R>d
z5Tx%#Q_(U0Ve%=_be&h_!ZXwzbREfzR1`+i$dPHv<s@np2$VL6&=D)LdH!bn5Inq>
zdeR0li0FdoM}(<2yvquhg~Pb~K2>NdApSkY?q`AdK;mBfhw^i66=F^LSkvKdd;ujl
zLx;xKT+_7J6dXnB4x8z>nSLmcUH<jZq4!`INf+mo$(1Etcf2h6?w)C4+&b>Y{n(Ie
zlxu0E3i*Bq_R2OYoycx^*1nrW)itnPn`C~__p`!1j@;Gui8(QhVc;^Xv!No6YM>KP
zQ1AZsY%c&5E}^azk}R#f128gM_K4%pSlt3rJl(raq!${%s+K~OzD~`A9qnwqbRLR5
zu`!(SwpYq4^9@F{CFk8d`fF!Z7yUt&^g9>{WMZx6+3NrZjhh2#mSt8cw<H3$|2sTm
zn1Z-@4a{Y)Ub{^m0*6f_G43j|*~=WOV_@G+rkL2QHx~|B1bad0^4D*UgSF5q*7DdG
z-jcV6d1Hn+Af|7`6H4F<_<mS@O-^Ai9)vyp_5?AwOzYgXH;K6|#ScK06zSY$=Y4E`
z{<U_OYc0F^W9{ke<7Kz4Md}Is+os+WYm@#lu8_+I^h-FI$Q&QpmiLC@ahG{P7;+P(
zVs%GR`;n{`6U;v6r<+yHXJUxRQCXRb=FO_!)9#hqwS+wjAId|1u4VETumAe~D6g$8
zq&HNAkI2ve#vOoMD7?W>E>P7_t%#U&FxLLrjdywqj5GwmEr0`}eww`I6VWC02Sg&g
z-F~~R?UnDo=gi@+wR^l@cFA?gMNtAL<5RHA{A!5uoVnku@~avm@N+7h-k-tt*u0-+
z#~j23Ip=Fkf+I_c&Q~pybR>2!+EMEt1+|O-WL<c6a_!3NTe>T2chHYixPo0ajcB3w
znxco%5gd16=2V}?c7r`XNrF`1wGH`%0K?+^7K_AnQ&0<N$SzF3QbBIhIo81ws<6Ph
zC9_n!;&5nFdFfJCpTAMnJoP$%lq&JM9E0hrxgnOtymUsI#aWr!|6>7&#4;o$V*v)v
zJ&=(_O&WH=3Ii|xNx*K77oIOrIL9cbEF#Wmu5{nz=T!av$%eR0Mw%W9=ik6CMdeDq
z?s}s%F(5<-DPx_$ez+-ys#ttB_0m8t`bs(Fw%{tp^p^TTZ3k<xOVgf@Az0cYt(4dT
z)<{IsnJ~2Y%;N2+_+sQfmF&i1m&2>;XSB<}zEU`WX!|Gz-g@PUqJbBH;ZUcw(X#z9
zwd4@wBpR?(>z^BiSc?!Nelw-?lF~E;q9@W|Fj=&{v0*U9A%Y79@dhs~HNSp?tB`R4
z{Q!-B6k!A&Z=eCd;Al}AznW~K!AeJV8bEb_aLpELUhH=Lx*-}AVIQ3F{cq032H7C5
zWn27r$oxYr?J$Nww+f`h*EM_O;^?*+G9tRz?VIv#NU7>$9+~$*Oq%}XmT!TrQO_g6
z{A1`)){Xz_e6JtHydOoxQ)r>sfdJT~v>_y>(7r)oPuiWs$F%Be%UPZ#YPPl1^7$~U
zE@OBtlwWdj(*1A(h`WtDb1K_Rm|91s0hsr8;DKr2y8HcDY&`gCA9V3FY)#*`0wjn3
zHI1vce;*;6Q$U}4fT_bys)2oNJK4f=?c(x6$3n-zw;;*u<8&u=wZ0@o&JvS_uL_9Y
zG)R#9M5@-5%u0zk=3*ENjaN=|?=zfKAXwWa`yyzUutn(GEX-nAlD<&_Mpc2?<XvE}
zmiLA_-RzQxK-SkHwtx;&g|!Wet@DWCV64}Mps<mTJ;%_JxO^l|bX!Hymh7A}sL3~P
z!u@opX(hzNMV`xbyGlvBy5nK})o8wngA>G8PiAH7)bHLPA~TM#ZqWMnim%M)4JXH7
zCSQf*a!?n7U&K#oBU&w@^}y$e^wg_6AQO!PqO)478U$FyF+gWVS5Ub45+@N*_fHkl
z;kxvzi}(lNPngg28?>)psAFMy_N!bbFqLr->*Tfb(0kn|p-1S#mLVT@KczPYa%m|O
zBL<gB44AeL#%K|z2lB4JE!>Nr+iZbWLJZQdnViMEFBJIZCN^dF(B}Gd%ZCJ<b{3lJ
zJb@!#dlujlIH;@C`nz>2NT&0uwV}5leZ#ivnW?D$r>b+Kc<PHWMW77E28wk+W{}4A
z@xBmnawwDLe)qIA!1)7G;@M%lALrbf?xE3UbBjaH>S>w+>8E-A@3squVCAAnTbxHv
zbJ_apM(v}^i<kkb=r!x}mGr{DVy;@xM@7n)(jBCV3P$&VuL{3$$-BZCOu+-1L(#|W
zsaD}l>D|^_BAoW1e@wQhtI;2*nL7UIC?Fou=Nvo^H^CX-59~b2N-RcKASTC7{pJni
zG@GA9s3E{c{D1rZ$x4xJifmhkqsG~N0pEVrf$D|++L``@11J#7D#Umd$laIMW6ax{
zJ{6xw_u|nXxfysGt>suQpr?w9j@TuHK?1gS`M{00hR51I{({QZxKb`y4<BY!9OzvI
zgfXQ0oEU~rP1Uuk_X{0CmmF8ZC!=QSDO~6nJbe*P&ZL;nS^|hTf=vFGk&;AJz&(ZU
zJ%6FdZj;iebuX)#8K(R~$aowq(o_I)y#8S|H7tm3%796tXS}c8R>HsKr6Z`5Pegg=
zdby{ah+<fH`tbnIX~MIUekzmcYay>f-#|)q{;0)Og=mlVCCZ300TEr%LA+=>{XnZ+
zVsqe0>`H{=KvUkDKR6W)vS}kS>kGPUVGPK8h+@ZKB0r9AGL7~Ku*gU6DEcNWGpJE*
zGt;l2M6Rjb<^~D<dK_@G8pi9gr805&4!8Od;-#b7a+>-}t5s1vbKH;M@wA(Z<woh3
z_K>|t)RBm2k?b4oMX=q4X7btY00%q_DDc_mY-0ds$t7UdMo!dnDDCS7yWNBZSK5J3
zc&=~9|0QO~k89P`=iLvmzA(E08Bf*krFDoX0~<J6ZAD7XcbjP+J}a%Wo!y>qvk9LN
zGSWZg!HC5zzRF^oz-ld?$gHKnogbe(Di1!*^fz)z@G(je{dC%2TB3c%C%+i)F;7J$
zCbMt587?=wFe;+ibeWTAI~B=_UQ$e^@b~jFSxk7#ga1gaQ#uO>rHNV;6<-sXqE4zp
z^y;l}joN+n{sKF~_Z)9~H_(KVh(Zxr&8aptM878jzMEYiw8eH_`~=>X%SWAz`<stH
zd&M$KIH7;)Ath7xJh9gDO%Q_;w5#3SET@B6t~&sy($+zpu~=<eZ1ZVU#<lc@9^vg~
zc4LGsrp<r-koN#fgl}xs5277`yizz_L9uppj#<|_xGs@uh0k6mCW@ggBP>!zr)jR$
zrl^%W6oWyAMWFq-#gF)dl=>WXKukAq?Q1=u%8>U=Z2?`s5Txs^85L6o35}GQj_I^H
z3``h|y78B-eZRvzpg_04MBU*cS05N7$JE{ueocdrE^lVf$T61CszDUWw!+-cjDXf(
zJrRjJU#ifyFnJeaqr<a_aou7R8^^?z?gPN|1|w_+lb__QCm4?;7#{pWt4Z&@n3#g0
zbLj9)ia9#fh9oDMaLdLALbNeY>;-bO94(%w`E8{A4X}mc3)(;z)YkbkXPe0LH4Lzj
z>Rd(r9Z{@KyJ~`E(idnACSt`8<2I#<BJ3*+4AH4M)I{urc2aC)g>>m&%CE)cOKo}P
zziNt1=CxkU*%HPw^f8TyOiv&(uJC{=CiL&Rwgvd}h!HmWgLH-7Q(!PIa|qI+Dplj7
zFP$aG%|CS3O*}c$pHuX$*%9LsU1cBNhuq~axzNk?<tt86NTmq%jg1$`qbn4TPkeMG
z%ZlUpepU&@!Z%LF@N(yJ^B>X<W)4R7_i)@Yt-M+q2`>2`MPq{Fsk1~p?BV$c;x+cl
zs&Xb$SoJg&wiJR4ekiOn#g??zQ{DALF&B-`$8Y~`k_-#lfH|b3p6%N(%WO-!g^W$I
zxz3M58KkUtyuAYR6l|B%Zm%T<lwY!!8;dp2yqG8!+q7krh>569+I6Q=_6yy;D7)m{
z$Fm~Qh^d%*mu21B&>f8kA^9UV>`I$MGE-S$EwG-xt17aVRo*<zX*YA5?m`&?+kmB(
zn>EpNsa1^oZKk|SZjs%ng0B079OJER4W+67j>4J-kgGz#IQ`Tt;<jgA_U{X?fkKO1
z3xKcatjwc|_{ZBYa`@=kA3BJC)5Ko>aQLy@Np{io5vTeznAy3GJS5}P_5IP)kyGpD
zcWB0`DkLL7@IYK(R~m;nmfw-2HEoa=M~F1g+@Lae<TT};*7*_Fc6Q%^XN&Mg{Nq4k
z?_F`fzCp9t`0Wy?@P6fny=Qw0#k;2T+Q21Znp(OeGAm)?BRvI3Y1$1rv7kEKfhK0a
zRRRgxG*(R|bEYI6jE75xaWh3Rt_x2CZOhEBGk-)|Q`V2-+El!1qn8;Y8b$aMT*I7I
z$13%BXgCPbnI?bf*hSN|8~$wD$-bw(eGGaeQCKv?NQ$E|>!0oA29NWUe!Ocsf*Nq^
z!v9149V3xg9{wPIfh)s3JO*oJr6Z@#BrjDcXVPrs*<2GE0KhGazz##|+uu`vr5N~O
z4d}nLC~fre;LP}Qa9;s8ztmi`7RQzl!u@L`gtNSi!aFf$qZZx)q{PI9vLbJ#6gIl4
zvT-oWD#!29X7HuBrj=UFHYoLd3@>n%uqcpNYLjeMvM4J4S0-Ea`rWr1*6Q{A1zx&l
z6E#qYL<e$#e(a5swxWj7Hp}n6q;@o=On0=z{q(!}XnMrOE$*54+3c=4E|^%LNZ3f&
z>x|B%H6(;G8c{PP`4`eMMbjdb24~_Kb%YIoMbKr8RS9~tPXCb&OnT~XDp+4(!WPz|
zQ@5a3wNt06r%iX7{8V8rzt77*U|r_(eqeyM+1mAQsYIcx(q-N=G&Lq`b?1G&QCF)*
ztpX*cgx}<&cloT7dga4~KrT--*Zr|oxU@$@lGTdT8{JX-VUZeoD4u%v9J>2<KcWRF
zVm)%1Ssgzlv(g#Uk&o%;#zE0~;;{9e4-?5Ftd-4@*Fs)YOf4%&(?UeCnEz5WzPw)B
zKir_%*&@;Ai*}AC5~pIyuR(SW4yJ()27gQcVv0)^nhpZar9PZ&bpZ4;qRWs;q}VIQ
zEX1%S?GuH+U4@jy=n-!9Fd(PDRVbFANTI6}0faf_<`HL@!l&HAcII<2E{~R|;YXs-
z=mV5dZ6gqGD{Ai+7N0AgN0CqsF7=R>{v2C8XW9paFAZi4P+v_v?Hr`qH6E_7QSMVu
z)h+7!&qpMSi4f0^tyrYH4el)I7`XNkV7YjQcUX%OEQCz<x>F6caA@Q9H@;>vNWy0q
zmU~l{B1*20&6<!ZM1FC{QlJfUbyE0}!)@{WP^>9z^r%JU-+_p7ufw1`$q&XOmJI8^
zl#8gws(IGWG`kwa#&j}HY5p3N|9D4AilF-E6g+jQLF)S&$p>SW$n^i_xpCw1v2$Nr
z4wL@^7F#%IU?6Wvax{u_!WvhbmIK|!YTR=Wh|8karlGeGqN$2tbd<^3+!*^ci{XJl
z+4VEwG$O_UB3h#}VM4uFq?p8zI!Qbh+>V6hQ-9gW<aFGHxhMzC9GQgptse@68*EQ%
zHhR#|aIhPhN+_jJO+${K*ppk>;v$#t+LR)kHoShunx*28;9a}=eD=$94I=v<e-<fP
zsMJo^Sh0Bi<psI|@hih7$3(L6L_HY=OEmP4+0(4p{&{c8uDBFMDVEFB7u1Hc1US}T
zTGLJNuBg@J?9~Bpd{FJ|+jOHxx`G6&zdwq~>pJ+A2ow<T3=Zc~9jDL*GjzS`?#tsE
zd5b$5FNDD@;u2y`S$8MHePrOC&2RAY6T-x5J=ug6U<t4St|bjfBii07Bik!WbI_I9
zpg99irc(lWuoLO#ddc=eyc*-wV<gz8N5br{gKxPH(Y$__EF^x@Q+$vUquvBYMRy%h
zBY_X<MyZX0r1&8PN1$q;{%q!Q%6dd7;fg2MQt-fX$&z@&Gik}ZnCtw?;ya)~d4eNq
zmA6k+#d+U#S8^hHw5uOZe<8|1=c_R)VRePuBo)XYW+m`WmK|ssr3{HI0p~0c8nNbL
zHJs|M@2|`MoYb9;4Z%YZ4~<{%JN5kX`jx#hhAddkgdGVfLJB%+|GmmMPU(5y7K<g{
zY={hXrsj>W@)d5tE~Y=?BisJa9b*0)R0ZC!$j3E?6RDr(&wmzXxA@W1Hd_Fb8}#<k
z0xni5*|nphfGqf&=|%Ut|2H3B_kPMp$6Vn8bx``l;mF?kco_Y(<-g^R^#vJWaj-ka
zG?yV=T8JH=2`c$%0*-he5G7trpoKMePPx{B)g?`lSV%p2wg;=Y(`=|aynuQ+ky-7?
zLh&rQ+2_M|rN-SsER#(z9hBYdOGz#L>;o;uvU#bGv7f+r^&6Ble(6Ove(DNLwwGs<
zqjYN%bVcYl+f`|by%4+i**zD?P|7*0)OFcDvBf`2Rtz`+Op6+lDGcd?ymd=}06|Ah
z*#YeG(n>onyCO!#w--9GB>jq40Enw!_RUE%>u_Nj^hPryYpc6Ozn)W7%Z5n>9mlnd
zz6X0K7=?6Afpc{9HO-~zDBbvdBK4RyUwLKTf{jc_vVQpkC^IZ2-gLcRjdN`3QYo5}
zprMLR63zjZap=k7OtgOZKak3)xHyq%K1oXa+XG%Hc?ZC~rBhxB2q+wd$yzk@$IJmq
zSPLZ3&Q?qYjtz@G84`eS(|S5`bNka>Szs-2?((PG)hwqDG<C;WWSrJh5LMRbAiCry
z^B-_goH!l(Od3ED26;)yx+K{gA}d-?{E`x5s(htTGz2~rDG?C$8C9w#)-j;HAR>2N
z^jwe85p(Q(-lwtx_cZ`u!&xx)hK7|@jlYI?f9gp|lxEXzYkC+5Wz{U%$$9yc$}ivN
zT~0jW4F1nJ@a0GK*LM?Mf@Z(cWfPGLU+BF7f?{~ANx~<IG>V*SY@*q~OW<E2;Ca4I
zNx49>-RbE5aUm=H<g4d;XzjGG1E`5%ClU>0*-Afh@mBZDI+v;BI%>bSPdWZUKhzU|
zXWy89o$ka@(|#6GYxFpn=3xQHXWjK7yOtk5iDz1CK#${gD7V`zZmCJVpiaH331t;;
zAz=J(z=FeqLzyDX90HTT(n~Dm9hagVSjXV#l0wQUH0_GqIzM;QlmAd4XrURB(7Kcr
z*OW_kb0iw{l}RAN8Uo;AbBdfVFV#nb`&l64fvL`gxx^ff)N!@lqGMQ=bv^cW7g|(F
z-`LUCLE0O#h0m-@_E~Noq6AU;z`69CtY&wP?Yr&nFc7`%NnIYP7oxk}kJmpddc+rB
z0H`U%cL)sD0~l6Pqjjn(V&gfK=oiqp<X7ivbw{(t-Nfr<kdECPL8p9EXRb%nuGrgB
zOJV3?*L5!x36FDoPte_0$*nk&2Fvc(!qf+fE(H+j<?1+%$ELeVkB<7_dm2hVg@IwO
zZ}S)CycOC7<!2#Ua`u~XLKRwE)eG_5H6UeWfkI1x<?}f8F6_GzL`NYdfRf%hfAQ73
zen889Bcqh-e(!Tf79781Kt6_lCz%G{*C^_B_NtN58+z(1)|L{=8}C|Hl)5h}4ysRS
zrX(x4yIif6IurJ+r)Y5R-cu=kt~~_@7_C#MaP!fsLhlb;K3AUzz8q&+dduo*s-_2C
zKpfg{`{edQv}jTb)mrSC{94RbkEU-S_YjENJnnKmZH|v&_D8y$Ap0G9f%kNCJr>AM
zewLNQ70F|PTBlWXzl!Ow)rNS9Q85+xzU}eQY4cRvO8>XD*AcX$_UXNd@G&J4Df?KW
zZWBrFG;=+|soq~iFo~45I)cu8A_B62Ey*EX-0Wm_z-9(gG)9tht$G0p2>rcuH;e80
zWvmr-4MhFXKDDa*dALc0vy}6XBacImWUnwK_au15pONOSbJ+o#Oc;3Lb(r;u3Ds^9
z1#=mbo~R;%U%WNSH7a?f!G2MAydxmsa_MlX&4A*_m?oO6i)lb0+1kk}DwusCKG3^R
zV=H^2wch&`d3!Ch)Us<u?Ux=FHl)}0r#zeozhdyQzByTyPp!acZ*4%0c(0{?lQj(I
zE^LfRufrf?gx4oFY*5X$x+q#v?71T=(!iBor;nV#01<T^#VJ|9rnF<B6rAYWzOhJ!
z+Dy0+_<Z{mg@1M?7iPS{NfIR4ZEWg<z|Le%+wPY64I$k+n!~UuuLf#{xVCfpl76#@
zj;&B^cjPX413#i(&>V_4B%1Qw>G}oXp}+CgXK#)=igbgZzXKP+i$~Eu{~nJ6Ql8HM
z^Gi!<xSNA~jXe;>d}?lYZY(L64uq?L%ydj?+0xbvoltj@45K2RRw?FpsSa-r7kb)x
z7^x*&)tBS#^NdCWpt3B@+=_M3uC0$jC2ly5Ux;{pI4^)^x7#$}^_dG1WlOlqXC5fR
z=89u3*ih>WD2Bixs=#7RmVNe;SkCUBtBsN7|3oCwqqL1n^!2_bIYdkh3gOAn1X0qx
zVYlc}5A-xH0P!v&&Q*zMTF4#$n_D~W5UOupm%gK~axrRXb0lVkaX}fpRM4XjPZY7p
z;lF!x_t(M5tU?87^NZJTK9l1uD23K3xwXo<3ylfK28NnQbpqFN8~2}qZKzX?7iZ^*
zg6D{9oiac&9r<BG@)gN7bu;|yis1>wZE%e~)$If^voAD3-MaC^wC-^JTwpg+Fn`;d
zY%ab^w4N!7YrRC`#|L8yiH4zrS51H?gO~AIs4MA$;UvnMTF;Vs>G|)b?W?2ZQx|+&
z7aV5SQ}ky7b>xNCygyVVcM%3ebk>Yr_{V;(kbUbQ5T;1a&zpO*4d3^cI4MIt?DYIj
z5Ya8{h7d6cfXoauD;Bsu*O!4t+5TTMVxr{srOIEDD;$#v%D8#$<jWrv(xSW^3-@tG
z&n^YOaF1VmKiN(MS<kFftl2{RGyQ959YQiJIwkl5fLe(ITfbWXEA~HJ4HooWr+9|a
zA-?U%^eD-@m%s4v@K_aB!4%xECL^{0ss~fZY&57tWwv|OmmjSskj*wJSori5dIO@{
zJPt_6`WP~;SAai6<)sjF=6ttX<Mn+w`j2#|RHQ)8!iTFPj*?6DrIrlCs{tEvO_f$5
z=T^^y+J4O#o%?EgtCRuo*}WHEnMj2$G1@yEOz>RW?cDzaaMS4={&g~MUy`?dy!v2+
z$JXxoSMq<cof}z6%9<MtYz7U=oCK|pa+-@N%G^x7pGR}V_ScA*`oRZV<xD3;`7}=S
z3{^Ep^lvW*UMviA4VDBiBJvA<4#Qw4U=>BxaOt7cOCO96?8y;oUj?Or0#*W2CPGEK
zO!nLId;xiH@BirCMi21)`GI1FJL_#~Mnhf1onZdFZsCcQ%KwH2wgufYaJoISu$il8
zQXD{}BN2w_a&#nOrNFIBM=yr^>#wK0q|z!q)3?ZzWKg)lE$SVOVHjjhHv(o1bx>h0
zPgfDYXQA9D3xo8$Z6k*eQfIX8faZpuwwKcHIlM&Dw?>p7Vik(zVEi}cbztBr0*h`I
zh*pv^a3a5TXDW*?9JH`VBXUyUzDzLpd}}{^OMhPVW1VVi2F%Tc0B88c&YmB5f)i~%
zXj8zt91}H9cK&|MEdr@Q-OI#sl^t;UeQRGYSN^2!&J+|}D2rqvZ%{Afd&3EHi3<S|
zfgG@5L8VN5#DQ~(7hJEJ6r#w_g@eY4(Fy+_ARLH$5(sy7L|4!phI3{5Ogav3y8aAK
zY2^L{Fc@Y@g2Ln_l;p9fzJb9lF*#XPvpZHl3sz7!&UF<~^QB9WnM0o$l3e~829ro(
zdiw=O{r&UCX+?K?mKioT+|D}<?qj|E9!aI7;Spxi=?vi5!lpy(hv@|W3;x41zFdjx
zRyX~eLiC?eCTt>5^u_ZGeTmWtaBeWb(+3QaaBXBdyT4AuL8z_@L<uPVYd{xASEd*t
zs{qUxSjZPux!Ju(B3?Md(+`|HBd4iXxuzX?=1EZ56JpoP+RyUzT#uKGhSRxZTtO6X
zoR98f?Tgf&+f*jB;~>68xqfe>){gs`$t|q8rQw#p`|5Jl#b|Zp3JbP^pXra_$)Ydg
ze}p(Bl|An*+yoAN)Ui_|k41V0mGrzjls&CHf82?e^fF=eMQC1xuQUR8MSgp|=z9iH
zX5h8{Rlo1Ss*|IE%bby#7%k%r)dOZIx`>X&`NO4mXTQFbf(ox0cVm^nLVkzCU)LBe
zh7?CR9aq>FXajY`0SGa<iJtM~Vg8d|u-?5&!{&*^%+Fjndj&YLTxw7$00F4v8YN$*
zGfR!E$nBJ#Pe7roQ`qvQ&yeHZ)Zg4YyCqp@N1uFpjc=KUQl(Q4lzHj*8)tg|fG-W*
z*I|So`Oe=f3MGJNxISxBcJ9a_m%V5;!JaCOY5_9p$@M;Hq7wwqL(L^UNCXg7hfdQ+
zKNkgo!<X*8P`sqYjDt<hp_09Cy#%PIet1G^K2m32dYWifUa-Az-aGbKd@`f8{SZrk
z)O(fAOp-PQiM@Uw7oeME(iYWbCt90ZbY0K+UOIRJ9Q$6@Z=seNU&;5AQ_j2gJd$NS
zvT(%y=;O^AM!KPh8u4J&zvmnC6V*u>LPS-^$w2m<fWu<Gd`Vk`XbQTjwDkbW-|bBb
zAaH0`U;&+%5Li1ZHr1>%x&XXkVB;tZQH@*!Imy9!ZO^$shO7juQiGji=RuwKz2_=Y
z(G^_Sl5q}`c6qYf%Ga;{HMXQoQe%usK@@wKuThM!naUKS$%Qn*J5EdVv<F}5ke(PW
zFH()?*-p~~oeB4(Si+dxq~EV@<aNF;Vk=X?@?+ytBMGg1`8J|{(0_ZYcr7+JlkO@A
zccy2CHH_riu%C!&Q44f`%dNg^^>nHQOOvRaAR6L~mAG04Qj#<k;c#Tg6T@A3h0vB~
zl*3)tso&;4M<vJz!n*(1FIG8JD?`jik*9Qne)jh)97IV%!?>4VrL%?RLfRf)>qcCQ
z-a}Mm$L*7wYd&9^SM#KTLz2~qI4PF~yIT$7B$n0+CqJymAtGHAjAFZ}ihY=I!r2<B
zh8A5xStS~VB4wYtejbBWBQ6m5v2~}?8Xr^xpja3y{Ex%eGg*sN?t+CAHlbb5n2RR#
z>$jQMwodcjgeXRY=L0zPU^^f}^}>}y$%S2i52Bn0l$Ony2vTQB1hiXCmL|Z_=3Vv6
zResFz>T|XYfQbHYl7Q4F?XL>|GZ!U}OCEz+q90-_m<Dp8+<u>tPact7GTT=eia0Bd
z>ykUkQ`<B}pfp!@*yMtFTZFBD2yU<GJw~Dl@hPq#q#c_4f6v7$|8VwS$OkMEPSy^Z
z_qF|o26j9>mlIsN2YE~8Vz1$$64fjQ@2i8dE2Nq*YWnN@qAZQc;B!ahHYW*rbqVDd
z(X#gM{TDc57oC2+en_OKlQ%H~&w8Kr@~)_mj-l`w|Lhw1mOK_Titg14vcopx`9*9y
zzC>*L<`kKxaL^}9{X?QXMmjpsrL)8g6$?;6^96v8)-RBL>=c9YQ#|7Zvk1EOWvQgs
z%rIk&O>fP$cIlnym~WwyK$<Eoey}8Pk9)>*f5DuGSwb$QK!Mo{8Fc=pF#*9qH|c+c
zFAO}sw9p9zf9G1`^J=KvX>Y2YBW7Lxha7cuK0Rb^N4O;1=$_w!vL$xyth<)8U5mUN
z4oWQyZZa{@bMEHYj|)m#{x1F@a@TAtZfTAdG=AvZ5-Q{i8b`&Tu*%ZvU~0cvL$CNQ
z+5o^*%LIpW4R4)bCF4OcI)(kM&s>6x>2*yOlJ+F3xcwTs-umTFaiqJO9)xWe*K3!*
z)6V*vU@t%%SeR*1vFtmA672){#hOlj4P?VR4ESHai*{33P8;y3Ww>#6uO1)quNS8-
zvO=MP5Ku_8|H!+(ch_{^3~_7KkQiynbWe7e?yC1G57z1^qT}>@?sDC2QlkaWT6kwl
zM0%zD1m2RBGVU8CyKr8sV@BX8<+f3^IP2&Se7-4UVe0X`QJiIz?<cFX1^Z!T&GF1V
zmAt}L_xQGhpcJyH=lo05Rj#PlkNEQKsTemuW9F&k=1O%MCe1XY*B#()*0V3P2FWzj
zCKUYKSd?NZkw1$6yJKSzCB*(b5)<{&lUl>Ay8%~tl@mMZe5c#be~@cXF<3Ul954Qz
zubdpj;uODbKPew{2siA7PqDnK@Zd7o`t;JlK9!q-Y3}zjXI+E9^wpxxO9Ml9KE@tO
zZ1&_8Jkse9-F|CK>ZZJiJ3<y6m61){*vodI4_C7}wm$Yvvgog+K(y?i=T<G1#WKoX
zTm#Wn5)RdbeOq?!uA6G!t#fPVePFF=<$)3+@S1IUdEu;=8QmxB_nN7^0%O^uh~>h1
z7wyV`)ho2*^T=3uY-Uy`Pcupc`o?3&F=&gMEqsH5B_+?tQ7v=}4E;GOfutJa_f3x9
zHcPrS*CP;ffS<IRdB{&HG?*gE8qv)$4!_(x<9026a1=%9n~jU}IalrH%oeb{U9hEM
zXv7dH<e5i<Kn!MZkE2qR@*g$Nc!$Ps)BOR)>y2mdF5%{A29JCU0U#{bP8J!TEA#x`
z5k28O=X(5`b7OZhpy6T1Q**W=J&kO>_)%HE{yP`zB3s-mD9`su=$YL*T)*+kxgOS^
zxJ2W~;2PkQ)O`X1<EgigJHTZV${<309bG6ntpiEapg;TpKRkQdS9YG}5G$(yh9Ers
zmRJX_(O@-zaZKI@b|FTaJFeHDO}vb!%(~h#l<dF0a{WW(b+JFgq1e_|<dG;r{E@R!
zCd&y9kDcJ^$ghyg<GTO;c`;MlZuR;u<J!9l9aYK2(5v%H*FVee7L2=xTTQMv-T#mc
zCxw5XHz{_Dy9Zrlh$@l!)<l2Rxr`-;1M{e<8J@|(r%;=!b-ZMLEsHD5X?7P~T8heS
z$z{^_6^as>F87A>b6%p<Gg;Grm(y=#mKP3=4i-V(@B2$JAE=Pl%!R8bq+E2t|JLSs
zxg(&H1zbv8YC1Z7Q*roq@Ed!4jzXWKtR&$i8W%Qu2$Yp`+sC)C5;{4b5oo6{@fTI4
z@|T_~N7&YHv_Eu=j}c5yx&qw&S5%sdCvB)D*buHK$zGFLi3GB}$S`cGqZ%3*4X6b;
zHgqRG+c&Lz+s*O6vrfROSjV>ty)q$}BUcz1=WKEk{99$vNG;Y``rK2d{kayh752(U
zh02f%C54<x?zdv=WA6Y5=cvLjI*b2}=*_)JyV1~a=*I=jwu!!>P=0-ii7YCTw&?c*
zo^a>7z5%YWS*+EeBpfNJD0qH6C2HBs=Rp%&S>Jc3Z4JApmTET~77r^RnFd;J`=8#8
zWQ*Nk2t1sMEVS%b8mQ<yw*M4b-fPqD$NcP9cdxUs;+^XmO9p!ThTWVfgS^<c`d|*w
z;k5B~;5Ybfh&zE#W8w^6w_W4{<0322%}IwjGRL^x9cZD(VH4aXKn_H9hJvC5p%bld
zKd0ujg*@l(gj^n@#Rv{ELasBT`!Wh@$0w{Z97J(E{#7#z3`f7|d}aJ1ys*<Aa`+ks
z0;vN0aBfiw_4hk?9NrN{_P9})jN*Yw8&rN5xTis6walt(-5j-bq_k*cBJQ7r<&uSu
z020#*06lsV55uuL;jFOE*5rg9Pr9*tCOmuMu={s@)5ft9GKL$zMjw>^i@{7SE?EH1
z3OF2aXit_(nsD`|Xrz-sZp;Kvyo;p1vwMxlv&)E|7e|X(x-H=+Vaxopw~|@9(l1v1
zPP>($?{jD3X8_9QgXv{JPa;(r0E{F`DC^R^cSQ}z$6&h#*C4fa4Zz*<L4mK{P3ja9
z4&rm=6Gy#$z098kvZNSDqM~*l3KV<7p9C^bertJe=R54wWfdn|EphxAEy&HyR6fSO
zOLZ41psCfzv2k;H%s;GqCNmgG&k}W}i1R)_UTNzT9QdFhbBSB;4b1u6SI;POLbdlN
zz_I{=>O>TABD|;t$=~L5S>&=}SX~E64Xhe@xH>C{Np#C)IH}&;gVt(kz<DSD7)Hef
zz_U-%<)*5A-eWBK>`)H@jjKj>?W9i>2L0k@KkZ{?)2~;U-~c$CErcD$VR=oGjUmnX
z@F12R8<q$TVH{;p`^H-GE9o?cYHMB@HdWk3fk%Nj2zt=!80f7*G%|hhi%h5!d^_;`
zY=1p@egJhfMy68*33@NZl^K(sVxw`+Ft(|lvnSzu@x&``jnu67Q**6~xh(^&xCf6e
z96o}(aSGwmC&p{b?|PA_m6kNt0g~x@IN;fXeSR+I2UY^=+c`e=8bmry%=<`_h`jwx
zQ68i&ZYP*qFKW82eB!YWY(T_V1y*g<&P%tBsvMW()i8nL;)M)9=*bO=NlhDiOi>nf
zZN8cZH(^Xpvs!!#3=aBr(TRv1E}=}E>RjEl_WTai-9$)rPFB3*DC6iuM%GXe9e%^b
zPtTKwJN+`<td}cq9qfGQFsaxFy$_q#V`-6Rah0cO#U)E@tDC<N53A_feiOs6Os-7P
zA(pRYSPxc0GwaMb!8kM1t`BkVQUPdU;$(RY)<U4s$Dg!g5OW4fZo@+JcV>VVbvAN-
zuaC!g6Lv?(?N4dNsC=GuW%Xcg&4-l}vDUSWeSYZgT}Or#jqp0iHy`f9YZ_k}u6{nV
z7rzOSn7ak)=I*qu>iG937<?WWcO8PWZ6^LMyM0?)PJPJc*_GV8o|kexQa7@0d*a!t
zrE(}a9~I1v6}!^iB^?$kxI+3d<&f%ti*y9pdr$iKa2_J`ui|^V60rc+pWeA}jvr&7
zJJ38PQi#hln*(ti5UDrFWwuM4mVyMGD%GC{BLIblj<AW^R{E=5P)YK;=LMnD&kF+I
z<ng}f6bwAZ7@ckeAN>h)6KmK{ucQ*x069-dN1b*B-$R{=Ac(P@1sM;rzA^prx!Z}{
zuxn)n1$`B8+;Wdv&((xp^{x_Q{h2yZr8jL*vCuy$gy<qj;@VxQdUnNh|4Lz*2{d>J
zC#fl5ZI6&*ZIH)e<{NUPnC#$|00~$1kJ)aiV`z^UfxDm)P>aETz{30U@rjx&iJ$su
zd(Y}f>9zD@r!8w&r0yUcsA9{$>1*GJ;7-PN>WsofBS-D75w?p69^n{4c_Nkg6gIEI
z#MHUHkacuRC46?ue{p}~b#c^ryi+k>oWSynQ`YkCR<&b1?fOy42Xi(-_@PmVPt+#q
z<w*{aPhT3k)gYv<bO6_2{56|x3hH&fjtz?>Dcm3sJEGMj9$n$(;VSc9-%_c=y@U;N
zrpM2gC|Z?0Kf=A16rFwakUKB0R38h`w!`myf_qbY!Pas)ZGiL@Bh&b6F#IAXmXX_(
z_8M{xvCDI~6L>-7(nN<p_<^jP#AMy;W-nU`;>O-NVQgUvGa%Gp1Zs#AlfQsZP6|i1
zLj0xs`5heTA6dx5nXeDS+10sZ2V6Kw|IWSlxEv9zIuoCv%a^z{Ay#TBLghz|to0;S
zD~2SQa&R#~sZCVs)kOwjy;0AGGH1WZ@oyX_EPN9j%$%yvYVFwdg}yez4?GMAC?(&^
zQ@AA$zcE1(#6;1n3AR^=b3QrhG6WmBp7KzOXqgODv$81H0s1rrQo|*9U2J3*GNiR{
zv2ed)$pqB;6yRLoFP^0nu5<i?k&1;R&*y`^fqFoNAqgcW)6M>Q0^j#3)tu3Vto@SJ
zuD^?^4>g*g3GTBS8rD_~;W+;)HS%WHikQ5C#=BW5nI6PNxUFs}Dt4h7EiCv`c)GAN
zlbmft>C_$KNp(?~fAzW2OY~m_iGMv1*}eABYZCx<ojxH$_;7p(qNwq#JE4M2v+!^w
zXZ#a89w^H+0hKbon@&1ZgtKqZQY#G)vZY%<rIW7YpPT72DG^DZ6It#8zfi%Nd;5(p
zO`0;`cboWPeyf}Q-<y&1`HMGj=akX=meB}*iv$0S^gYpIY{|6;{M^@=dk|P|<hQF`
zzC#PB)JeH&Z{@lquan3YJ_;=Cc>lqpc)4%*8`JT~`}=jVlrpMe_BU;A{FRC`*bp7|
zNZOOHE~5!i0?11&QsvP;T4gQnVqc1W4T!0yDtkmcSK?)iJNw?C%{NyLMee;p3;aX6
zPf!Jqmtyi1sv7j_J<|c9!9MKM%8BGk-nnFyf8Ody3jQYGOYlX3BXlWvSG>=Ok(4yx
z!gwwCW?RPxp7R6ngYlH6GftQcFCmLsrMME7KeaaNCtkZ*iC8jgc=2B})P)<Xl8Y!P
zU(P$f5z;B#xJ?W0_3st;C@+%clx@4u+cBb~R=V^4pv+)qTt4qYCc-k62N-<V`$V$h
z$BA<F#Ix37%@4nPQLe#wmsY<gDBB0m*I6f6<9{~5i0ExCJ%))=7?0Xrj<C6sHiZy5
z$*fS-waSYzXr|qiS=m#Ev-=@GfncT9=GKPEZSl+0ZYkq+F@0U?_<4VTQpJ-JYi-~6
zEy%X7`u-=aV4KRJUHL;fT9H=yy8cHQmQ=zZsgibytm3#V<$m#*)J)1+K0i^DLz>py
zxW7@aYPyigP)oIUyO>ut^DvC@iT{ZOQW(3zMFICV%PJ_HtZP+<Q|!$79kP5ad-{BU
z>OvX<{}5zy+4B+}omz~zC~X2~;<+&#UQjaM1!f_{WVpw(MDw2WsH-ra=@)?p9a`dp
zjG+QTca_a@A#-<}!5jB6{JbJ9<cStI%W{QvD72BW0>6-eC4MM?s$jxOusta~7l960
z(ew?&QLFBLL@Tz?2F@xzQjWEk=MV3<OdREvvi`rbw5OCg3$+mqk<mniJLGOs-o0m9
zC3^2^5+9R_jtv*5=T-|h<medQt)syCg{}iU$W`|+B5Y6kE%3(h138mXs3|I}2x_vF
z>ScuB8sN2%cLOSwv;!Mnp(k!T(!f!Fgo97u*s=py!q+tlj=?VkiZVRUB#?@4FV)Ov
zc>M);@?8UEhXKwTeHMoZ9}c;1Ht_%O^p;Ukw(s}%(1L{0F_Z!#4N}rbcbC#3CEX1I
zQUW7LOM`T$bR!|sEj4s^$8&Q3zW-;<E7sx#tToqpoyR`*{_G=^Q7#oCe{_QBPzMwd
zv0`*(yHozQaL7iMeg=t1hs7&@?*u*9ByS|DOfunOH^|Sxqq(&yrJW!9cy}2*wRgA5
zQL-4p<dSm-3Cnq+A0UY1e29Z#E#h}0QhcqFp7VquBTWuRvj2S34w3Gh2fvV=NJ^W0
zY5#%mZzatfByKLPt1h!cJk|aI<{S2l9`X%5o015R?<xuuHlZ66yC!wQ0eYAa%HhA>
z?&pJBY!_|8IJ>bcsxm{Q@{#U4t2u7`ImIio3ony+tw=Qt@+|I>=ta*S)U!uIeDz<{
zTmJP_Zxz#fUu%!@=f@$D&6I7G@ePu@7W+eoKHa$=`(nrZ6C%MCeE|lQd=HwOwk+X<
z(Rmp^aNpvA36XnmMB*ltMY74^FDlKgw_&NBHxgF=sL|M+$z$i*ZV2|A83Q+NHt=wM
zMeL^cZ>HI8Ol1G>CrcH7B3&6Q^DjEZcVAI^kmA0gU6xN)Fud*!p)=d}5`wxGdomaa
z72{4;VEhbPHX{inF5bALnykRb=pE(kS4Ry*gwCqQU(?7&@EvDb!g%b~k<Y~~8M1{b
zFMql2&7xGZ^X1+PRnai?hTKNsNRuycX>QP;!o{q6Uo?_aY7Ru__VA*Pp;niCy^UkE
zS;rD{Yd;PQTojG2btdC3+lr#BO4xaD)2b7#N|v|_YVP4|QP8l8;oob-E`8`IEgfR1
zfffzLaq!JgcI^!}mrh^Q+c#Dp%jeyCNA5(wQE^2r<V78h&q(PpJBz|TAe$}oEq4QU
zz#z)oBSBM{_q4|YTGw&43qmC@sp*m{$*VVqG#Li!^*<HPSLIMdDf(8Zi>u9Y`gv5`
zfA-78M}zBbjiQP#>AG;|k$0Wl#aPfD8ijj?NuSXuC1#M5@52}P&z(uW@_wb>ZAJ2;
z#K>K@__THHwaUTQ@bB#zEFlN1a@j7JFEH1CxgU^NFgPWF{S?1Y{?l2t_i17zm{gI)
zZt8j?@xYna#m@GZeRz=FUUu`Q<0-2&1*W7c<{&P=fxiRh0ujHQr8A~IK7Wz33z#ek
z`PZj0t8w{{u@d@5m%5@w%{eHi>~otNveObz*b|Gy(^MR?Z?pOjuX^iVmQpU(SuYs1
zmVbbo!M76DrufOs;<%Msqmt3&$S|*QeEKpZ9Zpc5_hxKUVICmL*<j8~Q^uvQf7tw+
zlj2PBgvt3@=VtW7OXB9^6#c};at!z0QTvF<%*nE=e`UX>{jT-a<uExCy75kib&IX*
z70+-=Ufxhu{;k|KoZ4R}*A1CVsUN!FaZ5d5yms+%H=kNv-K-JeZKc1UAR^c+gW69i
zmR{+6;EQje^J3Zy+136kDJ%5{>4bXMrEo4S$;Yiu(BkD*$V>g%P8+|$pQF8-<_~pu
zBPr5QKT4LkyRVcNPB%Dqq=(VR&(o$miyCaNZ?2h~+m5<l!BO<lro9X~*(lg3e!Ee$
z>986Y#tF80+eJ()E=a)xDo;e_OPUx$e<LedxU+v_8$^73-F4(b)bp_@1nLu$b4IHP
zaT%%15JvH+^bk~;Ge&JeA>)e?WE1&tqkPV8*<|?*tUfXrer92ttR6cnHI4b9WRdji
zc4j$gTK3pPL|D`@u&e)6Z;#^*=_g0D<OL=^qP{n<+Rl*uV-}9`c&fK(bYlk!Yd_t$
z-bbgj^fp)9sTdEAPRe)BTS-N`UQ_-d9T?4+oF`SP3o6_>!aC?f8}>{o(=fT1NfjPT
zPydtGNwE;SKnW@?noCJ=3kYEOr`<#bG&CU(Eg~4WIRfm5isN+e!Wn6_R=hS+fU+0n
zDdv3x6Z%A~EB81vg2D-QsOi3A79=oPVp|lD{bS?n`{+`h`NE_%7ruy2Wl<0m3NiMJ
zSW%9Xim4LjRIyt`v@Kd?5F)CgQ$K&s8H}OUrIcWMxi4nJn$vL)Cd#a1^@$=Y9WxJO
zTZ97Y0!*42LtiLuAJY#)r%hng5BIb|MHvMVJEoXps-%<9mr)rYydp&t%dQQ}NF&@6
zy;T<=V(E3efqy%|;a5*GVv^(9_$Z=n|B_eFtMd4cZIPnXd>ZSun>Up_kPK9K^9|==
zvX>xR)T^3_D6<9&3cHY2;5!}XB)B`>!Q}sX{F731<gEDjaU8`=Vc@9XhpP`Q;4l}t
zQh~&nQF9Skjr}1;sZ>Cawm@w)^2J8l!-RSIjw$f;z2}`<OW^ivfxI3a@6o>z+~c=+
z<++@rWHAvwak}GlBKt9{KxTBTGDhY-j+Mw>sfDB+)@3ps=8fJ9Hf>mxM=i4t-|nmH
zMSn_JI%7(|g+P}tt&6)M5_H^uwehuEEx{idAZRfl$gN2FP=uB@d@R8@{8|Q1U4G4{
zv2|nqb8PTILe5GlN%ZrRVM1i|&A#&tT$;!|8*hRXb;6PVj`;7%h<uapZm@H<-#rZ>
zc^tSL)u_&eD5d1B62H23wp$nqD5HD$YtX(Gg31;hOzFAleN+FO@|l4=5Qil~=7r}<
z*elb)zBn>dZxPa>NP!uQm8<j0sBiTi>>wstys9_Zux5M>7SQ3}6LpI}cub(jyreF9
zrQxxprrG?QgjH5mj_!(2;~$g2)jW_=%MO09l>QwYDqU7d`M&v=)TOAwrM6(*%7*+P
z<TB;pL*ulKaV+STr@TZR0)TDgKX^y1f#c*z#^fzd-u`r3RF?Ei1ulT4{URWm$24}`
z=D^gsl_txN?vBJTzS^#uNt!aSn#0Ae>8K|Prxcn7<Aj<QPgZXUQ9s8Tr5IC4=)1(^
zcEaqylW}=ewWNEEHM+5dQ7iaRXJqb($3ev{$P~%V_d=b%nw;%bZ11<CwFt)U8H!|>
zv>EkZ8>T&acJsb&OpQG(T;C@Z3cF*)?5eKyS{;xVZP`0)G#4B*MjuuVKOIHZz%Z=c
z#_;_3uHkv0o!NY-nv%4M4+vN0wz5x__v=@{1y84J5*Zn=4#gJ|@vd(-PY?rDk^0JE
zwb*?-4%;RUl^3b94KfJ<B0G({XQ?BT79SP_hC#_|Z(*;^d}+{4?b;t}`e@dS?=a|J
z2K_(}af$KQib=zK5Ok}?w7N)ZACFE@#7T;loLV8tFPBV;RXtxj#T|2-ocinIeKlSB
z9(KXYdz-Qk*#UGC_m5GkrryNHe~tsp?fiQ2Z!0D%Munz^o6fPhpw~u!2S~;5heL9+
zRKkT6c>W&8)bp7Bex^hA^Mi1Nof8y}06qby?n}E>e{1guQnC|lql}yxc^kTcFZ0iO
zP7mtJDXMXH!bp*_JH+b2p6a`zmo{m*7cg5&=n?rp?Hhf!MuHvI;jjV~`ZCt#V!d!J
z&i9F}TOobR-J-S7w<<Dm)dX!5e(}NVCz1NjUhBiX%oSlZVw)jow~&FGCFe1>u6jcg
zgfQmG>hZT7alT<m=9FBgGn88G)d*jva%Ae2Z~c&slgc_guiM`wrEE=FH7RtIJI{>b
zA+E|+pN6dZQ$n6EgC=p+taQLb(UV`Jq+4{j%7noYrcq}zBZ;d-Sci$L)svUQ?7v#y
zE8PtXCze?d?_SovV4Xf1<EWI7yNh20<H`QK(_a@Rc9lsjh@(}KMF>u^%EPEY+$*_Q
z?pRCEmmY&#k6&7JaK4vRtkvLwVAu#2Sx*jVi_~<N^c&U^Xzv04?O$$!KIx%OVH2cQ
zo*?g8hmK=`NrAKI2ZYM83-#Vl)p)*7#<*_|UiaXyp-n%RN-wX%cgTZ$`pUC}a|ti!
zBFoV8Hc%G68#?@Bb1^K2Ba;M)Dv~uBN7xP%MT)1{0%0pU2guK}cAtCFCsb1LX)ljv
zYi)M4Rew%@OSohBf-1wa#qrGI?mhdRl`~I+c`}-PMFjwFMAf{XcDGq9E<W)eiufMw
zV&eoH1^j~&w;nMdRJMWrYTSj%Md@5XIZyA}27caDEn1$SDlcIRWt&XqRsI#>6HnEg
zpc&vbT~#pEVxf={N+D#!&eLs1U2GD9P5!>v;LbhlnB!r%u4gA!H&4L>;ew@cmjljt
z^AR5&pzFs|?3?NE=aR?nvJPx~1N_P0Cg2m${|CvT?zoU6`+ry1Zu&Mn3{POm**s`{
zU|CP^Kk8>Jd$xE^8=v(KB<Yn%f#E6RGux+8<~KitlE_|!UHM04w7_sCXbOd5a|Zz6
z5QnerFeA0Z7~_RO{zPE709Ra+KB(Bv1S7tZ%=4=*uTx;jc}96C`6b2NS9$4xwQyvG
zh&!j5)#6<fi98aaYc|5k)csPO<rr4~JTU-vs?WS%|N4{$iQMjd-bv{i^gqf~Zx<Go
zW`W2!u3aHsU~PD?SSq#L+bjd$D~WM3tBm!6{r>A9Swo@kuyx=_OXhRl6nVsV<YNQ4
zoLv|WwL~f;&@eQYvcCx#J_jVnP#jJmbaRR`<J}$wHmrcet(N-d&L~QCUbBHm8Jk$(
z<&4QaNMN%yEPZ~unu;iFsE_={<Jk0b!?Ap#?lNWmPAVYRkJ1J2Y&ZINjmlR-pKw(w
zzx-?HW(-k_mmBzWu3Qmepc{DclY=<l;tPf`7uc&;7TYYf>^JO)ud(m`6*$3UZzI%X
z2R$&DqoP>Q9wOqe%$J&#oH7pZ$3Qa;oPQSp32`-km!~@oz2yXT720pCGF06FpX2oL
zM%x11Lujdv{jZHTKFlMZ4O(eu)#Vp_A>9Q3xndJZwU&HvYAfm^T;BqmqY3WHa+q;@
z|Nr21@w!blQexs7bRRgiR%&)O<@pc&`^&e(WNbO{*wt<PdMT*cusDou>bFd1vXY;-
zH+RYV7QXz2DY3k<yeXwjEl(dsld_vM>|bRBE)8`gTzfiYPp|<<ilM{(soR5*LV-&w
zxy5N1-17LhPO)*#Z((}VNEA>Z$nV%9^L3O^g{`jjsE5pKKkO|t<~c%H^ZBgd0r83I
z8}`?Rb9>!SP!`%fH^JQt|KX9#zEouV{EyN9hA^|AkaN3BN17wrGmZ^2lE=9$E(o!S
zP1-6EGjU<|@8erK%pJmmkm2|m??8z4NuW1XWHe4KR7u-(-e@T~NCA-A=3$jcGPn){
zXD3myk|&eqS)5zzDfH>$_j9(5C4ujmf}~{ZWWSekT*H5E0n1X5X2Ep{$8ZP&^FBBu
z6t1F(OOE{(D#r@WbQcez9L<G&jP0Ka$QtW6yGp3~7nb(MT#nvq6i8z8{QY)t6@eX-
zCo?ZM$PaVO!Hv5;LLE(^GD27Ss^d%C<L<3?T3~b@ML42kiPj);948y|7-N(`B)z4k
z#&p6>ed+HiD=Z*tAnm5?H%+~Vfm?3ai*uJ<{r42l<5f&)Fn1;eJ>HpC0e(`cSmmvH
z@E%}e1ZzH`e}up&p_46BdEJG@xTA|{1;4*&eaQWk9gkEqjX9)1O6kK3h_X2jxHU=x
zqP*%S#w<_xucfZ=n$qZfrVUOaNZmRdI&HtIu`w9g^pd+dCq}$n5Je+jeWQ?x`u|t}
zJ%BdvD!u3n+CsW~0aJn(H7EQI#Gc(kppK!_nb=7Pj>b3%Y9qGNDz_^UtU0z8RV{hT
z4pNIM<5PZ0F5UsH!t3HS0}1sT2?PXMcf?E^8Kav23et!vsXZjFXf?c_D%}eHMVtLC
zm)z}lC4y9B;(JnlW^i>%sh$s?vpHp<^wj#uw=<8&xfFqe9pg<sLFCnxm<|Q?CGEP5
zT3h38wnQ^0^eU}|tLF`#H-?M3BM&wW=cyQS)*s;eyje+IcJ`M&)^HA++a`?_it`WP
z@~O-;7){jM0_0%+mPG2pa8>{-q+)F~SXni32)|G%9m#H@+0Xn3HVsXO--LYLU)~Uc
zUfrtsb+XojL0ca)0hpHnn_(NIZtLg5{0mNla-o4{p(>#Yp*~ccu74|?k>NRXWN}pj
zkz0{yjwxu6dBz>n4ue%uGeo&`SSK6Q7_R&&JCZvFWd$6ayO--3CSl?gM4v%^#RWtl
zJj+#7NPUgNNN7h*UIyj9h&yCexL2{X-^>fip=V@>x+?MEDS!UNb91?N<O@_z>@@=P
zm`Ay7s%6fiHa`70l5m=7pMQ&f>Sl|gl6wBULGoBgfPek<)JX?L={23kKfXEq0<!em
zJDk#X*W9@$w)R~;?WkPb-#?t|z2Oe#$!EOE;<(ih{&&@5@JoM@xo(~V(j*$3Eu8n;
z`Cq3GIwTfnRYsixTjna>GuUU~&hzHn-sg-g+=H-VN%GdFc81QxMM=}QUTFLLX1vat
zHw~gE`Y-Q*Xe#RJW3%U(ajuz6as_tth~nW#K;<@GBP(T8KVBKd*v=LBc^6Q>`Eu}^
zj+J7^t=IYP7Kkc-3tC?0$ey>50<#tp1!NfWfMn&`xs$qBf3+RYwyRwi`N&X;FDIHI
zFY696EQdy{wQ$zm0HGRko*<)eoi=hrXV5h`EqY+NcJ0Dy?A(I)gvpxwCZ*fa{mU5;
z$vD$lzZtZ!;$*g?uo%q=8#KVz)n8cq;1L(Sp);EHi2NMX(#a0vzdh`9a>Pyy<-tR|
zU{iXZ7hW_~T5BzADm6p-c0Z8YcFtDfW|B#!EI-vcE%m^f#i3TvLV-6gn&C}so8TYH
z?F#m2^RVM6T2{2Ro}>*+iny(<vNtCIl56PmCs+QtW(^f4z5RUAv=$f!<baX458i7y
z?z=H^EZ^Qlf;Qv^u$V-*pZeRmI|BC~>E1LJFck1V8e8A#LE|PQ24A}VH%t1TyW^V0
z&v7**DTXDOxFOtgpQ;QU;<1^_9o{&uO3MrsBK<Q<(4+IXip7KSR`$OJn46bVppZcu
zO~#iTa@|RndyvpyYBT0uOjsiBDP6Ao+MkqF?eC+2**|Af_fPhFN&yE3b9%lILp67N
zuAq>T7#{)lP2|VmlRYc%+mEFO7gDHg&HvB{18i#-OTW#T2Cmdqmph>PziYVd!f@2<
z%;x^4uo2~UusW+hC%rpIsV4B?e8QY_c}ZB=Kj~i!EV5-@pbHR@XLj2T)p`y<vF~=1
z8V@!=2MnS=4xx$z&3jQ#B5nh1L3MWDVc)2gr&t5yC!n;>72IW_nS|BMy8f8=e}B!9
z4y;hg<7g)>z{lIZ4El_?{6`nOPZX{c1#fn%aM2*Vnh8v<0y<^|Vkr>$cfSCdEWwU!
z7hvsuATJ_5<kcT3EB+9Y@`Ww^-cHT+bvT)F_w^cQy$w?5(hN&x^ZP1OOsLdx=g?Dk
z<3V?P0nh&zol>fI^1D0l@rzbHVrS0Fr1K#r_oUO?OWjx7E$68>@+Mx}2hE3_dq9ol
z)9hmosY`xPQxaTjaNc?$&|zgqPQzeD%tF8AEjmT_1St_8p3zff-tAYmV!Ii~op$EX
zeNk~1a3-qbVR+Ddflad%&&4WdcS{_C#`XZQz;pn?casZX)PCZQHk!B`6)PptBZE3W
z=BZJjXxo~pU}|8x)cS8e{)WhscleM6rxsRT!f%&Y**pW;w-hX=XrovX!x6+Eyx}b7
z5XAda<M0s@Nt@BFB1OJNrA$S+sjAW@Sj<Z0Lt$+!Sl8Boci;_rETB089ayi+v~fT|
z;1KFNqlJF7v`W7B!X!BA0ys-dWQI5N%tDjG1dGCxXp?Ab-AE1>2blHdu}mqZq&WC+
zg#4Dz&UkUiG5Z-PGLDdMROIUB%P`z?fPm8{94I#le7=oe-UqY2_q`Q=>MR^(7qVRj
zz-Hp{Btwk5=O;2#*kSBHTe|2VTgM8Om;LE50dyH=*t?|tcDu@3=HitrF3kBrj1Q2#
z4xj9%XXnw5Cf;BA9Y58A7Q3;S1jLK?1`ESng1Kp*-W`ncIK|XwZGThph46~(MNg7Y
z<uI1kziX3(Nb0)%bHPPz;}?;FsH~=}BG`P85XinH@>RLdEitHU;smeoI3W3geDj*g
z*!80wa(FQ8RodndFB~&O?&+f+zr`J(Jk~xVF4Q@*Lc1(k+h!qM-C;!xV(=~n0;#R_
z-p)wxdW~R)^>2wcOLODDoXlr30Cce#x?Ka@yelhhG8<pr-(5pzb4C|$Z;w(Vo|c}B
z<yK17(CUzWMJVLp))5HG?dR8Uue{~j0}9%{BHva{jsTnOd_As{twMT!nlIR2tU3};
zzJZ|z@KHE)Jx7qnkLjjH*WK|2yP{tpY!ym|ONUR4-VhLtAfVw1DF-N1_pqnFKERRK
z6;*?Ut;qEkbZ@I+*N^WBbFnV6mXb4Uv6WH@(UdMBsspNbL;<6up870J&zrAm$-)b2
zDD>Nn;cV3d50jSOUYhgXZ2MpIE#W$gn!1amdy(f!D*?c?&F?S35tqJZ(v-x4V`zY2
zO%L(3e+yAf8vBzi0PjWE&lJ+I)^ez+EnII9To}dDo&9r`HaSvKrN<RA5H+k?u95fR
z^?1Q{n!-|Ae56p@wfMi_TY1iqFUZ!_Uf|;5D2hUqvfKkDQG(F1*=fzv^h<Rz9_y&I
zHJjS>q*awN$8=Y*-FzX&dHm3<Bm5w{^m-#%)K$$fOKgJ?hqki6NMaxT>M*z;d6H#k
zZuGDX7#<9)EWO@S*cysrL^d3CW$r2R@8MrQhWK7M{Ed!%?Uf~QX_=Qw2pufibc6nG
zJ{^@4ZMIL@1tl2R^SLFl1|gu-D&cMG=;X_Uu`AC0HP3a`f8NQW49_9rEt~_og+&v&
zpB6Ldm-KZkF}Ok?L`HC2syC~7ZOzT43kzHaHMyU}59VJ5GaN9;%*Hb@MQ`ZdH-;z-
z0M|_)4`#-b;pZ`z5Or{n-Jv)K+5M1dlka#%!3q&~YC}M)7Y#&3p<)@vAN6W4f;1zP
z2itMwe9L5gOk*RAi(FIph)5p?V~hcSZJ#cDyQnN)sJ#a?h@b*=;>y+C4~En)NUKQ0
zIR56a@2JYjpGP2s9SDRAbqYLxifIO%capb$Jz1B9ASJ}x%C@R2p^)T;Yfai$qdB#S
zU(q+WGcIX#skN5>0}U8Y3Q%D5Px_Fbc-<cLBAw+gSwB>MP0veLs>h5(Tacq=CiPd%
znfg^GbfloX6|!U^9gW<ko=f#*;4cr)_$r0dAp5ARE}iOYXGkr=ZpL0fP%ntLhLUA`
zh$BX72zbaKRD@@hK>z_NBnKn8%ZO^hEi&ZDFWu)HeSW)4$^T&Gvn?0aHOK^k(C}~X
zcD#NfY4}|Bd~(?XX5LZfAjLsS{A<4Mf5VJ6P4W|C$FB`q*gVOa<Iu|MP+zNV*XA8b
zdK^L4!(eR$EI8Ke#S<O(zgDB3&Y$eeyQ~gXN;wXtr>_38+<EA-HR*gAQB4Nft-C?7
zX8B-d*@{f0;9RmiB}}>3x1J57YwpD6uLtZu{$jI_f78f+FdH(5R5wUs)sb7*1D6b)
z>qGEZLI^)mfY;iHEn{bkQ4dlNQt%K`J>*lHApGRrhLg2{BSmzhpH>{YWzT;q=QCE@
zU<?8GXOysn@E6Iwz;O^tG!lb$`yZ$_bmB*t0l;d8;WR!u(pJSfLeMGbm;3(2#*lr#
zD}_*$+|UDD^(}x~e&u39%b*X>0oa$UE8vSjWs-Z`D=#TdWpRqM0FoJ%(9%m(?c5Xv
z(cTNot`s`JzrFixYo|k+S3N4wrqwhf?maE%9r4|L>^txp6}N*0I8833h&~S_fWHIj
zo7Gsuvn$)l^fvCHIOjTKGIN%HG6RSuV%~G!HUc!uB}3SCT+}N%tM{GBvLJMr*<3oS
z-!Uo40wr4z<N~f!ns{AlVAgt5;Ynp|h!#oCA5W+FLmIpuDel081%fQ<D=CXzxR(~O
z3f!5Xc8BY7$V%v(DGO4hnG4cDcttdH3%{vQr==k+-H*-jAP)IOvrM0P(P|IPyUxgb
zBB~mYX)^D?d>E=xH41Dzp|nCz(z7X5NlwXHCUUvxcYwDM)=;j_ztV|Z5y&bTO~LF$
z-ed=CRm?WeyGOU&5cagZDxRQ2lhPvO0ibj?KBK-Z1tEfC%0vblWG94Kw44dT7GbHP
zYF#m{o6{!Iv@NUDzD!t=X&&n&#$m>z7w&!>f?D4{H~B5`3?gI?0&4~1?U!;86qCAk
z2U9h!@E{lahWY|#9-1EjQ-=aj(JPM^P=PvNlMX9v!VEEpLjH1}iHVAiht!uNa4PJM
z<3Y26a#<z*wohiG@p}B?X+R>S$$r!YnhP^>rz%fS^dZY<109X3uTxB*^c(bkW|C~4
zfYA=O$dm8_G=0#)zl~y<;H;lmiM%<*>mXv$mBzd!;O}8?Iymf&#!=b=%L@F)pJ(X~
zi<H7x*H2u!>wAbwZmB8)g}sGN_d=h!Cj6%Q_#@wW-TZ$8|C$`a?hLzhq4(38(XWW|
z2*p!ulxqV|-{A~;8J3@SH!xAkr;|unN9DNaRcTZ@i;n#8`KMqlbE?)uAu_=e<@V!z
z=j3EF(pSRk?~TFKXCU!POrFbKzR)dCh`giF>`kcln88+Wp;vgDSamEx(g2U*-yD@=
zr3GkJgUT$RprmOl`xl?hRa?HaE+TiES2;IR9y;9`Gi$j4F)k@WexU9?wpzdFbnM1+
z?wsxZKa(b~q7b5JlcASrS7eownw2~*jVs?Fq@aHcE{-oyp?aYA4`!Ehgo)Yt^UIoW
z;>6oiQ#FHImO9~|d(r3EA9ZlC^jv4mDt~{h!Dtu#XxT6S9W|C(Z#T<ZQ%{PCmNs;#
zu)8ZNk~aQ5A+-1pus*jbZ`pEKcz<^RvikrX!?xj18WfRiK>xrk>WCG|)$2=KM|xlJ
z{*Cc##V(%h0x&Zmj+?}NbOx7q6cP38?~nenrI+`=O&Q}$v(|g<q|RJtV4C-h;jRZ7
z>4kO;ANzmHXuqJGn9X>y8B}jvsFmr%!i=aJ#!reztfSxJg*cswy2u+}N-Kf`)nC#J
zZm;`zxYQo-0j_oas>d%rSqd_S1Ve<S=~L8i{Y)`!Y6L_fl(rB%E{jNq=Zp5k_-)eu
zI?vp|!h+NL(*Am{ZV_1?iHZv1#1_`_=1}TA%7o>D1NOTkEdI6cYM)ug--dW*lo|<R
zF%ICln8wAA15E07ksqdeEOO*9*2^oPEyqO=8#QE>4ZA|CA($ao&qnD*aE+|cO71AP
zgV_!PMwP}AYKOu$Gp?wiBJSYv`Fc##0jWo#e|j2nH1~pHL3s74A(6j5#0vrX<&u}J
zBY?C+lu>Z?bl|A+^+%2O?Rd^w@VN=rTjWDZRHg4&&t5E0PqXV+bjiLwR5g6h>(kQU
zGaIyL#54QEGhcYu2p$sB2~!+<A;0G<w1jX-21x{AI&|}I!;uwO39#~^aMJ8kpApPk
z4xh#^A?eG!MsB<<zasz7E0M86?|&fYK3`TptKAnDiO8g&E27RsF2BxKuZ{sBz+_Qd
zyVl?2b1~kXIOc3TTf-`tn2F5Mdo<g$MyJ0zzkh?1Wwn#=<E5<O=HhWTBU1YIjw#Nk
zh^Mp-KMlhUIR0K-y#6fvJr>1e+`6op&YG?mFx<+Nn8=kN!P-hjX<^q!|3Vtzl`lt|
zz%ocxY|z6&1;tuXSHwhSx+~_=jQNYJF>~<cQ$nWDe=l~)4DU_=91{OSZ|@M*z;hkG
z`r*y0zd{3!GV#s-^J#tOK`8}l#;-Q0`C?hq&c`(pAmsg$KtS4G)AZA}`HDy&i@=0i
zk@3Y8HcG1bR6<MNBiFxDdruP<NpTOU4(W7mHi8r|OrNUQi@`E53BS7l<Pt$_PA@}+
zsmT&(&x)EC@A$mPV^sI(JJ1XDn|J8#`-#+VJkr}$=z;QSskdy<HJ89*I7{N`H&V1i
z6zs}BG(RD2p40A~lJrHCnLveA&uOPoG@=d`4=Cgc=MK_h`GABSQ6~&dLDnF#N2>y^
zr}s5KF`J485{h%IxQa%)yzX=8LhKY$IIomo=Af#3BqdLQW`#k4tcq}RMUyBpJ2L8D
z^(LLimmN}dst{%vAzGg?TgFQo2Lxr*K4jqOiMqXCwRfu2=enERzCfVjlO9p4(}Wlv
z93nj9=08*@(<q9ELMsZP2)r+`dK5H2Fv028Y2Qb!=P6x=3>a(F(cW^l2vJL5j|S>s
zMMF6<n4kh87>^vwPz@c~nZrk+B5Z=udi1M+PRXkZtDztd%aq|3FlcJ57%3tml^ebA
zlnx?(;1ryFdfi!mJ>qYhL5&@|j8;$%D-8)umSg!Pe%l*OsRVQP=LPDNKr`s^;Ita2
z_XIV3-FBdT16jr!W);Q@d9orX+q>iyCbSKMEah{)OPfs-zY2V~y|j3P?PEyQj7?_m
zTfdGMSLWwWh>eqweYJ^a=G}&SuAOUR>OrcM9vaQj;C7%0Bq7}QR*43+z^`!luLaD|
z39n{D-s01Iv-qHLVV^P{$y=<MX8-GntB+e-gnK$Cjx<ilDvpcPT7hq<)37L7-@KLw
zWGC2pN;v;z4hrIB$S=+XQO!8{;$*F!3;;Lpy9{wup_|l(pmjtYjV{tmx(1|Eb4kqw
zyv?KNdP(`_Kci9`J?sO18xPiv74Vjr+G-41sU6~PF1EG~60z|d%+?e^irCNsMm+G>
z2^tnt{vyx#EV+*FAE$2CIJIP?Shu`;^t)xx`Y@dbx88|u{N0f(M%1c)w)&nQAuXtV
zo&a@c^^^5>8|g`A2t^hG66$X=S6$=|J{;==5D<;~shBDYFmrKS`0Lq;ZdtytkCAV=
zCOwy^rN;vicyWx}SJ2Wr-r%8H18odo?Kb626DkDk*`+q%Bm?6d9RGQhBm;hALV7$>
zS=Z!qi(fo0?DtjtUj@lvO%dmnpT8j`#!*KEGrnLi1Mk52lsEnQrKBzljkODjzeo~~
zpR$Wj|0}=HGyzXhoRfDxei!3jVkchuZvk(=mS5}IW+J9d;%nDi*(-<b)$p(cIFfm<
zVR!X$RDT)l?$W-3I661WhI;IU#+*nB{WV*C`;k%EkxoE#AOCs&&d|ddvD+xt!^TkB
z!pNhgMaw38lbwZtmG<ePcls>I*{1$`bTpCM+O4>s%sjeQO2-8MK)3y?U-^zs+RR=H
zO9=GxKD(T-Hkf-fckb8Y%!W2%95hqhckGvLH)zZsnVdV4aTe*|x3=B%<&`|N0I?6f
z@y_`p>q-GIalF+GAqCENbkNg~R<F~M1Q(;~jal!Cd=+Q~MQVdmiq`|X^&C6w4?9G@
zLr1m1<XMJ^Ol}k_9<m2r7S}opW`+!j(ir?hk1B*l2+0B}hk}12L8P~;$h(Z(t2&tT
zsmr_%5|#V2XXwL#^y#TbUtI|NjlWd??ePF>@B{I)*Gl!B4s!7Gf%CoPrVb+`o|TY0
z;E%%DAa^zK?G>T3q-===Pp~6qq1?27eAE`P_LlYwrw-m_j-`)b&qjN_pAh7n>}&xf
zhuh70e$$&hG*53ZH$_cI^P{%P`kV=_h`pHt-Ax{Khb@_(P$~E|1qrGnM9ZTP`c_aE
z;erFs<O45-QM2A^{$g@5M~B;nkh?zPUr<2xK(#T>$eY%=`*r$eyJty$OKkA$>a*H2
zB5S&@f<_G!Wd@6T)nkg4bi<C%&`iFJ>ZBi<6O2UVMj~Hzu+AuX<V!_UMCE$($BV2G
z=P+1m=O%Vu=51AQi&A#{@fYqTiulY{{q){jn?WGUvTX|Nc0WB+y4y=?GT@M*Y|5VD
zPAV>2)9`IFs^)$lh(=t$L2PdWcMd<uZDyVEGhj3Dw_IrYb@+19`j`Agi&Nuo-`+OA
zM%ohd0jIincjg=0RJH{jH5~g5RzB$!BOF?lMmGV66DsUu1^hCBTXwXMkkoMmud@d3
zHUaz--TLKdF2wZ|cdKS%&R4Fe!+m@|0$goVY-+!8ed+1z-2kMwjZ4)Z#bj5<vcn7E
z7EcY19lW>7-|Z+r->c=Q64oS(jO^(_`qhr){Y!Yy$#SQ0ev^L(Z6`$jP-NamXns}N
zibI8DpfhpSol4l<UxWKClsm4r^^^*IONUg!zCJ`ui0KlPVsF_~oo{AG_fwD7JHVlG
zR17X2@0B!3*0jx1R|BuJKVhAu``&p!h`T~fy9+2I9&kiL!;TAg?>8x-`BkLcbEb+F
z?U*f{Ju3Kra|Sw9JQ59jzxK{K|NEW#_FUO*8X<&VbL2%zb7N}y+cxpNhvEZL9wtPV
zap6xUeGl_9dQk4Fi>BfL{^K=4T{xTvPe@?v#Ap7a7Xg~U1@Yo!>Ji+xbaHY+_R#7K
zEYUptT$q>bni43Rv{df87$nFShl!p&--cxPbbi5#I2w6eD({1kIMK<)&aMHm@`Jj`
zD>+0K)P)ge6(!(D&A}PQloO6^5RW5UBZ@QUzNJ%2gF>P^uJW>^czx;S6Org4m<e2m
zsYnpT07Lgm{4W^>2=0Y?jkWbUmD&}7+2UvInag1C`YgbypLLEX-5mpk%3-|zCONjU
zg><@vdKoq`@ie-l#7Sb@U=E3mxGfMvvYWcG7VT_2kj;O&+IQLUmQQ?!y{-ew%OHQ(
zHfN-q*dm-imm?F4TpV(NWeG6`m4wc)S4H`_uooR*dt2J{qw}>-939i%41(!Xo=G~$
znI*AE6z{mY+x#nkcMK>h40}m0o|EJ|UMW&Pf)bUVuEjBei;5YdHl}^m=fkX(pu5Kl
z9woSN?S<mG-fRt~{X2MDB%#<Kl@KPf)46+|IWo1GqCjD|H58RAk$YFt|1p*6puhJ-
z<s=A<r31Nzx@#f6Cph1IqD|YH*PCm<^EMikudCi9M?`ebu3nub&CD24qIhT)C`M^)
z5_<7Cq1T6S8<Kx+cGq1Pc`MA4RlyBSB7(m|XDK&K!+=qqztUk;s^BKbmZ11clI_kr
zUwiCKW?yz3nZ4F$pc#LZa3o@T2ssaDa4eg?q~f`3yUuhx7n^SuK-xb-?9|824mc7S
zS;ma0bt^t+etFQPp`}Ml#XNitg!lN)qE90pptdsLyRbi^&V;t)Y+E7aJQ%iVJ+|7d
z>#<<+xaV&!X4z3f#_NCe6-GqJ2=>=t#prIofyM}*Pn+~;o%^^p@>r}5QI4r`M>#|^
zcF#ya%+|CZt5%cnl+l#Di=q|>4L8FJ#r;m12Rxxp1;^FJ?U-xZzj9=u<ZX@Q4O4M{
zHcS>R_qeTe2B^uovsHX2&a_|9$jv;pO*mP1`NMn_&pl-pD|ioAytJ@`8^M>S1u_<c
z9!nZs03Fzs&xff0c)Sw6j)1NQ`6m)HGr-!)<f6bS6`Eq+aAfZvgSi=Nn=;BQ%pl}%
z^}ZB`z7q;@qJ@mB&n{N<QZO#Qm5x9l#=Kfa&%oQA?z#q}GGZ7KFK750VHAJtQIhO!
zqVutaFL&AVDj^!1PJ=uS)DpR+qT3|vO}~*&C=$UmsyGk!6qzOj{l@7WX5rHLB27Sa
zVT2q>#NlaBo;89!I=P6&c9f?v1>wXbeXG5va8zr>=1s_$dNtW?PhmmqsQ3=Ct(0~@
zs>nA|sodlC64zCvKAL6c<W?cBXnHqZd3aDdYzZxJ;N26XXOom}R9vdP$hwBdw4t9V
zFc!uecJMqh?bHxNI?vcIW@{8{EvU)Uy8UJf>()|y7T8F&Y)Ro+R-l*B#AoBH76VV2
zy7+?_uIKzO1&Q&4al5&Jdx;lI`eSO_GVVK~)-0o*kY@F>J^noDWP@~))IdSJoTC6Q
z{)@grU$4T39K8L%M`JQtza6M$p6x|c-4D``gROvxE8SJ!F^4Y)#cu_$j!5(Ia;mc1
zO|9_l<9=MK7JUxYO$z!#q>J9k^q7#OhhmgFKk)?czOY=O@4r;M)uLxX>dl)?j#%v0
z=!?dyXL3B$gjGu3`Qjkh$4ICj-%-OP^dm+<MRK~_O7MMyv*k2@!Oh^Czcle8@4SoN
zhW5s&T7bfV({?n<VHmYoD9%A3Th+6p_JY$Rke!eMd4xBqp4$bI@x=IT{GB3@6|5BE
z3TD|%HN)tSCHZ^9srerpJx0gtSQAAIa8ea(?$shRne=3OeWsIZXPw}KFoddhF0~e(
zzYobRB=`I-9_1)|^CtYB7U^!5`+0I(Un$SCd$FN%a(Tr$3uAnvd}I?D6O2%-f2&x9
zm5KzUmu@`oYd&!?U%IdhjyuYK+&24#&xAsJ_`=4-h5vZmuAX~3{pDsI|MnF)m2uYz
z6P1?Gd0Ip-L4Y*bT}-@XD%q|uFa8@t+Lx2%*L#2oGv4gwhEXR2H@)r~U9bSXi(Lsc
z9H}k^CrEcBG?JT2gE#m0H+IJU)D?Ux#ssNdOPN6)AveB&j?%Rq62_|ZJyD8ou<NPF
zq6teVUoOWI$v8<9E_a`MK!9RbVWQ>b;d0ENU7(#*2Jm++A6G7a?0eOPyJ~U1JCn0F
z@@o6@1?Guy)2qa}?HjxV-<f<To#Ry`ZexG?ic_(^o(+_0nO>x70-q?ZuqaC4qWONc
zotN&*U<m+R@|gB``aQ{r9_)ru>qDuGO5(;M41%Ehr%P3arm1Q3i-${{?Y|&26&%#B
zxN?>+QJqGC1xTuxBS9OH>hmTTYiKkZCBP$AbjmslNt5z2B!)<1(#2h92<#dtDz_O3
zn`ggse*iw$uZUMt2=Tje#fmBWKzX|@YMQ8DqHn{kwgfTLc8VP^LW(!;)sh{VeV?~x
z6!L1?U^na&RnL7-&|sFT6){oK*OxLzeNmHzQ<g4HX>cYtvLHQ*S7qGS?J)TrX;r62
zQlr7H&du_SLkbIfJ}An)I`T5Q(Sswh@z~svQSK*K`g|tDozz+|{#?nMx@4Z=i;rP(
z=2eH*uQ*GT$9eicXRhiJFm>gC0Aj=cDQI98$auVyfJs~2O@|gt+DZseNCmHVhoqbi
z7IuXWq6~XR%6b?FDh`GGV?P~~t%BJ18muTdd0+mWGVn{)UZVg77lq=!ygy9)Sc5w2
z>m_lxZv)N%xF|lb;`vKSe12GDig{+Q3HqBZTxb~WD-xg4&0TtZxtG5rnAtFbfUnJ>
zhu8VjM8sC}gwj}3p<3nSN`lhES2|&ih<pa3O!xS5J-Lg{PG6v=t5As}#7oopCjkf8
zmv`YmiicvggQEwZL)wf9Y7n{Gc?*^vP@WP#TMT`ri6&cI7^CmGMQiL*Jf1Cu!?y|(
z+e~#^$OK)%vG#ANOA%mbZFDI3KV>(f=O6<R`ZybrRD+L&*BauyZ7OUkhV{q!I4seS
z`Ov^l^)IwH&)uITXmv;iq#OCwceXX>^Zr^2X&Lq%KHrR`twJ}9pshhKNdq-B#su9#
zZ*`Sg#36<vLZq#Kcp;6ul>_;J&p8h0a#jF;T8-t82AP341E}HQKm==xcJVJ?p|D-)
z45NB)2Y`>qo8+G1US@A|%N9yx+-#88*_fKRIJv9Rvl|c=LaBaO$&+UNc`WZ)z*Gwq
z6y*{8wkD!Bn_aOab_{h+l=1@%*vO*|oG6zqy35Qy)l1X)E~{T_X`#8XRl(1%PB)A5
zZJ4OnMO~HykHXJr)FZQVE~?i2(}LT=$<lY`3Vi;PbKd0FC-7j-y<fEB>+#GeoT1(Z
zf;UgE@9#+pDk}~#vLNeH_fX6PAA1!k)^k%pv6~;Db78gvMrkl0li~A%VIZ{T&oF3j
z`$k4k`Tp_wSd&EXi%P0ij!GQ2H=bv$Odn};&uH`T$4Q3DMf}zlHgN2twFR;P?Xen{
zBC!_0i)>u_H;eyJH^FAjiTxSyo=sm@swaS<Kz%DVuFO)0FQk)IXTG1fM>Pa$XYoXD
z7$@!<YOduQxp!eD3piIwiEQnnv{aOQjn~C2d7NhNljl?BHZQY$I@<)N4pL(!^afA{
zuvt7SW#^tIQ1*jcOzl5zM~$hHl|mcsYlAq_)%T|h49e(8HEha$13W{KWf-@OW!@mM
zB9L$NSm?@8+5xN$DBEN;&{psKKnZBjR^5lzMxf(8_v;en9p_jsutm+4UI@jBi%Is&
zdL@^Gp}w3^EPkYV^=7FoF}z>>=qdG&+~~K}DXikZZ^Lt+KfPl^^C#6Fj?E<qr9|3>
z$}QsYucuzjJ4FY2>g0#lU#+K*t9J*|F|iNUSdK}Kuf5}9zhyHI!WgDcZl_!(?NR-U
zSd+P(uo>p@dPORjF<l~9bRxg-JQ72xH#&D-=1RHo8XRfjleDR~xZM-yn|KE)$NURi
zKGAF;EZ^TkEefr1`~=$SGqz2iN4bY>5>B<kHVNzgs2m+G>}W#?Y<;aa((id#<rDr3
zuwW*b@7LTJSF^oeWxz3P`Rt(r48Q}ofBGl5-!~uc+`;8WuursGsHg?{5~!gDwf&a&
zrc3(eWr)6=EokS$z}b<XzYsPKr#s#d=<xpFG+-68Q8Vz@`ERLv+m+W_<y5jLyZso>
zL4st%RP>gf9|~6jGq)v7XCIf+Iv4D9j?1fm4aiOMO!7P~y84m41IBx<L$6{-d(+NK
z@fjYiE=5DgY$jlJpzeqP1rDDq&28Y+8YHI8@RgZRr&iOS?l0lWmp6n!L{nMR-Hw(O
zcR9cgU+b67Jix%@SjNbVoeTu3L-RwzP1HG1a7rJ2pvjz;xQ|44hZxO5-gLivkBnMI
z9bt8bEE>yCfJ?Ip7QaL;9a0n?rZI`#fmAY9V7>gp2!mmGaFquo!+5=BN*_<g2>nAx
zT~)8{iJ*_eg?eF&K0#sswqCrVflR6NfJKL?17bWY%jiVqN_xz8H$10~_eu#N|FiH)
z=u7d@NkCr`PkoW&AbR#fo$VbKQ_h^(sIK=>ca|o)6AClhqPFyU@Tz`(iF)>;t@N^(
zcgfRn)pu5eflW`gH5|w&P#NS$D8~7tEP;O%mKRT;n0G>aS#+0Z5|x=9EyM?7!z^ft
z`vdk_vX>|^gTsC>_a~hY&TlKIF2c%V=twdsn`Fe&EMJ1eQLn?gI0mbz_MWW+ftLn#
zWdytzSQzw|>v}1?kbRKr?AmaiXa#z&6mH5ADqYE+oTXo)ip3p(jT`0b<(cS|{slmT
z5BuyHNK|E3EqE>N_qtUqoc0<6)#tMTHL>)N`c_iQw&`JBvZ+9Z!-{}Yq%EIfcYzn5
zgW6g`6~nQrMiXMk5w@`cIy$4W7ABI$Am~H)KpF1|=LABRV=Ce>bR>Z50|1)SBu1=z
zrk3~ygD^@71&^&R71w&Qbt$!9OWZ(-0k|r0@wuw0MBlU%;B8fr<q>KSNo}cox!bAL
zi=O;fO-RZ0D!dD~SL^MpX$TfgNbu<^pA|`|wU53Y?+^wD*!EX$5x23fI!KNrss##~
zHU3{n(D55$oWlVx(M|idDgJK_3}Sm$Ew7$!Gkkg}jB<5km2VN9f$~XUZ<MYbBLO2n
zeynj>%ODK-@lq-(N59!~1o)Fb^A<2E!y`Ix<w^Y0aY_7<D$r4C-0}|qwU3#hR{K4g
zXfvgM*dvPrPYJ>}NRtPVYou<edCf3aeuRBC<n1Kn@q*QPw%T$9ak^XnUJU|)#VYa@
zh>K~K=t?jql;tE_zq?PW`l>mk_oo$CWnfh$SGvgTn`|P-nrYZc9HKJb^&g%^>9fZ`
zkcwRwZay*$<?pA^LqA{?m;D}C37Pc-;1=ZSGq7Sohu|Fi24$rvG$FiJ5Y7%B(xsRA
z`Raj3XOCDx^NP+t^#1*i6O%v3cRX$;4|(nlgi^$5Ql})4aFnUGf4f{A*Ir^6`)KA)
zE>g<>jhK)75rhm!(Gp*lp9)rFAK=r|eVe=$d!TQ<IzeadjV^NaABqXKZaRDuPSoLm
z`-{CyuhJB~qN_n%#rTHt4=iui<kgT!SLHA@Qul^4p-T19owrvh7xHb;fT}V9MavHq
zE?45e8HBgsufl}XC0PWvTqON2;#dv1N`KgU3mAt^@4F4nHu0>pRj9OHL;QJrT#0?w
zt@KLtgOv~l16Ue1Uu=VVCI2DN2&lb1Q}xORsBP_LKXiY-t9h@n71}#H9Ks`?m4tWy
zB7Z>d#8X1kYi5ygvpnQHC%Fbrp6laqz^Yy%seyV_qBK|ikCQ{`WRMcWkQLFKn3s%g
zN&o&dWn%olRsHG<l#DO`{%@TqEgs-r6e(WtXYp~od4jQIPY|QXVRQ<y26xSvRN2Bn
zICv82G>>EeJp#Xv9!3WnTE?cSU$&PSpttrBzTV6Z(#g<=<)cv{(cpqBQZ6!ECtb>?
zceAxYVa(tK?vDuZe#d}cqz#)(4m}@NQ$oQF#-(|4fS3m<O2`E2TyfWQt<ylN<$<Et
zusl!jWae4EY)x6^yRhvYe1vZjD-?3)Z`7{n*#JkFB;?SCnN`F-%Mo(i04_qvU(qs}
zWRU|Ydz2PMV&ZLoh^-V2;n=^12)n@T@nVn>!U3c6dwTG+jzR$B6~yr^D1(*2UAjDi
zOKcO1cuGG-;-fB2wtu^Akd!71&*y%G{NSYAy?rryW4lNZ=SFv{j5Da<AcJ%=;H=;=
z&i;=t7mqSS%-;lucgdeBX;CS_oVM0qib@9cg!{B^XqoEl$r2p2B7P6!oB4UM)E;OF
z+K*JFAk_|*=sT~a1%eTtAe$5=g=a#pDNlqK{TBxxFEfwnefzelCJ!eBiQvvb8~l7}
zj9u_GKLx8w+%(WiqV(}9E3VpovekJ$LSnRCZQ`ys;Xdc8m4$E*Eo?=z;C=9Ya%E6U
zOZMhFr<;8*wSn5XQSaUaJC8E6Zea=B9=u$hhxuFxOSqnWJ4!o+*#6|W)yMyB!GFmk
z>@td4eBC$SaMUZY{qJV4M)gKI^|-7q&I0_BsXrnwlYhIY`cInQ%l=RCaHOAg+Q|7M
z)<ETDsk&{!mN%C|&@=RV>nNufjyH(d*pfnf;QmjkI>8C5!<|KSwFazp*t2*%tP$q;
z$`~RH&&H8D9*f#L<RCW7J|h?6a5~SP$OQW4s|^+u=TBooX~K3we|`wX5|)R@z5On@
z6)xP$9pUS_rZn3;bzbchy%!|%K4d;Oeu9Y%UzuW1a-rb3X(gNy85-!WEQ(?I5f+rk
zfDB;+ifr_korA{ApJ$*S^swp~|8YRSCb9Ijno9{-xa8C?3wxegkYrO+V_TJUn%%5X
z`I@n3AkAX4<;d!Mxpun@jDLdEhQ7o*h~}{54K`5`ptg+UGbM|ZH)vQo4kEkfy$tjL
z11Eq;3wi?o%9FyBDx)@Qh-rL};XS6yA2y>jdIjILRMf)3(`Kvba_WCzq^E0tm4#>D
zj@a}yeQ24D#YmUFNHmSudJ?Q;DnDwx5Ol|d6HIcjJ<8S@+{CWLgK}r71UNSd&oR2l
zafToMFKZzJ#V7`GvFa&(FLZm>ePX;<1`#Is^vo!SSYW+M`44nmJIXA9bClUelOVoy
z{d!`nZO$#%jrHma2ub>kE?$1{%g|E13GdOre0c}nt>}J~r|C=?&2$PW%4JoaBL<YL
zxhN)DD6?3uTQmNXHWrSY48>z_l*|7ta)^izbo_UYs2gK6fqP_@*-zZqwq#rIWueLR
zN4SW2*8BM@Vz43t6L$)y<qO=^j0Pq()HXrs;t?dr?@qIpc_V<!BpXYofU8ku*Lqi*
zul8F#X{O&C9DL8V@F6&xs0GZUQ>q^<D<^wD_0h9|n-VFXvjw_7B02mpt_QSP1K$-5
zV}Hjn3=yIz(vWxDao~5W*m}<v96$2n{ks#y7SXY`rz9Q^#I1J+ry8e7ixi8W@~|Fb
zoP_<%22G!b-j?8S+0G}jG#$Q!*KysdlQx*=q)&b~_#K_@dmTo|q^(>W1|vk#h<apF
z>vBL~Pf<+@T3&VI(%5I<K!#NdnteI{pgF*Vb1vKG0kKy#>O8$iEYU8cv66HcLG}3t
z5C}y0heb7-3&qVK)YKBsQ4L?^w|A>)h`q~tp^j5vDsjmQfKDr7%mUX)TLj9l5lF5>
zT6{p%Q{2bSvQoS;^RqKRqYaUz4ZA(rlMohZ;KUU1&ZtXT!G}Z}E}$Z@6@c@m#<nZ;
zIms0^Tu*d?&G#WxisXKaH`j#NKWTG=KHDL?V~|w{C7IiXAz@rZDVJZhNH0CvWV+=3
zKPt#qL?eu)%5?qO8g_npawjc(6gJBK9%!?`Y?WuXm1pxo!s#(d`bdJ<&ymZA`{Yxe
z^ObaqiH=ME8-f9?;bXG_%Z~45n{g@<30<MFc8j?b6LT(|#}-*}|1VPWza{4*=3tcs
z1+zZEoc!Yi7lvsvBIKv&=x;zP;)v%`AAU5}IX;;yP<z$q2A;BnPB$pb7r-5zLB2N(
zKmIF0a_-lR802jSqfdd(%-<?3y#exuL}4<9Cf)`f86l$5FK2%Nh;ZEG-}b?d<L5uu
zIc7}{!q=_z{ufrASW~tu9eT|@vvbpO8?1SO7s51<Fn>zVi+u&K{ZyD8&JSE}v-`8)
z6nPdtzGfx)gUe{XR3#hL27p2W)L&!$y)YlXPsBoq1mP-q4=vFPz-9~$VuOrc%-IP)
zmXz;-5<<k&1)~u1O@R1+1z|UO)@rxdqF$s~B2p7x`1-4kcWx-Er7H%*)<hA}M5*2f
zcN`y$JVo-%03jEqNIBxu0Mf-$U-zFo2YDrf=ghU!pQ((H7<h~_gq~D)K@oh=*w!Dl
z?>0IAYY0V0cNDng8Qf;1U4jhTD*@_u?0c9kCjWYPV!FQ5A1;J)*4S#r@ePLo$lv?h
zlfMBuzw5UcIMQ0gmiZvc=oz0HOiXyw-#JMgg&u4GPMGU3nZFN&!%#KSrr^34M~i=Z
z8Yl~&tAC11!s#%09^CPV-{Khu?gJ^SE?-SksJ+VTV!s-w$Fg7SO>X5+^?Mj6j2|^2
z@VxKJh;s4gkfg9<tYV<BX@#ioRt@n*=zAYYB$=Dah1+w_HUV!#GVh8~vVfx|6&B8#
z_@A(>Th$5lowN`N&(IpUW+v%_PSuoZ&gmBjDd*$3_1_B;?3LGYA=Pi8AfynN0GV`r
zq<m06exqQVgogO}Q2aJgMr;xeGl;r>182TrhY<<{lMSXD7?}#*p=q{FN0Kw+GA_Jv
z1$=R|>7avtM)YH-|Eo0jEU4sTK^(sEkUBo^auA;ECuG;nVQP&^<7H*)C+HSxKjmMq
z+eO{x{C`ZnbyQU0`u;t1H%OO&gfvJuf~24#ARPkI-8F=?fYKseN~d&4OLuoSLnHY<
zbH3-S-}@H})>4?6z4vp+b$u>qD+YGBDW+SvmFl{{{JSfn2CQVo4urMN849yl!HIAC
z0X%Dfhjq+(cUS2vx;ClPin{1=i*pV<OXHx&QYx9F^4!dpzC{S^y4nL!G9rv8nIzcV
zLL*4W&nJu!!*KDL(r;Sq>NgV+GZ(z}OEyK}K8ZI-c<(dSaWK`J3NJgQ=Lud>(R)G$
zqMM%IUhkfz&LQ|M0Jm!%9`}%K&7>xx$~_2F{0Rc}o_@vjMI?HWs06+5XG5tjQxBU{
z1B1$7vzzrOCF1d31=`EA9=7G9f;Wwi6Af)#(Ti%jnxb1Hm<?>!2Af|Ema|@($&HG?
z(L3W3MQJ}r{rSAlXwU_md8D`LuexOYu3K!X1_cPO0MRf0m<+N7i3!DNaRix#;_W8`
z)G>(5K4&=6hmFy)+N83W8TQ<gGa%6xiOc7f?!(mos!oKcNo`Z9zB2M0sZw>w*)_dv
zNbe>^vHOXwH^{g|+TE;wJM17{IK?t2`m5#ji#->$s%)c{^}J=ffp7;50BC>(HM5^F
zqVahD&wU7=&vRpX5MKicF}lwFe@iPu;+3vfZ#yu`+spqmB!YNC^JeRS{B@v$tuyMv
z9*k@kz!2{pd#m~2cj3gRo7&V#>m7_}?h;YYu^SxNrCGW5bZE|P=6{!r1TsBC<zF2v
zX)~a4Wy$5ehD@@mj%P6_?>ew!4DO_FoAL%7QqkrzANGiUuW`Cx5}wsBzcUhqE!!RC
zBplcAy%rh=h88#6^(!FXW;E4kbUS3EmAG^DDQCDu!WC-EiUOyh0D8ymIN%`mq#l$4
z<@DMxgn!!S_DQK{8OI`1e@r56pS9EJ7NLQ{G1c5!t2M0;EWngI82C$usgNj;ba=Gi
zd=fNnN3I1+#RdQ}!Qzm*0vabK+lF#Ro}RZ@<FB<oRpZr$2HYXg25P-ns4dD6cedd8
z4ZwXOiMUMnpDWNzaglVWgLJ#_O!0289#(m9o&N;kiu6`keF*Q6QcVdm5i@~CULRKW
zb4KGmbhdyRL>RTY&i8V*ho@Kb?sD1R-_uHXPhJQ~f(iRVRvgU3p(2T~td}-0f|kDp
zX>K}Gr^E7T9K#Eq8Ecm)L9Gjv_Duwl!!&-^#&A7B_${y@iMG5<;HF;U^4VXT>R9XA
zruJ{ia`O$0F26nEwvDqJGo$0yORrzQDjDtj^nOVUT7Lp$!?va`V$wEMy`OUl5B{tC
zeU72rI|MdKgI6EaF15<H9hU-;B2S2?I04|peFn=8IIr%ELNiLOzxg&2ohXC-K5pO3
z`U^Y`9c88n`-i{(I1P8>BHYVgcQ*(c&Rbk?s<hPj)%d-j>BYq}@*7Tiuo<ZR*L)(y
zv=&gO>sj|AeqyHWM_Rz_j+M(G3XPGaY$=GbC%+ZCj#Ss=+IP6dbQlAX#(UTKChEA#
zm{_>+P64>9A}jb<p?`(2{XnpEpjYVndjUa-Cp*J^Q%5#6NqJ87K%`xuIt-_PK$t%k
z*(KUvw!VR+sANcpTRz^liQB*=8XW5O=N{;K*et2Kk3nWveODxDzZfp$JKRDgHMY|V
zVunyXO=YRqIlH|z4_PkZBo8Qe?{=0bt()i%^=a|ywI}~bcpdPnjdwkw-=m_Qhj$!5
zTn*M1D5bt$(C{SD5mHWa0n%%9$Ope$hb)MZ5;R*o`-6_}=5|~(bsO0^DWpOni7N?v
z%+mDPn#sY4tO!&OK?xX*tzfS~o`CWWngdTaV|=(@XBD85@){s$BVWO%4rrqwQ`d!E
zQS&a{V-2H+kge-s^c8LW&BeZGq6rg3EY~=pbWDwGeZ<hAgBBv*tPQ=%N{3lsd<RiM
zM?|6vDGvxE{ojOw4f)b)H<SK7^tpe}zsbK|-Gye}yZn2dBlD{jJMqJv7K=-z+l<hM
z9WiJmK3L~8bUf%})lSYO86H<j4!14YOI*-iio*8!a-1u?*2N=rklOJe61MAr9Lh=x
znM~WOzeSlRLAXjJNw98fzFWJ{xTj?PQDcpKM-*Ll=74ncUZMs4EU^9yS`-=^9-a}6
zD=1_7Yo%L}rXCXt9!tueTNw5%t^u>$6I_RbyrLgm`VyC-xrfr<6`$?na$^dR()%KB
z0c~)?-720GRi76qeu89|RTA~5n&>!C-|D!5L836@)Sb<##`){vWS$P(e7^I+whcYO
z2f`ClsN6Ne9psncO3zEj1g*ml+RGW-4)#|M7y2*QzWqd#!ot2rH)V?o90iF5lu`ST
zc~0%}OtLl<A4JO-z{bZ|`)nVuo!u#JOPcGrdz&*X)YLjEPpRk)cPvHu->${mJ5IG^
zh$JpLbl@C;yB_OW-4a>xE5rr?zk4rCIoP^MMFN~U9B1)zkx%~KwzGNl=PjuGrl0q;
zY|WxtPA}He3r-fW)xpq_&%3(!Y22a(dGe98%wP4-`X`8t)HJvCvcIJCEuVf6btB`5
zdQv}xl<dCigXG#JM$_uD&q374{-9hd@nm^Pd_5py6+zFa9%!7#X=ICA+45W2C>I?^
zKQN6N-zRdB`Vt{ry#*hp5nVW5(zo*CG`5xytzU|Ez&Vce%Uk!rf`UI@AYt?G-q2?I
zJTT9%WnLiakURW31Xm-@buZLgz)mai>d%~Ilbx7)ClUy#a0WDZa~L&u!%U;GnEDI=
z=0=u@@CCv#E?~I9gGmL<qAu+i%=kqEOo^CsKA+e;tB^^+30@nPw~t>W7^zRAypRQQ
zC5Y0WYZlWjQGI1b%0O=gF#z2|&)>f^5E}=F_G@fRdd>AGFSik1lxxXHj`>A${Qem=
z7_(@;q(SP3923ZuVDCeN%GJRw74LU{p=F{dC*9;Ajf9F#xA8YMqqFeUCm@L?B=rL|
z=Tl90iq|GbV!4oRh4WvMElgJ3u7b-l4F109BD724+S&1UV0)=+qH+(=ouBR7m{(+B
zl-OXQP`RaQVKUhe;5`{}a1s|iz4Bq^Vo~ZI*3y;{F#hK@NC<4S;~Wi_$u;7cY!rRO
zPT=m$j|1|Juyi`f`)72LBbti}kDw?W2RlpUVi}^>d?X48n*l)F$yoWMcuwPvyor=N
z9Y+l{MCo^NgkCy!a<sk*ackG>AFl{%metL`m4bmi$AEgA5Js4=bai(4U8oPwxW%8U
zaVzN;b2od`>i-?}ZD3cf0*Ikcz<NY6jOWt`ohPU<yH2r(*H@!;1j(5Gqiu$;K>wk1
zWAMob=;cDJHR)Scl(A*9!N4+Hdrsi6yaTuM?SpeggIVb~PY;#sjQ`0M%i7014@!rV
z0so+%%uQ3#@<&$^gXE8DD;A0E`E8xyMB!X%eFnsr``RXr2WM~w^{2Y@3}hPf|KqmF
z2L$Frwul=@^+#}Oy5S|U<(o_^oeGXh_+rh0xc9sr_Ln(>F>!-*WcT;HT@J8d5Ga>t
z0rAZ^9OLQ}$&RjO{%2KKIf8HmLwPX^5Pb-Xl;aqINuKKDaOmUTaa_}ygwAiiW3RP#
zFtiV;D|Du1?9Ef=1??7uz%G!fqENzQS5(@sxKE=^>G7e_EeBUhOtXWaGrSV+Cv=&7
zuSEdm0B4CaKFTJnWUI|K@h%^-T$&AxTWP;%%_&VLYr80_ye6#*+x@LLMUKTNF4l-#
z`dABk5r)9f?8VQSxV%)%FdU<&i4>abD%3*iG$21l#Bwc6ayRHXGwP9w4ouYp3~|xd
zFM*>$y7)<)HdPZbf<KiL9LrD*X89wadr|Yyf>@>TSwxG~a6+~bHbOg|Lyxsp3!nrD
zE3y=isclw%Pc;hv#{$r{oGCZ{u91FyDfW1G)^#^vOx%l%0;z7<FKy)g`aW-fVcdCC
zN%5t1HT;ewA|?~};&(lqd+`%XIf3bJq6v6@I``Tp6&t$W;|<Jn`}6%@TOk#I#nlvB
z+HzyX5b}o3f@NiR9$~J-y#)B-s~uKlOe|e0uTuFE1|8%eb(leycfJzD+v7c4ElQI$
zw<7Ng-)C9XC^<6L3SdcES8ZdUKt^VO8!zwo3X`5@*XIckz&19LCA$lp^?h-P+Asbq
zx2Gc_Zeubc8u*H#q(dVgfS3bOW>T=KtAAEF4(Cfi#ma#S9*qsB@+AF?{`b19%_?<z
zu+YywIbm#mM=-?lU{7De!Fv-L#vJs6iq=HPU_(g<)u9eYaDlrQ&9R!=YP#Tt*;{7u
z>BI(8<U8_qpYQjeC(TAg+zT2FO~|^>v&<Qb3dqmql=X}kG@?SHK}tw3PkQ#~K+&W;
zMf>@M;G1+~?zDlwG(Irt={#jp!Oh!WrQ3X=C~WVKGW;WTYd+O<8vo6!xMn2)MAe^B
z?;lK{ne*%IH_{(ey{E9+`G>R&eK$RF<~Z~lH1;z0hF4M5PUUlc2ASel2Z`t=gA;WY
z(9A}8Ry0oEO%4|dyq=Wm7=((w;p%zIMy=s2_-Ul=B*YwcKjO6#eucY?GLw~$x=F8p
zb+kwwgb>ug=dn-`pW1e~ZG7486{9qtArfNkg{q6iRVX?{<P+tIu@-U`87Hy424lkX
zlq7dpBXeOtL|q(li$eQ^*AlKijw7m}#Pf5pf>92TWR8WST-n2C(|k_<Ml<m!Q;DsH
zJi@hAek`E@^(K$N)A~{6O~Q)KYtE7AJpM)^HL*8)DOeX3A~Q4QVzG^4kQWU%%~F!P
z6k4|`+qS$bW+xem7Q+jhB>R;6l(04uEj{Bito)#6yn4AH;6BsX+6P{*d{b3OG?PtZ
zzG~{0s$_!Zdply<Jp;kZ0`jKhSkw|Od$NO?%sa0`YvqM^t+reb@25UA7L;sG%rsfW
zZo#%Ar(UY6{UW{oQ|fq_qI;}RC)vi_;Ai$LKAH*kx3S}Uc%Z~aB(2?lOoyvt18MWV
zHut9Yrog!$%Zax_wGPC=wT{bMH-IY{jJ9TXB3+~YHtxvC^yCl;5GH8FpeV)w1jPIB
zMriM64wV%13X-NF%P^%~-JgnFCU-cBqebx<q-hMMSl9uZw{Klc100#IxpT0HYXrWc
zyktX-SqRDzXmMHB<?=X?B{O~lpLX;-aDTv?RjmV~U-)w=B?3*?YmueEvDS5smdXPm
z{%6*XBb9NSRGMbbKX3jTS8eOgowd-OgbrJx4e`tSXISXu5R%U0#m3r>I+R-i{b-l%
z7SC*(fu))|tCTVvA%7?m6t_*(CLTNswHF|d#%ISdk`MU!)XIG-eHQGSL%zVh6m8#w
z)|Qg!TkED)O13IrT-QA*Ssm?t^VLFc{<rNScr_d=<eKig+TU|cYi28X%4{c9y`avJ
zm3K>M3TT!Pn5ktx)1EUJ%ZX@~9mxY#tu}>0g~J8{MH@>US46t8q)qbkSaf6dQEtd^
z$oh9t$*t#plDoI-w`T1Ufb-MymWCSo2~76Af3>-T|0R7N9*59#<M%P!uRgH{v^`ua
z@==nIg={~M$CHT@h-ttLLT(_x2Y~d!sJFr(<wj!CBH${Oyg~F+$O$zDY8fX`Ut>Y{
zb#T|-pX1qfHSGdcdNHg)gP)`LE@J!Deo*@kQVIlInJ0?Iu<#4h)vFwYE6moI=X>Ja
zc@$&=!yn8V2aG`)7n7Y{?>r!nQClNB0DPs}Oppod4dJq+(bPq>#PdRq-NaP%x*kHu
zG&~RlW7ye4VFr)~OjF3lD@!39R`u^%Esv+{-&QAz_@79ZDh<}$8TgJD9s%tyV}{qE
zRU^&%e1z}x*>6Yc<0Zv|MH>5E?55g8^Ss@%)7L>Y<*m$7k=Df|GB-rtCP4pD_P}oc
zP;nY18HI)!gGSd2WwPmaZ@a05-Avz1+)QjEd{F7SwrFdn5;`~$9Xhytnna4X;=n`M
zCJud8Uf;_W@Ui{R9wv1gxlk}jd8`jQ|Ls8e&i2%(Lm}^l<=jtxAv(>rZeYRV#99J#
z_xHIibov?;gTv{JZAvh?Y`?-{{?mVevTzQ`khaP>CN+C?Q`zDUNqE`(>dj_~p<63!
z_%*`YEjo_q$rdHUFXVjPa3mC>gYk==cIG}bk7dY8hpH=Ri!x{ELYqB<o*WflMLTsQ
z;yLALMY+6MT>3j%YM={sBzyOvrWiWM&jEGfhBSP7Fs5k}`peeO<PWuj8~JC$B=_2K
z7JVbp8Y7cvS2=b3lIOF$^=r>?Ipq3SA$)9`nE#}m3b{C*>B;lfs_{V~^sIOnnizbm
z)tEm~%auCr#3qK*XE!e@M+D>U6T-^+-%93op$bDM59e#~uGwFw`^{_LS2g(;-!zcn
zPsXfor}Qqz#6@1(Oo+0nEf3djKUGy{RzU4!O3*9Gn0)TuqX3?4sVp?HcuG$m<q|CY
zx+8%$y$h-M>GT$fSPyRt<1AO357{khW@u^GMfimzCxfZs9$1LRH$xP%;Qa*k6brom
zaQFlFZGmM~qrLo21eTcBUsO^w$LoPY)1XN4`Z;%`L@LH4hT8!azkdRDz6nP+QE{Av
zbv<L3#C4$}p98#ox1S(fF+Q@%9V0@$(H7n#FNkjZ)d%yy0#oEmS`bZoW^$Z`aAngJ
z!<VQ{wNA7i(7uK+kmlfLcdw&;Z*^~ZPY$>cwb$@}L3H=?wV)&=_Sh-{q&l;ZJ&phG
z+D&L-Y~dA98Y?Z8=+t1gx%?fI?Mp4x$L>KgLVgRfu)h2RbxiBa=KE6uJln``u=wI1
z4)qCLCDqpB`CqI-!GbZT`9y@#npr~`kDT}Ar<`*cpBdlU8>arEYU5QWrPP?b=l8Ww
z3-M5KsaR@<5wt@X-;LDj*4iOzhUhrhY^`lwDl(^O$jq@kv51}8xx#?jc#y8T_bC=M
zWaWg^*}2!cC2>4TP9H=){j1^UQzHo+Se4Sa__Vo_za-OFy^yp)M1#9B)xtFjS@r0X
zFN+w%R4kM;S0#0S?w476kUjOh4{N#@zsY>bqoJVlhY;0>DLmW)FevrJ_D{pQJ0KaT
z2cM8A`m~1!aO1`yZh3<N1u{E2J|pBkq%(?Ibjld}YCX02{O*VCoOGa0a%+{ox#cO|
znMH%=ig=IcXLr)V@ARHzgdxDf?TtoK`1f>t{^IZ2hEB3t*-N*HS-c=oG0xBTZ$EES
zP`6+F5%L<~vF;gksH=B&FRpB<X!$Eg6V{leHRn-3V_p>33@)@TPq^YxMI?sbi=;rS
zWki<CSZ0iWtF<mR^n&vewY<B#h2;`IQ%Qe%A+`=raRMY<CV7AqAA%+A-!>@+JS(~|
zaLea!B*E^)2nN-;^!iZA9N-Iwn=A>)?rH(_Me(dLT^FrwSgL=vQ~$VO-cj1U_s#7Z
zgf%+@3?(yqU%b{3y=-WzXmt2TAMkx1Cw1CIJuVf$POBiI^YFw`xAslHPU*ImoKgQ1
z3;WGEz0r|Fw};<^OFJ_Yq+vOGrhfH2e%HrQ2v<%yY6pwuesJh5iyBQ^y&!*GyhRES
z#n+ukb-|$QKTPh5`lvs`o%KII5*zI*LJr3Oj)r&2X`?4JvD1oZ=T|}cH)h`L`5&E9
zgkHdW9%dh(z!F3HYwZe-+AA_+A;863ihnAxkXY_{wnY&1QmIq+pA~Q%xG^Sr<rw@q
z1KJ^F?brCfzI~7q)jWRHG%8>GyXsqwgn=`-d(=!=O$o`*MZk7H{Qc)%@7$(>ChRMC
zU)OY^1kb>%_&)6MIXCRHPg%nrn+ZuatBH=oZx*OTn~*y|=@1qb!k5D6hUNU?YpI8w
zl)G>Fm8)~Wm5>V0v|Af=p%C|~hRJwDnI`bVrO(7goc4(0Yh97MfmocGyZ*0pmY361
z-$`W1_giATFZ%GM5N0?7<M1*eq3le3Z~<GgzQ!n_rq$O#B}FLlIW<5I?(J=Wqu*>o
zX!nNLoOid$B}b?2NqmXS8<#(I?^&Yf!~3nEZ^2+#O(|dOl?YD-(rNLj4_>DY+D8<f
zi#(aLC*A4(Hjt|t)=P-8Sr86eG%C*)B0pCJ*fS>0H?r^sK2b-(qn2!hc~cD72)+-b
z3%^E?=#BLoII%)Ker35I?v4(u_>F2}C&*x`?97X2LNekqB)r{w8|X>Ujgkz=)X;1E
zXn1{XqHb>)SyJxekzwC>C}5J=Iub42!F$<8d(4WR?r<heYwYKKdssWZH(3<^esK(*
zbpvnm^ntW-tw+j;ycK^a^*!$sqXA<KH$pfImte~d8?&@rpof*rnI)>8TZA?&ZfNbl
z3l#Xc!iX6C9mE?w@(Bl~cWNVOkZX{TmOrh5_E4h7fOV@`TNL)$Z1!w5(vIK#NY63n
zuTMY<$haUi!C|P>Y71?W!Pp2AM;#C+IwazOs9olhakFez=6ahbdTP|#>Ho#Etevwe
zf>suvylmH|Agg$SuokJ^Nm`jFF(XLDZ3I0C5(6rj&bRlu2cVvmo;sR&-ix)K7E+=|
z(Ttb&lRA_1@_o5Qz!kA|ve4E@VZkWw`#ID%(~HGV0Gyqo{>9%S4UU%Ks#C64vU^QR
zF^3lfz%!0oqGLepxOVS9!Yrto#buu+zbx@UIjPqwA6xLjDFYEZgsEkP<(O_st~jN_
z{Rb}Brr-0DkpFHb@skjRuFUCQxPaoYN7dv6uEjtsfYxfT!V-1P;NJo9gU#m@ac{Sk
zBn&Jl6`%P)ERto*7scYS*KHgC20<)E2B9_%l4BF_bof~$g`cQL68)qO9^nT`{ur9w
z%hyLY2!_5C@i~s)HuD@YPj+i>K~Kom`OG|5HIt}lH(pioV;va2W1=VI75+tKgT5`U
ztR}lQ9r_`>h8oK-Dxa?Qs6jGWH&fj8l7{l9d8HbHO)i2)$d?ZgC2bMvi{0^1$ObYC
zhUpY&4;9#rU$?Q&_)ND%yc5-AV1Oh$P}poCT!*UOm$g1dSWT6-JQMAimZrJ@ILhnp
zW{6$IYIGgh{1Z++7RufSGISb^AXVR{eGf$bTsop)+L3$)SJ8XA@Eg+m*HxB<Go$jF
z?wnHguorD1+>mjj4%(3S$Gt?4<txbCXe*4%Mt867jUX+kbjQI&ukq;^_riiOlEE3T
zHirbI?~`wYtOQA?2o-b3(GpAf2f4T3rP0=?=_}ikD!1XM-?u*AN-84zQ?&uEce2F8
zwQ2ijB$!aQJ&bRO-iLH+I71CiM&1i_;zpgzJ!abac9S-KBA)^4X~I7-hP}>N^Z;{B
zsXK_qRFEYMyIU@J_XmOH*U&`za3|RE#IJW|KJTSWaWzK^KmPyLis|Y`z(&MtKM=R4
z^I|*`!Z~l8-cKR(F*>q27s%}6wXJIP{ES^KUBD2gmqLlFE4a!Y1aFmhzx6)b8Z^rY
zV_N&$g2Fkn((~;I<x{9~W}V6x<^1Hbwf0e<j%B~C&G0!5-w5r00Xg`?4_cEcj$&V#
zsgpVnewQ8ff2-L`tOY-wCY_aEL7RCFB7?cuLiY91?flD70QmCFMSQEyMd7F}rKE~v
z9bTkDo9>kOl8xH_#4qW6`+D5Tg>1ILM^B!97B0-vW>cKP-^L^&zU_V*RR-@1n>{&n
zVEcp^3%|A#C;knYYc%8T4{cs6ZznBSaP@UPY(GVT%yo1L9PF-zH%m@&(%(im7$!zu
z{1*A2!2gm3Dx7!}U|vStKMNimy_A@+K!#tdAx5)Cl5lSp!buwtJ}?(jRh`)JjdXn>
z<7q#67A*>t@h@$uCPJmD8T!_d4hfr(6C%{{vM&B)=3d%J{_Fl!pOA3)JN}vf&y8Ra
zQ&q}INea!B^uIf8bnkkg*gb^PdO$m|2u|B$k2sA9VOd`VHW8kgzZJw)!6$?XIIg4W
zT8Ih4zb3fy3xhttijm6{??(O^Yz$2gn$wE1P;VT3yQc@9a8yXlkr(>e4{RJ??(Z@K
zosEvM7KXNMn5@)cFI!d+67`sTdMPiu_J|4=*s9&A>>3tdmzvJMF73C|U#uZQQxKl!
zINrPz_Z&9)2dpD}>XSjd*Z5h<qI#SyDA%|vY0yF=oMiyELws@AOJwDWk9v^}$Od?8
zmOhUdbFh+!T*td>^V)yjzdbW8$MdtFQd(8Qcd#Z+7$omjPsv9pbkp7n*_PJb^RRuT
zcD?9WiOvoM3;CK7kYtb;ISepWsSSEZ`+5hIMR6C(EhlY{RqiAXZ{HhU+l<hsMMTDb
zBm5SKu7rgesFagzWJH>rd`(I~fP#XA@<w_WS6V$+uAx!XZPxekENxb_iQL@H!rdY<
z-1$=QcCUqUxtZ(8b6J>Kjwy{6H-v&l$FMns!4|umr$|DL;XExcT%7k6r^e6Nt_|T)
z1nK2q{F;LlweGI&FO*FD9Hm&7=3G59$=xPu*G{-`r|i!bKB$yeW%`?#N=Ax%eXv2c
zFqVk+RatV5=7|q#ni)|@EYd1VbT%LQVQJjb5%MBCsO>I22TvP54Cz8G9y*f+_5HI^
zZ#q4@G8{p}vFK!<H=2KRgp2Pcs|v3Bb$N>TXo<Y0=PQQ;wx&Afzc=K@aFE$9@?_;t
za<#;fsPepx5xLmYzACe2-yapuDQ1qYF4yrl9DMVtRIr?L`B&J*Ks_**)^55VSl}A=
zUPuQUEZ1G)UsBw6rSo&>6>0Ze{6%fh1WbTxE(RByofpMdJk&^EMM1i2xH!fBz2Whr
z2x_|z_H|(&AE(}ReP%W~3*uKOgq@RMsQ(?=Y*aO^L56k~!;?gq{O58Ec_R*NH9i>4
z=E_D2L$relOxv8)dq(c|L8`c;kc58u>3jI;k5LoA?T>%;5YDe>v^tirN@?-Q-22uz
z;RlzY#7S-^BhE=BYMBiBF2?>A93)))4NPU&mXZ#(pAyrxN6Hwz35SK1YhE($AbGqq
z{rts)NhVDlwLOlGst2g;j&}JMw^0zD#duInw~?<#6PTl}egp(xyOY8&7(a|KL?Upy
z7MvH_P6C|$AHq4NcD8hQl)1VJJZr@zgR7)xD6DH4Qah0&kv8RpdmzOpzF*nn6Xup2
zDetKz`<Xn~{xLSJUheO7r&54)b+K0%&b4m_PtAU6Yml?0pe&U?IOT_uh&P2*WM^mx
zaB2S8%JlWw?)@5M*9^K-YV1hA;N=I93}^`^2!Vp#7hur6kpd~x@o=N_83yRjr^8Gh
zC8y8YQ13TN)SA-@Zb<^9=%MOwaVQ0H0k>DNc<Y4S$gIg@L$B4ZS-FC&7Ub}?_DkH>
ztUJ+#2_9Ur*zf;rxhMNA?m19(Tvf&(-Z?JO^L6EJ_@%6;9h${qwPpnLX(sp(!54fs
zbSaAh!X@-JJ{rogh|oq5V>mWfV{K-KjpqZao363eg6FV<9zP`A_wqwH1nT*s$@vc@
zO)gk=t##2X74u{m)L4;J(1-u%mv<yA*#HwnB5LBMeR}&HNS3Y%lAMXGNcb~oY%O{Z
zmN;=R&J(k6G5ViH%zy4sO_pwF6@FymwJj_koVgfqANGG^2*I2%Ds%t)2A5FC<>+Ej
z`P~<TXoVNQCS7muf}+);SQ%d~RT+LzWwIvC>@L%C%O9XjO7OG2!V~A)eRpGcw7j$N
zEAG5&+fcnGVsdB7yml;Vm+zB$z^~|YD8;CgXZ`9e()sFD9OqGwg+W5B6cnQ#?nJ0J
zSpAd!<q_S0&v9|>wi8t1vf0!+igHo}>d3?hkMz)Ogr`%h<i<}4|8cnwDuSQN-~_Ow
zE96~!**`PRn&K`g1MjGZ{7QbR9pSf5pkvt5Iz`g;i?DW&)yLN)tbG#v0V0hQEUr-g
zjU%weAl-YU)tlgf=p#1^u7*OGC;BQzUS1>AC}1BgLhUzizRheU20g|uJlGe*8N$Tk
zy+ZW)*S!zEP-c{){|1E_$AU3~1|510I^Q78#mlb|>KJq{DkgE@P46YUt2hbr!SoAQ
zr!V@3a<F*FQ;OA3_}zqAS|_r!30#ItOj_PFoM^n3LCpzoNA<cs8J0{=oG%tuU;9&j
znyj5?Q63`8u!hz)iBUyc+?>yzo0;srY}M|Qw@~|$Q$bBSlKz1?mr4{D9;bJStbm?s
z9nA5txemk{ILON&Jfp7%68vaX#6$nw_%0A;(Jo=EvK|`q7QKqk@@?hX?8ic7IXYzg
zwtMncYEd_Z9V;Jxq}3-^lzPwW%2KVn{HRm%SE>Krx0pun@}A?jiQ`B7#vpnYY_M6=
z4ZmJB(CdcD@AQSm;xvd6<miM5AfA|=Z1%=)gQ})<imIPNns+TSe4m8`0_l6+0J%~E
z8&jtk86wC<k8(R&P&}~A00K5f#%3TaQ04)D0I>}17G;52#(#3+036__R+#}mzzowr
z0A<vNt@&S*{o_<jHO>Q$&YV$(Z)y$w?-Pt>kWHJx%TNCJr#1?NRqIifU5NOL1qi4u
zzp9vhJJtBo;!D2ayod~z_uzorN`4HR`r#5TFg?fo6pf4K6dmRa(UTmMRtw`Uq<c*f
zWeX$|WOQv`^}=%M6v(XcFOAUAefhAbfeaHLm1E-Be{R%F^js7KA4!B1N_h%4_#2Uq
zeqFfzZq2~svh}wT0W3lauzELho`!jold!}|c_7T98^o1&VxgkHhG~E1P4_+$zF7&v
z<JE~zKej-YVuzeyPf_8&N3BOZhz|9oq;ZWRrNcFqp%MsQ5w-y)2jnuF{v<AU;2tjR
zM_5|HANwd4^=F?FJs!-J>$G(m3c3VBZSqWeqQjG3s`W6K(Kg|?6M@v*M!Bp(W8>g>
zv_S0Qcn7N6%!6*SP(<>pEO@E~`+)GrC=dU65Mrz@tJ&yg%a&e-T=M*ex#qT#Q?4RV
zIGk|&{Wb%o+8dWBhLDH?>|(}9Cz3w-LCo{a<@w=);{qS$(b=4xPDeCPyzS1eu5dwC
zBdbz`IMVq3Hlk@{WLh#IY>|KoTT<<npq-CL;#=BE)8UFa;EJ2U{{+N}t7yZdM~K5D
zb*PE#AD$X$OMgT2B)jrqU;Q$6FXA*TTPur&5c_cQqdT;}v-5nhNVcd;M;AN=(1{US
z*v`jvC6*fhciEj_GU6t{UP1`SXgpRBM(<Y)u*Y!GJ;FvHPieQXM<)Md649KmytfCL
zu-UqD=O{Z;MvQ?92)~-`y}p}z&uwE^?qIKS{E$5%%*Jh-K^J+lqa#$zOfBpb3`Fy?
zVC<F+-<hC?jbKpzk*H>7>5^X%^?w>5pgO1a<+vp!|H}{g*|J`legApY)NZO%>q!FP
ztkJepjmDVgj>o-~&IbJm1Vp*7-A`)p7Kg_VSEL{t*gGuJL)HFVe`;LTIx*lB`#Xa-
z)O>!S8hjeyF(=nnhQ8t*5stVy9rc0_{;DaEeJ0ONH00#qkHLUydMF01Oh_Pc&`3Dl
zF+DLNa-50>hHRZ~ZU-Z=asv1bN&@)9_-=q+y5ykQHys*s7R+tv*wfKs%d7+ETghmk
zP?aaTFdrJX-s{=ODw-l2q~LEMXGI)09!q=|v`=TTYu4vOmtM?OBy#q8$&O>7PK87o
z+aeVub!`-DDCH6CJ`5O}T48Z?BW*>L8(<(q#KbEW(5^&1L;n(642B|VIqYKRa>SzZ
zc<9IWphLu#su43p{pIT3ojo9%HDoP@c{EJPdAHwsd&GgOm4IY>Y|>DmVDi`|$9P>b
zhqn7mzMJiQid8S+m8%a6i(Xy1O=`;@RP^HHkKZdIA0U>)HvJfPc%2jQMV^gF($RGI
z8MKrIQ924Drn{1kB*WHjH;-bRX50qdM>YOMSDro`#~ib8<`}KyK@W9@czozY(<J%t
zsq&vMV?mH!Z3;F)@!rqf!XrKOFFBFB!(gv#-H#1`Vm@m}pgV?^Yw29d3n>7t0cVFB
ze-XjiaiDCz#yW}367OHCus|PwpfBCzrdiotH&P3Igb9YpDqzMkY_}uL0Bardjp0rS
z_i`vFsu!6Db})cSDaahnDSFSuxS^Dk2Q_Xo{8qU5HuP%ri}O*=yD~lLDjI#|IFS;U
z*vq@(?hvhJYbTAI0~fi%EY~+Y1LT8Lgs80}tASz|*#)~=?)u6jYb7MkPa(eMAo97h
zF+Ht16gl8P&3TbhpQev*x>;Y6v{xik<$o`E7=7izdg<}P;DDTAu?YPoCSt|cF?HJl
zaCQpew*@V$oQ_8-9xc>c4yTKon34C;B-5dbWBhq_0L+0OdW}O?;zv&zP4Bl!k)@1e
zw3Y6E{t)@0uAOiGX8|m0%g8-NXt`Ekn4zZfnZcheUU+-iPY;0UgVk%S{=7eJD@b>E
zv=Z!?>?^1y*zuEfXwr_@(w}6)3KUg=3UWhR;ZZsI`{_iZ8h&%???h;I(kJ~))6t$!
z_?uTCoVNq-Y9Fft!{r=CP!Wa3L%7r)WwX@uK?1+6a0&ee`J>UT>zahyVy$$(#v9~z
z90dHg1XI|w69X?kSH#dB9m~TUaS@+ZDqTM{YGn?we_mtu|7x%mdt|Ad@1pLdUS^kq
z<<jxAPm!gVLxqug?dXP$7k!s(3J?4S-igG*q<+g@t8U<&E`u9P0T>-fo#<Sux!K@j
zG0=ReMbT$DBZQ;nN_boc(S5^QGOc)-8w!#a1~p`x9)$&Mo`;Db1Q^5G0+705Dd$;t
z`yB2nG5592B`OmOm;G@z*?xREZEpYAU0GItDO*X?opB*1%9;3jp_kxksj9dZ8Im}d
zUwy+LwNsfOK7SAJ!*a~1F}C<uJWoY04Aq}P_lIH)-bf3{w7ozMY6Zg~<mw^Vnxz6g
zOYa2IwCLqJg}}!asOC#PE%>uWil7b|L8v_-X&odhM7~;ACfvaVKMW&7=KQWFrxI6_
z6(P0p6mt9u_6_Zri=2Q3{~$f_!1K>35ppb;OiZ-sP6r_efCO!Iu)7S(75Jlzqg`n}
z!9b~?Do<dd;|0hH%1ykhWW_~~|F&u4*iDi|-H+m9xkcR$=!`zYin^bZ^m=U%XC$dF
z6JV)RP)3(G2s>}&t}#)qT8pRQ_8-DisXtMAy!}k72o4j*Ilyh$W~EaKtab+q={JN7
z!D?ADpY;^HhdNn{r3i#PU@OFLcj)ElK+W=sE%p*tQLL6B0Uyg^1Z<WtsrNN{@0=xS
zUOk6b-Ty7VGtpn7B664i08$XbtEZWbhsa{L4VDcj-$G68n=cY$ce$sb(a1~J_!6Xn
zo@iT7K$(*07~~pzyh1?BD7~Xj#~s#*ce~`dO)cVe{u4iBZGF9~k+Rni?(PYO`GQIG
z4j7wec+3R#NRnBZmLlaS;zzBh9s@^PJ}8VZ2=LRd@qJOu7^;hSI^zR_&JA5&hboKj
zs62=We?^-0VphAELpIt`bvWpm&DmZ4Oo~I_oaefXXSxiEJ;mO&(VSOUe=)NnKxzPM
z!%MaG9&x-yTwLT4`)4xf=R*Lll??(jm3MaIyVc~aW14_2TJ_{8ys7-YlgNf@Adm^$
zNi9u;Y5qp-KUKCI-5EifH&vK7>|^nNTFfs{@UApI)?5&TK!_#DQ_$B?+R@{s(vc0m
z8}VEM;)|C^@hv9<qB<hb1O{a$zf&Yfh`qF{TMdaPIvI^GEdaetguD2a%E2^luR5iG
zO)LzYTYngy%CrXRVQf-12{nVgrOD^RbTt;cj)q3yS~v#^rLZnRF&CMCjX(v1frR*(
zo4A*byn~M<egKv0%8cdrGOa7>zo|v9#d)FNwZnt@Dwm@>Vfy($x>=1zO@H|4i4W9A
zz=`dH-4@Dlj@B-0s=}awn~s7W6Vy`q{3<gT`41s|=BPh<k~MOl!h~-d6ya1FK}nWG
zj&6crXQqPT9!L~q_~mtPu8!4!=rs10s+3jCdDZb4%uSyL3k7cX5i%*M15_}Q-5c7y
znGmb{Lj!D?8XO(_|97tR>LDMT39sL?J6mVwnLR^H=JN-gfeAiNj=%s8C@ZDl9ivBm
z5CS!JwxRFE`@#=Z`S`yxDgWG2<TgzfX^n#fo4mf{vW^{s^utvqIDxR*c3vRGyF+Ng
z!>mZBrZDynI7m3oynaOe!^CbRc!P4ojDHj>s368Y7ldmLo((d69aOYu{sY$KodQt}
zS;Otu_n_tdOk4-xz6DoOw{=4*KI8A6$jnkGS~s6O;4Nw@A&WS(KeO(Z=*qGKPY-y5
zISO<{!LZL>K?A=G7FF>=x(TOAUC_9{e{R9aU62YV_cymmwS7mUP&+c2vTokWNdkYa
zs3Y^@AvF1?pu2fV4v8()Fv`*G;IEL@5PyCil@j?tj6{uO%#B*8Rc5yc@MF72%9`ie
z2ieOe_S!>YGcp4vp98vrajiiKJaq51jNWp)1sAd;aOi5@vV68q5m(eH_D~^`FQ%&!
z3Rd_&Wc>Mi7pA%}+H`ci!OVI}my&K|!o?F^E?v*ljf(OZ@hjo+<mx{yHb8Ct!tFr2
z2yCOC5M8i83;X~&iJGoHmVL0Wig(K@IdJVWLCj$fYF16rmwa}ot+M5^ds|m0H2biX
zYR|4Yrwb4v%{Emg`rIG)*&|7gpFDTucs49N+~wP-@$2o7EpC|!kL9pQWo{FilQy<+
zH<7FbMK!5!hXGy*CJ9^b7&Vd@sRM3b=ly4lHD0%qVxw;vd|bHw#wiWXEVz*+r)fP4
zXA_4j-hQ7lJdTJK`gzi4*Mqxgi#W;0>z+An4Op(~hm&xhmIhnPo}5Qn2(7f!9&Ob3
zZ1^8;6ED@aXO-39ta?+U$u8&r%suP)WArI!O1g}m8mZJY6X?L7xVR;&u?m#V1XI!^
zla?dI4iL?J8V4MtGmIul6Wqa%;^as(zlV1Zg}CX}#>&yeMrnBzhv8VKVqUX8?E{Ws
zlNgRCp~EE9+>{TBOCufS(q#jNv0x;=XjU;T&>B?>n#8&&{b$5COora6_w_?GSH%|V
z^Sx!ary*~(aJg)JfhF9sRIqdw;3@OGXuL0~zQ_DLV6T<>a_}pCy=3snD-}34XHAZ6
zpHPP9iK^L~?LG|D)7Dr`_O+8?^Dou8J9r#IlZbZQK0T!Hagtbcedv+e`hwuY*WnCJ
z)7uf(xd;byG6(paEa-)LM7H48KqJrdsR&oP9tb)w9rUA%6Y3`P&C`?{8G7VEf>7lV
zG$niqbtwp+lK6Q_V_?s@DI?cx<%~(B8F?#s%T!&d=6QgHNs<GX^4p%Un~(GY)MT(_
zxMiuu*Za+#^uL~|DbC#kv-x3+2`{V7fC&^P9^EgxU@lK2^Q4=y7YqF9gn8V(#&ll-
zke~eY3NdXt>J;}jEGHF<l#RnmhZ_)07>T>-R6w@-fJu>lWXY<W=aA-^=3;}ZetAz7
zB*KT~YE;b)kyUqJ_Zqr_^KK(D2FJ}ztnWO&1#A;#@cDZVY`Je;ejQ6@NuXkqa<Vhd
zsN085ElW;6Mc;2}Qf66t_@nEnLDk`io%^4w!l6@TKKCk6ofZki{{&K0p=3!MIz<-&
z1Wm-l1&|bwt_w|k&LIio$>pIzStYt*kA-EXc8SqCJCOAO>AY&x{sF3MzW76W()6!)
zJ>Mn$Te=Jo@+9*8@|%owy`CO{HvfRISy6t^wr8f;|Ma3~O7{d0)02w<yzpj~CG!V_
zb+4V>152013vx?ttY><>k#*CRX3C@6DDUQLI7`>xov4esV81*fgRUbBwT|%M*PBY+
zO8DF^X}wP;ywYIJ#0x+tM^c}0_}PwqCD;oOOHJ}wLBXb}Ilv=obal{SZnspm=hB+y
z^3D8%g&%5-zpu-3LY$+_!<!6MK;xNT>dy9+4E3owYC3yOFYbu`Quqp}U!6{P%}>v&
zgA0W9Bw?j!r^I649JTfSqD@W3g1)(*Lck^&zXF~ZKyT{V*Pn5FV9WpS0cec@JcgT1
zrguGd=B*m;D1AR5>qiI<fjE~}=sv7p1l9hfI=SqM$S;O_qtc3oz^=n8j9ah1yTJ1?
zvN><iZZpHWJ$QRoUGV~4W~Pg@xUejF6kld^wA0dJNgeQQ8(~EHveUCnM<IDrRN+S2
z`>1fwH@F;{b}^ivKmrmwzW0+Ij~B#{DT)CDj^_SUiCIRyfiP8~E0Eg$ZDD<v--{-A
z_&9RxujY%sW}vj;qqe=%0;9s3E1N6ZrA9jaM$+cLTauJsV5-vtee8jHYIXj=eM3lW
z`e;fOm28rJ2N)w^p<)Bg_6@twPF8RjhN>)vb%1oS?4_v`GOEXHXa)Zq!dsd!FH74v
zX5RpB8tx(8Mz@l9lvC1kJ<v4NqvS|?oa~Qe1$C|Zlz=_sP{U@TE<5ho^P4hwPk}9v
zB?$2xiL$)93$mOia^xzxIGcKX>$kIdXmBSIWoaW$>)&V??tOI8P4-8EXtRT=cDnx{
zvO13%X{X<~4U@qxXT14`$Hym*$4<ZB^hZZ+r(RZFRz(M##~_gDUaxg&@w}Gojz}|c
zukVJIv9)|E&5Qum-%ALhqM~B3q5Nk1XROW$ioaV6_0H8ECnibkZ&1B`vVl+-yBIgg
z^p`E2c&NV$M+(gq0k}tS6&`;``loAul`qbOoeaR#%zF?`IqB&Gl08;!>S`+@?+@y~
zX&Wno!A-&a?bY~Lp0Xu~b$8<Qe#(GLu0%zVID-_&CwYrWbP@Kq%;o0XQm4WM1&5qB
zShG}*)wk1FIZT#~@OeyF^ECmH_y)YtUBmy`xvF;f!OUYZI_NL9tq+jFg#Fb$2WJvt
zsCgCuM4`)TuL*fi#ccpJV?y2^82HF?-xa*h6IyCAw(grY+e0VAhSwOoq`0H3SME{I
zp9PEXt&DVz8E&=oDfyFY6`vOXqZSbndpL%?%Qehc*a9f+tG%!6bXxtyE8a_{2|3Di
z?|fx=^~2L-zz0YID!*8xUit9hihW_$!#fCgb1eB9*_CMlzs<Fyo1l{x$i^H-fxL8s
zqJ1WG#cNZ2!UeV>T`y@u>zxDZ7bQP^iy?tLLq<1v-1cW0v*hdltHwW~B6?_T;JNUd
z50It!o5`o>bZ@?bI3o%C$N{ow0&m@1v;M?U;Aaf$NFxkY%pvD99|4=H-I)qgUS>(p
z(_9UTvoD>Uo&183Gq-1K1e9oFplDM%IXUTE`uDP|D_6p#J^jE`?IydZ0aTvhIFCDU
zG%=~6&(|4;1fh67P-P^tX_cxRjK<9L)9}p3C%Y)v2(dTt3^+%%JRMBH4kS7d!6}}v
zT`UT9GZ_lyAHk3rt~4K{NV=^0lemL6SikNykOR^X&N`iYxRMwB^i86qW=6CVe@+7i
zRN95oFI6X*<ol{xQr~Vy4HFY~@smy>r!V5Cy$pWSH6i4|b5#FVL<XqHK)>*zONQP5
zOHk~x=7A3!vC&~z))DQlDpe#&={d=}WauqCkmtNtv8lVe%)#n#LV*4e)*az$B@ULd
zhn<qzL;04<u&38ho@uFJt6yNA_v1@mO*i(HLEQOE9t+7M9A*!^po=PDyCEKB5IJlM
z`YA@7zJLOy16pn)s0T}sg~Y)tAu9IOF2X=qZZUGrirkmK&YA#%nzjkd)W658s;5a-
z`SG`im!?DD>!GQY7nsJj#uxsBFQ)b0hMvd&+Sc>>k{=Q-7~{Rw^l*PG+CzTHW2_-Y
z+wPoG@iSCV@tKhlt%-`s011n_R5#UgmNZ}MF$UV(2G)LGOIlf>kP6$eI$FM4*OW`1
z%w!ZU^YTD`<FSA1-zR}l?*tm)I&QzE)Ar(%*w19&XJ0sjYGcOd1wHKA_i37b<O|}7
zbKP}+zWjA*snabLp(G+nrJ}6QUlOfPjTAtOq(bMIGR?Xb&?837{d&JC3p8X?U`=1*
zb+H#-;JZ_j4tPlW30ejf{~0fmWG;pc1<+2>!&cCwJ%I-~;S-k40CNwJ=Ga!eDJ(XX
z2vTnXR*G6ssj=R!=HVH8ekF~Y+3;X7QhxJsvnWwjY}wWDsL(w~(A9&C{}Aq;Mxkw*
zuNp1cc?TtGAMNhyQWRm&dc{LCHCTV7oR6qu+oPG}lp!)Y*5~Is*Ua3ooig|PmsSd|
zSuyAkMCTKxP7-v4S*fpT*bT??G!hF;!*~(WNJ>$1l3dk#LdFQCJAM7lZHs!un~yD~
zw8x!Xioqqb!~@-?qm<W1w=TUWZo1F)&GC&(=b<CQq<s)J|HR6uqPFT4jjKbA^ydYt
z6A1Lkr3w}w__VA72xLrnSBf>h^DRfp_32|^fU0cp^#|%p>flnNGHp&@lE29_^SEzW
zyGeA(MM;t_*I7&EdvXMw;>2-IE0fNExLNoXd6jIYC}pzBLJeNtPr-x0#6m`%95fGi
zy%1>Ud>U-2bfMhO6YD8GWvXn2;TsXlejxo$COwylR+X~uR{Urd{na{#>6N27ZA_h7
zsx8YHvsyYky@RTR`WQns&hN!D9?P-Hmo!_mZBPEu?58H)@!PI+$D8a++GHv4<Z1^!
z?`}<-`EvhI*RAr<aBldBf~h>dOt;3c@gcKs)F;TF{S&Pvt=UKH+|2B={;a48)#1sC
z64SOvQ)qLFaJWW9+2aY0naXTqb8z83sFpsn^rSC#a>wWJ_If{i6%hLm)%&PwhBCdz
zxC0fdI|USYl{5f0WWCZBKsfdU^%b~t;w;dkRHO!;zb}lBg+cM6Zp+z$8*T&KOo94f
z(VuqQLY383pPnej%V%rzz(Tr4v$QO%1D&Asa(PD@EoNMCB@&rq7lw;-8xSY#w>3b%
zEpnOi4=NmOd)q=zWUgc1ef6!<Il-0e-}mYOy}-<HH)&F2-(1#y1HmdT@&1WZgjs@M
zwdgT=4?0Hvs2{VpxeJdAvbXsfp%Z=W_1r&YMWw^|ZrI}OX$vlS2WD!I&+@3eZ=#By
zJE|Sulaot&l}6K2BdzkGT21^kUKLHdw7fq5b(wd`1di8lR!Y?BBAG7O!4G5*X|$<?
z9c6!25#fablws2VDq1>X6y8UNK}uiju5XgJq7xOBF}K3bTXih6yki3RMMeP!9C+UK
z&v1y}!J!vFkgUyeq}m;G-6eZ$I@U-rwd2&hlBhT*3w3KtwU65PG7EfEQqrS$TLwIz
zNoAp%Fx~WN4e*poO`OFqEf6^rTTK>D;Yr@D@T3(u`!0r_3}iXprI(d$hAN9(?GO8k
z>dle&9di2y@+^Y`MY+34n5NHmqQT<8AO_E$ZuNOIdB>ZRcXv-+K|e8~Y~P^J^?ttO
zDjt|tMkdM2^#5o}mb_ajO9jl|_hD|y!?JMuaAe1p$VJ@_Qf&Il$>H6<GZScp%M4yX
z0e7(}E}}k1)O98)<PBCx^R+ScZKj2kNNm!bxP`FeTJZQfP17jK?Myfa{-MkQl6>26
z^HLj5GDZ9JYDZ|-JWVKN;{B)RTc2YAGD3K`71U9B=e_`~RqgfLzs>5zm4x+b_F6#G
zobXDeapZL|10qs(fYG9%DFT7q$|eEhsOV8$2Y@kVAd|j)@<-_92XmX5Bo^8W8D!dC
z0DaB%3%f?r-vP@@=3FH*{!}+ANk6@A{;)WZaueGk<a<*c`^NoviDP#p!~7ui?5(a7
zi`5^bX&o!edr!S^?||0no2piRu0t+rBoS1^7pU=d1*Xr{O_k4@n@p7roP4hfI>pnu
zcZ}X+xBFtpa~T>mdHt(OL$9v`2WP9kqz8S#6~&#pnh$zt-cQMWDhyBQS!1#1H_9i3
zoOSyC_ZQi@=N8`h->bp1R^rRMU~#!w_1E<=wi6tAUMVfXW-ear8LCU_{Fg_Iy73m~
z8Gq+(_IZWU!{P1MCgV-Vw8RZy!~R$m62Hjtmgs(-QBX}jyKV4V{xl-jIZLC|pt<49
z0E+(-y@x`PELcf*kSr7dNhWO_Nn45WNZm4NYTW<o5n#Po9o4pHy&k7{Vvl%Mf;hQk
z70>;STW_09ercP#U_)~gbUX&560^3^mWAXpg7kv_ErI#hhG)Ph>{tok8*SN8T$1Bk
zFj%PumA&t$E&9v64}z9=_3+8W1V#f_aI7!LLN5W}9hE%+({5}mFDwrwc$iF@>$%0h
zfPwzBx^3$ZsP<L-+s5<b63$fp@OaGhvYx8}ml*F$uVmMAW`*n-`QTWvU{zqmJ!kG_
zZfEH5emaK|fQwP-y03}xHOiHYq2rU~LY?CfkC6i`IGlBUe#|fWR%B?l$+PPHueVG|
zl?R*jUuG{g<&|y+>&;W%>iE-GPgW+lng&s(9iCb&eb<7CBUP(atGQtBkii5{t)_~9
z(Og}BIKrV_ESm}5BWv@n;sS2oW`rFa?tvc2v+uCd4X&$(-eEhOAEV(1;6!ERJgK#v
zHwVk#0TEENMg0X=^0k<Nv|V{9EBYj*yZk=R7^JB_C6=6gkZWPcu-+-pFd_W1!rt&I
zgtEK#KDj>Hh$o_)`O<W03aq2t`|XVPIMRF{fd!xANj|Wj*Gkd6{7FJ}l{a+h<09^C
z7$LnC{P}lTT|u6tH2(7smAS*7w>oI5NfkVS)+F?i*wJ*KSHBC+rzbKg#XIN}dyACb
z@=h@yUH?&r{wx3fT#FM+9$VoIgs(&}L&SnsZL=I!+cne0Jl`IOUNyPSxB9x)p9F_4
z03FF*mB7IEha~SGmj6~=m&>mTxcZu+ax0SZ_E$xttL<fT><QY5y?74GMs~RT&xxXV
z7B6HiQ--~ZkWSltP&U{Gl*@x_dF}=z;!r)Q#Jx1OzF}Zs@II{6U^gHh^oTi+=c`&$
zB#mrm`*^UQY4#Y+Wf-ib*9t&%i4d~DWXm9jpqg6uzS3*qOvB$lN>c4#$c@BGpfyOZ
z9{A*)U0KOF)q(d<z@xF)S+9*U=RT<<w?o-PzJL0RVOu!Ix5T%^l&-o5@?#EJWG*fG
z>j(|QoPXA|f6E@MQ!hkxrU-^rIBn_$AMpj!B3zEh0rO7|&>1dpC73ZGR|JO7G)5Vo
zn&Q3bO!SEtb+<D~y5#g+d+4BTV`XIx*GAZI8fZD0vo0g>rz-$H__-F*wW$xY^2I7)
zb#Av~Mo}^G-LYJr*Uxdf`JY=^f)Q)y)MWL?zl80s5iK1Tn|{>EPLATRjzycB>W{q4
zJgl)odIF&8H6$h_W!}zX=E}gFc<Cn#4fl4k2kbi9`^193KY^2=Q}eWQX>ov4AC&;{
zXu4q5JSOF>@^26KsYF>iqR^ZyAdnNjLg~QVi@N0uRmKe2yDq`417ZuIuRtIqOVt;V
z?zP8i!bLoECkbSsYj}FW`&Zyq=K><IP|U8<2E79q?v7s`&Tsys^emZv0#*!C^7sn-
zqW(Jl?an&z<t?Y=|Hsr@hDEuxf5R};AP7S!NOz}7cO%^>Eg>x}L)XwLEe)c83P=h_
zH=?A{-3`*svu5vozyJ3u2OnVOy4E_+UqUFD(m>aps$s1|{dLMPnWZq~RnEG7+YqBv
zc_?Ez_6OgK!^k+O8`p+Se+s+8&V3_y5Mg=AqM9q7oktX0wh2tKjCjUM$iR&aNW+_5
zC1AEbtg(sT%<B>JIsKA+U+iHL2DW#fBuI7DW1<v!SEr!9YcCgmD<Wm8pSiD%cPfsu
z_px*WUo<bkg8rGcNzP^6YVRkVi)O9FwW=uX&Et<a1IG=7Mo?l_EyU^mJolwx-0LLs
z)dDwpO`R`k3}1fyYF{oL>`pZyH1j0x%59L;GbfB^FZfb#!76;ZbTyS%$4cD7%o}=i
zlJM_NCs{|++6gp_Acjk;?&-7Xe5kA_<kegP8BA$L`97^!85AK1(HL=lj`2@^{0O1C
z64dnG;i~A-Z~(|(?WB6`+s98gdLC>g3+agEpOAbu`CcKu|Mu?;%j3!S3>9gL);B{E
zoQAc9AYvr+z&uA&>bnpFcRq#8X`fAwxBnU)OmQj47P}j}b39GnEr*L69|BD43GrOW
zc(X8ev%T0y1HGJRNlqX?)E1y_om4KfT8Q<Jww~}>HNzAW^oeP{3I*GXp`<y{V^JhM
zrh*Q+dHre*m7d7hv4##N3U%L~%Le*oMGCv8aJCP8eeUQJ?@5Aa(CJ8hKGUDZF>en&
z0zni?5#Ct%RKy`LJOU*o59T!nQZW#{STY)?Gip2ayfPj=RYCN8LjZ3t6sPzlQ!Di&
z<TbXi0dxy%i6;EV2XN!Z0mC0F<}$^qG;Wq)Birs|RrrK`F2@s{5|kVY!-Pm5AH!$Z
zSNfFio$PMj+S%42;UIQ5<I{BY4K&h);2Ep6X4ZlG`N1!>^sZP1G&VoVJ&W`yk*0cz
zT~H;b>43^#tkzJiL=wlOxsGwa`1wil2{rRs)`#MaXiL$)$R3&6#tPy)1=P!{znsTs
zV_lWsN82xT@{FiRAoJlu%!waF2bD!6&;Av)y@CdE3=D&ch?TyVV~-u<=wS6owan2g
zEW-L;+kQY3v|6L*6$UCiVzKZg_<jhHWrNeTjbxHyJP_S0fh(Xy(Xn&`Hm!*{>MQ-L
zFKsw~z;U5O!6nQBTNt7**pbZ*nt+6|Bxlmts=j+1ip*E8$@!>iLNtMA#?ksIqeF1&
zbn6qHGXW~zqT<o|U=iRcVc{!HaLMuDhd}_ypXK&I%cst{e0=$`N84&3SAv_G3y(q&
z#*4TDy2SJ?&=GcEkdI`+{{L*@c@)apIa`$E?o^G{Ydo1!aDzp})dbw#7W9LFI2I%a
z40Ro9lg5?$NrjE!CSvL?@8aT84YUOBaN1kZb2<eaCTQkv7cU5?mKUr*kucsclROLX
zkkBQlP<yQq&^{!i=Om4k`K0@qso%USO0z`k!v=uK-p2m|kPQ6}j$Q@VbN5qc(N&i8
z6hgJ4y*}LY_RqBF)T6PIcVXhZ=SAn5anB4Aib<PJDHRa|ckMN9n&U5GoAl0qwjH@!
zF0<|#4L%#7R?pkt;yi|b?YsX?!~GOZdMf&d^luLr88)redzU=>3|oIa>DivQNoDAc
z<S3lGBjo`skeA^ob3Gs->LG7TAeI$UQ4m3ao?J&F<X6fWtAQ>_9DI^1fuWcB^)Uj1
zd5_5y;|C6`1lY?ILDod7s6n+2t+MY57V5#lVu!S}Opf9{XJd(G#PQZ2<#sVTTv>*D
z5vF|hW693MZzuen9J?o^A~X?CuR(xS$dC7=(&4R_q_hRJ$eAwM*ocJHYg#6Afr^|~
zP1`N${wh2Aiv9z+Rw4*A8@J!r>FhZY=j@M0QFKWFwXWEUBxjKC>FWi4xkVmun_}&v
zxFQ+x{Ue=+qOuDz_tDQjE7srTNl4}>WYRYIoP9)QK{eb)JD^MEg^v@{32`(0FALy*
zI*NZlni+z?a9{Xi@H%H*(ay&4pquH0UZfZ={MiH6?iAy5=k4h9iwGYBSsv7v_;Zw4
z_sL`r7Vx;eA}*1}WzDXr2eu#)HnAJwRjTzN%F?2I9ITZ4iF}QBLD%7pywu<$dyDYr
zFXZ2d5&K1M{J1`bJhF$A%-|*FiP-O{A_I8GcvjYwOOje@AG&y&UjsokG*HF5*R6PE
z^JaZxBXyKGiPJ!I!)h=s)ZgJ-g4%6bl$02k8@`{@>Y9*+76*}Vq;SQ0b9>6+$bzeO
zudubId#VjH7l!?6UWf9a@HdZtR7XJL*GSO%&F`NtoCh7Q^|pYA0-pUl*F4XH2E7M`
zUHJ=09DQEzx_W4TR{`lW?|R!c*EW*Hos{wgTxoq?U!awOF-2cMo>+%@Xj~Tq)8wmF
z^-o^&fcqQcZn-wEu(J4M+p#H*nWf^BUcGK4a7E{5d+-tx>GlN!P)hzPEfrcim+^gH
zAF_*S@Ad;O4epp^>&a82yPbG@#(dgtQ^DM*(}R%U=}tdZ5YJG!yqYH2`N<b|73vk&
zklRBgS_;5j!yUf1rT8G!-Ep9U8eF}w&zaxzh(hc_Fz!94c-&_$UD!nhUUnabg-2YD
zog#ZkUJJ=N_Q*m9-nUy(*oi={9Po&nS=lzPoq04)#(ZVm8f{yQbNs<Eo7sh6^-~z~
zL<GhMffL+d$7;|V--|UBqbX4A{#sW0yfXt9&ZXhT#KbwE(R}ql7i1{_CJm0CgX^qz
zLQrKSbn1q|r-XDHY;j{vM_V5vROP2qJ}6e}UayJj4TsgQrrljS=%uy1PIh%m=_mZ^
z`b5iGZ2Y8NBk$Fl&2sC3npCUI;c-JDr5*#7jU(kdzSr=oKb014pF4gE>&<JIewkV2
zj*G?`o8_<)_i2vhsByXusb4A<+#&@O%Z^3fi>g;+zXaxY($-bJe$(A98n<AGK{eI&
z+H#3k2#KPDl|frasn`uVqsDU`WjCfiD5q(GV@qq0Kk8RsV$)FTR|jx=y5fxi@#e0P
z0u%#!7}{zPOTfe~C^l(E{bP5gz6uc~Sv(8=U(A{ZMYam^X_#2eZKI&37aa|er{O|l
z*0T=eWJx>&%b-qG818(NHy=gJDh>D@5_W62SjTgI#{(T(3PT{dE|RIlUxr+?hft5{
z5(oW;*#LpG$Hq_)mG?&e@5HUYG?FOeC!n)c9t>%sZT%dgQDnSlao`3qXnZ3I%%rIJ
zxdYUJD;GF}_Z(;zaNk;Px!0#+;v_qn;#>N~Q3y##lW^l%5@dKn=}_>64?bGpTRaT@
zHN^IR(h*4%m?T^kzOEbTfSyjwd{kUxKOS}Fx*=d;VX;bi2=M4?FpcA)A})r2h>+ne
z%fjNYF|w%zS=>7d7VXZk;bWjP5b0Q<8~K5TFDeO?26^BbBiibfH`n=UCMErR7C0Cc
z-#F;(<~Vjbq}inm+S}*^CO9*_{dCqC#^%wuMW}LGE-H`B{72-Bu@!wWjsfp8Ze&zk
z0kDy158GW7pIttz_HTckF=2thnEfLikAE{lPPds&`fuoaTiMxn?shW47Dkg*UE#$4
z9cw`mQh~8t3`>*+^R}IZ5u0xui|HhlW=5s*pa@<F^z-^YPdz3VOM=P-X4QV8)ni-k
zz0>KE!=~(wcA@#%di9;LluEa-`S|OMy{$bB%y-xNkA+yNlZG_0u6aDIJpI&y^PcsX
z?r_hpi;{Y?(>T&R#gI1qYGvPvpgpcB3dEOYrZ=K*_cL=quadMC1{^>d+eg_t<<0cI
z70@i9F_MESlU73a)x7H-t<i@J4WhzFsGjZVS8p<&sEDJ|#z}zIUH~g5?eJbK-5vmK
zfo86_AN2JxzzDPE=(vOilV9R}WyXY{dV#UNavPa>bMpSZ82(j5-)OF~*)R}2Hh>1#
zVsM||{uJ(N+}W)xFM=mh;rf6?g7d>`JRh3sp#K{O7Gwj(p7vwafxTYnY*WdS4kcs&
zkP)MROX*U4gqh8Az0gZn?RRyGOLX?rhXwZ(C|;$3QpW|AOn#VVrvginOyms*G6fd4
zRSO84f}_y5`%8U_tXbfdACU{wIsI&2e9Rfj`qwuRM{kcj@^7U3`iA%3ckW5~=YLjB
z9X~wB`)tQxn8We(@9iqWxrr@5wDofSbjUPBKy&mPyLSCpSINnN9l|e@nX0gp$aa75
z3!^#9KIaSD{l=uH9-X4X&0!t8Kf>dN+8G_}K2L<J>f6Z>2Wcpvs5vR6aj8<IAcJUO
zTfc@ckxza+l*MIGA@AusI+z`_^?MHpeUSv~=#kGKp<|N!6oML}cQkJCKQb&wjDj+1
ziZ^}swQucz6qZmizj&AY&GMPBmnkzH>@4x<vqmk4$JTiKD=J}?Z*Ml`c_%A}!{(WW
zQeBoeKIxSfx9@stj2YG24qxpLSER6y=)7WK@CWISU0M&(@24meOaSd=f*#T{h$R;H
zOIiIphmYh8xsYq&`ZjYc-{V~heuw8|gTGhaMxlf^<O48d2#eZxA&r-`M9|w_Ks=!l
zU@FL9Od`<-W>=%?5)YibazrU7j(EqbCavdh!6l3>5iuF>+I)Se9K3D-I|Y-w3X(3L
zZ`>`|L&~pN?nxq+c@&YMBcs6X!bNqqAVeI@DEb&qxT4wQ?XfhZEG?-(rX8TZ`J`bV
z3_NPC7V<}jC%)ALnD;kK6MA%sQE3ul=e4Ci>T3Qg#zWd1<XUM0SqOm%|HJHe&rIWn
z7)f*<>R=KP_~URI2zL0#6wjUzEd0clh-{X1kSm({c*l+B%ltst7%TxRHqYUw{f_M~
z-YdeRTn!SqM#u_RPDt6VUUGp&L9y?2qE4BvOKwK3`vz0awvRelrkH$V<!%<?Ojy^P
zVV=SaL4>_gx?bFjQj&0ap?NI7cDO0oP>rX_GkgCDDQ#*a)v*g=Oay1u#I>eQ1yOuW
zYFCNxNkMhpB$aU88ZNt%;M;Hxo-sQytyDG%o!~KCH^zmj%0GW%=vpU8Y!jlDesf4h
zl2EM7PB`#ZUk8Xh&Zx>MM^4w083chOui%?{;ByG-m7?Ky&G^Kri%y4wir_}!MWb@j
z(ylGUdY7I~dO;eQP)qaISPRph>rn5DEa$soGew`wIxe5v+vvRoHzi^YS&QbIu(+KG
zsU|_<j&E6IKKtTNk59&nU$)%p?7y?N{i}dxd)|t_*^qNK8ekfmU{{d!<(lBB;ap>K
zFE^(Kv0TL&wPK1|b_t6ijYGCjQeyiCeF>!@3s5*tGwuEfD1)LfFQfi2dx#e$JFe$F
zCoLjX+;*-J9B3WmUXF$e_mm{1fJOg&9+|w&@T5Di{w$9J{;?|hs`iF-Y*81rQy_sg
zHvJj~`7Rqts3x608Smapl5a-<!47`rXSX8xY-2T7%&E%%bms+jC^CKhp39)Nq3CWE
zNl+ut8k^o1mKGP4I($T478t324E~l7TMbN4*NgXK!IuEjFHIoqlyMS<F{irl>|e$B
zziKSvAOr#tDA%LmE`x>ovtxN_sTinRo+h279~4&x-1!gJn7DL3SX!N-F0B|mYby0&
z>&Y1#{p&nI5+L|?EM`tBWuIN{o1a(6)6MhKCr{T?{v<P|Emu8S^ITVF&~S(&jf(U5
zd4k+3C@?BV+Vv@N4z#=4aUQY%IeIGPOxs#mR-k*Nx!cn}9D{ZD!S6_YxICLg+l9$%
z_IC>*Bcp-4@0G2=^<^sYHf1(_)KpBd+Uitt9?|!Kk$_yvLQ<x~NI1>z%eyS*G0j*L
ztK!uKGNl<en$?>CyK(Z*TU#x9^3pH9TL&ME35e>Y9;OVey0VxWscoXAWjEyi^N0l!
zJO>V`6jtppFg&gZgv6ofzo-Ht*dScGyv`<ENO2K6q%8`Cm?JE9-^r@K-fbP4GcSn(
z!t)=MnzrOHsb#^jiO1)>m>-mPMd6p>1oVAuk+?y;QO{M2J>CC666PlrGUuO^>Nuq`
zXr!C?Q0xNz<W~U3?ycL~6|^Pe&B&JFZvh;I)=S(&YD{~4j^lAmD(RBk?mH})Hkw|C
z8YZVJ)}q^vi;eh|HCwk2`Zs?D-K70y*73}{%j*R#$M?Zt<dZ)Y6F5FCKCruu$}O0U
zfwcc0w(nAL32;tMK4bm*Q?FgoU?}=$H4Xevv1W*T45<`QA;hb|AQN^oJ_fZ8&w&0i
z&wqz<j0>Pj#ePSL9`H4!?6BW|ML%P9iC&gKnw-4tR&U4ZAv&LSTs^fYAf=IIY1rBJ
zz3<jUO;dZn1I-b_TCDM0YGJq>Xx@;E?PM61cmylaHh`eqC%eU=?Gm&OyFeeza8ctp
zoeHGI>w=_#cf~CPR}GWv+m)|p8=9F?+*{3$H--mJVv@5z2J^WL(FO9NGLl2<m1c0J
zUXMhv{%`4i7lk6;+{S`wQV-_5!a@dNkF8`<c&ns0aHn(SO{^PoVYzp3Lm6_aZMI}S
zZytmtsVvn?^1gU)yPg|vdl#(Db<uL%*?&BGCNbRIL~ePPa27t2G$^*1$kf%`ASmY{
zq#z)T8)W&Kve%fdvB_=A<zn~p2<DODngsvZZz?RHy4n20V!7bleuq+Gyr8aI&SopA
z-**1+z58taRC1~F!}!f#1I48LQ<h6MBcUs8$z(#mvPk3le@_j-{=gfESJ^UdwDv>4
zaR1QcA=ll0xc=bXuhG-tB`I>2)O{>$Yoe0H{s&#tFL8b5GXJcbRuVSme8T!ioPEF!
z$>euTbix3EW;KLo*HK?yeT1TOL%>Hg*F2oT3V9?=!^^ALxM#^i#Le81sSd7UM%;a5
z>K(G;O<`$HX!!*~!bn<4pegk9XNH`FT9)kn?)ai4O*|*rU7tDbp6NH+YJ&`dOf9@d
z-O5L6qn;FpXEPP=-91V^+w4qKn0rNa4m<dH%gh%E#Yo|DN-G+1S<Du{)k$eO^&q_?
zzwuSanz}q1Omt$odlE&BC>I1+zV|fF>VQg_NOA=T{7XaRAS%V1I>nywBXI+y-v!Z0
zgn3{?1c_-)<upW?{mGL;YXA$GE$|$ma`C!W3XC&EjM2Pv0?K}}c`%UG%aaSdRvCR~
zhG&lS(05sUq09y&)i5&L0{h}VdL>8_8U;ajT}gY9M9h}t7a;arH9`uC@4qv_+kS&G
zM<z*#go3k89_+53SH>L}W8qQc0TL$SOdQdJBwSW?M%5ewv%<Vvr&%KqNzCeRUg6{W
z_F`E|APgWb`0MS{^!}&5S<D#2N<i7CT^ieT|4l)TpV%Ljr&1GiX5FZBeWNl3_YKXe
zU;b}4ltic3$d|_OYLOSzdLU<pr9k@vRJn+qFNC1S&!1jk4Ds$KMd>WSByC1pFdaNN
zY2J}qURreQow#$IetGh%A=^H(l1C&0TXZ>p{*|9&8kg}(olwua=lbn<K{#P`)&||n
zR)af9PuJok1f?||Hr!+iyQ1go6-j*UE=;t#>Z=K@KJ(~!;cgRYp`!b`{Sm{_Un9b4
zflAro{ifT8g9!qDIzekE#!eDsFScWJo9!lxZ=Z~7U2o!~Ro_68b{F`#881GJn*T^$
zA5mYXS$E88j303H^v#u~s-fbNsgJfQ+7eS9-6<M-?803$pj2Wh_VJ%{b_&+|o#r^|
zA9vg%sL|yc!biJfdTfxFu_)rH9e!nhW0v>30S2BezB*KUknXmcqE9IP`a3&Q!GxQJ
z_$lEEv42}}zwCEy2gZZR+1(Q6q<j2_4c{4DdJ{G{URX8@A5UQSrQJoEyMYCIU}g1n
zOGw!l!}xB#7F|W3r9biUWtZ+etBVPiUQuazEM-MqR3XwY4$LvL78f-nF2h{aO}_X5
zZ<j5QeCyPHk%?UJ-!)QscSzQa`Na`rHn~A6u;wa&z08yVWf(_7l-q2zJkbT+S+zYx
ze!kgn6A-!sxCECESHuu0;p?T`&F8^oAbn=t<?I3I9ZloTg@3*R%6=Bly%|C!5=$?{
zZ;Z$?;6ZS`X#p)22#tn=-m{jw8+Fmeu`=UnF~i(%F6zjrUB;WQFFQK7(G&yaHCh1l
zHv$q@R%HnHqyTpK)Up@$7cat{h9zX^3@|6pX7?=d8~4tHN7a&?c(^pJ5nmq9vHrac
z5ywP*cs38{VGN6!8a1gfl)b8ap1BZQ{?fSr>8oI=P|0$%?7%2GGZ@My&Wcz>^Aj>1
zuA|5A(n4-qeW{LKl2%FaC{T#Pzg7hU8M!##Vr<Lh7CY~6KCIdGRv_<|Aqq0yiFH{o
zkfEqNC7Ze#e>o+58}Xi&l>2AXH19>aL%?@yE{}JU&&R6PVm?Hk2^BEUJSpNb{*wp0
zU;YF*FRdxLa_OlheHVh-mggEYuK-7(b5-G*uRim{ktA94&R(SUQVu_)mf@L71cw19
z^@9eka>w%Rtz&Dwp~!4&7B(`BdZp_UoND_im+@>?hsh&F;cXe~4+&{hTF3#KrW8se
z9q&(~#%`BkLI5>J;*$-2MSS|`jdUnhsG{CYM*|`zqn0Zu?Sg?SdKr;k(9tx&J}M_G
z>w1eR)?7KWh{0gbOK2rbsJp4pP3w;Err;YeD=XJ*3iUD-b5SD71pXI!G!^L@gp@?e
z(|FVbkAvTh8!uExsvHt6y7SJwT1n~6iFnw24w)5m<ENag3t~1ORe3}E9X$wQGP&TQ
z5gKdoZsG;l@Pj=+X%Hj2P;CcU4jvbz%aNvlRGJ_>YH^Av4V!FkBFMRh&X?!Be?Pv7
z*zaPym019$BiO#%fo!z%3HEiC^ts`J@du>`^?ELMY<=iQU}Y$J?hF(r*e{(H`}TmY
zjj(`J`u7q&>X!?JSTe@K*Rb!jbwznGF;Vr*NV%2h*`v?R1#;0NK-q^#7I0aTg*@6v
zg3aFfD)76IWEM^7sr&drG%T0PCd`ja!vgB`)++Ut5ZK}aCI~P@GElq*Ng10UrHlUf
zZ4_Ms8RVQ4XQT)zulx@+m*t0~u;~(Zu)=@pYog;qK;Asv_0Pt>opCE?ngbBaHk2dg
zla<1IWL32c)~Hn7jqaA~#2308AorN)y_l>}pw0X}T-v++^V894uS@YyXYLqmnx9)i
zCzHXebEE4;^Z9G-<K0rX4^76eo__qYGs(%Xfcv2QU2bzRZ+2XH<hs;L5e2QwTl~%^
zZ*f-^eQZ&c`rv;y45NP3vZ+O`i;)wVy4h=%hwi`dMl?6Oq{0Q=)rqr1WlYp92tTYl
zQ1*>R8uz^Lu=&ZUQq2!+9(Aw(fq)hOcM;<BQ`0w&N-SgR8@dEZ=_Ht0TCF4{BYo0P
z-8r=gpid#dki`LG>FZmxClJS1xdVDo9}cFs*f@0jbX4|i8$}G!b^xNZ^A_!|coK9p
z&~7DJL6FT(!k8t#D%eV;ywEpjzLfO+cR&qw*ArW(KJxNWW9^T_^D>y>qPF-5)MG})
zbT$%#Vx#)UZD@Z$nwF1?bwueqd+47qW~%1{fkW$`C265rLqLhQyv>@SM&L|`;yMl#
zsf?W$AUJt#rkwqe49d6*@C`B$E#M4hXdKE&{u7fM>K@~WXqKDsx@tlh6m<w;!HT{C
z<fF0w1iq6W>`TWe75d_vaC~Yt;4(=9&VOj-6SO-GrmY)r&nk1V;3N1D^`QzNZ0kzB
zaWKCO&WuumZvt72#baPx)*YHZ`5~D!;>#w=3^w@Z$@KN-e&|%ExKad#V#f+B-?)DT
zZeVXpqlA&rOtc?Z0dRHmhm86^FZVxL06{|vC~tWWZX-M)VCA7i&DO7@C#0ATxV<P+
ze-NaG@AeA177OGa;vnW#6ZZmXyG|2zr`m)l^g6U2kx4H~4^S%K?k%aIC^Nb5X0F!_
z2I$$6iCsFm39V1mj^d5vIDFWjFV0-o_$DMUy4x~V_}B9~safUPBh_bm_Ar){Sk>5C
zpwO)+sfA!Nj@h+t^79tS@dm*l1G%OrlY*5rZCS9tVEwwUtDxb$$0k>OVG>;b)618P
zyY(m~<X}R8*`P~K^k)iiuL1}Bkeux;@@MD(Tj7QZOMT9Ro|B`p_~fBO5rHb0<xdX0
z!+21GXz=bg(BX?ECG<YD_1ajhxf@O}$HVyy`D3pka>{e;v8puk)Q6lz^eP5EV=s28
zU~sn0T^-v*8QMUzvj9{H3>-4W-@0;76nfi%B<1o+zsfJ1P70V&%o$~Z<bq}e!0E-u
z(M-y`k2e-^i<XWeGc*Xy(0Tk}KXfE;&picc(Dy2>`fWf`k+dCf^W~NR3&$~F(Fhv<
zv~53Z|GaeBQ$<{8ydz^O3G?>viK9%!YCL{aahBvqnQw!}irfwQs63>fTOp#v;0Jk#
zemO(QrJftyeT?S<yDmaP2m#5&(~lGtX9`dO&Cfa5(1oetzlqqA_5}amNCl%uL8EMZ
z@)-#k!07|ycg|^ryN@?VOMtSqKt`c62JorE_pjT@Ll5jJ8`m5&oe{U!&SHFzG(y$(
z#FWXKB^K)Q`wDAMCB{;gP3L$*yl?#2Po7UkoN`LYYd4fDrHwbC;ixP7>yNQ-)Efot
zsfMOwmAf5Crd4UwDl4t3YbW!0au{o}tuyxgjGN>x|5&s9M%&@krmEk{>##)`JAezD
zYZUYHVB7A9TYJ;s3pfU4=^#VQpVW8>eN3h)B9UJ)p^x!2#TYb@7+wDw5cyJ)Q<3-+
zENH3iG#ZcMh3D>N-i$;iXLn4D1>9ngjpvK$ZjXihsClhA|Mayi);m$cUsO>vkt&$J
z*BdT5zgIubh2!-%!)v8)zgS|w=xKS+o;q6W9f!UD>XfJ2svUiu*Wc~!2`m0FLv$os
zKG%i>ix=cie0~IeHyM-%$Ye+k6^m<7fj3Y1Kqd6+J9%fyKc95_fw%(w5%i*%1+poJ
z!a$f3LvQvixu<Ger9i}egXWAC{{9KXd=C2*7l-$}!-om|3;|3Z(9LF`qNoJn@Z<fw
z+TB1E$7phi%-WlD`jHrma_u%a|ND{L>Y8;;Jr?aTH`M1qjpTKq7~1Ei-B-Ct+ZfF;
zfz9Eh#;4(Jsds4<&+B-O$qPCE0mXm~-T-~!gkt~Kfl28<zQmx`e3|gVe4d~3mRX>1
zVi+j%ZQ6n{j)9~$I_}Bi1Zcb-88vp#8^K<;`Y6k@nXi`ZhJzqmy6w<wW0{><yFG}3
z><^sZBXb&Y&p0iOtCM*IAQK*0xtQ}fnti5jM9WDY-X(9hMaPG}HC>l{@GYYIy|lcw
z!?!=utEN{VccHX?m@(cbW^HR0^emaL1<RajU$`YVc6valc^&?X0g9h@hfFDuB<NK^
zKTsuT@I(Ua&;aB#a_jenGlmEKAc%VL62b_7CK(|C$3nwy%j{$rU*uqC{bCuc$dN0u
zcv}10WzTm@)Q$iCXRUW?PE#x|+%MgDtyX#56L$1hk{Em84-FZ}+Zwv&B55A7iypY8
z6~dUGfM7MluV5!ChtbbWlSEEK)$6>DUeVV62Hp}&T1gwz1cSW7kajA_psvP@{->1#
zx=s7#g%8UAX;f&w(eC`(94#sV+dU!E8;~PfDdsdM>J8E-ZT~KHX=%bghXQM&!rs2K
z+Z{M%wE*qc8It|CQow$U!)_=K74WlBRDj8TycmP5W(@QiO^Ny|Aw&3d8kkRG;pnX+
zTn99D{x{zBo^9Jy=s6l^KxZ<CF?&>z`*}M`l7TMJ(@nP$LvwKXGx>arMPd_G&7Ca;
zqZ!`I=57~VUT$8K7VZ+S!;7??amxRl4S^)+3;ASJ-)3Vi8X3lCk>{yAwDpLplk^Z^
z5PC5+i!RjO17JQ1*R=0w0SdEw6^Wt=1BsFDXLr--_mcZn@7Vd@aZ$J=?BHE{g^9C0
zj*J@bQ$D+$OJJ*QiMqXYr)P|-V5u#1s(bNsn$+mcy_3n`iNbf2fz6-S1D4+h*SKm7
zhN^D<%s)yNGku7sPp&#y*1J>@oU5NLZ;fws{bpGOu{nkjov8N0eJ?qDc>vMypRxc9
zO-KXNh9OVJJTmF0@1zc#8weicn!h+O`iUJf2ZI*U!5#*C&bWjl&XieI_A<)lHE5aG
z`26nt8k|qR=$wcq#Wwq#8Be)dpb59RO}mBT5C7~-3e8H$jl}UYf4`|+JN9_g#9D-I
zV|ij@_<blmGX6U_qmRbA2x_Piq%lIh0N%8G6m?hYFiBSknlh#YKeqsA2h??Bx*6&t
z$Ld|EWimtR@X_LZVlQN%{Sv`o^+F_bP98{V&QM)H91z)0j(>CRtY7Q!Vlw{gi@KJe
zpWlIS3)!xYj0z<zXcQWTt^9mLlM3GxKl=HEuzed@U;b_0-*76>c@Btj@&LxAE{YcD
zJW*yWf_?SfWm&!fbO<5LW`WcnDd4meft`$sJ_yo~GXs$xuD?`(LocEse=6>4iNO4J
zS->Z5-JK!LjN~e3f_T;K%42G!$`^dgCINdBE@b1^dJ31&jQ4PKMNhVW(WP?`86g<{
z+0!mpY6t<I85$@}`@KTx(0i6=Biz7GKSUd-@2^N0P!%ZKBm9QJ*g4wrt;$qLkyyg1
z<cPB9t7g}O$i}E$<I%X$iSG}!!gI+rLP*NqkH)$UH55@;NZ(UjHNAeRP(OKOt;%RM
zg}+{&lkLrA$Hu(IkrjLUsP^5fvy1owH>Qw<RI8CkhF3Fjp+~QB(SF%FzqMVQk#)=j
z4+8ol$cu>sopnN~F;)XgIT{ow{6L!`5QLc`&z7g?Jc|PPXAn&m@vRP@A+R-W8Z#|=
zB{47=QZB`xO&+Zl&2&*NzOMSZ9);h09US)5PEj?EDnD*H;5{a%;mDNLoknd|z0Fg@
zPwzJ86q(MfESzLHn=kZ4&Ef3K6sODdd2c}}l_tO+e!lAD2Gpi4Z8+BzFhq7haEv5u
z`X5k;Bat{lJ69mp8{xkaghqGU(G#Oe&@T8pg7RF2GHSpD^DQ%Y6;+95-2r2g;2=NM
zVU`HIs|oMa0yG`6(jNb63&Fy+G@^(4;c)v9F{>4VHJeCuv#p=%&=d$Dev_M=EchX-
zaNdqC7!&Kce{IDIB1aeUL|q)@`uO^ltwd_h2{5`1Su*{RS<nT29U!O-DX}U9w}NmJ
zDvi4kx(SAqDB@?fNE*_>_hLK%eA_@71pmRQktZqHWNyl#I_@ilf+_#PbuGDYG}kw{
z`TGa#Slj9Di;g$LcH`QAGitJ0%=zrJjFOms?~P;sK0bOGCls|SHkang7yIwNBe_D0
zf`3J`5koG(bx2musR!24a%h&7jOPYD&w97-9L%@Elo1XSY*-TY8+GT>wb4&B@HGzA
z^RJ9vYr5s;yEFA@s5Kmpey|!(CZ@-qcu;pxy)HfVJzZ(6R$Wwy+A>>FJNLO-h0yzw
zkvwUgU6=j0u`_D;6R+HVb~zEVD>xF3P~Ah58XV;|dw_iyQ?(L<eMn|^f_!@^2zoaJ
z5}Z3_rwgG7a!8r!ZF{N56qOuJI}D`{oA$&XAY_3x6jn6V%%c0P_KHe`9r-T1u3^va
zAMop$&5K`}F3amIOx=6R!W)T+dlAFMvv~0J<?i2SrpKF{MJAr>#-|+Zu9VzH0<-Q!
zO~)h3$AEYr0uy6<Wp?w^A8MkHB>Wp;`U_rc#9ea_&n`Pk`9z;fEz}bhrvEl+)c!Fj
z7BH)a-j0D9<6}f#M=go0nE87iwT(6YmbP*6xj{LBk2k#I5>R%G@!%k!nv4vz32}7u
zn;MjYIE2h#a=WdOitg){8l;F{AIQ`<2V8D4R}@f#Y7mwcN}-VbmjsHgG&3EaS)_+U
zs?XYSxM5q!hO<vb^_8&Op<Cc0gNaT_y}JX^^-ye5+B((Quh$@ZJ1f6VRmk>5!F?K>
zGNby$1zs4sG!*J}a2_r^(bPsKcG>zH)waU%YiliX=M{vZv^^LvS8}FF=%rSSO?H}&
z!#ncxWu8y^<wPAWk~>t2qw2fhpGK|dzX})EzPZIJUOVEhBP0DQHq3hJv94y~LT@?^
zxT*i1CpcZ+e)r;J`4pR6t0`2HBv6Pi=9tGiP|6>?Y}w!opuU8s^hcna#YfjUzDa)(
zo)haN6I#}<Ol|7LD_YhsbQ-&Iq>n$V%I>|J_VxQO@y498-3%4<YE|}~qRH_lRRI+;
z_^ZbjSc9~1d!_!P-m-OGudPIc^P?obj2yuc>BWDhFPc#pXiDJT0QvGoK#^u;@Xi1!
z?z9vKS5&|6&l*`d8Z)SwG)OP>poO7-f~ib_`-5RFq!|E5xlF#>?`$gKo3{S=tO-r!
zwxCdoctrgeFP0KIG?5C27Z_B+UA|Hm=QwuPy9`p<N?&hk2iYlUtQf}0pwn|KQoY^>
zFENxB%X%Pdlc-E;7)3%KvTP5khDL5d7N6qY!K;*qUBS7ZllOLpQUNi0PKcjwgA*Z)
zG@h=+YP8ZgNNd?KANm~jF*7Ew0S$p7fYe;$Zne?d2~gcQ)3>2#@)7mi3zuykPt7}_
zv^`CSKiIiec%9PIeO)NL0)S&3*cHTSe+GRGTLyVG`4=FbVu;n>1Rx|oeL&1omQ}iM
zLO4McxPM<ywFI;v;^Z2DDxZL34TL;1dGK$}dv9~H;xAfL2VA?*p0s_YM85(E565~C
zafGm0y+scm?Z)_m^+qrTPNxieum75Vpbz$!lTr4Mpi+W5X-5@G?Q}6YIz0-Xc2a*2
z2Bbn-l>H1K34c4bvtZ9XpGM0=M!0w-*tqdvC1IYceUm?qN_AU4*8QTC?6Bm?3()bi
znQ^+yY99&sUu|Z6eBUgm#mke;ur$;Bsq#jplX>q<R}<~9SzQLrKRw>sxISj3zqh6E
zL{<+j*MI6D?|$Ug-HwOqCxG{z>klR?1XaNdEamR2DR}q}Ji+1lwCHa4Qu^atL*3$v
z(Vo}dln5Ko7Jt>TbAHE~>`dd>ZNPg`V6#gUZhK39H{#gg1cNN-avpj)T8`L8i?azv
z0>VyCOn-ibkCH;9+GbcfM(X7;b`xPYalvy^DIKU2VUnB#rB4UuRT7ucZ>*2WYVrTj
zLkryKJVirZp)EtdeTjn>WF_aDgR4wIvZ7=IEbxq0hDlK$Q#=d3zXj;Al%7j6oUO_&
z&gXUyeJs%~Q8`*8HvY)b^+c03pknEG9q0@u>BCD-x1BV`X6iRr)^mmI55USIG~VHb
z(Mbe%hp=L-;EV<|FOZeh(OfffJj<<$4bo`qIL_T9uf|y9lBy1A{aoN<=-g=57W}*3
z&3c{JNBp_K^w)Nbd=>#yZaYVNjWMfYTC?saTf0=Jf2uLY`v7jn-s9qDMj8C|%jr=C
zexU~u-P{dn+%(2TyJ5Cwpv*JdfuJIh?*be~Ei`RyAymYfLhp<;>zrTG_Sc*CoW5y=
z?4=sM&VReZhoA2x^`paY&KRck&}!KPtJ198q^XbthP_|EPUSVQbUoMPqfE9EqA@KO
zMNBZ*Ac(It0Z;+qsxSdjAMz|Lt>?VE-<aFP;e6;qL!&M}2FA#Qpm|v9iD8Ag+|9%q
zxlxdE*I<w@1>}s4=}+Xr4W28Qs$K}+xd^LkZFi+*eJ2{oS#<thkNB!z==y+7r>HEX
zF)gdWH*>mJr$Zin-@oD-H&X31IWcs8nNU3|&%iezexPd|xy>WPco2M$^d9?-{;zxH
zj#+dt4RqMWN8t1=FyxVM9nzOn$anREJwF}ByIjt`+lX`8R)WqFcvwd<*Sd*NBATnm
z*giTFb9&jcb6!}|XNys&pQ+Tny*Kt_kuP(DqOV(VT`UbtYyb#?Q~|w{$|Avo9!jD>
z8vS~@R(0&H5Ux6Hj;NsSraRxP=!St%*_{!ApbtY1OmE^iWLtCgUtF07x-hqQt4hbi
zXj_IHAL?NmOLMEzCjGkT*D)wmQ_x=|8$6$ZN%_GNO!1Yx)>UzCiiC~TOM$zDu=`{~
ze|A_MwyrPSe>K)8cT!ibF;;v)q23;#F2AK*h92SUKUm}nz4xF1Sk|!L^_!7ya+|cG
zT5$Xp9htboU;Knb3TVeqHil)UAcXjx93mC^4`By+vVEzX<g*~16&>F|o(GSIAE-&`
zxtAxp>knM(U(X6d7O`@;0dcAUV7`0ZPZ~SSV?rze|M8^Za_cj<?GK>$;}}OycSdpb
z^UPAdAWiujBk^~kS7p!Ah>`@r_|I-~`t@Q+2H)$LE!;-~gmh&U^1&NIJau5VQ%6VG
z<2+@9hpYWWhcY6_irWURNeiMfYnX>i_WRPX=QiboAkHzXvrB=>3n6(P0>GMZU>34f
z-!f8YhU$yt=H|n`AnS67%O_rsmu2@<I{!45l$P}ld3RIim+Lp1BR`H5ihH6vt0Seb
zGXe*1RaXz`oNsQ<v?HfH3JDEAs#}lDX*R^|?`oDHXxIk_-*-DTZZfP>UV3tt-n}C3
z+MksHzw};MU37EgYj*U*k*Nv`o5A^j;cM%hkJ{v8%EdDQb3SSJTrVnico{c;K1y?Q
zmjMSJa%yUXNhXIH)YuJ|Q$*5z5K@BI7#O|>(Dto7${D^a22xUhbZ?s<hhK=|UM07#
zJ5~Gj#<zT0W8FL-<83(c6|UT&NNN69y#e<VoaV!(IA#<9^@ewtWUj03Q#I#2Cq}=M
z4^5XRdwUur-EU<f0Zx%tGBj9`gE=JvVtCyIF%~4;CLDb)3!|V!JddlCulsNiremTM
zCi?H6PUpWpQIA%&b}%m)+*?bd82xt}M!$zD+g%|-3)K<IKn9{3i37~R^Wy7x;sewc
zIus{~gP|w>*XMz3Xp4(jh%g{@hSKJP3yVtP^1Vy<XW6AtbR~0jzXWTa(>yDRxUbyd
z*S_I0s;gWY8mG&q#6%Py-Pd@uIXrK9<Wm>V@sliJq6<OTmiHsRVS5PHQac`g{EDYb
zp|KAFY;mx$Iy(R={p*o8ilgIHb&0zb5ndN2WDqE>IDcdn<#;Q#Gk^%5lcm0qIAt`Z
z`Z>_IElnIuh;gs}vRg!@4iDEcjR<wddU5gT4Ep#z4ljb!i!k!n32Ok^eaog(yfC6;
zgoBJ}B-(k~OVY-N4&fjk1Hzc*l^na4+65OUU`}eb^m!%@bP&vlRXM0rg01Fjw^Jq@
zDZjs(c)|7*j-PZ&2%7*mhZHjWjR|*?)RFa*oaw5%0<#3H-1}?6J_xRW-B9`L@Y3!t
z`n!Nu#UJ>V)Crf8{4o{~QANZ7AyhQB;1)R?0U6*k%7ej#&}<P0_AVinn;R7qO{A!)
zmELU5X`$;$sR=Kd>GxiC+MuQYa@2~~`x5=dZ8+Q`9<x4RpSK{fkgoo2W;$IiKQa)C
z^OsKfAyMt1v!WY4?~dx`bxG1I?bPqD20v!0B)<NA=kaN%KAN6`D;KL|tVx9Od41Hq
zkm6s1022QfBnPpWZo)@YW~qylT8=5;K|m)&osq~>I0yaYNEo+3`vPp{4Bj}$c;~c_
zv0_km2~Z&~iWGl}unsS#d|w^cEio7u45H!piQxd`k(_>-`%V%i)vcV%2!yYy3hTh(
zu-`C}{xfm(#|gUO<Wfsqk93{z9HHs3vM)Y=eXuJl2q{)GIJhaE6%77RErP7NYNuTY
zWXzUu{Sss|s4jlO89{T<P0|%0e|uxg9P8MU@aau+#?}e9{Tqej!&bZS@YCyYYof=?
zZ&iyXs=O1A4Q0{Z$08*=DpDx>D4<!k)K^`yn(7~1O&$HW37zPbuHl1xX!;O&kQQbz
zZV+_@Hkb$Bv!I#nVl4$T3)MK~?hJT(WcgON%k$$YN2qH9<~5gMyZ%%n$1*kT#8>yz
z$gtI2Z^IEaJ5&3!JW<q0oTyYb-A@`|+L8N9SS*gCw=*<nUsfLmtWoMkj@0TQHO<Yx
zw*f1chl+;T+g~Nt<TJk{%r|!;+1a7|?U_aD1Spw}8ycqeKc{V*CV`5;e7t>x8mPpf
zfe2jylvD(VTC0f=uum97S_cadQcR(`PKf(I%%kjy7fCickgfMVvD4&P&e@0*eN~2G
zI*p^QcOHa`rS3)3y1H}_0(~ES_|}vnX*Mt6>B#|~)0um*NN@EX1m8#AIlBN4hL8<c
zXan=J7BG>>M&cPQWaCc+)zw)KQvHR1mJ}nej|KPPYQaCT5@}Yz-S`0zHkmLyl1@q3
ztuw{hy@XBh10P%FMijbEE^$J0>G;YR$+!6B)X*e|@(n<Yw#2#uL9i>Zfe50j?aW9y
zhQ&KqXXX->hWYPxE@XDcZv2#Dl0Lf0aw_}n)y)wl;Stq+aBEFS$Qp@#+EeT3@v+B^
zDI-_tP04(@1f@xf$$;&}?3ugwo%j@=V+O;!o8k`mdQfcU#%HoWteb`Gn}a#=W&cUA
z5lj&HSy<Z|h})5_LN%}le+85ceddH=NW@GHA5Z~!vo1Z9$YAMUS|lfGyY};)l<u9z
zC)~aHR#@E>wj(mSHCCL3d(lPUx;cF5HJ3D_-GjXB(KyZbVX-Wxe;jm<<p)g;B`?`E
z`0;Bf7kSM2;915QA(39gI$oB_!p+OLj=7!**|f65p-0b%yQnpn$Vl=NW6Sf5pFA&m
zb)}NX*J82fa%$5^&wjAy(4%_0r+nsO87HztD^zP<DX)F%`@#|}=Y~!z@o%-D7O|zy
zFO1`r;fKeqrr(sFuOO3AtNuE|zhmEL6{4r39gzqBx#SwH+1@P3O!V|u{@FMJg4F;D
z!~stDv=@r@LQ#@|1g#<DM5mHJz_H5s5=V-H$I9yjrQ1Vlqj*MzwC<wx8@FEcHns$j
zbH9IblMX<fA6YhIrCrPITRpp7=-M2h5>nB;M&*bSd;WYUpMe5h$q>f6K#NUUF#l6o
zU$B~+(~%S`)d}iAsGgQO8qCHiYS#8->JgsX7~qlJ|NPRv%k(3qhYn&#9{)|`a+cX)
zV8N_%n-GOR#&~&Tda8CWn7gqYBbo-cAAmlL6gUyzKs{1E$XI)nI}v~InTBj>3grS>
zlJv0L8M=B1Zi+q+$PyL0#ngOGkp?Ezho;_fzS$W1<{$wJ74>yz1Oeekr8nn)soSFi
zS;B|v4vMWJj#(g!r9lLuuXD^tu%NMuI6E5p2^)bVP%oPH4*G3?Ua5lu>LQAe9`OW>
zMX~)Z64PXZcq%c2RGQ0^oyjENvU~R3bN}Jvd2go3g-1Rsy#yp9RBEkHZ1{ZwE0ZpM
z-pD`xjf9ZEnEJCyG%wU<O`M*mVjNAU)`R?0x8){>kgL|y`Hn#<G3jT%)ceAow~QC9
zneZ_}MytC>V-Z{b`68CE$eBo$dxPy~r&Yf5p|a}(s?U5|T3S1Ii!GhJX)YO=2CY{p
zn5sIa&;Ra<2tL(wb>I*(y!%fVhrofr*TV*IG0QLUU|da~c7Yog3-w7_&&?`G1xk&2
z$AZGch3QfbbW))5Ls01B8SS8-bq@wy${(1ec?ZJ8fwJ+%01<n&+fNMs7*Tohc_DR8
zH2k4hZWD!vNr(KZ)5^1JPYFh{kI?bixBVyOcqb|5B?`<7Sgy*<iL;4i!b@eIemHw9
z+}!>5e&LJRht<w~)W5ClsTw@ZWiENvWu+sG=&_3up(YE5uDx?J4=RUBPHyQZ<lLI#
zUW=<*<7JbWFMVnkVO=ys{CbgIjujsvgkxP)T6H?A%UQN?LDy_XU_7~V??*R-T11i{
ztKq)^l|ij^fll}&ToY87L7ZxC|J(-hfN_6^TlcUC3PvB8_{ddjcLx;%1jbVN_!*uo
z!;=evlV7QvnF;ruXOhv%#efyilNKfYI4kH&_hW2~;CS70(ucp+WgfbwmrM&CTi>x1
zsKc-7JJH#Ct$aHr6zPhnwZ0U?tKsl%elmkf#18&yKr0OKFyWMDk2KtM+P74so2+}>
zh$-`UdputHU3EA=KBaInkj>L5oAN3u3ty2|lov7LtCBZD7jjV8S*y@r;?Sxi{e316
zL7kH#O`VAv?;yI2B3d8r+%i&T%)kR1<l&bx!C-X&{JPFZl}CTq^a^CLD~{!FT(Tv*
zW9msU#WXCu8ImRrLL^b`bK=2p_0})w9Lp(_W`ov}f6oaSY*<B_@HY@AB4)!Lb}TuE
z_GSXpb}$ANPJ{jzX`RWxm6u%dy}LQXrrlnA2D1esJC5DYz!#TrUDkAJ@GQ>%iTPpo
zDY!jEC8|`-jR9sZDM`>O^0dqp#7xr&NEAsnca$$@dAUE32ya=s+MFyk+{kyFX0^_5
zzt-gRdfk3zm2g0XH5`XA_?1I0T2uLSM4){Bv->Wk=g9BZ#wEUKpx>jtrg|eK%}P9}
z(~SeF=u>e`6{9ZpE&lec|8n(i!0o&4T3gj;FPHob$UfcuheTvS7n<f2^VY<f0{;1<
zR_GS|1A?ef3tw(XFZ{Z*_Fvzbs$r*1X75zfi8lJpwS;mF!-j<A@I39}?~Oz1O3-vf
zT=3ytTKro8E%7i_3%AXboH$5{Ld>t(ULkf~jjt*~Zn5^gdOUOTL%E$Ef47{h<J$J9
z>>T+y7hNJZnp%Ci!4@oj&{srNl(MU@Q7eiUoSt1BPDF&qFq}S3qM7@Pv*ctb<LSpf
z^EBTll|ctBJ$l(iLN{X?+k<u^dyL_>lL~QO8uEwt*WBDvVn2K)BThc}R?IAVf9`bk
zz9{ogd1Z-tm9y)yp%;EIR^Su>r+gg+^vm-K>4I|(!&;T*P187Gn|H_~NDxUW3Oevv
z%h6~P1bR}zSD(eVM&UAGAzDK9sS)c}>(t{3H3wAai74pQ6jH*^8{A7^c)qM|(r0DF
z<AtoVC(F;b+Vl)5*>wx%TW-QI(^wVCJ7u-&A)y;Nl;2ARD~JYm3ZA#2pl)*i#wa9#
z8?--8#F)dBvO}HhOd_QJ{dKdQ+IseZW&Rju>Hew|jY1p+VeJE2ORy`l<@X@fW7xE@
zWKwN*d-Z8WVR2eg10bRP8~mP}qW*6Z3o5EL+{o#f@vryQou1P%IPgG$>1OtO+8pRF
z57fh=!k|SWc|FgbKt1Z#QOQLmDrE114<af8ottT7!QG!))_AD&2!HQ^|8C7V!VKR^
zgkyd2trGwUj{zeU;reKfEPn>0o^a7is!;a!Iez$6sR&g=DHK5mse47R<$Zpz_y}*B
zGHMq;RoIY9bf&3&j!2R-{+Yi%^+V4ZowE{cYeeH&%Zv~!laq<})`2GAw(77cE>F+C
z@6)Xalv?9Ywgh#gIwkNGw9G$BXq10?l#jLPlq1sAD%+fE$e(s2!QUL&<R%#5_kWxp
z>>;!j{t*yOh=>PBsU!eXnIQ7&*i9Wkmc!J5nlOaKzZ*brRcPL;a{ObAHHxcSXJdg~
zxlDpZh}#oHmz-q?Fd8XWsgf;N;ivc<%rCp&4D;)JbJ5PIKF6Bk@gX-Jzh(Ooa9=_D
z+>1nYt0A~!?qe{;&J)U8JPNDT1wz56T!jXupQ_#F@sQ;rplIP;T$Ye@!2LNzfo69~
zU9W0}njf?8=6vqNo<#>t9*Hz(XG!E3<PT{38oqR(5yBCSk<K<*rTF7s67cEK)bzF^
zki+^anhj1XAKJy6ZZfP*CK(B4D(Zgk9R3q~Z`!k8xRG`4M%&Lo+#JWpjywC4LYMO6
zp?`PQb8J-6Ob{0*4-`^qPSB!1(BJC4cKlJ22Q&-tDx9248&%{wo(;7`EDYz%VEx?-
zDF8D$yJaGPTOzF{LB#O~d1yQ|sEbDt^8iz)!QFP!nyIRCan+QN9p|S)rBN0W_uZUX
zE|Hwqi+nrXser_1jyrx4lNcth(B0XC$JY3&q*JATn@(Rj1nS%?j|0-;cmzZaZ!wQp
zIur2tPX=b+PE=Y&!_Cm%-n0Djo<a$~kQ0{gLe2T2lHp}=D6j~&>s7r{J>xK{ZAw{A
z+_>8@#_}cZBJtlmjtf+LTM(%AfhG9qW-$vJgiTHkBl&e37r!JiIlrtN?0h<fEAOdP
zR0NHxhWCCp!t!?Cr-vf5pRsU;(KGGr@cYi*&Q7w^E9)ZPBNI$6B!Tq|>uK9dlnL-j
zfC#3yjo&9q{jSel&U~V=&DiG#vR{vx>QPA9oz)1LI9)h*^LlLQknx(MqpBxR>6aO8
zG93ZadJIqjF?EWGhB&crNe4kNHfS>A=d1f_j>yQ40Wiq)D#_88KMr&SO6J^wJ+Y9h
z#$i(H$Nh{ehfG8R#t79GS#PSK!t9YJWu@((_uvU;h?kVV-02Jd<f#=c8S8&ja%&ME
zeHPJ+Lv9x)x1Ki+e=@9Jq&|$<{5_z<+nY8(igq`aEr<ZeiOJPQjS%bBI@0qry?3V+
zOX{l-cPr&fqWbN6)1jbWNUUd`Km93X4W~Mh(bfwjG!w)jMMIs`G+fkECpo>#!3v4h
z0Y4s~cLr&RzyKO<Nkx5x_B9X99(6(%;6|G5GY|%iDK@9ggS9u}J|*H4JUrB~^<*}5
zo!7W)S`8lCsVo%hSv4<HXX~GEJ*l|+HLK8Xnl7oyz4qb6LTz-3FO!8k^Am>4enyg`
zjelpr(}PXa$GIV1q)rezn)YL+bgW=efCKGKN9WF~cu<!pmdgJr)Y@mBW5Ch=o^U>K
z^vXe7%ES#TD=H~qQg-K0c0aVlak@Rk{g9eO>R>CJ>qEQ#$_Fnr9#<u{mjtr0@_GeL
zpW}uu)z2D^$X~S=KYu?%?D;qck4qx^xGkoRr|^OB!+h!0BP}4+7G-?QWK>P(-7vk4
zwjDQIK3JMxoRtMnt<(PN{wrklgKt{Qlp+q*w4%NzTSsRTls6W%7GYB9H+(Uf;5jb-
zN2n=5;Ow9IRnrtH0g=W>W!wujd1n__$qE^&PBKA#+j=j8F@Z@nBLb6V8;__QW}`Rt
z^<_u`DiUnW@B?9q4o!>!meN$1y$Oas$9Xl4X1b|-x{`P~_*c7+KWq*=PIukEiwPlJ
zdV{+}6#WurO{@Cs8xb8y=Hh9yd<B5&l?#mxovB7znSrCHsD(Lb5LDA-YD`t064a{N
z;&mRh8{nqbZ1KP86g>q|FWiOe8GQCiIlBb1oKPlB+S;2GoE`}TmKUf{oGoMh;Ilv1
zbU5&Wkc5dgU%m;XHlY(yPw<L*AKL_D3N+>d9b=PC8?p`3W(2JS{go6Lxz?CGnA7+@
zgCWQ08EQ`qd1&J*9cLSA(nFp8d`hwD4f&{<j2f}8w}20~a9nv6{l6@LrAAN^bPV>1
z{wfemmGU?^Rcj}zT=#RT%BS}n`F~(mN8r^_;-T@AHZy;eaYa-6I=^g@;`=s`hqGF0
z&Zw8%7P{jPenJY`x?#?0)m9t~e!^l<+4Vm9cuu%2ODXkPy+{OiCV#$B3-<@TfYSW?
z7)PVZUPsz-?Q$FMxBhI*TvXgsSv81UJQXa3j8GMFX1?xWVuel}0c&eU)-rFMk9~B$
zmj%Fk4*i}D#D7~~(Ep#M-|hcVbyi_ffBn~{gh50alx`%XyHh|)MG=tBLAtxUySqg}
zKtj5^OS)6KyWTzip8vsndB7!{z|42{{={1M!fI&<q}kp5En-_fH4V<!76AnE9q$(b
zwTNcb9cfrMS!E5r&eTe=Ws4HW&5X111DQI$RFeV#aw82mqY924)t+Hiz|AVl$^!X7
z!d)_ZT^)zO@}-XHyx~ze<+9VDZnZ#ed%9MT^SWO)?s9DY?9-zqwDo-L`k0rUZ>`sl
zsBP2Ta$k7UCd2JSc6*qPfK2{Ek9m7knheio1*RV}#7Up33Y#mmv?P6XG`{6*(*3(@
zYzOsAkz4=j$BRv$$_?qMIz^k6!aw&TAsm&P{UsCY`QJaL4EFrIHOBZ<+l;jFIy!cU
zI3$0QOjAnaQoi9Oy`a(yE+RGmh;p@MpN*4u*AZLat~TF(EP88rb2Dr`@o?&LHWzd*
zKR=hu$rsT;)7ZQFp|h>1Rrxdf@nM^h-Y$j(dPcGQO0ldEU-FD6jZ|07WNw-VWzxju
z;a;Pim6{&A?zKv%e~t0b%esqN8{GK*bEY8g(2T{r4k5ZAm!IGRmmJU%N3Z<1i^ww0
z9;tp*WSnv;2=@AyLTX&xJi^5hVY?SkDlah-c}qh{;5~cg>j-_*P7VuN$4(=epDklM
zhS++LBkE9MTNhHlFJ3{x?oUY>N?&?XmP+?j^h+7x)v8)Wr?xypQFn)5`P5`CshkG^
z$0tCki7`IcwxaZs4S_|valX-|%Z)9@PUEYK`nrxz&A?RWaW*PVVDVQ?lxle<gQEPE
z9JB)w-WlW?>3zke=D|!a=KBzJBHC7A@SR33;T=eSH#m9bOSLz$(CjJl8iZfYXPID7
z^Fq~q&F25Ir=W~+ARXsP(U#2!$%#sklFM8Ixep_bhm1C0`xqXRU1mxB;e-0Bh$Pu}
z`7y~Dl3VFs9)QupHV`w%R!Zz*aDG>BBRI*85k@L5P#0YA4c=+NU3!bT$NKVs{uLv1
zR>E1donm0r_iwmq(okn9p=m+mN&t~T%u9jQNcQVBa9Sk(LLMb8@Dd~JL@HN)H}&o?
z>kn4!F@WaAi3vgcR9Ht)l5y(y#GZP;RDz8XkH`>IY^xsiMxvC(TOvdh_hvUH6Hp!l
zf)@*~5+j4%HSt-pzvU#3bX1pYPtSIsy+3lj84<e)lfB-?t2|Vxv)iUzGCq^iIUv?A
z0gTT6TI1#?_fOAQVD6`{AP1glbH=Jz`wT<0$rCJ(|ALpJ;`jM~r!ZwyFr-DE`R4C5
zdHz3fm}nM789?>74L{{30<O7FGG=AuFzdT(|CaQe@i{*0B%#i%<IUMNk{lw?kU3gv
zoNbC!S^Oo_7e+GZ1bS%c&8DshlGNGmZTY>I>O!t0B=dL@BnRe-j43R6C2K?P&92zA
zhOa_biMni5b<bwAuy9=&#5Ro^o<!7UZ}ca+)^9k>b?W1WJz))uaHbp>7i&7a5vY1B
zCgpN3WFK@lNS}MGl|VhaoqK%uhuQGz^W;{CkYrqiOjw8Gj+ZIUj{SXUUg^PIc*H%4
zTzoxVp;7LSku+gbHKFTkgJbKhF^S5H*abV;dkwp_Bf_&SO=+mWewNK(DBB7fUHWuv
zSivh=YHsIx_ENOkP2u6dS13^%Pd^XH8K%}0i}M}MoEqlxjAq0KsxfMPIMEW<K(L&D
zD@d0Zb0L9xDkyeRC-%8-qjHh)+h9GjDZMT;V;lEZp?})1C=<xCz&((GK#CS>k>gBI
zNB(^PJt0=zTv97Zqe7#AZDLREwF}2GZCvA~D%GT`ObQBA?gHTI3U0dv&lNgGF?#HW
zj&zjK;Da?Sn|;Xv!+iO`Z4QMgk<ZB}+~(ZAtE&ntb>{=d!$PJvWPD#YyXlhJXx=6X
zSTrw4zLApUJzBdHsxdVEwL(WwT4$_Tzh8_YAHZHr|F&;<oAl{MJ0Tr9OWD_jXUo-C
z=K~ds?$;ZM0fVtAKr>FXXlUwm`BN8X=tBe`OJP6CyKxk2PRxUzDPS=4ZGDe`J9=!{
zvs3~0;+KQ0$FhB7_QdwysDr{KX!6hO3em2Y7pemk9BbBeepTz*RtKPxZ-TKAH;7BH
zUXnY!-3b2V(muMSv2SVPM7xziY;nb0u_>h7;AGw2AxCd{1vW0<S7bHXsg>Jf_X1G!
zjlgL2Lm{lwYhTCU0Af*Jm+(X03^8}zw!oG2jeua1)h?qnbR1*_pj$ytl~nu~IhHcH
zev);dcB!DUhlt_YV0v|Ml;e?esnE}0+xLAas?Z?Kx^uxXmG1U}OWuO}1q!^UfJ!TG
zp3NnxY#&|PEmF<pNcH|tSNbsT56T%s+>;kl%K?Y?fF&}2EO1XT4|bBNpp5NvzuE|A
zw`i_e%8^35Hd;=ToeAL5rmM?LsVvGID2nV|Nn52($FQJ<y#wa8j1Sqqp1@d!ZW;8a
zI<Z+oy%Z0z1kqa)CGB2*KuE`22T&7j24rUV+B@g{IGCzA$){9}Tjq%^Svdi+ga;}B
z$ZjUg&387*TV=z7K;t;3-7TmJhVKJ;vatqQ!;6;=>w}R-6W`ur4*~M6;dGz(z=HE>
zs`~HmJeno}U5l0$FRZ0<?$#LyOb)cCI_4X%T-s+h2O^gqUpZR`gf_g^YM3swkB}U0
z!@y}ox9pQ$J@6n+u%B6KQ8pi3o_3JEi-)Ftp0~VPW|(R`eOMSS%zn+RAyDXNKb}W;
z;AE2!&PlSR)$eR(rpFx0s)_%H$Ny*VN|VTynE(#{c4G#?_xSdCwsEdWh7T--MO<ZJ
zCiiMw<MvV#<3<`>;|}8Ik|OgGq_MZ>v!nC4mT}S^`E$6eSMesFN8-L}wrEUJHly}q
z8MV<{yPtNf#Qa^|yv$5Yj+R*Dd@t2LXfE<cF%CJkAVL2}iddm$Qety%P?41}Rxihi
zz_KJ!dxwy$aU@sxysA@I{?y{G|7uPRl%&Urq~$mI?&lE0uJVu1@Y)ByAr__gDTlji
zfCxU(GOFbwT{@v_?*x*)%@l6)l-pyVJ4=J5o8AB`ic~#L<*_ISSuEUNfPTR?*JrKh
zL65jimZU3wxw99sUuk;HUiT18xA1$FFT-=LGPh^m`R{I`c7f_7@fn_JKpA`ERQrU>
z+3iIPwyJ$Igj0crAaU+@h5;ACHw<(_PRt7A5riizsHy9_vMO}E;m30gu<1-j<&?>@
zH;u;F2;U;*t*B_(gB{qRslFHd(D7Sfa>~;>$;-?`-X}t$wWMHu|FnER$5#3Pm|@p5
zPBTSz5h{PSK4ai~JT}OLUfymbYIm6N%|f+hewo*@APfiSQU}t_JKLo2JM1Mv;@5YE
z!89e9ZYck*oRO4JKKnR9mc(w({CBWX6Zk>>;Oz1upa>|Aj*gy`ylG?x;L8qs0G%JL
z2|N<MKU22$-F#Qr`-WFsnVgeT+%y&@W4pqKPkQ5@@G(ONjo!SYNvankJDp6rqg;(i
z#>pTqskg6ppRF=Yys37Vd8cPK+J=Bpk%+eT2FYgauG#N&*rt1OBc|0l_*6jmmeLA@
z5`fH8hS-yB@M|P+&m=#G?p3JYj%)?I`8+{?hLhCtdI@EhY>M?s!T$-OogrBw5r+-`
zxyZaDy!IpYgxiMP*=pr);wGOhofn%B;=ghl|N6bc*NW{B&Xi_^IMDeA@bw3shXA6~
z=QdqEl2&n~o9{=iX(i`bhflrLv<0MnYB8Wl9Sw_srfbNIxM|DA)0K*WdRn164jtIE
z*A;`Pa2|ko+8b~XQWMiY4Ji;p<wbl{lsqWZuMAw(N8g=76c`#ANemckJH#J<sB-NH
zvxv~cIs+9x?aQr|dbsdunC#T`DRDnDs@bnU$`{N6Vxd}Jq|Z@4uX4SZwBs@Q-H#T)
zB-=>GUd2dC@-Rd!Yt{*G<g8@LCtd%UIA_egGz-Ddwr6Z0nTkOEPuH(tb=#E>K6{*p
z{QS0@5jMC%Ih64yql1q>WTi^`hV{!>Lxij&n2S=hJ%0MpK8TbrAf)i!6NGFvZ-%G7
zleFVa7wXd8h|EFURs3)-M>M*n?J;D(`=ng8+;SJ5!BfrIC8^}y{N%BUdX+iN;N8-&
zU4J^y_}<~>Na}lc6RQbVPRgG==ix5Ci=l+_r-WgJxl_bBioYid_5PqyD|)ScRnHXp
z*18t|%_1{|vuH-In?&f-Kh=aOmX0BgEYqQZc?hQitfYDZgo39}LI?vWqdFmFUEiJ+
z!uc8~A&}#HQc_VF>I6e{5-@zfK1&m$v`!JO^4*q1<!esD+m3GHr5!95vWe^awKaO!
zps*!~AN%ldMMZJoz07lXz4h%lRHDcx@)o)<t%Uugl)yU>PM)`IonU}aH8xcyKOOS4
z0k`nARCPwX;7b`cRthWzXE`ElwZqgZzg|h=fu6RwE+8~60<d>IeB2J&BvvDI4y1}I
za^cnnBwD0$xQL4bu!p*EroH<_@a|BQ)33#kr6UGyL;Udx3BtG7C}5flF;P$Qa^5TN
zj#)>f#VP4EOw-pRq!u(~HUxa3lK}u7>wLz30FxB11|0G<&0!MjX5dh(d>fO#9Y~==
zJ;WMV2l=w8tHTfNm)gOk_~@WKX_AI>xA1i5aLb&<M2+sIdGdWK>+Ns*sAp2vsl2fY
z^J^$C-Lx@oxcCFwYD!94mlhi2Qv~`9jDMH8-e><rr?=n{$;i?)GH({|lHe?MmXu%5
zISfYs41<PtM1-rbhWlL1SI+@M7WE`1)BZiM1KcOQMOXh%^3s9>KY-)KnzLU@$L>-B
zdKK?5FI%${0K^&hf=4u1d<2UBDC>TJ%QgUNG-+;&d70wZ>YGbt*GNPUtV&NgDw6_r
zN}Tj82!?$GyJxdaKx|dV?A@yjo-GMBIHb#Bue2?CZUq?(gF~&vWM#PnS0FrIFp;Ra
z0=AXZh+jT%VZT*#GX(>$(Y1RflLNPZuk3<JQatpMEb#AQNaP2*kDRY>^x>-p-pl5|
zv6=6j70KpExQGhcg{12{)aBbnlr3}!q0BI^<YkwfURI3x^?l4S5>uBGT(W!c@cqm<
z)|Kfn*0e-S<eW6%czeblSLpPdmV$PK;2dAJ66?DC+pSN5&GU*%$}J4Or*(U68N$TU
zg%_Fm5lfQ<dKt2;xbciW{47?3pY-ToQ8J{x)leQezq@|?T<vI1YtKJKR+qr7I3pF!
zMldiNa7;gTb98>6Z+7|M;S+-QN>SULH@NQducCGsv0OT7U8sv)Z6tT`lISMV`|GWf
z_x>IMw3}|pUzO-Z<1eOimk2$?NbvvU@YxF&*nA8#8oS+!JbZ7$AbxAY@t!oNhZyV3
z<4<y2h0cdxLPv}BN7P(88Z7lcS(8s)XwsZVY{5tG9n!C-@Ws|(x9<*O)#{8gJ{e{m
zGax*`T&fSEpV~hnv156IVaiyJ)K7R7m)uU#V-VK(_KbI)748BT>e?o`4m<_Hf2NhA
zDW!r2WeY-(DxABXF=$mFe>xoXJ<(E$d3#F!OD0>q()iJ17`i@tL^;7WIZZGm5TDA9
zB^K<erTg@S^u&{imry&s3jGtgU24e|1KMOiov|Q=q^EpX>U5vj3E(S5RN-2)kY<mj
z?Y>n?lZW}lbr?0F)`#~cyp|jJ(7wstfg$4-1z@`d7C!E{sp-WXw+5(z*%ubqkndR#
zWW&I^FnzV3Ka${#W0QX=zsOUDZo$c7;H(Y>n_+U0tDuo)1Ww<0Yc60C&s6?|CJ^&l
zWkn&bJbxs2$O<PFbYg8BAhZNJq|Xc*HLMnN*n0@w5l1?B>wi{P1u-V?pmh=;&FnP6
zi^!!GZJs#Z@=pkXrbg)77us2PLe2P6@uWLa(Oh4#?c{M+i2`^C`JVVS-9*-Qy%{a$
z5_}!A=m1GM3wZXD@_qIYE~AQk6T#n0(JWmgw7BN`i&Tv{&l+v8;!`U_3dT^^D)%b-
z-#xhOi8QjQkC^;tbLa5|7xKS%(=8H?8(q9i7M~qd*~x_<PPcIYFDtXp$lOqwoEusE
z2k9o}2(sP+9MRk5=euxaZtx#6OFIkDPYG4_3O>8;_v^ROOSnGrQ;KQX*n1G(4yn%C
z`tUqWRsGdbT_Md}R$pzo#7zC>Y%LouTct$(7d-*dV)^rNkQI!ImKw(9X+l$5-j4Fe
zXiUPhDsKpFcRUdt`|>f@rar-0KT@Bwz0Ei6MuKs~9<emf)3_cIx$5BoXXjj@cV?ZQ
zDTSz2cs(_`4itCoQ})}18U25UEPpDAW2P-!T2kEDXUs<BoJxL>Z|acz9Ru=E8D-t>
zET?`+oc$qbuDEEF;jiL}P;b(Vhj}@ho9|enO;h?^?8&xRlDC9K+zv1YYtP-kYE?1a
zzjwg4|7rhvNkEE*>a8t5&NrD2H%UId?N|E+;_){9hqxp;a=W;BIP#-JFRm#x-=Eve
zR=e=*^Z!m)=P{L1HkL0m{aHMC9{+dX!s)`1l*h4{K(R~f7VTVPCd}nwJWlvK?`0dh
zkn+FF4h;(~nMCq>z3HV|5@$H<6lM1UnThOh8N%NoNC*h5Re>aCFR|)t_Mf$<rZz?{
z*uHS`tx4pu)cV>Ps*@nEckd*?>~?PWy*1@jouhX51qL~Q8>3*wUH}0d9U)Db+~IsJ
zTHPP2<e>3~P*m&*6BYkb>?DZSt0$+Z<hFWZ$H3og?f63ZyR>(5>bmk11(hCcV7Zsv
z#e;CovMG|5PAtlzN>o~^nguu?1O-99&Q+H{$LhhYvkeC81SdGF-pFi0T-u<u(XXw3
z{1DQ9gBp-~>TZ*PA~DMqPJmfv7^n{_8$Y+DW=~IwKVm;#%ai*$h4dO%a;RQO5TQK+
z0f)7c)11{2x)z8s@>JBMc1o%V)Db5su*?2~;S+2Nee`;%8TAM=1zMAV7_B6pZb_Hx
zlMR13gu#fUpupf{9b6qm4#5$Izp_x&t~+Q7_`D^31$-fF*ziB+_3Ja67fF2lj#|Ep
z<@<Bf17qqG`&>q`+V!WKx%u)A0@Vg=BullSHP!`khkG*JbYaF7N(5cBJ)aP?OYiy1
ztHai9OmE4cv7kd>a5rR{ey!ak7;Ut(p5a(m3g}L-@uRg5Dr*l&8KhW3OL``up^E9}
zl@v~fw8jKA{<NCWlKtQI`A7hvOg`Jl=Kgub8yMq2%?pkjNAI9DB;B{&-o{<wOjkm-
zjY@NNrLC<UI+tAnOEF40Jta<g=SjIx-vEDpT}adT#hSKrUm@#FIHBxhxh|S5KkJh)
zK5{7B9x&U@w|5x~ptO?E`gtFMmSqR;*V?Y;`Y$~*qe<oJJEtHck?If&Vy*OuLtzQe
zrmA%O=9=I>PKG|hqn1elmIl&sb3+M2O+)1LdAo=<C;J-JDg+y*cicQUIFZ5KKMt6r
zq^r~NI)}@R`0=6Vz6Fg9iJNh|vcH+~+&xK*%{Sake7DMQ(%b2l1iJmYZBejaIeZor
zy8U~KJACcr{LQo&fhFqs_|Cg%#?zQ%@pZJVvkG)P22~0kqG2J4PE%@bbF|k%&CS;=
zQk&JN6d?jBqV*VuISii|N2llr$I45}KmO3rRCAnd(r4j%H5xZ~s70<;_hIGUbG`{F
zM1Vx;T5ii`17p-mE4lQ)V^B1K((CY-CRxvzA9NjTN|yzYuJRS%^U~VYo)x};l0ZiI
zkAOGDNNq*~oZkmE&gDJs2Qx;T#K(0naS1G$O=rd`Nkv)*f|Xc@kL*8|ubvDv%v20s
zcs_CgP1U@voqE~3j8@yLbJgFA?!z2zjYG5C!0Ppq`^z$OX55Z=Sppu;NAy&CR{8I!
z+ipgmt|tJurL0TP7Hpddwbogy%~w!`TT>km2x&2E%cc1x$}{+mjRzwF6e6knDo&d+
zjLqw}(C#d2R*)U90VH=KD_vJSeJ)(~1lc1g&C+3STG#1!wQg)(0T}QFIO-G8n!TW{
z?BkG!W1rHn4cDctx^=#Nn087U`8r)}mvcdQt-~IytIQRkaQ^mcsNu<}YnPvPdjai&
z6YpvMR_o&flI_dc%cu8GC!4bDIdQsG|B&r7u7!NZxU^Xl)}6IGU9ELG1(OrnUou$j
zyR4fJp4`b>PIp-8BF%H!FMjvFh&@}ZAKpj+v0YUQFKIFAGo{=O6}}<grvum3;>S`g
z&4cXbc0yU~YdQ0U>T}4$`$VVF;W)<6Ls{Au?N&GC7B0^sE?7X05!w8N3nuZoXg~c`
zUvQNX(WI04NaWNBTGPomJNU<+WxuRvpY>r%&&bM5K)LF(?CF7A?qF15fQ)PNa-q~_
z?X>H<)>%HmBu@-lWYvB$uq5|aH1Mfy8rrP)P)s!>kLxH#@oXf48aowmCWenT6>9@)
zZb{8(wjgseOQlYq-v>X|5%kp`Iwq-i!^RWt;l?*47KUv(nua9!IJ7HL>u4MpW>7tw
z_|Q0@xCuY}EPJN$L+M9fBRdbC{Cb84Y5IXoJ8ZJzrEjj#OjV*#N;0T$sQFX7bf2eg
zO|k(6=c51I#5N?7C)r*4GKKvU3x{d-qv6i)+Q|V;wmCnks9+{yi=2>v)H345sk{(;
z`ab>oyh~OiJ=<ZEK8Z}B*VGC<$_Ck1e9|}3i=?K6`SeV5%q~C8BPQWX5MLG{vNWE>
zREx|r8zV`$s~q<i^GD4V3I3_+rpd{HATc6<BlI-&8Xfw52E->)qC2$&qxj`?BJUhz
zQ(zRh=py(Lr5ot_nb1Szmwrj7B<rl3zPn3Nx3k*k|JpTMsk#7>iU%yGtlYGXRDpvm
z>EDM@w)X_n5YcqcMwj#X*{u)X(Y(`R*Odd)qXo+daB7`UqPhqA=myfoOmXFzhy^xh
zuan%~BAzNf++9nhPJ?-al&zMAM%rA*>q>O7{ud6ZZF#RNH(-Oyk0t6{qi_6(vxR(A
z_pCG2uBey+yMqh@%aZ#T$R%`4T@01<agGDz^=T43oC7E@mFBQv&{|NimiJ4V>;QOh
zQWz;v9#+aZbpx}a^w-fG{|5al4~bL)q+?b-Nls>ppFmR;Qh(0_gsp5`?px`OAHb49
zVpT}u3mYKYT=UJLry7I)N-fyy-s4FH-%fD{3zc=hsvlxdU-}mxdMq4?B`L`d$+pLw
znN^ySM@h&x=Q@i&lq7k>4fbCcs{J5g+0z(laP%nlVGGC8dWTc72B45Qbd`my5*AfC
zxg<VszCtRq)nD>hq@87tCMy28X8jV0c>zm;7i*K}ue7@Zbb?CLDQ32Ci?uP>&K%}_
zdBhS;eHMzOD2M$J`APqu@8F-LbJDXZ(FXq}R6YE=Y^Jbqno47>a!%86fzibFaNWns
zx596EU3Jr<H0ppqmu3>*ip;igWa4XgAdrGlKrQ_{3Bs^I^~`u_z|3WgOA;#dc12oX
zRR2~a9~&>EHWw0US5idXq519$0Tzkk3Kp~DE|x_HA)=-R%5KWp-xmkeb<Q@2$&ynS
zIgjO&2CI!*LX@=P11H#}Z+M>Xil^F?<e0Xlj?Mc8PtdGde|3;zyg4n6|M>mPkn>Iy
z%O!V&lh8b5%!E5wU^Xr{woviIIXjos7x&YbucbJaYJCY#OvaZ54@8!Ky<M?u=iBU0
zda=Eowt8OQX*^2h`UNu<!^Gfc(JIg9=p@|C(bRtunJXf1<4pGiP)LiHHk^bs=Z+o0
zpb@dahNDFbUor}88KOEs_jBM+s2wN*>SMkA&*Jzt0pZ^3fI-;Fgd^k18mSIsbcKo3
zTjj-1NwJl);G`tGM;Zlr?^1eQNj$G)=f!sAoWId5o@v8T!r^F!;=iVh>fb}qgQMkG
zko9p^N%DOFuF&;FJS0p^`w{rS;C@sbje^|qbP5rX9ztXY5~VzU>ja&=t+*i%ev!!S
z9tR=VGf%{Wh?>NoO*db60G1(X)?y^fJMWo+5XAI*bnaI3yJGze=ZW1tUs2Fxt&`BV
zEOh~h7Yq*XZ}lEXS>WOQ_$&FyqeQKq$LxZ6AoKp~pfd6^4?wQ#NIB~u!HFWDq$&pH
zgI?R9EzZ<HgOY?_Q4n@iTDRkjU)-41sr>c~D~WjKY1Cugk7?%qr}N#TP^%Ep0IZRl
zWL?NOt|;f_cG}6}R_6Gok%GyqOA!x}Q#K)|b=KDPsc|=A4-;Ab{)f*c>NNWXNWb%Y
zQE%8-^+N|ypwiM|FDSIkQ+G<ztT8rwDWLVEg~&6^C3@{|oXAlk$V4(!=U)9O)y~w3
zd5Pu)i(pK-g3#KRJXUt)K7jY`pHbnzFW6LG*USUHMFuQLL+SR(s^LmOTM8n~r&Y!?
ze!Iq8o63=Y!oNKpaG$+dDZ=%fm(r8aCO+*~^d|DaJX=fmxh7(qjxtf8&{OET*dP<(
zNC**-aXo6*y%SD}w#<)us;`um8=qS}{;2+t;g@;&rpZLUOn?lrbXe8)H|t|+&&LwS
z=cM+jOPsPvkk<pg<W9oKe8u-m4k<2|3>OldF}xlRXloqA+Bv8`Qqh7xtDb~*%W_Ae
zwH|-}@sqOpofwR~1&l5VU79u=@sD02JyA)Bh#5(xiWOFTYBmNbnScI=bVNje4j-An
zgPB4`#6T0Ct(=e&YZe5DzsPn}hS(S;?p3y@Q)bW`qvacFIEX{Ou9JnR;HsL{rl$-J
z`No^TwV18UTn+iXy8V9H)h!NETtd>c5L#i<Gr|7K#;su*YKlXDTVe+9P`Jua9X1?;
zsCV(OJ;*AZ%y&!yv6$p*_no3bNOLi6!{G>_RrwmoVv;r6@h%fM<DlIU8-IrXE0xDm
zG4ph_rqC<yTMonkfUcqu5!gwP@`U6+=$())IYhC7+4C#HYqML#Hbx|MOA0i6rYNeW
z6{pDn!==yjk^<<j8>J&j@tVy%)O*}v`9J_*84rwYGzZ+zYe!(h>E+KzBU@drKVtYG
zA8QXizTT~lNJhKcaVPxTd{TsvFZcd6ZJk<erHlPel_}5pLT#$eMp*QNcC)7I-ad6V
z5cgwyU>XbT2wuANIAiEDR$fDYzo#3^E$R&j3@5!3D|_#wU^xor8h(Gnj~Bq4PB1oS
z>fa&q3PD&L&fVH=2_~u}cO}otYutN3lu^ss@Du)c!hkMftJk71GCjM2?*vut*}bcP
zh%eR6{*{X4ug$|6TjteFnjcb9Z?OJ)kynbXs&QN`BD(&KY3o#dDPB{P$=VGyXuS;{
ztPCL=_C(WGqstSrc65s&ou7{rwiJvUGK(yf#;DBeA#NzJ<Mw`omqwx0{X0d;ox3{c
zY8pwa0yS41BQ7@6_(~?FkXsmY_Kl>H?CPI!ct5eBM&A0<9=-s8*pCMxl{8x|h-*{>
zhPwBtr)scl4Oi9Bw?C%KRn#+WZ#=hRk|!Ray9U;dH-G*c0<ojPleik-j}b;vM)5&j
zG1A&vO{(I0XNl^7h1>*a>p+ULN65B6GCTC9m2w<BF!P&NA6$A;`l~#;CYk~^-TAz~
zSM)YL#rv7%sb-4FfwyP=)EHgj9)r|DdslXu0<I#0bsz6L0%boSJTR>%yc^X2dg|Yc
z18r|dV69}#*(*;)smIGkf0j}oLcqd|L<zlYLqe+@)>(00Y4a}%e{&Cn?Qm4UU#LmQ
zVHB2B=|3ZZd7LHIi_I2;h-ze|J4r-KLP5Umtz-V=&;;=LC39(-7RVp<$F2YtqFn2r
zjwv|^PNstVHwKyTym6`(JKMDwK|D=0#fouKgUV&H@%GM;CRA^3^6YhLihB$Xo!EHM
zml_r-1RI)5<Ga_;(&#tP9*Ma$ECA{ZHoNh9BjOj_$qX%eH>UOG-5=wB$5jn4!m!X~
z+ETHehM>baj4Z6?54YLE`uVvH=4Ss#aUyRnvb~%n!S@q;RbTBkp~U77>#Q3+WD?zx
z8CJ%E0b5!F&at(8dNv~<2o*II8bK=X_7s00S4;^b%kWfX<Xhw-u0MqQ8<)j+P{v+A
zEyfyY0aax?$>gq`h*<ejORV|!aZT#N0Xe34MF!3N${Q!#;qK=}^5t~Hqaj#d7Ch7%
zN!(Q*+ElmpDSs+R69{6a&$>kj7qf+u-c|VXwy#^vo2WFjoD=DXd`mMs;AQ~3c+SM<
zOpRH1BQM{$3}XdW3451gF4AF=S|gcYNH=^*8u$5tIv^{+g;T(@$SLCJ5tOvKb@tzx
zedpH#u&b!e#!g!Q;6XlQ-e^q8%fq3Ypn!3JT&bg5fXEtDG$BlZzrMXfm}hA=s~xC7
zE=H{BFk{GG(}AG;H6IVd>WyT;c)auJyWHK<!Wc6n0t51PU*`6eX>Fgtz7r_QHF=eF
zk;o&3R~-trjC$XqXVI~FPm;_`wK)+CH)pqCKXuK0WEX(EZx4pr@>}EdB1TL0g{81$
z;PX2V#bkCVTH~x^G;{cY8t*-QM^OYudHEb6-XYrZA0LY-TXn=yaOkIxeRE+8r@Sz)
zQz6N@aloCuM1;AvV7a^prcpOQWwF*&O$S0A@W&Q`nVO!K_Km!NkH#*p#v>4biMQ;J
zWFziRm-~Vl!&|VE&#`Q}?%MtY=!m~~(k+}82?P?&r(HfG{USnWI_Nj%xf_dJ(#~{F
zVf|UNGPCt}%Nr58Rjf9#neZjXj5BCxpYZX81&`9nboX2NjE+r*64y78)k@*Vct{22
zm-};D&nIUt>PTYwOu#f}9p#=4Xy>Q>;Ke)@s@fsA|9K_gGpV9T!;6HK{E%V#lH>?D
z8L4UBOOQJwJ3s9f$maaY(qYIh3Xw@1bDf;-GCljS>0Xh$N5kFu6Ms{;6tw>ne+=kd
z`dNgvnKoE5A{DoITSK0<{rzb?`#yFiaZKNuXY}WRE&YwZC;dZk^R`uK=W6xkUr#%m
z!<WxfyD79h3NPw6ofXXlq_uH16)o{HRvrfTet#HyGNwssjgE%R=UmTQ&8-sTTywc)
z`~`v};upEL6Fj$$1${WJnzlw;qzUt8E%j$Z+uRbHZx>Vp_Gl=O9I^*)41NzS9bbRl
z5bKIFT<(0DYw_=FH{eE4Emi(^wThDKf2ox~L>%;|#EgRo0_-j)kpE?V{p-bz%=m)U
zyJM=!-IaesET>)QSx%P84^VGiM1LePJ{7h83tTWehW&pzO-p(ELZ!7jv)vEZn;Oqh
zP5&yRK0^X>La9V8mm<u11S+W}g(nVT)nEU-MX+zeX$q$?&lUpvPT{$#P=9R!3ZIY5
z#3}GmhwO%ZBS3Vh0OC<%{WYWfF?r-EC0MNyW-7W3nTJmQYUtoc-mu7FPS@HN?tOfc
z{g$Q-tHnS#$j-r-?L%lQ_bjRW2cNqT&5kP&6D}!p?u(2bb9oAY;JF~!E|_}&$c*Yy
zNy%3!!obxKJV^`fdEH9w%;>a*^#Dw)GEbi3W`X8itLYpBx8t+GN|kc`ZtR<vWbS9t
zHJ~dJ#Ha{mtw7_L{)?s)J;p)0#R2I!RY!2RtDklc{46wxfK=&rhgf&&x|jKa^P>{d
zSKIoR={#hb!}r;~bP+f%yeJqX*p@>s!gXWuXGeACc1t(tcCRhhq1WA+b`d-;8?|d=
zjH3t|9Rgh0hO0qqvIHA_Ej`<}1GPbH9@g`rvVA`8FT9->OUo*m3J5>w@#y&f3FSh(
zEHOo!Z$_>UgDu4;-odVHRm_$yqqBDM>31lD0AF$PRH)^2DP6$vd4d^~f7}aBzE}L+
zULXcZg3Xev5l@MwbyA)ASJB1?i)1}03eSlVp^8~c>@VDO#<zD}86ml8{y8SA3TVvV
zM-PstFV3_&$^H(_oPIN@C^)F0)KmT9giOPbXxhum)D|yIl=gYN<)R=mqKw=dU0#Lh
zx(d)xRt<P+hgJtaJM8J--`w|DxqY-|Mi-uoKwCU4H$nRMT;>tNFQg@Z{Wg(HiFpJ=
z04|5h;CzFCh%+Pl^ibACcf)}y@J_;LiZt~#hzjaJ+8!(OK|s|fyXaGY0-6a+{)8A}
zX1tSdq;D5$Sb($Wj~Fh<H2cA9)emYo$>?|3Ni<g=*|lS09n6doDNXTh_gK&3y{N6g
zkg9K;B8WffBNFP1O=K^4d5mc({iMYXC7L6wGW#d=odVxOPrS8_WGEmG<bAsA*o4gB
z+%lpVUcCb$7>-I0YmNS!U{6zC@H{)%ax72w4EUUtfmIGNMLy^4hUU$u(E}btM(MjR
zO>VUfr;{t-c*zGaK$%o4G}AQot~?h^Dbc<PKg}<{moLk^NppWMx`e{W^e}kn1cnJP
z{R=7sa182cim8U*Eew4Ops-BN703LM+-~c~;6Hh=!up0(QN-oI6NwPGlu`)Z50PJp
z64?MGkAb7W4Tx)D087&)O&YaZ(y2J{0p-;XH@16poROZXk9V=cAQL8qRo5q}S4w~r
zw1qqK`DptF;W?=CZ<$q_5VD6u^Lg40b}9s_3vnB!T&vMN!RR2*{cNYV9qSxnjVWma
z_)&f*DS58=6a1~7v%tw5sy+n-%tDa$*3Z*g=Zdt|^sMTkUO?aFKh+*S)lmnI5~c^t
z*%8%<)sjR+G*8}qcM@vm_C_0XQ-o~^+1zdVEJT5jp*6JSSd)3sC2^8jUh}{)nghh`
z$}CtOQuGt_%rtRnbdmPISc%rHH0{()nOSQ~a~bF73E7@XZoW@r5N)#!7+o>%{^lr&
zc2l&_^-(AmU&%PK^ZnfnQlGLQivQO0T)$Y-WEnXflcP}UFx&C%PSQ@-_#(Yr<uR>-
zwx7{3U~MN>i#n;ya9!}<Rn;O4De@64&`xJ1MinSw9f`IucePaFBi}!8N;m`Kdja%m
z6?8O*)BVOy%?m+|ylQN9@V%W67o0y|<&LxPZbP78sKJHT0LS~8v)M_$XF*#)%HS9A
z&JW=br-#9ty2TA(w{v>Gu66_q%c_Iv)i!u)3${Q?dI!-R)B9mMEGW5}58Tev%G_2B
zYjDi{qDCh)nZ<%@#olLtd^l{n{Ryq<yj258WS7%Ho+8i32W%(-ZS-<I#-IcFVJj0P
z=p43{?{f`1LFpwxGPvsg={aXKoGGH+27Hf~CLrQb92uVN2r1Q5a_$m@xxP(>8}uN*
z^S-+Th>Kv6C{=QD3o+#-FnNC~a(Cn#^?al2+7qfAWrXRY?XpdC`5L<Fi!7pK?eRtQ
zjTqsvV2~?FV{yR26v`YoU7~<{{bK|f{xv4(pbB8wUYw$BakTg`?|8ECu+iih9)NHC
z1V-oAc0Z5zq#Sv-K;)^XR-X}%*dV6==dih-rfdmh7d6tPzKkH{qnu(hOF$iSST*1?
zS|^)@R@y_?9u3-bzCJl58~C|W@5Nenjj%3+SybX>g;rAR<ftp$mpaWoHP;w7pEk@l
ztnBjU@Sj^WiUjK&&Zn9339jLPZ#Oa@w9Q*(2(%3$)vjTY<+6yW0rtiA*3S@!P*{|U
zZ|yKPSQ--ug+KXUlnT8~r6sWf(d_7qG_ns3lRoRQ5&03Pl4XAL#hc{lDJR&9L^m~K
z8ZLJe=A7=`7<ST)LzTMYH|uL}*V29a702_w;J-_J8UC&JsE@PIansCV*p5<*(7Jr_
z_iI7Xd?MWPL{JmK&h>sQa7klGj<am~!?>B`HL&N<DlxM_n26F#?AgFF5ML_bAKgEJ
zr^VArpr`RhT>wJJ`l`pgb)FDeRF$_pW>6(%`>@_bkxvg3v4>b?Xm<KtVOZGRU_k6n
zCJX0x1#G?}AT)rAPyvCQFE|Dcd+=hPd7rsj5ZhU%^9iS&@Wa&_%rCy(;R!?uInHR2
zKE=_CR?1jaF1?aSIsqF%^-1sx9iqk1>I>IYn2;8FaS<cOoDGy)*p-u--L!-R!1O6_
zDt3m*hPaQns2RnVm5zZhhKGd2TW<wj5S90q{Q^z!QPc8+Z^MVU8c&~5aW3d$!P|^0
z@W65&{fH4xg-7t=O59jW(8_fw(_sz?T^qP{<}*3b0}0ntu*XMj%`+a(5WkS{Y^z8Y
z^F*G7(=BiXUED85Op~jn#|OIY*q*aYZLBgsa!H@Vu=`2iP@s`*$k<5i@a4}#gLhjy
zCIwfJ=289ug@x(K=aM(}Wc#}p7z!>bMgvK@N}a1%UEC@Kf>t6`&X0)S!F+a-UQ@b3
z1X?=U)S4#L^ao_V<&YHrVW~nGOq5}S+$3BDDyWd#Q_Wkib=4{qwCC~5%&VL-tcs?F
zaud2gxeb?5n0NXKjL31I$D{}^+<bWsL#Tv^I!I(`)Y{}+bJlNsIsE~?MNj1=OwK*b
z_hiio(ly|Ia~93#adtd5CSn^<xTWUR;@qbB(PC<Br?%pFpw*VI`s+x5!V;}aPMcm^
zPg>R2*|N-_(pt;cHV;vBN~GJvh4vEmV74AosCpx~<4kz6JU(H6v>=!OPc*`V5Zf5V
z5y+w{d7{wMO4hetAP2Q#O6~tIiHTek)EK74ufM^5%Ofg~53|f7F(}3@31_sY<#sI9
zoFb0I2lvb!`Ui2PJJm>Jjsgz#)#&oLTypX41rdKQnj))fstRB3CgRxBWTm?~cK<gR
zX}Jx$2dubHsPQ54U9)Z|;=<ARP(^PeJo!zXQ63?mU<yo5Oi~E%NW#WIq3h}^A@5xd
zc<_{=fqqk?B~KbB#sf)*AH`2E=~sY?plS7F0OYiWTt@?>_JDNc7)dmLR(;bal4ual
z8}FJ>{u7|O3sDg;wIS$uewWpYQU5TUA;d_8m^Fh+?NN!h*ioIzn-i(C=F`>SR-E6r
z_<PvJ)L4{IvLV4O=qidOK8#}7Y{^{amFL+O`Gqj&EAjE%#%hm7lE(N<9?zWw2c)B+
zqALRLRW8$ay)ILARJA9QbKj6-lP|j^$D2+=jiwDM?XFnapQFd_irmJjH=Z*4+mgLl
zoex@n&V)zj<aw0gt$`vomq;3W0YOYr11@F`Bj6YHgEJ@aURnHYsXYs}(UhyC^Fnim
zHTiEwx~K;K^GUu!Sj2*(CnVDQ53XxP88!dnsXM<}i&q1Hhr+Bl%3$b^nz{zYw8Ar1
zThHjY*0>5ig@%cdDD~TDa%>CN{5erhv=YHA;1j7ClHQ4y4}~g!3-a5u8QsPfW#Ffy
zIOWQ)70!4NEX}sJ>@U6j_RhcIY`t>PPHy+&to<wwkJTw4dh9%VkPg_%a6X>Qw#8NK
z;qy0cv~?B6W%M&&PP$k4GC0;S)f;?1EdtNA7+4b9|1<gge-9HH-H64SCB8doYySS~
zXO8uEwPe-jAs^t<jiIRRg*ZphQCG9Shcw%v1-3@#-5!StCN0p)n0dS}0J$mzu+TVI
z(j68A@nHs3kF~*Q^phL%L%|AY4o8KKt+<=a*;r@=GvqL1z!q0*MegV|D*WvbgUPTG
zoUvY+KN>*+aFB)qQR*D5c5^!fS<-I-94uSg<67#2FY40L%)lCq54E%eP~WK6CHmbF
z>@${2uFzfIW#_0;#!ufRLBL^rS%%XpY8}W_fDr{IqPSMX1(ZSm>j#G*jw<6ef`nXd
zFV8%r{e`0mm`HGpe?(&tu#O8@Ew@k)2X~)7V$xPhikV$)Q~XN9VAjFlwtRS-_50{}
z|L<a>03MTS)S~ki$>`#{S{p-Iap3F9U)q$9yr1M<I1@a@6wTD?Y=%sSOQ1}a<UdXe
zI@4&qD(R%&x+mVp>OMj^9B&OLdc`Ctj*Ux3uuHqIOZ;m!7h^pA-E9WN^Eo;>QyuPS
zf`L|9s6<Q1NuyE%&&4l**a<cDyUq!*(Rg<2#&WCg>SMGtsjO+-NgB-)7gNhI;w<5R
zYa3ArrI$xVbu@))SqhC*ixOm7m}C|m${oOIIw{|E!mHPS0cyO705;(p2TXHgqyVq$
z-_Dz)w3hKX-_!d?elDpf7dkj9Pib9_+`lHQc&S+}2-Mz0mJRQNT+1@n!)s7XUsek{
z^{d(#l6uaMIFcledNuC<ElH*_>0BNR4svy3UTBLX)p8El6mCr6mv4ypy7)Q4!_`Q+
z&KIo{rO@ssmZ6)$ngzFC<unh1M1_!~q+M<H^3xBMaKP>@)fw<9fH^6LsFr8(bF5$o
z-8%}ePDjrWl6e3b^982Km3US^sV<o7uU2?hWQOzF$7(B-HAW%pl1TL^z@a@3d1Nv3
znbv0k3a?;Fs90NsXgwOIlWlxm9LiU|TzQ=j=%ED!u2W`eViII`K6c}P*G&zF^i?aC
z`Xk8)f4II1VBL)axf>s^KP!2MiUJF&Ul@wd0?Z$@&M1GRQmh&cM#dY8SH!d#Egh7c
zf52j^w<P`)0PiTGu0&`1k~!#B2z`P*<RQM_P{QS=&(Uo|$?bKFMecmbpS5{_^3g92
zL$4`5ZuVKFz(^blc6MRjS=u;=#^%dJJ+qzDqOxRl-7CGsS;;N^@a-Li8m=gbPu9M4
z?W{0WA4)yT`%MOKe>jMW)8)sz$d)(U*FvS9A}OaGYn;Xp2<mL^$mT|RL>@#=zN6<#
z&$ClS?$a!842th&BZ&@a4`^8F*Aj&r%k@&S{cds^C2oDJtdQVcKnFE8q~W{~AHxm(
z?9X>Da8ToGmKwF_lnyF34kRDp;(u2ogyLPPmQV^12|+ewDqvkjvcK2<mu#g#Yr#C=
zy^9lYv|!Mzs|?!!(^N&EHH~I#822>Ff4?S+3{Q$}mg?X)>0a;qv+f7yM(GO)vSyKh
z?+HH+JX$)yu0gtX!FE{4!f^|=PdGn()(PL;b)dH8>2nWW&y{NeRb_39?_>@rCF%DV
zjTe(Yu+8R+Y;D4X+#~AdT^w_qsXO8s_?pY<KVwZHSB#?!_vsgT2!D!3Dl7ch0^IWb
z3onCu3)O$l>--G{<1_<Q1oDH{uZh3yTbN{{-h2mt$@ZI;=DkciIyo{>_<65m!X=ws
z9xQCLm&R!%WgNV~Z4+Dqj(>V}m#m))%4p9A8~{f2#~4K!4ob!f(kU1Iw3yL1kdIiM
z;wPmB;7lP}&Kr0>wTVEj_QaMgKyo(yF!6N$%ckfNaL*q?3fOg@6KuL<8KTBA61Yx&
zaoWKbgfsv?ZKd7z7%ttN4;{}8Pd>T2mj+7AB9Isu`jFCMP_Rm&y{=&mhwMANvc=>E
zplrtVXIHf#7!(R77It9owiZ(s1sQako<Y|m<=iRJsQuuu3>!v@U%?eUGk<`sd?i_&
zkM5e+XP;NnLD1^2M-D{4WEzXKzKj5BpzN%348Qhw_&RofK79_J`~l!mK^Fp1rgs!0
zIsT}3fQ&N^6t<GP_&WiGqRKYl;5<)VMwQ3c_5`bRy(U6+)=zh#qSQDk6scF59mC42
z!er8kWdUPIwESO!jg`A-O6^Tp@OV6aSG*N%V+5N2N=6yqeV|9GR=+c=56}Ix9P?@W
zkJG=IcF%K2C5UOuaKD(@i~2MOTT25w{#;>2t9}JZmwgoY-1gOApCXQwg&o626y}XP
z(?j8sw3(_lUsj;OybopwLSwgBvrX<qnnYSM^8t-~VWfwTHLk^hTqLTm+tQwKe{HKn
zuhefBWam~-c=g@~Tsw58?9(zYNk|mR3gAk9Vs53$`N(EX3mtRbhD+gEKdRI<xKq^G
zwSStpi#GjBUAw8SQ%7Lm`*ddpxI(2Q0-j|9ilh3LLF5;Y8KpUV(yW~Y=E8H4y#m|v
zpLW!@qTlA1f4!c{X4UL6^1)e+iMaRDzi*A7uT8(dVz{i$fUVsrqdq}&gyt^j_8Q))
z<V&pq>ALqBtkyfzFH`~|0UtQ|G~H;y`~}q}nHTt2x&;_$x1eK{;~bH=Vy#8`Q=dJH
z3TKUl1c7Ft{Sl=7tke}r#)5<RT?Is0APhHxsvEqj+zi@H4b(%V@s$Klh8vN%+2P*-
zTS~-dlC=T?@8-%<yN>O+H{undISIPc%%q_wY#8;$jr+6Jd5v-`jURi?K^i*l7^%a)
z?+6<E%UNfrj&Jf2aFA%NmKS`&ngVQ=j=|?w=HjBn^S>?0I=i~YE|)x6);;DHRYeE{
zR_C7LG$o}yw|~iJLk(lx<)E5AKHQ7fb#ye){e0O9fA|PI8O&l{e#wN#hId85O7|MX
z@Jt;=R~Yc^^^q(Lcg%x-ci1rw&WMH#n&G8cn4I_y4KMh*j8r@PW$g=jmrPtOA{9Le
z1iDwrn}kOG629_zBA=wKG`Omlm8Uii+mFm3!V4(*QOzoyMgE5|IYL1g841_^w5Wry
z%S)%2mi0>z;|h3oFe{IL7?2>yEzZkyekaJ9Jk*lNT;LBFVjB$=L8FX3ca;xv1=CDk
z-B|n{=iRB&#3t@Zd*C8baVst-=C_k!TFNrZF4G0PoE&Q`!ZU4<JAsOk`nsg$H7x_m
zry{N7hyME5uR`xKxaVEtE)HhooWEm9ZH48rG^dhNH-e#80L(N*ftfSJi|WPQKrKDg
z9vp6ccDZgoag(n8aWkL|mn=1e?Nf}`uO4{As5sj#MR}(K*00xEpF)J3b=wmfQA_-}
z>a-Q<O~z7BmTjI!J}sPHvpyT{xG#<u+PE@<4gW!73qZ~~OzgIs@;)0GAZ7A~kr4ee
zxQOq<fN1pYSs8LE<ciR{qWBy>*5`rtKv$P6MXhWkZLAIBKsTC<_L{ZknL}$HV<jpx
zP%6(wU)XH)(ZoVnYYE=^oOIm!pvJtzgLe8ANHUqZynTCETBzOlToI`<%(r5GEM^Bh
zFCj&L0PC?=AIyviJ_s?3_8@Mfm|qns7tY6)jVz+PcK^$>Jcpb^*5ihm)a$Q0mZ5PZ
z0#$FkJL%sBMwe>E;0O?3U6FL;{YDGw{x&k~B}c$fMQHjqD%Fb&v4=Eu)62aXD%xT1
zIe=zyOLzhT3XlyGkiiwWDuR?_yA|D{?4}(vn#Z%8Wa<_>$}avTJosZ+=@?^@+dX&|
z=lAbB+q@l=F&95n@pgObws!-BJnH5wqEuHnku+@nQ<>Ql_=i`ICm~<8z&i$2ArKr_
ze$)t1&n$Bc^;mJ|pA^(+N*wtRBzlA&pG(2c%1NT@BFVQqiHxWdR!UMhdXsPVmm5X3
z#nJaBTt71kAkpwhxhNJC|7_1B^`vw_>NYWrI(Atv%>H19-P3JK(@{1h>;5?rdr6Dl
zQ_fW-Foh^4RdbBG(D_5or;J#O{C_NfR>A?`KK$FV=*#&q#rCntg!ya6NPH9DbRc%q
zg0%UU35U`RorBGuXexj>ro2|k)=p>OQ*!B7>&btAG~r!=RN@0qLm$1td#|umB+rNT
zh6A6p$S6=FaXqU)Rco4Qcg0u5<AlXLr92RHGD4QU=LL_o-1U7@Z?}(bD*6A}T9;js
zSU>x4wSkaMdh6lKqpv|Bq6HM-P?Psnr}}{8g9iWXEc;Ju(J@^uaZ4Uq<t3~XS(QRA
zm3<@CD@3Atpjzg`odyD;<l26X>y3c5z0^o0uWp-P3q#zqf=w8KKRrCV0S#}RIDSNe
zT%bxM`?E{a;3$YU?o8VUjz%dEDN_ip(Qz2ZX46P_6My!Eb!HBjFK3d&r`eLxd>}#3
zub9b%-BcaST;-DQEvPc$kd|b$Wxq{X4&SwQ(A>z@MXdrJk~)wRB#!iHPHq-(riNSG
zj&{dDD(y}1lKR4ff>)l@guVW#05*H_%3@zkT=K!}fgK*LVlJJ(79T<RDcWpBwkBR^
zLlKtziTYT^J#*eBpXZGUuwNr#a2$~*vS>A^{>%<Wl7uoz!Wy%Gw}I&HHOMtlR)`w<
zZuhkTd=HZ!;&ibK6Pt}r2h?hWaRKZe3*k~aVIyL`Ho-tX+;|o6Rs2>Z{ia;_yb)9>
zKL>;47ej+=!r2tbLh>`4lHQ!kx%H@p7Y47<$|jVv(ekw-i$rag^<o#;=@Jq=CCbtz
zBD~Wrf-?;DVBxlU03&yxuoQAOddxuNwEUw%c%>(xZsSrdi4{%NM)K*!5ASwCEF|^I
zVy8)zVbfiVZjo-YT<of+A5K@eoaEQjJ2uZUi?-x@>hC7SKmJydnoLWa4zolsMQK<y
zn(F#wX~OTwp8+<$Q$JK1gZ^60*8Y8!E)dsuoJq$v^2KlGzXV5Ga=6ze-=7HLcul?n
zIS!Or9YP{LKg1Xjn%-oAB*QZ>MRNjm`EHHqGdMZ2o!S04`q&(9A?R^y7PgO69zgMg
zCaY-|_Aae>7o)^{F4yb4Bl*<gkI_czu<MU)GOQ@b2^}G)c5Iou%VrlFCzV%q8LuI*
zidVp~dygb~;9aYXnFFqh(3U6)2?Jvx`6AI!Qd+BQYe;N0CUjnc2#y_59!-4wRpo2%
zhCoZs$~6}ps6P=1EQBVXJBU;)q?vy)>fSHC0s)J1(}t1hrw=jjItH~>pXk+)FN0k`
z>@PUnpl!5GPJh1Hrn<<B{8+OC*Uqx3v9x?1G$XTEB>W}z?;xYCd}KaReQ<XqE5s6^
z@&_yuyl996;=|H>_v;IzDS0J}D!IL)sJ`vX%7OnqLU@pA2dsP3IiBGVJ80T}!++5)
ze$HkP?raHAU#X4uGy^8{Xk}#hOOUdJ<{W+*<1BL5o`%M`rptkPvkXgn<-$m6a^L5g
zN@|PQ0IoxHQr4gpuG%oJIw@sU`4`C*0eVh2n41VkOq23D-f**6FxLt!RaJ70Yp8r1
zn>Ly)@DwZ?M?s#X0V3_q#&ofdvwsAMz*}Z9WG1a*-u?N8Q2YReT>sZi1v~D{xg65W
zN#;Dg(;=M&Y#oTEa*%O#gCk_^`3I!g(=DvJlY}!hjU#*`%HJQMKk6Nw5krKcCqfhv
zQfUH`SBanGU_UWod6?405>wWRY5cN>>3T;@a=lx8{$2W?x=S1Z{tWGC(0ls$t&D*@
zM>fQO4yh(LA%_)znj=f3Q*>7pZo(T>ze#QWXvS~V%Ie(P(H_QxkI2t+tl4K2?pUo`
zaOpp+=R6=5Y3eF}4^TjmOK}eT@-AN}m-pnkSUX|bvsnITeayKlPufoB>Fu|Ct3D)y
z^nsdEL<;vN8Q(!FrMnljX@nDBXh#Axgx$SAu?Dop8f^le%%78IUWXQ06PX}@u<z0d
z5K<0YydAs-)02R1h;Z^u7sj)$?WVQuV2M3Ik3Ad&Wou`8)vM%2*UP$0>BS_7uBz*M
z%HN_r9I3W!4XN|S+rP|>R5rqc<6;PChh8kwvup_-TR3G;89PfZs{GV2Q9TV+Y9C-2
z_@&<f3Z909^vv1&9rxR0G3(<KBCDI??dv6cm^S-ByLpXHp^4$Mb7%8ou=0t<*PXnI
z66=h@&6$ZCvE~bajhBm?1dZ}sne4fw1-IZCmh)8Al<QG@9!`6T@cgA(YLFOFn5$-;
z9a}?fxBIZRGhj5W{XeSSGN=l!4I8DT8>Bl`K)M@6x<NoBC8a^SLqa-4x>M=y?v|47
z?%p(K?dLt;cjk=F_{SM>vsmlCul(*-htIg@X{T1v>RV%!rIlNUS-wrL*6Qh&W1TaT
zbvZ-rrFal2dH|!-7bCKW!MkXX*j@10pdQXCwBWe>%82b`kTh@UCTsWy0n69f^U&b}
z##GMyGhK|~`0t|0%g@!^a=gs!QE4`|h<&x3_==`rZ#uU+ny-mSBU~M6T>n_}RilgH
zpF$ji!X}BAdu_IX-6jST;@Ow*DE@oU<v<kwdd70RPO33F;r_&{{ZL_+{*2qLsq*z?
zX=dubLrvLZM$JC9s6da}bh;g^V~k*3YC}ws1;xl7l1)YS!}!i~@WRgi&cFXnD*sQE
zgyFV>T#Er>$-ezHE=N9az{lxZw!Ys!yNE(g-M2p}wQHY{sxc8hk+<Ki0^5h7Rzqa1
z-@_wn_4(gR;}Z^<BXvC@ApkmktnW%b6mEgevPofLV0@O`SO#;{0|mRh_i1*7xtG5M
zjouC6h(|h(CV(uU9kRbcADwd5jo~q2(_&>nrf!{rXdJT%VK2BE-_acK*s?MDo(G!7
zg2aH}aAKAeQT!Ru{Sd<FXuM<gfxABN{aUaxgFz&IvI!V^2qhXOij5Iq(~0td!Fe1|
z4evc-G0^7i4L`jwwX>)Eq*lS?aesYOnL?Vce#%n!==S*{koD+Ggqr-R<H>RFu{}fB
zyg}6iazxxX=9Zkb?9E$d?aKGggI&h-@~9@Yv#l@JCEEfR*Opekj8*e3c#L@r=zi%F
za^Akjt=n8zby=Z3U347#i^kf#dhg5q9(61(_*|n<+Duzbvdb(z>np@r!G92#a(21u
z@@iWGkrMk@K9&g7zL`8wS*}TNNBjmho3Lxt@CB+pw_n0t-}FFm^U>wBrv3l~hM^wn
zn3mtf8zI}h$d@!wCI7@TzEi#-w><tnPxdE`xkU#bZ2m`VMy^=Vu324q&$N}W9>U*{
zBdrnrTgMqkpPsC>H*i^zFnIZUaY`a6New~DtgIs$G|_1|uXJjQ%~ziRUEQ<z8xQTM
ziT=2ZQs7&NmId5dzLbBEnL#j!@A7YTSflxFF?3fl<^9$A%~CU!*NPp+5?_PRRs$DO
zOS&2APbLk$ZE7|b_y{jHr-c$7UU$F_zhAfmP#yK7o%v!H8EY|#co)yzt^5Su!5g3m
z&l%E=<0-{qygQPnEv6HHQz<iht^Y4us83e4r+&V=@)V2fzCxqgVanNdK(UEbr{DTY
zM&tK<Ubv88=^ksf%h~|in!zpK_m|r=PL8TOMZBk42mU-__iT8SMatiWt|t!8M%E`^
zaj??JFdMEF^Ph+b^-wBNpLa*?LSw#P!BI!SwBoTHi2y7+dhCy>G7d5Wf~w$HLB2yk
zkvezD909CgNKo>^aH4S^6%N`XmGIf95V5)l=xWnDd)~?LSq$SVvUY7ojQ<`9@<}s6
z-ZcZ7T6BX-PO8ASU9lXgD2AC@#y5AIyf2=@Fx&!)_!Ix!0zUkE>U!HO2dnV{B9#xD
zr4T4n@t>2Izj<ZBdNC;e1=SPbunG6(wSo?+HWTyO&TA?wPY^YPa51RG2bSf$>JvM7
z8T9vWnao)YJ`%1pgOsTuV>W3YD(iM*xlH6iY*Iu|7OGAoBho;z#K>VQ@z(LWPxW?L
zi8C@bRWO!)u?i#rn~_t)#Ss}^Dg-U*R4PR`NPY+mZzyja^AJR$MuH!O-Eiqsn+$CB
zhGTNR{ZkTckaj1Q?@Z-)>^7dGjS`~iUIg<TZvZue_XAk6^MOhQ$MQD%g;w>RmVxJ9
z-mhFQ!xC};J6ZCuoK1$78MqUeg0I5(h{6Fc*CzUUJYS1r1?&KUM95fnN*2Sx9zgz<
zgNQ*Aq7CV0H$B`pvNvP?wjy#Q|L0<{uEN|LIwJf;ZqKFO9{nGkf&2|YOsLBgefaE4
z>Kv#inhRV3*2_k)_|V<Xm9(u7v)+{{Yqcj|8N;KMYLlj~#*$fJ@&47UecU<~Iy*En
zzer}8qC0kQOAs8DsO{?QzPWKV3VR*+1o@jI70RuHc1C(}PPL!)Z1dvL9vg|8#~Y;B
zf;}mL%ZLIg#hbm4-Dw-2#C-^@+IU`xi`$z+#ozU6Nfh&BIEK%~b!XJ|eS335aNV->
zT8zwq6FR-*j%n3-1MO>uHkY{CI2&#0CIZSlHrBBKY}aFDxaY;c2T%|YiY}uf8+c}a
ziN}YT&;cRUP@DyZl}aI<1_pu6zzXC~8bV$ox;zoci?v?O=wwF(jWD7RuL3iOG%kyC
zIuyI$nEi(78s~~`5DavE{gJgWirGqWrb8Bh`jmKr3#G#E-bQmXtcuF^LnX<R5Vu+u
zlMYM}HlKp?LrPADe!A`7Vfz1p;KSTLkmaA9>#y8t1;k_$jqqpd;3=i`{G*gEBm;y*
z3A&5@OwMyz<go03S$Quok|xNDgKo}itrNHVf#7rI*oLL}GC&*YJ1UDv^pP@tpIWtw
zl?wq)MCF*+7XmKg0u8Gh3ope-BuqGHmzj=DgXHo?yNpIiUf_a%m7vSG;*;AT1hS~$
zmc)m~;_-1bO<0bc;W5g4*SX9|_AI0p@e?aGC@lE(q7k1`dPBin;qkN!%f#v$;EO|(
z05$)qKn5)_=@G4_cJ{+czCpyIkOxiV*;ZDDcuS#1-EcJNT}D}IsVnfQRj=PR?xcGZ
zg8k?cXe|U<+=Zn`9xEykj>zsh#vNX)yHTM{xPv7(^dr8094|coN-~cm6@c^`IR4io
z;Qs>la-=+;4Qe+Q3<(RPwL*3S1)n|SDR2SesgG0PxrAQa2Cy8o)pLFb!Olxx%WJzE
zrh3^c{u(u!S-|ht6>v+wXJl}qWsW|}oSR4GSr#j=z<xXfnmFuY5<@)fYTX=4lx#{K
zgUY#gNk~AIKq&4=IT))@Gqk7#l6dG31?QJ!d>ELs+)y^G@kN9MF20?l_QqppxGpmY
zD4h=t>v%6o>DOq<t~JEr-iM^&D%xvtAjpyBD{*G}{ZId(fZd+_s()43{W22>-%R5^
zTk^d0!<O(BEP9P<_EMPP@ipQnq{@2CQ@HPj+;!fpNE9$~WadP!yy=3i>|c88$I`6f
z;$Ad23O*uCG>lMAbcgM1&zWWXycAr5f8FvVjmFwYysWbKw$5O*iSwvfe2`6>p6=Xa
zXleT-Qt&wX?h~l!c6lnM6b{m({LYAuk-{=^ZDM`UW=6g<AlpUq4kPmUe&recM488m
zp_@&m{qlH8aACE#X=l;RWHS8NarJv`G|Ely9@4SZw(<jga0%m#`_aNRJxhH-Pgz2o
z?a*tcGfw~v<FyQFCnZ?DgxsD*)d~IE6{Q+6S@-lw+ga{Z3L|BGLo(CIEXwdD1z@Pt
zK<6+Hbxb09NitNB>TPlY%Wy1LPFvIsb+I^|j7X}*28Q;zg4f+%LEOzd5}lf_Cj0-&
zSRtunZuz2l9_ikbjw1+OYM{eh2Fdalcd^NWrTZjaZCQrlNJu_7qkFO$_{o&dw*qA(
zdb^&%)y!&|3BXjwQB2RTds1hVDiHpXu}9`WY<mh;&<q;)o(xFF7m`iA@9Vj0IzS98
zFQA6Fpgy0yxvB!g&TK=6)Xkp*?jl2uzgG5BIx3D9+sdUiiO)@myGTvY<F4evqL~|v
z)U3TxGgo8zOFRSTirmDnl`E1*H(hEBk_~j5rXxSN^LL#Z7SCufxbJ_SYqwgZTK8(m
z;Y(#V_XZ}FBcI>poDLTa@uaz{H`ic-?4e_)OwfY54T{vyZ(XRGHh$m!tEH!PqN-8z
z46XCA3x{&j*omOxF9M70dCzvOH^3EFm1|Et01JztC-seU8vl!A;!M~>kY<O+B0TEh
z=Q;|nFGFw+dAJyBibKZ0uSbF?6DFz}b3`BZhebv@oAD-IL{`6>Bp4P+7jPO(V@ySG
zY)!qY0YqzT8@Q%)<Uh@OgmU~L{2>-i9i8kR?sC(xlOD%IV$Hqzolws%<)BKt7lN`a
zUaq#-5WvrE584mTkdNxIgid`EWjaQ0LHdnamc(~DrdcNp&L8Rix|i+ri3?I=LN6wr
zWa~X<Gz;{{JUZgd6fE~~#B`iNu-BAmrifRtn8OB)4eK)~aOLXTfSxG}%>E5n^NRpP
zVe^SDEoKn<oE_l?^i|ym&VTEXtiLt;GAc=sB5^rp9tGE`ESJ-nPT1i+2~8qmczzrT
zi6A+`@*Y=><jeZmeZhY~*cX4y|CqN!w%1;F48Wx0zATpB*A^D6Y2Il&qo)%dk*AqG
z&`1@a!UDl$P<;>^h$258yMD{&SW+}-gB)nTGWF!@z^5?>NflI_ez*Tx4#Zo+ZGtqP
zd?ZTGXh7^r0a>ME%8!xZ?ksCb^3G3zM=335y`&S%91RN0#it)|jm}9~Kzh@nwMV#F
z<{ZTZm3an0e3YO$PZ-YS1c9w50Fnsd^$z(UXigfK`Fy}CF8b0!Jl%j8_JVu7HJA(j
zM7zoq$F@5Q?ggqe?kb^Zd?=^xC<GN^hvCDPfyT0;sqe^rr0@BowaF~Bt&(O<u?fOT
zf+O5>FqXPi%^AP!EyBBW@bB~2RdBBU=MDsa7*oS74#=tSSqu(ppo5h~jC}WBnSQc!
zoXO@AIcQak=O;ljUrOP!P>mI650C}IxD`74G3n+f?^+MYajc%67>fB3dTTH2r=s$>
za?oZ}*)<+ChWA2T!<%O*UCJEB{wos7wddR=kflxM12^F|KzhLrT^&<d2@zr+KN#Qt
zV@w#~p^(hQZiU)i4RU~6Z3}m9X0k^7d&Q?*4kdbRHbC?9fS^HCx)X(Wu?_$?B;j#N
z-#w<S$yjyeg5Q!|`3?%9xbz4}tEOk;Usmg|X0J|4QZom(fS@wYW3=&52y&g;_`*BM
zgNatpmtDU6{1ajGt!CXS^G<kzj_ipjmrZ$*b%1v_=vsNTqR7)k?}VCsV>+5@;d&AI
z`AceWb$53+5l_~7sY`@5C-36WPbRbZTI&Pn-~}+*8O<_#(bR=+?--3S{?b1fMR2~I
z*$`-`<;vp&a{aHRjr+glN7Q8ZDj>=tPxz!!6jf$|*<0yn4rUHvhK}E1|2dn_nKoO!
zBkn3E<Ck|$7b1M#8n!^4DzrfC>1pz@W{{WH@an5$imq7;a=10+vaPi>>6!y_ZgZ=k
ze7=M2AzItUcssYrtYKJctKk!gWiN>CUwp2wQ?JVGJ@T)N_GJG&hkgIG_$X-hPcAp+
zcs%r75O0sP4=EoFc|YsjMmY(*c7@S*!?+*^%n{$jg-)ysqYyK|bs&=5Q)%Vcf08<v
zm+DcJTspgdw~=n<>0YuzRs#Ruf@@0t3h;j&pF<Or3=*Oe<gS?s_lEP<DV@!7Uw!hc
zG<z}OfK}`e9Qv8L_H>lF&D$ysehvg8Cj$^SY>qxrztXAG5w`C({0h%pDy0|1X5Nr@
zi4A!urrRA%7DOe&mRnT0&K?*D%?r)ap05gF^$PJ%qEDi*y$}E>AEb#;_SSJ*V=T_w
zRwAYz>E#jk*C{qCY~T1F4nAO@4#f8zm=$o^PXyYXF{_o<C481oQ=p~wU%ORC-JN}f
z3+($Xpz@RZyqu2pvThS-=o)J@7bWOdvf{BWi<BdQPA~W$$2<K)(CcWo0w04&_n_P<
zcHyu_>idEuI-w<rOC$B|YOYp&x$oR(PbDypTMqtWWj%tkG~+48Q5vo2KwZ%b`xi?u
zx42`MBJ5H7x#}Zs7s>r5S-vy0m3pW6Y|axjK!&DQ7}7%?D<G`S@&^}0VMaoSET5}0
zoc-|7GL|25H+z$3m+~i6&wtH9R8w;=I!1;^qk0RhwO&obS`|CimSx5Bj>ws}{i*YW
z1f+!Q&~4GXJe@|S<&XIV9Gj7}Jw|$(b-AUp)98!fX%yF^T(D_lRchkfeh6*MO$@DG
zu{qvY7Ydh=!tqZ3HzRPEB5-*4hn(NxeSczN0YYnHf%ppA1<$}*TGRm&PnKWzz^AO9
zL#8_i<gVECxvxK>$Hz;wYrg)xG!wr6gS?WIu(^(g?DDbu$G30#P5~)GW|KXi$}ue4
zc-mk1HwTROCD1~F!==-qj}e<9+uyWuGOZlG3vOSt_{DNVh%Z;xA|v!iMI3sX;_J=W
z|K-@u!}X}xPYGZ*JHa^i%PylWpHI55UJfb>xE0+LbPFGE?xSTAA-I$8F4WoPTP#$O
zARTrh{rUW>gPtJDZxS>Nk;b3MgIb0Fj-|bl6ryI$-|7PfDz>0&JQk9fbSRyrYZ_-@
zGoj>OffBScUgW*?Q&S>;(I56gO!iAT9wf5`$r$dC#*~vuLc(vR92C3s0l-3d8$b+K
zIv!RF{N`BhU^v34?#k!NB9q=a{!?u=Ti$`stTk>+<RNDU!Yt`TJTAATmSV5##MNu+
z>4M4kxvJlEQdY|ta;%8t^yMZ7!7)&A2TO=>VYQZJXdPA^b%0nBTQLDr5M}~eZ9@0u
z!T5x$yI`ARmgHJ1IDUD^Khp)rg#1R!xCgG-jL4#)ciq~(zfbh>GA<>0H+)Q|wYh;m
z<4j1361LvG(d^3U&GtFRCjFm_g0uTpFJ*a7EVb}y_Oa|lGm7mWPQ4X~7RNIg`KTx-
z9V|K{kIz;dSXT3gSN|?no`;V(eG&xp>J@c)?aM%@WC~(eP)ag9V)B8$o5rGC`SvFr
zk!w;iwR4V({*HGJl#n0XR_2Y&dPe6Te_IL2XY5_uW=wwR9r~wFG|wS<0{|J^&SgN}
z8K;{7=uS0Zd>>ww7}+b)r^E9Syv%tS{tDDql{4{gbw6<p`5_>$6F<=XGBrybxJ5CB
zQ21~=;6XZ#-rDRTcB5hwWVr9Y=4B}7f^~_8jBDz)dAIO8JyeGxhvAFfU)S@@$1~|H
zKh!wX)Jy(C=AEW&876lrfz4Z5d%>ZXT-)yTTw_&j1ztQCa^IghARl9|j_P}jX~{;1
zZeKvjgQ{RCI9^^BHEWpu0;fQ~ZIZlAg?%mN5FqP}o=_1DTR8>B{oK-7UEPj}SMzC}
zT7iR#P1)?_!Ltsi*P&R4+kq(3=J*|f-$yiGQKXq{31Rg#e@$<8i3|Yg%{3BP);;|D
zOaxb1k7mHRbG{@Yh)_idK9#Y?BXm#+6l0T(=S;KVv7MBl+~~Z$^zRnV4L(=ic5Pmy
z6KvaCA`I*7ekUVkjr3k8;ts-|H|3hm1`_VQ;lvJDH;kIWYHuiegkEp^{Xp6Gcl^ts
zJXy3im@M$(qE7Vr@RM|%$teg67+QZ-n)Mf<4@mNQ+R0k3Hja=vb+m+fgsbPw-<9tn
zp#_fCwrZB?8AW;l<GTd@CBet9OXI&*YOQsC?46?zWvp;_UxBreG_m<IocB-JU!U^=
zW2Yysr(%nD_>k}o%n~WE@s6^^Xb;hBm}C*<V|2}IFxI2En%FeZPp=3^9~XaU&Cglk
zy-N33RzJ4S5c268bCe$-V2KU9;!2s7yhRtnv+PV7|GtxDUnW+w|Jy#w9wo2Q19<WV
zJeAN&k!+jKf-Z|H@3pp{oFbMs8>X-!&~};0=I?SFdPLueADX9QcGuS!o~QqwhrK2?
zJlBk1cVm(wt=DWQ3+%piJ!ILQoAnr8#j8t1C2z6(b4^g&_<hEsXbb#u5<7s|Z-;`n
zjV>q0S1H*}?+-#|{D#XwLg+@=X6Jv^doWVI8ovXKQ(hMtkGSJgQYwLBCkBQn?6}+z
zLWdAS1-d7ABus2ex&HX-8=6=!XG7aCLtK8}($r@n2e=5RN=U#R0jE~l^6+obV`MK^
z{|7b|zeMEs%^q>CDk}S5*med`u;Oq-Ln{Lz55q~Eq03jw138>+?i8PqPuKC&ggrhk
zcT5&2;gzbGDW&p>%ZD&9B#i?yAymN&8xL_)*u=liQ#4xb{;2)0c1GGx>xXXQ>vlhQ
zD59Cwbkdq)({lCk4rrOoA8yWCBRLE@9l>D>O%?%3@c^=`wp;6@6XzVzUsS%vW{|;6
zX^P3=+ik<g35kfHpNZX_#yY8Ge4qkq>MUTc!97Wgx7-oE*;{E7t@r?6P?e5E4OhE_
z-kEC|`xxF1ps)}bw@@QPjEHUqhfr<@+?0VlsM|X38u#|h+0UEtZW?=e_q~<F$6inQ
zsT@EwrOh_CERqVlSCe^xS|Pca`q+H$X3;iFd1bnp)UB{$3nD1WrGRZPL17pf%B^S;
zM`p?M?uDNgMO)bE;yJm#j~wM^jqi$9i_u2coi~Eo>&f%SJ70yz<F<vL$x2A?L+d3^
zKf_?1#|8fN?kKy&Z5rfF-C>~CH66<m`lTl;RcDoT=5!E}@`UD%Fx!b%;q+pl9~uKp
ze4hGjztWG$D40N&`(War=9+6szGbA%vlHSFxRcv2v8G-xtH^;Ovr;r0cXrucxgD9=
z`3mc07GCwrQSH`0uWuHYKe_717cMWEl>VM2;Y{UQlYVNo;y+rb8#_``0!zq$`Fcoj
z#cK((%z|4ZDVmXUwEvRjB&lwo=7rN=S=&_II0he^nL8_;{pAN<_p1P5EX_;^)tMo6
z-)kxhgfIixAKb67Q|>Vm=*;-iiiHETHgMMZlgl{B^gbf34oAgJt&T}H507%DaOObM
z#3igI^A$J+-6NE6q;X)e5yS}YK>AT3D9YsmsV7Dwza%(1{dohquljLL^5|hh0ygwN
zwpLjwDidIc&3?h7Lx2;oWOK|_R8k9oNokOI-zvWc<+I*cu+sZZxJ7YG<4Ow@kN6^@
z6Ggv@2WGN@Mo(PfLPF@8%Y|J1b^1rT+cq(j-^k_yE>z8BpBai@rOpOBqiGWJ?=p++
zqh^h5vQNFdVt#tTndt!lRnn?56{^q!C+)x9zG!AR(7#=2|5_W%{T&W6F2Z1qxwucB
z*dqMaE1$f<ZyO>1bT^wXQX24X$m<i)J=Y`;R~_9pHul~j?OlKpz<hO!-;+&{9*TeW
z)V^YwmscF*vZdFYjb-^9y!d&Zc@8oB9)5wEjS8@`*e3CWsxM;oj$>o+i77uE^-g8}
zIEu~ad$t|b`w`{%oltRYit#H|3puYevOfr5&F>krGM#3Z+he(tplQE>A=R$v&FOAI
zOFT&WZyB3=0XuwAuI)iR3zF4$=I9ral_aTlq2K9o!Yos&DV+Xd-LE_p=9<3Sbca-}
zP{|A!Zt!p^kv$S~(Gxm&`x!tDVGd~K;-2~=o&At?nAw7X%WU%V6+8uDKNi=qxCFK6
z=pm?1madKy&!LwNb!}@7_2O+=Qsb4RYC$f4jpq)MMv=p2To&zv&3BX74B;v)oQ?1b
zB+%<0uNS_P`3h(iD3agutdhcJQ>1)wYKt3nnRH8EuD-qFH%kVz=7(8(MC(cbg5sCh
zwMMe4cR8-RRtRP5>QtFxRLot6xlP|&yf*-mo!ME_7Zug<Q9D)c<b23t_v_#yV(OO1
z<^s$-Bb*h(FHmo``b{>sWmHG798{R!2EAJ#NBHzjyje=OinQ`{3CVEG=1!b%7vA><
zvy!U2N_5|iCEfp!5_x=Fwe){Bxv$t_&(~n2IG2m?0hSO1Iyj+PeaN)t6fl(`M7S}n
z5bY_N@={Pnr3fVnR3X-!t$536*4myln#s6&YTN~Vs7~hX?m3>9SyPFTCuuCNM)*x(
z4iyLDjKrU_iIV;t;w~U`xxuzhKAxa^3%Sgo!TySzy0a&_W8Xrk`K>h&ca;9Li9nGz
zs^oyX&WyKLdDgqO(?Qbk&hp{oj}TO)b=Eg#2#<F6l%b1lS-sC@C$g(0$*s@(++%sl
zSu*q^xiS~I4P0vG%F=nSP;qc#qF>XznZ$}&V>OnJ<<AYL$9TnoZ*hF|d{+v&-dnss
zZaG=LKlc3RopC?Pe}8z^1ex1#DU&Sz_^YkqQ2Gd|zm!ceL(rx40W3uCHGZq(C=gS?
z?gsq70Qw-UFE?fJV<FVuyeYoAd-%p}qWCpbwfKt47lXtKK^I7QXkw66+cOa>fJ2C*
z0lvr_SRuUgUQ1HT7{0LJjS+kA_oh#sho`KcmLOXENnk5pE!LaO^y0gacqJhZk=lsw
z4Z>c+D^?;;9UI4v7lhb~o$@I>5n##Q^Uj0(5KG7QO&$RPpD-?RY4^ZD#lr3Yl0|?g
z;Lzn|c}sLsATK(<Tc|C#PA+)g{g)G@?SR>W;I$xC0X&|89}oUl-;#OF6!`^)xt(tf
zjTc9)Sn)p5;$pb4hFG|(Q|~EYFGGPj^pEEss^#p_`4g`53X704vG(7LQBsQ}W=6Cy
zSGBvr_mC4MXkjD(TK<9w|A=m0QzwF-yf-yE8_9wc>duEaqyIpPzROg7f^s-ul^ygb
zlnNfKS}RU&GVBV<{z1xWMV2TifqDYOYcvkZgFy$8?{IN@e2DI$A1;8Fv=GF++dJ^(
z0x@SY8r2YRG5oCmtQ5!=m)34r*9{pqznilx*b%;=D&fy3!&rMqw<4b;QBt7m!nL<}
zazW)gs9qvrRzB4%Ea*HQ3GAzFJWsD|_M;l&XINbW86qfIWAEHzX>wcT(|yW(?p}Z8
zGo*RRq@RunDrE(=ma0i4sea<zTs}oSJWgeaphd-A86d%B1RGZB4hX+2;#y#BJENU{
zI&PyVbM&y2_OMGFS&7r}ESrP2rB|Ajn+y?H=4t*fTE~w|9Ohn#FEg|&Il8^z1CSZU
z!ocd;VVLuwfw(0l_HhC14T1I)LjPM30*ql>{vN@HG#|oieWWCOrQY=wGp+u&X4zXv
zb13aU0+vsFC(T{bfo3UD08bnLb}++|HA(WiS!=Pv5XB=O3{El``a0h2rr?fa_<NvJ
zZ=wZ?m#XYHK6H^5c@G`dZ$xr}g@ThY?OUuq?^<u!6RwYZKyBhc3J%LX;J=fButv3W
zbL2iGC~es32R#Z2!yA&%f45hIYmrx8v2Bd-cML9x&^vm+Q@C$-yBG%gX#Y}Hc^Xc@
z`k~Ex74E4(w2uhothm9~9#70}6lm}W*1#k{BQhPcQ~Z(oBVzQK7ZhQ!jN!i?GoOls
z76gFepJXr;Z5J|{lWP4g4HmxR^Vze5*m@c;1Pdl4f6<z5Zn#+w!=OApcOC0?Q3F{_
zSb{!h2wy}&r)Jmt_K@#{@~NTw468J{2zm@(Te)>ZMU`ZBI64#gls%G1XU&)Ko)?Z9
zZ|pg5$8k6<p2Zv1Me0`Q7rc2CN*iV_-H+Y+tXt%I6MqPu`0D=ss%_c(>UpN>gzXlU
zkOwlHW_e@ace!OY+ALXmi;B;-l;JtgmF(*FkusxYx#WuB>>wgRg~$Yf+Vw$!E9K)z
zbz@%s!>7eW$8hTk>-qAez3Duc>pGkdp0L~U$M&reN@X!UT4(A)P-I*BQlK5_00XUI
zVBZYxk~lOSOCiJCazGEsUw5!;;I}WsoZtB6L0{^y6AUSbC&wTV;m|3OTLOU+dcReB
z=SuDo5Jd6rcCQ6Z(}Q<qrXOpJs-xmA*Y{~7v`SD3$S!k|0GwiIA?nu2oPLYKV3V=~
zOiCyXbVQ<|hrQcoTZ2DUSAcFIrE&`?Ryjk&%l|1|EKp{%a<W>oH6sGw9?NN`3rjSf
z$3Ji14cp_8_t)sw4i@}u4o0UF*uA!a71O0v+{b&74{@XSNaQbY5Fou?BxI*kPX5~q
z9O)jIrhbbI3xd2nXR=KS?|>OZ9vqYxX5oZP>V?s<iG650uDv>-(ZaC{gIC&Z{yfsm
zo$I(Ca*<__tV4e44dq?C7=Nc-x3~I$mS`w;*-h~UZuU#K!K7-1k>o``Z4}?w4288E
z_@7|(GB8Jsx!HEr9MjnM7}kKfhu{7^&9E^B?f;Gj)7#|Zn_8*8a34^ZBM%k~RqwRd
zX+wO|7v4f;Xi-|vHQ1-~D;^pAUgtl9@>Y{jnx0=a^{VA3_i8G{tR(S{;WttL5senP
z4IE7)U)P(o__uXfP!;OREF`~N@$EFnrgDY<#$D9x-hTPuLgf%YY_o?ZrFloC%xlh`
z_{&J1%Zb%`dV<?|LPJ7YLK-w&5akmRKtA^mCEYLOP?Gf&az<~}YA_L+;tPH|G$4YD
z5cj1Yu-}BDc0Cr$Qn8u>T|@RhvZ39E7|0pR1KbR@?eaJ%>L~)>RRoRtk6V2Jf_p{t
zjaruQ&C9IXD&R@HJRf4kQ)0M?jW4{BOb4F{efA2F$WZTNf%+@rpY^!Z%FD0f9E?Lr
zRC$zBejXy4AWxSo554&51G=;nu9;=-thLuVyhU%W^?>zSYC5qkJl%1WKfBTq#0@^B
z7JB*9uD*S4X5g+X_iX8N=}~pqOmPR20+gd1r(l;on+HP}j|D_n-a;Cab+{$aEZ?na
z_kt%ZhxDB#(lg*v1Rqa)o^yZ_Yw?Kg^>NHo*xSaCr$>msJK((DDRQ2!cfnBK%Nb)a
ztNgfuK}hpBZm?Hk7({Y90@Yfv)vM_;C-zFe7^obO4Hnz1<~WRsEJkmH9kMK?2mE2S
z^@!lJmnt5^%Bph|$`OwNUu;6T^>sxQbb@4?5+(o-&rcU3S}k~|GeSMrtl^?!@>2O+
z+F==OxIbD8)w6a>HeD@shTh=lQP>BP>Tx|9qK_fjWfOl7f`+oAguu)Umy09}9>}90
zH!-|cMLxY*tpkA|<ocfCTZ%;rmacjiWB(55FBR*?5)P6VOx?Pjx8sY{t3UHsFm--G
zu=Kosc4qE8J2j%$1*rc_gAk?Hs^Gy)mntQ`AO5@5-O~s2Ue7sluRkvThl&tW$2N$N
zsLaRz+0LyD-j@(QIAR<u6?mwNM~K#U1dRwg5lEEccTT<U_88qkSVF2@`RonIgX-_x
z0D#gi_@r~8rBj!SRqiyPsiw~O-OkNMQ~~g8W@woZR^-080}k+1Hr%aVqZe@NATm#i
zS-~I3Z~NjY!VR**>Ct2xUcUXzr}BfQ_}lDxzz!g5u>VYD0tbH3%a1f}mA4Yw(4ccw
zN)W596GeCT`Se>u`AXrCtSue_tWIkKJ(7u#*=Xuw>VSt!V!f!{s_!fWc-=-t?yaZ~
zKJi}fV-ndfc|n%cGsR%375s!=2x1qC5qN<<K{%U-rmhJOB?K+F7y}5v{^a!eOa}2g
zW+pWV*wZyoPzU^`y#x_ZY+VLUQ-4LsB-BeW|9nkcN&JoE3<JvEi#I`>cMnpoobd*$
znx5@ef^mZy6+R;<uZRtAs#S!hNGoVh;k0*<4bO>@)UCMLScf|8r19OrpB;mAXB>_@
zkh;Gcj$@T5=zN$t^tNeu;m@T>BGac0R?Pe5mFeD%1L00%rxh;G8#_008?x5U$m%qR
ztH{Kfr7Lv%PDIXBi0q#KY_zSY_VXOsP82OpB{TCg=k01O_gH$G6JOJPoKmR9hqLvm
z=New~l@*`AL$p#MO9Ub?_0E4K4th>rKJsF=8!Bi={C~6*RMqi-qXjU%!<2&gPhJl(
z)A+C8{z60T{&r}*(?B>6gdq_8{jucDrhrxU`_9!sHagc1X8qRB1gg-D@D5m%cL6p<
zSV_9<#|6w9Y*feM6I~!$DRg>l0%VEyh^Ran!Xs;e=OvsfEpVDxAh6~A@@Xi7cQI<>
zf2C-p(t(6gY*x3r?t6LP7VZ1~f>(hu%J{vIVoDiPX}!OgfuFHlbdlIKO7Coh0<-g{
z0nd&bHQtJMFh^*Avf~uCflo^<_fN&3ED4u+G<Fj-TC$iT2<RK%IIRMoVax&dzt6Ox
z&DZmmc_qmLPQR^S00}lQ7at6d{abu?cd97Pa|4*Oa-h?Z4fb;g4sFQvJXCsX4v14+
z%zct@>#nViE7s`+NzIS6-e{rKV0A+hKTl3!vu@KQr5<dSPvW=)=VK>y*Q6_~@1>i>
zLI6*{pwIE;jR%s)$H`r15;iidU=>%pf=Mq*S#{91mj#%a;4~~x5325XR(+9p^J!}m
zT3_d0-1<qPpz6`PR<C>W&Onq2n@s&aSN=us#XI2CjVD8II;veTA5LVaxOoB}lSA~-
zlHxkJ?p6vwsOF5t^Wupx-01}jgy?yet39cVO)TWKYHrAwvS$Nik8OcN9pcw{?j2l_
zLIQsDrCAZ6Vt@l<%b8-CvT#cqH3C0x=I?c<p^P$5wG}gAi~=sX0s)9>WjpBw@*>!}
z5&x(oPe~3&54#qEZ>c&GUY;O)?*o!wNmV!H$my8>|DjUHX-^lqexOd2f}S=y5l|i?
z_rNiMpZ^+;>O0t~S!rIf;$r(+!DvX$_#S=)8}C;TaiCG8`W+B{Ouu8OR1kA<hzdXX
zUs4ejJ^<1;Wsz}E6#R7%UOWM$3Dz<R9%`>tCW!aG8E~SexJ38IhWFa!XBC4~Lursx
zaMh&p%j)*sax|+a69^Q;*Qkr8fYX@jAi3$QoBOT7bQX*0B%o!resr80r<e^O<_Vyz
zdHY-K?~}pAWQ&K(oR;l)Cam~0+59}X_E2w$5p=As#o+V+v`&XspIr(PU=@+QURj2)
z^wq&k`~$TbbY#&GDoOm@O*KV+$*E-8r!SHGAJ*GHcmsxyzSN<(7x7IOH3r=N2z7;q
z!X}PZoelYt+fEv^*_}FK<G$LOq%_(+grmwiMoHZ2-KCAs;%%p(^wxKB<lGrwEB{7h
znHVaL_4`xIzQ%~2m-b(V15P-~_U!ImzTmu~oYB459{JvlQS-~@+>EO2?rjSW%$~l2
zJFPDd5`ti%C6b>gE24GGPjjT21|TDV!JrVDwIH<C*|OR3T<XHyfB>9w&dkAz@0vcH
zS^*1Db@4A^BbbDiT{kf1b|KeM5Id}^YQB1ejiHF<RUabwThx_StSgdNrOI8Z5a{&;
z3cbacX7uc$-$l?wqau@OmWjMH<!Iv;bbV?pw6^>`a#4ptS)BjW^qgPE+8Uzgb|%?L
z(kJn;#3LH(+m7kD**eTDpzs_VlM_yszMw+9KKW-QeT1%Z{{@WtB~ts^OI04z_o358
zNaIN3Be<$?X0CmcNN;??2lZa;rSQxR18n?sH1$<noLyloxIn2qjgF@<49rIuw)Lb5
z4yK9kjkIE;Z-H|f;|~+J{0H{t4r^$xP**p07oEh}(E2wFIo}`B$u!fY;bi<1pTV0N
z%Ru2vr#?^fvriqp04$zCd4H3Dfo86}u{8b_ug<epW8N*0fneZ$>>MSu6(C8-Ca8T@
zFXmj^97vF1^A>bDW_HcdI}fS`_8UWqDntJWL)?7B^RzVu$F4Ww7=KF88m;^l;Nzk&
z<ixq<$2kE<kO#zYVci{pqKt17w7zKRrWvYcej$`!o>DOGtB|keDwdsL1_felXTm0f
z{-H&xV~^8>I=^Ck81$uE|ITDZa%BDOfL$2}M~m?S4Rqv)?4)6I>687f?Bnx8IZ+fi
z$Hk%yWRvfVEB7D4Vym(@vg$$5-I<#|;I3S;sNB#7<1}#rr;5yhZ1|2r=w}yyYe)Y9
zc)1BqW;fk^!fpSKn+h9fF2iJ#i|tugY%s>W<UOY5FyYb$;XS6~1OPZa>U)~-6!|&~
z9;}uc%6P_Dn@{~a9y(g9zHr~8yAYMl32EO;e}3Fz#5KG}``>VV7p8es!#W`lU4@w$
zAz$sp#!`^Fm7BO3DHBzY!5)W29-}{?cEIQLy9e{6bYo?fMMuH=IOBHJx4x*yvcJaF
zI~Y=+%l>*OBjZZ*<7-ySA>_&HH6G8FSZJ*eI;Gu^UyD(k$D@KqczLZs-*i#3)QINY
zj4g`?i$@83s0tF>FaCq#s&5Y+8)_wSKg65&#meQ}_#(*0yP4og%Djd4^pM0BzsShq
z>`9BkxB=vlp4sYiRW%jk=Y&PNO`+A1H;ZKVWvzFE)R@^Z?fz)!Azq=XJjaq$5U=c9
zrOCB6a-AmU5^-`R?(p&sOgQF@K`cSr<Ho~QCGKLQpOn}?wYgKVi^YOWHLMWn_@l$b
zJ|Ol|4OP5RcSBUM1wYzB)1%2CsY*_}5?`<Yqh>GCbk28p5O-K;Kib7}d5lNb{c7d-
zJqc;GAr-4{E*b~ipWv%qDOz%Ux~?xyfvP7l*CgCurFFl^$eRK^9>vwDpyDPToiG{P
zskd3i*%3IxqCt6#DA3%n{Q8<REtiVM#<rIbufnsOqe4)H8!acMW&HXVKbsi&>GV@T
zA>6rbq5bdsS+B9s5N^z|*{;{2i6ROhcta@x-wN~z5tVyYdXWt-Hj(Rs(1%EVaDnS=
ztp>CStdk?kJ;iki?WT;^e#-W}I{EWF3j#30Pl`#y54@jl%?5&9Ze|qfHHUrJMbQ8G
z@I4u?llS<n<;pzf_VSi(5xMjG*B2laNc1Tjd|Ee5Ahka!lc(Kmj#xY)S1b3ulmKhT
z&!@8M^0jlDiKhUG^$X2bs*Nsa5E}at?_(b0-$Qo&?mPADJh@4{wiAs|kFzelgBGJQ
zz(a{OPQj8DcYAJ!bB>O)hr~jvF}sT}`{`bX_{c7Xsln#S1!vx(`<zaKtW_yly<EG?
znt^ahBC8qNNRaU}F7_3U$L@4^-4TP%yL?d3?L!J~QMEbW+P?_n-cea%iIytdo>+cS
zo+GOEbqeE1V6@X{i{ra0p`td#!SAN7)_oNRym}f83`(Mp)#zbh==!QfUoW0y$WClb
z_R)jR<{8+Rd`$@VA`EnVn#&5`MK|tRmyLx^+B6<?0vK#qLsQoTj4ZUOOIk-2(pdge
zA1<4;Qu`2y1K;Z7h8U@oO0$Mw*Zb#22_V98=Mn<Wd@!f9;bL2nbOI9vHCkw|7Hd0w
znr=IUk;o@>6LI3D_`YOvGMB}=+E@HsLVb^;8j72nF3y<qd<&V>+8$kkn~wnRafrIx
z^e%&hYTFEadb?XCHh%H*30P6x$sQ_gJZo%Q4~y@`G>4@?45wktS5HuvDl0%Wfc2#*
zO5{$Ou3P8XZyot^^bTu_Wbf2JLNKJJS%@EnhkRdgA?aHg{OqDX*Vwl{@rw)+YjK<I
zme+F=H>K+b(bJTPs6Al=jVOaFxDYC*vX1&Q*fx}K#%7tDPr*V5<CbMD43B)h_h8iS
zr*7fX+rVOB9CP^bf&zRag|{nSX-2#o(m3#)@q7OV9`M4PKq!U67bI8BW$At?D4QpM
zeMqARI|^VtFekAg++82Z$tTbzxD!^ygY?G0(+x=}#$h0%83+CA<{<mFI}ENn<?K9?
zH$RjyvKqLG*GF*I@#4Bu_#;JIR1hR2VXlJn68^$t35lJwKq#^z3;YfJG0B0GA%R(2
z0_>6W3A+S;t>#WJXNv~NVG~}-%9myVj^QT<CugXPd-s0g9vJt2tpJ%IlVAB1bHllR
z<>#bbMootMf$0@diqE$mdDj*}zD;k-2i>9;Ic4HU`FF^SUS4(M-i9#tbfcYLf~5-Q
zPj&&|iDWaHe4f3H_9OzC?mkV;fz1myPG%2~l@x~CSFncJwq`Wc?Djts>t*HV;|xGb
z!x2t1xU2}}<?}_n?#3%khC&$cs|~vlAqTz?B1_jkyzzH%t;V5p6Zr<sP=T^4km%$U
z-$cW+7pdI&1L@C>bc3jnBcD3je{Z=_pzo2#;@=874X6tvL?abX?$^a`Fwm@RIqxst
ze_ipM<EN;!9XB;XKRP~Oquf&P{;gF(qmalZ{(0HypDq_LB|xhLaz}#jUPX$X6hAAM
z#$d9>*H4##QL~#8Hau(k6<m?OnD_Atd=l*(E9dQ}dU@jcvlTs>2Lo5k&BV_$k~hA&
z8|t>6wC%xHCd}Qj`$gcNzXSoBgP&k}4FIGS=LC1ty{Ohl)5@-{^S==UI*A}tz!rDy
zCepYzPvy~I^CH~5130YVU^`UP*A2wSy{=lg)^J{Bd?iRPC2V+IgAmB;$dA_uH0wN;
zwPI>J7fM7dNr~mj69KN)00i-QdbhtFI|5@uY@qcuG3DQ@=S~=6_X02T0b#p+^MRIF
z>USWw!>07bgd8is6Z+NgQ!Gmqh=gfa(6~&C{%m}&yO@JH&qOEK*^c=&kva=&8|>;_
z%5e{qN}`fmDRpNHmW3-X&c6$F_;AfPDNo|rLckH=kvGDxIf>pSKDji17+A8oc_S*l
zTEG5-soKU~XAh0C291)C;m1z<RP6z{B{)1?nfvag*D);0TBk1DqZY<bGNY<3!i-69
zg_((o-k<4b@90kwK0!)mB?$zjlY0}D>?4><f0#X-9zM^LW}nw?7C8y+U%m8zqzpk0
zUp93zYViCkH*Rq{DdAGk-rqz`z|{QS1~0vU7wYk)&+X>wt&&I~1Ig#+qbq*9h6Oi)
zQXuy)z6IAG{Ccq~VlSumsY<9vkxJx%>Izrdr<H2VTo-Z!!#DXrAurH6?)0Gq-o6mN
z<L#M=-MbPfVvdUryPS%>#}Xq?z>A7d#D=^;BJ{2^sty=Ql>)tN^HeHGWMYh7?4f_f
z_fy6(sPR*;G?oF-TC(ev#*PFAr{3|>${1L)l$!TmsUpNscz%iDiQeq#ZjnYxnOYMk
z39}xf>Iq{ct#;Uwg`!u`!n|1CDRu~J5od9zk^sVZorG+DNx?^>{1%C19J<qv^1IJ5
zQ@v;xs=ugNDgpe~{q>+nlx_o)ge!!ARga7SGkV)BB4S0a#EVU{+N+=N9N~HSh`bji
z8a;Zjcj<{XBYKnH_0+)qtcTFza`&&e3Hd}k^%0u}`ng(z!`A02HLxNXaSy4td3zcC
z{MFE?iKLATKp4tP@YGKhs^s9ua!Ir=*+?-h>^c_v1ric1lmedfo<;@VdHIOq5bVP5
ze|>g9v9SP}uBohdNDWw9D<C&Sq1c-vm{8EU3vJ;<tg=Qy2NTPi*)wk;YVvc*^W6dK
zn=aMhDJZCLM{PH6QGi4F8wcfh@0Z7j{coDpGNlm%7UCqKl>q*7Yo;aLd;CvR*)KJc
zoCIskEDj_G<1t8ZrKe5Ky~dBH4GG|!(<7O<R)M}Ugr{oCb2y9Yj64X7zn=(2ll%X`
zSx|v(;14mne{vELI!l0?7CS~RJRVwPR|VQ?c^uz2{2_zSU^AyBIRP6QvS{H+6=E6}
zfi*Pk%6&6N3YtXx4PeLpi1NUyhD;iSVPawVf3g7Bw8|GNz63vrC942pGr+e*U)k|z
z3hIPGXILF+N(R^&f86adl{KqQ(S6|5RW3@x>@_-IEi(@jvjaOUX_obGwSq|O3o(3u
zk_0ec={Nfe<$l1C2JRrZ@@;^l#p^7rtozZQ|0UG-q(%^F?&F-A)N>r+mWT|vOVzP$
z`3Y*sC4hybPJF9AdnGdd&L;e&SaceP$q%J<jokHtm{3q}DSi})r+6xjF-5Y0^regf
z(^E)Hg9CX*S?#bCNhFowy9U5~Ex_~z4ON?bU64xSG_BdmYd!xSBnT67Z$s0(7#nKt
zTRUZ>1PgIlKwy)Asj&40I`_d}o?RWSWHjrQ)-<ibviF+6|1d}P)b@NY+L@IC0%Fq|
zosZs}EUqwZhb0sW8~0<+X<>4=%tkK*;l7eB(8~O}-2+oeDrENnTAS_;P*8F~eO_h3
zu?b6);@tx;K4$>t<6Swe-DrqHH^@cg8(Kbkmr|~)A7TV>FLXDC29U7A-Yhoc$!+^G
z(x&)A9@iz+6R|eaogj;3Hq9*Klz#>U;Xqq6R1_mC22h<WP%hZNhDsLFX~-7-k1ZWu
z1=lgiScERkrFxwbZeMujh25&k|AoJzLy!%u|A$ZT!8K#!jN6@S7-vH(!-JeMLH9ur
zuLNx*zuXunb59@2FF}ijU=qbbHWLf?AQX2a2oR-{EN3R%gRxw2|8eoD#(KQ=uReLX
z6WR$_cqF7GOGP0A2DTRn_7k8*I=llCkG`ik#q~*OX>UV8yQ|ya0;=g^cuiC-tekWW
zE*H{5>i|jyo`M(0zu#ysXhP}?PyT59G1Y2JTI=zcthP|=+C=TjMQvMo4;Y+>)+)mw
zhwrZ*LC!<)%ltB1k@)(Jty)lQz6}glY|KYu=j4hai{4tYg+M0MR_GnBnVyZqI&WTB
zjm}_Iy#XZ$m2Co0Sf`iNi?#lSlM-r?YybX|c^jsfu`68K`MF%JKyhNZ0_eVDITQf3
zgD#hddGFCfG^*?EiQH9g?1Ci-@mS>Sn1j|vEjlk)Z#Q3z0)bZ!aJTYgir)0pvupJJ
zfF<0cIh`PN@roRE$**VNEoCNFpWf`XV^MGWHS5Xxn9y=u*vz8Z@Z+)s?|F{adD%p{
z`GZ0Q<>fW1{lC*n6!D_LtpKy2P?Lu1h2^t7M{_4%tTj2(p4e5B;h;0Maz~CH0j^^B
zHNdpLE;@j=qprbI2GMg5-I-8{9=rU0!|PzO)~j*yQuBXG>dj(!&%#f4Z+aLQd47Z%
z;U0lWPnANE6N&4uVqs7jWxx#Vn7}wNh)x33r;x?2o)^SWrd$1&Lr7--3%)<#I!WV_
zA3}Ah?a|_%kg~oOio`Qg+H@>H?Q66~^YXN#uEBT!SFOraVIb;WNA~+ej{p|)Ku@`V
zau=@pFqrR6Kp2~`SS&1mtP(~F@E{Kw5z3)l6^icpHsL~q7UH!2jl2&Z(`-S~Mtv{v
z5s$^V6})}J#X5Cwr7|c&AAHf*FoWq~NIur=8t!=8o`68hCX0+OZcbmeTe0Xd`dxru
zJ5RwKjM)yG@N0;8*I*7{F~;Z3G6s5n{!TP{>(F(c#D}KIK~=REYISML_qG>y(c3?m
zN~0Tb-^~2@<Ma8;_lTz9MM%Yu6>=rMvw-)(t-a)!$Kt=c>Sq_0?~|rOckJNLE1h-}
z@s5|qiU~%`zq<96T*bZp=NtA7$2I9K-&c{16-)+8;_^TjPS2auYU_a5nnkXq2A>O?
zb-gTE#>5Bt@+QwQJ;%TK8J;r`2Zdx*?XryktfWu!g&Cf^Ar@>i&sn$&zjFReocFf~
zb@ueHTRnne`ac{3?TdbSuROwwib-2Ov<hj>X92y0X1K@j5H5~aWX9uKS$9kSuQ1W#
zX}yXWP)-K4g#)lMGnbhgT*8^TPE@^4Vu%Og4@0X#vj4%7L~z)Slq>-?c<HNhry1UN
zuZ2N=ErOHG%rVFoc**@9OZXDILY>|Hdd|!ONSV2Oo;=spJlDq>BR-O}bVWFME}tDZ
z?jnnsP}mOROrcdGa2I%gr%+wL5!1_5bq6GRETyO1p6P>E7<1Bh=$l$Ekv6BmyY!x!
zI2;;e18OqR#nWiy^znaMDm(UW8oMEBq!&_x`vWd(#P3sRp4=~7^+FJL>zMuK$mjV!
z18I0KqzMV8LFk4l=X3Mk0fJO}({fHKknnlUw|LYQmV<l5XDh`hG_%_0`6*-v@vF;m
z{RXB~9*r$J-)~%a$!qZNVB;;r@rTX}8uU(N?}F^xm|D8Jkmfe>1w?i?t%nQ$W;_Hr
zDh7tq5aD$wDq?)t{hq*Rf?a%i6RcGqn2RJIfSO+AAi^8xYlHz?t2=;0wkV#PH(d1S
zZX#TkhFKcgp6-}hfSO4rWo7Z4a1Wwf^Z>keICx6|zx!e$^}B{h;ci5r=>G=geM$F$
zj?@E@8auUiuU3CjTeX%)VZ#UgFBvQ(=96p`K@A6`P4Br`w&Ovzv_K5g9i1gKE)UAR
zIgbSebDf#epBYjX2-h6zRGU-3sXwZ^`jWAv8BXoU>G!a+t+@i73ht<VZwrAYV;YC=
z0iw^f_UkCV&CW1cza@F>FuQDY^CZhWsZxDEcW|A7)Ax<C;IbhGsz{gGP(PJ{{ReZR
zjZ;|92BRfh$CA#^`OFnsG+*}4VnWs5*<-K13lFR~g0}54-$t`-OY*PwA=#Uhy*S8Z
zt0l{vNyN(MGu-{Z4-PgKLV0gg#(!I-JVJc^uf(nFB3U&Av2Wr90|iytKVTSc;1SU?
z!Gw-WpvG2F9p{a0A(r0J`z8dYSoW6zFQ`9A<}|~E@>`J$g$x{mcHI_}1YtK^k<HXF
zm5g$Vao=g8g^FdaIl6f}Ro4QW_n-8vo>bR<cy5_0M^>4R#71aZwRReGIj^*ONlXZJ
zqSb?#YXbj4kPo-{1lAvhk3S{qkLRkS4p%YX8(1N?fsnob2BfCN^?#5CT3-%{r1s2~
zk8#P!vpg?XSU^?FswhZY{ss(7LlA<9&rq-ax7Bu7lw=J?#H$e+U4V>~hjV7f<S%x1
z^C5N@7@)IBw)Mn{_S^#D#wMx*-8^%G^5G^t9}Je|cyHFxJ5r>|btqaCT#FNwM>8mx
z+m}F&DO;@D^l`b*-HJZO{m~Hud+bri>2qzv+p(-e=I@ljcI<HQ#%rY1@dA&^vG5%!
zugy2<OxV=@KM0aAM^y7+fEAYJIat?*{4J&Su2uo|f`Um3SOEwGgBO%W<e|N-7gMgW
zsI_5RW>v`!Y=#E(5Ppu)wEfZ-CK*PM2PNz7YobR0<(<?;aiLzl0!B9WicQg|YIi}z
z+9!DcHJbxt8|>r#1*x1zm^JgpR6Rhh+j-%rG|Sa3L9AYrEz;LN$hpXHFK-O`4Jj;-
zZ_d%s5j?l|%f>|VUMQp}U%k~WTnbO=R^S&YOzFs__d!JB7FV8V9ifznsj1uG3Pc^Y
z1hKXoOk}g|7XYn)jk8XL-TNhilsD1}ZsHYJxF0n76~s{#02L38C5MBw$19`<Uw+Ix
z!`3K|_<){t`m-THUSlo6?}Gn#eimcK?#~cy!-W{ZR`+Hh2Zq65y@Dsf)a;>u1KhMm
z>PTP&HPZSjr+&6QkQulF%<S+r?m|i5)~ikubIt1E2|dzRhhBbV`AbpWT|su7Gpz`;
zW(pHTC8ykr{k11sL%(<dV$Eqf9HLGBmjk^G{Rnd0TMmA95x;%{5^fu4I*UrfK<qg>
zz?VTZ?N4VzfTje|OA;r1px+t}Q&x0q>KZ&+)_iEVmH7o(`#o5bIL{%@pND3eJa)R^
zZ%|(4#6(mVy55^+3h~OkG#pmTm5w1>i4c7*hd@SqH-<())tXIakPDET;1XtxkKq(t
zQHK2)fuE=N(>pOgcXPyv=Qi=t%Os*>OL(wo)t-1eP%%$F>j!auM?IcG1B2w&W5q~V
z(!>>73aYhiV)itu>rI6#n7kU97+AOXQ<c}%>tZa_j$Gh{ZlnDzKs4y$;NLWY+`dSh
z@$IwioZO)!#5Xjz<3DDrE(Nh({*!O#Y=%vuWhG*&yu|5C$GRX|Xa!9R!^~Rc?FOGi
zZ?XnQN`fZH>+aKKT(+UKG305VUhdv;{nEKhwH+g_^05`-OgUy&*kjrmoy+~7GNqR@
zgT%#np0J1AolipHChwQto0aS)Vb$TB;07a7K`U~N1~5p^>lC(Pg!o*OoDT#bh26^_
zVGh)ce$RQh`@({j?{c#2y@C`T)rWj1azf-<rz_9L>FN5hSB;r}t~}SZ3qhnuAmlEn
zf<=7|m<p%L@rApBlT1dYuJKy;|L{I#c%FwE{693EWmJ@Xw8iOWDCtHTq@_CtNkJ*4
zTe`cuOA(|MX@Q}nJEWw$TUxsD{(0}c%Wr;~#hQ7ZbN1Q$w}+E)v29qK92FWW!{I_|
zbqv>v^q1R^d(^on|9ce&T2fj~6vSBrPJ~oW{;a3TH&14&u`;lyZ>`ZoB?*RQKg{0Z
zSyBEiLj>;Z&;2enPS&%PdCJq={i5Ivm<5?vWtaL@{H8crS=LUEJr6fU(Uu>FjPKWp
z<&x~_(_1J5SPzxfD|e&KuE|m@mj>CW%Hl`=7Rz*f_*6R-taIJZww!btaN5t50q}Y0
zz!^=xXzKDwO@TboqV4WTgk_>I+k2pr46Un#I_%dDoBf0Q^E<IHrA3)JBPl#>tz*(D
z{&{UND4)oW&q+T%M`^VtsU|eFUf=pJ9(P5NKbQgf`(~5b%XR5ce<1T^9`jYKMWisM
zGH$?%!?~9Mmn@49mXC$_l`7Jd-#3-YN@pqI%=S?8Mz7XG*X6YRbbH(`OZ3$5{ouOA
zXvVPeSPAk_(eZo+<SL8x*alnZ_nTea>B)x5uhzcigqpdw(|PHR+@05~qrK|&Yb9;7
zwMj?E3w}9h=7C+L;9)S}<cWpZAlMzm7i@&u2~**mhZ`b1<Sn&K;kN@dbX2Btp_l^v
zBWW^K0Oc=p*NJbM#bv8=#J0GR{{6DwqWCTgNJXmq50DXl?AB$5=p`U_X7-%@@07>v
zHSOA6PN`8A(sv7bGA8{$FR1+BEsp9~C1j)Qq=NeR{c}j~*Dg{o-_kD_v$nX_-lu4*
z{Vg(5K^==(nJohL#WwpI|DFneWBZ^kQb8`buROhOU?Jpn+nZF}KRtb^C7Q7B1+<JI
zU`@lasrwa_z$x*#SGQmXP}|Cds##c<;yT`KPd%O10Q(gG_aW^2<;k7@GoK!VCt=sD
z9I=km<OgyAaP7<=|H)L?m&DqCy6ON0*f@{gC8XRs;V}?t9KvNC6Gk5%MZiP`<*eNL
zAYv;j;IO<1Q;-RsFsww4>NB8451eWeI<gf^YOZQDLSM<MgN9ca%N3}8<G`X29Bl@&
ziK^cyPYhPwSkbpKA{o^wumVj_mg!Dp0vv1jTY>2Fl+yCX0W3=4NtQmV?h0cRZGlti
zMbnEN>GC=|cUF`56yrn;Obz(bm!Bs3bvD^G3u58%R)MW%roS-W;cPN>?ie*c3FyVw
zAK3}_e%1n&X^sSa%8+lVnv4PQE84o_)zYOuum$iGhEB3O2#hIfe+~2d9ldSwS7dm&
za^#)QWa;T}^cVk%Oj`H1-k)Al9*IqvJsYvaph%J9mlkj2v^P?vQvcm$Sac(kfx=VF
z;2*wcxP&@$RY0zqNMdpp+dBCQAt=iZ(b1vb#oh;6-$jD&K70QFKH|e=lOQgF$5D=m
zvPZqWz$H~iKQzbh(xdjUuI(zy{<xRII<8L9Rg}*!dnI-qPeinmBccI?K92PW)};@N
zr<``<Y@CgK@GF#QKkIE>?)m(8E#-A~+v{&CZU$HT_rDPO3~AP>YMN-?mwxXMf%wJi
zd+mh3oOW@#CP-eTRfgLTX~Id&SdsNS7V{sl`68KH?Xm*AUGtI~ks?FTo`|jqUd!Kn
ziCA$?7r=^W>o|&IgQHXG)>nKLb=G2~3;t1#3MaC<!Rd)DM$~9B7=FQdkSAyWF1p{J
z_oI#IEHx$ysEt`c1~j`{`cq(*ne?Ox8iYo;-syddcdhaytsFcOF4Va4m>X7eRX<<3
z=$=&n^F)^I9-9BUi$>Q74*n_ap}|?xj9;xH-w>D?#Fn2VN%jh8GQ;5BE!t}NuCSJ3
z!!Ujg|4O6wz!72S5;KwH4#+hztgTOtPeh-%|MV2VO<qb(S%aw!dlEUvbzG4uIlz{3
z#^!$Cvt|HpbJP1`5BW_oiS1&M3r!4o(DVs4!?me11PZFR1!J?NIS|MaO}Zs&j9J`a
z+@+buCkf+Bo&N~{V4yTpik-on2Y=s|kgSSxU6G=zP&2In|B3#`PKSA~)m6r$DbXvP
z32!YG&Ew=(+d~=gsfM4CFO;{avP};ixxi!QOVMkLzKUDLC4Np$^E;r?`b=7enz!9a
zu6Z}zfU5=etwn&p`_UrHaV7VzJEAkzyau!`xW#T_(cZDmbyowiUu^mEa!%yE&2g*A
zGI@-g^|2>S9<oO1Nmq-AO2ZOrQ!a7&f8*!8JqZ7_&knpIxv)PTCnOm5z^Fx?``|F5
z+b}@8`m*K7+hsn#|5U_uZz^6a{{;CKneJ$f{EMhp)w$0tC4>FL$y<J!@*rrq6Dj20
z=a{s+RP663tqdY)WGj&3vA2JuDUGD__Qg_581x!E&4>+Pg^~M)`vwv0R}XY^5Swp_
z{Aiu);FMxl86SB6J4a$g6g10ec<c;k5T^*g2gQdI)s|{enD~z%T0vuj{aijavF7RZ
zuAC#Tzq}y2KXv6nSd`6d(aNp){u%KP#9dufaF{iFs^Yw3(?cDvhFCT9wJwI}x3RT{
zefv+kh6@7l{}3qo#m8AePuej1#$CqsVV!lwyzW5)7ZY@OAU9Lv#q=|2<;k<Zj`Li9
zZhZ&rHBkb(nSAq=6fvU9f93g}sMcm1QLfis%Gur?wC$Ml-6aS`vQhZmF_fy5PC|+$
z@7`S{vyMqIrlL2V53p6>DnV$(xnHrTEBjX6^5kq4tmr4UGv@NXb8i-6H;=jGBgIZh
zc}gaPitmDrl0J1)rkPY#`Ri9O?k9S#X;k%OTsX^7n-2q<InB?%O>X;I>pj}*VR|N2
zpYDO3o5>iKuPE%i8F^7K^Gqs`m<r7;aLS)RiFr`7y!Cn=a~;`tcLz6-5;JcZHfd~f
zu{K$%&AE;U3BBF4(-9`46m*pIzg#$X4{3Y^sx;nhE`W^;UdK$O5|oaA2SvdI4j*w#
ze<umI`CQqXxP9s#pNZ@bJqNon!Mukrby@4Qh?TSmoIrYdy4hKB*Xz2lit`SL*ETk;
zllj>(XY8AwBh2nKSx%<|UGA2Dvc*Crsw7fQiH8(!Fkq$1>vveJ8jZLxdhvlkeP)!6
z<F4@dds8z}e-PhP_bq)VM?-_auF~T*(rs+*FiHgq5Y%rK&%0V<J$yjVokej@>tbiT
zqRn_J@K_dxx{r}9=jV@z-^D)Fl9+UgouII0IrWlulNU`#)kt|-V$aLw<Cr4W*l}5{
za8biXrhnWGbo+zr1lfs{&`-DSV_j)teL#WX*_K}gTrVQ_Akz8M1^cEKpwc-I2&=fd
zNL=ow>k&wMOUrWnjMyr8k%$24JQItc>qfz1<?`T~5ZWxZ#X2x@W^cI76+r#6=_;4?
zaQ?>oaMsu!2+}j7pzjDT#m%0{qy-%v{+J(f)&jWf32Dtt8!uuTxD-G<vTn%h351O<
zL3Jjb|7q&BU&kK!*FtNIUXZy%3zwQC=PT{aazL<@W8UrlpxQ5i?O+vW<4vd~wo;DA
zG+dc}eg#RwS&-x{aOW`mVaoQDUhlsBq(W|BbQrA|kxZOF_{H;MSeyu6MG+II5_@qH
zWY9?=_kWwUq$<*=hta<*#iMiZjQ^!4r>gzosh;LUnuZ$3s_G}HFB3d9>}l<e6i1*L
zGDaju%CI6=*7LKoH`)l1>l_S+sUR}$y33Gmc3P2zCa9bOf3zGg8Ma6Qnh^}qJk<Lm
z2Fi`plG6DjWVhQRL+R(?YkC^brv$p`x5=&`ae8_LcBcd#JK+}w&4Oj;plVEeeq8p)
z#8F+>vMDY8RsAFQNg^RkKQ;M*_69-|O&v)yAB1?I?iT`Z<|t@4GT+E#fExwYTBewv
z?JV;T?+nH({9P2_%&itq7xht(5n{HcuS=c-vYYtR2^vIVw#S&h{_qDJr>H4e^r%t8
zySPgVa%>v2Ifstk2>!sg+oQ8E(_P?zL{E1-1wYbX7V=g2o-ZIL&D$b+_ekr@e-q}%
zgN2Xr53q5f5=5#=Y8x9NEu;IqB`?nfg<GXaZ(X|DCO3k5RjaNZyb>`!F-zWHMhIBk
zZY%`+frTIAl`7MvrtYWY@5&YkpvOsyA0+Km)dYJJ;<$98GojIvy0n7X>*?sdN^y?H
zDE2rnxKC_XT){utsvr`1)ez?-b#7|S!oxpXXO}T@Q-;lpSJ95{b^a2^qmbBB&PP16
z$W&GU;$4B(O$J{k2iruSuXlP5yFJuVs5oIk;FdA^wTDxrF=26jbTKpL&v<xdlGbhK
z$?<s6b%t255nEc3=gw>Y^|J!jH|jLA&5^*we;|#0=kwzdJsPeTb$J5@hQ0wQo*_l4
zNHSs9qLInF)BOo!j#T!fbdcVnH{++6mt1HDSsbCFAQ^m`(97eM9Gwr<Wrs#9bh^Xf
z(5=eumZM`DYDSA2uKnIAj6%KzaL;-Uzski}%~l!<RZ%g=Qp9VHA|SBpeE2$KDY@Qd
zEdT$zw-6p2-aS}OQ~lC19j2b1^mK9HoW5XqU4DobpgL&){>LON@T$PBX1!%CoZO5T
z4nIm~iw5?`GitA9LiM?p7<0yGVnHwKN3#jyWPQnkb_<yrtbE)+vuB4Y(N^CjvO1AF
z;K*%@P1JW5H)CaoiRqI?L9yjI7}`)j$wC>r&}G=2ixoMMM(dcW(uG_YgWiNTdV$cl
zq6Q2OQ-d<UW4)Ujr7Cfp03BWP$>46AEQ3LQTUDTvfx%J<JeKF+1TrAal7Ep)t&x(6
zHd(BxK)(vU-Y-l@+k*x2%U1_VXv|I9wO45dOq@uPuN`ruQA+pmtbL0dxCEtef>*$o
zIr}+U_gX1Vry!Oap&60w`%n`=mK5k@3{1Y1mU>aRFzCNOfUi)6=3$j`c_b>C^0=?6
z0I?Vk@HM=zr*k#BqWx0y6P0q&B`uypP`W39Q##_32Tv$KT-ceJS1n&Gzv#aByVdGx
z!THT-8^L3slq%9xZ=B@MJ~H(s7giUs{^Vyn{t-TtwR2(rMizt_N31p^mqUUkHK47S
zJ*ywx!QsOS+{lEQjUUmhe?D$QEVJB*o`6Gi@KkozG$AmA&MuS}^jRj&t7<$(xsE0e
z5PXe5Z2Ess--OV(X-O2%;-|3R>~pw7e0OEiyV;lWax|A{zC!<Kg@-h3Ww?<sh#><)
z`HR8zpO5Ij=C;40QCKFAW|q)m*w4o$!+r9HfcHN!x?^HlJ(!k~a<NPdnNduI%lZIn
zMITZMi+<OshgaIdkT}6Ljkt?}f8yBobcF`}77Q4rLd+kp@yA|FV=ksO9WU+53C*^U
zuhxN}gAhF$JZ`&LPc&hb!QwXsoDSSOrORBXuwTe8HmFb8kuIMjJ#X=Z{v-JLJ>7Hp
zaN8G|@b5Urpc&!CX!*QPe%_t&nPw&|1VDYIk5uS06CgtQfXGeSZ8oCvrahDt|Ef|q
zR)su~@qoTi<NGV2WTO6lYW{`1OXevRdhQrhts>6Z*=n;;&wSbnr+J>hd8tXKqK7yI
ze_LVSt-c3H2`f+<WWgVQP%69jNb<L&cx90}I#m6|6N$~Rqf|foIUJoj9qBu@yx&L~
z-IDn-520fDx2w^@SjFZ#iM5!qNdplKp-pWrMIn7x2-ZLU>`3ah3m~n3;xs*5-PdQG
zW3Ul-`UB^NSpbrj8hyDID6+{w38Xoo1L<qkQ<>9f&a)?KDkyQFkAO(Fx^UJkc%ud+
z8ik4XEf^hG5>Mqh6DK28UPD5T+c|P)9Xc1<+AN_gjwzR;9vc}UEZG%EcFT?F@nXfz
z62~7#&|+`P#Ryq<h2C<wX{3HR!qN(OSKv9_6x>q<seQ&0f{2Z5efV|wd^_#pW83*_
zbf25+)QF`Ha`$)58d8l`DwZdjksG&8J1DFT1@+L+o<Q&j!BNsjiEE!zBA-xvF{sH%
z!13IUCxL|ZbzUSAIrMgY)=*fJu~Z>jd+P_3iPp%|s>wEI-`)qo+qp$HCz>A>zeLi6
z160u9o{tr-)1cGKOG{Hly4z1QxLDHu{dOGmii@O3q6_5&NJ_7O3z0yC!a&L=UdBRw
z7oorTPn*<y7d&qfzf2=|uUASCREQt#l~xep^P^#P;9`#*AI!qK4Eg_il6?(@o9~ny
zm-_bX2TCbgH+IkvGnBR3?+|dvl>to%?FLR`v3)esk8U-4r4xRdNvftxln^z>iH5)(
z0Idk!4(H$Y+9zT~)Dj+W0<zw_$@z5}GRSwSDvIr<BBtRCfx?@!oxxV5X){T*Zb5XU
zTJWZwwe%jJ8rO%AoJsRY{OznKw)vo=@Kmvi^bVSi2#=rt$_myFKkdp*#FR*Cnm5L(
z?>OZP2>g;C=mNz_rLhfs4qwhCC=S2>P!7_%GVLw`bT{J_ELun!2=JLyh*2C`C>dna
zf!!Y91Qp#)t6X*{VjE~W{y;p*SQ&IqWW&WBIvEHq(|V<%hO|qZpRn_ljqu~*(#&|>
z*RXecd*4^t)6e-D(Ixa?PCDSSExL)!n};BCOv2-y_$KZB7ijqtWMwiDlTx7bOh#`>
z7B`>J>jvoSa>&ff#%q`;TSzH;E1%3X{@uvJxnbLRi(=m<%~e|h>&GEIDUHw^!FVd4
z{T+p1<wKlc{HcV}hbnDo2Yg5SItrc8veXCC{QRIuqqqnDLl@Tfs1OR~m_79ejE&#l
z?!;f8MXPANOH~hQ2B>q|fwg53iH>dfLmQC&{BE%R>?>yoHi}Ps$Bd7S<%C6!oIYbp
zHQ4ZWbsnyc1178d*>;3D1&$o?R8!_vvQ~dD%*NWY`6iv&PMEw%&TF>Q`{wmR_rxlF
zC17v=<7*nFFr8dMT62eEhq9wHF4%c;b5h&ow#23^GI{`41cVwN|Kpjo16>GQbZT2)
z=XXpEB6wb&4)2K5%gKsA89M=a2y#62aSW-`o}rLqem)CRCRQ!+m>R8v*;Sx2Mo>oW
z0B+m_U?0<z?BQiMy>MK)iyo|VqM6BS>1O1>;<qxUfu9#cty+5;YI$<HNr*H&@_MNv
zdUC@L!EQy^c8yACmuQBO%#Vc@uma$Di>3!L*vdm{qn39(I<fMziP8wB8&_NW<L;4z
z>p@N^{0+wqJ5;pH(mcc9PLT9`K<BrLvurt0v?SScIE3m~rD_`(iQ&2u|3Ywfqo#lS
zfx5R*Vj)G`Q@B?9b4MWbQ(1tX@@c&ame4>kS+f~;U<hJw=%PIaX9(q9ei9r2(Ro{Q
zFM2s^Dvi<JeDl<1i|$QA-JwGWt4DQ$Lvgv*iJzh$r@xJGz{(dRDJyKKt%5VO;tlTT
zAina>&?)GUpoj&{@c3k?SR|{k?PQd>8TS9GCvf=?+;i|Fm}#L>|J_GXo*whD&ZBSg
zg%D;PQM@=)QJDVG+F5~;I3ylqdh*q7B7Suv&|oKn8-&w|K;KjU9oM)@4EWWY3jf_d
zHzFp>rK<oR_o<NcUp&Ei+;A5NsxWSn8?sj7^X&>HhxK5y!v*CcL>U&X!1!r;j9i?$
zBk`zS3xHz0zbYoL;rIG;=@1GvjcML?FU&dziK+6ubB=yN?Q?NiI8vMt_=7w(kT8Kl
zyZpWGWV`V<ii>%&#5Mf4Rjl*YBryw5cF9oPa}V_;R2C^K)PKkS9@XY?GQfoGr#ih4
zqzW~_esWAsxWo@dfT~qtp8Qs7RowIqp)pp(r;!4pcE7O$SrC122q6h7vd!Xb{ju0R
zucB3Z730UY$eldwB0W2ud@rl<IZ=emhd{@DVtduZd#%0vuk5dFC9wUQNFUD5#J%li
zwb#*+-o+lC(&deM9Q0db7F$jqJS3%ia`WH3m+gp-8`K!guo+!Bt46!yR(-Xn&?mR#
zY=K?UE!Z^t)&2OFX=`j*oZMBD`Y|tt+30c3@mieIWGOr0eAf2V+A%CG@sjHJo&#Hd
zI-F*Btk@^=(&{DF%YnqDkZXSbd(S@VTX?8Xc!>zK1G87hgp&WjDH3VpbxmGkXE4kj
z2&}TNitRL|X1neQWM5L0z)(=t%8(>$zR^6cp@vv0&39E(o@E0y1nE;tE7q??U<)CP
zDg#t{r_<e^ahHV9H23zC#{Ke?E<SKAPaJBInnn!5Nk#`2%xFmy8o1>C(L5PB=Qk$#
zP|Z9vS?VCu{mJjjsY8c$9QQ{)!heMS+O3dl?d#ILBUgHq4*TI2&$d1CX9GwJY{w>J
zE89?6eBFEuah)dD3PZi(i&^c;-g5nU%K+`zoXOC5xZcrguY}JsI4oX%M#Gf*?*tYd
zWq#$0UH!Cr`JA2;|BLf~-c4tP_8(cy9y5E=Pg8XeXz4KtssaZuBuO|kxcg<0V}vWG
z`-f4O;CO3y{devBx5)7*IMdV9w*ZrGWGKDThsKR~5egPV#~I*-drckea}~;X%A$OP
z`+{FxB9_2`%c1STecWuIfI5aDu@wezKDqfA4BjYto$$%6>{2hMcWYOKTB(b6dn_38
z4<3LC4<dw;;ho6u42-&w8DQ-bP~dsZ;fNY#K{HqK8i^gDh}_Bs$U;^>=-g`$gGD44
zIB8U&u<qhzU=|eGI~@IdeYyjAo^*65(CB;P%%lsHxa48CBC=xjyx^K$bvv9|&dLzQ
z0#RX7m!;K@l>SG)t4LyKk6iXXqTjOD#L(hX=~3@I$&X!4QrcCZSWJF8-8!Jc8-lfH
z+^~VnuS5R`mL*E`6#JiAo%*%$eVRY-i6?B*alD%2CuMSC(a)W?TZZoXlc%>K#GAPT
zKN7wN)KR|0#vUI6#`Z1uf~z?+SHs1?RQ*Ib$5>Qm<g`^UlNjgonFj?1i@xWS#S}YH
z!-KDbzRt<&9T4zX9}TgoeJ8#vkMgX`FnAX?FvB6|hMGff47dv}^Dt+e^aC}*Wb!6)
ztsDbt7{|I3Dh6&u&FD(Q0x#}SXHs|!BD2;KJ^zTKI@TdKy{?q>F9m8p*{8Eq(Iqfh
z#L8gF=4gPDgoyKm8c*^XSZnNI+tRV*VtBrI;w=p!u9@$9RBSSOjVRhJoZc);ABT#g
zznv|8zN1vZ)fqEsA@9bG!HGrUNkO1Mn9c3G_XTx#NtkbBG?4J01xYg~39}$7jyM|H
zsh|)XDQOpfp!PG6_~L@<*YuY==h!F;(P7%?$KVUMnfd(-W(1N2BaV1C$;9$eB3*+1
zlSpjM3h*pzJ8|CI5oL?eDeWR-$d{x7DMKks@_MQLVQ?uo%MRvpgaZSgIGQk)ThV_r
zIS*}Mm(u9@_y1%<RCtCX231|FSL4s%p;XMk5H<8x5A5G>>Ai!RQQtPue9lYhmB*kz
z2#gIJhCmssdwpc+mzaWyhz+%k5DC?0;m#O-{Q8_D3#-PInRV?7C1jeC^i*OrJqzSp
z@Oe+WME4qtABVP)8YNlY!2ia)GaQ}>?Qh64Rl|(${1P<Dl9Y7!AaTBkmiwdbqT7yo
zuf>DJYKA6@5YtfopBS*JM!3cc!h~=%*vqe{%l^Ij!&VuEY9ew9u@;-XMrAoEtNx9T
zE)Aj&fDmr?=+X=|!hEL%6`3coF-mxZqqk>!Z4H(c2yCS>rso1AX>svU)=zs$c0(sM
z5}{9Z_Yu$54mmPi^=y4_dPJZua!uHT2{cXh-?2Tfjeje~kgFC9JrAEJ-cyYDL-Ru7
zn)!v;zvser+uW{)DgvV)$5M(UVYc{55j&YUzZN<IaK?;NOSJ1<X$K6sa-au;L!Md7
z{5HEQE0?1WYT~jEJMTOvUHG|oM_bwZ4LyzTvY|$9d|xN)OMO(g(g>~~$HI+47|*Vb
zg=I-md34Mjv~nu8p3OCGJrYUI`|Ow0PVQcYPi~Ffp7;I|#sAYTHn{!eNofBd(o#<U
z7&5mq7SKis`UM|YM^wqXj`neWB+D(j+{D?&KSWqe1PP-kvRsF0tgKG|sKQ*E+X2hC
z&<5I&fYg2O;sjZZj9}V0Kp)UPwgCIa=?6foqobutK3nY7ZUo*LjuI>Gh_FWDD|zzB
z#cM)l_4bg=5I&P5%wS6B3|D1_M1Z)y>zLdTvrMZ3nH`m#w^J=D5?5a*yztuua-Nz!
zDZEH#1Rcz-dwG!bgP@5nm%lLQF%DSdY|}_DnQ}I}`903S5)k!mQ~YO$Z}X$8=M~4g
zV%vgnpV)LUR|E>FipmjMCXhaPF0Ceul2*eJLj-lqPpVBL7iFGoyWVuGkMfnNklmFu
zOe5t4k3<BBPZ54^CQ`(QO8=1BLCb6}`RU82-B6wMrX3LnEmG%9IX|RiSGVem&C?H*
z5uw_0qyTJfA9JUvI#vI(Bi}y}p6&-FH50edB$F|r5Ezd;T}VHM>a7d{JU?G<FWg>|
zB>z4DstsofX(sbIt;^2+N&^P)qT%Y@nzh*-qw=+DbjC$l=Z)XbQAB2~UJ;p2#4El)
zv8DNBV&VgG42()dnXYs=>I#@s6k1it2l)L)@N2*rcsh$!YX?7okgrDaj^i@gH_-O(
zi8?V}iO*vACH0yv&>JE6ZGG=F@3n${2&)BDD+CD&jqF|*A^KSf3yX6o(hP$+nRHrq
zL2DdT@xSy12*Nv{qtWozm|lDZ$rS=Zf0_%auaW@EjrIZI$A?@|c~auzx`SMP!c7dQ
z{CwA5visq>$`@P2sOA%{qL3ofA|Ly=Lx~2lCzz9@K1vuhH&GVeQoVI{g7j0Jvko01
z%c+_+)$@9cKPj@67?_pbnn<Ny);`u9p4;Ua>IU~f95~@~MkIVlUOJ3r+M6r;FSo>b
zDco354&zD8B-AH8^7<6H%ah;!ZdN#cUmagw@5bY2y-d@!DaFm<tj=DEk(Q3+o9@Nz
zzTs*nVVLAQd0U|8niln`)cz**d-W%JsMX@+nZoU1;YtypO-L8Kk}T3-R5V;{iHmn^
z@MzfQo3FJ_D5`jHo!%Zj`}|;+HQMluQo91nt+`g*@8>!3{KW*-tx<1+8N|#l1iLQ>
zij1*SI91*HscvnE%|`P<NiEk6=N{iX6Yq4*9y8NN`yK6AefHQ9$^8wfgUmb?{juNO
z;m`OQz0ebIG#S)E3>Bjw3NF^IE`WxAMtzJB`eZlrgR(plGh!qcWrniiuyoYYbOSqL
zOcDj@hT^a)u5U^HTSAxbtes>*$fiwf(^zkW>EdqH)n)YGN>Zyk2)VeU#*;V=0_-kd
z207bAi>K{PBI0{LMx#-zT^Oa^mA=__irA4-KYvYgvG$obr1cP7?E1%kZZizMxx|d}
z;S#(L^Lo)sNrLmzYBbOhT4}#j7DDZ~LDjbUU$ozB7}HUZ3RTdF1U1z6z-Bd?dYb$2
z=i35h(sYs4cb(VN64y=%{U;Dq_oo>R7_SG=a+kXF;xJGYw=|}k8ORZ`tE<hFS_-uB
zpzuheLcOPEoJ7W;Oqe#~7@+Tx`vZWL6s*zy_!r0wy5<OzC{l5FSij_e2`V`ZUNqzv
zXtfqh7{;63u{h|I1`1jcz)Lj<sSRB5CnOH3m+5fRN;7K8s|!d<vb?Kf&+^_Yu2TGD
zJ5H^RGA&7ony=eCQ9rT4NqICZvcGI+`Os1RgsS65^l~X3skA>YLAx#y>BwC#_eo^-
z7+@(>2M%|+MhgDwQG3%hKuFw`KA;37Lw5fg43R~?YYeue!@#*Fc4OSVAQ)U^N|TjK
zZaiX|6aqO{$ex7ltqg|xSMql_w!%)GD|O#!&OQE&N@Xhm<{iVUkCneoya8veY&8ZJ
zwP2J7pFvLZM*5Qzc=c!X==^MIUiL8!{cCgu?q#FkUV9w-+30xx9b5zQ_=M4P`p-Wd
zVJ?3*5mbt<zNr-ZCIs`D_kKDEzN`ASlwOa0Ug5k%P~4GzQREqBvOcsqNQe0L&;`rQ
zvmV{=BP>*&s6M$>r!s2(@nzz`j)>GE;S23FRyQMSvZx*#oR3p~+6H@<wCBP!7`+s&
z9@lS*cCWwG_zW7{9@Guj+}0fz$%fIraWN9Qb(?^E0Eth7VC^=0q^d!YGxcc&IE|&G
zkn~Bj4{vYx9u5Db_z0+H?C)zdkzN!{tAGCTeX&*TE<5CfK{btA(@$=<#vkwxms9rM
zc0+o4FWo|9=4G6Jf0=-`-r$t~OVUzXV7}5n79Y+UuU{lC`Ir-L_d52~BrDXM?Ah-R
zY_`jeTla5mwUa+!3&pEay^rT;xHm~hoa@#))Sq4+kah$;QdZ#enShEpxm_lhQ#kjF
z+c97>P5mu;rkwOX+PxN}uGfh+MQ6Jbe!~3V7fs_al2`H-Qz-h&91Q_BU^l|)7(AWC
z?w0{CSh}%K469r{F03X@X+9+o;a-L!@f;O>-rn?Vvu?uY>Fq#;a7_fus{la^0VJOE
z8vjuA%_Bp`3V4N1BgEN~s03Nh-DfD!2a<0_%81roG>1~-5(kc>9r1?a=mf*1bhuau
zf;yN8*)Ijt5XZCiW2(o`97&#C)ql-GaKh2JhENF7NL=@Mgd>)LNFl6a_bpgBo&|lB
zg)`+Ba1vtREl$@!N=ApL3Ps|}BrY@A|Bv4lCfqK(sRAZ=EG4)|me!}z1a`Upzw%MW
zQHCIw_A*@H6&3+DJtccqJ6F(1r8f;P*ryX}xcf&dKO(?3kW~{rTIVr-X53!vX8?d0
zDI;WiZ$w)s%GUC%pFp^4j{tc=R<%OJj{mUIl!YXm!G^*7X4dy_)SIW25EIVya@LN{
zGC)Dj{DqrUOsJ>_XDNp?=>80L=`fdLS4MO=G2MOR8DZT#!wN}wRCPE*R!?*0n7nqe
zMo(qv+pbNqF8nF1z3ge)e{Rg`cb%-7Do@#ar9UZkgnmcB_-nTs!O(Tn^HM#n;gcMf
zzh8q?120SGbCuTlAE4LdH<wfA6P+WR0n597<b~as=P?^+WFc?~hNFK%S<x`q=$S>^
z>LqV{(rMwl&OTEe^DSO4Fl-Nmv|P_ja4C*7pABH}p3xk5f5G^n^kw56;Yc(9dODK^
z+{RSDPx*>Z)p_u6U~Ms1I!IrRGgIz<aB}n&o6<S%`A@$5`MV80A_47#UHjV>=kVP-
z4O3pP036LcyQs9aH@UXrEZ;EO`uC301sO)fvTd!%vK_yW3R)QrjVFIF-)EEPv3bVz
ze7cUC*gq@ms`BLz<m2Ez%ebXmb|L*e_*P=Z#sRg?{;fN94WYKlj5BdtYmT|aF)Z)W
zbQUc!oARx!_`%0NbWS`%lbdXk&Jy-_y1HALN}|iga(hAn{l4MfAJc`Ovym|Fq;Jn?
z3m_+4P>dinrSjUORZ|sP3J;f>E<fDcY1XK7yZG+oCtt+`oZA|X8o-2E`-yw^|1jBk
zgYp17p!b__fa5I8w0J8V$5-h1iRj#gZ?`q4-4r1n^ZhCv?39WmI-6C-yh#g{NcMmR
zu;X5Pl8dw2WkY*6^5S0d7?ERKZq+=u{zCuAufpV`9*J>mAN_oq|At<KMk&PF>y}e(
zh*WnY#^11EXji&e#y9sjprZ6Ff@=;B-74ZNq_uVXZ(isR`-hn=$4X<m(2sF#&TBHP
z;Zrc-En-!Qy&bD`m^_Q_AQGc*u4XIFfx{Z9c{`GLSE!Eeph<0gl?R+cnZE1*{FN_s
z3IKXhf5Ok~5?+0)XCsOZn-1EnKwL$sEO9(cpc6EhIHtR4i-CU5!{P|Oj+j7%SIH+R
zShF^t5_YVC{M`*m$Au$k;>N}Fm4rjE#!d0V<<p@{^a6Ns6%bs-R>I-|-+ux2F{L8`
z+gTa{^87)SdQ)p^DUZ#v>|d2g!d)LcHNUvN0n74JO9M&jEdd`tjQQATU6Pb`2}k@o
z*O=v<;%EPiJ`l~x0GI5}oANS0(F%$CzahBd2;YMk5=pd<nKOdfxd$rg*=R4SYS_B)
zafkmsUP<6co3k&(NbXL1C@h>oP4H~isgzRic!-pWxXbK0`mQQ2UYt8$M$A1GM6cWc
zjoS*C<F<od-fls08OE}WURQw%2dW2tC~BpQzFf{?(d*~CD`VS%f7<tcZs`NaSy;RE
zKHuK+Qj!yBso&>F?HDmM>aumNkzIM8d*Ia}Kj#D~h~kmIqgtj|xa?Hq|E@dUgP7w!
zPPW59wpRCK$?HMed+%=zm#ap!2k%>(GGO_6MqM>WuQD}81C<ez$NqBaktZg8nL4AW
zUdwvW|7%^EJ7Kp0y<m&<ZtvTkFfq^h=6At(^r=MiSM0x*7}9%p-ic-xP2S2C8Ya6j
zRVCq2iPc(0jpgrqMELI&0#*#N&puy@=DK!R{>VHouzI_KB)W<mz@YZRtWjLca8Yf3
zp(_w;&uBL!az|=$P|f1{v)xc=TjMRmt&p*k?^t8SVoNOUsqJ+fW2PCITKPI2H8gJO
z?h|jA+AoJ_t>2xSvEG(x;!0$gCvLMltvH2V4hHk?3%%wm+CTKBI2G@~rS%LNbMa(g
z35gR_!VMLR+)WU>OjEm>Hq2$awQq_%%*H`IO_}ldFX{w{ESmqu?k=A#%$mep?(~v7
zU^V%To<`ewu`Kt832bLFSL>v#Ou({%FCyYZ$0oYtHoixk5*DeQ4a*MUE?XinLbq34
z>BL{!r1FdROK>$&0SydU3vwkxK&5&y0W(!V_3*`^!7-%>xc+O*k^^8}Bxq%heNk7;
z60><&hhmWr^ZpFlZ5tfgEi+XU!nnSa?X-+d+j;fK2^l8eb;2~B=zyB*R5-t+;kA}n
z4U)<b%TW4aNx12@-Ts2}4ZpyWYfATENko22B~$YgkzvPm90(8;EWW?M*Y4j7e@8uQ
z5RjHCB-iK!b?MYKcTf|G(#lF0PV#fcKv*=d#)%RBZ6_ut3DhL^O&Sb>TDVPvycdK@
z#RbSI4Vzs)1YFbB(!O~G@SCJswN{e3!JrH*wgKF@u}b$|%~wUC9`#dMmPPvL3?VC$
zBiU=|?@Yqf_wA}SC`-?MHloD|ue+#X4$2ruE9bpwk4&nDSUs|MVAe<{*>7hC*1y?Z
ztrGdul0sw+&qpfnz=<8J^j{*EB&&=>SeO#@(e&*vY3Y!UJ;0Xb*&TA(8;^&}d5a2#
z)LQ@bkfTWC`g7dE=|e#w>FwJx7?VYA10Wd2w{#mf7!JVDVsW(C0Dl(5Q)l+kN#r-a
z^vKar+g9`?mGzz74utnM;?m-vB5U~jf^fQTm^8VEHnT>5>;lrbE+_BWNO?F<)qT_V
z_huh|C2w2wTlK$M4B_Lw$$I9+qLb)~;>-S6j~c}~c}r04<FHQE3B!NZ=f5y`ZvDB5
ze#LP^RUx1yTS#SKA$cFAdHGFtg7r{_fB}6I_dAiO^XC3L@3h$U>~8TG{p$Uam;k6<
z_fLZ#$K1MZeQfOeRN4YgBlCTY%X6NG7r@^x8=dWk;#9GAO(5195`Q(ry%R7#faPD<
zQ1dQv{A!=G?MpK9tEi@!8PrF6F38E$%pADSBuov9I0ft23yh4~2Orokm@OE5GYkr#
zkeA7M#_UV3mRpLw(&+TFaGHZmdz14EBboIym&|@OVMTX`4VcPRMZRq%3vPWiPg*n&
z@)`kQV>YBJfH-*D%DtdqGbdkX+tSC{POQ<RjzV*y+sqw4&J)RgsbdPG=Y)Fn3S-i@
z82k0E+gE_&R&s3uxcCi<9CWQN1lGar%U7&@!Wrl|>RVm@@DU-KtBgaNt5*zi(@u3H
z_0{a<WF=8ZttYfP6xb(Qyufm|rQVXN<hk{=p2c1dHMtN{)ZMbl!uC*}{Ikp~5U*Q;
z;pjwCg{{#9w?bGQSo(O)ekG>ONGUHbr6_Iw16azG6azNx@{a|G__NMkJu~ZpKdF_2
zgPnE`Id!vxo{u;BKX`|nDyO<OO*DX#a9;bx`h4Z4;Xd=v`ivLTCG}AXS!V_mmDVnX
zh<I-*Bg1=;ZPh#L)gEPDMZTrnhNvBDR{x78a>B8bfEw0I9|}#bb?S^8h_+_9R&)V!
zE^$<*PB${qA0H<@s^rkzZA5pPjk&rF5K-j+Y~WoCtqhZG0O-yx$<AfqaL0}`rrG!6
z?EUs02aKl1L7r`ZPLXpcrb-ZkWi^QO=sSfzN&8cbeyjsWHObVa_x8#|PV^VSvdlm!
z1A6Cj4z=MW6mh+Gszl;si{rgWXv}F)z-of;4pAyOaXoT2e??+dWL0nrL%8ng`|S?)
zb*g14H~YL-H(}SN-`|=)J5#}}+Jzl!2h_f5-L-*0_->TUZ(weHjl+gKA>SV^QvXW*
zCBL+Or${i5BvJsgKl7F&`+6ed|H3^n{&NDu#~|>RKiB(L=gaQouwHI&vA&|uK(){K
zMcc5uCHhe5RW>k`+kD9!A3EQk;WW94voJ`ewFSSPaV)~|BGcBrx2>JJ7Y+xUx*4|8
ztES29drDca345sbwsuokQMKg{h`ZIH^EI*^v0raw<#^GWPdY0#`ZBv)P&P!Pe3qyk
ztOoc8Gzy(BF$*&~KZ!EnQLXpBP``z}uKO^>nq$9sSZufPlpIh<E_)kbE}#UXjX1%+
zrLAe1eyYV(y<UEJpIalhPC34vT7v&7jsIb+F+7>bE#u1>_KTS^=N=t=RIX<@KX&v`
zzrI7wt@8(A``aHUg1qx?!(!o8sDJU8=nrM0xq2C|*AiuFCXzAMOnuSmsCtuhDmrjn
zd{^%Qzu*f9k$wSVGzWb!alRBt-tFW6mLt}uN76o5&_YyVT_qY6)yaDXf<pYDwe<%~
z?`tkRD(btIec|cfMwQt_gRURmk2(B%X4<2(FX`!K1g`c}W&u6iq7;MzJulQL6FU@e
z|9z|{4nfz<4IcN{y`z;ufMc{gDMEbeFWg>xijH1$zlPyoaTT8wdNXI%NFxk`;P-m+
zh~qeU3AHW1)GNKBNBo|LERW^%nk`e}Z%-J}82UAnXt^}UhsFn~u&?jv+wLfjl9q>t
zl%obI3tNsy^Fn+-tH<mgYUfOpR{WJ)lzD);iBH}d?UN9Z`~(B-hnaFa5ECoBU`Ou_
z1ny6d@H3Eai?&M7Gnj1*5=NG$$tS-lF?6)tr=~w!r*CjW{q+2ZV7T%Mve4Lb5sb-^
zC>mrHcLE=|fl17PrPw3XiJD|Tk}W3v=3L8dJ)X1TDxoKN{{LA3G_mcoDRX(DR{+Bi
zW!U5x>zb{;Et{ll9%WfiHSaOoF%fRq=x-d6iQ@KgdU@vJ;Co#4BGn&j|KY_CAKpsn
ziPO9Z{)igTj(s!da2a)Hg9G8on6^*#AQ@c6ji~q=QWp~12Ds`zN22m~kIBQ7XQ}SJ
zU|h<mM>U}t$Jw=>R^h7<kMcRJiuBPZn&chc;}h`nzKT-R`<gx?i@reYgp<tqdV6`#
z+3pJ_)#tUzW0z$+`vE@D+ZK1<Tvez!B6>z7n_<T{H{25%`&k*cia3GP8G;d3J7F=;
zbM<j+0^Rjx^SN#@-<?c(+^cxI?C&Te$&e*$q#MByXKKqG{@xp48>6(QSJ#JZq|$7}
zPb`}bAx1HhXaY!@cCrDq(Xi3AcyU2|xDn?f`jSsTLa-KMy$Zk7Ft4)UzfNXHz@<`t
zslMdp|2bM=Z_NpSLs1^(FMQN}YapA&klZ%lSuY$~IGw-xKu%#m9xKzCSc7v<lSdK?
z1GC2f1;v>s1;?5A{pWHW0jp6-#}6PFRIFW*y=a;pTN_XoB>8X%V3WhZf+n|sIjw#g
zOxWoQrgH{StM@@2@G8&{key3{eui8Gni`C!#QHBCVnB|KS_WDwkfrG%^(K^)sl_5W
zI@;1+GT%A)-nVm3D@#t-m>c0>G(7D3c^)RM7t~u*LYsw+7RE9hTIa?bbGJTvZsnUN
z-<#tf2pht4*ghHi5-rfcZW8en7dJr{O?Fv!Wje~;%4Q1fF1d(gdf81r+`Z>~77F1+
zz~#i>WQmo%xPKClz9opis%I=+>p?ldVlY2V|07>E9sYDx2NS;Mte267jO?Bj2n?Vb
z-|$}lzJ62M-7_v3ITeDJQvkS3zDeF!M=E9bpRb?soS+k%6#I0W+(h-aJO*ptJ(Gt!
zJV|=4AnNQ1Dx3*QUw;P%5ue$e%XZ8ye4^4-8ZBalW*r8*0FY5|IV(9PfP=5WVMkWc
z=o}9{&7>2zR#u*;H>fWJDi|)Oq!M{_;1kD5uhJ-JL{Nu}6Nu$0o=G`6Eu66Y=M7b_
zKp_!Qo?mr1v3L}5brd>5cf_-@S{HL?8rZGAC04LF+QrAH;rI+mfopLte`6l9lTOx?
zul&Yk@F5;aM-yoj%;vbZw~N-)0OXk;_m&WyihV2Df9`^}uo|+Y@fW#qTU}xf1Ic39
z;;Iqx-0bI-)gK$XBCX?BlP5tB=a=7CJOxd6eJ4*kgx^#v$iV{c%RF|Maa8jjA};Zi
zv~z*e9=Eg3Z=wScwb*D3!Sx}pqsC|7KyPvFC2Kv5<cFUn<;w&09M6gN=lAN=9Ctr3
z&7Te7I?QzqsE44f`0+*X=*n!C*)9ClB&iu0I#6qoTw`ZnbGmE#H;ySIp6{<#n=9sq
zIbO$-?I$}LRN9q`>sddIVIobb535F9dVTtqhpy(lXhx>Za$F`Zb)v^GTaJmJQ{lOm
zTjFPl=2M&a<MSd;36Hv?d2fp>bBR9FG=o;_*TPr-^fVb=qW5u%mH$1YzO-a_GkN_%
z;#+Gz*Yg1J`Pv%^GC(~8hIE?4<+=o-I?vCLe|EgxIYW3dcmKpB!(I{9G@N=-x*s|2
zO;s~pP>-+d_$<G7pNH}O7L>WgswoY^QW9cF4U`5%e%jP%4pnYLBbMpzZl{tr#rWJ#
zqUyN*D|sW*_arl@JOH@d#M^hdbJUEZH}2%7q{(Rd-}TWXA&~NUkGMtLYdk0p5+K&t
z6r@G)I5CRxWJ@-5dm@|CFw%|z-)ND|OnI{K&`AU~xjcyU;habzu4DU{nB)w-gV~=0
zGnnVl83D5rd`hqr*>sZ*8$8gF0JoV67LI75@i~_1MjiE}!v@fxzLq7Oti3ep%#P(G
z<}?6gs+0?iGV6AU-nbWfHHm_LaSx|eC_#TKCAv@_>3yfeX=fs+ydx{!+e5ic=t$oJ
zyGU;qGjExP5#>Ed%ol=QQAJYP5zs`33ggcWoXuZXy1K<tT285DE(^)MA6s7iF!-l>
zL_Fn0Vj31mI^DfWWH)(#a~6gC?n(JoW2cdvuKF$U&zB_xWWNT;bXeC5y)>TSmF;!k
zbQZ3b6Es|(N(nB}n4LH;(`;g+CEw$v2|9hXtqKtgRb-B&pusdIK->l=cj9#_#@z<H
zNTdO@bz?<TY3cQpS3#MKOClm`<if7Za=}nM95|g?c4Jtu+Ii3OThYEKMP4*zU!FIe
zRrHgN2)ziAFY9a%U2@roe(HFW2BBegu@WK*F%k7}ULa%2*Q2u3fvfNvpq!fQ(uRhG
zNc_7`x#r{1_rKZtEx!V$Tei>_sqrLSzJBRxXh<cRkIQ)+#_)uj)~Wc<S6Fj4zk~Z*
zxQ;mw%=>j)x*X+t%GW9`^iIeg`7ae3^RB<>#tEgOf7(Lj;0U&x&wG|h!Mwc^sYXCh
z?i-w@V~2L}XH15O6RgvWznN6N$Uc#D<EX^GP<d_Q!MfI4505KJ7K=5RtUp&?Cm@@|
zve+6X_-IjXq>Kg%CxpQzu64fDQ3v;g63@P$!R~2O;RC_!r`yL(7>weE{&<G<=7FvK
zmufl(sLO5lm03p|9(XDue2L|(f6<owt)5Yex~lU(6TN-TxMgQ}gR}J3m~@ItC#Z4B
zNBD5AHAlSFI>l@&wzNb&roYy@otxlK5O+d9!dX%PPrsQ9v(%f8t78M#VvXA9W`U22
zVXvO$cKHZ8oHH%G=89=Hx#Knz+zej{GyM6j69C<}|F%ANwY<@03E+0%Pj<sw{}duL
z^d(LAJ&k^AsmK<m>&MCP#8|YNEpl06>qjMbqOWAAJRJqHF#H<xKB6ymrd(pKry`k@
zjNdFQ|BNoPR~h83x9djvAm|u8n$LWCj#>@vy5Xt96c$lV!Tjub?4V0ceox4poWtMR
z8e*{b&nxxVaWAh@KS9eNgDP>QJCwk}-9Wmb@?4Va(SQh#`y1R$yD#aRd|N5j_pJMO
z96ASog36o=&4y1dADsdv_nNHqnP~TETi;=D<Yg$~eBZ}}(4|K1#@e(@1oPK9o=~0}
zkA=DRt5Ft1(}Qb#PxWpsI^1uvPq6D37)9bwAbm5uw=YtwXft5!$Oe#}c1nnh5!Fy~
z9U78E&B$@{83fkIYuMpAyCz8%IW~KMOiD!__(x4l*<DvxTKyY3)9Zy@x82n?1;ryr
zoGL2E5TWQ2(Y^l+81M*!MxA6#Cb%i`d1(2Z2!BA?+P#ae4dgqHnqfCxj>}LBx5HKT
z#@bVp?P*t@ePTx?BOC&O%^zCac}JWkppYZ`jdGj{{lXX%d00MKmE=RyVK+|Kc|{cm
zqF)fk^>(pd8$rRi3!d12phQU$Sl0Nn0JL8E6j2DrW|QWy!$_iwES3PV#||B5+K*ZL
z8d+$>4tEATzYgk>c{*na_9>xPpR^4W1&1J=3<Jdbe!w17SH|GuseFQZ?EFNuK-*L(
z(>1o+0`Uwyq`)~)WU<A?I$d8WAJUaW?RSo6{Nn-K20vcVQ*iZsVVkZ24^PInXA$bv
zg-_Oxz>xMQ3DC@fVp{v@mgpoKh#Cox&4kU?bE^qZbh7o3%5Ji*lwWBZdf@Jdh_`|p
zGNR!tJB<4VZA~Nq;7sJO1dk-1*tf}O(yG*IU%GKRx8p7nADFy-OwT+3t0{w4CY;{x
zkp>~V-&FDQqx04PX;HqMSkS8==oE2L#O%;sjWl`!G5qE`ll5$4lhC3se=i;OV-~1Z
z$lv(GLd9QeM>`~;SJQpT5x}9xwjwu5GqO&DGxap(%CBkIn7F%6Fm45#6XFGt=m{Pn
zHZws{ncHXV6hUlGD$=4?@?N|*y5;J~5sfH6>!r6CCd&^O8#E_}+aVX|7k|J6#_hYV
zwfI%FBjKG~sEBw=LO@HT8>!?Hs#@pw<0s;m+ym-l6%D02wKL$XkTTK_@*XUebm^ER
z)x7pam;>IPwJ;T&snxo}U}4>w!lD?onYtkL;jWkm6U#HZm4>A`kG1z>OlP}R34)#I
zs4E3;U)Ve}O?)_PuCNz4@^SEZn^0_D_t5b6jnUVvm86?$(nmR)#itStlGusBiU<j<
zjM>v|vh|&NH9gXMj(>$2q8@3>vmbvmZ`5DKllx_efET!(CCA;j7?wBOUC&AlBs&E?
z%rQ?!Fz3U+b6;Z*K393)dbGp#c*tQaRm<cb+4Uffe}!n^@ZtAe(f3fpMgv;S%Njb>
z%#^DOF28J?G7Z5QzQE^wH;v~zwo~83s?L60Ze<XDs=sZUr(eHuE-aOdy0@6`a2bAk
zH^QIOwxNqta>d4E{l`&_a^iCGa(gJ%QvT(OBiHYHerNlom<1P{Vzdz@^NRn>r>qUV
zB5w5<OQQ|n+hDPBUUjwEnvxl&i?#kPS8Kg*I_!N+)?dM}9L~%1xL?xVOZ-mPurm>7
zqBYHKr*=9Xpi7X_rYx>`Iw@Vy&4P?Q>9NstcHI#lfIMJHQfe|^2R}neLASQ*<+IJd
zLp3r(MPWNlVQ^@Sdgm2kk*0t%oO*efZ?CZMayj&6b9yEs;_>D04vr?j&P?JBwxZ$A
zX4JhMuWHrKHEgvmavz@5zD&XrSXn@7``Y3=5g)lscU$Bs7x!3;m5l*28O^7p{a7z#
z+(2#d)0DwG+r^m=ehGN7VvgpS-|QB5r+h{pl~175qE9r+t9LWriQ*TMgTlAw!x3-3
zzuQ1GxgGWz&cn}pydhUJPJlCIE}HI1vjmnw)tj3$g2VgDRe0T|Kw;f!brUWN(_*V+
z{<615OL4VCg1fQq-lt^ZZ)zY^qJl==Eiiwu10gi_%`1Vm)w%uz!c<|MP#D0dMaIan
z{p)#3sQC+weQn=F-DS@A^`p(en(PeHvf~u~w&J*qlky9esdZMl7^F5;)2bC?$DIGW
zzWeS6{!UnrX`B8x*()*YCLXB`v8wr+?=ClX8mD#>?Au+~h*b{b2myF`45!ekpQNdK
z<~rl?#upypJ!|CRcD6d>*=q!D7J!z462q34Kcb4D%ahO-9>8o&U-d3SVzpO;T$JAv
zUuY?4+~VxnFO{#&rf!ka^QMHlsx7f^Zt)<$nAbKxsg;wZiZjG0*#G0VqaianK|8M0
z3A7ZqW*@o!?Te=(`33x#tKmClC<~VR3WrgSbZU&>eyxJ*1tMfAAHS6XNZWdZ^vH<k
z+3E}j8^6Wc25tkQ^;2-*WjbU<lPkh0+ml|T6P*;$`pj<D`UK4#AU)*W6Da+_#XdIi
z?ZD12*f8-_hv!>|2}7q*?SMufZtHZ?^;D8<A62OEBp!RCX;Q{i83}F_AhRiV;a`S2
zT1MR$g&ZNj5;x}v<-ujD6kG;UdqQKK_r(X=jJ%~zpZ71Q*l&jaTKk2R)>do#3iy5z
zjcD;Z-j8+&n!1E~OG$Q1!TY+9v^||s@Kf)6oL25IMizcwBGZh}a?(tAHJx0Eg0_h+
zrW(XXiTxz+?d_0nDm|AB<`Dj~)t>FU=ljb0T0g#X!uS6sH;hV9l>A{i<r>Lhx07k|
zDqG{1*|RIUJ4f;krXI>pfnGEImYcLlluoor>q<mg)RB<oA-&lOS7`<orW%Q|=;ag3
z2h>K@>(bH`{!Q#Pi06-}-PU?3EkUW|G|_d(o84O-Kc;)I{vYNKs|3O;_I63^a8K^o
z$B;oqKlyN3{~`=%MbMpv-A*QUVSxrTwN2~$OO66Rf{|nPZ?fVOw{?0bIypa!KJQMK
z`@EUJf7O^WWp~v_j<HNQ|M6GQ7Iz^r9>Sau+ZUfnWF@Ms=C%kH5Bb+!_<P+BgXy}P
zdLIpkUG&LC%e^;)H0vJHeq_;O-!w=0ybr+qIGE`1eA*&q`aX<MUjGUjGZ=941)1UZ
zpu0Oab|0z)<i$*WQH>4Z!Y96K%9RqqpLYAhCqI<Z$`6~qr1(%eXdQY@v@fCm7)+1S
zR_3dCY?Vc;UyoRbOWD)rIXm{69TF0p8@jvD;;nR<mf!wY?Li2q@mBxZmOv?j?|*nY
z%cv;dH(C!hbVzqd_aM@(g3{eJfHX*V4Im;d4bm-*bThQl-HmjYAbDPX|8vfoPb`K7
ztTpfR+_CqyG35kUSSP5$Uy`!W{`dh459FK+WMK+YGbQMN(Q+;?fAA2nJGmS6usA-o
zaCi1__i(?ap7V0M0G>^s>!lNa)DzY#rLKH{Q%ZV*bU9tIZzeuE<S7Fvvluv5$Vi{Q
zSMt(F1YT2(n5Yr156@Cv$8rw<Z&3(yzK4#YM*TiB4&&1I)s+TC?@}cCS8>iWRR*^A
zQW_t%@J;+%&mDO0ka1vq!05A!;tuhvtm$u?xfa?y=50abOKBQ_UZ1&}z4KFkb)4y$
z=VNJR70Clo7<7Zh8W;{lfYme;jfcr;`Cnkc6JCy;%{FJK3ZDD&Gld%VW=^o`hDegr
zMbBE5+U53f;Tqhy!@=!wW#)hkZt<?043z^W%qUjsJFhm0K_a!-{ncg&uR&c^<$%^f
zWg%J!%kK10_e~dkI}l~;86OS<nFO0?|K_J4^C^5~F_dDGeUv&mb{Dt|j1KcoVm=`i
z+ak3u1l6^x7de!792Q78D0-1MTE0>{2xagQV<7YD;F$+8m0xlMlTQT_f#L^Je!xL5
z)}#)f01qv=#e$HD*_F1v`e*TFEE9NQTT^aNceHi{Gq{x|;C_7boswQMy+{Erh_%Jx
zo2(Fuh1${ZsyC#_cEuwx<~s}WO;2mCYA*^JaUUYc{2LgVmIy8S1&$;+DdKaSyHHVA
zmlY^4ytMVoYjs6;KeKU?;+VNbj(093=rRn-Vm6CzN{CEP1{(osc9DJ#Gj@0309nD(
zA;pmwDLP<VqONY}m%9@-<y!l)HRNxLl;A@Mgei5j*w0}<r)$65!Ja{657QnOu0BO(
z|J^Vg4HI%pg(CX)3CaDX!(|Y^13ykTEcy}GsgoAHoAoc%B6o`z2|t77?9x_5>uKvo
z3(6{AiE=M*$p*sGvr~F8n(9Jp-M~H;bpsfHk0yO7t=bm_(S#qVWw<OMVj)3sM<m7`
zq~@RQ7ubKFS0&D3O#SxL|8X`qV}Vb7Mq?OIH2%o|n_hVfB5#0Gv|-TXEPea3N`sC@
z!3J6i^RYN3Bkeb89RK~}Vo4E(vG5tOK`?1``X^aqC}f(N`xCou-a3<6Lj#=NR<K7}
zOm)XbIRU~Zt}#`}u1RCioh1QR3|4ALtR2c0n|hD1KbKg<8KB+#Vc08iH2#j0_MMxu
z=66*Ol7w@ymZqZd!W^(Idt1J1=>joG!`)hEG0R53ab|Bv51ex1SYelp_$BoP&g@%E
zWb_N8<W(DrQR8E%>tjJZtd_x$<fwg*nTWBi<RP4*2g}FZeC;*DcIn+4vdx0I5t)bC
z0mhbNQt`Te=ms)UQOWhPdh5rZuBsjV)Xnn&#6z0DfHFGWi~fQ<L*Fe{I4}n%0D9-+
zZL4jPK}kHj_+vUR*D&MENU8(pFNvo1w05akowNj4r9qhAVR$rgApnqnBKK4fFG7cM
znD{kGUC2REn?5gAy=?4mfa!v~Kgz)tBy(x%H!1b3wrf~Bx}>#>uku}P+3WkWgpT&i
zd#@?s-+IEu#vSdE-B*6Yde@q(qwtn2Ph-T7D3$o2U%Nhpp4Y#UeGs7p#MRxIAQ)la
zt%V3oh_nBMx!=r1rw&(zI^|)mynIL*s*2LSQw1fD*sb|)nU#QeozPC%U_q63ttse^
znMTKe&lV71x9<0TzB)mXdauN9PL0m06j;O6rPBa0=x0C<ZMX-5K`;ry(BQg04Q2(=
z0o|dA4rx^H>?s?9NU~SwH;IAD$zMx;+3f+hkLFkWaIU_q2J#N|>RZ2=tHBhXNC!Lz
zh7^Zy#N`O(fvz*4mbVNR8nUwnO>b$beer5!UZ{r>Aie#HI@j|_JPeiF^>j_jw2J!Y
zowVskv7e=atTecnt!&H_mcs6)^<<_b?|RBi|JWN;zWTK$S;7Ul)Mzu)a{$VLMe(h9
zmG`p6D1p`GZeEO<ni~GIt0LE<wxJd;e~|t7>#{VW`3?_eLRcqf&ziQd$8kER=*BsQ
zziBYS&m7oXA{S3yqQQ==m}zkQAw4%g7;pbq={H8vu2%=-S4WG<DJYXkg)3)9Nc&!V
z{ctfa0O<4;JJ6YX4yJ&mRb9u*k0wW<-{j256G6f=NX*E<Fb4E+Vaxs#0Q4*@^=!8%
zjG*U>WmELUhhW1s{Ec<dTNU?Vx^=Q1j_XweoD1<46g$XEBRabS<uN8wv=2igRya0`
z*M)`)hzC^V2qff^i)OO|&P4XNK(j+<5yxN=#lTNZ&?29Bg6w`_C*L{mQozM;JEO4j
zu%CiZHj(vgIF93LVThHt>R_X?OD&G^+(;mWA8KZmPV{?#Z;iojgEf61ou<#03E1`3
zF}HT})gdWTF>`na>bD<2UfHdlJEgDWy9#M8y#7SABdzLeUlguYQ+S9;)zigxUM?mW
zn?JGGCzcCkde0zOqI17^BUZ>?SoKTg`@<0U-`lzGZ37d_1pDf9oCcQu=5!;^tVk?j
zCh&=@mkJk{R{r+J#1V@R(>vwm8|SgK$~Nh{exsa`b!GLKJ8yYX-@$dNU)d!3l*S%&
z<=jW4K5@it^-)^>)oI7Rbr^j9*Zihy8NK)G$CGM?o3|R?e~7~(yel4kfw#H@{e5(1
z(skOXU>xgkurESvkWq^&lG8i?Ks?Yw;519XNQ<<4NUsCrdzY_*agvXaiXOk}wlKVy
z#?c5DI@j;Rm7OJJB{JiTX#UYqJOeu-t1WR~cw74G!>g~UIK}%91;qn?1y(Zz2YaVO
z5w5NyjG`+1vW-?_?Ghv7OLU97moxGj1wO3%H}+zt0ySKVen%^+_yIdH%wchMvh(hd
zI>Gfyf=vH34s;jX#mn8DiE;AOqA=#;Vwxx|=LM!G-N#!ub6KPBw%+UiI0&2l7+KsN
zT4~VohFzeX7BE;JbiDE)&RI%~XPIe~o81##zE;z=(J1=K83jz`e_dYz0OBpm<K+EP
z`@k|09v+_a^1_kDW6msygtiThnJB*+LCaX;2_#ZzEanYf-<cuIp$Eu+9rU1XN3Aq!
zlLItQ@uwkd+Xb!DK~@JrN42*5JZuJx+-_+aS#x4dmz~@0^e--BW?(at&9>RV0JkSH
zbuY7WM8%f2#TsN*kBEZt>n@R`RPJi{<j-MY%#e}soJFczjOPoKtzWTk?J`<DqX0aS
z^s`Oes8w#LU3q8<fY66{Z~BBAp1!cl#ha7k(drc-Zbvo2yaTpOtm5KE6PAC?HhM2E
zfh;-vn)QgIBfvz4ul*!A3%jl8)w~doK3jUGU;qT3m~I93o4)=f;+7A@Y2QlFZpp#%
z;hdN*dR1P_jZSa5y0;&J?m>?7FYIj<jfoMKHRLoQWmt8AtNr0Dv=K|G0Dv%sw;65a
zH0U8czjO_e0Q&$jmXuayQAO5_B(1F^(lz40<K1d=S!^hDu!7M3%fn?E1DJ=jEE{Vs
z6dD((P5PqJm(Y=r7Zr~e2Q7s=;PL*|7=^g6AP;8BV6G``_a}ODC=#67=#&&s7zf{w
z32%)=!!U*H!#)ZuT9{-_di1h4*aSD;1#-+Hd*B{&Ien3H-t(Z9D@nIcI@$;HH`2h~
zKUp0@2zu>wwjs@9iZF-L>7|JaYNZ7=_yWV4p3)m+!EO#s#6R5^Jr&f*t>z>8FZXC-
z$l+pki}mG8B<_`TGVd$^$%Eh^a&2?R93l(4>JBIy*^?~Wg47jcXYqx^Yg^1BJsebG
zNWx{1qA~+e&A5jz6r`QprWDE$ri)GoOg1$8)Cge82!D`R5GB=o*(IU7f2RA+mva|>
z7&zV_;1LI%prW;Njx}`7p?7wHMAZ*hgytI%&ecY<kpG^cX6yY4*#Y}8PrQ+cI*A3c
ze_ym%$|V)W=r%^;W`I0@v9}-XuPJEDoqQomk0*2(`?m8%WjLFkkP`o$gE;RHR~Ejs
z{KSQWs5~EPVm^-XOzxY+^D$$;Su|Rl?8blG4mq0ie^nl<n~EoQg$d(k&oeP_qi8Lp
z@EkEES;kN+Aq}O@aj`4}^e3$n>0Aa4q{o$+Z89j%B!t~p0B84|gL|y6pw+#zIeME(
zkBQz{pZEgb7AYT@UaD|p$y-Lf?R8ybTT=n!@+1PmZQG%bGJKg_vW5Yz!S0V<2{V2*
zF-h3VwowVb1OfE1Gk&gL-*g2RuU`<wqn>t?(T`*+ZsMr@;S~D1oV^05^(Xk5V_lbX
z)4#Tkkldu}1jI&H3MuChIjFR|uWPibpu4!Xos@&i?74Yn&av?*ZFnNq=BaK6R0c?r
zjMWff_@f*PM41glG)K9H1WL=uhsE_}=Uc9uN(>X}ooUE+<VozGk5hHBx*~Mpb!;gM
zeMWVNUKfJmSZv6up8oQH9JE(osTs$cR}JGDiyd+`(Rpybl=MmM^W0Xs@q!2w{4`qS
zgd~z?{5`89Wq3NN!x3${8<rBr&#Q=(#@=gwCQ6>4m`*dZ11OIsvwPJl!Sh>Ty8cUF
zdPU3tkrY1a38&?raC;HN`er?pM$X-{!vs-3D%p&yPmPOB^2Nd6SX4+;v5|`9zQ)~)
z{l9L!7_38yHT4u%Mk?kr`p<>K<s|5q;cBSRxI&n@Jn*mxp;i5?f?Kbag$Iea4Tz6Z
zTCJH^=1pzsyBX;+HRpi#`6AlcS5c*ldZ`^cfV2=OH33M3u1=rAsVQ^s6+lIm-}(u|
zb3UBo(LD^UKj-F#_HLkquBz~~0@%aC@6R_6?`F8si(Fm;e%QFP&w9K5z`8U-k*XJF
zNbO>v^Df4(mdCf-%!wXUG*rRtZuD820u0Apef)sQ&jV6Vb<^JfGU*f#FLjpkq-`aE
z7j26b2$>kPW;;$kM*d3>`?H0?jIbr}uM{J=1Aa1@Bp`S!UL&vr;(2907IF&g-eNm|
z@gW8&#tt0o0<b1ycH^WtKy<8ET7~ND=G9W{9Fu{tf=&0TNY|8lBk=35C${s5ZH$tm
z3cXxdRyUr3H@SC`mB!ggF@^(jEnT#CK(MxAgF6bGsztPJ5ciq_Tm<M*3E<^Asu+2M
zS~$V$d%t4od$<^_fgo0may>qTs50Fe878DL_XNQiq@gI6?IAh^w(3$&bg=sm0LY1H
ztFPsglt6voh60LPwnjUk0x?5cL7>pP*IwrzO_YRs*j<Qzp!J!e!4u@F1|~A{XL)gH
z>2wTe_Y#X*ybo-3-y?zK;dtooFVW!cB>|=$%RR?-)&NqV-T{Sl!HGID7gE@Qf2j_M
zG#xzFWF(DD4@bde9!vW9ovie<Q!exbuPpmLWtHI@wS`p#mgNF@5);L}xu1D3qjBZ8
zpgve9P29w#Sg-VxdWC_|@9yQ8-R#9Y!q^?G#9Z=^J)^m!;!lwlkM{y7S7^EQdE}EV
zt4j)W%Iiy0_eYDYQ&1JbV9`Q}>hDUNO-wMlsjZ$a_H*rG4XToJNu%b|89w1|4&&|>
zFMWIjJOUY8UKTJ3+^%luCd!n%cY<5uK~C(8Yu>!<>Uipew$*NYrP{~L54UR%xIJU#
zu7&ji@|Fuoa4;<)J06NQ{-(kjh4_=lDkovjTF+9$yRnA={v5K0nTf>BaaVuC+9^~g
z4f4a=WZ{_hjF%BR+3^%y6Y0$Lafmgtg3J<SI2b<>vrM`40HlVMIUB%X6VG@hIFF)y
zlr0>SnCDJ^Fc#3Ew~(S7=7}+MQ)crJF8?D+LO7Ln&D&`H#e3V1$C%~^jjscjL+_K5
zHHzYQIOB~n<7Q_S$=B*TE98qk+k#x(`^}qOe|7c2@EVfZqXw>76_Q85>J7iK?PHTV
z{$`mQ*Lf`xP2wz28h13Zeo3-lkVhp>&kcQSk67^43Vyu1V>o)c<6tz}mYjJF-`<hk
z;9?$+HMa~=t^Uw$;Oc%gT<slxaNyP}=oK62Bfl~)g;Zakx0ux&^=Q=IJDYFtMH}W6
z8HV8BZJNZUS*r3|v|w+}{LZ&Mz~?p9vajw0qB_kA%~oTFTfae!gJe}ZtPLe%>{}MI
zBTgT=XADdTH<CL#NF47($Rp^HIC7phAWM4q#8yr=xtqmQ(I4!bnS=kHO)a`QXR28d
zmF>)0^VWRqY4@fs@BGs{U4#_vRsUak0o>PKP3)oVg?!n3Yd{~629D6Jv%<E*&Tj)(
z0sKo6&jk1lGBpP6zOKR@{x0Q&9unw1D}TVBkP|l0?V2oN#37(vVS=(Qxo|+#gHR~(
zIMOv=0gn8Z`W$;xIbQG5*Pt}+1rlPzO~Ys}7LMaUI5mooFe%M=#hR|^bE*`VV{a+Q
zj{Z5SW0d*yGg6^_Pbd{StvEzHcbJZ9?gZ%D+^jp;9NMOVLk2izaLD0^%$(0v4A~Ea
zM^YztgV#FoS6MTGpOE}*kVHFeAuPYRZx4aQ)6Vz_qasZp>Ms~|1YAk|Y7YB*=b<sU
zMwkri^{csz^zm#dq+ngAd}T=;(Ys&WhBR;jW-!tN0~T)A@)x&crEDX!)0j>WQZQL4
z7WJ6<yf--akxb+J`YnHhHMovc)SAV71EIYWL>i2Z0_NrXEd<UY9cpE`8!E8|x-<!J
z%h0R2ko-=lXs~w5fY#o3Y%DMZE`#U@9k+}BwjU95ivyYG$6ANg70HfnF2TsYC<=<9
zLi5)10d_Ej?jN$=hqdVh{<iOKNo1}AlB~f}pOpq3Dk4^9R}0(g5y1OoSl(5(`a|ce
zCy&75jJ)-B8Tv&wSoa&2JB29;j@Q5Q`!!wZtgy!z-LLr$@4nlmCtIfIhQ}40*!pRB
zx(C>w4*2H13Bi!j&`9Wn8X~!AKK#T;j^k`mjXxKT!1POTGhNYMry3p3Y9I6DX^!~G
z=hXHBpV$;Kr+;~>W?;EFe{nR=TCpWk@~*U1<_dukE4?G&lV9F3ra1#;O~CwRA6HaV
zmox@N7WTuEr_Cp2^oY|saAI{q-at;h)H=g@rqH;|#P6&I*iWl#(am+DsHk-(zbJ>o
zs8{>5_R!M}e0^~?uFEh79Sa(MQoBzcZDUkIQFODya=I;}O~D6qzgZ?&k_}1oyUWb_
zV8P}2SZxBY`MScF?!OF^5!n!a`Zl@Svc<I-=EoTon#A|2D`K^^Vqm?IXxR|D91=Tj
zd12TWkE%x&OVk~=bwoNk&9gy2y!I=tS<B=tCdy!4f4x9W{-~|h6kmXyD(atyA*aCy
z6UXGq%hJExT<c&?pD`<~(R1wMnEM)i@}s_KJTYR2*nXwaI{6u1qv(T|v)&w5&?SL8
zoOy0{?R-PQnA_@=4!O2)Ip3wlzLO;zWS%g}jqOtEPgugsGrM&qVK36zy?l6!XjEUl
zn~>BII?1(9*}Q3{A$;*sBVc0U<h5r_Yo~%woMqZc=|3%pinK~Ek0aj(p8fvOs6rY|
zh*89WiD+ACe4u~Hznw&EFo4GzDziDw?+Cmm3cdhzt03WElDyw;tt6h+BPyGEnq2+u
zWIOZrX`Ps}28E>?-V60DMNwzk_sTLgv5ApgCeo23oGql-MkP>Dzp|*Gg6j@sma7ke
zTX!9wj=W!K?ao$a1zs1*`B-jPH1G=t7-)Fym7g4G9vl{qq>IU?N=dY?DcM0&>+Vyf
z`kNyKvf>X5G5dGI18@5>#eA^9r(^)mF}agk56ei!+GodH;Nj!dt?<fInbGRh46fNN
z@TUEp&i*r`Z5vOMANPqYs$GQ=zJnJ*RKX;uVTutX+rQF@e3arK3I=rUFO5`eAcD$v
z(8kaz?mXlD<(7s{xcFibKN85^y6CUb!Oi@2+mDmB_UqJgY!L6a`GK;*(yW@HF|ZvC
zDYg6SP3l!e;7r*oMko_SLW-0?W)H0j!nQW$8~cJyVZemR<^(~R`k@*uNsq(Nb|2VI
zUE$|`vZCmihMI&>mJ~dwgr?OUSVDa)mw~>t5}1Lt=NPcb@u5&GG*RDi5V?@A3@4=O
z#mm8IUOWf91sA~5Twp~uUg3D+RXzI2eED*35<LKK>ro4*Ou`X2&=U2~3n*aptRZrp
zR}2Q;JiioD%GO3+AQIy>de|*D*FpXY%}k!neOIiXt2&c2pn*>_E+8U5P#RE4UWd!Z
zbjBiUuRsL@*_CPpeY9$7l!lwf@8;E!pq<?{!H-c=uC;*C!xY}$h;8ysoTo~PFgUT_
z^CXZAfy*Ir?VpLtt4>Wc()Y{gI;Zf%P|KU*qD*{&b?A9W1U_1(p|1<f1b>y{!^yo@
z#GsbPo^Xr%?WMI-cMGsY+6(A3LI!J0p<@NH5?#_^{h=+}mGMekOAA8?nrJ9>nG-%R
zcEo8$ZehDzE!1Z>g_1sK*pTMU#c>>?!duOWs@6Ld50L_vSuOda#UwGa=4JoUV~p7o
zIpq2ITo-3|LD7;IxP9+M$NS%Lc9gs^Kju)dmnc=#j$X+QerkbY9@y+mBt}eCJL?Y^
zbD}2BX?){nQp~R#>S=Y~{QhxO1RByS7Lq2{=qIpR*)Fe-k++g{#Bpl@!@ts<h!e-o
zGEa6}DrUHC+I&@EOu(ihF0jm-I$wNy(ZGI?ebvwR!?__Yp{L?i|8mm@(!s-*B0?-W
zvt3C@7UIEeUzI+!rDsZv$Lc<ikKgFYXY`zIB&0d$(syQ;4iS=4lgXJ#2eUp#ChCqw
zd~8Dx5|)H3v21MAkRR)F&i+fA>X5F=PgC^Ytk9ja?{?Ae6BQZ^!8hoAY_=7RT6n@|
zRN;6S_l~l9p7dV+3flg?P^+)G{&-ft!21u2leH$e<cdR4(Wv;1w$i)FzvIliow|yw
z?*kogoAyLsaY#F*8hm(%*Qxo*(F!l*4{mOcJQsQBAfc%7FHPak!oHfx(G{ZaZCAu3
z&07e+eUR0<Zpu{upo?}{$7}H*?Q>q4Td}#XuAyMKaCPscr|N*g=hO1N@yp8fqL5ee
z+JkyD+4X6AGa&Q$tcrB<q6Q`#jL<g`niKO;vN<x=OHH}Gtux6Fn1Q+wqGJ|1e62Zo
z-va-HL+Yz2Vm343OdoWb?tZuw)~EO{FthoDY(daMyw3N65PSX+Pt%u&S#!xKq1}I*
z-Da{Go71?-ppVpXUbD#AkUZ-K?1wKpsi;)k@!6w>xI8ey22_>|P3||gAoNXB%n;_|
zB{odfwWVF-gPq~tf+Kn9pauYi=DMftK7m!e=MF2_46&WM8OAyZtKo4PP#d8Bxn??d
zrqAy)CF)j(ytx99IvB!4FNSzG%t8GT#GG+1t-C6sJTOIEcQ43}lxI#uB%M-*ur!c1
z4NIpHa?bZme-3S&ON0dUVTSZ1a&Ze1C5-`HldSJU9#COr^L?Xw4QmqcJWPFU9k*JV
zM(z`_ikU|+MUJL|ZM9|uTOl<pD=S54EDT<MSr?RS0Nl+C!G2guYtFwo93%RNgx=7N
zaA<C96MS+?wO3vO`vGuW*ZUhMCmQ4hM^MaK7fFQ(-3Jo5q-Bt;qt`VM2pdr<Mrm1x
z;0^rspN*)31vER#wBB1@^V5$a{6a{M%&zY5(UaMu;Bs2uN09MD10UZ6r{RifL&`uf
zOvzAuQuI?%&>YC4#W5$s61l>KF6F~#3Qk4{A)K@dAT_47$hmQ{f6{#V>=W)2VTmCu
znT7TZ7sd$$iV&UBGpXe^1_W1(6KC6kd{T4=k26`sI;R7@Wu}`VZA91tfRYYP0~>S*
zv&35aXu*gNqkvmEa+A`~aIfh!CW2KkzsS{OXLY~BlngjVUPJPgfa77WNl5xckFvnd
zcGVHwWm0&|`aVd6xwNCfNr428Dt3Z>)NDRt7Wj`ed?}h-c;xntBR7`hD!}$N5kGPc
z0CfMaU3XQ+`4CZ>^jbPWS+5!rb20;_^GmS~h1#yFVSn+86lZ5PCPK6~H&u|KgkN_P
z5dtE>x{<&AW*hQRAFZU!C`NPAnK8$IZuZ}l5dAUhZJ2`@$04ff7|G`hJrsJ;c)_iZ
zIz<LmK`aBhEa@pcN+8!pYL96l{Js4OL2`X39-dNKG0`0^^4gAI29f}J)eQwNXn}UN
z*t`9P=DMDLc~K~Wc(JQ$F)4#u$msw`N!d{eEFFT1DH(qFQ!)<CAU(Pm$QEy$3-ji-
zmyysgI^=PJmA!F6y||9-&n6tYy^z_c&^btE&+NkZ>`5RU>Xa9_Bu#%K_0oGSAgb6g
zvGq0oUm|jsyVj3f-{VOe_iA+V${yBe*DJ4fZKl=-n_Vpy&BI%|Mn9mtUkn}buc!MU
zZ$}}azs{LAC9f*rT)w-b%9pGbPwrRz%wU^_i(Vu89%C+rH=r?1r??~k_WPQOe7l72
zrC9mH4sG48iY~tLSJ~Pt6Oc{!6;V4)DyNa;Zbjd;V@$*~<}EkJk3iu13x%`?+j?@K
zspbyA%r4rSkB^A`$`L1J{-Ec{PBM?>e?uqie6rDGm$Jn&LcYAkXs$DKYW8{Kx4+U(
zWf;@01mr2?;5Q`Xk0DCce%yKKy|>o(ZmVsW&wTxKWh$5Nn;JH}j<@f(BTuM9+tABh
zTu5P0BQ>Vx(1WB!aWGfWjAXVAh@YZ0izm7#*O}wA?+s+G^EtYy-numuC{wg`KZf*v
zEcV&~mr%Pl)q;J;L3Wv-Zd__nopxQnu(ajwjg08t*eX=%pAViuP=l#RpVwU7>2S=o
zXdf;Bi)G}%>-Qz~dGXCfr#AQu@eq2*`kajWZm5;nI@_TOQT@`oN+6GBGWY+V5#odw
znY--I+wBfb-K+T&f$8V(0Ua87<BpNUZOIJGm=E-YvuyX-vY?((F%J*)LkXAa@_b>s
zhHAFIJ)C9@xcHFQR<Z&fS&{I{oRRfuQd|jZsI}giCgL-VjHV0Ge<60Ejz;PGC<v2<
z;=lDj?h@`B=I94OHn>Y?xbaSqTE_6Qkuwt`2_dd)|5UH3hZI2y&^Sy57+_H+nqh+(
z(j8W4!dN|{bLXHSEwSDzWcna_E9GfyFfvS1Lww)YV!2BPGw;2fU6~zEY;f3%OFj@n
zryyoG#@u&WNdB7W+|PkzZsn&}EzBdS2;D37A!T!@<}>cugH&_m(1r=b1>S2B=!SVi
zRZm64=T2uh4B<Ch;3<g2lm12q-hh1mCJW8USB9rJ?+*~wNR-FY@+wES=1_M*gH&6e
zLlR0dd9D})*^NPX^w|hwMqcZzI5w1a^QNYi;T?qRNBJvcm<b#MzvN&PaBAY4z(3d}
z(PCIc;vs1ef`2tfeZ?wT_VF0!T@+sW7-0r^(Y4-!eL{0t2@$6^M03T+tJMWF`pVQ1
z*s5*}_*m)eR%(<1eUkG+S2}5WKR!3v52eK3^l#RyXDg{(1)kTysO7cgn2gEIo0syd
z%Tg2)A{##5No@b9M`a@)7xh$*^_|B^7%efsu?sf5-H5#St-FMnApf-<L=YdsQ;_Dm
zoE@EKtGeYQpE^?Lf8DV<GO_d~?Z@(t+mF7gI6}W1^zx<FmsjYPlZ3)i?HR&ROmdNR
zpeCj%eI<p4_xmg4P69OFXzMep<mU-(Z8~988jWhy`)!ROqt>6d{`dzo#_F<8zE$FC
zmkU_wh$G{mn7nfNS?gJaLYc1RrGHCC-MwA)QU`k~Ij~abEzA24O6`niUF4<bsA$U>
zYs<kWavT_dTPefWHdg{*wq{x1-<%=7@gR{XnC)`bzx{ecktB`?^1NJzvJYzt1|4Tj
zd}gXMP>5Zc6}?yHC7<=A%P^)<Z25bbkLz-kv(AG%na@^g?P@0{zh)@fDgulo@@0@f
z!$G>@ob4~v)VVyI=G`(Q#ee`y&Q-U4p8-QkKSf|72^Yc6R)!;L&AB}D_xZB{Gnir@
zmyQaKh+v<FCynVVLEbB>6>G`R3G!1#Bv7YWj(}ax)7|RRAD{6CLNyX{%<;CmhC;5Q
z+A!?JS9Wrq$!2tcrT!=p$~@O7K77R4U4^)gJj4@kPf{@TTRneryp!}!*$Lvrys)sL
zRt(_I9~PS`wgJW`$c5ms9TNm_p91aTY3N!D?rI=Za##Vg<6hMx<I4=Jl6(v<X3SUE
zZHt|+hO{RYh?5)(!ZUe;ghWsS?WG^-{)Q;**hq<}gNr0nWypG+zb!cO7r_8A0)e5F
z!l2swA$@r@QV=x{0gE`WHcoNb2Wti)9nJ%Oz;naC19>9$gigmn=aH;%W#mC)t;I1y
zocYPRC~K(zK~^0|PqYRm*wwEr0kiz&W{iZ|)8m6ZNE+hvM&*Q2K56bR&+qS=u5^hL
z5_(7oWe68FU<m0gcbTtC-R&^tihA8?6zmZS@NqJO2HZ&H&vxXgw?Ch8B31FFJdAa~
z{rIu4pnVsbkUiwnEX1BbB0^eQQqrLfMcs@LFEKmOL@ah$WA#tXolgm*3GcWw_T33;
zLou~l$AAdHw}3?_zBz}3g&;+~Q{@i8Y7{ek)UQ-E47>i*nRMy?JM;3-xW((2vm8#n
zLVpyPO@2_Qo5UKXM+*Kpm3%>Ah5N7Jwu&i`)nNyhnKxH3t_bmym`7P&|0}}cq?k4F
z={#gFtk_&ocm~$751*MzxJC-c#Gk+NyF=jzuD;i@x6SvGau9`ItnD$5>USG%+{a$k
z2~<k?Rf-04q;KOFxpIsyzN_}j@>&1Q)2G7!`$R{|Lg#3bL9NEP%f&{u5Un*PxleG*
zW|-&aI*J_Zt?VC~Y0Hy9%#m^8#sir_vBLf2@@%Cn0Z2=idArGHhFQeUgBeZ5cPCNS
zJ6Z4TIet{O-?Aa3;~-t#jdm!<_BxN{Md*#CG+(fg)QE}KS0`T1pP+~-oP;VCP~Ja=
zMe)?N)w29hQn!WUl7rB20#X&B*Ij~Cn1i*?L%bVpq^f|x9E>2&KILauEd|*~rxtio
zrNjTh@rSf6?V>H{<Ryo1AAj4be3eg%EbXh!VNZY&(Yqbo#Pz=AtC=4};>*7(0%j$&
zJAP3$%}S$;gk(mJTGfd0pS@j*b)J(voPK5hyR@eHAV6lhF5VL`lmAy<vNM{+oWC=q
zb=;uf`sTln5g+q$@mJvbl6`yIJOiWQ1JTo^oKEOPQwo`yl{x#q=n~^v@-nT;)xzp5
zSON}oPWD8|d^^DAZIJGfV{Gi?ZFz8|kTO<K$A&LsYW+6vo5rvTw#oxFo77Q&&;!}_
zb=4Mu$g5`y&gb#xkg{3;0dA5f?0K3EdtX&DH@-#BG>fp(dl77jm+Ol6^$J`=m;zqs
zCnd;)7=f0Mg7N1rR7ei<xS+?daioW?kqi;@0zM`e5LX}_My_j7{kBW=o#4##J^W3a
z95(n87EkBc8Sa3BW)K4BLu)4hQC={F+Y_vV3XvjcQZza_FnF##w*6ePXc5Hj;Xg2f
zOZ5N_0I=ZsfK?B|Yp|Z|o7wqsuwwVKOE2&*9yNQ@dZEZPVigY38HTzlIeCCUhM-$e
zSEQpiUwazsUfPgRVSLw{ukD0>CEl~_f5lt`57`a<b?%i*R7w!9geJaaimV2Tj|%jQ
z|ELoM2}RS01?n}}(L&?2R{JKpiaZv&zMsS&<%edBMhqA3D7)qNUsnyT$VD8zA+b(~
zxaF{B-Kl@_{=9uz4~x<B(@zxk(~o)j>Tdy>4wG(I{$aeDEJ={YoxAc>pWjk6RnXDw
zP&>FdDP@IoU`pZu(`?i<`kW@_NPk4>5w!MYHXb&7^lvg_Zo06JO6MfD#&aN}7|}x*
zGjDX>7QU23(cwd}EWW1I0>T}(8L)E)M-4nNlqLq+jC@M1ad9uc@&87+dEexb6lX&b
zeO!?da&={38i|rl@}0hC_D3;}@o{F*l=&gm<zPx!?O~l?oY6^Oj7p0EpVdvC@kFSY
zL`~pd$Vi9?yRXCB(-nld1tOT+bINz4lNoyH?fbgK_{aNoC+z&`2m4~>OMO=$ck~Zw
zd>EUu+X<ZE=D@IYckH<P((fNhiYnv_{Kk9lo{@sS{oM?3BI+UePlN1qJ!!0-*IuU`
zU_vWgcXLS9<_VB+QR{c-?sdF&OpPgEqP@S^P`Nxe_Ea#=YJ-j34n7r^JMoAkC+Jj<
z#Gezb5`^%EH&jsaXu&e5*ZjVLVU36iK2Y?Ka+KBms)iJMsZ>>(=Ar*>>>tNKE11?i
z*C*JWVb9_uU=Th8Tp?{kzSpkvyoundi7Fu8uFE-j2Bja04E+uuGrqFZDs*i+od5a#
zYCQ1wC3nZY8j~noCRmDssY$=ihVjq`0Ey*r>emZ$GD(U63BZI*R7D8Ya9O^u!&sUK
zjyY1n;8bU5@g<fcsn*h`#d=PtCXRN6BO|)@tQ>R-j>@rnGaMfRkj?SB8J3&^v43+w
zvx7jVU|O`fT`1(2RiHis?W5}Z#%ZvM15gwr!U7keWs*HLxJc}vU(os>s5D<B1t<<k
zy-{95&wj+Kd-XygT(1^f3r2j8RC2<XjMgV>p8Z)K5A+Z88D#e278ONM#c9ZTP3&_O
zp2CPEd=20v=!J)e<9mGgDgp4=MBGsT>HPR}zR2o(kfaa$FB*`@AIxlsB$?v{rgpod
z$2oV)dxc^_@)s&f{VuF>X+MR;wgs{qFg%Kq-}X=R3*AnlngmSv^qsi=Zj>};#Ib<S
z3@6yGYK68_JW%o3EUO$x?aOERY<sa0>ZYvR-?*U<75an8$nI_DO9*XN)Y7;N+TUeu
zJ){}6E~IA8&Bm>uJ%p_cUa(NDdIZU>XuS#ASYB+FwhiabV(DLR@_Li@v@Wq9EwMz=
z2<dC-q>yd3EB+9D|2IO@SsM-b2;*=Gn;{+1DZ)_bBet`{4duVy{rcH3S|qRUXPFV+
zWap$zmN|QEay<7vV()iGIZL%C4!NNVSFf?aGM;U(Q_OjC4a6d2!8O+3x-G8jTpuAI
zdSHFh$ujQq5#+4IW36nwZhIWYX}_0a9<`fXSV6`v;Om|;7@M{~W&fca)Ik}MLKO`d
z*<@lw&%c6VRgG&;G`s(*wof$uxP5DG$A-2ExH0e4TY6pnN3k*7;gu*nn=5d!Bi5u;
zx{h+em^bu>OCI{+#~vG%4C4g)`s46m`@Mr7UdXcpnGAwMah7k&w!C^>_Hj=wbYh|2
z-r6F~jZfr?Hq}d7SSWakU(X!;>#@p2!sofUgfoG}jV~zX4jufFfJ;2OkSQv^AlV@a
zeRbCJgr(S^soFO*^z&yt7^0^@3d-sdM7XG^?8cIH*#Dm5IX5xn3KuT+YgFRlO~7cn
z^m_*($+m6&JX>ZnE7yNJtg*|Wx74ePK{gu2vNAexjo*{wZ{I%`ln&&if{xj9{rBgF
zI}K`kpKHO@nc%8MjEET%kuFnuyLEiR>^1;MAm3;2m}gsGX)eaOOK$yK|3TXOU%GIR
zb|R9_)g+2BL*x(JHCC#gOhsWo5&Q5!d!6b;Uy(oy!7Tcif7hE#PFC9XUIm2S|95c%
z1!OK5>M{G@Wy3>$>L){QHc;#`P5$eW96pFc_$S<pt(+ZM5_`@C;9oFvVMx;8;Jsk7
z;-N(lnFELcA%3{=KSBRGVd$_p`h(Q(*%Ge1c+%3+=vAl$^ty^w|M5~0B?ma%S^(1p
z68Y)2LQDyq5xMmLu>iz6bs!$c28b@ptzH-DD8r{NfC4M|cQ-KYLQkWF+e7#tfgKm3
z4HAfWG<9AD_FGB;U&Kd-Py~$)OiP(k_UesZ!za!yu*yyZcl$RVkPJQu3V(^VzYm1)
z;%9oH&0P-V?&s7ux&we8^bOJ(N@XDY(F*%xIv6;w=p&m!LV^S!{SvKmw&O_XkVcsg
z0ONkq5Tuh@26Ec%IHL+|e?f}@`a9MT{F7vFQv_+101veP1&i$E7v-v`m(&|;Ey%Dc
zK=CpGIOa2mes2l+$|K~XjzMl!eH*g5yG2k|#iH$sxCopZvsaXHijHvF(9K9XUB5}N
z36M$2Ug~!7c2!*3-1`wKHCcJiDM#)y4d_DBy~{s!H8X^Njm%Enk|+w*=C>fpAg)sS
zm%q3dT^+TTmHqS5up!n}_z%mGz^7^k3F>odMWy=$(;zN&Qv-aI(8Qc~>~Ln|RD<*r
z+VGB8T7|DAI%DR=;RU;s<`QFS-{0UOdlU&xy@yMJM%OaM0QE#yGujtpUtVuQ_BHfF
z^Kai;rKUU-c@ev(3W8^?o$t&Ky?KQ`SdU~6zdk7_zl1-LPrj`=F>Po+dwo_>Kp4O;
z1RK#LBLuO~EqG+VfT_!(C$&~kDbUjMdy&PSY~e-N!jUalkZ8HjN*K9$JttaEO3Fq?
zj2CH`i!PEc;GS^l7H`q-K{OKbe8yq9>?a1f%b~tL(dk=RM5F61_CWYIA|BYM3ijhB
zN!qw}*Sm92-)ePfl92ob!{|bY;`m3Mv>0AL21fD(#}@&g5QQRk|4?SCgQSC7dq9~6
z?jb5GGg(u)m`{dzZZuTRaU~~3@V}&ht_L*6tAGW2QWTMAFFh{o<96xr-xUS~lM}$~
z_G@P);fn&)M&(qYe-0%OrburApJ@K~{S&IykwoxDOX4feaM|}9;yjqd-=VSOr2e$U
zdmY1j!i)X9G(NqEaCrr}Y^%*Lu#B^|%8xg9-Mfb?7Xawp%lqR|hDMpY|Mw|>aYGTR
zK|eQqv9=m9zAVk}Of&k0xx3|nJP@c`VX{6KK_U`N<?$21{SN`;fzq%m+A5)S(g;a9
zPwp;rKfR{%WQFRRnA=R%MGhlF!?xFBFvR_JuXiFrhQ0U0-@&>TR&kzBd6~4+uwm-Y
z#5%sw?daz~s@GT$3MLxNbgoz?nld$KYdZD$nE=j$Ag~4v8JhXHIdJVOk)KYyOV7~A
zJt*P(=1%@WnVwF^dCeQdrNLtoD0>5B)2T51K^W-lA(AwPg1Yk$qp3tbehUhWG#%&?
zN9MMMc>{^NLPCH4$Y~>*=h9#;ffzt*y8z^E=p7?-%%>=%@>f>sR?47O;9~zOezS&D
zN#^@1m+QHGfB~OpfMQiPhT2UBs&cGB;ukKX?BhjN9mRq>WJ)$eB4PF_y|Ks>Yp9Vh
zM@{s;VZc#24h+%X0U?Zdw0)Dx5{f`jC1E#8Cc-854_-+D&>2F0&a;?^*47FHGOIfI
zj`9$*AG|%3N>9K7nqX~cLW}T6096E}_TrSrj63AuBHZAt@&Rewc~uAHx;0;(fnj>>
zzD=b52nm7NqwhsQhk&ZdqY39NPs;ro6O|t>Y@~z||M%_YVY-|P5bx4u6z;2nQ`H$o
z%F{);yyKTZbfs<n9#JPO+6Af`LPugkYq4Bx*0lEL{WKDs??Xd&=RL(cu{TEfR?Y8D
z`XyK;o1hk#Y(@<rqEdAjhQ=@HGrHO0o+A{LB?Q=e-{sBPk}(a0n~WbVfw{I+wT9hS
zjC@S$?F&+ut{EQ1f2cyNKeWBjD3N;ckuj(h3ruA{*R<l?b?dWxN_T7XCs{42qe2<@
zDIYUH8iDwklHZP0YRBDFkwlWIhE!BppkPOC!0>g@ajI6b_mIs8luuC%jcdqO+~1{~
ze)f}IevIOF@{&0I7-w)-H;92SMmIu_ndZG^p|us@<-x(}?MJaZlOwqN=&U<l+Ppo?
zZ<&LJzKu~E8Zl7t$+VS7#i|@N1vkrc10CUui3B4);V0Rl==CA#H|w!E=@C+1gWI{k
zmb?r;5BL{NUliIzJG_RqjX693mX-b2(tm0oZ!&cP0xOQg^CHsB_U^rBv&CSGv;&6a
zhIe%a=l=vO=`eo`>dVLk|NOG^*C5gN{FPbu*MCCiO~^B7B~5z${%mBhn~~$@m!T-4
zfwGzm#`QDR!M%7v{MdWrd8mnHIzfdi9bYWU?=s|~!wdG(p0f8fIm$+&uSuQ81cLXb
zIiS<<J4^x>s2|_;>b7I?Jhx^EVa@Jv;kPcSsfPv`SBpFI?{}>sP1I)cAWUPgEh3Zj
z<rW|AM5>gC<4nEeaIHc1d43wBj0&n~<}JV+9_8zLQnskB&OoL8TndDM;f;B>2qaRM
z)_{BuLBa||OBQ(zwe1zeKGZzL2=3Jbw8{w=&_9!LudtuZjEG!8<xfBRspH=zv}gFy
z7jTeJv5Q!NFsa8as)LV6XibMamYoMVH~xV?Ijefyo8-Jal&!ibnkh`6T8OI1dsZXQ
z%!0N#5lsD$>Sn9`&E2r;z=#UK444Ee@Q#G}zy<&i*hs7EO{-y#Axw3G{;c?%5Y>x8
z#-M7HJG72I^HnBq5&SwG!vZnE%+RzJ-+$HRo{e8L-#3D*aF<5Sr90QKv25@lNUUNO
zz0DRMGqSa7ivcINRz1iLhQe4d^kGkx1f)gGsmEZC$d&7Hv>-iMq?{b)1=5G0TUk6x
zK7dREZSfRvkOU88@%m{t=03Un`9+5%yEBqOFh&RR_G||vwhrS$pw~KESlf#IjgKrb
zN^hT^_o~-0k7dBqPVYx>%hI|!{<KyJc-EhD+b~&h3D^N3EIs51p=!0>ULRo=;XcM6
z?`%zu1jI<{IUd6TMj;hB6YpifZWns?SF0l4{wr25ngGzSD7#p+G$0?mM*N;Ph?ahI
zBXo*YDBk#gcmTb!#<eBO(`R%7yLl-GP*vxHDL_Ipe?~VfxIQOd6diw#h3)IB#+<Dx
zXHPp&7m`~1(=!{&n^&$Z8uw#Tn_ZK7-SlKUCG>~vH}CBmN<}|MqT$^JuKCp|f2@+j
zh7^mx`pWS>BRO3@wqqd@&JzxX$>UGdhHvBVh7$zMZWWUtt77~kT-YC>lQrmvE{_93
zdVB$lj6Aq&<wxO@>dRoY2A`HNOjB5+DlW*Ba=8O3&3z}72svB8^0Y6F!#vVbL~t3=
zs&cFeInhSvYoT~mc;U4kCtQ?b?f3ny`kg^V3Vqn6c2*sJk;;~+>$QVh@#JXTJBers
zjNQBuW@(DrlPq;-oq8`-w}peF+E6X@3(Xy4+jvA9A(?h3(ZHmD#t1n9ws+4e9VAr5
zB`c$~+y$5!c||lr!uRVg3&gjkdFu@e1xKWola0Zyu3JK^mfl=A&K&QvDf6-<159i;
z3xn3hp{fLuP5YT;w|2N0E6!mHF(vX|Y(ANyV9eFSKnhmvbu^n>Rg{M7zJ$8PWoz6@
z;-nU?j;2oz(dg-V0U`BD;tHBA$+_p(#OLxt8{E3olJBo)eu=!Sloklj8T)%LOw7U9
zpKiZWvOaaY`5k$7()RsPyaq(ceqlpNuJPNu(8tC1(l&o`no2Dvn|OEr2p{wG6+#c)
z1<qnlj9Md3IU`RzKqKq9%eoY@vS%+)B|l39JjK71|BNpAZzAyE?*nD1Zk;y~XGTY4
zX3LKB{$Ev!lDPup@*7V9LkR+TMhe1QTFk<<k+N2)-QZ95`Wywl<7dxJtAQ01=yL0S
z{*0i#t$i*PvR;1^a4;fc-blj?&=E8O7zHL4s01{2W1tM{Gee}Y9SVn5sZ(&!nV?;q
zqXbsQ1}-K>l_O2$4PIUEjj?>6HRu}QFY=Ov9m1zxM5)O2`+tV}w%Frb-z4m2S@gAt
zhwP<xbmoiz*7%udNN*7-4IKPg==G&CE$P_*7D?HB@D=qN!vfNTI`QYfa6%br#%*&i
zIcG%HmR7_D#N7l?QqXHO^`>Zo*%hL3Ng<MPs2^}6p_U`*FSg47%<6A16i84DEtU=h
zAwue<)&Y>XA#1Q=VhSi_0?mLaVo&d~z^ZRNvGnM-*TYwD5g@F%c!9!|`<ZkhE^)<d
z$?@{38wqR@mkU6$ZHZw^eU&i-5(wGKUgMvtC`=t*ScE)E)<X%F0oAg??k8?HXVSSZ
zBmCC@$w}PbNKO!m;M4O6_$8)Uf)F(qvR+yPk=6;Y3Q$3cwigZg{jmuUp^kBb5+cRr
z&~^Ab74ng1&rm^Cri5Z{`w674&oZFIu(_cif3!na{d&8fNc^RcMgi!*5P5wL29OKu
z(&?M@Wq|||EY=k{n_J#LCxT(0HNQX&LU?@E;DP7VlBtQ35};fWf&>%-b`aw6Qb#b3
zVxy!-t7>>O;hchqzo4wht`D1GHP%t4>-Qw>xeu=sbayJ4#sZ6XG8mH&m%iNi4Fxr1
zG}j%`4|$5<I9Mtu;vMDCFdlM|n~_+`?omx09@%J|^j6mbYWx;kl56S73fBG~pRV^^
ze=eg4^Zv!^B{K8oCiW06zeWxo)TxhYpZ9lMy2p%=w`hz`=e44zO0g&>*hV7!fQvwd
zGAlUrJAkvy6ncE{=wpT+QcEmar~WssH6SgY*W=f)N3|T=d+|X$5?a|8%3dX3DUW|0
z%5NN35BLq`PA>+UjFIDTxd}vxyz^++3n^LB4}bH>C8H?J_p<R?BholV{PqV&CPLLO
z5}uTD#{-Z4A4isT#Y!FLwfjd8BY7^monvmk!zgTvERpzm<%a<yZ!IHgcA3YF1qoaz
zlKf^?6RDhB-FJve9-(C5&5%jW>Z*332=1Lm`(wb}B<ydPyf5a(&grvdA+h!<x&_nV
zOP&jtY=?I0;`k8q&e1RrdSmno-xkaCp=Q!RUHjH{%@*m{;AS?Z)Reb|W{}TbNTr=3
z@xfXyuj@y$mXF)_INV}ycV^x(W5$0cKyesUlrGuIyUM$s+@kt9RqVoSQr`E5v_|3f
z<Ev%CELwtT(v}UXFF(23o0iermvL#tIRc(RP<G29*H`55Kmw+iqBjjY2|qebe9h=)
z{JB^Jzb4(ce8!j=lqz7Ns5O6dT4=MuEq~g8WGs2cdMntzv420EE#$}1yehFJ%E>W!
zS4{t{E<x()c{?=s8s-3<ga6m+147%=PdztiW`%&a4)ZH6H7k4wXU2_Vt#?i>;)EO$
zF!_?duTlrulGt;cMYlRXa?1%foS2hs^%t;MF8KNtt;MNBD0Ahy5)9Jn^~IY@=Am%`
z6j>$eMbV<D&*2#q2$EYmzMn&4$U41I-FE>%*axBM^w@wpJe^{MC5$m@;;OeNptfA&
zpaTOAH6YekKrvlV_3t|ChluA>Y(~<37N++jYxyesppx%f1^F7Te?wPD;BVC@YpPxT
zZ{hrKB(qHbd-#ItJbU4!`Q3K_cOU2Kb@lBG;Nvn31{+s9SI8x@_5D!Iiv$vV$)`)T
z1;ih3%z~xFz!X|o7Slg_DPDd28tBX`-Dp=MCy{+QYKK|h40c}D@S^eFrOPwz4NuJ-
zvhXW87qDNL`T;mdJ0q2<rAgg}DaBQ;=cy_JeV|-fP4_m&_QUJ<Z6_(HnI0AtJaZ_8
zou$YHktGt6E79u@hSV9E0Ufb(eof;Y0Rb)bg>H70X5jeWB8kh@<gEisYnolTHTaBj
zq|b2Z@j6>=k%v7?c&@~56tmnr0Xy0x&dRBVgT-ui%UiWoJXY(7k%_m$0H7wIy48vL
z1fAbm{+~gwqO8!w%V=Hhk}{jp^Jl3Mmak9oCPJtl8F#N6e~`=SJ@rS&EQ9`k%-0RI
zA(~F<_QPJAx=@u??Xz$Fm;%B=*!n<@0jqwgL&-vg$V~rF4kVm~*`jT@n#_@<yu9$Q
zgEKKs4r`P}iT>z4L9uN&2EkD-2W$($_}65vR#ToO`G>eo3*&M1eCcxZxf)-co(vwh
z=N7wI78n}Jx%Qijj^Oe3j1Sq4v*uhXB5%5?2IV*ZPFzKi7<-R%(HPyBy6*om7|lG#
zWhZ=TEm2>dI(eJAxoOnqdMuu&{^Tz6_zMvjb%zcr-DNJ6>5^<U6TNoqSd6JO`AqaP
zGI0p-`v<$A{BDzKNw&qYALVHk=*M1RVYZ$DI9%7QVd`h=x%i<7WyM_Ytq|lhs27j(
z+jI}K3mkZUSFY=CJ*@P}dHmwGZXi=vobW&GM2!L_S%aP?H>D?}W{(ust>m-IR#*0#
z^eiub-P{gVVrMSZCL);$TVAKnD_~hf)rIgLOfLm|A1$(ws5VWR%IgmTCy6TB(^spd
z1oBThSh~=^PsIyHn5lQ3_U)U<;^{6lId)kh-@A9@;d7gIGVGwJ{e-A4a0B-L6Z;;^
zJ;u`K?`0dtkWayh{7fUar=rlxV72++z+%LFjQwnSTL;zVp4sZcXZDOQp`jF5nXf%n
zV|QuXnalN8T@Pf9$5^*j*|aM-n$2g&z}()h#g;}Yrl+Qk!EHIDt<YY0%YZudWBUkJ
z6gXajw3W6G&bxndiNq)dW?q=)WMdnEB4Q%BSOE~=<aYE6GZt>U1FW?gOd6J}v8~e+
z-qNe$tKvo29Y1^p$$SOHuV5z8$I$x0>cMjo{3p0Og64=Vxvm)4@s;y|m_S=y;aTz>
zmZ7gZ4-o=eXd(Mjk{!?4XAx;4e@25LCYxR2yJPFZ1cy*yq(e|ok=gUj^Try$NmdP>
z!lZR@?MPmo;ZQ(6^L+s@kDSnKZRx}YfTw>6L=$;#x$}+ff36R6N8OgG5(cIXT60z!
z%ZTNz(OZELF~HXYa-xmb5X^nBN1R=PY@f^&5XD<*Ao6|k!ky-ysaqi%#XzWKgJK&0
zCBQV2f3=I7ZHCqbc#3{SlB?M3BvSPQdx}_oY+`->s&PQbMOPX>uDp>7ilCrS=#|FO
z;@)hU9tF)`ei6s?wpnWYS@v3{!{qq?KB$m=qnm2CB<f>EgTt<Z#^zBrO48W>L(^FX
zMAf!kd*}{9I;A@Wq(QooZiepe9zaC8OS(kqknRTQ?hfgOk@VZ#&-cPVf5<4;-q#sx
z9SeBT((C?k+kbkvJGB$})!!&MjKpL(c+DuxTcQp41zc$D=cAaI8vx86CE|*L7I>Ar
zjznNT?t!Ce?ef>4*>23^*Sqm3iFBU{ZheB#kCLWrx)}$gt`S3amkU3RLPquj!Z$s`
z!TC`~dXVLqawP|m3aOPK{*Quzf*+eBB_~<E_UZ0=k6U$&jgLk7Sxzj=WX|$aDQ3Y7
z-#u7tbR#PIguwE`cZA&G?F}hcGJC)TOoPkt(vgoPO%rQ;jwWLDLmFkJaFoWFOskti
z!JFLJ>U#cPc-3LuJnhYE-oMGtUCXajng-6q33A^M^huG_ovpSFykTSeD=_E%ODKg^
zcU~QTX}hJ-0e>{s`LHc$ow4-NS(izJCr##=5C!ox*DrEYtzpqH!SOt1un!wDQoY|E
z`qqy>M=zjnBcoKw@2me-XPNuqq=!r0H||KHqSaq-u;ebe9Z}wp+L9rCo2flxPMf{t
z!x5%UD?J9IhcVv)A%m)+h!{US(zG-j+{_|{$t67=(@J{k-KM*`;UZuj`lQW=(KcYm
z&-g0~<32+?z5{_@+uyNv%x`i#gfbJqW>O%Us@LZfGe)cou4ic7eVR9noqjjX?n&g<
z+C-W&?UvF6%Lm^$h;sG3{xIg!;!CARD70pt&u#<UxJ4h~2t`>8TQnA9)e@p(OJBuQ
z#fWd=>QQ4a>EH%;u}_R%NxwBe>Y$C0g^hqBlsP*@irn6>4Qzap4rU(^4mL#CkRwzN
z$xw-c5`zlKZ^G~Kj3k-ReJA&TnH!}*ScOhAlV|D?(1eDuzS+7wMZ5E??ppI)ap1}|
zi!JQ18T<28BMQvanbeAqg@Iht_W=*?K{Q11Hei34;57s@*n^zllwZKU`vJIBibY@3
z+DDDcb!tAQ|AnokYEk{)BJl7>@WFxgy`>+(H=E(q&Q1Yt=q{z?*!i&6tNNQYC<)dX
z1MFFrN=KxN8`df)l@uq=S67WZgp>y4fHIt+au9~DG?r;s{T3sG^MsVmf+%o_M?WW7
zpl8~eBHr3f7ol)+hoV=`T#5Xpc1Y%9Vv78NMWGB3QS&h__TzrdFk>{~`vYmfMJJ-q
zH%ShOiuLt3OfvFU!DX-@CT|as3Zr(tqx|JW`M4$bzS}AR>2}+>QYm2jSPL<m^$Bu<
z5eahyw5wgz&VsOAiMm{&v15C4k9u&i69mf~b!W1jFmT+Tm%s>$u^m7xeQMu{{gqLu
zoi*V)2jc}GEQ;bJ&U0^r8bp`{R`yujUH>SVCNY>XYIY5q4DS+tV^E(Lr|e{y{z!i<
z^7|4XM<=>UV1h`igc@5BexQw0vn+>4VpZqrxr4+?1e^$#!r|ujvHP>IVinTZSSvSb
zuv})1)7T78Knba;sT-5X%nU38Az#G<CWE}17N)~VP&fAxAV?cdLnBt4aBhkTThnh~
zFCwbc%5`bhM2k_#(u2OeWCp0MiZ~MbK|p_foGsOV8%M$TD^aUIA;r&rsX-dn-&BqD
zH&F{)WIpkmmw|~#Q!B6_PveBa%z8P{V{*20Ji16hko~cowM<p(vteQC4|9rfiX4#f
z#%^-duR(1Kw)n@23WkBq&PsjIZ~o=G5fl0CFG=wSeujqhIT)2XnLd(63i=V9XGF=C
z9O-U)X}51v1(RF;nZShkpf$LaezGI_=%)98PzCb#-<YU~`QGp3SXk?YXVTcdmqm9v
zk<|Szwc{P9k%#Jwf-vWJqNOaMH9sqAcO7JSgf1piHR+LL`X%O(RwCu!W_M_WcEAWf
zXmaeio{7jH$mAUc4HJ|6K*OTQzo%>1&Fi}*jl<@Zea1nwKkMc4;V;4xnKEQ4dCuai
z_THL@1W^mDVnM5oA#6@j^$>1N0=qTx7if7O<4gmj)&7R()g}AZ76idVAeJ}55%~66
zkERiK36gI5A=}UBBm=iGzG)o$FRybWz*mZ2vp~>8CCExt2-C!58Arv17?zD6MG0s<
z`Ec-MKtm~-{VuC#3c}RX)Ia^8XIz77fQPb1PAmvi*zs^wb~DO7@pWWQ@VqVQg6t>;
z^aE*=0%GC3fl5Mv5BbR{0tv<*Mfq{Aw^|5DH)#|^XgxyPNgQ{9xQGDL3B_6lA>AI`
zO?bbf_?7^`t!_#JmzTICS}YoJitJ{EJsQplN3}&>D+32}V-b9G%=9|f2VP4$aMi<R
zEXDtgz@OiDjnw_md{>P4@#9DMC%9@IUo<gvAQLjlo>}7uWhhoP7MSA1%KK0`lQ#xv
z-f0hfK=)nK;lb#LA&*dfI=SiA)t7NBT<(*jWY3eucei~Y!O5_-Cgdhq<_-?)LE#?C
z)!ZaSn+iDp#r!8gqf-Jv8+m7vDauoH98mXPGg`GlV)9VS=N(dzFSZBSqYf*IGrAZj
z$L7gua}o}&unHn|Wn)MR8$hHTeGAfXIxgz9!Pva-c|;jdAcVri5{m}5fkx;R(<#f+
zVMe({oyLECIX>aldvPXX&c6(VtBx{EnXI>4p!w#@w`qvt`M$c~T>zotJMW;K4|H?`
ztY+WfF*hk@XOh-O1N5b}`fEr*j$Jx2ntu)yx)VPK;(fVbKm_{(#^q__!ciR>n0<bK
zr>!-&jR2^e<^|d3m#01yX0*gKJPilTvXxhPpoQwEZmOv|E0uf8kFBp_pca}_T(HpB
zx&~k0W7<gDPsymW<KhIo1F``e8A6_;SU2<QMx(oU$|to++Y`QsLwObCBL0i1je}%}
z5s+eLt6*nTc6>#bl9TJN9fUH{iQ+zye~LD<#FO7vOwy98f0x<kOB3UMbclDz7j&I2
zL}UE}a`p8$B;n6>6kWv<BV9=zeBxh@e&n0lbdx&V3i>uxvQ=jwJ*+zk`!}-vZ7vIX
zD;Z8aXDhk9=a~P;TmI5^lPz?3+z(jcZHngxCr+4-olfn-Tjj={u1eAcQ0RzA{z-tl
z$%dwum<y8{54+*w+c97Fs<?O|araa6Xn0iCw~8o#PIIZDHQC0(_4ha_<p4NN3!{Wt
zP?%7vqLKgIK%&$5$E`j#bA*dSArGh(hTSy=X1x&i_(x+who4R_uFWW;*&vPwiI7EN
z&8AueO25l45B<d0i3(U=SPgV7k$!>gzfX%e&WkW^h%c02$!_RlS1=se$KofB;bqH@
zJr&2Mx+zNQEitxtOw0jii6(*vyPd1!d8UJd$rEFTbtwQYq=bCt+hiOD!BFeFz2Yg(
zdSG5PN(?dF>j^_M)#uDz0Imo<IGaM4TTCP{Ru120@P1Y23l7#Boxi9J?LhH3>=q%V
zUF~5j7lZ-MLS+~Fs}3u;0|eyKY`$l?=!Ff6{sba~s~#QYjr%Y@(85$9w$d{WKLH1_
z^P#S$=t@F4iVlkdfA>3U{?kW|Rw+&a0Q?IgaOve;!43*Pe^?<lU?HE7tu#6jY{P(H
zw-`}!aJzmZRUznf#oUv!>?4v)10NJY@`Kp`dTu1-5jCFpj2aRQqE*h|qE%nhc4B@4
zX7eZvI3QEni<><7Q9tZoSl-uNgn<Bt@V&>8M$KuNvv1k|q;zZVLACJ`;h4juV^f8x
zQjtqaNG%<JJFODt<%2u30cAS=|NO3u_Hb>k!`=V`o?9cxo`dssZDw(>(b*|<EoAa;
zP>3Vh%(5!M3{im)9XvF7^<65rmx2&6l#ffP9dNHMIC@iN+x#8}!G|s#q5l#@T$63)
zR=;1W4|drddXw1^z}RZ0Lw-VHPg+}?vfMC+d!L9u^FY$ZH0gBsy}>}B<6lP-;d|S>
z-0@NYi;Jdih)`&TD>|YO(Fe=W!fr}}pWIb1sa|Tqun6dgoy6!xO7(1nigwklE}=1H
zylYoXu5xKe*=qRakx-t{hh3r8k35883>GF`=~)s)bKL2g=PHzQBdfVrpUYP1t@gOX
zxs7E=)HlEs{ujaBh@L)t`3^Oil%d+XB;LjuJ0H%dNx6wumOt-!Af6N1k9uT&F(e(B
z-~5|_>kyr`EkGh}VxCv=lcSqIbngiwQDOgQmS?b{ly8|4Zcq3N-=NHT==d);;*wQi
zgktnD(W;Z17Jjl5tPhLwK!n?1XMPQoP=%sy_BM<pJGun3kvB)=sI!xx((C=lD}jF^
zrqDLEoXVj2%Tn^#Ti-KNzld}1b$^3uM@$sAjg#H;9e^1(usKYozrKGrKnWWf-0g~S
zs!%WCUs8HE&?UdqpRPP$rk_a!PsPQz6K|QT$(5r&yYfT7q;kdiY<j3+p?qRjc;<Mx
zy-)a7nxH#D9Ovb-J@qn)&&A+NO{&RIeJuG(7AuBbR_Sw;Le@i7+I8t_!{6_BZ*@5p
zr@fu!s2go&0~>yQF5_9e>hMfWt@;z$I7DsJKw;dQ*jR5Ad+467Izve875C|T#h#!J
z&<Yhih$8M1q0gqeU-<tFz?*l8I(NItjs2tUN~$|t&`W+zBJ0b8l$z+fc4?ceeHf09
znd%%m(U9c5)gQX|NX;6grltZkZpP)exakkUN(I0G$VfQqfgEvPcos_QwoY(&lX%i>
zx8By=jpi2v)XXHKgq9pLzgVM*R}LPtIiV|`{!vRua}Z+F-4P>34eNXh4nVB?JxrLX
zd&T{<&Y!AVpA7?tIMhUOP2QF_4#wvRCAcH%e7?)<KC_E?jfMfM7EA$n%bofKBkpuL
zm6L@Z6fYcKB$fg0W@C4rdMH7`NP2b#m4k=0QRS8P-i74|xjy>hTMZqzZ?CAZMHYiS
z9;bC7Mv@Mn&ZjE{ePSk775xLakcW@y!js?$bdi!B#@y%*$N8TnwVOuKBz)tq5um9v
zp(QQH$3!zw+jhHlt1rA=lA?&A10J2$wS^X69>0$edOt_`JNsM~5<#18ZKsH36)-N`
z(o={16iLo@a{?c1Tr|5v@l4?0s0nw0D5*DE)LG#d@byji$)PNFxOjLeHn#B4*o7)%
z`lc@R#Hmx+l-@af2jpmRov>idU(Pk4NT2!#j>(ZY-{5!Hqk3RhScx<o<KMB>-m!UD
zPxmYR@LJlYnk7CKc}ykifGV*Rv6_nLjIWw0J?2NPE8aOR@0Tf{mNNIaFpCqW(=Vsd
zg8lwJ2pR4jEfKHcG|)NkwTGwd03A<tIbp9<d4V(qB9xwiT`M7f^yqmA(a2;?;t5dT
z$`cA5qoAPlP=UTHmk&rA)FYTtY!@1TdM-S&iZ5A4!e!ge``0HzD3HK*HHd!?`3ZVP
zn+_&VQGSEu@(wxNjwzZwAdLnaEV_Rcim{lI-un*y29vDk6m1!I6;@&qq~MGO-^dM~
z>=nc>S9qm69mk3uy?|-`033GMWt@1BXLt2SvLK_V+a%TIBA&&%!y2=}-JJZ<?q(Lo
ze#7gND~&mld$WGg)f;bW^bd*P$}8j%3nFs50pC0_w;`5y10w#;s>tmghgQF}ITMYV
zRwKsGniP}Hbk4AuJ;Kr77Fnsd=OrZ6CDFS3-g`V$9CP`CrPXb(14ze$TxDuG&XoIP
z=pzL+np)qNJI0XphH|>7l?8?AR?qC}-FQ{8Hc&dJn5PE4+=m<aUrIfsEfIDxpc0sZ
z6(k?kTsOO5e9m0ZdHbrEpCm%>yz*qUc=B_JbryRxbgn&z(fk-Ds`!7dm3U3x1}@l4
zYzgm=_-<q)mQNI!$}3zY)^p50koFrxpUPWCj4E|U;=3F$_rPN8jQB!nE)_04<!qj~
zX6geE1X)pQ+)hq1+A5MWRb%OEnJcZ_-Q3s-gVTIJjw^dgk1KOXJjnF-Qb`<LrOIz>
zNmri`sMnA_iGJ!Ue{<vU{L|fqo16Md^!hsV97@9H_!Z5N!#+6Stz$JX(Qd^Ffx@ll
zdY6P`<DIK;WE5%jxa$4RO^&BI>skS3BF8f4B_0&ywX_(X;;ucm$ilbA8zEQ8LpErS
zCtI-%0qw7b$#?;vwjAWE{|~fxGbh&QxMsarZE{uOI*?AX<50cvat<u-W&pkluC#xD
zbXm`l!-K6@3~K_jJt1Ku!>8y?XEGp5ZNUV$JD^VosEBKrei(3<ShU9fCfhwsXN%2X
z2=4m+eVLdL?y?=}5j}kdOwkN%$*bpryD3<nons>J2>P$#t1Ef}Vg0a9RAg~CA{Toa
z9Pl~TGDfy^$lVr`&&u8>#AT4_W%HE)A-(!5TR>BE`rEQ~(-i6OdRwa|D72e62W>s@
z(cu9A5B4=|Qt5pcI#u)<XM)XUSix)y%4`2mNc;-7s2=45J4(Ec-NhcP#q`;yWhMmD
z?8{-$u{jP#7<2){i6SOd5RXnb=n2r$q)?fBB!OFbZCJdevYXQMTik7=@wESp5O+cN
z>rhq+uWUtOB*E2Kp~{vxgx6alGZfMRO%D)#6l8<7Zl1VKcs6RF-7x3H05Kr&@q@;@
zWl=-ZAA0>zD6d{49TCM8Mjl{PqlHYeG$4GtNZ@o=o0#kL13t9X!-7$cSlB0o-irdM
zsIR*9eR;pjs%!uWW8VqPwlZ9vn8l_gzKRPHJe1{imzdVh;oVz-68#%Q+*QvT-9JiB
z1D3X=z%FEbP;VRDYJmrK_T%aNk`IJUw!w~8W3^3JpZy+zWdY9b$hOVzLV~CUDR4qS
zgsvMgpg&SV06A~3!)<<HJ)>x-goh$QNkbkHL0>x&#4r&?6%QM!xDmdh$+p?0<8^IF
z-J~0-ewC6bjQp2`zIi95W`xRY@{H&ii*%LVQ38r^sf1DJ*5ZhVMe>m!@qf<5xsD*3
zJP3;-vyoI@@Wnu>(->7JKOoWYlgQ0`AaToyeO7jEqIK}{ct40_ExTtTQUw*Zqa0dR
z#8?;`7I|am=Q$I$7!KV&7@=}IEY>;eWOFY{@JS5)lueuez|hpB90PiPXZq`T*Sz+o
z+{5u&j@9<srNTUIjDC_mdPsN#4O9T#dttbgW0DB7IUfjhmuZ%Ezqo*LOTEItx)5Bi
z@=l$!+A$P$Sbm>b=GG+C>vs(lmbYkNEQFy?yq_Pc9H=cA7q0M`7LM>=*<P&VZ6lTb
zN}{Eb7>L@=N7H(@g`RJ2f?tnV>3hh_n{!EjyB(GP9XVUj1SNOg3Rl11eMRGX0Wc&|
z15i=s1~HfO5?I2LCoX;X%ID^7hz2qAuai?|QvBfnpcCU#MjVPDI<vVuSqg*+`1!Ju
zN_3v5NI22E4wFYV@W|eNlep-DjR{dj?qQsKS64F3e_Ob^Tabm_RA|8~yae@p7tXvU
zOBMJeCnwU;_sIlC`aPPx;`ogdW|Af}A9vO;7+PCKUVHRBcl)ZilWj%>S0Pf87d#Fm
zAAcI0CBD#lbwyTucmMF;v;b}Zn4gJ;)u>Zo;4Tn~@j0$8rV@gDUv=tYZxCC+%pbhY
zNb4Qf^t1|>V2`rh=RjSAVA&Hu*wV6}xjYK;hC#zyWCg!P;-HJ~e+rJk>jZ0tYz-Qp
zczf@IK$6QKH8D}}*p*g`=c)Wjta$Xdui+@NMKG4w<$7AoN$pB1;WL0L4<kL7eK!h<
zy38iV>^(l&@Qx#r(JX)Yci8)vPher0$><U@O%_PIR+SNJp5P6Uw-+Xbhyhz2Gj2v!
zSE-{XGxbTx<RE}B{Hazf&n4G=7L1TJcy<rJ7<C2OjADI2SG0zpAeWbc@=Unk?l%cd
z7Fo-RxB>R|eS_G`i9jYI1~H8fCF%Mh2##t0U3sJ%Jp4}}SIm3DAe(y-r7r;Tv72G^
z%o091oU8QFVO{%F$-!_A0ou<J>(J-V%JX>e$01LQnd8e_Z{$IvO}*ttC-zg=BrH%d
z$Y8lh4%Z~sU6Yq&a`0Uon3*~mgJs`&jkEo{0Dm$s$syyuQaIp~GiIoo;}_W?Nz~%#
zOctz$5Y+aHk+H;$)oX&hfmM9NdRpy2PN@U8M%I{?IA+-SChzdw$W)D8N`o26=BIj@
ziDz(0mM8f%U-C&F*Mb`RvlR;{h*|Pp?DTM3<-h+VTaar>pX|Fn`0(lAy*dH?9QVO?
z?b_j(F?q3p6xEM886L(5Q_@r7px<XL>`3bk9&Ox<0d+D&-fd(?caFV`&J`<S^HKR)
zTCGR*Wm6fw!vy`C_uW*a-1_Ih#^SSsRIw-kF(RsZ0t895d1Ben75ryfh5kxf>&@W;
z8iAB^vVtsx-o0<!!dMt&GWE<0-;+v|ang0dv#bV$qYMqU4=7$WVjDb=bD?TKv&()!
z_w27CHu(CMmrX&uK<`;EANU3qc$HuGiwL{!%EAP|{6&}~A6Q^ADCL?<m{Edj>eTT^
zX$Fv|hX7~Q9oXsin3}D6vAEkWR=*p|7St9Z_^ZZIgdr`un6T*rZ)S0`BTEtVdj&=;
z4;1?GE$9>o=a_OBCa|!Ys%AV@nY>F*htBc;A4Vybxa(;junC7no2&^$oiYm^i7x=C
zg!cD&!G4FuHrXl-vmiuN;PJmg`L*LDRbkkwbugjXlD;~4Bn{~U>GXW&q<iU}hTTEw
zLe!hqhO-qznbg1B?w{ei*4Y2T1;+p?`~aV13Ce~4#z0nRK<E$P5hQ;{&wY#BL*66~
z`r7=GF7z1&jae0@%Z+Z5j1K?b-yJO23#7B#O;^}Wie$W?Np78CRY!oz68s%TNWxjv
z_3lL1ML6Ut-!OCu{Y<V4ge0ChU~Gxs#y!U!>qWm?R@G4p(+A251^HXya~z6KVnJY#
z1By*|H1rMl>UO)aF+SacH&)|7=}67z9gpT}IFzEubW)&sI0(iw667KM7yJl;&b>1^
zOCS(yo)6@;ssb|rnw)#2%S~6rfh#~sjs~9e7siG*0>rB%afZxkL)+gr9qd2l26b0S
z?^(X0_bw}H7@_=0vQ``Ezd0ztcZbfl+$;o;WJ{@dD$fysRhpvUAy2!*Ne#}{5~!r<
zfT`C-34hv*S#Jp@R%3H5NkzS*R`9TruWWiQiSyTK>&{W)HESsPmeKpSY=SWRoG?&q
zan6h-!b2etOt#hbetB($pd55F8tvCbN(HQK3n13!js9>#m!E8iidj#Yj<a2k=cN{#
zT#UZ<j#JB~tv=ClI%mm`8qd)<PLI7Kh3!sQ{?EM5Lum4THrWoNS2M~2p6h%&J^)_p
z3fM~EQ(^*ph3fCVMcXht3uN8i56qlFnko1icR6ZfAc!YX=%g>kuMoCAx<Lwo?o`oz
zs@qEWdA74>(>+=7YiGgittr9`KYVfD0gdn7*Pylbmt|kPTcf#TU$&0yjxv$BZAVWR
z&!x=HKbMvL&<NeG@{mg(J=A5j@sId<Ilryuz6$@^zkYs8Y;(vx>tkOM_P!wLaoibd
z(;PA06|jHdYy2w96vO}v!B&SqU_#kaF^hqsaZ?2YY=9EHGCW8TWMFRyP6$7B1AL{f
zWA;@^xDSJNfS&pcY(@y|SCWZ%wHApvUix-L(oYHs3e4YdwLN!f!3g#NOoD#=s4FuT
z^v3FHrLOhM^Ui<Mz+*^Dwy4fIABJJMRBQ3xZC^3dF`53|3d(V;ty}D$+ZsQNx2$@}
z02a+YGQ)5Z5iBuCn!YrUx^!GTQU!c6prCz2+~p6qep}6Nw`nB33j7j1XS$S_-8{>h
zE34+k1gt@D2wp6FhpO-<tDK<$93KW~5Ys`I(_xvSLjY`eKQ)6^`1iMOLZbty*MYXv
zid9Y4^w|O)I1<tT=60G{zjL?FZh_4NALS;P!yWy*K$8|kUk-hJ>IF!?|3e_$BoB{B
z?>{R5#|$%z)cZDls7wA!N+Dnf#!bVr><W`#L0|v$=SItplKTDT;$Vo}@0#r1iem^2
zT#>i+g08NA0PnxYbJ5OHxE5YCI)-;l6ipWhO;~xoNEH66V(8%YuqhI@0K?%Xmz~ee
z7|(>!Iq6_sATzOSlPHiJETsuWXO9kt^*8ns5$<T<5w87?@;nRfADa}a@-V4FaMrxw
zjcU!!BoIFT`9Sf(xr>csQO}{r$6%R^I$Zf0j6ONHgRg3RR`905`pk2ZzA14rDdK(m
zg6$D+^Sk<|7F1T^rV&lm+zA!?nsi@wNAWewRppu1N)__lW7qqy2#O@SLNZs!<l_vK
zY4C0^1p*y6sLzk{p0+Q_9s-TWOb!oEU&A%pJN<W5g~m=gV_Tk_k$4`<aaes!;zoud
zw9y6TEivZWCLvVgc$>~Z$)Tpp#`fgN2iJ?gAr+9yS8^HiulO&wmRMHQ1?M2H^?;e(
z&*P?tS$mp|+;@IOK_2jt&0o)MZkh(rOAVJIeMg<df-atKq~~LVWW$(t^D&L*ip%J=
zCTo1|#CW`HX!<XTv<8cBCQn*_tU-AWaywR55lRlOQ>7Rdl%>>H84m{QV^s+dRy|S~
zKtE}Z36fm}ThF}N4!?*4JZJ_X6$VB=Aoj#L3lJeL*IC6?x4s%;b+Hwnm_UKGQH&q*
z(}gGOTNOJ!^#EzNYo|>&!=f5o)OMIKC%m)g)6%ZMiprydNe!IyExp~T@*&*og+}o{
zZ-A-VFZfdbzmjpl4rIR%mguFL1118K<J=f09(w*WkfT3&tw6sWIiij5`G%GDUzl_T
zuj491kSyMvR#q91Y`18<6_76$3|rfDv0xmOVuy^h!UFOGOMe;LKA0-31w8<+^npDV
zR&N!Ho3^P^&9XkvR{}bYy_l}lg#ow{_Ksz7b*kb2d!>LqiXz+v+rF$?d~LEj%j@S<
zdG*(;5Kk}EBdX=YD8~2VsZd71s)Borh&Vap*J~{lDDh@WY8gWNUjtX7RRAS&oysLQ
z7sn#jhX1t^F(624QinH7DOHh99Z_HZtJ7;c_$N0gr@?WJc+x`ap|4cA={hVywfXo)
zV<Oo=`~KY-(D(J@$%bQ+B?heK2zbc<4so{puD}7CKGWs#dW+@3%(0n}ei^}Sa(0}|
z3$7mP<|)zb5(?MUVT!%~wyqL{k=Oa9T*m|^B6tgzd8Nj5d`Y;6AJX|2gucOrik$vZ
zJVDI=?bhEzn>3iVOot3qej){<W`AaQ%3g9HyB37U&#(&n%PmDFK1FJLAhCq1Ws!*>
zK2m&=6y1>`uCkhAIkl}Y?_RAGI#FH?x%$1X*=M1*m0;q^dRLpWK8XOD(1q>vG?K;|
zx0N^i_d|l)SBFrXw3i6oU!bOYyP;0DzAZLQ-2**<+|b8pn!$+ON8d%UO|^dYZ6G6G
zSk4Wm^C6+AK80KBSHT2^Gt1Z2-aGz~p=DEp(tg(<7HCJ`>5I+Ep9OtTn#DK#hTY?z
z?N_&bWr*4T&Y8j_;ni~z`T298UBa7Z$}Dx&t*LfCXoDVDtLlo*kv7xCu75*Hqgpw(
znprJk;x1!rxU9q#R$|RSS<EnBN!XT0NBt^$9(Me1E+tJU?VnwgN~a6q9qzeqqwvP7
zX1er)R!Op@@8&=QOyFlkS-!$xmL(|L!8bdMMLQ7s1!x-LfpMnPO^)Ts&dLa2dHFfU
zyD{N-O$Ph@85MXuYE<d!z)ygD-u1rMhowO43b3dDG+e=F@sl4lv+`gob5KZOl1gFH
zlvjWJ#V63~#eISR@DU0D&m7uJKHnv*Fgh~R1qL8#qF59%HxKv3JAg{1sl4jo-E2YY
zshg!hH#0rzgXQs)I}S1N@o-4UVu2Q*BY9(JR2d165w)uod<Iq%;eeh<(V007&*&Ei
zrqIo##CD)F;Avs}B=@CnI9F6O0Ps2Xv>;Z`|4w0sdkD;{5c{r}LB%}xYKd{FA^qVX
zn?i63$IN3w3Gd9qD(*Llbp<y>tCAzsV~L<jPB=UYP@||(5fwF+uRUZ!(|}XCKr9=B
zm{rd=cw-PI-uju7$LnnMiWKH)?`MT?%UOD(h%yBR1i>9>{n$46_aDF9K>|mKaIGJb
zdF^&#vrQ2M7wMiDD|57i_O}=-0I|0zWr4Uu(2ukC*6DLR<|lZ^OkN{@KF?HlWTDq4
z9`qTyWdog#mCboHu12d9Khx5eV2`_NImbHnQHzu_#zS5MNhA7Au}03ePD1iwwy(Nv
zQ)>kajZ<O8QySN0`V0lhv^h|I3q@JC$FO&*5k4EFK)}x9{4$#>e)ap(CbYW^hsvu~
zpCH99Rqfz*AyELx;*zv_Q;3?gQdYkTaPohX1#}<9u7_K(^d0f=-j)6l_`FwJ{utf9
z&0o$pC)_FcjaTJ^Y)VUV-ij}W&Q$L;zttvUWb<1UL%R9=P1!#weUA=+gRy}#l5bB|
ziI93ePpL3=vc>7{)5@FrpSGRbKjoAMUrDAHNr;<QSSJEt*XjREo|nX3b;#_5*pM+g
zV0Zecdo*}o5>Xs^R#~iyVm1(+53Ed4KcGZi!77dNA{dXPerxL3Px-WEq8;k+fyaLB
zg+b8%t+OEp$ate8;5orutO(|d%8ej!IB+wC@QW>hBZvKaA?!bbsw+THg~5IgkVlQ6
z#kyx_N2mzj5FPaf;VR5qgaA0a7~J@(8REzKZ|`lW*mVbMi$?m=&A$H>(y~B&<_FOV
zdY(9=ewMf34UEHVkbvC1Oss2ybTr+!oRSPvi(7#&gd#~PPBBSrscz7$aet3vH>H%E
z)LSX>_%q<kDE0K=Pv+yk3-q+?v~h%V(aY{?wHZ2|b-yo2DmLw1e6%$jDw}!=l=C63
z?N5L)?E`#3QFD)4JmrYOKr3Kuhm`pNgZ>qC?yuOCvS~=>WYoPB(6Jt~{=maw=&FHb
zdoB+Thvg{Y-@kt$(raED0Sgt~cq=q(=Ch@mXez?D$p+oR0L>XF!(}RW>2~O}J>0??
z9XoaF41cJ#y8T;4DV(0s|3j?0foPR)DuK}zpK?0hO%vC=A$F%<8SkaN&EiATw<bRP
z<0k!!6NFPT8ilV0%r9?{e7<<TfKBoyTtFqco`h-Jl*Lm;G=j?Gs^BRUw#l1bzjbR(
zgfX)5<vMgow~L}amta$1#a(KtV0>V!Yqc&hqTGI^$C2Iv)7pa0-I<;3v?**yfjwWX
zahqXUzUe~)YdC+6`B`{QM0ba=N~nF9#W`-dW9&4(MQb(HTNl9q)k0&=PsV7aoEi5P
z?#>;?ug+is0q2BX>Np`PLn4>lfF=QtBl80<%Q?TtF3*j#I@^o!!_6<5$?lK#=s>;%
zvH7Z-Z;+yk!RcAHvk{`rm70aNc*O$r*^_tyGpyjUS{}KSq01w7If7>(JNrYh&AS5@
zrwmFB64hCo)!AJ)`awaz8tt7Lzhyai<Sz4+oNXdcx^2%aeWumb91Ix?n44=9*MK0d
zlSf*kl@&VDq~fae)}l{n3;k|d`v38JC{u_xy*`@(h9h9vo0A_5NQYkMskI#N8B<^E
z$b-D48Of4+`uPGD_r?j`^D3soYd?>v&rncPx0);#D_j57(=B;sMrv*mQj+#x7N|E@
z!l=&umKrdZETm#DO5^pxc@t#4Ll0qZ9Rje$MQE06VirMp`i+kBrNxrk^FzL`o^LU`
zO}1GOf#h080Eq$V|FjtaJg<FmnQ``mVP!BWztD`PriRAbp$q9P%FFz4@<eoh8-B3j
zxqcO*E{hwGVoggKRmSTh!aQqsb1*es!QRcTTRhrULMxvf?-(%me5+wtc9+1c%{J^P
zNkCdUR#scGkm}QUJuWzXi=_RRaqCEm)*bB%NI$Y7lPG3tTK6qS)X3x`xB#|UNvF9B
z){sKG6$k`$`0dLn(951l98MQs0=G*Zpb4KoWi{LYQd7{~&lLx#xk7>eOpSqwY{ACp
z<MBwa-yUb065zF7bs*m47H?EN(%&U;x8lm!Tz*3>-8Hd*#oqCk3iHVY-)96d-y3NV
z-R0@!iipj)L+EdXTjRuV7+kM08Cy5FIT8Vtt|gLi(F<JAF>^o0=X~n?15|(~p-S6L
z_Xi!`ym_C(zeJ<~%F9LrQ?@P$fwTo$^R`8v?!Bd;N8Vg-;fo776I=<>5wsCVtwq?y
z^}xpQp}uD^S)n5jjmzaLy}=t`BoR(#(gbRBsLbLeA&kF3i2q)w>UF}j+(<$scTErN
z!4Vb~r78rM1gm2A1FgamTZ8b|16+P=4}oL!Yk=*J5@^h@dX=8YZY<J#5iE9vo*{{Y
zTM=$$I53uE@<a58kUuF$%WzKwg~&`zNQM)}xk{i*5WF=TYv0HfL-U%ERCAR*^5CUK
znpoo$LKy;m8KFD0?dVowdd@MkF&hM7y(i5HGTK*f%eRA$oxSZ7UGx?y>}4Izh^5k9
zZff*iFK&{FJYcAQFG|4*nzEWVmP%OmN$3=ZTy|8yoinm*$u(DDe#n{^TegPm5Q&(X
zNB@VH*(4&S<oE$l)}|efviRL-c5+IVadB;;H(aNXd=5GX7^b-xrfA89{A%F_7W6`v
zpjEx`>SPv+{vK|d>*q&1BY6mm0xyK9TJ<+$SvP2jH03JR7)?Kefi<fFei85}M7IY0
z3v9M-^ts;0Q9ZV7(52G>biaG)iCtUi>4ZSQ09VZdgWLbH01{5AM>He}H2!-uD3f@^
zR{sA!W<hMe=AZ<aaAN|>jM8AB=rCKTG_?6<sYf>elkp9{&?E^`UQR<A@7nVVfQc}u
ze@6%02Q*Q+iNUtg3x2>N$F<-Yzy*z|<p~paKHeNAO&!U52C~qvhyyD<PLm$^Ljfvk
z`aum6n!FI)hrqyr&*pLD!ilp|VM?yzxxggtJK|z0x%Vc_A#a=0MhBNC@Z-A!DkDuF
zsex#!NA$rvIXJEE5CmH9R$u}B186*+cA#4&xq*or9JTgbLfR!c{B8plKVboCNUN8V
z1G3vsMzT%E8OZ5l4vS&3jIS_LBLF53kE_BG;5~^(fb*2g(<)&<{%sc<xQIl>y$@s<
z!~wAZf~ctnLfV;Ljy~)!0j7=c(;nHX6s#n_Qo%oge|Nr5)P7SfCpt;T2bykvP2ZNr
zBcr(zdzD9U4x|l{n31awHb+6DG?~I%U#F3(Qi>>IF$24`_8)!^n@qlW9qa`=-Iq>Y
zkdRd~JB6<p<u6E%Ix#(pbmO3b&_|R}Czkn$wgi9eVmX`!{x|6x2Qp2rp+~f+zGNj^
zk18(7PWrI&e7wUxv+o|Q(C}HhfZm)&C+np8bq~H81XvURaE#F^&;{X0=A0ok{zCg_
zAgv#RR=1&w9JqH~nq%V;M5Vs@ecmx!vSAS4__J|*oF95@s;SXxpahq3s+}>sh|h@-
zvL`pU^1~iak6?fZ>peC^p{8YoCsFpY3U<4HP>0L;llz8PuMR1J2}<sE)(o%dZ1acF
z0CR3r_{W3inM^7xgsIr9w+W}=AVjwK|J-Zw4aCk~oD$3XrRp<4x#avEhU=^*PDYT-
z0Zm2ZzNl_9$4$w9<UDr))cQTAuejbs?l+!+5H@Gp_8VUgL|i)}ut_fII`YsC5*yd@
z#W?5c2l6lb=i2Ve?6Qf~?YL|YvUddsZcD~+S$;=A?oK3=d>kktAtAB(5}5KZ%J<Gt
zXA62M$F3|w_d^C-JhE4GgxW<~{QoDy%_xTPZJ}Pw^hE#fqB<;r!B|xgT{}uXcg2`&
z-#l5W6IMebopmX28H#^b>rX8cn_QN_jkB7p*N;tlby7F6JtafY56qZ~Ni<a0MYU<;
zLtX%%a4&!W$SM}|YcsYD;i`wSweQqr0X#PNrGam}4or0;(X~lD5sH?!+hR&*C0@@S
z54!qr{&9>8zu}h>?qkcnpN@f~2zZY8updCO>%nR>_g^*|3G%QruHgfzYPb!+J@ZSh
z0G)Wp`p0(N<-ER=Ts4-spL>`fFvq%=`6AzP>G>yn8MqfEeXu9RRBK<Ct}yh(*hy??
zkLQSn)qzigepeYKt*XRht#@q%&GIB&=|h?L*y-6tfBmm~a`~KcW~@>se%$NQ?bwyD
zK`HZ!V3Y;gPe~EmRw)4ZQZIqAe=zx)ALXJT9}^EVyBq7Lnzxbg1Syr?!^^{(_i_-r
z?+-#ZerSTlcqJ^jN+_4g@ho>{0;0@1jmV?ai@z(QR1??@hbn!Ys@$t|y~HwT4l{0(
zb_@0-Z%&Ks!Gd0AeSqvtYOW32myMLuq=rG2pHKu|yx4??+G#+3)8s5M_=1_`%Q@z>
z@`ZfYw|`{fLtl_r1?v%AQ#h9s3I%+6eBAy;J7T2#2w2Yye14FFDjn(hI;m7OQ`Ipn
z@nTWVIxxGF{F@N{r=udX9-RQCU+jObk#?@>m0NFKYeY?(6Y;hWWG2XGO_NFz{BVln
zsU#X9J)tc*tThZDO1pF4)^?scntT@A4#)atjk$PD)kK@>4K1kMX9S^}uXE?Ff+|<n
zOY<z%HdDLWp|mciCZ>Zh&PT=Z<4a`o<eee?Nm%N(D<^?&%>PuP6^MTw-nljX_j{U{
z0uiO~FO6`jBv45Sm4F?ScdOP{od0Ssu-OFkg%otlEK_g$mRIMl%cYx%gxs$tyfHE4
z6ncNT!92A#W#<?WZU*w2o-?VfLbMib^B6hEEh$PRU6GWLQ4sW%-?mEm@GD_jsqqRD
zulc)T<mZ3G%uPAsyoy*i(f^oDfVuQ6hIpDiD7$uEtYHpBFD?A(1B`SdJp3^LKxX~)
zW-Q4ng3WsoCZ-ym3}?2-_XmnLaP~762BL|m#d%rkO41g39a~4Xv1L_TJsnVVs3;>K
z%C${D?zXss-=lX40opru-FLuiKAXmZ7#%rv+SSV@4=4~f<=y0g(<N@H)iGf^`{P|4
z%6Q>1V#8iu9_3-j9U#w5Iq^0hqE>|r!k=PLh>!v#<c|PdK|E~*z#k@UWypk~;+1c9
z&RQ>Kd(8rJSj({xk9TmII|DJ?d(CzDl7QjVJ00<_;??~Rjh~5^_BwQ~eI>A>eV8h0
z%ih<DDv4zSDA(6E>x~#MK$<y(#jha|EFLs9#$o}u@2=J)_?F$3mZ3jkuKfRrtK>Tz
zH(X__mq|MepJ`Xfd-#k!n3cxeW{oM^OA2ilY$J^127QABXk1-}c-YzY&LI!|iF;vQ
z`8ng&kXn1MZ-9#n(PmL`IZR9$9W(Gyc}{TAfH^&v<h?r(NGS&gN}XV3{~Wm<k*EO=
z@oF4u!j2i?MibjG<RcYuwl3(sbxNi%9OO4auAZHf0d?rmE8|#txXd@q6gzIa=)4DG
zeN+xFoXBN8ZO{xXsVG095+c$HY<I#yHV68G#82QV5c_HeVMO}H2e{FFrb;D2`eS`p
z8Y65I9LUh0K6)LE-w9t8S~~@!PLpB_=YAY9EEz#a)ETw;q&6o4Nj`O$z4`S#bU@9o
zIPDrVWN>}4ihtp`HNI#fK;#?@Hr=~hH1Z^6j#o-_Y+a=I1<h$7P|5x8<zEToYf4%8
zVNmhElO`ks2PiFi0R==19<x>+pxjS%SaOs9(?)|rgWh<mwQ|Vj<Kx#oxS<~nJuA|_
zji9oI1}eP|_{oLb<#zk$^q6%9EN~Rp+aSLdRk$)L;Dyt$w4)RIZ0TE+U!9vk)S#Ma
zdMLri6ORv-FHTtXqrkJ^SSYK=lp0U$CXIpCnOnH$zsghp@4L1b@nk~8e5CY9-Uh?G
z9B~%HXy)dTO@egALPC^+Om#i$Do}<(bUY*^r6rLn+Dm_%g*QITql`K8H8JJ)Ns$1#
z*CF6+NW=YU-r~2PVU-7x%9s+-$jN$N4UKk@qQ<b(tBpf+{WH$0P3kg&w+nBTNK~bo
z{}IPv@SEdeHH^7+!Q}G>FeJss#-@a#zyeTg#7cu^sx<?}OkSl1lZIU2RBOE?7{-A%
zf$(;nji3_c>_kP~jJ^4gE^%7>1z`T46n;dcy6`&rZ?%K4;5_X#=1lNxApGVK0vs#D
z?#;mD7=>ek!SLd_TM84NcuD?aR`AVby-5%_>pbWM^Bg@;LHho0%v5^&ruHyPqva#h
z;j-;a2Zm=xC5|GlUZO`V<JT#=HP3Ys(oja$)O^XErvsjnU<Vg9kCUVo!s*2Nk^HZ|
zpAOcq>r_OOo`-;S%ldD{7U!*xD3At%AhkWbJb<hqBS7Cgn({p|cyeOfGzDDt3;!P&
z&Yy_vTk`z`Z&meI4$1I7G_R4Ii6$%kJ4Mk3&}Q<;4ry4TT1!H4@M!(X4&$A~v732B
z?y@h9Bpuklzj)f>Zqx8|O*wW`P<|YCSRaPWkB5B6jxC|w>shPOJuA03umfg*+seb~
z_`?@{?WfR~24dW^f&l$1=p9dvRV}XXx5=3G^2)MLKlM4?GYnQtE=Qkf?vR=<#Q*1>
z{hxz&9#R`KHJ>lk<!u{)((&gbs$USxb(D6iEH*h5kMKMUH-x`k2lVCSG`ff9dgxl1
zj_;$tJ*+A9j=_tI|D8|lz1&NneMj<=&SUm~`1S_S(#ft+pGp8ul|(dtyilIO?{9f{
z_d>YFQj>Ioh&=kNAfCd@Xw+0x7eKh+Z~hp7!J~oyt{9E~gUrV&qMP5{k0QP|2hpW!
zslzhDNV?y<TJru^*^vK$2{apRK*wS!U`w1AOI;9CdkAAOrZoggfr&|otH%LRF{gZY
zH$PIueyv447Hb9y^60j_t-Kb4XfSUw8UdT^q>>gx9g8#mgAd>m+Wp+h1#hV}Wn;c|
zU3|tFV+pUc*uqLF+?jPTRvvQXzcvqAc^hNfR^^{M_q@lC8pm&)pSgsBO9uvM&)%EC
zNU9zFK2Jd0xK=!-xc`%S53~lyt(BIWKwL!qyW*#`y3y2A5gHnrkz{S4L7`;j5&OPS
zUCnXtNH5k1csvrfyn)f=5A_mE<y;ZgwNY|l3C(qG*lye8_e&q~lI7FTay;>;4Jtny
zN#n1BUYhtO2l;H?{)TR&_BBR`>Mod1?r*yLq503@_1KIcmq)rBnF*rXnupMUg9{f2
zmH-Dn$CIy$$?l6O43pVqX<g{TbtQ0t4E1mMBkwma|LK{^k7-`qaUV(Sw?|`lSlttl
zFt2LrLL&Lm_Qg7_9)F+QS89Ibm6X}G=<e+QOe=)~fXq><+V8%Z3u#`q2S|2oQ!JtX
zR7nlqK)q%CB%rmmw!}ox8~JWZH|wWcP!F0oyUFIaaYFS=3dtO0Jx8m{TOP1pVZHa+
zi7=sW6Iutd5#;lazOQY7QN|RQO3Zhx?%i(vEgmEmdQf3@|MoVfU*%k>p7_u)2FIaf
zjbGk>Zv-i!Ocr#OJN0&Eyb2_4eirEzKgBMGwfVEv!kI72H0kCJiUa>4;yk9+rpT(v
zO~GmFyo9&#Px3$$?W%Q^-}R4~lMh<5hmsXDpKqlqf?HMLp7y8ra{pJP%z%Q;3_i<3
z-Z;<#k>OWuANAyCFH&l)AB~j4_jW?^za9^?(s6XwfIpIPC&uN126ge*?O*PY=i59N
zP4p_+I9R>H8ffd7O_!^iKK$~&-2UMIxB8dLwBS(dc?UBA$D7eU&hpfo1u-D+Z7*Q4
z&I;>QU?cAL3-9J+N!?P}H4MnT+y=s#(xN~pCv}aW2_re+AwqrO#Ii8n4fz-wN==O$
zw{+UyNiyzonKIwM=3L@I_WH7)R)_oLg$q%hPz7eH3KQk-%X)BL6^N_n_Z#(0(|Ev~
zyaRC^*c|<sQyY%U4&*#Yg={94GXa61$>r*(ztS|1#9MqY8x9}T6qfBahv{=Afxm~-
zdNS{qickjQ^fQvKPBQin3$$hRg&STwYsgvJ@D~~AFaK0}rEj0fg?)!m-YKJAw##eR
zn@@ie4H9{{Iiy}{P|guT>8P{L+NX$-JVT|=sx7FFwRASsDi39=n-`ORe*t8n%Uh0N
ziTnitYHE-!4Y|+m8<bV`!#Bwa)#UVQZ>=X*x@i;_r*sr;I?!n|@w=X{r&F_)Q^NK8
z^`!8T{R$wV2@8djFDtf-Jg4xLPEmbX%9&HQj}Jq->o3IBX;oebhSTz#CRk*9X6)zY
z*>$@nXkYz+M8r!#gf&v+87mxGPeCU37-yURpe~h^(pJ~y-ak0Z9yz@*;?xmo0%z3s
z_i|_NGx}N<9}x5l+y2*3Hp0NbgQQcmjhyEp`D1Gh+iFB4I!;>{oG}{cJ?;JB2X{_O
zV>EN=ozI!4u;|Cv<}d(bCOf2VxGm1TOgbV;P87n@{WJOR;;%6Y7<-Ft<-ox54xBAm
zhNr9k%0!ewxk<&=qV@B$TAU*hjqhtr*)F`(AOd-fa-BCoe>@~zKdlLPk41>{go|IN
zX)};3$<3ej>KjhA92)|-vHDij<w52bFAa-9e=846@4X&PX)4K^EQI&(b5|-s1QHw_
z9tO!UDfOrSX7I^KNuwtxRhz%s17mrdO+bUjfO52*+Ec?szkT>gob%d3%%#o)zgRw{
zwuKG2vun*i(x}FDweUJD%QjflJBR*}RTEpGa<C;e1O7-OmhYgs1jsbj63d}3o<g_<
zmIAvWrm(yj_@{e%=oqT>-#k<r5ntQ3ir`s0$Oa2+@Bcc5IG{plC{i&J&;*`HX}HKw
zy)#Us5xaoUT@ycwLT3f0y(DF!LCCy;@;OK=sX!_Q#kBoO@bt_8cuI@PtSWH=o-PN)
z=?0>)TdZC#2PrPc1<z?*g{#enaiU52aT=XA1_0o?cp<uO$|y9ejMfIZkXfqIy4X&=
zw$^4?2jhb;G%P%P#7{ztW}PqO&%s?ikd|u=L>ZYUQd6V$PHm0`bV#6#7S$+KeBSGl
z!Z4a`j;=7--;?%Au!p*1Wi6SWi>wHyE$z+TTM1*lx1JOu?lum*w`hZ|`&~U=14Di}
zUY_|c=h%_1N;Ft~alRDkooL2*dXO#cmaU~EGLDWDE<6oKGuCo<)Xzx=rif%;D+@Gr
zFu8w7bBU%`>cd5nxn&drbNEK21v6l_JT3gTm$89`qa$FHGPIesW?`H2=N67o`z^W9
zucjJ!d@imGA>a}=SCWCrX!FOIOt=ln6pXwbH*j3H@~9e?PcL)E^lm?msrP|`ueg)2
zj|t%J-uzvo?}|&Z9DZ}{r*Jqn*kf?OL^cr9o{fm=nevIZ{fxjoUEY4_&T+NLr19Ur
zgN^o)NXCksyvAEUY8c#O_JPgQ>`5zK+|je@;R^Hp+x6J+w7(`@-!QoXhm4@<C2cHm
zQf;v00zM)HBF!ZHR}Wj4QVtgqw^EB&gXBUtI!?}9A84n0I!Ri*MH!yPm69QC4!PO*
zRfvBa79NDY)AXW$m6)FvD!zNI+!H;VHj2<-F_^eKRas+E7XP<uFEW6$MNhqY>#q2E
z!fs|krxxaIR6Fa7HD<+1VL5U_x-TRhxZzeto_c*Q#c9L~N*nj_!k_-qo%aY{DlVL5
zDXtsYK8nnG#1QioRoDUjmlh?VLx#CeVdfmrZ<_!*Zr5(6ZvS7#uJ6k5{}8|OPRv_(
zn&RC)&Jqnme#N*<O+?}`7a?JNFBtIJ=O?OHq52942n^XCmYZA(4Jb$Z`{1+QXbk(+
zdQ}BjI>q{B@Dfx_?TbyW&;C@exaY4Dzia<3wKz-E{XDAz%*>1VKA-5D8FK&ckpTqe
zTC`cR#uW#%^@!8TaEzJtvaL@Z@_KMfYH$YG1Kp4klwgV9G4GJV;Ducj8e?<VNb_*8
z`{&GbmDr3u9e0OqZ--B=rmZi58%9AMP}lL90U)76BbsT^iElg#E$p<`@hk$CTfYYY
zLl7d+nzqCHPSH~B6@d*$fC2p~{bJ}BF4#_;@K+yZ&Vr~-8I1P1QOET`PT1K;>ko7X
zK&FSA^>k4L3vvv<xnwDTLa{d!8ir!hhp-v7F$DX<pSk=45>Pk&QpC_tU18L);Q4(+
zE<|QL7`0i0u7>HoZ>D7SpyQg4vSqa^`(}PwTKDFj0{gy~K20R%Knj5ONzI^G>eud2
zq!Fg;c1Nsui}^U=wZ1Sy<X8hKxI_aj%5U&|C=~{K7UN=JG0hGTbQJ4rs)D}n$44Xw
z)P)JORJ>}Rd2Sv0sZ%9kul~wSVq{b&|HZqLE@3|ce>|ntx!LygX*qhPo!st?#DrmC
za&&DlAuuz)$m{WPABNUuoX&2*_C;dy^eoRWd<sM{eRsG^r;t#miV5=-X9HExL)(QK
z0Zs-s+sn8A9m`7tyl)*kyrWTP;2P?t#uk|a|9x-6fL8`iF6qGJ+R^XX{lK5jtBAYE
zV)pJVIZG=rB0kyh?`5VRBi>&~%>AQL|2-4FOzX@0;oYFf=ZY*nXR;BajhHVJT4K*{
zinAMVSDIpDd)bJ~(nC?#Y!+gBzD*2D5>7M_N!oQdI*C3>mV=fmlN58a>ChIOy}sqN
zZ+=CoORlKyboWS8n<U50g{}xi`#$0&U)z3NzyI>J+|Ybn_B-?qmhdOuKxb0@K?+>-
zVhq*8H#I-RH5A%|G=1hc2cz8MzW-+jwjkb2k9CBpKc%&Jn5p@N$n_EKgxUEL*D!Q?
z;Nurp6@t#J0q>y-SApY>53T!J+hWR`<8o1f?RxHg_DfjzfZ1R-6(SD{Mc!oAuFSge
zmf#>HkZ%NHc2g7AmCx|sf#U91=d(RO2AU!J(@>KWU3<s4;XOS96@yr+2OB9hI1cS^
z835oF_nXy@z@aS<5WD_6Uaqhn1-PpuzEE4ob$tbl(6Sd4y0|ueG-kQXx*h)f!0tQg
zEU$gj_Jv`R0ULps?s-I?KD^A-a`6^`c1j%G08phQfxQMm)l8JyIWGIZokFV(<S%UC
z$onW8&(^vYk(>e;Got2_Wa%^sJ4zC3NT>mqYX(wJjv6Z_>=;}h&>hHv6m04AxC$Hr
zg>>4Pa*p?QMD!4wrqANh2hE7Xp@{dJ=jT|JsKxmQ8CE$pwQAV@DeJx^Q<A}#$qHD+
zpJv^Z8;Zlw2)2PDMWwHH5HHr;oLV*d{}J_;L2;}Nv@Wheg1fuB1rHM3Ex1E)cMVR0
zGeCergF|q4cX!v|?wZr=@7{C%QB*NgR89BvyX09cVnD2&@mK=a=IgXHwC?~gNa2D(
zBz8Y*CY`?(Kl%e^C9W>9-=w<dk|cpR`W(kiiKyH_A#2*=k9zS4G(4)=*WXrruX`zm
zyKSVr_8G1pyRo-n&e3g0PJtDkw391-?+!kJi4TjY`(x?hz*`$O5A3oB^#l1mSr{r=
zcViB+;2Q5GiwW<`G;+MO2`$Zp-PsvER)W!?ei?F?k{Z{dtR|QJ?YvmwgwxK97%vfK
zy+IDv=2`Mo$9dKoM~CAI?K)TPU#?a^h;2M_-A8`$RUXFabxzf)nPQ>Ph3TkSY(0{E
zXY=ZU$sm6HFyCP}E0=6{dMq8+Vy1{FFr>o<kNW1Ns1GIPxGo$CX^ct8)Hm6qk50P?
zbtoRSeR>}1yXQjwWFlx!gq*m4pvvR0MAnprBLYM0{YYR=K3Hk61&7D|h9dY7Xmr6b
z)ISkR5zwbsE$qTl-t)L;U%e3-S)cAhX)YW34bR9|tvb>jn~!bygn<tq)6LhiT(<#L
zkLm&t$7y7rXSWHp|2>Lv1^i~HAVGq~e<K=7S`yjrQWc83ud}(p`QYbt29ZNV?VbX*
zO#jWe*t)lBQwrl529Azf9@gpP(CM>C`|<2h`b}6Q2@lGW-o5~rZ1IBb;{u9frl8|3
zrD(U-9pQvTkZf0lxUT+ZpUC#vEzI!BZ*vSw6GAS3e~dKNLyDW&Q|zjejZ>X;g{9N}
z9AP;kNS!D$6*ptRvek{N6~+-j>9B46G}|k(F=y7fzZmp`=V)!r!3t=>P#ZM><$P;>
zj7%_~g)TtoFfNg10*h}m?K<^ZAr;y4f$%ng>mT0fx;ouJjhW!Zg$H!CSz;zTHC)H~
zW8BncYlGIkQ0MI=JT>(GGxOO#lsVF~CotXu|4i%JG29n_yznQ5M=`i}bX(>=QS_5P
z?mLrUqy+EckRy4Axw#s^`j^cyGoOab7U9nS+`wX4U2F5<6@Iz+{DF{1zS$ehVIFnT
z13+-0eiHJ6q8o=?_vLNf><H)YnyY!!dzxw-GoyQ(5Ke#`o+1~#nc~2d|B#|rw)PQm
z=+<LV9NvxYG|pRBOd*JtM;nSm$)ccANV8Z~5+{hnbrBSfMjG=S9__0TjhV%x>-ztc
z1sxUWjL(!-ZFVd5k*yhKea#0PHYlFCa76bNf>pK6^8W=-06j11JKqXq3N2Qq`F4~P
zs-n#IjY1{T!}+*T6)*!(HX*fQ+Js2Ugi_*wT0D|ZJ_Up#kSH1FNVMJJsIaEFIsfQ$
znm&u=qstt{3S_9LfJ@WNwHIY$w7lvI=e`Uj5`No3cN(UnS-%Pkx&|K<3cX&=ke|C%
z9p6IWbY`1!>v;xxL6<q8Cp&j1_8SPhxWX##kn5mXhdcwH%w1XS<}|W{giZW)%8C<V
z-v`Is-4teB(1A87l_50DJd1WES<k9wDaQ1qY#`D3*87S{&@W#eL>f*`dq-^k^Jn1p
z$4EP3-RdU&fI@MRXaaY9m3p2Dpp=H@W?dcb&sxr9C$W0eaJjzK5W!r#Bm~<fZNLWm
zB93n{oc_SNwVfKf2IRf`u7pm_W_k*vz;kUVT|^%Fn7FAplV_shmxM%6P32j^fFste
zz9Uxbg5_u``C7)c!AEraSjiEcw9!XCB<VbV*+8as9I{_?rv-T*<o5^sI3O1$);bsd
z{16+7+^ejf`{4RqjsxO)!x!=|j}E=1L59#`YF7O7J>wCo^7pHrI?_<|+@;IZMN`zE
z8Vy1_jwbJA*XLTJSq42uH0P9IdPvhb5~j$~XSAHDN$16jTk?=Bc2OXYJKNRq>X9?{
zX^t(g2IBl>0WOL@uAzsw(<n-<6B~!0VHS#R`&3rY(5xiB>iB@<ieZH~4dLn$OTy!P
z|3W&Mo$kgRh9>rv-ARCgw3&o@vt0HM21cAG3C<>%(ELkf+>OrCnKm`#a8=Zo9>y-@
zPyJcqTLfN{f6ECP{rRwPHJcGpYpHq>soVbmPrzx%B&49gYu$<wo6r(W(F43j59VD4
zXVTv*SH}@;_AEs|Dgst#MPpXs_(nAdHHI>^y-`{Uvm{;%M|M_0Hhh<bP93wiyY{9b
zga>_;9!T@U%~nr}UbepHjk+>SM9su#W7aY70d~O~XB&YB$Y*tsw=XT%>nhEWjmC}J
z)aVNAEiD>f(nY2JHT}@Hi_QHzBI)R>B%JZK(6q9sv$n7~74S7@Aq(YV0eVHRUD~#K
zq_QVdBvWeXPI7W9QGl=G6wdxEs(aR2joRjTG>vnw8$+=&At(J+52`JB<n{w&KJfke
z{{8tHu*aZv3t1yp=ZX#N7yN`<ETn#jL|y`{zOc?tQ61jbR+@7hU0ir-)?X_*cIWWt
z=}Y!14fgtnN2#0bz^2a7J&PR)n$S&L?a!<Ag2L1RsXL&CVLG0{Umfs!@!~)zw4Q>c
zq#&-%9`HL4=F8Jz=$&(J0fC;m%f968B-%+O1In+sb+((KxlBXc2dhtGN&R8gc1ejU
zH4a&mzTQidNdwWR;j{;^L_g}yk%rp9MTdT2fm@*<HYN~L-Xd}iN|#^!nKfhchX7e!
zf^aJ}tN#1f*p#Y%UlSc&U)=|hV<(G~qUWJj>kisziqpRR3BnyK-B)udd^hm_z5SSd
z(z?#psd{<eQ~{R(mhJU!OWp{Gn!6JXN6W!$Ib04!iZ5I9Y!#R!3I@t=-eq7JL&Xbd
z_^)OhYHk;^P&FB@DYC>alHkIqX-e8itHD$Ow~|5}4N$ZcE{FLPTi0Foumee8p&M)z
z7Cl85kL!dybS%b-ptUCz-{3r7pCe2UI@O3q3K!B;W-ur}j9NES*#k!4qXZ`qN1MFm
zTg$*8l4-y+)26C41_UgOh`xLLARpf|PgaB|ic*3#tZN5As^>t4N@{9Wm3y)ZOf-k_
zr`DRiK2a2Ghh7x!?(W`|wG=khJ5@-b(qkW~@8Zdm_&vs~V@C%w*?zZd<D46~$xq-8
zfSc6NN2N?TQ8LUdckjc`o$8sJ@yR(Mw6t83`MbPb8^j#x)|hJAIM4`e5iiWsDMcNC
zd<ch<VlO9=7z!5DPXt#pqGkc|$sp?*;rIEpi@)g(vVr9IzG-M(#V`R?>qK}pf&;On
zDJ+?X<6(im97ykkooPH7(+WF3EWW_UME00KvStr+d>u_tT1D%lqFhWRDon=`(pp-g
z6Bl#nJ@`JnU$Y&$aoKmfH&WH3HM~{Y77gZDRlxlu0x<n#^aU8;S72|u+~Y|$?1F_J
zD{_^!%D(HX6oz)`LWYRPNE$!q?~rL%%oQe=Z}PTW_Q@0k2)U9HY#1W#|FM%ke&cpT
z45$8bN`abt1WoM(2}f!1Q~0+Thn5qR1_@o5jZ>j0y5bmMe#(**F6hNGc{8sU{TMu+
z;$z#Pz?zvWuG7k|`nUS`xX4jSB~1!-izfwxIZ!&ipG0hjci8wiHBtLwl_$wo6+*)s
zYhWrA*U=elW0d&Ba-loCZwF}G&Iu74uy@FGTMHBy6P9vurun2%T&UeQIsa(pn<NJy
zaX>7vJi)Mm3ufy4C#k-&9MHoscK!ki7!{vG9mVsy7|knBuhlNb&VTx}S43ituxxGC
z%5Be6XG@>LtCU(eNnr5@iF04)BQy@|Yt-Sc@Z{YPxXtXIY1NUYa|}EpgWHPuTpz1=
zaD-~4Pd*`7VJeZXPj!lXug`w&4YT5{C(vx&3|7$;>;M9NPQF9bvu9d3)C9O+U{=-c
zZxoLz?mZ{&CWKjoe3JV(DT90p?nBnFdT-NP@;Z48%KWdMzy>%t3XF~uG6psx>#<N8
z!BdAL&8Z@eEg4kiUj9#qw1NO2>Wa{`!S_xv$~TvFU~6}HWb?UL8yXkDc)fK;DIX3u
zsy3Kx!_r~g_Wora#&%;tz0O(+3lDGzGf+$4oM=z4yx0g;MRpY#aG>jW)-&s7*8z^O
zdky2@UpM^kC;9;bGOJj3zW;>@iy`l<k+B)5+DI!y>izU4nB4k7Q9O_=T=}WbKT85Y
zRlnJtYdo?L+Vjy{2P6Y(-6ky-0DO8=9B9<7mOa0}CAR5JO6G?lDEvd8na=ObkR9>m
z5A?N|aj!A%pYIB-j`4u~-&V~If4?T~f0L9@xE!ih(AC(|=9MUgF{g*J`f}0CBQx!O
zJ~|@H{RkBK#9yi>_Mu7y)nUd2=B4Hc=A3Q=1}49$qp*_<WtEjRDDcsopbE3>JnUcf
zbz8cVpUr2YdKR;IQ8_v+)Fu{POXx`G)r>}aei6xR7Vy5ITNJi_K3s2WnbX{<?iP5r
zo&K|)C{P^7%jMHJ65{Wl+!ox|B;s%1%fEqYz}_%e?e-x0EA-GpAORbVh{aR?CPcMP
zjRl=bZU{`56Niq1JB<h1uXbu~)qlXLa2EzH=tGI`p%8*%w71j6fJQh>m1l`^EWqAL
zHbM(l6I7UewP<PwSQ&`@6nt_7R2h(@)0Grv6DEaf<%6{;TqB3J=F}(}FmO|zz124C
zT%C6wzysaLT)FF-D35r>GId;JHnrm2)uS=rIy|@#J*hCY&2w}vL3Z792c?d2W;3XX
zSces8?9M>YjsTW)frrn6XO^|?9)l%^>hz)<1i>2qwg2^cLTK24FAY9el+hm1b4Pfi
z32WsBKP<PtRgsJ+sL3_WL{ZwWW$7>7(If45Qvgqs?-Xm7C@S0@t8Lg$`at}<E2Y|c
zhNg*|Q-DLMB0>`g78msSafo6#4svif)cegxGq$FoL4NztQ;pF%dXGp;{aCI-rV0AK
zg&%u(vDWkCXov!y1MO|P!BS9>#pU8{9UP_ZsR|_GX1*+jQC7y?x};1^%1pbnsz{%7
zDE}*|_%;HI3B!a+K&97#1W`C0zZTLfGq2W`zO?WKZR^!+Wv?OFNY1DFZU@@x(VKhL
zF9!?I)lE?_JUAhB2Z)nL^%V>&Y=zFxP<N!((KlHLhKaw3`$9QmBTKXu4sZR9WIIW9
zhO*FX)!qWv0BPdsP0~~m_;Dtrj~{#+94}%&WeXK_c)WO}^?;rBC%3;xId8B|vONrh
z(;6*=)D(%7aSh@%1YE(c3i=beJ*7x$bouV1I>sVitzR3IS|;re#tptvoSR!cFb`2Z
zP7hW@pmK}a!Avg=zio_X`wrw<J=jX>t+zmbq%~1_tm-BsW8vX~oP)>i=}N=>Na{pu
zpNT6gi(ACFvBaniYZG{Hx`JD%ykD%^Sj{TYs5%z9O-$kFzr-xjD@n!@&Mx!O+L%HP
zv1ssaN%fYm;ZR`QzL5qp?fD<y@$73{fCOqULgt)r8P^-3>?T-N(qU~n&}s$7>@M|$
zdRY#3p;JgM5pmG+M%VUS<ea{%@5SD6dQd+Sx5DUtT;OMvesqrBpBKK%6y&_3-$d#C
zM4&SK@{Nip>^~%;8CDw=R42Y4P$S$WwQPT%jYV@JJFI(!FG_UO@_a~fUxR@R73+IN
z_hQkc`2*I_@}h3Gmswf3THkCLDAw*~OzDhVWX+V4{}&zEp2JjVC2cHi;%L%m&V?~r
z;5D)*F}O~5_$}4Qm7@vWQE8IDLl^7XM-e>v-{hPkcc+YJFyCOvpLcDj(}laUuZ(De
z0nU4Z+4^sie9(y1F-Og2ct%ujRUfWOPGXfPgNtkmm)0^i0#E)VXr@GivB@#U{R2z2
zDY4bY*0%KWZKHCRzNYbAI)3arPh(|{UX$}QvJyb(%uj)5vB<Xzsh3#Rr?=JBOO>$(
zhFw=_j#7p=&;Icd#5QG#FNdk`ZHZ3<31k3RNzlID{;*YeK6QCa;Oxr``mu@Rz8T-4
zD^qZHn?)KNYclJhO2nQa6{4Nd@YsW?D8AI8?q|gQ@Zgg-lIp$qFh^eYxB<9Q$fM`y
z>x+H<)z<zX<klq?Yv??M3qV@V{$&2&E&TmKZfF7G^evfDO9~i>pyn67Qk!Idk6{p5
zi$dQMSxaRBK3ovodkQG6VU;+dfGk>Fa?30LC~KM%SQw=dy!>XkRF~pGD=_pvq#jb2
zx}&^)RJVvfo6^zb;P<MIgN*4%TMyu1ak`pJbS)e1j#OxNHuLcViaWn$=jv|eS=VUd
zx{e}g!H&QFJT|ePb7G8qvpAAF=dhG`8k!SXA<!zvFscElgov#>oC<J~P*r>?GWT@A
z96GH24@@hG9zy*nhBeHK+}%)IgD;x8b#1f^#BB<OD10-Y{!CQ@H9e&!+0;Z^n2)^Q
zX1`Q~(Humb25+(wSRhVrgW&wnF!?M&S_WR!D(Q|O-knQ<7525<U!ibj)Br6kxn&p@
zsRxs9;2cAL6mwA8LL-U;>5~tS?TDH1Ut#>-q@wcypj@{{zv8RTSEmsu28zyI=SLbG
zbQz8tFRyXtD8q2UpX2vv7|kp5bhv&_nD4HABw-u5*y`EKDdBYMJckc8Qhwtv@%VN`
z&*^wwXf~9fsil0EYwNk63Uju0Etx6NDHJvqzk*j1nt?R%u}iUVReM=(z;~RrkX5tQ
zbJBNs%YPiH9i!31%K@R@-<v~*59*>()$q{f90oey62%H~vS`OGFNUH~BHL(;;pY_^
zR_A5Y0u?7I{t<!^IVgtlhF-Iw=4$W~?L5asj;#T5#XXa(QkWKGD3$ZANhRML9f3)f
zisD~%sl(2N$o}MR-!PpjBObXIrE;?yIi&3}Yfz}Glh4D1Tx6h+-9~XFJ1kC@oDuNZ
zYjNX&3NrVdI!gY^H^YEngFGP}{ZFGOYoy+IYB`{4=a5X0tp>kj%@AV%S6{Nsh*+Kx
zQ3&rliivoLz4G~^UTs=d405IUG`XLzydaC~YHa6P``{Yi>6KEcw^^hslU@2KCJMMX
zkl{6IbMi)xCEDNk*-cNxn{W^Ic{iwOAKiTxqL!Uv1OA)yl({$v!igMF`>tOYv6E`>
zzSr<rPa50qb&yrdaqm&Lh__O3-p?KH<FHU&ghC}lmUS3A^2$*)@0|${UO2c=VSe$l
zNQ6kkuW<Zc`hbixWOR|~_^r<kaaN72uSi#l__3_yzM5a(<KFM3)qGOB3yZ$*YTOYk
z%NDF)Pz0lhclPfxIL`>*8O-!o?*|7^`09PsS)P)CTrGVz<DbPk@8@K+r+BMBNc8j>
z>VrRF<#kvm67s%@+u(2CpS}O&Wb*Vb>vn?S8VAGb`8YKAkM5VE3n$(&yuiNOgROHA
zA+F98EPZU<>J-Fn7wpefz$v*OhpobO<3Y6%UOji}6mlWgv5#p>uI!LoVXzY}Lpu$w
z?U`J^+6pI?eJ0&k<m=z@s`Cu0CtK#X`fO$xVT|x(>YXu@Jr;rNpM0Oa5B5<sr*B{i
zCuq0|@e)z#Wb*Hn%&e7eyC1iD_<Ng>EyOZxx{;7T`rApnj8y;r`e}D=R&rzIS1Pq$
z9i-%xogTE8_o*~|<<^?%1uDmIr<3@=#F5doA!FGThs%E3{d%H{0!-4hDRfZbf04BJ
zdyr}c5S>B%^}o%&dV(+C_LNp`+`a)Zfn>OMx88bQ$jRu-(bwiaO3av3QJxxy1O_zg
zJN_jxp#);(%!!k$rI8JTC63*8WXh0A!9_;ZX%`;j>-`DV;v4G|vOMfQ1EB<(Bz8-6
z9e|8Ov&Iw$8JR8QCj9xvrkFvWl@3&vw40v5ho5J@&_w6A5@wd!Se^L8)NGW}1^>2}
zIZLOa<aeoh@b$r2v@JQzW;|@Plt4xA);AqhYnQ$<=P7yI2CiRpD!@zHu3MP5{W^}<
zxFfC$-Aw|N{dXVW=M9WCh(-TvJ#@3e78RkGrY)zdn;i77uR3o4XLr~(qYfVKi+%bR
zYtQQCDZd(_Oe$Ulohtc<p$KQ^&hdMgHC0+j*8J-6v!twZFi<w3YF0Yyl{23@k<zjY
z-E(3H)7P!+U4y)5YfX8;CA5a97`JN<OJ*g)O6BG4_@~Gz@5?UC1G#`2_O2=;l<d-`
z<(MHvvm4<5Yx6@9RoGs<i^V}%q+Ht-e30Yx1@|~&GkyLV^=DP65799VD<1dw_`rZ)
zV50G_jjNi#ze77b_Y(uiZ>7=rW3~{Apcm}PpJh<QBE>(c|ExP*V|u9tCfUxGiAop*
z<tCYMgxCjADE3j@h(bNF9EPs_VIIiom>|5Do(&D`I!%ntyVafh=*&YD{x{QQWG^n~
z!+wVu1>;IZ<p&8(t~Y*=_U=n1Bn1H>%MNk)j26*1(bb8)9@OlvO{9ZIQ%YaqwOR0~
zB*xYs`>4*R3AEhjkg)03n}Yk;kvLykbVo1-B9l?7-J-?M-rhe7pA?cR_P)j#Q5u|_
ze%c%pA9Eg#G!*`1w%l6HE~uj3A?kprsDQ3gf^>0M5vLX*h#8KQ8J#_VTkhy|?^ZTa
zg$(cC9%%YcU*Zr1tJ37dej?~c^Kp|~CD)!rPY6Bu%NE?Pv)X%Hf!I_=kKs%z#GK<f
zO0=A=nZ3P`tv6Er)pRPv{8L00LGtL*HHR!Z#wAJIwn}|AKA!H=k$CObWpb)sJl-fj
zFtDFI0_EiN-K0-p1><NI5T5?PVKH*sSOG@&2Ep+!AS)?^a<N3kNz>*Z#|>oDt#$rj
z9BP}~o^5OXB%Sk2SSkrsI@AtPr!F6!kkGc@TQk2uTYCVKlIF`L1~+h`EwvO_WC#-<
zR`cxQtta=G#lTo~R`DcI8Iesy<K5;dHd+2x^zFKAIMQS|X^~D0X-f_W?$0!_$Or3j
z83<o3@i(H|i)O06t47pZh9a5Dl=jc`QMKG3-QUagKND|csF0WG>Jo<Eiqo9DuX*9U
zTRK~UPTLYqe5rkGB9>{&Oqsv`cgh0IK?L0%>4MD}bC4^zH-p~W=V-0;&0lo1poy#!
z#G)Yhl`#99CB8Z;5e`-h0x7R}dz^od`=p4=d2fQ{8Gn6-tDS<w?3W)>OxV94Gd;ic
zMY-(K!L6--K_B&;+MigHG1w*4D;IFaVG0~MUdR*ek!q}WL&oT3=zTHS7?{wK@3fQX
zqET?vupJX8#^jp$V<>XmIh*J@T0QAU4e5i;pk9e1L^(1zmkAV;6B8{xjyE%(XvVl9
z+04cOv@kj_wGa+;WMl$~bqEf(L4iWe9D6X-`q<MbR6S1BP5^?K3_WEcLT4Dn%!qcb
zx%SH~9{Wy1muxXX$~`!MH|idVb|6Cr^YU$}$)zw^BR-I~nT0xW4=C@IvlOJkp;#o9
zP1A5(`B7mf_W2L~SOkWcYk^ai+IcbUDlI7JLjzEEQUI-du8y5A)`w4Tu#>8S3%0`$
z9>i21gb4lp*(X8}JDlH-XLCtEbMm&uJ9TEs@m0R3U8xfHN@FmzvB-0^9evCF%pj~#
z`OtpAApe3<CBcpru&5q(@5EP`#W65<KYeG(Vl`+5=JIs+p|0?IbP+pU(0f)=YEVRd
z^`LSc2D(KC2gJVY+&nTb#5X9S!XS7w!|*2vkyVzl{*C<_84>#@Pdz$V`|(%k)+EPL
z&UC(w{M+yFkPK8@h*HO{-UG_#QQugM5BRvmvD>+)53r9EJmIhh$t19igwyJYTDe2$
zyGN=_VvCw|Dh+34!kXv5ff$U8R#Qfjs}}I~<R{Kp!AS6(f8rWkMAPY~Tyjrwus$SF
z|Jv^>e(YXrC97@;_^KvfR;w3A;G5s(W++Kn$>T93%6{pAE&V1GtC?3~GQ{}|6KzBn
zby^y$)^ndHx?oBd5CAzeEkkA<9&596&>h`aCxU&daFBY|sMXPdmf2nwjl5!S^sH5=
zQI%jncgjYxpTEHQ@PRf5a5gMNv_Kk3i4`f|qt7K*rno;ka}c%kM<b3~u^wLi{5T{&
z3X8J*U}yOAscb$_2xW4N7@rX^H!aVrg$v(FEMf#k8{x=z2i8Gkzsksn7{3??e7+T$
zZrUV58~?>Ua^y#qStED7tC6*k!8Sk-Bzk@eN`A`K!SZQ;B)V@}<Sbo`v>gGf48Vw<
zEjB?neI?{KSagC8<((5>ABZMzT^APQ>7ce)Z-Ltyz+}acS9zS}Bm;l{I*}}z;&5a<
z2LGIcL;ss@?8A@V#{%FXT*l`;`n<b}FvL9gRJ5W4w~p{1Kwq?e3SuAfosTahH+fh7
zpZ#vfwJGc-zPwwg2Bg922@ntv;<#4s;EK}ptx-j-2C1xKpTRW1s6mpvzH3Sv_JNy@
z0h0dJ#|$GSDA|-C?RIKb*d+RNs)!Uky?r{BgaZjr<l%sSxqZ9BJ6srN@dQZC|15&B
z|5O|4S0)PwUzTlLC0}5a5|Ch(YP3oC4yzIJMD6f9|I69<7pHrT)}?MS34Z)Vdy?p9
z1LRgBMSbw~S(+VRU+*`}>%k99g>}N(hSfP!Zm?_d&ROCM*f8hsrxuKm0UIu7^p_#a
zT40KkgD6W!kp+j<K(84-uM{$j+G=8{<*)r@xw<mWE4g-}y@C}RO=XPe@`UiSd_8RY
zKLm)s{ObWo4KQ~qgP`HL>kk8`0NXPkId*R(MQ#$D=bp0?qHYNKm$Q}nio(Hs<b#B0
zU+|HVUwlpB*-HRQ_HhvVr0Y`hVv@Q?veMh~S|}ZqK)DD}ktQZ3-?4Ozt#aCx@L+j?
zlcDP^2c?vd_$QA?RH|9z*-qkjMc?ET^iE5U>)~AyknkTGqL9%JzNE|b*;BU9WXD-V
z=flItpzQR)X~>`mGlAxke!FhB>%O&9JEZZDJ=LJoql|T>UVcd~NuB!!eK#ijQ*mPf
z>y^W$toJ!*pECbhog-sSFs-?RaG-}O;smH)Kh&K{4KeZ6JbE|1-hzKVl<&~23Sb5e
z5>5qAja)yyMsg=|$9Qh=g<LUigCD8H55O3DOk_hYp9|n`UtJsS0Cj`CZATWXA{oBT
zqWMwr<DR~t6)TVoq}FGA-sK!-$#SWZTbQVy5FFVdGzuAsUqO2uhSllG(Dg!|m?Mnr
zn12acmHhq@(^wFgN0Rb{l2W4vS!%KmON6)Zb{#MWhJne<lDvQb5Al6~j)=-Eb5=%g
z{NujZ{lVim*e{dS4F3QpIqKHL7zYyNzlYM*83^2QXCmkAUc`Hfga7s;(h4A1yx(*x
z|4hxR14^Y1s`OouJYzn!DUc;;XuQ?s?(IJ4Gz+>@)un9?*ZAR9?;TNh)3I1MD7$T?
zcTj>#|8t*V?ZjDV_fA1Roo(zZakbs~n**tTfGxF!>sRXM<5;AN_3!_9f*r*2ChSo%
zGKlMWr&7w<8~ME7G?jwyG-KDpf9A!0QlJ)|=McUKyc?GM`yPYJ@W?JtL{_{%--vzh
zDy&pwtV{Z-{1*=CY=>jfZw>+4JXkDxi~bOPn!vBgBnd7QRG5fm-hYmS0~RV`-V;?H
zKb~<WP{~kbDyG6W|9y2GW?33~k9<>HCla$KQd|hTI7;8qNpsvRI{E68Wf{dch2}`d
zZXtK{VzM<csP|$rYHdH~N-CF_O4oY3U|Dck|9GO5qL2m)Mqoe`Mc6D<dc5#?Mnakg
z8@Z^9)3nRn9!I8e8_LtEea!@|AexVKO81g#RfnV!VNjZW;#>eP!fAWfLqi2~s11U;
zI3_3m7JJM7^dF@AvI&1IrBx5J_zH~hY(7T^5;m8eZghwM{;zNvivBhOdktBM%Xs~g
zO2GJ0I&sCvKt&@O9ahA4Zy;Lx`*;~>R8d3C0Nr3IsG3EKhcd@GUv2GFq9UNdIO>bV
z(Y7os*Q{?A4%%)+H6!^eF}l?gP93q}G}y9(v<5e$(a0Ey7>mi&0v8u%Tc}~9E_OH{
zwq?4qeTsK`B9R8OmVhb${N4{V;#Nwq4r5A<x=fQw1o~HU7tHwRj}^3&6Rp(R>7(v1
zBq3oT6nqZ$Et2`Vnn-CY`PzZp{Xy4;!Jbsv{U*B&DCM2}RXC2Q`g^E&Y@Ix*x&y*q
zKhgqWa*lxJ>r7f~7CU=RMH=SvO&8)80Uusl0Z+V9QJi0HY~QAdLC~2m#`e!B1|CuS
zfRYB;z|D|$#%G;pcto5yvY?285k2vhiBglkXq>-7a$!K-Pc;A;yvDH9JG%E}tlfk^
zqLCo9JS#J}!EuL>G(J!ohgWjQ0@iHW5LYK~|5-D<<CrG(SMDmd6Rn0$kXCNX&Tmfr
z%cc&~lrmTB?fvuc?`oltHIpQ@|B674DMUeQ8~CNMj|x<!1X;SmSwqH}*C<^5820S{
zE-SRb3v|(hXGC6YVjj~KR7aXj;)<cj3yqR{8zmn1Lv-BmIPfdijCCTC2b9v4rptI<
z;2soNu&>w07@NpItN!HO*DJp-L-8Y?ApO3A=gn*i$cu4mFhDT#lxdRX3th;(b7AUJ
z%N`)=Dl^TGPn<Lb1g$QfUPPb&bg?t*KtD2J-v6%o>M%H~#BEYY-xD;2Oo2H{<o4U=
zL^^G5-jZ7udK`}3?m2~VyVo-0QyR0#yp`vqVSsih>i&1B#&YG9asEsfF2#K#$!{#_
zZ_bW6E6M}xI^?d?$Ca+*XGl{a0gLq-9KV!$iN_d8HX}d67i|aX?Vf}sLkmMgiQ?&o
zN}~f_hz7=u^=RfSuxs%bj(~=zT8{VVzWL$}QnqaZD?&mDum`KO{AuWoA7E7??(R_h
zKWPu(?wpbiRoT4H<jUD>o=taVg4fnG9Q1X|gSZwTx=^`<tz-aEVLPE>7vX7;L41d;
z3vrxEGeiZG*VV&@v9+HK>kyT9)F>bl3-jbQi{yvJpVlZ{ldCtdOrlELzeJ9VC*#z@
zhV#ygW8SR+Y?N>P@-TQG@qg|sWpD&9L!K6M3EKHQUYR3akuQ?fgklySt(&{+Ofv+`
zM|NwBEx?c^LG%HSD3Szn*1Zn^nt?ix;C?8fVDD@S;=Jh_>HEYD41WAkMj0!NJBwZ;
zTO`MUcp9eJ54o)dRy4?g?Y1IO9|l!-ACP+6&3rptqx0j-<26C8r3zY_eT&Dry`j!g
z8C!5HeWP-43s_`923)REN5fOTN^?JEtNK2wH$l9loE%484_y7)01Mdlc6^AW!ky|F
zbK}-m_;J0^QLgh82#8V5FyN9WG3t{r;#4Fr%yBZKsa__M4feOwS1UIs`Q1stKhqaK
zLOJXRC2TS)UZ^FHiZovn`~OaVA#}<keEMTWd(Ajg5C`+M*ap}h*5+Q>uGReiSpcM*
zI7kx6WYf?AyyBuVq&F+GP#r>*kWRcmyHS>n^SM18;6E&~tcvVkba$c<!%(K7ZHLM&
zo1k3h&FuaHxCLM2!?5~>anY!YkI%bNF2QMq7lRw+W?olwm>+Dd$&ga<a7~lyt{cB{
zye251Os@CB$)WpvzTy7YfF_1{)Xn<!JHfQ*>2zOBjq9MTkX^}CtSU<yc4y+{EUyzV
z=ZmphHm$m0Op<;-=VtG464Cp^M{^{y!JZSA%>XD5SuRXne=6PcM(p*m55Qdt(;$*w
zSqV3}@X5MOuvP-CC5%-#Oi*Z5iMDol^Z7r2<9Tr=*fD^0U2C%z8BQtPq|<4bI45#}
zI~L|A6N}Y&#&yC!4_B3=UUcuRvuEjPMiO27G@V!4IMmd>d8yCWbj9(+V*h=Wn`xq8
zFLO{b@Rt)S|MINyxdqBD78u_9{_N-F&cMdlw@J4Jf<aZFagZ#YtqWwM1?x|M$hp($
zmm;PtlN)zUZU2re-z%9`fDa-Q+%(gmYzz)~^E!MC+~M-U+=6_3Q7Q5UQ+XX=eubVD
z9TWboa!zfUF|xDBi1={Ze*2K~MB&&q7|3jQF?+_-x(jwBB6}*re#VlRp#NF51?-s3
zS|Qf^yU2n)k`V^Ret*Ss#b(m~<MPti*AINuk{=z3`e9G7gdNOY{}Z?V;}~+FIFg96
z3d*OQy#v>z2`1?%rGHKB|5ugKho)5m_X6q3??Bt8z{kKktt*cFa~Y<?S^je!F-^K>
zS0^Tb*T=B(@EyW<nQt$bAJ$?sumi>e%~r25!>qz~vUFziNhgE?PC`;eQ7bu~{L(lf
zgo9-)50XnCDwO}C#*CTQuNA7u&H)R&2L!Q9pVOFK7kpkGoDju_Ppb1Zfa5cEq9{Qy
z-@y^cRvybkJA1n1g8=aaP)lw(2s0l|W%oS%owCa#u<Urb-}HN)`dS?Yy;VkQ1SaCr
z&%5EBC|~8WuQ+7i;C=gT;HVP9bTD5-AON39xBdfmDX2HDEfcd3A257fHH1AcjE=oH
z@|`acX7)jtMj7aNaxTFl4;5PHe)jlb6=nEg@+Ga78ZS0Ej)f3{9Dk_K((pp5zoUdP
z+t3s*%6w^d7b|ezU0{6p(~0W()=WYmoQPBzE9oH<MMr}t-&jDEG8Y*r=>L$nf`jia
zb$t6J!b0LR&Ac|bN57T<O#`9|BA%}8$6&_Coj&3=_RVg(St50P14gxmVxxY%(C}wL
zrdVvouRw_Az2JM5geVT^KI~R%(!RRJ<P;P6F)DIF-QBP|28j*Bx<L^gLXD_}xa;VC
zOgcceTlS(^jdrK(O@S39jyvoF2UfEL3e#pRi|LU}L!a%nEP(t7zouo)20$WwUT-kn
z4pInkqec?Z0V)l7<=K4L;FZ5_UpEW)cG&sP+*YrZEd`x+6mIaJ;yzrxE{JRbq5OxN
zW2I4*UH^_TX36IfYav##tnDV(S%%=!$GizcW-+ZMn4*$Ex3FZ1T?KagG#}<EnAH>1
z^iDzH@@K4i(v@Ha$<nRuz3BND)ml>>yzDD?S`=6^SK6{XHdi${sTpb$ThKa3;fzIb
zgQdG`cLR3cr>)vcMY1?C!uA>6XMxv~T*y0@M(wSF3(f5R&s!6{bMV*A^4vyjy&7hm
zt`o#f@P4{iylq&UVmhw27$dMzzPoHBOE}{XM<Jw{NI%V<4+Ie}OS9@VLM)#*#sx;x
z(cf*S;)ZJH(%P7^j+s$sLl>sQsron!P7<qA!ArG0@Z`Ek48tyer2WYsO3J|8bv^J&
zChUp4P{2el?f3YxG304-i-^cs);d;t$EV;r2a>87lnm*17mXO&Ga`#&5<420bGwap
zeeYD2^=g1g7zN;kEo9SN3;q{&S`vdcL$JsSHDk)ynfnhf<_*~W21jm1YLf(U1j;PX
zLi)ro7V5y7a|OfjkLq9WIj-1}1$AXK88`0I+FG)$#P>s0IAK7&(3WMTT?owYg|s@M
zkx!@j!&^=MVy&}H<j3c-2;J8$VJiSX9@ZsQdB4Y`J@`vV%qNN5a$?9Xm`=CMh<!AT
zmvo$1AOu6BWbhh%leY|!7V%EP2MRo$raw;2Xc>Tl2W>H<q&ayH2FgcZQwnBj>Y=bx
zdut1>#WV_IKEApY4gn|ilLrgf`-tB6aWBP+bzQ`KI4Qo9LQ}CJmMI{bvkuD4q}BVj
zWT171Qv)9~+9bLKh*{&&CXiWE`e#eWmYw@peTm)Q4*HitKjqjVN|_<O^zgcdD5^<S
zlZ=;J_Zqa>4J7&`k#LNfZLvQ5;`iO<nG*pC=+J^ZZPk7+eu17V)C(-&nqa+V4OEJY
zCtq6vS;fPn{hNZ4{+l&mh-Moepu%yuE+fQ41-TYb@x25s;=B!!Ou`7PgF*4rv~G{P
z8nHhFSU>~i;Q8&)bN!jZ$P@N~)^Bm^D=Y38Jh(Mr3jO;Bsi7(FZgcWOUe@z7bh<<U
zI-YN~P#AFwE|bf*Rd4%$SGB3Gm(sB^D52@*tnbZJcP0Ms4;L_0+H66wWmXLO5!0}h
zpjjIu+on*@t==}g%2ICs5>wI$xooC1!V*Bhr?uW;5}ka|-l1`QEXHAQV-7p0<oiL-
z1G84IF0Lo<)k=7Q00*{bMt%gHf8I3vO0ijN6(#&8oQiP|`rtwW@Ax;;&$HeK-pe=X
zRA%?`B%0XFN4akA8dr}(nYddwu0_GFi6FMzj%CfI6w$gve7(`(EA)&1InKj_7vg3%
znmrL3bWl0c&46XmA;4*E@^KPeyL6LZxr0|UE|Av^`g_Dx3R_)Vs5xtTXfNOkm(gak
zs!#}Qcx4}#?M4x-Q1OBtP%o!(F>IW53f5<a6y(Ae2pS2wJ1?0}9sfrL(1O-ulYk&x
z>zJMgwnRmYfXM)`?xuRu=ql-At5?m9l;h_`cSFBhji&RcrBWc-$l<|pI;A--c~)Gd
z6c!SqJrF~L3tUfMq3CP?)4K@t*O|6C8;wH`8pd(@85Vb8+CBw?d}Ix9r8kp5IoZYt
z4J>E}H%7qxXk1r_;Z5Cms2=$dSij>ob<euS%<4U1A7h6ZV?1|}e{vP+a<Q8C*~CAj
z`yO2g$FXV$Af!$L2_t#1pw6<iP{U2L+5fIya4F<?Vxn7Yz<>YKv=l(MPvP;NEneLt
zI9O@;L!{o6k$MXu1UTTxkT{hoEqzYh5gu-98EEjK0NbM52P&f8xCid~cUi}T4$b&C
z9V$in+55F>4f#N7ZklHV$L|<tiGE5ln2;6fOg`;FL7Rp#tC7;HGoNaElI>P5svT2~
zsWnaE5IadjS#R@#+0Q;zovt=vDtU#Zt{|**0vXLq0EHUd&jibJ5fc0t+cs2>TDQhR
zL5VJA#hmR}3;+I|9GI4z`bE>U&b4J*zz72+btU+bg0^l@O6;#>b42#Xdtlc+SxhkK
zbXrfdEz6C&6VeADr&a<YRhe!i7mJs(D3h7>$hijk3aVY-QpUgqZo9ntFJSTKi*i<}
zOcs!?=ZBeqfrtNo=E|s9mP%&_Wk`n<2sHbUP!Y(|=lx;9+Uuxof9F#m1Xj!t)bnkL
z0|5S#;D>*DL+~gGQ?ZHY7U-+R0ro~y$e$+7ZH2m|JTTMOMeNPYo_?Y>WUt8YudY#7
z%2**`e@*Jj`y&`8^6yEl+PZR$w|MD8jlDl*B2tHiF)Mag^79%gVMS+UV&>)Sd_BkL
zZVBV=d^stlE`F`;gs+ugZ7z&;`M(!3AhBZyds19`FrS2hju%*TK<D<=L9HmL$VDn0
zk;NXeJMAo-e+cM_-~&^e<m)7Fs5oJY$teQ<nz&<9Qn}i%zE^yrw%Z@0GeUn{iV~T`
zl@?XuNuS|z-i;kPXF1U!=5F~!lg(F0xCnPo)EZWNA``+_ei=PzMG^|I{jo~j$6ep2
zi}>J^ZIp(E;oww9<U>h<9DW+NFODj0YV!Rz-9iO=vy!b9Ec#iN7s+!$X3qj=duRPQ
zs?lF2=vrDz9slvHra-<>B5uPkf7|~+psMDIGe`d>I`~TcseRl;u%#69$=)gme{8;Z
zW@x?So_nB3Z_^PNvMIF2qF03(Nnus0D(yHtoi`cm^0^oVqdw%!CQwaO&W!%o64!&y
zAfixy`i>^tIKrJ%`iFaSiC{CAGWZ^0nbOD>2*ZN)H_bpXfj;x`*N|plE%89~(@Ypx
zAwWOLwsFuBZy}zcutDirDA5?vz$#oAabi!EM1ac|vg(casNLdT4ugrI{0LwKOdl?`
z7*f|Q65tF1Ufrw-X_XUCY522+xC=nhyAoMx0}UAB5SJU8CKY%<N(bRrY`pQ>-kx!a
z0HWq8o_q;K|8J16M^}FkUgf+tS?%>crnc~?SJomA>2=lITy6AQE5D-m+4a(HE0$d$
z(CevgZg4k~Q<$hesQn)?r20GD7qaa`rr{;AiDp!fSU4`n^RuRf=7Pf_NY$l8<FG&~
zC0x#)%$DyM^U)RSM?jXk$h(X;C=jSd%DJvj1r5itL>$C$twc_$r4-M^m1;Y6MQz1~
zgIJ`ens13TIZv*r<tq#g+1dpI$4xu-m@+=3*C-n|t??KOx+|sA(9D@k>Xsp_Ft0ig
z8#YS>K5b8ao7ElEFQKwnTfs;B$FIa@y4#!obUW#BPa@B%4<{0p{EHVRI;#+21sz2q
z10wHORFzig7uQgps1FQ>BgDU}5muk5Wg^gLAvp$!Te+x!YVB#dFFD8%%sNWYe>JA)
z>4jFH&V|yL5U2BkGA4+A2ACy~D}z|H>42I@{joPS_2UsAaBhKEc}JM!>z=jfQ`Vnb
zTRxG=t)4tD<N`RFDLXgjB%q}qdvhEL62X<$t2NfMd;zLox@+qtHz#{Nh`H|o@@m~U
zuh<RvyDfD?V8sutHZ|kG80BXk-gWV{(S5!5QTxZ;R1O>h{gdVg3Z*_ooEAQIEwa>_
z-LlWybY~<^w=dxi0U>Xscq}%^8gi`{V!wOpaOjgO6WJf`AMFmVU)$)u))t*iux+^P
zj~fsC${f6{oXWbssxr$BgWqPm^A@Dx{u)K@BQSD8YPdnfnf}K^A1itsi3=&|Ec6;_
zq0nEfU-_-ueTyI^l|v3MIn~6AP{dyFNo0z2#!?=h_Ov!%8Ctxf<4*6p^qYOCJ?D{;
z*oOXm$b;`#8mBaenPbN1`)eoO9hF`Db*wLwL_sd4>uceOjI3QN6Qbq)2!Gq=(&uxe
zCwxAV5Lm2!wkhdHP1|LdEeeShdHx{u%T2ltUN-x7g<Ba*Bkn>=%*nsj3i(d#k88A#
zv1HNK)qK*~>F3C$9?af6Ja_e%i0uU9gbC9FFR_m@Rken}&u_dhZma7gz_ywnCcs8Y
zbfa<nIqI9VsB1mbJ|Ex<^T-I7x6&B)aC#-6XF%(^BO#84N2UY&!xdw;Tn<LMrRE?M
za}$6|=IUD0AX2%G-FGRKgG@KWP!Af4sV!=C!aXsc4HEdCLI6iqlmdmyL+o4b;L|&e
zlk78B=jD4KelLVY%o^zE$N5E^KyB`98zLR+<Vqk~dswg3GryCL?U*x03mT7?0{z^Y
zaSGzfS-HhyDe9|T4^{aMXysh9wW$>0+`YxOw!M+;q?Y#97w6YzV!Y70LwLdOl~;D^
z6@>FgEki%*fy?vOYgEq;y`i6(DydV}*uXh{hoR_J;1taehBa=0@^cG0S(XSKu~qcY
zoF;`*Ig#e%`npbGmDz=`P3!K-DxW)HO>=qQHGE6<eunqMGr4KJvh(zpkxISM(Yp%p
z5JUIpDS*I7Ta(K0SNG}uJPWjRSo=Sq&=~<r<pihIBpLsH{t)@uuayeMUon88+~%rY
zp(yZdBdP#;LclH58Il}GR1&7wx$lo9J-<`%21aKSLw+-oE25@L9)|4Cwl7b|T>|*r
zFVOt}Ct!4{ddwf+I2)10KzzPXp3rpi#d2d{x5`kItXb*pqYKFawayLH)vn|{Y2oJ6
z6s7k4(16ut$AWnyG*mt=*$3{e?z!2zvGm=h05AbS;Jv4q&u!2bQ3Bc-6pdswBUqm{
z%2Et^5!gEo-56nb-4P~nEPscNbL!5%?&n)5E5~s`0r$h<qh{8ck%5;KCLM~kuo6EF
z3njXAJACz}Mf!pf6au#cr2BU|V?VH^iMuQ{HJ~E_ZDyaN`(vVa*BAJ6Dozqe51{eY
zuIHvuz*G@tKa~qh;&Z|-NG96h8n9lb!#U(E#l|$V2|Cw>f*%Z&+F{jFb#czjT}Hph
zSB#z){R&8><iR5dB-Gl^_Bg`2zGn~e4$Ee~Bg99a1;c=IZ&C{KEJ|k^bF=aF8?ja7
zcF}w@(yxw3I)hioe3-UUu2~mb)+}qN8>M?ESINHpe=81s+Ysd6Mtt(OOK(2l@Iz8X
zw)RNi`sqS=g-os~%JzN=KL|&NjIM^fj(Ebc#fYfSvq3_iF}EdgA!0=%jH$t$_(G;E
zd!_31ZgON<$cz0B=AIlZrFCB=Fx4|rd1$V&)ezEX)A04EcTMpzdBjF!vz=}H2p1u|
z{O0%kyXxQMUC+ToM{{F{kW6CruF@n{&@VIod^v#bdlW1W^Y0E*lubi%Ws$<(NXJl+
zEsy>RgxtM^vcT4GE>-JUM<C#<N0_tEvo$OQO=nGbI~re8bYTSRHDUMX&~%^R%S}dv
zkE4Z3#1G{h&a{fY*J=d)@kH|PiDEE*en6l6z#|}F*0&11VmsR`S+M2~Ns*-q6>4+z
z>X6dZ(V`2btI5*Xip#L}MB3rg&nipW?fEw=TYfi;CYWQx88NZNssP3cyPxP|{Sd7s
z%z4zy4Q$_*Wdtti>s6vB^;xL<dlerCL^?+N5%kMn0qjIVTcYtBNuy5f9_ZlJ()7hW
zHu|2JlJDC-$i@u1$@>C_!JR&8wR`T|oDcqtoq(FA`|8aZ)c$dHi}joz^56^s_p_ro
zx5_6cT8f_3b@)qms9k!9ae~_YiYs6|3i30wH&LE$@O<FDO`vW4i{h_60Mp1#YH{dS
z1OJg_o7^)tckv2gT(-h7CP>~NW_~K>>9}^uN;q55IkAcme6A3KNq^FyDg~?#|7)BO
zMnA_t{h<c|()A3;c?B7XuRIT4d<9Iopueq!v(#;Qdaa%xVR+;C-GPRS3`WjaY<H+f
zEs81vpW6MXJE|=6Vge+y2+*3Q1Q14)`^KaDbN=E<Q<wi%1B89Ayp13B>^-TMjkZ2j
zonZKPEEpYVZq0$@e`ZkT60fCA&f?+P?dISx^CM@GEFk>Y75_ADIK?BY;s<{eX7>x3
z1Q9&@E)4vq<vF8V_?0{((l2Di6muKR2g04D)#cpR!6g(!y5AwD0$Gi`{5~a%8+qjM
zpxh3l5V@E*jdQ?kl*J0YdF8YmPf~x2>iRBVya*I?h7e8tE4^Zhp4*^2>wzHbu_&jC
z35jR+FgPQ(F9O>+DcPYT6pvl>1_Lg|w8m05@dC56PVL_dWo%oY|C?;Gqxag^5W{v`
z*fag54z%Z9iGcB7*dLza;kaX%U$!55SDT#2M}Ac<HcbaWprR64{iMWlXwR^IZv6*?
zSJgHt!~#E%MqhuQ%ra(`@r&Iw?8K<L_2rsNWzS-%kpdJ3PvAe8FQFL++(g!AqI|Vd
zq}Nz=sp$guL@&#(US)PZ2GNlE-NbXnI76kLPeQ>W8P$7w#8D@oCc+62JU4!O8jPm|
zOm*xgv$?#gR5;^yL;P4KJ}l14U!o)$G)lG`_1@D1I%?1-$mq4wfro3Tt2@N`D(Mpq
zpSCk+QYV|;cs@k)RP^4v3~W>3Pzq9DwSfNA(sZ5Eul2kFPg3(PzG!q(u)_Pk4ayIG
zd6QNS>&_c@LXOs!bf_=>ILR%z1O8P%8*xILm68)Xn@#^UX~CyOJ`Rv!V<LZG5~apW
zEijr}9n|GP(Cbr*8?Ty?cDfzba5=#`O@#DW7r9n0mN2)?s3A^n!S30z-*@y5g8V_H
zUEtc#S~OeUSa$wfKZkxeJiEYAZ=iYgpYcJb@xfDP+h8&yuDu~zPqAqsy7HI6i0&Ve
z0=H+$_*FjSH`&8An@i;fJO+)F*v`lDeDm#2RER}Gyji$kV=JP>=UgbI?M0Y;jX$v<
zf!a#bDIMF_+>$y95s;89#H2d`xi74MhQUngX}}KUf`7{+-CnKf4|s+SS=THwfqL@(
zvYkU7Shg|C`cY9rZ`Ls8qAK4Ub@o!Mk>d1%W;!)JU{W=nV(3f1w7kSqFqU>yEyznz
zu@ej1F>=n=r7w-EC;BS3r*eHcJ=oAjYVjtdUm#X53kA;~4|E@SY;;vwN)Bii^!Yoe
zVZz@8!b|Q`I;SLY9q&5Of!zgtP!Q$}P`K+UQpsa;TUO7|34m*0mCTh-7eC;u5TH8H
znjO>hWB@`tK$dy>ZaULcqu0|kyrN|_{_%T*>_WT321_j9cDG>m+2gHv<gJNXyG^TV
z3D4XsIYcQYelxk8@tI+8%Z&Mtmnp{V%J^k7@PC}Fc~O8+`FjBSE>R;ZmC21LE3OH2
zR<V>dI4u4wpubmLQ9I|zPCY>%sONknrRCx~@X=P{jvMN4$$Uldv_6VO$8LUdLZuxO
z@+7w759>Jg_-<hgJ7rhDQGb?m%5(aG0;UGqSYE8H;G_Mj)!M3ZgEoSb7OnEH8ue<`
z;;L4rrfqaTdYV5nNw*N5Gf%LifcHU%YIw2Pf(0Ho2P&m!sklA#TmFw%d*<s$8Ewo#
z>9UL9(Lf;<B9nlu`_EVd3@G91FlndOF0NYJJ(z6utKzMW+XncO>;pcLX{H=5>3yp_
zqx(X3SrF>=`pc#OHG=Z|;|l#LtgDT}!di(?svRR(6HTAwKQ7BV&IrKGw8#NMh=<3T
zgZ_%^B!oVC!sBr3Fbi?DOd=IO+itcQAo+6$p?<;M9M^!;JG5jZ%16WJfFTWdamtmG
zS1V^Q^LNGU?C^WO)1ghol)R{f=CHUqTU*&G;qr)TIlZ3f-JG{@Wu!QJ6bmfn)5i6V
z#O-Y7{?ks8$Ox><&zt#$I+2|W8eh^X>U4i)bspfL*l&C}ji+?d4fLY(Vcy>h@-hSu
zu96bqVp%EG^^DTR52q*o;tFg7dY99a;-)#xApAXNS|wq*Si!3Z7^lmny%OyT5~5G;
zWuK<pQn}^>OYgd$``r7VIc*o&5Q`{3=Pr55QD!~u2>m|(T74Ri9UVwoZKA@*ZVm#{
z{>Ff`b%oU-BHPHYoL2b`#smmuTp<{hDcDaEhsE&8JRN?+4}Zp+mf^4wdKcMEKh9F(
z<XBQx9zP>nt<>TM%fZXAvAT_lQ2XM3)Xt~+J>hlJ=V_bn`C-mGMIss82fLy{hop|$
zU%-A6i?I1&A3xy{X$z+F?2tCpW+I_zvIQdM)j!-$rwQT?<>sS|&(^tDzz9R5cSDJH
z^D+s)2VM<LhIGfeol2VrJ)?5WrDsN|RbZLJO=$0lWyQ3{LR;Uee5>RCVd^ZS;_A8}
z9VEdW0t9yr?oI>2-92am1a}Ya(0K6R?(XjH4#C~s=kk6tYt6rgU(j$*pIy7^sgtMS
zi*nnY6TT|JQMeS@Cl1o*dDi`@8bUX|S&U+*)2=yBj$a?8qCoji`8t@0ZZI1sTLGqI
z#s78>n2v#dU(@c8m_V{&(kPoSZR_u@gMnQ6cW$Y=zbe|86@&zQhA_}wyL;wL8`T{u
zi~KvR?VA<%Zsuw~-KVL=ppvaj%JW3>ybNa<X7~<xS3vLg9Riu<kL{xX(!0M*xA}ac
z8^vJE+TLa>isk3}--OwC`f0dET+-EOW<sjS7_!3$wLAXTI!;3?aSAD4Bc`K!IW`}7
z6f9AohI#Oj^Kp98<!$s14Z}TOE`+ogYBDfutk<AFZmVFb7`iA_5b}=epjSYOqVhbr
zgecX<`znI-;&k|IkOU#Sy0j@~0Q~cQ&*4{SDB4H{MG98j;EYEN8@x&@1d+ZG>8#>X
zbk`0?E`Vw(ggoVzT3>TzA!$GFLbwNX&l6pzv($;3$wl?bk{&CE`y39dbj0r6D7gk~
z^`NOQ4bto#&Y7=ytGS%|>b%lFe}BhipxsOex4dqt1i0`kRLAKxt`+57UeK0<)-4y`
z5(;9gK~sI%I?fn1^EtK{jo?<{?oV?+xuNW(+&x5%p<DcV_WSp{w9AVL)`>fd5{N&0
zJ!iC5|E@0j*?r&Kd_09TwK_%P>rnrl*cYBUR4PQ{K4}4-meki5odZ5}s*p`WLc-a3
zZ;tb=kvFk>cXhRy|Lpa4&0c<F#C0syo-+N;b=o8jRD0QkH8JAU_QE2%PHA^~G-~o_
z*7p-(vuCo&(ZW;K7XN56JIHuwn@4^rOOlzggNyM*`H=7oz6?ojn|c{7x+}b2xb0om
z{Yx5Qx!R;EmB>)KJaN5#&@WP#T{hL1f4X`6GQv7O03fCucycCzFplhpZrkV@O_<C`
z7NLT$F`0q6{aafrGD{;=1CkRgz({v{x<chh*tGg<YQ4{FFCkv>`Fwnj&JVe6RvU$$
zpF){P<EtNX?X08qQF+kzxD{R&7t2FgUKwbtTZYR5f4wkcZ#-k(5ef}CIJ)4=ml)Ga
zell&Au-{@Gq~I2`e`#mriBxA(OH-QAF`ST!Q3;f1cFXTyAVE58r>5l_D_2OsgYs()
zdqbP-r3OUfn)sM`_@V~`?bm=ZqEImOp~AD#Y_a-$Kk19y-kdf@6&lM;7yQuegb2#u
zW^Y)n;dhUiYh?^=$`VU|2U4<et~sm{U2&E~2C_s|SY44Rgp9z>%Bo9LZLHlzY_E*%
z%ap1t{J<nTDyK!6wiRH7;WV2GhqM1zcoBef=;GH=sI%?_b$n#*C_*5AiO*{L+hD#1
zfq4Jk68o4mV2}1uG|mhse9IX~U0yeDzUQd0U!%ndZ5gZ2`|zyy2hRaxVVE+>Swh+|
zN|+G5&~G@Ls}(7T-WxZx(Z;Rbi2Rpyes{jH?s37iZH8oJ4u9I-(XV?H<6TvkV@vhy
z5Pw7w&E~f^MxE6kf0dPyWHQTAYkG^6g>=)QZ?3gKXy*)x^kR|Z7LlHQ4abC0>0+{%
zi$291jt4_1=69!N3Z%7<J{+bQKG;5pg7NhnwOCAt$zL;HFK;;Yhii-Hjr)sqm9hgY
zHgjAKHmkVrG7FS<Yi6J4FV&q6Jgcw<$t}OlDbSA0_cFGY8`Gsq24R?Pwa4BLMthGn
zNVtzA1?Wb3hRI3vmWk!+P>GdjdC5V_`|n#m;F(Tt_pI7SN)~LgqLnSm$n1|C2Z_Tf
zP^EbiSLmEydn;wjOPak%B0aROaMVMI0VU_PcAwzM>vu--Mdad@GWEt!8|kSyng<ec
z#Bp>V#cD*1q*qu21R8IAQ&$}&#|U^Crt3?mx7AO{rrv-ewGpeSQcGtVwhiB8e6>hz
zsJfxQW~vnAY@)n+xQ=>vW04<EiKY+++^z@>2`pm)RsNu2LdGld3%eA?!_OoC3}PKl
ztEhWA!?Nk0=08R@%8wfDJspOD>nX-7f33DeNjf2B|I5Dkd1_Q*06)xJ1lQlQyKm*H
zakJKE5$Z9w-@}B=#Xn5ZA70JTLw@$z4*B5E0IO0HGUCE*`4Q9Itk~gfO;_Orq-X`m
zwS#(v{A@BGxhQL<u`e-hb?dDoX3Vstr?6%610CXxo_ZB<IUXIk-FHh`*!OiC-^O`!
zBwh+6b<3gGs6@(~Pz5EaexRhQN9(9;>DOW_Jdc4{g$}RjvQwQPO=g+KpZ+k%^Vcma
zveKy*#R-I4gl<%XDiO!t={Uz>hQM~RJ^>bXJu;nGn1^hep99u8C_wobTbr4YNKUs;
z>Mfl;MqX-DMI_1`20L|2z<S1oApNu39EDR2#6WN$&O(+Lf@X_*k_@a@KvvHXB>eqQ
z$BX68=0>sI!2<h3;enLra|7@0Fj<%iTdq62^tnH3(zG>(o(1Slfa5dzXIf4nG$o8_
zQamz}aQ4vX8NA@+@cv6#U6LY*F)+Yplo~NyY60Fth%lidq2G6;XIcX%f<@GJHE$)F
z(DabD;>5PvvbK!*{8OahUuYc|bX9WNc3+v&6!o%Pyj`VM!b-ZkAJDp@A+X2RkNw0k
z)=V6-m_Herj-?h3+Kjzi_Zi;`!<@W6oy{X54X{fp>9J?4>J&Pvs2f-1GV&2YkSJ50
zTP@d>uH2n>Pqpj9uydz<?P?n|2`I#i>u*fO*|Fag8-Quq52TLo0=kFD-847t+Qu&J
z&sYjR!1ZWt5_b?28(82zZ+R^@JC`3X*UKlfTTlY*jX6JW#OHs)WA<26)&MA#o!eoq
zMDwzuNst+~GyMx;>$eTI$}?>F;h%Taur9trT4n92yiiXCOw0wR(KKlM!J3QlRcqjA
zdC4fJh@SCv_1jebzaavs#v{Bw*XWS?80b{wOrrO9fe(8&G$0+`Jq7<TQiHL=ZxLVI
zsy@QRE`XC=tk8FV0#<7!fP+FH$CquKh!`_UP&6N+k12NUauY$;$?{SOfI#!gF85P8
z$%m6tFR$V<a@YJEbG)90s$K_3ac}=M+ZitIXqRdGBjVc=u@XJ1v+T$+-aYRpV;ZiX
zv=b#vFEb8Ry#=&B5#;boc)NS^lD*9>Q#r!K$Lm%{4wYr-!dnsa&rf2w112jqP+8#6
zwP4<noBtOb&`cWaUc8i=XdqT*SY@%)&iTIpsLM@hMk(OG!l9rP2<4k)y+&bp#ld))
zQ+~uV1sATWA6E3~FivF?@(lj243x^XH$7q3Jiq+bc$e>Hep#18*Ha}$?cv(&bVN(%
zL0RO=!S3UhlHqW8taR@MZ~>t=^`34|c7XN+2W$v2eo79raiJLzIP@*o@33{$aPmo6
zGtjff?m*qj4Y}pZB@%^l<h7grKu&MUO5QAod|5-*Jhz|+%e_SX1MarlA5gk|iaj?u
zwaTUHAcH1?X`B(>SRraNZO@<bVm^3~7=zk);a^mm<;=hCtylLw8sB7LgZ{b*YImhW
z@xJkq?!<%4GCvR2>nGwgEb-ApGL!r+a&-~A{1aKZ{1rG;dPF+7T($;+l{rP}Fc$!#
z%O<5#=1-X#4A2OrI_pVfNdSvfU{>fi?1(7>WZ~9C&ag|B$ia2DWqbMBM7sR4VVWI(
zgDDhm1s}VI?S)%qmiz6Wg$p$;?yMVj&96?0ua>DLD|oR$1`)Mrq|fGDXc8HO30(-3
z^oi%mXOxKy0FQ+%Z(|kX4Wt<FVYsqneB)<ODvQ9UjiCZbWqnhC%LFjGX4Zchtswgn
z8(5Q=7{v9PjYx~m>LILqIT0qJCpe!h)7%u##|ymG1gqMzVqV3*@-7ctMC=(WmFAWi
z`N-BvkC(MmW^DhV2uS`B3-f~`P73Aotkko&V7rL*9XZ(}8dtohrhPhJTU~&|qbfK5
zYV}yG2!=7yAwgzI(r}hQ1OcxaZy&35FAgM{<$ou}`$=+)#&o8*wl(JLdaPU}t^u4Q
z#NR{Q>j*WVH+?=BBT;@0pK;@hk%|*J$aw`ci^mMa?8BPAI;Pznpp<;A)%~awn`Z=Q
zPnlDCa0qURSN}D<JNSwSE7XI_W%F01RBKjO&0a4(XQS4$>k~F47A`eQwhR#I{!IBv
z5a5$(W>VI@Kw1s$h&#z>>A~m$Hv(*)eSaFum4?b&DVZ1xydVT&?l4T6tU-iv{=3w0
zB0;_4-5wGe_uwGdbG`!A;)K(mOeC^gCa^>Py~Oli*Qt-t^pcpI`xF+sfkT8$r?>&f
zJX9u`Rmkk?0qVa;cd1|UO-f6tvAmG)<#5l>SL2sJbFhnw%7E%Rr$*Tf){=A-cf>Wb
z=B24p<Y$<b*lD<z(<Zw^Af5g9`Ev=#*R6LXH6&<|xf03=oQEqUJ_<j@6U$xW`KN{X
zj(Xi679<%N(&4=3DlQM-3Se-O8`S&TD_4&0;+|95%P|MFWRUe%&f=9>LX1at7}a-D
z`2-4M{XO&~v>eT8@X8nyK$(fg2hG~-X5--65oTu3aoifcN2_1C&LR)rznvj%76;2&
z*>B?1r3w@mOM7qGf!#TSWBeCx#t-P`8R^stSh}Dn90=^NiLn^{Y5y?dw~#_;PDwWD
zv`tmmVI(m<;0fEJ^db!8r>I*+C!ClM<xM;&RjJu>3r>s6={j+<_db0oI7;W0wxZD}
zsUR&2klOV)=;e%+hyI92>38N;kbPY;BXp@Ya!R>oYi2%IQb@&}^xYBt?&n=N4l@Yj
zu_2!!qlH1B(<`Rg#)?Mgwknm|Mz3DP*6^>Vs0Q0D-bMe@rTYe&o!uEt(v*4air^da
z!GpRouKJJk)Kwb`>h%eWLrv#-yC`#suu804t#Wjh;x{es+QY-I{m|QYs(Xyd4gG=H
z7UNPr$=s^#)EC|Ty-&$@kcS*^TRuoJj*{P8ZN7=jX;i~%KxYYc;CAta>K>Z|w`^iK
zwBIE`hl@bwAg@0NPIj;%mANm-HoT`W!M17J+`qitmPSI$OGWmrUq!AfJS!CDf`(Q)
zV^u%zMuX<ZoNkx{w*E!@o0@g(rQ^_$3(Xl}hUt$tQMIpOHNO>09O?oZ+QQ3?z{iB1
z*NU=5+EvCOPOslr2q^ga+HLH*56pqHvL4I@XEyZb?qE9qre0B#0-E8a+o(VCfTO;e
ztHvlh#oZ=h(pAS?1G$`Bo`{5JSf@`veQ8RnKBK+WKFKQR(Cvs%rdAeZyq#G2R5wp0
zlWv#*YPhJ<S7Gsa8iKq1C2%6|u199EAu(nOu`t%nW`Ba@0q6v%vFET?$1ysi4qzGo
zF-rO!tpa#&45L06i4=5h`dh-@Rm%4g8oYmZGrbh*vyTh0&>jY<&M!>i&1l^g6r^=Y
zq&MC+T&BU$d?i34b5wGX&)Pi)a=?^FiR`q$VyWck^!`pdUK;ty7m(8R4Ryh~GZR2@
z+a1mnPGyR~5ZwNG89!U6Y8-C-)cJh1li0D<Zzx4M`m6s6z0fpR#l=03d^k@96OQo$
zAWOl+bAt^_dkG<U5MAWZV<J%rV36^_9uJphi&md*3&>=IvN4BP(WX`a%)JAw4yZ*4
zw$nup$LKfO)!bEA&M+%RFE!;<Yg*l4x0kr+hZR}=?@7W&uyJ6f0!H8hYAf79ZP~Z0
zcZ;B$&-#E>tfJ#}zu{>%(bncg^q02Zm>t?EF4o8*E&$@7;PB>xB5`{jMt#Ur`LLJc
zfcg$aHTF{{CnydSkO4nmxLc`GFu5NtNys|c?S<;6z(OI)<WIe{0>roz@wx2=T3oOE
z2U7sM{m~wer~qP&fYa?1?jl}|>=#EX+OKk-V_#n$k8ai;g)R`ta!%qL?TynP%LNJw
z^b*JXVQ+p3h`^Ta9GzuLy6>UrD4u;#+^~BwK0k1>`xYQdpl?=*ET4mca%Za<FxRU<
zi6KTFKGg6!w;3f=0q=aP8ZUNvyy$c}Fcnypx%F{1@*n@@QF*(0*TG`;p79m9cWSe{
zh{fi-`f|9b@$zUyzef3kRO>~plkSq_bj;{Z#HnA+;aX#BqRFhbyR~G3f~CNL=M1zR
z4_wn1`b>t`Ja;0A3ZUSyA-C0_>zgfMK_H=~jqn)1mJs2eg>PEU{4|McYN3D2k>}1N
zPDN&9StPza9M7QADA4DRv|C3dTiwQHq?E^q*Rv(`tJQ<$!^Yz9`VAKu;puYW+*9&(
z>a>I1QL&!R%*fgfJRE*9I=yAi+TnT>r7;brPkc+U1=vq(qk%sHn#9_zD?sVZYirGB
z%QN(lg|I%rsB6)is^TDZs{(ut53!U2zP&dcNo6ht2=9{0SepJ9GLI|;Pstah#>FF9
zYlA8MgwZ-CRYL?&LqAnnQd3h$&6c!gQ)a<82=l{8x#S*hKR;{MS%iqsV|d_rn=e#k
zp3b9ILi~zLub@0hIvKNde^jCAOf|`CNk!NT!Da6Q{xc8Pcm4MH6&HbivFs&G1JC;w
zvSaX&x(=V6s&9GFfF)M78BF$HRs+Xxvp<4e8!dj!I5x~rncDsAHG9aMgvTb0zR~Pn
z%6rPoh=-egORz)PiH+Q{7)}!z8r)Rp98SKw6O&ewjZ`4(@L{IpbJ$ndXcK8g+ro7O
zI^KqWrFO3OGRl9-!gsWDe2BH1b8-c-ART!JNQ?Im{S{OR59jTM_-R5DG>HV*zcqVj
z<)$R1X3+cRLQ7j>5$^za-yal1RD+Ae8>meXVb)aZsm<PVzbbilP}zmHLM#_5gqx_X
znJ~A*J(UZyk};hF{^GgpS7=(cV?hGGv>1R}A{MkKLGzpGBJ2ieV+cwOthob<5OU%+
zsoc05y-BLik(kF(uScp~yZqs_8qI*_Oh_~o!_TyGE=m9!U&&nq<V#`<`8Hp!KR@m`
z<y-UP^k6`azHL10+-x(VN0evxq^5h%#5)Yx77;m*P$7ue7PMrf<OBYy<IUQT2>zpJ
zEvq*pen|8d=l27$NAZx&Ur)Inql%B?<esGCW>d@dWYeC}TCYN^y_>i+Q_3d`KUN3e
z-c5hZ@e)-fl1Z~mhkH^g2gsF-blgG<4=vC5JKw>oO^k)!tVcGnP;w(Kc`42$81DYn
zJ`xCKQBROlgnjm@Pq9AE_HDn!Ga;UgP91Qc|FPQ1)D4a&S_N87;kBL%!)8j#V#65-
zFMI3D=HnzDAvKzJtZ__Pi(52094C8dx@CrctyL(D|2ku~OB*jn_{<B>l51{P?qXU0
z%xjxb^U{)sX_dr3#Jcs;w_R9!PX=ZvE-uIL;MbTt)yhtz|7j2U!VlDBv|JqGN!3oV
zE8cpncd~5cTh|;SlB=w-KO5z(C7ZNPT^Ot(m}1?1yur(lONSy%;LFv$1N|l4%~ki_
z$bjKj=LF+ZunZTl*pu%&EcB#F(jW7Bq=_w}Po<l^(M!Iilpwt*w|QL94MwvD@d3Up
z(7si=B;v?isaO7!&b+k-6<`vdq=_nT&<N@#aQRzGdGcuLWbm2csldBvHL;~Z`St1H
zuLF*{_9eT*nI!hF%k8mU9vKFVT_Y*_i8t^qF|}Ml8~GtyYej&CxI42j{c$6XGR_9{
z;4-B-ngj{#^~th2uWlZAbIDK(dI}#hPgj^15=(z3n51taN?14Qf;$xlh+F4Y7OI!0
z6J|>~2?eyqzI3PBn%?MBwb$sC#s?^3@4h8+SmdmYz}!4OPoIKwdEajL**ZR@zV}u_
zeL|dw>;vBd^o_izz2$#D(MK|xf3K`I7274+)V_>7#+?(q>L`Jdl+AJL<@m|%(={1)
znlTf;c!o8~`B63$jW5#WC$5E7z?fOT=~G$9+;*jv96;^Q8r(sQQ7lstB__Sh4c@LJ
z(|v4{QnHs|rm=M{vk@16#^n&pSw3~6+mST%Q<wsh{hF<AwJD-43Sw*^>ds_9EPVg`
z?jeM@D*LfAS#;7%DCq7l))DfX;*@)`Vi<V5f2*T1F`A<recuH<aCcHoaM7?Be{2;?
zLyG8kSDsGPLNUxQ7nFIq?DL*)ZK8fUQ{m$u2BdIUN7@O>!~IsI1c!2<K3W_v0JZC5
zshEyx{&1oP#@hqYZc{Tcw^{8TGQJpIP`%?o{SPb&+kIa%Uax%IAJd|zBcPjh+66QM
zG6)3Yf$wqRvP3$oI|_?_*s<T$JXp3Xqd)?6(i7$%@(BN(SPy`J{?2KCtHZv8uiEI>
zIVA8v@rg<-m{?U<Aq)BH4IpbDHa#8WXB<L^4V?R<)h4u{39-joe5|Ap2sB@QX1A|3
zq=!zTN_PQaG!`ZTEGu-_L4Nn!a5e-G=HI{_HhekaUW&S01caN=yY}TSq=ok$=`XD5
z2mZ6>KpDlFBM>2~_n#wmh#<+h7_}Y?vEigHv0MKOZ5m#YWa4oe7xauliI@mCj7#(}
z{D)lqWG|AUXgOo(8vPTX2V5F-WL(O2e^e@vV>r0BK(g23Z11L2Rv_#qW)U+1YI@6f
zkL^^R7H(`sK6K&dSYT?Ksq?+{*~bV6fVvfwdfugv>3sQlk_<qkE}aKXs|Cu#)2|pN
z=24&k_|+`%;C6lCIus!uwRPK5y4>E<gAlIZt%WOaGT&HVp*f6FRPXH}*8k$@J11y+
znLcZMz1;t;_ehN?W~M!PtJUI_$;)T?x>-MX5!=5?6E(7B?uXL2qL;ol-PO959Z*qp
zytUVLc6fky*^$M&(};IMp}cm@QZ$4AK;4ZP6?3^OSA#XeI*@X}GyNrr)8s}s)uev-
zvycC`-xX*7)A6D{yu>%3HVw;$x^4tu*JRCFl(*&)0FBGv{rSIYRvA|bW?pORp78t-
zHmhfEY>p-rX~_TaBZ|Pv%xE&um&@Ppv!%nME6}()KPY(K0X6pP+RmrzA=iCcoIHcv
zyMz>TtxSRq+C#N@@me0Lydl+><YcEPNtjT78RRt1Px&Ux4J2_(=1jUsU_gAg0I+!o
zFqGo>uoZDmF{sO4e$^+8KDaAST=bihp01}GZWWx5{o)sPsGIfJ;IiF>zicCSsXk7#
zNJ~pqRnT0lNU>3S!zIN!r|?$@LKw#fb&X4`OrsKnpm?6z)pa-6qV^#L{NKYTu>;Gb
zW++Od^ia4oT@6c)1IeLc<tuD~A$I}`e2R$xk^AEpCho!yTs&NeqJPe|J%HVV?&Tr*
z?L5}dFvb%~fsIL-aDhfzbJvIluuu6=%da7cw9Y9n`)|!Q=u!>g$iLX{StWa5NfsJ5
z_jmmYfRT2$H-XnUoXQP*?{2-IbkF&%98ndl$j_<pN7xb0gyK{?xP&k0eWK=HK-hqX
zjB~wZD)gh3szPc-r+{?wt49qkVu%(f$^}65?Z->CbVJ9)dW!kyUQ}^minLDv_<Mz9
zgBzy{?dH_`?I}Z!uF50ri1;Ij%~$lJCMKL~Hfmo72|V<6STrZWn%Q*TK@%cw(%u)p
z$F%szL%<(ic14BHMUq}hfdVblpkC%!GSVw=i)5OzBZ1yQ<I4l17|i$jc*J-@-I;?D
zE99RKlKj)uQw@F;^ECPpFtL%>CKgkpR0BWE^~*+}%G0n_t`?yZ{9T<l|K~Ek-CYl_
z@#tQ2&R~+&)a@!Fi}3(z>4c0coX_xs0f0K0FqspafuVIzMIfhALE^?fMT2c<!Bg1k
z?w(#4Ba$1!uJ<eiC8$M9@xn_9B{;mR>q!?gS|rEvS$GQm?01j9tVxqDbLL~|y6tHo
zl@=b)O?YY#i)0#|lUDAeJ#p=7f0sP|%BOer3QFf=?!SL%e!lSS8i)?;!2aZUPkwUd
zLi#&qJCS|w0IuR`cP6^9bhmLf?TLR$Un{DR$~(Y|%DcT$j3bKMPTC55J5ud4F*~C@
zQe&Wzr#HPT9353r($~P*uwKcG(GITM>X(R^hnH@iWZ%i|6fqgz6CW+pdjhC_ii}>A
z2ccLK2S5vy=I&Tpv1<9;m?mTUJPwKaP6>26O7tqPv-IE;#f^L96@IjHAfmvc9LqK$
zcr68Qa6Vq;f#6O!?jzE2e#RM0mK}I)I62h&59$8Gp*_WUjvp+(38|Cc`yy%yHF#u_
zwl*!Bp9s+HLBB7)E?seP3MG&j)6(_^Ld$#O2W7|o%xeef=K$B~N6ge0FJP@w`UDuZ
zasi`0$RFvE6CAQsZ(WXZ03^QrFRpLi$`To%v<IQ4@%|W})QkQ54E;>u=TQ6<S27m#
zc&!Qtk5HOgZ$<g80xZ_w`^y|%M)`kgLMBTk*^-|z!Vdv*XH+mKgmHk4=QY7$RZvD>
zwRVy-LV(`uz=J~i6>;jgmDUXrf~pmX45i7L6G8nJdT^!lXlElpc6+ST#AGa6F%0dG
z5&B_@Eo5pF?vE{CnCfS)^L~511oX}QJ!K$-X$;hN!l^>1l>P4!pM_=O6yC;!olNLR
zEMlzXZ-f@@pgpA^Zs0hWiTD5&c)MeN#BMd67lFz!kEnP4vgUq9A?+9il$RfMiS+&L
z-jzjAP0vmL0>$;`)nTNJ!);5JW-ilD#oe@m&vP^9S`pq0gH9zb^lDccZKpO_E?NLG
zE-T~Vj;(#BDWfx*31g-is7N@hz^B>q017=yV1UkcHvhCZrQ7?sX&ufVUvvaPVBwM9
zaUfWznG52-K%bSOcOtE-6h=O*r^P1;S3;3(rcn2l`F{r|Ps^HW&G_EXz*71`el{M`
zF+|}6%&7@94^;{^`7QMLcLG*y!;w&)kJ0aS2B20E+z;jVKrli0wDnG}H24mX{iFf$
zqPCw8E(SYP!trR%F@KzAz<0)ir{bAGIWJtzoy{+|#IM7L2jU^QrF-5)!XVBE;u}ww
zOrVgl;}?IF;NpU4O=%tIg6Wy0^0r5*SURIv-b{KbHXWQf>flF-PpO5{fzN)lP=s49
z10qYjHvfKmsm1sf>d$Z<;$Zxk#3ix$++Or)T6YvfGEv}lwRgA~awiB`I%i+kHRl1l
zemFv!p&v@Z*Sb|u2H0Qas!|dYIw@FYWOzMF0umJs5J;FW@>$_+!Bfm*v$O_n`m!5H
zax;cIL;7ofS|-5)fK)18=}cl{L@hb@mYIh-&prOl@Q8FG4jL|1qw2C6--S7KhD;;#
zbb7iwRj_W_8XdPoR%IPwr+=3ms8DY#qTA7^fEZF1!NEZwmQ?odY3Z1SyagpHL@L{#
zv)03d-r`#kBBkfeZgR&XDYYiQ+lBSooq&sV(3EQ|Hp4oAAODwPA7TRW9Y5zr3fyI<
zyY;~~RQXEJddnH{_?LXhCHlPAs$bzhU~>e*?|iuv9_8I(o6%fE%7XNqiM&s;%&!KJ
zE-sLoT^DJV;0KYJmlMh(ho+qCq^g~Z2MR>Whp;oECeL^&|5R=qeEK=1C)h4eQEcrb
zyR^mMe+8#{_3tW^g>)S8p=5-9P?w|lU6gZPW#1FyIW<xCaO9nFua2WX<kM1wL@}RQ
z%gGDdp3VXY^xm9Y<i56F(U)2ZXVNE3Y7exbmGB^uzOYT4-OV`Z-Fg7?Hvz=bxhM*^
z>t#;MMIYXy1Id|iT&FNZf69zup6(tW(Of3w7;AVN@0c^!*5s{G3*--#)Jwyv-x4Zs
zhCF>%^1QIrrTl~O0#U}Jv0@nxNp&A{hNc5@`cuDEId9lL)J}ilG>amz38mbW2JUl?
z`gnU2K=bs4X$l|h{(e=_Ef0BXu84Ef?46c}imG|L2H%uGX<;)b!}>9vSiA?QoO2v+
z_XbD0gE_Pk@LSp@8{#khe|PLYhXi`ftUcY5YSrV5YCs-GgjBN~CpKvR3Rx-oJF?#0
zmn5M-Z*G+Y^;p?}I_+D56zd=lP-S2G1$u+HFx;ZAmJE~Rb6d+Abt2V`ZzL&U0wpH?
z%i2=WubbI77FB_g*@1C#<x{@ooJ8gy46O|IZO>DZvIFsIPqQk}Z6?x*60N+4QuXbx
zYMBkBfmRbRxtl2V{Ed0QfYiaP8N8=s$2kLry;!O=65m++hnIy6MO8RB!XA2@+F(^c
zws{fkycb8Kd@V%Tu1N0JVSI`f$?2~RqWWTlx}Y@#>d>SrfI`&4SZdI!HFrxw^{BjQ
zY8NR4XMseuPzN4<Bu854|34PML^NtOVox*`iM3*$$Oc0l#u<1|ruFF^&<5<y0I82g
zHUZMJD}m)7vf>6nHkorwX7Vin$tAN%z5@*3%>%SjsA0@kugo`E<=6k9Na;*fl1ZCk
zmTE2X&xk~R-4JUwhb5x^m5Txv&vWJa<uKkx@g;6A8OwOm@dEKag-|mm=cb41zYdpo
zFv?g;h}y;HB6v^*1{dDc_8eb-)79wCfs$4>bl`R{vR>cY8D))qf5BWJJ4%0=gu`_N
z^5ULv3hN{{!Wuoqdp_)rX&ke_C45En6lm@`a*tutTf3;rvEDEz5?(_<x+-l5SiyW^
zwIi~1xp37ypZX?i&Qr5lqr<n!BQJtF!wVA4GRuF}jw~kc#aqu)_g+3^Y;<B!P#`()
zc}?+9L4M{}FA~%OC3`EV7Tqz>gy}7lyXU7V+69u%t2WGBFFxN09;B_8mytbaNuxrS
z5vZ6K@T*DfSszJFkJAoxy_WrW$_0nnoi1>v{rIj?pmnuo1lk7xP*I8`>>`1Pm%v3%
z8rc3_b6RT0JvOM@#IfvVn~th^*QnbijF6u6&%_M(Ck}Nk?(N(Osw~vzuO)U28<1;K
zC5_|bhn-3HmrJ^41lpZ~ybkV7Z8cxU&0joH*e<mfo%%CEnOr27YJPpi9@CpRvzsxh
z00y+f!E&e+<iWvds$A9T$IElyzRQIoj?0d+y<FEjN-dKwQ_N@5vN&pvb)fQRNMUy8
z#$QF|#rA&*i=Te>5nb(c7V(>i?>nWnJ?vLjwRx3=mkNxRNL5&m4Zc5ZU^dtZ#vX&d
z|6W(267%@lO8nif3FEL@p>BV^3_FDq`p0US8|P@sDLs5Y%en56-4Dg3|LDtw)COP@
zmJT5MBPO%OEeuiRG`Q%QDTUk7o&e)R4J7xLQ8>*63d^VH;L6H>Odr|jV04klgp#}h
z7Z<(qHKDBrOmM3=ziLjr2_CTJ=Ea$*3rW?EF!KH&j6dL?lN^{$ArLv`?5hYzqnuyy
zyRIbDxOdxK_;}M=oL`QBY?;<pNS1V&7iHsOr)3cAO0CQCEm#>dvcACAE<^Ku0r75l
zXdwB%MpR=zVz;7HC9VlQ#Q~YnkXTpV&i}+k{GErzp+~X0(g_3N6Tv+vPGzqQPW3Y|
z`VIl;EjGm;3l#=hXh^<L7`3R5=vue~aR`HY7{0&lDC%}bQ`AwEk|t2$nb=9NXz^x^
z9S<syS&BRt;jyD5Vkw7;<OQN_y<ch0+Sz3Sk=>;QJRWHtbdhWxP8S$nv}(ZF7HY+V
zMK)=}6>Jz!E9k6(_WX)7W@{~tl*d97%sGF0=%sZoeZOA}(UtGJp(MfjLIlp2a@RNH
zq^v|{W{$CcVKogWj5DbUK&1f}{V530_&5m9GW9+KW{`YB7<+Hk89a&m<+|zfyKos*
z1-B=#0;Ur$ihjV2vE|&C(gxHgJQ@UN*QLjb8VQMAWY|JS%)o>*_Lsx&HODXi96;4L
z@Sq4jS0M93!NDpuGJEyUH;BtF=p26CgFd*$xGB*Cs3<~TvchL<<IWVYgt*bMYI*?V
z$e&hkbc74-D+9>g()zV2so<HAg+Jwj*_#XqaCYIrDe)oamR+Uk%g$n;GM9WrFM~a-
zFAf&&z3LvTXM#Zbni0qHTev%ck<$1dO>y1+SPTj&kY&=-1#tPCZnhg&A6HXh%&ywQ
zlZT8QT=xi~CEN&+E!&gIo~M<Xs6}1mxB-{EnYUXcR!BxRl_$jc*4j%Jsy&#PwmXKZ
zIBP~SG!<5uPKBM~i3!B@CA2h&RDB+y#yX{;2Uh>lXHhR-snm5Ead)}s(SGQ%99+`Z
z?h-(QuriJZw6KL4_2Zdx|5O|iILxY!6A{gc&*87r#d@U`uo4J(G^k-k7DA({0Lj{-
zSzaW`k0z>=j?8gJuJY)HJl9nNgJ1V%Zx|1eDR`f6&|<c{rE1H)NRno~({y;h2s$Ek
z?VQ5t(rL5)N_5TM8}8TlC^=i!s<jyG{GDqjhju6M@QiVwv7`5L%D&gn8|+^7#`>~-
z7+-7$v_&K>dFHNM>3Sv+vZ$Ta8Ek#xc3ul0gKq6At&&cOX77<N^kjImN#R@D{<x@a
z5iqpj-|}+G#O+3#)&0%q3pa&Iz-C(myB&pqa{9g3Rf~_1+53)4LI+yr&BwYrwSl6d
z@!j8YA^d@g3-dUF`CAVT;fZsC*}O=y<X~Vj%L(+7%?OrF8y8{b<v2G8G0VDJa7%v!
zX%kcGz<zgRvfmp>OHI{V>+|c)H9K1u>eU9$v}Z>i14A9#nZCaHbtAF(d|EWdu&&g|
z#&mW2G$mv>(f!^{{IM9T*@wDk7opwj^_<z&wBUaepVtcV6_b}#vg{cOCmJjMR6^dh
z6}{PS8G@I&%jMo!PGwCy89Q<fu^oDMRE$#{M{awbO^+ilM7p3@KWjGF7}P_#R-2A)
zGf>2UzYO$*gcObr|0n2ip?wFO<X`@;JYUh%+OT07CDjc(5iPt3V<#k~a3d8ijpcQY
z84iA<%J~N*6K=jEY5>?LRcx4eW(g#q-L>`^;1s!i@B=HrndEuIg`Hb$OX8w0f$&Mn
z2ZrOeV&G9O#=;o);Y@Wusg{NJN=qPN5zhbpm(54Z>HU^Q?_vsuE|%Nbe>#i(LFBEY
zhU~7)Q*TK(&UY1_s`_VC!B1N`u4|@<sg|FJU1;X|YO+cp`l-b-?|Ds*v+Tf@A1>_?
z;G0>XQyVkB#%gnu@d|>TBY=JVY5SX3E3RQo^-8L?9qW$=QaZe4MLs``fIFr;+#wb)
z4en3T2zuZTm3$5@j9`dBtBCp)U^?W-8iS{_4!`|wLNWl(__LYJ8Io$@3ZLUZW}#1m
z7-#fd{Y)Ps4b!|ASR<c4MQOz80ISL3cE#;YPTIUaSd*|`73<O0i-+CR0hp-@66BO8
z9|w|rZE0Run-jPb9$@@|^{-jT0_S^h5NOTG2e`=6BPjOpT=udUJJZa%l351};y}I3
zGx{Sf6t7dCUdVK8d7uC5bo=W8G?^wt7oz}e>w6#J6LId00Q;r?c_$wu;puB|3-={E
z%^Y>q46cG22vXO-2rm(mEa%umk1H{amuEi3q-)g>d{BX;e-_;lYXB{F!^8MyBVcyk
zzI;=MMP!-yRi7ZF89m(>4RXCJk#o3i9eFTqJR$MPyO7bSm;rob-bGHiwYX5IOhsVD
zo9B6Q`v(nn;JAsiTpzJm{?Ki&l{I#SxgvZ!0f=;(2xz{X$@7pLBAk$iH-)$nBFp6w
z9v~SIwLB@9brvl9oA(d;!Rf4J)OeDF8AUl9P}H}BrR4^laPOz8SuD>yG+Uf99m-YY
zt=GA?8vOO5^qX0LkT912T~%tkt<o(4FMW19&etDf>Cs;NPtt=lnK9KKpOU!De65}w
zz4tq{RV*8+WQlAheZ{KW$E|R$+6|_*ONW$p3PDwhg%0@K4x)Cm0|hF#LlPI1^@AI;
zKa6*%mUsSj*~e`43+D8kb;eXyL_Krc)<NsRHP>G+HVS}5Hl4j9bFy?rCHw!oE!@MN
z;{zAaFpqcFDSyU2MFqFk{|*cBFKe0KxFc(DIDKaVyW$lOBjgDsJ?Z`V@!7iYw!Zg~
zT3jVL7SIFQoED{gdWo}mB~g$ttcOM!ANNjmVjI}jLLhOqO5svvfbyYuucn7TBJzd$
zP+9o&x>0?sHkCTyXg6V*_Q(9+=euEHH~}I_Ud>W--o_#vkgg2uCw-KJ7^zl9qq5(B
zoFW{a0D$yRldU`EMok>Pp8dDm=fhA^w1Y9Y7IYRkMK*!gd!vK(_IAShK;vQiU;!ND
z2EGi#qu)s#y>)}y|J@=m{eFj(nZ(5_%XC@_e}x=`hLB#7ro`_0OlCszLGve&&hVY_
z)T#q2V~3>~TXSX!yS9E0funH*9rnR$1cpSPDE5gB3TRXd6oiAq4VgGdMi6~H0BESz
zP4M9LJD^)s4Q=XI322ppa6-SF=;hV@at49%aA&Doe84Us$~u$OPCZDDcBK=EMxi=k
zyI4z>ZcliryXrG7eNz14TJKxk04e)lRk?sc-+?JAVj&DQVqUKt%wpek7bUt|Tg3i3
zmuI+gj61|gRFgk!G6hGy+%D!b1cyX9gesV_xUnLOhCDE*NaPP`2{St!_uRwVI(Z(5
zJXtu4#p;YFNdvd}>26Z7Hic3CC;@T(e<_BTWF)H7&lmhV9l!J-(&u@k^)Kwi1${V^
zp1F8cDaxb<GoO#}Ld2xu!rIS4B#eG%@1a7hRWr))yiL+{i2bawA=BOJrzKJHMC&22
z;mtd2oH+dn5<pl*PKP`;B!75Z7^%-yH&B%Bqe;6bbnw{XUc9{Wxu@I`c<pM#tGZZz
zQOY{74E?1x)|&otpdRd`Xt~?!Qw;;P%ZK37|A%7*Xaz3E>L>QuqOhiyPN3(x#ylgO
z`FfrTOsUOk=c&BbWzZQ2@w$KyIAz@Yk4TR8+o{q?df?>>ok$LVN7h10Zc+4kS$})Y
zryBZsg_;*?J><g*?@t3-WgW1Nn?R=LBjbJLA~oT~oH@uJ-Y2!s#8<2FA}qYqaec5J
z5-uI7zny-ur8<e`88s1GBWpP=<ImsT_O{s0Y>dbmMych8m+tY0HfVNb)D9DbOhjG-
zdsCgn+7Tc~7Cv1LoYLfzd)KDAvDC(#=~nHW`BhoY&%vHiKa%x9CEa~=OyDt*IGEu*
z<c>sr-<0{^02Z)ANY+X=%O$qeUBg~rSl*dBkKJ5X+R;4=U!l&9!;csASL{9?Kc<_f
z@CtSJU!a|@{5zvCo=sLfHqg>|<)8^#%7|srG~Z`xB5J#9!6Z~tl6R>@Lw^N=&j(&8
zTaK+(-8|fO=LOGKgI*DeS9isSa6&{_94V+%Vlyvx4y&3EC(-PeLO0^uLJ^)UMuQGC
z*Df55?wbUcuLaH~_sSWA8?iwSuZo_cvy{Q+-tzMpn`8fX`cNs;(G+}&p>$3u2F4Lo
zB52rH>Gd{1;5;=ZF3PkL<Au$K-m3VwMiS5Xrw)EEs+w-=7l78D-u{LeW-(ht0TBJ>
zPFjJu(Shw9Pwgq#-Q6h~eb!>2Y$6^Ve?QIEK{-JM*)^1_wLve;C=<rma-V)sk+k}V
zulq4vHc&MI!Lz)m5pveY0cXt3e=;KC1=#hzPq;GroJB^q_pbl$nynV`9XM2O#{?PJ
z36*KD+3hK4Y@>Y9KJHfb6HePJ-5kx&n~2?H*dCs)He1|Cv+B7Hhb$1iuLIwIBQnVF
zVRSgSASBqy|F$;^B!J7WvU~W{5^VyOsc;k7KDm#Xgiq7>+k!|qHrtT4#yv81dlFcR
zn)N}Lg%)5T7!QDjgEnOfo@JUKq-2qbZ?=`~<k(d}DxNd~+pdK*&h5r3#{E1{pOSS~
z*XPI5;?qJGl=3{?IpNARvcD;bO4e8__V9<pF(H;Sg)789Ty00Qn`bR?%0z_TaL)Lr
z*N=Q3_>PXTD?>_I%PhQ}EsjDU5Q~~5BamW8>A2XncSetGV9T6NG~?d&gNHw|n<!<X
zJ<WuSv5Xm-?vp%;cqaYOcajYmMFvA+w5qbky~Yf#Pev@Cv1GfNaE&0Ba6-*Du2`34
zzU<b&$PoZ;dzag+tY9rd$27+RSrAu;Oq$Ic#Gc(RaPX0B<zV-z1==|@#33mOJ&6x4
zg`|8WK-x}6nOxf!R*$u^@$0-TrCm7!lGW^z^f3}Vc@2c#FllgTGrL9C)Rgf}ShM}i
z`{llC2NN2Ud<mW5x1Yw5mX`VGMw)}H(`j0iaX)Ml1YF3mjN^V<wVi4JdhK@-Y1;Ss
zSaux>q5cBB=3<W{5T`~1en!l(QT9nSY3+@D)PE;GYd+5#nkf}0(c|!s%C0m6dIKc<
zqvVmig~V7)QmxxUs!s~}zKUorQ<}{$L413MJx<+3A|CqaC$GXipb~X?W+glp`fvm?
z0A#Ty{9CeMhv-*g5ejx<vV7cB4?iYNMLXwMJUhbOfyo}s&0=9&7snyN5wr68y=8(T
z^|#&tH}kHNq?aFzO5_`2q_PMr4E=|eu~2(X`#j_R_@BM9<jTWGgJ|ahzH%Cek!I9A
z7bIg^g2m;^`3r!8h2&D3ZwH6->QmmT3T}K$-Dy=cygUBe@U9t5HaK@LU76ZBc6WwT
z=d+=1t0AL%J6(3(aGGiAWkgEw?gWrMXGKSCe0*=dLX=iE&Q5S%7lGH!?HQQV8bWI>
zH?lNWKdmBGPrM>ji&%GZGZ`B*5VkFsVZP`+8K6m2!v1;U(rt3+5+va)eIF%g5PV{3
z4})!8m??rV{fd|j=(Y~eji;o-&yKJ+-;Oh7eU(Y&B1H3<`nbNFP^LR`J#5wpr$h~4
z6<`taI>|)!Pkn09diYa=P-=#JbDwg-hcDidRyhE)+M8ib18T_{OXQxduakH=>Gw6q
z88m!1oA2FbZPCft193F5+rx?DUn5a9`@}q?2GiQocGkgSN`3hWCDH2?Y+Ne5Lx0C`
zl4O5XN%+QpX`>2sdtD@$_ZRS#+1)yH5?>fCcuEqG@+UyI{TQtu{QGKFUEeI9kMtWF
zKUBYcUk`45|A?bxPu5}yAU*sqC}69$Y%PtIIF&An{%-A+{LnPYVnQbL15OMKETHp`
zF)ly{ogeHXtI3qBj3vLGS%aWh_P^H!WpLTP2Ks;7$FOdjkbGkh^AOSWpJ)xq1ve<t
zbY!~i<$s|b6}(p<YgLfNtbhF#r?+;!TEDsHT}?IY<sL^mBFAnM+qxh_VW!{f=?Fhl
zLL+w8EdQ!oBVnZ;>P``gJ&!S4U@7y5Nf>gbLvG#uH2(UP0oUWsM^A%k9?6<jr7D@j
zk`~y4+=7N|<sqlWJA9zn(J)BKX!-I|ZRKa5#FJSRVH7VnwAg=62qj{HAl`#cjq$xQ
za3i+6&LA)4Iv^<}sg329WOAwrTlQ6}W@4uaumI!&))cp_hP<L6e?tF}AK_Olef)u9
z>Ry)>36E$U*`W&I{#U!6zj-1^eu|iO$?#V?lHSY*)wl)v*Z8YgN&Kwr^ikkINPlGH
z2qIr;3$55fno6hn-ly|ws>ndpPWkk?2sJ(0xP!F|yxNH_(ug1uf+&*Ig;+36$&$IZ
zxLf?HP~ZYwR@sGv`EK7lL2_pe)Q!WY@$cz?#Hgs4s}%8yX0?T_6ptig=HXp_aXhSU
zx(rVR*qj#xGqA$hths7@uEp!b(|L5cloQP=PkS;fT*9azKKLV}Wn(~%dV`76Ah=t<
z)qORn$5j|hoPn@4FYb<^A=q7q)dVInBj#w2l*&7KVyle*<c9|u)6L^j5?)zO1NG3Z
zTq;%O4?Bw+%&xBnyD$0PTwbiq7U7S5UNc94NqVJZ?kGID(LL8_V<J>J6johDs&=S6
zH4sGMJ~?718j#iGHOGFjy}<4iW^lu1gsi^4v-FUHF>i|QzH-cLC%zb%oGP!uIb?lG
zM44^|)hug!A+?^-UkYotv+c<3{Vx>W2alXY&?EsL+iqXC-($m0gQed@;h`Mx8p);c
zQ#hKhljII3#MFb<66&&bq>>-<RVK;B9=*2ZVXZLq#&X_e`OiMUCGh2=ij!hH``JsJ
z^XIyr&ptH~A85M^EbzAYkqoSX=&+U;(dHVkogCdYzB$Plluz92#6|j;HQg9-VgK9p
z>7<?=cic6`Vy?vYbq38#<6>UlyK!D*srwM7Cb~#K)x>jO=8i-DvrSC^Sd<ru0$)Pv
zx)@e{#6E$!71*VjqwD8N#j|ySZ5<^1=FmSuoI+u4F_%FYtpZ%v5GRL?Ce%@XW}8Q7
zu8&zdxbXzAt2D!)yn%#5casIE*zHI(v0S-!7M8n3q$<ZDG`NA&qW)bMe0K&bq@(6P
z@p_N9a5gsqAWT~YJi*q9vm2!g2bOS8GPw?y`wD~ZYn=N+<@_2EHy{PE{jS4%zSiVH
zAIgSXYQ3KaOW%HTYm+8;yguxi;laA!e)a)G$Pjni52J)Jb!Z)UNb%*}K`8l>#ITmX
zePgCmx5F-XhNazezres10L7A<dwcn<kNaSrJA1-$mNhT`sq4?S<J&>lWL0}1VoS=b
zVwdx*emPl6)WdO9<pQHG27X^Vr8dYVkTgaQ9whFk=i9IDABIA3S*_6TRQg-B&ct(H
zv!e#j>q>L84;AWEdboLRWqiLrJz3LC6ja{lT_qkDk(KN|wwittgw+Ow?@7W6N!cYo
zt_GuiWZAwP`n>STS-NtFO#{#v%LeYC;}<@cisjgNV}(ki^MJl$IJnR%R0C$%hZJyN
zKBb>6;*@Y#v2uQ7LfN<0-yx`!qWAr<`FDVBa<n7h`M7`fM<8apHn_0Fsre2~XcayN
z!cSW^nQf9>!Cs!rzWSyoBv4l_0+wmeuuCg%|LV%H84|xN6*<oSi9tGYGs$GHB)jTm
z86&!DYmgN(hC9$s-Q`mMXP^R&hG2?6bRZ360moE)EN%Fy?^bd{{qaj_XiYvPZI>)5
z*Oem!FM`=zHooCn#CRK@20X_gWg?~zgR~E#(oW{)BbdVaEx)UPP(cp&ptrn6nj0}|
z`bGJOZV!3h`p43m&TH$QAi9fAns<XeISS?{cx;A}?gV$%@tWkh*=irVjR{r9G}aJ(
zH6pl{W745aoy8^*7;YQWcI2>OXm$)6)M4VAP1tXqSqzLy$UHUOKi64YEe~=3OgUw@
z*1235A~9zsH+q>uw&*n4JrXWlydcx)ShuI=7-b<pmEE8>+R~evAhy~g66M$Fga6@?
zC{?I2EM6Sh!nN64K`__6DKu8`N(41&19Ucb^eWq}zNVFHqDJ*A&ao`Gyfn(0$;Okn
zryM&yo-)`nn4Q7VuR978$PaZdhuTs~%}$n!^(O7d+8!YInGu?=&1|tj?gHCuRQp;L
zza3Lxv=1F+IyQdx8?*Ja4lI$J`WaPra8I;5>ZNGlTRGPPGCUAvky7M}SbAb4Bwi8$
zre|1)QxklD5*mY*t-)E-yR{sAE65y~W=KN`j|&<)=#qux>aW%G#(~Bt`~Zs5fp?lK
z#)c_!A~tTC8{Q~<U1%aj(7WwDjB<x&vl+mr27swbuLHMGET;xWG}{L1sO}skRlRm;
z8c^CEcJWH7qq^xVH3s6ThtHPl$B_uO8m<Y5<Hbx98TDg`8yRkRRw$+!Udb3+URz4O
zJOtI?>&ePlHWLT!=>{KHKx<+H+hqYpLGARlIjyE=P3gQk=V&L-7u}b2Aq-987%y{Q
zOzId>6nrk?cWE8<E3op`e8giRf>tksG2%|B!G^POivQ2paE=Or>33BiVJmqPc~kMn
zi8D;7O0)z0E@sE1GC*n!7F7?PMN5mF=R)SQm}yHeoMVUJ9I#uIRq%c8?m#@0cJRG-
z{?gA(vn5L=$kPI*mlT|o*$I1~?n<bn8BIBKotEki9m;!wnfa!gUhx7O^dY@@mi%zK
zyq#oL?@Lk3_Me}sPyX@lNlSNs;WOF4PY}oEypCm%v^ofSOYYsA-<Do#F2dLUiW)~b
zr_-qTt2S1om>qa~9wzU0!JiN!$j3&jQt+1wZ~<nctSA(bvC*ef#BplZ{W_fvBEADA
z?j!e1tJNkqeyHzvG^%1<la%D#Q`^(I(%7z7qntNAOi&`Izal8qbz~oooi9Nju)Ylx
zB7P8o7F!KHkH!&&*3O8Tzp2WV#D^}pD{s9`9vfMMsyB>Of2$#+%LVM&n@4-*DJJEq
zdO;VBj^hn)@W_bqU5@sW{TI>obR-Weuw-hkqgVQAL(yW`#+?%pQwMn1Bq6Ln&o56N
z1)mpFm)$js!;+lz?oTcrWcNwBAfIxbH-@<{OV+AP!qPvGTB`h3L~sbKkCIO}ds&cP
zhB6uQ9NwP98r?Z1vKTDmE*n&lNVfK5mg(;9p&k=`UyUK-hAG3CN8n)Ob6GP<iVKD*
z$jXdu2>A%lkjXHNvEg`9&TPMXSiQ43;0~fVbcJ@cgPd8mWvJBedX;UXbW|LdZl-B8
zwcLu?+)A_Xb-mSI@tjp)`6EmQqUFj84!VFy*3qSiLJu+dC4SH4+&DS^F2~gH-mOmb
z1OmjR{gvvYGxA$%mGM6>GnTrXnluGfjy4aij<cSmEiI(2BRKuxl0;L<$>z&}exvrd
zuLw)SudPovmC>GmLl*%csxUJp!%%*7?qIC}D5|cF{XM8-i}w9FLLlhB*EhsqUT3Sj
zK_<C`U$U*Y5z5~T6PVS@bdoDZG^8D|JbN{i+ud`@<Q+B99>X-T@~vUrpN**FjZg<=
z{87E86YR#kC-OvIaDUiIn1G*PpQM{-HSEsbzNrZ;Io0Ue24?aTrinRuI6PG&^-AVn
z2#6pWg1>BuFMf5uJ#K0ZuhP5s9JSTK?%Qwtrh{>tt2<=fwOSTy!XhRBeu%{$xY4zi
zj@Icl-cX#sk<FN0C&xImmLC(8-lexQo+rWmJky;EOO$YQJJ@ex@PByv%CM-uFI-Am
z7(zn2Q(BPj?nY@C1Vp;KySqUeq`OmM=#Y@^?ryk;-~ZnGmFIzB_Bng+wbol5@>5rt
zLw3hHzkWV!*t88}&A~P8&>q=-!VBiQg|YYgvQP_T&6r9ZHtm>1{|`Mh{{Wp;@AO1j
zuIK5#axP4Ye;cu3|LsQWZIyCBK41ycNjb=uOWTf+{XGzQ>{#^vKi2)L<Hpq=^)Nhn
z-$}_mAo0ah&O6(KY&kfHCh(#3bfbd$UP%0|B1x7DWd+CQUw&bQ%t8O~kz0vWqpWNV
z>R_}_fkH`N%N@}i&r-&!YNj+k;fvFJzm%c+gNWO1C&5Cq0#cYKxh<MFHiNQTpw%bC
zM#=K8w5Zu;U4)1@8QM3WBg7<T*{b!J(^=PUwmzRx8|=zX7k-h#URE|A-Eu-WZN$GN
zfK8*k!G0SyFW9lVRB6z+Ci><Q&=rG!MZr9hYHHX2xkKyUW_vJ{<9@N*3!_$*kWzT>
zWm;8YjGCqA1@RbXK|-j{CHH}jLaOQJvkF3hJRvBn=psHa|GJ2aCbunhvG33CXM71X
zj{WbD6Xx27%J}xbV`k)Wb0O|Q19=_(34T883B`kp9$4hgBL1C}X93P?6LKv(yw3f6
zcIOB0n#p|A&H~-w$A4s)ri8Fr9u-pu%JJOrIw9h{>x=tzf%G~o_-`jILPh>l!VV%u
z^m)|JvJB;j^F`3gR|NCu!NbnP-<<I-{{RaE4)gIgeP!>pYzNjJUc;x3vYs*OkiZ$+
zJPuL*0hpSmp!gJ)-UjQ{3h97{;D;}6cg1hulfze!?O1X7t*L4YJEw7~`^I6ad&3=P
zHihqpy1Y(@q@ND!l5lzX=$2p1Ncm4snM<=4nMgb@a-6JK_j9IMneM2#i9Mt$Mb=|X
z2e+{>#tC$a<kOwP{llbb8deQR5;wp7{9E(*He9Y2H2^Z|zhMyD`TgH5$6~i7CC}#v
z_x(??cw0qGtyyLId8<MukMwGm;?UptR@sLMehhRepEnhJV+20?f%^B1SXSr1yKlBu
z^&7T##&LhGhj@NW(-J{%zhO7WW{4qIu_0%EP*9I7PQzNb%D$uwY(dcvpJS<5ddk>N
zg`fLa(>yc!U-lDWUp{<x%BGUV*`;o0&gab?ia`&n$-*<|Myi}f@81xy3XoX|!Y^3N
zc$lhora33d(ncc^poA6llHd$xI-@j?zJ*RNkWC&Uux`s4fTG|%i&J@JP^<fOwfvVu
zBxLC@8RnX-*o3BDc*FuA@U;QFR3pMe+b%)djR2<?TcgZrlluGr&D;}0eBz(7F^8>(
ze_}|{!!g=2m&7#Q7iz)n<p-R`6#R=1741$HGlD@XXxfW76Ehi3nlZ%cMm=0=u6?1d
zQw6N7;Eae29aFb#7XZge>+=J9t4oJeG`9Sv{035S9YR1bgF2p~cR?FHWgk{2InV+$
ztPAepgVUy2t`Y)DM9%;<Mf$e+(wgp~4f?sjV41hzS#vXgZf>)nl*cL~&N3ML#!p4v
z(9A&1_bHAHnokYMJwvVE0F<v7B1CIq!_TUk9QRvCd0X}#&xR}mI?P=eKfW4u2o4Ue
z$$7rcJRZ%rAK5yS<W70KDVe2bL)eGI-^<P9UvT4vL!o)HDhUkVC7F+kKHdcQ*ChT_
zKdtP-L&8HvJ3mFZ4;KdFd#8FN21f>ZzzITi$i6#lWUHN!^oCH&O{K7|?eo(20K%RS
zDU8_x<G!d<MM%`CX&*)49SP52Wh&1Rx{T@JT;*JjZ07in9+<#vzA*0!&cJwv3JCgf
zvQ(oWff>uw2sq)l>6p^k`fBXDw>`_TwCI98dFidXpK6DMJnPRqva@sh!Ao9B$?6f6
z4tuiQ96kLGr<<U<R4wr^P6)&5QhgVZ+rqr_+2|g{yKwA;-HOJ!dwoEG)sNlD?W$FN
zBJYTUjdVY-!(WsDFNmE=^1CDPzSy;030IM&r`D)zeU|6wYOm+=s$&d&+Su%I$}5yv
z`E$+T*2pl^+Y5pOG@7Ol|Nfh&P6tDUY0|=<svcOzbi}plaud-8-w-7Q$rJ3^o(C|V
z4#v}5r(_Lo0<@jv{Z7SX<mhe9Qp{fz#%y8hydLSurdU!x@Qw@KvTP+u*13=;0WLRe
zc&Jg`eaM;rx6k>aJ-^si#s~wdll8s6@+uJY7y_vk*VIF3uH)uq_E`kT0fq92_t6|x
zjVo@$t|{)Ro}nH_QMT4fD4>@OqxEwx8CzdpCi11zO|SU0M_&339kP3X2k?!Kx8^C+
zl|Add-j`v0B_n8sM*zdEN4^6(-tzI}ORI@r!uh*fd?!D?Bns~~Y}G{=6I?QU&q6mP
zGNw*T07M-?$Ne^9CUrstMFx38R^C<vdK{ywmw&vv#{C<d!`>WVV6|W6WBvh+r@L_T
zBN05{xu=|vemGr{$LlJ0BF2e$NcmYjRSK0r$-74tdHyB8PSFk=Pc{c@vNnHtAw4^y
z(&l38jF4Pq;&L;|`g5hBJYjF)vxu_b>xuh)wW{QN?(}`f{(}$UXqx;%38)nHFJFgs
zC-_r-4-Uh4$FR(Y(?CSYqNNYTm@4SS)^y#x#(>8n;AfeQ=x;s=Y>0l*nIlP;x;L^Y
zmhNcFo!4gQpr9iI)f(>VW&HpR9}LLs9P#@ERwCq#Y#gftjuYYS3x6iHxvC6*lYyx0
zhx)1HnhV_L9lFVnobNQ3aK~|26u(Ou#K-RGX{<HZu0H9*)t9TqqnZwVd|1ucQ^q9|
zt{K#1uH|d1lCE1XQ2r%L%O3wVgV->YHIEa|3(d7FR>NqTLutz%P~0%A1}UP;Bt7Bh
z_XVq8za~5;4R@?2%1{?B)N7fg6eH~)@TD+!sPBoLN~V|*_PseB{ZIWzx=q%_JKQ#>
zqPK4u`ZgLV26fF7Oc7g7B>*x%KDO<f<eomPS?-?DWY?RViyPpihP=Mq3Z}l!c-+&O
zmA#3)7NUNWaWTOSjgctwpvzvFs4B3%0IpNlopil`b5g$bY6qLwlS?`2s9VBO&#m&v
z3#j;?M0Q^))1rCsM^lylys*-9$AEWszl%AJP)bH48q+vSxySOYJLDI3(yzz1&mPYS
z;&R!SAvdgtBhFy+G|QDIfsU)1#Za@+l+|s2XzhaTM-xY!O|L9c`M-`6$A$}ib<vsd
z_t6$2UsfPv(Vm^kFfjc__6p(*0P(4mQ#z5CKK)Pp%q^h8tosFLFKOH#5lfTpp|Wbh
z+<v7}LtpeM;=f+#O$5ydV~+3!r1H)!WR)ZXA_@Ebf~|N@HVJjQLca~ypdsiS`JVK1
za2WD%@ocan&F|F!dJ`p5I%)R~Lu40Nh0Iutur{u{rFX6(7{~oM%!TS4dwb-~(bYOa
z7<WA0kDdXlZhC=}&zg>;i??oS*4p)UfgBo!^tf`*>Kz*S{KxJU7b2;7rv(gdB!b(M
z$y4tu#o$ph=vfDkGiXWXqF6dWr$*$*sTZo9NLcMaNDa{rR9`zlTRa%0manx2uPPt=
zOERq|lg*OvTf?hvxC@GFz_&6vl7qf3KLF}4;2va}0-SP%OOUA4L+bzAoZ&(lAjC~}
z%ln;>r`u!Y%mC%TVXAI7zqCOtmOnKlZeC1zE#Ve^`KuGy16F+tWTBvPh43TKwI!=$
z3i7o>#8I76Yx9x|CEJ;#$R>S{Sv+!`JC=#qf_v570z0SYdI)Eq>+)0%FO_i?)EjU`
zc_%?B$DL?*IvPo7zjC#aJ^y){gZvhi3Jb55F}b5om2G#JRNW0b{;_a6J~R?{D0EvB
zY?oPZv6s}(W}rxLPrHaZGaD(On~@)}P*?tU(%7W&8(bU(3?TW61B6xz4A4{q7xQfY
z^T06xbZc&HfC_fy?+;B-M_hQ$l0<8FaVnK#vst64426;Gsa5pQa(sGIIQB<OQDaEE
z=?1e8*}7RZz=2&>fg+6j!;3FEE4qCIRM9wy>g($J|DrZ8O|w0zx$U+l!U8xm_T{xa
z|K|KTrs&A20D85Dom(t^x8aIxg?*w@4M#dXQ5Uez$IbMrPJ@H;8@q`T(0x8c4&R}w
zyv@EKnhAk^>&vnMbgC}0?}H?!*L10-;y976iV-46?*8;|QM3jO&Ci;*I_yp87eWHl
zrum;&N3|%a#KsEH;*zvtAFtr}s)ax?<MBiUB<05cD^{jhPL&QU`nXSL`vNHxMKi`^
z@z~{g68^3N8VN$h--s2_zvD1^OlO-qA3!4!U<8EQQgv+<3F@Qd1tiz0I>Z-kaK&S%
zwZAt9lw^{C<dwjc_U)hp)ND-`KZ@&;=D1XE_j$X_k-*Ouy}MU*g9|N@FS_!9kCsH3
zjNKM<kmld&Tf;_u+j@!p$y72WQI(Ly$-J#*Usx)w_gmnn16d@MfREl(qOAF+SR3j5
z2xUT<3JxR&&OrVe8R~n?`=GNA%d9K^f@rq(CiC9)r2f4*U9GGn+3wn8ZvMDU@w4lr
z<LKE)c%-c!z`gk!{HsUO^NsOVXEsUR4~8UzT!2LS39xVv_S55OCyx&F9BcC?&L&df
zI+4qFyXUwe$YKDeZ}DvxEII@Tv~ZM)ppcB(GdgFg`u?94!bZ4IaTpr58^wN>^Z6%l
z8G$TLL`#8$4`%+XIT|G@RLe<Z8Q4FF0Z;0Sg<g4?etJ%m)!TuuU(Yrq%y0BQoXegv
z;QvMF-LZ+j;jh2;llxI624g2KI4|xpUy9(zC&S*r`%cd(u#7{-y<=Y|-v4%__*1it
zX)GYIJ*4a`=982kD?w@rw{0EYMPFfjS`}=s;HzmWiCHN)_O&YLoUou@##@lP(l)ow
z3-g@Qb#7Kn>`+u`Xk2<%jq*0stwWqpj50r>TYx)~c)ObB$<F}x>ku#vcDN5o>T!V;
zTi!ANIonhm&({`#ZwGUvBq}&{Rbsb*gm?Pl6V@P}?da@+UIME84GVb_*}a}ED9r?u
zkc#p|I@LnjXk%E*`r?R~`dM<xU%!@^3iACKV+OY8`ACLrv*o8;?TVjltU?LXHto9J
zEyhvFwqObModLRcm3%3T%7*B_CT;u8u;$Uy5#_6d@mKRNGrt#-6?KzGjdeC7PI@RW
zdVmSDqb^0+C7^RF6=lF;15z(ZW?#QRf6_^5JFOK98u@Pe-(xZggd+Rrx|{C$s73VC
zxdAa>8P16E<oRwh+8q0%`vQ({I-*b^VDZto3=H8pn)i2Rr#r=H&<d!qulPPw%k1T|
zv!>ulP^<x)C>f9)4wqJT{BG~#L)uRVnV=|VeIuHyh?g5806m%yVK@Kg@sECLS5tV8
zM|-%?5bGGG(`36TAwQk=OKpm&xT>3o_>kf&cI>!hNe<qlru6yi#l7jp-)ZwHAZ7{w
zcaO@u5)Kdht?AzO+r(VM)TjHt>^3$2<0iLAWRij#Wk<*F{;j>5@<S4)4*$q_a+^e4
z`y&Z<(LRBeZCC#Fd(PCD$R3-xKU|r!{)hquKna|d3zXlMLywz%@Zq=*z6|-eP?Hy(
zn(LbOwE3vW{{(yvH4Zkv@DZRB^X)0~$QcM(9j&yt{sfE<Not*rzm<pdbG9j!7s$b(
z#^&mGdQGCK^=}`Ms$FJm2~d<FUZ*^ukS)@3C>LFp?jnKt;YII9*N7ZHs4x#`g%d-w
zz?hz01R#!Hc9q27>mKCGOD6B4j}yV4kf}^Ta~_U-R``9Z@37st$Ow}~F0`D*V(6D8
zbasW;s(%Ff)ozgWTz5_;!6Gf)3bPAEhy_nS0J5kS5*n2(e?9U!(sj{^9o~@82<POw
zMr-n7lJa8?7@9#r5*-TXe65O9sPE+V=YmZ3;TFuyB^H2DlIi=laKg6_QibGeE-BlK
zZ;%9bd(H-u7`9^*-{+5~?XcBM{AA7Xakny~#kYfp%w6M%eWaet54T5&x0dS~2jUeZ
zq6q1L1lhv*pZ@_y;4r9TGi)0d{r@Q2D!4FL7NKz>f=Rb*NjfKvLqWL&L(^h$bXleO
z0S&yg117=PpVUQ|DwZkc{M$>-2$yHXpDPWz{dg!D#Y8f)O%W7h1H0onFz8#1j~nWO
zyA0>a51(FR8ne75zd4wZ{TUy{9IQwiis}d@Q_}$GShvqEt47nFwV#AzP!5Ul-<`+M
zPPbxFo|<8rwABLQ;BO9oyO_ofs?&UEla7q$%pcc|sQ<ej)}Uc1BqCy*S3Na<BHE#M
z)wF8C^?*WRmH!C+H%Ak?+WLaEGZ0Op7;iHXDt$5zOKpgEL>6JJ5^R?-+nVf<`BIdx
z>d*L*>zXlhwmse9<piPmxV&*YtHILbwA$EB<spLn%gx=s>0rSZR{Z2oiK_{cn#V<t
z`)(Sd7?%y>S)Z8OpK97}XSFYakhQrRl`xP{Ra5pBHp$9k?3{PU++HKz>E?`8Fl<1`
z#DlP2j9I9El+4B4^pvjl*_gF)VZHc-ZgzeU#)w|blZ-x?K$Kgcku)ZSjZn%nfja)=
zC{H>e)N}#%@qY$1wPi@*0Whw6Il)UZNdcXtqi6d<UA_^pgJH-20MgrM;pnP~vUE<%
zZ@Cyu@tcm)?@cyZus*f`9&c>g%a_$u1}cic0HffK4$Fj?8>ol0&&VZmEQM8+;j;<?
zQvC(c@0jNXk&UW!0*U5DqO>ZNE?FWwXLVjhL5FL62vx9}A@!iGuD>AN5ibLH^D$IA
zwr&X2DsaqQbKQO#I0w8Pd%mV0{<+?n*A$_%Vlx*O*cz;tL_+bSn)CHl%X7GU<Iy7p
z0NNyF!CIur$T$%4S0H)2Om7b0w>`@h$ti@tm!5FV&}~4Z<b+vbhzV|{>FLD6YGo|>
zESQB(8=6-(bQxqyfc62%cbGj!sw#@aTy$3c(Kr0}5?TEzE<i0VfMa(vP5ZJ=N^hB&
zLy6K}i3jO^UQq-_v|u*SL#13jWUDDd4~C|shR!f@mir`mA|hu)Ri@p)M^Dtt!EVEh
zELnu=Fb-<aVL_jGe@dHeRGP+@!GlUuf&}Rs0e$9>BUbir<)UaT7|X1w0@-08(V{5R
zv}exp1YzZGoBjQ+?$3qWF{9cnXP$n3;{|xWBC`Q@jjl&RQ$a$CTsN;+l2exx{sknw
zLpk0k6AJ*9je;`9zOrp*{;$9(L4jV0t`k-E=yyS&Np{byIVb*}JFiu0lvp4WZFJG6
zX`H_+=xK5|_Bv7s_sXPJ91}I>t11uB-Sa&cZ!UI61WimKopwqu@@G$$@N@{iR~=t_
z6`u;+v-RROgW2Nf^x@4p+A^m1^(Fi&-pOuXsDmG~EL93*J^&gf?=8*Gon8Mh9K8V9
zf^viQW`vNIJ!eS*J^v@J&HVoTBvy7fvOTO<pgGQ>HUw-4|0nG*0~wS}zEQ~5xM5f^
zZ}}95PRuc+mJwN8vy=WfJl!l>51zoVr-&fL$3(^4thj8xmp5ZOj4$i@ts|NEO*$m4
zOSx9?#OO!^jHcY%t|B9EWpt_s?4?LIiP&4cRDQ{Z#7a{4+45xnXhC|KG!AE?ys=M`
zp>Y?2j!P-C<@up1TG|hjl6FpKwQ&Vf%Bjnp**{-Sv{TQI!Oq0r$tb_LjXU^b<V+lt
z+O(}9kd2mc?NeUec6dHI;OOv5dB~MfIkSUd;4kFvCjKSdn~3*-W8N_1H_Pe$*OaK!
z@*WER#Os@Iq>_a8OzEXX>rP##<Ao1JSoZ*88rTA39^p%ai0=|6;%6w5%F+acb4?ib
zew7rM7Pg3`=mL|0jYK3dhdxsELdwd?jYO@$aaoTJdlNZsPzl#^YxYDg{URUNQh9~x
zLWLEAK;yNiz?D3O^#K-JnSh*l9041QG}CQBKX(+)+%b{$>T$W0%H#N(8b^sKBxP%y
zJ;4L<7UOGC$`xX@<tTX!Cg|64CU93Gg69*IZW2`b0bLi$I9(R6!UbrnFqPf(U>EoJ
zCqS_0g82c%<YH2cO^_gev7D;TriT1M-@5>=xMw%0g?vwDXYOe!fZtOmBn`FgG^E(I
zw<&dq-J#$?kc7T5dYGa+5O$h(Or7}|#shp9U<{MP!jtDMnjY*-16Et<9&35O|3Wb{
z&|~Y@GA_34e%emsetR+l=QUNh+}l3&m0R}Q!_Q&hG5v<ml4-%ZZGc8TjWS^IBO5q`
z`c?R3;NR+&>-mPbd>sj(uhHN$jexEp2^)w(rf?5F6gvtQCw)&QG>vd+B_mp7sW?L(
z^BrV1^E)w$yc_8jutdRO)QZ{Lj8*ovT$%gs-U79q`m4-#yU@<j`pUR`2hZB$uNR$m
zLm3*K2hbrM+;js`6I;u$<3sZ>ylt_8@)RG)l<r)(={h>IGU%Mt-1s@fsC7~(J9i$`
zvJD4isJ6>n1%aB8*8;&|p=z3rynOPc46xvvi$!d~BsX=|j~fXSO35kC*5O?{V7x2o
zfW^s>!@i|Ov2=~-fKjM<3xCgI$gwt9xNt)Nh*|&Zz^f&EY(EfUggktIlYNBX1O0e7
zK1FM=cvmD8*5+&+0!)yBM!TD%e3(|JS365ncpwQ_`U*2ReT#Agh73nQT`WSH89kBO
zx|2d(?;|};!wbz^BHN#rPwC=Tm9)VPmWkZ%x5J*gc2dMM(fkEqdPMidl5gts#Sq82
z>434K%>n6QgjS0Uy{L`8>k|I+yUhz=<DvwxD2^Ct0)C*&%0(Uk7$jXI%r*p|h^sCt
zSCoqb1{(E0)LnJfM@bQ|7QcJ`M20`#0pxkk3^{!cj97j#?~txdJ~>iA*?w-zP3PVO
z<V-(Ia1$=S)tTkc)HLlX{|?yd1GC0=$i0XhvC&IAgej$WhcCNC@U#zXssziYsvL5q
zq)Z&jRj><>_mYJ<t$N;OzC{A3A#!`ac<`5?vqld(SM6%`cw&9a>Q;<0=I=FA^BB1{
z#Q$U8p;n^u;3`A_8>oTpDHR;^uh>!q#~{_ZN9!V5nF$hz2V0?~Hm)*tlVEmKhBaoA
zDNHDsV&hX6GF8laz;SA+7K!*l58#5CPZ!5lw7y}=Zx%mkEcXYcl|QCRxX0qHO`9`^
z$Bgk~0=FJ|u@v__l-Rej{MuN7AS0rN&_`r(QALp;iNt7j#nvNy7=vaYe_`CI!MzV3
zI!iK=fGY{O!}_`3yBjQstwE-LPBqKw4kj=N!P=B6IN|w^L(ALj<^iefX;)5VH!iXJ
z?Y|hh<I44%1`~E?9W)pNXLfB>lusb;*f39HnY<=%Qwn_4VC16nDDhahP3}HkFpydG
zz;()s=nH6VrYzw?s_}Z^B3RlL^7y-3jE6#p@%D3iE#6)?n|gcm!*6frxeCx~i`pu^
z1q_}Tv!|vr>vw<&*87>;55KCFSQvigwd)MfIT!gu5i~sHGG-jjPtC9Hq7$MXvCl%C
z!mUj`fAL66H_Md=H#CE%Ql0)66NaGil_Ai3nCd;kOw6+I;#O=CNnEM$BUo|oN9sc#
zs_SbBMp{I_3x`4pz)hSy{>mZ~QXSE?NygUPhlvl|?@RpE4Nw2X>gN-QR$UY-^br$s
z-(L=$q!v-`$%W`;Cy;z64t0<6E(34}o*zj0MsM1o|H!`1a3B@~rgu58Z$A+lLDa%g
z8Nr+9XM}Ns@|ED*qP7@>g|3hcME-EqE09C<o{Wl&C%u|~^UMT7w9}j@?y1RFrQh)T
ztnhl3kb3iQnXc$V>P}_eK>h{3__PvS`p##IVui!6w(|ZZW3?a4UQD*>=c~>0$}$_Y
zar?L;gudO*li7)5@I{?0&>43=sd#;{naIoaBg)5Y`=-2U1VitU$}a<v<xNGaNsg0^
z0n-`->V?nHKwI`YO(wDFkDud&RBKTt6-z$#OBj}aK3D_+7M2=}o8`SZ5Iu)=cs}@d
z%cK(v2U@WLV~zv{iwFmzh36PCNC$8BwG#-#oG+SQ#OtL__`Fgz4J$GvwBiqf$43#T
zkIF+!g)U6ia~t%G1I0v$YcwR-yry2bAM&yh$}NEU@gS6PTdIouCK?vogwsb|gip#W
zTi>=uQdJe(^6X3_4!iROU;C9#SaCY?khw?x0lai`97jCkOISbZ9Y%LGN=5sK%XBx1
zc6;K}r4yY(1rF;K9V}roR+m4;bD%3^9LD0+MG!$3lDLS?7WaTtm5Dy0c%fD83oxjg
zgLAaG?frrf^1OsgizohFhu2gffEAvkgH;6Ic&G7Y{f@Q*a`_e}ktMdyFN0{WI2wOw
zFcmi1v6GVUl8KWoD{VX4bXu%m(nl&k+3?mBTXPrGxu7q@_%wPK)7!heyi4T*`vZJj
zE{S?n5`7Z$8><i2$s$FK`>oF#n?(ys=h&S&Q7kDD=T>$+U-l9WNdgME`t6ZtbW}S@
z*Mv6)NY_%Ysp~4ZqpTYM!;GP3nM!ssN5m~)rf81u3uJg%%ve19d7Ci$P6zv#AO0oz
zgI}TDXtiMY9$|jV>`hYJ<Gd5wZ><qW#2z(tFV9p203!^?3xJ1^EZ`KmITpk^c#pCp
zo8uP<-iVAg2_F|hYWp~8*yHJ=D>aFj(vV+7qg*40v_EHF^)qq1=C5h(lGZvyCvzHA
zU~7Kzs*Lw1iVi%cj)Q1hl!bN$DsFp4x3uJ<M-#|?np=mHz)wQSYl<6rL55O+WxDtJ
z&AK<t0(86`g6rmXNDU;LDglG<a5H||vh)cjhkS^*UZ(_#ivL+<ZUjLYpWbkP<T^T1
zHphCK7pKoHXE`!r6l;{HX$aT!wrjy83$?Pxgo|Kg(y3vP9;c#Nia2r_+tVlHl&WA}
zT;9$F^XK(oZDeyxI!4KVW#83iT;3gv?*kG?9N|$DylE6b+0}UkgSF$sBI2mr8#Zf%
z{8v=XpaY76lUBtF`t9I|u;M)V?Y~!<&f9++I%>oMa}G+>8l$+Q?2jD|Y2X5Yx(vxN
z?JOMh5!I>j#N68#%CLN|IIj@eHNX@qjL^0(V)M-p4JjU&2+yoHD2@0v`F;G5k5hp@
zq!q9?k}cRteQ8UWM0n816!IK`30;AagV5YTOT~sa%7dscw}p!r9Qyyf05Ap0BaxFw
z^M3-x_tJV!bN}vGW;_t1A^W#b{xd#e98gpza=2!6*LS`=$iA|tfPcOT=OzL3n+Hh8
z-yhFN-kpB1GD$HV01i8xB#+h9m1qHL8-2LNhqlcxvSS4Nsc5K5`_+G-19D32KVf%z
zqKeU#TGhD~(JSO9Iefi>VQwx?**uQ)FwO9ZIm{oBmQ%<$M0#^x?9l<AbBf4Z@o;4N
zl30w}CcPLA)_8Qo!s-)xM*e=GPyN=r*E0+=2&bx9iwU9b&bKGaX(5mfz^}ImaMmJO
z=!P<Z^J1qnfv0am%MahcJjLt5BiQux5y9GB%9oCp8n(WPEYta({@egKkwe|8|6oNt
zrk%<Khvn|RMJ)X81<>%NqP;fLjNucaBasori8-awspHkGiC0z<(ztpdcqFulocIlC
zktok6_WQvjy>vp0jgCDJuKpXM6TaE6Fte%yi1mTohuYwQ6vsNzAas%wxgh{=1WfJ#
zGW6`Yjau=49QG1iXptEJCH(UMr(CR<RJg#W-N{Y8&4Zo5c8nrpR^`0NuAIr<yM1Pi
z2`ZOBB_V5X6`)oTB`OJ1Ie*EuyUM<_+cY~;AEwu=V0?3D@Ko!cd#go(DO~H@P@4B6
zJG{}Sbqt#ZjLhaq&ZD={bYbsTxFVelLi19<>aDf0?i07w=)z~a*R_U=jXKl(`qlP}
z<CQ^`AG(&!lSV~H@1G^n0*~SY^_gSUEi?~0Fazer8mks*NNX?JlyMyRT|tV<zexDq
z>WKgP8i*{LG6vn#^y|#y=!No5R@dUc>G*Jbu_K^Png6@m0QZxuShw-9xf2tN5G)wE
zp@n4Gj9;=b{)k0FO)AvwNUYfEG+e@Lw=9#m2{n`GGJ{*MN15+3)cbG;(1$VorpOEO
zrD(~>a}$I^#qkUbjD7L``F^4gK@9W#hg2Mri#ep<2ag3(qNBrzNzgiZ^%yrMwkEM|
zT{#(>A75OwH}zt_T}fr{v_~_ZvM=%^kH%r3zeOizjHi~X!q(vD6GKb;BF*z2#r0i@
z(y4)~yL;Tv*>1x_VJ@u@owj2#1P)8;0l_a~zZ<^vH2isWU_(qT&u<?PyjMF}Dr(~M
z1|Los60N1%jGZ`!m;x<n2Z$InH38g5ACmtCvSr=A;+KgQ3kkzCqbX{E=xU}`&NhVa
z25>%og8)d^Na;s&tOraLg!OPTbqQp>4#dvw&_A)oTcbR1%w<_*sCf|csr*pa5&t_D
zi&@6YiNGSxtZKkaY%bxamUy0)C<d}Ey7ea)6!GFyskat^PT@Ra2hJ`vzO0TvS;9~!
z_YBTH!HUnHZy=`oEUw>Pw^ZZP<sE!=%Ue8A$P%TSizGb*J~vk)cw+-}jJkmmmuVSQ
zN1EMolf6>M9nC_8k5S<Y=&+A*4+ZecEB7v?FI!JL;{TwV_ES>QPF0T-Mq<FM0b`i@
zK^&<t4|g&n!P_N!2}-~G5K}osA{T>zrrkC|CY`1UNWiMsV|n0tBqgzkM82R*G3CWt
zCkmQp=LjlH9mNB#OLD~G;1nu$#KZ<@7pYZVAEkyR-zmf$Ph#!<2w2x7;2gxyA+Gs^
zc}NuNyNENU>Wt1lrG4k@HX?Ic^6sDwVlBDlCKH(d$*66;k3vZc!{*fZ%GxFHdIRGO
z5&I{c%MM2W`kutjXu4@<6aDpxfTBosj2CC)CC<FJfgEbd3I1m%3*HBo83Kf<yAjy!
zIj$tghV&ue3)J?&DzOYu{qTRCuZYtGu7o3Eh{1NdOycL+`5#uaGR=D~x%18|bjDJ{
ztl{8$<^ck<LiS%wpa=+%P^wUheh_jQs<74(SuKKwqdc6p@klkK->q|RyW41|kZoTu
zC_K77h{^RejiR5Az5rL9Bh%h0_9pCi9yECFX1P~|_#QQig3*`oMN*fAv%eTigmoNB
z;+G3F)i<<QXFglzV1{m*)3@)a1($;RsP^7s{}4z|r~T>X`>BjMMUs>Nbk^Jc6Kax1
zxeCLl?GMzTc3Ph#zo-{r_9xAT{v|dn!UaMlmiZ@f$^N>Npdh71TQY2}g5v%3Uy$Yg
z+6~c`Yo-|CHsj|+HM=#|0f<kZEI66IgsW1Is(^t6L?1?ccn49M|DEHYY0<QF;duBp
z?eOJJ*9pT&HM-MH3O4oMEuZUQrHS3SU!Y2bh9y~s6>AT7g`OqZ=TeRGA6SXBt~&n&
zFEFkD1tMoQzIMMel74=;j&$&igR^xpdB=_oJ*JIINv??tf;$%pOn&c7;;h*7z4^Lq
zX%X}MRYe@?&<GyOrJZ*xMl|BrfWXRYn{$;!KgB=K+hrP;NpfuZq=}g8vlIWu-k0ac
z^yD@mlQ^BLVvm4lO6AI{A#=A;YvAARp&%9b=18&|i4Hb8QSL$5!LQi2fK3oPQ)_Hv
z5Y~2>pYE<6;)K(jr4G*h8@8fA_k5Lw>*~4nIrcDi7i_}MBfqi;F!0f7xMld)$X!;%
z+-{D{*T=}PL{>yuqq*b*oKeATNHRySfSdEtqra(0fV0dN7c_Gw?a_Hd*KNSrJmIYj
z*njwSm=p#iukU{R870pXu+9gc9o+E~i7;f{#sC7rMNBqqK|YJA-JVA(LwT}=i>I^l
zHEjX!m!~QkorlYxf0@^~Gucg}3btQe;p~uo_pD_VmIsOsF+w!qT;R_hB5qwD#Ps-J
zh<$r}p^RM~jHBw*zQ|-&tIVnxi<0vg7&LyL6s++YvR1hdh?%R&C&IsN7{J36mGFx7
zTSv*dov^S)+Z>ZZU&iy%cbrlG?gBrFY4?>el<-eKPge3{4ZC#&6&^#r<XHod;pW7?
z(Vs$Wgr`h$cVp;NXSXdwkRF3$>=}}nu?q$WDpoUmCqTHtY=y}RawhKX$pSuvbDp+#
z+q>qb{nONS!kElO8O<ocY?AUVKb>X<r&2}JMwkuH*RRs=B^}alm+iArn+$!P?l#Cv
zDp%+&oGVGaJKAThQnlzgsM;<6oPb)s4oY$A$bLmkmerluA*;%Ld<Gz@pG#7nF6W3Y
zaBs^nLe|yl_;9s9YYJ?U5yQPodL2aU$zW=AUIZQ{V4(u>q2c=-{vo;UP@Q6swvqYI
z7$@xESo?h-52H>Pxkl$xa(j6SPbmM&@SPKu=<50Q)a|{y_GA>z{u&Ya$_N?y(5k&x
zqMmi6qSLbM00~3sw5Vcss=iEplH?G>Xxh+keQ%6uSlHQThQ7~rD-;Ja=*?b|W<vL?
z4~}ehuQEG*Z&orpL=l@M?<=_V{sr1#1oVshzH27uJr`LI6bWkZ+r@2b2Z>+=$^r>2
zwby!q?Zk9~deq7h!ePSufgn31syI~vmCn+{H5pH#o1PZTTn4=m($jL?Rt<ssEi4L7
zzN-mQ?gON4Ax~?eBM*RH^QrPhe+W^?t$>M)RahWrMd|J`Qs_RkNT%&u?oZKuKD#YQ
z0@kG@9}IVdkVFRsT_%BrAo1hbEHXZeUj8o^>u_8?dma)G^vCN%Q@zi24|?Y@UR(CP
z`IPHjsXpM0fm__l0njBz$7{VqT!I&YnBW6Ohv$IkC!8q0{G7sJHbg!YVByb0p!9r5
zWm=tW`F19E2PxJtAjEJXYC;f2@Xko;n=457qYIx*^lem3OJ*rVbyOhfy?w*(4;&zu
zh6yjokTz%_?dB7YR4Hpcl^HfN^V7m$_Fzr)%V-0i_)T&iRv!hvYd4mDwNalOS9CX}
z#LXM9guyad4~#jO9;TWW`gyq|?Dpb74$=|tLgJu(52sZXKUXgAVHqRsvLyLL1P7m9
z&ioND%x1+2^dXkN@>>el!}xjT@B+MIAA^8{jqT_R@(Q$w11;)dk0}IX-uaPH&GLs0
zY{zrxCQi-KTNxd=vU#b;;O?b&7sIKigEv!$+a-SrAwjDm+w!cF4#YN=aMlizgth^O
z(wV$@bZ2D9NSkFBqcV`bRfhy_Yo^58aiqxFAx<W;B0lE;Y`Sk^)EUq9KOa7wB6;B1
zts9PuyZ=&PDGq3pbnp7I7%tfbtl}9W7!CRX>553Fli9xHu9Oevp1}9&;J$63{YiIR
z;I`RE=*j>{65Y3sg@FNOoH_K7ejyGN{(@s4<28E#x5|EfF!VXSQAU~Sz3_;A+^<<1
z@9|6u>*C;w7w;$YR$PTF#?$MAb%j`b=M}Q!)0V{s8oh}*Dfc49)bIK2v1uo{%mT?8
zGJ4>(lsbw8Cye&!wBW$M|0=|i`Voj|-;fn~z82X1XQ8G`ls}k8xX9=SC$-}O>@b-O
z&Q2SFkDh!jLTBKMxj@B&Wzu<9=H%C&y55Bk+hku<vaQ%UfYkRwICvD1y>?*cCIQ~&
z!&cBL&gI*5qXG5AKtkn7I!1EhA7!(Y1KhUfeXqid-j{+Co&ppHcnD2w!I?QOmm3%m
z3pln5E5-dKt;ff!{rEZY#lsgl0kIC*TOP7RA~`V2dDrz-=lo9Veo{4y6cx8OgrW);
zCe|`!@N<AQ{0d`U0pJ9@t{Zb>7VP4io8A4H@t(SRjFtM6dFxXX`lIfn$zn9!ADf2{
z(LTT<0e-cO-Boi6-RAkFUEzZF?NovZg&OCd6h4z8uIz1ZgUWIy12Z|004l0j=FX?7
zTn9N1%eXd;2itqsIkjWRckeOs$KlD6XgpRRxPCd(3?5Lxv0Beu1)e_y=^X=^Z3(`x
z2x*fjSrtf?vU|GZ+WC|x1Tj<R2{)4Zw?Owq;QF&a0zauzF>&t;KnnXCZXDqsV?E}6
zznv_P3lXR)tQ4@_5IfwgJq4VZWU61szWB8GLTLf|9$9bTrta}P7n4el!k+5i8<QSQ
z`}~$Z#DV@`rSI@{=_Mh_-~i6JY{t7XDO=$6>x+5Ix3DYu7@Xs@;W+j1?Q7y}nbvj3
z{6nnKttFL4sc5I7;hW`4#FK{zCUXkVm-7__-<XRW?{~F8=;N}(%1^A0My>scEBp8x
z`K|8M%AvE}purT=0f_!;St8E!!H?-*Dl$vM0vqNM!y98XL&%i;3oOT}Aq;id-+n-@
z0r-QU8f(*#nHc)N#gE~JOF<B4duShCT7FU|_I(QXp*h)JZMo#WMZ~6uL(~kUBFIm7
z!7l4zZrLNhoWij%I+=Wyk;NdoOWlL&s0(Uy%;#S$@?Vjn!xSdvF*!oH@C$nO3kiOz
z@PAYYCi>WL^HJT^yV>he0lzrwmiQCV1%1Ekwgn+AiPu#%VX?m)a|wkH&4&aoS_K^%
zwrd79Z|rvnVyt}4Ycr#<H*DFtol|5!i3~Xu-6_IQNqhm&Vy<x2;hu6e#u!5_SP4J4
zbx%+9yi&P=e^od$N2xEKc$`6`UD-Q^LM!_EWps(?SsnPQLf=7<q1FU1;w^2y4@-`7
zx;R7FrJjh_c-Ft<ZsXb!&-6waC)wQX<D5$hIJxmt89LZqHiZB-#&;}U(BNX7IYqSK
zOOxwi>zW@*zU5*A&c{a}o&={YZY|QE^Qe7aB6oj|ALi|Y?BHB*NnE#LcaVsT3Ex&(
zXkFN+V-I+5=t(@zgxBdDK2im41$9H-b1fN6z9T&*yP|vRUa7_z6D5$jz45Fat5K<d
z`2@WfTaI4n4`F?mm#6!k^*2DcuyPYGt75VMtG*BjUr8NeSw-tP$)U-uL^F@^FuaPk
z3VF<P?qesE;6&W@qkt<n6ZYQQa*f=INjfT>v|jPN)%~k%%anCBMX^QDZj+zyEQjeD
zsU4i~<zaBL!BuPV;6j!~*A3NMvk+Cq+KHD-`Y)CGi1AjHEJ;$dU>do}aByynk|LsU
zPr?a_YlLF}K%GA44XRs{&J^kDQze=#J>r{ZpR2xW?6cdA;WZhXn&1-1(Sq3UNG;e$
z=_UlfzBwxW6>kDfxGafA@f^?+Ah>Pl5o<(s)&u82OeKbaz)2+6N(>-9_?RQ0kK1p$
zLJ{)^(Pcvq4fZI0{1uS}uY0Orc7^;50Ax~&YT8EovRYex8T{=S(Eu%#`K$+BoJ4{a
zrWr!tV8t@BKNU~Y^Hzg){%OQG_|Mq@xWB7(nQkyideJ$gbq6t%ROkG#axhBpTZx~}
z<v35X-ORy+mu2Htwa2~P;MgUBI|Y<^bX7X7s}lKk{x4cPGXB&j$FRJNHdA3a0&5o+
z$DU!T_zkU_j;kqIoHQ6J>Ql>w&f4MvD+(%{6I9Z+EdiJl-GdW3UCbJ|0{#_svOQrt
zstA@-O)0m4Fxw50^eUp}lc?QBDmNv|4&cVW$MG#|lO;PkC&YTe9j_zeaj3d=iKmul
z<ajglAK9cnb?P~_iE_oAu-SFtAsyjgLCIZZ@WHpx?|8>e!2SZ*45u}Q4l}imgv8CK
zIQYs9)mu|Tv(k&CF!J5}GqzlgRor)O8vIh__>3lUx#F~FIqUtk&Ma0pcrpa{ztdn1
z15~0?5QagAm+k&mAR-IET<vDAHKQ_{y}>w(>cvS`Wq!{5SF0|+MJu<@YzD}ho@;kV
zTrIC`xZCtSJ~pVrdnu6Gkpa{W=YLS~7o=D5uDizAq9G1#v+MgG{g=Mhejfx5GE0E+
zGwQrQB|i3LGXm^JawYCES$G;*fryF!hG8vEJ%_J2J=V7LifS)~9>o8E<gl$}3>T~>
zc(Lun&cvP|)1`h_cvPu)-FKt=`a8g|!Jr!^+{kxbxvq&*k~aWy6U>7xbQ&f%TPz%a
z*pEdS<XmJXMwVsTFJpzeH6=S?(*!wqX8}G)P|~&;*sy%F>bXSYb~!V!aIX^&Np*o0
zXWsAouUK$_V+)2_H6tJB3v-HX#s*FkU%zl4Q;H8uE!R}T@Nj!6q99iQldK{8oGtbp
z!>6j7(h9J*WJ?!o&>!gfFpeJ+07*}W$eu<vSfe)YS^x6i46h(2g=KCRYvF&6_^BrQ
z=PSBkFRFb_zFWya72AREX{4$`9PVkeHqV~pXNws&6gdUCh2snF?28ZJUf<8)t|^m?
z(*t!ot-mqXIyJp^E`Q#i*xzxcDhRN7YgKJ{zGs6HNeRxt4Ef%s+9U{u^^yX6*&VFh
zlPyo%K=ER`(w-|B8O5S-|M_}mRC35jqRHU<NA`(g?I7KGABDNiz>rfZiXZT`|Bm6g
zU|y7pzz;!lStX^ch|lm)Smw8LZ+@z@`lw>!Pw4z)x~}bxsgH-;sUiik0b5A+IIe!b
z^&0Rafni>?44_&(3ijy_SOo@i7X8=&36V_^5+Gz07i=8je)})z)fAEC>ki+btJG95
zl#JvY)4f7-gA7TaetwhFr~tL0u3#W1vN`%T)$<>qB$d$HS^UAq@e~A{6iRrYwPl$*
z;r|`4LQs`vc;g$@c%JC+^?~XW;lFkd03i#;jaMYw?9P`QUI51av?pWLey>qNEp8G!
zGQ<_bst}GNdGH5?IA5&aY43jG9^?qk2a+;&Aq)7@-`In+D{^;PgW%hXiIgj>Z(?{)
zQqYtTGbx&t)Y<CAKmZP;Op=K?{eD~$3`9SqoD*20Gh%f70Zb{l;__v$v)oTu+lh{2
z&aVNF3(MmVB2;>hE8ef3B?b1_)!>;C`HS_g`6&|@`>&m5;T~Mz*i4N!HOS^#LnN7E
zNOi^Nv1b5#KWX@duFHC0p{7lX2c#G<$>C}jwb~tC9slpG#D;^jiQ0lJ>v3s=eoQBm
z6}=5OGDK#Jv`Il--$Kx3>KY4Sl$$(MLVysbx5g+ClyYvcj6GlS1sJ@JrZF$)w=XkB
zh^b3lKR=<|I}aMU3YSmmsba3eI4<j-$B1q<J(5&C`_ePWIVeR4Er@cy)azJv+<Yr3
zp53$RnLyj?oDAkn8uq$ZSMc07Squ#B5S+neZ-hJ5dV_i#ju$>RTBWW(Ha+}_6buu_
z4wyu)My2`a=-VneZKYb3V<vKj9V||c(--i{@Dme-0$T_WF_h4e(I{k?=_Pgr+E@P;
z=az(sCB+a^h*G<W>pnI9+T$teAUp5`kZg$|YSagLu4PKCC%d0O1pT@QhCl)7aI|0s
zse0*=ai?Noqq^+2*!9{~qYl4~VoKF=Ho7PA%?$+CLSMLlIJx|coW=P>hoQ}#rfD0<
zy80=o7efR@V!sFFyCe&}6pT9z5IN&XH=U8=wQk8xi7EK>2@Ic((!+QlWIFTDBH;(E
zQT4he=SFh<n3h7slJ4YD$?eRG9e8T_ufu?xA`0~c-;I1(e(N}tzJR*45L6<tuAl<N
zjjj))xOecnDePq~0MUY0N^>bQK5g)_=PwVEw(O~rdn-Y=Yps<-@>@ex(SO=NZN%b!
zr?Rj1ENAZQscFt$U7Inv&4~2zmClh@eg05{TrYt^lSwXr4L_hMcq~y051@ND2#xuJ
zTYt{8Q5?sH5DX-I(w;?3#G@>r^o1AtdizMttFzPP*;C|c^P;Fy<6+ypR=c^KnxqPT
zj$Z5;xcCvsif>s4q?X-?o<?X&@|BD7UH89pRcC+ccq?Xvg?vuGz?ts=^#_qQC~RvO
zXiG8RL<+IF<LVTrDPmqRy8K}@^z|;_p&3sB2*8TzLh$v;p5kmvUg2gbQTXVCrjFrO
z-7s_KQiz0mRAHOb218}wJWi_vlun0&?tDST6GOQc6RcHJcU1Y4VFQ&?^Lft2u|GQG
zln2_9F%nTjV$jZHo420&31A8{@<9y|(_oPhQE!g(i2iWe11)^89m$NxlD!ZzMd=v@
zNZIxqIA3MhF)0qLpYG1T7f7eT+l(mK4mAQw{;zieQgCD8?{UJl5{1+bemMr1CK~<;
zGTA-eWGk0?(uFSxwn+3Dg8PgdlOr0Oh6&e0tWh7$f0IHNq=a^!&T1SK+k9q)6ctbq
zo>(Ef%DjmVeeeX33e+m?F6GYWfDRoc_#O9S%V7o9%6UMu!&cxOgYeDnUcF{?%$&Xj
zoje*t5jgMIt~-#uK=cuI3zGD~MCcX%Vk-O-_^F9v+wZc};t_p0d;8wAbkOwGf`+|w
zkF@+-A{xo?3*iGcKg>M}G^!<{_|Pph^Lx1~P}WPTX;!SZb^A;_G?4CcW+Vc0zB-F7
zG!(Q9ytqw{XNA$55xsktHb+2IZljH=OQpd^h*v>t5QhgpH|6sbaI(<SB7&l#ZgsX=
zD}E)6CjS9`^p5~~F4@-eSNnL#I6rU0(fLhMBH=}aD3_nWi1d$q{xR<7Eq3tq1PwSl
zUqwaiRW0_2=~^~ghz+jrm0e+F{yvuyTRx_dx}9#6=X3QYuW_9|>JsVpxxduof<<L2
z+2ng3O=xp;!w5TIXD>cQJZf-RE=*r7xS$Q}(SFv|iw&4q5(nMi?LFK?4jrg=%Nn#v
z0Zu5}Kv!3H-v!XN)2ao@N^cjf1y?0QlVvXM&w#?^f(ph%aAGovPL*1JXVr9pB1Rk0
zCE6WundwyQ5dHOI=6BDy_S261O#A&#`bAPuaA4pokf^EMRNJPuk*j8RJD@HI7Z1?J
z_~mWFPN=;&RiX_^GM2S5Ws;My1|nv<_a*MnelwWl#U*rTU}9d(jWBO3Qc>3{8LdTs
z+Yv01Pi*<PGAec|_&9G0mJisaOD+O@XmsFF@kmMbdsnk%Wb84U&Y<1OzposR-B9UG
zE!3GC&tkr%@5od0l+`?c$CsK|7u6#MS_TUL92X((8-T%QN?MSh=y+>m+T^}2LUL#r
z;u{+#6=h5%n_?2csW-vDth57oI1R;+iAEG7x}L|$GO=zWjB~BOCu0}+N5E~DFO(9b
zlbRG$k$fla6T?+!fA9h3sm}cG7LY$v)j~CbM2WGI2k=O$^q-O_ckP^JQ|lSeGkIa>
z8$6TwUVH4Q%sh%xj@90IeQo}3Aj-Saa{bbxD}m6^cclI@K$5;yQiNkhVnXsBzxMMk
z+n(wT;+85Lc_+F@QFVfI(7VaiV~=mWKSwz=DnZ2i!@{{Yaqfs>4eBQ{dvsfgntB*g
zjH-ol)i0D1C{k}uv-$A~aR3XA%j#h<iS_k=_Z}D)hDlDJlX5%$?I~{%LO%!r$V>YY
zP|L1oNF0py@NR+&%+5j8d%y2>x6WJQw>t}3^YX!D-tq*TiO1bHHpskc=t^Y{7bPt!
zj#%46&AQ-SjrU~fO&Tw9cS3%;z8<(wgdI%Mjz~a3K8bjgpoF-<KF<HgVSLxaG2-gA
zy_8SGqUNW5N71v0R5&(YbzXAhRW8CLOt-Hv5bbA4ROu1$FR&?Ctyrl$>He9-MBIDy
z?hZKW6KcnJhIa}Pks`8I|NkJ07}}u*_#9&2X$&cWk5bP%-qRFH#h~Kc94UN<HS$Hp
zi*1D&-VCJ1p*3`ok8#9)8N)fU{>KnUXTDn51R2l`2tsQ$k*49s4!=rwjIc@-P%e(i
z@nq+p#cwjY#EnTSeY#Xy5oem^HWcFgM!803>Xdt+oi8oIY-uQa;r)6MbMN#U0lzo#
zeRyLjN93Wy+DI3s?JeWih769jHIY=_8}7yI<yX?pHM;i~qX9@*J%f?}K?;3qD(p{?
zhXK*Q-|7sPZUayRm>969N0=wwq{PYh#|!m9*OHbp0|B0@IqZWZi6_T=xsuPC_&YK}
zXuVy;<>c(%^>mdi<p-e=Ac}aGk&i|1f~n)q$af^<9W@qP44j_ZK2_}yWhU2(3q`@!
z(~Cy{6s3PKiPyiC)jWyF8kR)bOGFysJq8iDjca<9sUM25yEysRhn1yRObSLu7}7Gx
z7|J9>9grI?>zC+`&L4-oPElU&zX#v@-vr2cIB&4MZ){f~CO9v0I95^ac=}Opbewo?
zDD)gANebh;Cm?<0)LRnhl=y;S7>uO39_oJn7j=()-IdUFBb4T+wBQZU{5Mpo$2?|W
z&svu-bn3eSv-Eoc=$<XMs1y!!l^3Rv@pj<uH$Vj4VEfBQT(eA5BERcL5IfkLtIYOz
zzMd=O>iRW4h(wcN(87Z#DyARG<yv~lHMLtitJ1D6t`7Om?_A{VkU!1SV8>SE+KA09
z4{ym!_kAPu5&Qiqvq}Q$5i6K~H}B@cqjmq{R#UrY_wGY<7gsm0ik&H4H*3$iq=VXp
zX^02zXsXnpsvcw9$8k<Unv6F|#b19c^+HlaH@B+=D;>EnhBOJ)31f%(0ja#>%>T_W
z-iC7j%}D{G($X-a-AfBhw2+EQoKTJM6Q%x%8rK(g^S9a8e#sVR0w2hSZA0CUqs95u
z3M&RkD${QG?g2E5AC~f4Lu)u_1|2w_jy-Qb+K!u0N+Zcih1Qr*8Lm!E)rg~%<YqlC
z`M4bB3Yv(VuGqD>5%T_${u@)&ww!lBez38Ew0*Q#n#@t6u1Z~6Z}KerC><9RB0Bqg
z-ufy0P^k7YWWYY62?4qkU~Nc@Z3LNb7&Z^hdo!WCL~>t1C_WvD0ns?y8TMnWCL;Nt
ztu^d7PTtvKveAgG0syG?qkn!vk_|u8-KBq1O`PV!ia4$=xR-Ah;r&Oj*ep;+&4z@;
z)S+>2ftq*HEaXz5nLsdz-iL7LY*OHslN9|ObU5-uv_2f<o1%n(k;|l%6tgY41?hY-
zz0I4yLCEoEJVg+6L5HRfkMpc3+Z*8rB^UP5ee4MI>hwA516+_l(NAPpD|+;&ZOeMu
zMU+&+)R2;*=_>u>g~y>mnmHEC)kMwb;?>xmKoi3sTpeY^z8Mlw#t~Onx^_Gy0?*SJ
zT2%(V@(%0@A{n+}myqO#nU~`K``rEq>|{v#cu^U9BQimt5eP{Ef7m3&&)*OxaY(Oo
z5Y~cUk|B6_9^|q4q>C7muNepAIVteJO(ZO%!!!`?fA5xmQ+r@ON~<9JKSZ5XKvZwo
z?FEsL1_|j#TDperPNhK!k(Tc6?vn2AZYk+Tx@)MRdx*37f8ROhf(x$q?EOCPde-_a
z1oP(P-$m`Y{fWgz+>9j03>{saVrW+L(l*K2>D_Q<PM(guC(p|JYHh5}qws9xZbW+`
zE4tFH@3)kOIpJI?0ixXCb;`$uRg&QkbhMss=}a?SS@foOLVSBAT8mmm<0Ss-ds8lr
zMo9V;v2c_bC%i`U<CE<=eupjTx}A4$bXuJ$R)X3;YL`3aYY%(~RPQHi*><N5Aeu#{
z=!u2kf3?@FQVNL;b<=Yrr-{WUWTF#5r~*Q~N3nzP9tb`O{H?F4yo3VZ*jK%i%nI)L
z8EW&9+d5r02NRs`Yrdp$&kiiPkbz%1gYGp%E)50_znJYO6CzePBc_D`C;^8>ZwK}J
zpZN+MZse>~Ogn@CrgFZz@a6&G4}Lgbb2R<<W!RMz%eI*(ce{(StaZA~q!+umt=S52
ztalH}H8b|79P2uhPo<)hj7~eyf2iFZY7<8#Zpo4`mi84}neRI8650%x&=RqDj(177
zM!a|3`b8Tr_}z`0`DFw$d_#@h-jQg$zoh!x#n>G!j&}b-#8c!=|67u^v(A@i=nm}<
z+mY4a3sq-LBo>{<QN#=vGWn{FP)2>wSPNT0g7dH&@WuL_EZnm!I^C=NFv>oWFw>x5
z^!*-~c1B#m8Kx6RIcdSDgg>r@e}njIPGaY+XbLVKo@y{d%Eb;VrJ_lIrZ28H_xLO&
zdTl`;{>6e@XFWWFrQK=Ml@*67`Au5g#@cqKNr4wpKepDe|Le7Ciwu%m;?6+UT-kg<
zi)x;v*}dVOd5fE{81HelU907{<k=EEC0yo}Pe4b)*DjUNiyQu-jt0rXqyJS<8R1+I
z3OJ^)jy$?pY%sDjQ+T$5Im~AVL82QaM6jMfkkSPBs|6yI{3pPY_x1RE^m%j2wf6nL
zqtwseXQo7Kt=B)3@bpIY?M@R%&*0C0u3NFB(<E-N&LAN&;r*qAovv&CdPX{8R0xYo
zV@kvqbk`S;HqO0JgoH8*hPm>XKzlsdr(U!q=#C?Q)hRVt=9E?`k*@9UAd&=gj7hEV
z-n*=Rc}jNvDJ7uRY|$ZY-}x*7-@-$9b;}i{%2<5nEY2R$ht*MdHVd#qq?+>Yl@@0K
zKq5IHrD3#|J~=%2b&;ZAH~_K!q_6f+dgHWfj&7a<iXt>+6R$)uLRKF;*jRI%=WVi*
z%3d%}C^gcE%D;1&hKIj+<$viFhqW6fx2_bGIAHEYmi#oUB02Y{>12hpYc+Wjzq7gJ
z?!H~&s_J@hCCz?s_jB;O*gW3v!L5&FyW8PnM)`7hB(znTcd3@YvuZluo#p`4?OBwG
zjORFBn*X;I@I*YT+~o&MBpa9^k;2{IsQa1I){ig;IbdBPdfJwN06klVq`rQL29Kx{
zR!;&_#;dLe+$wB#>Hw*N@E+xz4Cwt_vLac~<`7+;A1vaHm(vii7i4CdWnX)|zbpVU
z%XaIYvc)3-AEvsm>5L3wQ00;bi{3tr<OCDb&}KNqVdm~vH;a(V*i1Jzsfig=^W|pK
z*DkF)HYLm&Enf{_+$c0?@<X>vYVB?q7aNwBGf$F;bsC4=JKU-Xu<9k<JQZb(JM9li
z4rG!&Hek*GQ6?6^)lRhc*y?dK9>_2>*f&r3B9QzH!$$Pe3>{5jrR=>GL4t3AgiuCF
zL!4UdBG~?Yl{3w3VKW(Cxb*+x0AQ0%fX%CF-46w19lJ>pgw(ieAofv<#-)i}J0_F=
zj$%RFkdy_|xBHKUJ?f3)0uY5%+c0E$EpbT0>8Pu{zRnPoFY<8k)0R1nQ&WP<b~OK;
zJAAnq;re(nS#@#$!gq9mM?GLY54I(}7O&R#HUNqtITC<!0PT=ftL3;IUuasejn@TT
z)?n$smy)ge5RdDBg=bz}ul61nI8{-fp)Bv``+<iyvGI&_8K<{kRM=Qgy3Q7S6*3pv
z*G=r$@)a=dkBafY3I6qbeOrJVJ&5v;CjJ&o&SU%Qx$zDG=7D?=P?D`NKkhdPFYyCj
zCWRuO6KCKoEz}TuiTWHWm&GA8^mmv1f;h8}tKv~f-}2rysWv*e>OR~%KA<lqQ`i&w
zvHLK_9z8}jZw}wA$|P2;K+VwkW6Smp<0`|2;UPlwI7{bUF6eTUy^vIVekfh^YKPA#
z2xyKe-P}Om3q+}%1CHo1Y@$m#{AsYM$SFD<nh5Q0Y<n@6BBPTaQa8;^m|qcn1Vlo>
z0>3xqvY3*#JWq|rX5S)D;LA8kr}|Bavgl4B*yNAB^WdSYx7>8p7pMEGuF+UAdak&J
zqq)|d+IlLM@iv<K97}xXJW|Q7QTC1>V+4-O7#F2Y76aC`e^1Q9s__I@*Kz`S;l^$%
zS5>l{pOZmsfg}5XBg%NXU|r@qPgKQn)ZPG2P-fZ5#GIfnvZ1o`!Q34_ivNV?9`G^0
zGt9mGgs&yVEaY|Y-xC7CeiS3SNK2|=cv=Kx<Y-{khV#gGLp=%yi=l%54RB!6d_1SZ
zmf78>1~$khra8^jZy$j-Wzi=xlt}t}bGpD`ab7;)TiZhl9U=lzyb6TwsK<63bqDh4
zF-nT~cdWvGREZ?MKLg$o80Xq_J<Sb||Dk8-5ytm>{~npvfpO*AI7DuOZ9$>37n|f_
zWI||w3SyQQzpn4m-E-hXz{j}bsC3IJep=_22r79WThSW%Vg9kh!&pv7=_YzxwwBd)
z#F*CD(*G<uMqu04N0whi#TU#E>e&2#3jDm#h1ya}%4R3K!_Ja)tXWyyfyj48#_uUE
z5dux#zi$C87XyFIc7V9>gZXk}g+h9^@-9LnHjO;SO`eB{c9XT$i1c(OB;+A^bwP^y
zl-nNPm;Os>N^T@LlPergtw(ah3@eUtMEd-iI8^fZdN>hVowZF)04I#dK{=N`K#Y9c
zxog_W`wMCfaD8N^o;-;OgDTYrSa(6bX|+1kRdk&_ZeDB#kL(tvfrD(=PAVu9t5swY
zb7d~p`iy#^N8BzNPA60-vBe05lN~(>z-8cd4qgDlJih{T1OR=&q%Q=MB-z8q2f8<n
z&;e;&W*m5h?J<2kMI`|((2SHUKXM^uqHo-4T><Z7EfUNX=ZR;Mg3N>Q>mkJ>{H6zc
zy3^C0@=vz?AyAqi@%Nk1gT)STIjk2%KJt9Hs)=bWAE+yb8r3CKsV&v=6z<Yu6)(Ah
zmsX9b)EnBq)TGkAZN+m4{ONNZ^4H+0j$wq(NPD<&_hl#t+K`M^);1EBTV`w&h)Q9?
zW}n<qwJ9K<759nYF{b`1ag_7h+1rv(-Vk6nt*a<_Z=7Z$JP|S;;-(MGkz+#N&O+C?
zNZ?rh0llQv<uOtYdid=ALYQK|ynJ9@_)pu79+ae*9bLf$gPs^T<YT<G6c1{6B?us-
zmL(wS;GF83EYTFD;sE%mfmxS@YXBLXZXf098qSeT;k{!v6i2;zM5#e0mm3e&0^{oL
zkcZgqd_Pz*Q8jj+U7}8Vj$zv!84miA<A7$ZuQpdT0uo8OBHyz0rn(?J8Af9!0td5)
z+A9ia(0@heocng0hozM?T8_u`(JlBFMADNhERmU>+5$H*zrvTwjuF|OJX-LfIQ>@-
zlH`y6lnguN^;u5)U-<H_|KIdftp*-E;K#TT`0bMUKU*ExARnhnRU9VfUeuRM{12sE
zzXr*J;rMKEZcXOU(|1)W>MTo`XWuqx%CoNnJzn-_XT9h}uKLy>C|?S?!)L+-7K62<
zsar;sT=CY+`<-|tcz|eBmQiKgCM@P&2wmETwptO3BLEVQb<#B8aZ>#SNqX_?lTC!3
zgFfG>wa!=wJ+ih19eK$t;nZ7BQ?1UovKc#;1Y`&&BEUcx%FK5Au4gf^77NYP7Cl+M
zfZ*x}0A_@c3s8l@^fbCAh4a~$o{teV)AxAXLi<1k_e<`gXI1-!Y_F0`bW3J*{e{*-
z6oU+J^W(*ceFX_epJev;KaisLTo&urgvSP6_a+vEe493M4UOXs?KrcoFE(<~WYUKw
zU3&cP?{Mg;79L6yY3Uk~MAEpv_^<CdqW<Py=e|1+9&fF3_;oJ9=|?LWxb~zJVUD}v
zVDw~so&B(3yln3U!7n#(H?MXYquX@1oxq~Lo1=j<88&`_vKBmSL36OtVdR!@PX#mT
zYa0KmG7UiV^8K%c6E|T_X})kF)Q=@hN32!JUi#b?w`pA2c4f4Cx_~aw(3iL-V5~Sv
zjznm``vL6oev`E=%qo{LHRE;ljz9#DHZ%8jhHcIXK?C=$<nrxA{}<4nyU_{j5TQIk
zputUd-AjFb+HoB3ME&rGD;qwm8}K2+UK8L}8vOMz|3FY&q(FFC63h)<q6?i*A_||s
zwLN`lK;v*Q%?>Eeo)4CS2YY!&Sk>9Ai#ZT27rQj>b$HZaiFB|tEDC6^sWb4L-XZ(3
z2;I|rL^q=ULkD)%a|J!nODv%)t$lo()`P-tz!X?`JNuI;iJM+C)U&Ztyv`>pZrd{L
zyvyx;B#G_kERh^I#VD%Az1e+=<ln}+!?G+5a*CV^Mb21Ww$dGtbcMbC@NqmE3utn4
zO_B)5rG}Qw_y}jf!yPh`D;s?r;rBcN%8uUwk%<O--e9kmHmCEg{%#<{jrqy!vvTgY
z&kp;_{M@R^8L6#e(Z%D)miiLJJen;*tL&hCP-{s;bZu}V;@;Dh&g4V3QI8du%C&N8
z3*I~cj$O;#96aJ=lN#kE9{(O?b@WitjZij#z-=kSxqowvDbnCs7JRyHG>w_0bm8zX
zOi`q#)BWnpyyZTDAj^eUIjNgg!v;_k(K`^`nx{B<8olj*Y1-6qCHqgNOOcKZGFX$~
zmRNs_^-?TFt-9p2!y7yQaNT|&=e8nn(uum$#pUySUOU2!+|}zzyw}Bd<(PENhwMy0
zt$9vzTki<G;Nq0o#AnS{jJO#}jJS>$6S`$}E6uifpJ13h2%y!td7uYoL>5w)4H$j|
zWY#Ef!8h07dFC&Bh$NQ)sRDaWDbz6c?NfDww)VaHJ6~7-+;X74rS!FL#!N5n@LWxi
zx1SMpxxpg&(AA6>Wi|{XQZWW!sY$K#qD9HVVTA?7swkq+rd4bn2ln0u1K3f?I0Vl;
zO??gMAjfYXWuRf)-4w~c(CW{nb5M<&(ry2K%oV3WwmAYpOl|~SDtjLi8kP3_2{_G-
zot<t0b03VKhCdMh9|9sAqh+6PGXeE$3Ah;LeWMWO|CK4-=5S#6y?~C6{Lc%}QhljD
z%%!hY^RNr1K0*xmg$Fp2g=7;p1gcKm|4BVxI-|>_E7=CV|F>H=k@VB$uxgfnT4Fl?
zAovjC*dF*Hj&<a?Jwpah&X83!eZ^?`G2CmF(;P(YAs^}&*UUdU?ycjt^laj775f(b
zK^TgjV_{!Q=;L?~sN`1bJG}OL3s2{8k3b{$?*cY`JHP{j=^hP~)-29F{|pC+N2-j1
z$jOv;{SDufuD(})9cX=7Fp5qL#io_*4MK&aZ9E8ijKpX@<&1XrQeJKYcfkXRZ$LoO
z?rKU(r};Cr`l1QY1H+B#ohe1e1V^!M0o*g1W)7UO4kM}^JI4wFvxQE8A!`R0*8GF%
zCX77=%6IF;?)8EHZZL0*K7+nYy(p_DW%OYQ^uAheR7fU6F?W4Ck-@<Dhd^|ynk%XD
zaekL@WbdPzc8O^nh28x`h81-2tD03z$4T%i$r#&#Z`F+%PX2tFl+N#Uq5Gm^{+dc6
zHxV)3QZa^>tuO}T#|0$SigOs-cR1TNoW<y18PrTGy-<BN$3u13-9!!JyR*&0GTOMe
zo~rGlfWf(etFQ~Ojxe<%*%Q~}{FQUrdTlWTAV$etramXL2X#H%VVdOTQE~7RL%&|j
z!!P%@(Y862h(BuH>9j9ys<2GkYv%`xw8OL@QpR2~GBYK~Ds!O6iX-Jl1k_RUg<{=O
zD8&QwBlYqRu~6$muq|d#`iWr`_DqdGbdwshgPG6nV(RpGWGCN3F_D^0?st|$u}6_g
z=pknnEi?iBAkiuHCD?(A#PR*TI07kCfCl`8rlUW1ujep?l9mHP)j{Kwa0sqQZ0)j&
zqx~Bd$Hp0MA-%4wp3A34KU7=w9BV$15Nkpj*uzAdq+IYNKmQCw$E(u$@uA4W@s?E8
z@&%6feCS79J(K+<tTm%V8Z{M%8y&@@ax}lwarH{GJw@H#|7Ze$a`S}fV&i@=Fr1}@
zh&dT0S(+Q;GFLW`t=s^!u;y|$%wlDL5TB%x<Wj1#m1bph5cm!c*TN->|M_m)|5Ze8
z-p>smWls&F{9IPu&PY2atEm}}&@RwFEGG*!$_S_BpasA>(W(07M~0A@UufOOi*<Dg
zL2}$8BS8ZoFO5p=48z|I924p6zj_PLPM0c{Ee$&A#%0g>oU@Mg%?Gvo)o7pWp4JZN
zkgc?Fd|TqUmu7(e+GBl?>A{eJIe5cXN{mjEuQYqq&aGrM+(@?FAzCtf7@>`Ht@6w+
zajxOn<>cDmZfTq3;Bw6oA~H0#*(luhcR1ClJKE9Uc2VQjMSx9)kA3GI%R=fXA<5cp
z<IB8S2gFfj#g!TRDlN*ib!Xydapv?3+LCD&#e*kPF3Evt=2Lo8*BD_N0)NvjN}<OJ
zjnq-<EusFkdN-l9PGv<4HAErt=0<SK1C&;HqpxmKU4W3uH)U}TCaIsV)N6xh|1nx!
z5EOxu@Qb)d?*)kIbGra)J?t9z6@-|bqhwcq{Cq#u2`<Y1CbL}Fp+*AYu8t}ScDctr
zG8V6N^JMyT{hM^S+a>Jwv+F-3+LseGqOFd#9?~iz?a<o`=wrV(;aC)(Iq-evnGST9
zGLdQ(jO>~6@(>ts7Pyr625?HgSe1USV93rkz<=kG4X@i6Wh&rcX}*H<JY9006iK|@
zV4X405r+V|$t2#q?*v1c{AYytra_*19gT9z1*@GR_j@_4!058IQa`M5dnJ3Adc3m{
z3z>)a$1@<&UE3c1wx_}F4|IFe|5`iJCxuFWzPLs33kN$@yZ;Rz=-96<0Ixm+jwP*n
zguk+~`Rr8=E`4eg2EQH-iqbSdOC!Y2E8jDB&$N@LPk|JD(@o;qATRL99a0y@j~$tr
zI0fr3S|G>TaV*JAc_-yYR@?WkN^k;gC7ott;~R~T4l9Ey<W_`^iNg%0r|+CHs6}d*
z`4`<HtHY4%vc(FIU&3kABFhNqjnF1iH|bw{^?8=W(TR`aFF6?ny${`{5YpKL6;_^u
zhRJjlzr%WIZmc32jem%I(Y&)a#9j~)k)yUlcuu}V)Yh(L3grv^T6@YWxzh*Ed0nGZ
znk#7~fot{babiXSO#Y6djl1XP{HCZU*G>{o!ofB!WdJcUGtn0Et&{npk{NW<671<R
z8UI%nGs>br|I^Z<+<A%+urdxoEMB%aSg6T!wGIbgoLgSpeJ%T{ki=rZOz`7RJN0J4
zxU1wcz+Bk{RD~oUE4}UoINLrmNHV6?`1QP(6U*LnW7S(5B3)ZCbba!7jHV@FTwb|1
zGr3wc&1$eX0p{hpCAp(V&4F$ey=7#5KO(Q1@sWK&>YYVuoo9U}CG|3T+3|QQLQz~}
zBE(q><<Q#Q{1k0m$&q?O_@QH|V2RW58_3V}L|q!b63aD=nLBl3cl~KujA%{(ruKo6
z!l)suzEbJb7R0?t{r=R!<=ZGTQ9^<t$?YH%*x51?43l?g4Nto6I0H_TA9P_R^vjy{
z<|%6^1=~vF#BkM(|0Z#V$`fYnO;HcX`OZyF9o8~DupQzAJq{kOk02RA@7l1~kDm+x
z>cV2XYu!fRfZQBLh?lb>P#_~2fluzd>X`g<L8h+ruiIl$)^kijcyt1gv>Ni|G#wt#
zD4T`qV`^Oum<_R2J1pYT>CvmKrPa%icYXVMhp%_Buiw_zgf%9SxOCGV%o3tqqK$*@
zm^*h&y_;6m8WjE9;8AwmWCJKI$FMD4lSEH@C!SenL$=jei>Em({s?rmvheC2BfoF;
zGhwJDkk;7(8pkiY1=sv#6_d*<TZcg+%|}}FCE6V7QP%}L=r(^=-bQ1Xg_cFvk6#W5
zz_$CFX)wB)ZrlDAY{z;if9e<g#Jx116HH>hj?H-!n)t4(>J8bSJIP)@E%$I{MeF*S
zefI~*0;2JJ3Z>KG;caqs2VS%=38fD=W&1+$cL@@3-})MGU=NzXO^SW8ow%|9-0ThK
z_z#D#xur_fReU$LH6$0cXepJg%vqk&T#&v^?PC*F{&wUzc*uq$8OsE#;N~1*FsMLh
zXZy%I#<9yL=~}2@%Qsr7c6{7SQ&&b>K1OW-2#9o5t5-7d%FkItrMTdZOs?YdB!(<E
zm#>fisi9TPsqSUm)To=O$!6LJ={-N&$=x*@AYD7!wF)I_rH2v-!=ZrC_Y%@sAH_?&
zCA9803R!j<L`a8AZRUrXDNp0EaIo@s5x_D4!^Gi$<l@6e26UIoGG|qjYuzH*e22lx
ze7)(Xg1l*veey$=^kgxH#h!IcSEp9lCAlxrTv4HX$AQ$n*{s3zcbB(xI$~hSs}`3g
z)8aAOuktb5q}Gh2dUy6bPv!#<S`&+;q!uHwhiozEm-ycCN;>QZ56b1FelNjZyrP<|
zzvAK!lV{_(n?xp8`%{}`0IJROq3I0me-%SOkCOCp)j&060Z<vDK~>jyfOCQp_5hL`
z<dFy-z<L0=!@cK)1?@D81@DWvh#{Ibd%o<Y*^F}X@38muV+F;Z*~a+KQVi-QZ(C=E
z_5izFS|w$w(`JI;NuOMrqzx}3SmgXXAhy`gXHw;fQl1h*!1F5`lZNaYAOYD_g*zfp
zH7E%^sgv<g(Cv<){kSj~y1?1#;kBn$L3;N{VNyb0w01J1v)ZQ-Gsd7X=+Eb4P;xiw
zW$ChG##?&=UvQWsO#&Q6$Y~{WCIG;@YiIDO_II(iv9|lT%rwaauFK~f;go<YFU=V>
zt_y`hgb;Q4Y16N|9TNIV3uZTBJUMUW?~nqX^tT9MLS{deVXiF)K!YsR3*|o|_vXw=
z<MP2>OtB6SQ68GiHcs#zF}lClGg*D<PzjnLXS%UktnHURs7I#a^9Y7qy|4xNxIepa
zE8~GCf{C3{Fndxgn|I>mi~;SVaFP;)NL8m-njjANGcu3$C5y|h8;PcJ+uJXl5cubA
z+xZB7=e5VVgjp=aukXa>crSSEEfM=hyO-VaAhU=KYF$&9igRjA#ByuZP~^AkcDU7X
zSP9A?S+?bmu|h{uOm&vc-d0%`8$VcG{PFLS(#Lf^ib=<K5S$WS@5YQL-zSef70i4~
zdlhOJ_yZubWptbZo_3?YUx$%iFmQs`nsk77F&PlEmqc$dqV_VJ*J>t(4q?b99==q{
zd!>3~{IZ&8FkK>?V27Y@ecH^{G_4gDuo#@o5i0;d2?tz1h8VFx+!%-*A9UgH*C*F7
z5XbS0In1W}mWcWk`aIVydtWXAMGQzx`MlXg`DpV3XeYcetxB(!qYykempRVT$XAOp
z*ALNTk>=^)M20lxi1UvD9HygTClo>>?xy2Oxlo)~BT{Rs-J^sgeH$XCGVf=NPVWb2
zt*fE+S~IUtvp<?6+GN~=)77*TT~DN!pLQv`hQLjXG3dK4gb3N_eCK`GR$DmZ|Ca@T
zN;@x=_Ok3@Hec?qUaYTQwW#nOh4xTGn2g`C`5tp>TCH@rah!kW4k2x@?ldBfBK|f$
zW6Vh!>4^|@U=DCYiin&fu2UrBmOACk8<q_SvK*C1x9K33%J~`X7dx5<?)L??<YpD^
zypN7_+HCppYYj%p>Sp@-=H{(sUb-wcS<nW{bUZf_`nh!_KA|PuJtb|rEVT3{jSK#l
zMH~tU#7m#-nklPOC22&G^3JxKMTx0tT_4ZXFxY}WKwa`xW&Q|s2CcYkM{;?oMn|Pe
z|1<cXO#lcs7V&32ZCY2v3i!VP8dGWIvf>XVM&KJ`0?^bhg2=tEE}$JZ8rNC@0R26Q
z1Hhxk-ZKDzW32~L@QtyQSI5&dqT89bnVMGOEZJ7sKAuO5#I<(fA<`Y-&=8O_)LXAE
zr96E60_%@{>yNULKz`8StPSp1YN)$PeT>ykq?QfzKN?A))G5W;%M6{OkZoSPo3~r6
zF>JA%U#A9V&_d~_4&NQ3OG=?3yKI-70qJJ0+RPx;C<{aMOf#|}V`l*vc8JfAE#v;U
z!0k#kLfLUAp12N($7;2eQ>=k775&ia^>PSEbRj_;Nv}$GLhf;}C7aFK+WTmf)zPeU
zW?(;0Re|in!fQt|U!mWj&EZfa;2v{8HcsLQY|Q#zJ;HQ>XB36xCdbQt{OL#JbeE%|
zRADsUqCnFMhAn&Cyr$Wmr#r?5uYdZ0{Uf005j48k_J-qPqs8oCN@GstX2E(&N_5_}
z`(y5Fld@}d3AjY-P-e8s|2u8Q`W#(G@4WtRmYLYY6#HOmn~eJOk$#(&injf-1Dt`X
zmF1iigu&fUk-2Rc`a`GVB~aSR`|&(kz2w4w#o30P`W`oSso^%#QO0+9!2S<6Rnl2G
zd8{X5CNUy2f!lnDZ%n0&H6TeVQNGkfZT11;yUjAbRQ7DYk58v|G>JeyQ0Ymx#?D6i
z{4r8I@2tG-<}Wl2Nugatbgz(jYW%Z!cU}Dv6#Rj$zAK$0Y_P-w>cAf4<37Q9(of0g
zImJ-bt_q+{jMjebBTznjCa~%P8w+4r1oX_2`LUKCiOp2q{1++*!R^a^!}p2(xJ?%^
z!WvepRabaWNq+J@e=Ee@6%&W1qd0kig(I?YdwtfiUWV{(s@r1K>s6a~t9D7X?&*2@
z`n=cV0MBA4x}<Dp0GvF?MGQ^bFRPl8_)@SO&t$wOc1a~Oq+qam9h|UIl#fElC(qpV
zXxH5)c$bZko3#e`k9eWSbtb!yUuwTY;7ioo#NEO-CK#v?&sHKmy(5U0TXKk0nkp^V
zs<leLR??t`Imf%Q>9q<>Q7BM7>(xpKw&*#AnCp0~qz(-2MW*eBpR@PQCw@|s4=YBa
zQ#Ge@n6ucEN#>NPbydst;IfP!mk>J~Cii3@v^`x`LGH~LsASRBwaLXg_F}Z8oD-V=
z@=LDm(_H9c9s1kVSLD6d%ab$vu;cd5E57H(bVARA{y!^1_Ad&bI!tb<uRNG%1viNK
zK0fqPsyH1Hl`$`_DDNc*C#%bS6)WKhRBM$JG=!oy?GCe<+cRe-JlswSBA~(@>+b;B
zq|T$jDU!c1ENcTm0iw^ku`3(q>Tx?AF9;(klq5ykue%*FmAMa}OpjO-9bTUh&kAfu
zY-C%n8tzF2w>289RwDp=I74N?%d<RQ@+e+Z7T?nim;&(FZ5AUXK!xBX5uo(<?UzsR
z1Ws&H=U@`qS|kpT=<gJ%c^rr~Q?fs+(O<a3d%8)Pd13xm-#C{Uc?Oai7DQJ9j-SeZ
z*sp+JB|?MwzxNzFYT29nu#UIH@)>-bcR)2T-r4w<g8F~hj{QnQJ-%@qu&5mv`}wYu
zO*ZSow6(C;>3c_P;}80>f8TR}B(viqeB&%(uVu|o&91#tPFl6w_7VVX8t0R(QkE7c
z?EJOkOS<`dnKh>t-|jFq95_RViRV5)tUJx7zTO^><&dELu4l6w8n2C@@cS$S*|Uw`
z`J@nX0NbXYcqbByMIBdwbM(>v)?%qXzd%7>GSV_`&#`9u0xIfQ6!caEklbJ$uStI{
z(7Gojwwq3Hd5&uGD5SDcE1&+p@p1vOAIItY&R*q5kDWUcav}p_NIkx8xlxNjwROj+
z1RT|Z@5~;YG@F<39V>JC{bv;UnUqg!eBJ)a%&mF253b(Cl*Ta=qWu87_ZB6#S><JK
zRA5rVQkT%WPkczIy|=qF?P>Dev3m2w>{bTo)JyggF4}oIa0GN@$}E3;-Q>}|7uUR?
zRx^2hltB%ObfhSnGrao`9`R&3>JN_lP)<e?4#HpGem$c@{6v<O-n)5CvQ7TV7KzQp
z`st#yJn!~v)%aBG?<Jxe4QDeVsT!ryQC>;6X0_AQ!?&HAWyk|tWLWS-sMN7XW}FZ<
z0X;WpZ&=2XPw?Nu(tr;hGtvp6hj+F8YUM^DM8MJ@T!hnq?=AYu(R{sk<i=HU6!J-y
zoldiYqm{eiWc%}GH}C2MY9{Hm;n`AM6@m%Ya8R^Vl5b;KKvT*<)X_3mvjsG;yI)Na
zDbv$Z{tU4A!l9OOy&r$L`ZN+Br)Q*(m+EC*nV0pYcjXL9XGhI_xp|uScz^EV0`egI
zgm3jmfS1qyDyCBrSu*3nO}>MK5{g+;;U4Y26bEd#iC|s+Q0NW-F2;+%V3-VH*7;M$
z#OfcJw+ZUsatx9D!!_E{tXpLkq@;Ozc7eHa%I{WySX#@4o00H*I54?p1<RAJz?Gpq
z1@R@=5qpj7la#Een`|T7#7}hKYvf8ii{9xvlB4TlDyML!z5qmukWKeLD?lDcNpy<8
zYxL$i7ABgeon%^Wnju$t1E>UUbcPenyH-nGm?ogYFU3@Hr3*Wgf-)ztj+MbRGa;^w
z(V@-)!B~1Io%ey{PaD!#w~)H@hR6`_%I6Y>FZh~-)Z&yA7lW~s6(=OX*-`CGL>(@n
z3(=@Uh5?D{+V<n*257%>XX8555TAN3!)^&gQ#SV8fi%d)s`Gj(f^s{VVaAZxp_)uG
zYhAy)HUi^~e5H1S>|M)4w5#uWY98Xo?ipZ_h*`F78xvK(5a=hQAZO*WUitam>&z{L
zmeEVAgh3Et@I*=Ur`SuCL6@Aq2i*r;r)NKfhn1=r`|%PcaHd*v9Vf_+_4=Ywl3x82
zd9vPf=g$Pqcny75>iW39-X|J$KXO4FR8|&89B~)HI511X#s=@vJwmW9k4*_#pU+qO
z#VAi*K~PM|u-kUvwnzmV;FAehER)taV-E`I$MJn_z5?ssIM)pM1COq*Z{%mdf^*@n
zTED|>^I-sLQ=#1e_>wbgdw%S?YgA8O6uj;L*}~t%VgW=$a`WbxMXE=~*P~5P)Tai`
zh=`g&YbVi!Ko3soI-=}ABm1=f?JuCQL;@9Y2bj2Gx?U7dOav#w%ZzR$u;+)m!kY$j
zJkjFl=AS6q8Uq|!OZF71mU*1p9zQ#mFy24fl+0E~)VI{+%<^u&S!}YCF<U4Lk|+5r
z=k<}8;MO0355Yke2oSUD2|l>f?nj)LM|X-witTzsmEwO7XvB>7rj6XqbjlcH>Kb}!
zeZ!CjhTe;i<oxebl*Svo^C1anI8bOXUXkXrP?&WLXT?qZ=}=Z+=Y0PN9?)QpSI1ib
zC25;)|Cu}R#|Xq{5g;us&i0$fHutdDX@PybQz4C82JEEAfi=ZUXX}BK+HgR7zki9Y
z(50O(z4+lWE*T-D-bYXqslM=10<TK)nae=9bGk=><?`ed@}Hq$ix`GN`n;FDE}8)M
zv|;x$Iqfy9X`Kr>#kocN9P>wlq2~|fS|Df*$OGCPRWuO{`9F%n8~Rj{U>gHuElH}D
zn<a|^0Q%TNh2oF$fh=GaXk)L8?IRnMP~;jAYx(#{#yo4I?Fx({@tEdh|FS<A`d#LR
zGsm584<N@Ymkjly3t4`L=cS!L6v*|oRr;WL*f9j(Wp4X}T*=<(O-OFU%_}FDlG-HE
zCyrqJ+<_5GCzHI#{_IigLu5j_IeGvh`RKt?g9T*m6Dwu^ZZxz^N>D}4*n%(sbyK@R
zF$uUZBa43lmkM3Db!B?N5zU>10haXnH3T4vUc=SxB^r+XH|fD=e`EqFUoJttUFV+n
zKZ`JqpNZSM3m0S0J$0y@jo15L*QslzR9D*9=&>lGwrJLcb#^9_=SW>}HL2}9*jVqJ
z_Yu-$rCRPHR-!MUA_J+bX9`1U?X0P{&{@2QO{kZfSR^Zuqa9woJw}o;L{PhLdU@_I
zP#Y*sT05DrrZ<I(m5|3Qa{X!g6Y7T2D8tkQNd{~naBR!txid0mX<qQyO!yDYXB~7I
zN4I#oaJbC6KjBppVr+4?-}QLTkQ%OpG@oW!-Fg2vn~=Rl{JaPF{TdYxO*h!QO93B5
zOf+WpdbRNo){)R?I<MMiVaFgvf(GH&O9<tOiuiaW|Du2d1{&(`HM+OXnYd<<@|R<c
zCL^4Zg>OQX5S4MajM9KmcgrpSN>ErN!5tw&3!ls0l}<-EXPE$xz*YaO8LLXs_Ka*S
zDT>-*7(RPg-PO+FS+2gSt|psAPd-56ZmF3i@%v-q?1O6w@yNdC<9(`>aL0AH<S`}+
zH&O;snSR(Q;qQmlPC6My(6%^T%Q^|xKEt+tEd6d-O6NTQ;*~B0%gf5LO|L?rD<qk;
zBD9Te*XEo=6=a9xLlG}&+%_c4Z!QT&xmigAE7NX}yAya3{wD3z+%9(P?jhSGhM68y
z&PTOrdU=|J>Se`9^GC73C^@w6uaQq-RsL?F4RH_Y73j2s*l+*$YFyyKj+UH{i5Pkg
zTxghiSPQ@|`U8F+6Z9y-L|C6UxRw~pXM9Onz+R6gNv|B|eo$nvH++pcS(gIv>3^<r
zk3rWIx}H@VLrw^?`2g=iFs`uXaj9@wj%j1ECswhEFjj_7diFE)IVNx68ymnoRpAQh
zp*T!XQ)Cr2H188QD+Rh@U5~>R*&i}_8}ur5d8aT^e>g^gxyz^R7+74sd?$Nqce50?
zJL+_*1Koj#2l{S-svk@J$=^VEHG$W--CLx&jQIFPf3lTED;+Zc)$4j5)}Gwf6-37_
ziZ2_BM$D&4Xd9;RfyM<Eh)Ti)FJ}rI=;SeF2jnP9zb!zK`=E@Gv-kq&y=fW(s^W_u
z<k;L_G|lckd4q)=1XNyt8((Maa)|{E#$Bc1$K!TNQ7@_L$7n}SH@h;0;XzxB6E^yj
z-!#Er;_P;p>1v>OuFhK=)aw%o;LIatcGK}>Ic7ng^y9yg{0Z%y3hOpVY&Z8^J<vRj
zS)}}{13iAXruA_nxU)vQ)$t1+x#yce6c|vSs3vf0+0f&#TH<&M>;H*n4L@u1;2nC-
z|61XGVEjR(Pl}>tH@mm^&sOUDX4^W11%CvMh({S9xu-L3?>EQ1X4fJ{<B3O42}_Zy
z|7f-T^pN>-xo~@s(KUEVT9I&B=osU#R7ztiPe;>TDZF<07#Vdg7Ry18CnS8*dP9=R
z?i{r;cZyar-I305qB}L2ayv_)1sz4Bds*mGz0Q8JTrXqkppd{zj_cc!7(456`f)*e
z9SFXA#UDMwWU6AWtrM*AjHx3Zx^NYZBxYVs>Esa7h`tj1<g>D{LM;y?yQEu|3U)`4
z1_tx_X$KYBtm7yiq}-2Z>XckE5sQ#UQF+KoBM?{*4T*DJqV3`K7(+<5rQJK+Z@Gez
z#QFUa+;?K72o|G1QH7*sb?Z>6Ff?-NQChjpC1&?I@sA4?UA{jJ@yE%836Pc;UG#AW
zm=}=&`!_}Tq>ac=LjM~jm&S%c2_kpCR7e$Yclr&6Vgo*Fm{*@QO2hF;3Nsv;66;-`
zk&YX@?13ytuo6X<rbbObdN3z8RTl7-m_S5G)^keTeZz+3XUeOaZ1hgz)2ei66WjJG
z3>(f{>f!!B7$xc@aV--7;aACOdDneB{nhp#jen07IPuXm{d>v=Yfpv|p7^8x;NY8x
zZX;Q+czPyf8HOY|dhKiP#42Ucr78e}Lmrah;b@m+t)>~J-GVP{RQdDFt6PI2PHx*7
z=U9D1OQf5>0}ua+V?SZ516dWrYR*-A8|VW0Qa4%daz_CI1G1U9mV?PJu;gA_b(KG1
z&bTW=t}ewR)(%5JXb#Y6{kq^*FoUCmsb$$*M-&+3pV7KI6BItP!>2=o4tmBOwM<!4
zoPy`sgWp{DHIcybLwsTu0^&^5ZecUA?kg$q^j$Xf*Mmmq>ejyEq!j!!`k|6~-t~o!
zp2nzwy>s!?uz}|_=a`b8+gqY?+)-rX;aP9v?Lw)Yq~-Cib_XoKy{NA@TGWD21t?I)
zOTWw{&XNu?kNruYJL-kXB{5PwRh2T$qgWQtLA^(&DsIt9FBx0`V8*i68h8bqO-A;|
zeNZ72H{jxq&6ot{mN{1e_?`X5>S16S{u1I2kR2P5%F%@$m3!Mx@BbP<M?K2|&p$hx
zQaCHA^M-u_q&69TI06Q~iQ?qK0nrSV%{^Co7~dz2_~{Hf+YidfVLSok(xJjLxAP}&
z7U%ez!wfAMZG(B?QVs@M1C&(Sbu_Jf=1Xx&?hJ}HCpf!*e$1-%E`C?{<&Mqmq14#B
z9fg#}_I0SL&wsoN#3LcK`l3^$!&fUbr0r)dM1oyW>H;+4rO>NwC6i0P7&OLoY22@$
zbpwhA(qUV%;W`!p;%1Mepydp@$9X@V!az{v#cQw-!nndG#S&RyFLMy&LLucHUKujk
zw;x+4HmUWOz-H=}ruMyCDX^4S2Kmld*cl8O@nrZsJ^%|Vt^ET)jN+1a3SnMuG?A%&
zjvkxdk9f}pZerOzlVr*tONPqPMYc~?o4rlFow1QD3^$3f@57YR%)t7KL8sSRe=rgR
zus`sBixC+!NAkYw!(<N87ZpqeXA20gY%d1a2Orbd$;1Kq-sR?*#_gwdEz=B>UGgjq
zJ;yH#n*%ox@~!V2d*}KjS^x7IVkjYEyo)>~n%el!-b)S#vYlvxi~eH`hcC}_iUoWZ
z*1B!)jYB1{{rK&4w~DA-9KxxYD>Q*1FsZ*NV|{hAgCB6$`H7!BSD`a6if3022)=t(
z=t>wEpEKPK=_t>HM-5|$=}-h^@K^%!lQsMqPmp7=P89(L!h0}f(NQh{O5Hn%R2~AZ
z9q~Y{hRfqrI{p0;Uo%!50yXS)6!f|M!x}F}&`*SNY*@V7kNOzo37d3S)B48H(Syd=
zWS9KgHF>Pd^cgSLB=UQT_Ab|`Gf<dl$XRcIlbA9M<`yb7E4k*M-^w;IsVbeD0yIqL
z3q^s-Yow+W)j-S>xAScrjn&(C>~NZ$v>5zIFg;W-5MPXofZf-zFD!Ke==yMQqoq7?
z<hfyQTemKPZ9CPI7r-FG{R)ePRs=v-VCgcPJjZ##Danc<g)R%bs|pKI{<E!}KYOrR
zcIR9-**vZ`SxbqS%mO-YX^HXH*0ZO?+g_5GWSow3$b&~_%@}sW7`l?~FBMU<z#6Z|
zN>AjQFm}-RMTPMzW1=(gN+v@DzxcCcujc!spPe-xHmfd6d3*m&5r*W#>+N>C!<ol~
z$V6Qel*)fFeclA1A#CpgI>MOj(UsnQ*XP`$sk~sIX*T;CSxs8K#!<qB-QmL5xEM|G
z!nM2jQ7?`UBnhHUBy;M+bM(|m61-aZZe=@e!>F~Y#-y=)#OT%ACk7?5)cFp(SaHgX
zJ0&qs65+wqzre<hfEC3+8!IYhce)oj3)uqU9%Va{(_$_~|D7no1MU?hf-XHf-eNM_
zH&D(AN|9Vi-0T^<Ojprn&)%AE5MGIcrEhLT9Obw_+5J~M0WZ&H5ATzaTR_c+p#GQ_
zig%-kbaGh)PgHy)rf+&ZC*OJkyaXpA@ViP<MS^d>pK1Mmk@#6#^aLQ?JT63qc(={Q
zV9PM3k_kP7bfO;6iB)V}l}>f|YU&Rr$Y|nN9L)Ds?iH1?1XuUansGe|_~-{+cfdti
zUbP_MzF?jZugGF4BAr$Tx@0QsT%8<e{+8t-yZ$d4(y4FdWTG{q;xy);l%9XLiVT$v
z8-7vY|JRJYr<@{2p^Y6NivqC?c=H29h`;+E7UlIJmXrTkDSoA<kOcr^-F?!3Mq&SO
z%{j&UcVzH3C@1)2HNYLzkx=^2S1FjD4dhznw^EwNnnL8|>~FU5yIim?-sS`$kqrk>
zlMed;27}d=7NhGvV@yB4;BGAcuZ&&z0oZ(aqLlv9w3@b}PH*rAb)<L57BZ3v<v{Oq
zKas5`uy9|9JZ-y1bQi|2v^KRF=rfstvr3C36PU&gKUZB~xf*Dke&W^lesa7_3N2#E
zubx$)BAGgphr8_TDNU~a3xow)Tth&>UPM7gv>86&fqJwA*+O<d=%?@F2fCQH_8k^D
z7n(ESCSdHR+%kme1m<P;B7oY`527}NJar?GYJQ3aB&ql7@1@^qdfB6WeRL&1dRetz
z-vA&o#aR6LAZpU3CuIo>*Sa5tmWba2s((qv$}Q8rxJ>nz;!*RQBKIKYNIyT_3Pld0
z(Rj@8JKy(AZ>*DcIoHIryiXfVvk!x;SAP$7;5jkUF=(+XOJHiFr3wR<ZLd#2H1Z8R
zTm)eX5A|^z@F2wgTx%`vxkn5bdLia_0G_!;?r>Jz_58t02-Sb-;ZIkHlgTEv|6$;i
zc`#GBtbQNtWO`i4v;ZcoiYlMyxyY%|2%ohg;h0;8bb~y5End$R60xbgDk1Tx=Jrag
z1a*Y+0Q#PkLi*p52NXEF`2ljfk@*PuZn1w=^Q1lR`COK#iJkZ4DbM~v65KO^{^f>!
z53zC4lhz~VkGyuqJf(=x6q_6yX?3E(pO#{9V(Zb|T+*J>04h%&-OsiY`yrl(93RN0
z1wtI5MojmA^EsP*qYx*|0BU)ytH0(!H9D4vv9#$DRZvz`eaf_>b=M47lJI>zGkg(1
zNNK$I9E_=;so7#z5c@t)#+w<kPi;>Q$dj1@O5CdsY74$1c?7}3No$uT4U936w76ro
zE2Tk|Fvrw5litiTGVSSP+O1ehlw4;wAh9yWzX#N?Syl#`;)uEA;*0P&!2gR`URlA?
zhtVk4#B7ay4z3q+y)fRqm*T2GagCy48HhHgLLgC-(LZ<)y0<a=6z!y%{%G|4ri0S&
zEz({J{?Q~Cn!wd0&rrd{E5rp@^LHWgRCV4r!3ly%e2*pmVBgl_DP?I3O1@D|ISK(6
z8S+?sO2|U-`-snWXCw^Aieh6PKuT7QslQJK^SK5V6P|%wG#?Ag4gfn`09)z5dQ;?N
zt-kbcT`$53y%-ztL44%$C%~C(B?KR<dDo8x!tzVJoW?myUs)LVgS-2|UyD0sA`&Y4
z?Eq+(Ye04G@&lCw{UZ+}`@jJ3zsp?nB50>c9MMy2Ux8nZDOP<Ap?<pDl}{2Z=Wz$p
zvzoOR`gv&Ndz(ySu~Ec|Q}g`CxwlZrW1x=(LOo2Uy&_rRW+2)`X^fmQff%#MVz?jX
zF8Nj_wKFmKyvnz~d1nhYi?vILp_NafY<t0<T`{#t5K9rmwBXpd)@>7l+QAeBimT|o
zX@Gfa7E1Lo)@IImb!YkXIo5sxs^6Nm*SixWa=|bswIwj(9B=a}6J8e|A6*Hcjh%iz
z)nOs=O~42_&bx>#1zOYY)Y~bxzXz};UwRP<?*DfPiH5&<3IlG%3q5aewJ|)p1z-b-
zU=U?7d?>E@=5_5OBLhFq=aGsDk)GCC99iF}iA!SNFj35x3#v3e5gv-oXvi{tWc%%0
zl(^It5Jbnc$z@`hW>#zc>@B&2d}=IFwOaMVw&gA@s+L!i!REwGx%30Ccxj}dD*VI&
zi=GsbHOZsX{+-vr-W>(M$eta^PF$i4H`yMqC+Wa1VIOORa(r*mV*;Qm(W;G?AmW0x
zM<Qzs^yZ;kv&Spq`hG5-=1OPCKysit9sT@so?j)swhLNFr&!CBg~=D`Q{dF%qI?24
zvuo|WfK60MCJQP{n;};+UlBH%axG*=Kt$K{0aBv!+j&qoa!a|z(xi<uWzxp<QOX!!
ziYnmq5c|QN@>!qMzD_%{A!E3*&^Q07hB89YQ$wFHuP9d{l&aL|9IwibE4TY~q3vVs
z!7}I<CbJ{Lq>`fB4pIhwT&0(kB2lHt{N%omO82+_8#_1;db93D#Drlo;tGvy0zEvs
zz}aEHCae|epE|a1VRRdB4`!suymz^~mlFuD*!~#$$nHzgoZU};e;3fN4pf8%_+t0r
z3Hp7K&vHNEr4;}CDO}3897e=ngu;)(NlHJv6WEpNQl5#(?|=hfc)ouo=iaarRK>qz
zFOERVB6L+$tlCyVGNc|xqO#j!pgoQ3-+B!_TanYAVk?>DNjE>;ERTudtbIhvAy87)
z5xdRJ?>5K2g(?C!@@8oNOx$h4Wza!F3#bm!xUxR~vr#fG*Zl#{se5KL7PagkK+8Mk
z_qmASf(ga8UH9L2<WEt0={N5Rpy-oLJJo08-3-7sgsfQ@7@nUuo@V=FnVe@8m9TNe
z1fj|e2Ao4~`Q}eQwF8Zif0}&+^Vy%j*^T`VC_x$t!1yMoeGa@MysuC^aO+wJJ|3Sy
zh@UINy-Yu3PDA5f<M(90IweWIqa3v#Z+fCn>+2$OHO7P`{0!9Xs14U?uzJSai~rRO
zRhL%;rC*PdvO$J<<FYev^ZF5el8isC4sIXKS;ZIW$j8cco&Idnr@J({m$=MVn`FGG
z_h?WQ&z2@PW)yPp^dKY?Wo_DQ<W26RYW1KP;9-f3H50Gg=457yc8>*v|G6n%MR!M%
zMiJ^hr1>k<GSGeSs<wbK*KRIDrf;L?Y7MSE^DR?rgSa!J{*XkZ0!7Zbfd?yOn>C~Y
zD}bM33@HGaNq!a??uki%p9f);_&WF;&Hf9<^&NSPZD@qAEj?Ustd86eR;ty2laT6f
z)8&W}?*vreW@-0#rvER#*?}lbcKf=Y{RF!NBlm@;{{;m9t^@UxEsJt^zQ^L6<7dM0
zrnrx=#Od#1<qo4f`34cyWeE0tM&}l*0)(Lb_D^%bVpTcX{i`H3CH$en;2d2%7LTPK
zf{$<9W4W&EMV%)fJ_?W`+Bs`UQ0YB}2L}IL>aGy&AG>ZO9LB%0zgo9=t!rwkIoI5n
zf(5BR;?K!C)VLqO8Ll)qq6&PoinELFI%f3tl0G^p%4+0O9U1c>eO=}+xC2{?369+m
zkAdA_I}_^Y!*`tf>s{VCKapRX4*s=hZ@|l<)jFbLVm$&0W?yX*9sUr@xAp#yYq&`-
z|CkWt+zu{D+z>8M(flCY4-G{$W%doC=w5)8)EElTSH9uv{px<#4G>cN<d|Ei7SAK<
z`KeE|1b=u<kr%5KrBuXMX&CTQ%$WVyd468GBhe_%?aLHOBIq1k3J9`YFB{wa+qBY@
z*lxifyM{7bJ<x?+Pr9}IeUJ6my46DqUo64j=`Nw^zq5i5ymu2#NA*X4j1r#k&zY6c
z7dD-J2Y8^YHz{uNmtut3q!_Ds%INo-Rr-KBuzew5J69z2&c87$VXJDLbO0eV5$pIa
zbw)dII40XlHE-)jy*}0m9@2Wz1XybM`g*+h!y5QP1=9TF7r*|~8al$koVf`#<}i4E
zjF>lJ9G%S;QpPf@{HIYh#8eUap)4Ikw{?R@N2zBuBg!#XB0~g@pmNE{B0u|$-t4P}
z4sHb}@h1O7I%tXr!YkJ36dv;Qbt?)NWNLp}xJmP;huoqn5<55Vns&kW+o48lI>yZj
z?%AdDtZ?Ki>4JGH*zUI%FG21^0#Ti)#vQ1|10C5wHN$hpp<~RuR@7!MaqW=~cT*LO
zyJW>V13r@8yMfO_*tgR&owL-g*_q%Qw}VI`Ubg1L(z2VStV|C3BceftSIUn+5SL<3
zpZ{-Znhe>ry08aE69izWcgvnLaZ&&vRJ{XujG7oko(|HBu{y5No#=1*8pdmVEv{<P
zSH2!K30LgFB1ZALW?kh&;O`06$g=ec!M`KYx2NZ;C%WIFgJK{Drc^8^y2-SS<m06{
zM%kM}Z&7P7Xe65LkuR`%Fnd%hIIX6Gz#<(-i%K13?=U3Zv%!}nMmiH4GfGJ-LRMUO
za2G4phLwQ(ZN>OMRc>X52Vt@F<wosIcpz_VldUfmSw9SY9Z0{v#Ncgj>LseC*4BF4
zY-Kw2#y`NdIc4UY2ILD@Bvw56Gvymy6A^lmaCpRgM>y}s)ceOEa>sN3o7*Pyd#mMQ
zt#CzpJaPDF6Xl83@)D(H&13#&+td4+;35}=oog?FXK2Hso)oAo$_G2O(X9q`IEjg{
zT(^a7@BFHrmSX1e=zONJnE!f?Sp24d@@Qb*>T&jqaYx?~A4H<pbdf4EXzabWg$KWE
z3-O|q5W-8ph0giCY>UE<vn4*bg!b|5GHZQg`_kaoa%t7_O3CsoX|mI9UzqHj1hQMM
zODVFHhh0ERUl>x(BZOubhpCNwin;EdMVlsJ?pmkq9oTc3CZabGY=GZ>b44iP--&=f
zHMHB$WFOwF(O^FHZ6;`@=RS-PtiU~odW83IRf#2NI$lNG3mr*Zj?^R1OfU4bpvwO&
z7>mEx=Qe?ULv!+k_h=eTd~5G%CtasFTb8cVu=`5fyZ3x~x{Zf86YjU&lOOs{K+sqW
zfc%B=E`;Yn*=m3`c-hX2;v^8cfPsLVN~^^#_u1oj%cb50k9+!mzUOSrw*t0R7`Nf?
zN9ynvy``;IgNOg&0%3R`s105it59wZHd$`w&S+wL+_HGb^&nvM7niYQ`!R}-ERvmh
z7!cD8kZPJgg^sPb1Fg224$3AHBtl;85d@sFg-Tf@co@xWCCQ)?mAs(W%-~8x{4J1z
z86E|a{y>&(v%U0PN}xD(LbkTkqL=4=VD#<r;*4%{u=CEvFwoQ@Q32oA5<gL~yj}YY
z__Z2;4J|j>0RI-ckRI*;{I41ek1XI$69zWf^Pwjo%Nwz3TBQL};YGz#Rijk#R`bn|
zF-7;6;AQ@x&qF|8P19%C*<h@@T8P(qT#UB5Hevwa{h1bhgWdZ3mf;ltI-f@N4CBxX
z=q1A<rlHgyj={U%(33~h6qV~sYi=Y;Wm)2!NX26QKRmr<R8?KWwoOW@l$3OLsvzCn
zDP7V?w{%K(w{&-RcL>rg-QD?3uKRhvU;c0m2gA)?YtA!{lU}nqy0_~e3H<6a=mX8y
ziz57;S#i0cc2S!Xf!B}@MDVkwD5Iy-X<NF+2=M$UEo9gnU^`DON@!OT0Qo!~{H_i}
z@XfyV41f?l<G$gzKnd+X`P=kR0&WgG#~wY088u^s@kQ$V)aWuN$+L9?g)J{RY_rKf
z-@qN5PYPzK`lEYf+cap9=Ky`G&fcBvD)xs(C4jt5B&g02EHotCig<A-L|X?F@3Zp&
z`M(id%7nQ6`keIKj?eKR7ly$wD7{qt&xKlMyx$EtxWtwBI4<d?8UIm37Ey^})Bi_E
zg@6h*zGIh%=HbscAY17}YIY3hV_alAj@8c%8TvviW3Lvc7q^mN{&GyQ$a)xAi^Miq
zY++=!BZ6$KKadT>Cuu&MY{62r@PS%EGiTT1xA*;pRd`%wc?5=hEr0L*#pW*myDk?h
z5`?PNHbdd-<-D_>-OU!{oLAv-w3GwqhRcR~RrG^aSsH9_i%75bk0^zM!(;L@%xDaU
z&9<E+_4R#}iqGmwRHMlDk95#NA{3VsIMo~CN7U?3R<r<*FMZ4B?=KZ-&cEeGWR)ps
z{SpXI*%l2I<(3}sT3<F=;0WF1j7Adm245YIDWr_olpv-8+r745zv1D4on*Rm`UC<>
z9kx4@p0i8a-S6srUTe?x2l^yeCruJu14H|0oBE@4()f=_M5(+(NB<9@S{2X%qVzvj
zMV#O106Ag??c$p&%sD$$dPBlE?H^;_19&Dc-E~wS4G)ZWr6y{|MD|CuvkRMh02WAW
zc0~%>V^!a!E(PTt0b(MIjK7h%mtBu9&$4~lD4k(&1044)eVYM+BB;qU0T+-1-*`nL
zjkE9IO`y<<JX+(p@8u>432rR-MUu8LsR|K+SpMwzn}FlTCDkTzs-f(eoGfA@yN$Yk
zu5Q=!#*@JAl9!c+D(u^$bQ2&bWs-+8H9nXroll1T0K<h8xnbFMXEZ38mDQarV+?G7
zR}kG>^ePh0jN_Icuq40GLdIy1nm{LWrVM`urH;ttY{lElU*XqxDzELaG}?XC5!(bb
z1hQXM>5S32Pkr46D)!va1THCHd-_yMo>t9uR^Yv;OF!pFfv+b&5<Dlp5`$hVVRiN|
zS@p}$T>t{`;7WfDeQyaLtJ-EPS48Z)1<LkOE3K}QAnPEdH}vUZ$!>h!Dfy)3C&m{v
z4>HSeF2OXg_ptdHifadOfa(@cx%o6bW1V16JSb4V?to9G=B76gl~jmZc%ktz<f;vU
z@RHUg9O7zY<)D%UI_eg@Z_(~KUa7XWmFj~FFw!L#PdpJ2C$KAU`-Oigjy{uBx3b+|
zADOS+I{8?U{zu478DVT7za)Q+{Z{$#k)cx1?rmPyj@P8p7|UAHZkZ*-OVd8bxEIIA
zn$P-)xsuYjlOa6#UH+*Qb4ADv5zC4`_4)91#QWT{Obv$wCca2XU@As?zt<bPpnkE*
zvH5bvC1i;^x0Vb;T86|YHdj7CL6$O2v{<>A#*^NH@qzF(i0<~N;HBD$07Bt|Bq5`1
zG#-qSj~GfM8GJB&AR-9`j9rHpPw(SY{`m;b+YGg{!o+HQ8C`+5iKsA;y)cMkXhQ3w
z-`W=r3@xn~50{TuR<zg-rT6zvPtuc)_dS)jn#?!)=+Zca=7K3R&cmdVTAJN>o#K{U
zWeM5WI&MO1`lSbWCBE~OA?&?p`G^=J{H{c;!6Bq&Qm*`iyh7Sf8hP?@iN>sREsN6N
z8sMK)P>cT^3(cio1=lFsUgV4hkH2TO?r~TaQe!TYq9PHKcOBl)(f~8r3E|gN$ttX{
zUI>T5T9@AQ@x!_0u~wT~<B{DvLrVjN*jy0(JU^@+4{m~GWg)VY+#mZmML9|#KJHoj
zFJ1wa!|9lgiiV^dn({RNdFn6R|G9NkJWwsNNcTLLyjU4NqC^*{uX7+)q&i(6NIQB$
zg2q63Cw)CtA>k3&pFe`Kw=Mv@ab8G4x@>sMO#TE^Il~xA^pAp@1lD~T>SwiGlX`Qs
z9Orv6mx=BT1+3%!r_zGe@SDH^?+4W2W?)cuvT;3qNT=JMBs#sy-$1oJ2S_y$ESw~v
ztU=h$9#fIrVvjP-eKZxKqSoj8gT0}JbC-=UHX$v=1)gNvGA>hkq0|v7(ZmK!N{LDt
z_AJVoW{IpZ2HE?ZCfmB5C`d{b<PF$$Lo;g!o2~4U$huUffv7Z~g{|ynCc6@0v#+D5
zTz3((j-GyL*Rh57Tl9A{g49|r2x%%sbugvhr<wR{f%?pSzngviEc|ug;T=DQ_tPAT
z4BK@wFXj{L5~I_SM}bE-!7wD;I7-(j7)g}?B3<a`AQ9Fc?iJeo`z5hT6oX2~ykod9
zrJy)LLN2I10iu<g;V{wV2e0RlF4%Y#oL_q43d<)0V2ZmmqFAFuv-j#GOfxce4q;oY
z29CH+>#JjYjOn*nUz2Z`!Mkv(&u?#{<{l(V6nMbMv)36+uo~4OpO7)ZFJZH7H<(o&
zmaHT<VlF6gVF+`su`Wji^y{EkI#MoyH?CESe89V5afRV2|3zlI0HL#IopGnl5}lhk
zL=0N7S`Ot<lbi9Xceb+mg9^3UGSyDFDu#oRi_HV!q;+8{8ME(I9;$w=*<|xQ7S9(H
zDU}men%wlM=?vn_z6-=nh#CC&Z8VWt(L*&d7tycSV42q#4C>*>-eN?V7NNsl9V%*V
z`G1)<ml4%+QY}!KVU@fv(kKpX^C{!s>>-UQIVxOKzwq!*%>_E!wmj6l1fbt%UEs`+
zU>B3bbXr3U%?lH!u<#1xfhw!AMus=tjsb^^Ua-yi>yG7$nOvm4G_<VHzs5N8Z}0|G
zlPqw)Tp(2@-FP*l0leoWVj7j;kv*Ttxb9%HWK?~8hb@<4kZl=!0#J3Mdi|?Fc6`VP
z)`4f$2x21I9x*5p5-Qxz(qi^*NY29w-(2K>VJz~jQ0XEZKbo`A`07<wOog`^wmS`K
z=aMdVz)3Gs%V~Lf^COqySP@YG0``ViJMw=i8i4+_hhc*1yg}_II!;l&oO(<n@Sc3#
zSBP7wyC9cEgNkfkH)SBFzN?{t#yQDLv1F-NZeZ#u>I8tKQGC=*BnsE5I9R2cdX!Xs
z2V_a>F%E?$L_AsRwK0A&1JqwZ%rjiCMe%s`#1S;O0|ywa4EGY)^?;}#B24Xc=XHe6
zb-X>kQ(x;IAy)j(xC6jdXQTZd&X*A0G6;RX4suM!NSkGJmFSQ5apbHSO7Xr;GAXR1
zRrXimqr4dX|5^%GU*jm?IM*t@5cBVz2*WU3`jaM{)9AF}t0^O+$BZ$|0iW+2@!z1r
z703cv*9+C3O>XGZEb=Bdu8LzrE*OHDBat_P*6^JHunuiVZjwf0snh_UQH@&Q(vCv(
z1Fh@W;!tQe!s-MC+r|-$g)ua4gmWreKh$VC7`kE@%|@=ie3hu=S=E$evszIBOXLpj
z$8BHg$;iBONjzR#D}ynVlHhb2Mx+@j;*fgyL><95gu3RA_WWKz%#hjdc^HbnT)Xv$
zy!QzLn96)~>%X`U$Z6@|@x_C$K<Vj{e%00dgRQMkoE8wf2>yY6z-sy#-o~+8Z)VPo
ze<0y}>-fd~F@p(eWc60hC8Bo{^b!7I!Y0+DJ~k7D#;@MM$7;hk$5&2;37fuXCagGK
zlHb7RKtW;}k8_@IrOakI%Xq>2Rv}j%NRgk)k5yw?+9V7!{}$;kk}r0N`lRt(rcuf7
z@nY+b9VFqRl=3`Jr{Ppe8CNV?Qb?a(sjxio=3ML~P}6QWt?|YYXHorLXJ&BejNnns
z%EkF#bJcD4EsGVK<#?KezA^Z1z-}d;bMuRC$=h51+4ENbWh(OiieLuzW_9c0nQef%
zKU9Hamd<5!f=#H(QpI-R!`@V3hzGRUoKvQ3hW0{T)Mn%p=LzJ*+vpl!5*!f-_%mzW
zG{||Ce3`V+oB$t|9+p_rsN(F@O(^%PA=>g-5aaA^3Hs2xPq4}duT3?Vefky^#g;(j
zC{-#STxPbH4G!ljS=)V>cFG#7dMv3^kW}mP9$(dUPV&oA73dVPn6la&g=w)R(+t__
z7Geg0$?xwFpiyGsuo`3E-4U)RRYdlGM2o*3M5>~nxhzCFkU*g$LP5hDM190wwUd3d
zPTo|2e5&c{SCO*TWA#CGC4Nf{o3=+q-izhUNz0L-Zc7^*SgGbU3orNk<p2Mh`5DG}
zyi2R&?~3~L8?otzOq1LTCl{FizMZ*PRv8)r#1MBdv=&knLjqoYfpHvls->j5dob^4
z=Jr9_w2B_-NvfwJt7kJfNR&hEP)0Q*024jKysWVgo<u4uDN$gG6z~B<f2C;FG&MOM
znHg@A$`J7&ao3Q>2y3#x%2BTXF`P3PG(rXkAZh3n*DuGe^OjjQun27oHHJyr)J<%n
zk}%C1DI5)<wW{_xbqOS%de9UfG4*}H3kd_i@Slj^_@I?&8`)YL=7+BN!^*(Owh+D^
z!cI_E@LO=5%ZdD~PvU&`;}{;1XhdCF2rOrvukzhpDBA$?cv~dphL_AZaJ0q_1oWl8
z?ub=HejyTgh;)M)<Ib<;aYG(Ba&lvQ!4F|lTaGCyW+;AHP(j#@yP7X1&+wG*tx23P
zI9_(e!eP?QM2IkDgBgLhQG@9fu0;lqIIj-CRL}ZO+Lj`d5?zUOJdNrhh<W{c6SV`4
z%?=T>N<YZ8%)WBfwT+*O3FS5qrGf@hk5p=X(~W<XcKufF8a0#9?fGu!niGr=SX4$h
zL7%{EJMg^L$8rfF+c4-h(e=SdU%O-5o7dc~4GQ0iUoS(fj#dXgWe@REw9ydPY*ZT6
z#`p<iT>KMm^ZF2@VggG=7{Nh^jM?V`nlR4kuyl=P`&KU3+x{x&{p8)oU=8KSzuQOj
zlEzPlCw={n4^*~cIf&L{^0^{@hxru4bOyO|AePlut2{Hvso8(+P+au(g;4qVM^<oa
zRBo?ltlupFHkonG#M_Pao@*P%Y1Aah@_2mPfkDbWdSAPBXlJR!e)m3k%#&=rlT-oj
zgUHL%i6>>)ZO<dDFf$#;4KOJfS}zq8J?!mb;Ps>0BR2t0m0`q{C{fRF+#g(Po0c^-
z4BCX>4J<`s#ndB>B<Ob+$Y=ZMWa$B@1jXO9f+1yW=L(*4KJq)eKOfom!=5KLpB;v%
zSuTdv9N+RI=W>dLxRn!+RPp$tVQzBvZ90D*lC{AyKP(w%suN|ln#>W;Yk@sO?jm3j
znQ<y>y<RZkc#t_HNsIV_O0SrA+)#<+juaZ1fxZhzH8Ym<iBd7-czEl-jDiF#)G_z)
z4W(2x1BU;N|0O&bj`zSW$6(O?Ciui)At@|SEE9X;u6s!-L`~?^z`|OeRDaq(aN!t1
zLrzwqi$4O-T%)k}c3UVnL|v6sU6>S{@n0rUZ&84HGUu`QqdLsgj9LDdJg@Z{Ukbzo
zt5nrQg<mR!cf<kRE`d!Cs)`g0dk-(Lv8QP!aJR$b2hkjUnfQdLmmsXGGvRk$5mO94
z9Mxy2RY-`oF(6xa{Ct;#_YKpjl&n5UDc)dxZQp<1^B&h%0_>N8(EQ*Cg`w#gp8g#?
z309ZfQug{eBny2PJq%Zip!K2)2!iC4;PL$HB3--J^l<u9RzrRwx8m)Fo_J}^cp|@Y
zfzkx%F!^K^kk{|LDuVicID5A+xomxV?@4b3^M{VmMhv}iuP?2Z{#ka6%RJ9aX!gUB
z!u!P&*CcDJjKh?JVW;Y58fL2^x2I7FcLroRg=^Jjk(9Hii;pHQ8;9ivtWYiAS6p`R
z9~z^pbZ3D`FS}&tWzZ4tXloGC^`ReOF>pAG_FqAEIE&N1>aJmEDAfDa3=kmeECV;^
zPC3h$1|C6PYs)(MIa{PF)iC4EQ*gPXsmHzxc7>7Pgc{*=4tIZr34;AOl&^;s-Jx8)
zU;1DNe%=x1kHwz;ZI;Y&DSxA&jB&`*W*Yw3Dul6PzF(yd8zm0s9#xz;t)UbReSBid
zU_pF3>iXm~-=bGt!_(mX_&9@DxKb0HdVi#QXE&yEzFWqOd5uVPv^69ptJ}(`{M++|
z<9vnlVN>FHHUoLgAsn6beGp2eNa!P9U#dq|@15&ar|msX|8UEl=b-<~9$okK30i9c
zGtmmSDi)O`5q+TkK&SP%MDoC=?-+sl7ysHohn4->ULu^>)t`Qo{1f^<Bm%OekMoO7
zqGfGkIM?Jp%P7sFPv&){ReK^e0BN;z+fn^NWy<klhM_yrd$$T(qF3b+`;ACvQ}G`}
zCy*nwW5Ceqn5N!_I@TnC&GId;RN%YSl){G*ouox=`w8J~@{LHr_my{mxukJ#mlwaB
zFbr&}Q~207FsiuX`QE=Ly@D9O-w6Vndq^kc;o~n9-7K%Vx1G`-1rj7jkAWre12uGz
zDt<?X_sKp*?l+FVhGYqrE`7G=%d>5;nbL3JK4zgXaMM^Yc#p>Cd>*?$q$o1f<+lL)
z`S6cD>DRyehk|q8iQz2RO5W=cqn@t-?DmHVg1=uI9A3MNe!ch<j|~StAK>{>yGUjg
zaPS;eA$leQi$!TJ-sXqZ%>wPvluYVd{OMKn+%Kk~9KvtChRmm9Cd*B=Y7RYLIzSo#
zMp7UN4<(gv%J3hIc{W|DqN8T^x#n5RX~@nqYs`T%=1>4iN#+U4tc$UrrFSSCJF-(@
zi<R>%x)_{fhRIo0Z)hCpN<RD>7f*p4KHY2Zzpg=OJ=fdDhDa0AJZ^<9s{3@$m+tZt
zxwX<~>89PN=}F~1!E>QH!J<gJ`L|)6VuopPGa~8alGrqk@<?&Ux3S-J9;9oXub$A{
zNRhB$_xrX&e@v#AefW?QPN*n!?!*r!zaEyOR)4}w{^C323~>4bRznP61KkO8%awTY
zRbirQ3wkvDn9)^86Mt3kg-pI1LI%hpwI6lQ4R|DLD+pXKbTv&7^XP1b=?yz5XBrWF
zdx8)G5a}px|ND|u!Z)x(*wVyKuv27yIM}?Zyvcb|4Y{1Z;vKwx_#B7X%og3Pvrox3
z<0kAQ?UG=y%$TyZ*UB4rzg#4~isTrp0-Y>2`(0!H9bs4ssaCitdAoa+RQf`-bCnsq
zzjX6-jLfgP$cXLngpw~B+xIRB43)IuH8*n`NQf;!+oV&bvLZ|4m{GnH={0%Ouu7pP
z6|IX+{z5zG<*k^#Gq3k{cWHjRL2d+khFg=$#AC1TH-m@ivI6q{D+qXwQ8R*cWmUOu
z`OD(6P2JmFprgGPW&V!fENpx6*$I{0N<jN-UpI0&!3ZSriJl)MI~lRIW^?2}&jQX9
zs_n{>e4EfLR01iyy-$p8EG=ej&i^Y9d`GzjGTrC^eEKDexHsu{h;vwKLik->>eWmt
zru7@%2>zr)Xo;Wju?EWf2YKpp6C4F!$QNgYEBHps;L6*de`e;bKu<Ry#^#{!Zj#82
zR-3d=ySZeNHS9i-U$q=;Qi$UGyxc|I?ctWol4o+gKI9Xp@-}0de~>5@Q`4NjMYNBa
zz1FBPLER;hy>!pZ$>AGx{g3cAeL5E9ShPO}PA3qL)OJOnNyBi|Z2hmRW*es*?>O~+
z2^9Ge>@R|!CQN!=excL}v^qBR`%vt=01cFhxd%nw0BaV<kbO`9<;NGKb<IVMz$)hi
z3%LXTO~>6KysDq)S8wmoYP;cMj?XUDOby&zL){)oO=kA&uK}+TIkpFds*$(=_+ptt
zx*^#N`cNMGO<xng+URE*413+QA$CEnxSO?7$&sX|DV{ri&Uie%v1rY;IPa!Tn#H7F
zVuWXn_+zDZ1&k6wA1mWSe6ZxhIHUdV`S<#tL%V6;RgY!H+xuev&Y>;1h1Mm{`P?I)
z?NM6T<)-PkW-}vqNA_Ff+~iA}`$$8pZv)<zS(%PLI-Zy$=RExBA!uB0zI<eTdg=w3
zym3FC4krl=Kd5b~L52u*J9v9kDmkY?eo%voN%yE4KKLv7@<}bdpJqbbBG}Q*Q^wPi
zVLY+eAO9u%hQ)9d&W&)O9+?Z27bY0f)zvHAm0`wi((T*7McnGnu06i%KK$0mf86L;
zc3z;z>{P}dvENxxelL7NnWl|Zhv>**kz#^}RnN58_U%O1^vk{$DfYcJ)?)c=u6ooY
z4OKhFFiBOQ&wArQ!ef2uAWrz#mclsEJ85d_gyHfUi<Hl%Xa!Xk3(V|h3zqEntD{aU
zc<$SCdZqrEIovVq3L;l>7?%KK)Qm4am>l^3SO6Qt>1dt2jz4%0&nzccrjH{Z?~F(c
zX@6*vM`O!8%9CUq6&G*uaWm$&0`#vD;dr~R8{w&Y=I2Yun=owdpY{tg7n0BnZJ~xc
zcVX{|5a;-ZiC5P+h3EG^w9(tulf&TEDyT5P%|wP11ym@rHA6z7UMtSacHlx0xKu(E
zFmOPB@k}9=9RM?xK`25WYDq8({0sR;o8f?<s}K!P3G3)iAmnsS05&`ffPalO*Ve>0
zhQS!;kWIk*eEU69<#{rmQHWq5$&8-f!e!f^7zTBy+kv9Ytd?G#f^B!`U}e(rx&7s-
z{OWZ%oh|!7OlJvjSW8RxDTw9F$vc6Am1XGIkWhPVb43$GKTevXyaETm;aq>NIR$!*
za%{EU>O-F%T{6x_h$8bWh}P&;D~KBIwgcA=EBgsPWz~L3f0j2dy52~<(G;H=-AQtN
z8%0GC-T)^ap3$tMl?zi;h|BG`)2-pR^76>N6zG=1CUQ&}wjpYfz3Lq-vgsSUbL?I7
zK4<~I`DjMtTU873k;9?t=(Uy$myH#Y$9u;1tHfI#BFOLiO5w#xA%(P*t~&f8<?`7g
zWljlJa4&T6^*n2RUWE(sY+UZ{lE?U6J}&_HytjIgUbFS!bpm~N?agE}?*0%-Y&`kE
zskcsGkuI%XJkexpJ(BMpmSewG&Fw?5Qx}L9f3LO5y8E;`gD^dfdXtP?gnsy<<Rn=v
zlV608E3f(ko%~qKRT?hl)7N!vy$fo_Gl}2^r$;tdp$EJk`BRmHpLu*-j)$t8JV45j
z3J;3r2q3s!22zXsIl&E?Vu0JJBzLwU4Sf&yjfbtqRe*jocA6L~&(>Db=@nh0O;i8M
z_lzKSzE)vapgjpn^nFfJ)zQg2zrbs%6y(<c6bJMj+XpB=RU6<4BsAVv&-IcaqNOhi
zpm;O&i*}%HSvGFPua#7fAx_2VXEFr(0Hx)V99uPYuP@j2itEW5@cKU&E2xHcspG}7
zQsHhb*O<l}DH@5nG88l`Kj_*?aUA>Rf#Xzgih(U5?>^y5({15G5spr^Nqo`eM(zU;
ze^r<9J<is14)8F$x5qQAj)H4N&!x8wc(ksls4`w2cX*8kD>I$R9ny9(2*=s=_9k;D
zjC<n>>FPp?HHzw66o~}+xdLMQc~m(ig<jWg|CKSYk0|WcEn1qZd}SA(pE9)2q&3m>
zz5IrzUSHTZlHYE4j!JP{`eN){X8egj<+9pr+7@xGyjcaIF-7#v!3%p&UFWu-#bPz3
zyW)9Gm*{;M?zhjxO{52g77GGUmp!fY78vF9kL(e$E=SOj*OUBB9f%yiTWFNyW}mkX
z@L!hAkOdK6oR>B>j%~zw7*-av&zY~DroO9VKJb~69xE3p1$rb(FZf2<FO<8&ipng1
znY(L==U1*xm&L8)&a(MqIKnEwuF!6C<kL3WL#MQ(S2~7z^YNw-=kUhln`HLABRj^1
z7H?1-WFJAZvx`ND#L}XtYPY~3*ZEL=NxCeS4P#xJ2H_GBcD2cgiD$7Kw?04eclbNK
zFj2ArS6P7Sot))PK2hWRYC6V8t7uAm%=}FlA?2zVjP)^bsEew(0(kkIeiGr>vpVrg
z;r+_xSC_YLgj5giYdSCY2{%LkR-Fvb-3jqOf2dHD*RcmT=^=WkA`Hd6{W8o;AuXz4
zG(I8vzv4g$1Ls7ZsAb1R=_#n>X=Qr0pk!XGy830ulvMjk@$)Vu#=xddG>7687Or%c
zZJc3IEsh}q*F$1Z$Es;jDbu7jQ<IHVrNU+XfkKWy@3J5gWSV7ZEKENXl#G`E@;jpT
z>(su0W%l(ye;jh+I<Cb?WBLvH@e^o+32DI&UGDuArc83ASN7xqqhaje6|x27M`>lu
z#!2N*hgoG`goA_vG|J#jxqinl`#CC0!<&C|AT@71xS<To6?0eNqMec+{gTg9O|qTo
z10`=BmjwkQ-xX0%TdzqSSL-;a-1#AWZ8>}9Oh_wyI7*bj^x+bT+&ded7(j?>TC<Ht
zlgTAk(8(!7qGL+wsW4C2F;08viBFhTtaxIPVa#UTf6HOyW^g|o+Oe-|+n@b#Iol%L
z49sR7wqp$aP<R`wT~7a(7l-CxCOoqJ8zG6q0cIKF9YX@Ny61m40U{`HQmA!@o92i~
zbzCqdS4GQs9X@^iJ2E6l-ga@!Xde6szB|6mnt|c~o+V`(p`JfyxB+_&!9wP>r6m0_
zI7FU|YK-Fq_l0gY8{<`aQDwrrS1z)2aX3y4vEuz6G>mCXTKB2TGLD?>zK1Rk;Z?KT
zX$JMhj)8QG(SihjAF9i^B^ef9-WQU5F(}9781|z)iTbAFGHuX1@GtI%VHHiZT(6?M
zq?zMLGy9{*`xEr~um3VURm}^DzV{!KaUQFdeihF>S}~Yu&lD(EuN_**&vf>03=fqL
z!yN!=QmP{OAGUX(^E3o{<JcC)lA;geK3MU?3%Lh&<jp1%S(-ab2-bIi)$}SMpM%EX
z9NrONSJZ!@s14l9B5xZ#9uHYj142xh#N#ts6Ev5qBq@;-5DX8p$$x9T{<&MZ`u{M_
zS;QSqrQp7mMNkqP@6|p`DmR2$pgA;r5^{n{#RB!VVp>8W>SVkJcls%dFU#jGoT;A$
z(1Jq;>VQF|76nke6!J*&br~|=@S7l<Z=*3Z&=XK$xy&Iy+bDoPrJi0;%T0O+tc`A-
z$yjB2=!KpOFc!_G*NraAMU2dS83`gCL&wDUhMSU$br#ZJf2-UZ05Olv_T6kWPGX5X
z#TztT#6qU&&MkK1F}xJNV)IcYV>R7-cn>0Ke>jCQcchlaSRrgCU9Z=+zDR7&c$|{;
zk?gbOONv!nr7?aq!MN1lJn8JN`l^}tdp-D7)<Am{TAoX`-v1+r{DtMxyu8)uR)8sh
zXoT5@=uc6KhO2R-Eyv?>{KY?atZ}PhWT-DiqHV2LxIE>42VJNs)8}4ME8pcg&Ii7S
z);s~@nild!=e|{k!3|x#wm~SnEJnwJgAV{$ZrXgN$~dWiHhVPv-%SUBxC2{|zK_CQ
zR-UBU<-g^k?@qA-mhDfDF2G+8j!tdwCq)7JoUi`_On*CXlJTcPy1|kJ*<sSiuny1_
zpc}CKzkkYK7(#7U56nCOY=W**o${RPUrgD4RD4lO%JPVGVt(z5pJ9r24?`^b?&L@G
z+LRH<X%x6v2QbU-56h}T5j)(CQ^C(m3lX^jxri)}l{^zgQx@`y@#7lw8QQHxwqwf~
z_}{k=tu_<RXvxi&5a(@r>drfRPsB&#9Hg|L_KVAt(Wn<uNktMhA^qJ4T!8@6lxqzz
zv&EA-T8*H3nC*gT1b6rJYIL4gSU*aqm`OZ1%I+5mPr4@b6-jAo8I6xgV<N3?Y^MCP
z8(d+eLf$3@r0w9hxU3^p9^e;1dON{{y#vkv<l(qkRt6L-H0!@>>djXft{V)4y9Ap;
zss`?$gCGxO0lGQWtM^-L2Lc9Llah?JcYlExW*&f&wYqlr+{azni6a!qJ<i-<jhw^n
zfgPYr59e<*;HF_w8;9c)=w#KlQ^R!>F|WSx-hyJ(RT$pu@<|HWvJH`<gt}-JL&KF5
zfXFTI&34k9;sFoZ@aGq|J5Zefs;LNR$Nd`-HzL7qtJ6+Wxs})b(DRw~wl;Z~De00>
zTsDTEnhQ=9Gky+~4x?1OPnR(@X44Xh5CRnPf0vE)kfk}r#P(;DE5_jF5hvrCI#`bA
zYVs5?68~_64knB3rdYLVpHRH`D&VN*f&hAz$J5Vh%sBwRR|8Hp>KzgB6Zj^v%D?t7
z3p8}tH^Jg4;Kql`WG?3*tP`qK@Z7FJr!?r>L_k1N&U+(#yc787*RVmcG&n|?nm&v^
z+8DynG(blkP@XXL41f<mKDv{~c@|w9y$-*A+RKLVL*IK_wmcZa)Bh<H0=<iBhK#Xc
z4JzrCE?`5g;X)aJZ#Tkxt;tfb^AylgZ#<qJfDZM+q0r)l6CNG~-v#-+%`fM_-pkq<
z!#Z~lXOD#ovL5?5!r!F_Snm|1<5bRTAO_GuEuNfK(Y@F;xZU;g+>fZlV$$n`h)j$J
z^K49X@4k;Wx$6z6H77mE0ya}o+~|PEY8&))(T4uh@!pO7;OQ<slE>2Q^~=3^w*3(+
z`_Gk=SLF>f#E3c)zwiF}-Q^Tp>00EAYyLa2+WMcs>!`%24-96RJ5@G(g$&jXuIki6
z5iZkwg^^<TV)0Y~*{6(0VV<v^$MM1!c+3)Od<Sj==B+vM4-3(?>_HH*e9CU|1m4c@
z_6IZcWl$0=4bQ3ny??CXGkd(Ft@{F?ZiVA;m10rHPe^K0W+OeHmxv;cxusd3l8#)6
zuk8pqp3m+A-S&do^CV{S`1XG&3SPhHF#77H@VqnZ-0VG{egRlQ<+@<HJ@?ZkCAG^j
zbK6_bdolMy23hXMnje~IgXZf!L71RJl$+<*eGf{giX%wjT7P(f>XP&one|vxuZ{}%
zDg?B}s?Bv(sa!w(Ys?UEXd@AB!eio!f70HWhoGU{{Ue5xv>Z58%NKLtOAluD0?Hi2
zF$d%u7`eFs`>pG!eu|H(DINFtFnk74dTtE?_3sU$uyOe6kl(!O?%93tXoC^w6Ypav
zMGd$jsfkaBY5U*>Dh*<PS2k)m$>WBdPlyuEN7#;~UZ=TzIgm`cxkH|8Gv-XXUg&4x
zDYjPmTg_@9UhFNM10)AtkDYZZ!z*Nsdohx)u|k?Hl}w^WVTh@(z}gtf?8_s%^EjbK
zxAgzSk<1bz2=?TYJYbWY2fjMN{VF#OtQN#lKR<O+(?XIA@x$R8<TVi&qksVvV?S=X
znlzBz6XD=*4nZb7{+vYT3mFrpq%qlH@Bp%_x-R@H-+h9}-z|j$3p~V&ZsZ7n+<Zbx
z!1~^t!{^4tf%Rj|up>gxM|lt-wNy9YJgSKy@*+wFRPMTlUn)3Au7xDT93;~~-82^(
z1o+1ObJ=OagbY#(el#KYNCZfyks93Nq+xZwNGR&4Hlg+n7jY-R0<8Yz@#s7ct~I$}
z-ALta%{@`~I@BV9{G?W!($3EQ$@v{XKZR++1)+`NewNhYs|dhF5gTfl>&v2v7jAzz
z<|M^A4fkCOyry2r(G7ql7W-Ml=+_VZ{%gmI<l#nJN(VOspYt_%F5$tblng}UbGDLI
z8{_K^O1;=;TFg>4>W=d(W{V%}Ff#-(ek6`a6AtkR*4~@AP$q@5TuCL@ajVn0d^G+`
z>LMb0i~nE1MGgYW4eO2~-xU&Lk^r*4QFX%TeK(%ZnHMsSkC#Vzv_QjO;R7RR^WnDo
zo5(8(7DkzZf}Y#tu%dZJ*0l8eW1?aYE!x$kYmLE}o2ts<W((=UYOOah==%JZ{h*3Q
zw6<+C(w}IjSGe@QK#iO#3{fu%i`GnBEo)EHaG7SWI>u%WBiu*Rlks`a!S1L0q3yWF
z4MWk0=DVZOK;vX#jOS=WyVa;tAo{sm9S|=fFQ{|iuL$roe@}05y2dM9E(r*A%PW`K
zWmhXQLeH#v9!yK9)(+DQMY+aSRAR?6AUCi+6KG#vJlG4wsC_uepi<;@6PP8gi$#jS
z@B`QWZy3nMNi6hLjY@CMI|Yb_+MNFczAK60^Aa>ou$l!QCKhh)pMGpoFCwvIhEpwr
zWmtV8mAlgqe*Hu<pH-{HMTqD<HieU4o^RSMqyuX+fDf(+-25cc6y4=`)<GL@=+-IS
zoqgLi=;nbY<;MQ)Wd9$xuGOK-ZhljT3X<<L7Bp7&-Ro<j@*1Ou4tg#ZcLv#zpU9^7
zmEy_&zNj3;x%7^-TnwyhW%5>H3R@q9TH<Z4-<JNr19AXFfFx1p#gSvcXEe#q{_9uU
zoy(^DvM!C$g&9TnTxZ7b*A&7iWfyp#n`uzIO^g+e2|>oEID5D`VFW&PZc?uf7`tzr
zd7T}S=7e9@EP~o39bio~HLeP@UMngts}lc&E=IuthFF;2kxO0Ly|0>ld0Kr*a=9z5
zUs)LV^|}P0HYFg5V($KWfmsf5ah%My1pMA_lR+w$ldxl|?A;2ph6!3mJsC=R+vk3v
ze$%KUtAZYM2*qY)bY*39=>qXR%I1e(uqY=iswTxhug=}Vn>wq!QdIu-_`%1IRW-Kk
zn#bz(<2!v=`o<T#$)3#X*>(jGojue@ozv`^So;ALZy|9XTJoK(vMVK4kz&HsP<%{+
zDqi8!$duoGXyMyo=tBEI^xS@N?%;WjG*Sv?dt{#I;eDFEc-!+rrpaWC_HMR&YgY8%
z*@48r#s(SR(zX%#V|%bp*n=ogsxPL$p%z!C64bpkJJ!Y{US#~N%4p*iYOiHx+q6p*
zYhy@qzVRFPzggz?0DE9$Fm8mS%OA<VJp0v%Hh`hT(3)QdMc`-QZDNXU&UuzvK>4G$
zYx%7%%7~LKaum0ke((eF1z+jV=VK+&{rdSLc&D9~Tpy)aL0WAh!3X4cBHGC0cSVEu
z)3~{&_4idbVOqH_xoHNi418lQCN^WZf8BHDjy$w93Kv-f7E<h&@~bTU%T%k-iu9H+
zvs?BUlEy(v`Ugy6^FNYPFq6SxXIroDyMg|y)=O|hq2D-*hC4TAq~0z=G`3&3lFSQc
zA~p@NZuhr3JQCY#9L!3K6Uk3%zqht8skn@(J>N+%-^zDNkZ68N9?U#w-gVAPx%_xh
zA4Xg>Q%3jBv4A?_eIf;=2Rolk`WG<@`=n?lH73!vUakU7c9wYJr1G}pY;z3S^}O8y
z8&!JAD=z3NA_;z!i*qDGk6S&D1SE%dv;lXp2C0!Krv)*=YnwgJKOnzzMOUO-Z&w-@
z`|r?iT*>R4MCJEUxi_f9m?=nV;qPFv4zE_JdoEt8Y+zR@9q*mzHM)+S5R@q^)i)Gl
z{fHbbu8om26HkGS%l$gri3$CoL>5(bxC@OuM;ye^{`VT%g+KM^jsB4cqw{~UB>&f!
zQQ7r*t~3eM+2@kzM7JPHf8{3NAx+gZGW}gHkSFyn*jbWt2TrPpgaGaV(EVb1Llm2x
z`8`bBe#h+u`Zv_}vJj|LFu8|t!cZK@;G)bnEn3v4MI~6aUa53$e(m92QWyBstxx#T
z<8`xFZPjPQmz<?$5q?n9-f-P@%^U7xC5q1mz-fFOrNjt38Vc{goh0$&_8`|InTzxH
zfYL)9Hc)*Q-00MzWq8AYUqf9{7kD+m#9Z94hjr}%pNS%>W?=pFC$6-@^aEx3FcKQ=
z$7HTgT<tRO9iOB!PZGA*_V@NXE*wD3Jf+!uKP=@3jCl>@35*AzANz!)=ezFD*=MtX
z7X}{!F-o~?+nJG&wfjKe&DE2(<lwdwxYBZA)9b0ISQIMWSSLO2IX7OE>16G=$b@s9
zi&gXZ6IpvT#dd0Oo=C#@!M6}soK$qbuRE!6*KxN9k=%)=Z;!QMy9~@53@1IGE^8Gg
z<-+rJvypEHBG>?`C>}95q^H}1a&_P*&>JcL{A2D<EVQ#o(6G}puyYY9QJ+*mhI@Hk
zK?Zz^Nudz#ca1yg)yC9ysb}d2b6*XQuqEFWYRiTE!a3rDu<J{MS@6GERFvg5oawGi
z)gV~x>9F=xB@E-+8ry7MK1i1dR%??-bg<c27;Um{5&XT5`2nH$^ElyMrPAnzhSHy>
z<DbG>UvwI;<>yablog?H-M7URa>a$`pVq@MbJf#msvHu2vWVIrrw~~(L>O2sf*VNO
zv_GTX3OXw7`JlvN{s!2Zfk_;vSF6g&3Hf(%^Pj6ME~<N1;%QLbbpNA5P{!Z)Q^H&3
zTyIYGBID4<k|c6f61u3;JCAI-<!8p`Y7t*B)9wEnQJh)zR_9wJyHcNxMifbv)YaWZ
zljvlXS|6sp0|ZNwa!JE$0Mk~23k)9fVLzZg<efJuTcX_At09*B=u{G8h3av4D%)7U
z@u5(mJyJbGJ=cLZfe@*=WV#~$8tCG(9QM1q=dSP+77I0lSl>J+{?lZfVGw`ui4PDV
zePba6w^()Xx?!xny`TE@0TQ!;SUYYDVh^F%(Ikd{47d)_%2+pzDEeJ0g$lV_Tc?Hs
z_aGnDAgFO^TuErIPj}l4>n5fFQ*zkoC8;j<b1ar;$0mwXzX$$PemjUS4T2c~;)Wri
zVvB)DVxy=HP-l+E`II_MlAyl6cq%|-smE;U3AA=~fq&*f`KdSf3|dm9--%J*MjLYQ
zo@j39*cf_C`9H`}<fi(HiCZHTot-e;mPlsUn;45Fm=IvHE&JGw;Y0f@fkdDFk>u*z
z1bU~#snp*qs&kxsIWZ$F<U&efu4QzEgI}%+`{FZxu215!9D;Tz8OYv8SAOqL#@q!O
z85zL%*PBkA;!&bWWUZ1Z@DSD?lA@HC|E(wf`w!9oB+ZU3=wL~bfn740HH8>=#?vDH
zs%}0nFO?^+IvQbS=+yTom0DA<2W9b~h83ei*Re!xL!%UZ+L%glmFMCOel(t+cH50=
zPLDtVX>*_8!^|S4P>^f;bDau8W&1#qv3+FYh03s&)6UWR<QrZWrn(Mk@>R|Ae=ZIN
zE+szaZ~N@*HdZWNr^isQfiY-WW`m6cHqUqASWReej+YnHJvDc9O{)Qtq;9J<QYWyN
zr@i4hSu~Rg{!cza<Q*8jFkht=+g8<f{M~Zd%-H^@uuyMh)pPGdr~LItbfd$n)2Y#t
zY)3MKN;FRUS()|kmYSgAa?*@1gi4|dhpn|g)Z2TbMLx@zP)e3AI`IB>*p)hSY<E92
zR=Z!;F=EdypjIwYm-l$ul~w!7UPYueWxwQJy-DzQ?AdT{+_A`JM}6|=I0dS0HUmxF
z$3H?wH&4>>Qo}FJ>ywiAD#H*HEQddTtXXglxnhlo?+bWN4j-X|xDB9*O?0!Q!dPC(
z|8KvUxFeAvMj$R3waPs{`rLj27h%4;%<g$%w(Y3?^G{MUZ^qRs;|SRGUiORePLa27
zb-JO{ztol`=md6c4aMsic$R7sufe<FtJ}xs;=V-Ub2>&E4wHx=&%8A9v+lPC-&>6_
zR_96%TPjorMDr78?7x&ut2Lda(bRQ6f|RLautQ-*u88dCAmJfG*Z(fGS;SsYHsaWh
z)as^BS_Iyk#E~z7ye@aQu>e8u525ninpZs@+JL*{FRDIP#Uyu-b%;eQ>>)9QUL~Ae
zJ<NbYk9TIDJ{9Jr31MANHwmE-T*=+qN33G+Y|#izy6b;$u$@Zj?yDoenoUt@+!6wW
zLax>lZ)~w*zKqHYSiPDEbY&Q|Ze40zx75<mi-W%@Q1$e!*%`xPdlEXy508ZoWBz;@
zjiZ@HG^i&W<-u&bIbdKUUr@7aPDe9UsMrtxJ+YQDC(HeU7Da{G#vh6O16xD*z`pg+
z$%<6_n|KsU^XVc(Rl)SU(auz1{A-)+C-8<Fe8;vFxi@{bbpgN0NAwsvIa}!^>M6Q-
z4Rp89`X9+0)~AcVjfab8YIEm2dN*mci`AnJx?k;MT)KV}M;Q5Z37+0%&2^meaj`3!
zT9zX-=G9jz&cTRk)tie}#tUDTjKQ+lQ=QJA3&r~5_}&Ouq?v)-gi)6DmBaaBgY(Te
zb&(bKc%@6^t!?+s6>&XVZiWr5HP<5nY;>pa9ZxW2nkUww>g7aWsZRu1g{<9U3P3Ju
z6YH%b4=9I;1>X%L8ybe+!mZ;0r9_@Z_V(k$*$^6TKlnuT0SoY}k2U~Tmdc{#I~>ex
zKO+}gq5L7vKpV2<&9+3sq(gzP2j(TspWax==qz5#A0kOa&*<2^C>43{`R_qrWpIMP
zu+aj~)T-D~+2F)RD|b-F;a!N&vztdmw8D3tYuM!vJ3psOX@)tmJScG!wq-Fx?aUt)
zqh$3S%@NWK8Qv0Wri~#P2qVq!`J&Gp$4jN0@U$c3X^-rxr&?SWeUJZX#C0BXXm237
zcUR7OzQT^mrqL_4yn2jEWdkz?Q2w|Haenn)Xyq-8uXE>-pJ4!*-)KcS5{{IOX7AK`
z9C^zR=V>c-OAs~}x@(>`fC-feHXy}}d~HX&AL>`@U(vd~X}*4H_I`O~vtFIDl!tQ?
zm$OL5T>a8Oe{MReUvbPd%A*vRW#a@lv7dE$UP1+R?JC6UivLc=PU7K|AFEu2CjP?J
zqU&c~U1%|_Rw~jQ+-@2vcN++Uz`T*uH!KU{LRy-;>HNPpQM7Nz?b!w_G=wUv7NE-5
zqFoh&6D=gvnK4vu=2}}If%iI51(5jGm$dhf9YQpd<g5ctP&$d`D&Zp>zR;DrgUxx7
zrSHsyT?FwL74|G3N=M4W_lJ}x$B#0@O`-JtC|faM<PFgo|FK}C?^VX$-+anxdZc6c
z%2QD__(DyHoYGfb>e$f<I1<KAKA!KR|KnJ%!nPG=@s;$9;J#*xM#7A9oHtDP?~6m^
z^PZq&p~S2ZF(epffU?ZZUm^!P%9?wmi&v$(Zr%vjj$8aCfM{bVQheNsMA_f1n}pZ0
z%Ej?@m~wF^X$#*A*91P;PnAT5<nFeCz&nd%Expp`n30NLoXWslQ*-0F^?QG~Pp{HS
z3fFkJrT`BY+?k+ILz_%(as=-`+K|aSX^h5)GOgI0dhLwdfOBvsUAXgxPANLLyi;iR
zP<D9=Blp>A%ks;UHXqoHQC}=o%zL!dD3vIai*=Ga<kp4XODFVed7U2eVE7~^2Sc|7
zAj?O`HSRadkS8XA?v*D<18$MzYIApha0olB+b||1t~XPnb`JXNO=OQVq;e)OUG9mM
zK1dBA9$+hIRQd}hNd^ezD`LA`jIwFPveY}MR*c57$I_zfZV048++j(!slxS!Imb{)
z|M-DZcW;3@JW1$o{B1EW`fY3Vuc&K}wCntmd&5$tvNELlGwogVn_y*{p=M(vDt@{#
zwt|My-mJ|$hsI^cFZ*($MHU+3GcWNE>NXd2BTv3%CnR5Rm1FA1lgZ7eC&e@D7Q`R2
z>*G%dwdnW<KNK?cUfTdVp8&)GeA0O<m6EqR8Bk4#NKtiLevQ0!QOolyDdD40UR>w+
z5Uo|g9*Jw-l5C9H96B%ZW_5CT-Jevs-~;iU-`|b<+oEzY|F2xaY&G5V4Bl=Q#fBtH
zt9#t+E-UHhCtDAL(pTDXCAEY2sur4_fl7s#N%a~5&$5L=YgSh)DDTamwb8jB_YcZP
zve+pjV>aGVIIi?Q&cZSGdRljo!d6*veL(GUo=13joppP?(*=h@XtM%aA(jost(?({
z{`i0voTf@qySP4(`$B|V(Uq{9d!c-+FmdXDgj4md=n?j!>o@q->rv8LNi_5f>~K0T
zY#h^Qx09?<@(!1Ej^qirQC3Ly4Hw;$@_?gn8wo`>LGs97jXqik98AiePBfHys^5LS
zzPUp|@`Xhe$?}(24<D}nR<13=)mh5&)yts@3VWRcjf6twIyLCdAXg{Rp2;|(mWu8P
zRkL+o)(|&_2hu6Bp|<7md>Gao1{B=Ot~AR0hr4V{5h97ya{kTMntyYxn+WXO$DX+a
z_9`=&t290oO_MO^mXvh8we1t_xZ+H=TY*+J^||FX9QuGR6L8{VKU6*FgrtYp1EltQ
zVd+{sLKPk2Q{69^NW4GpFeoz%9=efn4Kq4XZ4cJm_lFy7IB|ol2G+J&)*~_FA9EAj
zNv&EM#Skpwrn&oR+^-Qc%$w_K4Idi_Y^SGJhmM>cOU6l0+PYRV#70gzYpGN`O+O7p
zo<Tt=3|xgGX}e!3+u?@_K&+M^pi|7bEZ_(bTDpKBtnsSq!Vp{pUl#nSJ1(HPEC-+=
zp1{-b#2c<_XJN-Fx%J^tK`>kTHvp4)5y3(!FUS?~dMv*tI{t}NWyI6XwNk3xkzf1^
zzsR-*KmELAy$0#vLL&L_c3FI1+7!)ikAsDyRZYxsRq71Qv+qH%uFFGV@h2+uMFUeW
zBXKgnEGQpVXC=3Jo{C^za(2IKl6<*JdWD7j?-r~JLxj+alsbN(-f~NB^L|o$>kY<M
zDsRAclKa%Xm1Agd9PNNR_iB9Hr<wlNd?vDI>hEpnC4_```vm8IyqpXMMa?(zlv86o
zA9<v>VU;S$Xs9PD6ms`z+BGfCqd-RDM4AC=K$3{<w1mjj=0P~B)h~ms1n-PgcNz?Q
zqL@-jCx0NnwJGI_xk|DliMa>zzC|fg_lFWKeAmNDz7601)4u*^3i!QxG=6L6QNvJB
z-}^!b^`7$RrV4AOyo1PY`vbu4qBf}Z{b0DPP}-zOU3I@n(+xTTtvwZiu3sWe2AnqQ
zLa#9JkyeTuv{!Oph;Ll^>+BhI4tEI@KT{7B!AvLBw?sD-jR*rgUu1^)3aP~K$Mv`*
z4xbI6f6;)K1N!8fLB!D%Ha249_{R#KILpZuaNM&}?(9NmP}dT3SZs2e(>IOuH(bDG
zsYEIp{i}wV;FP5SUk^j4e)c$N++hzuq1y`9V7Wv7W~inSsVp2XA>e0|?SsWUzn2G`
z4fb$pgS!P_%{J=xawtArA00L3)J3+SC<qV`a@ZHBc?xda<|b&fBw+V+LP4nL+w?nu
ziD?@e>XTz8Zy6o*^KA{<OSbvRmz&1YX(cyNQ118DbP@jcVDoS7-^t*JTJhzRy1dbN
zcsqnHg5R^$lXXzj@RRm%D#xTYRWr#6=4IJ`_^cbPH~*Z*z@yw!<*_2U4;vb(73ib&
zt<Bcm!}aMm*hispLf0WAYmP1t7Iy*=`i$+T^`#{weE6TZ`R4q^q}AGQ*O5rC>Qrv}
zR5v%>TS3N9s$8IJYq7y`SitFJn3J0Qi1F^<ydgrqqKcKtRB6CruBD$b&vS^I<182Z
zb2tC}@9-(d?qry`<H0vn0V&@a)q84W)|T!mIC(R`0%jQekEMo*@r%j8`tc!Bxdl|+
zr2tq%g9trCdQEww=-Sp6YaY*TrskUG@-Wsn@A`dJ*^UC{&nI$u{bVUE+68(CGV^|3
zB`Y)w7~1rQx>nN&zrm&YI|GioE5o|)2F<|Hj^YHq`SXtEkGU519WPjRSUW2$IfEXW
zwv_UX!7{UH`r)uIW=gZ{juzZlQ8Ae&g7YlBTUsp3OuqEzas~)4)2Jm4?=7i*t#fBK
zcwY-Oeh_Ba{N{Y@0%V?7r;pXpWfQ-bd`6ya3FBPwIOI3vWX^IS9DzZX#m5y!%O{EM
zry6Pz3Zf&_uFd{QIS(C?|785gLU^K$?&RiI|HQ8&KFZ#usJ7g^QG|$#ntqlqmTxQR
z4{@L#Nyr}vwnjg|x0|Q9r>pS<fKk>xL0!J3t>FY*;y@4=MWEb4eIw|mU*)=BnvcT|
zKdVYNAe+W94TFHv9gL2G_b(Y4>?SEu22#l+yDSew_#tqj>tJFb6pk>K+csRFkRh{g
zqL6!%t;S)_%vyh$t+&##OWQw}nvM6@yhm{_B!e<gx<3X;2RlQZXp8h>rk_wO1mmXQ
zU1MjWc>bt|5q)m^&Zz-}_p%Y5yY_lqEm=y#|DE4IMGJZvqfLHre`BG1PoUlf_^iyu
z`KgqDJgt8PlPlFl9>KePBElBzL5wMDh~SY@m&pvx#4(prH4Sl?kZ#{@7FcToZ4pER
zothB*J+>7;=B3$~<^I~C35d6B%Pk!qsct6v$n3Y7m{r#q(&t?l&{<~-4$ksgyOt_k
z!le*K!sGqNe9xqQ7lC1l$q}XroW~8_9%Tfo>f|jTdtsCoVg>nxVD65>u}5h>OR=i%
zJ-li)OaV3Mar_CD98+fbff;=miy_7wnGP}oytiyY<u)1ZeVP5R*<8iX7^PAzHBZ!H
zD#P0JpqXK{hR%2R$A|Ed_{CEtWYY(6sSpjeR|8xI7kvYw9;53H3fX$fg-s5FjVIC{
zGzeEoQIH|Ih?f9Mo*naaqxWLxf6sBjj<Cfp7LGAuEHBl#pv_h=`JA902CR;b)%$lk
z<r@_boNYTrv92LsNk5m5<Nr1>PCP+RciDs8Q~O;WTFN58I{fDD>=&#;tAImH+NW9J
zx=uVSwKo?lAYnaNWBPXl3E`Z3dlc67T|r4zyu)OJP0!?~I{}q@5bB+;z46GE&=8Ls
z+PI{$J+AD3rc9~6p4oUe6A!LGEO*4|veEpO0@vurY<Q%C`tHPZSXpkBorC`OHz!sz
z_aTCoq>LYUf4#${0Mytzm!AT9oKm(z+gBi~hhyYS#UBtC+V;-oJtKe3KY3Ana_A{v
z)Q17?4(z)JkkrD_c=8Kh{v^><g5d{!zUFeVEi6l%U}xL3JIK-iO;KVsag3dg{n;ea
zDM91S;{3NkaA=}ZgJGiUzIj`2kv=P5W%18WmK2I(V1Ug;K2ha1qd#-x64HGlA*m_0
z&sY&b&s#<tI&BUXKz`rHVjG<hX~DmZ<<BU#uvbXjNL8PxxZSt?BsVF13JA=L4i1ee
z8(MiWTX;9*!Ex#v2s~hyM?W`8ES=-5_Pn?^uVyRh+uc-}Zl=&+PQ33mIq!lw7^M@J
z<}ClY>7cz#`bGj#!WBn}53}G8v)pghg^MwykhzN*^sUYQFBm*9pd9>eio4)S7DEK~
z5x(-~eq%i>PAP>;&X&J#y{N0h>xNllR#d<K(tFPCST)Gb8&L_Gd}k}LoB5^{9JOZ3
zZ?iXN_+UL9|39YQGOEh9?ZTx&kZ$RaZk0~y?(UZE?(S}o5GiTt?oOpaI;Eu>_Qmsl
zdyhTVe}-#$$92wg&SQ=}(cRYiimxdf)~-~&YYRZ+|9(^A>X1jt%j5bqb2a-tj-Oj$
z)bqF1QHej7@lU3M+ROeuFSsm@ScJKk54^yI9sZnLTay0U72~6=4y)($#pE@E6CM_J
zYv75~Q~I;Mf{tIi`|0O6eY36NC?TOVCS9o=C;I_>a~Mo&xw@KG2Mf)=cI`~o*d>K&
zdE?=sLN8f={yaAX9j1=3ONn}YJksoC!M_%1%v<bQYFK+nJPwlkp0((kHr*)8qZZb-
zG<se+|DFxb@rvJg*nmg>8-^$14`e3!&haBRTMO&l+DU%x!K|3+XPp$Wa_@|%3-#3p
zh>JS_Y=^PT9@~Z)iZT`kVRhiHVLLeFJJOc@lYKGY@!Q8rdiF-nE$f7}BRl0=|1Zzf
zo1sS6pIerH#7wFqlBw06C)$zx{xH)C42@_JSCE_&nS>nlQz{hR_%}Ft=z2<Ox0@F{
z-QQGU@=dy2H5m;`jl5MsuhkHE|5wvZamP(Mtbz^;9@t+{ld;qdmX^z5It@mWX(bcT
zUuBH$NNjqj_oJFb2^Jxv>~1GIJXLUrQ17RAy7~>(zcQY)yLJBQ1i?k<D?tM`_NAyj
zMm?U^2aJ{76g9m)Oj&{HEoA_}wn3QDPCCBWdu>B<7h`LG?C{?AGxr+f+wDN5T!kNq
zC9<*-B5v*bd6904WA;1XGb9hJ6pT-=nOg7tHI1*=s}0L{y*z0r@vpSy293g@OF*U#
zstV@t$oddzGPSD^A!LkY?hYM&luaxQ1`m25UJ=f%<4Tf*51Dh*8yE5$>`$*jp8U_T
ziFy($$DIZsoY&vOOpNlj8Uf+$rmp;6^MY?`WgGfxA4PEy{Y%fb6IhXG@^JM`1G+;+
zyZb~J<0HL{C%@drT78DxvT|b$Rk&kt3kz4Qk9f&<=zhJe<sejkyra&S=cC=GiL=-8
zm)vG^WnFx@(g32S8^flxX3h=9hfBp^v!4!JVqYY+Hmpn);}L{V9f>Db^yyI}5f?bh
zc_`O!_7b0tTPUu4F0S<6Kl3vopCaHS>ZE@#j~y%z9*8geR+SvaR>RT=&|4`ZdtHnM
zKX&%kfuzll;u1bT=&AQ>3g^oo;@_Nj?pE-B)?7(I4ozeBRBfDvYkmJjaZ@jMv;h@(
z`3ZQ|?vi&cPLbd{n`(Dfz<!wPoZqV-jYob_nTU|zSNry7%V4QN`HZCl3lQ&=E6?1q
zqtgb5j8#@2x?W*UxjfswuRKch9HUOZ46$V%Lo>ezHT7Q}Hwnsa^-oJgQWDMdSv2ll
zA66;|uUf>gpyVE0s)pS2jm~m^685#rQsi&pxWv5sqtdOau4_79)l%c5g2U>(#wa!Y
zBfh(&zVl0ld(Mz>d)78*TI?P}Ld!R-bd*9}b*k+)nrhIhUIkgu4g3<+e-~@bmsq95
zc?x(?i)EtQoPj^*T87i(9`p(E4(PdVzxvF<W%dpAt9{R#?uI`_`WtL3YgoDZXM+_P
zv4)l1PQ=u0WjZ#JBqOL$ugjij5`m6K70zDENDo(;3t)?ARh(`}-yZBY1180MCL`US
ze)s#-PF{awXM4iDfogCLe*bU`!J|64@a?Zo6RaHJEu_>^5D9xZUK1;c|6E&-8<IfQ
zPiW?yLC+1&PAJI0X;!wLa4W_Lw({XuLSfK`$!%-mC&ClE3nnXA*g|v3iP>AcJgmLo
z*!TB~)=V+U-TigiN2q+>4k?3}6&Vk|S0;YHok0(K&B_r&AJKZ+p>1yyoF(DQD?ef5
z<e(!KABI|p1UWlWe^dHFD3-vk{xe<Y<9XEO$qG+pnMjV!`=Uq;G^Z=499SLybwtW*
z?s@w^pY129PCaK;Vq4f9_IhbroNNy(`-xjGtf!xRk@vD=eXi<=kt-VfBy$ZFZw|@n
zQ;{1lkO|b1)^No-vM+Y4QY%-e#J3)@+t9n7A`Tl(+fjF70zsoG%@YEPIVlT?%BWX^
zDu(`#N{zv!%;Byww?x#cEXIo*4LX}J_Qrc_9fEt(ZDrwsU2qe%&Qxe~<Z2Y&C{;A5
zZWru(joRfhB&DG|Nx5>CC#V)V)=Qlq>mn5zwl3A29`l*3L?W}j><cLC|F-7vnDDU+
ziCla>u5s$9xXW^!+az<mKYb|ZVBiN6FCv=4=>0jE==SW9(*v!&6JK3-_(Bkx{&csC
z%P+uu%<55q5s6W$t8tzBZ~V>bzdwVc){no8?0r1i)&?C{Tb{P{KfsLwL4`712_xS;
z%9eh=DHM%JUuA@=L*ldCqU1VxW8g^W^$1zn3t*j!NJ{PcHj2ei0qrZPNs~&OSf+LG
z8u$D!u*5Y-%DVbG(5LvYCA8URWJx?eo{yit^YBkdggM^Zl*{2=oa3GO8!!^pFoqvL
z%GEfy?_H$REVYI6IJ1=Fd@wVtqfiD<u@5lgsv#~A|KAqb9pN9J(TWx}?>4Y5GG18m
z^b3!!>8icXsWnrH9mGITVnal#<}W}E_-8Vi@Zq(qmFx1Cu#M68N=};|Jf3d>b&#4c
z`TE)1F56&$ZMc1fO(gKDP$<vnrj^_|1eidPT?8UAq0Lf$Fl2?nKyVL1j!!22!LkvV
zeT&N;BeLakyfh0(e8A25TRRD3tv<%G`JKbzMi>eErmO>|+derZ=We=jbg~DCyWm?C
zH(B61caCPcZ#)H_NbkC&F<%u62x%<?MB3Xelief<Erd{&-YJ;;(iK`TId_y4McNZj
z5%<7_X9>JK*77;=y74Fbz}#Q%Qyff*s2tAGjke;P@4iwp4t6P|zA9U}oCEKJHX$wt
zv-4m1yO6+2rz(o*-L%-wQ~{{bG;svFKFj$H(I}l(SJS)A+9&HyGg$#x7E;8sFgFjw
zdmFOEbH!I;3*-QrX+YOKh*OBq@TFBtlvgf^GP<UXH2lbXV$$!V%TGSP|BJ6w>+p$d
z_ZaeAaE-QRbM&gTCbRJ~N&#R1{7D;W!7cigQYwN|$pO0RfBU&#%X@T=Gwl0$P;0VT
zrIc%TbWgx*F5;PD9wH{WCTxCT_gY@idlggkxD8tv9yUp&2gZqNSUy(0SbZ$lD%gIK
zB@RdwgMiHhQ2i8VR`2K6>zP)_-vLlN35|@=>tmTM(Z{xXfr!RX4QClKdGZAQ8)Wo!
z){%$9^R|K;#U_(q<B-C+r}P|i;qh1Ob_;Aba$@WeMT*TWmPZx~m+}RKs+J<*-ib#>
zs(2O6%ZxM~!F$k_He=hOU#rxsLI}AXBVaU{%Vg#vQ)&dprY{ihbVm<!YTWs8;0L~X
z9oMI+VmDVmn0*-R&GYw^tf@!88c2dh;ZPfQm?BwtM`0z=HrjOos%JS0Y4RMT`iXhg
zz_0!KJ$|Fxud$lz<o~6;<KcrUgoo_3RQFsON&EN)frTs}Z-;@2l1wsJgb=1D34^~9
zER=o%+`ix|V<pN5M1wQc-WTu%wk#)tjz`I*1{;EU=RuRb7>?EILo{h7P_3+Z6H(NC
z>GyKu=j#1<Huy&bVe=2zE5!^I7v1gny*%W~WU%$0FdmU0s69R0aBK*dN>a##19^zj
zQcPEld!LemJd~B*IwJ~mawjZQ7c>$^Um`5B;s>y=0b|z=U|grXZn-`_RM({(tRH|h
zGVY?$5zwmB4j7yYPe=S!CBie-k=~$4C;WKJw6?%IggZqF72M(TAjY`=>cqX~J(FpM
z(3}ecIaxg$lFB#1GBA?fFYc!~4g>h-;641NrwL$gR2!Bo>7!7D<aIYPLnKxs_>+-g
zcE_F6#5hKXp;u>4b@HCbJ{!>xe7dZERcEKbYOf;fEk1dfdR0UJtn*IRkvFK(v6jrg
zg=Qt%B)&jO#*<RE|14wP%&37YF*m^QBQI60x}`^8S5bq{oRycIg~Mztlc}(9;ZfP=
zXwC_r9v$lGvf`1;=qVqVTF-y)2uMuZUYRTe>z!|JqMDw~%+TM?T4_#?)OdI7rnKkD
z@vt!+&Re1WIKuhEnEC=N34927SOd#b^dqS`>=*B^5RyMa)mzQuU`1q`e|0@x8U~j7
z)ZR?(Ha2o|Vb+(tAe2O~1fcinYq!qP=pRAPC)|o2!ee;&T#5~`8Srgyx1&a4I5?*8
zV&l*7r4{ZF2kHwYpZto@67lZ^2DUv1?aksKtDV2!<ykb%cScfViT!}LD_thT1t;7K
zNLR(N7ON>ID;V;tYYCn(`A+ae1hsG&DCMK;tnfQUIE-2dR=AG<vy0iC2uRoHU%-Y=
zSJ2`G<a*RUDmB`!z-(c<XPp1POQOgyh_&z4Q-Wto7j9lKYy_`x>7(njLTPfSFK2w9
zU)h3HJg3hnZKNO_p+>Mz<TRAPHvW~;MdhUew~1gv?u*tsBi;@QzNVL1KKFUaM^N?!
zM7x15pe5GSOVYDXpwXEjkWs5nVgi7s?*M-CEs0EG?Jqb*L^4z+0oGalbpPrAQ3(b+
zgV{I*JaQSssR??Nt5-o!_Zbk$lydZ&2rF&t&?jMV8o^;)`%7Do#ZKcSIZe7RXkRh=
zF|?Dl#@FAxJ2i-C4B|-nl)pC%GKSij{Oo;zbh$mwu~sK)P~KF)-PHEV5yaGUJQ^xb
zj}MLa=*Lzc0_h<qtp1g>tpb4|4VZIF=6k{uQ#8j90o_WplUF>aZN{fc6NaL3ZbTLa
ztf+}plY7g}P9bgz)<g7F3+~TNmt-t12M1ri)${yKr}6yj_P=LC0D|Cxal$Nm2T~hZ
z5XHZWELP*t6tsB)wBpz90A<dRTiDd2kPYNAij#`cnX!iQ{CbavN4o;ss7%LSz3n?^
zJ_5;E18df6Pwuh-{X0`9Oq_aP6YVleajR0L-k+T)rAi)k?D{#vsq96ATD7s1LDkCC
zIDJ#}raUGA)vn*3tBwU+DcR+ybEBBOMI#%`$Ha_PAOn+(0}~2_gE8@THVq34R(-iR
z!C?yjD=Y@@Z@e*W|Mjjoyw_pFLVH8=m;6r`+u8)rsr4$Og53K8ZQt&u?!Z_I{!Xus
zBX21J3qNPfyXW7J?WiKQQs??ajowaw?52EQY{v5cjZyohxJ_nK=*HQ+hv%Eo9uPqP
ziKx-0LlHz_FUE>@_%yVaz(`hRuIJB&;3$E0EzfKC!%Y3VyXJkXawLRf--10@y)66y
zO?sn%bSA49b(H}|(R4@t$PSot-Ob|?QaeJNX2M5EiqAkDmc>7~S@TN+o6KPV2-yaK
zH1=N19SAsm(f{dxN-zo%A;xH}9T`(HKAXe=7@q-8K?=b=*XADGe+E`)H0+YpzZHtQ
z&h&FwKQ)w=`>WMVCY_~w2|985HH88k0}T;mnNqfFL%}5M?X_)Thhm@iCbGwi`5q8F
zN&jv5HUg5QqM?H!EXQ{L_GK3|YBZwPrmVp*J$?Yc`y}6uh-`6^8Qld^+M;1kEsO0k
z=N%A=$HrN3IG5|~A4LGmrHUCe|2BUG(9$BCQ-;;|6vu%hP@#2O>)k;y3{opo300#(
zk7VMn7)plB*L0|)1CNeEyY90_#v{PsjqOzMgsF-%5M$WW9#6D>oSr?^@!c^ch;lMK
zL|)_+u&S=ZO_lIyygry69h!(|^9U2-wa1Y`H^Ib3;!X|`y>n-RSb6xU=i#$PV_lYV
z;=UyNf61FS6kUyUou{fjZ&H6DpJ)%d{TlzY>T{z@@ZFdX29~m;RYPu~Dev?&6qLJH
z?lq+87eCR~=ETx`P@MSW#6G+xJuf}V_l|(<I0QF$vi3F_os@on0BPGpnYLM>*%!9H
zU|x5Q4#!SEzH#S}`Sd}t;ZOay%g*vznR|zp@{Ggs8LH;|#o#X4Lkb+bYyy2pNIwp>
zhvN$5#W^pKACjM-h$n|xeh9jGgDCPJ?+1`1(d<uSz(HLb4u$ZGz)AN#tD{M{Fq4ks
z@O!avp;d|X`+pm~Hfx1j1J15&$bLn757|e29c7oz)?O6DhASuj@U97Gaa2K+X$G)0
zdzUGtVnf0jIt_WoQQNSxwLPRRemwnHrwMK?{>!w4K(X@Dti9j-_k*qV^qvI!?P;1x
zNbe59DYa5jp>)~^*Z||U!v-D!46RWP7pG*4#)t?$USZ`qv08<esg)8Cyy85P0_(!W
zX#**U)7+xHpZ+uC2s)s5Ng$5I<}|76P(D2s{?~$rT(Lcxjs-)?DF`K<%JZuLt2COb
zhmz?Lz5jd~grm&fd1%x>03^d$4?!RAa-9*`>5Q`cCpP@KD+JKyk@OX8BiR>)+=B)%
zwkgZcg{FFuk$Mb=w5)%3^f}G|cc#@lIUs<01pFn3Fsh08#P+!~gs*YUx3zhz1?VV@
zo%wS4$GhZ@-+aGR$AUty@Z?t)q&Kh$NTRa{;sn0s1O_Ud_tKv#nS>i}!=omh4Gzz{
z95?(Sp=BO$)1aCH<@&t_cJ>ZNajah_$R#N5pn3bQK&jgt=X&9$w>u6bUp-Ja0&#@q
zUiT=c&F;4pea&Ekl@*Nn_0AFEG|XNHr;GH_F$Pr8n&;3WOxe2uriEB-jfD0f22u}>
zbI%iLgKUpSzuv{*f9tx}pk%lxMAW<JW+^KKKTPABmCux^`so~%`kku`Q{TRBbfj%~
zNnr$;@f}L{qSTuXycZZf;3C*gH;zlNGxxSKewaMXWY#`aQFrZZYH#zIkXdYF^Frr{
zgA&y3YxsXz0Ie-M4$Ue#m0F#NN7Z&8?vA%i&mUS_O44P&MpcYOR?Q8Q=vO6Y$6C^A
ze=1WWb%^7lv+dKNarXN~^`*nyhC*e+>)1{6`bmpX3D(J+FzN+f-G|z-aJ1Ww4=N)D
z;t3ZOfyR<e;Ou`dZLDC3Q{*8*jZ#nf_DWJ*5Ad$F7Q)FWMCudP3p{sQfGvEu9`F@d
z@u6KgaNo@O<CnC;ysUU1b4-(~gYOX3?5i3X{=%B#;IilJE<hM_CaTxHvej_5`J1UZ
z#hLwwQMWxvv{A?mgd`#n%g3u9*G<bg0?4hl61{q5a``xcv%u$Vy>=?TroJ-q{JryD
zN<FPhfi~oCoF}}ueNkx>PRVcXK8m3$tTWj{V1|Er>@j?;niSvUhV<Cw+WU$bt7Lmk
z#>}S93s9Lh$A@6E)8G=|2<=~0^55=K0uu`FANZ;Y#Qz1%evNd*hC=j_lS~EqW9E>C
zL%PARx*W_zSQ_|&Ks$Rz_YyIt4nx;Zh^ZDAtE?j+kgS&LkTU!Q6pdt<ki%e`hQ$G#
z-}XY>VCx;=z56kvT0_*g=^vepAcXU*s(MCBlL+<S5J>^TFiAqpy$=(d6m&WcoFQ9<
zjwHycBWK{if=K!KbIn0aAs<y)d~9Nh8M#J0wQXXOW^voO+tv3cQgdV??iqxHFQi7u
zj2YcKqM6$qCPQc-t`x<XPC;sX7XR!AM#=th_7xunTe6W~!{BeOmY;5aQp}Tm^*MVL
zbB>RI>`vCFU+y*m_EGG+EAW#3u*}yEkXul;H12-jtEjJnT6U)ovA>SMh3}w<xI8f0
z+Lh%E^WPf|1XVWdRN6{lb>rOP1kwbc3}t#B?$%P4%bJo`Y+H|`^Sa&2>tH(~UKf(p
z)p)MBryVKlp1^n`TDF4|pOGe*BmQkDB+(N3e63CIsn){Y){$PdoKl?FqkuubS;yD=
z8TamK8v+0L``Oy4KE0+Oqdr-97X@~i39}x(15*{tOUzo~vG2HPF@^R>?g#VHBGW~V
z!RGEOc!ct%PAL<)Sz~7c5Pp&M*hrT*dFmO`h`Z~3^%+0?Hoe(bm4f=$#|Iy_mI?)9
zuN|>Tsb>Cv@18R>L8ibqv}PtszHI{P9QS+=#byu~u>J#9EQF106Xba%L@2gnIQ(f1
zPe*4WA(60g%u0$>2Pm3vtzYY{zu?Rsh|Te`R}Qfi`ad;ri>{8`=f1t|L^)qwblL_Z
zN(yjshz+Y2KzEPQPU?H^7fyW_M^$DA;_}UZgk(_op!mjHMdR>YTUC}GT^YtAKw!v{
zc>_Xajr*C=qU#@<3c`Zso7MJDC;XP#r_hYwhd!yhr3IlpE3B06@-;-N<p;ab2pnP!
zts`Ro^JVpW_I4r}%M7L9+2qE%IXmHGXY5Gv(2a`8T4OyVmpl2p$*SK?k1o}LMxVe)
zE^pGd&r8<a6tWm7X`Oc}`<3(e_7v|t4f+RSv*@F8!+}-hxw!6hG^>DL<0;aP6B=_r
zZ?0bP$v`a(dE!qa5%HECr31)pbv5m|d3ty#uwY8$a@wUdHA<#1^owVP0zS=;-^M_p
zp*33^3$D!(m_~<nNZLTFOJM38kd0}8Hu&ub<VyoPJ;S{y#AEB~GLAx_-{}C|FiSR&
z>?Gt<XLU|kp}?}vdv4lD4t(T2?XOS5sC+XF(3@hPemi1Qul;M6A^)vlsx%qAltO`j
zHK1h#V68BtPE0HItNRP6^Av5lk!_MBHG~C9%e=;KFv=2xP3Cjk++)*^C?2_h7Gn}d
z<jMm1#wIe4iFrzo?m&-tN(3Pi4S568tjWXoCquBb1%ekt<X$~Z$Upp>zP7Due}oLY
zC@<I$FwV}0k5gwmCq@(TuuuL)OL;SVV(ww~m-&DG2ye7?f)IOtwX9rN=mD4rwCIcB
z0ewU=h+ROr=SVX|`#bf-M=CI|RGjUa>4Te8QH@q=Ddnb_ZsLnUb<+H{dja;~aF_DZ
ziXnV5i(@kUvMD|j*t$h!-FTYLLmHzst9zoatajCA4@5-xKy9%i9Ks;;;X~GQ1OuA-
z4@KIS(u4f-$KvN?64YDa7NRw+4v#MZ40kkkH$$M&xB9Yr$Q|sym)aul=qfi658w&r
zL>N@n%4$Ww#ryWPqe%knS~5F~PHq@p*N2V&IX~<{N<&Z%PV?;)+BVaGB3cig0NHo(
z22nvs3|M0<`fA^|tPF(9tyJDj)7Rg7#fj8uoR|s&e$u1|_tF=8Q-$$$w!ZbNOyhN1
zH$VRyj$1gaOx`YMMUQLe*UEc8LlMZv5qTBuVv+Ug*}*dbr*AgT%P&a*d>}MjL>fJ2
zHJ+)uc*l9ZYM!t2DXDo28;Z+!;UbJ)8LfQ&0Sm6ZV(mnB`?&EWzR<5h9Vu#<C@GPo
zW-17UdnnHxu$pBS?|?9YJz!OiU(N5ooJ?}w{0LQ`luV0grAhc61tGy5<6yQdEn!E3
z1eIav<eVIj)XJnyjsT0jYQfQo#I$g1!=Dwp{N;C1lWieG{4I-?zxwz0(y77UaV>J6
zp|9Ox93Yt`C$j5#)N>l*Ip3743w;TRj<!y_!q`2!6#f6Hl)QkHL}g|6b~8vkkAWiI
z$yq(s>flhV5}gxN5{)^2b|)*+fldq^`S7Z6KQI&nO=X_ynPJoD+gpw}nFG>6`;%8N
z2EWg}hR%=|EG)ydLnjh~|LsPRb9W%DYfD~35n-<H{SRq!Q|-FRjB24vsHDJ=l+wW0
zzy=Jf##?DKuIn{FKPUaRQKnYPPb(@L3d_#uL%&kduG9UE5lIZAdOv0yw*p{smM8&3
z6PbHY3o_ErBx-pk+fO4iPiXJ<=bP5WG(rvVHn2>l+&{CxXruZ5yo?}?X%PO7XT%tF
z-o(Aj|1nGeq^7;xgxo_^WW3x?mYW41zh=clQG}8U-c!8v1e;vf_H6HM{qJ^ZL+iqR
z8wJUr#W@OzQ2EpHD8kF*n_AmNbOt;sF@t%wOKQxIs5C7jx7JhCHOcwah(d+)pLcVs
z_PxkzXdJ$yWh#{&I$`NOkA}cW%x;@^TKjKofr1#4rRrmn3%`|daJt%0C6f)DvQHpX
z@l#a&T;uW%(PFhWz<RzhU`)SGB@n@!L37dEnHyVWq<-b(sv^46r-fHslip-2rJmMH
zmCvizy0T?@_E(*kkLLZI)*D0?rEt+0fTkQR@B7X>a0_4sWL8d;AIuBGo?rca+ip>v
zyXZq-VBE#8f!V+O!5cjK@paYoIbY3;PYAq#C)z_!-`5TXybK0pBvTTf%LH2C{Er?$
zlgkRE%wdRU-ZU-z82k>oAwDIS-z1apUPm9_+t1wNqA%kOZfD-v7wbTp{yhY4tjCLK
zKa+8i!-83VDkY0D#PeST@H=e=0_gSWZiCrwL(+B~UnrTkb)vU$LDf22RqIL1XksRi
z4m<+<m#@KA$-LFKUpRP_R)K#LZ-34uZ!G;R$D>t<m84%y!Xb&elaczXedbrcZbrv}
zbT~0R=NtZ;<tR3%Ikil`>%7tTnq!Owd_$h=-JCwlO%7Ct!kR>Pq#|s)-<z~LHY4fJ
zDW{r^Na?+S^K`}VTcbwJ12~yE_L;A|GETTA9J<^vxhUuMt2c?H+eTf4iK)K`V})KI
z0c(ZZC0hZX{bz?a%%XL=4xim)MYwmeMY^@H)Ns!tgF;+$n4xMclb`t<CfEP@{ZAqJ
ziFOO3c4G(j9xuCCNFsht`+|b-T6b_2u-`3TF7T2M`16qH1LUHmT!^F|oJ!P&p|WYj
z{s>ltz?Qx-8w7&!TTsP{`}ecfrDsz|-}JCt9_ErDZUK~TI^s;J>c>iqk|VyNxgmzv
zPTBnVp8e{ociFD8)W1CjBHD{N2eHaIZ>SIT>ffb8*P0ZRB~1!e#A`mO{7O#soDx@g
zU{}D<);dSp5?MVE>$%TNRQ!HdoPPrNb4*}UCHUq`0MlDIDLiQl-wjW*^A0V1#AHtV
zr+NSZ;RJ#iG*LA?P-Gh$6glVu66>MgC&EbWj$bJU=;aj*3Bp=+pYM!9V_(D8k)6WK
z^2clvqK2u|)sKd;{qUz=d(_qGyxLOw4~Q1bzrGrA5KFBfjo-Ygf8t4%Twk8tZ8&oC
z8C%%e_j})|&O&iZM+*c2z%0-m-%%+s@A|gT;50^iI}v>Rf_9@t?g6r4?-h`OQ)B7S
zVM}GuN=8eJinR^!>7;%g=Bstz5ro3(%B``NkR~|Xf;16&33k($XK}x&`AwCzN~KGk
z0E;w?W_YKTw%u6OEY&(sk{i)ZiFQ@Dk=fR(Zsen-yjE^FY&z52$qrvY!#dLH)r%NL
zPETX0q>O9ZrIDg~i{f6?TG;8*S&_fvuoJnml*b<8XLNBfsC^$kNY2Wjg8t!kMH=9<
zw3UfhNz5FluDK6gdTDS9zs)ICgs9>{e&50Q@_2D9T|5^9{r(7c$SWBp<?wKCpcB>*
zgMj(EDenQZS+CJ)c76Gia1aH5_)Hw@;(q5TW`(wpMSrU7uwwun1lIkt`+nSgX>QC1
zI*w@2ONMD$*YxdYd@D!8+plqajsF^-0u3(0hvc=*&i}N+gP7&_Sg&39EDk%w<Y0^A
zdiS%^^;z`+n2SB+-%Vk5&E^+FdFoYK{iSL2c5M}O3Gg5UZ_WbT@uSeqq-QfrI(e(o
zfq**?TUwV@^SG7rBhRT^v&1~MF7*vVS46H>$Ta&SOT`^-u%u>F2iTR1uIW~_Uu*pX
ztDC`C&E9<9pOb2uCAC#SBxSe!>koo05B`Z~PdCz?wEF`qdufDY-=RaGTz_2-aooa5
z$_2R-O5=!2u!CNdXLbX0+?s4l4LG#H1@ia}4~5!)0a^jIPaw_zdsSGbq<I%-YM};i
z7z&|>0H+V3R5f*LON96YU)jw@?8s?wgE5LHK2JjhDOY}$-v`A=p>&FwMwcoC0S45d
zp}lUC)nZL#uq@I5D*$gJf{l`5Jn&$%vJ6)E5PBbr?N_IY@|1+?0V3&A&W{ofO4WPh
z?GLlwF}phl`oxWw9KHySZ-CGh_spq={D&<diTb4Qg+P@!ms;O^Z8C8Mez`IJ4DR&6
zr1*4tbj6%#*Wi09R-iO<`;5K%;Ty%mbd@bQ3*y2ol|ALJm$Q*>d8z+4QxjNmqWb$K
z1-wszhXM*mXVz0Ik7<BGcOZ6yb9mX#%>+|BedXeX8+P_R^>DO9K}&gE8>&xhV}BW7
z1CER&j(NUMoEzDp4{E7Dgu6PNom6e4lO^wK-|AeXnE!PC_hU=OM$PEL99Jc(UvqJG
z)fyiMdNYfuGOk}YSjI5Q?snVSj<%!0Y6{x@)L%t3+u+69Dq;)1IDcqft0hkAG0so@
z3oSl&)`U&W5tlMp+I9uGZTT8D8xGW6nVq$~R4$(5f!?;U^Z>-Xmfsgg!Gj$qtN+-&
ziTZt@`YP<dsvPCEJL*3)L?v$C4&>fn=)Ku_(=6UTEHWHvsmk;1!T8X5Brd3&kOs-V
zLT<3s)w}Nj-O<qcv4?Pv^u&f=?u`e<m0R2M1j|>a7fn19n>luS6M>cMW9?RrD>@l2
zX=TFgee<slVk0bX1W-4gSj6hGv_g;+x{z|?Rwh9h-2GVts!^}o$p`Sq|9GNtmi^f@
z=zO{!#Wj!1CBb~XK7oZH<Pf?%_nXAg86BY>aq(DBgoBV_Aoa<<$bj2wO$_8aCI*u`
zS2=y2)(9e;YlI2z44$e=+i`9jn?Tc_3k@5@n;6yU*k0Mj67EcVF7jA9AQRFJg!`|(
zrTRxcGO1I|CDHv=?pINa>ZP%O(rJhXytg&yit}I0-Y{wIW3v;L7Zr>u88yiqz>V5_
zgUsXRb-(>`{z+>`d8H6T6*#LFFjVEIvg+QPjRoftFl?h&N2gV?C5!NbyQafVMY67H
za=Ot@^Aws!3~j`d@54`DdET>N+9A%(bb8}k?SqN;PEmyj5?8}AwdDo<Z6Yhq`0TdP
zX=5$N_FpKp!+q?xV!A8T_r~^C+}4mur<xu~tA3`x%^1(7$JVZ=3m0u`^Q}b53K|zG
zq~m5Di>3F}pWRI<pHSS)$#FVcG%7@sape~%799wpI=0>;xE_-XeY>W;;DcjA0@R%^
z9niM>1{QXH61{)9rbZ6A>TxX*U$7d8`Sm6`w$6qk#5;YT#zqwBR-K3sziN2#@)DbV
zL(S7bYmkXfdlT4_US~%!vNUr67Ukn-unc27W<ZZ|om}{7EW6JmlOvDq3jQ6jAMn%#
zzt*;GYBWZv!yQDy8uE^=n^Nh04V(!1`9q^DFaciv#W*+5C?@=q*@!3?k`#5-q2Z?9
zToHO{vI1F&ey5Q_v0O-=JV||J@kX{A|NNyQsU?<<=OVOFd|@Y=LzQ$|KCQ^MYLQ7c
zsxj`WyxP4-VUp!0@!h#hHUOJ>H1Hh4Uh?=J5OduE+>-V2+dFzV1kU10@2%Bbx$^TK
z7R*WGrv(2&S_Xp9UND8@;*QFchcPFS*!}mAEV_Z*w#m>F)8~I7>_3Fw0R0FLDOYCT
zL~r^I(g#9>8r!rbHbafeMVZ#pK~SYv!nW;943H1CN#AI*&iW()TcPmGW=;uJp-dLL
z?9A1VlGL=BDt-N#%+!)Xgs3Vc{Cu3D{8?pO21nqNA5LdA-cFQeO3=1zS9MNMa%Q6~
zZMHbe0UTg_KF_-TsMYj#Vb8}Y^kfrCx_FH2%OZ+a9gmU+L&Y@Nxf{?plzQl?C~v(~
zC#eE~V>6jwl=Ro<Kuc4_o!WXDdqy^_q^RmIRLKV3Nz^b?kleo=;O)eA5n!<VCfe29
z77D7^qFUB4u@6STAxJc0KHuc`Ibv8<<A3rQ4K~6@+vO&e<XQ$CrHZvhH%Gu}=Nibi
z6zv{Zj>2J-lB{1@;C~;Hb}lj-^D}1Wq<N26v)Uk_M7t?@=I^&ZFVv?`Zm}ghqKb#x
z`L@C$6)%h4Bc^!S@Zv>AC(Z+Q54!Vm_g|;w*egc;m|T7j1Z|I*206_P&NeHCasOvQ
z861w<xiica-=djkZXsFCaQ=nsc<be}9UKQYypp|^{K<(Ul$p9tKI_*rlZj1av?;yH
z7;ReK%1lKT7`jlIN&$OW3K?n$HofZZROp8Vo4!JvU<5gUNyEK~lTK-(J}~Xy9yp*<
z)APMErO$nc#Z_4VwduoPm&dpS{7bY4y8Q!v;MgE9)JS#H;=6gee1?qw!7IGvP{+e4
zE)FS$-|=5Q9Lwg)=qY&0B)^u|Z69WFm<+3pmquwpM#FKyMdZ+IqDa$U>>U@M|CWGC
zp;yHk=Kg$t(jp0{y1m;)+Ot#aZ)<jLvBmbEYtP5$?WD`*1`jn{d_S6-?0O4JvH7on
zUNq8!5fvcpQCo5ff}Rb^()iTmdg$%+%i)Fi$kQUvj~Jg4NiTg4LUb}%031iL7rv*(
zUn>~%{oh3o7i}Bu=aMM(*F{nJsuWcUh)gbeZ-Tu$?k9iJ4`yTW04*}IiAumO1Clhe
zf7^Y{k)Yb5W=4}=StX8r=;8~oOFevL+D7+$|A~aa$4dwGoUNs@{peHp2CRAMWh-_@
zwJ2fmC?Dd9VP?uZUu_2YO!?7udycAQVPbX0N*zGs@&E&oXg#S784|{8&r|{M)HZY#
zR?b-`B3SGJU{O5qjxQA?MHqo3wCD=a5GZ#y0T#x5oMV}x-h!!+@3Gu;xWn*Je_rWr
z2af70PGh372Xn~p(qV)<4uH|9#HFcVH$lP(0Sb*1jZ_$ix~Zi?<L74;f=N)SCCU8T
zi%eh&H}jKK>fZ{{Sb4pJz*!i{gM*Q&Z=HqYC7~6(z#KI7Lz!Bd|M>HOib^S(1-XVQ
zvVxF-`1(_x?F~#IQ+iONWx$e=0$Jk4|GXRi;(0J=_Qt{gG|FKj6)S6<w~{nW(Ix)H
z2WmCi+C&vtt_W$cFvfLXO*_x7&>FD78|CyuO@PoIvn3HLt>{7%`)4zr`)EZr``==(
zz)4ZvUz$tN4;fZJ@nX*<Q!2>!II=c2d(8DAJMD@nwQs$`J(km+3g%v$#r`Q`=@a_V
z3jS>mcYgMt5)n+MA{B+~4*ZBMf`yHgA825wQH|S$61gC%sF+-e1uH5dl1#1yv)?XU
z^Kv&~8auwr)J*9%47<DIw79bP!_M5?eD!hF%XDU$3(4vqC2Tppx0J80z4zxODfJmv
z140o9OXCc4{dQrlW<y!+h#2D}ZA!atlbn)q8-bs1Do*<ME>hes{SR{ll>Sl|p~ipK
zyiTgMtDv0@eWvpBYm-$y<h0AUshRj`0wR1|2BtR6XFnFXwNXS+;gj8P@Oj;<E71^_
zyN7t@XzU2zeYpfL!2MI+k(@DyTDpg~YQEL~Zed_`q>FmBy`5^I3ePk0!ihJAK`vEP
z^JaFPg`)AT%9qa@-ib}jl8G29M8XT?hH}G?Fq$oCBPmzI;&EXl%2JKw#mP<r0w2P9
zyw5qWqD6*Z)Niq)he!&om)3Kbi6%chOq7g*smevbs3Mp*)d%6Uh3@+&sDA_Z>DSE<
z%%t4cqWoajP+gzP4&oK!jbvyF@hy4D6a3PoM(tmwXy?bd5wEtw5@4d4SaOMZT+3y0
zcr_;kUc587T`cp;<?dN}1y-f~lWG2H*AQ0!_d#HWzdg~cA>>TpGVr>McyyLOZ^%G?
zR~O0Ih^2`F!QE3sOiD??z&Z(m<PMO9dEnV39`p@jg|_yv<+HU)o~CB2t4+nmz{TcP
zRDBB&_^mdO3|KFO;NHT!$F;3`U&v;F!G9SD(SfE&f5BZp-vhfdry}5nGc*H8BdKlV
zLPgmcs0B=4Dqp=g2{d%uyc@Kcj+$iCOzz>=hG08=1!u2zKK4bc&H+-0e3=EJDtb5J
z_w6NiFfmZ=rt??@;}b|>to+Gvne`Ze8`L%xRyd)=SnXWI3bTuGs|QHzFjnc-dcp{5
z_?U7$#~&;1B=JsfK@noTeU#M@0-v|~b?1)%j-ac3SM*cz4*z6qjSYTt9P}7^HO)XH
zcWcLVbDT7nvN3y9au)HEu4#@TgzrWv@<sX=c&I5~_TX06<9C&2<}@T9PG1R%rr`O3
zFGaxMxQGK5ScBsx{kQ}>&AvB7L#;W9RxPM#J4Uj;x*Zby8J9nGoE9N8>s>-7HtRD#
zY=?IK8NTwPqjyXxE*?^A4owN-i5+Tu^26O(m5n{(qk}ChxC|$tw0qA-6Lv$CvSNi=
zVi%xPtw2x@E47DNKHEPqIC63Yza1~7{pc4$83#4bibF}{R3NjGPdhSn)c?4rJ~a9L
zIh7Z78t$ZUM1S_XL5BB~xu?})hj&%96|c73sj?h*oJBI7W$m|O3&EA`L*G>7X&<ZU
zH?n&Xjz8(=pQhCGY*=(-AI;fwmEWUsk$y{`JRdu8;Wx$Z3Z3T9ChHAdp^Z{|ChvUt
z7xs0&D8`zv?fo{djmT)gmkkIfQR!6Z*UvY{_17~8AlJ!pyB?Kbv&**B4PflzEyL>s
zfBLwcq(ea+j4=AflY4{SX7Q$Bg=ptaN!eQ!0TK_lvW2`1Mlb~Xqsf*+w33oPAxW%4
z4paU?Q*rHb&6c-E;gpPv{@QiFqRXcVMuS2xPSowIo`^dQkN$|?j!Mbdjdp8=$)aYX
zKqX*8!llw?cg0c|mdvr^My#XW+Fn+hPL9T=cO4vs!!zH0929oCj^_S7ScACqp#s4~
zF}_2m!CKQMBZi-lsY-Ag333WaWJvEPLRgBFFvQ*1{jb(o4qkVVSiNi&%LHaBwSU#Y
zrZVNcMud>|)M7a07?Dadia5yNnl4)$?MT<A75MdFk`u(Ig`)A_CwKy~!)+bnq~pQX
z104Q8%YQM}LfIxq(OB1G^3-T3#ovMmatF?*Z4h%nYOx>Ih-G(WL4m004Y2_y=SN##
z4gd4E)&4iYk-8s3Tq)+TA)*1Qnd^7a^E!Y_R&VeaxLDUM$2Iz)FO~B}-ehwS*!nj?
zKqSDGd3_0igc5Oj+vCHuV?>Lb@BOjw*Cxym4sAZ6V6cX4uZg!D^H3_2ZYks2O>)Ls
zlN=J;c?bh!5`LZ6LZ>mav~SKu)^+BPUpCdacr2bD?=t=T2V``6boo!J(X7`eqGeAR
zwHrjr%k5|Q82R(E4_2DQZ~J({4w+X~AsXA(#At&3zW_m*%QA(H(3?y1T;lL9Yy|q;
zACH;rv>rv~IShiocu>*JC;xc)fwyg5vx0dNArGn23t~Bx=cRmZvM7!pg@w5m=O+KO
zS%KoaM9)|5<;yv3yM2$hskm<i8JA7dAK7?NexJO;Pptu@a|N<7GzuefXEv+m&+`(6
zZmAyKjvWm{nQLXr_#MYc3jBlA4+7B!Vr>f4T`%pTVVK2QyD$jCs7aFIl`g2tAlE7$
z(wEk}uY^45B?BG_<k;28iTXCxRKw05!jUA5G~%dKl6M2|oxOGD-i^8z(0+sPqN^cX
z_7yo#<F)PHN+K2*3a}Yjayxg^r=D2vT)4$sY;f)*T*;&iqx9J0g7!FiIxO+_y^?qQ
z-9$C@*!?oR+^RNGIlE?MKqlDF%||bJVOVV8PqL`@&q#RjyMK7Hm&qC{tqy<ZHhrcz
z2I{SQN<coem`mbMKO5po>KJ%BTr<Q+&WGj30^7nzC6`gnbImRD!l%mv$0Pe$|9zXh
z9l?)|{w~D9A@cusp9XM3*uS~?-d(=`b0abh*57>q+dR*Ej5q#{YA-Zj)XbpEu}Ck0
zbINv`5~6gW+-&-tW=j@H@`GhPzT<T0HJgc@xd)wLO?al0APq7lYq6tj*}bl+-aw2^
z(dioNHm=8Yv)`phwI8dT8|;#_%19$WjNb9mn;L5iT+Lf$RY|EfT}^i1xuh9&S0#)P
zs?wI?Hk&R(T5wm=eRV-h#oZ8N!eNl3Z2DConS`|n#UPB1ROWv)PdZqrcXAVHrL2y*
zS}2X1je6pu=-!yeN1_eM+MotUvoQJ^3`6<I+Y3dKCv~mCp!t9IpGgdKbj9kO{G4Om
z4@UU>C}^A}T;0juIjS+jE~P)<)z@3Zh`oD|jkdUv5AkaiG-@7lFrVrz816$s%B0Y#
z)-`(=GZESoXKHJ#_Qqfa*K^k~1M{@gF+1k55QLZirp6dQK*TO(5|NS_*W@;Z4#6i~
zK!oIh6R=d!J<T){1Di#Sqe-9vPW7wL77&){`k^@JJ<QpU!TY`*0zN*w)fazwvS6!u
zB8N}A<N4m|c$1#O!-Tz(HcQrk9a`hk4414ruR<g6yI)32aI1F?x+$Kz**3|K8aCw{
zL6>j2Tbszg7;RUh31EBB@jUBA&}oeL;+~BW6v_(!?g-}X*;<Y|z4{Sf>$o9xGpY@G
zbm3CzAm4tE7<C`JfG>|RSZwMtEEnlrXo_wE5Ae%SeenMKxRB$G_6UjBd!;YeLdbE9
zI?sw1xA+Spxx@3y@^v`<c)P5DY@oAAfOxwbf~)uIu`j<o%bmYz*aJL0dd)O(Vyw$e
zjW$wSz)=KY0P_BmlZ`Yr34FSpu?@7<V#YTEW23w_yghr4Q9OKWCmID`Yn%%tXJ!Si
z1cdJ=8<f<;Y5EmWaw#~tjj7(E=Mvv~_}6vz7ZM${3N4j1P^8jsD{BrG7#5iabLIQZ
z3LjM$n>{=G`UYsCT+ftFBS&iX=d;hBg#9Fd(e;Om8e_BV0))rTin+<Y*CEzF<|-9+
z(#`^7$(qLt<f(s42P2s)#F`v-S#`c8m18Y0Zi}5e)viJhlLbVGlWxn{<(*w0<(AJ@
z_8_3^r8%5rC{Rx;Qb_0zUA9C@>zT^}ya>$i%g?aN6T~fD82xx#jHpRPkVSZjODCD0
zZW-<_YjWQ^$h+ql4r_&kkT^@YN>#x%4t*_%Gp({t4Ui>CWKI&>w{aorey$}9cSK=n
zU~SO)-*FE7a^>zGLS8iU7Zil+XgCpIF=tUx5>rl#hA-{aABm0R%p>0Gy_J_qO(~Dv
z1H0KP4eN-ry*cz}iDtA-V_ukj;_5Z-{;J}xH-xc#)c5G8m1=H?RD!z`*<*b^4rRMl
z6VLH8lwIdd622I?QF1!-Q7V>0zk6;)aTM!)Vl%r`KL01o2*~ww#Tj#}(M%}mZkUT7
z*ZJ<TiGvsRHi#c+Anu`P|NcHLQ~80RiyRra<ZilEt}D-XrNW4be3Qw7m}ymJ!1elx
zHzs5m5OOSsYA_fLKyd7Wx*;FZ$#MY;9P0`DzM7=Me@=N43FPO%CuwQr>c0m;f#1<8
z?FAe@g>I*-OP24tpvr9xP2VCH0KAQ5nf5ED8qVbrBv-kAaJ{R3^&S@Y>VwhS=dB8g
z;Q%F9f(9XEXxB*SusAhJuYfXt6jK+C1}Y)YLG;!`$sXRC8XkNmzeVbTGEC#)rNGa0
zzFBsP!$E@-d&}_>I6Ih(Y=DOOyoih=L=#H-mMs5=bahMUyOW8qJ!nI83>I3G6Z0FW
z)#NhzjXQB&+olaTp(qSoD*tMTGXo)1LlmZO<5YYOvaP&}e@E*^-e9eT?hwMer-JV_
ziqC<MFa*2?r7P~FEGTdjtWRmD5P8-(@|R9I0$p+tv;O1Z@cvL6B>pTgrw~W@TaZN#
zB>(V@In-!}qa1QQ<L6V<^?qzkp|*?UyvDAHIcTO2$%A>TM-bBq?MZPwAh9;3h&DKU
zGVNxyM*>yWI(*s}!*(*SV~BXvE*6@Ssg0L{tO#!V+>vr)w2_xWV4=z#H)CO>YXcCF
zQdimGBXKS9ydjc%FKWPag#$nKkgG=Mb=tQBv868dAPCGF1%MH94!G@i_?RQ!kcG*D
z1mf25dE3+Sn$IX=MX@K2%X{5ZqQ>qzs~WnD5C&_L&!4qk+%gEql|OfQnnvn8y6Y`g
z8-S=ly+p$<5v3m|(snf=TKP^riAU%*BO$Z$sRn~QjlnfMyk4@M?USnZ>1c5eeLVx?
zVTYXS-ouJnhNf%f%oiQuQojW;y^N1i=V>Elj8iu04dR*yJfv3UcC#m@#U%H#BcEuP
ztU90NzkcVBHJpW&-sZ*ZeW-Jnd~A?il74MGk3rSV`US^a9P467!(3>EJG<UpcRqEa
z;?1xt6p|dZh7&g%-vnbiej|~nVy&npm95u@D;q!x$qe_Mu3}U|Cm@{x`sN%fd|H&j
z)2H70(Id<k#ZrniY_oKv?pQz~ha9mo-8@ugiCrHf^1%dGz5}aVjeXgXl$+B(Kh=w$
zfT+Hv{&jR4LMCjwKPdZzHr{?BH+qgIWi$3CKjGSri}FsI#%jpJcspYgloa#uNWZLU
z4=;GG!B;RjqAjV^eNkj2I1_{U7*cQ67Dp;D_=)yJq8%iA)2;@4H>)xO30M4NG<%hu
zkn55DQbs9Zq`dDaFfm0NQQuT}pT>g8Y!?%&Pr~aiL%V~kG{WPcRBFO{XT$0^0WGEW
zZJSThSd}Plu-?2m2wJRTlpmFibk0q=Napv*A9Y=ql#?Lui-b4t5KVpUHgn3ww_}bZ
z9fKJr<>qn>{@8$99?ce|`ZV|_<h2LOrSY)iy2zG4>`C?)`I!cBC;wrV)BxZ@ZLGF6
z(Y_|$gWxs0hz=pht2cmx#_@fuF6XdbU^zxitLroW9zy{E+x2FEx&2R}YMwVVqpM5~
zA#=-La^Tu?a`s5DUojogXt2&W(bqAK8PT}snJ65}RlEjKsG>VW60l@C%bO%`U&7Oa
zj^%o~oPKS&AH(^TLz~u6omZ$2u{vxE#S-MgqMTcvYIok9NU2uqt_}eY<^JjBi<2DM
z4pe%NcPFGphNpd;n=3f%w{*1a1w-zNq({CVRiG_i!nJ&on{)4v!kwSPgw$OF8gPr<
zc{w5$O<sW3ZZp}}?~~_pMr5D(4&5$Ygf$HNuRLC!pLRf`=9e+$6TBda4+`@6?XA1b
zPi&TVoe`MSmU#xfp~z29G0)<B?&hSc-wnF>Rt*oNpKZU9?@LVbvExB@vkpM)LLZ5F
zjf+8A+~Fv~<zcL<;{-QNz!lsBmv_NXmfHiUu=utA6Ymf%rAxjXe%)~nZL4mh>dkjK
z26p7LO4xid;n2SZAAHj}buNfda1%(Fdhdi*&wEs3#|Is|97%7S{9|&W>D<vsemPI(
z@R<VN;8dOvH1dgD6hX8w!Y&lLaVh>q`&P<LW)sM~O&Rl!yjy(;IFD<n9`8I&@H4Yc
zEwvdP(O;`de(88t`N}I<<M^!R!RPyR?8&#XT&q(tb%cku)c5%V^5vBs^Amm9D7}I!
ze8QUkYF@7qh7lItDVDzXRkLobSA8^bwN@qGnxrLvqI@Y91C#0mBjd}MirP`4<WHsP
zqSG76(N?R`8qj>5@UlR5{d-mRq5djkz)ek!pqc99KUPJ^gHl}u5jqt*(kp58c-}Ys
zd07!YA~u|jUilraH&#8L`EXRa+KYrO@4la|xGR{gKD9np8FL}4>G1i~Lh;z*LWdW~
zAN}!`$o%oK02%)2lSR->TXmc3c&pKrnWWT!I8#EJ$xw{2=~0>S&TwDC`FebqN#8WZ
zb~+7A0qjXg&=q?ef<qpN0`z!7g>d}ec^rSItI1&tM)arS&dB~x%J2IhFgvAE=(JAS
zKrl_^@N8_`d@A|}r*0^5vjX&W4(-MTo5Q7`rKm7wE`^lW&>MRfFEE_mo1#;pTxji5
zgueYUNiY6SFYC5-j!ru4{e{$X>6zodT;p@^GdpJ@_(}_AV>*+u%#MuHj5eP^&i7m8
zlrdS}<kQ6`soAZuG@44PSG-=M6GV20H2q1~=t@Pk>h}J7q0#h9;XVY{W_h&}+|mk}
zpPRAbX&nu&`4nnID?=Gj54~}^a?fecPslMv<id;5-7OKMubLf>Ee`DB{-+Q8?t|=5
z$4?OzQ+~41Wq_aB1W$p&ej+bsbti}@ZVi1FH7iFK<NWG#hUIE@a~z^>7`Us0+37qu
zf5BIK_$6qRZ3Qy{1P`gdc{hzl$LehnF$2@@B^aFzIBEAS^XsK#=n`Z@q&yTsS}vg?
zCXG)f7YY2RAtN8$vSsxE@nLSR-d*hkwur};b5gchqvi9TI?*T`PvK8rO`9&*cA9(J
z_A)kdIZ%~Re9(mmqpm?S<-wQT91dAmonB*xY{N5*QA>^XGdahIp#BkH+xklvL&(L(
zQ))Ik1Oq>HARVo^%+beX`vqbU5@PF}EU*{19U`n(`$p1#W;X0*`T+B5@{(kk0iI$S
zCDzt}W`6Y)8i#cHJ1}9v#01W9Gnsy&7c?xtZ;rTuQeP2+h`@k2MIaTZGeyt5c=`at
zi5fuFC9wJKAHvPuiNhM&v7z-ioyQ=}sY~5n?(F^I<?FBfyEh;N3D?pz4xE5QgT4y?
zz{e4RkT>oQK+Xj?f}*TwhY4@`wuD9A&8)#&n{p>;LUTP{!yXtr=XOc!Wy^TxyhHE=
zvl^xPK`yTCe_u{=`;gAyZWsc0vZJ1ruk4qR-zn>zyPC-&H_4H7PHA~_cyhq(ePzdG
zZZG@e!#Y;;er158YFO&?pP!a-Op=;<zY9<t`i%-a_oiwrcl4E7q!V@6zWr#JbF|+>
z-%u}?68bS$Sq_)~p7xLdc62+bB~3lvCkw>_$@RdAT7IHK+fqkSYbm;9kD2;7skcU6
zZTN2T9wKzL$*SWIdF$NKJdSbOO`t9`9)kjR?#y53$fLWPy!gQbi!76!2W{ADQZ=|V
zpE|u~Z!6_UhRC^b`teg<+2@=VNsJqF#bL2Cql>RTHIJh)j?Z~>=citSCcn))A4bdk
z<oLMmC(sq29e1L4uijsarU>eS(|EWUO5S=;y7e%4GJ<C#1)+hgZm9}wMIAiC-&ek^
z;K&>tZ*oFgP`~JIzxx%aT#&pw-?`#ti6=LxzWf$SvQ!~>LpB8(MC#xBVinDoYhtpb
zxUCYLXX+!5V@_mH2f68yaxixa=8W)YZX!Mm9QJy}&Yvy(q=fC_<{#W3r1uUjRiFG`
zb+IkUHK$zlVUpnouY)(mpT-rc_fkIJ60wrA?3PHG+nZF0J>4U;Fe!)sDw>zs>-#3B
zWQF@g11yE^I}Y1&A<K*ko^{?iDLtrL;)o|iJFfDd@$#kvLl8Of%co?g2SBZ^59FwH
z{>JTcfB=WgK(q&u!}3~;ga5%S+SPoVXs-3>4QY@L;5IgR;d1Noc$xioxVPW5_rH_)
z7`QG^i1H_On-ZSEo|(GYZZwt#{+B)LZU=?dFUcGatsZFpJP67>@OZPBu4nN7&}#p7
zz>?yZAObY$4uD}z;ru^Don=&1aU1Ok0RidmQo6fAq`Nz%ySr6Hx`s~a?rxDB29U0y
zOS(b&9^Uu8_pbTCS}Z;cv*w)hfA+KYZwo8>)Ow^Nmvr&i&w*k{`0S!$(?@8SY@v?%
ztjEhODzHnVimS9LZ}fGg`gyFp-eQ$9If|Q8h|@&{8`eulDhdgCpHv#D5t}Nvj&kk1
z_PoHa!z8scRCy1Qu|fB2B=aYuibTGBoxy1#`8C`~vk*u^ZjAnif@_KI2gU-P**XBC
zuK49WgMvvE@;2uu6+YM7XqVq$!nrPg??BYr>P)xIwoI?gg0i^VrEk{PvLvoZWYMp&
z*cX;TUA`vU>A}GCO<|D=nglzQNcjcS(NM#|&@4iCH=?Ng=5}ErkZvuu9j}{26*FsZ
z3>=2j_@{lO=%3LOAr)P3Fv+4O5nFWtArEc%67H5BDRn2F0Vjll(g`L^O=aYv3|N+K
zCP*4t?w&ZjF>~y;pRc|Ea>>PSPRjOsvxIbHzXoX)P5+gy_dg~*u5Q}ayF)lLiFV7?
z7T2?4Sk9-6PS6-<W<Aao{XK*Z*4)Q9buHWoIhgWUf7y<QJDojJ7e(k;5;YbZXlm2F
z9;2;?u%8FSk}1!4{zh0yoqwbbpL<x!On{)wf(t_Zb!Q)%UtD_)1A=n3dj47PHJ=D~
z1Wy^lKZ~9|MZG^!vT7HSkRDG;ayDo-C?^xru;bRSAKq8+>~6&RKmqbsEr*q~8*!st
z5(uG2HD|@IWcjaXWAP3Cbqk=Ka@u~#inhch?`K>ArXSR*(&s^Np{LowBv8eQ^w6D@
zK}Y++fF06>do7=H`i}+8{Wx8kGWF+5@iD_x2P-Fw5c>{7M!<(Pfa)G<ouy_dp8_fI
zNy_8xmVza|REQxCD^e?f1Ao>{RLoq`R#r1JmY4mJoV(is627w=Imu3yt(smMmNJ?E
zC8%&~IzCAd^9majnUo}|5z0p+6v-vw+Xe5I)=%w@u>iJr{qbxeoeq;6U>Dfvu&54r
zNDGpEqU)7QKTxI8)BrmK760&I8$WM>05&yd&Zk&w@NI)!676did7l${&gG_@10${m
z;l1maHt&Zk(z#*ne!4NJ`i6oT*JP0Q@1$|neYXM#N>7QM)a~^-1B3r+DczGN@SFas
zf1QrYyk?!hxikU7qlC7H&_0nZ(f<Z+bfkrcO_Felx8_wJMniET;IT3|b7h(99Y?B2
zYkwSO1-nWAQLVR-J4VEW=`SnEVT36GG^oQ`t$4RuyDMP93kG(@d3>S}E5ij8HKL8d
zP$}VRiV~&FVuGFodb%pXY3(<vz`M~Pj=j9<BkDEM{vo`$E~)$7lNp*~NoU3pFXd1e
z_R@$0!*DEVO31LD!>|XGsmyEJ)F-9>0{3}(03zG#;x=eB+D5f_HrO`2XxI(r4<jm?
zPfXFLM)tZtf9D*o=U`xHPwAZqT+AM_m;PG5>-8gjA{qVk_AM+Y5{<b3ubiimGpNdU
zUPS+!*U3tsU$t0~$Z-U_HlYYQ0?U<Rh3IqQST{0Ca?(Z4@%h`cQ$6g!?Kozp27v#&
z4PJ)9_Bxy!3wGRW8{NoqHq#e(N@87#eP|&~d2cyZXk&@csNv!ALe(>%!~6Cp?%Lfp
zL$+OY@oG}w+)Z_wTlF?mqD<&T?1A}Oy5rST7YRCeFxPu{qiYCQ*firYCsDpUw<@9J
zW|*KbqIz}AQ-FM$Tn#pk66>GC9T}B7WB0NJ2TNntC)$gfxi>4M?q4cWtA83+AIFnL
zEKkRgzL!>Xh$hw6c8Szv&SOEQJ&_L%LS;{g+3ozWwVk&anz_y-FYwxFCB%R1K5#2G
z<diZ`+)23y)i)@g5YW;NIIl4=+lrve`2*IS(-2>BX!I`l3J=7@Q-G7EZB#MCVEW(E
zoT{+bjTx>xP+EQMm(&C>{`7qb4quD~0IV(WEoGqx`g}*}so11gry8tPs@_8$U8ajc
zDV|&z)}`l3Hv;v(Bdm@Qt!K8r7N;gdEYNh}w4QvA5kow_!o`oVQ~nEQ)94JwO?e<u
zp&sZ=^B1T)AAW+l#mMHCd=wcQJzEQ!s7RZ9(Trn=w;ktRrIrj=dL{kCZB2?(YKnI6
zaKOcUu5+1z#;kK<rGbe)7}TLLUdksu@FG*>oFD6m>*LsSanajpVDvpbzJyB0d^H$g
z(tx=pgbP0Y^cnnE_o2X@>3$6;XyX2N03ZrXQtVMfFz>Rc4&d2=0tJ&X>>sGL)awG<
zA)yw_o#whDpR|8m^MMh#f6Ve#b4i?B<2?TQq!5ewME7JH5UV+c8;KxM8L^3g(sxhZ
zYbNfGDJ}i~HRo*j=n`EGt1M6W;)a}tv0rYKdjh7R{sJ%W9MGtHVMEY>4wrcnA*|t5
ztMrfP>*%QQM1860T-J8Aa)=GGT!N@Fjr2f`f<u=$b-lk?y45D8vpS*#41UM&aE$q~
zx2WC%`&vjL)F~2w{#2;`{9^pAB$c-7Atcy^2?)nH+heNwo&WZvFvY4C!m77G+DL&v
zRxpfEb9(<5q@g_N#IwH#Q;SKx^PcLj<mDTCUYnSF--DBu#g$<9M1llX648Rm;~fS{
zw-5#g7b0;1?Le0(iC_ywD$>wvAO~EQi)Z%h?H^W&*VWK^o$3ae6MzA%eGmJ)>-rtx
z2;%GFE6at{w9`x&WjBSP3$Y+1w1bSsV^E(}+izt}s9Pl3Jz5E(HCJs^QDwT-J)?Uy
z&^H4#Csq#CMcX}<=*aks`3s2Wf@7UgE(v&j00|(k=Vz<$ZDI@g>fzO<|EbJQ(^BIS
zzj<7M>)3?b`9FYvztUm31%7$|Q&nQvy>sOd{ckZt3ivyMP@zXQwVO?<v%3JRN~OcS
zWeeo6+xSM%zi>`#XMiC<HzoR>4^L=3us!ARe()fKWSX{~2m|6N9ZO>zpxX^VFJB!d
zT#3w5(5_(e1mwM7wt0v%ozbcteG=>Qhk?LTspusGgvvA@-L`n43_tJq=kf2Xk+xdp
zn#hjKT8sP#Y|g*<_i_ERnbjgqnfixR?s@ESfXS~d3_b+mZ;Ps*xr38!QdP<8m3QN^
z4m7`A1z*-;MrRS`k!_Rd=*kdEAVwZ-0W2Bbk7cnub~At6uy-Se3D3pjjpRpy1Mh2Q
zT5CJArrK*em#uQ3-CYfa7vqbZ&y{Uu2XZixqx9J_*sVH6;(?2l!LmK0_oD{6(aE<H
z4<Emubg1T}FB{+Y<AbhW@U<^W!%#GfLwrN&cZKovKgE)dGIh206oM2YaA_!lW<L6P
z1#5`jwZ<GlwWLE^*=ADiS@~Zyi=fY64ZQ=k+D=!A0gV`~U+!L6Wd5|!`*Z}%=&=o>
zur(Anzbd7wD_#7(^9b0jB;8-b8+=8dfr^`5jZ_Bg$;Q2#QwVuS%&Uoy(jW^-*gv(!
zs<$;y$nx15jG1ONyggnnnZE==EKcRywoO7zkFr{-g0L@#KYI&CJg&s^E3-S3SP5s>
zMS62yYmR19l(Pj#9`7!j916=56DVH*UXhr^>1x+A|4sjYj_ts~-JP@-Mq1zBCVh0a
zxSn*__jMIJE(W}<w=OWF$N;WdzNn2LG~Xd*-z5ZLO^I)1I}YKD8Hy@WiCep`pqD@m
z4f*+D2yU7A^Y4{d)q`%ENotnb`={Hj>Kq9<0($i0k7wvMaRb6mP4!>@T$rne#ly}I
zt}#TJEq;t5V4JJPp@v;5Fxi45R>Ck$g|k>_(?G^^3xSFIID_0vc%nAeVRr9?uF3RS
ziu#XKCg>JRNrO9WQIjVx(FIpA=xo+;Whk3%9hO;!I`6!Fs@KmL*svm?hQCTr6L2nf
zK;e8j%Tam&%yvSKlnnhhgLlvhU;*<VdGRgMKVD|NddWE3&?yc+)9B4i{2wmJV=b8+
zZ$BG<$$S8&<okjKh9TYWJSHdbE@m3BO3Ce>hX+3NsO(-13GAl5Fv0r%i4Xj}w{wni
z)aoqjLKDJ>XMg3CMszu$%My2yy9r3&S4#FSy`iq*!hp;sNE(K>yNsx5z}_6cbaYYV
zdmPN=nPvL2)N(+H(;cWO9Y@Tf<8?$M;^(PCre(?FKYTH>k%P$$wkQrhtMtD;IUZqg
zPxVE;>o=CUEosalpl=zq=1Ed7AxKc;=}GuaqRzD5t1`&F>ru&)))OW#b4v#%kOMdA
z{tUyWJ5Qt^>g#4jZ(`-m*%7zm?`{1;@Z|{Zw?%O*Z_MizcjX@9EPNsUw#ec~(-6>e
zRs4}vdQxUh^`-l6mxRThCOh*?zgO^!0JVIhZnGb-y>Bey*u)YO+HN+v(P#b?rx~A<
zq(5AGuy(*m9QM{bP$@gDMk*sRu8i)^nVFhQw3NkpATIgeW}V$G6-s0HbxT0K=|{Y_
z@-_R9<*>N~_oqe+brl)6+q0cVvlRt!+dL`8A{9(uk&m}WM}kLv9koXVtW#6UG+w4{
zFZPf;_8V&X{KxSZyM&(ZQhRFO<(x&YxYe;5wf`l3&3-V8y;R<0i|_9@tOjNMqJ1cU
z4;quDpkqbEla3>=+N*;!_&UF%x|RI-57-B(|Kq<tDeZx%KV>Q>_tOkj6<BlU7MjNz
z{<GV{9TDyHsjhqw<dpx%fCBDAa7%f$brh@Q<~(*^O+9GoKD}vuRc6!qUGq~gtbY<_
z&iwD<5_b(bCBCo+?r?O(NDeiklR2f1al0^-toWoUZaMHLVT>7ux&x?m*yPi<4mqZQ
zjOrdhLf;-1vl^e$JCj<=DlcI15fMa;{)ha|d^KlG#OFuE(-E~H9E|G0pP!$aLvL&h
z_@Sgez82r!1C8XdhCnQqxG{XrMLE6<wO4W+E5OY|L;2dso2<{%X(<YkDdjNll_2+8
z=G^K6$A07}=DHp#H_BtNneN}Mx)$8`IuXCgM1}q;8-KVyOqDSG8xvw6%jXO02Mq6*
z^S{X^at!`!<Vb?oEkyirM24h91Vm`&DZRP&I5u`|c;_KJ?(CmvRR;OSVK37kLXy#J
zCi*FO>_7rdLxvDoD6CmbM%()*rbJvDa`Stm_oFd`ojRQzQpG8{%w##Xlt|5FDfZQE
z(#~JZyTW`KF7a2bxq=sg4h3PuZMP<bl3p9;r0E5P%$q084W#rK&%ljmf=y#%D{+5Z
zoWFV<Q4_;$8DQJ|nnIZ&^t)cab7Y3C_6MRp`o9)$8fDtJc9|s>{x1sv>4^bBekVyq
z>|XxF#=k}JX}qN^*Estq@IPLjURy)e%`bnRlqD6O97LFrn33F&kf3Wm?&DZRx#E|I
zxE5-HUO3={+<=RSM*Df?4zdAaR<Kk&QLf_ZiAMtekUpD-OL)C(h66qOxy^-S)c!a7
zaS)kj{*w+`H_)xa|FK)(u^rKDptOyGD~l*XrpOU0YWWdky|BcMv0tVdr2^){eL6W1
zpK;w7ugv8@2Bq>gB17XQfp4ffQH9ots#&NU>c3V47IfVX@wYwas`6)GOpjvYoRZ$W
zV2*J`!0oEjwEf{fTcMrWr-d@yDU#F`HzlcXOPGsES4DoNH>JquMvP^((xSN}tUgas
zaLE+2vm-zKG(YM08MHs<4<Dx%^rkVQQm_Q{SdeVeZ2Vv8GJOZeKNqH04f=LI$k*!d
z^RyoNx1?7efwI$5Xa0BikQ+egup<xeq1L4})vz6mCR*b50Vc7lNeZg3bgiPj2yh`~
zvXie^HQ=N)P1zuuoS@y!e5GqhQ`&c3KA)kZZ#&%)UPMxPOf*WmKY%e5$b1AKUfJ|V
z*+}npJjek^OO_W`2rnIFxGDZ3wVKK_pW<pO>oytJ+@9jU2%k#by8PywZxvD@aA$C+
zm~Y+hME<RRz3<}Cwt9zAJ3jOsXC2(^xNH~o4f<ONR=f0{)7d+@p*uhmEDU_OSsFvM
zq$EX`grb&&^@v+8u#dt5uh2@jkeFHkZ)(p@b|QUe<rCox%j&`T38MDL%VJeR0CDDv
zlqm0XIU|cZaJ6sjhU|Nv1a!H-zwN@=7v^0&Fjl|Vb&1On+5Yn}m#H35jhP0Z?`!I-
z-GG5v;KAZ^4Mov1lG0A^&2P@j6tNGian9z8XRIQt>^<D_`1C&!;P4Amk#4s@3hn0+
z7{h5qY{SVh4<nBzd*O+Y!8+0(DCnjuP+gwkx4M*!jLuAEubZZ1QgL*6s-%y_eWd6Y
z;5mplfxYQq{#jobDgpvFv_Qh@In{hCNgtkUCGmYvj0sIsJ7eBu(Fe=xl4Z}8`paC~
z7t*~zTg(FH?U8vmhnC?l92M_x24CcWl1#&F5R3@f+XwhJ9fuSY6f_v3jgsU6;%)w(
z<XSBkU@1rP_!_}L=G?%i9HDYtRtvB^mIIsH5BOr|vuY@?l`gYWqjdgA^t@_nyUb(n
z^t}XKkNNyAwxdH?OEn(G_~pp}-QM0`IkAQ&$ooaz_Q}Oap!2~dUUQP{5!i);0ps!`
zA|1K!HdfpDAPGwd;9bwG%6@%@v97~hN(&o?V%7F`8J_hz;C<Q!l=UzOBMC=~3(VQO
zlMbN8BgvpVpl^QqtqNvi-sJ1FvyXsk+6XL%bPa~V^f)j_ub^1BUkC&i+_uB#uD+3|
zUjYJ4SQc#-7TOxEVp<-{v>6o}X@(x~eV7*)XI_z<Qm5)<cU;dJU}R;hjl_3yONGb8
z5{JuccS-8VN}!Rk+xQIhLnG*v>E1_kw~flIf62oZe=`^JhxOOg7{F<xHi{t~qX;3N
zGq24Xwi4+T99}uG&heG*@IFKFuao8nSOmxIA$2yudUmPm9FQSS=GO<u>ZmS;wBBex
z<;fagbM7n^03QJG5xnQ`P1A0$QtDb9;G@CB8d5lMj(dRibD0gclW)=YihKf`sx92k
zt5CzGEjcjkv`XJ>K=*!#O5L+XX~5mZQnvoneHmY7pHVjh7hPPBXpb>U@&wy)j_@ag
z6$4STs8`3WK@LaeLUE8c={_SecxLtK{1nWkZuoi2yh%%uErg(`)H*j%n@J|UQNZ)v
z9+!~Ug0&nQ?HtluUG_~123^*Vf-z3-ky>8<uIUc(5C9LKW?*@~f<e75+;HW4D>e1B
z?Bm;Y?V?B4p=jLzWHpZ&G5PdUn33|6;dLW_xt+|EiP~pP>Z=&Ay<VEo0od)Y0P=Xs
zO=2(=5Rq(0eF2=0@+79k6GgyTXiDhLNRrH4jS1VZJQbG-dtM<fa>GWST7EB)H%mfu
z+-xeBbH)3$m6wvN$(G10Uq_yoN9wFu_G2YBm0eDqekgO`HolzT`f%rvjw~tybb31z
zUGCNSx=&WZKpP1O`iHStC(97xGQmeSr55%6D+BWBnY2+lmuU>QLqJPApI$Q^YtnqS
zE`y$L7w#!gBoM@+o|m`cG!q@Q8T@NC8D8dQRO42ZfzLx^&E&z7q9(Qc+*&@)sS7Xw
zBtELElKbxm>0J&HbI40R4<EcKO0zWQ5-V}!mK?FrG7&nR^y9p154+ei$mDjJFp}oZ
zC5h({-ZdI<pCYo(qVh5OD7{jl!}dz%@2l^@84IZOZ20f_7acv%(6-z5=XhLwfpPP@
z(NJbranA#8Im>a)m1kxQHzf*H@%J*w)9y-vDk--Lq5Y%?!tNl`0X0)0YXFj|T%w#!
zYB|feZLL+JOePchMEYUI6=;mzFGbsopSA)`px|x?0L$!HI7_L@Xj4!1q<6vTfC~on
z%4ig_+t|ZEO<|pJ;rZ!`OFyZWnQ1o4rMg&5*5!Zeg<Cr2yBR^31&l7?U5d^EKz5<E
zpw|eCNrt7KW+|u9XQT@#trG+<g%-g0-<L3#^MesQ7nVs%$py%OJ3H?lKtgh#p$#lv
zuIOcq#lAYYLL=sO0WA+H?%^p1vCq9D=B8&nz#jKn>^ve}x(S8OBY|8N+knA}w5W64
zF!<@(&(LjR9$;w*h1bnIIlKl`WEjT&{Z3&sIT7<7YS?S55&L!l0Tmg@=ry7JgCs9=
z$><Ti^<e{bcDX`2<E}$)bKqj^7QYKU_Mx(QfDRWlT~}P)E`1ArCTIVYVf8pzGyK?6
z2i#R+>%I1!YwA6YHR-h{iCU|(W9M7t%mXn*d7X}g*mSD1a!h_9?f$83>l;V{Bq}Qg
zwkEln`B=TGI5+b>ZgOp@%XP>vJkRV5QB5P0+|`<_Zl`MpmO!J+NKWNwPV$Ud&aXv1
zKI$|^$`R7Y?e8qLR>b(8_Hu)-IzYwotnc`~&W?ST0fI$dtJ>w7BWfqt)HlZ4Ue62n
z%icpbp%5SgX)lkZJsO#O`&ML-{)56xD*7GA6+kNH2TFG>BRPg4$iGJ(AiHIT+Xv1M
zo)zQn*o*`Fmr<g&Y5CphRV5e18}ta8dk?2Q-T**nF8;D|wF`7PoB*rbk!pX)jZi)$
zbXzV^kmQqoxfrgLm4AI9;AkwoB2eY5_hc_r+^kV9XP18b)nME^wv1bDh}FH^gXP4-
zuOrm`7%wmz^&!=JkaT}jxUDo<W8*P@eDPMx-R_F<Sd)oj50bH{o;qD-9ua?-srS5H
z?1A#8W`1GPa4#|D^6+`|{tHE5`?aT1w*B(zy5Y((@!sGwpMYg#VS`S77E%~gS_g_B
zH~+_S?m%Pi&SXMeFs?P9L-6QeZTxy?Ka#?YXtA|sluJ9rLd1z7FGsXq+e*pmhe(M{
zWk;0;;=Lp94a0-sk$YVrr4NN^ZU+D*7;oF74W>Eju<c<}gCaqMDZ2`^Le&a#ig2Rf
zZEDsx|EPPo!gwR1xX=tX6~UFt0v;t#!nsKaDxN$!z=7E4H3cv_KeQrhN#Kc<i-LqD
zBEEj80amXz_J%+JI#!nG*MZqm!5j7b5T|@zVuVApR%<Y0S&Xn_+bQ6zz-*$bY2ezi
z<Z<@)OQM^oP)KE+YIC;^d;~Os#$*a_Xk|`j{Y<e#q#JZ@zFr@3UrC@btlmcF{gU~C
z9hhtW>>|hiWHM#5_Wck13*Zrw+LxaYk(n8{>NSmT$b=J%M_=i%If!rX8@gFM?=?j@
zy_m&j+{xed{-*ms&!t|up-^DiZ>vzPJ2G&AUtIwN<wVqaU3~$<>ecOAR5hn|K&^W^
zxqRDm+`N3{$ehQK`b$IrlwM~**Z7j30e=-W%GP6OAaSnmXrV0q@|J*uu8$0{+LQ``
za3;kE@2yf=3t$}A56=t+qH|LzJCN%LFNjY6-j}1ZY8o)VR5bk`he%p<z|Y-$yi~l}
zr7sw;p@MpiCHlFIs83=Sn5V7<CZYsb`yWuS2*8ksiV#sjN2?dJ2$|4D&P-_TxSB$l
zWAO3hd{}a_1if~f-`8lSYEI9{gF~gB6YR=8$w=(TyPU<J*+W-c4}hlsB((zMu!BZ}
zp2ht?3t%FU_Tph_SN~5H1y0O9lBy^fOBr<b=zjq^{4+HW+|HXw=1gcd-0AtL40-G)
zOPO@<)Hx#?d}3YoN5mk{nq>BPT=>#0FfjRK2e%<~y_b}Jhr?<6ulE_zp0nr=3(7)T
z^$@YQ-fI{rqbE4w=%C|cF~*hN9Z#|wC~TwtI#T{ihRichPy@Q?@f3=By*Ae3&Gbhc
zB?{M@%)%hoa-#`DEsfn~HmVvU9%JLIX?5+gt}A=K_u%ZclN4+ol+k_0I5yFR@@=P%
zLeGkNhKo9awkY_+;04;X)n;A&RnPVr5;f_$m$L>)=brcCrOd_in<r?Cr0hzq>Bef>
zh%bF_4yas9*UnJaSoHKZsnW+eimw>ALSI#1RPEF5JCpPSV{U}n5|8IQ(Wo#hmf0E;
zF_Grl)FRMORa2LZpJ_Ae>kQJC0S|7CXzqgqv0U=M4%cwNZuL~sC$bqW1K!G&n1Z5)
z&xP3_KOA0|q=DmGZ>rQgR-48CK!Cp1XfWMq7Zvpl@Dv^RypA84M6F=%3E`^7^tSNK
z9RLusjB$n>&Q1^VOS_?0__iccQ%_p564<sf7M7@z$M2A}wS?z>)LO}f8_}FVS1mIX
zLsG)`44M^3`%|bmEv*rx9S16^92)^>^dA5)&<`7dD;@A_)ZsCt#-(LwTc*Q>CPsmJ
zl_;dT!D<W}3Ios&AM|U}wN@>?{id56!9^?1-FL-*Ocn_x7GMcO8ew2Tc#7G=pia2t
zPtV_>d$bU87GM@-MpCvyPpg}S;HXlI%Y9z$^vz%aY+ZHh-~`{qOj$kvp%(lH$Zj>c
zAyQrf$$ZTTBv27K5wAlU%qGXqKb(am!!cN1Ts|i#y;g-bTB&f@#R%V80M1#Pbt7Ba
zKM2~pa}mCINIHlI+g)Kky#1WZ;(~p??WxrVN-S`Fgza_`=xOmF-*nftp7)yT%dZ3-
z`tpk#DveNfroAb;@)+i$=&O2r&f5MrANPdKRSyX>>6*B((mJYslMbj}b&K44%SLsZ
z1s*jqyd}O^y)3>po^A<xJeNs=24C%N0ha*W3jB=k{=kX_c)~>fIBl-}aP#Ru{sfPn
ziNI>P;3M|sDe80NbPYI<)MHV;yUeXdsvfzP+f1<3)_7us2*GfI($HRsN#J#Igcabs
zQTq)$R|ss(Q-RZgklld=(`R5xa{25M;)&o!Vd*)sMhW6Ut4j&#`w2OE=B?3hk{?ae
zJ-j+yGXV{3)H3)H4#05LnC3_l4P!6rO3(2D7>Q$14L$4`PPb@G!u%?}Ri6TOm4b00
zuOm!cH3=&4D-cH8!O9p)Tiu{2_%Bg8(m;djw?M#qHS`nyENTV1$%I1lp~GKFYl<rU
zz0GWGt@)6D&MnIGJ$ia~qWfu`E?yt^p8_@X_`E9uW?%RM*+lgOBuo~9+Y;d)c*zs9
zd&m<<p70e?y~jZrMqvBey<gcf+XYTy;Ga!HYtxE6@#96sy?er{N^SPlMGgNdCj#`Y
zH=08h2k83cnr?RT$y>uG-vl}RrUqJ;^J~e?YTih6=QB(0dkdMG17V5RaRL}9hDpWK
zb$tC%1-fHw1Tc?Dfziq)SfV+I$~|Pj7TAt8eV}Dl(~BfWKXZr<d4oxwX|KDcQ9Y;b
zP>4{@K_Usp3u&$d{9N*A#+<$h&aL}EE#FLth`4Dy0~2(o^^(i-4|!#{sg`0J{*AL{
z`ojq~k$?;nPA>t;$!mL_a>lIiG-D3{w$UH%A7q$Fvvvfd?s36G!>$EFVsx>hH3f>R
z44NG#^EfLTrSe5+Rq!vs(cN>s=I)Yha&yGJy37~)Y8;+Xa|O0I*ISD;g_3V83~5JS
z^#_{)TxMNW=XdatlC5UE3$P~%&%iWz%{Q@4Fdno$AsnuPIi7#)_&<wF34iaxQn?nB
zTJ^&9LX_0@tLGY1AX6q_e$ntU(O+@bA6lY9fd)1IiX3_k)JAN-u9wX30ouh?R)1!A
z*SHZW`<l7k7?-r%@2(Mf{=Nnd^TD_H>d^DHp$s%Byh?>n&~QbQi@|xmq|*jo(}tw$
zH@+I8dnep5Qf=W)pLgdT(|RUb>PUV2;y*8k&JJz&gb5YNA9AW!x_v~0cHAu4Ul&(7
zSzi<rX4V;h@g7_<0EW({S&>yW-iZ}=Wo9B`CVt@p1E)4A1IcH=CgG7HJoL&mXVP=c
zZ6it9ioTZGt#{C6;DZVV0Xyt#E=e^MyUF~p)lPE2A>NJ14WR{|Yr$1DcSwX-s_ea=
z!FkSVI_Akp@f4YBuMlG~C7{mRLyYpIe3r@b^2>3($C4YvTy-AE*-!%gSl}S)@GvOR
zqrXLu?5kY5BIY&R)q=iWVE^ZKD(^K5q=1LMul2#BRAqk}3;P#h9DmdLM}I~?L#G*9
z1P&^=^HbVmuTG;DHyFK|pTIs)5#Wnivp&fT{)8mnp<iYX7JLHQxf==4ER=@}fgo4L
zVr5*3yAm1tW1wPpw{L<6@vQ2&?Ua0Mx4I%qD${#_pW2q<+#ubT2?0`_O`nr=(6@7P
z$=E3WrQ7C4xUTx{-Juafy&GE)Wn7(K#AJ4!L{ST`g!YDG2(%Id2|x+AqWb2E2*RBH
zq?nm9C-TDY<Ay%GZJxVG9cBuCUj4``D91S@2NIIU#kDooDdrRW<W)n#ssP;C+1W_a
zI`}b@qAwJIxl(7IEv3wxIicE|M4<@3X!~C3*i!Mh%+14HKF?Z~W!vbddD)u4txi(P
z?Mw<A(j)WBQ@w^4n^N;G!TW;#?KZ#oWi_nF5xU&B57dOp?EOnk0B?TDj@E3r8+P}9
zxe35cEr=8^@)?4BF&<H$)A725eRy5ME>5@`e10R)TMPkc*(&_E30M|-&6Y7CZ<OTT
z;M(v~{}5k}H)rqLB(?E{VHw=~^j@qxWEpd*)u>nXR2IjwHgtfNkdhY0OE;C>aZH{p
zt+Ce{`W0>oRVL@xzhBh28#tMPq%pMV3|zYHkZ%MG>XPgnDlcZ!m<kRF^g~8`IbZil
z^n7Vggje>qeGke?4q}x1>XgkZqX`c=5}XP;L^t?Ll(do&$YCg2jp18i`J2T<;g(R)
zGb<IVsm3SWx`C(XSGy6(FQ*$e*4mfirtUxy({Pr6dwqtM<aEjW%a;`leDX=KwAwD6
z|3krFwT1F;y|DL8TO8kt<^hkX?K=5<^yEI4EuiAck)fq3!<4+%SRAjCF^z_lP0^eR
zZFMQLb#+nrqMwN%rVZ>e!l>kQEIvx!!NlYBOTV9hhMc7%)spv?6Gjri?Eib_;0tN@
zIxY{TvJJ%8$AFMQ9zW7)zChjI?1m%$83>$8%Z+gETiJ)+x()bVaPFMiW)^zgMylUl
zfa|Sx(S6Ul!?){4S*P=aVZ3?#l2aZ(T!QoYw4><fZN9_Iop(}h66Mr~j$>dFlPI);
zzx#}z;TyM)_&W|_dXqY*J!D*+*pE+-PLr*H%nzKaN4~}cf$ra#UM7DG9<`+4)xsA7
zM=m}Jd9ka>^Dt2E5aXxz^sK!1pD$??H@`r=wmAp_MnM#N{Fd2|Tj~yzhZRMQZez<H
zKiuv-*HGl3BmqjKc5ScgqeA{({`86=uq?;muKlan=!JfDysR@Coxs+PJer$M)jbkz
z5^||K4`S9+H)!R;ALL51&f|{oC-tqERouUQ@}0U{;j%c{3O=yud44W{1f5gJF?%R7
zaZ_GO@iqs)1O>hTQ~z6#k*ry<V<X1l-tbr|ivcYlfL%^}&8-nqLu3^(@tbgBJk5P5
zaL{X(aS0?b;5KHY-AO8hi6mR?JM14E73bmO!Yu)zGtRM6GvV5t=HaHw*MYNLr%=^t
z*N7R6d7iHGM~2AVF_2;%6|jSF{2$-v-z)Y|=#N}SE#HA`8C7uT^ayBI@IQRL87I{I
zn6YZCfRZ{<^RYU(>)3rh%jupzu2*P;9N(YasqYe$dejLX#qxi(`2*P=0rX1aF8$M=
zn{sd;kYAVFIq-Ig@w8=#)Qe4GqhAdtk!Il5)G~bA@~i2#{EI?-Vy9-jij}j=hq3|i
zJ7Rz~SpHi<<p=(QPZg&XVJf{o8`~Rze}6aN>=)9rTYZ6|M(~FaxAieVB=PJ(#?ZL~
zCx#VN##^9LBwyoN0}<`mdZ&$2C2=t}C86J*nj|i<OOM_lPl8~gjZGbW&!dI90(XFq
zayP2T<YaZiF#2O0$lM;%i|6-5AKB=XdZK9)G*O+NjkgDXGjjQb5Q`lt2u6u2=%t8-
z(-H!d&)XK388?;hK4&Asx26H;VHkRHB=XPwYAKECBEMnb5&3mY0zD&L5TrdoO`74i
zv@@M5+de3zoAkZqJ7L)>GM*GJaa37Mri$hF?OQ%;dZ!{q!t3;=I)8*a&sXjkDk@{)
zeD9_ifuHqr2R&XxO?v_j+B$Q1WC#ZrVmOvs^JPXI%>^I^b%=TT6;rCIV9rvbi|mk(
z&$z%IKgz=PI{@{YI|M-4$v!wtZ}eY_E;rss3st)v9su1=R9b#GL)j`obRip@%!CwU
zKP7prb}XaO0*w4FfVuon*<?p_x0UjU;5xWEFq;ag<FEfOfQgnswnMB_;(jUJRy}>0
z1du}b!Py#&<XH1$xsP4*tDIf8@G)GpF%kB$t1j-xqJCQ${({2kIweM0+L!P_=M^($
zY12Euxw41ipno;1bvU$yl*sqVx-6Lpe#fdq<g-oOL}qB)S;b7k`1cMf)W-{^aHqmH
zto$77Zr!a6W3;=uiWyN;%O2{p=k;<HL`@&Wzuor&OKJ6hW57X@NoCy8GXkhpaRs<i
zq?SgyFH^+!EBin8x|Eq5bL#8v0V-jqmGWe}@3n6@-+@)>s_hO%DJ!HRxvygv&+#27
zr)|8~^Z2Sug6EPSgKU43KO$8?Bi4AkG3c2)I730yc{^*arSfHI+jBJK(lcO+R8#C?
zD`2ZmAzKtxUr;0?x4foD^Nxe!!%EuZwWn@L><9H7>kAR0xZxMPUe0n~^?KK!r|6D=
zKa^vB#w{1ZWT(3?g8W~bvvaKsZyTxs({y%hkml|8i~Zs#CgQEQ;GMX!>6lYiCc5zs
z54BSr80rAPO86{<B1?MhID-7V$azfMe*?RW#5s)wQg`VUseo)-PA`ZOmWi(CzA1G7
z%yLpjXS(0N9)eA4rvR(_P>&l(stSkbNTuW$ZObBKS1NA2^kG>=?CCv##hghV<P3GK
z`i3AiRp!e48I3yF(9<kAA81~DzM$4C?%>c3&FgAy&>~5@*XCcU#Q!F!@~;d=^VzFy
z6n#%%XG7F_vp?bjs1~w2B-^kFZ;9P56PL*6#Vi8U?Po8kdtWtH;>t(rN!DqM+RpDq
zavIWw%R4)4^TiIQA_r4|aEZJ77!g$@%WU3`Tp^^3+@Fm-cfHhTyVXO+931>D>FmvF
zeloJ<PC9TBAhVAF(4U3F7>A#9UEiZ?d(~<eyxIJMDm+Nh=i>!LavRQ+s*JnZy#i$y
z06s(`-dLtnyWD)TGs^__sE`$8cZH7CtQ88-2jl0sEdxp@Op%f1{ec8tA|e{`-=I$c
z%sZQNm{Cc($OG6(t~DhjiJ`%hOFb^BziuZSVHx4a>eX0~>cikq&coe<P<|Ax>2VWM
z^|_QpEvqpXw?+83TB*a?l2wL?F0r&nn6IjAek82}m*Wap0RD`ne2N_oMEbmzN|PRA
z128q~30vKX^rAehWE|Hw127T#8MEt<-4FZ9NRDtdQ#PDRmXmXbJ7vWTZaD*0DzeLI
zAh~NhU#9!%>+lxP1vtOgYP4hUJ=uvby?YBC)nlCR#~pRxfD(j)!5TQmKASSbaPn55
znFZ@s3(6VO?f}_workj%8Ze1{G!%$iErCh#*-k~5jABQqBABpgbibJ$Z+U%+;!$Fz
z`K*UQ{+s4))Z8Ob=~>frsN1&tF!qQZ)Rv26+v*j*ubB99-es-Gu3ZS2Sy%z^kCK@q
zp*=1ntWMPYf~IX``dhla_wz2)9|-ay52&{<E2wL~#W)tEMl)ZQzuSzmsH)^!wEX**
zHM9NoC?nWa)*{xhZJp8*uR`kWVV$^n&Qt%g9%R<tD-2HD5pQ&X5kT1DSw;SzjAVg?
zq-(vN+^?kTh#m&vOLgYF>;?LvMqiQp0Q${<e&jmqD~B_{5Oo9Wf062h=nP~Di6ttG
zaa(evme~+*wjzhjyJU6FNlq*aN?lw<XR)umD*yvFA5>%s6g209xFs1>!M!9%@)C+c
zi@`o?=U<X0oR+86a`6~5DNzhR)SVBp>^YLRXg4!2en1f~+EP?I349?wZ3viqxz)aK
zy`=!`k6=M}L(w*kHgKSN8(2w|gF+qPf}+cY$(ty&xueSE>Q!AomD@cperlV|&NOTm
z*>*zzSH(eVFml<v8^dLV?BwCR5)}HHf^0cHp`XCZ-i+DpsfHG{UZpJ<noJPYDej8Z
z>wn&SY*9%|0>lONo{}0M3QMj+n3SiK$$NL(-73urc&lHN3jgml3X2N8bn7?X(?^FP
z)dxJkCo3K!O<5RKn^rXD02awF>AR-GxG<1_C7v-4@psrwlZwG={iZFk`&kW<B!;x1
zK-imw0fP?{@Z`^tEEfm-X7YwcXSLW&N}#5@Jh=cVU71cJhd-O&Q^$n)WZ5f@&zgz6
zjbQvi*C;<@zMHK2q_@%50HFe#7HqJcHRI2m4wjQk2xvzc-X#)Em6sK$$SF7K4{I<w
zsh1Eb2?Shp3F<}iMGO2E8V|1t^AOhGU;Fzd$@r=|7i+y+Mhcaf#zwp_;`J%JVR9+}
z!I{z2g8i92wi{TU-;;fOTUhCMpGy^}WSe165Qamm_y=$}5OxDzY+_QgK1@QI(qa?1
zsjaq#eYCXiov{DT6|u53tb2WcR^@pu2_MCPK9@<jvp5eJSns2SpGKi^qyp+4TCdq*
ze8nWbt-;p-?jhoj>kRwq1mK3M*+bxPRU!pKf`QMw1?H=Pkf<s7B%E8Do|PXH$m7eL
z4SleN!hi_eMf_tgsKq4|6?60u?t8rzB<<%=0F2jb#rdl4QoUsq`6yi5m(RaZe)6t)
z?S^evF?&YVKkw8Cu3>_hC}E7EnQj6v8t7z{JJ*=q$I>9X1+Z6_EGUI(oc=cs8{R;5
zkPRk99!C<=1W6Ihut@OR8CCJzSGt;K)M<8oaW-RC_kgg9-nZE`ri;Bycj`aYv!bc?
zMapc{hb6~}X$2g_E;`-fQgTN?%+4?|SFJ$Wmd_4?=A!io07vXpnJN8y8&ACPFyEW%
zi$7zM^!EoJR<$%%n8O4>#C(FbcUB0)*P`*F>7tR(NH+s@QYx>Do6uEX(lFMEPoE!7
z=kGY;7MdM1Dc&m*mE;Ds*A8-^V9*99Aw9+U@?<_^=J{TjZ>|36enAIM!bc>QggCd<
zO2$a2=UIrR=Wr`)3n?fyGLtC<)8gE=2~D#ocqyc}OBG2}e4eV;LS3l_l$}1O?Y4A_
z3HL1c&W8Q5A@|P2X1G@wl@1pvUW&a(ylq0Y*14?BIkw+CHowwS0oXODL%0;P#APf>
zz^IYkVWeSoalY!sva>&34ei^6PmFn7iGPCNW5HQwOx!TU-p!bo*cN3F14=IMIf9SR
zKV4!KcUL=3lH7_^5<0?H3YO)s6s);H@+=Cd;I~eZT278UWuLmKMHeF@u#)(+Q({)$
z!xLxGb$Fd77S$-txe;3484_pDs(!tZB`NVaZ-qM~0@o;i{v3Gcl+mT~ng{Z`VWm@1
zwxOa)Qaqu(iEx*MwFDGUKQei-PbbOb=K%?s#BRBnwKgJiC&p^%rvi-ANUW&3QsVx9
zqAYu`WLS7$LRMY#8b}wt6^$7$9V59*n%Opyj-UQrxuw6j)T`&or(&Uak`Bw}Y{c5n
zwG3+!MZNnec}%XM`t5UIqxY#X&cOdvSY{;75m*{)u!UPD#Er6@KaW<}HnE(gf&HOD
z_{Y8IAE2awF6~fjIlSPIY|ycos!3oAM6cGTwghUAb6lvUTE5SK3l#vaZC96H@i>8R
z2oh0X3P!b)qH1SCZ|FmQh}F34ko*v9?bph*jIFQ7o4m03x)d^PB<<K2mTXdNB%hXl
zIfPbI|G4QK*G5rB<201FKV2eIX!LW=Ku>@2l=Ae=9@s6vpw}W6oAO`q%yc^g<WaZ0
z*KsL^TlnUAhPKfQue3Mu**`j}Rp%>of~v}W^8$h=JE*oE;cz(g>t4@lD7WYNel(N-
zC4TQ{xrNQ<!FCI{$8OS72`PDlt|?c7?{es2)4m>xjVJett#praA<v;Zx55bvqP$(F
z|6?|6i+d(6X5b7MjW16@J4V~S;R=41O$xW}m!g^G?9af{Nv#9K$(jigUsA*OB|~U*
zof!-sK2@xJ^lkaN(y)<minD*N3tI@+u8R&DUrB}Tg>E9EZe<ewC9m#-WF<VykZOFd
zC*E0?=2aC0Zc>m=_Jya?cRJFBr8*rt2R1tDcx?0s_6`5$2<B^?4&SZLPDZe7ckrHK
zu}0zjU0u`Oy+-lb>!F<>aqgT{BUuoPKF!JGtK0`6IEXpxY1EPPb<&FTW9ejqzkH7D
zM7CxbPROlq-aCuonFoK)E)Y+7w(q+=T|1@!lPebeI7^$>y=$N>0L~BVwt}3C1}EL7
zw`g6otq2~ur$%*DQ-uo(6=hf?DV%Iu&pFq*5mbT44QR*LXqT{TIRk2kvMBfpo`|*)
zTY@IXKN*NU<SdHD*NN5wcxcVsUx4BLp(n{Hh;vUBhA~iu0cPJro`K!e&|7dkT}-n=
zTd73N7)C;VYwUMmc1zZTmQ!x7{@+eUPuAj|b2f_W*{ClPRU~(e_7;M<RxB9@g57*4
zA|*>j9|A84c)U%YO@kn2-$@nkNXmEl+p`e@tEC^<Y<FgAKr$V*1GC5fT1d(sx&=V7
zCI8HJT_B5T1lZl~4^q$Eh0D!FQRsU)>T_-&?3iwH7e^`JWS_|>q!L?MHNkddD8CXl
zJ@I1?cbsBpnk{W2BOqOevxUc<q3CGm+g1rh$bCtrh7ef4g$rOSieZ$><mIX}&yFbG
z8P@Vz#h|61HO30&1LsvMG>av&BiSxc=$jJD(UfsO=TY?1U&SvS|3;+Kc2#v#GmE^?
zY-%g_$)rE9Izpq_l4?bTjd1T!g;sc`U|Eax&O3LhKi9j|*TP#Ef%N85unMPvcJ$<a
z@dB==A%!i4tr=tcL^O|?S;$oK$Q`mWaV{AYWIKioy$wjz^!~B;C~1}GxEyf}XRvO3
z;*$fz1btHq7mxhLl)htC0Y+bbnq{b$Me#l8ns~*Sh7eh1d66DNMs$BC6oz7Rv50`L
z;udzTRxWimU`l?1w$M!hYmyVXL!+yHvP5dx)o)_O{}9^2d@9f<=Oa({&-En~iTuw*
zk~f?>zT(sMMRCh~*^vGq4xb8|^B7hRY-w1}DusmL8$W3u>yt}Ti1gh^|7CPHQj^`R
z8{qIPd#G^RdAH0RL&(vmhMxr~5~Xv#7wd(&z5LPx){h5tqug{~F{tavC%w1RcZ|0J
zR6TT)h(G}~{*TOiBhmELCfOFe-gj?cQtKD~!Gi3;I~}7PHr1a*>ILB0!~gA)q48A3
z5ZsB@Fx!{F7`OS3{~Pf+K(uN*mDx0G`!<`YdD5BdyGyhO)Uv%>x`oMQtZ@H$+Ho1#
zeA#SncNkO0XcPxOip1<(@EeqAxI(+ybv1|jt9t+pj{412yh4jEE@r7UIl;C&U5WF~
zc`D;KlLA+4z2Rn@Kj8o42NRn{J|;5>$mj)~51Bn}2Z_Qw-WAuGMNCC7q?!Xui6?W+
z`4iWYjpLDCk@@0V21k?<+0${Rg1wXaDS7{$$KCt8?HV^y=JHD9gLAILm#OlkE`qx{
z&X%*ZbA7#4v}OMUgC)m9+cjXgbgRZlyBh$wmg)hBUA-dRPnCRrH%I$%-hk#E#%;_&
zfv|wlmW-mfsOS5PjJI{=P3EGOZiR4lmnkD7828Sf+h*l6D!o7$-27ee!P2&Qi0{c(
zl@O~7;;HcSmhm~_(7YDHW`LXMbZl!M>$>sIR<8)%K2;IK&9L|!=n{DFEfW^A`EaYD
zT$X^++7f85Gn?|ok7dYE+1JEX(7RiaYO>n{ovc-pY^Y*Q77eN(%&RmeL?&`;@k}&M
zkz5o5;Zf=y*1hxk$YnkL4($Fr^GKBXtC}{!r|Nk?1K;QKSg&TfR2*&;TEM!aMybJk
zU>i+kn}7U>PUN7Jg11iY8682ea=H?DlOKT==%}o1=-Tq3%bm^TwB9+NqP3***fz)i
z<(%1v_B)`Z5V)Dh#;M!{jR&?cpR$}fQv8S&?F|CF>nq2t<=q3#D$;ZqkXx~?f>E<b
zKM-K*dVNttU=1bO<@Bunq_#?_ldzo_V2KPBC1GDkHb$GKGbwm-dLC%1eW50kLYw)O
zZ^ci_m$dk?wO>=B3fF3tMyndTPj;g(j3&8jTnAa85q04QeqvS|cI;b6^^~VKx8~pb
z7BLiaLSyBp)jJs7*Zi+0Wi*5*j>@At(A(||^-j+_u2JXEEk9129*mJ88M|Zfp{#lz
zSOlOGIW}MmN5Y-jrZE1hTr-LokwMOFVoCzcM|R#>t%cg+TF0>yPoy9it&S}Xi2$<P
zQ}}8omz~3BUIrG+<2g0hH>yFd)(I8kt~`-KqLyZqI<w2#ythCwnEW(?%Fq&>lwTE;
ztY|w^n&7n&@xWN*W1j!bL(^33*y%@Sa#1L*<u_1h46Yiw@OeMZ($hXooN!_}=^#my
zK>11)$!%L1b(nxD7{std);WToT6RUXRSfyo5eJ!`wMr49pU;|TbxrrWTY5^`z3|G^
ztG8SYk`q3%S8(Vil+&zdYC5m-jn^*t61WDs776CWkwKQxI8%VN*Dx&M3AYyEc3k2P
z&WRJbNqd2A$2}PGZG4`83IG+MyzorSDJi5dPo4xQ>E?<C76P%`FQ~0j6V*w8UM&xh
z71NioOAH6Yq6klck{xPLI=&LWb<R88Noglz*HB@D11335KgmkGnPOx8kt31OP=^he
z)1ME4qbU-ORR1}e@*qKODHdfZCB7HKoa8e<MqU_Ffdy+)3es8&xqQ>u-6au6Z?za~
zZPK$CeitIAj2B|;5SHv3#ODNmXfcCoI1fV;m_AgSR`#!3&wY@Vg(kiK%A3XJi&DIr
zzYMUf>@p*(p%a62{R$G30i3rHMHj!Hf)6=6U*egl4p;xJNvn}lNQQp%z?D%#F#llr
zEW0b_d#cd(bQ1=Yeyfx+$pCH*%HiE~CMs*SNbHNf+=@b*ji3WjQKi5jr6_`lnv21%
zJ*qDJPw0V*mf4^OC@bf`ImQcUgOw;A&GC{6ZG+XZwkwUyuuJE5VDsvOfM+&Vx`?Cd
zssNle9#ONcdGFhga4|=UzCu&}*jEHa7L4GnFww;5=M=m?#}kFwl3|u$ZxBGwlzqnb
zjk`Uqw9HPnv$WU$NBrZpy!ADj{#sD!m-X2sM08_?2^BQ={r@uH3ddl(W3*!vdE<^&
zpua*v4L49VYXgPRP)*X;HgS6I?0iT=NxL;XfQKV6@<@QBaeM;GMTLzxcuhcvdd-?s
zZ_P1{>8pxKu_Tx(zVz+#n8v$c_hRGw6JfzwGzaiLLaYMbBiy~+QuL?Ri{1F-l??kQ
zBu0vNr#=d_%*T>d*a<9ll~H{bba`H-*uV@jVRp;i;sUqX%C%+jK3o{%Pv757ikq&U
zngYi9YSWf0U5ju1H$-nNHU5x%w?6xGHYv$F|H{EYw4+xLmoE*_V_fGw4RI^UUqhRU
zr<1UM<8*SKAw_8c=HA(EE^rfj4;Z^=AJtP_!hAtM?$^aV<W|!5CaS*l12`rTUp;dy
zyk*r-0kHP2v#IM5;)(Numr}%-7mzd6F=cq|KqST@HC&gSruz;)H!k9O;>fyx0EMj}
z`Ms*K%QT=chn*fZ%^O$1&W)z9B)wKOlS$<^%-I90(@MPtX%j9d_;y}O<?*iVCstvg
zeTgCeRmuW5w=`-_H6HxE5|*C)%X730j5yU%7OK7%#&u%zl9ru*Ypa2NH1h$Yk9@Nc
zw@>HBIP(D-EBl>mzptuSY`Z4tRG(LC8!v8G3z)qZ0Ov|YZcEuGhX6MdOB^-F=%GOb
zMC7!8`((>4&Na#Ke@mim*}AS033t+fKNafQZbz4Nn-_SJ<o&)QzRjOkX)UjBpEjKA
z;NA?lL;2GMdLNDTa20;R?S25;htZ?1>W31pi<!myk8fu4r76PDJ^@drzE&#>S6ZW5
zZdymHIGLM!3nT<E{Qw-#4*yHCpL&V)fac;cz>Yz85K5$eT#x+?gz+vl>-iaU3bW`>
z;ikI$F?^pQMAfVOcp{==rop{VAASRwKf{USbP}%AQp2|T96AC<RawMiq7N85hjhp#
zyGcdL`2q1hn$lO*)aUX!vjuxDYI7(}N54hlOgeq)fTf0Zvs0jcxegl>$9QsiY`En^
zEWCPqv@`mvNmXRN?f&5Tz&ILCl3e3bDhGMY^?h)ks}!q5eJ1s_Lqh>sLb=dW4v!#=
zw#C5FXf2||vlf3amR#w&#C)6bCtG)kB7TIas3el0L&_J(0&}i$v#OKv^hZWsCI*q>
zY!)20hX|*icv=!8Y%K$7<gizMx36Z0A;2m*u=XZr5BTSfdU&@|#d>r8i$m2#w}^m0
zYU2u*RJ#PWFQe+lymdR`%5Ic9jP!K&pGTWXhvAT5ZgpY3@d?vn<R+QzR@$3bvGQOt
zt87Hzihtm?g>l#<X9wOa%AeKps1G2FrJ_kUmg(Y#e^u-NF~K-?(PEa;lkUWaGoUK?
z>)J(X>J^i6KSOqd_?JvC&}58>Mt*}jY^P~iNGr>^wXwdz%5M`Wb6Tbg!<NF;7j^<2
zwuiUxsEQ`rMTme%y)Z}TIk_)e%-NhjMrfTId<l>0Ms6x*<VC>Zn+ax>GCDDZge@2t
z;ezV}gqSpS?^ES6WT$Gb2ScUqRhli5^>guFWM`<XF_$PiZ=M=P*nPKTy)y49bPRR}
z+wki?w90hV6WHI{RN#l`zw_<+`syjbudFoc(7tCtEd(m<Kmh$%*{S=^s%yk*%9Z<+
zR;JR|%om=Wah**;U?S8sQ?YWk)~W;=ZLeTaTx>|9V9N(}`TE+Ev^2$#qP{ZI>FfS8
znEv*!U@jnY+fWh_H#ra&OIZbQMWWEM?zHg<*9BpY@I)~1CCZrB?F@^;bKf4f4&X&&
z?Fee?@`>*TB4oveBw53op{cdu;Qu3=?QdlGni;X`YCT?ymsn#%F3=FHb!T-}Q|~12
z@*zsq2lhuVec^db*m_Z_Vlel{UF5&YUUNr`_L~~s-4p<eyG@9sy9M}>WkD5&LNbuL
z5>wP>Qhose7=DZ>S0@S9O8^;dPf3jLFXZtz&(a9O_H|4Iw)*Ga-}kxyho-Y&iz?c?
z_zVNmCEX?6Azh-<NT@J$mvn<PlG5ENAu!V2(%s#i(%tXjz4t!*8_b-2&ffpE)^G8c
zlo+0&Ck!u+404{9(F8e@#Y=3xHkyg)I8&~58#gp?5cp+-XXR-*v9A3(ai{Jdz0I2N
z+4sZbb$BeyjJXr}LXEs4`80lIue&|^Ke%Ov&lrCl&?^|X30=K8>?2;>mZitZ$Jtl`
z6{uqKD=l9Ulj}BV-0mCKD%_^PjGzA?5fh=Hxz;y%nkAu8h$=CYwiy~85(<FNt+#o4
zo6a`fWX%cCBxe390=TgtEsxW#{S+lB9q$cYT6Zm$$OyC5NJB;78*|~lozr9p0|Nta
z_cZ~y$uOsNBe#dwx3V6xUjk(D<GQO;dRrT3_C%vp<p9zK4a8eaS4y4RBGhAi3{l0s
z{GwI`<|SHh;w4n?X<@2<HTM0sVa3MJxCJL1DwWN%_(zkkl?rY`($yMi4q+P8p&uu%
zFzCNT;xsc-JqjL``)PXOQcL%o#FrdX^GNqJ3p_?$z_N1%30y}GC5j)8XckriUzVci
z?FVGk22<jT9B$;Zkqc~p9whdXmb-7w9O$n_4E~}Lu;FUON%*W(NaQQ9)PQld-L~TA
z!{4c%p(RXf<C7=@Zkxhany*4L=WJ~Rpq+4&$zSbvge<06>H)}vF(6i`1J$D!NA}(~
zYlw#^SWy@x!_kzV$wzuY$>r8Y-5=rId;)IL<vWG^8bx6@gl##sk0-@|y7Z3z@+3jQ
zV|fAA(zsl4c-hwnh6#N9vFGf=)kFX#hJQ2F=yDu;?mMd*6b=CrfciyEyQWa5PKCGl
z6G@PUcq`jJe>|RU^F>=Y*xEhjZ`^FY`Dbgc6(O8%NnE%yGuHz7^iNlYlmjk-^p@mY
z1P-xa12~NANDj3g4d28;(w_=4cX+7{SSxo^#cKDz-0y?yyIHyW=^2idq<97A{2g&D
zUhnT5x?J=Do}I>Dl83hhkN7gBeF{M8X~^g~;k`<OGxqIGg`2&lR19qSdz#|5XV+_<
zRJp)1*WN~g%Ulj?=uxjGLo79GwTtsVr5xO16(zpQHdTbG^R;cRDMzg<&YcU5G6YKV
zUBJOL)duEWUAov7;NF_goR@uG(de7KTai2c)*@KHG3zkk`)?bx^7WTdJHN$$y<Ls`
zdOl`HXM~YaQW7s|-AN!ap)kjm^`^~w5F!&AP!7YArG}-Jzaf{$j(WiSqZr4RG8mPr
zrbS7h`0Y(=(|)YQI+x=Z4S-ro8mgE&WmL}nN+Iq5#E%<RP(cUuuxp?d_K(-V<V#r%
zM<&D)F&pi@Ak2fa^i@>X9ZK?y`$`LqAx_XhtC)47uczVGwfCzcw-NZak;{23rz<S!
zf^TO~lqHi*0PeJ)=TAYVQF2hTu$uwiGfu9OkHD;G9=}R)0vrO}XUl(fWiiN=J!v~W
zw<YJ~Zj9OXX^RyVzc55AjSbx%i>0=X=!48xfAg`;&=S*`T{p};P3l~i{zAmribahx
z79kgzKy-rd6ntt@A2)ZT5de(BF|Ir^_)T>xS1>`A5O;3gp57v6-F;HV6ak0)llPp9
zb+lIr0gF#RnM<&6y~^y({wK6hdXRF|{W#e&a{z+nUKmCy{mGAb)wCKI@arH{bpK6D
ze!a70l?(ee%arVTt6ra<Ku5|>ZI%8}z=m1>OS@ln;CiY}3nM^RM3$PS+S2c*w+9F>
zDoGzO8Glel?_OIz=;3X?D1f2Ol**N)^6E(5Ad&Tb<iCn_eJ98`-N0CNY-@G+Ewj;C
z?h~fiL)mH$(Euy-DTF7v;-yTnnGg}i0Tf{e<689Rqzk7alDmrkAi&zMS2+1x7eW&K
z)1PWWX&+JPZ4?$C^b|>MSIx3L6-b;Vax%twk;y(bxh+@>_==O>0553>f~h+p@f6Z!
zx&O<A!pQx$rVZx@<ft4wt056Igh?j&<wpk4bZduhL?0n@{gOIEr{zUQOX~8;-TM_N
z@6qDY^xi|0s>KBrpJjB1E~2IE***+n({vgLD&amdUBc7<75g^zUOm}i9pYi2ZF=V?
z#JbaYIcy_O$`W}pBnAY49i#dZMIlK=rXv{*>=kb2(=<?~Y*=wggX~M@`Q4z-D0%Xd
z+fI6Z(ynMxc)8(1EDt66Ng~+C>c*~?q124Ax2e?9<Q#*@D;w6<Xj<|Z@?}{aT$Ai!
zaMmR~Vl$ioAh=Sp<jG<q1Xb$4?#bs}(xp5<pxm0CIaikw{z^$IA9^24(5ABSQvmNc
zhGtEWFaCA-`oZ6~J#Q4B_!-?a^vNb<zBru_t#QvS{Mr()aR4R80kejYD|;*7|D;Pn
z8cKI2!zM_f?!b(>V)g0?Xkea}jR#65Wx&fFjmY@P$#Pt^9hGor+h106V$FklfLb;M
za*vaD&CfmvcD3!&Su6lnv}PgVcm1a@MH}sWbaj@<Dngo8w0m_)QO@zqusb}aFVf6>
z`cA|LPQ4|dMg6a&>eQf40RDJ0!7;MR=W%1}O-K)Aq8A%)IcY~FUPWqMMLrE8SC&=%
zwp`A6tjKjXn`-Z^B}(>b{zVO9-m6Reku=eCvmQDh!{N{&$F`Thcm>BbPyI12Z<RLm
zGqdHMe~I_O^l@VNGns9`?1b5lxV%JIn%7?8ch+*lDN*&yV?~wc42>8Ym+ZK&iaG>@
za^Dv_`<HumnA^xVb@<-=xULx%qqN~mp}`wwXA%*!V!JH)%`*7+`N=u<fzPItKL~E*
zZ!E@bg}mT)F96$-j{bZu_aD^ZF3()h+w+$^-W?-59PGW@^`gI}5imO47dZoK{V`_X
zbNN^QAEWOye_I;n2+KnfCg2+E4SzBEF5XO9rhm2th4Znzty?0^3)W-L@q@6a9sw+>
zW`RHsgkn6CQ@{M^@*3=t{un-EgO5l9UOrc0-L7^0(##Z}MH7UuIH-8~nUaP3b^v8?
zjBOJ%_0ew;C=(+Ez(ev$98~{4Cfj4RWseB}m8L9-hG(6Rb74f>@CTlt76zKdBl9IR
z&aLs|vi0^?oPOLHA|#z_Rj)SLP_gLQ#OM?=SYBE_8)X@X3)QNk3R@dRCo%u(0WLAf
z8H#N;P*AX4JG=LZ<R59Mpcw_pLB`+pfY;;-52$%9yb)XC92h50?*>yr*p`3>5gF~f
zJfP<`YfFLni8lk`8@nORh9!`}d2IW(Eu!mj8c?&_TNk01a++j=n`#R|#F!*e*pzzi
z*ymbRZu<(lg^Okk5}<kH#hSAd#8RseLpyK;g7J#Vwty-1qN!RjQ&cd`G=O#=@OkWq
zE8W4RW(`ikfiDzR<PL~7x>56x`wc3`v=PO>g5sM={xJN^n)EfcUd-804Xb}klHHxh
zf`{?yjr>@zS)Fvo{!pEJh!#&x2yW@of{~nx^};v}yG`@V>Kv036Wt|SDGPMG7cJWK
zY27W);9MnHCW8t6$SU$^WImq^)HaZ+%YKT!-4CNM-Mcg|swrR+a02-l-VFjh-+ADi
zLc`#)xk$V#+ywGiilG9v2Zp6(8z!>l-^IMQpRG?2`Uikf!iN+{Saz??|9lAah)L)A
zx3z{n5gNQk{gN5o4-s!6KZ!wuQT1#4m4Ix0N1|D4t&LvNVb5(yc+K?!8pdgYQohe+
zi8HdJPf`Cinpg60eeHd=fyo$ulSE>Oj#Wl}*Z_dlFSH1is1FH&<btY(ed2&{fGz+f
zfmfpH6!yPmk}a&m-VUljeel@0<a12BJg<?7{)v#g9Z=9~C!IS!pyUvZh{q8vfTH>o
zm0sEMJ|e{F{dO<I`=0JdRgK7%+P43(06s4{=^$df={Jp?Ha~C|Y&$P4vj+=X-cVnn
zVH679^q+j_b4oN3i8)}(@I@ggNsyWkhq1p9X9hjj6^*eQnt3diG!igpX>4Hbhy0X^
zm&q}vTZvES^ZiAP&ob-LZ*FrM2eKs39;S7x>Qz10R%~1b3e*M@1x1)-HV%ph1b$fb
zV=6w;o*#WqxKa>~RRZfOw&uWEsE6A*pL<umg=BU(P4Bc%Q+|)xZ>AfCqb7@XV6QiS
zbUx14L$NvHzgqmSvw@NaV>KC4JOGfq5`iQMUDXHPge~znI{+@8^Urs7-zW{j!Y~BS
zzfoxQQ`)wTMZR~zGa?EyVD&QSn-&#x+>O$kUn!<dni5Rrnj>UWugG<#jl#(ifw?B3
zx1VnGm=>z%E7W~#`v^&uh{noIwTH?ZXv6!^jvN4a$pMwe=8&Z=%HZ`BUhr@~Oa1wW
z-<{j+sCuy4GAa_r(Tv8$cRyb3?La7BmC>Y*sC!=NcqN^U)*--$UNKMWuj=x|7V<ge
zdQ%x0Lt?|7gYWAOJFUbi*KhUF>A5%Agt+&th+o3^S<rF=BDi#u>L&5Ma`8Ssmy^}M
zcf8YuwvnhwT?Z{ZO5aJG(;xsbagv1p+4NX=6~<H|Ch9ZGEZf<l8aJ!l8QAQrUEVp{
z*m+g^CgDRku>#K9tDns=vsmTbKWU-VukrBQj}3CFZZnV7haIl$bz5y#+#4OP&aak*
zT=tXeKMy|)6U9MN(m~_kK*$LXkOc|CE5TERzVoGZ{_>hc_I(h>R|`z@tu3TG+lTzf
z9$8EhX{a*L6MQh~N3g-A=MXU*a3Zx}h}O~m!S&x4jaCsr`dX4UAjd5%7l=x8(3hb!
z?~AxamD-S~Lf9uoz+g~+ZZch#^M1*z7iMmXP2+(c1nh%(L&`9=aJGSobi|Y{A^SDZ
z@mnit*mH6Qnr$s)rRZ%LHU0H`(BgL7DzLijGvVGy(Sl#h{>NQXW;*^tSe@4le3<;T
zE(UJv)Ug$sz9*{bl=C~;B-(J9t-m|w3jR9d-rByHm)8p2VyWt_PghrPHJ$2P5Tf3e
zh^1U#S=AEY^x)ZJLDzxBEndc_zUdkGQvtYukAKN+ij(nUMI1Oui_#GZU<r)M2(<B|
zB|8}zLVmt`saYDv#uPpg7m0YyY)48Fy2bQK;BWsTKnb;aXOg72t%F;qZ&d|++6$Xd
z+!#>02j5MIyXST>G9;LW)>za|BhP#($LfAS)+o9jKPvY2+)}dTG+;~la>m<+>BT@W
zjac4014!@bGt>)`*4^>H_@fJc1-FN=z?PIU+V{!1l%{PENo{=^Z6-aBq9B+~iB_H=
zLfggfPzLw8$SAiUIIRG2$pI>7N{)${^m)a8fb|F%fu_AnIGx)OtJ6Ia=>8dV-M{GY
zJ%0OgLWi9x-c9c|`jX=n7^+JdWQ-2UioHa%PcR~pP6)Y^$PT}>=4|aNJG>P}FCLLt
zGTGZU!5;WIx)Su%5#F+;@RemPX+D5AZD6Q=6lQ&IF>P-nauRai<J`62E7>WQlgP`t
z7Qud5`>;$=A274KHZ9=gCSrZPfcdo3t#dc=&?tA!W<5%4QD<YU&3!k?xAb5!n6LbJ
zW99Tyh*$>w=~Tf%5Oh!KqpvP!$<QW6v;4ja+}Oipl9Klw6{j>B@}IS3=kruuX#9N(
zkMHithxGR+jd?K9>(I$GQavz5p&fr4T(0-?6BD#-NZr1Nj=0A(`!6CC(H19EHA3%g
z)MxxW%173kSqWA*N@-+1zslfNB~p1bX$la5>L`f*s~rALgwWA%DFQti|C>rWIYd+^
zB%cJ_0aFq8t1kom>iO~Vh#d4J#Z%T8ZP!3ky5b)`13-THEUvev?5>{Bu}-b+uVFSt
zwRr%P$}a#u=r2hoUPaBsBf&uzW@;b;tY!VFQW4+6tvK3b8=fri*BsVxRZikgq<YGI
zv@GN7<F{d@X&!Br($SF1O_2DJ3J6p^NV9^eClb6Nghgt&7gl}?L}ILC9rIQ+Ql$k?
zAm^X%U=iY$Kb7(Od3s14epe&HHp)*8+eGG3H_!}6fLcj=5Q_zU->-zd4XcT;4Z9G;
z6Q*;5DvB|#9)njT?}2gE7hYnY{`K6IZhJP8kE9L(LL?4YhE}=V+dkD~1GEX%ib97~
zi?5h2L*5#B>8GbYSJ{z($P-}a;A<9^HU{y2kkZ^P8e<q=6Q9^7!l>%!i3`RN*!hFw
z^geMATdYj9D9QC7_q((ity=ZQt?<Z0hKRrE>MVcCI0<6PW;~zwB9HRUS1{8TwwC9K
zLgt#aOY7~Dw*O}A;l%oYFQd*P74+_;@AzSSaMVbB>zjfA!w5loLaGK_@u{>@-A@p8
zBltQRayaA*C<&&4_4@8RRQUuids#q0Z^OhM1Cb9TQ(p4a`g|{j&}27`(9>6u!4Y7K
z8u?sRPktmL!4{xJO!W4EvfBxC3vJy<(qIUiX-};da<4h4J%0SH+KaDV8RQpj>wUPT
zwzPur(0p&SE>&bi>es83Vv=rsTa9BLb<Jto21kXW3N4oGL$L4%ApLbTB%OEUvMNvt
zc(Y!uBsb(UmtB9|%vF;Vslh~r0vq`H>&+@g>!lL=H{TOyaz~!!|C)g)8JT4(bHkf3
zJ|0ncCH*r=zFpkQZbXV==j^Kt#!<uR$~(m@y<Nl^`*JBQ@~3KC_S;9>HXpDx@T5(-
zkvlwD)yzrGpvAqV;H1x0uwBQ&_n18vw<9(LSINxl+8@S-g*JRFOgn?4%W78lq<_p8
z!4Z>!omtrA9UfKI4&Q?hFaL=(l6c$;qU^grmS8TW|HxDvu-50p8Ii^Lewm_pas8KD
zx6Z0dgP$i78sKqeRNg|Ml(?riYwwB=Z&Rx|K#{coM4ni*^`-3J3uQf?9;V7SEpZXy
z{3Hzq*6u2)cng`YJ6ThVkn1V+eBlo@TPb#ORC>BD7D<}7_=vM#5RxFaHsNolZArP?
z+Hon9teU8e#0_J*P>3zfr3~XWnKUncT?RWfoFo<7Z!pcY<_)z|P!jYoeUZbbmmsW?
zo$F*X>dPL#Xm{)tN=-L2_j<bBiI)_3&oB4GCgBV!Xcnicd5zC!g#x1VV-=d8SsseY
zMk51JsF3@c0CJDD38&`r8c{nHygd>QSP*EGZ?TG;?`N6%M@_wg%32kL(gFAs<q(K+
zkO!3=p=cgwee?seVK>yq9bgO1TLb1RQQf*-Q5wWmTH;#JcY&vFnEdc=B~PO4L7-QH
z7sSB9?+G5vVR-U_xb(4V`P^+DF(va$cH&cL@!Vp2g<<$AK#s9a3=h;eM;>T1`8&gc
zG)^v&vm_BF5@z>`KU?vYZFsdXFDpOW3~Z2^L0~uQ$0XBVw#I)kp%L|zsh`3UL1(kO
zdY{^6U+H#uc};y)o1!^Jwmh2bVE4Wq0H&MyoC3gYk^Q#>M0_X;Mt<1ecp`d{+O!jF
zB<=?E`8GFhL<r4S)#mDEC@<>`VS6>2KS|W8oWBk<pS(Z4_S$SA6&t&pGv>VBexzuW
zf#x$<MZT?;<{lA<b>D89Swh70O1%?xeCW#3ZAg_`+{~}PP*95SGd>^b-R~`_3%cGe
zO<{Hl4Zxi)@Cl?^+>L}DkLtLZ&@B1%+%P6!@@~z70+Y_iMU!{NTg(3rD6dhCjht0_
z@pxR1SKBmxy;kRP{0Z!Kr?obqQY<}oZju(lIo9jj>-%W{+-(q%_mLAxkNoFHs&W&b
zlKO4pYVyb_+um-A_Qdx<&ZD~IeVG9w6zgDv6vrZtU1t&a!t0$+ZwubWnjei#=-Azh
zH5aC~UTX>N%z4%}FEvKC{IZ~#UW1+({;A4kkoUe`a!Em?)+hJ#`z+1Sz+GUx<eyWG
z-E*+sJvE*0^9Z7d5fcx`Iz!xM*0v&0f==cQ2wQKn<;<>a-K?-Fh8Q>7SwGxxGWB4@
zo2zf=tGgzszbP|i6Ob;U`EaB`o>@q+A6HEYAB>)d*cOfD4(Eebs0%~OS$r>4)}+MO
z`{iFJ=o2<57y1*9AI0o}--}R3PZ=wV+#aiOq@WB;0W``OZn6K2B&83dCOL#(#u;u)
z*NB39o-LsCJd<!6FfWdqxDsICJIvKEXwE1_53+kcnKI&Y$xPPH{2SU(9icVcd_JPM
zB*pG=m~lS<Z;qNh!gb6FgDL7XK7Hmh*!E_mw4D(9vkux<NEIFuxopg2`I127uHAgT
z1Uf?txyu(J3c%l2-<hw~JKlI}Px=2ViwlFJr`;9lEGd|zFf54ki>H9z=c~JI>U?qm
zB`U;e4GUK7Tw1;WrZBcw1HllM4;%$Ho$G|fddJPz87cEp=fMGgtgsBH+Wgt^^T(M{
zsqo9qr0n6<+_8=6tay{LKY%v-{b0gSB7yBB!*6i&G?}?S0>t47ko+^eTj2iGQzOuY
z1DHTn)^Vz`J}$EL?cYrBlzmO-B?<m*@V>8GVIfU4y@s*aZRq$2*jn}<T_=P6N3llf
zQ&=BBMx@TO`Y6L6ek0Vypu6<8q)M_#HJ^WBUZ$jkmk-avmM9y5eoK*_q)=s=vz5$}
zzc>7`0H1%kE!R9znyUlDZ07>CFMacM@9crY1<CD4yH6y;GZ4#tPw?s$y2Ip!=by$b
zTMi_#{tf3A;hjCaH$zyAodo&NCMNE>4z~#^3w4H^V78KWb+|-|Ctpc;_~7wlq;9CF
z^K4wbAY+9=uI3I^eEutzY@^Hks=0Z2A9qkL2%h5%y^9c>gSJXG$$fcKZckpf(KPX`
zc@Wz$%lsm0AHAA@Jyw`YG)nS^ch`Zh%}5wX%If{L$bGa1fxgc9R~|eX?uXTQN<ZEP
z0h8hRv?(SdmEQ4W*9^lCILl>gA$gdUUNIlBX9qjrki}KRX{-3UnRu^JlnX4mE9fsS
z%xxGo8~UOvuKzveq+YFrZFl_R!wcD;@#Y^!_dekzI?msp^~u5i(CS0o|0`0oXk>6n
z`G~8G->FN(m$|LxtE&3P9)W!U)b@Fl0~N>-_D+JPb)f}f_Zyf1mr1|}9o}VEL$b)9
zRv0vdCNwFA&H@^dE$GEHFemii*Fdw;UP=<r?5JVdlvCNsrSA_NWo8;u#cf^`2kc`S
z#Q8)>*ljd5;>+6a0vq+1ufCa12i29q4HHjC3>(9?)iCtClAY)(48^TLH(~8>y<1$*
z>orLkgbA@%SgchMNUX0zt(;i<(%>lx9cUu@c#`WbTYD>&-{^Tpu)M+|*Q*R=YmjE$
zpi=8tCploA9*1=)=^!q4kt0hr<_v}f@QxFHKT>kp5>{(49iceBBcpO)<(qrlZN<*b
zasaMHjZgt^ZKG<3-zxYH<xx?FS;0<#QM;eCYzprC2*P&M*&-vT>N!y7d^xt&^8+&4
zEu}Hgwns_cCg&Ow1}}h%|D1FH!6yYeXEg%oRg9Vk%ad^0A-MA^_;Y@z%)0HcE%nA<
zS-yUJ#vI9<C}uF4-_I(+C+4^{iB;Fz-3>%Uh?kB)!ZpRAPi@~<n6jYHZ&fG(FQih5
zD1oRh>i)JQv>rh<X(5}?@w$b*u5cypx_N7eeIk5MIlQsxYj=3S@zSxn?vFvP<3YC{
zyl1GDIXyBE(g3+78ZmCaYmj~T?m6{Gj1T|6j5@#(m8M?)Fh<7D!*l_Jz-`P*VDuv$
z3WBLd7hPNRkWQG0eHI5IR!$|7VF$m8p*>~&jUi5Cmw*J}(Hxqoz7CRy(ziYkL0NFm
zPl8a72w5w|ySWjse3EfXuH@0Y`3tbQGKTzG<=AIA0~#_`cD(LMN-BRDQi|#ouY2=m
zdRC<`v2Ru}tLzHB=f74geZ_HUDYG2Trcx`4qSqC^T`Xtl52Vv@@u8i~1pwq9M30@E
z5P$cZ(23D?fn3Ij^M}E9Z{8v<{PNoGj{=NIC(dnPM6*_ek==nZzW^c1qAEintea*a
z@m^G}AQo*S7*xg$qks+BYEeny)3hN#KR!!%zC&ui4Fo>mTzGGOjfLZ}`lp7~ZoO`I
z;%&eE1n{eVrB1~K)m7|X4-6^hJz+O-T9<<}-$eA}%U4+(#gg1N{lN5@k)_>_Yp7@_
zXc739_&r}6)Bq7iM8fAFx|M}ngr*rh;#0ZAfE3DQ-ersuwgOntMWFip3z8_%A|hfE
z#6jBeo}?{@7~hc!1g#=z)2RA7jtJ^+PkN}Ss;UEM8=ndKpqvKp5!QIzK5z_<6_AFa
zh=<x6kby=iJV~=u;KxCX!2}1TNSg;T0GZ>bO|(sjz$?ly>5?I6>U8Ob<cdV9{`qdh
zLCNP;`leq)O3z4zr--8gSAKB5zM5-q+iZMThdy`^TtE)ZFYB>y&l^`BKOJxvH=UAI
zSZB2wRN^p`4c<dBD~QxkIMqxa3a`hu@;2Y%i2XHz(w)X*$yBgsMcmL)c1w#pTpVIi
z3bJru62F<&%*O%gv{2w?m*saXCRQV-_J&zNJ2~M_(yJ!Tk7H&ybk{^Yu~VOY5CyzO
zSg!ro%6Vk(ruhPne=hxkefVxU9I*E~!j80yv_A`XQmw^JySQinIW`fvnVFNeeS33&
z#Uds7pJT0=4aO`q1O}2jp(<bgSkvhd<;E*Znith&?xo&CZTd08R8~qxY`b>cq7>}V
zAVRUoLEzDDwNgL!e%Vn(6JH5<)Ky>sl%Le7JU8jE`r%>c>Y2whdVDnfBf`d^tTjp>
z<+s%sz%RHz!2&%`bV_Vmx9vVy!F#zag6G56E|co-7o(5SCK=ot*pBL~E9=yBHAk&l
zJ1R@^@`8X9?7gFP;3EDldgoobK$M2^wL!?z3WHnvdIbMBWf{YmJI1TXGqCuF<p4{6
zcb@>*WfWDE%VsdFQk{>w=LMiU{~@%?IRYjBp%(`PQI*(4D7w^QX0;&=3Hui*U@8#j
zbYE4q2DY*aB6C(MI6`%Hl4|A~ZN7`dT*NVb?jbuF%-<LF>XbUVWnOlq5WWphrTgv-
z7mug!)5KI)Y*~ogQu2M%z&hGXq_bVeB2CPLd+t)ima)3Ql&baq#QP{FA!rfKVrcuy
z(*uX^D&J+Px7j!&Mwy%?9ab{Qy+VD@->X*IuwS&eM__KQ)LLD%QFW1{xhej#q;}d~
z?(_CyxJ)!3F?diAWz*dgS|JMzv|+a|Hxe@x4F7a&Kq{~)qr$8U>f#T*vW4WQNPrg&
zViSiV-`M7)xXK|9z=aMGuwWY_0PVEA_1JHwURNP9fW5#l8Hj6~%9F~&3(@wQrvab^
zvUj<wd+~2a3YxJ4Di)0bh;0i+OO}-oj)nIYOJ1es`ve>+OV)gLpT`M0q)0p%sV>+Z
zM?7UE7exJ!U{KMtvBC|V;)ZI{*X0CV|4qsi^fw#_ViGTMuOEO|I>9?GZ%ev5qz2I*
zm3e_ysdjn9kyF?E?gS5Dl@}u7Oc5>!Wgo?G$+wigdFroWMj?0-t+L;l-${2vcz3X6
zA(S6}qp@SH$JAaOb%e$vj^g9zN-*nh66tfQb`kb4AZGn4YhegFB`D6%`}RGXC{SsB
zJ*Q<Xp&;X>^Uz89kuVAi)frlXifn5~1!pGZX6E3tgB4>)Mqmr)L*j#mbOcsEi3kU?
zVE?HBY9Cp9{(jb-q{jz+zdrf{=jy{5c8YargL`F|?>i`TC(sWkjfF)u4IvI;ptw||
zkoF9t5NnC9P@8G;_jGfmFhKz|_-ZH7oXQIsIDkemQe{BO=B+;EuH6;zhqP`@@`N@b
z$6&9qATW==*@(xlnz>g>@k0zS2}cu@)?1Yuak2M8vZ+!y1c#6uGNLi|vFCA$L<!3{
z@FX0I<chqAlUctW_aCjNH}`OmLu|}xP4J;|=8d7pn(2tvVWYms9FX~P=rg2BP_UlX
zl!RiMd~S`ql}Rc+A>TrX0|E30S+t95OG@tT7<!+3s<m#YsYBt3w(AISs2iqdR{YrV
zIjP37(zW1Z;ugIQ4PqFixU}W_uwX-dm&JdcO?L-MEh(TWslcZhR8)S&5@7kzl|!zd
zq|3nzDf0d2$<N0?yKLaq;7st@8OyLS_<Z_bZOuW;w82rn@=Wnp;aKcdggFXN_Jn#R
zSxH{_LmFWCJz-Tj#9P$V)8y|0`U9fiaxt;8K3jibhT?5UVG@;RNPLR$&`(G<anf#h
zdZRh1@UCy=Y@n3kG@Z#}8R1J|Y`r~$T-|;Vv$4*km7uLnuH@s?jF_~xmR{c%kFM_-
z*iL`71{Y!0t|QHiuLGrsG-vHg)MuOo%+>a5Ro(KfICVez8t-ow9{o~Zc2cajSTZ;m
zJgyNg{d!cHq&e5@zVpv|)zYFK)9Wd_@xE^w=Zo1ws@7!BP;;-PJ*lHHZ7S1red3DU
zSdQ=G<h0CLqM^5~;=`Jlb^1o4%W?SAVtDgrM~?o&1SF8rER%sU8!f!)GSmJiy#0s-
zJU{-{KJa$_&zb`ws5s|To45Z~fVZ?66Gm@mDW!~l5>c1#=DYVbV1ZY|q45cUgPIT4
zCja>0Z=w%ufMccryWVS0d|z=oCx&T_CXfzSNb%ww&VZT@%b{^h4Uz#5`B?9_#Is;0
zi9d(}!)&n+1*b<%=fmd(8Q;GttS2f!*;A6=hG_bK*MSerC`KCn79-iqKE9?764ddJ
zeyhkXzi@;QwTj>~x!og_y!lNCn=%H|yW+1-qKAeT$)T(e=M!?$d$$hmTOJoM;ihX}
zxQ|$qC24Ug5=}Fb)um1Z0!+x}qqv3>dCiiuCVT2Saim$7JGOyac~QPG?pho{RK)T`
z2_?4ZeHmF`6F2#p_Bux7VT-C2m^_Nj(;+DVX|@?@CiG0I%}j$0%#M4R`jse5@pj!1
zE$~C=5~}oM=+`E#<xc8f1mgBb>JdlEtq-xktn82A9zMI=v4Ry`!9Tx)+1=)p`fdRQ
zzbW^q&Sb3nEH5^`5yaEvQ}Hu_x%fg*Rv7O0DO^$isZ|F$-v!+0fXL@zli$xqp1t{*
z1aiLh_DF4W1Ka9H$y{#@>tnWHSL!U`TdpdyS`{Jt2vuKI3GW2gYb+S|R;A)(gg3CS
zd5_uNT3+yN5SL~MGu9r(x+&c2bbW&nWSK43^g`*|9-EF?!*LXafqJO9oTou=*CF(#
zw++Y>Kd-~5@(;8ra+<JvIEYC0wJ|v#p@`{!(ES~D06pAIS$^VDjUNr=w@#}<`O{Gs
zvG(6%o-jopYvOPy?xnvK=CX78pU`8M^_ZtR8O?*^PMIH%f>O1^toWLiob0MUL@I2R
z&CYVLc0O`au@o(bP8qP%+ijjLw0e6-qWN74Sri3PctN*V;Ly%`Nk34Hpo%MQ;2%6P
z)f$Gkr%A@aJ%+M3?e?_ZHndutv8EJM(pzmjPNp|!IwwC5=$2d$u&*#p0A8~iH@D5`
z=bL+}KgDi$E8dxSZbO#DM@7tP16zMYT<5XJvAQ(82RVCqNLN18&)77ans{|I&z#bK
zn(y-FKhzd;-amgDmodHDptm|G^*ns-Jtehs_d|DK@c3+1!)qq!{yzQqAkkuFdYHMs
z#O~p>m#<glEy7HyVc^Qhbb1GSLzi&MFuq!~m)H$WI&0~$m|CftCiZBtX{kz>>J1>>
zNwy~c{ef8QVHJoeyrxtt+~c_6K~v*9`%3T|N!GJ*<ey(aVyXDHWvO#C6X-?Q@xx_q
zy=7{xoFqd1T0Z((XqgU&sozd&eg{OX_xS^i%}6x}$pr0YGGoQpEZ>;v0b7o>pGH81
zpL=<80}*Ik=V>a%JMJt>NTjj1P{lvfE>=)0-(QykoFk|<<-%VpxuB1fW<Nk&$(ruE
z;4ld){>UAmpiJWyjf3^p;#ip1{(BB1a$}sg7IqbNnZ6+r2-O46O74&D4U#yzQWWyB
zYd$-KQnlE5Y58G;Bu^{XVue|)-)v+$|AeL|r>%h$^As`yQ#kt+A#h=E`cqSpLTiAl
z&#p2B;~7BmjPaUB*BY3A&WvMU6lTEBpTG8bN44Ru;c;Q(Gwen$b?H_fmc)!I4&k+S
zlM<@BLmtqxzFEt-!(|4M>wV)CXD7jkg5I2i8Y2bwe{0Q~yIH~W_0J;Rn&k_F7Jofg
z3Qu^URLh*tpsAI|ZEd-#vXVTCL)mbtJMT<N7KfBFG&ZAc@<~s~Jr(LV$T#5JYVE2g
z?V*Af?gm@s6Ej~6JuE(WhY96{b={t^CAd4XpsXa0uOzMqd8limZm@6ZAodYIG1Fp>
zDelA}Jgs4Ro6QJVSem9+rgwdhyv&$>)j6K>($MojYyrzYINIbe`}qYluS6{uN~2ZE
z)g9U;7)h-!!fQ>i@@<*7EnoD$mg$V@$H!=(JMw^3754PK{CnvchPWpK^1DGBb(`Ko
zWSt0hHMiGCz47BnL?O_iYrse(NkX}*Y15pyoT!;!tLt6r^e`$TbYEKV(C;mBnXuQ^
zO!6RPles1(=G5SQRd4g?vVwvAeEbyG`9-}Ea+2B*nJMT-@w`|`vUh$^Y89=4Xnr}4
zz0_W#y02BG?CY*7=D@O`J^zpQsY1qk^p1ja%fx#zQuE%OZe_DH-NQQd3eD9z#A^He
z9L5ux=@LJ^0t*fC+g@LK@F*Tmaj0}LpCR`&z4XyyeFSX(0u5#jh)oUAcbaCu>Mz{R
z2h>;y*;q@i?BDYSQuB6R)6?_qidf9>NFdiVpW#o0Y}i;r%F!JYctN(98(8UiP339f
z&sGIP;PFLjTSfTC@^^gnn4Od6@JDZz@4E~}0PsV>3JDxa)pLgDmHJ^KNcYP*S;ZPA
z*tM80L#1PWU28DIq5%+SKAY_IM~CV}#C?g1R6k&PtGjh$82dWrmofK%JJ%zb%cAl`
zFL4#(?)ggqUcIHmzKl`oep3+O2F$Q12$ZO}IRV4XgY8z*ROQO5_Om&W1g3Bdkqae+
z<z7zxWiKCg*pg<_@OK0b41IIjTW&B@%3>A4Mk*<K;lsu^Vf0%mk`6fS-Rb1xwV+dE
z@^)1B8s-#Om5fic{h~>#;2L;z5L=3Lt(;tqm3_{!;Al4%J?%$;N}CmQ9xnGY13J9Z
zp}Cb}goxT-u_oC80`U)R(M^@71b+=?d&UsKZE~o(y+h+#UbkD-pFm&AsPy%{PD6EF
z1-R5RS-r&n#KV!WEPvNB%-nVC2ph`tcqkwndv(^V*uaOQXkM9(5LU)ANCYcIS`u0i
zA%VUE4LAI=4@(%1|1(l<QNK5OoZM9d<RO&*G{%ugUFhnnOXNA(T+;On?%i0AIC}zg
z=w{M;9s6hFwSO8`-uXYw(4W;sH>z6>YsYRz1hE2gBnQ+66)g*ZKX#E9D<UyX6r}&x
z6A)&;4K0D#>mc93O^9{ub9C*lff!^zU9^iK!OVDH;=?NEO+k|0X^wTRZfCS8Jj7R*
zZcht}EfY<&&z^v=uzvv<_U>OG1;>mB0LvJ54Z2P`r<5y@Uxdi>NPG$iGXm)E7qGt)
z8h~qAgbTzxa_0Upm`SNRS&~r|3wM&Qu=KU0vDb$0_K>#q@7aR3m-~(R=c_2@tmc!R
z`}9X<!-byj*;|d+rM>CSbH)T)tOfZ>rmrSZ7IL@JYoI^>MV6Vbv1X)bHww0HVu(~V
z>u}y$KKI69e{Vg_R67&FPa7F&nUt@mpcJEj>S*#folEp;C$TBkBv2!aA_!!5T(Qbf
zU0wEKx5+x?<+5dmaUacL`d-Z%Vv2d(oSalzI#Bq!TP-MOUtz6$)al~IS`aU^@)1GW
zR?fSNd@@)law0L~z9Dc8ZfmB46P;=p<I#M8E<y+=EpLxtx78cA3O?<DB=CCX?f%Fp
zKN%AF*8GtEBDDRq-(UcgA>>tbRCOeU3eYT7oF8C+-d}uWQ18Y(B{u)c2ZFs!Im`G~
ztNsb#Ur>m&Pz?-OhzkSL&hN`ZXZGh%#b?sWuG1JK=d$%KK7$pRKWEjOF>>D{2>ue!
zT(5W^kMM7xRaXVjt>%QX6-j*?1p2Ijj;LxTwQ|?PMuoe9od_{;H&9kP#r!<!KocOi
z3~*8p>^y?~Wb?o7CLfQo9O0c2tmLs%g8FPH!=_|_cv_XNNz=R3^bIWVSCvGc;IfOK
zmuf8{i?RYo7(N&&kED@qLvLq($A3Mn+R`mIJi#i0KusZV=m!Ru(BInlr~~e8LjezC
znBu^|rt+?MO7hiZ+icSiuRXkC6nWDk;4zDPh9b1V!NJ)e<%LPOjhtyyiN><?%E#<+
zZoh|4Z<5*hpUyUGcbu48uicw86?ok~oo%Jt1lAMbYl1&+of9-nu%&u>jg0Zj7{^3F
zpY@ybmLS$JJX=h;H|pncE#+|np3{5z07^A4^13_m>Nn)>Ug9xzi`LC}BQ3l>*S&s3
zaCY)UxBfQd3AMH_qCGQKQB(kK<X2eT{!{TU56p7X>ci{ya#FcW*iiVijyo_=crjNB
z?BS`amg+QLkVjEC;dVPL+V$)S!5S-H6&jI_6fFx>TuI&jI7;#O;g@-rS(m13mJu+e
zBC`4()<#KU`iL=$F-nHw{Qy!vCzse0Vby)U=@Dy6pY#I^(ln=FxNqj>V>iLvMAH$(
zT?X^UaH|%m{rAXOd{(jtp2y4Fav6=%pG0F<fEGA1yt->3B>cMxH`zQp^Uy*`suv~O
zX+~S^0HiLhA>2#!bWtE?PDZ=2J!@pvcvv$qMN}Y{JEdRosnX-9{0=s+Gm>$^{Ja|%
z{k?U5EVS0k=3|>{udMn#L}!pmF&ty@m!ib*&8%VMR=?%rIAcLtj+5+CN=~ido2P&_
z5!6(?X9HnCkEiyg@irte`^W3EhF~OAo<ZE0At?9wCh)l_61$EdfJ<S{yj);Jk{xwn
z_)OU)&OV2>myR4n8`0mjk2zh~kKi&V5jcd6-wT+C^@bMw@<QkY9f7Ikmvp*Rf27|a
zh}}P8)J2V%x3@2#ABzMGI28DAX5Ok62sE1ra1rzwuGAA7@f|nywSu1-OmESY;x`yy
z^3y8-1GySMP>FqjC&o@+0Um@vNqbY*&s2N^K)s>#B_5$uP+Xb(sox`{jc~_fMi}o}
zOx(g~q<<55u{WLg9j~;uuIT+hpMO{-+EAP}4r^<kXCWVDD)5{bv99Q&(?|rkP=&)}
zSbm0uk%IXph2CvdeGPekY|%y<k88;Be)y!LgM9ix(O4C!U)zkHu|?1S#0J^IARGWX
zm|r25H|VmFKGVZKQ25RHm)&|?EONJ5hY=AcQb?yZLkr8Y{?NDcAwHX5Td#bueeijV
zda+oAE^UFq`~D(G4$=h*R^JT2z{(ky3Oohp>ze1qSCy2X$S#!8B%AXMLI^il2Hzo)
z<%*lRZ}&&<FeFF40`V<oc-@&-Ti*22MCR7ia(HmBoQK<Peg>?X4#E}abp~60$3xbK
zh!&_oSg0g{>|hY4{xiOjrUkLCN77Q&81;S<w$4IN-f!4LlZeB1_h*Q3gfQ)2qs89?
z`vo!wxb3;A?T?7P#81V%-kcs*^G?mT{jVHEeAzD9yyaUBYYw2L;hLq+XAl(YQNR-f
z+n}$gz``}|pu)d?Da!IB<u;MGh8cgx7X%u=refSh?m0IxsYHag$VI;4MlCW0v8N1h
zAHJT$Irsp3kTAbB5ka;Sl&WB@l&t0ez6;&mDUO`Re{~?+UpS_9>C;(E&}=La))g8h
zScqd@J8>#|#k!uzv5Xb{6R}&rzJopIYpyWAT4Z*?h-cBI9f4j-Z3kH*<dE5Ia>!2P
zJs}~Atr=%$o-Vi!a~ty-n{5|nQ#@pIptnta5-z)8JOJj^7wKf>-&PQ^N>t8uBE<TW
zyEkrUl7^nW#=V-RY3$7#YZY4BDa1BstR8lXb^9^%y0k+ye?hJ}eu}pm(eEYsu^5Y7
zPJWIj$a|oPU;#(Qp9lt;FP?HuE5K4`Uolxg{&`Otu(MA~8DEag-KzF`fW)xt=wp8m
zFGEwxA3$lA?^eQY0Zqe%JMGMnc$sHh0!x9hsl00q2pAsQH0;s6%+>ed3ggP+@5*_~
zU&DifH|*4H)rcw9K-D5AZIJl{>~5%5LV}2?(?`nAzzqPsy;c4t<||OS2r`z8Al)km
zEc^j3>T!r1s4EXy?|yKFau4#QmOFbG?q+l~$5U8Ac~1Bx!G8^Ye@w>9jJbWBX?@Dm
zlY)in{|$Bo_q=(WVtTA{JgX<l1WrB<rVcNs2emRFjVT7=Go$XfCv%?ty8^th`U%8&
zNY%xg^+ZXYPMA#&3uBt{w^o4TC;NRpwBD(c-{2Bkr@W<eY~APR#u@aF2x|*gIjjQW
zJ4CtWNXzry%S+7HKzeJX)s0)VvA2M=JTBVd%2bb-6b`CWf_78%-t9?vReG3uP<`q~
zg`INJf!lk~(VcF9CPDX|h|?jiM<Xo|3eiX*1frMn|Dd7_GZG)N4mWxdxYox*8xd8?
zvXX8O(5T?00N^%k?ZVe&Af!07*oM6r@-*tJy(pF;W>wC5=FUKNP~|?KVFx(gW;e0C
zVnJV!;y#DVZbl9SAsQN=0hWhQIH&TYHV?{`(>W^4_Ir~}#M>U?Z#RcuG$Zbh8re-*
zQ;rKn+&*ZKM(S7*8QfTQdrTA8sB0rDcGTc37jz-aHfUM&cdg6I(K{NZ{iY2t@4B~~
zH>y0xrhMn_qo10t$oYKg9%4nA%4<8REVb8g*NLMzxrR}X)@1Bu3J+q)jAt_PhqY3I
z6#ZFirhgkRm5cI7=%-hi*e^Yz+_U6T*1xgEM1VJ%-&dSY3E1X|WBvky)sQyxnjsuT
z7syG3x=)bfZgu<G+xHWGHF)2L#q|sM4vREd=xS?-=Q-ev`}>8N$Q6Gk#U?9OmORMW
zdBmY!tlho=Nmh?RCx_BXb!E*)pw;8{&omjOZp#F3l-%bTt${v^JTFF6;a7`<38<^&
zR))K|x}jao?w4MFViV7_wP$#rkbbn&az%0oUvDz*sqQ(vuB$!ib}-N2ZM@h?&QmsD
zM(Rn+Jf7Ho>h*^npC17h$O+!?rqIV?1OE!ow{9W&l|-4;6f_iuB)j}plyKLApR}F*
zk1J+{+m=V(1cc)Xj5?A=1t4Ti<PW%*j+C(&q#^02`CO$X1-euaeog3=Xl9vKx~9&u
z17;|7LI5f<9jZTZ(y1Hjtbb}<GiMT!>GL)XiK9q1mMiolK7AI33vfuU4Pidu3f5~2
z>7t;Q5*7qY^9xjrg_smn+Q)Z22xk}@F87W3e&Qin0(wjT!4pp9%e(pEInt4XodjE4
z$}M>ynJBN=&hyMT1i^!GL`8Kj8>c{C`k=ES7bo1{4=Sqpv0F+0a))c@_g~Yj9*sc4
z=2GY$X#7eC;jqq4Hk8smYIPbf`n%B)?mb_YdON{wj|mniAoqSW+$6~sraEOw3IF6H
zb)?pM7mt%|ebX@1jHH}SYP0|0ckU6~H+#4YTWWdxmn108yVx@(w75ato04WXynM1_
zxeajvrw`iaOGpfw3o5hIn}pgffalBnK3V}nkFS!B^kWG)n*7`Bb)?r5rB`K1C+*qT
zB($N|Bcm5rruShRVtDKaVhwxny;~oU5(k<tqc70mavb1Frd+63<sL;Hz;bm|Y}ns-
zE*bqSuWMT$he8l|=zEU%u`xzYJh#xbwor=+T-l~kFA;S=HbM;(&<T4=MS_KqdX-~y
z#Ij1>zt(FO9p|OIqdFkyJqMWOIiA6mgZ$<FzrEo74?c8g+!J?e1bYQ>%2%y8rFAO6
z;14RRA&Y{qPtpJK-OU4&agEjxhhYJ!9q)jC^TS_JnUda8B-nB#DGDvD;DG&RD25#l
z{U=9z|9Bjw-&ZtMsNkaQpYS8_C%f?c+2;$M*Gqge+x8hLyMcGOq464YsRyd5o&xwo
zA(zPElDy`{DMIn71pOV?R*&L@qC>166vvfYpa&DnlEB}JcLv>#-OCbhRIU5T>4hY`
zRfFkIwR<)|hWHli|D7iX@B?UBg5A6AFz#W7%|EL`;Jy0n_d(ka9ON{K`c`y$HEHhI
zqB3!rO91ii_c<3TR>{WDp9D2csf8qVsb7N1yZy8RT?)<<`8Dx|b8_JLy+Z(^w!g^(
zlA7gdr#RCpm(09s;HfRKYW95C{or!&NwGmYu=K#lh1OMIY6zejSckv8D<V0AmTT>F
zN;K;L@U!*Jo5i%}%xm@EyIQ1fWcD}$&C~>JZtv&Tz!k^>tW>uRIi^MdQK$7$9=SR2
z->?`gkJmiWWBNbJkR%xDT+Yo`HeZYjhMDy|&>|;)lm2&3sV379<kjC&OE;hQb~6am
zd&hd=R)+89%4sQbMZqqT>|SF|*@VdFc*Rw^srQC*aSoDIYQ&1o_&=l1qky<>*aL&g
zrZ~49u;)_>YYcQ809!7N#&P}5u<hreK&84Hrnea?k9g~zTo^bey_`iDl+n;yMqJ_J
zM&Os54Y_1_$u%Jyste+KFZ&p`AZ-|zvuk`tsex1xv@mmPTn@LR+e#q8_X<A0>2!gU
zJ_AmdwmzsJ6o~|H&)(G)AT?HC4nl$t);mqqq(hZTz!2EPqL&(C1R-E$HZ$`LdvsOa
zZ0?%iNu&eTz1t?$f1h;Bi+w!3p6^#I+}eAKSVQA|T{}^dAXwj4d3bBUvV_UH8N~`S
z>nztPgFm444oF(yD)Ec2*=^%z6b%99aJV0#p;5J8s4;zRB;Yuba=)K4BuCz8xnn@J
z2Tlu6+T`*Xh_^qnbTX~JY6ZRP(n#>T0<DUJ6o7sHzV>dvxY00DMD<;Pfg7;pzzB&N
z#Q3$)l?u(rPq8bKN5;u~o$tNV-i(2!pAfT!{2aPhS!)-^|CF_(pulABFwbK>61yr-
ze>jqV-=zw#m^P<7C));(n#8mVR=D5?n))P#QDC_RDZP<q!a2e4iN+oQU1wME4C+eN
zoh@;&^PdlLr<<j^_*3JtzMqw}JdYAYG%^yW5zoY0{mLsADSRf4AlDmxwLdHvG(=mz
z{d<LZt;2QdbL@j5`8V;#Z<S~iO0%3Oe3C<dL`w9vd+p3VvH0(khE=83m(2fg*~*WO
z%+x1~BQf2j80Ghwm-s+#DXP({>p>te`i~TeFFm!Hh4cAEuc=-GIZOk|wmO;!{v6=d
ztO&le1g=m;lSbHCQA6`cdZ<6g8R~nn%ZfTfn}Y76k{}?ocCuS#k>IL6`g5oLuF}`k
z<Z=5b*be4#-Llzim9jdoS@x)fyq~Xcvo2QQf>Rg=8(SBL79VBS4lHxZPQ<>Jy!W!%
za{2_)nQ$KIx?Go|sQcS^f#cRLU<FDPz?`tzyztBYyw9B_;+i}q%>Fi-Scp2-_^@MY
z9^o<wt#I-?%nsi)m|PuOSW4AjWe=tD(#d;&8HI<0b8DOEAqXRm5yn1U-a7(CG`;lv
z^f=v^-F)OE<t;pV`1o*llrlw;=q&n}8tcJF4PM@8)L)`FE5JS{KF0-o%Ybt^TEM6M
z;^ju+(1VOa=eU{EUExHD@nKa5v1o8u1s4|77ybF$-|Wh`IsB5BfB??KJO(N$^wU7?
z?hh&ZxL6ILzw?;WpJvl~WXbeuC5+6=%fx-)^nNmngBE;Pa78_&NbUcn!;zOs{GiJw
zohoumG(>P{YX+FWqG+lo<Oi&lMsN}xTp?SvdRZvCG2Srmza7R2>XNL~`T}|LJYy^$
zSG%Zd=IhSz(6Nv;ip``esA%(Vri$|tx7xAAiT%7_8SfGTMdMCK+Q*hhha3M@415v#
zlfpj{Q|q`2c6%k?<`O_F?VgBVqQWQ6)tO4GF3JdbgqwCt(D=rPfEkbg8KN*ZRo{tE
z2oUZqkH+{*wj$#B+K=iEzn`*auX;`#AxcWKyBRBBi)Ay?CA=-jz%A_fpaU$P<#ROK
z3)JckI3$BMyQwjO>%E<QS_KIJnh|=r=h)*P0aFtmRHzN0)vL)F)>R_wQ?h29E2|!I
zv6A0;cav2OV^dc4QA*(m2xw@qDso9hZLpB}*$b#hE)C9WwLF+?k#F-gjW9i_toKnb
zRq+oFl_Vr?R@S>zF{jFdPQaxS1!?xO3f;3NtC||=+}7!i<1&KJtK{pAn;EwvO&uz1
z_%di|I>XQzX^(8yNxx0$^@du;{_a5Lercatuqm&F$W%^5QFyG4RKzcrm%DL9=~OpC
zU_nNi9t5wbvg(*GjDA@P!x0{XQWq?pq1ZBs-A_31^4KVmAw~`8RRbuWL??#N?|U=U
zHFJVP1_=VDJT|spkyx&7?%whyizEMR3A&9Xr7b*;A^@tooKUF0ZMfY*5{Ap|BZbX@
zsU;IZw}F#8*7VLUeY8|%%{IA!DpktadoQ_sfDF>qp9)mfOieOCGb4!w`iJ<QJw8#o
z8IY9X<-&`XE~Hg>A)#d{+<s3Hvi&D%ozZ2QNmX(z_NcrqXqE0hG{2NC%wC{cCCO#C
zjtf?G#y%KNtV?aQT<GR@dgvlpX}H~4Xbry~<aWVBINHIjQZr+lqEm_v@;EGzE=y^W
z%)m$rulHiSm9X{~4w%3<n_#%1^rjNNrvl9f$2NoJ(T^!zgBHo_8QjUS_SPv*2vB}(
z9rc*KF@AXeO3^*zp#%5Y<~dEs>x|d>hyvf~u>j5SQ%c{UL%t1FrJBLd*#uI%?5J0i
z9L#w@@ljQ`=w(mVn5LgogNn^0{d8k&rw|(b$$RyQE2EgFn33L|!ed!<?Kc~tBma|~
z|G4{BlS};{O=rQ@M%zW}1a~PA+$mB*ad&rbAxLp|DQ;<TcPPc76lifNF2!AoySqCC
zIC;-^&NcZ3xn`c3z1LdzqR*vAt=X1%EMpxeG&4*@By~4|4^2AC{1BT#JJWBjAny1$
zWV(K2mn=}GH`Hj}#+tr6E<r})Zx`MK&Uy{pJTbHa-%dD9tMY&BfS~Hb@eC`|>f+^?
z0FXMk0#C$cggTln?qQ1xTcM5;KMRnv2(R!WYD6VTpf6BJ_g{D39!Rviq&jki^MpQ1
zyamt!uxzvuxEPG$F#ZGKBHWoIpz{l005xzH6$X2ak`lots<i}|dMBE%+vJ^y{dBv+
zRIC}}QsdAv0QdKu7A??g=;rWUTqu_mFE|>Q#A9TcK{Y!C53`s?{n1h-19?v5NjmjY
zdVO$|LGqN-+g_TYc^_vIDqm;0C}dfEmu97-8@EPRZWnKbb|p_bA-5~MXj`0pU7{P8
zrl1A#SMJk0<(Ai=YutR2yGn_5jN6ylO8N6p8T8D`t2BgZl22$C;+B>)?gk6F`J~q9
z_8fNLg8TM?^pS=C#p*(x6nM!MSOdgExa;C-g}1Z$8VYSyQMxNZIy>ZIdNm5qV|{?;
zjOKhYxsn|r$h41Ak1lcFt>QYR(#*Z5pnkDhRBSS^xpN8$4fQo@eFf!PHf-YvGAlfz
zb5$S`&Y*WsLnC}gb0~sSlnIagjQ>~DTl%a-AC#FAJUWn;29kfxi0|SGxyujomVUKV
zQW)8Y(rj;x3T3?!C2Ik^&#0~0{oBQm*;KFA@DJvJ6jT=gJ_W>#CmkEmkDZGR^Za*<
zjCKKMOUrcOCZ+$Zk=t9H@Zr6(SYh68TVTXoGY~NWH7{;U1HNg?SZ<7><8h~}Kv|S#
zM4CWW{xCw|h$UA_P^7=B85+_4%j<0Qa0i|d@*L>07d=Cekv7tkrqAsE21whFN{Y~3
zZ9Q@4H_PF4nvgB;AJ-?jjQJWMhEUQlP!QdSp1kU^vU|AMJicV7<UG`4>vTep=J6c=
z{bGMPlKOeQ-S^0E1J#{;4+OCj>)lzYg+b;l(`Hi??%-eWo<~js5_Pz^`0>QUm}j@*
zCUpY`zRiVkt!fgw;?1wmRe$Vl^&sj!9OoIi-;MKU4>^r}$o^73u+Rt{){s=ES+XN0
z?6X$3Zo>1KEoPF03E{my?bx^aXwnoTrod~Um9!$^0=|S8SY8TnEx-~kic7da+s>BK
zs_~&<^ddmh%Qi$<LI)b`Z{HKUBWzLqa2U$r+;)?OpfO19C-c*R_vp>a^2Sz!+jxxk
zVw>;uom0O-(Q16l3J?%*(R%mc;rpd)4ekzd&ye9tL@SN96iQa=jA(RAV#T#=nfpP@
z{43Yd!#3x(@Dn56*#^1E2u}gk`de7^|6bX6f(ewUB=HO37RQ)Rd&gQ4oU%9;BS6~{
zWW^-uY^E1FT(Y0oW$?s~5x@aEITzfHOUe_KCJ%5j3PC<p2O;|gz)y%)FyoC8U(~e`
zD5BJLaI-U-F(F-5BB%?U>Ao9BSml8k6x<4ZY6V=kLWy{e#a|!P*#U!wB4&~DXU2gK
zmg+{mhhNe|!-%VLc5Qni@Qpy0Ms;*;(5Y4cQY*Bw70r`Jw1RHr0)cU)mjwi?J4-a@
zdj79XL-st;FFn5aUmLXSpD~NbFtK{+X-(r4S0{bfhs>W8%KNf~T)Ja+Tf@3IKslC5
z<PRB&?bB2EU$W9imDnXFMyBv4B8n-Ci_%k<;KJ|*ypnzILCr$baJp{5ebxgkMc*+g
zNA3e0^aaj(Pxf4KU6lC<4()WH`PYf#Lvx5nrP$?H>{c-Hyux0D?Ws~T=j6`W^H|tH
z)b&(N#+IocuX6%5d8TjVUnyRY1a@ULeguxHzrH-X2f7qGT%(3;QQjd;UDAEHM1WoM
zrA0`nKe=p$+Iad+UOP-zY&W`4vdWy#_N{b%{=xc8W=^`5oXYA@{BSzzOmAGOJJc|5
z7VWdZG0ABt!J)6V+@tlCc=X(`DcLgr)xtrgxqq)wN9hmn5hGKaajkdnaxt1Y(Yrd~
zz(8OBdMTOS%p>PLIk#Yg+%AiwWEnT{3BSxmv5k)f_q;QlY%df?^p4w{#h-PX>Bwuu
z2SP9yX43LA(?gJqrQM3k`S*^wB<&{@_TQt%SjI-;s{=4IHkR3fHUNs*8qrRBHFghQ
zf<5BE=Y&c0#*t{c+BlIi>cj)I^#NQ8feFlY<_11Oyo5)5R)4Z^=Cng@H)&10IX@tQ
z5AgWl2_BOFxMIOQ15Z(`8cBS7iDb&r;D2Ib`s3a8BM|^_cb%z;e`{=(uUiXUZb_=s
z5%kv#TK-WkI%jdes?2_-ds6#7*E&@1*^qT)`O%=g#n?aF=%u{0(pI1=tixD+aWz-O
z*6ZP9c6ZKe`s4FB@u$Jc#vY}>CZjgq@v}I;wV?aSaDCK&h+IA7PK}=|RNVc<Pe>j<
z9U&*|-7R_H2Q61C4?df`Mz8qUime5`1~2>jW>_}=7KbgKrB-Lctqc|qHQrY2NTekI
zG|?k!3+=j5w?H!%tvPyxtl<lUK*C2j(0;7;Ov{$E%crdOOXlFqNbGNBUO(WZa+||?
z(DRY8y(l_BeHaFpR1Un-*(>kOvFVgl_4f)C4rP7R&XCXNe#Q*^g^!Gs>=MGMsGI1k
zG~_ZkgU$!C=@(b!e82xR#f};OWpBHJmET#H9TOi^mt2Op4E#iHEUxLT0x#FThAfNz
z_Lv;O^q3KWxm`EwQ!6{IN#~-%nrfW6GS=L3ZT&!UfGDFfqA?oj`n%&u$uiT@VV;8A
zZgCk=rk!e#ZmVb3gIC!wB)uBDPMy4}`hLGK{?=@sdCp7zr2+SqK6eS;GmDe3J1a!5
z;K#kf#-+>HfcyVh0Dsub>BSF+arpx0L-3D`C<~I2VSRFfCsCu9{+MJ-CAFiUt0DPc
zKVDH|;Eu$~5@YJ~&ATpHWjeM3n-t=)*+6Y*w2gEn$Ro6iqJpbYL}G379NjdijlorE
zVL)^}4LlGKyNi-w)umVb0Ed)|AsxpFCS$MaM+REi)ft*WS=NH-Y@+69dLqr6O=?`}
zsD3ZN{*^fgZXZzRbtPiO&D;iOVFS`V^c{Xpt>R%pjDD#Gdnzpj!tY4J2#X7=fTCE$
zEJD?;??010+9TtgRpaO<Zxx=S!b(sLKStNpL=d>}${4Z3n;A(j^&B$?>EcgFQ6+31
zztmB$5lAV72tqjly3sTIC&@q|3A7o!q;T;NX=pw*yl<UdeyW5T7L`{1xP_5hig-%f
zg}5lyi2@8k`N#IXm9Di-a*5=*)uzHva7?nF>2ev4Q6eh1I-yaJsY=%YJ*xKE_JJjK
z&>k*O*HgSn2_k-r`WV~lpT}0GaW1y=_BoKpv&J@Pc1?N$UiAZew7w9t1_xHQ-CmkS
zvhO*aIXVHnBLqoK(wcvcl^S2o?dvYmZZUW)?E5&L!7c(8MYyBuGOTMf6J!lJarS_r
zD{!ajka~*qn8=k%(0}q~AC9|EprRP(E#O?OIs7PWyG36F5ku0p1hO3C<5uUU8RQ^P
zl*%8IJ4#3xEY)z^XuTzPY_LQ>`b{oihoFA5L+zj4eWlJr+y$Spp9oxUSJo<s@mTWr
zAU^`n>J>RceVC5C70N;%wABQA5qsbszh@#{!;KvV!fbP(gOp^(=Y;GyQf|g#!?;%6
zILJ&r4@L+)x&Lti?-B>?oVo^mHWv_^B-7l(I$L~A#8riNEwwnn^^VQF+GSu(Bap$F
zSrl|-yx#`A3k+PB3jF!d@|a^keo73n=Mzr{T<1JK;=exBslzUk|9qs;LYol24GM-C
zjboj6Ja`g!EYUwdvr`!x1a<grGQW;-pFO#%cRWrzU;loYajkrqGfK~8mWeLnkMB}$
z_b=~w5qX(b|C52!MSctq6Ujd3_)l>i{#mNPe1t3EAAQc2TLr6DrwjQ<R^kJi1dQi1
zIV`6%@azBiEmFGM+rSU$Y0ul;OlLb%7VA>+l*sax-7Q72JMd2Wac15!#m`h(7>Q8h
z<q+#f6jVrBZdSzvBjuFL&v@es#V&<lNWxXa8qf0G{u0LW(t0J`_>!NbV<vH{`%e@}
z9XUZj9m1HjQ<z&&4aG5~B%?CMu;FO{e3$+&uH2;SW=!8Rj4O@Y>+Or<cxcse_(fZ!
zhkxoxSQtkP6CjNing^9nMsmUJHKo6me>bR$MbQ5|k77n1;YiblJ;0)@TFXRq#L<Q$
zj9~1L#qJr~DVky85rT0Bwgez`N|hI;=*E2+aXig)<#m{^GF+HXKtPh$1CrBn06u>q
zqFMH~CKE*yaY57oK!aIBWJUNY@N&+nXcn`*R!i@owiBC4^mc6&xy5-{n^wS>k6f_i
z9LH0juT&v^!fH<>0g;IrnLe6H)uxQtWOI)Yjf5yq>wHP@HnSFcKZds;NOy}*bqHST
zAhpJy#D^aZVa7ZP{6~Ki3}O)}pTQkAgo@+G=#l-AsN1uv1W?i}O4hrEZrJkUJ>ag?
zw(l)*aM;bd&e4eh>ivlUdD}LYJDN82H6YtGawh$h4cxz_=;ZgSZ0%)B1-%C2tN`Of
zCUDTAXdZgVdw>3G<U#6FXyGetPu!kzTHb&<M4FaW7EZg$vLh#_zWid$hI_w7E-^4F
zitu4SJ_uH*ueg%~qfscxZ4lXVntj0A29k~F;`CwkF%nE|;F^^s7Iw8!U=}zE#p>>&
z7?bCZo}Wm%Aw^t9S0}y<-LI7C?xNY~5f23=y+StsYg6XMLimK1M-2TO@%Zy4BKgr=
zg%0O*^bp|+v?!nd+5S1M370{ByaWO90TBv0`kYA{D5$kCU2Ks(XJIfT^fCrACKHEy
zQ65S8pzE0C2;^^XMd94Pih<VXd+%{Cjo+i=T2MdJrY^r>Zr#hlK81aqV=-gH-mZ_2
zRK3Us;~5CrMXtRr^#=UBh|V5WzcxCo)+I;GpZpO4qy;x$ZZ|v}rW40wXHgQ?zAtCA
za#G89VroP^kkg1RkA8iHgFDLU36vN{IN_m+cR>TR;oF4o5=c!xMm-Rl(h#yZ!5w<B
z;i}qb>*H#|CqJD2<Ipt&M}h&F`3g1%bji>J$+G%cecy3$==-e4ug46^?Oft0G0gJm
zhb)wTP%5m{nl@=MS<h`(xYI9M#Fm3lpj0MpfA8+DgkvFB#cOT$kE86yla*8MyssNU
zVq>YAaTB8}y85T@Kk0Y$QvNy1K-{48uuy)#8ct<cIkVDH38NBl!56ojQhSLDy4Dr9
zay?t|`UC2mRckKpY?r!3UB7@o-?#94u>heJ;FJ1fE)M_W9iyiMjz32Y4I7ERzEjO~
zvF&WL+hhgtEjR(^>88I{9<RiZ@hSx|hVv-9VOEG2|C!cVH)3xyb_=I^*)|XSO|xpz
z%tI)QKeU=(ryTH~t}3Vu8-#sZGn>mGKK<!4zco0k&FOoayMp^*!rjrG9T0Gr%Nr&V
zepxeSh20)P!i+W?LP02Hnl@c_nRpX?kKWz?BoG9Dxc~Fc{<24ur}X%6%F{hCeCSR3
zpey)#8T+buhqj!o=<Z-M+KpJ&Y$Dc5|BrN0;g6(UW*>kD5}_pq(x%`amKus1sKF&+
zWdzWlTkqE4|DhQGs31tNY)`^P<$s5id4d25C=whJX;J?xI0FJR+d!v9!?Y{Igxgo;
z-}%#0f{ejbU{R1hR0vQ66!=RCKwFGIDrds<&xa2SOWZX2K2A+LAY{k&bXmG+NuDIg
ze1dW9YX6h(3$uMw=GV|mOm>X6@k7d!yjfdX!{K+>Tb%O7fX9(2+(B<JC7ic}$9qtT
zpYu>*6U2DDF3c-MqJJi$zk00Hfjr4SAHQcs%t7)RyQZ^xJ|z6eKQNSY7De4D2{K+J
z+W55SN@^QOz7iYTF+o?nLp<u+`4+w?CI_<&@UNQBjQ8~=+}YmUW*g}183UVv7UM#z
zw{f_udfy#f2!-EMwChts1o|8acE6MB?o!7{LuF`p@t{x|q?3Eet;{Alk_w}{Kaw&p
zRgVUxfF`4dO4(+JmP5*8=?{O?rwRuD+HlDQq|m%NH;#05{Sw3vVZvLuYpl;%dN}J#
zdzk~joGrJAE%WPIQ#Y+1OhzTel+m@F#UVc=zeM^oJWcxUmDB&oJ@dm|aWqo(sdX4X
z=@suQ*4Oe&rT?jIydcN<Vs1;Ek<6k;ctyR&?-|hd7<r?uM2<EmIzruDom`)lE!0)Y
z))RhAh9Gfw*YUct244~dKHtpU4J2jw6_kXJ_$I6JX$Aq#6cEo>c;^fYT>&B8zqmp!
z`HHbkX2xeN7?n3fi}6foDVu<97G*C?=nLD_FXyO16OyQ<L=u;zX}<}@7@j$FSK&d_
z8wP|aauIJAr!hU%<>*_(f4S#(^v@HD{ZCg++55PUcWHs^^{h|j&W5~qOD}QlO?62R
z`hlT4(G^R{rb+>;_T!=Om2zRcK39798qC-B^<-`-dSaI=aS4t4=jpGPKdZj%8xQT9
z@8yGr5}Vcn+oGBG#o0Wb6N2{hNrRw&I65`#0)>(~;Kp)Z2us?4{oiPRA3C;GNdTJ{
zd25UK^)X`ID9+=SZ{Wg;?>ifu=)|;23qy)4^4qt&@vMF-07PAQVy<;V`qlPsW%p#$
zE9kRh&_IZ)fBcN~j5pv7g72rhk=f~jsjm74@@HZ{b7&4wGV@0C@!j^1q9n@6tZjF{
zbf%xgdqn@uVH28*fFUG!V}r72>W{({&a}ddjybJ8AlxHDFfgB&8IDSc-bk$04&3>M
zQqWX8*xlxM+X~+ny_=Uvkjm^O)AhzaJISth+yu<`_YrUCsVt21b$iE)E01q7-ze#W
zP}3G7)~0IxAq(Dpb_AFt!tdw6H{aHL4S~2)dio#e+HaXL4<Mgr=tQC4&zXMn95Z9Z
z9hb=lvZ;l{;dRL^n$M$*suvn!U)#})z`L^4eY)=~)@((uqIlIJU>DMjN<mVYXhG2M
zql|I8X}NU^SDMf0!w#1HC(c1nnU9#mIiAKtiCGZ*LuPn$M0FjX$re!8JE@P=J@|EY
zl|*+xDCTaE)kAY1jpX;RT?IgvX39y&gutWA3e&$+2;qZ6S$6>fU?#z<CwVqr%j+L2
zIqQ7`p9J;azBr(7^>+&WEvzl4alb_(AI8xjxYjllFQ-Xkqb|lUy=V~kz%x8D=oJf7
zSqXsO%#Mq8^m`2;pF-8WOjd}2`cM1Q4X@Z8A}~8(+hmK8^yidyS^5W|G1G-iY}29i
z+1C%>i}k~5V_D;5f1%VthDG??mmMApVMJ+PZt=%9syH0_J)En1L3WX1*K7NCLL4*a
z#)awPyOjYJpZ{|g><J7EoZW7CZM$!~w=F?O3%^|zLL>vLzFJ>_`n1&S1mPl?JI2O{
zIabJ<-P}SxQtSUFjO^U!9rmC}P1ft~XXn5xat~{^*qEcnt+*@rs?f<Pq<ba8gYMFG
zAKo1Hv6sEJ(K@h^>ue3qA>a|~<ebS;k$eEs7JU9<$<Y~Ug*|bf3f5*BA82HG(zDLp
zIcwa(2|C(oc=B^@AFu2fLV0`F1Vz_87eDxstc0Vjv^p}rp1s37T^fATa3&TVk=j#^
zWHD1<D|&H{ve{niVsK~;k8a(ccVN!DH3IHr#@?N+2014%{~gsyZq=j8e89lt$p})@
zHv5$PxuK0@`M0gtpto`WiiW`FeGS#8FPgab9pavuL4JqiFKzwe*DXdJ83^0AZH|5&
z-s6ALw!wkz$}Q?G1q=g<GR)UuQF%Kl^?u_O-{XzroJ1ps;?7UJHr4Z{>*|g+!rYjZ
z+y<5%a(XAbirz38RK;t?xAyaF8NU6$2@!qx%!{(_U#{+dWYZeGGx$fG*@Fu2iys$l
zTx_L}HxxMs5m$txR%lB=;xHGPSAHWOImGh4JpqMR&qd&E)=p|DMhLRVrlmW1dG~*S
z&D;f!y$B%R=!9X}j@8FTiJncPCeVa{*aZ0S$3+B}<ToVTwQhF+{y*t9{t;Wm|JvmW
zU!(N;F@_U7os)qhg@`c4EA8^kq6;`)f%O~1(Pwa=@EiQtIlD`T3w+dmTNevP5q_X4
zuWPtz_h*;>O@cI}8Z{=B+C@J^WKjmPUk(Me_>nU;KRmMRrddE@gDo4~;jCzTPiGx-
zXARD4?SXI`!9Q4H|8|&d_5IN^)u_wkC!}GtAQs)r@LfJBB8f-}S3oDpmp`d<@LU~k
zbeMUZ;Ll&qWvj4Jp@2T5!>^th4qiIAh6k1KUIA^#Leqdv7kEPV7oU5PNn({sOn3fF
z+xFd?N47gERwmQsoiO&^j*Cf{+YjFUl_mGZRR2`JfAk(nG0`vl6uJvAQwoL0-*enP
zg<1cA0UXZ&nAC()=H?J2bHq5!k%rVuG<QLT`-ntuhaS_Ys22!{eWc6X`7={CG&{Ko
z?6Lj|=Hq~WR&A0l2EMhlQtK8fPO6IOE(?B#Zg9qNU6^)P$7gs3<F$Vr!R)&Eok~v;
zye{0|copY4+T0D}!~v33?a7sqXTx2=xv8Q?new1C%TIcl;@1<>qU)L9tvFYYF+QTB
zw(6smcArCA_<*g|x%8hg){QF#tLz2R@L%Dt-2slv@_(5IdVG8NtKe1=2{L;{v{1QP
z8WaxHAjUbX_qG;8o@I#n6GK!`Q498T<G`k`D8S#QqwKK>)b2HyLjjPL?n~jc6}F(z
z3R>!Kn1Byozn#^fV04ECO5d5?m8rkL2Z<-~my7scdE@O;Tq>om++G`X6kX|Lj2SxZ
zd!Zox#WnFm0P;Gpi`?hzl#9Q4IoJ2FlurJk{Pi3Wj-Wow^vpN(y4cITn@;S(*w^k{
z8w*@!g)h0<{cZVv`8Bm#I!B#_I0J_h|M5EV!7fxK>Lw?$t;^Km7SaPjFEx&@{LCUc
zeuOTxRO#Ad$d9AD>IqwkuNP+q&ziVr<<O@7;+RYIIEoX$ku~!$p*=s0j(iJ!=6@~@
zd?8vOSoMSaVwM;<yq9;+ZH1Rwcx_I7+WEtJ&Q5v&Vc9Wo)qEV+#6=J(S4ynssgKIq
z{JG~b(lPI131F_CLdhQ}>ap!y`p>6Rr7Hw(hf)0t1g;5iS|1`Q=z0WNzB!Tc>qW=w
z9o$VJxH?gTgDY!nTxz~e-1km}5R8g4?LPcgV<-J$vnn7cHaIw0uLk}qxIp5G0Cqt@
zaW!M<e7<ey@NjcTO*Ti&0-tBu&@{uh|LV_MjPf9Xj80)7%tjDtd0j?e9SLo(Cn=7c
z-zh5@`G+68TzUl#XF8Rc-{f2KcW2TM*0;is5D;{lLoXSO4Eu3;4X3KO(3QEQ7?eFV
z^-~8e4^|hbg|K`Nz+V>tYBj>0gq@niX4r#~y&_DzE!ki&ud_EHAe5jgpWwH!8s9;d
zjlEDcW04P@X-uDNJC5)%N33xtj}yFi97U$=?C?fCL~Ka?@T+l=%u)f^>~BB%H?^bU
zsm9CDTAmVX{ANM?5?j&TE8~imUUsqyH6CGJr|KAp6OZ64C>Lszg_fP=U}onBvL_W3
zpIL>Foxi}1MJ{ex-w7n6E>T{1=mxt!`LIGYPy7Z7DYy%KDm?;YjZKBl)j}H702XRU
zOln{bo9ATp1=v46$7e~B_rp(h{-GDclR2kw9{y*9f}xv8x4PWqtv}Mv+{@F{7ot(n
zxk%5Z+kl5hioUQJ9Rt-4bKt!o@MPaPOrcO(Rq%WP{^EfrSidX>EXGpE=9^R9ILKtC
z7B*%($O!a_OrCndL2>_F6RI_!G-{oXLbLkcws{@m5z0IY1oGBOC>9D&&|dR%E$+2)
zNrPk{2EXp2V3Vts$<je}u6;hcn4xH|J8uG<lS+tzy*pX5rjq^7S<>0gU}tiNsxQg0
z)D3Xt<l)_T>qLYP*46vNNUo@lLXOI}V$q%^t}{=*a7p_>AX`?<qINKrN>qQ(Z>CS4
z+^RJt_RSsK+`Mm?B3SFdu)9lC<61EI`Nv~Pn)6PW_3{>K<PIT|$*fzU!i}+Nc9o8a
z6WtI)sq1ucHRu;(?7s+Zmmk#6)44A{d^!$(W^zSv$^lU?M=`rNBW`LP(pb8?JY$2I
z2Lv@ITxxuN1qdBo!XJR_NW_>@H;e3{cn7L~8AH^=WP%e50-gNqL$zv9ZlM?2amxU3
zo-h;W^uuWkcwp9Xq$NRhL!FR4D=Tm-$M=w>ZRn!;U6A0@aqUW?JXPrxd*8HacbF{+
za7|FNOw$W^en5fq!0mdeR+dnjMVdhnQf5%LEC>`Y#Ujn%uIcix2^#u3qQhsD?a+DH
zoW?6jTO&wkiu~<ojibesncf68DOb;@&y%ub%diXRnj89p@Fo3aaF$Z13YjbkJ5Eub
z<4q!nk+hhR;dN?RFNogQRMp=0^?vIEzt0f(jY+G;KBW(T<p83(2ff@f9qe^^RB-Ok
zo2B;4Q~cP+2zuk$BZpd#$=|ILNBnDqyU(A;u>-0-zZ?_z@N!w)DqrHes~nR0h?tf#
z2?pfFdTpy7pWsnnNZr4sc+yy&R!b0#=#^)}c>CyhesZo_)vp~fK<^PdLM6!LTJ2;$
zHvAIHTrN<ZQo06q!wUQ{csH^rBk}utMNy&yKlk{;imI!4b4Q%O;OMm!d&8vfaPw2K
z;gQ<>CYKi++uy{={;WLADBo$f=s$jqevEk0R@+bX=INu^TkR)WZ)j_V82Um5vmzWR
zE!fqqY~g&Zey*?f1+CET-tQhud_g`@J1mQW{IoBIbnAWVxzrrIjUdUAK6PM=EJu(t
z_<tI|$9CuQ-CE^V1<B1s_3t>nNJV)Z!fgqpQz8p>{{;mKlgYo^YkbMS2Fa6cKoBrR
z1o2<s*8n5}vvz|*tHQ)P@b{sj?XOIvRGmba%o1M;z7$5kgV6{8NxBinr{3nGDMJQ;
z5+>6mdE}<R@DupJtrnN(&RSlV^xj`4Sw55)iUoL2dz?^sK&a7A7uib&P`DomLzWQc
zlNt<%u)CPKP5|f4OviUoEx!y9n{E)9w81@9U83-uA4U>_%79c<9bk`EhE&A6>JoHL
z?{6t-h2#>3OerBD*sPEp4Ri)Ou)(PsUuglUsYx?}j+l7pT{HkW5o^UX90KeR^B<{i
z^<e)xDERZgHePJWCTIio49@|kCeZ-Z26!gjk+^QUY+wo))6M_26VF6d$Kt}8D7P2U
zMhE<5Un<8L@7G8Dw4a|!2@s{|(Kbjo25#nzD+6e{-gLIi;_5Z(e=1+zmX5>rG9z^k
z`vEY6lRC|u;8+otg2D2yG>?v*eMM+?2i-PaXyO0>i0-jbVYD0Asq+A!yiNve0r09P
zaXS$^MV$U?Wcey8CGifSUo7Bp|6WPB?hiV@jPxw<G-*rqL!&}aH$;c+0-LLS_WQkB
zPfZkJ$@ng&kG0d}YDhv8K5zy_!&(h_Vdg&AB}2>=Zf+3D$%flgf0@wECf18%EJcOd
z_OS+7=t!Bh*9PN?32^gqq;LC~o?>!Zou6<7?X+0H%a$^m9Fv5Uo*9Ne4qNvbq<GH{
zg#Ox~HN{4PIEgR^O!N<H?l=vzHT=6#hB(a%wxWJsi|B#8`8r9?8K2Fjj<RnqQupMx
zWNE#n2X=<rQ86&Cf1lK^9(hy5yoD1D-s8|9N}dia5_gh?Oz|qM0%E?M<y+9CU3`vX
zOWXXs!mLx51sT-tL<bOVXTdSxA2KlP%`N2RVv3+&fd5=^1#pmEyXgC^-m5>BH7UxM
z#o4wF^d}<HvNFX360zp3U6+Vx2<;)4m>hTsTJ1h*u_tAu0AM1j#j)9iie<`xX#PZQ
zk@gso7+tc2E8YTEl!iwAMU<sCvL3CMgrY3?;UMq}M1Ic0MxTm>^`@ogjq<ipVVyi>
zH~%g9`ogq&Jif%FH^0m8FNbGeF;5eC*X0pt$PeXWn~g2#j+%wfzAdob*XjTk;8vhS
zt`~;2G>aepO$=NeoU04<Sx9KXNhs?L8nemlI2N&GZvpX9sS3z7Q(@DKG&CZBD(29r
zP?2)4MYmF<Z^_RH^oObSY__z<G-qWQCH?*~ft;5Bw0Qq2F|(D}Rty@7BXg|xReRDR
z+XNGgP{9~h2j(ZG<7XONdePl3z=@QR*U74WQ{Z<ma*w5{@_&}5{LM!$UY#>7&ArA(
zgC5y!(eM6prCU{^>yd}i?@P~LDbfRc%Z_Mnc_6jjxQCH96iBa;(Pu;tSZjXVZ~Vh^
zQ-E9K|CFPTBSW4otKEQTp<TW#AjbB1IFRPQYhWBG@h?XDzYX1FTMw+dhac{c-0M`S
zSEu_4_ZeWL0)nge(IP`**}A%(v>5B*gkq>1GV~<(6;}K{2~XcFAYK*^bao$l5oWvB
z(ok3Zj@XH!M#0P#+XbDKA217;<NRXnI?$CNtCF<6ukT^D5y}XPmPdLE#jOhYYZlHL
zf=tpGYp8cm5`m+Ex6}C@U_yJ@g(7AtKOHSgCLw}o1N29T316pWP*#tleZ$s@|L(f+
z<j+za&iuA4<SziNBq0>s)0J=?bo1L-P*6zc3gsIvEnd%8oOOz5VI*cKY%v*6g!VYU
z4C+z#GBg+*Dyd9xzE<9_`irb^r$;l&SQ#}R<cON^G0ozlFhKs3ro2C5>Kn|N|Dt3M
z%1>{N5KBIiS(A)iDhv7ByNQHJmkexU{>^T~?M`uzPrzd9fgg9ZSti`~*jHcVPy;|G
z*!iSVq9Ms}1pB|Fs>8@Io&$ti*~Z{7#x6vOk?Ux*8#`H@F$-7<c^)U=%P8NHN~qkm
z`#pabu9V>;37QEl(u4rq)l7zJBhFW$XDqh{*rzm8L7E^A^je<oxI8i6LFeeGHvk6!
z@#G|wa)Y6S=b3G|S0;}9F~a`F@qPexhlmMOHkQQ^2gpCfM&0-t>qN$9ejdAj%*ck_
z;z*;jj3irh^=?ofUY(0?TYnqsoRM<#{i+}M0w4HjGrNz1VTfoL12NS^s-DnN$hZl2
z+{0UPeuoeS78UV__hQ2RWFgoJwPIr;9a?6T{e5qgXbqKVe^Y~7CH&3H!~w_Q>&9hT
zfK`VNX4zlffGjNS{<V7k^w%VI_*w0l7g%+R*ei6XQl96Yp2|r;`r*faJk7Dcz-@6z
zt@-oU<!|*^ZibIQi=Z-tj0W=km<KX*dV4dAcxb>y2fQ!lKm;of+i4&?qp*4v#U*5f
ztjKA*1$z6f-;^lxp42?!3h^_-x8N>*b546FLdpa|M6rUPcQONG)lR5*3D*M1D7}{n
z4sc!>l1ro;4WhNE=`3snj*jufl=mG`P5eu5ZaoTIcyimC>HI6c+fuf&kumsJ<9jhq
zf}%!x)}QY7uNN__HJUI@BMSgTC}dm_c`q2p@B>f&DpNllvEg7FuW13ZB=#IdUK!t9
z*1)p9`Ud9&)m(SH_t^6Zj5j!Iw}aPc`)=|Un~Qi6bc69%+M=fM?On)Xm_tC9I6&1~
zrsX{{b10Zl8l%j31K;h)?9y|{l50?FIHHCpP}<BH)pFV`vWc~Z@>Zjft{c@qLKEdS
zph{uTYb+4gPs8814jJ&o+;Z`J@m%$a%t2V>wp4sTce1gy*Y3`4$osdvacQ$+PS-s`
zS$A}kn_j+!+_{4({gVZ4T@aQyp16#@hFJUh*eba=cq-9)8>LQH;&0i`0>2b(yciwz
zD33jV0Pn$*4m8t3i%dmqZKq{#IH;6!5)>Ky%o%tc45grzh#TlklxH05`YV5XSYUb1
z5WeaDKLXJXg-N@dI5~@3UcMkrY#oi_u5IrHv5&?aU+2PB4f9P6bjm;CBR0DPM;oyz
zG<{U4tv?<OXK3iR&6zj8iVcb2<4km?<QX!<-j2<9(pJ)-_gEzwUMm{vwO(CyE8w;j
z9Oia0xSNz7nvot$!JhdN2!Anu>@bvFnjG%x8QxrqPJP2J4?PfG_%Px85z626`TNci
z;V2zD0y#U4B>QY&tBtxZr{5{E$d6Mxj;Wf#&E`)-RvvE`Esc;Un|MasKD39u$ogp>
zec+LEzepuhXL$j$P2m$f=6HToYSe;f#qWutrHlX(MxEJ2{nUL-^48wK9$)-7cK#bJ
zT9-N_@f40HvQ@=vRcKajJ{5eF_pDuP>|MczS-qE9`=XAqcmHw$JFPa{yPM^Fm7!Dh
zUUV)ubly{3&%68`7X_aRIufjR`Hx@>TLkAd)MEaP;Y_#i?9SfO7yj$5wDdZAu67>r
z(OtPUX8C?Xuvcwr>Ex9`3FRctV22H--=xm?4CYoPG^o{v#Sb6ghtIXU8fjv%R1eq{
zb%#Sz9K^hVK)9XffW+o>#}IR;KFd2PQ1(81*!uEQt@vD&bsw>y?-J%W#fOG}TFMVV
zM3nu2^6C0(^5-EKh%UF+o31O6Jrn6LT`&R?Jkyi`QP*xz8d{v1D%fg(*v|NfuDx3;
z0kpS56s~LV52pJ%l%@_#OuKx|Db0@xC)$>4xW}z=wA2K4QT&}jVGjgHQhqp<Guv!2
zOv8S|1ddCOk>IU(*N^PQNu!SJ<RQ&!#jm!6Id)9?-9fQhW8dNrmt2}e>H-zPFz*Gh
z^EW<YC&7Z(TW8_Kz0py3i(#`+<-Ns$?5i238hXEeicb=rLS&IswawhQAWLO-8eR|n
z2kavY1*os@TO44P5AfGM_bZaio~+*VS#>{^Z+s^6gRjLtf`k@nBXjKfF>=*U-Iv6>
z1f=617we1|^F~nV@BEMaM_UTUDcii?pq*}(H8mgIu(v>+IhTF2Aw9(H?7S~^90*b$
z#G<CkiD{B6K7Ff1lh7RgyNYb@Gb?0Y(e#rTFX58ZsJ7C?k6}=RVOrnhOU29KZ=I{a
z!?8zUlh@qcW!CG3p^m%L*(R^MScjEN8a2W9{Ch9g``FQ!X%8YNQYmGaFaHIB*_CvS
zB*KlLQHQ#npPHSeGMJ4^JLIywFkLE->osa{>@QT7%$#FAnP{pF1Ku~TQv3%UddhaD
zT5o9iEZSylcJu&~m1dvywD!R-nqA&wq0wsF%iIm98P)sde+H?Y4vXx+9RiQQ?OJ6%
z^BZIBWL|eUpPqH*%8qegE!;cycdxcD9bal2r^aawO8DMybxY%l{(wn3F^9c(^WTz#
zmcBE`qAXu4=X)k;Z|XmT1XaJ7kq=a0N6Z*x=gQ?+^-m8BPx+9>U4)(%`WDswQivS}
zX3W1sAe)|7e&6&%UvOin+5qxBc;bF*UIy)imHTZu&RbYAE`yf{+INQ@)O;3;Sqd<9
zXaPM{*%16pu<J4XL(xU)$h0Z#hv&#R5@?<pn~SD0vq`<EXrmtqkmmh4(Ics>!g%w`
zC*<Mtrj!AeY9IHQfV65vp{W;~^lqyUA;^b8esw*P_a~c);wIli+PevbX8%~2=GM}M
z?NTmA+K3b+hW$*oRkZ!#^I}>WeQBabc>?BHuWDx^cl(}(O#cb|7{MVQU)5zt6S$84
zu!V{*?+00`ZDtU!BQK-;P7Y&B1!L)J*_Ty^>AxpT(63<oe}@c@PPIZ95osM_4NWw8
zIUhLtqC7uQe{{0<96wgfTSZJzE`c7EtsH!*N@0V9(Wq12ZwidOb+k><P9KDs*kUTD
zKtAI`ZQqam=y1!a-c?+cF-gTtlQoJQ*&LJw^HbzY2BBQ!h@rBr%ozuLil>|;o_>AX
zt=rwgVbsFZ4may)#^2_x<(22(kp2v2WPa9VIeCv|ZTq>$zQ#z%jPywFr_{+n9M0xF
zWjlSJOuK2HNL}RQsWr?hCX6qoP;SDl_>=p!%Dgx?3RgN!Yd~^QTIN~U1=;8`cnR?t
zZ*d#qJj%7IHT+RIs3tN$0E=w9o?(~6DgbNLr>xD@0muDMT;H_P{Xj!^L#Kb&Zui5c
zWGz*W<jB@T;JwpO_IUV(d1x?ok0DamCa>+q3HRJy%XSIPECGZ$;$Iq~DRpG8%L0RY
zjqNWT?Q=av;XU$voFUrBkBf<DbDy(&3~8xDea7~R+g8}rW9Ua*zYV*Wac^PiIMdCf
zBi5Psd7!a2Ut=QFeBh5GjDbf<@dYJ17@#PkH=ST@uO7EPuK!HbbvsZ!4y%_hA4PXR
zl<`+1$>u^uZsBoG?ToFfaq-G4P?;xs$tQZLv|;^lZC(=Aj5$}!=iW$e3gjlZes<J6
zH`|?s6wJ%Hdy}O$1wS)w%|BuvnRLq-jWz=Xc|!b`%b2<4=#^!{LSt~=^ipL<n?**^
z`&7RR2b0<RLIfD!qj?1m^bQcucCr?^j48hkl6|NDs%Kv1`S-FmarEg}aE+Ig3D)gK
zyYZ0d1*WCTm1T@ndH$~za33wYp0{>)M2OMWk;Axo5Ek(Az*G9lzAWa)UM~4l#Wt08
zNM(((@wLTiQdY;s0NZ~cw$+QEp?4Q^uYf3K=lbdWsb96{>~CG_>KxCYUn22;B-%^`
zv6NQ_-d^FKC48E~5zIh6`clVT2G!+q#(BtMAKhQ|^(FAn8>KxmbGEs4a&NTW^&N=y
zxNGLm`MzFd*Rb3ku%c4y;jTM8Ip-X3Kds1Cw+5fAOJb;^10fUJnHI0n&SXlzqb>Y;
zs6pVX$({zrmZv!1zm7hqb`@)i%5c_k*7|DDE@s2#X><#6TJBO?P|#7JQTusOBhytR
zuO^smin#iHc}iK?F|R~VI>Vw+nufGpd_WCHn>RH)+X4?oO$wR_pv3uB%AGDxJ`fJ;
z8vhrF3V5K_9`6Q%?!HMD(}#W&s4eMG0->Uz3+N{mR^n06ILLg1`gkvusH)XnF%*tD
z{6u{DD<gpE0+)LF=mG(5Xcyg~-ulbCa*xn(j&5AwcK0=J-dnUz+J4XKK}=>eS_DjZ
zTm$XBLQv$}w24?2fIh`uzga5sdw?hu2=)cuRi7gxLGh5*sWAPzsz?Ax179(L7_B`c
zjS$9Zj)K2GGv!*lDMTbBI>RU|hf;gf(DitxSzejh$;r1fy%o(Bfgjw!2w$na?RvV%
zuP)A!J8?Qu`C4haq`@OSZ*o`XE^}w5;g0xB#N)&qwmc=(rOo_j*X?S^l4}9_?9b(*
zzK<ihMWGMgDihRjR(VE|xhuT{;%5or{8;^G!eN8<JBnVFCHecl3xopNv}T2I;&wtW
zsF6PbS}B%=`3}ol|0!J419!mYIVGnn_l8N+2|b%rXPV+K#?`8_9_iHsRJO6zHoqg$
z7d)HGRc5T{%+TsPy-)ZGfT+C6^>2uaMDmn0|27s$nFo*jEqBo*5j}2>7qt8FglpK0
zzsY&B*DuE!qFnZULy?HTNGr6YK^nZar6`}HBgX}-<JGvAX?R~_i9i8aF2~`NHwAHT
zU+4oua?mOkY)aEA(GUd548Tr@0$o{!I%`3ZvLnmn&oozww+s^7Ba#_Q`2v6a>uWXj
zm$u3AkA5NRNeYBK;$6uuGNkb0Du$ak#2iO>@DXQ-BU)K%$6pK*dXG3;cXSCqwt^1M
zNai#zO}45;JL7(gE=JYs<*T;KRniZAks|7GRWJLyA5TA%i^zwfC2uxq$lcSN`62?J
z0Ofa~ki^-CBFggl#9^{w(0{jO8lu_3V1CM%<;;3;4P7TK^yU^n0sBbJ;wQ8y#$`z(
zWrl<`@rY*6DmI3$p}bAwQeo`*z#K43n~dOX3E;J$9Kdiqbw0>jHVKJ&<05+GFeRrm
z)(m{$zVfXodrk<z$C0%|dl0Mf+`T(yNh$c|WXo7Ng8Pj%>m3Hez#k0>Q>t(nu~?ck
zh}bkjTP=6u8GGn2eo_7#Rud%!@FB~1DRs*E0zp+%wC}*WF&xC|7X3%lXV8G%!xs)u
zTdrw4`~2!-X>bSorfL}fP?_q}M4`I4!_6|V;M9$smP(&^=V{if8E@rC9`BtwCcUUD
zNWT3?PNh$G%zd`?9n*=`^>7aPlWz0rn@_*>_;R;rh0O8qqRoRgn$<IV1NL*gR+_y$
z#@m)}A$CJUcbz#1Jyh{0Mb+ZBort(4lPLC>hoxVGUgXbK{WjgPn<fsg20gwW@oBT-
zeO~;4n5~27iWkC1&t!gJMDa{uqW!eGz;51ANM?qM?oxR1E+$%(tJ;V}sQ2l=k@deT
zMiAhH5X>E;2#36rr6S@t&Xw>^4_@_lk>!O0?;sSJfb3EFzzGc2vOE)1#xclmb6T|2
z76EA&bmk~U!DMR?3E&REV;cJ&=@g$I%7#RN;?jjJiJ;SghH1Kh2t-~NM?$-oJcrZU
zSlt~iYa)Z8K+_O(FrJZwj@;>j1Qf6cjFPVELZP=<$Kf2WbS>Sw4uV$E@GU@009odf
zNb~rk3iQ*hSjgPB74t-2a4e+W0IX~o<cof}?EhD-&9)3wtY((E*RZcarfZd0@9L^H
ztPv<thNc1f0kNj#RUnIlNk&EGxT!LQVGiR}W>gc^O}St!RkeYsQR4Tx!ZaG~h@F`Z
z3i<{mJneO#Ap@7?L-^G12){Cc#CJ+l(1!U38rtCk&`v>hqdN}K@`vO~3%4}t%^zo-
zZ<wCx17d-9=jQs8pw+I6EXzP}U<=)P{uw<sQsXuzE&T!FI`3~r(?GC;d}~*@KY|J?
zd*J?3wHmSl=3j2I3LEDoxX~eVBdU{h!&jc>5I{>Q(;<;7w*W{q;Z=-->f)i82rxU$
zZFu!DqDav8b!bI4ZbL{`K6fgyJ@WwC?IKixJbw7U7GXu-@5wm97`P5Jp&pvU{z*8F
zU#T6xa&p*D=&@bpeq65eS~5foaro-ncO>P3AIOAaH1<y2RK!$hSFL?M#Qa!3GVFnW
zR+!FZT;QK?sMmQ^ECrhK%ysBU5HLGky2o5exG1EyPS4x~J&bqcKYGZPIm-rlZ+yM@
z5bn3k8_Pz)CFY0Ykq5LRw4K8LM4vB+%)b8@_r32=(dkQWFKJ;{rtWxxgXes`KUo1+
z_TO#En8r4VxLXkRX}*)cja$ae5Wol@_zcX(KRr2^t&oo6>e!>K!QoRv$IMR{XW_OA
zz~IlkTkyM-g=`!B2;CfjU5!`1pxK8c32+&uY-!^49vUetN55PCh_GnhZgquF0RU+F
zn+I~ZZ7ZaDm?2kvsJV}b_MyQD7#Ia8)_^2INCE>>SX)K8eUz(VT(Ajs&$~}OhY$Y@
zKa0aAeA)Xql~&Y2TYyi?cE88=cF&rsnT5UF3v&%spW^~{0urB2b{N(C`HA^fdDu~<
zj$+xO$M<I4q8odO-$-LSdBW3etEw-RP(JO58*x9?<5Y>`c-Qe9vbDD~MoUXI&UA0T
z7pQ$IMpP|UEj4^G2IB*U^34!2>`5l_#Q#pceWHYly`wF=rA_Zn5_J=-<sj?p4Wxgv
z82B?%CC4j*xfKU*b@|`*-dcqIlFlv9RFDP}`jvsynvpOl5%pvi#R7PQ{5eTk9aL?^
zqt*)Ua(AjztajP5yjbnS_R1I;Ti3BA&+%IIX@fpmMX1M>UvWh`ZN1Oq4*=1I8?E^s
zv9%5TFsiJ<oBIv9x-76Pxxnxqi7vurPxfFZ2!j7^=%rBDL58gW<5o1ef2twxjKI1+
zK>Yn$IA_fAtr+W?zI}O-b={R32*C)6{V~q%94BW(H-z4Z=dRTT{0`TWbH{ReDms*h
z9f63QT<JUYX1*Ajy??!*VEcG9-d{|>Wb{WG_Gr%7OxVyaeEe8Vbe<}XN%I=6g#NhB
z7M**12M}<+(X`c1QIv=<;!%8t`!(xE`ZATaz=UzlZ&an(<vDHiLt$x8Y9UreaRcWF
z^TR(V&XJS0(zzqq%SEmcYZeZ6AiE<|d8)$&3(>qoBbzG2emYJF$Cn8DI${xdGtsv-
zfyH{g#)=~6@qq>ar9~8(iFMgv$OCDg?y&P)mEyF!>r{lY>A=K4+WY<dw2M<lNacZW
zyk4pJYNSM8v`+DC5ryhTZ&Weyxx%kk{|lb4EogOhFUZVB3GC-8A<shXlf_-?)dhmm
zex;V8X#@?i9d0}cdr!61i<`{<Do!Z3IJas-S$6D9k@0rmabj3a7bq(CCD=!fR($k6
zj<1JbH@?;2Q=+H0aeV7PR@H@(X&>4H>6;;W!Gkc5w`szykahbg|8(s>@H1HlQqLYT
z%@J~lE~tY<+%kD;n9*5pgBj7?w6C|$LodzJA-)mvI;mUjX@)&RW!uN|T|K9W(iZ!*
zmv7SFk?yyU^q;<E;otXWBH0aWz0J-q&DPKG54+bDueF1SD>x;_W(|4_A=G!{;?ji9
z-W8k|oXHM~@k>Q+YW;nYNf~d0esp`mTycOaUu5afTNvCHKf`(@S3SEYTdKMD1!UCe
zewfaY7Ic{^+d_yrXe1%@ozUHAyJp~+HP)armMonjxRlFf`=Y3)KL=o0$+M!>Md?3b
zwyM6O7bFfl^WAvjdA*Q-x>poG-hQsrwRAj^QM(B}57fEgez{@e5eHdaYlj|nq=<Y5
ziPxEG4wcykSPc%p_1fQv&#bGbcA3Qp%wBt6eAnO~So8`DW*1qjhc2~3ZQXbB6N`(+
z?~o$38i=dx$i@G(t6-+@&K91PeUEo^vRygTzlrF*_SvD0u8ZVna{xJA%O?O5dE4Bs
z)Tt8EYwxTswPQK%nM_34<s{tR|JT&slEQ}h(j)La$O!L<W7gE#2C?Jy`OLiI-Ik_x
z$&nnZxswaK+jMiSR;P9$5qP(FD@X%y11N-iLS*E|GZdh;s$u^gBp8GVu99%t%2mB}
z<CmJ(AU-m0_C^^%DTy3?OP~S?lp(i5Nr>6OoFGF$uNFufP7mF(dl|w0!otvx<vp1*
z4E)sdn1Nt&+VL95t}jZKZi9dLmGafWpEJ_B8*2jRA~6Vr14dd9k)61lQf5C8mM~6$
zpiWtbAiITR5ZjX)fyp1?O1w`9TLFF%ltv=roS?nCACII*6e<cLf$NgEAE5bPa(aH{
z{*-+lsIK^q10}3}Q({Ywdu-Qg6VK}NURFShncd7*X1KqLAgKObIFN6lB*i?7w_NJs
z-Cj1tYTCER2n$O~RnR6JpeNHkHf?49ml#Q>;Nwy6=7%`qne7ab%QkurL^PW<O<vc_
z(ZR_{%V2k<QemV}H->NwtX{WtVr7$e13$#yz4d2<X)wV6Kw2meGV)UhYN%XE7N}2g
z(}<8&dl{+=Kr|ct7ron<md-`|IH@<^U|2$Ntz7Vsvt>Zr5&NPRhcV-+5y9RnjW<RD
zL?e^>HabsvxpZ0@FUQOel~E{@Ps^t6)2>d?^K3jP$2c3_K{W*)aXU>10#nk+<qMzW
z6QMr1&u<+WRhtSYhbPJ&l>ev#21h@DhC3ja5hf@Z)I`68w}|XfX7NH{KikX$Xyw19
zQpCBY$Hvw*exDq@mtS3Qv7#EHAd@Y%0;`X>Z=4m8z3hh+hQ0n8G{7=w@pZ;sEr1R+
zH;Yb1+46W9MBdD~9yu>Jy1v8Zc0oI08rWrFHkK}1&})$%=+mnmb}fDsfphy(18Awo
z=5!JWLK%wN_6^DTXMb#@`T@M5sI(TJ1`f7E&;#V0tbI(!;+83EytidCTOS6}JASkb
zjztnzVO_ylQD<EOJ3lB2w1oDu+=s@fFB8CD{00nY0J^u+hgqH#UoUZl37dvnUlPMn
z_iOK*OM|oxv%FR-4qrxfm+ISzJB_VTP8aUp%=+Z}!G;wvke4KzTtP~ei(V+pM_yA&
zfiv_5KsQ{~Cgmlhp`~kjyl12}4Dt3!i{@j}syU`&_0piHpF7(nq%g*A#$08TQ}lHn
zQob{H!9Yvwelb#{#`#Tf@#=oZ%hTGc9YqnW<zY9|{Q%;7@%VbYg5Fv=_gVafiL*x~
zmeY7LjPI)g7QXZwURk_1nz95}6fA7mZzSLKeHYRQAyN9O#rsj;{5eHOqv+$a@}eD9
zdFjujjfcv+cD=QharfLa@AkcA_tSt`pMY`hleSy9rQ1H}cF}$NjPSXSW>_*!nsokV
z2>PNFB%;0~nv(pfGOJ8BXMF0+of~i$Hs;rj`vN`>QG7hvMBm2ipNU;X6v#j-(Qs{D
zR>5>8+pBvtk<9rhqO+jkIEhK}p7oH_I*Stlj-zcw05_uk0`@4ZuI{wO(XwGy7X%+;
z#L&+!a{OHr+Y=loJ?V@xcfrg%)y{qVFx*8c<|&OpVfl?0kL~PJQ7l3)`&KRnQSwh%
zWc=O1D);K9jae!sq^|=fxJ3v&4%2pMd_-s3YNoHuh?Ct)u*FC|aEJHlkx_9JXM00E
z(0vV4bmxq}6|?JHP~A25*Fz&#&aT9YFXh{|yy|OC!<;8UZ}Cj@VA>n=*v^xCa3lWn
z+Z?dY`yunJ|3lMRzct~1ZF~a+X%z&KRHP)O8x-jVrALQIOq!8H1XNnOQBvuSQ6f2F
zbi=68U4t>;+2{IR&kwt<{Q=uK=Y601yzX{!YuM36te{&atj>>ekN7A-&V|G-G7H+_
zxA-X~Ng33ZkX>g(JxJH9SUx8~V*SEpF$W!(Ou*6Ylnkaux$T{LyJvPE+Iyd_(zd6n
z%!}U^2JV_7O%7m!=qLb7FY5P66O+58E^T)_!XvDKbw|OF3FBcy<<=h*#M)ItkC>Fj
zKV++lX)y^3y#qN|XP^JwcqR{l<k%p8GL-TVadFcJuFfnp?WDcR8d9r)Pt3S43D~1r
zSr-e)x<k52>|8j(+yk>QgE`R;yf5sWycX{}GOSkGz@?Y7@1bAcN-5ojE)X?=zX-<p
znN1TE0z2puQn{Z}&ZmBNAD?^G%b=fqlTio*d{F(Q<>w9ua0hASR&6)17uBS~*hV2z
zy!S@AIK=te@!R5TdT#o)gHHaO3778VEqwWY_|bEAEX~%Yt&y)2K}gvHq~Q{R#;hmH
z9e>NyP<Eex2~)3YKb^3kSbuNymh{0=;6dQ|1$oiF;Q`n^L0x$U338j0mrxX6xIz0I
zFg7=ov9y8c8aXV?&wkM+m1;?7H~*n^+w*jz+#{6!c2s$Qntu*yH3+(pJo(t2zF4L`
zGuF1n#qVO_fBPE+skk<;SuC4%v3>H_<jD_jzYDkH(R$mgy6lu7_Wme}l8xzm2m9!G
zF;2JUS(^sri|gVFrZMv!lX-AVb61i#cThHF+`mpa@WcE!i_Y1@%QApsG9lRa(8A=Q
zHzVbCrEB=`xAFHo;F<+|wi*MK9NFlli~5G4q2Oe3GS~3qJ{`hv!AkCwkOVDB$8Dz>
zShaLSEn+HLWB2s*8n6)gdRC`cG@ERvn@qVU{fqWMkx$!cmWL^Ojl6Zrz>B4kX~@~;
z3!gybJK@8ygamTHYno%D-)jGXJ~?`Tv5Ca@zW4iwJA7VS$_I9YyeCOuPY%?8Gydx`
zVeY=K4qCvwzmsWxQJqpp=|A(&q}t=`Z;{;1w)-Hj81^~z5rQtJq%!CvAtw>+eOD!z
z{5|16Xw#+8asJ>#!n@TR$D@|npi<|b4szGCyN`TeuTbOFC|l)uBDu@I1t*|rK}Qoz
zVx210o#QPGxhtp=|224eNXfT+ZCiZJ5hj>Iw^>>O={CJN&|~bi3zz$5*z?;Tr95j+
zsJTnU-VGiF!(M&@GeOu!>iJ{k&`pRWzXe{}D`{=<gay1Oc<~FzF+R?6C-Je)ef2ha
zSmQ@ZcgM_kRoknwT%ei&Q*Yx`w!yzc8}z$&*(gE=o28$78pF!_-<mYxu5(^Y%S*ko
z7geIM8W}x44wu;|O_M|zrdcQ-tnzQYc_K+0>Fbbo*t|lHI7K1slTvKlDP_(^(&qPu
z(ubcd!IU7wZF)h|XoXw;<h!0{B9ld+g}YlcbD-Z;O}0oqd4GV4{3B97&xx~{4&T7D
zVr7{>f!pd9l9+V@24&T;_@k6AKjvJkM>k%0>jpJeP6NrjVQj5ezw9{1H7R5D<f`uD
z;y^Usu0x3`&&8YP7Z0Z9&XCG<6^_T^+g%oPK=jhd#!Tsx<raL3nUBXNOmhUq0m~j~
z1A7K-;QT!I0+0Bdvp)1>TwY>PzZfooAz2y4<T>)1&AEt~HxyuP?5lCK{rqH$n_o<3
z!{pxjMfs_=lSS}tB>aZ_z$>ZBF_{LSc+Ldev&Yajjm<h~kAZ0$H*fZ}=1RJp4v8#0
z)(`&XGzUU_emj*D9P9^OEzYgo`tQ0Su;#llh96?F9cA-2hgU}zrKC&xzCbpNcRQMC
zWNEqb*b&v^#|+Pd;ogHFbdgJ54n5I=6wV+yPe4uC(YlYCPTD+@?)$)ruRxdDK$hvd
z`t2X~XHjsvVNB@t)s8ux@Zb>`a6lgB0sFfaQ|`?Eg?)YXuiqGsp(3Zxu)mPz1<iC^
zZ*%%xG=Oi>ekQd2QJJ1JWIOy`u|_QGd@2^bujSLMe|?1rZfm_>Jd~9zAAW^jT3TRX
zNYfG{i`hDf5$T{`Itt&Hs7m07rLVN9hzVZWjMsB?)MhwRkNe@UJ;)fG$lO*Wg>Eab
zlSlMtOtO-uk0sC$_H)FtYZ=SzJ8E;oX=w<IeFs1vaKG6=gZzhwLno|=#1)h7+2g^!
zS3Lt1p}&?!sRvd|qF9t4@JBkNPzroZ9zSR>&e@f`i3SJc6{N0-4V;w*ObviuD&|}b
z(#VF30|4>>)wi$TIV9&58nc;JREFUfM*#}3W2<Rl5Es&>Ntb_q-J0`yz1(Xl&~jYp
ztVd$SYGXRmuYl_x9n^pPI;0Idametg16W|jS>%=0=g&O;6b$ztEvbKT-Pq>++trDN
z+=lmt?GryOU`nu*vES4pV(GF+ELrq#Ri-MB;$B}PxfM7j?EQXpiy+@r^7=-`v%7k5
zN&9&jI4KWqpA>y>$XL{Nr6_B0Yk#M7JG&c{EOn}!p?>DJk&Hc1b5B(?fPK!`Yna+;
z=I;BXvUcH8|LRBB&1=#50sFZAo}_Qb@;OWgIk($6O7yYY>>lICaVe*pY`YdB1$Ls<
zEz_+B?`0(<y)&KlQ}$8^2>`3c`1=C=;GStvWWZbzx@A*_t#ksC5^?FkQ_e8)E_5xk
z?=-og#m*Vqd|Wthw@*^FH+>_!TT_+J{G(pcE6?|<K0I>oN6NU*2rh^2V99qbb?C=V
zsoQ}EdN1LE&$0zaw6VXwXi)S8F2E+s>a(T0R&(mo&KjmVWH2;-$|nNs7XuE5&2q<T
z&l?~?*u@Re2D$>?@kRqfH|^!6>dro@PZM;KucjfkajyNJA2+(T1f9>wkOB_&<pMRY
z({p5kUw(=W{!$^C&2j2^#fQPFUxNy}R@x4mCppU-{4dm&mlR|Jbzr?-{G+lMr-=HL
zB@dJzu5u-`)sQzfY-W{@$R4paaJN9IEqrcC)v!62roDA&36J&*40^%fqPJUO{t=S{
z$V0c{$xAw-1@8F9ON;FLqI}qu8*JbbOU-M01-d72sNm7UrmW9I3YncXC)KVl;m&Q~
z*iHTD{u9I9hqKS3e(Fuwb#%UEx<~rr0@i*F<9@1Rkc1&Di{bZNa2|5X_NO^&!=m_U
z?{N0bnZB2F%eiZ64l8z@iIJEEeUnCezW}uJ>b7{#It0hfO?sNz(JFV9Am_-8<Flvq
zJAOW5(2oO1#Tb{W+{Os0n8%EHWwPWlKUm?AJ^ogmYyX2E6oW$1<i^y=;Cfq4WoQIS
zI)UtV>@!mk@mSS(3{nFTLc}CK_kP`1M+#<isc?zMWS6dS1}xNgCsgj_1-e)@FgY>V
z9mWDFedTp~;vPf0tJf2xR1ml*wfCXRf?CuL@NnWk)v1Su{zEN!0y;|u15#IxLL*sg
zsj~qMDrT@C7$WjWL$1m62_RbUWI|ut#dv<;(8<e-T4*eWMOpX*zLDNwj%52_?UZ9$
z5*Y9PWNeh7Irvj3z4J%K8^D5T07E`v$bH0TlKB?bq1<#CUalDr@0*tmSRn%&ob}R!
zv1`oyQcHIy{CDnQcb+APy!^K~$}N&aEYPZS6ShE}nQ?zgXz!eCpfe(fpZ4&qXzsq<
zUtPuatBm%hpC@)Qw)(MD)2uCR0t`0TG(>=X*j~-jSrd};u$CJmpZn)P_kG5*TPNP$
zOhjcz*}>4G><jcWN=V<k22lZHp6syA;SKgp6AWWUy?6Z?`F5J@0T=!^5v<ty-!thM
zIy-_V|J=`pQ|q5;N`cb|J-5e!^JIY$QVTy59c<#P%<7Hk0L^(w^<D4Z4x&ERL*ytf
zVG!N;W86(;ePxv0U?vT^njU{bh)C1#YQSPi_UtDmZ)~ZD#ApHg8hDM_mWef3w`cSw
zLJqs~1Kq!wsIQgw+a-bT#gOCs%8YWyec~+YMNzy&49<xoLE=wizOJ;nL6rE>hEX<X
zEOn)bPt1!Fw6wT+CUz(i;_s+<*nwZ6vnmi1Oum${S#0J$;E4vIhH#iH^YtBO87<dN
z^{X>bQkm??Y>T^*6B>S89pXRm-Q%+omB35>BdtjP_KQBJh6WioZh6-q-L0+xu72Yg
z^%j?%v`Tg+{{J4l&AQ7jOYpjh@o($d=QHf^k7(WA=+>rfOu1$99|mpU9qjvH$>-*!
z*Od0hb1wZ0$l8`^wVlBMT_W>~8R~=7VpDHMmE|y_)uO4p5V{=9&I`VG4Uwqs)Zn5l
z?>P;(;(t#LXm_cIn#kSh;T`4rgj2h=tv@yPCG9?%n8$8!H2BK^(AEzmHB<kg897)x
zWTZCNU*oN6u#<(uuH4t?k&R?n*r7w9sBwMeK<i&Gyl{7GiD+$~H%_ZA@zn^6=GJ`|
z@chQBrm;jS)&v?y?e;WpM28y=P2zK%R1r{6m%8@#<x1*$qxLKSKj@++#|4K6?ZXq!
zm4=KZWWS5)wsY&zC1C%H2);d47tg`2bExB6h3p;P-5w5TIWM;_#PiDF&ftRmaVIo1
zXh*_;oWKjm8^A3Sc9|&U68O5h_}QQ&V_NokW7{t1@FQ2DAr+0l>VI7q?-!olpknRh
z5yNH*1lx`&X*a1#aLb%GM$Wg|jSZc(_X}EXlm=Zd*frhYo=r19hN@q4^&<(U;uGPf
z7Si^Rl$J(KQukQjF4p8ugf7#TTde;=C$`zKaWvZXo$nuySqdXE?Mm?U&BvTN8S4DR
z&vQUjXiMw#Mp^RRi#xBSd>D?1yw6)AGgsM`<4siQmc4fQ18v_((a~DR=uoWet;f9<
zgwau6{7-!5hSLjFpL7a3xY)4_@Z9sS&%<RY8@iDK?mpADSiW;`eUj{UJ_O~O^K?P|
zvJ?0Rnh2iGsrqtRe7Fdf+VwCDKj~T>Hbr8lmShWev&}F|E~1dKLOGCuNpubKRiAQl
zn4!(fKt#1~1rhUkZ{wJoT+en>hU|c{d3QmZMQo$X7lGPnnvv+6qV8VnQgXHN1;>(U
zKMg@%3MmY%x)zrPS{|3=<3(3;=;Ij>ag-vK%|#XZ-@khD<m6*b-`H<Tz{3o-HU(*)
zR>8THO*F0vs;OW8F>L(^s~#9LigDX+b=KQid(}d3kvs_Y7^jnY$6WCE^uYJu%#3d%
zJN@wW)~>~!->lBTyg%}Ksrt9{Vp_(mB}EHS*PBg+y99#eH5ta*SK{!d<|>JxwBAMW
z4Sz$)kH`2&YOVF?1nk7^;&|25jPE5DJCyb)1-apNo1ljS@DB@@Y1wf>&IY%Om%|t{
zd^F!>cf1Q8y638W^|6mr@KNhE%|HP00=^o)FatWlRZ>N6d87@g9gG>*4O%+QM)Ony
z7wBnYqmRs+x59f0U_si-XHgE)d5PT^nY&As*b~`u=~6GhI-(7aC|OP)!{)Q3`FUBd
zL%bjJ=;LV@@ZADE!q%;VKf7u80vGhrYE<;@;wBM){YA8FaNuEiVBceP>4S^8jK^{V
z_y?eNnM=EQPdlHR@xVC`eyqv$2r_k1<$kM(jsRTW3Nv<86&qOEcw9#6$;T{t_vCVG
zO{j$)*-l<fXDgH#@{>%18y^=}aWYb`Bqyw?^$(5eFCK4=4P=1&EBsv;c|5Mgp!WH{
zM;}{u1cSTEb^3Hm;lgynE+%L+yz4EOCSpD;lk_{!o6Jw0#6tbsfrtLcK9I|^)&KMo
zAaN=IF!5VnccA5C%Vylp5e6+;xZa5J`u@JRAidNNjhSw5+tbs_FeVIG#%DHKau82s
zU-&`C?T-b7LMV+kX9-5E>B8>mJYad+9SZL7j$rcpplkACq(ir@?Fy)eN@{WPIH9%`
zgUt@9&4^U?9DgZ=RHduX{cFeCCh=dDPfq^&ROk{f&=-#9Bvbk?E^F}@@qJ?@Nb<(J
zmNfA~G8$CY?d`$gZMi3BKbUTMJ(?E4H4s{|l7Te}$al6B8P{5=HIm>zTvz7W>T@t4
z1lojzZK5VOvrm+=Cfn>lLju<eh--@C>0@SUfglUaSc=*_tMrs;m>fR>vgk9;s?BNM
z-OJ}ddWlnaWWL)mx|pDq-GsrZ8U6pY<t0F4#^GXh3KDl2KcCV)&|JEGs;B$gRXk`L
zccf>edr>>h59wDY0mOv;#!a7I1#-O&^fZ>1pOHcfZQPNcXk?-u0#d?YQ&JycZu^nb
z!<Q6*p3js=zp_SX6l9M=D5C)5W0}2t@w$QSLU5bb@%$)#_`LiNo5>*qnmQ`X`zc4*
zltq+9b8_djX^+(NmO%ylu7v;rE+$bmO{q?4h}LRNNq~^UxK>)p6sf$ZhRh)ZVLGb3
z|59-F@t85YnbRfMFm5HTCuz;e206)8B;My)pi$KSuP|)lSi?gq;4sC-1i6#_`3*wV
z9@8T|8Omq(MDA>$sv=<Y^@|Q$GoOUq)-wcUV3kDT)yrSg+a`m%YeYP~ym<9_PDHCe
z{gsuO13mk3=iQr(i(*dRXEP^HQEo8eC8`G1D+(6HyO<N|Gz~<lA^mmtUqZPKAwQ_9
zmG>3i-mEQuSxgTBQ-0`S^R6su4Iz<tJO6GI;Q^z2pMkoQ2#yQZ=Go;FI|{~qBgV%Q
zqg-BkwNKmr>Fw51a8s#G+$*~NngfIq2oqll6S+GGoF0;{J~&B78PoN9J;3Nnh8hRK
z^QmZ|i018me}nq6Q}H?2?XcXZY7sIHf=ajp{y2}@vXb#Hv_(%3xcQM?X7+_Y5*u{A
zblBD^vqJ0Ud13=zVDdd##ZB^_i7ek_KmGFD5-UQ@)*bkU;Vr;+o$AZI7B7s&ULySE
ze0MPh7-lE8!+9V2Ft?#lJ~_#kKJ9B2$fa$z`Li{&^v%Y;)a~in1&7?F<0#|QE{LwD
z5!c!LrpMX@)2*_~y;D%eZ37L`pu+nf2HRDYlcsTG>)Gp$*9M`b;erB*z}@(sdIjSr
z@J_#^kXMUX?+SjphfhDmb%ZC%lR0MqoP=MAyI2dpTYZv{$=>0Hl2!9)>VeV%ye1vL
z_IptEcGJkd8g>4!^d(;FPzwWZw^2xPDF8r|r&PF#yY|~!)>GsirsA%dn9Y$Y5~Arz
zFm!Vme8*11mjO5xd1VY<eKN%^Z?CBV_|6Qx*H}SZX-PFznQk-)Hpi~gidmVo5Uan^
zgx*gF2qtqkbiV%C^1-^-kxH$Eot+Q`+FN$Cel*h6sYOK+&(3rD*f{*h&u@#LG&K^_
zb+UzHA%iv~2mnR|`1C{PFC(htOEur%W7=TdRqRBOY+e2emo`N0G?z%GSmK~@Exl5F
zs;fMwrvZmbuO}X`XWIupaPD+Bq>5B$ivnnfSPi#zamiC?@_ZC^+jFK;%Q$hZyW0u)
z6l+bi*6EC&I@uM2VCSh7sS@(Mmt&V$mu>|h$f%r}=;}0$56nuQTWmp}$R+|cp+D0r
zDfHpL8WHq&QIx?zXP3@5wp1ixki7BTwhcz;0(;^dTa?lLLod03fpt2y4!sM!57Pwl
z^%Hf*SaehTg-*Eet5j~USKimjm`<1Q=}qvQ)cK1<>4JkeN2tp86h(;ljidh?pw5mt
zXGA*SDfMmn&vk~dO}lBZTO&MJL|c%oTak*rSUuo&h57a;WQEjXrYpXqV2A|uj!G@^
zeRyM4>-5Q1kFa-ovh-G4MGnVR#UeSHn{8xzBfg-mF80lD3yd4`jiV{tagV|^iu4RJ
zx2GeSEW1Z}yG)r5pck`>uWz8(#wn@eTbpuskUVfyR{apS`SEx5m`5Ma#*1;4Z_}}*
zH}EiFK)gX}z`vSw*<Cf}_^~nlHC=C4r+%L1ltWJ^?uQUP(2B;$t~J~B2RX!VKHvVT
z2vxdfjuh2|T4q${ju1SkubksK5us6rEAcc6SJ4%G8{%T)?IEe+Z1_E<{~QJnp3Uxh
zHBq3YX;XBFc-7w+pq)MLyHA1~`nhzH+#uXTvSFY)S2L5U%hcQdYLR04HC`xzw+nIC
z8{ahtCaDpODhrfW(>Z_#(|%6swMoVW;L>!acoHqNCD-6Qt9SORhxv^DOa-_!FLAH3
z>)ogfy~C*LI4LWdh|zO|lNvRJ71QeAI<&6wj^|~Rk>R@PwI5^`RS@xX1@>^+tUe4K
zj&_SWu5KY2*wlo6%@D`)5$-MIKFMm!IH11^zDfz^YTg9U(tv!RO(cdkOtwrZ!mbO&
z3#qXYYz^#M4V-@HGof7(d7mL;hpb5gqpCrxt(k2ThiA=q0M?|lv4V)Y+h2y6-4(~~
z(0(;~!N_a7CJD>C5=BhV5{~D<_8Db96dN2+urXkPsrw$d_1Ncb4XWPr#Ir$N`1;Fe
z4Nf`#wPnLMs<JkKQ)=P+)(t1^g>F}-FgIqx2Tz-mW}rwTWUgVF(pel_s-X%}^HW`{
zEt^fl<wsro%!mkY>Zp&C&{TM{Z5A6(1P$Yt<|iC)h2git)r9$(yU3x(TfCiqNMSH{
zTZU6=9;@FYl<4+R9;MT8z_nDm)55yQkc9<G6Lqe*iD)2fFs<=&;o0S5*T!X4ilxl1
z2@aiVLCjCra_)+c0&j9Mrx?zmdFiLO{v~{+EiIQ846*ZOo>sWet>Tc#MUx)H|H^4^
zbkD6-UD&Ys7WJ_x?^@l=&naW2`#ri{<Fpmw0!nF~K=IF-ZG@+MxIIMjJ72V*-oHTz
zos6078<vmkd8aL=FQPxBJ019=HomoAec71LkuEHK_>%cQu`T^DU=x>l>T+6lq9s{J
zccLVvihO7}$PO6pwo2x;<I@s0*@g`2H&8OHIs3|}SjKg*guKS{P+<{$C;VTs^IxmM
z6_-zvDwacR0s!=sS(gGJQVlRO@GJY?xOj6NPbyy8)iV<O$WpN1_|!KANK=O#$$b_P
zD&7t{v{iG<C&BGnft+{z-`8aT>5Ar-!U;#>CUp;e5)=vK*~vS+*}bf+egzOcs$FKD
zI!vD#eaG%fjb|lNp@C=ZU_&Lf`(5yP>5oKohbFrRR0R;d%8^;wGu?G;6Md-d+ewu!
zURbfx`z7uXTL?aT2>Tt)(=fX+%VXiv>qOO;ald@5tn3MDJ(!a>)5X&p@*}GM39+f|
z88~-J>H-yY6Tk6S0<*0YD~Tg*?W`!|1-zXe%d@aWUjl9T@EcREEb$cM6{bp_*{NmY
zX~Xdotr8>HZt}!~Pcx8aiSKQH@$j%Rpx&n?-%&rHNu@<)+yY9iH!b=V01<XNCqZX<
z%phn9{6)7Z2)RMbag_$ZpNuPOF4QUwpD>m;3Yc^`m-yEy#<39j1(<L1Lre8de`dhu
zN?MR0AE0QCS(?*glGy#8z?R-L7=8UQr~5U%C4j8-XDfhxf(fu=2dka?B>Lmdq;SzU
zp6Ee++Z$swyZn;oKbi$Bd6)Q~r+V-`bU*ZJ?dmBtOB<d_bCs8@!051Ae{6y=MZuD4
zpKtX8tK&N;b7f<g_(>T~*j}FmR-f^OoChM6L%O2tS$02=M`;Cj+-dvIjjO>5!B0QD
zBA&R?s&9I|#*Y(a2HueA6ruq;O;&GZ$aXF5GK*X(Y8i;1aC{^RS?K%>qDy!>@5){c
zD3&#;6c$yIf4@Nm$nD56ju6vtrim`8O`rdu;BtQ~*m!22mGq5mJu>Jz3Gy~4V`Wj4
z_(?CF*t1jBYNa0Co)*y(qX!j^hA;iSnj^0ND}xv|fUT=+Mb6W?h#<PMZc`SL45w|8
z?FB*;GdxlGd7UhI!OSf>=eR=FLS{l0QqsV@p(U>mP{EUsY-dBZlb?s!G9;#K^htCO
zq3;)p%!BEGZNwcG5os39zx8#ybSTtWt>)Q;kPH=Lq2^W{#gx*8_`Y@R>(J6|DOA#{
z>L$Lds+QWn3-;a_r!Lv}8m~P+`+iclU^llagB=l6stL+5EB9<}c%QAw8$oq?4o)92
zi5feJMVmTIT8*Qb0*?L`n8{xCYxCKx5hx7J5e}48Y8Z89Pa;~j)zO<oPe}d_v~Gp@
z#CUz-j;I!rw}G`wmfuYUV5#KDvJ}auNV48uSNA0>+VI@9Zv1XPMcLM|2SznDVIp@V
zZa7C8pRqOvG%M4eK~=}0^84lf2xgF(Q5bmjMW$Xk5U}~v_>?Fva-I+3z?9VUqEs`>
z9nJ-EXhocr?0m0(xHa;0R-Eo!@M}txbg>7tWy5jD_?v_6)tygMO@@QTQOCv(da+1i
zgQQo)LF~|_+-G?*{SLE5nQ?)2V=!z(@_zD~hK(m>kn$7a<kT^roUzQ4q9S2s^H&=~
zhgf-!{sFUU3GD9(VgEgk-y4!CGbDrReEw#~KZbN2mSmX+>c}4;D;R>ZPE3z~`iXC{
zA|3fKS5xlSU$yW2PFL02D;)6$uF7hHN2E_Jn3pn-XDCIL+xE(ba_)wWyQlB41F`9W
z%<lg-rnG0r%r16P_*nj0V7IVP#)Byax2}hZ^$6T5DqTF}&z3-9imcj|`sRSe?Me0D
zSULBb44iPNr*u_gjz6fud~Z-X=BQ?)fcL4V-detJ3-b$s{|ck^qttsZ+MjIxon{H?
z{;wL+q%1CbM6&_n4A@C%BNFhDb!1a-=kWX(RNv8TdGy9BdH|Tz8eSa#JgVIK@H$Ly
zxNDB!A!S$eUkNpUCf+xw*aIdS5Rav<WEyAMpsoP`rkku2<;zb&4M0GKJxC5s*dd5c
z8lOads2~nZG4MKwJ>Q$e-ej-mk?1lbH$*k@soTVdw9D!rs9r&}rN~f5h0<<D`l$<^
zC*R~M>BHi*v?a}qCzlBUc$a|G*U|>+W9*CG&zNwT>UoM~lSgb$EGkozKZ4=jA_1_V
zeglbTJ_)B}CA+Rm4hdfJW4Qh6Z(q<CFecvhJD-{sN-Kyz^hbNv#~pkqT^#5$Xp@}v
zYPKK#nN8=Vy!;dra#i5(6*HzF%|)VFC{*fMlg_<NdC&eNvo5p1-|CbVfqDoqGO2W{
zC}*DxI$11TDY!PSC{55!1p=M$&A8t8R~YPEbURK7i2jihyjO1TrAlv>fGH<0kk%Ms
zLeDw5#K^u5UP%*F^4IJyi&>}ex#<(U|G>ZOwV;&xtx2Z5Tc(TgNbcn!Uh4IZ-pz39
z#kdyVd62;$$l#8Hp?gERpuqqQ^NszF&E}h0Vmwa2T$Cy_s!k!O=VwM*l*3Ief(BJL
zFUmGEXzejj{>Ce7r?AP?khj=T-Nj%o$#x=X;1{B<G`i*_4doN*+C$#7@^MsvFgTQe
zka*ARQ7$DM^0uB%%8*3N{v&|J8H?Z6ulbB#sE}UF0PgzZSV41RQT5D6-o}#^qNX0?
zagW4iw2X9NH%9lPoYcR-ELo4I7zat>jAr8r{Fk~~E-gx)IHtDeyy!p4_%My_9_VgM
z5wph*iHt<+$-b)(>{sX-GjwYhr-G>P+bR4yp#*%u7Y7|>`yUvWegj&Ma+k24Z8;vt
zhP`yN6Coc>@bvOO3tUCA75R>ISAM!z%0o7FO#749w4OTmc~-6_rsh$@Dv+#yhXxco
zbdg3|Q~wtZt0NRH$uVB*V`ThQWm8;)2c@H#@`kzBT(j}r|3}>5JS>6stJ-ExJuKaD
zPmyVo*fS3-dJM>Qxu@7Y!MFN#w;s3WXlUEN(~bqRCJxCq;no<cIV#%rLRk=n9ckD>
zU*8<ym%mvTrqJ!|d2Kp2O;IZW2h^nuzWf(SwLGTSr>YJw5qSkrl5fk*+YkC(CfO&J
ziS60oLt>*hK7w16JbE2m>pCunYTA)|L8>TAX^`jZd9!PScHt9!7#6WXH6#LN+_jFC
z%MV)gze6?LDgXOyPTzg<eY({dNEvkKB<Fg2lW@1VuzX<%F83biE<d9k#dIh%7}u;Z
z*8$i4@3Pv*uss$stye`v0R>H{-B{<_tA#r!KA(}Mp(44n?F~(4%)C9a*StaE&;#it
zdz)Ah;|u55=NJ=Z-_nv{Y%s_*J1UYHiy3=WHPbJSb5FI9B@U~hUMw>)shCp=|Npo(
zkOXMyln*5kO(!5+UQ!x}e<rJlm-wBqX0ac_%cMqEJEt0>=qYt~<VIZqGRDmt3r5j^
zY=LikFMYpgz6M%a`EUSex<_@D8Byb38On%?UsqYKAn^*m$w5>5A2@yPy!XK>EA(83
zG2BK3fHo=>g#O1F8~7~8c7K^MQb1yaRxa{O5WBOs-&lg0(SK{GNb9iG&S81@8%tR$
zd<k9neWI<AUwK(GV@<eZ+!Ro98_?M;(kN1J<%sO7f++PPc`|BIb_Qxe0MxsAPJ%$C
zTa7@XlG9eOPj9)Yirc#m;_%4!gpZPQGudhp_aY^*E44q7c?pma^Hv2O6r2Rqx%aWG
zw-e_xX7zh43FpgQ5q0dSy)xw15Nc5@@4ut)X;k2%XNTd`m$S}X8MinkxZD(I0@Uj~
zBM~Loaw5<xcm7rH$LeQm{VY)8bgiYsP##&1|5YbRMtvAh2x>xg|E*p#TH5K!woiPC
z_qz-{UnMK{fL<xUE_<0z<`>h5wByus_#3XH9UdvK<9FJ%qtk5{b;`>vk~~lf8f9af
zKi|$tUF6ta*u_YeS^#&vsIzZXV56YYV#uI7rGQFE;OaLHnx?G;_J{INZ`0PXV4eEu
zx3aV5Q-?s%xvmt|wv_RL0u9YbWA!9D`_cPgN&Xd-k%d%%F7qJ-f^(RcLX5jqj<!GA
zjkz$x5bO8M+HeCNY$1j2&ZLKUYiaLfvP}(tm$l~w=51tr4_y8o1bZ%sY8>@dZLi`K
z6w?I{z90MbA***oGC|aN!d1*fQcNwNukVfSk9%G=ofz>&tf(0gG1;RTFE+~-%G_6u
zYwP)<4cBGYExT&oesD0gpA=2A+(lKarjK;vj27NmGLOZqybVR!-ZT1~ZK@D>TouvH
zSI9N!z&+(8CZ=*J)kbT`Ix~8DV<#plU*Tu$qA}O*%SB02UC*MgG0BSj9^URFvMji8
zntSX&N#CB0_BccJ&mNyy*YW);eC+2SDTJw6U>WAuVIkF*FaYM-{Dg0SZEWc0*Hc=4
z8LIpe0{Y^63uWi)3*Hs7zUGNH<}MaftB!I|xDVinD-;Lb@#4+t%Wl>8NAvkFaIte)
zG%MJEER`h9F)VKQ6VLDyT?hU}-R0Xoy!k79!Bd;2Jw89)osA1?9*PYL*xooVs<ZpI
zC6ogRC<Qy$<ceh@yH#30J$MP<?+c)OGTsx^43~_R5cSd9D7C2UFyC|-irNEc_Rqe8
z*Tjh=D%#4D9!@q|&9H3##n{^wP=07h7oa&FNH_ga4~#Qhyq@L{F1uQ=2yVX03BE;c
zY*5aQVMiCuM;QV(G&4-)R#N;!eEahbG3a0{c&LD{p)4ESM29}W=9tX{>@_sd1<%}^
z!0sI#bo8wiUiE>43!1uZ%BQiINY|<{e-Qh>MWUcxW@TjX%x!KHVzIvPifS72?zCRc
z-_$r5HpvT~3|LLb`OtQ@!~eL!iw+)eN^yTddg^Iz^#%vC6PoTx@Dl#!m1DcJX9N4&
z_K)SI(HUXx2Me^rJhwz*|Ftx-M^9c<wh}~fS#b<LCVcv$QujrrDH+So&q42F!6#2}
zLY$v^hpTMp3<0fY<nJ(Ic-R84cJR`RsKa}ra#>MVC%EL$I0N4=PbHT{<rBVG%qtA8
zS?N<hq6Q>V0_Zy2*o%9^*(70uoR2wk0mLCId--kwAAW>AQG2NT@_E8<1qz9L4a^rC
zs~kO2%kAY*N<KA0DQY19o^Xw&%BXeK{Fj-BP<a+_q3^KD0HWu$eVjbY^u3RCeynuo
zi*<h|mc2*I(%H~<UWT`YKeo2?9Q0YAa||lUd5^P?)1_<Hc8&7xh?0YE#;%zI1pERu
z?wTbi9!^lbwZE#@F!%Ch_lApH=_c|<?vd3xsb}*%LeEillbBP#$3>(XeSMmeoi2xB
z9?%DXV%m&&xrlaRv;IOwAW=pQi>Yi39b8hjA>JV@9?BrbJL5~w#hL!}UVHEMq0`bB
z-LHDZczQq@c!hC@-RYMk*WLn<)CT>SeM#gT4UIJOWd~6qIt;$nclyJ%FZ!3{{D$$&
zOF0ak+K%y%{Vq2EL@PmPWSaSX(Rj6?nEt$bb+j>{?8;=&^5#7<-(m}{9jLomUEWkp
zp>n0=ieY}MdX39sZ9P-KaKO22ePp_uWgm&V<t1S2oRGF!I9?dR>S7W{C9Av~Dd8fR
zUM7oPCW!i?hj;yt^f_-hg&=aPsd4h;+Zry(dj!14lV}&~^}qlXg|5q*8)Nu)$m5um
zOcD6ADg0tYeA3zl-n)@2wR9X~XPy{vVk*nc3}qL6LN08K2b%TD;kagM^JWv!3L1Ed
z@cFmvKV0=5*<ja_&@-gr#Wab|kkE8`>|<jX7_TV;GRCp2&*Z5cN4qbxw1)A7svQSc
zYav+q8daz{A^?}@T8K1Rs&ShU0fF{E8seZ$l@s=-kGNf9zy-?Hy2sIOaH6;_=9^LG
z$G|iN?h=k(c4zu9mqMX~YO6-|(wtG%r+H5z0Y!UJiFTTF1cJa3_khxIzOCpR>G6R-
zyQPEl4IB2Pf<L7QWow8Tzm64h*@$(iJ-r%bt(hvg0c4kO0bS<eq#t(qWKeC#0#~Cb
zk42OS0`g$V(`TbG<L_wzH|qtWTeuCN`6%oBJ1v9;7-zu;(Ps&Pgt>zyw6!HBj-UEa
zswgUJqkl#<j)nL@CJ<+Ft3`Xj9lSSc%6j{v$a9dB&-y(c;+YS!R;O5=cc&`olf##W
zO=_|<t>gkMjAYB1lF1pxN-?X2v4%IT{@^kNrpexiyXr0Ycx=jk*L=Q+Cpp9SRorOO
z#mq$zlb7Af(%Y2<Rw6dS?wb}jHL_5S)`L=8h3!(i=aAt9Z`41d4!;gwS4iYWK1bF9
zb~@{>s_7PN_UGy^jDOm!aWgi(gwglgM)S~6y2aeS$xu$#MbAc&+@&#fIvH<y)mTgK
z<BJbWw_ClUcekw-B4BJ5pr;jbj*)G~4L~=JL)vXO;~P%XHvt}t9JnHfDX?$}!VcXz
z9bkT#3I`uoB1G>nQ-|*!(gn{aeY+U+%N8spuPTpQJ~}o_x0aWe`_(#b@Qd&8Ea$K;
z!gI*8;|$-kwq^u*yN^t67KEzX$4@QW|4+NBQST6+F|*EnjpxK0V%!@S+TB_={4M0}
z%_AKs73Zj7>b@EtQ%#=xY~l*t+l*zsr+|W7>{P25T>-<dx68dRQkmg+9UN4?wm{}e
za^qs*hhoo)H15Ut-=5}J03_JUX#Q|M`w?)ul)FR#chyY<Jp4{c8cIwkq+0JpR2*@S
z10XpC(0r?*vXfQ7ep;H;q}QkeHBQZaKsAN)c#k^IRu@1oG=w<~I28dj#z-pB>eTFQ
z8@De!`HL?Iaz|<yV-sK7cHjL<UeHMIrUlr}=E^w!KAk)L_^sM3Eq4S6eJ$Z=N)AS;
zoAnDnwu|h_Vup*u_%-#o{&Ef^D_-hvaFQmHK|@OJ?Q`M7s~0B^l2mJ-HtD76;LSZI
zt4(R4iFf3cI;ULrHJ9zE-N|m&e68@%1zjO(^JDUh<euVz&?5GGjh|MJD!|XX&116&
zG&c3Z>t^%X4pf%{dN3=#<jBSQ`rq1u7jm9gvq|NrDF5VkPIpDUu0<u&Tc8}`;Zy1+
z`Ly5IgC5-sb5qKKmK3@5Y?jATrTc9}6L~Wi=7A$Lm4M<hE5#HT--=8>#&!C}L>7N{
zyP=!@5+s>0LFFRwfOnuQ@e;$HTMgL_JYgumc?9U14BparvDQHv#p{#<-Zs3=kcj+I
zmvepO-THzBfvY+`iDA+%gTrrjV1kZ3A%m<uI@Q%Kz>}8KVbiMOx|)0#KjX%OzD#)P
zU+e28t7k-x+(YkqDCp?%zWbE@nt4!wTo&UcqelE|-+wW(?|@5zgFG?UNCaG^*;E->
zq{#F>*xilR1oTZ?Q>}D;C1+NC6sOG7HG}<`_2fef!C)HTo=$_yk#h~XK(@EhUDA3R
zAr&WjVWLYj#o;E<uwDB>BzcG_uW6RCt#qHWd%w<<GvNSvv*<m%Xgv9`Z(~wp(*Tt!
z01+1YarbQFS75&8wD$+3kNtx`f`nU2DZANA6D^5a1V$7`S~r9vufTdK+tk|-X!oE^
z|APnJ3n@6F{D>1nZ&_8QM2ob7yb9D|!|_rA5~f9ZtUVpz>}+1dEGnCCY7f+5*nYm9
zyUn=uZM%4=wLAI%dYdBRL{qZfs_6xdm=pJNMV{toI%n9IIeCIQasknorX5)Xb3#f5
z^bbS>rHs{R;gyTpB<;H~*>CAHB;6ee<jWRUBx1fO#;Wu8#$umQU8P*DedN)qNS)}F
zTU$i)-i*?3Hb={b^X$mtKlnkPA+xQ;?$VV;trfm0sowGteJ;~}W(hFp)Mpw}z!t`z
zafL#=Hp;vti>}JrBSXF63lWcBdxd37kH3fQ!wf~CZSFjCo9swcR=rL8NygK>vdZ$j
zC6ishk-VV1rfnCkkJ)#p%A(55SM)>EbNr&4wRa<#H=Q0C<2ha*Pp=UWuhWXn8z(}k
zW^?a`0g>3|L6HoOrPG;>FJkR2Ejc1N${Kuas=@F>6rX>SJu(!$l<8SPm7zZzY<pUB
z81%bod6Qe4a2tbgxUHjuTjL4qX<-OFCL^TRpV_fid+|Z7?WT=97=WRsyF!PP*@8xY
z^&EPdE;w>pQx4C<cuuDLF6La&*%pK{r9@;Mt>vEKoB!#Wgv302Ay4Cw)872UkRu5d
zOE+RNn*S+G<ZPC!ww>V4g@R^a*&*z};93K+d><Mc=FVES7QN>9v!72+jg%Cg%_T_I
zu}@N9SuNDJ{{8u10_MvR+u0X#Nki-egomo^YQBY?{KyQQ841Vddr}fQ`2hLK(#>=N
zvN!S8?!~N+IN&d-Xyg(;Q29~G!yh9TGNkb=0nb*s4v;v{tI~A9AzbfARvgWcro4xq
z&7;o|U?`nD1vSGUs>bqufOyI8YYlC4E?G^Z^>LHrP%^NHVReE7Kxn<W^b#KIo<wxw
zR%ffXlWLqhgTPs+O6<f=w0}Pf;vo8<d#mp~lLRx-BjkJzG~+6!N#YJ;FHlXPK~1A-
z(l>EoZgrg8T$(5}Zh?%}aS!qp;o-m(NKX=eFswD6BSq0XFw9V(i>Mu)i%g%=Gfz`4
zUsX(#(xS8I?Z?RyehOFSU#0{>bj&UUq_{K%srLQ8UBU74R(sIYsFaFnhvet1y;5qt
zRiA6ggT&Vzx~NCGM%xL1=u?vWHQBbn&tVA6hmR}x&wzAz><IPQ-r-%ap?8I=6DfuG
zGF0%^@^$Cuh^oYUq&AcWl!SAakBsum`h#BYL^(uP7=?%uie5~3KMCGKoEi4?OMTv{
zt+L)17c;aGb1{r5asXA%K6ErvN~Kxm6*M}7ON7@iK92~{i>)gIRe$&xG8o{TJE<;j
zk$1Q-J21b)Hc8EG+(8ay1&y_(((XD;eV>wyt<}uPa_0M~4p<-2nxVd&tO)X|;c(hM
zZfJ@=AiiH1%cF%qnokiyFE3=!-19ICm_;M7tMqdZeEQP-{O04D@u_<(`=q`fj{Qd&
zxTP?3bKui{Vv|H9;3)Gu#z)L-t%$&D3cZGn@|XJt-Y;r9#L%Ig7vy~ic!r*)yCt|W
zK?y)Cvv?eI`lG~L3Gj+n5fZ>Z{|d%)l?D0Y#hfJCivKaDv>jYaGB+dHWu~pRGn}!>
z2B+`Wv<yhM{kBrKt+wCQX2{F06S0JE`2$+F3PRrYP4;l^<BI|0MI#fW^m;-JoB>2%
ze)<=p^Z4;_8D~CGe0N&WfKJ<Q#@WqJ6U0|(B(i$U<^r^CRPmOC+GTN>1p^Fb?WH%m
zcSrVIM>va!www9Dvm0#~-jnB&ru1troNqzqS7dt{-W}0UP1P%zoL=0xh2e+%lHO?#
zz~52*v$(`5+MR%3$uRKwgd|SHi*sqfwAuS)BV||vw5>PPa65%I4>Fl9R-IWbX&F1O
z&gKiAO29$D|2pHGCggGU(Y@^IxuE?}%;DKD<MZvg#|9*Z3OiHJF~7(XmIix}j`?QA
z9_tD@QZ9LwZe@N81-#Qux*iKo%;N#*@*FV5?2Q<HBRXtj*M;F#P)~GvPT<QqsA2n`
zUAr@9ay2PU+LFNX=}QcJuFu9xpE)6`qQbX;TNE64KA#Sg+2R{AmAeVwIF-Y}uVzDo
zgeO<C1?nJk6qx5UZm0SDEj8L={FAr8b1?KyM&oB{@Hn?((P1uG493lI5N-QBaBj$0
z^{T(k#Xv3;_P^SMdk^vhU8=`rx1t?nnggU8Rt8u?`XBXG_1GF7i!f-t&<aPggr4)^
zS3$>n_}a-WF5Wm=tUjTd5-k->x;A2Qws~ZFkhcQ1aTjlnUGzZ7R;<G{In~u=qW=0^
zWuH)Z+_{ip3|j`kkZ901l)uI!rGu7#V(j+&e~FkE2tQQ@M0R(IQX9MMskkW2ju2Ls
zM}tCKG%Du-+O<~Ru;@I>@AF1jX9eT2p11AYQXK)PR_?Ycsj}#PYx*gLxzw(PD&M8>
z)dKQev2GBj%N*cmqQ6_&8twNy(HIiCz=2vG_oQJ(Y*|W9Dzpw5WtJdLs8hW@DmeB?
z>X|wn8FYMX?GIlbV5c;_0n0vaq~i4N<N8<RbCkRcaoB1)3i0dehZkGo(}I|(06wqZ
zg{_^By}8>*;-kkbU`<$*kJD+&p$(Y{))#Td<nLS});Yf6PHzyRcD*c1J<Eo~lCoKW
zjrxO)4PE{O{0^Q|_u@=UW0M6|o24}JaBGfV5?mIOKR|0G6{<R#=a=t9Bm&z3KU4fl
zC`lAu=;61|VX2m7kz@)XhHxvM`!2NEvi#J)QLWVs-}gi-0tNfsnOM3+0oANQ*xw0b
z)bL^V{ZqLX(^<msle%?HBi9c$z+!UlSk^R(Y7?#!MqbLB;g37cKF*Ix&0TmpYk~Kj
zY+hBY`y0jZ*|Es~b7An(U!rS2D8^AJ+5nI0F%3qx+-o2A|Hgcu_Y0>_CYm=o<8>t8
z{p-TB<C@}n1!Fz^D1)k6vf=_8qrH;^8@@)t&b=Nom7MEK){0;o<}Qa;+mmeq5nyyv
zEK*nuh~@RWAN23Dfs^!PuLIp-VYgkH)r8d~x^qhw9cBt6{-i}uZwpVASlmFjjJ@7g
z{`XSb$-EO(7E1C!hn^+=aW&gDYyri`y~K8BTu$e0e&vP?97YF9kGlYV%mc8cUn4Ka
z1JAFtgF)GWhtd5`K2?@WQ<xt{sr)vvZJ$~qhx%Lq8tR%cRAO_L7qy=)EK<FEPZy?J
znvW@RRnt;Z4GB7a4{CQl*ZCGH9+tH1&J3*=g%ae6Pe}{$jg9Z;jf0>&a{%JUcRTBD
zy^Mwjr&3IAk6zP5t976BHyr-S<np(AUlDEde!kLqf{^7mxswj-9gxH^N%t0CSa(AU
zX#Wxc7XlT5H;C!P5GR5i&aAS5D~J=Vxt%kwo0b)F@Q1bHk)==8R3E=drHyb9J^0;7
zYUw=c73S4qV6u8SBMNv?kUh59^UGS*!HALP0G~i?73@qWpRaqkVve<@@=6oii~#J`
zS-mgcay|tGl0)^clsKZTYasL;<Ijcs8uKgp-uJj~tdE%_h~B&Rski`^NPC8|lBwE?
zc4`2UIOE$+JY}AyF0xFlRV@jK9r8fcw3N^1szP)&Rrj^hzLP%>vvB^!&|m5O4}7B$
zp*772Xo|m1ziYfsClZ?@Z1!+e0unF`#4<&dn=f$-0rPnvw9uTfVJ_C7vj;n1u~IiT
zI2wdLYXH`M?3d;dO1^}otTYk|IKnQyuU_1>Xk8CyiQHd#=~KQIbfT4MJ&yS9JL$H!
zln`r?Ij6VkT4rmuI4F(=IjAb<$Q^El<9%(;+Useu*uSo10*x&sDWcQLnZgP#1%rp{
ze($MY1TJ{ZBX7D__D6$#5f{&x+ti!xHWcq>gHG~-O?T=9CHcy2qxiFm+#e_N&Wn?O
zEWJ*Tm8IEU+eTJLuWy9=wEJEEC0){OYv0DEq=@g|EISQ%r3CL<@3>zCFZIt$2IKeX
z?lcFfNSOwgtfiqhjCNSA?Cw$m#x>0oQksl2pT8UXS(d5v8?15H4{mhj37n2&RtO@q
zv-C&;-hiGMrZ-X2o%nd54wc{)jyC~8%uxSV0`n?#VqJFJ9i+w1LVeG@ePf}nZY}at
zB@gc_JtbTCr>A3_nfIl#*cWFwPd?M_%bnSO&yL@r@73%;_mzI;G-0|@STB*0dfMs=
ze6KwnU3*RDzr@|f{<10$#_64p$X|Lpuuk^m>o0qgOQ`{$s~>`9_UWB)E12CtazWq2
znJ=p!(vESLiWEZyoQlU$S#{qh?njwK2p!7glBwBswcJj_Wl%!j8C&L2{ZdG`jQZB2
z;*<X_hVUX_$BkQ`%aFk*dDqd{!@B7LrQ6HkLknm8VLi?lz=yr%+b8!gKch_&k*=Ai
z65c!Z?+nbkp8+d(>2ji`P0V>Yjl_)x1Vp945c9iyWs7uq#xS;+-q{tw(t$$7N2D)k
zwmZk<acrl!Y23pZ-}yClCe1_6QZav~azkuQ=Ka{`a<pvI+}LNCvm5Uz{YFCPD($O2
zH675K+T9G4wDkFt27X}WvKDlOJZ=XSU~SWTfoLe0mvjH+i4nrHa4#&)qxYOF_`XxU
z=#R{)^r@HpJA&+FEJc;r3DcYqx6YFdZ{*-mC&{q)A*=<cy`|67f_JH*u0MSzj9xr+
zbMKrG7}I7n2+Tq5az7{=(m$8VnAo|ugPGqh$}yK$IhR7OC$CvGv*da~3kfRIHp9IB
zJYE4zeW^=3p~wyABj%UPAZW$M3-c;FS=vw!65MliRQG|@9oN9B@J3{LG@^Z*8@^@*
znO%Led8K^V={K^q<ik>hONUH2_G}YsuLI;D9J0Kow#=V4L!9=qw}tKdo1SKiY-uz$
z0Vxh8&C`ZU2LJ+an4-@zD@$<{qPkpOvBUflcnKJ5GSto=rxZuPD!-VQ!&NdHbq@YD
zFBUr&#1H_0%uu=8uEj|Ndmj&nSmR%ElD=MSub8~VO+Yg_x3Zn9XU6<Op>y9UQyghr
zx6CaxtOsC4=lYIgy`V9CGB5e|F;I#Pi6N={rhLP<Mw+Q63J;0yvWosgUSi0-Fm+qF
z7;&%Tu_br&Fn`c-q+O_BpeT#EQMpqc?v}Cy(8OkODzz+*5DouLFV@tri3=L9>K8&n
z`RelA#+10mS1X1u3!R(CD)5p&sCQa}XqxoOx04fPB)49$Fv`EmsD{}dsA~0Wi)2yJ
z+jKTxZ6yuusI>j55n1Nr35fCS9j#%N6Sc&fXFKADe9EQ3yJL&bsLnS_HRQ0Qfj(H_
z-mj^DT_**VSoux5o&e~W(@bd)RIZmd1TDX82yEw*yL!QU33wIue!TiG$RD|hlya%Y
z>c_YwPE+97I7UoFyWMUCRvZN26efNMt81rz8@8Il3*tSVg6wTYGpf9oBL2z=Ufk5q
zo>{O7Ep$&^L4)X6Qbm{r&Ck8`7@T}gmk^y|*9i~^VmBzcaYDMT#<q;dmOdVdhMalf
zJ@Glb@biJW`}fiwfaKlLewd&8tp{z7KNa$;G%S`}nE4l0bkT3=<uGu#CB7nMsn#Uy
z=$}(X;mI$ESE|F6PVUaH8Ru@VuE5T6-hYYae6oV)7CK}C5`R_-8tvHPt+sH$OoHZ~
zQ`2CSRve@;<vYJ$Ef}_)B<bduo|fD!m+;%ZwV@eHj$oTiTh)yaT0l39hnf*K;Yu7Q
zJ{*7zzqRC~RDc-m3JR!OAco#WFPkWPF3Y_OccrroV@-;{DbxEBNuGpjzda4A^!k&I
zd6Rf0f#0bUb_HIWv(Hm2Eu?z>diN2(Y4#@JR&(!4NHuq`AMM5f1`_y{bCjLCa=`jH
zjm8kT1AvL@>xj>6G3g&d?&&+Kggnm_f1bN19mz_m)~ootJJ@7V-8|3`e*e2eLbf<z
z6*TsXqjJ9LQ@d&qGBAjvHSe;z=F^W1M`Vv?V&PTBravQLQyO%5EjVmGm1o1_<Yuee
zVB})gcwMC-&j}TL#&=THMl_?amY7pF4>{zz)NQSSm^0P+Cs@>`yT`fbUlu^?^J@N&
zrt=JE`wiQ0?5d(IrS_^-R7>p<t)jNl+M{a33S!Shb!cnvqV{TQ#9l#E5n@x+j6F+;
z5sCHY{~quA(T99<JkRsH@9R2G28Vh)q|2K_%U!Z7SxSo4O2l)VU#Dj)rV-N;e)_xN
zKIUo#!x+tVW|cZem_Me$AiRoP8;w3<CrO99;0!h_RP6Z)skh)5x&Q;e+Fw}N<3rKq
zGEY91{Gxaf&2NJ6@6%M`w_XZNUGSGLYc5TAGLuZ);F@+dThqN@vYoH25F#zq0|9##
z9?8ywJZq`{tR+M&Pu2W0(4p94U@eY&;{lofspU)djiJzsHpL?4_%dCK9)wd?XtX(V
zX*k_fawk_iSj<qCo|jG>u)5QX2=9#o6GnvT=w789g=26wCsPD#JA{A9<zyh-OIo(^
zIiTG0_fv8<;v5{W#1YoAlBjrbd=Y{IOZ=|g|C;u4+q-TnXJxZzx8rU-VhQS_k|mT|
zuGKRIIXAK3tdtAiEnoA@w3bEV$KF;fA)QLc3&7j5ZVLd%`qb(cYqB!GSygB=8;xUC
z)wkCiS%2%WJvy}FWxt0)A-;-gs*)l4YGdV>Z@uE=mp|=xRyakZYk*%K${6Y|T1<s=
zPRqXX?8#rHJ9@J5PHxedz6i%~v+K@RazXFmZ#O0&K8B5CK@;J?o!Zvt33^2PCy0gB
zr<k>DY({{h#BhYwg5W2uFyHcTp2=R}RC*?+<`8m&=#_VLy=bMqBs=0GhSFgv&k;==
z#Xu6^S7p28MMnHx6oq%6aH%=qv3Bxh95m*#TWIP(X^FjpbI`nd+6&%F_V)?G-bJB`
z(XBL;CZ^r2=IC#t{_5N8w_#3NEvLzhl}FUQZS-k=;<)!~GT0)8ij}Q#@Qor4ei(+V
zabROkpk;BcoJPFJko=(uHaTXgm*;skwZrm@D|76i?2iW=or{dm3shg_1(+PN1!B8y
z7g>t{%cn*W>~`esKVh<gV!ALU)A=GkvLpkKCqYWT-W#Odd59%58IU<hGQ5@$_uy;g
z2^Tg<i_8qt4^+!Np(nHFU#jt;DvyTU=KVbA1^8g_@rl~GHf=;DS;%qVo?BIR&H2A7
zgQt?Q5nCyN9p8$G3(obKa&l;Ho0t0HKeRdad7EA7$n44b0j9X4G>b#=c{0>rk?YKp
z6^C1zO~)$zBeZv&GIR+`&Xc(z4g$GN0xdq^yZ|Q_?(I8m=i;6qNTHWe{7UbXY#h}C
zAQ5(~t)!a^be|LYfa2$C1Akn~Tzh%t$q?~JAb6?t;;7$8{_~aQWslT1AAb1TXMIX*
zufeFk`)K?29ow~7DF@CxFWVyc_x^qUnz3O4@FoztuDg49MR29Mi~k~-J*K*Y;j!3*
zpO#tu3W2=z{~<uznZuF;$v@Ga&0SP>`c2f<pNvh>&I5MMxW&DA4>_c{^a<^hjB{fQ
zUxPP9wJ)+bybT?9<b$WciYmu{E#+R~+^i{6%7IiJ*=Yt251eeyN^B>T-I27{2bjEC
zz@DGYH!xMdz$dazX~*8cUhsc-fV*bY1Fq#}#GKtr`NrR8`J5FcH~d<Ts^clVz2a!6
z+@u`$mFzyqCQj=~QOx<K@O=32xhr@h+br%d`6H?QaHgT1xs}iP_(gu5-|^)VDTwnu
zi9KRR+)c0{6fKG{PmUKWj{d-c7iY}yHcAUFvDW>NDIhrZhS4_Vx6lfUhlrhth4UK`
zTjpZsIOY_^vbuk!Vh!6tMn!)gfg)ogj>1@}7oaK6S(bZ2le9LZv$L4I5enQ14`^Yw
zPPtP{%tG0Gv@g(~o_rN!xKeNSZ%k$N73?o55+3jF6*^Yi*z;&LHw+lmGds&_<86Ny
zx?%TMm2d|1=-RP*^8YLVo{MAO8O&wh@XS2L-AH_L`&nB9Egp5XDZHx=l!HR}2^OsZ
zbCI-8kw&H6kV!1F<iMhsUBA(}(W0@)QsbxPr>)ZS%)&19t_IM0y??ids1k@5;`nWi
z2HNmF;Qr-oBV+J>y{@g@x)h=wb~Ua3^cTVhB;X!Wk2&e2(d6*6KSKh~pN1|Gkhf3}
zTV|yI%<KYQ)y(S(Q@Y0%Tw5$M?R(I0g7|kvDKeW_!8MZe2Z;EHBKwxcl^)w=c-CZ#
z@RT!dN#|k>PnUX`E{Z2A(Q)=ZjdTbx;!cz@RQ?mJ7KpIkcr`Q3WY8kRVqVv#%Z<Rj
zvV)OIu0fQ~RCnvf(d3hr;>ocoC!8Ed$Hi=h(Ht_10vO4VJ3_CCZf92-zj-zct=6)t
zE1REr8dX(+AE6pm6FTlq$I`hAw6VgX|GFxWVrz$-Sn-5E;1g%VK)GxsqQ$h-hBUQ+
zZ?r$lWT0Pb!0~?nYbW|E39EMWXnDkgyqro6nS+dCPpg0W(Vz$^mH-QvjJikOIZgUW
zy4;8J{?ZQ?X1ovokrJsaE#r2RPq1$vj~QF-GB#0dwm~-*mZ94|%4H}v!$X91gtG;D
z8A1eUNIY>_xHv@t9{||!+sI(N<vU@I0Y`oDl~uH{h39?2^ccgT#543?G8)c@%l3(w
zAjx3z@Qios(p8^ASLh-V2i~0<U^L;yG1IRTmWT$7BuB0?MZu^c88=|^-sYTK2d5&R
z)Sq_tVG2h+RPAI-`;AZwv$PZQ>>ft*Uh7Z(@=kFRk0Wl?q)*n}?7c0JKue%VbUHt3
zo10nOdH!Y?;Hc(xI&R17;At;vvYL3&$V3S+Y`Vh&pyst8ho8zsN$=g#a$=+9anXl5
zU3oDv=+FhU8!CEM!xt!oGZ|-|XHN=G(52<BrvhK|@@ChO!>7aa8MgBv(KX-2s~u^(
zqLBKT5f9PjH3D(65eQWLjq_HD2S>WuZz8!=lI^y?^`gx0_us!MuD}6AAKn9~b98TY
z{s|;4B#G87uUp0_%|=~?BjkebxnTbr3V)V_%I4ntVMe>mgi)4w=h>n=0|vafB=bqS
zUAJMbBXzHlvQ6-7@MP=H9l7~?>@?R6bGD+CI$n_Sc`eFtZYx!OQ}X$Kx#LH_GuL$D
zb2zp@kYW82zWIJ%BHeLr`Ih?XXP=c)TgPmD;GuOrq+Hbbu0fQ-@gEaii=l_seQMC?
zC`G%*`NQ{eWwVu>&d2F{Zq1($#eus5KP$A{BuyChEND#|aW?o&u;-*nu%_*JC#x*4
zqyT-!M-4bp`e2N^ZJqJ5?qY}TvH-Ahc@kMWW0rf>>I3Tm<}N_@q(Sl9VRGV5tS-%G
z3$;*gd5)W*oW;Q1dt(h%mfNRu=K-s<c(*Go47Sj|i7~iKH@Cv_RpH_&m2T@5%9n{^
z<WL@O0Z+w4JaKLAk>tMPPwh@=i-u%<zv<yFG-o2rOZ}U?G@afgbn`~%-|(oE$^$n*
zU;pk&2+R`0%3494>{NFZ|0|)_PfG=Y;^AbQ@>Cm|I-x3mWe;Sk<VNIP6ui!R`OiSp
z9q4bV=i<k}%o~n!kh+tD0ErHY)QQaPKQB%fyA(28Fu7m!=3S^xvEsnqMN^ai{90`v
zlT#6BF_IFWac{kH!w*pi&uEw%d?nVHey8!qyMkHIpD&9B(N~K3^6gWzLoSkR=!<y)
z<lZ^Xtd7*&*NVjrP~vDIe`a5KLHbXAp7D&>h77wmBAEc~9JZ~#JGboUjjU40mTZKX
zyA27`!&=ZCc}HdaB#RiGVvk*C89Ym_s4&-oi@lRf>nW9L{b`;!C^#n_RFVNio6sq3
zj~DQg03uW(YbJYtUky)Qze{XKDp!$~2@Mb4==Q@dQTF)*?Rtrjp1w!T?ctY)8(rAk
z8J>&%Pev!)%<BZNdFWwlXrmc|_@r9IH~Qo}muI^D0&Epuone-9*dz?v73hWw{90R*
zLU$`e{%(5KYmx3VFzOA9l+S?xtD1Pji=LfQQFY<e<Z`>YO)JqB;bzP&%(i?S9gIb8
z<$Ui3)<W)ytn#Y4E9m)@vo1xk%m{TE=ez>|;q+&D#WLdGzoNQpY2kRy#QY;G<^B)i
z(bFhSU%hH;{<w$-L}R)MSBP6-SNLcUz*(`xBMIhvBf6p4Wl+M1me4d6XNUuaWx?aA
zP9R-;&7#TvZRJ>+<{{y!;58Zj#KsxyVGqa_@5e{FAK$+00kgBq3Dy3cr>YYKaEe5}
zDhXbZhlfVj*YkjrKU@&!>(0v6S=PrZ2=W~<3b%Q=BZ8s*Wq~pe#W57`1r4#AE<WF`
zyEwP!YGshG7Xj03#@<8|AeQeAxcvRK{O|TT$e5k?RZeQi=Y0Br@R&WIIni<d62W&Y
z=leZm=^F#X!}}=u2wOn>R@4I#If|kfDf%Oe(@^ReiUM-Y13d$lg?Wlb$4m3qG)<+b
zWbT>`YL{-A-@JkMJddEw1kkk9oswK1(aXKJEmF!(25dt?iMo-m=lkh=o)ZLtW;OeR
z;lx16!7;4rnakQxEl->0wq~!swXv7^Szn6=P`&b44&URheN-7yY8Z?*S)bhaM7Nmb
zS88CC<7mZSZULJqn&YI8d9@?&vyzIV1yHL+c_oFe*>T2bfx~3isrXYSErkaZxjRib
z%)Qo29Qu6dRtqKW{jOZQf7xb|9%1O7<W;UkmU|zg9dWP6vUhZB=9(iX`b)uM!Gz&6
zqHP8ZXWb7@d84zBw?#Ie|EV*-b`G-bU@vav<sf=de(KWUnRX;(3c|KqiZ7VtmISZ)
zvBxVfuwG+%T@N>iS;>EpD9ClUiq}HCr9Iy|(E;sb{nbBYoPLFTIp0uYJ3C~(l+hq?
zyfVDi;`SP2f=;Lw&wXVlX4vjhlVa*YFJ!%Le+L^5`GMSM3n7ch9PR+2U!dRVITI2&
zE&|Zt$+DWCZT=Orx(6>*ofW7$G(k(EwbKEH*X^>>O6fl}woK_49#K^F0nGsGDWgR@
zsg>Tv0mD1CWh(c?XH_m(wdxGdy2GzO-=kxNY8nY_<Hm*fkL|uz&M*_h{iXc#h`}68
zM^9GYG5)NGNLJTBJ>Q?bnr+9!78WiSB=>j@TCmR+b9}Ufn&V_E#a)x@t7qO6&2J{h
zLJivikyeE(&YXtF=1QH*l1BbKNXos>81a<XhY<Ud506^2+F(46GQDmh6bNUwhYJq+
zA-z{N(pOVr7|_;6^R%ol=ZZrczw&$Om)D#+^2RIw##(c4zkT#DmG;$M9y2y%1NUF~
zHi_jrKl-z0i*fUY$_IvR>3%mkQ-iRKIJu1K?`AxZ7l)NA@_X$8K&(n}wM|i+=;GA-
zZU&QNuP=!fclZxKB0inz0oNG&KeDK>QhCJE0e|QH?Q_uRY39OTn!QQKCh`#XzsJlS
z2ugw(6_2S3LD#d+Otp47i743EC9Yd&?qKe=PE)3wZrHJJxZw|a^K=$lsmuB-jlDGl
z6}!~X{C@Pl0We^k&q;M98G{QRs}Ioq^*kemjyd^UpO~i1#pjT39$K8U!LXqXH9I4v
z`_Hi0Ii-}ZasBflMK}x4g}#eA@=wjcl<iT<DHp~^V6NW|+Bj0eJpnxu2=uT!+_tI{
z&Ta}k>oc5ly*D+{Q?Zl#CQ=GNeEDw}ohV<fEMD+!2RA=3Y!N^lOHk@RYY#?Y?_I5&
z(_>L$BYT`EYje_jx!!^}Zk;eDXL1s<@hL}D!L#e!2qW?g5&o^on0a#R@23O!N+S!t
z=RR)skxksqam|V7zwWozJABUwu^LZSELQ{pmQrt2iV?nH68+ylgkJYr@kN~YE`<oJ
znU&ZfQYN1qRmg6eU)y>>`8kw0{e0^y3u^?_;XPzeO5f3lBAgSLarm9m-_I!ldT0rQ
zV+3<!)Gs73YyM-6eznrp6SsU3dUXAygzf#UfF@7Oo&<lg6fMb`6y4>@4UAAfx_AEP
zxTGBmJyfo=>w!}P<@4YAKZHiGkKIn{w&oSUPA!9Cs1n}F-AqyiFL-}TN$9u_t$#v$
z)aRZqEJiSRvDr>z)Y~vhd-FKZhwS4HMfQk<;7y~i$A2Hi-|y~!8WQ=~ZK14<CaCPK
z0h?*$k0hWRo19bO1K_IZ@UP=^e>O7avhg@p6-!(t{B+U@&Y*SrBU%?oP=ON`>etnQ
zPigEc$yTYp#gENc?Ru$T5eKrWswB2xbn53yRhIAkZ@)z_PJ_t5e3n)ai`oOn1d|vd
zHgWt^JvfGIv1?i!E;UG@WDlMiRVTu+DjA2L&ZmXcJHLu1w*3IZfF5%+1;F~*38@4Z
z&t#}v{cKczic<>Zwap$ubCFdooX2#N0~ir9ri+Xo%S<_;SMW(eDEr9zR~Q70PKsu!
zX>Z>Tr%M7w1VlK*i5b)c_X*8r-oE!PzI9U|4+sgM{NAdEUirG!aw{UcN$B+1?EAXl
zo~mmRuiAw^4J#2ka%&<16#$OJ!GrceTv@-2Sn%_VaRX3ub{EY<bf4&Rg`Cv}Y}WoQ
zh5ilpu9*$!jM8pKYsGx5Zj?jBTi73gF3aMEPjef)rEUt>vjjE5F3cIIJd!k>7+}a?
z3ED~5%#BNZuMiW_QnXvok9N286p6!os~qHWri^`#X&8Q=EEz6ZbB>L6u`9@43pQ4}
zNFOP3Iq#=~3^Tb(5qoA^e?E8h4+Ior0g?WPeGthf_ZOS~TimRLoP4-~?|;9Vc>M-i
zkxIrYY}6P)S5CwGw(i!PE1i4iuwgErDls?<z&5boigkcD@*9vC24@fuy6-N4jikU<
z3?~e|nq32h(>WnXHR4<roDbNO6X+Q(&7dX|G2f{1eZhM)c4}YQD)&*ZYr^<{h)Sb$
zvfazPLw58t8==W%=%gxad-JsfzuZ3O{f;83svETn$)}Pl9NyVfual#EI~^5sSmfL^
z_{J~USg7o|&6cvW&6sKT-qMJ?6orW7nUBc-tNN`8ZO(rwbx`mR&DFjqn$e>_C;hL!
z$+&J3W&9QJV$tZ@>LZzCA#wLxrK&k>QU8rt1=JjK5fiQb5C0MUtqt#|ZLAUc?ZK;;
z*sDbm4EAUY=z<SFXPR*!H-FlfH<0Lya{^<Hr{+$+VQm;;XBpk>cBHCQAD>{(ii0Ud
z`3`Onv<6GkCiuziW!4;VRqbmVh8Vwi98!slJ+wm_;<v9o<m=w}-$ANadw?7%oVUWA
zV058$dAfo!SOl#p4bFQaz&nzc=QfRCEDTwJl2AEhL(Zw5JemzalZyTA^(yOMS7v|j
z9e0tBP>da(A#)dyfa6n=3Z20V8`vc?!vw|l>_*$3x>tL9cfSbEWQn&xx^svq?6Q~k
zRy;`$`s4F0bwksKv^Z9(kqKO%eS{4XLGyy6Y_eI>>jYEwQ_Pu9f7}$N`4XtKa4b1P
zbwP$qSzBQ#Ag(sf_#GY0>0yau@OXo0=62wJJ<JY%@TU)~!QGuDekTOXwXLa3ohTn?
z2<ns<#V<s??DC@Lg*gXb`wm-_XO6`^lg2tT+BbH?r#I}u^?#GJUa#GHy2F?5Y4LEK
zdi8u$3uwubC+l|ceP!2Wno_Z*vZE8t7sXj))K*C-IX_!iYjm=U)f!)3X!1s25yQ(x
zhN<y;5-%sT1Vcavt?o<Y-uxTZ<)RiMLr0=CEKxC9vEbI>lRx2hoMYG_?|Kod;bU_n
zS<pP&ScApb!?LGdF9JI_-1+Fz!Z<%nuj!+tNjtQMMm(<v=K?uhZWu}a3SmnV5E(D9
zY`2Pb-*#AEvT0;HpBg>|sK{G-aT@X_u>T`{hS|zA#aQ`n`EAV5*IuUK%lGprqxWl{
zha_X_%Jktjx?gI$d~e(3l~cwRBOVMB*?2|gN7Mh9p2yLaH6AyqVcw|rgyW2$Wc(3X
z#&Vz~nyUMR5-Ld+s$-Q17XnzK(x3<MwA0Gg*?Q0)aIe+OZ2Y!$&l44@+sFUD03^*L
z^`H^l=yV+Q;xF)!dhAZq@lb2ux;9=-W8HMmVuWg+UyfHLyh6D{a#j$xG8vRDBY-k>
zQth2x?H=R@Dk|0NTfKytMY$(pNo{ve1!{{qP71N%SczK^$vi;x2L;%WQd_@#g2v!f
z&+9CX7>yW*nlMQ6E}0rH44rrGNPC@)sW`P2S8xcY9{OxW{>2@1G>_v-L>_={tMNsr
z(NY%i(AXxn-}8T}6pK@NZOzTG8=jU(xvS9J)x-Um(M592xjZT+`9pTiAU530pRbSh
z{+!=gx+?5g33pH3q2~dhIY4e$<>Kf0T9oZ4+wt(oM-v=CNA&`!Jnlidg8q~IIF1V%
zbYXmbEHUyQh-H_R*opRzrML-rQ&Q1Rbf(}QNfT3uHpHjz(kQIT)%1B!?zj~;o|sJ!
z`vEvDF;8X;3H?M)v7;k-o5iDYNcO#{OqvXcu4YEAXkn|*Zz=1v#?|n}KDh<zq%*?O
zF3=_@KwArgtfO=@NK^B0suKchrD2F|KJeYm+ar9zu0W56r+ZgnjTgTR+cCCxVQ`hJ
zKernx_q1yd^jN(?>J+yt+eMs`T?aOZnbcI9E<5;OxMyBnhw^7uU@&K}%AQP5R{CA0
zUilkS-96;*=BS(L|6BC<vyI3F_<uacFF{2l`$Z^fPw?q69shMp^6%YbTBW~}+ur+L
zihZ-5mX1$fTvnE>)BVkx(Jo5gRpkJ-xV<*UHESk{(`88^q(0yhjW-&RweM_Ijv6Zn
zK8UuPn<ESbdcG-8#__CgWPSbm81hn$=}QD|0b!I9Gber~v@(N|W7gFF$Ku(kqser=
zS=0JyqRBVvCi|2jRb=vl{&n9ZBm}eiBEl}y$W4aKlWoIe3D1TBSj#@@1C#eobQOi#
zL+ep=q~zlI2u87_`O+@DinjY1lg=_f$kX%ky|8R<$gdxRAAo1ByTx-?#N76V=B-36
zS)Pz(CetuFCu>o7wN(x6zAckZ{f9$Ws+FKm4iY*uzmK3yEV-IteJ~as1s@(dMg1Ej
zNQ}>8c*sPV<^t=|9o{sdfDV~_Z;$1>+d|B)0?@-b$MdIb)5?b#!{Yub0U@$vY4<&F
zXUfVT2*;pC9jksFjhWkCz|_v;v%S#N^}i&n&p8@y{X=1^D@zUz+{I*Wd<&Zv@dY2g
zM_pAZ5LOu<n;~ZiOQ5UXcNKgj{>I*5A<wbc+R`kBfLuob6_A7unya$lDJ4eFoRcFs
zbD*^IxClCYE0zkYIm?Ckl2sKs-o>w_uK7Ix=&l+^KKoj3nTPI7-<->(^SU#W4pE5N
zK)O4LGsV5Ag0Xw(xh(OM(Hpt^D)*;vr@A5ywhZ5}y1AMh@$@#Dr*INTL&(_6dvT4T
z7jGMaZxgt6>SSCQg+Cxh+x`3VTeaDu%dlicoEl1i8~P&9=((pB<}=BoD0&|O`Wd?-
z;Pnrr)JL4g8@|fVREfsU>Vzi#Nq?>iteO1GDx-gnt^<IhfA}<y8v({SR^BJB;GlD7
z6O<o8=24_UR1$>NGP<he;9nZm%I;rIlbHaAUCFQ|I;Vj<rkM!az)fE+Y}Ln+E>iR-
zUf~%4&+brlPb!wGE9_rtS&i2aWDbIoA&YbEB!A;d6R&deSBND=6FOh+)c%#xwf2pP
zNybL`wbSJ&#a&zf+~O&7Nfw_kjQ)x&?hj7dj&IXB#VVNT=a|Hi{V^ZS70Y$Ji&BW~
z9M8Gl-;_Q#Vu>z^+X=yB?GUwjt}i}o-Yegd_&nQv$u?C^_2IZgsQjxZs%5tF4?}~;
z^w=z`JLXM(U4UYXoX}oU3lCjEEF|k-&oc4E(RR!;Q9mM&eOF(<I>>e*gQBoKMAozY
z1xj#M+Chz&QI%prV=_}?Vo1;RbW`k&i4`dt^jek4v<mGjUL#i>q3p)kjbLz~`Yov3
z^^sKLlNA5p`pwycyHqHJU}FB4ZELtG@grch3EWv@tWLA`h1}Z|y0)|o3);p|+<igR
z8?N@oP^#q**pFAV<^!Kg<qj%T1mZ;0jMt>O$)3A(#Xfl0VP4p71}pMn5_Zi?&r|y3
z@Hq=mC_~mV%saWtKG~qd0p0&aBX^{>p23C%JV<H!DTdZDe|lY|v#;1=EW-)G!(p8X
z$iJgN#oMrd)8evk$Z&chjq-JQkr{il#*t#5SUG3BBa<oNx}=i?>i+5%y9V7J!F9A;
z%V=ySAfDOA#p8xYr}3*z^*EdQ^O)fGa4uTjH*Ac@6Nsb)wo5icC(Bg2!{>(*{tx3y
zB>a1ubZwG&I#(Zz10P&-h^z83TP-#?YTnSCO%Hjl=<v*|+&VxM5aBey%2BC~tn(U`
zVOK{_0oW?Mx%1lA>*3+x(X;!K)uRQQn=x-U3$}j006dCD00{7XxPfD9${ps2Ze#2m
z%{3Ckrr5A_<(m6DeZWR0KqW0?EZz39=hl2(jeumYlj+sm_?@4OKfJIz4#Z9wp>%^S
zoXOOA+7sL>rKcaq-lb&yeP%uOU61cz!!+a!*D9a6&uAE8&?2b({tkzC7QjuG3@xEL
zF`P-de%=>L-YD}Hl#nr8Vjmu_cbeJ8W()xW(qzft+RwadMYeno(i<xYS*f{oB8KY<
z{>UdF_C;J0XfmlT15y;8!89&So!9(crsAJ78&jNuq514LQ8%<0T47w?a3)gT{s-;8
zHxr<G`Cq|^QoKkBP2klr;_=nRfjJFgbHi!imfAm@g`K+A4~DJM3DK>@AsXdHvRK*Y
zXWVKhx0K{)xPxB1%EeyPY2g?pp%g(DL6h8-A@$&uzq&G3{jL|;dd3C>)ehJP;kJ4o
z5xf6j=qXU;c|W-QZhMSGEMvmCo<hipt;(&O=7Ip<+ZI}M5uttV2RBPOSF|_y{ez5y
zemDMq=Jl&SIZ@NW<nX;Gj|@lDuL<6_f;zm^qD-QZQc@hycQ+=BZ^&Ezszk?HufM60
z|6*sEPN$(iJukEncXqwo48oL*Nt|teJe~R#;b<;Zb(S`9gXMHAoNJ*dkO0n@GU{d4
z^=f{{;^kVSl#(jKQjm7Xw!}AcV4WT-NP_ImDKTu&{;uW^xLJGcVLY<Z3bn9+pPwy%
zyg+0d_5ebcG<L^VM4J7wq(a65k(fP58+dXOcSa_xT-b)nT9{i{qLQ53-dL_-*NLBe
z@hiV;HqMM_bA%p%S;kwx*DC3YGe^{@TbQjjUxObxq*rp?&Udt%IVg`Cb!blX7gD17
zkO~?0KC7&4+9JhQPo_cfCs<&pOLb1V=}`kx_4Pk|db?xp$sI|g#aWNs+|MQghKF-h
zb>)5PBvGooeW&Skmi}iYaoQ%%oXbB<Co`3Mqfzo(J+v{i=`VQjXLjBFLh)tM!^$|J
z)O8E!%x67ed@vTxUKAk9GLDAWmG4e-ZM!J4M5HJoiRJ;9oJ?t8M)xNHlZIdDbU)#=
zFM7|j{8IOfSXiT<?e!JJ;l=S~i5KGMQ~mlP^wENSb=R#+%i8VWr!jOOV{6X0Yf55~
z<q0~`D^C%v=6lAI=3k7497fBV9De2R1V{r7lO~)ErrehIL>{lOYk}EjOZ*om_UAUC
zXXqypR63Z3TPwCGITm-F?3ZQ}hLiB|nux+rI|3~O5^iMzJs<N^?q6CAILmzS_hQMh
zu#%n}y{g)O%HZGBpB7E96JHfC4*x;Q{smWzzd-6LIJazj5gx!-dzOvGpH|4*dL`u^
zDOuBGk10F7E=cmE<=Ob$?=}}##<8*hGCzcZT<?y;2halZgT51SHAlv0pSyR{G}4Ik
z{CJ`*OUS8=YFS0t<oTOJBd;rK)ZpoS5d1{0KV1B7x&U{x2HDWpER^`tAJ3NH`EciE
z6vS^!nB_nVztDG>k_9R?AE9U}wHo0mv6}+Ou=JM`3=|I+zBV?5_5PI5og{>h$pFX&
zqFt)1?BaM=7tFs<DdSy5b?uZsPe~#D*QVEOU|0mU{qP&GmDrAN8xGvcIWGE!BARZz
z1F`g&3tfMxMd;DF<36ozldXpvbeC%l3ph@uSwHCs`;UW%R+=P_Elo+BVK*jjkxUOL
zMLyf@ZwJiA2Denl+#aw2%5NLko}C`I(RF~TBPQ?_SR3v>Yks2(0cn471uCJA@-^GJ
zKNm5+!E9INvi*g;ZG~5_gxzkNHXx7BT=7RD%R^&#Rpi?w&=8Pa3vs~}mfYJutdCLJ
zbux}9bH=4R=_+VjJ_C-i<`QkV8Ua*;%#AZJOn8q9eEm#jja@Sr6TBb@JN^<U7m@4#
zu<&fcvZLSI8g<d7q-Iw5aHIJJYmI75?YUCC^;iY2=7ziKSQ2C)$Uv7zEM)nSfh|FG
zWm`uw!Ec5jx!??uB#i<O<Luku(=Dnj9kp+|B`->-6i{Q6ED{JvW$xM7TOJkB?Ussp
z<iUA5DZwDQBDED#n>Am)54}3W0PAyH8Ix1&a6f~y-W39};#aL<*o9>Mq~`BOfBb>S
zSVT!T@d0Kj_^TJge-CbTHuKgSBl(q+HF(~u?lq)XuctL~Asp!+Fc)s6g1z3!K1T8h
zC2OBK_r`PPncQ4|aeJBGueYdG;D&>+i-M(>aR?xWleUB21Y0fe<4-$fyB-c<ije}J
zqT;^GiYvr+u#X*)QUXC5+!K4Ou_m~O2cPB*#7-sA$;ed%JNNjxvF(c*LtnYQvptAj
zkIYlH;Cb`A;a$kEdI0W0`0h?!V9-+Z3@CgPKJ%MVp{Z?~tkDzSbV;viuT0$8s53jC
zNVWG=SpIa<jt$24>VMRt27HYR1N`a5_cs|w3bd}L`tRnD|LS~SD3f*z{*vLv$%EUC
z?eaz*WZ%~s6)y>uq55)h@EiRq0!W*{<Q&6rD5t)0TwC}vm-{)h&~@zIX#4IkgUhh;
z%A^U*3p##T_>lDMo~O&ry*!2P9_EB5N3Yd{&*nlZSMKe`(HdV}04Ne#bVQeEZkV2J
zt(d?txA0?R*V4DXuDL<FBeH9(6>TPjudDIyIu-qX!3?OX#pz71ry?J0zP>G~we)^%
z@Wp}?_lZsM)FZ3vo$mnH;pw)8BZ@x5@Jqk*MOsauUqbi2t=_=s5?7Y1+NKPa_7B$4
z{kXG2NEnma4=UDZ9hTpm+N<*SfyX(KK35lbjFX!6_-bP<jIrzy)kv^6r~mrtUAosS
zo1FA3Hs)F)C`VVV08<M`73mz<ssK;3=vXj`QjvB}b}8GKAQ$&I*(<}a2ixIH_%@~I
zrHZhmtdH1mv%iy9d)fXkbocwp?exJw3Brdvu|9s;-S?s`*-Uel4`;pSp>qnoj-*-Q
z>rncu;(w_Yd9h)(>l4*qR@P6#)ly=jKJ-^{KbAG<8I0r1W-(Ly>GY>B#>_6LXE7Y?
zo;%wf7~V?ue!PM{+2(Sk=Yp2HCeK5aGtEvjY`@R}?I4<rBm{mtF`83|e83v}Ara{}
z+ji1jvEw}BYwm_IK&}RjXlxAm2i2~I=bj^!-BfXrF-?hv{<eSc?WMph`xHE-N%3X;
z`F2%Wc<x&7XoG#53$tm7g=NI`TWvgm@V4fGgL1!=qqoZspmD!6(V4lA1wvl<(msPb
z41DsqNr8T9|Ep|339Wwa`<_3?Vu}o<yh0&D@r<^~te>h$Ozy>6DC!~kM*vE@HUyjH
z#}7FXr^h|X?0Nep8D#q#{r8Ii_%Iyr#$@a1mWH*^Y?3zU1)<`|LA{}7A%GMuS!Xt5
zvt*o$v!b(OGL^SzC(n{$+<LGL`D4xPlvt<?+f*)7p3FVfjNBYBvA&vul-0Em6zA=}
zr^vThevfd+^vfZnXqLOJ=~U>9(pUePmTT<@e{|6{3)&$CK^ViATPFaGor!aP7$**o
zy>HKCZ34@JO#`GoapX7MWX|1?7DIIu*I-2pQ+w-eW|L&2HTq=oTBl<1E;1axDLbo@
zG3TE_=DK3MR>xLB(?SzddK=}(;R{ES%TSAsHjT~zah0D=FEM9nT60PD4P^`)4Z|&?
z*B2Lnwx^7(%+ThvJ1p1DR+H)XwQ1w}1BT=oM%o$Fv*7a@{tF-&+KUL=Ph@nx+KvpY
zhxuIk&PWfz^gqw4G9o+?p|$n#l>`F>C`Dkl*)_cOBz?}PVOQp=s>r;3jXIKOg;B|q
zN1w>S*Va;ChveC3ojeP%xzYcJjkbZ`0PI<uvE!gq{b$GMLs6Tv%|ntdi0mrF34L?O
z&jLsy1<yVv+(Owhce8wOu`GX4>yzLm?Dw@JM@&VCj``kyY#Hmzv{wy2QeC)i5*@$Y
zhEsfzPHetHb}lnsLrIC%tlwAVV(P%|@f^v}Gd_d*c81*@<Uag+5;%mcYQvnVj?^?X
z4i|E(Bz1dTA`Kgn#42zH92LHToDsX2L2JB(!nRD$8mGQ!7hop5LJ`Y6O$?htE37QL
zT6jXGwfa`ga_~1q5TlFMfzBgV)Y!m3NkZw|^7Giqq7!M7^yFs+|3{0<H+Nzt*ywJP
z)Dq^O%|52u4~NK*nbEXNeq3K;)#_$Wc*)t^VAK{oDXl(wtD_B03QRnOTx7eLK*Jvd
zqkgb;U)YA){M;w}!Fbwy?Zb!T0GdfioCQ(zOtj1V`Uv&+Qt@{I7eiuMl~=*JqDkV{
z$cKzcC_YJn9q(n^E0JvrT_fjW`^nss`GW4ICJBMTLlTI*+o@s!l{f+BM8075R<m`K
z(ubn27w6U#-k1n}fhj9xxf{{F>{3+iV_Q~Nkp^$i)bm;Tu`Xvg{Yl`bP|5t86r9bW
z-DDflGcj($E}9jf*1xQ>&o+)R*t82Q8X%&}HQMDv;O0ocGR1X=;@G5l=R0&3fEMC_
z>spf}INbiE*$NxT2AA(CHP5le?r+dau2`P+QN@N$Z3rR#;WmvdF(lx|hv5Q`sSBx`
z<IxU|{>AzQ<(MZ6_;!3MfhVDMA0cIpm8V|<S2hG%Lt`HYxcen9WvXYBsP^v0S9Kcm
zA28N`2KRT$AP(Ect?-?s%M!-No}AeL!4*SaUFgc!#^PeyJbih8>%)aGQ@h0BLtBY}
zDd?vC_Mk_EVL8u~=kn&ZlxG=c0v{1N1ID_sm1JpSS6Qpn$(5ITTkZrWDO+3Ir6~G|
zsaUsdwb<IiJv95iBJQc+Wz-X92t+@XgQqmNa891L;83Wq)bdsql#B5zVOr;8YUZS!
z<7;ub>(^QL#n0@VQaL9i4~JuT=w3qJdnC@+$h~sD8!Q9U)%~ERwk)K-&1O*uN#pM;
zPYcHBMHr}VOoZ;$UOKD(Y@`jj?c6dPD3h0P<DmCk&w?DKq!MZ8&*$HhF+7%WSR~Hc
zCpSxdpo8yBfH#vV_0x!bp6gDm)G+N6TT$N4bfuY8Q>e^m)m)E;aJp;LjL=jSR^Rzj
z2<$(LA{j2$EEFx)p32IeR)PWIPnOB=3dXH_q1`#>%^f!oUgb^&dZlF$m8u(3(9Z&K
zx>j3k&#6BbRs(Z-!#VL*V<xs;z_q)8`VvaPkkZz~@N^G;`_Y?%@~H5s`|FP_bQ_z+
zZj&AwX!k*__AkjtiLeXsoC+Q%BS%da|Dh7H2MUKj5Gf=ueLQ)u*QMW7+f&U^;`1$x
zbYCy(6aIqQyfWw9E`tQUg7X&mg5%Bc@cCh%ctcz{+_C5z-}w%Yf;M9ky!O4cp(1Fh
z@p~2*)m)a$1x~(i{Yo*LCMRs=PDdLIv7b9Tiy`KXf$rDN32iYR!z&@o`0pxctY6F=
z2vMF}$DOwO9b%@BQ=aW7h9mC0n`vJ<PfuwI{};(vhf>|N|KP>*z0$xCZ+qGs68T8I
z<VS#6{|l=~;@m-Dn?L`TOV4jvt2C{pG44vW5)TH1&ncjlE3$J~&O;<0PgEEux+6}K
zLiQH@VjEX3qmsML*O7YoX&v}w{qk``RQ4-1^%hy%HEC^!U8mYrdMeuoBVVu0LbPa|
z3%-3R^nxB{6&`(!_`@P5@<~KO?<HxHB;}L7HB1(!3ZF`i?5VU&{^*3||EEjAb~^oN
z3Ll;m7?7Lkk~uP{_%=7{s%CycUX2G3IFC^=fQ?_bm|HWnowvPu!<#A3dAx-5lx&<r
z3(&!K{tY+At8UDJTcMCAp_^b&8|ENd<u~)#`;Bw(Hr(LJJaHDkzANY;{G?=OY^vEa
zG&KuT&@{cf{5g;mg(-9q-&-AW!m^5%;i6<Xu7mSZR*}6E&;E9jISF<HbnT*h#D3F=
z7}NNey>8FN|CGy~GTeoVd1J|Tt5MdVF3_!DVQ;K7tg(&iv42lNa)7SYvuV8-1s655
z*|(pZS)<&w=9Fp-IWOvpBT_#lq2j!BtWa|zH2<A^AuYTE!DCgQ20&|ukEG+f+bjqV
ze$Ts&fzy+^Rh_;MMH6jp`=r6lya^|Qhw^Y-IGd}5Y<vp6SX?>f(Ltc~$9A3(*teRY
zNW0wIZR@CIi>aqN7va<Rj0UGeu2XPpHNHqW<0S*<{9wW7ZWeG=z(Aylr}+yQVYpjj
zjVEEAT&i)-34{GwNBmx=4Y3^d%79+PcuPx=R#RMDrf4-~bJyU14m?&YK7%$f^AdmD
zF(i=BFgtAbybT+U9bLXGzm!c<nxwV(-ADC3mbsDdK~~xB;Q81|xgf0;7YHJXmHy{s
z1EwVRs34FIm4R|hGki8{Pi{Dnq0F1s^9K^SCgJ63LHb+vE?F<YKz}6zDr5s%dsK!{
zEdS3{;^1UxMtPLF=YC215T|dK2@oEhV}rTD$0Y&J2Kz%5dF+cg%%r4IjRflbM-u6x
zQT0lA|FG?|N`ysDw>t36VQ+2j#lPEWr$QsTIpmJc@&FFnhywU+jNgukk>baYUzc8;
zXuoZmF4Tdano*zR%JaBwb?!jaa=AV&9C}=)wI#<bhh3$?aB_Ub+~YAjxMgs`AVPPO
zoVUZgS8hu$A<HK#LY7$WdG2Prvgh6mc?Tlr+eDA$)=gfdp5A9#UY-@RBcMkQsvW-_
z6x0?fbqEvK=yI4$XWvs@bMZVJt<vkE-?!kV^Qf$Z;}?V;5~<RnaYxtJ^sL`1lXm1~
zlM2;!crDnG7M1vR5knSkwJ_`<%dd3112d?NN~q+GER8LS7Gm~);smM`XZ4>4pH>|D
zB8SS-ZmFVQ#o4Kxi6U_?33s$iH;a<jo)_6B8cquP9L8blJlAa5WQ3_tb!)U^#4XZl
zgAHsyrTc8K9Ym)*34TYiLK&viSaCnNq$$4!a8=i&f5wzc*>|^pd|c2;bsbXZ7Z^vO
zx|U+{g=tt@cA4x2zf*y3IoB64-gPAmVW@^I`D!H|*xs63-0Cj9_4btFb<eO@lAV8~
zHHk<{%vif_{hpfyrW#uFV)p`QQd<8gFYur_)sVe3SXn{TjV>j|;&kxj4?n<p!gU8h
zN#$;VdwmRcK!0*UyR6?6E0wwJAb%CYA2Wa2V$P(-GNM$p(|r5BEKbRH*@-h1G3i>A
zE)5^$7O^v%xj=)bmKQc2oWATs?g4ik#$3ynOOdrpl0Q7<aP!J$9ge%N^=ap34~NWL
z3}J(&`;XoIrVaIz%_gG@;}qQ}?>kEbWFqU*rnn3Rkq*n0bd*)VIJ1^xuGeNj7ck^@
z@eQye4=OMSsjvZ4Z>J~7ro82{qdwwle&OC5&y~xgWW}wwjoI;{@Rp?3RI*z4nmjru
z?b0tx`l3T#>x)L}xrl7&sp`K$T<2n%x6DsRgC?UyvRAF{dk4Ja)=8%_N28Cws2lk8
zyk%eiOe4sq!Ay1OriUA5S}*W~-!gRvoaF3Rc%|=nU7eQhI$Rxc>_RYSPl8oXQ{irb
zj33taSjGIcmY#9P?kVlE;!jm1PKn_wb`4zdzsDAGsu%eNgs=9sNWZzVZw7Au0i#SX
zhl5_OFkt{}ZY~eCT%e7wiSsyJ6$UnMJ3JPBKB1(Q*3KsmnDF_r<8lF=QCVa5{-uy_
zt>0LSGluP=^Y#s?6x0mNma@V?%_ZG!D4^%;i@w^^ot7<fe6N9W_wIWp0}=4oRt*;A
zmdmM?jHmHZimW%u6ne*pp90ph<%wjkEKdOp>H&Dg<<YpF<4-%~_9BKX^i5MVjv>mr
zWuV0<W$x@Oi(oA2+-B?e%|Swm6nsZ34~Z2sJK3Wv+xJ|8C&23Axxo$x=%<AmH&E|a
z&sy(u{LsT5=I-GXqqF>!tfLMtEK}cVuO%c|G*z+$OHhUkIrwmnQi#MEDDBqaVrBSN
z<o1pVS+EkPTcwlt=@0j13{su_{beo)%J!a*8~t;vUdjfN<&lll-E{k94f}R6&wn5%
zxm|nGb`8$8hI->SrPv)u`UYn(Q<;6WW0~z$oNxUb*1^ca{(@`Ia&8LTdo{ce-mw#q
za=dIOOq;I@C(pk}Mt(0MWrX6Ij*Hw!_Db5D<oSZkPi|^t<_D)1P$n4NvrY-6ddd7u
zG@mW?{l3||XPIUR1!@UKw{K6v+I@$-Kqrbj;In_>L#o2qFjcIQ`(S2piOP5mVNg0B
zxSr-U{!iBW?AycjkL$63Cra1vGYo1sK<-E6pWcE^N<p$5Da`eV&MLCnp|>imdW`K|
z2i#`qI!wyIxjZyu5ucQ;>_k~-8Y3dhtI6;3^`qI->@qu34*ru2KgvGe-DJGUknp`&
zpsn(8EJwoFKCi{LT?WY(lxurXCP}@oWBwX3qI37CS9UoHy!VJ!T)?_hD5mB{p1#7m
zlG5wPPF&NOI}Tv9bfu}re{!;|cv6vyc*C59J{Vx*&Suj0%jXb4bPTnS{M@J?8ahi=
z;felit&zFPr}q9oh1E9MS`2}9m_cQ$ZhcVB-I|mMZ$F5gknjs@y!BCQkgd}_5m9$U
z-r=7re3LVeN!7mS5u`o<jr2cMUgvK7`}<+?LaN|`E0TgiYllOXdhs`M{W03$E?7#L
zj}h>hECtY;V1w(2PWRiIK1uFS5&q`=G#f_FO#R&LGL_KaUsLyxm<+)8>O@!#+6;CV
z{*pYW`L{&<;cnP+RH?&}^fss6vS`ge*49O;;|`DWx(13+VvKbnd*0kMDA(rgdq<BO
zBi!Bv^)NKVRK-QTM_g^7A=ZG7L|9@GF(XX2GhrTc^gho{NbJ*%SQ=l>)Et%zk5x?J
zcIWO;%p2EUGKT-g5}EwTo3^63b0|pRrb%%qIE!QK>rq^w<qYxh<Acf;yk5y8(N-Vn
z2ZG@0QL_Et$@%Y0p{I_w_4kyF_r^CX@p534KJ~UmuFLLh8VLP+NhWG&kZZZ82Xh*y
z!kIOxRB<?Fk~;(7U30g+oViqmpEh4kvnmH^>?~xkhX33ppIoy5Ql5fT3*cOrO*E%s
zow=SS&G?4gb5H##{i}_7(^J|{xfyuV2tGL3-k5FqRi8P~G@!Yqm5YWz*A{_4+J$fw
zzVo_^GXQ7HPFPU$`ov6*fl(VJ{iMLbQWDLBX64s$s;%hoKE`xws5!PJSEga(>~@ox
zkjakCscwk7*!Z`~d7FO84(X?4!Bv0n_D9nO?c{t`TGXt3<buQwfJ$|3ED+Kwb-mTS
z^BIsz!3UAuuF>@xE@NplocR5CXgTs<tfzG@&nc|I_P~3bT=_@>(DE)fq;s-HG`h(G
zaxIYHbNw`O>^INV)n$0emC`xU>nJd^TdWL8)4p?6*^-0*TB|y1cO1}Yr}4Yvkr@49
zSR(|<J@tgb?a|||K_;v?P?qBBi6g%J;clStZHJFp@)bypoGFebJR=PGY9)t0^6PQy
zvP)uXw^s+7to55~rnDKyrBF)E1EdA`;@zbRaLe!Z<EJ4ryuMONuagtyI*q0xZvLAN
zV|wsdcE%R23NGB~T7MSA*MRHSG_*3vk}#BI?WAxQ70%?^?=K2-1-eiFwbHdrv!9ej
z+N8~{`t);toz!8M^H3!d`zri52&3l&%3(AT*Pn4*zsM4eMMz2Xcw~)>vR>f$x5!+s
zRlP~{&gzqtpdWqEkhAk~Fa?#J$YLpKptx1^;S>ZaatER8b7~WNoSPLss((@v^HS3$
zE31Q$KXg<XX$hT7UsW7iYHYO%B)i`<G`m#g;&3?sYVM$MLAJt0y{b~-BAO(a{;-k)
zyU5eOvR7OyQ(<P`t!GnT0R{`2;bMDg!lHMr$}~PwS3UyJTB=>^knQyRV_iF_o-^Cj
z)U6Wy)tMuT9P0jiCWuB)+gy3vZ4Ke8@G`?|bj(xb`m<a3zjN>XDYVv4Bb><!s^2o6
zrl<WUab5cvyjW|xUaC?T6*#|{cu=dK?B8LoBB;+0Nj5;%N7*}_mL>bgO+>c|pmw<T
zP-f6eP4_Wn&-@dy2wng(CF`;;&?yI$`NvnpA{g;P16pq=g^Xw3g#aWb1Jv$!qK58_
zio$#)>;a@C$GGZUSKXF?)a;trUG!4`qZ7?Ds!tR_2s@IfB9^2xLb%OP3@r9M6H;S{
zgd1yZKe1a@3Vf~Qv>B>qu^mS%!5a_2M}S?k3M4E-K`wKlXR{dF+*x3&t`)bf<57Nx
zXY&^po-yNVdPQE*WXo>#wq)jR(jERWep>yuj|V8yaOuC(M3Jt?-^JO?=untC?;y=-
z5m;E7Y-Uxm$1V$YwCbtqxUG|lM7#D;lY2jUboaWNJGvzGUEC;m=MFtC_~MZwz%J%e
zgTS#<*XQc`^+<na<XCzSp#I2_)kVy!=7%e<)Yg>hvHlFSBfN7XD(?4y+Q;NEDN}~z
z^F8svuv}b*)5?q8k7PzKg&lw6a(q5jw2hi;uXD=1e#|?2chSHBTOM?1V|lO_(wB!q
zpXM`$y67*60)x9rX>usdNTUq;32@}{h=VA$Xnz5Ps<odgcdS<Xd5^>04AJp~OGD(;
z6`T%QT(Z-l1*S@jDvi;4+&!bz`&a6-=T6SPWcZe@J7=ZdHx^sOBc4A{MiO)Djx8hC
z#g`Kuhpl_fIR|_ZAlJ{NF>VAc3~@HoEOEGY=$J8G<B>-C$bFmLPcbRW;OVGeFMgG6
zbr~8GY<PF9(btw7k9Kr11bv(rgdvo?hxsQ8Smb~ISi812gKIPw+;^MjKK`y%;eo4r
zik=)E3i_{BXIfeLm+j#5?^ib{6_v!=xjv)F`?prO^7;NPXsB~W%tP${ULR?m*~J=R
zc#edz(>kFXYS<ChR_{9zSZd$zYFhY$!(q7J1OGwyV?)C5Rp!?Pb%QWVSS~PV9F0G_
z!U8R+=4OrGkJ{Yosv}S-X4d@}BW9iGJ;$e%Dm&eocfBkP_r16sYdU^Da5fm33CZ+c
zcvt}>C;O<D5z4Yzkz21!(-e5lREaF{6CAoYnJvxh;vTy?>WpGO6D=MlI2M=Y&ZJ(%
z9Zes-wtRBUo07NqXf3_f!<1w3o)AEVeE(WXn1GAff`W#GR}Vc0`+Jr5$}w0DLRn%8
z5_mBVoabwwu^@qaT)=BC$zQaCL!lc=nC2fiM_+d>A_cFI^Vq9Ll?{!LYHPROunqgn
zSKU(1Q`tyA!IA8%C7#saXJPKeqOr2wY*99oIv~RvrLZ)~8Az&0Pjm2Li!cDc?+Ku<
z?Dw5mY$ezc5lvqDcHY{O$upxKi*|J6#_LLjt#4W5NU56L<(g_;iYaq;4{In%(f4=H
zs^_}B>e=-0r2Ah0<F%P)Cw*sewnBtyW3_u<mn%hE@pK8?Kldo+BZX+48~p`<k}Gfy
zBDSZd7D28K!?~$v1w1~ozEC(y4&34>!+Vf7a_lRey)J#cY-kBevJDuMEvMKKn?Vrl
zOR?ach%~#Y*pE2`>4z`t#x=_Fj)iwU&*w?dG_32K_Fbnq!)5~YO=j?i^XGOPpfTM;
z){Z2vx)HMeOx<gT|7!|%-QSy;;&h`r)t&QX1Kj948=>BWEdF|{4ES(@sB;sTo2j7~
zi1|kmbW?t)z8ZHgJR%~W!e}#N;GtR$U;^wm8dKWdiCoUNd46|M1*<k~fgNvP8B76i
zu_;Xyhf#O|LPwWBQdc}ZN7NiWBCS|;T9VoaU6L=xlsDBV7DWmN>t!p#>i&#YiP+U0
zkM7?B@JzE&BvAZ&YMQ6I#`>J{=9u&XZ@e+I<6(pi;Hhv<&>MQj4lTO8Ai&2rT}=8x
zbATgGA<CXNBY`9A4-?7C?VF}YiY|u3zQ%;QL_h$YtBJq7wn@4eI2ag2CFx!^9t8A<
z?{4E`S#MO}39|hSX#o+R^0Oaa&K`Avxo5wbx7Ukm@+^F$oB!L+rtb4FBP^zT+?K3R
zJhe7pjvTIDTTD{x$#Qq4li7ci?HFmDLA;+TIZzAQC3PB)e<~Y%fvL<1ul)~CXBpPy
z|HkbNq@+}oE(=7uhDa&`BBi2qI%>k`6c|$xR7#~8ogxhbgaMm?q%;f|F}fFYkB8s?
zc%J9w-rdKx`}*GZb$zb$R5(K%?>1sHy#`phj8=|{c$9ysaNS1!)$!&&QsCfD{ODII
z;oLA1KA0x#2J5`aMHqOXVA#AhCdEt$vS<%fUGD7I6%N^GdogPr^!X9mPVBbJC428t
zKiKilpbLxK8Cc+CeDJVqn{$U#5D(ICbqX1+#~)gsZ!xNktDT(EjrtJIE4J$MDkD=8
zNT8TS3yT+Z|Nb+uOY6650rVD(+XHrS^@&H+(Ww8JGsf*rAcKB6ud3damv!>nfADC^
z20Dg+pL58f*`~4GGVyPfSfX;p;`_irb%Dl43I8t1o$a1m6x!CKwz0b&<$B@lBIEh^
zXi>?+7{9u0`M_Afz+$R;bS39^j>6A4<&=s#;gFEqofAy-Zj8foEuCM~3zfwI!s(aa
z?kMzov#YTuYh=$W&-s56po%+abmFOSqdSRmq8xsB$5$3Sg-pvhe+*m&OqBu-Y6tO;
zjdbRcPSTHKN&(IDD{uXOFWiT5C(=AL5r1q#Jh}cSVZ$Q=whbf4We(vF;!DRyJW*pL
zbXogCx3y21S0{GEKe$@Z-ucGU!<zB363?b~^Ojn>G)pIXYV!lljMj*j2S>E`3Z~z-
z?hHU$**-k9d)CR9y`wC*yu~)lKQP@YP@C-d%we%@C6=*2J(P0cG^0Zve51r=J}6n-
z`s%&pogpVNUns6H=d5dQnKU$%a&SKm^zT-Gs}ED_deUB~tD}qJ{hqB%N~WjGjL?-o
z(st<q<oE-S;S8H_bG9O;$cgf4C**c$JFi3E)04d5qm1bOJb&2<Xdna~)}gmf?eRu7
z-(MU8OsqY^xiPV<U5Swyl#)4ZHp4SKsxGQC8~&{~*4}&KlmL|*09+uaPQk{Ok-`0z
zGX)=ev0>@*Wo`HjH>Y6P<+aE-yx9_1|H?Owmd(D}y<sFQ$iHv$7DNGU+IBuf`WxcL
zH?BaI+_=Y$0aZ=)>ciE;J;yoS-#;Sw3x409_CCIXN$9kh?>Mfax#i__O^AYRrIq0w
z0zU0ZARU1Wr@%+oRqfvUhQl!fnfAjh3y8g^7}qUEXONiinH&7g=^%z9+Wpu8aLpty
zqFoEuRrdfe5RTy)n;E2XqRtT57E#Xvw3pCSxiN?@X@l*C^f!W0GYS!X3P*mnLZt4y
z>auPuUgj=~7x{@&S{;XkfP~O){oroC&kVC)gpQ06eD7NBy3Vi}y41nuRlCHA$ilzw
zJ!rXU8csSn+KPMW0bH0X7+`W+!ypiQ^YABMH83#V23PQ~3uwh-6VQ|pNacC&_S)rd
zIipA>@*M8b#wG4!%OrZ!gkYrK_C!NXKjC1%eouBkt@%Z|u9_f&dg6xb7Pm)K@cr9@
zD(crMr`F3$4F8V>0GHw2bR-J|UWE?L`<F`asCjcJklz5nI6mfJ<lzr#ng=HrYTsYA
zuf4h;S%SM42ao`AR2CUJ=Z+CvZWW+-KqU{QwbI)^8(M<_^Z$t-5AYY2UNqp-sWt!N
zji*xKJml#8N$xGh)q5e}y(<dJPr+QbS;4-tKGYYEIqCLVIFE++DmsMKmi1iQSkdrz
zqE-fE*ytSd3uAKpiwt#cH{d*tJ>aqf@I<EsJPv}EpBW_IjQ-tUm8)-Lxs5>9%$ef|
zvTC>w&5<wq0I0Z@S|ZOyLTh+te5j!DqSCI+jBOjS@_cucD`mC(I6Wkg=zRjsaFWs8
zV<?GTaa>BVp?&&WT%&)TV-*=~a{Qwm)w~NY#Wd{*44e;ZIvGEt#7HR6)!1z_U6)ij
zq;XE(*9<B2e4jZ0&9s`7V3Nw)eQJUz0I8_?Z%poGo*ne>qJ#cayEO102cZWpHh97J
zSLL74U5lV#lgr&%oz?I^X0oSY3X;G(1x>Hcm3KGNPaoS9pO3|A=3Wqm6_{5SI?6&6
zBwo^9!!1MOn0iZkYL@?Q6vR&qStoY=pt~q}{K~dxl_*|z^Vm}f$Y!ITEW}cvLGpc&
z!<!oWe^xfMavl!3PX0e*^=8s@zF@vo&^l|YZd-kv{CA}DF#r32bf8XZ?!1Mn-}T>P
z?V8RGTL}T!w{>!3&%nmNGtZAQ%Oh<g=geS*A^4YJ!DZztTT|!61Bu9bm+L^{N+f)v
zUGupt?UV~dWj1!^^_eo^L~XS^cw@5UnRfRqLX$qqpam-OVY3i+f)XVqp}H>gG3bl$
zr7}~kFm!W;l212)+HtoEX1)QR<qO@RIm<5({RqRm*c676gDA-JjjGvGPcyk|4W*g{
z*ifR}RoB`N+rK5>u+26+!+hB=e%?$Uo!9f>p-JGtl)Pkgm~f*b{nn(B&(6>n^gvIL
zOC6>O9COh!<BphHVi-^SX4=H(fzfAtTI=jJNjr6+S+*>JW<|R=sh}J&d$*C>4=R6>
zrUof@Ap(h-Qys3hSBS2&z55XgX{t^O@=Fi;V+GyB${=na>2RK#!>8qA3Li?$0*4%-
zkBiD{gWEIC*D@)PvPQ|5+L6nz)zX#M1YGX{n2~EWft9O5KYd*78#sDb2GEW3MukW#
z6A^SN#{J^Yj!!tMTDufKUf^`pxUsT`s$ywByb6e7zlF#{$v|6wM_Yijpi)E6FY0G7
z=~p<{!YyV=XkgT6wfuF#*xpktDT6$<To*FKwha<xZC^BlCE`0y&Kf$$F=|U?Awy4U
zzxx87%KO#aRlks)f<38iqw2k@C7|*w(KRAGo!d+5y>}ie&Cj$$pqG`ytv86%?NZP2
zdB5AYCx)`*={<z!_^R^iMM<U5kR_D}<ERw(z#a?ZqZ+de=Fe>!jON8nlCyV^$!%B9
z1Mx3{uBl`lI{ULlG`KpvBG^46mKH$gs4Vr4OXPurB?|Sxyd|koDQ1isvNK;bn#7V4
zX6zb~>AD<17xtC5fqld&=9{SW-bc`Bv^U()Q=nbocmom<3ixuXoW6Y4iu+Gn1l@cL
zo!3tT3N@zHkYJv~{>TmzyRBbOHKmDoSvFVjx8M9FqW=+-dEonZ?_FO;h@<nFI%C2k
zB12?t=*g{0&9y9-o^(BWRvUa`pv7bRrNP?)d0MOHw(apxy5rTvC0n6r-v9#(nwQri
zK1M8uBJU0z-Z?+Mi(5I|q0YL>|2%KBF+Nzo%1FDF6gL$?X^bd$3!0q7f-d5G?xmc$
zp?6{-(GML)5@Z}KWkm!6jU5kf9f7pzUeW;cPTHuM#9f)Po(-k$MmM3V#@?GpbVI~>
zj`fqrj};f*`)RgMS@S5d*ZL~Wg|XfDh%U(`D445#ar)<WHV2k@6uvx`T@VbjI6r3J
z8eaThNY+(Pg9q#JIPui_3Iy>VCxASG3~PAu%7w(Y@y2oK`Mx5O?+u5w#WM@%_v>K0
zYkt~LsJ1|vKwLO-488;Bi7Rjh)Cf&7;m!+|Li84_Pu=YUg;;7U)y`hKY_viQ{vx_9
zEZkc8_J1Ktou`!6`ZMw9J$?6LjGJ;)$4Fdrt0WlG5@(oO9%FreJZ4gF)%=%#*#?Jv
z5x#79%86d%DECA4>~~Oh?BuN^&rWPM&KLPeWt8c+*VGtVSpw>5@XQO)vmiG#b(L%!
zlJ;SIj=+Hb&{G|6A7`Y`ZkGmGOprhB(CVYIqu0VyMrXa`e3j-h^TeJi%l|#<X$D|Y
zd7cM-m(oS+@*^VX+S~V}>q0J9=9?vABqZNgc~0IS_@I>ec7(Fz3d-o+8oZdbO$>Sy
zc{J;MmKL8gTN~W+TiP`-LF)M|`PlpJSS9y5=IQW%HQy<tzifQBT+E{(2U6OQppPWB
zFiKkPg$#0dCTx5|RgsDR^@Ge|kwc%)Z^aY7!V5%&^XHK_{eVQDCEWKiI1_58UoA9Z
zepUC#YVP3}0d_IM@H+`61x-g>l!%5~VVlj~pG0GZ#g&bc3s6VkSc041;`h;h)ueho
z|Dql%zrUe}>m{+FB|(RhlsEn;*?#jQzkgg#ekazH?djVFCjoU1<>J#0<$g89DwF!R
zXMczL`Oe#jN-Jr@XFZcM=J6+l>5%hc8-m2)!MTa{M33cB_jJ0uPcAX6L#0eqc>0>X
zn-WB{&<Ew!t78(zW~Jy0@;{W0wYm5mQB-;B-9<BHmN#UU9e!C1g>m$}Y1)1R-1E`w
z91poz9ZC}wGi>4b<7UD>Yo+*lCV_3%%Itw5v)cZP!0Q!8CYWq+W}Ua_U|I6wr;oew
zaiLbgKR%l3(>H)m^bN1(lpUTA#_mh>M~wK}`G<=ju3KF#@TGZ*yk-T|_jRBy7DYg;
zuFj8DDP7>aOqxlnAFJ*mURu?E$YLAIdnZEmy-W{-$`LOT#cY+q(~LDs(W2&$P`5bE
z*~&|AfjWBne#L=A4x7{7xTkAn)=XDb%2tZe$XLe@7uTq&#HLi$8*fZMsCc2JHoaVw
zh9z{bQC=^YOg!EAY79>etL4*w76*C_Qhd13=P0jv&os_4SX_MoC;SudwWMzXNZmvD
zgH!9DO<Y;~+WxuB^{dbEYI0!rZP-|=1fX?KusW}jE&;AOHU2zo*J=V@q$nG{Iu@C@
zQI__w{8UA}M=3J4$I;0I^a15nYR0EskiYihxRe+9b3tR`Dd~R_dn|Zjs&pgIE`_st
zG;|`24~8S)mx^8aeX*Y%K6=*m_vbN1)in9(Wn6b#Yu<PUZ*1X>;3`J*>2ST%8%%u}
znk|5GPD^>(3=h{D1~c7*r<B37jGJ9-^d<%fUmYvUoPE~1wJxsUl}qMJIptnn1tB5(
zmWZ}BZ=j4X4cyd0q!i^&lRT2(GOQfKeH0}!TV4i$yq%eMEFB*bi;y!e)qR<G>Xcy)
z*G#W3Q?#3SU?U<7E6J~3epClYX`u2j)vR_tS>GMjQv?`|ksUsUzJ3_2dnOdp>JMv=
z+Jo1a3`&6Z>rod_UemFYqt*1y&f{#$4cv|BKr5iIAI*bTJCFHav8ca(r&(>d(sA8Z
zPq>VVW%V*3+o(C}!t;tC`omWIjhBi5$hT%$l4yHyySN&yVYUgzEd5>QLzHXZSPc|d
z;QL?H%LEkIkW*9KS3N4J=rox8x~B?m)sWLwBx=G}{ws<55AE3v!B-K&<OpBS3p?48
zt#Nd(ayU~S9Na+Fw{bZur~7KBBA3i3s^9CErLxNS$ZT9`r>jcw(Y`jome<y7w0JWx
zN2!<Sb~V-NPnixXui_mc=&Q5u8`rZso~1MNuku{>C=msGGdN<GGPW9Hbb`9^8Nsi<
zPBaEsSOK?uX%@|wB56vDhwTk+Lb0k*e&(1jd-ZUjXlU9+f@)7CbXh=i(oCGuD<NmC
zNE8s=?Y-5Cb>oCMmDD8%Iy@ZR2}A2tMd}d2_DG%6i<e=_MzLyKR}nb4q`6o3t8r5q
z-1`^P7blr2ub-8o5$YQaO_r4wI~H<2uT}5cc9|#IAJ8DpH*_9&`}sc8iqIzO57NF~
zbKY2FSSufLqUTV648xFoOGT<RMUVnG;l5!K^>3>|8SD+{`_Ulhn7^(1p;=bznO!WM
z_9>Mfdm1|&YH0&MOkALW;<KYTlN--1nXjTN6}d&pI>2iG^L_Vo<0Zn-aCgsTK;^z+
zLT4=+Bw31DNo@9qjY5Fgez<@kp>mv;Udt9<wKyVYtNh(?%t*bordm#D6r{Bcstxtx
zaL_P#WMH8VPT+`7*s|j0^b~b>P>3J4kZtcrdKG7AQGS$vH_SDdmzuebanMhaMmr0X
z268f2^bS4{D&q`{({611X(rcu>Jk^nWLC6&mxML^vGW%Rfi1Z=Hl7g8I7o4v3yZC$
zSBHtRTnc+DQJ^wbac*T7wO1@yKTS~^z7@8_^uQrf)9C?9Nx&=VJ_@~6<2Dp>q-#So
z2q8@tkCB3pJEpnZ58wD_;#Kp47Iex<&8IUYpI9}*mKE^T7)*yhN<>;y!!?~}Rrae^
zW0@t;dPQZpTe`8Wzp{hh;s6xIx|ru&XV+U3mD#T4P-r7>Ft@-->D3;1-M4f+b*zJs
zZ@;hME7e8cPe#l2Tr*9?R9#BETS2*Ni*9Me1`nn>KrTL3>)K|f^UcXLJ1|5g!2e48
z>ApqvMf#!AaP*Z2R4Bdqo*KYwz>lt-{}8&54B45a31O;ks3D;T{|cTVK)#REjSMc8
z(~FIz_6^WDpE_TinT)p*z4C1q{*5-vEjNmWJ)7Mlf+qg`!$4D)H0{$P8=pkuj(Gi<
z+m|zyOX(x}MX1kfYPZ@yX-d85t)($F`{R3(ph-BxliP_To0C+cIi0`|K^y*ZDkX0v
zga)wpU1>TWHRWBKs9o<>H(5+?Tn5|u_zS&p<EHFRQ|QSv|Md`}RV)5b^?*P;E$&~F
zu)6flL}eC!9D;|oxQ-ZJESWZxv#aA?Xs+%yS8-ZJ4hoS&6f2w32Ry`gFGzg%;Z+mQ
z4N6NFL#@q4%Dr-GE@$RNKc`D}NYpW<O7S|baQitPa2G>`vuFcULFh2wmPXe7J`_wK
zJzm&w9EN3quF`7*;^Ud^#*!<X@F5+4UR5txAXr=IvTQ#Deb~+x5hj~JuQ8(ygXn(V
zt`LwtPME|~_HTT%A9lC~z$AmI)1QA;=^D4xIWyd@7Dn|bJxaujX?)Xts@TQJ0UeKb
zx%o>l5$%=mWcvC|jL?JIwpfWVs(*a#qJ&kw-2BY@g-09Vua(X#)NkKuQ@ixD)YiLt
z`=bwVzNUSd_m6@Gndl^|r}W(hfnV29>|F8qSeRcEY@LYdyANFOSst6$Q+s8QXY!{G
z);eBjD4mG2m9eS0YTKI7RE;aDEVB%$`!SP#gVgsca__UY4VcAgiq9=A4^|zsJ+uFJ
z<LbF%_Im|BYz0hCtx~$VkZaG`fX1{^iLY~Vc-?O1^jp(|)i=XRS^0sj20Q)<#g_S0
zx$snrIKMs#ocDd(fi&1ZK-}t1L_3brHkG02(9`3lH~;L-_V&X8o73_n=xz1^!Lv};
z-AHUcxBdo5#Cq?lhuLYe3H3zVs>WnZaibY4RgKE=U~HKnT^8$-Z3!<oJU=nR36TFU
zAs+~+l5at_1H}+C*ubbGwEWM&JJe5{a+{~K$^>f9zU1m#Sb&-*eoCZntfvCube!~(
zH7;n7zH`w@@_@J0U*)j^MJZs0)Ok%or5~+%%K_$Q%dDcHmN(-P#DM8{XR`O~#Kl8k
zGqqzLKOFErjT1ozyl1jS1c%X9qy;vd-i;nz_z(&;y)a!@hq&qdP>_O`Y&#G{C8IpK
z04z2TlV&qfDeR-AUrX<v4w}y>_i)dLD>T}P-}cQZjTGoNZ3WtS&=r6O=v)Dmmqc|R
zE*=09AOnnoH*)%xvg83nL-Kh+Ef!0l_vRg|MM0ii+Fjmx%-^nkdz=!d()LrA)cx<K
zaAQ$gNeKMD5Eq4WZeV(vskvhkiug{qXFD^stRwJI`(j}39<pIkd>ZG<kVVVfm>pf@
zvP2lj_qywC_qDwZM>`UCY^<>U{5;I2)5xN^^nJI@6kHtmW{>Mw5Biga@YkjV(%&vl
z1ig$<udnSlG<DlHlT$TT1w;)x$&_ZUOU+4AzG}4_q;efC#$=YFQVw+=jxPxz%pKwF
zI7EF`<1*Qbr_WnmIio{wp3EFVhj?EG9=YL9$<sYfw)?$qN;y<1DJet-`4*Q~C1r<>
zYVE~o-2T^C5M9S?gfzy2y7W`G%4DcOyTMp9r_ueld^c4hlQoCNhHmsFOTgAL9Cnq|
zjn{|xKYXNd-3dPH9msNL8cbpy{(0I{)72`NOTrcIX5a18{3kVIXG)k+y^;ZVNUbMp
z6R_cOUU9NjkGI-tCf(Q<>9=?vG(`2Z3t(1kcof=OY&~ohKr}>!>`xb-TyRt-)3-Z9
zpYLndj!1t(DYnJM2EILL#2?lWHA2tkh(EQJt4_)pDb+(=_;hQJI3jEF{>wKPw`Q65
z5z)kk@`3pE{AhtlJi!-ClD6smGEWBArlU!y8{c3P#YBrW4ya#xX8j#i)L^vRNJ8=4
z2Ir?T#rL~&;$Ze>M(2=g=*xwh!DFP=2=bRR5#`o9Vmz#z+I#L;ZI<giq$p?+HMD6e
z<3)!mz?zWo0|3U$&00sh9@UAxcVH0HwKb#SF{V1U_kwIh-}P{B{Tiqf@QfkZLA<x>
zk{^WOLCWGQccC(roV2%=+cl!xz(8^ob-uL0fjQHI5YsZ=pH$Zv=@`#?-z-vp`|R-*
ztAfUwwjqVb=^rp0S8+nl4Bo}qP~B=ViM$m#S;N`#=<(|h%2ag&){LbPyR>Cm=e+2i
z@RrmCktGXG6zZU>oaFZM@bsf0?B&5%g~sNSrCU=iYHa|##N&VcY1;+FtO(T-r7TyU
zvx{jyxXc1B#(FQSN%F?cwT_IVC?eUYu0fYEx2dt29(2C3s_kzh+E178N#5J`Dz5Ex
z0vxlpKXWiNJ=)zxLU7D2DqMEn{;GfqtTQz8IS&mztR>Fo#;!@=Xf03LbgieRk@S~|
zLEQ+H06V`*F@Wm4%JtGe$rzmys~exM;>;>6?oij2)-dfcvyT`=uo$*l^DZnFwiP#u
z{SeP!fRB(GLs&vVB5B9iG9X>`9QSeD%K;eO6|c8xS6Zv%R`oWVuBFcx(;K80Y>0mT
zJd3hadCqYNV)Wa~p+Mb(EN<-I7d^V^EJ(hKOtlwU3R-pB5q>PmBQa0)oL9DG-5V<5
zjIP#Ow@pO{9@%q8Pw!3M3M{<ok~p|OV%HAnzZAU0{kK6x%T&uWznnt>cvDaS7;@=7
zlvl$Vg6>85@kE!-zB)nL0hFKQe(0rt3{rMbc77BL4MrUYCfhjVqPe(tzH?%pPzCZ>
zM1J_D0ftEG+N$UR@GO5;0uZR$b>wZ^3!xZb@WcQSd34`4zL$|(D%{aLRC-s5gz^jr
zDI$`PMo1oEo~!uAHCz-$W+oMl;7PsxV9L(wTUC`vxf*Ub>Qu1;6Tgvtf1n-bGE=`u
ze8s<P3+8D=8SzW#KzzO7Fvf2HTSt{B%0&3K9CiF)H|L?g+N|O%e}8G^+Q!&%uQ$MG
z%MhhdF{H(vYRoHyluireoD8q?>QolP7v$4$(hdxy3WL)c9hmR9Vx+|R5SO=DpC`3O
z!&%p_!y_K`1Y_TE|Lip%X-EVjf%cVt!adz{GQO8S)BFa*42&{VQ%ml&r%LX;b8Ak)
z8_ERqhWoGUPdpe|_K1~}+O}u2Y#6EYY*D~S3)0vsZangm-_ijin`zp>rFee+1xZ^a
z#3shEh{KQZdP0Zs?M69-nl@!@c`N|S4FUf^^M1XlMt0e#W4@RpVKw9~0cqwraOX|M
z3a17$%j(xF9vtzAdv8T5<muLbD-@w#qgyp{L%yfEE)v^)_MgRh^mC#9`ebQGJjXXa
zbLjv!kI3xl{@&Xx%0oGMD#^k?$L}4pI=DMPt++zCZ*S2*))6{|2x(_O#(Ozdtd4A@
zqj~YWNWb+I3*57lkRfj#w;!3kt1M{+jmWE7sv|eDxM%7dO(Zc+xUhO(Q48QaxTNE~
z^t9^ZxV(p0VSpdR5ZjJ)EHVnYY4*E5+GF*L0svWAwHR(VAj@Lr9XtKuPv!$a-5|(+
zh;^1#2nyxYo&vtzy2FE}{)^n%v-|xFdBn&^84zR}o9Ub#YUUw{o_*c>>KDGU$R;&}
z3jEP5Sdbr^WaN0H>Zr>I^*Me^$I`XhHSPQ-s#bGPUJhyv!Cjg$Nj$#ma|-_hWP~tV
zjNFlI^_U+G-B^)TB^OIf%B`K0GMkG-v*o||HOEeLr|D@6^sRFA26cM3O}3|#ahEbK
z`K1-2zey|@v1GjXnkDXhJ1**QP=H&;dcj+%o#`wfsSMuI<dO+kEB!99Ad3iv!$Lyf
z$EEl=?{4!TxqVT#ok$zi`DEe|KG<@r*;j4l!;S?1#$=W2&i+tR=7`lvpymth?_fL`
zkwheV6`!F(BvAhPV@GPo&knHe=$w<k3#8$uBN<7O#15v%*Z=TiCV_ZP;P4}@*Y)Y>
z%KQ2&&%s~oD3?kG-8LaL*gRZop8k^b&g@`?qd8Zp`W!(Np!7R6NCwZo8ORJ?6(;}I
zxC|703l`x}*YTouaEIAXRT=AYm3$4D)ZKo|YyYoq1ifW)qGMWXQ37y@c4%3<oxpk}
zo0NijD{$<juD&oUN0~^BdRrs7f(<!p{QqZ!k~LL3)^FwqCA33CQsm;;@MGANo$MLv
zsJaBIy+n^_XK&=;*@$@|u<=vBLA;HN%|Fr!qe7+K^2AQ;nnIk8YM#r3xg-I`z;;>m
zr5o&5*m-u6!>Id|OVY4azwhiuo+!)BcR2Fj{J~}u#^;iK)x6sEu{Pr?$KI`Y8PQMJ
zX`*63!ENXFLPPB7@lHcE{xF<4RtBpfj#T|*oMzMwOna^FR;Re{Io%r@yn5M#<U|T^
zuAkXopI)Kl0VFS{_gI~A%d6neW6S-~FAY?P+5!8m0zOU&e-BkP50AH~8aHmIiFlsp
zrTtKH;!y{eQ}rE^sG{ulB?{l7kBbcb)WoTLF)HDmduoQ~lf*&I<dY^B%jzJ1GuI5t
zc9L(|fz`+Zp#En)+qox_w$9rRSxd3ynzh5udOhBAlgD-~HmhV^@|&9m<Avf9RoVB{
z*zsXE;J~Z5YIZw*48S;qXZ$Z4WzaJ*JhD*J;6B>V6}bAl=q7>GNi^_R{Z0S4zx;z7
zmG)yl$mG*FB-6q-*2k(#7x0oRZ(iy?*QM7Ec)$*Fi(CJ$bn_`vIuC!nZ>^sm60yeB
zES!;jvK=xO4?h+>b@>$aa-REEmM0$mck72s;%!%<p9+b%CoX~Q$@x%ynVk_eHQ;2O
z51D@;>o6^RjCD0K=SA)w$}DrTDm7l~@n)^+vPiWKGLeWfVQ|GZWVF>y<xkMMyWHAj
znQfcu4VNTbIc7^$h8Wavonh;mYnU4(`av~q;M?8H53(BrtMon1V?i#8<+{sbPuqg6
z>$&=&>>E3|e>)S>K|dnK9+gS|(q|J3juvoDMq!yVXH8tRU2ZqJmht?VEzmioV)%}B
z=ClwTAKH+(59Fzrvz};lf5fAd%G(&78F6NF!jdH|hx@V*%%heX;m#6+-$SJ6;a+9*
zI_&<f$Ht>5ZCZO;wIYIv&djbmPJ|zUCCGnZ*ZUS0DI6UGBabI}zCAo1q+LL1c!5GK
z23|655042!vpZ<}TzX;=y~`za@ZzPOhB3$L-=jLnhZYM*I;?E^L4kB{Q#z0k!JtQR
zkLXt4L#tJ^tZ@zhZuB3y>3xq)-bd|y@DXBx#fx5{`{?hbE`{@^I==M5d)jrG2XL_{
zKT>GS7w|$7BP7i-Qe#46K9-yWP<`v)J<84ht?x9<Sbg@PizQr?juw~$nCTTA$$yts
zLpT0|_ffJ2s|s?cW{x#j&=8YT2E0oWi~#1m&YY)O1j8R+(BUZAR5I^Tou_#ksmQzP
z;=WWK+WT7J@A2jp;O?GxYTg0_Zt=j^uv5V1;Lp6Xd1ibrO;^BsR^FIP%hXtE51Ck5
zr=D!PSvFcjWUvum(uGPG@SIci4_kmu=vNY1MgwI8NL`<56b*6F)jKOT3eMdu?dVrr
zNyAz5RsnNbtC5?(ylIn}vSLq}`}JfnpVm1(ck-&zNksUj9q@MEjHSH~-iCIvl`73V
z##<IMD~VW@2I+RZH&fQ%KzI$b3wO=JbGq7`t4I8u6^;$7UZ~p_(2$>@&n~ErzCtt7
zxmKb^c4vSONW-cJA(0JK!3YQ5s6^HO@Mh#0Ad0VQ>gsLDG8Go7zIUGys(PVMmg7#j
zGOJ-c?I~3P4gEWAXPRzdv}O^?&pxV-s(uI#&F}OjzhES?K<6B{+)raBzH^*_5?S5E
zr6ghF?e&$OL1O&i+F*tise%>kXAM+4TY$Dexf|f;Wc>%SqS&0-(Iw?|52(FndoJC~
z!k_8bOmZtx=C!I7?j+KEH2oktr^c@wv>cgv;?+k)hRjS%|H8W$FUa?dpKKzmHti;d
zX{Hq#`fYai9y{s!M`~M_W2n#rO&;ZV!ai|-t#6}`5Q94RoW4=!oARS$^pWJ)nTtP`
z2EQHyYm=TXS3aznPVuLl5D_M4qr~C!t+jROrMTv&u&5Jkirl7jyEefv^C$D%RdA}}
zm0Z1W2Maa1(Bl_!n`bl+5dAGH0}Ki~uY5Nfw)f%idR}!D8TV#?<zG=DSCIYj?0)SB
z2qg)B(LMUV#{uP9h2ataVPPy2fl%8WTDf>rsJPU9$^MtfqJDHD+JVcJllA(5wGDeD
zk5*{zVVxdEd)U(8{k~?vSkfokGi!FukgI-Whw0bdew=9Kqw`M~(PYMut75wjEqIPc
zeb3A-)HKz{Ie^b?3<O6>!$XrWQFsw|LE3s6S6Q_Cp{0Sq9u1~aLoQrSVKaoso!4XJ
z#aa33Vh+W+Tq*E%O6}5Z=CP)OregGeNt<=j&=?AK-f0+I6?&S~JY{nh6JcD3;M-ZH
zgppw7=pFa;k@Iqe#>#Wd2_B!c=Xu^%ex}W=l$vximfboEZNYnR$Xy0tES|I?`qfd=
z3)6=16b!ZTX&b50ul{YW<YPiJJ-vxD)6T;$#vA4I<HxteR(o`gZai$Mfe8&l&gK+@
zi^bXPLTth!i&yn?v!~hTJZ(U)PpSgSeavygEkfy<a6HFNb3SlFw3Gw5WS@1oWo1&_
z&9jhlTLAV1Z?pUD_yjeaX*9Gw*X-)kpB_7a3q98@9XKLK5J9rR+uz7bVSYBC_>)_5
zrD}UdVJ~Z9MvQ=0-L$=*=7vZ<K5b6(D?<-NmG*sA&mX#oey8}vwNH;4OLY5*ueta5
zh|l@^CI=4L$Q6uK(q-1DDSt3&QK;-`e`Ny_((3WfIK;_t?Wiqd!_8DB$CwZ)bWu<T
zY6vpKb=gPqN9`@swa6<dNgUlfm#7?vTuB5}zMp3hrm+*_-w3t=J$-v_1b04rM8}7;
z^OShWkC<==Dq|o1Xs=2cNn-A6=G|ZGvCC9R)NYmBc7>TEM@AaWmE4va%nP6^u~4H=
zJ{#mpK<NE`vs_--;hfT^^ODlbQqVV+SOX{z13rQJddMI~S=zGxmPY~;I7-VInr8(t
z3jq$7K63&t>@8)j-><8bi>JOFG9gw*`9Y!jwb_%fSv<9^9~jR(bEARAbclnD?+%*3
zmzCBGus5EHj==gJ@xP-Xk0$kQ-Z-GTQ{2C9zp6mmfFg3E26-Dem<vQHi}e7K@ysVl
z^~!|Gzd?hmV|a47g5{ES;N7xCc@m9s^>!(s)MPYMQ4{zBOVVsL$hQF83;{0fnvq;G
zoJA}kqFD*e&Z6t3p^TkniQd4gqZENAs)kj@*&?h{y+c-qc8ib|&FoN8v6X1ASHhcg
z{B_JL8w{uK)EyV3^?h9aF%|zRV^;Zb^pt^li-rAFn(NQe99fTTSRRb5@Jz32i5`hz
z{#Ya84A&&#u^E<2xdLaa-N~;p((;yr@5V0{r9&jfim+ZCJM$u##%2KL2^C<YYy0wp
zpT}&4^{GBxl1dK7>Tk|$C`&fRbySo!#({E4K*S5Pj~+evMVWhaw~wjXN*y-J;m>~Y
z-e>7H$J_RGU+*0Z0UnMz?UnLht|-IW0Uz%<v{u0uw6qE~y9k#%i_{jPWY-lo$Y#Dd
znIc*7&2Z;;gvj$<4Ti~Pj@j2sp^cd&4b&b9vHu$BaX|Cj=t#0rfk|P1W@sqsdX|1K
zkLp-^xSjbq&7twx+g6-AFsE$0-~E(_b&rSUn?>t&@>>GkW3=<xFZ`ZA&KkJBM`QbX
zGRm*es*N;Y!0Rb#yTG4L{zy^Jj3ZS>%+0Iz)J8nVB-Ob85n!UcaJq<AXN4wf3gd<I
zKQg^UzrxsuC?>z&xnn~x#7h_i@ero8GFyAyAMoFq(S+{G77k`ymZ~&y*Hm&Lj13Mq
z|4`kLpfDzvVT&m51|M*2*{Cms0K4{SuA3j^y##d&qjToUu_h-G!U^T78o>n4;I+(Y
zl-lvIKOxii5A`y|YNbMSUm<wlb?u4ON`}d%4P|TPz{$qP_&g=jfaD&s8GW*G){^iG
zZF4N3y{|ok?)(#Zgb6x-o1fM#j}q+{iQK?vZDuO({-h-F``Dis7|kGCc-ns-eks*=
zQQJ0=yIa<sc@(xSsC{pCKcUj+np(J{t6hzY-NaOcT_pSD3@>%8-dnZ_4Jk=o^d#0W
zW0LX^a)ATvXmgkl>v8O&*5x;4Wc_C3$XA<4?vc`M=Gj(n-!j|B-q^lwqtViHaJRZt
zf%S?TF$SZuWb6F}QzFjBShJrFi2d@s<ZOB3mO@L-TwVH}wZY`UrHi4u%Z-20e|3Le
z@&E8y*|17HT>R-*Q0_&b(h{k6d9q=FnQGa~tFpbGom;#qFvI^|%&n-a9W`I!oX(`r
zYBs&B;4PbK`u4-7euP1hU2Fi}2J0_(h<|x0bCd3@E9lqBl3Uel(|?3E?{n0JRSy^u
z^H<tOY1Ol1)FT4D5PA?XwJ~#s`YQOfA_iTvm<ec^mxGVi3<wSg1qkU$6q>o1hhMOl
zG6JAJSKJbYmtX%@5uG9{-4xIur@3dRA7J@mC=1|YnXu$bldY<5tLXfMmD{nwKIeq<
zcY`3zw8n|e!}<AU#|(0IYxA=F!TLFcL^Oj$0}t9)yEGI%Z4}hHzqB@|{U~kG2>D}r
z7B%QRJZpx2Zh$udUix!;?#4SpEXJGD%`jM4PE1(Eg^N0Ae1xpJQE2m8k`PX3mm1C5
zlB3!EHshg1*vz|=^aRz$p>2ZzBef^@lB({cSuoD}HtkgB)um~WnD~>iZmyQnL(;wY
zzNagFUt4LjVb#uU7C%ER?}W5g$q5g!66)|U9`==6Ym-s12#4ia?ZFnK_tZOZOAt7N
zUB?J<u6!}U)pQm=Bc_0L2_*_-_j?F>0k=D9KsiIIw|Zae^i589&%tg-pkB9D{dXDH
zoA>%-qfO6}oYd<<Y3`blllSZPgHBw}xCgHJ!`pKHHv3B<_nc%8;MB$fwW21oqrLOk
zOs9=cyji<$)ObhAfXyIrjGC2Ydh!5--%+l!Q>n9SoTT@>Qn*Lg_)^Z&MA2fyErwOE
zy_5zF_zOhWz)BS&9mCeKR4i@y0^sL-tK7*&x(ap1OxkbkI87Hznvg}lab<P>Kpjh9
zn`t3nuS1Re3>9wdq#lal!FX<sxve@`S;}ebY4(`yQCkkg#NkFSc!GrEXv^<5$cyAh
zwd11DIViJaqiNxG#MU(hp5M97!kb-Gi_%%br<|c(9Pjj|M(Lh6`+FJqW?S+BByjwq
zpJia9MNXKX`vVCwb7gJ=RL}VqCI-xK#U1K|Fs5J5SabOg@x`A`a`KBCfRn&Nz~bN4
zAWmPFd+mVnxrCfQRg&g=zj$CG<}1h5{i-O*x_`S%1!3z%_34rZOd&3rn!9MYR=>7i
z{gY;p?NSHChmH0ri)pztO$kN<JavoCfAYa@il&l}ja<v#u~taml}qsfpF~qhZ&Tu&
z*n5+TyKhq7?2rb8r~zN_viwSI*}ZfnxoK_bHaKGhR=IAso=f;~PqU_T9${OSiOn3m
zKTzJR_Yk-b2#zy;23+h40BJc{D}W(`Q6@Wr0+{D|gJrlR(GNjecMK~pgp9ncK6Us4
zm13hUgJTF-Q`FT)PSW^FGP1}pM7Y%;6?ixQdt;0M_zA}BRPSr9uL<_}8HBb3k+`pk
zp4&Rz-5L7D!iS{2=Dr5(vzo)`-dLhm8>U`7vLg5;5!6EFgpa)?j#ns*93f@s?LSFG
zL$&kVQM^3MH2)@U2(<*P+a&o#p!RIeyT?w-VOzy#Hza&Nz1|WLJuy5#?LTjvPF6Oq
z!=AKDR5^y&wzpnH$QM5{0VrgI>ML|da_j#FcIJ|}owufEx6wLIu5Q;YzkMn`zo`P|
z2mkY#^-AQT3X9lHBo%jbIs}UKfd5w^!{~S$Q~d($ubN*3k7in_cT!VSlbCAke?~6?
z&1vCPX}6|313+K>>bY|+x0xS@1+$l?#5=`}tQ8-6S7~*StSGky{~e@5*(f^In(d}j
zU5sj%oc|@GvC*^PT@1(p=d*u1dgCo*lri^CTWf3s_hs{C=E-B!DUi<i`Bbg}zM;Ym
z&74{O9G=d4&tcC+t^Q}|?0JZPXi}WDdFxETWYKyKqG|bzee7g{k{o*IbLO|CA;G>$
ziB&t>p`@raXEm@w41-ubSZ&D!4hk}KeD1))n~Qq#DpjJWC00+voPh<sSL*(nd-%fA
z#*=aLsTY;_<CDO^2+RlDzqjfw-&a0|!EN3P)Ig!f-kB#;soRfF900Wa0mspo$IH?_
zh?yV6P?k2zzF2o}yc?Sgy5$io0HFTB9VoaeId*ebQ`~1-xl2pOxz~m5;T78)h|wp&
z_euuMEE&o4-bL2X3CnMVH5B@+_);&yZ`F1h$;1#`tW>IqiRP%jGD11*nflwS71UaN
z&yo%qmK2QY%bd*VxuM7TVhl6hkGt1=j_N9r8J-dl@s%FL7dqDm4!Je@Ns7FsjK={)
z!uYsqogHYWy4lV6*i}^u`1;>AIu~ygBlmZ64ae+{EWwfGUIPj_ZJ<8@Y?nR{98bn&
zLZ2^W1|GgMl!$Pcsf{VDS-@e?z0dpPMD$Z*sh&*yOjk%};Tl9f><v9$IjrnrZTxXC
z&cbi-&=f-953PhUP0r4I_te5gO#(qV`Utt%(JNAKD2BQg^G-^i6`?y=;7wc)PD&1F
zR#@Yf!3Sfc??p8SxO#Ja>zK>63xJofi!BFPxE0Z<Kf;uO8vS>Cwu`VTh?OC11GQ%A
zm`7Zw?-=Z$#2WXpaQiWZ9`gf9c8alwJ;`NLle_ULk@_pf$)sT@!FY;yJlfA{qn`cn
z9{MP;>bg@;E?gXY8^DG2`VyJhxpWJ04SO>TKs>COxquwUxjbm3`ix26Z|dY;x?6><
zWqFwqzyZG_sHsk^{%-b<bJ$qz<8sR_6M04<zV6uC=DZO4OLDA_jY57@-?2IEDfE=g
zYRWU@qW(Gv(&5h*CqA8ghTFf6TAEnYL3!AwiIrOVP9%wPh(6_DrF}AQdtd6Bcwo1z
zR_GA7uZ-N%!>(q_V#^b%Ij#@`XX+Xu@i<rYix|;(b|T1-r8(Y3z=d~*-+3UBtIoC4
z5XoiutS1?zs@u5qcJ=Z-T3#Mhbb)>`?3Q|INvS!m)P~4nLv?+yQ3r(m@D+~d^j0VE
zR}|*U?<?n($KEocvbIOo@7@H@In^_-ZMf77kFW}6%RAX84X-joYG7Tz^LU_YRi<<Y
zy{vg^YG>>NUVvfo=)labvMvbFaPk40f<@39^6Pp2>X4*@;9=V6xntdhtNt(?5)v`9
z_h~DRJnZW5@2TuZ5>75?Uw<Yql%J3UZD`MkHbnCNC{EZI4sxl%+7zD={UYL(@nwq#
zwGsO^sw<-M(V3^K*h;fx$Q}u`Ur4$ZtNL^F-ewSXCxGXC34V5`;kqk;e}gd}t?!>P
z%9r)7{w2&otT#Yk$X)XNvU$jd2P-sGx<>Umjfuc-$iwy_NHFaaJ|fR-G`Z-AgN^WR
z&w;D}B_=gW5b{gB{#V?#F}MJA0T<?4F?3A=zG|vgvhI(R>Xt9VlqlQJyVQgR84uh&
zwuR|Z{&u06ZOngJCDB5EaX-qIz`5tB{q!wmDlPLQu_6E06o$|5qmuXK_D$c3<t$D<
ze!CeoB_`+m@?Vb6OS8Y{a+82Hxk)*-jlF2H&u?e*_<dD}KSxN?c^1{0g!jj@V`C+2
zT}GQLTGyV)kWRWj$ukK~|1pGhV^0&S*u$?Jwvqz<&*vE>&6%er_s@#(6e0`oz-_Nh
zcr*a}(r`UuKrqmPk(uh9!z0__%ibE^)`)LO@H6@HKz$mJ!|gX2a)YYsorQ#J8Nz*m
zE$!uO-!2j{p9f3Szco1v6dKYylgBdaLTM&T={!h@bz-TBmjPsrQ17vXQ{Yu<nXAvP
z!LIN+uW(rT4EjATSoG$fY^VF9Vk`4VGWC)){$%FB7}K~a_Of}W4P_03OKr803sr%P
zKZK9Wdtw1a`Ys?X^E#b=&@_F@Ee~P!hdcjdH<C3tRSn<K7t#|M^1=ht_M2}LykEkw
zlOf!Oa|ajDt)`m({ODL)dBoeeA-ifu$vXTXgy!f{^rf+#%RA1ToVta68t}1p`Cx1v
zlY(pnERlqAvqly7sHWcSbtr&51UN>wJ0qAF=;&?17zH??x6h>-LxDJjZG(0unX95p
zL<w?;XUhs1&{r|<MHFQCP>h6;xLuq*@`d&Wz2HaY$oM7VkIcX!$SLF~Tl^#0myw(t
zu{_kDF?ul8)lEx@-qP)f0OKRvc8=Z82Faw$+Q@%_LVG<eNRv7tVxsYhD~ltu6N>=4
z5_s2je_5qgf7OzQTcK7SQbu;2RxxbI7&zooe}*XA4}99AlmZvMt>Us%<{qiG-~lSP
zTEkj<@~FAG#NB+ZX%6q2H#|amn-H<H%gBsLpO_`-CEPKa69B8>zYW}oYKtv6qGf*I
zd0V>^dPCg_eKv`1+7^fL>b?Dwu7gml?)9ORSdFttY7!I4t~<Ml{dpECbk4#AbzX*#
z`xVNg8rXxau`<@Gj81ODK#`pRUjQ~6A9k0HdHl<14&O?6<NA76m~I+Uf3P9`n7ZYk
ze6PK4Gb8nB>QRPV%DJbB(Dxv-jXUV>Hd&>mICq?hx?h8kPZSt)D`Sw(o~rg@4SMGF
zGyv?)cT<fNgg;qVW|QhymiJw;SjWC&v(YiZ8bj#BGa0H2&xMvaGO2TC6;AC89xdmU
z-qZH(k7usnq&~De(~aZny}8aDz!bZEezAO0aXf6uq`!z}`Ay3k$pT1)S*j%-hVR#}
zWLW%pyC{6<cBm2<O!UsQSaLpA49N~&oRrd=so^v4pSf7kF;Sc~9}_=}1ah_}fn9!r
z#1|PB!{Nq5T!i;JCN9*egWZXu_R;Ub+mE~+$Yg*v$@Tgg%|+B;%T=Y4pH}((F=)+A
z5I>Z?d`*cnBwe}xhZ$1tTHU+L_<1WeQj|2Km72?7VgwtEpT|b2Q87vw`l&HddgdG9
zZeO-aslkd)N3uW&j&nTZA1N8(Cy_n`(ku7(&Efm5tG1?N_)9s}iyzl5>>XSAd*0D4
z*GAgrx#MAmn6xeN(1nmUNV#?n7$^AmgHN;jhR-=8BJ9ZgGeVM1xJrj8$AGVC)MSYh
zcIedl6N~J>TcsCK7(dSfwXC1&JiWgk=uM8JS|^y8^N(iQdztlV$uh8U5xT5*;B`5$
z#AvvcD(T4kqXVDhMnD&}QY>AGF7JKBrxa6cS6^}5bW=%T^ctb=t_SH_#W?=ozs>8<
zMW0OmgQP1!OKrvNaM3Bh|JJU0<j9khCMg%V!zP^Y;#bWU=}3+>LDR|#K>(M8R5%IR
zkly|90ycC%8bZNIf&1BqE*L}pe#w2D1c;l`kL6r7Z|voZMcsaP(k-6@mxVh~Ue|~Q
znx=lonrc=I-YfWrkYY46OW7`sx9Qx9^iqC1d+hDh&RG~KO-aEc_BmnQf%@p%Lgw#l
zE6|Lsf3G1f!-|B77F<qxCZCAx2O7sx^XVU2XABD?0aK0>0aL8wF&I$_N0;HHWt^v|
z>u}5k(E+w<SY^_I%C}+kaQ}hXX0IE+Jaux|L3|PR$)x;ogyo=Mx`CnLvoU4jkQ*3o
zO#e7U$i$MhwY+boU7zUs;c1*q$^pmCFD{7@YJpz3;N(oczz#Jz*<KB>N>6k}G}p4{
zrpkw4^J~#p<GQ`{IMocB{SpOC0E&B?izOh*a;BSE$k-Y_9_>r%P;LmJ;yb5;xkR!~
zNUTh2#ML3T0f#hZX3%?QRQ3d{r+VWVcHxrmYH3}xwt|vCiGPLyo66V?2P0qe2KqT5
z@+kY}Ex0dYhDn^e{gU?+9s+E56}s%crm$CC9=s&#q6&JbRvz$I^cXK@GAqJ?2x7UG
zd|RyCG}*nePUuU!Jzcu9z@O?h4F%bYgarsO3#t`5s*`?V)Q0RO>)0g`ceW-RB@1y}
z?z1+CP(%~v?bMLd@(EdmSjpGjwKGsKC}EpFP&qT45dn)JoieT1Y*CY+h?>rT5!KPq
zee>!Vpmb+8ZqI4RnE)a^$S%*hAqn|R$m-1tv^%Z%3n9$a(?s1BZ<hd9xkKxhP#*oE
zl%2X_1!9-?#+dg>pA8J|(OO?oML#Y|*&PHr{*6BS!wRdQGE=6uVp(aJ*SBV4IlDkV
z{d!9jC}8~gqd*-7WNIH`f9NVxmn}eq&m#8%+E)&nXg+;SVi1q;^zw)gr=cC_^WVTY
z*Y1=__RrCgG%9p3kpZPN6f+*A%m)xj{fWKU_nY@+gV%9S@l>6zJ6~;X$4S8!D@IKN
zbqf!v!mGLR)2Vuf{g#!~pI~TN!p#(FFM87D(YKG7uMBb~24^AUA0xDg7?aIT3P5bY
zA?gzb1jb2!nivi}RPfDHF5%eo4d^WwKesbW_HX|kj+=^?J_lsjhjkb+@eh$|h2Yo5
zN;BTA7wugsAjM&G#U+bPFotVP0Hi+nbh$PWZf2nd?hlZLbIgtmaP27dFoy!)L0F-P
zYi>_X&<7Wd)1T~yK~{UFyvJNxlP}AP%5Rc|J)dgh$ec~T%~rmH?^!HJ=APzR@w%jl
z3NwHC2tB*Tb%je<wJZ5Q<iMtSjiR#pt3?S=^Po;(#jtzSAh#}$YCOn6>U5M!B=q>g
zsjb`-fq%ED5uFbeV=c9a=Rvv=cFdh?=l1H_IgY3;b(<q<x9tEM;SQ^O1MCA;`O#`M
zY;^GVFSu-5u>IsCqCYxlFTzA)EbS9oCr}=i9DB!#1zzWKv7E{_)-O9W570P`8kFCc
z8s3+L^Ko(eMXU{%!TAyxrM%`-9eI*Q|Mu8Wwx22Nd;>)_rwJ6$yu>kr|9#BawEKZ=
zf|xkhd5UKqoF27Q#U%MQF20YR1A<cm$yLD$1;LV#C3Ct%MM@&3Nf+}tR905)(c(L?
zWRN$%cp4`J3Fo{lm2PgIW;*rysDkX^%<KJbxJwsO`TM2XC2H_va^-FV_gGP5rF-P>
ztInoy@oSE<htsI|Nw(%~chRihPN%D<rTrY1raM&*JojFjyIWm4%M?Xynl$f0e`TID
zosAH-H{QM&izJ6Go}B+Wp1eA1eMs<iFghyGoCFHa=3rEmqa>4S66^o&kAHYqx@&28
zy<+v2+%0wUJ9?*D_q2@QasNDHnc=NZ?(rXB4Kgt?@TN0WhTmg}HplVf^RAG9nJS0O
zDhAlmFVAxHYJl~xiSI5>ZAqECS7`tK{uPiMeqnSp6Vjh&lu}$v?GU;&wTi6WWxy=c
z5NqcA_GOs`tM$T6i#}$jgQ}~CRYjZAo+qJ^8jwtO14O2YMVbC%$JFNy(LDi(D&2QD
z65?ngtt0IwL{HIRePP}mQjVueuKjHMvCDMgnqQsmp0wH&JvOC7jj~l^u;sWe6Cx2>
zw4&n!ic^ZNjdWJD>PHT{u`$!M%J4LoJ2j$s#Bq;b8t5ot9%F(%Cx@Ykx1iGf-C$zh
z(PXEeg%Y0mlqXRMnk4stb-D=!i>*IsA~j9>+sg2yfaiV#&!gTq2742h$M-%obAeqQ
zV+Lzjpnq6TV~#8$c`81rI*XO~E(}a7m(tiSp`up;|AaEM(tgXT5p&z-zVd47%`gC*
z&hQG`$HB*5`G+^um1l|+(4k~J<?N|g;9T!=c8_Fd_AW_{8wR&Hq-($Y`5SEpJ7z&p
zJ?I<rJzFNY-bX{^LW)vtpdR<{{AlLDKf(6q=TA4DGwyoTh+YX{<czCbL^s8lra{w>
z6SouG*PyM@rS2n<fr&@P#gF=fe~qqdu)QejqM?dfZnm?!$5!>wyRnVW;%s;`&c6;a
zKu+@CsE&7EhGFj<MhG}SRB-=?r?Y-*@(ug&MkA%7bczZHNJ^I=Ad-sGDN@4djsZ%j
zq|zXaMx|kNY|>prVhlz%j2?^)UcT>fyzf7-U!G(4^W2~7bDif@sYB~VODpg7Dl5nW
zAbLnF@!{v0wpo#Ll>&l@NhfSd2g}EmgN0oa>Xe?7>+2zt8_tSaYjW=8aQZ~GdwWua
z`Y#N@)DXYv;(_}>4jVCYBMmsOEnf1sxp@QAXEbxzN1xl}v9tt{ZC2%|zZ;;-Dw{0;
zo26Y(7y`tC0svfQ>q|9#QcC?8zegv^so5vBJKiN|?w`lDGNa@Ey+fTm5*;uI4uf7O
z7AB-Ihq;0u7gf?gJ`dT3Ji-AFU-(iE@wa-P7lEh|HGM(n!~P@EL(c>%g+dnJWGek<
zq@O;}EM>I)<EvPXGBxdV$Td($68)Tn>^K;ZRi840t`!QO?X{IB-F#`d$-wzRwP1L$
zaNu~1;n9zpc!nYKYvcrEBS2_N-AJYg6;<DC<feLHIg!-)X|)~Mj5bD39{pB~vX_g(
zD?)o*LNq2Md=m|sg5BgL$$GTq9!3{ezfgEMVl%5_Ko*7`qB~*T3RQ7Pr8ybZmbL!f
zn&Eg?eN;Lp;D_@@Nrp)c#AM41j7A-QwIc}bIP?>@q>>MLh`Cu#byt+fDCFTw;x_*K
zwz^?AoJHJ1vmY-?SO}cVvLB1+WePnMe1GmE>$$tyn54jfJ|3H+P}0>_bJsFX?ABti
zEO+-b#gA=7<o`#05z4e)KZTj#L-vlZCzMLUmhwSvS~7Mi8zm5I=hgXj!|9Wsg;>+*
zh2P-NfF?5RirVplO=ZB|pQGCGaHVrAy2YGsDH`zRJkCk4(u31;d5Ydn6Cs#Y)#X&l
z4m6aNQFs1V*0_GLFB^w#-B6%HJkYK=&q;$tJAK^_n*K`*TVoHi%Ut}jt~M<refkq|
zOgWfCg7T`72Fy{ku4!0CU2y-j1_zGM-fzT?C!pF2{YqgjQBaSOOKszDI?p5Fj?hEB
zyRsu%K|)s#(p?eBh#&lE0ef{_uqlxiDtF;e)lPPfwM9or$@Z5P5gML#Bc_8N3p$pj
zSGCxB+GFPs%R|O8!wGJibFJCEFoQvU)w74?DYeU}6f}rwjtSayPpC`s(#dxwWy_!Z
z%dP)o0W=qO&5x`+USk#>LFmo>qB&UHa|;bx8=gb0OTJTu=sY~tqS!yd6~_J6beeg$
zuY4tG;;V+|_sTkst^eV@$$K2E*)aSr8m?gXp2kkdNAn4%E#eVBti`~xU$ht)?JFT%
zmox*1+2D%fsHUiTKJ}4B*_0gq_V{r37hqmfBHF`$S_<$pHiX!>fI09S^g|oixX8nK
z(dT<r<nt!8c+HCmV~Xz1uFfwyOeK;{(9ZQC_od_$*x&X{pR!RnSmBKob)sgPPn~~w
z#Z>XAs7(lFb2E?DycqI3`nE*YTIWDD<Kl$)#lH8~@n&Ve-lqAkUY3UQx?}M1e7J_P
zZcS7G?W`#_VXaNa0pJcufHmoe3&;7w-)bgx)&k}Ev1OV_SuPEacdi{!kV~J^rE8Nv
z+jtQLV&YcZ43wDEMMJ&c7y`I0a+D+yKvtpeiia+aj1{z1X8Zk4D~l=Qxk5S^5w&C!
zS>^a5o&M3~k`J%kK+yIiIhC`Zogq!;h`yJ#7aU5|RXpv^l|s4}N1q|4fuNlJ<>W^>
z19|+JLrieZ<6ojYY_^J|W$g61_XDYQvVeKxEYTX*yQ5cS>)gSpV&2gnwd1)|%rVFj
zaWCz2A;R(T&odV5jl|Iv2M#k9W^l<kWx9}*0d()C_##nzmaQ&jL;U`Jy!qY5<`l?_
zsJn5E1Cy0|b1KfS=bpi0rIQ8d4_2XvU_lPg{`9_}A5&^h-($r1qYH;q;LEi0ntSgB
zLt+}o10TEYaXY1d&~i{lXiiJa8E2PzuIM3=rTS}rZ-Q0*;IKS(l-iN(ps@P~>S0)U
zC?Q#v0+K}91-<IY*&`JL+KeC>^$oTCfn#+=S?!(D?BaVkV_OE>eZ<5en`zi}Yyf;L
z!O!XSl`XmlHDUjsm-0**X$uChX0Wxo+exg{-_HH+6onSe)M{AEx&B{J!fN>G5)j@d
zYSLK~JY5njthpu)l6Qj%-JlXWd^F$34ksJMsDwohCl}WhKB7`D(fe)AInnpvbBmgC
zi)Qa0$J!*nYFsOJjCEr6i(kn`2_-iLyJvQH*}2dZZ}tS_zVCUq{?z-Ao9zV{R+D-t
zwV0p$=6b2b{&-b`-W~J3pzNIrz<JJj-Sws(9w&tpX81dVxVmh;`p$X%;`*Q_U~vEi
z3GHNcZnuycs!GCW{5(5+wQ_XHS+%@wRxuFrCXX5B!uz_wRUt<4)6?~LYj4knISA<~
z670qjkLqorM?tfc3BnZ2(O33uuw6T0`qo=;hl}jfc_;9C!u5H=#w(31@{%yh2#4-*
zu-^Gk--53%Id3*Bu8y?bLq4;jtWQ|4t~~E79+k2Qt@a|34;MM|@(fxdq~M{O&@4%1
zaWrnK;g3j!vOhXR*a(-oW4O2wtpp_$8&05*{cflc#mn6%@Cik)XJ4W8>X$T^^$})z
zW+5@5=LWRO726NC@-(?JiKfJ9c66NmfcwK)ZoKae$jfz@vgzx$MRNxNw)R~@ClrNr
zjr?TZDGrZ5KobQ*`PJb<V7rVsIjGpv#cK*#3#r<d@^YP(&Gub(@dcQJRyk;I_G`r!
zXEsTU=V|ugpKQb*jIXYq7I`>WqJ_FQ$-;z8f_6CQ{<L}#VuqRKXGPB&!%js9@swHh
zl+K6El2xA4d8Y-C6J4HIa514i*m6L^SCY=LK<JBO+sbsMYoQ6|gmx<fqMbxD(+cI6
zr{2hB_Cnux#bupLgYdWlhSe0e59XCY^E*?|2>&6KjU+GW6HeI|b;1~?R4%5Fl0nf~
zsCGFfvAqKmE!!z^9kKOu1inbOoYCu@#SUjxjLRP%v%%y4PPTUqN@K24tu-^n=lMp8
zPk%7j%DN{L(|n-q_>Z}7(Lh`QG97a8Y!gakmx;uLSvR!zgD!hR_N%T;80D?kutS^+
zp+&JyuSw#?7l?pF_M*bz!x$rc@JRt@ISUjiy%9n-Km-PyQ*1_&VGX@Xp6Sqn7j5jO
zKdR<uZ{8Sc-|El}J3zNaS+4`niN8=u>&_-t=4#nKL@Y=gee*0(8yMk!$1-?MS~gp0
zZnDKZ^I69p$n;@<NE*K@`@(e3(WfLpU`O#ZAAGY;Bxc3dVwfTa_NwX$*SDTBQAeMx
zfpR;}Q=U3WY`6_S95%z_NJ*rBj;;qeM^Ec2UaYdz6C}5oLj{SFGhXmc59;DZ+DP9N
zC1kHFh2@9cD!Dk-f!#~GcseUb&owE?;!Ma92<|3LZ{=h6g5-@?Fc#5YtC~t2uFnO|
zhS?~WG~t4oHV!PZ9Hs=W#-2;T+ZxONscT(-;E)}LUQ#;84VvdYvuzlLLRX{+c9%k<
z<1UP_p1zxUA!2f9%vf%naZLIK&(=P*Og$WKE71tNFP=0}toOcWT_VZRlhL6x!+&!c
zKY<cg-eu`ehB<L{bMLKPdbZu%oF35$23UjS0gW2Or#1K~L-3$yOCze7daG3NzoTXX
z`r)UA0a?Xwq=BTx&ja5_FLXDc@^&^_d{fP9vm|mx42~IctbMn0xoyw~55ISl*x<?x
zB9HDajRiXDL;u3=ltk75Uy1x9`uT>@xlVPu?vbu-!ST6ps2%C;>4~=GVb=w#f=L;g
z;L#_rja0NKWWHHH_+Wk?8Cnm(?w<{y_w|BzZMT2B!fOo8<}B5!{7xK3{wTK%qO3~~
z=m-Gp7-*$7y@BhIx7SC)K_^`$v9hiMr)S4kfvwiHLp}5R^+ieYckh`hFW}duAe#*|
zX&Vt(*CfD8ACC4{=Xp9zFZtFVR@20oe8NhCje2jB35(5s?Bw*Be0|!*3WN)~^K%O=
zt|p=#2UmKik0pcM!h>MmU53N*X7w(6wf`zTzpSgWT0_TgPl}Vs{0F%eI47Uans-6}
zhPO#VG^0y1fx;&S5xctZj}Y)qd3>lKIP1;P>V`?yOMJPGf6kSl6WU@|fu>Qa92IvV
z!OJ9WUTuruK4r~P2D^OrW|_A*9IjIv2UNer_k0rtWSy0{6a&A3e2+e34}&U%+y9lj
zKC2Zrc^D_RZDiKIV}WTVnnBRrsiJ*8WeWmptXM{&pLyYiyu7wK6aG-y?#(!zsckzk
zK!$LNv09hn_2c!cf0S7?U8*?VROGVavUl15TN;EEUy*Igfg&HNipO*VdRB`f`a8W|
zc<Om<mtR(@g!7uUN|pwGwHdDUCIEV2rC8~zbffkv8%GL=Ov?%Rh{3``H7iw#fQ;Sc
zbxxxmIu<)qiW<j;JN2m>n&f%VQ%z+u!dg0B4R3oglr5%9gP>sB2ppISiS2oaoT^vW
z0G*$ia1@`KP1rvbq}Hc`zwIDa0t&6Q)%8ynda>+jqLVGARx2FDmE4@Pj&Jj=L`9k$
zTOn7=>7&;~1@)rkO!e~wNTN+co&d_3A?0pK_B-Dk+g2r4>G7gNdN+urPVYLjy+Xq&
zvRJ2aERNlDK*7l?MDXI1+d$mUN9c+AAv2<LF$a%6auG=}b{jp#wbiSP(fPo|V{XpI
z&RhBz<_-1*r5*fUc+iw-r!Ttxkx!bpwBSoQqXF6~fq+*mKtz5Br*!d})fT<vNuiQ;
zk)f>@!1jY6k|;on>JPkk$!^OTkySI#XJ#ZAGUYL4yLxx|2gerEQM21ome}$B*gN>b
zz_8S!)sd{S97{Q~DQSNsFt~4+<LV%KI$--|5o_*>%IaK&bf4du%tD=o$@B4%sObo=
z6RMPh7w?40OL5L9-zpCO{lm|R^^ALB`f5@RLBuhKu^k<cm)ec#!RvY@arOY4@?ev;
zrT+%fMNXuGr=!yIZeqXSgNd|uC=Oc!*`Q14jqRt1f-(e5>k>R^9KzIN0DC7MP8cMB
z<(Mc18Xi&S&_Mx#VAYN~g~3|wH}@|)6{Y$!2A!lEy`dIEytq>rZ>RL}2wgzgj!qqE
ztD?35vwK(&rPQD<Buzr_BO+JU@G&qeb5Qq{D$<vg7gT-IY`?9GUchxqiGC-I9jAY6
zO6aFKQJO&9FleaRhR3#L`{{Dq6|2`)rYf6mA>vDJVsya2SjE8eVL4mKhuyh{4gbAG
z?gJEWe)#?}IzZ=xaMH3_b0tSi>RPp6!vCIjuQ{s7EI$YbyvAu=r?KVy^mDPg!G%6_
zo)z(RW&W^q$O=1TEmhWR@lw!F$?Sf0BsD~*KX>qvF<Wuv<N;7ecaXNzbyaQ&2;92u
zt&u3pFANH#m=f$DUe4gvW(99r1whf$D>5F9QC&Wn44-$NOG&uko0}gKW$*^Cp$!2Z
z3HUDFhL{~wyXS#P55kC{L7V8{Mwo%gOYkxZa|$zE0AedVwnBn<xm96EwU==4xrUNq
z=S|-v=5MZ$yBlUXIv4)i;uBTJL8UAGZphY<I8I#gXzA)O!QcP-6OrsJ?B~nTXYCFC
zczVNLqs>B8SBxLXQ*R=+>PdNb=z{9XW<f$ljLCGx3u|Vjn3)K6<qrU*a%+)8?fg^Z
zbbI}^ClGWuR_>8#Kl-8HC&n0$5?hDxZ$B75&7?vsJb>veQbY%uhuv+Y`ym7+4*f|n
ztFod=O_hM^YMsr|+nYUnyxtF(+SY^y?-Mhol>SgsR-Km4T|8e&sN|yBdYDM2`vCsX
zm^zdfB=^1~HcDdkt9A6)to<*=3C$p-EhZdiyI#KBNkhhhuqM2%9z{8Z#OWwFGm<z+
zZXY}cFU%e69asGF<Fx_0BE5%>cR$9O*p9Vc0FMkTbqDER-F>-DmG9(}bK+9S`Dtg2
zoQ-Z#9nF;I(=NOh(>pxhBnVxx!9|B)Tk-P~qM@v;L0&#@cN!Gkvcy3f?I5t{*mLrK
zae74lrb13KO)9g>ZM~(I^GYX`N|T(Y{pCoD$B`TDdb5sg8_o?M!A;wHu4tuQRnFgD
z%>|054FsJ8Mf(=dbaerAGUVvBy<}84Pnb+>++l6%k=D$7zoZrMTaIF8it||Uk(rT@
zO*g$DSmEU5{#!T6xw5pP0G@5$<BPbOXgh_}Xwe3n#<T3}JncM!D~&W+<|6s++<&M{
zcetFS^zj06Zx2}B;^5pJ#uXJ^9(A!ST=O$EUe#F@P%XnIWK*s*s=?;bHIV6iKHPHn
zG3PVGg#ECcF0c8-c8IYrJeYR#kZ}wl;wt~@sfah$Rb1BeFA`(x!-p<saCBnk&@~9v
zlnSH`UAvth9Gk)#+HX6Nh>eK5tm2sSp0E^WW3{{(FsdN3CCUXdM_BGU-EfybIYz9Y
zy{Mi5>YeTGPOOr@<-6YIHH}V=YaL%a&05Pek51>nHunBId1=-ozl3aE(o-%kwOso{
zNNYbYD&IoCfxD*(w0iy)B4k_oC*9BG*bCH?!-5I5V0`ZK85|5L-TS9>;f&pa;L5Fj
zfgMqEo*CtyfMY88r&jQzJ@d|#&#F3+VS29Ki%MHsCVh<UG`VAeyt!73Z{XinpRJAP
zo=*zNF4aPbLxlWHnZ4dsbSftVP8QUss^uXj%rP6Q&sy2rlQqaSEw*;uKQ7b25=%bB
z>X-Nv`3ncANd?fS{){tp^m(qR#dDuIQ(k<v{5)dH8SsHjsORS$kau!QJ%3w=c#}|G
z`^^f2MURa>KxU7{I08jo9(LU$QCk#BI`JVY#BP8`GQjj)XltlJv;Kq>D%(FQ;kTA6
zpUf&Q*+YR*rXXHa^Rjcim9;VBxJeg&78PQRr{3ee5rCw$!weBl(0lRa9#Iku0lfv=
zka5@r4IOFRnvln$O=~xuBH1hZqmUnQkjP^L^`_>xc<nhO-Td+9=$!f=CpSqC<~Q_5
zWu5}%PxJ(P(3E1*ndqEfO&rr6h)D_*-y;*e)8f%Y_&!SX%h?t+cd*bKE6Oq&SM=M*
zKz%Ey=SXVZ-t}KG_oyZnz@62#PrQd36g7TT^tX~m)t<pt0b_=v9nJf#H$6p<eW(*4
z1R5RBca(pYVDPx}f^8v9>M_Ajs}~h3%KIb7w<6_)v~>gS-}7tiHmA5SCepqFf_BSN
zpBjr{-%Fox)s-7g1U*DeEX>IKtqD$BYgrA~dQ6v&Z3tr1Qf&Ni^fz$w5_;H5s3+M^
z`MMBz@BBy4`!z@6>5dTTyao0_(Ii)=(POLYQ>*FL0Kbp|H)w>KxZ#ne7UzltXWoE1
zG4_YzA$f%AjunH^a>H#+oc$~yh{1XG7ieqr)*RSxp6f5s-!%H7y$1FW-AwaRQ5`e*
zgTn~>4rA>GDmXn1OSrF$(x<ZQ*nKDYG}w!<=kqP`_!m0VA|$jwG^8bTyP(1KoOb2T
z*&WWy7pHy~#I)x8C1IGEp!-S*Aw#w}9!yB(m2BF4;PL^`uiYSY@&>DVJrc=ys;97F
zby;xh9AgB-)m^;~4Z^*W?F|cB=HYuQJe0rpY>PLdYbAkyi)c!dS@0wM)!4U{?QcOt
z)vr^}yNUZA&@5dBtUP|<MTESsTtULsdau4nK|N+M{5J3x+2(iU<8<5j-+MpZrEKiS
zDcvz8jEaSy_BpPqoXaDMYOLh{(?dBE&XY!%RPKE$2b>e3<gjvd`w2m)(^nR#{NkOP
zE8k0@>B(9}Ejbb-Pwp$z=}y<<org_`_U*}0%6}z_r@slpY0g=v(dGJH3d;*+5){kp
zO`q&)i$jIBK3-T0wkoEO83ZSCwo_C_IY0%nR8(1NH|YqNzRL$5Ld$-JnWm;a)?AES
z7FjM8ln4x8<x-tG=mzs&){E`ZaCH7&s&@m2wk?&^KRFIjST>z7c6r(^J~+580%DKY
zcK+>WJ27J)ag{gRYiU=&Z{2(!`?q*bBFF2X<L@`ZK*}qn>nN!+vu|ABNZqArc{hI6
z3tD^m)z6^@^XKF<Enr|1#%i5U$!lymcVR4E2v)IL-(+(<RlITxxyG}gq4E#$`>s2^
zQO-YBS(VkV-BjKDMmNe%5X~a>Hi`nMqs#6+AD%&#OK*bdGDX6Ah{o>FhSWe#s-!i^
zVxlHE?0eZ@6uSSOIabPhR8B_-E)|fVpw~o0fMWK<GdumVw!S(Kk%h5-3}l^Pib@Ja
z-iqG0UFyNpbk<>}{5y#us}k#a<uG3PCCdAWh))__ZbKl#+H59|C!`SZ*bi=SNZuuG
zE{-U3PLcta(T9s2jTA3IqI?NcRiV0>cPlZVCa4pY4xyE}CV)J-AE>-V9N*V4B?*oS
zO`$>VVMVV%gE~l;!U9_`C?xyfKW5^#=|2{ktjqO!Cp!P~HkH|)Am}YwW!bHdH)PP-
zhGb&D<vA=e=rqX6Lo4)mDQ2GY{Fie@*eQ5T1B-zibO)XGhORB#hu-`Fp8kVeF@~Z!
zgAuAvtT9{Yxj(Zp5c4bn4m)W<Rt)@EIVzehscfX-Kn;0M1bADOis<cn#}4PAA=QMk
z)&<!wlF2^doTSGdBzQm{#b!A98`r$<)Z*$RQ#Yv)k<3{CaK1IF(Fo2uwduJd^56~k
zM>4TE^59qgz5}!h7lytr_|+2m<fJ-!Gp2wWmhG3|SF#b#AmkiW;IXxU*Gr=(y?kDa
z52B(pqF2Wl{I-|Kq#B<ST=)6GO5mfR$Kvn%8XcU1JoO)2!@09liya8$;09L!_|+WC
z%_vn@GKS8gcF4t#FVHM!Ol0~)Ux$j<8<u}GToOs`5;mr0azM{_(aJ#*-|El`p_{6l
zf4mt;YiYJtEv%mE+F>t7L)+tNRE4aQjv^BxPdlyrrIbtRA-d+}*{wtgKQN;HKQnxa
zK5F&Wo;+g59XKfLn54wR#)cL;MRp7Qe2U|?u=A65apgjz{_ODkVLrPhmBjEO8gJ>d
zI*%Q6jZ`j7B;Fs3Fp_QkrQm>f>}>YrfX}u^HUdUwkVOy)YDACFo{3ox7PKH=f_7QS
zJY^eWGI=wBj$%H#y9S)-;M+Mlq+EeJLph3-MjP_7%^np3%a^dNyfo+9p^O|JOcryj
zXY91a0vfF>EY;z-B*m3r)a=*ZrR7x*3c<;!8#WAT1y&>)91n5K_g3v5O9)_Gv98$3
zDY*&$p#Kgo5~q$rHp-~d)>;KO9?|X2$}7`0wdMqmR;*-?NB#dqrNdV}QgFrCVq`R6
z#H*hEuFxCxJ4}>Gl$ZxgQWL`OR$(T(W3LLJqRX}JTwS$^-;g1r4Bc+QE37-}m;5C^
zYdR8GQ-rPm{`7rhP4psPXoO#tH8(<{^N6`PGIxN|PlZdx>?#@@CDF4$ug-i&VlhTE
z)Y*qBWa|S(_|z+bB^85qUP>AlT#o;KXl2+kZP2N+VRwt42~_DniYyF~8-4cgX0tma
zbeS2lh4D-4&z$KA3z7;tvBmv|+-6bSDr~|qqVcI$4cC{KguR?&)D`3P!Jfbt<rwA>
zX&)8__O$W2i5B{KOyO4Nr(1fVIA5aku4neqQQePih!;QL*UT0+>ass5?l-P5<P+mb
zQD@7HF#i^vuO^3g^`$*_kPnk;&mp`7WU4dMfAJKuJqlU0p2yt|z&Z{b%N?cF-(sF1
zpD<1}h);UErP1;{ENC-$Co5rdRQ_1tortbTVO$??&<%$6200b<hQnfz=HT+1QEPsE
zfCsL;D1KdM(EdzA-(Qn>U#B!dGnhx)Uqart;51S=*rsj&XwpEh97#*DeXi#4()nCb
zh#k>Rvqg<AiL}7Dfl$gpvt1Jo2=~7;f{S!7!EGy6R0<`TKz1bfnKu&OwfDmjv%hAY
zRJbPPCC@^3atuJmOgfn{kZjqqvInJHoV4kILqe~w2^v1SUTJCVWw7ZTUCZ)|3TU{~
zAqPIW<V-4bw_3n@k(bartwF$8NLqvuV@~oJu^R2ym46_Ffkt1<bS0UHw;j8PP`g@t
zMgpJQ^rIa24oU&<xf+v*h5bcF{~+xk<s%mqn`_@L?ZuCtRhyiXOrFirjl-6cTGNFp
z@qx~$vbc{s_%O*Sfd$zxw?#D6TUOA9M!cU6U=(I-3-0l6^>K#ucDO~%6-t|7&jJit
zh~hKWMzM6@gU?=67fECUv#Adr^bQhjGhZ4OUT~g21~+c7jM^Z>h{|B5e%399RKc52
zFmj{qX^T;4#*PK`#|9ZC6bX4)?)_$(H0Q7tdwO(Ud25o!cw!nhPbi_>tn@U~<xkwG
z&EGx_^JgFBHI1E$PPMW?gTp8yz$-A3vEws0lh2K!552>J&AGSPU)u9Iu%y~vKHX=S
z1T+>1Ki`29+>n@BI{`jIF0tFV)y+!7sgcsT1FrJ=1Z|}32Sl@bt<U3nLzgTG<vHiI
z0$a^jDXwEyhi}a27RI4|9igsmPKylWAO(G{-LL4Y?rqKk>t99sv!dS*HR;3dfL?9i
zr?Yp_Wrt?AgX}X_5U-Ych`mi^D9Y88EhS{?JT<~tJotIni19x#Th2qFLys=#$+-$Q
z;;u-maI>S}`ku(>Fr~0@q5&;}hB|%KS9!pQ;~>=a7ICb`*{#jx#1t705q6VmF&^88
zu-6qss+_p7=G0nOPvxI^A;#^*y9UAmw7FeC3bDB`$O8dSJ`w;aj2LE`CSy(|@-<4L
z9yx2eaKEHobIyA57<$A@l|1FB!#=?h^=#uJ26i6vO3qIDM0_-ew*H6ex_x@J`#2xd
z*n%;x%;umhaMEFzi2s`~7waJ%-NuR=h=YmORI+zMGQn)3xa@!tWCw-^-H5L&U&erc
z09(l|GdG?{2lG^p`#&zjZwt&d_6|S^@lowBLfN{>{1G4`W&H{dx61-am&>w*4ma@T
zMSvv4i3ZujbE2>k1b7YlRU~!Brk{NFiSDkR3;L8YmN;gdgZHV2*pydneF<92_ylU9
z#8uJNo!~ng&)f-}HJY$uiaB|-d}P2IYnD3cwakJXaC#|@0ocTfzEdLG{FA*d*HUg9
z%m=$^=%u+IVgQN-bmK|`et<C{p+yCs&R=HUJ#Y{R)xpYh7e;(-8(<*m$e&awAZb!3
zxhN9t!M6{R#{unU7}FuZ)@{tBW`}N#D!u>c-w0ohB$4GWnw&Y<8QXp47!?O6H@vxx
zNzb5|<q<q*l=xoti@d>_b}SyZ@Jn{&Tw(A(VM~{5!4n%zzss88u1R~X2-cY7r2at3
zwd^k*abX8}SsP~|cW^sPMg>&tTYYjixs-MNN<dgQRPEtXY9z6a49RZ>u^3B@%=7i?
z@sYZ_x{tqdGCk>lXi5O?&bjq7Hq<5ibx8Z@5f92!Wl3I{jOOWC+kMycV#}K;b3YFD
zvn!)<GOBUe2%YOWNNT@|d#5aO_2V(TEo_71x=0x7CwJ&X@VZ`||2O;$RF38*?1cr*
zEeGM~z;8RRQMI9UN++bk=6MkRndoGH+E4^PVNMwDbX`n@3^J<Lds6R4&R|(~=sM^4
zn63J`3;mNVj$*s17SiU@JJ}~LQY7|~tYGpNKn{|KW02uZ?WP0*4#cb;Lg}kJ&s*s;
z2U-yQ7Yh?{X3lglXP%14uTejzFs-9WVL=^peRVcTh{<JE78NTZ|10cV<*x(SX5M|k
zd5W+S6xz~o0)@mE*`=q6Djhf8>ER;|$%24im=!jKRk?An1T{l$_*$1u9S?kT$D$zz
z8IU-8R<^vQOoxt5(dWkavhK%FiTbf`KMp`GPLLak!k%RNS;oPerW?Nx=mX1o5$Nis
zbr}-goEM&<UN<gUcVcloF;9=5Z5%FM-T%pXGr$Db_JfEY2RH{jb49qw+kOUbI%s9^
zrVpl5%{yn2*<$pr)*Enh^2bKF9cn3thT&{e*@TOY+MwLlw0j~40)u3|oWYkZ1W*q6
zkc|s`@%;GBbgLnxlTl({;mqrL0=>>Wy6blBJi1dEd{8f7sj*C%T{k1yFf=+e3%XS=
z!5}m!xoaOxMm+|LA4W3_@8n<ksRu}xNsK<WnW$ktPJ{~WReYLZduTjb!!)ureHrq`
z)XdTuvJyHZgnp_i2`8Z$t38r;3oyadvjQa(*!Nww5Mm%p=QxlBTUFX{;EL;x)Zvd%
z>bkUer)tp(>RzMF1=_Z~>?iZ3=%pGvmlp5PtP%^D(*7}4aAnO<q*YY{0l4^}Gpnqd
ziS+(K`Qrx4DY+GHW-5hjNDe>|ksP@aFQg<5JVKkztp~#+Z_FgUhO3DK<@4!`j~B}#
z($=fJH-e5*v6}TX1eYUXec4S`7)Jgy0T+b2sKQ+(Y-G=(cdw*&EB%i{E<<6PgQf65
zjS##pfk&^!;95x!rWM*?#5Q9UF!=70)!Yeom1)#$AKJF#_OIrAJqe9cTKtaPNw_|0
zyDSMVD0_W|lubyzX@Fl;q~w%(3&XCv@$Y{QH=BnTn=wNG8b8#12hv>BGN3-nQ_NT0
zG28k~r@s~sQ2JNSSEHl<Wz|o_k7BKumG5(L?MN+3V%nW}{gt9ai~rFC?i>>1xdz3^
zZL@CrjV5kCY$qx-TvHnlSgM`^ei9b~0^t(v#C0O)f?+Q%usSCF2|jO-`E`$H3;bC4
zfPca~!sMYQ>c(X0Bz;k-GURnYt3vifDK6!!up_2i7P!!3Q(6TCGIHH`b~!GsZP)Hy
zJFi6&-h+<sDd=M?M<{qeC4hgL-GuIm>LJl!o%2Z-Ju9YZidlr(KjYFE*Z0dw$*xZV
z_*B)_u^^f;-D^aq#Bb);DGz9S*QYYbg4CWPa5Jy8y#ZXJki@>E3StOL`D?rVE?bu>
z%MJPc-0I*H<K#gFp5tRLs{ox%7ihF2P?wWy*S(B|O2Bnde(6{>0a#31ZrJHGF^9w*
zM<m4gnbHRikRGI_zOd~(Aj^s6-+mlGH9U1jLlHZk_G)KF&txo0bMTJ@K;8LR6Xr2s
z{@PUjSV_Iahu$jIj6e1_2r0iK4n4Ku6tIN*IK5^NnwR%-fM#*e8-mp@%OtHax0slO
zwFcd(bu3d_@LOh%l+X&QUo%TyAU!#ouvKI=bd8hC&1nsy3Cr*|ZK2Viy@OjfV1_Jn
z>Su~x_xb;e^3!Kpd~GF4CRj*gcy(d~f?4m9P!xG{;Zvl0cO^#(OHNCyOePmw>gCM>
z2RBYL7zGDCOyadTZep9T&Rd;_ANr}%v@K`82SWVJ|4ZFp_-fn=WcEEg;(><iTT<x{
zE>EiTewbOVYI{n;CDb8usuJ{*bY|pFfp#OMktZ(047xcUK_5>0{F_>h+Lk!vVkj{1
zUBQ|<yy|s{Mv}UnnB;di7~NobTQTK~u3|KJJw#9bXzRtAvFUozvvuK<Y){)LUTANJ
z;PN^YBj{c<{64D(tFSiw4?H1!J!)#1CZsZ4!XPBnzJ^6%Ak)`Zyox4hV${wpOl*;p
z+~PvLq9D9&tk7va9D`rEiX%<Oe&d9oIn65u+Hk2AN~b<BtHCZbC*z_ep7Sc+FBNe-
zUFmUGdfZa&CGzQ;-U;#bbyH&b_dwk-H8|XA+fHyuLh93^xs1Zsp0Rl%M&;D8rz-1C
z9;>XmIZ!QZs}ZiwgWhp9Lg0YZ{b@MzVYvEo_6Ma)6DdpJTpbJcocE=*`LkRIHyeqh
z%kWJN5b;1+zJ|g@SFmJ0;didWVw>M}KfBR+sm^tw_s~v-5F$M{ZYFum%6SDxr&Q7c
zl8#&m>|xJr$!a6n7g9_}H|%A2U2adF^(0o3=0JsNp6T`nmf>nQk}gSIz*z98w7m<H
zt8)EP$szt=?x3wmeDv7(T(lv;u{;eT305)7dIf!UyU@Y>UxIqUP+)gU%UE7_$9*$R
zH#19*x$IpV90<Tv8G8pIIoWe0U3g!qqSi;`Z}9r~HG0Y!@A#}3DC|{SFpAd>EfCu%
zv^f8-P4cEa?=N@~owH!I6jshU?DoXzL(bu6&UY~2Bd8N#B<Ng2w-jv=7j$D;Gl)C>
z&-ciA*bdtj4k7G4Gj2E(!l8OVwUhG0&E!mCS?HJvRM}OM(N3!qVe2NmdGowh8z1Sq
z;8z4<zo8O&8x{L7WAVqyr>oUSO3)&@#&V-_+R3iY(uX$v9@iq%@Q`>>cV>e6@1<bF
zM&Io2%7E&_{9)o@kIvF{r=#Zmf5KBJ-@PF7JlIu81ric6ZbG1;g&jQsFqea(ub2K3
z;woFO|A46*xdBaG8d_#8v}8>E!3msR%BFvpA$zaZ!alV&tZ|11Xr!S&aU!dbJJ>$9
z0_OO~u%(|o5=j4J`Ms<QSmSa--5j)TKL^NGW8c^C*)7l=hJNO1TnTTRnjbiASMqDR
z(zI$D{Zn&QPL(ruoq)YEzs~iWFNS&hZC!=?g}R6KW`Rfz6u0{I{_Hq&!IP|D?^M?<
z)8-~P{YDfnc?p*xxVERTqU(#!{o4HB=i+Pj(T+Po>vT^QTgjyvg!<1vp?<J+=VcbA
z3`-}ISKR?q!+;k{+*jtHow39XbHdAYP04%AA}nE!fcpSJ01rM%Vf+PI*xf2t{cpr!
z{wlKJ!(nfIQ_C1BI&AdA*tqUW6{lB{>r*JlpbOEB`$14q&lo|mR$N~GUwyOZ(`1WI
zCE139tRcYzfZiM2G0(M9&<4=W=ye^TU#EbSg6WkbkdlPrk@s!wUz)!rUWgC@?-0Et
zfBoG)VBF~UC)}%);!pSPJXgB?o8`~Rlf3EMT*@lj!8=MDio9ODL6F+frpV;BeGF>W
z&Rz$i;5;nuycn(<bEV7L!8yt`kQkSqtY9<C7f(%EJxiL+r2K08dDk;I_cn7b+aYn+
z19_*<en|2@p2Nha-gf6-3Efv)h!c<Pcc2I0%Bsd##&ZWAMgZr)Be2y83xPHDRw#aj
zlUxyr%J*OEpgZ7sf$LHNkBTEXL0P`!Rg`Tt_~NH_(N=)+NgbEp^e`u+G1Z*}2}^|p
z3;dXGAE(Cmg6L=_GhifeDweQeVZb}deI#}4tgKTgLsrYL;JHL9^3^?}WcY^$PL(C0
zC*oX2S*YWvK<Isc>`@`%Z!{;@w6u2Muz8M$iaf>Wj56UXxw1@IK)zq$b+<f|GgAYz
z#;>5BJBLJ*w{9~f-d7es9Q1lt(T!(q(-peQKsnm;wkJz^)PsdzFANkm&ZPBjeKqBb
zIYGnZ@U<(zrJEiM5C?9(b|b3i7i2~73sVqe_QVN21H#S0NE*Wf!0nQ(RFMb3!;svN
zlC0ZZ<VJ9ZiF<A@zsw0-EeG{z<8Hl>`*vZo74O-J%L9Z*yz_7WoH)_b2suiN?4AsK
zEc+4=>;zspM>uaY-A&KJ4%q@IBvpo_-Yl$z^bTDNdI}C!yAU7yv04Y)J^kBI?m~6!
zXXdC!U?dc$!4<M7>Pm74tK!l)TfDyQ%4-fXbjOm=KK}dW{UY6SA~p7ohoAWd*nFjG
zSgG<NFO#htP&_1^jPEs4m&d--qYC(J7<s<k^X<yRD^v6qN`51|$H?b;zrMhNlG`)p
zAc2ri^1Mm4@jT#;q6gDGz;3YCa2exT(uSTB(W+*aa#N}uk%Dk=+s1N&64Ewj*$}RP
zuTIBz#b)FUF-R3nSR`DP+)7`We;V5S7b~(hmRH%fk8LJ!wquWG2dw>_?gTDPb=wt5
zk?pg~EOp+VR$0IPJ9iDaKGhqg#yZcqitSYu#a-QuOn;}Wc*-pM3C&f<f6{4L>Vo~!
z3VE=YPx0-qxJT3dwG<-box@zEJ5f9g#sF)FoEun&P4$8%M2(qfOxLOG{!?&oJ_SH6
znS<~7>yCU0VDbY?H4B_l>I02<)SQ*yUVXWB%fRa<ISjP$FpG3McqL8>6l~VYLf%5q
zwvvmnzv{lsxOO7UZ#w8!9yDEyvI`7}KTUMF7kYb&TK^pIbZEfO+}8xNQQ~mZl|Br3
zVR`vw?*4SM1G&sMndD}^M?egvwSZE9{Be$f8{f!l^BvaC2Y1oz$?*WE^wP1k`)U3+
z9nKrMv@w${Y9ffiT>s^^qHmUt+mNkpV?_#Gn1%5@QuhMVb{X#B>};}o|4bb_#KexD
zcbk;9t~$7S*bxc}$_#)g#AA}Oh3+*#ZhXK$DJ%c#?dZ5$1kl2KCj*r&>~a=GU1cG)
zlOzwj@=LpPO9)jm7x#q%@623wPg$1SLd9I1)Ve7h%amcdN9DFnLE+V;B4TI@+^ys6
zkz`KUCxp0zzpqFbTv;gTFYR8FYdskneNXlDN#~54k!9$@p`V@QU}Gj^WhisVZ{(ec
z)bO;9L#l<mV(!Yfa6v&h@z3|wfEe4~9HfgR$~k2xUn|Y=)~|(q)8_vqa8_wq!8*Ql
z{{aXC+k6|}Tfi{cW;leB><lIdvwlMkbK8aaY=>WcQH5E=B?O2ret{#_mrS;-<Eh{e
zcG#)V073s~w(KGY?ogsHA{yGyIS0^Jml!LB-=8tI<+fzC6F;!AarK8k70wt-guweQ
z$A$Yw7QVTh_0y^C*Ha<*QH;_&r}skpRhULYSt^zzbiVJ}W&1lAn<aVFVlLN(_B<^I
zH<V*3meNcEZ^RdW-!tOVd28;xk7oBf2*MmaL)-Q<gf1qjb{;mh;obqres15{v3v)y
zwVancFm_yu{IjUx%|DSXVwBXa5cb?Sm>BxW^4;Roi7-WBGS!1N($)K)pB#{%M_njz
zF|W56S%vtF%~e%@!)`Lx%_arG*$&lh;B;Xt>AVJ4eUxl68J~<O=e{e|@rLh{h^*##
z+Sxt;5e+7F#XPg<54bhBMX?zTuyYg-Kccbz;=Wn_mJ9#_e*3O;!XHtDuH}9kpgMI=
zS-ZFOZcf~C*vKz|^{)^x&WW#=m`=V5$NgOCPl5lEMm8d$25#J1vOm59JW?6$6<^bN
zI%?eag0Z(XM%tg@@?V=d4s$wf#X0fMK0aIAm(P5gh|FG`<R0;l6v?I&MsG0G3Z8c`
zE_YZMVxSRb1El=7tiiO+lF#`?W49w=7<b$D$3oFzG1lch@uCL%#FU!Lr$-g%=WhPR
zONgN5s6M$==FsCT^jk#!ffEywD}<HE|Nivv4n8H%t&*~kL@-VqM3%@7;-s>R{Vtkl
zwzOK4({cWkWAhC_hdcUC&IzLZO_(&A9rsYlpG%`)uE^@!m`6duBprCu73g!n?(Hw{
zn+3i{r_XbC2rn6Pr6!`Ryx*4bA6U3x9t-*>N*xu+4;1Zhc0%{cBt8bm9IIV_Z~?ma
z^2<(^cv~K6D7|jeA7T2H`0Opd3-qlXm`GDqsO-7iNhY(+Ks58R#a3{<!Sc%eu-BBh
zH|1C;v2I49{Uj&udtr)Vkq48~<`&>H(E@#6=Ek?ILn9}I^ytkhXE7CDeaDNe587)%
z!;MMnGPImlh<5HN$s(%d7K3DO7uS!^#p4TFUCl!60+QEW*N4~8+;bwv6^pj_q#DLV
z2oaJ&g094Bu&pIFJ*srADLyZBitxZ)$6^wfH~ZSpmt3&As((RuceQ8SCBIfi6@-}D
z`qi_JXNW8hJuy0GcI=&9l{pvZM$ASm%+j`hl+HSrl~c(LM89S%>?SdH0LVKG@H?n{
z7k@sM;l8YW;8!n=p}x1O{d@fJtJ<**?WQ#szjX2NiP@))4zJ23!N?%nj&kuw@j>eu
z4u8tDFV?i3R)#+6pPIB$9ldNS-QBqu4Rm`olW4R(5z!zhyccLsl{RwEWW@Kg>^7{n
zFF9eqILq~xPiBt|T%lp{u2wSyyS)?-k)0p@DLI_oHb(R^tO8w})l9tK+<RKD44K8)
zIhRh;phwCZSK{|erLJZbj^?htuBBbH2gFcWYD^8gQLIf?6*x8Qtbni)fysT;2lGz=
zr3aCpjhhdqmny;%?IZLQJ?h1WG8Xr=JvOwLFxvjqhn5ny#X-X_bk#4I&swl8yiJ_6
zgqq9!t&(V8GrI*i=^k1TAT3w(IO1b*c6!G`hDxu6*YL9uKt!aiL$C;GiTGM#El-^)
zE024*#LWAb{(9#29q5cjw0`r}hP};L#;3m-9pf2dof-L)8M*r|y^Z2<52k``yN-LP
zk>uh$B{9Vrk7Yxvg%J5emFY@!r|Ya81GQkg*96c0)ZBu1TM(TmJ+C5YWkz5hP7K?%
z3=IKF`Fy0qcF`xKvn+WW%l!i*c5(7vCDBzWp_BD~p-pQv4aZNfzvW1Qow&C2ee3;K
zwKJA>DaahY`8!yvIlTJ)q|<cG>QsW$Y9>ERTp)5J{!UJUR^D+<&cCa3T2{wjxR6}f
zQF*$e!oENq;=?UnhZvi@;p6h^$R7vdq!#HbGamLM_j*U}oyB`ZU2!kVeH>7B`1Z^J
z&?e%xB<BMEu^8zvM5-OW<MRB8ye(S;``;1x^m}o==0C!7!FL7{L^Fa%>AL*F&N^pa
zDX{iUGV=)pGiv9_>oJ7mo+C19FE%I4ZtP1Qz1iX25sX&uX=S3nIz|>|$)(MO8wh8v
z=S>?;{VO-i(o-hWr%=o>K}i9j5ODghA}#LkO7GQNMz=h6$Y;8F`ueOvM0nc<jbRsV
z?wW5L)D7?T(o<M7v~n&#2x%aFc!O4!T5dg_=d5Sh-aR*;_NwkKJ+3)TQ2Ogrt9V(}
zJ<ePRzI$A+);MM~RFha6N$wCMI3h{fZMoj;Z06`&ue{|FUc3d(D*7bSE{;`e7vuVr
zWprtPh<r=-lSeD8NyXvr%8hN!mom50)MxR}{?FP`N=k)`8BS^9wkDlu&~^m@!I{mT
zmXQHOOr}Gpk>Dr!R>d%Ime6Xuh~sN1uVz03rJVIM{L1>f&;%=|V;y8}T~a?r&%+OL
z(8}64x$Qa>Zi`BAyRMKsxB{U*Gq!GC1q{_b=Xhw1w5KuOr-t5bSE~<r*4Ot9FrRF)
z=~16vp)sN?wuCJEBU5Y6c{I@aGWm3G#R6xJ-Mn{B%d*HdR7pMa&`_so*-T(WzGwc@
z+l$!GYyc$bt<|t3HRUPXVj}xTiu32<LdX$M1AMs+H5{b0U2*Y`6ZJfree>~!o+^^&
z!y+~Gon73n&Mfz+@(#86w2<_#XjX5eitA||d5b`OQ>TL0=yAOJrUv)G**CPslDc_)
zJ!>oGo9loE0)_68-%>9Z?Kw3qEU6v#eIEU_VhrH*yf>1Ae*T+@r6-v$g&5s2=SlQI
zK@Qg(bd~zUoeu)59K>5B;7xVv$-5^tP&48}nG<g^MX~H|dTtKlJETpN%&}*xH}a2e
z<}VM-=a!8rqOW|1(nUv<X-2{rM#}hPwv%KMj+pKuG_R>Q*Um+!AH<on=JqlF>Sl%y
z%xcnr-p9|~x*Afc5g)U8mOzH0+$!c6FAzaL=M8iH?y#BU*gzKHal&)bb;7)?ag+SL
zmOSCyCbM7wMce>gUh#zzlaR^~%}XEid5_-~6IcSNhI}Dbhyacu3$3I!eI1GW$~IfL
z)^sXNjHzWsaI!TqLiSJmE1Y{d<US~@I?Z))3@GDoK;*3=`Zi+<`F>LF+Kw@zoE8QQ
z$y9rcP4Y2`n!EnPxoOYSUYe*bbEi4P4sQZAuN@#E3@2Cej2V&rV|rsSda01e=DT!F
zM+wf0&0}PI&T}Y6@0dxstxRc;eDOic5lMp4=R*03Amly59Ti@isS%ZhF0Rv2FJe8p
zZG_;842}&HFFGl?`DDiBX`mUBA5YD-XWYOozZEs+oVoOAwqpypqq;g9R%L_Q$T^B6
z$G!F5hq_VBi{PD<y0ql%<yEd-tY;*f*(Stodt!4}>;(U_(A~G=P7|f&z$cD>6?oJm
z)FZw6lz6ka(xD|fI^08*<0Y$IS0Kej9%@)x?Td4L7&D!ssDhFQ3x({;-fqH>n5AQ-
zNey?vhgidG4?4FVWAB|<4Jdcd9UST2HxH?t&*?U|900ZsH!0Xi9|ZVu?qL_)IbM`=
zq0H}fMQqW88?(sMP1t#kav@L5Si<X#vb=FxYHH^8!@hPC63%~|IJsph{r7#WA=KGz
zCStTZ@1gg5ZsAzhp3g;4Lw9$*vqU)Zvs9)T>G4c-zcfA=-DwUP7*{UM<2(eFY6Q<Q
zUz=!{&rUHi(NTXerBSy$8X(h)y-0p0|LTcWu3&f#$-v#snY-3tc*7^m3r#yp2?L_c
zq)93>BJ6gG{zZGI7Th<IY)zZGu#}N<fdBDE#Pd8L1wYk!@B3I~M+zDWW{L@ZQIo0n
z{y180lDukWW#$eM`VTt!y>mp|9nD`N{qE^dm=KduyVq;+QOMOB^aJx%XW#d7kwjAE
zemcARjXL7?gk8(O4m#2tiXyfS8dn7sYc4B3B+0-Z#>YDfpYG>sijQJk$SqUTd=*q1
zRU9?A?c_A(x=Rfgjn4;<j7axS_qS-lOKQ_4*Pi?M6hrp~l9>WDc)BkA!TXbl!~JDD
zT#H9>*(Ku3-u$0p@5=#9g}3df%-p<J$DxT!Hv9EDSq4|ozk@xLS@FGRl#?_fr2YHX
z>>H+KF5F#n;z3a9w~sKeYw+!|*MZ8MAt$f(sqs!xe%JL?J0{t>qTN^G#nDW&6T<|I
zq*=?H_Ai%YK0i5@hkV=mcc1Y*%*U%-JV&boRh5KQR^zT>#TbiH*`4lu`d7+xPs^7r
zEPOGLoK!S?JMt^yvsH5G>ObF3(nmh{_V3ON3`0mO{n$JSF5!G}U8{M*;%eRRk6Knc
z2v=T{`K(WkYa1iO@;#`*J<@@Ey}c_6ua>z{Q{@<bPy2n_ThXrjb<;}T0_Dd12>Y<0
zNjf{yyxrX0Le%rwy8=$E{Jn5@#VaEsY;*llE%I>;8=&hO-^B9d5xXjLbiRV&ZufL`
zsKuNI<MYE<v5_(ol1EaQWJx&|F+oAcJ29FH-*gy6P4IccBB`ULg`Vj~$3_xX|2^cD
zo;a$Qtj;JJDyrfk=MBSe8--8@a4^Os*Usikw_f{F-0j6iOt9oX9XEn<J#hc)9Zuba
zh94#!URlgDzIkA+t?w<U8$ODyD-LzMUrfMyak7k^RWV-ejF`PY`~zXo!rVQh3GQMr
zPZVXaaITtL>ac1;n;FYRO_zrraPRlDVLj)|^eRI)nXq5OJs|Ck<!@jEWuJv79gKqk
zBqJRhRDAZ7#h-1rO??GXfEraxzCWm6DJtuRv6c1;d8rKn`|QVFtG4T|kA@W<joksY
z8@e(}y~^~`QeKhe-)-nuaZTAv^)R@3r?ff5%)Rf=p0C?jSX>AWI-^Ncjko-JYTl@k
z$gBk35x=BuyDv5ybg}Pxpg7pF1*x_et@jTRn-94lcWzQks|3jTlNq}mMf_&akn-Dj
z-A^PvTy$#Cd{m*;s8BhNQgFYb+OJs2;c-lvNgU{qZkAx!Guc(IAfN0EHtF)aC&HOa
z%QxcI76A31^a37h*VDuWv*t<12inLj6L7udoA%n-%wgZPDr?hOJ;$DegWRTjLvY4^
z*u_0tiv-f<mQcbnsnpsjb8+WaK-zjz%~Fu-)CS6q>}_Wq4}G@DGG%V~uq1k@ya9Q;
z0iHQOU+hr1%Mp6c-LR}`);I?aX$e~W9ZkFlIFQ9D_9SsA1T``v9HeWT87Ib<1Do4R
zT-SAR5PD+3Cz>2U8W)_gPhdz5xcEB3g~+ZwDxMyE$VB8$lpSmGld4%|l}GYvrHkwf
zI7hG_kUaDL`~lk{u}6A$UG6dP?I&6WIDHtccg?29c-9ZU4p9dZd36-v4A5EaeunY_
z%~|&;^Wu3d*Nu~@Sj%Qk)Gh$-MDR}w_Z15HT;0OfXIe+p4bBsdY6}@EW+$oBBi){H
zDZ~FvvNpSIlUPVEashn%;!lJ>7=7qK=*kIgW__mvJOfs#RCb}hHqMZcHAc$X7O+Gp
zra`&%h(6^*D1PQcSKiRA0s;4%x1^Pti*sg0Mjr<<hyYu;Ve5AS^5Vz87D$mNs)4(N
zh0UkDj`U34xF!d2h{O*s4@Hm9y>2&ofd^>uQz@r}-CB{@Vgb;A?pTOlNDfAWW`I$J
z=1$mH+s#48(%bS}6V$JbHb>L5USCae?vUaEc0^=FR$YbOs|ufak5TEITelCS1mxFx
zXNIJ8l>P^%Kv}<~-vPj6V<PJaqBN|XU=Z{ejOC0?=rGy9%X&sFA`aTW%3<KZf#I4o
zGse)C(RqkiCqd^zA7gxBK1<&NR0@$U!o~Vih0Sy67h9YDma;|{qqN9Bb;5Wh0;Gk%
zn249~Yz(6R7A3|o3_Xa@W7D1}cVy!<E%Fz;_|f;2?}T);q=~fi`={yj>ARE@dggjW
z|54wiE(X}x#`wef9sLWQjPtysJ^=dBz8Mo4r$yw2H89f1I7FKG2GFM<>YI&J00>#5
z;yVFQjrJoTAn)P9S_Y){0I#@wSeF9q2w)B!0^bS%g#rSGz&ZZ_!qK1Ea4zs8ecP-Q
zhPr^Kj9zF?LZ2{8$f3OxZ`?+RMu0oU1;z)3V{<Nmb^xq^Qj6$w$Om#TylCHyv8<!9
zhDf?=Q4Bn;<*$5KmOxnoWeJoe(0B<juSOoSQ=J;tUpF3G8Dd^6fp{)cD}N<$uU6@l
z?>eOfWC@D}x}t<aft^+ijtJ9$&m?rC)S<y6=rH+avW_AG#Q~FWb~H6YERLceOLyWC
z4?+sU83GML5JEZ1KnXY~lMpaaL?9rMCKOntk41KbT6XVXO$Q+uU>R{Ssb_(WX9zk7
z=<MccFKxKthSi-&;)SvV0Rq9iC?N;}Xdq9iz*>}t{GkLu$Y-}~XApc5a%eA~CmsIo
zdv3wLOSoTPWzU3MJH{=5_Rd<Yk2~&@NpAeJ&$uhM8s?6E>zy{hz1<AB_kaQS?t-GK
z)c^oM07*naRLTPGnV@Qo$&DKOfLmt$4cz$yR&q1mdeePmin!DIEGAvLn_@!i0@enP
zeEUs@A`pSrj^7Bo$`E=H_-?k7d+!dX_s%M%>-YVdd&&AvGlaD`6etL~2>7W`U4$nJ
zMHCn8&TFKd0TiUZ5u6dAi^NSoM!`V8Zj?wHeV*OUC|psHX+fY-UZfS!0*Z^cPsVxp
z>6vS|6Hizxu>SN@#-5or#`LnW$R4%)#!dY2VAp4{E8WK*p>*}eueUv~C`J(MyLazn
z*1rC0yO7s%FTGe%Ru%>Bp**7B!zZpE0Jx;Fq+%t&H-T}Na?~{;`KHj`QDCC<qFvG^
zW93(|7)oEwmO$Lsit?cSGX^mBU>%KJ=3PZsTyaHMFaq$OMa)aoXNw90S$t|eqYY!B
zw6plvihF*0hvfs5;t=y&J31&VcA&t`+A&|Wd}#Nyl}3SbMfqp>W08bB0Fq@<j5#{M
zckN)80-Sj>^G@2B=GR&Lc($O;xj(RloRg<lcEe`^_u{~<^C%u|^~(#~dIyYl-7T(b
zb{y&6usQi|fBCEHx8@p^wr%sRi`Ezv=5XD5_H>88{)YSI(iN~@U+V)4uCb=u`1>aZ
zi+sy&wUyiL>TBFIn_FYS?`~U2ed59k;+Q-AIvvYc$YwVv)<x!K0tCV$GxON?65nCU
ziSKSv!U{%50HxWC!lD=QV^<UMC5!6-flHcuN}#j9vGj#p;#CD(kn5#<(FG;YS`S0d
zTHUR-Q-Al&pI&#}Y@^*qQ%AZ#o`1c2{oPrh3R1J_DXX|GPhQjAdGZqh5N4k-=S?=8
zIc=s}ZsR50YP<Arlj#5c?QQqkedEn)e~as}V0U-#_g*Me?{=K7+v~b7x%>b3S$E&}
z%_4BrUeNQfKwo6xoW!Kxt~Lp5$4$&PzHSH0!a+Cr!78Clzx16}3&0i<0N+1PdD5)g
z^>8aLu$G&0OAmL`eK)xMcOUJZc=2)f$m10YeCTQb>>x8TF9P6!6(X!0O_(qtlovpo
zZMNOUjlJ^$_vQUJa}&3HD)7B__kX)j{yXa%gHDlo7vZqreBOEI+1@_yj-ONk?oD{%
z6L;}i{|rDRI}SI10?|{Vk0E}*1%LzrHJ}T`5*C)F0CJEf%9Zq@|HLofSioz5F#zNN
zN&;j9*nxZic)%(e`e1-Q05|}6q7OaI=1s^p%>PI?dL02=khicp#~c{F333?UWm(h(
z6w=v5-bfGD=g14!fU5uwqI1P1KUj}~)aIx|>JyzYX=m;W2v49$`U#*i@<2JD3m|{!
z0^mVeqc^7>C@a3_=)C!!lMaBKfMZrRpctSRz_7Fr^iu%h0Pmom0}P0c6at({nX=%B
z6|GdB0HlYkM_Q;q(xv>!g%98ntO%m>rd$NF;ay71?Ew<f1^}aAC6E(RpqOVGF7KXv
zBO_3b(wmEqT;ic!Gw0(6s4i>%2LO=t5e|JO*73wcF6khzfd2q20l)z;6@RC(N(Q)6
z@{H0R^WYg46)AVkNqI*6^of+xu<`r&9bh2VFKCxoz(j9HKdVJ{bZMv)Kt;44ESRxY
zKtAF=6~_aAP8&fVN?T*YG+|hCVXT437doz(=)35PnFq)DhBs|ICY}NMMh=9yR~X8h
z^^zji_z6QhL1vDL_Db8JEn*pszLBMSW(?$qfB0k(8=KMNX{{*BPx_3pi?M*bkT=SN
zaf5ZDMu@Q&9`F*u3*7-$EII8#z&&);jIG2+xiZEuhSwtW1OWB;CIFJg*aP1JfOdeD
z8Dl6n^(o3J<w?0Qmav9*zySvY0Dl}OvI*8c`9?5Sk!G%?7f0VtT8V=(l=cUa4@muw
zJQD{xIpjrnF@7=T5H61Z)lx2uC5(pviTSS3ci>GsW_+Q&#qFB!DeHIqSPu~4=ep~z
z3!rrRIRJ0bFk5G&%xEhB6InZ~M}R3=_eB4PJPbgTZ!!HnkJPTnf7%c8!j#X;5-3Zc
zEP=8Fij@H0AI4d}H+;9dYV%OB)Rn&GmJ-nXrCxd~TU6@#D&LnS&_N}@E+`ZYjez!u
znn^UEJqAc7(+CV2ECD+pm?F5a8x&%~%!FGMPqOta&t;OIg7)x>!U7?H9gIwv*>%Mr
zjRFf7g$YMLptwTNVDiso8A3QhU}0w@iWCSX0%_KwBny`vO(>8kx0pz;b_vDLb)8jK
zD2@=wC6vi;qb`C6=~12=4PVbcLOcQzJGwi9Xk+}E5qPky7l<}s0yH$ughs5xK=AEu
zN7<${&}7inyLWSUr7yU*$pIuD(?6bQN2yM9{cKV3;14I7f_{N;B=7nCmUPEYG4u1L
zsE1H!vBQ?dTNdeeGeyx6f1R}(IC!#uJxpkAl*k9*Kup7PEh_pE!oS)a{e?b6e=bT0
z=<HxF8n@!2Poh|EhEP(WfMsDKj<*?J?5ssWksjNTSAP2+aKHQ0_3xSj_8v3CzeR|0
zg_Z6wrRq$#{EGjuv1X$6ubHl!DH#}tP`pE{tg@Q5gVzix_U&+=_mBK2Pqcd!lbHJ#
zQD)i+sb}-LOPgsAQI04-Y1;s*VgLZjK$M>%KwvD)uv5Cxc!@{tTm0mTS2JUoc+9JA
zUSF$~U*;MpnYXr`+JJ`DXPV(vD;~XL%mM6#Qk+F1%@-7}T<RB)7hrAL6$^EpL2Qg*
z_irhrc1G#8<;i9T)h};QB%=gpfm7Bm6hiGU9tZNS*&)cB7NvZ9Nc`p2;>BVZ?HFRQ
zP78J@y_rWa&xBa4VPR88XN8AZ|7>hN+%`Xaf9iAtG87!ATjx=*yYq;{!*7pU|K|3&
z<yLpm2J5>UcFNwtj=+3&-^CsFsad39K93bR77EBa7LYGob{W`yZlwBG0$3PE-eUgu
zvRP=ETZwV4qrP2?oz$7ER1_%_<~)G+YAyWa@loHUZfNf;nzQ&KD$;M{w>>WXi?~Ex
z6|dqe-8sJmwz*(UlT+>3wjSNwTMvKWp1Z;}SR&R{M9Dcn1M_MMt^Ai6x=5&pYj(QV
zee(P?w@&{d?xSD5<|Z#;mY3gc&3J2F0ouE9|FLe#)%w_mL9^Dp?1E+uc9k}z^TJ==
zbUh6`*3zTJ-F)C9ZrXco0TyjM=iC7Fg8&l({(JlGinSfaJXyZa2|C;3^xHL?hbAAy
zeB<jT*)<gG%Rgw<L(+e&ZYI7y*<HKWeQv_TpV@6U_iy)7mEUJQC%I{U7T6AefOKW(
zMgRq{kq#XW@&E+b>ynEvaSIRZ>DJqJ(Qwr5A9labP5Q4{I%{nUjPvL#M92n|1FusW
z|Ni&@6a!d;z83u|zygl51&{@xj-&Sgd;y?Cp5;0G&}YVkAAnDQJpeH=Z)Ki`)jzBm
zkq3Y-SX;xTz0hu?v*22?F)}f6lMeuE`lb>e*XSq#s&oZ04<XN#19{?mjuk5M1vmyh
zB)~`jX~<vNq)fd@hl&mWP!wsW>;$Gn?x)Vl4*)LoRBUho{Kzq@0FMCe0SH4Uz4FQ{
z2S2nAbi1KmTX{0K2Z%!X(gsKeX%M&szl1@bj@7Ibv`1M2*1<pZ1}VLA0V*<|BAozV
zh_fD%KR`X`&xo76i63byN|Z6O3-YdxK8^Y48t^EfAj*`wiS<|VL!8tjegVzt=u5qq
zi@Xoe5ugqIf_TZhh;~YuqjRPI&_)y%Kgyq;0UD#Pv3aQ;ct>Fsmw1RT03N`w%xeLE
zQN{p`vg%blu!u)`(G}9JSf9b50M_zE@jQ_<&<9!1pv`FWAV8w@C{)7JSB4E67Wz7E
zmv%}#)B&4VWMx%xF)vmhA%CQuF^+Hm!#GMBLkG18>yWhhm}qk^oBRWxl(6nb{sAk}
zM)HV04=|4Pg|sf{_sFV@L9`wEZcze!MP5lG8`}Wa(r)rLmE)KAVsIz@G>ZVb(GHa!
z(v6G_u#UVk=2E}NiHu3`7LhJ~jO(;<z^=$Zj0e;!Kn2#j;6ptU9xxi;7si4C0|o>T
zH)9(_I*1G15#v8~4Ila)?T&o&qkaJcl2-aF;UGYKkWQoG+roD#wPK0BgdZSi)`LhN
zpg+b|0LH|bMdXjZ0I(5oG;58d6+IvILb}j-F$QRjg7RVvpzioqU1oq)+Bcv=;$iH@
zdMAB~I%S;%z%T2J`~czsx`q$mQ~2Z&V?OI0^fib&q}|q{xISz7E8mqRP?kVh0%Zv_
zUILm6FmKV^rExB0h_VEVl|TvHD^^aWuMR2!4RFn}W0xC30260~egp$e>anuNq?ko?
z0BcOP*+B)NJOLn6YZqX>G%=|s{Y<Pi8D@8y25P`?Ow@^s1yKYPgeCkEj%Nq~0M|$t
z=|QMq@{J#k3_!SmP=<9Dp@32P<2aN~c2HqTp9NM3VVgxt<ueY`$TgrnlmUPaD4UKZ
z6pRQl<ReQsZzetjag;MCeHtYeaF6`pBX`V%58YX2b?@vi*l`%+CzutyiiRBvFDY$c
zX+OBTDOsoG3vY3l9(@+A07Y%WaPAU|yW=KRj)m3&_DpN5tJ*OHx7aSi5oYQg`l>0W
z`tH7myUjp-YnqI1`|jMM);^md+P4&m&4A$4<^aATsG~fPKwKm}D1>QO>?*BCiVHy>
zQ11X6J7R@fz3_UEqE;Z{_ViPfRwxuubdoj!78FmO`^P_C;##`zXiA8|2CBOySRb7@
z@uQ&R0klV+7Bj1kEHEn$js(8%zIzQc+~2)2?kjn$!*i5!C}J7Q=%*+!?z-!)u+tF_
z^%Mm)na_nUDLZzW=P7%NWQlS_!H(jP{sN(t<2i~zlyE4Y;_qYpohcdz_08^k+I9>u
zARfkV-l0@0sr@B@QjqTqb%r95dgU9>yreUT1p(@jcE~l0V#LiH0tGtYWr*F-Si(SI
z+tGbhV<_>U7)L<~0H%>Ko3JIduLM{aKnctO1#N)ECXO0I;mi(ih`v6|7EExdAFMs|
zy<uTN^CRk$b`n!u$MN%8*Bs-;x0}r?j6KBD3~}_4h&GLantsI*HMDO)aw#2UbF7(`
zr*oF>=Z>|x7F2ixg5}=|(2cy}O1HAj_39C2w^*x11HK6?qBDPIe#qP%dB8Z%aTpis
zgzo|M!m(6*Z{}xW9Bp*&#)1y@Pk)KO(?!aW#WC73KwFM#OTz<1U{RjF+S!CPB)$<W
z60;~uzS=Xl>Fo5D{JW+EMqIjfune~5UMo~1oHEgXF#b{Z+?7IwRbQ1CpF;_(vdz-L
zY963HXrs+Hbp1xW?>;*|vLa)m{5BU>z+%g7vZUMSuvM!m#j4t!r#ulXvH|8>boqsB
z^I(g6^rBaq0`0;0)q6g0Ph9niTl?!Pg(H05zWc=xUpmHB6EVMDFflM%c&XlQE$d@_
zj#|(?_vbg<I&Tbfef#%ym;C9X>hXaUpDxIZ`c46!(A<o9A4f7G%bjrI32w}xW84y}
zF6{bmw1gXb?(+e(hc!klEJBR$=tTh&Fz%y=1gtXppwWuTExdGZx64JFyUkzU)tz+O
zcLJ~iKmz&_tSiZCA{MEbf1-;8%pkB1U?vFt16K0T$q*-T0Wd>v3s?gg72P5hzK|i{
zPdpGuIMQyIM?$1qKrr;GfV9x}VtH3b(((-h?7<ui`MN8J`2+GFI%i}t=7Q)6IT{fF
z4c6@d$T45gT$s9}93jA3+OWbsa{|Kh9-s<&CXeVe(br;e5ReMMdp2QUL7Djk_sC$(
z?PJpX9^C@*<5JFS`a(Wnb0B3)J|OCcd2CA5A8`S&QQM<_bj>DqblGe?1rWnLCX3`>
z?*Lzst{8Arbi5ER(ng!ZdfR0NmeKP#eOv=%!=fE?uQYuMLmNhaiA5|xgw&1RDb4Vr
z+}Oy<`U2pk7yyVr06M&*Y=cEhdzSLSpPrE}z&gk}^+0=sONlfg>(frCbAY25IiP=`
z7p2eS5%1}Ptfc?|BQF4oM5GZS4)UXQ7sexw`J}G_+@c<`i1iJ|3EC+jMnF#h@8}bh
zw*X3vV~hjDL7dcq;--C4E;?2<j)(Dw_(>l>5wZ>PFzYGA1;LO0NIS_RZTKgDF+cg|
z86Ye4zgZUfh=a8g+BOy#ixOipz%3D=Wy%+RS?RzZeFu=?0F&z|-#mf`dO!Ma-ia{i
z-f2UOt@sxq=P(vQ0N&6~^PX{?z61d{MdoG98)mu&<aE-;`VnI}#~L#}GCncB0A!|~
z$uIc?U`jZ~KKdPfhV;+|WSx%u0S@NWEs?;YjEl%t7}@~53$Ri|+hM&)091@^a83Ut
z-Fd|KgfRmkBI}6&Tgfwhip_6~cL45?*VuGT9Z@cTupz*OjBC^@&jqk#JSYE@v-+97
zBeYH0%>b)U`ePn3jzfx*GSnEJ7p8n(mOxnoWeJoe(D@}G9bxCkRK_r0mw-(GYLXQW
zGve}BmO+QK0TX(>P2!v8Wnc$yi{l}@?fCKr9IEoN-7emgLgC(oaBqWX^<Dq|{k=_a
ztM^6Qv`yec|1cBV)vzWoc>{ELo6LIywN#&*a2l=&0Dluus_(cr!P=7!uFYDHf8c=!
z!nG~9dJ`zawFx2K1cf%?il((0e<oP8iOYm0Z%QQ}Kc}3V;T?yuMNMzJh1$(0=BaC?
z)s%nU6c`;AhY5q;6oTFa`KEauIpITZN90z+bIote>u-3on&%f^XWejbN3#V#v<2u7
z(`Wj%4?DaX&LqHD*VcZ%@zBE|T<_(UZ}S}9$4>ezJX>_F!NJeo|Ge98J$SJH$DMcj
z;lqdf$HtEJEA6^#cz5yi86mz4rcDp`tBo4fhM&r~mJ0**yITIzcd9qrZ5PsD0&7EY
zo8s1+a=_c-xi^8fW?02#yYReOy7UG_t?55aEB6Kxv=fh~g|r%IqT%o{PgAIQQ@Dn7
zoAPWnUP`aRoN@Z<-p|;}etY_F9X!zdxA=!1de9FYIx-5oh=1<6H$(ae_tsk<`xjn#
z+s2(r*$f`Mg`YI(xxfQHH(b9-2y4fVhcKpa53T`^yeR}i88KFy0w;=N?w<>!Wvena
z>q#M>26pn4g?^@Z@un;f-x}IYGe1+XSIdy_1_rF&6Tg98yn$B2w~hCFCrp{yP+X=E
z^0r74>d=&3)%24OQ_6Z<<O%OgNz~?@@`($XWdv^vFP?9;%BFN@Z})Z_xEWQgjGv~U
z4rOJ#k-MzSh|8?^hIx*GaYB2coppA9HRY#g?4?Z@Ak3RWw#{6JcBJ-_*VpvEbZ2i@
zjg$#}o;E-j<|?Kzua><j+C5_$ejr@@F|H^M>c<q~_T6|cj1QWV>OF3eI@EX;#}o5t
z@3k$IcvGBvQ>y#)d#O1Uaa8A4(cHgY+(XYkCxjE9zC+fpmQTH~@q1(ivuM&LY>~2y
zd35$QV}@Bp@RTQU@hwoCe0OXyu6nQMbBb%$d3^jG%}W<^QQM$Z%|})pFh)u~w8f(E
zy%WDI|9VzS7Lp(FNnNwj;SV_A0FV4=Rv1G1Y!S&D_#u4v+aqVBan-tKZh<U{AL_i8
z-|}7i#^;VIzc7Gz;=f(49pX=zx>NWqyHy{*{;?}o??+y?PNj|5I&w$lr{vRIS#EOj
z%Kpp$yP7vkZJ{m>*#01Y<SV=Quin0i-}(3J`JMkT#P4|3y4AW<oyy(&ADgzR!!P}4
zRX_9>tG9X9Ox>*d)h~oN?O51$^J*skIXMhvf6PZC{qn<YzP@u`zu}21RqN;5?+*8e
zJhG!d=D&OT-EP{zFS*fzZOV@>uWyc7<njg>2)aY^YZi)vZffW4ckw;@wfI9{-p23$
z@Rojo6)R&qe$Xcxpe^t;OGwo@AnzdD&^D%2`u^ds4)>=Vb(%L|hPMql-mI}z(}PTC
zzzlDIoS+L~u4BL{Z=3q6GKT?9f`4?a=n>&h`q7<mZNR<o<C<@u0b#rW)4TyZ!g~Xk
zcyzqHC$G5t_)ZzXCtMp)#IL;a%HB3ldi1<z6)9XJm!PvVP*9l5Dr{%CeCrKt<qZrH
z=A^`7n@l{qEc7RQ=d<FC=LnRE>cD`0!9TJT`YZ#O1)YFd@e6Z>JMOr{8*nQ4w+$@b
zfHoeP6QX<}1BH0}A(QfbM{j|?&j6Z%H)&$-&%B4W1EHgaY(q?VP8l<QGGK`}pjCL6
zl@FdVZy~?vkI*gT`BR<^d==`{04yO4;R&PndExau_GjR^kT$b0HyaQ6qkIiW<qZtk
zM&?QRQ9h7ava04mJmeQ%q?`J|mA+AH3I^2i_uqeiA)SeMvxb1qn)+;XO$1qvzD6Ax
zur26)s0TA1P|btVj;lEJjCKn#CXfz2$6xB#z;WI{RKdRiu5#NpJeXIblVu&jfPvls
zbYYB8|Hh92K|{J3H|Vnl#Pw!b-P?x#Hu9^|l)AKO0}qGv(EgCS2&*45FlCyC<DR}n
zxe+(xi+*MeE$}noWk|Dlr7q)D7T@%l%200lKE5}AS0SEp7_M1+O4FC#Qw|243wa}t
z$}cWuU{)Euf#znbbHzoQVjNeQ+Gf$fi~5ouTssCmq{YCLVN5~KZa}=?$4+Yr?+joY
z+6B+~E-5@?IPH*nL2ro;LSqAdXrH8m?+xu%;pCDQ`Yz)x?E*bWT>o*KHjrT8#kh)J
z@yMzt+KK_QLw<>0erd}F*z*RGt$wTYPA=tT!0u`oJ!g%QyxZwI)$g+6HZWm`i@qcO
zsms`GAj#k_{-<#~ewJQqtV(&7_sz}4%W^Brt!x8j8;IKgdT_pFtjEO9%j>e86lnwL
zH#_gXBE9FmwdX}5EB%y>vR)g|<P=2^N|$>6i@vwrE#7uIw&9mfP}BROyzytd;X>~~
zFz230x9!9U3JVljTKqu(1$e{)sO=646LSJ8F59hIz2+H;6)m756rc=1!3NnbqBefT
zqa$6j9_Ve?(rodd5MsfXxKw8->ly)ODO}9Mc6|9}1=PHJ$MFC%L17mIIrG95eQvv}
z+P#n&1NUCDMfAnj8xj_Y4|%!L9=aJQaP|H656=#N;2}TZ)mK9sxWa&Yhrjc#|K<xX
z`V|f2Sxs{*fUg#C@A#?H{XX~H>le4h*>R?rV?p}R|2!7>Uj6&u`%z!n)nEViJK_1r
zi!brJo^`fA(fr_VfD{w0@l@lr_K;o!60)7qP4#Ehos^F;;2@1B?;gP#z*F9PJy%>P
z`-n&PdSA~)sb)K>z3mVUN(GdZ0xHxCtM_r7W}PV1MXb!q@`o}45X`Hue&{EDWWu_E
z?f7l5!9u3+`=j4x+k^ZyS8wE>d}2HQ_+#7p2OrqV-tQlN-1`}$?RP+U{@8!E4gM<c
z;qy&DdGaR)NP90VB%|;{aR7*8Zk}|;<*YKza#2`O4=Bj_F+M|pDN#aE7V$5uuCn+x
z`;0Lc#Z$B470!0XhD8k93Ew9D<Ox4nd5iPO!iw!$4P^mHm2y!T$)!zdF-h^pZqYhu
z8)o+LC4N6AUE3zwP?jhuA;y(CDSe%t7Ul?yy~M-#OZzbkrlFlMuVH>C1%8~CX4*v@
zzPzs8M=19cIBG}IhPfu=Lf$->dchy>S)?(G9G=BdNOQ!Zb!V2mI;}6TpbKfyjP@{U
z>;qxm!Ul!|pLsT1Yi^acm)Y{rI>*`ml1oE4=0D8En3JVp>)lgcEEuo|fnOGJ%_>f~
zo-JN`UyomBzUSMZG?DIc<Hm*b0Oq5e5VziUW;z<>&D@?vKb3J_c-k?b13Q-9n}wNb
zStFmZ&__7hIh%>NwxgbA>o+Wj>YJ%}kb?y7*E8B8K*)GODX;7R9}pM)RC6rG%|>J^
z#ap_cZy)OF@(%-FC;kJxv(%Rs4!@AM7FPxD9XWouAGF`nm3~x$_p0Mbm$re{avyTc
zihlDSRzOZ$4j$@9|7B-??8iG+fnWmYEw|mGe(`mCdq9}{mfm7vKX9LZ{?KvTRMWrm
zZcF%K=M3^amu`GRD|wH{xz)e1bP3$EGRbNa)X9<W4X?iI*2^|eY`>WAx7k8|=vk|`
zsk?8zu(jXgmW}+Ve}BpMSfa(Rz1+Hf?B5^si!M^xJPR<5ILx|G6%5yX(Qf{Tx3~A-
zd}f&M@rAZ^$@g8Gh10ZYqa^)a?PlM5H}}0<FMrCZr+9RB0CmuF5*DBwAP&A^+}n|X
z!6gpOE!mJ9$AfHO7P&mSG~`ME>jDEJYXW3o?kRZ!P>ulX@It?etd0(bO$PY069GaT
z%mvI+SnvbD0$@MDKLBnWsRxYnlU@VpqD%l{n3cA$;nA!}g|Y**1Xv{v0|*NF8K4St
zF?7G=6&*4}egPwy<*ER%1DJyTnYlRnvS0n`R{=apeXxNFnTu=ULH7(%9}v9IBe6MQ
zCEMTws0yGIeyLwbpbvm&5St?a%aA73aheC_EdXh10rm)^F#@m-G;-v~045~O%$K#{
zE#;ryiw|iwYi0rPnU^-gnq|BI9HKp?>kZ<CKLG&E^4)Cp6sLi<hOQGGz50{dJo<Ww
z4Uch{TG!I`t1Z{^o4zyahGEXmywj|Hg?h@;PsRC!#~lXhWZd9KJqXatJN(A}&H9`-
z3yt2a_l3LxuBA<Ay(3K%bqQDtKp5i+x)J8!@<%<y{McX*@C+cF%29q(myKF%C}J*8
z-2*PA?rEPg;*f^np1LKBezbjL9rRZ!H+aN9&C9d+HTw)XkTR!yr~}fWGyz_=(;7nA
zP(FZ2n~f(g9AizKM_p3}b`*Q4U)ofhF2<xRe#nS`O(Eo=6u?Iq#vrwCfU|()#EbDz
zWESeG&v}o01Q3_@1AvmcR$t>i?S+0y*-7UHz*xM=C+(W=4MctfN=@50?T0qWW<UUq
z;t}@&p0R-)9*n=N?U_~4;8))xHsI%Nlx3{}a3AFbFST=IM&wJzUCN4giWm7CqTJ|9
z@jI^hF$bXR8PDmLB8}s^PhG_?KBb#IUn|R{ESItklx?8dHo!cv$OhwP`Iq75-L{#2
zv-9pt;9l!wsO$`#-x&}FQS#(Xbc@yjf(MES{4+pmw;BSFCf0zsSY#PE5bIPf?1oDh
zHH*ZDFu-Du9a9kaAdDhNqOgz!6Zl98CJTIYI0OSG<OppjR*2usYzIClK2SoykKK<b
zuuzb(3rdQyBI#?^4?rCj;N$QT9^z;08Uk2|q9uNwbq(l;-LL68@<n}-$Mju$?-9Td
zsMuAOT4HGrZ-7}SBl31I7UeDA9&KRzR_lx`5Fhf&tJSjTJ7oQ^aC~N~^*h@#>p!#h
zbFSS7zXtN#_rCl6Vry5FbHx3PhaU;=vMlZq=Ge(oyd8k&x3ltD%>>93?AeJ=KkL`*
zzpU-*-q`=Z?svQS7T?_jQ?s@1M_m59YFuAvRSsw6QEddl9%2VumcLrh!~=mA5G;MB
z=$L>%`bf-Q{NiW2);k1mJKCafJPJ==WEY?vFBTN#F;Gyv<u}ijKXwwLyg~VZwXQt>
zC>>EwG4|LID&}`*f9)TK8Q87@+<W+;t^K4=5A^rlyS4vh;#aEA^}d!nb9#k`pRu<e
z`|pFS{&otWo4mBljn7d?q4=Pz>38Yx`RAVx3-^rAd@uB44A=J~E8nWetat5w#<zsN
z&>lV=l|B?glmp*ul}+qov64lHtny_6BQFnm=@Ss{#1mGrHa*near+i?<nNz%Zs{-j
zZ3O^H9#QgA7Hk|yHxKYW&2PQ?ymI@zpU3IWyC#h&O>x=D%;FowJsT8IRB|1M=RWV5
z-j!~#d3i^bD+{B1e`wqEc@|q)2yLVuP=ssowMe))?eTRQ7C<52Ll%=zsPmq=4nO8H
zx{l{o_yK^0Jiv9zAFIaSet$wWY~S@ORzT9Y>)kWArQGBX&=?D>%sKRIPIc99Ngtp%
zEThb+X=Tx08=mT=kwr!Tda`DN{DmbU7FwA{3YdqbTNY+xek?r208!rYJ!H<ncTaxd
z!y+YCC9p(<^$NYK=caA3p8xWFv9WMoET`}>?ZkgeY*5*(7nj`^Hd(L==mFR>@N1Qm
z4(u%O!fia}S6(++Zms1F`0l6)Bm7~nZ5x1HW6mAz$2?K7lm>8aiS-xo3w^Oi)oR<<
z{yEFKUbpVf?|JJ+et{Kc>2WqR%WvCUux7(`$u}Zi?51AV{N}QutpF?x^<1`*YPr&P
zj-R|^u&9^z0YG>CHeJXsx#5C-;Z=M1z3<qh+UE{>%B%yKCB;*wj`H1IWi!E(_dgvL
z?FEYIu~>^g;;rrd{*P?wd-k1Gx9K81^`%V}=pkYN`U@{VUybv)Pj~cN{%keh;#z|4
z3t&t=y&B&`zGZg2YsfR-O=NEJ$~PB)9^XmywA?dSvLhG+I1N91pZTUUuS4g-GeA-*
zE6EY)Aps{y#v~q2Cjq<%=mQy=Jj!C9!grPnI1msX{Q=^%4D?I@_ed9~v;cfe<3~@*
z{FwO%<pkje0;nPo9(oKw{cKKW{z{nvPGo+@CQ|Z@zLmMU=G<6o0xStXEbdW8_@j=9
z4}y<Q_TZi}mF0NmLG|YD=)M8@#h{urkKAj1!<-0`m3H1SKZe-+n-w;GCO+t*A?920
z`}mr+O4|k~NF4DyuEmSfa?t6qmH?<U^{0F0bO0!^9w(VY?{EPhL3tJyW4~MjuwxT2
z>j~6Htuj<v2?xN7{zZGCegTZey2v=5I2<wv;fNExBjNbP{^M(aq?{ZCQHG2&_!qD?
z_KOafe##FbU4)k()?V272`CJrt&(T>)!M8A;EJ(;bdNKj9l8Uh1HEZluL8!g4nP>v
zgN_fP%_+}v8IP34W?evKfW8!;${}?DZb2;Ov)%$BS1T>Z4d{+&g8;GsdtpH_<rCji
zKg3(B42$NAI$}*OE+_6G>P_(yUktLP9<hqYSi)v!(ge7Y{-U_4GyI4bIz!&Erb0i!
zS}WjFJEprDMq&Bx&~~tFM>=A_C~1WRDy45K94=!XaRcIpv^E#}(Y5kQdr7V3%MUK!
z7TO_(JoKFYM>znZ)3ekSP?7Hf@AO^f8wH3H;(HK>i_1+`0pmVRAAmT)igAZ;4V%#F
zQ5;8k-Q--{>aZ-kvJA>LP_}`*Hh>YKqB_bv9_91-vwhY3hU<L^+$%rCT{aQH3Prm6
zt(gIeNk0oHgk{GMiW05?^c;Qk(cv25=*T0FtXej}Pdxc=Bwj#VvP7Y9?0!VCqWjcE
zv7%=voY;-Y<Xa0wC;<>CP-01;!2*(YA;lAQ@#!cx=AC!V@d8+Zf{MnA5&}gC{w1Jr
zjWDlzF9HB85}(qIV8pRl0%+=a5gvJd5Qq`t1j1|&PXYH>KqS6h{&YiFB>vX8m#gIp
zP>x0CpH82laoOK&V9`O_Z0lFF9cZH-crd*G`oG79@EoxVkdH7emex2QTD(5;gO94u
zTWm3&=X>1xH$SkYn?L7~N5lOVKmA$oGs<=hKk(Y?{tSyBf2-JT-<YSaRSr_bu`5{b
zin`)~GV<)R&z@~@v>qRnMJOHX`BOajV@!Z*_17$Z0BYIEO`lD7r)K$2pT%ilXCUJr
zO6Js`?om2oxrH$Rg^r%%{_u>-QMogw0lcdmnHvClGq3gg?$h7D^`;$%F!OJI<cKBw
z)G625^-X^G_I=H@BT?+y&%E2uoMC4FTfu+0H{K8}?AX{8$2YfJqoiS{I{lR&ivTFT
z0PeB7pMIDAjyU3o4pDk_WEph?aIYiM6Svt~{P?EPmeaDOjnhX}AGj#IvXpbP_;>UB
z@7Lc?7;hHrqV_o_UQeI8hd<>9mCXQ3Kgvoe2Pqd8a{%<|ee9xSi_7@7+iuJB7l)Y>
zt^wa`cW}M*=v#+kmIV^#0`bmll)3a-7Mo&=6mhxKi=%wsHG(aR3aMgTWuI4Hd}~>#
zl|S6nZ+g#MLY7_PaPhSOW-M-KarT%`Cx<ypnD<Pb9<JlL)zbD&z3j5ff>i^7YvZ`L
zzTm=H;cf2IF9P4iaTmRYxA?QbgkryF*g3%;)^D)1N?MeUIU!w20}Eg*MC-nuOFN8@
zKx2`c<DOzbCG$cSOT;TH-|;hzZ}KmfO&*N@y2p*f#m~#@t{K1Pm2zfG?M%ulr)7pN
z5&%8JFCJXAzPHNmt()&kU{;%co>hJw`3zu}tfI+1`r3p2iR1V3gAeInP0N8#4)rYy
zR%AK+VTFx!a6RUgZL9uxw(<8?_QNk(yUnw_G{=k6EJ|Q$CidH{r}JB$IWWYt+P)R5
zv+?`+`FgRzy+XMFu*)h39ed2X?u&Qxi>}ej_gu=3zBTKJs%t=eqaXXS|Nhjy{p$TH
z8*W<`>J}{doig<s{{P)mu^M#9p@&p0$^QD+zYa$f!WaFR=1c%r08aqsK~7$JK)-4{
z#CQ7C(f)=@Z>px1c@uyIbb`|P04NY}NV-u01JUuZF<E&9l)@%uEYk_Vz=mtS#Y!8N
zoB*~6G=|O<VAV?o2+{XmV7GWt4A5K(<Wanx{q03&hlr0fA|I)r0N4Xefi8zKBmV&Z
zl{UH53q)EW0C9j;keSh$UVZh|Aq;><z*Dj$2!IPY3SblYpFDYT@COfg1BihDzn~vQ
z9}W2LxZ{osKo9&74*)U|^J=9l%ccDQ1_T_ay36v%GnKt`g#ct?zsQ6D$w*)PJiZnW
zo&oy7I$OHgJoeB03!=@WK(m+^7QmRNslCQL;%k7WSoaz*U_iAEiVvGCDGOv=`awOt
zHhQXe-gzg?tpQQ0FUIldT5)g>NL43v&}VCb&f;)C`N>a$9*pw9@?*WcvRRX3fB|u`
zVLk4jN|#)8Y>XWcU_E%#mUvG;CZFiwkRwPx{S~<)3-~CW7&IWC$}7Ancf#SyA1>_x
zuoyrwToF13%7^sutJPLwe(5#x0f04llaKVrxCF7Go4hH$*d^WcU+Ro&$-|6SX?*cX
zM3f&gW6UePW~>=MetZD6QC?~KxF_uZ-jIFM_;eq)7d?|3mm_1jfWGi0|EyKvm;4|*
zktW(TeS-QI02@E_5Bxxc2Usfu5AbH44$|74(g07&7fY)E_^=MBcj6ZU+CRzx;|+R`
z%NRx5VeI|jgAYQwNPC<%{E=6_QINuCm5q)GPK_0iM#`FTN8w|ab_ro+lIP;hctyVh
zU`QTQ5HET~2+(dEj<Ex9H~B-~O24Jf>k;)5r>nd!Wzez>lx?7F10CB2*yx@olayuM
zv1L84m1n(gxZan*J)20)Yn4Bz<wp}$ls{7TbW|E;@)~~Z#6w6*c`N?(dA)muLrB2V
z2mYCaqcA~`kw6J3LDvG_(ZLYH2!kcSSUBSuN&$|mMYz+^5r6^n7D?-+wWHo+kph7L
zp_{rQpO;^Ld125aiYRshksfvuaa02fWAH&y!}ITc_q%Nt%9>3(Wlnhukfr)+mLK6z
zC?Qm8ajv~Q1>A#oH(Nv$u<oc2C;Ih{J|>h$pEcGD&qqJ_kl%0DT|)fN*iOAoS{Hqn
zG3y_z8+h#OmtXbYx5ed=S6t~2HJ}~5hF^MF8}Od^3%J+Ub~vA5yW%$b{>lD`_s07%
zS6<~`Gvy5aj{WaTe%~E;@L#k#d({AAm$m};s>kJ8npKu62Q0yzamE=z0jPIHT!qIk
zfT~L_xuhX;!dPs?1ipm(m|q&d-l4DpFpIMIlv7TbO)yTw=^h0&fD<TIW{R);)pF$*
zVIAd3z2mP@$YOyIfFH_2c#8+uKRwfSYqo-#&OdK$KkDnt`pJ`iZi~=AH7hdbgtYSS
z^wZC_zFWCH_0(Cr@J>4Edj{V7NtFk@ZvOKu3qiH=@_D?A^RD;-deUy<AIciWO~zRL
zXrBT@E9{)^&MCmXBIT4-H*q+W#;OZkzA1bo=?B!^rFI+$;|SL}%Hpz1*SEI0m;dem
zt>=&Y_DcTPV^*G{e#aa=pcOp3r+@C*=-8_S@XA;N<yk4?9b*eefuQW?+t0WbmtVc|
zjs14jHH#fAO0kfXDi~{}L0~ETC|_*6V3#~feHM<GC-6<E#it|QDgKyO*0cD1XJ3Eb
z@z={Aiw0P2V57`F`|K0uT3G$iVr^D<+PLPvEVcqxl|L3_bwnx8w84WpHVZQHm*q0f
z$JSqd{i7fH<35`j<}!m07#&>ZHOzfB|Iv^AR%iayk2Px_kKJ>(9slP1ZoBVZ<^MH{
zYxFNK2*>MkRIWBhv=_LibOYRF-W&t|^=?jfSzu(*nX*<o<2IUiEx(=Z0`AlzVIy(H
z>1IJMYrKj5G{QT}FJl^u<XP{^XS3vrvMgpVi>!9YnA(|?R}X89UM7HgHaU4^zv(Hf
zgzGQv+3}Y1^Wra`n{_I)v<IMShbz{#W89tZ*{{X#{7tjkv(@Il=NG!0USU=n>b<;K
zU9)43DYxFsb@z*`*3%#U#&+S@Rls0zdA2OlEda&hXL;AOJ-q|eA)t_+;jVM|av`1T
z9=UwgZ~T6~y|(&j1DxzOtG-kwC!Tm>=-#9D9aSy6UQ2ba){g*uxCcMKl|SUAZTw-c
zZx?`>9BsRF?`1-`MHgGNda?z;fE2)__;3L*Ku?W(GNey&?0eVdzF)Ul*2)0fAfLid
zR-OP=2#gZ*jIWUsAU1C?4<u~SQ#SyH0*Iik#XO5#$Kxm9o~*W7gijGa$TC=KqKu@s
zAsqQ-F2#>J0N^697XAg~q6`Hj;Th#hc;?$2mB?ll2yhFVT>(ELQ$ftZ0b@|E9RCSG
zk1_+`!ZWV1aL6Wh2pLY-$er5I%DYjcX0^Mld{ZBQna~Z#bMP#`Dt~lYP~L_w+EZ-#
zG3F&6Ito(y_t=lFX`29F9)JAtP_FRRJ6!QX2LYv<D`LM~QzsA>)!4kQ_u?a<5&C<*
ztL0Mf04K>SX{8KP|KhFZfOQ1Eq>WLRdY8I!Sn3o&8N|j)<gHquLiCOhvN&x8AW;^;
z4m~@30YE`m7stQS!aLGSUlTYAURiln7>+xgG-*<mC-ZW`kXQ8s{4xe$^$>6t04^N~
z`m>+?EQ}|-XFY{JC*Jj3)(il$vN<0j?fjHKp3zrG3u%gfln-%cf$-vZDKi<E&~t#&
zT+^3`k3Nf4dw^f^Q^ci>$7zbMNt>*)kq`1jT=Ws@2Des!%8DO3oj3p_pxc5Nk7B=!
z?X+*ec~hoL33U~}Ctdt>jBae5oG}o&6H4^}#7UV$@D)$SOZd^oSzm%uT^fE#7i&eN
z4Ztbj!gP$lA36xyR%%UH@zLIi^A59=NBQbGF6|uBcoz3b-tTXQLm>2C+{f?fpR&xD
z_7~bUl-EA6?i7>yM*K|IGM*8qyS$!#E>@OFStey0DBD1@ZGiEdxmP-tH_NXKH~+Ln
zeXHxaC2-FsQDw*I{4xO0g%YA82eWwcs|7&nGJf9Px<#18EIL9MJE0Kv0E-|@V95ai
z6QBq}9Lf;Pi6ejjRv;Y+_Ymnofg=DP0tbsw?8uV!2h69-VqJUF-E4S-G15u85mMMK
z3ecjSwL;2)1zqw(IZ)4Mopn|Kp#iRBp-ekY*{!JKAev1l{5kRiAqpPtt#gz$)D6m=
zEa9rzbP7lp(ij2vcn<iE=iB}EqOj<E@MDkr!@n^q09<G@C?*u|&>`#ji>zI3d+KS`
z1?uCbObgdNmzt$yReV+8UaP?SZCgbAnE~@ovUty(Hr;>6+$)}W*6(6w=hw8h{2SZh
zdx||Hd<xuC*`i!#F<NOV>MA__hrR+}NA<6FDX;js6mIE`wKNR(3dh1JU_XwzrcGw~
z&3Z<gg#hnKF(IB={^Mt<Kl%gX1t6W+UwTbF;|GNY?GTG_sh=3Q2gr%{haGl?`ClN6
zHI%Eynvj9ke*gwgIpwtAzXJT*%j>w@ISsg%mLKhnzKCLr{6Sa|V2tHQJBk4YX_=+`
z()V-HJxZna9+f*M<tI%G$~8c=)EnPY7BAj=@4WzM{qA>v<{y1z_W&y0aKnXWYx^m$
z&+C5i#alueA9`@>HfdBlW77cbkufCi_#NsPAQON!1UwY&0GZ&yIENyR{E6r1mDe<0
z7L3&A7#o=zq<-@5^YX&n3a~Q3u$_0_xiG)Hc#1q{e#N50?grvw14fZBo$ZIQEsi6v
zykb9HajkSPZ(~yob2#RxD50r0+ARw3F=NJrBXgJ|vJ;=hHqytq4Uc#p!(uMqB<9P6
z!yoek%?)eK<Ke?NqP*WUW2Qf$b<VQJfd^IRMVbShXzy+_Ki_})S-+9>E9N>1yQeAM
zzx(`)A<VL7`Fi_{FA3Kj1>ED<BNj_iD*%drPIg(KpzfH{>j*Hl1=7vJiuz9cyesmA
z<rb`)Y7xAVIGG2=M;#HC%|rr^H^Qr)U*?~h$8~iX)k|CXzVr1R?JZ+A8pUnAbvoZ^
zPF{Lf7`b==^=$sbRXu<#z&)i!wXSbJgZ5iGl>Mfst`fdsyM1LhztM(mk66pA!ybJr
z7Wnes7kM7*fA~gc&HvW@cBOsb7U4O+_yJfcP~510eJQhS=<5K|RnGcmJocZ*LZ4r9
zrM>~gM_r1ST!5KJe>l=_`|GtbdFPcgpc&$WUYhZo$66!D^}=`LW$QG=D~*GBAs_~1
z9Aw+{eR{9_@*I#(Jy1*<Uiabyhz{ad*P>uS@hIw|Cly!@U<H5_z({~dvH&FFOPP~S
zfx-kVME}Ec2&;C?b2t{4c`|cq^ieShJOZEzfDuF-5VAFG3ZM$lC<D@qeu;J_kP|u-
zNT-V^jj;<TkfU^|%lLWLwaOomAe36#Qy9_%aRjHHDXd)S{bGyB@*BHs5`yAqx)v|=
z@emgB^e)c@*aXiQm?ppIwjnm3>sc+Aa)z*o75h`V;%DgDb!0JMN5D!&*Gt%7EFx{R
zE!A-xM|_QcK%TUTti0k^<%VB$0069H^^v;6+86DYK2DwT6A)E#DX#)21HOWod*fH(
z<X<j4AwZ81Ru|z#nGi-}rToiPe7eUaPJX0UmIdKSKc$?M2F5M&r@k5U(6#E4egwsQ
z#h)?}rO%Xq;$)m--2vGIlHb_HKe8l*tjwB65d#?HfieQbOTMUQZ7hWsX-wlGF6Fb9
z8;?Z?9duBeFpM*lDc=`Ba-==x#r%PK7buyuAaCLaU62SpO&pFn2W>*>j>ithCgKHv
z46z0mhmEgEQx-TCKj`fkJLNyCE%A<1H6YgU<X0eA#t%J<!&6RB-ue<2`yle6@VK;h
zS!TsDW&BR#5nxL6a<wQ9S6+Ah9KI~`vdqgiFn_cG#$&#@Wqr>d_1#hRT<iNCzbk=z
zHZkg`LNEE`mn9a=iU=D;%P^i0bDiGDeByWU^CH&>511uqk&?w;j<DpoCKfOe4r0Xw
zN)~t`fNKXMosC6Ko+BK~bUD|u#sLVBomniDVd11mx;omA%7}c$$9a<edI|v2f?03n
zlY7kR15QZOS%g;_CcQ_n!KxESG79LB`f2nYVG;spMOc+bqkK~TTVMFAupm6pEH&~@
zi^@A*{d<4-+wb^|kF5aecz(<)ulhHic{-$ne^{#`3~d5ob@$ezX$K#2Xt16~nB8x=
z)pHcC;uU{@dp#{I;84+Pwur6w7aEA~Mmq|&pDkv8|F5_DGwuHP)}!WP;GW8XU8?My
zRy;*rg-5Z7G8M&?1aiI0<AsGEOu?fBl+~VEVf2ojWhg-fyo0~q<;BHw`ZY&aVhv3G
ziiC^#)2C^ZC<sZnEDwoa>~c&bz#WtTD12iOS{ko_djj44>O!-=5bfMY35KE<#S7pz
zh;c~qopAgr0d#cTwHpU_PO!L_=8?3b^rjwAa&UwI<)U96Xf%tD@;$e>D5U0P;9gn}
zfF%HH0i?S9_J{myubNWJtU&(q{5`Dh4-0*Wew1gqF6EbZAGeM8I?sQt=PRvbN_b0S
z3fv<M3q%}W#4$55_$cO=6+hoe!0{*^S*(H59miSWV!oYyoyHG<TE}NmM_3?eG!5d(
zu6E5g^sG@{jrb?M>^5h!M7kTk5k4L1_r@D<44WR{sd-~Z@^25XygFg5!b$^kR_1*u
zlvyYwFT@AP570c<e8*XQjPu6sc7XM~$1)j5gDIWN58|WNc%G)Cn>}NWqxnp5ZT<p~
z?`)gPoMpeCFsHHm)9uHmgbSW{(iWx%dcyWFr3!PWZ@%!NSteTEkNDjc!S7OLi9zXT
z)>T?*U;CM*WcVOAFdvzlcD)16;~Pxc<Fa5*1|YE!%bRidX0KJQEC3=iHOdDtovb~I
z7Ym*OCpOA2_Ah=s(=~lVR>tG!aX4KojP6S}UzadHl+E?mU*Bc{FH8(N@n2q<uDC-5
zxHs&aL7~5Gb?%@5?BRJ{nDTkYx7`7|E)mN8%RgT|lsj@#tb6LrG6zV7C6{_}tasE3
zA&qN~saOrG=YM{A9|QLedU{yha#Xnh1{*SD$ZR?UfOSVr7!mmN9kM_ubAkAd`DkRg
zr!K^&mWw{7cdy=lnm5bEpY7<6{dk8E{wp^$zo`ZNTnv7w6@UB=D^yT>7rd}u#N4Hp
z?j?@5J=a*FLO-1H;M5WHcg#1?@x<@ZO#wo|N*S^u02=|e$UjyB@sErQF;8ZWjD859
zon&IknF1NbuCC=Lj-PA5T`_6nOze;JaSz4M>s@0haHpMi3NmfpyoULk_@i^?=;fF%
z;1)>p=lETET@P%;W+I4rYZ^AbhYw?2{7l!#<dBYfE&^f%aE66Acw~XhkVPTJR@FaX
zCV1zSo6>{JF|{J;N0Gs5rA;7Am04E0;7wVHNM{@`N1}=VV5zOee$s2?ZAeB6NN3tM
z=|_|UKgt9^DtzG258zqKH}4(`kt$bQwKeg=%^M%CyY9M>RzPh^k6d^`+(SAVTl~mV
z(Vx;TV-D0?R#^NYOCvvDd+oI$Uj8XfwOsrWMseX%j&V8Yns=0w%1wM}E3q|E{E=4b
zkZU&LLMju<?rK|l2jBR(Th%|ha*^7Hj@afox;5g_kMy#B2F3h%FP`xxc61R6E0?wf
z={|M=%|e`v5kF6_1rCp8QPM`AfMQ@FVU;#Oit%Q~m@oN(;1fURS{7E*=fAKMmH5V>
zm#anU^J)0<epcHm%c3lcvJI4NpuKH??^w}I7wwI!3_tI**IM82_+1Iyizb9+2k5*G
zzye9F<F%S8drZj7au?y5aAR7UYXCIyz{vtio}gAMZUh7tsR5I)!%F;Xg<<y;i|{Bf
zp!lc&7LN#vsdg#1SiA-V$Y9S=bi6MjxB%fm9>3R0Lo@HF9|S~57B#p>5UIxxWt~8W
z2oeyBvD%TuPBC4hsG)99JfMJPM_4`ndJpiAg*z!Kc-|3Z1$aV>&du=43}b*&(s#@!
zlft5Kf3rNuLh=q*RqB}Q@7eFq|F|<e$Lh;EW|8J?JN^PC1_C{c*yQ!;r=PAK|H+~=
z*7`<2`IH@t)6%Bw_PFiuffq;gF40oiy&Du4Rv$M_pXpDw_=xv>v$}V81mxqHegOAa
z+*X;h3yotVbl<2;UDGD8vPD}bd}et?apDJx6$VAZu$YXZ6Xh-auV`8U5XE^c5|?<(
zFAKFO1lZ+_61rBu*?aH3L;e8nh!3uK#m~4-fqMXmHruQM*u=Vzb^yXu3O(`IYtN+u
z;OJKuu44)hYZDfh-<)7^4=ZITMX4(+b5b4vpjfoXDvPXV%IDndqD*64BM<Xx9#ubC
z`MdCfbwZnb@rB=7KYYSJ^2j6M$GC^$js8g+;m5Uj6>*s-uw%7|-&%fZ0ryBF;|I!K
z6u*FI^LF(r4wVCW)iV|_Soj6z983TJKmbWZK~!MThP>)KtoNU{t2DDyoui3JI~Iun
zXEmBO@giJ&-6)@;{<Gr9dRCNQNBC3xv>mRYl&}2oJibo-b=7^5G>{MGF3i_B_UT{$
z`d0vfW#xhX93QC+XhoYAYUOD*;GWH4u(AiuFe@R~*}Ur9snh)LZoJ8l8v8()-+b-v
zdwriZ*7Q65{*U2)RqMAcW?_M&a=$h1W#4De8ncyutu*AlOUsCbT;>MMeF0oxZ6Po0
zob;TzMb<_J^1~wL=aCN<u93ysTTW~w6OfW|mgAISo@pJ%?{tk!$zm(_EL1V>>mBnN
z0R{Dpxmg;I-U0N~Vwawk?kqWEekqHrb{7V<PW+cwwkdEAf7|`vS^?bCf9v{|wDS0t
z&l_vk0`7h3$E*77HXB~u^x0MAnAIk;;@;#3mE)9G|3>4$>MVZq!n0<9ds$@;K<}7|
zmG4cpY+5()afG!1e8HWvQ@CG#_^cx>6$XG3L>w&W_gT1)|6u0()$m`rzGBs1{)@VN
zzo6G%d#(B%ED{#LLVIO2fm-scofk<*d;BoBgV@x7WnTa-S^VO91?++!bac49i{EG6
zXYtH>hBYsA%*=7nqfsu9(xrIrFbiH-)Q)+ee}vNbV?XIN8+f3USL!dmXFi0CA3wt)
zHyg9#XIa;jT|F=gb45sC$1FZPgOBPU^~1ajYQ%arpgk;mrM$SOd_-8Z<5?r+j2>O2
z&D5&PtoV2a7!*R+o)_0pvqBzIDudBZMCdy6;^Mj5n*8ShmGC=m+&Fm)@}kmLFHeL;
z50OOxW9#uj=MPcuDFHsi0$L+u(${Tsexo#P+O+CvB3WUTHcoG;Ro=Xd^Q*FB9V{g_
z^XKtOd7!&bDFp(?Vc;u3o2;hly(}oEfHuOAKT&MKkIjLQ){fG2(+(j{UrOJp3@{)8
z$xsIV^e(T@vpxr1b=6g~@l`zH0XR36gLSko>k#rEyYwxHb<y~_c*??J{Eln%<<Ko=
zbV2U~Ob##V{8O0HZEXHsmRDI`Wg95lKqGB{joP)=fE(de`kg=8UY*Z(^>qo{v&m4|
zDLTIln4k*S)t>T3Ak)#8fax%&FMe1{(*iOAZM-9yol*4`(!~b>gJaydXP1+NPIk&5
zV6bSUU7@6f#VzeVLP-L#DD>QO&xLF4%3?A8$tRx-LJ04%7$@O|xL6EhN2St%6+6YF
zXPxN+Tth&5;)y4`0EX-+t+j(s=>b4_-+lK5>p5ha#afhc<Ox40Vsz9gf*%%FSn!94
zLwstvECK^~!YUg6B;?czQ{)}>Ct<QZJk=J`#kXTVom>UQ0jMKQ9B&IyZht!prSJM1
z_#@x{z<=$od&0ZJY=<MtD|S&qu?40c1|%Kx`WqfT-E#ny@IC6IkNvV6Z0O%=)k(Z)
z%8Ap>0?mD`U_C7CUGd&{KkC7U!td~R->ark7WdfcMmt6LYy?~xmpKcY)O&m!V7)S?
z?;)T942gLaxn^f_e2hiCxZp{AQZyIwpOp?KX!LEAiRli()IZ}A$1U7-*Ik9%v4DF3
z?w);S`v7t}_?wj@i5`CV(a;xAtO0t11VY?&(+z^(x8JIO7}r^6fpGtM0{0m6C<EFS
zR`lL|_uWwbEG85k3(_*38}9Y}!-@hx0e*~|xG4P@EAtlh=Z5^XCyf_hG{oBFUViA%
z*+JqDKKNibc8Na9@0C|x3EvNuU%hxaLLvsA)bpSBJ`cDTj~OT(IU)*Tz5p0L{T}4;
z*K@{Nj@w{&e7vZ^+y_PFGRrIz<_%n@3PbVeOgGN2;$g0{xmj<GH!0{{>DHcKt2}WT
zzbP-~U$k#+c41>gBZ~;1cbNk~)i;%SEc0Rjv>F4IKINTz+B0pOV<Pvr<9OvK%U#dr
z-A7OSq&jC|&ch}M0rvoYZ)l5xe{S6fv!MY?4|x0uf7n}ZSHl4OTiO;jMqYVUc(&w5
z8`q38D;-6i#d*X(mUv}#n0c^v;uqo7m44!MXI-<9$s#0mi_FwjX=CB3J#rCYv68?s
zLs~fH8^Mum$Un3}<%Kyta}`+%r%&n~E;6MSX8>>i{qKJd@3YDa|HPALMFx4OBLInU
z`pWCtWAEHjK4C)9iT`Sq!x#7ZLICu9=|`(p+y0JMts9<wVdDj>&uZ}~-?gtD3b?oB
znFIayTjm4z8l7tvsTZ1E(QniyC$AjZMG4$%Ti-1UR*ty+<}<^Z?k^`85N|fno_$lY
z>QDem{LcdO?HsNr&HOZcuc=eMYtV$5<NbH0?&{y1@oMn1+f8l1b6Ii3&uDl20HVat
zi(I20jW=Ky36uKKk*}$B;M8A7-y@6ZXhb$1#+KvIc`{egMrQQCMdD}ffZmMyVM8DK
zSLz8DOH6tWFF;SsWh8S_2k4+UwlTIIr|^WqS~h8mt?uclTsEBo)&Y#7IZu%^%i0qs
za}@C}Kg_2f0a4h%!*Q8<pSs$74=^qdw5PF(Q+^<TEh(S)p3_VOpp2g@ZRzIP*iU?|
zv{CLUXpcH2UCn?1iG%e8h;%~I@5S-Q*Q&ew@4r9zuVoz@8B?TFgW#n(EXD+&T78Rl
zNB)uZL^t1jbDMG}P56U1Yab%yzBujaHR+HAGSw;NAW$0{9MdqQg}C_1Ivep3PCv#f
zNJp1aKl%}#{-!#mtw9Q_xWmKf-}&dCAJQT|^<4O$eDcYeVaO9YB;M0sA;llNieBZb
zeB;8OG^l)Ji7pP$HALOvKMv2e+5tRbARw}g_@*v;E0q!YEcnQej`bGUF1Dm7OPi!8
z1^_e8LDXf;3!YkYyTkO~+^g*?K3o~3$O3i4H|?*4VLXR)?0KzqFm%|E+9~0YGa=T~
z;<V_RbwvmOsh-6y`adXs#<j*JHaK!$0{61#Kxw&^_hmViZJ=xeUDXEo)|SA%vb{CZ
z-ogjJz`qi>XA_@B3bypyczH4LXHi3dyY`eJ);TyjG3EjPSh$D<75v5T;&6G_2qyy7
zAS4KsC;vCybW?Tl1>qC|{DWTrU>t>j<sAlX2qg*&ukV;8HGnBt<AN6p-B=+LUyc()
zKvVkJDbx|iIEjBPS7n57f<T2ZPrd;d6$J-K0T3%*5M=c6VxbSS=t>7x2@$$<U(cnC
z-+%x8;9u#j7pC5Olm<G^pglYh7C9ynLI95AZ3Eo1#bVwqvEfG51>}A2yWcPL#Z|*S
zN4AaLeK&vh*=Gl#T|4b$0dK!Y{}cT6UZG-HGLA>rvYdy@(Q(jBQ*_;J7GL6bL(e(4
zYFTfH9oegQE1DvtyQPuEZzwheuQ-ajOkQLiu}JySPZ0P~CKQbWWdnexxSbY>YuRO&
z4GX2@v1nYWKNhS}Ca^OPss&tOQJ5dQ>BOf<I`R8IKUu@HHdxBr+Y}nB`-^^S$7I<3
zuDi@`d4{s1&(S8m|H$fo*72UJuinc~{cMjAwks{}F%Dp@6s0iA87XzJxT&(3TV3j(
zc0idkzLU@NC*>jF8+O5??B@s39#EL0;O81%Y54e_woZ9cmZ=hJZY{$KaBok);|@!<
zX%Bh*gYWCai67hd^S^EUXXXDp-#MwS@;)nE`YaE)hd=5p-WY%dU5*`r*on;okG|_E
z-@JR~ODNuHJ17C+3DEV3BaR5y9Eq)CR`Pgs#dFmWa|QAoFA^}`bwwKHr8xKoGKRAN
zBx{TuWhH=6{GFRq^G$ysZG4+iiZj-cAC%a%=PZB{z-4XjP`;RZr1Njyy=97gfO?v<
zXr6QYlxhAWn}0(9eDS-zS<JZ0tUvcL%QS=Sc>3d~P7mSM9&=cDuVZri7hG6X+8TAq
zbGiY7ITjm77~dP^_j&qPpC=8>dt++`ot-Yg(R^o{jR#PuEGh6EjmK?3mGMF#V;Ur&
zAhJSM+<;Q4%UWrQ%PhVoOnP67&)oKoZ@|2qzpki{&<H#6U#&b0+^1gv^K5q7EV*|3
ziw1{hrPaO0^<lNjI`7>IBP&N11HwAu&VBr*Yj2i0)^J3i^euV38hs8(rs&*NfU(s_
zEtQ$RM&q7W{CDqD0q#+M2R=Tu;j%e?@{a!K4--p!_T9)VpH>Tedbh(B>-wWV7~v;O
zAMa0@`t{InKb$$i&+yZIceB7h#r^>9k+yyB-P|v<a?fzR)79%`^H1Vsjt9sf4@jkS
z#IELa0Nu2SA%5S;^_5p%>5n|}$U<ptgl`eQY(fN7f?wuw5PI7pVezB6hg|0Vg9Z)q
z97oEA0d&o5D8O>BtjF!V^Uh(;#U^HW#Qj}$fE<P{7daVz5T`OAi|SMt!g4wVG;G+g
zu<?|6OwqK<AM<voQC&6jC`h_?fwI`RBi^aY_{MlNY0{)zKj@U8SPz}fKPYF$GysoT
zpdB{UKmrt|;nQ?+k8T=@-w}@bVIvlVT!##gpCUZjL_%62))i9T@jchfXK_h4#7Q@)
zAMWAHkIl2top;_@^+$gOOcjHuvOuTEakN`mWuqLmwxKxT#e2#cpcf<%9AzwjsY}}6
zjT|ZKXYhvvv{M-AUhvP4b`L0zcxeCpKKS5+>PB#MX8e>-_^2PCmsA)4ohiYaaLj2%
zv`5+{aR{_Y+(mx$Q#Q3jS^nV-VZDcRD(!N4COuQ|*!qvOD<3?Pyo3Bn`jvlNc%WyZ
zd=-YYh{zXhm9ka)lC?sBh3J`imj#$(^PYGy=4-4H*p4`|fUy@{bWw;N@SJoBtgVR`
z`JnxgcK!H{K%98NGv!VF48s&qmX032l$m(RMHUpHf7A2W6@PTN5bJ$<k1h=2^eLX{
zyQeTbW3yKtcn^IrzqD-CCs-%ak=r4nI{%ce(w)_A%CacSqHF_Y8|dgZz}mOIT^-H4
z<Tr1&*E*l?>gy7?7ft-i&d@oX0V{ZU0%Wsg9#4Fm<xwOYM;^qc!P(ha1yh+Y6Z1f5
zi!JD>mmyf;51~cCGwz2D9qRAB_ue)<c#d_TTFQ$!&PJ}8{3CebN~lT0vy)G|k^mZJ
zEwH9x^6mi@oO8}OLFpu6TlFS4FKpIxj#ZHL!mRhrJfn`9373Y!+FIU@#1y#K!;VQ~
z!5OP~xO?6H55M@j>xJu`uK7bq56Uo(B8%hM<JP|gKlqKGr`KA{W`X+H4=4JWrntDz
zth+6+;z}X>ai2{ME=TjmVTYZ2UU<ITcH8+|TLGykc49w8u2Is^#`d=bQti4dTHh?T
zL#d!zD_#_vP^_q{<zMemHlwIPVNo=F3M*I2C@E3(Jas7(l#RrfzRSBuc~<GCJ^fj~
zFcbLz_h#M{^1AWHJBOogdA7j@vlIpJLiqx5&HMM?s~m&UtyQ7F?z$DLdz}f~qdZYK
zqF^EK0Cq*4F}CHEVMjfW^FyBWga27)ofUS)YS%f+w)BU>RtmN_P1>!GLXdGEfBe$$
z@jd0Z*Is*tUCUUqrLQPG&A3v=#p&wm>lC=RpjoI`WDx-QwdW)~dN@;*&H4^^@9sjl
zg%|D?p5ylF)y?|R0znzwo_y5;?qMlFJ9Fc)gdOZ6eODVvL#*(J4{eR`xCbA6uxHWg
zkV6hBJf<`fce8#;Ct(3bGbiGju|P`8X8F#~;g~D3@P;B8m$J|t4n;f)g7}+>@|bog
z7ypb6)Pt@&x;)_zPsTE}fiw;+cwmuP??^Z4=R2Wia#_^NI~w~l`+fu1W4?p?iA8$Y
zn{WBPy?TcD88ZcZl%<Q`p7jgAnpx2%%zDREjtm-c`R~GgX9M?GppZpy@~3fPT&pz!
zz`e@%+~O(^U7cR6#;~~8-guc~r?Lq=wfI8ar1e6((l=9Wo-izs3tY>4`hogrd($~L
zhi5@7tGwpsJau*2!+_R_|7zt&*4~DlGpJ2}8-B^!0pPRju&%SVSu36M`n%P>zLY=y
zxv%?e*4w^KJt*IuH5MbYW__C#Z|tsj<O-optantoVIwY=7`XS12e!zpzu3R7IcE3R
zk9Y7#O&C!)jKa!Ya^WTYBHjA<f8O~|Kkc0v{;-#a`xB?`;>WzQZK$i0rtaonp7CP1
z9{u3fewm?*gzFKPu3a}y=54V~Q*qXF*~|&$>CEbd=RMzO03)2V5+AQvBy30f$?AIm
zOPFtyM$ISEcqRV>jzK<xWPu966aM45Jpev{d+53#=F<RLhzB0{$D$SYZ16!B#KsEd
zFc5Mhx+=gz08w_`b=R=5mUp#)RB5_(k2NrKyt>aS2ffR30ia-=i&I6giX}fOA7liq
zk&~ZT&XIq)<Oh<4{gjXVX;X4Mf73hGDxjjr&{AJ{X`}8Sbd3T55>H;blm_(f%x~Ya
z&DO}!01Rt|6DUfSCjqVj{1m^~MJ`vG;>{+sCEARREfrWS4$C!ZqisW^jlAi^8u_J;
zSPKyt6MxK`>se%EBmGLPfKK`bb4ND#i!UzUBGO9T{rS&-4(TAj0yz<mF^T>|KVaR3
zA9W!sY5?s71PlyX|B+tmQvfr7XWG02FpIqMBMyL+Y{HG#hvIY=y{@(S66@T=M|xG(
zN&_y>NJmT@b4}dJH~GORgots1pNKvamlM~7XZ?r#!=L;@v~$KXZRY2M8xd<|v<=3e
zw0*-1{WooZdeGR0@d}7Bob(bl))N70g8=0bj&zYG=~83AyhBeSONv?R0c^~I80+E<
zri3MZlqvcY?&F`@u`IO5@3>ZeRTrsj3Rn=zGCD$d#%S~$sjR7ZsDJvLHjwE#eU!2T
zxL$-5u5{bhZ&@B?d6aFSYy%zH1^`s&jprR1PswZEX}3kb;rJ<md&vZ?>;xU(2@uve
z9&m0dTg-Id*}4mjMIt80q+b*Cth^vFAyDcaf?*2ONyEqY+Bwr%z!r8}q8vfVB`au3
ztK2v}@wML9a@n=11zErZ?3$9mGukYjpj1G(tHmSl9TrJ2N1pe-+2>&p?0_)<z~fe1
zZIzk67`P`;-M7ZQ?0cAy%Y6*oJJ*h|z2-N+4t(PBAAZ^8!7re?dU;hG9Dj>n@mX@?
zO#+V{uDZrAZ&qmVx8=`ffdjX`;KJ~X=foo`fA!)^-vdyA*e#sCt96fX--rc9cv0ri
zHP>9zP+S5CA`Gj1YNajf9ZE|`K;A4rdd675;$<Uoaa1xpL611%dj{CMuobv>yXAR@
zAm|SnvbF#BzlMe9E3D9?n*S8IH(`7QxTmyiyz#;zOlJc3)?9PVP&V<uEMSw;G^e{*
z`^=*gPdqUk*$&v2d{D==esNya9#Bg0ohce#3fjChHS#=GD935*%Ig%kckewzLmLGw
zmzR!37ws1OJooJIs-G>kFh#k6RVRPCd-XSU%KwJq;Aq4=UPYeQ0`76_5Ek%KKB`}=
z*g}ntZ}3aolAht-aKjCs`=V8vXYz@1on6c*c|`(IrT)wN;&bk-Ih>B-0>G}a<mh6K
zY-iy|*4$7^L-<MOr3%*`mxUARTT1>sUVu^K4HyF6vnZK{QU6JwbI(z?n(N5rDBR^1
zUo7Ms%g2<1!ZIiQ{lypiK?j)Cz0U!-mzNhkXTd>>wn}s9X7>#iEYii1X39!qYZ{it
z<h+GX{L+5_<}uew!;Tm+B7kdI5R>(X)X)5Mul7xzHU7*``R|ClhJx$Ff4OOF-8eC5
zzoi41XQPt_RN8y%^O2XY6P^S1adkFX<i=s`qI_4s4Awe$S%3VC`}plPnl<0;igII9
zVj<vOYyESiGS=I^xMy4I*<F#=IW>+pz`e0wD%9RvY|{~z^1l6U`|4XqUB<e}nP$kT
z#dWuO+S!kv`EGDen!39mH{*E&_3i2pdwo04ak<@k)ozynb^xpwEqCTOmtA&Qo0B(+
zhK>F4O#$!%$<kZ=or(Q+^fgxLlxF}+SkWh6%(sb)c?lpC^g{q<I6gCLV`rRR7WmKs
zpyOmSH`dkxnE*7p;DQT+-W0H1y}Te_qKAI@<(KRF1^5KehWfygaHGII^iz^~<Gj>M
zquvXkMIGMJddy`OAM|Dbbtq?r&2mSM92vk^we(DsGvFXBJ!V-hXLC4m5=XfbM^<_i
zH{d0JW>`m*jHP#Sk#(dKug6beXa@kY*ubf0c`m>u+MPCy0?1<Cnfm9H26R}+MCe(c
zdg`e*epsV{km0mB5FT;<0XhNtqkQluFku`Q{erf;#~ya7hmio3v7eYH<wm-cHb8Rp
zfmBz(1{nO{A1h-JvMm<=aETK?q>rC~h4=$Z1?Y|+04VWB9*l{20fhm^<616hfBf;s
z+mu(FAFh#m$vYd}m7gN+;K74En?4y+QvdLy-qN_T?y+Q#%)$vyfH9SJ@<Dw|_D7#g
zzo$>>v?%<ld;~nBuhCajo{VQ)Gq&ItLieP!0B8d&DUc(5kvh?Pxr8Mx_=TsQb0U}Y
z)*K-lw-M4QzN{lMW)Y5O$YELinDLhWL)lV>+Bl^6=(Et;Yp)&3p6ASQ$lutpV?)0t
zzdTo3IOzhOfaLT{{9>2QUi=u}`01V2MF5+|;A8O;s4&ZVy*5MAerapsseXIvsi#&g
zT850}{8PBntv#<P%cm@#vJI4NpxHK{F`e&X`t50!XBlqZZ;v^j?b#lez&)Fol^vpU
z%YaGr1{-Y9Ddmi*?)Z2uKnPePitAK^X#A|mH5S7#uZ>@JCn8WN9FuR%$D?Fn*C5Xk
zwk5!8M^oMcX__V$WFZ7A#nI6&$|WZK>?lfiDy4BLeF!q?E+71&TwsA#;Shiz?T$kT
zLfGIvR>=@7xK4L7z4X#cLD&_aESDW%lxNnvW}hJdL9y`I3@-qkP?oYp{8HfF(Gx3H
zh<M-6c9OA(eAI^%gL}RK_iniSvNn0$^5?$@?*>K-^WvT5VtEFhhrd$+Iu0~t&FeG0
zKX%e*e(yW)3cSB&$4T5eV`lIt%QG=>uUYFeD3Kt9->kI7&(y{+BM&t?MzD(w0UVx1
z+cSP&ef3q3!h|*)r>E#ON(Si6GtUeHe7e}Ke8CUJ5`?m_DF5vYGzndL>7^me>8GD#
zz`g$qN8b7wU-i3vWhv`h3z?#!0&05U`QZVqw7>%0O!4r32+O~TAATh~Z)s^ezIPUI
z55*az9gk8DqD15Oc~4;DdP(boagOl}fMwQ?e4!X49Ak!l<cl$lyr*MZ8b_^r+9CkJ
zT7Kepw0Ghr9debXA}-$ntWW`<0dNf|->Sd(So1jU&bStEZ?nx7@weT&xq(v`3uW=*
z3nNT{Sow}9UfT0VAMEPidUGf1e|G%k;NJd;$F~jsvABnYz0sp9ieo?XeCs<u3i%^n
z?5c&f-g@i8WvzGx+yfYR>t8o-6CMR2U`qK-UG3~gsmeTooyWD>V;Vm1o_6s6**gz7
zyNc?MPYMaWmk>%4I)qSS3@ss{L+Bw0(n|m-3ZY38X-e-XAP9mK1>vt0Q87{!K|@gy
z5K-w!6#^kZ+WgOV-u_-@XYYM&-)=U^o&CJM_s*R;)921P^E+qK`6n-IE&e)Yh&rN7
zwZuuo(Esq$mHx?3n%rsl;<@$wT&X_P)&NCyBp36lKuixm{ICZ&#9YK0jCn<y?z9K(
z0DaM^lvcg(piiIX%KXB3i)mni0l++hcbv4(*+9ALuDe==jpG4&%@Sq<+?!%_SnB{u
z<a(n0KCm>1&o`d*M2j-_HvjdG85hFdW9y&8OhISl6<3DmM%O%|-`*>2#vI&UI$a??
z-%>Uw*o<k$JeQWKS-?Hf&B`O2FKnhMOnlYcrj2s?cDz}x4VCztkd;PEb8fg-y<vr@
z**x8xTz|}$9qFpo-8x4tAHZ1aem&x2Z0$w7gPt7`-gP$%$<^o-?`!|or**a3o_+kW
z&wbTze&Dch#A*7zqn@h)X3}&=T)cYd5B+yqtmqFmKByl+cw=7}8S3SrzmM?qEZx?+
za9oc)7wZ-Pdq=)$3Y2D5JbqWteYftd$K=9m_3{g@ZWf8Bd%wi`^N0KZvc!FJ$}Zve
zoBJR1gu{R9>(*|QHXxHtHrb>MAi&COExa$h3P2tyIyU+}WL<IXwbxd(&{j_!)f<+&
z(vhIZMp4=Y6e&yCSXBnBm(s0NUgV%@_^jtjGzgDVJcVT~jv}S}b(QQ=@@x55yFwRv
zF5ox9DxGTa()U=|lY>@#T8+YDnTvH5>&Z$=Z&)GZxKG8)$`fl2`YH~<ox*Yj5QJ5)
z*aBS^4Z;H0161|QGtUHCdHDjUBMZ!Vw3G+>kJ<t77=SCu&lM)V@(y_^itxy;h=T=w
zg;9F6JM%aD5CX6%3|DAKo|$JkQJ_C~DFAzbbyNP4x`Xe;(O#9TCz39pF4oq7xB!;1
z)&_tV=Z!Kj&yYtzqBwwQq^~_5=ukcZ5-B6fTGTsb=12YsM;HKQa>yqERl;8qhy2Is
z>mEK58nkEDkG>-iF|_GJl&8{<^mxxa0w5O<WE`F{%BtLtfBfUX%VwpGA4hbPzZfK@
zyz5FB>I4}k?L)c(%2Aid?WrT=)YKV{@Z^*7JY+^Js6XnOzPgWDt;cbq1baW!k<#KQ
zXvw_+9;b4BWMTAIELsv5x{8Y|0KS0s21JWoRqp`zQZG22;6)xEfBf-~eoRMk;1h8e
z!_b(}6)ShlyEujC%J|07*R)5VIJ{@xJoeaQD~?aXA?Jgik7fVJTG@L-Nimj(i@r|i
z*m~=&TjdFQ)CGV?fR+L;;y-%y=#XFjsqM-h$qnI)Ur`4|A1L}j(FZ!d4<M(H=lP<}
zJHF0m=Q>V4*SU`caIaX%bm&435DE;b7N*S}R50T*yA5r$gtSQ>rv;-Bhl}53-NSI`
zm`|+6#Y`>2Cs~eTBSP9U+-s8*Mv9GU9Q0wXxdQBqg#${PcvwzBn+(5>sxPHiOB&=8
z#ww1Zd-6*e^$g<%!=<`F3zj;QH5C|Jh2^U9p~aU90pf7n!>rZQc4_?d8D<ZGMxMYW
z4WIR_{A0>pmbS9O;m3w@{Oe(TR$&(zv`(vcyWIVAzj%)pp{@xo2sh%=%R-m|#-J%)
z-jz1Mk)AB>5q6**v4`I-zqr?LHRMYn%=N~A4nE<;;J?DCQGTPd&k6U0tHrWkng@iK
zI0O*t2Wc2RtNz%qA2Vi55ISh=C`_d*f(C>T)c||YP+C~!!!foi(XRAPf}gzO5tJ6d
zUYKZ(0;sj0p{<F-QXyJ|(j@HGZBc*C)z)SP7~;3v=#s{v^7+Q=+Xw&Ny*UzeKC^UX
zf|u!g1)$&385;!60Pd-r<N={7{SF}<f(r6YnRT3k^4VF}IB#j1SQ;gtXqb~{e&m_W
z2pqy6@}BkU$nqc{lEu9=&mHy5yi1w5Qht@0_CxrFL&&6h%DQ4zEiZqS+98c2;2t2n
zXP(}|1hyqmEtd1JtQUs|xHskV-AvG5nkNC&z5d#$R$-i5&>w%?y8hN%hP3#155i>4
z)c~C8od;sz-eZ3l(JDQ@{k43f2uGda^0e0DvUc`8YbVxF-~H})Lmm+nGFCXU1}ns!
zou4ATY(_V)zo8}0MqRafqwmqKS!>ZaZarf-j>kRo3H^t)03bfT*9hZrY<fwcp-lk7
zV40asK`b^fS0j{0V4bcJ_nGjdKYq$Ie^SfZ3Yx52j{AIyUv80wL)r-5l}4PmwZC?F
z2)n_zPWN}%8ugNCrS%y~6mhs&aZjC*ZVUpO75Qjyn(zkn9hHagNPB6<@tY0YgZ58;
z@{?9ad9mRVZyd$x$M<ZQQC4_S`P1(hi`sCE`JYC|RHN~fr>=BO`Nuh_Y#mY0%x@j(
zs^xjb?HBdqCT<siJUx>2*j7InSORM8ym-0)SEE<F&;G7gs+SE`+sGgN;QpbmzjS=R
zPOEE<$76hsuw&>d<xlT6yJF3d#}&3MDGA7bZohKuEnql)`#o7w_H9)s-Aa9;SD)@4
zz~0wh*}`|9zcugED!hHOYIz2P$8pw+t=HS1@I@*83DbTb^0E7GhWgR>4)K5a<6jNS
zH_e|i<tyQN$uE~ceVbmidYd!=yuc5@C)AQYVUUF)+XV2ybEES;{K>d+<ARI{%SW}Q
z-=1*FE1(OMwc#D}$`H%26faF%{u~orOZYUto<)aKC$vsM2^N{V!f*xL!#Z4kv>U)R
z>JfPs{-UKYdDl@UCqy2OO!mw(&n(l-3!mq|@4own&)Khj^{c=OAS**TJ`RtpoHZHX
zAz8nQ-|HS;0T2$)dF7zIx7>0|0OLq*t*|=Al6kHY4c^g@vBn3lf)zHvPRd7oMbQXX
zjUM?zMkotl0HXkE2{07X)ICavoF0H<&p`fM<NicB@4ox)5DuAl9F9ICt9V$T!>VH%
zpJ(!if*5Up<<)r11FA#$6Tln19rb~APo)#*1wQ~^gr#l(_MtRNxnmxUZwz6ei(DA~
zlrR$}Oz<4b3LV-DK9atUObXx@piLYgG=O64N5~PMyzztYq_4!M8o@6EJO((*cu@M}
zfilWr)lPJv3E+<(d3)@!$HKh8(Y7jorE9#e67vRe9L6n%As%_;k&uScqn|1tTzQtG
zUCFoFg}H$K!Y}Pp%*7a5p#4RUGD?via2ysRsZYSjIKl%e1*E*c8MZ({pYr1{prQGM
zav&oYxK;araruz7Vl@w0Bl(Mgcg!s~WTL#&SS3FBgDz!d9{><5{Qv+c07Yb(gj4_1
z(a$KN#&nby=5ywC#-aS!mqJEDAC(-QcZ!cyPb}~uL)6@>xVn-)^~xSmssM(84(7gC
znN_qkHkfPEH5Uq+<OxgeF(8okL7r31DR0HKwzX4HPenZyeW2(AjrIY)Io0;U8>L;u
zoAdf*wePpy7r;GRj1>c*<LZEoHx|>gqK?rLk|q!gNIerM+JLei#{wM(qf0pe8{uGV
zSY*RU;tvxOGqy03Fqmj{=@<eQ`Kbny>PDM9Y+lN*Bd%yQVJ)m0ZGc2Dj<V8{6&|J=
z1_$OR7Oo&*gMorsL?bN=^a2Bh=3*9I`N1UO^xWuGOcC*Mv9O?CnxX}x$|iHVshrYV
zWbHv=qZo^K^O$fTyl(;JIW{+~U$N=TZ`+%0ZuMTrx@~f<S$B)H&*C(cAGF+H_6H9g
z8fYE)_B+1MLJRr{)(^Xza02>!KJxoOA0g_k*IeyaGtD{tZ+PWZf5}HPkDjX+xQF0_
z{*FKb;ZvNy_+EL%Pe&dgz>LFGx@QctsZGC>b<0ZO^-gJV9JT!FxgwxLFo0l+IVJAv
zqNB1Q=)%=1#34RHqw%IuxXwE3w5mVR{O)(w2;jUd;NJ4f_wZA{*v&up_pPiACKzDQ
z5^n+dDR1$$Uf`bc5Yylu0RnBo+)BUWr};Jw+u6^6jL>2we=omW(tbq<h&%e|qr<$c
zJWF81Jk{CxRC>%I%q4oB)mP?gzT5m%Pgz&$OxnY$XX-r9;$~i75^xUy-ky6b6RhS@
z2LlH5^c<TTgZBXL(Z5wkE3MSazw&Zv1A_cscil}k56$a8`tV3=?f;oYglEeFN4_WY
z3xs)Dpx?awkyZ@c``!4`oK-2j@|Rv2BdiOkI~;<~I7}ln<M_?E2ZW;b=NpYc7puSs
z_`Av&uT;*iP=}TBH>=-iT%*~I#Sq3jYdG2(p)dkt9fPI3Hd3d|8N_3bm6kk8FFLLW
z+H6AcJqP$tnh5K;YNLjuycjRaM~L&mhhg2Kbra(VOUCoBToT$dwv`^?mG^#jjMQ;o
zO!Zs;@CM(_=G+x~_VA-kSpquuzW2WW)HVRJzU^_^opDcjIwPIVN+WNBv(fZ9hAwOV
zZj>JJ==<7)QGdao&6i5nPYA;%sWy+}JOJiqEWtxz9iF4};`g=OQ%=6IwS=AB!ccFr
z+%vo7y)*O6e9)1uVp&=MMOfPdbTsJj$XZd$yUi|NBfMLA_a*1Zep<19Z2Wh|`ym6r
z?2jJ5U#RDShxYB1G~;=Y{+CtGI1KMs-)Ct*?&Iw|psy6<nH7I-@x#iVfO`PuLb+SU
zkB*t1cZDAQ@K?9=hyH7G-(%6nKzsA9*u$?hdI{fe$Hjb~O&9cszO;F(ba%RQu;2M-
zgZ(adm)2CfZMD0{G9T{72e0!7{cVI_ZLg(+g+TJz+vE#z8=;WPcWwLax38)s0_Xw7
z41gH$-Ox$<OH8}kJ(kq~5Gh=HXo*JFHKhyi1ONu$nXK@&`iU-p4_u>b7|~&E!MY;`
zmq>1{JOOUu)DxZw2VewnnBI$)p7X9KW3z6L`GQ*G#x$YBH~;FZuMT-YehxTEVNir%
z-vH2_WHWjfUy<>^lgiRzUS3ch!IBj~AH`Q#+7&13-||<u_{tiNv;gf5H5qICE{+FJ
zg^R7u#bI?1j}6boDFBqenk*b^_^cms$j(_$LQ4Q8(M2hpu}MAw45^+0Cjs!`1SA}3
z>sVpM<%%qlaO`)`Pf`U(>XEv~0a#1dy>VT@(?N^yghk00&{Zl!0B9y4A@u0Ox88bd
zxsJ&@ZG~(V#Yc{51&oRe9&jD}EXS|{s3Si7Bnr^+ajtP0IU$Gi*ylm+O8X<f#+n)R
zLVVhqF;72)CywKDPdxxaB=592`J<c|E1=AIKb>m8d%^-Dq)pQBgo7ru`H>FKX+C(S
zols0CzgV&(@6>~2o>(FT;J1S*@GD*IXJyet&P^TA4$z@qp@GZ_fGyVg;K2Yh6Q6RB
zHgZ4s`B?T%-k^n}3~72;YXn%zcZIx3uFnZ10uiz|#+(qxg)hTVH?${)HjwYZD*?QV
z!)gzYqYJaj&Af(-<G`EH$H*I?Q{K_H)xQDF;;P9D$P05W?Wul`l}q*o`7Q!bm9Y`g
zCN26UPHi5)V|)WvjKO=<Uo{t(uek4=b$e0IMLidNVD9V#e6yJI<GH)&3v*{*sHNSj
zeZTd-0PfiWsg?>Y!ZcP-Fh(#{v!ec3@ahP$dedhyjV2dD1<YDsfBp4g%&_Q(8D$X;
z*aD_efWTCcMFi5-H7gG=ejNM2JA^7QDzRWBD^4SR@rMJ(sM<!F!jt!`BMg)W7&SD5
z#K6JWAy||3C^l&U3M%~NmtP(jEuLY<E42^I4uU!~t`xsLS1h=|fTERGZ6hu#9n}f%
z(DvmxjM&OXRvh^)ZwxD&!#n)+PCx3_+soxy-Gpin7-&u#&|6*q!}7ZoO#2W2?Qi=@
z0N#m~u5o@4Qt0>q-V=|bayc4q=YtOR`#$-UKj`@v{8k4Y<S#a12aMz0rV+Q(e*62&
z%;L<+7JlR@r-u9yw_e~L$0#6x%F0{n#}S-pZJ%_~NkPC=sShZwu41qSevRuuVY04(
z8qYoV+=doVXq|Fw0Y^E+T7gPiSJK5AC)QvPkks185Z+>ulOueU_n0PrS6w-%491Hs
z?saPs{0|;HU+MjfZ`jqeaShnC<(7+<>4`4aT7i3U9yqp_x|h}`!Z+qf=E0aY_i?=R
zT}M1KKLLK?NB$7NAy@*q%{PEN>c{t}7K>CJk<UDufL;+m$3mFAu$|#wZ~GyvRQ+<L
zj@hh<wUW8N@WKn*^c9uoh94S;tp#k#ak==bZ%_Jo7uzW6UXF|Z_M?{Ye|lmI6Yf52
zpraoC-~TEdbArb2<Bvb%x7&7c|MXL%jA`1e{O#sj#+*(5D$UynD(OclpkR?jY;$Y+
z?jfI_e7vs-Tuat*RfbxwY2ES-#F5qm4?Iv7R;S@=q1};T0AvGpr(T!`D}l2+G9T4w
zGe7e!tQM|#KXX0Lw~Dr9u4HqHH7OSTSogAyL-@dWrp}ttFA-$m#+zVTn@<;9a6#CV
zVlBWrjZFbIY3O%+Ytl6!Ya6YZxSnlm7>>>j?JA`R%P*kv__)&5%_0Kfhn#e>?`H4U
zJn$fY%qO4vjm|pTkFa&@#>T6#24^i!{wmEA9o2^zW6TT9s_%}<XFYV;42#3Jw;!=4
zW3#9|3OHHy3ox0@@HnsVHnfR0SK@cNM?py&Eb>b?RN;-P@m-1=Us*@;jg9^0lzZwF
z3nz0*c{-{bVZ!Xdf3@U$!*8z?06?qm+1lc_Kt}+z044$4s)b$=X684zmbw_c=9m0&
zj~o>0c!%58^S%1E22|BTqdj5hesO%1Q~?~se$JF?cpiCa3COwJmJ7FjZ|_=#pNpPj
z;NHQ{jr2<lnXgnAEj;{u{d@Yu{<DQ2`;X21JWI{wcWS9GKz?hDT{ZyrcD-jv8K}p2
z{OThc1xuMb{&f8|$L^vej3P2Xyos!zto_cNa-`q+uEBn(jY?MchF>t#s#yHp#)4UV
zWM5W30XzWMLB=WN<*abE`XReyU5MP3IB|YzrBN-MXaWcUgu)S|0(Mo3t9X^d@D3go
zAP~T=YD&LZw3H6tP4blm{!^GVKC(9)YyR|IrDs5wIEBfhulIRZ05tTs_&xgr0J;FK
z!FM3PnLd4b85|d<rF&$ZIMI@SOc%fm@@HiCv?cH2H5oLpNC&7dj+b>0Uj!hE(-{C7
z5k_h1id;eUD`2GF#WdMJAe=yZ@F;p8hmY?CJR-lb97W|}oE>q*5y1)?vQFf&fYu~a
z0@#E@rheaj_hkb1m;*=${(0K8X`#I0L&zU}2GAuApkZ8Zab5|_y6)hE4-WmD@Z_7m
z8Y@$gC(^@m8;-UV$PgOH7g4m9^)=cNUJ$+veh{D-4r_npnP1jeV2-11_yK%`rv==F
zBTX#BF_$qO0c)u(=$Cxokd<?dX>f9b9Ag>K7(eQkvI3$5@R#?4&)^*ZIlyG3orZ-5
zw8$e@(!sGmM88&kP(oE6$tUe3Wq9~k!qVqxFZu*|B(L;mK!en=XbD`M&JV~;IYova
z3flr!0(3-<PrCf5I~0(i4cLynv4?{{<)a+Rd)&8pPdEU6$my{LDkUn~pE|%nGX@1x
zC&<ol7>c00$fw97eKl@R6ahsCP@vjbSAe4AjeR`%4IDTy^fN5v0Vu|wzOMdGUa?%L
zdQ-gg$~dI1knzWR2#gUN-^X~rK=heMaH>Q31I|T3nv=xDQ|jn*+KBvKa>*rOUJV3V
z{-yaUp6A~6XwmjX+ZTPH<NAQ+YJl$X++Ngq$JP1lUbpeLy3&15D`?@08fbbA*rWww
z%?yflb0(Z<I{j({N!PTFss+n^Zw!Pp0Gc!4o-<~^nZ+4rmPwp3JkA&bXN-xn#lJI6
zMrT@I&KOR2|NZv|dd8eM+w2VY26%C{>DekBW3rtwYt9z!&KBOznCbA|w0oQhippVC
zhh>j;+|U-b0p-jBiZigCv&Fu%qr|FGGz}?dCb-*#GX~ii6XXnt=ZqmQzc=kpXAG3%
zy)ntoKoFt+6u^!daAuh)JaV79xXh$&!l@7>_D|zfdsbXy;GJzoJ7d(VMQQV1G;DLu
z8MEt5do9p1t*16&vpn9g{&N3ehq~>r{Xuv=Wzr|^(|6x>FP(mxyY=*MxmVU2=r%m=
zc(<~Z`2{QU#8+Q)qknRTd*hFPbiY3O>u$#6$!*Berl$4f`uFeeY-7)vAS8q_OERUf
z_WldI`<12rbxZfP-~ZmdaP!S>{vCF3Pdxs(`@>_8xr6rD!}Ybk{?JDg-O^iZ>E>Hv
z@iysH^Pv6hXhmn*ZsnllEq1Cu6K*-v@(g3s7)@tKsKvo5-ZLg`^V87=j78B*BW3v;
z!|zPk5ay&beBQGG!kjVG&H#zd1PRWB0nUyHX_GExHNl7b%2&SPOo-qpmkA>C$hPt~
z!Gp77LYxVLoLK@Zzf0@ys_(t%1`S%wZ907Eau{x?W}E8V(@%fqcHZS-_tT$j>gJud
zdkFK}-+t64udN~{4{5!T2NSG06XG}%)Hwsj1$sndPABhKF3o#Zm|FePJQ2?XcFur#
zfdqBOd}?42X99L-8|WdACJ=K5+;avHtu?7x7S0y+Va_zc(JaeDoN&GEXM&<QdikF5
zW7^T-nReotZ=DIh%VB7*apTUkd4RdYI<l?lCr_Rh-qWw?-}BDfBlK}a!tefHeqiI_
zHFxg0582q8;rjPm%pG&g88)xn;XeIzs+(lL2@~FQZ@>MSGwpaq4RgTHfBy6GbJh**
zU|{^vk4(rJ#;3C#t@-DF--6#4qAa+;BF+F#<>*=cwEQx<N$ZwxAm1g{7$#^7A^AQU
za4DpphL1>f+AU>c{ev?>tTPacvkf$7+T5L%hiVBK;47@bSevmfDO~L}Kp)3xz08^o
z$C{Qg%NmR|j|rW_dM>=I;~#55*3GPS>Fc(s6lk;lWsWu_2xo$3XBM8rTGNisab`Wi
znWc>IO|;`=Dt=FQ>fX)WGb37W*aSiU<eO@n63#X?!u><GKHuZ?Z#%xS-R=8Dj1`6t
zcRlBw*S-FS-@7;LyK>f9XN5V!fcnlhPC|Zcvm>nMS<BPEI@(cw<^|>{)=ROgb)Jdi
zbcK6(BF3dzRB*OQ7S>Ab$(LDwaJB&w=IQp(VSZ&!Gi%CWJwrbK^Pm5eNmAdo!Rl<&
z(Aj2Y*<Uw&zgW+*_OngJ&~ET?tUKsSa?s&DuHH7ZO>D<Ew%&M3yDMKAXRHb4#Ikke
zdT%6euRQdwTW6pCZr~m(y4N53pZomv=_N8uq_$Rj^!OLujHxr+PB#p7znt$W_uO^=
zZ6ui@PDTx|zFBP1#oSBJ|I@9$(V}k6G0VIEjDN>H|HIC!W4_Tija9zO2F7v*2$-4U
z8O|*-sJB~j*pk6-y7h;`m1>|{i7ki4af`3Ipxffyb=)0W{V6;zJg}D=bNfba$|qCZ
zZKED@Q{SszZ|jd+(M_K+-OZThUEd*#yLI>P9}@cL<xkz?7rp3u&ey~BTCk^k^sE<V
zj@6_wOsOHXrvawiH9xq<{qU+A-1a@Uc8`3#o$KDSl;LTUTgJJ?6+c2BNadO_Y3LcC
zBgjk)WEbQ%W}(WN0#_WYu6qMwIRn+ZlTSW5$h{0qQeLyxMW{kx^=$)sIRl~??>VnC
z5K^0USWLnIPtFwYoGFFH-mUIM7f<A62BdMU6B==}fhmxO{F(BCvtx_fkWoAFg7r82
zB;Q)*5!RHf`QtpbArN`6j<F-Z0u6b`^yQa#RXX$s1Du4s%g=zHL3W93$$(eRtXnw)
z%LKVQ>m`MQw(jY7251SgcjV>rkJE=PYdHhOIRkkG`IUj(0xxeMn2-jtM9JynxamD>
zJOfq*I>^vXp}(A_(ndxGpTL^kfK@^E4IR}J@&r@p4bO}R0|z<y3HU0>)d<T^G`Tmh
zkuw03Gs}5F-h9FdCxq-N4009v67nuPts|^qk?&L2@LS}|jwKGf6>C^zWT`{`MBWUX
z7<jHplO_dOvi36=b7uL__3z(5$k%@KqaOu6E~cY$vL^yP@=Ln#knoS}m*C8Ddgw>2
zX$Kg8$JqMhlTVzT0OSmS=lG`B(XnCN(Pv2uo{PN-cp~N%<`Mb;e53*3!d{9gK9{pY
zcvI{N&(xDChlRQ}1=-+dz|bHsG~i~aH#{ku0cyiKo^a&JESQC{ij10Pm67n=Bl9-!
zlru#$yvmOK4`kG4@z5#F^2>}H+Rv259Q`wOto!LZ(1PcozZ&4p833|XeyA55>A{QO
zkXiGL434%S4dl3tF**7JdtA(Ov>WwB-!VX1xRN$<X?Rlh7;t7`F1$BzV2}lwvTl&a
z(zc8><mBw#oO8}Op&i&GW6p*T$I+(n=d>eod9$1s<N(MB_|dkM6WIfDbOQp0cm{w7
z<u;&a$S-ns19yh;K^xF-$S3m$X(LZ#3^3Lx1A8U>7*p&6F>g~=Ip*+(jn|{UlP~x{
z^24{l6bpmgnLHz#z|mfm%TDugc49&3hiBNn9!}#kJlH?dr_G>GJD3$qX8_y4J5omG
z7ut|KFs9YlXgAuAeqdnV(2t?bSfP(GH#2rAul5|Me^as#a#jO5DhuUnnz!<c;-;v9
zq6Uf@C~BZ7HJ~}1E9;%Y6*bVT8tB<9LIs_o2I3lEvSMQe&`T`<u{drs?loyPN{dA_
zOpR&Rg#|odFBn^xH5fTI8%YCZ0taI!$D$d)4@@izel{!P#WHmQvyNkvo*zsA^w{tL
zfCT^!rlFY*03M7CjCsuHW~D)1U<MHM0GNuw7KG<R#6~XygV@1@!Jx6xZyIZ5;0)!*
zRVtI}2xc56QbG#iHPW7<M_hyerl}YhE$G$bL<evXf)GGs2#ydO=v^)JUbx|hZUJNP
z*eD)$%Bk+lRwox5Q}+A6{>8m#!XAKo3-(#oZGP@~ZiazdK7IEcch5lwx@qsd2Pa4+
zgk<70P20<vmA4>Rqt4<5!aNHs=+-&v>+bJAx-rC?f2k$i8@BoQh6$GdZqh$LwZ3?<
zrP2MsgWM)3om@*X%3<i+j1$wS4*i`Mao!YAevBm;UxYM_55|$+DQwnN=^%6fRD%E~
zFP{9(3S?k(VbB5737n8cKhF<v8bVG42?AA#zOD#+5S{{BL?{iARSa|<UHMZ72w(yI
zA%wt*uELc3oC#Z|Sik$A4RDY5bPHrY_uTuw2~eh+P@^OOI_I44TfaXdCY5o|xGeQ6
z#tG$N{2~k%U=g4kn>Gl3JKCk?Cr=385X8#a5d*pn^@X5<K9vQ?P3e^*cG68P=~LI~
z{8}x0wa~3bqodyoh^BN*>of?<;(kQ?GJba0;b9AAfWB@UxNn^{(d~c0Z>_(1ckH+U
z?%3m=v`vn-8(HU_|B?wae_(<w`hRZ|Vx4Zn>-7x;X+YY;*E0d$q4s?mWB|WGAq%(P
z{(xI!jSWrk_^b(&w=rSzZ%hdHyt3DF?N*-4@K|V}6>LtOW@D_I<^5x~#bydV-2|TN
z2jMP)IDr=%X%_^UIE2{z5J2*crcZYTM;qZN8|xN?fo!U<7DB+wx0THWgq^d>0jfbD
zjgZqUa%9e<vnp@1W4g38Aa#BSk`dq|L}u)owcD^JWG%!-Rkig!YZAWQ0zR=;rq7t=
zgs_HW0|mi0Ki0vlH*o?`F&+R{A#g`f{!QDwGLszTY7KU3Tb>rMe4l0Ce8Q3*;6Q}w
zeAhm<bl<e~uj|*p9Cum^0KA87ZsI0RoESD@0EV;Cg=0<57+}NTfEM6hBjY>}tKlE(
zUtA12H|s`Wd}*VhBh#-Ti**NK{lMl1^@u`L>>4c#n_10pY{W=`giSa4JpB`ZAsch#
zPwO*n%4$xeZ?lOa$Na;_85>HmQ+WL1`#4Os`>c44)?Kx<I{STA**ZH9MS2x#z}SSh
zAD!g>cFQYn_^E5T=YIN{d+8^}m&&n3urPl(`6ai}ajUyg-}{mQ_Y4p|lf6*L;@UtB
z3^0(x(n~Gvp8e~?Zgn>}P?-M3%x_==B<qf1R%L=b3%Nlz`(xu7a6I?(uhPS<cR+tP
z`TZ~4;6qn-i?6a^pg(0&Td;H83C|%BrhVewGdH~K)<0-KDA5-m&-{L^zEfYf#2O2^
zUmWmf_r;smu8bR8Y3W_MxBo*=wYcU7AaA4NS1<cd{dAfefAZhl{7dw9Fa7c@_s$;x
z=|C30YUkCFAptt$TlVT(uevL)xx$Uv@?f`GZ<KZ3aZ_6qsY0Gw@>6ZTW*x_xMRG>?
zg%pTY&U}R%_zHj<tQi4kpri+Y1-=8|RWqHwrO1v@FaWH^_rID$?uOzOPBh{;@?%|q
zOcd})J!==mk7=6qr?4K(QmT%_A-`pe0^Tz#(g8REKrV}B98d4bD{Ft&-SDIGkHhBO
zD=d8lSuXs)fF$Za0ETE!JC!9Uf+2GQ{FC+-(W0-CC**pP4~v${Q#jTD06+jqL_t*Y
z!8#M!HN2`>S8D~@1MC8?4(}nQaK+2I!jA(09BF_(e#kn>iyUdg69B@4k4M%YJMux@
zlP5qqDEZT;01ryxU1if1n#{3`ANrPfFX+Z$*pGl8AYXu$s5i;RD5qHu3+q@wbM$HW
zxYV(K(Z7HHAcLVC^c6V(Oym<f@M6$3t6_Rw&I=x96)ohKu*hram&i_$9iljC))WK(
zh%!Il4V0+i=@<vp3!q&1cJf0$1vry3IQ7SPN5Ka9B<+U-m`OjN-%xMJ(%={2-MA+o
z?A64eTHe#2saIrFgri@YrM9wcnRy4kk#gbapX7=06gvQd(1`mWvOGXm@D=2fczKR^
zDF4%bDGdO6fU>9~_zL<9w8<xqz5u^UABkmc7!@E6;~ko$4=q6U04XUOj(FsewxwUn
zG4H`Y(=X_|^kF+;!4y~L4?)=Xp|7B5#~vATB{Fu}iuMN#4h`hs@KNN4y+Fn-ZEGh>
z1h7BP$iJCaxJO109r!MO^b4-!n>{kx49C6;vUA1<z%}ZI_M;CWE2fW9ACw7TFZ}~q
z0Og>rNec&fkZ|yRyu-0SLI0)S102+T6|!a0pbf}B@koa@03eBDPJ%XoY-D4xBOLP_
zfIapvpv&GFa}l)JW5B2edpRf@#%}D`u_2H2f7*<`&G!|>S?V4DC3^@`Bxdf&bJVqD
z2zg-&|Dpzp8YpU@sDb9x0P8Q-Xb&4`yKqGfG@}N3HiJk(qo{$b8UX0TLK$Xjc5^To
zFs=ZcVBTP05Hhe4$l@I)g@rndItyvaD5e@FfQ>4EVhAg6F;m7ppcowV5$3?O%E6F}
zVHHiiYxD{o7$|-?7<|#zHKh%MhjtZAf*g#Pm>f27VG3dL$S;;BU?yaVC$>%&mtVBv
ztMUMog@z-J`jvkiw$^*nM0kMqAAJ#Kvk?a!7<3pcv>8RGk-R;5?sr{p6Bry~!h?5i
z{*l}C+h@2b2GV%y>hG7bW@#<G_8=30zvq5x8g7&S{VzClI#4<5<OhZga3>5b0+1{R
zKyd3zFEb(Uf^NDow|_QJSzpsK+uwu`Xq)jvtMX6AxG!%OXFhpzU$@Y5rT#jl29OqQ
z#YQH=7S%=C&hm%xM<bQ7LLb8ET~^-ZC&rs}5V+v-;>jN&2tqjoGyEEHFu!Pjas{*}
z+LTZCXc*GJ058JGBW$e20dPU6f{+728Nw9NOzRJNYWm*2d*qTwCb!?deQoUYbXQ#Y
z5BK$>)^MkuvW(SrZWI${AjtD>z<>ckaKM}gKn~#mLK?~sKo!A647SRrSpOT9TQMm=
zLNWw(<Oe~OHqm*9kR}!`DjwIA7JgaviGMR)R=T23`m{4bCd!lNsB?{rtT23g7(Y0K
zasZ@fCr2Ft%wh8{bvx~}zuW7p2nDBDd-Za=jqYmz^R=vuAGwP!`jR{Pm<jHvBm3ER
zsC7)n#YZ1aaVLFqReSNdTWG<%+<^x$-hbixtuVkk#8L*@d%^h`Pg~!Z&rSU3`)=1=
zhPahiTEVnv`?yUu?qiy%lWdN#`8CJA{@P_0w=@Q)O-11OW&3&e=3DQ$TW`DG1mar+
z0V4uC07n8HL9SB2;C<@!U8V0cH+MBhU#3jV2{`5lp83(Ptby22ldHG<ir2^$pd;%v
zrP&$dzL9*+E^$<U)F*2megOAqbJm5-SIlSBdo7N>A@CF75tg$F2KbM4quNb#Bm#Ye
z^(euhjn4XwwI=DEXjWi9pMQaJvVUPBh#UfZfKymZAif>Z69o6q**a&+bw6-B4;vnZ
zo_WhGAfdi|$(QD3SsY>=B845oDesEYj4Nr9j%ahG{Hz<We85Jb-Zdk=&Y+?78=Ea`
zz_P)ERU6i@jg*Cc$a<hXj&${1g$`>u=&Fz7r?J32d?3%X5A!vA+!<$_5%Ld60dOZj
ztP3_$Ceo<I0bl@(8jq=3;uT?v8fd5nTBg`}<}dM6)2C#tWn02Lc*0-ZRu``8hMqFO
z{rS5u+XQJTww|IJ%EH_niFt0G1?F-8{?)tg)koiR{We|7<Ob79Rnmm|t!1v_n?L1)
zY3|$K`nEgs>@(dK-&@N~F~Hai>kBJvvSj#?p_lIeZ^&Dp^%ixTerurn<=38ZQ{K+#
zFLPOg&M-x!M^1mC4BW#yC2rxhdb#xvUdg@r=l_LY@N5|`gO6R&Ex7Ew_RX;&G~EYK
zY<O?~9{BpxrUcr<EaP=^AN=zZ_v*bL>Q&8S4qlt@FY*iIeBDgMu;Eg}+~%u{bkBW!
zgnRzZ*WEh%^$&gSy(daC%bAJf-vO8byn>7SR{Sl^k6n6${11y{05-@c-&X(;<P#-H
zelhK8^eVj<9RN-M?D(d?^2#ffqLkkO;6Py$keC24qDLUgjGUPD4xl*{Rp8-kaiW8=
z46-P|ivT}puUZ`PTjaBhT{+Rzb9~i1@(jp>^|mZt>0KN??_Ob%5hAAqjDlb4P$WnG
zh4LE?>zyb@)5hWjM2m937bE|P`OKImvSYwISn-wRb<u*S;FJh{fP$n*6vvP6S?i&2
zgrzLNF|xEr80v?1gnxPa?YBceCEeHwd_;b!M`X6}B62F1o}tP90%-yALH3n`YzTu)
z6CNCy5?~$Z!+#632j~bu5I`u_sq90fbxE4k19d_ggvG)*4v-yVjxo&s4Hm_azY4Uc
ze3KXYJ2FH1H|u*@1%nPUMCuHXDRHT9cyz`J;|!1z;{Y%p;|&^ssc2((9oFE0py)#=
zhe<gS+E_uOK2iF7*fzug0ul!x8z3h76pp?NXpFH(egR|AX2>(iFLMh0gSO)xd1GBo
zy;A?ktI0R>4~~2iK6bIu2J%e3l2_^vrQ&Lie8eX$DLnaxmjGBuokJTZnKrx!JT4Av
zi?lIi1TaT<@-1K=07~i?Kl(m#3B!C!J7EP<Ks3b%@Q6dMP2EBpU>o~i^auJ2z(TIb
z!5PEM8GtC6x5+1b3h|&tJF<_$7+{<-o&m-p4<|lz1mIK2Hj%s21_Mk!O+TYwV%?86
zrTw6Rg*{FJ04N9uk-B70gFZl;Gf$8QPKkgvV~epUKA-VH+fzRBOkT++d?2*gmm|NF
z8NPeu$dMs^_6F&L(85ud)CK(m*?@q1_(PK)_vDp$^bh!8JF+=^$0#d5_Hww=MzLe=
zXS@U2q)x~e^l<cj_-H_^N+*sR-^YD5_Al;>8YpU@sDYvenqLFV&wLXeE`WQ@uiUwm
zVmJHHR}`sfaWI$GPf_f4)Bp?+0!K0B?I6|ZQQ4r>q93LL28u;D0u&fwHU=dah%<-g
z3=4hA4g&!*rNz6#!N@`zrV}k1G<l@8qp+ky-KEwkM5EbPm<a%PY?fAX2#sKt*i3~%
zg?WY<fO%sxQOB)8LuIK%FMfwfbC@|8toAykjnxu1J7M}@q#JPv5)f`+F%E_m#;QGT
zA?sTcO(4<T81Y?xaj)xbK+XQfI4@*;*he-SfBw;j?w$h=aIcv##VQdgQJdrd!N7b|
zE(v86sGAAtcKX@fZdDUd$i4maQ|^`xhPus7<7>RNVXTGN%>)tN`sqs6r?<KM3b(-0
z%eW^lzQ{dpAj9b<2$55rBJ6>&mGDIV3YT8lNT>ej>u9q{i!BYOXVGJPh@oX%$zS2J
zuB3&QBV!Y$99K&`(V{;iz(F9wrYHiNtoV#!7;hMG%>!BC()5`}(9A`U!hC|pVqQGu
zN!RaPwT@X5Tg{Ex{SkN9-Ld9tsf$ecd^Z!C7?7ri9ltwmjA_t&H&5@oEqd$zGj!+z
z?oW?z=bkdq(#fYRZ9<M+OmMPm5C9-7j|F5(yII$)vLZ~OA0t#^&SP!`yeuIE!l(F1
zlPns|%3q^&&_3ljk<?Kh#tWLdc}p$I4KM~_5cgRQVHlt=w4SLCz%B@uD>(#=gsauf
zih5O=rM|toWwmu*tu^n2W14J{Wz`d{uCO*un&8&ChaP^%=F;DXqklLG1R*5?P6UJq
z6cO|>zT4weH`Qq5d?KnvYuEcUZwSf}yaG~XbLoHs4hZvHyAmlPcbOVU+nbD`aKgOD
zJfsbdtPHa;z*>>b8s<T?&iQ6CU$Xh7wQn33K|JG$4JFp!Y{X#&53O{x^1o@I^ArlO
z)Tfi&Hy2$bd^-RNvPMCGPTu(LvKck-kVD;e-@n>DblR!zsq@csTU>pOTYu~!?#?lz
z-K(~#TFvpjX7hluP%h?UHe;LZSVu!UbyyE&a|F<sHWKw+$!eoCeph&AqmNBHHYwPE
zU{i)8lk0VCP_hnY13z5@*Gr4=)HjMv+MpB-Hc_y;!Umcgd=OSFVi(uHHjCr;&AbQ5
z${L>ews3QP4QPyaR7vT(wYbf%TGM^$(3R~t&qv*xzx_Bj%GzJ+>$baMu=~SzUvQ6~
z@o!t0S<uFpcDd=y4KJ84`PM%3muK7?ufO4joY>E8cFrL8-={xx_l}+5CjQ+lRoCg_
zxk~qJI%7=+UL?4THrx8TLGIUM|K@hMZ9TWv?kkj2{out<+{0)5-OazGS$w<i9XHQ{
z-Q8}tZRnOBw1~T5<MHl;Cuh!`<w$ek&HO9(bVt6hZ7BJ~*Cx9c@3AA_x9sCS`R`}$
zXFE-B{tNHc8oR7p(SVRx<QsDMDy?ei-a{q?dBHTZtl!GwC809ft5xKd2emhkC?mkL
z05Sqp0g&p-^RIHlPaNWoz2YGE>Lc&DF?Vca>!oMilNY?2MXu?7%Afdu6h;6l$-kKi
z#x#-HBL4wggwk<64&`9hrN}Kr6Oae%Sm*%^VeJM;61iP1PIQo)vDO2;ge*ad(UoYR
z=!cw%wF~l;R>xWAL{xh83Dy>>|D52t-l8Mxc$6Pd4eRi9od)lKTmyh1?*ZHZFp`W=
zv;a1dFTw$o%W}|SEr>%QK3;>tj{?BM+8b-mYNuk*UMOl~Ed@*N>|u}>9Q6YDi#)O}
zq<_Y3iu|AYM-GXCQY<f4c>*E(r)OlH0D}PF061b@7oVP@@W|OI7l1-!LhKKa4o48f
z7XZ*i=8f!uusV)e=|G2iMy5@fSnH~9P`A`EJQnSQd^fhH9@i&w+yMrtqHh5jM7B=7
z;-Ckx3Ob~7lqp(?2ZZ;77b70vDL|U+AHd_0_XDh*Xlv4fhg04dSEA25+L1Ae+yyzU
z>L1V*;|v)LWk7BSp8;*!g0_Q?q+EcY0P5jb+f(<1L1spIhy!pDI^>Z$jFo&s#47*d
zdd~_<{*jGKX;WdU#Zx$}4^p>MqLe><2jHJhL7=~rcful*gg#>&c{yW+{z|>#;G41f
zPTwXR;54o%+Ohw^oWh>fNSoh~6$46;K?sh>#aIRN9QB8zy$OqP2g<5>0s0(UOh2H#
z=#S)?{*3G%P@5cb0_5`aAKFHsaM~L{BNk8DJHi3fLvf#W=bNVfKpq&kI#EWn;pyo^
zlo7|3euAumeJ;{uZ0hJ}EC8}b;#8?&!-j?W6fLfdUD9FR0C0yBO<mbPVEzD%2aR}~
zX?(!n#*VgQE(IVcfUM{-{*k+7$)IBzgfCoC14RuKHBi(*=hXn9aQ2G}SJXhI8erW~
zle2&ooe_aj@hj?}u0CLzG~O1^-ZX*giZ`qKnlXspKy%(eWMN#`B01#Wz+c`Ne{al2
z@HZe)X1Q!ZAN-Ax@y57#V}4tOvxRf{*#xp7Ey^7G^K6WqH|D4uwxjFC7&32ytB{^)
z?u53Z4zkiUhNbLpOq@5tgs0x@*tFnpNBw$ZB*MKh_uiNkZ@?VizkmO7yezs@Y?gn$
zeyU5-B~PZs*DzfZ;CR!#@&;%s(-8f7^F?^mSo5~=>+iYu-ZH)3%PsGZn(&mr>xF;#
z<369_*BX0B>0Q)U(zHyd;f?7o(@5X*eEihu<=;LNp7h2>djpJmV~|5UW8vDw-~9Vm
zm*XsJc}?jK88RgFe-jv$XjI4}ag8DOrm^Xbfi2UjrT$Fg(KCijKvh#Zq-Bh@H&CrN
z)BoOtFy-{pGUPoQgC5F8x+XBnOqVoF0N_o)<xMb<877OqX*7E1n>L|0!9+Q4wbX^^
z8Mw*&>0|tEqnC=}m)daPz~0_~d;U*PZ00xJbhw2(GK4jdUOBAjthe5L;hmp;nBg_w
zvOGQSP1Cm=uAV%JW<6K>tqFL-&x96X{xBhjXI`<518+i@a$f48+tFc7KosV4^3J@j
zp9xF60WrOS?n0lX9Qsin6JAy=kAX_6hT)rI$MSjGXz`3crIB|vZQn4DE1!8+=6wSK
zR87Al>-PuOt{eK~vwz#t|8D$n|L{X?fB5_9NB9}j4)wqO)h2fTHTxarCr{enKlD4^
z9Uh+j^h3k_yYKAb@4aUu|EmW!_T0bz+Gs!ix5EPMM;_kPKlqzr;T>_a(o6lGczmSM
z9TR?!KC-FR*UWD@bz_@8-j4C`CQ$aqq}9~!?X6Q}jL=wXt={%+S;TL-<)YqX!JXQc
znlB7o75XRnVV-Mm+4ABuk1_`H-WUFz)BYwX_RQP1iQ;YJDXbq^FIB34)?|-9`e?=Z
zM*T^2Y*V?sPX5I`_t^NK=1+{)Hmq|_u-{9DR9}DHb-fAey;*YbX6Ym31zv>pC-l}h
z;J~oH+5L_?egE#={HB*&8lL;xnoQ|cy2kyQwJlEJ(|pUn(Q6((+lcT69P_rx5z^+W
z@->>)tcpk84a8Zl!&>v9x?!HRO{co!XQg8RY;PMbZD`r%l866G<0ucYKWi5Q*_6X(
zg^9zr_r9a)w>N)9{N~KXVWR24f3>#LJWF-=yZ>gWAN#^czwp4ybN!kJ_VFi7*})G#
zZ`Ja6s5PA;+{}Js8|&USYRh%F!M9cl^-A3i*rhsek=CW=4CFhu*<Ge<KxDtdiYt_#
ziMRE2Yx^C4y1w7>C+qp0?p)u}-O-;r<%p1e-z^sQ19n~9uRLZ6zv#NXTg4;p4!5rt
z!Y#IbbLQu?9JAx|f~(CF>g4#(w+~m^kl%h!4)fdIILL2z(>ngxNu$c|*89c^zW>gP
zhk9OVx8xhtvi5JJ9>%xGAma54GQ0QR`M@u*>OB62sTcZFF8ZcldG{qlUJOiFYTtIG
zSx-KZQ5l%0+^5uM>!DF=Sme6!&;~Z~tm$fvr*LK=-5bEen__P{F7hj!S=sOgWGVYA
zPOVq?asyfg-csw?TH_*{RoH6vr}qZJ3He55VW6jSe9@`(YF6L^9kW81O9T110qwj2
zgMz%yz=<IavS`v#o(%jL{0+>LyVf&+oi`AXH*1OB6hX^rnlhv}K&3ZDTu(dIl79m)
zdjsisQ{*UzCojqmY2XY<)+Rru)b0&<*h+S<cyZdXp8@{@Jp=58{3B;lScPMaY?fO>
zIOI5b&y{g&U?y)z1BZSR%h?T#RSru(f^WbXc&Y3U4c4~?;`7Lr8FvOm4ev=`c_1A7
z64W)$K$hMBRjueCpTZf?&6{$QH?WmA<t}fEj3NKX(#bz^KLh`H11|;HnH>k}sRsk}
zwW7z`-jtYvOhtL4Uqe@QU|=xM8Xfrq&VXTo7T-5ywF+mzwU7@}2J`$3xEJ_W`Z?($
zS1^E<kL%39kRdGnfjFjM6ljQMd#?uE^YrUR=)~=k6`%1%AEplYw$mPzlkyrU&KsCE
zl!<cDKJeMhW6&ZF^#?zM+)-nYv7vn__V*aG1_}%^ZQ5D+P<pfnbti|M)Q&Coj7!>^
z{=l9Ydm`}Z${TG%UtMy^CCfCSO@G6Y52a-QR*(FYycp=S>~EmBAcr!miXjj5CFJmw
zm%gI-T#;WGfH>rhXY~OCM239uZHU7&=8!R%a$&3Sp)lkF$5=7Nv{rG|Zj3SZDGZbz
zWFqV>C_d$6uaRek*FEC~XMox8UU_Bi!LetFjKGX=gy;C5=oi<S>+7N}in=KJK+y*}
zvJbG1=t%kn?Kz|W#^35n_h=SWbVY}lGde_Px1+!fot<ub(=+YHuuw;sV_HXHvW1~A
zbJ}5?AY@>3UZ6HR0yHr1Y@BHWDc(ebIU-L8VC)Fba8I~ZR$0ZHMp|GZ;ylA_u<<Cr
zc+oDuW?$npV3c5#VUo?VQ!C*G=?aJmvjB4k$SKZ`X)k%x=nBjT{$}wcFfL}^+?z$7
z5Ej7#0I*m<VOmn5eqoeidX3)GW_H{_V8$Dz0pn{LT>&Tqjkul~p=p|0p^ONtvgj>i
z!oJO}xUy9KDuB3n<2EZV-K`!4++$Pwu)jU)$85QU|J2&_ic2mDY4lilVZX@QgZx5k
zt`&a0`Yz|q0$_;u^$$KYVf=cn+Jbh&nZ|D`|E%=!Lnr~mkI*cuyjk%W7i?6}c3JPU
z{Ag<!X@p7$9u=n6_JYafhXWLq7oR>)8aVm|;c7_(umaAsU(2+WMm^Uo;2!apvdy&5
zCy)03eXF$TOj^%9_ojdTxvc`osDJ<7RzK_cb=O_TuIu`}zG`J{0U+J<qrv4o)l!yv
z@|%VQ+{=7vn&4zHfb>lJwUux!jnf&=^c}_~SLU~@AMZ>PGUVH|nS;=axt$+ECWJ%r
zMngUM=6iskyHcD++XmpE@|kxf?*LWVw8$zue$)?Fgk+WS*~q*3=IiGF@9qskpByn_
z;fnO1dg|W>Hub(=ztUVvgS_&}M+T}lP+jCc`)r0k@4RaaP~Okz&Ev<7JHy(qB<NBe
z7h7zAfx1yFsJsEK@QW_my*zG8z`dDk!?b<OqEryV^F3!RVaIBRxq^O_h86u<uH<3x
z;Q36@K2so1{XO;M=H<Kr>|ND1-_}{D?S|=^YxcIeZe9cW)K=zN@<f=dGG+PIO0QBl
zfMTpqDupTD&Fq^UQ5R|3vZ-WN76N!)n?70T5)Yw1n@r3%%qwiD0r1aLMBqJZMWy-F
ztFN^I?j1j6nm_RG&xIL)bsHO5T0@uyyua(tJ41ij=<IX+xKBUxKX~)4u)(v<k8bkE
zPX64p_R7j{mY?!r7Ak_UpD?Uhvcfjg&n$|R0eWVY+M7j<kR}^dW-Y11({E;3Qd+9h
z)W6y@-_4khs1`o&eVl%LpBFyQAEhTYfB+=6M`0+Be&OG-^IBJ}-)L#mA-5IF9f5UL
z8oh)+@~y1`m}kii=Bqg8bf3Rl2zT&vBTL~TIu+Bi`LuXHvku_9B|D*BV&GoC9qUta
z(%i<zIbe|f1Nw)$HaqR*I^Xod)%;f;+`#X8&yX^JOaQZ`H(9{<-FzXx)P`*p!p!1Z
znFjeR0sVIH^Y!o9%71Qd+itzP`9Vi4H?v;9C;|6Wzg#!@&MJPHVGH=>w^_t@>(RPS
z^Xg9Rn)g2S2hbq^j^3F?A3tp4VZNK4in!ldd-^|p{)iv)&HjF?YX|yoyuXbHsHw0W
zea!;UcSM?euTeY(go13Mku*|y70S70buFxOStB+=lQ{6(0HDmGb4@hV2aw5{b*!4=
zkdER3n8N`)Lw46lzC{brNHt|dKwc<2BJT#wg#%Or&#bhO9|A6>ee&`I@COHvt$18%
zTikd9sm7p~I81s^z2Sxq9U5p+9+io(fR@a<e8_LCV2U5ILmd1^N;iHMJwRe+F*?XB
zDL?Wtg;N-w0UqKA10Ymks5i4%7y8K4Pe0v8QBX9HMJR6ox8T1-gD}tl2nBF0R^SvM
zh_EPp)30!5K`*q2XdsIQz(n1_mmu$emjkd$8zbWbD1~g-EQW=&0C3^pT~PEFEnVR!
zDHF10_7f<#cr1XKya%j@;uuQdD4!CBJr&X5`$ib(py-O@yGLI_xdw$>Db<n(`Vryn
z$l(x2{fahcKGKi)N~68k_5k;)rA>WPZpsAUka3{82B=B-sRznKy~wdYK^%TDK#sbl
zAK(ZBfD+z|xU@6!ZTb@Rt-P^EMBCty570kwF%S+VcpQqa#A95MmRaZv;{(}%=y9c=
zB5MU~&D^bbX1OoWf!~)Od>H+LJv;g#vR39*oLRXmha)`m9!~oe>i>*O3^x!y9)HxY
zSvCyvRizhS*#k9;hppuP8k4#bo;)J2V9t!c7vzP$D4L2x8E7*ApJp|&{GKsKdn2C)
zFiw4^4qBoky2Z7&HC9niMLiXLpy&gQ^Z|Hyfa;CJE#k~QeJ=f8=RFs|JzK~W1EcwM
zzycjcb9Sv;7VR()FjjVySJ)Us!v&^QWg-j=D;t$C5fc0`*kS-03vJvhue=iC0_?*Y
z5_Axji5^z~ZZJo&UuWI3poj5_t=RzlpnNbCXt}`*kRQNOFlxk+_8Zz#vgSrTp+Sg6
z3@on2WdJ0ClW-2^4=ZVaBB2|HZS)?&87|hotd|zrSvWJD-;_K-OQ5%k%#+n=rMy;*
z+cKZUz`c_VxQBb_*^-q!0DC=*86I-d$z^b#Kz_%3JjsunG}$jc<jdte0Oo{wrY`{I
ztduw2F=mJZ11$}{TJlUh1eLgIX~gdltl%zb0VLLvmg3v70N##A2z?JB8vbc~1aSyD
z&^*KeM$HN*nz~|9Q}u>cqWl}Z<^lI!dv#O*?Ev6`Hcn~y>3iC{J%ZoFiIc3HU--`^
zn`UzhaPRiphLpo9PP4AGC&ESM4g{wN8E^<Y0Qfd5om%OT2ZS5sM?dCiggVSUIOaF*
z(;xl_e$=1y(oFkKUbxu57T{hSz9a4>TuaNXXZkM=z&ia}^`~nrY_B(e*IrYywnv}b
zepF&rqGdi4O}>{n%@g6F<sV?KDO09+J2oY3oM^n-F&O2(ruO064L}_SXjk+rUHkRx
zSsphja4(LhH1M+{Lc`_+=>Srqub~`JtM-3Pm-}Om?jQ1S+NrD9x3Zh>)vKHT&9Ch{
z)iR#neP_q={iKg~2>?FPSZ0|X;oUoL@8Dl}erpqAR{qW?URS!tbxXeJe_bhmb7Nl8
zHleTgGq8GAU7)-I(3y6VV}k|D99Xc3;~+3+ZbW#^T8wvC2|mn#ds?p?ZU8*)0pr=h
z;bD9chw+65KAU-VbX17D#{LI{up6Cqc6c6f(Z#`1-#TV(wztKN)2`>9@k|<NJ=8;^
zmawFgD#{QKD~HVhuho)=t`-K14b`kXHkxPn8rCq1PoJ%&jp3hgC@3gyacz8jb@jUI
zjBk+U%8t5b)^P;HT4CEo`~lAl_s4v+T^V$A)Z1J8UVUaN5VWDu-34o5fRQ%8vSh(0
z1$VWH(~fy~PNc;ggf*CW^BN0=0($isQNN<&mRoJ<fBB0C{Ng=I)@;@~sE<GJubcY4
zAKTdP`Ot=C+N81ERtx(jhM1zrm*?|~f2pk^NToX8{ntYStwaB{x$nJPWiU%6I&<2)
zh1crUs*bn6bzQ&E8uNrYUTUNHefI@xUza0G1US=39Wj@%p_CPOukH5nf137)-}vlR
z{Mv{14f$Q`;5l!tjf`4~v|05~iyr_E9F~xfl>$<NN3JDqr7(a*0E-A*lovK$lMo(2
z4!^wcvA^O1JhNj;TUiT^!_{&xS~uQ!W3a}i=d8HML2<H1h*ApRn3z}RL<^kc#94HZ
zzvF0w_&pZgaN`ZE1nu};daryUgT}>{!xR=EksZ<N0l~!pJKhU;gcUo&3Gk!1Syw=N
zx7~JI00_pifH*D+Ab@ZHKLJ7%9b_PYn(P?Y0QLcFhvK{5t2{i*3Kw~cEkW}R>*2^e
zk;ldJKpY>?B(fj&K8Oz=rZqA06M+f=Is!m~F5n@Q>j0dhsE7g!ZAp5hg#&a%nPNpy
zz(#=OII#c+ze7J({ZLNcA>YB#&twdNbZHyFgIJT}hoc{mXU3)a8sAg^f9%1)Pf^b}
z<otjGnNuk%dk3|SBhqi=M_o2*aj#l>?4{6mFp|JYG&lfa)D7haY{yk|6@7>{q`s&(
ze$0>b5ymV1oHVIRept?vLIHAA`aZHWWQN3v`w`Yiaionbm9WfJ(8U@Na#`k2l#A&*
z^c(ni_L%7F+AoXC%)Ef346@RRY#s-V*s3JuqCYWTKo_|*{g3=0ClFmM;Nk2D<>06B
zNcs5AB6p<TC_i!Nlbq1SHzbQUCky~b%D`S2&vCsdUHT-Be94de(FfT#6o4|0%Y2PP
z&Z#&kL(*=56E&ZOKrR2syTTUN#>Q+>Z$-ToeW2(A_4WbH|MkWz!p}W@E&f(lx-Wox
zwvZ|YM)T`nb_MRSP-k(x<BqlgZcbJ$)JJ$P#ucqEX|HL4PdJ#P7=Xs68Vi2W(3LvE
z@`#>0@(TE<+6EWpfeC>DiM1EW`-~YgLVYNYlnKxij!jD#7=hAYDA0t`kpg*|moQ^6
z$!z4d2l$~p>B>eh+EaRN^omw;J({bP(t%k-;7}<{-aCXF)wDR%xG8Y&Rck+OcE?wM
zddGb7Y53v4QgZYGbYC`2FkC9IO4HOc0!N%oz01Ga6;KO~epM|jW0Wz6<{bi`YT@(V
zlU~dWXN8kCB1bJ`y-)o#&JZZzYSs2l>7>tjz`Ym$VH?E;+*xwT(k2@H3}Ge0h8Z)a
z`Y%2k?caKH`*PmbUw^(bxThn4d!)@Ai2wm7VO^|cnU<}Y&x{rFz?jny`pgT;4|!6)
zq&eN__-t?9$zwH(d+kl58S&YWNb4`INBX_$yB;9P<(I8p?vK@gdjRuDlQxbWLSeoo
zd`|($;W)~I@VL`YKfT=lsSD<Hgw1?|&=9Yserg5oL5sT3IzhAnJ~J0+o?}CX`qKMq
z*TcWIqQCKm^+Wh?owjPZ&ZbS-&GTapAdi3wxnFqUZhqp2JDYMqX(Mpz7rXg4UN6l9
zi!IjOjt}0k=CrHjZC1Qz-o|3XtVp{^GoNR`f+sHS+swzTW7rhaI*~dQV3_dIbl13%
z@&%g@gw+~52JYd{y5(2{?s3123BXx@bI*@&w=4jxY5)krj<Dl#d3Vi47y3)5P4~Z@
zKEv;kVf8^YYPs^QW5bHD%uVrzR4wt_69#L0QjSsGwTIs96c=k7F|S45)$ep|v^;El
z#2bTH{;2j{qrV9#S}9$=59-gA!WHlG=b5fhH;i@G86BM$=Esh7dHG*_@Vx$OFK^+$
z@!mE8yfgB$HGJ>oO_pGQy?vh8#2@qFwpA^vahxk4u+1)Cqf}23ATTf8!oSq-*-StD
z@WWg6FRZq4q$+iK^#8W48&J1Uw}t)s`>f#)eQEOm#vJ$YOfV)iSKE7OzrZRzJpi*>
z+aZgG?rw@Dd;f9ckWY?Zt(DfC9&WMqdWSlpuKA6)c;*p<^_HD)To%x%7COYs1FfyJ
z%TnRE+;dMmzic6pBas8$*?j!67X9qA&%)8591&TIyeX?brRd2!tz(f9HA+uup!7=q
z;9apyCjYFf#vL*+KrVn&vchHgDK6`7Tzu4TR`^POqJ`Bfj)}}$+u#on1t-9e;-VCY
z<5+NhI4th*jg8Y*c-C3C7__EvyY04HP^Pc64i_EbV4WKocGg(ZX&fk}YyGFVs=M^a
zSmh(Vk_L`rNz-+!XtL)3_zAuT08UH;Sv+GFfDZW*7>zKB&pjY3<RpNX02#&a;8Or0
z;-C{B!K`o`U5cVAyajTVG_S~6<OHk(R0r>cayLLM*0_Mi0At}$<VT^4I*1JxAXlRP
z<p6D|ZxD|7>=CdBG2Vcf_zB>|`X7Z&#-bc@d+LTfsP5o7DK~VG^8wfb4A03aIGwIU
ze`D;$`yZmwQCCiJ=_uNw%ebRY0Cq$M#h5`ptfOwJ7vxjSA&R3b@-&Px;0Qx|=)@U}
zOaSbpPGfK>&(H!~Dmw5|v_1WrzKQHjN8fVnGT~5ydC05-;xL3kKcLSb=gE@K(pQu>
zfP~OsF9}&QP8J502l^`DJ!EkB(YJB&Njn1XrG^0Dt!1T_u|d8>7vL*S07H!<y_eFu
z<h<#7!eJZ$Sp|KWwozO_q&UWj{J4@I@`%&%#qoQLiKMQX`)qMq#eLW2Q;YT~+NbCP
zv$hXll&NTgS=$C(z5dhhb>4FU+_MExS8vSOF@J#4V#{r_OBvY2j~xtloOiUcFz=2Z
z%#aq~Ff?NNQ$Yh^v;2sgC*X+FY{oqrevdrzNUO4>bXA@>uL2nW+LAvExi%{(gP0%a
zqoD=>5ZYK#fmu(};TZ;nx`v^K`N|5@h~IVBT~}r*8;P$t2*A(?({p>T^r=Q^Ajpws
zTdg!=;9d;Wi|@Dm!F3@|-Apj1pR@K%(|GT__sZIo>AUn9CSUbdPv53L;9||sly>|~
zpTWt(WBjh3djtp)#%0A*I@QKb92X%fu9^KB`U39lYC?6TH{s8l1(42w0VT^Y2oxBT
z2)Q&y0rUan`R9w<gm^=TE)bsaYc_BX!HArMA<P^6>gf+EUn5s(A}Sxc0wji>@}sNL
z;92*bd1XG8K&La)tv4;&Pjwbwp_4iUfwj`F2e_xS5ah*9z;d*w38S_|QyiBC9+!YH
z24_^tb3MR4K+9}C#PpOW{A9t9yv1QE-5+|$^8SV&mNsV2K5O;z7~XE%#r>Uk3=PjK
ztu&AS{i7w|-t?(s{ETU1On_dR(<e{bwVW1nf{xa%lxA0d7uRW5&+FWhKedVaHX9^3
ztVd7>dawH62mv`PHM9B0*wN8lY@#4+N2!E3t$=&>tz%7t9f#w;nCd@mIYN02vz&m|
zJsUUz?yX{*ScJWK+6;ezS<(LTsi%eK)h&H2`6)k*T-l6}#(ivQxDi_Q#?g1RS!>^V
z)1TGhvAQR~B3ITx02-KM+mko?srWlKjRf$oL=&YIHcTpo;oW%KfJ`@)s>LbZw{MJg
zRQ;v%Sx2O!`LUy}S$SS)&6$?LcDQXl-(wMzR~Q&eVZZvwMge%#t8evn^MGAT;Jq?P
zX*LJ{s17>%%0_E^9GcSG^rF?v;ICS(?$J&F{SNu(W@R8Iz%h=-?XhUL0LYxTpUrta
zYaeHP_pmChe5F@e-2(`izMEU0v0NBHdo9Q0j=Z!5(Av&5NF(*he3BP${i9d#H%-0N
zpMKo8P1&K;->|@#7k+l|XRZdcgwhnCC4r9W%{$;E;<6?HWQC<L05Mojs+U$A9?%~$
zSLh-O1H>1<&$^eqiM4Ll`$~RT1?D(M05ixTDuvB^r!^AtUL2sgys-G=QYBy%;BkO&
zPCDtN07#)OQ{@Y#C8gLbAPNA)M<0E(qP$6TkWsN-OfB{)|C0Y@DF<Vv5GT2k;^bYC
zlf%c*_W<d{;glaKmjXVE-?P5}K#e{ZgY^ibxZF$LfxH-STMAqhhz=S8zoj(TE5K!)
zG=tSK97>P0A%H%Bn38AEZ&`ygwip+<d+)tB^ye6iM4Fsl0niTzZwHu6{em&d7^7{-
zGwIM5uwDir3_ngv!SQ{h|5C5<8l65OAR*xZJyKVMBYiB(;b=#A2-3m<B8%&kupRYd
z>;WY1DEcv7PL9H%SVtWq*T=C>2ais@kQer3s5gEn?TZF=%lt&1akTmI#~&ZsjCWXA
z1oTL|0T!h_X&2Ip=`aUlg^^>M=^Fy?VTn&x@?-^3r&ux0>HAo&lN>?$;tJ4Ic>-*T
zAN?0co?=ik{Sdi7`68S^ck!{b9NUW%pi}*UIi9)#Jd2CJAF`S!#YDwXJ_X*R&hSg$
z<44|b={o?hlIO>90kQIpU>;A6RRB~?9e`Ekr?__9xGw6psN13s6n!A>13IZK_ABm-
zJ`(qV^n0E6+;+z`2^kYD?u$-VOCP`tb|v%nwb1D7F#Sw>2Ic}r0Zl&g&#|#_KF}^Y
z`|Pt@`RP3i`pyvGE4_NJFz_%O)#8a^fcbzaL1QXQr~$J8V+TEe4={t=lTOTto`3%N
zo?n`-7<3phnH^8V)cUME6CO){Ss;yCX%P;lQW{zH#LEf`lL?b3VL?`yEI*YAlkJTl
z^<e<tpJAKXwo%P*>_1-$ZP3FQZ2h|D2n>`q`9k=S9<h*>U;GdT)yf#gX|S=r#THux
zp+M}Pmk#|BN8a+nWckOm>31;N>W5k3DG%*Oyj18EhmYz0{O3Opd80gWm}cAyxCh{F
z^UW6t^q-utc>wIpGmkS9{{IMR17bojC?OWswTSbtm$nUYnho4TfP*y}G{mVd1b6iJ
z_G+HjQ)dVg<q%>3W*`sxQ6H%gvmW}jgpK=w!eiNu_Fx{Rj&w!n$TL3#U1;6nct+5~
z+=@e}h#*x0B+;#O&B{lm@XdI~_fK_AzXQaMQyHm))OxMbz34&%;ZEN@^wDY-_lQGV
z$^j6Rzzm_dXvSB***FBr^ildN`Ppo<%_<7_V;bBqwNz;wF_zM!a#yqCDwt>Qu-}Pa
zJ1pd*5+Im)hx+15p6CNgFR%R$J9Gt4UG2Gt&3~2$epuln-aWrCtz3Kl_~XC&i68A<
zw!p`6xdQAl4*=Ht-}^iHzdY0Y`APYo-LD9AS;GL<sWfNgwbSfg{$}N${tYeai8%r5
z5m`cafNm(ra4Zu}+Wc%1NfV#VAz7bz*EX?EXj#`lpK+(>6Yc(}_ulvW{CPq+{&qwA
zE<9~V;cRXSC6AhAJ;IK-_>z#X@7wV~SDVE>Eb%p?%xpT0H$WD&IQ}R;_Ny86tI^Qr
zRvHd#q*5wM-^~fnSjmWid-Ng7#WzXi!y+P^K>+L69F;#;j@VM4<N1$&{A2hw#9=DU
z<#9Of;T_|R-mH0ojsBEJ&I%XPW}`I=5MM1`OryA;J!Lrb>5g=9znI6?Imds#y+3a9
z_I}TYH}o8D+r7nVZyb*MgPt7`KuA5zx^Ma}eO_tw5+Q6Eyl3&#FvauCI$@ogTH90o
z2uKA0tk0%1t+S+a0TvNC()g(VZtai$VB0d-Z`XT<gm&$@gejlSTfOa+ZhFPim=rbB
zcRcU@o6>Q)1#oX>yHYRxc37<37Q-)Ctuzj10hpCJC9B==TlK3;`5#Wbz+Za86@KU`
z144eWo|zZEtNR1|0T9$)fEwi#pbnWT{A67AbPUGtD&3PW09v)mk(Ca7Etae*(aw7Z
zpHt7;CZ(f12+YGZ4HG{Dj!Lb!={;aGT;2w!(n7|FQyVBgAR!zIdGgD;ijIzXjV;b8
zJiH4I`3>z6=UD(nl#7rHR%<W%2rdQ&DIMj7XO6=J1c*!^_7~W$k_=K-!bk@Yn&|5)
z#c04(d{^WjUjfhIkfEn?e1V`a$RNIsJpy=S9P&2ekay(WIM%rM$8|?~qD2^FFmeB5
zd<sCvco1+?rzj8yhxIt#%Q7Eyu}DT-e#p>p#HWuFmg7=!<XQPfR;W4v1Vz}m?)9#t
zu8qj{8_f%K!I+`W=##2j_#$LbalKFv`~b}HjymOv6@1dCj=uAq?}Yk9Q5rufkiBgC
zXgKN<fA}yQZ6b?wDuenhfL+ZY0`Cz<V^qAH#uBu!@F+TrLB_`+ha3{pk|H#GA>cq<
ze6j}qILX~4Q>2`LexVCJfUsErR^AIdti0ieoCAl_AkS%El8ghZq*?FrV;liu<Uan%
zs0dbKV}ISV_dtE>ISaT)8|r;=ZC$@beH8Uk^ns!ebW|VUdnnL$N2O8FoxS^SSUlA5
zuK?~Pi=(3Bx4+}FNy1UHvr}ENSO&0#W{ZwGLJ$y_;i{{y3ZO6q3-JOu4l}#mr;G*p
z=XeIdBHRNY5dcVKg9&B>N%aH6#xVzCD9{!}n~w0rjhSowlqL)k>4@PI{d%sVb=YBt
z1%U<3Xv|2}LyNF5hHMhIhlcV5JzTv)HN^*j2ym~OU?{JQq}|h)^!Zm>+4t(ZT&upE
z7e{FWvcahz<b}tdx&jCXGaVlvk{3s5=$MIm#uCB=9NL21=cSSQD^0)v2$tBu&FgoB
z#iAD*-f7>@qbuMQ)`QUCYqZT$`tfrN+#5Ksx4-TO>v^nd`56;T`}BiQF9^6|EkNRT
zvkie^!%TQ+z@DGoIn)4_cFbrC@TVS&d#Xd_iQ_GFEMh+cKCfn0j?lE#`}SVb^q?ne
zjsU_)n;*iL2OoSelpBBoK(6+t-DrG{E?^vxFEk|i4oWD56;GT%Z3q$piUWK`*h;^l
z{?IB$yPCO(c}<QnrZT5jfPjDk1(pQF#wI|m<;be*TI1%0L&Kc%Q7&0QjLQH(>b2KC
z_8<Q5#FqZJyB(uZ0;I+?v+_pWNtla}49B>>`R1G3lo?<L4ohCpMKc&j{U8+QNJRQm
zrTJtD+u#K7rg8I31bka=>#hB9X6*}55Wu1J4mq?<9^&$(_lz695eVxlfsyywds+Ya
zWIM9N#x4Lp#y?j0pm*;*8<q0rKeX#U26CF$=C1Dk<BxU;{;$5WT?hw=Z;ds21;6|5
z-Keg7rum*zpHWnx&(U|Y`W~AfvL-sG)<H*=i?tz+F$mb2bs`$~0`;<KB#n8kFZpI;
z$@yWk==_;kBRS=>FZ>HO*4Z)|`_jMs>juJo$;NN5KFit>f2#)Tdw(;K-oelQ-9KgP
zgwgju5c>I-k2o@n(d!MYcbv_!gO5KUjMr7|zLD~#eS@_peFIu-U?HeE>#VZ^_>i@Z
zHaHqdt37epSmyiO9vWRaZVcR`O|%iqI!N;=ZNa)=F9T@;%!|uIxOg)YK2V!Z#Agl2
zW)$D!I3D+U$A%c+T;_7-2G)OAZMf;Co7%)79(@IV2`4}*{_vhz>F|!tCcb;z^TSFF
zn^N4TeNkbGYy0IHU7-#`N9n+Sp%0nz4?wVEKN%IS9CKPr**S8RczNJH(dfIy!Xez2
zS2xeXzG%;hSL7ya9A}j+2JS7racP5G;XZn2q8~SLl>J&CfqU@tBLl7O^L6vxdNpTU
z0Nm3?x6+TV06*y)^RCz<vm9~Qx#@nHVGD$Mp>FsAjGGnZXG+OSceTBi_Sa24*IzvD
za=*c8CE(tQqh^BP^Wt?ye|SUkRjItS<Qrgzlmw6&<0SJ!S+f#|wHkeBA=j!FE`ASq
zC0;+o@AB@k{KUHSLQ`D93RhmZTKxsM1JG6}4*YEl(n`YuTEpc5_XrR02Pg2DXb5O0
z*}eSpuCnN*wPG9&zz$B}oA{lqX4P76tn+YL3QMAkK>%Eq<vv-Jt5k2YHioPin(Ck2
zGdIhzu2q`x6$NEEjslI}(H3&Cq6wfq9DELIT^(hLp8!?t6NnZ-CAt0g-#_?EMoOEZ
zNGZS{<BIhxv~lv+vC%PI=>PPmKNacnDJP;07>)SQmomQOz5<OgrU|1{97c^ARnE8a
z-%;1oPd~lW(u&J~QkfilIPX+1$S+kN+#~1ZJq9`Cu!b*sC_c%>$K?XT#ECZR894xX
z_^X{Xu2AB;;DQT6u;IgphcNUfIpQFvW6uF6>vqZu*CUTSGQ5jH67(G$GRZhBew0=7
zE^@eZtjda|z;<zbWQVwTK2pEU${TS372*IH#<bFV`X!+3*dNP*&p-csE5G>top;_D
zXv6OdtQ$O{e{uNYzB%jQq8^KSEc!sv2P*Xe)-<!~dtWJE#k;Q4Z==t2#oGe7XA7yK
z<2JVr*kl_$dh~2ruUHq0wSqAF4}c0L4^Reu1ttJSV|MB{@y1SkPZ_XC1cM+eVSx46
z$ddK2RI`ydfHULr14?0&i{l#DXhj$hzvrHGl{Oo-Fj!(1)3CKXix$FT9Gj!H#LEf;
zGl+)Lgb5ScWdTrm`oI7CzrX;ZQJ9rpo}c0)#KFo|BLG4)J1NWB1_;VaC-qml_;chM
z+HZi{QXxqik7sGGp%GT?xDKVmanran&pfltyl2r){TQ2QhSqw#rP6~A$5*m>K4Qd(
zaz9Mz^NdgdVFn=HtoQO0ZE4XV2u0|m_szbRfO{p&WY7L>OaJL7yBcWk(Xc_h@y5df
zxPde|s!e%|^RU-f`vkxafS+A<UNYQQ1Ma1C#+yYU1e$<b(Y{5vBEe6bR-^Z6dI0hO
z>+zGY5P<{&BGTqZd)5n%Z6tpPLjWu@zNiD`OIHM39P@&Ji+oW(61eJ^M+vqFqdYJN
zN<$Ps;;1|T11KK?8^)u`oL&*$Bj_Oy5^giTs59k{EBWU8#kjuhw%a^q5@<&G$-1WP
zk`<<s9|COh9G8Ll;^#kq*!uAI!;!84(%yXI$7Z$jl^{rq0hr9o2wo-VQ(BY}hhP@>
zxLMcIJ6-$s?Hkf%Z1OFS9p820@|_28L4ENfJ;mp`%rY~<uK)P^R=!Vnv%<OBTK)h7
z2OaXSe+NL&@l&RS>$pji{W8{8<@om9E8iQfynBuvORbv%x>ficcQ8wXR!0Kdacu6f
z<4PNT0`7hG>7G^@e>BSyYykB%AW1L(!FwgZ;X}V0?mzpqv@RR9-Qsh^QMsy{&bYFn
z!x1R}eGvANW_!m)XXLvprNQ`RoymL!$QY0$R)w%OK|U|K=%VsETlr>l4GU(YMr`Vj
zfAzI6#-`dJ-fGP?!o9{b$KAeQVEZ8^eBWq~J^g7@r~8*}Y@cG+JI4RcANcp@LijDO
zyvkp5!twsH$)APiemn169%D+g(iIC5m5xJ=`vgEM9T^mZ!z$5l_B+7fC?N=p7U!?o
zY0V0Hc^kdV_kiafiY&0Oc_ih6v>sUZ^39IRz;~Btc-q)s_oTr)uB?NoV|XFn(@!d`
zQ#po>u@D3G=*zXP`JvBxgM2GLX_@3-TxWhqXUDqG9M@6T6F9cB<><~sUToo6qqRr1
zIy>w?TLb`7HS2A%x;Om1Rm*+08tu9DJ#v>k;2y`T3iMTOyOuUI2!g%aF|=o>JK9k{
zo0pndpVchxk=DL{9v1T2t55f)rrpePbT+MJhA%j?{S3Ib;kQ?sC3VAml2`V<_t`s4
zH$R?wxnE=dWy;6pE<3U<fIW{!SMle&s5N^dc@lU;igB8U0jIF05GV*G6s=|JNe3R3
z@U?!wrF^EfMy+Y-2*|uOQ>}Cqj^i0|)hvghT!~Y>^t#I~y97(ffLUTtP#lgmH!hV&
zvwp%M--yHK-T&@)zYF0}%u2%uXs7m!-vcVb)q0`}YeHGL%4=8Z4VNnQ$f_V~!MwQ0
z3UC0|$Y&f5*$$55VAFSTnc!t{fBDN_+Js}185bYHiHt-JfD*Li02!uf=@?YP#FhZ>
zyYD_l3s=UF@&%75ClFv<wxf<ZO0UB;-fN0wU%b?~5^aE6sY7n7e8=f1PDfs6^+rB>
zHCc`_BZo{K@>t$!+{bMy%XY*Ok40D%nB!w!;pcHF$Qdj3Sx#U(6q9jk4`idbc)w2X
zB$KDF$WI{F7_3WsYyX0|T!27;y^6<`a^l909ox#EcPPfCyuI?4cLEW>X&j3FaXRTe
zVPfzea$z~fcp9E(ffN-kzEWP4l(XU$e$8DQ7xh`xXVC|WKA=9p+&ink_sUCg?V9~E
z{#IAIpSkMJ2~{+3*KFXf+AeHVp_#?PwX5baD_>am0Bi;DC_at|fF9=5*&va{9soUg
z+G{D}hpGLn^k#+M)@B(<Wgva!mGa2!KmI(Ud50z?+I$Eg;{2ve4etP?pgD)&18_!6
zA7KoZE<^*S1tzVMg(HQncE#!h+G7ZQuy#=`e5LnHf!erJ-Bb#nzEd3f3r@$prQ!0P
z6`%UUU3%%I4V8zu(vB3(yfkBf+8e7Ofa&7R#5hdWJr+y=<>+0tG{y8^d+oI~0Ru%B
zP|h~nY*TYO$_Lu9l#4N335bZmh;TTiDH?j_8v9XaXu2{^I%3X?)4KSgfdRNPWBN^Y
zH6fybcuqcPSIf^#%~tZ4rMav8pY%;TF2dsd^8Ss(H3ja)=_wA!QXsGcu!i6Q`tf*H
zxJIr?6P*4406+jqL_t(p>0xyVYcy=Q;}C9;CO<6LQ2^9HE5FxSX=Tw$!&M7|Fo`)k
z{n7Ujka9c(z~*hY-L{-3<&As-600{&=FAwlr})+Ks&Kk8Z;%(jXw(yPfbte!4?g(d
z0N|r;nXCB5aV&gXW`yJjeHk+V2bD&%uYLNg?jQK2S>dui$?v>#x3%`&)9-9xntSfK
zCxB=XM4<)E5gMu!#xcU?)FFr@j|!Ip!&#qD9>5j|>HwGvTtS^tchX>w%ewT^Gmpvr
z&-=UhZGQBi-|y*X0`Lb*d$PE<*;U{3yZ`og{%fzj?#KN4LBGJN^?#F;ccnjg@O*y!
zZ-;rnx_95TfoY<bfPjGV1`OyK$_toJ#|``GM;Z`uvldGz3;Xxq-O&s5H)Glu|Kpp#
z6ym(~=Jp-duFCtIbY&d`z?1I;mN1#8RKB#$<X>E?^G{ghTyxDe{XTZwG;1ElHb<An
zU@7h&xcxRi?vqc$n44k)fBV~h;-5Cldc4EpGi?wnN^D^3&KC_}KIDWG{h4NEk8#Qm
zD}5W=cWk$tZVt~^8yN6O6V#KBTG|Ui|Cljjs)mjG1XkggpHVImD7%)l+8aiBpkA2Y
z1qx#guXpWDV^+s!BZdv>tbEW`d^_l0vVI32gq0J8S3O_}mvsj~OTcjYE^q`FVNv`L
zFGO7`e%2Msl7LWoC;D-?)Q?Rsfa-w%m|x<smG0T_L<s=?hdx#*Z1Jx3TokuKeBTx8
zDP89@`}x>Oqr#D@jh21vKQ;?Mq#g@ZS5m-|7of9^&X{REve9(s)Odg*PCW6%isO0f
z>(?%WzmEHKl&1};S1jsvX1!D!JNy1=laQ|hxHohDNiFVeczV|Z?j5|>Sbx{l8~jaE
zFZHJ%e5U7!Yx>u+o6UZ}y=ILE$&Z+SSqsEYa#8r}yY9NHrZI?>uM}KVEw8dLmMVEw
zi^qHT7OjJ7312M?z+&WhmBImDlhR05IM$}P*jj$N2153PlND~!p*?Ufzx;B=JSe=Z
z9A~Y;1=^E^F~#AEauL?7V!ulF<OxN}tT2*EU|C#i#5f$X7+eZaR2Y<<aO!XJSDoo7
zS}Ee|eSAgfLrzu-l`qL><Mm_=w3D1&)&OI=d<&o>88&HfQW0VFo#41toRnA<A3zaf
zL10Ju6Hk`f0pH>PJnB7T4M$wua?34Oe#bNZ`ti&WzQjc)hKqRt(d?}2tlG#2_$!NV
zYBQA?kge*AlRxASZ-S!@RM)z)f243+rDQEYo2(fEzEe0^@Jq)P1`BZPLkVohvuKgN
zK-lm<IOYfF!taTG3<Qk%bpXCN(IdR(5m`@VKLeS!XmQ2B1r9ky?9V+G`*2d!)_Z{z
zAF<N{<fp4F3j%JHU)B|?hp9iZUGb&qJDyQ=%yWz{Ss2WUTlmdfrxkTk)J4$;iayY+
zKEN6+URO1XZb4_x=%abx?9{&i?%5)z==9C21B40jB6)VHTQn)rsN>2e2mn4z{{vJ(
zvjq(v7VdF*#E8az++)EAAprMS7E9mHs%JK@(2xfFgdl=^#AP6_SV6)c4ZxHMQNFW|
z31IUy20Rj0f&erqWyT+W1O;rgie4jE<r_hiw7{Sv|9WY{1mW64gSZG|(9ViAy=uve
z^37)9)mL8~H0=iu9^6nJ#SD2pX(P~s;mZ>kWzoVaBjbj5DnnMd_#GRd4;d&+8k<?+
zV?P8@9I01JI*O<C7|S^FiLgrVxhf3zKl|Cw!aD#1`|Y=1t8iIyu#N>t2q8n3=58FP
z(R<~Ov~Ius0sqe1C9vL%>BpJ$WN&}@Wow)8xc<5OtTR^+ASr-S0`4g-@=Bc{4C6<=
z(We>nfXgHtY;T$3_y~V*x#gBHZ;~F?9&yaKny2FM+{gJ%-|0Co4C&F{>5uX<2WI^^
zb^!qk0%(C^l}>uiOE(Rt=RDw^-q&)iCr#QAKpwzi+L~`3b5>k#z<kU@2sJqhgE32e
zQAgKbf4#r|{`+&s2>GogKMHf<1#7i{&UQE8bV-XF8UP__f7}f>+|VYy`1q9&$o_GJ
zaI7H?9lm+w8(==)BKjIfTcCN5ARj+{t5nwjv~<t4PYZBw%Jkj+nNuGPfFCS)^)=x5
z);HW(1_iBo;6c8JZMu&A=S#tFp9xR;o@O~Erd99#@Zn}rpat0YpMP&_z{7?89$#6;
zZ@2B@Wx99$%!H~I=EM_L_V2yBqaC4mnLqg8ulw`QThk=h_l0n>vPn3scfR<K5(qlB
z>Q`^xX2<aAckRhL0%ELIFb;G?R5R$dCr`6u+_=tI@1TT%Kp$Z~M_KFWS<1GD0k(hf
z?t5WeO*FvX@9nprZNA)i<Bg$zGmjFVu*;Z0|7inPJ#Uugc;4#zAC|``;JtmHe9G_h
z=Lvq)t+)Hlzq*&7WSbROcVJ^s)<~c$3oBW58|OXieeB127ho9t5&Qvb@%Vj5-YcKX
z_u4>BEqruj`kg`hoO8};^UYDYNuPcM@SA(AJp$5>^TqmxjVU&40OX#3{`tX@4q>#G
z=Nluwj{0K`NUhb<uEYhvpMFE)^mGr9lf1EJXY+_n@J1Axu;`02f#??3+SbL4%h-B;
z+-JH{9cq2kQP*~r6>DxbPO#Jmu&2ku)q!J!j#w^)UH2PpE%Uc4FP4uvAwJ(7c+pB_
z+2)2pp$@6@1y^saTv;hzGv5J%PA%u8^ks2xo@EM)dvV<?w`J+*XxeYsxvN%e>o{IB
z@0m}am$%M2;?N_*lymff<7R7%d(EmdDnnM806+j-;RslOBgpPhVim{;U`AfLD8}-=
zM+q=5OqRcnxI`wD^}doH;2M<R2-A%39Lk$n*52Yg=(x;0MPN>D!Nuz+WNNITVmeql
z2e20VaSw1uAe3rlLbikh5R}qkU5tC{t+(3L56XPFSm`~DuV>bT0H3hJCBO8Fd=VD|
z#<b?6oRYz(@#uFb9^$eTM^Gx2%agA&&iT=A;jfeivQd;JaROo@6QNE4RN|x*1-J=E
z|5bdh(3M3nfK@Rd3tCrRd1Z))%!%+SGp9fR?23VolofjLggD;EeT2G`)4EsrVN7A+
z4IrR^V!T7{#o8Z7TDUB^Qe38vzMoaVJ(RI=%wehvEPzwz)D=JaGFKdRi5qVxu23hk
zK8dXMyz|Zr@A!voRC0Cza*9XY)0gOXv^O-^U(#tw>T^<lM{!DyQ$ZLPI4nd0m}btU
zjWy=syKtJ{#HW!Cz){>zJMC2Nubd8ri}yM-2XLAU@z^KR-je8Hoe>v<?UV=hzi{f;
z(BQ{jqQIi^QyjGeV^8nWD`7wR<dYzy<kSbfM_CQIrk>L)N9v~T-FM#&?Sp@%BX?6;
z#q*rOR}}47v}4f+W?diP+Z9`>E!tw%wMAE~`}BLA_gnz?S{7Gbu@Ps-JfTHZ%Z5c)
zFB^;P{rBHLSk^;032=`EJx3Glz4zYX=)$}*AasD?$$LL5{KY7cHdf`(Hd8+I4rYdp
zE6OTAg-Ne^4*&vy4T2eIA|V(NcmjGb*=$-u17-sTJ~pME(ysLxZ6-h@Fn_hi$qEN!
zhfs!cXT7iHr@9~<0)|G*hQ$pSRJ7rgW=vat&Af^};}(IFKx^`^los`-P3}tJ^$sme
z+KbIfnXA{kG#&W^_{3qQkudU4!^h7G1DF(THMI655KuUUVXVSD>lp0xy`H%$4#FP<
z3jmv>^{jVs_(twiI*c2R?Re>>*ZgtE4)f1Ey+s+sv(ifWn>A;iv6=^<m;O#WbrpZf
z#RFSt?Ct^lD4(<sb%7==+J=l<1S;{7r3zc`H7*x@jX8iH>9OgKL#r2|EwmdQi)p?P
zq9g1k9ext{A@D#5!?@Qk1{bGv(&xB5>AQNL5ej8l+^d&Pv%@2t;+P7KBB8CQCuyAm
z@?{*WUUkJ<XWmgVd36Q=Rlv0EM=cTBKL*lCNXU3&9w05ir0KD@p%dA^4?g%H2$>P)
zAta{F5y%5f<QooXn7RUVB?mAVp{43j^NpV8@7>McI`w=0vZ=QO0MPvg8oJ}X_x(N_
zZV-;el{K%EpZTlbq<=rZ$e?xnz(Wu7N4)i4zsoP~_1$e_K(y+)F1K8dU}cZ``Qu|F
z{Sk++Xmiejew(cqYXx$|5+7l(z{io;7hn7>V-W2)ju{3jJpNn3it4e)p5YHYWO@J1
z6S2CvdjR+yd{9X{eQpEq0pbC$iQqoVN@gu>#F~fs5i4;zt|zaL*FtY@4x@TQSk9&-
zN0cIDM&Qk6mFi}qtx-=j;N8cz0dUu}8U8o}H%Nn#jR3|vVfkjUK4CNK2wTT5xxt2E
z-0uCvpZuUBj`Uys!(-ujwHEF9oo$|=KOh)q9Rx_3xTz3c`KafrvJr>%FW)0+`^WF<
zp;>R(G#%#Y)URHeT`7FL0S51tuII=r06D$^%6D9Tco#O2c?Tf8PoF;FUSa49tW(&`
zA`Eo+Mx`6ftZUiK%1T3i@fE9DF~88rIzV}ZZhRl7Q`~0@2;oneyW#OuFlSe)Gp$oP
z>e}wICg8oEi&X~itu=Pp06H6b>VWcCX?Gs1|IaBjn18V5Ua36W-Z&Gu2MDZE*p7UM
zoD3^Ud1;KkZ%AnC#n$UxI<j{TYNxz1*W%xAhs8?3%a$XmzjQ*s+&Zj<UcF&hBf%F5
zoI(8f$V#4dEakXk#|3a9Aj6e+Z=WJkz4>f3JW3cbAK56KI3AXyIJ)q)*Io;4#XXyG
zPdxEN09<he(1uHSNYSCb0iNLGSM3TI2L2SjyAk<1%JevR=W1zHd(RpumI>vpIjAF?
zKrbm>);+iw;01^t7h4;JXGf_bjSq-N0J*$8D2{+YTI0!|tALoYv=`H&u5mj3Bz_mu
z002b$2q+f6<DP!V*kb*UOV^DYuZ+u6*5+gYKvwj(7z9M$Bmb;_aal)sDlhP>xXF_z
zx2j9lK=K!WiZ}vf2~?!hNC0{XoJBakRRR=}zws^LF5bcC$nqKa7oQAYCGeDZA<^PA
zBOGlDU`gO96ghG5X8^*EJo3m;4*DZ`11v}!e&mVr(k6<7KY5pRHUTK%6G?kk{9?YP
zv(nG%!?KV^T~X)M6^_0IPYiDzTTw)=j?<NKf^skW9{?wD;@gp5LyvZ5F3@-*i~y*b
zGw~BYPkrb}aAY*drIj!GAJ4QWz+KA3zLtQ3DCfrYNx#O$fL0wx9nWR#696uhG6M1l
zT+B~uPa;0~MRm&e6i45S)93UO(Uia9Aa}r_eD(kAoe8{M)7k&s8xiwVa}hI%IU+<v
zB%+9!guYc`h?s|n5L9XLe^r&1qNN(tP^}hGQ!9oRebrbsw1$M5XF(FVH_7_1?>gsq
z^4yiZ&)MhPb8qgw+3WK;XYIX)XU%J`^{n6XEcwMY^^b|K@Ma%My~p9<al${LJ*=TE
zsW*jJ9`v5u^JgB&>pidcybpB!KEQg7^-$i{UB9hsX=D4XF87=R_vX`ry_QDp3~^X|
zO0z*{$Zxqk!ua7TR>CmZvfyQ7%~c3eLyzVKHr1ljQ5V8#`4VG`jRimq%E|SKY}(mi
zsa$C`n=t?#lAa<Qb;N-<06u_SfPi2eaUg~^3&0?0^s>5>E}EdEkt?lymFU47N28*`
z)k=pAAz&jIM0O)6x|M9=04BoN4Pm)>wdPec6`%Hi$;J<0s{CVH;c-VBfa=Y<r^Vqw
z6Xq9JFaxL~Tv|N2qXh+tUO+?T!rL?z7vL>w{?VdiueFvxan$zZq}D8Lo}mT!5JrRi
zEKRw!#)e68j=u_m@xm3YFsl0YEo|y<{LNM+kQ189jvTeRJNl?KTKq;Gv3e<t08vTy
zYp?F=#+|yUXMl|heHZ2gbpePe=_--dl!>@#CZyjy^UO1WsqmZM{3d8TL!y$h+AA(<
zNsIZKdLs>fFg0MZ0K%Xj@uO@UGG*2FSW7;tjnff<d*%AHz`D%8)TgamEjDRS+5iST
zwq=;9ew4<%z`equFU&wO@c>!!?ZA*;l7ZpI4+B#UO=vKk0oO9;Fm}=Q3ZoYAJq$(;
z4TuAXk9H8SgEYwJSg+9)yYCrimo#=={oYmXri(9k*I)5-H~!L}x}jHJQ}X-Xzg~2g
z`)}tM|6MuROX^?#^1t2b9%wY=iYr}%mpWG2v)X7N)Y$#U6N4RE^ETROu24W@W1+5Y
z|6>8Tx6L+a6)m`3b=8%AuDUIN=v80)?|+0pJUGl<b)}a)_dq`Yd_#xM{mw5a06xH=
zXkcKz1B}YLCqA^y+D2mT+QTLF?W{ENaPzJQn5U5n*VRna(NuqM3UCjz{f{mRHbN-U
z#!DEh5gG>iIbOTys-CG2xRyAC+3bW9!~15(pD@pu1UyB*&|FZ@w&5_B{Q~O;zE{vj
z6G}Y*g4N_xK#NM?kZNeo&#(aNls@w~YYX1(IAmXvbeL|#Xg?%cvh6_-+IKPXv+;3b
zZz3Tk-mQPyGl!69d;mO#Hr&=~HdfwpJDovj+X5klS^yF4RllLbwc%e~%25RG9r4nR
z0g#lcVXYXh_u;FAaNB$@2k+VX=Uc!&1~=PBZ5W;hT(F)yb;j-m@RtWvEwO&OAXgl>
zUhf>13s{jzv)I;${d0$q&u+^U)~1PJeVG;{_nvzefqTB~0K{#6!dyOLd(?xzVSrlz
zM6mt^h{5-hAA4kJJiplsE%h9>yc_z9jYD2n*k-W~+g7Zv;3dG%%EH6UTGw_g0t|xJ
z4RA#9`F;S3!ocfd&x*D(8;|uUSD~UAUh%TnXz9S9{f+RJX1VyRXW%Dd1lr3QH;pd*
z{YeI#ML0lota}vaU;p}70F9KZe*|>UdQTkX39Zl=0rvnxVi1-UZEV^YW8+$XJ(E5H
z8}Os8#x41?1_ul?Wy+Km^zm1{3D~7PDz0qSZJZ5Y($<f|h7Ak;fQ%#{%DAH(2qdCC
zOhUk-iw)ltO@n}NtUF*e{8V40FU`<#J}E!xpy|wN*@GW9XwaaLFZej358w)Ik!k}#
ziQHB|-(G92wMyXtUJ9@$AuG`!&j9TJ>j7ruW*q$JBV6%|!CiZ9!VAa=`02?fpA6yY
zS7_A3pwWzR#oWX<k~Ka?U@rFI<W-u};QtbbF^U^Jc7YU0gSr5$DUJEcdwbgR1GpEL
zkK2YOPo5ljW7H@7Smrkj^+cbQp`EFBgs(B=pLs=qImQfo7c}2tI0I4}!*2s12!EUN
zBGSPC-jiTG`%w(@o$bfEKAO1FBu5|M?1DVf&t7=ph0teMlVB!Hm{97gl$moa!f|#d
zo`Pu6ujzlP8~B9+@^W?sxXvo$fcJ?Js8#W>nNKkEUEWm=goC)@gR(2U{NOhT)GEK&
zX1)M)%C}9t18D>0+)1=#6JOdfLxzffxZ5ysI{Ev8wGPeOJ8$p2542AoVBH3<&k8=|
zb>BX9-x=!Jey7Vl=fFL`i0KRs+8Of5WW;9N9$<EceA_&-vD8(FY&HR%;m^SkAObeQ
z7MKM?VDrs4ueuMl*TUJBhs`@zGNFneZA5^ZC>xt*q^!_#0tk;dfRiYzts6bVoDevZ
z%@d$Mu8H7U1j4WhL!$>kHkc7?tT1f+1qf<Q`C%xsv6FU{*3cqf<hfQFiU*^C4X!qz
zs;jKF4*xmC&5|Fbi$*ad2U!qgP0NO(PoU+D{8s|h5f1<kZI2eaEcql1{Rsw~0C;Kf
zR3`jk0KnkkIW4^WWGg<Ka3t&)yOLnVPySr_z-HXi7mYL<1|W<m0IdL#2q$`JajJD^
z{-iC@Bu3tm(1WQlm*LnD0D50e_1FIf&<((w<lh~)Z|eZ_#6L81-Ee&$_t>LDy!O81
zJ+O8HLm;#dU?!XAdX$f`iuO$oMmaR6T$G9aZquse83qx+X+RJ#FgWmrK7ErPQn;0v
zA+@BNHBL44m!_qzTw2x8FGr^uze;JMH4#7*eU~<P<dH{$WHpl5%pJ7VNUx#8elsur
zaqm(;7Km)~&HH<1;2ih-^RId)?ee9*a??!(>C(_?{ojlkb3B9YI<Kwq0XcafUg44S
zxptH9IgDHS_s1Vk4W2K&Fxi9k9tw0B)66&2!|k`<Q92xH^zYBip5xZ`2Qq+uE^PXp
z`(^XBZt~+peAw=;kH1=Eb>EMJw!NlV!R~eV5$<pgB!bcSS6>HDc}Cp<H{BfkwmfC5
z+so(WATM>#IL`vk&^5VlzOkEo{+~lbJ^l5O9Xx}&u~a9UZ@#1Z=X3sGz|RGnZMJC1
zUF`~ER)9uT($Jxv89wJ^clV$AyID>9duHace%)5MO1!Is_u@Xx0ULl$4nn9i`io`K
zsvgs9)-*7*0j2Z(F9&{4qmjF}+=sD7Wetk<GJE*<dk=&i=f~pxeq8Q3c*nqqSD&U1
z2}^(HnQsr@oj!hTy1GC7VVoZH=Lg(DeE)y<`=AYTCBJsv(XV?ss349#cw-(zNQdu!
zJ=(|iJ1f9K{>6x|M>>^g*nE(7rSP4>AMj=@5G1YQXa%>xaa;Z@WsAe}P2)g5EiT_S
z4r8r9bAtWmSwEiXYwV>ktdEe)jl<;cE$4`O)&~c9I?Ssl-Fndq9it8Z>XlE?0f@v;
z@AKG2K-VTmuNK1fJ$)_L(7o;2cb@e9nCqx+023<i2a5H#$IV-W`lGHFU$-_MK&|zl
zc@Z!Ke6VtKj(D*E=FD=9aydF($#3QDmvAS1xJ#-12K{71*SJ{w02{d$5dk{t6F>mk
zs^E)Y5O!qE16Un_h>t!l05^`@>u9&z^_zwKF1z_$@Igmw6F?}GhjG+ad83Zd1oyxL
z4}>(h8Q~$X<%l}em5J0Pn)TopF;-ZQ0?^VvBWvE+yRt`-HZ_gAEb#@7(M<-`#7&|P
zKSe-YOA8J70*(<6VM%z4Ni=W+jK!XmXG;gZ9Do`4LjZ3u@Ty}vxWnIx-Q#zJa4_Nn
z*|Y$i(T;H(-UZqNpu@Y3XTt+Zg71r#|Fr(b+7ol_wbz!?VtvQ{2E!gjG;KO+JNW1F
zmyKWu2EZY_cDdX5xUsK*_W~f-?rk`u!BBqMim>oXIaH?4B6x`#{TsvCfYQKL-T=qK
z>yRH{R^|?j0FU@FK2Qw6J{e$>+LW^g&LN-ys0;ob=MI1h0XoqaaOeBN`dICWwmv{i
zjB|J?teI&8>I3@RTtgbH>11Ah`Q-p)0#r)7*=tARdQ*AY+U^LzJ=HgT1rQ_vPp-KI
z<OWDgU`GI_0OzEYk=rZ)KVmp5W8bZ_9?pFLCrgtb06qz3OQRq21H5g8x$CaGLO-Gp
z0z?IrYI$UUd;y}-H)H^1a`wR<5AYy-9NLV&g8|THY2e5Fg#on6JAFmv(3}NOn7LYW
z40A8%7r0BCo;2kpf%gy`Ca#nQsDDvsC_G?JSKpG~BoBtRbAkM=&DkTyYTaa?3n-E^
zF~*3(16+*_btM1X?znq^ylwKf$@@Up>;rs{STE=8+cn#_HSHRIuhZUh;GSQ&wWgx;
zxO3}|jh{3PXffK^>W76nQZeKk$sJVMGtmOvV}s1%9gRcwFtM64vbhK7R!yANgk@8#
z@~}~1V}`~EHrH&xC@&ijHawQ`0#hUH@EWFrq?_36v4IAh!)ArNqBTO&oorCpbhBZg
z+-zhdiBxO3Xcsn@#ILoV0q8*WI~)F5^PzZbNC7R@dSFj|azK6Y#TQpqCP_Pzb}a~g
z7`8B`#H7hmpL|GQjH>LFZ1M$Q3kD2_;FZENhrlF)F;gjQl0V~;{w4q<Vdx|3C-gry
z<Y`x0#5Bo2%vdA=VMIu?i|EDSYk7wjS9p+T7+aFawX_OlD9p9rKYM-m^ixAT_-;_*
z56xUQZp-KH9=Ho|@ksab%m4M`=+&yOc56v%?%Z$PnJ)p9!tjCd4^spNpR}hbY_)Yr
zJLtz;29p=M^c5I(78t5Du&YhS#%=90^Dlr{0PWJ=3R6qvz@556;}`WM_cWU@%v&(3
z$Quk%Xr#^6t<8Tu;sc7|+rUq4#W#mJxNqN~{<`Mvi@?1R?$ohJn0I&YzH^%gmc8!3
z!7Bzp*>>A4>FaSxU*1LhccgIriF32u#~(f5K{u<pVZ+ueu5rKe@%~u??`h$C!wolk
zX6>q8!`_A7|3B`i(OY=w@r8XJHuem~)kCm-_g&Y2pVkV`1GgXIR`l%_KzYrx-T!Oy
zfWO)Q6Y}}^<2(9%^mn&<3Eq``TPO_n)@OY$aqJEH>CZyACuYxa|M3iD4t>%X{kG0`
z*mlW~=C!}t()I4$!#(%xPHyIm{Q@9&vEm`$kIuis&G~wC2zT7E1N>aFqigWsdgu%)
zGTi6PniaGgs6Mc7yX|2wIlYm4W6EwJ?(47Z+5!-0^U+nGlja8MhC{Y2WmbH~G@EhO
zRxoCX&v<8Sw|2dgB_CZa4BtTJP1YxT8<|IkdNAMUDQ|>vbikyCXr^FT!@-OfNMCg!
ztpLC9v@}l7^;cKV`s8ExTYt^&#z&40ZG44K1JHL8GyU<$9}kCHw4>;@)>fYp9kdhF
zD)1p1wc7Pq|JIszz2gDE7ERWHI#A>Q55W=E%kq!yxJ_bzz4Kk>IR**JpL7A8@@@}G
z(duC}LE7}}GY6%tf32Hn=XUaXvmW&VxB@}5deCYM8k+0c%C2{wzw@{L?$ob$cS~<v
z)DBz(nzcx$=(fwXn*=b>N&^al11-Y%akQYl!w+CTkfq#v-`1xD{AzFD9{B?RAwY7u
zIZMF3h92z$Ov_St^EFJd@2|C2%j<GCIcl{`?Otu(<F;WQ5e*LepMU=OFtxB20Tcia
z1#Nrqr1-94j{g3r(sj9;9{Ww#ut>)nn+Ond?P%8ZyXqje<NAvI?X`yRhybRrMupb_
zuLuwk&+v@-rUTG{Zw9a>O&~f>gLle-fO4(EqVRc>v=1kxRmlzBK42cg+4`~107A>{
zcL*y`oP-|~UpD(<fMtMgSi{HpqOK9ji{0aQ>JCA4dnTbY;G3{Mj6prnKyxCd9B2>i
zT<lBGlm#Fxt-k?G!T{1!{wSY7i2!+UCtu1N@9^a@0@o@oHp2H9z?^coad88%M*g@-
z0A9WLL%6Y*!Eg<!;=_N&NFz1%j&?ivYWBQ>wJqyYj6hA$U|kG=2*Y^*f-9`M;nmak
z;cH4;B)luavuEa90H7Vuj4AeT80Mf!9>~O+o$rgT?8c8<5-1;ijq@4G$N7bXugDMJ
zSZ*sJFBkw`@O}YVVc;90ZB0Ne)kAyR9Raupzk)tYonvSN#ux@Sg!&n)%y0A`z<aiy
z;PC>6#L&j@_yEIU=r3H?s&=67K!bfX{B-6-Xu%5vz=x(s(#GxTtFI2=J@HYLroL4n
z0sjXum2)T*1OQ+oZNjRb1DK6@ZD^dwh$l;Ul>sm*@ANVH4Pa8<nMW`HP<7ryKgS3d
zOgQ~GU%_avs4>dgj`HKLu=0bC!8l@k$W38n1Da*tfe&m!;GFYW4f@PE%-7cBz`eQc
zqx#J4yiW2ykoST1>H~Z`5I|_J^zw8%Pv5rR>T=IHaL+8X@($j*4$fvX*4orr>y$+`
zn*s6<;Dk*ql0Y!`SrAL(TwFdF0!hpOg@b9shEebBWmBGb!v$aqL%GNY3?^-4*bGsY
za{1H7f<s3n-*o+!je{f>MpxQ$1>sx`V%7CG4#L<3u{o5KpiQfqXY!O4yj6}aOi!*6
zKuWPz;2si#Fjz=Oby_Y>`N14yqiPQb<yXmukplo#*Irgi7e*DD>;%rNL?3_hg%p^i
zeQ_(74jXSkb^Nl>w_!MRCx0;Nv?z+xLQ)RtSlY@;rdc}iGt4K9y-Fgc8^@{kT~|R^
zMp{}LwQ9qHp$#K~D?5;MhWTalnU<anQ|XyBm@~MF0103EW;{--Ne9VF>YpEUVf4TN
z<j1oZDmGu$(6{kg^9&=BvMaB$VI=CR0_GYdP0`MUQ8}<-@N-=!^<~}TX8^h~Fj$$>
zVV+|69@+X*9;}VO!blRdHRWziTr}^&RQlk9X>R(oOFeLGgqt%9?U8`v4)MS56yI_4
z471r@io5h1;Y<96|4s95-rocKi08YtfBwDyo#Y>6zVz=e`<LkjhdCGdu%5v@`yl^2
z(7XLt0SoSKvVWiAfA1FodCl(o&BTAh(|^u~86H5hD*F~Ej|t(9dHbDU_dO$FL*|+C
zgx~8Aw}&vxZdO@CFW2fWzV1e@{{b8OZ|ncKiMMT28n55^AAgOB&&SU%-NY@r%F3=`
z(be5A?;Gmge0z8I_M7+nygE0`Gv^msWmWgSuajkZ^>%Og%Ar3_nKC8#)3^8reD&2#
z4_In&AH2VZ`{MI`+>`$p?6o~r_gz+9bQPNe9S$xumoSDXn;5!rITa3fnAm`HSnn_g
z@-6GgS1`t9=?L$(K9!$)ye3b~cn^*RAiJuU*8keCQ89$!+mEq_h8*ljXk|^WMUi+r
zUUs<~HD-+ag9q+yHTKlfnBrRk5M1eD+k7Csj+^>|4cD4yn?`Hs<Z%~r=M=A(v~{8S
zrQJ9@vWF#vLxX{?a$;|mb^t4yx;}}%h8uf5zWo9PasV~SA2=#JYXiIQQhxBK&*}h(
zclnWqy-rEAa=UH5OR9I<1|6-g>2Gam+M>J%KDccFl`XerQS((pa{B=SLStP2b3L%O
zF*!_I<gX*rq%Sg`Bpn1N<<|oD7Fn%ZY2HZ+TTeFuP5@j8;8KqMQImHHZM^=sS6txR
z*w)iVBUdf}FJ~4sRPt{9>+!7l4Il$RE@-0R)$)x1Hn1m9dH|=;`Z&^SN0+&{=@iEm
zxqIHaWyteVy^5e*PfzjYw|)Kf*O#;$)tVR87XTLOD@jP(3OpdV%Qv680%(OH9{{VY
zVee(X_vP}3HZXu02t)(sAguCLE}r#cFG4%l64%n<nR>t^36&}=JOy|Z@H6F>WCH}E
ze(+1;1tS23Ipvg7O6xV1o%IzaiLP~{{-}QmB@<sX;2~q|x=eB5En}oDPI2Uq&6*ML
z5PQ2c9ywauISau^Lm6>tf652n1|A4*HgAkOz-8=P(BNp(<r(2;*0F$x6xW7f9lG0Y
zy9GcLd`=5Y12BaFH1x+m7H%%!JHwd<MuPkB1~FFq7rbrwl<;ggzt9!Oqz&&0^YFtD
zm-2-G8wP<H(WSoOKTa+JSQ#^DSwlbz!yb$?h+A&CB>*=?7fo(xxrRSFapJ^KH@pMb
zLx9(oU(e7ZEkK7D_>eLH%XIcJzlAmCH*nAPZvc|?X$*4?&&+}FbLk7ZRu(Od^f7pe
zs&8!CNAn+T2iQ*rz#d>T`W}XTI%9!bZ<q_vut{D7bd`2WfP8>-G0d+@4{$d9LIw?e
z%$JN`z|rF2aNRR~kTU@Q!*>6N)_V;7M0AyZcn7$_ujQsF^36Luf;bNAAB;4-ixzu7
z@=m<~dXzhgElB5*OD+jlJIh^dp}s2jBpY5nfJi`!(qxEW82t%;lY|h(?+Y39f3&HD
z&y-#g{oJkNtZDML$lD_C16`*Nz}w^dXxC<W`*xl7ZB1M1J6*}nfqQ;2)|!gW<Ib%=
z7Wr8jjvc+;P;aj*x6VKR{2={9p4p6?c;bnn&%{zZ_yY_AfK;h`?Y(G*kst;Td9&%m
zSim41r+h#6+;c$+PVYM0vhUD<iD3`BEM0|V^93+P?=V$rIhc}qe>V5fN=i#<l(PYC
z>6(Ue;j;L_Tw$}AC2Ud{HpS8mQcZbax}tTkR!uE1fUffzqmuHJ<i^GrQ%xR`3}v$p
zy)5xy4AgqXi6k;5F>2ETu!j}{uJ6PMkegIaxzXR*c<ViD8Ep70&ur*1V$#`sXNg}f
z%nB>45DujQoM2$OS+H9@(xRUD!L-F7(I;)3%3IcSV5*27b1Q&0Bpw04q)7^9rLDYF
zYcs!5k1+2Ub1<BgM{MR2r0Ox`)Beai;|2x`G)No9Hd;^Pd{n#|%f}@Sj40>=0>lOo
z$bkz?TAOzKOjsG1H-H1UVnSu1oG{Xum$M!&#AV3(9=F-W7hmPUb>{^z+SJeQ@!!x{
z9?&=2HP1Z4yN&Q5%{RT<3~x{OWu4(Z{9sS-exP@I&wc#ip6(<6`{ZK}_;Zt8)7OXi
zI-c$8c&hL3&I8geaWkgv=Vna*X#ff0_Qe;oecgFPn=zk!(&Xvx6KF$^bOHK|YxaOY
z4=DU3xB>VZ?w)$G@SR-4GyDiM@Zz7iwLDXh=S1KhZRfw0)X#Swdpv|&V!f73&$#Zs
zv30+2B`DW|{%`Z$?vCI03-JZq9eeWHAum@p|Hg;&*D*CWxz%<mTut}zLl1_Mo&3qC
zZuI0S?zj&=4E7AqfFvFI3Ue)e8e3^^Th#7%*TnuFkg`w!^vQ2QvDG)JKwYK7SYqsB
z%GHD7CWR^ImlQsCo4c=2-5vbY(_!o|&X~<UFAm`qi-3Ow8T6fCp4PgwyYKV8{lOz+
z=`{Zv`*o9>@YQtpz2gf4E7Ub><1E*=(-(AAPTody+o&VdMWyQ}IN0HE(dHFxLb_^<
z!_5Ej*O*)X_*r{C_IdOJ=1`2TXriww3~@OiL~}$;hj$MD(Uxod_#WH${JCTGvH8^=
zp_8@YUt5)H_`O@ZaUPVEs!gv5=IeFXDgg|%{r{F<7qwMBI|^NZz}0AV-~Vo3zaI3+
zukUF+U0Z9kL)e2oNX$FXH#)5zw>xh=H)iT?A)g&lQ(IcT+uD7Fev7%o{<%Y`4c9xc
zuurU4x@s5JHV7XAc(Fh<*0u0y*q<T{N4ko`Ggl5GkUQn&H{73R-R^q!UCr%(PodBC
z_-6Y7MYGh0&3BgYNnu0-O;PNz(FWGDXV1iMGD69s!8aexRl3SkQ#Jf73_KBd1@QX-
zRRGAd>jcrkW?cb408M)c!=nw&`e%7Q>7<iNg48NgmUu~F;1y!v<Kl1W*=GP(2)I~~
zN=(mcgaXJVt$PZG@C3XtZl9pP3V4S<YbDH|yburkJjID^c%p#M#J{(B1c1gK0q|hF
z)&g*dNdm=*2E0SSGOYh%;F7{{27&>U#?1oc58f8_2!GOowTPp5)EoN?fKjnvF~CU#
z?2`nD6(7Mo0D<HUfE$7#qz%YQz)J!4a7Vxl1F(pG4xk4<{Ui^lv%+Q)ng#F%UDY>0
zD!`$D3{_`nB|{4yM#B2E2Vns#F~`t0)CZs_2`x*&mv;g7;2pz1gYU02u{mF{hu+L#
zxHDHVCP@oJzGMJNVo<ukd`913-Xoss&!*8{&$chLSDI<*pqUd89R{GD3;;L!F=LVW
z3{V|>Of=Ndr)fuMqMeWVk3J+t1n@p(0Oj!mOpF2OCWQd-rUBT|2RUEEu*Oh7WKD#@
zPZ~5yQ{X*#pzNpMXK@}wo|(TTpiW=JXl_!QqgfWCb4&UX<sjeE{KuRD&kT?uX)<@8
z<AubyK3AG70W@P6XM_=bo~1og^@JNg;sg2<Z`j6zmYw?)$J)$4Q>RWX%`y0?oEneZ
z?2%-c6D+O#Ip1gIeIV}xc^_z>KEV18r8aqew@-a{mU>S5RwudTz`f!ks<Sj~N6jZv
zY=<6tXqTIav<YN0O5LNrpM^VG)>;GD>L~5V#>Fy*0CQ;5!DdLBnE-o;S;A(8jTRpt
z42%X_9yUMHI*3L$K2T^vlXM#ZDu9v1QCc>B(lg964rY`5s&#{TfYGL_+W0m+Oe7c$
z)-CHZn@DN!$QnoCV6-50rT1EG+DUDdM1%f-J9QQV*^<J`4XM#A*I6h$QgUh1s|u55
zv$@AeN;VD$vk8NCfY>kTokM>Ns{51t>*c0&V2IU=CUh95w3|%5#*NLR<^mWTfMC(k
zY5lW4)AvM+t1U3l!&KAXvZfb@qn=1V=?7z1hJ$(fCx-m#$GgCyG3~7HFz}@%GHaY_
z!%;pA^9Bb4GPWFCH4MX_ZyfaFI>|a$SDUw={nTqAM01}1?YhfyrGE6{i~kPUv_M<(
z(5+h!54@XI3P*aqdlvw(k38~NaEE~=X7G9EUFZ)(|2Me9_(sAO8^d=D0|RlPg&O_$
zsBj=K$MYKj2=P1q<1gIuO90ULJmgWpz196;82)JSTF-w2cuocG`8=v#j(mM`2;0qH
z<s&z1FWPf0cf@~QahtEPj_+*Gy8oMXsN401d*=ze-33>>iOt^+z(VToyykzn<+th|
z!f~Z&_m#?P@6g;%J1hOY_FSRV7MJ{F<C44l7Ze+&uLZCIXys6a@~hsetqVKXwAEOu
zHcy=?{CA)DhZ{SyIgA~EdyF?mFJBAF!8{5hTl1T)a@yN7ssBEEjyr!=;Slu_fBl;+
zH-`+dw7m7V&$JtBVe3}UXN^VK^`za^X`vat)^U{<k9M$rzN?kqS^KC?6^4T+gr4Ls
zJ8hkfyW$d-10t&xLh<wdF!%eZI$4lx`dwR^wyMj<B^m-K32>54!}j@wir~GC4k>6K
z8+cLc_q8^y&h~7D94qC0gM(Ib$G$bR1pHcL)dGm2QoLIH0TaP9(f+`;VcK&@r`Wzn
zPbp~K%Yl2vxlEwdLkpl)z?Xaeb_>^9K%8w0>K|SVdm#(h!yREq!UB+BZ7SfN`i7-*
z{srfU>F1~`N4l-gSj(OK*{*Kjh3mUUudQ3*JF{)e3cw$53P3yys<7qbx<h!UxWT6Y
zzyjD0qrIj07=R_&KcK}MgCHfs(h>}^ba}=<=BL>F#o^<3z%9yKHSsJR#beB4G$+a3
zhDB4?`0?Wd|4o{zY&bmwD!K8-8|UdS@`t9bY^6Twx0Ywe+DIA%ZTj&u;7E+XIQT0K
z0DJtTkxK5m83sWP8{X1}55d|FaAJJ@rRYe@p1oxR8qx?Zp?`&!4LAtRUVxYEzLU7p
zUMB$%#j$yVKG&U+H?;x&+@OO2#0F>$HvoX}<Y^0lV`%D<q25JTpizK;fV5O+oO{sT
zfG%~*4(BA$0=x-m>eyqC4Rio!Qs3;SxmJ`qWSj_`1-Owq2aKh1!pDWD1`P~jO?d*a
zM_#5(nNk8_;U`UPT=h-4R9En{_>m{TR-z+2sV?Q$9(G3o?%8_atcE(2COH7D0?sie
zsTcYN^CROCz$9+EN|yKlI_YN!Qvvp4E|h`@K)HaDCwYJ<AX@mUfHAqv2SeI`)&u|*
zU!OiFV4*;lq)Q)y$1NZ#3JqANXf6?empEMg9`B`8PHDj8TnmsM{Al$7u5o5gw%|ST
zO5f6WRemWu@l>ab0cbIQG0yQ5*c9*{pvail4M>_ktJ|<dOZm||%2F=7?6NSX<fl1^
zI;AeGVb97q6c0mm6gRgQ?0sh5{(1Z7eW3080DCbsO6PUm_I2Hv>ezm(%RT47J->+R
zOby#n^U6k+&09y!U$yySBd7yCm^f@I1>S=(qs4i(bmvF-a*J~vLbLgS;lTz2)%E0)
z&5z>7AUC}u^`mRVR5mt1Z0G?`=$Zi15kO2ECjd)qh!n4!4RFt1<xwtdIX^U1p;}yd
zC>OSp9};R}P*e&V`?HzDSdB+<n6!65L~5h7Fj?K%SW7x9DGnOiFuDpXDNO7}-jWV4
z<8XSHD*Rf2d%FHXn*BsmHVhmL>gnZH&Zb{u>e1KAr9=P2z!WPNt`<K4CSn>BUt_fv
zT4`|@M;O2o(62QONjxgQT&Vz4qgHxp`Au_w<&{?g6NB{akFvoGf}tWKZG|eYY|U$F
zX;gA=58xhoAitXTly}+q!$gva>jHP$rczmI@wdRe-G>#l)gtwZf%zmb4zyu7S-=Et
zd>drckHk>MzgxGt-yVI>_|75St^7{**SA0$)9Aqzb3B6!U{Lrqd3)MhB|w{id#m~H
zCH_OMxUwqX9%-KR`4=Is(ziCiy-}~e7V@_1kY(MAvyS(`y+4)ms<1o#;%cAnX!rYT
z`xNsw!fmnT5+U4CZ@lS&dn?ToM{xlSGxuP_1eZJZs3TS{mG!i7>$LE*;kxoO<03Af
z<^q_Zv0s*VGzH6~UAvow&VnB1*mvIx8uu7`7&PgrPP+SpXtY~Oqw@|Ptp9I6w-fd<
z58S)Y1NeUF?H`}>{Zbs(redUvPC1)?0297km|Fcf<RGr>TIno^@n}PP$XAa#c<;UU
zilFWSpMdX+z9F*NPY{kZJ%^_@T@FPrz4X$cK}VXjtUqgcu78u8zR!Bc=D<{-Ve6hh
zFF3p#eK*?5ZmYi3xMYLd?Yhm}sbBBzzPa<#Zi6*92!fCEq*nwnZTQ`l0tjt~i#K!&
zBEXBQ{{RlnQ$Kzln8&SHXF;oBQR`m!mFo-K(;P?I98xO})?IFZMBtn0J+_Z|vj8xR
z-52!t&5kQv6^&*}z<cYCs7>phZ4-nU`L19Cu#t8#8-}?=bw6RkgrMPG@39RS19M)}
zk#5Niy1N7ZvQ5ZqkJUSt*1b62S>9DA0P6sW0C3{E>T+=fCLs)U3jl{TC%_QC`S7D;
z0HOdIK}!`T*1Qy_1CUAD%3{B?ca=fl6~f6aEl!%d;;~+#oB}+_UGelz*;x<A+N=}~
z8-YNKb+gZ+B_0I(fA;kXgHiylmehNa&AJ>w5bG_jv?L64MH{V?XvPv4Q|<zhN%OMY
zZ5q;M2VYQqL*Zm2(2t4p$-GIw(3ye4%N8hz>o}z)oU{Qi0pOtxBpj+Z${T%;D}mwR
z0zk6-bO|)0bxiRD(o-G<$YR}W&u=IrYg+hY)F(VDc&NHv!tz@w8%B8~Z>+_YFXD4f
z0DqUT)Cc9FuL2BJp77(G1yCVCQ4DnoJ!s(%E#@NXg!2m318oRcGHKtgFxaXG>oz~1
zI|^`5br9DN^-f)L=EbcYv=_iIg_o`UGax_0t4~lq8TuGJHf-7+uq=Rbz<Vfl07#CF
zmc{$;zdy`@j2R5yCjlMQH*^Iv`*$=+Vwg8L%lhws{~N{+aR7}z<h6K`SHOIX6>iZ|
z-m&Rd80IP7b)B{pOyI`%!Gia=dKaT_D`6M|7-`E>eFD^Ae3KWzc7#=(0c2!el9o-y
zAq<A`!LxO@@03U5Su{CYqs~76+zT5UqZ<iu>WYe1sF(bg*FoL~@;=a!`v7ZIfav5g
zp4U5azVp1b?YJ_Z>aveHa4-MR*JYv8)qs0!R9OVGnFajAhDB0z0`###$a1~b{F>}~
z_39O*m`FdqDmTs(;3;j$SoBLWC=M6D!(7qTkM;w^ra*?ei{tSQC_})(B!BD1RgOp`
zTDPpv$`{vFWGz=3ZP5boh7n^SEqt0g%p*xXrG>HXO84G-?=8z5w{fyOBP|w7{wgiv
z1Ik2;NETXzfkA`hT&3_zgR9Hf0CSx}H2^3yK?<aU21lEoXloN~uQajYlAb9q8hcn+
zCHdFG4aNk&)~2I!k|(ZP=5QB-RI09cZB2eHeI!}g^eYX3oTO1Ledbe)=m31B9-zgq
znm&-0w#_$TU^dcMNYnmcHp>8{Q66NA!OJ_$nOd)<PRpm<+Y7j-^wMk&9sqJtR@DJE
zb2LCV40EupL)^r0is{Df5c}&r3Al$J+5s_0lcSLnH}WINZ<yTD5&^^78u}_sG8p+v
z2OB^i8u$2yW9;{UcS+KZ^Veyj-K)`qCIWcR1Ak6y{>Yu%{D1Dhe;?u&U%y{)-{;P|
z0{u;nJ|?_p(YQxG8og96bdUe2a6OgHqXq5_*?#F3z&%^1+ns-bJMptG+;Q)}>rR?F
z%e~xuZ~)wWbE!sm_{*=m6Z}=VT$6j^XH!dcKpX3O%~c>UIK}K!9u`@o!MACHyXZ%~
z1HF-ltX7JzFrCHb$`>TV>y=*)G%(WU23^}G<<fz9%lP6Dh;UOpV3IYE;y}j&!m`p;
z{H|`l;@1@$`t=3noZ$}<{3X`zbU%g|N5{VRz8?o4xI_Q-La9Cp*XVz1dF{@G0pPdm
zuP3<0{1{l$uX|ckUt;V(|NQfTPQBWS`Hg%52GcmL7wy)Rx3k2pbX~432M0eG9SF%!
z_04`^kO$Jp9UFs<rdGzPzDxT4B=rXl;?asAIvg@dy94V?f%Q1pPx_XUAA2|?I=S7p
zOtg+vzoFx`;a{7UwQ(W0>x50+SxpDJ3FrLA?Xh!;5LFR~cHBEV2T)L(=E;xYj!2&y
z3aZVQKvaOf>IK}>97tZ!u%~y~NqGb)7SES9oUY0pbjgNQm(kMcYR`R6Su^x^0r%G0
zJ3H?oZVv<*x$YfM3*RK3;nCSTL*ownY~6qX*agtf+i$<!BAksQ;NBCn9&zh!zK;9$
z!`p^DFS1(a2JWf;s4D<_0E)^1&}^O9XV&p({AEuH7)rN1#8Wnc%(3P}(SZ-ZUWK&<
zZpxed(`<zUV280csHDY7a~CauBzEsCcg52?`yqf>Xx+n4?r}KsmBt5FSOAS^p8~AI
zzSIg~y!F;w^IT1<c(UOMV&Es@MqZSkfddByKfr7Xk1e2MxxBIV;(A;_d(viSX+wuA
zfHAhta0fICA2J5=C|~qD_!RIkEzePFQ`*7i*FK|}nd?F6FO<*vp*fGZc0DYOVA56w
zpbulUjWbsO_@Pb7x31d-z=T1gp2`Aklo}9Dngrn`Fuwqlg6GCqVr<Z^tbyUhG2d`i
zCtxYQcYyT(F9FyCfCN2$$|o8t0ZcKz$^pY|`8&&VM+5Gq)f;;h<`>!pFduz{xvHE%
zJpGEfi0=$P^*{Om?F<kQ{x4uZ`agRq_P?AV2+YZiM)ZSm<Hm*lr!zSE3WmNzo|!x8
z&+yebdxF2mIRrdg#w=$o^drLRY)VQcRAz2Hp+E8+0N6*GeD^4;u9juIvhM?gM?CmT
ztd9UIS~@mw;;%6d(AdYm9y)}VAA-Yx74<G!DtEaXSpXC=*3p8fczpMn)6#%@j3tc1
z=XS^359IBVw@cm!x?UgP9ENjsE3})pbJuI<YTGn?lr#M0z&*css<v|T@blIgzz0c`
zb>{lvAYbaRQ2~zODhU?UI#h!>!^8`)gAD`aWuwXlT8FZ2Q3u-WXQfRZb)72Un8I~{
zEtxB#X_*GJr4EqfuQa7y53tu8Z@dx8K48FrpblSQP^S(+0wxBV5%M8F*)d(Y$2Pz+
z7>XRCwdU{&$u0~BqH!8o-<4;WJun^CS!bQZYpu%B19U=p(6o`YJW3a)A_nGcxirf8
ziB`EV__29qV=dMAmBQoC_+Yba!3c3Yn?8L6Rq*j<HI5U%!}!E-%`jR{;;S?XFGgA{
zSr^l?`9Z@PCJs}Nccp_AD~7&PD_zkdKGy)iP{eR(Qc0S-T$+}y!ohgd)zbUyvrhmv
ziLtFPS!}dOsD5Gitg*%#fziOclqG((VE`f1e_;k;V5T$w!DzNWq<{f9S4+R1;X4X&
zuUwfJXSUw-OkYsHp&tT10Qen)X+>LM^^Tnc+yh7svzT-U19My78@>gKPx>(Vkql2V
zFy0gvyQgPNb7-}w2hMHe8T~!H8}0xuEc1J;2krs(dC$)=*rztl)xdY~!`HhWtM?4<
z`%L^(h`;GE$A<T<&pfL|dN%F&dC@fsG)iD2f6zb}3*6gjU`ya0{xHsGdjRDRKm5r3
zX4V|{;q0HdcbiAJv!~wg-kW`hoA%k>Zskqa_8Rv#a!YR5%Pqd{dhXz-o({CBf7%i{
z%)8~ZoHqIc_nU9(8^C+-zO%cZUzRS?n0Ngbm#w3`qrqX4zmmn~C#erw|4Pq*wh-8W
z8GQTgx0mX*QoPuo{>u7>@0h?lTq6Xtd}8&w002M$Nkl<ZReij*d5zQT%<o!T|L3Kb
z!gr4PTXvI8!<En<%_&@+%UC_^g%{nvcikP@rJw)KKK$utg)!{?cA0Qpi@wmB`rum&
zL!axdES-9_S-*b$0=N^}^`dF>ndO;uZQAxZOZ@f@!{Ji7G;RLbzcJnf(6{jrDEYR(
z{v0>HEgGMiFWE=X{sMZlo<oBv^8&&(#8X-VMsNs&I|s~3LIBWYZ_rk2B3r)vx$}I(
zXsfn1ZQUQxp11qeTf6UneyHoY=W?!Lp~6}xDcv=PEgL{V!+ui$gCvFNs%|u$RajJi
zptUFH4(XOsY3W8#8bJ{0Rsrc8nxREfVkilvrD5olRJwEM?jeWf%>R7nyPTVui@o<e
z&;G4<y{qYWRr;yHim5`}xsS%LVsX57FQ(xb5He^~-l~gz><^)75a6GR#q^T3Hz(3G
z;W2oxZgw8O`Tl6n2ypC;-25F<a~L_cQ6K>p3sba~!$@|tGJniDPFp)0`c9IBDCJpU
zJR4o{h-gyCnwQzA=Q2*lUjfEw<Gd^$G`MN*v}BDJ9LHxr9Te+eo+W&opgpv9FqZBT
z?PAvFs8_8!#m&aoDJzgsGlP@7ug?*u>y@dAwP+u?NN(V2>)c^jiRv&tjdJLaK-%O_
zW!F|fpEdQuY7OZ}+Kiwv<GoBWXj4ad)D)xq!(<wu6b8#$6?PTovQqGSy7ofGPw~ot
z+Jv*T0XmPIX<qyL`NuB<I@8F_G*wBK722L!krUvk^u?sktFPKQDxH-NdCNb;)V3)A
zUPtIkBS0Ht@~?hDIdB1pBz3qdI(YNEWRDgt@P8)>?p>qRBxrH&v6iu{h*_O~56}jD
zTft?`Q#9$cT5SduX?AiKW_lWG#mY$mt6r1SwTY%rZeW?=v~e0fl02)MdCsM7T_uU{
zJTlE0ybAbd;|nC-sevBY5DyCjf+yN2N3jT@KHaR`SUC*lQd>Ni8Z8qeJ6wa9XyGNT
zOXCe~*Br(%s8+>|wB<?YuLi>Ks@R{$0z|X*W83a^*efJmb6GreR#DfQApK}uxn_VS
zMXaDaMu-O(e?>a_o^c{h0EA)Z&|^woc{s^e&~$i1@N5j>xPGYMwMQ&hKHgbC<Ua6q
zi|BekQ;JoqOkDLLTGB4hB~GGNFOtj85mN4Nv1ID)yXl|fb_tyJs~UA)f@Rk5>`ysf
z8krn5K2@qTrRm>wUnKgsc8Ml0?SF2y3f9+Co7pqQM^|+)_xJSbY4d{KRRyi?wX($<
zO7DZv6QW)QyI)+Ky4-Tg2#JrN2d07d7YS_fgafsO<F0HM=Qb9gXWbkzF*q6GJwf8Z
zN#y2o_*&o2g5JLuSA^{d1Q~3FB5Gz;6T6)If9Q6Z(K?_xY(<`2b{_k5U!F~iny6KC
zZSygqn<`0l136)SbIbNClZzgTHsu-HE}Dp_<++_sD4DamyS-RrmC80W;X%*AKnx>a
z7TqNTyd2|@PF19)_TKExUCWV<Jbeme>HRqbnYJFQSuLX@kn1Iu)1%`UsL5CG>g9s6
z->_aUx%4rV6coGjIOH)|csg&=(T>F5efmL6^D^&?gBLUp@Iu>Bs8$+XVYOJYC-nN@
z9qT|esuJb-kuk*OwUPGn9XG#ttYrJLqy##jjdS&QBMtue-QsPI^c8RmES2`;u{zs<
zw!^~l5{Foom)=l7@nDuh!^Q$2BCu>eu!3Ea?eucl!6Xp45@)xNd`W!Yat51W_Copx
zdPYc{mD-)3tzWhp1&e~6M?x;Jd=<}5{p*Q3vL`Z@U5JIVH-u-0Tv_kw&olcTk1E`Z
z*hUwT@!wh0E4qjvoLBoM)RO#vxMBt<>gGhc7nZl*T$f-Vtc(vuoPFyb8ORr*$0mCZ
zo+;KhLySkmwgJOsG9PtB9rk37VhY2XteKdMe|h(xSE!B9b389Q^$f{=q;e%cModpI
zt;#&QJB?8Ppb9n1>b)~TogS`BDlnP1!Sxob4`)<1tr)7#h9$9A#IZ#~tIiMHwEYmN
z*;j2Z>*tZi;@G1!JSigOgjS1GN!CPmB-CT+M$2PV_bA_-ih+EQz0GNaRUDbJ)75>T
zON7+zkK3?zS`><LrT_Nn1621yH|{1B3p+~r)@}Av8cMZLSY+^`fe9XNI^95T>4x(Q
zSt{tBY82-~zUZ^C3FYe-jreZ!L`Bue;u-q&$xrQSSbu18MCwdE>WV(q*&IWI=&1)Y
zn-4u>&y3ZC^_qfGo7u&v#E<uN2#1G&C_|k`!*zJ_VtUJ`nC3AUT~kJS;8FO)FE7GR
z@48H*uHJ}tsM*9X?4yh*dkwVpWv30h4>ykK0r)t^8jVEW)qr2W+8RXa$L!R~pO~GI
zt%^x8J7kT)*i7-6rG|o%cUCo}0HTo=QHPK<rRGpi4T6b(3l+kj=JzJE&>FtK)>t>S
z>ZlDL?rG8-3XI((j!rS0SDh){Lv<CMNp2B+21E@??eXH56|4v!?9!E3e_w0SwUK+{
zb+Y8!j3EUj@2zRTRdMOu<o3iKk6qU8qmAp@?+;gSz>TT@g)Ss^djaT5&itYPf^M~k
zUtoGfo5B2^5#$gO7!FuA87!j*y3}D^ujPDkCR#7&?RdOeR5j06l0SeB$51O81s5il
z7dAVfRMwkmEaPYq9t>d@z4>Zj91G4fxoEF+K=`P-we6gBXP1tp33jCx{EeR8x+`Oc
z@pDM${U%=^uEQ@e81D#j5>QDR*Ob&={)=Ko>1aB9Io86-{5l!B2H^Z&2W^Boq5B|U
za&YNxA#&Y?>MG)%C%jP+AUg&YV*f+A1ETEC86#=q_A?9$U%4d>m*xq-0iB0-B(D8|
zY!Li|C0YaYyOl;w0yc=*Ne@&iS0nyb)XZg)Hl)1oJD2Vy^R?(LPBI-`@?<J**i(vD
zgChhy=R~WHEh~DApZ8#$D_|6VRSWE6)*6;Hw~f9mXb?y-?4s}SCcNb%2-Xi>Zz0Nc
zBaJb;p+bANv>Cl3qa}e9;@bd%Qh#(sm%vxT<_x^@?cEY>voIFw6CDhqxp-gNyfvk^
zSV<@1=8LEfTe$BXTI?VVt5&fM-!ZqM7M0@4p3WBNo824?IZnTGw^LzWc?94I9_yFo
z;bknC*vWpcw_eKfX4+}+2mA<a6Y!SwKzM!`sT+edC1iq8-=az{?SGWRDDmHL8B7}t
zL>PLhptP`v>^Zz~UyAJ5YtON*3~zz{eMl9s_e$)~fH%Cw(<`H+vl4pHrPExspsoZ4
zHGWL%R%WXrts0c;dF7i-4RlQ;%ps2K(LZdstFk~;{;PA8Vofu|1z;<Lhlq6Q85QEi
z!Jqt|xnSJR9dC}oHvd*v!7kffTqmWP5um&bH;1WS_hu_}{HH!WH!Jql{8s3WRG^R{
z2rIRH#niQxd<BDzC(sSj5Tt+WR@O=77P+t<0Gf<_))`_Ejh5)O6Njd*F>fj@hq@%O
z->X^_Ny3XO$EXx=d#RXn4XI1|Id@<7J@v+e0>R`&SO)o0?AJz3S2~5040OJ!w)|O*
zzjDT})PE7K1Nqss4~$df7Ecq=ck9}D;!YAiwnCf?hC+l4@C?1}X7g^umf#fbzE$X}
zgnguIBnk)LM~D<ko8NSL^^HQrx!*Nk?AJ%#KFsa=#*QPAXhgW;o<(&P`L7@<lzUlb
z#{%5hJE|nWPr%Mz;$<KG7wiTk+fiPS*<&}XxZV(i6l0(Lm^N-e56-I)V`><WXF=Mn
z?SERtmAL*C{wGK^wjtcMI~CtYm(GWKY_;Syaeu#+fmjcCXx>?V#C4RtLUA1(2yjYv
z=;b-2o%ZY5SFG>j-AJ10#Iu?DWqNegW`{0(8hv>*WT4b!;?&U0U+*K@vwU$whe-Ej
zT;q5W6lWcHZ;dM9G=+4v*9J@Ox3`~qGpsURon1PTGHrKj;?HcVj;Do)?YyUrP@;DU
zZbo*#V}v$DXT0<lMO_*-`^NZZ?)z}JfzQdQ2XM$LO{)xfUQ7k;N&Zu8022aEt?r-L
zwJQJ0-r%5}_1n)sgn6Xr4N??n`-SbRibVY4@8z+g;}QHa(60W2dftM8Sbp}dI}t$>
zpnh*LO%DlY>Khr!T5r49$Nlr@yiBY=p(A=xq;%Rv@s?i5rp$1}XM3e{yhJIBr!~*|
zQrOpvB=2sP&PC21<$Z`AR?VJLB>8%*VCY4ZsxNxJJHaVT;7h|``a9aM>wxj=G0Cxw
zvX&wJBng#Pg-LF;)BDy)Mi)R<@BD+<MnZhj&M@D?mPf!Qi)KBtl{o5$$erE3k7Cy)
z7^302EK%s(v(?1<;aPMBo1E#GL;fKpv3Y?%=kQeM)b#a_>~pIa4TH1l4Xnk*qCjF^
zV0#mfFkif?70ptz5ND)J4m-mosJ!UY2&ZB40eyog<lb@r_i~yY)x6Ud;~!H8<)&$A
zId1B;5)+;>Hqrg;^B)+98h8DWam}o(^U~&=-DedpEg4{Q9DVpg_1R^8o{&U39f%nL
zVwP*=aVqovG{mc+{wrvdIT&>1GJxTWb^Y&y@6&G9de^$sexFqT7!#+YsFV#(!vpab
zK?h&s;zjlyr}M|<xM^W+7~B208l9{P9U6etR3f*ha`MfW)wVlnH)?Bqf1;pfT!ej~
z!`;gScq2L?n6c1S#NT`Wpg&=;f--o8FQ<TPv$696^k>_j;$(x6(d9prnJzHBBJZP5
zpUcicXZf3|^^Ts=b#zd55dwvwF4RB)6>_k-%1PyJ1@cvoph7P7{2f1%m%Z|rL=<sG
zcEHUp$p`tHAAB7O8%Cgx$Ol}xs549<g{LI3;(giK{8+fGLl^y2cILoGl7Nk7_V)6R
z5X_aV-q#4;AHpZ-SSr%b4zPrK)jwO9{iiF059gRfR4CPs7qe2S9c0|!NFV+X1AStp
zaSQ;7*E5^kBoP2awoH1N?#*sRLYD+6jd5gQo__$rVUSR|-Jer$K_O6Y0h|II&r2A!
z5pf@bo1b&@IifRphU?M{@sq{-{H(c2Fg-kF+TKrT40gARo!w}i<?#GTuW`o%Ie27w
ze0y$M)>^=C!kw1}i~^B9QuRgIZXN&IhqsuX`k-4dOW1p3*d%qJ6abH8#7v0i+g!KH
zcmCO2b7=gTIK3sYzMN?Guk{=fa_+N5((~n`MOWmvFGK_ed0-j2gs-juQ2Z0xqZ5uF
z{I-S+U&<m3IUv;4&>{26%Ai7Z^oofy3!k49P9Fycuab5;B|_OxL>Tbge3V%_EF-3G
zOKQo>=n;Mc{inMhp>aM|2aX?Lye!Dq8_v8-WSVSGkv!*80`r(#{(2T@yILlz{<NPN
z7Y-LwIWmrv%NQ6LPtLG^UtRM&Z0?irvwqosik<kZk@svjt|34$G+bnrnKRN<Tr@T5
z1XyG<kv+H5g^@HZYxB>{{ZJD6md$umkC;v|1RBkcXWsh<L%I3#>2r+?r8^6u4xA7b
z9IIeEyrQmY)8gJASSA;CHmti`ik{j_35cB;o6K?I&v@4gXBPA_yhaBV5V}~2Pz#{{
z7GtW^%X0%Z+Qd8$9>TH1ubybdEBlmyx!Vnfe~Jv&U*HyE!6|Sh>v5Y&`tLyz;9K*h
z-txtABCO*a?m1};K*BUguzE#z`&>rt-OHKwt#6Mf|F+ALLS3PoB9g1!fQ0q5<#?_N
z*BLJ@GcVLiD_!8Fj%e@1f#8w5=&)ec<EUSG#um`!Dh}1o^>7g^Ie|KR2SLyv0C>{t
zX_L{qCIjG_`2eTHJ?mTrV*Hca%=`QJ$F}tmXaBzJb=Yp>H_7&EGsg;9O6pTPuY!kg
zMiQJDC;P<_2go1Yqo9Ac&5?qAw|7JCN@f9Jw^MIYngz^;Jh%Ne+2j08Ay@x4k053v
zmsUHyT1wEj{8jJU_tZsf0k8KK>T_7&PRl)u(YCjZD#Z530Ukq?jON-89mC@)FWh_^
z*f)bbh?v<@JLv2y|46yL=egFFut7sI*x1he5jBk6b3!y+)QvyO_Sd5Lh!Qpi{Yz^%
z3f(W3k{tjk)Lae0Ti7O6T`09pC*(?T8z-8#INgt-lCp6sh=W99H?75eCWfLBcDrxO
zw30IJb{cdnha*9B<Co_lO!qrVKMBfy8feTZE7`ttD)krF2NvlQW?m8u_+`Ktv9mz)
zy@z>Xewqg|%9|<e_C$%hf%Nv(Se=VsUXuFD5s#A<H_Q&UlVxeso}ZM01YsYfv*-Gf
z$-94B8;Y0Nx1{i&{72B$fo};e-`HIkb47n=NkQRZ!;CX&$2CC5*C#<t*y(pK!t4g_
z>ZuN0=jFLJS64W(lJ@#Rs1;xRAj-=;fMCLoEs^SJNy<X-WY*N=929?=3vNc*fQl)j
zRI_Id-Cr34m==Wx<W~Yd0j4W^S3L1dtZdh-MzSV05U30ySA4#i6~%Uuk4LlIv3hiE
zxw3*pqrLAd{7!iyP`Uouy@}Ihh?ha;Z0qmLiL=H4x*ERUc<=b-*nKMU>Ayu?io;WE
zhY}Q{YJoZBuJvtSw>JJIf{Fiv7A%{ft)u5SseLz*SmtT2!PMQW;W)U4$npm0QNaDa
zSqk*;iwuAbvT*zqD{y%n?oRr#uadLoWHr~KKj<0o39yuuPtQA;QmRv*MDG)efB8W-
z&c_%!f1;RJYTNOM<*5Js=&Ar&+}qyUcc;NH8C+_ReJ3EtvW$;~?2-v54@mMpO(pIG
zEEne??C_?2S_(u=b7<RA!x<jeCn)8=_@#S^Fo#tU@T&g}?ZE_QdUqMuLlelkLc5t+
zOOxrH7;tHa7<4Rv?W}``#glR%kVZG^`A^biN|bc+Gr>-i(Pba<5pX?@olJE9&s2A~
zj1NSbNaUYb>8B|`9u*weAaI1Sv_1$Dy*fm^wnBLU;sg-S<f#d+*VD{UpPCw(&@P=`
zO=)JpWvsWGjcsPH=;P_K$=<LXEd@fcwIz4K<`zV)<xS2o?La*&>RfXc+-dF>{%?qE
z{9*VCUQk?I+@tYE#PI<kyq(%&a^^X7E7!LVE_ES+tw_S^HT&$cug5t4J53SCKBAz~
zKg<2zB%jV5JIAbMSi+U05@p%XCWnLFVl0Zs4*-y1pUunFkbYVxNy!YtPGt6U8i9yV
z0yzG1>DdClSSmz+#<QIPLvdyc$Wp((OoRZ*bTwa$ptU#)pbiyjmyQUwgvEyEe&Tc-
zEat+U-MdLq*7vh_e;Oid4QKh2vy$sXQu$54vZ6{)iY3*BUBnM^AGInHDhJbfcSqkQ
ztc{QnYkaE96iecs;hY_{><=PM<~8RRS-EUdzt;V&kgCXE=Uz$L7qW{%64Iq}V{FL4
z#W?tOPdA;ME8i?eHp8<cn!kI;x3=JqpMv*aP68sE-j$gzr}>r8Zn3*U86bUE<(rJe
zsd5GVspPePv5Vq<y`xlsnO7q(bJvi+5<m7?SbPLwz*hN#gmz}=htWB```XoX1*=rb
zfEmHB-$~1^@E1LY&mPXV)p)NPv5&mv=Ikq8jo2+VJ9a9|TAMs+t2obBO}Ko4kNx%>
zAK1DXyST#s;iAp`IA*a(&x{LjCWSnjWHLK&yk1j-<YFTnJZ`QUkFKtl9hRg=k?vkh
zGg4h9i+;6*C$V&%6GKO6rov5nT7Tl=d|CXi0wqf$_hCEBx4e0J1Z;OzB9Ro4SGTaX
zA&?ITlJml~Lg;QLY7lV&jkS{dgiL{`V9H`$X=VaK|6dnFCT)igr}F_vL0c1xrlO`>
zi*$?P0WsAvOeuOkfVGB+8N!)~MsYnc(QT~lYtN%@a!oDD#p1#&+AS)sN`M3PKBxh?
zG^-ETJ(M}1bNn#z@UyJEy*w_PGeOjtp7vRs%4V{e?*dvRPauGPR$h*%vMSh}bs1vk
z2w(K2zc`F<F_r*8hEL{k?5U^uN39;LOuCKH>Jgo5dqnxEdbVZOhEZ1w04KHj9p?)x
z<A+buHTUw54Bn$^!?NboXMQ@hS-J?Hc|h1ds{5b9?!cp}hjk-mrhH=Oj(OVj+HT#}
z@e1*0whoP7{wcu3=tB!|Rf-4>{7bQ%8x@vitX1Op;rMnsM4pxo8PS2$D=K=*)hTeO
z<Me{Tg#CasJsB?ZJZyIZp4gtLa-n@=UO%Rwoqpj8mil~qEgvw%fBt)EwbFIS;?1GX
z^Zl>v)2J1FVua*Q6}qa=%5L0xD$%~f?}Rh>PK}Z1p%1a|>fpDfQ2wsQ@$AfdENc-l
zyW8BXknOMtaS4F^<QZ377%nFl;bGWgy6!heA+|M}E@o_>>vtlZqRz(#!7BtSB==zt
zILAT9dDEwecqB69K~-C{E5_T}j86=%-@r=+Fzb3cw_RQLH?USHEiMaf{%tC|U9EZg
zmBak&X%{5vSk_~}FqZ!%fNc(}2Yx$#gE8b-3ce^m|6N6)=O^WR_=Sw&k{x=9p{|a{
znhvPuU;EA(7G^!{(JY|+MyPu|@{c`?a-I_iG7!!3$S)8|j^gl@R!`6>wrRONY9@-0
zTdJa9`uUmQ7Q1nE<dBSdK(_XTR3w=5D}M@xBSrQ}53$CONbKEi;Rdc1=5I_|dHie)
zjnPxSF0pV<c?LJdt|q;6pyW6VNfU{$yjNDg<Z9-3@@7=1$objFm3i&xDGnkMCRP@+
z9Se680Y57O!|@!<IBJD}*G78joxX7I#K&NBBh%erEz|zL>7hpa{0HnViIlx7`gap)
zeUrsHYBT7aQGxN5zBasE7b2%@mvA{z_q3XZRj|S%rv4CwAjX~AB!f!=p=_}Ld|*)Q
zCr<4e>$Kih9lLQp?Q_hX!kam$ZWBMqmTX{s6x1Vsxj*awFxz*b?_g0s$PpFl&UmxP
zdNf`zfoOPY&h#@%_k*EuUUAEJCOO7Q?0HNTCea~ck#;G;b^sfO7@^ikdYYhh-7_<K
z(ZFNl{ZzEL`?CL5Zu=1Y!z9;M#Hnj|A^gH#^8Zv(ck+c<6zw@<mTRQYrw%N0bNiu$
z{B#~fqhewkD@lYY9X@~+%W%R^icQ>YusqgDOov`zPV&c(A;T6{jdunfYnSty)I{{g
zKj-4E5}s}V=sM^!gaS5g`U2Dq8>Yq!pF|8reG=)m{pfFPh2^I9iR51Ogd(;Cv#6Qd
zkoyVtJQR=LMq+jGMpu>YP;mZ9VNcD`7r_i7bxMKHYv(qB@5lB>5#!&wTy<Gyo_ENR
zRq_&9E%THmzLj{M7g8tOsV}=EF2tUl9+J?hFDh3;&6O4r*c!n98_?xE1Rms3Dg2y2
zGQ|LA04Tv%Sp=Rz%XpMN_=wPBcUg}AiWHD}7Q6q!#R>v#gX7=Rvc@Rk#WD0%1#I3~
ztc}4&!>g2-td=&ZCY~vAKDU=%w7))>El$ERTzi&%4-{GVmCrPUNP~X|lVXN26;J4s
zwP>fSJcvB1JUASFo_HJ!%bL?|d4W3V8-~;`iU<s*D0hG)3QgDAe%~2LY$9L_)p156
zfgy?yXWsRXt*GWMvj+&E-vr(2syEg(-^xB**!os%)>LIC+Nkq+kl>+E{c+x5h5bxa
zc6S3QGi_H6&Z%FPM%{G%euE%=e9+7ZE094royf@&r27c)wVfvz{k-iY>R9~T$8b#f
zA{YlvUdPgJ`Y?Dl?4uPTEW9Uu>KUt8W~P9r4z;i1wD{r>bM2$3C>I6s*4Ie+yF2}6
zTls7f$HFq0jesJr>|=!E)k9yCnJK&~W5T%M+w4%9ufOu&oX7RiKcL?~am``k@*dxK
z;+Ir7%QwrL#?`iNMb^{Ta~10+vs5$8zml7kSnUo~ST9(QhX0{gySC_e)ry2F@EBHb
zn*b!~O`YSrR&nfDrcI`j=EiGIMu&ld>E2YY2-xO<Tq?dPgbTV^x>V?njK+$If2lEz
z_(zP7SV&Umc4(3fB9U3H@v8QwYS@CPa#=C?=ZExeE1n+HJYgY2@)QrI2<%@$1)om4
zY4e3U$j9u;hU)b@U)aI{EvG}99W#s_-K)>-=5@cg=r4)C2WhF%6M4C;)5S&#Q?p-S
zcG_yDy>4$^OgK&5m|nUzB$>!{g6G>tNqlm`qGpb{jL)VLIxef7aV@s^+B|EBPu1ZY
z2QfjSM?f`8YJ^a%rt?)8HxfsSnI>JG7Yh>w`35Do&&>$&y~~6H^kPJjN_0u{@d^(b
zm!=~;h|$)b*}qNU@H4o7>^{-bVqtcG#0VtzxT$>cV;LKBD0~sS%y-#_+jbG`MLpqo
z!U%R;hsX{ag~lzH5Hj_}EdfIeNZ!xEa~njs-T9$~d6|PZYBtp7xH-@45&+gBj(4S=
z8|f;S&#D52kAGomY6ov`0$fM4L>$o1>Qf$uu~5#h78=j$^TePxqwr+-jAeV=bL`3x
z4vqpsri>DU)qiJ%39>oT9kHc)6IZS{+g7EQqRBx5L8K!(?)kQSgz$(;ZUAiu<hRvW
z=y5F2nayYRGzBKz;ql=sEnyh7kjS@3HVx?ffZHWBQn+ac&+sm(0G<=c*FOh1jInTf
z9`hAhH8$FpMQ)*jMrO1GCZ9v~Z^UWwpL4m7zWkSl3w=HgCW)SdEc+{@B?ZFMl8yMD
zRhaxUB?<EVB70Ng7&xk-G3c;IcWyRJuyZ?mF1fa+2x>DzE&$;cB<F7`)fGj_`C)(?
z(kyB5`*fK;8A5OeVH31S0J)f~WItTXpB}X{gIWwU8<lDP^Mx+Fvc&VTK+^vv09Yj2
zX4^gv@jEY3FkZJb#X;qCHg<*SJ+zaw{lVXo_q`6MKs=D&CU<6^@0PFbT~oAG$tzE6
zDU1v%AP>A|q|l_sAihd32sOU;P0>+LKk@2jWdB)3NDJ|Xf6lZFU_`64?b`1LDmTx`
zGr@=mF(BJkX6V;(_qDcmQX<kL&x<}sxtUguB2~|3#mWu9AE6+34USi}?5wo-6e20x
z-^1eGVxcE9%l)SVTAXM11{hXJs+T%j$3#KRLmd`WOa(46@9$2?gN3hg%-Qc){VU9K
zR&i7X<H}IKm@<C#{dJtr$Mpg(-8ZsmSH}WcZ955KgR#rd846K!9>jCNxlfX0uuHUO
zOrJC6fK=+9%Xl_=DLc(y@=;NKCd4kbXTy}^qB8XcI7fb9D-Zwv-(d<3GrOE2yf1m6
zW=$Yq?v8bp>+^S;`d&(Z8!2Q57`?d!My4h661eYS|9cN8wD|#daiE=+9uPECP>Q9e
z$!oP3G;nw!uy|#1-e?)pWm69P1L)3k=tgKp6<(=`KyBtzPhvgQ%UL3-<gPuE)wNq<
z*Ra6)XPh}SZRAO;hkdDAcw~D+59TjfUGUD9o6ZQpNnAMs^hczyMwis~m%==mLJ$+D
zzgLIPJd|YuE+;g;p}iiAA8X!6wYV^lsEg)x(Hz&=H`SSbdOm}F`-p5I6LXxfigW8a
zepK@^v%C*hj5519zP2(=xL}u+H9ioYin;#c13+e0*ttm;9c}OxVwy7mn!${IVjFJ7
zrmc%(POT=t{I}djn&XzeSm85jjBrTkF?oso)C|Mfa5IbgF{>gb$?|=c_*JzgzTq7&
zcY)jB!ts-V<&}!x2HcYat*9r&r8?8U>k$Vuv5c=pz4-MAMx^CJB4cUz&CNth=0fbP
zZxujH<+fYP2%dVx=AZ8nUk5k6q`ie{rolgKqnRDPd2UO@()UkSPFIeHTla7z_+`hw
zDF*7@x(hA9P%tA$+#H(n^FOm88DGu_b7}tIYzc3<r2g}Jqz<HiF;`+*lJ9`7*wbtY
zjj!lPY0+0w?=#E~GwSPRC@SGP4g8j>Du7YG1^AH_#0R1!S=;H)rt1b7jD@=d0U82p
z>tbd8dTj;~zz0iz-J>s6hT}T4wcTaHu^Kuf2T@m-{>VSrtNlL>ZD+U`uh(9%_L`Gv
zY|_bMSg|1zp1>Y3tq5g%t8YxS_4j+dtOLK&?d!Mg8{E(a<JmOVz$N+xZ~c7v9rmko
zH>$X{S^bjckrhiGoH1`e<!oa!7diU}qIz^L+G2>&$?0U_<0>V`!_?||6WS(-F^5=3
zt4{k;tHQ9G?R|wb0^95Zpn81dnx}sAm+;j08`4h4@h2b8_yS|b*gB+r{SOZ#2bxlA
zDVNm-{{s%uZ5evzcc))=7q&l%?yoA|HJYhChRZJ_PR3*8Wtkr=?dm5*uwtqv!W#w5
z4k(4FFw=XyU=MczpQUm6OU8ZZ0+K@k4c;K;Cpnx@ySo?33JzGBB(LgJS8J|V0SN_}
zv%Q?hHrlsnE5(c9cQ|8~rCJU7pR!l0zy(!~FF4)1-D)4CxCkyjqqK2$%^6SSHC*UB
z>{DH@<#nhyaFub02}fxI;PpFpQajvoN7bu>oo*N;&qOzwI@rwRg~sQFHu*-gD7KjC
z@qqk*#n(FNfW}M=(HmSvDW))m8jf(E+!2sSay*OPG#Qb`Ej8d<lCF1RxKwE2>o{k>
z^`F?xc!r>5TpaeGK(CBROsuwS8}lx%?^Q=8_uj8SM=r47puVUr$yy%Yj|8Co23(wy
z0F`Wu$3Zb58=mZw_@a)2A09ijCRe<paz>oJN6n+g&%yC9=X0Y$x-9EHNmVj_0k1fh
zFmao1h(bd?EzFZOoBH7Gje9+kQ0nrq6B6%4v!Ai4XTlpAc)H0c?`0FmC@j(NXMZ!3
zX<2S<mdOx>wBl1X@SgtBA-GO#6$WyN3p7s4_?RkM=oRpeUM4WRp6E>C-#**_R`mvC
zI%u9s-PWnI7A}h4IFF+w{_$WRv2%N!zRhY)tSNr|rL<&<#*hX>G1uN6VLg~iDpXiG
zC;wC{%2w`+r(i`>eEOHx`of`CQInCJ`2(N*g4+XhBF^})tG^Vk1MszQ-GMNTbfM=`
zB{I(2$WqE7W_qdihnxMfJx%_n=oZ4#2eWO=iLkbz<iezXFuvlqiHKD9M8rVjpVY3L
zvUumIJg(0w(_YYq9qdhXzQnfJYmw)n=~IyH4E5g5gupsK^Wbk%fuBW+(7C1tpl8zg
z+0v6|bJR`+BOS{e`AK)0C)uNgMS7<ID0hmQRzp!cll;vzLK#}D<$&A6@ni}-qgW9)
zY^W7{jDVV-8Q@H_o)lSyo2cRbyVk-qkxz(8<s}`iNjWA|q&?QtYe-5?y0W7{6CBU-
zc<!+EYAaBPrn5Wnuy#f}&{#a-gBd_;;x5$+sMK|k_Xzrum(`!2yn)TC-=WqopZ4N@
zK9^?;gbJ4|x73yH_J7h*)*qI{jb%=Bk85;d+3fO<eQ~s;0v8}&{!jX3VnhwvxOwY%
zNP9=G_9ILh?9<+S?7=5}X@kUWi4eyhU7|9hSQ8p@wXzmz4#^Ja_22_7`g(U9(!2pI
zDY6oKM8r`ut`VLCVsY!2SBH6SNHyhJ(TD{_N6_-ae(upi+}+Uq0+{?{ea~dmtfSTU
zs2r<7&s;_xjP+d3#E#L|J>D*>vAzAMC}T!!tSkvO-d%U$n2lD4wpVp-9j(U^xjmvF
zd>RKpzYr{*!}Z~2Pi`gF0&&@;^*a{|T<ERgSumxnki*=S4FaHy+fj^`R`cB1H<XUJ
zWKj_RAtP8}38ktO_eu=3t^tP4iQ^Oq0w5+_J*%8<)SJIbRW{e@{?_BiJSlxXx>oFL
zd-3ei&_OLl0Rw*P;{1-QHI{k=Mhf*v0Q|k{uZPDbsWFXm$?L0F*v}!iRYe%gIl?qu
zqrwy{(?#Bj%hEXpNV7icn1_X!2GMNmnNq^t(`M%9CrjSW-O6#P+`iYQl{9O05rZ2$
zD60j<<W)wM8vu^|x5qZTWuCvac3$tdC(1X<&@1t%U<ISZ^3cM9v+_jSKwp!iml>Bu
zjhdBAJoSYiopQJ&6K_li5EHayJUcIKC=I<ZM_6xHj8<>fQ;E!_=6$YO!{}H`ABkjn
zZK#Z`A3Fn_CI=*&7?*!EGl^fHEz%p_HC0Sf+qid^Bo||syTCV9eYd{7_i{tB3xM)N
zsUA}(N19kMLSNxbG+Fz9UL+VL2qGhxsrekEcExS+oJsD7ss(Tmkfes!_khc)!KPv{
zi*3GvjYF8&ogv?qAqTZhzsw1VoUw0U1!y)V!R)=_QeA!D+{vQRRqwUVW3{FjrF}U$
zcWT=vR*lneQk?k8JRb1ucW0cJR+jNkTUet*$5%dCtPuZJC+n~CQ@+EPrxUW#!ujvN
zI=o>%V=NnZMEXI>5<MW-$M8o(${`7>lB%~d_HJSM;REOl2pDW~)C!PnZe<3dY`CVy
zk?j0_YAa#pVi9st&lI;Aqk*B#s}M`L;+g@}3@w;TrP?V+h{-)e(4KZfNLZ1Kv^TrU
zF3U|voT3dS8IRfP3+|`Z&2@l<lT0J8#_${yr<)(0VzbU6gp0SwwHw!<xA66V6f-9Y
zVg?&wUi2mX&KHET4-k9I8Jy<X6~}E!xyVR<%?D94t>3&zm_d`yr!rb<Oe@Avk9RyS
zXJRHLBPXpRmahMK={7Ul>m<zAzxMKSO^r|OY17$?5PD9;Vf-%V0(6IV$MHyx2l7|t
zhcDdJZr7Yv2+iebQKtHg?_s(@odHV#W{_*YtMVH!^&jC&7MGte`GAxljP%~YcMn{1
z^gEs0U(+a)*Loh3fMwj47{%%?4RA<*h$1gQgkO2Qv*(E4IzCt$?YB_)@nxJ{nt-_|
z)p1<%p2i_~4_n2;QgjEb^=&b6iCu+L_)5hBostP|_7V=Vt3;5sYcq@$HrCZKLL>9;
z);LIL6M_Jr|6fm^tb>_{(VOawP^F7|QzbV{au>`N^1K&o@#W@=De{xuK%4YG9f*`C
zu69IrX=)9wY2xn^qHNyESEKr5A6x*5$y_?V*Y6bfh98PFYl&ChbPR1UeCC#u+M|lp
zDwin}&ORSOk2wko1i(Z}I85fBU{PF0Tqj?WTiwxDY{ngjl##bxgNENHdK%6Otz;2q
zzbA&~Jj2#*_>a)K3MkH=Ye45>q;f8R?GLk`jq2A&c)5T(fTQQ!GwYx=yK?}YN%CzS
zHcs2_K0e9v6%G{rDkHT^oZ^E~W(`r#Cbc<2CaQs2)mlT39zr1Q&C9NK&v}9_-(IZ9
zO7k7G|6KewbDal7F?xF`Rg_uW^M@x0Nx~-^^Z;pq_)Q<ud7^L$tj}oGbor``S700f
zo^iR^X1doFL0t2>nCLx~!9gEW;2<hn+`>5Ubj&nptCQjC8(I&iEt}gmN?nTLcEPpk
z+BXn+;5Pz}ysYSO?~SWUP7Y{vR>jVdvU)#nW<Zq`HS_%*KxcGF0DU3wK`lD<N`=6&
zKb(X}k-!Zd&<Bi9f6~jy@%Y7sC-yGzg_Y6Frw=!9r+neEp&I|UstOOK#OFVr@RrcI
zxr&dz{vdJ2O21D(T|9MQyQL)4terk+=isoXYo^i>p$dmI2h=!wHI741JGPFvDCMMB
zvl(Y?+pSeRfY1WQ_2L=;st8O}*htA+EJOLR-pQm!B`O46e;FB;tiA~6Ft+8dK9Z_o
zDmuT94Dy9x-I}NArkD;fJU-ORXHgU^R1>cHZwP`HUuJNx5d~OwNF7QYh-Ng$C{=uL
zwN}SQhG2u_Bdo`aK29k@?tGVDv<~<O%qL}^CvHiG!US+I`RfFbB>E`>%u@YLx*Tc-
zfV}sHGx`$9F}O%C1G1GMm`%jK(fW#E^X??XEK+<UUg1S}*duqm9+`<(Lh`9nR(Bi~
zk^|RX!T+a$_;ZGREaJYl1#3Xufn+IN`YHcpu}A&L6>HL$MK3IzudldF5BelqRMlIY
zx`ygFEr1p;8Q&<S_wIzacENIT<$Na@uo%B}{(MJcRjU<cW{olj$>PG57Jq&xl~{YF
zW(!FF$d9~6rkK+N|C!IO3$HI~fqd|N1wJayBteT^keHejAukDfxv*Ak@X3f{dt@)o
zsWT}q_NKU6$g{ktFGNo^J={ol?YKjs55%HFEI($DZTHZ_5L9-?19d0?i4e7DP5$Q=
zih2{NQ1-hS4YPUxWv48Kh9f>pbiiQpcyt;b;%wd?mpGfks;_v*xeLDlas5rmJ{-C=
zl7;`()N#Ziw}tn#>F<Ah!F(<;^N%br>a<PKY~ag_aQnE}jKVQ-NuFrnWHukpVV`1$
zsenq_*k|&RUwS4sfYU7Z*>Y!xPhDE_zXb~7!lg@qgn{LeA>x0gsO^h@uadyxk-XT$
z)bMxRyU4f8(4fC=35xP`O6sL-im8NpyVv1#jzt4gL-YEoLxK73R)N5*ZsTwF6HDLK
z^9Zi=e3g}r0G2AbJP?r(XtDW=>Z&ph9+A9XI>U<1gzE|x)k$G7yxw5vg!fM8l6e>K
z-9h=p^Y451QTwloyEaqIWG3^ay{Zh$0e^lICD?cTrat`|IQ_B*UPMP!x(l(nN!p4M
zGai@pwvPQZ+*8D=X&~&(G4_#ON0i@o7oS?ridsmWt;d#6G&Ln@<{>rPygH%pO3B2P
z0X%b@JRxLYw=fom?!O%Ba;ofp#Z?ci_K>Fosn)ufukAIPFV;vItt?e8!kk`-s`+)E
zA2d?V;caF4;*Fm~@zyo$AKll9@B0Fu)t~I-f(}i}o7*sc7%iDul?cW9K7uP&Ej|Ta
z97?Vl8bbrFgt-xmnOm=XnQj^>oEK-)YC!RtE6te|*U3i9s1ui^IJ5TS%tOk{6WV*H
zcHbke{Cwo7#A>4E;t5lr_9?g}1q+d}=vQhTJ%ambqCTi?z>71m9?=qmJrt9={oC00
zp5Qxf+_d>xj>qvp@$HpY#8pW#{$T&76cgT>5Bv)=g#lZ_Voxdt%dpTC>2Z<a-)=<h
zSrgHVNweRzy4L)z;$EFv*z}gb*SQ)$=hEGi!cVcjn$36*lnKt+GyyOwoOryIzDczO
zk4@7oGnVM4guI-tqp!X{O=%~<o~LiAwU*8qn+Kuv_u0x^<^?rPgv+=)S`GV$#qyb^
z$!2<ARelxz?y;{v0w5bF%9PX*pMP0#V0A!0IS=pb`$hO@m?3+aSq4-%zDKipr8Nl3
z%|0r_M!*IA`K)KA=Uul)3<>)^yJyAaA_zrG#YcZ9-&Jnmg&i7ndHrQ~Mo`M(>sGqY
z_yHaq-F(wADKe{~0x*ZvmFKz8qDY~>s6~jG4HHz;c#}IFgy3gj2cn)3`J#2k%9uwN
zjJ01P3!uI+QNp47E@g_nM_dr@6;tu}6duWzCq_<_g~FxmvvC&oQn#fSQtp-yHH3Oc
z20|;kR@HvZQ%u#ci|PU1f$kX#hgfg)?~o4=XlhHX!(Xz*fBR|N6reL-ZU5uHLFmws
z0*|=tRi~oZQl|Mfe)J(9B<gDIy8%D?Fi9}HB+Lo|c#TZ3gtW@u?BufWws16BSaCb)
z-PxL06PlTEv@vF6;PKD1RvmYIO;O8GHlmSxk<axICkG8g2mcCEz{6)uSWEFX^PVJH
z?+^dcsM?FcKltGPYRv(m8Zxm~Z_#6!BJQ}*raner`>3O;{c=q6_VQ@TIG9|zQl7uI
z-li+8hq}w=C^=jS<7%>Rw8e?=XrQdY-r2|FQq3afA8kHzVIl;DHzJKP9$s|6;9)U-
zbhu5~iR2aWuIc!QA@@n^>J_!nT9)7zw&ULs-!LKAUhP+f#B+oY%6U9}FhB$sP`Y0Q
zlvp@FbW7-!J_Y5Bgb!p(ZqxWJIeIt$S6e^<g^oNvZR&YH4lc>ozAgIrzl!*N$qx=5
zl?m-Hh;rWPRmigGHBx?et^PB^k*(pG-pzpTKoE7sO%`=gt|0QDqAj7s;PD=?SIoVk
zIbv`o*Jv)GkuB3Yc{xg2mddD}$@GAX$gfn{g~LvWHK?_l$XK4od_kChR9-L@K6yf^
zp`}8gea$&XzZGeazLC9E;IrKn4CkL_i09a%YQK6@fE00rS(_Z@*^yOuvqYDh0gx1Z
z-_H#{I0Swpo+v9LQW;YWzrL(*XMo4&sHkX98R!3&Ld(Z4Bd@i1jnlK|O%ps4iZt+@
z7KP&?|EL%k;{VnO=spkS=rxseJRAKmk`&Wo)3#;cO%AS(uW5V)$xlgYs_J=vbtuOs
zb>_R&p+kSZC@3rm%%My9sclHvP~UeHR+30d_fuz4bN8Um=v6T48y7D`iQ&!!a$716
zd{W=o?nuqQSUxj?c~rKt?Txr%*v)qUjtG0&Ia{?mx^imVzifb*InqE7*vHsIURT*t
z7x<!|4PSL-b$HE+-nctHFStM+h#!p`)R>HxTMHeK65T@vykqb;s3b1&7V}D6k6R0!
zm1Ew+gspm`afJeI7j82%1nY1Rc8xG{=nt<G76QFZN&qAR_IJs1!69vnXX+y9UvsjY
z@91x=2JBo)#S$Bjj~N$lh0S-q40NuN9(Do0YD84M=P47D!Zs<L5>A;6`TRrCs9pBq
zYW-*2uYCUJJPY+R=z-JmpHD2t4*Gvoc$JdY*sN+@`OKBRamVSUXurR?;My!UwcT*1
z!~<-qM$~#s*WSI|xOlgGV@}*nVRrF*W*oaIhS4xl(_ha}!3squN*n=8h97<JUE6<B
zz9Qv3F5*$LJiXB>>@vo$Q{mtD=W({J=Z{knS9`s(#CcBz<KYLpIky$lQ$NA0>dsef
zM936<KFJ*NN?pIH4FzK94I=bGaP>OI(l<=Zu>ypC(CWNKTfPmhwvp@|vMb*JhtMe3
zjE?LkvmB)(b}$7oDEP9ngig0p#)B2nCX*^h*oqE2i?9jdH_GSz9lDLk(Dx8z0|4@O
zR-$GEaTC;t-k&#|j6}Ob=RivCB?|dsNKb*}&eajLV>ItY7`7Bgnajx?*ojsLcQ6#o
z@P@mEmw=gmy!L!Dkn1F5PBd?*-LETO{OzWEGlk;5SK|PO86Y~LJookfFAP}+QwoUo
zyTp9V8e9@=S%cMXqlt^wT%|a4&0(IJG?iUy{^WJs%~L1Oz<)mldhra%<BZJsn=bXu
zO*+>#uUD@5m$d?^Ll=?c)M`+r0kbDs9$F{;CY^kL+g1HrOQvJW2Qa-}#9SdK()h|~
zUVc>GVFG1$Mywiz{fx6>`^$%XE+^f~sb~cX(i_ZSz)HSZ@YPxNFdMplde`<tu(oI3
z77;DI7|C?F#(T99N^<<`43CzTj`9W0YyJ-dxOIZV+?v6juK}bA;*D7JXA=b{EG9w|
zPop~_k9nGSGf3W+JL4r{c{$|Qgd1>&v<b2O{eP>+@rrlYsd3IKZt*eMvV5~{I{)Wm
z@xKAsxetFoyK&6EIV!rK4=njlUA_crbFtCD4!?%x>Dk%1hc|FLd(3(d3C|=6Sy#^0
zMnQN&Ez~<+Tm5ukcbcy$-3S{}_`yNeVEMiyx(l!Y(iil*LV9=hop|@@_hQVDTjmKz
z^Rp_&y4wf?jiDRlbWuWw(Z=HcrW{plKY4_VL7(VMBR4x)-W3gLG&<V`C^LL8%K8(6
z9U%cHi?cnqf~e@~T5^y%-5%n<Sh1U8qb_7<*Kr)Qij#&F^9X&;>1x<rY(`0)P^+bW
z=_AvV`Qdl^?`$;c%y>X@=A%{j=yWRC86o=W02FEGG3`{$T-?jBY}sQVVQSSq%FGou
zPoGA!ohNqcxstPD`tTd##L>x4X|mH^oKgvr?^>nSQ`<HMzk4m7EP8O&k!yMlB_hzE
zZZhry2uAqLGTj@F4^3xE)nl)W#r`quCuB{%>6*P`jf}aRRu!tay8bM)hD2tFTgi&v
zm-yQ~xZLj#(magXUIK@Oi6QOT0kPPtL!RxI?c#!gD_iOdBPPfm9!Bo%S00S7QG@Uh
zlSD|D{~#4p6R|bZ&gbiGos0~`KSsahgKX=oW#1Yi_dfKVeN6Vl4`1JDh}4Q>-E9!|
zmc`L2v$T!MdX~RwRbXKK@*)Z=5Fv~Fb~b%7Q<SoYAy?d<P9=L!<a?68(!R+Nb>kZC
z3_AML9pR#44Cgb~LWo#j()>KaBGCWBYr+)nOxd6$Td(eWC%V+2q#Sb7^w<_8^LDeO
zk(L$@o_!_zICOegcRo&dv`&0~A*%~Q9T?`lXX^2TbVE9vCasM&G~7rcSom$Pc`MuA
zPN=_gV06C_wlax`n;K7DUbPr?<9VwsQzY96MnwHV8gu1_uyv~BeqDn{42i}Is2lo`
zk2)?g?(%-MMYg^XG*;sBy(^1ZmNX1FyPND)4mHy{^l33hf15wuL$X@)(EYdl$VI0Y
zP}GM5SrlG196mPK$FiMO|Cu~qHCM2Gu`iCvEW1^yiYP9?s&E_#=P^>sutryowsQ>1
zs=U~L4DEw%jqSUn9hZJhhDV3#elW=I4pje5H%);g18^bSCp384ym5M&8{(hvdFn8&
zHod}72FCp4!AEvQn(pAf8lmhx{`A&qUUZc4tjCdM1hzF!$47=}pqyzsi_Vy8fb7(O
z7mEcwA%xPjol~Ls6#K!I#@~lLS_EP){jB|;chKy9SbOu%42lBO{?t&t74fA5d*B`m
zI>hijM3>9IzkP4y2(OX8Kc67_Nk!0UVDp=N1Pnwkh;^b*BKwV=YXluuBj=Q{6&|db
znq)r6HP2>?fvQfpbDO?)bv;&fUgB|Pv{Kl;_NtwAO(C@5TiTCdkQ8B){4!b8{gQ+#
zOIC_x?7Ot{id@7N|I&KP8Wt3{3s}B3%0ayu;te2S{H%TAV+@qcH&J)NgxAbZulDKl
zfA^||IcfSmp)j{GS_0x&uDm-NHccpTnJcs$ZNYZJN1q^`3!K1Lb#eY0;=V4u<9l`)
zjEBheN6Vd11aeQn?Xa4s1lANbSiPl3-k;GJrKtc?HC1P4V*I@5j4(-z-v4}z1+iP0
z$#`?<<kTkI;?!{HLlo0(b4MY^=-cuLi^-@;p*y>5J8Har*bMW;?`ULIWEhiQDDLI!
zJRKZU@z<4;?ETi+x5_JVYYDNP*kvIGHlVzK4N48<vP=0rsHE#}-t4=^s@9|~X4?K?
z@>Gwh;GAq&p;+9tufuJ7%hIXOz<=+c)tA6fAH`_u?^itkR9T5<u5UB$Kcx6(HG@@g
zlOf^5%QW-92rZtNxb#rG`ZX}F)byn@6qkpCxdR)}h*p(B!ZF#@aiJ9am9s7QAAvQi
zkoH>>YH@q{XXFIL1cTHfPpH{2#PeWZe3DyPX9KUgm_Q-1L*9+P(>z*EQ@VN{cj)x|
zmIbr6i%1fqAb#@746^9KLf{`3Lr}L?v^Zx>@Fna#b0=r4f{}J39_LMoQLP;7CYS>!
zJf@&Oz2`IdtNTY-r3eAp4{8-%!}hB*PoOf`s+{bv=H{z4I2k}=*V#kFki*8><d(DD
zwGKW>h)xn}?~=wYv)O$5Yx3sI<=DgQ%IF3$w}hy1ECarAM58H*b@bd6Q33eAR6Fgp
z-luhj&k}Ub1bTo(*oEE?HafyUC`9?Oy!i;vA6R_Nw)IK-PDnJh1t4#}^NvG~*Vba8
zKl2(Jyo1TA;=F~`D?CG{^WYOC0+8xb4~ir{d%4RwRFYth?IkaB;Dk;mhztqM{R|;D
zWa^gcWQWsDW2S!X<MXU`*-uACp;d!|_upf_5_rg&?$(sjtewM5(-n@eP}{fI7|0Yk
zzl%yvk!%`(OTCYV*Rb%r#G|#X3aQiz@dnK-53T1zE<<Lwm-M7dz@wO-Ot@M*i@@oa
zfP0F1)#GGD<HI#3@$ABX9VGpV=M-fDXs$=XW>-OnNfv(Sa(|RsQ4rA(RneSv>wLO@
zv@^pgDYU;Tx;Hf=@6WwgaYNxuOz5X{XZ?*1^<XA{L%f^#s_p-~0G7L2^v$1EH+GE{
zrdiX{wyg#{HV1_H4SV-G)K1m$?9IHKErv)AH=16d7q=nzx4uJ|J+7t!a|JAI9o{ZR
z|L!kkU8Rqm>vs#HnhLXN*R}><0QeGxj;dthg#y$o1Y7qFIeMEh^FA|h8ub6J%DbR&
zn_qr>Kv`xy$!o0{jqKtVN!xbmn^pKLX0PPG4N%)ZaP{7xb=sl}TkX&MukF_2mNT<7
zv+;sydh5%EkADQWN!I=xlhu6YW~PFsZIy#YmTt<y>N$eer+ZV2oVj?X<!UV|62x^n
zayUE?Vi9vyD0^Q#IbDlh+Ji43E2jOXcuYx)YPOy5bMgU8f-9L~nVWUSLiF}WsKU1L
zpGMn<OvZ_H$1LmBAI<^>10!HWxh0XJvYoE_jCOh%OxHU$wtMqCE`s<oXRQ-=X7z*P
z19`(N_pQ#Oc~9dbs&^#Wo6}FLI4m53wSZVloPL_{8{=c)<i0IzjR<eT&6uMkzKB`?
zZTuEG^Kg!kHJfyP67xx}iLwDQn4y4Xz`Y_K?c$9r?;Am{2QfMQ;tAr53hc{p2OLO1
z%aLyl?+qbBl%1%s<=_G#+uNB#ve95Cj`ZrEF0&R9jdm5SB3B0Kc!@ox=!Y}pvOC2r
zKztOK6nQCf`GBnY2_#Nkc<L3-_Z6$zwS_AmKR#BNk~YtoD@Ey&DHT1O%pOWX*i-XA
zG@bQd)9)L$H%7-0QEH?hs7Oh7NrQ+eD2+%-caI@0sdR(1(u|IcM!FkDNHe-)Jo|i~
z=lKuz!(Q+EzOU;%k7M_kO>9rUK^DKGCb=X95b;UOyI_e8knx)2!G_O43$<039WmLN
z-=$d}6CHVJh08<3ISLHom<58V4a#5joL~`-Gbv#v_wgAb-zyaj%3%uMX9=bMrcIOP
z9M~!YJD-XXalio(!+YvO$?*{30)V$;1j~XyJ<fAr(kl$_JodEta2~elc8m;vJOI4!
z1ki8Li9`;;R>j^fQNI?)*XIgzLt^Mbn!96aJY>VyW_#-CB7Dux1;@81!=WbvSL4Df
zJZ+m}3$HwTzC8@me#5vMEdkGtaz;>|ldf%4`0u~fV`9JtTRl3}#2fk36zlmBQb@m}
z#?6&h=|wkvE7R#e9z3Cm{^HV|cU0(nnrrC0CW@q6KBJcm?cb+HjBV+UtM@h}(*ZaA
z_EQ}U$M0o*jz8NBPS*FQBWd6g5~HJn9VHw&<mIX;n#spP$j?WKKabO!+?HBiFS&co
z|38+%R|CJyI^#`Sgm=yVzT^MlyMJhxs~X2{0fJ=uX+yu%F2Kk?Dz6nNJWZ2WuoJl%
z$SIplHoX&T=N}-2Dce@fTAAj3gP2gtumD>|Xz;6p$hboW>%fq;##CeTHBcLS_$iQ?
z<%~=@k3N|MsWK**4|6rqsYuD*OV)R25wx|~VqNbe?D!?f#CgWU7Uj@<)UCsh9R0~_
z-uEc98>@VXzKbW%>xg|8B%5^hmI_80DVfZ*zJn?#K6UJH#0xyr^tK?CI__l+S8o?-
z-m)=E&BNn~?&ywh9by+eSM$*oQ!WEu)~x*NQDpFki|JvZ%`5-myxwx~rc~F;s&k@S
z{!{SIu}tjG54zB22F}tb7vE|zCL1cZk7=&fA2qG63@ykTw`I3H#4p&$5HY%a!|<z;
z5WxLz$tod8i#n9^!u~`~gr;!hixJW>Dx_@enNndUls5=CmTek-71WTVz$?9Bdn+0i
zHXhmk@Mf2RU3mF(pYo@(xI{I$h*pv+|KEaA;GmxM*U_F3*;M>j*;!}k`MQ^Pc><gb
zqZe$YrL8%&dEC2BEm&0(!*M(PlW@0u0_0fq&C}-dC<kbD8k2a$<<V&F2Sn5~dOswy
zvRr%-w0E6#fw1q5;c;)u^v~H{w4)Hd-M>*~Zs1Qvxj?{rlYTDi_V%NKIv$onhz+5w
zhKd^`f4L^+m{a&>cXQ#p4fo!*x_fS6^Lpa!7k-ar`Its?U&P#$+9^)${6@(^1IZn5
zy1WIy6)<)JYl%aoZ@fw2tBwb+FrKYJqm8L!3lk)iy^5o}ga0^hgUFzENB6Vh<RVSq
zj}M0J>2UM#zdev+20nQzBS*%ZgPRw+6cJCu438ao9J`dKCxdGQq<H)L0g3XXD^3OP
zgrz_jGY(7ItAmq_CPZ~L$L5mH{$k5t{2^bdiy=|%%xxEo%qz)Z++{{5>AL|L|EFY7
z4o#n>1LyhmocsNezU92E2nx@Q6luOFpEXd}ek`B7-H=UrX$U4l;8n=E-|bOPTnFAp
zbb_u?*Oc{;K+4&%5+fh;i0DyJZ3C6`*#}8u=<IKnh5e=kaQaeKvxX-@ZxY@DYsSZ8
z<I^uaW<NGB4O62ZyT6(H7c80tjiRDN)(@OHWv({&?4L=gaegbS-yyw*2TzG)$d)fv
zeTYaJg7}0<pj@Z62`32;mBp0vJ6>-xzZlN>Qq$~)cV7=(wLI=-Vb#@f3-;CNmzU@s
zUoWDnOP9V;VfY+R5;x6Y`Upv3rT({R>G$ijGaR}{k3vAj5_ftlBu(hFiOJn>l8!Yk
zYMHNs$UQ;#$NzFxO!cPe2oUv7A_KOsOrx=`z<k`p={FZ8j|Ga}6VE@sn>hf*ht(Lm
z31qpDIhM}`*FtWV((%5y!CN5|{(;p=7i&_Sz?ChOhUD>}$9ii<3CkZMLNG+=rPUdT
z6qo0_^EL#Jmjqf6G_q<P_6Hx)u<=U;x^@kyClHadH}Dc4e)Tk}V>d%+gq&d-1^Rf|
zUFUthh-of6m@NON<k?3i;t?LTK|Vac_-a$YCh?DM0%t#dWJI@cki;kcu<qO#DY~G>
zS#pq&X;gPY$Vk`~s1x)yv}x5MU;D2H-OX~jvESKb0M#un(P}#)Nk?!Cu!SsI>>sr#
z#gSgaQfM{oMT_H=7JG4+@KU!c;%mk*A-<y=z<ARaL<~9RBuG^qu@~f5u!~qh_7Qk!
z1G;t|hiuihItuMD_||AN%uaxIEE5nqW)AWU($eof^2DDB$D+EWFFycmXUf;QGyn6<
zfqjeqLn+pWd$?e(@;N1ll$w+Y^=$lijJ(FNQf`{dHdV?BR(w<4h58nJkaU8_CH}u~
z7o2ll8X(8Yfk{1>&i}l-GQ7=t`&#STSXaC(t=x>ENZQdQf%JsMmy?v3Ww{B&-W`S@
zb)?R*KL6sIqywo+y}ym)xvh?o+->s9{mbNwX~o3vzNwghnR+WETx(sSP`&$?%ja^G
zrzRt%83R9BxBW_6u?ItP24)RNsAMLKSL1TVJIMS#r0c6;FcCi{80O!Vu#NbmONOdz
zg$z(z0bDod2_$Wy9r)!bBbE}Z0EVo`&v{^bd<L|n$nu}hlThuFD7`;Lg}*YtOT9e}
z7{UsGrXL*dsT;{yu9s%>-&}j;JGk%Z0xoO=-n$OW?E>Bgl}VYe6m{k9ygN`n>)ILp
z5oE?v<~VcYR$Y0M4%MM`$3_m2%QAWtfiE0j4zP3H&q2%@&o#{2Ixql!EilzRef{wM
zD<|a$ou@+6R&*->Vpc^kfR$lYryqjuRt)!+6<?s#l%Sx&tCu$$%Tr-2wI0dS&=lKd
z(_G9c#R*|P{ZJ?Yl2g41{^}Kr*aGOS#lD;S^3OO%W480~!CkW}H{$l#F+-Uvw4FB(
z@FAN)F;v`ZT%koG@|VPp+mF_7T6oWX9KXj3+I0=xKGZM4Y!s@6%A8s%SQJ1MYk?YO
zd)PI?HnW!R<w~ff5ne7QU9Y^TSbYSQAFxA^=aYJas=)87@GIpEHO`-78a?^OxmCvd
z#D!NlTwceVmYiUWatTh@YvROLPZ%=ld<{MgeC&$lSbatPY*B=vq04=a3HEaBU&Cf5
z2`S0ek^B?k=xN&FZMdlbjxU=Zl)k9rWsoRwH7~cWm>(aPna6d#l`M6sx_1x$Wm_Fd
zM{_|{w|nVwkBFb7N1{9PXuk~zBR40=O-}lyRhuSR+R2Ysw+%N0+Guy2N5uc~O~e%r
zKHuF5w&Mgp%{<0Z%HtWBNw-XlUoc#rqbxQ7Avc+PgJ4e#9^zhG^9I|IT_>P(^sBqM
z{|Vc%_ql$0-|kXAGfCrKa{_yOHzmBZp?SOK()4B;dEx#TEzJlWyB~$`GGJDepw*oI
z`_!-4S=P~(C=sN)*^+Pn!Ochq1`OK2^PD@yg?XHAYU4y<y#tONWG0xarh_0(w-3gb
z!Pf$0TEw9(s5s3l*`@=^1@O&<D*~H2z1A2KM>&Xo`GDei`0XQncn7m2J=<)V-LTRz
zFEICA3SAUPkbF;5lhZDu`@5XcUB>fB;Sa7J`R&;}^q@sm&n*od`@vLG1Zr|9y+0E$
zMWDLxfH04|)`{}_gp}+`$e8qaz_>i3U<y#O0h>pX(~OKtjq2d%k5Z%^NVtH#LPiG+
z@^*h!_31k3`TAU8oX)6p5fN{c#!YMs1!djh3y!B!g!IxxN4~W5$!dvipSz~Q$?rnz
zeXE~AT*wKY!Y;>FHm5wMCL4`ja)WFfTP?k&&72ly&mwta;i-oVIPYl-7k^-S>qW6h
zEl^AJ2#d5{IiH7nd}U$+H4%NMIZvjzM{DNzS<2*o&A;&0X=QCSBRU56ew)xBP(M^Z
ze)?5`#7jZvfCug!E{P7p9=!<8`9mc!v8=rQP>WX#m)ug#8et%27gDfFPa685wty`w
zv_;>KM`HfO!UdDAT3y@#r@{u(1moNd?0Xb;$6geR5@h1pWR<+4uxLWpk~E9svMy&l
zh(2s=TWx2;V(l01nf=>Rp2m*$^BWz;3}R<ztC5z?aGc-6T#ZxBDq<~3yZd$5>#_g*
zx>~3&b~CFCj$8N16Hf?QDk@`Y(9dI<mYSlK#2+GX;nYMF&kaN&X!D2mFQ^Mrn|8|e
z>fzE7CS_m03`R38zdSdQU_B&2kebjmo6afT@NIasBM&GXOO_;*BOc!riTFEPq8OT3
z--8ikGV1yB4*@JYbK|PImoHH9QM&;Rv}|uQhbJhq>6acWK@S%IW0i_*D8Ou$z#K*9
znG`@qoz#MoYxb}>NB7O{qa(9H?|2XFq~kB0_)u=<=qmzpVacQjrf_EQZO-On9GRaG
zLXnCD$PL7*%|^@fxD)fv)P2cob6Jxq4r0yvIwXK8MBE(rAI&Bi%i96JA0cl7&hsK=
zbU!=POzH%HC75xcDNG{^p#jE<bgR$b7VZi12K4~odHaNgRnHK?hS%M`ivd1PsO6Hd
zqrqE(kMtcgZxLYddD31_q_D8<q~KluaO<kE-^~Zat-IRwg+=Rnd78V*G5TMV>$`q?
z=mCK+A*^LZzjXKBdovJf+En~xKM<!8_;sDO6$CRtP7Yw#VXKEut)dsKS?iw504LxR
z)m?ve%ag3~hsTZES{;7m)8l~Pv>=c6z2nfDj+1_V)wy?0f#e}3f-V*>W6O?$nMHk{
z4Vm@P%3Qu<|K;hF`F&Hg4#T<AT6FV^+*cCIMNiCdpR-OpP#<M#mAcDW5Ee%pVGj+y
z_oR_?yUbHo*<DNok=SC1chmJ3N?IHJfPerhwjF~b&k)lfHeFqth$!KunJkYbFX-M(
zrEGN^70OB%bP0R_W+#!MgZF5$gFn~1>p(mb!rKZrXU(hY3x{TBI&NW3ZHk66m9A2&
zLO-$=W@Osv+ZZ;0bn&roRvFkk+0$%)#*>GT5fn22H9hV010XrlGLF`IEbYVM?FG~1
zKkYDXYqr10np`!%>gaudgj0zTljJIp#=X4$WZ!5Hu(rHa;N&b>c!aMaaGk%<KO6ZR
zMt<7U3=k0-92p`3=Q?yvzqAa<TfHqNRV6UVxn1uOXK#I{Zu(h&<<qGn7l>dMXRMD0
zY&^^#sSdu+AzLG%OR(xVlsgu9L*jOCKv0D(8rHeQbmVRCcoUbA%kb0A{To|ZyAaVF
z(Dc6^&yr*KCCSD0m?kvJI`0H|gFQ7M$za}1-bnqvpV)KbVB5Ftb^iT3Mp8Q@eJ8oy
zWAVKT`6k~7(i-wtt2<wi{v+C-L?pzQVW6M85=EAyNmC83biX*747`uaE%>2*)(d~A
z>L@U${5y$RJ2+{(G@l0>c=NW+CN^8}U-tK@)iefZg>>s}f#k3sLsR^R=U$sxC9c$r
zVgqGqce;QbyOZ6t`G8d_JwGx;m*6yhT@GdRnYez|+%Pn96m@?tjM2m&OPhGbH8WoD
z=B#P1E?UhUIB|EK|I;0JY|q}zcdegW(~9Z-iW+l6r3jFKA9U$mG^T_kXzn#;QSTuw
z3Q<@`Wc{u^;tS`qhQHT<qweOjTI24#g$uvE=LnN>_WPLawYv}FP{v#Au|WV7sfRQC
z2EJk=pLam@_+S4m0;KDkMb*mlPlgN3y>kCpZUXRQm2tvaPzS~=PKHtc{QYwImdU~{
zP%(AFyzbkFw?4T^*q{AXL)vcV4F;~QtGsIH*lHbp`MKwTRH;*pSB8_Kkw`3n5OQ%+
z?@$r_J)mwt7sr~<vio883&`D@VVAy;oc{DSAgu-B?p*EoQBryC)f#W?0kO>s0dtvh
z)>F3(30Io&)9~nxN{S;Oq@otvom77Hhv~g)QpcZ5lSn~M)Um452D`LCY5|aThq~lu
zJByI+17&M6rMX2ETN@>Oq*9uN$#-q^S$lQgI~&Z$i>k7z6|>T#>M?Wf(%RZmL5B9~
zgIJ`l<WYo3%?Zy&tr!VY5<bQ)<=P5wGqSnLHFySwrW#8BGnzN%|0h@iCJDBzYw{wC
zQ7q@y21BMNkH6s;4l!QP<HlrFxfR8o=&2%Ejsr|s=aPS~Ti0II;ao&tI+CC)Gd@k1
zR&z0l&RM%}nxorxuR)AF49g@m({?!zv&*NN0nwHjOg0w;%xxD@XFwGH)H=ZNwfJ3B
z(^h1}oi##aR<r)>?fqV4&+QSHco0TmX~<qwKw>vly8G8wr+d@EbF=1$ggpH`{iSmA
zd>^U`!cN}yS09ro=cVE|3K&kMRn{7x*Z*?b&QHQ`)0APMS^Ye7V6rTlJxl7b&Z2>s
zYqHOXb*$a$wTNjKjGZ&y4xu?>;T$b|!}lV~%AzYkI^wUM&o2gWrkG+KBF`G>P{1uR
zkVhW3>1>I+XUf}%5zgSb93_nFD1TF)l)!DrjFnYHg3d{Bdb<S92I}SFgpo?Cy%}yC
ziaulwoLuMzxbZ}?d>Dl+ucus)c_dEv3N4JwS0{4IL>qzataYy8Ee9ec+UE$>A4W?)
zQlt}fGCTM#dzgMFd?OPX{~Q32vr4Yv@zF$j5`sE*lPn|wMUj*PO$V(I&vXejdDF%{
zrzOPxFrH&M{LS|pLY!bggeVho<N{FtdH;Iw;1;#-Gjh<p$lVQTLTO$fGg4+7j@;YN
zMddsE^z)Clp<U;(7444kvQQgN*o*Ny^6+1}B)NTBXh>zn8&b7Fu|XY)d$OGt=@8H8
zxUajf>e3#bmA+B^$DjYazUC>0olM`93vrOAA>WG1x+=TJ{3yJP;-RHqB0x`ZM9wno
z(`>|CeYQb)1PbLC46)M0MWW$jWC6dQ6Rf?m8+k{l$-tUV>$J#;J@5SPj3%_SloXhr
zUCUUW0jG4=TbVKv6lDu-E~ZZ}8W((k3)Kp`qbW9jKzSn~Urb%RheS8ARPI1I83@eK
zTj$PsnTE%}$ugHv@)qv(;j+xc<zw%Y6i#q#Cy=m5_SIO(?abk?*#J&Uw?!V42glL4
zkPyq{2g-{BWv{v-oWSC<xX<lq1l|S2K$uA0U-b}d5_5{ds|9?R7sFKjgrr|EK?q@d
zu!8r{(b&@Sd~RT$8*Xx2z}~Pw=H#Ero2&PFH8Z>xz2Z*CW8ocbFu6W$*hI5+F0hiS
zB72NYGTHD`+OX>!Y*r*MsmW@KLEIhG`MO2tXJgb__pq%eFr~|W+cF=|Qy?SycFWSh
zjYQ@pgEV%o^i0<-%vynFw2#<00;0}W77kF)^GvWRyxXF-QWZNH;E%h+rt>Ub0&z|4
z11Q2LD%%UT*BF~}YC;;#nhR@4x>jCmy3dhsNV)dj%vxqKSjlRW>UZrHWxS#Gj!!v0
zF|*I7&m+@*!rAhYj#m}H!6N!M#u=cvE)K#jxHP+*`bRkiMGSNpUo@<TV~2YiIfl<2
ztPy%!Z4(9DSEGu*ZEUaKh?-Q6#WHLL2;#Z&!LCGoVN@>W^ks7=%r}alApL_=e&6!^
z>nj#B@(Z84wVdVJJG93&DAPwuV%QA&%k6YjDHNkICoP2bqRG>yP+YeA3A~o_xhZAy
zjXNWeanB2MmxSV=O+z^y1{3uU0s?QB(j4~7)1=19?^m{=Gfq7>WIXk}Bb1H@gyTCN
zDM0g{H<(_O&)_KVf$;Xx4=55$aIKZ-V1>S!S%Y4tP+$2C)2tM>G?gM&QgawfJ>vFJ
zBgv;?%W;N!^V_j(kA9zNq8R&_>bhRJe>l`q0w0n~!z=GEZst?}L52MXC*2vqdS@1y
z=HG}ARe|woXr#$=>8lpIha*OG^`YG)z%!9-{H<`={;$3YaZ`AGO6f_LpU`Z=*BJ|6
z`ZW)#`z<8f7o1musY7bw^V-G5rLEq9U@1oWf6{C63a_FA#UrgursX)Ir)P&FvHD^w
z`z^v=SwB+mG1%y=7uwc)a*p{tChM=#?a&9wN}<D{Axow0V!PJ_4}dOGp^46adNvNV
z^<M9U4>K0B`ov3EccbGud?^ZqjJkC5TibCLkqGV&nx)Jc8r=6f+Yel($5<?SbAhcR
z3pH(@o+2{^{7lIu3;aVjB4$4kSnXKEB;b857H29wbMGY35Awc2B;HsQ$8htbJ{r~C
zxos1RBYoVGW?S8iYwCxkE}Nwb&26pZ&kpQKYK@0=U$`uEAI{akN;tH7_05BWSF3qL
z4TV4E_rv18^e*YJKU%Mo$!kp(Q<5nE^EQ3SITlg0{?Xs4{|6@IkN-N4wP+hpOILf_
z{g!5(?KI;d<7N;Q5sa}i_EY}#i%kL%q6aF10e?lH1{dBIXyOOKx5y-B3H(gH<}bA8
z3d!(8A9d+regU?yT#09lH;<+eFfFzTY~7PQXxIG2N-)&n7w5ZictXfRgV(Bq|BZfu
zqmap~NYAb#x(Bfg5^8-i_p&6}_HtTuRpA7eQ7xpYWKOzR{gMy+<t5=}W#~KKSy&*i
zcaNgUV`jSHPXa4R9l1D!)Tqn$=4cfVhXN!jqJ)bgG9DuItr76(>1qG9Zo4(ko~-c3
zAIA;mkxZmJ;kss~gebh&3V7YoIfwhSxvMon!ZaI?CtVc86>fDd=a|4LzY_4y$gpi-
z28Id?ulWUhFx@I$R##ur<+9!M0dQe)2X(=By1%<1Fg3DwZ=-L%u)6QhGB`+Da%Mp}
zoZ#IBjvv?+qTL2|)2=h@4TrN%F|E*f5(HX*DRX65yjXx~uc6;40N>Z#Q!&{Y2ev>L
z9KH#$&YPgsYCQNI6B>4&biSG4!Ur$y&YsOB7wf(^IhI{%^4dZ7gl=){c~mjo*jwLC
zO;o=ZE&&1#@ES{PnxS{scw~9kiE#w$AfU}7@h`NODFd3Y06v&P)v<p4;2ckc*z=vd
zEsz3})q5tBtbhWS(pkEMH?noR=PM-+?uq&yHl6K{1m9|t(TohGY&^G2)3i(*d@xNF
zH8fEe$^>Sg-H23)zx=dGV&7e4yD8+h^PWJ`Ob7R(?A-F9|4=gh5F2iW-F1darRe-i
zanLi-`DfzuonqpTJn)5_YiS+X{WeMSD8I|5*v6qFoz`qw8EzqlQsOT?@Nt~FO^8%d
zrXZH2gq9FyONh7S!`DMJtT}hKf#|(PIS;JoNNn8o-!q%J5qtK8P22HRD>K?1WS&Pl
zu=iVBKyNxjJf84erhsyP^6R#&dUoJHQr<06z_xA2j(RYnck&CQXn;1Z-!x9xDsI?2
z%5eID)r4H=>7Jw1sXs_hRGgXAE?t<M-D{Wh(T**)!*So2#;28T{nn{qM`FTF$GdyL
z+5Ie=e<Qhi@E8Av>Q6--$rXz^oD3i;U1cQ%W3w|;t*q;s>t|>7Q7E!crsb$w1FkK0
zoGY~jIfa;YCgeXQ2aScaS~D|Q>LFevYC9dzMS6isLNH&a7uZLuc6g5>8I!4#DS5lI
zBYmrwta(6GB4+y`Ucr^^>492z&+J;(Lel~=(t!|qyW#pTQa`mmb|v`Ou2~KcThckE
z^BH8Q^oyB|h&ADN(pJ<Iv@(oqNOSTya~SHEh?z0ta$Lu988D#mHYf+ph_lS)0~=so
zF%6*}8<I^+tSQvV)G7RXFG5~E%z-|)6a{IdxYGVJ8e%WArX^edzTLS(E8P9@dq$rW
zr(e@p=j<7ud55{zGy6m)`}N}_FgXM0d5w$EhG)}d0s6V`G*N`9gd{e+Hzc#VVrczb
zbEbQS7{Swe*`D&+im<2J(1Es<+BY}Ma$sY*Cu@}he9oKe#<*j?=CC8~&{W8c81OqS
zr(S#oiE@-zig7=A33nuHjrL1JR5a|5x(pMcT<Ht_uJR$SOIK}Kf}}1t>Nh$XV(9l5
zp_wi1A1xS=9w&i?Sx!8f>09`pm@<D{L*45f{%KF&jh&$!<?BXZ=4d&+1IDVWoi1(Y
zGNWkq#MJKHp=;e7g;HZ7^qSK0Nmbc24oPEckGf;IM#jnp%H!Mm3Sr%z@P-Fx>sdjQ
zOU&ZJ61F^;IR8bv?((<10aIPUP4!Kh_z{P0eL}g<<l<f7;6fL5={^=I?7u%yZREbz
z!?byQj;5Sq!pCv%PA5mVHAZ22Kein*{NgdS`YpVayu#i3&&^}F(__)^!hXa4cGA(~
z2JO&LJ%nvA9#qEr=_Xb*STJ66#i#7YY3doEx;BZN#{DBrDeVc-W^yD$G*wiXxC-ts
zcF5~qFnc-!eQ?nybdl7<@$RTfxh9$-kt^^VOJH?JrpT797dy)R*`q$Q>|EZ^c~kQu
z<G9s*xI}EU^%ueuzFj`?_rxfDvre%oV|;rztoZ8P{;2?}^4v^fRJsEFmBuoAbHSC4
z_P@d{zuEh`H2tp{aR<b^#^YF5N2j69X-M$!&Iwxq#_;)yr)}qOXE@4QyQ%v{WQXeM
zMm&=ptGwS~)Uwae#V^(nzo#y5TSCr2Ht)~<OBAlaZnfd_#nsl-r{C$4Ic;G1wWabH
ziE}Su+f$jkj<emy69-I&KKc-9zZC4#NTzA@cQ9FOT=J@adhzQG*pW%<zS!o<wo%`{
zx^@3bALXWPl(w`UF?bETQ!X5OKKCm=aejX)MY`&%z!G6{xensL!xXwdcSj^#pPzMe
zyml-R$c>66>s?MjG`F|h9<JG^7<xBdQ5`&fmwY5GXn%%lZFYX_d$JOav<dKUo14I;
z57bZnZXND2xR|yO+i@LS2|qnlSd8_<hvTR16*MX=-1yr&Mev|{25*qZ_NNv~jZ120
z&ID49**8~Zx(y!t-rbEK^`%*#+<0CxmfY2B+B}_k8zG2$(-lqEWcT!Nb2(>S@aS?i
zr8)V~V!03*XJSZpkbai2IJTl*A#|9$TsKpSvwwGiBDXno-fefjdh6Ll#DdG$Rut==
z&Lp9(JWs34wMVo<6jugZO9v8WSO>jOk~-X4=^xE3>EW}UtAEbKUeDyI`;T>yl1-`^
z=-G4F7oK}2daFSR0Pngs9glKhReVE8f1<2dfzR^I*g(P*E$^$nrg$mS(}Bh;K7i%~
zuH4V9;~pTC6<h6YesXC!L3XPg+4oz-Jus5rB#mFjU|IKpBJ|Yld>lVgZQ-!T5n~D)
ztS~ksj}Aat(rtneff7*cWi%&a`A&8m038ZSXOJ)(q9zPv$%-e)WRMWJjsXUx2V}NO
zrC~_8&O;*)Y|0wg_u9hM7M(-dmSC;DttrPN!la#$^Q!nZ7-EIAqm0xoJ|)XAcz5|M
z7Q6|d0pW_ZTJQaJ!{l5O0>2}E;X6;j3?Cnm9@0TW(b7|;hl6eVt+XDEEoBVriU)Ow
z`Mc<cS(oQ2)J+MgO7$1ge)D%n-O@9+XNDKI4j6QzuW5Fg=UugZR~D&rs4w<?Pib)=
zWOlJ_nm$9u!a{vGTkYAO9-89b!mH^*_02T%9|XBzjhsa85;2zgr$Rb)%?wWHA)pqq
zZdPCHU%<M-joQ#HXWn*3)jkT8<g%$*KjP;&Y`K4I!{frCK0i9>Sj)Z|=U~&oagW-y
zPmsKBGWYn{+2TI$-<}RYU0}WH<YwcK3fMNwB1JD{1~v;m2@J`3Zur9Z)y5OCpoUf@
z{Ef_~1}kqTWVApEWRC=SZ@$7#bjBCx&#fuZ>czYYWbvgyeb@H_R6o|`k2rUjw?SjE
zg;3Lou1-z+qQ9G|_${nQ!L?#7W~`qm5yO8sOJ2n?z5knAilf0W_Ab;ycK#Co@u>mO
zhGffqZrC*{kMFVE6^)72v-n3}a{I#FW#;hFUU>oUDN<+I!ozT%|8z7^3q9pBtxqJ*
z<@{3=>T0+glgGGUc`l?v`s7DEH|5vUsJ4+IhRFphMfp2VF<-6&<<igZhif{cI-I%i
z*7-sdHLkSon9Z#sFaCX<N_KCsWXs$Zr!h<Zr>eWH@`O^Q2mbxk)q6zAhG_qL7MQY$
z6&*o$);taFxyycNc=eoXjxpR=1-`;9&DOSMIq_%pLsi3fiYWJP-`^jHRIy?lCpV6V
z?#l5mB9DtN;9UL!AIF4$l$l8sbmXJLmAnnCLvz3a!ru#4I}faA@)&4BCsO#veiu9y
zn^AUuz(gduAqrP;ddhXR(IYtRbQvwtb5%gji?5vK)r(Ba<dlmIu_&9IWK0-~tL5D=
zhkMX=@G5h#s`S?2H-2N&U>#DLD^K)bV{FKz<%)~X9g8`ay<zg|zh=paOjHpVFuy8X
zOh)lVvR<p?!_B1g`cC32t9N>7`g0@`{O2QaxzbME*43u<dan9ZnWB6Kd`8*}26KMc
zTXq>vl}*cvFNUdcb0y4nCy#w>E9(<EZTTC{F40cLAl=4;*v!NLZW#CBT^pDH>+!qN
z4d~sl>cjQAHyIms!geq#O{6ksJ96pdx`n6Z<p?p|g<e7ErrApgR|MasJ|mgG#PGO3
zEn-{Ke}g~~UGc9V4PRP*0pF*@dSQ(YC9koz1Qt%GfzSG#u1H}DrEh9b=2+mXgTHU7
zzjrqT^UHerlCq$Mb~W<$sz#?)3R7{wwfOtao?3c;%N*Twet+8;?oX)uP)ips?LvCS
zOymWJ{PL4^WA&M-$blkZZnrt1r^2aCmo^%IjAIz>+?4C1g!GsV3i~G;8@Z<)qpL?A
z4$fHEg|YKuuVYu7MwCTlYlqvG*8uxS+RT(2UZ;8q*VRJ?zvyu@!{8}vc<89*9^kj(
z(vF6jb+%!EXqrvKGx1c~tvL5*I(40lUcPHl!rwwj)-|`c{oLoQ#+fC{f@GWUQuTKx
z8+*KoN;$S`cG6#F?fkrYFi7j&qjMj<xZ|}?y#7ZJ<&?R$skdEm|6?OYZ|#MlfU?=h
zdu`6BC%&x?vRayxyU$(Qv4g@%p^taH`GJZ*YcvgnMlS_z+h0L@P1U^0f)$z;<mA1t
z638(YhsI~I`HiO{lg;g|`C2Z-bbH?lgqr%|uOsGlwtMVdb{ZNyw0k$(c<c@O%>D?a
zh$du}#o8}O`C#lk$2O;>&0-yPbfB3ddMf6z>@NjG_N;H{;EpSnCT#xe3ep#YWwTjA
zE3SyCBhYPX&+NgB`K_X~9-j&p$N^c~@Aco`FZbDcfpS>FU=I8}dyPHNXPBC4V;f-z
z!+ce#@c}$QmSik-E%8i=Nr<~del{u2-hg|(vKl?4o~s2XJ$>W}jAy_w;oIHNTsr)Y
z^Bz;$rN=k=wv}gR-z-bQn%cTdk{fJ~gaOSvIHm4LIx<0ZCAWX0Fx_MX_(N)2edI<A
zieB;J0v9O%`(=WxIFCzC8qdu>r8%KP4Z=Vlb+wfl-70^RtZ;VehJFh~5Wcv5LM+6W
zB=Vg27bgURAQJbY<W<>u1BC-quU~W4=8(jYP$vDw1$3O%uWna4bRy%PN-5srkFM>Q
zqgO^@q2kT@0{>DfN<*cCA-95&MD1)|-*{q7xtU2*fqZbT8}KFYloEbe$>e*zUD$CI
z0*sLpDf3CBdbOj0Z-jIDo?3z$(dUK~xt*KV+G#@kV@DnCVoXHuOAokLhW(Ge_}lio
z|Ml3W9v?c9?>V~=)@W)scKmGZAmol<y)7?q{adGQL*(nR@!_)n)Pb^<Shr2>=7ogc
zAnlQzsS~7vdXtJ>K%+0iMXD^*VqYCar$}?=HD=-aEYtSNMUgto!FMzDgZwNNtxhA|
z&B<TwpT{q_g?1a(^lC49_Fd%U^vxix_@?pr!;R;<X4<ttNBNKE-86$vPI_G~>wNkX
znp)ETn-{WP18AZyH|iDhv_|$C{d7^=U)`;B-?F?P?KQ0XvH5zwB4BgmgG{+AqX|y-
zcSac?M6)<{a|b8Rn<PS!or5anV%DwL@8(>C>X)@%#u+1z)wqDhA9-hU*zL!E5njlI
zFEoZ4eH)JpXug)4;#PQBx*!YmDY<pYl?5dQ%stEEsq>9v$P9%Ze<v|RvV}-wKUnzO
z&6wuRcYL^J^2sjulD(8tMu}b`E&iQc((X4?d}p%BKyoRXvY)C<ue!|zaJ_H<-@pWO
zc@&{qsgt&42`qMHcjIorvk@&ZILUf3hcxfvHmVWZ;E~#Lpb5Y`={Bn-OeQY)acn$f
z(NQc^te6EQSqqgam3qqsw#)YBObUJJu@ku~iwk?okdGHQC~y}9O?WKqZf2%&AEMIz
znC3G44Sm^?B~pe4rsZYTV_n@ADoRkzYhj?%N5ae&snS)~^iH?{BAr&-#VefyX6mNX
z{m9G6nNM*;ktifwjEv<z14or;_@>*d=Vn^YW$j7>^qudB{I)847*8}Mjyhj1PnWo+
zXaf~+>bpjU8&rYaPf$s&nN?PuroXSoduS+Cli*ic9tJ1st=s-IbJ53N?u?*U>z`n>
zPEwv_XTJ@`pFJ2W#8i!`H~r8ZD8PTDEjlK2mf{wrE>_OqMNrZIL3{#hBi(pD1%f$u
z8mNsAf904<uL7OsNhWKr+At+nx_>2Ds1^ge>H~e}myYL`5u5jSk`3Py4`b%s>Us2A
zJuv|R2UOU#m9&+;_|l2beRIAg-JqO*piUg$>P73J5{9bjI`MF=^>U2q3^6R3Eom6n
zlpOHSquJ;4p|MGRf;{6PQ~kU>R0Xv3@F4!)k7~h-@nmYg$Qrw%J}zLY3Nuu2$Zkk;
z(Ceo;k|*0b|6a39?QV!3zMa^tJLQn{fre=|n|r+Eqvg3C?ZaG9hUzz0LYwT=bO%zV
zq7F+7G#zQ$#exqKRqjHhez>)Ws<$!SUy6bDXq7WLozZouR<&A)nZa;`xZ6=1qD~`o
z9GwK!_4zNrWzb`mn%dDugf2S!EcWKM?6MLv42#Z@MF^Erp^sC+^Su`${`But*dv%<
z1|VNs8nn=k$Mm<cAZ^YUSDFa3V*-E8U?*obiZuNLeuR3@-ZTiTqn<$yMW&VbgdH>V
zTL8)%PGtdlpTnQZrCf%5Ez2$6%KoFZUi3OiG%~Q>HCgn-=C<!Z>A@cLN6w-_8?Mlu
ztfRL)Ch#eNh(kQY(?8@v<ZhsQxzu9NM2YVJRoyeDj&7bf`Z*kB{cCZO5U-y$I$p-U
zo(G3uKzPSs0{GeBLG{xtJ?$y0>U~p&One&;olKC`@Q$32D@E$#;k@dx?Pm;FXh}~Q
zxPH&-({4E80yWK3lLO?n?k_Mx<5_0^ZB)R??g%Bn0Dv5Y?R-|e)q>o>bbhhM_Td3-
zcb6bdQkfRAAtNck$%N9E@>dprms<yfNJ0+-rj~2&o^BfN5P<1u&cTE)I!+?Vj))@2
zkS5puYI6Pc+v0sLxlmQm+p*(*9#n;^SgQZjCLs?f9fn<AN{vTTEiCsxV@OatNU*i~
zK=~dfjpx}VKtupjS+apqhzX;+Sd2e;{e(#1dWL`U@fz<`q>^kV>B|u*DaF3CI>Ijw
zOKvFIOW+(qnb*Ds!oRR%Q)IYvl7=r)^>Zm-!*N9vcg%61f7@fE0V^kOy65g5f|nRF
zUvjYGW+F6amCHto!s-5r>qep61=wJfV$OvFB=%38B8TW5bD<g$W=|$GB&+KROD9pD
z-2mGsU|rPb`YPI$cetYz|ALtmjLjYF=N3AkKOJF`Ih-9IdT2Iqw~CFtxS^A~*?|3o
z9m16e?80Yn`U>L=9k5Abx1nz30JJ;UUKJxu^;9bE_Tq-mlwue+HARksc!jq73H$h!
zSV+I>sXGyGzJ_T67ur0cZ*pOUpPG5vkGQ&!1X>asZ^k`SL{ZNo_ng-Nj+|!Tw*G?>
zL;_Yg&ly~m={)b$IxH7)b$334Lh;|RgAVL=5q=ord_ME`iVjVtN3DOPy^rGAqz`$L
zQ2^%lX;!;@eK0n&n0_@2x5m$QrC0Le+1pvGEz12B{eR<a`ygNHWinTx#cxXsqq-fx
zSbK~izl+qDf3<B!<7XM7akoGZAlueIXbQ=A``-@{_zR^gQ}tjZ?y;(*qnF>-hc-T>
z88^7qMs<VX8C|ZT74kOIShULcP=-9ZX}^)PzEUOFv`sDvk4G_eBOA~0wO$`UDLa73
zgN>8VyVk~ZKA&PUmp^Ovi;L%Ah`#GM8aJ19uoKUO8dW-kk7vPyUqK6Zr2&6bhF5ky
z3SacxSl+9hw^w6}H3>rsv`aDw4U5CHNSArq3;Pp7@l8eG3ao5)@xpKD!68kCsMu3<
z^Hp{hGWoMMUQ6>4VSh4k;o&ChZi>)1uehXEHWOgcM!5VU%VbJ|`%~d=T8Itb>}pj9
zpaVcx^jaPYqv7)kaQmLrgJ1D=IssSXO;F6qr{_`w3PNM25uWyc9uY{(#L^_k6YFzp
zz#IVf+jRpf@W2g7SLql2p0R@;C0)GHPp?Wm*T)k5ONCG**KA1jRswfBRg<EBIr2u~
z{V@(fhU5<b<EIWVeL$X>*xA1f3@Mpye(<13LO=k~TX76<S*~ifOon{!;hOKnwK~HG
zv-%!^-RB%fK+7@9Rp-uOE2)2w9tp^FaHgpqfAm`O#ButN@W5v_%wNifdw^&FJEvr;
z`Q+A#1_6>-gm};l;5Sv(TDuZt1}Z|&OE*4?>n;-CC&rwVza4wwwH0xH%mD#_AEZMM
z22WSK_6V5R1uof_>;O-WYr5;rz9HbdQE9IP0p|cTQT=q!jyh5jen5e(!VpMYG~e%6
z4_{FtZ26d%)Db`ZAj%i9b*Lkaf_#^odFZAt(*!?kWgoM6_~2lMNa$ctVoLMw76;Me
zktAUEZJNUi?LcRb)`?=vs#`iw?pj=oq2Zg@n8XnAXJNjZ(!bKosf?EBvdi6UxsaL|
z<1gOOSza0vuTeo$Zf+L}kwE`TFG$;BtK&`fP$YdQ(O6k=2dB)HFZm9z{>ZR^2idF;
z$l;Y9Cq0>ajBL};lh3;q``TmOMq|<P*E;~TFvFvXwS)3{isqT3lP;RlL)}YF0h`Ay
zeXJF4qig%f0cma$D<a6%Lt;l(9156Zf0sjE({X`7k&h&`druF~i82>z$~B{Ko&#;%
z?9m+cU97|?R4+oPZ|UFh*j!NBhs}>ZudN+ChmN6f{jCY~%ViSYe-9%IS{Ne)LpKdE
z74AQ$v(f!vFM|EC!hckwsb=fXt1+;`_e7Cnqqp3Mo6peO_)>WWhO@tkIZ_`j*C*>2
zM^8_>9jZXq@J&Y%>Fa!rpS1&!`#+5P$W6td#vJISc|y`%n2dDEINKwY8PsjNz{P=Y
zEXrcW9r`a={;ro(2Mhp=c3`G3YL^M%+XCb5Y=`T0cRjctU8z}E+5^CYHiWFa*mfm*
zpwctYj0`g_%!gmf0km(v-o(Fn8db|l9H_JI9n_`}=-V}%k)mk@92nywwaN#v1&ia`
zDFamMOL2jZ7vp~llk*uj8ql``9=wJm2q=Bv<Pr8T0j!&ke(=2<qPdP}{~O~$I+wp5
zI@lZ})SQ@@$h^QN4HKeSEMfMr9etfG*IwWpako;3gFi7yLCgxt*7ubPxH+3PW*Rdu
z&lY%`=^-${;`&>vz=h<TvV`Ay_*4{h1ep2EwiN}DV8^QBkk!MVq1}#60L&O^x63Uk
z`86u1bsfwlut@}0ZE*|?0r&)oW1SK>ez?J2N^0UEjfzMnSyfm%p{VIwTmZQRQ&SEM
z8eWBebSMX#l0Ivy&x|i`w@0~eUt0+{^4P~+ie|*j-YwZm<d#B3*?jo{d+3W{vN(rD
zcj59C-5HysL&MK4N=xT6_QUo4PvtgG)h<J5khxZUaRJi(G_cLC+4P~h_vG*TfPM|d
zjD-EoJ;K~l9<n*18<fGNT_MAf01hOwM#q4|H|`3IkUSx~FI`uolkM4aEn>Q`w!Wh}
z-bnR5X?}|CRPw8+k-j)2`RNoRjg-8UTs2?Q0oM!rU7x<y^hJQsI<gj-5yn~s|9d`+
z5^NYSE$@LUJX>&f6y^sTYIh?Wr|0rh4FMP5@zgX(gusn`3_+a{#&%ZQ1C>6vCDGBZ
z)mu!sM~f_6Dzc`&9v$^Cg;ud9$omQv^T|JE2Teb$3hzOFG<AXS%Zwi5j4+o@26%f9
z0+?)POv{|!8ga#BtdO$?MG?cRcEmD5g!p-7SILOyJV)wrcqs-=g986%U&0Sw;h%#B
zfXuJ=D>d#+XFkei$=k4THV`v|b<rvCBI9J@GuYDD>1?xWo@6~SkhMZT724pj3Xp1-
z)|?Lmn*xKJ@PZerY?D=-Am+h8AZt-jA`=|mr~3)VxK@#ebo{^KrVEDHX6Xt+l$25Y
zy!*%8I<X&vXFqTadz~nXbU%+&&$E8aGgP?HH~aaU%5kYd{R{&yjs-S;{TK^=;CFeX
zyY)FaG}+~|-el%ziE@i6p!Oe-SSR<>P?oouefF?&hBR7V<7YZXK>|7;T;6*9wU^UR
zY*$QktUNy|`CfquQoxQsSWL4M48H^daNbhc@Nwxb>7D#2(B^dHVL<SsQgZozht6~}
z0Gh|l20G9F`rO{VuYW22XYo>KL^Iip*VZJvnDa~3R;T?>xvbB29v7Bb1aId(%38a5
zVx7V`mzSQ{ls)d_pS`BD7_ce72{eXwF;N3Aqc4X!TGOoYwnppn2J>fID+5VgpJg+g
z<K8epx+pFkMr)>z#BcXAAL!6stV(DKp#xuaO4}aaK*O(8k>@!b8zjj3@C(ulpMx_E
zpQSGU(>8ZZ*xk*QV0V@_4!S13Yy+>JdVKvV$KSDLaQ8{pWxwn)rldA@UWEfpyp6L2
z@tJu=M7AY;_62)@%$G@EqYKr;Qbg#4!a<YE4o_@DF+rh(Q}onSql?9$QNjxa6yBU%
zN=>p*>L`{1_U!;71E)MQ7Dffa%3ZO&g~%0sil*tGU|oSWWSKCO%i26xp+8fd3w9|`
zDsu0Xp)CZ!(xA3IHX7jitV_>rSht{t)>YAA<d!EBpj|44vP~l6pp>Q*a~Js=Yp`wn
z;L1ubr<&YL7u@FQMn(DR-o$$cW#=$)t2!y7Y(hJee=dbh)2d~?ALa+g5orXy9)}2_
z^N2E0hop4*Gcue7mXXL~l(oB!B;(1IPEHx_LZClRMeg`m<(Y!z$XLKBM$@-Rd&^BR
zvynKeYYL4f_j0NJ^3v8rZGMR}qvuTcBPz>HZ@Y2WPZ)(_I!WWh^y<1F65N{Qp)SXS
z058`<ZSsNJPnoCdx%kgj_hKkKWoD>bH~yE2aym7=%y~7k;W5#pe~lJ~2<p$rN#bVz
zZmy&6JS%h}GJf|hztX>D_?Mb*=9<UCj7>!CCELVP6Gq&BpC1&oce$q;{g?DNc`Wb;
z_~0cqw>yXCm_))G?E@=lNA{$ig!vT<Y1muC0iIw@NvG9Lq;Y6DQL=({duRbdi(~J8
zJ(?@E^RbEG`82;DB_mi)-ASTrlh{)IS{+bVlBU$l<g4nL+!uFFaj40+?#UFiR!fU?
z>thm<8z;;@9>{#anoqFT#0#*+cCNAmR}yhXS9RzwcL@zv3phi~xXc!u11Lr)G(skL
zzIE)?@$IEqiw8ZYNM4l);)OH07CYdEm}e(g9_0B_otK~n6hRk7E`XBl-84Zk-4c88
z!QUeN(CgB2<H3XDo|?D=I#gr}#-aY8uIn2#Sd#ieGRc;wV*U$K@HkxP2`ODT?_rQ=
z+S*q=Tq(YEL7-<h?>U7tfn#*67{GLc!K(Q7Fl~9U$No2^Az4&=_)Gub-LTR>&c-Q6
z-~9J-dMIUh^}>pi((Id`=PlaKx$BBI8`mpdP#dQo#7H)~TFB2G{;1agtyp4ZkDLSx
zaZ~Txi@;((@1N|Km^Bu~{d)7itGV|LU{~l-?*s%9hE*K4R5<=U`mc2Y)OJ0+bpx#7
zpKZBlXP9%n%^wXO6oQfUb>Ju4kbMvmDz!TMd_uZ~8yg2FE6CpgY<FejcW0I<tT)gf
zzP26&r%CL3Q!%Im6P1~M{%JKCAyQYMU6rT;w!ht<k=&&%HA+^EOf2zq291ES<q!I@
zmTYQ%h-lOqa|v*_Uam4%>Z#FRr(J*IIwDkqn0{7NPsLZKQlD#C5Pug>lgHa19KJZ(
z9b8<u1TaTRe78O(4J6#IhWkv352)uYn!H>=jY6{pXh>-CcK_)_aD{)NA@N!rrOq>d
zV1eWGB0)h$>-Zz*Rswux#>}J&)cXNDcPZwrnaRUreh+(%tfVOmp~w)?*vaq;^1STu
zMLsi<RQJs9Sm2!d6=U&ql%TnvTt#6_z9W8h$i9gac=7eQIEr9L!;SU@mZ=s|<Z^d9
zSo~UipE@XTG)Bufn_ONqvQ8C#IR2qwT0Lu}ykT!_r1m>%@OB(2Df27&DO&Z-t)(6c
z5ZR;2EKyd8ZdpM7^XuO&yHKN}q`2qYOI<)g`JNtWr#sj;SE9Br{7Lt*Rf;|?kQ)8r
z(j&?**`zild=7wpm9@q7q|(&WQ8H4(Qh|!3rLTXMd+2HTuYuRWyKZQN$H5h8>=-9o
zkFQZF_(=LLlruzPAl;~u+hgLql+JG8putk(6$;=N!Lzh?f3o5Z^;T11ed#BsOZGu6
z@VF51oL7~P*F1%F%rBQt<MygR0=xRpi5$Qic&c~uFC10GOP_2q@{TlDa96c>NLjp$
zKe;AP{ywyJ@9TDU3J^_hMfh+_YEfs>%5@?O^jw`)V3hCRg}-lIa^07HL-%G(ygsY#
zRG8O%A;q@;q}3E?0d%TwU20ewO~ZPc`*nr*{?<3N+{Z9jr6R!JK(=3YC2<;@(sCyx
zHnW~8aEv5JG7>a6HRa~xNteBo-!(dnSOr~^IC<FV=XM@7weyFz5sC|WbN$5M9~8d#
zU0L>VpO9@n56h|^+iz7?@%ao~P#X*`y}O1L3afVG`surcF{AF{_BEHNZFY9+L|k73
z${DZQg)n)Ei*sFTecKN9w`7RZ#HTFgqs10~?f_8Lej1#2Rxzyytv5K?Hamsdce7lf
z?AuWp24lDdSUt*C7-9_I`<}=D$0?9E=WhukkZhgh4?Uo>3fF#3GIiD3Tj}E)`vfaW
z{A}vEF1$qTW@n*n(<x!X<X@7N1|s<pisv+JRH4}Fo59CFHlG<R;QyZmu$X(Ca|G_t
zkZ>{>qTnSA?b!zafOB6K*>X-FxDJkWa)=RLBGa<dKe^(5&*h$hykL||WmSwMJWy2=
zx_uW^Mne#!dxbkEFOs*A{st8(>oahVeSCFR8W;MTu+9AD492U{#sI*dS6%Qu?fyfx
z0TwC1yw8s6UO@rAE(_TaIi$_iJXBWN0H(`Z!m1|%;qI^6fAAR2_A|b1rm+Ho+0>7A
zKQahl<9j1Z86Hct8wALlXBEo^e@xL-r9JW8U=Pw0ag6=oq1`H^n>^r~l+EyN5?uq#
z5-o~4wp)99RAswC7X`Y`Da+ch4R8@7)ni%ttC`Hp5}(#UQNbl+G;|>lqQG>R`ADvp
z46-bMuUF|~DdpgF_B%&vO59UWdVQQHuDLi<`F?8No^1QV;1^s+EZ^MNY{SRD&$^K9
z>T?O`UHo}}eRMVpCFnw|njWt-dJv>=_xMcrG~gHc2*Jm+bxBH|>jB+A!&~BpgWNEm
zt_{{tx9cgm8;Z+DIt?^5wym>~tHZJ{biZ_);w=QK#0piOX0K+KlE8om0J+cOk6}Jo
zsFH3yRpf13^MhQOtKq8A2OO0AqE+n{4eJ^loMj6$@tp%N8>Y?Vl}Mw!S3wK7^}03&
zI~7wRL5Q8-f7<ckdIn%$8<jwfeJt<)r?{rAH!$r(zY-V5;2vVEqV=4cRF*U{RCQ(N
zNIsCEAhRB4GM_ds!78|Ok9=7%-|^BZgDK>X#_wQ_8y8sQU#wThzVzU0R&4fNemT?d
zFM@GVyZVR1oVKO*R)>ABc(-iwC$|n=SRG@ef-Qd4+gI&C>YDXP%=C`<Wfo^Tq3fAR
zg2jFSJ87Y1qq#i^XMV(BxdRw8R?o3)B=(YDo-}rx-$Y_{$M2HXGL=1I^l#IGnK(&3
zHGjt%9IHC@(vO?Dv}-9i7p{XFk_f}k=$4af1@_yLBWZ776Xt+@=LZ4BkLwG4ZJRW<
z#Y+8e{a*LZKXE_2o=VdXwiGx>u!(Wr=l$d+@*p%|_GO3Qe5G;eFivqUU*1%G6md+V
zwY9y~d%9o!bAZE_ei~>VM00J>{~_uu{F-XR|GzQ15rNSl64H}y6cH&wk<Lj=cgIMj
z1?g^(?rs>;-QC?e^1J7kf5O?>>)iKsed5jaF_K1XVV|&sLi6CT*{zfm-*@-+Y(vV;
zz_E)RhR`kIU-+>%xv$h##`l>>JQ=1!U>n)^VM5SP=W3G80@pmCl-Zkzxrw7~_$tc=
znr*os*VS2Z5-TZyH`7Q)eTtu49=38}(<ODsMqM7DZ`oow%BEhlFX`~egMrBS#h8^{
z<RpZ9{MQSu-M^H_(uhV?X`^j%KC-dJN{NvU+Z@NU)8TOWx92Z>(P+_`5tkDc;jrbK
zF*U1iUp$;&)9T082e<IN-MgqR?4Me?Iz7PCTLGCo9>=U)H!amgNbDkNi<|`7evq`S
zvWkgm-QQVrr`sgu*VZ)5|Goqq0d|4BKnd(M*>(c(L))!oNV-oC%~K(H<nXdk_c)W6
zD+9pb9NVc7ze4jMGaTzkq2?UzS3KJ7&x43YWLIzn?JCFZ-lmi!|Mad~wOt7Q5egSq
znPm9wjjz_a`_FXgd*qO&G%t9)qY2G<+($0ZE?xwTHKP!|V8GkRf|YZ=PnuLz2(4}3
z{i_ie*#(#^g!2Rh2mC_&Xh@Nxlpe9?P{K500JnB$()Zm!jrfMxbxElHXgY;=1|<CM
z8Ygmfc6U(5d^sEc9q=iO3J|l+jPkxa<MAtEeQ+IF6eDN-+Mu<E%3%;93+A0SM5QrK
zM<T({9|@$M1AKbLiCU>WIBzk>pIrXY2UmlRumA;~FP$F`rjVC7KoAONE-f8lkZr?o
zfpE=I*2(p@gnrM-)v9e<ul~$Mlaf~5(v3xjsz=}z^-7-m#LhN2Kxz%AXFD^P2D=RK
zUFe{*pe%-zPH%Xtau|}4I83nWx|rMi>S%@h1mzg0<9VcC7JLydpzkxl)v%oPl)zPc
z?R#nR*(O!OU9fFk+{=HurNU=ria@V12daWFur@lZynde`Jn*u;<*xVdenWYvF#Db4
z7zV`@h6Yk9DxmydUe2rk?d3%J_R)05U>8$BiZ_lWCLB&dcMlrwVTy0}n=CTN5~0wD
zPep+VVwUi&ylTMTU*xb4k<}5l1?+vKU_#5hu@n+{g7=}f58FBX%&}<6&D}44vnF?D
zycLzV0Ec?%G8dOuC?fPL*q*-&q~%to|H2^IHh&g9*%T_fPWy3bFl(?sW#8pQ)##q7
zrBxwumG4d3r0(&NQctfOxb?!YkYede_QyarvcG{{>1^(h#vpp76v40rRit8~69^k4
zp)!BI6fvyT3wJfwI&1PRyfSl<s*H7mz1=oWiRMS&TfFon-U&4Yo6P9@c!34N{HuL(
zT>yV4$v=j{<<<_aK%=t4+}PbOGdTH1jK)a7zneQGx0*U{>GWFMq(jK1^hP5B8aAWl
z->1IQzLaHSQ=)d7m}29uY|x|r<($w0KL56dQJ{^xAh*VfkoBP3n;MTF2p{=z_0B7A
z<8PYr1O`CDz>FABIk*Kb<@*~K^Z*qPVH)vNDh$2Vvgm{(h-_^p(jO*AY`nXf2TyDo
z=JdZCh)dd-4Rk4DyrefI_YuRpM8%ar??<jQr)Uu?&y3c98#UqG)lD({qMc=skfpV@
zh21VvHf@mHUMcAWAtMJqS#~D`ZTb?0MI?$2z)Lj=_Z0n`YFef}vA2eKE8J+jZWluW
zAEsL$FTnNI>d$-3*_|T|<El4}+v24nQzFQ(C{}$P;$NVYUMF8OI%^955h%M!25>wi
zVq+ETKbn8I=Y)AYK3>p;h8>L_0!I{Id+rzJo1mZ;^;qOcI52PJ&`t`s%|vr~new8S
zL#SA{a<f4nTV>9)#BCnjyDFX}JwCLYvGruH$jYNTkgT9@$A9!<VBKQ8&b9(ahHIR3
z?|JH`2^GBeFiJazU(n5WZ}^Xko?&%qDQ__5#S1*m2vN;!W^U~q=wN6Xn|?OS0T#@d
zrn+^}A6&OtP5Hf~hMQ~^a@PH1qH6)AQ?3EyfpS`Im(e5(UjNyw8J;hw+qOxI?q%{N
zb_GOD5npfBl7^627gbBM<ZO1Qtr55l!@4=m>VoD-%2$gtz*nqhm)o{FGj%54p0>NG
zGQR~Z<`iWzrLwQyoXw}5wmj0)jn;9lL!4%6ugj>ukF2j`TqkIc?~T>X^{$GYd$sdW
zA|7d1Mo%tI(Vtey1U}EZjbKb8fs~xQIq@g0wnr9cj;R0|6x8;47Sc4gRTxI62;k?v
zHea^Hl`9tN2a$;9O2<^EB)#nAvX*PD$P7KP%ek_)t7&cMQap1j9D<S)wgLWwL9m`=
zQHq67u20BYq;cE)T^|2VJL#7C_KcHTn~Lj^U3igBS@uGQih#q>tb4)pVyMfhcFPqt
z3l9^W2L0^2@EMQRW`fM<V*IvP=RM%ViyO*ml}XBvs~>V@gcI%+LJb?-K<^9{81;Bp
zsa)|6_cRCC+T<`=K}_3IU1ZZFG1y$ml@&Cy`h^3{zka>55AhEN5V~o&ZN)CsyxH1(
zki0@BR|bW4Qi#vxR({^7_X|Ml`_;^qS56m@{fEIV;A(&Kqdw6vBD2WG9zq(9a4?|$
z5Vkr1_|Ow+$JLnP(9ML$^35B&sntm;8|G1sbN>hfVK8~+Pb~a(db~QiP{*XRFA*`i
z5YsX+ZqnV-zVg2scgs`Ca*II(TC_E25rU#f=y<OB-Kd$iX#ok+391*?NymrYWVQsh
zkM@rk=$YESy+fti8oghG!CMxvSofflN91bsi+$Bq%tJyp_<j&iN$l#|&-f?eNWF`Y
zby8<-F;b^5lhR`tA>!QmcYp)P@Gb@cP0HYr11pzwW0eb4PTS1ZGR|rmhh^w+Vi~?*
zRDo$W&|fAvc#>~sV!u7?#+9fKYLdMGlC7~f?{@puWjmG6JGq36+e00E+uE5lzbcrW
z`fI|V^wbj;lRU}QeKE{+@@%?!5JULuFqCt8A6|yDj!!#yQK-Q`148*$=rge=LQf%}
zP?y(fQNvpX6u%fDtmD;1EmdQG$;_DNS!oBc_+8!OefT^!KQ+A=#Z_o%rWDVGnPkhU
zf6bXt6ZMVI`CWA5D7d_Z!NX2ajxXy|%~{b<g3e29#S7}8Tq*W6YA8WMXlUrn=9L^9
z#8Ir|&7X^1U|y_{!^-to2NfE?!uRh%8V!Q=Y&0+0z}p~XG^y9K{kE^MRqAXkE)eRE
zCne1FQxYNhos<qUNa&3iXYA*a+Fdr9nILX;HtOwf+fIny+62}B85PRy|GJ(wv;e7s
zo@u7|#N4AfK8wgMDp;nx_+^pHaN!^EuvY-HY6kOOV{G!uHeKgAM(zycjB%sP+^-~*
zj97c!<eldIg;RmgrBo=>2MR!8wb`=FDY8K(JqNEt2GXr>X1PXqE*RY~9MMk6`|L^!
zI!2WW2efK_6mlB)b>-wYpOK7iS~R4(;Ahl^b8^B2VrK%AlOG~no;YtGw_=`yJZ&{l
zPIXL>i6MN+Zvej*#r^Bi8>agWJa(5`ndh^i>61Gf!(f$bn&)$~MUl-Tmxo<-{fp<y
zii<JIvfxcH0R?eK$M#O!d<PhzYf*wgp~D|t3^c{P_L^Rz&uLggkljv2i1yQ77|XvQ
z;Fx&aKwrEc1V=ZHH~Tz4Wu8DCdPoNQbK^hyuW-f<#;fU%EC?86M!m*Wo>H18wY9nI
zczjT7(_`B-?PF|0NUWHKeI1w?%>+GooKQH7rA^qewd@?J7gHzRKztVf6mPHD#)i)g
zJ$3(U`AaK(^>?Q!PP$1?U_PjxRlCJ;|5tB{|GTR9RBd0IQtbQR&=12fXl6M-Q?*s4
z?=@XjyO{16I*(82eNQxwOXRpxcx=5Km#<<hnbVAv3+?LD!oMCv9ikV;pgR*+y*!^C
z<Txb02;k_fKBE=1oQVySZmFJ<PjIEY&2Q9m9D3o(H9gMMHe7jc(-O5KJY<({MS@Qs
zOf#|KfTs~-{<2RlyD<48xK(VN_H2#xU6+Q^%ml%l6M`_lYh7?SPeSuey^C~-#~>DF
z<+nRH+<0%d3V)cu!^Nmc_<NWvreD`GFH7#SqbldM?u!?nCcY-jw#<B8dY_z`%k0dR
zCcgnI%AAVJ)}Mdv1`oFzJEBzdPBU&q9EZ+>?X7%UiW^J6<zq(%L16j0shBXUYLW_@
zu$pO{pFNX~o&yn2F^SJu2$9Y3A&TX={u5-4o-Wz#ANwc7h|ln2xVrmcu0}F2TJluI
ziB%k9zGO2`uo8(g6pf0HWDz!}B-CE^;<?8i51i?o<;vGocAMl2Me)s@*w&S7x-6ka
zfp^>!igk9yn2Z1ol<sN9rt|_Fw@4SVKwKa^E%(T-(?NGU5DQ1Ceqg-qrnhry@#4>3
zaS42`#{QY~r_21;`XRk~(PFusc}G!1_n}K~1klUbjyFNsPp^(#F9!r^JoLgA1?Ar<
z$5f$VdD7$JM5#bO;E0P_=lmivOEKLKwePanR0DO61rv#GY5Ls&u*T?8)o-QUSPl=b
z6m+<*Qu2Gwn))w-c1of=<28^9YkGd$<6+a}PthNiD6e+NpSUQPh16I7zQgtWI4qoF
z<nPIi(aE(bLJIPr{M{?ci?*n}zHl+}RS7eLZgRZM=9kjTT|ROfhkR)%T^q->kRzn|
za?;hbIgTf#aH~(7DDUo;W4{3D86bG+l~HxyKJ4sGf)nq^|LCVTb6B<2I%j@^Qg0gC
zkVU*x2CDX*`l^2xl%QzyKr;a)#{QI>vZ+l~%P=zUYS@dT!QU1A7UUSvoiY9n2Y_B8
zheG{{75{3|k_t04AQha|4B~G82Upw+b!M>C6_9O3HIsCI@k@Su!<}1<&C8e9us1?6
z=V~(oFDB?#wD$-!LHB6)1rc@Ll~YFab5G4<n&j71r|P-7H=yua)Avj&YJNWe7qIsa
z4sjEkBEJ%4;@_*;vhk{Qdg%c^f~|y=QDu!gow&GeM`7QI*>aTnzug67y6at-(bXnN
zdbuvX+?OKw3RRt%Sa^hzjHemr9pGGv-smPx-iyXxoLA^{JgsVP3GiTo&}~)FOBRmZ
zR~>_@o@ckR`T6dkfM1u+Ngav!BOWQ}3OKkI>ZWGA;pQ=<#z9~3B*VNFHCb~(09d(B
z_WMN4kq#1xgdokW-`c`fi?Wo326w0bRsQ58X7(-qk@>1yT~W0=kY4dwc#qbLD2gr&
zDp{UW@)kl+T08%Pp6e3lT&2W^MXc2W>6X+hQ%tH3K35%PDr=NOY1e8@X$C1QI?d?D
zh<?$J-Dg)yL%`2Da`bjVpI$DxACzQ{*sF3}5Ffjly}{iGKmdO^;C~aOtM6<z4lS*5
z2GoStZ;os3TDd*qb=cIKL$&5we{=uM&HX+2-;xoyI(Q+)H1;$bukz?QP`26zsXzJ=
z5{oZdfSP21cDe;T#@cm>?m@IZs#RT62%BZ-I|OdY_S0HeBl%mt>D52pOZhMTEgIhF
z4cF!eY<QoICq!*i^ggtgL9BI}F4dP!X`la^zujh4nHTfgefw`bb9h=KyKcq6-zjX_
z`)Mz~ww7;{`8st^K%TnL8*OnTWKn;~K%Jai@}!5X7W4YJ=?d>`*=+%wyZ!WZU$#I|
z>Y=G+Mk@)(ped>G^&?_$XR)c7rIN%Z>}l>#LboCUP#2bInK?OY^mjKV*vYI6WUY)<
z#7oDx`6B!p*ObGGN-8`1!rcv<ftIn_(47?#r>Vy8dT<t>@Lh{6F=k5`{2~v}H?1WD
zU+4lJ+GWQQTKj3S^~V)?e3fgO*oFk|DEd<&wFkReW8+IJy^bM#?5&wEl*)87KH>NJ
zSmnJ?o2nAKBzH9=$8G#ex@F=f8XjXkqt?9E1tQf#4evO#Y$eaCFdK1Pd|RB)b`&qn
zMm<I}%Z9gzgq6)FZ2)>R!vDUox>l}`z<Ah<ExP)Cv);hVIm%mD+l^i@gAAV%zk2Jj
zwyn93do*4xwS4D4^7?4&Fn82GH6oOdUI386feW%#1aK+J>-2i)3=~AooU}CVvGb1e
zikz1|p`Qk76YOUO!Ub<7-PT!KG)`aCfl)D(P(cL;z99UBty<h$=F=A!IqIBY=N)=1
zBUU7sVuq{nf3nG&iWJ$-R8}1f2yK6E*z^1Tg1sGo`1W}tzTNV)_q&$6nwzG4Y*T++
zx$h(7l<1{EE!sRNjI>EUy9r=hL5s4J;3xz<grY|O+F)tJ{M73Q2arN4021m)PwQlD
zm<Xa#&LIaF|Hp^7u61BQl^of!jQa^35r|qmo+0M?8hnloP&TBcVMB-6wKsqRILPnO
z%*yU4q>sLYKB@wbt=x{HxKFqm_E2QoigRi9v2X}T{q2FceqVjF%Ieoc0Gl5ri5q=G
zH+WvSawOQB{=VAG-4-l4nEI_=OtsH}Q{P)hv#OUtUD8EHyDZWTEup-(6K8zq3JBLk
zUc#PCrc!K*MqYjXf1*3Z3<jYmG*x%fZezcmZQ5#DKxCi<a7=>pdoiX<+*uH}D{mT~
zz|i>qs7`4excP|(9iVDHq0RQF)djMp#|M!01lma=0M!-0c6{xthWK%Cxxs4O__Lzf
z9U((7fV<_t6CRRF%kzyPpkocRbt6Kgb5^BMuByuFJp3cWu3RYoixA6yPzvDks}sv}
zC?Mmj&fk+hHkK1nH$U^2!75FCK|<k|1a!>!2OH&6Y100U^Kf2DtI9qmzMjav&}k&^
zhGt)Xh6m|r6{`~Z@QOHy5h`7!BZ{=QLfrVAS!i4q9F~B9_#ftkp8@0c&@E0ZzPmTp
za!OAXkB=ulD-Nkwf}r4UC^J2FmkMp~`xQeYiXEd^N^+b&+X3+57eV6%y`+lkaCO}o
zIUG1GfYTfnwg<1?#GeR-{QC8{VJgY5(|}i{c*W#1Hr8}Fn`#jp`*9h<F0ARSkbJ;z
zU@ea1#=*$LzcEN|Beg84Xrebz^DRT%zOoeR{6g+Q;3Uok9>4iTYKD^S7Seq(oCVVt
zy7}+)hVfr+&|0BwL|3oV?ww0fMve~}#>-1YXdjic;U`OT4RA!nc$?^`r2k>&%uXv@
zNq^X61}b!mmfnU@msU3H<t6t|7j<q|Xet%x$p+gH2jf-?e699PzxWewL+h39|9eB>
z?jfjd1*^RlZ&-}0PXw{{cfQ2B9&&qCVIyk&{bB5QOW`Z2q4P!aza)zq&~S;<c%OHa
zF5YX~W!DM6cu`8a6Rtd>6Vxihy8r6Q>YAniTh}5J01pDq1T50L9^XP)_q=z8;)e4_
zUgN@fS0^B!yXyPL1P1gwReXf^FEtBcuW8L02@``odpcgQn-#Aj!*QBCc-ES0A`8-!
zZa)8zk~{Gdf<dCz-tu^@I}Y|9(o^3=nWW?`kjX$Ci!iP(TQ^zhSCkmA+jDmae7-bn
z(h<pi1W8Pz6q^D9tF}WnwvpKl10-&`qWIT2+~$K|I2Rlk#QR4Hj*;YTST{TjTA?lV
zjK1Dvzw1|;FH7+dUYt1-D^0#_D4IfRw<`?;MI}yWjK^N2*3keT_vNa7Z2gu9KBWd0
z*h`*w(LRmNu<pjyDM;7*WvW}3e2w&2OefXJb)=q&aH;w6)Tcq#y>=n$pqkV!qy1P}
z?ai4F<x)IB=IAds@&~b9$?F|tFaDvM)~)DUx(razmq-T<@yN6tAb*aicv44&;JrbY
zDFO8o^v2ZDh}iRB1GeTkIq0dpMdO;V#(Cp?&bWJuMRL*DtuP|e%jUx9P$8jFu^?GI
z^cu@g?k~o*{V5V^wDTwa1l2ZJ@CWfU%8NT-@x=!1^Xz}J2<9qEn1Md-EFN7PFU-94
z9Ygw^4szn&Otf)Nd&(1*TfltinEBKc?;-P#SsqN%`Zu7R$S(sw69~rXF!p_Y8ngx}
zFJ+ODC=+@AZ+$^6q{}<>5f9q0D^YZ-byH4O3a>(2>>I)l3B`+iuAcZTf6Y;!*+bqj
zYmX9kXvIC%9<!eN%913XT%s$I7{K8?Q&k#{wi%S%A6lN_iog!#kc6r&BLhYicDJ{h
zS|^4AaGdwW_8V&GVeQg?zDSA$c5JKpxl)JltLVD?YO5Cg5&3fgjZBBotV?1i)hiV>
zJyu{F11>ZZ_Tu9M^c+a{vMZcLCl-%3fxD;d;$XE1Wih55^+MvHcWz1@bs2|x`Uy-4
zwmxqkZ8HY^=taD;BsCDgpZK?PH3I(Q$wj7ZGp+ra&@5BD3Y9C4y5<w|Ei5s#r6m91
z31?S&*L1NHob@?WnB%T}riy?L91ZV#>9tn0MAq+BH*vgF!tgS$0QQ&9fKt~q<|mJN
z=U?aq@GxpBk^gu19Pgsw#AQsg)B*t5=aEf|`-d>Pya+wTToB8Ap(cK~9dr0KYpO2Z
z0WRhSIxcusAvBW5MD-unNbQ<4Q^mFdrA`L4{f#M(vFXf`5GY9yG$v^1C}fB!2%@hi
zV)!Tsk`i2aEeO)$t2SfW+QDVn#Qn2Yik$4D;(+HVFuYXQw-nt_(teW{PV{YXUpqJ$
zbIp?BB&;Kv%?G{Tf^3*6_B6uEm)%rS+EhWzG^~5|^F6CV(@%1)fGRd;YAd6`(xi>h
zobenEGp>mbn_+JlPjqU{v+S63_i*d?(makE2=ldsP6e2L^JE0@ScUP$E+nu;R6yyz
znubZ{zs=#1r^w43Aio$+yzZ{PVLz#6dt8tt80OWE%PqPL!#c#RC%X%SIGbjw4|jmy
z4NH;?C$t~;F{k71<#Z+eN#;Q}nHO?Xr%|B%3U)#N`{`ldi%{{-4`!DNvz4MP3QU8L
zQ@g$WXagRBzb@8_ME7;fb7Q=uH@p@y+!Y4FhNIb!tbD~x0TDUNlOslK(fxi+kjE4V
zkCgXe)GVYF&$T#%Z!SyzwOO*s7gG$y;nHY3cr$?m$RWg>X&`4n$29uBV<drb7~C+N
z@M^evLdbB{WLi~NalRM407G|}?fyj^bBSr;JL+uSdv->*VlL3|AJI1gp?75-Mr{iC
zAkzYoAXI|$z7MLtLT-{H6KyWQ<Dw_c7b!54t{Z>Iu*Pp_7(|c7W>KQRqG_CdQtU$5
zt=1nlXp`H|n55~fBao9=I^syx8t7S<$YZ4Neo+^>iyH=L48ImgH6Qu3A91&;9dEo|
zy%kA$xlseW`k`QCO3|KTZi4epQ$m?5EONG+7hzI#^6ZcqxL7L{+<<m7J7HyD<vG7f
zvKYVQ5pech9le2kLe8t+!-5i=E$rEqc;a7ieK)3X0(Y{&Um0-TOQOx|ZN5O&_F0*C
zGd2Bjx9A|Sve`JoQ>IO?O5ki4m}~-@DSylLL$`#ID7)dvkl|i=Hb&6pqrB*j`^>n@
z8v%!8{dyW}A{UJd3+wIHfd&WDKa-OhTTRTR4f9^N|GKQ90}>84aZpeAc96cJ^W(k5
z{;=?Yk;^(((JVM&owMly@82BwmsDJu$F*hc$&SL!u!z23-E>xmT`PhGEw__*?rtw)
z+%OSY-8Lk!LglG=Ur^=GVY;epis20{<>si$<jTE&>wtCe8O|NlFp1CFDX8oddjB5t
z+qP0Qmd!jlG6b?CWc*3{;Ptekv+ITePVNGd-zpWCpn}JKe3-#r%=Txbc}w+Hj^9E>
zq}(nKO#mw{ugqN@rCNM-xH0Yo&EXw%{)cZP<cI;fJnh~*RCgc>se9Xd?)CQ6;UD+_
zdwS5h$8x66EL8})0{0%~treVLQTdH+^>IajgN>w1^<wkBN4$?0PaQ|=mW*#S_s446
zNOgVDbi)kw<*14E{R*y=%i)8@5~7ODcImfG+_i0)(p_DX@kMZ|1?TSzE#W_z;#U)e
z$2l}2iDfQigB~~RnR<vJnd1mOf~KP~%>~8uw_T4L!DT_*Jo&Mt+(S#-i!NfRnxHR&
z#76d!Vro9RdFI<*CoLLWE~7Y<O-1M{=>l?!YYHO1JVOuAqmjFoBxowP!=1v+V#}P9
zc0nKYm0F<>yI6DN!de9Mxc2B-GrW7I|M}eJX^i@;gS2$UPb}Kr=XNZZbFM6bt(l4V
z0q1G!dRNxS!{E7>=G(n;$&v<67#Pw?jyXU+O8)^u7iFkqgCRSN>IaetYhk!IozC^-
znM)38k%@&+?__3=oU$X-W(*aQOL^$Bct&-PcZ+)pgR5a>KiZ2|2le39M?%B_m{)-e
z_g)j2I|>Fh#}BNE*WIUyH3D%S^LZQ^{@gnTX6bT4{GMk$WS|ui8g(Hlp|urrFI0l)
zt2W@=`*eC5k6-_3Rp%6^B~*p&`TRq6!|h(iU8}(7`GDH@`UBWz2DR-Axzv@o_Y|P?
zKxcXza6gU>ws**#Hx=1a42;IKk5W{?_3@9u09cUgRC+i0Ejq3vlP?$<Iz7~!!=T*d
zJw+$ipHy#D#IxN|0KD!?hjB@z@tP<$NNS99-mFrgI3Qz$BB1J^mbVTl2fF(NG&ox@
zQ>kU}&cmu#;(Nc*>qDtNoQE&Of><8*!x*d7WO)!i<&%f0ghj^}Q^Gr&?q7rJa8DLd
zhf&Iwe4}6zeS)SZ{h}i}X!?O?#FWF*U{KXWarw+Tv}NCPR-8vG{n+Ih&h9|$zk(3+
zAzSviJWi`WfF_l=K$gO|;`vh&ZSlJv=(=tI;!4mE3YH%k1+XmiMsK|G_~EMb<2%MT
z_Oo+*+y@~URVpmIUW`9{!>wt8lpD(}6%-8RY^Rw5rHYZp%Y{VVc*ATQD}YTfwezf{
z<|kU0MLQVmy}PrRB|r{1?@t70(hkDo2*Z{nb{<ON!5WHEl2q}<5ybtzVL)M&QdoeW
zArbS{3E&Jk2HLJhk%Kl+R|$yi^xA{ZzYOPz7mASc(@!#>pC*UtLiTQdn>K}T1FtHg
zFiw4e8Yr`zR^#TYBT$cdWeIMc0-gkV%Ln6i(SD8rH7(0ZGloRuf|w>*kel=k*^!j?
z)c4ulcFd^`A-mW%d&f<Jn4hiR*px?edfz|$yZQC8){SR)1ICOXFE$>C(!@V$GLcWH
zo=reMh0=xz?qkDCbS=ObMRy85rXajsCG;fAG{tNUz%<}9i|HQ$-$?ExuqicP78apj
ztK{9FSPeL48xOz*)|w30b@{CsIr`0=dmIo5bPw%)FRUy1y&7*2GQl0!trSeGyH7m&
zvRw`QlUq|!N)zmHJN4sxBex7xc;XlU7c}}#18bn5?CA(^%<&d%hCXyrK?;`vuf9<Q
z#&fuw;}FM4aonB#mZ8}LoV5EIKb(0L7*+O%Qid4-O1Q@$e~@H*2+R#TgH)2<Ez|3t
z11WWG@^IB+Vwcb#fnRr$MdK;Mi(CNIK~3=H9`7%L80}-KVr6{aba29xrw?KnAWm|j
zhA1?dVDgMV2ls~omHP=81mFa8YH&fT*EfX!o)SPTpcSF^Bjn>5p|pl5s<o`cGf^dQ
zCDrjo6a3F6>Rw80t4*8|EvlN<8!|1xdb;(OR-FY_mzin3H)*Qvnr`TW(NAYmXY_ZT
zjD4?WM<c#hONCZj3$D2e95*|3@vJDc4<c0r8pFp+Q<s&AkDcr6t^I%39)HVoIbAGJ
zyuw@59`(E@Us?*MC)2PZHT~fbbDxek=QGd4v^&RhzY$30e2`-Q7YNv?01fYzo<vP8
zK6vdo5W6f*SXB+s)m>UIG`X3U9{CkF^G&rKR|`$ujS!3oGK8f{vR}nt4T;~Ukd;K3
zYIqEvaJCq67#{kpd3#u3Em~{VI9oDg?Hn)3du?Qern{82(eR+O02Pugsbqf7XE7`N
zaFO(+0@&FC%7ZFK-l1VonRmJTmXf*yu4+#k4hrS{qN^rwU~h+z=VsopYkQ$ngt3k9
zc>EP&VEfFl_=jK&{m?K8a7D2n0Qxxna*!I+p#2Y=LrIAEy(*0v0lufgL;%mw1aMP}
zQEpvPB+X%12x=*r&uzum4o#e%wJO!Uz=EQB<qHlD&J#EerKk^+T=1_tNdB7g^-z|J
z?CYPb@*+<zOFqSG8>Y&iD<mnE#&ax2Kg8B{fu8W&F7`(H?3FI<j#xjpS+3VMS_d_m
zrZYP*j~eOp<mo9MCq&kLUT=PkjFig|)3N{81W4Jfk>rwT72ISueRAq*TN1kv+pcZ&
zEU-X$HhLU(zEIB9xfR+Prs8hG!)VK^V0^tWNB42a9J7UPmIYR1f1kl*B&wnE*#*|?
zvKV$0-Q06EZe+3EIj9BFLfSkayS_GRcTGJ{SOW8?%YjSWFWM2FF<^bopcecG=Ia{Y
zP{KvSvOaH$wI9=Nr>v{Q2~1CtD0;B#Y#v3Je$^2X>8?PT$ZBHv;#AQCBU>VVL!!wK
zQ>~pvBsrGU6<+hg&<l@?9bH^4yMrdM24WnJclA$C*9s`%?}h3X^J{@==_7W`jER0~
zFgn67zf(^v5`uRFd{D(QlR>NvwG)FO-#=(5A-{NK=xxeDVj`m0W@>LQM?@_Qw<)G<
zNX2f2d|%Enn~-0JU>YzSl2A(8<-N0u<7Y%_d|#ep&%bNog9sG1)>FkK2Z_<l{%KTN
zn1KjFyvRH_Vc4-U*G&(z>Cv2??8EX#t8Jo4z{flAOM!_fvTFlOoC*olpQaQD6aus~
zMpMc#mGh6Zem*~w%{6clhLL><c_)?Ers+ei_1xiwK)3=s`%J<&6N#y@XR19;{j(eE
zev9jh6VxspcmkWi$ynW*gggrhOyTw<%q2jK95g8NaQHFggPmlq-Qt*K75a7U@FP4#
zY$<_Rz((ft%D;tcM6mXcROzL8!+4is4~?CDk&`2lzct#C%S(%ECpO2N2lp}<x`PWo
zw`KQ7G(IjOOZ90Mh!w}1I%0iRx9X?-gOEp22d)2q2>o{&8620u4wvp1wbNeh;1$EF
zyXDey*3pa}Z~?HP0^DjSH;_~ENAVYQCO{ZE?h6}V?9XCXlc&Q>G^>=9T7XZRBc(`X
zR}3s|iE_v~T0UkvF@m1b_3ib%0Zk6xAd0ULd(zyuleXt4C{GH$`iaODRusU1(5V+r
z2|yX-!yR{`a@7A}fa2@q%W8n~=^n!hP><3_#Vg)1N-U-3_6iU1jr-zfXlARytM+z=
zA%_t$0q(L-pra4mbfcHdF?nxpX4MjqKtg_Q{K=1Y3C$x}Xd1Y1^o$-Y+(A-sJU18M
z?qBz#x^-nN6RskF>HFfpwj@PuU`kdCfa_=4`d+H&C;>1DMOZbNMQ1+G%FGb|L7IUY
zJ?WErm#0h-)7M;XRtrcqnF8Y-Lr{fOdXCPe$eo7Pfl87%!Z#5~Ru2q!oo}0ViH*k-
z`8q;1|Fw-<>jHcS41VGJ1^BN}Da2Yq%KA@7=?3P!T9j*Uct5$XIn^IxG6o7e*M@gw
z9IBHHzC4ngW>JMCR1_22<c-{wV+jEN7?*yWQD&2MR@#uR=Dw8P=D42b@Gdq*GXfe>
zLEo#>d}?qu00cdMAAI+Tr<X*+s%sR=4)~e(Il)xc-??l0Cd4g53KMKxkvGHJaw%ff
zE8ypN%hW@sDLLXvdAMH`)jBRWv6^}b4eDfBr;I`SWc*pNi6<SU9)yaIH}wWtEEzw;
zO-df`8;z<Qy74|95kl)gFrK6t#EUg>larFk!F$v3a@zwtA+-{;3#S9J{qkB?Ev;9m
z*R=@Sn9cYS{jkwn7uz~=qx#X&^#rP5g|y=&A_cf7wBsk4XL9%xkqq^bx9G|P@b%2O
zo2&=Q1+`NM434-i@i_F<W&qnp*3Q)LH2pnqwz|;z&K{vaY>~99dn|SYP5;yx$6c1<
z6eO}9K)lquEo0F-J)hHJsavtucl0jqB<W+zO}_U&=MT*uytAgps0&*Qn;6KW+(E%*
zN5FWgQMw%yoo4kLu2t)LZ>gD`0-43o=etS#S~)=Rn|GD*{xM&-3up%?3wf-(%RV%0
z=JT(K09L_K)Q`UrFFUZv0Zn1X)t&1~`gH6TkX8&)9*H+zu!Tf_@X4jkgTeeX$`23=
z#%ZFUS@YJY;b+R&Kw$u;j2I*cEWxc$^ua(8X{;1J{?v+C=4l1Y9T4&ph>e?AGnAdJ
z>891|UvW?3&DTRx$3BLaa<%eQGj0%MP_DE#(66}d2f8)SYY7pgHbd8KG#^p~yopK+
zU~`%vh#8k`os$>}A#<5|W#I7bh@3%1+(=&2a|=(8Zp*&D+!+0YTosjAt6i~*#9LS>
zkH^VIo?_xQr<U#ahSiRx<ZFI~8@*irK64eh`zCfpM*7v6t+MczX>-PH58pfrSG_|U
z-~e4h@>Qx?Qtx!$5?($c(FLgn^ZGXY`*+4uOAtIHdh-y3RxS=X3f-$UsoUu^n)X%o
z=t~%awN{_yfT|?c@9{LKmtT7d2At%GaZ%G45UzMx_XBhSu3xpr#Nf`t3A(hBy@cNL
zAI7Xo{VNv_Mn@MW<Fs`_5iCHhSAEd3vR*Sdk@4v=x@wZ+om1%o6`-?*nwMjkrl)V$
zaH|9R{RqNfJ{XwtZlFJ1t!ZM?cX<^1VQ!o!^S;ixx~qk%+P$MsfVpu*_`pjEP`Cd{
z1pl3VkaPYr3nj|oCS_Vk|6kX`?Ai3uXI65_nA7;z59*~fqfEU++^5M^xXr9f-oK~F
z2&n=h{Z~ya%~bk{Kx><A#pY?~yVw9oUf&b?lY~bFyOdXzOV*PyZ|kf&ln{`^)gIaN
zlUAw;j;=?VA&(XPE|@>iCm=I|2wBzSedte436M!lUh}{wfBX70>Yu>VCG{PLDE~gb
zH`P%@I-ufIR_NKcSN+!v@lKs<X$$(em7YjoDVbpsmeMKpiI9(gK+o1jRnf~ZwA+GH
z&9#Ge!`ukM^_^tml|f`GUuspe6DJ;~pM^m6Sw>_s#$C{%X00I{Yt8w^DLS!+oL{<L
z-dnwAQ;|p|-6jIKJ~y9}xIF*ndM(@LIT38#;oY(nk{xHSUVJ8V(?&M`Z8{>gx#3_s
zTd4T=wr0KQxh~^5n@_^wlUolP?6XS5mC(k0(d<fe2s_RHnQzLJ@ygZM+Nbq1(T@hR
zW>NC9Dyj`?NZQKWsAs2Sm#fvB<Rr?6Y~T0tdb(*BGoLUq#)-P9wS`Xj=jpl}D86FO
zF;wV%Ew=E|BPQUYR#S1&(&0oAduSoEN<di?%$A4t<}5hPDyjv-@0U!S=CNQDY`X9b
zBf0)<fYMGr!xrz+hh_X+6vc7nq|xf|Hi3_F|NM2a*6`>y!im<yWp~1fwiH2u0;}P5
zUDh_fg@l~Lq37ll9nC<Q7e0pAt`p5Km+``4>nSmrUrv;^UobTs;1O<y6M>PP`gvuh
z2y#OBLaZ*97-HsoXq>ECU?l;VJHy0%GS-SB#x+ZmllHvs03!N0lki1$(cbR6i(!BV
zDA3gqH_$bUEO>41b)MMG@&dG6fKY0RHw?NP-k|n7Z~X1HwVB8rnt7jCYH+cO;l4?p
zVHmyAb9;hXjgI3~8Q8GatWXPH{LqaM$V(T<_$$z7XA@SRv<va(&QmS@zO1T#=>|%7
z1!V?!6OLHei7N_^F7P?hE5X}~l9&ze&>Ed}s{}q~n-0ISfeg8);VEq3nNe)x_M7{5
zIrF}W???A~P^8Q=9nK>dpTUz%>i*T0cVQT&Ws@O0vIxtha^|xHpZwvXZXFkK8ho%C
z+?E^w7cNV((@Vf@uiyn7@?30@OpGT-TC2)rlNb^pOZ53_5{K+v6%%hbFP@vJb=jYp
zAKsg$S5xXYoAmg}u_Lo>)22<{Je!(`NUQk{-rk_|dDzF>>7FZtutR6Jf#h09MGIuA
z83L1<&oT5>vB3bWQCbc<+RPu@kA0pA2HHu{^2%O~G9Ud-basrZvRSw_=XpSObzQ;b
z<sb0pER=j6noiK##D>LtPfRQ*n(&$a==yjDYcO=f@e7*G8*m?QY+{%YHzw{rgOlbk
z{Jq_}UaqnWk7aL}b555-G83)WJa9=I{!4{yJ=g6>ZnDod*?g_W?o6qYJm(8rS)q?m
zkHcfKAG$7^R_TH|>Ec6A4W=^g5{9PF>p0rCxpDj3WSTj1&)(~{&;0`{XUtS>g_-;q
zx7yZvA_4S^KZ;_ftxj62oNRf*J!HjrT#_^1pIgsUh+2?{EXplT=2a$M>~SSA;EEoa
zuL!1ZrIFv{;qgr}UU08{sMATAU1_(cF9kYwxrfvc9sISo+sP5x2x>H<&Tru1lsDZR
z!cRAO<B`jYtFfmz3yl*@{T=W3ap57hG*r$i8vkC^b41(w8kiSbwVOUxcUm+{+xpiS
zn>HW)DYmGtXZqj>k7cv2HM2UBi>&lMYiJptQ)*Ufg=VkqMsC4n8K1`?QlzPMlxxN`
zN@8<p2&Ke+kMX#qT3vWInf^hG85h$$ywOU-6<I)&)-dtejNc%v{Mndm{vWSNrqww?
zHFX<Ot1c8MadFBgu))1^1{~sDMvp?w^v=V4s!D(!msZz`Vks+E%TCdkk;wY@-qt2J
zGj)+De7jwqO{>VZ()dg2^}(+Z=+(W8ad6m3#Hao9-Dm5$(g@ts)wLRBCRa#tPk(Hr
zm``&pkFT<s%JmMD0`Yo<4TJcKeAv?QXhfQ;61vs}dJ;BtR*I;%JxNx)?S8Xvp@%I#
zUu$tr0z6v4+4u)1`|`Y)x`1tB_oZuMNPO*tW4B1gvH}bHsr$GcE~@!`k#ux(&f~<%
zdP$z!$ewL;a(jwEl=^s@CUg_s2G_7XzS0e5V**$rjF15NGeI{hyR?JK80TXxuQ>*<
zVmra3dHYY14+*|bOsW4hEu9!G<u@k<ovx<!PzH^J)9Yi8@Q5;pVX2$s8LPht_G$q}
zyzl|x1q!0Sz_uQI1`{9RpD@|dE@x8D_+t&Bx}34?F{7`_ceZEu{8X3IAs)SEqNDCi
z09c;ZX;XBHY`jbZnZ87SdllxkUZAKbxRk8+)}~$8hT@x)4hRs$ZAabh#-4WY9QXGa
zcb0Nr<HVur@jolW#Zxn=@m}niUsUu)!%~d~#n_0W<A&)8a*Rtyb4TOw6D9*{oBM<B
zI&64884gGL0M<?lUNdGDZ+5^HRd1NGaMAuZEgUM~(O+0oHmzN(k2Ig<tayz#R^`QL
z5!iT7v1Yc$Ejq$9yR^LCIXuK`-$Tl5_6gh!c_gk|hWwKkD`vzi#3I!ksEkS4AWz3L
z+S{-6dV9UT1WQwsdS&c_-0lFT$*U~qNDJNIqgTj@6OF&odubKNpoTUc$B&dK?EARF
z<`h)5owy23F4rQf!d8FxPV=CgvMR|c_{oY^p0*=eoAw2vFo=nIG2@w#nTZ4Bd-u=o
z@COi$e{JC@{vdukC)1l5P6e6%LQg4wQ_@}ch`pqMQ~^bP;9+M_ij=t|R{*4hV!4`r
zDB7h*0MA|4TaiKAl~l^0sH7G<*{3r}?on9)095QBNsh}}{U}!Tj=061pBJ+AU|~lF
zn5d4Kk|t0l#4Qmu|BDBxYX`v0<CxPrM|Tt~GBHknCGYl0I3vq?fT%?GreeuPrkIqy
zBiO-|SH|<sC=*Odw+Z;Z<P)Q@i7t2K=;&IcOOBcwE-~;c?wpP4A71=>KL{>Ha)vDO
zK$yf)IkF|D+q!OF(x5*V_+CMA)lh^x=z0o<EBB?Vp>Ur3_ECTan(fGIf|yDctH2S9
zuGe$+100}H6habQ4`5=|w%9i09H?=WXqtxAUes(3xdAAn;D!Se48KN-<5mAvBi*Kn
zsU<ke=t_~Jh3QRe46z2E59&xEGBLnvL6sdjJH5@u6d3Q9xZBvQ<8(PrH0?Iw0$AJP
zC)Ak??pO2NvB0A-F5urH8ijtoNn%Ifhr(rp({sfVKCuLgOM!9UwP6>()*jE?u6!-C
zaUXMmJ0`<WoAG^rjC=1m2>>z}@ImRS=|84RTXLG$FtT5v%R9>}9%^FPz4<l$=TV`)
z_ul=ymT?7Q=c2ClL5`{5_*`J+dS{-?_ki7Nj(MKPK!O}X_$;2id7JANh724$clB2c
zwD??mJguxqEh1~`c4e0A;#aO(@4IYzo0#uTv|6RfPFKSxOz&>O2L(mGB_Oe1$_*Dt
zTg=|wE<^Gy!Bb1g^9JqoO}u{X55T5X)m0Wh!PD6MzM0QT3+3zfmaTX$>aRw?n}jCv
zY`gh9AuetsbDL?*3VrwH&Pjen!pufMz#caQ>1RMU3noO#xF=E5pwBFGettem#qd?_
zxq2QUb;lt3Cj8+g$v5Sf@5+JSLmA=A9`6oid7slcAaJu{$Pae&X|qW%ABD$Zi0(`2
z@~<RBggJ)~Bc{t~y@yL4D;`bkEP@!z%O3eDi9#+W$AgI{DY8LzrWdB~jL$dw#5rD|
z``Q@N;M{xFqnuL<Sf{91(0CI~@Q3p{F5h}rLW9vt-}gJj(5{*QM)6W3|CL=+{ed`D
z;yo&Fb>C)b%>5>F;z_9^2_=5Cm}z2(Ir+%+Bvp^{{L;NFHMs5)CotQ3@zY30vE~!p
zBDP5m?Kb`4Dnfh^5<{|DW+?qE&#t_^G$c`fUSgiP!P@z!j@`k~H2j>73zQ5I|M!5d
znm_S;>*PsuM?F1vbhT>o)GB=i&Q+dgn~#L>+{Kfme&%?(E48FgJorLusl6kbFbFgY
ze84NuCnePl6^|{|9FbEmOqQr^%hMNE6k8qwE(?3Dl^(|ksp#}FSv2ZdTu+Dd8kf4A
z$k9efdvD<Ui}plm<gIg^s{N+hrj#fGA7I8>l*+#~``%R)-WNrC_li<tA}}GMAN~*E
zf!P~m$j=IjGl)sm5-q!;nKcR-j%(m?kS2{q0%2P`1;t)102jB!_<4~rL4cUF7tG_w
zmxV&UTTeatLTyXlsv$^jfO?U!n^$)anf@VX{VGbj-mQ4&{a4qwWx$^(EHS4&>lYE>
zHksvvW3#iVeBSRPdqyxHr@IIwQW7yPxpOTe{-N}0rZr@d<$g}qW4)~M7@!>Kv;V3d
zm8kWv?6>~7Nm_IbIOJ&sP#lNfJZYb<BJalMnkXU6x!u_3gu5B^Inee`uwoiNvxy2{
zK&$AWgfHdg8UJUrW(-ZR<NU4myw2PrdZzylxh^IpL4%BsRBHZX7jb9m+jqMoEIc`C
zi;t>GWv9^Sw~TrZd?~nUGOK#Y;Yszn)(f*Y&lZ-Ya2h`d-PN1Py1V|kbD5HN!eZe)
z{;I%$o8zS>gO+S7h<A>)R+Asc9Z`+=9^NbEm6GF1b4?Cc;hJXkqLp%PO?7x<m@e!5
zp9Y5IC3yS<TtnvbBD{(E8h+}jm8L}JujN_v=zX3(Bv!WF!fFAZdfP!;PLoW}sukP7
znLR)GiR@WXNw=2iL-OZrW%QbJq6KG;1{rcb9epxma%1uX$)y9pN>w!;)0a4>_^rQm
zRn;fXN!qB#JwSO`5NYxtpV5|nBC(Vt3O{)sy@pUZX0LA?wV=j4R_yx#0_Izd4?J&i
zWTE5$Lcl6ySs|c7K$`p}GkTztuRGRLQdSSy(DzQIkuXTw{3X7IR+k@+HB>W<0Kr&%
zG~7i!;054LFn|ER)6<|l7@yTwDhIXE(0=I>!!^8NA3ylgg5Q(TFtrmB%kzgZ?^Kpe
zg}xQ=RXL3?Z?gX1m&l|GxsmtH6A#Me+`rl5Xq<=uL@`6W;+d3A<lkqscGH|An+rgP
zpS#*7-x9~wc8SQtL16)vDnk<@V)(UjJ_!Wuy+DfhnI^|hef~XIW!3LUsb+zeYMc9=
zF-!S`?HsqHq4KHHzwpRU1qPK|m5{Q&_D`;UOm_A6vc_hau28K$kAod$y1(Qm&&x4T
z*N>W?sMlqlW!m1|eg)KbGgB|Wsgx`%klA&`@#tIX@9EF%<-ZsHsOz_@wCAC$@cJSZ
z9-7*(hTrs+rdSnvMxe*81^9^yh}$F=&p#u0Jn&G`?wD8i+7S5A{B<BGR;1X9PQW=q
z%6DrQL!mXW{k8dChUK<MC&56F#wrs1rx+TeX0l&QQ?Yj8Ql>>tZQK2uW|3c~WNfSJ
zpf2j3`uW=BBv`dou14o??|x&XYDenp)*W9dAcUeN<IywmI+5ePG#+Dw{HOKe%*Ene
zFwOR-iN(V^M?T4s+tCw_sfQ;8pcgIcs&|EZSn^fi2rVfJ<(tuf`g@6+hsuW{5d^LK
zZ@=}nyLoB>r};|9NfcVT_IJr#+L%M}O~QKfJdbTO@f-ImX<`lrbwmvFn;mW%)Aw6*
zkBbPh2k9^28_oCk#4?D3G>=WVaJrY)!g^Y6tfx=4IVnwf=O=9=Drg*9wB66pL*P0+
z*9Dj3mPnxtDMFg|7DUSaa`_<|VFW`H`04&hut_WBWSwF=27>Ra0^k0}*I--SjL1Gg
z=Z!e`MdK!Qx57=`v_gDOq4pbySzCALVxcN;e&P4*^Uog5(n5_Nrw@ph5bl5z80w9u
zB9l!;`8ncAsv70Fdam5=&ZW{FYS-c~g6QddXS&(irP%nbK2MjenS_uOpTNVSzKc~u
zcUdU3#H24g1|<lee7pQR3T8PvK)J1Dm#&hX4Z&M<(a=|4%16rQ@l^k4ubEUN?^fAa
z*%26Sp!vi$s0;IGCezjs-<*Ov4TC7*<Bo5S_^sEqN@o69xn9n@J>@uz$7e6WxGc9g
zI4ggyR20W}9Q6t41UoknqcADnj@GOc1V&b^Oz;U*I4@?+H*T!hZ<LhEp&tF;E&vjy
zc$AH`6N@C!^Z<-L3IY&kr)0bcVHAZoZ{r(8HVC>CE{OJRe_f#Kp^7N?BpXXCH^-v!
z<;6P<P&J)rIR>zds@fS#y<S=T=9O0~eLgqZF1E1ffy8}n511KqI7`!Z=%k;M;ZWWg
zl0<}uW@=Y>0PK8GEBvMjH}F2Y_o0L{UVLwi&hfYnqX~ASl@zIpw%TyPkxAu#uTRp?
zK^`Y~NwiBeS8O3#S&IB~`j{X#hGkzWXL=L+@Wb-X)^bq)^~-=St0!UX2Y$QMbe%!Q
z-CC?hbWPl1{EB-O?KSPvmacy6#d{-10eCzp21n7hr|)}7P^8yjjG_-?B=1su4L!$*
zv-AZBXT@$7H@cLc1&vW3MLgYh2p@&)5?$H~l1Uqa%g1|v$wjJbtKbgUnD<z8h3`b=
zwp2~MD>bnzn<$TK{Y;I}!=F@JwoH`PyTfFMkuvZL1KjyRL_M2b!o&ax$43ohLmlhh
z=rX+s2Yd01<#~=G$p*ov$TR$nB{AB6R+ww|&Xe~GjsAKx$jz8>g2+Q^_iUZJs8(1V
zVj(E_xQU$y(@%c^sQ4qS>{I*-aKvT{-`%``midAT<pyw^M`Q?;=hCCB<&bgv=mr-i
zJdl){0Tz$MHC~U6n#*{zwTb5={b!4>Y?Ux%D*Q;}fS}`v&mva21HWK-Z=B<Is000-
z+Np52*Mh%?0^5!NywY)jrst941LGz)uZ^a&*r}eJM_xG!O2VmMg~9IFi_ffnvWd>U
zN!|=Zdj-((HA|RGerAl<S&wY5e>flDSt;3yNv&rO3T`S*^z9h<X-Mc+E9hKv89lN+
zOwzzRnZ?t4@D6<zd*Y3AWF<%$dkuR_ps$xnjs+veJ1`9>nxqh#i{XlGU{3G7ErU>e
zrxM0RYy{dO#j$v3K=~M0uLRHbk~G2M*rPqKSc3i&chpbwu*laKlpoNiUuMb8Z1fcX
z7Qb&EVP9q$F4$oqt?XPKKK^mXf^HcFTf*)uCzN>DMPAgo1C@6*Q=+&YMI|EI0tM4R
z?=#KUaCBq#$|nTOOE?v{!pU$@*-6WlF*FE!7*_wBx-zZjp1s48(%EN{(~`_4Y=7sP
zpjz{_z0BNAo5-3)ugzjr@6DE6jDQ-JVyW}iCuH?XGswI4)1SDH(K|?d<}O*}YLAG2
zIa7x~4d;gM(NyPl74~<~E&slfK)sVz&E-|!dHI6ajoWH6Y+rnUyv3pF^3J>_EAoVT
z4RQVZY38nlQo^cn$8A=GSSPY1h!ZqqE__PCef_$w?@j)lhfi3k-yS<&DX|k2_id3k
z&y^jl(e5>8^@onop_*kBCLI-ycUp@rv>vZkp3qZxhnqlzRF@a}n_U{SY1!yaHJ7^)
zl`lww*<X}%OFdI^4nH{BEbWzLh&>lIs&c%dS%0#1^6`55#rc&NX$1PaYI3#n^M3%Y
zKvBQlPd*;w@57^VP1adgtUMS2P)-ZS0tIB2b9oQcskE?3HvptiDd6>F+^}9^af5!4
zF&*Gi`lV#}@8UVkBRk6>jxc8|yS$caDNo3sXBLN`QjQ^KY{=NU)OYn=$Zu6olZ`{S
z-g@izqAq@yvb1_i+vM9{C@(CYhQ}rq3LE_j>ja{FR<3+}=eq*`u=vN~WY&A_zH>Ww
z+Jc?j_J7>KTUR^axvl&H;(;#?b^AY4x6J~U#xG7g%N=>}Q8m)E1z)U-ShzIshP8VO
z<FD0Y9**^;JRC2^wQ0ACJg*wz^}DvBpZOj$*7+{y9LRuQU4eUMRnKJ9X=n?#=8)zN
zFr@M1inW-19^b+*B=^}n2hbx<udclvrv#Wa9Qd*LO`M-TIyf(T=0HQY80Oj!Z0h}X
z{Buof%$EKS^$LLD?eTBBy&lS0<?*urcIUkOOfKkM(CgVV8`NyPhVIWS?BO4_DK@SF
zmH<3!anS>?83Y~5g%@7v?!NnO-}?}bFl>lHM`D)zEFRa&cDdtn0XC!o48yQ>xo3kB
zb422#wtsa`<Htr!%4mM2#s$0vKoL1u+)}Q{2(nXZN=+|~Hvl`Vy`6sg=|%HE9|PHj
z(=>j#p>LpW04z)b?a&b!A9&GzjZaoNIDVD6qXFBp!okN*oZvny96BKMxK`KF!WoD&
zix0YctIwv7=Na8X*5*RWi={6sFIV(&SULn~7p7%-0)U8aD!I4{uSct`V4{~|v#fzH
z_uO+&yxk#(9O8Le{?LUocVe7MT4o8)(&amXPK;-M)D7yG&2eoW2%wfZAOI!+dW2;T
zYnBAjsX(~%{@KLJIst@n0^=XA>HSGV$z`<!?&%l-`HcBXfB!A-GO|q#t9!JkVq}IS
z3dtGEefVSe07BPHpMe`E?69r^p+Bb`Vtunz;{c33u`<2pRxMn+O`5uGuUbDp-r=~~
zz5hGdkENQd(*WbjKu>2!4ENZzogZhdF}PViUB3AM_8Di5xNN<A8(nw*HGP;6xJO?_
zKbIuyX9~$R`mNYDALSe68|y%<1Etmh#$}A*_->XOCx%;f>sRudo%Os7d6N+$R)STx
z5;WAiGhk0c@hg=N3$<7>D>Zy6;h6ZMY+~XKSsWG_)4(C&U>EBn2C_ieXmP_U6YmhL
zKsrpCnY<@W+=ub%dmm0l;6Zxk#-e<hB`J=}wMh;;Yt6)dRvxfql(ra*f(1(5b*p)S
zALZtk#T$3C-W1BMQ1~=I`>?U=6H9MtVG6kcs^UEr3X}A+a<NM>JW@CrF74TNU}9-7
z%|CPlJX-34Bk!I`TNpoiZ*`ZtY>Loim_pCU8U@#!Idf{IV}J=1o`rarABwMrw$O9s
zl~;N*`IbhhS3o)t3a?V*po}~7%rlFI$7&Rm#g}%=docx?DfzO(m*P%cpiLQNS*)=7
zV1P%$mwMzOX@uzjO3F$nEzh(e<AFbm0T6v9gmN-19&X5t1qR~NPutPV)J==ek&;>Q
zSEM`rrIoADl{!Z|<Q)_WS0BF>S-vdZyc5Xe&#qR&lK-@CNERR5*-a1eJ|O_DCWk`t
z@awL?7Qk#*EzZSd@dNiUfBl<xZ@1|{x67S(`FkwhL1$`stm)0KR^C&mAi$%<ZP~Mz
z5C4n#-?$F|+-dmmx7k@|yMbr^%)4O`Z|v+j?$m{g@?k6;&s6`6n={w{PSR_xwmbK=
zX62M`7W%lj1M1uJp@;Kzc%x&F^ZdAOan8AU-aa3Tmb&+r9HPSCOO^iFi|}c(vv<S$
zGyhXg?&*2KZ=av8<!-uhQ~%t*e^d9=lUw`y_Ih+keSmwG23L-Xw(&^l&OP5&h<*&~
zt`OsnWVp0vTO{H*DE#evofftfH^z<hZ>5A?*<m=+orPPDU}n<--)P<?{b%@2mnUCW
zUww6c0nxr0S^Tg<K_6f;x4dM9kM5<<Gm*W?HOg(7+^BEXkEO{a0{525DosAo?fWYq
z|KVGKfA;v-Ain_Eamy5o_tlGw>+I0ljhj8X#$w|7hj#Xhn=H_F9bNjb+m3A=)>+os
zzO`%IF{evu*|(9mAhPe1TRM(lwfgw%<Im;qn^&}OqqoU{hs-~Jt<EP__v~0v7Cf;I
zm=B}lfR%aAX0B2<+^vXwl{e3UsE`AeVSF0FVsVW$6Yib2Zkd0l$>Z5}y>k<9eP)aQ
zTV~-6ULn};y6dj<7PKu5TZrZuW85k#bH_(w(f6*~?{-*yB%k{{vBh@^wzT|yU#^US
z$;+-=?VfLYCrlezqaNEB4?U(?hCbz#Q~Xh_W+@-w4;$Lx^{9dwnBN#U$~N<DyY05#
zA1jdtJiGVad)?&8ll^8Y08zvT$d_e(xx7W0!<r)R@~}6D*T`pGWD3A3ExeUA%`eT(
zEE8w(OU5z&=rI6+B>f9LTU;!unRT^7;S2d;DUESGvL>AIPR22JJ`Z-Pin%4j^2`Yi
z&@Q{|k}n%zF53)(AN9j{rSS;ee*W{H6D41lLcioa;jp%zbPu1exZ(;AD#R^W4ts`=
zZ59vXa?hNHJn=I>z{9-HtiBliEfj9lN7n#o)c|jnw((?>FKY(2vDN%J79Fy1X6=q=
zK#t6#EFD{4fY&Xz+)~40COiN{nr@rtP%nty6vC<_X*2f&Jjoasy$hR!=?@G5NSNWn
zhx<6RY37L`u`WO!tsIvBcwI4LhSeFkN1Zuw#wh;{2<s2)EQN?V8-mKixOQ*AXr7HY
z8G~nQPu9nwGrI4-`+P9_))}AVmCY%PaR;2(CI6k}8#Z><D93mQ&>UdCWV|{*C&pP=
z)nhDt#Dozw(k1O5-!stdaer=X&Zd@pTGc!@I4WoHI0WtiI0vYnB$G{^hsrD7M;XLA
z5bHp!1K+<oVB;{XMi<+Z9N*vf@BP*1og}~6S<lNxJ{ckElX#4?dQ>8NkKg44qu2tW
z<*}42fTnQql`3!X*g*<$M4Gwnx8Hs>%+liycn`_~wxs13Hvk%dcZ_#KT>&+Oi+H31
z(9ZZL^MfU?+itrp?{DD>U6VY*upG@{)`>!QyJz=Lv7;>jXZGybvrc(3{&q!436-)l
z)#BI{MMr(!4PXPv%&;53VpkNNkbznXg$G#4Q5l88gnsZ1pVRJ9*clajH)-(!FQ80H
zs~0G-(!io=eApRWAIpeoyv&W=u}~>sI*Vgh;u%QC+zMS$bV0>{qGqlikXaT#jtDgh
zkHW|bUy8f&GwYaX?+ZTc{57hpg+GAM2COn5XI46CdA{L>8!TymwPR`EQH&3}oQ<fH
zkST>Nz72lU(q55nW-ZL}W%*2AksrYL`VwFT<ZtP4Z||cJh+##R9oZvyvtDU=t82YR
z(PVXix@8pZI?pOcPhIo@%(l%HSNb-`j$2P!PcB-r)OFD#(TV#)H8}7L4Len<P?gH>
zbiFseSXhHhrf<FZrdpBcSrag406qoTjhj2)1Mf)t<ayuZfqQSL1;CKTDX?!|b)5yE
zcd{O{3(eMGhrIEoYrOV4HPYDW*4uraHaz+m@4nG-$GgG*cTSBkr1?%Ya1XFur%t6U
zSYu`3O)Zy|*{)}t-phmY;8nHs0sWorp8@Xu@9E+GzHQs(2kx=h0MU0_+I9`=0B-bU
zP$})&!W^u}ibA#!X}s%tHDLGT$&+j4v#z`vIy`c+^0Md#b?MT@TVXEsxZqOq#9}-Y
zF8Za#v;LR<34av!SmR6!8{O(QUu5wyw2m3&my;iDfNxEfJOJ(;e)!>;{#o&&d)?}1
zlvl&c%Xhjf@DJ~dg~C(jjmfo-#Nwh<KLgYv9E+HCycf^Q^BZ-;hHmX`Te)LD+TQcQ
zib(IhI=DVRUBmwX_x1T{M~5}HElytFJ+$a9*HwXk=HK^#HT|)zVZB@Lpib_@=_5VA
zO5I!#`mM}+z!d;uR%V)ES|M<c<A*tpHt(N&$65!ls`sOPJGyRT+SYofJKo%1H@*yT
z+g!fP;)~~1&Bw+cl$oO<lj)p1XGhP|+|k87`Q(%Cs;jQ@VH>Yq;g5i$E|Hdn`ypRV
zrpVyVTmO-NCnroD>6%J2KX(U^XELI$angawpAAb`oWq~7n^{4&Q$y%dEG}`dTx8G8
zAK6rcHCq5fjGqB`nSY@x;62_WR)o^Rh2;wG32WbIfJkX^OLa@~%>sI*@x!7MWkxR(
z`ja=t8KGP99_#RcMY1f3C&OnwGhPA60(dVgjJY!&=cELC&bspcIc0_8Uo8yv1JD>&
z_|V7MNg3u}=n6kTpGk{!X>n|0C4R|wW8ndvU^TLkPL=U*SZ^&ZSLSG(fMF+Q*t<f{
z#ECQpYPC3)5BOQR%?(S7P;z6t`BOhR*(5xk+CW92j{WGPk9KS(CQSqNnYBIKc+bp@
zLSQLC!ti~v7Qu10mT%@)=!H$+jc$dx7Ug5}Iw!{f7{ro3L_bSh$oO*4#%<cX86>c>
zSvv9Buw%JUz6O*;#-aPFDD(VLxz!f;C<B(^_y#bZvoaL|?x9yPOL|%IpkIK{0n)A@
z3@+HlTuTR?7P_JnPdw45jb6L|fSO~9UVAJXW3G2_r~J2-JdeD7LvJ;3*E=?GKia2b
zKL4J7%DBnWE%n-R-A0aaE8|!!(~@TRV@!<YXyb<;;q2M2X}~=!Zz7W<Szl;z<F#Si
zc9dt7XRHIU4m5Nf0JvAGv4Vz9JMvq(^*Q;?&U%i(Jsn`i%2`+j-z9LbA>R#yeZVt-
zj+iLhv1evE&W=gtXg0`9?E?x!X~iS}VmBpznCZ6s0m8DQC;%B4Z}aEM1f3l~p_|=j
zO;pT3>uS4RiI0^N!kJqb2TNWkXHcH7+YY~9{_>Z;Jf<iM!`r>(3(#W-5DfjpV{R?H
z`6FxFF=%dFEgZWbG3_7v)#skc%FQ?5TyH)IqSSOP@9dfkcXgH$pST=NfKrs*muYc~
zKgUx;Hu+2o8@d5H!zxwimUh3}ZoB#8G)RYCrD<W3Zk8t|glWfCQU@T8&rHU(XXC*$
z04pZm=GPEcj&*_9v0E2@C`aiVcpvO?E+sAsGeDKJ2^Jv2IAJ-lMhO8j3&Vu>SQdrs
zIG41(BMhH*AEs&L;hj()!uFYri=rR8>86{!LONNNFnpK>?gpYEK6x&bPRPf=mGoB-
z78j_8Sk*G1Rv13K2b2uR7G)z!Q<M>u(SC)%ks+Vuy*C9dc}e~#6AD`THo~Dyw)`cp
zC|Bu+v4oKHEA~toI3@+cLITf#sEv~O;u)FqeR}rUXEW;~e3I|FKJtQxm4W(UihIi!
z`7E_>3+poRC<krUj<*l<SEl(l;NI}dF8AeZqHfz<d6j!C0MPYT(D~EV^2^!7%vs%A
zK)z;u;f|j^!!_1;5Pi0_)1_MXUQ#|-!}FHz6fkF3tmW~uX9MqHp)Vw??JZS@X4-em
zQ7=oYWtXn*sL4}Yh4w|pgX_o}Zq!}5!@S$@-(Tj-wDl#I)(AtmZrkRLAe%Wu!PyG9
zYtp2w#(SoHn`K#h$9C)xi%p>%4Y+sgG2Qf-Ts?+K`MIj+^vLbpLgBj;M!LCke(aVm
zR(JIa<JRRK3ki%7SVSmhS(g45vbs$BxAJ7A$+wnrvp4}^p&%=4F?TFCgo~SmH#ruI
zYyJQ;o2=8N-gz)SSy%cS`ch;=-WcohoE5$<?)0zl<p?da&Qd5o?H_;ox#g8xp>%#|
zevH|Z?e&KygS7He->9R24buEs*bL`wX<?#UVSPZ9&&rpN|8T9qKfF@|;`RPXwnf`v
z7p?0T7y;7}XQSiRaU*`ep4<9&ee3lba(<sYU@u&_tm{1%zWN=Z*2czmaT|~A>L|le
zALJH9xB69|8ppM*toqA$80#_1yD4#b^UETz$unsIjpLCAp4Q#Vt>0mt-Ep7pkPkcZ
zPaC?eFJ9Nb*Ws7+P3=#UdE=N(04B_TykGSqH`Z*(0}K3R6*b!r$GIMV!tw6Mqj&e#
z8wt1F)%{9JFPZ-8{CxPqN95nl@t^JBx-{<Qdjy*U**FvsR|o<!nE?J_9E>$0v-pJ$
z!>rOXzp>L*(1~L49Gx0~z6Tz7z}cocOUL-8T`SsT1NSUG;6aYRB|c+v(_3O8n=vNt
z7B(%7Qryrng*t^&`2p54eQuZzc?<c4es<5MZs_8RFRphT1`sz|H<tmhD`ff3%0qgV
ze%cki7T^{@dw}!U%!4&KHrJuU?5_GLJ1xV;>}h<GZs>B*4HudVgn2ZOZjz5ZGe<Lk
zn#D6ed#0@DtL)kQx!N(X2OV@!ULIJS0~BO#$#}R!#5X_m73dKsOqfu^-O?I3aG;-8
znRPIW2OrZzQ%{gBL_Bm{v@g~Vc(ytY(C&i|K5(3ngFEjX;`_lfV||EwevEICBS1u+
zY1eElr0iH`;lvQLjEVm7zyJNO4|l);2YB!<ap-da6LKu+?Af!uzm>t#iPxInsfLmV
z^N)S^-Ph5Va78Z$?^Rj;wSas2-N*Va*6R44o9xi_WRU?G)8t@o7MJ=?yMdAxF`47h
zFR^)l*w)onhE=}gq37=H-A2c*<Jj0?V<NjUhGpC_@bvEa{93+k<4XRynqR5U6>ZDh
z#wT@k8y>y3>w91)Z&ldR<>+0;(A!?Qz8`Cy{_W0=v6%6;%@;?!TXRf>e-gN7ax%TJ
z$&0J?H^w(!S4{hka*cA0bs*M(hN=UA+1Xt4T^ZtNsB%O;#oI*qy)JZLsltpDB~VZT
zs_ZG~|9$q<fh;TUK8tFNL3Hs@)VhqQl1NnHAi5wgs=N?g2o_Z$`EYuCr|2YFbiva<
ztCB!;5-vJf5LKecM<0D8dK9RSZ)I1-g(&dPKkAv}q97{s66B}KHBse=s6s67&W!>)
zL@joSDrjoBm-?WSZXc8~>h3y!Z*CzE1#5^-5(OFPMA3Vx$$n7<o~T8zFldc?_);$2
zMeSvWOjNOx4}(9vDYGg9L_abT%3Q%`qDl}^a7sRo`K#3s(OrY`;)^fl{pxe0iVaak
zmvF5QDd<vE;Zagl%ew+OMU^_D%CC~*Ss?0#et<<mC;n&db~XO0+z|x{h$`4*?%cWN
zUF&M$-+lL8QDsuT&ejT8?V!pXx#ymHL>GO1y9n!PRvb%%I;=;BhyufE1jYZ8Pd@SO
z0sk~I9;y(P%PzZ26bvi613WEgz1&n8DXI_=1r>^ddW!O}{HTe2QH8arl2&xFw`e@t
zxmq-mYp=alRQcumgfNovRba2E0$uc|B;UU1Gt!8*p$c-JZ=G2CAoNRhAxl$_UG{yM
zf;4=2XqRb3SyiDY3WD?f7&4<i(l^i#hGe{PqphozL{SB=@6!};>v`Z;N}hEwL3FpT
z?@RR<8BryssB%(N(dhfjw7N(>b?37vAkD{9<)^46DN*HRKF`)qlaIPkShi$5+8k-B
z!aM){urSC08S^bru&57Xeq8xh(5LY3;lVd2i!2WcKFSJG(49K3fKyS$v=65XXQJRH
ze@{L_BHz^YXP$XR=m*G~)j9IPyC)9sMHR!|k2+CbBE9h4+<*W5{u`@1!+oEo8v;a?
z<Dv@+{<}D8)F?00wDiKdtUKI&J_*OSh<sE3C=1`^0}nh<mT29EC>PZC#IZ8)m}5m1
z=`!mtH_JRV%=8DXgG2Ot1@%*&ALu*#^X8k$ozthu9v^+;-4A~M1Apax)4mjVFPrOk
zwuAO_JOAt7-Y@(^nJrD+d4{f4!(%7v>*!jYAKYl|Z@l%EwAL(EHfbu8-hW>fv~4F>
zRW_0%-+o7Q3%yK!>@j)%(%;E`5B^8`=vV<-Z2d#POB157GJjsKyuJI}FSEbcQ$Bcq
zN0m#NtkWfzSK7Hw0{wWTTeohWALAkV1p0$Cnm2E;w$DX5FL#AJ^K=_&+rCWS;bLjo
zvU&<p{yUBTXKj})(pVI9v;La#m0CuS-~RTuzI`#KpnndDw$!6Xk9-}ce(J(_K8Up^
z9PFMlLyS)p94M+F_U}a(T|})#c)wEqD7aSi*e#(Cg2<m*-4)&FQA+fDm@=)WhiP3}
ztYd*j+K*kcPHAJ0JcA-P$??LV%{J4z@I{eWfj9N4y58nptKF=Qs6_}rj^JC!xR$X&
z8d>|NuhFB6eV@(yR^YJc0+o+q?<p;QbW4}Ot+(DPYC)!!{C?=Nq`vVT;5(Q`^dGu-
zl;#!Pq6B`h64-vb?WO9kPL^!hT1KC^oxJ^yjtzBSQr?npK~5}gfs-{Vn#<xhTgrg7
zw~+OQE)_Rky><FtbEH|K7?pL|mriC@HId4ZEvpkLF>V-4zg26tXeDD#?=4H_FO{Ww
zutAv)t`<#ND&KrwE<^X-Qd+#wQ>x}OMhYXYxO>L9j8m9DFt0(!^qr6vdCAQ>KkL<8
zs??a;qRAC9V#DpEOouQ|r^##V-c<S=(q6~QI?h+wES_2=3qN*J*}Pn;ZtpA=`hHAi
z+i%}BwFr!_=vSFL=*A0COH`uAyXL}bbhY3oKmPMzdF8rE^4JLz@;n&#GuL1|y7%6D
z%Y@h8l_3W`kn>+j?<t>|Rp4fWjW&?m&wos|{$)RD*1oa)=p3D!E)`LLg3rCpHJAep
zA3j_Zz~J>qjMb1e^Cqj8=m2%It0-_s^wb3(A6@9V=bkGHIP>E)#%OBEOWx8sif;VP
zOUTm6I|Mgdp3_^ZmQ+cTwz}m>siINs7fF2PdTKdM6m;Rk>BbJ9Hy)T%8x=~+!W6nH
zFj3S3TRups{FuMnydfW1Z^FEV?skv94w*7f)Xk~pT<0tA3jM5Fj>^wB(wI;l-TWr#
zBeRG;2>mNM6UK~cjn3B@o|)I04ih~u<I1||B1m5?)n(43Y&@z~L;SqX>S|hEnU9hm
z@&F~@70>i9(4mJO>aWQ^%ujSfr>K>}y#7MLo4#+N+>r6LyeUvn)XHBCKjwPKRY83<
z+%1j*486{idIG`I@^r`{hj=&Inp*nzx=z*rSV!P}_SE?*V^rp@tTUkdSF7`)8+Uy?
z>a1Gq^Y{EwfRg894(JS7Gf*I&*MCsHJMOr{&rKDW>2)itVbI2qsm-(0f~21lqjSIL
zqKoqLV|YO(7fUBzSBwOx6E-eTYpGuEqMM*a0hTK!!S8~re)paJTd#bWOqS<m(^I<1
z_)9<b-}|iW&>ul*^$<67ceE+S89XO-L&mqVs*#L4_GFncce>nu{hiWzcuVOvw4*GT
zy+}r1wrpPb!sQcntf=E2r6qcO;rv`%_6~fuv<!I)@AHnuZ?^b<y=2k6C9>v5ZDgbU
zyXPa!cx#S4dG6cNe2pfm-+Nycexe`pYTD$mp0d*)2gy4(-tS$Uj@uYJ4LGQWA4|P|
z>r4w;>uR#ojj*-+qC=EGlt7d~l)$PZ0mhrG*D*e1u4MZ3Ri^|orz=7N@$0@Kq`DgM
zWTMRC<f=*xWC6yY$YS!$vjOy&C^M;L;U7O1JW+O`1OZS7(Q$@AI3`$72wq|mK1CHZ
z5aIQxP=9ZKP-^P#Qm@=((v5QB?z`{yc>)YzfIq_9gx=zsqQIV$SMx_%p%(p;-u{^d
zW;K=XC-wlQ$PdIvpp*{*N>#O_=HsY^E`Q|@;K#e}x=R2vAxqss=7E3)3?vLn7|LX1
z`33ldA^`wdeaU!{4}MJQ>O&|}boXLOQK>&DbeI_FF(M_!u|NP6sfTF_K??(a6ghy}
z0Js$bRa*H0!~kwG#gFkyyD~YRHEUL0$&?l*bh9+5rzm({d+oK*+b@DrHvnMu=+VQs
zp&9|IA1D_}Fv_GmQhlF-TSE|FQIuEkpD|-bQI=LF6x@2WuLl&+eu|OqJom~K`V*80
z08OB<%~B@HNPAaHa-x=r3bI96Ltlf^ysl*BAWf9S0O{aGf0Zl`^_77ipi%r-*aq-L
zy-!O!<PC@mzVsW^GeDB`8>VzGmT$G_<pF<y_H@_2f_++g;2`fR>_?4{S_buHLB0Tr
z06ppv3BK=U5iIO4El#nkl_v~`+~~gmB8G%Q70@M%PxPJi*=Yn=g|@^mjS9703%|Sy
z7T%q`KgtLQvJmlY0)$N&4d6$3<ccC#tuJ_h6W>G@Jopv@ghL@tJx6BLvr@hr$$S9V
z0f=eD!V%vGcmljrE0#V@>IZVq0=pVW(fFXChBtMZvC1F?);-m<nfMm1GJ%?CU5Dms
zy`3~I*Vb4pvTxHp`W=GLj914Cs=OPw+(OD#L3rr@p7HTe76MTE<n`BNrh+VY|HC!j
zy_7$r6gc;Y2F&Fo7lSgNHg~@Ku1_C1U9W(-)={8#uPukj=zH&#yZ7Bk4tnA#>AcCt
zvh9VrMVaOu*YuGITZ^=u505)ZzI<)G_rl;*-}Ud6_ut#iKfm(wwlZ+gV_w!MJn746
z``oiAm+UV>{tFf?Rr&I5EtCBZcw83&`^#&uj*`xumH}Y4-+o_R@Tv0em;T4VY-#N-
z<Q?8$aKQzB!38%KkLY&}(M1roMi2(8Q7`Dv0MPR8$B!TH>kZ!_zMm+?LjwHC58xhs
z(j$*N;=jkm`e@@p8(>jRcQgAoM0&K1l8L&g?;XHihra9OqaE<Vi&DkdMg4BsYDnH)
z2b11ynL|D9l*fnK=70M5lXCku!wWLd<C$gTrN5W^_t`ra&eE^G>M_kSf7VQytbHc^
z1&eWjpjh}~(+Ha%vc@*lcNUX)CydkRGZ_mpzc~2dgY%0>1v3~u^Aeyg@`I4A9tGsz
zi5}0E5A#F4;oFv+gPAPq(jQrzc#RTR-6c@vbkw7t^YMpImCBA~vgz%e<jc1f$dt?H
zNO`mBk&F6IyJem%(eXgv(>ln+tG|$M-d!SPx%Jk%iAY&4RZv*RBbrH%J=@CL7tWA{
zuT{yM`Cs|@fi2FK%oKiKD6nw<X3}Xy3#n*QF3s1elsA4hLl%EtC29dIHvnsxDQD4a
zF|GvEL0<&WpX+x;<yv+Y(e)g0Y1ylhZ1kr!q+9`2O*>Y~hquj^XUBZ1@3>~}^pheL
z3fwGfv&_9pH@g_ok1{`FJjM8t@f3hB<_wGpcHM1PSu$70G;_+N!%n&|{%Tuk{y-nu
zc_#%h@+vewb2@-w0F;>Lz5C94ven=bvgDO^(s5c}*>PA;QA;)YfwNS9xL4$L&wc-t
z4rS}hyoXxpyA&C;#bCMRFSqzjTj=E&2QY7AY=sVvaX3ISHmLADd_p(fF~>n)gx(YR
zqmu$qM100>YT4X_C>aw2W@2L$b76RU(y2$)T&+u}5cSn0X~W&u@L)oxb!E}BRnqkH
z7P8a!J4snntxH-4#*O^Y)x7b>8y;As$I<${vFWqU?LL_SeWvYEc67SvJt>>%YEpvN
za$+utY^amy)W}mQ#JH9?=pxw=&D^Ah>`GvQjL@^d1HBr+t}k@cO_C2~r0o%&c{>1D
zfRH>x=&9jBzr&_x#^!94W0NL2V9JCJ*4{5=M&E%xg!+PBBLB`)@=ZF-*^x1601ib5
z$ReZZBZvkMKyG@JZa!}*?EKtXkLvZHLV&+n1c;5YrV({lEjP-kr=IG)$rqqt^1wBz
zPq4fZm<{Gel>HplO|U7F_ZSj!(9hDhqu=A*7?C&1X~gDK>K$Wpbn|M7(aQ;c^2SDM
z`akp;2HFDrhn@jH)+5-&#{05yJ$a-2=;YD)QMb{@(Ep;#RO@?wk(c!d^l0?O)F*(O
zEdD}(_RwJi@?(C@{4LVTlK>mUt)0^6Qm4>gTOC+=Icd0*%2dY%RV}Icu|BrlS#rkW
zU1hu9^p~+~UmypcdY~__mA@`IK<9|8Ald=?AqWEpr;IyQF1_p${UnviTeDx48$Y>R
z&fnqpa`@l&l~4OVC#}2Y2C1)HJ4t4LIA0nytB@CeJ=w?4|Dgz$eY@s8Gj75-)j`xM
zwFj>^Rq*<zNA~irfcG98J6;;MsFcd474pG@Q{~I?x^p6h9zOLo+4<T*qSlV(snaL=
zZ|&o!Opt*`^_FeU-$>rS^|6##(J4wGN+3!gN?>)8fPL?5oUl5TEtaMJ5~!?y)EM)J
zAc3Aedlm%)eBb53f-p)I7L%DsGx25;fHH%HYZO^n1A(v{#)Q_$I!=2|yD|ya<7fS3
zIL-11{w!LugB8#OlYNw&5c%MT@)KYX6LluvEH0z)z;YMFL?<mTxS?cY;Vz4?#>TE%
zlotkUI_jvSyh1rFtHlq~;vQv;5orN<LXm{hMvp}CUMPOpd6@(Nn6kupl3$c0qz~W<
z+0=)OAB(>zEeh@AOyh6yc_#+aObZvfSsZpTvS^yLY!ik}y9cxc01h%<VK{SRcRufo
zx{yT{7BCbFJL(L|yexV4=+VQ2WT@ZN1L~adD3%7wJd``U_ayP&QC4EXi+7#I)7${X
zQh%_zhl05w2&FIWggQmtwzRT%n?E~R=?B<BjpfH;L_f%mU6gD9*H8?j{6Z<l4p~49
zv|*H~Se8RkhV`f{qV1rlp-%z;XZgwspLDnIER-Mv+S1`_aWYNmNje=ncJ%Lwx`N_7
z1YjC3(y)8lDGEb9R@wKBD1FH%?pSOrM3fCnOelx>(I)_KL3UxfVST0lqEBIfN?%MJ
z0r*D!0ytO7JF+}k8SI%W`KAn%2c8h`j<f*d&?gfQA}os>$SP}Brw^!q($X?NyA}#V
z9!jM`eBeR8OChX=r4gV%KuIXYu(ANr_E|vSyNSXPpf!Ca{ReH>`u{Y3h1@6$?bW_V
zghe?`9|LF$@GA=r^k;-+kwA|FPG!lvf_PWdnNcG~$TnJk-d8Jk3v>~y{lG!8<*BF1
zTetjO-oGdJJtaH<L5MW?_R_xj8j;?YFTdpdZXP~NK2^owE!tn;cijg4<tL9ks*1pF
z2%Nw^T%kW*G|15vWpab&=NP?q(&wXEHkZ$IaSIyRw23^hK<lKomFs_hsdVhnUIw0V
zrT}qlzD7s+Qmy_ye)d_i%>_B2c%uv;>F2M!EP8&h=Sj9c_+YV&8vUq${?9|($$j^3
zp$n?d`)A}#`v$y=#XV$5e+DJ%*;!|8qziz}<<}R!Dcf#$uKeoPf0fIB-(K70vgyOK
z&pxmHit~#DY)W9vz&DZwoiv`_G5AB8jGe;q6MZU+AuOO^u`P|Lm(&#&=osVh4q0sD
zn*b%t!ZV9nEX+}_=@%J`!J`-;n0mrkkv@$DFo1u2e;HpU(@D!K^<~Jfek~o=?d!eo
zJM0jdt8H%ohnMZN-}UZ$JYaO;SF`1zgAZE9h8J35`_4zIj<e%m|0XY7ahbq-$%1d>
zf&KUMLBc$j>!RD}tFD%(&OJw3Z?K{CRp9wczq%k-zct;pbun*i?LVK;?;1p0+bF;W
z2HFyJi1BR}S-)!jyi?jM;9y%+GQW7On*{VIyE-jS*Nyw?6Pj-u?H3B`|LRl1Sdu7#
zx=SFmJlnK;g>1U+j`Gd4nexPQx%q0yap%)Emdf?Y<++!h%3OR5!)M)h-nyl1aDdK#
z9(z+B{^y&r^egAbfPfzuU)Gh@9MW9+AG?)~nHI`xH%*qA?|dz9JpO6U(q3Jetw!Mi
z;B9iZqUQpd`pRs1?v!_B+G}%U{s*}YXn}W0w*X84-UGa2EM@bGZr!^{mnPk$>!~fJ
z&5$ZNw)KT_{*f2TtT}2my|Tj3wEzq;UuEMC^A5&i0B@R?H<#DHT_F9>>LlN`m@Ct6
z(@iY~94#qVt9PKUWVUo`-$*xlC@`+9LRz<K?SWE!)0o$ygTT5T`UikLSaxDu%=nBj
z=q>o}p{p@#@r-GidovbBkH(ys_roSnzd=C9j8)$#SZ{$2LDgzypI^0;H8yVHK^hE6
zo3_(=fDTWgr^ZZ_vPQXk|5rb?deonkEzmiYwrTVL@TQFe^kVZE`UuE2Mf->4_`_V0
zItf6DQxQmqG8ZEJZ6gGFJ!Hk)0uW@l!L1Mv%7I0DPU1i>gFHiESE0D(N1D7d=4N`_
zsh?-yW;zQCM;R#}mgQ(ilo5Rj>2XC5#O6`PJLHS;HS=pWoTB?;E(4&Lxi-8QhXe8=
zE_H-!AwpJw%k&spKYr&V8RUj87JUt4ePqhUOaNuf4FIl@ZjvY)08?}!<R35^I;4<{
z7vo&!`2bVHcZz<W_tc|D4__vLZRq0Ab&@7JG5~Hy#AV|e`U`X(mN)W^UIV}+R_x$o
zdKKbOhndsD8{Hjc<({%*&5+Gp<c)C10x%-FIm*Ypn7qIfT?#sfDGIP-E{<*#y)PS^
z0MnvlWRoLcR_2$?fs=VdPUuJg=#dZfRlG+|q##Y^^}I(mL9)3XpcDFXbaSLZ`)Az(
z-jP<01gPt@JDY=$DmqW}{k(J9>#9ahRZgv@HIi>1Xe?s}?<se@`VU#4-*D)F{SK6u
z%3hG&Zr)rLpY)k`3)`)CDDe*fy)^k?b@szW@5zpT?k`IgF4fJh?PQ%za;HMI-@a1*
z_qUlIDA%-YBYFG!soANOrarA1U*iC<!*U+qLjLqWutz@3KYseGZW?YRUrnAb)1I1_
zcQ14^;ihS_;LB&_CwC6fZ>w%hJ>o4XYn=OjQNEA>$fr@-H!H30O^X}dq6DG@q6DG@
zzCRLRY~E07=HH)E#hlbv0;}FAvA!jZ@#-Z3Ceuv*R@FftlLRKG5XX-K9ATFm$}E&4
zC`p*`0^*^ZOt?{c5YHy;r3en?S0Nx518IP5Oqc=mplk`#z+wps0l->JvLO_qC|m%o
zgy6Ykey}c6=vW3OJSdS++86=MNE_VaPr2dAu^DNL*~kh)VR4A=*fT;I!D4n;F7tz@
zxpQPM$|Dq9^(FZAQENq5YvH&>Ktaa87*7j_Vg|XTft}KLkS2-`K!<67$Fw+>7K%JO
z=7DhV3dzEu3`60AG6ltwxrbr7Cq749pu_^SV8=@&!`U;6FceJy8c-5s@wM>g&#r62
zr&-<P*g+JAfCd4kS{&oS)!a}>p_u1=*^a}c48nND?g3BIj*t-wN$Rtul@-_gQ5w?^
zps+*fRgAEZiQG71;+0ok$+rm>D^M6h$ku>G>|`!<bP3@ZgrSr}Sp$_K3(LQS2QY!6
z4gdx1iT4o_@}b?cgWAApC_bSiFMB2*DA&msKk60Mx!9F&3idFK<ULk5QBtBfq@B>e
zl0W)fDEyZ2O`$Jf(SR%YN2cs@M#)%;Op@vIOuP{a-lD&=V_#7CP)7iuk_U(~)Bbs1
zX@smyE@}RS{KD|$hdK%niT8v;o-|Q*BLe`DfQPU+M}4GjponE5gESblqx3ZpcgQ>I
zzSO)~e8RI+K1>5SVO^4+5uj9dkE6H;u*!S2MFkc)*g2e}w7ewWEn&j6STsTbjeMwg
z>|W<P&a<@{(g}GZH^MxpmIGdD-9|bbbg*o>%dYY({k~VWX)Wid5`D4e(FkxTN^`(#
zW5<ro*A?<cy`cP`Dd=>peuDw-?V<hLbhVT=^sKYJ^^#P}cgEbV%8L`)*4Oe4E7PNj
zZGmgiVmYRnny^vYp;4v$n?*4lYu%`0gq??OEqiI3_|?+fBFF4WAIL{qCTOxM-JjR7
zOBz*bJu%W`RW>3IKQh}p9eUWaa?_1N<d4@3P(^W-j6La1-?sTKV3~m{-$9i6wyD5)
zZ9JfzzkTw_=~CI~Q5}nXq6>d-$nCdnowvHzTo>m6?ooee_qI?7$d~%fMuhNP8E?C0
z)d#+_{7`hWxRNC518ti07+=w+^6jJ^vIrR-2}E4dAs>uu;76UKuLjU-Rt}PBB%i5c
ze9IV<@hxHDi}qcpeEwx<+P(BU$nm()Bn81P)iKn+4nI`N^>}3BkNM-WzG$KP5q7p(
z9DMmV7v{3TOXcO3LK<$fUz{aVbunp*zL&<@r;Pc-AB8UfeXLnVZlvG0ZGL;Pyl~m2
z@|9YY?5c~u-G&Ym-|CgeTDrJJzOeYXcwtT<Z|{pdo23BydVq|yUHWvREV<BM(5@Np
zM5@08kgoyavt(U=?yFZ!<_V1Fu+Uiyl)rkFJeDR(pfm|Ano%WB|LPqXc2+;x;K;Rd
zi=B*Hsy{nVp8t+?Sg)1ra{Xp<$DYs0f+@v;Up4xrYF->))<UiJ?!MK*GOyRY^7Qzp
z{5;a;R0VSz<Ojgs;IZpU?@^uQgNLTcgu6eLDGz_4-_{bplLZr6^*zg*AS@*lhd#FH
z-dwt>{QHbq+aJ67zYE`ySuf<i?YY3|e;H>n$78(2xSa0}n{Sv0_;;*h+>bx{SSt6J
zFPrSLsSN43o80)z8|7b*-Ybij<~AuY=4O0={)xE+V^8{Jqh38b$iSYhWyX>z^4zN*
zm~*^VOJJ+bbNaUmnJ>FE+F9P5_=X=}<BdK5y&Ad*z)I*P(})dN0DmCn$ymQ*zRQ>y
zP#?dLy6@c1AN9v^gPnJ8uNEzrSqpk_>~pecZmtiQIAKN@t;RjNGxSC1UMEhRSi`TO
zA1FKH(o%F5mNqh@F0v_yI?Y@>Yr~Mm39kUJ06j7npnUZyBl2NP%satKVwUA#((Tcs
zhu6*7COh1in=o%hmjH;2xh;S)=F-e}c+Y8sj)%FuZO}{$6S{GfucZON%q;1nYsQj2
z^D^c|=#c@zq5q?Q2#IHO2gnT_JYfKJ@(yf%Vd2r^a6$_kbh!_C5tcF|H_AZzyg%wU
zbKBqj?sxubmH-LM1~J+OCk!wTp<jbYn>>>rbY})8B|Sie#~pW^*ZDE$WDbbV9~~)q
z=Z9{Sv;o4ygZD|_NIl{iU?=&Yt^uw^cg>s$eG2#Jc~~Prw~j77)EOgBHc|r4^&sc!
zc@|;N@7Y=gdJs0EQU3w1GN&gk^l;3@c^_<`#_A^W2B;Zn<w$_~&gOaM$X3UxkAO<i
zU*P_IBib_ck$DsMNvcx4Pmk@|NUIIn%e#O5NbBcL(!6EPgfDe=klR739%>=uXHAfQ
zH+o3EaJs?XklxkI3@D8rI1QY~I%MxYz2&~Y<-TWMIW;^05B>l3mvhBUE0;}vG*p%@
zsFKBBD$x0%CUXD6f6G^moOGVhU)ESOKtzpWZnB@o%I(`q7NhX9rc<SgJizN=*4?+N
zAL-LV8ns>qbYAkH4zx7<()m@g<>E0?Hdo`Usk-k2)v-jX(i1bP9%wH8TCF8(jMj}Z
zUG=n+N192KiHl{w9lJ_}TE%wy$!)Smxh%d*E&DZFL;CHzRLXQkA0Q}ehpgLB$N15g
z)#-GI67v~@MG1rwz{+qTgB4-ecpoJYB~Y3KSR2K_5aYi{Q34UT7wbSl2{4geRf);A
zO^zr76Lfn9L;-Nmp0PG_<&{_Fi)!vk9{B*l4PgLg6(R=H>;h$SZHgAs0OZ0X9%ZIk
zt7B0WU<bPu0TMF#DMTnglf)#8G6Fav9RNq{dNjgn5|mXw6dPs@%0MTCXQ3L91Ih}H
zBtU_Y<YBTSype?g<i(`ooO8}uE`<TWEfmPehO!ucOE2qc;Q$h_cu(0%A&Up75@lei
zA5dh0(u89b0FD};WSWHQ*|VotB%#PD1Vl3)fI3l96;m!Um<Dvl#0@2m#ZTtJp3R>&
zf#NR&d|DWeYv5Q1u7JBNj`?#nH;$%8p@!m(BPPr}3|rTG>L$v5EEsZBVlkfPkK&Cs
z!9<q+pct_`5@jd~H^8<KydZ#oMkwqc+AhjPc1dRufIH+$`vKTudCLlqyM;mdXBHw^
z_~5;wR0)Y4_khAs=mA`z-5WT~_)y>Mp13@tJmLr&@{Vk2<A8Wf;c01v@o=;9pmd^r
zvqKmq6xOngP)?!r;fDgBx`-ka#Ucu3K&YfuA0;BPq+de$LpdN8|JZSC1P~7JVe;5b
z^Ut~(zchdRIA#~15|kB|{sILtN>Z##(B}{bg)&4Pqt2jYMsWon+7$hT(Fg@1V%3tc
zS;TumX-mC=2mKD;Z|Zw}D5<1_f4C3?pb$kpM0%6~1vH8`;?gGghVk7qO4c274`oLf
z`oNHQf9x)Y>^n_bNlUBLvl#%$@3>v|QQ%%1-R(b1`3=|KAM_~M2L^5`S1Tx#`c3-<
zc#g6h5IEmxBmAjPr|3e~T&+vg)qC}wKuz^M4La_43tH=1rR}GTx~MYXYI|h$U?Vw0
zt>*n!t>?YFc!_+|)Jb#A*Oh8*Z;<xU+B$R*wS&B>vfNAEiQh=evw_O<rD}k1Qw1P4
z*S>4vM^j{}DuD};vdPoUfBsyLrFvb?Jx3LoN|PqdmCLV~;_C~*HTpx|C5sM8f{$iL
z<=;&H{FghtH(lT7pMT?ldzWAFu6~D?B^Hi_*pPrVP5N5<)@Id#FkR{c;Q#`XH~KH$
zZI*Qat7CvMSxg2DM!8t5U~CnV@#b3y(RX0+fPRd6#<x1Gk6~Q9r_W_^1b`rYE#EiB
z1es;i^7K|q3$3c_FnlUow(`Ke_ta{JQA@P~?etwY)&-v}PdnY5{R{YN%4GSVx(<2|
zUP}0qmxa#X4b%ml>o?laKTp&?dAo~#>+e}SpdHQCw(<)d3(eQXq-NSbLEmcGmIQTc
ziS{Y4YG2-G^o~;5xaNGo<V3k|zWHY9(W8f71mk_M7|3E2<Ha<&;AU19)BJsA;-8`t
zuy;Vf@V;ooA7B8!N%ZCPOA)yD1FQhC$VDZ<LhSSB={!x3KO1xPK<!grk$3(yRlh;G
zsB<SQl9_LREgSr_s{-%TVpaj*9`h!?3pQu*JankA)`-^ned{aZ|Ma1}@b9nW!>Lm|
z^W?ZMmtx9a<<YWlBhBBY(zbgG4@7(Kp3mfgBVPZ(Ti8oVYspt?DfsA@{qbw~ee&!q
z`RK`+GW6%`=Yw|a*IH)2r6bhJa%tM8k=(J%^RiSIk3(W}9vjqbVUDpP<1?Gv=@PN7
zZj1EUyQA!M%|O|*^^fKGpG}Za!^g<I>-;NkP1!b>F-K!Q3$Vq;38ZJo?uBWu$ou+^
zJ;He5eO>b!#trX7K5P=fIv8`5JAab1#`E>``Lgq_yU920KnCOp3uBCz8T+F5F```o
z%3?7eVlHfH0Z;=h#Qcpp5r7EZWrxk0$qp9}P^;yOq}#AHy!9XAKBd6jPo9~n$1RuZ
zh7%pbY5OgO*cgMpsy=|WQuq_b%7lK1u|M<kLK~`$FM2(IQs^M4-)v$krdPALb_MW<
z{L#BuKFqJwD{To&XdHi=#v^oNZg2Vk+7R>OU;p~oUiV=e@Q^nreISQI8+OrcQ9sbn
zl|tw;`u6Qx3kb-(f}as#nE%<v5_7jJr{1tJ3%w7UaQS5sWk7#2Wy+KqL8#}{TL5YJ
z^Mj~wxG|UK7ZUm=^j(xUso!F3m@Efz0n9N!q-|LmxMO(~+0jPn)7YFzxdB}go-(r8
z5Ph?4gr_X%``HjpnVCnjk&3x9@#qh+R7hJf{Xg{+D}CgRz8CPV0R@?lTKU-+iXM)7
z47idy2~ZE~j94~BFGv26Bfm&1R|3>^TU#Mzz<ksx1HZ1^+!RlZwIn-EJuCFut@}%p
z?yY3frxRq_r(a5oHqHG!3!Nr?*7$KE+dj}+9$54TIla-9vcP>)gBAT4ZIQJh^v|?u
zzxJqO&MLLUJn!rI@|#nBD;G9BMi$Egxv<e4vPA#QEBjg&uem^89C)YvW9P@Dymjsy
zFm>i#a>B{SOWERbdGfYrrCG~mn_6omT;Rc}IuHPzl<T}&v)lcY*0SM|Ys=#KOQlJh
zO8x$osh*}%?i=<$Sv*~JeMw&Gz!CBFrK+(`w)uB&X`^c5$99?|m9Le{_IK%8U+=l{
z;&GpQ%eEamG?&drbeBo5eIiqzQ$Vv?C+2%cA3%RVe}ruL2B4?nT||l!SWyz7?P8oG
z%P7o>N;mS25?Gxjz`7peQs#Dr)|S5yGT<8%t^s`?^0d0;Ap-YSw>R2=<zeE;KzdbO
z3?knEb(lyqSxO$q1(<@xSBOPh0|fBQLOz4tVwTJZTNnATgU<j%Oo&ig^z7NwD;ZEU
zp|~*%aNM)|4+<4F;XQsR$CE@DcAWymVZx3A0I(Z7^PW&34`3&VyeIkb4EV-~-F<*S
zSl|W(!ooHm5Tms6CwUSED_kf)khRIcc+~Yu-9+&M&;?4%LmE%Q5r^`T$5P130zeW)
z7>YG4gPD645Az4y#%@q3E1xKLsH?O$z%dp#$=lq?0}Aj`KH{X0ya7{L{3Ks{Hh*@j
zj!>`=?>YpuSUBnf$|V$57RUVTN?EDnfICr2U>Ptgti>tyin7Z9Q^k0iKRZlqXCo8Z
zVgvw*@{tzSFIeVx001Z3Nkl<Z8I33xJRvN%01{zP1<^)qN3gwXOCNvQABs%g9XroM
zKm3zq{_r9l-XXi{0idDmLt$rOh<og@$9hYdDC)6VXMLW9u{;r$dw|0zi2;`Z#N^mx
z76?$}8(Cbt5)b7xiU95@4<K5APPA?624o95+ymr-H@m)3=Fq3G8=1OJpJJe8!j?iP
zno&#=w?~g29#qK!Axa8<^xO3NNwPAO>P|Sy0SG#|6BL=5f&_4<S2QIV>oaL$oesr5
zlUn*?fJi7+A(ZX(^8gTWN5Rg(mpV#aLdKK{1u^$ogaQz-4DA)b9PJ*Wj!=e>ct&YU
zzS$|BJpMRMPGQ>k^X&?WUF$3!5RQAZ%t{!Z0ZQ0+2e-5|a1Yb5aDYgSsM~}=J}fpF
zV3Rsz3l;#Mp!xc{fBY-*PgU%XZrMUk(f7E%0*Tp;j&cDDw+7nheWLWj4L~eJU8R0Q
zfJ~Qan6Fg<pGD=`2CD2G>9xID)7waY|59c7)SvDvmt6R3DgR=Y^!wc<(o|)5>%v9y
zjfOk1S#FW-hKqh9znwZ=&ilumQg-Zdg1Yg%t%u1p%@?%Rpv|POT0413fqPxI8tUCj
z{d3;=@2lm7=30--<+9%o(uLId^3Qu_`sgfD(bq%C_oCU9mtOutUVDAn@pap8-&WTB
zk!nemesR{@s(enbm(+Lo(N6*><vU>D&ty8DS@4H<Oc>e*ZHGk*0QW3bgk!ZJiU%Un
z)_Q24z-9&7tpWGK^sJ8IMxV-$#T@!W(x4tB>vxzA_p~?41bCc<5&BKWRz~^sG!3n=
zviaDP6zI^|d%vfE?Im+_U~dc0^~LjZZI=5MI@VaE8xvZsy^f4H?|fM>TX&gjy9M0&
z`AaX#EL}*?Q*|2o=jxkpRaO-(T6%zB=!aYYtS-<6xTCb+zy6&6lM|=SkpHwED{F5)
zSdY}Lw!Wi$XX?fXeob}LNkx+;A)EZYy(>0kKsIjXy#c07lcl`_!iV=fM~V_yQ4-*L
zkv67XQRzj#Q39)+1oU(M+_{rvsaogV=??>?vdOFR#?{NlJ<ZzZ#w!cH$XQX#Dj#D{
zEcZk9J?t{FjqLu{!7`xLcCzUGNpj7<|Ckj$)xEO4f?FQhN*cAOly<#Z$mGXn$Q^q<
zBlD*&j-b61i8*~mlOCLr_gVO*T4edZN%G-;r|TwxDp@#ZiHyE{6Zz53Yv;op@WfCB
z@Gtf<{_E!d^X_LH^D}=1Fv9!|fC*!{Lv-_tU$xdc)APseW%s`gmceDa%bUOXNUpf#
zN_qBw&-q1sW1b(QH*W0528^3HQWAhaRM{jqS7CwNlMbKq)&g@b4f5COBV!N7iX%ph
zC<){h#$z)IfE_mM)t5-0IS2q912rajF(x%#&y;`V#^KG{SIWX8Kaj_ddnezv&>sO7
zVr~}_x&{Lc;coAM`?sEXM-JTQKza4USENx#+SX86@Wr>%wr5Le-6gkC?DaoSkr%Ie
z--9}5ygc6@olD$Oh~rc_HH7gd<y=u@?}fQ6V@`Bl%;`!YfK-g#nTwm1wNm0)7<3zK
zaAgxVRG;xI`ViU{fQyiamCM}FF#*hCeox(dT&=OAi(}4W)~`JS{lms@fN5EvJ>$(L
zJgmf(A}`8HdqrQ3z9EayWib8^>sm4_{;i$&J6zqn#s@(%S|oZ;5JV>-x*#NaqIV-f
zbfOz&B7{U3iB9wuy?28UMv30bjOg8%L59IN^PclPf5G?N&vmW+>$>*7_S$Rj=e|Gp
zwgMof@GC)Jbo+R+n%W{j>I*f8{M=s3Z@2#VdmOG2hvKpEoL3APPri(O($`BX`}ihs
zyCf(G@C@?zW{hr(2dghZythL)?ga{@l^A2kJz7%*E8D1BNax%?B^%@4QFW-1zoC4a
zOo3b02ok?-IQGH7uX6hr|J_=ZsGk=Fm*qq`k|1y9AP1bxsv6FnM_J*Iqgp2(GbFmW
zhCS3|FH)qxe-#l}sxOAs$kw49&%JLCtk^+Klr`D?d03#4e^^qk%@<f=jt@KOfUhq@
zH_G>73AP`n#n)~v?p-?*t>u<~1v-^Qs${LrHNi4Fds>B~Io24Rn0bk<A&-Whp_<Lq
zqhlk-@KA?Az666w!Oi}@cm)o&efU!?#xi@D&VpvXpH{(F_2A2WG-ljDvar@HQXBkC
z7;lf76iwECdTn*_7Y)Dez>c>~)R(IC`5YE%4v=Dy#WH}<-tLBiyq*OK#{tSf|3P@5
z^)_gyI2Pasm-3j1o~0KcQdZwKz5nc%+O0?5Z=`0ARd&~?_IP<b(fu7_Dp)qk_G9H;
zO^|n~B`k(ft4aP&SL-cczEPyB9or^jA=ltDIwnA)ss5X9R5go?F`WEbslPwoV2VTZ
zHUgM&_K4y_&X1&XM4sdT))}}I!pA@<&7p~&>uH>uq@c!8EjS<|lM9<j6nXPQ!K2=r
znu3rgLUtr{KfCGe^M0QZ<q#BF3XK%A7?z@Eih!LQ%GXUCgVGb5rpxsh2@v2<AN6x?
z3V5CffIki+CQV^5D=0UkZ%#BKkdzYnI!b(lJo@$En92?}4S<cmL_amru7NuAmnyQK
zTHpGfoj<?vEdZaXV#3NWJ1?UqOI+E?75)8kzg$n1v^VIw?hK^_Hw=ZC&@eFzDmw`B
zZfdmvtw#IeibTi}FkkIn*ekH99(1Dhg3HQSeu$IHIF9lu+MYqQZZ+ro>s3AQ({X#_
ztS>26M1eWq3)q=+Z>u>`H{YnO=3mwSLGp(O)vk10*X(4n9@(pcyaiXDFn($KZatC~
z)Gs<{vcRuQzqck7Td-7f`6BBr{Dg`>nCR0_K0^s}7Rwc){unIqEp;_;P?JFzaLxM(
zA4AHqPVPaMJY|NHE6@D+=KS}&x(;%#+n>$-9!`>vb(ltw3o(Vqt|atHu5%&{q%i99
zD&3jbFB>YjFSoywhvA^AhGywEIwIx2VfkOFs)#%MsUA~c7C9Me)fh3|<n{<=>{pV|
zyrU_R_=mrQ2uyrK{e4^>6U1tUI&h6-ndc`T4`3UTG4~?wvGgw6@*M;D6lXMl8be4L
ztuOug&jeDM-33PTN*X#Yk4G;%QKHtK9kx9vC#unlysE;(V;J$TUvlb1P+p?Ci08o=
z3eyZ(b$Q&*j`>N<w4L?B%NEl)$wE>*>_kDk^p#6QsoM3`A-F%?yfbU+)||04g5Mq&
zlBM5ZIO2k&ROQ9kP)XOeYwJGruZ?Vkv(q)ts>6l#6tLx#<BC2&=2y-YwQM2NoCPr=
zy4w^*d0X<DXW*&vstbJ>xanIPibW(}UFl&AgUP^MJpf>&Mr$zRW{M>yE_|91*UaiV
zrp9rJ^}}?>)YWHOl)L3=3k%Ow(kNErb2;kzm6s!fcQOE+8MUqYa=fBvtT&TT)H4)6
zwuJQG(1wU<TJ({hIGI`kT(fxu1)Wmu%@~Zf;mnfnIOBS{&kBBq^T#}ZV3|ZQZbT;|
z#1Gfl^vpjR?^THE)Q2!D1pI#KJYFQ_IeOWm7<>l8*QmfbV(FR}gJY>{7l*^p?gloY
zFOuMnFC<Cz!u~ED*B-4w+rd`&v0@dPl8v8y^_*oBt#fXBD-#|5oN2|jr#yjU)|zci
zp1DG9s=Y{O`*`##7SGYuq<6xG8k(Otd(>{F-Z=aFGW&m*2D*3%wPsOg7OO_X!T5c~
zc!&IK9QbdD+25<t+cK9e2kWg*uZ<|Wc35+nu5&$XE8~zP&Up-o^hOtgobX&X5S`u{
z(llt1I8bx95sP+gm(i%)zsd=x$ZQH&O@k|y+~N0VFYm0=J3A@f+UP-o{|vpVoB=(Y
z`uuy<qjdd;Nn=70Ya<#T)zQ&lC_J6N=+n_K8ZNv(?)G5l?scF_<F>S#B`LQFZ^d1N
zpR*tsA+BcK;`ePSq_@hx&ruRazr$z~AkO#zoznU<_#2vD!$Sq=6pzJAsb+2ClD|Ch
zS3So~daNTlwe@~k;x$<Ha*FLNM`<XKg}2w(ls~R#40D*R^}Gr&Q+y&)A20Ix87sFO
zjw!}p;bT?F2<j}51E1R<Nimv)cFHPU6dUE#J5$5YT^P>)LFp8RC4eTH?F++s-nm_^
z6*~?NTPWQdmNgt)f-(%eY6vd)n|kc3*#Zh3a8M+?WalS^f>OGR6UEvYeZZz2>TtWY
z@mH(0;&C!BLX^Qrj@s|z9t8gK6mYU&+C*-D!yJ>HR^#3ynGhtbMf3G9te%qJctSB|
zoleb^$qPz_D?4GjsMy($Q(1g}!Jl9EeAGem9%nwf%HLqlVYaP##GdCxHaA>U^GKNk
z)X$kw|2F&gkgAq_Uk{R*6)WUM@R5rW#5e<&<M+fhC>GfcQiIZ`LgLAB0#e<1{^8{Z
zb>a-{@7?4{)d=APP#H(P=}wiSr(s>U#J3m4*Z{dM&osghyCDhfjy`ln8jmu0O^|}E
zbUq18OkH;#p0X?44APAbrLy7<;+<g`>m*)Uk)w=c^@gcl)=lt0L?W_jPggIrgNw&c
z86NPpC1!Kcj9#e~Y3Yu3s|Ql5kIig<7<|YuWRr}~z6?UwU}Dr~1N=JnaSE$#C}^(H
zthb(`9+?%ak<IVilRZZ0>qtiZF$l}gS=I$>#N=Z_Cn~j}9}lMWLKQbyT28{3heSu@
zwJKXSh^w0Ji7gvM*A!>Ue!zD!iFe4ap1QqRF^MU71}~a;S7D!w>$ADel?S8M5M8GC
zypa|>rbX7{K36#!>-{PI@!9h2EtlS%Y|Y*SP1CPM?u@3wADqsW|2&E{&#D>xEaI}>
z8T1_T_>}PX`X#ZF<*me^`ztgiDm%Q6aWC)ebd_)k+*Rj8V@VR<n`gW$)iy0e!bI|9
zt=?h-XI=y;qGUmSK|Xl~V+oUp23@`xBy_OKR-pI;E_hDNI>1I#ZtOLeOTX))Ths8d
zk$w2&3qQPBlDY0vU)}eTx=@CpHVfHxExw~T3EJ7EmeN<<J2#MRJ0ozGP|%Y@qn-TX
zLn9fZhf_C;jU&H{{E#K3+d$L}md#`ZD}e|My)@`+cT1y}Yli5TLwZRHy8cYE4c&%A
zqF-zVVT7%ExbXLf+`GRi(sp_3b<u{PO`k2oL?O@=-u(J@?)pdAF&&}^-6UyUde1bF
zunuY06Qj!~r0H)yK1Kr720JBVJVMP(NXujpI(t7doIrH0yL6DRyNn+-9GqYNE>RiT
z5pm({n39hO{)PoEc$FqTOb=y(N)-UziW35gKUd~?`A+O@6hV-`gD3wQe%(d}tWgyY
zzU4&Xw~38nAJJ8B!lo7y4SLNUjEape3HJAv+#lc3wJaXggJ0B(J<uY(FKd(&)R8!T
zSywsr7^jT8k!dSqRQ|+>YbM&U{QxLBL2yusMupo3bEYTtMbmP#U1c50`T_ALJ3mww
zOMKgdW$&Sc6r0Ox$G1`<cDxOr?iw;581C%SJx|%vn0poYuxn@wD!)J@qgwh%@2cc+
zCyNvPp!YqcLDefRtIBo?g@rjZqPl&~h<;huUq{wxLe@y_@pBvqxvlj>GC1MwWMZZ)
z==6O({ZjI1iirD^98GDv-&EMdIJfxshe;u@hT2!NJ?+z{9L!(CtfoBu@mDDKlQhRx
z?-U_lM-l7Y#wS?$(KgGy=lv!XXXhBf?WwTW%-j+90WfJ_Ym-eF0)g8tJAAZZankhm
zMTMA^dh9#YZd3CCBo@*1*^gp}ad7f&a{9qV8Ua||&#kDJ@jWPA&S}Kb|Evuqyq5N7
z*=*5DVl*;qIs3_AOCh*r-e$jOIRVINataCL_=o;*ad$KR!|dO~Oj)C5Cj);!Nyq)X
zH7F){PGu*R^GfVBz15c^U!p+CO0jD@6PM+nH}O!tFlCzrdOgOU!9hR~zg8k{NlT{X
z34N_Zypb4Q=kJ!&%CHXOyI^^%vC}I~dusx|=I}^*+59i^1SW;Dx|Dk(f3Q%C3Of8V
zyX*;8!ZpkNogBqn`khTuRk{(nadm(1ZQu@Sk1V07XB{qv3LKl~a|ET@3e+NSC^>63
zS}P3IxYt&>h~Wdb(WcnsqGhLVwl&~90#3vqFLl=g5gC>gkpi1=tYaW9$L=!!y0)=X
zc1T5<;k+hMBx|}%q-v!0ll?_#BM~cO`45*(@GQD3N3ykTlJQ`lfKHJnvyDwU0^`Aw
zsLJFQuNG;&dHSv*4rX8(os}{LEzr8jW%7c)f!|S>C-^${C9mwwP<pq)@8;xXl2DaP
z6UR;mg@>@EKxlDTTODiYi)HiLw!M)yL|lGf0QMi2aS=k$a)V`KXqDC^Oas<gadDof
zfcT1XTT@3==hAeJP*=c~`;|7b>fSKNkq{n^s*&F^-ACZq^G|Iqc%3XxU(pknelTKe
z&&MEYGFFPW7<6tj<Vt$_xNv<kKwQI^Vy|#Zb<u_Z>IypN|501tPQDg|QE%twbI(V4
ziH^c&DMFgy+Q<>3`iI_Aj18`4EFK#vM#zK4#gWnZx=YqU#G3)OyD-&~;Iy!ZbE8oj
z8fMPzVr;EuCwGAQB__dZk?v%*j%}}$grLp2QEFj53dek;Z>Bzf;0*G-84`;IRy~mP
zdDZ%f@3A%$@^D<EtYJ8O#kVIxuPwfVioJ*!-ENHxh%y=CY~?_hrj{;)Le;HRuAay;
zY>&vHw1@M}voS&21xxV>?(15y`5D9C!w;PM%`56Je8$+#I=qyZ;dSPl$M{Y7w9i{-
zqS2_Q(!iP-yYqYOyY@^_G3U&&9d*-ULP`Z|7^%j&OywyEDC7h5Iq@3Tk$}(W6slp}
zE$MlzKKQ5VwvQeTv1m$d-x#Y~gl>IYy|O|xhfJy;etFhLfKPSoNFl|V{JQ3#_)lJ3
z^<%<E%Pf7=oE=iVdw#Gee4yt*=3y;?b>+mV>(8nEeZ3=HX)zP_)`qiVsK1e*w`-^V
zezXmoweSyVvc;=#va>(%mc7ZYgyWszp!>4#li!4r8?W1?9q%}(cgePprm*+j*I2wj
zt%w^m4u#Rs1cVA+3%_BTDt9d0eUV(j_tDGbL}7VzO=<b(?AOZt#k0!;Ppe8ONIUMj
zsCct)7GymZrBZ(Nbfz-`w0iZ~1NE}+WnT^Tf1p3TyO3-Bl%pssWzrH;mGQ9C@-ITp
zKO)7WBtS+%aP4SSNX!O11c;62VBSL!F+Oe0?^nvm>sl?a@=dXz;(Z?pTJ2KMjJO?<
z1E&k{r+`1!j`}3c)m|VsO6D_(@S(mPg}%8Kh27+|CbA{^wM%FkO=kKx1pJt0DK_qL
zhETn11jLavzgGc3qG;REB=Ph`ud&W<4`_5Hq}R*kR9SCyJqrbx=lDd1)nF_*nlaW~
zn7gfvtx=sE1H1Iv&f?YU0aN63JOCr$dz&=v5+ivU*?XLkgFShVxy0!VdNGt;)SBUs
zDSGoO_TqGl+msB>^PhwR(PBysz^$n}7K*O9@65j2nE>c!;<Ps4ZDc<v=wIFc=v3A#
zQA}_yA?qf_t=4tN(TbF|R9(+o!E?o`SL=w$&{m(FRYdKAR;qh|9t==T&U{3BEvDpr
zZ+N^@8A8=_=7e1*w-rvjQ5zB5(_9~dlUE3u-ifmg8z%HY4i;wx*;cCBW?^}}CGx|X
zeA)?Mi1o>2{9*rJW%hsvJxSd(q*4$o0GQt)(4@6)K85rgoo{6?(4643Ac@hKhf2`f
za$izhmCGj%AX%{|f&fWjQNQWT^*B6p*n;@yLzPSPB>Swj`d@|G_L(hB_N`2A|7a;4
zR^Tzja<9iiI9;ZJA!BP;g=v|8_qP0b-&B;S7nxxEsfCfdXqMuz0<3=UAmG5a5;j<=
zvYdjZ+@q>}wo*Jp^8HZzG_cy!X_i6BT$&~w_!I2N&lR&Q&Kb5Emn}XSC4<X{BJO##
z?~I%|<jQGX>CR^Nru_gm2uycEPn``L-hKsZcd|0_A05FQv%Ny-%eB9QI$?9ZQyn{W
z8}{<uF2$f8%!byG|3pRLv4r&LaVFx+PFpjyX#YpfkMz$2le$)9O9aX>t6*1W19)S@
z4ROb)-4A7b?RAersg-1rF@@@GWCwIWJ_;2pHMDv&$9R_~L|sCD)Y)u`1ci2_gybzr
zDHKFDJ#tmO!7O~b_|x=hMa`3j+bcuG#&?$*I$Or9exM|RO-_jpBLfdA*UHxvvv>Dq
z296om1F=dgYF0i6JA<XtOP6OI7YLRQP5na~XFHM@Y+u#O;*mH{Rw|}HKfq0Uc^0DV
zB>NY?3@W|riq4OQNS5mAHDMTpb_e%@W5`9utI60E<I!_h3~XYwEHWV&p^JlWajGkr
z8X@bhQ_H)Qde^JxM}y8tyX6D)I?D+BwMc)L310TIUu=C+FS|P@#~2iYU*y05Z+*I_
zzJ{)RBOSb36#K=^*xqbEr10FIWoBZCQ|9obX`$;yN0U?Q*FowHjxO?O!Ymh*6&YYe
zB<}vcO4(H|qOcM6mg|>~F>hZ##PqhSS1g{(FhN*1i9n%H`6GVp$+xHwYXUt3_R)r^
zQV|PM9Gi~J$#``x-skco))GK5M`!N5z)+dzmX@OTz*F?C62lhX_54y(_L|~2w$QgL
z6maLV&4^1VSojiS$?T|S%ta^vZLN7@C4*rWDZnaB6i^sB@b0yH&J5)q9p8Fyk>lnb
z*>~X8XM{zEFdO7<29~8u@Wwuo`uo%<i|;BXkf+-PKq4tX9e`5k7vG60`PWAA#ctCy
zfIh=wL9F$ytMFc(w!Dmwzy^cMo@RiwPBBNT1#;|O-WtVKvmQn~Oe*>*NX|&m8X2pD
zWuUgI%8L6M_CUE=fP<a(jXFtmv4-0PRaYNLra(mH$dU?xOV&1lnbj{l7&8$3q=69L
z_T1pwm5k^Qkr<0YmU%Q*`QaLC)#*i?ddqb3;&uk^0`+2+eSrW(m$u<M?<0N`xoI+d
z3<AWM2)}|ImZu@FI%80$hti@QQ%X@`QY=}l2JM%tLxGMs3nY=OM_)>?%XZif%Cm=y
z^jCAZV$vy0)G=&l@>j?}iqYXOJ^vHdr*o9uqaBfax|}7gchMOt56z{2H+cr`X-&G*
zD~4StN&b3lE$80}zxvQlSqlLN4H7*YHt;9a7x7TEyIeR`9ON0~nR#zYZpR+&n{#hh
z8UMj*{Q)u72QYV=*ai_$O(H~*gANfc)A^lnuId%hz!s_<G`|72<>bBfi`Y{_cjnl4
z)@qB<ApviSyG7`GU6(sTzz*|6-D2}LNJK{8P$mEKN#qv6RJ~`PqmjPBI5uAbzb}0^
zL|LHdiSml`WpmzbmAKqR{s#U64c{5ho(YDCZb?duY_PHmz#}^2w4?5f1;CrOResc5
z2N;dhqcG4+?~jyM>Fp*dkN5`Q(-LjB15v7!Ft+lch*XsMza0rClgH3((-XVey8GGN
z{l38z2^P;GZSyAr%<#Ht@f`r40LSjyLR&FvmHOh<+`+=^%7$h;)z$AYl)Y9HDtQO4
z&vey;eifxyl%N3f#OX6@(()7v#Er(*`npGo`T!Mv<;isi`<w%k0EoVW{81N4L%>J$
zPFOst6QEptuom;&lmts0EjgBt7Bcn&cqT4FJPU@}in5D3n6vFnaNf!?iL;?cy{)Wj
z$bCd2&^2F}FR(j9mf)+fnl-(CgfIAd%I&sDt7*p14SB90^`!0#f9E}%^{OMfkKODk
z+3g#bcJH2B2Rfb{m`Uh#NGdM}m1|eX$8(%^pE;r_5DD!>4^)!F4(Gje@qe_e6TDPH
z&9FfQrITwgd8VBiUL*IT`A#356Ic>y*8z{sg?i3yo`Xlo11a`Ya?MCvp^0X__u*I6
z3cua*8HSE5EQEEt)?T=2Xq~WU6?B!X73-KhvvhL_rl?=C5eP+1)9|%XPv;oDK__A&
zW_6pEHUm$Uyj-1eO0V|rC8ZmiXHm4A?&L31G?Zd5y}3^^L|W(K4NSBA-EC0#QxhdW
zBQ4+A&}mX!dlM@Guvhdl>`&H4uTXbw0LcBc<vus=vuMZJ{RAk-td>|YTt-fTWiYdZ
zftjL^euiQ}h{E<HCbEwj#I}aehJRl|`HhA=M$W$@!V+su93l71>ktFS9cX36GN={&
z%RmYZm(#H|%&KMcdaMaK(zYiqs0&>=9o1>|^NxZ^{#>q-d{-c6|Fh{Z&q;ELT^bor
z(<uP~H;z5)EQJqjO%{C@Vw!RM;)c3!y@!-8uatN_e%D2-%;ns!#ki{VP{~^GNUsJb
z@#mLFD7yOkmX&jT8i~!^U|U@d6UG*KSJB&}OQF1u=V~GgdKCcnC$Q!ft|uAx*VhvF
z96r=nk5>eQcp$23Qg~ygUgOPKn@v7bra9i$g(iC2C#}vb6d7gl1;a;JHgqq@^yfs}
zGY?YS67P+e)OBLIlsB7-z_0NM$%jR2@07=5_3X;c+FgBbeaIpilk%3^O!xiNMnFc&
zDkRtIdI8bokL+iffRS1_qsAd3EOlVTLaQZE-(&9C9?oBtRdy%FQceOwoO1(0XRpKB
zLCB%h+65+@du%&O-Jj9u?7vKpweTILIVJ0Meq*)vait==Ua*yHF(+E_MrK2#5~}?;
zI31!eTroz^6|?iZwi^T!l^GcU4i#zi`+&_>#tev~e`Db6?8dpn<x(kjJ<_b8x`3Lr
zC_h)cEAWFh-2SkS#$PHWqs?k1q0pN%-@D`uJCXh;5J^lGobtkFoPc@A2pG;546GZr
z-5lApoJ6lkg<b|kC21aS0l!N7=*APJ%=P~kP+>@pP2OJ6$qsK&sAKc^BM@ME7T`9A
z8l*AH`m&Q{`E8QkVDxKb1#9ky$GG}k?rR-fSPb<ezA)I?ZcOm^qNEmF;Ia~a15r~-
zeny=~C5DHLJ*&-?)7hk-ZY_xs?RN{t(w>Y~F|Z%Nc}>acQCzc=48~AlU+v=%Hk;(V
zkLH*_0(uh(6v4JFn=my;S#G<0v1^06DO6ObHJOV3v+5FPK`0IOYLst{3jw(d;4@xW
zkg&zh>`Kg$Kp5_zSnVWerb(NdBzA$x?fMgs49!)es82WE+_8z~(|F(@U}?&v*GIZ!
z+jjjebimQiFDq!bq#=N(haOB;?*OT{10~?ww^X=_-vr1h-c>f9s2Y>(suGGfkl+}k
z*G}g{OlHa4m!dLEp&U|^ISAqHi5yK$imHyFZ(W2DnX5d@JCB^jUtNF|*|4`;MMo7V
ziG-)4$dj30Hm2NZO7xi<ccW{3DJMmuqztO({ULH1D^F}14iq>xdjgs8qv9L-RNHgY
z(`!QMU>@TBi?xI+u3juR11(y_L%kM(-Dlybgv;j@P?g+%{@{xTmh2rxpeN@;Q(@N<
z69Z$uz>lN++FBba3?lL>Plh{1+{u`zk5hcFOF7QX8IfinGas=*ph2k|975tiCd590
zF?FkEZ$4_%7S293rtpiz^hgi$3tKXHqJlQud|HqsdAp4Ux&o=|b*BD+s2MD!r~PcU
zi{;zk$8SfMnp%&fE0<|CW&rQgmU$W$r0b&}&%9CG4~!u+$>TWy9ZJ@cT?b87<|fnN
zQ1boG<O@Xa6<xD`HvK54x2snQR`bHl`iO%|i$P0sfWlaa;=TQ3z?@3v`_{?sf|jeb
zKGr4rY2Tm7yR#d_hxDm+`$4lx$G#udyay)VSAFs4`~7_bYEFIJPul{)d6LXU@oI1$
zmQ^7L$hVWj;*unq^=GL`L`x);o$XgnrhNf6V0T9Q`yJPxyI`i$#z~Fw*GH9vjo5ni
zxo8lM^w%lP&VDEPub;r16rXfvN^U_kiSrg{ezPC63>{>wuQ6y&T6h_B96eyuD5M8+
zUVR=aJ|!C>U((v8+i#wUVa(jeqodjoLFB#|RE&Qgl++85av4A-)c7ZZ)Ed>-Er${^
z)7wsvxks!dm)EjP>+*QJO@k)6aEsp%3b_0y`kE@4QEF&mfHrbM=UDO&$2$bJ#D1oS
z5V9o02Yyp&eEY!4(@l=ce<GrRxAog2<j~s3&>tch2g!WGF+<`#YkjdpDWonQh2{>G
zqMp!tqA$72B~|#;z#=z8afMiRVRw6qDz^9Lj8;#@|8UIg@8%`gdpii&UoBtbP4qp~
z((cvuGbM|+YmoPm&5Q{Lj*7?UvdW4}WktFBjmnd;5OTnU!<K|C10p%;vh;uM0LJu4
z6o|Qgc}nu*?{Qw{jJ0wn&*vuS$__-q_A1*hC1pV|6#4@*Aj;;5Wk5q5$8K!eC`J)Y
zKp8Jy4zM`DpxD06y7Jc$;ferZJXNfOW(sYWLWj7ETPDfgq#i(?%m?0t+#V`5Q(T&l
z`HR+_dI_n~Pa<n`K%ToV?T;U>SHZVEG_W(POHMLpQNaN;><j*$-esH3AFNr@Jwy@y
zPFv^D3ug^~%y)L*{h!jFDZ9(HsFn88mP(_HC4t9Am8EYG<Nl|3){3+`AJ^9<uOMJR
zdF-}aM_D`EXL3<HQ7cjF9}eX|U-^(}q6=%rwxIQI>YwQdn(Tk8s_*dVcifqXexXL_
zNdH@9sKrH$wKIyo^KX^ur#sPIuq6KdRKx%9TmFyY@?VXFJSW+I>oVTvt-iy1`#o6j
z<(zm4|NDQdZZ5_6{-59d-xzIqH3|2{IIsHktA9En|4#j{AK+iRDP5Y0$~O)x)Ijip
g|F;wV_h-D))N0G@{aJb;0O5G?OjoT^#WwtZ08~@*X#fBK

literal 0
HcmV?d00001

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/guidelines_of_approaches/bevfusion.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/guidelines_of_approaches/bevfusion.md
new file mode 100644
index 000000000..ef3b427f2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/guidelines_of_approaches/bevfusion.md
@@ -0,0 +1,35 @@
+
+## Installation
+
+Please refer to [INSTALL.md](../INSTALL.md) for the installation of `OpenPCDet`.
+* We recommend the users to check the version of pillow and use pillow==8.4.0 to avoid bug in bev pooling.
+
+## Data Preparation
+Please refer to [GETTING_STARTED.md](../GETTING_STARTED.md) to process the multi-modal Nuscenes Dataset.
+
+## Training
+
+1.  Train the lidar branch for BEVFusion:
+```shell
+bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/transfusion_lidar.yaml \
+```
+The ckpt will be saved in ../output/nuscenes_models/transfusion_lidar/default/ckpt, or you can download pretrained checkpoint directly form [here](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link).
+
+2.  To train BEVFusion, you need to download pretrained parameters for image backbone [here](https://drive.google.com/file/d/1v74WCt4_5ubjO7PciA5T0xhQc9bz_jZu/view?usp=share_link), and specify the path in [config](../../tools/cfgs/nuscenes_models/bevfusion.yaml#L88). Then run the following command:
+```shell
+bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/nuscenes_models/bevfusion.yaml \
+--pretrained_model path_to_pretrained_lidar_branch_ckpt \
+```
+## Evaluation
+* Test with a pretrained model:
+```shell
+bash scripts/dist_test.sh ${NUM_GPUS} --cfg_file  cfgs/nuscenes_models/bevfusion.yaml \
+--ckpt ../output/cfgs/nuscenes_models/bevfusion/default/ckpt/checkpoint_epoch_6.pth
+```
+
+## Performance
+All models are trained with spconv 1.0, but you can directly load them for testing regardless of the spconv version.
+|                                                                                                    |   mATE |  mASE  |  mAOE  | mAVE  | mAAE  |  mAP  |  NDS   |                                              download                                              | 
+|----------------------------------------------------------------------------------------------------|-------:|:------:|:------:|:-----:|:-----:|:-----:|:------:|:--------------------------------------------------------------------------------------------------:|
+| [TransFusion-L](../../tools/cfgs/nuscenes_models/transfusion_lidar.yaml)   |  27.96 | 	25.37 | 	29.35 | 27.31 | 18.55 | 64.58 | 69.43  | [model-32M](https://drive.google.com/file/d/1cuZ2qdDnxSwTCsiXWwbqCGF-uoazTXbz/view?usp=share_link) |
+| [BEVFusion](../../tools/cfgs/nuscenes_models/bevfusion.yaml)   |  28.03 | 	25.43 | 	30.19 | 26.76 | 18.48 | 67.75 | 70.98  | [model-157M](https://drive.google.com/file/d/1X50b-8immqlqD8VPAUkSKI0Ls-4k37g9/view?usp=share_link) |
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/guidelines_of_approaches/mppnet.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/guidelines_of_approaches/mppnet.md
new file mode 100644
index 000000000..f56b45c6e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/guidelines_of_approaches/mppnet.md
@@ -0,0 +1,73 @@
+## NOTE
+**If you want to quickly develop your own model based on MPPNet, our recommended setting is to use mppnet_4frames.yaml, disable `USE_ROI_AUG` and `USE_TRAJ_AUG` flags in the yaml and train 3 epoch. A reference time cost for this setting is about 5 hours, using 8 A100 GPUs.  After finishing your development, you can get stable gains when using mppnet_16frames.yaml, enabling `USE_ROI_AUG` and `USE_TRAJ_AUG` flags and training 6 epoch.**
+
+## Installation
+
+Please refer to [INSTALL.md](docs/INSTALL.md) for the installation of `OpenPCDet`.
+
+## Data Preparation
+Please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) to process the Waymo Open Dataset.
+
+## Training
+
+1.  Train the RPN model for MPPNet (centerpoint_4frames is employed in the paper)
+```shell
+bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/waymo_models/centerpoint_4frames.yaml
+```
+The ckpt will be saved in ../output/waymo_models/centerpoint_4frames/default/ckpt.
+
+2.  Save the RPN model's prediction results of training and val dataset
+```shell
+# training
+bash scripts/dist_test.sh ${NUM_GPUS}  --cfg_file cfgs/waymo_models/centerpoint_4frames.yaml \
+--ckpt ../output/waymo_models/centerpoint_4frames/default/ckpt/checkpoint_epoch_36.pth \
+--set DATA_CONFIG.DATA_SPLIT.test train
+# val
+bash scripts/dist_test.sh ${NUM_GPUS}  --cfg_file cfgs/waymo_models/centerpoint_4frames.yaml \
+--ckpt ../output/waymo_models/centerpoint_4frames/default/ckpt/checkpoint_epoch_36.pth \
+--set DATA_CONFIG.DATA_SPLIT.test val
+```
+The prediction results of train and val dataset will be saved in \
+../output/waymo_models/centerpoint_4frames/default/eval/epoch_36/train/default/result.pkl,
+../output/waymo_models/centerpoint_4frames/default/eval/epoch_36/val/default/result.pkl.
+
+3.  Train MPPNet (using mppnet_4frames as an example)
+```shell
+bash scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/waymo_models/mppnet_4frames.yaml --batch_size  2  \
+--set DATA_CONFIG.ROI_BOXES_PATH.train  ../output/waymo_models/centerpoint_4frames/default/eval/epoch_36/train/default/result.pkl \
+ DATA_CONFIG.ROI_BOXES_PATH.test  ../output/waymo_models/centerpoint_4frames/default/eval/epoch_36/val/default/result.pkl
+```
+When using 16-frame, we can just change the `cfg_file` to mpppnet_16frames.yaml and the `DATA_CONFIG.ROI_BOXES_PATH` is same with 4-frame.\
+We can also save the paths of train and val results to ROI_BOXES_PATH in mppnet_4frames.yaml and mppnet_16frames.yaml to avoid using the `set` flag.\
+For each GPU, BATCH_SIZE should be at least equal to 2.  When using 16-frame, the reference GPU memory consumption is 29G with BATCH_SIZE=2.\
+**Note**: Disable the `USE_ROI_AUG` and `USE_TRAJ_AUG` flag in config yaml can double the training speed with a performance loss of about 0.4%. 
+
+## Evaluation
+* Test with a pretrained model:
+```shell
+# Single GPU
+python test.py --cfg_file cfgs/waymo_models/mppnet_4frames.yaml  --batch_size  1 \
+--ckpt  ../output/waymo_models/mppnet_4frames/default/ckpt/checkpoint_epoch_6.pth
+# Multiple GPUs
+bash scripts/dist_test.sh ${NUM_GPUS} --cfgs/waymo_models/mppnet_4frames.yaml  --batch_size  1 \
+--ckpt  ../output/waymo_models/mppnet_4frames/default/ckpt/checkpoint_epoch_6.pth
+```
+To avoid OOM, set BATCH_SIZE=1.
+
+* Test with a memory bank to improve efficiency:
+```shell
+# Currently, only support 1 GPU with batch_size 1
+python test.py --cfg_file cfgs/waymo_models/mppnet_e2e_memorybank_inference.yaml --batch_size 1 \
+--ckpt ../output/waymo_models/mppnet_4frames/default/ckpt/checkpoint_epoch_6.pth \
+--pretrained_model  ../output/waymo_models/centerpoint_4frames/default/ckpt/checkpoint_epoch_36.pth
+```
+The default parameters in mppnet_e2e_memorybank_inference.yaml is for 4-frame and just change them to the setting in mppnet_16frames.yaml when using 16-frame.
+
+## Performance
+|    Model          | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
+|:---------------------------------------------:|:----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
+|  [centerpoint_4frames](../../tools/cfgs/waymo_models/centerpoint_4frames.yaml) | 76.71/76.17 | 69.13/68.63 | 78.88/75.55 | 71.73/68.61 | 73.73/72.96 | 71.63/70.89 |
+|  [mppnet_4frames](../../tools/cfgs/waymo_models/mppnet_4frames.yaml) | 81.54/81.06 | 74.07/73.61 | 84.56/81.94 | 77.20/74.67 | 77.15/76.50 | 75.01/74.38 |
+| [mppnet_16frames](../../tools/cfgs/waymo_models/mppnet_16frames.yaml) | 82.74/82.28 | 75.41/74.96 | 84.69/82.25 | 77.43/75.06 | 77.28/76.66 | 75.13/74.52 |
+
+The reported performance of MPPNet is trained with 6 epoch with  `USE_ROI_AUG`  and  `USE_TRAJ_AUG`  flags enabled.
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/model_framework.png b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/model_framework.png
new file mode 100644
index 0000000000000000000000000000000000000000..a827609c2b3d3cdf64d968c3ef510464b826e761
GIT binary patch
literal 104083
zcmeFZbx>Syw<Vke4QVX6li)4^f|CSyClK7--Ge)X;2zu^g1ZF|?h@SHbsqA2-<f;o
z-kSQVzQ3kws#4X}boV*WdCuNzuf5hj36hZ#Mnu3xc=qfWqNs>~+_PseKb}2%-VYB8
z{=z%Y_vzU);%A})9~GRQ@3p~cDZngl?Jsc{ijg-(N<l;p6$OGRDKT5UO&O*ern~4d
z%V-?Ru+Rm}h`rCqo6t;%NwY5XnB6y$Z&UA^XOiB(&ub_<D0tkI4Sn?bK#T$p{o+5p
zJThbar}zJS8Xg?{-+WhCQ1HJVz&kVJ?SDDyi#AzV)c^fdFT9rX^8V`|KUgU!{BMhT
zS_C^a^?zRJ-*@;J|NV0PG>=!c`Q9viI6`jvPOB$qdPjIM6)>%eCGdB}ZA8fh9}4*Y
zF5kN)+#(2bFR@*-t-Aun)zlvPMcZMPYlTpR67{2nAbm=);&W)v+<XMXpFfYqY43u+
zS)eeZ+-Y*Q5G{QD_RnT{^KmXbTli;-+R|iyef<&sf%vLv%fIcgjS=z**84Hd;>h&>
z|7HDOb{GEJ<#u`h=TKUXC@IN^UV&<0e?6$vrc;SVkS3t>1(U4-s_jt+8}_#5PNH>F
zP!^ptjS(WKc+GlUmt^&RT-#*HmH20%$OAm2<A>F-67H@^nIOB-STO@;A%WMjrr}3o
zl)Gy)mGW<vOYVLovI;Tq!sxv?#oNCN`I`JgiP_!+Jz{cA73)pnMLqYEUA0JmJgU5=
zyRc9h-ei!5h6ri6@KB^U6MC}RD`FH~d62_7TBKJkKf^2x3uW8i=DVYHK_m>PYyW7A
z$g0?AbIH1uPE04Oh^Xr86MqFgI#-oD_mbaeDw84cKV0t>LxuZ<8*h>wg@4;4{~u3E
zgo1($m12P|^53>w36R^lbG@It-phv&b;DU->+#Q@e^4IX6v*wNM&2bdq_#xdr6e)(
zFxes|?AIR&{|N9Na5<N~uuv1V*|46!Jm7vUUx@<$V2=p`I89n#6m?FC?oZ8cTBVBc
z!(WQXQW?X&xu;7%>Wkvd3uAv!B0vd+uGAD1((2CbkZEC`;c|PoyzXFBZZSei<Gy9g
z4nX>`%c)(rt5mU<v~5Sk9*RfaEcV3#FY)BI(M-J#o`33^4qtYp>d^khXFiq$Hg%V$
zy!ZBOSm*N?{!G-|5t)}5Ov0RaR%BLxLD2d&DgX@)YuNKu#9UWl#DcKspEqB!4UeXy
zFR$pl+nzD-I#G1NTz8U_pA2xN9e+kcfSIW<z}Ssr$T-p>#K;yTCC#-`YY0Ev<NFrn
zpXN1TkXa|a=oE=a6Z`uujF|&h_%EYTX4SEBVapptT%k`kG*7$mffBp(@cc-%cE?nO
z{a$jCA$}0Xk+<syQ(R^N?u$EK<mYK{QQFvdm>*jrz7G3{zPEdzgg|+F5Is)|_&WTm
z-s^*v9WA&hzW2fkEY_k_zT5Rut%n`^MLnC=Xb7;B(+yGYDdq>DEZsi_EK(3(b<EW0
z^T-q7NJ|P*1!sI4tApFGzwF*T|JL9@#)^?`rtyGQy5tFkEOq~Vs}}l8h6yfJLJz8e
z7RuS}vGaT_?hYdaDxxrQzcB7-ZDN!0PfCK_9?!EUjJZ?Az*?)cxPI&LDr7>eVQ2xH
z@7Vb!)sFcQ(c9~p>Qphg=aL0;h3!?box0&}{RIAeLx}Gh`h!Vd2=eKTJaYL-Zk>}F
zed3ooEqcw_eF(M#ZVm4pi#eA=dcHeXP5Hl%TsUq=-*ins)Ufi!>;iyuTbe)sug{k2
ztg|mpefaQdyP_t4uh+uf(#bpCh@_hW<{ar}I9CDEdtr^Q8I(^yXz2q9er1ctePgL+
zy)<^G30q%+cH}(MVosvjctRR*dLg5(G*ENc)W;*w`>|3P<xcyG+rmJ?IWaL<PC;=!
zl*VH1`*8#cL0Vw?F8b!->O@27dTaRL-b}5(qHW7cXn#pAS?{(A_v4`00|ONQ)Jwll
z<4UpR8$Fism)i>SLoOyXeNNlqh4C!5Z$QRB)t$D(x76gZS6QU(XWyP~po(V1@a`LI
z_%g#=%v4-Y9tO8FjQxg-4@Uv34y-4Ax|ii^0hX<dfuZ5)hL2uVV#7ajD!k2iJ-byj
zd-`B(sc5LzvZb5J>1dK-L0qEX5Ogi!{0R8u)I_K@1bfFz)b`=)XvQZE*Oli;o14~~
zotx##MNeVml}zuB?c7CB)p>YoWz_@Q>~x@k;9%LcYqH!QN0cF1<5AUT;`4O73;zi_
zS6`p$PSP)<)bEm}JN3vHte#sKLOIX9msMAi5Sz4ye_-rGK&o);=aiTsXt3!`iFl2c
z7AQKx@}*{XK>w1vE$VX`KP9@}*ywe#^~`ydzyym9T9lEfjtQrE{n?MHZ>Zd^V}dM`
zQ*yFut?$P)Hf|_10C2)Xw}gzusdceB@`}W3V{f^@r%m;TBbp}M+zA9D5&XzHLV367
zo)Vzpf+tx?e8sPFbPiD(cqD}Q?5%Pg9f@0xRZDvov2nG1O>?iCORyUr+#6nyS}Y-O
zyM9oeKfbDD$t|F+;W>fuOYz}FwLO9onU10nk)*Er{-kT1YRzKt1M%`^Qp6G&D@h2A
zMH8IYha(EZO0KPOOUfzfo3B(g5!n@#X$9Y3#121vEw7OlkW~yP#fB^!=XE@LtGiOB
zY<_18akF?&SQ8ca%joUf%xgMGN9%-42q9!8FucnoV!3AY9EH#js_iSXupRzSc(_N=
zk1fbGm4t$dL7_pymC9Ge5cQs2<IYWobQ8=Bot2RyB@ddkp0KHXQf6|bLOMWRrYpft
zJ<QJ9ZGZgYpo+9%(KWW!IUyr0ey4$a-QXw_T@!G#Bj%>@9yM}AZw+5sYV#oLr$q<D
zv1`-bLPhu}zcUD1w4`{6!huPU=3nEl1RiiSYC`VR=itS;4xC<p$sSc>TsEBYPvfX^
z<w_NzIHt<4+MNtwQ+LfMz-`GY$O?3yo0RvsLg9@F09B`A);`01*h4_5XEb?gKf_9s
zK3(hEz)yleQIU+RW!?*O6={1Fro^sKCcA8QJ6zwgsvsVsy8HyUH5Na>=eWDOAmHEd
z<Ps3LrhPEeVIG`NjERa}hz-toag)sb9jq_0|Ce#Vph3^!shr}Mffg}E)_NrD_K67Y
zp54oZXZ~%YrMd4eHf}{FB!!=%=+Pnk7v)OScVkf@2c!{eYi_%aYyd((EtUDr70G(@
zw?X{c@pSI^@x6w?mJ74jUbh9E5wiO-77j{HE#BiicG}LnPQW-V&`;8;w4Pt`29r<+
zKMKkQ1_zPxQYFOdtyg6~ev@}PZsSt9TA+RLoG(o}rQLY*A*^b^3}n6L2c=t!Wb26(
zi8QzO^@KXR6?LJFHrz}vds+0cIpKa5>#n~is&pKqZnU7lMnT1p>}lr5=DaYOb=Zx_
zC}251P2M(BJ43YYHBNRt&0I5A?>Lzul1Yver^NnHIs40GDkgnXQ?fK_Sg7)gu^6)7
zbb~QQVasDQsMCO=0P>>LkWzH9u+3*f>mHC6vVbZo-uLQMndz4QXKA9et~#PWVY^PX
zt=i@yg!dA`1;T?SS$X98+dgGsH@0G@L7KhHoWP?L4&Cq9mU2A=f2izL30bB3XLP}I
zvVhBdt-p(3MM$nz^!5ITF4%@jATAZho9uMaWyfU<o!BL2>rt9CD85m)vKv9kFfUDs
z(s~e>HTPo-$9dfe+FQ5(38+n9!M=dL_mFT9CPkH%Wyucsw5QyVtYXRC-<G0!T$_yT
z|1rMHgpBP?Ley}B?<-4Bcu)!@x9f*;^iLCx`{dCHrbC8T$p=g-qY@YVPc3Rg<QZyT
z3(8)xf>541Y>6D~-_SLQNf_im^boXF%odcW|6{pUq8c>3y78Eaf`KPq+D3wX`CIRc
zzQI`XoXRkOA?@DJX>y+xKT={74;)y18_D7EY%qU8k?Mg|kb|n^W<@l-;P%qOkms{x
zynvwfn~;_pU;jjEkg!=QMWnfgHV=cBOe>b3(^Y6$Fc4r=cpTX(mpr^iy`ip}2-^WJ
zQ5D;=ANn_7J3Qzs(<-&sBjb0=rw#Y4nvgPc#>Pft5|S=oYKs}gw2#Q3`+YP_9WHvG
znq9O^tnrxyP)4n-_>iX#r_EDsikITs_f)};iyF!2_O;1U-{r@QhZrG!!3fhewp{vM
zegIlCji^DSSM5*xU8uWgMFcL30LEIoOx#dok<pk1uRbL@+((}4PBCl09iBmXr{u3~
zW%NjhyJNyMLxOL&Gm;b=jlNOoJSf%|5)x=7@O|MY0S_GHEgP?w=E~*mbhSPSoWo((
z&w7N01t~ED)B1}YVFM{u5F>Tojqc+U3aA8V;z{A^R_{4ynza#O;4c1H*nR(&{Hf=l
zX=}RaJ!Xg>38<iJhjW9A`>J`gG;Ptzu~M6a?!UHw<4BGf71U>V^E<mTWg8fbn6?ss
z87&caWd1_b)7I2rWR{m(@)K8B0sXPaH%n>$dhHedbW}h1Brl)Ra0rXY_P!|skiv(z
zE~##qwu|oJ^}(<EkoI%ytG>ni>a_!&b;{=vL&WET^Vyy5QJgHAV0JjCYl-Q`|0Sel
z&45-3Ph0TTvBLTIvNKn2+1#$e_}KBP$s)0Uo=Wf1e?8fLuqoMgtb;PxyO+^!@_{`{
zI*l+Qw>))&Is~a)cjCoQ`jq|&w56xSh)>@&)8Gu)jylN7X^h_}5JQ+QcX<vatlpQ6
z+!(lk+SL-Zw5xrw8&P>zczIl(%%b>N_ET>gkD2lj2G+tUEk&w569_&`1lah?KmD|K
zdx*Fu)UAw01tk^Hqjyba8YeuNmTuoFOPjC+WnbD(Or3O{UY1|<BtYNuCKj;YAvvo~
ztvF1+LBZ#G;^&%iB>~Fs9`VGG*0oK>vMGw0zl@C+)K$jcY&oe7xkxa*3?i)(qd-TH
zlUMTYS<lMJh(28M49lvL5DIIEVeI&=a<(q)b~OOygqso%>YGtaLRjvIsG>2dptEcI
z#g#gCYha6d7sGMAdgJvn-5s|jo4@wA*aiOv&HMPOy`L;KR^4z{?i%QVZ>Z?dLI#EO
zt>2XRyC@AONnZbPBfMT#U5S#1x8>MPuo<>DQ{zTfN7fFOlpyD%_u8q@aU*<x-SG2t
zwrNV<R+Gw5-5RYRM|+h2P2+(OcZpDQ>dkAA=UeG008?bkk6FeQUPGWM`P>4L)11E!
zu<dU-tn~VBy99zON=cAU?cR0J4i1+le!~$~zXQ;00-b2OzpCdjBd0O?l|>zq;uvFo
zqL6|aH#k;R7P}|kn5Nr{4n`T)FprMm4Wk*7Ol+%J>}J<PML>P0ZYCfEwy00yz*WSn
zD|cw0=Ms^!BLeLBET0uM`a+FMPgnPb^9u{!q8y>+<WYmq?6xMO=7>@RYMP;pN(vgI
zjfbW6y1a?<3QIms@y9Djl91D<MhS;+_}>zdkdcIF>SDvvU=8SxHq|Vj*N(7+Sv$^4
z=Mpv<_x-jva6e)rYQCYJh*`(35AQXpj2z(%z&r2^9UE%;7Hg(l?(3N9=?&H3u;Re_
zI>!X}_@!tl`{s+1rd#dT-&IG(tFTh_&NGO%XB&vH)E)5-o6cC^E)Tj7Pj%$b{qo|K
zVIf>$4dl%q#DvHPF$g$+Rc%sK7;H<k@0yI*6GUYcbas<5UY%9e9zXPm_V<y~QgbFg
zo&CGF+a4;?-$G1@{e*3D=rEFs*PqF9bKdQ&&^Vi~KtnpT&NHsnJP$JgPi+azIx}OQ
zb;u~m@YQOSK7LTMsXu#Twq102b&z5Rf7NKDM6&>wB9;5V&jgunSvPbjN(SYOwNleH
zId2*oHqDi6aN=`J`9V(tWq}y@mhs#@G`gU6PXP@2!9w`bs=-XrCHwKfE7AV#ulekj
zSeo3945{5N_#@?KM9$C68!m+vSx+1-^cL<Itly89TaVP(ZE?k;#w~uDZl>cqeaM_z
zwaDX_MW3}hlm_LOoSNRtTU~YfXv~Z|46*3yh99)dYMg%rKctg|RVzX_5_(AfKHL=9
zkVqQH*tSGLfGwZhJL9#7?7JY6F56Zb-Xqvd(Wf^TBm)4Ew)@1)0~<kV)<0S|kiv!o
zNZ$gg;BdO(X!E7$G=8K429XZ<2z_&X<+N6r)0UbdwDD{7q&d@5MCVGaY;uf^oTG$q
zDc^*%$4wHr$f($T3kzw^*1E*}cg?k>q4{OF`W>`X{vR1pex&CF@AEO}kB!1WLwUTG
zAH#7yZBi~`Yu};nzrIO{v*Oq-S2ho=zfl^hJ#)#>S!IHx%=Xzdr(7_Lw>%`vF#gJ5
z;4|p-TMY~hZC8k{iDS_4crBfYHmIXN?Hp$XiXkd!3IMtte@;7EH;n?FriAAo>l-9I
z(NBdeUsmiJoNoWjDYI^hLpq2vC0)rI-$m^qsT&0?ZIW@)rxp@<gfH?-3r{E@?ajB3
z3-`#&DtuTB9~|(w6)f>EVg{*y16Da*dj)5wPC?EaDSqG)`p_}lZ4mLJyd-w!u(9>T
z(s0?n;p#3ssA_vOs)PcPQwKPu&I%fi``|u=(Ed4K$k7;tYf+no2E#ekPU>N_Hz&HB
zmqflfDvbMA&K#Oe*Z6==1pcBNYFrT#(y$?jEVl+-qtakPSJC^lu6?uRIY-TO^@!#(
zj%&N{EG<iVINKT<txe3yp~u*Q(YK%+9+tghzfKO}_!6A)yWZWfojMrdUsaqEeI?h@
zKu*IBTYgjHUPMeZGe5Bpfw1}5=4Y{LSjnz(F<ZqfzO;a^PyMWk33Z<}2i|mr0T~0Y
z9aib!o!9ggbUUc{&R5<2({;u?{@OoGaPt$2tp$W+U9M2hStkY(j+rpBySRDI-|4KP
zu|J6{)mDVk;Vr5D^|-W{G@qAPy+SN&eKtVRcRD}5g}=HN5D@wvn;@yL!bG1N<qdV~
zWOH{8edjS$e`J$*vz?T{ec8ItzPrn)V$L?oTxs2qJg%T5Bd^$2guH0rdEh|abju-1
zW$b&+Z80Avb+Kt-xI!?sQdG*XsQ6kn8|CQMdU9%HT4s2Kqq1CB0Ca4JTGcb~TveXP
z5%YF1x3)C^<CK!Z&O&JcnGp}pD=juyj7zgNdelfwxS_(CF=rGM>an`1!#Xjwd2jin
zDi1~8O^*3kwD+zMr1`YUCgXWs&{ZQL64AW0zVXZA(RX00J)Z|>pe#|Rl=&x$wf7-h
zynD=ydwJD3y@y2KeCYfH8lVr$0ZHE8SIW22|Cz9T*XE=Hj$u@PIiEDgU?e(yclvE+
z?NNy+gmV)C6umY^Z0l`03D+YNhf+-;cYP`@rAL7;Qg9Mno*KTDKM@qi*e@r0*!M?O
zWmsS3v~8+=>}mFKzDzfT_wz1F;|l?&^6uTVW^(Pzti{`rQHYUesE-8QBq=i+VO08V
zveRVO)4_MB#g#86Tp{@OFU{-EGs%Y4JeY$EPE2<S`;2?j)m4@6y5;}@--{ekyF6<+
z<JD)uEJPeLj$7!yi^x5?9~X5}Lv*{Gl&{+5`1&i}2{b$_IETxpwX&>X<&NCF`B)2I
zy9_Ay#zQPu_HXG8mvR&p;xHm>?CGeA=X9v&E5jz1JpD@o0>h<Ob|@BeE~zrX<@jqe
z8V#&B-X2v)<4=cuu-a38+F^^NSURoUf-aWq-8W}4fn~LTm0ip$a9(gk<{%x+iv4*|
zkFQT-2^BwdfdHkl3}^}^Zp%0)KP^|R76}2~<P}qrfd<fDyKJpcI<J_vgJo;4eEw?O
zbb1ffH^p<cDH>y~((WR6d`h9hc>9HrniIw#2(lbrwp5Iz!A?fVmzu)?Uq^%QGe4n(
zvnc18x9MP0oe6_8w05}mAO|k%B!n$1w4gx-$Q6BR*_h-T&0<0=rowW{Z>KIiMmx`Z
zO((_e4hb&M2^Kf$l9X-=&rwmnS{JUx!@K&ES+fP?TcxCw90keM9pO|%fNwtvaC17x
z?Mj^2oKx#JxB-2@A6O96H3wZA6*SaykE=C0HAH*Lg1#+Ni_MH<WZiP*>jq!aZZ$_Z
z*>Y<McQbCFsPK8*Ao0Macz5Y!liBjR`E+S|_-bSOR_~6)StCo5Q?rHZzG+sZ)<&#W
z1jOHK*-_R?i>MIu{uduMED$*o3&bYrRDUIs@u5OsMf&?Tf>jT@b*e|l=W&QC%@<7U
z)N}c3Qs+62oo5~Pyba2iPo0z-%up?*JwkVanJ?BYbUT8{AVlvl<y#LoGpAAwd%Er8
z!%e5W<_J>+KqEXtt90V9znm?6u(6z~Hd(4X;5f#&3{H|x=C6Hpx^m6U@xTedqwI^h
zn&(ocnn(YFa*<Nvm^}dr%JSN~9;-Q$s6a`P&V}jL1_tiI#?qYCQ<wOY&igYW&SWLy
z?RX&SEO!MQbgF0u82{Qfv>o#eBF)YM7c+f#im>Rk^M+b1WTwIa1FSe1F4dEua6(Al
zo^>K#3f}>DwVUq)XgFSY@Yx!Aw4c>z0KKzfCzpslIZb#jT%FN*!6JwI_}Q(41stUQ
zlAtvVjoij<vqbB|pSJtPhc%zQ`KoYrRkovxwI1iQ3CHr8{X5L+i5Rz&?htVQ0Gw?7
z8UfLVA6Dy23i>Nb`d<_%NAfBfp9;?)PfS|COb0<b?3~rtT!k|(vEr=+Me_A~|71QS
zAR@$17;LV+Dp_A1LM*zuKm)yp?(ofeGRqBf;;zT&Hc;H{jWB6Eb16x)44BXEtoJlp
zARUZ(l-wfmh0U=o?;z+OWLeu4Br`Y5Dmm6O@MO?qijWq7NGmAIUS;|UaC#k+g>)|6
zL#OY*;@R8L6|+yjYKJykzewYChw~^60GTV4WN~jKYA*OvSa)!K*qRHEF|2mY;0B2g
zOK}XoQc%EVlJ&C8@|4=4L_@^mfE;Om_EX&R+~tD8HqcSWPbOYP+rfs{qlXMi$-kt+
zIU}&9zhx_a(b1}+Dmr};^UA2Gu$yIh;MKPK4z_|~R~97#z$o2u`^(cslBvu9Z+Pe)
ze;6R2Z5m*6f=Ja|v6(y_9FGeEl8}2uv#<Wo{a<0yQt3sEX`se;VE*}07w;<|_hr+H
z-4Z!nCh+T0Kre`z@M;=H!R}7xjl{X~84{yDlS=E!nD-;fn*1h8Ncl4yESsnG383qs
zz1^^d@aIZy63#zfU}X_Ga<^oOw|FI6%~FC2oG;}ropo3iS(NZd7iM3*H(QqY(J+F{
zaX}2YH%#aF+{w)yLK%6~k~44LvR~eCskCgt?G(~XS8HROPbh-MepF%dIAx2=jI^L&
zWZasdAu|4Zc;!!Az|fWsI}~K?0vq>}n6incVk6@;0zfWPNmeo*h1y;pSLUxB&JMNB
zn=4>qd{4FI9`y5mj=O6rBGLchV|q>oga@6}bBax)@gBj#<;I@#g4Zh(1BZY=et7lM
zKWeBxpIbi=ey`!6?05ty(cjW?(53X`Gzx<z+PH7kq1^2OACMnvPXA+RZt0e`+#}?d
zj5ps4^GYaJ_j@ByA<)0(Dx?1BcI!~nv;WGlG}IOoG3i<@_54u7OMx=!`HPDCXnr@e
z#R(m#pngk(D=T;wxg3BeG@Mb8FIv+95;48!V3B%6inU;chZzz>vdy)nm+E$^i!Uo1
z|N8rP=cBHgyu$pI&92k@;*ynsgfvErJE(Yrrbo=DJcSKo_X)D}5U?}D*ES4o@*N+T
zoVTChsKra6y>fvCtp*KHzkk#<`$%o{*6@-8ZLMtgz$2dj_M%MhV2WjYqQY>_;0e2o
zS9kQy)T$wjzl@iHC>HO&O`6Z5#zued8#{FCYx*2UW;unVM3>x?y05x5@+jaD*0aA-
zEE-W^+7BzivCqW>_JsWlP$1Ply#{U%Xc33%zdR*z@W=Rq0^fa=r}P7w%X34uzzKs~
zjzOLD;rz|Ao7-VVyOgFRCF{|xexs7P<uQPWiQ<FX&nUloE$zOcEtZ6ns4qF)pOcnX
zDob1J5Q<#L$FlCtXV1{7HEZt&O^Cef`=Sq^ox4fq^arV$D39*fOfsZ@v(_-e|M9|1
zxh?Vx--W!u;Lp_FLhXy1%G3>qQdL-N%HZ}^**HMx233mKZjzb(=hLv#fkZafm`D#L
z^@j6JP?F{qdg+{BL4iOI#i#-$_Kyt8H2(sdcHqj8t53~TYIBe`kOX@fYK9gRuuSb|
zjfq%}d95_6|Ifsnv~NZ653dW%dBIB*hG>sJG&fjsUI+@YZcn0qBliO}q0{Xv8&3P4
z=p|VH_J7X;%yI^RAQuL*2K%mQelcY>XqGFyeby|L2U8_Yuemx}e<Ml&y-FVjj;V{B
zeC($w1CVZ9i3pucVKED1HbB33AZ#fsFB_b`o0r(Ve#s|zW8HIV>Vn9D7&0Mb3rMo=
z@BAY5Bftf$7#(SRh6u&~G^_i5`Y6%l59t9wL6%_7_08T*7-VQ(X-O8a71z!r62;6@
z`0d<Y49*ZlP7_4NpUD89kVz2}OHNbKtJfri*z%Sf>0thot^af2)5^O`lEsh(hz@h4
zZYT`4Bcf?`{kH#*ekMcsE{A(?sG8>2QgztWK~p$I?xTp@7jof_T@zB;x6H7A9Wi>q
z7%p$G$q=inpNd5%1^EcjrrKE~R|~ifR$C8JzJ9v&fHIN-*75n4Wc<`BxmkZ=49obG
z4z9yRo!$OK#0KZ(UPoP=rntL0;(o&!F*f{Yv^I8;YI7ikMN4e`4mIeSEnLrV=&>Eq
z61&<XjS{<A9r=8L2%WQsiW=Hr`@%Gqwv`d0M1SW+W7&WusHk$WdKu3W;%9o`61X+Z
zp18}Zg+kIDx_&#%*S>lFRH~~EXMHw=G?~M%vh)%wl#9=ZY!JjZJi_vuOZ>|wLN@-K
zx-hCc?bGS4<4fD(%y5L(>ESc*xggUULg6XVrwc!#U6UPj3oW{BXnVvTzXaYIUfM5$
z2tIKW1Jm%>9254pnT*@@;ZwU$2Dl$EK401j^k^g8xGTh<>o7xno_yq)Qa&m1MtTZt
z*qnkDel6@Dl)hBKpX6LjzvxuYjL>3!YJ297`nG7U^$-43-FujZnq`YT-QLjrqPq=U
zohZ3q2jfwMKEk8rhbW_S4g#_Y68<If)>qH8X<Dx1i~Bc6UW0pXM<B0w{07qTK^8r+
z;kZt^j4R`cRa%o5h~s$1eTU~V@*dbx&m~eAkp=<jD!IW0291JZbcCp^0{)0Kd&a62
z*Y0$a2hKW-vjX~ttcwv}jNmo9DUJ2S)Dg137vSxGii+FOaqy%ibP(u*!lmCfD{^)@
zH=w|WBfbI@ZMax+^B}bGjPuKD1Q?D+&-a#6wSM<=Y%(sC5r>OO?fb)$f)gxHE3fs$
zqG?WPyI}1Haoo9U(z$9ux_oL)>QRbSQ~6w8&IGm~X9OtPWy;0)qQF4WcA@M{zSPXz
z{2~8jG<{c6nZXy8`vSg&iU4bzSKQ5LXVNeJQoOhCPawMA_#VE^R4HJ_K&EHG!Xfa<
zQ85Yvg8Bd>L;|sT;whp0Zf%_ZO!l+C1|l26&3$bOFzRH{U(1ia6c_7XE=fjCa9b5z
zGhY(vP3MmFyoK3ZvSSd=tZ(!!1D4FwsLW9VHlrDIZn8W5qt@5q%`TPCPa^CzAZ*o&
zF-7V(Xc%OI+?Rji@EzUYB-mV``Gvy`0ITwK<{>np?$4)Y$j-=$=b3x|<S(~F)G5p~
z4;&<d3r0a*0?=uT8ZL6+bf>}+0wXBP$gz9xs~8i<e0*V=S6t9K?)bY`jq!jS#JuKf
z3^d_1FMcD@2oH*tz~JtMExpH~{jGW_eo0ov_3Zf3%8KpZ#aMK*=zhS$?`>2WdkOnu
zZ>icxhB6TBN`ZXDVihSp(@Ss`h>sTWSJ5eZ2g`*}5D|?Q?+ZO{ykbGY$}0f9&~$_Q
ztCp;ioM5VmrQy3$L!jscg@>&k<Z9?G=jYneZapPHcUA#4k}xTV1%^*k_F_l}<g`>x
ztmJ7^l8nnFMQh6z9<S1RBpI<=b*fQwOfZ4k{1{rh#!`}Y#;Ymm96iq(0=iRZH;n>u
zgnS@4?~W1yBP|EmC)ln%0^dg~fyzS|7ge@1`l&$t)irCI*0YwWC#lG`pbK#PBU8Y5
zI7<722oa7Q{oDRD&0W(0eG~v9;C(mlMOPanu|vs1&f0{4CvE^!;*B@U`eiNr6?gnz
zxCj8Ar|E!l(R@%R!k-A-RJJiDr~;cS$H=F9gIK6X*T8L}JD-Rl&B_852?!=6IW63g
zz;R@Rw9_il7|)ph;%+ACg1eAYikFu6NP`2>RUs^7%`)q-FX(PhHlg2T_UKG?n%mN!
zXI*n5jvqOKA2dkll|dN~;fI4lS0o!SsW}w~N}4YY>?87o&UzZaCipR0V27?=n?pPb
zff_y3ykn3vZ%gN2Qt5cg8SbCHyJkKopzP37@=u>MlKq))sO1C}Bq8ifTtisyVPoWd
z!%thQL;7a7L&*c@4SfYgk^x|=7x<kjpWS~(C!CIG7zqOZdiKFx`vOd~YDJ8A0Jo&1
zH7g?E!h~NR-R752Qkc!%q&@W{_pr#Taajg>z!C)mI5E+o5=i@VK2xjRs}8d2vi|9K
z@pA7}GmUYDzb8iefyBdxUoI*nL5HLpV?OOu<Ooxhz!(d<U>D;jE$sM4!uKUY6yzPh
zolg@2p8LgOKe^xXpgy=z`W6_oII=%JW8h{_J@3a#-{m|G5}iLE7Z2q=e5;_istgZ%
zKdz*xBgY!HHSP4bH30|+oCYS9eN?@lhryzXir_SQ)q;#xXW0ThWGAK|iolXL(9s`M
zRQ4OJPhX|UCf9_R(|G6s6##vIDF~<-9lwq9^0h$kkWWvyR#Ma4VN#h{Kus>FxXo&Y
zzRf{!#afU9JnC>(#ffJyQG=+xzfViM4IGb5ndj_3%Ax}q02nNOhfB`>Ph=hyO}H?0
zRYby4(j`giNs;Kwa5HGUdM0U%NmF3ZhK>LY595O$R1yGilmOu92fZp|X|qqvAX;jZ
z5rNPwA|k50+b8IDx7PvYRe%VO#B)35{+p_5d)^03Mu9f78<k)%q(<|jiZ(Af6jam#
zs$YRIV0!j3`uLIB%cm}2P76@H4bF3($lJyEp~b;nr{fEn-{&Zx=<}Dhb@ZA{1(AS8
ziTyiJIYGY2D@fo2J-|`mwUVZK8$&)l4LL16Q^E%N14raYx$imGIqg&T?-2sj3<=;0
zvaV+XPSk6$<d-+i{>4VErvv~NrZo$&CVm!u@fB5M<-9nEcp5@`SOu^(#B*zNu8!t(
zH)#zNwx31ng8b@q$3eaE!LLr7cZPOn%C+gdzNRq(+c`{{xNz{evm1!+y)P1h;o+vi
zKrUISRXKD083<y(^M#4n9vkuFD!r(rxm#5$u~5LoQzUyv#x)thP=KsJ)_(FIDG`Kq
zDusVgQ-l3sG!lD?zI1&A$~%VVEom~8BFYL9w194u289Nbqa&o7;7YDr%>1gnTxefR
znQLVfv2@43<h#X%k|qc$D8Q#tT-T4rAn4Ys;reA_%l!m*I%6xA){_caeI`p`ozW5!
zY{KY3TJy|pJpvk2?NHNI6X>vv=bHF{(heq><TVw$)8{3NG><S;<AzDLF0HlZIG-f7
zztTFJSJ`66kipjV=hL*O;R&5;Z8;fi_j{;LvfN|U^~34by9iA(U@C-IF|Jgo;@BK0
zc9N2=28Ih(Dl^d~aD*{JLCFg!Kpccw$Q^Z|2tPy7y@Y{cQHUY=#|!f^Dk@DcZ^n!=
zDAy|g0zfsyz}>qr9ck<c7+8vE<fsH#((?3%JVUc460lm>u;gGS<|+#vs7Cpv1yA!9
z3TF7gZTNh06_W`{^cnj$I?%L?+ktX$G=udmI=1&^ZkWOE8C?~QO>xj@I$Z`eIG=S`
z0!_UtYpYd*5h8=0$OQ;OeTi<L(HNmTn5zMfqBb`A!JMD=7G<!QEpKT6FpuMD+3<ib
zhhKcPreKDO1Vk+RnDtJw>ULnNgI*1ARYd8`C=i684?kOQEd?@<<MD0d{Mq;}glF^Z
z7rD<WQt>3z{+=;_V_sRnVgvwB4fNrg%YXiYWs6V}Qc6%zSZ+>%xBo0IF5XU%v_wvM
zdMXxCeH5Lk5`aQcs{CY@J4pT6&Xcra6q73Irt^C4eUbLycbIrUpt4kG1=!y*UAt=}
zBc9`nGNJ%-TEvDoD9HY{?r~-c+o^AR-g(=|!E$ckVcy*BYDW(gc8>7Qb+hF-h1giF
z3gsdYt>s+!KaL#jSrxJCha$%nV$dM4j9r~wnJTpSh_1d+Azi^#V4AA;(Ea0vwfQwG
zTLS?YNt54=WmuWHwaBg|CXx>*;Ym1Z$FtkFeG!~tRfO%|c5-bCGCU_4DFtNF(|{8U
z`u?!~@unq!(HxK56lpyPe2_}tCYtg5Tz{g1c`?BKW>Q=-C=mwt@0?Ljd7UD!l(y|S
zjul|jY+-~u4$sT<HWV(*H3TWK*ETl!$lg-JHjsP=MvSQOLaat^=tiaJstOpj@P5vx
zC>Q_`XEMb_huJPAr4WmGZN3o5y&EQdNT-V(Q>b9#wEI|{{W4aVeUhaxX23z+NLkZs
zcvWY(8w0|=`squB!=i+e$LX7w`6_1|_7Ha|0)C*?sGV2G*CTSKoLaa;3hBHzfOaDh
zPl%vhp=_?_xbyL02(dQB{;5?2Ga$fO%=#AG0rv!}U}X7dYU9#H<>Gu3m>3d39}l#Q
z@n70a&nd!=qHAya&4l$DM7=E-^4K_fSh!0`&kXn!|5kVP*TFQ#Q`2OM<t8ldP^3PT
zzA)E2*Bk^)I@^K*aN=S$>qmLu9CuW?4>Eh)nc)goT;v`eIrFeg$;fw|Shlq4m4Dfu
zKO~jdcRlYhiD<2|8H!Vgi~0S%`c-o#7;(r+mRbmsH0v4>>$l*yaUsvS<KdeA>GF~;
zpPFN!-!Xi(*K|mK6cJ12dkc}g%h2(-qT;dxTbawE*$WS%K^Qk)rJ)4D>Ei|0(^5d<
zQcUCC-LD75gIw$sTm{obe<!XVS$7HwtytMWxD^{=zRGFh`dOj9{u;PXVBE`aDXq7I
zL0W&W7H^x!3SNc795B1gj3Qq~{`c_WrYT`m%a`&lwO?aFP~9@{b;RT%E+6y{uePSD
zC5nkybS3CAym?cHQNzQD17cNu>iQ<?a=O-pN?1<zOK3xRWbBhuqN*N5RVb+nyj>1f
z#0H)h@?aWuzC<W|?e{|y5FI6gdZ~;O{W7&90*R@}Frok(DcRN2+x!-p!Ve~yKv(v!
zHvp)Ci~;nKX9&RC_-Q@CoLYBF4hjdLxX&q&4M!W&#)=Q0p$q;|E}*5+J_wCq$}i{(
zWYPsgiJmskTOyu<alg%4AqKxYdZy$RMOZyFx?fo;|8OUO*)Mn3szBYBv<2$sfcvG2
zL$P{$VqMy*!=!Z;x}yHD|GJsR9Z$m)gYM>Vh|v(Bv3~-)v+}4>WtrmZQ=R_4{w$8~
z`H8_dss^S24_t>UO&8=Q8LomW_+76Zx83_F6%;$p?zy6Xf{<QU-zNtgQi+2^vZ9BB
zcc0pbb7Y90VaKn0mn&D^XNjvZr<;o%CZWYXLYs7*rUZN$us*vDmI86)F$HjVFpeZE
zujC872r$(TMrIv#Nha3G6yoA!)HOcYJHG@JA{uL>()|2&4vz-~-{93V;8-U+b#s7$
zHLYr46b!5gD(oZqviETt!6z3-BUvw<)WYJyz~=A69=!-FM!hpX%HU6oD9cI447R}9
zOQh~y8XlM;*RGf)*>c=bx%(bHU`F3!`4+ehTb%DS+!H&TU5!MIJ)YDA$<{-XfeD#m
z;V2?70D>+b5he9xbmUy{+>8KiL}DVl%k5;Z15D(NjHeFn#-HElfq>3IlfskKlb{u*
zc|#WVKq91lQm5|rFe-3izVH?J19iANS9dYFl)PnFX>)>NN=QHw(b}25g$AR0I<plz
zZ(bRC2s{lj9iE<yVI5ZP1gOLV^31VaR9;1j4gc?b`Dc@46<Yt?H_U)v9ZEI^Y+BTz
z8=AA`6gi}@Z`L3GQy~1ORt*-5wyzpBB&cvyIw?qXsTp`fmo_`biw|F(ZSkQfDjN4~
z9+$cN;coX%f2PfIo8}AX{D$W_j7Ck;lf9^1u0;=bu~y+aN3OH_+DJ?m9bK_&+;ACX
zsNsZD;y{R^l-Gj~XmVdxY}m?d{-}LnKD~cG!v9912@dI_qT*ZpNV|gxWO;sbM81JL
z=nh7sp?#CJx#30ODSL@~f5@s4&=C&N;B<k|V9D!@T-qaP3mghC(>d>c%`tyb^X^(_
z*3=h+aDMm|a(Hf09*UqMhyA!S_i=ZC9??>&ZgF!?;uN{mkE2!(3E-jP@Vd|fLL&`i
zBhC#8*I16RRQ;hU!)uQnQiG-&_(MJys3$kha^4*kRE_7bvgI$;oG$fN<e7~MK%-_n
zw+by0(-q*f`9L#Wb)~vP(|=T-j6YlL7eTSu*DP7clubAl&YK(l$4JH8!6l6uS5WrI
zb$-Cj_{A=kn;^eE%=5H3kQGWE_iw;lA4Fck>_yKd{?~j(B|IrycO3Gxp1}a=`2pbP
z$tcQw9bV1JIC=(#AVM)J`8xFgAG-UVbnVD-ycZ(MQ$mol(x|pryZhE=ind)vJ}9@`
ztFhQL;o>(d$Z;R4TyaZIWNd#bx?CQmB!aQnY}@IdE5cwzDyRDfcXBAH*A*G)REjv-
z8lR!GQ*Mxf(;&}?vA%wC!2h|@^3+7_oK*pH{d8eYy3*<<3ELlxuK&vuH1)Vk1VB8d
zMv0vTcoyh+m8TwjIQHAE?}#pzU-0g_Sl%7o-Zt1S;6bLbGj;7kr6RwfP+@TYAZ!cw
z@h%QTz{Y2S$BSm!`hF075H73RwBY{u@OaBz%40Y@^udnM_qdLp$fr$YkeqI&+=WUb
zA^_(RUvRbyy@1uoe`@#7?+eKyRmLQ7L+Qxjb-Qmd)_!i1oDRARcPe-F1~`Thglqc&
zt|L{p3@g;1DOlHOO*bSq@A7pivqu_DEeqb8^|4Y3_8;6&mu*q}ELD@RP=bvD$vxGa
z8Zu&*L##fPaN8xE%vlvdPS6k)6*)Ay{))ZU9j*R=jFJUB_2h~eH4_>!qPOy(ZFzF=
zjdx8is}hv+Awn(zpPwm#lo<WJP_EOpdPq2N3@h@*+V40oJW;aIyRxG*`dib1BlI2Z
z*3tT8nQ)O{G(d>pAmh{d?Ap3sx$!Z6_}-#_$u4w&@aXE{FsNq7K|$insXhLu6M0xx
zv*vDGH)Kd=>e9tI#n!VY({&aw4U=>|>vmd<iCZkosMHe{A7n@Aa~M}r+0fU{ova#?
z*9`PmY&d^?rfz;A-Si04rv3M!sb&A(-l3Hfzf<(AT@5uU3PUM;{o}num5WnaHx|iE
z^Qng8SMaSAw9E*DTCKasRu2Ql6{BPDFC&Q4It+Ag)*@zVbzWZMv*BHvuo_8xS~tPx
zt4f1v_{n5`;&kA?CQ7Ts=a!7b^^V-Ly&TH*IDg{DOWAC(sFWB{S%?q`GWRiek`n#u
zTBHyDOiKU*uUkOw5;niKMrmA7=F$%nIi3|XnkdVTldge>lXq_57YjGYpQ!w#FK=PX
zbvtkyHIU_bufO{CO&E{n@Q4{-t?3yu8K2=LbGtoDV?M^n8_UD-^q)*|J?eNLB_iE4
zP_O?g{`i{W%=t+6?hfi`q)B6vUbQu1Nvd1BCX)rK*ZiOrY~sl}oTcr`(Q~)gg_Fx(
z6q>EO6LoHXxCNQ8Sw3};j6h%?kku*P-6O;?BvYgvoUd3-_lilc9xbK}+&`FJ9L|PE
z^qLtQjgbU~QiXjWrhFcLxY&NYpn63otB<zz-V9QmxZouh?ES)<f1A$?5+wv#xeMY?
z=4cEE>Ub0q5r7CqCG4Fk*f5tYqoETDwrJ}HQWEr}rGE@a-TPAcji7(i=6dfXc?)}d
z*_N^dCXJLTp)uC*AL|B2Y{{yYOIa}rg|;3omJ!{p{$xJuZ%|2}BNJS_4wu}+VbUVH
za8Uy3xS~RVlxk1lGt#*b(IV9~D|u!45-Gx<Rs2!!B*aIwCAz&_JqhR0PSd+1lL<Ra
zFGquvfeR|_Xmq{SvNHf0HsFtZxfC<TF1fANDK#^Fnqm7XR3LTI*RJ8Ty)&Au6uz+j
zZQ^>hw>WXbt5uuU2U{L$NL=J07^WXkno`}O#%*)iJy(d~YD%={S9x9{gmfC$fHk82
zF290VE?a+#a>o%0>iqY?*d!mi_W`Ca5nknL6o#L9-e7#n)cG}akP0_lV@x;qneGi2
zNf;4DV}t>AK*{cw218=ohjahIyqPSrcRejW{o7hov+FEob2w>Vmu37L&r#Ew&Y&A6
zi<kTS&^O#UQoSLtB<Eio^?rvOYOy3-(~(#DePjsR_#G~}re{#*IA($=l_FBpCM_eg
z5KAy!Y0S!{L<dIA$k5RV(fWVO63M3<s3R1N<Z(na-&1KepL6F^8L~H9QuJKzXAx(!
z>{K`Vz-i)$iwK~5#%@&{bMv~qc*2(suA1)-$J`;gQW<4|a{5kMopa+Bld*1z20z@>
z;6xhiQoH0Sz9+@>&N{zS;j$!$QGt4~ebcIY=CKC@vXT<OAV558I&E2|af`)pS3*~>
ziXcTWrSYN-vD#=uVUj^r<YPS&VMNlv84bG`+X<`1L@Jl-IfbU0j=oq-5Xt)4OQeJA
zm^i|^w32(+*RL_#x}x9RF*~xgSf~YnTc^3x^$G}`a=}M=*s#1zp^gts=m85un}=a~
zhd(99Ef;YsyZUls#Am8(a6q!=jg~`Pc3@<JO<zU?ZybLXV!CU4s8yHpNIl^goLv&_
zz%-}Y+Y8d=o~e1Iev6o;kYKa7P#%-bH%R=R>s6Wt@?DCF{Z3S=WLzrkwK42&EZtgP
z_N&ITU+!S2%V)<P?|V#Y_tWXXM6yOH960xCY3N&a+GIZdt{v8G9w$mok0T_h03XrJ
zC9S!7MHZ8p*l%T47iMz<1%9@_BwD;jSvc(uB?oW9jyPPjkWFI;C^Y-FqadDT3{$Uh
zQM>Yj<SEfKy}TPW7|<5o!z_7m`FYi+tQ*v(Pi%Szhk#n>CSmaE645=8-W?98I~Lkv
z#y`-vI0Z*opvXh~zW#>K=vVsTaZ5OEzTg))?G!DCPL^%}Md<2@YfI5Qn0hW=zOH8t
zvi1Z=(x8CF=wt53VcASk)o8gphU_C*F)ulEK`K8v1fpz~6By_K>&K8sQbv~V=KUU|
z3^YFChT&1t#<<yipEJ}Sg^k^ZeJHbkVd0|=eq)l4=W~W6VGWW3-E5RT`AwQ!aBV@g
zvFD*K%dP{T*chb<6(UhKx)@bSfE?~MOSY`cIk~yB+dO2$jV;(mbxLb^usbzLlDBdq
z0=IX6`{eg2DLLzl?4x631nt(4N4<>zhH}Sy=DoU^WR9w&$8`9RAhJ4f&o2w_;$IFU
zuXQe(Cv%oX^>0eOR!yhoT`!zkAGypq>A0GjX))$-R>4`**I(8}gwJrke6FWa*Eva6
zDT^S3;Pu1lZ+=tcsBt6C+5a(#mWwa;0L2dFb3W$;E*lA3`fKva1aI#S5+|KKQl&@k
zutD+7NbB|2NUDS$56?S0$$n?)BI7PuV@U<1*!Cd(v19)Yfkg<Wifgqca_{dJ<SW-6
z8CK(UL<m?A`XB?3nyq6zQp^CTK@S7*H#aby!(uiCll?=%4F-WmKAjmE;+aI(Lfg8z
z_OJj<1t$qwuq;@D8}943Si{L$GgS<AP8XzvaUre0tqLYBKgD_RQ$;DmWTV4SWgE@r
zQyeRrRJcvP-&?8(52cB-YEXlQ_Ct{`;T}BPk-VRDe2s$t>Ep-rKjWqgsIC_DLDf4p
ztP^-_B*g5|-+mbtJv>K`R<5KcAs^DaZzr0rcY6b1q-^e_O!*!jDI&|{UT^lCP{iyL
z8OVL&kTkwa-c#yx)rGc<Lvq`PHCg7;EboIZK6YP(3DdEX;ibGUr7wk|g1kZP#jQRJ
zo?1<(6dNnMfkVN?k#vhmRInTm1z3a)5qk1>tUA{93?rjbx~hEz1xAYaDF_KKsMG(T
zu7!lr4Eqc>|AH&!7PfG}RGp+X+?~ZRf`uZi!AmRqMOnY}KCoIG(`s*-H=Jl*7-Um)
zkn{&C!GMFNUJ>#2kW1mnl!Ug~>{rSScF$pAsPTHxN}KtsInS@@#HbQ~?u+EQ-n_G%
zeZRK8!FW);dA--8uY7;si=~;50O0LiPXx2`o(KXnHpK40mXBW(KPbm|?VT!Z<5?TD
z5VC7BtN3PAR7`K9@e(6A2*6{E4qb+@Ec@I8%IvFmMu}dx{=7b87KR&GY2V|LIyMyh
zryEUhU=julz(ZBN06yAxHY0}9dANFe`$#o4q}ZG}X4FC!3_8~B<f6~U`?G8l3Yik<
z6eJ78BW{V-hBWRO6D2@RQdRar;t|P{MNY{uPV{dx>Rcfbkx?eaY<X59Jk!VOe6`O|
zXSD;Li*zS8>u398SD>>S6-_g;0>e}bq(K?iforl5foo>;Tk__&f<ZxrzY51D|D5kY
z`w7a)I7#3TI_YyeBDn=eqruf#tuP`rgtdNS1O+`^Erzr`L(E4G9rHCR|D(39k>{a|
z6w!2Vb|r<?>?wQ*J-?mW+jZ)xx0JNsflsYFz2;Y|P9OaOVCA_JSX3F9DMlms?A87{
z3iosxZmibogpuNMpP1zQMWO+5g#>r@3XOb@j{SfVpNo!m#lL3(5JDu2x__6;v)gM?
zU`Y{f^!ZiB(<zB{eSTYFV*XZV^*0L?e~;7~2Ci4x=&0GNlzCvC{^2Qo+x!jewnoSa
z`B?A>A*AIFx_wFoq7JiU_1x@`s2K?nJ;;lX$CUSvtGF9!Hp?TZcd82_B-UQA7QXyA
zgh&RpsPGRu_2C^BB;d$#V_q{lWQ;73%-(^+KQ`>YdL^Ei?(r!FF*|x@Z>ML}m<0(J
z31VrF&jjCBkfG6XYRIZjcT~k`-h$TS0UA00RQEbmz(F`vaBA_90G4}reN?`?lNCEw
z1(^Rcnf9cja%80Z<{AwfH$2A}+~}5XFT`$5!Q+Zcvt*~&p!Zb^lfzZw<NHLCAyTB|
z!x@$&Tk=Uzg8ZFdojSdP`9kYnDY@0f6%+HjQATWtuaH3Gc-c$!8whc^31W`qO&|f@
zq!uHt^HyD4zFZ*^7;Joh>u8}n<fv(Fb%|8*?j69d_RNfYr3S(#I7Bk~a%M3qH#6rt
zH|`U`p)?LbJqcqCx5sw~4e>Uy$ePYbxgB1kEP!ECnY_2?tNk1hX>&uW7_~E6H~cKe
z?T!#U0YpkcgH|~qmQV{_e}N}bD766Kgi=gH3duBX$Z&i39f#`*wsYM~{2=D1b`s;?
zjjWCrJJNu`Q)W|c6?%pn3{QrETP+lN@B8ft9$~J*{hg*K3v6i*f6XoINWHuxf(=K?
z-eP?u%)4AGy(NpMOR!H_{%s`|Lnl1d3}rl>f%xF{aZUB&R04DGA$5s_oh7tKgHTW*
zU)ilWjWOJOSIL^yAjGeA8KqA#Po(&!SD_?9K+6gqJQ&PC)gIXOqn5yo_wbKaA{Kxt
zpYia~JT&v=mVJrv0D^oA=tmuy->i%6)&IvJ`ipQO@*j*n5xtf-mK_>?4GxhryvLJ#
zhk}AF@Ii(V6vnuN^Y1YU;dJk@vW=l(0#>k4zRCqZVyaK|)_xDBW59~)l@M`)1`FN&
z1AZ?d-tnhQn<Wt*9zssch!VZhB6;c3+c(e}8~3U{+*1n$Tr+l<5FoE~|KG0x`)@Dy
zWE(-(U}cU7h$yqa(f$LX6LeZ3#H6&|c!E@d%6jP90>`d6VdQT!bgyVAB}qtWlL5<l
zx<>FzF=oWSum5`cua{s!{->k<^Ob+Uc<YS(m%qL6`k~D7?=Sp^uTx_Fzrzy$zfAz4
zMTtq`J@@e@`;<0`^SP=NCU_j*ud8#k<go+b>)YD(Y1>K_-uqW_!zrJLYwNH>$-kU6
zrd^qc!dnFD^GWGGhSPzL;)9GgRi0WQeabCwtETz>O5L7v3&O&#ATed#>jH(`U3X+1
zMr@RMM&nKCa)X=P+7u*^N&TS4hBWiP45Rn?(aH3-mH>bDXR72Y1K{s~MNmm*3Pnwl
zPFe&m$3`XE_+yNbub#W0!dvhf6cQazd`Fo_c}L+cpd^I*;$v@(`+^wfrTf>GS!qq_
z<@sJ+cxbY@SjGO@%&Dd;80fdP+83p&?Ijp|Bq-5VVzTu*h`4q;5&xXkiH8tOTgxqU
zlR9que0V0F<JQ+D-*e>B1aBc)f6%pi{#v5|qE6(3382PGZ_|KnC@pAeRmiHtiQB@B
z5a$?E$<q^DP0zl=X)oUJs&zvh4yaFVu)M6vWxtHTC!1rDBvu>*@D}W)Jz>%l<oo#}
z8SlWNPj@Sb*qd#6G$P<W2I&b{aRmj2Wm?P)!9$mMEZWhha{4dFZW5tOEc{FXmyLwv
zN{F{PEN~QOG+qzo^(Gx$VnqM;TGad0O#}HF6;q(j0=P&GvTO-`)uu^FUGkPy)!`#C
zrL)xGT=#LxE^4$g?tegle?tX&i2q-N`p+MAWxV<K7ycK_d@MKmUvK^=D#_6P-*wo(
z{Fs<B5)g!oM*L(4F5u$+R6cqI|LGkz<v?nZPlv@SA0_D8B*z#N=s*KQnf-SwmM1su
zI6<LVxjaIK2M=M}(2Uuq><!p)6Cyu6_fjtM_-AjfqW*#XADz98HVM~DZhbj|eAdT%
zRNNL=s7&RcSBN}7=9A2?MF8F2<$Zu<h0~7SgU^f1-cUeu@|f52V&)TnUP6A@xxqw_
z{_D)u2FGrTPCATJ;#6I>mhK-eZ{cid`iBOzL?SMdIjlo_G;su%0to*;LJ#U}XW-vk
zN+u?>L$-MBYKe4Tj{}5024WPz8)HH?Rqht_Jk%>gvB(F0JZY1*90cqHY%&O$Bk*F!
z3>sFoHog75Z})W&0vyZ?u8S<4uBwqmnk(VN#{RXQH3u&1Y4a=WP$4io`6XHo{5KXB
z_8O&F0Udzc0j~Ye|L3$UttaqQro3MZY~EgKZXONM#SdV*@NkAA-NS-CpQ*hv%n9{K
z2r-mm)*qmOJeqN0@_8a4gazs>zsiIU3T*G$JqN;3q{{=XY8=Db@`YrUW__z61K<Ck
z>Mfw6?4teQS42US2I=nZE@_YkMY>bE8>BmAl<r1CN;;&wySuw<fd9dJ?^@rtmaa7n
z!^}L-IcM)*?6Xglk0QL2M%BM0=%<<7Yq_=R(<qXQE3F0b3!D7<zjN1?<A2ARWsFfr
zZ@#Y+aKIwMhq~?2ZI+~gn}tyuW^*i+1i>Q)B+L1~n-2Ljn&q^w)E{0-eVt3{nX$*1
zGG0;K@_a-cf4Y4?yK62>#1-4;gu6n70k*+|>#AV{5_U<DT)}b`=Kn6APW}JAVluWv
z*77%)Kx?Sl8%56Ul@)$789Ty`=wXdiH=770qzwNFra@=jda(NK`X?X@cO4-XnU9s@
z{d=PYZ+U#dzjZ29p^<b7bpc{mM>B0vh(*1!3S{)GNnH%{N_8ey5tQhGpu3F8942Rp
zMqh7supJM6N7v7MYkQeBI@LBkX5|>*L25}GUsp$_a65jBsG<`S>sDl{d-d}xxqh`8
z&A|TUv+~PXqSXgJ3e-%iw3ySaq0u=o5Y{A*cSn~w9e!j?5s1t)@1Ri4QtEdW>y8s2
zXFoH}$*r)tSB3if6{s~)7QG)@?Z|xiL6a_3#CJ@}n#cY>m!Jc*0PslBfc%rpT8X)g
z9ZxElF9>5zI#CC6K;-XaVh#cFoi`I?R^-0f$FRMP$aco+K%*uFsPg|tu=P-U+R4QG
zlt#!?;5(oDRp+j4+1J%!4EEK<>7A)3n?`1Le!z%50kNzbZ(gl*(IFAIWPPPx?7hIY
zgBsL$^7_4ykSAE5pgyE~`?y6Muvv^8bKF5*$1S34!5qgr*R#4s#ymM_$nUNu8F^L8
z+h*2A(e8$O{hC?okIp8PknZNzVU?D@)|E<%QfjdN={T(d{=s^ziKnxbR&2wFJJN|a
z2%n0yqjk52D3_=H(79fp@BX`NTMw3hi$FNkkTv-fh_Zn02yg{s3dtHja;3wB)idy+
zXmh3sk(MeueT@Iz)9dfSU48v!fe}jr^5$P`wU*=M1*E*Dzw-~ZoIW!)jpeQao8m{*
z&%AN&avHIa)Yc)n{ocV|6F@d%7p{=zoSTw8&+~lR8-|5|?MK2-o0MmDOir4IffvWL
zweu+8c|FSw<1dPRabe3o_;+AfNFUmR3fN>D*bKh#OCWRg`@w8=sDFb?nRBlvqps9y
z`|%cNap6B4y)3&0Zf@tHJ*r9`ey<hZnq9+8ndh6gre2atzy=^{gwnq?krP)5>j3`E
zOcsNDzCS~e&+0-UdW4SytoMr-7qw5q2)r(P+}rX5-InDBoXK_#+EqIvpiB`gBx|An
zU8m(k1VRJ(WQ3a+s}CDSdFv{4b(sFTg#NPzo6#q4-?a%*!xA+xU^Otre^8<RpIMfq
zg#?%0bPlwQ?^-gQU;nrsY}QbJY|3MB^EkT(+Q(|8$G!yN@#32XhUh@HmTaI!g19?0
zuu@s0+qr)lqgaP)*qkQrX<+P0=y@d`Bw&l2{N<_bfQia>csVF088Z0g$rG!lUp(;F
zBo2oiZRlr<wRK1C8|Z_Q?8Ai@*do^&A-#@sx0alv)A;uqy=&FEu0Xkn@z`51l68}A
z<=JqM;P+(eMn1|e12uq^7uz`084l_9Zq6I`KyhDjxLtEwa=+8(ELH29P!J-n8zkuX
zK_e-=sx;d8c=}~R#=7^rR|CzLOw#|24F9*Rw0V5uAaGYJ_Pl^&HWDK5#xqs;?7vQO
z*$p&dKJmbjqXY}|6vPDXLr!?4e`OSYnvm&z<K?IlXxzd9eHg%GZd-%b@Zlk(y2|ZF
zvGdMGn||rFwnyysrP)6W`jaq?a&p(sueWu{1j|`(i@hJ;xw~_BBOg-b$*MEz@(=$D
zNezC=xY1GA(gu@B&q~faZxdL}=~BJrD28GU_IQtA{=p*#yt*Iu?{m$;@3@Woz7XpI
zKFWX6!7L$o$!uK{{*=2<pL9t8_O04u(y|)e;DP%R!$h0ME*jL*+f1Oz<8=U!r%Io>
z{VkZfp!)%NEXN7X^Xr7g|75uuDzblf=7-vwNsIF{EFt~V>+P8(-P_f1mJ|W^m}uP_
zK1xXFhP=RSlcP3}7*-6Kv6*q@{%4u)vtmcE?~jBg%-CzmXc>ryt!*DGfJB1vUYqwl
zQT+IuotuN@I?UhG;eZ4_i{AQ|v6{?2+jc+3P7=?0??FnL4M3QXvli9|D#FpuY_Jz!
zwe41iQU&W6PniXubxpFe@p^%J3riq}*;Lrhn6VU(L#(Gd&Ta~?J9c`;isDcjcUK*x
zn}(W-E<7WK`T6ghE>M61S)ke=`0vW#|L+1@SHFL<!3b3_7_9$PIv&II+L_|vOnl4d
zoW@DR8Kn1^<@U6A869WJXN+tj<nxdC)j9!n>`v&6{4%+EEjlEO!CyJ74df2N`cnU)
zmK^Q+0EJJxYVTgZd=jR?2=m<i!nlt9nf5)9bCeAFezfbcgVo;KC%fIZ8GaKGB&k<^
zr=(`9_PnYwen23BfJ&!7@YS&7n0l0M4xDCVY!IPHi8;Re|9x}r=LwZncJ|pqsYQ;;
zJMoqS=&$5dWQZ-_K6ppdVGY^wY`zl&T9w}A`9BcjHy=N1x$5rLq~jSOfu3>05cZV$
zW9=(@Zsw1+ZawCe(^?Qy!;CnSK_Z6|@jlV9lg8S=3DuHuw8TMRRi_6c(EpI{{{v&(
zUamsP@q_Jgkj_QXM`1lF?7%<?iS_le%~gBe+a~8?osV6c;Tagxz5m*O0cj$&FHYg_
zc3<Z?05=E#>Rq<blTwra0Innu3A(62rm>EH3%x-u#8LsuFm@!RpGaFs2yLK(vk5O!
z10sBO#zC>Hr5g_h1}>qno+7g#!_iJ3-8!WbMs&A7%#XbP0Vx^qKvYPM>OSn+zsyj?
zmZb#V!Fw><x)0mB&Db;95u@h=r}sDSsKJN<>1e?ktA4uwQYB%)<hfk<V8eG&_Wnc;
zU|H$GIpuseK7_N{Mf~|1^z@7oTcAK|acOT4jQS7U+w%)5<@PJ*i+jNO&HK+EN?g0&
z5Fupu1}KMFoVqu;E-*d@x0$hK{t&U%b_ClP<~u*r1m>}WX+bTnUthZJT=!q<e(sN;
zQKPm)=S+{LZ~tDrRs(Jh(7Xg+XTOmH;P6oe<U=4~kO@SR-8-vPdK)6}dO_it1nh~b
z7qoQl^hkeNOl`&2RX(d!R8jajst;YyMz&gwC8{8cao-vd$_KFcfE*pKJY;|IS10RZ
zr}9WI8Ju*3XQ&_ar9bdq2S;Ce%(gh-?H0zvhezaP(8V*Jw9-BO-LxG6Iyca-v3*yr
zRxfK$-TAd3%G9};IPZY=HL!mQdT-Rx<|=;7G=KdgPYLYqY2CM5H)KvyOzN841$l79
zi9H)tj_pL#aWTAm6HGI3`y(yDPWg;|@P(3Edj`Q6fvyC?G+;_n2Y#?M1e42}S2qH`
z>r8za2$$P|!+9#%N)F!rel~)}doy2HG#^}7$IU;L&WEr>FZHKI7S$30FVe-NBtt9r
zE8ve30u$j6np1X7V5mHsSE(s0p9}Y~ekDwN%NT)Y{jFtiMU`EsRz&DaqJOhfAOBg%
zt9wb#;-pYRC~>663}MXa#FZbs&!hPE(|GyzX1VjDtceeQ(!{x`Qc@Se15pmJLcoBx
z_v8~9Y(mpdS(0QU?!6hM5g~PP>bl-tR5nck3a&=%>v@COp1HIOXpX5e+r#A+{Sv$v
zJA=CV_d$~pt~)yT&Uk_5NfJQC%kArgENmeid9UcVk!DfvKxb9>Cu``iREF%EaM^7J
z;Z%G~4np$P+4uRNFfiEnmzJ@(l;@hw=_d-5k#6WhPyb2ya6o{}N>UrR1E@EcJT1T@
z0~t$_<LhM9N{k}w_;>=lGkJDPif_8le}ce=+=X-*4)?bkTxLrrBY~83b}4Dt)8A{-
z*6{muXu}*fIA}F{pI62^sr3r6=(KRU3KO6KkT`{by2_ZFvRQ<@oT;I#4J6)^-u^-T
zJ}ROZ$WLkg!}?2u{raEyd}Z<G=t66&`oHy9HBSIA8F>6k+A@$Bs2rx6+CYY~pyQ4I
z#4FQspHKvRLDi=Zeq>pNF%u!2O+-L0C-|Yfp?T7&wk78spGBRk&zcO3?`Ii3w&9AZ
z&`pxv|GQ^@zJlIVZM@>g?DLH69l;Cn4@y5$BG@Gseg?Fs^*0T-NbWBopK3-O&J834
z^JV@p=t-afmgCQ&Q+EOWMP8)*arp*s(VhDPz6=a52;zD3_Iynm@h@4JVYviJ*TM>T
zSgs&c-<7=a0?{vVo?JM=4AF8j+lpW+mUhM^w+Fov0xSwun==Zq7vIwLs(IU)Ghk1y
zYk+*XetN>h%WdVxZRvJHoqGzrOF-C?KUj*0A2lA6n0T=!H6>3m+bg=vT@wU=LV#2Y
zAN|9H#M%@jQknS8Bgr&zK%1#Aq3RMydr_m7t&-R7Wt&?u-n%f2`7eP-$#AsSuI-=x
zfw<NSSk?#C{EwZl0c9X5)Mx#&?l_}fw?YM;^aTGw*+&hG_Ov>;1ua07_)f{}`J&NF
zP|O8uE@w?fqy~aqjEozu)W!vT##Eth04mhZ)*YAbx8ML}`98dVYEZMSU$g0}OpVdf
zsb8}`zBkm6aK9KoW93$?!yca#)T<>U@BE%_>nUI{P7;u+Uz4TLD{*UO^Ej6aiZC$#
zHw<sU9iIxIfhv7x$;G+pU>5Y=r+8*ZhE2R<xySzj@T^vMVyeoJ;9t7G@bI@?Od_6?
zvYI~CW4Rl7S=L}b3G?0ItS=+5-ruA4Mn!@Sq7Jl-`ThlC#@IJE4j_o+_H~Uu9V?ui
z_QcQBxd}vH@5<JAzyU6%#=h_2Y^@`y1Q0|#Fwl*FDd)-NO&@%9lw~!YruG0;uXH7(
z!+MM;-6!Xe%~QXZe-@SF+kMC5f}`_rZWMT<8u3BN#`Z<4wxtGhYVgus%4-7m*x}8p
z^{UO)>>n@~PkG=`EaZFFEqd@w{RQ0I6`lyNoIoMS4G`o~z#zQ$;funRn^!7#Mi0x{
ze}2alBs}YQ$Udk-0rg0VA6M<_YAFd0s<js6;TVGn2ox8dAAw30c@M(+>F^J>>;FO`
zrp08o=Oua6;cRQv&+y&*j&RtZ06PxTXo9tqQn2&XY_^HWqzE&AQKC7)eiMh=6Jt8d
z{Ix+}mx`KW-5Ec#Cm2C9c75g~K(e`fbwtyt=pGPS2B1lzE!L|ezcqXEe`MJ&N<bej
zk)J7hpxXi?D9-A5%2h-FUaG7CCNCnk$o<kw5Pt!?&;@Xmm`)&arOY<We(gj!>R^7b
zL~7bel_^EW+2l1>xVMjwp9W&m3J4<d4XGfXlsWIhfQt7Hka%!}>|wJhy@O{BGRsU3
z`9^bxAE0qP3CcI0e$U5reC6P2bivy9R_|C&bN9`NQ7qlMX??(-`2*%E0VtO?_(NJN
zPVEUcdT&m=0bYIBvfQc9g~yGZJfT2G8;;6MgW+2b7fX`#JafmL(d5kb<qm+i&NnQM
zK~a|)vjeT<)v?J!(GxK!0JsVJ>}tF~4yZkeZh9~w8N(&oIF;AHYEy&jac6$-muBV9
z06T(2`P~l`LAmN*xa#;AFyA{{>aT}USZb9Acz&b-pt;9ly@i~eABL#2g$85>VOueF
zACE&7P=IsGMUc-BJ@G@uQiI$7Az2j^5tx9eRRBWWNdhatbr>J7Gnvres|0!0Py4sW
z^@ldi&(!MiUM*Ov4&SBpT_%kdtP5b6^bZ-$?f5WTo*5Ls@O=&@A<wZ19UDyo61B)(
zZ3-7Zl(zP_8WDK)fmu8CLrHbxr?rc)K|zp(a;m^rpf;}9f$1@1rA#Wx>F7Dlg3vT`
zZvW;@G;A&kl*FXyr{ofps-*tfbUc<Pg~$EB_;%sM4%$zJ^C5YuM_rbGloi*JTu=$M
zld^8$UZE4JB<-+nhW^$-2WaQR$+8P{MaQ780~G~hU{K5)cZU}cn(dp_mlNg-5m}ZR
zbw3UU-og5WffO~GC!>I{e}A(6&Zj_VKc2y;JBTAEMNs<o<7aC6O#9DsE)Bu^m;l`s
z@io}F9y1Q5@^_^jCY#SylZlTyG0AwR1a%)$(vyNTQg26R&x@bJ-<TdY2?2b~8te1d
zDU+TPt{VYT!;k9#9DmNY7W)}S@#~X$*jTX_6#$d)(Geo%xVOO7{O!pdTI%(>!gC2Z
zMbH_WAVi^9HXQ8c08~3E@B8rH_OJ|*>_(}#kKrwph{tJzSUw&Cn1=bnFNPAZ4P1yg
z#t%4}3XNw-R=n3nr^w^nP6sHj{l9A<CXYD$6}41rPLQG5E=XSR^8>MzWi{WpSiAmP
zBwd{S>Vi4@;0H@BH{5D7(f%lH;GJ*K>DJ)_X68G&oF0+@vc*#`C^eb8Rrw)b4C>!B
zQ+q8*o?T?ab()X%(~q9_Q;_1chgVn%MC&I}m+ZR}pbNFCP^`v+^lm^O#?;x7@Pyf1
z+MD**fXS*|pVzSOxgv}6G`rxvZE;U;AOaTO2U~O?rY!u3<-1zh?p^O09MA(|k@-yN
zTk&4`mbNR*@RPzR;=~HfB4U(<dR7%CR<{lm+DVx_tL0X)xKPoP;Ljz?0lzMjZ3XtK
z%oI}J9|GJ)h!@Xf(3zs{xOe|wD)4iOsS}cgZ^J$VV6kP;r#?lWCX?DVNIhWPZ#Wox
zdjmwC=K@Fvjyj&%gW38}^kC!|NNVV;LGGtn?mdbc;$NTWpJb+aoDSe|z9`dxy%=U{
zu*uW(+cUmi*b%SMXuss@hdYBFD`8|zT*0Cq&avK`tb)5=lxP3Ay?pfb{q*a*n6O(N
z&%t7IB6#muNTRUOt{N`8g&er~Ka>~X-xm?NT<o;p8Nclv8X%^k^B0my276{WcggH4
zSk+mMC7+8gKyj$9$$D7s&4jeIJkT^iGx~ggmM3hHS%jap9wUlhr)>O8m=(wB<0y#!
zuNHvfQ>g^t<4`3z4VMh*eIzY8|1B%Ckw(NXDqM9uPLO1WrdV7G$OLuba<<8X#6<CO
zNE>L+-7^Vn<hjMkaA%jxwVEFcy1uQ$5g^O+YC7|#3r#2YMb3>?94i{2TYOXg@#%GT
zXr9YrcYS(G=eo1q?u3%}Z%~*-qnj-#>0-UHR+tU+=YjV^XNd!<P#FK-j}09ah3nE^
zm%fKNCD~Tm%~|0u)F(MeUawZo!-vv}|B)yOWsDC|4$Csi&$oB&c4tiGi4x$#3-|et
z<@9I4T*j*xt@;?^*X}&!HhkUJmK02U{sk47QeQ_wDEe(EMqEqtGwk<=63UX9DkCHh
z&XQG7IY<h)#!TyBvJ{UUs6UWRZa9`CuD#?r<OGgS>y_@7a*HU}+!cYWktIH;Fz5UM
z>Qst*iOS8oxSm_%%6q*=10hA12H3Y6mw0S0bJLdE4Y30k>>_P#G&^7#;Pcf+7bxcf
zd`bc5Y?aR(JwK?RB4~Q%Ve>uBJ+!U(@eybu=g$>)?EWumx(9E#f-6mWJ?k&((!q?V
z@Y&`>pw$%o9^WmOqhl}gSm58_%nVrf*&$Hvf*bx&mTMs9mE8UG3^(!VrpngYQtspV
zS7@`#_svwIS>b~o3YUn92CX1)D{kOkpPGh(D3VG!s9xjvVt%TG{Bq3=gd&xhQigpV
zk27q*Q-+$3*Hv9srb{C3oaiR^I5&=jMGI#$-Xxhkt9D_c;PzJSVj<b+2yaSQnav)v
z7|=~+<k<6AX6)anz4X6n0+qc$h!EuEt^blw{Ir7NE^te^9OVfEY{uoLdwM3%I*cM8
zVy<v)u&2d%LM10JC5w1aF9%eWUHEv@d;7LNI}-!9j^r(#Pc*=+L3sNva%Bl?iL7qF
z38;AS-ZX(sx?TZIcyWM{ZiGk4<Y=@{fqgQFpe!KIh&|DKGFwOxNc=OEb{x+jVQL`N
zSHZ}V2SkDB4~q6c7~>pDZk-9PXWMU|mX_lIeY9)kHjRu~EzVri4kpuPbv<Q4k(5vF
zdlzT0i_soB0(AK~1^s;=bumy|G`Yu~VR?&yVca@E)A%&TmXohT3gp(*`vrw`IHFvz
zmayea4&2;iA2z%IoDkE0Ya(y_G-L(T^t>lBh0)!#`;&9zm4jZxM&<(K;H!g;XoT>H
ztW5&#H{lUoR3LH05gcdfhgWYT1Es0)@kS4@88Q&4rc&$2bBF%}jvt_Z&(+B~*b{ic
zi(NkRr3`q9g1d&EvA7%*oQ%Fg;k}l5D!(6ZwD?`(bh%2indAF=V~I>xR47aN5tC~&
zbGT3TRV4&?61a#6dX5<OffN9AF=4yi`gqag`19M!ri90->>Pa1eIEiz>}q3L?q0Vu
zWKN{kETnAt(v^!bL?jdfZYlq>aZ|KZ%V2c}B}j+dyOgbjmoLzJYjdPWr|-&q?V|xP
zfA7z}e31uXj%BXKjt(-|WDxdy*%hN^T)LU4yJg6asmoxvpM<iMl)(cr&#FbTK#dAV
zjx7da7nn%Y-0|}~zc)TUwU=u6%5=D%Vn-)60Qb^-$&2{8I`J$Kz4&qUEhz8#$AdnX
z2(uc~scs&O`WGh)K~x3qGJlXR|9ndW+y(sfEp3lgB5W}w#uVNmDj+t0^%Eh(zG;&R
zi?v^aHzaI`ULJeM%r8)`L4^@!+2sx7IdtY6NN%k*b}%2w#Iw2+lmb%-s4rGPv4f~X
zx+~4PY8?02V)*Xg5i@fK;FV|`-T^nH=hXjRt;v<pDT$rxxe^Ub8X&e@EgrST#mnf=
zChk7dOv313+CO{DNB4DNV)1Lbj;Y{u0%mBd_r{hHSo{2Y?A8+cy@y9e8&taJ`0t~J
ztDRorJn9KOb7bZ_C&83EHXkt%yd!Q+INRZ_jVH5sCccg&TsJ@XdeY=8u-aYh1A$WH
z=U(IWr6nlG35i~e>)zQ;*IO+vv{bM3<5~6+2UB%(nAz?EZ{X3L^eJnfJ5^qpDaZEl
zz#NFmn_#bSUCmPW-nOI?z{~xCZ25I@e^>Mf-H(j34Etk$Tid?{kiqV51~u-1AA%&{
zt}+r>X*$#FjVVare$gew91^?rcgQNj-JbdLKKsgZ1es(8!}{!>bpoe9n3HMyBo6b_
z*NGt22ST!H13BwgR}1*nj1RsUWz=W8^;nWRi~u_rcZbz!N%KT1q{gKde&R~p&)w(E
zM?Nx`o6*nF{U~gX%Mm-Tf-z{8GarL`uw0b_sz=K#_grI>mZfv+dokim_dQSh+GV}&
zX-XxgTMzA(6Udn23$xSOHXTuvc%$;Wo<3+z&fNeV%o`h!{pQD*5*hCd_3Ibp9LG{~
zfo#R;=M43wDm8M`Pu+xC|2b<@MLqd6FQsd_xRkZN#xpc@+}?>rY881`TzNTAIs8Fj
zSnM6jEApJ&1{_OzJ;QfhGhOvUr;krdOFq|&Ue=-)pA?s);T&`y!IsCD2<maO(9H&V
zw@u-`a0Ko@K6p)CLn7s-msly%i_s{4qcuW9A<0ByMO=EE92IvDSnB7k;c5dWp9*dc
z&6P6O>NodxMU|iQQ-o4mcPH90HcZe_2*n}(%fEAfHDbV-eyPuo$bN39VX1M(QgV8N
zsJA6LJQh3m3*o9*yDNG(jWuW|z{ooq$x1`~sFw`-;oFCi!Y5mPs6ThT69Wa-ned4X
zjIh?Ruq#}kox!w8*qOtv5RABGP4i8VL~OGUU!bW1845HRMT`odP5z{Tc8@w&N_5yk
zcv3z}^qD*YQ)Og3K{~{Ml_&VNIVu$V#k9)ZZk~8In81}MSV<RRa>_JlG5gtA^!dvd
zFurX<6%6|2Z!k8DVm;2_;e9hg|8kN0oz#+p=FB8imq2DD5=Q_17lm{kLPNvdy=w?}
z;|rebT;z)3e4|a>@ae<Vs24G;94rhL6HykgQ+8HPNHLiQDvZ%%?w&nMUYEPZn3`^P
z+N7?X**I<N>qtsg5*$q0Qe7;0(=xl!2KH?%Lh<0Vmox)|lup}}mTqvt<K@SS;Hr75
zod~M=Vf>`=nf>~)&UqTmIjXh1pwfh_L1$SaNX5EQ45r9T^++wgzsAt6O4Y*{;J$FV
zM<j{J8S^x>JfLYxNK&~+CkuPYaqz`4w8agXNw$PmvT#gPUKT}{=T7b>c#m7D(VH|s
zT%<#}T5ovH73uG9v@4XO$CCE?5SkxtU+l**8tio7B*;;E%g8g9Iv(<c04eFYLz1{U
z4WsoDb;X!+@1S6yjUKaIYD2lty@}=I<P;kI=<CYD@8&hj;lVXbPn56(vGr(#1fqB4
zqMv^JH0Yn5jN~KqdT+|*lH|Cz6pRWnJ=^rKv0q!!K^RH}MaVyN=kAQd%`MG7oUXjJ
zLBk-FM$b+aKn50T@U><B36y8b`++Arf3Q`L{j8<0SC)#3Ep<xl&vA%qem*#VC%<=C
zVDM+Hn=8xi7T@U=a!zP|G|?hlj_r{wCsi~)vCuZV@alK=**ZJKpPR<DwRPhzH`v~9
zD<=CYqL*8zZf4Ivz{mNVAsfwxm5j34h!;(kpH(6=C*zh++HAm<lCRH@=+thT%ym(*
zI$s^CmAnTl!$IwK7H;RlJqeG+3F&n<fkiVkAm~oRGFLVwv?&{XKi<2GQHDc9dSu~Z
zcihgwVLhCwIisoyLlZ(N*ET<&{JZ%sEGvuDGLlG$(8tFoj!AbY@GVvuhx>(@pTEEQ
zpRbs1Zf-NUf}Y%~tE<J@4RLIyLwXh`E3IOZl9hJr60B@&<&!NwJ_d${v-M7NR0?T1
zy|=V<oldOZQ{7QfFuN188@SWmQttK;G|2i{)$eO!y!7Y*LCYTA;HzDl{1kUOS%mCB
z5t~Eewga2Te}ll=gsS=3xU<n=+320~6}vfTrrucaZ7)pOs0dSB3Y>J4?E1FE&i>qB
zul@JwT_X!<xq03hetmY*oy^S3ecJM?s5GavJ~n-C=IWT5j`jn~X6qMtYdi(?NY(CI
zzVx+;W`>PC0+ar=>k#Q987Fa^Rl8m-UY8)$8bQC-Azw-nc(l97jfQBQ%zq!-PhG6~
z7LJQiv|8g$sI=Zn72n7nB6St701ektfA1n555)aHOj>UbjZX!&rI&;p-<{GQ{KNV?
z@#pIjznxHHT>jxW(5`_IH`1bNGgvc^OwC3=gm{eNf)%8(zO(jG`Dqo3oxZ~C{rh=g
zm1OK(x!%Cp*{*+-KV0@@I9_3H^l#&aKHx^#EPrG8!bIdLO7^@y;kZgWraJD^TR|Y2
zAi$fgbbnLbwR8&aYc*$Qe|>Fv?@m%3wCu^$I(jI*ToWmm-V$8WI&MP+V$gtS!|hS~
zbou0U5Z;?`xs{92hho?5SC&ph9El+O*vsRa>@Girl??eyqT{5?>fG5iyI;Ps?7YSi
zNe*D#FwHH$+3dankx@2&Nz7@lt8;<A+4B_j5i$^MUi{l|c#q;hyAtC$(rj0b^Q5QB
zJA}TJ9LwDPfM8##R%833dd(dv$8b3lBemanvF+8&*ZDfC(3cX5$}z+Bfp5z&3FF1i
zVpx7)VwPF2!dSi%?;LZwLHrc=zVi9U+W@f<!wJykqVL~ZEqFn91*~6R{+TS+to=_j
zs`Th0nLD{F$bE`I>l*`DyyG_Yh~cvXW7o@VaVUPL4qKTwcm6do#)(8K+Ul)bM9qVL
zMl4Qj<9zM}>(ujmdZoxRHJLE@xJomdtK*PF>({U_L}VHtU|g?=!vDkZ{h<FxgQm{$
zd*gT_{(MWa%&ToBkfAL9ge#E$^5P$qj_vsNef{>ViL0=rkdp3@`>{0>ItI{mFlm*X
zCa?3MF9Vz;RZv9oK)y|r>{Rb3+`WBf>`3VE`b#`nJy%${fk;Z)ewkh=fv!M@x@S47
zitOI)uo5#svRjX?N{*d{08)zvM`fC#y;#O!1a_Cu-+cqFJ^oBxV8)3Q#-GX0miI@R
z80D$4p_bFW%YKtzjd$;D^eq+++~8M|k+Jm3)yg<L`z)(82<YB7OKQt;R6n2c5dGaa
zQKcK{yWQ|$PelX*rVkSMZE%V3dY%FET>0KPav?QCt7^KwVY!RrS_8#f8d~n?68-j|
z)vc|rEb;K3p`k9-7Vn3Xt1FY!wa%QpJcA;O>C&6~`}50b-AbP)Z%e0xneQtrdaA{0
ztyhQh<(LEna;~o27E{Ig4|kU{2uMhjyu3+>c${Shod}k09v<j;c>P<0$?A=+EY%T6
z@~~o^c6TT8S0!3KlVI?QUuyOURw4dgkt#J6iM72v6V09^(JYxz{i*UrTN-kdoyTT2
zM-WhT7?LwOf(FL__5e~PF=l2qolC?<=<(Kr3ad27%3k+Qp*b=#oq_mxf@R9{1`T~n
zsQXC_-aRr^T+NcSq+ElZ-h1~2{d=~-RDe(>4TRx+{lod(@}u<osj3j`%!6rYda^+y
zl9e6qEhaUj9(a<>GPQvpIs^H{RQ?wanuWhh#q>Fnm40AhD%z7EgY6~$wvKlBJlX!}
z(*9j`l$Za}K9(7&=lwch{hZs$UFWuTaP&sRR$wGcq|=Fkm_MvKbdjeSXyzriK|gkD
zNX@GN=z+L73gMQ-d9UY@1!Qo%Kvm+uTV?e|T8@F=YPlj2l7MeL{=D}0d+q(}5wYq%
z*PB<FJ2PCbo<gd>y<>e6!a2@R#5e@)ZQ7Y)wv5!fsK{Z&8#b1>YwOQns^^9Js9}T^
z$ZL<{yn}{E(4YH%>v5)T<0CgHPq;;{kX8Id9~R?$dV$Y|81vm21uA3Pz>VQ+Ww68Y
z6@fTA#`uvaaU>y%8rqh%BWQre$|@9dE9`nNPYpuY4zDE)LgEwp`*GC3U?1jQn)MG`
zPgMTzghvmRYHx4G-FmQNZha#nNQp^t11p2MK*$);0suli_*(C)<;q9?mwx6TPBb(>
zpn=Gu@Q7*^dN>uo1w%TQb%GdaG;&clbA}lDVHFdjFJsrCd=nGJuMJ$H@c6bt|030Y
zfkp95{&~ZlapCz~2vl4OujSIi86>~BU?2^ZzDp9s(sYg*qK>#i`uEs9n*gDywdeVL
za>sWS&Db@%2Afl#D9u=}y6RH+tD=k1CF`D7;p^!1zB!8YF-6^On?frs7?9nK@^dS^
z9=@X0UuONvO7YU<B9TUWDG?<JFKiKjlF}<>y17l{0R3xy<}j?NsS-)w-=j+R1q}rk
z^c{pkN6o50G@<>-bZ$Y-&>YYv#En5huCYdhABKWvqC)l=bCbKF{<H;G;DPck8e0Zk
zJx(bB7f&7fsM+{*#-`QNzqvlGyHfayD0_0(taLkLb>VX3Efkvswz_S;YebMGvAv<D
z(rt4(BGDTiH!^6U_}y3(p;*x*%R!JiJjo4%RBT6rdX2u!36r78pm<!#%-B~aY6<y<
zH<g8`ngCpUA^Rl?V`!7oPxV_sMyb^e;E#1Yo<mAUcY#5i2nmdKs^P5W()XHIQS#J5
zvI#-{b-0l$LbY{unF+j3v=kH+GiPUKB6jVFTq8wam@IGa@3XP_oav>drDqTj5JXt)
zH>BojY@$m}PHZt~XlTU5#PsNBX%RaA`o289zV23)m3<fH{cyK>bR-Y)`<fW*p7@Q^
z4$MzyNz<zA=K5IwWv^DuH^N2+OzsNx{wdiFG`z{3mwk&=--3l^1A=|v5gZI(!g10Y
z?k*E$p4p=o&QFiIjc~UapqodwF8P+WQ?9S9$)iihGSBg?ZQ)6|-jjQZ&JmM`5@nB?
z;kfOS<s3ewz0Dt=g?2V{Tz+h2qEAYf`R}wzSqhguZc2^eeO%)mirN7C7ad2`K>Geh
z&vO(F@_y=%Z-))P7Qj(qVa!TiS2%Qv*eej`-FPzQ1-S#LN8dm5tK?Tsc_va|gDwig
z={tDg&f~PnuM~g-?B2VK81JZgijXQkC%}WjZK?SN%%Vv@a%r&+CzAn!=oPG~APc#0
z1k7m;|5Sk{Nx8L~(qo?ZpYrl^cBH(H2HShTSvWXX2v9`wn?-BUd_Taq4wIpXoJdYl
z81q}9)sMt(kQmm+<_Krk#s)mLOYI0nOm%=v@0m_gl_xsv>$t-cGu7d|FJlRg#pCkL
zvf56olG|(bL4^@dme)3ij08TB!iI&^l0QcY8F*h(cFMFEXS*3Rs0@e}DuhtHYS^bW
zBErrpNu8{^EC-%Oho?8PxrkLAbj$(G=-V;eda&Z#_A{4<@db)@UI<}f`ZFoFRUJb7
zxKAf$8tdrbbSIv3^T>Q3=Ju_Dd=6{D$9KtW=~LVut}K>Ao*H1ly8gXjv^7-UVQU;W
zwY3NQTuyO=6{*^vGk>)Dk#f-)+2owgD3$x841P)q4!<`o4a8(^ZEvW{Soh!B?QQK%
zw0;PVNsa3Red`OU8)Tg|Xx;KF;o%Xfs#Q#%fooB-R*yyWBIt<9Aa{U|4{tFZRe(gm
zQ?~AbaAt2Fs$-Ku5gUUN`yX9T#lUar>``K~MAdvz_!whwuVqmDo`?fJWO89AxX&*h
zEH%a%-a9+&-RSo*Vy$oP5Bs1H2dscj+Aex0Gt&4=O{wF%RNSfFeg#*jmQZJ!ye<it
zcG8_O7nBxE5^34`LqZL<)2W8KSb%0mV?mGgQgU$4M|tHI+H@|QMoE-{?h_vEC$I}W
zoeh}}PEv!_eXB&PH>A)Z<b1?n1YU#HVv4@hXs*g!tG6Q*y)M>ny~~b*OfqWYqq4HH
zl(cjv$4fXQqm{qD?6W?jA`*o^DXn7fMkouBjRUv2Zx*qtq~J->J;pu8_+hfwQA9|u
zX9!DOM8=4vaWUMp1q39A|Mm_-t_hQ*oy2K~gxv7XvFOPv48Ld?Y_ANAs9&#Oy&q(1
z1urQ?ZtB`Umr&8w9K>Q;=uiFa)A>iQJV8kc7qK~0Nh&sI3SL?S-j}}L^So=lSoh-f
zdq^lH(vUT*SQiTRWSW;nOICG73Z-f|&g@rlk^nIofD_<J6i*u_6~FXXaHVgaTo-{9
z4p6{11u59PuGeu)u4xmX%c?=SMlWwgJTg&3>b%a~f5#cjr4$UbpR}&mJg&O?g+kW+
zJdtcPqg@oKgKJ7fzw^724_Oly+@PateMfoz7mDIXhbm%AAp&122H)y~0vLDr7e(p^
z>jb&{7i7#cv*#~;5in^VE&o5W?O-no#m}N}J44^|;k04HBy~+zf_Hwp<hhSoKxxy-
z*=0UQB30Ds)Ht$|?w9a*-Tx|&+`r|dJ;1}i5iJ^5(bVpQ_L5Qe^uJQ?QZ<)GWvJ8g
zd{fyQpqk&_Ihd;JpXyH*7Mmqvq%Lu?9QKw`?~lglS10be+B1vsT}0R0o9u$ZUpWBM
z>S|0k`=0=ATCl?JQDgnLe>%NMlQUx{nH3j{%jK~kYn_+HWD1D4*B^?;GerUsFk4_r
zU{OPxb_I5wVY1gyV>^zuwSH&jEVvTUEqN#XsxWAFJrz9iaG{Vt(y5X?k@=du5d1=A
zt3rQNGo@eF`0}2~mH{X#*|yHtTc^X-hK{xyAIT%*M3cXiomSSACCGyDWVRf7Ddv0-
z6l_Brt16?e^g^<xG_$$_KhdY$!BN7LIjex0CWm6PGZ3KDu;mia@z>UD{3EznySfwp
zFh91$oa4nHkufv)h_Kw|`mijh*2lL@&mxDc&jIHD4lbYy(mQ+sk-FYJzJtrCsUtt3
z<2W-syrT{supp87qe7EiY_XpQQRHbA``|z+Q=~Kfhd+ePzgvOAcuW>RZZPNqAzz6U
zq+Bs@sG>l#!~7f~z5t1$+@}ybcoMV0@`r|kU(}NM)8{>O$0H6S%t=M1#TjM9M1hlE
zJ5{H8*FSwxUD-Jh0f~FI#fl99@RLldxl@^#TLmkFHSP*iI365xEr6|c72yYaRq4P0
ziLDZ$g~{iyPpbmDva3R+#LZo&S5>nezlDw>7BBcxt?5R^xl~s)If46Z$Y^!vpzWD}
zkcCC<Kc{lk_=T4x^1)y-euAO}#DvK1$^8y>coPvODTU^PAp-weF15SxL6gTx4-cSO
z&{tlLa=_<332X~zq(py+CeiA~(s5l(YS$2EI%z*^3&-&ad;rRicnswjh_F$2>5Rs8
zuLandfzncDCb4iqOm%J9H8JxIuxLKrCj{_hQiGOl6ga#JL$&A?fM7&JgiAlP>pxtT
zH-X+@24$7`q4fPmM2r4lOhIQqBpVF?h_DX{aQN4qV}ql_?F_E30l-ltM<g6XyoO?`
zhzg`X9g>V)TdiN5`U8fzwsqM5hZGEylR_-{T~wl<MA7=%EE~P|xw=u&&yi{ZGxYQT
zKxg;`TUTY>SJaR0ySW?*ykEZN^YFF+5dpzo0pfJ=pQ%D>M&kw=ZK+h8@vlA^o@6_}
zlq$ndKYxOY26xUANq#6I)r;>pllCykD6O4?FG*mh-+I7F)+2uuE`Ln!+%PspLqipx
zzkw~4+5#~ciO&NriH(D^;^yJi4-6f#(V3ZVCD4>{&->19b{8NA^<#N`1Dgn5W?E(4
z*1=|s?<;|+F{0J7YKS5-qNqE}H!Kv@J0az;%<zy>MFywncr1kBpGwe@2N<W@Ww{4W
za%w1Po+0Uxjv#c5T$<FljlD{P!OgVdX6JaAOtVDHWk+iMMjrYdbYDLkv3T0(b$LHp
zD5+b;OF;W*Tm1elxUA{`^3gm8U6Ymz3J8%;d`9&fc<Ht1uA$z*Qx_qB|8Tq>*7-PZ
zpfU<TRa3MC60S~`NB0mxe*1$mZ}s1md?|51z1txP=#*^6$aTnEB;*<@iWl!7xDOkv
z`nDoM1T0%NZ1d9Q>ZCtJEgu1v2k46oA+=RXWEqv370G(LXX<lLg)HPC+k>ax7-%Rp
zeKL%8w+4b?f?NYx#~riWwY8&)Uqt2igh65<NQH}(C>l51-WmAT%S9590W_Z2w{BW8
zKzO;)hPRsc?Wq-GjXoed@9`#=7HzQ`A2F+fCJh<qpZ5fKYek;A@QW$8WELo%p3m~}
zvgtoUo@HFU?Z1C)A01!!Kb|54*H~p#*Q3qSlEH%H^DV^eP9dQ+rjRR}3tQ>b|14I<
zQv2$aX1vc(XCuB5n9t<qw%4bIJyn`G?B<+NjHLj-T-IL@41~})*g88%!na=j>i(8V
z@8<IY&~oZn_pA2B@npKtqY>0p9R+PG#NR@<|8Xr|V?UB)Y7Gn<Ual$3HkmWX0r+p&
zZ@_XsjP=xayTJA=eagJLrGMPk#7GkgPHlQwBXC150`JX90=T5G9rQ0DVLG4xw-{z?
zpADR6-T_Xp1sfBS<dzw8t8UQRsRlb*dqZE&*OZ-wM>8i6y58QhIURWh#N1Fco-eF>
zo-QRrlPz<r>yzHP)lE!Kf;n%UrX!o~LWCRnT64eoF?PcL{)Po9N`B#K!2(uNOrNe^
zR5FW?JjNNU_-K5w>``XRHAi+_L?2k*rnGw<&tw2x%$(dmK{MGo*~|2A(Kw6MM3#2x
zf6?}xT)ia*(;nTX-v4If2%e-?L$l>R4vR1$I6htIw|D)!mJF0n1!k$K5V}fTXzaHg
zZgS~%B5g5GtUHCY{ym@O4|2vh{aE~+&{zFs4vt$jD5(?SFI22e+w*On(@YSX(UhlZ
z<N3S%=)-jzwtF_)y+ha60rrAvHNEoxR|~KYi}$`s+{%~-z!m?z#Xxx*;$kl}zBn%t
zr(0t|w>@WN>qaa<5*br^_4?O*Y$=_D<bL(bn2njX$sOoEabP*Hs@+WR10f%6vJei-
zfo=9?7Uoyq$NzyIGGM0j`hPD7C<S2T2c~uo^EEpH@OGeNS>|g8yZ)_9-@EQK_AGwY
z##KREKYjV3f4mVvZmxJ5Y;e3Gd#%6T!(V}Hgu_Loi*}!TVqsBSv^lZbq!n3_6%HnE
z3sCpSOQ(rTe`fP>p6)HOfg>fiTM~QZgoe6@Q}vGTY}^j~ZES6wH-{OWhB=o!pp1-Z
z0wAnHsn7$r?bR>V3@tSud61i~F&{I(gDL9-N?2|MLLzoQm?fKyMH7nTbB);Cn0d?I
zcQ8Kl#ASLYl{!`Q`3{3h3_C7}9N?6MnSdeKfPiwl&Mzr^kio!4El(I2^R?am=)%mj
z&GhEsKG4<w>mM+YkmTk0PoQ|uV*67dil3mUWcgjc0!uO|_c+A_B1ZDD&%4y6zYrVm
z9>}>#`S$%KXhIiB=HW~n@YkR`13oHbZMR0Km5Vt+x34b6a%j_N94%5pRHSBU;Vho&
zITgz=Vqx0X6~qv&gS;x|gvnrMZ<o6qQl3K~0ACSN<3qf7hqx6jV#ACE8NtEy;RYO<
z-+7-M9NFeKX^`1FdmuYxVp?Ibu3@rtiDj~U&J1{Q(Ws)Xq?`F0_iWV4S)hh}F>$n|
zEYODWdH<X-8zATnoVJ{Y@an-X6^HxC(urO~Ew4bn?9n77wHh`VGELvmz~<d21Ww9K
z<(>m$ioD@yr3;xf8c2fbaV)&tEbHZ5=J5+~;?w#No+>*YkT#$HkQK>N6#h+7rysp$
z=rLPL(A-yff*A=A{eakMYirj&IG?VW_7vhH$Q~}UEnn%qif0KuU;wIk05Ln~_r?n{
z5C>yR-UGRsHx(&TUJ}w+wGPfX&4dpN>s;y$1VH%};PKL98I!wpaaCH|03s>IKtNY#
z#EMBjSvFOiqRFt?i4St(JhR<b<7!{}OPHFY{VVA}!f=mHk`k9jRSi%4%XxTm0<bD>
zNmLq{H8DRY;qhM-Zv^Ss9RYV%t2M|C4N$oiGuN(**6dl11-!X9FgF31!r|YftVbm9
z4~`ZBg7`)bk?A0njl<T4(irlQ$?Dr^`|h{LXQs~f%SbwP4!EC<<ADO@h{GS|%QZmC
zT)_}4A!_YTyX<wxC|P4e&cqhkGMwI<O+??mIFAexhtJ(RyhvSJYG0Do9Y^=1nOQ#D
z^+jYm=nA3MqCn_T%m;_jLq1lkr6z7}-qpR;P?7v;hv9Li@wzWQ)!sFJZRgu%ePE-X
z$S#W+cGGL*{2PN8I^K_Fe|YIl*akZtWjEsPiA;C9k7HeU-vZ1wYWF50jW-zxt^p{|
z(k;<Ie>(l<Jx70G;jgyE{_wkcUPt$rL#5V@ce||4Hagwaf0jXwnL1cJI?a!chg=Dd
zWZi2U2YpYp#M}`yHA|7bmO!0`$sQ#{Cek%P=S3f7e4i?>-~iqtBH4}ZC#JC$1Nfza
zjgSU})f?fbajshNd(!#p?(uGL`K2~Qjd%F>E}qfku%cA5n?XD^>znJ{h3k?xCx1+P
z<@s7oZCXAy+{j;GC-?y<sDV2Ehe(8Mb!RQADf{ggpgDF%NG@-z<ZV*3YY=R8z0N}S
zV7ch>09tSB=wxa-Eh%GSCf8DUsz2LR)KFE|=%oLwLQ8jRWIqbLh#{{X2XIhv+}E4n
zNlxB|Z+#3PXK|boNRW#Zqq-7-7e;RyP2*8$k&IRv=P6soW85+U$0LOUIqt>x@(Ua;
zJme>o02FHA^eo(NDFZW6_O6-X$NMzN?8!AeT=B7t@~ZuX;1ozO{A}#EC9`09O>?xR
zOtxsR>PT%mA+qxk`Ct7^>uZ}b@tNrG5qp1+ex|Rk90a98-qL!1iMWcI;j2?rQI~g9
zA|W|k18gvbw-y~lqJkVk_V{FHSfB?1<qxQz4h-gX@{`k3WoJZ8_Ky7;vXO|y{XJDc
zr2PKf7$;EI3@^s`6O^RR?UwaCbGsH71g}<=trG`$X@j1LWZ+H@Q#cK)GHz~XjqZV6
zJ6JfpmnHyTk>^->pxq;G7UHz`O7nG<PU^C4rcnXhus&SSm=hA{<B{;IqJM^m+pb^5
zCx3W`28fMGwI27V<8^T9{MW>I@ty;T=#sN{gQgMb*Tev|J?2EDf9$&{tO6%Sjb0FN
zCdGlS;;4|A0EX1156k-5y8@1|@55gijhL|mA|198!a4A?FT)OEE<36S3PJo|L!T$A
z_mwy#u(&19NFOMS?ns34*S<786`0av!~q$hs`=RpebnxVIKG_*uW`sDUq?!`Gf`0U
z$GSxebPsPH>~$S@5N^e9Y-0UQRe&Ob1xWOfQQK&K&gOfG>Roi5ucWRUR(6vB!t!6e
zwyZh3=^TBndSLGSZpi?Eh!~a@WO!lEbbX*Rbxnqa9E*VTo)mt_`EADf<xO1{ng`m?
z8AuT-YP_tN7OPNTz7ITv$ThTxBtWkXYHA*tGdjjt$7IEK-SxGam<+_#*qj$6WyZ{N
z<1TG-7la;U&tyX)TU-haNZA4*dlUX~KrLdkyz+XN$U~%FO$z=(BHUojiz#ExrMI;c
zQIZQ8r1#loOH^Ma7T|Avx6`9S08fe-u9?TW(Ec1ue?ku&ZAIl`(gj>{y3k6O$>^zm
z^I5qCiWel93bzCaFf5RrAzA}`{%vN*AMyqLQDOraOu3MQmbs%}`PBXF3gSIuRaiS(
z4_>oheFYAWw6(kLXlMF1KnB;-&mTJZwUv)J0BP|qiwoBr{6xY;7TGue0q}rK8U|pI
zxelr|bc`SrKgx>9k2<Qn?<+$yI1g)Guh@6tTnx8Y#O_O*PA}ey>9ZvBc&))>4V$QF
zD@8Ry*jkm!1O9{qe`E~!AYwojJYyX%_+1EAaU=lDFm3b10b&k7=hz<T&87|-PLc%3
z`T45@<%n|WlDg2NMkyh_eQHye)o6}^yS{c*3s|P63c2<yOmHfqFVnnzfgTuEnM^g9
zC>Ob5oJV5L9w65FEUWc;@liM1B1vbHFq0-#hiZ@RT~GtFgP1G%ZVneeMe79dO+JM<
z){xR?XB?nizF1WPfGKmWhC~_{fWBcMoX6@6|CWv=|B%HIOHLx~4uHO4tZ||P&#V!?
zkO)Z7rAIme)uEICj%q;CJ%FC=$hei>u2)6IUr$i}CUw;84?9&q>#TVpkY-8e{z%4^
z;4jvMpVyfJ3+da4En59WhnY<@kgVezOe;WefDH&*-&hM{sGRMXEtVa~lkkM`0}Y$<
zi*v9$3uw3us%rA<MmR@1D4dMP13ibfHx;!%hh|s4l`~SO<l4u;DU>WME&e+%^sVE_
zOA&}CVMP=H0TNVpf4~}&&R(TLhlx}T4-<MPiN!VNd?c-J&1pIwbvBlw1iXBDO7v$L
zweh8BAvh#_R&&_?>l4zRMUj%@o)A8ji^B8F%S`ICkFvf+0(uvGXb(6koR2)#$(5*%
z3VD5(NcL=`92~O|<FE#Qt4;-0A{tzj5fn<rGRK$ktmDvnqk+%GKy><(GE+&AF0Efh
z?)KOA$jgvKzLZd`O5p=ZKP#P}Yigvfr&N3^8B4M({#*qTKn#HrMV;e9A@gZ&;Q>o8
z%^A;kyanC2m{JD0ee23mizX3Z$fW_m8nc)nw{~B{1TG5o*P<d<>mh7f8JXJEp<wmP
zQy8$oP(|A4h!e2lt#T`>0$hliAb?}5$x;Hm5=)*rx%<W@{cnjvi?FLD|KMg0!Fsmo
zlgL!LKS(5Cp#g!~rYd_sMrpu(!=~2lHj6n~_CUP`Up)@g`D8o=?7~ZDaS(dHkWG8y
zHtviMkvj@a^+%TjQQY!&i>=x1+PE+5ZF#~VaX$!(;CdsP-2}6(>)%GI24~e0r%O^^
zv=5CYsz*a3b8Q3^>{{K5-?Jf<$V-|vfKIRv=+Ji3@5XexjaL}K&AQ>iHvif9_~1uM
zgqOryr^|Y)^1f+;J00<mK@sr>dYO$A2Z!fFuTV^hsy70OG>F%JK+bCkpdf0L14n2G
zeI6EW4>6l21erTED;&%9@brGB!T4n)A|jZkV5T(bD8x7WyMmMJo>f6<JN*~OEzoyh
zYjEp=S(_y%8M&`~J{do6P)p+pB`9zi1LyrDxvj&g=zi%o^zn%vUiONtR8LG8q{IM8
zDx(ZO&2|0d9F_B`y%NcJenbwirEpvpx=WBBF-c$tQ~5o=wJdP^)+gaU)~^`yI`0mV
z{ce^qKc&c;bIGB&boxVG{GP6YemtkKDu62u`gVS*G^e=~smM}1dlY3ZkD<5eF8kl~
z`UDLFhhpa>Q}d*E_X=n-b4-kPun`B4upNJp0?;=QsB{z4s@d>SM<0eqRqCdyw1UHi
z)4^Q87VmBrO@zj8kKi5Jo}ZCjZ|E1R(n<SEtZp4j8j1?=wLJJ;rqi!%93Ah*vZnP!
z^%neFT<PSc9zGFBQO^Y+t>f3<z0HsXf+ET4Rj}~27tGc+wuIA1A?3y_iDs4spqLU9
zSZi7WO}i5=@LOFHNY?5y`Z`EVi-Mso-t|e&(u+#KTRn%$trybM37aybELRFyFExLP
z)7jd1Bmf^#d0V6qaxfA~qGGe9YXl}1JLg2@#^82&f*iHctcb1SJJs-?Ttu~rDUj|m
z7e~s>=?^K30<{V8qFt*^M)*tp^T>-`x>zNxor~z=@^dUYwN|8^SVLgqn<dX6DlGpu
zl-^6ubWT3sqAe?K&Vt<hK;axE|LF&YQVb*#xS|NlE4Dyx7G7pi`2e59#>IKoS)@>Y
zxBdzgQh^T&-Ajld-Jf{@B$QL|O_fzWzt2WDMWDc_SC7poMi-X*x)3D>cUn?1v*Ln0
zHe`~L0aoF`I^69^;=S7$<R_3k2-~|VO%Lr6&K;!ZTf%FPyv!M?4zcTYyFqAlB;rH@
z)Ip^4SXa)8!ba$xTIi=lcCM)MmqsYe4Ta}P9~(HO>|CYJ-5qtbQ~;>yc$>Kt3w%x(
zv%6HlB&g8#Af4+}c74R}Mmo^HumAih?&sN7EC=@C0ZrYf9&PmC?~ueOQZTJUoDXT5
zU419~JwO>m-y%fvWNU4*!|5nU<Ta?40Odg2bsjdI6EjkJYOs&OnHwhQSve=9b?P{c
zXFCE=!&}f0pxq2_Idn03s>1LAH*e9KDQbw3y~gUW=F<<IF2E&kHmqH5@7RFpS+#Xh
z2mw?vfRvd19jMH}085wOWw_*E;fA1s9{oLms9JLM#&+8C%TfmWK0Jp}kcFIh>fM+x
zSFvVVta*T?r_Yjj5^X7S{T*_8fh?k5<<c=bo?M?qi*}bgVl#%X#ozdEwu8eRz@h-A
z{e6M-PNXxnXFDl~&B{7jQf_u6z}EuLT@VO`x24b{S?LS{$rXKx1XVg)_}_<WaXgj5
z=Lwy#r}IBCgBWgkYl6zB%GhOgEG6Yg&(hmuw)_X{5UnATYi<-bd{$P^NR!a($-+~V
z^g~>)yuiRYS9CTQaD_CV1gw_Xu$<Adz4cdM4D`w>x66^^(Z}of04SO?SQW8;uP}@+
zyqg<-pmC1)(4`;=LVHT5rNK?HTST&l5D*bY@gg>i)5wMRUV^lZ{B%xd0P!G|#vL$0
zw|L*&zx|tE+WO9JD2FglWtjxXw`vz>=oq@(!P!N4H3H}FwO>aA%NbDY?m30!&7xOK
zG4&R74bAsK!VnXcFDl|zPMBda1#0AxK|~a=)$#;O?Jnuf;hhGQ)iO*+uB-o#tgis8
za%<W?7NJOq0!m0pNQ1Cx1q5j+1tg@qkq(gt0qF)61Q9{HJB3Y%gdpAB-T!RPd0)@>
z{@1nf90i_d#jKfo?wK{C0Txo8AV~<$QwPn-xCIyUX>4P)NWdD;>wEc(RNmvohNJJt
z_7_0U_uZVSv^%0^$6lM7t|ldzPHq-r3~zK7vEJEE2k@Pg6^;9=ilt*k<=`gx#<b5J
z!qGz>`UEaTh^6Y!j^MD{)hoFkd+oEKJh}C5d|;GQz@c%p!>Wm_Vcw60EF<?X1G1ov
ztj$%jpP~A8<@d<F?^}$u9ix)2Inmrkqq?U^B_-&X;E?R{b_#tj-?T>!?JY@*-!}nq
zSp!~S42$YB>+~^ea%Zm#kff3W9iQw^nR=C!H|sBLsT3NM^HzDzN(ToIAC5;zSMOM$
z3O>Io;&<s{GSeyvd^;un{fU5ZhD8-Iulv=b67xyBW{LbB1&R6opnH8$H<yRkNdqCQ
zIleH3jOy*{U!Tb8Qc!cntQxIdzhiLMn3E3LxVI)V-FYe<D-bh8=6qG0)^Ksr<Y0N_
zIeh&>DOnV3&;UGLm2}DNUVGZF0psuNMulqLxSZgiw>xg@+o4?w7n4%$ejK>fg^zzD
zRMv7_t=on)KUNxy8P;wHB(?l(Z0)$g2ah(*qHpIDQGtXIPF_+8k!CB%Qrj%}Nyk#|
zU!euQ4XE|WFB0Ltk;Ekvg!py&Q0%CA!VPjjzua$o!j~IVc&TE4_oQZOb*d%y#hU(j
z;~*qHnqf-l?CP>ikoWuE>&9$F8TOQ_<I96BI5wx!_v+-?06kEc5A_r)KK~6xL(;n6
zzoQNS`NDU`nLPSNhytHMgHRP+#Ha#sZ)^)8o3rY;kM@3`a$8aQ`VO2)Nb2ccd^*34
z26V72u#dYT%n;7{_+5feY&PtKPHfl_UBE9C0xDMlGK-Dyr+l6A8>q3qd56$LG<0Gr
z^IJBglHK-+NI)0zGb_qBZ_)wdfhaF$o7;%lz%doMahA(9e7JpwpFL0L736QJ?(%F1
zp#M;x^K2kyM>&xnz$j|B3CL~!B5ufyl~Hf&Y7%0^hodJTK3p5`vzF`7z+iE@G{!bC
zm<r!tR<D9PLE2N03<+sOI}#X=)D@oRM327ItM#{5H?8t}3o<?y0|vi1=j7JoqcrCB
z<TF-q27MmswlpTpZj3xnkyq7h9{h^6XkwyLW5yQPNaj|$P%R$Dd7I#NaP(<Iiw*C}
zW%BA>exA4ml97s_X~9cI%*<tXI95x^y}Ge|WWUWtqbI*S+wJJ>>wTAE8!tQJ+*5Ui
z%kOqf$L|p$Gv+Si8bkIZ+<S68c?Dd<P&jb&rLCxtFSH@e8;=tosJ6Hn7<nf>Y&b*c
zYF1y{tn%1Pt~d<y8y(<!HaB&U`lLp1!0S?gE3^U%93?w|x3$E9)g>3OriD~(4p7kk
zEB&GUh%%Rt1G_sJUtjFs0s^DmW#vZYMUk>+)Hx!&UKaq7_nAB@Rx_TQUkph~<v>Lf
zIp-D^ypmF{J-De44%o-Pj{$qIn3PV<2mT_X>y}^nzRSo}3dhu?0vRT*4GBsLr>cUk
zhwh_Jj?9p$Qw&P#$-Dr@iz+c0;c#j_)!E(;BXkx{i?1}*ijxwOU2lS3mj_+~6lF2<
zS>o}sA|0fe-POHcXpT;jsA1g6!g(>(iz;~MRVAk-^zq>KTNz_cpBE<Uv`Ch&I5TeU
zs7k5zHzFuo$m~^qPU&4MRCs-^OD{adEfAhHZJ6?rmBkPB12auApBby(LM{S?bD^Wy
z)ZFLn92Cw7ocGT0Zn1BC_EL}28r;tBh<qHt3V<;DEZlIzMQy9=w8342c%DBfy_$_4
z`Cg?+YsV$$__WnQv4u|;2~JwN9%7&Z-Cz+F@874N9A02Vkw3!(oN7!RgM)nQ-41;F
z23-;cqbG!*8KJunFfVKz7LbUS`@o_<+lUiCpV1arI<qE+)sQ+Paa+L)k05YB08p4N
zQC)o@g8gb!GSd<uvQMqEQDZ*_1|Hg}i~nwH?v97F2TdnE!TYz|{F`(>wA2#J3LG*z
zd*MA+m0+8bHT>{oaS(@vTqxew&R%=`Gv>K?05&I6jn4d*nO^+mVo+;i-{)+GjJ%xa
z!`~(l#6oeRe#=Q3fI?x3KHjChG4|sLD)7XdJW7z*`hI=c7(7Bq{Jk2~^tMT-S%*uT
zEADR{DU>j3ByOdI1==R9Jyr@B)q_FeW%aEGFJjze&6Th4J!hiHaS;Lq^2pYm0uKbA
z2?y%Ys*(0Lqe-&d{P<(h4v5-3+dhSmmBZv~JaJ0u%+13CPXPQr3-a-ZIAE!!(0&4Y
zV$S#K)7k!l+5V)+7bFrIp-9>1s0Bjm()ed0xc%^Htkf73?)y^L4Su+7;WQUE6h@8t
z!k_^2v9c3=AN2n#FV(iQ`k0>oCVx`um;}}t<om<=HB4~~Po`|yP=}Wofrs%Xi>4+<
zAG=Mg_jVhi`(M5fK#^|0i!b4k@><U};z6$wVbCv6&9w4}RHHFNLTK2T@&4vNG91eG
zVNX+g7S=H_(r6%apR-I|2?Qtr3Vo<ePONwVBZ2D6cZ(3b&5i>!G+n8T9tv;pJ)gy6
zkv#BJ)^e|JI_3`{9grD|?1oTy*Tl$>{PrC#G;u>hH3fB*+A*@yq^dE?T9cxmF5fZ8
zoOZ}dot(SV(#ykt|283Te?)Qja`8ps28iCD8!zJPuO5($<VXKrl#J)>?~~Wi44iCS
zU866Hl`{o{z8J_3H~WHx!#P3jP%bt_*XyT=!&T72Rd0*%tdSOxh)RJ37{ycMfqunM
z)|zXDE{wF|E|Q%X-Cq8=vL~nvF&Ff6{nMmu3h1;k*&G%XZzS_RoFLBbMLVNcIfXLI
zl1jpc&S`IF;&^m{elghlt-qbZ>NSfc#-|3@3EV!PCLOUbk3)j4G5+L_$Nb<HUj+{n
zcBHO5Po0@@+-{LVZEAVelL_*gmRmF_tHnKv-6~?YFI9XlRhI@SBlg=1hulyUl&a8l
zPkH~n807;(dhEu!a2EHE+Y%UhkeNeD=pcuxYBi{s3GSF~p~JK!ii2-yqY<UG`v8ay
zXio^<X8r29UDf?W%KP09;+0$)qd3TN7#lE#V81s6p5#^4-aPwec$VHrcNmfsD3<m3
z9HbsEJmBJdk1Jdc_ak({hZDkm-?wBOfP2xa?%_>XvSKWI`(y4$QokJ}@9{JE7N(*P
zR&L&L#wKY#%}$`ZPha0X=u_R$@*^DXdL&mL-D6h>rSyKeUC1-M(>Mk()eN4X!MA;8
zk3Mu88KZa?VT^zD{#jDz%jx<!X?wS$D@;6v#y5q0Abw5e?g`=H91(l$5W8i*$m>C)
zGddPrYr_sykbX+!gmf#ES#4ca4$6eh-av+iaT*c4>Wd;7&c#=awuXR9T_5SWSX=i2
zk>NPgxd1L7+sbJMk;jpiQ2@2E|7x$#u7<O<5t0{Lo8>9Xs|ibeX=BzkF?u*LIpH&C
zHd$0*s1O_D^(+GwCZ6YiNmOV48x`&wHR=-Y8tz)?6)BX&Q2W?(XP(`WAmBs+#BN;r
zH(SWcK;uOAQW->N`nxYNYOG(}tK7vK_545ww3*|263YgTWkVFv3JFByba)+T!Am3D
z>U@`wELoAZTG(uomuuYGVy1C=1v-Ar1iZtzSpWO|uRD;2to4xwZkx<V5z5bFiGl0<
zw7v$@JwBPr@?|$Nv<GerRR?GNxbqejHR@Yf5HNx42bAW#vVY|Y6-4CN6rU+bOI_XE
z`V8sdLqfQa<=}o78F@?VBJazr57j_rA?YCE0x1lM<BP_HYQAq%>NBVOcuH-reWH16
zrd~!6)FiAED!6~e=jHvC`A5m6ZujPFyWaQQiFKMgX|G&jfzZuMWM>^-1KdAztyNVf
zG9o`svF*z4x!>?Z`?Cl-eV~H(Yl0>*yvMztRhQK8Z|Ir@-1+sU`7_ZqljW-CN=gJz
zMV(1`FEwmyS_q6ebXeMBUJDF%soQg^6#U50COME2_*2JhNDTM#BV2d>9y`{_ZPl+@
z`LFXe`1aYte{v9ejLtaThzOtwLcD5EGd5G(y^`?U{jsjbb$y>9Hd1Wf>AeiaHP5d{
zHWzNX*l(QPupM|&BR?e}+a(N-L>Q4-UNj!$Auu0mgpOeirNna)?-JWiy&JR{BzJM6
z;=?+suX1N+K<YwcB<F6iNjL3)z4zyk12?!6#Kd>4|HgyU<sigS{0l3UTP{O+)=cvD
z)b(|T$15=^2M4Y{^KB6)lcrB(yVAz;QWyA^9c9bb8BPxqGQVn-`;WZ12KN2a<AhxV
zW6)tdLtwEb#kak`c8lh#o*yhWID9uA@?`7jq<O%R@JRO_+ge$R(dX=mD3A7ZU3aZv
zP4c6H)1@ZY2WJ`|f7`9fYJPKn{peX1*Ya>-h)h<r{Bw=RI+6@7Sy?$cJ#C*vi_CuE
zHL&ipDX`}gAr>#+sy<;48gsesr;pdSH_)v&Jxz)6<}Sv&#BebiHD2(;zN!{`vqfa&
zmRKR$PfFBV-t3KiG(1Z!UClvf!}5AUT_1jBi<sY)^XPD~AQaQDL9}S$T}$Yz;emkm
zs->FGjQ3}PGstzP7H^6M3+Ufwj!CF8AIyzjV&P4w&rXndB0c%@j>Sylf}t*t#<HB6
z*gfJwrhzKm`=&8WAM!qsePi)7C^j!}UWh@Bs(${b+uVP00pw_@GHw=F?B7xT<l=8)
z@_A}Sd^KICi#a6a)RcFdq}CHUhRxVb$VgKPWZbNtCeZ1imZkY*$i5|4K+LnF{!?u_
zygUDaulA%}1<%Un(!#JMh6{%b`iWR_ZEi#-v>63K^Y)5<mssCTn<(Ae`DSO>ZN_PN
zRIRo;{`R8dYVw&56u}|#l{mwz(b0n5qLqi^8q3*`G!rvB9AZACH(UI{HYF9~ipXmj
zC9$Ar*xM>5=RM`C&zNZ#2(Wa9xX^W{@>!hL7W|y{GX3a=D?JT-wa2Ao>t=RwXKY+@
z8EIF_XL~-Hi<=G2&hV5dmYbrx+#f9Rf{xR1I9iU0K2S+{!3wv}ZM+-npf5m_Dd~*)
zY`<qlDe7hc_fSZ#`vN6d>8gUseI!^R_XjKLi557rB?+fg&G^cNMXw!FoZmg67A~+t
zG*-^d=9_MzVfE<!m=8Es@zrKsEQ}M)R!5}Fq_{uq{jAsp-QHn2E?=m`48TG-Bjhb>
z)(QBE335xa@APjTRF=T?p^UEgy5CI(1IeJg(9Y%*PJZ@F0hg@o%-IRP(>J66<C~5|
zyo=9|RhWhfb;onVL{GM=`G9nPuY44;eaD8;#T0HJ?WW}D%tgyMR%*JtED1;_TQf)Z
zG<lW^Xoe7TB!7;Qy0y{D_f30<tAi_Bqp9+Fiuk!U5cYkPRcSGeL;C3S2cF#>M=UZe
zN?+|CIWFXzej#@~<}v=i|GaN`H(^K28p);M68<jSS39;b{xOME=XagSgXcEK+gpP#
zoRR(Q<r^0L?i-jDSNkV+X?_&Hu-rT(@j*^KhL`SZ!eZ4Ap4I(=q>26q_f<B#hrXUU
z<BgzWebRLoVtsGbgI4@iwK<t|D3=NkRjX_xBc<a^d#G}EeFUsGhhsbSqN%Cl$wW=w
ztCg!CJ4+{>)h)QQx5_7Fp1>uzz+dELeuZC2;5S8pdDe2<=du)H^LwaBB@Z{Iyc;O6
zo8RTpvaJn@XEHF&b|MYf7lx5WBbnD^c6JyYr=0qk4-!AaPET&oj7vPx+x*5dS9j+$
z!hGrZ#@v>y!Q|JV)|DS|`6GNzTX{!x!-d$$1-<q{+b6QfyKM_L95eZ*gVoyChx)%w
zPCZZ7>6;8C=5(fn(cLgI(E}|4nXoP>48lwDmT$^6gb!-g`pWPJ<7n;ELNTLpAD1&p
zi3xkO;A9Lm#)EMtLFWZbv!Qg6Jx#0>{-hM)6kW0KxFKJ7eSl=^cb(eLH&FZN?O|Q6
zp6Jp<uEKxj1{p_Xu7}i})!QY7D+6)$<_S*A$wQyNWziHombxd+a|4;bzoYzXW2itb
z6<G8j**nOu$ea&~C9Z9OL5_#y9=a!i-vW5IgmUgOaU_r<S+Sav`J?2<oRXRkc|ZCF
zlgX(W+;Vxbe)@v(Md52Y<ig}gO!BPPFU^-8YK0ca#~!6|X^~3sqKG{$_h_a&{WdK{
z&vSKcFSwzRi*$Fc@IRPi4s=*U*$!NH!Rj;uciST>(RvEk)=Ux$#>uDeGd-hH-27`k
zBx^Q=(DDcbxgCTn5Frjl(Lw~nJ_l>DOctSAP?21<ne?e?h>3iA7vqi9kDo85ZFU6Y
z*1vZ=FIetz>V5WX9eToTz*io31MB<)J4DR^a!f)}#!xGOJ`+JAJL)t^?z(?G1p-n3
z0XMy&Zh=s%!8#%XZ${>cUb8ay#`lzO>8@QCs|PDrN~<*MCs<E?ox^)Lhy`BVBCk2-
ze5HI7b<JL|<?VEZ-HK2weT)XOr@&IDrMZZIy+z8Zd;|<2v>W!-wrcn8A>y8LWySjF
zE0^2}rG1^|T0L^18y-r>Br<b<c=^tX&cA16w$YYFce@v?YgUNH{>Vw!`j6n}(0J2T
z(qUyfyJar-oA<Pm`-2npboEX2L}tPc=w)xUV1729JeCaQ1F{^?;>2UmenyC1x#Z4@
zwdssAKKWwqU1D;pTpbAJahh=@=bZgVUzzwfL+2+E_w>2uUb4D<L-z4Xy6HfX`L&YM
zU3@r8bq`-#Aw<QVUCXJm#m@fiKy5PrIZyo{hVS60qb9>l4$t9qLvol5r|xx%72>Ti
zFXCkQTHLFHHLz+a_3|wt)ETc{*qhPU+CbagHYMpEJ<A}WWN}v9yI(-wnzrVw@2>DU
zP>owsetB$RdTMp#Htc$EusmEIAZ~C*$Xt@}l=VqtaqCiY*X)kzxp=4eX~$E4(zhzj
z_ZPWdR8)ELeMITjTDrbpCv}G5hDJZ*VXV(LAt{q**w=fq)@f*{z~s5IWb@ijIP3{E
zz-^@hyVD8+3pF`6nVWU5$+c`4c+B}&jkZM(Nx9KNSv*f#+s%<P4*c!+aXJlt&0YtC
z6TQO6Vep%=90zgRPrrNlc~4iWatrV%t8B0vwPU>xZPf0Kpke9vW*Uaa#)k||HH{5C
zVf`T**|Va(FVSQCIqQRYjO(s`Jode0TecB&?3q-;l$Tv0z;s4V*_C~_7)h?HiEpfx
zpW3)b{j+^#hixnV#W}2~JmvRE;QrD3R8GO<HjI;fvzKGx=NuhlOpx1*{r9POo-T!4
z9n6%P9ef%ezR%1oSIpRL)VjYYJ{L=LMMLR9E0yIez&8+>WD;~+;feOxrHJ~}6q_y~
z7Nm|A!s|()D@RGs<Yf@hn)x=)89Tj!lB4AM(dIrrF~<%GR>OOMQ_EeTb!A>T(aY;*
zBn80{yL-&nGx2g$b*?*g?4hQ|A=}mduW0~(x7*ElU#+zo&mH?dTjBlD&M70yb9C_#
zi#PsPay+uR?|x=h_B}TGxZ<R8*3)g`Z9x;d=pG4<7-UnUF$X5v#lp4WU^i7}+9|n&
zM<Q;dN##3wTGfrzod;cG1!fzDdNYM-iY3V~pI>CC_P``>zb2~lYm#?3<M772XgT_E
zCp#t1Xt|2XDAQyv5R>rPQ=a&eAcI0rywau#@FNt@<%c{c_a#%pbJr%hrj@O3%V|e|
zet9~0y2a)<u+j4JjBi~<t1S+=dgapPOYYc>#pa7y_Xn$-1mr(FefN~^HOzk9%h45c
zoBZ@X2#oLhnt-Qt{$Q7L6HAB5r2V`sb%}m_5VvHmnbJO%xenk7In^|gI}p>5KkwE~
zw~0<J{$os(8o$}5+{&qvlkcp#VjuyzGBGSP(p-|qNDB@2($C?}vG<z`SUrt1yYS@Y
z(4Lu(U2LiI5A+ZhJg6qDh>$jbx7cLR$C{_4-v9c6w@u<o^5Ny{m#lwrmGYA@W(2K;
zhlh2f*|O*lzrBWmcKnj4RNVq4G&C88)*IQj2wW`%cET}UKg)<AicqQ3qYEfpGEpnt
z^QdI>WZ`!Ki%f7Zs6)^!%q8dztOGH|@pheS9m*xcj}>(<O+3Z$)1R<+VI{@hov|w`
zo2@zP^h6{v=Nu!GToW)AyL9S%3lQ%(Sjxl6+gP#Gij7+iYEEvPri(%IXxWTwF)d!&
zMCvXrC+Bc9w8Z!zrjl!}<{&f8VCIM059`t@L7gVb=G!J;oVI<DbS%Fg6+A%*YrMK5
zFLyj+!k7_pDbF0k?+rvH@n$yejc?GP(DpGM;J!^q>xmgZ@E)og^{{;#@~u<?j(eqH
z9XBR79eGsJ8$4TLzT;@e-jgjs&lqaJnXs#{E+0V+L-?3g`i7<OMRYK7mDQI+2;gZO
zhqdSOPQr|((?;2hub{BNp~LuX#N+<NQ-8NUW#x4@J2KYD>9=O6<@?uXQn^c4EG&K`
zJS7l0MZ-puq>}G{$*Wn`prt%2@|FcH1Ig8V!=Zb^b$>DaepNpuHT@8)p;nv6`Uv0X
zS1NalbiA)|JZez?`ZQ*>4;KR(o_V5!hdw-dIbGf9#)Oss=lFblvi*^7nT+&0n~!TU
z+}yRW+iN+oq>=SD<)|@X^E9v$2|DqvRP0dFP+j}iAl*F~C%t=C>RM|=f7WmF+GAaR
z(5E+DTsI{p3cu)Y3hi1yyf~xy*C5s}`G`<lz1BZzX)=Cf!>dua@cRaGf$|yS<U1C6
zp9q%IOG2W$8cbwb7k!P-9iTtsw<<LP=VGR2tUfvW&Ox;T`wOeq5xIAXVUNG6G?vA0
zDzw?J-JQ3j#1F{j9xBeG!mIyq_j=I-GquwzJAF}!uSQ7s!xjF|8iTuOHo*1ab;|3S
zfVKNX>zBCl^zbq+$^^Tv_X_PVdT|?l&b)_@t*yIdX_jk#XD9?0(=Wheu$sf3`p>Y=
zm$-hSabcI$YFVN;GA37x3l9EbRu<i8b)3Kux3)N?-GWf0d`~A8#RJqfDqb6N3<*tk
z@wTFCe8hnTNF^3K$Wcj4OHNt&X0}?<IhOe&3)+^<p)p|@DGd%VCrEnUHyJern>992
zY(R{<^GtFpPY>Vk_L9l|=E!plKVc%@M+wXaSV;(<XHFvlk{zAe1JfMyLM9hcf&#GN
z5SzRu&ZF|WoG|5v(cbEabR4_SV1Z$Q=`ZF#FGX!K;_|V(5TE^WZLxXCWnW`3Or3cV
zU1?_<H!$}T_V<0n!P%gRmY#gNkh7u`wMy~jXnaE5YROBwZ1bM4EPoPRMo-5)2lIHl
z&}j2x2f;Rf`nmC3#;egx>E&OE8d&|si_I04lp5XkMp`226=MZlxGL;d{_o_yQ{-R0
zdQf1{F&X{BLVIhrW1zSrtaieV{bf7#M~0vvy5IR>D(nFwAa|GJZv$e`l@s)8?6v!7
z!)M>HvLi2_pU@UA0KTD(^R21Q>R2)nv8P%Zt%lzOWvKBfk_U@r<mK`9MjXf$)1|d{
zmj{=B7yVnlVLjZ@V3g0EH4m5B@=U$uD4mIZF;MBmQEYBoD)mJ2O2b_uqmOW33N?ri
z4_`k)cq5mnnD}B0Bo^WvBf>G!y&{(fggIhbTE=h4)@iGnK{u87d>w4`|8MR-K0g0C
zCnzhyLC?gbzdBr&Xx&UaY=dMn`(21RRLnyHqpsUzsV!!cx#8DC84X8g#zvc$aW^V5
z+LN0t?n}79@@T$im42a_7dM+vdD~T><|R5TTD!F8njiEsv)n_P=9iEOIR8kOxznGc
zf$U7;8+=}3F@nGyDyC;(m>hLkoB0%@IoYcyr{5MyUuZEBpQ6^3tnpQy73b0G!$nDN
zDq%RI_X*oYa3@73nu{>l3bwaGHx6Af>ceOWuHuD0bKl_cd2tojn@SErBIci&U!M)3
zBf&RT)0x8s5snIKYHhHdPE*0$v7E;EJ?S#3iW#yXx9xBmpTa13;48pen6v+v!upPm
zpzAEfNB(N+pDzl}XUjrQ9MdAA=tfGzSMu|t2JJ_%zpV>{1m=TJy8AD}smDf2M4Ar)
zFZA?>9NqZbT>f|zt?K4z*76cciVn9I__x|Q_;-30mzT<ya&!lBwOwPNg3D^vo~EYz
zf&S@shETWr`o=MVwBIExuV5*{eCq|_%M)5)O*v(}8^X0?iP9w?A2xK<iuet#mb9Ob
z)%RFzbosq8*XwVr^lTA~lb#Lv{?6N-LR1*y%zh1?R^Q{``%AZ~TgrXbC@{nsF7-@|
z`_Vyxll*!n&QGzCX|d2;;KQXPJ_m!P{%isQg4}QRE5F0y<J&wVqodg!R@Fxxr>@_V
zP5cf;Xcl@iZ{+=e^m2|84JnBUF6{5c0iu+kbbw;OK=%?YLas^KCtx>*KPGAWZZ4&x
zh8Fy)$aQ;IU9AvFRUzZ@QLS+4yYDwa-M#4R1h>9^(%2Jx-7y=b)&2BEa7&E;(9+c0
z&vILw;X3C^>{A&SPr%}66`yYNTWzhC-$hd|VW-^P1)+?M9#pX*&|O!Y|LH*owkbWY
z812!%mNAn}t8ogL&^Dt%U%ZTtk7mE@y~)jw4x>Oj{w#jM0+bh?JAq$MtGq5P6$yJr
zvJ0DOwEKkt5T9(RZ>No3H@f&kKc%?6H5UgFdhVAaVCBS*;ipuR>hXA^_`3H+_sbLC
zXLf0RW{$DC`zxnZH)V8<wp25h&r{}9ADV#t3RqOEaP7WYEt|7BJ(%0lkd%^g+U^#2
zTFNddUCu`m@lJ4J7d@ethD~f5;@)!8D_N+;EMTs)L-^A)oSyC9vb5N{QOq-~=`n6U
zD|ANpG9tMrkm81{VQ=BqK<>0HS;dA8xMO8duXQ^u6QUyP3sPrkaF-S=th(#3DL^6X
zwhJ-F-CW&udqq&>r#bje%jutW+n$GbOFDkh2Zr{;FSuY#>bn-hmRPiR49RUeW%hm?
z4X|O<%XR&wb|{U~$rCZD4!%A^+Urv5kL6=`ZsGy)5tn^9Ez{5Fm_ShSC_m_o&HdQ%
zo?LQ!#acBI$x_o^Efz}Fu|Cm2%wd4_>eVaPgvv_(bh(ty{f()OBX-qiWRp)#zGh97
z@#SO`THP6I`^&GPJ|R}RMf1|U$MNNSwUW8HY1WrIeB5Krl(N?oT+yx|Yu;N;-DcQb
z))`vV2bG!unT)@xa1x%mwyt(8Ic=;a47@n)rjqFVK5L~-gm!Rn(4NQ>(VZ$D5g#82
z`pjy6kn2q64<c#&q}f3G({x|-P%mz~!#?*L+@fZ)7)I=7l$OR?M#RLlfT$A-x$_SW
z4ifp+b(XXG#nVy*&_5rMrpBKgB6_VIj~F2Xk9VV<Qufna;pA7L(N1)^6R#a9K`Njc
z(~T#0X{<JBJjToTz8SV0Dsys3M=+oGu^;!wf%j6$f#r34>}t@N*gD^xDj_3-qLciI
z6Fk_?8Mbqj<E<6)&$4-A-uT|u2If<zl@-fXqLn0vaZ&}&m~`1<7dVg2+Nzw$mEK@g
z?$cm>@+z5$^df-xg)5_enk^)9!Zr*W;L2=nZSgF9Eg%wb=FH5@L@E0`!=d9K&;Kro
zRU;CK)CK({9n0$Z^(!M*!(Fn2J9lyuWl5qVEC;ysAN!bTZqPt!r!+mio)3l|`9pTV
zkLq+#cB;zKs%8)6eJfm!ej9I3<`*$Nt!GiS^9-aw212v&F5zLL9vX5!5(;{O8~=rH
z6aG<uuX7x$_C-}yRgh4AhM^*_t}(}G8D!0Lq*Tr)k*-tGn~X<q2v@5E@->F?%<nX?
zK4~?pn<plO{GJ6+d??gJIl&r5^SD<zYRb~=?vGyY89RW>64ElX5ALJKv>+#_X=(j>
zWcjYct0`9BCONUekpBME;~`h<ZC8qZv77YL<>%+KVtF|#7rUT`j=5g(xndf?FjNIS
zcaIdoZYSV$rGvC~0EhL(4#!fEGokk@35rw%BPjjK2@^S2(-7Z8uK27p$+!IW8+V~F
zD~txtyhP^80Eslc;`c-H4f7nXjAf+dFJtea-oD<jJY4E~_~ojaLn|%Y1JZvjOgQM?
zLgOBPhhp=s!7Mp>mE7ph-jSTk{xZG&50Lc3rO}_$3b8_XjCo&0-DGq+Q;uIWjXOJ5
zFDHF~1tk9cTh^5Lm|HvImKzg_IP#UY*B}m2z5K)*{5|*pT(!4Q>6k3he7TO9!EUoy
z_76DqURz6QS=AnylsnjA+KUjf`xn%-ng@p=cOuJ2bf&ONw@sP5CtXSq#mZ0kiBf7~
zHH;4^cIs>s69P=NcB*pSUZz4dYWi7kK3&*F1rYloHNHXJeYwq5-MybmTH+jX!ua&W
zgQaem;PC-%b=r*f1sQ1kIkUdl94yJf{Gd1;{cv;SeG6&|8q~%;+D|rv^YI5mdk=ih
zX3RK8bHvt%j1bzP`jYeI4e8P|Aig9&9KD}n#kvH9$Ju^>>t_Ab{+&CY8Ae28kNCki
z!#3MDp;!~NCzsiIB?`w^XFT@Cg3~sgLcWcaOQi}kci59ypjnHeX3VrI-mr|h#hIHv
zhncDD45FtMeV=K(meXv9K#j$ly!PRrG3ijxTh@;a_#>x2Jg$)Cybsa?D}Z{#$Hzk<
zoLi1Jteo8T?<@j_^`{9Uy5{%Bb-%rVv+oyvU*503WUG$lrtOz65-oRl`t;1)qu_~3
zv`?CMTQ(AOpJ1C65{h|0*7eir8;8-d9$-wA&cLlkPp~8+`UBpC&EQO6`$0?w==L9G
zJ)10;S1yUZS{@|N7?LS~s)EOmy_6`+Uwv7pmAW~wnBZxl{w4X$K_;G)z&oug12<Xs
znMWJ-n%noUpNNIfqE_^}78A;&!mTC;V8o7f>A!ON$p5;fFn;y4lJEr~zW#5NU-GJ>
zi-LqG+O&BVhCMJk;8-GOYh<WN>SuP}#fHl=sy3>$b~1s*!hJBlk6CE+D`2g`ri_Sx
zRq2`QCK-r8akeU2q8I9Ll2i`MYGC@kp-xNtmn)VR790J@IY=Eu<zTw0#kyPoC1fj0
z&+w!ADPxK?T)^K^B3LsxXj$7lFzv#PitY(~JKPUo<gIelE!`n5c~a-y7FMk4Z|%>_
z!C0UUbf10`pchc(s55D0bx*_U!GG1m8{8M*4SF>v)px!rQi>+nsMp~B3EK29nBQ~y
zEu%@Bl+*F-{Q>3NXjtzC8ym34rB#@X;Sz7J#NRfKPE2mXnUIjKPk1M)1e_rO#<|)-
zw*=^8rkUprR|HW8_U{jcJ=)v2M!*40F_FR?8AzG2uqk5qMd^<uvb14VvGDw!m5F%?
zMPgQyi+Kiky1<vvHE_ZT9OFjMG)~yz>x<Wm)@!4lHZfksHOIxptnuCF>;!it<hJkm
zd(UUp=-V@6u)km!@yn8x4+<(1VExD!A^bhJ*R-+&Yy{2EyuF7uzh8YE`-};(5ds*;
zSJlRN^BelN@A+!4ix8!IY7@CO%$^kh41ma(AuOq{_MgXN<jq*A6t4V^fW62U!bVP}
zUGoSsMydA7-2cq<KU1ERK@g-ttPWh~wDii$9wGuGeps{|B}|}rB|6*hf$|1N4gJ>n
zxQjcu|BvJ`@!@M99V)wS!i8AXqR8KYHfDUnN;U@90N*)nO$>rhs9n1eLBuvgf|B(P
zN?FiS{cdHiKcf71(BV1d_^5&GYnz#U{9|-4Q$&NB)F-cL$l4o|G=EHd1SaJ@90G_i
z$L{rBQi@)lotDaJnv$XhweM{{gd%><mqLUkLxYT>hFngE{a@n4$H(<`vhV0r5)#|e
z8y?vYh3FNUbu@pwg^#r#U{gL^*tsMN-VOMV2PUI;fRG5%rj?<MW<1|U!nIyxj>w;7
z)45M_bz>taRPlvsNW+3&Z=@~HYGDB;*MrSHWLH{#nm--5)rvW%92#h3=i?WVxfwL-
zn(UcMJ0bA|ip}YKH7^+@d0@{j6jivau1>~+8mZNKV2Nejn2I<I&e1jCk1DDE@Lajv
zZv_-zpXq>+SXU`-OpBDhBGnO$8;e9Bpe>woH13G|i)0`j3qzW+CO3-oawg~Y9c>9d
z-(#IQp9+hYTW#Oqo|EqM4k;?W@z00E`T7Ip-e=LevG+jDw9IU5nq&2(?d5j2yXas8
zNmvx6-xwZn{=*&r-W{&`3EYXNxJaAu*u=IvAn>qysPV7j8JWO#{Jm!S6A4`f8(Cc!
zE;X>$zMfo32yi|N;9+##asout23*y1!xP`g_FMlQ3cz3F!Y)v68{C{v6-tvrs?awz
z0*i0qZgWrAxvazOJgIXLTn`HG`$B5TZ^b8b{nc{T%R!ype#3nge1)HXcE~$L3&`|L
zRUQJ%vG%!dLLw!eE$`x!8a$O^ZgQNJiTiKfyB7ul=RCc0rB*{n>T><P?HPqDAyD_a
zv|Y7-lU}Hq!0AfS0`F-Aom8qf=S>m+tEIKU{6*8-CRusr2>{5zbeU<Ed1qLhT!KP0
z)L#H9rOlz7)W6rENn4e~WfSXawWcW4-9Z0o{=>R(sT=)2hYuJQPOOHP1JlH2>Zs%c
zu3X~-LjHZfYfn1N9%(4^$Zu&mMuuuRzJ(P8>Wh|vrr=6yda|E3(LeigJZZQ5;B}?D
z2m&{hB}`W?8RG2!BHR3A#gP5dRfk=LtHZDp>)gGHmAcm;KJ*z3biUijRS2I4NScNr
z>0?gAibtIBO#fNrsocc#aYr*Xk3H?GF2~^sfp4S2Apd;)G>0sz_5q(1?61TO4Y1t&
zPcDGU#)1ntdH6<VcS*3d<FOfI5*m9s%eTqfa$7l!20w6Y)czf*3*+6r{ba7%OSNj9
zYIj!R$@D-g2jJYlR~_{y1s4!}toE~y-y+;@%~UujJlA;iW35G76>>|ygI@_GPdreG
z;Gu(px_M68>_5&=l=V&G_j;qRHE#JeLp7A5!MLZ0@V~=;gX?1PEfkCz4=$*#uH6pT
zjZUezan=-=^u~>W=c0bB^J-DxU1hbG5e!NXwnjyk_RsHI84ZMC6y4O1Tpn(N1|FHr
z_wNy{Z)u>ox9uoer*VfGgC^QFysK!C{X#Uf-ZzKRiz$*ILSAx+n=wP@X4RDsxu65n
z9Vk4$21o$H<q|_V0w@Lc_s3f#^`j$qO4TNH7GY<`mqLFlgtVmCtreH;nf(RAcSD)i
ze6{mSo*>#ocCsAjx?Sj0POsQg3q!aHWs|OGvCK9YlFw%R%y%<93?&Igt~}Z&pRn?8
zo1~=tfQzClkd#anG}uVteKBhM59M|^k7~##-a*7U4e%0*Im)>d`oYzKqQ0w>^|}Ik
zfwhzCKifVAjJZ<Oa(##>xaBbmxldX<Au@qWH&_;xak_Nd-b|%w6iB(xjLbSqi~5O9
zBcyhV4_+JW^eHns&Pd#<+@!u<)W>Oigl~l)LE(Y8=u5~M9gq6DyT#@`?RE&O@?jNO
zLKy4&F*&eHaga3vSs1LO7-t4kKOghE_uSmSvxMipKe>5`I6ADDXWK`3Tf#AKOdLCn
z{|$LfBC888JVLFW#HM?(8J_xD7cl>`sQb<3kGC9O?XJNLfMq}~Y}ou>TTMsGQ|*5O
z1c=gjU?nKqI7z9~(zSUsdY?bXH!?{>IT-?+30tQ&<e07W1m;ZRQAek3>H1nP!R7+X
zq&fB|(W+CQI$a-E>J}T9Rl6RD_4jHB_&7~{;LBuYYL0mK<ky9kAE2bbC~n-lhqunH
zpeWzG*d?g9Q;%c(IUC%uh&)5Bx7=))6Jsb(a!e(ZmLe>zQ2H{3V6SpCq?AcDB@26>
z$!lpmHh)YnTjB>k5AYvTmY*}yKSoZHzW7IOPQ{yY%)Lb(RZ7tBhyO%zZ*H%6WOj;_
zZW2;H)hrAOMpmP83T&6@4Q;j?GvOQ!mfI6s3L+8&hOR=kIHYMYQarftmvQQR9hkMv
z>kW6A?)!!Hbn32`Dnh6UQZwG_ml(_0Lp+ZOT${-eZbijslERX=d)$u*ow3M5pzZFr
zWD)WFRb(E&%Mp1d<7h|to>r>jS}H3Ew)a`;n*WVh=e`aMVOPhAoI9%f&9)dveZ{77
z%w<QIFr#y4sZlHEJhkldqk(Upw9<yXs?YCYh$olK4L63Dq=@Z{!D1{jApP5foT+IO
zZFwO4r*!%buT(&gta_F5`1+3PN%xD$wNZ)I&TLUtW^RE{vL?(qbx9N-b}a&-RBn4G
zOXv)DD1_DIw-cN_PZ+e2uklZ}b!!rrHdRq`P{o@9|3k&zZOkhFa+NX+12S@6ZU=x)
za53#EqAeq>z%QDN*rQ`qky28#my$i|T8i87d(mpq<LRh_@5dk&2nk|!6Lvp8ojt{B
zm>vQQ1_X<hiY(ChpTB)MhxGqF_m?R&AN2jgn*q1E^ShoFXsYeRXPm%Nfb(m8>LE}&
zZk_RxN2Bph5P~2;G<OAK0Qc%;1Q8r5z?_W0RQ`F=xztZSqa%V1hsLiigXESv_#HM|
z<<|YL9KxeV!X5ST{lXP`tX*#Sdp9titjj<gX)GNQxmiY?Fo#0ky-SqTk-Ys!@trGv
z>n>IVuHOuGn;inNI$iJtX(7SS5g;;X&ik&fTtGf(%-Its+5O#OK0e^LDxU@l$*UzB
zZEha=E0c2mCMjbvg}1W**y;1pBJjcOH5#EwaWus98-|#fz>KtJtlK5ebTO#AA{bqH
z#z4Ax6cL}~H8hn9!wa0$K%sWnjE~_Q`NZzO2N8__L)d#$!Ir+{NowQ^LiBk|J2C`b
zJ(w>OM_E7l!l1y98p|n<Bn`M|wQQI`rJg%*e->`yyY^pb8=aK0K-cOTs6RP6u5S0R
zV1{DGbz@IKCqXn9NZ|ZL#|VXa-j{T#=_zuu6|#Q}<N1n_aG62U!9&(@LoM(c04AW~
zs<mC7L_{DsL}(ByZULYrb@l<mLf)X30q8(bLV}g37UiSG%Vt%sLVCh~Z0$drU{wmy
zmYErs(>KT^T`f4D_<c`bbObq!c_XK09^xzhL>&AS3Wt6BszP!L3zbyYPf7S6lk)cj
zy{R4}i1e1?^<80jwf%isTjmM{HW*hfqKM-B6%3i{J7C_%VvKpMj_|>8|DWG>oX%y0
z%r&8#8OUJm+o<39lKu7N>Q0P;MzP=8{fbxUoz)Y}t`hYBvmA}7f-S)`CA!@143PB(
zc3Y>F68mwzHh6BI83r5nW_D<_#m)Zg!QaBGY$JB7l~|QT0>T>&`9|G2H%1$&cU#cj
z$jE@NJ_#h#JY;;>*3{XNbCSgTLIqjZJqC(pM9i<lqMxwWa>|fI--u}mGLAnc05f|z
zJ|3Vdez+o{7o-!e&%>Wy+PozD;o)rmXR6pRF-b|}c>N3V>59uIGRp(G@lJArwauTd
z(GOKc<cO07_spPF)V|Q-7lzW)_Kjr-LY-J67}d#eh<UQ-xn5#S8K^%@=p2D~6!}yW
zPs!GA5r>{Hx`eB)ZWNFkgrh&i^{K)ha&cPT#`2RQXC^C@B9|x1jdkf*Mmy?^;36S6
z|1NLqLL7-C%%vkMM!wpRTckPJ|Bp<NXi8)2f-R7(LbsVMD(MEgo*~F#YjkSw2Bseh
zOPt~i6=QNf04qj4kNP)%ZxP6fqMX3J;Bn&ta4M<=i6v(TfBljr?by6RzNzbT(H#Px
zC?_cIR?Xh+Ux~blILRJwpVED=^B1kZd@KXs!bCg%`x#+Y{!b91)FhPn*m(auS{srh
zpI*J<Rf(9hKecOCdFFbG_eXzN?dKsbQm3bLI363L7`GME7{zKAT2=GkC{#)EG&D72
zV*1**_i^STVEBu_ym`D<-MlF`2iF7Yytt2P9V&NuzycpXg}Tc+aL)-mxL|FfW1=iV
z4@v!2GPS~mS{yd3Y0S3*Q~cGz5Crp{Ccysmw6<&W$`(N9^Yp~OB@A2cmbm{i*Ge5L
zsOI$Q^gt9=_qGg+8uIl_Z7vX^k`?r-(Ks;`beXyW`YUg8D-?0)9dYo%H-$540vtoK
zLbI_j_|bpC;d;6DNeuaX+xr7t#pb|pz`W~mX6?#|g|>wDmxBo*D2tMjDhS-^OW-ZV
zFsEB>Ip1Dd1RNVL*dP`@^nnB0uW1z3Nicck#ri$!Zu44P$eb}cG6*a-zehzI^DL=6
zUw_Bu8Pj2Gd?0)HPj*xeS`ySlvjJ<x%>^l@I)M%rG$^WQ=9=JL9swY_(X>@%-DTnO
z#@90wyo6^=pQ?*}(|Ya7afl@61TR@TRW8fbjx}~d_sO#z9frUl44*gD>1o|;H7t-k
z2@Hs|lCm+|QnjYEx&9S-8lR#4!-zeJj`LjS+e=F-dws%?titRTQXdo9J_@dcYQ?lI
zRzsR;%_BHdASz$gwlUL<f9n2VjdnY{uvC4R2zivgZw10`ne*bp5rC^<jAzRtkj+gy
zzX$P^a6zETmt-}pRsN<M&ee#O72_nt@?2?$?%`t=K*EEd(}wlO%79jQ2TIJ{2})YT
zIlRtpZXY=v1)w_`cdkH6R35kAjNUXo2VzgIWI{?heX!DSZnI6u0CfmYPfyvg{Jc=Y
z9kW`K1c}@e8#KA&!E+E@k0<tRwWgJs5wa*ld4nHUUX<=|E26^OU#zy!EKWL<P?EOt
z&0WmK8BGX%s2JwVK~}!OfVu`c3QGOgyQ$Lg*_!Th#pk=l1usc)M(k*^2;GFK@ga0p
zK5IKjiqn&6D1^$Pa?o;O?uhR6i21ZN2GuX~)X(Nk2`qlsb`;1quIyM(35}&4)us*(
zt(qs4I`s!2G?*=%H%XmIUSUDdf0%zslo8nqDSi`ueyeCh_ZF)%$Y)POp8@&S?r}g*
zR<omqD#()1pv^YfOPuI1vhvS@{e23&*9`E^QwD(Jg}XG94?!C~rF$adL_gHeO#5Tt
zp%NtWLM8`F7w^}>H)pQR;?3kT+VI)kV<5ram?q_3|J~qlBPy&Xu(~0nWnm-NxtkUx
z<fwJsoE%Z#TbDvG(vHm_oh)&q!owb)zSrJQxUE`ahqv>f&vkQ=WBWF5Vz;6}SY&*2
zZa_xfVUQgC@8Y<$Lc2o}fxx%X*^pF2!U$}qXg!I)HW9>|3IM=P7Y7+U)zEy?67w)c
zyCtHhn2L`pN4q2D8uVM$T|MklRX-b8g5U_AvW8~c$n#|6Y#$P0hw!d9yCcpMmS!*e
zCgld;R=^PV*NKHe?u`~dt&jQ@vRDaLx9<~n0H98cUg7QjG<KJFc5kNTs?Qr#GC5$3
zm?055BE9uk3DFK%-qX{HYk4ae62`<>w*|sL*Yvv;z3a%To^6;TrzOF%Sun?$%snRo
z{<fhLL#mAw9=cRgLaq^QlOwL=K-h+)xC=)2_@L}oHf0zqWIuv_2(HQV4ApW+Wv<IA
z0ga(?h;z>bxj0jKFi<7BL&W(u>vUI+-Nq)s1nwbvE-}wUJWtU+$k^K7kILXV-7erE
zN;)EGdSIJOQJ`=7kt<uRO@@P3{)KYgn_p+-F6$T8bO#ac$F?I=F1vy)h!%wXZs1!=
zsxVvzDoTANhWOuK#EmVPe4cdUN)FzST$b6je>=6jaiaNa=WIma+m?sZYA27e;}6@K
zF-7CSPlVQ=!b@$p`CM5yHLFMXeA5E^hz{#rpw04z_*YFtLSY0gT^r5y1RI&LI>e@}
z?f&#kSXz=a1#k6bhnoEhJ1>ewFg%|HLS>Yx6I#Clmo45|cdn47HCNo0_$J$dB4eiF
zu9ny0TJPOb9ua)+Xr1jqN6TLxVi_#czAnDIny%)Bq|?g2Cx<g%A037K{A-c{P3Yvd
z2VqZssax-h!o_|+w;^sk;yZHC%uY9@8YlavewGqk)z6U6H)F>p#$a9eL>qfrXUZ5S
z=u^zSoAE5-mLmkTz0h&U`2YEzrh?MHFfXL*>;C3y!b4n6*H^3-)A+r8`^k)R@W%O%
z)|l?puQ}zKUh9mEa*yww65p7cnaE{KfN8wjSCBBuR%M^!(7XGZ1>rrE>=;n05I}%|
zdChSA`*W4EhKsT(h~~=78%DMwLY-n~;>lFicEneeaz@@Ef4?#q?cGS^wfBBl8%j#h
zI5x4Ktc9qYBMkhQHXUE>WxYI797ek2(cGPPmc&`QX>t_<QY~Z1%nq$Fx6(rlyMINL
zmG7C0Ha6-Ip<U0Zyp1e2w$_mABp|_;?Ag^&-Cc>feDPq~K;I_kL215#x#e<bs!oS9
z!a$Bp%3h2&q^_&-QEPrH0V`8b!m3N4^0n%zT9y|F7C&8jje1EMIRBpge;4ZPfto4I
zvj1w~tOKT}{t>RQRke!x>-tpPvYogRjLXLa;&*dh1u1nVF@pW;&zCr{=<8d~T{G3t
z1l)@~`TlQ%Gmy2~cJmxW;&+8op!CGV=clRIH%<E+m>AjP(^o3iN7D9S>mNu;fR#bm
zKV^+F>1$!iQNj;-c$d6zc1?k|j6KSf`#oJBi@Bs{)eu)$LKLPj?rpNxer2R}P@q(8
zSX^}5LIgdm$<NJl&BDqYLXd*-t}ZHC4lgPfhdje2=VzTKzFNk?3gNcdl1aX$<}bEx
zU{hSaI8biFV?omvIR|UNlkY&tj$6wr^B*Dkdl4Eunl?m^RM>18hMt`~Y|gxK`S`#n
zn_gB)9dp%H|6-UmW+KZi!n-~B^ik@>=MrYssq|UX&w#YT_8xK<?-rhfjt_&<myy@+
za;>h?(X=Amu8N~}Kn&wiKz=LY;;C!?&mMpOkWYv_8cFT=TNcsg6F+WqdZdV%^RkZT
z5%S#0q!&Mk>UU+eK{U0>%2A#4VqVqLPs_7wCW!Xn4))?Ju|3?Ek5o7=!EAW1bP#xq
zp3MW-$+5E^gzi=bPNtCz`$b+bd-Fd>U~YI~gpcTGekF~Jubn)MJld;2eyeqo@_iH~
z3wJ^)szK=@!t>bGwT?nGhVHR&oSy5WI*f+efA)B@&&0YR31`C)L;EQ~59a~5CjfFX
zB;n;Z*TVa+`f0~TW~v9j|G^qaiK|kK(~_y{M=X$r&ewnPwt-cKaY)=%N0p)Pi=34a
zk!{A?skM)3X>`IE&`}-{fflKNU__=1ca-0~{Fu$#6ZhhubBo#%tIpPkugfAvDRu_5
zNqn_&p}Mf1`o&@v4Da`JVQd@wZ{F~vFfv<-@2%*aPU8%enh81$s+L~y^HH4J%qsn|
zhC6u!IdQHHIx)40K&wqbH(U&#9ugtDzx-yl@NVNZHGNeo3q-tWs3$&ML@@Mz4oc|1
zoqbc72&!-?ux~Wc4&<#GO7~0#b_y8%vi)m@B~wC0k27SZpju}&()#^@Q2ZFpK6iE)
z`|B5Q<UG7_-zd!7&p-7$5T%Wr_W2fl7wZytHo<LNVW{Q#Z>>bepJ<y&?>AWCL>iL&
z*{FQ%1o29(uz{0_84ZmvHyAm))t{T7`oIAVCPZ+L`+h`JLB__cfZJf6GtarOP9)}<
zk%Ka9$umSc%aZVTCkP3p6iLla(kW)%Cal2MZ__<`^S$$l?$Vc=1O^-1a!%zQgrjcD
z%nI16R02Df`MAF}%U8*BtU6B!m~dLi!mO@9l}102RC&p*`*mk{#|g92(fU*G6vC6j
zLWUqcWIq)@;{FuEdit(QiTOv?8*pt(zU0-^)Eqg@cvs8+PHvERK`&X*XKIs~TfV06
zL2P<cQ~l({S1(%((kqWIs~Tq_mfyc@Q_gzq8n@~FSTrKp`3=+tv6+v_hC`Q@BCWl6
zUygm>Cy$yss-?jxPy0aF{_9a(J&|t!DS@b+s{}(|j$EeAO;)Uk8e!9pHm5PG@tADt
zd(YDcAB^m0753B7voQr-R5QKh$`%GHJ<sdSLhqR@;5#~(;;)7~A9*k+=53ZnR0PRA
zqQddLAqRus5g`PK-S}uJ_Uzy?dygE)K~&iA#xfD(Q1ge#$pmXlTdQC8BptkVGwQi+
zj}SKO92om2M%hpgojEdFAqkF2p6!%Ldezz02P_qD;I9hf-$ym1o!kh()O{4*;q`tj
zhPQr%S6{1*er2NAcIx)5$0-|jHUay13x&6*{vaa%r)+AG*t&=JT}s|tpitK53T#HX
zIZb--C@zOYLIGm;pGU{?F-1#_v@F5YLM(oJ$t2AYQ|UMG$sT!LM-sA4rE+8LZw0&!
zXkeB8quY+V220JDkBYy3V6A6`mGBQ?k+JDOYow*oB|`J?>5o_5O1Cy!W1)WbMb@i7
z>EJ)hS76OM!zv`5f^m_#>dT!^ZO=c@shJvh9Nz|A!O&YC880(r^?_#}d9R=8F#5-L
zqj>w}$4?xo$(SA0BqGD_UY;@Sozc;?zVK6x&EaQ-UXq|sszJ{)yGnD!r~68E=4Zvh
zu16DD%PLiGBE+#b8w%y-dAITS6AACnxi@f6jm_=>*|uWvoZV_VD>73lSV-!A)8seW
z0@r@d+l6N01*YX2&p|nE93-Sl2)IX1d<&U6>xqw>8r50rzkV!9Uyem0CSRO$8YZXd
zd@=h0=^<AA=E45`UGuTVQh6#P7hNIdnq4&5c8^7kQD5Ke-;jl|$9QamPmjL)^P`j;
zsKc*n>!d=iTRb6+SC!`n&agXu*5`>xj*2ZOefXTilrkCI$Q^xu_&+(d!0XN%w)%yk
zS$$*>LjJq!26xfi;^?K!r&V0^e!E*>_Ymcl+{G;ZrWRzvaMl#io_tY?I^p9gfLh<9
z9yUbJ?G`T>^|QRFU6vEPE3iY2?cpv6X9yvUyhqJQ(h@k>(|YHpmt@hR^^`^^E-|6p
zN;;Ejg&y;@npjcjTf4(-*^%dU6f5-VVnzt*L@0xD4&|@wWSza-$@)WIW`@T;@vIzS
zr;F<FxDVzAszOnv@h)0U{x_W^qh8wae=yU2N`cAhbH>w25%*B-*j^L&o7@7&3^YjP
z$u0%S&4yp(1@0S+w~MPn(6afqp6jJ2=^XN%Q3}x}yhwJADG0N%!qKWcMck!R&0+sr
zUg)cxZK)5+cnVi}yl=~>L1`l4e3ynU*NwxeM%sIzr+2-*#V04;5E9^vJyE%=x{x6u
z6j$HWJbBS>^2dc;^O4u7I!RIs0+T$#R3GW2%cM+t=E4XM#wY4X2J+&}h0Y!xX5J8O
zZl0Q`WRnT<PP2g%eI+t-T2KF-`{rc@G!#VBEKTL65=xdZYZX1^2P5M>{_Uy4X1|Dt
z%Ekz1{Hu9MYeA<O`rE~;591{J5Wi%}1lko(cx@L4wAEg%fnuABnc1?2zU!NdRVT3K
zWC`wSZRsh|3J;-4joMDxSx11kulVLKjH^BG_4(36-ICt+MnI$WcQQiE={-y$n8E(j
z>ZOIR(4EJQs8gzUv5aX$Id(b>c_IK&-dS^w<>KCL0W?tMCsdacZ2E+!G;}bch23RS
z<*K*$;~xk86$jS4cA&R)PjI%%;5m##>8*VlDn;uq$4+I(oQ7x>+}~1%-W_dRxzckI
zg1)bR!oc36aG*Uqcg(tFOOkGEt=#ou-?&rShP~&2%A$0O0`;(s!d8;sRbDtL<)Ivp
zMO)a9i#C)#%l`{GdxWE1CgcNLC&d_QJV@|VAv_`Kk=UYjWTctOp@1=Mh|OBX81BvI
zZ-k`&5{8xt$2Tu{2<nnoMZF5D{-m0bus%L9iT8pbK%q5iut@tRsA8#;XTQd;VNd92
zTVIfr;gPl%yB*RmU_rx488``$uKVLrhwJ9B2pA3tu|-U%$-QAU+88A}xgb}aDjK(0
zN;+5(<Un?(%~${0V2v)vmQc#~>muZnU98d(39D}5A>3n+pkp^^wA&u;WQJ5i+<qlw
zB<-K+w*Ug$N}wIn(evCnaxgR4Y9sfTrF)#p_3Nc7m*Ld+(WI|O+JbO|)%$Ze0AApD
zRCw`mZ8dDml^vIlMrJCI;wIue`k<}uf_(Z@jx0ksxcrznEYF&MquUO;$9II<8rYbf
zkrnnw(kUhbmizfx23;CCD}7z9m<mDt_H*fFZ>YcG@%RPcD$%B>T27{6lLy9+zD<PQ
zz`NnsMJVbS_+V*cdY_mv`ILAeLl&3#PUy_F|Hs!`KxNrA`@<FpN(o4ZNZ!&RA*CQG
zh=S76DUC>nNJ&dKNUKP<lr%^RN_Tg6{^$02-dE2$=lj+|LN?d6_w3mdznMLg!aMo&
zhdPfX5y%1co;-w`r?D#X$G~A*z19H4%1}|{mmRK@DO?7%L)@H|p*Rpyyb6PNemNK)
zam+m?ZqAW4k>U>#v44i~eCck6eiTJRgmjAaDk>R}@gS`L-^k$wYh6kT1aO_UtP2nc
z>b`HrgY}O;zE-)eak@v3d<5&V(qva#s$TiEqJ<uh*g)1y)7hn&*|{g+e+6xPByz-b
zBcs9yz4JVk4TPIEt6R&jDhu7gQcY{4ep_%D-^npO@*;U{?4;{fmzwg&<JibN<$8rX
zxcG@{(V3I##t-!`Q+Q#-?5-5-w76FM1%~|7;$xjbzl8HFmhZWq>?!0_A5xrQ^m3JN
zj?FJvwz)iPikQglR>f-p%frV%7!H!6AgU!;7q&TSt#;GnGfQ#nG`w5^{_c$^46O8V
zZ-gl|Ezzvf+bp@K!hV}Z9i4QnjCk0DUwT?2h)#Bv1^NofKQ#vz7;Kv6)NEh$WIN!5
zWl?{Uzk4=Zl6@FLOj-S)zjog`87Wa9pZ((JK9h%|2HAPHO*@-PehF^@aQmc0`t=f*
zMd0f7y87|Q-!CjjwHy2zS^R0*7n@~HA`^4x?t#ViYgoymNe@n**RdH5e7=mS_Ikqf
zr@13-ahyhL3~+^1HXSoUdiPP>)6*UU{0!>^?`Ju^?+b6@5<JMfXt$|Eqxeyz%ZXyz
zRII6~_jK<ngS0=3pcUlBHksw7TMe5aWJKiokdnr0(Rq0^9zi4UmcmZ>`{2j1Jj5?j
z?#BNbYW3qRJe&!%NmU)+WAKbeK4Ht=+oz_v{e193%71bJV%<V}Zhro;|D)ewvTsi>
zjl!_LXRv75iAMzOjQ4>~`jY*|Mh){s*Hz4jWp14|xkt)eugQcvjzSBbo8S^hy_vl(
z@}%tjJISFv3K>cJEA#%1C@p&^AnFbs%JnkRt+_cKlQ$<N`H<rW7!TW*eQ1ngdDLos
zQxdfKN`Q@ZVIeyD_2%RVoznu8_Sgf8;}Wx(nIEEHMky3)P!ElBy+>uv6(>Rk6H2rC
z7&p#MRr;4>;NsmAD>C{ZvYAz%^;=v5p=y+FA%~jI?NhnJvjb;>(UG#&yF*1a#<!Sr
zm!TAspc)U`WVr+qs|b=6W}f}znjraEIbt}`IfnBtIOTihqb=al9PShwB4zk@#ZR4s
z*ABK-HX0uiIP>{}3J;44la&!cENP6zvRw9gnj?*Q&6#J>C9*SG1YnCG*5=A@DY-()
z9hj$OCqv7dmQO?7FOWvIYj1C%{4^bkE#bjTG5z(I4hFeN$;875TN9`~+wRM?_A`nu
z;rIsbjSgWk--4UIj4!+ceopHd#?uM&#C>?rMkX!Aea05&r64>p5hR<IK}N@P$NT&1
zxbCm>S}(cHJaAC4((yUR$QyHZg`XBr&f1W0U6S~I_Cm&akD@Mhx=j0^km>Lx>|1Qz
zrl?cCX`FS`&s5pmVPogrT{NnJs!hXc=V|mka}^KWZ9j)ffdkd=&$7jWkHxpgalt~}
zSq@g=cDe0iYmS`H#{>DFraj(!r5N;|<S|F&gUNMj!}kXd>P_XbR&Mh!;%Q1U(6%XM
zXveV>t-ijR9P*>g?AQVQ6<u|0=dN&Qf@`cN$4#TrS7;f=lanc7Q3;J-f;Hwh)>iSM
z2C-pt?9G$J1yA{+1zS$4$s0&_{6kCG6>#5Lr60}C4{C;~r?Nz@rZSvWsw;LR_)>Fm
z_<><6px$l$s-UH{{o4%!VlhiehM@Q_GEG9SDX-VWdWMEx0)@Q2eWLP3MZgEk1-17)
z<cD%eU|;NLTz`RWtGXu+eoLo};p!CM!CO(@G80nZ!<09|!og-)aE*ObT0WREeffiv
z6u(3K>qqlXZ-LX^#}e~s=o4O<mqerG@yAkoTP-^fhW5+JQ~|LZ*li_;f?()4Eap^y
zQM-qoeHR5BxR#SHVS`4y-m>NNe6Fgm*Mk80%AcLr#~-NCy$(t%`6#_(!}3QMl6N|_
zxBa|;cl`dpc*o;^^G+iR52MG(3KN-j2s^C9az-z0bF*e`E^{4H5Yp}+wc__NMb{ps
zk6XPScUo}WSBQ=b?DG?hQ!2C?By*-p#&7v9sXvYV3JnX>aJ=s|*;sMJCHE5n+b?JC
zFP7bIpXyyx8nvM}1_|oW=zJ|Lfw!p%?-f9PQ#G{w(_Ff16}}yebIHXS-#(>YRXN(d
zqSz7FWR@zF28@6|P`qh4RymLQIFoEn28x)j3>QSYsPz@R8Vw%V+|qk1T905`@3o6Q
z`d&XXuct84lDI14dL}d_9nn~SPDXWQKCBVt<HNyg;Y>;)qohh;!rJIOqW`hvt+s*U
zGZ7gV(Hif&o@YELK<0Zh3rrGpY(l2&yNuPxewX=6CmEEo>FU_JRkvrO36ZWJ8K6(%
z!4;IrHrD!kX2UHrN{I&R;H&T~3Q9-HX7s+PI1mJjc4fRe=H)JH>hgq>FLO*w1|{M4
z;Bsn9SI6Y=rtU(wAzKueQQfvCv-ZeGpMerbl;YT;59jq=H$aeUlN6VGD~=xJO|<!%
zY98Zp3OOg_c&v+}J+MM*mOkbLpJ_lG7OaN_A2!nFneASq3vl(s^TFQw(YvouY)(BT
zO>2Ehj{Wr9FUx*A`GH;{+@8p5E-!lS1c=AfB&8_oiK+5EpAJR>`9fnx5WnyKvg;_$
zCanuOl(z<n6g%T3r%jHnRia!y#UH+6?@=wB8b+j%4R}$1l9u%ZnsBZr7%FeBX7YaK
zsSbVYA^!eJ$?fBkoY^cM32Xa)#W@*g59E1$J$>I*&fJzx!qy`7Wqq6RxCL*B6*Rh>
zb~dOMCUMhX*`^Cl)-P2nOS5`Jkx=%owQIa9Q8w%2uS)XKbw|JV8<L%g2EAJjdWLCF
z@zZIfrLS>1?3|javSUkx1qm9vZL{6h!8_1jwa%#CV#^Go(A!!#{-|7W1FQw`cVh&O
zG=O!GV$a-8P;8Wd9I%n&((L(V_O3jq=`K?Z_TW)c*9`G6MczGAjgEc`VOiJ5+?!IZ
zBfKXnp(fl~<B`SAXG5v5@<1Q|$3}Yir9#`Fq#RD3Mw;Q>AniKTv$|MdO39qN-t0~u
z2lz0D(}~)bME<a^%(-+z`DC@p+Td|mo=gb!F`on}AbcK}z>!gRaB-9At*dv4hzje-
zpp3#vc;a{IuzV08;ppN=^M$sXrr{W1>vcUBJero|%ZozMd|dZ5`vD#W1d>FkZ(D^k
z#mUUxi`vo~H{soAQ#5cnVNtSOl?DH?2BhoVL?YpYycQ#}=S$oViBR$2I%-i^^I)OA
zAd-P+hcMBCgKBRO<4Mfk!$fqxY1YuH-|~v*wSJGt4NlR{+6&KB^iKf{+859)5fj-g
zbC<I1a(3{af;<}vXf);HBpi!o4<;|SpDf6lQ?UCT^hhxfe=Bh2m8Mrp0lS1ZXl1yu
zfslfpo-yoF^WJ)`mDaqiIQF8u@i2Z_<@9b*`J@3-ur;Sr(o!==T%yOKYyc0Dl67p5
zv<+-tZ>5s?p))(n9GBfWhK#CRCc`C;87ftq^|tnX4V@o^(^kutZKFl2g74PpD;r0o
z)~EVP_HW%AvF5ohMA}wLS?#UnzsA$6-{QfPu!pW0?0{98L2fZ~q%F76`-L^;Xeq4h
zuIaGv7`9+@EXw#+>nrR@#d*K1h5d#sQ&sLHE9&)&n9@>yw=cVLGbPl|Q4&5)F+~xu
zKN!z*-P|9s`$g9IEX#q^ocQn_q*YC&(tA%SW@~ez2D&C`_MNp<V2^pLd8uL&9W6Dl
zj1~m(tS@?~frUpTWN^pF&TM{`YSfDHX~}JD7cLJ#w%Cs%gUTAi89H>6+G$rC*b~=&
zp_b--BuYHfK@1Wb3yfv+nLntd3P`E}-$m$D^;areSd>|<RqxH>Zs|#G*E165+FFdV
z&o{~e;jH2@A-?gH9ix2jT91O<)ZU!ZwlAYftW!>n-?RkQ<X+E=g%p#f`_&_xx$eY-
zGC}^e>!DeF6ZvWudiy1^WrByDG@pMNf9tgyYlku+mQDx1@_(9-1kA6KwjWVOP1&w5
z@RaZPaaSI-2%c2hvzUuYR7)0S-Q$>B^f}PplNmt`viF8~Hs_vK2vfAs%~IETipeF?
zBeu=JAI?0px+9KbuB-{iBTeD^8oEc#>(hO!k^E(6CCSc$lY$x5=l9IXokr_qMJcuV
z!qH_MryWLemiqkF)|}!GHRWdO2_>fbrebq29Bl9()g;yOcLjXQmRjB$Ic;bBqR!>h
zPeJajb~>lD_3gT@a;Xl2bwrq`G!~O}`Ug)F6aeZH;4Gpvk1*+*UECMe@9zr~a8<(|
z`TjhoX#u^Lx6Fwidv7&Bt(9Ze{`L3$G%}%<Jkp^_0zA#PrJ1a^SAU|Lum8MM+Pxv!
z)Oi*Xzrti*_4x+C^xK;$y9|iM-V|=Hm@+-;3}fr<h+VXUZL4F;PHB<4y7FunyaLW1
zD^ku^=s)r|M+Rr5<>k^Q_J-zPF@z|4PYT|%T$vCWNlfBRD7cx$mZGV&w>}ay%t<Me
zEFU-|!2XetpC`9@HNTYZNvbQg_^9BUjx4u^)!L}Bbd6qu%7Y2=FE2{aN}bMjNur8h
zHj~PYv;}Y1G_US8OS}?0?yTfrdK%dt>plGDYGT^zYeAy@Ezb7SC54frkzJhrot0*K
zi<*YgFyXpY?+5$c#p7DlM8^c0futU4QE^v)=x(qX$dI%du2;TQJ0`W5n{yIpJNB20
z-dJK7QMD^~AEt2j^Wv<T@%Rxu-FLF|c;v8Qn<?p-*&@*SLa8`&uFgSWZsE(tOB%%N
z_@(!vUUa@w8Z(^kYI86jFe`f@zb>C>f2><#p<J&}x?h`JM_Wv^JNayHZQwg~sZBF8
zq8yjil_RYFc;C`37tTo3<EcQ_IqR)~8*58_sHH8@ykn;Z2x8+*VrN;-k+&iu#5@)^
z!GYMZ7`?y)*C+~X@PfuVOfHd>qF?XVIexn^o<Rlbxvc7Z^jaNtD$z(jA91FFm6OSk
zh|Z(%nVdB<K8GDP^H=kow0wLq(1#s)ZGOa{J^I1!(yzs#V#YXbvpO@lHO*zz{YIn8
zG5_VSJ0o;5(f8pFI}w}Kg-1~=jjN+}<LR<-+#^=a<n0Okgr)r&PJuz>{MO?x`|P?+
z7$b>JYkWC1XO0vuJ5MtnC-|uyKM1y3yjv9`xW}jH${BJ=)z~jAE}}LRp{sn@K|`WB
z>{A&p!}73vFvj#n`MY<~_SeAD35&IUhxy2`5G<<j@Q002Gkz>Q)4X2|`GsBiQ6zSo
zl&rhrG;zrY+^MM$F*78X4pv%MgI15d9Kyyktd5t_vHU0Y!z<EQ>c<nsp<QQ?bTMgi
zgu&3ToYm364Xm}1hmAvlQaOX~^IC|=Lh{j{ef;%a_~j-cWx+vPFhgWt8TmkdNYcFX
zMb+NQN17lAu_9ONZzz4es-N|1vfMSB&p}TZX}Aa0C+UL*B^xS7k%*!S?#XD@!nq`2
zBVv487th|R`>tMky0uob_)9KhFWt>~%20<R8nF9E4!1JljW`nE(;#BhW1_CUcr?_i
zYhU&eO&dq;D+VE>|Mh39b7o*%Ft?xEFmX)GAJkU0AI}JaLEf#Z@U6h5Xq_kg4rc7c
zWaT3;zIiA~+TFT8{P=Jei=W}0x#(J^d^Rett%XV#_;2~9lrPLHd;qKM#8)fe>L23>
zx1%Tz3v0$IH0iy!#8nYG-aF^*iu5h-qj@++mFiy#{!mN@s!bT#W`gS6(c|gmTzXa>
zA9n2V)*7KWkJalbY|By1w^->(p%Lcz;jPH7<`|@Vn4WzvZzD^}RITc5WxOc}2ZXfZ
zkTExt+I@o|;}O8%2J@V$4DV=W6Cm9XXbae|INfa7*4An;?bjHOfN<zJrYeH#T*|~t
z)YvN&mnZu!c(~HiBSrGO$iRkGuk>WMYI0hGEDSISL=M1*2p|lAa2j#|pPH)Fvs-Vj
zVxN;1^%)`f!W2UxAPVEs{~cE(e<JMxKhx4;1&T}g#>t1e^}=%%<hK|xk&g)nux~0O
zZDyQHUfM4;?IA+!X&T0@^fU%(>9p`N<1<-l-|*1!9^UY9NtjKnCW1Bu#ur1FL`2#b
z0%$(+xCmNvRTDC41af?~6+`-a<bb;52;_hs7;p_a070f|0s|1)mVX9_!vOhfK7Ggm
z(o+8pNVhb~M!q7gF1h$W14xhq5U%MD;19xaeZ;BL8)T)#F2euHOOufx-xofkq>B7s
zB2<*LaD0jH7hp9hX%D4aEsR`gBYuAk6qSOdq%a_mPyfLiE-#5V#Dw|&wICQBD2nh3
z2QuG;eUOKJ==(j;s#kTgTbc@9z=+%^<NzdnkpmQYi{MFC3doH@&Hx6O1j2xH{F)x*
zy#KrMPYk!<*9hM~zefHnC52puwDg(m??wIoNyOxuB=HAc<PPAA!K86{0-KUyF~8{@
z{x7}dfB*KM$==06(Eg9fPCxlQS(ATPgJcHasxyZ4OIJ=DjIzsd(i3>UUk%2X5H{*I
z>L&4FW$^6upiGwerOFG=zvxk`?m+dep?y+uOXVwuesW9_aT=o00!KoR$%dM1$>Tn%
z*C1*##Y)xC(1TOnuwel7P+wG<B*G{K@d>x6i99rr-3Y(F_f~gMvhNxWRq6<Vn1(PM
zm*thn)oB^2h|db8Vpk(Oq8nIEl-@_h$;R9%E;}E!;B_@(h2ZE_DqaO;8?x$&Cj~w7
zV1iHl1_Xxmd0+BaNE|M`=2IB_{QBegS3G|qN`D{!M2st7Qv2+LHN#k)t0;KHQTIh+
zwv<bPulshN`{+0qvSNSYV-1)8@`BBuFRbZ(fg84%)E67{)nn{*#a>0U`U&+cN{go~
zBQrJH$HuKCy%*0u&vIsN9A;4)_iUm%j_h;Ie;>$Pn~vMGP#~hZ0u!5hME|tdRd;Bl
zJ2*BlmKx)7bJTie3(`b}gqbdxL|Fi5t%gg@@apezi!(NM&yT0S-mk8-v7?OaiWT>W
zT(#FZ!5}+!8>|~$CDBqP*stbxpM&Ir4Be}HY-M7vnT8D-Din|*0KHq^UWuCP{W?*B
zq7xBUo1LXEX5-iAn$?YElN$X<r-#3!@ac;!r66esT%qa5ynE)N>+xkIE+u4dO{8i<
zXCX<&bvqOW)Kz|(wnD}jhY3B0&&cEW%1rn-ef1q2nydj^Mn)s9R+LQa`08S~nF%mg
z>@-+GU}VmD?}h^(AO1kO)m_pk*4r5m5wUia6vJTF56P<aKpdp!ae`LoOun9>!4s|X
zppGvZ2ZA@)t%?>#E&~vY^7)sAOjuThWsdXNl4N98yhwj?aCfDGTqlhTUOn3r`ILw{
z*=V>c`62w_Bd<6%OpN-62(L(7(Dy_czKYjqY0c}{pj6XuvFND{SHV>g;o)WI^^nt1
zr1<fT-gpV7^F|8}__>GxZB_N21?!<53dvi-!FFqu_tWo1aqJZP0(jX~Xi!@!bJBnM
z)6C(n3#CH{C%ULsG=VDPv-?wY81CAtM%ho@FVtwG$@Get1YWyxfJw#1d_DW_9h?as
z&u>LcMSUOdzZu8TQsw>-a(1DH>RXX1S<m{sjL7{@IG5W>tf2hPHQhn(pf}@k+7L#D
z=r1Bbf0*~uMPVb?`T{1LxA(tc$Bx4zsjhI~+&2k~Ggd*PdhdQg)nr7b4cfkAT<KPw
zHc*9@n`syyBAPm&c8T^a{R`P#<{8GfF)kx$RS_eQ&CZ4q9?p<%$@wqs!s|NNWHIXR
zOZm!Q<EiIj!@vWV`pyVcmtvC*mj^-5iE+7}>!~zSg%F`;7^nP}%4`@iy$>0YV(g#g
zdSOV8c;ZAyLNo<ZZ0P=3Zp-}(JXDZG1!5TWQW^R`)ZhSWTv3h;q@LJUAo~AVwX3jK
zzA351t%<807Vuo0OT=g&ikmu$m^^Xdx!HHkGxVuE@1tHa%C_SCizG2<|7*=KQzBG(
z=_2_u;tXGHA_l}$=}Cj%`~u3#D^8mP@&x2@X>Xc)BY8s_$s5Gp?%J^o>7dvQh)}(a
zQ^G|+@%^7`rec$YEr$FcSu}TJ6;M3x7tBS_U_VR(F|JtRiNL``W=egtK+9tyEuzCg
z+aSHf)b?j{|6S&{^-Q)%vuVf?z;7xrBXfU4$yKARLV)%Fx&GXj%_>)7F8S!gexqGM
zuDFm1n85uv86trUw{U*brT}Pj<<iBXzOoqPLjU?!^7oo!;f&l$hcyp>#viaE2@f?&
zXUNn0&d+qXV&*q>9X(b1n1XBd-kWy|_K`9UK3++coi}-~l3$Nc##<mmEK{sPX>1~B
z09G_a-gz|pHR?yo@P#}5CU0>rH5Zj8>Ks+1zC*+`ybGR_FL2f$v9&mfF*N^rDH~r@
zBU&;wt75xpwEsUL;R=m7Ki#gm=K{Y{i9w)uDhZ{!W|{EyCMT%b?|gL}cu<SsWI}Q`
zuB0|8sd-tnBH#E>J@)li-v?cR)21uRg#m^Z<|t_9ZdSLWG=4>ZPA)HWkOJ5gi3Yn+
zx{y;{p%fB&m)_en{n2i=c%THk)PtYrm0p+alysE-OgN?QJJ}ESzq^l;4OF}9UbRJL
zG(aQWh|9w~piD4Tdr#Lg7rBhC08*i2bIuJodB(AzT(9v2+DN3UpgT4tsid<G2X#{i
zt<Wcd`Qa~iiBt0`VT)`BR!HkyILrL)gDPCQ`5||i)>yoYol!-2&B_Od_I>T=-d~EI
z74_lch~VIph=avy?9_V;H}w0foR~{F%#;tUm{_!+hHl<!@z3%UX28jH*CSu+#oq%)
zXf7~InUW4wFQfoqQs?mPhGng!BvUiqbY|H5XF-=h#$n*q;{Y?WVRVMaC3w{nl8G2S
zr13(>x=s($`%6*pjGn=U6Wj+BxI@B2g>i-@f_5a#5evB>Rwv!ALT)Bpm}<qDY%z<O
zh>h(N3q<GQv_xxH8!`b6N;P!p#v9K`SQJ<t-p0jk>v1~30~m0M;3jDorl^EvhL`4J
z9Y~Gz4Pt!|>_kD!K^Jt+UC+gC7FS1<fu14t6r=u1eHw$e_8Z6x(<X}ce()4TF$t>J
z4R3d2B78Bl%e~wXc!j}@w1F&{vg__NO1b{pu`BAOxY!hDJZRclL)>1F4pW5b2hBT*
z%>IwhbZlmEQ_&?IF;}z(QBff9%^PW^K-N}*2+|+KZ!R$P(^VyCgiN2Q=twaunchob
zw_`F;=)^c=@nphMKvihR;x~dUgtQ-TW1VO^F?OIQ8^RBnjr;)S4{1MsAV=HS82s2U
znCd?;h!824z%n3_P4<6(w)!9MABOjv`RS@~|9O8~3U7w~Q?lNoZ)uVl#&3<~AziMP
z3tkrs)ux^z1+}c74iOw#dHj0VT8|Vc4XI58GsJ+zBZfM*Joqo5${>(XL~D@y|M-l1
z>G#_IefJ=DBZO<Flp|%WAdT*gnc6_c1SD-eC2K2S2+~)92{OS1fk3i&y)X^q*ys%3
zIBm)M+C+3jbSRL6$@YLsn`@;z46>B*D8k<0yuGdh-dOqms0GBwWiP@cW$>t=aNKRr
z+8BtSSYGk+#3dtnto^qBHpBtw={XZdzF@cp8xI5DOwxHysKa<jkMzSq^=j?pm34<q
zfMniGIkI{P@=YUAdxt}9?%ZgJZ%M(5ewVzBsc?+AP__y<H|OUkAulqMjUC+=I_O$H
z9V&EShSsk*^w^Xi*hpGhB@JiyHS^78<sC=Px$efQw;mc;-N^HJE0v=XJQKTn5Ycz*
z3I&UD9nL>9Y#QWXzV3r@fMj*HvlD@>f&xo$?W`1Gihl2c^f!8QH&;H8wzRd*B}jPp
zti+m^K?=mwy8vIf;rR3liq8{#sw*MvR`v%HoHG88NHibBX_SVjSFv~-PyLl$?BiDU
zq1%$AWc>kL)<6%Ix{p@d2;Sp|_fH^X!K(%#ERg;Orw;f{Nl}kn&9Q>szhiJsx<FOd
z?IqSY_3oYTy!T`(geacSP@(zz&Hnu1QF*tL9wY=R6+?o01bFn4pXI}>e=XreDJLqK
zuQ#;TA0xD`qUfq}2aT-ATnkG|^6w+TCQJzP!De&VL3>Og^rpUuNm}Wnw6hLhU5ZYN
zwA}1Wh$sYWFo`$9Aj?xhleqOgq0onC);Bxp{ry95><^lK86!NY8L1O~hNp(JXrh;<
z9^2xf9k4UUZ7e9W;6s4=0~o38arY9*mtE<ocWG3&DUq3g5ZlRkx{UR(IjUi`A;d#3
zE%A1a4Q}-Wj=%{uafTS66Ww3?&EUv;XsiB7FhT}5Ss1wDy;q&O(_RVJ={~yUIg!MY
zp@$pvrAQ$y>E2^!9fuK!6^fvqfUOS5#2G?5A~%4VmEPYG%2g06T`E<Oz<wWN>lYoW
z542-5?4iM09l9jaJv}yW)N6uP!b+q~#2d7B>UH0h0xSDw5Ip0pNS!6wa4BB*n+cg_
z?6ul@F$t576jnORscu)Y*1OBSn|<2PK0c?cXM2FwO_ELnTQFU*X;A<S*YlW6x7Lud
z$Em>xD7I~S{8@ixtjxRS(L))gz+t;)Lb3oKe`zGzj)h4LiJA@yO$rdXN26i@ty90!
zs%!PIsUbu?o4}3nqBol*1LBa{6K98XlL>lR-k$xY0U){;;KosK-oYJeKhtC48QX<C
zJqDbO`R%Pyp6xd8=QPhh)&b=3=%Edh2+^$rNLa)sWUepzI9PPI<KF%U=iPhJ8D9T0
z!t=K7wo%`zBFpkf@VfzAG^zv5r^$Wk5cmVHY2L>e@8gp~DiO46Gx~=43;x(v0509m
zpQ1fUxN-}_T@=ykrU23X=<(Zxc>l=-h?E5ezAk+JrqhE0V(uuA%sl4sfz^pSJLQ@6
za|#OKVBKTew|$WJnj+Pl2ruI6?ThelkrDJ2{eB^+W#GzprCf>c?k~P*a`=Z|-j`1?
z6}vX0mr=NN+_h=>qm}pdTXGY{35E#unCjrFhsabd4;)Y_zZ1SD=MNWrD+a2p?(+AY
zNG|15H-s~F9O_slo)*kW%(-z#);wCAx%1QFHD>jMmVo**WIoE{?Y=MZ{9*cq)in7c
z`d>%5F#r^TLnGv5hHpa9)%0}TuLRO{DAMuW2;ILClD*Z17~jfxz83(FDR|P>+_!af
zh`F|&Cz3i}23s)ufq&M=z7GgUGN|jPtQ5Bqks2BspzJMZ>G-bK(?&g5y+<D&&Smbz
zJo%Iza$9d<)HmcCH|l)mF_nI%ehGrrWp^twNM}Fy-GW^BiEg$|#Z&P2UQU%>@e#uz
zAolWkl_E2{wBx#<g3&vZ^m4hYjo8IW?(E%~oxuJ1i*{D*Z5^Fchy0HDPQCYXR^6`h
zV350qU(_HMdK1`2_@k))HHO0+=3hCVaxX{ndFt41h-}nks22}CGqXpDi|o)IY?qT{
zP;-ZL6hHlABrjy~B<U^7`CI<1o_eYCh>j{^X8pFO`|9<ke79vsI9SpTZO9;&$J>u|
zvu7P;2dZBp#si(aK9+^5|Hu~3Fb;`JXpOQEsLjC?PEJd6&3f!9-h`8+Od%n@B;~47
z5ih>_6|!69L+@^9a;~mszUO#D&B`CAiC#O~Kick{KK^QH;Q^f>Pk>sYvrlxW6x?Un
znvIhyFzkIPpl-t<FBfV%MHIyNb&sk<?{*`)zhAO!t~;E-*!V9PkIDHZWm<b64d9&X
z?Kw4s$B`*;`?ZG1Xj#z8Xe%;w+t;u!DS4+Y|I|s6|20^yaKT_E<5=P?&Pf7jUA@6A
zkQL+grwMU45}t(aN14x?-ZlM|*w;VaK7yFOZ_=r!%6&@d8kuRaC;}@QbDE;4CfdG)
zyh$){HX1KrdnFtHF3H_Y2n*wgOn?%j61Wqq0I(uJfJ~=zuZDKXzM08Y2dJrkqJgmO
zri55?)UBf}ZhPKL@Z}B++q&AQR`ssAJ%nC}g#76=#Gf)H`C~*No4lqcw!QcF4*(zj
zwX<CuGXCe!0d6p_+*b)&9j%3|jl+{)5;6RX%sSF^A=X+vF`wvvs6I`1RmiD0Wyg72
z6Q-@wKpWX?WxuZ|K4wphF4Zc)uX}ku)_8EI%z`U-WGtq=m;UfT({8ilY8PJAv&89R
zyD-NSHdVxD?#g&umvs@e2UU#x9Q$FVt<wCB01|0U{@nL{CpEFLW=1wxh*{4DVDl}=
zFbF^854k7|w`?jL_*HEvzn#c6=V>pRQmhWW37usObKJQF$riv5KK?<%tWhWNrf@;P
z*ZL+eDZ2jm>FZcj@1rDOwVj#&w7yZHJfJu`8{Zc+>)aWOCe}htzs`W>ck6ckz6PF?
zjjlgQVQg7`<Hxv$&}dmM65aLNI<;d)Z*NIP2?Y{oMD=!68Lk^zs}SJJ?6gm;JCKE%
zp0H<f7>!>(P}<Bm*i28Srt#7rq#@E}Ys#xQ!W{9}6ga_^JDGQ5v+KivVsJ*EbE&AH
z=YdBMUqqOjwO~v+$_dZqCS@9;whSM30;0=A0r>-o?5F`$waNtN9YjHV=hoHH`|OMa
z?Q~EQ>Y1jf+AC^f;z8v|vA6mvD*^1@uW~B9Xp<@>Mw^O>+H};OQ#Wn2Nn~svjPm5`
z?KyEhvq(#Jq(&EVBMFn^4O2K<b?hm8wW!U{;%ZZGQMUfoA7(_0zmQ>YN&-bvp(^)-
z1(yJJ8UE=@s2Iep{lrCGt}DI)`3$67o@<?0(x_Pfsl|Il*H$3O)}+O@l2h=YzI@4j
zQNV3nmdVwPPWE-2@#kR~5miMNyda^lRF=x1qAvot7+L{hZ}UwZiF8A>F6d$mb4H>x
zT|m{vKyM<#I{)#@x!fMhB&r>5%E2mSB~0)>5f82e=&S6oMK&sNApcfh#nj|mo=f?W
zT<*q+V2xhm$-dE-!TY)$sJNA9Tv)twOhMHL^J)6Cp-+VD@#jyM<OGh7Nk6I8_{~D+
z(AomCJNv|4?1bm>&#wz>A0?UVuJ#;qS$v`@Ed%qPZ}#(W3k$<!pu~D|dUE9JM{{9M
zO7<n2C~P@cN1X7QIEZv{j;;(_<-K4@nPf}IW?u2@EK#hhy%4#oRiRE3CK@`69~r8k
z?3X9OyZkkdZ}mGVTclV2ykRM^sFfJ4(&?JEDhcU8kpZ83kS&Mxbq3-mVlTxBBF%d1
z3rtyRmRZ&JmW6frSscSLh#K(xmoIqG(|WDW5=3n^Ox^o7v6<dYO--K^N9h*my%cC;
zR}>bZDXA*X-BeI^x>)+IF=c>-N0SyxbBxz;Cn<AXB69v@eB#0WERd)^5WDKWf(!F6
z6QvsqOEj5zo^M@mrs?a0n=w3WIQ0zKsXXv*T<v}HrwEoP(gzFK7{`x=0!}k1TK6OL
zhvT;khu<~p0v7Y{J@Xye%<~oV$K^}50s7NYn}x#x$@bsI)E)bEi@5s~3T;tpbFh%>
z`$F_k+L10&AJ#|i>fEo}a9i6n%AZ2tDR{=65A#j!^mbuY-PRq7=qeO8duPNBlK4kH
z<2oj*;+I~K>&@Mql<S(w!XOfgylTMn=3*(2SFVl<ca59f<%VfqbL=LfvsxlljNz!P
zvjbda%;KLpYGQd6VJmNSwxo}yyV{!ykeo)uxy$PvAnfVgv265Z?9D1Kz9Po$iah+N
z;rg*-*{O|JxmR>IGSYhLXE&_}uyggM(1p`%SFa6~m{S#gi0=8G3~%y@w`FfqwwFIa
za}kkNMFqAj;`+Hc$j?d4v33h;b@Af`da7*nF*ZzF2?$XV^Bn>+utG<!TNL2U+0qdB
z7o7Qv;qT_wMa2enn?Spq5en9GreUiP1!bAX+U%Om(~q>BBK1Q;BWpY1Y`~Ww7$l8~
zq2r|CcBkTd+|+y)FAL9M`s_?pWFcb!dzyh>NNkALt(9_Bl2W8dP&MFopw<6rq1yG`
z>y@#Eaa~ek?T4qjh&z<;*uBY(Kj&)yN;%}>`8m0r`BYix&G$)NOLZ38G~-9iq)M)}
zksYLx3=GR%A6%NIwlfPjM-#}uD6_>e$MI<Kv!G&;in$sS^Y<uM#N_NL_HevvVO*3D
zVe6LNjbgMKF_*r&CZ^;>+BWCb-uLw-SyWuXXR2pGL>{ZWE0Urebc(F8a*w77P8Nw*
z1PufZY4xAwz7S&|O3V8qj4)pOw<bhgjrZ{M<=|kq$knnI7AD$?yzt!V&GbCMwt~%c
zgFMcrf(`gW4Yf~F&YbeI0QHQDtj*a<&)9wrFC3Y&w)Sruya^Ic-zo5eY3>cvE~|1Y
zb#vPWBLANOrk}-SwVbMdVt5MCQs=%2{ag#Xm%kHM3cYoNty&fdr(`z^t1Q{RHRlR+
z#49DyQo-x%w%n1Mh`X|;bM_@FhG!dvj?CrGF{J}8e^1Yot}wf?`!x7^KWlwB*WQIO
zFpwhqH*_0T9nOdfoHz3iu6L)lup;IIo6kk;l&<C*;lo1G(kvG~2~T}7v1nS|>`ARi
zO`vykyS`q%LG)sEwISt3KtAlr9f^R+6U~vvZ<DEuKL-202S}WKGjTh|BR6AySg(x>
zt33(jcwBQ7CBr*)g{jRzZF?-XcHH%J9rj+13l~*)<LE?2Xi`SR-Mr*x;6wX`ITPqV
zV;5~X<Ew79??LlL^Ng=XV4BU^PnvJ<Eu;OJd&PjNk|-0&MP18sL5#um3oI4W7q|S5
ze^BFNZ0uB(Za3k%CBs~excu~0($X@vJ$kzg*VJ<Jic7rP*3USnvH2<fxy=lple?_d
z&FLuY=uE(|$b-E4MCbRklvRxj2#HWNb?^QpX|`@L1+%;U<8BE{tV+24;+8Nz!G<Mh
zSd@cr2e)`xaeKwWLZ^PK%eOJbxx7qxI*9iMXbO{oqM*4Wl9Uj(b)jRlq^e^Q`WFs(
zqRn#1iW>`I$cxhVd0uI_`NA}Zr?qoknsBjv2J&<eqvWgY_6M$ae1hjb=*m%3;bOg7
zFDB%e?^tZmWxsZ`YdEp-6l7_kP3KjnxO4WJsx6<Bsa#g%Az2HuU8`8rnd^vs%tSz)
zn-*{;DvrCbqV+4q;aRR)hfDtp86^qQ-p7L20aV(P#}?M>cL{rb3<dNjeb2MP%Qy9T
zp42f59MFDFKu>lJ7sCb*-D}~7e<K>|*H5Dxv~GvNS<qp1CyPV|v;a76>8Zsrjg(a#
zVKt{WqsC8fq5qJKZeBa0<)k>foUa@{k|K|aMJcPMK%GU<G2JM99?7c}Kt&Sxrv~Sa
z!}HWV!=)I)?WR<>Te#Pr)=WOqa5~U{<3i+bFCflDlJ28FOlG-a9B}UdEwm$<&}g7C
zQNp4+_A92z!Vl#_Uo9ni5IDpB&C_Z6^W6pMSW{N2&`WJ9Xqj)VAgOS?NEcaLAr$J3
zD^a?<L0PZrRBA=snQeHK1G=^3Cb|!TnC)Gb>pq*lhc`}e^P$d}#K@_IVehZ8?@p|O
zt!F;uKEu<qVKJ<1=Ls>e0a(DPomFSHJq6aFz}j72-P=E3!TsEyGqpNTG25PWc6GgK
zhh}>`_5l-tZjV*wtF_(Iki-ND0Rr9KslySs)8J1krLmdo<q65U^zs~9I4@Ss1f0&v
zZHl<V6qM_3+6lKzoYHBreNo%w+dtX4#GR{ByY{YGr%XY<d_&l^?VvswBy>ri`5aGF
zV(rmvQsIV&3fE85+qq?wCXq}tyKsKt&FwSwWjp<Dx2SoNjCk8eRB|`IVV;ZIN$@w7
z{Hf2jhZbq}YU9&<$nz)dYQg7(ub2DZ<M@laGYo%6tToqW#y6MM%YCyY3+<q4o!aEh
z6}A?BP1^T<VmCAY{02S8<U;R8+QDE56rQl`NYd2%m6^o0J?R8GOH#>+F<0n-UpmYC
z$<LZ=aLlOqrmP5D4hS?eopY1P+%|tH516j6ESo1=HxM}n#M(1VXOMSPK!^?mJ5)9U
zT_WT>OAKY+K!ZTT3uaKQ@3p4cCg0&58Z{L%#cW>$^}o1eG(Ad4K}{0)1PD2QvZ7dU
z-EWayZ^b>{YFBn-yLWp~RjH#=g_HMeDr+#$8evkS{IKq+aAm*^JUS0{nrDFnQN1~3
zA>%K0MN>l1DyHGQ>2GZsYBBkHrua!eE<4CP+57$q-S%}saY-z2Gt5Z^#1!m3?>eC!
z7)~N@r^|RZTEdhI^{9F&E3f`ntY`=83}fkLkhQ))rO$WR<>+HR4S>=b@4V{)QdPBO
zne~v6FWI-0@8Mu;>L%r-8T8ioR}oql#YH}Qb!d3NX$8t+=om7!fnIF=i11O6zw1fw
zw*3n3#yvFA6)e<r@y0t_MFrmWO#4K}?hQ6Xwe*7;YbM=%!Qok`EaPk1MkJ~|cmenJ
zB+jOC{Cy>QlbVA7&!<;qsrhT(nW;oJ&e{n|a^cpl?)Frs2Q@9p(gX<Z7y$(b$39B=
z$UkF$AD9)H?_6sd<llT~y=Zu)qC&E(Q+sE|SPNsAgxmaf<7w7Fkt1KlcV&Y5Cb>Fg
zg5f(%l=p%AsG*q7%938kl$;cifPtGd>a%?2wvrgf@8vRX8x;T4?LbakFqr+|#Q51N
zXV7PtrVu!NQb=O((G2#;je!6U^!#L~-Y{WxRXaetpJ?m&x#dqW;n`$Tmh_6kgkRU^
zdw%phd};8zMvYx3kv|@5RnTb$S*H?vQyrX;s0gJuZL8W&&^OD|UI43M(a4OB3?uYO
zcP<%7=SNbHG9?V17@?q*=aipx0J~{dQse^z^NXZP%&DpAzLE?N!Tdx&E0Q@`jDg=J
zN~tPhRn)I;hQ@?7??Epx!KnsxAJHQ2yYJsC$)ggLL?vi?Hb;r@;{Pdywx9sK0%jqN
zeI)`cv4&~(uS2T7;?yfFZ3P>dhT2U2dRCGJVC6}H8EJ6PAj*z(zp%;0el46Hp46*=
zY1%uaEcee6MdW!)Jf^L;A#G78fZaI(sul@Q(1Ss`{dy{&$ZCp)*PMrcde8Cje9F_~
zB5r`bHkit<M$DlnR7HIWIa#Ss?oA(irpIsf`B&btg9-nq>dy|}_PD)o4EDHa-9-LX
ztG=3XDLW@px6@g<kHdN1qY)utUBT;)?}}CgLK~@kK@g~!@PxgO(R>Jo<+!K*aQHK<
zxovF28`o@ZtgInDt%W#@3L3^-sPHN(vK^^vo0k14<Vbe8^*d{Wt@=QTsK_HxL4I6x
zA7kT>^tDm3-qU;UL&79PK4gUfmkwV?`MS7fG(QYQRDmuT=F_Srea{Q26IQcr>}<3K
z|CH1bNy5cKS$t_iOV#~LN(89UJ@`OLjj^I_brW=!6rGQrU$d`x9nC<{b!hFE0VfOQ
zhEvTWP>o-(g>)hHR|^`UwG^1P!?C+R>jn0k$ALZGCXMYAofTmW#geF0YjI1UUIuAY
z^1RCSW?*rg6ay3G8+PxW@3nA_L9vl4v8I-}(GI`-oSU3%fH#g>+Wbh{!7Z}m9J)Dl
zq^2h)wpU(q1S@;4!x81(-!vceqFoWRy^TKW6nv}Js(ew{8Dp{ha3$wwyD72@$jtkP
zO$9A29Z->#<eq9AI?7k5bL5Q29Jotk@9bKMaQ%IN#ulW6wN?rJmA6_eoZ0s;i^~e&
z2T&!KCkH`~!BFshueDv3e<t)s`<U|KsyD03&ZHWX^EzgeEI-nRO}@{Qd$KE}TEY!R
z^Pb>=B`(-<bTvispj>0lCaRFORZpR{B$UmnJp8E%Hu>Z*;IlsQHdvH0Zn4?BcLiRJ
z=ko2(mxAT{KF@qmCqvlgcNyT{ZkI?BmuVfocR<|3xBA00a01BO26r2Dt!(4id07Zy
zg?fWt=`^O<gDrhS9S58R1214gnk<QOZ+U2M<_iBVu#cG9B$69<;x9~ejm`!4u|cfn
zh`eNaLzc{{lh&DQTIFSb?&Q8wQdd5qy40^0mqjDvsR*BS!``Kv_NSl79ETjh3z3LF
z)+MOizXv5GPbQBj3t^((D|WZsbFju-Cavab1o5V<njQ;=)*e5;MFqZQeb?9Mn$gYm
zFUlL}b{dZNB;sArtABFMRc>;u3!D>e+Q(b8f#bn(mN$K1dBj->eLlXZo$-k_%OW}5
z!J^{>?E881zrH6p3_FNOGsu4c^uUW<i|M*9h9*5O){lbp$e>6A(2)~mqcr}ap{rHv
z7u(&YX+dfTC7#jHMmXv6v)DF&_9`4K(m_X8@oG#nm_<I6rX!a2x)!=-)(1^DoqPsv
ztnr768W#yiY<S$t&g22GIXF(J`L?=?8~6~4r5&7ninsm6I$$_OMe}S$F#eHwRq*-I
z>`l|`?75g@*Fb2>MGy$5k*!Im=97f=SfCwv7hzfflUEJn<wSSIHJnzum=d-x0-6K<
zsYvoI;z0-H#TanSHqD?S(aD1MIL^wF&e<@h%Gn}4)A<e_*!=$a3c)&KYMgASro;bL
ziVqfgzYeK6>_ldL8qbS_+R`^Yn#mjbV+5yFG|q=l?G;Yct@}l1BErL%u%s0EUb=zF
z|7pA|(rdjv&FGtkjKG<U>9Dravs{(V9pO<a2BL4u1jmmBNlKf47tKrfj<}=$U1~m$
zpJRI~pDE7s`($d2bq}8&A-*<0*6^~h{S_qZ5<471dpgH}#GT9LE=#gwu5&jOUAco{
zE984P8FD_(boX|qS|1ph4pY?<)w}wTN!5I=t=t|NSIM`;TI~P<OtB^XJ;w52Ga!)*
zQ}ZRs@K#2eb?0s=`_vl`hx2rLm~s&B6UDpH^<CAT5!dJQ%nxx<R|_f8n+k{pjY$Sk
z8dn4z$pj0wuJO*yys~t1(VHX4tJ+yn$KpFABa89@Rqm)aPiVC(=%(BFb`WXon8^mF
z8P2mEeK1Z-OXpnACMMF|IQXjt*in%^lekl_BAC8tS1+&l?L}tW6c>XnxA4-$J=7ZH
zcB_0(PmE1Zd8Os~>O+wS20}YdC8p|a4bxpIc`3NM82mkhL+x)%4L8OLgS*u=MMYY_
zRS{R7(;&sny87a`s<M+D;ceN#1V?<`Op3<NfLN};r(qfSg1Q)uH=!rlD)W`gqYp)?
zOrx%s_Cr30X%~^`)5H6nWk+<t&tQW<5sfWJD>6b*Quf6ByA0xTM;GC|qf=;JE1709
zN;(~g)K?*UbvA37n`e4KxO}^71&{`G<Ts`!YhRR<D|R^VpayH5XgB9~HGDE%o>oet
zuPq=i*!D+?^?yE&0-o3rJ`!^Exo^lX_;&P#O-b?lJm)isA|4BUL!C{c1m=!*w^K2s
zdK|=Ro$0>5vhOiHShRF~uJVX!QtQniXQKAzRSWr_HGUWCo8G@reme|0eL&Xf6<(Yo
zi#GJBEiyN|MG*M@J($c=N@t+~@y@>~M6QdWp?U2<e&A*k5$G5Au=Sa?o-9*$R*GGE
zX@4{}EY;DHQs#DZ)(JCR0u)ECfU_|ABGmTA{`eno5)>yVS!o=ra8lO_aaSJoD-D;(
zQ2>-~gGkb|UO<6UmCAaA7{eEaMyoVnx#W}(RI3Xlu@ZLC1Ms@39I#5P%RnmdHkI|O
zcR`iJZ~5aC1XArA*e?=l*GpemUV?i>2`8>M;q-282FwrUEnPPqo-Fgv0SEstw+s?j
zEH@CsI<s%(j6RRH3sXa*aLRZW1Y$7;i7`w*lAUk4o?|f4a(b|kPOn^QLsh%R;t1|h
ziGGDT{yt?xwGTxb{F4PV4Ou%e&k!+e!Z3e6#d7A_&pJ@xaJlPj600O4G`1<9P@@sM
zv+Tluwh%n>R%XQf3h7!#d3CHOSOkS<XBRruUh*w8y!uGQ5t_gWrcU)w8*6`QsV?SQ
zgJh03hOLP1ooRg*56=8#%b6t{*VZ>ATf}o0cVP*nU`T9?=;Nn|h~og-%#Zk>S9CFc
zPia1%$-k98zO~y5FXntt23)u_GZX6SA{(T=Iv!5Vu%_vyKm0G;1ApNX#P01!Mg0k;
z;Mv({Amq4-^fW(`l~dMZ$4hHYbzd(R)VRiY2l3@24HZT3jlhS{U`AI3`GC58I1%_e
zSrZ>AHKf5nhMc$mvaF|I5G!-Sw#DgXs6`ntatTh$P+V!K!n5xsBqrQF&y++5HR?Bx
z6c(AujxOjjkwqa3t=)PD8B@qr{Ikb2Z3V3?OjKmoIF2I>qdB+vdR|$VF9Zn#Tq+pp
zyu%Hi8)b}x*NWSW6q)@>Oql>hXvcr){qHvmm|yvaW6r)MINw{=hh`g-Pu_R-5rEOO
z@B^T2hVG6?*=76P+Hr+abIj)R8B{PqLR<f^?mq``0h6Fw(8D3OMv{iC3A)y)-i=<x
zOk(r;;X@_{tN`!;ibYLbrbV?(0pFkb|FeqLzF&RcNBT8x5%`CK$tk5Y+MuCJ2Ug%<
z-v@TQk0XX;kY+yB%732Fv=r#P(=#2AXnh6Akp90Z^4H_uo04bC7MXr(B&tsfb8E@l
zygCg}xzSca%x22f+j+!}4)WAAJBu#n&o=zCAD(*GZA!rR2a^=wcp9R5B@XW`Drzvj
zJTJW{G8+})_>1iSPTN3*8*ay;`z*Y)&{y)jbP*7rDt9r~oBoXe5UhMgdJ-@00!(xx
z@ZWOK{}opYk19Vc9`thhHtRT_)y6#(p{kZ<BGF%@UkAx;<>U<N{=E!A)f1;}#j~w#
z|MKG$yunIG;)<!N`LiY$rT9iXyOa2!NV@;>(m&rEg@Vl~eHD{R`-fBR=GnGeYF1SQ
zIarV?8+(kE=Og2iEq53)@+w0?sCa?jyGz>N0}>y$IL9a_HzPec$cRhiL!k%AHqUeY
zR-eLwB5TCkZhpWZsaAeM9l|o02zYODb`qrj(4g+I%ii3pFK0V0SG2o&`;>h)A^`T=
zlt^q}@&fnSZfmH1zu@4Mhls<KgHP-foS*K|!3VEiU@{7wz6)#<2Czw|LY>Thgcv||
zWLN^F;+{&eC059j_Y?`M%$}D<X}+kk#r1hR+R_mO{}?LsVx6m$d&^GHMC5r+#hHs!
z+Am?Kc0Iw28BJt;a_^|6ol)e#C%gzlp0f}v!W732YnYgx3PCxo{=a;3{$54dPoLkW
zhzs1vcehqz02E(z5JTfH?ytVeF}t(vu$+H>8(NbZ{~|P1-9-WA4JeFhn-*y_^?z~!
zY{{A^9XO6D95Gtw-s?q9vmO63R2JI5Y&#PuasF+srKOdcnepnR+knY9y859{rmwds
z%nw#N6SXIC;8W)(?P}en>-n_;r<ybP^vLk1#X%@%PK^g(51tkY`ApV+NB=fKIH`Ys
z8H>86($AjD8z6M1W-)VPH_wCuavwxyyo81n%DUh_TCrvh4cWC&oZ!X<@GVbVFGpLp
ze_C-n@GTS2Kted`=GeWRXY8a=ZjXUj=K)Z}lrCwD+WFHW+PTMS8N-q$93+=F`olB(
z%fTB7)=n-o_~E;mHr-GDWw6PL`LYrGJxXNjjp;qMrHT4MllChmrW(x|5s!>Vhwj@s
zF#;?K@W$z9Z+R{)oW;TN9RjSX`-fxHysKY_jm$pMBY|1Fq*KNhX4x|L7S+L$8YI@m
zfWHD~@=*U2ME_qsN|5hO&V3}+^+Bga=o!YKPxZHCLI|zpp(`x>08E>nGclv0Dlp`#
zZ0dR5kG~`UQY@|@06t3A;z5V_AoLmm|8U@!6cd5I9dTP-+*C6Wc;5?Nz#x3<bI0$*
z>+lty{nc>-$=7-GhbyBomD@i>0Vx_w!gIC0?A~dcSh<Z#!kuksm##l=*%^2)1?>u2
zLHP5r5T-x>J$KLbrp5kIaR8IwZb|<+o0G%+gMA{S-gjwc!ox%0gb%6~`hur5@B$o>
zlphRWj`+VW;VyLip{~dpFA6ro56ftK0l+KoFwI5#=hQCC{jTwCEBo~P!JoC(*+FA6
zpPtWDG&2#AVOo4xD6^L#8*VfrGQoe^HcSSq>L<8Y0<Zf6zq}}Tqs&#8bLaEN+a&0}
zebI`wPZj2RZ5hS!N4;tc43vM>Wp=LEi`lvCy(a8?rFoXBc3y7wr-?ZoL0~~efc~Vc
z6m1NdW3n>ScT%@=Y7>Hvbf31>+SNz3CFIr8FBL%`{`2@8gH>tnY<20pia(v&9vtlP
zvG4@7b)4&M9GvVt$Etb)_<EMmY4nxgt}gfHq}Dw2tJAxg5IEBsAdVUi>`(fu<<Rei
zZzRCh#V&F84i1UbdFno%-Jm>!Fz0^->p$NN;C)fwK=&VW3UrGI|A~>2=)ewMTxer5
z;WcP2NqvLOd-CezMq9E!Tl8BM?%@}kD6!A~nm<r#ZD*YMfY${Wg?It)jz}WD@b50{
z5J&lg^89|6www+>7W|qY7Yp^BSn5t5`BgdTJ7C&^#=CEL73^F=IDIN8Vgo_QsDX$B
z?JU661;w_UZSyV--$IT7twFNf#wzdO_c%10*P)u+e=H)P*ed5C+a2$f`aQq`95&B}
zjRz_z+#KoZv7+mck&4&|kLG!L5_BYN{aH>}du^Gu=UFLnRDat{n470CwLenSb8KBB
zdcAQ2$<XairzogF15vfQ*bN2bNCHf1=8kLB|DmAg`eO)%8>^I?G7<!C^-!*V$Ty(=
z6f+p_*ujiJ%!A%mGN^Gl5rUDx<|ScK70XQ8zd7k2%x*vO0Gul%js3)_G2+;~BCZP|
z&1M0s=G|w3Bogo^5bM^e7$K_1ayT`9w#G@&{JRD@j%~-Q{Sle^jNN$q!@!v+%)c6;
zRXc(Cr*gD8jKtr0=WM)#k^<NX_;mu3)2uBr5?m0$qqYz#j#Q|yy{YenO|Js1*%@h&
zczT)(VDx<vK%@PI?ZX-=D_i>!pFd6&jGtoC*Z+<Uufzs2M&=pM$^%aK=S+!M+mzhx
z9jm-KY_%Zl%+;jqoB#(5!m7z@<^T&x()nvJ5cvQ2H3p3BKWcDL^V_0C3Y~vHTzqTa
zxGOV>gBE@9V{Al{i>yhRpk(?zoW>{8j;KBf#}-$<-WzxU1hqd!LOFkb9N}udymz+4
zq)NIRbKc~SLBdIqBpjH41jRh&&mRj=)Q3&cbLPH7?kQkbx_^hWem}``-M{@r1nw|R
z04$g9JmynrIc_E+w5Sf&X+AljAXs;=mzB21II#<N!t5j&C<Wf0xSmSU{eiFlnGn?=
zXmO9nym7^k2)cjtG=SaKzEw{7M8J|2g5?l`#$8!LLLITijEkmbbW{-Q7kylVGi)VE
z=R@NUW!kBc;Xl%0s~f@1=vN^G&TbpUPQVgO_^$<S5>&G?Z(~;yrZhzPhXj&|7pa>p
zNA!i~BZS9gO+*C~vf<hcuMatgcYK{ZWJ{2sVo-_}08Bg*)3v>*)+<HiI`J$c-cbk!
z3LICQf5`siJ{qC$GdL{g07b*?ck_ollOt@>|6~KeCeb9Z6QpL6No~HVI#`&cS1vT7
zFjFhVxn_eG+z2TU!_(9dIeDPl^lHM&FmyauxXN8L^lpED9z?}y@FSH%p?x3HZH!$8
z@m<^b7_6w@8e>`GXquc$(PCnN@;soj=h~lbrE&;hhj{wb45LL#VR1}TtSrRvZlHTX
zYKFXgZz}>peV5vQtO5LV4h~R))Kt)V8AKX{mTPUvY?Txrut%=`=B^6!)ld{u)$(v-
z!*ilPR9g@{Ayxs%3&3BP42YtjHd!v*j8Z_DF9-1@oVr?)p;II!c>=uXx|8JuvC0*e
zVOwzI{+GJ-^REB-MXj|bcwE2tb2<`6!1Cgq3pM#f#uwR7S)K$<Tf}7q!oQr{cD(FE
zvwa{BNW?%SJ1U5y|MLGo-;DE+XLqzauE>G75xbPIYMKSg|H%jc=hqDjt!Cxt+U+C5
za=!YyQ#W1k24=o|=|uvdfs(tiH;eAx#YIQ4LP8^iPnFr<!^R6BT=yQ*y87FcBHLt4
zs?FMApo<oedCycp(R-i%-?nwNgnH!UvCfl7v5;{9-IAZYYs)RW6g53neY_^Npui#E
zEvN!VlJNgg_ZDtdZd=><HbJ_iLrOr9kXn+`T_Pe%gM=Wkq+97m1f)ekKmkEmN(xAe
zG)Q-McYkBK&pG?N-}?vre&2PuFP9tEv*t7BnD@BHJ?>}CwaQqT^-5V0+9q<}={*9x
zEA+H1^l<Xm%9@W0TKxReJ_K68_Gu&u2G(HQg~<+Pfr<{XR_N1UV)$Bj%x-pjFR(&R
zA-p8F0zJwVh5LE|58}`Za`3Q><LpU(e>!W!zw09>y|?|ZXZbNb6`h@vucOQ-H(gkj
zN(lVMOrTY-b8<!n3NeUX33jcEpjK_4RF=Sgla^GJz1gJefs3L&=Yr>Em7$KH`@Z3<
zS}{i&_B4tEukry0<?QsL8qapxIPt=gsW~`%a!K3GwwIn#J1_382(N-M3k@xPdi$tU
zXh0PU+`4t^AFT14SpZDw5j407B^PL`gMwo!0MLB>{czICg3&OZ)LyEx!Yl!8<!*M~
z>51mU_Vb{*03m1*DUb&0KkGHOk3Z~pzvkd*97bR9F(yajI9mIDgt(}0@!V%VYyFO;
zj=An3bvb}v-m|SIShud;FYdQPZwaG*m3Zz8{{*jm{@VZ{%3Q%Wc0Z8j=)%i@8MYr_
z$xBBiQi6rG58u+>fhzWQTB-ehykl_9q1a>)4};pC5(6k~5mv~czSQp3F|RF&x#~@Z
z=PI}!TU(kt6=tlBMSo9#{d|gZ5gcEfU%B`AS4+!3O;EpeeE{*bb|=8PqRIdi`<Bx2
z^j;PL-qi(*=L`uPZfOB+R3CLJ-uM|2<IREU!Ib+xo~D_dZGHfblzq12Le%GR#`Zb<
z&*4Qd(VI<ly!La-j~_q&?}`4xf!$IZ+++f#2`3q6bKyy}n+*Ij=<*KQd0?*s{~F@x
zfryy6K|@?(`<Yxa*kC|rfPDYYb<j&mV_z{k;=W!9_PjF|pH6q!^WbqH4wrr;uz$(8
zi`4+s+@hHbw7sfSMC?AVJ%F)3D>V*LxF2$aw%dMd@H+VS1~|!pl%%b3{)15Hb!mdL
zFFs~OND{ckwSJexx^>IacI?-QpgvgW#lqqdu{6PcQXEO!v~-mRh-(yOha@rhOh5$b
zOZr1sOJQF?(Vh9x`?`U-l^%bTI%Wyim8R8ymKl8`P8=jy$Wh1s$roj%%lWW($Djep
zGx<?d=e)rjAaV1zp5T|Mo=8@TQbCX){Q4T*s^U*+Q@syvqCFHaZ&1d&4{U;1j8>vz
z610OrKH1BX74w>9yA=RuI3zZ<LsiGS-#afa-}dL=$Nw>cfkF$uUvM7tri;jkW4|60
zWIQV`*{EUXp*004HCx?RuN3cYU8<(14ehFXL<*ar^&mqPlA&8#^3<IpT*_ZXch^zY
z?Z?XJ1r_lxh7M;)-Wm@IhyqGHfx@8A#M`)S%9ZH>4uyi5{)NBuLVo$uEeHu#cJ}|m
z+c!<Kdv^7%+OHF1>jJq%WZg9xH(kPLLC8CAnov?Q`8`v2_vAbK#$k`VK%o9lCn(oW
z7|}og<r`WYe2N-3nfU1m(oe&(S|$`ipT8yPpj>Dn4>EyVd}p!VzL0|oefI;7(JX(A
zC;y5iH;j`0rr$kWrtlHyinY(PFet11*~JtZ(1yTk$wnmt-4K-zU!QR|7A@c^pPWyz
zw+;|6QO*mKMK&dzF7d)?0_)Iwn_LDuXSb3mZqiCGcOk&G=c8_5(F62n8bu;IMEAc~
z_T1#GK6&x$*ROwvu@Qd%=xAGKniQ-6hd~v!#KE|LqqNQd`D6u2WE0wy1|rJUNYU3}
z-{E<}EWt0h)LLtkxrm+njpR3}U9nu5uLA-|S?d;pIEc|D??+qQ;xEV?d!eq0d-m)0
zC-NfvKXZOO<~mRFmP4m@&^%Q|M~C8{<g_trWot9@x#?W_w2`ZMxj!}3>y!ojSPSgb
zZoDQRDfuH_G44OLYP-4ASCEGK>~DbxTYUjCqKc6W^IiW;@|J(Y`rn(U>fP|_T)Xf(
z9I13UZQo*e2%L-j*MQoY&R*||L+spv1+`<9=b&sTdJZbX+o#dmk0taiulzG0H1qF(
zgBYV#g|0~~=s`mldQ6w0t?_hthZ(O@nvci`{6$cWYJ)K}04y(%$!pKF!o+6ef9+8Q
ztd{<JZvRBOpSLP2$y8kR33J4>9h?)-fLST!q`{UM>#I|34d<tD&TdDJnGN{)7SjAF
zsA1oJKPx#t*l0krG;1pe-qw^uY78(%i6iFGiT8hi#AH&aY=+2oE3b<PE&5>Y%W2N?
z3M~_Wj8h5YW72hVyCP@H%jB+|d?3iOWPc%v9FGJ?AAH#NH(1t9^uW$v;Tw2_?Y}#v
zDzxys+)`9X9caO%EJDgijkPsqA33pi6z|lY`RMH*UGHRB*J<!6A3wjw<HzX3*Z=j0
zNe;{`>msKFP?8ft-b-yyY|lnzZ9CrC#wy#wSBs7U^E`q{2liBIt4rb|J|CWdH@|Aa
z#E%|8{<0jr-?Rb;PW0q(fvVG*a7Ya*4qqAbEc*Jj)$MRx_CHhVHxts7{#D>N6a=wV
zKk1uus&yr-rb&r@?a{WomW-&mInjy+s3re`<9*_H4LH)j6~5bTA&q`=KLS10lVy>O
zAD13zYuh(7$Ep91d+^wYaLCA_mN+-c7}NtP6R;eue)0ENGZ0}xGAuNI{Vt`c7%T|T
z`{EGDe+2TN5oGrj_XVDRjdZ!7y8R8a65?<sn(g-O|2hP$3VlK(7$~F-W?!2EBJ8l{
zeKeKB`gL_;JG&c%h$ott{ymp}CUv8&OqbK9>d?%3SeC#o&_zU*j|j?rV-D=5*+z0H
zgQ?f1QR+dLO?05XbE*eh=<sp|><tLU|LZb$BkFX_Q~Yl*8E}whK=)eAeO^nTKp$;B
zvPq4s-BOgL@j!s17K}wl7okpQN@sf1SW8|P0kVGaGy^?p=>ODV#&r?YbH&3pbx3NP
z@0+q<B?5vAJc!ly?DPU4mNlGPakur|CLdTG@5m)PGeeQ@Cj39oQajKOmSzw+TF4MN
zJDA3l+~V+<*D^cXrG>YH?fnZ48JOAO>^U*7Lj`^B88PER^2(IIkGNddbD8w#6A-w6
z|Nej7r^j)*@DO^#1o=ra&Ag4_JzLOZUFc8kYrNg+z0EAZah~kL(FpA{?!7P5Y@eu=
zZRh^P?YO%fjU&lF7up!L%p3dpHhfwB=X!rCw(U7FD3m+oqO<a}hrO|RCp#uWPXN*_
zXqjVu7u%@af)Pl{96_SUh5>zdC1Pk8_`&=;E<Z%^f`aB`bnV}|%UoI@ZF+I>+wFGY
z$f+sgfA;U>a4&do!E@|~3`~``QJ@&!I?_z=_=S)D5D+U=a;x5#*e@^xk-#5yVKXov
z?e;&JJ24qZNDsNic|1D4VQ%_oc`D3~b{08wiu^Jn#S;JTj~E<M2HUIFio8DZF5n>A
z$kO-wp6&cU+ZP>j?qA_NP!ZZ}IT70VA**wIIMv1A=_^i`gLBX95Bp=-uhog&2rpd7
z{7A0&g+1isjlIo?x{m`i<|`q-*B@}aH(pQ)*`dU|#vJ3v^kM&9^3m$Mis}`!js^jV
z2QI1e)t!qD@oi>9nj}|zYhK>l7%qxeL9mAS)HKA}^OToO&P~ebJ*CK+yLq;-bJBhy
zSsSdn(tJonP7yk0Aq|h6U-q$QXJbpt%=F(=zDbNHBO~MD>e?l%rBz#7YllQipnv7!
zt9v&HP81T9Z_c$Q)FtqmBjUbU^>^zV8#8cKpWL}vW2=N3Myiusm(fCyrHs8Cv#77J
zL2b%q&pQk7&E5>i8u5b*U|uF`>^Jak8@8<u&$2{RoSz+Aa`Es)$%K+lPEGmz`3>|Z
z9YnuCBGU}2y*Qo}hoxq)VmXgGl3lFv&$8oDCqxfs;?0}HL`4_)0)IIT5}UGF(u<ca
z{rSP4OL)IvV`HmtY}~RZA|fIe_Y#3Oh`(n*-$o5HGXsOq@81tdWG1tjn3%Y|PF()J
zIrI-@e)RN2SFDwXrlzxEq0BpEvwP?XFwZWz{rQ#B8RZnA*i5Ck`;Q*|EMpC+y<IXQ
zdGFp|f9B@Ts2F&VQ_^B*2PRuvTZlM6zsrr0bdmS$(ky>##c1d}c!_%3=*D42jQ21!
z;aB=audtk+Nh*?%oSsehF2=t{$08z<45J%!TfWVwQl}#+d2zZaQtm%1perpRA`;JI
za#c<2kg5HF*3-R}!375)IGCfwrUbRn&~$J&(>2?5v$*hteEhvSt(WsPu~#)G`VaoT
z0jA8XEFwa}SG+PBrL%9JU}9qaXUYk5O=05)1qavY#Bu8VJ!$VR?VxY#o}B^~K0XUS
zf2`oRNA%u*=Ja3honV-moUDQG$7+jK6H}nl(kff8I);#h#g2$G_{#`=h|1Un336TC
z15vi2^G3Btv9`5Y^atKYmzRR~e!~P`_7SgyjfKWMENpCuB7gH#&$mx(E3c!Y?(ZQ)
zJ+rZyOSH<T7Z;}stgn}T`1!HFYPQsk1MhZ$A;cp~nT>G=EM2PUS~ZP-MaXqpTGeOI
zZax3s<DFYuYlrcM`{v|~IFEaxC-9;K#;1SXtCETe^PM|Hf$;gUFJJ!kU(sRH{Xs!m
zPOc|Yi7dbB=otiz>-#@z<D>K#V)4_bPkDM3Z;{iD=zID*DmX9L*x0lt-zCP!$9E8-
zfj|`LWISMGx9Vek-u-9EZEqp#nRW<8*}fiYJTm&PrM^fct9(^v($1fzCRH)0Lh@sv
z&jh|)<V0lNR|Uq5kpT46cs)5%7by>TqhTZUPmJ6lIm4&&5s#g@5vz_*<0DS}&out$
zmoSB-W&Ziczwglp@9)X`_pJZ@8}Ft54xj(r)Sq8pAo&0OvVV4s&$w^Hh~nP6aK7FL
zu*W%(^hmv6CCadS8!bA@x2ea!y#jMOdgrUq=h@>MyY0x?by+@tHu1Aq>rvay%RZq=
zH4WkK>I!NaMu}@u-_ia-I@%CbYO_Ns?>e(@FbSN<d|G{xs)N5S)^NqeeT%$qdrp(o
z8*=YPX}#+q#`Z$?Frlsko~?f45x#+R4<GuP={blUD(6c+;-aE|5KZ*aZ8PhK*kTe!
z4m2;WLmUu&7+hU>vU@iRx|%))P0s|3P>(mU!x@Yw<k5ruX~tu3xN6*bTLqI-@ok?O
zp9<r5d$siPeVH4jh*`QpHOn<5M;7U2vMc|({uSwZ-nBIrSfsccNxi?Sv{D%fPUa<z
zi7A}-iD8Q=sat7Y!sz3awd)F1qOFZ}774g#hkU$085%Q33sdqpu>QZY^8ate>Hlv>
z3&}}8?_}dCRuTTTCW(xMtzAt|f9jXgd<R8Jc~eQX2U<;sA1o=8;=Zfxoqhh86Twp=
zDxdw~)mNU9;*Exu$nFvK(9m2AXAxn#%CR46U3}rp1!v(o-506X;*U9L#m-JB2A)5B
z+wT5$%cticQ1%Y8PqWn3?N74%54PVRNEUay?6b|_KK?LbZLFRvlNF1$>I|oB&g^EJ
z7nU=ZK|7TS<NOBgUV%Y~YC-mREb(Ft-)jMZ+=LyK7R$U(^}ThW!|&XvU<e(}BUKSo
zBh!eHjREn^jm`OvA2ukY!~FgXJUQ&*p`Bl~%`+lW`43n48>ZXoL;=jnNK=DyE6Ccd
zNwHOSd*L8&qi^wWdE*}KZH<>ywC)r@Z2sp40jfgW%}9{!EO@@FCB%$dn=ooq_q$u!
za!jF*(<7X*LeQv0XRvGw`>KIG<7Va6{{^{cevo44Q*r~z*2b!mL$huFx?rT?=e*ST
z1Yh~)`hrB_QqJ9ScjCX_dgccxAZSI`hU=Idn*MPCS60O11T$jEFEmNm$3Uisul?Q6
z^!yx>^_Pam<$H7U58_Dg$Jpb@Xvi-JlVs!y!@YLCYxu3W7Ii89Nzx&YQ00Y;T&l4P
z^q2i)(|VTg(g8rR;IGv_;~^xcm3pMhQ2hXe*o5JJ&DC03t$%v~lHI9QdN#0w4Hq0s
zrk2*4{UJa?(r+9nBxa?tVtGIK^Vf4YPyMI37iMLR|AGD~61ffYc7jE!$zG;&%#-x^
z;9s9Dc7YzE;?HO2k@9Batv14ElMpIdSYH&E>*3pqr1_9mj3uZ>C&Yq3l^sJicmJ(7
zrTPMo3sa722c3eW4!ulv<mA+ns&2sr@-R-I9Dv^w&XZ)+s@-3ZGc=+>QaTcN^XY@D
zF{y2ttn=jjvkexB0c!WSjW+eJg{s`k7Tl0CM(MD%-&Ji+ApWiWHjS5j6Fc~Z5YM&h
z*O)*On=BG#pK8!%y0U!w%#oRyhF4h=WMILR)|GNCco7KPwn^*fwi&vP_vsrrur3Ez
z0||#Kxx`+<mpoP9u4ro{<2v_0F~PXYA|sLAGd(r;%0T|IwsK0%MzeFeAUWX)gHuv$
zEL~M=jY`Xc=oBTb%QBUhYGWV2$7W=phNIiNV<&MVWH8VP3HROJ>m2d!T(L6d2Ru1B
zQs&%KYl~73jJa-(@CLS5V)STyZVUf@ez3~-`vLb+md?n@fz=7oZ57<_+dpxWMZR3B
z7!B~vSt^BdL(0^x1=yRMY;Ri4U%e^5fs?=X4)Y8nw?XnK>nTjy&)vu>zjW4<sDbAK
zs?-CGHErw(h+{U9@27T%l)_MFy2@)@=Qn82T`v6@nvX$Reo<ItYI$JJ8xJ70OPv&9
zrS69Wqx=ci_FL05yYC6*WEQ^Lr>zceOjOJ--@Rbtv66a14YN~X+ctYpbz9nbwXG-A
zc~f&QPrr5Rkn1n3b!)EL(Uv&zH+4_?w+@x?38{O7&yOr<&gfz5{=LW<tU^Pu1(LN$
z=TURh+IPP`Wp>nh3PYwQk!fh*dTryIKT)3G&62L^e1;ifk5Wk5=NQB&?s}vd47JC?
zj@yYO1BJjLUgOx&Uy$Arxwr1N1jyLhPaN|-V^{p5bNegI|M!!N@R$ur^t$)kA3@~F
zPw564VSF!cT8YDCBLEP8{CsqO?Nr#Q0{7KK$p@p7$Avnd;o2e|7cJCK!`$Bldek))
zq*Rzwt9LN@XPK4XiIT}^{aN<wz#!VpX5%_n1gsyj<0X#5nTP}@_=y3d&l=3WO|7kl
z8mJ>yEfZy}>=|+SuRn;peqZmQJhC0R=52qCXY{C(J~5!}S&i8@W7Ob5CZ3J1>|WJp
zgkO_0GwUr{*w=7Rx~#)*IVYC;&s8my$yNl|ee55LoxApVZCxG32%m|x=tyMtIExJ2
z#1A&Ct+cl=DH(>*59B@MQvo`8qL`Qk=mr1B#pM|46j}GgtgfMvdvG(onf_>ONw!Zu
z9@mw0Fup^RZV7LL+i*$>H~3|PGDbZv9L_^8F$}RmqKEIyg0BKincALRfomUlE)ZgR
zk$t<)Og&bfC_tD}(W9@hc1_H~Sg1@6?`2ln$6J6(FpW#Mls*-3ny(KPG>dx$RTw-*
zC!dpdTM@J~qtzqQGLsb4II?6OKDA%sAAc&im0iRd`E*@OP5~y##Y_ys`s*i-_W4ze
z>=%3WMo>^0I@EA6kfx}rndn__+x-R*Ut~2KH()JbCUQ=Hl}vlcbamuMZ5i(;PWJkm
zg{6Qv1A_2xS}|lZUltkyzYOL<?8z#Zh>aX~bfA~F@Ub%WXl3y1w!wRN5Mej9s%9d$
z03C2>hWrwcR>_1$|EvZ|MgKnA9qBBwlZ&vR0q_H@MV&tHqnm-nc^}eQBfnvfPmINh
zOK50ueoarFMwFJjZ^i9vPtsBPkDAG7kZ{KQO80n7xlDNS;SDcKVWP^3hE^;on3qGn
z<u9+wu-rsWd!J%wBnkh)op}R286MiISFkxaoh~&h8*N?c=zN!D`AGTpgIJO;d(nb-
zzdQ2Jn%nD~FFt8?*<>V<fm-0Cd65v4lK>Y`z~1w=%-trPiVglP6tCR|e)xyuiKwrZ
z&uwOw1?OHTeWMF}z0j_lC>mF)f2yHH(@@rQXfuYIp%a$29Agvn-lV7Xv}M-!dUf(x
zAk_c45}6@)`IaW1N=9Mv8+%~hhBYJZA6Z3wrTQ;0#=DG1UzTY!`u6B&+fb9Bt%!o-
zqRw9aX~0iEeO=yD9ulnvqg9py`#;N<p_JMZ?{MDZC+#e$<;W-4S7^RVB*5W@t*OLI
zA)c3Ype(?n=RV!}=t)C&$vo_qp!6IjT&gBN_p<<7b#h$(*gU-hp+%~mFFK!LB?G#8
zQ(iBqCM%i7y+d$#%@)Kzd4l-*^g3Zm`5%HpX?5#hE)^e67RG!dr&i0whXrM`y6(ux
zCLZRT?nOpp%<x6Yr%16YgMrO=zXt{+Dt^D@Q}&(@H}umLjPoz77i-gzor}1T-{tHc
zur#W;lfRk}Ot(an{-@@u+zaKF_00IPw%jiYUm%^Egm1*#UkeLjX3D}Y7)|OJjMhp1
zHK1ama$WyS0-Z}xgUVW3*VEF{K-ro2-h(C`*r^HrNr7bOkb+(Idqn>EQ?}14xCnB+
z(K5Fq6M%w@wog(OKZgo0U_drAt11dAr9HJECZUjn<i0NAAZo5=7M1E5@3~z4bSIqm
z@Egb6!>WU(42Sx6{7MM-JJ^Ax?@kj=kBk8ZEr^LmgWvyj8H;zMQQ76UKH3_`N#(;`
ze@T!?wS@TU%AA-rxj5bNHG7=7$R2;_z+al%a!>bG=+;2x5xa6*I7zCQs&fAr!qi$%
zO2X+Tp5Bpeqh5#SOXqvsc&{dTMh;gzfPO4e#nBU-@Zga1`WG0a`nEmGEMmo1>G|@w
z6_$<?!in&cbD`&;IrVTQAwHe=k>1^?8tw6ys<uCDN{~V(f<RU@9F7pMX%8+3bUOaP
zJc=mCJMWYqY7uq{@=ekvAfZqyd8Pfuyp0GlTfnExgdZCgZvqWefGz+roz8=O97gvL
zdCNo)gp|$|CrOiv*CJET%JC`vfn}{)ItfF#-~EE5Fh}uz%H?PP>KW87Do=~#*E_<I
zu;uOsg7xdqq#ud`H$fBRF~fRszK}Ivx$ff-tIeIw`F)5`nZAf!>hY4;UsE2+w^d-n
z6OoR`jxpw^4gVlq*ttr%oHJN7a|y&EJq(=e<M&YrRJ;m$&LQf1JUg#D&~RVGs2FWb
zU|jrQt1DX*Tn7*6O|8;N8=p_^RKH-jFxmChdG9wNP-ew@k4RG7a7|CsOW4>EDvzu$
z*4-SJn4hV^yzKoVQC82DXZUR^i+#(BH?mo;McjzI(lh|D*4tRP{XW(+L1kcK^E~ZS
z6)CXFA=n`hP48AtpRnDvzZm){_D9eoH(V+u^z6AGbrRl9aKHQg`R#1py@^hBy&C83
zcMV4MF^iY|WG2-$^;=)9_xv2&zXWL-LNr)W+4zT6bg&v_P)-iQDkpdHXa4%#VrT4F
z8P~4@f<P@5fCe_#HNpML@5qu-TICLuJ6wPiKB=zvu>Kdmexo337@=lX1~2*G)-DEL
z-@~5eEgpwOQNKNotcS&%diOjXuZRZfDo9qRL3S$kEP0){Wqf3iAURejwW4>-pQEbQ
z(UNaE@Ilyae@g;2%=YVUBYKE!Lu&R?x1oA*P^DEq&%H@V8uzmx&60BV%H5T7vG5*>
z3X8nYwQW5~u$vae&F*vsSN?S+urKRspXVN_QxdMA8drX~tAJYu_H$shNmYqz@PwsM
z<*SyPBdvR1)mTLwF9!mS%L5@1lSm6IpQMGdr?uHvICymoLv8#obY=*5<ga&HuUnup
zC!!v`)@H*ZF6`U7y4)<Bs0s-<Bz@vKfwE+3%YvYgKdeFWp!LIcQ<@u|Juk4-Xlh2f
zrSG}$UGwBZ*GijP*dNNO-aa3{J@d0Y90576@F>H3l8U+=IH<aTR{Oi&P$Y8S^k7DK
zV+Z$rc-A1Bh+_=w6?K;-%x|;IE(Zr)cU`$X<+2G#<ATyz5f)Cu71~@`eCH&vIFoCu
zO>lwc=8?cpWf!Vn%fAVX%wGSvY`485Yo|~c|G4??SKpHijG)D?REd5SMow35H{U3w
zaKPiJYod1nW9*f9fjGMyVsthB9#Zbxb2OzJPz5%SXGZ)gsu1v7se5)BE;kohZ-aU<
zHKB@U^H{ZO(F9^~0~af!>9xiVl_()ZXR>8t3W}xZ=SX1$uUxm~$NYPjzMuLoB#Xp*
zWoTkXL|~8%v9}2~S2>QqiT)M|J`A7qDGH%U(iJBvpH!_UPg<Z?Ubd8zD-QzTty=*0
z1j&Nvx-=g#4?1(UAi-cJhKY@Lkc5e*28q7_<Zh^xK%9=t9+B1EuR>r)gqg3b;%{zh
z?i-fqD2$hebqTi5?0>O6ErFV&Zss_(v4!g|^OR_eF6SLbYqjGT2uD@AF{N>heyE&?
z*AA(e%~hvp2$W%uwm(RTDAm7&22W}Lh_B5f#iSK&AvsIrp3};ZXDmC~>XLG8-nA8u
zCRtkM$lW+N)oH!id0=ZptIzuKQ)aLh-5IvM74IP=C-hrK1Eeg^*p9;A1(khE$g~x2
z%hxghFT9dFAa3F8GJct3`?3R#%N2+$ySblU55Mm-)O4jBoNc=%i$?ip4I&W<c*lL7
zN~s$C$mQhz&1*QBDmHyyRbHNW=(5f1Tjx#p^M_9P2R>C91=CsQM`TqJMkjQX0LYt*
z*!b#tFW&AIIoV7E+KNx*B$@MWR%Lfm;2`*V`-={GtBY(`1>+wl+7+9(8JFYJws{tK
zL{yY;k=@s5M~b;kaY7+jYg5uS)-{?mus151U%6bM>%KotuBSU#W=sf3{j+UEZhp*{
zr@XXF-)2s~`|MO??ycFB`(9m*u|VNkwBV!j!6$$aU1Mpt*$4MVFWMw*qe<`w6^T6~
z^vy~{Axqs{kbBZM@mPACCzd8<{vae7cKFa1L-FkdwYj&mOySD(t~*R8k{KXTk1GK$
z*J?OKkn4_a>}+PJ=$x^UR&OETM5KD-XPIrmJ2WdgC_kG^a(2g>+n5m{^Wu(6oi^}U
zM9(Ro-O||{3q?54@x6KZg;kYyq?x6oH65{k`F+>iz`Hi<^EKi|Rreq(bCC!TJXAnV
z5PRp&RTj7pwW{U%!kP+1j~{3;KR*oGe=5i~T&?Z51W1B9Ean@w0m}`J#8=Z<#tul6
zX|nbEUIJufg3jeTw36;weP#l;y&~U?Snt5MPMroAe~TYDT9pHFMA~Lf494-Q?Aw*V
zlWB?_N};RpCMOPq`XcAljI8hRdw8}_TKY1v<BxuVQ8NyU71;e~Bb2Q=Cri^0NN0QW
z`yz0yPi-y`n~OI5{KR`t1IoadxfIimrG}3}qMZ4(Vdb0p)O;!?!`=QIUb{$m1)Sp(
z8@q9XJ6Bg0)X(f=-oYE~qZD^{Z6H((PqS^i*(?cS8(aaiChewv2&7lsBqpb5S&B4x
zT4!$CrLGx_+F6e)+#QaDGg+C@d?y?-x)zkvSq<x5x&|%hv3Tx%97glMxJsAH@YNlK
z`n>l@K?6WQja+nwDnSDXfE6nM|JJ^KeT#U|n6|Djs4#eDn%Gog?t$S3r%6Lw#zQRw
z<pFe6hd~Pa;%%?3Y=M44q3Kj`<dc^&uK7Ae0?M*q-~rP=YJ6(C_NZfLeypjXm!^if
z>6s1ld?(&kB<+nXIq$2P%8Z<re*jQMN%N9c!xaSi=m>IuFH;572lS*wtd99LSf@ku
ztU~m}uldR%kE3mYu{F`b(j;WAi?sFGl64OKzV~P2Ixb4CdF+WAm*4DZv?(bx>JrY=
z*`VCjdWuj2`1!0PZq~<cK+yA9@cEtM{K=NMb4eK^P9SP_TNA2xit`xK?VT@I%WG&5
zZqKzb-o72K5PdTW8fy<8kbt7I!2L%Q>namQm&btmDvrGmNM?;n2JIl6&9{6zx=l7L
zfErqMK)^EzQeluetGVfkk&^hIOK2>BxsA5Ui?8p2Mxt{FBu8D*?+5&0`8KU?v)E9y
zd4*(Ia(KAi?0xG0)x2aFVb4c{A){6J__pc#_^+~3R~JCm#H=W1zLctr{5nXsQT^jm
z?pbfmfCpH7{Pey!-ESoClzQm<8v#hU7{Enpd*qNCJJ>h!&Ar>1v)t(}G8nUpB>3Ok
zIPT^Pr)d$ZvniP*=qkft#aexO@&!w+`d&J*n})WQ>bM3mcX|0H$PC+cf^^0u!;2&C
z+`f(DFXIeu02q9+>Ww2w5&I>lHrAH09Y+k8SdAr{nRq?mO`c&>N~VpkhQ_^}@D{`u
zBepTGS7_yB0q5#1td1{+Yn3GLgnXdVsOcY(kF3ey{>E2mdwO4WWxTQKn?c5{Th1c9
zbg<KwZnfwfwf8)TOJ**&vX#{r_x-W)C`S={plC*FE0S*Nm}@+9zB5;TMI1*3g?ie2
zydr;$qot()#Pd><N<+_Z<W{XNY**>CLMiI;3V%>WDFiGj$)Mt%j5aqz&i%Xq3qUS*
zs7iEffX6yxwrcJ^l>J*fw$Q6<7)PZn_T?9gt6?fndoj4|w<hp%La;GIl|EEnqKY$m
zX2Zl$#h&s8WBSW#g@2yQIWy3!bmHhzk14(rDaK}Dx7Vwdsp<|#<OXNc8tB^ZEG_D*
zwuZ}984b8dJ+QQ;B8H=|<d^sCc)CB{<}x0Bb7*R#quTwG<i83lzBbk8GS`yGynif)
zi6ZJ9mcz3R{=x$$&*5Q4I(S@?)<d=6JS*)MS$Z{4N<43Bqpz2Gurm0H{rY50N!--h
zY@K&{(`)v!)!>qWlC>wQA8ec8lR<&1q37U?3aRC7^<E);_A7LAW6xyZgQdfwJ;k@{
znJbUZD9j_WP*at0K}+4yRT-LgP#t=lbu%Ull@gOV<LApKNR+%{)Nm96)xq?*NDYlx
z+f)0%93Fc3)|O6od4F+l)21FqU4c3yzkJrq<DwLDf2-k{Ko_B$lV@aZ1<SQl&Zdze
zNkbPL$f-X8N*W4IVxbo-nJ&z0-OVRCO#}jDBT&OntRi)9v}r*?5M-#>nVC;+AE;yL
z7?Cs4<M*N`#7XsF=?Yc=Df8)*A(ogKv+Xu2Z<bqy@O8i#Cfw!RyHRmy?&>Om*D_aq
zFOoh3B+Ax7;sg#)ch1C7RoxD+4`Mqaq^RKGcP>eQX0K5!D9q6HU$KI1wJlr(GFq0=
z(Pbb{1+R8N#!!3qD`dfSP>Xu-TU6|NwO`pc{{dQ03Wy*mY=K=Fu9p{pHqF~mHfXJX
zA|@z)Mwz1;ScgCE@iWoCzzFpyXmKnsjo5~nn<2o&T>vGzs;Npd(%fIrF}asLM!%az
zHc)a#1z9Cj&5+f^rjl;_JvZ<*1er<?1pTGAZQiuGgVLbcb5^W9^kJB0{n>8VRMhbi
z1f3RdF_30%DAYA}={Mr%@|2hJ8n%)U9qFm6xDujS3MjUL0-q-idIdY7<|5QB%A0%m
z0&n%k`GS|R8;+s+FTSZaS2G03%N{{N$A6vK`skS$DAUNG)jl)<r-lUm=%AHDHEaDI
zlyv7g%}kNxky(~#v}t{A)jU-8%D<y^>Xl%1BLLHO+P6<pJ%RfhnmJ0?cjJ2Ls(<Z=
z&KZ>qqp4>aTgl`i)eoo$2j1<mxytqE?9}>-9F1X(h<e)n9IFn0dhYcO&=FD_ug6vj
zXdxg_HlM9mGg>uWRoqrBT+BUGPCDiks(jb|JsMPDIka?=ZE!<dIYHG_k_)iG7&daU
zNGWkm!Tkph>c6{g%Y&m5TGh%4cl;L?ETFBkLH9>5Kra!5%Jh3nr$nk{DCAzDm8<fv
zuXCm@q6@|SSByxrXO{3ZGARa`AU-U{0gtRUzQ;vL=z;*(?HW-Jgib|uKu%AO=TBB$
z-O)ofNG3~@{%B)E>+LpllGq02i7sPs6)3M1)L(zP0q9+9y85UCgk)AO5h-{a6273~
zD_<}CS~84Pbgqw-TEz1Ns*nj?Kq;V8Qa4Juf%e8E-<5EOpTg4zgB5F5PM=SR(7gw|
z?D<X}1aEmc&$K_jLb(TZxTDsB!&R&z-hs*`r4=V(hzw-^Hr)g<c}U%lLd}I3ks!6y
z>eQS!7yG!}0xi`leN}DWSNc^+B^-8iKriD5ec`zsBqhM{<k8$}<NDte(kwR%9TzW#
zsM5(~3v%yHl99WfjxL0AB5=r)evxsB|5}SRcvFR>@GU5dnwgybbc^jh)Ba^yZNV(D
zEr!YfeVL?#cb8nA_gz+237?@8hr-;5uebM$^J>3EVoLrcxjV}Sp?13)=_&;+O<Ns{
zCLh_@8IFGl1gIo7`oEZ4ka%%tA0AB}yQ}LtpTQauB30eR1_b9RXlIr=xi{Ab=wTO9
zaGPO`dy7Rv{B#R)P7hIdUy-SST0H<%^NCLP`d*i#Bh4oowMS~>z)6|KEN}3hA5zq4
zY$A3l?)bwXHqOSgG-B+-{!km#eEP>*s|`+Y9HTZVbbyrfp0a`Xbh2pq+TDVseahY7
zYKY|8<fviEG>3pCcaEx@{+vFj%I(H(xYF5zuf0vh1wtvzHAIh$xXbU`anR&`x^5md
zdSvL}n9VJG$zF-Loq;GC7Wu=1>CLRHtI7V3i@{0R_zl^SokQw@s7}>Uph6jZ`-nEj
zUIcdVa!8=<Ur$|GEOI{8>!>S}eLwMK<3h-oXx(kUm?1PtL$3JLh;#QkGXgs(sE!4Z
zoRcoH^efbEj*QmVK%R-8`O5E@cMS=)7EEVGJip(9dY&A~n>t~4kzwj%E^As2H<`Kq
ztF+59j_GMRVIe*}d=KJGP>&B%`JT0e4iuY;B|Lc>&3DItri-|%6OnqyId8tQ$u?Uw
zsX<cH6F0Wu5I8STA<U34lUh-4DUAozc-Fd&!Iy;9wP;Liy@O(O)TXB9A|U)ead@xG
z^l`YvYGODS+Kvk9@;F{S5B}))PTFx%qB$WtS$;eMRIm)&M1>@PmGrFd<i1`06kMLl
z>xBL9S+Vdy!<`$C9F>F??1#!@>d~v=^#D647T&wAb}V?)S)!7^T3LYHQiIqP@zd{}
z$HD$0eC+{5(FM*VJ8ZPv1GQho>Y$I>Jxs>?+0$#Yp}xJ=>5L9nFA=Z1hja;c)EeJ0
z;`wH1^V|$wN?7KxfDE}`d31$m)NT5GxN2JetVzQ8!0nh+(Yhi30(;n7=}QhL<n~tB
zR$^YUjpffE=_Ty`nrGUn#8J0f;C*%xjRUosa*y`b=Urz;XF<%rD9KQIxY_Tr-$eyl
zy?L{hx5<$`-Nqm`K7322Z(yXjvluU(pqlN-%PzsTnS&}iP#{C!2R)J<MG(tq*!4Nj
zT+R~TzreXh`}-}~Gz~#LBuws-Vgdrvww3-W2G%<f0;f&NbKesBUo1^<s+zCV{O<Nk
z$`;(!nzcUJ8Xw>wg$}Lh9D6D;31O(zR!}ZP+tXR#JEr@xzzZl6U$FPi`csb;lX6x6
z6`)c@Y<qsgOIb_dLQ<6*KBOWCV<MFPq0g5CY3i#H36hcY+FNJSC7RkgjZlzeF29@v
z2pWP?r+@5JyK(%G4Df^+=<r)-+C)W=)B{TjviGm|cN02QgpWuGAD(}_wb?Ay$rsK`
zQGtj%7>j$A{yM1;n-AB#u-O-6b=L%U=%FA&7x@WN#_pX(oeprACCltD1dc5z))YGA
zWj#!maa#5TYFWF213nQ%sJwDx!)SDRbV7V0r5)*5CQJT1F_2v)t)yt$C50WEMu$O<
zoaXD-n&x22XQ!>Y5Hp+d{!<o_xdhKO&|ft4?Lr!JUL;?Ub*vTUj`@YU3;ob3jvIII
zQs3L<glv6e=c<*sw8XB2AcNUxPotTk8&y+|yns_~L-`ODERG8MZ1pks*56x1kN@v2
z0zD<n;I(>w=kx2Zh6B}S!axH2b9#8zBIHWf-rYslKja93SV)5rj;Y_9gD(vaRvWR^
zv5Yq#s(T3pYMX`4XM=YJ+I+K{r}OKEkXC_|jBIa7LViLnc+W};?bEi(skPofI|QJA
z_;Yz(LW9KV^MMb*BJ}v;4+~@fIWtwxd7@p9Yd%58CTfHc!@@GJu1E0Y=W)*WATdMl
z1fnlhjIlsQ%Mph<PEj|~3tgQ+`xMjbTY5UfTw9Xd<HwZIH&u+`q)2dqL3!e1paKSp
zF$%p=JLLZRa7c|X>|k$sCc3jzRcL@CN?osj*d?5r5D7m!kGsfvBAB8YIW|!n33L)l
zp%XzBsh$;G3y>;0`G!CQ_Nnc-uSH`)$%+NqaiQUwC{*S_AcBXdvw{pqOhD?NzVs6$
zK=+5C*FX*O&FRm`5o(7Hk%-3$bZ^3ZR_eZsoZcEjB`Sl23SBrmC~TsRI=2t522A7~
zPiUa)dadlbVR7=nHYI`1?vbFHg<416)E_L|F#^z2pUAZ}JP*h;J^7NDQ6bp%=t;Pm
zA?9-jA<^kIm&?#YfI<3|jC%ju3*deS8qLtd3jhR&SRa*?e;}eGEb!ivGUd(HJAAVO
zAlnYP939y2(s)k}tf@d9kF;n}1c|W8ro;%P)0OAcEto|}eO*pnngi5W>G?Gg5iVSq
zBj}RjUCm(E9(5CEE@N5Xx8{Hv^y@qoxvbp-5&ow#pmy8QA6~x93yB$8d?p|=vxX?T
zBSnva)fGbTR)Bv3^g0%#=n>Lm9G`mRyAQkVJ6IwXyrzAdwDPY6zFo1Pk)*TU%I0;_
z)OtaxUh33na4jMvJ3Rg44!KAOXz^x1tW0eTng%TKCns0s+!<|4(VZ^@SQcLJbn*#@
z&Z#yHYPLkC-4d$un6rWQMf=&FqY$UfI}X=Hug6JC$h|lj;msXf(?#~kZgsr~6o&Ya
zALOQ%@nk7S1+jLs%8HPQ1<pJ-XHLj-b^_D8w!sVCp^kLl<tKx2l2wkI7>wQVOs8wL
z5z%EB=7Qqe^m<3Q<}LHK$QS3N$$H}R&&%}$-mWUm**i2nPF8W-F#wIqXy2JGx{wXm
zyuaP+t=F`T>G&_16@zW&_Qv-3x``NJM5))cif+;g>)+4uc~_2L{l15N4jpe8{IYC*
zXQ@~YQSOOLCGLr9?yH%_<J36bJcf#bA-zPwwI+rJJCL_Lso2L|-=t=1JkhT&X5MZ-
zF}h-uQf@B>Rk6DM>pJkp`hbR#;*L-Mw2v4nIbYvi<GBs|+%7)h8jTbR_haAE(y|@^
zD__vCe!L2|M0Z%d>zvV<aXtwu)*t)hlbz<j9d~HG_(Re=8=*Otn|<!D8R&%EGbKg$
zRm%1WzI$9s*!qEbK$2oKv+JC(<nWaYlnzdtwMWXIVf{CrV27$0TKsxW9+nS(CT68n
z=k&26%qPGckpP2Ip_0)ES{;jP$*+h-H;o~?{(ks&ijJrz5*j<qpk`8Ys+ENUNwnnR
z%i`4@^Po!=ai@dchPqC&oV<g%I>{SOx)xCAi`IIerTQFqUCfOUe*AuEN7-}cN_xfK
zTz{h7IyLM~JIt`42BXf><!jI~R5zuDhS%5msW}PgB}18s)@eb*2uyDE{2fCEz=Gxv
zkoblc9NYp_l4W(q*hEe*c2b3%Ja(1CzpvqNHATN8ewdbtLo7-lB&XZ)haEc4Tai_%
zPBwFd*cSyWF32Q-uqdnVjeixo5TJvy2=N53oDP31;LOf|@}0CTMb%x%H;SdYMG`gq
zh|gJQe(U#^$;XfguGC75q+TD?T?4xMg}ItHab`V3zh9!7NLTCY1Yc(sh$u=q-fgEn
zvLLRakK*v&wtWysGO<$StW8WZnxM7bp2@L(xqZ#+^hs^Sj`1i4m%7Uocl+8w(|r97
z^^ZpX0=t{``0X~Xb5cV4RPA#e<1@d{=<J+Mlbsiw2FXEa?I@(NLJBN)c}D^H(i`_|
zom2s*xYM3J+D_@NeS7LhVu782QzP}&>=B8hBjy#=CqKBJ@+bZL(`PtF?rzL=<Z+zy
zJw436OD3ZMg3t$O8_OpgUhWERMu6iJlrY(z6+c;)J9B*)+~p95y`#}+U)xdR^#>$#
zFTnp1pjH*VEu*336$0&!mVx58P~NDMWQiP5kDb+g)p!tlrmbQ&?2LW4Xxkl#xA{Q5
zCD@x61%=QO02u7itL!eT?QHD6Gr6!94vlx{KY>pO^a?_io&|voEH_*l@Z#73SA@O_
zT3|vWu9AexqRyS|5Q{A>ORhV2-o(d~gCDxNqoYX&?8UUSv?uZZD-*Gvfs^7o2z6JW
zg&@jR@#floyHRtlcil4{w38*B$Q{|Xb1tc&MGf|2Azwm_(6U&_i(X;C{|bw-Fu>9V
z6r3zEw1NegCYtWgR%$KaUiAX~oepA4U9-7KZ4@o_Q$sFY5Hdu7^#oKjVQ}Jr*$L&|
z8#hCvk}az3%(Zisj9R%(<x%U+cJQLXmw|zC7AL7!Qcs3$l`X_)Y{3UuE;xq5%8@rc
z<y8NYsnLPz-ofqCYKo}qWxnwDA6*!P9P1LGGy}TXd|<@in`sVT#aIKKNC7a;*6%I^
zqBc}VH}P=N3iA`8)${Q)M@CgSbzuEj9dz(&bRKyy(DUeh%;m^f*x^|bpaM}}nt6}m
ze1IO^4V>F^ly+KF*ssX|KU~blK|TonU1$)Rj9#xt*YHy#)=>CEI?0Ag4&;EJ;_qJh
znw22wugEm=oVCl@eFTfi4dLjc#23G|?OBC9V|o(Y!Odp(W%xOGfqsE7JSBYuVlQ1f
zSj`{>PJMA6EdlaEbd1l!!ATehdJFi95Xs8p3gDDT?$wH&R@c^&wU?ry9rOCywYclo
z;5<8(K@cZGZjOb8hg9$_9Lz&#YKh|Uh++UaZ-lw|L0|4Jo=Ei!X~tbXLD2gdU#w;l
zT?$VVM8$_yM2ffTj)>S{u6Gmr=2Eqfh<rz4F8`(>xnjPiUxo0icWf}WX-KG0`wOpz
z=Ni-qQ>{H*1cC1t+QEuYlc0SM6YoM4^3Q8$Lvp;c$-+OcZS9!ws?s$6yf&~P!4s<?
z{PWt|g#%CQRT}!Wj-jatp8f@T^y_CfnFkL@1#)BQGFc&4_8k9eh9(K3%Iea*5*QDz
zc?PCm5C}~uI?)8<+z(>`NUlw7k=3%*?3zGVda}SQ7LqPPJ>|C7#KKKtK6ar7ZAS%J
z3tI1x+o3()p1oDe3r}xPbwYd&sg3>60fp?j;+AR`<b{j185F{+^wg9&><D*;=%Xbp
zkmm1$JPF-tOPh~PH8v&NPquOKn!xJ1ccu*w<Ip*JihG^x9**g8#_2Xb4fM0#!~97q
zBcZPTg#nNHBVyU+IgR7Dhe!5bwQ|o!h)aC1LVRNA-2+r!zE-$7Sf=!%Z1;5EitdoV
z`s9X8#sSy&)Q+taU6JRX$?;al8?<dtV%u{<@Qp2=J9R(OlX&1kB&Vt2@RxHacHN}Z
z6_4FUgT=h8JWSt?<+QB4_=6Y@oW$n(uT<+M=Y9MnYh#jYS+Va)GCC?_dbmmh)?0{7
znd!fgrQoLWrA$U=O>ckI(0d$&stOp&xu1iDuU=}-ekd$x+`0SLdy|hGLN7ad0@`R*
z=O#29NRyJ=xz0~G;SlAZZeRD#OBZU>r<+@8Wy}-Jj5kWzIJ}lEA1CAfei+k<+P^U*
z(|D6;E+~x=#f+IGO6@zooLRxwCcX4N%E9zb(qY8?59X1R@+4SNLlYWs?5U7u;x(zI
zA5ktNw;jyxU>M0SiieS!UrI^v%_DzvjK|Et8QjiMBY>k&i9t+6C}ZA7ARItjdDZ9|
zUK(nm(+=la!l?wC5XxW1SYCcTw4+DXJdIF;d`i>3YsvaOZv`W%lywJ3H>#;q=aGX*
zK>P=sKd+^&83Gn)KfzGa^q%X`tMJD<G#G!2;+c8SDp9kdQyFyrK=jUWm(@>lhac;^
z>N+uUn^mg^BF_gHGs>o_vJp>f4=-OCw&<UF_M!OuWvRyZgnn{OT{@=942zcnVP5aK
zdU1NC;ztiEJ2zzSO}(xDE~ov_;{=Nxs@@327bO|XLaYt#{GrXGU*EJ3Jx=8L{g7mO
z_p4FiRbGc$z5vzu0Q91MCpY)Rb=1myaD7&zlqK*ag^Fk4)nD&>)b)Z<`IJ(9ue)Fn
z3qP%|8UKh!4OG1#WbvhG5uYE%f0vC)vaL#}Sg%TgXQVdkiPbs>R|ZjKi&;qr&hVtM
z%h}=l;R>pwbm3@N{_b<i#Ad&V_;^Ch2&zoWuq3gh(<l93;B2MM<L@54`#MKBRB{Dr
zCfsn`9l=^q!-Ne|<B5*si}hF3!MSN~8;5S;z@pTo8ByB`xRa+%M)CD(^^81{lU5dq
zD5a%VFnN9*>;Qg#GKSdSOa{u_cx2p5jO3Nq>$p-q0%NKMSu+<tPTE~rbr5|?KR_#M
z;dcq|72KY-C$7e(f<xbw^$#6`@OyS1A9kAOirmkY<r162n16<dGGNJh5RqDO%Mm9f
ztC~Z_Mb3~u&7E+YI+#&_FnZ8ud_J?cS6*)KCo9B{5O%S@Axh5BPT=>1<@3gQ%esjm
zr78K=w>KD!iMU-xKD&t8z}w4K*2K3b4c2Bmt7xF=xUa4AtGze0&_AGyF;|?Jt2#Dn
zzV(M`v7v?4bF<TQe4Xt>T@Y_AZ1S4scMa7QU)?{^UCL2BJa*^$;yKGeX~k~oC_o5j
z<XI$M6D4?$T0v@JMuudjmHV_Q%|-FpA-=^1BECH34<Ix*khcq>@rCW^6guvem|wbN
zg{(+iU}YMSuqgE<4(}$>ZgUNV;EU>C_|=MG@SdG)^cY94Y@%PSGuE<1R8c!?WQRa6
z!C|%1;c0E2L02r2*Vc%-#Evgp-$fMtpB(SAA+4}Pr|WXO)?1l4Ngix+yfb-=ICrgt
z<aq5yTydzKY2|vHTbYg`66(8lC<P+)>l!r-G-|D?EB)SPxt-x>1}VxL{jA5PaG2wR
z0s{;R_q~)aH6}O2?u$!RJk%zpsyYgaOs%5RHle}RPn6Q_z`28x>uO)$?(Q23t07FS
zkAiuS(<QoE-7odTikIFf>Lgxwmw(|zbFK0EjE$~@HhMDq!2wi+Z@^$aC`p;iZh_<{
z+BoK6RguD9pyn!6uw>2bib79QuDE+>{?~7%7CFMo+HjSx!zXPX0^Yo-84$L=wP@2b
zI)hj_OAjU9RqbBHxry?(6dVg|ww^nF!Ng=zbh#@bA7h|!$?C25!JSOk3MBvUNn}Z<
zYLBwRTwZRYp>w@q;Z=JkfV_`yv##PmbPuddQY$<%<&!8@A9}3ICrq`#dPm^ZxfENB
z*;J}Sd5babT+UrvBvYc<6#U&4%TDK-lHcmFai#o4S7k?M?4q-%f_B!ZSYW<#*&==U
zi@k6A-LcnDUMkv+ZQo;&)T09w{k%?%yVPwTw+oDaA8I4{>TY(J4$LHY$n_sC&B_ga
zUl5I`{~M)bQ@pQ>9;eg>bc{ozT;?R3lFx}KZgiV$=v#T>qy5?AR`)rH<XwIa7bG51
zl((6jFWE_~Ag;L23@74%DMFez!LFXgXv0<X>o);nB2o!wo#*r)cG|Mo33;x;1Qg5(
zm$#dDh)ZfO<^0-k9p+wedpWv%)?S3KKh=3gQ%d>F((3sY2b@H+^bA;ceKo3ppoPm3
zOt-`mqWu}g5+v%H3`;huKYaLLD6gn!+#1j0@D|8$p`wC<QDYbd4jq5uEfp!d)>h2R
zO|kq;G1xX<uWLu2K#wo&Q+{HHflJKryE6jF9?^r6W_zr~-W8a(NlCq_m2EG1h<Tp)
zEU-@Oqn4l#|Fzg~L3D+WPi5LgQ!%5zQ**G~`oe|k)a%ep*?B@I$rc{rpf{gUJ<`<o
zG}QzfcfNM`{6{IaIaKbiUycVFEgV>W*lW2;LQ|HMvNBle<K$J1@0*pdAntb2zoG<?
zu5hhoUtx`(pv^Qr!!t=+!}BA-tBC_Rx&FiWPu{~cA8UGtEIwS{@%c-7^YR}0_~o=e
z2uyJ&&pQvrFTVOqtN_CopsJfM=05p}7?&I*T_f&<w)j?398bBh>3lM>*Kw!_n%t@^
zyM0o;IMU9c6(;W{S;g%=v<)uC$}7CTr_m?wER@++G<<x$&J3T0{<9#!BGWFV{IhC$
z4$pPhEoW=#`GXgI{Tt)Xda7p;xGxzXE(g3Q>Y1~9P#uQG(LJuylSvz;PZn4iZoZPp
zPN`e!7&C6lDM)adUI#qB)_z2}5@Au)q>`4>@T`u2gt~UYo{JvA7nY%+NJvR2BWH%X
z0(g4Q?u~lLM?aDP<?pX0%vBi~iZK+~A~2Fflaah;(>6$+^#qJh9KTG~l7&mR%MPWP
znM~9b(_(5T_FWN_1=+a1-OBO4K8J-mc{Qx}Fs_E(4gHXJ=D_R;XY1w%2<ca?Q|>)~
z9GovDtxV14Srn|G%9y!tICcL{g77@v<HV)IDhIu6)XVWm+6{ZygaksUN6*OtzXMdh
zzYL0ZiBM!qCvxk)9?DPdYj%fcrgb}LF-lRJsN1b4s$<!xPMfZqb7H$_XLLSc_s6(;
zV&gs`iM14)jQMia!As}4k%)>0{{~8B2V<;=QfW8SJr>Ui?mZjb=5qh_^ldIkGXW4U
z`4|U<Y$|mGm<L^#E=1I??-OnuqSIn$l-V6d0*5x(oMNUp86DM$F<#k*MVFBbMT2#B
zx6_xAg2IP8W#Z}@rK#{)?KB<jb%d#Y5^Vn6d^lbOaT%MgBjhnzM%RkA?2F*G;8a+0
z_p{jeR<j6Mw(uLdxOG{~+pfn^Pb`rFhS!RSV&i=nIiqMPPUWc(VJ9?HhfZV5Uwin7
z71?ma3IZALQ?i7vpt9_HTPbjYd67(-*{V;Met)8Fo~#R8R8&;N;x%hEgs0P)n3*+(
zs3+{g@oRW^840MJ6?87ezi2BsoM?96IR3sed~!Qi%rn~FZ1jR6kHZOHmFv6?fXd3_
zZCrM7*Kl6Wa#AIA?yJ?~a_vR_d50dH0q+f|>R43f=U)t6(MCO=jRS)Bw(6J~NKuri
z4OXIAEj(xVsCwa2F~=!RWW^N~-Y(~Q1ddS@4{K(pV+yqUztiHsy}`aZa+|ZtWfloc
zsi%F~CUcaHxZQk~rJ!r%QPYtBdbjz2rD>!vS(|CCx{kw3nSyf)wI}QsYrONh<!}Oy
z>SkRkpI;u7T*xcWi-&+|c*Y34LN5R4vB(<fjgSak+;3lnsCW=lZ!qx+O$vv&;Tee+
zOwVt(@jcKshOJ$1eXVwVA3iIx<a4+YG1o*4%1grDL{zK6b>&fI{B^p@Fi1dJ+vA2K
z{-{qJBOTq&sdey%<Cvb(gMM!Y*rI!w7>Yf1C8Y<)7+f1Pdh;%i_i93C)<=RSS9D5v
z&)!hk)iV=PKWkD?EWsc-|FG=9_v{z5^~n+=7@t}1eu;kGLPmsnK*4>)LA|N)HB_y+
z{(XXSvh%2DeYROwLdBqh6^a<(G50pfs|ltLBpTP)a1vbTecO{|rLFN}?o->|j852$
z8@UH?V6AgR!F*i2eTW_TVz;it<jxcBCwY*}$a9oJ|DsWMnAvvw^qIBB5EV#hiaXat
z+Ru9_lcYSK*J5Q*xoWQ8fpp^-y7oVwVK<oJH+@+`2@aMVK5@`9Kgks2+1yG3%_#Wx
zZ#>ppSo;|VEPDNiB{AtK<I8ENVp1~NlB9J;F=pZzNlVIUuCiBEjP37vBP*^0KPc|x
zh9F;1)a_7?xzC!(GdI#gj%>#f+IAJ?>!rPk;KO-hLB0wfGa9#!LT<9KW)k-Eroe+j
z55X&Y+LYX|5lq&p2c!=Y-35r2bYjiYtuu3ZIz$+^9LG%*h16MuiiaFVig$>LsvMht
zZq-{zenQ*F9%#(G=p_#L%V^SxSrusqA@k6jF~3m=rrP15flfgGbR$qaeCHbk)KsCp
zvPZ9Z!o<#>-9N&wGcC4D=S^r+jz3fa*kv+5lnmLkF{x=eZ^%1Nt${be-lV1sNGs1H
z=)52!2YYcSM2g&c&yJ(d&`WibErDG5CNJ6OA*+BpBIO$&|AeJg11ae%euUpcZN_bI
z)I;S1`I)nNzrYliLdE3)+{qQG!<tHR2L}flNk(em``pmQvS)I<&W!2xB_@W%tTny{
z45$i<ijr*p6Bu0tm=VI6BqklHGUr{y+y|ceZ(BXzO1W0>Fq!QbO<gZZPiTdlzujf9
zKHYm<Fg^T#+PluEsMaOxRYVXFP;ySTq7nrKiP8j#jlcyF$vKB6NS2&wg;qe2CSyPp
zP;!tAN)VB#WF!cZbLy$S_l5iBt(ku_KW2?<xfE8P)8`9SwfC<2>Uh5?@tlz8IGl2<
zKKe`nk^T15mN~Tbm%y+01NcKw5)s;ngX7q&zP_eU+xMCc0?WhT)QM$pf_L>{8(;N)
zzQk&|0eDmWtAWby7#(u3bW@5V^TAR-&gFyoGg}3C3GZ+HC06wkl3ugTR|58JArAh6
zS}fTCp&Uw<mh$<53|mB+M_={UIECNJY?Xf99J1E$mvVZ|y4h(@CpnIt?k7SvL@KsG
zFW)+^m$}bCP_~@UX8_MmZHCFn4u5+3%ydC+2_ZTg{~oTzR1@(`(c&H@NLXfrB>{|^
z5ufI-1kNI0gPOK6h#hbIh%%ar8cMuBpRNx~B^%qo#;5=wBX4Yt$~?~~zJ6p9M%$Ko
zqH3kIqUcVD`0`%Wyr?_5X}k@1XpnNUvrJ^b=XI~@ytThNcLNYpNBI=>+1`CB7Q6hB
zptSeE6B!NyWLP&DJ(*m2838-}V6Ro{8D+t0#Y%5$LChL%K5q^9a>~gwp~R2VheK8=
z=m8Fe3k+?wMosP-cjshQ!cp&O@be!-F9$Xkh&#ItH(#e_ObXSC#;LkQJ|lDTPJZ5$
zK3Cb*(#&20RD&FLR1~AX5Y6JSns>eQjaSmx?x5H9f;p57T_{eTD+z7oJmjfQI$FV-
zmeepOtxs>xuI#)|1PFYY9H36tspFwa&2AHpbr*O`WWYLQP20vQNWET>AvwmgpJ|;A
zDO|`{m+7z~11GK-Sk7WNF#r6D4866#=vV}|bQG1Z*yy$E22TPMKP+c+8&bYq459Zn
z+u|XBZxFra32z79YO{gGPpwUf7@PeO*yC}{Z<7<#o!(^ZB$*U!m};wM^zc)4K0D9&
zcB0UIBCop2<fhnKm`uNwCg<m`B;}P<v>Du`J`}yY)ZW^62Qv{HQ)oH2vmP0%-ehsj
z94Hwga8{8#=mEPsD-!l%a4?cU(*~Y8%fho3&mA+aIii;i_~`|61fiFLFro$b^Y8JR
zYF1=m{+@pOv~xFDcO@MicJ{boy&23Bpw~jB6=`XY?^hyz-?{Vr70dcxPGP`3xD>w)
z3l}=<kBEv!DGr=)*jYQ_S!fgtGacNtA<Y2ZAt!6)*fTg}=<2fXL(X_2VjZp8F&Fbi
z<Z0^9p`%}1pFM7z-Z|`ehL#{q0oYbp)sTXREp=C=9l3eFVf~77uWQpLfolSC6D#*W
zK`cde%$>{ZT;xJZl<9V5E`en~Y909)-xove=M0@o2+i86_m`@^dwz4|xo{yHE4LD2
zOF|?=1_;gg`vC_Gh}`3G#GRclffL|&EQHZrzk9iD6?`iimV>Q6^ls<e=s{y=wGZCI
zPXbTzy;I}LO<fIKfG9nF50%V!^WdHu9hsig21_F%4byJXGBRn_m+!px^Kf9T!I^*d
z+phEHpLPvEgJKy6gg0CE1yed0go!a04Y}9NN{J@ceeQ_j)sNhIb=YuHj|SMv<E0jo
zOfj(&)eB3A4(brjz<uJoX>kl9olRevmw&vQ*E26AA_RPhuCqUU-<Ux+R;_fLs*azq
zD{l%35x%0hTP*4A^E=?oang7a1z+@M57v;Xp?0XUQEabeWe896W`p->yVI=f>u>Dg
z#NAEk=Wc`}_q~edHZQFDd<T!nY2Ri8_Nb^{!2k$fGLYT4`hWrO@bF{S@1JG`4ye6K
z5+APIf^VWnS~zggD(v$PzE63KtB!6Rk*fmfLuK95oP@&51QUx8X$ayR-_|zN4P_&N
zn4e!-9g4vPJeX;@L2l%Ac_eoMj-SX|y0SrGvJTzsZAb?F<A)v{gD_6GzJAJya3<E%
zhvNhIkt;sGlwokhZ>k>0LjY0Ks3pp!5&uLtZugv9`aQiqvgPybnTI`K;^C(4-|LFZ
z`v!InrO5+uN$N4PMKkMVFD`X8(%D_OBFqHN#H4gsY<=xpis&q#M%@mAYbrrgWfdQi
zQxK3Qv0s*~W(6|T?m@FSRNiK#sKKiPco8vSdBc}x4u6@#c}iP71J_7Yx;K>`k`K@@
zcUMY1%5w5Y*|BIa-8(xU&EHi%wbA=Mp&Mj>6x7kZ|IwVY!5Yjd@;#`&cP0-i%wbfr
zM_uZEs~@L!YK+JM9C#97b>1FyTT!v`5Y1e;0kEEH%Ex4|W@(vb)(JV_u_5~adCGIO
z`7&n05xwpY=tpSoU5QY^{-l3@z^XKXcs>~GPN(>!FTCxn^O%KaeEn=9Rz<HN%ZyR<
z$64}kdI8NqS{tUEnll!DJH6%bW1W7+Wk*xGF#Iln{Kj!7PbkKy%Y3mUc+Hn6X>4E5
zEJ3ScF<l1s-fbB0HXEJ-=F{^@h6y={STHw}aK-XqLqm@U1ybFc)Cd#d$=9VQ9cX&S
z^3=A;iuW+cqvVVs;fdGAX~@T_*I7VR&0P46yi<MCZ*#`jrC;wH(^AIRb5e%jf_et1
zr+dzS8R8A>85f=xhbYe6ZW9=^C3|fX7&ht7Aw#vJldk_B;+vjd6zo=pNgpe~C1Jto
zhv9BRAp)-L{@n@bQcQ_M4eb{zwq+5<>-Hjvj9xJ@-Nt@c*q|bbDYv!x0h<sQ<-zQc
ztpD$fzo2X-WxE4e4llzwT`F|2$awI*7=@Dq!G?Stw=Z?7^fk;qxipHIpjYw*;?q86
zod7;}j>Z8fsM6IGkSm`|I-+|E0Tv{^ky3`(MlOws0Ic0^VYN|_WF~BIai}h_*;z*Y
z)hp5vAh934-=Iwvn21{Cc@;nrV7iic>=^AwW%-*re>7==)<67Zqdu40+uJYZQn{xx
z28}EA0C|PMW3>hssaaxTgBl?c`?l$q9z(u*71enDB+oXJEQG?Cr5)}=#q{=<sIi>I
zaU*bBgjfjbt<cuNWk!)L2QZx@`rKO`b4ttFEdYNAN-glUUYJan{<q<wX2L1rbn_wM
z=9t(96N)pL*(=ZQINe`VFFiU$_XqAuM^Mrd1gtz;&@ic<VOv5~opSJVqyzyt-xd+z
zx{icLX@e2=-Z3Nq^OELZDdDHmjVNp22lJ15D8XcC!}bA7xRzBC_yrZy(s=a#kxCu4
zH33jVWpK-s6X!)SELJ)s#qF2jqEQvLhWW|4L~OYDMG|O2rRt!?qzzRBsPupb#nn=h
z=mqz^c!mi0maH0a2xoE-bV2+Ox1SNXt&&{=E^e*Ev3LHuo#fn&id0=K1EXNkrmlC)
zOO4b6CmNH{0_L+X&oACBNv6eBs~i=oTaYsa<-ZRD*;i9rH+1N1Q_f{4ZpRP23_dvF
zC!>uT=3{xzyOWPwhw3avdHqXPy(L*7yP4oGA!raR=D*iR6fvm$D-f77&riyv)!lWB
z=999B(04{(by>^?o}P!2Hz3=M06hTL;08jpqS-HvZ?6uY7%(lqBldJ6k$*aRKizC)
zUVCt7vlSf4{jGRLc)5lU=*i6$--h1LFvGHT(2;?J1%P_4GF~g%v$1RMBta@A$aBap
zNlIUN8rC7XMlpO6<;s^4J*b<g|Fmt09HX9!M0I9CwQ3EsE(9o^8$x021K-2M+oEM%
z+(=@gEup{Y3Z6Sxq$NG@q=c@2bD^l}#m;Y?1I|*N=e-W;50y~kUv2f?&a78B7}P%e
z*l=h;Aw`?ZAz;n=<K+Hlk#pu<sqN#v7uL1K7mAIGZC5YQ{OUCAd4+md<$99wgwpuB
z_h5}DZ(ptg(D}Sk7XbkH<RL8@W}U$m<*CoAS5iO0+ojC_+_MbVB^uh-WmXiiLGA=M
zfHZ98+>kw%Yb)P^$iik7PEvJ`cniVhP62DIKvrVKoe|#R_ON{>D~t{0mNU&)cO|Et
z$8Le*4xSb8SnH)WyQ%LGU!A`Z|GY%G+La~&Gpxj8Ns&>Mmmsz9WTN0r%TlE2Pl|2R
z_hDaX!HZ143T$3_uL{}Bsj2*c)>2wB<@r=~wDyUr0e-^r*+)JDFhK<-21nI%m+L-&
z^99N<q&EV&qDKtG={Fwr3Z!*LV~h|$k7}RciO=!ZkcM#Ao(f+J_Lp=W5f?K%xBKfd
zDAa>F?g_pW9DvRH16SEz7b*&$!eliMwWWIm=DT#$b==g2=+_zN@$Q-gX-ssp9hv2D
zTwV6Y2d9@RXVw<q@aKU9M~aE&2Gn=j%UQt|fiU!DCVipL0Fu7uQ$?i5d4Yqi(^->=
z<wvMDt~{oXdeo}sy@2M#!TE*NHGjQcuN|>0-yF?!+$RXP_rHDT$=RGaFdoHN2~SWG
z0h5v!nv1n1(NvjwM^;Es%!W#imRW|&LY!xg;uyWUOvhg&GWd_G_4}`~86*X$cpn-0
zP%ws^nf_6bo8=+{*|-<Ig(l-K&h1u&DT<Y^l*+8^A+Kv{-)$=Kx+h<E+Ztk(BCxw@
zr@K**xsvl?M;N*?KW&%SF+*wXkt4i%Rjvu7MC89uDag!+z*Fqi=yuxEvBBbu+-~(d
zF=iXrYiA!n>;SdS@>{e3A+@49>JuLFt-rU#`C5eP+EAT0t&uZxaNsAiA{S`y?6x&z
zoruMTYMrjfe1t|<*~z=#F6`8bBu2IC;iCzExrI*Yx3gUj1C=nOWJ!>(3qcMDDpWx+
zJEemqN-Ns-`u9)ZMt<BXDAio@bCMp5(28b0+PGmzdZy?q!QY}s)gTvcYwww<hLCGy
z4Gow0tcHkhvLl-S8xovcWGrV|u<^JCfY8*di3*hGsv%5uQ_mg8%-9qkM$nG(K>#k*
zrLX7ogjEzu>9nxK%mf>-0S|m}QS)?&7SmXgT+pP1iY-ffsH(GEuiBNA{p)b#U~r{R
z=^Ys<#$v^GR&Xj_<}73CEo*?hKF@@ojM&$ic3L}w90_VfqV4sO?zVRN9)6LAdeJ((
z^Nh^4Zz-Sgt<_OSn&px3o3>0v?el3KL!Gg8(~7{?!$DLkS;8~3OL|{F<<|e!`cQbQ
zz&QLSuL5Kdt&QASLE4~3I%r)=I@)rbk=Yl7HWvjuJG8agH1Pc`#y;GDSN(RTM_||8
zPOFN0ca)!@$$<SLhEVD0(l<uxL5BvRSV?Q*lM>g9fhg<HELqpbpan0ga~E18=tqx8
zss;j(U29>BBjqe8d$RtAn!}rIi>fUdj~0#gDXdhfvJu9yUqo&!$oNQ>u=&Ib7zM<F
ztvML1p8N%nJ!L4nd}T|ijX@;X^K`QpR*n}Mg<<4=i(ZDzTXZK|*U2(T`H3Yy;QsoX
z^9E_c6Mh7@uNt^623EdMcOM5g!_qG{W?&nr2SSSK$`jCUYM4vTM+V-`pdrqsX;tBc
z+h%dU;=>vo?XfLOdD2&;)}03-n?QpM-OW%f#%|o!-IXWQdj-kG(U+`^y_v_ld_!I)
zga8^e2j!DG72EZddFed(f@=Yr)Tc@dGn<EGZRBY}P&l>zhe9mhYlD7%k%M)HN>Pwb
zn<CNtT!atv?S>Aoo$XIbGJ(K44dR-1TGca%o!KJIGQ6^;V7j-!l4dHEj7^Wwb@@DA
z65z)yw}*2XVArSn6JOm7_idLpKi$ezsdVYO^bsBV;U*Bo@3)rnoZCk>_=s~wKxnwS
z`_$O==hshykXG-=8b~KHykNd^EAJkf{uj^{NHkb2hg<5<vRfRLQ1&`cr|{%$Pi&AC
znyz2Rxs~(aqJ#Z)=-4uvhaLcr){5>@v%0D$y>fl@XzDe<T<i6OT?Zp!b|iqAMb}wj
zw>5<h{|(Q(hU&23Y31pR40{;1ZrVEEXaOVdw2zaL*RJe6^qja|l;+ywp;o)6_h&jG
z{U-kQRQpJIFq}L5c}WG%1%M!QGjq<Sjd50JpHiTByjdLkW0oK|`~vF*M;*oJb@T`z
z$8P*_Y(oY%>*~pigylS5ABE&SQm}PD4`ddEhRDh9EcYlp3evWTk*zosWS#GMZYKg3
zdTZJfft{TNQWgVBHi!Vd{N>ST>#;BZ5O~duoeQuv)Of)R@bOwq_#9c@;W)8<xx4q`
z{0?Wdp%F~6ce~#(<oP$4y@|ngNGG*R1c8U}SIu@;AI>?#TsQPvk_|#ehuHqs#S^G!
zP0!KR0N1@H?4Db~g#Q5mZ5Rd)j!sJNE%-YaS45r=w-<j{LWhJ}34%yCd~!Kh&c*#q
zM2P*H8oGLv;yhlKzV?b!;~Vasq)Z_ztwlt91Z&@KrR-bKx<f<u6k2zEbotE%^R_!y
zQhUVsc1SruX6CM8T&SXaVt?Spv52wdsSi*3h>iz9=;Go+q-R6V7cSW9mtxM0ulqzs
zqqzLXU3|9KLdDxulmI@5mErF*-zJr+*p537V@{|;p9{cY02^(9It0JJt<sYMxqb`0
z#%V9dyxNy6sxE@7y!znw2s=`R_lCW_W1h)?)g>s=0C_9VwrNZ40(#}vSuNjekvPxF
zLT$slw??-%b=WU`SSFAk2*cDHZHr0<s#i}|UCYEOy1fYsRcO|FT9yiIt|5DJZpc~?
zuK_gR$H@won6^PS9?Q*ENNEt5EkL^Guf^ma_9<%V214?7B66XA0=Wwh4v>CMbVCQE
z64n|wyhA^qA4a-SV-`|aCKN?z5onSERwF-DUjxxPDpr{TL~#9~Y(k<e8@e|UXfo+(
zgyRRn;9$ozk^^aWbRMOwEfb|cn*xWJXT?J8#8j(O{_<SZy}b{C>qyAPM{iA!yOvQs
zOIDG)D^LkJVBW3~#h8-ckaBm~v(EXNk?b5|e=HL`hTSLC&uRpx4U>kDK2Mfw>ps2h
zEe@KAv@>&06fYsF-_fCCg|nYTpoPTV<eu+XaMgZyAtHFhz^32@msk@Va&s3Zj^p7M
zXT@e^%R)=KQI_a!r+m1gq*(>i5nyqKP1#*e7|s9#%WSeJeV8J}r23E+c{t&A#>oI0
zHHd<`wTEFsC36!4-%;#Ydd>2^=r?O5A9ar_R8NP04M6e=3@T|m(!%OC@#I23s*A%V
zVURr)lKKJ&b<*ZuoCiSpj?6;|TPHh?4r-*I3LRkzG4WITc8;D}<#1-sw`|Jc`FRn^
zgmSD}F)^lr8Yf=`-2h%juOCwY_6E7W`GRT__RZgOA7m+6$*{OeL2`r64J0U#R^*#_
z1a~&jgle?B>PRS_5hkWk;*IMfq;$R&N5eUoyQ!&>vC2IDpIx-}DQ@<5fZJummBl6J
zh)il1&pihc9|B$7jNalNbgSO8L%cW)Pr&xELl);YKu&M*1)G<zxp*3G0<Qs4S7mD%
zGAUfJ<^_<gvnzPP18YS{ZT3%caa{}gT8q5(O9nemB>^&jz6Jga<nZO25D96b4<Kio
zFXDV%{r&H-aUXrL7L*AES?Ao<2=n)0Zey+DBpPD&(%vbSXq?$%EOYYxPUl_JxFcE{
zN|;7Vb<KQosBupf^o|Fx19>21%%IprRYy)3qynY+`WnJMg%hJs+tu#5-GJa2g=&|#
zWn$f}F*>s}_@Minq8dgA>W5aB=PEVYYkxslEE?h_M9)IV6S<a8b7ah2Un2)z9dCE}
z%p<U8;C3K1a$b^-0=*LUV1pf5?o~Y`>wZ~bUh&E?zAj>bGK5Usy8xt+{*~$F!N{`L
zkKrH>go(^&Wu5BvOMKw$f+4B&e1~T*PBIZxEtoxyHaj3;f}p}35e|8y9PWuFp$qY>
z*@3PUt(PQp&2Obe;f6VraA{2J!OO`sYU=O5(B|S#81598Q`E(e8;7?fD;K4Tv3cs8
z8Vnf3$a0AN#@eCZ>04Kq9?NN-vN^|065#$9ALTt)^Sjt&BGo|-)WCUL%rN|X^)Y#K
z9Gxb*jvz8z+xP28kp?;<S<OKoG(PyFa6cTwUIpi@FSyO0z{kPuNg<6cDTEf&HMqF#
zpB6G6S~8QEq<?Ljx23B*C~Dw<E@-^z-A<oOk?X|0u8JeC*rR4qmRE~+;y7Iq0d^7h
z0^IEUNNUZs1V#H)^q@H4k1mS`vP)L<Pz43Kj9Q;50TErj@ko@}+|HkkcmHgSIG$ss
z__zO^x-^{HsJh+JFf!RNvMA5n3@{Ng;?#fb8>#c>y<OyrhvlMT)j1?@oawZXTS<JY
zq<C&+ACk32;e(9QtH%*W0au^q{j)7EQE*~3;~zKDxP|OaQ`!440l~L{MTjKeEU9fx
z`Ba5BJl<KhbEvy@nRg1HRnDJ%BH^F$9P>Y(F!)>o-<9JAcZh;<*rBuXx?48?{EV#2
zm)?Kvmixy~83rPM`OKfgl;xPCqxj2^{{Bss3jgfr`}f}w1v^1n=-+Pf|F?xH`Cp!d
ztAzcqBfjg;mgW%wA>cwLR29g6mIA4*tFjT=?Ivm(#E{i1gAiP=?*)y&nx<XLxWqD{
zHV;61_svW}n&r_BZ9N<`{S%Ag&7~r#WzVGv4-tRm{qtup;f7apsnU|M4R5`Bd>xE7
z*Z|CFaEfvM?fUvhpA(^YvPZ`d9z!gJcWx+<;duS_xo2q_a*8tdJo*SvkfLQlE>2rd
z`og+SJ2Dp4GBBgaAy#y9NKX0ZRfc-foBu6_`qy1{#Q9%%ssFQES7_Zi8GGEF2#<6<
zusPkq^bh<0TLah+8NAJ_K%{c04ic4lk66K7|L2bo+Bus1+lBC3_n1GLcmL}Y6OCm3
r#XtT1UN_5Z{@vhZKlA+$-4;)IaB*<WYKNog82nLI(2y^bGYS3=u`vE9

literal 0
HcmV?d00001

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/multiple_models_demo.png b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/multiple_models_demo.png
new file mode 100644
index 0000000000000000000000000000000000000000..fd9787eb35a7e68609496eeca5060c4017ca2005
GIT binary patch
literal 235379
zcmZU)Ra6{Z7cGjrdkF6C?h+ulyGx*Phv4q+4vj<b;O;JIG&sQ}NN|VK$@iae#<=&P
zAF8Umx@xbz=bCe_wWCy&WKfX^ksu%-P-Q=VQiFhi?t_4Unn8eqfPl=O*~kZ9fOJ)p
zk$|Y3Avy*hz*vbZibFs&Bp|<-!h(+xoj&WjLO`Gm{{29XI#yUfK)jdAeiGOCYIK?h
zYk;%%{QmlSaqq2ZC(b4&*3l`LA<cA(_i-5Q6s@H5<7t}*BZCKRaBvhwVWiyD`-d3;
z6swR8O}2B9x_etAs~MrgsVplIpS!8^sa&m4kls*ekOMik5U9PqT~kwYD$P+#R~K<I
zHWr1Kmsh7qVS>e|3t_UYjZcR~zePb#4#|d&g{5|D84J!QG+bQ5$cSVggvuRT)8Ft}
zPELXz6BE-CuYg9w4fz8n2vNC8J%2k=cA^AtNe7K2PfsW3u8ZZGJ1NyT|24gD{V12Y
zc6f}Lb_Va~eix-u_lwIpvGbk%MpO|Jzoz08Y7ikbo^PF!oFZ*UB0`To{%vZcxAZ}%
z5LpHUpUE?9ILk_^o<`1FaH87m)K@8i8!Zj&GiJs6{xPo@UI-PHpW*x4FC04gKJqOu
z;>^wmsONCNvS9I)9b;^A^pk<>$Y0N&a?tVy3NVr$Kdm#mU|((H9U1Nq&2X5l37W~!
zH=Hpi-G+bmQ7WmwF6qk-4%8uKp%Z<)x~|kV{?SY8BKMh%6vhUHz{Y!h^68+5D)@XU
zOqhZq-wBzE%W>4_d%xi1IFBTVyt^tmhiMt^$?Svk0k;=fh6o1!CiW;2<FWqnzvlsl
zL45jPhKd^RC;my4GH03wBshVfRiAFEPfrKG{2EsWLz}%G4IYcf)JU%x5`ZvYy*<hz
z3QU%+TwjEXKH>*I2$57WuL(#fzeYkwIM`^KblC`wNSdY30%L?w_Idb6e?-#b$LRyU
zDI(aY=j0pa+Rmx2R<h)|1f~~G1q~reOBF5f**@<8##Phluv<<pt|YCS|2T)s;NKFN
z+4*|#H{)`{O|4r(Ojy8nVAkJcz@($AOpA$GG!UU6G^JLtXd%Kj!9NM4-cjA@Zf;oI
z3#bjG9J5vxYHX>^k%8z^aGyPA$KcVMwr7WxjMYA*$tLm$cXRE{H73Tu_Tyhc|2TBV
zwU$qV&;6F171eQrXe)t1Sw3Ax1Thi;lhJ`<<9@M`0oVDFpYLZGrwC0Em!043G}9@Z
zL_B=M<jJg7Y22W9!uKPa+LA6W0t>UC7Voab7{1Us3tQI$IO8dFfp;plLw4MwYO?`{
zq0s6bfhM_TkIfO4M@UN*(AD-*en?z*B(w)B1%c*xw@k`J;2qyBX{?PUEY!5!?<#G>
zwdQale2U_f+SuD&vJmL&FR!_ZthK=k@j+#STC>7kC<-&ot^QaRZgX2Zchd97j4KR;
zPPIy5Dcd@D%KISPvlnVq7&C9{=Cc?l8%7(HWpq_H8`WY>t*_cmiy;sZ$pp_kbo1+r
zbTUp-=5ZJ(fc5)8LTEU}Ln!bx$3<x|4(|%dMhfRfhkgV>8-_H8guz9em7b#G+9LG=
zM*>alhauqYdKHr|lNFQj5V_{d$w6#X1FJ#otp3`Lu#2Z=E(l+Cj!})bd_=IaZYi~p
zs_9>X384?z%#h?`vibmoC>>}|{`Bjk%H?$qeE!&Qm27{=BD^h%@Hq0M6~y%Q$v`=E
z{$e6=Q1*K~M=;_ys*8HLe(MuHxw4{;fa&Z2S6cgMmyd$eqStgbq>R@ocPt8zKfOwR
zES-kvbKId4>|!^)j*TpABqD6M`x*}Ld=9-3ZWiEi{k1Hc^qy~snI!;N-^-N5PY<U?
zBN3%O|9qbOtF0fq%&^;=TR_0O>;gqSBM2n-ftk|jq{`9s{ERceN=X^keXImt;M}x5
zXc9uouaSDq!~KG?^VKi!cP72L7R=YDsS}2|@J$JUk-HgJvsOvlL5~99Y70&LN=w?T
zHK^q%gnyDUd0d*Lr=BZyWo%fSeG|KEn6$Htjm&0j==f)?VN7J?pWEBB&30%myV<u+
zL7S*qeGWnS&q{F{v--L@=D-Fx)4(EAev_GK$>z}8L}$xanR+8MlfztN74ts-)FQ~O
z3Oi^kbimo&{U?`x?Xk!4XzAxSrTVfz$Y9`DGC*<ayJI8}-JVndOxq8LwI;GdcRwl_
zg1Wch0Bq^kj3E?LC^zYXUw=>#6mI#CzbJt&itKd#z9A6Ce)+y5<banxNNM1q$Zm2+
z$P|H1ey<w%5xyg*r26+1w-%-EnkVM9qDVJ+YpM~PDZlSi>au;~99rZgLk@Z#-g7WU
zN^%GV;P+nQ+05?Cf#YOe8~41|%PFiNi|550`3?DYGZ}<5vhd#GD2zqiCe{bHdv#@4
zR7D?F`%!V|*TDf2%;KdXmQA0@MXSxsTf`U-Lv1z6QE9Qh>A5ke0p8oG@5LEAM#<zr
zcyF`i`0<7a!RT8Z*A^42-6QoNl5T3hy^*~YU3i>WyB%Xp*Y7{vGqk7z_~3Mfs0?VP
zblZNfF2~8%Vjr@s`uR<Dd_`!wrQ7--OPi{2DpmR<prB_bY_k-N9cJircU)S+&3%3}
zhC1%8760p$!<hvqbjR|16JQ)mkIb`&XS>qwQx}<?)CBLc4FB|u$v`NXC?>Oqtr(0e
zZX#mW9}STS4Qm$+H!r**@uu3RmX2GE6Ubi=D}vOOAu)o|1j=6XcLHWG>xW~drKKf~
zylu}+Pf!1N_lORFlzfj2Q9TRAD}o{5O1n+)4T}44ggwmdWBdkDo4VkSUcqic@8xb_
zBJip;g2FX~&?yA#p@}gul8(i3)xDRNaW24ESyJm@dco#Rcpwem`5AVmmzD%GG&y~9
zCP$eoZu;!Wixo}LSAy&H&~BYkVk5laMTaETCY_{bw_8YJE$O8c@$c#v>qN_gav`~3
zpd4|GRhoKEm44&b?`FHk0N)gx&Dt)4IUQ{-7BCOxBW*Q@!qg~Qi_Tx31*TBN6k#d#
zpOXX0r-Kc7f$9!hns&22g4dpgGJ{lmgUlePK<===vsp!q9};cZ9~OE|Q1E1?`wO-i
zj12?9k`T(?JO-e5tUAqSW>&Z56=sQJ3bNrGBsBr)LF#uTL~HW<+cFAMO7N!JTL13B
z7ef=|r(2faig}YqvIH8O7|U8n6OeaD>~1$e=adyFeb#hL9h%|jiiM~2WVl&30+B4{
zC1@GSf^ac-<FN?8a@@sIVcK7=@W(^61r<D|bRYDl<oN^Vbca!&f=9Ny!hRS^$|U(w
zqf^@n!*5VW^1?KbyQBZhU)CsIurS4R5ERIL46(A~U;Kcx%p5}B-#HsDI6np~?-tUQ
zE^O+y-vq6=*sC)lj*F<L+w-}+V@`bFv^DjUmkrT!HoHzk+6#df*!^Ot<(&821e-Vl
z6$gbxe1DE)=7UI1&e+=++f`bo3j8e!U^H)(ewc|sn;Dz=^8U(uFq4npGn|d&ri6Io
z_vt>ro0fGg&;??FfDH%GMiaUpS}(lGSj2ty{6kI<zDftU?;dABGhIk1mUNeUiW0%J
zDprfPL^baZuf$Bn+mF=&i_lb=;)70aO{qVUtGEpc4?&Od*z%8Gb#2UEu{)~#(M{Q|
z(LYrPPiVM1)JyZJbWcq#DvRE#WZ~xNB-<I8i?}5^zUP@_(pq9Rt`KS(@86TO{3$)U
z!QUh1+(XscE7;jXHNUB;mm$H;QxD+$nDyY1koGIOtX0fJg%Spi8xF;m1@2#lvb%T*
z{$GOPf?18;{&FD{iu+A>W{}n-hmMZ)zjVW6Y8#%mtI`sBn1p66#SKY&-hkzGjk=hr
zIO+(S?~|(W$7{0ECr4?0^XsYBOELQPPotrA=EsJbFaXQ@J-L4RS=~Ft9CXbG0i@1H
zXz^x!JsDZcvXuR_X?yC<s4;mr_SD9<hW;BAFHyApkpCvFouSP9ni#ymr~1=YI|m?S
ztiq{^Jl|_20+W&zT5Uu3wfx_}ikVLX>jTyv;0SR5i+xFDGTL`pcI9{i(v%)W=KNkM
zm?sAi*2Gde&28=d4NPcQ6CWdrI*vrx`W{H=B!et{<v2t-Uq~C`69My`wpCg+WGN&^
zMuXE-GP);cIaR#@`N&DI{4@tBZldOTs<^ni4^Rnhmk>{YCL}o-k!8i07m1Z$usJ3_
z_2|{XJVfqyM2q(6_Jc@mx0>>p1u0;;4{6arg)2)^nja1;9v3lI?!ewP72RXz5tbY7
z^ci;=mhvNfYTw-q>n$^NoT!xg*#pV{GRza<Hq8GwLTu2?O$R)X(68Ij*F6}84SG_r
zuBQsTmO%9@r~bz8J4Y!r{n2D>q%9^=5K}==ul%9>jZnpX^)iYfX;5tNEx=wPFQ%~A
z1hH$V+stv#V3wYh?@t!wTm+LAQmeqXsvJh?_nBciNG0ydyWv?wMH~U^Tg+p|bB(0x
zdKWSjNTRI~cTR1Wuaammb_UX>rDXH%Jl$1UYF2)_%4T(!m8Dwf&|}@H9Hf=DH~`8z
zb}+zSPV&9?F4@nCVimVqfPPEFBCqCaN}W=U^``hRT7S_<{y}n^(~D^z=B7c8`4s7k
z02>aU$mV(vhpLxw7b1VyKRHl7LL5esQ>XR0^28%Ui{LwhP)%@Pz4TrRt!PKR$Ythd
z-$|7HombD|e5~DI&su(;j7L^JW>A&VL5v&b*^)k<w14G(T#+*D>d_R(u=}P{A4iYU
zf%ekajJu}ZU{?Yu6zbVfpjdGYRp}lUs@3swdqOdO7N#eGD<IGw*jpefIg2V%>WoIs
zUhhWP!foNA!Uc=sJ4=m#h<DT-_OK<@%K<WBI@E@f%KQK*<Zk751nRdspvsGiiZ(q>
zW`~BtFjXFQ9lwXY)0xA0uU%(n2A^CU4*g!efaHd=8$1(W36y-92cKHV`Fjf2ZZc1R
zg^bh^cwgTMbDzupei5)*p}*XUX)S7T8D1)QZ_nrKv+#{<Dh|yf32FTsx%3ZR4v%zW
zu{-jMarLujbpF?GPZIRkhps4KWO(onYKR`Lx89ZXaO~Dxkl7$RQaT1{HXP-^6V<;$
zxd=!PX4JUk2pErpx?DY@W^E<9@M|{4ulSgW?Jf|gnK!D^nu`w~1`*ooqP>lAQMb5*
z_R!y%@+&G$KqZyVG6rRJ1f)bCuYlhY!nZ>~r!za<AW^5W2z?+d#D65WFwg}G{8vbD
zEuQ}3im-#(5kbC$P&JrBbLW~c#J^=Abtc*1L(MfQ_8~e)Thx{_hWfO0%pP%W$|g}c
z`&-n2WsbtPeFxSEaA4)sk{0X|D7@N(FxaBjxAfZ>fhomoCH?Dy1{)yxY#yV9PN`K=
z1T|<WngDrM<S?5{n_}|V6{*C&U|Z2D9(8LpdbLtrQi?WsI;GJ?{^{64>7MGYLMHaN
z;3%#O5hJ!KBhxsAv6}bSlYW}=I(oxcTop`j;J!?jB-%<cD?MAX`;(q%OBnf?&-Z(X
zh`xDr)p+D2L<fuP{c+t?Sv5Q>XIJQEIsU}6$3zHz;^vb54DVx$=LzO3oTL;n2Rabl
znbIlOq#^UZIb&}L>4a_rHzqeON;V9?lc8h_4599pQj<ph?^xeIVkxO3S;ofjh)keL
zsVOqOLs$+Gn!C#e_v%RaqoA1@lNJ<;qZjVx&J?YjO+!^QVNjr}V#o2YW~}l!i~Cf>
zy)N{bikfjf(<WGDb#FAz&d<a_q5edJ?LXjMD&_)@<X%(CnS;ldxVIj!nMG^Mj3r=5
zIZ=feG}0s-3RQeN<_ZhU^|m|s<?($8iF7U8e52;V$>}1vL3fVD_5~41PmFQUegt`c
zA{0D=;)&#q5N7L{EmYmCb6wpTHsGBoIOyO3L@j9pm+DwvCI!j$zDPnKCNu;FkkjY?
z#~#Ki<OaYI7BkPn(E&Ep;7nW_CMNuuRB=*K@2@{FB?FU(&iJeyVIO)%jmBJQh1i~J
zrL8$7S;NM<+?V7jO0ZxVyGv77n+N*-hntz1IzAtSsat?$b)vrZMlUQ{!7M`J%qyDW
zH^inR>HBNSuggmkDSI;64N@8n$XC_~ZeyiZfcsofWBrJWPJi|QE1369iDVPgfqtVI
z^?SrEd!J*<oide!Q;#27TKGBlZ>p<)Fk=5*08|w%+cF}n=gT*<7)Q<@i>A8Z1mp7>
z-j-{08wAx)9P^6UT0AA9hPsqlL^wwrBg=SHO%%8B!OM~XWXAJN7>>ZBQ5Dy7k$qej
zlK)1`QN+*j`WXJ0_2lW1MV!~K0kvs9L*v*we&GZioy#CoteL}~#SD$`yFUxu^WiO_
z4Fh*S9LEYg;aNU5+GcS*k=B2Mu#B$dW}YoOYiEiLXu7z7`EMPH&$mPTj|TRF-3!y{
zK{1gjG31(d#XhmtJs*|=5x)tASDL%?4MEFj%PFz)KYz5B>iL5dv92IkVgIphn5~+A
zBDw*R6jJH+0@zC<LkqXmwt2&9urN(rR3BN_(RDAvU<~6|5woWoh=YoAs6>~RPnI4H
zalf`ReI+I7^IZ<cUHMuF;|dh3JC$@0_xxRiJpS%M9_OD@qN2Y@$~YEXorBhm5PX^S
zn9a|^B>D$|G3-jv=CkhlN=Y8i*#*lP_b#n?yt<*cA6PKy4K7^=)VzPdZ>mvzo{>l2
zxSnk5)63W+VDvOAAI#y)I#x=iWUeN$Z=ONcSm7YDkMmjiv&Cj6_uw$*NXzJO&7?7<
zI{z}_J?WEUdMshlky^^gObTqZr{Oaxg>v){QW)kAu{RE7tR;bYyLvN{9>tfW!94;+
zgzh^T<GO|y|1fJ1x$4#=l!`-Yie)Xn2TZb*MJH3%;JkE`;cZlFfM{5Pc=*_Q8w-iJ
z#HNfkN_mQlElDTWa!N<^i-UE5h17JUfxE%ZP3x^&`OEOxf*OVf08=T~?Q*(oTp<we
z7IwR1z(;=>bGVMIYE8!P{!K3lYv^b}1qB7=<+MBx=6=pk0ievFO)VYby!+c{{<n?;
zAF(+8>kfxkW(Ts?tx52;>n?%zUyR}HKYz_^P&W(va=B~Z*%=b&7=M2}pdAT@nTngq
z+98bnofcZvxIYmIs$Z^=r!cP`t?g2g&h^ocX(v}PXTjmz@?oy3E;FxausggYQA-wY
zSH>gh47o~LyZEvW#SEeI>j9QK9EpoI5qttQsD%m5bfIL9ed2a>XRJcWbofpkWgnt{
zfWbO&EWBoB$V2G;<BizbPbw3AdjDs!oWrC9Jbpj&cjhm?RPW!p_(Te#$$|+<?mSdo
zzI}O%vVIjvbqO>nR6vA>eGNFgqing&vs|?L2A%at9VLWnJof(6Fw*U;_&1JKR?cbG
zf)Cua<}CRB>7NoE_TF3ewkqRqTYpfzC5j1lCAc!haCSDs&e=iRzcWmo-f2Z#!Ay5g
zo=##BGh!VDi9Dx2Dz|e+c2gKp`(%ZpUlT@=0Q!#2yEdL-T2D|i2rbcUfy5I#vL@vh
zyNZs$gV(p97P|G52lk}+?JOw`7|gwC*hT9j7HFg#Ps~(k804xFND9;z0n86y-*}IL
zTAm3)>`NccpcP?b@YRri*Av0O{02wuaj+M~O3H#|T4V+Zcj)o56Th4|I^co)*G-6&
zb;Mk>jn+lyQGplJwI=F*2y8r9+L3{JE@fWTlp6M1(cK=JGc>xEldr1Dfw_H9qhK7v
zIO<#Dc0)A(I}Zq9z3rEH<S%CZb6QJ;y_>y$7%@fKzITUW9eWMm?UwyOwthe>`R{i(
zVhK^5LWjTP%^gLnRC~5z_w0BBB!CSSrVmy`!(f#BclUs{qJ~6gnTIc9b?pBmrrnNP
zB9`AC<N8h5)O#D*q4Sa>0)%uMvX17dbTDBK2Jx!LN)n0xN_Hvxx9)0Hpd`tXa{qnc
z<35Q?L6#Vh(c~QTO8<|*!p)d&>dzqk-!C!~Gx>bJ%WClIZtlEZ0Hv~DwpLQYgPa)@
zIPb{R3@6NSwNZ}vZ7Xhfnd7BCqAJyt6a`{F#=b->UrHw1YFg5obK_G~HD;Ab>iW{=
z_2elm)UeD*iV~QX!p)GA@;klZtl1uzjFjWByYgegTHD}PO!sD7&bkA4_t3Opf*$K(
z8Lvhep1i1&j4#*z2oiFFBy1h9;X1+;2mo)Vm%F1=I0ZB&!0YR4YHI4)g+I%7w`yu?
zQwyz%zoffYMJXvIKYa>4`*b~R1)>5#a2=ekGy=R|*VhdeEZu&{y1R2T%`(29ot{=$
zKOK+4<|oQ3n)THo;%<OV46eH=$ofWqq@AgsLq*l*^$PUWS(s_m<dib^{d`Xya6S(Z
zM^#1sEp0Oc!`R(}!iU6|?rev8n%Jz!5ES`|oV7A|81i>pl6Cqnxm_?<rc~Ke-)+St
zq)F1(<i{-k%JN<^4x^<jLBp3InF|xIb7Iq7c8u;>;MOwz`r$t7yBtqjc@aZLKlUm=
z=Pgs#1CKMB%PzsuUisq6ZR46`Ok>RW@pNPK_{t}CUI9DkJaq|!*H22aPL+RscY3;r
zTpFt*+ID^X=ipp!k*2q}qr}LHf`V{$pr>}Cq9Oy?R$rXuVR>(E4zmvQ=gp=4bnJ2?
z|8^Z|B5yS$%!u@Y^6uF@G#h3lw--hikLO=L7J}k<Y|Aort5AvhlEBKx9r`1CD6!p4
zXfQPtlVoh*EmZ^lGRtzK3mF-%pTbuTf{JSn3jNY!3Sjfan>R|l@R33TEcr|X`S^g{
zc|GKyc&{Md<X}Z$12S8n4Tc2gSE>omZ*q53V{4OH4qGHLp<>U>s|fro?#I%!uV+S~
zwnIu>k331j-f@C*=oCsF3zlOuE?Rn`p{k-uor{AI3s`IbA{X=F1|bqy!n7jFx-tpw
zG-OVR<VvnZ60O{mIa5Y&^GxI<^47k=QX2JjO&O2ck1jxXJ#)@$Szj$axYdy5xXmYs
zP$QP=0=^!g|IUL@O`_^T$~)C<9SH<Y^%o@~QCrE+z5bLHHCvmX`(wwV4(nFmuX$Y=
zkud-VO_y~>{0UYw;THyZ4&K)8Rv|qu*BhPLDRtU|P#eRa^DpVgw~4({;<~=MfZ30Q
zEo<WSmA(&}#4MM~$G!blrzyPyWEib;5yfe@tr}Mux{8#2uoyPdM*afMhv01N?zt4K
zfWXB+FZ{Y_w`a@DkyRH7oSuAdJ4!piyF&dTu+rl+#`39bU_$)+ydenZ9g0EEbdfCM
z1f@J-e6Nb<n_8YoqRiwsK;NZgFxW>2ao_%8F`NZg7;;6*b%d99)Fyo+KE==ryv@Qh
zOcxhHj*Nbq?94fiplmC8q_|cjPKU04<=7+sH_-Teeji-OpNVsppmd-gV4Ly4hmh^u
z9{_jC3uPj-hkkF;Aw>;ke_(A836@k)AfdYV7lfJMgSZ2YRcwDlPuEP3)kKM@*V)QM
z-koE=ADNFZUWM)bp++mFBWP7iFm4qCnpQ+W#T}?^LvbljuRGAj%57mlA27-K7{M!s
zac#icLi7TcB!C&j0|mrIA?t*&tEM*^y$>5zZ`}(&JY|29bZ6KJroDZeu|Hklen0TI
zSk1B_l|D+7I7|{jPQJIFSQa&DQvTH<nKO5X0=C@0Umrb{5GNw6xtb~Z-u1DIU_L9?
zMJs*eO3#+@mz&y-c`LjO8C8xG^rq+G@py7`dHM>A%MCDIP?y;9d$`jxyFJdR?Xm6B
z9ulJw1{qR$uf}IKxgCB`WE{DPWcq+=2#78JQH?57m!R8{<=!L%S#$eCNPyN-aHXZ2
z_TwsEimK?!D?b-zzzvwTU<RBf#k}=TNwc8EUgq;Lzdh{3>Qh)wWjen;-D*N^S1AdF
zw8(xtvJtpJ50G%fNz24?H39pOM9|YnWdJdwpE_!?!p~>gl0SoxylE1zCm9ISH@xKp
zlyB5QoT$BT6Xqjk9ngXQq5-SQby3o7;+orQN1E5ay@C2Kpy|D=gT^azo3HuGcswmz
zWFX_-IhG#*`sQ%iBAm~xN-jo*zYK-0{}bTV`li&e8(q)@%g?YF=PCGR{M8RIUrhN8
z0!0LlSn?lTFBSQM_f<muo+ZI<U*}!&og#lZz|Q=LzwCWL`T_)m+7Q=(TE8)fn2{63
zcPSufXCJ0D+gf#XG{J`PHoB840NS~He?H5gfhp;XE!gFDg*BfuNZ`*|%Jt*1(2UKx
zr_yBnNpfYAQ`@EIaU)KODmjVCStfPnd7UNdZNFZ$!D~P}lShyldbiK@rALPp*2_=c
zOVU=#Tt8J7wxd`lq58+CHMG0~htBn$DuO3WT*=M83Ae<A3XQEGd5Me{A5uag?(?ys
zKiz=e!Nl65_HrIsY@V~Vzs-56+LUr>ml}wP!rH1`hEi6;d}{d~m@f4Q=&I}m*nN44
zD%YyrH@wHi#Q(s&OLf+^D`avys7I&${5%%xBP(VhL5d}*J?inBI?khC1Z050kd5ri
zm@P<_1-6wS_DHFj9IAhn>JZU=Md8wlX8vDBNAOi)gGXA92G=|O+U?oYpycG_DUH{y
z&EAF6yHrgrEw%TsV$T=JM#rt5xHtUw?Yc)RIB(X;+*>H8Zx5Z89Wu<2Au~b9KFC?5
zBYz3&NUdTJap@<$*@(?(8`V~0&y3{7dNVr#_xCN<b(W?3UJF;mw`{U%gYF?8d22%b
z)9!mYRrOZ&pAMENo?u&?8B&tC;FB>ffwtQbP0zXS<xS|IUVLr7E3df72ZwY+=z!OY
zi>AY7ABJ%}8ll=L1{$4!^}aqr2nEBQ;}cpJZ!5r`$_(R~u1aDyQ}3fq$;^*X&397N
zE5DAchO%AjD3K#obLOn5@&g-29}*3Z#1cP_iXlU+iCv?CA;yN3mJe1Bq8^TH%(`-1
zkn#j{2A3$!D_0+^7Xvj7ySIb^uzT|k+yt|Q3yLX7aegu2PilwJgSzrIuy@IuE{mDi
zPTCBELMT(P+Jm`*uL^7!{~fgKQ>q~!sNkW#4gE?pUE~WCz8U|0{1t_G`U|5Nded#-
z>@v(fjvZ9oXXbBGjetfD&fLf7$G!5n1E}&qu{z~dNM@is#N1~>ZEN0XyU&xkd%fo-
zn?AZ<xZOSu1j(TG5~=DznC#)meas(J$xA7Mo)IajbMX0j1gvP9k3`hSwn1l%xSk}`
z+FGiKoo;gZ2-21^)YUJTkf{k1bDZ*1ZcfDA3x^k|YMTMMlQql1L)k9{d|kcnFvNwa
zCp&{+I_9keiW$?@$n$%S&Mk6sAD%Ze4ETIEo)P3Y#{e8Xt6;mzAuPj?_sFxk9tD+I
zQe97u8NOTyA6hdmR0{`R_yrR&fok-cgVp1AB4M*%9QW5~`j+$f%Xwe~n(N_SbGt9z
zY?s|ZGv=KRxZ)DkGOU`0lo#~=AV)DEdwbtp?6z}ZH92;3I+EL*K2(Qgn+Q>tkd5BU
z4BwSM;Y`p0R4G77CE60%aJJe>jSH?fn#K`Wnc%+k@#;J)b%0PD#}Ivpu*1<C&88+W
z`XDJ*`$3eokM;Qrfp<H`q__#{FZ>0)o?aAitg`sL)TfIMIfcSitvek>MA<;gr+02D
zzy`!~dKLLVuxIWl<iUC8Z@o~nGi(jdOVW$$OD;;^YjZs=n2Npx;V%vzQr5Kq(wtxU
ze@Tg;D8>!HmPvCn0Ddq8nIKIz-IGUFvHsWs)C#tlN97KtB*6c1!^2hd21mF+&;m?{
zu<Na5uZIIyDw$#QL|qA&P>}9|JeXoqh#V~azu3#5($C9R5l@bf+ktTIuCX$C*T7yv
zG*)av6krJgf$NH2XC1KW?O$kTxx-$ubnNQ#F-nPKh^6XT-@=mLwp@YwULLs#zM-?K
zJ$bwx_e6EqFf#Vsp(C*!%u=5>NI#x4u{oXB9Uf(+8V6}X?m?T}GhZQpmkS2{AX_^y
z$l0__Oj6fWgGZ&$i~RtYBZI_6Wn;kJh7lV8vjOQ`^sEPJ+&sN81t-XViS?=m&;!A5
z!vAlyonNWbL;48&SSy-e{64%*g!?N!lN5_aUo|T9iPaf)rf1psH@3D~mBBRU@H%p+
zPdlcifvg+ZErIOKjaV+meTct*8h?#=tp9$cBcg@m3WPR<KbT5qOaE}F`+vQ230)$(
z;9g;sX<MLqkD<y{%xk@@4jN-)vXTkE!7p~uat4-uJq5y;C-rv=OE&KtPPTDfQ+*5u
zoARZEWyg1x)pM`ViW)-OetJkjvPMy79m{O<3)8YQT%Q-p*_9YRge>;k3rBccoFl}-
z&f5){ksdOk!&>YN%`yh43bDcFmzyE%*8}*kbqEohmfS7}1H!G6J=48nGtS0+xw|6Y
z{lEJ>n~HQI6lD}yj)knk`qXwl)#j%=t-Ed>+@S9%JlQNcT(=l!Wqr-f-X+1h9G+VB
zSdb8pqlPB|+wVL!>Vy*|P<c1;V{a1Gy*f~uAMQybIOu<oLFNposM;>hcz@TMP4PkD
z0r!M>u&3j@E_~8IZbTw0lev(}!Pv_UZqh`mUZ@_UoKoD%4+ff$0$5(kaTMXDN1eRL
z4}ju7hN8)&Qc@GqIr)B;sOEnKj<e4@4qR~4iw6ELM}SMRr6naZ`NA<IHPZ9zlarI`
z{Jp?JtDdAnnLe<7(yq}Po_cZyOCKK7ZWAJ}MK-H$2w-}k>Gi64Ahci0(*<Nsdp@g@
zf6lSU8KOwDFr3Ttl1!sP{`ohuvhcD;>0zn$ue;q&d7sN!-u<Zybo$qi`g$5-G)xTT
z(mR62k5=S6Jn(<EelPewv^5&wxB3X5`7uxr3eK37?Fz9?weo_;U~s9pxFR$-sV3a8
z_d$D)23+osamr?My*c?6uo?|x^wmz#x5&D;5X`PjMF@1DA)+?bY`mqvPn_6GeG>4*
z?$*=w*=4-^wYDYZHsfpSqy_otN06mF2KdnGz*sa*upnJh{2CgDErC6&O%0Y)07#RM
zfK735?mFB3SNI{QhV%`B%|;%pAqOpoUO{v#27d2_KL`X8k%1goHkY-c*Im@`b!fs$
zs{W=OF0{4PzTXH=fqtgfGtf!An<~tZExlHvvfc5uANHZoz(zV<&7Ym`xIp;Tb3+`S
zeqq=gv0~=}SMEmxb~$xaiHh16o@Wi&SiwvCz0a;8Vm@t2=)XHKvJ4e*)0WVWZORZ3
z_rjs#=2W^4$taz*iEVZsMDm|1s7*k89F0^XkG2d<;}MC!{XWZ?_e0)E?I()H{G=^%
zz*A?2Bm<=;*!%%7)L5CwKB}C}j%Pyd8moTPj1kP4VyO!vT8xvdr~%t+uaVH*;hAop
z^M(1jrSRF8xQYgeQI=Fd;}$0rE+WKLY%y*G$RLkza$$o!-E9*us{&wW8x@_^jV`YE
zuXXLzvO0P1M9m>kaU%`3Q`dT<Tq<R)G-!0!&1P<t(cM138nmN*ab8M*HnqNHTG%;&
zi&?ux7v)9cvMVZMvP9)FkhU#VY7Ul@+qEl*&=+2{ake!t7VG_Dl;5O%ia6QhYG@c`
zmnw<Q1tejgd6G@^n80BGJK}pKkGFBAzMn(UNnOnG0Zuk3i)1jB06fw_7ZbSbVaR?I
zE_o3sZLS#T6ppKXiF#I<bpCx^Xw<A<%J-0LVCF}!!Hd5CFEo`bsB3clG$7xGkbQfS
zzmUi0eN6LDt+2wpa}3e6$)MzHvCY3u2=n)tR_-m_eX~hFhc>(D=Kaz}U-|7G178yD
zzm$VB*Mam4;(36ktsG2=j?~u}<64XB)I`9XI+z6T*nsyRW0+32zsO1d(IJ_kx&seK
z=<a0qup|R*{iFT095%lK!PhUTo3;jP0xWWj>+fb!#qjT$r`bV9jp<5A3MVxcg!h3j
zYnez~cYx;LeA%Hmd^)hO<ga~F4T>M5?M1Oi3LZXPL``5hFIg<VXLG%+K)f9VVH}-$
zx`SJq81D~9M4t$9pP8QRC6QAK=AaBowiYF11<eKYVCi_lE1;hTREDo5-nck0az|is
zbQAUxbt}UlaNvL_*HmBBE(x2B`|r`5)VWZNpr;XA-h<!28;4Wk)%Lq!fZtT2k9AOV
zZK+0oRN++!n+;SflVDkvy@kiDH>7}Q^Zs&;&bjz<fP^o63Qk1|XVsyHmmH?ATGcpg
zZv9$#yt%WsJN0Ur(8NZ1$R?aKjj)VRp&}H24XV<nSY~$k=wHuteR0r=ucH%kZmNUI
z@VQRa7H@2yWlXzp4@RP6SvK9K3WJ_lCHK_d4?e}Cd+xxl1mQvskZ}8DLTJeuP9kt9
zvQ#H$n_LG3yo!37Wh@XJs5YYCd{h4sUMC+LAhjHuzX+BAnwVm+*_Z4MF-cu|e*lQA
z&;)D)0BMIH_*Iyokv9@J72x~Np12#7a-)#A_pPmf<}|D`n?fe;O=;^540gE32rigx
zBdR}WkLuwYITi6I$tSbe)#r}RbP0|d&3?ACXg6Xq_*fgIxMb^d+0wgEt*VMrK$=Bm
zHP(F{Jl6AX^BbBnro(+6-#2{Fc%=0kD8!b{4@@Nn2?~;{&+X6cK=@kB7xr4H1T5!e
zgDqr8T(Y`EkCFs_MAJMVg9ljLY2q$2!Fr0md%A4qufT@Ny?`(N>gI4y5jtS&%iEn-
zM^h8m`+}RQFFGdX_y_Yl{Fw|KVOD%R+NWG3w+t3T6`<6oPl<y8^I&yAQ;m;e2c{=r
ztQ<UO9tHR3IDj*S-#4M5yZfQ8KIUytlJZTT(;VlX5I{k9LFC!6oe=#I_JJxB3W8g9
z&lk(n@l6Jr7E2pQ>&nUdjWt7<jQTcOFoeb~o5e)B%djeLc~~1voVDZ)3;!fdOwWj)
zpyg%H^XL5w(*F!nY7-1a7B=u1TLdmY9xoW-$iTC50=sx07_h_5QNWK<6$&MVJz0|3
zNkaf;@<mbhr65C|*yEc{T}j|<TY_p@>?Rwk3g#!BUNoskw2Pwko>z4xPfBRvycgG{
zM@fzQAiSM)4kv4nZ7iybWRSFV+sW}caAi=k{KDWszW=ZW75wrcVkueF>=yE40jXIR
zBXDkbUYy`Z8iYpGh8eL+FXX}%hk;qmTRzlCvvcxo-_BYrL#Jrw^zAQj0Yahj1t=Yz
z*ueFl2zwGt6vD3_Y{N-^2xT3)xsga6Bm|6p=Zopw<0dx%tn|b$)&yJ~D<g366bgw+
zVVGXCK;`U`i1Q}~-4TatN`T$9FKQ-1BQ0u2Pv~F3!Jzq-LB7U6ph^b|2Yz|5<SCoK
zutWvNt<5TV5sX}Or`U|11@Zw#jeM_v%640^uSJ-Z1abUtVYz>B<MI8CYY4!saZO$4
z?&FNx(?&I-RJlkM*ml-!Iir#@3EIU6+ddus>|K_%e`6mG@BMIamQsA5CE!ycL@fx<
z0Md0N%tLvGgibEt<b9N~EQa~-uE;e7S)*2diO%n;(68w_57j@vyL)~V-A_Q`f@6&?
zn}PiAYsEK{6QmyjN(j9ukKC5Pu%&gdRC9H4$*eqRHNF$4>;WUi>F&7PA7K)j>H;oA
zMp&Krk1?GMY=XB;U`7crnWI|xwKYQsD<D)S;#@w<P6@>tApUd0z{FQ+L=N(f)QMMX
zq(*K)&J+!&9{GP?rMZ|tDp_M^Mc}T3HS7LTF$h;7#>s%?-ChYGHd?bvEtIs*?Qh1A
zWxe8e!-4h2gh6WZ@^|BfCvJ3g-H6oa<lR31l&%$>KqtA?K}OhO7G&gi#s3XdXp%R4
ztgBR@=Emt4>lrWEDTexX7W=bSvf}81%kuv%M7@7ajL2sR-_!A$%FD~a^`)Ggk4#{e
z2Fl4d(Ur>a&G&vyiH=5+3oLBK0D4}kOS2AJQ$SXr^7NDJ?(FRC@6RgS0(ktrFGc%6
z;)Rofd|WzGg=J`XXrzjU5D~G#D2QVbFi5PS2w|8~C|F~tkDw6rM-6HKE($|XcX0~x
zGZrT1?Ch*Kq$4}_9Z2H2xH<`lot+)nOZ_;*PuGiW`3WH1E1O{qvQ2x)JN>RZ?iEAo
zA|EUvr<=m*=n|nT-;GFdWQaTT-EcGonW0neX7w^3u=s6peeX5DC+0qYoM6|N+G5Y0
z*br?2W3`24#W4yo=aQG|4?;q4&rMKfBmIN-7x#!6!+eZSIUW?FG2hG*40~iS^uMEj
zJNXcoy?-A_cDL^3-I)$zZybqs4gP?ct-2i(AT@TZJ7$v;Ej!-+T%*p_qRP===v+;9
zvwi46+<B*_aZzPkYhpK<d8&-NEf1<qIYhhN;_`MMdopir>82cHB(DP6*tq^;G(F-0
z?Dxb7+&M%ki<IK}$ud{3yarvsLt+XcdEcKMV&k(W2z_<<Vz;!9Zejj3#(0~2{2WQ<
zJvqUQBul{VIulnnwEKjA1C*8h3K^ElAqTpw8y~8^xyIP8Y3&IWs`w-7q^LF8<flD*
zj5zwGNfGQ<F*8uZJ2>CgQAn`aR`QynPm}x|<uGN4P+=}OB>FGYYm)khux6t2-_2Ym
z3P^~xxI$#iq}qOIWaJNU4Dw<%Z52If&-@vmwz1SM?43kkTc84W0O?So!3A-)ehB2}
zjWYN0UEsXJ%~F9z5O}5TuaFz@)k)G;?=K31G)~q*!?z4CH|2-GYD#5%Ec5RPObIjd
zIVaxM7<{X#F-TlgF+*j=Y8p8p&vZ>$?u|K622JQoNAg#AvZvbCfd+s5f?_D>1U7DA
zRtJ03D%mowDEjBqQq?Qw!uV|qi{m#TC!MQh%x>teRZ=vkq&L}{R(v*go)paSM&-r!
zoGn|2paML=4Z&;_7xYxrWX~o^?VNW|Tw%?n5PjOCS{N9a^W#GlctI4$cjL9y%s7cv
z((~KPxYb4_qTjBh6`saKL+v(~r?jMS*<}IXQ>c4BPtA027{=`#n{KlJ2&_$lJqlVt
zE`Ku5F2;GW)aGSWrVwODTp%#q9AL!}J5pMjHmVQ?&E<1@lP@MuD#-UWwv;|gQhp%C
z{;*}_A%Xbx@+=(;$IFK-a6E5e)k#=jP(~=!q4;`?S68qR9Gn6yMn;qa%`{tYOr;G8
z-|pf0bYQK_G#`U|0~qXykfxn#WkbSMFMjUpgK~d0M4a2v!9#HfHPzQ>@J<hn`_Zh(
zDto#;C7(!GG$kEX<MIw)z2S{Uf8F!}jG`mj6q<jsUl=-FD4|)6=C?j}D4?EIE;xsF
zIw>rKWYCw19*Y83#rLEwI<W;iIl0_Dd9EI7NeH`jW76jpgxx&Yb3Wv$<+C5Y4McV<
z9=RJK#0zuTh#t1)Ivp2wW;2TO7J>^3R<AvNhn+cF2Fm_pw)4j#TWgXjm<~GovLNp@
z-#B#EU-6blblz?{N%zg8(PXhYE)etAll+L@Bf-rop~eo2Y_K(59%L<*vTmH^jnYIY
zP*!Hg4!#zU#+gL=IGW3!K_GmSh#ztVJ##u-Y+7f+jc#jE;BHq~W2Qjlq0G(;UZVCM
zyR~s}R|w0N7a>p0s*lLcTw@8X=4|Vh(k4zAE_@<z7{#Jt&=D&ocb~3>43OeFSzn_#
zC$E*v-SruCrCwcs#2qzT_^627@fi39?Q@HZABrFL^1ahP-H_xF-4N&eP0XTeCr!bF
zvx-?^C83>L7F==Bq3#6&iKP_~e^5S9a1{9VGIU4G_{Nb6;tSC{nVCY~`44Pl4_WVh
zs!ldm0Wl_s4i^2S3To};VFqwH1J(zTZnAqqM@dbyGVkg4kF`IGS=yKmLd1(`v!o0V
z-gL<6D*Cl~tf<Sv7=B&Vo`g`^Tc!MIFQbs%My<Ct!+qwJ7aHq3wm;y{Illd{<eETe
zQ*o$`&Gopsg{iKRf#KWhus=@m*{=H*Ha!XU6KI3*pv;@cuu?1v1m6anW#hd$iJ8*2
z_^OUPu1z+w&IoJ5{R-=vt%U<~e>txjjP{hCgWJh@BbSt1*q&TC3`RQOLhoW|bW~c@
zp-P6VtM@o#gaN}T#jNdgzwIxDf*agRWF+sELK!-MxTg)2gMau?c+Bnf#AeNgTRV}M
zeo&v_B3;Ad3A_#m7^dQb!{se?&c7=A_mBrWouNb-?!&x(Nrm_*{<AkVO>QkN7o{{6
zh5439c+@^}^*V{o>5jwYBgz&u3-&8rtFbW%gHH}5etRD|Dnx4~ht?(8!;h?i%%2$P
zFRc`XbDwMg3tse{nYpgsRv#c|;V>n0$;rTlQiS=CJ}D5wZ&PgYl58F&n*$F9nxzo@
z>Zl%dsKPs=CXx1E$oqFbh=fH*lSdL)<>*J@+usE4_#tP@Tq<NIy|B3WNr!!mR{GEF
zv)-~~*jj0#+AayJ_Ox+s#wzK`x<Z<i@VUk?1wB}9au)t%mGkL<DMjI>QEos4`RHn6
zUBeMKVzNkMy0`6Yj0D+l)H==#Bvz!yFZzh2jRSAAEJ$HA^=YZ8Z0Zuvl}mHMZ{`jn
zBU7WJ5m`@OEuI%fmM00En+4nUR7cF7e`X9IzzXE$+m-OAJ>J;Y_FWM#NG&Zknh3j5
zn~}gO>}mwoY60EIaH1o%D?!uP{P;yyHl69*NS`=-9F)2&#`<h_%OT>SXfD|y>?^EX
z2O?NuZ~2k>X2e8<05x99V~el5kFL-T8nfERiJZpsTH+ZH!tg;eEE%pSKh=bgy~&x5
zS&zdR&6*HjANpY1>Vb12;<_5y%w&KkhE>XO!>gaMe@0RDk4KxIG%%}x!UuK+uIIQ^
z=_KAl?7nr56V^8BW6*6=j(>)nNO)s<Rzcw-eT!Hb%U7@hC(BQX!QK@wG>Gj#x&V?W
zncd-Xjl;a#2x;LXyaG*6R^FA08X~8`+Tzcp1qn=KFX+PI`#S*_5hCA^yIwyiz3;r<
zL)<(w>a&Tn@;}szG&&4lmQ(T!DMpDjU8>Rh*5jSGqduBLxB9Rq$a%}m5TU7*LKwmN
z6_9~H3y@ssQsg_b8&AlNhYr0^q?2Jj4idXoR0YA|_U(7h^J&qrosP`Hxvk{)!h1}s
zo>qG!I1j$z>xcRvCzanby{J9<(4CvUXS>;rbuSz<3COGLjAul4MeEB*-nYm2jPar$
zo4APNrnkJh{qwKSK>JL>FJFNH1R9^c%uIsAgX>uVIyUqTJ421)Z<v%zwBQn9$J>D5
zScE|N2nqXjK1$VZm(4lkD>MNcz#6z^_UAs%U<B$b3t|vz*Xx(ttPEA<@%IF>%qk^Z
zMR`6r@-SDVa`bhw`d`ouVT5hSgfGXzZ+Lxr%d7(JM;V=^5hQ3vn3C<6<ba@l7-qJY
zJJ1$-Z}0(TOE4aR0*E=#L}nVvSePq;iyTmhc;?w^tOPoO1hd5*ts1@l<{EGZ6BQXe
z7PwsG;NSq>o<TSI<3|G1pR;j`8x>xv3PKecrZ8#VNOB}|@b$kUFp>Nn+&7VkjyNVW
zyV+!o{i^HDP)~;~O!romRUd)3O8O-Zru&{cXKipLSv9~MmGd=ux_1kq<74k>!1!;`
zF#Zd|{OOW%_BK1Su)p+ew*}x~F6Xk^G9qblWpkiU7+2T;{nBnSx;6kzRJ$v>>I^@g
z(mPSa5Wirel&(P|;tD|Zy=9$R|0z{ub^6-cjiW(kWi%m<_~CKCGvQ-9>NyLwpuf74
zT0eX>U&0@{qw9;PHghYjA&X|)E%L{yTM|DD)~;v1uUFL{s#+a-wM<TqI-Wg(w!-Uy
zd861$`EjDo9~fs4|FN(iZhtZrwdJJk(d_4YQg-*KX+mhe>9#wxJ&=Sth&l&V$7kI~
zo2XXy%*<=6Nj+e>S+OH>)@^eZH^NUDa(ftlJv<ktv5US${H)jU+wS3(7y6(&8!z2I
zlq{TSv;IEi7b({1SAO;{k7X3WiP}GB#{9lk|JhBnz6Ntk-L{)aN(9;qRYcml8u^tC
zNx8d)sycK8iC^VC^x~u4R9KrBJ@i&(BP3{kpB17uR+=*u%-Cmps+LnTCFil?!Sn9h
zg$Tu)=HQtz1Q@fh>mwW>zg`sPK>z;;CU|d1IVrzBR)Oz;3mcdYn)8n<SD9aAu*`@+
zBHF3rb(X^<0)Nt;0pWvXYPHxqqfZ&VG-%jt4abJR3)tKaJcn})2gx{^j>jTQlQ}7w
z`yM;*g<-6o6+5X2#rH$ahzz+L0iS)?Ri@WgrorC!1zjN89VkUD^;h2D;}<&TW9UuN
zty=q3*V1Ig2w}@537w)uwQ!hj_SUy^EB|GKfGz&Npeq3?Ga6i&I$a{|T$j{hKV6k)
z5T$>29g6#D`!<c*@rUZ_AjciP4#s#Yp+mFlZLB$Qz5MLmW*<LIkzf6kj{4$hm;(~S
z4O|cdZ_=12(%9+yNO97)GJWi`{opzo6$#Cc`JSJatnNn24(GCP6L;1HCrNSeM@m?}
z^Oqs*Oz*4Rh*Jm=+4B`MpJt7I^Q!YM{e-wd6@?39By#Cc*!|F=@=zQr%mPXXUdNBZ
zu6v)0o-W^GHC5Hw-#_bE4LdY+bRG_-KVp}^wQOQnsMHE);OnZiUb;l_m?vI0;Ub0e
zT)}Czn3vxrYUkkveBB$-*c*`ow;nfxT{^uM79WNvN%yz`s0Qkb6o1)MF<ynATW-<k
zxo@r6%YzOjnD_Ni>H4l$M_CBvnAnBkO@}b2NpfNsX6VLZ!RMm>dK2w}m$bF{Uya;L
z0vCQ?RWvOZXOJ~eF+GkoqO;`^2s%E7H~Crk=BG-4&KEGtjk+Q<1v(u?MX$>auHbiU
zs6QO;N)=!!Z3GG&i(ntJ_V&9NOH+%tDQ5Rxnjs?TtuvbWpw6nRN|rQMI8Gcd<L?Cs
zfGa3d3jF50{@aOZ>_?9BfsW#?!J{NkIN}OuN?M$a?s;6E#<;W1G8gVLwdk3RP}0jO
zA!_lMV4lL=)~thm#_yeczqf)!xcW?r<B5GS+g)DrEx%zaT&3g3#hhe>0n2ydZD``5
zOm6Z&-B!GFGHSAZY9_vVs>J5&v03bbd+u@jZn1qS_asjSukM__3#MKNsGLFlr4iwZ
z#a7P=d9713llp55a%)3ZPgZIxZUqG($qOc0!El;FnFw5^G$M*WL@-)JV!`3HH&6II
zK`B84@OhA6vg_bWtqj3Q!38EHBGm7G`}lO95e4FMw@*}3K)Zc%Hki+HzLM$<L_^`l
zy!C_-I0ZE;@F5;V+sJgm6ZznNPWzp(ZcpQeaC}5>=~MEpP)c7&scbyAYs~iwi6612
z$zI}LRmWJ7Q|`-qEuMSOc>afnOI`S3*@eXw_+DI5*g?iGkaGfZdu87&4(s+}J`;&j
zCC^Yd)HXe;UgCe(pfO8(2@Y;?CsuUiR0bJwLBpqkw~Z6GnOT_0Bp^bBn6yxGo%1!u
z3e@k~o2q=KmkeI2D=<%EJ>7Egn?*4M*_fJtvFfY{kL3|)7y|3O-|&$6Lq^vhKYAxL
zu?c;TyyXAT&42`hN*fh_e_HC-gln3zSX@)TFq7$U&oOXyZV4`B{iYR<MURS|`1F~8
z#MtZ=P-U*0>+X-y76feYj!(nP5Pjp)vA<d66XZRR9~}E}lA*Cy#FkS|4&KZ2j`<sK
zYZCLZLDKQwZZsOE{0DvB%`=6vQm~eh;BwfQTdksJr6R9T$HPVK*|UPX(^Im<jFtgj
zd9M|k%ODJRyL@9~@R-d@E(b`e%POGv%dNTX9jkJ5O+ZO5T3J0F99W=ZxP2uNN7NHE
zmj>5kkFGRY<K3W2HECLoS4!h7ct@|kj+9fC*HxC`Yf~zVz)VvbcmqO}((6|CxnH9O
zuVRD*>F!TT80Qxy+N+|1rEfwV%1X{L2S~D+tA)89<%f}tMsabV%ce(N{x`(7n<RSr
zKZZcd>DsY&$EFgQ0&P1ETWmaygD#l_D<*&AMbblI@B<WYOp(upKq#PU?e}Gq-Px&h
z6ebW&M^#EnQ{QuM=XP7$*pA<I%X~*cN>ft`SCiX*qvr}qfaXBGWoUSvUbp)`_!Wrh
z5-lfZL+BAAZF2j+=@Smt*>!EPK^(@Pf782Q-^}21Un*DSr0r#NotxdqWUD*c1mI0Y
zg8prPfmz=_z<7^D@xE$2v7~-^l4puQwu-h5pr9*B4FZOoFCwD+MP({5udrh^?y)HI
z&ws5%C%Xm{0K=!SulMa<UhlX+9y!0>ZZoD!!b2fz>HqA~@U0$lc_RQ~kGLFxYLdWR
zUGS927gXVc<~dLh#mY<(%y)w{ru@O|jK?tPAwj~oW0Y0B!nbkz$l*=mgRuAaCN~!b
z)s{*Kf3p2smfK<nY(_G%>Y*9uxJsSbe<Xd)8c_vFze?NJk@M{JD1ViFC_S|Br6M(p
z^Ogg*;TU;+PwdbR!c_o?Y(ID0CP(>*L)2d^AMT-ZiF=-y87C)b7Cl3lM~}&riT7j%
zdtu&$BVUMt$nXCTQC}SuRrmc(N_TfjDBT?c0@5W&Bi-E%LpRdR017A|-6h>Hq;z*T
z((m>8{?>Z`WDUdIxtx2>-k;hi5m5o0VF<^f+prt;9Mq&!6H(Q{OAy?7#ADHYHg9I`
z^6p>8C}mDoKp`NJ3@xf(Mx=PC+AwxOIL3j46OQ(g8C~(P2CBpv_yITt0k+||3J7-@
zG6C8i&QbD``zfJQ(a&VB31eGa8m=Ite$E!gHw;#43g*D#iyeLoEJWY|Afla49Sr?W
zEH(W!tm_n1=Py^MVDRQaB?v4F0#412+2t2j!8muIpU0*2;kyUrx|tmt$$EHB)dW(S
zd$`+MvRZQryGPUrl(4@eY~;(@F1G^!oI?;~EE)UY-K|vuaLryvX!KS*nIPbCaOOAI
z%c^7Rzud-$JHzW+aVOCn;+v2>i785CqymadHc_6Eh|JIAf#EyUUN}6|#|5xUdmGxA
zo*kVL4FR|;Q=iFu|BOlM1ip$)UorO`&ukO=m^k3WT|-ryo7!cTRt;v01{x10`-(a6
zU9D(9fRO81YF!QbFo#|a`9du0W5R111gWNNtJ*Lb!osgB&4}zqC_9s#l0z)Z#EaHA
zsj2}RAMl}<k(jzbG0Q(rE7rYfp9Gdv7(?CKN`N~^<LXmSl1~>2uRl+gKdI<(T*KuH
zE>8iC6rP(#0+deJ_m+jX_AUq({w+N{04?vU1ijWU92bP$%IeU8&NfbNCjoWsHPM)3
zpvpIb4MzrLMk>TgniT~9z3U;zJ091)(QI9taprY-`g?5D`sgNA`BfSvPg~r7<rT)w
zd<W_KCr*&q-?5|w3EnSAgHbC~gN*X3SvStS2!A1sucr5HAr$P4zXE8f&aDT%HGHel
zv9{{p!*Q_(Zu7R4zv5dEaKIBhluqUh;5+dWXv`!gw1-6h4`a>U2@Q?SalCGztU--_
zQ!Fn-IO36;sx_PT=@S))r8&E|xj9{J^-)jynb@?Hmf<?&ifMnTKD4GGzG4r;ck)5M
zJ;EzCtBJ-v0UX|7&>4LAhC%e3nMgQ>?d;FgvqKnjdxO=GaiE%0JuXB;iRyT-HW9h{
zesLs$FjTDkxRXg$Ug`7dZ<n15%jkZO>IsxBSktZuAH~RM0gs>@tLb0uD}!>kMg1(J
z0vx<N&1;H*g7!TbzK)MJ2WPK}qY5jQ$71vI?M97;>VOK-*xVaQ*ihN+aIwN(`0LQf
z+#Y`5KEtZ%pNgkWy~YkBB{u@RHIEAFJtgU*z(ws=tc+$pRY~<X%p)jl;JjS-YCZM^
z=|=}4CA~Ct`BH6xusu!BBUWKAX)`K}9>ZweyemGrYt6JdF7$$ShWP*8z_egl5I-KU
z)5W6Z!$3%@{?~CNIpNfO`3J-Df#;&Fcr-cm?DE5)MLp4n!!O!6GUiEIm-uS7nh`ZJ
zjkoZ2XCP_ttrMAiv*4d%PhK_Zv(lFgCYz!|<>N1Y@-F3(5lXSc50=P>&gF{VWGE7g
zFLT8h0o0VPgpDl<mV9KBVq`0~Y$!A69Tg;@L=4|dCe#93;9hOlO(l;h?mI0e5`otP
z&FSy>(rmlkfbT(^l)G3T<Ey7uILpr9JXxrq+*Q3cFc}y3d>a52TpjUL^8$Fta<9z{
zY`99jBZsaur&O~K`)Nv#Z`k9q3UK(VYk#5RN6=zxM^-hp{+dq5FX<25FQ3mEl!L}r
zzH?JCRLwgY&4&PXxp(EnO#RTORt@2v9<(2Rt=$~0!96E$;;X25x&zKXBAuyWO34Ug
zl57dMv6#*aRF6~C@ne?ckTJy}l5Aw&fb;U3kW8f(S3#zXOM0y)<8e%J+(PyEBM3OH
zn52gyUp}VCpB#WKq~7Vkr~MFFelv_|UY)mTVxy7sHR0;d3}xQKCZ*47q^C{g46x<d
zFw&z2AD$N48)4ILd_R*k<6_=^Zv|C4Vmkrx8?dGx+!x=n5&kj^`mPtKa0jbJ2`?vt
zq;p4R$8S{{To1pJi82`9<JbRlShu6#9Wis!)Z3PrNyM>l&h&FCg9GgQ`(a8|?iW>_
zSBamow^h6h-2KUjutrC7MS6vzRlyBS?bWrQwfHRmSQWMt?peyLw>H1boUDv3MRooe
zlPE*g<F@|??((&^XxS82_-r>ds23{dYrFu6Zn9tzAk|nY&$RdPbf@!p>g;@l<EQks
zf@$#{;!Fcdte4aIhU|(gfKldY7F>zYz`}?ebW6|dQccK$&VIUheR&A<?_a7*CbY#i
z675vLVig@LXjf;iowj{YtT_^0R2TKr_YI>Q&)49^W4t4`db?xF$UiZ5>i*X2a$XA1
za%BCqqde6nELj(qaQIqstXT5;82Bpd2_tX=4YKI{5Ebx{TlbJ*pnU*3{%W+W!o7@i
zb&n!?&3-w<k9++cYuj}G2~3Om<z561F%zeN1c9yi#RET}08Qw76~iON<lAe3s-&kn
z(kod-eWK_cYA&PNAJ~0+^7LKoGp!ioZ8+b&lu^3mpkGx`0)oq~iV;`*dc|3$q5S<^
zf;4SR+v#lfdL^Cf?iT#VUV2Gs9ZY7q9@%J1^X<OF)0PZr({GOw61o3Q-6JACKTE{}
z?Hx3sT27mBKCDvAE@K3ZxklOKUv-mIcby22;xqFzvHFO1hLzl+@090)yoO`zoD*C{
z!l$NdV0rv72r$vd4-p3-sP)8AO?1>K|7fUJBU?CMJd+K6R08vz7B0X_^*r%Aj(Mka
zU20Yx^j6r@Bjhft^w$5(e^WN}?#EypkCb!W5SEFbJZ`<FhCXLiOcY$KmT>9qsZB8x
zpgvpC9N1pI3|G*^1dFLEDWb+IF+Wh3{rIC`sH3aIc&SyLS0p=g(um?+sX3ORV#D@v
zmCEbXd2}TSL)I~<Nh4<1GMuprRoA0|nMZ=#x@NirzsEGDy5e`vdHH(Gy_<)A-~-+}
zJ3E}ce3Hdhb?|8^>vMv(EOO;!bswPH0T`N7?k<`B2}Cy3?~I%u@ZXk>Q2wi2?R>2a
zxS3f><dRUuOa;3sNwIIC%Nue~<ImAvjMpwSqKc6--HU_Sj%j9;nKkqtm%AC(!57JV
z(n?tJ`t^5y^2XBFxt=4QIa#FG;@+-2c3}E%=?=odnwJ1<yqTt$kMYFm>@Q^SuU(y=
z<?x(uGuyIu*HvTHlVQ`3+2J`Sr_wV8{!LD}ZiD`p8|WPp)AF3QKN@AgtG8_o4UiU{
zAr`tSa%FOFP2bBKl@I_LvTrkB<R}dfskF^XjZ&Bcbbn21x{r7u*n;Evk(Oh)F5@|M
z$2bJmy0yjsX-gu2!{0ULNV&tt0XZ#49Nu9dth*J+gQ>;6Y+CTVumyL=0EA}XAf5K4
z^i%suG+un_wp5Y!j-48@V^}HyI*9>&xkYRok$e@ck_IW+)ky(=IbWBmUf9}OI^r4!
zA?-0c6yFaVV#(~BPXTFEUZI-t1;5N^){Stt&e`4+S^{0qt#^ikLHIP$Wgd3x=db(D
z2BCmU^4#CT>#H+<9iQp?Evd=*<>22xy`hP@);qo3qJQ8WHRXRE3M=Z0FpEZ9_Wnj-
z<?WLBptXEC&H&VWwam75Xi_ApKnwNO(<A~Qpj{2Z`zqo*D(U_mA7Ukm1Lqag$U-$i
zIV@Siw&QIOgy)10d#~<RwgLX_*Pmo%G`Y4kaAy=($>D{6%6YQH;He$IJ1wv@A~*N2
zu3x5Ai6piA4u=5le_w*<^gO+*ursK^==z2QIIFS^bc-5o`9fRUC9!Icj!5z~jtxVF
z{pEr~PD~5~%ix7UQucIwAHtR^IjDr{697%$?@8t9G!>O)_9-2=n}fagyUHny%oz|Q
zoRWHOi;rF;Ouw$eOK(YS7J;&GRIRr5t6zTg9QM>I9V^7Uknbnps=Wr+{93dk^<ew`
z!^l-ApUrBEd&KN1#}}O=)c>6$xlJ+^OJ{Fc_V*gV@y&|k`KR8OV&gnKHle93zO>@K
zCbqVJ8+KWKd{$m{G27K=H5fSL5yv~hz9bs@v#-&?p{^MBiUMFP5E^<TC8?N)wzB9v
zhE8PKK>>}#r-!@K0-e*EO=Yxe=r;uGvZotpWQ~A|NoQz;VZPk$dsw<bvC?WOG?bRg
zmP;%NDZ$vr=2zd3k(=g-k}_I^KpA>9iwdCj5)z5V^(g&GD~(T{YcH_JoiVecD6a-A
z=7?}*X{o#=`KHoY7DPI%+#NS_AEA<Xi)Rj<b&!~t7*|(U<9GD*I|m2ex5qkzA5?;h
zbbfp-NXM6anXfjTRGa}oTB~H}i|zj}QoRjx_0Lm@rlOJG=f32842UKIlH9%}y$1iG
z-{BwL%jNFlZF7218jG_5>yEK9vZe5s)3SZS&d^Kdpt~Gn!%tQrO<txky6ab*4vHyB
zjIg`J8PyG)dt!(L7~Q#dq!|wL^=pOsjm2@vHv+FWC8xyR6Y4y5y2OMgqE1a&^rj~b
z2C)3Y_!y&HMsp^8-Uu4RjK2bDXKpac%VT-S!+qn4#l*fCeXxnWiEMT#>R(*SDAT*8
z;;<NfeV(<D+m*(U+Pb2z*LGI^cRfrXS#f%^`Z3xol;<wjqCC7Syhc``3$4lD!%p*s
z&Le+Dj`R?We64xiz-=gWw3VQ>|6S}CtQEK~sJ6x#IQJG9qID7+wm7-~OaaQ?v+oW#
zG3{Ib>(}yEyE-7QlPO6pv;69e3pn(&#bCy@oncT${Cs-2!<4DE-ae2ep2e|7sYd1R
z$bS>odYdTT)}!@6k!qC0WWZ%5&MoY(^!0qk^5KsupeQWjLU|5X;dHb|1`4<BY#I>Y
za)pX|gDk%`szO<<m5_Crp|uIGrD7V#i^Dg3zSxpG*||nyR{e#bj+;?^kqon;b)BHj
z#{5q0lG}!}!2#}t-=~I#tx5aA+=M2vx$PgNl~&9ysYf$ku)wA|Z62E+YzVqam4j;}
zA_8BE(XtShe63jw){ym(_?cRps^jw8n|w=dJ<La6@eK~u%`AmIp=n38ZsmWyRAjvM
z6S|7ECc0a6OUb^)Pt8{&yCjwXmuUxx3yx0pN(Fkfeb)+a{e@ZsV)xB{6;Yh7xYk7M
zv{&R_<LeJgE!Ei~I*8l?=?3=2rOi1yhGu%^I?Z2<00KL{4>T{Xp5tQ$DXW#kvLx8b
z*~!Hq@KGfBR_p69@xzcUZY_}IxFFEh_7|tyaBWsfiGa2A2VI(A^CXe7u4woara;uu
zNI&ik;s?)a!geP8ByYQ$ju>qQMvkz0j(@KdKmv(O>GscU*$=uml@Gc3I)^L7-7rRQ
zU=)p6(Ya~Y&{2fWJunQ4+tvXa5KCq~a>adI5hT6uLwO`7*A1zV*b^j6>Gk^~8wEXb
zE2u}2#jS)zFXNf=4c4{*EJE&iDc6ItT=&*+)8h`Or$Vc{Rr)3VXUqN~qQ_|Q40Ph@
zz^Ut<p@W0UBaR3ioXqOG;h@lg{KB)gguv?y?a6^2L~}-?05Nq{kMi@LD58AKz4SaU
z9rx>n2s)J!8mb1$Ktu1g(xijix+kUsE$No_QZOxsgEUowIAcuD;baMcY`$iWU(~W#
zmm%cE(f2;`7EX_unOQvGN!!4{Kuzu9{%Vg~S~L3RVG2ZcG*{fN-^~B6K<q~&&q@i4
zxY4hDj)uyp^oD6w_{!>WQwC`f(-&k`d^a?`+G}Wx29~j)5^l;B!bn%kC}B9sS4Uv)
z`0sw6&~Ingd4K=5A?}1a7;)73jd5FidmV-#iT#3lZN(t{CR`tp%MeHI-e@D5<I&!K
z+_#PL|KISRiP$g&5WOi<6rb0a!@3fq?1b3Dmg{a(mSnuW)w!n+{~pC=$D{63G|XDU
z0^Uwu+&XCJZL1y~h;eTgvHPRiU_v;F(SI+v$r~1-k#-@sJt00eDsm9#Cs$_)8o$D_
zGZSxV(HrxcqUY37GEcqTXUe!B0G?}E(2{RUAEBQIrJzsG3TbCzqS7D3ag}90!J`hg
zp6U5XXUR*0VLW)ey2489&$+8w-k|vWK^=&FKBAz%yxUX8w$|%Rqk>Y6{fpM4Cue->
zP;9i<gFh*CSR`DboJB8HAp$G6zJfn*50FVPfE6`vq%o@U*n+KN@#l|VpqvFhl3+?-
zc8<}3%s1h*zE<pohsRwVG1Qu-_OFXY6XA;GE<$(>7C!9gkeuAIO~r{v;WU}-<E-DM
z^3K2HJn6U~7X2k=Bg^4`=ur_@y60*{dF+2;3hk$&5wlK9O*3la+(H9`p1nXwYmB>n
zdrvT%<Ho$MCJx6WZRHYnZTj)EvDIvQtXki!4->%ucMJc!x7n)y6kCc+Rt3*$4ES}>
z7bZMZU8}&9XCBaJEv?<Xz1HUDzP`Spk&(UaZPl{tM%n@%A3a=iTwGi$xm55h;Dgrw
zSz7vgYI=HlVgl>(-&utC2v*eA`utf6j>Jy7FY_ocv<uW#eYC$1;a4tzm%UGfHx9Le
z3`#IW+sMeg36)k?$8FC*4Z~qo!zA|MC*Wd2+l%~(RMDOE6C<t4s7@ZsYeZWC?P0So
zh6z>{GZ-5OCm29EX`5_j-`Al|2Gb{z4i})2Uecr_N|a#8(*(-b4<hk($$`ODM1!F4
zJ|Y6kUr+DCS98Mg<%npvC3vW<s5S68g@uK+X;CE0zu<k_xPmETie>ui7V^7LaUSt!
zgpm2QE5gmwX9QNB0^TwdMAka-VNw**?E9lV@EGD;QjTAU$~1oWC6{SO5b1E|rI58V
zTH^J!W%wQ82#a>G=SAejIH7}Uc@?>!o=~)hQS+Hd7Owwi!LM4MvM-`___pY|CuKGD
zBxOKDP`C<I7(ztl(gTu-CETx9@i}VG>WI!Ap&y0(@$7}}8ZhZUxy8wj>RWBDgSE0l
zV;~hW<92_3=$(sR3a0E!(r%*Q_@(>#Zw+fqNVi0Tln4Fc8j90+ecA<Qez!t=2ZtSf
zWz9Nh;Q@McH2!Wo8MGZ5rcy?;Yc1_w_0z^;siI`NrRcBUlt_0gWM_tl=BFUNnOCF1
zNW{5enTfJtf=R%Oe{TE_f~G!-*3ip;7w~$gLN*P;qeB}MMDEGQX3E>nAyA1_N*|$t
z&8A*I^vL^U7Z?PoKh_7ZQZA>1HR-~=0wI%?$?PQsWDEsa|B?XN4f#TibPo#Q2t)7X
z>TF`%_Ag)5)<3k~QuxT-<yQN|#0~tOW@qka*Cq+vTx_~$P7kGD`75v8?DIU`kb87~
zA!IS<xn9$r@y_`aKws2etK}S{fP~(Jw2;r6dXfvH#sCebW1svz;;Q8)?V!w5i0(dP
z_M_2x0WGEkMjM*XSXXWKgor9{y<v{OiTua+q1O_;8$8BoJL}aK31Uo#*}s+phm1$-
z1fw1wN^JnFDby2`kW+6p*YZ0UujH0Yn4a#=m<A+5AKCGfbP9Di0Yy?5Wd8J&dLry`
zOUmz_vSw1w!q|kY+w&{`@%RQ+ropw*V9sTKt=PYD&R!Z_L>35S0V6XL>oR8xVrt`>
zEC-SkEKyNg?WfkRCVuOrp2|y(0>o!*-(H#}YLaF2Jk!2@0T8bkIG`NE<u8kZPgpG&
zhu32Frv}W9?YGSxuS_%!el)@v0C9a}lwqKd?1aN}jSZk|`$+|R$SoRxhhM*6gwZ#p
zDg7<<$M5%VOC#Y`;m^R~=L`g&k|&uN^V>o{XmM5-rTAHLB4jq#=TLW&>Fh@A;H)BI
zz=lFWpDLw9*8W-iBLJ=s$kioLM1JRoA?d~5#`sI?lGKn593Q9zNbHM<9ui~UzCGMq
zpz$>a%%$jSao8T)SWhsZMs#g_UEB@g79HzJPM(BhwPeQ!6BIneYx)dv&ziNGdzONb
z2xIt8i3p`W%%xs54xa6|R{K8p#U1Ryq=}Hm^v!{BLLYMNU{?gASdOId08*Gh7a)%p
z2CK#C@8Yu7loj58q}v(i@w_)|_zY9SXYq|}FIbOZEhzMCvtSE$e=r;SdQJnYN^fQX
z*RwN+k{TVGX?KExY#>-011364!@lR6n+UDw41OwNl~P;PvpS_OXpIr@{d4XyeC4Dr
zi4FlSM02K|14#?WoN6}m)o!}&acPn=sL`MXg|y9gpr;9oO=p;zqiG1+X(S5-d<=r6
z(F4hf;+b{KEufSHWI^Io*pu_d_j$X?N07Ym>q1LjjA&o4No{@tnRPeG)POI>7nAF7
z%e1W3l<>I=PZ@kV4!x$njcz@>rnD%oF>i8pw>eWIbU_p8U2!{b=<I;lKVWsaG);4X
zwXjG|uIV+sd!Ur?==2W<Ki<Rxxo5)uihM&lp!+8?kZ_<95JPqLjn&@E%$TUB<C7Sm
zjN(}R!fXY2dNMPWU)|0^ci=@(Te80ozdql*O7zm!Z1RN_7{qRWsL%w^eBq-zb3dC5
za$FSBj>Ru>=5PRMusOc?tC!CzDl#@>wx=S=v_j~o9N<23<5vcUkBG@bvrv+S4u;*P
zQe0k=cGa^tR0(CropiI6$Gvq^kCl(V*=*5Tkr=(%l9IGEAZZt^HJ00PC34#(>t61|
z8~Mb;p%kcw^i}0uc|m~Sci>8SRwcj<fn+OkWeKy-tcgx637$Vj_RAU3EUDuA{*H1X
z^A>SMWfkC~kg}n2f?@_e_yt?rvcJ1bboFy-5T6kEXzQ;*2)V*fd*q;cXFRxWzTfkN
zbUFa>r*wrT;F9Eik4G~IeN(6D^CskSyEP)sXsWWPC58DbN<N>@%N#eB6uT%|^EJ=g
zJL~g4@H*N(h$s&sHxM@?naKO-pnog<E7`4!UnrV_c*%DM0b?cP{^hs=(cZpl8YFTu
z`6gT)XdhSoycbwEb7L3a`b93D+^@U1ul=-g%BHm$8{>rCocD6mD@m7HYAfyD#1$h{
zRJp8+<L1tq`V$je*nj^3f&z|CH6)CRP&otEl(xu_Pxkr<7?vMn3#^l=%ByU!MccM>
zp!`Nj8)5Uigc4pMRvR}t0@E$|{Pap6{@x5wP7^(x32gD&R)h0U8r+|<s974e{)Doi
zqF<5op`t9Fx_@&wgzQwrowxtd`8>Bh%JuB;Iz(5arinp$qDsmrycG5@pFk>%*DY*%
zFQdTio6?i_kTrxn%>{x+<h2RiV`H7AkyWNsHpw^3H67`$uGo1ya{G-7+uQ_Kt_Ac4
z0DY7Y36N_c3J6ry^H_zpIfM=zy#r$m=g5a9(@f&CqM4BzfQU1vFJk02384O!6*o67
z%ev$P%)GXQf}!|r85rb`qlOyY8Y(U+VZzlhr!Y<=QhF<kKRX>)Uu~bow~}7~RI85g
z1O}dFbPow#5b#DDIL6Zq%mfLrL<n!^bH%xOk-Gd=gTPxK_gD|YMn`OrqrcWo7k5R4
zUzxadQ_(8k-M0@wp&`1W$f~PClHONNTeTyaa5BFw$E!s^yk>QP+m)Qv50!*!=nU8)
z?e*}BYwa1{u1}lXSd~rw_<ntdO$}`SmX9p^zF#`GZFE@hBQ^w9V1fG&#o;$J6;x7<
z+~XOMHkON30#$W&A)a#vIa<U3%KcM2I9RBE02l(Vyvi-s29Yy4OP_=fg$4IN;S>)%
zSCfZ^<sxap2NhZ*)sF6;!*%RCRD=%0%`+Izr#(g{L&nD0`Ne92*nQsJenZnWGz5f+
zd^!4~2GAocqnPHqfSyURk%oMPv@?-*KvS#f>mtcc#31@3y5kCyJk4IX+Hc@p9ZL8g
zZH$jl{xXyzy1yIzLrw3lGOKwx2Kac#F8b2NT=01ag$&PQ#3w@`*{RmSD1aVZbtVAy
z81b1x0(jrp@YL~^HYQ^tB#_V!F-|(xGoTnE$}sxdr71|dfc{<yQB+g_Wn?2%6)GP7
z>txA9Q2%!!H0h}rtTj2)?;>zPg?qgS-%L_D=xgHj_E;WARQ-26*K&kxl}{B@-P@dg
zD91S3#1>X-0Upi1Dm-r8NXF2-nrnnRhp4jw@$U3nC%I%@ZH3BhB7C>Mcp`3X*iO$H
z$>5F;;+G7@%vRdPgtM4gonD_2aoitQ;_pI#{!m)H5^8*RwT^zmLbuECnynt_x!T}4
ziucA@76p|Bl@)~%AWI$GuqfE)F*9KlN?}nJy;HEBiL*m1AjZu_DOD6=uy6uaFInaM
z+Z1h6Hj97+Dlz{e30mXK;J=!DCn8Dv+;>J}<+JP+iUmS4U@7f1`l@J2lx!e<In8c`
z2je3R&{ATG*Z{K?XFx{jyj7nQPxE6Pm-b5Y#|`x2Y8sQC$WIN*Z5ViMv1@y&_-Y6?
z4BRldu6?+E7IL#Rg|00AUNV~060$?YP{U|{fWw{iT<b~lJ`UT?FE;}Nyl~`uT%Hdu
zcSy9?!>&rqZ%RFSqJ%G7`hCT5UWAoPM12cvY{&pHkV*D=M)_+a-t};@H1;fZ<VkxA
zG-B=Y=riYE?7EujW-G@7wK7Q}%X}wtVbLcawcpLMh3$`aBF@u;{t`qNd4qABZpYh7
zYJj-PolMw$Dts_*;FlF?R-uyL2B&j>;S4-l(yd^^wNAz*JI9y0iL}_~-~}X_YC(HP
z=xtBpd`b(W*T5vKsUP3lGt>ola@-@_#)wlf*r@u81!G?oO!BQ-1~zV!wS!<&hKlH+
z$mKu_Akmz;Xu1$<7oC*fTiQ(iy5K5Nn=lXG^=~)iLL)7lp`Wr@!0QSBb^UKsQTm&N
z{1yQwhTHue>*?aY-d_E7ANPmr12r|Z7!M=76KFiH+mQ!t%&no0%lgaT^;}&{c*#?|
z@I1N_#=QNR_PyS7J8aJnV$;bL5KzpNjS%ylfm61~89E4EujPAkX;XRe%R1swogMK}
z!pc;g#|LAQP9dk~uSa=ewf*JD^G?^{Q6XqSd>5F=J`T&{^qI2M4X!2)kCqgsvD90H
z4H5p!S_2k^O%>6~F;92Lkm_WbAT^|Me>7?mIj*1xHsCv&TWgz)Zalz*_YY7Eh4m(_
z3veaj6@%VxB^cc13D~QBaqGTdmSRxW!^TF5!bvWcm60**4Dj!dCE3{6;CEPd%H$Zx
zn8cDsPn_g)&#e9#k@GV)?V?}7oTH9|itC4-<HCk*a_dbJO+N0!?ob4zpt1BUwQM>c
zFY|m%bIn`gOyOn>Sy_~)&lybIV&-bILA10XCLc}+D62zuD^AP4SDM&KRUY0Q7MKE6
zEFkIPz-&3Bt`VkjG>?5p8x>Zu_RD1+mGq{!b$AHpvpQ^MP)^+*b}(sq@-EM|#P3MV
zVN!uR6+iW_x__NrQ~}22CVZ>$dDFvssaf5jb1yL@*2B%%-@>1xhvu*65ZMz=cZ%Lf
zT*12(^fBTsi_zGH0u!6*&m$7ki*joVwxC6jyF$d;XK35BqN!EyxQirc!tHNOP$by|
ztpmP*N(OyVL95vk3ULGzNtTAg>j5VZI_0{0h$pZwPSGI-K{%-zeh)z^UkJhRzDl<w
ze#ZQxNSm0v)S%Y*TzT<B24jNj(E5T5EWLe8SfR@`NvQU|7tOk^IUd+SYmTZVddCSi
zMpB{M5V{6C6NIo*j$p*{8`$H}P)TEm&z=-P2}Tq^DB-`~xy(jg6hC50>xp`ehGZ{U
z+>d`Le&MmjpUyX5(8(N~$v58`h&M0xsv;z~zB&4n`9pl<@Gt;yxeyX^X#}Q?<p?Hc
zZg%N@f*KnekETGgx|H)oh7_N5Jhu8VETWNY)j+RT6XO5=PTA*5i{gNva`N*3-uV1{
zw>FB81pv#SyThzZ!uh^R<lfuo^w<6BK=H1-<}e5D^cczG2Q`klJb&pG^czuaEaH&a
z(X>IhHpp*Cdbd-T?JjEh$K*tIq9+C`A5AzsjnFemj&&Ym-dgjn+YxP0SETRC^#)^G
zp8{{xN@CP<z|L<EYGNRPsK*7N$K%`!knnuu6h|;bh}+>oX=!|8@>qnR)S0~DtL=Hp
zo7R&SCR}ME2*wFJ-PuHKDF|Xej!@c^10*&_`WEH!)N9F~`Dxrddy|DaN6}{HFq*%A
z$^zs%*LR1L!E~`c!zVC*ae1!w_7$b-X|~bqo%vB8=Bi{d_zhtEP)J)M_8){X4%4?!
z$Y&THEMqGmQdE*&p9^TeB5reuBs8NC*T0LCuazX?xm}b*Kb_iBfMWOtiv}bc^!PjR
z3?{0V)n%MBTg{se!n+XM!r>GXl~55?g2Q!t&GB(pg9L2M>y_}8?2uS7j6B60)weqk
zTOGy~MHRejz$iZQ$9x9q1z8@?4~H47i=IWc6JpOqZ^}iyHD{-!d*-;eLGG3ptIWzG
zdR0fGuP+Wt>{ftfPsAZj<-Fm3p&1w=!0^U=Y|SG|(QYj043vIRJAm}<@vODb*a~x8
zbo-NUnbpLKheY4<P?x1zr?=jxQ4-sU_G$`#&-tvsVux>>*&|_NLca6H<`lF3aVOz=
z=hAr>Z6%H83&=MBS^FUi4NB_uonDRES;eHS>5AVj>#D0MFd5)a)D_`LhnR;Hc%-)(
zx6t4X`@ACW#EN5kPF`Q4Cg&9C8$Cz=$r{h}Nv?~G+5p{d=dWyqDP%w^w2KF1!Km|i
z?9b`(U9i%WIP!Aev}L90L043=ZVl~)+zLf>3i8LBKKBmBRb&4aT*CbLbALYemaLg`
zD4^Sq`)tTK0j1=}2$1u{x*Av+(U!IKjoz@XA3mP0ByK4M&c{8I!*aQQ8%Rm&;*4KO
zth2hq_!H$iP_7ab+7aj4Niy<VXTp7d7%yQPqa1>jG{=*b>Eq^d4Qq|jV?W#b`gjzm
zfSH5lFQKR+V#jUKkP|$6-Af1k7I4*Q*K!GGviie!;)W<1^i`GS1MYjHIyA{Fgw~MW
zCZ!=Lj8(X{EKUoA{^5P=s5a)$tC2RQ$<LbKH2!>#a4na8Y#!4W9u$vt+0uk|-3=L<
z`D_jWIU>6!O@z0l4hI_pA!#y-@nTKaPI*Q$QjTM&3|367R%^M|C<FL$C)DI+%A!=w
zoU^{(qd=}m8o5{1@yFFd|HemlRLV9MT&)Ad`@@1ght@W}y)lW`NWib~OYqUgZoT2Y
z)xoag*4d}~Vz$JH<>gL{d@>`b+*|n9o0W<MmkJk)#b#q$JU`l7q}6-Uhscb^XVg#K
z?GfR>a#y9;#j~(-ud8@bs*WJSyx43m37^G(tBo#rM7fwU?<XRe?U8E2Jo~u+HRVEz
zHyH|!FhmR42;mR>!#kWz&;p@psM7Z?#Kt6li<T-e(6I9`K>xZLaW*ffhBRlyqT~Ez
z{lsjd550x@SfU#GSnboPVYOelEX*{~owHIrKroea?k<@GjDVFGc7^;1XB>p(h7B1U
zQbU4}#N4h@whNlB#vcK8iHVf}L}i3dQJ$O{U|fZ)!^Z`$W^&rLFLxLM{ToNP8!(^q
z1LT@7SoY5(1j2y<GSD^nyI_Q~r^bHhT8JvtEfg&C)DLE`?EQ=Ln@C8Do3RQEC)F8g
zjJ7*Lr;2Uz*n=>p-udOrtP6sY^CjJHTMO8$y|8me{WUBAA$&glA9DJ*gOL06tMl#g
z!r`F}1w}64l_&^pYZF;S6|~YdG6NvfndS8vyAELL(+nVo(y$u*_U)T0TsL5$M^v+4
zY2hVM$7e>Sh~~>$B3AkpyIUv|ERDjm$C*Q_{i1$5f<lVWVz<B*i64p%7bp3KrlGc0
zs@9`ORh?Ubj{hTMp<=S__CC(Cj);`&t<_t3WNKa`e&Qq+;$A#yGIHwJkI<Q%jYN0_
zc_`CjX9`LL`Tv}{cOB*T?oI0IkF`zLn1M1(be(MUDoz>y2oZo7uV$PO_&RhM%6BBZ
z*~R#bD44*WHX%G8VwEF-cRf{L@u3)oYS3(KulHS=B`O^yN1!fhpyG?}hv%seI4+%&
z74NR~gkefy#R}(PUp{c51@3Z(c~hOMcww_@J$0X03LjSq(yYohBxih}(mbgvoS^&s
z<~sXeO~KInj8SrIR7-7*<=*=HUAMdNYM>?u$I`3LIg(>Flau79!jg{ET!1~UurV?I
zqPY3PmNdNh?k7rXz$zwu_{x*~UwdT#kJzgkPn5|TbX$E3-O^A#)7@_=xX-(ed95dY
zMg|(rI^VbNLNijHwoBnDKJ?An=fplFP&9MuC{JWX3&8LKHjv}S50}%?20I@Bs%OlS
z((RP(>nBy5fM?C#G5#o(pPQ6gc7j)5pilJ`OLa=#T0%CO$XYqFUPNwFnF_L~tNA`R
zH}dqHKJkGL(l<YdH2DWn%xqOU{`%eEz3rIy#mH>&({Lma{0}Nz^r@rL|E8=WQcZLB
z45UcNmaAY)(BZ~J&8L4@KdD6XSj)+CB6s;zq5ot41qSrG_WDe9%=`2Z;yv^zvA`zg
z`f1G0^t9b!t=ZOslau~!vq6ONQ384TAMUT}bG^ftm;lJ^w!bW`ta)X3k-pzUv{+}q
zSL;;~{&itvfIaRc2XnW5Ey-Wi(7@OGQ`X}k%4+g+RZY%1t<_SsYl{N&d*X!*tlTVp
z$vOvST(^=B-(2}$r4nA&USBAiX}-B<dvGl>C~G|Z_#dn&CXcP)ne&OWS`7WO0oP>}
zdg}x2yVt8MsXwDgv9T=%JRSzxSUO>*2nA=`VFY_X3;mo#U0jK(O^{CEd3>2-PUhfG
zgNkf2n6e;MJihQ+<@MB^I*^wt1|~cehX46{*aaK|Iq9ExJqiqTY<|JvVJ)%5G5GtV
z$SMJ)U<nv62Apz+&c_GK_UQv}P7~*N3QZp25mvaqs9DJYeK`jte9dhT6gRC3mloIW
zwZ`R5H4fXkoLFT3uMtH?Tq&<Bg>W0<FyH0aCS|m4`|1i-*XESSGXe9O25exUT_rpE
zkz#In>>?3yAahr7Y@q!X(9j1UB{WaNoOJhKcBlex!JD^Mt*|}%GAf(#(~%c|<f&VM
z-c6f*jQa@DzvG(&v}x!mNu&EX_7Q^$koF@o4QfFP5)R43Rd&-8k(5w`a?L5#8$PVP
z>vGxua%TJC!-_Z2BShrzR+xo~n8Thj4IWau@^|<smAtR+`;qFViFZ-W4?$1MUuea9
zp{JtKM$$O}24iwixHi>9A00Z}YrrtNKQKr*ceh~dHDMUN1G7)zu}@#^rjBMiZM056
z3+mCNc>e9Zy&dyDg;>m-FcoF_7d;d?6i#VZVeeA*GPa6<XQ|PaU+#C@`wqlhtxpP)
z9mn-F8Ze*|(Mr7>e>gUuDnE!EC_ivQZO#f$x9}@DBM1vfchD3$&KFQu74vE6+}oFn
zhmTnD!3?)0Z6RX^V>YXZQHMHU>?n^Q3*B4a7zMskc)diOM9R$<69Q~p@#(=Iqemtt
z_5|e#@0Tg}zXFVBoC@7ZD(z2__fLKxDn?{;(~%TQ%@L2h?|bZHVmNMM?3evV^+;@p
z27?JLHx|zuWnDyQg$|t<&2Em$itkrQs<%Y%M|gOV5OYBtXd5ix&8}eiZ7poJkZ@X1
z*#TL$Pzw+o0N6Z44o2&*&W_Z_S2L@e5baPG{7nEqzf&rO>-H4u{@Z9<OO~|^zunz&
zvPL31lwi&181vrx_nX(*y4t&Gc+f!opVWX*7tgJ6p00VxQN$OB-TbbQj*5n+X0r0b
zGE0Uq1rqTq`bM|P-DVkQEu~hDep@3}3`a)+b6MM2L`7dqZVkZvoY}yq^p(p7^0z#o
zYc2IPwMA^gNSGYNeSB&yv%)&D3-G*QK1Yo?`t4Oo4i2@|D@6&BIYrOvDn<~bKyonR
zC*-qa)G?76gc^B2kqB`hn{yeMgR|a9gRceOKyizu5?;0ZBv0;tYkU~3<cSPt5|&f>
z2^XTk9FE4Y57<q>9trrLjys-dFJ>^9ya+_ZITUCh)Hz}4O?gp~aJ%qXqmZ~<)<r$O
zXSzw*6^WI8`0niIFu(mbZ-{|P4JajtlN~{1zFxlftYdx#B}e<9*Sk;!EQ~H`QMdhB
zONv(aHBIF_GU6;LINEFjeM^3q?r|=-=PNj)l=Yz>5pR^}Kv;LVfhAd0P8a0-j)Dk?
z_?b?ZJ`Z8Oi5#A{BG=z$mWNDTcRQJEN+ZIrf^VR|!_?u%2W}=lD5G2?Qg6@jRG*Tp
ze~-u`3}pFhcmhmZ>ZVL$e;*Vny9-~h=cp;5tozO4Bx)Ik4gBW&X~o-~sTL)@GwGur
zev!DT7WB7<?+y#>!<B%UgmWls`P3Q$!W>$d<E$UOZ%HbOXY$#xd9ubSU7#eP=2~iF
znSW;YI-U)IX-E_E<3qh&?tf$N_f}^q*dI!59pI^M^mq|W&m%kDA5KQFFZy6gj+aKf
z+<`b5tYA85Xu7F&O1+n6+Z3pf&)(G@VCMMj0|x4A%0;N@w~r+bfEZHk-91oMfV{Sh
zaDeSr7xUS`@>ekVutp*clAafd3s9+Z2AEz8LgN}X_Gb&qZjG?h)6iOPH<3Y#D9R+Q
zY~2pqX89FS>kC|_R_g>Ha^G9EKi#{_ON-0|ypY{JIFu~UEXP6E_M5Db&)+obuZi<o
z#c}7P`ZKYJ`J7IF@<xn<EMY~%fV`-O7@>d&a)>E5Junb4zXPSqdeHft^FHMI;ImOa
zPH4jq`V^(H<|0RPmAm_UDNN;av2bN(T9vn+x{A=Y^HyZihZR;!N=Y~wtEy1Eh}||?
z{>f7%t7=Xj@I&guvfjt_PL)>#P!X(DG9aZ!Es-aDd(!8Et<z`s{sT?ZOW1lk+3aXW
zcPTu;6R9R9LrnAVp1|SZ02p`;Kuj^ySk9Klhp+%yq*Hx_u7yzjb_+QTm^p+c^bsmZ
z?+y}<KS#T%rO>zglAUGoi1Tmfb^3ae?G$PR`Sh=EhjXPg71Wnddwl1FTlf1vOcV9+
zr#CFhgJqW%RJ+$}&k|Ml&*;ygGmU@n;LsV<{!C$(^t)G+%$&biC_-0Cx$RfjtC=KU
zl@st2FOp0#cGG@oWA+s%d;TAWiD19O(L3zSWHgUSii8Lp75l7=r*Cqa286GJs0$qk
z7>WF%I-c3qM?>NU92|t4eWx$<5|M@S(^HI6Q(gr(`;W#L&kB{u64t;KUCk=h@&O=F
zgh;90%b8RFjS*R|0hA-h-HU2Oc>K8^N{49HNl(taX`IabRr(>N_xIF=6x`bk!Q%|P
zq_Ei#0F&K#YsE-QVe66nk?j36VnwyP!0vCvk}RINwbUJa{OTNrg4e;gzleZ8B~g5X
zW|tw~UJz7fN3vjARju3G<S_Ss^VcE}8=aAyHhDoWibR~XHV*+*%&led+uF=~r0VpN
zpxSON=w<Y<wsNvrTQ~DPJL=-SLbP!8^mn#t9#242I03BecNJtNem+0!)iRjBuK0!u
zy0}tsz~U<cXYb#}@OM^#rDgB%)8537hOh!)&QTR=X3LUg=61OgOANmN#~P4=QA+N<
zfmY}_eFV!E_++jB^cx-7+&OGJ#*qmW!*p%MI;ZAYl0P<$y;o@sh$8{DrBCaV9V%F}
zA$~?V_wZ9{tjGeYF1NQlQtB$^cjup=A|TfKHxW_jVl$tCu<M%{n0qFza`39ptufWR
zcRrX^tzhCBtJ&L!W=ah-+Zvq!E=jzG>HAp(3a=7|W3>=Ssuq?94VV7m6fB@g%n@Uh
z5&^kW69D6F@W~OVz~M~H<;3%z!Ls=mb25xi1%@Zr2LORqaRr8Wr2bYL9P6{SdqBGs
z7yqzJwz+0g48dua%>d*i^wT$cDE*YE%x@B-Y6OOnGKd;-4Y8bFo>jK&P`Ig6q<6kz
zDXD@Tm=`d_%GB}fdGQ#cbj!HJ=yg_>3SZBjepy7zFs2W)UI)1b{rkdnt4P~N(&WdY
zy$IlAd9zze^GQH#$!s0HaC4)q`R?5QjRW<*0o9-w7*O&vHB}Y6Ma^p}E1NkUh<X_J
zuhPWuc^pMCi#<zYwBvp;dN!&Ok7;Bo=85r^>+Q70t`n|SaEaBllxjAVQdDBEgl!l6
zTvC6;jF}6G%n3cYavH9cjvz;lSt3hqj7`k5JCiv+e|y`#hmQZ{Gx>?U9Xy4qLpKc)
zvt0za2H(IJL8Y4Z(C3pP@t8Gt^tdmd7`kD4s%c>#EdG`TM-HL&-c=Xg9;RSX<MS}v
zAyNR-&Hy?1w*B`w*B1@&FK(WOo!qFSo`bJ&Tt=?tAOmTlkpgYsb1p3`!mzZ^WXKJ=
z-3|K5U&*z_C^jq3A&04lVG&x>((d>BHxVvx9@su<{rRM-r8Ya4s-Zdq<tObFA`b1i
z_^Y({6>ldsCH2$nr+T-CVdj0kg$dM&NMF_He_CguV@?`V$S6BngOP(IK#$Z_{G&|%
z<1zsEJ+NuH)#5rmhylPK5E=sPE)m&~t$ZzhW>`U$Dsr}<p8<peMq6N+U_9bd<B5^1
zZOhrQ;k4<yYI0>We69R<zn*of?o&hfl*l@rvgazAPX@oz{fGZiiu&{d_@9NH-KgTl
z7uNUt-@d)IkhdE9A_)qqFNKt-<c|X{?;anIDn5S__B<u$zOl^w`W1(}FN7EOx^-e~
zq^mZ)Dn~q|hExOzzP#brg7^I)Z`zgR`sQx@&X<2yyaS>HxZ1<sX+V+YCoVE>1OEjP
zkzrfRkh>p<z5pcAHBaZJOvu$r1xgj~1U!c_Sp$)~lqgu>m~TYC@YaIwuOz)F|ByXZ
zQy};ER%B|&e-TK?4%?}sKX%5`7`Rk+Q^UJyQZbC&;gPd?jtZTB!3M~5x_wVBKP*sV
zS@rM&mzxKSmMTY1q+7X#7P&Zs$@i=e{f1IZR`JM)W2p32WsJf7CFuYYe#{9Sd}~R+
zB?ShSqeq?|zk>N}A}6M!$u^<4$K2V{n7Z_+veKxbfv5?&9j+@>A`tzG-s&Wl<h`EE
z;OLj@%tK`16O*^Eo+Tcqy~=>a1(-OpIOLXYv`FQBNwdF#P++~c!{grkanIrFeP~7<
zIAkIT(8NIUsVEZV?*NIm{h_GX21fAiCZ@N9vODQx&Id`Yg;`JDtO}uh%;s3%g;Lly
zWT9&{gKZ6UXpN8sg*3fM9^T61V5Q&{?F;X{A*{BHD7_I9-g9hDj4-ChGdLqT7fl#^
zE3x?76yL0yK|5>_NO}mDa0A_CcYcq^dH;!)rij?<k$DCfz0wtIT1U*63!pYuF$EeL
z@<ZW$?X*u=A|C@(_}-8TnrZr-x9jd5kXuUiBG{)A%^OyipnZ*Ul5G4tyOsn*|2pX5
z@9O97UJEWX3(X$b{&Y1cdOh*Raf9ZP%0n6UJ`4HF9kdQ=EGrFEr?hpoC+C%S{{&Gl
zU=Ft*qtd1DVV<0pvv&3>EbAco;eYOCP>PFmhT<}<euHc%s)6SsuY%L=$W5kPAz$z7
z-8b15feBo~WY@eRO%lxJ??39Zx}jfgOF^UhdM}F-ro^#Nggnuj?wd;r``N&Mfff*e
z+lKt#fE1xl4G-*?CW<=48ON1-TyBzszb3PjEsH4tBiC(4JIxEmkG?$AL!iJe=#)T!
z0m%|1Ikv+n{f@Hx7$nn4FM;h`DF+%M)2?@5hGR`p2QrJ=I9yDBHkDrjG4gdJ@Fjgz
z$=p>41+iEQoZLITKxRVi)C8-jACgCb@t?}{!rgsV9hVlusw{n(_gu93el_(pYOmq0
zA2S4QJoD><Z1Nt<-6>k4LLv#!D?rd61ffF^l%Hw8==h6?v8eN}WvU43cB-tarhkb(
zG7&F4Q28O}_wdnDv=iFNvof|^Cx%W4?nDKnECMH8`l8!s+GJoJMDjfI>Cd9pX%+90
zbIkYc5;C#F-{MKt^LoWfetz0f>XC^51cG@WLZ<e#LrS2k7f3RU#)QBA9C*YY6zG`3
z*OH1012RoD^<rNh6xt5jG6|4B>N<rY1nef7PN$->M8NrIupnV$3Tk&DC`H_u(Uo6?
z01oxLgnwaB2d5dchRr1#)qRVZ9UR1o=UhQp#Jl0xcIeNZ0}b}y`xM#B`C8kGbL~7Y
zPw)FNyE%FILrtx;nhfwI`SnSUo_JH+LU468ap^XG03NM%hZ|%DtNlFVL9<uB#TO;~
z*eq?~mo)6Uj{4-0zYA*#@y)DS+<FpFZBTG<Wk9(Lx;?ioz&i-U-GqU$m#FCWB_$^Z
zbdABYcko-L<t`0g6H8zMeDpr+xc+22wG6Y6RGXw<xc1E`q+qshf<g1A-r<(0_cXoY
zyDV1!1)Yd1K@+INEs8^L^Xt|mpg?>H2?+@;Ev=A{P@;^W6<!T2{2O5CdLc@x@z5v%
zDOR{eM)-L*pnk+lV``WoKbUD-`v>nK40YB#Q;PER5+PtXM>t7=GZnb8c~AkBw;u_w
zxex+2{eG@=K!6zXA|RS<1_XvY1@(2jxNeG|8+J)I&`s2zdhwWPmI?e{9cqmm4M7q*
zr8GiRf&IsfHFt=D(|;y5nJEn};{}6tJYz;CJXlmTY7mv!f~SUVrr8RbG53#I7#Wd*
z>C-0YhD5~Q`j&;~7{Ps?w0k$7^A6@O%nS>SlFJJ#r3*6oLj}(h=lz`JVTp#&^{Z$1
z;j0Sh--iX82fS~P5b>N|6{>0vFd?-e0EwYuI#40RvO2AsV|WbaG`%#_Se0K*!X(09
zbjrBe;%b33s-t~jtwq|bHr)V5b$^u%hz&FcBex`HG~GZ^uGBr^^{5JrhpgzPKqLL$
zR|mp?SYv|YH7<{vkeNg`<CbhyP7!>94{8I1i{A-SDc+!88C3fL9TZxKiQemUfA@l|
z>uvx6MF<GbPd_|9v-aJwF2SU9-D5nNO7(wG)kVi}EjK&q9Tkz+qpPQN7)JB`iq8n}
zbLAJS_8ndG&U?Ke)<0{{&nkK>n<A*BW<sni&NDwDr|LEEPrIbeSc@*Vlj0{I$|GWr
z_@_G~$~VuyGka=cqjARv%|$>hEH=<60qiDp-g|3&_VQ=ELCbqvvX9J6R71XOb4BT;
zUJ0gQsrE}zrDRzYK9@4zqz1^0kgN0Bt2Y54Z6Ap9ds-=8IUWruRQJaamBrA<r7bci
zf6d~c<XB~YR*Z1iyz(HQb0LQUgRb9S0(jLgQ_BI)X*Y^oI!Cc=oXk_AMg?6>;b-&u
zhKi9@mEIQ9&nv~_pZWNXCO^w54<Iv-(6ymGRK7k>#BS`0I!~3m2ZNTdG6b547X)P~
zge-TrUw{x9<!o+Wsl@OCXJBmV!_QJvbHOfYC@^w5ydvtP$QawLv3TtXMvh>``Xb^P
zYc+0^5irG{{V0X4j`xunOm39ojTSfqr>SeXUSzJwd~2`HF3D;>xm*$4oN{^3z^A-r
z=|OmAeY{$kf3|6D{A21+x4e?zpN!D{;P9)@%ec7x(t<YzPU=2M)7D2SZ*w`Fdmzt6
zIYbsP>5>af62UnfYz7FtvTEhv=_9bt$%^ftT9Vk)3BofIpjoQcW~AtMlU-8wpSMk9
zdE~t(jCXA`Qf>4oivFiCglR)-5d<Jh*mVUcEBu*M{BBST1W|%%zZeG<kR7UlEZb?0
z4W)U8`la~yVY`DiC;;`}1dT=&DHBLoi{4~hiMFILbl4$syr1u)JFXHz4>ZuWeg?s=
zHHn`>w0E|z0D6bhj2vxAEJWt$D}yRp{VL`t3;%p#-0S{~lMHV5-`{CaE1&Ev0rlTJ
zfvhB`0Zdsh(0yx--1!^PXBymp3N;-m?%_Xx?FM$;7m107^nB(DD)0{cbGTBE4Fuuj
zgRi&=60h8JOj*v)BtSDYIdtpJ6zBSov#FmuTx#bP^9};}6+PUIQnVLyP4HR!P+F{2
zE%}WC5c~=Z+JWFcD5%1+fs0!&B8`rnTObuwj(z0XoSk)9l&D&t(r$X90a2nV8VQ|6
zk$j*M16VD0SPq)K<)Q9?J?oJ69thb^+=nbK9e2wQa9zf$0&cQMj-5BEU@hHExrsBI
zw`c0&Qt<a$<LuiCl|zg$@>``5FfjdlX8&5~qVx}H4sAUpkfUtLyqAYt-fgtEKq6sO
zjjR66?ReiMEG1$^lV6bDNkhlg@&lf1oqEYFsonCJ{C9EZ(uj1}8!?NZWJv4&ojaq4
zb-&UjwMQ%N!U{`jDnCYY?m_5E_(%+7s8%x*L4ix4$`%>_q;4`FV}sTzF<!wy=u@}I
z-P$E0HqU<X9TOVANr^jbfUtNa_$3LDWF4&;NDuDa4sXRJupfv{EnmK*0mFi~pWB)E
zflqwIll{)HI5g&I>8A)@6p8W&0{@B0bw1v0s6nx@ACs)KTV~7!{uE1v9$W-xXjpgn
z?g#V+E4OfICBRGD&+gw_+&Xvwpe7cnvAET@=XEt;q$5RmtOvPJIHz0r$Ue$0TO1|<
zXcd0kC4{egYSA=clTzpk_s&jvt*rQmG?5$pcMR)9#8jEV_sjxV#Bjv>q^8*!iJrx^
zjwtJ5W~C#)X{5-q!~Uoeu=)>|xD(+V{J@mA4ZkM;QrhM%&}%R%*<4$Y^O7fc?rqCV
zFYoPwQ&*aswUElYQm(o9(XGJ;>O|GPQ-8C|_RON+&a(WV*Md>yM)hTf6FTggSikq>
zC}4G^)niVVKi0(o#-#hSfB?JBQ9H}g3N=?6=ZEVYVqAU$jB7v0xmcPEusj3C=lumK
zN#AE=A2H=Eb!2Rw6!*K|N`JZy^_IXcc$QWzenW|*j&z-N5}#e^{|%lRyyNI>>9})p
ztw{L+jZw?<Lt6wg+=2DFv9z#1qwXWdE?PJ0HZZrcNJg&v#oMtKNc;q*5<{&2qH>G1
zk3K#8Ka$QeDypu5!gNYENQrcZq`(Y~NOyO4cb6a~DV++^-Q7qH-CZM%bbr_P;}<Y2
z)?N3`oY-gYXZrwH7+|*!VGg2?7v?z!!mBB)eL%u7r$6TgO<2!nM&E|Jx;S15%m37#
zd|q5Ax!;CXF??H!{uv;c&wu_tt5K`Ffk+YS=XI1%P4}<u7(5!vI)C4j5X5eE^h9Mv
z{KhoTwD4mKhCru(NjK$SnwrQh#lL=%YluU?N87?T2vJ6s;jb>wIDhB*m(CWzVA%Vo
zDFC-9TO>Qi!~0_~g<x@5-Yo>?4ME=XxjK!&SiO4_Cj4fZW00{Xw`40;kPp=L45d6W
zA{C@*6X2f>q$r_Ez8D|efkMReZP4x3ta#)qJt>C3Cljjbglc~Z%ngA?_TH0qiC*o{
zoe-ryroE*CQz*qVhS99;Ex(xaHI=iXa8ee*Ns5nP;HR09oL{6opT@xAj>VL3LVA)s
z!A*oGMt<FtQXQl?JFkD{!8A5vz=#jUgej&*me8J`9Jt%$iY>XI=%)a*iLG+a`LrL8
zFkPd>I-ZX#ODztEoOmlEZk{wAATzf*{}txjkWk!jY3J`S-DXpf_$YuKJHIpsZqi%h
z^;Tc$@PTJN8?LM}vd#^htcbUR^v}jl(?2~miF?7Oo9$L~3K0gWr<X~C5GYwXvpjl+
zO9Z*9HKr4R8`A#j`&fkW+6LKX)Qo`^Sq|{ZE^>Zc5;~#g!nAl#^lvowUDQI^QhfS6
zX<%R<OD7^l=%}XzU`kmwD*06OVAF|qNPV7n)=}mMDK~y~E~5uO7UUkm4i;Lg1fnjb
zTY{{>c*pH<ZpXz}O!>~vP;;g;I#mDR7=GNVMo$S3RUYpynBq@2>RSRR<@`Yi*_Uz6
zDkFTeCvWrY?C{t@zyKZrGG^!94Bu=My^bfiMdbzp$4VhDjKE<Cf<6ZnPzxJV&V7)8
zj4)t!WaSW5h2WYL{9*UXFw$qR3K(IkR8~_T;t~K%jXZvjzLN&Og~;M+pUkziXjU{8
zwDskbB^#>RLt@iz|B@G*oOb4tcZ~N>5%4npvS#>ID&8BjRYPTM2s9;qoyHs#W+>DI
z^aRpD2$#>NCoVpkNH*wzweSAG+4i@4*|tBNU^iUH70)mdU9-p?0hA?Uy8nQs(yZ}`
zl-7gnHSFJM1b;gL$2A94xdsilA5-fpiZFR%%>mX$w&&HHp(^C+$d;mq4eh=)mv@JN
z%#wsp-;PM$<dqiyUIK1P*XQ3C&mnY<hX;lb<))2k%~w0HYTE1=G7W#291J+=RkP0Q
z#UsRH_r;@RI--mG4%>|lY}WOh_)_rO1wsr&KTZTaatP{XtlJ$o!=;B%#iqL7wp56^
z2w!4+yJ=@D`<_MQJxTxWVuWm-8{f6bZ9M-d2`;JLUccThtES@GFWi<i`WSaxXHId)
z;#uhS522w{e`k5&HdG1cpM`j9Y5>sB>bU`(m0gW)nJ~O<J~8R7oEMK(Tt%Myv7Rq;
zDM{;9Zc@GBRJWbR@dWu9Dxz=_u==CR5#OdH7!odr;Uj*L++bXCy7Jj<k>>QIb}oE2
zta!NLE@CP^r@E0*bguR$EN!ynZA66uBO8ttC;9y_8oDkP{YtNh&Pi6wZ`2>6G^+#5
z#(XAiW1lUDqQe|Cb~j2UK3F5Q9e7NqaEi3)G21V1WG^d64o2pArUHkI*&8koKW_`J
z$rM*HJFo1PD(gCSlQk)uzw4;sd6yBlYzezPV|kQ^7<leVfhPJ4{xX$XG-4L|S66_<
z3~2qY(~k1bcn|Z(9Ua30#2oS?0VcAlITs_e)NJjfj>Xa5VMV&Wt&VVSL<1sD=os`P
zwc;qcgXm$>;FPSzesoECkYe36=?2^tlw$+0<!c4yTD*Y>Ba^KVMjSe1qS_ew^`0T#
zUHd|-hcI681yct(nUdbzOuD{g*gSsJ<DNh^IeMa%w-D?D#_1pK>m3o9%8@rmm;#Bp
z0rvGwZ^hAvARi&PA?q&&*kF2*d!XP8jT9TLRTe;(*%-Soro3A>_FpIK<Se$2&8=s&
z@o}2Y2xJG|>;Hg!Jdby?L0YUdb=JYVbXt{*{=C3}3=FDP_%m<m?gF0MVxeFzM*34N
z0GVyg8;RV~eqq={Sh3vCAChG*HAj!Zt$8b9L;cv*`R~(7t|NL6+Xd>O18jKBF&TjE
zZkq_7r(fZ}Px0`XRO;tO0H#Gq><|*iTH>IQC8f~o!Vi#j{r|e3Om2VXr1<-R?}&GO
zUuKsZZZsd>JU`;O$_BI3!9YO`8}K_%bd_}|Kg^U^;JA5!$%(cdQcMNhrY{B1TU+e^
z?vDI3-oYbdn*9u$f5YnrRRSz|cxVsE-<2TaHeHo@A3l8e=jd~DbNl`)*An>Y@bGXf
zMRZg`QZk*90$>m*c9z;|B)a4p3j`ujOFIJpJFeqBvbU|{oP8&a1a@x@a1G=rEGq1^
zQXzwtl!&IotK)t92mzuyO+|nakM?5~CNl#^KGwx=&WN$~5CZGOEjclW`Q7c4f*6jz
z#4wcM8OANeBqM_wHNUhXK8@gu2&sQf1}9K1SL^dz>la<ksc7^{)!Ri81o7BwYTw`l
zP1OM#i?2qnp4Ho0IEo^$Ju0H)mvUv*D)R4+%CBh<_3unE=ks|8w<FXi2+cp(17XC)
zUBv$oO(ce72CqifUS;ETN`S0i76}V1JuT7}7CYkVeF?U-zJUUBR>zjVYJv+5D}Es~
zEWZ~6&lod9zrS<A)RRG2tN6-7bzt$67gkT~c>~AIlExd$B>sySmNPvo-@BFk_dhd#
zd;(f8Qz~ens?>l!*MVTX`$0iPvdJHCGW#T|%p|FB6F0v%I~tD%Y-etYZ$3jT1j>L6
z##WuV;oCi}obHm(Ay~P8!czb>S>MfkvoG?eJ<qtl{Vi9B0X6~#@<?{|kVLQUzJSL=
zmYs)nELjakkoqE`k778WV9k3618wc%Q~>?OorPwjX%~FV(^gL^az)t0SFlZGbtg7-
zSd8cO$c<Y+kS*_mE}S=69Y|#F5)fXJ68-PQWZA7`9H8ex#Xh@g_E1b+PTL`_HE2@8
z$A1F`P}I)X9*<+aq~Jp8;TgQRxYh5=`L?(20t3na0ASjpM4(xnh9Qu+vQWe6gic0O
z5{Y?KtB^&HaVu^*Kui!uA|c5&u<*0__2RG`>-D21>THBDw}pn5@mImrxuVdHX#tTl
zXjOi}==CGN`5H+EfCn1UH17ENEyIX;zhtVg3(RLrFjvjF?I5Uu1y`^7Q^gz%(b{t>
z$ica+oq!;YBM9!O{6$2?Hh_sk@h!%p{1*-LVvjcnc<R}(U+(#ntTq?xd;i0qi1RFE
z%Cym9MwpWWd`okXH?N^JZ#X8aO25)ns6h!lytUHCuL3q6ACAk~`p(WU?FElUTLVTC
zGY_QLQ9^#^gzqA_asMLNK+sP)1|K`nvs$<W{LnWp(VgSP5pez$)d<9Fb=H^6UInMa
zWjrfolbjY#rZ$uUex-R#C+CF5f=^5MQAkC|w4U7nJ7YA0eSj^^Jz8z_l|qI;AYH*B
zHl5C|*RTM1J!5r`)VjGH(DfH*D25L3G<3C@KgI*blFy8^$AjZv{lSYd7b{K|DH$oY
z070tZBf5_9J&acC!fDvco838O@lO`1bt6lAfVN2mt^65H#lpUgy<?iV9UXk-Xalf)
z5=c8DE&OEMXlsS6<;W-vd+(CpP*UHmeo}M|lgGCFB5Nrw;B09>p3F$9yknn(z+6Eg
zkz!vP84*;CwtNnO8~Ct(tukSP)>;Gq545Hrj(JcOYw&IUJuCNakfP|Pry5hM<&$%a
zC*Z7jB}1V542a?S1cdACdR-4YBefBNC@*YLGYkO+dij&*AmrmTZpi79YB_+dxGgOC
z88tUXNW5W+h$ta0P>>gea;2OwDEC2flEOq(2||r^_>Wpa4N?cNDBAwD*vt&Xavp2F
zHWmYcL<Xg@^M+od@{ktC@F%YcBJMusdcta{5W);dfajw-VywmCkA3J57Ct_rRKOWp
zaGDpy!;;3To0^-;PD>lk6@If(Z>R~$n<y=1(khsXiHX_XHsRyrdvC>2v{q+1S)e{F
zt$JUep8w2ul1AYIq6Y%-7^p>R&_vxPhgein5-%~4-5|0LfenzCP&!~T0;)2kLgFSK
zAHHIz++N=uW#kU5xja$lUpE~hSPV2sbupt>Xagr3N$8z_#U9Y#9l6sSBo|{?(<$0^
z+e<T6xtqKv@Dm2iKInX<QFR;B#Tz^<e}b?QbQO&5v%Hjo(Sf#7ZyFrzo9|z|rmKG!
zy_`&qnG0`9{ov#6G}g2lw+TVfkx-j9cQ%>bx$UEmg-{-DNYAV~XvoaAytTIe)zZeM
zpaWfz8|a73YZ%A|XkMZ(ulhdxB~J^fZE6wJb@=F}sx#wK6u8;S%IpSMa*+{ynpY;{
zL<9a@lX=rLoebUlU(z8AKn*Sa{JrJ&;*R!m<tua8Mj&&(Nex4>SYM^#XK>4}!tAr0
zr+ZG+z*ibE0ze?41$fD$)?~4$>y}Uvh8^dv!z?0jWE_dN*BJ}7ou7t9%B(p7zHiki
zq|tow)|H!*6Zt>INk>e>+hBTxTGe7qm&<mg&&1Qx^|8lv8uOTRn-TzQTd;rc^w{_y
zFOK(3p`elGz~n#0X<CQQ6%+qvt}eY^q<h#jtTi@s^m`Q0!2Va_Nl0wUB`?GO^-j}0
zBW_&{=N~oz0mls;5C${h*+K~a;|PR>SODJ5uBxpN1z(lX5?T91`+?QjT(34j)aVsL
zZTejUgf(5)@nTwe>=xt`osS8p_r}aKtL5dlKieI=hss;ZaatOn1d0KEuf)N3&?lW^
zOyNczYN!w;M<B?hC~9yX7I?zdepFi?h#K&;Ru9J|+c^(6dr@(~!&Atx$sEZX0)RRs
zDY9{tq~rEk-rKymSLJ(|f9}_^8|2RtlFYc6@J3Y3FMxVv@=wFQjEt0n3;1y%BAQYy
z7jgek8hVKOujS~4Oap&h6Xo;^Q8@tpVn;Rl$;`Av<}Wq+8$d8=2~-y~X^Ua+%N|zw
zZeEem(p=UVLtmq56Tw`c;Ii}HYAIG#uXM*3YudF^0T51@!p0MFjq~3M#GdvQ=II}D
zQSoErmHhr=8*S<h_w3+#m=6o+VT6rzK30m`hQDBKEaGWshjgYcBgg(y?zWZW41`+3
z$stX?yU5uSHM`-mrROdEA%|D^%HQ=LWF)h$S<V-yUF})iqCSdf8xX0grRkBp<0*xF
zzqMo+>b?%Tngkw2<tJGPxx>SRMAWJtunvxmTXv?nirecSySppeLq4Ui`bPWm*<y^=
zxxW8cpKM||a;K#&ifUK3$RSkm@5acZECVHRqitkcS7dK(c%}pZf#A28EvKC1Nz9+0
zmTBGA^J~0oTka)mg&zn7F5kRn+XKP7lNr$E(NRy*5CtjJuB^DuHb>PyVkQz=sqhmh
z+hAjf-^-(TI9|~KcHvXuOKYHPa#wlRCss<NYm&+hKBLmzJSj!9^F?u8Q4H=5t=I13
z4j2Flb8HYH&>qXRC^UL_qqDND#S<(b_U{f_ycI8FYdEubXZfv7gWAU<*SOWklFx%v
z)wS}on!#}%K6hPPO2O2LmE}A1kKROX^m62V*&+%J>C1mqmzhRLbF^7a@?-&~8iqiY
z?Tl}$HSw#^IN%rus(+51Fv^}F9>RhU|1eYN#@Zsv@`b5M5)WX3i1rE-7vSzeWDD^q
z|J3WT_TCL_nwilbHT?hGaaf3haUbGyp^UdqU-ZhNwH78EGddOcm1Pl4#|U^))|L%i
zH(-CP#uAAq21b|X08$~h_(Xj!{L}#c0fAc&@@vv2)q?UM8zL9(Mh&;Jfkzg<rodV*
z>!Z@(6L`8^FD}OwAEED^b6-k#3JwpPcOKSmpr?PC8md3|psTQEm=!?mQvJ`4Qq`rj
zo#qm4*n1gn3MFhfJINPk6B%%+U7s};fI9~WVEPlfr89fvTaw1;EAd42MrH(j55y?I
zVMtF*VO~70#P6AH2r?x`HZn#!znJ!hm)V_lChAKjd)L}y6yzc+Y?U{+ne6o(9^3W6
ze|tyq54%W(aDtq=7>-k}RzR)QUFzC$T<B-Lk2Mt_o2ladc12Dg6IcCyYig)Oh?d?^
zgWO-kdDnViBrO+<D4F~}<O(iswa>2geN8VGy@49}1bg`2R4-OSpBr;2g5qxs8~zL2
z7XOfU(ubGEM>PfU^f`ecep-l>L1UBb!Y@yFcC9zH?X9g3eUVtXBK{A5_v9oc2O_?n
zrBQf+M%<9W2nGRfziSrn2!2*o6s}Fs^4;3|LTg))tLEb<Txwz^=KT-u0kCMT9S^r1
zb`jTDDeJie?iG3t>#8grx~g_~`d^(W?Bu-%pDHkp@{|59VF}Fd;tv`4Tk)Fxj5?4Z
zJNve%VWcZt*zxKjd?g&RE*=`9N#2ZqUSeSdO{cYPqshTF_U;F5cxO(EauRbHj?Sn5
z#YbxmKN-RjXyi+!i!Ty&yt1u#8m8vyIUYjySrGys%KH5MF;xn1Lhy#Q5V94S5<L@h
zHD<!U*bI9~f<!Sua7fgQie+^hiDfx>{xi{Eis%hza_~3?gR+iIYLU)iq5lBINCjx9
z0jRAsPvotVC%(Q`o!Bp`4E6hsQoz{#(4g&1DMT>3)!WkaLG)`RO9*D0;ZS&j(3}=#
zcF-U@2cb@#4h@*ENj_I3JN==~ZYYi%ur~xWq9Q)SvoJG%O~+lHY$PP8Z*JxX3=G;U
zjrQq``OkK?@1fmo?&F_klPop{6sezZns%0{+8OM?0DbuQuO7#kbZl)p^z!*L>c+n?
z6RRe3O-P9G2{&q4Izgsv60nQm60mDJ*NjW8rvyYoP3lEg_Yh96b-Hqk#&V$tTws+G
zbEpQ`4*t<+ROI2Lsqx+9NCB*Fps%%^7e#tr7iFptuxkNk)^GqWl=KO8#`=O(BhJKs
zRwE~n(VkOf!@s)YHbMKf6H@E`!xt!m;2qaLQOVptr(50f&oULbuYFe*!f|P9J0LA6
zyZAde)YP17H*;gv>a}%BZ$q32SrQ+6b;+fJX;X|=y+38xiPl3<oo<mxT1WJ2_3QWP
z@zK`Iv{-lLqL%@nk;V5b!zUdP%$nAwUtfRx+=-MJ`in5+$bR(S@o%~4Yc!ixOV9|~
z7B~5Cypu96!9ynZ>0?u8zV3%Di{i<V`6{yY-S+?_2!QDfb-tfPs>;SqWSDNz+{6>p
zYui}OmKORlLi+g)rQk;H>Ayqj&_z}OkgCY6rcZOD&*5ifZh^upoYRJt0coM@>a?cL
z20e*ZT}?jprVO_R`%x&O_n-VGy+?_&e973yEAle-7AJ|xk!KcHqToE=&wmdEESpD8
zQH%00%!t*0@$x-a7|=%xlDRcZ&2Dg4B*juQt(akv>q|foD>^NIO#H(5t15piYVJ0x
zdRWeU@I*bmt!_euN1N%1;UETXs%}^%&Ugu7Atc>jD7K2Cme&5b1Aj^Ish4^v*SPnq
z7@ZjSmE>jzQJDT_to<(Q%<}o(Q)~RVMKh8BKc>a`;5<Y7JCP?Q^W6KR$u0t5=>#ko
zFPX!hy#6wM-y+aK*P;Q&R;#YI|FdJlLjF};{2Okq0CXF>O)sVn@C~p4R(?zpf&z<J
zLLwsZ0$_nlLj!N_!s+epeQpQjL5eOfFO{`_udc3s|K$U)-o#NwMMeLSb$oq%OoT!{
zK}wU8@mY+S??7<TyZielj|GR0bEQ+V`5y1Pw?Jj7th$4Guk&v>7TMEi2$mK39@527
z6(Y>~4&(dZaEX`MVOWPI;-S2G0*ZU7W5Y@d3*qI;T|Sj8(!<5zlKlia?{wg~%4Hdn
zDvZ%yFORV(!8C|e+4;1Bg(afl>QIbtV7edHP>^!dz^X<U0UqAGWC*slkQELNjz#G@
zv}`l}<ZCHqSy;~gtrN$6ufmkZB739f&qzoBMqw@^w1|nBnHq0GM9{WmLELar2DjWY
zbsbz-;T9X5AB;%coL^h=DoM^`R-kU96~qOORYLtMomd!Civ31a<S#P(hg#m8me?D{
zz6^;pK|!{w{Gbk_e^)vA8U?Sw{i(Ypj7M-uMz>lbT?W}rzOR`8L1F_<Ao(5g8{#0`
z;cnzNB9h6~-3bv8%g|};FT@(kWYzx4t<vek)#Sm!@So0I_^Usqu|L>R_1(k)dyuch
z&!2;-?Wux$v<q*YWm18Q#pO<11s<K!MzkM@IC-o)Rs<yoGs7jc7TN}D1HuU6X&@j4
z`M-k_yenkRWs{p9oM|ffT|-BW{NBAhG=kRdUG|I8<E0SbI{@AlkMgmm$Q_rG<LhZ+
zi%u{4<0*;DuT(8(=S`ZmIyMISzZFd!sV{M&vF6-;wI(vKjc&hY*q&X?JWvK-ua$lG
zFQH4+mYCyu|8f8B3Tp^caLd~LcO9kdKJ(v!11-9D&xq-{I%;y0y9lKSsURC;41d*8
zrz`jIrR`-(nF%4dZZ4dxze^!mlN}W_c#cQV%m5Vn4W2b{<Z2nZ!)GrFoadM?USdY~
zcBtN<uvPkyvw2Bj^D|1d*9c-3JXkSldxlq?v@w_Y&dB-$cPd^jlY5X34mbA>t*`xz
ztgiyqx1?M|il;z+S;aEf_3h$oA=sL1-%<G=2VOHAP{LFtGQ*66Y~3I7B~4Gv_jIDI
z1t7br@FJg3a8kXW0@YN6$S#!fo1(}@v^ktEf0s>LxFFCN7zTs{e3_#<b&zZ^_kSM|
zH_N9toeqD{##oD`ugt(A&d+mfbqTC2|Jhj?>ic)*Hz1n=Eq*t-9b!`YRj<c;wpfO+
zim&AKyjjgj$%b4ml17o01wx}yi^zNE)A2@+HVy%H$o_xer?l0M1;Af!Q(K=?l;*e(
z`7HkK#fODNIGk7Z3I8zH956p=yA|8`y*@j2t$>kVULoq#d)2(7S-$#!l?C;^ho`Qp
ztmCzgJj=-Y7-F^dGqxt%RRr8kLiy2}u(9e)q8f9#<aIHOPr`;|O1iJx`@!V{^WgR~
zpP(CF+8nQe>y4x*i6ju89HEuC6+zqP`oyeAs1PgKcs!gPx8|!5w^Ze@!(%639{h;H
zioC!Hn+^zC{@3MzX=uPB*s2qA*}8Y^+3<_CF$oE~=;%@U7r9aUK*eEevQNXqk9P7w
z@_8q5YyIz2W_Kvsn~3>K0oOxLd{}#>^8!T&_};(as>gLj=v_sL)LL(VG$W_zHTEo|
z()1wPoON0L^&HdZAl5Px{WsXF(>AoYVzoRk-}Udn(1_f^FDUV8yS7(N3`ezx8Xt%9
zH1DGO(bD`$BxtVX#~hpZ(3ScW|Ltkn<o1V8IzD-GK(k1G<FV}|l46eg4mxallWm|A
zA1er%{51n+q$jV(;pr*)R$?K?zx5{B!1?J{QM5v2<=TOnKE(6v3~=7<efjfy|Atv{
zzu{<h?sfA7faHr)%mQYFwI7jcK*^21P7R<q-JyILWqO^d6klcL^}n4w%kvDR@?kEG
z(`!L}zap+d{czI(7?DYQy{bfA#{9Y&{4J{-glPdW{<y=*A3%wZeHqTkt$hSc7WT`I
z&>o!vAZOKzNufKbpRBxqC|@PwH0IGf3B*XNIBnrq8qZ){hHmCopUyEKs=8jHMf`r6
z9#qm;utl&DzvC9=*x!g$6!gFe$6THsIO=_@s_YvgS!uItdvFpz%n|wfsi!S2^x?6z
zL{Yb4Z}%(iNGF<sX>kuSmDhr-J>F=)swFvc?5b-q_R50iJ1b=2i80^u60?o&70O2|
z;<#me8&PBM3T8Im25yckU!(QLNVlokW;U+;<BR>)ZcaeWwgmMX-Is;d*lVaZ4POKU
zvCiwW;tE(U6v)N`^e*Gi2HU*0tc~arm+p|b9lxPUtdM;1)lfdco64ArPTh%*VIrFG
zTLM1vP~{LvBkgPd<;Kfdef3q>%Zsm$)nP^`(T57ayV8Zxo`6N}*_Hkh760`*yWJ=#
zQC-hg#K`P*>1)0hx*^=l9p;0@RzX78c)C!ed@eoH6!-P&czBO9d7Il>>oW=p5Tk?D
z34f{!mu%f{41B)sGDQqOrJ;|$O>9>eXL%EiceLbtds5Ipvq0^0F~n|AdS_zQhqCT}
zem4KwJq-Wv_JEKvI&3m;$gkIsZ#CwlS*RWkGd#Z~T#4Q}U8Z0GJv!^7G($*Ia<-&$
zSL!64Y|R_(@)a*<uSPK+Y5u%-gFu8v$#mF-T)JL+K_&06j8lS#82)k?z9y1i<@RH}
zM(uclt1(*#fDhb5e7-=y_MJX<UZ?|S82MGa#3I<0(-`@gndUgJW$|nojGPrf#kZtk
z9d~(4BRtD-3KpOx3Rt>{OQ=rlJA6`RV87sK@>JZ~N=#Y@PinO*IDd<LvOCz9l4wNN
zBT#viygG;Uu4cfKP-fbvT^f1%xj}AGO>Oz+XeoZXW@i$lc(a+{k{6(jj7rU80`;BL
zG^~Z~-55d(AsibGn(R?y3n8yLyvu<*2?FRLY`bmoxgyr4MF3Xxpg$eh-FYcw5cqbL
zzs|M<3wT$LSo${uRV@^F{s6jp-Eq47E)wu49xX*yTlZ!pP`jr2em>Xld{*m{4X~O4
zT*=0&ufJRuUm~NxXL*hqc<(xdeoyu&-eO5R*9<lPL5C>d9IWF!AkjsCUFDpljO+cV
zseIv(CXz_`c3lVx6)T7gfAh0`A7tH_WVk(Yc9PeW<71Rf^rhpE7RP6W`=K?W_Rxs{
zIV+_2{zJ2`_j3(#dlwq<jfZEMM@qQjz1yK%YH1ET{0SSF%Z^i0#mhY9OhFgaU{=5K
zzY)~4mHEO6g(*A(o&3LF+~4nw&`gWVZ{bOv5dCXGbuHu&?3D2CQvzI*KrN0@CU#7G
zRD?94ak^cN4{`M~?gn{%%Qy@QqsAN-asiWR9{zQVDRM<Y<v_{yqvU!yzXsj!vR{6R
z$mGsD>NYqz_k~{quc&`uPnw0VgE#Iz$+R2_`elayxQ8LR`>Y$|5gmlN+&eq*i;mz@
zB8W>pR0@C<qxJUjDr>E-56dpPbxUiA|516KTx$1(P|?SZi&vLsz(0CU)!`|8ute4;
zMV7luNKotVV`5P*__o5Q11u|4SCsWXS^TVH{$?Fu+97Yuu;2YV{sfSLrdLL9p+&4G
z;NzDzTVH@8Z;_@UP7D-muu>xMw|@<m&$vixZL`u{UEjT8l%17ou&0~?EHZXE2*&Ev
zcPDwYAFu0Yift_?pf;t8@pqcm*G9#qW}5HBS>0lQLlR*mGOYJ;a&b+6>>y2TNvS%E
zT8bu=#Z@(wOJJJt<hfBK+`Vh=AgYGEv4^+2-AIJ`sH<RZPmJUbUsBWaT9o|)k;#2p
zI8Frk+<y?x{S>D6Ql?@ii_=r|UBA*VmG^*P&?6efdPu?zsrHcs2mz(7h0=lW*WaTQ
za+BPf#&JCtP5=a0I-t9Cd<oTmvj>>EZ7j#30O{TW?H^0uS(Ds~SOax?$ODr6kxD`>
zL{Ehi{7l`3T{CXCj!7Cat?;u=s76)TrTt4*^Au{J<~#HU#OF}-;|&Sp36z-`IX;RG
zg724iWZuCCmmxAa33{MiWJ3x3gk8e0UBdoV-}MUdwhBu}oA8+4Lo;zy@3`#FqFGPb
z^ccmD7<nKmkBRjqT8W!BI{w?>W))e9Q&DdFKN*r`eTyCt$WqTb_14t~f{6lGdBOC{
z4|6SoDNi!VgM$7F!G!-(8>?$$RPK~mCN4lP>>yw10a+~#%ZvZ_H2*(1Fi{==njdia
z5g+HowkU(ffjS46rI^I;54=S^xCLN^1vOlFLuw%G0&O1#&KGuPh2SCJ$TLCMNBOQR
zhAKu~=Uq<It2f*Wuok?}!15ii>wx9{$~^$sQ(pes;fD*2LQ}bZ;j52d^wCC<L552M
zG79wmf_^CS6Xgjx$5!5sa>ywL?_6*Nm$<hIOK=WC)lsD1YawG?0M50O4`cW?L16$8
zi^Ggtq-hm6(i-l`f`#Vosilas@9~wSWxH`+hq#Hm<lbcNT8diT-OU9roi#tB8E<kK
zZCDn>N6+Woml5w2hwF`)#@v#32ue(Km&ccS3^F^IKfkN<P~Q|Yr@efwyCpDE=Q&$B
zdwuM%Mn)rC1HST0w2Y_<N>CQVgF}L+*YrI+kv)v4->ESDVxSV(sI!b8+JTD{M3%g5
zIkl~_w6w6a@H}g|T@vv17@g`6yuznuW2;1$JQEl<|AVOWYX4oQF>TPFGTgMRp5jc6
zzZF+yaC|n1=JoaqI59uYw73-7iHZ;%VukkjnT%<OTgK|m-P2Kdc+(0-%030tXy1+l
z@3L030cS80_p!dyAsk`F<uoVng3{cPOkQv_zkAIQSe|8=2PM}Uoi`+?UJ1raZil0M
z8@@t+6V87Vhu0}?<1DNLE9b!5aFPs|mo&@`lK*CdOVHjU7BH+m1yLtvi4)v`j$Vu@
z(Oi(roPKe@Xa7>%bpMd5Xh>IUyN}ISneWnGn?CB5mH}Uggcf)23hA<__!TCoO#$u$
zhb=;y+2V%g5ZG{jV#qo*QZa>bN-;zg4&k_ofmP{b==Bj;GaHjfmz=7hUW@kp9{9=%
zeeNU^v>xq;+hFV-SYy)e0p4XjjmLWmy9P$o-$9|SUtG3p(1NAbqVw33XE!RO$t59b
zYSOV>2|<Mjp7z~Ln<V`}=JQ0#=4<Z=;X0SLRnmze4HD_XQ2Z=TI2#UMF}y#5|Ghst
z4+dr^3MtGnPsB<$)>OCAu~PldAb4MUB@)!MIk1>1^4}WWywB*#v?M1Ii>VZAv3cT`
z%H5YYG*;=ev`**OqrEBYc1mq~h>TKmQ-`-5TbnSMTX*Lb)j^M_-}Jd-L<eJezi}~q
z4Z|BcGIl_T`{lMGkWPWl1G<<cZB7YpV_sW;T|B%{$1a5ZOtyj(w=MyxR#!3p+IXG(
z{2_)n_fHFbQo$zrO+WFN!Lj#dX-sF!!iDZ3o}~`E{t!RNV4HGbs;e6sEDg~d)pbxS
zj2!PrS<Yk8<F+K?Z$%8|*qw85_`nbkgR$+-hquUqWA*X3R&<|K&s+0q5*e$mMRc0%
zp>Z{OQP9ig-wRBgRkny7%hIoi{6>eep3oqqE1awGc&H9-AqpwHq@S@}Z%g+T5gh54
zCI%P{bcb}7tpf%U0ZJEX64~@d91$RP4L%$1={@kEv9i+fT2U);8Z$HJC#7Ns>96nY
zLxdIRg_9!Q6Wf-ieUv9l{E<ZDIx(}ARaW6*y`v>W&}>Q0C-2@KO1D)Fqa`{pCC_0W
zT_{M3FhQ72@`IhzIPM1DEqG{-n1M@A9wiP1p}2H`7c2EU$LBcdtzk5X`QGIbIKnHH
z7`BWGEr>aXK#(D6mrEi;?)iqd-Eob;>TNd4RV36H&F|yrXS)2PP=ukNj2I<?da(2h
z$p^DB2}D~r1U3pM#s~;Jepd6zE%X#0*(f>x{VJ}D;Gq~ZL@+b1<oBLDN9zlTaDM?U
zdMb8PZ^Ifcd4jC=XW+dtmbGof>q7%s6FV#B=NYFLOOTT>BTNl{@2Q{X{P(He6#|2z
zfiS-mRF8p^3!}ntcxI$W#`if@nrWT{Uwf~BzTYx|t!cymdm;>>X~QpAX-~w5w9L>`
zIxSA*itIKbw}ga6_-cu~^hgR6o;na3Pu?wD1)uf9OME#l^hYpiS0acTy(=6<Ptefc
zQ64*5W(sF8e{)!+8ZPhby#^hTu#BH=iVc%Mf^Lt3pokATh^<O`f%$jnWzIGA!Sf85
z&B`a4Kl2Hmu2I&op~V%!laTdD41bu{EQ|JmM!48er&Qk<0B53;Eer;m+8MB_;i4D*
zAyCsoSMYlw(h_5%W-ct4@|Gsn*XZ-q?fAJ7ogrx-P7OsQg(<Ret<;8P3WPd0v^U6e
zrf&ZAJ+wM$1tEX5rFDu?_d2IN7a-KV`jq#`-bg;G!1yO|-vj*$?pZfq7jEqqDwFp}
zqq5O>+>s$Cb1aPV@AqVIYX^7ah(1H<kxWPyQZxH*oNT9pGQk!`MW9;>>$8NcjK=l|
zh2v4huyTVCd@H1RLqx&jE#J$f2|`zWEm&1w`eyGUoDv!{wR+Oe>5_|$s#d!4jc$6M
zWz61)w=G$p3EfK*GD6<O@FEM_s($&_XF}sk*k@c*5lM6tF@1v|*wSpCIQprV(c)eC
zKD#{QpQl$}T}=JD@6xNOoaIC;uuQnPLg9T8*)he~vcP5W1Y?d%3nm?4rw_tGmjsYR
z&Kf|Dq5;fxA?l(IATKh%;x7q{gpVS5DV)fi6&i)1xk?YdHdT4Jr0g@DFKTdzFsK_F
z$Zn+aeg5XfqKye~<K0$p_WZWW=W?I<w4?J5WE_|M{N7cqQKHI|6IP$bI;Y0Twf?Qk
zPx9nA$^HtG$)mmDAJVi4@Fh2ez1g;A`GHpGpg~Y$F=+oInJP*9G6{`@GYhy;Qh3k=
z&EZQkwpi2hzSFkco7F1#gEU;LMG26B`^3%K@#>T5UD$$UmVT0tAXMSv>bh;Q<(_|_
z=-jgmozZ5PL`U!>4z1;OanU(_pqJnyZY^8zh;QY-q{t(Ofs)-h3UAd;Q`YnL(xB^1
zi6R;gd;*+22n&JKOmo1cbZV8hjZGxG6V~^Utt$q1#jEl<{h(v=qaI;8;uWF#j51Hd
zMccQ@o^i+R<}sRmrLoJ8whI`3T%YhH-s7T?!q;wCt&H!bXj};-b({daL<^NXk{wSR
zD^%9LXUYr#FNC-c1&F>T!QQY-h`MnW$Q!I%-;!6{&Pl71#bz(#VVlqver8AcX<)pU
z^IiDpKG)di4pm~!fS$^}Y7pg_&dxAxYApLARMflQ1WGui`Jm+!Og&ob!g?k}S1Tif
zuS$#Xlw?=S9_|(lZwvwn{HpNd<*S?U+O2;xh&xOn8>^L%@>AxhVs0P#(t1kv)+9Xs
zP?B|Vz8v&V11{fcYiymFovJn1<kt^|x?164eEkwJ&m+h<7BMuNjHkG@cw%TG`?yj_
zom_C?Pd2GrvF*g(Cni2(Eg6b})#eXm0xWbWZeFmYee5^}5S|#*-nipZTIYj~s<cq<
zNp|L1VhrOQlyULQiTy`W`&ef2N?A?40)?~oYv5B_yYd!xMG)zDCsk$e1yNknvI*f~
z)6%TdNpiVO(Di^I6Vm{rUA_7K{JLA$asMoC{WV{G%5EAcgksL$<Ss7R{U*rcC~aop
zUALbsFqGMig&b!u<ctib3t*h6Al;KOXecPvj{*g%5rmIJNNa3POL+2lR-5+yNlasm
zb`d>^S`d_?>#loJ4Lk!ryWv&@Oa;d;4TZUZSAqe^4rrQmIYIr|2d*lgbeeM8iEc~T
z=x$x51av4o!#u)+sNsds%eqw-osH~t={w$<&(z^tBF;oD%)_F&ZyFsxEUF`UEVM`-
z?0(ofhC--B`LV_?qGHQHIrIjQnK#uRFK^Z(4fl^_(S2hr9OB~{Br2oQ=5xJjsIG>e
zHpQ81Phl>Xyu){xAznD?i?>#TH~b7O41Z@M@YsWXOc)Q%zx9zQNuq^gEh)iN$<&)=
zqAIRs+i9L$@|6>t;UjMm0c+`b{!G%nf@NSSC?Lqxv~4tMsZOK|s~H)%cKG%PW)5zX
z>*KOghYh-op|OHMN{Z(=>Y>H_&>E)uqSUpI6~A1$e(PMEVHv|Ga?$0xXo(e~`<c>J
zU43G+fd!%A7h>qZ4Wnehw&H_DZS_5&#uCaxIBxx9{k*}HYQ)3BG5%S<i@YhU`N%zJ
zivy%^TT?@&V$l4<d9z1uJC$*=zOK$Vkg_i`<dq1!UGc|XF)L6<vaUA1UJOy@SNXC*
zB1LHG5Gkn4w1{oZk)6v<-_U!T9wo6_*)MC$+7~**6-aj(QGVvgY2tz2^kHZp;E&tg
zs#7;hhK_GdD||^4ip{&#^Fdnhr3W_dm&9(v47O8Eh9$!GIS0XI5ZU%zue%UcB5bc>
z{*T4)_V+AxK1t7pKfj}~LV|um=r~ZONgxafYQ*7jA{dqeS`ALE>3>|=Bcxo1slBoA
z89HkZmn6gX#4@Kumg7|ju<ny3Q(SkIYx%gqemU9oN}$0VW^x4dTg=|uDmY^s@mR_Z
zho*IE8Ze|U8Gwep9arZImn^YWc(M^p#X%Z~xCEc+de4*QxkK~TJ?<@JaMY&?ICCh9
zK-@3_17Hx=Pkt!uQ!-!UlKaJ<&coC{N1o~YttM4FCDN5!B+{K!w3j1P8?a65pdu_y
z6q&RS)k?Yi+)vJu$D@;Br7*z1K@vb-!@Dxbquz#*MT7xwRbm(H%&OR7l75fI$O)lq
zo(W@l3(@OO;t~)?KjXedx!$Bg%z%{%rprr%!|48n?uNLDfYfR-db^1HEx3}Hj1dz*
zLSme;j+O_5>-UPc8v-vq@b>}Q!ExTord<I;$FXf%!P-adZGVv~>OEO+j{HzoJkStX
zg{9dI#on2Y>C~}av(iMv|C>G4f&-RYS#HYLNH;{fdpF@>C#wlp(A-p%FQ<bL{!Mm-
z_fp$Q7BO3%CqMd-wi{!%c28`r_%ag~88rqd75w_3HWI*?{zwP$u1HGr#j_ej`)sfk
z>krqS2DcUn3yaBY5(tdR#PWHM6!wU%uO_e3^(_4935&k}pV5HXJ_;ng%Qt|3<!C45
zOyAl3@3kzy6~7DrQO7TJXM#jfVFBV5pBQ09Z0a9JUq*h^K8c&!x@FeQcO@t-x8Dxh
zikY0@q-?mg#CYih;(aVR4C|mIothYuP;n;*dkYs=-WHysU$Gm3!C<xc`T!FQ3=Fli
zrY3YEHYMTdhfra!^Poe{xd8KEgH|`jIhqPlEn8b#Nl8Tay{4x6dY)2goj?vtOUu$B
zwMTQrhk>s|z)=k*Dr$aZ(#=d}R8&-cKDGPaZKM54BhZ<wsVS{be{#?Wg<1h|Pj+_p
zL&3{(@nE(pX#&FszTQMGRL1uK7HWL?wXK9=CJV~X?jA)gDc|SLl;*xnthX!5yuaW!
zJ@GIU`?LUSvhvNH0n#bZpGz8u50~5IAd=m5KUS8DSZsLGt}YKXW3PGst!u(8imy9K
zfS{72e}vJf&ql2_ie@Kxop{dMR*JOy8X6Gi_*MAUHkcz!<P`+LB$_#YV=s7(hcPK%
ze0d{cL*-HX2+hp?+oB9>AH29dYj67u$_sU!n!j{Ji4w&Qp8U41HbnJ1m*#s&^}?{r
z7OB{F`~ckR)o`N0iP0SykMoj+*gIzID>!d`7Px~0e5)C6hy1GVOkX-8Zs$D1lGlyK
zU)t{IhXQaxmUhC8IJj--3yU+~23R&2n^j21oG=5TqF5Ey8l}+On!spon!q|2Qzh?_
zxT^uwXGF|p^Vx#?P0+rivSnJarQ*e#s49teuF`0Yy6Gcj8`kOUlg`T<X}mP~Ekvv9
z9^{{LjbZzR12qR0c3-bslx0h*nT=Q=hckpW<5{9%u{4!$Bnk6Na1o?=w=o*Slu7lY
zX~h(&gf|)YhdQO<qII0C!qhRSh<<}KB&angY(mKqkW&*7Pp0`K;PDUz&%KXwkgp+v
zY!JxlIcL^GoI@W!9Vj=Sp3@PhD~s-GmG%C&-G&#Gpa>qZ(1`LYB)VM4_#Wj-c$e0E
zh_Q|Gyog|fQD!I=(aNB^v)RQiK99pg%cWXRX3E)#+ersrEXEO0j^Dv+2-sABTPlPc
z)Fe_o7$85t@{tI()Lo+V#qGq#ZSO_~Jgisg`tl=t*grH9%OH0%24#mNA$Be&BRcV*
zsKd6hLAv`|UeXzJAwkrANwx_<{1Bw}m@VtXfS`Vrk2;<%x6KUM#Q>LKeGDqJJUv8|
zCXQ<y*_JPCMnq+wK6*!t;x;B^$HTZZ=JIAW@-kLT?D+H6`+m)>AkfUFIF>#hdyiL~
zf8-UpCKyAB+rux%wA%}_KkqG1NpKHsv^HGF{bv!LCJRxIFGd6pROUv7X2Q0T7Q9y^
zwflEJ#R#+ZsO?Pi$`ZafW0mNBQKOrTXO|9$uSp6yd6YY36BnjVPFz$F7LQVwaUC>a
zh(9Aerg0*En^rR8^piKs4^<?KG3MT>oT2RvtuGCvn>Me~_L%W^5B)wId}Mrj-p*!$
z{n;ng6!VdVyaT%(?><8s3X%rm=zY+EaTg2~zV-ER|3H!wL6ABeMnCQhS7ayYnBku9
z*-Z49(8p<}PZ~D~n2#1)`xp;!pH6W7KYt#zJE%Bxm(KmpvfTXs^eOH*=kppDq1D{T
zrSa?5l?=&pFQ<NvH@chj^6(9en1x)&JY`@d?|}7Qr(CORYG#|mnxBa7h(%+`PsX>T
zsN|!`lU55SrP?B>h0O@O+5Y2<bdGy9GmnF#BrCnMKLznf3t&qMMaNMUkj{}X**L1i
z>0f>6)1VR^1MAw~lww)3HO0rByoOL^d#<s}bR^d9u*C{8EDA{JKq1;97kPBRO2&TS
z#C&Jh5a0dxne)#4*MSK1`Cf`8drZ045a5u%<P2*>oJ_IPO|iqF(=M~G^1bl~aiy$9
zuKH^eoUAkg?Iv=MVu>!vMG*yOT$K--$w~;M0+HmA%gy}+My}APBtxBTY5WIL#Jz&^
z-m|yRk5^BbNs4aUObM+6vv*8pJFQV35U6pz(ON**uOew5UOf$gJIVXqS>x})AV&ei
z?^>}PnaZ~Ni2}ad60`D+`rd*=eD*nksRa%2SHP2j!PHqm^vTe~kGb!9A*V9uKxGhg
zn_UdYp$F&P?=`^XP|q<cj3!nhi8uxm&K0+S1oiIr#w{&A6b2$;MvS8pcp$z(Ae{&q
zte31-<vjZ~&a`pyj>~C~faS#UL`JE}l~8F`<V!;R)^03)ZafY)B|QtG`TW}nSiv4h
zozn_tABI=kh1x{)*ot1_CtJ+D7@|}UxfY8ifsl7^`}eIDUAs~TyK{u9zJ+S;rq;3Z
zQ3OVy^HjU7)f9VV()1bf>G~W8B{g$A@s|)Mr*VBpX|354JIxX%K^Mb{+b0a;wCVuH
zed3}@U3I$gP?|29$GzQauv7WwcSn)5wv$g&kf|M-uNhnIu5x9^onK>BiOHRk^W5{?
zrlonDuMHA<FWyaK+ks<hPZznNNNyH~8EZ;N%i-%D3)*j;?bfz@nWrn8m?2KJAd;Jp
zcl?v0u5Y@CvwD6w69H>J6B<n=++mhd>vxWvwnG(Zt~c*($6fX`A@~c8VKX*=PQ^>m
zy8VJ*sDldU*BCK#GgH={V{K2q(KqwDqhVXQL>H=d^HUe|!>na*!>sp)M<)yugV}XD
zs#3NtOf^9Y&TeQfn5|^cU{D=?5y%s<2(&|W0F36qCjG<ay!gs|&)C$CWse`0dQ#LF
zffb26`M>E4t(C5W01EfMaGprWoNLHk+Jm6}&K0e`Wya+au^*VWh$jtNAAtk$q>i4S
zsxA8p4q+MrR4iG-KdWwO+-lpjQqt|WjulGM36vn5r}?0v&VFLW5Q)H^EitB1QQm?u
z-j??=PxUZ01HV@01@Wz>WoTk~rUS~q!L3DUYiTJ7hnbn1|DqUx);aC$?)GP+;?Zql
zI>^h*=j7zf5`_J|1bkDrJ;7I^FL!;*%cOoK7=G2q$9A6>k9WL`n$J#8F$1J&WoarZ
zV9A_wihll7Nn+42G*mRgoG>G;vidk=+x<&!(!K;%5rHl(C1vZ65syje6nCl$^PJN}
z=s#q(m+Uk#F(V|Ktg;af4vvQs1+8evTG(k5qy=|Eh4>pr`ocuBQm>~4eE<b6wvLc*
zcwh+LfQ;BcfF-y;EI*_!R|)r4mL}GSM;4K>h>`I<oINP-LNDtFf1y=-Z=I9LOia#-
z7l!M>1T6}WaLpeQbR^n~GB^>Ha|s<R*`r~Z;L}6_0f8+{1(PqBLEbz%p;!{gFI(?0
zuMl0}ah$!wx4`U>si~=E_=p9Vm1!Gzc%44nD#Eu9rAE5TPH=s4iBdXkQtW{sYCYj#
z8GU@K1huUCJ0{_#3(|9z4A2Z-X1A8pG8zw$d<0Q8sX#Aqsle@5Ia12QN~=B89eyU4
zmJ6J5#5FkX<wF(E#mm(?<bEIEKXwX|b+rTIMK#L!%t*Wvlx(1}+Y0)rz%^~_)VGZf
zl{_Gc2;c1zxFQOcp<$5nhGb`YK1Aq8IcC%-j;o49X5A2)CsR8(FD2vRrx~8PKY6%t
z-lQ+gswAt;CaNvMmEG|jTmV9UN6sh>b=%1<t9s@iQ!m89b5!>$u<v|u3!HxiuJw1F
z%P3hvuktR9DW3*7eOAYTX=X^QO_76L`ZMg>OPI%d^4=L^S*N`BKXT|e1qjZ<o+*Sv
z?gol>dECwdd#2M-j095NlI!XQ^J=$8sp@%bax`wHjl(L|yv{y|_87c_#oUe9H9k{8
z@d^=sL-B-bFPL=Z&BWdggF(y-r@?4my+EpzeZcUCX4<p=g>ZwAC)k3`Jqaxnz82lX
z&?iTGDb;!$>?xNAL-lY!#M`m~%ir}lUFWaPaJYnA!unU+U9%HqLa&H`7iYrSQF-0x
zk3G%#<=;nMZNuQYWLTq>mE5T{!SY18vy7n0CY$e{^+D^!3J9%5g!h4yC7Y}BlHcgF
z`{K6r%wzHnDjXOW;By;H9N+p(iwzId?4O&KBqgB0#QyXsh`s&>3XQ=e8{hIipLV*b
ziM0LpP?QcI@g|9{QDiv?P-0$hyF@GW7lw8N*1bh6AAk80e$^oVB`qhWB5Z<n@)&ey
zo@{nCPVjjwvN~2IR_VMWB)@P`a?TAaKur2DAuFFYpH(9icq$!eHSs%oE%XVBYTIBM
zolfTO{vQvXKIj_0bL-sY9zt+ny^QiagbYIZld4M7yzb2dC8iEeC9+z*JDU|Y1}A#_
zvpX_}WNtVou`{|nY_D1}_qh**ZKUFLvFW?8hlTT7*7GdkDugV(@%8*iAzsQ$ug~{H
zRWTXgQ~xk1yo7x+gVsyw4FOcY8BV9JglLhf@?~I}J&F^-@G%heV>!sfDZDeA8$|ii
z_Cs$sWe)PaQ3j+e)Cotz-|$)`M~LbsC2)QjxPuseUqo=LHy%4)M$Z1e@!gBn)#WE+
zT6qfD7G*rIxBzf165)iesi)zDHet(T|I~+bkE!iVd;`Y&J!k2H9QBy}?QpJEZgPyb
zgxbyJ%dfB}RZ`;8&YN2@xxs(nI;Q#bH1v-GUmrKf!eZzquVg=Z)m+s@cm7HVO?r&W
z`}0*(kAdlRCtfs8#Jk2%6WZM){!Y;lOVDp?0~Vf7`lmj_uc?Nd+Frh6v613Pc@8wX
z)iUX}nK(m6TY@B2I5@oJLs}my$kY#+l>icc3%RDsWgv&w6UjBGs&U$O;NRiL6JNcI
zZ~2;~5`R?}_<ZRIEt~wi&kY_LMEev&Ji6ywW$}A(^UsP&J6qam`!iT?$VGYg55x~p
zF{;*XN1nWb6&S1p&v9U>LV}D)dbge_Ic>!5-oV@|usMXg$snB4wLAPS?SD#EN!ALI
z+`ujcaXRIFe(-&hPM~FvY=b@fc|(aZ=#T1KY)0|77>936{I|_%3GY*zvRI$qOvEy|
zb>QkVJi8olr~3Z!N4Gl7)?xCbM(o8aH-isxD+!D6H6%v-AS^zKAjm>#>^N1>ZpZTd
zZ^4HzU%<7`uGJ+Z)6|H5TH9wF=st1bi(Z^sChWD|p#ll$TfF>A)cL4yS#Y7V(}YR)
z4x~xs^F=wbY!0d1wFYmK{h-_`9O=y`yV)bmO>m}b@30mtE_gHA@V}Qkc40rA4-qI&
ziiAjszsWZf@s^b#<<OXbEI*DtyOSJzup|Fjeiec*rdy6Q14IP(8J!W;&4qWe=-&S*
z;57a7JB&Hrt(?v_L&q_G<F$Y!SSxe<JI(tF7|z;DDMw|29|x>fQ$MrmFU#UP8Mwdm
z=u62TZSI(qz#g_-RpKyxQ<-X2((w&nIz9;oMea5ztdI(L`(rC%HuG6myl#K5Gf4YH
z_1Pmz5W~13uFs__D?;B>q+6d{$*0oLV)JsoA}u<<d$l8-yKzX2B=o*)fx`REhwrRw
zwG4mxXJt1K;waf6UAV&nR!BG8W8kFO6O^&sZKx=)UbrRkp@(4iX_!titAchuVZrqL
z?ZoOwOaF3J%jO;rh$na9kF_E&{lj5^#gh7)QVc=fxH&NDQ42<#=VM{NxCd102M^MJ
zNrwZ@E8z2a_utjnuF;_Iyl_iV26ZaG4TCF(uNZJZ<yq~x#yH>ip@lI1VAzxrIeB}F
z<Xm<5&V>mzN*}r8vNi!<>|Gtqp<SC7Ep?4P1ifDIsS`PN>=j(dO6Yjisr#xg&L>(B
zBjkO>$kKbU`d99(I(UsWm^XcgwD+N@+1QaVi<dHWvxxvY+r+TBZ%gHL_DwtLM$a3(
z1M>W)9sILm5zr2`gVuN&JU=o1`PiOE1VyJcoFEK{Knb1W*HrV(FUpx`+nlH8v8_SQ
zCuWAh8GGCG!K+Z8$f|27w$t-}RK0an+f5s-jRbdhDemqXC|VqfI}~>>RtQks-3ij-
zPJ!Z9N}+`mFYfNe_5Aw0-?z>>f3U(zh%iZJ?%Dgg_edk{T1`1|d|N@e3Z$NX9oL%$
zw$uGGez8RV$-2qoWjnm+_Xae5Z&x>h&*eN0t>r5JBMn|aLbCpm6ldIzHtEtBFOp+*
z@g7z>pVZ9_MYR$43jr*hL#$V5Q>K}AE5GJiv%Pn{9(r2=(O@M|Fw1u%AiTw|o5)@~
zDIsF0RVjFJbo>{0Sw*4Abd95Af}*z6r!t7<XU<e~V^!N<rx0*akgl4s4VC(Kh&?-u
zpXBTBa$FvtlulsEiF2c9mbTLH>yuo%VgpHC-UTpTYfi~2W|>5clgyb{gZo_Wm2#+)
zXZRs2%Kp-H)r~;ahw*QD|3PNNNZd*)-a#(TMetU$*~3&i(_Bu|Y{vH+v8*MfB*`Aa
zkti-$9~AI{BH(!xfi{3o3)554Yf9LRAe7B%-Kzr+Ufns0mXPe_(ZER63!~Ino-YO)
z2<Ac+z^p8hXoquuVqn(#r!&_1ofjF}tipxLELD!VMl>Jp^-N^s->Uz1Ef#<4*?6h4
zF~j_x3{D36N&wInrbxKwnsX{#tsMAf;NJzPcz0yyr?ylHwIx?T%Z@&^(J+nsXwM3-
z<KYwNzg^Drxi|YhvE`!=4L7q`<#N=A0xK&<HJN)X`%u^GP5F0yF&wf?aY70iw2M9v
z_wUSZQj!~3d`1miu@{zQaHEL+^Iq?vy)z>p6zPIpLJQHnJw2LVe$B1PNmOF?=pO)v
z@x&iR@rm8{)LrN*_o)Jx3edDTqfQnW*>){Y`hXXkuW1l@z>DcfcS%OfcQl)YFjj=%
zGY?+-)r6|qn!tL8m`ga;F4f}xw)D}=3jQ`opKz5?$;2|6w8v9lDV^q|6A@?SA8vb~
z4h=3v=OwncXWZ}>5>YJdj-r0UR{b~T%}{<XdvVz{@~h5|)QQz?a6W>nH8gX=NYrAR
zEwgbSyaFzjpjw>>RF@?_+dZ8?IWf3OJ2B<I2Iw{k<rkp`#@xgxS{wMQ4_`EY?%Ime
zTrUPxg1BGf%%Tg>9-AJToSwDof02Dd9(Aax$GDNDB{dr@StY{~g9s8^_o3%O+rv&r
zV!omp?JH<wDWzo8p@fZYS&5YsQaON+=p(;xWln;`ilGiKiZ#?m+n8{I7B~q!K`V$0
zd_~8UDLRc0vg&6ACH3ka#>{`^ZAP!k11FQvP|^M0Kl}!fZ;_Vi&A@^JAK_Oo31Y<}
zAFWdPv+4qpl4BvPX2^OZZhuTeaoeD%=~0Bq&e2Fgsn|@b?~ywm2`%Bn@+0ehryJff
zi4eQZV;lN8Hb#j-B~Tvr_>pY7b2gxAT;?BE2W}guHn~Sb_k^XOPBaxu78WUY^Wr1N
zZ6IR~gFkxEW1%SKw^pkH2#~@a&Va>?Hp_ZIW9p>;{&%6~0tvE{HK$tHFZG(a(#^W#
z-7z3V!OOe6(nWkl^i*Sudk!c2Q8Ez<SY`}e&s*e$takR6`%OV}-}2XwtT{>Q&C+}$
zHStkk)~RFYt8p4xNX(1%H}G~279~$&ZmTL9S-0QU6USDv4W0V^TStT)i8C*p*XyGF
zjf4LvVv}U)Hkj|e2Ie(5$ZTuw(H;@pnA!u^_;Q!x_khuzr>x!gC$o(?9d~`(e<*wp
z=I32R-}^i((C92WXqCIg+fuM{u=(~>B>HA1l#gpq3r;pSh8y&=Ga>Gw-00#eyI){H
z3S}YuNJWXedhb>OC4<jnzt<X|!*rGBMkZIzFURmLp5s4NkCTL~6QpDxwGe`$^!|Pw
zFv_}Gw3=8xpOE-EG-WKK7M_%}+V8_y3&uI)e0M_YI}G17BP68K{)j7vo8oh{J{Yyw
z<|gg2F%*u10|>glK-9v+&1tw7kU`h!cUrgD<zo0q)++OIpk8noctarh3+}fYo_Nw3
zV2YtsjE%E%lvV9g`bOxOm@T+B9c<m<T7o1M$)||b|1>y#)(ETl@s^rw#<VT&Sm?CT
zeX-|SDY2!P^O|<*#;3^y+KIuW?jhG(Q8F|_vim-YD@moztV8{+yvl6ffQMrHR9$yd
zWG2s91fgJNMA_}~$YS6V5$gRplYopILThfXcz~f?{*$eA`LfoL$)mevD0(M_%%%m;
zH;9+L+bk>Y(mck^PVI_>+l*~H>tJ)8e(SjDS2X;m>w;pJ0XWI*aI=FiyL*F_!bN=v
zmdZz)q9|Bo9gJ*PvU!IY2^U7l+_>a|wp|(FUJBRCg7CZ%F8k-DpGD4!H0`C!V3Yxu
z22l80{#<wZXvs>$&|2!6{=y3e!omI_*2J~WZ%*11Lt7uvJ9Jpw-gS%J4axaw@cMMG
zbuxBoqZ4ygzq{m=Jt0wXprEOfAI;Rk30B~jyqT}P!qb^b2)LH{?LB$XKB(UFo4PTI
zpWUcf_Ol#p-js?_HIiLelRWdsT+xOX#<i;x)zTaiRnH|9UebApy79ZtPTf36265!_
zlOJbNE*@NxDC*n;wW<gBaA5L6A<b@oaUSi>D2V1O=#~0Vr4xAnlUd*<w`njEuiA!;
zmMEi8V&fmGC=%8DjkA!j;BX*Ku7q_p;+r%NGIp8uM7*|MxMyX^00L(u$D_{pd~;bk
zH|?g)-Z?vlK*d4+@hjuRrTlJA`{xr2)RnA=GFNFIx0_G94yU=V_%1~M@<{c5nEl+%
z(}HkHIDWZ=VI%a;^isn8+s$ZPe3R|z6pn>bAL%gryACivi6pMQ#qP;vTAxq*O_x@}
zwKPW;P^^^^pHSB8x0bC0QG1<?hjWj(t^S#+m&R}XgaW*G7A0W2^y0=xx=oN=_zAI!
zBN^vB1v_myhn@Wwx)ao;0MD2=80-!KR1<n%nwEXe(Lh(J3-^co6Rg>)Tbaq<$k;1g
zDBpLE9NxNg4Vb%Olb;aUOt+BKtV;P5=hZ~ky*xKhQ2cgIy6Rgm<sL256(I7#Dji?R
ze5<V^+}eIu%niyVvPcA%_q^r`ESdY1e<TU`MTr4MYNaZEjW6^~W@`!+Y48%wTs3M;
zOui9n590=6T(4T@xlj?P{14(^dZub3|7?*(I9OTSiM_DM@&et6-U}Ig&RLy|F!!Rp
zLhoV0YXOx;j+6MN6e2_jcHk{_-C_6l6z-9m3x0Bx4h-DA9H&;igLlLhCfQ;f{|bO#
zOEc(4q9}TsMvP+Sk1kwDaMK0d-i$zvbw*kIr>^=;q4O0iiQJM5O^YWEFiC@dP%j*0
zfN#v5rQBJ=J#j`%U>KKfA6?PBS4(SwtAUU7K%uK^+KLKr{b^(!hWF~`4dLF-CrAv6
zlQ<502gX0^ixs9S)0gnL#U(eij06xSHRc%JFV+%EE3rySCtsJA{RhWy(*J=N=-#I(
z_pQ2evTs5zewN8H^`0dDe;`XY=8vJD!ZNHip`v2%93}G<poz&^iS66+s#@fhUY`dE
zZ8&h0=v&m`{sAmZumD?ZcTcpaEF`5(Jv@w%sb@+7n8MF7y_*agcBnaVT}4@VFGdzT
zH1u!XS#*9=MA&cx%Bhw42khW8p+_x$Idvov4}Tv^oYi$h-mgV0hBkPj8O1m&-?~f2
z0<;iWfg2R1D#Q5B@2(7H50RDPKRn~1gY~qv683BEB4*J5_Y(3OB({f?nvkm~vRo6Y
zezLNCo|R1t)0V-G)YYnii|5tJ6BR)40jWZ5J|;L?oz;pITt>`l+y`!1c%{3VP`H^C
zL4AFi4V|hSye`@e$!%F?FhAj>?QD4Q3syw)RBFIwfBBp(#I(QlaF`WIJ~Nanz_79Z
znUfXHC?wu-GZW=Afe~{kFnZT&&|h|gKR!_z=&f60wxyyXLNb@c)7sI}EGF}7YN}Q%
z!H9H4Z<&bk333t<1(mc^ijtN78%8rI(Ihc}x-h$iCUqm-I(ixsz}d<2+cEFxD_D&k
z*e?r1-e;cgiYS5Z!sVX)ET@MS-q*bI5$JKegdD8x<$yKZrwgJJ!*{Up$;HtMc076C
zN`2823+T7k$!YXuvt_}e?fNs@!m2Fh&}e3v+|ag+tE}Fy)*9f3pw`1MV#7tjBH?@V
zmgw&+CT|D|UXcTnIsSPrfEI<;p$lw7wtYbzV(47SxuTtGU2Ck!HtW+hqb%+3wDXnn
z^}+m(#8j8DVa2JZ_?`4Hg(9o<SnN4Bdu)};_y~3}L6X~;n_@nEQd!OyNqw}v5`ZBS
zbo(iiWukhlt*8X-xo^p+9_5}S<Z&8$kt@lw@$vtAQ+bK^)JMXBbA#hivG?%5{Ji10
zSIXcfH=u*j`VY=TM6&<&G8kJd@uL4C|CMA9GVj67t0&b<eJ%6%hbu%ajlnuUsv0#u
zr-p;58}44xAT{k5Sa!r|)O-*bH9);&2H840%B3s2g#*#u>o4ZhuA7XYVJ^5CTfyfX
zAs-<E8<&=gll{E2M28?Fb(@5c{C<wcD$ElEiG0ZDjl?AVJW?NIE7|!mUvHf2D<XAM
zrHTuzQ7bZaP!~xC*T?KGDX-+GpoZcJ6lK-1I^;W+es`Cl(2TBky`;$nN=!o3o4v!M
zOz_y}&Wrjl)P0g~-rcBnFX7Ve&d0>QxMO}i?AVBzasQI6Zd&5)OV!{Fg+rO6dtFIt
zHyEoV6dc2=lm3Z-_({?+x=)P4r~)40mvo{2m{qDR3aqJRn*evsf}*m<E&sYMtvm)h
zNRCnhVFzQG7>VHz$0BV7)VACEHTe23l^spp8!;(2MbhFR<D-}8;Uucpdx-E0#A%qd
zA9S)8=lO)jVp<({T8{>!DsmRsPQA*1BS$TL@aqxOL%f@pN2I(o12r3uCN!o#<hk%f
zB_Qk-o*cr(uM8qLRHF5JRtRJTTnlBW6gD}iyjaqR88}P0<H!=o7I5I%JIblXaI5B}
zTE?i61oc~>eh5bJEv`rus!KpLjl{qPO|MM}F+=U}1SH>S6C0NV!*Jt$g(P{ejLiUu
zlxGdVVo8f%Ij`i3%Bs0TVWf~N0_LAn)J)%<AWaSI-{s^vR@`(b$Yl7zZsj5kxZ$~0
z3#nlqo7R~5NQkHkxv8v;$_$#QuWbln$l48P6~hdux=>PQ3}J<#`7&{Y+7}_hEdA&d
zB8^&z!upQJjassqNA!8QNJ5nO<!Th7{wt(_k~{nI^Kt`c&EKxiQr|YP6YGE~!H(q(
z9uAI%*!F4_X7by4!sm8`o8WP)m&VZ=FvYd$c$HjU(o!f@amEp9IxDuMEFJti(62sH
zMc8~goO<DX{T3_S1?V6{bHr;E#nb2Fv2k%o|3h-Mw{3y4DIEvI2W;`hnLF9~9vvPS
z!kv;E1gyphRNemdH;<`W7&S&XUZVR6(}ZMnQZf?u*h5E5MFdel{$k@(e|O~)AJvp?
zV?jmuw9Xdx(u5;e7svb-=^yz4$Rh#X9<hj^+@vBJWV}}e`|~&O`)c_LN(}#|wArLx
zs7(o#T5g&Fyq7mrT*IRbU+DCeCOYpnyQ<dx1)Mf?QSk;u9vHg`w|>S)!;)bvpPmAK
zm<SUoAc7O})q*0)5Am@#{;)x<fNw`MoNsE#$qW;n*H0CncvJF@k;HPpW#;mRb1W?F
zTtOTGSW=F}A>#(Jz)TKjFF*hyhvTrM>bT`arDd@kRL67Ymp-OaICq7>T#f(7bWojP
zKJ;0buN|tSFQ5a+Hvx;aEmAe?TNdlaa_GRkUdB8HC2u#EbENJXMdlTCU^#u}Rc0>H
z-xAvin<Md!{T7Z<MZ~jOaAIQ(-w{&sTAv_XCQqV;m+Tbi|9^rqBCfl2q?+#(Y=MF7
zuqQ&Nv?M#d)Q~G%@(yNLaAGUKp5O)adQW^R?7c6g|97t*H1DEPW`rjU18rFZfbuAs
zU!26Rn!|X2%^S_YdQ*%qp2yQjB8FB<I~uRN2<|1%KQ-@_G$Lip+)#2=3*%{bB;H(2
zOSOD4H-uuKqgPNnJPob%1gl(qaARj@AN%#|Q(f{bjjFu-$N#}3Kxl)&e@~OeZC<f*
z>H;`m;`B%zi$cCf^E&0~TKY;4`z~{Jrr6ln(;2IagoK0<A3s*Impxt1!eBf>w`Wds
zby{;dc2vL!npMB(;4|D~g*J-#Y?`&6{Mg@pFAM)FWk@5wk0d&I(&y;?^b-vl!QW_U
z!Dkk^Oj^5_pUD!JfBW8@G->VxPq5xHP8RKei13Z>AZJP)lv>l(RG5JY93*v204x$g
z1g+i0fXXlEP)g(Y3}WD+oZ5dRB)Kb-mqwGD&3%w|rMqkv&OYeT@c_2=88oQh!d-Kj
zrceuxxblPf2`4jg9c`sjNj#!G<T9Xz65q~s&|0Qe1OQIt<GzIO4th&J=B}@F?0xAP
zaY!K<LU&~H2s&t+t{&s#+G9Mz6S4<8`eumotui#o8OaMNpyNArn(e`2J<nUkq-Gs}
z7I(Q|#Cs4Qj<WC3%^WV;jCZt(m-^<|dl}C6`PUENocRqIuXoy+7T?Z7Q1ulzUp_^e
z>@$#@ee6CS{-sHPk?nH(iaZdBBK<Bj140PU=nHxgenvS^LQ{SeS?10-`M_(k;1@R3
z&HD(CshQ!4x8GN6;h*ksU-y~=JUzjV!)y3$3<_Xg6s;j*px>!pU3S$Zt}`dE(kHBO
z_hvk*TzWn=Ki8uAWlc+F`138qtk7f3xpHo3CU6j=0#Rg7{!sO_-9>-pZdc&r)|c@=
zX~btoooeo&-BwqT6aa5VC&ln~=equ8^4R;%R1UySNC{hy{d_x7XVC;Gbstswrk9wf
zR}e~6VmOhz_l#)7l0S%eyDu$BqH++~qXt^iG(O!g2>gnY40{H{OB7IB-W~gWIYj8*
zKROf*<Rgxt{-h;*=3nhJz_=RtC6GoCE+LE2bU(VI(9R5Cal!zjM{Kl(H|F-9IkIm>
zLo?=(kheO{;tQDKt-?in&YN#y;kQOm4k5|&-%OK(x*%}brOIn?@0va48nw+rZb`xJ
z_f1#iH!cZ@@i1KCCFij(u+U{$UwpA2$5yBhBC07GS4&L&ztW;c|At+oxfQX6*nizC
zH0+|@(d2X%9M#m8=(CjA#u{wFZcu`%8EX%#YGtD4R9|@uqY5Nd^B$1^48G{-+wK;v
z<Kax8KSSPkB7z+jLZC1psBrkT{_%sKinm2ePR(_k<iSla@`V?wzglrUfMx>j&7Hxk
zM!>SAucK`Ii&{SO%Hj1QWblvPapxFwJ!@IX7=7(UZ}0O;@ac>+BC`0A`eje1UB`R=
ztTMXz`9D3C5epp%!wB89m(e=zOl>Xb@!f{vVWPKkA7?N*m%H@e73Ln04fJylGnaGO
z2?YZu1lE#ZNKDox5T%9<K-Fg1eb>!3=L$A?tti`C*30Jkwg`jl3(X@#ON5TrI!cq!
zTz*^d`Q<!iTCivNP$Wv_?2|=^elPqvPinv2RJg}6To8I%P(CFLGT>fm*gn!pOc?}4
z$S2rwBSDa(D=2a}Ohna{JUxCYw{2OYXI>KwZGl@qyTz>pomBoKAHpwoeB-Rgokh*x
zH9B{uuQMEG^guauByJMdNp#wbQ3DCDJ9OPnJble9l_e?646n!*t^Jl+2{k9RTAIuY
zKiC|nD1KHE!l)$oRA!T*tB*g(mt-}|LVp)uj!7;`-g{P~+UwBlov>M$KJ##rEgJ9>
zh51nE7q8Ey`$Ap^+hOLu#La2G>Q6($d@0*$C3wrmm@%O#8Ub>qdK^B(mN%M)+-nY;
zxAep3XFsYS_V>~bkTgbX`)&N-NEKNvgBgnr$5k~uH_mvG{(7k3`=PzoEEez_(!%?7
z1?b7@3l?^;uCT>kbozvn>Y^3QT?qGt7h!`y&PMSsDi%4fSjpLx`OBl-cBAB@T0zN|
zLBNqKiB0*`DO}uI7upDR>Yk@hI~na=cyo1ci%NtNmq6oIUkt43u+~cJ^2z}b8mDqP
zvTcT<FD9FH!fkQCr6~tRoHM|z0w=C0-TV$plx%~NK!+0D(r;v{?fiQ2g}1g1^PpsY
zUg?KKLX)d4@&``KYcss=sQBEvhW7MNgQKT(_I-16D(k28gByGGihZ{Px>pSx9Jk*l
z8*l2hR^&gD@Yf{_hFcxzr7twh^~`;1&KvU-J=~nxFt+T!x``qI87Ua0!Di|ZQ6(&w
zL@3I!K#<`x!MESd@{kJ3kM#vx&D)c>&$~;hQ6A+qgJc_UVt$hBU41W-0mI7_dEI)v
z<PTTo4ZM2vQ0eLTJB#wd{0leoeVN^$MPB=`bPZgs__ed;?fLfppPn132^_KZ;{$P7
z7gKm^$aRk%vE1FwKZ_G~f4@0V%#W<@WP6$jajg-(eDAN|yXFOYil}fI9c~beqOKEj
zmp(HD0!+M~nwE6xCzhz&*0<tr(Lv})jTB2*x00?VUtg$$*+D$KVaQsalc2?J4J17`
zhqNA}!<r(k?0l!-`|(8@+F;8qWlaLOq2R^(j9-7!dK_Q6bstV2mTDrFYO~&~c_L}O
zg4-B6)?XbgKAUieJcH36&jR?nOV(=GwZADFgq<;8JZo?2Fsc8K+*EhT>%YJ27HNvv
z=81C;ZGh0td>K&p-6I7Qml9k*_Nh4t|B+g_Ps)n<TzRZcTu|ogd}590<9ovk-PMPp
z-IHHNen*vo3s~(w;kExm?AULXvHW8`?((;O1OHMOrvUp?A>O<$4WyoZz3uLgJia$!
zjHNF}+qZ?)4*=u7tF?`W`xq#(n#THl2GI2#{TI&b1@Cs5$aVvEyR5Us{aEI?=%ajW
zBj}wwirhP<1m&)34%IwukdW$IiJmw6O97fFPi@6?;&xyTRy<zw^DW2Cj&=wFCDc4M
z<!^?19fYTOc`kO{!U~jt1HVD2NCJHd!ja3O>$Kg=hyWj<wc$bSjWm4O3(cyprb!6Z
zHX%5B^)mjTYuo;M&sF(LzOcx3xUp6|Z{uvCa?G@Kq1XE_i4>NRlW(~K!&Ix6FA#_#
zotX2bfXzdMTC~MS@Qvh%=w1&XHT@#==KFR|-*+Lp#|3_<tT(g7HPIr}sF@9F@d6tB
z+WY!uP*13VB^o%P&LVO0Pm6q%l4qh&8gJ>tnwEZF32S029E6(fhl5Gc+39VHJG-?3
zF)Q&J++3kF$}_)`bJxY!E5|=(z_S9yu{<qOhfXpA7I3j!5zjZ+V|sIi!DOmXgeE|6
z=7j3qm!{Va|MIm@!e<!oqDqZGn!LsQEC5sF4bYni>iSxw34JPR0qm7%o)6S=UxbqY
zYkUhY?{+<OEfn_S1R>-yRFjh(t{hQmk^sU4P%29F$KT0WVPz=_<POr605HV<A0gEH
z@=W1}YHMi;5!ck#(<9EmzYoyR&~Qh_rd0aTkLlUm+}xkTV~J20W~`$_V3sW~X<*qm
zJU#u^z0Tw-0%n;<s)92Qd6WmUv^32sU~g!cR$$SfYJRS%!K9oFZ~%(M(a?erP0U>u
zT*4p=QB-zytNbZ|!1?m!OG`gkr93Az6prH+i2?S<bFqGa6i@gR$xn<)qL<%BI}6tD
zf+KY&;<k2i!K%Il0&paKiiJo~Pfkt%7JUq0eKLNdkXwMQpxg|TAo6KF0|U}5w;-Q~
z>tm7S{xdnA{A~-7hx1nuH+l1Tfyr#AOBqC0=y(L75f8j!yTYai8nqZqhlIrb46+M6
z;J1-}4RVLFl<PF3D1_jOmyr1cBBl3J-s_U*UUt1`by<GTYO<@88w4uBi1#QNRv6KC
z382Ky-osr1?yN5Yz6vXsb8^~OWC0i1R9;B;Ys{AuA&8%A7e^VH(oB{*UJw`b6M{=f
z^Xadm!!_K^i5C2&n4fhe%NPg+j$fjiSc(LYQC@C&%gVc7Kk~g%87#@a8Ge2`4QUp7
zcocVc*VE1FsrkvlV+y5dgG#?V>V#}Cl(O>I>p~Sd_jXXczfj7E<qKMiM+IJuSnIoU
z$+kJ-nmsEJb?C%V3tD6M{77&dV&{;ufvDV`tGJ&!2>2iLA|m%iUSeHQOXBYBX<x1;
zug6&@$<))uHIOi@pUNXhKVn_0xw0T)Kd(6&z=utdpCh}7q#XJ)NjH(I+)VVF(SGc%
zq%RlTVoYtzCwizWC^s==20AW)6DlKFb2J1BPBQ^GNfp_MiC8GytAZG9gw-y+G~t1}
zTt{jFYx<)%Et|S&%@#$?J5S!ph60;L`yZo2rJ8v=Tl*o;PdJ$H|JVRvC<7SD1%10B
zN;Uwu)3^xt?D*Qw8|Z))>6=NRv_6@(6fkS@nOGL%pi;v4R+rgkI~T-`HZ7U`SHVn2
zL(kN%a*i5J{*$G1DR*}*W6O-nr7G04L?f>=fK?;+xPc$99X$f`#~>=DyhrGu79%Mq
zWD5>j@v&2wkU}IMYqA)#4Ak8h3qeE_*(M;D$|Ts8fR{Fd;!M-R`)3gRvL6}ITCsCg
z-p|IQ`ZmtUgtfnaN`ahYkv7S@61*>pdMWz?0%$D7UfL<7ld0KLi3=o;Gt`EJ&FMD3
zCZnd=n~St&R4h^n@1g8-6erDrL*$$6*AcA-=$|nJ_G<DYPX?J_pF>K=O|mq#xAE|8
zHc10F=|>gq_s&xC=ymju6F@|*+50SX)wfVQz~^<y(fIiqRqy3~m&V`ab)G0jz5eWZ
zoQkx(y~v_{@@<)usNoGds>>DrKP8?mDwNcnJi6z2G|+3VcB)m*SxUcQ{T8F!pS?7e
zqBhfQn=tFeZ!NhpFYMTF2*+|$sYYS3KGQ`cr`qMzu}zdR-N;}D>=HZWyedG7eBg^=
zy>eX%4)1cDUkE226dZo|X)5DC1DJ9^#~g&KNN%rJ=5>wfTp)FO>OHZjGgh*2laxsm
zrfsL{#e7f=yodW=L@k@kU*H03&uR^=3XDbBmK4Lpoq#C31uMa9F4)l~)6%1+TN~`j
z)ikamAOAb3?#`)AN@h<`w)DFiZfa`aQiM<hQy9wx;0FyP4r;7MQg(XoDOc8RT4kw;
zZ+~fdH{DW1Kg3SwQF}4gl~H)pOFK__8&aE^vo4>6M1CnyjQ;er+Gj~g;BCh(=<#QS
zu{ik>58h#pW{m6`V*}ma*3q)B$cfdyt4};GPVbHahXAL{TH+7PA?HU)!c=P{n}I6T
zFHVtngo;5(xwLFIY3SZoG6WGsaHuR6L?Z>-u^%bcz<Q3X#HrwSSR7FXi0qn?^1oOa
z)0f)j@!6Ft(X$hnWnDCpHIDd!FZ#I%0$d{x4>i!ZoWO2Vi$l?fi%5ru$_sXHG$=(k
z^<PCmf-+Etf|a3CdQI_0XhTfX3)!P?l#+QHr~!#^?Knqec!V!b;?J(+KmB7wFE2kL
z+&;Wzwc$b06Tg2gaR?$eKriO?rO=<p3<_@$?#3+T5^0Da6(x%{iVbZfO<>*cTW@K5
z8?eb}X>1`y&G(vaI?|(4?H{mHE^;Dl;pa(ey=q;ItghnTBwd1nm5sxC(_>MCOp^%(
zKJMT(@uJ_K{0x-mi8q|w;rhG`5CV&ZWM6m7O@PQpkXwe7QBg}D?TD;537pZs-u4=V
zdp24b_@f7&BP4(H$k2|=L|`li8$W^&o~lFh!)={`t{b;LG01ZUcwC0R;aLo&z+|cG
z7?yAx)f|}Qg5m~Dq_Dk7QwvGapfpY9Dfd0w`O=#R;>9pp7+~usi2JjjOuNi(Y}HQZ
zP1L1_IFY#bv~#uM<Ra&HdetI0H69{R-FEq?%@zpmw;3kPIJ7-d3*xe<bHh3{q!)sB
zJWd-AFX`V9KGyEIzWQ3>KWAuFpei@gtt}gO9A*aP&J-#$8&~eWh?r6On(9@Xy1;@B
zC1AjhZ3Ja`ais%z%QF66H_o9j^@pbOGo8%M5rW6#o^E{m_Lwy60+k>foh0ZDllxp{
zlt70!iJ`8N^B99{K>>7(J55lARfwOMz{nXR3~X$u8ij?XyUtS;^XWtRE_*!d>D@^{
zmOW2Z0zo=%_#(ish8Cn4Cabe3Bd&-&%U+s=jTynasuzC)Or3XV`0g;;=|TLrieD8A
zfQ*GFu5OtB*Q|EsB&f$2`=cdU0`fAFrvCI)q^2G79-l^hUg{Rnmkz#VMPL+4Z>+fs
z0qGO&A%5y+TJZi>@YBN)(W2<F>$2Z;_iAXwoH0Rc@M|CMMQmKyHxdwei1K`wFt!n(
zMNgQ~xGrTYRm2kE8d{PY#;XN9+9H@zxoA@J%H_s+L3)6e_q>w@JiPsW6Nu1jhb0Uw
zAM-nn>~hJsiFVl%^{!@cH&*oB8x}d!pRCJSROEirFbMe4N=JF8j@{;vU!X`vDtTgO
zY&+Z_DgpiSX_0ajhUd3zPdDo!STvV3%y~Pd1)JqgRVIG~v{fPo-+UOLbP<yDSPtvs
zO(XQrB*$4JV{EbEE5;{Rf*@ZefGL8bXG18ERdKHiZDX*jR{zvJFeL+IP&PXZOl|S`
zT4yz6KI)>jfz++u;hK?1A#JCW3Q%++l~hUTzWuOd6?XlMY`>>Tc9KFqkgpzKef%~-
z+``%q$!NqW_TmofA}Z@|Z6mp`y!_RDrp$o#dUnIU3Z~8$|5N^uE2FeQ&;ZM8Wnd>>
zFbBVuC&Wm=)_-n{AwZcPqJRTw<_opE)O6q~R%=C%(ti}aHHY}jUZ6;ze)IsG>$KQh
z0*t(y;Js9EUXU_n-A|k{yg!m&5mmUWo`361+&bC+#Vxpf08^p0D8I&kmQhVqjt$Fm
z`L3=f<fMk8w&gvoE7m#Q^~%(Fg2PuuloT#CMV)f?!d)JM4MZL|`2EBMk)JG_%+ezx
zxR_8(@M=MpP4wJ18P<<|fx<s~mN9`ZlkX{2U{pz4$SDUXc&(i?FxlxL+f5F2U6?pp
zZJ>Zt{+3N!8&yMb1gwZVr;!j~D}YH%ffKGaG<I^D5?+kx{(HmB(JGNo)mTT~SG^5v
zNa@~gq^)YK-OzVzjky;FIBD-uIQi4nE1K;PToNbOOhWPdKOLT0BFeRDAW4#QNa(#k
zB)nQ&1ja-Y)RBKd;8%V-Obqt)Pzid@dZOuIfDQYcJ9k)ZQVG1OBCTW{IJTP4m@nx>
z2;MiPKP#t)MB@GGoh9cY3aaM#LVfzKEm_b-b;rn_XF+bEMO1#XunjiL3T#OF8YbIM
z5h(}F8be$q2@m%+W3$0?rR>|ap@+J)C^RIJGVvgz^C3rU3Zzigj_Pk&s$YNF;A%vp
zOr#{5qn3RE=fDWyCzL18<7m5J!Bl-~AAi4vaJhAxtSn3yBWrVZ!`+#1C$Y&Kqqp2k
zx~;qTE~&&{#>qxStKnX@WQ=(~0LUvXD1x2%40nLrD?KLR9@*Jggq#$zE(H}^s1dZu
zj)hAvuE*`WKqZgHmZ8C{El$mMU;AmK4cJ$*XG%{d1{9%S6jSc`7ra2Z7Ox;MbVNj*
zw#?GjtA;GbwJ=&ADO~SHO*5;f6^Ip?ghQXdhWm}^?Yr@lvCnf@iNo=$+#OlKpV?)M
zIhV!)flgD%=Mwxn;?+hy<_V3z>)5!i?gG@eKv;+MnH1&Z6?%B|DtjrSJoH=9tIGzo
z*S=U6^F6{M$KHwv%dFrlT3SArftBXE7q0m8VD|EiN2fPaqQBm{@-$>SGSpiWnkU>I
z&!AlaW+|4=TC?1P;z4X9nbJJe8oaG_zag~8g1b!l39<NF_>Ms_qJ-<`P1}k=R8S=!
zSHh;DGic5Th5#il9;a4>Arqw_B8hPP?&+7cmU$|+9sgrAPx%(5lnNwv?n9rDZ2CP$
z@12MXHix{BHY3_g7lFXSUG*NMBSCqA`-wDY038mY7Ft$d7AZM5L~2*fb8*!9vf!Jj
zV{w5te2^3j&2%Ly)4Ur%R*Ha{-EAmjs89%c`C1xgq`33!tKidi44yC!P;bF)(Y<@;
z@__KF*heqI%2b|lmKc-LJzi?4PvoBKCNLCyiaHVDdn&B`wZAV>uNdolVXbQCMhj;p
z6{M#bD-!J`fTH<cpnV@S5PA<`QDJWH6C`>+0%Wy7u7B8kRsO~)@J!0y;(rmcsnD0u
z)G#4Haa7$dD-Hf1p40c9x{i*=w+cO~c9Ow6c5WFn3>)>=Jm=P$cK@bhiRXt+K$=AN
zSIdYsgA0e>O<qW-9o%}aK&IU9qj5fhK2x1Htfupg{m2!R5Z+HI0LS%~e)=O+zjf~@
zJ0`Z_{xsW?R??k7IMj<_iAeUAqsdw<U%i%@u+9%o7P!Ybr9d=yOPt!(h`WZu?y$NJ
z(V~;IwlQHC{l`-F^#P{5{m+HRlKnC&|2)lgw7jYC{0%rBz)8AA6V{*?h+q~uP*>Rf
zuBM-+><P*9YwygBRtIMud{P6Zd(=6$Kk1C~OhhStzF&h+^8fnyFRNM-v_B!B%kV^)
z8|Fx%BmkDN7!$P^PF*Qt`#d<ypP2ltwuzjspA}dHOl15sN&As>?;ercqw(9GsFw@F
z>tZ*|tT3YxWF5Lopp81|iuSR6GKgB4@n&!+@N@(4L8a?ql1Qczx&y73K$}n}3KRO}
zTwv6+7Sp7=;NXD9D4Xw}D3!zSq|vz$uzRnqCK)_J1kzPTN(0D+uTG_JEL(sH60Sdj
z4nfW?ep|zj%hhi%(6bS4`u>$RVpQ1M@HeC%9OA<(f&3J>4V{`RocD(YYmufEZ1gNq
zjMU^G2LT)dYeB}IqzsIh%>wJwZ4W|VUXNTxrwTKLs~a#3ePBB}5@5Ere-kDr!tIXS
zIEA9_#0JN0ZBi7vtQaEc1bBXL-osCSOkDp4b!b=b!#6@f0q^UgTVKEdVEnBTgg%@q
zGg8iigJC0_+_z{!Y&(m3fQk(sNNur8rxz;zy)-=8jZ$7WfAUxKU;Xk>_1pc#GD3_D
z3{%J$OU|Chd;fF0X7?-pe??55M((%+({~4kUq;y@@ifwjEHE?_(FB`lM#E6}!59De
zX-P;Tn>_ve%O<3B)Kkfy!Q@B@ir8$fk-WB<NpK=L9OteeSzGcO+6i~i<bg>){R6Hm
zc4Mh&XbAficS)J`yeZ^*LgEJ_^AZXvuPx|$l|bxZ#Z7MTPZUs(p|0@ez^vLp3m$&>
zNnJcilw-WH)~E!aj+cSaA3m!nOfTkfw6I<xp#3yB$@gEY_c+t)PxPIanPf9)2)nlm
z&;j0C3Vsc?Z0<WY;0LG}L{=b^e!%v-3;cKEhTa&0<^U9cmoexi+6(*q^G&)|KmJ<1
z2ox_GesT=goBnG%a5zg~o(yYds#an*1te9z7Hf>HzYsA+PaB>F_^m1Q<~?y)S;#WF
z@sm^>j0+x6CxoiN(#2h+b)nm++|Zt-Qd3A|oB%BJIR~8y!SMv$N9G;QG)yvYT5w*Z
zljc!X^@#8`(3$j~%L>GztYrn?E1#~Q3*`Tcz_8!V{yI`4Ym8&g%+QLItRbU<<4qh1
z8kaR-B5Li!XUId4CEmHA#mEODa}0bzz<)x)1p`Y#{^Tm8muDNs<&q*x;|CPAhnoiX
zN{9cHJuq&$apZc{$QjxpLF^fcNpt=V9dndksN9_Pe;`jy@3Ru>=+z*AKa=nOQI8VI
zN*?!?ldMe))YVWbLt%x*ALoHzKpGxEpvpgD<%k4t4+zY_+~x4Nj?-WpSn0{uT4`cn
zz?yvZ82>>lO%t{j^4Di!F_UVSdh?|UJ;bC?domx$Brev2rC&W!%9bNw8Gp(uo^Io;
z-sreov9ng3i*coxTGbh}ggXJZC)D!|7miJUOVMI30VS^SPHNb!ZN3gnk^a$(b9&d@
zDM1q5kP7R(zG3dIfz@*q>C>cUDbDYbKs&OTp*In~2C3tQUcTy3B~(+C>AaB1B%PqP
zd>UiJv%#_ed<={-JH3`VRk4(7;=Bc;G|z=oh3A!OZfj3zl%)W~M+gPS&0aX!_WX;u
z`++Q7EgN_jLCycSJqgmnmI_^7Py6rh_9;|TKAumJ>=>~qzj*5sC>X@*R9=SZkaAyj
zu)zAc8_Akm5wLzzT33r`>uQcTq2JE)C(isP_iNkKptE{WtyJN0P)$`fbDqs-Ygie+
z)yW6wp20Uq6NU|<gaiw&b_=Q!NgQ~4NP*VO{x#;*fd-mp#~iZS3&TQnhsU$`q~cs&
zaSJ##ND0LH!Uh>Z+Hd8JXZx%U4vurSe*qmJ5>`_}N1Z&G=JOBJh(<27FqQCBx+1;f
zua7~yf1<LzK5j_B?;?pP%hRAd^-Ax!-NXeOzFw@NAKDH1-I^zCJ?KjMp=x%D8CAuA
z+q6{5qhlh+8D2D_tYqB0tZnn>(>}ZK#7cWLy@>PD7w@qX8{-hgc9-Sj#T>QOEpe;9
zRolPIa`~93-qY-uxoZ{l8~keCH>&L3?%9;5_Vho1lABFmh^z~1CYZ|NEdjrQcm8bJ
z@?ZuMvWhCHu$r30`|<aI3GiK|Kn-LB4UT2jBRKen%TO31Ed{jJqbF<=ARjDnEUuBK
ze7&-7t;nr|gvU*MRb)1T*>m+2kQDAMatj!*6$^PFv()SmM*SR(Y`MWo6&pUU?}a%c
zRsqpGk-y>5vBeTaPkX(vXNq}C6~FCf`S6Abl|D|H{ykTh(J*HB-s?qk?qcT{R@Zxu
z3T6>nSWIVz0|m$%Fk$K<D|P6<Khyf{7LAw`iyj+GW_<gr#NqJiZG7$VTto}e-YdQ(
zavVPcXpQAXSD#rUR5Vsm5AG9WxD=C7Ew`b3?vr)C3rx$*Z%PXpzsvP=?00=HaWc<N
z(jPRJxbq@-flYaJ>ToI`a=ljqeZdyeAQIeA3zlyRvW~2R-fCRH_YEgh*Ey{|j{x?K
zH8FkCww}I#1QAAOvq#Z_?SV^uYk_#>)JE2~JG~tJeLF%fAQ?}<8nFs~eW7RrF6I8d
z$ddyZGP1!>Y!CN<#ZG@NENTJaXDl#6o}|F<eb%CbXID0V1ufU1Y@w*R{{8WXfCULg
zxd`d=U5_d7NQgZCv89K0QI<c<?Xbm~t7d}62c)vYuSJ8o>B_^Bm2mz|Nc0d}BtW&V
z|3{g8I2YZ6E`C&YF{2nFJbH+3rR;h`uc-)^$!>p8mLVgtf^d_BTW-xA;-4gc(hy-E
z-89rho9xDoVQgi1%4$ehzFb3PcN!&ab(v%aJoch6q_EoNEy;xE^!HB1!eb?<Uw%(7
zljX}r1&5x6%}=QpihBE@Jyf7AtMdr-F}M!ha5;3em<T;*NNzY;!~7t}HxVZGZ_eh)
z1uxcP=t;Gj@Q!87thHoDq)Zw^PI`H~+>vdV8LsjF&Fdz>YI2P3{qn6waIwkY>}vq<
z68;Mr0dB<l_Xpb090cGe&Ri230`Fzml6;UdOY?JdI{M)0dgu69?Mms8S;%LU@L#M)
z22#MfED$=8rdBAC1KbXfn7z&WQ{_66pZ{t9PaF#90>2&~AOC|sj*gEjsdbL^iS4nm
z6H7|y4$vK>yRpax_Le*S{*fFzqCe$o)uxGnXc6UDR4vKRfG%E;t(gCVNoJ!Q>1l#P
z`XFl57s!GLJX|>}LPJ6gkUz5H#=ZY%RuDIYYc$%;1%xN}EL5&#NdhgfyLfxibPSEb
zP0C)A8VEY+Otf6YsdL8oFkHj12;u`g0O{b7XO+01BP=24T<|&S(yF}jW-hkfDP@eA
z&=zVUC9q-h2w{RGvdC>LIwv?bw4FQ=-VzQvu|`P;juC=(>F_+rMDT_pKLG3)inCau
z%AQ>t5a*$+#Gco}MBr#E23DT-VM3O8NbCk3(jaiIeAJk|Ap;;wKw1n-T=>_C9V#Z_
zInYgFpI~f^Cdf}Sqt*3!QW}Um`}9HnZ#f(#7lz?yj27NO9K>s0@4t`Pbg<*+9L>AO
zfbJ67nCLt0bBqM1%}-YOg4DO9T{v^&%hkSPhrMX@zjC#KJjZ7slL!zAP|y;#O>OCp
zB-J?FTgl!qzD443P_yg-8`m%WVk{OglM_1gC#XS|QglzEud&_Bb?4m^jpdMiBh-vo
zq-9=;9V)j3U$f+wwc|$>kF;>>m9HF((E3vFVTk(s+?<rf*m#fMAFg`^s2X`%{NY9;
zPUHsbJc*lli1oN|pQ{j)Wzj7|mmSK%9pTumQ!%g#9N4FEqs_<t>5<-<KNOUb+bp^l
zbWtw;U{3h};$gh8#;j$)N+-DS`7(xpReAzQxJP~p%KMA&O}S#(`prhEO=A_jFj$R#
z#`;HDt<Y`9cnx6iV86br*r-=WTgWl(-VS<ITU8g{2xc!d;s21?@g7E5io-ka(YLY~
z1?iZ}P9h37alYe4R?i=)BTME=YZxgjq@!u6ZMZPUnVZ8}geO3{S9Y?OvI4TW8qvI3
z;N~B6Sqi4xo1YfZK6UARt6<WzC=lcTkTy&<3kxRC@u?h(dPoR9`6~B3XuN&YMq@uh
z{xXK@*qR$j>{1YTm^XH_<R8ikX?-z7_c9K?{JLi>uF{vtm~eS?ng`#7vk$B5k}*h(
zBiMwhCgoK*e#C}MU8^a>WumrJ)t06-QwZez4t=L~oa&U^l5!0A@j&a7*pYf6O*&hZ
z&Hc;sjdUr}SY$(j;n&m5*6z5$@pr-Bb%FR4x%~fmrvCay?gs4GYh=H8m4CMgDRWp{
zv(PIGnR<-P2ICid*`#L0YZ3s#p`YIeWn6s3LUtPv-92nF{xRraJ4J{r4-28Fx_iGX
zee@b|A7fD_@C@8a9~hFYO9ogFmBZ3ua0tKX8ECz&M&04yUM`j8WZtrIGPBJ%DUBsi
z405s6GPDlG_n=v90kA42{#r6>!OcXr<#5qPx_bS#v1M^iv&#C&oA5CA!cW6tg=uRX
z@dcmR4#ZWMTq?)eLje6Pq!~-EzrBe%{hwH)xUa%eV8p*-7Veg@ZBM|uhCabZfVY~%
zryM83#MZPQgQK$%HCkD8^8$n}e6aaJeF}|EKO$|#(~c!K^6Pf+aojzzoXQHF0zFN$
zqmzB^LlLhq#;y1(W&y`ck=5KI`le0p0U133(qDP?(=UQ<RY~_1KuWfoVxp+HzflmW
zv<YM_Fl7#Lzk@r`x;3Wc^;U#NB~a)6iGIG{U7hzE5j`?E3KXyoq|AyaSs*NrS4clc
z%4vU(ZB0|Yxwb=?IzG<s+%YsIc;2KlvJSL>x_s4?`(M}&0tHjdQDc;h)Q8qgL9sGV
z|1{?V{4?f~G(gdsK6TBCHG&2?F1y>n4>l?JV*L4!DngPO{kn5t096GPNNlIO5a#%|
zB26atV}zBr@<2RkQcl^3D;uI+GK`pLMk_4sZ2=;|&Pn_!WBwz)0uDuuxDP<In{*;z
zr9k#hazc`*(Q6RZM?ZH~<r~)r`7SH@yjr6sgaxgY-Z0sx7m`LLgsFcBeeUGZYy>KT
zffZl(SNdODw79(fuppuVk)T%EKUkP8>sj&py?|I=(qb+Nw1O>j2jx{ttd9D}W4$A~
z%hoverGh?Y7C>KN;HsR@nDelh8y_9<9i$@Fqe4T)0``R+(d|ej@uq%`v`F(fTKr%7
zFhE_GU$xLu1S4`94zi{|tmbV8LZlc1$5?jhV@3>=d{dvdEIlQVao=fiwKg~|q?ehK
zqgSCwz$I>wfDH!ZDKg!!B9(O3VV=owsM=tehO;fFFD}1@@dRdnjIwY<yS|wXCICHz
zow9oC-uS?YkRsAHG9izKx@qFU$D|KHrq3dp^7`=SJ)*6kq(|hfL|X$XccCugmaRf2
zZ5H1fQ(Hb8ihhHagdk$G#rxEYk#i-td>E*LA9v_jLlr~p#0*71w!v%2GXOmawjCm1
zg}Nv0z&X5T>74*UUcz$KJsQMiYksQKV3zfzHof|y+6HhAmEzAc{Yhvq<W3R`_W{v8
zJ?r&c)YwRH+)2yG{i&%V-6MSs2;#2qmAh0Q_BL6uB^Ex`eUMz9KZvrY)+ab3Do7V5
zmnANn62NAU6u)Ikrz($kI{LIee_@RYGA@lTJEMqewb&;_k%Tf++VXY9B`{bQpv1&e
zRI+kVix^7<e^ot5g*+aGBvHOwF`HJ!$wc*D4bKlhvF<}<-USMTVT30{*mr~{dYByo
z)*R9CQn!04)YrQDv3PsqmDejz{3Sj^K#0sqXVh*SANuo6=+*~hWHrW6I?*VN2B`em
z+%R>qe=A^o@6g{7ei^9XsT=e3&!Fkyi`=otp9q{CE3)-0d%N&$jUX>1BPBU}S0~ik
zN3gx*(z^c<QgP8T0%}WkM3{^&L$U4W!3HKH-La}w41s5_Aq^y^D*nhKcNEsPyKYdi
z`YilHNqPHR_$*bDa`K)q+_3i7#o_-a%S7#eHat+VNb;(yt1pp>xYKfQa&SD~ZYC`(
zEC7jQ1_NxtR>8r+zP>`AL^7krflLty$C8_)<HhCW(9=JrVf1w23kFc>JT^7)!NbD?
zu_p!v0LJhM^`FDv3AD0E3gD{w`T4oqYsbZL+Y48))lD0SMgZ9!&Zsth@JJ|#uRasK
zf4UbmqsFI^{t%BLP<D6-oVyLYw%)vye;5Bqf&U9s0QqK7v3dAr!l|^h&pw;mO*^Xu
z6Ha1xHH!&NdN0h<$l^3Uw@`eCvMcjLddP}?f&zMltC+WCdV_)^+8Yl0kt(pVJj<)z
zbxqC<y_{@A$Uyz-Wk@6+yH?N#Ye!`#Jw`p+T0)s0L&CK&^%Q(#DG_BBL*dM`i`O0=
zO@UP1B^6A0nqjCejfAQsdni6in!l_*$Bm`JHubAv;Y{A$O+<dwLM3Eo4t~9~8X9eO
zs6doN)|l}*LYpHiV>PbX#jxVgprqv;@x&gAO$ZM8_FALk(ED}qB4f1w+~U1GD+cHN
zgIssdHG`OB6HqjZ9As^)I9hmqYW9Oh!;AK7E0_wQS^T`sPsGAv<-bRRv0ArI9LwDQ
z@PM6L>2J^Pe{pl5??>nXL4s-5Dn>MD-g-p%eEA_=crkIF0Sb`@_4A{Q44hn?J`LnZ
z%Yl*#Zxi%gtS)QC`~B@zoQL8+#o>g=P>V}s9bf-=jE=A&+#(vv0)68|S6XO49c{D~
z2ntJ&&W5dPuv9~>EH4mcjX98}knTTldd5h+P!{()FCBIXWlwlcW;d#9{@8N#KTOjb
z;;d|XvLDYpKBR7?3w%&UAFc9}q8B9cXUBM*>?}I<hkpL+FIy2Yx1_G*2i4Wzf3-01
zH5<FkV(4@R)b(j)jl1P=Kw?#>4)t%aIl)b_sn_h1J4g(Rr>cdGOY|318~7P$V@^Wu
z?oEGsG=2vC#ypKk&>#IOXs1A2Og5J3IwB%!?N{|wx22|7Uoq~TrLD``@FkX8anf;N
z{qEm5bsDUyc`)=iAg_J!)N-<d*9+kT{Z5uYCupeO7T)}zZOL9JJN8XwGJhz{?aLLT
z0q?hzCp7rJuX-O{LllHxX>&P!)rsQjb{n?ds?K0};O{W{jbL*!UCM6r3zzRTmtIrX
zwyWgMW=5#M=6Hsu6??bh#Agwfda{x?j%!}15yr2Gho>yjWOx}d0;M@QQ;gsOpJwOy
z2$_>F_~?PY13<7@;C9%e^uGD$$?|w&=l<ht3NZe~E*>N!I?*RyQdbXRGm%_L9DPjo
zSGka{pB}4$o6f)e{MQM;8*I+%79-|ADE-V*-6(twyPFI!>J-7=A;bezx~RhRHBR5x
z(4vxWAEL65sqF-pthqrF^hmia=CeSU;?^T^LYHX-$@Udy?5>_o=GCKccYD5u*l8H5
zPb<!7o?Rp3i8s8a*U=`qY@7>_`!iRSJe;~u)g#)Wb~vrC@Ap+@6^2{n`^;NwO6i*C
zOQEw;u_)Eie2QtYl-=$bNV{SMq<78D-~?n8M^qWW|M}wks=h%P*nTQMFdk<S+8@na
zmDap{e`Ld{6&ZB<E-iEV6r%+Zz{K~<-G9&<t2vf}1|){nQ)&3sRD_1~){mH;N@$-y
zvTfQ`9_S4<lZ`dyu%dC!t>>3o8e|Q)e(L*T)uh!)bFu#!oL5X(ht&*}v}zRAiaG3w
zO*7h3YZ9(iVV3?|*<`t?C_;&OERw1L`O3N?<;}$h&B>O0OKeGxXc_+!H-ShZ=#>dH
zp_c(Cl~|xEhRm8}NK!2<r1q2CLRH8e(^*FztI6#N29*Hd7;c))Fq4$tG8c8y!G^D5
zD1|P<0`nPynLSnC62=)Su)w%F7-9NsMw{HrA+V)laNe{W%gUwT>&pDGa?c4J3VxNj
zt=BAhDFUl{DCY&O#0jhyB;BIUy6waVe??i7^6H!yaz$-R5GIVo9n;nNZV(DQH}{{I
zhQLrYiZq2BB`*VZmgP&Wu0|F6@K58fCwN=YKbxlxtLo~Gu5h+2*yk5CIhZbyiVMzs
zhsXvNpbElZfVkUjPLdzxYyL2=2mPhF;chJt!O}~OYJKGYAh7C)(XV;5=s?WkmEF{E
z{@wly0T*&n>eb)Y%I(NJA?CcG7-0t`K9@H_74ZVE$>;^-iZP4T|GleUYW4A2MBnT*
z=<>NL(>SrZer)=wDfZpO3nSe|d+xa$L^x3=@irU?^bT)neSd?cY2iQJb15rFU|%|M
zZX_zMn%nb1Cj*`hIFXIsTV)WdnlzXW(@{O(kKF1f-~YqdTSZmXzER(>X^`&j?rx+c
zq@}w=K)O3NDGdVBjevA_r=WC-G$I|+lJD*FKlr{e-jnwXaV^H$Yp-?3b<O#kN#_Ks
zl~OGdMSocZR_x4#hG2Uo1j7TuKOjENV0CS&7bQ$@>=D2pq(xXzGL^^jcvH%m#(s#n
zeOz=cFeWqXPv+>ZxLImXo5n+u>E%}zom@{pcc2S(J&Guz4VJ9=K^)O?-<~uU7k^}e
zzI<QiVs$qj?8knL?5-5z{J@eUYNwU^s#d=&tB6lCMc{pO^{~&P6w4c`?t@YSQbB8y
z)T1}uwHVbjIGY!dV`t@g#wG46t3>1^1=OHq0H(aFMP>}Qls;iyx8gR`yrM#Ok?}Ui
zPyM4Fv^$Jv-&2~TMvW>;-tR_Ye8k7T;lcf3537qYP;S#Df9PR1BNQ7Z{35K%plB}l
z_KO($=8bWf?ot&pi9Dm*6xL{SiA!}Zx7Mo_oFT=$OCDR*$FvyBBRqsudFp4^`h={4
zlDny(&bZ<`4aQ`hh&;~2C$dK3*PB1;)VHrcZIWy)Fe!>TM%@AJ<9iD-ISc7;d~DMa
zYd+B>Du3$JJk@GHaoHWzQn*Hk{`X$|6b3uq*%G?h-*hMYu2`Ki?)J;+HVc`kf<w;o
zFC@aMu}rfBO4S_`HL1~*oKyuI6oTpZnx-R}W<O~yr+Mj9PpJ#@IT)qE-0bVgq$Xhr
zWESuhy>IO`!&n)TxwT)elXx>7N{O<0K9vpeL#@(0;VmrCupeP9Qhsk~K2jGSfXGNp
z23j@1QL9LMjAXD2$$2wlan#!D8fAED&gT`l+(D4J<UtsPpwfQvj+EVKik{8&lfZO1
zJ$Dz}-h`Wi{j7x{aOuuRFCZK?E4#U6HODrk*SqikS#bq}C*(RW<M(e>rcw8$wzhDx
z?hh?X9?)yQmA4yv7M)*n=D9r1d!G-a|9z*$9vr}Mq!`W)xbh#@E+2fSH~?JVOJ)(F
z{Z#6|10L%kIT{i61g)RD4*$l!?f_#8^uM*w_a+Wx_drV>`mstgbQbKZS7a}+^VZYf
z#EMVAW(8wDDl*xOG0E>+G5t~Dt(2zGN;Qc77=zt>SN_KaZfk36`T5Vi${14?9-oHb
zmtKnR3LJ%w5T#|(mm>d6TCuA68s5~XK?A;cG|y!JUQDYhC2V(~w8@+_qU*9n>C~50
zu#YM3|NWIwJ4{x`OyBvv4nA!h1Gu_r_~m<pw)O4bH5TL2%0|r@4@rrQrb~B`k}|cO
z%!f48N%(sBUP477czf6XK4bA!orht5D98GIrpV;G`L$pDzkkuBTz7i}o^1|>loSh&
zbE|!ueNCcE>GSES+2g8UWQkM?EwlfmTanpOm3gQH59geecP)r9C<m+@Q%xdNq$-sp
z6u-WYNeP)W(A*{Z?xGa5n5h-kRf~5ALk`E7QNPR#Y`VOGRyg3JzQElpWnv<1Vx6*^
znP5`ObUMx;y2bMgwXu=Nr1TQpU`F+hXg2kSGeqc-)@-&8wrmi1F$0%pK1TYXC*hKL
z*_BU}DPe0C3P^x~4u{Q>PAVUaNpjjArMpY&p6U?kti{(Yj8p0iHwxn_yxtv)-opB<
z+hV=pHYRdY9bS=g8W!6f&%I|MVH6z)5C(n8Ycy=~pNaD846Ks*0i0rS0s5rL_rm9j
z_-uVhsj-13xM{kCSC=yHZCA?*LLX~h{^)$>aj1w-ygqmwjN7c3;VA9?TgE-Fp>B6R
zLoSM6MJ8Qi9b-qri0Ao4(*-C(Jf$pmKSo+Ref6Y~@=3WlC8W|U-0Wbn)ygOF7fk@c
zs(oYoN52~3TA=X7@7etj5(QzyOJbbKw7ERaFQSikaK?3$DHi-N&Sw^cuMfdFIsMV|
z8Zr1yhDT^7NB%CWe`oGIqw%E=z3Y}|YYMJJ_F$@(Y%7UACl6Xojruy<<b**8*96p~
z&F@{S%Ls6KPSBc`9=-TIJUqt7$7?IAsvO?GFR!V2e)#KVX=y1VGf=9QSB<xL+N#ga
zLf^5kWnu`;ZRm58^8P*iEtO_$8AV&ccHuX3Afbb2u7F3nsN2v%84jM#hr#d0kX%89
zbfJ2XEHG(s;8A7dQ{T{djM}yHCkrDRg9E%ujqBgqUk*v#WKldNlZ1^5aXlX@UDn|p
zp+6=njK3!|&fW_4mE=H)iiHg*3OgHD#4|p*Tzp9;s`^ra-Rkl!1+bvjssg6>T2sQy
z&e-tDxy8Sl>w{{>7InN=_)qFMO2zQYwXZ=#t@v?y;pOlAZBzc@Po9eAeo<D`zY@a8
zL$X5|;u<BIClE+zrVu0wC^m^JgZ=b;&vGm>A3VY?d4AP8jx<}gKpNIt5$QkoI6<v?
z$3u=soMh0kF4_?phQyv51@;Fs2e<3mnrbuO%ycqrrY|OCNwuJwr`9XF_Qv_2;(D!`
zCATawV5Qb5NwhI++8lio8Dn5CX~@3TPKK;XfF7%;qt4`d@&Q?E!o0d*p7Oter`Cfx
z<Rn3X#l9*1u>bOu;NyRp5vw=<Zkp)~5PnnqFE1lmj>N4>2Kt0xzyFaQAhDm!{?u`6
zB>=H|(NYcDloB{n_xkS2u3R2$*EEhmr5h6y^B)GgHsJnRL;N2-)1s$dGJ%9U_Fqxm
zei_g3e}@9lwd!};CyWa??6cUrgor%&%jY&T)WCLf``H1<B9$CANFc{oN))gXtfEA2
zTt7x!J;y4_T*`xI7$4QtJ)v>07d(^De6Ocwzk}2d|9RpkiQ3*<FK(YUOfn|vab=`q
zqyH_PA7%1A)(~+d<Zj^1ALs$=&fzR-X$&*WMH5F8o&*CAY^Fm!Ie;$yIIYnUt?_E*
zhlK${=f`uydb`aNo=bex2^mX-oz>9L$8&XA?ybcv(z8Z>zC72g7YIxkEB*34at%c%
zvj_VkL}-nFcD+HR_gxipMCxGWwFa^68$!%@mjG#9!M_yW@odJ9eL8@qD-U^<w>--3
zIi{M@qL%(hNv2RSAmq>PQjPw!TuQ=HroUp?fW@-HLpdAXuvZ?eFZ0w=BODyGy7;n+
zdUOwte`t@^o#Lnb9+%aRwKV5Hj}CDETUz~uIEeXdEn6-=6N`xyVJNEIL^Hke7rBsg
zi|If@wC^yk+tonm*(?O;n%AVu@o7AIsm=;({S*Edfhvow=`?)2mjeH@JjFQhrE{ry
z0m=L#o=ZbJQPLp4k>`w&CViE9rgLyn|9DrCNB9!nNQJR}sf$~ex$WRB|FtEd9*GX{
zVd4qez2<r%`r_)hXtwEM85@qjUE(&LI6H}k^+Ip3$%-O{x-lG1R_aIZ`>lHRuE_a9
zkD^G7xt3gOJtaJ<22PFoG=KR_CYQ;SW@)71_9fT!4^hK4By@c>STB`!W>`Wequwzc
zo36Qpz8ba>LnkdM&G&K1a8#__Vt<*)>xq5>D%c|;hM1aoJZxtKn9YkdDx@WBW9AhS
zV_LTP8$#m^F;=*{=}i=5h1AwskH)jZ<BB8^n#jh-B3dd8@f1Aj11r_WP=R|KUM+3=
z{0*Y*_O~39!Zdd7P1WpzWlG%DN@QG)OdY8YJt3X6_(C2OPDvt-Qjhu@X2b<xv2?&l
zxUbWwN&3H0!2t@kt%1T-!DSYB=h1iL1z#=jE^*wb#0#+y#6~3K@h%}yOyr<AX;Vv8
zt|!NJ$B;UpjSL~eb&Gvz*;nGClA>riMuf|aMXv0{(yx;B5i}hx*W@I>RvHpn7~xtz
zpu`9kh!NNaewYSC{pIXR-!Wr)M&!5BAQ3l(`);XQ1kR?gm(87T$lNGW@uQ1Fh$HL*
z{Re;8DecF4g@d6Y9@L+NONYyPV8IqzPQ<oXQdZ@{&aWJ?x)_v@y2yzU5lnJTf>d0V
zcQfUG@X~RVu9FVEGIy&NL-g_jF6_c(*!1IziZ6tW+si^@JQQ1YtNmt0nR+0)ss!Dc
zGV$$M(+kk3OTM-v1<3{uml=}JOo?v5BXgas7lgHEi0<2jJv<)u5=B{KIAuo^7=_V0
z)zXLx=%iU+VwEu2o&44V&RaCYn`7sEWPV=qMz{a1o37xX3{r)_-ScmEO#W|~@t^Bv
z;h(<+3{LloasNa2Hs;O!zy9i^yvzUZ$AcufWi`vk%um1459R``pcJ4ocH->nx`hE(
zQByNHF%j{xHf;VM^>@FOpP%2y*Y}Mslbm5wb92P!@Nfh+6m)b-39?V>ye1K!?r+wE
zwksN&^Phm5=d8x4Eku;Bh<`40wa+h{w0J7hthTiH(U~@zS**@@!1hP8Sl~4RJUr9q
z<rYwYTPY~D3JeU?*U#wa5UrGU0CpaA{0S3<FcDi>2T>Xu{1EK)_3EZ7=)16_{@y*}
zC#7XDAPY_Q2GjQ<X0z7U*9#R~U0vDY_2Igx{#>@8H0ghp{7H!l7Y~slRHlMJ(03oc
zdNzUMPJ4y1G5mreHZ&B@tJek{dS&LdjBnG)ez(B<_g_C#Nb^=PtO3&%#Two9j}m{}
zCx=P4z-t$z$q*GPLIGGxK@AO!m4YM!l70jde>1`fdS8K%1VL!c%p#&(MPjdiXY$5I
zBb|G$MB0MP9fuT+_<X^;uR}F7&XQpA7(py0_!@=;xy8Zx!yC=HnfW}TTjWAAW6qk$
zerH4y=Uik<?^w&80vl<PU+^lkIb^zKYapVVy|cobBY++_Ys|d%_G7$oUL?HUGc5g;
zeBD5_sl1sP&RbjcTiW6$Z%kgm1KxI<w>im3@7~`CCycBqcoH0Qd@>_7mr&U<c+MAc
zDv$3QQBl4#PJNv6>1P^n+wYK7^YHIEgAHkso;)+10S^Hp+*O|pjPLJ0oOD>ZNjhKE
zP`3Vv%Qm45XUZ4-+A9UAeHoFu@uZIox8#)U3y5zZyL&$iHE>^0CbmwK^jCFs$Y9wR
z;F?`lZ8H}?(*3)mDE+uDe}>%l5i*_MQ6n(&szY=ILnPsX&fK|tXSO=~Tlz<3C}}pN
zI80EAXTKuKnNmal!W-a-sO2eDbULK<LS?}f8u`nBPIOD3esL1c1ieu*R8DC3j6N6H
zI%nASwf_4J$!`dhWUn#gXvGi&%aH1ATHbAJBUY$0j*p<54t-HxCzy+}o>pteCV?ED
zob<AMW_#CI2PHLEQ@r<ybuMvs+<+e#BAgsjBYWE<-f;V4M2#CS_+zhf<^y!I!LSv3
zuC};1j6iWCZiY9z8%JS~oa+v?FCQ*ZiB(cfNqTqR%{Fz`3nf+#+)NPkWkGJ>6r_0X
z`QbmJv%>wC*#G+ttqOoXO)dGhHfG2glCe2->(0BDo_h^`%TzUqHl6)|b8dhe1P|at
zhoqY+N%@<veRwyspBTPY+Z22Ragl+q=(O`Toy{X@D*~X^_BJa1v$BQkTkCwrtWIrj
zu0?5fWf&pGtHA+yLL2*-!A=t`-Po$cC?u-g4h-3|>+pnYUK~?WlOKu?c!T9MMW=`F
z6!JA+s~;Gq7_F!_jSkIWry4B(oeJ><^vYi7a{HUyec){bBud?H*wjfBx~xPyGtNz&
z_#jw3SRzLsfJz5guB~nanKp<E)lQK&5%lWv32W_p{A~j77hyi3x|1XxJKWEgs4Jvv
zEh*<MVdqomL7WPjfv0>p;016e!PN&Sl}>%+KI@k>b9N>Ha4(R*B^KLWP}45E+6(-C
z3Qu8Uo6Rt5(&1sUWWX||lD#r~hN_H(Gs)(p!ts7+AHx^@Ky1`<7Mfu5j6_FOk*y*3
zS+q$~18u^V31uc*J8Br^!&g%Mp<jU{{JB{UWNGVhor4gI|J8LjwrK)gn57hd{LJHy
zH>oI|A(0i{UrxTDzgnzFEcr5?WAI)?Jk^$2u-tImdLnstDqtZbUm%eCOV~F|j-FD1
zKDJBAe9N_ho~K8r%KY^995+CHtzASGLe+|(SJg>$nzAqx5HkO}w=N|^6=2){dDZLX
zKrf7N1WzsgTi7e}#t(;jay<@_JC(R#)njvp+&rBv#)=c)|9)8%8JkNO5lMly$mx^T
zO8fT8_8wtgaCKsQl#@B{ZT@QG$6BMbzz9PlfrmeaCd@t<Ca`*tJnAk^V1DRE>8L3g
zXJly`w8KnIG?fWEN>Er`!YM=B$1Zi;%2bNsHN#iy6ZZe(kdvPMdZW+nKQv{kd)#S2
z!Kb$bzJQr^q2n6;4TDGoEDtL!@DP0A*tuukkeM-5f9>O(9>(Pq$P2=KLIg~19-*{1
zh+0|F_OY9j8Z$_5Tg})59{B*QScfOay%R;nu;pSyevq9S&HbfTjL<8(k#iI^;MCf~
z{d)PkZ{P>`b{GixzY^=$xm{2`hQRM=M>ZsgpChPXeIokCnSG5BBjruM1HF;3rU<Eg
z{=2xYuN^Sv3Z9%YF19?HMruCWr{yXL4v+2_$?xh*ykuR~?x)K@xHr7@SScykpEQK7
zw~phBLm@czUN2iRRq_T{X<*yMtp6fn6Nk%>Gi{z1HH7D3inC`m^k10MZV}-5tn|PA
zbfcWVqg)CR^|0<>)G5N=ZiUS)HFb#|MSo}MrPfwxCOo<_uVi^ggN+VXqx2U*y$ypN
z{=^=;^tDwDERA4N4iM``$o;e4OR!v%TkPUKT5oJf5bTBKNrsODoFBj)Jqfx2+I3^e
z1WM->iA^W<VQgzeZY4DQG}Q>p6x*umvVMWPvwzvsZPy-iGg*fx`*<seH$nc*0y~S{
z$lvUgk0deStdVZ9q7TF!&u-T-jEzI@xpecT!Xq2vSfoA}DqPy4L54F`vjS=;P!z|G
zl_h$)bH525-)@!;xn{NlT&G%QB7n1ge)KnMs-DMVjYRM|Ow13fwDztanMh1SeiSte
z&b^xR56h)~YkMkdQ;`NBUKKyt;USEHk^ry3w$dDRNK_;2?scZ%X`-)PqZEE5)-U-j
zjD;^+$&9ZhfAKR;+8%FisT*R0ORFq-+7y3AGr#ocvdQw4f3s6`N}p_?4|$e$cNl~7
zzo(@sv-xpO`|$H&zuQ=8)lcf;vhItA+10c>87$ZeaCb*+!&(R5N)F<yFpgiYxK(27
zcT@|y7!i1PIRA|5*gEi?5n%`vJTEexV=`2Z^V8l1fcrNZtnkzKFRz`Ts^fv&PV)>S
z^Q4PNuaGF)gluygoQ6^Et?Zlvq+JLpdy|-@ZO8<jV5PGm{rN+ZR_3-o9I%_a$UZ}F
zQ9me+8@2m?Su{ZwiKvT<7|f`yC|Dj<Rifm3!h^8e@gR_?n=ghbNssyZyOMmHZFqFe
zq`Rtw%ME<+n)(`7O7XyJhE-FUc|Ir%7a4aM^afLQ-TMH)CN?wp<HU7^RsZ`4d)!<5
zMI>O_D>+5TRFfOM==}>=@3U+CtcJ?OT;5DbHB_<-jD~eR29?TUCmvfEDcQTYB^I_w
zbNrbNgN|{Szv|uo#BKdb66Cpen!?gU@4ksvGjRE}gmPbAyZ6)cFTq{J9SmsdQ4d7a
zERhT4>TXB{G#p-c4amjUr|;aN7NC9R4~@iVG29FM`~m?mAnCl;HsH*|)Mg5LWe6RE
zr0Wkvu--0pfUTz(yn>x*kmB#v-S-jiu(W$K;HySt?B5lPK*)`wx5ls&Vh;cSloXly
zixYoz^2=^W2Ky>axj9=|CGSr`6}?xO_08Eq4V$u#Q!$*h!>&?T<BSyoI_mxWf5@;*
z?0&y7<FU8e>x`fJkB)slybv$?7@o?9YZ^2KGSl8$5hiK&aTc*rl|sq`>WPVk*bu8Z
z6Mpu%Jbw;Ce35;OkLS&*W}Ufx@{O}FHvmgxdLe6vEcqUznS6jXU46=i&nxO`Ce@dh
zkgj`w=903Z*BuH*jJIxYGu7TOaCt-W%m%-ZuAw0P5E`~mSU=5ih7n??DMF^}J}*(2
zwcmPvTz}5-y?Qr1qixydeG2lSx31&1H@~$er}<t8lXdMi2s+O50R!y|A?KrY*wA{r
zz)ldrA&-T}s;3-I1b>2yfY%gRmtg31`Q^0BAke33lr-e`-K+Dla+z4MSy8i@<*r&n
zY9AYyqqh8&g-y%0Tl_|GHSs3n!{^gpYN@s>AmJ*2`N|i7E2IYq;i3Z$*#kQ3&xn}+
z;Gq5?<nPhaw12ARf`9F%X+y@tr~2M@lOnh5_OU9-Xg=NSn(D+Xl7T#oZ-^K%o~E6!
zEwRp;djWUZmC&7FYMI@;TTJqyB>m~qI)vbT$m8OAkbG|U_hD|&g53!u^u1>gkL)w_
zj>0=d)a(okL)Gzu_`)&p7V@V)H*U2cdohdVO?dII!ig2gAN*8pJzOlelKfaD$-y;;
z)rpjWC!~oAlJp_tP77|kz~8Gz|8+hQe3zx0zPSBhKtA%(gJbhp!Y>x|`WqA)5i)1c
z>hSC}rH)Ma;Ko9K<?uwx<9|PYlQlsgi${~_%Ji;>S6x!-z9#57Yioa!vqspJEa6Wf
z`nzhA{|zY<O!7z)g`xaRPae3k<5<@W_)WbyiBA+tE4p#`y}S9;t-(AfHCbl(d5gfz
zcdPT_jTs8H^7`{kzdIDM-lRrW@MXST3Vq_!Oa}vUq`A-M%Byof`w~nXweE7nKHyaZ
zNbgpSI7cn>EwrB@%6XjCp&t`NCQVIi`dw|^7L;Wt1L@$!<9uR5Ow9DL5@b`&0(%$R
zgcbUBi~9O>%_}yH>`~~=@z+_l##_cwR5r|VBpLD4zasu(8)-#_5nv!}3Q0DI#^y)A
zzaoX@g%+w)L__mTBCx&Jk=W2)OIs|9rpWz(-{pdNU*c4T*?UQe0*&r%kBlSoNa(5T
z)J>??Zp;ldhJzp0Gl=6z@o#%1rsd-L%Y)UC`&!9tzX{d7Bp>lD`TJg2IM`2;t|ia@
zGrWKs8W-H9F|;Jh>x{EUcpKZ_o4ru$|Gn=vBXpXlo+!%-af1y3>C>$;UoPn#s@vJR
z*7DW}h?9dW0rR^wCVczkf96{7R-JHy`QnBc)YyO>@?S3P^RNF^{vTQYGZdg+s)1xP
zV!da)J%~L;oc`VptqU>!Kg9i`aTabd;oi4XLkfWcq__a+QNp0`PRG?C4_p5uqI)<G
z;}SYFaE3%bM5XHrmg)Jut!X|8070>C6Wp7}q)2grCsQg?Hs2;!;uwBVHuK=8KU{68
zOu>1rLtj+M_F9L9K4!@X32^4KC_gY}z6$1^@l?%FkMQWFY&!WQ8HtZ2Jj~+%DD@dM
zRdgbRgqFf|Gtf8%kLhI>^v*6&V7hGF_bE`b=H16-7Ywn>tq7L*{{~+U%KZ#B$*1Hm
zf_4tTiz(})7c_x4&3=G%2(~f#V+y{Jr6OpjAXoEQQ}VLh;|`w#CoRZ}Q;k%68_rxo
zePq6s<otye8vp&vMx8Ew_FgVDsM~X69AYpIytGONuU40Bf83>LE=)LrFa%{+{rw>E
z$r+c?5b^@g6Vl3TBf;Xl@XEbiO2)RzB|m<``s~ioQudoLw#iB~fr*o8&e}?GFqM8P
z((S#(&#L;(x$3`Be{7M!9$D?V<HFzzHR)ij#Jq(gq8q%f{o8T2CC6_eh|h>;iQr8L
zK)^w$?4I)ZNtu1Jb?7?qCEVnNtm8{Uh}+NB`{9y3^8Pq9a(R~M6@|Vi+ngv{AM3lY
zZ5$q>7^;}V-e(|;it|!SGYTvtUP90dU~l-erNt|Q6_x6LzNhTeAfuWZizXRXQ4IU2
z967x!6o`WT5Y<(U#y3bc^VEQ>T?awL$Nsj>;l(tSz&ht-Pi;TLYxD94zI)LwP`K3`
zf1D4KeFrOTtFNu{fR7vtI`pJovP6OmIG)M)8y<5$Q<q(my3{}35&~qdq~Q9?<7Hl(
zIzvd6m5p_X-e+DuqJY><9lQwj-z~T{$-{I*+ZAyyhgfrrOI^)FN7Fd!65AkESKoEt
zj_~cv5*B6FL{^JrdbPDF8h1XxdJSMsH-0ph^7>CfTb9@Lbz)zn<^e^Y&t~NYY5~##
zs1QixfnfAY8S_8Y`ByeNe1Y(nEVOjZF@e-mENKGKN9h<x{7-Y;M??Y9+G1@HiA$hP
zdx8kIWfetjge1U&m?<pk^C=^}sknCk1Bp%x^8zABikcc3-76f1quiS$GNd1u7k#GS
zw&4*OY3a8p;}ZmmK7E;k^wHNWgo3D!IXs@x^$26X(#y&oEielx$bhxEVF7QyHGE<3
zgG&HpokeL{ydj^_vV<v%ztnZ<E2)+&<X%2Y!7!iA(@uEyj;__UZ2g=@D<l-phO7(E
z2HEv^MGyxk0|9xGeg{7&1DQl_1kZhxg#6Qo^hrl*yM=6?H51LvC6>D2AHZs=oRvJX
z{3W(Y6aOk_v}MKU__q#_%qHK#OVbMl+Cd`_%K|+bHB??~WXb5pMo7fuk83a>C*F-a
zIE!q4e;g4<<!A%R4PaCA=|eVWo%IBagy(+BkP#X>;(3D%6({y@KvfG$C&M)2ayPki
z;S?aNX5%tanIf}tR(|u#lbeK%2LnDy9X}rlBJS8uatRCZ{ES;66%)yPp3zhSw54jY
zjKITz>X1W2+~IuW&Y$~gc-z`GwoYSjuaa9g-}Vybe2UCovEy@DiO}(?xj(B+WG))t
zXMsvmZ?!$42jjY$JrDR*vbzm->LeH<lH=}^I?duPJN;HxnlEThbCzsVmy8HV0UY_P
zEk+L0#!$qAv0}DVRg>>v#1yCQ^gz8FB>OC-hwg&-Yq_uin296D&TF&2y_OS(rjY8$
z;jr}eDMN>eLuL`l9yX??@zre8J`*jUp^i1^<Zsd-TAK9m$!?yVZ=(9=i&9C_YQaZ*
zyT}x1rX}mb-n-;U(Oa4sLMEwh-wJf&JGpy-w*hmN^s+HquS6VJ&^UWWo_5ECLIfNX
zH6(hZcKRb+c<1Fwm0&*)PflfX=~{mngkSBa`m%>mDEbb4Yd83-Z%R_}<~`E{g0Ceb
zYYQtDJhb~S_I(W%EmZCcNR+S58ZBF0Ds;B_i>xi>NI1Zai=R4Kz3alA6l=njn75tB
zJ;3MaK&WR$i{E7SosM1_ozGo2|IxLUa1OLDoh1=b63D+$vt{id?%ITX4DOAHqHU7>
zt|2n{#_}uw6IlfiGwUmrf340KZu-+oCOHVN)H>xVLmaJ^kc+~{2P=rqRV|k_8aszg
zUhthWc@Hx3wiTyG`OSU*v?U17=?$d*eiv*{SVQx6+S1hH(3_fD!gi?jVUl>~w!^3H
znNphI`UrPmH>RCb%Cz(WJ(X%IpKz%_9S7@&IIb*Kp;qe!?-QOiDp^H*1U7r~BE=R8
zu(0OSNjM;v!cD|ztw<xklWG8PL@A~^v+H|O9bae!H1u&au})G=lhm>Z$ack-wT2<b
zx<Z{6#qDP{v|pf|MbMXkthj>qLeigVkC<hMp7`*Eed&5O#VS6igdC{_1K&0P6ey5$
zx3cs)>;WorD<;qr{5ExhGYS#a5|Smu#aAZz82s6JQB@dE19;NCZJeSo6y!vv7ZJ_c
zBoBMgG`qTqRasWjfp)&g)r0p1FSzPd)fxvu2n0%ipN!g?aq4wUkMVrygV)*b3@0xE
zGp2q<f|ZWWG?mBm?YlWNzQ94321NbwrU26^imovtV*-rgI&tJ7whf<Uv39<n#}ZnL
zkg#w~cg<G4CiPq>T+9z<U5{<GIx6ugdZBP<@zBqTGBm?x_WVOHKG)jDSmad3hFvUA
z?-0T-Py1L6R~eDHtu7GhEitirJkXk3(W%1+n;ro0b1yN}(X2`xgG!EkZ@6Xt`V<Sc
zRg%&>9qBk1bd3`VR)aq6y3sn__B<P=dgwFsLe=rJ@dN@;Rv%!fZq$WMrgi}CV6*lU
zCCxa$<0p|{(lxg$%D-mQV7DU`T+oxZQ&qfnjNezgzehK6dLp~z^n~fD0XzWo5ue&y
zcsutnr&M|)+Eh-5uux20j3L~LD<x^b0{|0Hm&ZCjiN|C0&flgH6W;?wv$+8Bt4i;s
zprBJRw^dr2(1p5Yp)+i-RZTW9cp2o?n4U?ImfraB?_26uS55oY-w8R;+Dw(1)DkM+
zAFbv(6H_@iv2!e#ox&(|J~%&(UHybh?!n#5OqVL$HvihIXfBvUQ9#cltr?~wA3N~c
z<YHP?$cT0>>=#DsVGlZ~6%&fJyqGh_B#!3%p`j$-Glj6i@iM_lxEvP#BfkZ}19Mdw
zz&t}9i#WNSVB)*J-k(lBUrdUyq_u~2zYq^b6!PQtsECD!45Y$*x-3SZG;~ldL~rzj
zg^;JakSwdTr7G>%j5P0DDrw4NYR^VxB5M9bZ3JEm=S9{%;n89asm)uK9Ni}>_oIdL
zPc}Wh!-`x0Z;d)u!bV(81K{PXWT;h)9UBou7P4(@Ep?jOR4g=F$bN$c(1_5X5>|mV
z6WpFM>8}HAl0i5kR#2Xw;&0O>W0WFSSV4h=VQ+y{FXS8&JfbDNJ~)>FLEW$E3E>Wo
z3$x?AvbQEu$*QtEV-G=`hDsr%F&jL-fI+=I9%;v*BITR((LH-`fW~gNvODig<a<2z
z4IrYY^DHXb#8VXHo~lRXAhv6_zAFyu@-Bws%GDr>7-DX;aDagvcfNhxWc)1jyJp2k
z*r6o4UMCU&2Fi=fnYJW$-mg;+xj-e7ZU~mUQtm(1busyO?DlnF!#)4-e#mtX76)WK
z7XWO?n4@2wym^tdx!_+<inzw*V`8rzjfT6oEVQF(XFsJ;P^CExrYR4>R2BWPj=9*N
zrc{Gw!d-vycPM<4yw;^>@wUD5eT9~rGYBt;txGMS^=FBt>{KDw627c^p46%BEnEi{
z`EPDKB=6_O(SnS)F7sXnsG4Rq`{PzwI>164+Ps@UAWjCpg50D#Ti#SP+Ko5eFC5hJ
z&+f6F*?yAX@rymzv8A8?`!f{T$RWl`0?O)%IDVlwA`$H{#wTAW8529}---5t+b9VW
z*TzAk^$>X2i@p8%vzb5SOT{9hID?ro7}>zPgM8gp=|`-Uvzvf2G+|s4M-_L4Fj`7T
z(Q5_9U)6Uvn)?Z6g0P45_9ichk#p=b8w1{<NIoyj_nKJvi79Jr7znw`FVx14m!&CV
z)A8fG2Zm_7^CHdH%W|{eK?C4{rAI(Zk^ag?ae}PDykgROwf|C8fC`6UYV1L0Z{zld
zL6LHCoecK&i!}e%udob-xHKKsSS)YbB;ks2jV06gDNp&B5_)x#jW(*`VRQxVEQEYT
zhcqr7qTzno!%*G>vqM+E8IghB2Ls%gp@R8z)Lx>2S6H8%q4<~?-@3kHNpsCc6xZx8
zG3d$aKdMM}@DI5(k!5Xp&#i3YAz`#eFy|R!68gug<@b+|7O50(KIrpV*V;xQS2k;b
zsr3Sic^T~#E^Kb-i&Y|2Z&$lG#+AO+3)7+aLtaQHeNUani%7SY4{_YslD^I>*F6O7
zSNt5o5C($zMa^Cx9+Go4$RBNm#k+2XGf2p&d~p-eOICDXvxZMFB|h=DJrRvj>1vFj
z)eRmyk4rbpEjRvrC=QT20VIUnx>RIM3KuQEyQX0Tg8nwR6){3^5{H%wbMCt%v*5C_
z9E}Y|0bTr>vddd-^B|UtdV6x}7>W%1s&CsEp~+9(+I@q=-+lgqq&@nEQEXwW^I-uL
z1dq#b6oBE5QvBu_5WOv}_}InyL1^d?HHqUSl+>B;R!8rwrUCY>HRGI(cZedREIuOG
zC9J*n_W=2uY1RDcbELEZ;v^MA)RYJ`7JG?hz1KZL%8q%YRjOnlA>|GTQedW#kd5+H
zHzI5;akz-dTEk!}r09{(;v+Wg<9XPpCF**AfXtQYRp$69-KE3C<(#d=aU(=J*n&{(
zDql-hfDD{FwHN#nlQ!-gzCF-_YFYpFB!hwfk2BMYF`6azw?5+E(ZA8iXT7$(KwZ~e
zB8056bN!K*#v*C;+Hm2olaAcbLf@dEZynXS>L&pFd}mGpJ^Ri*6Ti5hYv=dk0H#by
zGl~+4sf7b?EVB0MxP&m}Z<-~MeVM0aOy9k8r{DL-BrK$r!%@n!U_Z%3(hcIl^3Yc!
z&;0gOD@HQFqAgS`d4`-5X_HyYF5Z4mOARBNr(z00#H`h4u&M)&i}U+c`7$*0U%SEI
z@Qn`U9ew($FxOwl^h97557Qn)+g{>a!`~MBBTa5zKFIew7N)(#Dd}?3e>J^c9enLZ
zdE$O|XzJ>6ae}6X9CcjSqUJIcNY$r?^oZlOoR@!ik`M-CBB+@&5?JW>ZuA{{==9_B
z)05_$SINQr_>!f<0&4y=unzT`)7N-9#3=I=DP(87#PLManwi1S6_x*;MzD7W-|7yy
z8)3~VNyu)$3~XSqKgl&GXcYs8)0-hn*1tN2sP@@qE|c=!KeeVTrd&LEGYV#mlBbxV
zD$O$*XiMW(^QVrKEZ^X?H&-pgC1vnnS_rpGzKmJ5NvXq-uxFj1N=5hArM}sWzfEcE
z_07_Vz&V~+o}I#lI;!BWy+gu_?26EleIx@Lj0onl=*aJuEgv;Snshevk;3Sr8FdGw
zZodGt`(2D=a*$R*6WX3uzA)PkLqT#j+T{iTuum-|W%grLNRvHI?ot0%*?*3u04}{3
zIK*~w7)o;3LH<P}n7Df!8$j?AJeI1`zqV9~HEBVMjOc*|8I;-mvbs_Zn4*JK{Svd^
zGv9<atMFG2zN>_9QT+#f!eRc!Kh#M5>TuujA1_;iu}{C$BLN?RzAO3X(%|tUR-E73
zKVQ$hUDjrQ@T5uKW5-DrmAaw<-a0m5WjOPeUA;{+p`N3x@P3cJk|}jqyIqUgJse8z
z3~z?tUI2D-Z?OfRQQzddT)xTIf0R$HjXXsG-*XQ#ATrSmPL#xrqK@bgFEYX;*Kxlg
zMkpd}E%)>zjV1q3f{#%{^+2TvBwed8ZU`hu+3a03a@J*spwY*RCXQ{x0EB@5_Ic+C
zEMWczio}L9L*z?%6V=p<LoL;+6M8L92P_<%<2cER!}JQ{b=k6JS|*to>-K4fK*FvQ
z)XSoGd<KTdBo_QyH9j2r`{9Cs-EW{{ps^FpSU`8-;z*7KHe>9qTBbXf?{#GU{$fd3
ziXlgFA1rd+|KJmbyUP2OkrTD^DO1PIE9uR0JsaH+@1b<>xWC?TSNYGz=QqES%i}V3
zy|R63cc%C!D=(lw5K=0Azef8S#98HZ_grz&A1-8pxUDUHtSFgJlVAJ*q~hkg7^OSt
z6x4ld5Ii|Jq10aRi7Yo-QxIEagNclS|6`@7t>9e553+Bl@^&&^@$Z@3THp<goj%^h
zsr;*Vlv^*x<CBoCeM_T<#X%+6K0iqRFIjOAISiGUs-e>5)n%YGjm#WKPBFAc^MxWB
zu{pG=KZIYEA-yW$kKTUoW0z&Y*0*yrr?Kh*#qeHezu8Rd!X(v;M>2)vS>ieA{-bVf
zn=CX7PmA||TQ&(aMMoyMuNRPbMq@MQ7pNhSBe<nnaA-IZ6DUdJ5B%n<(xW}#Wf(Ai
zfC@U#L<pTU*>*Y`7W;$-MM3~yd8snn2%RN|I->hWeBiT0e{jqEL*Pdbq`Dyb?q|X}
z4c#>bJoKq*3LSdl)(A2K2v0VNiLJg<hF(G*4GRn*t2ejg2qISwN>f#WAO-;J{XrrR
zU&DsmUEq7$8pyRL5UIccg1?%}2CqS~du-y8-@jY|Q0hbdh?;dQCO}bU{uKYeU!8~+
zr%@{+SP|pL755`b>u~<>+)QTV);|XNlX~A=T_;T(M}Vh?mkV_J@eDCck{9Q!M20}T
ziy82LUH>FMlydlN2|aoE5s_PX$nRByNsMpvovAr}g+$uddT&jWtOq|(AF9gi@K5X~
zvc#dEx)M$~vdBQ*k}kjUiMx`JjX#$=A#sU0+dl-CSfW}MTzwIW`&ZUtUZ41+M++U9
z$qDW=;ROqj#dlx7Ft--v9G!vC@MFUG@2R4kPeq@WUw?Cs^vLkU`zH%&8cQOzJhY<J
z|IeZCgf8ex#S{0}S<n7(6aThyaZ*ans8#HW{W`>;g!Y)6$Iba<EP0w&*N^-`c_n)v
zpi_Rk0ZHO2HV`j@j%u?KJ+;q=`7^(j*4;p3c?h}pp^C`P>R4s(Q-aNIn|x!lNPjn`
zO;nJWxuFi#fvtzM6m5YA<X>gg6oCZlD~E@ZG3*|;L;JX|dMi^1h=7L(j*YnAjX=do
zCV2K0^yzx^IHV*rZwgA1a(2c3`@XzqKKNxNDziF6!#Jhd%o@*oG(A;tS3p^yC40Pd
zDyL~F;070Ln>pUO5Br5))>orm(yTKrzt^E3{vKOj)5T(6vML364F1{so(U#@npD2@
zSq8=LEFNz!9+)c-VL!XXz5)cv%aZYD1O$>TM+98bD@ESWlTKI^i8E{!{e1)_BCbC*
zhoL8UjD$_`TU$AM-GR1x!%vvVgCv;>>_ZAQ7Q*-fP)}HW?Il81v$82UAnc9>&yKh+
z%~be!|7#LW-5N@fXt<^HCyJq+`EYT6OgsdI{dBW1Hu-wQ-ylx8q{|MW9L~uF!$~+3
zJvRIn4*3NOrhe_9MDuwQHK;xZ<L%|<M?xM6_LaTnnzK4~50tEXZj-i!{ubjsk&4Z+
zzwepMh$OA&2$6JC`Wo^<UhK;TUh11XA-94cqsxZ0hOUqy{uje&(cxm=gs#syfBW&b
z!|4RbJ3DWDIZOc6=YN;WFg4>_;?@-aTH8CRZ&p0d@5TnVL9=q4fQ?ev6OtE8^MMDM
zFR!r>bYY@zi=KX9&fCA+`zadp<*-Uom^|@u`1G#gkF>?niob;Q$@j~Z0fAh!cBt;Z
z@9TjBk^_=l+)J5$JsD<}N4%^KmUX7WP+@)r3dOw+2ES%m#0dQ#gi#+5(M@Nj@S(${
zZ2z<>(c|)tk}z8Vzs>!h2pwIBQB>dpg+HgSsC95ip!u8;z-MYIAABWrZ^dBFR5i$O
zeqoXPtKW-P@Qx+(NBk!KiXS`YFB99du8-zLLNvK4r`?y?2H*p~m{tPRB-=ID0-MtT
zPVbCf>nfnl-PD)jRPRkDaQ0k%QZNpD9EzZL5>^RIaYS)fKCynzs-eCfJk9tRomptA
zo|h7ACIE7h@?hcKY)0j$s)IQ7e$y`CEUIOf${(6@pQN&X@&%;ZQmo86^ToA8AJK{3
zvu|HMFxgpYnF)s2y-}5R7n>gEZEN+-Rm<<JYl^6PY)-!6`G&nh761DezQ%O)_<f_q
zVd3#P`~K+{nN%>gfzT(-U-jb9z=Z?1A_$P@`?O%2ofaXoyvl2g=o!X&%;US)6gs+T
z#paYr05)c1W?*V=op)U#Nmp0rb&|i$oSWSg#y{lcyj}I)X%rPXUM1ZA`|Y3EG(g*?
zG8kFi`RQaYEJSUg`Y@iKre*K3_cT>;Bq8wcHG*<}O~wF3-K5nHE+e9OS;S4?7$xXo
z0i?-fN)G5uPreKR=x0ck3^y_>)eBfp=})^**4h=nN9{Knys_11BRirK;;PJ!#>u`5
z&+q!KHE+yh6r%jJDR0U#B6gy~ESFX&KfiNkBPSfdSq`B$LisRZm|_y;81S#Z)#0)9
zabqQ4cwXE?!t><qP45>Pk;T3*QeN%bzXUb+r;`F;KkXK_iEdkdlgu}rp2gny<ci*E
z*Tc*FfS%bVE>Vx%#$aL+^w8$EROj<_4}z|dib8VanYtlygh^2Wm{wc;CPg4JT`>DA
z0)<Q+e<bC9oE$sV+<*;Qfl+6ig1;SwAONa6&D8#-&*aS*8nf~2Dq$YfCPczf*wt;e
z;E*bO1;=%CsOiGLKe$nSY0!l@fD4xXv3ghPv6lvG;WV4)yRbUk;{B_sh5pr~#;V}3
z(O2nPp?91KFHvJ5EW^L&Ay8UZmTi*&*lP<7jGfF)3O8g#h`I<(DdaW8NtZt7MVl#V
z8gbd%i%rrLT{m?*jE-l4E;6Y^czS5{vB)mVm&!fVao?eqW(@T`KO|cG@qGsTj+2!_
zp)}SnAeFShC&lzQm!Ejj<TaMf1~pQTp10KO)bzIJTDb|C-^9J#Q2Y_g2K$sZGAWXM
z$Buu2sj8-tumMg`AN}2Byx}~S=aNG_GN)ub^uCoYRr;c*0t9w+J;Q~d36++)?+x5Y
zdBs>=e&1N;s4Vs=MEJYzeM}G_DYx$5n^=Iw04UG~$?fYtPAi{^e$2EFgMdTN+(}|i
zI*h<~2u-dVg}KKs2-lfn+Bkm3TXC-7$BRzjS>OLLoDl7QtIZ#Qd8<1=^@2$55HSWu
zOlNMvG|PQuF0ilscEAoN0oow)ggmb(1mVhUGIP4)UY{#!ZXi4w7uwA7g(vHY#qCJO
zv{d~)YCIa*`)A$DR~P3u{K?A(-aq-rWX=;O3PLcdQ&2Yvl>0BL4-(&nz1KuJ(6TpV
zcDpQjxp&XPkGYAHciE7I{ar|hP{dtrL89R=;fVryPM=qwRdNr>JTK1|zG{6J_O!B-
zJx^&v8;Cbvg-mpYZ;sSBVkkivw+5TW&7;!Y&X-H3;GJpI)|QCK1g+KzziJ8!AP%W`
z!0-N!$R~0xR9#=e3KpI&ZCFGCx<~gY2{*f(pkH%;W4K2+T^B-zyiQi4Vq=Z!-;M%k
z4J{3gGozT8|NhM9o74661HyMAla`mYedQ&|&BiPRrL<=AeMp`4?|}p+meA+K?v*7x
zU2z=4#c27@Q-#aXYRFxGSukuq_qfm3GVXy9XZGl2!EcPV8o&M|ZMRC*%a|5;$mikw
zgGFVD_<Sg{0csUuMk-GP>Tg4KyQlGx59Ky$rai1joA+hb_}{kUz4LtX1>IoN$^aeg
z;4Is#sLk8zQu*?j@CC8(dwp4gDuG1RfS<$?rKgjtok+u9<9Tm(r?OQ#9I{uhjeNgK
zShSX<R2u4$IIu2IALus?iKSiHmg>qLBegp!h&G6^pt;RVawHUs7+P{?n)?sfE(ou4
zA#Ia}duw%j$ab~~t#fT@5Ghcvp|JefBi<v%QL~~7kCqfe)G8Jt!bi!D74{<)vXysP
zxO=rEJT1KISn~r{V%%2os7<uuCJc(}cH#(#`n8v9U48`x1wzI$($Yr8#x5fP8HepM
zH{pZyKM<cw-@W^(lmARC6ee%rbJb_Buh|h-YQhmMvP0DRvH?Z$K>vc-20=pRXHBPO
zW9Db`7MNc@?e`Z7j<$6uTf3>~1{~drO;s<5VE;(q?ENr-V#ybGJMd+k3fNx~5sPb5
zzlwXD*W)S_wi3u|gur89$+M;}9d&sIjr{OKY%s1buT?}@S~(mfM<%mrf=YUc4AmVz
zY(Mhb{644xP$fB^n6*lT<=Njpc|OQZuvFN2S_O|n-S_7g8-Aaiy%alM4aGDeAoB?i
z^}!ftY%N{XQm3EeY5ZiySR7R9Yg@8D&XLCOEzbMMoy%7Io~qECZ<Td<_ubG;SPXTI
zWd2CMD;{G{aqfHs?#+rOr%dAc?)tOjXvUfg*6aVq$_hPI@r7U@$ov)O&`crPC2%}v
zUz1NyZQCDB;%OHE78?Y4>-pw~Fm(;(<L-*=G6DDDC<v5`#o1$Q^0uc2IgeZWZPWXB
z@~;(@mIl0#?0Wp7Ln)>TKdp+5Gh<-Y(-E2%KMtFt>dvkew5=5+>)Io{-bOI{f$cZS
zjDff}gEw-q;1Nk7hu6eAgsl@S7rzS|9>+AuR4y=N>+{<o(s=YsUSj&EAAn&)?Yrc9
zGJb7$8fr?@s>$bS!kJ$t9vkp*9$TT>7x_KS0Z#G7=kH-ty*MDHkeLeo1cdrsYik6(
zj?m7>SV7!+>Qh^4ggKF+aZn9~$v04=Xeff?dwbkYkqN_;srsfq9p`$lsx<<*8#%Ft
z!1!*zS^rwyg#T#s=W*%D=z(GNWV>0@TXy)TV~H6xR6ff~R}7N5zZD7CAOIUDXaf(Y
zPB87IZ5+$+1F$GakD_~ruoo%S@i=ovHDpk%;@EuffhKNDeT@=l_(O0(P5&sagR_K8
z=$el^NR!yVkyi#b!x;URo|ox+Cq4w`b2%-%(z34v4}4OiQ*)LFGn58qHmWZb$RQXp
z<p;{=OU3WS(WNOBur(GI@|yBjpFNWx<1k&1NoTCQu)PDo;&lnx1`jUl8LkfWT2l4F
zlra&3S)FYQy%|1tpt;w)EX;ru0jx8AgIMw^2SKOBp;CavEDV+f>u7qu7Z?F@7>01F
zja1C!mWkUAjHlA>Jz^(kwqaB}Jf7}XT_1ls7J)4l;$J`n@#UOK)^$X0dv?9~Z0$_w
zO-y~y{si)aRjKL93(=D#dSY?DwiJKDW|)01;+!C4H*2DRt)*^vQ~z(PmYDG43ciaF
zC{&$Aztb|+Gz%OzDw%y;ND&5W#W!4avHk{Z<>jJQscG2-JMiWxJ%=?q^f^N32q3>f
zApN*ML3~mc)xG7vl{@E@TYz+U>Kwu^Eh;&Q)sWxzn7;|qpxPJVtRTwyTAM$r;!q}$
z+RhC2OvDAL5R2R6@sIRYv7{HElHP!euz@LKfpNV17-LYt(&`kR6v|#Fs&nLI;CzFM
zZjL*Z!bTr6`f*wAxEnnO&t@1XPAI@RQ~E4^F^15(T6l;|B($2ykC`kYCFRYFM4~iZ
zT066DmuGruaUrmo3I=g@jO->E+Tr7~+~io;!Bn2F@~tbEeH)`%eI{Yz`qQcDwuJOz
zzVC9-AhZL2cD~n2SufyOM&}dSOm-RA)EPrDzH%&?%7}gY4p~V&7P?Pd(2<9!XGc?|
zw*P5)RQm9JmSf9w9z>^Lx3f-hpGGNLiWI(1e2PWr!r%L~MX;M=4k{ED*q;#TrEMeu
zfo{p?ni?zxxq~`5%U)8~X;!L<s@P<LR6}B-9LW|{lM{!Q;k5bm&VJJSWF;g>&RKh3
zV2<D>9xvMUVE_=4mo&{|1GD+}wbtZXhf;NJzO0_{w8XDO4GWHH%(lBDGQR)i0iwEB
z7(Vzw#`VI&`;g9pIxYcHj`PjyESZy392~!YxV-;(a{oc*CfpL<ycWSU`d^^0%Ku2F
z164bBbDICZQUTYzLjb{QOkz61zmoynG&&abykbom5&VdlCO*Bt1F8++`K*1So5Dx0
z;zm$;BtfAchk7X?M|H_-NzLxme~LK`k{2sKk<G6lnK~Is*SUTn<?}D$&->-wf?z-M
zVZxd4Jy$_%2B<!evHwrvZoLj1$$oiQiu3G8J~M9D!@oQ=^MlYugRa>?0O8A}PGPsQ
zH7BPm0mowB;wxNMzvuY>MHF`X-EjPO#N++V7jQ&fT^(=(23nYQ`xp2s{|h=??+H=^
zpghdq2M4dO0{y;?ju!m)Cs<jF{&&D+wy?~<1DehHAmukde@38_{s+m^!^8NfNd}xI
z*BgCs;6DtFjL80_A?nG<z|C8h1BY~%)i(l6%!HB>h654Xmn~LSR-<EM5n*BKi$RvG
zEG(|>?w1!AMb*jIvsquhe5tGB_6YxS{SDRK-Mz84mdhg?xE6s%mUVs7;~OG6NlIFp
zyfO80si2>qA2~Vs0jg%zBlYxLG)woTla^pXmM|wu6kXsuvNq=&a5GaDHNT+AeLYA}
zSPF_wl-{zI)21EcyjM^-5-AtWB?s!NZwE=wvpOJi5zTY<F*z~uRmh-PeAvlTG8TO1
zne`y$2Ow*Aka&E5k&%axrP?#t<1ip=FOn*6vx8(YyD!!wf9x;Pon^D1m9y+p1j)Sk
zjJtQ*@rM@swla0<CuKajAS+7F5q)P(H!a^E%etKvJE5a6RZqg7oM|}aRtN$)g)E9K
z&j*jVD3`oPO$(b&wVc6^bjaQ_o%gEfb$MOEep}(`JQl!C#eJG|e181zT#ik7i*HfD
zPE}Bo_{3MbVRU5Ltab9F3?VlOUZC_*-n+nrC*u$o9m#j1j67nOzDOE15(JJc0wtPw
zxxmLjvq>zS;L0?6IuHpdDPuTEC>ir;n2-LB<3-?K+_-k}8$0SUKwm_p7)_&%4lE>(
zWs=p@Ii{oYBvhhFxN*&PN5Bdu`tJOcf#5q1^$Q!ML>4l-n<WGiZI#_NT$~^_BWX<T
zSNSMWl`z@J|H2=^Td14}S;hZfniKHOyRQR(xdZ^)eg|cbfP{5}e_2g>zPIV@tL=vq
zBf&LJPsVMhCbH&-AD7%VL?bCN1FovX(4{yH$URHpB^XP*Fd`tw&&B-h@UJ{}{d>(K
zs$i(aA35lh5JXVdb81EiM521kiD&-ck({ZCdmz+)3&Vqn)z(AQkcg6lQ9>Z`MEW^`
z6BZPQj?~RL#c||)o8@W3g3eq}v;3vX5OR#?x&)FP=Tywi|IYDH`&2aq%<8;dg+=l{
z1fg|0=@fnxfj^RrRsPF#<jXb^!Z5E9J>tSvGoW2F3pY=0jx1v}`yx58AG!*~^T+r7
za3io`D@xmFyw!v-G1DB(8?U{#CIybzE`33Ju!xu4xB(Mso+h)bSsp(@c!A)1S`?9b
ze_A?k{>-dUDGf5m9$ZPj8+W~tp#B`?R&a<T@=0<2tM88E<ZMm8;c$$YAXyLwAHQ*Z
zRE~TrBa}-98t)UiB=i@Lt%O~voya&kpJ-==O8xjN4ZJnB&SRe0w?thcrTp(=&ul^^
z8%veTaQu>t)P#EwtzebfN*s83VpMlNM8L_;Qr&&^i7Hd_G+>U&?meRucL2x=bmoHf
z%<9koIW=H@v>hzCalhNxkF)f}#)?um;(@|)Ec{sX$R4|yRvoiglK6X21WrZLiM`hn
zrx2PtbqdrN(K_&~uhq5juMzC+k`z;+mg)~HrWI1?nr{JDIUakIn4B=-pplRj&`<9R
zvCZUc$9s*wEFGdfW;pZv<(+%B_`&csf+x})Tn-YFD$<dHfjEzGXB1FD0a6(~2yMim
z!n+_)xq4v=a@@vQ*27TwG7WnNhj@2*U)RXbL?k~@1aZ5rAxAD=xaa;UjB}<-vtDss
z%4vUE;*}2n7lJ2X-2Te#_XBm&aBoy_C}t$&Q!Ps1%9-}V+rIz9)HjD$?nd4A)V6Kg
zwvCy#Q(IF`?Ne*2scqXfr?%axZMXOQzVF`WxqsxJoFpg7o8(=4?X}nT41fR^$|0qO
z5g@m;fC)oGCqsZYJ;*Tv2OR9G8krp%b@nz-pj*MV8DaJ=V&Yh}Zp0j7b2_?^@Ov>5
z3LT^0Cu`^t>*)Dj_f1@&%`njZ&T<;dZl^W~)emBza={|0ii7nIxWZIMqy+;Q+Tu0K
z<i4rWWFs^#ryiOdH)8vMWrxF26`6wMPq`bKIcEo~lmh5h&dYY8%9rPz<h8LgwCA%{
z%mCa}ZxX*BzB_>PwZ<wnkDBWn*bbEL_ACxUB=PTHhX{?^`^6ynwP@C+28-i$gavn=
zKMYb@CmAlJ7pUOD59$IS5hidhzwStG*%>}j+z<<q<NIpaOUev46a<Sz$o=O8oah5v
z?)b;#3U)j1;XN+E=|@7@W!XsPKn$p1WdQtWnu@!UAp5ikQ9h-Dy8{}MDQ<v0-SSNk
z!X~t)+_4T*3}3^|x2<%E*xc#!&VKxP?8*C>jLyEA0_hK8p7aS<-88=eK2n60Hun9)
zK%k<drn#BGONptnMKh@1Y^3wJwFCX3*=BkV&CtZGsq2!R%*Lww`Yex}roQ_^T)<M)
z@;>;*Q>ohOn%K`nlN^Gf{oo=#D$LA4ls;?Z$)9^#GkXd1+Jw#~7rPw_7|dW&xh*;k
zdb^mtLX(DHpD^RmG&Oj9HDLi`bksep@1si!RH4chI3t<pnjHB3!*!IG=XyO8yQZz8
zC5*{-5MAr89WHVko*k3Bwwdoip|R=RTwcpWjLMMJ<DfDU0T3eZD_>nNSR(S0b;_rk
z8spnyJ3|&?b=Tn=p>ewqn@H<`$b36gW;nbIH}u}=4n0^<lYq8T%3QqU4bb2YHJtF+
zwL#!v;gxAVgK*!XH;nfrXh(uEl;8f<JA(#lOXZQ>4y~}sN31#ws^s|7DgoLDwH&+`
z?6_U@?%n@!3Y;<W;$#2G{p-oAi<`U%d~S1dlc{KB6RMdOIy_ib-aQPog4_+2o(wiW
zT>a>4YO39x$f!(TGVAF@_$^b&`7o1%<LYI?+rq*Ui_<fB2nNwOXhnR}i67CMCx;U3
ze&xHE34W+ay0rAX*87Kw{}%yEZ-Qm;Vb!X;rQe3r$6p1WY>&3jV-4|;EHqP5U6zM5
zk_-fhF{{_HHnlSH@>kYk%!w?mf5f_fyp+{PGJ_0*4K;o99p2MFo;qW;6M39LRb^!O
zGcW2!no)PJjyrEwPA?ZNKaWO;U8e=%N-jXnU$+nErGlFs``X(dF)W~peoX3gTRc_a
z0m`&ZrZ4aSba6mao=uTeo5#o|Z~K9~Ldr`~@EAryL*6mR%H8k*r?BAVcyGOdFj>0t
zwJF1>#dq8rdHS?zeKvVL`#_bA+l_%@=Or<R-G4R>E)`ENDai;teM^6FbyPO9Pn_#~
z0#yQ=&-4aP#`|b!UZj8=45@qrrHKA>MrYkf^6+$Lpo6V3J)v|yXfCZ7^lgN&E0ge5
zAtbRihCy&jEBhv<W@fl&UKO>ZPc`fg0#R0qB4~(Gstb5&E)%x-XlyZUDQt5WE3r7<
zHH_Ac2aNBG9Yjr(t>~v=x!LG`9AT|n2}Bz_GJrE3)}X7+qD*}Okq8aQ0DP{z@sJ``
zn3_xRJazh+zubOTwwJcYf53~>U@RLeQ<q!71|eidt73&yq1^`tCph+#^f62@6lKl>
zKR=HQaZSOj%roG?H012={(j=k5Trbyse5swroIb!q{jWZv=0V?T7`24lg$tY+iHMw
z@InplDCP<n-vmDI-xe!7ngHFn2rWH{{257W$Vh9f*?&IF-AD1}ZgygzkI%B&Z-E-h
z0wsc?JaW7#D$8Le0`b5W6s@9)a|LpGiSiUs4FKb7%AK|}H}@Tb&rJe8Q{CV;`#|J&
z-JXN<p8_cUq68CzG%$tJh?^l$I5qK+7nH#B;RF?$w<0D*!9hAw=5l*0Zp%%vV8Z3#
zV<kOEONySK(|SBo65qgS6lyS3n)R6o#)c5`urqsK%olxFQj`(Kpf4pbvDIe~F<^Hv
zCF3K^VW;d&@Q3rDCJkr|#CdYP?aw57z4N2g{xL`@h~`+s(L#JUaMY37e7y#_3(ZY6
z!B`QsKn1i^SjwCe(a}S|n8=Os5zbHynUV4a-j&urK=zzX7hlN2@b+8F$xnNch`SN{
z1(%EJgLAgjQZCf+ILWyfb7YPi>nUw!5A?Cvw)G+eQm?4tPjQfCkJ^dO2hHa?u;xHn
zsy5^xx>!Kv?%Kk0K&(KdGdJvN_x@e+lLEHy|Nel*o7RSs*!0<YP3o9hXIww6<#+su
zkD7z#cFYb%QgcsDf5FRdkj}-*w1==?>NU=4WLiAx2PK_UsVZ)QL#_JVaR?>TVcY}&
zBeOH+(&||CncavLFKqZsWvvx>NW@cg4u_B<2)L0s$`&CPt4=k<QzXjSeZA3C)<;2?
zK97R$+V1`0*THlE>s7<J%)cvq@i4i!OqScd!YxiuS$NF|+-`eIgNaX8AMrGxQ*wfL
z4C^~PBr3Dvk<uH-ghggd@4D*^HJt`7v%coi+__&WRrT@1HHnFIBm&nOEA@>E&T*<s
zE;zk@Ogw=XidMG$T4r*@#pUg%Z{Lwc6&v_LXi-s%#lL@~v>i`TW_sehySvakVJ+}$
zfw1@iJ=fUCp+g7CInot|r2pDk2M79i)#LX2KLkb?_{uGBl*b*juZ2lV1)~h2<<Ket
zRj@43Hpm=Vmx7{Gs559Px}D0MK*$LtALhjV>|d*>P|oDKC_Pmv^hP}$9#Udw_FFf7
zDELO(RU)s0PqzZ8S$&bt`N&hSQ+L8&*kFJhR+BtR$*czV%P<vM(QVYORF4RV9&)jc
zK{WLlOph~+=hgn;go?)v0L?Yu7q%CIj`t3L)OO=g8+W=^tv1JZDn<O|CS%{1Ik8IQ
z7Slp^>Wsn|MO27YXV@VoWFI`FZ_`-8*i{>HCM-hSN^HK7XjOH+T<FP&8_blnFxN?H
zOG(@5Eo=H3E_*FOfpCW3r`a6|-;EJ*YDR`>hVA&T*%v2oD}cv_%+m8Rx^{%Af>uLn
z1zX#9Z;6uEtQKpZd#Im{{2SD%V6ND>bG5{oBRGV%sGXxU4A9HWFoY<iV{q*QWhWe1
z(@)&1OyJ40d^x@CQVy$)=kbP-*E|)@mTN4#Z-40d4~}w!gL!~7u-ByCP_Pm<8r;2k
zyDG>C1G&0FYvR=R$(T60hF6H!;UWN>ckB!qyYL&CD4)k$s}h@|dVs0Y&p^@@eZ@{_
zq$aTfR<XgJ{>#9DBlia(7h+@*!olwySEAilMUh$4x)z}q0GSY^V4_R^eTZB)>sKfv
zaI^lYFgnI(!E3<AFKJnW-e`PY?VV^c6nw4Ab_~b6{b7v|M@ge=<+WsRWNw~UR&$OF
znpf(eFWVjsm>{U0p~ZopGqq}n)nY*vz43uroew5_yfoa7w8{uZkkq}Qy`{l*ZH)Wq
zK3T+_<99O@vimVIrSOA3qF-QakS-a41Bf)7;?$;n%#S?=^M^uVOXN9w$G=BQctw8$
zHjCe@zq@nA+z7ryq7VyUIs=gMVO&ViUQ+e1%FlonO|qiy(v-Pkn_!#@QsC_z<bX}A
zc;KaLJkTOpR`jVGv{ZwZc3Dvl1<G7+L(rlXTf5w$J^fglzN%%$?_}WUH*NU%n6q@*
z@sndDYi0~3@j@?FE@RE3xJp4M+w-r5y@PFHWS&$G6ldmXqn#cs(Py1HSF;6mt|6(E
zorE}sh^Ec3rVvbHU@{<2M|0HV75)7nP=q6wq*?SCk>*sGN*u+THvA{-!<S=-`>SQ-
zMcFd94b8!)_BN!86#(1xk?!LB62)pAgEf6-5JijT>X8FAPkaf^p?Vxx{TgUQq+aNU
zO(py`Jp>me8-KdLX1F0G3UW01&scUK8uEWkLv%>E(Eqn(ukQlrTI+|0r9r%aAQ1_P
zp_W!yNuN=CN(#;c!qDm2*-d5&i=pPXZ#!wNt1Bz28kij1+`|}gUn3+dWWY62z*3E&
zh)+Dot;46NDTXCP^6XDx{L)%4fs+WJz`~aU4GqmE(xjDU{v96i$yo=<BYmso<@vdm
z3vKV<U<jjimXP3gGczKgAX0K#TEv^+O4HBf>pz8Ttb2VF(MFtxgdisppS?>cVntI1
zCYVfKQc|#Ajx@tU25M?(Dl@7G(Dv{-s)$c&oru{H{vqYpQaH?dz;Y`J_*YTd2pW(P
zlhKZNYYuQ=<~d3Lu@!@Ha&#0(=*Y1V0OuD{6XGAD3fx&&g}9uC6rH!pebN@}o1BcM
z$>kFi3-^%yIS+bVj9O*(1_$Tk;1-63aA9cw0oQlUi&b)Ba0c`-p1jjF0{iF}7Frx}
zHxm6kc1j?h2XVykZHl9-m#E|@s+t0(cl^LVq3t6QFsBCr(JVtMFT8y>gxZo<E3IPG
zT_DS=IG~T0=)3@TVu>fCeHB3I48j`mH>Ar5bYCForc(AoIhh0|?Aj}lK<y|a#$@E=
zpeQR|W!(dl6B7%IyYUPNCVyC0BSJTcWi7BIN?jyTMss>m?8Lgc!IYRIS_#(Xx_4^T
z7@+wOpu|N-N1p&T*XsJLnAh-UAh)Tjlqdk3b%3lJI3ixQ?A%GfE{40W4}Q1+A!PH;
z;7)k2y`Wzn3}<Bsjg<s=4U&q4r|e952nJXc{c|J=&Fh~y(&)w|X)w;qfStcPvP9A|
ztqBcvb+g^*J_InFW_z3?(T6KW_ry_0jnJIID;|NI(9)L_|G8r|3foYEYzjf9HLs;0
z8Q!Kt`eP53)Iv6olKU3VcI7M4!?6U%BHvowD|O=Bsbyss#cPgNg7g}T=><4{T`gNv
zIHsChNOa0JmtiSLlw?U=?%ZhV8co|ea#ePceQcPF`BskBnSv<>7ZPnbp5d>OMSIcf
z+Sj}}(s>*fF6J?-090nMo??{3b3i2{Nz~j3reWW1u1n0l|8sc1%oI0DFQrQMk-vq;
z0OnhUu3sq`tmwsNAU{9kuFY18B$+eK=n64I2DQS#V;Wiel~Xe9pfVex&30aczfMQ;
z0{)iaD^3mDnYjkX-fSg9ClwtG@%nb`@?!)DjYH1+y#}rrxS-rvZvcM3R++E?-hWQv
z>AXco`b!gUSW^}(u`6~%-s2VR7o-{1(}H?h6c(*5B=&sD1hWI0rYZqNm18Y2(*D20
z9gJ()6^Nnh?_M&BF!!r-HT`qH`sZ#_Dr#?>%Ob}#f#<V0i=HipV*P7&YnwIOw{%d<
z-9Pixa4CL$A0Hg5Fk5WOX(UA}Rg&kny(+sqSfEZVvIpbOL;Tp@{*aE4D_c`R_W%81
zd^UB2Te*wzMIhA6GrT90D5B*T@QQ*DpF{jZ>!bR+sX6VH>2I%)T9E-Js|aTCdTM05
z&t3jAu&n!hzqb$?fvd#g9(!s=dCv!~mjfKx-nm9}o6Ol_b$GUvm`+{jp}?9>#P(+w
zEmwyyoJz3!m)l}z^XL}g7A(}xpJa!P1q3-WX*`D2{*pV3mI?&Di)okbn-Gmh@iYzn
zPt_u~g&YpsNUc8zGPhAb3!yrB8ms)15dGKcPrjj%VJyeO-2X}Kto+sZ@t%+y75lv^
z(__edFXvl@PRs!|>W#nySmt-d?<p$kF^2Mf>wi0@7+S6)<w~%9!3dpW(c~CD^UZ7!
zsPeS?=XJ(cFT)7p?%=P^;f>mFw^X`Lk3skSWI0EJ`CpIpsORG!SK|ni+p(~m@0IW|
zNpV`j-}T&|t&0MayJJGC66>Ycir$=Psw2nfvpnDWy!u_p=w39anutHqOzP!DOX4<Z
z`Wgyi^?C3FKBu$_jcuKJ>Z*+AAzV>r(D@IIW8)Y1CjTadM~B7QQT9xS%*~?1gS-Q@
zn~strs|rpsn?mO|s9cqJvVGW%j;|kNh!v4WVLewke|vK#s(6L*K1)F(9m!UT>Vczw
zArfwk1Gjv3=t&o^7CsK-V18A^O~>m3*EZxaJucN;J_Vj3`J-s9gAM#MpNIpZUJ1Lh
zv_jBx7GH>aqEhY!lKgXtqp=foGJM(Z>=MHNSb$u)zdl}$vQfHD+ZAD4{5baTCsRBQ
z=XQ*3dyg?fNF4mdmThML?x{?psxPnsC$4=h0Ua%8bw4&wmLGQVJ&XW+%fh)BQY2SC
zVzv~#DuSN$Rh4YNvnU?+zVo?eAz<a45S+HP!&o_PF5ow^UkxwxDuy#rzm<$6=SI^Q
zD#48cSxp|cycyg#BRxVt<OByX#v5ubTED1@;F0$6iN2tf){>#wU?A}@d^N7Ml3Awt
zEE^WWy8Y7q1anh{E^a0SI;<?$^0U$uOn)69O=uPq{?h5@>mYRA28GD`l;VQoCSe4#
zv}ka0N)V1#fFD$%+=B}WrnjUTL;Zfm{)(?ZIqlA-{-ux*Zt{qqSMKwsVY@uVq#_@4
zIGm0s=kypAOczpBpigh$_Za=nXA(;<Ug9~M36L?bwS-6eqplgGVsG#nZyKCUI@re%
znc=R8CholK5QN6=nHKi3z{jfd`Y{j6NI^F%t5$Uw;v!<bhI&=ogX^p#qEDR-2-Vj_
zmE*xPoK<i53S2+Zn4iI+O`pD^n1E=J;q4C?Sb7^e-B=jZFzf{uC83_(6=#4a677}m
zCDTH0AIte3R+?%gXO`sydu|JH2Si_Aj{oQOC5r4&zcC2j_DnhKs5X~+W1Zbi=}fw9
zyX_U%=D&VUM@z^!K7cF;0$#C&;rMtR--NkZD{t|5T|6vKaE_9MMeR1GiyEVI*NW;o
z{uufdIfO5o@aJCf<b6UwkCpw5$+!}Bx=CNp5q>(`HXIl{orn@2znOQfA{_jeILD0D
z`W6zqLwtgjl<oPjwf*rrsoli3KUU@c`c+|W;oQ)k9AxR@hrqA<>eRpYosdEXVh^yF
zkn&$6b_viM^T#WBr<YXCbwK3ns^++#Uyf)T<OKl^KMlJ%$xzlv{<OAHIzXV(jjFZ?
zBXfZL2bYR^gozTaPiEtcpVqp|OhfSo2dM-vXqC@2GVWtcR2-xARTPpaOTc)*ErIT?
z3&oIVO2{VE@cw;f;S&Oek7>J+{ee}oPhy4di%4+r=-O%C8li7o$QecH0giyp3$c|8
z%nOy^E%5rjmViYDKEhf+d92D7Rbvsa#ckY5t?W0?U<8fAP$aAuNcS|F9Te&|<^Q#L
zwKI@UcX{@C`J{5&w%$$WO^O?EEh#Q`;G-4+Yy}OT#1vLaDZvlXnHyNL>xTe|&}`bu
z;A=c+|GCvOt@5*6$CYl=uea?l`^66rQJUlezYmzwm6(0(<Y1u5?@azTt3IU?_!fO%
zma?C0*1ETixmLo~2k|EL3C$s^jCU{l{$Ei(5gYR?A>xpeTsn#aD`$fRbrpf1M1qX(
zCpcK^e{0iNjgB*VhxkPu_#L^9h}y&BI^!2bzL0-=-+S$aUd-_idyC|ArO_nQT;!Y*
zkrn28VUuxJ$M;%^Ub8Y2GyCNQ8O9fkNW-#|+vC`6c-v3K44T4D!Yjscv2GiNk;Yse
ztOQ~Klhqczq~M51JGBW2;dj9$QBWlYiA9)-DyLY;iM`KHA9OfP52PgoQjTEpVu^T6
zA1oX<XTkdi_}bk6T|)U>bv@bk=VL*^M(^N3<vs0MeqVQvL0db`YsV8iOSkkF+d5Jx
zhB<nCb0<{<lt2}MhwN|V-ej0A6ZseknjSne!R^LoScIN~;>m#Rhtt+&E65zyT)rx3
z5EljRuH+fpsCQH!iwz{yjkN<gO@QvTWv3Kt9z?7nooz5Kq(va>TM*;~{Ja8dfB&5R
z6&mCM2zV$`I@8xGEXF|`aBQ1Ukzvef*V!b#!z%#!oeksLOoo*D$FF9SY+Rb4H=l#`
zLpxNrT#gv{zpIB8F{JnP<n@APcT|mm!RyhxC(tpB(I&9THh9&v5#T_6OezxKV-j+m
zzo^T-dEjnvDfj5yoLN4Zz>h#Px_;<zwXld}9K^gRD&=)e9@I*Xb*ON^e7}|tQ@F5I
z#nY`)|Fy2ey<Ty;;*U7p2x>38mb4z5V*f_VOKVqxl>0x)qu%D#6~5Rq2ej!cG=d*s
zhFHIC>&J95we0_TZzCGlFK6G49U`|~%>>rED81dloW`x*OYu)>xNUhNe1e#&BG8w>
zVY!9&3}3Fo>b2+!VcT6=ZM}EH^>%fcgfekK8B0?v6V3Zw=U-kFs=SKsF4PtSb4|UC
zOk<e;U{?*(A#=hMsINuS%6<wa=Y!c|qf#dIigG$slhT8>{{=)sSBp2$ZMvP^&GmY&
z5n-oG;Bt{b$q?$2$NT6rYg%WC#FwAKC8a`(vbF9b5@?Q>(yU*$;dG@iIG>zm+)-da
z7F5Pidp66roVgUzNAI`1El~&3yP&1_KI{WN#>26IpFb9mM4!^NVU7)@D_iAY*&vCf
zfwfWNiJX2(X>JME9pH(OOc>LB40#cnp%JIVx!y{YxoOnsAQx&#7ej0Yu#bjt$onhu
z|F;6pYWT4MoWBIULw$mAcB6=3jGkg?Sn{HLxY`NSpq<G}G>65Z`wpvPZ5^SXg?}EQ
zhjJ4kU+7@XQ#1^-aWK(HEA2-$`YF;tGeCbMaaeHHC)b;NVOS4s%|-)K6oZ+u_S^l%
z9yz8_*7adK^s&r}&>sWTi)HOi7*0a(fx3NPWc|&~D-)EagI;qx1l@Oc;O2ZGm+prO
zdmfFQ&0+G6J{M7r$T7h84WT}VmFe)<T$ABr4r{wmuv4vkt4X^<Sy9jxq{}^t6meQy
zm_qxaGd%(j>4z8FDd?s7hAir&yRaYI3pK7l=SF1|dqL?aWsNhMcb;^g*^4}l+ME)K
zhfMrwTfhI8@(_%z8$~=LTK$88r*$HA86myNL<lLd^-+Y;HJdv_^*2_%BVkw=P8JNO
z<GgWQ%E_Q-7imXGHg2&utW^b_Ge_!8t)z}z!_Z)w5a50R34nTv&Yo7J)G_T^{L4#p
zyIcl==XuXM`P3d_ExGfoW5S)On&Zf@iY#<darxkB)M>1;8l3+W>vkgt1y1wl+l9*d
z)q-(EGZ<1e!kx(VR(3h&s-odaf)+xhGGA*;_4g)4^;ZH+*OuQiCO>B(Ue&37W%3jF
z6NVSHYer=jPZjqF!OHV`?i*0lQ8Hk<9t%!5<qa`|y|S^pWe~l9dtZhZ1ycXsu#j+`
zIlh8A&V+B|J~OkYx)t^@9k@iuGJCCoJg(qkst7XOYzet&*O1kgobL{7NvYuPHvMb>
zc|ow)(%IK%bN7S+FDKp+{o^>BO0C_I9x<)G<Vv6w<c%fV!Gz404jitqjIx-&8zVJA
zyR7#hm%w&@_;G*-V%%Eu8*cgRo0fPH*Il`Qw)NAr^jqJQ5W|q(xBQ3!W@DkcL*)I<
z7#RHK`sgPKn)F>Qe!VgPUi~oAyO!Rt#B28^7Behs?)cs-`#sK|-=WQ{L@rmx>1#?|
z_UrcHb>afD&Wgrr=C!<}NZJ%jA#vcW6uY2~v)tuv54{!Fa5+7mVH-{w4iG194B=Rp
zY9Os>rzgjI_$lW&$WPpa_zj_b?fDG1U?qng+JV#C4z_N(jkm)z=M2GrgU@L0U#kE}
zy+u^)TdLdNQa;-+w3Y(AkY+l{XE%0Q(>~R}tgK=T)tPI`_nT6|tq9HZZ@o!L4Ty!w
zkg9(R5Pnu*=xu^EhDK1SJ`a~1HW^1G@0Z9|Sp91uRcNuUK?`gg&!n1_scB`>+vWDp
zYi^4!53u(B&W1Cylh}F|Ci0n_>$7JcNkyCT&&u#*(c+K=`go$nY6CxqYL>5HsIWmk
z{%_(-mgnLG3$&Fn9iO>S!H=KZpbo7@|8}F|!=?^4J^7vf+Kk{KU^U)n9x)2tm3x@0
zHcl3%nbb$_46O28HjW%`<IgDMJ3DU~W7M5=$AgKHK`qYw3?Y}t7r!m{{BEb(N&_5j
z+m;hmc|g|f<bVg;^zS8&`hh^S$%6{tSjG&%r~a{H3@J+Db!&g=Wym`ni;fytwDQu^
z`J%)0sE*qS#(1l+jlv8|<1Yxa2kwXFBuCYhAoX4R4?DY!&K>tN^BOv&dE&$G8h`IJ
zXu!-)jFt!~G;qp6I<vsTmFc1&Hrud`LV9`3`0o<w`d<!>X?^!B4*%|DZZzvQwtRFL
zVr->5?K-0b5itC4l~PD8EkM^k;PG8L0~o_3w$cA5{}h^Iz>Q9~1CGu*ZF-xu$TCSa
zbmFKg);p-H!(Qy&NtU+~x!>Oa_UDk12=AalkR`h}(t>COlGYO#k<**q@Otw7@=DYJ
zH%aGV(~<aCMGe7|K_H|VI=tyAAope*GYb+lbERp(f`Vo|iXz7y1bj|g4@;l=BVhNp
z#Z1=^I?6|!M1%6eWHMNj@8VOSw@Uy;zx@-9$m1Ka>+`Ssc!stX1MyE#sM5#|DY{Py
z;#B}nbhxA`<>``U|6*~L>?5YTe=M`R4Zj}%1eivY>Tr3mibJEi+!<GJYmGF3;~Pb&
zF{t!;Wfi~}kH|HM#_h!?g?Sb`zBl|i;#Dmn6rn&;io#sJcc-ZBCY#X>5T(Adk4+24
z*VyDJ7W4IhZ$e!G=(@tF&u;lu>_3qQ{jYh-fButUE3C$0#nsPuYxOseod+M-+Mw&N
z<HZzVn3_P55Iw)Thna^`Z*wv&16!eA6sHD!jJHBw9It*-$BSC)F`F?{HLzWeV^@P&
z$M5dQ&YK5|ocbxAZo`HeQR%b}d{%33Xm3t@zpFK&)@F+JEuVacl_4DA|I+{Di8qL>
zUe5JKvU-jaOG|NJzFm{P3+-I5ku<~XZ2`9JC}LnU3t!i!Mh9W2hGV>(XT<K^j)_X}
z-fe#d9)Q(*hDcJN5Zx)QB1M!A;GhV4jnh!R66R3}_cf*iW)!$#2z}MEeqGv+y?8fh
zdH$uRUyaF`BhK9&1S^5@0f!AFgS?Y`XNtLwxaoq<VBIq`Mhjx_0<-$L9N~SLGDy_p
zb$he^=g*(B<(ibFBrF^p4D{OL{-iTN%UIaMo2i@QSg2-?4b*L9Dc-X_(=J*vw)&hL
zql5&X4E?$yE**6RkFl{hU8w6o+2x(dFG9_$ouqGjDc>OsGA8xwd0!Aya-T&YU?h-&
z`8!~aC+4W1p1XFm)SkvuhA;W=vAj)}tDj&6`3|t^SgFD8@1eYf$$^Z&LpH{m(HB$0
zM9Ht-f|sis2;8&QTHo|6!LsyYurDS;T|sgl{RGc?=dBn3|DzbzZ<cG7V??TehsZct
z|EMlE!gu2|6;uyQq1*tc|Ev!=dizNHF&lHshzVi@YY@ly-~lj~raMeQp~w)i>F1Yn
zrWMU;N?iT7RWu;evtu^#(_w5VSb(<E6Ewgvt7JOl`z057IQ`C<kIGeM#`HP_=kasJ
zn$VNsmGAYT<m|}Zfzr%TqXN`SfGLYeuln_3BEn0Fp}+9w=|Ee3Xo6G@vtPz+v*z=b
z1`8?`v6spN5;XQB;mKNuUB~T`%U<UakURab4kyoAY&9=7ohOHG)#<B3=FxRUKh1U2
z)fCgwEiu@~i%Ff+UAan{x?xGoh|Se|DG_)dZaL~y^<~!cO+17V#zn`ep*6iayq>ys
zcI=<Tj;=w+G|7A9&cya1#Lx~^a)O1#OqOm4A2TUR$G|7F_m!~O9Qux2+-P+!Wv#oY
zsdi?qSJYX(YlBa}6*Gc)?K+(umn7!F1<4E{+n2Z9syEyFa6)(n6&ngY#I#_pLT9nc
zbC#du_3pZJ5f{YUN6yF+kSYVhdMnQPOzQmRjP;J4Sit@vru1Z7>ta-(o0`*4-viz6
zPngP+ov~@jGz`yV_YL^3-iyLSE_t+J7dWV-5?JpR>A6+!yRLUK_~J#x0c)8@V_aob
zVo2uh(Vu67FXL+vE;I|^5Bs}_h*+c#PYPccU~8Uhgrb6qIK)2ruh0-tOj6+B;Ks2c
zo|io;S23i$6^#Lt{eu_=G}-X9R`z^PjK#2U^fpi%r8^1g!+<W<UCBorvxW(+t%<)b
zQ)`}V-bxlMA)?+Og5)(Jg&kpL2T?FEe{M}$CL{iLb;QdA6<W)d_Jodwkb{)6lrBf-
z9(9ylgP=)0jko=}-{bC|>&Cy?-ErE4>Fl0F$=bM?;Z##XI;fZ;5r0x~aHEbykA+M|
zNeAlevj8qniJ$F1mc1&qwpLFv+>h7WdjA@&4PQV%R%f{7<eXv|?d-IBUWL$|ecTfb
z4-vN^l7)nqYWIkIvNk*A!rb>O0e<jg_n%*ky}dRW&%*1x?!@g7&>h`5@u<!$K(Iie
z>b5BjEVDbgWVHBUU=r|5`wo{kacDU}hfTD-m_&xr7=S@m+nWe*yDZ<loONQTY~CwP
z5ZzC2zaDCG1~z=F8>wb|LRiUmBE31+->%X{Arbbu+7com$)S*m*=TcyK_7`B>hOKz
zri>vG>9$!`Qj@dZnk{g(9chAYeAr`xPOyylnHGx4DSk{^ZV$W5JvjB6usLEE2|aZy
zPsdk<w1bw6uEybVhdk%1%R9?s+!)+h8@W|&!D;*xJx@Ft0w2?}M(}vAp>i&n38toR
z88ns4Te!&=H>WwI`9wglBIPuKGK=IriJg7V)@SZn07VUZAJdkefLl~{%Oq1iRCqCb
z(VmNcL_|k&taib!bW|B`8V2jZAM6KM2RQ&*C6@<$P7mB&^brud1R~YaesWL$IBKAH
zO5IFs6F|vK{Pr!Y)0PMoJ3kf%);e9sE?jZ3#R)QdhYz}WYR#gVCTc5#x9voGtz$|T
z`ge0SYtUys{w_L<+etye$ocg#?x8!6h;b~x7&0Qa9_z|ZW1XMOb-i8Q42(tz+|zoG
z>DHnGVIPW)o3O(hx{M21z3wH+CuZ52QuEz4h)WQvaH#8LIHdxA2Cp<8I%r!ayH7>?
zQzBrTmwcAF=zUebqUf}aUUJF$ecqG$)1Tq~^pVSII&*%1={XQZ@op2&uqDUemftmC
zXrk!e{8Iyk*{OE{_1(^O41k~8V(5`?bC9X?js=Z_2tV3)1=+7<1xrGz-ChbOZryMa
zm8E6-*JECh0RnDJy%SNBOmb%SY3zn@Eq@8Fln|MmP`)Q|^Mz?!YR0ko5jGs?fhrDv
z6`Yw05%wHB^hv}!YOy!*lcct(7l~KqhJV_i(?Z5-FreXCN5_JjL$mT9A>W$)=|&kb
zLv008v^Jt&4s$b@=)B<`@7*K9Gi8thA?Gd*^o`0fO)I}opw>X71#^O?ZEPR_Jn5=U
zOHsF|sogE2Ab03V-O~ll<_<k<D_9E7d~|7Z`AL2?{o`e)UCdpiM&Q}eexdL0r*-ct
znos}E07zxA-y+EpKAukEEjwU6Ov)WoR}rMg4XK1QLHTWk0C@Sw3*1VNQ#7bY{JG$a
z{Q*+k|MSKF*Xv;hvfhn|>y}$zdC%92%;eZ#jwg?QUCZ{_fWZ1OijYCz)y^4nh0ju<
z<@9~*>GGGMpBx{QDMNTA&qYt5*3^9oZ*M@gzimM3>+neO2}74f<yNEBxSNk`FX;~=
zLKwa{6or%J|Kd;6Q`i~S0?efEoKA!+<V+;a=O)Kn^FJjpAP0-2ZxPxZ5W-fE^Gb@y
zI#jfkQm6VjP7NeF9$fHdCj25HdhBpt<9Ll6T?uAbzaVNt%ZU9#p^r9nqeMaDk8x*@
zE=~mzR!>waGoJ8Ry51^lgD*Nm>R`p?F7qdHXk|6vh927uG&?6wUNBiQairDn;ELa<
z*v!j!%b%s%3VZw*Hx8@~TuWrilL0xC2QGRX2=zcC?Lc4#5*Xp6Z#RKil8{s8mFfUz
z`X|>*T5B>oZJGY{>Y)BC(WeIz&a~A_Kf{-N5#-eR%)GEqXr0iQfgs{3>WrVY_afrt
z6sA@=K^n{zKRF}QGy^4gfHIjlvvZ1AOJB=!uw=z?#=oX7Qo8U69=N_D3HaI~wCU2P
zmOh%gFAHzkt{>>SfmmSD^9Ez#T_N$eVPbJBFTb^SYY?r%{iA87J3>)Cm|D-*&NULa
zFRlgr)~x3A8t|f82s_(p*`3N&f-NucmKTN9*Q9q2_LpD&;%Vzi%l=)M^0?i&`Pp*N
zHPY{RPR><yYk0yYfI27{!+~QTGY&~Bk$JgG;&keom0D>sdZ{7LAIir=>?KVp)?}b|
zViS7ki#PLo(N5G|X#&D!IHUMFV`yD@3teq4j;DUi6NaPn4QTO9pO=@S^KF`e49D#w
zDL;Z~Lee#>gwUq9`Z=J5L}tZMTh`_@%z#1O^Lr1am)1ssP3JFcp`n27G_dDA3>JL1
zKB{ZNS2+XkQ|R1XN`Nwgu(go!D+BKS*lC<LN&Iat8q-GozO-G`!82Wp2oFSoaSW^_
zLjNf!7zAkn%2Q#jV13B1lB0DSE@V0W@YL(Y|DvrbwC)cj+6r@c&T7xODK>v^2TSdm
zx{uxTJYpK0O5h2DH1q@n8E@$L!)p7)ZX>l{bC%>=&wq|Lj`_2R4mk3^H~#t>m^*8t
zb(yBaB|93=FcOB{@-P2)SKT3XL*sC`<(1~U|I!WPu-4&(LJo+I@C1ee%v-Fg3X5Z8
z_AUbkLJ`L3WJG8=(s?EHl(ZJd=&m;)gi_!@AYSYkEb7r$+~?12ffSW9gRa@b<PkJY
z?@^4xG$<{Jc!Y-(6RkBxF3KUb8I`-QqOY~Y(e_XWCp^!*mDcRj<^TE#gCzT7`FLX8
z3%2@z>tWz2YmT*U!%$UTJH>!9w}X@#Je_*yc1ob=o^Nvr6pC6zKyp$a0`Rrm_y&iV
ziZyV|l1zEpcYhAI7^3gA=#oaqCQh`hZ2o%n!TsX_F6{K-1%&R4ypDK6MOdVp<tJ_k
z;@Lx%D{XG_Ll7n4{jHnhY@$CrOFYJL)|#B=ssbitU3GQXm38rV?Y<!6F_XrOedb@2
z?n#)ccRtMh1<UA|r}${~ex>Gl9Yz(v?t-1nWE)Ogu9(5+3JxQqP9Ms}1ugv;tDEwM
zw}Q+6Xe-zIqMPxr&26S{P94pzS2@b4+hKk-nAi8Y&w_C6i%XP2!gRNmG2`4$8_d-R
z)*`H2%Of7smiQm#Z<8wuBCwRvB7PTi<jH0s=qj5)6{|W>9L6G5SM!6xDiH2!53Ely
zatC!&(^C8!G>CW892E`Am>uL24pxK?oSlnaj^47Pe1(j1IS^vgZhcA94wVXw0N*kJ
z5nSps=x?FN@r<)AFjs3}E^4ON2S>s4jx9|Oqey}-7ulecS(TE2cxi)>-6&sKzU99(
z&);#|*-M2)r;6Lh0Hm?bS6a$g4`GY$+Zam&PswfOV?ApL$0;=vIZfKD=L7o6p(zOb
zf3E-j{(&^1?e(D|+;&WT)eRs9ywZGklNz7P!V8|r2GM0b8T=^6TUr^wPq`$`xqsg0
z*klby<Y2EY>M3BGZ?8n49DlPX0YYacB*>xzFo|j2DP@XNc`VnvRSeR9aHG$3l?#!~
z^17=Od-K78d_BO^>A*#G71O1_(<4A4nHCVLlUiO=+Z+|VKV|68CyxBz<3M8Z*ll?m
zOZ8{>azG7A31NVEOlk0TC0Aj^V;xbnuR;q-9LuQ|OQ|#}5Yu_?E!yxdMiq&>N%<~^
z^;8>aXXIbp!TcZhnE>8yZEy5DVu%-G{H8LlyUlc}BZj1N-SaIvzKy|Hf0(_&b$)@9
z!px1<bR^=(!S7cIa8Or1Fro)|>ooq;jJ7%*FY5i~OuA6)bx4<qpt0O}lplo`B?xm0
z{K)uohCDAhoj5L^H!)EvWN2^KzP)AdiCTsb%A2uM{xE#&(?amw_lW2>u|L6;k7#<{
z(OvR--44lVu|1ke|BWGNCtepO*OzX6Eqsg$IP7v<Pe7y+_2&;-<`}8XMS00`T%!DU
z;Sl%D3|HL5KJ_=K<4rIBDrRJaAV3jJLU&#k*P3MjyEYk~$nun+_ff-9<Z6mpe><aU
zF|cf-+D7_=3^3Vi1&pDSt`FGPxiB~!@1)wK*tJ~;GTYB_)bmvTX7fRtfd4C{RPfa#
z>n-o~Nq4OXsO6>ZVZXJvp1W%=?J$2<Q9#f33(h=#!*V3=!|r+}bd7DNy*Viv?J%#U
zZO(<qrs^pzVSI&;+RO0)PM3Ng^m^IOj<c-fYZ+SF!cBROl!{R1;t?Pp*N(`F$xRoM
zM>@8qF&Ff!`g!JAoo^BeK{m#BNhyS$yzy$v+U&0=1H7)V_Cf8GbiUUu(XPXr&(hb~
z%7-bA<?}YINy=*bC^bp{&}2$$WN)1~`vb?fUB>VCI}Z`=_;G<sRKdZC^3ni%w2*#@
zqE26w0MyZL$~9^3&>eb)?TO)Cd4tTXUi_|Qgsq!i96Y^l7Obk!#%wlGns-AnWi<>E
zfOTVtIwN`{mM;h%HF=<cqs?Wh-DDR27Skf0hMl8PsTQNRr$;-mUr62VSJ~Qvz}RFf
z%TWc*N%Mo-9B~<~#dZoTePAm9@)}>l%~jBV12lf*0HqpbaD4K;e_DpHX_YgDO}cm}
zpy&2?G^7i`DRyqd3a4bS)@nJi9-mF%66sBRrbRtrGW;5jZ{iaR&MNCtBXzcWC?F-P
z{<izzL#AFXMEScw_tLc|Zb?zUQG}YUj!84E%EchRtfS7@*(Tf{g;a?P(1R2D-ycs&
z&%9iLE}IH;*?u%v=AGvEC=H<DSZ4topLzsY_IW`CU>j=O5bn3cFTgg^FXaml)$$T{
z<9BVzjTahxFMYu-@~gQ7==5&F?^)f&M##4*$fzs^d3Y8^g%Yh*WOjVRw$crensKut
zNyH-^Epv9R$PG)nX`n?mJt4a_4gDFlnk)FwKmZ&MqLfb4q86oE_>2QulxBms(`!D7
z0i8}-6u$2$ut?l+@-k%iz90E!q8&<YFXbov?Z7orJ_0DUu|wxKK_$?S78Z-p445I@
z89UmxD+<mQ^TQn%;^df;bkmqMYiiPgcqMNrnUYV+QYNL<og4+}nTygAp#6nlPPa4h
z^B}fb=2B&{PN+d*#G5-GBY=~aEhDD&V7bwlUYy^(**bGQ*LQq*M=D%#0r6M}vO%Iy
z`lrFxgmslw*rRNSVzXGADmsw1b_JXtk~0iVYFmwtB@Du_&cEG8Ma_L&yr%nF#482b
zs#NCd1X-}kb#lFyR*z@f@M_kr0VP7ZhT>anRRDNm4V<>b;thS6W?!kss2=1H7^-8x
zs!%&LD}b@qR#szF+v+C7tG*A+j?}j{U<Jm@ToWX|uH#Ak3iWzVIwe44Y{r~Ko|3Nu
zh{hIOE;`J0w$46JDu>*M>};^da~K^}5bBlr4#<$1#;Z@MH(~rF^N)jVL0aSb=7i`G
z<MPZIHC-m(vDsAPJuv%i!XV$NFG|(&2!KTK@5&fru%-2Sl5>#RhpV_OXVqvm)m}5h
zDui7IR}KmDA7@1j$w4ukQV@-H0iiO3l$F*&FMKC-!5>(`fC>pwi1a>VlS?gI0k@3W
z(xzftxUA0A*aSnzFUv&rmADsdXYOz;QlG!fR|~o(|9wto<wpxajQue7Hy}5$H}+dQ
zl4#J~JJ++Jlf&|Cf=i_id!H*8vwfqL>_BD-Rf4`2A-L+KLI&s~Fk!Q|sNyAFYO|WC
z9Bopuzk6m>HhcK6opsgMqz!)D6Z$UjO$=<$YBpN=R_@L$-!jtP$w|yIM053*Kq>Nf
zbi!(iDW3Ik|LnTc>P672_U;_*skiXvgb0waoJS|9M)3K9mb|{bm@mY{Ec&;e6kI7t
zv#!bq<(hCyyQWjA?%LT}g}whNJH{~fUj660s+nyq;p6miyr4MwANc?c*bc3?$ECdT
zehIUeUgP0>3J59BR>p%cdbrUNW>&9;`Eygy{Ap<5_K64m<%mQ?+FO=*#*D@NozCH;
z=bgfc$<ZAb0z?6V9T(q!7OX;TR#Cpwh!%9t-|r5RV(~BTY$2x_A!%Uku_m90*zmTp
zWsX1G1+`ryts)ifFt|WZkqE}v5FY-Tk<*z!^}O_IR&M8xxsaP4jyF9vvP0;YVVJf~
zv{h*U-veb;aY14O>+{P(%a8<a=%|X5H6{Zgqw89z-LkeMB9Tb`5tgyuY&Cz;Vz-_$
zMO&gQes(1V8Z;-k>RpK<%EWM>z5JIYWky_B(+YM98N1KB^KY!XWWekF6;$*OBlsK0
zIDGJ>U7P-Md~zMF7FpXI*lx(FV&8Qw^X*?lnMM96M53v2b<i`k7{jTnH0Pac+P^q!
zwfSv|ulZqc9RkN0zla9dv`rF;*4{=){Ci91dm8j@&szKDI%_@a>l%z*al?iWW<9>Q
z7IMdIYAE>P*=IzyYh@eE{p<L8^#}P7!u1<}@<|uaoQ+aVKy~(?Ah6f1;3fNYl!7yf
z*H%7}g2C_bUFbUmfSV$k8x_kPLv*Pd6#QIV*?#>9Z8ubSh<-#QKM%(lJv&YqjO1o^
zvdX+;FB`O<roYm!_HulH#lUl8Qgb6>TW#e-&7{Bh#Z#kjIz0gGC#V{nu`VWc6k2l{
z&-Kfx;j89ro8q^*k)W0SItaBX0Ew#^08VU*52VO}YyakP<RO)QiG~?<)>%i^m%*<V
zeC)KN2VgLs%~8H@NT-g98(-Fy?^s|*Tfa^RSziQVY8+iu3>XBiWRpT>@3(O5yDIda
zz;OO<E|DV$QYL3KNM7Ka@P6v^yIDi(m`29yYa(Urvwk_Otd9W@`MkUsgnq0(05<KO
zm0b4`7I|>B{}Gt68(+qFH;tWAR{?|+^@H~W`6%=k4*nd{nX$yf=UBud-zqN>6q7^B
z&bHUDg64^Ljr`kBESr%f*qY`b4S_frZnxy!3RPHkFNm2Lh+_sSz^n`PHU8b^D`rAD
zZWIfhCM|-l4px*1giM+UwJ6@&Q2Ns8l&PCb;NgnvU>@I!lb+j{txbQj;oRy2)kDq&
z@LI+!D@d=O5Lp8cSI1FjzgYu&o10j5%9`5p`hxtgJ9&*5$2XK3F%6YW%)Wpi4?N*M
zx!Q5hxcN9~C;D@BqDLSSqurn&lJM@c3Cb>~<;*g$0%1AVI;FG1Bzio*SK{}!G=9&f
zwPu2mHfHgYm4HvWrs*;pqi|6vu`a~N?Br7z_E%K8HH}9kzY`LE2MEfyDG;uZpkSLu
zod0S(n281<AY-0LGL8?|agghi23-_xluu+(0KX>^|2v$`8$RcuAenYP{ZgTz&X-)A
z6CJM#<)L7R@io>vWQUk|o5xRev;yCs4A_jCL#JC$9E|W`X(VpG=Wpxul>p9$MQ2fz
zB$5JOX9mO_wS6w*VfxyMIqZw2whIIbJ;w;Bk`O`4dwB5g$d#;gQ6|etz1fXF<(J!b
zB}(BzcsUd#U(eN|aX_aQ9`cJ8A2ka}mbS3aVuf#%0|9eKGDpf=wvMX>4N;!rKEXgG
zeM#E_jBOWRutrZM^pvFh!;t8pB2PX$%#h_3K)i8eo|jpYniFsE`W)IkpQ73Q?FZa<
zV{*WxDrN4;;_ArxrPediRWwQY#2UWxTS`AS2;kSn1MMY*AUR=8EaQIIkyD@lMLM?@
zN&T6+Ol8`DIHvNg_Ak^yJ4Yet6Ssmy$z&wHSxyf^MW8(mvLHDhC6YindqqvF`XlaJ
zbQ$m0B0zLklqt%6D%PK^r8ak`^DdJ|zW7>u_oSGv;eb>*HRn(u+zWGz+ai_dIOK1N
z|4?8jTT)*K$lx`Z7C{=Ep)j#7R|C!EvjeE{bknm}=)>J-sUyeXGl#w)HMGq_Z&3^T
z7Rk~-e?rBi`)D}DNabHT*W3<or4YP%ZB#ZPLd6EkQmP~f`7SO*FK}v;dzw#935sKS
zJtUQ<13<CF+zfXzW)c}FmcSyC7<O0&1w{XWZ)VB*$n-Ir;mYJck-NjD)1W+E#5^Uy
z^)DMedL?QKn3e@dz^m+avud#hj^-2*PC%s&FW-lg0~Zw$o7JNy{o}iyyakBs&PV?f
z?=Ekwwv*;#tj3mMeFsXc?Vb_#$g<Iufii9CxAMBNdN}B4Tm&LYKSo3)6-}J1;42Aq
zuMOmtjpwuPYDXQ<J=Emn*aPiV^uZg;1E5-?f#r}DLjywK(()*PXsB7oQ;RGs<U#G=
zCjM_0)(SgmFu@a3;WNx)AjF7^G}Bt}6mxy}KLM!)-B^`bS~>q)MNcJrC9C9MEjBN1
zVZ&G%myysQHkTT9lPG@&;YFDgI{U5X;uljOE}rhU0HJo6h~F*uQMH`SrS(DE4Im(B
z%bP1EU`V=S!9p7;ZmAjz_K{$t#q~?KE+TH#8B{-EbPv2VZ-P6{`6g=TWc>rkOV$$x
zdhS-NfS0}?8Nrn2ay>0&+W*}aq=f-VUlf;FV2@K%qTxbB>eRP5U$Vw%%pUdXSlC!Z
zbUy`7HNTGlAE!sOSA{}EMxf=M{&|0i;Dil8vc7sIi2+<J_{je~ss5FUK4M4*3A|4s
zb0J6?g9YE&<WHb;{Q>KzT_Ne@7nZen^Tlc!LJj3LOjrIkT%>qER!^lRC$7?rT6}CI
zdrE}4)qR5kl9mf1iH{c^-hwly&V1i`B)^gXS?THWO7V{zD_H{WrDfY&X6EK)baQ^_
z0&eHHTtna9k^mpqyAn2$jX8bEj6cMcJ|3mPUrRX9XY`wybVn5kMG_{{IKt9jFQOsu
zRV+a?jIFh`^#Z1~NYG}Ncgp{_-eSGQA&2XyrnNQQgMhW~`s2lVNogrAA)(^OCxNb%
zRLJk&ia&nf{rB6Ju<xtOPA}jTr+AG8;W!M11esVc^)&=^4j`SXR`#^(mQf=L0XHJ6
z(ucB64i5wb1UEM~XltDgi9LOx^7mV32Hz_*apbb@N(f74j9>gLj#}wlE${cgaL;5%
z$reXkA2&S3l~We7LArdB=N0a|V4Z`wS1zbac~`yQ_sbSYq31TNS4gy9vJXxd9xERb
z$@i6KBHva&_x+w_RUQ2XhWDp=vOH#G8gEwaW@HvxGFhdJV{3abs2gt~nr@z>h@MVC
z)()XYGH5myAt2pVaC5JIS<HHh9?v^mq{muI;~gJ|K2KvV)gE451WBc(&{l6C_KL!w
z&5wfX5^aZ$JB_RE{R3u(5A-p%kyDCD7O4vNO-h$~nzdi;tmSc_F4y1m=<3rt(r5lw
zj|n{}ibK^|6f@jHu@j3B44Z93HhTA47bUihS>L^df4jq0ko`Mg9W*m-&v41Kr*=Tq
zcx%cv{GF}1P=)~a4c{rknXUxmr$zfWXHc!RQV6!cy=bg%gHUm5S;<E>T{~*ks&hu%
zDu!nc-LOibucTvht?rK8=y|!|-1L)Uri(P|3u_u{%QW~J#Z9H0IpS^&r(f;_fx0;s
zXBzDB3XkllRP@YPY|`%*4HPGUpMbPs`ZY;kW!%frztwKM8FDx)i%3WpOG$A2`wqZb
zCAEQB*MKaJYcvNzZOw|m{>Z5aa{s5#rElip!K6jgieVvx8RNftwMOf|YNL9=PN;7`
z`Qef1tcT92unpBGg{foGbA`r~#@|iJlU3-zuynW!87!EJ55J9METVrMXGsB*xA=&f
z7067{3iH$w#<aV_)^CJ+-A+W-*=z!_w-F(Z+w^V)nF*-Ql5GQq4i8=b<`ZN|3KB79
zu$!4$^r^q{%ZWBfQsxR!pwHxPBhQeaLyuqc6=;0$hK*89nQP!cQ(Eh<_FL1jU<n?8
zLK(#hlRQMy75szJb3?Mmwd4`G=uJ`ijqS;5&sfEKMP!ULfVkcg?fARKpI15+7#CHe
zGN_Uy@YsTMH8tLj?qI!l!l4OJtwu>(retsMu1>!6&~76#P#e<9jRFtH#UeGm_ONJj
zplN95-T0ak_LbEl-czJG02J4)<?4ZAqN)nrZ^)IvMo`2aL@852tS=;)j1n15Si@zL
z-;6buUz6|xjPaRecY8pt7}8xjpDr<n-griJY4NK!v{@-gevrut75c0Z>;e_at-kzD
zUBzPqbCDHO*dTxm43O7e$j+sN)r6cv$97e(gy6Juwu-VcrE3mujm{#mK(m`8v0<?C
zMpFQMY?Hk`TGEpG=++h>jugQP@Ha}elG5Bt5j43H&!A1~97+;pVkdTiO696PiJp4w
zd-Pi<9|&77n;DYGpe7q{?{&JK><2YWEG!3cXFxnboe0Y(F(gQ9SRk1T8u{+=t40vs
zo9k33(_j>)L0L|!mFg%gumEux){nyWsrg_X@tD_Zg^cjx|A(o!42rAkx^}y9cXxMp
z32q^H2n2U`g1ZK{Ai>=og1c*w#@#}Y;0{5*o%?xD)%n#0MeU|x?Y-8VV~lGmFT*sM
z+Dh{a|16zY>fWE^|LMdcMAdEou1?oClQ<9s#M)%~CBVSl=mtTGkxXs@Q+77)Fzo_~
zSia}dW@HHp-mCODUPg}AcVtEgWP%Cw?<q++H|iLC6Byz}R;T&b-rqOZE)K<>wErUc
zewC5R->3P^+uEb;7Cc32DDa~qT(kaI{)^V=fImSEB~<7B4^1P%JnRqO+6NlvZD?xD
zGw#4EwFt{jH-Ex*m$Pq-^$M}aF;xYw1T(s+Ee&pr3Dn|jrS@gXp$fFwRwvji@$TD&
zh2y|^`ThQHX!BpvZMw`9o^?;)98PTQeoGOR^1b5Da-0A$p3v#9Tw6cSjik&_?DFC4
zaE-Q1PvV$xBE03z;pZVl*sXx9AnvlR``fPs0iMTI{L{w!pXS5sWzZ1j!-3hxXz|(6
zRI5=0hd=~7G&F%_%3*HJl27^D@T_XJb&0}^YjM!{Dg*oBVzOfz?<f5~xN<flRU!DG
z#39psKUfc3_<Y(1ZM{ro_ot<BPD=bK%Lkfx@0)*(lK4Ol`MBV$(KytpvrGQL!=^#9
zFHsq%Nh8ILw3ZBeck&liGUGMaU??CjypqM&KJO(?pG+y3R1awnZPxxmo6+UcbDrWM
zivN?J#D_1zUK;A;=muH%%ul<4Jpc%_3XmZGQmGp4s>jX}6i^w4ptVbs)p47EtUwU4
zEUDINoIoy3hPh5rSXuUqr3zYTEIN()#@g>ne=usb3+;Y>M_F0c=BR*=yxcAddykDJ
zE>eaX2xSQV1%m{Mjtqno3*B8Kf&{8XKbpNq1}YFw?EQx$`bDT@!AY|b77kvZ&;}kx
zEr4F3L3w>-=Dc+|&@>pvgluDYAGWN`XN;B^HcC<dO>;7RpFAnh8N(``<D0oUHdzIO
z(MPf&^J<3nJ~fMj8{_d{)sIH$#roF<=@EZ`CxAE1p&aYCTGh-zGC}3JABa!ko(MZY
z!Ln0#v~j=G+4^lwSsOB-1FI|v3;;+gqAqdPVd}+8U!wUEukp2z&j<y+r8-f=bl8Z{
z;OD;LqmVY#;VX3b&9PYTqG@JzM}Udo$3~I^AQTIFtnGP<EIV>RjFJlFS`nMHSXr#b
z--6w&+Dlb)(<9Ef#H_!(;3S1`#I(WCT7(u6O>5mAyuHB2dYh5+WjDBQtGa`|V&&Ho
z0ggkn!LMp=|D22g93%294q$T(-t6~O|3L!Jn>L|$>h?=0Mpii9zHsojCoL}h0}ZHi
z;=reg@JCP{H{6u^5;;rSA+AG<(MC(?!J`x`HeYDdhtO5lXSSVY(Ru%!$J=l+Ny#M#
zsF4J)X{tSV(1;hG(%R2<H=XRK3;V#?M-CSQj|)mPD>600!;1=$gLgmJ74K9?hk22v
zzQrB)|2X~zMC5@y40L;!_p}%8ntUL)ynghapyfMsk=ZZVyZhO_^wlV9O$>h$t{*Qb
zN#03*5OUabu48_7#DOj~dIJ(6{fXR?4Sd<Yh#Mer=0E8*n{ER(B_maNF$-SyG%S{_
z)0^Mgn9d*XBdavpmTpPt`epwJD@9r#I}PL<kWkx*1~jd#^HM*AjyT5Mc4&6foW4^H
zk(1WhTz22)a8HZ17jPdlmkNFTO?y%Zd;0x}U3mqalnw}7V*xBl4l9Tgn$m%`IuzFc
z89$RwE1Z}Wl=$10VL<$MRf0Zd0?!USo%BZ5p{I4?9@}&CSW>*fTp0ds66#?jade#h
z#dkf<b-x(|e1AAM$h5RNWsFI97e%-C%^3yaw{%@mM5+zA4SJRSVK!r1oJ0>a;ixdi
z-TrYj!^`BOc{R-sUPfeT!dc<aWHcpI>;=OJbn7C6Sm}Vt@bnfxJXCn8DJ&j(0+d!s
zSdL1RwqQ6iz7H!CC*&odU0pKIc%pS9gy^8&;V&hGk(@B^k=I%e)p0dNdX_9Mu=a{3
zd2lwGc#geQ6u<8a=tl`KaTI1;X1J4h(^52~v8#{<@oeI;T?_ek)RCEN<mpR|M9(y%
zos$*6HP+skAm-t+4oiTb9AH979vF%(PXh|^65y%Do(Z9Np7dCBo1UhQy6)+oieWci
zXv&I2{9Sx!g?VB!Q*i_FH2A~%pi7Z%>*sJ-^zJEX3T%u7?Awlq8O$>yFL0^uo8ZsU
z_eh4u*oByV*<(aVAQjC?6u#J+i??kTkV$$8#W|S$9=r8U+q;A5sAc?_*WCh)Jugxp
zBndp2K#cT{*fOUyNQAWea%Z{$7d3B9_ZPUeUIr&KUNds;>$Qu7V&r!_KU`UY!&api
zP~|R={_1)1iJ0MIgoYTxXrYjb1_C3Wxt;6MdH|mX_ftv`#!X+%o#?&m%S?FPLO!}}
zF{-`s?FBum^0A~`^iSv*qaLLjrSa!b5>0q8AuJA$Ig0(xVXLKnkc0ThFxy6q5@S@o
z3v}UN10UAUk5g4EetwfV!uGO|x`PGMlCXDWnZHx}X(_&I1>p_n4uVBKe8PK{UhUN!
za1MLBwNww8b=|=0)izulH5QX}qN4n7VVcLBsBf9p2#>MjqII1Cti8Xno-n9525C-T
zj*FxII*;(>GHRze*_dxC@rIW-P@##-gT0hALEZ_!87lWt5PpPJM!$zs3T}JG&=y5=
z5ghoOFZr{Y6tWZ4uSZn6gpRYb-<Y5-+^xKO0!IRoDw12v(@S%%{9U!pk+PTTh{RBe
zbN5lO?&Im9jifFQIByT9X{WW9@+==~i;@s`G8BpCu<C<Vv6S|FY45%+UkT<F<@)f<
zVh#d}gF2TkNfJ|J*J+Va``Gjd{#}`OOg9b~QY{0^hGSbvLqaF1Y|Bah5B#u5#t<mI
zeX0U%JlThtCq8W^Pp9qMPpD^vsm8(EF8M6a@;`t5=2n<v18D{#Y#wZZJSbFkx(@hQ
z3Tms9fbFOZLm5u%m-Wwfsdx$~Hv{r?gXmAErB&;G#H(LxL4`nUqiecH|C<lC#f`Y-
zA*<c!^Kv1#<W=yQe-7pkfFodf$du*pzFryLDtzeRw@huK%OlY=(LM;CVe_4>kr>#w
z>-vQDkcsbNcKxS7CbAUDVew;>aVFs{R-0V})W?`Lg04Gp|LtlO3jEr`$<pYZA}F7=
z1!kl}ls%8`K-b3ey${Qd$k@stZ-7^X0K3glC9DN92TC<Xksmb@FX}Cp4n5b_du%6%
z4_jqH3w`Q7Um~Hm%I<)-&IuP42XH4V7b6IF?!R4rdvp<HS<0NDE`JnY?JI{8bAn6*
z=S&clKL2oUdP+B$UN-#A)B&r)$7!kla~-$s>Ue-)vv@X*-hNGZ2Wo|HJa);)`QFb+
zG8Is_HL>sQP3DiV-RyR+p9Tz}X;C2n5or~sb>N`GrQCZ1DjlwYEqZA1Scln#+0QW7
z9bWemje4Z?Xq*N-+pSe5Y!UT?=GrsA00#C{j?e4UlXU`m%X0=^TVD#7gl}`tK#hpi
zKZ0;kb51E5Ww2dpC8R_)JSWf||DvO3PAP5qQ^V=<Sst!mekGYiCbn1IR5h2_6FD0;
zS6k)xGx}_mwPy6pmssZ=el$iKhXFL3Ryzl0bgmeY0R^uw+9+W&1hkAKaA}ow&-Ij0
z=3;w{F~mu_$z&v&7Z5=JYFFwr8faVe0%!7NGufRNa7!q{@UUGm#yM182fk98_$Jjg
z1unJ!eGOF{?tkbG_^OTS{J36{hHRyJ5BTJ1h{GeR%2MaVImyJ=(qK!Pt(GX%$N<rV
zOn2z@ot@JWmVtTn2H89&ebr{ZmMd^EqZXI+(I2)^{VQrzM*U)XQs$->jQcDL0tdzS
z92bV7?%TY^xWWGF;te|gR|M5SWh@*~2fn5{hT@L@uv0vq7WqVJn*V7OXW+nC3KupE
zIPIqYL{-O96>R*#=3H~u1VW>>?mr@}SOT%;wuAOXywRUg4Z@%@<dh(fwyFV#f}w9n
z*2u5V2aa69ubhB7Pxvhm4(Q<lbX{#e%QQ9raFZ)GI3K({7p{^=oHV>q`pG%b1M1fG
zjAl*x1>3-qSJ|+S6P;{?&9KUX%O%DtQgm8;#d9B;>9HUzn23i{5imRvFs?1;n%IsD
zi4t9yOSrd0c|J9UFc9_T!R4vj|4<krJ}W?_kC1&5`COx^t!O9-kR8V@YQhVBh~pJq
z<!$dbn$mZb%hGW_a&nw^1S*R%bE&Jie)lK;w(Lvo%qU0)C3MD0G$ot>l)t#8P#$*E
zR3XbtRssh-1r4YOyxjUG*C>{-MN-jX{CR!3REBq~ocC^p6p}AudLr?Ml+CpBV^pU_
z?`k&r!KgLF_i#S*`R5UBNopsS>W-00&sq`zLS8LBBDbruL5|{fU{-f{NaS?g!uxNJ
zj4=Q8GO*q+(cY?C8>%gVmHxpoWLM+=|4@woh#9wPKyOgT=kA1%SLWZ(EXn}D1i#AK
z+Ubdj(7~_B_%-?+VV1MHrly4eFoPzqsHDU~U!SxHAY5#7i;8v^6hJXcYp()`jQ5?s
ze-pn)X2!;%{<{<l$2xIeFLYn5^L?QpAHf4hPKh8lF)^{PBq0Fe2tJmye-Pi^+xx0A
z>+Jw^jvRT!hO-Io-8?)zE<S;nx6}6L666(j;n0*!h2~xU%+fwY`3Rz!QwYq9LuK%M
zCjwf4rC^sG?Ddqya6(jwQB+jajX$!ilRkjspr@xFr3X*4{QD%(=g%P~o)}=bGc-f%
zgklN8cf^KKxCOs=slohZ@OmV&uxERo*+zAMM`KpYrgbEEXL;xa)#LMe@8?S>ql*+g
z^IhPgARaR)2+*zm3E(oedWEg~U<u9^TjA%RGxp~mQZSyeb$2p%4A3H3b(_V<f}r7l
z{WSvU52oca!Eoz=4FH{@_+CW^CRPR2niHM?_#^bWIg3llV?idkupk@!P<egql;aFc
zOu>poT9o0LnZzm~U|+_11dB#DYlAq$v|&@ccTC|Vk6M{LvuU<4;vqrjYU((JU?LPl
zn6s$1^A?`2<6nVrh3H)Gk0ZYY(gD<o_s4~R#RYh!u8!B~_*dZ*JVoKCIoA?RC-dgM
zzPMIY{k7#@;9qTMEoJrpD<)g7Qs&5A_Pm;?y8OPP$;6P)pv+1XXi_5ak$9lPHQ$Qf
zjaQQwTzT7&>*xZDoyAU(l}hH5j@s@J(uwDrgOK42XWpCvh9SzL?T42;(K?_T87d`<
z^!cOl?w-fsz+)!De;2}g*fj{&x*Py!Yee1oiu>5AeW<kIR$xs38rxBUX^bhMYNx`w
zsvm+IvPzL{FuPXx?N4C0LG&4aeuv?V*SJ@X0snEGA|#tVqAr4h!KD^+<2jV=vvHHe
zqh#f(ci9<O=!EkQe|tuT;l{Xh^9!M@S3L9UQj2{>mj_`38blD+>Rc{V(2{v+l5Io}
zKlF))T5Vmp1&bN3Dd@NakKgZe;H?R4ULUvPLd%}rxegE~Q})?aCyrBsq}(4aT2>Lv
zyLXW3SDf^&b>ssmi6YPw{NsaiPabb`=wrrQb~1lNGv2Gk3?C4qJZ%;=DoD*O`(dFY
zFXG80z;m{^UC!Msv)3sF-;)QH3L|@nnT^W4s(_rKGwf+l1KodBuF6h%Nqt8IVL45&
z=$h+KYp`VAZ0bHMGwEoDgq3_cF)pSDzuGR#_wMjfYST*nWfJ)6T>Jwe+@UaoG~|s&
ze*^6T*=c(7FB3h8IdVl`9z!aE{s4>uYkcy<HGvP6W#Om7@>XTxKwfjiF;X|ncYV?H
zxX6C&Av%-VFz-+jh5{}CHaD!661`5WJP2_6XaER|LWBICr`r)Z*dk-K_YyAi3U*mV
zopP%+E><aX(Qbi@F4-jqLdF=x!mYFj7f3ak_hi?`;pW8K*Q(G&6T{ta+_D#Td0wm^
z{1@Icr0cHq^U8W&Twn^G>qsZ_;KWRvrdBDi9%(F@3*}9x@Wzo)kJ7W`c%4$J;s(bc
z;}WC>HH;wJ?kaD#{v{kMv+0sN{MCV{1!I6}tN#{p0i$`v-2MoT#Z1EatlD;6zY`Gf
z%9x*5=3wSuOf-(2c^ugewsIN;YPtdc;%V}LXZAbZyEBVz-n$AbonO3oUgCow1<E3z
zI7(2B`gd?6(Tz!g9|=or99%yfIIVvgf%dLYc~~~Lg*8i#<I4|{S}C!QjTmPq=P*ml
z>i%0$c7ocW!WupK_j>wGmjJVYg5~tw|1HMw0N?XW^n{|pI+|<CpOBTVn%7-Y)7o_K
zG%)}k9k6KsPInQiZcWz}he4Lr;T;hI>gR?fZ+MG6!qtv<*0O=tu=X#(bO0;CrZ=yz
z=6ZeLo+Mt#6>bfk4O<2GGZ3&#h>2P}ZKu-`N;JgK#7xroO*9#B>@pV{A7S{3>iQ^Y
zw^kH1@^pKSE*RSEU2q0oofIa+AW;u}CGDpxF~<3*N>yNU(ew7=xEooi8Qlet8;M&O
znQms}GR<{qT_^h5<P;bp%KqGw^L!ex)`WCnO>q`H|EMsIS1^w~c{>v`l0TA<hBW}f
z;Pq$_S?mK(8ngGd#Je?>7N=R)HP(>29RT`LYQI6~ba&U8FbL2y0=_rkdy{wbIURB+
z`Lu+V?m3MXxy86^w1{&?C{Vb^`8?;u`p^KT$JL$2#QkrQzXHEV>ayU@f&H3qj;{ho
z2f0(b3O<@IG*0Rd+fNHtm6_%hnYQxxc;0VNk%@w_<@oKnj}fCa6%E_^Agk``cB^%m
zBpGxT#!t$X1Zg(u+T^~UzWo0C)KGl;O^)qnnT<h}G;>G+dk=jxfK-5nU|^u^ksyk;
z{rU85`s)`mV{6t_K{a+_py-OlO1&5TEZ}os@ZL5)vxDlstSIhlT~y7~!wQiyHy&*^
znLy0Of^F&B?JR&6key4F!{-YoZipwE+nldMmxkoL(qpcEhQItWVa3T<DM*%yZr~Qb
zm89`67?nKB6$Ua(j({1Q!Jy8Z)`CLRPc1*<W8;L?$VAr*QZwfBQ``PKqHS)ae^;Ic
zuK@&vq-b8Jd!U6nIr{a0Wuu;28xRZuvhrYUIT{=dz>}RgwJG@Acx3?nC8xoRKv)h_
z5<~r+^r%h$P4CIT-$|>5I06EKd>`f0cb*bIC*UOa6;fiaGQ3L_DyY%*->KbT7M%3F
zo_SO^ql3H^NJ4Sbb1_o$JbMD*<F-gR9n@*yd*0jderzOaIWqzv`bQ_(@Oq0<{_{Yq
zHYc2n{&PNM7Ik?1@7(Q%IdiEpARF>985D(AQ#fcgCrSYzKM|C+O-S?HvPwzh7*1rz
z_M!Quljk*9NSd=d&wZZR21nJq23F)Q2R`~UdN~<si>TAbE5RRqn<;7&Qcfy7)36hD
z($E>vTA1bBo{)0Vj}P^8j<?aYB>Boij6a=&E+&%ar+%mBc^_mbZ6dR+?1g&jMcn*H
zYgyknoI>dJU@L%9T6Kn-E%1e!N>ug}6qHTew?ZJXmrKRt-*;tvLJg$t>DYCBo;AJ(
ze;18`0^NM|V9miCOJ&MLu)kH^fZ<tUNE={%FR<fR^J&bc^D+ikcM5Mh@*T&J7%G)X
z@1>ReSBU{ZloFO^y;e+&tZQ7_E~#sixT~KPgo+03RQFsh388(-=FDFlAN3e?Gu-pL
zDoMo1ATI7Sl8rxyd9k22F$*8eT$OsYgGPbTZDjIK@jUs)B<nz6!3ZRMkjhA)eI-AB
z?!A@A>rMp{y3xUeKPNyl2zqg=V|$C3c}_3ZGU^wNntoICKV}a3cLRxdyzt4>;x=Pu
z@mKV`XDEvpXCU!=&4~?hIlRC`o7?Y!6C&1?7%eX|b!{p!Mp$#AlwWx=oH02km1vW5
zTq?v|r-ny2y;cyWV5+2`Dopdbt&eDYH#qO*k%LVHFecy+VJ}B0Z?=;(BiW>n(bf-=
zG<CARe2`otd0w`g5+bDbF_0fBLK>uIqRz|1y~=Z*eD#gs3QYlKNvHG`jDf9z)K*jl
zF>_jnBC6s+s@e$E+4+CSjZ7uOce1knz;^K@MoA{2#))g+qyASa%mw9ujCV-*ghz5a
z{RJK^k-ssb^T{GPqNps&-Tkj6>YDR6kDvXHU4#?EXgNLiQrMm>pP=%21UOm(FRqIG
zx=z|BdIr^2K~p6KmW^M0=6j&w+F5S`J=4P-V>Xgk{-h%Rs1XaX4$WyA;JL@e+oj7C
zh|BG=S*YU8G`hM+p-jV+94jZQq+-OD)@SaRkM?-IKgcDbE7>0VEcX1MKr$l(wBnS}
z{Eb<c00br`0Puvt6F>dSDOYkniW>0j)z})`nLc-w#Vq-OD9+YBqMUfRtB=ijB2tI}
z;FJJh*@J5#WiThB$WMS?vLc5p88hnv&VFr&EeM-v(HaFs-YlhYZRS;d9FOgmWZ~Xr
z9rp^?9}Ph?QigSZLHm;DtK*EV`6H&CEhln8Gm6$*TM_<df#`(lN@=_c=Oj@N70++|
zF^1x?)gXFGjg5cnA@q#T2$tgD4xa@)#HQ}h-#eS>wydLyh+q++W$!it@TdXiOn$uT
zgXu{W_baXkq;4d6T24#!^&nh}@B^JcJEn?y7vcE3e73LsjTH#M{oOYiSh2E~W+^$3
zB8sw!VAv-^iE0qV{xzLszur-G(}xB8is+xIxsDQD=M(ZZc6&{WRz}NfVp^AX(5C(u
z9p!DDs|@nP&PHw8x&Bk&25FGYcJ6lNcKgNcM_M|TH+^Ir;m=_kN^Kt)5vNdc^OzTw
z=@C5a_A7`3;ez026Ofc64D&#r2RrLQ9ht!$=JN_C+`rbgJb%F&F&15NEt95}rV;PL
z>Ku<iKc-^NfnOTRG6T<8fxWCJMhkqHE3*OZS_^~-=A;1|Hb7AP`A_P9FaCxEy!HYF
zMRULrU?;|$H{kf)Zo|X=_?66dt0_?ZueFG$o#2%Nql@?KcV9F;rGO1*zz6TwRw1~v
z+*D-BuT>pNy<}Arc5Gtqa+NL|YEq^8Yv!0fN>nmhAIi0N65aNPL)Dl?+u~^l`5l67
zy^?sio)@MkRW2h^hk<luGjSAS8P%szY1pjRT!Y#A<5}p(Umg1=wkllgICXjl$Us)M
zpSbwX@=u#l`tN2zKWaq7Sij;4qgy%A*AB$<p9Cx;2O9^+r=CF*c~(?gn%@b|wDSB2
z=kmhmmerx@D0pg-n>Z~=TcF3RF#j%{nsIt`ac{H*{kUjJQ$G}`iq2dFv?y}cWIPL7
z&wOPH8);(|3K3}2+2-OSEBV7EfDh656Uwn+x=QHdTlu>vLyhDs?jb%<wniKbjdaZB
z84nA}aemBC>LD81%Z|B?q_z#SqznPcrinz8@y^Cy7Lw7`GU$iGhLHEDoC78%8jARK
zO-;quuqWY6Z(g+x*RG4Fv2UMn_{PNnzQtagc`2Cm2`;uiftLx1o5`%&2t7@3YG6bm
z9#m~>WIB`R@F-dn&h?bWz(Bu=Ev_r1*dR_AYO|*41~`G+7Yf{Iy8D3@^L>>sP8FT4
zA;S~!b<Yuf;O`!LR>x{Sl!EfJuq?W#FgnjR=~UALSsC~d9$7SNx{)--xdkSulq$Oq
zbaL8Pd%!d<(~5Vnt&ij6{t@TBH07Zo0O`D!?T`5}?Za^WXog6*k^{;puBl9HX(SHZ
zUr3^Rpx0=<_B|)taMxe9(LHvC5$|Z9GO6QdsL6!XIEDzdSyt@+o%r1WwvrGoo;rVa
zK)wt7dM)YO`3%f*@F5trX0x2j8mXc0Gp4au%avq+{OkGTr|?mu6fg70u!_>_@)Zxf
z$I!A9Rn!=LaKqVBgpLm5nX3?BA=i6z;aZe>w#BU{)6<7-T;z4+)5tfDf&x^qU$#Uz
z+_ef^wF+SlT6~go^_r7}dd*RgM%o(~U)W<67C%%TpceylOHiW+7;7?r*03YitjSYw
z;X`!zeV5&6!<b^gEieZ`qJTlsL>)f`DZPKU%qHYZpGL2}kwA!?Hec}EaPXVBv}74P
zz)7%Q$>LN^i&j$7qV%Jjcf5op?o7_djBayqdv_xTIMw96couogAGYb^`J|!R6^?kt
zk2kN?1slg3Sv0MRt65jMRnt07-|zSUzzXUSTcUTU`;8wtym3V>YRa!1;$Nv(KE+ox
zKO{dd<-J0~2xy>J<LH<adRr|RhP;u|3>P{Z7xwzP0Crx4+wCK!yanYNb&<{wd)ps|
zI?dlHS8%Cu8v~YC-v$&p0tMX#OTbruKEmmFgQL%;dokomfYsX3f`@3OS_>x9`@CC~
z>fd+wZMVrzbyI9z%6J}Qyb;d@T<va?y_hl?wW1KSZD&k5eVQj^krU}U=(;;wW_(2T
z&Qv2-fl993rkByI?8o+~bB0Aq<oexsA)QUjpIG4EgF6m&@M*JU{g=y!fE?qOws1x>
zRx%L@xqD^My_QDAm)@Z8as<Mj*x%X46gvlZ_ZLAwW*QJuRBtn(GqCQ5f+voFtN<T^
zvV$koPe+aJB~bO7E4)C<rwg;by~PC3W21+6DZUGMa<{-C{0{Bg4ylNSitCHkSZ`z$
zVYJ=M*-l0QHL1oP);`nuC5D~}>)Ec5MSl9QSM5&wFREjbqJdJ(qrL?Vall$&AMM`P
ztROepDEH)9dK8Q;1?n%MgtBDmUXjJncF|#e74~kuu+atDq^O2r7&dv4k%3|5%nxue
z;z<>0j)*LH1CksF*FCTw06~B4f_&6b1pA0B?=s8?L-OLQ4-1T~XF+U<4<^JLy(MG~
zfrStHLc1^vXk+eop&w$~vi6j`{``ry``)v<3o!nd9caP3Vtf%`#jR3p{<6t+xCyAN
z)G<XP1p|P;vw(OQiG?%V=({Rz8F3()h&-_@(h0np3VaYP9w6LrPnhDRuNA)75fwOb
zad_e~{g{zoU(+zlesN;0cRwIhgmJ}P<g`tOokH|S@Q3;)x?i?UylsSo@@r0Y^vyvg
zs?5B5+DtHShXna0Os%xhc7*}o?VCn3(^G1+m!M=pZ2dg9xZJvTS0$0#>`zJobN+<J
z$3)mU9&xN)g?b)0T*&q>z=(h;(D%!>V#?kSFmj-|Xh+2S{<tq?Wu$}4-tCqJ=x(e0
zzOr$12`U-<T3_Z5%iFgVv+%xPG+-R{0ngCra~<~$uUiC8sK&Dp5F4{h#MXjJZOdy0
zY7m@2dtL`_=mLqw7mx#*y7nt<3DeW(-cTZj6@!kv8Yu6z;;7b^QMY^*+uWeK_r7bM
z4nDX)+g;s7A~~;>T+bf?=A77078sKdC$Is(RdkDl`Y>PH9%{5g5PFK)3C4I3siv`{
z7FFE?Jjp)?A`0eC0?aJ}eo^*cuyo9sNqMyGl3X&A^Pz~I_H5MU9;bB0^FJE~8M@87
z^)U-5nz4wMps8cSain)fQ{q^u>%-pM>YRKII0fq*#IgJK3o5c%laa8b`4e_+BC=?1
z_DH%GVynvEpYf5&X)K!pkHA(t+#RF7cZ#BJqJFeIYGD1ymoN<13$>Llx<L>`RSZHN
zYz{;fFy=;wrg<x44gf=u!`Mz^@>2VLb#jO6jhQ}yo>McVvsK<~K&!!B7K{st)tUoF
zU!L{#29r^qH_%3=*n@~jKo;$@J9BS4BwQBtZ8vvgZ%!zfw_jl1zNzRN&7`3nu;$b=
zfi5Me-hF3H=V6Dk8ORBh2jO8uG$L3{c=P&(c6wFW6!uVoEPydpp}z^K?+@Y++8?2)
zf<X{Tq(r)!q?l~_V8tx@^wvPS%vKXFQvR<6<t4kN8L_!xW2ihEzED<S1>eaIE{w;K
zgt9NcV4#^dWQ6kR*D1-@x@qOQIBCpwqv592i<xZo1-zXH*ar8TL(^j&SxfQ2Q+a-L
zdq?tqZ{w`7_Ko$?04_dQk%g2cdpM*jYa=u?6ln1d*)<YW&DLCKtR8=42Ipz~Ubrk7
ziS;nG3qx~Yg2N+`f@fqpY`P-^{4}zH<$z~=mFK`oYxH`O0}?<$0|+aOj)dntA`GB8
zOEmq+*DNX878M>}0yJ!j*A-d7h7OaeeYAmubMI34-&#S1mglm<8VrP+4&JF6+pS`2
zUm@I|qPTA78y|SU!NHnsBCE7L75__8X}en%J|;hnm)g)x%7*XfqX>sqgA<#izCgn1
zT31jdYpIVP58vk~?B}#_$7YC?(@pa(r5^8VvG+)kqtfWM5&79WzvhJUF9&k)_1wL+
zM*?3bhUO1TUyz~d7EC((Wi{Pfd2VCFP>C2=DOSe1BybJH@>A}Jiz|c-Ce%g-zpuK6
zmpC}^d(*@x)TIr=NJ;K-C<DQbeV%WmA~K(yieoQ`!U16j@Pq6+YpRPikTIZ;EjT{(
zVy^xI5)mAnxc8)`ndgodTsTmJ5?F=X$=A>zR3Z^I;wg*mj~S&Rti-c8TzLIvgB7%e
zlU`J066AJ+2AAvgHXIU%LAI+UdG;fY%W;_sA~ngva6s}P+u3BfEo^AVURmT27W3vz
zCs}iLLhBFhWGb^7z$*Pk;9Sw#*@Zxa&$oPd{-iN<w7y}XLumVvu+n8(<zR|01fMeb
zKm?0mu#O@kPo=x6Ybm1Zej6-V;;y`8dn_jfGyjKE5d($-!t=c*E<9VRCxQZWvws>1
zIm?!<Fy*Kd(Xf)a!;NoL$Nn7Jct=>gLB_|BIwwhHQOm{IVBC*jz>F;%9ng(9QJ4>c
ziRZJzmwrqz{Y$AK>P!anP)PU3DPF@_dPJH}Xm%;AydyAdx4(v(4Vzox_y{ke$Ecsa
zZgd@QFTI6(1xj3DeEjt3IakWvfV)d7gk7}REnCV(-)d^xivv%@ck6>~`V#H2$YxyZ
zsrwn(ZZHC1{`JvDH7!mp$Ry{)|8;P%mHXDXQ7Ygh5$(DO+qKZyseQ1hdA&+g&8*8a
zWe*AHBy~AI@|-9pgz~N$pg}M_=QH{s=)wOwxF?^4?}iF-4fYG>#+(hSl_WeJKJqkB
zZ%ak6<?q4XB-V{=tjhXmlMq;KVhap;S|=lY2zOz4dzVyy$Pq*FDmaXGcmcE^!SDzj
zT-<6uiT3J_70|wuq2_mey1+^Lb+|)BbprR1!}*yru%tq0s+9)|3y=|yEcpY=)Ozp#
z>4!1=%Q}r-R50f!aEYt@Efv{%@Ohm64cHd4Oz@h4$H)8d;uuBFeSn*gE7Y#eFl_WP
zGg<bHw@eJh|6c@aYug;bl|VE<7h>7zqu)Ll)YB5tM~kM1RE|8aTm?wzng`A7F^XM2
z{&agYzz!&}TE?(`rBJc@#|;85-Z-msK{%d`c0zr;@;OmDh#_l$PJK+K!4`z(!{{WW
z3tMez{&1ECa{xCUab>EGJ1fvY=Bv-=z}FIjWUTM)c8dl7`k2O7D?!{78@kxHWbAU$
z0F{*$^MNXl_)*29_w&n$L;^JY{FOqx$rP5rp434;h{g!N$(ia;pOPft0C6%Htf5q0
zhW87(rmZWP1fvI@-SHmYENKOFyXcD`S}9xC!Z#u~e;-Q4Ss4`E?iCleBIR2zV1s?C
zQVB}5SU?OJl<Q|y5;gDMTXNli2Ll4F_%_G9y4`1X=VYJ=j_6X)1qKiV_J+d6AuICc
zu?l2Hc?tyz{}H_)=H7w<rcQQihXE&I)xbKoE~CmnwGxI!&$>vvBPRe!JW`#_i!Ag2
zn6yOrw-HvT#W&@iH5@I%t7Ew^B&1lD`TUlESAux-y<T=njbRAfx&0H7t6NrIrRw|M
z2f!qTP3{3i^U9zxL@Re=qv~r_C!S3U?(7W(e8q_e3Y;d2bK%t$X3O!x8-lkmkk;3Z
zgwLe(T`rG3z9-+^{+Qxr#1hpO`}<~}DFvjl4`7ngj2HTktJ8gHUo2+bUI#2^M!33-
zPvkkq7~9Z^gMK4Gpo3daS8sg{7Y=1pWPg)pu-IpdT=|v>{&PPKDN%49w8b1WOq(K8
zL4Bpv)N-seo2|?thj<k5$=kh?Fp3&&2~j9-la7JMRHPlj&)uT4Q04zDuKjRyw{_&N
zM10~_#j)H%lh)PZKt^A&Q-0L<76j4%r~-;T7F(*0Qs3->^7&E5<30hUo}bbz)Y5F7
zvkD{1ST~q8@Colb)2Ts8atjLG43J&}$!jvW<T12R3!#;|Z(2Wc7~*N!)p&dIX0=?L
zao*EWvLV2w+_k!-jY$$wQC@83j|Ugh|2Y&}{}<zu{)W+orh$X8@f(Kr&u5wlkd<Bx
z7}dV7bG?YgCJ$pt%N57sGjNa!8@-5iRm0!-jZ%Z`!qPAfGj^I3s<{GGHrwxio4PAr
zd~sR<Vx$x-DJt3^U$9&d$4V8I1d~~m@0G8Ek9kCi`R+bJPb`s$wq<Kc+3P@_*wYK_
z5CsrdX)+m><l{9KAVgEgE8>ik3DgujMZCTVfEA!vg#1LuSo;MP0x6@>a}A;PB#?H(
z2m|pWGGhVwO({=We176XbhTwbzXvSI!i_3??s!=>6Jj)dor<E?IVOuHO)s`9sKx(o
zOF+{G?1)RJVm7T;n|nxqki(f`2&tyg)r|@*F$;3ges%A*+Trj{IjC-)f-N;Kk~pI|
zFy46U-pV3Zh448Y@ePbcY&z*k#@~+L`0W>mh{P>j(te^63~-JsAqOnN25SPF_Ucj#
z8qV)Q2LW<DL>Hp8er_j8RelgE<uypuJ?(ewm$_u5^a|%qg{O>r3QUK}VU%V4#5H;(
zHony8Txj~Xo^bTwI84nq6+rT(v5<vm)C2Kp4v)ZwmQN6xpCzscpX#IA$gzxcr2UHU
zpTn+Bklok9uedRyYnM$qwNz}dji8{?mk(Ob>Y*S`Y5C9`boIwq7r4hxc>Yu2FbGIS
z_1seU@IWtrnayS@Fki-6WbOy&WDyfwcfWFP?w7(wGRKuH3FN_597HP&7iud3DklLu
z)wl;dd?yqqNdboa+@wBimTn{Kx%=h0@B~n57)&dh`}Re6JHyu<gjvHmGe8^+=#|QI
zOzZeaMh%@C)QCt6E{Ms~`rxQrtx)O!A_|QqBG_(<OkS+_f!i*F@bJI~FB)G<)X~>>
zH|b<oY#~7E_=8|Yuu*U=Ktk6YHhtz|>Yyc4H*!dIg-QDj(kj>$bq~M3D%nfjn;$%h
z0yGrJv4mh6|EA)cG;VNf^OW3?05PKIZsov287U?|<(A?fYhA*Di>YC0z_hzLSltb}
zJVc)m+6txL+@BB8@=jCgOWz?jaq8B=r$JlUV-tgIhdB4IL{h#&;U8S&fL2Y`UBR}r
zCQYXiTb~RjB%%|>#0O;2H#Z{RA_@VRg*T71kw8|lHY=I^rQhG$OIc@d9z5Ey3d1m#
zHd75^OfX&t+#&93FkckZ{#8}V*Jp&gpi`)Jlvqq0f<hKi+47N(c&Q0zn0cobELf`F
zC^Xk{aE87vm`A3cM(ZXBzo5ER5=M|gT6*$!sKYLqBO7!~KhcoTPXbENuTJv>6^1dS
z`g-)BZ&0l6U`Q?INJJmhhhHqIQK6^n3lE-uMDrw8&LdRN+zdsYLOw@CM&LHr79t2v
z+)|ivVL$};*66q~MDnIn`R8)TO_~@2^xkr#O_f@h2B`GQhxIxY0AOL*3%=huLnFwx
zo;Obd1NHYOE;$iI7V&h)NL(&RK3$RHL87|MgrmxXp$+DLs9HmYkyqCW4**<zP3|2e
zhdExS%<(brAIV}JNKDAwMp?a;E>MfV6+!K<PU<O+p0pi;Uv$eAfTYlW0ix}8APrkC
z;_2d@`3N)|)7wFZ<oR1Tkh_Ld3@#rwfFuzE!=nz9B<Lr6z!xB}iT$tH2#jh``x?uk
z;;L6iRb8~3Nw1Ife;^B?sDy8Gp*Kq+?ParoD~FI6@o-apI1|qr*8B|fBy!poG1{Bq
zzl*v~r)aL59dhJEXS2Ru`uvY`Vfv&E$nmnBjD3n-$98D|5SeSUV4cY><phZZObKTe
zEY;G+Pzp6xUUps~OX%3G8|qgrUH+hxER&G?64zcf;PZc}tR0oednqmYPN3hXAECc*
zQf_6?C!Sus)nitI9gLtOvt)HnZvvQid{Z-FcuWWWql?_*J@t98GhCU+J7Ph-0_9Ge
zVZC$K2%P47dAS}by5PX(0<4eGl1X1>q*L5bV@Wn(zJ~Ijc2Q7L<Lj!@($O}taQ!33
zPWDP~ZNlQ&kBY!X{~`~-NKG_IuY<<}@Lzwl1bYjb{>Tj$o9&Wy<EJKSCwU`$X=FWZ
zoT#F#7qZT+D<jw~Ee9YW97K8aRp{8NDHy7FmQAf~D{FoeHBi=RTKzMYky!T?#F@~(
z)^RgA`;W3nDs#9QMH^m-%Jbi|Ol3#SSa!yQ2Ex%N{HFhpF=3U*O?=-&mDHroI+N~g
zv3sz)TPzpYoW)g2uM)_$xcv90F!W*h@e8GqJhMX>MBv3u!CM}gCeQJ~O-P4GH?+JX
z`a=fz-ZT0XXhK65f>+&kG!>HNJ-KrKKBfuCCe5I0oE)$elM2vPwvKtPc20r3U%$V<
z4ny;ISaRvx1nxYv264&bn}2O<CvhpGr=*v}I(Cq^wwb0o?9W<ONfl|@;!y;EWP%Iv
z+bP`y(v$yRQZgInrn5KgMA@+VtQi1{*sr>f@uerIu>Wrb*#gaQWWbZ9<aUQO5(XNc
z8o}uk49v~VjmcY^X9oetG1i-SeJrv*#ZuqX6PI*gS_1EFmy8APWt~1~-SMxh`Rc#8
z{gGgvF}i7Z*j>|tPf3~JRvfij^%dYjue!2WgRPt_VIRwTz3Z`7T8v!xV6`5aRQ)ni
zd;oyE00)P^m5ru0_k+-2QQv2S5*)r?Dz?1{eaHFbQsM&~A=CCF=VqO!M!&^f)cyLE
zO4*RQub`ejc=@4-ZW6!`@tCJH9j*v+`&Wa3y9X|CIG_s{6=T7RXd+tk+<<zU3u7#}
za2ZzHuQ*=lG&1xG$)npWq8i@zs`JwSv7a)z1xCi=y#xpc5pQ{`xZ(#IO)=jzanxoN
zSzi<N<3tWj1=svkSnuAeOm*O$5Aeg7rMPQQ^V-t)vYG97OX<dMfR~0Cvo80aENO(m
zRu}FB0@*N)k7I5Z)(eS@jk96;Dkf#mKuWnBs4sA5@n`XEfxVKjk~<J>)G?B=kF3TD
z)#`e6(D(zC9g`%$0b%JbN?V|d$tg0OiRhBX?zY)lMcM;A+JKKW(%!k=rl6Y=%Lkrv
zuR6Wis-^V}<`GGP1$+a{KA7`=b_)d@7%_B{GcMW9bl0su{i4?Pb43RWCxs71jVPsd
zh)FZNlQj=im5yX@MK;M1gMy(bbQ3TD(DdFaV8bI0Eig<1DhAFm40Az<)82I3Gyc})
znaYKUP>>v9{91m@P{QgEp+-As1`@upFv*06w#NQ$#?{HipuSB?aiCRWmU9_3q`5->
zv_jJ3Xy$ZFg@5gyGUJp<E{R`m66r(c{1JQYgeV=_ccRmnsb>+pJiTA*m|-93aA#JR
zy4pxT@k}{9*lvsA<tD{$$=Voh+N~s1By>lt8EDV{RNKv30#fnYT*>4VgEGEt=AS`b
z9dMHu(Nk=WS;Sc^6ooV;71d%6?G?pIo%!a=u~>}u)Y>B4OT23jG<$e57DOr;e|&zC
zv3y`;Y0By0`t$doT-5kdWwRv3*CqcK9RT9)uj#oF_NU*M(6|_~^rt@?#QSvG`Y?Y!
zsQ=Db%rd>|$Euk}?i6U1=xIq!V}ZJ|I@2X=^qTnH8o0F5*rq>^?`<cE0@N57C4KYq
z3sT9xHW$ate!t1O?snjTW9R8C*L<tPsrx4Oc^~NNsZxT7+-p}ERSJJO#kvp!G?QzA
z3TJIbl<#|{a~Lz-d%zLgLNjyJn5pu|QplX%=C&Hgo1$qob0&ObJPFHCrJFT(d{TQ_
z3$ou=3Qcf^uK2S1PDU5`OvVIuPMc-PU+HCb0}o8f2m{o03(16Eb*>?a<=MgYvE9cs
zOADJ*%*`w+Qz`TF=skZ|%~Pg<K_cnJZ$VffQumkiw=ti3^ey=8fEZe&O?dr0I(I$K
zU#(8XF^M0RZ}F{%<~3HaLEepNjW3CAYWK_*u!BsQEA%~OyGaSC9k1{8lq}mF2LlQ#
zNzHqt3R~Ms99Ko%q(IQD8(0s-He=t#-+E7D>?<tu;!Zt9H)mw|=5(OF13{(RUu>`A
z<;AIf?xVE*{yXj?VmHI)PU_6ZOF7EV7W-*RABnPAae{@-7S`QQena?`TAydIQ$HUL
zjeCU*YSp;>Anqr7FS@#cUsJ}e@gbkW%!I=^{cjH@@_pX|rq$g=J8aEQ2Y5J5x}7$D
zn~Z?Xz)Qn$xZS51hi_uzTiW#*b2Z-w7!k2rBA;%uVCRz-D9?iDp26R4-PoG;I#>zw
zW3~{E=9PJwKv)cSppzMt&HX+(9Q(;996@qyu^&vTej#+?t>%N&rr9XgV|M<srB=67
zEBwQr->~8Kt!JTQstM8RPnx!OJOEvCZ;=)3HDR+I+Q~2(cJv^^4KF7(MBbAlc)wEE
z6-=f{z0nx@dG!OVip5eAOz9p+s*Cgp?7N6J`Dr5YB?{c=`h?MHyC8^XW7&L+y;>X@
zu}Wi}b+_<JtL*qu`q@+HBRWari*I-S+Ha*Nc1bN@7>)@;d@XHP8T|BcW&gqsZCqob
zg_Ym%{`QnKB>2m?s;;{>@}NW%9Us_|{HRSi9UEtwnT@*IKKbwWnI>e_$IN|mHSL&M
z$;4Cv0<G2P@24I|1xRYdsRF?L{A~}=nBWW#8k5N@SgZi#L1%!V;*lUqF%XOD^TmkH
ztMvlZE9O&oauz}F2p;xx6lkFE{(|TdK6-I$N<NP*e$Z*Fo#Kz9@X1IN<#I6e77XD3
zLg)eD-bbEhvKDvZiG)T{dYMhXYAch>>l(GbQ6yiuZDd>X?$aw;v_p`u`zsx#IANI3
z3H-8+JG4B|`Z4n8+XPaNG#3R;Mz#Nb>9U4YfvC0aL1RtqTDJ{iEId@}UcIkXL;bcV
z03(SoRW@C77%JE}k^aXf=CeATcZs>5Q0doBV`GArE+o8ffdjguO#5ubwHTFAwET!Y
z`iamIP@R0csB{<l&z7DKA1|y&$EpWKjo)#2;+p_;1UN(Q@2~A45BQ<+v>2|?k0{wA
z{ygv7+`$_I_?#F(6i)HEC)OSq+DAyRn;E(sFrtdJeYV`xW;1@f_YX1=p;JZl6TF@l
zpd4mhsEx`C-g{$~Qpb11dM#bV@93V+H$XLYQi@qnac=aeEhhy|lvPup_6FcfQJ!k#
zU2`kiHKC0EXb%8rUH^P~`EOd`ui@ojG=o6EavSt5ex(Tym@;UH!400IdHEP<fc1S<
zSnLz#w*(sMLx8gIDEuw(wH#3CGLLBT@L`7Tr_eRtHN<gur@sGBL3d!qg7``FaOF*|
zmsox`J})==YJ}i+h9%d#sj{4X33RsTZ=$YNP;%29>wONZ@r-kw42&DgvVKG)_Hw&0
zF^YXM7C8FfDNP^mK`Wo6VS21;Ar)u%{1=3yUW+Vsgy{4mKi4rz-hmyGx^SZ&t)o1C
za)<PFFOvzKRQZ{~FzslHTsKQODG)eycEpV5sy(Wqt*HB~&3J3dtVDXSy<7Yg2uCYg
z8QrSgPc`)JaC=X>6_gz<nRwtBzv2Dx*x9@MKu?YbM=!m_oJ)++CHnNcAff2*+HNPh
zUOGMj_8WmNPLg384b{|_@%68Lkr;lCn;w{WLn0MnGWD<;*H8ugr=l%Ja!?3Z7r0!;
zYU=|>Zhz#!i=m7Ej$Q!k?bPq=Hp@QU<@twJ)x=*+pMhK>)@C?Ya<2^r7<i0`A`clK
zF)Cn|1!6iWQU~g*{X{b(fT=)*ceRpGc)-TFOS|<BrOGj>hb@x>FnO@MmM719|LA%D
z9U$B97XWI2&gp9)IOBhi0=rM*ZwSCT$oThH1z-i3h*l-hFs``(vtu8~WdG-WxJ3p=
zMVOneSLSM^FN`{Dev!18#(k@}w9~#$-%=?Z7lLzrp2K^4K4U%sXa%kY&MIYruMo(W
zqN+coiz_Ol6V~qlPM^@H)nvmB-}@FEJApGY6qa3cg{evhrnbDeggIJ`fa1ISCLoM&
z*t=MzBmWObpp{q4&wIO;30R?dWmcz+L^rFxY#0(km_H{5zNXtf-JQ<QJ37+j1+ZP;
z-WHdn*#mCtV`jkcZ1L}VfbW}>8vNoyh-j4otjRVYB<x|gP@(Z3nc!Ps$x1S#dKthu
zfLp6Kh36fzdol-$827`#!hStEvN`gYeQci0<e!E>>bkpM;N$#>UA}xVF*U6$FVFX<
zuXfaxk{SpEL(|aElx?Nk^swaRiu#RDPpiC0>g2Y}m8wKXMSbjfzAGuAQTdOp;8|MF
zY6yT%8<7u_9CfFXU*{#QTMSypX%^(%YDkK=yro7@I%_mkb3@o_NBo^H8km0#)z;zm
z#P{di9(o4NHCvuSc|XNJ>7z1!)W<zd4aL2k$A{d&%Dj_Ot9|_5pUdI(CXI`g`A{F_
zI~Iz1QXI=9oe!(!vhkPJcnf@U)Jcr}&RtQ9Y+dg6e!Ymm8*_e=$V?b@{L?ZV9PH-f
z`$Mx-oVzk}<@d_KSWmfx`?e*B3BVBen>-Xejs+F<%THz;tD)HvLudsXcB6b3_kg<j
z3c8qvl7fNYZ-J$#Dn#Ie8Be_hUPPv&G!Mt4C46J#q^oV?NC9JY{AeA+{DCVX@Q&Z+
zPGbFRpp}lXmdy0&U4JM9kE$u^Od;)I*jThzsh-6sMgI4;-)=uCB%5nyLgdV55=9rd
z0meZN!^JRs_Rma&>Syuv0C~K`Z->Shq`o~8*MmfolT``6gE$d|`9rxR5O;1CRk)ou
z1pmAnWT-ThL%~cIoks{9QL0SMShoc5K|`TeJ98e{>#LIfrX~|+Cglx@A|HzGweO>r
zE5bM4!Lmy-i>X@VVx>b_#THf~@a}RkMM47F2YyhZzL9I~{$Ti*2qK5q<}1lu7$ojQ
z2=YyeA~<@Z{;c-Kh8ML{iA_V!mkz^o4NP8@`W840yrd3&6xa2YCuQh2edV-HSRMt`
zo~jJ)6&`80$SW<Lq8A_;4qyO64*IBwN!%P9NEybVt>BX1E=S1G4mmpNdGeX`07BfW
z2Pjk#BZu&=Y(sx;Und&~u^24YA|1B!BZL~>4&eTU`ufhQ&!r$*6=5$<$#&?w>ztds
z`27zp)WaUQH(^C3Ek?)mUM%F@D~6<~L!b*s1kk?x$6eC;34qhnwTe6tY$w7C1Dezz
zaX`Q~Q^((F)TH_JHTrKLoXTFxr{77MoY@V0C!8s5IpO5tI6Pw+0=-L&uhwep^#Lx3
zei5wwNU8~>mpEgYg1cRRDhR;sOpCv4nMO~fpq$l&0Rs+#ZQsd272mhIvZNze@d4Md
z0#Vp~rACtL=`pRAKjR#DcBJ!0U%3!%`+ooIWJApKoiWE6L=u-uHol~Vjl*GuH(N=A
z+h_priz<gHuy)kuA0qHWE+MjF_0acQ^=I$$Ldbcn+oqhaH&+567zDk*yv|t7=oT|y
zNvu^1RdK})hI|}q0oajc%M)7`JoyUoH#D;5YcCJIRB#^ak-C>9!G-c7CwzF39q&Ro
zrPAM#kI$9bsBWf|`JXYYG*RQeRit&LWyrhubcPW<U-!I7VPJ!myog|c=hl&DWG`Z2
z>CxEmRPbbwKU5pQ%6eV<Xve(^b_!#c?4=)iS<kz+p<j&4DPje{X^nKRYn#0li3(ZB
znJ8>Gtk}A#tC0I6#yoOkxlVoY0Hvo?N0|NM^Id>FM=8`SEBGRZ8m{vGczu~}e^*F4
z$MAX`E)XR(wqPbRThjIfATP-h+-kz0l2!3r%RkD|P}F|A#JrSeTw}f0Z8|^$WrP^g
zl)wo3gaJNOstPz8G<8ZE%ukShtglv$MO<s|{h!}NI+)-Bn`Ku>N`MhmOA!vPz{0P4
z&M5I47<pFzahaLDjqx4T8qiC&FKoBgkz50>!q}yDE}(&-L4ezY0^YrSiq&xU%1Yp_
zPAbTDxL6I)kG#>LR$ZV#jpuDG+j$}j>Wwrzp6j~vL=XUpP?>!J(-WHWuNLYh`cq~6
z^#^0=cddbbj%BSwvWLdYwf7r7{&lf*=O!pQu*YJnadS#SChhyMwV1=R%~q**ZjOPx
zC~yp7Ha}{Hz1Rsrfb!JO7A#Mwd2s(`j1p*&>XEs;HianOOz|jg0t?dl4rdj34usiw
zryZYDQ%Tza)vP>**OO34JOVlEDFD#q<iJ^4=k64CTy{S}2JQl~&@jf<HH&VJX`duH
zg0mZmW{uWx8_GQUZO)ML-00ad@RGS=qL+*p?zDHxp~LHitC1CIW$fPhR=6jHJI8PO
zA7sz9CjbfY>w<U|WznmEfS1!&I!}jBtA;O+fN5KkAvz$a99amc+W7S3^o}$5JA#U|
zm8ilaQO<GnyX4k@B>$vsVyj(BuPIQ?OA$K31+qVM_L{hqNl?0A;_!JA7QE3!1k}`<
zT_giFu~3U&_~>QG!CZ?BP-+dANN<0h-fKJ0xwVVGkShZi3w=Z-Gy-@Q_uh#m=FZC{
zT`B}6EC`m=1m1u&B&z~}@<nE8=3tC9S;8f+paDUvS~X=O3(<EDPc@S0!iG`CHwXb#
zl5<x-fy0kGb7993eS}1v83WSaMSb%T0pJ^>BoN3ZNIWWO#!#{$(U&hHRM)7&wa=0y
zGN1XfJqPo13}y#NgtNw|5_qV>Axl`LKq6@a_wf=*7@1lu-uyJy#;nN`09SOTGCp)3
ze1nTJ;``6d?z(MHIcK3V7%8FukFU3ks$+@TgmDQD!65_>5Zv7@xLbhW?(PyaxVyW%
zTX1)GcXx+r?!DhP@0zt{ew}qrcb~SZUAuNY!fRopMZjXrR`BOEcSI&gf-e-(d(77k
zrzO0xa~gTA$Q4*8GN1giS$DU^Q@(*<J!6q<=dZjp&@&>>U9C=OjM~ulNB2yAA!VZz
zm`HT43b&<xwoB-&1e6kN06Z59^X6oX0|a;JS4F~F;h4>B{H^HfXxks$36Vhu6CH08
zGgYJ@>d*DGE%^t$T;1>?U3KLa*kAt=Hp2AjbGgSMCV|mVRd=H3qTNoXgXnH(zRQ`v
zx-9zCUtx#5;at)3$?`Y=!l}0JQRc|`i&-AlJb`vt5h2YV`hr;QcA=-qcVgza6|Gi}
zz=9qGw66~#8IkaTsS#-f$=7derR(oKx%%zkO!ssEjwcI!PB$er^LoPQ>p2}dAwY))
zI%8vh@!uO+<G3<$wa*~U>p`8w_C4=7GZ~sPU724M^<P!P7>_|O0XpX2LqMl3)1i*a
z+wy0$>l*R%&`FxxqR16#Y!-dBDOx=lSZ16BnYSKQatm7pQbS_LlC97DR_wMd*&W}J
z+}72g(nj-EDaZ{#bG@|KKAre-Nlo+H!1;8N9}&(aktgIiTn`Xm5ry91t10?4NID80
z2=UPMr5lvGaR!VC7C@U2r_Jh&0|K#dGz|fI+*87ul+J$;hRm!2AiP=XGw4@&oAzz*
z7C8Q-nCZ;$r-k6cmU+|hmB19?t5cn^dIm9>FZrP4R|5;>-LVjTBP?Kn`tzuO9$*mv
zV)xLOQ$RWMckKcOW^m>p&OXauzCLC8c;%(ja?c?&ch#BUD69HOV8u_Q3V`I%71Hz6
zTVjIOK9u59_^mlAdk*3WLXBL=%y6{_$l94DfxFYMTbh}|F?Pv__lzj(0tT+b$Y0s=
zVfe*5zP|M&zdHT8+jrO^1#yLeW*tE^Pq0{x(&p?W=XdJrsw#2n`JD4%&UwgbTd(Dc
zSR#bUh(EFTVkfnc3QtZ`6iKmx7C&pUu}+kZ2&_vZDxkAe{P0C%fPe0k&uJ<}_fbQ3
zQq6*Ad;MeK2rBuqcuxDP0gwdZ>uhF+4)C#GP<-<@RBc=;X8(!I5B<|6f_rF95S@^9
z9GT6Eb9_|*q_!_l3R$lU<8Wm$-_;mBl>1FBz*^pi0&HAWNgB>2C4QwdI_FnHSEj#l
z^ZEt9AbKBGN3bGqjc0BN6kC}6fg+nq5-^v%j#rY}E4)-kb_}D7@{i(jt)>B4`hp*Q
zfs3Rt?8O#{41$1i6W{^av*svWRS~t?b$e;BuDJ!mhlIT0S3`kE3zIF($|?AQ2yUDC
z4as}(M35*FQzY|_*=mCsgFj-^dqOW0h820}$t+%RL9q_)_!q7YRyV~bY0BDiN7xBC
zG#UP&9@-Kd=aOh`kzT{1h7c8fQ-mq&WLU^w#R*qmY%W3oVg<D{4|S2=2zgocwkLW<
z=#Vk&W7>Ek!oi;8+he}7K$jPob^Ne=XCa*~$a@e983P}wG#q4if{wX*wg8od&U+|b
zyiXAl2=Fw`GU+f=ng;-`bPk#EAb7jV!e11>zhDeXh!v)^?ojlXsG`=M#623PANMF|
zkK_aPkTSPBT4{nTpu?^$pIJi3jzuyuYC;1&&67BTIFEr_PVNKhdd#+fTghQgQ3}Qg
z4#Hv1nVJD42|&0xO}-x@ABCI{RGj3=Z1D;v-r-k}6mLmjJAlMxQw%3yi5wPzbp;DY
zNCI#N%G9dw_!ER|9JO7i!Ah*Kn~X=KpD2tGTW?XINYas}HQY_aKpjhv@8o(IcibNF
zCjhOus}8N{)3&|`GQtxIQ=I(2+6p>-&{bFvn0yu|$0f2s%s?pn<p^)*K9E}m0|wJ&
zG6BfKvJL$joCNu-3W=B37J}QD=z@@J>(d^V;`g95kPHFj!N}$)4vZh-l{!@31yvIk
zY!ZT2gw{~VdaIrUA?ST3&lYGP;ZU)|?y_I%5j*I7=b_97$wmNG=3uFw3rC8hLuSCC
zF^l}??|gftV_uSPm<d&x;7<Xou!VoRyAjy%oDTvq`+m$7{7QR|H3v{q97qhV_&&4O
zQku=;LF=7;_~`c{BS{oqH8P{L#y(=$_tt=y*v3c{Z6(Bxy<n&ns{B3dE?+k}9^$W(
zq4wR{oZlXEI*bNvj;ds*iCtL*Kbr#F*;YX8&DAyWBThvE%9m4EVXKJYV4E#E?S2We
zjqE5QmSto7tw*`nI`Ygk+$wPw%PfX^_QP8cwk}6i)%7_i^;LC^iPV~Sl6|BHyO#U~
zRpDq~rhBEEbdUs~Om&zj^uI^nq>uoTUzI=1svsuXX!`&a%GE|tRs8uq(lVA=I96CU
zaiuEjJ|e&IOzm5W@HI?5K1M0{EXj&BV8&iII7d_XdU|TW*G&=!sD?QO_&ilP0AjOj
z)Iix!!Y)F|f#1kW6=L=Ih#^z0R2zEY6ri=&%m9mwwNEgaY6V*0On&x%)Itc66?rPV
znkgiHCHeR6e~!A<Q$j4SH68kF-7wSIn$-A%3a9}D1I%V%iLeo1HdMPJO^i%bfE!E1
zg159s3~ZPnPlK)&uuSZDz$<`QOr`>y_d{Pa4Q;4epIwsah)WUDO%EZmbG?GnkPnr*
zPpw)xF6PWxaRlFjs3Ay%T@j7zxFukjYXqu6w>=^GmPa(HNvJ@{`*^bHw*;1HR6eGb
zQa67N6mY+NC2iikAQm8R$qgzHQx`8|W%;4({x;_E&)g6YM>)RVsaRiD*=gGMoLC+A
zlfDzuiBRXdUm>o&BCaF%nhwVFlXg_niEuN?;ksXg^YX2Gumk6KIdnpbxran?T@du=
zC7gTj_UF}_LOXSrE{zfHB52M8Z9ZYV8nz-7J|ahDvv8ZE$)r`uG&={F^z8QM+_P5V
z#HT9Nj{k2G{i;DtRaI6#vb>^VZvGN0Ktt!6Tv&MMXDuly>1MRq-roK#NlQ!16=rmG
zbu~9Xznw&@-CkN)`1e|zIIMQP2YCLAAjwHdNs*1#n_zrs{WQ|14}KMQcXu~QH{N|J
z5IN+WVGpvKb!PLKTr(irHhj<!5PZoHOeVRn0e~X`C)9zTOlqq$p!bD5x4Meoj)=#G
z%wt2;0L4U2(}zGS$@mS4EdT=1nD6f9a9W%%Mr6s#7(ij@U0z;Z1PfwB_=C1zpgaGW
zj_2XCL@;iJR|Jz|ff!y{S&7|M1%2ol259TkAPDwj<KyHXPog6QSO?4>P3Ojy*}(kv
z)w;Z-B!>{4VUy}K8d58o$G4AGcLVCBS1m%@qi=Y-wBM{7^MjdQyFMxgoEwlD_)s4B
zN0wktH0<LpqD0gQ>KsAauGIub?Bs<=+f3P%=5BD04wlJ^jcToMEgQ^R-t}&7!0V85
zqwc8G01~?MV&oM-YEKeI02DGk1wnI)(kI(NzX^Ww4Aq*Ef93<OGWr%ZbNY|Ql!Ur1
zm7HyfDd7BfDb$B6G;FR=X=%wmRMJK1v&68T9Xe0#$54s^a80)vo0o$uJw*K6k-u}O
ztU&-~Uw!hf{Mdy%eWLb7I3)-W?0yW~ihwiY+vn$grIU<k0%XiOjJJqxfHXZXKD|E;
z>(6cK1{Bk#JjIVm`Dm9mHXSaqr%Q|EXp^tja%OZxd%c%RKh>_Rlh>dcsANV7OsQF^
zPlcq|Qp%aiNl@m2C|{Ar4}2G1>?MFt@Dm>v$18m@tzj8sG~?}vH&d5G^AIe>hyi^q
z7gkYnpHr7fmm>!3=bdmSR-r=6B0=<1u)eKZ2|&UOs*W^^>YR<bmX4^2$yQ2Jxjc*^
z%*S~WLy~022$GjwFjX-`G~FdHD38xj^ARIx-lDgRVYh^@JKriOqI`mc;581W5YdsL
z(-&>yiww}+jF7V@Os;oy5JLGwB@8A~L#*vSOe@eC3lSUYwaCW3J$NrA3sf>|wWnNR
zCx%~H&DdqII|pqb2*q=3_@R-Y*WO7=Sv_A;DUNlwm4!qs+OFa)WZw^3Amh^yPgYt9
zjc8<K8efdO!V!XDJ0sdZ9fw{22!ivxV1N4Y1|nU>ZDop~zb4}{n_E=ST#)~}F{KG`
zS(buVWa%#$C^F`HTgu$6mQ|42lhdn7?#4@xYPGbi_sP<@Z!bKW_nPqC4T)$s!y_#=
zHZgFn0KE({DWnwVdRQ`MGVo8$-b$-DP{&6_*$I;hX?#G@d=G7W$SZHPB4n8d2dHoA
zB2P9}bbEi6OLtHs5nCUOmG5#opu)6}KUbZ;8Lb+KFV#yA6K}I<c@7hQZ((9n92=4A
z>=Ji;F+?#bx(GDj2+@PwtrM{9aQxKNMN`OR7qZ^vLAuV$kN$5vse^HSl=B-S+WG7p
zt1UfE%wf@8<wk{wZt$746cNcmy^?L48&!#ZK|7Enl)O;#YDptd{}hvIfp*Yc`OM)Z
zsxx)e?zh6r7?-riZ=wi-a5v=^{rl-w)1L0pD3wb)aCI4pBIK-Ay_Wkpcjbv0Mg~SP
z_R9M%Bl}}G+b?F{iKyT42y56iu28DAH?ZxFyI;qRug^-pm?Ui3q7pgnhNsm{xTHVF
zxsp&(*tUUMtRFfxA+8DrDX8w+8W4cgW@&wrOr(rDd}_;yHD7U$R>t?BIdA2;5gDu|
zKIf8;u)e&)U0NQ2kxn|vV3}Vgf8+k~9HjrVH6Z+R#zqVs`DLkh>`G{mrrs+ecEw;Y
ztpMX7Omgk|AkdC*W1@wX>`7`R9p{N)m8h@Tsg%Ds7wef2=IOOjle3<qMbl%MZE;CU
zuQS~lOs`0#=+_}s7e_s2dx!2TrHpqlI^t24=hu{Ah!-6XsYxy=J*f$aqfv%BtI8M$
z%>oU`;(P55lY_HA>6!aO;VGM-l*Y9oF{;uWrqUF_zNwO5<mi>yn4J64Q!^`0RAr-I
zsqaY+#!k8|D2PW6EbNfU<)>gPxTV}eE3&-OsA^gT5lvB5fg?#J=!CMCVt{vV5F8j8
zU0%DGdmzAMGb$o(5wn3g+9yFQXccGGxbn3$m)L-T@J=bGvbkw9JCan#Mu%3;o_;E=
z#Y0%P92jDgr{jvrFp{@1mdyw1lh{=zZBzTB8vb|FA>}Lh%#x46oQJ_807wdu)>VQ|
z75PgopzHr2%Oxal^K+d>lG)g$M`y`L*Skmkh=7tmEZSUeu`#EuP_@eW=XtV+XJaKs
zGA4yTsaB=l!(j05%_1HAK<GM06BxUVP~FQbj)#kziB>TKsG5-={AB6BRmOxI&dGER
z1R*|w%C!gZcA}~xwts%!7yOPdGFX7#%~<45VepOpbd3-K2>YiAwQFEE6-4>qPeyWp
zE|D2v2(m<L0t`R@8ioVmpMD+~0RanfY%HKdi0PF(z#DhuIL(O=sNzoR37;c1z4(eW
zTN*zf2CgOTtC19@PW=49PMs$^k(|JZ^9-1tuGsAf7+iauDl=)iuD24q@Md9A)e;-<
z)<S*&$B;CX4YalKT#aoObmC3rE_^cGw1H9Z4o(BwTbkOODV=v3%pj39?^ZVcn^9{Z
zCih8mE<i^-eMB48m>R)VNUDfuN`s8Ke2xqSxHa35=b<SvFe!tdD9Q-D%I;d8JsUc{
z<|&lG^r{m2|2~_*s9gYz%eh5xLN>(m&;VfHciFM#5jS9+;@Q6p%{!fSx|F5!#^WcF
zfmX&=y*n{*I+DzSvDV;x=F1O$B6A*=yK;m2Oup$BfCj*yK%zoe7ey)Q@@HNxE-AC)
zAkUjEF9R)W^uo?s@bWFQtO36{T1k%@S6jg68_$?nB%TFNVkU7jTn7g0C0g^2ubH=2
zR(OZ`2Gg;<CV2y=g{G5{(%&Wm5<kk9lW}Ck{U$J$$;QfPMp_BxPlWvTO|XGYtqyka
zTa%-P1eLEQZ|s29{#Yr?WN5YAo`d-N>_r<ONZxDAY6*>AUZswq9bGl&V^9~&RyWE^
z)}<m5ONzlXT}uXfEH2ghSm2mK_A%J=PI&V30d;ew!E}Qs3PC2<CWNJ7!@O0S+JT>Z
z^CZ?uK^pGTTT`2?9@u|*$9py`u6{X~#1unJFZmG`qjrxfDKQpABw6XpVsu+(4{#5v
zQd_aC+vCTm!{?U)6g0STCDx))mT!NMlPkEV)D~~-$O4O23Y+S|7@0(|hE)|sLz7ME
zOoOS;NgT?naFRv$YEmfG-248n0;oA4$%9tE2er6^1^cTS`B)%S5-*ADXQE7v9r^c4
zBkr7<um6}Ap<ZxNUhUYL52<0wL@7T`6~wrvLC1G_?*a=B&Q(>smzv2n!}N+wk;^hk
zf63J6&=dz61E#D}iLIW0S3dIw<9PE346u`klW{>S$u1oBPS3IYZ9BqS5Q<Y`94NnQ
z7k}6$RV(bG$1+;!OfbZ)y!Ztluf`BBCHT)@c{_h&R;pO3LLV|Ru%%WW^p)-{i9d_b
zBQoDR@{?!fiSBX!I4SiCAd-B?nYy?F%!oQmXVv0?+^VA$K$mimV_>(-RGEs!E}t{6
z2uJ~1qCc*Lvg!~cD+{fNQBQa>;c&sFc?aAgII#K6(yaGw<f}}FHY*i3sugs4NyDeD
zj%ZbK&?43voPq_N{r!bCv*k-uOm@cr#-qKK5bW6eHeFnm`G6u@t(<8}&W|M~P$#oE
z#*_eDs!xxSv+NzVxh2Ws4$C^<xie|otN=6hD^E0HJ}RN`ZW^0~A~6fQdP}jXMogY5
zHL36B?XeNm5hd|TdZ-a40nR%_n+75)W9m4f7<HpUf0`kTJD`nE+ui6%Ex<U^*M&t^
zoK}#F<XnvsK9xKEQrMbhuaZ5BZ0Jn8eKHW=)1C@69_|!3DD?F<5KK1EYli*WLO*(C
z_UAD{@p_6cZT_ZC=bwONwF+HJI<%E+PFU6T7j(A@dmtj+vM`<5->Mw;93CyG2-cq;
z#MnWQalhB>Cq{*Xn!q$iLSEv}qAPn%k&4^$v*Ugj^T=q04J;NRk7flU5QMIZBl+@X
zRLr`vWM@bfy90Rph^n6nrAcR1j?K@>n}SeDN$gl&#!Bck&5oB;W2(Lrk4MfS<FLsM
z4cpqI7kt&!6s7jbb++7d<^4-dX02=iND7k^vusuIvL~D<^=XdXk<kwoW#MNU#rQwj
zjp{IKIcGgiUB?~Z&42H696zRaWpAZ+LzRr;10L&OG3frv%b|3UQ`|K|EDrPuQI&mb
zO<QK6qiYab-fvD!&jXjq;3WQ{>Jlayw)4%q6}D?mLCqsDQiN)}xUWt^1^+%Jz$A#e
zdd7OjTq0;7_qz3a$`raFKHh{JZtVOTX?So+E2BgeD`$hUvRRy5oMt%4ZsbG~Z!pJ6
zi7p|}tQU%ikd_932EAWn3F?J;TrVQivg(XI`J`K+PE~=YStg80r9pI;(WJUEai)Ox
zS*`+Xau=%=zO!q#SQRLBLRAQa4d+zp*1?j+S)9-(fAwu&_O63#o)1-i2;>$N(^;1n
zs2VG#oNFb+=#rnNJeog`_oRJ1frNxHyB9#1rN>g8u+!)CvKTq8CpP9WdaODa=xD{`
z5ZzPHdIdT=r8rvIWapdtJC?FQV(OS-sjh$Z!fjsQ&=oEZoZ?~|->&aE9sc4b)Rm!d
zd$Uz=|Hl-pqxBYM!f>{VOzDzOyn8F{V)nry@hEDxSX4!C9KM!vCB|%G0^!LFDEp`A
zwKfSW6;849uC{16WCQ>KFIpnqN-m?+VQ~1yqxNdkVb6i$PZAupMs=WAc<oCvyO7*M
zGlj6tyzTW8TiS67{IKrr7wD9>2zpP^!!OeWHQ-A95~U1u#Xsa%8%A!39Y;mkJyV4x
zSPC#Y=F$)Mq_Fu!Y@F~HFeZDKl)qiKICQ9}RPg@>fP+H?w6zb(`HmCaJA&13)A*m*
z&qQc{Z%kmd!=S>d4VcjygxD&{IRD5mB`cMvDA)Rp=n=Hk0#)`-2lBJGlmkjyI)64%
z6dOHIS0-9fIHfhBACcel>j@LEc7u_DwmXHP!IC>jIzH{yy4uR3yxRj6GjwhW{!c#8
z@CCG4LU&K9tC+29nkv8y23Lg??p&3mN<jPlO?h<MOdrYlPjAH*1F}4Ky$Jo3_~}kQ
zA;%};{Ut#cvs$;p;zsVF$We_VcWQmV@&z$7Y#Su*5{w~qQ)Lhw;_8&kn6UgX7d#Gu
zc<q>3yZq}h2AtBb*$&QUg+))q<Ohq{_KV%^sWbt8=&*nTCu9KyXXfkBVoey2kzI<o
z^+AYQ8yuZGZqXyt4>tWM2fI%NDeMV8g~UswNn;1{u#caifmt`-GsVCBb^f2Hku6;|
z=bQDed<4=KJlo{$^6VwK)$<Xy^2)=h*`6bU`{EE8EsPOWm3zI^;QB&Mp+d|NG;^*S
z6^`VwnhFaqVZ8IHRWoN*FcVeVH|Y-&M&&r!M22tla-@?|gyU&~W}$m;YFF$M2HX3I
z^95DIVRWt;0;*A&mLTdpJ^bg&nff<e!9o#PN6{x7TlxNLPrDSaZ;#!Gr>Fhn<QW_U
zL{6(z$E1v0u>bHx{<(N|V&l-sB#f}bxo=WLCUFLO$&U$4iDM~VKOhhVz%ck{%rBW|
zTWKaw^iW6csgI(8L2BcXZnEC|2dfWQM#n4PZ)R_n5-ElXDep5i_fBMZ?kWMGNO>3;
zK`I+>qErgGs}B9DUN(!smoi84S6Q&D8rBTI9@9&Hw3-x+Wd+ATr_Af`UEHIn2z0{7
z{Qs5j7$S3joZ&rL_l}|{u-bU=fFg5WhT%CDa6V0z<OK<cKD28tk_m2x%eM!g5OJ{a
z8`bxZov*v42`3(g{m8qxC7Qtx{Lw~_Vqlf%HTS-)G6K*Sla6W!kb~H+zNEFR`jB{f
z{*Z(+$rVwg78;7W!ogmTw8a;8!)p%G7<NXfy%8e)`jSVxpIls7umU+BN)$DD+G!Zy
zu{!LrOPEcn`ghOYLGPXlFancmK2p6vY{o2V<Em?KL=QhaZugP>mKt?Ky9D!=oA8e_
zS2{o(w3EQ>Q@n@HQuK0{iaTFLMGnfYziEYJ{lf_O(D3{>7v@FFY{>=WodAbM*d%TM
zA^l-d`62h$L6do1^3|6NFb?_O+3{<#x3p*QE3LC5yDx-}9MT7V=%c*Cw6|0Ndyx8x
z$$g>&uR8!F=l@6k|C3^KKr#KLX^8gEu8Q!f{vmf263|K+t3CrUGU^go2C2P?W;#GA
z2!O3G&}m~Xp%gX_!QNw#Y?sv`r!30B{5eA#0S!a|C7<(0)}FQy%b!Q!-+UIFPj8%`
zQ)Z>{cqJfJK{7#Q%mmym;U+p9`?S?rYR#>d=GFKm2T22IeWCW*{c5=40)68+&Z+PG
zVKwpP$3wE(QZ3E4>cMQ+M#%WQQQE+Fi-3_vQRa40OUo6HFGroq6n;u$mYj~{WF1Ex
zO69GenqImXubtIxpvP?i<Cb4+ncZKAEpS!tXY9_3jMR$b*!Pw9SHJ#~`Qn2wd5%@z
zL}R#$(M!laF1UOFJluQ2=F?onngV@)xgSA!36TBAUx;Vq79fz<)0m^ULRXJ-!<UKq
zXX=sxJ|W>`sNZfNPS6ba0)22ZKISW)h<q~FOX-?(HdS9JTfj!)f(Z3d|5c0m=S7Tz
z9@K8F!DQ47sdMSt!X^O|A;S+@NaC=jIc7eiRttLDlq^<D!x6?8mH99&6PLwvWDush
zLYw8oe#<Xr*K1jMo%D-&)O>5TTlwDC717!(`U$$9rNJ>2ihF0V`Hb3yWk+T`1f_*V
z@n}ucYjwlYk8i@r)4Nq#$DMc6b~w!iZeE@IBz-^NT_E;~D0f|NbKYx@s3shv6V~Zy
zFIa9jVDV#!p&_pRUFpBny{?#J)e<}yw%C_kulOg3zoThHT|Ie;frMx_7;Y;~0=HJH
zz19q6{HrQ@d#eTp);NOE0lVOcPEYU0Q+(>GtKMq;`ghzha+8y-VqV$_*H(qS8lqR5
zX<dR20*V~3M%W}8gn(jq#wk0~_wDaHY$Fabu37}wnUtG{<j8N&CxF|i9^6_#&`n(5
z799>bh6ih+wLO8%K3zT}Dm0$(#v(MRCw6yOX|%NFk69@B_>yCt;<Cxo0>WicnKI$G
z22?5a8g!g6>Ov?|?q|IF1HqHu_rc>wsOj!K9FIGCOV`bM6{gY{UR>9A!<Su1U&T_t
z75k4&TOG^9xpUlAKg}GE7p%Jn^bBsvs8*>k_lKC8^p3Tb@rqMG8Ax}$RbYMh1+(vM
z=X-PN{gJ84U4~WWpiXa$iCXaED3ja*{TU{yV0|XTRa2DuE<irClDbxxG*-8ws5ktx
zr@6WLix9oo%<Qbk%R^OGR#tm^dwu=gZ~`p?f_9sg`)3UFlr1y^W;asM3HpktyOqjS
zn87AXd3q;V+z#6Sr;8QuZ|GE*=Mo0Q=+sU;f^gu!J*-+aG1739gJR9|$A;a1(L1D<
z`Y8Rh$%#HyT&$uJJf!@0>fJyC?ivcn?58b4^JWRNBXx)4Yi?j3pnSH*BRqDXNJdkk
zI>df7DmdnpqN``9O>DAuywO>92qsJ*FqTwBc`kIER3_-SMlz15-UOn~C8TlULoFf%
ztoPTi3}-qHK*#=TId70&&xkR$fR{Qu$!rWiK{+ImV=KRwS!Bk76=5ggu}qX>uwl0u
z*WR|xmhss*yz&!2@?ilwvx0g>rwo#HeUOJL0(qz(dB#R6XhCExrUM8=Xj~qaI6X|f
zZcxu56dUK;5qQ8%!)>i@B%9n#l>7;C9ad!p@}F6~6RH=W8ZM+;`u+7&2CzBv^E3Rc
zz^*g>$&Gj28_G71qQ+J5)Hh{#v8CdL25ABMLWD7*iY%6Ici7f46mxs^zk0vb$u;bX
zp~MaUihOu^WRy1LV~l({p05BgJ5R~*>+LCcCRcBOH0G!7Hz9QEtWZuX*TBI!Y&DM?
z?#f$q7@*#&_Q%A)r@dH5xqB%w>U%rX=ngabKABuQwYaJ<JT_}HcyuEpwrB$Y5~n;_
zk8v_<hBQJAEySqJ#k^MGXB4WEs%gmj7zr=`&SNzEZHq2ecf5|@Rs0HRkrw+b1HY0F
zx#CJ*nj51qC*Z$*ALKU@4)B$URLyk6<PA-r6DZ_E)ZsM?4BJe6+7Fl5R3%d|J)gtM
zmq$hmv<I-4IZIa;2{Y~1vJO)mkr`l#Xl>}|PEbX4mJ`^r|3ntKF_93Z6APnhpzj;R
z%v+}K?PkdP8<MP{$<9Q$`>f>1J7Q#yLem7anMXAwpWs;w^^RzHb(L<2BJ<18t6~A(
zwLne3**zvQfc*r=UXJ)KE*d--sGJP+OvM_-Pis3_zhWvM?nreNw(QnAVh7!o+MA->
zKv2n?3W`ev&a?7iTP3r~d5(AS{ReHX#e2bFqTwRwn7|XD_Vx%+DCgfMy{vHd1&qGM
zpLffKy$!QDkc{UIjyc=K7XV^E6J#pjtMzZ_m9dEw!Ld8t2eG##{o(l-7ddDz=P8cG
zH$Va`jseEdKoMf34hZM~L|^C0?NFWSv3LIG&~hx+|JWQ3YH`A3BfMf6ae%i+MW!;|
zSH<WZ5Pc?>q_#UMLCgRqgK5rtjf}boeCv|WIa3CrttLtbXI-I*xT^Y4NnaeS8|zML
zxQi^LcFj<7!r2Hs+wHICCed|e@sL)9dYk*lU7MDvKbJ~4%4BC686K2}!lxhZ;mZ~P
zMjAd8%2-OGz-6;#Kj2ii2t(*xtL>TPqvhxvgG_vd(bPE`C)%_EJ(XQ3;a~T<H<dj~
z5m?u=Lg;ma={2@O^9THq<1SzgzJ7bNi~~q~r3^!qv>h6d$Vj~x7|Q^*7b*Z8=d)5!
z8(b}>P`hPpt@bhp)QwDWp&L37D`Ec2J;3eH|H++lfmNRW@WHuJeLn$cE%W~v#%MA@
zMGgUPGu?BQT&@aYAVp#CMZjEVehuJISfl^2D-+^6%J2HZglgNIoAB}a(91OW-cRnb
zo;>XIz;EFIzc1ZvM|p)0acWiohx_{W`Wyg-I>8%eU}*T?&%jGl&#1(t)sp>pw|~FS
z%qW`v0rZWZ4FL$Q_;b^DV&dP)M1b;f`yyZ)=K1=BfWs67*oL)RXm@zxvsq{6<PZb?
zV!(;2v@s_3r8C^E&k_H*0Q2vofa{qU@o5Tx<@0`t-|)>)U09y^LL@*|#8Wb0#Lc0)
zgQO((ObT2&^d6d5>1_@~;i<;co$nXJU{x;y<gCC|<k$85O4*&^XtC$KvI=k<sI1j3
zgaEretg$nyDSMkR_g*Fupc;r#lYo-^P&H+XPw-d`*lz*e-c8}eio>5etGw$og;6JR
zU!&*tL0@4^MpI0c6U>!U7DRI$M~IsvO}r7k{wO9iCV7y2z;9DISepw)abZeI@U^82
z<I&~0x5Ay`K2pxyS9tGq&fWJ#PNc%ZxQ4Lx_gBlR`!$oP7O*KdXgL&NNOU+1MIneP
zDhwh1$Tc_g%KNq6Kc6TugpkqEJO6w-ahG9f#8eUDll|Me&z>sIkTfF6(BXnw-O^mq
z<)9tFf--oTw*J>U-K<Ds#3NSw+v30>FQRc~Ovysu#Kh_yd}fkyXD0}tWd`!9N$+jw
z8w?f90mc!7Zk@r$qx3G<_m&a8u6a~}^!ofb8$2efLht?ds0%U*fhI+A1a&YWnycVN
zh=T5su0#BChTP%yxoG~3yX_1Lo6eqOie)(dnU{oTv4=GUhh>0|9LzQ-Hu{J4YSSG)
zAErN+`ycv;&m}5Xv-wtH=TanhQ`Dbky)<PGSifdAtdPut<IK9DN!OJ#J%!w62v^Lz
z;rjcbQ2Cgmw7m&R8f@u>>bP(jdF1`*a**h1S5-*!{5-)bj^HhVu;?JNx?ncZhjhY{
zZTX;qm#?z=Qz>bm3b?&&1JN{4YTi}OZxJxTS3O)tm+-T=rSv-8os1y%GAN^U<OCWE
zi~en2fA{P{ix>Otdu$OH&S=Q;?3`)vGf#(eKe4c7hKb3u+EM!pj{SZ;b@&7h&(mA<
z)2?9Rj!X7jlj(0+@@~;R<xgrmaFIq&@gPI~%-DSx!D#&(3IVnyNYbwnKLT3LLcX>|
z)1`Ist?q*^$lV(x_PtUoAt@@^x}6YbM0rE=7H_y`bLwDlT$}OXR&718OYw2HXdE4)
zLm#*QnHjmB^}@{4#L7<JwUm@^HWmZdCkkOwWMsXRievmmniCd)4x5(uj2kP0)e4Ke
zz@We}^W)OdTjY+cTc#=?LwAPY!9+_nH~qNcl}Pe9i}jny1&zS-BW8adDK0(r6}yv#
zS<!`2kA7cvA}WLZxqG=T1$v-^%9ldd(XtSGZM147((ch95-0h1Aq_<tcH<7I3ECU1
z_(KV$bG`3-SaFg_ZPC_YZflw|y|-FQjCFMX-*eYzGaCWJ?C<XFF6$5EC9hdsWlbFb
z%XAAiZAtUsaa?PaGP3Qt{hsF_Ij=?jshj);hwksd5W^Nl)cm9c1Rr@l2F}8=d(cvM
z=37Xqn+ehv>1e3uk)2hxl8hvMBq_q}h^#G}Yky+=U71pph~!~LIc1)~zDN=rk5FVO
zRWL0vsKr9fpA08M`B@1bPviJJzb^gKkGR15#SYqnH#jgve~8AbvNea-YQO%*+1(f#
z?qBb)(r$9uZp62@ai-<g3A=_16<f-jRq)53h1Ys;lE*4_?b14IR^h~s`eN7(!W@jy
z*Cv8^s{6Gdl1r_FD=)`a6$yd=_dQC$xi81@fc^Yu`L;^cS2cS~^hp{V`Q-U{N<6Jx
zSDU<}yf{i16wR>DElI@#58)Hfkr%yL+5to4BXz2c0VVZyvHsJ^TACa~l*ixjHHMVF
z=WSK;xV~`1_dYL2?4)q%vn|dUjUAM`+8T%S&E~C!1>VphD>NO_;2f{;7bXDub-ish
zyLZ$Rt}zojg)rd8WS-rHxS8Fbf*w@osTchUy!J=;&#`D=&({h%`X{f=tRkhkhIkqV
z{2uLywU9l-#D|`iq~$dB^K+fd-_P^66Z%}uU$-N81&%zoUo@dt#}_{-4+jYa3(+3w
zebc|8`SH#E%lOh^o4uc#DJ;(CN3kB-31^g9IpOMkMaFcSXhA*lYJ7W=^*(IMdDc4o
z38nqD<gA1jju3n1hs1$>dJX5<3sAjWgyG_jf&iQ&l>YdX(pgag<>O4kwA5St&!m!K
z?q2;iFO4w!F(4rCH{wG4iqpHyG}N0Z1$pix{KpP>>whja>kL4db;upiqL)O?BBww(
zPoo#YhvYSPp};1fD!>C$*F#gFZbN8>?tM9^f*qAHl(-Kwo!z63{LzPu5{whboEWj8
zaA~TA6e?nICwQ-dy7yUzw}+l1Mc18sK%vXJ^nS=|+#A#GWa{4VpcGWZ{Q10;?da-b
zXj8gI?9@wTIVH++LHlJgpdn(Z=<!NaQ}McMIp_k2L|d$(&7ezEG3Y&SKfTvIj;zFZ
zd?=s<8!D%7Tq|(Dhsq34g@EW|@v5Sw_G`rqYHm2)wi!)iJ=M-qf9eI9qYayMKQI#@
zBCV%oeeiSJQ7sb%btTnNe{6>$$Z^nyz=`ia%=&gWEprmY^etZjFG~e)MN_nEL$mQM
z#rhSgYR2xUSoBez_cX%~>O7}VwDz1>mekpFHQG8YTei0MLLu}`qd347khlrx5N0V3
z3n0k3T^F8w&YnFHN*vSU?XFhsu4X&M)Y;I>(+a?<Mul3yhUGB+%75M^m-7}AS*#;h
z$Zrz5mhx@%CqXLYtJBF^eg=O>ZgJT?Q4ohmp4`CiktIma5k{aPjsp$RtGylkz*H&2
z3nakDRlBTIi+Yb5vHOp6&@(~shz6b{-K2gtDOht<Z>!<`f8!J^)Pw^D_`&%fP$o1<
zCNU;(KB}?WIr7V=U3w{@EAmtvtch#tTdKy0D>!Bnleq|R^|xF`m8vkpCOtT8KE_Gk
zoFZ@>^(yjZikDi2ha|FWf0P0bOFNb7x8_0hX6~3{<POzIBd<!I1rMDbiZfkY{#3h8
zkmO(t>`{=3P%ICXkFhiAQi^Z;&$v!cVbX!7+OESe1R6C(JFGET;xAuVGEWO2_5q)X
z#@WD;z~`^*U?`yP0ULwKaDd}Kc1kw>KX>{#;$zDZa;VQghktzeIEDj4@84to*$n&}
zVkg#rr~3DoLP0vH=0{;QwWQ$S;3OaczS@U@gNuoZa=Aa#+_m+{sjXGy<NM0ZjUNmO
z4ppjJm2C@7$j;9G`h4$ocLMCL!arYjMDx=JwYILViSlq`<D!3mqc()cb$4levfe^t
zY_f^L8b7vNe7BzWt#FBvxi-X%wukrq<#&1Eh>E6l-9bTGT1CkZQ2_ygI?ce_n==RQ
z0!g|~Aq2f6jZW<1xM6K!CG|NjLw3HsAC)0RcGiR?0fKfL)<-i%#U!Qbc4NA`>DpcO
z>XIG_em|LaAXJo^61AT<ioGnd1GJu$-S3%Zb8sY?ihjY{*TB2dso=MP;PwAhHo$;Z
z!UGqz%EQ;HV`uM9mo{H%jM9THNiM6nFKga2gBFKwwVgHf3d52XY_|kz%HTFOfxjP2
z8;IDrWd&OfJem;IYwmYVQMLeVf8XMxG9!w%ig!#?K>c(GaQQv+a@~nrxgE@kheGb|
z?)zhDQCXDv2by$P9h`>jyNXg@ar<aBzvJ7(F3hj2;C17fwR=KbF#B~l928p@-W{Z-
zp1i)n(PS!7<AHv$buC(Yz$u>n64dRUdkAk&Zk^_v?DnSdtZOjK!)3E2l%oZ5LX>1|
z*ok$phclZUX2#tX%y{C13$KWnQrye|!JS?eY_>q`!(d@<9vP_c&~R5a6kGAToNu>l
zKhsRiE|*fNq<kNujdOmoX-h+cT~ki5g$*IgDeHHQ-mYz2VZCrrv5}7*TlwuxzH0`)
ztCT^9J7V*GM>*Y(oTAai*W>zWcA#knSL6iH-<I)@Vw?SZwheFu-kYrFtZgG|oEooa
z9jP7U)xR%~IgF;BGLKy}7n8k{A_lyC2qA;j!z`T7T~n~90Z|BgUU`haDY<V@xz4M-
z&T5t`Xz3o6oE){T10>Nn9JohAyvE@Ie(?ETY(K12B~TTIpsDUe@DM9*)iRC~_CzRR
zpe<UhH1fx6X*|#ILBx^~5p`vaG=M=L&6dp1PZZ&#a>Wgw{LLcZa`qOg(?C&@cvEgU
zc(TL4(SV#=$?oIfWOll3Xf6ldCH6-z`k%L@e4Js*Xjmfki*_uHQDsm&A>c)uID{s5
zNe~=GX_GIf)YPq`S7gk!JUJ5rajQ7Y)GVm*j_+Hlp(jG^i{<z{SAxx5Sylqh4%n}3
zb|}5f6en*{fK^!G+94ewK8B;8+0O#)pDHJ{zZ2>ndSu0Ykr|RrA?vW?)n<ht1X9qh
zHLipRws;_D%^yfLkvf*fY4<6Y(N2A|E{KnJf?4RP<+RvUmI>inp>7s<ZAv+E<I)J4
zQVoSm9P<;7t_Wz7be9e@bouSfh3tA^X6vS}3JAL@)PYo;6rUE2NtAal=JS+7N^uZ1
z<0NKTKuuhy8-6`INVSh?Gn;ywB8(J7$Wv37lsV3(zt6S*WZtJNHl1Miy7=#A{s~Y2
zuTb+(Nc+D>E1^~Rn~;doNN59;rckU|Wt}F2=Zx44cV(N_$jS5UZW*d_%o@rfxpn8q
zC}+cJmepS_`fnn(En|NgyH_1ZGgPsDY-%IWh=jK1RYDuMePP^qZG*TlY%ML!74gW~
zL0e4iT^jIiz>s4F*Sv|^avl*B2{i>wQw##r<1<{eY>u#V)`j~&Q87q(&hTM+k?^pp
zt*Mn#vw9JO?$X~kww%q9V+6?GsQeB=$+4pN^Sa{;dTvGPeeZf1fpege5p+%+j|sk}
zE)>ErQRQNw)1dvRNwZ+PnRUJ^TJ?yop&fZ}u9Ub$_3SV(s!MXvJMet|n?YM~Q~_zT
zk%Cry;dFj&pMZp$7*<B?=68h8e8%I?5z$+)3SJpck<4@Dil&l7+=J<Hwf#SXNkq1L
zkWCi>`tBqJ={1d4?$oXo#b%X3aZ2+uO5|`w0<xg7c9+ymFX_KV7ZY~Aw^%ON-x4?7
zgsTtM=_yRhFix~Psg^|SF88M=<2xEl?@XdYpF{JKv@hxpBTcV=BrQj71vA4b6<#+@
z!qk?n4@(rM#ZHFo7)=ab677qn2G8e>U%dPv=JTzJ*l~>M7+=P4JE$V~Su!LZK{S8J
zmHox_+_$ivlfY@EcQKG-C-Al8iWqxUWY*C1-?pSC`qen!geS{-Nuh*xv2u?Jx#n%=
zud0yZlGRj#jdf7G;UUGqGy5lF*885+@kLikQ`qO8v;~ZIpqDkHQ$y(Q+K&s*uL&uz
z&R*d@P|iMelpAgH-W#_g-}3&toeGjV(}K3Dv@#AK!K}3;;&>vru7_b5WHz*s2_lGm
zOAHToz4%T-rET|e!}HQ5?*Ex)`%3K#dCg{7Oa|Ic?z?MxJ8qmpbB8aMbOvpSQS&}=
zu$Ls!8IM!w%B%_>mIBzW2b{P&DBN+?@LtWmoWPL;>U|;C)>?~SKZG4rqhvx~z!iCf
zf>R~MDLU*hjmdn|F?>bg%+^#p7*P&J=^wWq)7A3d3ODJIDG_aBhT?_Cw|E1X3Gakw
za*&$u7)4~3nVX5*V{0!R@6R3R{5C$%h@37^glz`TD9AB;hl5?V3(CJfZ{(wRwfn6Q
zkr=-(j;*@kjk$%y(Y1)#pH5P39iU_VykLDr7ha|!fMLjiaMs<x%G0DEO<0pJ6@4tw
zuB;5WIyS!!W1b%hN}h!ILO5xmQZ8v{bGnds+=Q7|O_@v?V7Mt7?wWTt_l3ROA*FuV
zYDc$ylPL@Kr~vR*EfDhDAu)NKj2aJm_t>i59?E#X_rjn8>q<uk@tC|bn|^gH=Y8g4
z%P?=gavU8bFA3jSf;!Q1I}-ovf%@QG%JF2Kd@h}k#u^Fr`r9^G&-TgwU6YUHhgLOb
ziB$jC@i@9aMr4qYKEJ%{vt=8LmMiXEBFN~1&Ene01{YY5o>M{Wu!2WD?W!9t>GLX~
zrxWa69H|?P_)#cRVjbS?X7OXg3?9PG9jlPn5Er3KT#%bTAuHSnzDnKs6gA~OcO(ql
zvCGj|{7vBCxR|*XJ8nVG^+?;r2`fm&Z1Ly|!_@+JRixjlXS?CJzvU~`lhJ3_f_>le
z1x#~4+_mU*xN|s#FWR!8&Dnl`kH>*<)#XUeEy#iqj_UfXqz*fk;R?S|XRXz8y$Zdl
zV~P{6(9lMN-+Whj8w%6%>MZeO(TlRZ<Zy)JS-|#2KU+erB(=i#JJMwS>g{bmLwHSx
z{~3|?I^^N_yklOtz$fr4<<Hx}0s}sxO4tmk9IVI@B9grs1aF)1G^0$?k+Z>q-L@x$
zi**NIfn(00pbQXa2@|lIPKiY`j5hRs$QU^`NV~h=sOG(L<q2rNt74wuccj|vUBXL@
zQtt4jxjk}p>D&tgX#O6j)z2HJA`~aPQ{Y|;&nh!9zcPU%a8ZU;q0cjbV}Y*qwlKV%
zS9FXfJjiYUysrxI*5ffQxU9d=mZO9HCE;8U&QY12El<W@AlLBc!R^>K+W36#x2R%U
z&CgF+(wa*-ukmt)AK@Cb%;)n0pZ!t4H-9Pk3perxy*5Mp_ZaCy1Ye_iKRY(P=`A#m
z7iR5TM1pYwXFI>%a$P4fQNM!!#p~s=f5kf&{JSp0u*=TN?lY_!R@?sioCWb&`&JZI
zHn+`O{nkBMO`6YJRR}BmBx?*K9<`Nzy0Uz|?Q;^4ban{aJm=VHJDzg>HS_?nUa>>=
zQk0&|M^p29eI2ik-F9WMbA{gt^OQ9vh+fJU><@Ayef94d-J@B24wlq9UvDy6KUleM
z;f+?}ly*Aqzm_Rs@tIHeTlWZYF&JG7p<AhPK~I!$y@r)m-~MfC$DB)A1cdA!IHxy@
zRdo?H!QQ`_1Q8$|g*|ohTQ%3t>-_E-wCmzcwhc-t_3fN8rJux&m$MW`if;T<z@DxV
z?-p$&#Y_i^Hy(fv?n-hB)!1@#ke6vuxjm(%s^x%oou<}AXE&DK8GM-FdWTPEQDrvq
zX4goD>EaJ@%Im*KbaK%vtd-UFgtX#8)o;{4S&z@rm)h(ZBm0JX?U&vQ0ZLy~Dx)7%
zJm=>UVyP&*F-D?)bJJ8vX&75vW^DSD1f{MM8y4y#%!V8L^<-hT(*uI^S1hRgb7G7f
zZO~BTV(Zip72;Yy&c20;-XC#oogy2vF8h4y!%pG@r-A{B-7Wif64m|p=VJnIwigNs
z0kg*3v2XV`TOIEfvt_*6GGDUNTf5TSZqKKjB8~c5Xgr{jXcmx_gvhL<-x@5Q>&W41
z&G#xeHL)~>^x^vkaKh}w*yf8h14rlAp;wF?PKWgvygWi!CvxzV-<F$4zse2ry!bW(
z24nYZk^_F|=}XFt-klE671`IX<y=sP2KJKj+JH=Blgq+I)iigPa2(TYzcjksOc1Se
z&Ev@H`|G)vb+fpC1yry&Qbvt_zKUYYnC9<tObGp*hSP<`F<PCMD=7<32iWGQ2HWSu
zj<-kF1Cgx8;_~8N5<CM>WAzlov`VN$DRRrZJ#+!7l?a1QBd4?3d3swFV32sO#3R$-
zb6l|I#8gZvA*2?UTIV|-a9t_aBOlbEqy!50&>=V5yB(_X7@jw_=b*oJ$Mw%zla|{R
z_8lgdxnk}&r#^AIySnh1%dBXh@-V9k7&;iEI=7H<!3JPu#^dBSZ%?5ccuZ%Ray*}Z
zez~?PaWQL?r_nqd=UuR!^F5emwI*GtxJKl4#YkzL4lfN`SgJERx0pYNAYg;L2*8u<
z=!K%a;MBbud2|156<hr)Q&c)>6!csukThHHvo%}dc`<^C{70*BIgx}tMd72t=>7zl
zPoT6t3TsB}>L`u{b3t7cYmDN4%c{J(01x#@+*nY_?I=7+jN^Y|RB<Huw&7MVz<xT#
zv<^`aD=|>1e=p^&B5#zN*k`?9teS86>O$4I+~T#r2F_L9e!LWc`5n^PqE-IQVj2iB
zqUtP&W-{E6&IV!M4dDC0T0yG!>zqK@zw5K4H^kFVI3TAMrgn;fYXma-;gQbdAi}38
zaZaO$iiW@|Xs0(%vpW*NSM?8`gs-}mb6W3$5PBxSVF-~VjcQQlnb4W~nB9B+mL1s2
z?e>85Q_w;k0Zjx^q$V3WN)AtKxcN%>JQN(1{iK?J0Htu6)n?FheY2{?1$K<23EpC4
z9=>3|HFRZBkniSa&gBSK&V7c$E)5gDoMm<oj1U}-=Z7vwh@Te6iAtOba$IpERwZ5g
zunx4zunra&)zX?BcyCkVpr=KG{7<=HA$Pr3`<w62I9;Dkm^W=U4*UF8ivtW<q^Zu3
zkC-DK?B;cffH=Tz(>W!r*>XPUX?tb>S?%+D57UW9XI*)*<4@JI^I6B=3ay&xo0h8U
zL4Hr$MNcAoc`Rxb;U_a<g}_5BiX)r~ctro0^z%J5kE^9wr|--;QNGdD{RACjWF$GE
zVpK&^T8%F}QyJg%!lJ2-*r!yjs-FgBGIs+sNWDmaao_5$K$`m*d$~fR^>|O)>%HaZ
zpgVJ=8ZvmF%;Jyjk2ZL!6PsHPAwm=dR(c0(joR0lXl^yFB>yFaG|syNRviz?7MfL7
zElR{jZ5GaxjT!1eDx}@o(;0`+q5_{Ll3eeR2;S+u@xOKC8MT({Cdub_kuTLsqF=%_
zcUgJQPJo01(@m<VdRNk=MqHuZyt``SaC;rKQ)el_k;*HOe8N^_uK<Q!XrNqvs@S6C
zk#IT891|WnO7(L_v+Mlh827U|n=N?ZPvmxR7Xhn|WX#X3HepB^wJW*Io^<_HkLN6D
zjk9IznFr7W5x=lFD&poC<Qr1K?BHbue-QT_N~h~J^=NkL!y}@^K4$lWtkpf!IGr;7
zT9J2(P*71m<zk)XUf7;ljk-?oH|XFI30mGfey=sfcYYOa@B~lD!-M{NACFdH#}0B{
z9gC(Bf(8CM3;~~S$aS((_UyAV|7d<8t<f5wqRMqMvG~Q~DJ;>vg@q_UThZ3Qy0vp5
z_}7)Dz-tce+e_F6_+jN(49554g*>yNIJaLE5s}~U=>DcSZ%XrO`5cDKfBG@>vm*0N
zrjf!1sorwY@w3->Yvx-od0A=u46V9b?k=)YnGzw0)xZlvyG|gj);pj5Xg~3C=gPkK
z3j&7R3`=byJ;6rXQ<QFF<P36+IAb!8v)3M)vB__Gae{9bPpj16xCkf8)azcD8<6av
zTK&29X7@1r7T6cnDKtPW5SE&8+r{jsV=9o`O>@@B&O3?+TDFy~Y%MvS?RjQ>`&uwU
z^AZ{6v{g*pbjsmm$9>6mKnSS?(yCDLW+u&LIh}PqLv2DBF{Dgj@SF7w?=R;pIW=2_
zqCsoginW<{gBkCazbu>7DDqRUmtL<Ptm?5oYfrugE$jko3qR$jW#zkNovPF3o7|SQ
z(dJk8^XfuLXAt^mmtpd)I9xdQHaSNxr~N<AY(}7c;#q($P_4LI)BG`a(vr*Jx;1oa
zBx%TAl&AqWSR%Fl8HzmZCDr@3>(ddcscXpA9pV0tgC}EKPD7jSqqavjuY*?4Y6Czi
zl`|MBp-5DBP9N*Dp(lW6#-bo8Fyyk+aqlb`NR_uZPGV@zmro8zG&koaM$M3a&)G2*
zhEqUeo!EG&KYr|D5PTFOc@QFf5YFoxvuC@n<~mg`kIhc@YmLrN@(+Jx>koUImk3_m
z!G6(||4pjTLU!__CB7in$TmO7;cEd_>zyPI-HPjytb?X`oYLfyL&?_~-|uUv0Tldv
zKh>>n{H|$vpU&Cy*jKiG(%)hPzF{4Q1-q5E8X#n_rO7BpYfDZ!4kk0wQU(;7gbWP8
ziVs#h#V!GnyU&D_43!5(TuX=E;P)ndLAlPD@s`y#kS5|sc2`Gm8|Zc@yZxydvrh=d
z-EL*%g<K(jyASG*n}>3w+6|ETvX6rkD>0WwQEQKI*(`_g;LSX1wCtOEP3WTkvSiIZ
zE#6!ovO30%ME&)69OFn*gM_!su-Zu8`pcsATV5|iM_7W^vtiB4nLMpVgc|k%U^;cx
zUk)FQ%L5zZW7M}C>CV9UGC_A;)Lbudsuv-nN`MW_HQ3cMS#-kpb|95;i7+kOu&gs$
z_SWU?m?l6f1RoqB(cmHxrYcj991kd=y|;>8F~-a!jcC=Iwnygn(M1^XK(!{Ax3mVU
zXV7vm%h%olb{+28vy(^};4`+f=`AV0ad<A0$zq<Q)sd<@CpH&G&!u_`dC!m0dS;i%
zn9{>xU~k;E$b;1S9tm6>3Eqs(;WzqjKWU9A;LvNyN+UQ&wBB=U(mTkiwQUwIBfp}=
z%supRQ-*l0Om!~TBHTQpR^N}oi9@2}v~DcJq$H8UN!;u6;uq%=(-ah1G7S9+y)8vQ
zOUf8XOu7ag?A)3v*9mqz<5HQS=Xff-dDy<^+YMOI%qbYhFD_?Pl~)$vXQw76FY*2B
zcpV&sgpn{w$IN?sW6^V2V%kSsgvri>tZ-~nB8aAwP=$$za3QT;dGnKALT+AF)%Dv?
zhQA>mPWs$QbyN5nCKp>lI-)cza2IcQ;0PBSB&p0v|BI@(42vV!x`lxWL+~KMf`s4>
z!JS}1f@|>L?(T!T1PQLe-Q6`f1h>K6-MO7}?t8!Y`@xTS9=f}_t7=#6wf0)oA?s0G
zGscE%ajRbOfrRF4l|m?IJxvAX6xca_Tz{w;XHW~aY=1#qOW$cB@_<d+oD=iSZ2qe~
zo9P8UgX_p@XiXj^j|RTpvv6pieMclNUDu)eQ*2)tOvT7&q}CGZGlEg4>&6F;D~zge
zt`YDl=HomT)~v#sHu9Oq=uFa<ULEO>-68yRT*(d;(;&-25lHQ>uBFP^b@xiNix~o>
zb&c*`7#TEWUPS^Xl5wXs$EL%_m|i?OeZ_7Dl+<er6OLSV#;{d()(yq|;vyIGq)4x*
z6!un%P;^g(^A?O0D;}!m9L$Bohwjs^q54=ar$1DH!M}0=B%6*h#R#q)L%w~B$K7pe
zGks8*(KV3(C=Aoo)h-;3o~k#`8M1`ELOhOh_S^fWck(IuoOSB&TaWY?%$$>%Ug^S=
z@S6z@A8o4ceQ$U1_7bW3g;>qQ%yGae3O!*g6W6LhG8Ew*n-T~yp03nKact|uGZmLn
zh=9DH_TlZhhqFvX5QzToUnx1jqVnP2r8ovz`0OG7^J}((Wx_6+w%fXcpuu+Ab~u@=
ze~BF<IkJYeE{&ychHhuQVE#$>>_=>G!F#kW$^K2t(f-k$VofYu+6Z%|w<{^?^UTjT
z3?Q#{RJx}<3h58LbacEWp$-~;rAPEu#=%5QA^CRfiMfBLKa7|5>5wL38LNM92no_0
z$G2g(*wLTsakCl2QQ>HfMRDx(`rN}QHm)DEzQ-M8py7uiKF(36UHfvS)X|cViBpjW
z0ng?*2!7M4Ld*i4G>V@y#TRXlATnA23EwjXNpuubA#gCHbuS9SN>yCl$bd)2kTViA
zpGCj)W)BhTm(|;*@0NUSi5YpQb^BXZ1tG^rGODxED}^!~HP`f17Lf$IZX2^Xh1v<J
ziKXamDwY>+fAbB#hv&E$#EqHK(fz9X(IEA$VC=q?<}ZaaQ{A6#^QTX96SS!u#7$LP
z&br%_RBA`;o{M}pl+w3kbH6AFv!dBo7o03Gu2BoLB8xNIjk5mg)&Sf1s7#Iq3W&?m
z@9z)~xjC3srRnjC%EZU7QC8pA>YQ}k&bsJEXdGET{1HOfiuwMs<fw`5di>22w~Y7s
zxAB=bCxAy%;rtvOu(^~BG3|KELH-9!-cB<Z^1E2;I~^q{mh+*CXP4Y-c~0xa)~=|w
zMF3#%VNmT~VlAUpSgD*9nP2X9f$SG#%UO2vkO*m46(`i<IQyo|PPx={F!3!g=<J(f
z>S-&n%%(a0*Q)^~>>%1<5*<s?db@-PLL-TK^Y`F0>9+;Hq0+BUmyD76SvIkqS~)o^
zQj~^LH6K)GhGNJM)Os(d-|~v5y>(aT1QER^1uVOgeqzi^tix#Bq`g-QZN?+MM+ynO
z%NUmNd_Zm|XUq#IB#5rn*>H}Dv8s^Ng1yFKkoet3X=$U8p&>k_<7I-CAE9C8em|36
zkdUf)&~7G3Iu^^@YqX7-@lgcix@UvG7a?cZq&d-vVJ}V|d3eRH1Oa>2I9Zx7joop$
z?xRx{)0(3$tm`cccczwJehyGm<T}4%7#nY4i-J&Q#ifkl(Y%H-Ah!Br2jB*fl;8s>
zA^7veMmNE2>(#3GUOS2v($wp_GFQ(9CXR_kGKvQE50x8D2dXyv9nqif@N(5K^EI(^
zUMZ*6l<09#1w;?qkamae$%F$b_^3<|B46;q?p2Q;rN#WixXO7vm6UtPA|!G!Bz;ka
zHw)`=?Y0s7q{wirb1+fqHk`tHJ_7tGtpd9y*;`b6Hd(WmPYtIZOt}-Xmk6(h^_!&b
zqQZo2`?*Jh=SR`k$5d-3f=@pw$fmnI1p#d>Hu1O!M=sF1qXBFD1?t_3VS{Iwuw^sH
z!u5!`Ne2ewt~n-#c2y65i8~}bmHfVCROGkVWMZ3;99RE4dr$v%HRUPopvw4ny#B_!
zc=C$uOIDPtpf%Y3>W55y$MB|cJqK*lPF|IsS?fQysI@=qzb#w(Uo`vQ_WWA&(q=kq
z&oe#-gHBW5juP7FAZGWn3ib&dMkUM}BHsQ{iQJ(kX!YL;z!4=hL2+a^{{bd=ku$7u
z@|g7Ik2G?Rj%R&I>OC}FE_Nx%a92#<zFQ1Omv39Qo)C&NRpOZV_$%o(-lw^CQ(Lc>
zRjtE+t5b(8=%6E-gFc+SID}K5V$B)OIHw{{5tsc3q;Kc=jr(=#@BC--$~JkDmEEp>
z^5h-dG|o41sPt2Pib1Iw5wl&h)#_X}rPDsRDpd3A^$_s{cRUHFcs%AoRgX?l6$u85
zTo%c0cjcsJO_*Hh*KCp(3j(?fa~i9<g>YlTVD-*;;)kEpQJD<~i2<-DS18%HJUOyu
z#{UV)dthA4!r4MQ16+DJk89+p7c#$1-S+Fj!DQqOWc4akIGuhWwaI8aQ{SpvYPWBn
z{Eg+;j%~_{?PnrEI`Ya5)Yx(GI4r0N<CK-izTf<Dx%yCaFa*<%V!|?JWR&8E`n&8U
z@jPuQT%pq#tr3v&3LvTz)zwl>h>_wM94w@+XRam5(AJ}+W%HYsXRt^HEGe}&)?as8
zYea+gsvi8!r@#bG$Te0g6?B<xu%~?f)*AZ$M^|hlmS#PFMJU}eLJFL|e%I-_t;*4)
ztUH*itC`Y+7?E8=MfdoSA6le&Arxeyd}WJ>w<_(G^{LGv8w)c}(jz>>mlgt7|H?Z8
zW}`4Jlb03Dv%1%ftlT^Xj~y`7IdV!;n76Z~%^&w3sPOg#b}oqWz}G|-(@R?36PDUs
zFU~p7wkm_x>9029QBu3wynil{=}9PdO#W)i3D0|Z@@5P4+%O^3A&gGO$s|J)D+-><
zDX4$=mL-Z7S|JocXib)z55thl8sa08&O16@u+J;~J_IH2@HnFWcclcn1*JA9IWX{f
zs&;O4M5rJ!hI=(ONAdFr)?V`g&?N8KVxr!MTcQe-Gqj{~BYA=q$tV)6-k524n2L@4
z1a0L&SKJgm_k(<zpya|bA*MjDVWFKDjsp=F-a7ngZSP(E&ZdflFy*VG8~x`5xr$S>
zPyYB)JZ7t}FSmm0k()EtZqum$>9CY02qV&rH^HEt$8=3q&bvm!^xZCv=qKv!N76`{
zMLD?G7aN(fuO0x0G%fu$yZZx`DgO<Li0luTo~3gJg0Wr0&p(gY$#?^1qmMsw$GC6I
zzq6rXvjZ_~@K*&xuHIG;WU=L=+Ms_?Lk=_;)<ECOcVE%oKQix9$=yb0vSyn8+VZh8
zjj%fpzWL9w%YF`*IvrGvbsTpP>pJ6YPnoM_g6mnWtjPVejax15X*6*VGUS!muwY5S
zJ*IzUv?q$ue|NUuO0o<ZW9A4N4Li+dqQN&0&qt+N`u2yz|Dv&cNZ7t5JwnpnMH_J$
z<QRjO_4ui`k{)gJsF*f@GAWMiv*vLuF_$#%Uc<)$n8Oz(_6IqIV2nBMHKzBh(zNE3
zJoOcMnE6QM{@!Xb3qqieT>Bbs?d}ylsDjxIB91x%rtk0ksg#+jpl7g>MN8x<3{1ks
z0&j$OUUl9**#C7=ZW$4T3Ms8Ud-5M9$RnnAe=L?GzE{()XAT*=A#u%wpCP92SO!*8
z)sc(0c2V=OPpvP+Y&^G0cj=s@oYJ38+N9ki+wuvYw@c^n!aS6TUCV4YCr7|;Pbkk>
zkp6`4+=^kss0$&$@rO5cqc4v}FU6nfqkH;tQ<^Yp_}E!ym~rM`*>YdKG){4k37|qi
zQ5uV~WevOf5ZES#Me?YehBB3HJm7aXMYvV+{1;D{6cq2Uhi|~h8sddcq!AJ~4>Z;I
z!9=%x5u{6)+$v1UL`AvkOkPath1E$8;Kt1U+XpJp!w6pE*>jnRM`eRiL|j!%SnGrT
zJKedYg<6Y2_oa83AOoG>qE`wVJ#En~$#*E>`uNV>eD!u#oN4y~&pV{c5DL|pIkSrA
zkk8l+S5kbOR58n<Fv@ihu=e`mC6&t(f0pN~F)%T$Vn(GTJverYdjBTHdsE7EW&Vr9
z6?PN@C!?GqSG$(&G2Tm`akq8i9uE?~Rt4Og+AIFvC(JnAFK&AzO3FI-+1+6^OzKy1
zC&wf1t;dTZ>LZ%J$G`;7FctZZs|g&io#oxJdFII<VsX$@Xz>O7yU#U|2JAi3#rVn$
zm3IBd_VD|Z=<GmXQiw2i6eHHkX9XIs$ct?!Dco7-pX;$<X*Br!(iP+*XCm~B95J%A
zvuIz}8zUEi71Xq7uhH?Zm9QQ~$$>lfxh!`j2jBjNru{V;K4palIw*2M;{L*Yfem4w
zi+BHydkWD(?<#?*e8zLnUna~%4J7wqLzb6g3>g**RS<(Cpx&^G)XCBL^@``egmR|+
za)y~;@gYK>k50FHHuM4km#oZr2^4;PM+F`bI&DpiZc4U3O;+1F36?UQ=9NN3Y?Tv6
z8a(RMW_@lz5cn7?CaPlLXobRN{28*H+k7a!q2DQqy<i#HLjTMed))UR-M6qWfU3w>
zb8tFl6`Za$DVca1E%?%Dq$lotLqJ1S>G;M8R{=Bsl@Zg0%-$o7SvTrq%VPw7|DoyU
zv?E~kWPH!7$?w6M2I&emO(?N$w6~R+B2f^k+B+uBukKFm_i<7D&HeHhg^ijt^}yGA
zB3I!3JEjI31t8LLxmex17i(#ifc`#B46J!g>#2nk^K5`^$mqHr$+L*TJZD2#dNIgJ
zs1%H68$#imH(xZv2Q!dz%s&q;TTj+M2tfLys+)U8cNjv)yoh6Bm%_j5<&=zP$QfRj
zU*2@lc&iZ`2t%rzBbMvTcNYSv$nWv(cs;$m_v*7gjALcSBt2*x$Ls5#l2GLqhJpKc
zY8t6E(8c)ap|4khXD5HW6YVJwaIn_O768ty@zSzq$zYF>D!t&#1%md=$+Y<S6|JxE
zX1Uq`X{(4ck8}KXA08|-giJ3nzeHn}@?IqTeH7z!!&ihnhw^;#1Q&<`rxlCA&j9JS
z3zdi>K?Ia0YLa5(-SXY}KzLuN?UjG&XyXNTks<5{4g4&<)72aV-eLX~Foi`a@^TZ!
z9cW)b88inqM`~GSgj-W^uFo#Sy6>^W9Vx>LX%44*g#5#3@vW<uUO-IdZ)?qWEGK=4
zLKFQu@wld{oHxGwwe3MX`y?2tiquM?Kf#Laiq6pQ-XnMJ3#R;tBE%;DjMjJd;CovG
zk!_3jzmK<O>P$toeo^-%C#Md6gB!!$<ze!W55E;t_)AoKyqW_{FoC7Tn$HW<4QA3<
zY1YL>YQ+Rm<QgB~fN9b-Wgxfn)ng*})MptE>|`zO(W+}MEV-SUgy0zGcmgiRf?S#f
zambuv>_FEb%G&7iAh^PW!j*N|7V(EmqpiOji<HSB%Bt}bQZS3;iAmW|%Y^7-l}}4o
z+qwsH?(0gCm6}s*d5XT*08N)+u88LC>0*yWb4$6X92S1O?xT-wr%*|{6VbGdFL0=4
zOwt?8jZ877S=H{e+QxuN*KH^*;-6P?(mp};N*}1e@yls61mqUrCK?npV)+jA%o>P<
zj#~SEZ@9qHwD`Bp#irskrvfMQBpcf#GxH<|2T^|hLV-(#?C~PfTclx0RIK!JT@TO-
zUiWCZak-rTvUe8gMoom92g*APTwIJt#f=Sr_`rwTQ|L`*N3+w($XxVSGw247Pe`RC
z5?JFfgR^2!?+YxctVdv9uUKEN53{^)R$*gS5?9cS@5?jK^P>rL)$7dz@=)MNVI-tq
z`$159@p|IjYlo`OrnB~Ddqda2eAH*YW6=jE1x$0giEwv>e70sFvY!z0(4paIaT-r(
zJp);v87LATgY!lk`|eSL`szdOe6yShi%)7sezjiP)FeBw^LUh%S8X~9;#Rqz*4Wx|
z!AB$cn9hYeaav3nv5)m`v-tfl?`lZUn(W=Lq-$c<ku0|QbUm99$wfJk=I!y+cgc4N
zr$0XVhidV4g25qRrr+*PZ7+|DiRhp*HxJh6kFRT8pE5Bt<j!6SG<?;B<V}SDLB-2U
zjb?Dvtn}R1Rk~0Q<?-D{<_UA0xR&b=)WyF!gwz!^mbiFs(+#G}jfw^m^N712?z5L3
zFT&Ni%D$($cJ6MB+DWfGo*^?yt{!67(9u7tXnFHTOCKkxne!Np$+I&s2F%KZxl@{F
zc9j@e7XQv(sj$f$J6^gtyPzASMb<Q**2`LlTSB@D+TYJ1pHJ+dI-zC1eC9!2S7|v}
z6_at@vo2ty?FPzMe`eP=I$@tDO(b^Uj(obNTqpCv`ib(&Z<{asb^lAFW}IXaa@@+7
z)(%8TNOS;f--*Z+VGxmaC`*V~Xp9+NXb^dJ9G@U#hmNs`EwseZ61BWj00#+_Z-$nV
z2Ndg?yQ-rRNbp2$?XR9{t`SXi__X-Eo-7t&>1INDrf_EOMFM0LnI{&}z3#~aXwJN?
zT@8F2SUZ<WI6HZ#ecY{3Y6~+*1=E)l*Q}o9JgQzA_q)A>3$j~}10La8fn)_eE!#?-
z;O}>c?tXU)BdxH%TI%HpC$>7rPmT#adt9R~Mthi3W~5fHmXP~}D+2D?ISFp9_mv@M
z!NqDE@@xEUILkYlhKn+qgt$|0s}VFU&{ub!&>ZcO)(7UT2|hK#<2##EJY7}B(Ji9v
z<zLb#HNU4r1MRWcHy0xfpMEEg<5HvaMzdbLTAl6^`YV@}d*UPEEsL2kU06YErtGz-
z;Gsa0i}p<;(UEJLV&S{SGnOeJ3J5Du(=WzBPVy@Xf4~(NQRf6IZ<q6tttzoEO>n~z
z>uz4J?-UCw8wXs8a_u~ZBoF8g_Q%l8EfBeHAzDmi_?d%5!k?mardWvZo%fEkg|)Q2
z5Tb}wZ9u3KB`X(M868vOq2Wa?+FY7;Efhq&sV<PoEN*w<!+!IfdxFINKjh<^PHa2L
zCY=U0ogIV9))h6bXUY%#D4Wjn=i5IqSPO$^Gm(qpCStxMhDAwptdehe{=sd2h`l@J
zYUXx3O+eq0qAA*Ku(d5ZxWp?yR>AF9mMRHoZu$yxJGy4c!><%P@=I%co2PF~r>mn@
zz<I{C!3SCOW=Ai}UGiv7T6WV#jC3F16^1*B&Eosa^pxc_9lOSddEQ!IVT;#A!^q+P
z3X=RLdEi?_U<G!XqeHZzn>X2m#Vr*zuv^$m2&lt4B6FGOi#U3BUJH&2;FRfXKWY!N
zq+0&UVORdcjSyz$ue!KxB)pbAQ%!dBeRkvPS2@2qW~An5;U&cDbxl*hP*__vN!?eM
z0gjxtyW7>^`+G2faGAVhtum!q2FpTm;_*VWjmdQ?apR9pcK<TJ@?74Ko4BRTZ~kig
zX%@#_k5H^V>1fCo>nZQDAQ+oPx@v0o2CVBv@Pg)-+L3{1r^46Zw+$VXm<R5O(#tpT
zDypBaoc{=xEpZJTtvGIoA37fwSfSvX>($?XyAf5({G$Ka0JkN1#6GO?veE(ytF+?Z
zFmu~TLt%K5xJOD$z$^F;yIXA1-PD4GTU!Hh)l&E;qoGA=#M^l<?0u(pyXQzlb9cCB
zu1b}T@+74Q$()x|iI2k9`CmQO1&Z-M@C=@zEVRS8XnmG+cr}Mg9YUC#3Kp9fRJ&O|
zct<Gwv3<PE?0BQi5HH)JFW-qmQ&7>jh1T31a?2d!M}~V{JJs5<omqZN-eXzh^Mwx4
zd<)2Tmq3hKdVk(75B~`H!4~=;d&2ug%ycv<B^VRHI>fwoTDQOJLO3_c5Eng+4VI&%
zPm2mBi?7|`M$4N08n{r%up{7IS@sHDGzt@@=ghlqX!2SPI!bU|1qC6pO|~SL$SXR&
zw>XXW247wenW!`8zY^9*bG?E4ii1(NC;*WqYy}#Hb8Um*i%V7I)KTu<w{ANw;>^3g
zQ;gYK(Y=Iv!tYp7gTFx2Y=mh`z9kR^$26m+a4hyX)VqM6IH0t~o<P*<&Q?xHF0}Z9
zHACKG;`kSv$h0I+^IeesJq{jfTHari(j2MgC20EMEOG1TlSsg{T92qwrd}Vr8nz#$
z!YQfpLfCNlOC{|xsi?ML9Y>(dC6TEXYTGxX$L}RcaXm?uc2?_XmV3lhBkEpd@f5-Z
zn_*p6{FM6wiD74+4>q}m^@}}c=iJHEx?15+XRv3`{`Zo$-d<GHJ@5zEU^D)U#{x}{
zEEBGUsFM231SDgDwvD$(GcK(^Ee|}meo5Bjt}>TLZ!@#_e5gxJwT+#es~R-6s9Vaw
z3rmyA>5hCryV}QnBb-BgDx{XsRe*OdBE6=(2KIG@ya#s8A=ktd1>PpCkE6xR%O#E~
zIE2+bc5K|m?p~n12ucuiYO-eJ52@16pmkKxWRFCO&!jDVj^^Uo)kNc|wjc?j0=QYB
zqd$sa`NsxvQAZ@tVmL3XzY9wvov%`_cH?rpSYtY#Udf6{t+1L3X<+W~Z>$|88$uh!
z_V*5?(46(0)Ux;ErPUv)z>DU$nZ?=ZyU@Q9&2u5yRtUHKPJUJWPNj>5q3s}<4oyiP
zO_H+<p5+i=R1vAcPpO=0pbqP^yeqV%_%W!Iz&cI&kb8b~TGj0onC6RF{_aVPfK%HS
zZx*z`MJS8<5CexR*~$KgW>-@f5y)+GRgaLA+P=)ZBZJ>fzm)ubKtWyX^a}aqj#ym3
z_Pl*6G_dpQQOgz2_FA$nQ|Rk%qLr8I{YCL9|6fj!I=V$TB1K-v5X>NzAQ=}%QQtJQ
z78CO}MCDYC3o}!-k*M`^4?pvXf|!9P^1%LXK#&{*6w(jhNsOC0r+4J5mOw_`4dcUN
zq}FTTJ28jobgpF6qWJ4wJ%MVk0xqW0b7jY32tT7dTseP)l(}W1xWTlZOL0Iom-%fq
zm)0L$6c)n5cmiE^T}V8{dc-BmOaXl-6QQR8wzbh})(4B-u)x5D|Bv;ezDPZ<xgc8S
zn-!Ch%5qxawMxqr|FuPy{`Y@q)mHA(zKOM!1`}y>1f{JxFdy*heBZT`m%~a1pux3%
zO<HFT_vl0QI*f-hns(4=|FF+m-WVl9tFGthTQRkVC77m8O=amb#Y3Mrt)t6AocSqx
z!Ef!X+CnF2&w|xX3|Nk!Iz%drP1jY*Kf_YkQwY~}EoXgxrp$r_yV!AB(fY0K+&5%P
z=BT-so1(}ce<bh>U7)1o7C85S5(`y27(J0?Vo5lk6=PX>htA}GS=!;1OK6o2{hD28
zBthtos+5Y4t5=MDn~b&@Wk;9Fho09ZciEEE<<~R&s@BubIYIajXeeZLC7DrCQNQjN
zcQqyD9L6w=ey|#^hifa8d#N&8m61``yQj`Wwy=XjWwRR0`-4_@E?O_Ig3~oZJ#+&%
zhbPnq7XWm@(g@g;lP(dqH-?wwdIv!m(R#L~B{O^mZ{R7(;Tyhfz$yvZF2f|nl$pX^
z&#Pz>njO@=ar5qxv)lW$-`kX)07mDngImBxW9T|g6is5#o3^&KS!Pa()t)xO;Sc|;
zs1;lim~s*PHnq_->eUjolJu|*=BW!P#7j<I;+}gX+S8>tK&+Lk?S1FQ<+P*8b?`g$
zM^<=|oT9S=#?Pos17XGBIIE;zE!2o`-TCD22jN{?%tE?Xi&mDW&E7JxKbnSj5&HiD
zaytNC>Wia$*`NF}G6!aLTIy<>spk06eZVR!$!BT~z-6Of0QxH`B_9&>1>-XPN$wWy
z*^14!WKp&<dS`_J2ucm>`(qupT!b~|K_2KLMc%WtpNLg`Tud3N%{+m1R&lZ>a`<O6
zHS$e^!mh4i7)z=13Md1eto~Mln@c#t1VgSw)vM)fguV0RIPmd+4~>{jvjN!9XXYXz
z%{8{<6;wq8ykfIf=cXnmHFlu(`)I!Coc+-4u!dTqus!Z7xTBIxiKzgvLvz$mke!_0
z2lw+Bvv@>fo8xRM$m(7>&FYs@pNd%fpuBT0dhA8SD*zKh(p*pxlUN^AG*JDy$PoSd
zd@uOp)~=sC^pV<Wa@-9L^<{`e3*~glB|;G)6t>a$Axh;<i(Mc}Qc-Svdwxmv$CI_-
zbe#n;JLVG>Ti$cE7S&ERS)SRar7|z3zobGPhFjnElNR<efr;}nQ#Bfsj*Jg_jsplq
zu-X*sEVi0WaOdwWT;*tXvPm15p&|=baC7+sKA7O^{X5p9UZj!5=YCq3cgF?xX8gum
z<;B94gU^&jMTU(|3hyR-J`{PH5n%!<Gae@4my>z%UIRG(<Cnip4D6aT$Wj5{;v}mF
zlxIBR2|MZHEXVQoGU@w;e7?$DA+l-W1y`0$zY?q}yhi&XCPt;Orya3T=e^AIz2xy9
z94^qkj$e}!Y`!@>p9|?*J+vZEh<sUezUg4y^HKlkvrTX#C{^f_GSLCP5J0*wnp&0T
zRYY_~ECfg0T?_K2L_&hXs6(>EB>qOHP0kl<PWDW{x&um<>%JphTTjiVAe@%l!jZvF
z$vI8I0inO$@V+-W_g76EcM{L^6f^xB%$7OsK@Y^CL#tUDU!OZNo(&Sy>T94&*;9?=
z^RHwjx|jGMZJWt|bO$Kxf%N?)KnIkXMhX6|)<2w#tszV%Q7kI)j)LR|;s>Od?%|?P
zpD9AEuSi^DgX8p+QZlk)?=V|QQDFeCU`~zAHT5SX@!hQZM)cUs0Q=+{Ly4d9f3<z8
zAhQId?+pz^6`Kxj@^Vlm;UaJ`b*&!Q0s85g((EAR6$Nx$4H=MH2>ES0lbwu&x?ZK4
zeA@c*2zD<FdlE1asZJ;f*r5jf%t}5TW;(AysqLrU@B9P(pgJmocjum`z=zZ=sGp@$
z(FE(FMe$0jfZGE))wRfCAL&<T(CKLKv-JA1aq%57`p@hq+Q=fL{&;u0d-&UY@^YDI
z^}W>t&i9qNLB<7Dq2kZCx5dr-->*H68Of0S8eV>rDB2+tb9ok$N(Ke*@c54^WWhqe
zSuHJuci3dTuQO6Qj?{STx><(|a;5M|gv{cT?HkPe?BtdBayR#3FMz7lw?uAhx~{L+
zh$3%0HSG76m}nl`*Qm%LnmmDdg}iP-5lexn<PK#C31vm8s1|EMdYbR|U7o_9x-k_$
zFQ*a+Dcbu9=NJ&F`JP6aXH|H9v{oqU{-pnT%D(#Z1;dvqd(*MzkTO#OH3M#*=QOAK
z5F-Jz=A^=a(knFzHaSVjL0XIb<3HZc<-aFUuR@xJ;o<vwDYi9(L@9#8(DCa%p=G{l
z&G@Ez3>&MO>{t#8EHrCT9@wuup`pVle_2q*JTYGHa9c$o_tfMy5~|;2B(%bODr1(q
z=<2OsT0K4CguB65`Cx4y#=I<1A86sy%P{LQXdzYfYCtZV162tpF=h)3uhkox_dQj3
zPsTkl&flgrhB)6(M*s6?<G;s0CIb5?z{sI{QOS5e?}f!B6ILxo%N=UkK2^@?{lKt7
zrn`bECZ%WmGaxwcw7bB)s&GZgaEUl?5fTRWi0eEcD%id6S4<5MtsNaGz=>-NvK^mj
zK)Kph+(>s9QvQ|$4^h)mS9c8bQ6636*A=F>+6@Z}X&^k48lo+>0D#_z{K-*EdQiHw
z_^1mJJ@rsv#ZHOT*-6uPR@aUXg63IfkR7{dTh^7{_*dk&V+xku@Gf2i5|ZPC&0e@(
zOgmIK_BQ6!*%w-)MGXLdPFkNz&hX6?TaC42HW_#8Ncw~*wMyqFtC;RLZGebgxM-5`
ztkFy<=BP{rr{KIBV7E^V@}_Vlm6g%I9D3bk_TVWzr7X^bT}2K4+$swd^J}~Aka<_9
zMjU*94V{(;*zh2YkFL6(*Yc*hz$rh+8LZ9gU{tKuD%gN>i~K2_*!90VJYP@dHKtKk
zFH5?BqKqPQKG4dB3KeK{&xH1v9lu5I-g3Nh`sWVJs?7&eaR!x0lPt|vfS7BczULku
zqtkzWkq(vna1km-7RoZwk&Oog2NrPiAKH8PVNz-{v7xFhlTlcJ9C~@~{cj})gDxN+
ziUo`tc6`*^o~IKp(e>@6b4fh6hW<_i{{oc<n7;-*(8?P?74z~q|1?$SF~u*p8a7H7
zIdtEdP*C*0pR-NJ(edfrtf_kaY|Ki-$^5z@UCr?CtH~#WEFTe|9jgFE_7AI{#TE5=
z!j^B??C_D%s4Bwu{RK2O^Iv11KLMTT6+@ByOz~w@;m>$cw4LW<&I2t^13l#Vv0N+I
zRGosKY)J>p>F?wv2-C&ux3@wfod^hN!0+IBv$pnM<w@~5ilY!(wqxdVLsR%lSSP~u
z(>v16nfY2Xz8^!Tg>tk?YhKHDYYgPP(xH%<+9Hcq7aw!aGl5qH|FM5>9BGtQGr#6_
zwgkCtInM?2oa29Qpwf1j=7J(G5D(aJkhx_vZrg1n7XP8{kiJK~(4I7_7*r27;wa(s
zVp?72QSCARd!D*#a%v-x5DBQ3!0~enmcQoPcyLswZ<}5^`2j7Sj|h7hq+RUk6ysV!
z^|BkilqaoCA9}n~o`6sd_$R1-CO%G9IB;mmw^4dn6dDku6u>1ePKmnUOP0w#Dmh<@
zuiN&l?+;-)`C8sXZ?B<j1KX;k)z;5Lf7`J<xW*4YTV3*eklpmVb-X#SDwrOnpQt6d
z7dGt|dJQrs1j#JOpk7KkcX_Ch+_!Z;uH5|qX-mu=x9J)4`W%Mmzc4W{DnLo%b$^)h
zw`HR^nj<Dxnno(!)f=C0<3UxrvHXg<T`#_o<AFvCWzES!$1cD2G)6VNxDY~z5=Otb
zH~h&TwyC8raMSV96$DvvO(wq3vW-0$Xa6?obvz&(65{NQE`w?PJNGzx@m7Yx8gZ@9
z5IgC7y3<_4@#J4IQgIx?AE8GmmHVtS-3|1-<JJS{3P6nsY*^j4?cm=#fa1Q6>9Tl^
zVGj3w;K*!?rXeTbm-Iq3bz9C2i%IR{u!dl`K>Mm*%OuA`?4pOf7<YnAI`WIdEbrgf
zcx{T0=~{Ex6cwe1dr6opuKOf&7i*2sMGw)e{CtVAu*fS+NLVNy?*xjT1U~}nqId+v
z+7gt0_FMnQ^oatpDqJg2xW`G@6#JCsO%=I8d`IHe;TWd*@w$d7x*ethx(&z7&5@}Q
z!Bm(;lEN|dBXD6kuV=S$<SI%SFlN+s=Jklx0Bs(oXM}gm=wVsxB|yPUK-tucCNR|^
zfvE<Q^Y%^&g)QpmS<;#Ga0|&ekKEu7VL<0&7q-v>962EGj{#J4B7bu3<$GfDZu%;}
zgIuZO@WiHIR0hAkgxhy)GgH5E#vRTw+AEl&b@b5=|MUuh>MggBga#t)!dE88gekTu
zLP7q6u9i*<xoL(135yRU8sCsQG=FpS=PO0)p#7jp3}4Y36|qj}^b9s^>t&qM5Koj|
z;ml|h5cLVDtq(QWDvz(oNMQvk#(?6my~PlgiAamA%OMCmixI=G0FnKGG|B)u^SyY~
zt`~f6Bubp01VOBAIr5P6VNQ4*A8VkXdDtA>Y0<*n?KIxUam;Hjn}kM1bQ==5h(=so
z3sg-jS)N&Y$o$N}EK$)>OzEf1E%jr~{JQLw$7+YuSz>C8|MR-nvEHFYR(l6bo;3nU
z`p&vwY}V>bxME6AY}IW~FI~1Hze-!+FWvX~paR<))?y%xX@6j+0-+LiLKNJ!`1fLA
zapEi}7`6s<(!@sa1vj=DuUJvpnE72pa!OZi{mm|6<e}2sB`)ckv3PJer&OY2w$l=i
zM$C1q`%Rux_JHUzTflSbg1F!$gu3`q+bv=>eApzL5YJS8p298@0IUP3NR&s)s>FxQ
zFXa7W!;B4{D+Jgw_6v=F%-B@!ZPj^8D@D;n;o&~7k!8kfsoz*MI(!4Q|04duvSnN~
zcaLDZ3jpEM1F|~_c-7?lZ*hn4ff)xla)K9kan3t5Y07oeaspAjU5e?pR1*?(cfJsK
z*x^{HT7Bj$h6G(cqPNb#CO+)9*gUCPP`C=>N|QFb!%br$3yumEw3DCJZ4~na)n7)K
zFggl4uN-QjorPF)i-zpnnFakNGIaa~Oe`kqiVe4ilc<JxwN+a-B}EbF@gV9n7V3a5
zDf|q^&2S?5sYAr_`(Lm9ra=ZI-amZRx*Q{F6CtlNei)aDg&OLpm~rA9F6J&Ggnqi>
zkOvYyZ}_1rfmb1US+~D`mVilGcRVlYw)F;e_|MbS#SLkZ){MB#zhU)!HH_+r3_5km
zwL@i_$oj#Z$C;T`>4zEJSlSxv*4s9B9{s&C$&3Pfp+Uf8U$)*sk_*P3La;!UQh+pT
z)iQHlrDe>T&$&h^deu6(yvr19(uS=lf7z6T&OF&-fF$WANm+5J+j4rLdLy{wn_8Up
zwHXYfp!uO&=<Bwp5IZxL=PqLHNTt|0!k!G5<Ap=wY{0(o1?|)#rM-CWnnmE2a_jgm
z5(+*}%sCsNN98hmEs=})($)K^J<G3{^!km4^L7;Lv=1o@;`?eoIp_%!eKxDz0nCrk
zWX&9L?$UQkv!Cl6j#y$`e)k|%INb6$9{wOn%h9(Eq!In5(gM|s1xB==NCI=XrH=?o
z{umZ<em<YMUrd_){4p*UQo-1}hh(4(^Pm9CHlf2g1lt?)wf9!m3e|B9RPCy!T9D!8
zW)$yg6m*SGFVN><F(DGRpB*i6CUS5&hpfbi`hlN;`qvx&k6mu$<e9Xdn|W&em$QD4
z(`>fUr2>i3m>j=5dZX4Z^x(??kwq1<lo0nIx<ho@Jb$h4LmFzGYpRq%WMRtsPkv6&
z$(-_dKkEd{&fxv92g(|Ts{BquJO~HwGZJP-B`rR(`uiH+@Z?nmUTEx=$@I`~VL#2i
zKo$>N(W6dOj)`@`7Ls@v;XdWsPIbd97le1FqD*0eZ@a$&RTUe7ZwSiL`YL8Vf_V<S
zX>7wZW}e;<?4|A#J-cDm8Q&4&>V%SB^yi8t)WTKZ`c*McboWfJrz#MPl+P^yrU1F3
z@7Z&STceq>GK6{_8Q((D#&X{|=K6@kR&2?S+Qqq11gn=V@|i_O`yN$dT;klU<4)J7
zV5Hrb8Z>0o6aN4ddX;1-?tqBmxq=T+C#culZj>wPpEQQaX%K@6X3jdo@Q=fn@ct89
z2quJV+7-~qq#>Lw^<V-NE#Q1VKvlpu73K-(D^}Cq>iK0-2ZJjdH2Rq@X(XNyR(Y|(
zk;ij>5Ny}epX^jfu0{zfWAE2(JLx-l1z#N5*ZMwzhkvcTS728Ml&XO8amCaT#E@rY
z*)~LXzOVTj5xnwvN&go5W4)GuL&1U1=<(AjZkGU$_|Y4!PV794O<SxE3jT_Ym^=BN
zSZ1HB=t{qoh1R^RCm4)B6H68S3@ab=bNKCFKE+G|AsCPs(eBKi1+MOvOIhIKkJ?`G
zcz_l-GDmxL^ow~i3d+GNuEe(?9#OE{n@H+F9&r4;xr}bqDb5tN(&GJ^cS?1my+z(_
zEjUYPw(wH%iWn9mYv;sSm3)j@6Fdwgw#r@Ctq8WF1z)ZV{ZR@rpMPl>`Uns?ytn#g
z207Io`F3<_(;e@7t=>x+AM~Uy*`#y8iCiSQOoo$=z)AH=<i~%!-VaKRSwB22?upVI
z+ck~CtD)at+hKJ9cB4AjZ66XJQbUy%0GzO3Z>4JArCz7`Fu9RxzdL;^T$k#^D^_BB
z)qOu)RDY&-8}J9UlKm7YoA%ne!#BJ2F;<@4E-;<T<xn&9&Vf^@g75B=-Hq|V3~eYm
zd9`B2j(03qPkIP%TymlUd)4is=#d#Yv3GJ~Rhg7ND`tVUWlO*0Ru$IVtF%@DCQIg=
zeCXl5^2S!(kPKCYl}SbD4!NwMXRO2vPgS(kva2#XhSSqQ)G1|@_CoA;d3Fr9BdNtm
zznI!*wF(`ti>*Hp5Q2GJqy07#)j4pyk_ZAiA&*Pn@kpH8dhi>uzLq)*in8A<qpVVY
z^@mb&(JeS8gvvxVn!JUlvVM=3sqIMDUtuLW@n_7X>MC1xN3;tol4<kdT6Iw7@tUg+
zLVT8Lm8z2#%LFsG5A-Z<=Wn##zg>KL1MRY&+l+3$!sqgYEX^mh8zesJ>8n@Y*6;gW
z>(#EJ>~>wG2sG%HXfB1DCE<;0t{TLwJ=DO{Ce{SRI|2&1TJt6E?QFskYAvUdVjvta
zx2J68<B!{ZqxJld`25Ct+`aA=rGojRn$OTTX4a2nYBtp?j?v6U4u=Aseg}zIc5`j_
zBm4>!qg^5i>k)ai5w<oJd{an4=p2)qTgTilRZpLhaAc)_AgP^7kx<hzWasa?+{caw
zqP$Z#y6PP@`7CUc;F{jTKk0;=8K3qzN%x|b>4~cIR_k-JKBs}f#KSZ08vU<c67`xx
z!UTydOjxxNxyBQo@Lol7=Zwfk%@9l&9RY6$B4f$dG5fT`9=ks(@xy@o5L0_N)=D6f
zvi&KbJ{b7n$i$xOb)xP}s`4>edO<7R_d8-Zw!{KCf@>_6=6X|+{RLfP7qWY9%=r5(
z;&4xRol=zcb1j1{bzu9SL1pch-h_~-2pv;M9=s5E6w}8_Xnb|*rZIQ9+b?A!(1&(w
z82JgSw%w_!O&Gyh9EpzhK5{&8^=?Wje<4xZ5CnT82LXJTzvB6l>u)R_OIh!P#u3@0
zij;Xtswi(A5vux=Xs)4EWp=x*srr$DF0;pyYV1&d1azHtPki}j>zvBUvxzSM;BlQ*
z+nXY<Eeuk`qVnUH1V!b{1B@bW<Dp`aTVOmLAz=M_M3MrG7?9TL)K8#l@<Z360%^4e
zclghuh9fx5OsmN>Mf`rC)e{hWf9YA~R_sK(MRaKDhS^y;|9vB%l2Qu3#&%V^gKG_U
zG5VqV<Goi~@)+C&0kxJvj(=fFRp@gVu|F8pTD)n3OryB=+#vG^ctUVu14(<Nbyi+o
zB%rDd8KOaI)2`kmI_J5Cx$AGcRuHzWu?EhPwi?;G|FINfbqpCc>l2mG>D!zpinLSO
z4bJZ(K}J_v8vlf)?FGmn5vgtXyh{dPbhr!-XEMl$RLfVrRL2HA?Kak}Dh5yFMBU;O
zu2UF33dlEJ@+a5RD6hI+Wkk=sB4CO3xaWq}W4NsTt|kdCXv=9&uayl=&jXCnabZc<
zn*?Hu`u-(*xIuX;7Fi`Kj@J0^A{L^^;=g${o|UArzrEiNK4;#W`9^yE9;J4-=m<#b
zb0R~N(2<HLH*K`BXetInG41zJn^eUaTPgP|4l`Vp3^ryO=GaU(^sO*${@e#i#ZNQD
zDh%}Ixzx#e7CUSxv;3(r6o1Z5tL&&@{UtF-^XD*y@f#P$MF`&fe1(8x2T+bfsOxic
zA9VEzu+n8$z%$F`bXb^`>wOkRlouGa;Wg#$;}x5=wS4*j=ZwytUiolG)=pZ?Rmk*+
zcvakk?(2O-tSy?8fn`~JF1yd+J8n8=ewmjM<e<t;Szxg+5{yC8ww&7$GE&&L3an{K
zpzch}FYskzOBJsgK!&$My7NOpVs;qdcicK^IV-Zp`&tbGBJ93b@VZ6qTke{D5a}9$
z$Kbt5#KO0VGol$7#VsZ1SSDN3AdPOMzp;P2v|8m+;q9jn?A6XC1`ifSl56`NuDWNW
z8;GayIIWt$-bLNM@nuhLnlMcKyDEq63K(pJZ=i=zn`NQd&ESiMc*8|L*!W04lVRM!
zvmVC7#OIfv6SIX3IQ#2P)l31v6Dw!nbcVMc<wS2b<C|zrwX+<vy{GA4(@%aPK3+Kg
z3UdLdFM@JZDEVxmaz&43yL<o5Qpy}<tfFvKLQHsF{)XJf2ZZnJmc=%3gAZMz6ukt&
zVb{`WC4H2#8`IK1rO7r2J~0Qs*~Xv|V+WK^19t}5@V&NSJz-u8P|k^>Uymq>>MqSO
zV5W&;F8Age<=rhgy_F^J?kT{*LgFwQ?3#B?zo8rVCp8Wb9nYn?j&O>Oz5?))cJ$6Q
zM*O^ejUU`8GKXth{S6+qo@qlE_!N+{X(-`H97NR?zNx_NM4pFh_1|X4wHh`h1@jbD
zcK0*b_SGw0-HKJtReCaQmqgdaa@JR(OTr5kG{Tn)#zQQDL`G7Mu2j_uZP2y(MK36<
zDZyZCtmvfcJesZ(<Ma|e!k9~ZJ05JpKUbZSt@qh^DCpzIRf?Cai$O#n!uI3JkL$vs
zrTAli%9NLbZx0k(6nb5wBV-GKIQ{-|)9f9&64fB;@Z{)?AGjdU#c}vdRdL;e3O<Oy
zWM0pn0i;0_NaUYR+H*;geT%0%Zsx~1Cf?lb{O<nzt5?L&o;JH7Y$6$Zw9E0P!C4+%
zV&3i3X$OMeKEg~LHVWG%5<>1n5h)bXF6jjk3~bWq03G}MD&>3Xv|Jbe?IqBGj+-Q1
zI{Q`fFj66vd0^B0&w+v4=4=4B%`jdE<EJB*?~T_RcTTaktM06c1WOftPwOthmR!1q
z2bJD`6I|LC-i(MrEM&0(XXSh@_ml`oUV<j}P69rpB{)T6|C=UXw^^>z!M2kLl3p*1
z39u%jK1K=e<*Jnc%io`Ix+Bj`A~F}!FBOumTCf6pG8tqxhOp4qP1LH(NX@3rqi?6C
zog8Jq;r8ZTH*g5+5B4O_MmY|j0O;*axqpogT&te_NRJ}f5>OSst$4e&TMEd2yF6`p
zlGD!u-2cFC_~JT&(%a#6-X{B;y+^~&IIF#QwS=+J?&wF0`e#_-1utp}i0Tdxy^kn6
z!_7ghg~Y~hYT*dMu2ll<zVKnykM~&VozMWPZ9rH4ft-@a{xUC8Q)sL0wh-o7`9p8N
zt}PxG&Z2~1VR@dd>CSsJ@oOa!fZ~L;DT6}5iE1RHtPYO1W5sJQsPc8r(Xg&614~xT
zT1W0Vom1@C#JG2;bRYG0?&{XRpohMU7>PS*!|FhJd->av;7+%JP+voHRxXfu>U|!V
z)92{x()}QT)cVC^Va*ADz18f;P~No(i9syf>9Pu7{88fOXUYL?ra^x9WCzk#xn`bN
zYb;vHVQhO0D>6l-E0i?D@8^FYFR?%pTONgn+IhePm$6uzZ#@st=2EFwTpV!6j9-qF
z0gfD1PQ@PbKBVVoyy(}KJ7GY<ZrTV=ypnT%drNNH?Y$=8nG?@SSVw*@VI$C0$%`&y
z!gRnL!W8<#xuM2>uK{@8Dci9Bh8VX8=AM9?Lk|Z~9aUy}gls%yN>!~-Cdv@tSAUp)
z3L{u|k*cK{!;T@sabo(VtKmJHm=udf*SVgwOqH?;2!B`MT$@iMf&BB+TgyX?*<d;5
zHI1mi%akuUQ-D=K<?vM5z>C?8J5aU9{8#v**y1+N<#Ze?#x9dbe(q$liNwqXu*Cg`
zzRbjcQ|@u1GywO#e>_bZ^NQZ$Q~K!wv?f+9L!+C(oLRs5>pbjDgTn~wH&teQ&>I80
zyEHMttpXu*n7reokaI^@PR}kq2629SrdWP6LO89Ndq#85%S#Iprg^i60LH)|5p@@-
zB|=4SOzltfxVcAk?1Y@veghP?Tya3|XF&#lioziNZtvu&!E;{d<XYMMO-de%b#dV3
zBH46<!?(=VF*%nDt<7ZFV8kk0=b3aigx1LBS4P@hB22O!hRwLdrE{E!6-1DX*BdPz
zBZXz}FYxb`jc3X)^=TKw<D#xU(EmK!N(Znyz?Wqb{U7Q$`F}wt)U15HEQ8CT?i%Qw
z&JfgdXBBWs%AU}q0I(UcWv>svbaCu!xPbgT9>?`L`S1jJFmuG4!g}R}4)9=_T(7K8
z5|}$`+Z%t`h$=)N@vG4hU-gLYsJb_j5Z#V%JmD7f9MkU@PX0G4<maUG1SE7y_5u5c
zhy#ZI;({+KV1hMx11~1QoUTKs8*kg4;mV`Acb(EAu7?hOy$37%vA0lA>!0y|qWlk0
z%X<F59>O|-nVkE^7k7t?tK{v%87{@w?RfEJh6Pu?VZ;2m+xu@1ygWaV=*;(n{sZ>_
zn7I{z#DnFPm?t6`4!nBd4#aF-&jSXg3FUjjL$&mQj`*j^tiu3?Kmx?tQ6ZD*w?ICo
zeGT~u2J)M7e%JqZo9SAV+9k;Fg~kJN4<?YorN-_ckdIu3$IVS6H$MFSg5tPnp}*fa
z)oWa@<cc|0MWA~5w2@BcLjQKb{B)qL2)6Dcm)XW=7luo_q~rfVe^H}KQX)SbR+|OM
zO<j$Kj%wGhO2*9w4jm@BYQL(Z1L$xQ%w4xU{7`Y!a()FoDdI&+b83_cdk93<LoC1S
za;B=%Im*)Uj7QAU1x3b6`Hbfcg^ccQi9XvWj*fgN%U$_@Kz%^MxdzG+m~yZE8<gDM
zf%{*A$F77ZRX0{c*vkX$g@Y-3jHXX;3$27U<vBgKa#HK7ne8!^;nlL3g-a$>X{@d2
zM_@hq-1dTGoj%HyO`$l;#F3*OBcj(gVI++ZVdTP!@C~+i#d(nYcwp`<YjuTp)IBlx
zyTA)k@ZICD@e74qD>jHJwA@rRa<Z3B2u^pf_`LfMnF8=R54Z5AE}OxqY`K}`TezhF
zt1x#gBS%|0Q=sTw_J7~t>NJbzAF8aT8LnQ#C;W?oSju~quyYt-9poYnvwRR(zz;48
zPn#Flk4*QvG$C}R;8TIm;0I1@gRDbf$Z>wB;A3<V^t2kjM>rpa``<iHN4TPN$i8w*
z9wI_Y9|9*pt^bc6H?<UCmRK%-Ek5?;$wQ5L+v@&5&gK~fq<s-uw`q5OLKWeihlW#Q
zTmh}#UJC??(U;4}$hTy~XZk3_DrnhlQviN-xZ$)XZTAYG=Lamw!5@84ll4<MRZq0x
z=iZ(qUL&nTA3vD$mHTDw+xk-ch%1~55sFeE9$M1`qqkb3t$AsA2LUE_l)kqoha~J1
zHp3DdAl2mx7^XL8;?Vovx(lj84M2Z-U<`Pdeu|BaJuj`Sw1)uhqNekDRY%9WZJyti
zn+D%t9Ssf*4ULaiX#KMuCE(c^Z}53}`o1k3hYoz9;Ur#{lWdV-THJqjieOZnaCWbU
zE5PzynUS^Y4R0cYW@1v3dQ;~cz?ao=esK|pBQw|^0QeKvcY%!ZVtWW4a5K%x-UR#`
zNd&!1|2@N5R9u{x*SYk+ZP^<f9K1VQ7x|y(bsnuooJ;}NxWR|_rSQ5~D_{US6D(a(
zV`F0-9i44Bzz1=1g2iUF)nq(LjVsA?M7`aU`|=IoPe{OYbab@Z=ArhlIY(z_XI!>x
zLjQAt$ynN4g{~SS+dW_>%8I<42Ar<!bToesXfu)oeFVa<bD>WE-a<JtEiDbWiOb<^
znRbgaVE<X7HQakj1UxV{A;D^?{y5X;#mve|TjBiwU12Z~RcAg^ESD+p`UrjP?(U|Y
z<2VK$a~Up|cN*~KPL)AdqiN*dCo<*pc~FnZynRTJP8j~i{6(yfxrwwUU#>(;|KG=F
z`^>0o`FyKOBZ-(WEF%DPU1q{h8P38?4!tXj_wgct?|OMc1Rjey-il%1)=!28@2+<s
zz1)rmf!ALoMJcXE_9n(xaqwpkP@~S_5QSXRVHwOP-@9dN+O=ov=>;JVL+|rTh6vMp
zW2{$?-c!ch12LyDqV5?p8R>?`!;uhau27*5E*;N~_qC<0Vh;X)o6UXRA%h80h1_I0
z#B1@+W=oA>R#T6PKU1AAnSWZdGE*{S0H8EIlq>W@9K=CVfMKG|H@Ge}In)@{?`1e#
zAjqXEL20-eqrbF5`gkYB<uC1G@~8M<PcH3`wwt)+zFZ2wsJ6}2L*?4njtdm<4(4td
zuAy@66;)jXE4!x0vLpP;`Uj@?3GrYcBCE+0v(#wvjOYcth=N;Z%<E_8&BDNB{EQu4
z>0q@}cKCZJRSDWI488j-YkM&tnH0>aybP#h2GW7k3X2{xTu=9zc98+x%7g7H!>L1E
z4%-Sv1y<WiTTSQSy>ChT?)2!lZ0d%+6q%MA$>%y2BhxZ#u5oM=5cEO6#<+Y|ZE6WG
z6(4bVTY;);*=k#7beg*a6XreAWUOkJ-_Nwg1iC8K3x9Sw&f)p{$JHJ9G682J6?sOM
zw9inZC6#3u2Pb;65O+(raMS1-ZJ!4kM%MCG3#9H)2gw#ou0GgXVHJz95VEV0BVVE&
z<k_I711j9hPiQHl-&gHBJ$|9;%yZm0{No%y*`Zub2CKZ1b%u~>m1d|x+0jAi<7r(7
z@6+abQLdQMWeKDN8bWM1fG}4y4t|j8^fI1TCDYQrTjE2x64^mfWxD$I_wxksDX!v>
zfg9=rfzkt57?Ix@WPK0;=xhvtT{Z^(;QujE*~SK0A|$buROCl2c@uC4Wyci-u%%r>
zNPnnwlCiN!dC*_<7lWFLdPZXV{KR^`m^nY8{QqwFj{#OEb<6B}41lY6Z9C`+zs|~Y
zp`>FDUc>(DcJ^!Itf$%c*9s1{Seu3BuQqSM459ox5JX^sCK(js<C(@xA3hY!9`hm%
zT2M!)^H|2KG{Aa#1(I{><_b)}+)NsNW$n(uY)h7Yd>ca1N~YMH+gmsCDML{P?is-C
zRr*Jfe1y4e+&_25<W`~m6&Zae$Ay-f0;4ZASWM<tw&<ea^MBZS%b>WTty>#+2@oK7
zfMCJhB@o;~aCdiicZcBa?(Xgm!QI^*8oN8^ykC9w-nxIFx~r>t(|h$|%{j*y&%=Oh
zZ5_W(_S<Dm;@M<sHC?ZsYj-oN?%2cn4|QB8&~;m%n*Gi7>(i{*uW)Bo6nJB0{Py6V
z^A?e%<Yw8+7rvRwoov&5O~qYxf~XOi1QB)2UYh-*eJvWsu%68)HW#nWqn!hEd`+rK
zRx|oaP&)`83$*w*E*hSfw)X^{AwhCVfiHo7Xd7s&d*61b4KgpfX3srht=SeAPVNzr
zSwz@Huy~tKbb$-3Rl;w#-p2~OHK`%daa#I;q>>IT0|WZGB#ggd^G@al8LpLL=(HWo
zrsWYRoUZY*6jxR}^R`L1iC#&0SWi9nV6>a9kyfgx|HULe6tMv|TE|-sC&3mt2FRXJ
zf158+{%|Zp{KadLTm77TBKxGNv2@<%6#boqOFrWTehh3FjOvQJ-=ICD6uef~M~5s@
zRpYf75>6`b3sO-K<f>&}z{DbdgW0C-_2f9%-fO8+WR4r&QE5{D(mZi^qjr(nqhccw
zS|xsyJ^jHzdUh2{_6F5Hx{3A}V&0FrpU5nD?RWE9k-jj()-?w|@oNnHv1fN~-ke){
z+=KVsUeQ2_M%Xubj&VHbqO=>mTIHPuEH3PtuIi3wAI&EaT3FrTd8;sIYTgH`v^_6~
z$PQ#KmvTcD!M=k9oj1Qw$bbIA?lm|GHmxx`tb0K|Tsg;QMc(FdGfrF$BoayTg1IbF
z6g5NLD3g5LsDyM$>-635c+&^QKS2ITC7+Z1eN7uFw(I+*_6JbLGLhO0by=>MOQm!E
z(uh(Pp?aknbMVEhs@HqP%jFq#iV5g70OsYDIdN=(j|bSy>2=?~7S%`lG)MHPM26aP
z-=8!c^!IVbihfwli+C5-r7!oRth>1ycQ!4j&y`xjPK?yKA>po7+Otb}IdrkKdUXrv
zqvNu<l`i*2w!tlnhX5f;^R`YXp+hX>oW&|DK)G1XZ{8B&OGeHq<%q#Pcs!xP)o<cA
zabO^&lSY?hB5Mm+9`Sbjve2i00_lLc&-DfF)_xK|k9!z5N}iD0ez%~g*+zIC$)D(V
zB8q)ZK;rU|q1Rc;pTN)|&hy%xBU7dz{X&-_APy2K!FO|D-=xz8ksQp02B*(9H4fjy
z%F|}qmf${ife7+Lvj_(eb}Da}sjui!_6Fx%O%Fjq-Yk-nWX8qbD#3?JPTfhv<YQTP
znQ`f(4GHX?+_!@WCn3Ane~94{L;eg#a$fs<?-yZr{b#0$rBOS47M3fd_)EZE9Z~!G
z>(@$7{G`plyf{{eSyXCLM0!lRUau)3f)KOh{Cen8@E2N%MD4SU^2&~<u8rWU;E3tm
zS95$9pVNbZ(wQUYbn^2~gyd;7Yiirdp;HTe8iy~Z!Tk11*nxa-stWCjm*ae_MhA|h
z4bFHZmr>1{<e(1Hg^Z??)}XLo9j95%<Bldy-+0j|ZdAkdN|+GMN+z*28(LcxPdaPA
zi^rlD4;?q1^n=t*^NrQWg;XF~ob7qbS$othmy_bBQsM$*E4=2m(z5Q*w2K4|uRz%~
z?os!gq3K9|3g0%pRa$jd7uSkKsHb($%VZn3{I}5+d;B7CXOP~+NR&H#k3bzFH<aEM
zNV|Uby9@$ur@>=nHZ0Vk+x9}Z36l&N`3j#OdRg>=FfVBh*-sVKDK~xm$z3>O)byn7
zsEcYVtHVswWex}lU_kk_R=H$Xh9oLiU%eft$f~{LFW;vaX(nvjj)uenH?8~&Vo$^V
z_24A$5y*x6*!*~RShmHa=x}_AlE9J=jK6ccfqP!}3?9oH`szD;*gvPsOSc}H)(ft+
zm1G@)z(sK;4*l^7lSSKluoq?BoqjE~(_*v2Tdiq6nMKyJH(lwRt2uMnS&ZY&diCK%
zsS&b2==FZ8E1AjGq4GQHpA*aM63JdCc!uy(a6vVRMUR=rTOfgMUsP(@rNpIq7sk|6
zGJ@2{IdzK7#yTVx(+!4nox5&M=K&!?KH_t_;TpYe{#bN!JHtN7ARH3W;9|9$yoT)B
z5czwKS+kF=NUtwQj%^7oSOof)u8-P)9hME3m*vx-WECT%UhB_r9abfxu{3N?!3C4{
z6Bq92vZ}3n<iqmBg(jPgL^+DQ=-r*F$)^;pMgE=6BoU$@MN9NzCsHynt+9bL=zyPv
zmQl#VRNAgrbTT9h$_puOy@H_d(MrzeFZLu$)L*M|xGlw{7;)-I1O|2RCxvYY&09B}
zz&RZqu!Ewf&TEN9c3U6zZ6$c4#W6a7?=TNO1Pl!;ZB%_MD=s_5N%HA+@OkS47@;xD
zWhzcRwnG273AU}la;HT)wrK|=!#=P*b~}MhbDxr0+SoEyamH5h7!7{fg^wCJE>4)N
z7v`8j2|XN7a`7S51R*(WIDykB;_7pI_`dw@U_W|x*eP^LKP7XoWr+F}ybo&y)|KJ@
z=q%C3uuQ%DrZ;i}x!f!hl&YqSFlgG~fc%3MY@<Z|Z)v9C-8jw1d15&4?#6G=5Mc*>
zAu^j*b1|@X=C?-Vo$VU6{nNC!1y^&o3?zWL^gh0Svhcv-awtMzy;uM%5;Hbwo@JbF
zUQPBEP(Xk_HM_O$aW16&L!2Q1hW=u^g0252A1SFOTU}2Zc(xPwhsKx@akn|kBIYfC
zxifG3X?te?bl@3i9l@;Y7^Vr_?`n|OFw!3vEVjv?f*g$rx9=QI=7|>BC!y51#RLgG
zS&R(7T4v8w_{jCDkD3m2kn5e07b3Fh44Q2=1UDDH>p)vlt;^XUolf!jP@U0pDM+ou
z4eXd*3{W5)=_qtVlXSeF*|%!rY67Q*ABqzrh;BCsW5`YY(2TmC-(FJ!9^g)q8E5F>
z7y2j);^WX{(rmK6nj0J^dw|QJ$R_~_tj*Wmo~sK?dOA(NM|u}bCYjbEO{CK6c61t@
z54gkkJ6GfnZbwQf)uig8xoJy{6f%4ZVc<XIvFkHls5AU%&7g<i{>oe=J@{}sp3dWj
zgg%#I75#&*JU6%w4C`BG5#62I<bHkkPb4Hv`LPm0+wdk$EyttI3g=qMlYPZt`xupY
zH`Do?(f*Su^Wy%*Jz*;BE-4E`3PJ^G4(pC;5S%heN9)fE?9jbiyh&aKMx5E>O@7zs
zr{L8VDGfX(y~&$W7+hugi_Q8Xt{yrd-j}FZZ^ntxR9O8ZqXg5y>h8Lyuf9hbO;Jby
zJOf8bfZUzjFY^LhtLOLDg|PiiduF>Ja8#NM=gj9`H!MC50ES<`IAn%Kg-~sF1jh4G
zp@O>=B8kDq5U0iPB6_l?Gpy(<4TDaJ;-L0wNpCmV&TTg2QZD>q=*4(WQ?9WZ{?=*M
zM#eCfK_t)`Z|_`Ee`~q1p(DpzTiC77Woix^EJs3*nFR}CDD=A>WgMc8%0!v^mYZvB
z(j=iGPTv|La3vMT^M`>}7b<WM?I~v;_F@|$ha2i;@@BKpR9By<1=4DY@qG?yv%X=7
zh;uM2b31KR>-PL-Z^{QcUy05_Zxu$UvGG&B4SJ;_x}<X^bED=`!M*m|%foyYhkJg<
z=~1ASBhxhb?P3n6zH*D>1?#6<+rOfEb)3+&!lJtUzC4ZZZBRj_wn+8HBBs{hGmO-)
z=e>{Pkt?&ZVhHxMJXo!0ec;CUkh*Wn8oRD#tQ5<RMBV(R@(`_P?2;VVxEP0NT+MHG
zkn8FK7I$_Z(|(T_>-x6)97URf`Y%XWL(8yEHV-rS{;JeQfdu);EOY}s2x>J|#o<s-
zia6FqJWhk(roMf#vX##3xmo$mMH+cPx9)IU647(y)yt%Z8+6`wC(v{?`W<qMfqFv~
zv*Sq^`*IWrIdgmJjcksDa?#ZlrN0+O?S|c@gZMe{RV^kyiZN>TdkM2$^7H3apY>Jv
zo{T@e!z0ZWi5D#izkdKXB!&@yXDmTl$r`3tj71@Bo<#%m1mKj?O$*C#QM(Jnr2o4c
z=lQPyhQb#sDer~f^ViJW4wrQn9|Yn1;5J^(_6zBI47#45(-R*Q+ymw0p9~V@coiVy
zNW)6;TuQ9*bV|}>Q``AG<Rn^8<+AAqsE+Xqzeki`ev8^Cu3<ofH<M22U!F>Pm(*EO
zN)j75I*{-IyTHEjMe}i<&i*kti@nq=nzF>zhpDje%yy3q|0ZlOUvY4<LI<@|2(s!E
z2e)9P)njvzX&a@^<5{;J(s7m605c)ILKKR4M(ej)@5IP1GGwk|?>b`1I!8p0K3$;;
z$~yuAlNax0|Fn{7IvGW2?1jp}xY!1pT68U}@S>pLZ3Siora-!E>ji#xe7%(aa8&jB
z3d<fQp>H(J&=k&bmZlk{w!Hyk&sgb^4Tdetk9G`Xd+%!L^6|<{6$!j8DH0cd|K)=V
z!&~0HACU4nA5)3Z=UM2j$6%?y2O+f>5=)uDHN=+X*Kc3kpIP*#cjIf*p122bXQYjJ
zs9n%fG7uE|W&J|wo%9&490LkFM}4@!YR+KKtO-Hh;A>T49w%6cvQv?#fV4a~+R*+9
zvvvMA82eW8H&x0>9Ra-ApsV<nY~k|`kC_b;%f%fCE@aoQ3aYC5Zan6#*|9&8auTtd
z+Smx#JB9S!l@Zz@p~<Zxu$2~?qIsdO%w*JbfK+H8F0X>yAr-A1^%u%GF|cL4K6`%{
z^EjXP#@kzRIl#1okP<&FGW6gZaX7Pb;cg|e;!_u}r+NiKHvLGh(P8wz<<ouNja5oy
zCI~KDHXPcQqe}x0+ePg9gxMd9n;$!TcDkd>Yy{k6X_4n_K2?rwC>Pigshz$k?h*{I
z^%nTLo=2=EwaG!pPmbHot_<o&QSh>hPJC+hJ6p8PbPh?a^JiYYzc*5<BF-1LZXgU8
zOR+CohuMEhd*<>Ew(K00V2ydqr0g!Z=sCk%GPhmz`SsP?BG}Aq-SiTO(euR;lVkmb
zIbg8N?h5U5TO)sevo3=cKV<aJm6T%|d^pMD?2hrxKy+eHZ5!1S2J_%K887js73TG|
z`g1AJM*%LIe97q6qP3l^sSxe!4`@+>c)otUh?uXyUK+|gT8UoNdGND>d;)fl6$_a`
zvQZ-hM|&nO%P$)A=?LVKF52tIlMHM2c_=!?(W=ML{Y_?zwzCOM$tc9h);gN*qZ$#h
zK+S?R_Uhx6{s<jvtP>l|O*d46bG#GU>uFU3K~!yH9hydTUJ~U~tN9cQfbuH2Gs2nX
z%MYePxq@D@IAeot)C1G#9ucQ(Cz3fW1GeAW6i><8YV+?!NsEeGV{ejE>|<Nht6FA)
zf7vGyEDp>?ZbO1mZH?TnsHcMzMQ<Ao6t{L%y{^0zfo$CkK-Zl?l}>)5m3vWTvlTX<
zauQL3xB;z`($4vu<-J<YDF=3=w*zH44awJJTleiGIbOzo4PaoTGmVF2W1$3~WVMn_
zFg{D6684}?Qoq1SNImrDn}_z8AMC-zt5%|9V$=|+Hxy02oMw45f>(Q@J4<(B4O|a0
zPOpP*)o31vqzSV3(%#^WH8#eL=40K(fax-XB&_&%sC}>0W~xs6DzC2$7yU32l-06y
z^O9eVt9^WYs{hbc=W(nc(0M2wby97puR2{6GJ94Qr(Od_?Cz*4PFrjlwS^^!CY+C#
zTiG=SR!GQ5Nxc;k4Rrh~65qN|yo#qLgI*9^y62lJBE#Q|Ce3b3S}u8Wgx9$0)n|(8
z;f|+(dm|2=5YB>3%vlzBK%ii{8-ixgK#X8Q&}GFOJ;xC7b7&8d=XcnVcRHFw$><Ue
zWiUk5JLQrghdxl-I9evX&X%$waam2;3iq%>vR+_kf^U5}-cjqDuQ-&x5&H=t4_Wly
z@Hr~HOZtky1nIC_1O3XU_1C|JmnHDHgWYpZnd}cAE-Rwy)&IF@E?o2FW-DJOUV8Zt
z3-=w^3q&CZYdUi$fh4~e@v2mHBJhzlx#LSv58s20EFtkNS!VNjPs54Rd%@aO=$Ctj
zP2eC3<+AE@!euvOMBP+OJh-c40Icmb{b?QfU_(kAtVlN+yYQPT34IG;1*AJ8nqIe<
zM*V2MvgnP0^Tgg^Q=lia>UgY+U6W-Ua<a3onpV{za<8*n{9LbSerP%O@uo9G)*w2Z
zCM{?b5ziU>vYnjqn@;}JQ3RQhkiNLM;~Au$eqHcl^|DFk8CdX3;lGRTz~_;;=ehKh
z8eb<n^ZYjh76sZaBHY_)mQv*qQO`)VaXKSODNOU>8R=Ix{&yPWjfq9RU-lP`NVx4s
zig;C@i{kA$=t<L+9x&sR_t3%#g{q>!T)3Trd{giotB0~Bu6}=j%6MJUntcl~Ut>;^
zMzND?i<6{nXY|RtReiU+S;N40GsHW31uLfV7xFHTN+7&riImY7@)~lYi#3Y}XiDDz
zN=Epw|N8N`iqJ&une9M{(C!H=63aTOLpX=|G(x;k1voC@Z(70NuXso}NLEtfhWb=C
z)$Tr8{d7k}8ukj-b=IRbV$LX~!CiM$0y3WiE9$H16_!mcs6)pHoIk!yeYQEC`hxij
z7GjQ}G1j+qDXHSAGPT_I0PaoxJQ%J7F;r8x9#`{+-21IzP?tl~EIb!#!|(;knQ#^a
zDA=3YSr<5x2+DEDi~Tx$M0>%n*}UG@fFBmm_-yw3S%c`INdK70R#1*6EH}y}cvJ*A
z_;qwPQm=;4IIQwpfFwjN9qNNk9)0xaxCNxBWo{LI>&RJ6pDA~RwrO@5Tx3O=uZVSa
zA7>IBug&{u+*Fpkkoz%I2X@i(QaIO-Gwn%HKQ8(+XYU%AwmeERms(l;1$|_*5P5N;
z)3)4~E?s1niqiRRZI&$Dfh<v$b+huKDxz=2V{M~FO|e<6f50~^9u$W<-Ig<MiQ>QG
zp_ptMJCF5^j-|;54`QJx^FvBEHGQ8rBG)H#N1p%G8fn5!Zn5g4*<4pJ#nh0n4taN>
z@S&rp7cCL*FC&tA!u!zNxA`&JC7U3EXo{p;I=fVdxv-^$&8H&-vTsGXQz{?A^!rm{
zf8A2rfXy3|wi^sLLvh5bVeO#2^`}q50b*VM?#RDDUpwYAUWqVUu<distPR4ERlai%
z`B|X!7c>r4v0uh9$XcJHJgKV!rk3(ihOSW+NAsy7b7OGcLItP$?v{Vb4F}ze%_j8Y
z?&Un}`vASElw;~(!cd2}W!HDf>0Y_iW8iJ^v?FRyy2GVS=;tA`=&faLYv2h2>x3vu
zGu_TMz_u*0U|RQ2VUZj%0(#4$%0$pB#Y!<7+7QgC6w-hR`T-qSIPeQbc^Bts^G7nr
zsd4t{xAY{DBCJ(20c0SH$UGazcFqIYK^_=-r$u-r`dW4VuMYGZKIFOwFqg&xJR|Hu
zfRDofnA%<f_9$8bpf&uj!7K_qk@~-1!8YwP7j!F}BfCTo!36qkQ*cB=8%~8s-(gh?
zvqF5(oU&efq<1mOoQwJPXB=fs_oDed3D2Z0Tk{~wTwkKud<j2r75$FF@x}9u7-*h<
z|6<}lVy}EQg+jk~p(66BvND^;%u3DqL_CglI{Y^suC)3W#eQIrQ9sT6unjA^QuV$M
z<7TzQr;rDHtc-7K%>SHNDnQq_dBAzY4V*?tp(4Gha98P)qP<U3wx+As^LaykT>LdL
zy-7=>{XA@FALbp$uQd#m5Qk?t=4711r@?AbXeT41?a2}Br2+@E<R5wqnzudBl)afs
zzk|wnIF*&-W@#4^%MsQf7`NUM)8*+}^EH<pp6}^ltDKFNYkAq#$5S9&ls!>_kknS3
zqu$>;P8nS2q$2<3NH*ZrXwJ=?T6eK-b~VQ^&PM;9)w|m*#rn^S{}*uZX~Lgz8UW-0
zbFBaVkPFs90(gx7|1)#Mz!1B?Dzwe{QmGJ^1-C@yKPbaQ0wkUkom`oJ07EZInGCM^
zM$1*>sZ2#WjNfW%Tsqx80RaJ{`&GH+<>USR1_NQ331jonn#24buW^}~GF2lZBgdOv
z-t&;Q!V?2Qfq}M`b~ZK)%*;Z$Cs7WqX8SYU{O?cxbwPF5ev{*>uM35ji6j3mQf}-C
zw|YxlQDK49%t&Xx0xrAR{%|rK$-&cPVZQskmE_G_kJPN2o`A9r&<ZQI`Ss9aL}CM)
zlpWE$?{l9t2IK4CPYn$W{*4qdhcrxJSdMwJD|~#qQ<#m%CnijB?Mz-OG@BQck^gb(
zZfJjdYZB2l+GR^w?+daKY_8u`Fi|J{2drj>R*z9Jih-irOxgc~E>eCvXd~%%I1h&0
z{u-e$Zc>%G*=)-RIc=%U3vKw7wWdW)+0sL^!z`_DD=8EP9uUM|=gB4f!k@G&K*JZY
zM^}M=c^OjnmB!y8MWu}H<*a{Opi>OrM`#Tg4LpHRETBj*GnJg;ny>87>4M=}EL=(Y
z@8@A}bJYt!slRSbjNPWdI*os3V;dbrkx@wlvbUieS`O6OLzwi9IoSE1OW7o2E5c~L
zQkR6Bm`4RZ&aQvA8~GCp!c-Q&Hl~(_#~wr3_wug5-O_qtpQ6gjN(a-;l>)OGMacQ-
zg@psD?+!MYy^8E~)pmq--kv6tQGnR5Wqj1|KZ`;Q3YI2eOmRnjSU9&5rq%4Zn;zC`
zUJq-iK>K71KSWDAVU^V)chM$S4Y)@-7W+)@n=4;TV7dVOCj(4sXb}ALd32Q)P2To^
zSHQKXqRxzLY#Cp6!L1Epfz1q-uV(Yt$p*v>So2c8I02bSZ={yol~zSfanbpkV=xeP
zljkz!&xSNJdGdej_owBK`JxJI=>RTlk;aQsLq5LCUox8n+1(6(rB?ihAH`cw$GFX!
zeTAcjPFqKtBy0q%TiDmMkeV%!++SXqW`6xY(47AlFVB%}dKF<*h%jDwk4aB~I*^ZY
zRqmG;NOj;^P^2q7il?GkLqRSy0yNmhRJ%`ZzKfhFdSLyn>9jnAyL^=H312iTp$Ien
z(q%kEuA=_U_BnHCKPl|{mp3J@kKCPYZY#GrRs>1$Crzb0UZwHNZ!E4ueO%6(8OV2&
zL;E(ujHoz_r3obVV@~{Z{4ec0bIib-Zsk;UT7W?k*re?z4ERO9dqSZirz$)39O@L0
z70ZNqsU#*V53+v8X>cL%RI|OD06=+u&QPCk-Vvl})#v>l+h%f+94xd{_uhU3W*rZ@
zJ<FCD?8G;&d$Q0G>ci1NvNOztTjHa^@Z{hu4(ggO3G!jm>8y@`zE|}xGx|q}OU6X#
z<XincXU}Mi`aowAk!8!(^yTPo^D9EB8?C371r`iQgOOAH=VbfW<0`R13V@N2PO~?8
z+fcoDd`L67vz(*=pFZFQ2Ro2cf{;yKH8PL7x5sRTtjP3onebbpp=7mc{=c|9a~ZzI
zXl`Eavlz|(8GwHWwU6G#4dC)tt+Lc<rp$@bf781g#gm-dr!^ek;1$(V10JC2;e==P
zn+YR;j^TV?<MFMT%~Nt>21sa0px`454U9+6{6u4CSgf^lIySz<et~v66#FGj+ZXhW
zr!WQ)Wr&wV{H#^i&;z`ocA!l4i%EA}E}gpK54AcO{tt+{rEA!dF*Hpg8R=g%SjVp0
zb9F09mx}<P&E%3sNdN_Yr08?HXv~IPI4D>bk}`bXJq-$=t2_9rv<vwWE>zek&|iUH
zcZtSqt=!Q9{}X5$lE;x(R!ZDumu6$qWbu2OK7f84AdnJs6>r3fr9DRFmED9ESk@!s
zr_egIgJ8<M00{v7_ss(3u8=nCSqU+lg|Y1MTtY|}B`f}2Z&vILT+ZX(Sl8PW_c?{@
z_@5izs8Ye3u5Hg}o-tgN*%02_$+6%lged?W4e&Aw-HD#Lshb(J>0}qrZ7`91<(Bdu
zZ69`*eWg2$&^ih`X(8&!d@Uzq?g+&*Y5toRz0q_z=Q>z>PYfGf9xh?AnzP&PsI@pc
zJRc2(_1r%zdsQyh-Kd8w{VFBswCS}_Be}RVo5DUiGSIZwn`Y;QcD)bIxHuMDxRCCm
zW_wJ~@&v=3*n^NopF`>W%)9Rs+pUd{8yXS)Ih**iSDv3#a#OMA&?NjJJ>7jc=q1bh
zaov^4Y}A67YPq^kw8>)CSl9ex=;IyRiGyH&`rbMfWD3LeN;Q*QFCZq44Zmf#{9f*@
z)&Q_@5WNL;J+CXvO})4mGu<FY-8DQ>%aG~K@v2wvpYnWOpaL9uuT4@fCjlWh^uec1
z3xrv~9wz;`x$l(&MOMz?&?OyA2WVgec*)TM4Q3i6oHSe8GQ4pfpt!NJ_X^ytwkfX`
zY|bZ5m5V?Y+O>yR5<&i=h6n%XYA?C<?<VWV;WdcL6I}&H#@W82O+t%m4d$EnU$yB8
zJ7w10kEPOaK9cQf>T3!!Nj(}X5|r@V1R*LhANH|tI8vlrR*RtG0pC?}4X53s=xCt*
zXi<wqLX6~j6qIF1%V>Y<QhV5wUs&}jJsB$!Oz6nn0Y-Wwql-5uPS;u<F!bqohIz#A
zUu`kgu4@UGJ6w8QX?Z;;DYVY^ZF7_}g@g`p3HW)ftf16mJcI^@kw!`S@`r?$!oHxv
zP{=MvbQ(ZleEqIO;=NZ>hM)$b$V>#U#=k_I*+{D6{)wk215-La70pA}LB?2AHBxGV
zAdX5<XqoIpOYz2Po*cEE-P7w2B@F0~0PMx4<1Je8Mn93(D*(C67C%mDvf2z)R?6uL
z`HXI+WDj9awTRgB9>i06SGD#1j1?@LRfqK4q(_SeB2C@uH!8QsR`P8ztCw?0LX`ON
zN2BGX(>pX)jh9GwrIV(c%lcD5?h(OtQYlOa>5Gbk+}*ao%aSrwoL|oRC=nE3pe(>K
zDLU%-cwE#50CTW#;k}uoc+nfmGKt@a;{LKgspr{kG@j03x|S=sIszioj2FA(g<Xi+
zuy70%Wb`Dqw6=WgDV@vP<!;;H%5_JIRZD8GJiY}O6>aCOzaQ_f_CVC?H8h#WZ@ZO+
zI%SKz&IRN7sJb61ffcLHDmCYKA<tv3{f3R5l(#&dhgnJ$%2xaxFOZ?lFCt=En4IUY
z=qR6<T#9!ngA-Oae;sXJCz_~F#49ZN&sVO5VHE<mD;hKY;e=mI#jUv1cU#9cB}%i6
zz~c(P`E1@h`*Lzu^oH;eXV|=Hh4f+EQ9~_L{n1i}bE(AK`D48Zs=z=wm|So3*=j>|
z|L5-?(B0B4Hl|Pg$L+m(qC|Y6Y}D>a=gLg|K}fR<4NEEaP|Cgi&ywhq1$`d$*O3To
z&aIk5GBw6b=#!h>xQ|ckP4N5G)@V$QGiGOAC3~bsh+BA__zi)5PD@+NKH3a#tgIOd
zmSS;#r&y*_;|Ky1?gbGke+BW8#IaKV&F`**r#t{h$II-Zt$;R@(pX%T{ki=1<L&<O
zwR!g18ZNqiXIMB*`oQ{V%{Y6i;*gG;sn8<Se%bbXs_R{={>|+tvE-mldG(*pp#<lO
zmB!1J=pu^fU$R=;lO3=QXUmJbCvCt~BI2M`q~j~VtbChotvJ2*IHy9nT4tml`6W*c
z42Y7!s`C!#o4`B)MauA6E%F^8QUxpgn5PJp9c$EZS*p17!CaNK^1S|G+W+KB^2a}z
z@?jfe&0&X}rgqfi)XRUM+jxEk1G0+$l+lNM?_i9|%9o)He35YU1o`rB8GToTVA95$
z(M$i?+s`eZe<_|{NjKA!r_ZPNeNLxf!X)5_QSiQfhwi#?<>IbLHi<}Eko{$mU0k2O
zUA*-A2)>vJW!xd=apN@_l-&u@OC^licSwgNKfnAO<wo9-B|LsWd)`O?)AdM=kIFj8
z3<QHKkk(<Aw=}KW5+G1h#JuW!(*Ch=yWT`piklibItrQNRK})7Pugb1i(9A<Wlqg0
zWjJ)tEl?A-edQkTC9*Ds!CG$bI>M^M4OMaR06UH;lVM*<4#(mmz;)7t<c|jlI&)In
zdga$iEn+p&K`|8h(<yeBs502mHTTDoKbfHbsS2O+Y`BG)o^PdU)9%|PuURdXt!xru
zhYTMeD6{sNde9fRM#kd2-rMD`P-_XYoOoJtC_#MVfqY92DaI+4O(&8Q<2*>#BVk{Y
zP?%=`k>Rk77EbBds`kl!Jq+__Cuq_FLjrU`Fk|LUM+;+bz2Q{qjSpLB3IzNm(nFO-
zi0rvg;u*2PJX-7|RrjrRwng$`&0;-gHlmvs1->Z#w@eymtMf58<q>g>UHH*jSyy`s
zG#0I<e;=VTpiuz)f=XXZmmBSy#53Wyx_r_H!5BGz2zPX;H#DkT1iRVIzp4Y3?mouR
zMmxld%HCJvBFetfd}C&7ui=G<jJC-e_(z@u09V^j?7<z;5PR+?6yRp1dq~vA;3oU{
z?tk{W^YPp?-wLVxCo`51@_FIB%Pu~qKt0;GS6`D22|aE@Q38|OHG>H5;&UCgIXdFV
z0(OA1-I3oF0s09DaQ*`}RF$@mn|kS-i=kSYq;hBeRM+H~r=h(|?~iqxyTs<y%>5l?
zQMey+*U<Zx+)j;Z#|`JKfEqgGh+=x(?LL&#a~GaiIG{O*%=zpu?Sq5J=8tw5{c=sA
z-hzZV<Ek}|5!;s3nGo&(4KG=A`n5354ESEPDebiq^QP_&<68J0gKBaTXaoKj`M>U8
zr_K}!x9$TMzlDGL)oHv5{^Mmz7rAlc+t0Cah})qhRTRHJOl5+78SJq1HjlUKt~6zS
z5sewJ@}1`(&(DFLkwdE{(fHwa^QchXnDxjCNE0Uf>jw96SpwmvUnmm)3>$yD0xui-
z`a|VpUi4NcDe0nv;I4X{YQJ_KG(FH(*ZOd{9;1bSpFaF0VkWJ|;kweEj=Rd$n3p}d
zqt^290)w5r{&;ENeC@Uy*3h0LTIM0bo>y+V(!L2G+WD;$M~BFd7RYpfDmbs3EsH9F
zohf)PsfBQisO|3}@%9=Z7G)hUH@DevcQkib-L(bGNf6gM&b{0u<>k@(4Hl5+t%riE
zG8sul8EXzC_Vbe%U<Yt7hVFdm!PS-k@;Hpj+~I6v)~KUvgoZ-pWSyuuc<5zR+~pw+
zU?J`#{#F|^vBrV^t#a=AW8~}*<<jjME}6UXqK`iPDLy~()0DjPR;jWDH}od{pL;uP
zXgMNt=D%Lg#n9B;4yVKgCDb{EKy*ULWWTEWSV?ZL6?PT34UUgy$7y@utr`%3QIC#L
zx4Mb0J_WaQ*ZgXC;uu?%I?FUigLL<L&({Hqq=uJL&BbE$*RKE|l}2zT*dDkZGks8o
z<Jf<8jO21W&5ZFX65V{?7Ns|?EpA$2;;X^3xEo^ihvW8u_geIR907mzJCq#u6f~ky
zugW(sH{wAU1kn-4bOz2J9p4smRL$Y>f~TPJd#oCNvVNMOUUz>?Se(PN;zc|Cc<lNJ
zBy5HAxoo*DrjPci?WBV92D!RE^1ZQ<U3A=l=NtC0BT9_OIB7WL`7ut%e@Cuw;Du}7
zCl93~3B#H-KpK*{UB9+IZ4?Y!Y5yCquhdjqhseK{rakvT*h6R2lvw8PY1NT9jk{55
ziLG_4E9s=oqJ6U3>PbN?jQl`{+d7+(bk@0CDH)A?Okblc#+QQAjymYJ+{yjXXgb9I
z6LT2~vmR=14(xU$^P1;(A26M4L)}`;JmFch%_~kt!*|`YKT(Cb-vchL0my7i1dx+q
zM!+o%0^&&ef}$P6HXxM=+{(o4dmR`J2}sq08N(MQ?xHrHqomY!smrtJ^7LD;UH<*A
zftfw1Lwp8A&*<JZ0LO5B#7Xex1n}GE+Dif_^s!kPvC>n0>@l(n9ma!*ZF{UCej_qI
zL)}J|nm2&7+;6sTWDxUf)-htqmq(8C@~&tS<BxJ2JJTbdw>K=efBH71xHPG$B}O*?
zof9LzhlBk26pS3eyfY3PzNR2bxRd%vy!-jhC6LT#&hs%d!v#e66^kI(N17bq0A=k$
znR9#AP*+tq`y|hKuwP=x@_}EkRvv1*Ng61!B!Pycw3<DNTcfh`%VXwZj^dQ96=No-
zUY-WN!AcHOnjBwtJvzKZiiWig!rE(V)06HVa!okjwkXPg84?mRg4>$jnmQd<jS6_2
z(b13wLtcl53}uaJ?O=qG-h_(XAcW?4Y-Wh|+>yU$^=BDnL)xiCjbKY1R6+7O7Pd&K
z5H&ony!hasX#1vBb+I>UQ?q;g83^+M&hqsQ8U#Bja=3RshIrLud<O<cq<ZIs{z!lh
zaxufpZvgpN7=YgGkCV%;eZhW>v1q?r(M#D3LLBH^rs?7?Z=fK`qpZTqg0K%MFo$zu
zNv~Vk9W_6L>*F2wD4o0;c&MQmtP1bmUdVLIHDXl;oV@ozlyjf%_x=&E&yZNwPJojH
z1IxedV4xHW10W!GrRzU(h+Mn_6--hQK}s$0Cv{{?>?f_(4*+J2q2%II2`s!ek5#HK
zM_c7dGK}sqy0<B54AVA!QD}^QcB5cV3)n<3aFX3x^buj_j4`PDFIrxJ(2yITzdy#u
z=72~Y$R{2;98iUG1YWiRlq^^2!&cc>*)P1k-#ID1j=CK5vJr3v8e>PLceMirK^_(j
zTjnu?iW0ol#_6l36qW|EsrbJWgp9+(Xe2?KlR8mcI~|kltA_EH<>K8Wpgs_l&2ld}
z+HYQtYX1JP=2~fGMweG!^RmK<EPt!JGRU0RhCIvIE(9lam7IczK3f-S`3|D3DVpVW
zIH~>q%~sREtc=alA2ofg-Es5dZBsi3b}BOE*hkO`qxRHEHyD5_8DN<tC;JzO67pzl
zT?CU74pUvM<jL|AdR^A@n)d<{j|X6RmUtf0=^NA9L~owd4s`Kv>HrYm<ErnI?IwTe
zg+2f#;(I_yDPGg*Urh<?=IUCk*UI|$qi)##!-pYXbUP0r1!;mrz@p#tmvxx$K_U3U
z)1!_Ga$Q!%?X@)oQ2MAjA6e9RRBSO=s`fDgHvjt_Y&1DmmejVVDsOe^YZ<lH!-XvG
zw}yB8djwddJBKcUiWNtsak3S8Y$xE@Ic|!B-%{^8Fxux_y)kW-urWR$21kARvI$6e
zl~sNl$Nx>nY5p$4;hvkR!TOO1m|+M!-%z>Dq$58Db~nhXUfVwIg<vXq&IT^e=Kbbr
z_<p*izF{#{d?@i5XXtl*6YUUuboHE1y5*pj<u^Xm4c`dVLM4u5Rnbnl?b0kBcS!vn
z_r}N4&?zB(WET@zH@sXBdJ3@P6N`Yd^H}!S(p$2zt3+W`@<f%sIq4xRA1kgN{RGWU
z0xi_*S5TKqKgi$#R<f~!#P&HpcKsn0WW2RN;s^Bx{^w~(;k}>YqvFTp(ksF@z1#2A
z@F`7v6mnwi*$DYjzokB?tMK7Zn$PmSLP{JcpcatvS#D3!WCKNuEY@$L-p<!d4L4D`
zE>aw#<*XFCd_H!&cTDtsoUHHsbatHq)kHY)`e>$Sj@!tbaw@jltoL#lU(vX=JcRb!
zL9uQ%$mZ4b=sw@y28BV?e&dw*X}|d1bMXyCQkKv}MG5Bu<Z(WSvnr%^zzlV0DLpfO
zDYqU4r4W%IjTZ1QR%B0QJ@>hFaepiK38>)@tPSjd!;rG>#r<%f!!R%RZ9t~Y=5yW2
z81a)U9CZ~;5b7F=kp94QNebZ0{i`D1vP-(-o;E5z@2iP&Z{@((FqozJc|j-kcGM9t
zqy3`+PV@r#bSgkEXT!`1Fd||o%(4SmKp`MosEOyFGf^J!Ed76>Z!CF4*uW#%f3N;$
zS1Y6hH2gupnesnmL4G>mQRcr#_hU#7y|ZBeFNs|KOk_fh!^}y3=F0Szyy{NizVl_l
zPM`8vHSlz@?kEn(wru&nFvn`16FOm$bhK^)p`3S81aR7Jt5m3z?aGa~Ri<pyIh=J7
zKjHcvWXt`FSS<Sv)aHu6&X$gyG}_p;RaYh*m+>7clO0cPsaF916{OuTQr2f)bMD<^
z8CoHjxWh>8i(UIPw~i{9C}s0(jAedAAij7$KR|8ZfE|hJgyvy&sTfQ-AvL_X(mZ>)
z)Ce-;9dv8W+<?@P-<j=S7P+SPA%a=kkgSwPd>B@e)LYh>vhD)2^1*lCX(x%UrqJNq
zH7KCp4aB>N#2LY8dH(q=<+<xA|LX?vgqbpEVb-5H9E@DX(mVCH<Z!;>MO01+w`9xd
zLVv?KG7w3(zBU>9{%tz?R2>FnN2N-XqIcPP%$CCk@fRW>D4WydbL!yy(#Ku8<2xn}
zv*SWkxnG3r8X1eICKZ|J&1Q;hs@IMw&LQcae}6gACyq%)*o#1(!p`)r7U~$#jd$V$
zhkA6`rH9qxfIyu$*H7d6`O$g|UmWO8?Az1;x_wwJ>S=t_W7iv}d2PXGQwb^(nON=!
zOQS$T_7uL7j8{6|8}TVc_Byz6m(ge6WTF`$*bzuoROu1*m($;!G&mpqzT`#P)naa}
z^y76o&19y=5B097vTQ!E>D$>akj+>qteu)*S<c%df`-t}&{S%)YryRIAOiqvTa1(i
zTsukUlN#QwI_IIH6PhLiVV+G1(nD@W9>xCy*TXQj;7sy|GJdO4wD1z%dwT?uF5e{}
zJ3R6_ygh`Lr;{~Kuv;2;2D5!YaIPABh^Ca!uKV;0Z1Gz;$^{uuoG>f2-{-BKL8ZH0
zs))!x?5B%Yz8vjIK2Fwor&T|2jl6F?H`IP8M56|g^1p3A27!J)FDDsXW*p{`%e<Jv
zxE)X8X`rH}mULT1Q(HIC6ahBjz8t^SG}+B(GNlH$8q1FBJ=M&jT5s`+?mpp}wjnwd
zlvg`Tvz^>d<!_I1Kr&&LUN*l@{Y96yN8{+!4XbtrRc@y<5iOU}FSR&dFQHyk?Q{ln
zzUiTcl9ZDj^_`~OqrGQ3<+9gC$z2>1%Q4f7V|vdzbqJT4FzF;M$o5Q}0>GytLOU)%
z!LMJj?p?E~gC<-z15w-OXSGK!%e~yH2}>pIDQ9?p@#c!(`}a5A@Fml~@3#e{r@yd>
zY}Tsr*C`WqbpYEsh*9`6t36ir2d61R{CecvBJ)gmys(#uXAeey&5GD2S-<rnJR_&@
zNm(ABqE*2=#nT@N0gVrgSHR2ppF~RC`Dz$0t|ZVqH_(bFZv}CPoJ)Ag<Pv$*ql!0Z
zom)2i9V7N|_W-BfW~44WaV-JhbDGuwK#xz_GnmSJLyaDQLkG#`cbl_X&j6j{8TTcH
z`w%gEFfmI#<1Lz<X1pCIw<3bhr=YjCog<G2rTmfs>&5?+vH$YwE{g+kOtJw_=Y9UN
zPO9h7MF#{EA#E7{SIQ!Y!n~>LrYfJSoqp0}{${80;Qx8&POTs1XPPiqh`5n;3plKk
z2EFmO1N&woq%h%<F9qjk8*Nn|new=OqS5J0?)tANRKoLQe7c@6*Dx0=#tegMV-`~V
zHQ~H3$XBbK>T*M3%iVL_=UW2cIn8m#NVF8HlWL!uAMc2~Um1tyN%tjW+#)Y+?1Aa=
zKcp(25;Cy;WGYzjc$wdDb;Sip*&@dOn)RPJr~gK1cf0R}CGMd%10ck4wB$^YZGmj#
zMfFI_Y`OQ5B10VuLAM72L0&zuU{qcYeJ0(~0~<sD&VIY#x=BMCh4&U*Y=$Z~i?U>I
z_wi<YLQWc7dio_g%dYTfwzDm86jT`ICbD(jD-Aw$lU0TntuW2>h1XMS(7kq!-mEya
zagL#Z*Ew_1R0OaOcFeaU{~NIMuRAX>vSop~p|G}X0emX-kKS7C;EnITy_fCKQKXki
zzr&kKSciSm6~{_`bd3t+$vNcaWC<t(4t2GT(^o^m0~m5t!qWC{#~}dZxDx@wk|TAw
z(q4H}@;MZl(Ug?{(sO5|NTMHG>8tNRT;RSU^IUmc;AfWO%c;Ya{sI6ENa%3|vf1E1
z;eIVStlC$xr+j1$xCb^pB2NW`G%@2s7d4eA@uNFs+R|kKWxmKKG)PUEaq_A3o%S2(
z7iF^_x9R~V$x&Ko7@0yL00cW2xs#;!hDw>x0Hd~_U1h)|pyd9pD4;)c1U-?E%Q3br
z^?F!PkZtf121G%xQIc(GbM@{&p^n<@0^2fR0lQzaSy8cT-S7$pIq~eHsfk>F*zh+J
zTidezY2}d(-b>80tjpP`1!(F4ga3cD>OZmqvX|pbCv9)ma?c%r3up3TxPAXN*ICJR
zUYfNmF~z&ocaqkh*}ta%+g27uLth)td8@t}6RNMnZJyQ9rFUf~Qy_2XcX;Sxi^b{u
z29O{Cq;(*Dj=keb@;AD876UQ`XX;Yw-Yg;U%R;}HWTXFT9e$TXcVm3Re7SXX^jHty
z5s3Vfg9^2w<t9mtYmvY3S>AjL(KLg)kWKNTHxuGry*w8PS4SL!4VSD4mym38V&C?7
z$UG~0x#;-(2iMOPd^&D+F0Ip`;^1ngY54&cb_eaV@9HF(fufk-pURr{M6d1*=4kko
zMwbZ|Y-Qj$t76Jv)E<_ztd})?ZJ!R5*QlAwGM^fhFj%M0v}NE&3%r)0^gb*Q8Ztq{
zu<p{`Z=>2`J-DUyG-lWaw;g>lvnjE`2gL3e2zwT#;tre+DH>^=3b$<2@X@N^i-_D#
zmo3|3<X2GMYaN?kr)Hf4gG(lEqM|0eG1BjuPI^u;S7q+EvHcuT#=J1?>NxHm%35(v
zgvw0O9ycg3|1^t2dCPU+VwCrT$TF448#{<^J@hH6?m|Xg-F-f#&8MBe?cfdvCF(QP
zp~^5OAHY#$C<(q+e)c&&h$<UuZKjpvj7ss}UHAS4Q0qps?sE?Yh{S*L(G&qtJ=@NJ
zhWi`dO#)GHi7g``2ek$m+_{ZR)vF;QZzOygkILp9fVzuKGmxu%LaZ`4jIp>Nl3!rN
z1Lfho2>{COdxDxZSFLyiQ}C-iK+vau+nA)85fz2%5Uu&Zj?&N{g9uxFcoZY)u^^qY
za4ElfaD1FECy@zkKXFcSTk{<ckycZ@Jjb@P!xFZ0DQmRyFl35~eeQ032rZ8k+s96j
z@_t+Xq>N^n&5Q12h<>6B$$0&X2xLTIRXuFi6_OIJiF%lj4Zmw~_)W82T^Z8z!BBXm
zfybd2bVOiP!_C%zAvQ5A+37)qDMMcw!tZSUSkXInIsKPc(jyOr*TeG-W7#XOdzpIN
z_(e{EaJ_-zB_-Us`Qt)96#GJlWZ8*#bD0|0C>b7B+#jJm9+pWi09a$aFI$#HI36Ti
zc(R~O*Ee=8W%Rra@MP#s3UJhsrGZQgC6KGJX9yj6kUV}OAeqsGBmgC=&^!{AXYYG=
z%{@M}0j@G2B5K#UNH048|F0H%Chcm{JkS9J;tpY{O?Lj;Z8(u#^U8f;RJ9)bdsXy#
zrw8PVep4s-Fwj8~TJUqmBNd>$Ut5Qq*1VSRtuCf}NKL^~qw**3WHh=MlV?Yp>R(q%
z4ej;)1?i7tyXT>3H0=Bv3aE#NNcQGvwfpPa@q;N<drFWFs?)6(>vA>#>6Y<@nZ<t!
zfwoyzxGO=c6SmykC*@g8xxZidhRBX_${K<2+J8jVF)!o@|9UE6<QJ&aeQ(yTuI%JN
zj<lZIFr(YV{yoo=l_GvFQKiM~DxZ)Z7I&SNAKj}<Ft}Ks%KgpQrkjP?8!{gjwY|?<
z6+`5Z^sW%XM<~rTs0tnil;AqCj`}j#Y((kpiV@|y9Nya3JV0xGsGEM)I7B6f4U|P@
zgm5DAo4u_>^j&`%QM!uzLq?dblQ0m~U$ov}C{K=%6&GV?r=e?L5#S0mTm`P{{JITG
zaU-+6RIenj7Id<G-5&a4m5aY?7<C}CtWa0vlT%Vrvq}jIYT-DBb7qv}8~g1Nx;??v
z4d$#Z9?Tvf$<1RW61IN-#&`2X=EW$kC?pT>THxu77)Q4;*f|PA?g?{Ki69-p9Y_3?
zu7bH&LrlVH=>8>+AZbedG%zLbw0ToeAJ3#}{07)<p%3GHU>zBzb!9_B-&W{4V25#2
zB~5Ug0aJpP<`<6Ta9Nj_e81vUtE?V$z%2J<l;nK0p*~?|^^^F%&-YJj-e4s{u9xQ(
z+rS36o)A9z<lim_O293UAC4VX;O!stULv@+{rYV?QGJeX2^);aj7zt-6%J&KX#NY>
zhCYckIh<bm=Kkj1Kx4!_PuXB}9}6k2#k#mGU_tIzbY#x}GNo^v(jEwa$3XtwH&&!{
zy$3y%t?!5Ge!hd+io3CDc@zLgR5R92%DsNRT$=c-KOiN|uLT3oUx`GQPj_(et6Ajp
z6VAmQ?ss2SHAVDBJP4(H!YV19iQ;SKCHTD1*?m^Wm%Fo#>&-5h>nl{b8I{i+WjUF<
z?<_q6Z3N9UWL3q+rVTvD)fH)7t;Z&n0jbAFhT_4~9<1`Wm*fgk<Gk{y^c(p3#%wFz
zhiGk>R=jPF=W~_PhbRPO20<JxFGlgUb&xQ)ouATi*^P<I?U!Q5M0Nr8`IkB>UD@gP
zU9A$>UR0vfA!@MRs5*5MUGA!oUzco0sj^_koPh?lA(68PFZb$7P=n&srMzFEXf9@o
zHZ!@Wz&yE7jRHgRlvf;&iuE`&v?QB^4lC1|E`o{9r(44P5<7D%Yy28}T8mlExZOZ&
zHOvGuSc!he?w9QxA1wi;z2#fgPIa}3m63hDM{$ef6ZVa%9X>Kr&UPt4fw(wHu=@7$
z*zN1T7eDW`?(th0a-9Ww$b<4Rvb557`Dw(w7oDBrlD#o`3pe#Bt=4-d0A5^N92NyX
z`4?Gia<a?SR?j6l3IUe^tFGVqLBR72Y#^C|oAc%0mU$otQ+%ZKxST8g))P`wfZS5T
zWVWU*By0h&;ok3HsM<5ZQa=Y5lklo&L3<|*O(KmN!Vj0UowXB}TbS@zXS|p12RTvq
z7=FTe!cv&qj#Y?ZCZOi`ut?&A9IQkk)UzO~V0Syt#ONIb2}zeL6HKNaq%~S>s(#Mp
zw@(4I+n+Dz_GAH5rS*6i7=Bclk_IRR26yE;F_~V4^{00~njm?_b?je;ZqLq$sL=M<
zmirUwX3R%E=cz+IuOux`(Q$RN?e>K=+xgo1IL9Zhv39%ZzHT!RN0A~OV_W;=T1E}=
zG^ZVGf(@!3#l^(hmxzGJW%ukFdnokB9%HkKmX|u-zVxI11Do=m0KG%ZA7qn<<JVCJ
z6oli;=)z=wiN^0kMS3ZC7pZtm%lm38&sXkFHl+0f210O9l)@K*h?t=7M1o!Ue3b;Z
zF-)U2<6=T{b*>MoFo56l{UkbT$LmS$Xd<bZ?5g95;RsZBb%No_`VFEAGicK_5-;cK
zG*I+r!>a4rWOF?iLsp_-|8uL;i3#HXo6Ba2TwGz#E0Eu}ZMdDxDkTc(CXZc#o}|n~
zXkGU<C<M+}!U|*x=$AK$MnQwwPH`FyzM{n$cb{d_Q{{Ouw?Tb!v>*MU<0P~;X={&P
z5L7a$eYlZl{n71NR2^#nHT$>m90w<I$MTqRDNeKkGp=p0u*k}IZ_Z(JW!AwC7jVRX
zQ^bLz|4Ik_2lkA2jDp1L!jSV&0KaF8aMC?|#BduXtDCn-Pex0-Sy!^V5`(d*l740H
zFr``DDH*+(`a7Me37^vr4+ZG3=k&W2PB$wp2+Qx5f5iR_YN7kcJ|-#wdr3P**%7U+
zduLZG*DltrQJPov(4Z_%C)@uqYn~0Krk9`ceN8&cG8o(HDw5Nl*=<VBn}1Wi$%=s6
zX?!tB0-xUtKQxWq`fq4?c{v7!%-7hIlo9cC9#`~GJa(&eF2_;Jy33u6MQrj+tj`HY
zD-T3kUf_LH52Z%CK)^*wq%T`?gP>0F$>*wJQ$F||+`>_PBbYUcs|jE-UBTmF+1o0n
za)=px5nhUSI-#-BKqPxanphr726(GKy$Kr@+>bX{o;en-IG=M54{{s6w%TO7r&=5E
z>zW?chXb>Yz<39v-~neMN(NG!$17;7cBaujg2lJl(up}*Q`$`dq3q{9$M-M6K=R|6
zhwPuMSn%9Gw-9kXeDUyt6>DCtLP-}<Xx!K$7j|%)ojm6X8eW`-Ek+ZdZDkHVqW!YN
z64q}(g0Y5k#<~Ly^*QG9eNL(}$B}FSn2kyEy_t2DJ*Nq|k{#2WEG2joR!<6jVQa9I
z!~Hei2iPOUJsl8w3ThtEz+PL;K0We&c^4zOfL~S@`x_2#!FchW7p0w2t8-nc9({qf
zlhSPbQ0q9)<Lf1jPQP(J*5_PUET|^lp_tZq7{*Ur!$wlT-?c?@IlbOww*pMJ*_e=<
zF7@ofR_V2{5@XZaoS2)(si2HjRyLp;S4my9r@SC{CbiOQEO@($_fm4B@pW?eeBGxU
zPF6hid#x@lx#Jxof855Gp#t-+PBtb-`JD^o8g5VLOpZ4{xA@$0>##9alaCf_;$$xf
z&2wt}LrIlbQTif{$ri02l^(^1)c6{uAUOQv((s~YEdDAedq#@LtTq$rCnX1_dj>-4
z%}>$!p4*~TbI0?>QNRQ?@)2Puah0Yh-`>hNE0~WQ7(w>zwKTW}lAbX#YVXi~tpd*$
z?fS+ws{y27pQpVwR>jPfPeY<jg?wlNO-RtH@~>XHR~w*uZy?4!bPkv;gag4l(1Dj*
zvQdB!4fFS#q}RNu%|hdSs~>M%^FX@gHI*+fF0BdMr^#BC1tCbBNZBvcs&e$Zyp9Sb
ztvH?hAEw>{sE+4}7QT4!;O_438VC>w?(XjH?!n#NgS)%CySqz*OMq{G|M#lCcdNFF
zdw1ELot^GEeNJzWW!)x`%kKYK%oKXLxrYQ$u{2fL#FE*K?gRssfwJhp0s}~Bfik~`
zj0fWJAcl&byvavk17^10^BKv3NOfZpdcV1+Sg;}BHlU;iEsdsZaAF*iSYrjU1Qw|B
zE0#h<go2Ked4-dp{%o++tYm~mf51A+{$J5b3ku^6x`>}bvN~Tv^170Ty2a}rN*PaJ
z*}a(`s-za*qA?a5u3jk(Z|M?lC&0kCo7tFXW~fSw`X?qXPMbYes=y;@*m{8a;v?Rh
z5qYf^kvb+S>?3OooAp=W?PFzmK2$Ga6sphA)0%Z)F&-C3_zpZ`yVvtttU!9ovGQ@q
zjGfP}aEF~hqP+aJ+szS>rev`~C`e)4&u>=f!1<>y3qt4ZyE1~bj%eb}SH&M+HrpcQ
z(DB;_pouES$PP*J=_$|I)*RA5u&tC$>E0CBOCe)GPK_pwQwY8I{q#XyAYStNFGm^}
z96<RymrcJLh1;PH)dqs|SIE~9sMM%t?RViWya8X$e?+DVa@D2BO<0c)TfWAx1<5?Y
zuPa2?a@EO+N<)W|6l~{2H?V)(<iH(&CB(N)2jf&rTtf?xx1*W1=Q5Q?2Q25*%R9~g
zvk9hOOI&OBDGkwDuz=`twsW;7e9#9MU%Ruk@IzT!8oBMX?YtHg&I8`OLF1yBg~q)M
z3DB+QqkF`Vq^0<?j7bL$sGiQH9jhbPb?Vn#eAcnk>{Kk#pJq4oy#?lkLLVP~c56~@
zz-M4i-^~yUjy591pZG`6ybheJn;VR43#lv0-RCs{>5aw@GQOFoHZwOfSK#x=m%Em^
z;gpl6TWNfJx=%n~Wpv!~=*>d1F8dAx*&IYQ`?V7~210aDH|b`xfbgdp6B!tW7L-w7
zX~Up5^q*XOU*hK<bpKFDWX-gtky>i8+lzyOqrPYeQnGTK+mFThn^y!F36xIFw7;`_
zDkQ79M_AMt#1xhmYkPLPzoq;7k{rKxrd)QfdWq+G<f14gM#Otu{^fHWpwzr43PG~l
z@**v?ITal8gD1(X_~Pi4eHw;jJ-BZ|DG&z!+hMbJiYg3-N*xJ1sH_OScp&HijVTFJ
zpwv`(M$ad2_it*Lw2UB31~}jNH(!1j+-4DnbE9#}S`c#J-#$dS&SKN=y}x?LQ(@~K
zPx*X566k`nsEVHNuf#0qz`CWY5T<lu3Bj9(UW0Q<iHuQIIw9&Rjeq3&?1Fn^!s^?Y
zrQ1~q*Fb2OG&2veYL7$eDeY+bLP!f;z<}Px5Yr&0<^#-BV5BBsWYUZ6rtER}Eq4;#
zX!v3>xLeOP)|sTV>TWD3+6BaW@-v2yu`$SP1=%fCLG2YTSVV|_AG2}N@f%E6;6+Lx
zC2-bwh}>Vt*9&voBEymjBJ`v!1-iw9GjQ`Rp2+&CkG;9mI4}#C<YUGh&F&!`)Y9;T
zN~JqxWO|Hz7{b{{+jt+rF)-YcHAC!y;_S+TPi4{1gREADL`aaT;`ENP?LQgxlbejD
z6uN(#!kCX^v)TFJcd;{kEDm(SB*)n;ht>*Ecp%fjjJBzzJDwetQAqljK|Q78K=u`u
zb%BiaOdb%MM)A#$MGaL~f&}+3Xz<&fDimvyevQ&R-NL3Nw<vzs9cp}%|HIghhiO~y
ziRsLO!zJi%*!Q$JtQgVIS066Zbqk$a83rCA_hY@O<<D?`Df#QE3*$GG6Nl~wM>X2h
zOd4EGbiv<{d1jmc{BPygwqFO&7t87eD0tj{zxblN3RU{zS;&xN8X&QIBTPf+z4xIb
zA><dT-4vBL3Q)nL-o>Fpb~f7I#bXUJQQFKVtr@)Fkw$WwUh<HYmhhO$)~;OkUbX1;
zc^MEe8>j51ldN5ogbWjS>xRsHpqY|ea|??tNjb&G1xLS8F+^Ys@VQILFODm<OnVdm
z(n2!wxX;>U`AC{~%N2Z$@T{8cKMc%*z^a{8)l31+3Z+3Hc9^bPV{CT#DsfwWfB1F-
z3l04uQIm(v`<jqZk>YnaK3q{uB_!Vz!7f&6Th((9>4O;&EPCT7<znnb)T6jKA7*qy
z#6w9<iBjh{K@p`kv!mm4-JO-8`uK4tA&4yGE6FXTG!qc|O974C*96J=-$VIXh(zfz
z;g42bjER4KWxnueC$?>zrfaWXE!}8kq$O1jUv3rZDnw(udof#2Ym1>iG8sqoDhhlf
z6%|9u^4??jt9u73D2r_GwA{cB1*pj<ET0j+Q*2XwgCXRRg|pAWWhHUBSj>9=^CEbc
zx)%MHh){vq@2%>&;*(~6<&^-`F1f@Dmhn55`Ch{#GkaM22>mM|K|C)dD*=9IYFr)z
z)lf3WLUjSe&-*l9_!w(8V|??~&KvvL$HhZZ1t^t<ZP9l)PKIsYYS9x$q{0&J*ry|m
z#EgtiDQyk1{oSU(k)(%{&8o>(AkklGhssEx{$Xk}&vcL3Ss_Xz*B}fQhg~Re??`~n
ztRhFLM#EzxIf`M<Y4L-xehloj!53lH1qB~*z!u@U4%A0&g@6kV=?CFTqy}DSMFkOQ
zr1xn2pm+zmUxPC|KfUg7<_Sf_79qFh2Vo88*BEi{ZJYg37Se&&%6g9Jusd36nnTfS
zdrC)3wHUXDAJgzlj_W_bdI=sJo<Zi(6BBqITlIPrbP9xPuik;p+X&7e%F9%(dXNaN
zZKaW<3pT-kD#noZM3U-IZFc@#>iPKanj1D^bK-;_qZTLZv=nf6-u%^JL^>a0l8jj`
z5WMP9@q~aJb2B`J=khH5$rG7+$y;M`Vk+QnJs(0>0B31L%H~2KAQ4I#IMmmD`u6yh
zRltWKHSV;&fonwEBydsGP3O5oG^}mNi)#4fr`5TfZnvV8j++us0LegDz&ZzoHH8pq
zWn|j+E#s3std+;kK}4&IS3ewQD3*0#|1B3LVUO%PjYX9Xl0Z#Ah)|)DU4C%9in*-j
z#As1e2>KM4<7;00U4U8cpb3Vk(=vgF3Wx7U!3UaI*;%G`8954F^Jux{YcNw{oEq<E
zzV~7!NW8~jf|CFk=X_7`)aK`*M|SPvWh_A>e_+o;kFA&l(Z43r3p%+irFZZL_Lo)L
z32J=)&H&4^vX1bDA9(^toRulC19$E_D5vO{c7TmIW#zvN_4*4a9;Vd~vKkL|rPJ3+
zrV*oba+<2l?952){4BO@;aRNMEuWWPcOP6uv?(W(mRRrMVlfg_wCmp*>rRM8pFRkc
z-s8CsjxUBb<Q(AOGN15FIn0%j2|s$LBua~`jxmxixvY?K(-tSQ5QDJ9)9>*+A<Ec&
zNFKMu!Usla{Qmuwczxgky=I^)hFJ4~w$>N<LD68Mdw(b9rOIP`ULz-HN^S&s2RG!z
zR%U0H$L5<>*5Q$rAEIV?02lCGQp<0nO6|wY6ce}M@V|}V)kpl*d?hm{Gh{@gNQ-<0
z*bBFavGEP}$E)=qqwQOK+`LQ;c44mSZrE8msj*88K0engLYL?DVL9XL@-FZMM`1HC
z$+gCcnt?}cR3*hHIJt}Qv25?N9Jygn5}@$)%f``zbfjLwS_offf5Po`DlK-J79wti
z!4<pvx5M((Bf#mu9oaojY42pG3SA#oE!F%|Q)i7WL$6iW6t$busyAP7@P$o(u|o3C
zyd#VUCXIdCj8S`kzf~auUY_W`8h1NxZjB`Vwl<c8{=@QnqZLwOG6Md$DR{`v4AyTk
znUodfbtfMOI4Hkp+lK`WV2`JBTE6^+)iSld@lK5!3qUzF=n^$dkL7&*3mbV%z|(QN
zSX{8s5<*dAv~n%G<x!xNS`9T)JqOyFEC73@vTTyeW3x*xv<gV928R|XraVN1jcUR_
zY>JlGqag*$LVq*lyD(8aje{dhoXniOGFjS)hvj$+Nk?c?(1WTsJs{agU1Y3ef|<+K
z$H-MLzV#4cwe{8LjXBlq_LaauoPAKe1F=&a)8&@k7^nMrf;$O)l)O?sh6~&#ycXf-
zYUXVthd?=}EQ-FSE~2@msK51h)EUVHGKG~jnEO|^nETrTW$Iwrd1=zjHpgHBGUX_B
z-Mg;^U{~$GE2_}l?nal9wbWuoWP2XD?3SQetZa+Wr0RY9BkpcD9E}{1DNC8-H_UoC
z2wyx3SsO{6#gu&Y>#a{qu2j!UVrscnPG(-c^Gf>iZ->~-Zy{YtMvnk+WhUkmihbeJ
z97`<Z0x&=`CG-!~kFC|fw;kDPq1sWKLD=+tLqv!>>IzF^BJX?5fMRA`P@~gtUJW<-
zqLGha|8u!Q!IEe{^*aP0qrHMWPgjr32(iF!a+=_(++~@s%@7`?_@63=K<h$_RK@P^
z5|8!o5^}%KwstM(YbBcEmOCl>GGjQ9g<(o-vf8Qt!Q~^Ia($JBEDcJ0<q?j>?RnfE
zV8giNX%y@0o^yG?KVPy2b=hx3CxtD>{#$QP*~ve~(nQ84@I6OFoJCxD0=-&+87L?>
zM>9|ZV5rIz;&hqvzduwVXXUpCq;8Hvat?f6-QQXWAF{ZOq)+QF`Ox=Q7;WyTKOIc|
z9HR+PO`fv3AZhjCR)et}Yj{4cG&JIITM=q0lbM$bF&6vvxn_ZHNG{3kK%5RRm8)jA
zv5mW3kDh%LdLMs47sw8}G0MV~4!hPjxg44-?MLmCBzjA{{3E2g7IMCgf|{uR&qe<6
zcZrdKoP}K(3m!z2E%e!<oPHNL<TpK@-BnI=pF9zY{7oi|#ZS`g69(S)Dq{zZR#x-s
zIkU{t5Fy}NS`6~M9@o(vK87%zHlb55{9?Yn<3_SEEUut;(<VSQz1Y$$N8>TK2<rNj
z42h0B@vl?rG_BnlwNd1T*RP9?9O<Sy0-Q2{NA6`NM<z1{Qbh5V)O*^H2z|t*Gj-nR
zHYveR(-Ne}#O1;als2jA_|&z~rvq8~H+r`P&P6;XAlR!+Rj-4_%Ys${xrof(UAvPn
zyg)J+T&ieb&R+7_AY8-g32@eWpP<EkT6ee)s{k|1BwK~pl(q2%by`o!1V(<C<7#(V
z8DFDFn^t=o7HUJG!q;;z(A4~#+9D)#b4|i}wLK#@S+mnW%|+#wnn#<3{$E6f(}NW}
zBPAg76x3>^8fL>cq^c{plLY$%A-M$$e_JNamn`Xsu}cmQ)xQ;(?vr;Fz<?%*fIjuC
zs0PVYcqG6158}5CVecn9wM_d>tvbH42rs<qEIEVTHzJ`=9}D`aRPG|5q;*pCo-3hB
z+&;?zdkp)}D1gm{_RUu8{74NLt1Z5sp}?Np|GS6aRMrEqK8=>0kQ*A<O?&c_?Xdfc
zoBA8?<VucT(5J5djY9_9JgrjIl=#EQnK$=0te)4~l0sEF+`Ns_|7-VHw4UEWpTk@~
zpK1sRK3uW69f#P?ZKSL%G?V|IR$##0^Y?P)ZR~uaq3)iX&s)fTaGn$7=}}k-0Il))
z-@Q_Rwf~`cIOcYj%3K&fS(M|#J#qkN9cz;?8FdX8UuLWS9TD{<+?jyKLUc&2E}9`r
z)^_T%V&x~haEmAhim-%E-p7;63&A-RaZfbrjr=E^?p@E03zYe>p87xs9AV2h0TK`7
z?!Tz7o)H$EmF4=~S=HdVi|1&8S3FPU-QQXC8lNc6uTQ9te`fc5-!h9G_xiD%n#ht5
zi%;;0xb5u?teNK}gKov79dp@{%;9C?M+NsPw-{t6p>VTrF8n?V+7bjF34vSk>(Z88
z@rB;D4`dU9@Ife(yA;9{L9mX>QUOdw@V$+wVkFL_9fj2aZ(jH--vtwuiV&C$R+Zo=
z+30WdRV5Qip=KkFf+Vab6=b5KZf9o9)dF0l{CDMk82bn(_mM-4&&#fIp|6OuSMq=<
zO1k|@H}WIV(s)?yE6?}Y=<MG`^>G*D?p$yMFZA0zxCHr>g4+&YA5sucQWaNI7njVY
z6jf7`Q<`7T)7_xB*P(2aDgR5D9N#7;p`jtG$uqOa)K)aT+$ANWVxpwvWN;YD>a5z|
z-><)o;5Ahp!<vxJ<~KGz`pQ8e=(ats6P}<XzsSz|vt8bDZ&^Z998&?UkQK9fn~H>l
zq_nK4+AhicBVA<%Op%q9d$4HrGA~*}L!q4abz(-a?urXtVb$t>|9MDDR!T~8f{wPr
z{G6WeZq#6xng&R5H2f}XeEP_yo}#9xIzKDT{V;2>sxE0^@g4CmGUA5MH_gxpMR}E2
zA&;FYD^#`AnATQA4>Td5W0YjTW?53doUV=bDXELpUbRiRk>`_A(NdnboG;dEF}&jP
z!R6(Cb)w^HS|veJX=|QI+=#>fQ9bXC9Qsxps;&|Zmz&VL6gJ3lR7FIw+9hSer@En}
z5wW%5?C>-Bj0Fl2M<00JNK4;Ah~tfpxaIh}zxYzS+M30>q?>4DP*;@FQkuP9uJj<x
zTcbmwOEvHHp=lbuvX?^#iJ{hC(C!hdG-gbCvXr}$-u&r>?!Jp?lUlF56kshv=vW^3
zoWr_pi_-difmg5cLcbHhOJ2NfDWvTtaEWCo-|tXxqjl^i3Ym$*BVtA{AC1-e&!#O&
z{bRbM8=b41)npChyqh=L7{)(*La!pxVRbxmCw+uCw{xKZTm_MLwPSL6lSRfhBI=k?
zcrUG%jmU;CXsj}lQoNc%vC$)`rolOX=pOgJaep2?lX#_DqPZR3+)5Y%rApbJX(~12
znwoJ<&a)*c*jh8yK;D71nmwM++cJZr2+yQA$r!xGuRA?{B_tA<q0=2>@(bEr^8vsY
zE{Z{5njD7u8%|ph_*It<(a^I`6$y+<xNAI2TS^I^t%#k;r=_&MVxiMohQ=4I1z-!6
zfM?9LRP<jS%iWcapK|4E5d$l;&Y_e#1$v&D15cPQ-a9@%W!`D?Ev_qWl-VgCZ1~aS
ztUpMm%|@ljtb;}HkeZRe*pu?R$;*#9g_O;d2*vJcQ>+2LFZa8wDz)u+W>36Z@rUsD
z^Ga|;tC5$^6joV3@&`t&A1eQLK3aM+YKlu>@^nrmW$g7&$e*|@GV`ERCUD=@FcP4r
zBE%t|*}3b39T-(l^2;SE^_7q5{=4QC@j+y${tk!CxmTx@41am>NAOMv9cd2m;@yCs
zlLa;uPzET^Wy+@CO|OWO!Y?^*ist6q(0o@aU^A&+Js&+trT@nvwwE!0HEu<szj2+>
z*X0z|k6nwEZ&k22M<=E6O9(jnDZGcHkHxj}HaD-1)=XQ<`lsBxUY3*63XXlVu8e*x
zw|K0XZy$xt$bomE!b2Z`fxBn<dRbM|vm}?7+ATiJEmt41qoU<CgdV?VYI$aaO^%!n
z$_s(Ug@0g~UE@}2(63+0R!=6c@$=~Tg}{b(X|*b<h5E1R>%5-1KT2sGe{Ms{Ly$X?
z56;`l*=BS3dPS$ssFW=pMi+sT{g=ZtkqF+1J5ZAtbf!bb=orK0k-8+oQKY$liHcqm
zzc5?R((mSMyXjJqJKuMmjo%t2^WNJc41SLVxe;v#GpGxoEovq1X+ab$G_B4f@;kY&
zYiNx;Ri0wdFHmP08gov07Vp3;R~@;IAk3$FY+ju<|1A%uG#??XTA52lq06R$K+t%t
zXTrX6d<eXdRD!`M>ku7=N^$oONuomf#B)-#`T<(sZR8t|>anW|dP2tD{&zTnirZkA
zbm}1l6XhdTx0!)!B%+4<<{8xp+(4C$6G`RL=N-Zrn`D`2s9Cz7zlr2OvGc?vv?8Nh
zhQO`hWwl*iMj7kD``@EGsNphGzyIN}0M85PKc^|w8MCak#PXV${CiqJZ0t+X|6v0S
z(yWaQxWsp(Cx)?PS4^3-s)1fSu2V#LsGq0LiW;03wDq@OMDr**qo?c5xSUh@_9L%!
zyN7=(Duy2Q(yT1w@JK{;4{eE#a~^kyd6kdoVTM+#>3_Y;K`JbfRTI%8j(UUo>`nl-
zzNZs9mOexv*1F4+^~!4gk9I<WRnXLkS&~QEqj}&>x=<8M<UJaHyJSEV`-rEND4=s0
z8P)i>l?=vm^^<pN-x4x3Z*l915`v33HKbq~S`Ql?@=XMMLvh?Gj>JUbhOI7w03~FT
z*^-056-4n{huM__QFx<AhD!jCmSaG}I}5Q0T>u@gf=E}Mt-EYYAKL4LW(1*dJSuvz
zh{=+V7b7vT?++t)`NuR5w@jE@x~7<t!?JoFYrbo#@w+*Wq9RZWkcW&SdB!YXq>PH7
zSg(0ewGhPqO0b;C7$>^E#HECG>p#cygcorD6-y`Tn&p<szF}~v9Hy{+P|)Cbqh_*G
zB^+`f%#BZnbJ5D-wG?C7sw!BMfu4AF%+dD8;@zz1e{**$h`=w~ajPAo@jcUd;qE8M
zn*Kx~3fuaP{&&h<QLh*xGNW<~Asl=Gq4Xm6OanPFkx40Yx|#*%3aP<2PB~PKxBk9K
zCZ;N1a)r#(j|ng3{dw}E;5|&Pf}Z!;!-lx}Xppl?^>}@JmTAPK(@#tsi?NdelUBcP
zRFb-XSzNQp-5Vo1Rg}adW9!6qKBwk1oYRJSytkTGo`1+XT~ZGhwpdVdE86=zsF|Ro
zmXikPzK;JLkdX^^RzZn>xVy|JE7oAf=^`$hJGJ+JY5UUVzb$ZY92E-#_;1b*>=Knr
z#K6ThW(cgNSK1G?(@+&~&rFS0#x)1$xpvWq*?oo)?*-L62M4JiKM-=dM)^49q|{#U
zgh=US3<VI3IO1{hAqsi%8WG6dGrzfPRG!>dE~Wnd%IjX9oX2Ho+ApP@J#exOZ&B79
zg23{H28Oyw0WpckCWD8IYzWMP+4kjz#1gKTel=B5B@%WvffHBMCvd=ZxxqIt<N`^|
zhBvGE>c_aM=9Snlt=PkBZpTIggPr`shRB%dHT%WUIxLBio_iaek`r5&7NR`d)i)~8
zyCB-1brprxeR1@RI>pIaMn`Z{ioHLGb10Zw9D^pN9eBB4_8Zg|blKi#ravPPSgz=&
zVo^Fgk6ZnK;>zSvy*|O2;DXzdSumoi#l=B+G5uN4Z&;%+k4MeQ2^rUu7UX7c<Qj$v
z0xi)IQ9Wj6G=i<2tJqeIY*1iwTRrXnA{p~D<a<g>z|1Y}b2u|bWSgkgfRzvC=(7Ur
zc{*nj(k}fI?%FK1wtlg#a_RB7mR%30i$FwqSSPhrB{9@U{FF864d#HSG^l+?w&U8%
zHzhPqJf;3gqlbm1NY1g4yrG%Cqcd_V9`%Pf(XXw;vL@$Pd>MxT@sK&miIncrfRd;I
z2cvDPsaPIEwkWPjpI!l!&9OYYTK!C+p)vfiJ=tD%O7YMuGO@Z@e&S!NQwqF38mA<$
z+4+F(S-_;C%v9wm3C$4B{D|)y99I~OfGis~$NHn~-|TEvZaaqP#x}#=ebDNGTb%H~
z`mepC?9bEp@VKLP1Y{K_ug_#YyueNl0Q-xFk#i$p*Ikpn{aVopDK?&Ra@-fFdMqBW
z8!NP&Df4re{IN-p;GTQB#(lBTHl^)B{g-i$qj6;n@6*UX^sdY-_M}n%mVCoAaf|oD
zn=is)03yIC>5kh9eA3_2->9p~H|cJ*++KrPQ~MXB)8|%Ip=AOV6zvULd`U&%R}Lo(
zq!(ikH41}+gKxtU5FVFHyUJ2?qFLpt`ua1P$!X>z&q)weVSo3(W}7_A%!JlHpLiA2
z+x!ICl<U8{w+&61zqOoA8&j0lb!whtt2cba@MKZm>KlR@Hwy$6m2j?&gOXwsC-L2`
zT)OQosS<W%bT$vA!-tm2Vo0@hjG@@!DEpS@xTr&r6SQWP7}kE06bZF%rla%3gGX<|
zZmQBU+rps<85<JMtB5V08-QnJ|M#He^03_3I}oP3J{ccwD?F($mOCi;X5eoBo@Z#y
zXw}i!FMWfx8L|W2KPhKG5{r)!NDBU3jD`P%Uvp8^fGcFhGLsSbVHBgXinb_PNj0ll
zu9RYWBv5AdE-aa<i?QS0b#a|}<!0W5Wz+V4{O6tP1{X7@%M;7z)#h@^Eu||A{?^e;
zItS19HlS*wELSL?1msCFyaZq)2D>2u1$HEG80xAT9$4=(+aKD}7T|iIy<~H6I8yE|
zgjCLt=Jei_%Lq7f#u><9KnQ2*X|Bsit{(q&#8O1)t6ZEP?#(MUYwdL{GHW}xxl7EN
z>?PJCan|SJV0qc~#a=R!Fsl!o(4Nzu*-Qa9p9&l!5Nj)X-y|>Pt1yd}@NmCc+-<xz
z?mA14BWzcsmDD8-l0hef{)N<XD%ngE{D_5TW8-a%@)gH*(1O^=-8|oPZjtdeFwh4P
zBNa&mc|}FxhkG8)Fm!)VR6Ny0uFLY<_~hLsJylr>2Vpk8r{K~eOLkG&+?UfVPo(#y
zf4rbZ+dg9oo~3v&1u{%=B)zYHD{xW#yHdtS#~obH%E}9cNQp@wPopb0a_e#b<yL}D
z6@Df_6&-rMO*>^?-XyklmqmRi=(go^&We}NV-aiGW&=;n?Wr=grTbm7;I>Taj1TcI
zs7_~N+hp%G=p_~x5b`x2A`tYjNoXhpJRQ(Cetjm0GerGCiYQvDW~X3w)HOF&9uVnG
zDC>4AfS!y@P*XTL|5sDc?%K0vV0kX?Nmud@J+CREi|>nr)Ko#11cMKK7R=)ph=98^
z9fXT&8Zf`Fg!)DJT+;4&zo^#W(lBu>O&vX1{-pj5c&SAO`4}r;;rJd5L;_$vJqIDQ
zso$RW%l+3&BVzX6@Cju?QMdQ2k6KFWGx9z<qSdek_365V*Ynr~>AvUFf*r^_1*|;d
zkm8{7+||NUrn28*_3)m5+bn**@NV>sEp7;E2{>gvY7$Lt{*onv1H4<``_;JOcX7=!
z@+kQTd_z|n{6m{hP6*1F!I-0$+ddzIo&5uwDkAc{Aaq>gf+k1Whu{Yp-a`I2cz@Oa
z)YKx+2Ra4NRxb^H%ed3Es=-s1mx@qY|6hRs&$;-|fi*D-+cd>N{o>jH&rCA~Hmz|d
zd(0)|1h5ed9?sa*d{&1*0L0Z2eD(MFU3P1I|D^hK0Kl{K9K8ZmA@D9ij)^0i(5Z&+
z%W4&qn+Xc!2sf5Q?^Fe)_Os<RxNdMG^_h!aH+v2)S^WWcgO%jkuHWQZ$seu|oyctQ
zo6wn7V@B<r(n4RBzHB;#!eCO1Av^qwvud^LcD(-@_Ov$7rnAS^dSl<CzV$0P`{b7#
z+;6?&F@|#+PtL`2+pJM$HeY0dqg8_#qPtywa32J6O1@Es^THmVn4t%1_x3;fXuWhC
zPX`YzB^oPbzd-E(?l%K(iKgYiHESo5g`v2qW*ojLUl3DJw`(AJ<(b<%mU0*v98vF3
zse-tXl5TA1?x`&NlIs1#&zH+4I~vGjyQ?B%xS$@d`pVne%^tlVui7ixl38BnI4QpX
z|C9F%P~!{HEa{{mfx&KMnbH5~3Oj9P<#bZ9P<n5)^@Zf_cmxPh{bJ~a-oC5qvKVii
zg$e~Q`hE*#l4Z8P(mHTgKfP#Cmt9$PJvC>c;E4V0<ioZCJ4UUJOqg$NNYH&#SrQ$|
zIH0tZFgIaF^3YS;g2$T*flk=?w@gmTVLRycfLkD>(5D=w)c$);O*p?=j&tmwUd;tM
z5_hcWo-rdl#d$o%gvYjNZEO0D7~XVY!?K#$0786U3GJ^!W@onw%?fN5b?e6DtHkfm
zQGnAv1Wkj1J<TS7w!qv$A_C4u($m49rjFl{u&o!MysGMvKoFGY8L)T?wD5)PpAahp
zEZd*OF$LP(q%m3@`qq|gpsn$xFCd+t4`8Pe_}NW@elAy|7UjJbq$398X5QQ&8-V7y
zR#iI)e#JA0%ZumBuPCUU+WJmIWVYtNy|p+L-@C7=3?PT%(|jHD;Cr_ht3$chFyOZB
zkaR4%5W7F^T~vPJ6?Rxq&ZlK*Z%SrK_>~Gl+kC#g&E)!a{1sytUMR0QZLwdJl!2K2
z`T+UPaTk)tQ4l(7QCW%ruoiLtxEP(rAO0Ol8Kt9|!XrVDu1&t}(caWtMkV>KpPOt7
zMhKR?8dai;GST3(93n7D4(~p_>p^1_Ope!_{ofA(bDC`7_FEHqaG30YoorKkzdq><
zTV&Fe_dVY+yHUkt{gn8MRs5*?uw1TY9PU>Z&@=$|DiPf1tHAI<CUQREH*R2QV=T~F
z;hJ+UQMThqxQwrM%xLmC7OeU4kGOYFl=AKgBlqfy>CipPprRc1j*0vXefQ^N5=9XZ
z=%Os*f;53a9Tr1=fc8L)!!QtkXh2V~`g10_G8OrX>DZ(gT_AWwbt$@1Nd}*SbG<#X
zGjA?GWZGB^S-?WlP8`6(6<&W$u~PIwX1)}G6W?{crks%kXeu^XQrrx^%zuJs^GW_q
z*MZ{_BwP*X2|L5V&9mTW1475d41_$b23)K>4ZFRsXc+G{+x_uArCAv-uK7tHWEh$~
zw66^KE+~kz(S?{W=EAg$<h?70(qD`{IS2PGiyjJ17Vlr`l`q0zRox7)c8vAaOL``D
zZdDN;hW+K%C`&~V9w$wrS8In(?Lp1zLO*IfQj9(LmXF6ACpK8H3%;p7?bF+d|CFxh
z+UyaoFt;?r@s;Oz*bJ}2-a$v4Z9E4!)(djnJUoP19)KxfN?Cg(K=s(Q{3zVnk5$%9
zFt{6tzRFgc^(zv&z!HOS)&XRUgR&#Ab{u8#Dd~bguNI^nWS!S~ZS(&R_rQ`POqJtT
zQeM?!Jkl0A6JU!t-ld1)bt$_#?ZoqRE<_a!u*o#Qg$q<+X2ngBP|2cP)V!=tqU=QH
zbq*Njl@^vwddHzbh5$VlFIIu{=hvNuY1Hy`Zm(;lcoL?RF0)3BeYq;9m$DJ9TcGS7
zxh|CN&FSVo7BGxop*S#vcPLDwKK6asAC^5Y4rA=@3bYOJd(d;;&Zm#ud~x!osy0>n
z-!@Xs3fas>>p#<q(|N?@cIp9l@Iy)x#!fKjsf_dh`r$ytdIbEZMvIUEyj8lu`7&~$
ztq)m5)4wU|Q!g)t3gyMp^QBDcCAUJ=E>Z|H#(B;<^HC#$|2XRmq&$u$hDN86HNQ#Y
zj_i$s-r42YlwVe3vNNk2{h%quZP9Zq3>#%?{*ALmrIs?HJa$o)N>3Tekz*hxa-Y(A
zEEC7NaJIL+q|!12#JT?wfI~VI4r)Ix?FuKzuW=u6-e1@+AZHitwrCAwwl!-F<$5E!
zY-Hh8FJt)Kt4QS3t?1rl#s2<8ZGP(qh*hWC7REIX#;ftnHab_4kB|)|#xCvlCt&Z1
z9sHI9=hb<p2c6rLPTfF{zL(=zWVlbNh62fkY^jdV8;_-yqol@RK!vh$Uvpel>3mHW
zqI$y&Rq+pLW#_S&28R*5ef4n7&ZF<I4{m@!pwC1u%l}FLI*Vw!__!-G!dBg*S8K^c
zXZg}r$|7M~R}jcaSX`iblc_Zt#rsD7cxppiZtt}lN_Mug!=&9MbbM;_3d>%lr=kR6
zNB3&8+<U}ZV0(Hoy?&bF{ew43<+M{-fEI5iG@SHr)ZGsk7Y4AjQn*}|7ZbZI?Bwo(
zM#RaNLO|Il2kI-fs1bl=sIt-BkVtYr2<vLNoaH3^8>#ZC{_;Qi7IOCL8^G&h9SN3&
z^Sb+tO*?rY<DQ_%CIU*q|F25`e8<u&F!;NG%}YT6C;e;u@{ZZJzZ1*X2mj;8*@4&c
zfYZqSN5DM*Qi*3pI>>WoVVnb*c3G%s$#7j$6(g-@jEd7}BeHybL#uT5>BdLXqonfo
z&<O}dM}E>Vby-sj_tX&6*{U9C*Nk_}fx~S%@!j1@G)GSnr#-)RTnZvh$h2wXI7F&O
z(wva|;*$h(e$GkQ90_J<ZlZZt^|-#e?UJLlXrjAvSN3RP0eOzKm50MG4n!WSo$H*d
zF<Rqge&arhs*pjRbv8%bd%V)3punn7I~9iEWp_<uHMu>Ih%Nu`t4NNJPeFOP=fQdM
zxeM=X08%u;VlqaHwYFZ@{5b^TtPQKNu`whhq;d_g&)NKP_vq+hw=d`%12{I0N{-sU
z+c5;%oOd?=cVIw3AFwWCxtgX=9$33fXq8rrshOGD|NTGv^Tpp!H#`6P3K(txk_-{h
z(V~fAK&G-z=MTBPJP3jeTIme-v%P_^B|7Ik;M&f4q6;v%d1teS23PA%EG#Vl`<(!n
zPdbe`AIN6*3_)@xK0KO81)XlR*s0Y3X9ZfQZ*Jb|^Y(b2tO{Jg)+oQApup{B+wbGW
ztW-9eS=H<9(Z&Um;8Zwl_zUnm1`5jF@$vO)-NE%%M@7qgzE9ZXq%<KRAuKE`dsKLM
z_<0r376M$FUTX{tjN+mqatex4nM_rcrP-BV?4tj>(>+n>bR0a@|8u8{e+uq@cSbG7
z2fbCSoB<tN&&Kxod=ML6ytj1mzf*BouXUb%emonU#*r*tR0;lL6*kkYfa6oca0yVI
zI5SlFr&523+XwE2&Y;<dTOFw)+492~)?jD*KCq+TsAB8UiMm@Q*}0AOinc06`4}<5
zeGfu#t$Cq9tU@8lA$u^_BlBbF(ex_<Ca0E7s|!*O!YqLFdaMoJ(6BUFQh)s8Ky^!A
zb{nk3Z<eyMDW{>TvV&vbe^jJv4X3z-vf6mPsrox5G+3bX<P||f@<;bPahYrA{~Y`(
z7hvJT&}w_p8V(%!R?gF8`bs6TKHj6cFTl!AuCuW@?R)CJ@$JCehoR$}k-QLm%MN64
z-IA`A{3b^Ip?P|DRium`)5f=Zv45en%@38?Z<7e3R%R7l$IAVPtY+^}3`%%!Bt~me
ztDGE!GDwKXQ^?U@seV8>6C~SbPwsfCtjsI?T#=I430>wYGAqot8OP>fI7WR4532&#
z3QF0bOjK$qzeu;Z&}hKQ!Bi^4;b1rjfZtEuvK*spy`=oB<s&r_OpB}qyHVEYQq>ey
zX<Er2sx$yQ585)6YL|PRhI@(_mFt|qr<s41VwM^X|J|UP{k>&G)vM~8gW6buj+-@f
z)UU2rk5;*h9!=Lw>8aHtUSFi8(Vp^NQ?yB2&1v1#x(4c)&+<4MS#xW1M{{>`-^!C&
zz-K7bCRzOM7<$0C6f$A?ks~UG#K6gukQ~}^QdW*9o=q3qwTT;zrDNuIV}Aw~Dq9p@
z^`}wYk+ed2;8lYanrho(jPA<@5tc5wLGd^-6jE}Vb%?cB`J@YZNW$Z}?zc9{NeL0a
zEv0pXoFh5^E>|A4tbY;Kdzo3`ZM1B>!~}Vo(Se_0GKd?&MkC6^0B$QB{Iqdvzx|eT
z1{Dxw)&=EChA0sj?zx`JEE{e8bDEXQTYy$_YVBDaszWW0nT_okrPx@YYJrQJFfM_B
zp+hg@_#9{PL|f%*C@zMixQ~Wohyj~d<Mb99x8X(p;eDz|OK0kFTQ*b9zfqs`iJvXA
z`fil+{?<-#LB|uPoh&B6KoHi*FU-V%%B9xql_;BiqjprOYJqy7<T~{rB34KBy#3&r
z`zw0(B%=7d`Pfkj>$;&ff-X|slv0u!q3N#}9HW(s=iws_9!~w^d-J&B<&MdZ)iO^g
zfa#wHQd~*_X`~A9&?SK1e!xVW6;jcHG1pejG?OF<ZYwI`vnYy9%o=}AZRvO^E|x8t
z1?Yy0unB5(-xPpcUflOYFXZ327K?ZYf(*OG2P6~x*ZFm;Ql&+B$eKx&7@YNQaiTzA
zsv7^))47#I3jOnyJ2Ix(l~N2OII&UpXtw~UVu1sfy7D%^EEV)Cxy~N#*zmVaq@<nD
zacr-@!^MHL+DGA~WL;-zGs-g#ADH}*i$_VOjiJJ)0LTZwsllhY6+Iw6JYpT)DGA7A
zysF3f{3$I)wJ5Om#N^_*MU8&%M9Vc*%}}#Z<@`u!9$?5iQ<Jc8B<~;BbCO<Fv1}T@
zeg?4>|7f8PY1dsLRlq27DvwC3oiHFSY+0Jo-|6(@?$~9uIF?JuIu36a%PHFi_$S;p
zG!FPuVpSf`vbjDeAWJLk`8yw$VifNU`LvM(ML{C=lin8ZI={@IxFDB}ww{D`O^uX#
z6(`uN0#+Fk(-DcJhy43khHZZ}{31ft;Tw8K9$%lxy%;TyYiy6Wdg&8<yL_^9>T4gG
zt68ii?i}b{2>?CIz-+5jMgwYJ=e$vcFS+j`BHTR<4|x(<1g|*KD+9NFFHf$k?B>x)
zg+9$a=K`5;%pX=u^>@l<6p&SM-xsx2B}d0XxqjVc=B*!nc9(|<AcTH9cETM$waIwl
zk}$hkS^ydJ(g4UBU%Xy(Xq%V~lsmLEI-Sr7(7VK(jhOn{a`j@?1zp<uL}zQZ{LSS^
z#cTTq)=+QQ;c~T!_b!jbc)`$_qfk!}1F-kYEq+;j2Gn#c$$Q^#Y5aXt`|_C<M0KJ@
zwCgGgzUDTRTosZ7$|0ckEvKTjPND;>m85ht7QX)BpQL?}voqOIRT^vmQR<)hAL3Cb
zJm3NkgTote$Q%bvY7KsA+d887@~1y;S7mdHIiY8h+q|bW%ZON*tg>_fK)>MhU(~m#
zD89ijeMw4}m%tpj@MHOYai1}wpY?;VcPFM`Qyd1<UW@k3Hp{@9{ZCCbfk%}Jw{vkj
zE!+gm1C*s(%6>yDipgh2yK5PRTm3gSJJg<z7|mnu%g8=CHv-AKpL?5_5xf^Z0qel1
z`yuTrd>qpXRK~8=v;ogsAVdkuA>(`6Wjzy`uU)bC5Y6oQyyB)Yg6U-Xu;fr->i7my
zaca?i>-l_Vdfr{2S@Zh5o^?1odpcE9w0C2WIvbzDatk>(oy8#*Xuwh6jQ$8shG>>x
z)8h{DwjZ2i?WZc9Wg^DuXh<&orEKRP)1ermM2MQQVWumR>oGxTb~QvZm{dz;6>i9=
z!XR*GVE8&6&PEjH^lypwQzo?(3%?IsYn)QW3ewyZ%}`;23k+9ngzrZGQ*5m3gz{c1
zjos-1r+H8B@~C_$Op2=HDo-_C4H3k`YtS{(I8=6Z(^U(u23Ge|wsW}HAhNtg{&=>K
zX_eDgu5yt2-|C<YSjWvC`x>gZ5(DgPtD;&tO)ViPx};lw<mwwqde;fXuHt?r7+PHu
zT~Ba3HQIvPuIcwCeV%iXqu|9W5{Vomj%&*$AT9W=WoNKIRi~Q_-SN15!HF|z@$W}}
zlex3MtdDeM*xSd#@5qGouE4sgxK2+j*_^YGFbCuUnf}6r8--+8sB6D}ZX>cEnKhGZ
z?y&HEFgd*`@%q;DC9(poWt`sWo`(VVlSIrY%ZV|A8;h8S!;U&d0T3tMlyD_Tu12N<
zaX{wL_i`cO)M9<CAfLNlEq(fdclg=l`W!ya0(o*kcuwIe(3oKbcpWxgRQ)^pvF0cN
zlZD?);Ov?xR(mIMdAXFXDH!a6D2E~uU}jMf_(5HFynmi*J8^70sG5VAOSd1_v`cPz
zt$f<8Bv<*RL3D0PsCB+ycSPkht(MrsVwIco81n;$LG;>RI@Bz*F{MNxqWz}<)-RwD
z@(rgwVTtvQ|64Dr62bS-^bR0vWj0>%3lZcb27$qzW?M#TJ|pLJ32xp_k((060Ls=V
zukb&h<OW!{1f{V3$t?!lPv|nUXe>M{GX7Qc%1N6-Qiz-!;~I3quNL;)YW3pIjZFYe
zHW!}~k^jCm>e-j$3HQxMi%NJPPo2O}2dteobwOFd<b~R;K8B;T|JB!9!az0~u{$RB
zw*7Zxp=#R{y0jdm1MURQW!e;Rn}BmM!qemtyVC3yyOQ6`rtLUsl$=Hx#Uh6JY8}5`
zZLDBT5HBu-tzupqK)!V`G<cp)aI0ddZ$*AqXpMZn-(jtbBHKuARr%T)M~{Ai*G3;I
z4qE3IE)^sf?~<U&;HF=9GWck&Sh#(1y<8qxV4^&pr?XX{p<pxfnK4-1Z@G4!4WQb+
z916>kVd7t}vx)^W&Jn*z;ozpgePG6^fKg{6=g<>Z9qwO^P|&YAxhiiayJZFLpEu{k
zB%b`+XuB9q+b=GiXupHA<a<!7bB2lZ8mbDI_`e1yKEg)oSR7a<sz9Ofb2RM;53(rv
zol93=T0PHT$8E2gPc-GwocF$kK!9)0^JsAsA|?^sIV)_J?NJH?ny~9+*n;=oNLm|2
ztJ8Q=y_P4*%NR6Fua)ba8>P|wZN=q_YAFUj0^eV8X)zcVc(`ve?Jx9mhWA6@a3IPr
zynjY5pPgmGx~G+bNN0SU?+~>bpTG>mLQf7fpY3SpTnH*p1g-mG@bX7=fhrwVJdb!C
z-e{gR>r}T&bs*2a#|sNNYE!Z3{OSz5y=yK^`Gh%zkc0$9QA~>OI__w$|5R-JhpEQt
z3ylOhZgxFIhy|YVHKRUwYDQ;kwHby%69x;0j9r-u&Whi^L68T0z^e5bjxO5rgC8ih
zi0gUVu`J@P*cwmLf}-vI!Vc&bvg7!Oi(@hHd_*b9OD~PPh@20j%!z?wez(C$3{`2g
zUNBZyhU(buViC(F&Jl%u5qiiNvgyr^Bb%}Rr2t*Km2gl~De=4?IIZh9=OZ4z)+@Qy
z`qFjxn7@|!NMtAEdfi!-D*YP0P<ylBW`K$So{9|BTYRtO*Kc|_S*`7@boUqoECStn
zL!ueukUO_73sHJ_DH?>2ofUvtY`bA)|3kGunn?}k3kQWy*vX>US`E!(zvXR^sphD=
z?7Om_wsvQapYi>WTEj}t3x&V9k!udB@uu6PxX6}?`BHYfQy@I><k|n=$9NeJmnU*N
zC{R?5t$ERmcS-|K2Q@+IZpWGC0H+T_t=YcMAAdr?OyXeJIR`gt%Ynxp*8U)`-B4=W
zh)HEN`hn|`Lt1lW(r8$5`TaqO16Ejw^f-u;CXIVgmsa!*R?t0DLV4YKHYq7g#Swa0
z<Q8}QIEwIJ|3~IbShn{@dXU$3N`?GL^_Ij?Wky1im1dtUSFSt~m_pV;-jIdBvlxQs
z;|XfQ=@C)lYb~59V`ZMjqSsef{6=mFRkA`W#*%^x{mJLI9i*~c-W>M<6+3@uP&y!V
z6c7iSxYhUVYaZ?h@Zu1v=f!g=7}AQ`Rpe?&a`W1qrB^Q_qtV+9l@&+#)lXWxRr02s
z;1Fm)5`z0GG(ZU&|E4FR;T+Zk)RPCqk1Omo4*@X=4a-9rM^ZXropQ-Sf1lHG8Cgh|
zc&x^DJPDaeF`R8vAkQR_p!j_=t_qMix~`nCZyfE-cd_Q_LXpw&Y>GP!pbJT{@|Zem
zQ~wnRE_Ms0`MxGo`|Aqn$V^}x5uQsfbgAawEAF#+1dJqw7>xXM+DZ7v2CNHo_Mwl|
z_;6tInZ7Nb(oa3Z((yGk@`p^_&@Low4h4`>^+6#Z{9;o6lKkZl7l$E@Ouv}hbCQiG
z$>hB@H3!*?3-r7wk{w%s3COvqh!Uf7qP88)%99fk&nAc7&0S)ua7Z|OoJhlm5u%A{
zSVY;N?B_4>z<=@c&+f#EyongC-mS*RW@Owlhev_0dA-WEHw|CmkH!B22-rY+Xp#*|
zp^4254W!A_yD)5{y!`Vq!`jlMn^^D#MrI3oQp1h*0xCVBR%257jLIobBU$OqSt`-t
z6{MZ06sa&^KEUm;h~d(emb#STu~%M$RUYU<@bmYtX?6W&8)(M#vloi5jHD0LuNH<b
ztjATsU2z!6$ZVp=-f6~`Mlv|vs#;*?0E}6*@uJ-M<ThGjA$GL;Y)!^kG_tTM1v``b
z4Kv(zJov*R9g@g<IV1=#?j+kS4<`4kr_6me4-@Pc(eIL0$L9@xAMddRzk@1sn#z+i
z15V;7M)cNWa%jj)%grq($S4o4@oWX%;l7f*!}fi{h`NM^E^UGJCroW@gQtpZRuXdQ
z*nT`XX6$(RJ@XGzMqKay-jeg$=a?d|-PDdcex6gzE;`2xgQPIEO0CXbq2Up`ot@}V
zplkOFt^H^MJ&Kr&PD=pVd^2N*!Y|JT91gUXFJl!jm)%tP2G277;sP^Uj%yaTA`|&&
ztzyLz;#giM1mCV?##ak1uQ(~Zh~x(hPtg~^&>&5cV4#Z&8wO+apY+}fFuMqho|6O{
z`h9L)VXnJ`7=0fWCv|o--Gv#9FDoZRzG9*-FWMN4tiT(L5b?(5^v(TcLLni!_#l&z
zsHw$c4e*2?G4mI%2`Zm;!8v4gVH9u7@l`1E_AgmD6r-Dv-0hYSfjdWAa1H-`UZ-0M
z+Wo^ISgdat>v=s*S9HKX>v?FbozxAFYF5K%$u*3e1=l$o!h9}(yW*q%og|#|hM~_M
z#TbEJmAP3<H2huNPuD>MuZCuQ8X|yF0gD!_zAr4NFdMXAB`%Gbq(o3pj)w(atl02Z
z7#=6JQEX|QfRO692|taI+lQ9^KFXVelQc}!5h^OoKglvYVzk}qTdm}SgqAY(cGDi>
z)<I!cNg1O?QFPIvaSSmPR;R4CbM`U?%?)eIY2PgL3pjAI(v$13bXh5lCi{^p9J-ji
zVQ@hv`b~!hB={fR_jf6|4Awn7PDd=ufuErVwrD2~E*}!={yXhE;kw<CTU66;6#mew
zVBv3!>XT%YROJPkPoYsVo21nx$nfK|g<tKzE7x`AyC4cnWHVtUsXW|AX2rD|I37bQ
zP}tNwyR_oB&RKVrP^a@*@IC2ypT^uRinThp51O#7%Coy<rPZM^40;0jP{}TS@5(Yz
zvq4l7IIG{Y+JqQA280OskP)Qls!6fuXtcNoNct49)8WS|fXNFt8t(qG=Zw{#`*750
zbKRUAZQiw4`JGD-DASSI*!;UFlx0*)b;t1U-n_w2CwV(X&0+Hb>|KvZ?Q(tVSh51j
zhpjN4s9d!XG`m(@@Y&;)0zd?`$DVM|poMUXMuJMqLOLs_2B2dyTBy@TS!<%pX+uXv
z<OKBnv5UMLMMc&1$kS{xYOe9jO*O=&IMtSl#&;S|b07sTe(%^H&|5=+WUo-fq@1gd
z8sf0N>o#y7MCHUTOAzi$18DtPrF^N&eQ8kPw(s4;TEzng7sy%c7=;C2<c>bNcYB4n
zhI+#pXS&>wK}_un8qt+8%Z`QDc*xz+S^5StKd^Bk52m=U#DB2PdZ^S2xxdOfN>X`g
z^R#!nDl^*C@581RTHS{M0hKBrRW2&Z>>@&b2?Y<?!FPTzLX?F;?42JtHuP5ef(u(Q
z5KuCZ2YDJ*jFqGWWH+=2q{o4X+*)MkS>Zx4s~r*EbHSZ;RQMA0#%tqawxL0|-jxUZ
zpwY;XQ`GUbIWs5NpP||A4CMu6#Wv95mwil~_9Mw+UPQpMog+gqxdk)Vt5q;oa4?nl
z_>&~Wa3cT!tDwWmmHZ^p;r1XHp2_aHg7e=f`cwxYRubu>_`L_^RYr8i6F}Btqo@RL
z{TBjQei25FRVOJaN9HwfzzapZW=pl?d7SW-?7Vw&g8Q`*`}a6i5uY%9Wdq0_Um@fy
zEXlLWO!2e#>?0ZEjmiwonWVE#KKgTZE8bka5LDzcDbA!7&Hj!l9#}1G?Zm0uPYmhu
z6YO5paxgGHs@ciTg-?zvmzl%LO(535wmXu~<-q<yqkbRFA)D~l4=AktN68MYIu+vV
zULX|jfw0)}jF-ZUNEm;MzoM*%d>QR@nIojI2yO~N{a5{7(Q1kPE}B4NaQoiuAO5I8
ze*(=YU+kW#4Oap(#M!Fa&i0ea;)WHy-BiUguu}kl*>fxqqMgu0_Es`(e}J=&C$hDW
z)-|{>54-z)Zh&<fa(Qx68+)H~Zo;b$P(AKq)!}mze62LMME<H#dl%~8m@p8mQU9un
z8AMg6Fq9-OGqh}lX+CUmi;oZ?JO;pNHnm)5Y%aC2%^|yYDD4H7301HiDwFHs5D3Dx
z=yW5#^_yIz>=NS}C<TLmuq80OGul45Et6?S6`;T$QFE0Azk&izzMym8ZqjKRB8=JJ
zg#SL>bGC$FhbCuW3kmWl-mg(ogB*HZqQN)6!@euq@C>|SZYS*UJ^)JkcIltkAn-6Z
zuQ*;ZnVF+`y^vWVG%4IcW~fOon?XH9$ZsaFdZq-W9!_dH;Xh?$-taGe?#@ah_&RGt
zx6i0YeKW1$sZ*+VBgPb^d`=sg{DPkZNQTOQB$JW*Z3cy!O&0TIzy(7F2Or!pUqUaB
zgEoVC5+?R$wdrj!YiqA``~7C7M|cJZWA#bGc|sr<Y$k6JKV7dQv<7^${J3m=PiWX?
zi8(Gk88%%A7z13*4>%y;9idfWxyC~dm6Ip45j|r9FWP01hra!9T+X7M!W$3-^Gwxz
zU+tOd#SBAEfA#t=r?%NI)7F8kyn(%E(V5B@S;v=)00=!lFaNY8)v&b`C#-U7Rc!o!
zjGc8<l~MoY2?1%8ZV`~~1_?n*x=XsdLApb_yHUEkySqE2Q@V4G?>oPl`E&kYEfx#7
zmwO-1bI$(m{n@s__4wPk>5R)9;hIBUf^b?`T$M$iSqz(TRa5An+ah2B^7ZjZ28`a_
z%a(xO5m(9O=9EQJhT)}ni@ysxTv$|9?7jh_=gZns<8+g`gp&%oeGw%qA5k!ZVgxQQ
z%x&0wFs=2U)Wr0^s=EVLWD<@bnVHiKPjT%$o3wHP7C)(4*v`>iE7~v75cGqnUN2Y?
zfx|CKc!BgS5qRG}U999GtSApH^{$~1SE*w3nn6eX^JL#9k7%PC$*!|7wns!I8I0d0
z8QyY-*QV#x%OBcOu{A%iv0)W)eM3JO2Hu4=mH5S9P>gR*8c{o;6^Q5mg1Iu9nU?b~
zEfWLH%x5c+(BgN%zeIQ1lfx`GsMy=4vpe3xJ#)C%j6b{}>=5^Ru&`Hba7k6B7T!B=
zmwf&1qT_vL0~w=9(Q<>N)Gr}^!ttk7@zHYC3f}X}D+uamrK0(;zLOCOUon|d<~;!g
z4}AOhLp5tTjVP|V55x#R%M^)gaDiZB6pstU)U!*hE8POtqO*~u+_7`4>+J>4mGAk5
zU+z!&UeT)aV3V42q2wx64QDCX^>Eox{|LnEVLEDfd^I8-d~cTpOMP4LO-Rq7m58O=
z^$h;8ls8l}DhV?bD^Swr?V)PzkhZgKf88yqpb>I4-g_l$l>+rH5Ja0DN&3rJ=akO4
z=O<s>KroVNOoIDfUwe)|IEVU)nY`#*@`#k?r`_`Mig$=5`f}?Xj2ah-T@^$jESFzt
zXDyO$VQvJ)Rpm<DkTuNgbO_%W2fdlWG}aO|P~x$59Bx$c$Xf`Ahc<@{(jjaG!J-5k
z?skNUlBEJ$lJ9J3e9~CgkqEl8gY$b5rCK@4?|O@b=Aep|3scG+w<^k*>+}(K#725b
z+&yW(V{cD4K#WpTgmh9v^F^~Dy}`ya4u|RBeXqk45do#oGZv>_Q2DljwOT=!4gZ%Y
z?bHW^y#e)JHPWI__KJwgYS$gqA_|BUs{G!0S04ke|9V|hqyNO&8BwufA|_UDmnf!|
zF^}hntPMe=%F{}{20R#5^kHtwqi~~=dB@6}iV@23yj>*s5j~G;8D1{WXhFjPd8G^x
z1ejZ1B;YgRiN)*-f-b=3`N8usff_nQv^75=r1xazk^JRRq>j6&^fkDm@~g+5Y!!wO
zVJgM&>VW#Ry~NALv4y8g;Tf6&J)$s@+cws9omYJ4QP!A#@sg6-o2`$zxjrlGML<{V
z?N^1I=N>-0TqxXB&>e(W?iVL`U<UCBO@ljO*knbH)K3b_5ASp}{qd%0)24tCP$$2=
zbBqgbaSQk^vR#Tlx!B?ioHd*2n;f1oD;N||9X;X#H&TD3iydUE?Qh12=b+}|^Ff^s
zR*ky$JRnW~{dx_%uZ@NtZ{~a^_0h;VSS!lI%`d<+(GyWLRe2d^f374CV&5qT7Se&R
z_svlXMVu5lR-4z55!8c9B{CQv03WOec@9{2L{7efAb?asCRtgG#)1ZjJ>aYPMI+S*
z$U95HAE=denEwI}s=NfIh;d=WG>ZLf{R(HI`o<g<D&S*F?P_id1p5-#n9`*q_q*d6
zRsH<DX>0fiyvEpuFoX0YXt~g%XiX(x3#Z-xFhIh+@_l6Z8~A6vIW(QBUim@DRuM)L
zZq#Y;{R3DMrGI%3f5TM){)%<cQOF6)?_m7n2()wzF!oq8L-DrS%7IKF2l^UN^8#R#
zRoBAuGIm%vxZOwO-yV#cejGO}D`=hTFDw!;3q(u+{^4*GxTFm2k6feT){g{>i&lLU
ze!Ub6kdH{H^l?cB)+!0qTc;$SJCgcE0>;H-U9qpdQ0E&So*|Ytgc8=2`Ohoa!O$Z*
zwYOHR6H;Wss==BlF&QedwrX23@NX=}x%dqbvOji@0!=+BT|1|JnixBD{!9A0(EPEU
zYI9Q8r1V$lD;Y2d(7*<Xb&~%E1-+>8vR-qob&1(l6WF%uT;pB$bgrq7>H}-}lNmxQ
zT&f5l=q5yoGwXhfORe5`@_Uyl|IM`XEWAAAuhONjrWY+8GS)0v;+gw5vnT3=aeWMo
z2-Qx%PfKwXJ2E%?Od64!H%iOBW(LWvvqzWeqaRBoTdlr`AcOeLO~f4$g{Pji5h|;-
z+NoLUY@Ak~L01C<4?l$fk0^y0d7_<cX02vMbMpb=26GW9=2SLbB2+5k*MQA9fs&)2
zGV;XQ#uPMCx)_tjhH2&Y*SUg#+iC}#l?w>MHu<h^@m~-K#fUUWmNN>pNlT&}WN#6M
zvLTxSFzpMepSJPamGX^BIN9yVORY`%LUJ$JwIkYO2Oqkll?#&ISLr;4;|7Q>e3K6S
zjY|_bO3mH2y$~w$^qTlk@QeD;y>}&?+(gcT+#**nR;au9e$5YdpPTvwLCt;N)H)L_
zGWsPWch|*r^5I+VZc}&~FZIXB>stW}%mq9QuXI<!=KebuBGSDoFhrFg-D`3g%}A+5
zbnI1j8>KpwSmM2Pl}3KDEW>d87z+*Mv=*WNBCNs!>97=E)N5SmH<=?GidEW&xq?Zg
z@wYBhXh~_w(G+~KxbQ3P>1-YxunT}mJ(#Nhvm;Do^ZuLh|NHEJz8Iwa_CFSZ|MM+A
zDD7y#E%3j8;J@GINiqQ;6_fyfy55eBiTS+MPQt)Y&fSEP-O{qMxahLh;xc<Q5)~73
z;mB7TIooKzM<J7;C^~~s!dh$JY`Ya096Z2*Ufsok_}@pi{OCUJuQs;3yQ`djV(;B-
zW@gsr@mS}Yj;s2!*!hKEA*PbBppV7tPt!G{0eI5M|NIFi$sHH{OTRQyMK|IJ2nc4K
z9&v-Cvp~hHL;`K$+5^`N&NKKW9sNP5#JhWY{C3-7f5X}8Lbrwn24;HZ-pn}}XvJz+
zwwJAnS3CqRX|$b~{`VD$4<b%Fo0qc?QAby8o{|3P-BasP#STaM;Rf{j_`tt*IC@zO
z0C$A>zt<_-u!{pZWv6KNC{rlhw%UbhnfkakFjYZb-t*7(`Zmb#>N{_<xZl}W6rP*c
zW#r~{-x;m{&maA?;BO9X9?r|G@Jumtr+fXdXQ9?NV3c980ay0uN4Z-D9%cAH9t`FU
z^V2f;k^+7{ujzi+3AW~$-T4$vv!Eqy(k^!{PENCKdh;qc8i+#kCp<YZ3rkCnlNGK6
zZLiBvi8ud!e`{9HpwW$BREdvl{p#zT!zU}bC{pQ24K4Ko6X#y|qA)9lM^iZ}&!Fd@
zzhK0}5bjSP0CEL*k<Xw{%bq8EVcJT9KJr7DG_Wt*@1!hPbKlKp*J)QP%e5^1(dwRH
zLp3$S_WsXxwWNIK13&*pwKy?xo};~1ABJ5+M8{C08gaPW+`Y=_81?%toLmVV_}fDN
zzpg#tB4cPR<G#+hK%!H3oL<C^=U7)<b<uE4Rc@YKtyjWSnvIY<wYlNqy7MP+S&Hz#
z<h^WpxH1=X!F0K)EQ-S_<wiu_?aeRZ*x6<)y&?O44*N*&@)G^oRg|dRTDyxXQAK%P
zV6~e8`YHtu)3ko5+G;xI+TL)De!NYy?Z|fLg(CjZq!#tBhrG*~=}x~k2WefDM=SM{
zyV6y!6Iq(|9NP^M+UKtt^rhPJ52Y93sMI$2Rja8rp8Azxw+epY^?!_%RC!pm-mX1v
zdpIJ`@PhxHVtBC9@+ekAIl%vHyiz0g=dIHPZXngtP}yJF)F572g9n_V^+Mo=f;I`E
zsdZOlc2eZgmjPXSyi#MKiL14Co=>#RU#2I~m-jC#uKf3O2S4hzg8E2Wc+Ib<A@(-C
z|Nc{A%RuXGtcO=tOHh1aqBeK<`DEQKPAjWrFH4N=W$v`9wx*~|oYea7(}9Very#Oh
zPx9r6_E>d(0b&=W_{zPjyZdaR{Cm_?KI<>?FaLm1nJSg!`sg<yv`bik|1_zhI7G@&
zT!s4mtr*p3#hX|{udLR{Xw*f|z1f*R`?`FWQ%;%Om(eBdS3m4@wiz=UDc82M3gRiO
z)7)jEVl3B{e*-8&508P~gS4C>p)QP+E{vnN)K+J<+Jb7j_u$-JiCQlOzHxPVl~bdb
zUim+Xk0zPPsJuPN#;-@46w!HAiPiS)$}Q!~Vf_UaNu~BqntLYWC;F?aP|2o8!^g;3
zEJ*HGxt*t3xQ9^`+f3$p`z5H;nOPT9@v&8eIs=O?tx$HN)PyGPR}w?-iZ2Z(3k8x%
z1~=&s8hT@`2m~I27G=w1uQ2AQrS8(q*71?yV~0EF-kaCgh(5FBwB(EYG+G)eG;sUR
zGE|w8ORi+B&YH4)MZxhJ^(*c!$D=Z7$ZmgK6f+uj&gN1XHl`NrDr%@bZVxtDoKc=f
zD!o1sT<x1zU9L4xq&ZR!`WSED(NrBCYtq6QmuaQ7vODg<b6##>TXz3Qh0H=Pi*?Z`
z$Ndo@TYs%seLiuE!$hUOhrMMP{Ef2OF{Q;s@bG4*o>DX*dMP}erS%NQJ&E_+x#k!)
zx$>}P>sLT!;_KERKQLnK6l=Y^YiP+B{34jOuyy+a8sI{?oZ1V*!a~QU>g?yM9Fx-;
za?Yrzeh7*L($3e|*5WeLU(SWXpS#4EEu|I@T!lHFDD3*5v~)0#Y;n5cy+=X}q4^;h
zk`W8=EDi*p9SByQFX~I(M2&VVh2nM~7`D0cZ73vhpLjdGeg@Wt{;hzgO3W}`f8OVF
zv6>3l>xZ9oKG6TOglgJ!c?M_2T^SHH8c$|2T*&`&2VG2wy8QEr-Rv2{qs|v8nO$Rj
z=!J?z=B@a;{ln|a=_|Xom&+kE{HW%-weM+z3&{}!Ki5Om;Xcj#D0NPqDaoW4B|)6$
zphi?;=Tx*P2M~e2Ox&Y1*&m6JB4T!AXS}_`x7#EXU$HBA?ZJ)35<YHvJ?X)J-|ng1
z73d+9Kbnq%*0wd4n#)jxk{G?nWXd@-)ip})|LsG59!$QcSn#y>hVqHWWyJU0?VWC{
z9zkAFsclRQuDg~e0n=xmusr2zNY=NtUINVRVRlRUF%FU2tt4Uams|lokR_q0yvo&1
z*h+IsbvE#PcZ?TL6hIRGbJumDKC~-M5|r7eS;$DtEG*b9CfIG`(51f3==RLT<w0|t
zv&YB!u-RM@Yu#jb{!rQ+bsqMMm&C`7*lFXBTFvd3<KMRrMBUMn%Hwu(ODqeMJl5?k
z+2Kh=Z6$oQ<j?T9trmn8TAd5hs$aUTM$%@2+F1n3J*C2U;X2_X88w|<ADX;)9Q&L)
zXe47c*jBsUw0eBZVIqrl9QbgL2t^;mevYW#W?YzIDx{YBzbWBoCZTuhT=IjGx6{(4
zlGZoYap`4&3OH@uX4XyU$9UZ*tygI$(>Vc`2i#pyqf`o^%l@Z}uXvdw$Vla;q=E?w
zG6Gg!6ZXfo2F`<~f?=OwO_mxk+R1qD-t3jHq0X<K{TvxtMVLokbA8}&(kzIFzUq{(
zdfX5t0@V+WZq{gaKhczUNrT#ibZy}>W6!!q?e$oyZZi2VLOMvk)CepK7t-AD-q-9+
zQt-W#6S!=oj;T2B*u_V^w!|Sdru(k0O3-Y?fkq&Pyd|~f<RI;?Jk}YS&+i+yj!n?4
zY;%0LV#uZTIM&%$_*p_+Y-|bUr^~W+dz;`u7FD&<b-1m=c1LG7c1pAEH`%z5vJGKD
z=@xSQvX!lE^!*(++;o`bpEcAV-tHIQPhow~$q*KBl}~nyY@3cXRmh_vDSls-+T+nD
zPi~LB)uyjg?fu6SQk-}1D@|%M-VGk6!ygjKWg4sSl<@pOkJF!~JnyZcqiG<T7ka@o
z^mo2?{^t)}7iJ#N?-BXX8fcE=WCT6MRJG<1p>mZg9l9ghgehW9qX#ZeHCz1R=~h??
zAAal+e#Nf1y23YrD&YXyejkxn{I!68>(f7*I)(r$V#Mi6dUwe<n*R99=1>X(>@HOz
zF&&Tne7ni5Z*4OiL-6rLBTbT$`mm0P&apJa^!4o*4HPML<%s}4#6E7mPsVU0x^DD7
z4vFa=RY6gJqFA(hQ`h~;Y`MAfh=jL4LzR$`1L-8P2bY822H`E7HE%cRN8L%UUou7~
z9WD~lN)rjw_`~8ooktndyKjf7t}oyRTD2F`RlJ3l7oDkfpK}n3o}aLC7c3|)mJ6gH
zuKWXZ33;5Ar#a+jW?t&`De`?trCT0w<QgF9TOLok3p*#%TAqr>R!_$UYb~ZbAN?VG
z6U$V2Z7w8j)dHk7o5-zrk-{?26R9Xa*<SEAN(blUoh-Oyr+mWd_Sjn|-Im2Y=<95B
z=v5}=$@hsh`b<Hl*Ner3d+)n7|N3-v>b>nNMV<&NdG&Xyw7+xen>P<P1Pg`CJF5F!
zLt<H26sxsT4(*TVI#+!(<4sPd(corqUSB#tl!}>!{rgOuitKxS25Uzsn8Y6p^}-$Y
zT9zGg_vo%T@YZJ+Y%d-$*oBIXuDV(p!c<;gOgrK;dp-WPT6sbcsRQGETHfms<DJ4+
zVE9Q!PTOYmY>G+Nch>3zwwbd7M$fg0vk?;A<g#hOW}AzVb=y!0)%UUy21b6LHrQ|J
zxIqQ*Sk#0?pa0@!k!!`pMzk;#c>235^Izv3EAIbLwet!rM<%WOhRe^%POiyA*-zeJ
zt8FN8Un}CZS3dsM=x%MHXcQ!^+0pF2Iklx<rCYS&#^9E}jfxnuhZ%oYub&8S>#_{5
z*6<>xB7M?>I3C@Rv;H<V!_P8EYNEzAaL20X1G73TZ$8}B1+pw_jrS4Qvh4Tu;@8~*
zbeI2D(m`eaxU}Rz|IXW0N8M*QoF6}Z%*%;dptLrqU?Sn++9$$}!yz7ymoa-8f=eQz
zhwpGH*}>gH8r&^f_v9KYezBsqXZ>o?bCxE;vEHZ*)v!)PxlL|9>I}+dST8zAMM`2N
zB{L?3LuDwyE?cml=`l=2p)@<dUE=QIuV4A$o{UC^F43$7eTo)ScVv)gwVG(TvR)`L
zzayCot{2ENI&Q~eq5Ltq-eqDmz1{sa=Mrbx-9sy0&N=NSuZpYqu&SoZEIENamL0~!
z$Zt!qDScRpG=l|dh>#ODTOKmSe-7W#)L;GC1REy8yNtiz)gx0dvlQyOh)Bmg!&)VI
zS+u}fo^M6KqMW|_E;g_^B1Gyo2~&Jg|J+m#GhO#xfcaZoybOl2JnV*Bdild<RQzg-
zzrN`z-7>AO_4X>&Ut%jmw|+q!zi&AG3D*CxjDY8bWMxu!;B)x!yj{`??hyf)tRi0x
zk&wsl^7W1P3PTeK+Ih&`)_ZE$l)F82c0G^2=}ZFrBDPY@#=8KZ+_MV2rU>Lb@6ke*
z=4sMN(Yvr)&#r+Rb^9U=53A*Oou0>JgQvYruYhCD{G$a#f|kM<Nf~X#F(RzA59QYC
zXTJzU_{lzeI?!8ueLnSKv9GhZ6N?L@o#YKelG@zH9%{R!-&=E&{<5(Vns}RDizC*0
z!mH-_f(zk#@KPLO#(fgIcPw;UaT*fg=Qgu(l-_iml%iPE7^1_I*&x{$?#$?My1jXI
zG`=PMecJh?yOScx<;e2n`9TK5bjJ)Gg4dy`J+Sg#SWqX?oc{CO(Ww`dyb36clvb~~
zf+vx<V88matEO@h?E9x{H1h4aX3D~_x$oWpykFi(h-N+h?Nb@gxZhOho&VQ}R+2sV
zchNEF@1jeLcP56--=}%>r_U2|JB6&<bPS+4dyKH{3YnW4UU%uwzZ^`EoDLWg7%uif
zlN!--UQ`K6tt*aCp|~Rn@jt^?v=R5U9=*UN-M!?OJJKBs?CndauKUzUvk?pMe|Uz9
z;jy=SEcwh@s>2|QGp{EgtG=Y)MMH1QYbWwB9ReIjgSe_e1TH8+>8&SvH;7Q*=5BG=
z$!KNdyD875VUixbULWoIincTx@8I6#<?)tP5QykHZ#T-kIvq08y`EiYkZr*FFm%^O
zM^-gfH<cO>`CZtGYmm)cw_c+B$%450<(5e{gG|O2?uVKK|IEX!>+z@Uh_J~)lsM{U
zp99V$R?83h`(v4h1VW~KWUL&?msA-L666j`gW~^QbN&yp*GtqML<jxU-O)cSuX4ov
z8EnTVb$|4vw}f9I&J+3vIPI^HKMYu+Rfi<gbq_Cl41K3U*8E{%i@SlHNQ8%TdcZqA
zQOb|TwQ)siMZ$F4#X!YF_Td5IB&%A+A}}nJ(<WR))g2s>n&Hn8Bu@HzV_mP=o?Jw3
z@-`F6ZeJd?_(ET9_q5^jiMJIZix<at>g7^@K#kH+BCR(IzuiVp=%68cA6JP$=x~x9
z`avBUzn^5S`dM#E`1g*^*a8#}@(^W$qN4bsr=+;VdgW{IvcI=;kaX&0QWRkPHQU<I
z4K6SIc8iS9w}MQB9Az9|5hC~9j0uLZgkFA-N;+v~6&DjkAabh%ZHUHQ8@Hd`NvY{j
zCEB|_qp^<kYp-@}bCc{*4|eVm_ZVOjJR;|33RL&FLakP6MBXo3z4_bD(`v(@v(#a4
zpp5%YZi<<h)uoqWv;98Aw-VTv41KGpbM=%B+do44O_Q^Q=aq8?{v9B>SP6Q4T+bxc
zSN=(9*7yU?&sDc>$Vl2&1&n=WSKPbhiDd5(1@_P|@Dp2lP3^L>(#)37V{FnXQ+cFD
zS5f<Kk{U5g_&zZH1y!K9cE6D7PC=Hrx@IbCPmD;;G>R7}a9PLAJB2D*{RGX3_yO<7
z`Dt9GrJQNp1#==+QmXA_4tM4O6p%d4rR4%wy!pmyt;l0i)Cx2H-ElzpqVv!tadeWt
z^PgU-M6JH`7Jlp5m#|b~M51n&Vl>Q(4N7k4l3o6yZ%uau`(dO;YY!M+hq@s4L?Z!z
zlcG_B9h8^hv{Kk%d^NfJLUa3p$)24kp7~+|nJk#smiRuaebAy;Q@?xm0rFwua%gt%
zM6`SjH>R4M)mU>VVk<~HDwkm}BZwIL)6OHU)OWY@FNb5XsbvWnBK57hna(DHb8Ys<
zd@(ECi5HPtTsJvp%YWx5i=8^Ms!`Jr2envexDEGO>qWKXBPEs7T9`b<q1dY~B-cG$
zuN&P&hp0I;-6Ht@bW~F(ao(iky-#d;#<}?Ja%i;D#Jd5LDrh$suv^yc<2_bs?i{pR
zCc2nl&G*I%Hh0=S%e*JWeMDW$DK^C^j81N^SfJHbMI2ZR87Fq?#b*M(QRCY45!8J^
z){FjhH&G1^0IK$J8wX-&4u7aDu+`+6zn8HEJm_K8x@Nz>319f*Cqp`(sE@7u0&!G<
zZXI(X_cM1)<zx5n{B;Jd7uUsb?IeG=T4C?8Mit0m&5DzW<F0gYYloUC`yujNLwXB&
zKD^G(PK{sD?68ixa8Q&uQ@vLioeV&Z*zF${d}mwWFc+O^uuFK~ZiRn#@YrbT-qN>w
zKc0y7nZ!nn^Rma^w{Wny%F0XqJPt(aab3<_Po6^n<_gQ2Kh<>yc?DmT06qDj-HyYl
z)7I9(fZ5g5bs^(@PmVtc&)6v4TIi;(8^FQrV|ZH62Q2jv?DsC31N5<!ItCLR8NFk_
zZPG^N{2M0@ot*O1sh*Km<Am)}{`-Uy1{Z)O_0{FGpa0%~stqG#-Ee_nMNnyHu6T~Z
zxd#q#(4R&x;I*ChxwtNTQ{FJsaMzx4X7;KT%e0a5oU|cJGWbdmyD7yt?AUbmK^w2d
zUMCW{+|%K4x+ouxp)+c2L&RjfC*Y-Gv;(q62s%fo^y)>ZxbR{{6PfdK8wIAQHsBfY
z7ym*y!FH{o-S5zUtCi>;*`ry=zE*IF$)b`Dzw(I2j-w!7yM0$YP@M_f)WT`c!G<{2
z|AII|Ki8WO&!!e=;!qiQpLP3+rWuunLL`d1BHAN3FCibRP=Z=3@VwFm_rUHDYLv>Y
zUGV<Qm$2Tbj1S)nldA=h|DOE-<$5_@&3tSl7z~I#sB8$o0#c%K{v0Y$mNZA<C)$&j
zJ_<~m1(@vsF^X!&9pafM1dzQ-rT?osX<Gja*vWwiDi1gmAcM?oje7e3rT?WU=LY{b
zwuhgS0KKoV0|b=~7BdlRnkkk;O~*xN^<9t5WRUMCu4&^g%*}X*(I%G%{{oS9%UAaK
z$(O+EQR86$DD!z}+m`$~W3t@I;)q-J!O&Mn0rCpQ4C|swqdn2;#W?1QL37ik3IZZg
zMf~d*MM#Gi{h>|u)C=O;m1Vj=O}C4~(}Dh0anE6g#l#R-FUE0F&(hpCfv(e&krb4q
zH-GG@$(P$KHTR|3()42?9Y)B_HZQDyr_NlNUs18`EigkM3&Pl`xm;~w?nY0@M@__8
zlfPGr#==BbYr2*1+`?kKiZ|?W+$OVvM1lXStVrP)lK{n`eY+O>6r#l{*!m2SCDTUH
z8W^#Q@dsmG9&J9#`f@4`TRtu-I#$E$oCFruf<hxVmRQVS;}g9V3X>Gq4Z%`D%{Es<
z(`e){=X2phN$T6I*ppZvdwmy|CUFz5S(D|w9yS9Qm>=$!sPyehqZ5vA_FF{%1vgE(
zT$qGS#T{Y9Yv)KDVdS`MF{S=)$1r<a{DtC({L@6FpLY&#Ct>QYRHsbu2txu-0!}L{
zk4lTq_#5T5ZEzDdt4W)G-Jx1y%q~g`c38jSA+-2@_7Dl<InQw2Ay~ej+n*6-cDO(y
z@}&P|Jf0o?P17eal7`hDQ#$-VgwUV2!72uj{KOQqI-a5I&|ZJA|JY?RCSF%tC#WN3
zeJRMp8ao^oWfTLG%$d0lkGHsUw_AVSNz|ov4z%UfjON9U!*_cmP32@D#f&S1=ZK-|
z85sNM4KIZ}L9zBuW$)!%_VdzrOb>+{VwO>fE<>d_<;*7g?Hy-GJR|Is8u=bU%!9vL
z`IUxKSUUoCy(^B-SiNk}0=LO%WGpi^*LRYBSD!{t9E%#VeVs2NcIv&5vo48a2Y3^I
zD`J3L_OjPV4xT(sl#p=U!JrNd=IoAf#x|b`)4bC6{lO0~7KY|Kb{|}tid$<PN2^X1
z*{K9F9|XGOLxe|^j`<CiTBVF>L^LqUYVGbIu=CsYWdWtz;~^|2b8m8YsjvR}(`q)e
z=!};Bm#^HZ=j_}9-9LiH)aVcLkc*9*lvA;nyt|M@gS61q(FG`woC}63Ju+7C{JE=|
z2&XR~S|`vgBT6l<<xFFqmytw-BLKq7ompj?kk3f2cxN#}$)(Pa>bQAEg|eb0QvVY2
zYO}3fS$I6A<fP9Bzr?CPM_LxTAbbh_SnF3-$Vj;e7$f?sK62o)%b;g3nWhNthmVXg
z6w(N!7Ju;IElUAGcaUQO-URivh)T~S+4iHghFV?i47%OCX?F<To?riYEkE|w(W<Rv
zeSxsFWrNbBavP%~1Mlf4QEb3^8@IQnofqccR85vxv>=bq<`uhDsOt`r?8~<h4ci29
zwgk^wl~#Yy61fw%3vI$;n1(kC)lNNlYw(OShOX~I(|2R~aTX3Z*}s^+e;{3mK4_FJ
zhJ#mxN*HIAe|{2Zm;=|sA;N_H%HcHow^*Gz8_lAlMs)K_1D9cZYc+&GfgGp9QMidR
z^_xtapY7D*_!vvxzNlczc6wM3wO?f*B21r|8wjFPVCqfi&slof_cN?&+kT35jW|n<
z@m|}9$PUp>4rjDZzttbAUftV??B4xkX^Q1LPNtneUzd<%BW>JwF{U5ty!IuN(ZXW}
zzfbybUoV{elmG=tF?!@XYPj!UOI^_e&+fiehGC@7V?Qmh#?DTO;Gd+Po{=Cvreq+`
zj$|4Do*>(&d*JJ3|4OG{ve?U5Byq=({we@2ddjeqykyPoH<m<nekG^<%b&GSI7GQ>
zRb=K@KgS-f@*4pRPf-RJ3b}4vZpf75+`UO@T1W=btl=%Sl<}xT`gPlCE!hY?t?gah
zZzWbGw`<lgp{Vx707e771~16jYEQ>2Vz5ZNf_!zK@)i^;nwmjUqks(WrTqpV$Av}0
z4l$g}3yBVyoI<VGYGMYwLu@fxF7`AVg^=PJPf!CSBu3YIw`hdSr|Qf0zJ9+yecbr&
z0RNJW%M;y2#2o`Ljhcga&ws0VeqMUBHI87+ycadabkX=8>hMEmqgJ`HUS#Y|v70St
z6L4==O%0?&aM=GygsJw$t{g#-?o|#XemmbJ4vCjf#j>0}|7CSU^V9eF2d&TC*r7*q
zd3%W_+fuCD7&@=?(9qPm-H^b3pgRyI)40zz!O&f1p7F<ItH++gVLqltf9-lf&U%<u
zvE+Jd2gk1CH}H5rr<{XSDMU8%1jIAxvogde#su1}nKQC&@cAdvnc;7G2;D!ox3B^d
z9b>j@n}}Z}+AevsJ4$i00)kSVv9@-Zl%7BZ>O?)42W3Y?PII3BMjZ?oo;On?G{($k
z34!#CX1p+9G2;ByE{Nu-49ckCS<E8fNE=t1zM9bkxJeN=Y>-hIymKoc3*W3$J||0^
zW$8O#D7xS_y=Xqdu|$PPLHx1r-L*J$#tT+4{Nv<X=lY1xH)e4bO2|(4-Fd*1*%*7x
z$(H>Rm_tfOp2z%o;1fV+g}n3Q9!AI#=TzQtQg|o#7T+7e$3tT^^fV9H9{6sE$LR6y
zV*#=0X|^^Om^0IV-!nq#@|r%9gDH{>m?kZlT~SQ@5d<l-v`==7s;O{4?R-1{F{L}e
zMnjEXtV84n!`)Aher+(+F!6i7d_7$9nOFavi`K8L$&GWOZQ{a$hX`m~waQX{KHeTa
zW8dV28tCG=L+lVynH+zlE^2DX+xKEv02fACbB3VhbNUPQu*l2sJmHpmQr$@)ZiS&u
zT@OYOiZ4MCRqmGrFQ*5%w-Fn>8cb~z-;eTOO#&)>2=Cm^8qJ_{NsioXH_`u2_FjGD
zVG52px8Gt``8sHu2Rtmljhbw5hH~($S}>=DrDgekX`^aVVOK5boPI_jRhv<TgG5Pp
zY_t2~A%_a<w#y8MUx`9|=-M_5ycv*!_$J*D$h*^ON4W`131+OnXPyg_`Qi1|91h>d
z{soqcpW)SV;{?Vor0T8-#dmKs(B6aXHe1Lub-i_wA;g2S%NlfyedI&Xf`|uHx^E>z
zPzLJ>Z%|^gH;H1z9hqCFr-<^rKJx7t^n;j`vmKe`7QDl#m+U-Z79y~a{4#FP;q>W*
zcE_a;G=dJUX-odVvW$!@;yhjQ`uv=4wn^E|i|E~zlS?}*(5E~ELP>T{HeX~kwE~+!
zYHHbyN)sdCR}0~6HH8Dl1}hf^l2=^XE+tDpVNgYDEZOIQH*JFj1?G$3?!;+s(PR7!
z`JhmDlAs2tG_xb=1<4$8q;8*K6^GzSf6&GkD1_qEFDS@x8L6Uz;32w1qa-Hab~RDM
z7w1RLQl~6^37)<aLsOQc11Qw}9DxzACS28x2x<q=cWz6Hslbqg(nxsyrFyF%g*dTS
zq*pHciZ^g^&JFbsw|F@$@!Pn0gWV@{VC<Ta2HyE8DijhL76-7Qe&ul;oL)H7j;lyr
z_+m3r+j)NhQOBGzn{qh-XO$ht3FSjwt_-;TkcrvrIa{GVT^QH7QdJYudR=$FSpEK0
zU=(ozcwLzCy41Qo<%~XjP)5EPO-w(f+BF0`nQspMFk|y)9Z+|r1&QRfhGez98{Om5
z+tuo@S;?o~Lye+@WznE1d3SRzDs9N-N5Y^f83pl-itWJ%X>^xM#0{HOB5rwK52U3b
zbQ4b0;U7oP6E+!AEFCT;WsF*-@d__@Td3WBh@Alygj>0xf7o})C74G3)XC4Sa!MH9
z7ewf-e4_B>a`=7CKIUy|k23)HgZDwX)q{gNvon*xX#2|2j5a5w`Q4%f8K)_iGxDX5
zYgJL0t%{-}v0$a_>mMv5n!a0q^JiXfqgEHcojzoUeJEV7+cE@Sbr=N@J?&<ifcPq^
z{!Pm1>_h6;z?8uUw+@3ht_Qe(H5Y(c+tb!7tVHKi6{4G?0y+y9QP{hHo5O>cwFt24
zGy3n7b?Q49yem<yDEHaO4gl*uqhhsN^pMj^W<en&UY}bsz%#M0aYM0w?>)342ve~L
zV?%m#i$c&>*dk!Og{1iILC>OJDexTNPigv5a@#(6q(;aUe0#Dv3{iLyPc9WXd9&N?
zMvbtbA6bVmoP%^J5&B!%oK5E!2X~VgxhV!sX3F1nj3ogLPpIdQFviPLYF|l>eG%0H
zGT{B>`udNF)Zgdz`%O?*P6lA)zPDx2^&e6<j5A*TTzq+ZkO|3c^cwC+JTO$1r}Xa9
z9-YK*&))<uWejw8-SFD}<x#fv5zSTQvSJL1T)gXPAWrACVprC$a!Ckgv0PdFL0Pfl
zATpl4hF|=a`xmTPuf6<L8i9wXbSnkLdhD?OoBVQ|9Tw-K(y3zq9}TF=NGF$wN<C?&
zqP!PWQoaQ)8}W?=2>nIhSAtaYj7Z}?=|Yym3>Kug#P_8wM;ZEESx+$;ReNO)4)sKD
z=W2?NJ7J%gd$WreUQ;8piSOKeBFt3%jnbnWcv=9PRj=h?>k}1A+_rB4!}a95kCS{u
zuv>dRb-kY^b)5{YnIah<zAb0mkKssPUWiLIn}5TE=!{iOU0{}dQYEvv5NvQp^(#Ap
zN^Y7Xn#3<<^rxyB9&fFB#-;hrJ?+(VPTA6Bw|uz=-w`_JPUNi9WR&cc|F6#!Yie%4
z40d4Qdgz+3J)>l5u5`9CACRP_ihtk%U?SWiK=P^cf4?!#_oA~FzT`G9+><&Kx&bRN
z=anG_DO_k|^>a`?PkRu(J8UikNCcFe63F@h^We8=KPGAWPXX^02ml(6TL}Y-zR@yH
zI53$s90UR!GC`&r1hN5-&?18tPVSt`+(`*bS(O>0v^ekcI+i0M2dO~&Fe5Milw8vE
zraD;)FE`6Md>s}?PE&EPFH|Lr#kOP!*{4N`u=;DV)HE@Yuh$}uZ7`}3r3FO^o_{sZ
zX{t6CRj4i9ElbFtjp*ekr;?K^U9a(n95Hw2%rlPWyXgG1o-CVYpMMNFYRm6>EA1NB
z*3Y!sdD0?e0iX74J+0@L5N&zFZ_r9-X~!-?W1b%&L>DF>VnoZJ13y_VqQXTCuTxtT
zDLQ-iacxRcN!+*eR#Ky0dy{-Df=Lh@{SgQw$1BHIJ5-}>kxd=0CVYlP5K%?DYhkyd
zuz+I2%7wKrsXWtw9>1J#F%<0i8;GsSNC!tJ+!g*&Tc?BP&f5NT;;E=1p`kKq*fpn<
zmtXuShDM#9cj`KiO>E2>HfiPj%p5+RvRHmYr$YA>9gXliS5|>p@pJq*a@kv$&8}KJ
zk*K=MkTT&?>2z<%1gYMmOvlOhiXffl($>EgJ>4GRtsTTBBeto}TzvPd!T+nwX}~1*
zw^pcXa!ocbgoy%#tu3Eb#a>wn2rl=R(aA-f)9-_((o>#R9s^oG7}DV6LvU8)eCvxt
z-fnr6DOYmz{VqT^h;U7~aC~=Y{q}<c@F;xk;T#2qMo(LQD;jbl9yYLT3%gy6nNHVf
z`Mw)+xVS~gza!zw(oh<&djQPeVF=X3b-tDGmRXiE8v>zU6Q=?D^=d>9iFdS|R(R_u
zn<~PVI?!sVI4|47r2OKfh)hyS6U2O-w<Le?I&yf%&vFDT&pt?}#RcXZRlxf_VO`&<
zb<DUYJV*uY%B6@DGW>3gYcbzOz4pl)yN}EpIp8(C!IlbqQaxl_EN_o&Z;K~1-ir`~
zdP4CN**{V*3_`l=`#a?RBt*ind;?pAwADTnXdDV<4vQ4Ei9OHAC_m3)ku{T)sSXTd
z4>S1@+0ii}DN)`6i72mPVq#R{lqUUQPllq!rNk@LhBL8ZHLS{+{Y$FVFqtM~3n*D%
zm;EF@CM;bP%RuoV3Ru`Oeg7tBwa|l$z6TSLT3sNrwFet<jTjY^aI)5z8<G7p*Pa+R
z@Pz@|A@^<FW8lMq$KUXoA0=bXbMu1h49nvqV{?|@Iufgi_>JW{aynoGecF6ljozr=
z5|1wDhC-m$C-+037iWaz{yl`6;Kny{NZ6dFPow_ab5(wlZjnje81<1)h3-e0e|_#Y
zt7!N0rB7H(9ksgf+KfJlxYlI!40H^h_pZL|x?@r%DocBXBA`$HimaZ1&~IoQ_@JN_
z7zlPGsX@c!t%Q)HfQ(0$$Jk)02sQ{8WEy07+(RSRCYBJ$ka4q9eP`Te9XgZxKAHet
z>=?Z{y~-)%mDt|7<^n+qz<Q7zgw^&ENT{@opBvC}p_;kt<soVg&OhLCHFj3}io%FQ
zz6~a*WXuui&W4dNXSMLW^I}Cs@C&SB#a5TkC3(qyRT_xmih~`8$Hhf6GTB?n?dI3h
z)63XuMMnOs2;F}Dd#+yHll1Lko3t?>$s_fliht^W&9kKV7bZ;>d`<g-CZ8#$diIJ6
zrn(xYsv4%cs!0we;A`C3p9{!Llo6d4SR0%;87+SfKI88i^ek6m^m6&B%e9ZjDNL?^
z!C{Ulp&IZEML?yIN7R-lOSO1WSZz}}n;Upoh<hj8ckkafH+MYH(24Hh>hPDgqkrCh
z|1AqN&8)Zf)2J)y&qI@>NX2#w+TR)T@^ty0GHpo^bboVBC+!E)_8BPnNLksr*~@%%
zM{*W?LO0X;N4Eb?Dn&*17p>di-^$swh95yqe9Fh_mZ(*cjz>Lt^zwRz53;2=0w;#1
zBe6mHeR7t5P3uPsm8x&<C8aa8D$T~G;7I(nwM6F|8w4Hiv{HxW@}X1OPPoa=!bS_q
z;RVSv&re%KRA$WM(sA>AGZz{Wz5<5!Ae6{-YCYX08#<XP+^hlpa@fn)gVA92iQPQ`
z7HT^{WqmpCC+4*5|8|H=9aQ&gEiV~k^h4ceQDeVT5a+c`rLjw<w!s?~f8^A!Q4!7T
z;$3-C;FQJuxhHW+EH^H!oZz940H;*sW($?!x*ynV9y~mD`V%%t<Lxa_pnP$5Tfdfi
z{_<3m^N57JOOo!f%QTa4jUK{#{K(6mqOCQwS2CzeIj<=p4=n9PC_a(aECm){{3R7~
ziF%kL1#z)h?B^;MO3+{uZf&n{I4kck2iqh(G8fPcS4&44H5&J*xbDJScOIiv$m~y(
zeREaD6r=2wuK&d;vpZE+BNI~&LaZQ+{S}OO@eBAE{v6K+kG=nFqDasr4=#NwydNnG
z!qQnmd2jN4kb)xOW>_f-52QbktkgI$C`6HzA*z!W!@LxkWe9lLBlLsPZRa&N=LdAh
zxomNo!)Gj%ZzIZIc*nEaLbL2T40{_DEOS}m+0G0xEd$Nq75#ilvv}VkGjCMM0w#DN
z0Y8+GJDC%?`HVdU!VQ~4Ny)q)n$h~Yg92xQRsjMKe{Yz@TO?wLXB-jWq?O&mQG?o$
zj>f85&>#QHVzEgHkrOKRvXXda=YRu4Rpu0!QGo32{j@L#Yx28*L@bQ%{TkBwPbaLz
zkKn}n6Qy$hBSk^2U6I{!#fkd+m{@}50mPr-Rje!RS)nD~It(5J5=?eBG;d7a?tSDP
zj=!eSxKi!whqfgJl*En&{7O!>E6IPf`g(!}(W_rdiQxuu^Vc_T1nPmJA2LHzuI80m
zd!eDgDG;)6>w<gO(A-@3GrK;JJZDE}-_S-ukq1_p#;)L=`uE@rHV;HwB%{osIf00a
z6Wd{{q#>_V4{rcrG+kz_5voV>Ny__~62?5L%8X%VWcHtW=23cF*Co+SxaU7VlDs<O
zjvy6Fh!H+tVnn1hD}k1niFj67+<F>z-jdH(VJVHaXIzU8wXAOIi?PH5<nrV7Hj2fp
z@M}3Z?6SoR_*+bApO(1lX3`?i2NTndC**=-`@c69hXm{JljdItF2eBgoH_1Q%5E}8
z?=dG#(<b>-?jC2+vU0`Wy#vQ>)v`?DO?5A4l&k3>&0GGCaG4egoBW6N0KebzMa8}M
zRYYXDmf&Eohp7@9qQ|FbRAyAQG`NRARnhQHn3uqDN>56`hlgY?-69HkpN@U$VNc{-
ziV3nwd=K;kJmYwU)3yuYS`+5mWKl~>!vJgt)Lz9|ZgW$Ey9s~tJ#XiPbmqFfH;lNo
zXd!Nyw35kZ&;`}>eHUSd0~KPw=rgVtDk#-Z6%Yn0j=cuea{Cy>+8K8NiLq?#;NTws
zjRvqEwBBq6OBa@uhx3?Lbz?3_pS1_Y(Rw)Vyq=4=@p@#lG?+JW0`McrL~KR6ZA}Hj
z5KVQe%44t-Cg=z_65?k?#$f<{6OL$o%dBb9Lg#7)ph*pk!LlH-Z^aT#f(0^NlH&m(
zVHN-K7c=W(dO7Jpu>zFXb9g5d1{RleRv=Y?BbC{<B;x`{dn{*UceAA1uEdF?^c%I+
zpGvCCXkif~cuNW0N0L5ku&at+8%pCi!?$ZxWJKY9_F39}dPCcLb0mb(F#8f*QD>7$
zDU9@zn4dZ~M1_;@6pbC1^vBz_=tbHq4G&`JNl=x7i3kW103BdE2V{&TK8Zux-h3QB
zSvt$GZ&Q?WS0zMCF+j&)`qtM0yQPoCKl`7C!C5yHRtZJX>(9x<l3yyyAz>4Mj4^Bf
z$su7QF==3*Y{=3<hlIf2HHmx0*VVe?{6h`7gJMJVMA}u|gRZtp72e*OrpDCv%;wnC
z)26-!&s%$ht0^G{FzC~kkA-5DEbaT_c_8YKLB_OB-}QcnZX}voIE0NIDo)pf$A?bU
zGZtkN@}r{YELj<m7jyK!6t_90;BTtjKza-#5-gS2dnJYkIgsOuyx^^UBOt`7JtU!4
zJhE5CCWnS8;lZy!r|edotNu2teLodqv|vC<0v8!O9&z9DpO#6yB~*4t_6TtMAt==y
zNpKv#A}9U;1tq^$)<AmYJo3f(71bC!+fxK+RvROLjen<E`*)c6ZnXyVkX&+^>7e<h
zz<q+kC}2P1L-%6^pjXgc;V6gpo=7KzB%ycK1nJcFd*>P~VL+q9+(U<%4ecX`X@wpF
zvD)X+q9)}h(yk>z{qAINc<2;QPDAM&{Pw`9k1@2D6R-w13J*cY*N5j>T=BzuK$b;X
z!6iTP9_(LnS}6ljyU3$ZPeL`I<B{eCkr_3EBTG>q$T!qE?#+)2>e5XeIJ0tb>W^2n
zvb>N=d;1n4{s3x}W5PBYS1V4wpArO9-vX$P$xiGShrC8JccH;EJ`_~W=D^0Y$f+Ky
zk9`wa;#Ka|&?Kw6s1Dgw2WUc(Z(q%+W}ta?Ia*6dTuc9I*U4rm92JuoZ5}EKd;m7M
zP}t(|MAI?V-}21PJy4huTu$lg@0L2OAt$6hDVi5U?=v5=<qXLQ0cjY(+v~<-+)Tu&
z|H0BhXDHWNtVJ~H0R?M^s`0D?y}fXy+}!F+&y2h%;tku(NIE!b;1I&25t;agSK0F#
z?z+gga87Eur-ff3VW)>Tkhn||5DryGzjvSn6@DXlr9<H*=!B*uEb~I3!o;bv9+7Id
zQki{$OO(Vt>r6+Xg6+EF8l4TMPK44X6~Y9`DLhivdJ1AO>ym|!X#Wd44J~fR@Ps${
zhBONgx^G0tmkZbE^oj%UdTdq)1#*hu5gFv-tA#dIxy1%XBL(?{T}fhlI;XqxA?Xyk
zK;S`>U6<H`TxLU@0LK>kmk;{StM;}hI3#Dy2U+l@1`&R0xkNPxpHj7uppA2b6hd%A
zK%>SX?Vzy(Z3XsX0PF{@)k2EJGC9WNai!ul{vfk-h*j7bL5Nic@ELNhw{c1ad?&A<
z)KaBN?1dxj%|^sO?mR_UM|gQOOZ4<tEu%mlKBf*>e5U;N@~o5nd)`J`46x47h#>b(
zV7#T=I;G#)GH`d06P2@4MX-3ok*mI>D(mP>2R~h%ePTe${0{G{)MK>9#B{m^+GUlZ
z>dN@t*AF;|98}k%eO{$`r4X$C(d=fD=GUw{NxC>He+(we9TPTLxSQT8A)jL6n$Th|
z<TEV7#cou}CnO(5aL1+WE+ifcD>cMkGtwJ;B%TqiNaUk_!zkq;cQfmANcsK92*QLy
zUZ8bNrv_6!8W8ek4H%GGEr=NUivZ;4eDJ0P$#FWivjMj-(^ea4T5Hd*?I*yL*}S^v
z4++XK&{!b~3uI6%ju>VG)Lsq8B6)KKhlok(4ZMTp)-#N^6`~-17UYAdSvW+veCxMr
zml#UsDc_W?Y&t65W#qsJIa5FbAkx4DJA|;vMsTd#paNl6Mt9@vij&=B=^dC3QhFBQ
z4-8mo)CdN9E_TT@HX4+s<rhv*an>#@>RI)C*S*EjX|8>o#`3?~J=j4`jjX<)USGKF
zSAFhF#G=?w5(cE<LNgd{_?-q_ZGCivl}`|OD0;Wd$mt0qPZ>^zh3{ht?|2=7AkqD+
zpg^S%pqFjtY&aNy*JP#wx#2jV4gC)#>%^PY)E+W7&aCbri(DZ@Il39YKG$nXTF~&Q
ze-;i2gttYgj3yJXCXzB8C#0P8oM!uVLN7(yt=M#XT;iVEPqylI9timW`78?(kgdS{
zqqk4Bdk8FL-RDewvQl^f-?|%fM`f@3oJyKNzutdlEpr@@DG&6UQruAy%jz(5MHoyt
zEr>Xq6NwwQGx5n=V|Q`pAe5L80C;vI*NWo=`6-{(0R>^Y-~Czh1>BIItd`6Q_0x(F
zYL%sX8!(E2+>J|IRcHb23KE;=oCU?kG7$P|YY~{ZMtw)WzBHcXyT|v)r3A#L^Owj<
z5frL1F5Yi_GoUA)Nqk6XRBruZ`4%*U86>44^(`!K8r4j0Kuk`~G3p-DxEb+)Q+sy(
zENV|!C-mW*&MVXvAJEH(=(Fm??wdM%9682peu#P$m!xpx{XBZH1FX{dCR|Xd=sqM;
zj7DhryTKu<JBT*5e|W-g>#C4r*?mOB*b<EQP7JAQZAe4TNkoemG(al4-+2a~V%zUA
z8#P7`Wf^Cu5!6!&_p9m>1hh3g6dsm?85QhXOK;eH;zCC#hj0ipTIF0xGDFrM1vhAE
zA$r-Ec;<l5Q>`*ejqg)wt=oG4+qKbXO9k;26JdoNqA9*G8KUX>KW{*j1}GVm^gsu9
zuj+ItH3ts4SKKRNr~3$E`69w=$Eqfa`{X4wfqHhZ_M-~a(}R%+m=e{{d$TjyPl=t|
zk<r-5Y4{OP+goJN=R>$&9)Ly*xcTg%&Q`*~Z*^6*cG(>Wp7d7FeH_pN&73z1THINq
zN|PJ`Zd04CWH1vH)}TU4=)Fp?YSG8m=YHBc$)y~eU$P?#*2)4FED#Q-=RQ@WBkGak
ztYVo}$NWYi_|!irEX&~pM339ZvdPRnZGR$PqONK9S!n0s5UYO3|5fvJwe_FpK!Q-M
zdCX^26<;c6aQTC)r>;`Y;E`RE$15kV&Xxt@DrP%gun&R#xA>e)%J%mZwT3x_FRke~
ztw7spbq$Y7KUn!{ncH~%qw#OCqON)T{YK#^#DtI$g&1eya+`Ryr>{`GOE&U=+w!7Y
zgt?1%pdGOytkq|35>`VylZS2cQeku<c@O!J>9W?*HVwJvn$cWPz@E)BCaoI7&BYpC
zs)L*;W06qVn4A5R@L$2<muyq?Zu#!p?>$r%>59Bp(B6EC%JGf2S1kiG-oats1M6Qd
zNv9Hatrsr$r(cZMuh0F)i`lh2>L{74b-!{ipI9n#MhBb&56gN#jA6Py=<{5eE%Mf^
z7o{H~z>Q+EliNMb^1cF0G2MRcKCKtMO-IQ65+2OtdcFWFetg*)FS+y14y95-O86o4
zwnx32^w)&C#yI=(k1sQR-bw|r&|v1@&{aotj>D!jKS1p;9o?Z6w4jGiS#%h3D@xcB
zp`gQ4_;BC`;qgapC!+aus*YG@(T35mJ3B7iQX3zI-12k7Zr8KlZ-5Th2-Z}&pmf<f
zwV!M7EnJSUYQKb?kNb~rf+?}6OS&aP?T3ZPST=5BkQdwz!ObX74Zt(vZ3O`9gehBf
zh!z@0ITbxA1>2C&=BHUO9Gp8X0?ns4Sxr!&;1qkrY2nF+WiNv!;~kRDf}Tby@N<BC
z8R*EYM+aB1)*BjULN;6wI98?scAbB;)l%hNhJgte-Cg=t<+v_KQ^3_e1a2N7@Zt8Z
zVbO5=D|kQ<U^EN(msbWKvqg90xQE1MCrhdD6UUXMQvWx8_WVe*r)3C&I-&Ih6&J_W
z)5bRYbwd9N2k^E;DWm|?emNW9_y2VU2lyW4X?gOJ*flnqGe`-cm;Wi)@2<ZfQm!O7
zR|1dnuLCvkpLHmEGtod#5!U&(>@#nZl+-{!wT7=U`Tz5q(y84=x|}pI-eK*Y%=Plv
z3ez}i{vXc~7W96a8|J%5`tc$4)5O+E24e;Msge?8NOKf}mAj}_N`8b!1s```cEZ$V
zy?n3Eo21<O#7sBtdYi|V_-3p!t|-rEkF&h3l7a>OVAsv@Fa*_5sFw0Ynwy369b@wx
z6nQ!P$T5A9Uc&bJ;}LyYe+!;{PJ^qv;F;iOV^z1_%!c;kE7Ccw&^f{`Eg$@d5skqx
zZWsQI86jF(xg%5s@KK%fUYa+U|K1z77X9E|z#Fl?FxTMEd)wBAFL`Y*@k0ONf8de^
zB+Q9i!?yb%_iEIKT>`VqE;R$<BqfKBo5%f0F$|xyu>X1UlI;$5281V}jou)oU@p|n
ztUGt^NBM(lSG=9#Y7p*=hATrkN5nlO<aAKCiKQ}`IYxuK)y>--%Y9Bnx<+}R{-RH1
z672kEz?<@lu<`atoB!Xt3of62?PB$cXOYZ<Y>!WGZj&4CoG1MMXvIPsmwd+adLA~d
zLlW>Svpad;7d1JUs8!}E5u#N?ZdvlN_0za|gR56=3k~<H?M@flV}AgJ@%p{@$-hRC
z5sgd$uFrLNCVX%3-D_OkQeZ}Ojv4U*az|VAfARH}L3MP|)-FzPcMb0D4#73J26rd8
zySoQ>*WgZYcXxO9;J5Rh^VN6%+)LGl+Q8loEvwfWbIkD=kUj#4)rl8T0B-bU&3Ur7
zQ(<<XF7x%_tt$aIElMfC%nv<tv80gL09rtg2dUlK;+$wk$=JaCe(~ZN|Fke(tb5Fp
z=4X5*m5s88yjpjjPw#K1t%Hux=<T)v#*vobn}o|YpbI>YW+<=DCQ|qoNPG@IqoEt#
z3O_aEU1<XniT~TmX{?GVW8&#^#=2n~nr0Q(&_Iu)SH)k_<V;U=p&|Uv4HCvFyRhv;
z!o6bXO8#XkkRSI6@Org?XS)e`aOrf*@m?8&k3z;Jdx42fs_nbh6|Ky_Lple__O1{_
z2bvqmN#KgdAM8=2Rdrz_<Vy2rLcVWM{JeYC9E2c_c;_wSO)h<7OeLzazwrPI{T#S<
z7a7Z7K+``cd#`|=T|9)``l}>>36Q9BOHvX1A(88#dYuGeC&0k)RoFXC5^(eJ0RIXP
zfaAo??MD5_mjJ-NX8_<APa^sa;sp{11=&KskIEX(ZuPSaK;|*<zMCt_Qiwx7e7I`<
z?{$_4^mT*2(EhXf{Tl*e06I#Rdj&p2>mh>Xd%DsMoJgKV)kb{oHnT9NUvPT#_sP!k
zHwhJ6pn;H%7?Z|BK5ordX%p-y-m59v8St1m0p?Bs#0<`fZ&BH8B<B446hVZ3;R^qZ
zetu+^p0d#70QH0t7`Vjfk)};XV#ub#(S>5jwmxh^Y%G*6AY2%gu#;N>uTvwZ$m10Y
zvTA39Z22t+TT{`<GQr8kR&o8o9sk$g8x;*XWVX_NArnf1cDX*HilBJ@S&J?kcCe31
zf0Im_a`emD<zF8Z-fIblCOogfC0@EfIZ%ZPwhZXmc!UD=9A++b3+vP_whGtyq@3JR
z!<!5o=uv;Gj0vk)peCQIrf;0Fsf~*N^E{qbErLv6E3oMUQ=FNEG{Uj$Z9Ez|(<HMc
z4%yKltMOL4gGkCxmE|*9^-nDus9gW<zJOiNYgl;AhQiNGAzfyv*s?nSD=ub&F13et
z3wUX%^Yj;FU~3$&;**Qe?PJ4bInP+F4_I%9h<t}F@p{PPlN*XZmWQKGH?CCtN|rh)
zZI$0PC?<4Yp+a<Vno5iThwU>G_0UwSdh6S|a`)TCcuC0?$9)L6(`Mf&K;rFl(WCMa
zTl6m6Y^X7@7RS-+%N8o=8*BN<PShJV9d5~s6p`bDz6qD1Oy$#l56QI){4T>qJr8o~
ze>zU6#7{Iya%77^6H4FgaSE`cCy=DDa?Cb9RIcWLYy^UG6i}XBRG4LV-t+#meQ2@+
z6cC>45)g#k-yH;ZoMu?zQZ5l+!GR@zJ5A&=pG=|Ti!(0MmnK^WRxN?jnu)}N`9W74
zVr#(Rw$Rx^=%I9|PI|6aveqfx*p)N=&ulO)0$9kl-2kxILCX%na8s*z#uI-NnA<VJ
z#H{6w8w1!gP5Q0vO@)shM4<{R>_smj>F~KbMdhYVm&$L-3>)EG_WsJ>ja4*mi=|Q9
z&U!05Y+A~82z?;eND*}vXsDwFep;z*E+)w^steSpur=5dp5b*|!0V{%PN6>+{_vzF
zrpD{KV&TKN<Y6^~b76G5mD16yKWc!4R2bbEB>jfgsjS|YL#@C-3b#mj&Pj>>>ho$<
zF$EI+0VV*`9pK<U(A|E0jR(gyn$(h+=a>q-Wn*w$c+Uu`d^&*Zsk~8=Kw+rLFN=!M
z(U|~r6JT-=Xd{5&z?Fyr;I2N#);7)!f5A~J!Q=f^UqS@r_^8`VpjQJZ7-L}&`K|4F
zot2!28f#N?e?;KBOI36pU(1|EaPpxG5<9qt+x6@K)1jLs$ww;|YukjvGX-1b77{Hx
zGk4*}d1kd{!-wXnj|y!GV1b<_nFq*R8*;47n8DmufV0e>if;9<XxyZS-Wrxl;1%6i
z4YmNX8oau(%Aq~>a5+WHPx1;N>lYB>0^Xx_MsUY6W?E<`q<aA>>&~DkMLI179sy#l
zZw4Y72XA-3{gqez4ki#GOdtiSy|O%S3WB@M%R>^aCsKS>30aNiEfj>d`*WS*wIXe;
zBuPiH*|<gi9<PmR(c#`H6{fx)eIX@_hcPdon}Nqnboi%@=Tkpi(dgjf+}x7OWlx&h
zhd)D~quK`tV2QsR!D6WRwS1q2vCCpmym2ixLOx{^5|yB|)bo4%6%l6BqiySaV7MEa
zvHSOs5Xeh}*_K;u|0v5Y_vX-_MF+pDx9tf^?7+?!`HV5GV@_|~>%%>u9;gVHVMz~<
zzc6JcJY8&r2rlvXq>0@2-csG#NX)44gy<U?F7>PZ;ga7(ttYWu9OM{}DCi)G9@_08
zja_{s;%h+}k?U=>)Km`&bahPRBxX_y(uVuxmV6dC$2lG@Hb1Rz=kFtZ{t{i8r&O+(
zZ0Yy*(aw;rBO<OY<0EcG6ci(9b;l)LQwIJsO2^dfCfm+)bAU$K{&2cdk=L`{n)=&{
zk=+{&)K)&*Hf!Nt?g1V@X^;oN$W>kTyCiI55E#+34keDs`U9jT{i_Z{F#Hf`ta1uZ
z=aj?Hi=W5RC$_(CfKpLW^MHbjFe6}m;bJj|p{6eIO@{3dA0?y#V$6T858nDZpXAm%
zKy_DO!f(9khyn@dRT#?qSPzO3gq9B6ZGdJ5nEd38xPhucoA%qs=_br`?ea^zC5s;b
z5rfxxH5{(8G$k)C%(J;lv43H%`8B_LVb+Y2Z*9m=N7YmLG(8edd+;DfNl&|c9UeAX
z5p>ac&SZJoay&k;HuZ;_6jA4LzG^66Zb*1R$VS)#vU#K)%WkcYl7gLuZNzdKyZg;X
z21!Sa<M%!~a<4do71S-K(-kFM#e8&z`pEJurP2H7kzVU`7A$t;hTo2T2mo0E<HT<|
z<FpR*l>|6AF7KhraCot+Mh26H*1w6E46n}z<}oe5a)q@rnX4h|sISc{`K7kMcKb?6
zkKkr-k3O;60eHsa0Xcd4OS*xhYX)Vvu(YIDw3e0MGzviJN#|bzJP9csHj(2W2*Anq
zD@(KYD3@V=3OVUdio&}n%3D1dj*&BtlqRGqJ7n^UjG~yFl&Ty|PQz-f$^tIowA;Sw
zyzxH9fT$Q3@B75iKvl76A=jVN5)hkDnyU6<QFRPO2FD_Xp8=?77f~?`z4l2jo5dtp
z*=3qsX-jHic6(9s2^}qCl|&3@bR$-GZaCt3ZQ7>XOwjpJqdK}0(U&y&BH11y{11R_
zbC}_ro@X_EOk~DQf%spAFFInSmE3Yfw^-s^Z9!>jvXGtk^LAIZF&q$4pHve`I$}nP
zm$8Z}gai}jA81E8&li->Qg=!Y`tg{J(vlh{z#0e~^XM;{hdd4`YlSPFURYmoD%~~g
z--25BjHZN-DMc~ncjea^8AxYwJpj6a#GjA?aYAsMg3idX&DiMC%xqnF2xzaGPazKV
zc!wFy$vv-T)Ki>`bWVuMvcBuTJ#Wy9Xy(X{D2}!;P?zpVR*Wiw*MB|o9_!=EVBEpY
zAd7!^x2DB~E1oQNLSgdR1W`_(ENz13k|J%at4Q&C4`~8@<g2ZovA>K`frtV)>5<UO
zHYN@l`ZE_COIfuwH;<Rmfi?Ny3-0HsZUVCd8zX^y)2YYQf2+n5krQC;0AR9IYhV~m
z_*L!`+IEKo6Ic)S*{UA%ln?g3#2SCFkyUz#&*~<`@2m_?dLP;HrM`#2|HAyrzx)>n
zZnqP4PC={fKsytO&~)SmT$&&;!rXwzg(uwM%r<?*Ca<(}G-FPK5C$ls12v@PE}|_t
zRdg5)5Sw@6JOGmh7}re^gENa^0r|x9SgL}K=;WG5S)eu+KsfJH6Rl4@T1W#P*IY+V
z1P6eg8l|xEpJQ*rmfCNSphRC^fQuN`IP){^MT_z2L&CviLRSEE=T+VwCSsg^vubaf
zS%5)eX4uenhR`jtC4%C5$>UmMxJDlu6-ywm3pHtRKJ?5`sM_LeS71Q!;*{TpcgYVS
zlu6AMmzDo>M*`o<OZG7;Q;s%Axfr@equ~Y{9{(e-+P_La3D*c#Vl!|wS8j;pNzcUl
zYU$W9m!RV3%-mjonz0Um@U94rL2qu1*x7v6qAkh4S{lw+7XF3S$8?CF7f?M)q9iaW
zc+qs0uceUtRO9o_nfXEa5xnjKhx;+YgGQ<%>v;Nq?t&YX4z9ydy`)A)k*L+5t7yma
zN?t++>sa=R023B-selL8q0}sDLG$XBQO_07n(;kmc`7>Efn3U>s>?x__`~`xtkAr&
zaUF%%=6QG?5GN!8i=wa|CS5b7u$yuq-*8~KN2eIJ=_!G{n2Smr9`oGl&rzYpCskmd
zq`aFS%HzRrHD<xq24Gy=cPiOBf^mA>xGhYm`>Txf`_F1`x?rmP3yQt{O4^Xvl+^C?
z?7>lva5$E<<5emn?<?6&ucHPC2=nc>bK!W)4Ja>|hEeQRU~*r&X8TZ9M{MPHzwN4$
zJq1CqYkyw<N%s18<m9Kmk@jd7=(ge<5JtqTcG0!|{2gVkUoYsMZwyR484vQY+A0XO
z^7xdIkc--x@3*cHcDz6SCJ9y6n9o&r36EYWC{$MbvfJUJpr8;33X#AX#NkM2NSB0}
zJZWOV8Ig_dLo)&JZwnQ!Udm9HH+d!(*BgZNbh(4*tj&@E9?vQwieGx+ekR(2S|O^h
z>K3_XWcsEF0c<K13MtBCj7o8TPIdVT_=fi94KLW|DvA181&m2y>1<nk1@h^t#aU>6
z)&fff#5RQe=fo8^IjCv6*sAw2WV%1Q+nLK>=ms&DAv;h;wl`Hg4y_{shE#Cf7-_Yw
zOJ-R=osm*R-XRV^9bNry(P$=`QJ#k^3%LOz;tlSnM36ZXqtdjm`cyWf92i2a8Z%XX
z@mYuLz|qlSvZ&lfkA9i$-JWkHu3zyH0J2kU4B?)(muy!b--@nnN}}ko(@bP2)h=dB
zO9y+RiB^<Pqx@M=Ea-18XsU>|X=z)ku+p+uiU&J(Rp#zsFEdom_qufQ$LbfiGbPKM
z{(MAxY#UKpx8n29DIbO@&IMCIp{#K575(*8%i{Ek(|wMB@C;qHSnxKay5q@^&ecZw
z!z)|nGaj~zr{cg@beUq_7-XY-fdZuM;9u94M+G~GQw9)&7hNz2d7PhW+#m48o3?#x
zkKN_CF!h|L#K=_a-;4EtRiHEQU=#8MgcOSfAaiX=kxhs*1|TB(jtMHEpkb8&7}S@4
zR&YvS%?Ez3{!cObhgu_8zy;-bTqeLKQCo{sOJP4nJH|5Z@0A3{gT~f-+>qW^u&Ht8
zs$A5y)knunXQMe1cp&_9G9m^Hu0F5PD$f6*+2jzXgPviJnyORF^p8Ejou&tkviH0f
zeaY-InhKAMAMkcre(uhCrvgh`tU;N!Gp+;-Ah_-Ez(zZ&T&Eo=ZMO{;?iLWL9;_@y
z{IWY`NAEaW2Z$-_q#c?fzfIj#fdTzIC`*cobn7o%h&U72=L;!p#z|d`-}<{~xq{PK
z%m3+23>%(4I&O7&Re??6!EM<Fe=j4AL{iJlPs&el?=#t0f&SqiaCkOoj?VlAMV`_f
z0ehuX1c95%rb9U%rAD@LVkdZF=iMwn71>y&yRRBrftaP*d^Y%~0z@6~0$QsR%M2}q
zw05$~etrUF8Jf{)veW5kU{ayl;H>I=4>buN#_NN$_EnCw{+N_A^lxZ6O99j?<Pc$J
zfr+fx4%*mqz(trCZlS2hV$2QYm1k521qoS_j*>V4Y*uvzaEgM&-d?Hpw=PMw%89i~
z7I$@OD9tK|ho^k>R#P=#PLrLZNn?x##K4^nF}t(H#$tP1%%~4_uBQ9-g;DD?4`ZtQ
z4X(N34EOXn{p2`}GwU9n9^E*pP^{=4o(|At)mM3n+YUKoo_>t_>U^rl13eP|0S{f%
z(_58Q*CdtKbd-m7UE`Ws>)JM_bNBxVlu)^v`MSP0xi5kOs|mkMOnFnGPB0&c^u<gH
zk0HrbWIl`z*Vc8v+l>r&-MyE7UaDSqQXMiLaH3iE15<3Xhm6yU1;uer1p^$odEK0X
z+9>+U1HFjenX+c(-&uP#|1>>7^*JtuQw17bXFIo~!(FAu$x*#`BEy#;b1i<um@dp~
zJ*2}w!qwfMPu&g{W3It`l>h5Rv?TE}h2eO}piY1OS%TY02B?|xJv{`WQ8(C}1aT9X
z3#<S77yqejp1=uOrN*=-Tn|Ri|H6KqCUW;b+l4&d(gmb2tbY;+`#+o@@K6IlofeHh
z|0jBDL-*e$xJU<RD^+%2_w})=;hdw&cC(gJ7+u8RrchdL_hYo=){0f%r8q~IHf#P&
zw7fD`J(Aqlx9lQ7n`7HeYewW{=%J94ouV47E%U(&Xg{>zfV@n$msiwORCnhs7`8pc
z!xOT*R5njtC<M`$H=f|$-R*d?$pN^+*iCi*=kKZ`lm6ZK)!igmqr(GO^tt5RXZ5c5
zW^2pvz0bRqC(}HAyW88K@2~#{_lJz}+v&a(Wr5b_D}c9~%VQ7vZ8Y@q^74NmdD9i@
zHP_eI|6v4i0s;eH=Ro8DG@+)sV>9+Y=pc|!20Z<d3!09o_g_R?C<Vg5tUK<1iEscG
z5E6)#yLSHfV<+=jA#gT=bkhH?msosL#dz4Ogh8YD`~196ISa%Gn)grF=ykWWv;f<0
zc)bS5e*p8Z0TvD}vcJC{8i`Ov;y>77)b+>vYcw7(da;jgZu(55vnyotmBpBE_(L6Y
zU<!@*g&>^GmnnxL5>UuzYIG(8*>m5Ff{=&=er03;Xu<cZ?$2xv8?6U5U?cV)Z|<O=
z{tQ3bDs@`xJkWsb!_YxEi5UEyot=~QR!1Oj@S*rWVqs#B)p4!`+K}r1@&xsbY*}1W
z6dt5yLSvKn2Wfxau~((cS8*C2T<-mTKD+ENiUsl()%t?9>8$3uwhKGj=^Y|_jLvSh
zAOZ5<NNNRURv9d}L<Kp9bFio`g*6@!#kfEf(Yna;gjI<FrESxg)0g;E*!i9ZgxfWk
zvzMfw<b0{GZfx&{el5#qP$6F@9zl|v_14-_V-dKD|ApGC=3FJ(5^%G0O#7EqQT<vV
zyj3)xv35!KE`n_Hy{a00lKhfJ7_NWC`!45;h2KZ9A)`9Iz+b1uh&><3p?6q?=@+f~
z@2mqQy$YStt^K;5RiaeO0X9o!nD|Dt+*P;}G+Z36b4ACI$us2VjZ2xm$*{}rXHTt4
zkV9q|gm9~$UpRVsgmH7VWre=cPVKDMWzN1M6nUJ^8mbd{GlxXfBKvM%dTy&Uty&>~
z^}EUI3{$VCbp>px+3~};((7j1)<?+GwJ#W|Bj@6H?6(4^L1}b=6Os3$NE7$NZ1QEr
zRL|X2*Z>Z8&NhUFUv)d`6|&4A@jz>}%hen(*O)e3Du7s1r`|=h1WH%>nS=ks29#W$
zNxh9I58-{Y)+4J=%~|Y&$S?~(#7dE*8l7CT=iU*I{L#NWMs|BnX`)Kd**G^{x(vI5
zY4XJI!`Z@@ly%93Q;O}bl@MW?zPP`=f6zJTmd_PB!RnPS4Kw3>v$zk^I4K#-b<-vb
zBpR{!=oqq&bb_E9I0OhQozh~yba~{0726tc;P-h-+$QXa1)xBpHL!t4l@#mGugVE|
z0)_@WP&-_}emi3TX-J;?egLAd>fBA=P_3YuFq!`Ch|aDP#QVG`pf2R5%e@6<c}xLr
z@D=D$*D6ek=zWV;*TT<Loqs^!m=Y>5q}_K_O|gFBsnTLp{tfvawh@_>4pFJvuAVd3
zA>GHIXQ)@Q(mkPnVXiGs{)#%FBd+31ml8j9bkO#NpwnYq-v|x~IDWve_o)F4eg86R
z$pG;e6hQV-0$(pGF2EXq22^1Ekk#)Fz}>hXOxnsKPAhLv@Qmp9u687ELE9H&QytQ1
z|1~61F(^aY${bvt8XE3dgH8ZwcJdNl*Cpg57WLC&23selKleK3fadj1;8<HTF(+y(
zLk_g}Da~bZ$zcFt$J}a%Wkt~_=XU+2l@%D%AR7@t3T^EFNuebH)NEqhTwmbpTGuL*
zkniy9*1?vlX8);ACXkm5Ah@&0+%K|AoK=x$H`wNqD-VAwD#qa1I?&hEqvrtKW`h#o
z?6(x}V_O@$TIi{*`AYlxW_75UHlI8br@EORYJxymGT<%)Y;Q4zAmcVZ>>^$PS^9zn
zh$5LlSI<KI(A{zcZvtPFp_c-n?kYZfA16PBHlDv~xJ+EOdq;%%-I7%rEVBT&u=d%m
zC1H?y_|~cQ4YZ9exU8m$GvCr|vk>|v#fR3fx=TaOtJ9}4ou$^sj-`>@uUhZG%bUG>
zGH;V}<_VP(R7kn|wnM{&cU7UUD-}rkDsOBD0_nGQ?Z$QVd?KUdR?oSpi66e=G;q~F
zt-Dzp*j%wwnqDVl9yFhM<n1&^BBg3YUn6keb`ZM!5L_V2l}Q9>j9l?x5(x@rG*gda
z5jH34Be?lWrzy|3e3fhfO%gK>3{G#Bt<gu_k3mj+KuwSL^!A``tGr0crDpE*o~bTs
zwA@1plwoD;Q-`HVT!j2pn6!udBgy8IV`InLlvMY)31PbZ$VCyIqrG8%SDa0Cp)}_S
zXK)CNTW-I|D+|axZb?uT4+R5Rd9<6g$tL?}uY)^3lji`M`+ycZ2U06QgB)1+veQGc
zT2TT-N`sWCqDs7vt#w8Q#0^ZC&+K3Vt%-orD6bqm$?^uMjy{}z@}8n>=N_Xt8dEE7
zPA?ICeWZ6EFGpM1^+rmBar>SN`q7YF5BJGKAD7e2ry>Jk!x$TGe;us8ay+0oq~QV_
zu7)=XsSY{&a)4Hnc&SFpp4^vwO0S|uKJQ709PBbkO~%Y@^LooWVRG;QX+Btjzktrj
zzzUq<<bY>p#3E+i=8YmN{r!%D0A{;)o{jjBAZ87Zl)F;NWokXEQOo-ipt`Mmus34S
zGGF>eZR)SDS{09t-`ESaGs^T_&~pz619W?9v!X(;M>(EkdNf}9{!@I7xhUO2Vj@dY
zofF7<knw=`1TAVI0K2GKJF1N3HyEA)*qi?)d6$eDl?)nHl&ABg-%5)r);G>7dbZ@k
zM!r%H#r_eQh^e&lx00q;p6mENwXhH=6Rtt$)e2F0Zw@{VF@7=J%0sKe-g!~aEL4IU
z)tV0^g-7Bks_X)_WhK_H0KY(eM>9eO+5%HH1?jRvl`Bm#O_^YThe!3+$<p=wFFWI+
z(ij+<t<Cya%aAYpbg2PA*#xXAEy=_J5(u3Hj-_z@ZR{EsDdq0-AkK?`9R*lW*EQX-
z%rCGG0WE&NBP!_Tx5Ac$<rs1~Rk>TJbxRBihM61z2jILjoeT<&g@uz7N(DvP)KoK>
zYEH{y4kLt%naaMTyzA`NMXYvg<4t5bNN!p6rI)*_u0KwNe9c#5F>ZhU4*-53PH8Rc
zD=jnvO95|F^6`-&U8jfId?M4Y;^e4{Qn0DJtZ^M*$VUcA6jtnQm*}%s>pfVmjQiM$
zAx99(Face=YYJRgKUR8GXOft?*x*8<=&R?xC68y)hq&E^jWMh`cCXl8dw@5PQpaT;
z1_@~JkH}CP+5@ASj=9sqJfug8DCqPM-5E4Yiz^jTNU+avozJk2$>#6^(7oSsuTPOt
z622T6;T<1f6;0sq#Z0NR*@gpA*!?gP-I!s0=(|%Pq+b*bI9k&*B0yiMcPUKAnRAO5
z6!3T8h)p%(9V;rE(!;I&tpYy%5k7)c`th6n3&H(vpu(X|R{&J3&z(AGdU-`N4xsh)
z53K>#*zXZIFGzN-$w4TS#$pqy;8&IyvX>-%3Ja%4cxb7E0I*&yG)xu9Fb`|=>Cj;|
zj61l%_QR@f90|{zgcSj{xg$~#FFEnE&wn1ZGl?YB34JgA<_YA_PVYhk!j}n<Xi#^a
zL_+~A`scWhm-e^D$j%)yNuzf+9K`-Q9UV;_WlM=)-?AjE^gul3RM%~-lUEwrL`eW<
zPv;^0rFlb6sUt==#B{0d+?|XuxX0S}{p%t};U6cq>D25S;iqhArnK#~SPTelB15A(
z1J}YpyYcrasP;F29Fr$)a`XgvmMmPp$_lg)&2B$7v|v&*uYfL8XA1yR0i-5<iG`#Y
zph>!W=l7Q48p!c{Xn*aIqz|>&S$txpg#3~t-fXml{$&bWBCBi9+z`3l?d00sQ#XNx
zXjNwSyD64DA;^8pKE3bzny~a3M8XXF9y+Qz)9={!+w5;b^WNfa^Uu%#afRs#lGpYp
zY{Z0)H_zYgi%I?#aZ!xuC~5!;6T0<6YTIQ1sGCFg7(=HY5JmpwoI<?uUV!WBSkE(|
z3UEULre+|J{Z`nUSL*tjnXNIh<Y78~zey>?I-AU(fAPn+Od#cb>qqDfvjoshZJ7`k
zjG9xQa<OL|4&wsWdEag&UCKaY;=<ekG+Owz>jL$%PI{kHZnVETjhdv+>wngZZvhWk
zX&%R13xLz8Mf}RKIB5R~2Hfq|Pl2MFe|WK($e+mJoVo9dZ@Wrt)AFbt{0(H|HKO_w
zO^(n0mLMKGDQ7cSf^_`vT?fvG-_8$^pdK%Qo|<>l&;eba8MED8B5*ca`Mz&(?KI&U
zf;xv9@kVN>AFfaG6lNlRW2H9oq})JvAP{Sl-#0$P&l`_XN}^J?(*p@)7HBMi4T7@F
zZJpS8@qHmR-3J;P@YP6|2S8Jk*F>b|$55MkZK`IaA`}Lv<V;p1f$D+|ZlOVdJ_|#>
z^twG^NC@u=U^=A^x*uv;KXt)RxLGcBQO4>cXXU{Rv+6gh>a`%VdS#OGsspzs;3#b0
zlmPnC3VZybUraA-@xRn%M0~luqM<BpwxC!QzqqTa!u5Nc5Ww~<(yY<He?~fXATKge
zae^NlsL|;73=c>@1R$mw)*cM#SYOy<+ehx#6MWs<)3OmjrS4kI0aOxDstcQ}t0*^D
zCpXa5UI3`Rk{+x&k|^C30M0YC4bLpMV1N>SBg=Z5EnXrz&r$5t>?cP905jM;OdFKI
z+~GK(Bh_~@j-TlX2INRDd94<S^U>LTuvehN9Y_n<)yjnjsKBMCjf&3zSK;zdbE|Z-
z7}UIlsyPgOSYu@!Q+}A7?)Uy*G$;Rck^~?t?siKtDe3?Sb9@I^)&#NPcEYU*;xpRC
z&roE&86l+rrz6Bqm@DHw^2;sUL7^78J-rQaVJ2q;;I7%fntCS8Mn0QeG4)lgJwFAn
zUb~;Vo;J}415qJBPcAQ4J<$RrVQIeR6C3!26I16oxu#|f$)E52W&#}`Mn?g)Sp4Ml
zB;DnRA*RAxI(io1t2bS&%=}xuVin^1`Bvd-0C_IJ$W_sS1)1%}5*{8c4DZC8i70qi
z@8GG#by)F9G-nqfvFJ#@5J)Qe>cXe<q^~1gJ{5w(a%G9uQrf#CTt4L=gDGe4C&Q*g
ziEvt1Z29Hx1^e5lNJ*eN6j8AU^)IX6)t8CJx_W3_DJnHVe|oMLg;MRgEgd}LwgVKL
zUNZ1ERR`@KE!HDm1Q)k@e*VUmhLc!1nWy-pD$>@!w>3xEHv@?kx<$rS6v6j#bLNYV
zSQFf{x&7xnQj~pKghNZj3-y)<sxEy`<mW0>!f1LX{<&=TJ6~4Xi2etQ0kcJ8J!l`x
zbT{pII$hsHB`t^4PYPBkkta=-H68b3>HH=5{?<<FQOfu2boeEu-Btx{j?CIm(;P4#
zdA^MUy~K|SXS*@Mk~fl$0o`|xzsBymW$Q)udUGA^s#J!AbjYp9P;DB-{kZb(T#}}L
zgnI!SiGZGqr>YCD>M*2jr2W#NuDk(9Qh<?BTk<SCr5uARxTNo*B^s!p<Ky$GEEMWX
z?CiEQt?D`RhF02`oZhFy>J;C&mz8NCFOgQ+)ED>$d1t!VQoq6u9yWp>Mlogk4Po8(
zJ0@u?Fg!BCOB#~jMGl5v7}=)xbl{D0i$+q)Ib@n2(2zu0ijp9cA7IzwI!?fzSMKi8
z6*p-M&(29Lm^imJ=qH32Bi1I_5|LcS-9jQ(<BRmbx)UyMci}!?o_wp`6Ky!iSFEL-
zqbBbd_`spG26|VRc{099jta0z3{I>0-mC^-?z(fd`?W|+I)pGyZ`=K%PWb8RW!&U$
zB^X6+dt~}^w=9y%`?>pzJanpQ#N(gF<OwB|Nu`5&&~5mZlvBrXb#Ud1S2mQUUYxO3
z4T+OdB5cyyc~s46eNqiM<K;stSv8dHn_|gSj~N{E5k5|PxBl(F&Wm(<Xf4OG&59rH
zm~cATe>u|~8D>LRsPHw1ehY!y(y(G${%Mq=>c@q8fAi_5%B;rprDdAdN{+7=4O-uI
zt!5BxFE__8Z85xvfyHs|EED|9VKIrZTVYlnY6N3g^)<?!g8m>Gu6Cz{eL2fX^o9B?
zbRoazbdZ-;_^@tpe9?jIMfSw&z0l+uH%)+`Y%e0|_cvh6Xa(c?k$n(So*I}YiK^dd
zFyUlH-hzBKKRBhuk5}C^4cQG_-xuapw@Y`CuEDhO`SyMUXm8x-3%O?oqKJgJJSf#O
z-O~0WmparxcFTNN@!P;-AX>!x;&Z($N&gyMpV>bJ6XU~JAKrFYnZk*!3g<{;wM;p2
zB5zt-g&+zcBf?Xm!b1^Ltl_(3PXxLaR@~;6Lh+<Ys4X*7IZiwx;X2YCXlv~xdqWIz
zpVN&SOCVjH8;R^HS*8XESmv_ZN_?b-YSK+Wu7}T-MNczMcT9)a=NK_V>CqOym{D<k
zJ(lKjr5L&C%~B$>{R$tX5nY&vlc&&A?6=_`l0UgAL$+E-9nCbo#PLbfkBABF0Xmt!
zRcK0s{{z}r(`-x#+F`j&+L$gyQhU6R7V3y1Nw|0(&31=J0d?n(Up>s|-2}YT{Y17N
zg@Sfw0~SLoH)C9$1o84Qy>Dj{ORe9S-D;w3j!mjT^j1$hCLZb^9IPf1az{$$tQDLG
zQJg=zk)?R2Jg*Fl0?_Ik*D`X?HF0&pV*z1&%r>lFF#>J8%_lDL1mEuk1=c=PGWW;|
z#YZczpcp^qKHn6sPdtFrmrobHrKJZOMqFY#&z>MeckhXn(g#uPFw}}T6CEEBmnk<`
zh6vk4xdDzLVLQ1;HE@DOtbNBC5=LUxY9ls<`U#2%{sTJ(`8HCg=Q|!(@D6?^*%+&N
zE;p9BJ&#r5+E1q(@*b+ooWu{(<f~%!oLizvi@LQG<u=&u+BdAZ8cW}h(F@Iotu$Ht
z`LZ8~{*^mkp?fJ@h@r2@>6W>z(S?|h2M5h4f>SZjCf!1yp@in?j2iV9U^hA)s0gnx
zPTCwd;;TM-tPSd|hG_UBG)Le&BBMtV_qaM*W?be}1W$q5U*Q867$B|IzoZJ*;>t^L
z;h4Bo|7AQ<iOxXjmT|P&SSx?xbB;jG(a7Ip1e+VN`)T4@2?AdAL=tV9>+U7HY@-T=
zm{9F?98hK%;B__;rjyxd#%0lyg(9x}9bT6R&HmaR3sc4+<O>+6qd~<mrsryrD&GK(
z?N<F_x{?;`w$G@7cVtYpZqDpB+VQmV!?e>k?Vpv&x)N`{YB=q}#}@PZq5H=fn5m0)
zyo2}Vl-hA&)zy#X%q?3YEmQ}*Mz)c6%{SLzn}-?0-`tr){N;TE?lZlmWH?dvxH}he
zG-D5|WA^}?chx!TExhpohUp&KoP!Pw)(Zmk<?V>o;QFa{inpxj9McEj%LJ=zMjDG}
zubZ$Hy-W)&zKfhdj?X5qAhd6w*_E!(jrfq2B6lEQ<woN*s)?(FDl}-0vPoH{<3<1_
zzgTFmMw7b}jvo{u<(LlX41UX?6RH)`1~;Q_#?qwO+GuH(Z6(pKV-Z|7Y*9+oa{S}g
z6KoXGMl8y7%BaZIfjKGuWm2P1&T_;*S$Q6!#3E7>%abbmI*>q9?m$D%q(^t`QkUHk
z2WA`s+x(|GVQM6z8y%BEBF1w+D`sFSm>sC8ZF=>u)nw$|o{Vx3Hz%V8xtgzO6SB7H
zDZ|F(J_~m}0Xt13Gk4T>kuL|hIm%pNzS&B_+Z?j^7J4kf1^m$GSZCIn8!za!->0*u
zZF{wk`b@AYQQ0VTNTOfIo)^<$gQrLj$AvRDMOJ>$hIcU~1~S918_K;j1x6qKTwpzA
z%r2*f?~1tR3AL#Pi~J2gp-`a<5B9Ctfp)gG1{<I0##&o0ZfygE0mr`8U$!WcZ~IjC
z4Oyx)ulGXq)d2roa{O!>WuZI<)EB8;2-zd&o<*CuUyE-J<C5XlQ_UaTucB4zN*<Un
zl<{#sSfMbvW!UC@adk*zsp)2&-m*iyuM!wWzkjC2FXS-Hhio8Y`x5>b=;nzCPf=nS
zUOSl*C?2x*b=_*^sO+?R-0zj#jr5q^oTnvLM7E`(=MQQn$6pMt?Gno`eb3~o+wv#W
ze&UOzQ9;6GlY0;vA$MT|vho*}zXqejsA<4tdGhJdsV~aifH_LP#=I7cb;|CJGYy9S
zw0HkW!FY|+YR+GTop-R<VE<z3TWRh4=<MaSem-);-nFW(I93FEUI@=lmnr(%*0>+*
zSsztPJOv-tvF#fQeCORSP?-99fAO3j5GwtL?Om*SKHN4orOn;BG`Az#o<;6XvzB&5
z5SU3B8vo#Oc@u*bqIq}H9;#jG;|yv6XWq1T8^Q>aQ&6z0)oJ1bu2QbV_BdE9+$8*B
zM()M<%PvX03$AOJCr24OUehU`)OUgAaKM9hPaRdM!U#E+(u30{mLXXcBN@$+6__Fb
z(P(MNer4_dEr}~)bH7vv9*h&@1R0=k#H5ixPU0bo41WPi$P0Gjf2d)gDdQ)XWOuO5
z9CIyLA@o6G#^XChOYi97e{qA4O$6mEb`T+4lxcHLTkXg2)lRlep<2X$1#2K%KU851
z^-~Y=q^fE+2ooWDV2eK<t>#-8>(So}Gq$*+E&cJhDJ+sTED#6h9Ku#cinaR<xsKEo
zRhjo5SJ{BkP=YHHcgLPYAPs&A(_b!y>HAU(A!L&-pL6^UmM`6I`;>o3J>Cau-z|#Q
z;H6POJ2u;2yIma`4AA-+`D?sN$Ngt;dIL?{CmLG?R5w(vj~zN>8BI3FG=Ggcv?!6h
z19HcdKf@^tI@zABUvFe4p{bE%q6~6{1I@<#N*%4t$Lv3lv7VYUpcawBg(~q;A=D9W
zpp^q3MB`pOcDK7~LzuRZ&CH99DpByvMf#(bU0H*U7m>fQomOhLz+OX-L%NDxLJ{#H
zCo-w_FE;lqf$M2|*`eXr#I43jN!H4GGl+zR3BC4`TwL9$V3JzYYU<-ckti>rLGwGO
zA=|v$v&HL@;G4syZ*rUqSs_(>btji<m&v{Ru%b%Myj<E~eShFsZTH?PmPPBw(F$p%
z4L7%2LL%g7kh6PWu=)#!Mcv9@q0a}>2NHfxZXcX~@v7);2L7(5cZ<5?Nx)Vc;==HK
zt;$^z;`A=|i8pE0=_-r(stGZgyG2&iXK8-kF>cr-H*3p<zwu`4@>Sl*G}jj*dR@r3
zH9d?Od#1bKHDV_eY)}53-=2*Xw+71NyM6tKxZaLAMR5t*-hPwyF4POX-T{RF-Q{>N
zI1H>%M?F^^_jJ@>g5%im7QQB9`Gm}ogM5T<$}`8nbm_OuK3&#M<wHAuto^cA3%T3^
z@@yLxefl;rI>!|j3gQkGFVl))StQ+zL$>N5>&Cq=G59;_aRvOn+nmJJy<SPUaV};0
z7xnj^QKWt$sPy<CgnRX4ZEW)pOpfWOnsAF;J(IWbK9X)%40uy`#Su0elsrk^fxa`}
zLzDPXEe<(#6{h+01=$$y>~g30r|#mn-sVYJmm%0?8r0TnqV5mRVoHl`ll2;8j_Ex~
zv{9j0|5|@jCj)SPie9J}T3~BiU+~Nam-<<!*%UqCO1qm2{i!yd3pu&_MUZYFF;2&{
z`a`S2OjICf!vPPzl^v)JZ7XT&@=YLHD7kg+P<*ZbN5}vB9oh7A1CNxx!!SESMatV~
z+6(S7$K!lLX<g`mw8q-2XgHBLADr?<;R!-%JzwK;l^(;6gat%~sO*XW?-u+==nR_?
zT}x`wJ05ILjE?7oFGiXFY>IZ6$f?0)9oPI$%%|fn$th=#@GZ=VaL{}+W*hbB#}BWa
zSKQ+X4933@Vo8Wqi|(ij>XL)XstuEK5DWwcLw>T>xuLWzr%L#pnkyx@l5jVYu!i2E
zN+Olxi9eb!=VGdFivN0=kLg|H*F~^GaOIWW2M>Ws!8nB9-rn9o64c0*zu0Ddf#P{o
zi*1PD7@~`TnN^+@v1a|%K#w2(Nf?Z9|D+0xo5N!@??~%`1|*9^jaRnUBP9(>Z$o*x
z(9LfTgP+^pFNQ`4mRlQtjA9LG!>1U%BnCFbzm}ZkGTjdtX+YwaM|5q9@v_(Y6NUhb
z45?~iDUBS=hN<b)+o3#jJ=Z*1j>X)t!DpYT>`<DUo}g3eQkmX&*H4UBJe=c(_IF|5
z2fHr+VBmv2JwS$5M<1G@(az#b*bMJwirvrv_=pbQ#)VT~-C?l?kM7ZD!iI+m?0Fm3
z|G!V*!L4(d|NFK+(=P)*1UBFo{`);v_*(x#FF5%ef%m&9K~4@1#2}Q@<Kyl9eL5N%
z+%LY6ap*)uTfuNxP`xT19v)HA(V@i(+Vu{5gZ|gAD(dP$0oI*KpvtYdxHxd^^JS?U
zs@`vy)pXqN)XNl&KJ34ZnC|)`upQ$4_Im!APE^E<UHH7(BUh`drPbEi={;=_NWQSJ
z5X|m`$VEzO#B`X7Z4TnaXOrrGtxXK4rl!`04wYBr`*^+rAme6t;n}BiRz){3{`>RC
zAt3O~$2kp43kWQYPh>B?r^+5x^)P?lgCgItLU|C+YKQRr&GY)}=SB5x4%%1Y?*1FX
zy#%MC>%jl~@1Q+E1Nud7+SuLE6q5J4%n?^l0;2o{$PoO;uScwaoVwN==IL)aLV-Q+
zNiu||tI+-dGZfC#MMx(QP*6VRxhEVmi~qB?)7*fa{aow^^Knv=6Y`>^e>}I_Q!=A3
z9y)o3&9fDV>1cm=gN1AEm-1SY{cWy;m)=2tTEX|1fSX4;*5`P!Khu8)<QEJy$MpXm
zChT#hKTx>;dzb=op!ztbm-HU8JZC(A!@!+gIp>u<-HvTSS9xm)#$0w?S{<Jgwdh7A
zr7wQ;DZDDoziaT^Hhj7t3BmpQ3r+c0t-(9bq!dh@U2PFC7q@6jirbGBpUMVxD~Ti|
z&OEO=F7f`o+%fvyVCmJ!_FV(EdsFYhBNy`D*~FTiW)JmZps-o63Ce$-xAj8ZqkAPW
z>AB3y#$_m6_dVfj&rWJ|JaDxjmD*Y48qq4A+mzFwY!ugE6!NjjwQZ+bw6y=v=@``;
z#4=qJ{ho0P2YH|xoZJ|M&R#@y2NtuKH2*Z$G@E&*zrT)KzTmU0ybj`tf!6|wvG{bv
zf6m|*z*4<A)1-;--C2Kp@~4Y!=KSi_YV&TWKfU>|r52Gqvn$`!kfO}U_#7?c6+L;r
zNy)kr0)0L>nJrk=sUd5La_fZ3XOYwC38whQHaF)f%Xfh&Ig5A8su3tMyXc;A+SPDv
zMaa*I6{v4Lr+f@&_68ZbGBn)xr90BU2TRC9?7u7Cq<@K#rNr}KY{=tRx8ttSu{vuN
zIG7LYZkvgT^rgSuFyCvM9rRw<_4`#fnyS7@946I#)iT@6GEG$kwen*9i@ao{rW7uL
z+4+ZKA*kBmIvL4y$HVlG<3%sBUTxCsdt`U=!*XuuK~)s11fs90appg2`ujtvFbzL`
zjOMoRQvI%zTe2;C>MD^!+FZNGG5GHi|8JijFwU@aCxg92B9b^^=bcAl6+3*fZk^G5
zD_vQg(-z+nmHn|r!&Os?f}zsM=BP_nW<zDh;yc!eg@<Yc>2&_|__g_u5NS@~eTA!8
zcv#sQL-GvkqQgd#=3H|XOqI-3$|7^pbpH6m)cRrs7;MwTqehJpyMl(8r>>}`11XKq
z+u-6D-fR+uX7%Tu?a3Tx#T}A~ptuAIOB0ilHg0=KYpO!rl+{pRCs2fjqp7ekHZl1*
z(cN~zonJ8=G}FR4{jR<i-&S{j`xYtp#h;_{M{kLRZzEBb&+I-Pra_na%53euV77Ch
zqQHBlyXyC_frR9pc7_e%;eL%VW=mIg5reX{vZHbmqdw!|J2kuLy1Pf(mPPwA1b;(V
z*eadQl%PUFTOoDbC-O@H+~&PlNWzNDw>?C@%Z}xM$$9pgAhsXsLDm&5{@;HJH;tfv
zb|ZU4#Wl9ZJck5C?Wzc?IxagE5N${dJ9LG|RrdwUJH0$ZJL7P^m_qTqGb=4FPHX*0
z`h+c-&+iZ?qoysdqhr0oxRo8pnqxy*U}Hww$es!7P?DCi{TMa9gGjosY{HlHjCgxe
zp~BXTu$WM*)5H2%#M+5Tzubbe8)=!wZIV4@AujLZ!@6KSr<Xe~K~)2};?Z)aoMO@?
z>i^0(9DgVz8$Dp4ZHq?X<6s;Sw25!vTsQlMu(RYbM@WK~6WxnTVaXxG^_M)wx<aaK
z@V~Ky0v8R?-H>iFMrrrZ({>xUd-_L$wZ&cUijD?i*Elb@4)N3Vn~YbCO$k3qlXquP
zH`~iyy2Zs5UL1)UpNmS~c}|{kd|rsAvXBrTbCVpg+{sl9ukyS%h6U*rC6{b_U>T{$
z7ChT#vgP-2TEu(D*LYcE$A@iPkybw%RKF_?UR`@bk~1@piexlqRI1zjeMtRmJJjQy
z4xFvw-}vtHeJ-x@5VfwVD^V|Os_NLoos{cvlYlY4u8q#Zc`-(PS;&`C5rIup-Css0
z&Dy5I#JqoS006#R{q(eZ$#Yz@U9{=;SeV?D9W`FLlu70KTZq^t`h+|x=+Shn^#BG9
zr;7rkCa}Wq+&{WKan}EUr7KYi`<0(wY^fB?M2xySD|x&2)9dEvBHd;X>jL@@^3;~V
zhc5?7X0TQo7+E~5!S3Sr&t(A-aPpICbR=zNYct6VPF-EXQfeCBA3WC++KGM7=+RRR
z`K(i~la2XVC9(Amu;_xv+*?t{XDqh&^au?RF~u*pmG>n<-{fk(rO0IVA64tM`0N?m
zYAWY2v2IOkiq!A_*v)!-7n@kz?+_p1%xDQBTg<d>e!SO5Nm_EzJks=_uRyU-a^f<M
z9`_2E@ap}gSv-y?*vTj0K$Fw)O=l!vIA1KB9F|8b;ZB6(g6DZlsuC*Opk5u4HgO~o
zvcafa@sIx*;tsbdE$8n$`wR(uGR-urXY_scg7HxAUY3Z-fhS;A0L&D^hk(I)EgcwG
z4rF_^@`h@H311w=baolL<yPF@@?xqx&U@nokzr|uUxz+FShvp|S@F=QWpa8$)MYsT
zUZ3$)hJ~X|Yu0wdg-704GAb~3d>*HMzJ@x_Z|+h~CA058c(E8Ep69S+F<#FXtgpq2
ziayijKQbb%5f^rs!qfeIe@S}-^C`hSYF&=NZcXkIff<sIwD={O+1A4zxL|tbKl`F?
z`snz4kD1Kw<S=E-ibNB`<6tvaO-j>No+<6<n4SOjMCmU}#yp5-PA*ka#ar`~4|n0M
zok&_To;1R*d$U&4ZKm$RNwVKMt`^kr_c*J4&g^<$rOQ2RI>96|WiUx=4cpUHZ9mye
zekX}*GoVTI)lu?T&8gt5>cX)14Rmet)M}Jl^a4WA`?yA?@|+dUmM!67zN1cIQ=F=i
zOI2mebZtPe#Bq|QLQKDGZZu*&Uz+9=Zy?xxVu?%-9Qqmc$EAoOj6uiGq<Wju{MOIw
zPS4#p{6SjOYL{5OC-{Hk%N~r9k~(_5PV>z*!j_F$G=7$^DF$NOVm(8NIZMf}t>+~R
zBp&p8YPkBENxcJd>kgJq&;i}btVz;+$Lny|a6<yu;%#;kP3-XW*VYiR-%?6V>sOJj
zDzy29^|!;0OL8BTvx7IWVbu61Q*o_x0h(k}{i81@tSyITAITeGhYUZ8>sgE(?1yV~
zMx`F84h<I?EV>goF0={prr=X^t>mnJj($AH`nKA7hW$6Sl;xvH@`pND047o7RTtnG
z3kA1WRLyPeErbWEjRSgRq3lHnR3+v0+tf5w65+m!2aEZr%P+Of*D#}HmkeGZutZNL
zg2!~WLSnI=2_Kepwn6Nj-0b`Za#Zzo)F&@vby%g_iok5-qnGkM&dpX1={!SyF6$E_
znLU5M!ujNh$KpAKE&nZ@^G5xYcx%@z&Ic>0YTH2&_yn{1nBew-kT$DoKw|OME$VhC
zcC|S=^lVmbM%bR<yj$bFV3WG#r!7XA(-A?licx>HI}&n9uw)g>`S`;<@HQqJfquFD
zO7SR}qv3UN&s&uqgYi{Cz=FEt@3y}4oqu_8)ca7=IhSJ-q$=CP505~^bsy}McGCCx
zEE$5*U9qd`j94TAOTo2y&xXf^M?3|EPU6{dK_3P#SM`;u;tB%usjAi8NY{FWw>0;+
zsY*28%eFPIT?_4*U|!9kaP|YvQ{pVgIMQrj;e6w&_{NX!3O%qvZt6g$o_CzlkAE<)
z)o>s!Gu)~^68rs)0NoQ>SoU<?Jjp(`wqA;z>Qc>hx-6vSHSAY`SH?2=o7dc-jGg}1
zri5Qea*<MK#-P+fyCX;jL)qJ`Q)uPicfCor3SX)II(=g^hP8cFBO*(_{bD1q)$o8R
zUHvryS9aen_`f601Pk0)jG}3yeZiyIPhV1?JOW_w%z<@?0Xi_0;C?dze+P%fJ4S)~
z|NEwRpOCsM)>0mDc<-ONP?JioshO*uJM(?MVJze&h^x&0F}D=`{<EzuN+XAJ8f$GD
zaCkmX1bv5YyNAm1*_2S1;K{N+t}@RSYV`RwAN-3&z*y^X8RxjrayM~&1c@@nJSU3~
z{l6J@AC*^?KQMt+)`*<1s)XQ3mmFY7ipmxwBum~N-m5n0^a8(O*+Le4vPsfgOZDvj
zHZ!l`Heo$NtUV&f0bd}bz0+8qJTB*W+4<G-MtC8lLILNvo^fUey2jY_3`R^tN|Z5f
znI~t49JARkCtu^|<74nr&a3F)0>eHAa!;$|50;*I21xNF<9yN~umjb+-`{I`cw&P6
zW7-2r7@MSU&LV;L7@&&_aLk-ZuJzl}7_l=mF<jt9ivHi-U7()}SN`gh%2mN1#^jb&
z_Q~SS7d9VH>pQ>XQsj$sGjQNq^v(fxmgZfrY7?HN=H2m80pC^^ZPhvI$5O$VmE*kk
zpn-S%EtzpXWyP{v*0h%5C8_PFv6?mpQTvTM_`4b9?PF}x=7rqpXn}%1s2IRPqH}(N
z2@mu@SagVjcj`}5mzFs?VI!A|0w2ufmGG!~cu6>07BT`B45R@F0L1nl!ATkSM2?}A
z7VAUlkDG8Uf9O3$=zNOY<!H(M;oK0>@4-(05KQ7mGrR`Vo&W>iJkawsHZl?AbYU6H
zqGq8_lXPJ##3E>|5OdnyVSHR0I<`e9-10?^>64(<`iBg`u)F2}QC%~XSB|0?J6u4p
zApHH`eG5AT3h#Jki3;!d5MEjxGc}scw<C0LfIZRp*(v?bOe@l%vSFqLg`s<FPaPC9
ze%CpKzu-pHkq_@!JuRsz$zoI|0Ef2m(oXf0);wa~>0|$3uvGTbJz?}&XroxxNQD7Y
zGTpK~Zz-wFW%WpX2ZyE^%`FohB9XuGQv8&yI8m#9ILn=fT|0TocGf#v-g0c#GrRni
zPWV8i)<=fp#F?U%SMOCgY4m_eNg`W#%>KnT3ZdaE&8~Btfg|fu2U^+BuaXB|aq>T4
zqhRk((Nr0r&e+Xx8jO3*Vatnua?gVds@rhyKhiBk`lm0zFdYZ%1oe!eKeW#%IbhN~
zLW!I6mXRai%C$2jucuWk{mNhZRq4Pg(#&k5(S`qiuR(KQe|blLpdF{;gp{hh_j;(+
zT{pC@?#G~BKF@mH=!SSd?^lB*gUk4+r!JmRF=6+RO7TQHg1=K_sb+X4L{QatU3a(s
zGPA8MxJSREi!*iRSZqa`;#LoG{@qHwN*%AyDkou_0FQJe4JXYwqo(77Xljnv9v^3Z
z0(B5Lc7O<NzO})p8uD#-doXQrw?03$M@?Rax@TEz9JeFjWK)YXfC7dkGRZpket#Bg
zPZ`$}oPN9Ng$Pt$O<B{luHVV*o4vkuTm4_u)G6^+*;B8&A@G%ZNOGi59GAlI5=KlI
zc9Zl1i!hfmqNhyh1T1)_5ViE^$B4?MgX<>LSf(@)#hJFmKUZZu6Cq@&f}gJR+?|Vz
z)#uuPgl70Z3oG~uSElL0ChLbW&|3+PgjL5deb1OxcfkWVgL1RQ@O}p5h&rr@hQ!rI
zwRVq4w}?dhq^xT-!|2%o``wZwB&FBVu2g@9@^9|39ug};-J@jP@}>)D$kbXgmSI`t
z!}I`>b_$F-27?2)nFJll?UQ1hLa8IB_lrWVg5XvAbi`!GF;Skstz261IK2b$Ja+P#
zdgxomv--)Nfrvo*d3K8U#z4M-BbjRr*0oZatm1+ja*1+1B(7Y8H9}2fg3u`}tID7W
zJ$b_30NK=6osR6;%<FdI`oEV5e0N1$OZ3F-WWSHUNF}}2Dh3TS*%fxkA37{4cXR}=
zXN&1YDV)qfy%>MHdd5__iO~3TR>&<{Ng1DMA;|kF@cmgQ_hZbMBO<JS^Un%Z=z@^O
zp(Ju>S(yGI&HHNDiP~r{N{X_RNdumvF{@3<gj%X}^Z(P?S4Oq5#(myGp%g7r+@(07
zxLa{|hu|JGxD|JbQ=m8mclQ=|cc-|!+ot#4S9agsFUvW6nLy@D_%qM*)5)|dG&&>C
zoKu1i#+#&_onipf=;crDyL95|vsliSqKadw$|moxS(T!IZKz;;-}{D`W=WN4(G|>w
z_cDE0H3GCc`#psTIV$*#c9mgRj@t91FR}n{e!@E{WZxV!r>vBqtZw1gnjG*)#S2!7
z--^LUilAs$gNt*vP|K(6Mp@hvi!?Gx8|E|$1fsqJu7vf!h($2?+DDCEUUd+S82yc<
zkw<RbU{-z@&L<8O4*|4K%%_16r7ElHV9KJB&@Nfo3;RYY9-Hu+?>XtvW_KGV$j)9P
z?IKDJcx$iiZZa$_&`;pEaR-l^D*;`3LHLO=C{t;(PwgQ)*oVC$EGCXvEXoMKwjrmo
zSfk53Tittk4;tf<rf}Vf*Pe7HP%gssGMtW;ffCeJ@0vWXc7?tSAQ4}Q<in?)(5tF`
zgBpk<K!HrISxyx(KdOt%V=W&xYWhkR*;<c6c;BTizi|oSIc=g%Urv#*8ewxwm)jql
z@lFyw84Sttkx4QHX1`owwc+e12>dSM`wV<?h7dawa}(x#kUMn`_i=(4`o?}9%&?`T
zYqzFoqDbR0<VhFJOUc&BggNGGrG8L_ZOi@rt8Ufr0rh}Gq}-bG90~EB%0$;|WuE{a
zP&Z`#)+sCOB>49Pq@!ctjiAJI=pw%TPl8te4$*);4v2E|WwshEKb!~306eH^E1Iwn
zkk6}7A&|1HClu^Q6DjU-;d~fJ3WpHwX2gbT5r)xFhfbTgr{_J^F8<Bq9TN`4jupY8
zc9fCr4Wi6oVaj~ziaYIta&q<K6M%UfFW9^3M^ao4FAT$kk_=eaBnp12bHR$5FJ;Fn
zB`4Qu;&a_IQ-|ds?aA#!(6@UHF^4jU$jZ_i74{ys!x||@yu4AE8Np@h!=YFY=?&7)
zZWy1%Qh?7=(W7l2PN)`He>iW+2jgC2(Vm>IJ5KCq7Sh(uja4>?UI&gLQ#0Bsv<;-j
z;a}s$jJ|{ORQP8V{Br3HvB$05*;5O_a4ijSD5X>iDslKklW81H6Vg&7+f*PLo#tYk
z<dt+x=`5A9BA_fblo#GsM@;Q~|3mc*Gn{%BkGENjxDCJclsvmF3(3w|0HM}gao67k
zjmF+pY`t7K9S#I@oVP#x)#b-~Qm}{6l5jy`+Gx1ckbK%zM0583#di@k4V~lO?r+OS
zX{?f<ZfpCi!wz})3YIXF4oB>f5@ed1xOh{8{rz!R4UrqYCu!j!I|2L8hx4Y2H(`RT
z-}JFC&`mRV7KT^J=$t~D`}op#5)AS73=6qoy~whYhW*^z0<10ky@2TP8Sj_m-1?Bo
zcgC@TDFRM(ErCO4Ta!<$8kW~Ysss5QJ3I_8_nEH>!g3T0CHCitf=%8f!JsS&u?mCM
zSTW8ayl6s;lx*vEU4tysdQWcmQrh;j@>xp>rc*wr?So8;-GdoHr*F?qMvpUUJs<g6
zTcJJ@k4s&C3{;w?g~O-kQ2MS)K|4q~TN;HHUC(|;si2dczESW^$&u^hm@KwDCq_X;
z^tw8=vTKpoFE2CCI79PMRd*)8(EC!zMF3RCD4esq@V}6F`uNQ*hTiE}Pfc~B^0b0a
zR(G|xwS*@AqnQ*5iaK|5+}vE?kd|{?aII`MQhZ7?*`k~eahDN8?h6;n-@DtCd9McU
z@vRe)Yn7qbHNjW1_NBE-z*;45+KQ?ymtCD9z>tB)TkL|{<#op>#kQc=EWg{ToHVZP
z$MSXlH|>rgXc6&2xj%KcpuJxQNqVQ@cxu@rja6_R1E!cqY7%Mkx5Sr2LS?A3${zy5
zQO+1~gYW@SVV^OJA3ad7pQsyC)Slbmx?j(|+gNF_;S=e+@US1*Ef&q|#y}o5BC0`n
zc#L8yP7^B{1G*>#ygT9J`l)^CEclKN?h5?~Nthf^c`>cUcFNYP!X?jfrRyg5bQ=}E
zQ}G;0RK%~b8QvkfFKvNsWqKK=vAz09?1V{?d+$6Ai?b${ExsMMPZ{&M-l+4Vlp`yF
zG^-M2iey~elDY$Tu_OsnE_f$~=VC^o!YHkv?2*US4K3r{^#U!SdD?iQJottR{~Rki
zo5g}6BN@$wT<O*?`cto29%qxijZn<u#B@M0cfcetProX7M%1b~5vB!6tmREFMoL~Z
zAEqk#dM<T)*SAUbqxa)kRT-V=-Zf}XrphScEsxt!0|NE9?yU0DK(`Exc%iCE`i+UP
zT)y`Z1dsO66>IV#d0B@t*?P>TOy+=*!(aSN@u?4)DSfBak9s^LNp3cA#xos_I`kwU
zhIY_Db&LhhyG{#KckhkzeVAQ!4D_RTkQ(>6dlT{kMAsjoE~-f?tmMYEAB}&mX#A>f
z3|0I7Fz8kH<pA`~w#>Wz6psQ+avv*d;y055oZ{D^25;YqSrMHj19MJ|vff=6agVPb
zU`eO@pl|M=0kbsGb6)ADDmgR9^zLyig<q~ADz#M8#1N?h&ChuBh<5d<rR1d^0H36i
z&aoW6Rv&EL(Sv1cMsARayGZ%~0AqpC#0}PFzHF;UdUgP!BbSy73al5_UypOP+C<Se
zM<^XP<IqUkFYPocsCeaxQVU6TFXu@Pz=+R}o+Qzih0syfez`_-{lv=t%=u-9E^;>+
zAp|iP(~iZR(8M<uOR<Hh>ootDQEm@6Fk>V(XH8nx>@QDBAhT~=ao|(-b#=b&FqY-^
zpvg9oeEc-!$LPm_<IC8k2JKz<q#NL&uLj1fPl|#&9<SUN#dGZ^=1=@nmXSd6y!Q+h
z4_a$Q_#!o4<_hZQ%B|I67Ew$SHPO<gs90_04l3K+^MqGi40-d|X~l%+W-Mw{x#@I|
zNi1n4)^lC7@X_3vBSJ#8!)T~IwA#OGy!v0wQYhP54NQOzcm)9TNv2Qs+&Ox(>lS`w
zG@#VM7_RQW0yrFkG(Y`rU3#FqT$#t{O|dH+s;xqd6bT!``kWg)sUj4qNebW!D6BCm
z#4KswQ$zV%Qwz?%FNgOSW|Kz&dqVcG2@;p2oW@rd3L}Sf1F0RW*70G_i3oPHp5t_6
zxm$Oo^A~Ft3d2MJCd#|X^9_FHRmFFsQR4h?shs|B0Xvm3J!a)Km?#{nc}%nzEkex0
z;#@Ow^*=XU8MP1>Q5&3$698C7Wcb#7COM)aOQbj__l_d8GG>|s^ed5^Ug-$5h5Um_
z%T~Sn@+r1Og%6}b-+waCgCmWyiaL}edvxr}tETum9E-CNRed?=G7LX4DOX2N#v{RN
zb4(>IM6<mk;*bcT>Y|;p>6dQNO95TUO^|%D;0>Kj`64=0nZ98-*dpt6HunwvAPv`|
z>*Wb==Xa;rp?R(&v(*Jvw8}uZiIv1yZSv+f@DJPUtaAp54i0c?MxeY-?A^}7k0F{V
zMywetR+OcwCL*l)l@a6#`P|VhOS&iNhWO!eR39{yjpZ4rl+Eh@-*H?4a;*>R`LZhC
zDzx4!Y(!d$%{Tu&UFJ5V)1<h&PE?1>ImOT}>|$e^FgmrBfI-Y?XDAaffflURifcHZ
zQqedUVC?sRjN=^?&Yas$zR)Q~h{VQ~9H&+e$_HZ~%-C*R&t}@@@^vNVdGL-_HfU}R
zikFL}5~40vOTf_TrQ|puO_#*?SHy{J;uLF86S(Y#ifav}BYK|}5rjG*vwbFke8M=O
zapDs_OcgAMhYyJ_U*1=Ar2hQT@9LArWw_v+GQT`dI-dNZ$a#bmk(l-zjD<A<<0STo
zp8QEah3l>q)fndJ89Bt?D`trKN)DOL0ig~OA+BxDWr1L$C3-?TOUa`h(50kvBZ_cU
zA$cTn94!0XY!R#Z?)U*LZJuP2m*^!HxRHjO9o5UqKcHLA!}_Dn^qcTjf)@RiIm7HO
zQl)Y;hFGhXWbzyi!v6sPy(fjxic{!l%yp73&g+Wj8a_Cwh1e4564NzblZX@Sml&LO
zbHRHo?=Ru*340%q;+3~E5!qx(F#J3GcyO?Kca^}IS&<7gwCxTKTD=<*H035d@&rrs
zjrQiJdso)+i<_E{cDBkT;fgIJ0)rkOvh@fGTzrw0%fr92&<|%`G9q6c<4o35?nfMA
zmA|EdK57U&u!i^>Sf~fSdgz%Q!`my->j#0D?S3RqmMJ!4;VMZdW-o7;F;jm1Lq?P$
zUoB`T&te71SSMD%(*V!S!9~lc7~omxL)$fs*#F^s+@O39q$uxA;%s)D`KDz4b>SY~
z;C)u%nj~|%n?GXlqqv^fH^rS1Y~x3&g+!1TS|2464R=CjyEw&U0W#a()n&WP@!rMZ
z`#cx$4jj*d5O6Bnzid}~Jh2|_##B?!QbI8|yN(zMR<1Daj!@zPHi7Lkn1pIBYkrAl
zXQzedA9NAfxrb{}Y6p2*Y_ClNL=mm9oV1SmvLT{{^{GO&W=%{rW790(zy6i=U^vE+
zKQWINai&?o^Pmw}l%7-n*C(&^6F&79l$^@+*o4E~8*V9sg3SH;XwLu1w3$(UoVhLo
z@R;m3sS7XtG|TUJt&gt^W*Tk8;20Oo`Y+l<{0)JHxbkY(>ZG`d!RFnepbv}lIi0mg
z6MODu{Tl9jf;|$&d7*Eb|7>q_&u0E;!oT7LPP@quw+W7B-!7Gz5thBL*V^ykV*A`u
zY^=H?g2jKhr0K%&bXHf5<4r%+pyN?i1H}CCr152AO5k>n0f7hJW~6y7LvN;uh6~kS
zhI|T<_%?slt;*op41r03iPmy2f?vb9#VS5=uGtyx-4O5wByg3Sc<TW*4_+>%K5`^w
zR$i<cY}c*QGDBMN?oCeoiH_CZQR$V1Eb|*VlI^XNB)T_z9Nff6{Y!Z64C{AWmkt2O
z1|cL%!$`^D3vf9|;?j#l*dFxa(@*u(?@X?BdhORjPn!G{T8|Tdjo4ARnb_kDye1N|
z$?_G7f>Ly4qMtb*v4c&9&yg!N@p#^twDZkdIh)+|BsKEhvARH4`>sFJHk#{mIy~$w
z_Hdk3{Yh1KjG4|-$aHK%*EO@zi5>2n#D>#^DIn%~QL$svnCiOlj5bzaEAF<EvjM;L
zS||n^Q<wH_MByqHevCTqJXJw6K`9>t<}Yed=8pPI>_7~W%JuhE$S?}XzZ7;vj<F<3
z{J~A*0>|%~uzWuy_jO-HtJh|46-wyp?`wF;5d8Ve85?H(G7ZkKWCNc|c?o$vGghT;
zBq~8&tH|e!ZC{~nAy5kfGkLCYmK=^J^~-(fy2I=G!#RM){Ux`ZTSmP*-It3F9>0|O
zyXskErBFi{IvlYF=+`gPswU_>pvXW6cTAvwt0J_0LWVEOrzOK7Q8i%44el;UulsN9
z5f>dgydy%%oMiN;D2EqlNj~3RNdyiCJ?GP%U*%@oI5e}CuC}4xrFT$@7D)c@{pA<D
z$22k<<#(Twlmo6X4nuHan~?r{F{EemLEvxdam_BM{pmk{7|$41ZzcT|7i2B6-a81)
z0*w@A)w}K?ztd#$3J*WrE8yRnxtUT=Y8RrKhEsIlr^`tr6;8+26;0=VvfWO$oJIj#
z8h2Q`S&g4K^*HCU#O%>}%d_FLt<)zM=J`-MKj90zjMvwuEP&J6b?1d%zdp3X^E{aO
zKJh0!;O6s*6xGoje-3YX*7pjHIs1!h^-JZMEx&U`;G>;}a=^gM^lOcGjo=Ncw9lPs
z`Lmht@=62mB*+8!FI;4;hsQsv@b$n*3q3ffrZ|+oDLyDJ;(`O&eW4mt?A$iLS3n5B
zb*MC2iIx#_4m6f;nvKz$YRH8vlaY_(a9Wz3?&91`ezh+$;S=}NiG8uD?%7%KT>K?|
z+Y2nGFa6S@p9?p%;=Iz3fO!hI2{{#Pv)%ou`Lz@&<Az#Ym1&G$`K<IcH~s@W3b-@B
z&j2&b00e|OK3YTCx;wk{H6ct_UX6e8PGU>rj|cyz@G<TCd30hW5z!R~<C;OGzxhbo
zVn|zo{ZX)R6BDBrmGO*T@Tk$2F{Ds?`FNBFpfm_{9^lj=UGu^<<`X+l&e-DfCleJk
zNp(1MpN!JTW%qk)Rm%cOld<Tm*O=~g*D<|A8mfh}a`HCvTm0%?=iO7sZ?1opX(>{W
zd5fYY1dAm11zR7P>1qmF9!<}!<#fGMBJSD7>Xce$X<M(FL<(d<)<Max34Nt6j>sWV
zP`iIZ?$~%Bx^}RS;PJEpO_xc0*u9ZOHK(5*B#(ZdHrCN!D6xT~Lo+(`IxAGbFLqO+
z?npawO(#&#U>x!_kjqlfVb&?ZzCiG9_%e_#K{-JEBcV=H3lF-@6)cwaIr$CKV5vk6
z5soE5L9U1jL%}^#H^Be?T5s||=>QW;u|4yP2=xV%bcxu;E>KkeP_A6~b7J>lq@g(h
z2k=C)SiGu1$?;=&^NpjaV|}Hc%5Ms+N8yEF#D5eMi(Y7IiBEzo=w9~66FgG}<vR^x
zrT5={N1zU^%wPu{*_=9Po<OQ7m+2i@-PI-s8GrE=t8Z8E?#}DrK70f*F~&Ar(ODlQ
z&T*o0xygx&*o^#oH(M?r!9u^&#H8wi&|l<{CpYo=X2=O?S@K*C(D*95wwR%yYttN4
znwoDtzfY$FuyZiow$z1@Hglnueq{h<0SFRW_h`wN0LrXvPACdmI`%?C!-EtYcBT&;
zBMV6-EG}-{Q1(Tu0>D#C!=*ph$z<?I+rs|Kgm?F+-p8a2Yq=juaL3%{8&%r!dgeSx
zQx&gwmhkUF6n3IJ3)V^3hAiHaBZIG<lxUSw7EpsDR!rI0KOWEKYs>=Xv69^$w-4qS
z`RJUwZi$RHu;25jHF3D^COOR<@D#%f6giled*+>tz6p2~PVG{2(DR)q@jdvygwLpp
z(U_)fYW{7#4QIU~s4q2Z<>NTmr6eFr3(UC7`>xl9hC7G9Z;_v8h}s&5jg&3d+^Zc@
zG}GG&+peP=$*@ax!oBek%hYN&=BAGuXDv#7RE8B5Sm_FE|2akdd=}J%8j)POl;F5N
zeB_SQ7SKQmZD_Z!Y~lC>vflT(AS}s>jEgB%o+KP%q<Wm|gAi!X_nz|9le2l$AH&R>
z?muKTp)$oRiU5GT4cnClb^V~&3KEmI3+}ezUrVD(kGyi@j0Rsx({vsSwt{w#zfN~v
zx;{{E$L(tO9dlLF@vY7;3b?QBn-YB49d?x$1g~0dd!+RYv3%40(a=5RaF4HP+-@KH
z*gL-UY#v#5>_z_D*p(16T(|l?hDfWCKpAO>7EKmFnWiI_Orrax%yDv6h4{^e*-doO
zJ9=HO=d8i~kg+9z4!7^ZU?c?&L%Ocs9DE)jAMcV2*)&|SY>IV_0F$xIxxQ6m#Fv^u
z(uKm`Z3MqrE!Fgin?I6&u`0KQVIb7YmPF$BK3Z<gep3durE8Qa@Pb!|SKqV9EqY=U
zx0+c?v#GjA&p@XAE0;mAYVxB2;-M=({hTa*g%>YVinNz-qcI4oQ?oOyoBd*UG9?$w
za7?xY{wkq#V<DfyS9}5s9hK$XUYM*B8-xcHP4^&m6N;uf7FOSWN|xM5;{bHeZf{*(
zT{|4N2g5(odwF{U^qOnI`;$L^{&d<M<>ulNm5|8fb9Y>Abx%o2d520^SzS#B000;n
zgMtFy-AGWH(v8-;Dy&fF=k#11`ue=iz4lwi_M_@oB~B|WqB!J$VLA>8KlFFgmu(&u
z*&g?k<y9K3eF3SkRs-GDy^}`?Rld9)J0&-#Nf5CDw@7XGlhxM9D)LQ}J$hz6gUan(
ztQcb@9T_j86&|BZA%X2pjjMz{cyPpweKYwI=Ap&qyh_LD@xx5nr@l{B+r86g)^hR$
zu=ijbMwwxp@r=x?TI*FuyEl_@dD?%kqh{o+z3>lk7cya(pY7)t<Yc#K%D*h7#pc(v
z^~UwLt@JPSsf<uf)3XpF*Sd;^CLQhsAaGY)P*#kh0_Z>HawX5+O41aS^FCb};H2*I
zycT5oTrjkl?Q?zGN^38GZ3?l|tJ^FdYEHzD=o$p(q?cKMtakkOh<KMm$C?v4vcCuP
z4SN|6YKOX0G?5H{Atg@mxp)tL#S!phvK1m2iiSJlQ@Mc=jx9}oj2e^)G{}lL5k!ws
zM`#|eFF+CYHwDD{YQw~Tb#(D|6%xNd#tRq;VPZrWVzS$>`Yxzp6&<1j*1qf6*-|kt
zs2)LXJ}cg$^#&qq+}H7kX=L>1xv`CNb-BSjm%n+JDU_MdKlij3Kl&@^mc+f2DFCUK
zI=xmD9!H#_&UFA)>iBX1BMg}dIq&92YJZDKn5cl{O$6e7)wF<iKS27p%|tMHaVaRf
z2!+W@i7ZUi0x9?A?E}a8+eK4rsqTF2-|w%2G=LXxd9G<wciVJlnP@r^zxTx6{guyu
zq&7Llx>(EGPz1C)A-o87EcO$yYIh4W{8^r(o@Z@oN~B?UxS!w7Kz98(*6qT$mXcC7
zW-Ijqi%L-B#q{xhnlah@bR#L>rfH4D@hm(AxRVo8xQ~$+NbN1t#o<)4)Y2E=rynyp
z-eF%i#sGq11*qlM%!qXdfR5o})!#uokS*uoGfCi?&2jpf&IevUo*BZ{0mqSS))Oi<
zfVMbg+aQwajuzH-L4vu)JX&@_2X$gtw-}G|le_pX0?*!>u7Cy_Z{c%(Mb};+hg!L>
z!L8vK-ibpi0kJTozoSK+`Du})6b4^|Vt6Q;)(y>v?<MXP6er?eS|vmedooG}8rSqh
zo_n1hq8{dDoV$;rD}3If=Cq4yHrjvT@(KH3t@&^QKo<#|C%eqZwYv$BifWm`bgO2{
zMx<J%FawVN9*@gmq+FIz@~xN9I}9r$l-yd`vOHiBm2nRhk)I3F_lnmI_<fo?pQe`<
zEf^3K*fNbrq(-x60`1_0`_<6oEBzlktsN+$wVFm@;~Kp`><snIn`A~T@^;-;bru#I
zO#Q$x4tj9>8%OyDZOR1xqqws{kx!~0Q#r*;fa~3cb&$g29>?b#1HSci)uYPK7=loj
zLlV?7kMXZ^h{7?RYnF9s?U_3!AlWpFfCNs%Rf1FSXvk=9xE}`wDM;u;VYIIh^fXkR
z|Ll)bCz!`)R?N!jSx)P0t;O@l@Wf34itd=HhbIJ{<z`@rJsVkC;)@^e*vSbg#CliN
zxhvE^3H_XXyZEG&x|^lc@A^X^S1sw?9pQmSd6)skc`tFExNN4I@UTs=siw-L<XbY-
z?qSbq8JVOcnU}{VEJY_;3P&>kQ^%tG9c2lWNyj1|K=SgflV8W@k7`g5gqRui<j3;|
zB=!ypWm>XnAHlY>Zlj;8nTc!cIGI$@O&452LFymG6ADE<@E`e3bTq*ImMXO}lKwI@
z6a(DMX719K^3FITH9q-JU<X;Ta)E9X_Hgz!9BZ%KUuUOov$P1Ga-q@#o#zCJ$2to`
zS1Mz{$<Ns+(>_8DaVErQh1QGO#`ZDjTKqOyG!L?7JK#Hgn4-8|`FW6n3D5wczO-GM
zECeHz%dY7!3$=356qL6aJnx`TNsy-RvEHV`<QiI^ts=$9K-YhsJ39yDd3@*J95&Yg
zLE#Z9rkBah-@)xO+@53zL4i3UeeP7)zoLhX9J8LZZWmYTO+GZ`CF1ZO$}}&o6B0IT
zZ>4uq-@Rv>WoE;MPj#!RsP?t|Ex&|Pb@N6FQ|OZ86IbxAxW47Lmgn-+e?~2r84(`;
zP=~IgUhrN!w<=d>_W5Sa%h==xl!y5nlKFo4?_l31tE#u@e+T67m>lDpn^3gzpSYVD
zYUSQ^Q1DQ5bzz^qRXWLi>F&UfB7|}Yb-R2=`Aa;j32G~PXO)j8#(>#xxfIuJ4IPU@
z8f-Tqx`%5Xm%KC(3^5Jz=EOB|!?_2Ne{I=6D^b7_0ISnu-+=3tzzhN3rfF|283`N)
z-%4k`;u18&XRd>zFZ|E2$r0$V`?W5)^%<vGSFJhAaUd{#M^x5e8*l{-9fW{;c;qD^
z<91#EuwA#_M$6UpRtPuXS0wT6$x2CaF+RfIhWX3S&}0eUACj*(y0*4<d3o9IufHAe
zB(TuT3iKP<?5KZql!<|X@PGaSv=DB3dK&rdA7^^NJ?^{^EAH-Sx{RzW8$0_*e}ALL
z^}$bVWcbUO+Pb>Lq@?DiCU-ZtgydvbFc{h9=b!WMc>g&)0|Ud$t?#Sr`R^WBLnuWr
z{e#EZ>R`GEdR(Ji7f&<jPkLea9rSWIU9)$I_Qa@XU8id1^mJ65Qk(bYj13LF^Ve(X
z1nq6cs%qA@pZsO4=jMEb3I2HY*Zd6$_Lkp0qjNsw+8KH)^el^jtt%@l#ii^Wy)!SZ
zSzi<a|NPsKLf?ItfA3rC`D#aIPk7PMw~mnCwmFRinj{)tRN);FdrP2Yni~!!zGfC<
zl#f>igjefw50J-Re_L|t3*mEIRq!s{i^l4o_o6X?eXfH!{pejJGrFp~Z}!zgF5w{n
zfq}<TAOd>~XmIi|;lhH?!vgmykkEr}!=mtpS+^~`{Y;Z|<UupG3>%3PQZVf^Ml4$<
zW=8z-8xCnBG>4vy0?~>riI-#)sSu~k|KFkhigGm<ZkDB{i(@g<v)^EH&>`EGgIUC?
z;nUHOs31Dn<zSWd1x+yiwsOK+Zq5z@;KL4`jt9ga|Hk@$=^Y{z)6z4@d=ESHpC@RT
zi{JvR*3<p#&H8g8P$J-uw=5TlV6O^_@&8<4<Nxb;WSL)d+e*j1?&jhOr+58xQ}NT7
z^SffGGr9YbCiO%uZxEV%TzyOEN$6c7cyLi7M}1&`0=f-Fl1r24_}S@sE?6=@_>zUG
z&{+ZQgjs=9jnm*ly+g$Unu6jFJH!qmcoxkFT_D+q6-&L$^VPxZ2j-Tsz5-0p?FRM*
z?B{wvVw>8rTG$mx_AkS_ReN&de{A_f^Z0vL)RKLH<TX%YCBtojZ~w%a44pWiYT(0L
zz;?rJZ}^3^L6HhcZWcc^q!KB3SxA3EN3)0KypCr7&7h5FwDp`Lofb8p2=u8Cd~54!
zis+726#4KysyR@fgTpK1*priCGA|~{hfD;?))22DKu8AYgN9<QLVLo4+|lCPpZspS
zR~S-B3Sn9hd0HX{urU`Z9Znv~!WNVI3(I^eVqE%a^&MH?6d47>THicYy9WMbVFa)H
z(}8}VzLIhKD5dKT?eR61v^I>pFrlL-#6Gm?C`{2&;zmeTo2S+!EsOzPT^VN&>5S)4
z4;xr|Zkd&9cjfOa?-PhY?3LnYJ4(q;J)$k9)Qm4@JH}!$GSNgd+~N^Eij*T4s_r2M
z%7;qGJO1!_5VLV<@Z0XWX7ryVd<v(|sHGt%ipxz2GrE4>83$m3Z9JL&gIi2V(0H2N
z<GC8-SH#;F=NdJ;zIbi2B4lOj)OBr)Lqc~hZOE8P{Nk<z<Uuk({lJh}aWvBJNZ<T$
z8D$%-ZmyYVv|&QIfi}%U7bQ$;2titg@n-kh8wHyEZ{G@`*${9*$IbAA5S^t(X7|kR
z7FBA|k0I(6uEiOjA)@a1F|k^V%`{d<+r^jpSQB3%O~`S=nwK?<yT2d#o)5&~w~i|E
zki+mkoJehBd|X+WOR3Pn{DotXf60e5NfLSUA8C!D?*taco=ZSYH&M2M)=5NGsst)C
zKv?Tk4GWp`g>1?=LED1D<JJsTLK4J|-!nc%1`D~?&SO^a^%*HSa8lL*&X#t&h++6C
zc-4~KuF?GbyF})wov_T8F3oO9vX9Ol5wqgU!f<ku`fBn+1=IZGj?J}Fzg4iDFM1O!
zrwD88qJ3dOq2X{zC1c2rpCx>Ug&)nh2kdPtlS5M;TVP1~s<{WL=PDH`=Rt|{*!W+o
zu+Jt8SrRrYH4I%pv+dC(0bI;jV!jJ#Bn|b2FDFqGT6Aud4#{F*4&2U^AsVwL?&#+6
z%4z7nEMqs>wCqR7(G=p^UIy!F9ev6n7du4uL_iWb8IGOjNnQ{&3~s}CR92a}JY3kd
zK5V0%EJ57{vn%q{`K>5NPf5iahgJPLB>YwP&_RDu17M%98)3)YiRb9~A^`7lBGA*<
zTHP0<zD43Bv)VN>GQt6>P7SNe^@aG)@wL^D3FJ}IDQEdCAG)$TtkjEijk00$lkHC_
zd7z0b$WksGt7;9a)n%B4!V!!j+q1c0_ZTr0oYP2A_J1Fdht;i^ei16OwoZw}x?a4<
zy3RTj<Jhb6)WprkpiuqP)MFAC4U=E+bxbF1Mj6gS-K!72D>}i_%Ig9rcfhXtr$RiC
z`m;46-LkCzm!3udD2};g`MxZ!$|)ssTZ`s5jDxK>KlI61k_=8*DJb1yh#H40E8Qlg
zc@7kj=J_c*Wy~Cy?{9)qr`Yc9G5L^rtKD@~HOthrqPpjkM>*W$U++=jU>TD;!1Ytw
znHUdAgx2>-MAVE|KQ2yRsYw~kS~z|jt(Sq_u9FEY2GhXmu)^p3e!+#~2-^yCyW7$B
ztc5F`W^{0Ry*7~2b1s$Rm`7Du_cqR;afCchcl=#%pJW`x)=I781G7ZT5})L470<Zr
zTBr9BMT8;g7<S_#gK1vbC??o4YA!NbqiX62<>ZFGh&4szX|a2DgBXi)P4JAqf}h^}
zySMo>@p>x8K|NJXj5McI)tX0lK$nz2o?3$->WbCLQVp6{_G;h;nUEH`J0#BilVMhy
zvGF`r)FyFu_dA;0H{9jGv69W|>&Z>a(jT@<#ku8tw}P5Yl$(3uk-rC~?tidEORQN<
zuyBm)=-D)ZtMWjSKJ<g`!J^yS$0`-ffqvNfW&EJ>q^DmqEG=HO8F{H$BIWLmIZMw`
zyqJ*U_eC>#W8zEsm<(oa-_P9k2h$^<?HZz-c-7i;HQ!72oD+t{x1UxIT9s^5lR0HM
zlT(T*m{J>*4_#;q0`Ln6QWD8|SjVN>=VM2OB9iRb#DkzI+pTDZ0h5;fS-NF?_zo%i
z62AVJ@XzT@i*cS+&BMP?O0}15ZWdXSp%n!OlAzo4{Vd8V&i+b3=AXitPMV|3)CK{L
zf-tw=yu=AF$e^}aDy9Nimn&2rt+xCexnEAJ$+8<?nc_o#@H@4oR|=+ZS{2dKnap4&
zaJTim1!=6D@+9UMg`<X*D?LIZiPsZMO{x*E;b&*1%z+#~_N}w;LO5%RE_h*&3&!f_
z6Q;jY)oPowNG%{7V_wgtZCJmPQd*4vUMreVZfiDy?q!~(6Q9Cvc5dsEu+%2<GB1uu
zr$JL`UOmYwvJ|QGW)6l5n-y>LV^Xh0h>{(4T5O=(jWP#4JJdJHU`A)P%gLNVv*(;_
zsi(!eq90+$XbD*pQK?mU9Ajs|KJWm+`C4b0R%0`w0!G}Wpf}sHWsQC&QYlU4S@w2@
zI_!n=Z?Q|K{JOA=+O6^xX7mvpfHAW<?!p8$pyG}w*AK1YkQJ!Qt0+VFJj1VClU++s
z_tBIX>Q#A_>Q)6g+zWd7U>a?M3x>g*>)?r4!H<;a#_TKV&XATH6A)fiw|nUg)H92-
zFGX3>lw+p%uVG^YPX{*rE^CcHd!9R&ysB7m$IMq5<zme_sg}X(kiXXQ)SX?1I*40d
z-qKtpGb^+PZMR$@ymf)3^44EXQE8aSh|Z}q;Dx<M0z^?yJ)PA^Q0BxpH!!AOc!-Vd
zQb%0}&;3@Me$)X{8mW_7ZEd(rV(Hw^>D!T$6436s*BUci7jlSsGLdY~aK#RvI;ld6
zZcO%JSA0LaX%bo5wLUAeYWn4`t_-87As-tvmMIVxciYH--n}kITs%9~JP51tc}M9%
zI`P)C@_WB*COs$paO|WODTc+cO)=Msw3Op6d|ge6R%AZ69!ld6_B-Pwn&ZnYBq!2a
ztPegptU5n6T#L)OL=89+lJ2_{D7ynk)YCoTf>z|!UTnGvYn_78*CMY`KUt*B)_vV~
zGJ&?U%wzBE^T`GI4-leIxo#nsbtWT3c5?mbMAwlTMhq<y?eY$bnM?*$ia7NmT1x}7
zg>|BB)$WjW;4Jd@qv3hRrtUQk!jsB5_lHtHD`=fNCUnm>LGO;)b<i?p>$Ue=`i8}G
zsj%WG&H8i;!GKA)=8Wl-3h%DW6!H?KGzNU5PNVE@BRXm)w)r!;MOhri?L!A>KZp3c
zBB~V6u;|Ww8pp?<qA=2~<*I{x*qGc<6LD;7VsD`)!{m=1+1MFXdB=*5m#&dz`Vls}
z;EOw`(G|P<7*x8XUg*5yyK}MdK;-^=BIzi4kx+e7#k_<7jQ!l^_oA=DWs4C%9rn7x
z;?F%kg11!<)<+{+z%<<ATwu0{m9H`bT0t~$`YbEBh?;G(%hmPj#e)FJAz}Ii=eQZu
zNx9B5cD+V%@+zoB>^iRt*z)F}k!IP%0OIkmDaLK{@~T;D$&~H#fp*1+zO+sOpiC}@
z+^4SS1<~jgZNIJ=$p%XUma(;eMFL%@Nbs$lZvSMBWOcOT8s)_rNKR=+>|1GOpTcvB
z7s=klufk`UJ((!!*vETla7Gv3d=0AH);n8aVLZ>)QrzW13&p22!tkw;1jIMu6d^*~
z+S#wBVOoj55AlBRy~EFhwklBEBfgvPIE5<$-MLIHB|;?{;CMwIlgc#{7v)P9ECK#&
z7pdB5{_Dg_$O{#@30|ut1}e*@{rLALtLWKy)ycIHYxA>Ygf;fjmi2Ka0$uLH>|eiJ
z2t1nG_^nq#`RtG5sL#CrE*wa?czfps73|}ia$OF80Sn(-WKjq2>24PE%)@)M{>N2A
zX%}rdHduc6PWaRbXb4#oT72*)dZPR11t`JN@P9k*=NH|yT4Ep|0Uog_B8B!?aA<<#
zQ0q3+DvWB(hHmSCnHacxBLzUo)IFKEn&XLt5uf&%AGMOgBRY{(tUAomAhYB$YY6Q@
z#rC2Ti81JKb>B@ZjM~hQrX$pa{}C^t61@jF0V)K}WaU|vDij7kkij*-3UOy7Xa;$n
z)%X*)UbcCjnF*f1e*dJ*+2*VN9}#IN7T;-mS?_r0vNQNQ-w%PQxLLEqYn(KKqSwE}
z-2Xp0|G%L(6rBdY`5RCDlcE3bDf4@y4-c<&{@`E}D5U-i`XwbMFIp~Q;Qzk>CRrJC

literal 0
HcmV?d00001

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/open_mmlab.png b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/docs/open_mmlab.png
new file mode 100644
index 0000000000000000000000000000000000000000..f09edbcd2221822e06c4c0bb3a1d57fef70bf3ab
GIT binary patch
literal 262300
zcma&Oby$>L*FH>xA|eJ|f+8_=N^>h9(ji?6GjvD~J#ri5&_jcCBdxSS4KdOk(j7xH
z#CLK3-uHcO!Q(rgKbYb==GuGhwa#^}bMG~QYAW(1*Qu{#VPTOdJbS8vg++wK!otxe
zA^<+gNIp8j!oq%J_2h|~%@g@24z><XnvO5cz%u3zufQ7efAKzkEP{o_8ewK?s__iO
z+V=9cscBmm2itWgca0ApLNrYMzBRQlerIemZDWjzGc=s0yfytD3y+8To_B3a6Hz+%
z>YL7Q(XGUvH8g^wV7m;A-6~wH>@Q7CJF&`QIux%<uhBCaJYb{gG{$;GflWtb?top|
zcYp1Uso<@Bx=#t=j24U--0&_09)usZHlz6d6TY-<ZapL8Qe0$ebUqfk{@ur{thX$I
zzKlfAH8`G2S2+91fYl+Ma)ED{c``NbiSI=2x!eu85psi)Qu^0T_0z|<Xj4L7S=@R>
z<bNxX`D@d3V>AplPx@D9CIa>nb`ux(T<h<+g!{Mw{*t)g8y>f3$%56)DC0w@D2cYQ
z%@EJsA`JtN0n6Efbrm4W%2@2cYa%Q>Y-%ig;1xFTCy7n-f3M}RS+Q{c`5Xre>%A2g
z-v3;q0z6;51YJD*-!o1c&i`IPgiOQz-`6<W7uU8=;%op<*BqbeIb&gQF<<;)dt^zu
zVPQ#QDLj?Y^1xo3xE5!m<2sFb@gnm16NR#cp6L+?Q3e<%?pCLHp>$MT*tKPDllOHh
z4F|`#clg3tlEYPcT_O?TAN$NRqv}GWryt^h;HCueGG!0PJi<W+JG$(7TKQ04o1*fe
zJR_Zl2C?}olQnx%`}JO_KKql4NmE}A3=O=jQs1c?{f&=}cS{=Uzj%O&Qdk!5?9A@|
zcb5a-=!lI2K4fI{$HF1{FCIFt1D8XZ!3z(_{;Tm|;}J#w;{PwdoRNnExIF$IB0lQr
ze=}KN^o(lnP=6Y^w9&PsCA!EAi6^eEDoHo4%uOk*DXD$=o$(*dlv~{Nvn}`JsLx_9
zN;M_cXClC97JXC(H4C<#{PfJY_Em#`kbu?!)`-}Z@6|F&d#Q!UXTT@?<D8ow5_#|G
zR*#p~zerhHLfqS-p6~ZRjnFwZ<5t%0PR+pPCl+*sl-Aj;|D`uL?sCm5gYC*KI<+pA
zU?pbu^PiSrF0Gq($Hc7yDYd={ntA0i7zf-$BP|3w8RPx~^mIYlh8$@p+Sj>2!inzf
zNQk&%XqX<8bXq6q%g-d1E4OA;Q^h&`&WwJqTG`<sKFA{R-3?-f&C9VM*q-Ohx%-H7
zIZ-*6_NdTUU4$LMoy4*E#XDlWszKIQ%_31xB&RebSA^n<Bewx--p<p;^aq3WH#8Z^
z*USZsskBWN-<QxO4i7Y?41PxybI*CNM=?O(SH-{c(6dLtVQPGs(ZQg)$s*y4vPB~F
zb;tN9;V+^q_L+WzPZa&dl%4%gH`CIbUM|N)|5|@Ew~-;?Ny!_aL`Hk3{QL0sQ3xNd
zP?wTzd2gG%XG7WcyfCPA1fnnQxUg41-eyRW`BLVxyTal*04sLZyU{+rYDK`!YY61$
zsu`pnHe!CTQGGa!6Nu>4#ZpoeuuM}5NTQ}>yPlF^Ol7O7mt#1@h4c;p#g8#o7n{t<
z@NfQfS%34mR8!-M9pmof;2}H1iJM>kVRq%})6N(aQl8#ACZk^Hi4s+1B&$n3s5joY
zHzPKP3JiG2P}1kgL&n57D;Cu!C*%7h`gVQSeV+EUFgY#=b|hwfy{>m9Skkfqd*&^7
zeE)j?>gttiPWSZrpugG`mF;CpK-$Sk{!)PPG<&k|+zXEFX%2u;n`d>i?#$|N#)y%+
zx0f3Q)ZE9>Z5i4j$W!ha7X7kyI=8Naa@ZY#OqRa}LSo`CJ<*bOQM0SIroO|dmfgZd
z^giG!6m&vheW%~itMJP99Jkbnm&7644@r$n(-5qxi4wP3n2o*1@)uaF>QD8jKG$ZL
zRM9TSiosplg$=yGrsjplJ))bNZ`T*S*B1qXPMQ*AoyOk1J&Vhxi4~%F0hPb9>iNXC
zST*+)o3Q@`r6HDTohDL!_ZurRUZ{{{wz9}7DYoB{;UxQc+e(XdP^XK5I69Cs*6rra
z4xd&}aYsnUFO|oG7I5d@d8Pw?`*dot3a8ST{=Af-V1osumrzw(5e<2}!5}CapIqVl
zn=5mFK?azGeQoXTAMyqxUPBgty|*Ri+Q>(^0ekER(LkbD@lfqp2sWFs?cP&uK~xVF
zxOn^IXi`qlAH2Bin`+epNgSCR<Yp!tM<?+Q>cX3STcmo~q+*BZy%@%-((=ACbXDjD
zK<tgCW@H4^gRkB;+t^<^K2I1z`ln2DhlqBQF4AQ3wZ<95;r*dzthgi#e#~4cF9~!+
z_h%qsVMu|6nAK0|_!e}J;+-|OZ|PA!Pn-_a0T=zn^+ldxzy;DK+AWGIYgXbr;*}k!
zTRVdH9iL13fch3fgHOnmry8wcomLfYpeuLhc>zR@QVi(y4}n9Z#L6SW$L?2FYvj8Z
zA+$_e)(Vn!{2-e#tU+e3QJiB({^E`@@;H`l)TAwhZ*ooC;fbgBl;{YDfq>;gcZL!A
zFdnAG?<V@VRS%k?xG2<KD+Jn2i+3+Hcu+vm`4~l}5preWC@!$08QF954+}SqPduJe
zUUiW{+>+Oas>p6-=mkq`i`@00p&*J{i5{~<^l<fVuj_R6QGqAji(Vu}&zl~kp#;p}
zmd7il^veU%)Kc#h#k&_pRmIAOiVV|a_(jJ$TgagUWe}^acvqJB=q0>MH-_n5jHs}F
zH%a6VXy${1sU?`tIF8Hmll=MkHDl+~uiV<&1R1(Z&-9Ue25{5((%o0|^>>Y=Oh$#p
znMi3jBgFQ$mG<MZPpO~sOIj{`gO`7}Stenfh)!a$Z*)6LLX#;w7}cu9cG`G0JON+L
zvPz7=aPU~~{sDD}l)|J-B=wgs61S@NX7uin5~#-k@9(8G5U+t!CU`4&pwhGIqtjzQ
zjH1u3#(YYO&1pOTDYZb~LjwUvh>Z>WMzVA6qEz?xqT8><$2z1-UDP1wv3KODb_+ER
zcT!ewjl8?EVDVTmJ}LseE08RW1#7=^Sp6jVEG`Ro;>kV!>`cqsq#=P=5umgmq#x70
z$3r+!6TUB;qbepPp>X3GwTLj-%P%?%oM0HgJKcK3prnL@LH3e(a5v3de(Ynky^nYl
zem4L__<W6drNck2nkI=EFwNr!cmD)HhCxl5bf-?ORd<{kCDSc!^N76TEo0-v(|+uj
z6}3W4@^0j|uVn6P9xY|rI-ug|2AWddtL#C4b|$~Co&VM6V`d?)u((Vna!-X=@Y%xp
zD*3SzKaiCkAh^qpEOf7A2Sy;VU%PfSQ2|y_ye(3|pYJ`ht%)X7t?EeY!ny-j4l3L;
zUJ#rHr|Z2sAws87+o@H3`Ly46wDeGFoY+IGM{EvmF`8c9HSSet(0wz^vOrM`MQnAB
z1n+>$2|8^jKR+|Bc7h2A%BA(-(+a$}Jf7Sq0B>5^N_TpG1pxe;T&@qVv7LY3EIj%4
z{n7kq3H#g|wt=Q`MBUG5y03+|ug=4Gp<j}F-d36jsxs8?HZ1$?_P$LZ4GTdI4$_y#
zL)LheYrAh|r+v(G)z9YIM5o1*L*I9cmOGs#5lfh_Ty|77VE}RZyRzn<ULGZ2oMj3R
zT?X#mk9Mu|V|J%C&cx=W%{Y=8YKnSp4d}oX6)m@4>o%|DsKkzUIK9LOy{o8*6OIxj
zi}O(|iH}<yn4$uah3ik})geak=NT*)uxC4ITHXbU)!|BYAi0vobG<I?a(3JK%^&PC
zM_jSo$=kSZnRqhNN5{wak|$(?{}q_YF~cy`)ByScD*c{(bcNWqG5?FYzC+0b6&R_R
z%-F{-z+}=R&p!dpGzhS$nNzFo{uxCRZO8Sk9fEEzi77GY$t^VDkry&)(8aaTpDPkw
zRq7bec2YIJY>wPV7lT{2cH;WO;O-LNSTzffJXLz{ZM&#o#Xw2VaKj8nv54pTR9Zfz
zMaKG+&qJ<SRRa?*l%Zs)6zyA5KgED@S^UM7T$!WuPM&+PJ<YCkUlOcbO8u3uDi*y{
zfg~+9dQh!59nw&&*K626Pj?v!MS+qd`z9At&Q-!gr1ZW?Sar9JusS)swR_ks*0$%v
zbrX$S?+EL4<00s}p;9ao&JY%LA^dd<bO>Mf>y+yKvzK2MbGwByWcb|~RwJr>lkFCc
zk|xI2Dm6*}?orZ<UF7s&F-juJRImf7ElhCh7sB`z=LiM-A;wV>p93$Kg|K)wAo@G&
z>}du6fYWw3@}30qW@0MHsdcdeOoi$7;IuXbYjA(ni@6TQ_&V|94`t7|i%#<wvZ*jj
zS+}4KE>9&I#|9UH!L2YS(D%e!jElQh)p4`!J)L4wHjr>^8Ou9=mSESyEh{TlG}YVB
z=rCo;-vx>=*sFZmQ@8Z?G6jH9-3P`O@>z-Y!5_wVii7(-11;sA9BblNs9f_7WXXi1
zClJ~c#K-eJj2qckDi>8DY6N7ql5^?!NC9w|HBVnGv*=g|LDzUmd6x^xidpH5rw3g<
zCH+CV1Bquc6JtwN47Q-2WU-BHa8*jS=V(^}0)^<})@M&NsnfF8uYX2e<UoEIwykvm
znnK+mLQ8#hkZPPTuRSs;hH*E;eV3J4AMtPBa2T1MGlj7unM9bX7FVIk!;+QjI{%M8
z1txda^y4Ho_@*0F(N?tzxULA&Qu2RlLJ;`|_Ucymb0Y>yLnE@i9~k>-6^u%0x1yOk
z76p;D#@_Mc*)V>GM(;dOs#L4~r^{%rrg?!M{qxhNmjedCkCp4<1v2*-pt&y^Tyi-Q
zu~_hHb+`UTQO-~qDZuVRK_Tyzv|?2qFfY17UC)=YD9~&p&Q&HG{i{|o{8J*6oVd(x
zr!J-b2yu~?Vx#MCwu3j1v`RiCZ7bUA^x>EnQk;I5ZJZ$kWynsxm;9(;+?XwEUpvD`
zngTrvdfN~veA%4&oYHAZl_gl1D|8tf&ssXo!t$x_B9I5fRFh)Q$ix^)19G1giQ{YP
zRy_Uk<N5Wbt)GD_m8t9e$Y=!kO^8I~@>vG8fOD>9)lvujh?XH&z47ZK^mII$kDTW8
zxq)K{VbAbruE3}-lW7b3ST~1&I2VZ&;sR>X-n4jw5?vOl&#wbP31jAhSAa0i4Zxc0
z<RrR3(7?)vTW@hK=9$L~=G96hAbCWNThrI1I+-ZbLlWCNkp7gMA^t(smW|IDu=|f2
zY)+p+z3gGhcJ;tewHf+cv>OWGHq`Anrgh*#xaV#(LEG@7%+JFl#@gb(?UD~AX_U!n
z?|?&N1NCex?8?dOptBm#9LenEWNH!T0Wh(*PTl1U!b5k#S>N+v{vg9xt4(k3`+hOQ
zT!T(Oh0_~d|G=gxX3AMEeGTrhRXqbjmTBdLL<U+?DfU&BZR}Pq_9zCP<FnWSe7YKl
zCFoY+cm|Ar{ghlJs{`*Vx38e)(c`0jmQg*^Q2gQH$e~cwper@28Gl#ZEE0@<x2I@s
zAB9ra6qvq(mI~yIYLG~o<v%dlTvr&tb&f(?Roc(T2e_V)w?(#AdproJ2Y=-POG@#V
z==K+s?u&b}HL6e`7zU|K8zx4JWccaI=B3|zZ@QF7*|U+lY^w(1cMKQIs4Hqif(N##
zEPEGi4ZSv{GkYM}J^O=@Qpw0=PC)GtD>meEsD1UNP)`7b`9*RW)Z1?Z!z_ESOnB)Z
zz}wu{Fm;lrhhy~?^BH3LS(OeV_-&b%Z(kLYN(3OWs7UR8zT!Nv?N@!lDV-it!Rx<L
zZ89+aps0Fh>0vE(R3+^S^-uqJekTM$xAzN+#AQ0#{pmO+Mmb#Y+V@{yd16%=?ip0q
zWjD7z80bTNK*a2ctF&~yB2S3ZT!<+kZyzhszwa>A6t7cc1PYyuaPOeG3`rCZ0YsH8
z?IF54^f*17u~E<YCxCiVrFpvC#GLj}jFcm&i2`LYCVq0CyKv$k^V&zHZqiUklrTte
zxUUa2QTfm<RiL!4hi}7Ww?{ttQ;~WrfX@yea)JWP)5pKrg5u-~ylB=raKP!~Z|bx;
z<2b5^mH2gv!-ZEf5@NekZM;k`SLoCD*KV=yy!o7awbUW%1i=O;?;pj;s~~eymsuA)
zOQ-SRazBL=x0%t+gDy_ml=lQf3+*^}D#udfxwAS%l-s$0l5T=<RM0ZpsKKW~bFEZ)
zACBMRF&<NFVEvW5S>7JR+Jf`acAJsf&djwCBnhGC$ynVF+}+QcVp(_Wd-o8nO_Bgz
z9pIQdj^P|6A--%<)J3tgb6Xwz4<?1;JfTk7vQSNpFd2brh`tLl{hJS&^+Ee*6cXoE
z9jHh?`i&iTM;D`A?)HAc!g2;;vNZ#dznv!`#`D1Bv4`BoU_4}F%v~R(R`4~zAPA-M
zedz;1*IoX$;nuvKNm~j0zTrndEk16eCZCas+yQUDR$ASM?j9dEynJ0w*jcxiAA^2J
z!5M|`oLESHW#EfMw^*HTUc>(bRq7=7nD&5=m!5tW_l2mb{=m8Ws`S$vCexRNCx3Mm
z!ikH-)a#dRP$7H}qtj-3L`8X};>O4j*TIGqrx@Hlt0}gW3JjRlBDCjwmqkozhC{#I
znbknQq<YoiBMyUuw1mUYdUmJozM2_zznVMdVhePToB(zmfr*1!>X<Kar8EG#ws`*l
zTq{qf)eh3UU0M~6I?u|RsbrOu@IFVc)_mzis1qpMHz+C8EFN;ZNL;<1V*JQVVtxMh
z_7v<dCIG7|=@!`Wm(0&ApMoTAOQr3a+4c)2`eetcXN%Z+-H004e%OJ@=^y0GDDTmI
zSYnZ?&UxUfY`0f)FL*B2M!Ov-*CBoLi1wrwqONUY=#$^2oj5Qs<jykU?&T}FLHhLC
zE-AciQ)-0k{IpbPxik%%l<HGIIVoH2GoOG4tK?k!Jq`b_x-<pCJEQy=N`_OT%IiI*
zQxd)R57s3f9@tEGru0di9oba0Q4sqpq>7&_jar$5g)~F$xFr)}U;@<EcgBiTyjuh-
zg%lGO+eodOIu{UyG1Zn^o#M$p?=EpbQ#T;i_-l)WErPF<sje)J0QWuG+P{OgMMy&0
z1B^kmj5wwmlPJ9%YH78nlz4CKs+*~^f8;F1jjYG3XMaxOUp|$VFYyL%TKObLI?&l7
zuu0C!RI^a5n!YUcKcrflA5|YF#X<{8O8C@T@@=?<R71P8?#$;==n46Sjh`Vy(Xhng
zaKYe31MnrD1=G7I;z6I=l9(<BAW?s6Q%X55J)`qbkG;fWQqndJu_;!?Bfe)W+|_nf
zPxUgPdPr2-enCQ_$8Mxi6;;hHpqT|wgUn?3O`uiEVQ%}F_`7g$kgCn~NH(dm<`7=8
z8?(*&&11T&ugg2{u4>!vEeHy=!Rr^Cj<ynuW5C3Vdc2pyNt*6L%WI`TXkPLKfMFfq
zS)!(mnDRV{KAF6eV{WST{-^_sTx=+4FHSVWd}|9<Xe@gwYSm~h3FDi8SQ5}#9A!k$
zv-5%^3A*&G)^9G@KW-P=jr%G{)%~$o2w~e&h=B1_BZp{hNOnUC{f^eHP~<BB<hUoC
z0_x5Mxg!Zuo3n3jiQxZEtzgbTf&KXX-#EX2e1TeNLim!k=Y=OHPl}u}-C5~O$q#29
zl2e%l$Bf;M;DhShvQ4g*pR$v9Sk|8=oIGtG3Y3<gO7r%aVmrzQ;;6FTu6NP~POF&e
zr5uza(Cis88h}`<ot_H1`@oD=yIQ8;t_;7daH#&-sSXG+<+oV?7KEGevHZToX&b=!
z@@f^4*DjM6f9d*b;+{3Ck`<?ad+$7Tc#5;cmd!{JjQ?ze>9%P)x(-xgtB$ib1SliS
zaO3=H?yq=@{OK~>aN6~)w0Oc4tC&anwLz8?oCj1dkyzB2@SKNLBl&jKu2azsM3WFL
zZIErOl+RQsdb9(P^X|bVJlq2|IF#xQ6}oY+0Q`zdTD_mH?|P5WQB)49?Gx$r7~9IH
zhWX&0Q<v`2+t}DuDFkP&S>+iL0Q$h2L5ljd$MY}?Riw^A1u_7ms^IR&yQ6Vtx3&4!
zM-U_p8P7o6Q0zG2(DH^SBvQ}$oi4FJJ6vrb@egu-56}K;KtkH{@p&wEWijJmI6|5&
zCK7%38OER*qsQ{QH(aJCTQ->KTlm%0ZmhuVhMv#jSZTybYq`V4UDD1*OA>PUk#(D+
zR37t>=h|;mQqo5hVMgaVRB5AX-RKYvUw`fH^`L6cXn6U~VxG(JXJi~3DUOK`RLd0|
zj-7A3Kz}c(^Des?>U*iU$Oi$2i{Zwq{R5MCf;ptF#gN*IafzuM*u19Soq2LBI}nKT
zlI40zeAYU2SK};@X{twzHPvS>-1}I}YhLR>n(WyENH#p~eW>0irF68`P-_Du1D$tZ
z7wKMSE)jQW*<1m<P@*$@s&X|iLmHa40TMAB7KJ|FSpNwHc`r#OYo(x2{O|UHsrZmA
z#!1!fJHV>Jjgck%N4J(3sAC(?Hhhoz95y0~zurGZ3Rv=l;CEy)Qo$43cX$K)as^OU
z(WsOD#5wRPcksruOIt~hTwuBQcfY+JzBEW8Ml}YU0OkyIqg!Q%pVwM+5}guU53L!D
zMFIC_gM8hxjkbA6GhA)ac9+WG#T=)By0HqJY`zCH$XVH(fiF<RnkoZSkHQM8>*Aj6
z+>aa<iIUb<qD;onpT`T}=4e@|v28(i9E%y7tSNlbOtuCR8*&r^b>5i>?ux`;+*QE-
z9ZZ`#flUC&f+0uP<yssYZ$)YP%xvO94n+6s`Ot=NLZ^clgJ)*ynH;ljShlbHoMfi;
z*wxsNXY?|L`9ashy-zx(q9(t-EQ*3emX7K<Dv>cjObXLaNOX!K8_(bl{Y?o(AP%AJ
zu-`Kek24;%MfXRi%qzC?m-Zo|U80U=I6WqVVtn3fgey_I96XS1^N@_eh@1|a`UK^&
z-G21(l=2C>01m%y!TT0R>LjRR{v5L`+z!aGK4%X|!xd!sVyj2MyT6oDMqlbWc2@7F
zMEi|4I9!JZ6<=DnZ2gNJ2=Z*dD^VuRcx{&i-sT`V$ijUzV|;Y-wR_haQPDGN%EZHh
z-#p9$$9zH)z3b^0UNw*`c#G(n6l5O$s^jrw`UGdFSRa&%$xEDHxfeV`lRd@lbyw3H
z^^{to#DoW9xV%cb;isYEevsOzS#_z9@2>#-NYDo=B64LwxfC#N`jvjHcpvn({8*;@
zgKlKH0qC==CaGtss}UND67ExqM%HMTmWAwR^L3+${~5|^ELXVSah`DdK~ZBT?#rN(
zB>WCb8j=t;NNlO?wnuik#3?$DFh$hwz3nBIo@)hEzt+cSN5?D6#M6iG=DOP<wjuRk
z^K_JKqy%orPEKN}z{_d2NP*Ah*djE1gS_GJr%#m%h8V5r`G80~wW;jw&K&po_k)h$
zSk7I4%JYqpgOZb*<oE4*P>Z|UimhUvuVOCwq$$A#cM0Gke;@R_2Lb$%Up~#*QCITV
zv7l~75i{RlHsLu<y*9JWwGV-sv=hv@PRzbq<FwoPF{z>VrI>28Ah<^)0n(q@i{jXN
z*Yx%p$fO_ScgA4M9h|u6fs3gbVUD9-;}_CU!kssfIZY#3GV4D(6GIv6w)X$^JNL_F
zyD-d(lOxZc470Wk@@uSqQj9^=qiX>v+mx#9qlGqslWvQ#UXx;JDQkJ%P~}S~%92{o
z08X*q;s3EW?_1|DV6nd873)>%o-OSso|u8Bz+NM-u=&ZO=U4Un(^9KZc2gX<Pv)Kb
zxoioq45$Whniq|Dv{#Ho)F0dwrs?CfBSAR-h+dqf6=R20PicPOPf6ogO&K=BnOtU9
zjd1w+fEeAjQ<c2W-N}6|igD{N{7IK}>Z7LBl5K}9KYmSrrEN|+&c<vDFT%OiT6q1>
zjXe18_fi7`Hp%zK9+vnWZh)&gy<wWbhO3|7;%D1YC+UWP(L|MJxTYY27K*Mz*4p~4
zCd#So)s$lcy~@t$=7f*+Wp%e2|B7(O<C2?RoZQ!d_Ml~D{q<LxAW>GIHZvPuLE>v0
z+?QU}<dFP+FX?+1o6k&=wQ|3&7r@WrIeEl^J-eD(qhicZJ=(%c)cl9s&kld`u4t$j
ziU|G5yI--nfzytA%rd*vAhBYitU^^N5d4?a`xz2g@a;I^90lL`Djg34%)&@Utas!i
zj#7Ur#l3X{FO{|}BDe|P&EJ`hX*N)K@oN^}Z4Lb0PJrVIKKxW!DQ{d0Zai7sObPG9
z{VwTYOlx}GvQ+-z$c$CA9*nd!r$0OU6Q81{&s0iLU&FT*oYRmPe3;@bkDa~MIai(H
z-4U}_1XQY(C=b5T_!LR#i6iFW+S}|){@4cyZJjQ`<x*F$B(4WX-s|1mqXwwfqzg(0
zT2U|6TyMG6GVF54)3?e#hssJ6%-!dW*P6*LTkmaF5gfhG6@KU5hj`}(UUYjX9xV9o
z;ZIKPy%Hzy^ckBC_{-<1U+pmZ+-2?wm#CfA698bXbIVn)j^Xt`@@}naQsipmVd2T?
zS+~mxuXjopmf81}`86JLGBa0OucR`_tBQ$gy)Sm~>lF;;JW-0<#LgOcTq>r0F$Hs3
ztK1w7jr(-U+~-=>4Kn9P8|8RN!z8Yv--rrq1y~JDI`{oQpvbf74Q3;>kICpGhKU{)
zqbHuzU^PWNCrdr5lUGL~UqDOE)no4pJ2Naaz@vnt4~|1!6>h?U?|Gan>%=JcRSV*G
z>u%?+jZd+BXJ!V|@m*Sh$_uH}&V(WCs@|2)`l;GkdJ@w_1|8BN?dVJ3mT&c`7@u4N
zmn#j+K&+_PGQeQ0kQdLnM+=%=4o6&H%1rm-O&*~``KlG9N-~()<cbgJF**hT3!cY>
z^V{Fo5mk5eFICn`i2yS6$#sSOW{(S&3wY@6jnKAM2CFE<*_h<f%r7npTbc*(j008*
zqaK`8J-MO!wq9EUDv;TFMbiE?9{vQ4#tO09cwFkH?QfvycLh2a;Ze!@vujnTVn5&D
z?&{6iZAC<zlJjp1Z8{22{&*p4x4*B%0l&=j4x#A$Lz8ii#v|RtUAV4`qGyZ@j~hAL
za0Vr>YR)~}MJt2HA5^@Alk6K^UOc-McLOGDRBdl76i>8;E~%;ywDnr}VZQcG^3v?G
zWlXyT)&h(!<<^V0g4|m_;{Q=Xk5;znCA3N0wmm4k%CRUp*a?34RESrN_(SK4A{XnO
z@>MbUM*}C1!f=xma2)E~?08>lkz-kunly8oYSB;nX}pt{%gl>tM2JKcbF}{ES>>Ak
z=gx`AL&@b=k?6rsULFv`$RvBoajzr^AVxm#`08l1_nCs&W1E#y+16z|U(ZyDU9kX6
zH=6jYRBMboEZ{B>`JbHzUg>?Y^gZ6Nkw5-IUrCj-wo|02dY&f!`g6VRuS-Jf|Iay`
zCar5B);t|k;?8o#m>J2V??yS`Hr$uJx$g8>v!y)R_S^<N730Qwkky*#F-`}3ZDT!Q
z<c_431AElUA|3i&Qx<XE#>$K4gAv?ca;=it$CZ^1+g0`AzRSHa%fB=6IL{0Hd@@er
z%0ftoUbA&rjTxG9m6qIEpY0tLP733~&wI)f5);e+-J?RWs@OHd6Kby>j$>k(3VqRD
zQYU17>$3}>9q1eRW}g}qAG{~1CkqJ+J3KYTT*Q10-=(`~a`RZMxPPU;<Qu?S*yt^h
z5#a}ob>+%u2mEwquer!?%)X{QUVs8?@N%mZ9Y>OVzbcDeTLr_LlYlM=MbXNuOW`C6
zsAYfs%S?c$<uCI!2Fw{|{fX+%%Vc$khxyY4>Z97gJ?DYPcmD1Dx$@X&B}Mcl?2A{u
ze-;n$pLw`@Wryo|nT});+@SX{ql?#dPd0`q^VKS#@Gwz6diA6y(^k=|I{!uEXGkWI
zQe;z1L2Mf`7Sc$1m%n{D`N4#+fJb(YpKgkXw`b(OobV<1(Y(-twc2~Re@~P^DE9x|
zG!*9EQLr)2?QSb}-{Rda8Qc5`shfvTTQZKZ?LdHJZ<Bk9D!=?)WwSFP8);0!UU|p&
zH%$+S$6YGB*`sC_LAouQ1g|@O`q+6b96s2L*tle976SnI+sU)8CZF7Eu<sKAXtOC1
zCWkMZ;o`p8Qq8SDZtx}?PrGGWWsZf_by~BaFdszLJDMn~^T3S>hm=mY0IjO;h0RGw
z3s7nd%*gTP!(Wb`+1ey0^XB}1$_vnnl8(h5c3&01A?4ENTOYj9?QTa5@YKi+Cju&(
zwlGGDz8+UV({7%B9-R|P0u>vTmu?E#E?4XVrh9!jgfAVNm*ki|JGQ60&+hvh3%3n1
zHUR8<``e~elb*WO{LT(pUihDzC2FaNUKNR~uX`UkBI?$r_B;H1<}zJKN6lhgO0n08
zB1>(l?ndF5)G1BX8QY=;t)%!**EGr)dD?Mw3iX*d3>H?6o^CR@IZ$$L@RL6<bB``=
zz=qEJTvnLmn7EX0m9hcEkIGXpx#S8!9eNKYVL@oVqjR&PmO%uBawA#dT?(%Z)kj!~
zg8q~2nb$4qOlsyH5GhqPDeIK<P*ODpdJMf@WQD|1#n_=}Q`d}o8GiRCz)S$!35;`j
zdj;hCI84i1ylv$R>P{5Bz#S3A70z^3T!f`|JY(-l?H1mP2z3ek`018eCBgpFxAU74
zq|C$+NbfAFO*r5a^2iOU4AFuRiL9<S7KN5#z{K7wtNu2zrn^L)@oyxTZ(mVvZpAsl
zP-|iwja!Zdw99l{`!7NabG+9i@#}R9<*8^hpfp`oeDD+n7-NMheZuF^BTJS-NM>p>
zE-_9Nhgv6VhR>+ok46rXW5FKN_+7e@FPoV+;33a6nf>HnJIycuzGW0gi+$!`=R~`F
zNqhx}Xtu&MaH0STFz~rk19%oARU-1MTS^u<Q~q(Jk?lc{He_$T%UDmWi#!?nmdS+R
z!*I)PdcA%po|J?VIUbANbkKmJd2{;0jL=MZTZpxBBPqF-5Fh)*DK7fQN6n}@otjIY
z76$@Qmqju1vRDJ;{O9<d@8#wwfHR*%<51~_p3wqKuZ$H<Tov{Tks%dD|IFLvINMud
zH@T5kCR4tz>lMi2yaC8H%19;`zL%`ZGA8rd1p1+s8_Z(<TOAD>1@_CbtBMKbe-FhA
z+`mI)YquVu;|p!{M(6eMt(o<*4L3(oW^xhaYW6E#PcQAZt!+RuQgdX~v)#Sny0ZAF
zjRr@P*|BfHMxxucKVZ*=A@*1&?{l>+vl_&DdimaOD;<Y-F<I;gu1n|xOx7CD_SW$b
zgI$#Hl#klJ%gwuD+b}Ddh(YY-58EP-wq&c1JY0D#jrtX1tV-O<n;fhqBX@)R0etJC
zhS%G$ZptJCdIdw=#l8LlkoxEEQGP2|2Dq>FR=VS}-`w%S{~3EFM3|(GLo?7*0&0*S
z76A$}+_48=t$aEx0=LVELbhYx^psTy|NYIZ>gb+Dml#eIyW{*yR+;y&x^&gr)}?|v
zI;$q7i_8vP>d)MW>b^8J7WoTceVuvr;B?$m*u@dZ3s)m1tkv+Arw-gVg}-L*t<fRI
z3gj!uQGEQ$4$6_{^D^Avt)j8^dtbK%5RnqBX|>oWr#?)MC_#laV=9%*NYx~7UEJ5|
zdjF-k`s@PCnrkBbYEm`5jSqUZ5R18qWL`fNB}m2*e#Kg2ID{V7+7P?m%UT(O*ee2=
z<eV06aTJSg5-9~XW!nsTHfCh(f5_U#=Fg~USYI~NEHe?6{k?YkEO2qhF8JUTGxG%E
z*ik>&8N}RdpS2(eHkN33h+`taImEfiF&pqwT)Snmx2nvSonuJw3Fey!DNDX^qr!=8
z?>(Ft9AUk*5I>6`v9K=Is15Oyjd6Eii9sJO@0_H?UdYv11z$@q^B10Ae1g4*hv%(B
zVQmN9H*(*ENn)@nNW^j?EX%hG>zg@<J=j*Wgp3jU<%1|mZotMLn__HYkwLK#(xB~D
zuA_$C7PGGw$15vT;g3j@6TUI0+5DS0030P4km(j)^;=#Is3<PS3xzH;%4f7h{&Oak
ziN@r+bi1vQ8RMYp3coRX?kS#zP!#18430JVah594H5^U0t(w)r(1|h^c{6N&5?`Is
zhWD-9%2|H(2NRD)K&d0{+F|>ddV?#ce?R_ry3SSrJk9D2<T9-wVs!L{o!2jhoWwVH
z2M&`H`?v3a!$a=rPrn}st0)v{P}VD?8l^BM)73ig-9IvwF}23G?{DQkVn(cC=HSNc
zIOcNl!SY;oUI~EsR*$ncQtOj_X7BLc@VwaveOA<J{+(hU8S1`)F!9P%ZtyD8kpnqK
z{|2pG`lj8o)tUEyPed2t$8!ybu$FM1nY7YcmApNYutGP2gdiKuH;`UtJZkv#tNA?r
zy(gHF4GV^Q<apU?)7dsIa`6?1nL7K)!DDgcYu@{x{|Dzyc$1!`8kjCFOCF_lf7<6H
z`M^eTsw7c3Pnx<CHRVxw(V)RxU1Y+VC|uPxHpCeT?otN7_^kxoIsvu`#T&^4FU2qb
zPuKB%j}2Ngou65le29x9`VhGHYF7^~O<Y&V<OuBeC(>DtZp;rV@o!VIaA%xSx_y?_
zm-lsJ2YjjYA74T??_{mpI@EN9giScPOilO%py$47qJ4A<6K0q{V&b`e4gwrEpTFg#
zIpro5o8PT<c}2us43(9^dH6|@WMsRsa?N@u(5?V9e$b2TlyxKo3;nh++XMyVl*im8
zG*`!-kH6M)`+$D4XJYADDk<J*T3WrWglb=eM{;f?DPu3a6eh$xHFY=QzKm%;zI}~b
z^7!7?<jic27)@==y@T9!udv&!`~ws(2@a%0#F-cl_g^0&aFcuaBYlTXG|i88GbpfA
zV@F>#TkU+xp0Zr;(<wRW?H2mt{fqYKV`wnYX2EGgf&BKk6EX++_c@sGYg;Dme>!#>
zCkex*o4|ZNTR(c$6VX74RYBIomnw&2_w;93gJp}whte2<4@+YiIP2TNsK2%Yf6>A6
z?hd=GkEVOoIvmw=Wchkg$GlIG6pE?M*`xvi+x^uYy(mfM^G~V)=AfBod!v2yR7JGD
zBt*7c!1tV~(t5-x*W%fk%K_EVN5ZhSAk<pK7$BCWjt+1E2Q<s{|D8VLCU81|4cZbZ
zi%XiCzw`j!*((&)$dp7brn+a&mhop;8)2Mg3qgvV1jF4BJ_hwn3ExG`$*{!-d|2MS
z;Y@yH$cjYXXFI`6u>@Zh2}`-TUlw8QTWuc%1kvu~Is4B$lP2K@H{0oe6ChFyYqVmP
z=UDer)15b#&kQVH>^aXX9#3%ifr`YDAcHxK9L-#@nZ)r5`N$+Aw~?sMWMxCMMG|y&
z<t<+|@4n71^52Jz%Vhzq7;(yad`0N7eg-IaM?Vf!mdUbU5)8BDFfa#_@I)Y$;G~6s
zNVDAApJ!B-`c(gYg$_?ZsgAz4tpLHZ%5kB1uT0lp6ps{xn)<IBep%qt`{DshTIp|X
z%PNT=Y+uspYyeCA<Mcv4C%r3q61O}{pfDMqiryTzYK*OQ@)Ksyy8UZZF*M|;D2AFk
zS)h|6@{yR!0^&1;fwiJ~(X-&lMJW7Tg#MoTCZ;{C58a+K3T_`KfaU;4o~nE!#%o?i
z6dE<DeceebqxwJ6krsw`STZGA6PI-TGWPJxP7@zjFToE;vc5{wDDSI(6(F)as@TX&
zju~XLhzKNmQZzeY2{$>e8<zhrb+@YfP1EkR;X*$5O%HANxCz8=3iy1uk#uzy0Z1kd
ztLnjCHJg_Iw1xo(`&Ao_mRa|9wu?hK(X}N;>m>oAeWQq*uI<V4HS?>FJcH!<M&9S%
zIIe)l7v=QV1lCRAcD$*Oxl>X?!&xco=Zv};f|OzqS>;Y7TyDA7RR#?p=(haX8OpJ+
zx`FHt`YrU;<Z(zuIW3P%W<^CgRDQ$Vf3Uz>f12}vKDuNXYx;5UDVyvK0#C`3U(7J=
z`}b6~<f$uKRi&f~5@@pa_HA}ZNuhYzTGQoI9vZ$o{G{0)Z)Ya8#!Gi2lxmK%5hh%I
z=&xhhyOc|HLiqdcBuH+_arRWH;VeSRMuVfxkzDUzT{Ls@#ubN2ca~`ABTAMsqCl1l
z8yQArC5pI8j|@**RjZ$#--}@v9^16VLwO8r-_sTrZnvYZD+fAHCL8P{K*>W|J@*U#
z#UDCtu_4j%BSY|v%iETwE!V`0;4!Rk%wMH-tK27{)}O)kNeI+~*iN)=t#t|6tnBS_
zq6{96(u6l`mBX(S<MciJv|O|2EA`wxkI&=>a|kl*e*;cPW$1osDm(G+N=A(DW_usn
z0G=9aOyWUhu=bwaN2Awa*am@O7)zR85Eev2h;@4qZPd^irN(jSFeGs~(jj>*<|7)H
z!si%nx*~;i5yVlGsHC&}&RYJ$1lgTg>G$+(VdSPya?In=uC4-#LtFktVXjC7$S#cF
z-`i@Px&VKItYytM{YKaUcaDq4Id{tKBaD#9eaMho&Re6)jcZy_2&&#u4)AJ_nD4-r
zADu-Iyj$P-`ld<Xx?$x$5P{~fB+;>gZ7<Q_5Z}8#Oq=RHxuL}_dpFxO3CHH<bqZ5j
zGG6Hn+&)Y{>pm@%gjh97%H<uBlrwLWzGXSf$X?y2VR7o3fud((zfid3a@Q8>t?l(7
zO2RLi9OM}m&0+ipw5zdq>c8i0F0DM?2pGBVvF@_EI|b(cP?bR+vQs%LK|g&QOd4G0
z(F5dy4!f#&@}sYU#Ne;AZyNtf`p@=}>B5Bo?_7LO{x@X~@B(QgqVX)hI}MbwRefv(
z*Ms9<zRFYyW;Sif*L{um{V=F9ux7;4L=R&3N^IStB-O`W_ZE;oXiAJ8xl8TZLNPtJ
z;+b^iNWY><*Cn5)b?zF4<*!-k$TtE<ss0|%fh)3;pK`MMeP=p+a4LC((g{EfI`I<P
zz@OBd20c70ODnXq^C^>M=rJ$vS`(QtIkP7Bv1p9ZzSTNs#m<WB5kx*aX_lBx==p9=
zvsP@^RgQcqM7X-pXbEmvql|08B`|GaI92#BKB|TfEO5SmH`kT*EyA(bb7+zp&d2@0
zYpfeG$o8K6X*~S?%JUR*4*f?l@~2MWsHd&2YPS%3_57m<dTX%Z-iD6%bq{${eY~}8
zMRHbt(c1@16Ggs@;`XZLW2ur%Qb*l7${nSjqL%M{nyR|!raAelnFCg{Afq;zDG&Dz
zXu*F?4i+xa=g~f8au%}bS*>93Iz2AXURmRNLeEn^?^ylM!R%rF1SF<${Q;fo5%Vo6
zqGb;b?cf!C%ADBavR1`=@HZ=iXR2)-2*F@{po`<vv&pnpz8qd^!@0no8~+EySTMGW
zLz+RR%jEz0?0;T$-}8`Wm@=Jp9kDbZ`$Q%ybO;Fu;eAJ8beNVD)4F8j@^Wu)-t6`9
z!clHijJy`nIxbiIK)H#x$1}1xdYJN15r>13uVUGRzUkkSh_zHY6fNRc1eulfoVsA2
zvuw66w@_4v)W-j43!8oN4D+d2=MqRiMDO+w)#y{M<wgfXpJf<dj71IPEA3I94cE?4
zIDx&->{B>Fx65h>zfsxhn!k2+z^8{Am>>F*K2EdMy_y9%^Xdx)dG=rGbAcu?Z0Jd+
z)sbI{$is^!v7%K9&*@I!vE5qYw^KFNvi|3yMKK&-U;oP~fU+AP!o+yqLR4a^G}0B#
zt#iFtM$vlExjsf`b@j36dF^&`n!57-V?`fN*g$1ho<qT_V1%p$P#aDhkIaK*OsVmU
z^=p;1K`GpJ7VD*Ao(?^jm?<akBAFV`<N3S*JD@zIE@{`=ux`zhJesbWMXy)+PAD5L
zANut4`z4;tv>K?RI)X!k7lqnf6&I|I`Z<LPIyhI!k5;3Ei(`F~anWF}i$=Yp<3Qu(
za;osP>dKS)0khKYJ7prpYjt&P9GOAdrehg}y6cp*v8avBU4*MoU(i2I*M!}55NP>E
z_e%M{=B!3{5eT#0##aI%2FGB%RI%YlT2gVepQ6tj*k^Gbh~ebuasfyY*9B^-lv0hH
zPczA<Y5m2v?vutZ+_A&ogDT;0AW|x63KcErlsG=j(p~EY7;lGE1%?R52jegEE`0O9
zzXkB5?_z(m0G<>rHRU5GH?#O)13}NNK|b1i;CJfYT|J_ZvtHxdF)G`;WS2ua3lR6Y
zC&I%v@w-4WYjEnkK>Jz&j|EfI^e}ArWvrsFivCh#j=uI8fxMpeE&Ap0wf;u0{3#O#
zzuoA<<f%+-{tF6h2T<ThsB+%@YyaXQeHYDMyCUURv;j!H^wib|@j^e`MoQga?5byX
z;PB?<aQz!)%}uXvw8UW3^mNnQ#f=zzi$C!L%lNUh6-}XPiP{_ork#O-43p{eYsH?w
z6U8M+jPv5wu*<|dCN+r5(}R=^P2WUz2x6pR^~1@Wn2FI_W-|nWou`n$<Yb=TVh-V+
z61@*wONl%8Jv`Mom}_L-&kjZ`C+27`(75_&G1ON4EYMoD`;{aIJ(%Y_D>FMZ_G%>T
z8n)%1vRdtvf0*sOWmZM~TE&eofjhvP$uH%2P{1JUfTZ0-V(b@pvX=a01>~dc<j?dH
zS*O$9g+i8px7=mR0hBcFb(YTWr8R&NxwnQV2RU%}KM<N|B(#ho%Z8JzLsiR=^V3H8
zWQpt@t**CX3f0`r3#vS*In+}IeX#n`Zl)3X8M%m{j_&lN{cHi>sx<b)BN^Z*!R|HN
z8egB+wV#Z#G3wjx4qLT9y8lB0resQU6v+=40_l%_0#*AGl~-q^v3hNlZz9T<hRJ{+
z{3$H!IB1~o6iDKR=u%|};Z*fSRx~kg_^bQIjgE=^4=g?t&J~I(`vzNx<R4Fi&6eIp
z*Qh#`t!NvjoFBfC;XDU+vV8WCD5-IFoytzmHxMDNNMReSt`n994wjbxv;p&cx#$om
zepG)+GG_E|g5p)BEDgZ;=*T$E32L-=o2WQCs7Lk%n%1%;Ka+#+t($GV9-mGgsEnO-
zv`w9~n4EOWUp3~`ut6XUVl3~t_U^yldquNu(}Lo{2S^dfabOf&RtIiK+UUh3bNN<(
z@_JSy>$1PwQ5;~0F5}Qv-H9lz4JH%Ghcg{#(b=I7FrvK!O7+8U_hk9cXg+crIT%DY
zZ-!?z7-<?*jL`bjYXoRmyM9q(c)x3=!?iv1AqC~Gx({@a#{qr!7kx-U{0fVj(A%$z
zvlN<-bOsL`g6;`j{6xUf?8N%9C^PY1ppt`84&1Ug9%A1|Wx-`4aF7)H@196UUcfkp
zA=GwPs$HPLR(yC=Vz7mP%+6HlT-m{F4CKMWU|wFMEkV)|I<M)q`Mt`Zg?+RD)L+Ln
z+0aJ5n%9&Mec`hsV8c+}ZureiuugLn{<XHW`1m2R81_rG>U`L^{XS0-PGa#C<AEfw
zr=fZm=(#q-Ut@IvV~W26tF(F#`b&(4l0HTXC>a~KbrW4kmX+9Fqk{S!bgR-!4(pMA
zmM6Zk6I<y<nWJYU1q;Cv$CfOhP|vWEp%I6*y~GmzhpnFPfG%f)FqihzR?kdlwa_(}
z^%L9Dsd}_waC&KIjeX;)v6SG4Dw$Z(RVw>%Zn}+FZn~A;<05oW*3O(zvK3?p=mI`P
z;*0WYjxf6Y^)DvXba8;+Sw>0gk}L`cSBhkxuxA~r$@xKtSbC4svjYr;Ok+zh{_!L=
zRxJS#Ta*<~7ioIB@5m%Q@khJ5`oYo)$~?uqqSYBA=tHBP{vkjOud&C3%G9xz3)t-|
zJ?5c4og1?<x#Q~zbm-*1^Ox{lPI<qdX`t|W_EUH~6U1JtY7?L)&{doK*OqHLICvWp
zqn!V*AM0i|rs0$;lL4!C9s`#-Vbrx&^qs1Hu3gMsBJ>&~Tx@JfVlh`{56B9tL&ggw
zGN;LBHIfNPNJ42-e2${9MCCLaW6O2qlpf)Lr#>(8c|=+1j(&2h=<(^^7_fl^=siva
z-9pZZK*3n!8&Q~qQj=GZC-^@)G7ZH)ENNYQn2z%=i2i?th+%$Hl*RG<Zj<AnGBp-Q
zfMJ<TSr5=|T<KHCYuBNV$Z)t*c_60;X7CU0P8T0*B<;3>W|}d3$<A9kjF|<Nd2gYr
zb9_7U69Q{0Nb7l{*bZpM9zMp)R-9%@299aFS~-Ty@6xMH-WI5KMyXTvOuvzXe2*(u
zbdtf0j6G(Q!l)IXOf`hIh!GpBZjEKO<Eyii8*lZ#2=A*(Z_|*Eag0vYS<8G$j8BR$
zNS&>=Y6_}9Gwq{>YST3q%J7qm?2wS8Em!;NoD)3FFMuE#XqvxJOhw^A?gg#Cv?!>0
znr}CTc|V`J?|xjME?lP~A-p;pzec89V$9o^i~6^2uy~<!iu2sPbZ`N{q`7Oh17FLW
zOgrUj&}k8rM`#%Tn8m1Zan5}R4WptNJSFx$LasZ#A#2dD&`a`|lP0TMAkcF#)IH12
zOlrPVS8R1i!$>^z9%r@NEWIEZoo*s%Gt^2-!WX6-)U*po3ik0J8$HJLWGiycvHA(-
zAmL>|j?KAQf$S(YG<yqrLB7-@&v6>ot#;9h#`Q+@#MhoM>%jh8Lg5H4%=qGi9;9$Q
zcug5mr<_t^c|Iyivhdk+Tl{ECF&0x`z|g)MGdqC>`?&NP$=A*~9;m&XlL}q|=W(}p
z->+m8&k*sI>p0HSdu}WF1vn^sA#ov@AAwKbJVt-#)(36Yz6z|f>gr7XVsWBZ_MZlG
zp7;w4&t~ztis66bkf!uYQ<>PBD#_O{i8Ld{!!w7eUwB7@R+_QmLz9lC<14EZ&fM(1
zrsnRouIP^Zy<Tbj#czFxP_nYKwu&yN8Gb(o^IGrAi(Z{4X4V$#<xVEsVHIk^S~ues
zTJPW%S1>r&n@iP_gNU@`O{2Qmoj3O-n!8VHH%&HX$UkP0E?)>2dEJ%m2gh%5lhF~t
z@203Z2+ispjhP*|*V->j+A9<y4t6R=I*JziTdGqVPdgCZF@n^c>YE+uUqaC-9Tb?3
z?mgf)Lgtwh8Zu-!<BvUpW>zD8HC>~|a{6tu<vD}0Y7PbaylZaCd%Xt=q_NYzUI3~r
z1RWP2m3&dM-KY8YF)StN^9S#*su#E7^kGHE!pM$?Gy?m42Gt18hN8BWa)qD}hhXg1
zAW`k_3E2x~r@DHw7|Qf8AK8qudI-{PP}0>pZ?O+6@gBm=E@69JA95O6fEaDXj>XN>
z7IvLh(1!1u3E98YUdSk+Sg}wBpenK{dvz$M1bV#I;`5h|yx{rIjqfi+`P^)9;ME5m
z7Yd5sd1wap1GDm^OiajB;tnP>W-L?`X={Wy@zt7a<yh5kMwX=m%)nw1^r*y8(!}@Z
z9tU`}wm5w7!06Gr<BsRiHV1h}5L8s@*A&@aJgY16TX$JjqY<H(Ug9&3(Xk8$zlE{Z
zxrgV+A>O{{dJf(H?iSl)sOlEHdO#)rI_)Y|G(gbJa1De$+*7<^-;AXbt&<I<8CzJj
z+TtVN*%6g=Ty~IEEiD3-nR$fm59SvZKuhRbi}EZo>a!y%-J0c4F*N9XH_&8t!#q?9
zZt39TnUO9v`iOM)vvi{i{<8}h?_$*)&vDsYKGPg$S?d74=|DXcpEgDg^4Sr-?72!w
z2{iq#MW1XJ_wkt{zHJs&9n7qROqjSl`NA32ece{Ul{0Y)UMTbBY<%6pxV}jrwE~7Z
zN1YtLP&^eZoO-)iv4@QPZX`OZlp~%GQ<_D5QnhN%H@S13GE!OE%x*rUu!C~DiG=%}
zeyGEY+&l^vE{v)yH<e?^nf}k#uIXPFN>TXRzpgOII1sGp(Qc_CIgwc~DH9|>2l|E7
zDN8hhVTi>pn2&*|JjOQApT=gBdjgUdV^O<TR_Al0MlY+ZXpd~Y+{c5?pgJ^9)1kzn
zlyo)2(s0*ezVwiHG-v=N<iTJyF_uH1y|LFTG)hrHv4WsfxD}@YD;VTP%yUxTAT!xH
zRI#z1nk%049dZ5SDoSdlz1acTK1#F@ovZN!<lC)i{PrKG{kot{;Zdg5Rekb<#m{cq
z8wbl;!U@JtF~Z%wRNxTX^ehG3md?35C3>9jx<%{9Cz~ZPQhukRNA5HR=IWZ(>c)gd
z@Vcx!^@R%k+Y7$G3e_4iVIn1G)fV%sM@sI-@Bh=*3mbU=R2{Z8qe&c>^Bhnscz&a=
zjV37fp5`UtYnj%j)%?PFy-<&OJgA1((F!>2xR*--w?V@cIjgr-4bU}J8|y{m+O~m8
znR&kefC`K88puu6kh<9+|76u*NBeH8CCBV&PRz;4thnY*zf2FJ8h&PGGH5Ykg)}fT
zs>^4P)(@<ydQDzY-DfV7b{!|t{-+*zR}3OU?b}hRbzbUfP_;R;t$35S*<s8jJ94ve
z)!$p1p|NjNxX?@S;CbVX5ud)W?;UHL#x?bI444*0`^HzsQHDKyL;EdG^e~d(1tiL_
zTIp=Bh&xhqvr%RPIu<+^h5#XK$WvRp{1z+t6=x&mRuCFfNEfZJ)!C{^UzG3Bdka+3
zA`$C<qAk}Qs8&}_uRHtxOPKtpn$^;qmPu4+X`zspZy_i^&z8Z={N~>#sNy2QdHCO+
zPL%Nsbgarr;#i<z-DUL~`~*YDS;b^I%(D!iiaoTYIR-__BDYwf#PEpM-fU!YWmU)l
z(L1d37-{jM?hT|D7Q5OrcpUh!BoPfh)f<(xd`6c0rYUsDVR!Shlx)|5!&JvBIVuC6
zB?TOC5;$m5UyyPpM^L2Yjd@k_b2xh23tA(!0Ih8uJ?(XfS@vCSePLLRroZT>;7&Uo
z8{|w%=PyRtCni$Q)6wVydwta^`zLLcWte)Mj!g%B=8Io(S!mAan5pj8Kv`HF;o1%H
zaeA&3xb8iwqK|3xu90}I@Sg%6648{T=Te-+e%Y^xl**+Krau5JL&7A|3jy#E*jzvq
zZyOjlTyi*OpuCV&NjqA$-g5TzMMbrLjMG6vX<cZ%tidMV^}c;?VTw`7<Bl5kn$qMK
z<fAoQlPv4b-}e7My1qKD>Ave<5eY>QQ94vWS|kK%kZuV{38iyNN{or92+|VL-Cdi+
z21s|;MoHHkxxvQZx9fg>*L~^z+|NI}_-^07&N-iR-sgSJ=RH#?wl}&+>CvF_$c^K{
z!&z9)oUW0v{d3&=BwykZPHU0A69LVZq_yb@sxBDhz7&Qzff@=C?Xm{dQ`QZkrSb~?
zS}zXrb}03lAr=nOtb^2@G_F1Vy1|D^3K~7YZLijWR(OFNbp6WLUqI>zEnaL=dHhh@
z%Z3XA$zL380QdN=8RhhP;;Mj;n|JkW`gtXQC4tkEX$@%n5Smr+8`LhM`YU)=Bt2;V
zGk7NQT7Au$I!Ofj?<NF38#|O0Hl;Aae$JnqB!4}A*T~;e{f#>{nE^?EeEJ;(bkMVo
zKtV}@HC?v-=94O26n4kKepj`5F$_rWS4@>Q-Da~3;(lCH3UFO`@ty9YTN#i8*VOuY
z&ZtfVVyI51#0ZQDFP7fT8JOxn7x+pm*guuK=<mw5p%pHlKwM$g@*=T*APGGrdx1?{
zbh%&+|5pcirTk!wZ`yE+YzF|LV%~|9f_ZAOqUc8g3g+3yWg5Q|`Tqz9V8%=YLrBM!
z_XD!sqW$$Ine5h_@b?Lz^b>=f)?4AkPS4cUP^;z+E83%)6~oN4!*j~h9U0`7kom@}
zCK}=1QpI+%{t{R=F71cZc>isPT}9~c7<%SQgauvu-=a_NS|`X{>$@LzzKXW!qhoU5
zzw*>ok+Eo?|2|uY*{E!v4OFXsJkWfV%Nbw?9xhq|<_Y)fP82Y|7zA6X1xVGuIWB|3
z&eF#H5G8c*Mmyw-a96#wQ)NFp+>=7bOkZmp28cD^o{_9E<}EEmZ&9e<EGlR%U?MQB
zSQfsPi>&}hD=7<Bc;`yewJ^;9xrxVjQ5V~n8|?Zm{mvp}?RlD!zTNL1BW3CA-eg94
z>mpely?381vnD5L>)`CuWj5E69wT&29++k+fLi!R)*yfve58OD%OJqGS+zXlg+M*<
z6ou%JLb9+Xu;JnNw#FEof0O934<+o4)vic-CjMtk87eQe(O7f_9k!`5#n&AN#t8S7
z`NVHrN!7EzVv&fc+k{U;p(qZQ%;U!|ab-cXmi;;Ic`H%HIl64B^<rBl4<zju4UM?5
zkjc%KIf8CT2^L+q>^E<^E2!_{0YeAz7wh($r_$dAPc%h|`Z(k5+z!qnT-Y`g!sU;N
z|5_VJSVSD9A63Q$0hqjiID^m<-`Cz%8|Oc%Ps`K0*FFu)aAwXTWPJ$K+_KuWhhDE`
zW`&>qaCbGM$JEWZv&LcqtQs#T9v?)e2=<r1G&|sZBO<W#xQD9D^scAhgps4RDSU2%
zTkhf@;ovCx1v_eXM)=&5x+U4KM75^UQ$ENYvCkv*K=QZTEkHPa&O83EMgP-O6OpY|
z=K|-bx&j6}SNZKUP7+3MMTzg<ru)IFxV{N&9a!uVqk^art)|4Ilxq*10jbRyXlg_*
zFmLT;zLuSg6bWI90**^`bSOr#3x3uxm2{s<FUu^tg=2xRmD4@vLj?G_)`=oOmM`wo
zBKpNrcn*CofrY=q5kj?hpIHQJi@&gx77zfYpP8?Uq}}eWKipzx9Hw3@-Dcg<Lg3O#
zhkE*Dad~IcNY#7?wUAd`^K3}hxYC5~eqH^_rxVALTMI^(B!lrSEa1V!iO>x}7y*3x
z?Z(mQj3eM5J=d%jvG-5cj4K|Z9j6~xxaG}O2V%E;W`l%N$psNA|M2ixu)j8EDtCHk
ze<NuBMM^&tA&SoUl`zyM`qt@+{t5~2_-ETwH43R+L;ICDOYuPx>r+p7;dq4XA$Dck
zJ|tC7{P8hI-w;BUMbl4fi$0gi#o<dKPv&%FbG}c7-XfFJ%Z3cMFJjb3!WB{C%mKA-
zr~U6uo1sQL2rgB&&H4hPwnW2GDk%iW^cT~Z(7LyS8g7}xPc^HA$hh3<VZYfr4pVee
zvaKbIy;G9}^p$GD*-Zyem9Va8Ax=dcu&eV{3j(P58dY>Ak}04rF7xv|qz0Ef$h)G9
zrG-2!HC}&gL)X<`_6V9Yz*N{5G#Xw)hd5t<eqEoGW1Q1E+7{d4GTp;<tf#ex%11Ug
zciEik3E)oMx(jwjW~lzoNbmm7n=4rvS6QV;WyI){snllGlddE=T^hTmFB@qZ^%#t`
z%bsQypLrf-qJnF7p8_SaOv{qUv6s&!A%#$5(|p(KUcbNvIut|c@;wmU61ZM)!iKHX
zc)o}4g4mtJrW&mUfa}OaNg~iBz09Dp0PyAvHab<Vlb1N%*)N(-j>Nz7Q_R8=rCQeL
zQROIH@VvJ@!+nKapcDKF8-Nxg%G-?eiP4~)tmbt#wgeod&nB?vmmQjn&q5zob^P8d
zGdxfa@*M@g!QYe|*@T%D1D@Ik)F#zWa_9=?KrJq&%SN(z5=+R=(qzN8+6682cBOz9
zeZ!Fn1vP+-%~$~I^hMBbGMYs$f7LwffrrO`9y&u^>(U#dvrDfcRB72G*Q^cwY>f(;
zSC2+}3{?;5CKk5yjn`y{#OaMFBXp_&QdQ0A<*F?ilR#5kbJUvK`g@bU*%$g2E~sO6
z?U2gROj;bpqeOTdM3B@Os=v*(*fI$*7L9^=L-{q`pa9?@Z!wI^Z@vtGdUBjpf*aD1
zL)dGUg9W3a2q=N)^~w!K!U~--P7)q*=Nwp#IZB7?5VRvKGlo&JJeB87EC;=z(LGti
zw?uf+q=*~gGesD-78(h+gUG;O%Oljntc=TUcdvhVzy2!pquAb;JVbMHe#U6|l<8`D
z%{ufV9+KBAXeJdq{V-E7Y0%|Bp!vD#62=Om<8J0ui|y#FF|-OwHz+xk3tYDKri_99
zqfYRDUIqDPK}6NH*b~*wH>;L?ZqDx%(??R6GoU|Q=%#mk3Ri`0a!uLX9UyHjdsG@j
znlsZ{=++lv)h8=z03N=^&fOI)cN_pi>rG=;q-u1&3^fDzL`by_jAt{v42YLzd`hh=
zn}NK%U--`YRv_1$!tiF3+(QV}WVZ<P8mv}WF3NBgQh4rC08D$tLo`WKC7-;!<93;4
ztN*2y-^F=EAA1v(OPWO|2J9bdZV(W3W}7sJZctsjXbyldqgeNFdbzW+Ky|B%Vy9ea
zoWmUJ7o4r$Fv`Y9@Yq;Vd8eID^EhF#eOdC36n*K2+cG1p3N2o_Uad)9<E)-*xzf_?
z7cx+-X<`W)?5@WMI)?;qwVF!IN*UI#*mnODX$FFX4rhX?mh?XxLHqR9^PwP#F%c6A
zrPFuuw`Q{9qxrg)auPem5Z0Y~Tzzgb!#H#qkS7&V>1cDsY6Wf@up=r^J*aQaKZsC*
z&6qWtoAMI?>GmD6PTA&6*A_i4Y_v@^uv1LaZC{d{3*2CHROsCdodzfD-mx>vTRX22
z?k{w+v>;9FH#mNu6O}Y;zPkulGB2fz6lL3pLxJFM#GC5BszIq(S&W^)A>PF(P&XVE
zM>^;Tt}V3kVR0<~r7$(KFnn~QuN&5=>VAr2IY`fSz(<W)L%w>kg*EA(h9b6P?Y+aT
z>92=ZH$&~dR2I}6$mSsqJpeXW&F(!HKshZua0>`VlE?Q}DBh{9G=a&Vk9+rjVXxBM
z5(M&%>oH86wXjH=cjnqo|7JyjXXh;)hN#cy|3LizPq$m6$>M8!J9u#Kaw<Bq>?qXI
zMZP>OaYW=s9{=<cIP$OvLyu+>Bs*N#lr1I7-vV6#z<lF{hwvG&aI4~Vfy`-0vq0>+
zcdoQxY7sO&U_i{qtxTsbEU3Ks#n^s<!EG0-Au*c-S4H>}y#PKYW6K6spvkF;vC($F
z9|G~l{D$WE`S6x}f4QzzjbxDY)R34-iW}dxFAowyZAb%yTLfLIrLLqG8Ni$`!?iM>
zcx3mf4o^5+G}xUFqN7<7#Lp+iPIpwJ{FCJ@d&`c394x6R42F1fF?xa+_?F7cuth6g
z{ZhxZ>Or`PMGGdc^rat4Ww9?nzl6?smw;-&zbCZhO|*$rEw1^N>McE;Kl3y*fibH?
zgi*l6kNRc$zlRdR#edDbOKi93{!KFf&%SK1DSy?mQc?zx@xy^a@3n})XC-qL$Ng=p
zgQ_<py=^M#tLh|azz}~Up|Ku1WxG@YB{S1M)m>kUHA-alyN$590MC-@*Xt0#szWJ_
z!lMKvvZ`6orIO{uN1#>|ZW3S~J#kQd!HrpgppB;aoKvD295+$Q7e;N2o41ex0Of@#
zBT)nQ{sSX>@4yQuP_h4LzMIz71(=5%*uv_~nWgyTzHK-h7xY#!OYE=hf==IoTQ{(3
znRq?CyNM6(YpC;zeCgZURBRIL5IoZ7v@^iBKY5|;U|$1GIz!7mUZfw1ZL+>hscibv
zA&6GgpgBD7&G?u~knY&EH-N~AArWu_=3W!sY<R&MzNBVg%9{sbzHqIqxyC(Syk}Ct
zbG1{zv;18?39<WkSJ+%lh25^&a*H^BeNvDZn6>sI>i1G({8y<t?(?wxCv^YkKSMq1
zo&8_?0hr6ZqA}k{vYVgEK66TS3#)lGcrCo9W^Sr}rdz}y*CT6;(aBWyvniM$PLhY7
z62+FeqS6DhA$$IM)9>~80k|qz*Zsh4@oR@RRahFZF3U{Ct0vzkZRW6f+!ryps$Y?v
zeGX+;A1@->O}$~0YwSsolx4rmtJEAe7dtyxUgUykJ;ts0eWq&3*YG}?y=^B%J~+Dl
zo^lomF+sa|SCM7$3T^_|o^-b9^Gi0bnt6>bha~DPmNL7()G8>bpXOiz^j3{kZq$^d
z=R`}`pT5ApO{i1zuqzKk-J<Ncq**O9S!?QW2FpvhFEH3{IbMav#io~HjOwbNyL>Id
zImi1sd)LDHg!!UWRuG3XoT??o5H{E=r#H~$6lG`C;-nMX#VY1q#-d`I_1_ff9_SJ#
zvc0u=T>qIyZ$A4=`^)dZ%ZgD|E99dS#aQscrCV%iTHdZPyxR#^jQnu)X%iFRofc(z
ztv=NNK8!Uj!rXG$2&$*yEkkHWH_dnNzkk_$ollTzfbkBp-zFl+05odsidhLq7e}!r
zJufqHl{1pF1WRCxi%OVkRy2-X#Ye>&hY6JklTZ&iDB3^@(D(2XM$@zVq;b<27Jq=H
zkP+9BF7QO60H!(p%DD4tRWqt?bGpV7bss?o-HVccezjtu>S>}tGAa|f?2_ZzyNT(L
z+r~1WwtpqflbV(KM~p)c+X#Ki5g31v*fPU=Q5J1pbLrb#<|u|!&0t1R`hro&EcLdl
zu#r9DalPCe+PnUU!LDqs?`ZAIyqq+5moQfNb$}_SCZ1%2FJ*e7=A3Z1UHw?U;V};)
zuSD6Qv(FFS^5*!dOOL)HkzAEeEUR`P$!_b}GOg;j8eQKbx}D!PdMDE5PcJCEeeKh3
z{u_AyeP8FV9&g1$U-}X^lUEHpkFi04N6gq2?i_u_tO${+SCyHP2ZLstcHmccL<M#X
z!MfhSC4<B2%9@}$$hlh>+Nk2oWIyH(vRlWwEMqNDPFW8g!(tAf{ytcpd9<0=hXyxD
zPfa`${9?nm^FbX<gP<+6>zrC3jmdS-0}Bl!8jZkM7@<+rIPJsZ_LFYZ!Pfh2#smF=
zilJ`9h<JpJu~B7B1#P;4y|-5{f{wG9{h4?0v}KE4iv;7foW+m#xjP6fnEz&Ph#hP}
zWN{Ah0EImYIaq9|F>l0%YANfQT44b~>{>#eNu)pW0z$5KtSSgo)7xMY$!hk6o2EPX
z?%K@jo9|L<#X?$PCV{okPEC~e8lI#67?-&YtY5a=vP08W<`6_)h+iY~1V_6CA9w1`
z`cdv&66s4*{q*YZc(z_AXatEIR^D^?SGV)qA9-zZldOT6OSTlW@O#d(MgjsV4BdVG
z$FAlQH186&mP67L?fuLdrZbJ}AcUEg5IwC(ddH<=w^~j@I<PaFg*iT}W7)YY$%p_*
zTuPPXj$VwCfYw;{U5UgatSbc8&JTSOklxT2`m)%tgPE2}7Z%hsw)(wV`N;Kfn}}wB
zuJ;3J^h8&a#_6G@+z!{V-7zo>oq&Txl4nV0_JNHY-lL5M-pfal&8#GK$rk!N+Y;Hq
zgz5R66e~XV$TRSz4;?=00iB;wo<?7M*G6Y-vx;Ajn5Q}D=DP#oy8fP^4Sp2EPu6AN
zeaK*6_nvfeMoh{Ux@fX3m|rg`+hnrkn&1_N`+T)kX~E2NfsqbC5PX*+XlFRSDo)sn
z+sMqtFH79;z{mM1GcCBNSnJq?lvzB2Jjpm=Hc5dF9Z^jW?0ek(Cmny~d*tBJl=}|{
z{dYP1h9okN_D!-0&;NK;D?TKA_MHtQAVR3eV`Qw<glXp<$peF?%?VNo?@sJzZK0X9
zozaHLYFOQC(dTs?VgYxqt5m-)tCBVn0O#-W)(dKM!^QJjzSthSI)@y*)5kxlaNNM}
zXi)U{JqCX9ua+6(mIL6z@k&yanb`W8H+b6}JN$u?jc$sHjP?Zxy&yUT*^x#eFwRv<
zgHzVMVB0Vqb90Zpyu5i1{b%d~BbT*DM0uXwmDfAc3<=_nBXJAPQHv0xToNUZyY2LV
zYiNW*C-y;j+>}pAhqh?UEl#t<qTWQU*juFP1>8gT{rqre85rO+=wcX}N5Z=*p*q~?
zi>jZJs&uHVr}SI!#<1R<V{Y_PVZ-f_^gAIcr5>fu11ZS^Wq`PZGgkA`{>6ntM8wOD
zeoD{lM8mQ~-p@Wg`Ic)jXA7HBNam_DwlpNVXiiXNZK}hsVSx6y!$wxW<|%2w)a<=%
zx2!Ha@eVzOq2ggpB8Qug*-4!Q2SMGeMAJ%?a^OX?iT@^UgNle&a+iTw>2^yve?#w$
z(h}rbXGe<^w+0|i{RngxP%-*?fcbi{E;*54P=)=lbbE1FIf6>a*x<eVO)?^dyJW<{
zcW)98aS};zdp=}yA-O~R#~1(eur#Z%RIQl=p$gI?wNrZ(=g1-cJx<s8`+U73Gs9?_
zT3S#y02puR*yOeNoThtmfvY9}h}8+yAJ101)G`vf-YC^xo0^y_L+b9DvsEYbYC#>y
zbW``J+SJ;lG=<A+DT4p;n)<OhUGk_YQimiU;-s5WD_ruI;M3T7WupP_7-b+Xspl3*
z7)Y#=$yi%*&vWJ@{3<Z;n^4EEFY8y-G*7ZxI1nv2qL_yYpv2>3M8lFqawqsoxaIyb
zovT#?>Zciv<=1QdF4hRvx0^1ALRnu7x*gz;tYzJSh`a+~!=xaYXQ$@iVVAkG0m+dc
zk}|ge0?<1zMp*R_l&GZ9Y`U=hSY9;w0;(<htK@ypBD{U_g68_6k*o{5Oq669%S+1c
zDqWkmeZ*B9`9guy;|%_o>7O#uT2o!)J#OZ*O`>-*Vg5jWC(1*8gSX8oPm;dT@%(N3
zf0=mWR`B_T&W498gW+#(`OOu36zqH1f^u~-Iqkx?JralR@xIxNUzS~*kWk^e!$s|F
zsQI?nT8l1uy!t0xiJm>ntkWk$sf7c(K;VU?5VhUeCp1=9$Db|>O6H}rq_P34qgGXX
zhC>U4JuZD0RJ5w2Im?cI!4(<Wy!((SG75AWr@iw9c7%xD;tG5!m1Cvd!gAl?7#ayZ
zKiU1xOmjB6(MpA+!S0;4zZbvOQ$QR%{;~B~?(9>_4;k$Z0qy~Fdz3b0&Zm(mh;cM<
zT@wSi=(GZ%&{h}oOc232b5d@G6=wgJx)|rFg=d;c$Fih0{`sD-gP|ds^!xp;57{E9
zE8)jZ1+nb~TP5O@T_!Cqj%2$|V3|7VZ;Xp|T%Tm-BWj+jm`~-#WwESfQN*O9Ye2T{
zJ7RU?tO3i{{~gx^1D+(_T)JS;|IL&)e-H6JH-#mrugp^3z0-Vb8Hu_sz7)iHKU)Gd
zfz*(_Ka<4kD~T})8?cY-;rQy@e#^7GgRAp??gWr{WWR;d*bS|-=r4r_-rP)Xaku+Y
zf&T%(vA;?mP4)j)(p{k5zKC#wtILLleEI}<L~qWu6DPf%8Sf{IkLAni8_NL0UumcM
z@6rF20hwqrq>otTCTDUi-H6{5)O2?Yw<*;Ix)_i?HB(qRy8C*bss;9bt6^~A$zd8=
zo2><TM2r(qIcS|`MFeec$Ts4K<S@UGz)bvD*^9|tD+*uqyYwq7@>X(uj9=S_Z@92h
z_Iw_8&x+=2s7O}`wa@C8MI{RraINk#)2YhQz4nW-624y!X4zaHmsTv)4RD6@78JV!
z7fcRPVYsXIYd+Vgmn8Pjcnna8L18%vA!qz!QO}R^vQV!IRtFNA-#-2KD@(J=OESLO
zSz27zPNyXjY)V%1Yt#7Ho=0kcG@eNoh+c&2$#?%EYj9V!Fm=e>eEM_MK`2pHBS6`O
zjjJ!kFyS_^XH|VQO+H~D7Sy{8n0tnhJ9aKobh@|6ZWYl!&sz>TqAH=SJ0EJ**aat#
zredw3W?aYG$X1#2($mG$T8m@fyzNI1sk>1VGjegt6;Dn5e=XAiC2dF*Xzuzj;O%Vb
zi$hwA<(IJHJ6RD_&fIAk6}7H+=gxYEcR0BvkJ@}ghD?51_czIUq*C^t9iZ1p*(M$9
z+O1Zfd<h3g-pvKRlanRRMcYC5JB8n6-9!1+!gSr~hGSVXJAS%J(=qbx{(Q>uvs6(O
zd)fBee(>&xOJ8*xUry8rM4;&(0YnQ}VHDX<u=$2l<y2NTNIm>;<&};0P3sM3qFNH^
zWP2lT!T2;x=JO8;w|+P8Yj^4%g=9~hqe#`iP5uAV`M+E<EKP)(X0lTgmq01aH*k4;
z;@~qrA8FRG9{ZqkPu)zCL#EJ;ghYcf)gY@u^vzp>)0@5~qD9<xqX#{BUyiDfHjP(Y
zt^<9x*oCd|uH-8+EkJUtG>o?eQv)t2<)aERzBjfgHC0zVxhMYi@$B~z^mo1n3zOc5
z>h@<h9^{G~jI&ua%Mh8?Oi_j~?H9{#(KYboiu+_ZpM72JC>XSn<0MOyB@WudS=B?A
zIR)o_0heBCgOHxk+m2Ni^A}6(h(dR00Pd%YRRH!qDEzbg=?`PD9JV4Ak_u=koNnMP
zccA=Hb?)Lc9URN(Sw0&3vuXf#^7=aV7Ocq3sJTwSrtEF5RyjtKIZ?sr!4sy_4?Ve5
z6}i!*yKgU&uTbOguh_0A{78j5qopy*VpOp7=Pe}U)o8Kmh5eLf?Se}ZNK8ufakF1g
z<J6%XvYHF_hmiqSgI6xDoEq^fas5vP{pU3N{od21ag1yYa4WW(SD^?tmN+CrSi<R(
z@viv_tJD#WTjJN)s_d9t_^i!}c+51W@!B$b<ilugq5~;$DU)u2oO49C(8H&*=^Q+(
zGX3@#;*)@Ea_bF{pzQ8}K877Hlk29-AK^4>7KsKu1vIi=JWqpjdCg`c&$+{*hO<#G
zygxNBJ;tjavFz-1{hDxl{ew}e-DiFv_j?ra7&%cPQSi><8Hqqb#<-bo(0;=GQr{d`
z?Ah3cj&j<f+D$9JJr{)TSx74Q;%FHE!{=}Vwkz{4yP9#Mh6Qy)vS4|-`DmEr?#=Xe
z;ZUfek|Q#$T~YSvM_WTI-Gu}=dY49$EJTpya~tW?a=A=<MnvFPnE1$d0Io@_k$F42
zE2%%?65|I8=>6MnNC?^S+T|A`3dt_6JP?sjty--jv_3y>5dBsjPrCk!bO3Xy8cFcq
z*8S~kG9umJ)_@~1hVVm84kjLs2fpefKP4*Ff<}cCUyvIHnU12q#$wfhJTI9#ee}G?
zeS-w5uv}|}(HU^q=u+Ii&N_U$O7nFXKXjChWffC1Zc(lFllvTf9b{WEvbY~3RNylq
z*>3CxO5e1o+4DU()Jid&Br8(=@vUZB@JE7Z>6Vz};cHcpGvY@<1v`V|eOAswx4O*$
zyc3fg)BBvK0TnXe?bm6Eb?y*NZyjn5jG3YqHVP{1Uylnw(o5b}(hhKzV_FW+t!B^h
z7snQe#io4`ncL+Gov8L$_k%8GZ0L_MOz@p4X_rI|$j9zDIz`!sE>`rwT_c2s`{Cho
zPUA@TLs9rbx19~;fXOE&X)EWZc$sl~HcwZ9Q17D`P1$0S<L;KJ5+j0u=@wx?{N;25
z^Qjk`4!|0a^A%m|KTSy&9O%Ebb)O#pUoEZBO4fWagxeV;8{%P&`<&$cu4ChVykQ<}
zfJ-nwVp;ogg>s#!g{eo=q_RiNc&OW)-ZlMZ4VXqH@@`Oktt+tSggb-6`pUvyp-R*;
z%pE;RiNSI{7o%pxmriLbvCR@?dwsG;oWw?)_89pFOVofzrP+AfE_6`WfaY-&p_Y52
zS0#3eTc+iBL=HHjxX{Hc<2s9qu55dA-SRjOPrCL^2-)PlE*h*Dc%xM2P17gUHrc2K
z{r9a~%2^BCxhMW5{^}khItJgMzt(l<Dn#!4D_*PAkodS(FuVZ8=Plen>}~ornKbj{
z)6<tAlo)YW&D7RQs+uy=2ueAoy6EAVB!N=)CE-xF?Qay42keNm)$c`UPPT%EP4WJl
z1DyDny<s}10OOm;_qGrFVN~Nwp)=fMT5ofjzHMCo47je`{lr=E@WeW@9J>1vDIgfI
zfR+&)s9a=fHZT)1c<bJsPq|~ME|;hw_<L4a;%AS!(3k7i6|5%xZwKIsGFkJ6&H}eV
zm=c$q6!n(k!>`YF1Mk0-GKPoRJJToFdO3%$@+i4Kh@X!O;mEm|ddaYwHCU+08{06g
zTI<H2D#q_YlbBg!;Z<BebB})2*RGME4W#0L<tq*!qIY47*<JkMm8GV2md&=68rIs@
z-P6|ilua)%3SW)LTT{hz=B|(E)yoFn!Ic!C*c}!oHxHz+#^d!19aGqzm82i{GDo>!
zG&!8(N?yA*@q>c`WNDJb6?mAjd*$2S>)-$sXvP3cNi${XV_GOiK<KeZ0|>zzd-atv
z&#OWErBrU9Wec{2^L@ejZSSpwt`u|JyM^;*MgBDD(d@6v)4$3T<;o_$mU>choGgV?
zuAMrBa(WjjV`XpJaL&3|jXVqwrMuQ`{`Qb!YP6v!L|iw^M&)B$a0-{%BI#S^==;kK
zF}hY7=bO*0Ri#!uS0kQ#h};5+Tqp7|Y|w?Q9?WvSeA9QmHqM1r<nhO1mz;oxtG|08
zO_lg$@nigdKQ4F4qy>m!XL~k?%AjNFjhj+#n;$q;(v1zLdVAIFvghdL!$yB8vI}*-
zq)X}(De;Z*y<f96q;Wt?WMLEiiE7Q-g&_8vGuw4@B(sd3;)K*iRr|>rTei^ar;ASq
z5nmF1rUx;#iwi|$JXf_kdLt6JO<nlrcFPCdZF7M8tDFE)OX{>2tPGPwlw)+!Vv+`l
zzit{<4#$hS9mS@jKPw^-Ci_x*agbr?i^Hln`io$)Dm%wU=yYh}En*!yqFsY&{2r5Z
zK9xu_rX~Z%hCSKP(H@DG4RXTqq0VQ}Z0FUWD*RWf3$t?Nsq-?2$xvtj{BcbEjU(*w
z+evgA?YOAc5$6+=@7^3%!rny%2zb+(&$q;tB?|0$ayH3hQ05gkG~?NbszayMo0NMC
zykF$>!r1lro2+fFX;ant)hP$yTr8j5D%KSs@_#mPoz-HxP6pB_Tc6GKC*&#oC{NiZ
zEpU><{5wGU2co`f4L~is38$+XYRB~gh#pnn3itl}(bW*I2x7C}T8$6Wkv6(No;XJr
zx|*Q%#P4}>pT_eX^YxOamgIV=@h?t|8@&pn=TB8HXkGEb=H#_^`VRno2lXnwRb{$1
zeRUXchj~p;{Xov>$Xd90(i&SvMVNQQsP9wosnM|;g<<5cO+hB-?}Iner&2>i9`I1R
z`y6%~n+v9j3Exgm_LcrD8tRyMK~q!8hx1X;&Sh{HAKd?<L&5b0saQunX&ft2{59z2
z+eGT`=K_Gez)WCViiEVmnIdEk&97?m>td}Nuz0?b>A!(4T<{)8@8w3jgrS3^ceGwk
zXTV)1o$ILrwC@kYy~;~4A0=pQSS2GaKI~H8aOF2bZ$Hv5_X%nPQqQ9mq(=hp+K(R5
zGLFt9RoTb__o3dql;htX;aCDvlxWK(g<%MXIi=72dMOuC#v54_J?X!G9FBELe}lVu
z7s}3GQS<tvkSyLOIz1YLkiD4r-_Yn^FJ2)+K@Wp`x2<0C9T#)te2r%xBj+l-nGk)S
zlR&MgCFXJ7*Gb9I{82&^mzZx6d8@85FW=Vm;6;w9b^@|N3Z5U$58zxvIe*>jnCIP$
z0Ks`JSm5O%If+j95R7=EAbq@;^*%I42I;=MA9k~k1Etb!>l==;HV5;twr=7=INOuv
z`Hoc8BgmZsf24?gPj^*JtpP+cH#=ocutC=hKR^dIBj@&_&ay-l2p5F!zQ*4Cem)3z
zlCq%m#31dt*SJ9Dcrqo+uMw{I8Ic~iBov_!LoI&CADX)xr_qG?XjfLk16(dK7tt(c
zK6-VNIZ>*oa%R-VNh2}y#@SC1?PCcJQH{{yX9C_|aE0&Ns+AU-G5xfp0Om~i0%o#u
zSeV+Sslz|S+@{#%k+RxaWx!3Xqrv@@N12XK7p@I<yLTIUOoG-91ziFDy@vwnh%M!J
zvz?veX(^7$(TXzAQLdUlQW8PJ>*6Bjj}GFW140~J_^}oDHaFtdCysk~d*TH~dA3pm
zNT`M(FST7GhoNy__0g8ptVX%M2S-JT*3&x@w{mVh{DeJ9`@nLGx&HL3+7^UrlQ$~=
zn0ez#n=it|yErRvYVsr}`6}R~glCFjB><m&uWmXMLNDLdrQ?oj(S9y1pjv+>mkisC
zABCK4mRbUP(~bb0iEsEHFuMoL$3ma};>DubKSIx8GV~DwZ|gArSH?#u_44oh3rGcB
zl!B95lmN`h4SillA1vc?WKKq=!Mcn77!g(xK;-G-`F<#P@mTfZ9Q}o@Nqe6<$35Kj
z6}!D)eVD}y*?e30!{u!k5Wr8+!fO))SMG9f^a11w-)OXRoddfKV?)Ke_-APV;lV?&
z;Z*x|0m42UL~Zhu)p?lHos@9^kXk7|v9mFf=u@DJ83xq7V@bijR>$6YIJwq6<z4R}
ziwn*~>v8-?({MSMIsTMZ{)@(c=XhSrFU59e&bXg1hjfQMa8RNWR#h)j*GRS?FOi@S
zA)k~q8I$M{sy&h{=EQQm<{RY{j8nLdvH9AXww)4~WP5g<+SaX>5_4h|Gfp?VuhH-A
z4gZ{Oq_ml2hp46%T~#Gj@Az(-A$B#mbSsqyO<(1)6$Mi-U)(*o2k(jSPpz%|DZui$
zz^x(ud@PluCg7*fBT;9r`r>>Mw~2D_^v9FiSo_S+SC65Bw%i4guG5NqVRMAOS?QhP
z-RZ4z;pdb)Hpt*tdq&9)HD^CU<(lwAnJ2SI!E-sb1=fHvx!Sx-fl4Yn+c(YYO+mX!
z73A^t8>Mh|RMPTMVPu_##Tj|Q==6ij=`!Xd;fvt6m7(4R03RFyfA440PusZ!k<7FJ
z>)8Tde;da-M=zp((i@q-s?9Xj0jhpjsL=lU3ciM9sSHv$_LTcdb`q0#Kl9Z80cmVj
z$nQL7#w~&e{50PFp!mJN_=g9Du@bSJEnh*b;44ot>VxX;Iyqq-G+_o0lPW@~+~8lA
z(v)0Ay;ZxDY@bhOC)R|bp2NjzYzSLou&?pMIvqNZOb|Xx#J8CdtG@9kdgJq_RzlRR
zd#(;gDnACc(<XfPHm6hluZ5%sGCZ`vIU$-LqTd?MqEN921g{9Eu5d&DjJIoCCjH>T
zv6}`YR@^FmA6nThcD%4qSJ*{sGq*ztkh#fR8Z%)jYT$95sPNWhP>_1;si&e$rpxSv
z>}huNJT{^OU+Ozoi%$Y=TOdHVr1LGGX=Ch2uJm+GsrLrDjIYxSXJ1)w>0b*;+I77Q
z-GCJNiA<T6&SQcE&1<;?O>unO&x&R69k3hb9?Xdh_+u4@*)ps5Nkdmz;BDx$SAnZ8
zRIi%Fx(3Cj7nIWO816p^!gQFNT}Mb+dTRHs29;1RwP*$k0UQl>N*a1V<axw@pe1AC
zi~vj0#5aFnsDEA=rXZAsl^_WZbk)HJXCu_oBKgEuFV6g|ub=NLDDyOgpq*ozYd+Yj
z+28B8@S=Fq_MD_c@B-YF<aw(s^4Toexn_3L)ZJh>^L0rlm57sD`5K*ozMIH+CcFDt
z4KC4TD}aV<0U();L!}_L$MQMIOQq#wH@@nP<{K;VFIy^U<KR}EMg08W+d2jK?H%1F
zHPs^-&}wn_hB_?4n+ZD+Ac=%t070wzq`?NCTaN*!mj&A97Ya`2OS_LlWrI>J19RHh
zf;jsipi7vYL!Ap;lk9hVy4Bf?kGjLMGn<i8T68-XAXpCxB<Lfdo_7apBno6<-nYvR
zIk-HWn&~jd)(*+okNTB<Iob4!?kt+WMmn1D?bw}*G7GR-CD7AwZdr5#0d};!l|Fo+
zXS0z*qNEgMS;)sC9JOS#lJhFRT<+}boh2oO(lKX<26<6IWWKPG+xSD*Mb1$+zf<3P
zF&=R0B~wqm0;BP0594+n^50Lw6DzXStbtSi3<mB0nxY(-gE5Q8ts0MFU+9;No<1is
zC4c7dn7XthpC&C)fZ<k7kC`f@lP=P5jiWs6t90@}9K`{176b5><*(!^H`|*tTQ&_!
zt_%eackijW($q@MxK`g`vs59Gak+=Maofy+nvceuh~-eB;23?G-)0YP`Q`bZGHcvU
zK9#QIyXv!BkMu`g8rG5+&(mCr4`PUI)nR*saAzwI%9oXVGO7I7?o<`jZL*K}o@Wl)
zG#>Z8J9xJ(lK^EUh^GIl7sk!2DaZvJ>sXC-W{O`$>typqo9bUBV;ZeE4f}<VyoIjx
zg<-xTGS6GwXM*tOp=+h+hp0AwmZ@dt#5ddzO7gjlzt)?sy<Zh{0R=C=v8Ga(@-<d7
zOEP#evLm?5`>G<7?$vhTXDoAo#6vq{(KGKku^f7m76}eT89BKn3E71QAs3Vpw80Hi
zgF<7okuBfyUwt~35<{7D^IL-#D<Hkn1p0*Azt2hKUEyjWBo)*l<+(4~oiXzVhRR?i
zGCp7I8n2~8{!!xp{jelOb~!Fle8C&U#maY~o)g<aquz&AQn~!zm6Q966*=#%vJp;2
zXuEqaWxAQM(=a{@66n2t+<VlmI(f@dY+rX^CgWRQx*ek4kL9ZR%kJ{aPZTIv8b%8x
zI*+!zA^|w#O#k;GGCHaNpo-H6L)3BD)v2CnSupoXp=4t&)%=H&=%Z*!<I%;f(HB0A
zKSSZd>(Q;`@Q$lY>08m~MG9>2Z|}gHgOGFX$o~OW*3pf>1_4E_{(acHYJv}pmbBS$
zbCAb-(Rlbgf*>8_+-nN|TMOXA6W0zx&Lb)pG1j;W{I41`J*s;p_M2AjFQ{4daV*VY
zv*zawmYDjDFZ211xf^fV4EBZHO;=+Wq*gLzldJ$X(2z7xwgBUPqSjUt0IIFx9QeLB
z^W{@x5dKiX?W_%A1sx?}IO?*Wt+Lr^7;cm5f8$@ExznJEh`kG0tJMq{H7&wP{Ms0W
zAvc0(yJG48C^0Z6;<JlOi*-h~|M1yw4|j=WgNEA?P^*AB^$v}P-cR+VZ)N0BSxkJ=
z^%x^Js4u0xv#^}{(sXm3r*wO@*qJRzB9_@gc9%@BTiu|h2CeLqsOiJS6YiBzv%I>c
zBp`8qvZUD^%Ky?7V5}&5<8X5`8b}G;7FtS44Yx=FLEcz5I>YHdDecC0X8Lk|bDXL^
zw*=3?1o7#a*K$7k96@&(vqEM&v(I}>?JF0<>`qj%xS5#6tq~R4^y;P~-E9kHcdrf&
z0%^!XAPo;lv=8BOXj~eG>MCfw+Z_+4_{j>bU*`&1znBkNyg0ysqk(95L5alVBu-ZU
z*;$7E(Vfp-3I&ust#B6l*yWm+PVV)?)B5}{U}RReIT%UTR*;}}yu<V?;9@OnkFg2a
zG8hTNc+56)V?9?tP$^7aX818qP*31u9!FY<B1R({S9mLdi{q~H5TOsB;Q-L!^=QK}
z2`Jg#CTzjmKUdfGk48k7^k0lt7Tx^6&&TgH`QlHqW|R&g3h19e9Qh~SXN@H=t<vtq
z^m9Ds>J3|pGqkKBtPxsQxz2;d1E<R&7sm2I?X5!@0ub$2Kj>KrLP0=2qj3R4yn98T
zJxhi=)?AN|rIf_2_RN&Zke8=Pmy2EL))ch~eS=Yfda?<hIYLy#t6h_BN3c?R_gu%d
zgJqsF=^E{?Yk5k3o<BAbb6zn90n53pi95ftbQu=1^^PhF<$MrYJcBH`gY;5WiIs_n
z=vwvS_pMIKOIS$r{>_w)mIrm)vwZI7CvFHDGh;fzjI>SC`a);IHSRE^!y-NwIegL1
z1je!Y8}8&&G7qsn$$XMCy#4CbkluS{IP~0qzW#%)*KS9^RUuPN$rAFEVNPl}VXO`C
zty*GH`dvBL5w?1reUY|PX+ij8CjI*AM#wRz8(V4R!jPOC(@_*NLz$j6qAphMNv+KL
zR;$vH=hVUSN9g2NPv5J^6IN>d<HNo2d{*yX^)bl5Q#JRj-iYUFWgwHj_XnLT?MCeJ
zY~4wb{>mTj`}+arN&R+_1tSq^venniK4xHQI;4@<C<wpGsr==6Sh0+M9upUXL@(cm
zjhI1a_E2ix#T_wH*Dg7Hf>naF7q03%wp>~)#<#DQVy2R&I*u!YH~n@xv9p0iBuw(N
z)J6e`pY>7oGuFA8**Y|1Qvm1OCnbpkE9{M<Y>|*!&$6`7lVYhVua6quz&G9*o-&#u
z;Jxq%{=8AWDkag$`=`f8<y}Z;Y>cvRL=(Km$7AW|L66Fz_Ar6#awo0;;{U2XM=3}?
zD_|9xX&w3yO>TdHry|S;uv1N7wnnt~;*q>;mZF&4u{h`rZh!?d5iAQ(`}T5hQnKyK
z;?3ME57w%GEuc8$9!t_ap?~$<-9RHVL<p>Y@@>6bfI0Cc|4gt;5?L1F{P~-kWR@%y
zJUUym5g_|4y?l%t6JJ_}qm|7#TB64zR)}!X=i%VyDii#pWSEj-Hm^~yFwxT$hfm#Z
z>c`Sz)P;KyOQ$gu-}rM8EvNH~_0Y4^VTRs`Lgvc_qi2B`*!a-2z2B1AO=6h#Uzh<x
z-t+AbDDgL_-b?@7iu)Bv=o7jY{4_Zk`L-n9#`L}T_&`FLi!t5V>(y9ePnayZzE_#)
zfDKKYxnz4aQa0lLgJP{~wum>C?&<5zyz}Jx5x&1_iRL<a-?#h5dAag6xL(X~fw#D$
zgzdVEVzQ7w`!5vSQz9-kejRkYC)^plqk^ol(HR$h^~+9b%Ep}iNddeyrZ|b+*X-s2
znS!%S$!M~PkE=)$+?n|()&eVarO7_B_^N=WdFR{ln{Qh4Q~!k;cJE9>Pj4Wa7B9$$
z=j$UT_B1tbU;I^?#J!)bS6H<Ga0VdU1tREYNXva>yCvD}dROO`1NY)%VN$at-JkL+
z@o}X+Bb7icAGfljt#9I2thM221<HV3_uI9T?JCHmJy!)$8Y+(?<Jcg4ecEH&vRC|s
z-;}?hOP3#@{@9IM4J)s8HOrD^^UYmwPW$7rpGyNBBGN4i_RB>EceV!=7z3>q7Gj4V
z-gxSeThVRg8M86e{H*wbvKT!}ua@&ik8@&Ay>xN?{~m*Dv%l`X673FIym-77>y%Rh
z_bYH$^ehRJMbdiTuRT_K(YRIinzMv*o9n3m>sM3u+Q1_RP?ua#`tCNo*Z3%Z)9Uj~
z>pf}}N1Yh(z*uG1y`Zh1Hof;zLe_0Y1f|39MbeNxE*TDr@TSIO`5)ivT%$7?{Q~cy
ze0D~CwL}fgT5RWdPGgf93F)o+U{1*eR3Ms|Xoc;uRumuK^a5F-5Is7X>thBs!+gc<
zKpnDOyQs5_k<d_`Cd6Y(vGHp}g}47IPFJD^0^Y5A2+*L3dI<X}z$2r>mIdT!H|t^$
zHeKAc1=oTrMF`HFI2BL9UWw=zWZ<b6;L)iYk9*B>vPyxifYjM?z9VQ-_|(s0*5GtG
zwSLe(vLzx_^-!MAPxg(d57(#IRCs{kKI%w@78kEQxgdA?B5w7`_>YX?(Im+W0~;>)
zelW|Ymx!bM&|hK!*^Z^(9wKgsO=#;(1O-s`UFw<T8@O|=e&g;iYYJPH>3>kkg2V?Z
z$%n_%A}Rm1Rt?h=p;``QPw$(mH^$lOPbN*5K1<2DLXk+#L~h$FqVogoVHaY~CMMR+
zI<H|k*m7iv{ie@=ohZuj+_a%maOXV`4^;NJ`{j8C{~8l-B1;_E)VIsKXLmKtB4@a;
zoGw`6XJRa*KB>D9=8^7#8=^Hw)QsL;p`k>3@{mj6w5|GgUgh(rO@8_y^VY_9OI$bM
zde|%5s3Sz{^W&tpF$icBy*lc7!{+LTT$skZ5GxP+f{*~v7DH(!{`}^XD`YP&*yXZ#
zkn^vUcb;e0ch}(d1TqbVEJLMz0Dh+L=TGR^&L@$&XOSS-{CR!=)M!?OvR`q>4sCbV
z6NR~%m_8lp^H3Jb;qc(tzwFM(^qfR~At%VKNVy$1(OYQQ$sF1?Y>`>xdjYvut?dqo
zOc{hp538Ie{ly;nWR7y3P^>G7<(5=Qc>bJ|O(Ofr90#!ac(&3>t}U4C<?@^QA?AYB
z4TDoQ-5FsBrT*ri$YV+#4YaD&>3eX@w${DYv@9(ox^YzPk1Y4Z;jiQ;5s<~8^WPh$
zYr&X#KTtjkS6E2|w|1dfx~sk_$auN9sY6wx&q;Oh<~EF}JwI2ejMFLc#k|lL=GE*7
zsaqMHiVlmcz3f!wfCl%k(#naY0zdnNn2%#!(!=YdP1qVg#0r-Dn*9Fbfh`+-PH@!2
zG3Z%&HU+q&zP;mKcHS%1a`~p?B#~~Xd*67S*}V55wI5-fW}9j)-apOD>?%j7TS!Jc
zxOc;~quC~H`X%StfD>k34hnS-esz0r#OU07GEepbhcyONzTNsln9l@^j5ibr2N-t(
zFG8YSGtcV@B@5d&Z6Oqc_|@!yTLIzc5domB+WM)yj*Jfv1K`XD+~#F>gbd4pdbf+3
zCAtCx-g_1mV7wxzHIV9)j43^K*0fyP^sBo!&T)AZw9eP5$4RXl1!bC)LTh%k&Z6q)
zv8y*8JjoKaLtnybT0I43k>WQ#9}^30qd1SLn>Kxrg<WOc)&sVL4Y$3S#O4U2D_0EQ
z7xjXE3+OI7IZ#pY@Fk*@j{^h#V3BU0H@Ly4O+hJtBZ+@iuMN`R`3t?YOa(iRKR*BW
zLu-pa@dBtDuk%BvF$TN$g+w^K^XD(k-j36<tD_PO<Zc|3ub)oT<>ZntcJ}t9H{WW~
zaR*;&4EjmrVdKk_>Xp^MWVdktk;gW>^M%h8o`ubHLp>FB9))Ul;?kAzuXku}dKA^o
zj%TnrHKs~AC-lvu-Z0bVAKyYmyl*_suWZ^(Pt8y(?YaXl99h6*`<}7uUr}+$`R228
zQ1FT8j{I@dE_gs4#D#N=Vv@2<4;h47q|ToT7Q^Hi@M_%YcVF|~v|el=bXA)sGY<Y2
z<Dwbf=h^1&GU3PBp=|T#yNl-=2ozpX4hqGr4>KoHHkwJ!ELmzc!WRUqbG$p+zQtzC
z;mDjV{VX$UIW${VUFEf1<IAr=u`4vV0CUYDaQ#J!Ap17Ec+H`wbtm8yTz;)lE;@u&
zI$^XBC8KYPYIzi+ShVwH0`Hd&=Nok*9ua?Mhuag$KN1o-FE!mC55kpNIqJJRkX<gI
z^86Un<YPWx{4)O^CWzpPG&y3*mKI5nP5<XVAjdxsAZ0S?z0G`Nu2oQLvGRe|Luck&
zxj82{u1=`M1na|#YxN8hK9n>y(k3fCSe|^qpc!Y7R9h+K7a$15()~>MUd|z~%^}%O
z304bYW%-aTh~B*4ZbvnqNa4f1nRwO_R3z|7vZurinl9V&A^8gb;q_!j?OC=9yZY|=
z{ah{sdj-iTn^(Vt0+izPDZsi=<GAxT!C}&O#J6FOtjD6<-wXIY6WA?Tb5$hNAotQb
z1r9?M`9UZQ!7-NIWB^ONA>xIgzFBH*e3g^14s(^bVtga%8*EoNV{*XZv12V7fZZ&2
zp_ju50%n6SY$w+d!)GBFK(tk(ZKF?wCEs?<dZ|8*qE{dK2uW&GJMSn1UjV=#;7I-L
zzUmNkCx6+&ySk#b_r=9ZtvnWz%&wlFw9ZKK*ShX^rG|(6Gz_ehr0sov^BJ#^vux7`
z)@DihTv*RSv@(pFVS(RfK7F9a<8C1w9!*<BZ#auH#IaQNDkKxF@6P$<iO3bKH()Q(
z4vTaCF{+-sAEPUnj!;bv{@(=O5D5{LjE@tpRy@cZH!pW2gJ{_=p-Yd<qFM7<v1ry-
zk&k(%?-Q2C7iPwlqU3X{R&MdK8>i-^!f>d;O=rBua~6J4&9h~bVq{{Fx2y4wHIQvn
z;^!+03U-}Kmep_Xg;TY9Em_nnZGC&dWTN1+tA!x9*llMc^l7Le@v|@o*ER^KIx88*
zZ+b=GPW`d(Yq8$D#hisFGFistFJ0Pex5<HCJY)@~v|6?7$1I@nBq6Ukr$T3|Y%8Nc
zh-3fo#*3&U2@3Wr;8725czKvuZD8ZH2WtzQw)SbS$6_(-XQ!*d<6j7SkOwN-hwZHX
zHTa*=KzH1P18VO4WcX|xNmx0s)HHTksu35`_JdPYiq*4nqn(AAWrGd*1r@zYUh*Vz
z15);dZfzFT3~_J6VPdyYiscS|P@jDCgH?XJJBxYIA7{m@A=rQmpInRG57+~M&4Gmi
zH*I|q?3*(i4?_Lt`|YLPVD*Lk5Ej(D$7g4jPsPI`Tl2Q8rHal?-LOY*e-y3~Hk@2u
zCwLUxTvG7YM>;>oHDnfNbIkk!IRMxGnpG4mg}?qUbuCbe`b0GoyNs5*SatwQ{Q}tr
z3_cznChX{NeZL5In}6L691<vhG4_yWi=7jAt}y!`KZ`e8{b4u<cV^A&vADSj`bXuB
zWcM7t?v45aDybTFB`-Ja-nD8LenuVtnEz*PT>Bl(`q1^aWtP1LTp$FM{l0pRcN{EN
z6}qetH7u!MP^}Bud{o7EEC@Mo^XXOh*a{(<Qt2YM`Km#ykm@AP`S8A1h|b}=WFhYv
zvKg;QXUSQeNE&>4wbofda%Jji&(`*v8P>DsujQ-vD%3A&vjNGPPHlo2=yPaF*CyG%
zR;2d6435A1`qRymHt}ZvfKE-T@ACnR?I~k8??yMlhV`omz}s;b$RcL(A^{n<E{2Q5
zuyz&lpiC=|s~nXdl1w!-JJY4-dn<4$HdEDt4bjN@b=zc<@Qqv!aQ*Vm6U|I<*ycO+
z<l4bmIppHvqnh01-b&;Xe?QwnY-{AKw<3vMmrpfirJDMRe&$S;)#Gy`<Bt*Da~|i+
zUpH%`ji7Ex{``@jRPN)pU;JnZ-(sn?*~{pYwff#{f@kxeJrqbqFg$&?(J!5HYN*?|
zKE=SO^7PN0s&(q3i@1yR;~Z+@f70o{Ltx2VW$CMxzcmgj7jTHiEqZ^U>g67<pWpi!
z&|(*$7r0LS_JRe|&qMxTjlB16oTEN6ub@%6bTx-2f{)?3Ghg-&@$)TQ((NAgmvNb!
zW{WU2``O7mbsAoGz8>*mW!3JFncOu&FlN%qb;zu=8m)QtA9i@`w)Z`De_?GvlJ@=i
zv?P+0&yHLnA-h!883KzJ$~xh*Y)LM-ZG2`|eKfB^%QbO7S`p`lJ@JSXJf8_Ah9~W$
z!{*^VC%kM0nxv=BJ35yFENWwTVr?FXzHMY))L|nC+Y8r01-&h@3}`FKNpejZNP}zN
zT?**(2KeO`c$5cdI}3&^9wmyq(q&@iknNe@wKq`qKSb6hRi^HUtC)&zwuhSaxA9qu
z37C)?6v)WFJpKr?(F0LZTPO85g(6lCZXWiWR9S$6hR;>$a7Es1q3)?~vr-GLgt}g>
zg+u{RZ=~?Y$t}XW^!su?)5El0hJ>x7K8GZ9J;tH~E?e_rAynHS^hd2D!!{cT`B*m4
zU|?>Tk|^UP%R?mZA^WO4gU<H9J@glcb@*O>f!otE!?x>xJCT651CL|7!E&cCb4ZK-
z;YPaE{Z);pFBW8vu0pdp*0k=nGTo4U>kHNU#E-m+uMN<zI^)dNxCc^mxs{;fWuyXO
zDB)P4sJJ>>BJ4k*+jG;q*L!&iIA7K>px}nuvu1V1h3>SvR^Q6BZD-~Kr#@tSgDG+)
z0ls=P|7ug6r13g-#p(uJV3EX3i{hFzukZG{62CTu9`Z=Q;b4>9TEzW!sb5ZfIb*PQ
zwel6}X=#XaQas$?6BQB5_5DQ4>!+qAk!6vrCC3_JV9&g_mz?M{5#RRq>%WX+DQVf7
z+uC%fgxRB$d1x?42Bg6v?^?ZE_9F?MI_6EK)WV~7(}@4a)?0=}*>3N{3J4+y2r8n4
zlG4%=Lw8CyNFy*HoiihXA|c%!(p>}6N;^pR&|O343^Tm*?B}=t@8jP4(|ug?<vxyC
z_qtY`=ebsz1UT&?c1ELRv#j3#Nqo|?dq$+rL-afKuaIZ>56^|ZdHLf3E4df)b4SG|
z7xq2%{WH}Z)R4En;$2aFh`k73r(<9qJ$$09LGzSh(^k0i<`Y<k$bZ_Vy}tWF9e7*#
z^;KEJ%5;nARfp<sQuo4qnI5p6RUYaaN34t?jx(-dX>xtAn>$NA#(MCm{bGWOd?$i5
z`}BflF>Z(}u+Na)oh)o~@B5F=Zv&-C{G@*t=7TJM$uY#U9OeI8j`3ReZnPMxnXcI#
zWp%Ss3kQ%&KrP)fl>o<N-hjh;1mA^N9eT-|Wv1?yNd8xI%yAH0CMssw13cWX$>M_M
zU@zt;EL0L1yiL(~Hs`6IJE5eQ1*)<>Q~F?O{1cOO1Dx_zdsQ76>xvi4zQ8EgJ9$}i
zo5e`8vWQ6(-}M{x_o!ZuX?cz5k#X*-LJ_jF7F+&lEq~;>=h1KFUS%ZyxN?QNG<MfO
zx%CY8!L=C60zC-3L%^WI@o*mA8$nZYR&0agsbJ^skFI%TwdTts9{$J?ix)3J0-EG2
zlpJV&<sKx{o|u`hsHh_dpR&tj-IlCvdvb_n@KMPuA?dDegTULVi2IAEdANeOuYb83
z&0`C+W=n&;bLH7xQ+-fk(d5^pJN)O*E;*dBdTo-mda%>9{L})!-#LTS!B}7UCtWX2
zA#24$;5~B^HMGq2Zz$v-hy<@z|7bD9dbs%VLEad_OU><0Vu8ud7tiRb>Xf>3=8tPG
z7`Gj7-ouyKjYu{Q6}+fyCc5UKPZe;u`mE~^O|EyHuvDevMK17byZr)|-OaL&LUDI1
z#g6}1PW**JFW$qN^`t!eamF8B{P*GV^MlJn8uNKfp*O@2)i-}3`a+KWqpY@F#`yEE
zx;H)-xLNH&1U79J)>(ib&-S+(`VUY0o~f@yZz`KZ()B!eW9KOPl}D7c$h{Ve(l4Dr
zgOUSOL}vLPNP4P2{T?xPse0FT2T;jqmuGn@W?XHs7@3rz)mt23kSVD1YZ|LtsJ!LW
z^wnoC;adXm!xk^$+H(0*@wOt5iAz}f#L+sB5M-_FjURXn2NY&~ByvRg6Oc*rj_JD!
z=*qk9^@O?fY?c(&HTT^&K9AA>ljXf{188nlN8l3ZxD9`HBJg-`u=lAUgTGwfgUsIi
z(ev~7K5v@^cx5DJ==`wEny?3dg;`tw5u(fnkgQA05qO%&T4>d=oDZL{imNr=ugzbg
zI3s`Eix*gW&deIANtL|~`iW2Z!~a1=3kE)AQ5iUfnU^GeL2t=Yf|-qr(WC;wW~#n1
zi5!Oy4&A!Mo4R9aOb0m0e%oNfv<gy+C5l?$GZCPRLN&nN!U_0n@Qr<>v(V>+6NlMH
zSEcA5j4{wn`&(13JD(yMu(k6o?Jv7-LqZOeU#S1WLH=RX*9~_EBnCtpa0fd6R}{tS
zreLI4ZAMNOs_VQNGZ_LkZwr^<Wz1r`EkzE)bIuih!GyUl@-og1`M{^0<kz9tcSUk!
z^{6JCUqCV7So#C2x`}f1>1y9)68TFR$_E+0ASF3kN`p0_Bqv<}>r&mOa`U~|hsL}U
zP}94QUz+;8r)gQ$TJ&U+AK88zT_R1dR*q(Rl=2*`sp~%g4Qc{V4-mKhPGL9t+D_6C
z5efXtZLLEcYb?OqJA(9*hPxJN`5#ugT4e9(=m_fYk7(AWJ02Bz8U_ij^l5r84(X8R
zHC*<+;V@BKghMph5<~lIqJL?M%Ga#F6-&X4I)c{Z?^Cc!Ek!)<KtfJjF3l8n0g9aD
z7nw*^M*#!+dJV}+Fq*g=&_xL0P^2}ynLA4zR`_g7%8oz4)akeRh|`LKf-5#q&})zG
z^XjM)&SdH=7pD-fxbsg(bJ<NJ%mYEf8ynO#1TI4iFV_G-do$GpObZP-!hgrO?~&B(
zm1PkC-N7W9wMiEG2idMBovXk3C~&=!oUn#&L8c-;4b}56e@mCc^QnqYdCmV>FMaM0
z>~4?>=ZgE%{alhS{l7f4qxNgZs<ICHRILHnjxJnmNAD>dFy7I7QEYTYz*0Y+m+90p
zi*;`&iB^|^_QjW0BTDimSUCl#-gjCD+!MVV4kghq@>J|ot_p_TswsNqty(F_U*QWU
zypD0?DKqMIGkA{gtqe__=-KJBfZn@jq9tRPL{VCo(f>(1;3PWFMd9$*$|X;0_a@oE
zQz59PR0DiWVUo~n3o+Gi2xEU2o_e55ib*h`EIb1~1HreqW-0OahI)QI1+6OUyS1r~
z0fQ5$y=oEh_S<RIdvvGypl=7_=d3W&<M~R-L<lSbLR4&pe(ceVNO6H-`;vSiSb75}
zZc}hKOL&lYQ>zgAd?d_nWqGnj?oc|mrz|wrkG*=vtg{@5;%u5l(8YvWtQ6Hx3Bu*S
z$C|>6pU~JTxQISUzN8`*H=&L<uFo3fFuLL|J^vY5YRLwnm>N%XmKf&xH7@d;p}9){
zbUra-oK$9Ce{=G#VD$(df)VYlCjj~Sqb}BJRX%PzCC&#}I#+DAI%fS2Wco7=stm#E
zk513X?n(T!6#YMtugwB);pDqNRHAH%1B6{x7q8xWwxXCq-1RG5SNdnaL~#5M)K5^9
zlX<|BBizyK+El?|JDnEK8NI^*pZ7C<!$j!oyaw>xwQDBN60<uYsZLU4dO77TgnFf)
z-oY^ulxwtBv(xb<HXX_?T2igV3TF};?~bb|oVO0;G(qO60I8_^Q{_BLzj4o18}+1r
zfVSfHVoo=1PpusFu0F_d5ON}NaleJ7X(#zINK=l5_VR=_UKevd4G3@*_L|BqH4~Yy
z(4<)_<5q&vkJ%g+HeZjuz`v$Y*Yy%Zjz<OxaqvF?!DSR6zs$Cced4Pny20bs)h{xO
zev7gWW2YNZux;Z9)_&}uj{lymFU<pMg&G_nJN+zaQYRftCOK+anOpWb^k8~|Vfscd
z>70Ia6)fbe)Z^Cf`Jh*1AOl9UP94XNUoVV$c_EsOZ_{?Luc1WOY>_v#J)>^1D)Um|
z5PSm$FcYbKKmAPyG1Z{{^4e^Liv?PEvDSfeH-zATkQ(}q<ku60k!4f<N-MW(n+g2G
zj%jA+i31-*I8eO7J^0qxt3P`PCJY@n1b@<o{@oVxF}nM{V;!rO$b&=9YlU3eb7gqH
z#Vfun?@EhR9~6!bSV`5gR7{P$_IE-3das=&jU7g@S_)1SYw|4srx6ZK4?)SllF&SL
zGfn#qj4xi68q+<w<HZ76wot5x1di|$c#@}V(Q63~mzjIT1DeUcrn7oA_Tj?0yw6^6
zXQp<u8+z&*<(%cb9dZ*jqN4ZPQ?_3ad7h%QCS74rF>XiPclEPBrVWW8YG()<e0wXa
zT9m8HQ*&?Zetp&Q5|dmqaLl6~2*1DuZzNQe8{+f9Y*7vhK{q35khQUDw$vXt*-^R5
zyGb^lUw)sOA};01BvouB@Y$zpjIkbB`?=#;`^^NL-RykjEQNkj>bL!Qh?%z;M_G&+
zCnq25r|8RF=}(?Q?3M_%sts~)YkA1FQjb3#-lOX%dhuTp{@u$T=*S=1e~AL3;sQt9
z>7)n~RJ(<_RNW&8kbC~ScIw64W%yY&BdfGd)}<@Gte+?J1tg4zb4;^LwX9A($ZW;C
z`7bAi_-9;F=q3u2v<A6w#8AG8j?PC;p1)0moAXrG{t+9ox}49F=>3)c#+?5tTkyGu
zN9%h%dTzIH;Ruh~<x{y?8#5PU%n|VoE}<YR5#_Y`$ylu@n-IY|+Fmt(dF8JLLwL}?
z@1(iVe=8=6&e+^FYEb?dn_Xt-7}#{xwr1RJD8OCDebRj8PJU<CY8IJrSKz)Wi-iSZ
zi~N&Bbs|Gkm$}=u+czhJQID6s1R}4rZ1%pkdD>K7U;KC^tFNa=&dz+Dm=QXbOK--A
zq17+e7lX2A92@gk9lWhId7(hn%zk9&FdzMduCu`=K%*sNN&GOzzI_X>b5-~*ZQS8Q
z3Pvx7G-DxxuHf#HxvR*TiGgvIwI*w~9Dl!%6R~;~B6)Uh#gh+<g@oU|o`x?p7VsU_
z>NOge=+@Gv9EN!6z<%PG(Qf^2qRA+JP_!P2WeH0kx>||Xhb1L|7}$TByyt*?>HfuO
z468oBk1Ahc`j~P4J?Ycjc~z&GG?i#)+AL}!hLdpj4H1Moq+X+EQEc0H(FJmOXly;p
z?Z4PB!}lm|MIPVRv~KbGc+;h_b;B3IIbkUG7L3u<N4?3K;9=VTBkmQ-f4C{YO2_{&
z>feV$HHJf?_TzlS;??#uY>}Bc<QygDr4-7V@@CYYT!&wYS(sJt{z<x`5XH(1Lgs=v
z6XBOu2I>ez5BJtrUO#Z$w{l6}eFv$7&3&k1oK9H~-`(x+C)dCn@<{a!>7Y6KarO9F
zN{`D|4`MQFTk>6)j>M*hYr_-k!6=XBy|PP-8D^o@M;6!uF6`0bBweb*O13l>imw>%
zg6|sO=Zr3yCF_{xsvk{G8+l*Fi?15@i#5~S1b;Yail#E0;&c=TJ|ZnQ4r;fl*1TnO
zFBRg;4@ImjUA)4aSH4z@yB;~|Op^WO+MmpA(HB8&-sM9sp<~?lduPwSeDeqqQ1`y%
z`TGsB#{Kds5v)@PIyO$uf)Ttkg{Mv04Qqxf6JG5JrdPJM<+|Gu2qPJ*QtOT1uhxrw
zYc~>CreD&)N<v#hvwX-ZTPZ_>I1lbH*Mob%R4nRQ&Bxk7tXw_`<?oHUfa+~;?sVSM
zMp{mRv6`qfBWbXE1eoOcYL@BovJpCblvCo6gur$obBOEqMhYi&^zjSU-*LshY4AEg
z)ZpG0xz9g4cOBX<4(AFjKU4nCZVw|)R_o>uL{tlE49*QcpJI1pyv?$a;Y7c#UUToU
zC!NCc8QHMn8r=$J-v$T9uFWyE3-ROW^JB_2bNgp~+8(GKhA5Y;>tCJViUjL%T=$w9
z+C3Y(s-h1l&1UUTJ<R}|Tmr_{pa7+`-*rb19mH1z62x4hIiUMmZ`&p~O1IZB>*ou8
z`pBe*?xQ^<NapOIce-&cp1FEIS9=FWTy7BeCi1b%4=)jf4l@>n51lrQ9E{!*X*aMj
z60vK-kt8D+m0iWDorvPSH&X^t`PmkvEd}G3-`LH*7W(v&_r-iVlJI<1jlZ%#7DsBG
zT^AVRy4a%i89~};aLY)cZp^%lmor7Kx&?J|%@ae8H~1W9s{KqYvMDKIcF(XZ#4N4M
zw4A8m=2LiW&-i&N)aQU^nDH}*HSs8aOu^*Fv}Nm8nZ4YQmuALU3!{OhcZ|h_BivlK
z>vHS5lalkisF|fEJS{oi2t1~|7JfoMeDny#P2(v$s`=Q#lydx>7I;Gdx(SGhBBC^E
zN8|`v^&snx{2v%TB|io`-fjxhEuabq^i94!j--yiU0rMJlPw1Kt0}dme<k!ET?6jF
zx(47Zp#0<iGV&#q{%|QT3dN@aSucP?6}V$A#p9Kpm{vq2*AN}D=Xk|Cy~n@Tx-v#j
zDz$Pi^=paW4G^nG41%Bq$h6fT;0P`FW|3+h7WwEUc-Sys-VU2m(m7FLBo_-+4o-PI
zm-u>sD;IST$x2h8JtM!kJ!6_XwLNxh68SjFw=oN%jKwl%idEGuoi-}BFN0Zb*ukfw
ziJ{RiCf@3Ves78`^+MIzX>)`;&S!PzzUd*vrAV2c-ZzMT2H1HQM0}KB(hskiXpWMk
zGy*e^H<4iG16iAdL$<t;v0B?tXyDVICkbto9wsEi^d2Y|h{4~)Fo}iQ(~7xx95&8U
z05Q-I=#2QUm}Dkx3cF!ey1&|f!GA|mm^t$2UyRUmr1iosaY(qGpre%IP&csrlUZIZ
zX@@beayy2#RnuI)C9-QwgiX6nr7*zPv$9@@eFofe`g%i8#8K0|FaN@7%_+%qhn}eP
z9WFVURePa_f6opm`=iKHNX)E+IQs|Mc9J7vAG{B0=oeth-d9iS%?HgKBf2*2?jlsm
zqkNHKu?_9}-8L*2e4)48qa8Q82JnVl00)^&c5Y6DBLB>4mx>>3-E{jo<h=i*a{0UX
zToQd22)R7MqO&)!132dA*9PY((~_YlFDsY6B~d8<VvJTj>;@)ze0bfW_lEACo3fGz
zeJ-oIIB4nQ;<3bq_NR{f$UX8QFWSXVEIz{tibg{&$-{EN;`(aD+jUi>gO@;d0xB3i
zJ(^WrPdW6I`83vIU-6^k-iS_ftXXtgwXAZ^DIv`>MopDlzAV<9dLaU-6Nzk}Y)d&i
zH&f>h;EW!G15L5+oRp88MoQUjQFJMzy_XNW`33ztKJ|7jf*B+4oAq@x7CedCpn7!_
zohDchQXb~XewzXfFZcSQ%LN-DA8H#Q5KZn1*I|k&R>^#9!$E!8OI!cR!qa`oxL@^Z
zNol&lO0K&16V5NsyuVzT5l5m2i>WORcuzir)SK&AV0LR&GdJrWN1vR!WEkSo!z0hU
zcDtdo73ew}Ray@R(}~QLTYI=y`HMVDH2RYVse@fQmaXbG_ntleoYM{~w2IfOO=?@0
zr=!7pIZ@GCi#)S@y5+<=9h=G369|BBY$iR^MZJo6*#{t?dL-Tu%;`l9&#n9nAyL|Y
zG}PW`iW;Sif4^_{<o3oVyVP9}ikOt`&-5sO<}V)H?yZMj|GdBdrD9kTr3y6OZ-bx!
zkli3S<RnVGX&&8?m(kBAtH?T@FG^U1ul!rxN2=o6Ljefe3-e-D4_588um}AM$w9n5
z8+G%}Eix|g+jbk91!-|<c)#2?sV?}0%z+XXQ)k!Z67G0h_+)ti<g{D?kcTC`sE*HA
z{)j9bk#rwk(?+~^>Z<vaDddq;EOk9KPV7bcr+1#$(t@=MY;Os?TxbQC-53@;-}kVQ
z0XuN|pzo%N3~w2RoxwJ9OdWf#sUT_Hle~+_<oB^YPQ~Wf)prBujrHPfY+gjhJ&un>
z#IuSzC9lZ|A*hun(d-=##9Lo%IUek<VXn#V%~Y=%)o_hZlgIcl?{7SwKR*vit`bCE
zI>4u~%^15Ufx7{3<Z-vo^xch5GQO9$FKGCs4vC1A2~SG((~BdD9s38^($Buki+|}9
z`H1l3M;P7Iaw(*>&dSxSW(j;ht^OoAmA|N*ry_Zo+GZ2tRVLxpV=K&!+OV+NbVu|@
z7QeEXN(SMBqJCiDN%o!w@@S~1=5&O{uF8gYz@mz#hH&TILsih3`}xVa$y>95V6jeI
z)vZ5rCXKMaD%5Yo!T;T-zZ{fXS_q+oW4hX}2hJhaqliIM;qWSrC&OMnU!H~H<u5iV
z;e@&rsnR|C{c*0JmD~n15cXO<l~rXOFB1ra_a1=X`d?P7z=^ViZYrL!vko!iMD3_a
z3oUwP$rM@J16JRiM5hpfr)gdewH6CRA+P*s6{m+x+yhhF1-g}CjL$eI_yG;G*45FR
z>a9vP$^QPs-;8_okI#KDGp!uDvGE#;%k7=z1#=zd88tiEfT{1oFCQ`qBDa3j>{N?~
zym7#gChFB^2xMx;xEebfuS*^;v52k?@^0+`S6h0c2+jt)o6p{9yeJ6IBXylJ^IF$j
z1B!dCkU*n*DOD#fwe0FgvJ8Idg&Gy)FCVOvc)j}6hADBY-m^~<8jDf++rW$G%xa#K
zIzu*-31L&89S&MkH<Nt%!W;_3Vi&Zd$K+B{y~@)TxT``&$M3$`>rEUN9<6B8NXQo2
z>B<*}Or-0QPRLI2hf9;K>wyMdgK6@#;qt}<!6Siu;ce)l1z$sgP={;5svdLvquZte
z;9}NX0M9Bdh#-3lfDxGt*{GY|<@zP$D%s9x+>Y?%)?{BddH!asu;42`|GYa7l$idH
zE4l>R6WsXD#@F>nGX3}Aa!~q1*T`bi)d{>EO}SW${z|&oBVk=hk7V3@u0x)t$8w~|
zamB6la+Ju!KCN&5dr4k8wX7?1h#aYu)|;eL?gp1eG&}RUmk8PA7ZPrOU+Ct%i^a0V
zQOh}Lv~TYFjNU!r<t@(M+VXI7c^ypLro_pe{!~)&@s%swucIFI!!q3h9vDSvSDMmW
zda~U<&P`D?R;fVgGpUs)0dxK0zS$td1@aue&~*_h-uM>2?0Sd?XYw{si6~v61HlaL
zu!mSCZ-qIbsmxdHwsCUeuOHvjOtX~$rpvr45ar4l=hbGAKb}#tIqH}~4mX!4qAw{`
zIQiP%au~0?2v=)1M=Bx~z(D{Xxr#MmgxhM6s6mlIHU*^twRLp4xkvHxX2@T_tKb8l
zv0d}p))gD><EP1IU5ayNj-Toc9bp9OenqvBw!S{|VVh{xrm+j8<<~1q^jCQ*Hc?Au
zyGOHQ`d=f~Q#U{j@AM~<YFi-zxiZ%_1RCouXBx<Dao6XaHYRW8x1T;!{d#>kQQ2DR
zae=H`c6HTBYWryX+B}}?eCcKqn`mX!nj(D%XAg%S0akRg5F<>;Ka5!@*>R(B`0%>w
ze|3FD@dDFMzvta-Rn9k}qa<#Q5DN;l+HOU$`oxQ&^sySsFIAgk&*-HpRkieYnssCO
z*_XMFM=8}#2Q@rujG<~W5V-i|Rb|s4!sGe-Rhl!gonpP%VZsq`fqU{#Qr#C*Kf+|l
zYh&LuSHI#;7I9R64v)hD-3V_P#}R7Z_Nm|iIlZI(+4QP^lIcr983M%9O;%tA+)kVH
zntK2VvkBj1jT*|y34u%`+>(!!;Edwyk1Xx9ARU+G>@9K1KKtT(3TzA5ic~MvG5!{$
z)*|`*Wknm-);Z>4D;XLB6UI);?UxZbr`0YX;{83JmKuSxzDr8=tv+dA>A#{ySX8Ff
z{KUq#-fE>!6??CZ6r!d?9Qd%zJi!mqS3dW#BSmhhcfqUN$}Y@5(w}Dhy@9Bigc*KW
zXol*EUw86m)`Xc%y_hm(=!mG{1(Mviy!w1-VZ&#3!3I)8Xxz2-t9m)8G4eFc>kx8i
z<&FsIWX)gPrSUr<!T1+jxnEuulEtm7EJ8WPoObsl3WEntF4;gU5E9YI_3G|=;844d
zFY0S$lYDu6ZN#mi*erci>bZ%XuTv=C4<9c6Yvf2_56j#iX8-HZ@m*nQpP&tj{^1Ss
zz((Q|TsWuqRH3ILnNCj;`4TRUN37afsveFqB2T9=xXk56|L-&!)w)lk-SRByCF@*j
zyPIEmKd+3>iDPLJV6FtGy{c<sl<&!$BBNI4#?{5`dzpM)IifkCcil__qVqR{)&~Fs
z67~v6t%@!es+Z{4>b<U&AiglzuermLHksLOV_TAoN$(mRozlGi#wsZQ@KO!x^38bB
zXL(3axi@z%a%=k4NGue{*Cm2X2Bu8*{$k5bpwld4kcP<!c~3H2S5M^S`G4d7`ILM1
z!xytvec&@LBA8jX;c<vHUo1(p&Ky(%9*>^%F01QZJOsYwP&MW06!9wnmYU|@!?7a|
z++8RM;vKNj@UD!gpKS4(3o_ibK#=D80~EdO`>cL>!jig2KLv8s<qBD!n|oX*W4%<b
zW~pw5?qxk~eh$FS@`HezGo7fBn+&aaWT~i5aGm8l$eXuiXr+0rm@1Oi%cP}4xzo%Y
zsEAG3*^Zjgq$7G3vits}OWY5VGZ+7Pm4;0TmUbgbYP+UabO08Cr~+H(iI!xfDk;y8
zKRQohgg8SWa=*SmV)NgJ_k$07_C_h-LvtSR^TNfG6vPh&I{l3zZM?NRij42Bsb!0T
zWA@dVUohOh$9tEaX#sQp(PR?flr!WZ!DH2D>p|d93$h>gmhwdEPKqp5>F>2<qK^Z)
z116$E6xMnw#KD+D(dDidN%ihft&JyRg`25t1sX$fv1>;%2Mg70egZc-47D5PGvlWD
z8$;8yxO_Lt@>;L$(E>LE*5cl`nhjEtpzl(`NZ^^PfCdZS^pakPN~+mO`>Q}R&TAes
zP1T1J;A-8QQ8aJQ)8wZRqWF)7z?_vIX2J$MLwq%yIQfcqdD^O>la);>i98I@JkFqD
zgzT{>D2ArgcPnb3u27zCq>Z}w&1@xGZxJf2n4fKb#IxmY{})d$@#lqjfwf_Op#j<<
zgHHO}t?wpZQe+B4a?A)OcGa4LNj{r2`c(UvQJSfeJBbG9Y1)akm{q|<Z03FLkCxS|
z+^^iBz%cEiNzdM0_-(!o!+8x2qEoKaKF5c9+SAuC7@6ypi-T2er>=J4*C56iq${li
zla5xiS`DXP5%;nQKOb&(*n?s6UqP!56u&_<A@YN_i!}lctIy%s;T#OE8!nmD0|c~=
zQ^X~-9wS57&-p(5;kBhNqOA_}Wti&z`1N1DEUd6(9&iqWw_l85_4~(JAP4`gY87>i
z2eT4`_iVpu>fobj#rK*BW`X-$Op$%BUeCPIN^p6Z;$W;gIvmYPD*`pAx)(Q})HU^R
z>Nom39lus401teY83L^oyH3V^E!O7uS*$YL59}DWH8aT<-0r4UP%iY@r@~(FjmmVt
z!p6tu@~EZ?*Dy6-L*8!LrB^r11Wq@TK-_6Fq@1zK!w+p~ke$Rb&tG(_ues>S=B90~
zG1|Y@Z#opl>uid!>MNpe=A`G0DpCsG&LuvuK&OWz-;SbeZg?u!%g2Ni&$T`4<z(9s
z<wq)APmFcNM?{aDk`@TjO0SbZRKv?Dt$RQ5;B0;FQWxBXRYI$B9e1wT)z_kvEg>vt
zz`QE6qX=m%Jd6LpXLZ-Swe{MDyQHu6mdEPbr}3Ewwl%6P()Qy`sb>Npk7))wV55sD
z$PvKd+qxayA0Lyy!RcBtk>0eHD((O?b4}v#fJJ2J*&A`5&M=H5q{oAP%*}KcK%BpL
z$<2&7n9JCnXT+NG%s59Cs@(^-06qf}K_%B%S$XVL`66nR-Q@H8bF$>`>%QH%whM;?
z&9)AI0tD$Ae^pHZ;!_c9|GdZl1p>X-#dB`ISVpuXQ9>>^S3_VQ)E<CTFBaB+EA~yo
zUZI&%SwDTTS6uLqx}*?gws={lc(xchH<h0m)-TaJ(H?G8f=YyAMWpLJ@OHPYZ)asM
z-sdeaudZx%#Z_!}@wt!cdYIWuT87H-iNAeuhgPCHRQ0Lfb@YbY%7e5fWdlSe-({5=
zY^p_S;FsvL$UDz$_jYS^-BXsnjpOgNo2D)z`M8wywr?j#62g|PE=xlDPnTs%d;pKD
zlT2BXRTg>P$~Rpbu0ma{`7!H+o1)Focd=*7PbAoGV!%;^ln>LoH5v*9nK^eihhrxX
zYl{rZ7DG=u8?9BFOvJo*y0x>su2|bCdM`h>$DWtBgEc33zLL}<N{vp6J@4l9EP^|J
z$i`#&6*F=l1dj7u{NolQa_>}4!XoTR+*N<;RZTwJ&GpRwy?&+*^XxClsh`~)aV(r9
zcbv%52%Z#tp3)Z8i?lqPrQBtZ+fG*S_IYBT3e55T2%Qvm+XFP&Itc>dEw=J<yfy?g
z;);yCsIAK!LpOoRoT!EI#2AjIM_Mz7nQU<J*9<)_m`uu5;q^KCB`Q~Eg`__jq^A<I
zgk=Bg;%<AeL?I8^0JJxun<3i(_z(mc{*;4?VKbs+-&%_)Y)OwkH2rZXE4g0Xvb$RI
z@9bL%X>lrJ(p65OA8BrKd@m`F{Idi<5KtGbWvjk8c3QmJBewzEIG+q`zU>qb>>qQF
zjoaMXG#iR@zd!v!bzZy2lV<N_tEJ-4oh^rrqT?S={Qwe8Y#=Q8aE<wC-FuyrN)KC1
z`7IGLl%79%ZHPg3!VRwxt%Iu7*dW`Qb7ULZfDXEM_ZB7slaok~DJN|wHth9ykQx=C
zW>n_+JbBu03qHLi^>SvE&RjccL^G(u5xY+>l5S=m37izWaf3GrHh*+gJeLS{nxXUU
z9m30z^!8ujc5{D3C8#S8u}r9seE@Mvu-=zi8lZoVHm)=s^R;c9f~DTnI#XFz-Zxs=
zt<=A>`%}M?2oihOcC6U$eiY$uE%dFI)V%-R=%+<-(u&q3y$FOpa>1PE^W4^f2g$LV
zb%us2g$yEl1tqC|N1(-K%(~w`>j&j^9??l_h;OAHhmB1Adb7)S2Y-q9RML+nU^si_
zj2Y(RW^sbs#)G$9JI4^1hv`kZW}QI$;I-s`+|Y4%#V)XeNB--6mL$U}(gXG?DxD#R
zbl7Ywfm>o}jW0ACcD<1m9KFLHcPoy6_K0Kg-hB<GDmGvUApGrP9_zKf`t`KtQZj1H
z_?Ov=yTi_HtKw*eW3cfj#M-&j^TXQ!0*T7`Wn91p7(5WzS-W<1<vaMBLYii4%O7kq
z>7I~jlla1bjllR+`CF9y-ou?&%I>}D#ri+PLl=+}A|4#-CZ=56&GfG^tx~~^;f?De
ziS)$9ICAnxN4(?*<<~jlP0=Bm*GGK;S<t!|x2*SQ{Kr@Zm?xq6G-t<;#ZL3j<1cZc
z_PpTK_JA+Os|^OC1zL5YHw5@zr6Fwj=XV6U98Odq)PpaQX8~n;Lg_^YLr3Kg1G)un
zGmHl~Atj^EMAT=2L?hTb2|E;MA2_9Vi4rdNm2jNjZ9v_f6o-kgBoM7valuBtq$+oh
zjcf@0??;v0FFUhI;^=c8C(-k7SUmn{jy_r#KMEaRvASMzT)p#Zdi8UQI^+8d8@edp
z77!5}bMPfn?Kt8d<`WTRzPQ!+kJf$I)1H!@>F2udHhF)9#7{fs8+bKA=Ln6K2^EgZ
zPmJ4cY;1Th{?mwq95~d`e~uZr`!}NG9@eqgxa|&y98WnLgjg$bsFj4ih;wtRiC81G
z*;y&WFn(KVM#yTjz=nBRPcwQ}^yVjC5enOt8{LwSsr{-Hl+ywv4M0>Q^1QdB<$qz}
z(Ih%Q4^(}nn2r)`o^OTn-kIu^KI%*-MQ&Je7@7TX3)3C6qqJb86{?soY}~#PMeqe1
zQE`eiSepsb@?R<i$4HUpOmc&(hbv_Sbd%H`vX&%);eoczmYsRw^A)`W^d>fy9uqwE
z;xf8^u*6^<Z>>e2FUt~_nvOG`?Wcz>xN6!OZuG}W<Rcr>12vE7j2UCqs*)KBqcG|X
zDJogZ1`)Hl>jbIW$&z=UIVZOJZA6!n`e#biM8ve`=f-3Wo1YsmiXb3PmP0(3<qvE~
z;|lKITBpBw{=VQ}^)NOB=W{n%5@+t_YE^n_vUlt{+ShL=lNj!vA*~jvLDU@L8TERG
zwTW(sGf|>x5o*C@vU}Z=yWqMCm^7zay7w)OWynfp!{T(~Ma=OlLE9+S8z0s-G3wUu
z4s?|Jj&!N5AnVY|S1};#H@mJrE{_Azh(+W=lAh%EEMGjI%#%e{ZB)w19C|Os@jvDl
zKe&B!vFJ-}(DU%Wu?Kbe4|rpA{ue9_630YY)3n-{K34h>HeLQcosYAGPBBZuS9|G_
zEcmkg^K5dp_XU*pL}RQ?nF^!N?jK+77_~qfRirH*?l3~VSh-kO+|vTm{yhuem$!I3
zp8Am4a;-`xt{?L?`48ZPn3aSbz&%s6Wf}G8c5}1>#6wB_c+D|${3K*^$S8RPEK8k{
zBDL?&`_8#v{Yd5g#^Ugl1$A&!d<*ebB}G;)CF<3Ue7m)#6K+Z3!?vWG*rJ&6#@P%3
zU@yiC`b1`|M}w0t*V1IY*5~qRxGAjR)@hT>xZ9KL4sBN#XN5N9DYq44HK9t##}yz6
zbXd^-;B;Pd5u^J;W!$FEM3bZmI>D2|GbxuQ%%`-qVimCG)p{avetr_g6Tt~_%O_q3
zE0bNW(xJ`}=LB@3Oe5*#uMCnTkZBc&Pr9i6GS@^?L|(*pM=4<Km3ktb+m>cj<jYb3
za*Mu!I}f(toH$)e@?sqPiI#u`I35721GER?+nnT^<L`wtqqe*B1RronurnjnD8fNp
z<^~<-CTQQ+r>=EdUlJnbOLx%~Hcrh5AC1_e3&Gyc6?-M<G;<U=zxLp59E@;<ozV?i
z^DMwJT;xx_#x;G%0ok8e)c@fT)GrRF^I1-d^UW79qxakO@^&EG<pEJA+L-$GSe)Id
z8wYJ>DLH8eYsHtkD@6&$k5yC_IcDq)*OXMhGk43jQyYAa1W4=T#lCs`vhDjD_6pKD
z%%f!1A#xn|HwANand{Wb(-Ov+XJU|zAjtOcP*!38L==d|!9b(Q6L7=9Xr5~kvOn_7
z%DNv6r%g=a5CPom0k`R#l*(i4@MNDQKrKY~1t>tm#^+Rbj@7dhBF=JRWV=Zqv)o6e
zr5~NT0b<trtWa6yo_hP9PCe|uj?Dd<`%T)$?T9UKPK+sWH%X$img*hF_+_qP0NiNz
zB3rw$?srAiQciKj6Ov;n!F)uC-JAdk-|f-+a~i~`%M=aWin`Wo72AykFs9zJMEgSr
zcFAxOwEdd>BJ-o-pzU2O@;GbeR7UbUpfc&HO1au>*_VZ&RwrB2@thc=cZzDIYKbWY
z%C028W)$F#3FncNC^ol-(h^T6`RzfoS7zY?V(BUmOqCC0u$<eLa$Ayn(woO^&u5qj
z<^`+j^LER5TR8Cv@=Irx353Utdkywaj}QR4$6SKwGj9JM=bw{W-^9mb2Rh$0xY3-K
z2&@KaZaPF$ss0m+=%5Wmn)YYL{x?2>RcsmJDQ;Z{dV{U~H#xm&$rahRLFo?}3i*`^
zl%J0*77l-q0@Wzj?8?T?=4ta=SJb&L#l&klED{#f2((4#d|l@WD`i_rsRq&^uk}Nc
zk%p2>%&nTXnJgMNYbQay+?3fv?LWmCo#hTTqXznZyJz(RPGf|LYd2JXCm@p+^e<hS
zx3V=-aH-twyOne;?LJPJNA*K1YRHD0Um~%MAmmF*{6}%fJQs_jGJSXdQ*Muy@2505
z&iMw%2%=9Q&BbqK?2p9t9Mn>pd(E3{aNrIUDys+JUT{@t#%Q44`AF&INZ{6yO17AG
zY4&si$^jLptyTy4I%3sByYf1jSG);&y^;hBcv~CLKe|Hsd(sQZcf$;IO+b1)KRodk
z^AN!i4$?HafI50>UgS>9nP-V<rC}p?UGRi)ZeN%~wFq8EQ>F&Vci@asUf(1#^{Lz`
zh_BLP>tHK?!L2;J?kqZQ21QgSR|&l!3a^j2h#sXf#ZHVkbr^i+L=s|gmtcm_#sT#+
z3%ix&-62Ckjj`X>9`~-u2Oz3Cz^k7us{uX$*a4whzVfa9!RvVa{8hV*$eYo7e~0Ej
zf;eAzhX%S<19)@Mdr%fks<f_zX)L!GDJ)_p<iF$Kst6ciZPV7Q6O{t=*Hza#O-mXV
zIn0<V@vx+HtyLmanlZCkz+6!z@H#Vuc3%7}9~H{BP9?vz+{*=Q4``^GI>~~(J0Lr~
zR@Dya)rnH|&Mf6ZVythw4f|n)sX}j$Tz<l9$}<-nm_71F>g7T5F~eQ&a6@GHUdC63
z0ehBZ4G602*9UE>lMxB!<iML6rL44VKJ{8-{LiV=wwLM#V;c@2*%C!Ei?wBhNA`?T
zc)ZC>nTCqzPlJ#0W=%od(ALRW2?P_a+BLHK&Gu3$6Ez<QBiCv(cmDevz+4T$U+ETf
zV!xlkwTf7Q2B$)|sx&|E8zEoY*Q6-~^t4ZL&D+TMU0Hu#7S(MaT|DXYTFgsQ*HyJ+
zN)~FxOu(@qGySJsIlY9$4x>9}IISenJ7+&H?dk0dMIKJ*=OHe(K+h74+%6^Q{5{h<
zLYAD!&G?G-(_}aZZuSf&4z}hI=%B_elo~HngJt>cx*M5mt05HpnAOy2xleFn;ecwx
z^O~GWxR+yLNyx*&d<8vy(Mny)x`}r?(-9p}dZU*t{Avi3PR!T&e|gv6#%;WZbB2m8
zeI@<BOkVq`V-a%*#LSH<;|o8~u{z|w25Fmn*tHa)J_<PPsB0wfdh6&NM87xc|7NSD
ztL0DFU`Dj01z`i|VyZQHR6e(V5ve#Y#t=9Uv51m<5qB>)Z`y4`jJDjjiH$(F>`2N0
zWN4{0!c*<G+tx1ggWeTfoTP7-`;n*l3%5(j9;0|&<X%efnI|*b1P%Ad(+smB)|;);
zLe+xRE1=?WgPK+fNu(tM>AhbXxrV>Av$n8)mN+z)b{&o-u3;AJ)g2poV(jmqYX!Sg
z%hn}qRhq2U^(l!^i2oi#x2C#i$tc}*6#?!8dm@RgVUzyPPP1IWwvF!!nd}H+JLRdw
za<~IW4fl2!A`KtyieR9(M~)gGO(vqyR8B44$g#SbUzfcVeXNCd1AFVu!26K|8k(|p
zEDtx%&iqP=VdZ+Cp8g!$ee>`<$57vlV#kMuA2iF<`;@iqx5emg6-N}92r+5A9ZyF*
zZfr7ckB*cyJ^1p(91&Zs<{H|YC42l~>8#1?<x`>pwmzumu#m>Rq#1~umnQBbg%6xx
zfc`E-slHn%AU_L2+1q+}svEOgy`qAqL$@VKDG9UOwP(JJf_N3l{mu`WKNF_1|NIB!
z`2G$K#s$q@^a3yN4;TOI(2*j&l-S(Xgzg%eUrAPyoT$9K5`Z-12(*48A-qM7vvT!O
z*iDVy*!m0Gl`O>CK8~!Ek(&6?X}O0J#bb|M_6pX0xLv;}m{7!Xq<YQ=4q(VcQpqmi
z26!oT4>e?!(v{n*GmdOut#&?Qnj13kJ4w%nwHOeHJHd)|#(KcT8kWb|d|rDbNn52o
zw3Nt9O}&`5AP%%J8GUtX5<epVN*#xoc<Ra_5D1)dQ!c9Ld`ebOWNIa?e@=4;`oQ6)
z&yubU0d<21TAS?-O;_nA?@uNLYZ_O-Eip})2-5JN3ZE);3U#NS)bf0CDgV}zHceSJ
zO+z}<$LmORAKHJpmwo1_p8c`Shp}#=o^yh0XISX>hVi5bXOQV5$I|afSq9mL#DJ$w
zJ5{>dvy#q*xVP0(v;Go2UKyI)K!whp?tKUBR!`x(FQO=EQ}z5A9KAJG$5hIM?0a{=
zofG$!ckVo+I+x<#HdcEaIi5qe$AwZm5%Hov+b$$rbbC@W4=S^{S!#E3m;C&(?Sist
zSGoY0!<ClU+aLB7-Ll#hy=IYgf7{aga;q7L3lGn{Nk^FbGr62A33J~)w7$C9+?=c5
zG-&{E55nH^MgC!42E0p<`v00bnQnnc+j`n{POx|dgRdg<#k&c!D%i`NYrs<n{JJMB
zR}7i4k`Gxjo9FSqOpMQm;$E<2jjI`Z=H7<oamS%#p#ojTg7PE`G#9eYQ7d^8N07r4
z-@Jh(wCW_?WdKe5a>^yo@7k5Cqlmko1&4mQ+u&`yk)Mb(bPI<g&s0Kd__7>o)L1XC
z4mPsTOZSpM7D<9$K5?)YS8(KA#XyaR!V92*ZZ=%!Bs;nQDfKqecK#p7)6uU2JZXq-
zzwxS8r?#oaQZWp74-RX^Ph;hVnwr%7Ndlic{Gf#~dhEV`<NBQ<64+N{m2fR~d-4Tj
zK>wNlgfq>`hisUK^J5wH`yOQk-#rkdRF8U_aO@e%-G^P^f@OB&;(HoiMN^;V&qL5h
z<~6H}iNAZ+_Gsk#&@&1j3O#*M-OY61a~a{h2PQX@j$bW4nfr{IxyWn!lVcoPs~)$2
zqqT#)D3Q?P;05BbU5d#*-~Rdvph4yN2P6M8B_=xAnDlq$=Ax?X#-u&_TANGe5&7IA
z1QvQ?qWSNXr~M<=$;yS`>8DZm4?u3lM>jWnt%kKOy}k<#?-f5X+#;cu45JSXOx%;j
zW5iLVo}v7v%cajWn53A%t(9QBDfs=D>Au$YKFaqcT1q8agOYkqu*cdpx8B4u!EzaA
znL|TyzxZaVXD*(!xL}z~_$wcRt3nzs3`?6Azh~{6q0-OJuWn%bVJz1HyriooJn4sY
zsae}K7eW#@p$L$0=#*YT+w;4p$ZgN1cB0eN#^+Y)7Q{F~9nW9g{l7=L>|4mDd#re?
z)LXJAc=&;TKHNWc+?@K~IHUG8GWF^b@Z3I&D!pWBc|t(QaAQznMD>=1K!j%<mo(#B
zZb7z7^90w^N5~j@0liS>`b3vtoyi{|P#e18?z-C^9I0tHQ;m1&F^Aif&;tp~T%ho8
z?`MHi!6-Vh>u;M$ghso;Q0DRij@_RWy(VV?lh+|@-<Sz3s+NOj>JzI*c1JyCj>7Nl
zO{@nO%^FHXGg$fV!pn_VrWRd!W=O@q4_D4A>3o#_@C<CP)q)}CZlUf999dNmClDi7
zIgQEgi@#E;ax)02*2+C7aGuf6eG&B|GQ?mmU!H{njw=9ze>gj|qjmFW#E!+4V;%|@
zCqFX(c<HK5{T0&v%^BtWrHAy#lBGE`>2)>STz9m3T4URni-r#KBs>Agjd?fwtE4>0
z-Lfk(Pk#cqWkRp1E&-9wK>2@`{Jq)kzky}miXE-^Kko7M3V>5OFNlNl#D6nBQ<do#
z-x65#w>?wAxpUr`%SSn{Pa!g;S@3SbtXo^xjkyTOxj0{9C!eVa6yl|}eU~=<?xs~^
z#4zijRp8w4k&h#~G48PTtlOzsfle(v0eumJ>OIQiUcMBjTCI6;73tpQ#rnmp$iF<K
zUeMuv>e)ooo7IdTe|`WQcpKRRA1$B$^P~9pw;_QCJ{}-~-&~d-Fuz~ej+;4Bfe46R
z;m#O7H}J3F$W^-22o%mQRW8tpl_cl(>MwZ|(ggaU|4wus%E(5>=m?+eRQ)DH)UK$Q
zG`i4o5!gGHf-)z>S@b=fK@?M(3va_5Wlr=QWxuwXo(#mkwM|i(GLZcA6rkZoLoL<l
z^dn@`2qA7TDyUA~Xg@Q0ko0W4Jc{-X0PbLZne-;0__jGCY}p)y3jPc>tyYyF5Jgfr
z4PU&46_7d>0n?~FBglw(r`hS48}C1AFM+o+Y36n)RG!8>BCMO8l#cY_h^l|8vR&f%
z_!0dURAQ<}Lb_FAl|ndt)&=%C(SYpzH6yIq6#U-nblvMWF7%iEDFD;eI{xmpUI@a=
zYTaN-yGrQ|KPU^HBGp0h6VZf7ixTs<{VAdmSjBSiefQjGu70w0Zo(GQIG%%DOyy8p
z%kfbaCdUN9%{KL~IIZgaK|xaKBLO$vTQ{W0SZ_EB&6!u6@|v(~ABBpY#fF1UNcbbL
zg37JNgHG?mFBYI<;<EGhfM8qfxXkM<ElcjBL$Q_KJD_?%PtB(?NN>^2g!z@v+6H);
z>@an>sL1`QLG_Gl?!BGlpQe@WfDF+w{->w^%kQyT)|<`)u($tm!Zo*jRJI!c3|j=U
z`16XJVMJf(_CGJmpxz4BVvJL-5i^IqH>x04WO)K*_1A4^1>8ya78GaBm~S*FzhnDU
zi7jD6A3$IeDcOobpw2w?4Y>n270M|dsF%@IR@V<x$5TWkszk&bbd6QClgGt&Yg9gS
zK*Ai(ID?cXWlRf%ag985%cF?70URYyx%<*2>2)H+eJtIMBEd@(5EufP|DtW(w3j)Z
z_*QP?BxU7RSFQ4{C~XmwU7$lA<eSy4XKh`M&noiblF7b6;JUTj0Iz3(jmmIXg=s(V
zs61Sosl9wNuq$igh3HFbCPbrLiL5B2Q`zPZDG8-*)5X}yBz^(PUtC@~=TR62{TGTg
zYs!u#7f%A~#Vec_eSAoMwT#U)ZdSm*rDj+^L-o9$Lgjz;CoS&Or|_j4jr4*NpzMHU
z`HMT_=ZvDT3asLa7P&XH;L@i#TXN>g>}Ox1#*vy8qes}@CeP|xQ>fVu30Us+8ux}H
z`%>N-z6RSG5%35+fqY~|_pvbdt1x@XkrMF{%uXOc<d<j0&aXhulmxdY<tF7TA52P4
zwoDDWN|~fv3}Kh9{PO}2#!87&Z2sR}{}0C(mRqXcG@HdFLC&&s7vl_iWuxE#K@rAq
z#k>4!oGb)J5*dA=kA<<T_3~px9ZYxHoG9UiDl_o)b6lZ9ga<iSmZj9)qcldj>l@4k
z#Ba9qbWsj(AGD=pOaL09bf*vFo>#StOz}H0ip1<rg;5ku?$%RWsR!3Qsm{>K!m*Oc
zxj@Q%PND*xA@kaIOHFs9G`x;rQOF%q>^v)aXLbw)vK6zo=n?F9osU~=k{mJ*P5)NP
z7=e4QE@<)5gYt0x`;V&2b}BsSg95*7?I*9?PPLMFN;Xo{$2l{vPtv&bmE0n_p5u0%
z_@0Nx<+wKfb}TIYuvp}A1)~_?0@3cHX&CBP)TVmzfa*Sij0H?hsuc@dK6mY4(Nrv2
z5n#@5_kr>@Tu|TEu-iDjK^>jI*;9vDry@H5-V4r5P(@{?)9rC_-d4QpxlijV7r67a
z)#3uSQy~lfHIBq}<<w5$a(hRsmN$FrfP#EFTpC4(_~8aKcyBY;$Ux#kSey($On0kS
zBSEUL4mgICtECs5Uj&UFxSsKiVy;`>`5MN~xdfaSAQ-0F7hQiNa9(tzmUz}<$0?I=
z4ZpH``KL!$Ds&u#nQ74e`v1fFKTK_?utbOcK?<cb1M701oaGU_#sYldLqy<<jE8P4
zo!i#807oLMOEP!%8zlFn!t9w!NAIh7PiG|Gt3I<RX?<1{;XU%;ab-SL5S*!fCFx1O
zOV4o-OXearG=$r$U$ILrwS{CJQu}hv(rGKFJv%9co!sE>l8RFXJTz%&n{dQ*`_!I=
z(6u*K(WGnTDnt=s{JxJI#k=%=p!Rqit4vaze3zll405>ZKuc8B`_95?=F0VYWm@Nm
z-rlmubak<N<(YBrnZ86$;!73m$TN-pZUQY!GXD`uaCsE@5kH;b$h6HBUg~JWn@l?K
zbj#|nM`*6$`6%%oRAH8cmp!^GAcFLQD*35RuhMo{6t0k(bCL?3e0RGKe|7@6Ao1$8
zV)_>K)>uFk*_NE|{P`)Y5{Q+oZ7$@!9&CWa+uomejoqg^Kj?O1JLk4K<_y}cH%A5i
zzLV6de6&n1)R;gE59P`~)%cYl?go??)#HHW7TL%=j_?#XnG2QPZHU|*J&Malcwh5w
zZAO$Uy~%Q;{Qd}pUF*4b_5RyaD9z)xC;9;jkf`$Q{H52U@|e>0<IpE5s87-#zMuJR
zo%OW%?{{w@rN8>%^Zh$#`<HQt@0NKhsU`dUkE~5<Dd72GJ7xrTcU_$OB!goNg{Hn+
zw5HB$ul~@J{=>-k`A*q*Ysm*zEQEQUjoH?qx7p+b>P>k@`f#yj0xOla>v&F_NRD9j
zH|4JeYP+Ov%rQ&VlbJ|U*`=65^Eq0-oja9G8x5PYyAf2C{T095(>*C3hrE2*3Qi2$
zAkSE}(cB_P10~-<8%QhQ^o4f7A|eUJ^@}TI_)a;!4`&=TIeIrP2ZmUBlLyPg;%W)d
zo^R!QtjxW88PT~sBt9m-+ci;CGhJr9cf%8J_dSCSQ}(ZR-pzNsoKX8s1KP?zc*<04
zF>t5i*Y4?RFOPwxLJiuRBb<}(9Pz}e7EM8v$$Jx#ki60vLwnt;cp9VeZG>{dCQ3pa
z{fKv2his?M7B$oqns1PdnvwcYFW3L`Y9j!xwba-n>c5iN?3|S7tuqN}D6XfQCT9tI
zH^YJ^hUMKhJU5vtIsl4I6XBBzI^0`*25K$`1Pq(=NS4y=aWp^C%366ncmHehWgI)t
zi?L&Hu0>Mkt8gxuh-@6sjFU5lF?CCDXte>HUe?LSq!RIPDmTY_Xj6iAWsdtHPU}Fj
z)2w5l7fSl8*MFG#z3lS?r^xtsq<>`X|4z_+@i3RmEZBUy;wDDpqhj|3|CqigVJ2g8
zdSC2Afm8Lz^=+ISpu!@j-s@v2c&H<l6rBpoULAF6UsLQ{_1DB8LOL@-BNAQZz2^05
zUyhvxQ39J=>!#cn;+L19S0^TX6sgVXZ~W991`Y2U2AA+1RV15NJ{V~t!Qw8oYLlU9
zeN8#f&OLL+1a?_=2jze7%5EEdXIT*`hOCbx9sOH3W+}N<AGTWYsG-HzMyosO+d8#T
zPaH{e-^Kn=cglz$)S&HL$xWAGKF_ZpP4{?S=+xOMPuZJ47^yP7;6J^UT%tY~+RqcV
zuV`ABfy-TBw3w5a9BeiA0=rc8GuSEbGNRa~*-LC$X7-Bra|Ks;kzuLJNa|(|gX8+N
zk>l+=?FD~4egF#3v;ky=joGWh{Zdr7CO#erK&m$CE741>y=`B<?a!#3cFLeTDqHch
zJ`(FUXKx%SRfCO^&rVnilxb@(H?*pqC-gchY{)xDLLNmT9VqOaWNXk3nIO7=?#~a^
zVD1Dn;0Fs?i4<v}+(#-!*PhqtxvQ_{nOGkG*~j_o=C5Z(u4e~_6u`-q_<x3H{6L~m
z$s5#?G>J35z#q>2cgU8LTgtlbj^gL95%$L^>TM!7&bVzv{FF>P-Cj$`vf7H|M#dP*
zIkRHQ@P_ctl$-{RxZwZC)?3C!-F@A|f+!#e($YvbA|W}1bV_%33@F`5iF9`iAYDTv
zIf!(34c#zwGcfRQ-SK%Y|M&0ZcV5mpXUE!WuO$!=Vr_pg;GprLY%AT4RQj$?!|9vo
z>%FDxZ|zvt11-@VzSsa8N?OS@5Ut_lQ6^3(mGO7bf*k!P4JN;^?^4oRuIC&kcAmcV
zDXL=G+iS5H@I&Zb2eH0N_y`6z<f2&E)YYN8$RXWAzH%cFwGGcI&ONw_O!+3x&Pd)U
zj~rCLz`;A7az`Tq#Obaw$ozpO-00g{n0wsAE}h~5Hn+B{L&FJ$43!uyl6b>f8hmKu
z6;k}d22VP--j)r|e+(>IcjdO$WD=S|A}i6xFjlt)-&m&z3CVTknd_t%v-20LjbI`R
zlV>lNK|xPF)zvNrI5X{JM2+ft(X*wKAg5ky&hAK!)W}uC`%!&mlx}Wf%@j;#6G`d3
zJ$^MyDpsND1uzZ-nB!x)9*EWEdKps5Q#w&DSI->5j5$!X<wi^yOIZ=K_6)q$dK)(H
zOlw&EW0-{RV)T2hvwMDBywm=8%8RAgf=Hm82Oz4k7X}yENstZS>wGk1DSu&Lh^Jvx
z_iohV?=md%+sXze;omv?f4+7w0#@#>V+2-Pdl@*VIN%8#PPl#f7bb%J72okhaYesN
zkRaMkQJHOu8(75pyXd7f7G);|__P}e$gQaBA4>}+>TZ-Ft|d$}92r=d4fYS+F`(sa
zp9hP<p~Iz5s{miqI6O%n$`_EGRQ~;@pGU3}JSrBR)+2_4K5&Y)gmVrO*gJVP*n7CV
z$9#=qe~;;{HhivF9u|BVWDe%>Xnj^)xzJG1ZWLZ;IDG<Fvl@H6kOr?jl!5))XUuWS
zb}umq?)c}hlsRQSKGgR9Kqsl53(d>@o~RconqV$KXV-pF5|xr3rxt}oT2lI!>e-UP
ztZm75k#I*XJ3O|8$#io}Hh!2t9~V(4FO#$pSF?9TV}J=Yv>#vgHJKEijqZJ*%fA#~
ztf0I<pmo-Dver}L5qO9bsP}9pCc04@d4tRvJS5C@=`Vb7KpcK}AMA8T<hbn6y!NNQ
z<cHws+nA0U!k#veosq8U{TDWJ0@$n}#|PE6$L5h`p}83s+rg%T57x<%*Ic7NK9rX7
z*;;@*1A4R=(+aVC2kT8|3nOapd*0U|2o(HAN|pCg1-@A{21RS%J6ji;o}nECy*+hz
zz1t@O=uD^?aHG_iwuOIKY3sPZCSnWz18=c51D*b>4x{=qkF$F%kU}0x{P-UVwVjs%
zL`I&jce61<l1jZ{RH&8i_e#hWwC1=n!i*k8PM`Lek-o{fXb6TCx^TIuv6xJM*N?C|
zNBLrGjQWX|fVo;UMb#v!d+a2j+2Y{iVd|Tr#D$l-TGLHWZMUiM(eW$f(bk)RD{^uf
zUCC5$tY~Mp!lG0gRc`}Ae6W=!;)GTkZm31weD@OG2N{P4MHBWfaBEOr<W`&WQJElc
zYqK3(Y{0PBGn5>%tQ>}oj}a?3i%%ZGGZp-36Bs&9H_w8}BKv<6!#c~}Zj{--qlA^>
zjjB+lZ+ztX4c1<h1+{N<@&Kxs3huvjFi^l_`(DjnOh|X1mgF|O9u79FV_0Rgtbb*J
zCkL&2hzCK8Y&FZ3%F9|_nI<P?FQxe14xCG8slhd!%d0p2V0s@l!E<iLI)I|DQg4iB
zT&gV%Do18H2ykb0tCi0#<(JV_X92ny!gmXGKTXz_N6RaJ8jKF6lPbq3ocQb^V#owe
zbhQqafp4@(g)#Ds)^0uVEP8dn=b5!^$(I)QqP)KSY{5lW6w_Rd=pve5f@r&bIg+tM
zM8oPHf7*NWe&QmA3WFYlH`1WppT5;6UUtb1>s~C(cJ2v(Ih7KoA^8hOdxo?ORv`ZC
zr~d)k_@vS9Tcknre5?s%Q`yFH6mmLrJoiqNfd%njN)Oa7Sk1zv9ni~|6?4*2h|oEG
z@->l>UJ}*G(U0x(P332pcH8RpzBFU$Ntk2to&&ntLN6ZQ_gdVFYR>wKc(h_FUpi2o
z9EO7MhtfEpj`wM~4rP1%rRY{8;l8mYmo!rGisSxS!*6YC4$Z?h@vxLCMIz>PaQ}o}
zq7o35Rwx4(`Y)f4+v$k=-gKeL=O28{oVXKgq`=L$yXU{BR!OP<NG5tJ*8nU8oS?;v
z3#k{_MdEpMgT87vM9Ka%+C2!JnQOsaef3TDgBIEDpPKQ~_+UangI;r3PiNMuTXEmq
zGgp1G(@(s<D8k>SZnS1wqYETy#j5@7XS=;lwYvsuzf0XPn2M(b3ZHu4dti@8HD#fr
z2+8V@7*fvuSA{+zVZOup<7xk6vivGi3@F1sHa&hkzAQh#Gmcgf(jpb&n{UNYdOy`}
z%Q5BYlBTx|BaT@4*}Cx1#V}nFBlF-FXnlWm-uposu>}fZ*-a7(++A?6COlC@z-cr3
ztnZgNa&I^guOds+HJw{b1HN4E?BH3rMRI6HT<%<sc%9YsHCr`ZlC){lWRL&*oxexp
zMn)JG+^PNNn(ZsfLj~4AY}Y%*X><#QJjwI50CNvp{G=RJ<POO|*_V4ts?SVkZ5y%%
zq}RH*<6rcdLj1*PT$A{QdMUb(78nq9)yH`YaC(^)Hw&X7rSIdm8H13U>tTiglt1o!
zevd)$g#rcRH88QxdE=<;C{v!D?(}ZMAC~2GNVnD>-j)&L8TnVRZd`W;2Mv&+zx7iG
z9@So74kkr7A!WJEfCX-de#0&GwcrH|amqfs2xy99yI2MO!F5RcMnSUQL0OL`fi9cb
zL%=eejH((lE{VL075w=o@vZsN>@W{<uyVyqc-^f8!MQv|Wv>jxzP?=s*x@><)=P=$
zH*-I3Cx$szGGM-x>4N`tx~nSl-Kxm?Y^ng(ZnJ855mtTzjKfTb{?GU)hiMd^Ellb$
zyz<-e`zN2AGAQN!MF`j1oq6oNY(YomSUbn+ZP&vL8N}*g%T{2^b?uK>_?!McFrbsr
zt4+BLwIYYXl2WLee@VTw*X&R^ZbP^Z4_=e-9%ww<Ta<X_{_W<NulRQ3mG7N;N-AS`
z7*f4*A3+#UH!_3WWx>A(R~H5q`S4-TbutuCwFK{vS+^LFl*pl9uoDM@#jKfi7;Hv;
z|7yNg*Wbmua}qh~bLIVA<^9(eJ1g>2WH+tG6>ps?l0IV~&X+?u<hbb`81$P~!mZce
zOMU?j4vBPVDEr=$#8pKJEvCiwiC4MukNgcUu<({Ct@E7*a$U%&zY82XqZB|z!<}wG
z>|uiz3_Wf&%7zb#VmjKe0Z05_0%daCRlg#X?S_MA(`S{7sM&V=xs;cb-gAP0g9&|#
z64vDb+GvXI=Pr7%=episjo(T@cH16ux<8Vf#N~KaStJ+8iEzH}d2;L)cy-d~c(-{4
z0IA1i9ukqZtZ)40&<qcaYKM0yoCp#`Y>M}jr)c-|j*Dc?U}F<YXvh-)_1?4bmOD1b
zRUdfWI#*!>_fksBP}Ewx662EbG_r(@;Ff#aB4OjFS+N8L$P}SLi5jRzSIiBxB`kbU
z0wX732eUu`cf?+FW!5zbTFj{eza83pJLbwK+|6fm{$j^Z5pD6I`^j0_;ij)YWQ6x*
zIj>Y0Y0M$TvmOJ!AfggLNI<Hh=el=GOxtdejNl2G&q%$VrQWiO7-MpUiqDSXqzzMm
z`Nq_AAVydK&e&f+3hqndtODTgD_H1X7{ERsgYpw@Vm2I_YDyeLjDUX%&khP6KRO~3
zher<ssdH|xYp&qAZQvL~Y$BIUuJV5$HM=k}%%HzO+Vt7~9i@n^W>TF@S{W+j`!yMb
zUF7FvUh2pK<3&O_T~+(h8LU8ksARavY%M6CzBsjbmVSj&%YJ7<3K2HAxk5q;7OxSn
zmJCoqXF(C*%%%T;4`oJZvkD|_8u?w;ANrARr=CSfuO`hJLLYYHinxi=zww}M6*0)T
ze8d^|@pvtEN(o4MQJLnT;}(*>X*2p2MpIj<tN#ldXv8t5gnu`x#VvXD>mfR9yr$br
zupDyZC^A)_{FQosr5&go3!gNUpvhg}P_uuX-qn<Ijf8)Zv0OjbV|R$dkbJEi9ANaP
z5j5j$>aivsNm6vM@yCxA4SKeSR^DTi&X=v$_a4a7Z2Tr|cL&d&H8mqOB8C~D_&ag?
zd{;U!&EJFV`|(5v5$~c_kaD5+l|)Ex#?Bg*mU$|=kw*NMj{oVK;ra(wp+>HdZ#&I|
zFOvp7QatrlV(SY>LeGzy&KnZsmS>mQ7BDrgrBJ*d@Q;>TVDDv*f2Iy{iM71ZG0?WH
zZ=)pVyQf;b8~dQi+`Nz+kUQvP8L|?cbKP!9dHlfnW@#!lP7=}Xw8HA3AwcM|SjhZP
z8nNw{M`KPjev|k<l%;dYC>3nLY|id6&GIchf@iW018_mS{O=kr1wX*A(OEWB<PXLF
zP4uhxPjViEVA@?T|6`xG{S?c4E&IVq@gjM5uKuTS_34}C($Yi+v?1@`WM#=LkOb^D
z0K5t)3?dShp$EL^;F-p&nysEw&94hp&~bUzu5yWVg~hv|-e}#)hZpZoibTA;yq7^U
ze}n=MCTztmGckNig;)0;rC!4p+1|^uy75)~eWhZ8@Y9~QSyqAZm02T0(>axcIR&4R
zJw7+z=5V8fxm<FcyLt!HD)x=781@^F+X@aJq4Iq_O7Hz0QH6PllJLBdU%M<gz?iOF
zJ6}mWU(T9rJActyC9DzSIE-{)9=HKf8inMsC=9r$2&NpLc3>SVqP6t62?uavH=C1i
zQ5sk4pKJ8KqiC_o2*c+$h}e0?s#Uo1*59-q>vSiaCi1InPLJkfXu^-rIBKVH*bns7
zJX@^qI?Sg@`n!No$bCNs6hq*iZ)*YP*IlKc5)=7VB>%y4xkH-iHlv&?E-K*Bg7EVB
zyhzihK{vh8gyk5Oeu@~kVUDb`5J$=%Ea_!k^W&~jN>?2F6Dj33)8DsMFDd*dX^h10
zw4sDwJw6xRmko0D{cdD+?O*0XwLsnrX*+gavHRwlLZ@;WLVky7|L;&g81-!>RNn#3
z^5Q=<b(tI}HxfygxS%o5UU0Ay77}CACnE_bA^pTn?<=AQ+MhROeste6@xwh72y=mR
zMf|u<xWA((dP1}66ydU6?yR&;0=HWY_VxaY>>@7jmymdV`i8q7Uq9KnzDMK@KK1V^
z^l2F{DR6;Eqhw?IO<x4nrq3M%vh_;XSc-Ka2Z<F8^YG*pG~We)gi5nv<L815H|Qcl
z8$=}sNsqqmf+xHl)Km~&o`Kw@I4(Fm?4qhDi(zF&cf;eqR~nxO7Tiqy$ZB8~e{zUa
zC!IeqN@bec_@G?B^nI(r?5hZ>^~^(Pa;(xWv`9M|!wh%d?e5xyLK{D-<Yx?-+29Qt
z1Oo`uYqsyK^UKc+Zg99u#O3`QY^Z2f_H<e5-0ii$qzId^*v~}t9?oHX%>is-QjOtB
zDE1?Jh=E?6Jp#`3Wt<`OC`U~&-hR8rDs+Qade^z9f{ohhF44iZM$<^R%ApcM@_D_M
zo8FJvGe)CoFv_b;M4bqk);mJ>C9;x*xprMp`HLciUMwH0UN|aZJEF~0F*Eh!S1{+n
z#Knym`@@Z(#$YjE*9fc@>W@gyz#6hK<VLw^OMrYl<}tfCPW<FM-Xpy6cgfx-nDX1?
z-*@1D<NhkyyVXr~s}RrMAcYtyQCyj%yZ%esTRbnwh4c@6Zgj?nSYvlmOc^pZ#0i_a
zQx)|FVH;M5snYv2#t7OBMvIp%`mtG4A4&Zs>+Es0G}rA2bom3Q*Uoxlg!aJ3`wvr)
zw<t%mVrhWvs_jwdSROnM9;>YnFuH_;e0upH_H#YwR%OZ|we!(Hi>3Ks2b@!QwO86H
zT#Q%;Oe|o*65YwAkxuK#RBD}THo4EYZAt;0cN>TJ!59|z^<Z@sKU`TqYuF2J^rwnn
zBPCpMR0}5yY$6ZCeMl58e)|sxEckw*=hP*64dbWpcL9l(JBB;#Hu~2KCns~Lb?x5J
zWjAa}<Yaj!8RH|uc%nZs-)ZrzabaauZBLR;xszr@<6GT;Njw)S(6Dh`08vF3QARp*
zmvlb^;S?<dZ~865{weCQoDXjKQ7)rGLHrkmw{AGUHX<7BKQ5o-+RGo45duh&F_vZP
zXQy}`EQ^_%vazRg!_`qO^TJ-29|*98vh(hq+yBtNs9m{!y;~8D8EVp;=nYjPrM&v2
zaHMsCYn^tL;<st4^fggwu#R-!V%BJKDzoc2yVs=bb~A%S*`@K21<suYTLGEHRtR5q
zi+|%8!Y<2!8Cn?_mE(+B{|)7k1l(r$(Qsb=JNW-El!FqWbpLC+9?(zMwq7LAKIMLQ
zk;;k@m;VfxHnSAydq8Uvi!$rbQG#NgpKOjCHN(Nr5~eu=ToK-+rog#nbiH%Tpgww#
ztg9?z6g&cYiy2{GfGLiYMVKDcMLr_0%!m;`xI^$_I>7wr0wuSyl<@w`grr&6k6Y~S
zD6^Is!tZTZU1n_Kx$T|l7f2~}wa5cAq~(<ByO-;}pWVt$P`f0sx1xKLHYjzw%>{S0
z9*-BVN`k~)$Udr@N6OY`Y&+c6J%+cTQn&fx)XGeJZSmZKU^jF;Ef=%-?In?43i^qQ
zCcjhJ=xw$beN)#S3>O_i_how9JE2t%)orh0Ztm53kTf0MWC8tn2Ke|9@5zZKfY1wf
zZ)a2hWrJ@mP3Ovtx^RpmfiA<d+larj)bmvEnk)EQ5hGVxckW!OU6La}2*hYcRe9P&
zalfC{D375ZRG|2)zk=g5=B{2c#9uMsx~&L}?Z-W*2vEc|JQ~}({J~Z)MbF6gi!s)1
zkbh>4`+2$8FQ3BtJ8+O-tovge_?b$i=Jb)4e%<M5&qIMF`Gsp!Rw4A_vgPE6BNHxc
z?C9JggXZBo_09UYz}w(B)8_SGFuOh`3LG)i{dPFg^gld(`ETD;MUq0P;}U{33^zit
zP&V^5!SP~B3N(!5MvK-h+?=v>GFis^=7go1?#SrVs^8Iy?nQa~?+ZgMy~u-qz+X{S
z&%L}_@W*05;QbWpe~i5iOX*l6((pU!a)!o$e5<#WG<k>&rG0QkNB00()M46_DYpP}
z*%?(Z<*z=^+ngQw52enlNb!REv#pch%fL#+1&Nr^{m<iu4U#2`B9{tegOtZH&A7M*
zMc+S{$Iy14!Pl0Tz}r0}86H#-YL(=PxRWqM5Nl@MwPAm5v@a&`Pt9tKl5Y*}VIito
z>Jv(z?vU2`v|B~WzTk&WE56OXTuws~=-ZcnsoJ>sTe&~)yF&OTwXH7H_A#01&yez{
zhF#-p32nUSUJc?X(e5zJhrl@31<OI#_hlkc*Z4@vKN&PlKHJUsc$h*T*7s5xLP6fQ
z!mp281t3pp9V-_@?dk<7bz>>1J6C6E)E-l@Z?L9FqxJRgIzPjkrUPF~F^To=hJ8<0
z`^IN=Tc4bQ#bkhkSq6|ieVUV0Igs`c2ZBVww4RiUb|cz?c#<ULurJVdU|e%SfYB>w
z<-Gez*Di<sX80+ym(7^XpuuSEwgNxB|3mt(qHR@``aP-z`&Qj(4(eaS^rt|uDnv6T
zOE%kl%i~8EcVA<K!VtoYF3a49&JJMOyy$pGcJ{0So73i$wjd8=E>=$Wd+N;R6?)Il
zpsPwsuP4uHx|L*XCr#8EGXeS&KFnANEf^5$>->59!u~-YaYr%6aPh6S;tSDCk~;T(
zeUI_vwZ%)*Hr}o;&Y;I~baeK#awg18wU?cy?LaJFpC&wMLw`fHefMe~bn>mW!SAy+
z<8iv^nq@BPYvo`YRiY+{cP|I<_}u+fJ(u`CCyu+Ru%8@qvXAV@!ydPy>xF!eOXRNj
zVgpRgc<N=KYNG<>m)!5|wgP=WA<ys>3yf}V)C{L$G-};^UCr`(G<o@gCqP(}?E*b+
zK@=$%57Sj_XcXVPO7$SvBXt1Nx^WZCDg9!nQA~1xPM?`XVPdg4RjBRNrW<8zcze|F
zAuCnKrq_4Dx0VYS-NF9%tF^<aaFRFwkSJOoe^Lt(jr>mbZP`%LlT7M&nXXc(uW)c`
zXD+!G9>w-+T^Sn94xyw>4fnk=gk9ENGBR9tH@*T1s6DJTow&b}n|G;j+f*8VGO0QZ
z>z=N@V93v1;r_bRlWa4{4hVYYqHFZ9*biRCvl)0HLB*1i^p#QP6>2B<-v|5z<Rg|h
z^P=o}{+_h{_b{egsza78UPu-e`(*^<;f)6bf-71#49u0$O*4<})Bl0bFlW+=p2Qu9
zqbJKfE;TLdWj-G8-F6T9#9QNxw53UbHOuY)?DCMKGQh4%(2Pq-H-A_S=-mi9N(7Mx
z++Eho4}tLFwAETH1C<V1O2YzTTKRND`Y5@(erE8m>~RLm-sCnutoIk|%oLIHuiR<E
zuaxnRj$x=-)*(q>!}3JB%qz`h_K#2vyYL=iMG&_zPFym8{$ygvPXOYJ`+R&Tg>Op?
zy2oxr9OB%|mr^^<1sZj`uOou00=G+?F~N5k@Z%T!YW#vkbD<J)Cf7E@P#i*uG%!PO
zv-_qNwrDMF6cvwTWIz80w-5=Pf)8eyM&UoFwOT^#XARV{)KJ=!_Yn3UgS{Pa(@TZ7
zNp)cS=1TNbhioVGwV$3mIe9a1x%3h_&O0=p{`o!qvZE>DYM?qJi>`3@<qDoLGq(uW
zXNwqJW}dtY=#+;4K++4s3J`dvtj&EIRol$~+F>ytv(!aZihxQd$W<a5YNIDdaqyWe
zJ_21!SDLSUV=>x#tV}}bh<0CsjP8b_po*03%k(>L)i@_!+OV1#(Tdy%;WOf;e<$t~
z(tsxszNQ_2DUts(ecK@jXq{GGmf^VWKg2I_mZg{%c$PzPpJe=MR^2-Fi};~2YFvUW
zFOCdtBIeau$!ls+X)(GwTc}Na_iyY1izOV+O+p9ue$S+{P+a($VWaT*I?Iz+@d;B#
zDs(jFpD7##&1=YhD>vVW(y(*CM`aqmfsLJCm;6Mc+eEd(LVQw+Dwq)di*VX)Dsm7H
zHY#uvo^MwslL{(tF^lpKlkINi1o86bWPBesz+QLsU3(3Xe5Ny)OO{b8kqG$On`#HP
zhAOLvh;NQPEpCs;yq=mBBMAJ?@q`A=W1IyUI6K#xpG)dG)t!Fw(TKjIBxM`y+l8j=
zNB==J*3Hr$P<A=D;ou}kSa3-|9raFT?wfzkF#`sFY-<lEu=-Yp51Ze*ACk64f_=@O
znYs-T^r~AS1!Ut-^Z4JH8#W@&DTQH|u7uRW%A8f9M@<P}BV?m*)04g44`?5_r3&;&
z;zBfiUEstG*?2Qz`S#7rB5mm2Gqo01mf1LS_Q$3=FG*5>WyF4$G@FQJr&v|pqD%M3
zb(#B1kkBM&eaIE*3UML=PnmpM?B#;Wb~eYu#0SnOj7ZT6#hBPD=H4r6C9}UW_A2rd
zHfKm=Ez0%(Okn>Tn|p!WI>Zp6u+Fka&X7ByTw_HSD1byePy1xmphaEgktOrNO~qxB
z|M?2mldj8zEgz_3I4a1O(Hr^IWLK;NVHCx85)1saIy|dMHVozNepd8lK_dvhsNcw4
z!YHH_vGsG*9DrPfZSKL#RnT#qw0*l)_F>A(;ZuFQH4b2e|9mHiYY3cJjvA#o?76?2
z&MMd^HmaM;W^f^2aa$rtn5b?q_wfLV>gp2;!YXgD64`&;wsKr_xSc~~b6cl3p&a^V
zEvX<EQ0Yj(C5+f}J*r<&rKVp_=r_0^r*}fjQ6|mvoi-GOAa93^2vAYKuy@h9m~hnF
z{o+x`>1uFwV#eP;M^|659gD?_z%98Trn+wc`r<R8>ha>_S$Hp}7|vI#$#O5D2HM_e
z&PIJ+GrA^(jl#*bSd<XUG^+)1^Zl$Jnu3#LANJ&*$TQaoM9X)4qb4ZsE1uA>;&}%4
zD2}@U?xn>Xv+dHMBSb_^k)&YjRn0B+-R*PI%MZDl3(3H|*NIff)nndsqmp5whbyF<
z;pN#ZslR3Wwa0+m%<O}8775Nl(Rr6NQ>d8aAv%Q2k909c>CAopTNofYtD_X;0G{h1
z|EOOud87_Zr+=Tl{EvSGwSyA?pZ+g4gAw?ROXCg$k@U>DrnINH@!oG7q_2XL5bT7z
zI7B-xqat{@<TlO&@pV!4N#p#faF9cRh|r~<QhnnR4^qXlQ<f83!TW+kMM%9c$bQ_F
zesK&6`XDGIA`SENO^@6$`XU$*#<tzX>%FYz?Z!&v$G}fRDzz}IYz+G>*_^&qjyhV6
zKcheU?#B<d(&n}=v2-fUFqbm|2sxxzEvPD~emUB0A;Lbl9A?+(_o3_KFR0(-pkay5
zY&quO+R<0p(mgw}cXa^Q3zl}vAARqV3Y7J_Hxd=P&Mqw)kc^^Jnil}-L0OF1<+)E-
z?z4<GEf8N&v|Ov)h!2|U7b(S7&&L6&i;Xg~A1h}Z@<&>V;sU28l35K#1BfK$o^PUz
zC&`8f-GxULj=!6-h*3U>Wj8iY2fhh%{dpD@>Z<KHz(ez8)Syuw6?0ysqEc#YA@M!V
zYJBTe_+aj`KgTS9Et_TO28D3ps_^Aqjcf6mio$svwOJrSa=Y&ENV*be#_-aj<9nH=
zYvP)k#ky_G>su$#!W`{P)=tM1aDHy|IHe3~0DAgIvx5e|m@$2BR&qT08*m{a`zM0`
zX7Kp_@&80{qBj9b`_|S<g#1YPSYfp6lifN98Gb8z64~6ogMNAjusJH>M1J{REPh0T
zE%6Trx+K=nw|>)m@~>vx_1~D}s`1jrD6-34uvBiZm3gA28$O(B5w;^leGOt6)t32@
zuZ2&76S+{cNeLsqSUPwuw~XjtU}|G(j1mdSZO?FNw|z|YujZ;}tQ9Y&@$*7-IdrRw
zpT4^dpWL6r4=UTUnc*9dhBm%7y^?fweHyGmzB<(%YB$VKy5YJ*jh`{|lGVBd{Uo)K
z@A#Ux^5Fdp_k55<p#tIlHlZ)|xa_i}=ZoQ$x<Y(+;)a&u{mEXha=!-I5Es#l47LO~
z&s54WuK0wTi2(b>wA4znx{(SEs-K*c?zRq*yp+?PJ974^hkX~F;2<h<qEptD4YY9N
zaEPj7y}$hWfRf?liOKA4`4qbYeD-Ga8b|?Gxxw~6zqN_(x<laHM~dN**V+kVIP-Tz
z^~=2{(>o<rNVC<8-sGq4aJ#652JLRa-7;dLOD4UekcM@RM?}f{mSbPYwbpx+GI`Sh
z=VBWcKM{Ee;G@?J#o!L$A?{EMa63+8vp|H81fFH>6v}Spt)qy(eDMUk+5P(^4@Q4m
zan^TxV|oAgE%>jmOnH<GT+sE65~hY*v2&~}zm<BooP+Fye0%(}i`-`RwE{>m9qp~D
zJE1&nynL=xqyG+3PEqQSr8SVIdF}wz6;O<)Z_#<y%wMSLMz<0JIPbh#0tt~zJNx&d
zEk1t6A|q(cwu^kRQ^}=u7<_voBwKuWbvdhga#detU%GZ%&J)rOu1TuL$<rz%pMtAw
zRUV2D!c}NoWXcE8#0<{_WqlsSw5RUp9a<lrW@;kNL9WZYGw{l-KO53x&$Y-fo*kIF
zDQ^nsPmW2YlwKtwq*oU4Ttas|3QRJ(?68~PLD)%{e{+x<-d~WVPH^G|d8`$=Wpo@s
zOg}+fe11sjHq>rnJsiq6hKoFO5@~eTHo-Ci5q;$;>T{a1LI$5A077{?tC-Eg-+8!E
z3NtxTv|l&dI;8y`5+G;=wmcSb*egTsOQFu*cW6}{5U*MCf@hVk#50}bB<4#WUbqB$
zAI}!QODX%s7#3`}YvBDcJ<+NYofs>-Pt@Bn1M3s({(&W_4D`y?czacZbJu~>&37|S
z4B{*0uoH4TMn~XJCU3<#_`g~JLctW|w~jASacv@AgJ3L1_jGlI`h+OlhY!M9m#6N_
z76HaR{QrK@781s7LBAq{K%D<!sQSeahiW{5=+@j#J##TvF23ko#FFW5!;c|Vx4oDp
zLcMv;X=GO-OI?s!nxb%_K}{QNl|#G=FM-s>g}{-nP$4oq!FX5i!ki38aBSV9#UG&Y
z(q-kV=ic;egNWyiEPZL^hpndk;l=#-BHs<3bT@vVwuW7Ohbiw@)Mj)lDH_lBJj(j^
z^W~orOY>U#Pa8D=Gqs!IMGb^_-eM7PKj~#mFwfl|3i{c2KPG+j)Ckn+uN$V-$t_7d
z6@GA%A*=Yg;FJvJvzD)eq|?Ip2Y*3%+wLL(4L?CrSB1m4KTY=#(&clq;(`xs2HgJQ
z7~#yCid-P-PiJ$HZPCE{jX#TYc+b;_?~;ye5FwH<L9EQd>`I=8GkHn#)4IkuVSagH
zJ*W*t69gp|%h60_2qh&tm+~kv8gKR6Si4mxe$zDLbe+{GE1627T=zRg^)9?!){CS(
zl&F)<%A_GyJvk0uT5#|?`2vX`LcTI=Q1yO`m{!dz`l6_<>r>g>xQg&$tb82^{kD!+
ze`{jN>C!&uHQ1WP@6xkyGvQPYjlcZ8l)+pC=kD0<e$G&Z^XHy1{qPKiCA*Lpt2}?;
zpHGkT<pX~IowNtTI{L3)rQih-|3|0|6RF7a9(hHUg27wHk7<14KwVX&^`xj<<M^IN
ztt3~HyOvfkppYz};&Yi;-XJdh5$_&z1(l)cfbTVGRdfmRmq>cuYD_U1&!=4h(**Mn
zg!3p!@(yu~sGBbGLq)sqr%`s`I2Ad3VLzkHfGyT4H)?dPah+KlVCkm>hSdAj<jmZg
z71`GWsxJ6nUoEr`XwC$96qjsVYUScerkItP@hCQy?@i6^{c3-pH)_Gp5FXi|P8Zs5
z%%Z>x)ACsd8UJ?guD@6-$T?OO@}K_kW5#`PyfGCh*L+yq|A0QK>GNlsM1(gMjkDg=
zz76jV9LRZCfAE;hCGV%VyiqeR+ztq)`S6CnvOw89T2J4WbV^IAq+K;NcUG%+1zv16
zbzigLHXoqqKcMp|$4WAx$n%^vEk9$&VNxv3YD9NzhdSH)sM+9a4HJo3r&0?Db|?DE
z8%-E{YFWMopN;iPH7c7e=iz**FIvGu9LHmxMj0EQ5NY*0(&2Z6{iVMT-vGLn9*=~o
z=NilwK-Ik~FGNke2|_`+Z%z&TL8qzrEy{UbJt`#aEju}pr@s^$4Z%}I;KATOk*<Hm
z^I%cP<SzxY>}ssPV7LDwQ#^_MeR)|N{~2KmqSd4eNW{#Obz;qz1MmnE(^?_R*!7BY
zRy-5pjn(Cr?@rZ;U_-x<{rsD^c3)dbe=s7LmB<7pz!?%<$r}*6A#NxFEDIr26!*Uw
zGvmuX!@~Kc4C<Sd?V1uNz89`mcm*7lQj8@FBa<5!mXG79bx9qai_e^2R4Bikbql8#
zKKK385ORI%2)OD##9)%pA<`TNRuB&!H4Jh1PBHSbOs~K@89sR=cx%!uE5RK>Q{<n(
zcJ?2WqiTcjIZw6hvGOiXv30s{N(@2C-%v><X?eY#ohth9bAK|(<6d%@zlnYgO`u8#
ztme6472sEFuOxF8i(r9!#&}ck`VZ_ZMLwJPu6K;Cm3ENjSjhQz2ZGK>DJN0_c>GxR
zm9cjEKElJs)}00A(LA=ykv!<$ke>Gr88sqmDMuIvS`+4*HzGbde)PHc^Bebh4_9j8
zYn+2z*Hij^B^O)JB^T1R*UJJmK|BAKrq1M<r^^vauzAShHmB-?LDoXSRg?ke+U#4h
zi00%5UoIGNtlOY8p)Wgfp+J`|cX2%~dCL|b_fObQN{>gk-r<kB*B=%i?)y_3tPDrQ
zatGfpe<c4ooAJv~u+3v8&{M7c|6M*b`e*s<HmfDf=im3@zk4zkWQJuZ%L{dm>x7HF
zu7qF-Wm9HR*LYiH!*^#36AX6dq*=n^5KcgUv5%flU0d>!Z{EsO&EZ@ahL@?MuYT4S
zH5C8Sm%vx-S*zgyGoRJdorcit)J@xG%~y8_Y=Ft4SRF@sRr!ob=(+_m7VOo#%5E@!
zFco~<5R5TONq(Ak$MZ<7lbxX`MX44Gr?5mPzo>{%ul33*UJ-&ODKUl#!)%~O@F%Fj
z%Hf78db!G&25#L_<go7eqF8I_PlEupgsLmgmt)KBksG}?a1~Iyg|A*!>I|w?q!BR#
z`zUe4<s`h|=sE4x%oX!@gXpH$Tq^z9<~f@$aHqn<URJ{r-Z~Z2y8sA!3N9wlIYJHg
zS0mN&EKX^E-Kmg5ffzInKWTl@J4Grf=cEzhWlPGwgl~+Myv@d3OlH*e4VE4bxxn@-
zo@d=-+Yb#mzw*G5gGB?}*&0FDCX}A9Fx{>u&{5^ty_ZMnnT(l6nJmHdBl~SZpQI}-
z4#}+wl$TOG=Sr00#GtLL$UGa9m0f4&S2@_-23QTd%9XpDP1>KNWQL;m7()XhOE8O9
z{yYu<A$4uX!|j2}gdDoJ9R81?&|?w!y(H-jlrkpe**Q3A-L>kG;&3I@ab-^C?-!rR
z@Xtl?Os@$0D?t3ujP0Z7>Slybyojvky9IOxhF}4t&%E*Y`@!Er{`?kAnsx~aR#Vki
z;?vNyp{u5H>qPQxMi8Ky(BhaF+IN#Y`Y(n@XJzc&Y&snww}&r`m$b{y0<Ry8+x_})
zT7#gCls=1mAM>W7PQM~Nm<19%Vs*|Tm@c2h8C7iA<LZHzwGPuM!-gQ75UYwH0XJKN
z-~#X9WdE5r{`Syy+Z~pm9FyMwJW**?%}NJHM7{i1IsMagZJ!@y2k+JxPaT;q;da6j
zK~1n@U`+iD8gaygGU*@fqd&|JZd75t07<Q^Qx-;jLSeU#`qP-#;yayBzHcrIgknFj
zL7#*UGzYwLQC9Hh<H`Du0enHP2XRn|9$Zz5t$8SQUR_7;y}o1%`P^REj~X!s^u4be
znzF(`qF<UU*Q<DrYOOn*zI{xJE#lp<4qzBxbe;u<<G=Stby=!A_?;e{;JnI7z0?22
z;&4GPyYf<~0?ZT-I=Edj6LEf@vUg+^uP{&tqH3@S+ltP9(e>$N5Bg)_+&66o$Sd6G
zzPym^BZlm@>gnn&&$n68l%%JR3vSmHAh)OYZ-}?N;&<RgsbF^K(d~Iyjag&amOfRO
z{o&vB9Mu<29PMKs_U#i(_5V=J>wiY^@H{5EAu*cenKFAA@245hLiuJ?lZCds`3#x1
zDgjF-eV}=4kX<4EGduX)@i`x3J2Olwqa{IYLcRr9qQ%|nhN3MiE?hmgiw=6rJq^G1
z^>6np8T(bZ-x33;6+T^*|90ppHvdQiS`gt((ZOG}dx)1ANI$jbXIg*_qRQB_!WYxp
zd2H&i8xA5at%x^IAVx<&8s`Yh4Ou+b+?_O<Kdk<uOKvumQY5l#y1luBA3`K93kzmB
zkbl+b5$E(ogZH}{7;o`1<k87djo}l$-}>;c#5S<!B?IqW1d(!T5{GJ9&1Y|NlYw`V
z6h#%G<z9%uVgzSn9UjK|nYpk^SyVK3N1`8fKYO%;K9s(>h<$gi4tymIIncqT?AsGx
zscWDi@4+7Cl~7g~7qb}95zDcR3)$<8q_EKuW?gg~5lidbT_|liZWVrYfPMV->cECn
zk@0{Bb$+&QdhsSfmo`O&^P8?wp|K#U<(Oki!#<!c2nj}5ab=%q-T)xBG$N^qh)Te6
zf1C?sAr=8LV-+9zqq}2P1S!HyIwdr2^nR|iJ3+Ue8t|2$Pu+vT8Q8#-z?|;Dbw>3t
zbeZT)(ch#<eMS_xa9;??6sG0>3HC1oBBGzknh@av>R8rlZ8>9PndcA#lx_JPHgVR>
zzE&7jM9$;jivUDo)E1uaI9bHSmq3q8Jo#~5)E9BOT@TY@QGsDyg|w-U(tQ2zoezr4
z>H)eZ_{VK48On)B1$J!LA}1c(98}jRC|V5IuiN+9{E$5_Q?&%y`0I7BY5BYnsWFSP
z&rjd+_w}8Jk~%B!?PfSQT`12nr!<tgfg!(`9(PNPQCo&1lrYC0=1`X#mu}}tsqg8p
zt<7@H-pTaHm#c(Rng$TI>oi+{B>a7!MiMpeB6@k<xcm;%O?>P2tVUaY2E8Ll=pvH|
zUSC<SFybuso>C^YNTNUk`WD2OsH8++$kCg9HMd}a4~bD`^P7~J@+$K-H~1j!s>nb#
zJeS~c*%tbOxTCkH-N`>YvN`1y_7(Us(8}vIF!m)BgtgLIRuKQ}DI&tC7&Z&fHPQaJ
zd0$&7HZCbjz79(75vi-MzMDt7t!h~e&Nn4gYuHwnD3nhDg;m;se72LXEnXqc9F~qI
z_pC4==ZDz6%)md@l$T$R{QR^eaCNa-wHw!Mvk^SQ`^_o?SnVf|fEVYKen)DS2H&BL
zetF;u{Ff=s@BP5fb3ePzn1Flbqelx2$+&L*mn&iX=GQJ7*$RXHMfJ$d--Gr4`ug<-
zjW2g2<@S;plyaY_=89{+=2~grADbCY7(T6(g+h2}K;2KT|HHv->a>6DS@^LeSFR(l
z2f0~CN@LKA_3e*g*SVZP2iKeJu*&W5ppk0|Z#VCQra{Cd((CK`DY8Tz^ucfYo+tK%
z4-K=xesh2-SAl*Z0r;>21&C_Cru%VB=+T4%Xd0K#0^@L#*M{H0p|jZBD4fR&QK{HW
z)f0x}x-aN>To6|&fVQ^Bfl)6>+<Cl_6Wt9ZgD|}`hrSQVXFwgs)zxvjmobT-=%0>z
zEm^{LZ(n(Q%!+Rw@3T$Xk62fEy(GQZZuz?$Y{eeJh8gVF^X>0r!mgl~<McyAq1#G8
ztWH+n#gw+nX;%d)!y2tO4&)bXqL|ZCQruE@DqTxN6&}(ERnR^w)oTA{;b4Tc;;>G*
zwSy%u$ZxCu(CA(U{hnzyxPw6W&(RaEMwrYUXaiilnAQJbrGw_s1<E+t$DPIv1q2?r
zinuRIZRb1(SJoS)2w`=`#Jv5YZ9<n>K>5f*Va~Yj%m#V)9V_FHW-=2ZT3N{7N%xeN
zma~0iZ~qDgIV(A&q4a^U{d4p#jKIU*!S}0nBtNzCxtrVRw{ZT(TKS%Os`5tB9b+`S
z^?y%b{wMUOU<4$k3efusoN9kAr+tHvqfZN2l*1Z7h*V=L-A{bxXDv<dv?P))F)4tm
z2YkM)3Bs(hPL@Z8<-1$gXh_^YX`ZW$`a)r}q-5jWH;7yHv<{co*LReBw@Dggyg43~
zUVxI8wwclN{64uN#+e;AuLrL%FJZO}R46b9R`XVnnwfDF;*whtD{<&i>sI3+kYiAZ
znIW1%kbPGFWM0UCHYbl<RVi)%{K_!-hl)hT%B<tv-c80zkTnK*+#NyDX=n3~@s*_w
zw1u;<%<#)YiL3>VTym+yoL6Nv2_54plD9D;J(GTJ-ara}G*bs7ku)0K!VMxky)d-~
zFD!ktoPl^Xh>t4x*MOnfM5q~=6?7L~cUY*5Sf=XW_DZ_&v7LeERuQTV=S#Go_Wd(V
z!*d<YYP6{J>@~@CNBuQf$Q@-l89vg^xhD!%CMsh9&Mkw4h-lsCeztuC4u3E?x36W%
zkVXxgaPLJ)TZd)l6n!>xZft@6c7%3G3^q8Vb%-1A67pN`YL|Rxz0z{<hJjZT`a+nm
z_sdcOM?*+vcwYB}3h1C2Dwo)RepU9bqy3G;WD@;X0hd-1=KLRm`*Y+?Hy{4NpVznW
z^u)qbClOgD#!zwGoR(?6+Y&+(H}O)7lBV>hEyrd$S;Fju*Y8i2kp~WOzn4=^=9J}R
z_|ehs*5g)BR0=5etH=$(=|&&PAjfy-m-0xvhfDrXd&p4iY!AaPAB=jNObTt8OR<Jf
z({$YpMbIcOrwg@<Y!?oL+!zX@VWTdm+DyHwl?Ttg23@0Cfi!3;A#ZB@2${H3>1tt)
z&=vSP!&<Aw-i^C&g(x(>R?RH>U8=Drtkrx~N7;VE_|mv#D3@r%jYuF!cdaN&p{a<N
zf3XLgwpD%&viR{^%PVy3b9B4*Z;hIouVnr)CBUz5dx3Z1=)D_!>nu-_1%>{_Uk#B`
z%NiITHs5MAz*4lHxD|0cQkuug={Seh7|+#4ULxyIg=(i-RQf9BWA8nQN??mP$7XFq
zC0CDH98g6Ko`&R&?|IphZ8NmlKaA0}Y@-XS$gOCQr<y?6vuyP$rVf{0jUNkB#0OiG
z-7FOzSw&zEX-8V+(!3*iJbf=mNp5NbVq5TM5Lhq-LdA-DzE)i(K^=PL3mE@t6%N;S
z44d%Hd<VroJcja|B-i{Q`xiChDSUrM8xl*y;K|}j{r_Y7|8Sju38B&co<z@6GQYkN
zwCNi3XNV5}s^GxS`ME!UHZzI!v(DLiy^v{yL&+If0`FqWf1c*}Ticn@g(?uEvMG+t
z`K5h-31p!PYs0$PR>-B(7i$15(vES(@4ZorIZ9I4LDS2r^w&Di*V-p#R(OC0N~TUM
z6wU12MNyiQKrx~FuVUBmIDBUO!3m}Z(+>N0<qbY}9EQ#BT}|xwg7dv)O#N@UK01J!
z);`59*TkyVi<st>z~B1M(QPO-a+bNh`%<?-g>MwPf<(KyTibrRA-=`i+AJadIM<ux
zN{j1Hz$I0+#tbjs-g@K9_gVc(Z>}M{dAAxeYIN5qGKIZeWr`p{4LdIYucke2AQ71l
z)?VsTrag&8%HD)u5J?sJln8DqP>t1=E+jx*Ef?NZZciV^P<ns8+lv5ahxLuBy)kOO
zOg{V`b9t_C_hL>f_s{gj1<hoj!OuiQ+w59JTf_EXUOamsGi{um-GeZju880?_vpQa
z6_2qQ72E>cI&0_%)vi=^y2znZ>L(vA1$vDy(6^&Kj&K-eD<}5Pvh(OprPNlvY{i=K
zI;X~|hYf}eM*g)vKKSjQNf~CXklEj-{?7qpgoxq_%3{T>%1uc3=g1N6jEQ|$Xpv_?
z-lg25{SiM_!<kx9vzd<DRFxZ&Z7x2@_aR>CB&K1?y?o3>F5gwA%9S)ZO?|glJy<Xf
z3G8|R+rJ_b@w4^b?!H|a|Go`eJbL_X#)Hk{-y;A+yL^%gwF=Q>5YMnBx#8j{IOex|
z<azW(Gc^*a_X8&AZG%3&87%B8n#FcXyn>KO8ns$D%33n%umf0Gu4WzmTf$WGSPb^)
zG_kDva38)DxV@eiqks4`M3|iUXT1=E#EY}`pjfZriL!7KOCmMMoBlnkq0uy82CH;$
zvl`_s9z`C+EGTZsMtyuojlv!ROeMq+p7}`*aXFjwVG&`J9%vAHxYFQhmxQP;*ASjU
zq{EL?Kz}|M&)3hXjo>2>k<;1zR#Y|~HJ_aHSDb!CjQNyCer^5^O=8h%0Q=nX+I*u4
z(e<7>fhG6k6;Y-c5UvHayWA({%)BUSLMT`ZxL)n4(ARC(`(0Z&#K@1n1-z>~{OH&}
z^WhXvqMbIf#?Iw{v{^2D95sE5<pB+v64KprxlP)rymEu%&hcj$)dExFq#4t!J(=^0
z<N&VgBJK89P=1mJc%nFs^a?2^{)gTxQ?#R>N~P~EHiv!~3D`CUzLI1AJsVPf{^?Rf
zk(WxKJM8^*Lx9OEo|GhWkh#m`aI}6zXj55E%epG*;bv`hh<>-e(=G#&X0pVS5?`HJ
zF4-5EcMofAyhaf9oVK=oD7LVlYE?D<^e?YU?DHam&P5L<8G6@YBbaEf5_JE`9Lg6F
zyBG;-(BiTYh#(Mf^NLm*gxHTev=baKlon`aeY!8Gy|8#DXw<TCNRhZ#t*df1@1CoI
zXXlouy*0==+rdhs(Q6Q9JgFd7d^lO!Fnk0wmhh*92z-v+Xt<c*Z8Ob{V&^5(!c3ED
zE$k*iws;~irqW|2TEmzH3$-INn#$Z1<V7CX#GFVGK{q6L>Th{Quaw8AKe+SzOs1*>
zv+8_{r2so2S^?gg%*qW~7zwc33!&LXx}icV3!jZ#t5La&9p-2-9F~Io6-a;Vy>Zm0
z&c}t#txOOhdd}^^RjF*DvwmPFf|qCCEVK3}C~N0c-G;@aV-Bgm#V7dk-Hi%=iQ`7$
zN3h`SMe1%&vxt_iI1i?9Pz&ug@NO6iSjv}I@rOx|63j()6uiBHp{9kbuW*#V_8a|D
zO<@2rH2Z(_L5NTSJRQG?(K~(T{Hq85huHK~dgb#7$m+}H54gH?l1jkoAUd?qn;FrK
z`EijYe{Orx_%bAi8?7tx*7Nd*u+#R&AS~wvF>!Hlqe1Jqs4085l<RfFY&Zu+Ivb3F
z@-pyi|Hq;qinqjQLXT%pJKBJ=!~(+Bq1OdpE6Od-m02l0<k`;y$+W;VM`jZRTR<6o
z_||zas}EY}gu$#Gd=f3@Qq8t#O7G+Llwj9sHEgAEsRV53rDrj3fOH@~`tro34s6Kp
zZUJ#ke94Ir<-nPq-oIfk-ahV=Dl+L@Cuy-aH=@Cd7?ftY;A^gYyU-JcP*(7M7Fw8C
zLD}wGXWi|nYr_}+eCd<mg&w7ViK>^r!>2mP^H;kmqZ`^4%N@{(#Ss)_g%90$n|c*&
z^rwC0veU0_tZ2`Q^tq7#*)eySdmm+&@$9<23pQ*?QnUWsO=Tk((E5jtScFAkq5?j4
zFwFxPs$p$ye?QF7YkXe(3Bo;qZ~$BA^?30HV7@dPgxfMiTd;?kRmEUL(9mQF{JMF{
zz)WiqMyF3MvRCsd&n1*6x+$+nqhq%^o}3)&XWwF2S4h8A`@3O!Vyitd2lIw^Bdz{=
z-2ce9u?r#7hN@=1oQUx8)exie7g*bTj+WQ7g8t5;aQFDb2tiWi?uS<~s>v1tpnDu$
zx;1%v?*2&;{R#|-WQn5IPn(OT1H3#>H0G@>?LT(HgSZ6`E;iXwa*xZ$A`9guE64#N
zUb|t3NpSUCng<MhD^81?YQlzC606Zn^t*I%kBSOa$+jKF{UH!Lp8Il*UA9{Gg!*re
zq{^xC$+6vx?@G6Zu#b^9xU*L!Y&+i_jUXG7P}Jq?E5oR_xISyq)RezY8Sqk*=cov@
zpEUJamannBe9pu=BNgieN2tE3u*(-DXYoj>81vJvO!dZCTaP*D^u{4cy!69H;lJY@
zY;I7210zzYXE_i3a8U@?N^)Gr93Q6~7&>tiqIWelqDrmu`*=h8Yr<F+(W3jCfiyzr
zacNFHZ^nc?snN@<O14Mh!h3w0^_M8T9-gu^g?BDot<3<XJ^wi+<=t+mvT@VK(yze<
z+nHK))NIPP3_GSohH^43qW$eswmViU88*zB05VS>bqNvw*}h6CX@7V^V=KL>I~yf>
zaC|!WTV6+~gP{mZ*T$YjGcoSBUe(DL=tk}<ezm$OKkp|g&ukPaZnxf^=tWMzI`4fI
zJxx58rl~zK5GH@qlQWG_mY1P9=YsA30Y86A*`cC4o|&J^QB)Z~&D$rK1r`SG6UA@D
zc*4r_8!}lxCx@j^eRD0PBq(M&v?^kiB8isgwKoTw@Ul@Swd(GlaY!*NF~6#l3oMN%
z?&GPSd)$s=Xe7F{VNzQr32+q20UO|Y2tRgU1bfFjy=$R?B$#HRPgw{nm)2n?*a>_H
zMUHy03bs;xc4PDcF6KY3Qld2LGQL>@QoAJ48rOJI&PaZJ`>=56cx{(b^^JX@XvPSY
zYQ(Ewb)$x3Nng+3vDOR6tnb8k`UacDd+*8`ntb=+&C+M(4z0ko!GLjQ=`vCj`Kjs-
zTVtZO9}MubEIvuLUM+!zk0g)b^<;gn+p@k2pD%B);e{!$k}xF92P~O5e9z@vN^1i}
zhs}o%b<KlkF=h{=wa(%ZJ%65>iKsqk1z5kSX6XqjomZbHQ?-B?zZ=bRNyQ7#rm@3%
zQ#xqxpuZb>wBvRxJiXi-|BaGj*iWfb1|lL>xMf;@SvJc{rslGiQ)_f#=Rw~HP7V_Z
zE^nC<1RxM--t1$BPzU(#C6ow&;1w|_=N@&wte_MDpZtzc;g;L+e7Y(Jw+Jkf+r639
z^K&yn!hgSZzOpB=A3iIdj<CP@5dT|(ufBdm)vwkRHA@$ldCPZ?efmlK$L8ljN;R5I
zIT`!Q7o%a7c}>Up;kC?b8rt50!*YxQ!=i^y#ntm6t&6#OdEsIeS39Fsv7gPm#l*7I
zIlq;oo3>pH*g+`)H>tqk!(p6XkEkNTaTz01*_n?DO4dDd+o|M98(o(Y;;=#IDd&1{
zQ?`Hkfh?0=UFB`f8qYK!nx_@FXe5BNqyc@poXW!xV&E>6tdMzgqu1zH%Do~*w{ric
z2{`+U9GkN<w1TCsMh6sb+<qDNPRj3dP8|S!aR!@Xqe>FScFV2nG5-T7L5f<P-)>sV
zk|`=wJgHLt=Jb2}|3}tWM#Z%?+a`DdK|*k8+}*u#2@Xkc_uv}b-7UB~39dmKf;8^l
zSa55c#^G_^z4yJ7@BZ4qdX3RzuU$23)~s2muKOC({VHo$g?QP~%;>fQuo|2PbWhCB
zH>SugmDQfP&jngT;<ex79YArL@2)EOnmGer)qlp`)v7?;lAEbK?D=XG<9m_7Pj{hI
z!fF5tHa{W%^KPpZ3aWp-B8kuN)r<6p?GnAuS!H*RzkG3S4_2IaMagJq31CU?v1CyR
zjj64v+TuO3mOd@mx7zS=kj*wXeh6^nTXk8_<G6P`!RTA=eHA-S>flI*_hM|oLAZgn
zfGWJvj8A-+cI8qQXY2l-F`ee0m~K3F82$Bs$8<J0B^Az2&lg(!4)az*?@itoaF43i
z01lPll1ZH#%297hW;k8PAq8<ttXcc9$>^Od7%<E3)<L^hVd*#U%?h$o8ah|oIR-NM
z`HQOT*zM%n*Yi^@j2=OM+WTTwo(WJ>{MxR8Xzh!Y_D;rC;w>)#KG{sx7EQHgv2k}G
z%3?8W-Z>KIzMPO7awNhtJimW6{dDwmt1X7$?ITPIVv;=iyuL%!G`@O6F1hNqe|EyN
zNMwd>qr}JRE$Mvo!{bf8<X43#D<2_Qj7;jxKa8s0mlC~Zq>l?TE3Yd^)pjb+QdOh!
zWkA-ZisAcIxtzPidGxdR(b^9wowrl=9dAE=2KwOxJ3i!K+0FA6tnEnHWOmb?H9iqu
zm*1Gwchg&_XJa9F!nlALyyH!Bt+Pu%XPY-3J967=I!?ceklSLFo^k%OC~!U7$7dNk
zMEHv*>4PYi&nWx|@1%i1S?bEu2f>`zWnc0~L(-o0^Q3`s6PNRh>HQkA6go>hnN3#_
z<8Rr`V`H@G9m{f0DHosvAIEu44Up|8uK#qs;4=BkM)~nW(X(8ks%kEHn$%g#-^D+)
z0G%#6-0${2m-1f!&*%DIMypYwUH30L{8$d%^=E-cFP1Ia(1=}wB~riR?S{`l@};Qi
z_STMvgIOqbho*Z=Rq9OX=-TzIh+Ea++P9UjexgtezjdD%mGe^<EFw-9<nurYSP-e=
znU4Y7b-8U1J;?v5t7;^;{+n&{ihI?P{h>Jd=4Z=Yc7+qg+Y(DEuz2;BDr1ToZJ!A$
zWkXiU^)qIiCX@D;J0*wjE1RIVOtUnnl<9AockWl&T|kE=&CsbZy&VcIr{LCypOc&5
z6Q4Cdt~zHw352Hy8D)EA&`sT`;E3vIHVF0+H6O$5l?bpSWh!Mc!#^iTL7u>>Is;WX
zCoSb?i%F~}3Hg#xCR6s$ANdkJ(#`Wi5*k*mk7+fKCcR09Prs~&NgJxy9Bp6ffB1+2
zMacXpvc%Ais|NPu=h<$w$$sTZbup-()K>SG=;LsmG;Tr{qAy4Q0fD_-HU~@_e$#N|
zYD4YSZ@eX`DjQ%Y!5F9dLV$XSQAQ~;aJ?pnVB+i#!1ajti4o4x7?<hWBj!r#>kZkq
z5EHqM-o4rulQ0;D^ALeV9&QY+pQ1Li|AxmX7Hk&k(E++E1$x`vD+8}<IeB91Nybv`
z+RW}3WPrbfFOH&Y;+L30PTuN0X4#;I?C(9P?k>RWq?Ug_esG+>$4(i!BN(6TvfIq?
zo!L8TJ>I7QK4f_hNP!Q`h99QDS)a?!Uqy%@8#)+_qi5-TJI&SfFqV2g7PQNEr5`#u
zuuj!CUl&R%WkRSkzi30E08u{P+si5ZhuFD*{Ga-6Fk0mC_b>!L4hRyy>AiqRwCetR
zo=6(E#{k|2V)Q?Jeiz~=<0Fo2?t8llSoJNg8dvq(=pnk=1-?k~dgehV)eKo@|2Ybw
z`w>Cbp0Bvqui;x2<(s(WY~CM|pO<|9*i)OyWL>I-Ex8S(mKh9HjiBZaq%^j>Gv0pU
zNlcCvPGnX3`cr!THf@R}Lh1xR#gW?vwXJunlW}cGul@+k(KZ01y=zF;xCFqjRzG91
zZC{~y-@oq6VBp-mJw-69iic_E!-!QRJ~QLUre|bq77++r@L;P}D{2Zqr?Vwflfma`
zqfO5CWepeHTid4NibR_oPP{COdy7s$q#S?w9z<~5pPTVgGES4XkG4Gunl~=x+(CcH
zS<hPY5y%r$^(L8Y{R&6@UiHJq8G~42W1gHC$*|#*!OFQD5zNHLF5-=yN=#}evMP~F
zQuupkaM$t=+sBmHPn))W2ylxPb%xh%wi{!2<+Aa8Rb8hSjCyZE#)T`JAa`DuH>fWg
zeeRrsEo{2W#YEGczF?l3ljD!h)I#M#j{80(bygSJ3q2gY7@6c)(`i)cE*^JYG(mHp
zS93j-5g>utVF}ma4E7luHUhTxbu+X}$UdXFr8SrL4wS9;V%<lBM;gB^Npjdn8sB=Y
z+0Hh7-yL_o!|){6U~>Dy@OJ6N7<PNg<WvklqO0w=m2t8;s!q!0I4m{i3H}dql?@2i
zVtqr{8SH3xa%i0z-QPme&&uZT1<{)Oo`QQP6Nc)-UGi@zf{*Y*aJ(cb1x4_Vvd8Up
zoJiXl@S(HtaNO@Q8WJZ|9M>yE8RC~IQlk9x&)qx*@T#@!CJc%va+Uy{w`&ob2RDI^
z?~tZ<k}UO+=Q0028Y$pgxG1Pgxn$r?uBm?_O5$w$9cw42(pFKTAxA2xT`)hG&51!u
zzW*cFPs&tm{u+}s$xn^5dqt^oO<EC5nF>T}l@#X@JU*$hK}yYzl0-ge#6z`*D%96U
z^3{8549Yg3vs-0Ss?tT2<&z)|EucOx64oW13%d2N>Eu_IXpj~7HcamhH>7zSL|Zu@
zS2_RAWU9vmqEVQl>#k|5W8dUEVeq6uJPCGr7*2dxOqB&p(w41W4fCcyy4k1c*CrSD
z(xP(FWxZu**Q2$u`l36DQ4I+oN0Q|MTvf_Ucnbs`Y@&;E`A*W8DoNr!xe*C(s^5wI
z+M*6h=O|*3K657gn7G`gyram0U<kj^6D{!i$KBa9U)J2o8Ffjd@xJ_g9EnZ;@>J@`
z*G)G>M)^cU9o_<->lZ{jj(We<i@?xz>;=bYxTEKSKBUm%_A*9&5lftWhGbm0vv_*<
zZnnv~&`doFKYIYAzo!1>hXTEmF9%sh3Vmy(a7#{pah63APEnu;yHh-M?Xi~*M^p)N
zkSW_A>cHl0fWf3p-c_7$b!Un4!kk!vWQ#2*UwN%XAB4>!e>)eKJFOCAQPO$}VtUoS
z$fqSKn$Y)2O<&W#g#GgGyyqPILNa>9(Vl{pK{Y=2$jUa?&+BJU$eN$`{RIFBt(NnQ
z>$z)LzBq|E0m8DX?VlFoQYta_{VX5uEbNz#Z#QV0ygE-Kpp3cCH*SR@>E{32_EeV$
z?%vD$y<GdWGPEdO@yr;cexTPQ`<|cT>Kurqa3Gl|NcHn_v)$v@*(Q_tt<Yr3L^p0{
z?BGE!Q|>?+pGPq)!FLMyA~;L%cW%gTw*(^V4D@qQu034Izo-{gK*umBU=Q0Y+Nv55
zfx<QZExt+V4*9CzAdxGk+0Ls?;7j=}tCz5!mU0_0&xl0n4vuy-L*O|9uJ!v`ZM*}Q
zk(n<SdY4l@JXtXNrpwzU>dwHMrJ+tgC0YZL2;xO$@$zstjBa`pS@%dn5?5jnWI!V~
z#WIc4jx<_0FE}r<rlwsRH`)iz5h&hSxKT}$A0~ioVA-l@AiVV1SeaxpC94&;KI>AI
z{xJs?Ri6HF&v?pl4jz$&LT1jE_!GEfB`J8XW@20UV@!)}#N-XRiTZ?)|6KZAhYT6#
zzSQQRskrhq8BUD9aJoh_(C?rany{<CC~}|R5%Y%0zWE;^N!a78c1HrC1(eqIw@qq+
zS&+8TTCRPb!APOV`07Z7%`EH5YSeJW?)Q#dpN~*AucmtvE9PMIE<GS3)%qGTI4HQ_
zHmM2yer-IgSZ_QkR=w7H8#a8=C|_;0eEppK9Bp-~#%fdIDCtk#J5{mmX{zY;7a(2_
zBx8I2{lJ(rAaltr_q@>tTs)r7$43XBx_(S8Za+P@Cgt2bepyxbl!h_-0B-hmx`%x8
z{(65SQ)NFl^S?;XQyP@Lj*cX4FS~MVy)jH`$A&cgL5MGH@=yr`KT($!J`1bHeJklW
z($k3{n?=@%ks$b^*mqFuyW6=Jo@p;vHg?d%SPZWFV^#~nQXQoh?SwW`Zt+@e1N6T0
zdKwCNu+_<eybcJvz0>zGw%XCX*{|vT4WjvR&rPXiqxH&c|Dfb~IQO=(v$T?KBo{r;
z(hla4lu>%1xU*)nkA?0$^n6*mQ}9VqcjW}1ycW)DHG!KouvI?qYIc~mNq40aR&0I0
zL_PBTTj=}TXqD{PVjoxFq@XDHRR=2bW>BZErMg6nNMbG7?<roy?D!c&mJ#W0LR)Zc
zx+ZMc&p0*~qafVwp{S!jsMzVmu^hXNkH6oYBc-n%KOlu*U$-0M%<w0&gD#K|^`T~}
z$=$G4>Co$OR<sV6nf|d!G3{3Shr`ZO4(0kS#oN&W+B<wz^QQR>et8kpbpY^Y1cu>z
zwJB3<^^At_v}BvRXm_&^)@+e8+Xh}@6>JiIb3{<qI9cX_-&Wn`aR5s1)J#Z-G+Rjc
z;Bq;`R-emko+kA(7d}C(BZv7k<$L5Xc^`-G)GRG{&tmXPG&K(Ji%g^_HpF_aTc$H`
zpZDZO0nzR3=RK*9k=GrLFwr|QUL1$Ek&rVYc;=KUj2c}h7nX~mwAz1lFR4WhU#vlg
zlOBFNY4iX@PJ3bFPa9y!$-}nt!{?7Db;HhPPpp0qBmGx#$9|%$qnD1Qpz|~6A?R)&
zYTf$OZ(%2-VUlk6_be$=0Ehu03t=kre(1T3Mzsh|F@~yR_M6}f>d{1!-k12CsWwYt
zh)pP0R#ZI0na}tAl|(bAnZr3Zdv<G}%F7haT{d4K&6h}sUR2Y2iwPI<*}AW-&|d;K
zn1!@UFJ-x=DKX^;J=v+P4>WN%++)wfwT+!Qi`sM<PkBgWP<#cM^sD(0Zqkf1+-~o=
ziUIb9hxf_@L}N~KE*NA_`!v~ud9mzl+gy>*`|-MA^iohM`cuNYt>+|G-yihzpBUf1
zl=XcY)`W};NJL<IsxRJQd~Wb&;7BVCoBJnQodLrNQ+BjIm{f_inlXpc0Vm#<f*8BQ
z)XL&<O)3)}?z2L52ECtc>J))B<^4n%Xj&xHdRC~EOYWK+y>D5h>K(ibwic8HV(kwh
z3f_Icoy>&?RXCm+c72gB4Moy-KeJyeSYj=kpMTxR93#Z~$5{Wxm)8G)a0rJ)p#6SK
z=juv+jXtYeZ9Ba35A#S*<-0F+m4?~P6XWKoE<kK+KS;J);pM!G$+V9kE9p3SINPIX
z-XCMn;h!Zcl_2ai<k~bG^NR2tphwQ!G$QtS;u^O>=hYI1(oz?HyU!slafaG@3{Li}
zNErMbV(7>LJ6EaA+z$b%j;s~G8_?q!4D|dnY<zV6`5g4<<x!QD8BRO#f0Nu8E*ysN
z*#m6t5qRgdMZoer>IdU(%Cx_kAc%Dk7d-r@dHT&LRDWjh!LLe!{dpJUYkI$OBGM{+
z^_%e|Fg3~|QTgK|JPE_b-O2NyfnD?RY4^)TbGv|D`#o|({4V>H4}dshK*g6i{PFO=
zvD4}sI63q)0g(Jt)e+u<B5wJ`7sm7=n@Lyiukg_(s)-1wjO9gccUVKToDFD!m20l_
zE|KJaDqd9`u-*^$Y}Er!0SSA}?r^cn$0<n#n|kaSe|pE*zo#H{9DTM?7QQ}*WdnqZ
z;cs}MNTtQqPf&PC#89%~<p~QB4x(jF<q#16*ZUfU9b4u^GqStHNRn~HyK1>lxVMxk
zyVLJ$vE%q=0H{<YIoD8bYEI=5kSV>Ya@)P5NycWXem}Wd-z~3Qi_!hEB4G8ZDt}wk
z>oK(>HrL^fF}Ld#dw)k<YCM4{zvP;@E&1iK>#`mZd^VqzmnL|@HA{RlHxOd${9cqq
zmy$0^5GjKs48l_*Il~AvUdsKtMeGTYlI9w>#eZK@QZC%k%jikW?1dU-))tT;P>hs(
zCbP)iP%TUUN@k)*jGdi?j#=Q!E8HJ%*fG|6Ls(hy8qFf#qnpbI;=3C*?g71-==I`@
zzSz0T5j>d?O9%d}L0$9V^?E)sdH-AkvZm-g_Hf63gooTW@(50SfC<FGMzWVqCsn^&
z@o?5%t=E($o^b~AoFMkCT`eBoeoFhb)dFcc*X=5^`Y{Q)?R8m!{WQ3lahsN5Rd(9e
z0lrv%bos1giMM5ie4heIwIW66f9&vOp%vgf_-{}(9NjfNWb3y%?(0%QeiP<*tugN6
zjlH}m^Agh@0(bVfJ8m9R*Su`*floI^Kt6YWh1*-qo5^WbyXSfltI#M3PQRDTv-0Gu
z2YMR5D7fF1aBKKGl8p(%$)WI1CwL=U(d~vx1}T8wSTB9YW=}^@mj*#(jpdA^WYox7
zuBm?4PMGU~B@>kzpscalEU}wRgY1JO=t2v@3smEDH@Z#;D?anIW;b_Pnk)i$wmL#!
zR-$XalSzI$?C`nArzgqu0#g#=xw!px)@UPhZHda3(`5E<BY`vy)Cmd~Gu@xgn4MgT
z$0-i^n_KsP?Kci)496v9lz^40vF_~;Ysd`mHHod$I;ymv55KkdC}Wh}Kxw*WQKYn<
z1oB8b`ae51K374|AMJG79vbxL2{w(OncbeLuyf^UOSr~-t=ARs*6vZW%hYiqTqyW6
z0E}93iNZ~oQAwTQnfn{+;60r;NwranO%ZAGgV>I}b$(h@d$TXp5K_DEkpHX{?MOyq
zu)&>b)_uBYm-83wV}DOaoJu_8c9YDIe16}{VvXjt`T72bQDsoQgq;Ok&^1}O*Kzwh
zQP+TPCU&yc<2BD-&l6+=DdWB)oR=2+@-m@!*P+){JGHsYtHtjZF<|?zw(l}Tpl&}?
zMG{gfKVu7$V?xAmJEPu(Q<L@~dz-BxbL;)He(251zxzz%&a{}qjInSgq*G@mNL4GO
z`sVd}y1cgjS<x$oNOSfImW+IVL`1}u%OBcY)}O2Ue*8G*Qu&mZ|AQ_s>ca)`k!V*f
zr?%S$pQAK51zE0%36fz^4l+7%K6&QSyzn}CzV^5~;kOvyh3u}<KHeYb55EIpkRdF#
zY3NhmYVmu!Y{qIb1W=URAeh$&cIO`w|6%)0X*Bqx(oygME<}`E1?9gVOl}AV(&<Sf
z>*_?!jn_hVy7$wxJPk1rx7?yeHQ;-=cB>tr?;e5A7hN~8d%$sf$>XM1b+yb_k~cL4
zcz>OwzzbJ#ZR!}#C}k$wm!Eef&au<;j#U+<l!fRJu1Y}4h#u7#*U*RzRfUPkYD9lX
z#6_f3CGVc8;OA)~I|pAZ4wb1h6XL;-WEQ|>rfp4bU(>qmnz^j%A4cOO^qV5#Iyd$E
z*yfeQ9rMn$RxkVehfrpC^mGOP&fpuwD!o_E3BgeJXT#{2?MIV}me*yuw|J?r?<%Cm
z6uJB*h$nh-{tCi8<H9?^O<19s{V)qH&JW6Evhm&WuY{k9?gW)Dr3rK{*lmYJm^k^S
z_&((_)*47tJeP;%a5d#SSPMz^bQd(FH(9;D;KJo`fw9S?-79>=@jAVgm-*7@yYR*$
zG64Nd#xYT(4!BT)7V@hzKJ|Az1|;=0>BJtNagq<QXdwX)OB@Z?e4qTxoBK<9@^iMG
z5XIQC@!Bf_oJ?p4yy^klGm7ch1Y_=E+<7ng%V6oGW<&kb<6+Z$3`g)T-_noo8qKJw
ztkw{>qren@$=rGM@!O&nK5J<Xd^i5s#n=*zCS~LBZr`jz58r+(21f6pEafS#h!$~_
z`&yv)Y7p+9gkf@nlRazP5d;XDgV+EYpX~Fe;hjuNOK64Lc;!5m$HD!$-RjER$x^i|
zMjp~Hb<o|sRvu64bsR6|c1ugcroK$e!oP3N2e>GlfL7HV1de9==Kxr0u2=cv4miga
z;BogIyGDh9HmGYlv#I%cw;HtI`=`6~Wm>EM=Tfs@-#e%+qtemiIW4x#KJH)F=mXqE
zgny)j*4hB;`K=T%lR@p_cdnm;LW=yEaZO6Gv@34fwD}Ln2X5xFgb*{{nXtPL)MnOY
z%r&yy%kMGa_H1Sy7ks6^l1dV{WcQ~b)?9w{5a5h4hH+oU3AaU<|3HMxbw8eU%iC4k
zONYmI4T?(IfJ*Q`OlGo>N#SPNrL80V0i5#lEk9w&>)zEe|F|L|EqklC_*qHjmKKX)
zLp1`Mdsl$w{RB(6BA%eQXZa*A_jkj?hx*ip*?GMwMdESMgmrE;f^XLeUQ?*afF)?7
zMNA8sNtLS=w24fCUP8pijFqS%LR#*=nItBNhGnqU$!ej&Mm3j%gmHwc$;vUkw}Ak`
zHQH<dF_fIv9HDr-o~`PtwlosnL5Tz3Qf`5CN?49qKF&l*v?#^au?v{s7qd^PjaJrg
z?o*zo0!9oJHAnMN#+4YBn=)BdCOb%Qs}Zj8^}64mO;s;nVyCDv9ZYm%&0)CUb$?8>
z3zy@ApY|>Ac=eO-IxSKtL41shO<x(5+m^0Tra~~Pr2t8bF~#VB75C?@RjY-M&Li~?
zS)=C?!voH~(pYbJVtLU$CXF#h<(LLp%K8mQGRoydl+3eSG4&>XSKhUt5#^hrzz#f$
zqtdt(&%>lccpLEFk8<%myJY@Y7rU*W{->Atoo~aH`O0}fC`aIDGLMr|-aY3-9K7|=
zbRr1vnyX%$gO=^#?WlqW>;FID<sd$6lrp{#AvyZKQ_t(6-*Cguhkc{9fNG;rhy`d$
z&xJ%G6qCyr^zR10u@8n&{V3}y4otQ<$0*#)sg_i$uW1<g#zw|~`7v1Ub1OcvR6!id
z6w5q&bl6Q2&#{#RR`5!U&bP)=gVm)ky1rQ1W8R0=dKpR0zVLyV?*p;vr>UH_HmtUS
zJ+c;n3T7`LyyuBLy~WsGeO-olHEX@t51pEk+V1r}+U>h%BTgtAE~+-`zKM>(w?iE0
za<Tl;0bg8f&}0u#g!tXzZt#qek9MjopMx@zGW@DT&lU2hajl<~Of;5nJhtyC14s`G
zerYZXnHL<wZljglgimBjA1k-9Ny<eiZ6dQMf*-?70slUp1f@=y{ZPwb#&m#g?*j^Q
ztU9R}C74ul-1QHvO4GH=(vJZ2$#VQhto-DJMk8X7KTK?ENltz2{)BOx^=4DJ&>0!w
z!2_XMf2rwOwCywtTQCd{4cCG%-b<s}2E%uY^YpEE2q!#ByesFRsc2w(lx7vsbw~5k
z4BdkL6aAPoH;In0dtNF3mCA$%Nv8*ip*-Q3_n6Dkd=HnXSbbQ)j8)Re2UGiJ4bYzg
zw;r7O3(s$BbS4TRGBjl-5?SVrXV<LPDP9F^q~i<>jmQ7KF(o7ct%d9O94}79f=kc$
z-KCHd5tuim9{4Qgh!#oxq8_-}=!Zfro*p=%dMD4~_hZegGoAOgyp8fHxnBJbsfGVO
z?d2L8ieNfmm8ZskJ_v7(?}=ztNeMfx1TreYD@&Cd9biSmGv!PW3Pf6GjC9Kng!q1g
zyLGP=Ppmt_{DR!UR<R-ZFmBIZymoAlq7@+du)+BhuMQ9b+xHW;`<B}!v*X4cyb%;-
za5&+^5?ZDDUJ=nAZ{b5q{Wj40=bjvjr+FoWTG&QyRf_gu{F#l8|I{|A12xYH-_5q|
zB#X_GBtwq<$}#N}13n1W?WR_(N5N9N&QxlKK!CiNeTpI7wJ!$OKhW*)n!i$o$dt5;
zG@qP*>3b`JgNpr1`(VhA-)0};!{}{5byZy>BoQ(X@EwrQH`|;fg{dFVeMckPzdbg{
z`NZyzA)bVr6|J32sXiF-_MwGdZU6TEW0b0PaxYVDRy%H}(Ts_?M+DcsHA+y>hcww~
zJjD;V#u6Iv66^F^X@bSswYrm6SWfmjtYtG&&iSetp^LhzCE~c(kCP+!Anx*A+3L*m
z=H9fj_%mEwR$9Mt7qNd)^cn5pDu<q3jcI6uGg{H&nJc<gQj1xgcBc3+#urt0j&-V2
z9x8}QJ*~i54{w=?-%o3aZJZvC2q$)Y(=>8+Z-~_XAMo;mVY*z%RGtwx={z2$h9OJJ
z58bqo`R4}-DCot>a)rwILj~?z>)|RN=-TFYg%kJrAgBf4Mt4)){|C;0WF7UeSDnok
zjuDl932|@<pyhFKtTbZjKe8rog3`Fsy8>F(Oim~oG}Yn?xX|*dKGX%FS<U2kdJUUR
z3P9fp+f3jJa%D!RnNscXF!>e4@4ngst2y>g^AkVOJx%y+rKi$LTDbn!Kmk5ISpQa6
zf%7n~7UK5^U`#;3xk)$*@{)L4AeALlLxo+JZlMD>510&Qy(hgdC0HsUpN7(9O92z#
zi0w~wKKZjkqH}>+LF>Q9zS@Isdkr9&QU-nHv-oghS7M@<>DHUtN2zH2`J!C0WR1be
z)4@*o_`oG1?|3_)QIwi4t&3%$pE&XcQ`f=&cmZTv9oU9(e4rM<cI0qMC(Cv#mv<d@
ztT^q#737H=q(QWV2L_4QY8wa#lUVN+i`Z8bIxneUh#|g8JgOb$vq_4jp0W*Pxf<-j
zFez`Ez%=xYCu=;S{3bU|x*mL6h<2M*e|9BL2h$K_L9o+#;Yl2BJ@S)&nJk4llH^O~
zXqu8HqP{s@ccXe7NFDq35q2@Z<KZSyer&Zv<{MW(4mBbi^9tW`4Hg(|?i?)=F}KXR
zrF%B^RMOcIf5pC1e?T}m_WO^Sx4~Vp?LC~oBzadl;wycN7;&oWu;yY|cC|3oC&s^W
zMw&FjK?FzG;K4Mj{d|(#>irhdQ(O5Gg`^=k=Mk!L_94&Ud2b0;d9AzVdz#(cFwCmk
zgrcEF{x{nF04F2<>RGcJsChR{ddUnN?qLD6i1satUf?}Zdfi>4qhnt9orYB$se~#V
zI?>c^@jDZhXOfYV7Kq0FA^tALjwXFeblHvPs=HqB`lIP2WUlC=`~z|(P0%p{28H#3
zGy%nI&T>r)bg9k21R%{z9FNI|E|e53;*MN98R`d+QIdRBA0$F3ZI{S>{9Ot&c~c>u
zq{rV`k@UJ?qy{fnMxkAcy<Zn?C)eeXo%zysPH-=lk@f4mUfpsLHa`bG*DqD|=qwD{
zcNT*mRtRYv*|!o)M2-?5wndfh-0F9P*L4J~N3Ek(sDR55kL&M9zj=K!=&i9_Q_+{f
z{HdW)bNEBFy%%>$JGY=(Q`IwqZ(k!ZLN7TO22c`IzPv29NYkDy`XFAj{yBgaP&4QX
zHnCP?a89E6VSs`n>6XU(F5MKJFD}O@G!2QKQ6#;x!|%}z<G_6cg-NTfXvJ$c61E<8
zc1NuXMEqG*7|~Y!(!Y|X&2>t6^IDHyP}--M+ZBr;OWWrc%k6UU;wdX$D}yG_c@-T2
z*XZ6}P_?64<Y6{=RWeAin!vYq=jjk$`lQ9x%`W-&5bu_YVBLgm(%Mi|W_KQCb&=pb
zo_9N-aQ8rrv`VHPUZ%3)-zY^(JTHfibLeFgoY&IxnT2u_=pqWE8Tjn7Ld6+qK8b#P
zBsk%_X)@jfdUOU@f^OFFAWF-k>XrXK<0bE;At8J^F?6InAgv;;^~W)|xq$e@PJRlG
zOaR^DseT~UWKl(3se0Mt3|Uq2muBz&=vZXXYKm6XU52b75bhctQ!vv!dO2=+YlI!c
zi<&3LI=6(_dY1f7aY`cGleN96u;Z6m>Cwo!(P(`&0!sp~p~*u`q>;&&8wrJUR#=*R
zic>_ThqP}EZHO4vbQgjf?MjLb$FP$)YvLbuM9K5Hc+|sq(X;N9M@U23Rzz-7pbX)R
z@<F5gK8@Fc>19WVeg6<fRD8?as)W1dZXT;(e)WySnst8Sd{X;m<|7}{Z5SR-yaelG
zX=hEr`*-*mb`(DR&#|g^@rsZSN`%`uV7lG_9-1YBd3z7o`P43&#%-976MiP4L2*$4
z0vkn~f_h&;&AYPcZHg;q6|jel|C??ZtIYtwPE0_|U5>_(c`W_bI9@Z<Hg~Nq?Bi@K
ze1Mn6=9PRvhaE~h3%g>pTvr-ZV-c7;Mkv9-O=*jIyDa!j(||rmB$v?4?!@EWnFrI9
z1Lop9@=u7Lr%|=zfL21H*u4Jv8VP=Ml}@X^zdie2?(7)wI?HX?)xPEqjTqqj#t-AY
z17BI3R2-Pozh^R~O!!T}GH1(kY>9gN-%z42Ms$TP=mFaQ{KM-SIp?dvvol81TCXC`
z{U*R6O0W^<5&yM_(asAhq0le93V;d539q4liUps}{QB#j7-CU$Eg^wMzQwg7BQe2U
z-F>e9BGoMyLXq-<NJl>xmXSv+Tv<hZh;*TC7>@w%=Zaf>b}S<G;`%B0<)@DJonc9K
zgMwFV^oPU3sX4F}rAc8Y9y-8OTg$invbOZ_N<OY~h3X+V$#A=yUknZnKvxu*P4-n}
z`7+@?jCVpMZYmJS^_@tiQsiT>6!LXqzMp%c-#%vydm_W$-gh-&BkJ3D)f*#&J1*H3
zBgDuAQ=SK+Q)~N<-<wH%=yNjCX#Oa|Pgt1^MsXNePN~)e@rrFnvzAGUO=6?^sLCl<
z{PG==qUU0?%DX7L6DRDUai<6@w+9aRkQ{8FX(+eUlU$T8ds?lc7Mj7``;SU#*+wx0
z&Boa2$Y9a4h@0-$5t4CrwBHZ0<2a0RFTSE8yf#CwCF4!!O~?{NCh&k_1<k8lJ>JjT
z9xf9S6Tw}tL=c&I5*cNAU=HMA;Jub5vd>8^r0QL0&jI%wK=NlPBTiH2CG_FBkcM~p
z#yrv*xA~qa-=0}fn3B-Y+&<>m)e%?ez>W_6kF2tJM0Kt=8s+8Bin6t=FwP=4zV`Zu
zW6G*a2T_ffp7>a}@QsZt1gb`{{43KKN@J#F`(Y82=lj9Rub@P}Px&yv$636krj@IP
z1j;Th?yc0OV>{rLOzzj`^%a0kT*dZtS;d!sft!DL8YU$KO~c5<8hKJCku#YPjB723
z8u>?@S5wp>t>IDB66F3DV!36%FsV68tBO@vMRrONAvDT}%;CeuGoc&bmp{_nGA&P=
z9!z#-YXSH9>%ID3%tP{xLTJq1l>b7$qa3#>>Q1+d69GIwVJY>}IZ(JO`CCPhZzZp#
zUDsa5C#fX0a#7=3k*At>Z^t*uPVfmiqZxzJUvtzAkpe!p@X5O#KvLWj<$R6-YZYQZ
zs~{8pu7swSPwB{Q{wnpW+eTRQD38Zh>)n!Mi2+xC#g$2pN$;Ou3w;@6AAP=tn5QP_
zO%zk+PwXzWD!$D_d<VZsLw{wJYLZEoyHON~RInuuJi}3bFT^%y15>0dPz3-E&cghT
zx?IM^P~p!BJ7qc2W#~W7yCFRUe-2-WqAIc>8f@@$W;ZvS#U+DFz;j)~3QuU+<Z~zF
zbeTF$AK2D{2Do0`Q5QH1+0I7(goF%_2fbw<MQVq4Qtf@)>D0xJ;3-1q-G288uy2^f
z;-Pn9622$!zRnq(T>lMHpgFH}K~?hj%Q5uu<MTOnM-bF*nU0gumNuv9v3%YQ?DSGt
z99=LZWaDtf){XU2!xE^H+&(lK{|z^p^byQq(&>2mkj1Bc`zkBoxnNxLm5v+pD%zJV
z+TgAiA=+Fg6zEg&c=F52{9>$w*0eDB|CP@F9er*kx<X3wEk|19rCB^q2LiF)$`MOn
zI4Fc6et3N&#|~?fY(-V)q7r-$mk?uuPTz~s*lQ6NV43oc!*qezwSV?eLe|Mj=7&CB
z;{Z1BL5pXZ5%Xir7F!7|QsYkgqHn>-l15XrBE${ZH*nAdJ;LHC&U@^2#f7r|*mpE1
zYGs_=-}u*$lq)k~m<XKXO^opHD->MDpd<bw$Jl)o#ZFJaeHdCzhabA8^pi6{TeLNP
z(>irb&(p~ufQm)GI%wok-rlBYh?bUwPDxtvm*`uc10B{mh!~O0z7;bi_9Jm2#%(~z
z#9@2FgJFbZ?#boiW1Ne+fq4&-*{m!v???=IUF`0oQYhDrdIej8d~!>I;wI**r1DIU
zh}Dg(xWv7NELma|6X!_6isJxxjtoLFHVtyRacPN-d7xN?q`)va-tYY9fK<0epUuNB
znm>*QP?-9zPI8(n!lwh%;HO)5!<9xxWG<73u~W1^_BrlJh^3U#rE-u-c+V16?{N(Q
za-elgB+*4CKKd4>uRN5!d_NmZ^x?^Tt^2?fw@-`b@TJXqwlQr)+6$#xZ^>(70VOzR
z{FQ9i2oi3Xp4o3vT|RRlY0{U!v4SB37GH~f1THz1cjZ*I@9945+tXtYw3~GVmRcOe
z=2I%PwbpN))_I0U`ql4Y(6w~gjezN2QRg3yMTr+q>yz3oyi~yh3lLR_K{!CEGg9HD
z+Jz*@VMtui_X2PB5ot%MCa?4gB8>x~d$J^mU8K2my)HToU(rL@hL?Ef*~_Luvr;``
zS8tW_Tr3}&(i1d?+_vFEewFI4F6l#RX{F4o{4N(2hv;inwfWW;RxDoyieaE5ga}g2
zsv9gCGZe`XnPi%t(iWZeGsTuG{kSTZb)JG(WIpnWE=-=F)$4#J_DTE1LZfPp;rQ<o
z(d1@&wz5mt8|53ISJQ2eQ%&=%76o48FX}Mh=CBkai*(f~h20_A8HRN}L*`tcSQWcf
z1Mky>@<?${DPyorGkeB$T0Z5oelnP26ut*wJ-mgPR3b6*Nmi#0C<D?N3ee-T%Pc{Z
z8EiCMK1*fZ#L8z78B)JwGYaiH*ui;C6I#AyltndZOi4G7MZKShs=sI7%{Zg=Q0?Z8
zvfSnwGL|p<+Z5e2(jWDbO-O*>wd9^y$nJ53)#>M<>sFe{D#C!dGN0dtG=6XQg&vk1
zhCVx%Z4v8AP5v+Ol+6Y?saRLxZo@ay_N})ce~d|b-B7o?(X$cZqLB&MNq=1*U6O5M
zV*8z@`KJ2F;$57!{r_j^Kfu)obt$aSU2NSkLo@wuCH`*-3yJXiq``;~-o*&HZUEeO
zC^wxvUCAs72w9bB|NFNuY;6rbrh`$Y!7=#yw#>O^FIekCjwb#&hS`V_7XMr6JuPI1
zl}WByO4}*7A~U_Eyui1LITN3`Fla8Vh)1{aqS@I7F9&aAp47t_CTcsq4wU;(xR>V0
z<UyAO?uG#&zqEWGrwXlk31oJZyYOXVL#o3mAK+Y0Xs619rCYc}wfD@{oT(jmOLoaF
zvw??dD*~VVd6mRdJ9btqPy0novb=tG?hSm*a_$|6Xy_{S%W;R5P^Lta0+x1EIvoZs
zC?BWD!C&)})EQv}HliFhk&!KyX#-As!xrvC>|VZ}g(OSExJ}#G4MG+JJCPOk9waz2
zNB(x9_u#`6lEDG|QI}^uA*)_w6XT>7V`cTX32sh89Fin{U8W{7)bA#D)9&&nzu>OW
zOK3NS1i#`8Y`^ju)Qz<ogLYvwVFP8F{#?mJM1Q&op)7yf{TPls79o81#}m^cW{i30
zRw(<@k>ak&Y)xp*mhy4uN)!^L-;Ec?yU>4IiC>1-y2Q_(!la2MZ0cZ==T0A&(_iQ{
z9r<(V{9-IyckaVDsV5fikmVzSKePYdZb7+gmL%D=IxJ~9<L_jl01wCMScvxA-CH{F
zP-t-L;N{daXEmTx`G<?_^i(}_Y0d8%`DN0p_K8cAHR*49KBTK$(m&)6on8KFLZ3M<
z@Dowwh72nYVz$6kPgVyiEfY&ETJF1c)A9vLfnVn)%y?DeD9a7o8925woMNqW$^s^#
z$yrkXFf2b%BQgM&;(%TnQuL1Zb@XL9GRcri<TIz9aBQ5AaGUZ6pXe8>52RkH1<XQO
zCvf6TJ+*JzK_f0zgwhDVA!R>%?d!?je3H6}`$qf%W1C##faE7+opbTN933d)5+x%v
zGyny-H7lp&%eEbC^0e&RCk4HePl@v62-B>!nFdkxi*06E9y?E;g@GsUT5utOY};Nc
z931#t8Mk64v?IUMPccZr`Sjflskcek14?@sVi1rGK|LCHFT~lUA+sqPq^gT0-_J-S
zXW|kKI&^-_LL{l3&^Q=C)|$Bn9F{syT$<r}a@^@BGVM|+S4lM;vVl-W7BP~#>C(yi
zI?3vXWj5}0BkZoLAtF`<=G9SMwv5$QcuYX$-6V{Dom~YmDVNw7{MaW>N<hi2i`f`o
zob&P3<<<QHIRj*%bXk&BK4J5e;vyygKECu#^u2e{=GrYRLB_zk!lVruqSvgSbX0zV
z@hVrwfb_9cG%y!HtLCmGj47i-c8w{rYJJR4qp%22>~R144M*Vxl!H{wds6hEg3rU0
zo{=M~j}MG?jpFMAC^joEOLq58L{@ztu1W2QU*g5z7y_3D?oI$;unyL^VfbJa?RQ>N
z@|~B?Aq$c1x^Uw?uCxzs*{y^;%ZFbB;~uQ>31B3k3pHRWfpdR40}drKv3ZscNstj^
z;();_na3<kJbDmIEq1P)1MBAsCCx#?i3qA?li3QlHSg5#5$;Z>gYwj70$_zN9I)W^
zNOWp9m3bDbGp%QlVAnj9j(!A>@T`>Uir-1C*VuDwrGz{72$sfSf83LDq>}1Zp2E*F
z*g%|V@f_)|?GBwPf7$#yFAZv;sm`oTPdQvaXU0Ag6BYWiaZ@%7Xx!s?65~I@)bLdU
z2tyN6YU%v`D7CD++wfkMO9<uzo9MNhy9cHcQ!EWAb6ZM|Y?F)x7)XSRYAq#3grCCY
z+{nJ)m;Q7jdGODRMVoXtEL7wW1{Cl#$6iGHzaNAbspaL+5H3$Q3|-p(4xL=SivzW`
zb=0-x;Q9z@>9xAZwOupw#?>G+3Fx6TjT0?S$A>+bjv0%L#hAva0;_miX$F(4BQDc-
zJHT$EL<X`Jo3|CUJDt91yreqSVeb|jAl2{KzFphS5AHA#lXfd|=v@DKBZw@myhtuO
zWJDIfyUFy*iP%~pybg#y`=0r)cdAK+qHq<%;dizb3cG&pL576o`kEya75>(xLwT5_
zoi{lVTAr)$am^3@<oI_Z_yZg~oT`m67UNghZizE^1fTNppsj^kQE{x~NYT_6iJAqL
zD1}jk9&Va$bYknTjA0yZzVHmx(#@9aA@uzYTx`d5=HjZ-b*I;nSbeqb9B7r+T1LEA
zv>VFK4Jba<*M|2;ccZrV$BS(QPqn+JT<N`nXGBJlXfXiT``5TT0T(F1y7v}?q6_C+
z(e5SUDR9Z*8zn{JN@obUB7wY%lW-n<(oY?JQ>0hr`8`En$F_2LhjbH?lMcMH>WF-2
zsli<RBT^w=m&hCDIBTul-g}&pdbrYVVf^As5*~1lIe;p<Jv}C*rDwHox*tAoUcDN<
zr!c`aIyHMuN}h`NWjBt{Bx+OB4EsUoL=#gPm{{L!M`l8!^)5JB{ejIilMO-o8nLCe
zYQJH9pf#&<&XJrfZ{{=}K=Ud6En(0WvG-tqnkmnu+O=Vi*6ixNxbWxxb>jQ8)mcVz
zq2Eor{yd_)5mHSUleEXi(U!zC?>0>)HAv~BUXOn}sE@<eo|mVF%)@E#p1^e~4Vrk?
zqF17A{&L&9!!<dxTAkM&nqJCo)`$Li;U#vgQ^TQr$|J9>0{Gd}xBG<`u8odljFUiZ
z4bSETdD>q8&i?ArU7uFz>i_A@R<GTs!z7-e*tRd_gnCw|_R_wj;mVF4)bDN_Fl*hW
zRGM3-_aC)q7A9N;eywCtjtQ;+-<!a<WW@D`!TJ(-1JqbyGa9OdQ(u3izqrxLlkgNH
zkssWeL`LcCXPbqHt$k#1*SVW8!L|8-@yO{LIa%847|yMwlOWz(PYNoTC>8)4Nmm0j
z1-+^bwCiCw>#xXy5xYb=a|mIMziq`%g9Vcp)MbiBK-bxO#wtuU>hC4S1~vwS!}4Hn
zknV!u_y2$yUL4Kr_*J6`<IDyWeHbQ@2xTovot;(}<so|W1`G#-r0w9NfF{0kQs?%4
zy<@SDa{Xh#-vd3QUOVDV><LVB9F9-H)l46zn+dn~gRKEZo3rph&RHEMXL8oaagNv+
zj)$_zoJ%p2ag4{jm~FKj;_0CGXAUG=6QO5?h?z;=yoM0tulTj0BoUJ!q^4A($&fQt
zp4~qZu!9;l%<$!R-E8th$#M#1JA3^mG(O2*s2A@4^kE@vm6Pn`FAbA+)&6+x*B10=
zpC#5VwwBXqDX7$rO)srXwy-<f{Gxqa3w-X4Pl%2crr&Yr_pslZXt9#n)daoS-(4c}
z+Wel{z{t{~lB~ribZNY-5ZVZj2GlZ7mYI~z-A-A+48p=~Ijn#2MEMH+ceYSqgj4@k
zIPwpf`R(y817;1-60GF^>6$Hua09^eg9VH?0&cUF8%Vs&PyBLo#L9+0|NAzDbVZQm
zv0hh}Z6tQCTd)Un`8EO>e791cDMHXJwG5reGd+W1QB_LB>hNl0^V4owEaicFm&_-T
ztd~=u7yEqM5+Kedd$VS59t(If&g{3y3he3|5DVZ#hMx%VF^AbDdmJsMJ_4^oQ0oIz
z@Uf=wf3n!pSQ7egqdD$8Sg*r-oSnTxeO;IQQVt`9oqptmrJ~*LAiU@LeJh&-<Lo@n
zg-d+%oHp^a_0&zwG2;Vf)@U&MrM2^ktFjQRa1q^|fBnd-{|bD^hkJ-jy^$ocAo47l
z>@#w<dF5<luvef;z?^$&{3_5~=qLl0h~`Pd?|u0oCMm-7!{qvnRxBqAyM#dlsr^u}
zgc7v^WbFG9Ld@56gDD)HB}GL-S<3ufzM_gWOUi454Jm4H?#EeHG=yZF;6QzkQQzb(
z5z|=CuV`fl6-s*`KjEAdRp>9wU$)!a(7^58gLd|kUrR3DRv(jU_Ajg*L`i{i*AKt0
zJXX=A?=^SX_7j}4*Sbe9bdP*1@e4++MXGrp1-kiUV3wQ55R2W$7o`GNKazIHfy1sB
z)9-Sh03UT^mG2sGf94L0FT?qF=YmZTMPW9EL%F@C#b=t;r_ARv-E(&8$mS1c{S@$i
zCKLvI8j@LB(m0S$;|y~5p#LkoxC<lnOVQwl+!SgfqXM)DrsA>RFZP>^X-UE<A|}y!
zPexxd3xAlWSDwu8l48fXw956|nl?`>YQKUSc;8XzfrV=5jC43=S_ljV7Daa>86+Cy
z;G2GMv!knYh}?T^rN<aq^0W%y!<v;VAqu852;;gBoLck<gZ}KNsCqj7l_MZkw~u)v
zKCh*=6NI-NWpy^DDG2r|m<w?m3c%ztjGX~+=Nz)UVQ0#P1&oA?=x4CTpH&L9v}=ss
zcYryTI2T~-=Nw72!368R^vO4GO~eIa^2_5~VHa^O52j}<lD4@!;yHI@=rR5M?UQLt
zn}_aY{i-mo-|Sb5=mb$HUr(K1Cl(6L&|D~}?#6LExhob}R7`RbqR*P579IsL@jOnm
z1cN=|9R@zXbWTNcW~Ajl!F6DVZuPfcl=Tkg-;JLv&x_n1uxM5G)RNb@>?gX7>wFRA
zW1GFrylK654K-;hay)X5DJ;5w0Al&1`P+lE<0Nu>+Fz?Y$v}dYE0Tz}>G4(xq{b>w
z_`^+&He#L6Z(F=(8^yAoj_a$TUk@E)%doJ07wYgti>OsL#*U2IH^H0fh();i{|fD0
zXk={MyhAU&Qhm#{I{<KM&c57_Y`m*8JC)k^sn`~p>HAV;{waM40{LU8>)U^IM8wy_
zQOc-7#KWQnm>m2$7y|cDpDM!2YQC5B_hBC9yO{b6I!0qWo022?;N}es{BFEuar)_x
zLF(6P-!EgGKaOIVSSQzPbFhH1oj8VNJ5`+_;9y3EV3n+B%(wNa1Civg8&-~|Z{-EM
zy;A>@alDDm+8%m!%zGM4R~uRDv}o1KA`)CY4faf6tb@1wowc9mW3CWm!e;ZHOQ9E;
zxvoH@Z#HavOJ1!ZLGkM!<*GiB9%bJCS0slTRlk<cvLXv@I9e0@ZayHkNB6xJr`wz%
zETYQ5=*h6e!0kX$W+S>;v8#~V)#>Hfaa0hzHAPJSxR$k*_8P1ykU(U@bMrk-vP0TQ
zw*Ic>Shb+rwO%h4{Hym|@jKbPoR>}PnhBu8g4mjDDug*f{=IHmc#NnmZwQ#jTF4&7
z(%MXEfl+lbD#CHfqzZvHCIRrn@43~z5p8ic_RVXMZv;O4c(6-R`P#cI#&;>4G3TGw
zcd4#6w-m1r<dT#Hz?jayTkN_e?NxY*07lI?urEmw+U{A)Vbh{V!AOs@L7OBQ90oe~
z@#$57CZ}t-Pcm<89Uu<u<y7KO-uG{j6&pN?LSGEWOSeiLO8c}P(PZ(A_FU|X#B(UF
zxiP1AzjXbuQwY6&@v#?2DK-9=X6K(ymLo!4&CzdF?^+B61wf=k9zMNGry{k#gd<s$
zOv#oFoqp1)t283++)(HCYNj4%(g;~;VpGsTYYw{OMAc||52bA#p!(;13EUj{KAQ<V
z<r{XhE+=i?n}Xa~*pcuAsh?dE)9CL%lNP)L$hbZY>x16kvfjm`N+c6)r%wmFDkv}g
z8h2cxelTjiFBRrKRa5fhb9o$bK2d)o9AcRowgB3i!q772^hNuk=jhn>X9{&dgnQK7
zEXo%@+><!mq3XLb(2$bVDy2CK_m$3X^;W%%pa@sTjMh(Qy}l6!=MBEZH=O<4x1YWn
zyd6?ekiw{c>o!3fb+^)=<&$7O7Rg5?ldh_371S~JQ;daj(_$v5niLq$@FlVI7xPIe
z>LkYg98q9VP{Xj}Kk6DDl!D3rNu!tSL`q5RlRPs+&IoqCJTcHvexeovPao@byx(}$
zkUJe9c!t;65Gpn<uT9Gdq^&-2CV}0oI`i~yW-beT8KRUPItUXM*&?aB&R~*Hik^4V
zZgan_bDQQ)%@BeIhw=0(n)XikzcDd#F?5-(=@+*`QsF;;-vQ{T?)N$zAnf})jB6gD
zFdYz?!8QSE&j-pPE2~#etV}SWPY+Qj0X_@gfVW}FT&sDX|0X71UOfhaCfrbBs5O8t
zQ1Wa*iej$03o9LwE;hdqy$=glaiJ`_WW!%pjcxKXj+1a|EYy_!kItHBlzSGW$ScEj
zKhwE?2tpXTYSLE9<SM6bFM-8qr8+{en2;wFhPOX}QkhQKW0dt_$e%~P+Q8EVI;`1r
zJrs{DZbWFr{?r?*NhCPgp%qQ^W^1%KVT@<$JHBkvZ7GVkcy0&yL*NIvc!Nn>RPaQz
z&KP--=Hg)?K{91YLxPj;yr702`K-DdexHC5=bYr4Hc_F)tGBMfe)HcJbm1Hc!Ae9W
zQ?H12ZF=sgS@q(3N@k{j9w@ISC51kfb_Sa$WLlCfT3ja9i|oU;@h{AAswaP_r80EX
z@+(c5;qlB^e#>HfkNE!1d-iP@)nSmyO={dksr}QymjMRpiP?fwKP}FdSX9K<Tl5!I
z3Y;tx9DXe_ICjTK=Py@uXV1@aGq%a=xoj`DU>1NC0MXE&X6aI-6h_3~6DkNjz0%^_
zqIM<^=&n-cV#tnvlI=J=6u==p*Y0^pIxg#fk4q(N)<!I`)47qmU~s+c=mT;Kjw(MU
z^~K^%*lv*$LWcw<IE8&rw=1~*KEH5HRdTDNm}%KcUN-x0iixcrg=vY%%niD-_O$5r
z!WLMMI&)^WYu`LjbP+W?Z3Fy%oP}0?{Wp^QM|smu=KmYXnn-JYD)o`CX1oAv^BnJ)
z$u!2|y%UyH<W{~Geh}sJ%l;-q1J|=#XsdjR%-P2!iFcflELzhIT6mMpau`pw_k$Fg
z26z?M@nk2Za*}D78*vsXANQG}CpG57_A*K>Hb4K+eFo=xQRg4y8{)YT@=6wfd-*Z?
zt>Hil6iFO*Sr-y3@p6;Pz=#gdfgN3Qwv+o$JeEU_E&>R}K@!)owQ~ti>Y<4QuiQ`C
zuYNc-&;pNN2h)!QrP$TSB~^l}K8t;^9+WkHq>$e0XLk$`UjT5$#O?1)mmGz@OAoW1
zoYGLBvHZ=7SfKp+oNcV!b22e-qR1YY-W;y<r`QzMQp$(Nd`Sya4(0S@z$jK1u_ajR
zq8Cloi}r|-HL$9>3-R~o=-rZJJEAOn>?M%hD6Y5(n_r74l?VAr2!Gyj=V<>g6ktah
z5OeWs*WV)|ZVLNXk~9ap5-M#1a6(Bl*Zj02gK3+a{H}qex;<ZM4RqUn0-_|#G=T|R
z6EN83Icizm{}&8o-Hcc;SgJ!sd4&vznf4w#rXC|pGWSg4m={L6Yu~Un!YA7J@bBJr
z31)zlM<JSl-v!D4;p&|q<6NL_;TVl=+cqXfW3)kIr?G7{W@9^TY};02+t!44&ilRh
zem&<8nE7FzHP7B_@3q$MdXRbfJ=MV(LRUt`L=ClZdvtpa>(IB5?Q`Fif~~eezwq}5
zu>~MrPqG`L8Gl&`(IX-?p!!zigpeo2?8y0H$2{@PDsZUQ!iA!LQ6l2R(iW6I(B;v?
z4t4+|CG#H%{OLn)YiVLQeiG&I%7F%uuUj&Gj0tJ{vPcn2J&cHO_GJS#y4>(B7VvT-
zzjpRMKUi_zK;#u1eS-=lr3|llP%@Bs4JP&ShLMNt(vxuMjA|OvO>VpaZ58riE{s!e
zP6m2tUuT<MBliqwcJh2yw#g7p=;Y>U3z-GXE!$o~)!rhIJR3gFs1HVBa0R6J!e*<n
zL#Ln-HPxcLSIle+spCb>BoM)lk`zYG5-DF1Rh~^)UUquSGS!`Du=eLYcNg=XOzLz-
z>n=C2n$F$)dZO)*peOM0VCgKC*n)|k6Qk5$FKxcZ*vNw9Xw)uG91b>x@(s}bA|}e=
zAWg$`Qm4u#k)dAKMKE&+O2J2WtNXw>gyy~x{C1}R@R@3dUWCFrN>)|QQ-_C^2E9@T
z!$X#j-q3sP`61bpKWvv@!Z@46e}DUP71~>&&RtADzfaUAJkX@I&9+TAQ^D_X@wCw}
zJ|5*wPjD^Ws}^}E6Hoi1rA6ow$Fe{{h$D4g4B`K3aQ~_2SWwvO(d^&c&TTgDPh;z2
z0Us;nd!-94-Dpp8Yt;iK1m~3*rr7Sq^#2k)6eECR2RuOwBS?XqFz27|8Q2RPhGV<V
z9(gTEKY!N6?DPbeXCz!26S>1h+5ffhb<>@%0%aT%Cj7fK=Y$y6p{p_So96`qcz;f{
zEkU7IMN*g>>XWRtJ}87WJ({hBN~*rmc<TLOuT>+FtKrRf%C<L=D=OehJTIzax<{1r
zDx}NP{L52}vTRlmtaqK*q%`@c_~gsGtiDONEGP~GRROr@{v%pNhH`d@>+ZEjanKwO
zAus7C^}yktm2FEtlYp0HNzNS@bGz#xD(^uj3b7BI5;V^c1y07RfyalK=Cht#6BAHW
z`e}xREthB|4pBOZqrq>4A|vrRp*sNkh-Qjlhi9oM>7G@YqDb=nTuKUt*hzwHusBwe
zor1ks&+4g|R<e|5Ctkv*VarlpdpyVrjnHXwgJf46_#FW)U>x_RfY6W`{ROScJlP7Y
zqr{1lW46mrS4ic+z0T+I`vz@LGVdo7u=QJ!;_C;G<wTL9Ndc~BPWTK*l0#ORyD2#L
zJuZnw&xfbDN!sVsXN$1!k!zDZYR(tUz|Tr%TTj!W6@%)uhCQC2aoD&O)15MN@VfV8
z<nD<3HhM&iA3%pJF13jM1_CWqF;c)g(WsH;QgW8ps@rRd^a_psQaf&M1+F<<Ko_ug
z@By;xUUM~CBk;?A2g!;62xi=Y$EPQ9K!gmvd$)imG7#EtLFCTVZ$U!SHUTUWLgnj@
z;IWD(9!^CAb@-Cog5K9d4Aa?oYvuIbsA<LX0b^KGTjI878Gy@Cys<|z>7psV?w!l$
zMPb@vF4&4#Hov{&Xhi#8ARo+C1RYDdfnjP}$kS15Eyos#7euWAf&PiT@GOnpngthA
zVx`Z1fR9)T_a0!-WKI@E7Aid4kRV=PZ3_}Gt@LtYnEQgV8bREs1hh(llkD%kzoPL$
zDBk-F2m3nnTy7HQC{JxIU%AOWkic}ZO1Jn~|7#s#id?>NZl+B_d5yKqSSO`y+ExM2
zBJOOM^(g!oToNjoshZ+ioL<D)(AM>yp}glkmFb5txFafjAQ?xEIpOC+mBDKiPs5hg
z^sSP&6C5yzcs!Rh;JDY75sJIHKaWE#;jfQ0rnc44tQYL=6>KHArmFU0CV-z-b||vW
z@sCVeSVG9lY!o9BZ%YH><LNufdk>en#PY4!#jLwkyZSGT&a%>-Fd*9amV1ijekSBP
z>{i*@nB_IO{K*L$8O82L38TUu<=4p!GYO?TGw}F=@_5#*0vlqXA<9Ts%s>B?C)h$^
zPeik4I3$j{?NFzI$arltTGderb^wa^_5MT0$9+xXZ`<Uw6~e{dzc&h+v~bm(c`R2m
zb=A)Hul1-m`P~y@!_A#XY(HcV?RDfowW;uvM9A0T$vgidWCa5ax;qQA6#6n@nbyZt
z(5Z+Z5JjTF39EBhMJeXp9C{tiL!NRcAeQZS<+1yQHE2J9VH_19uUjInq~kTKgeoPm
zz3Tu=(d7JB9eNVQ2A|?*JmkfU;JJ2rK&##Py-fBpmbSbc&gv{3bPE6=wV2^jAO2?g
z+%hm|Hw661QSj+{vM3AmLz53EX;pDelQ4RIkU_oo1Er;tMs8!}Zqum|S`Gb2wILUK
zxX+xl!I7MSdozA8o;V}-bN=s3CGsoGyg@_)RPYn6gB+&QbB*a!b(!JgnphfeHCz0?
z6|g_m0E<6ix(y`Ksz!QUaX!6LE-Aog#IJ{ESRBFnCQkTj0u7p^5Wv9C;mu&RPGlp-
z%Mh{$MN@R(NF+a|vJZco{UTz3kh>24>9yf~TiC4l4$>pY&)ci~7{l_&9pc-F&P?*v
zyR=pywjd$VwKE<+4a3)Jez3sLQy%iX7#TkWZIyJ*JMeffx-qJ9mikK=p;^PVCPPH8
zByKTw&A`(8SkRPFVJ@=HxCS}BYX75&mM4LT+>uJFXnO!sr`@|Gj^Ekz9Ba?OcGZ{p
zkk}?Kt5q4MSN1P_{}%!b;1Ho*yu+th9<LRAy{BSjRB$7Dg{1mYL(oT%^<T`-<(`8A
z^fY&B>u8IJxp%%o2|)QR^U1AdRQW+dzr~{?J5yu`+Gired}sy6P3#qD7&6>R4)X5i
zmNW5U$H>lT?+?{}C9y8@0C{2>+*d|1oBr`=9O_BNGhXB14*E62Q-OhzdKiaciiOcz
zk4oN+lbI$C>5)vAN_7ex3`SR3?=&CA!W$rb!%vi2pC)-0MWKk?u|Rm}VFNVU5j5%e
z8n#^{;mEuv5gieeROshLy>h%>&^}}qL^S)OEJ&s4blSwD=GcM-#RM%%qRpZE2^3aT
zD*F7of$ezxF&K0%lWP@sqVsIQx18JQTz+^roVvhLTyGiFi5m+%;qYbevz~o<4<~I1
zok!{DJS?=$v>dG3&R5|a9<4He$nhOXlxP2~&k9Go7*7~eXPMJ4c#CVaG2SB(@&0xS
zlJLPF);v5`behIkWDa>GJnET~n%uUl^}65WX!{%p-)>GB&z*(8(}{f$5837L9^O97
zx$42nx4p!i9`^Hh%!Mu5S-l{}vmEGr-&tW96)nh|p7T#!wh2e`@~)ra%+{6pzW;Zh
zFE0Rr@Gh0M-n_l_a{W0&X;FcFt`n!k%%@%JK>zif=K9m)v-H6{WdpYA-vcHwaZ&`y
zR|fMaEr}gzE>tAmAtlu1GR;_)HyY5I#Bc?g7T+O}9{EJp=v)L@M*^<Kbx|S3P)46~
z0s36v=Q52FdFIs|o)06|2y!RYKwwC8btoIYmjd%s;zjZOoUqC5SNxNXMq?$A32;%>
z`s7*Kg97!Ik+jCuFvK+9Eg;@lq1`n=c3b_2Z!V%%LizbHcBcEsG|63{QeSO?XdIZ{
zW0fToHow!or9tLg-^nbp@ZHbaOC1+1vyy^|9_!$cw^$!XCI0-$3{60d$T(tWm0vju
z@uJfH((MVQ0R2`t!As6f{gBk^wx){xnvWr>E2ErL{t(*XLW-Az8gysj1#W54>l}~o
zS+KF+G|279w7}1HaoWT~GAA`@kp?KXLKJf1&>J6ofc^4PxhqJ~9Zah6md<K2$9d?m
zC9cCq=~Ky=!Y=zl(Ei=bJ33dQp@72v@&pU@iqCq4*V367@9~<>6mNE!Jf}po>kw^n
zjzH+4npaeguQQ#F3hT=eRdlW0;O`W(BB6T2Q?Rep%EySDKp(2G&pbn-IavP4O_Tkk
zUcDG4K@R+xsxyE8gK42ay*<LS6yAo#m2uaL`^ynW6{Bk=!#ke%))b&4?`=E>z-Ros
za{up%k}@3Vpy8FIupPZJ8tu^azObeEc~I3K2lcbE0`dfte2BQ`U+K!pjk-K4X!NHv
zv$3t83Z2!06q^3<=rIdr*jt-ukwny!0Sfa&Dw0yZjQVf66PePsLRO{Rhyx-`dh<_9
z2yM`i*qjSHHGEL3^wT#=Mg<#_t8^i(VK`869i=zox`s7|r66e0p9f>_me}?Xp9_b4
z<B^dk!>%tMC{v!K8Mo_(*t4C=fx`fz5RBtOL5nogN}%&PEodd$yNYI%=Ql#<jcrH7
z>NRLhkW`aZc=##Usag5my4#w;s-4orBCD}}ifpViOv<~oD%)8mH2b=s@i-}-VN}Z6
zw@7sfce&{6vwb^bePTqY-t{zb_=rf(wpw{?84qrqH<YZ;a&F*Vw04ZF8Nx4@ITTNp
zVRjcCFgRA4?_pyKH&O&`V?(ybw|%E}YaLxaXZyMc26>S*!2-4|jZA=#OkQXVue-oZ
zSUnETYakWO!Dk$ly?w4B<C((VzVIXQ$m0DzV)w~gZPGde2gS~V87INcgJ8FjwTP|)
z@8ys~H79EP9NIpXS0E<y_ONhIpK^571i@`0@!9f8^Ai;5OJAZHLW;J{-^-r4*zUki
zm4lLxvk})1bG^)W#`#Yr=m8&>@rNr@ANM#Vjy4UYe^bIw0YE7*EEG)ML1?4spkgC5
ziO^o}1)0GCYMd-};yB_3&RLCkIs}t~eKE=tCUG{y`{<N#nA3=nf%B-?5O(|@MxMCJ
z(yfp(1<6#m)kt@HhN)Ix*9;>&&q%zZWX;AvH^lDJqRr&D<CT)OJv#uI=FA)_RI#}5
z7^3v7(G}oH&KvS%d^9uc^YT906ApK^Nb!0GMYk5~EC-E%Guc6D{j@G+hs@0GPT@~*
zx5U5iX&s66pk1es+_9D{Euv7smmDkk7OLPRIJhxqfjB$9vj}b^R1KpWG}IR!*_tDc
zeJh35kTJam@EGEbM?e~WWRJ>W=_6BL0i>@#53Vl5Q+^lpI$9%=HbFU&1J2m4_=j=+
zz&NJX_69KGe)i*3;G698{Z&L~h=CEQt#AA*D8)<_XYV@#I5<@>taoUzlu0g|gOC??
zB+~ZC=?RH7O8KfH#h)0ofFik+dasRkh*>BN_-eB$XxLZ(s?34*LkOn?#TMVEnFbN$
zGqOW+pj%iad=$E1$bh|AOw~KZ8z{7n)P;J*_qAkBT0i0zjF-;__lS-iiWv}&9byKN
zzN=@_oABm9KpXP!#|RC2@*OG%IPV9M=S>TOO<UmQU<d&PWLLLKLziVf-kd)oH2!+o
zi;&rq!<E8GNMc7E;1B0>Et{E<g(onkT4gsR=utQXEK6d3j<Gd}%j!Y92jz6O>;*oA
zA|%UV5z_y@FzU*!7J+PyMB)t<TE*R?iJFyZMKt%=^~Y<pL?diFQ@$O+eeslb5PH6I
z2JNw(raB+e?tLpT7Zt`iP_)jj1<5$<`xv~?hldmScQSwkXLOS2m&vk@*+uLREMH@X
z_+<4DW~5cE9aOtE-Fx~EXw?4%Jp5_WSx(+IOPiZ<{E1g+6zzX|cI-<xj{B$PamnOe
z$(L<@)mEEiBt&saA16S5srZezrWcyE4xS%XU<xq04`eAcH6CUdP~);IuL0-q%*><N
zb|AzIE#0#>SYw>!U#y)!EaZb>SZ=(hk`;pyCt*v@JOJHosh5?WQ^SO*v~bY`gQFEn
z<9QhVw!xa%GJgTCRJ*GH<}l+vtaM~WZ`KC#)<IWK4`B8B{I<h`MwIs;M)M@eCoyC`
zR<ZR&7C%fn9*akDm}Fqw+vM@%?IkR!uS&^Q$<ArB?Gj#A+pJn{?l@mMEE#|!czt7V
zxZ;C?b8g*2T^!#~65<cjC-vJrb|YR|R%8E=N_g9Di2U152T}{BqG`tQjJD%cRRCK&
z(T9vSG_YRaz=iRSDU@Dj4k;Xw{>>Uc1tf}hV-=`msr*D)zWX8lDH>rrH=s~VL|w6V
z*cml;yGfR?F9`ugJe~?+)7eNXX6>Q{=r};mAHi~yEq270<V&B6^9?)|CtgL>FYr%R
zG=c$lAKaWQ6qC(~680Qz@^k&m&O+AtvcjB(y3cEi1_;nBo>e)K+}{0`Q2>?OFzJJ_
zC%oJ4Sker{?dG0OLR?FQcF;hMkiKfunk$a!A~~TIqp7!i?+}|wQ@`f7;LgEoHv+<G
z5`aa?DM)TA^ib81I1H}c{Fs#IZ-D(0hn%MzU%93Blz2#jR-JurZyj|s@z6`}EeS^I
zyo#w7<clu-Zu;%U>|n*{;7Kjs@u^T=`)dxu5dpONb5D-RjM<6Wkdu@@2|QBc$muho
zIYp2w6)Wnw@e*JU6`7buLh^TCJ9%z!R9&z9HAej!E25(yb=RS9VNO*{p0~-HF@amX
zjhDmQZ`bjEgs0G3_gWN$&}=A^(H`MmqkL4ZJGU%DiidR5v8n9b_l0awu1<r#x@vkX
zK}ssa>s;bWNgnQRU)LFj31IHQHAjgL<<UQUNm2Pk<p_l`MkxF?Z^Ha`hlM{L;@{*U
z)c$Q*fI;Q;%B#2@x&QV*R!SUgO$1?L-!S_>l4C1KDa?z9Cy`TDcACD5ZO*`!^I3$+
zo~O0O*R`T^9l@{ngx-@vt3o)9upe^<m+z1^D8gq=+Z~ft7fV4?8%K}z_Ze;t=KAY1
z*<LQ@UY4g9(6K{epW<SL-ts;k!XI8XUw)ns@-XVl<b7fMy9EVh4B&zP`R%N%68}MM
zFDgrAzeubC#s|kw_kKfyU={!m0M5f}8`NmCGZvO6kbhrF^!Ozj`poVRPbdD(XAbM!
zC&#lBr<3l~-8$#Hp$MD(8qi20JP=&^)Ela(Cg}7zBBWM%gllkKf)DA_A~oP)>>6M2
z%fa9nb7A*<{m|7)ku{`mi<u+4-4Zk#;b!?3B_FIAK~w#opB9#^x$Oed@n+EA2%Np`
zXdmMw9;sn3iY2}gtbz4PLaVMi^F_H9Y>H`BIv&s!Imy@?h60n+&r)-(^%B#tWK~Le
z$X4f(#YRU?Oe<Yvaxdv>sm#q^XT+o(ja~S;i06}pA@Y8u%VLVkWn9o1Uv3-MgY90n
zBR#qO76J>VVbG(ZPM`ZS%HG&=0V`iP#U(b}$Y}L}3#+7_wg;{_+o^->)Yc><_0=Yi
zw0k_tamd8Vn6?a=eJOz;HcLHDWlu@yyZy$hmJQ2U2a3{9;6VoS#XLSCQNTZ4hpzu6
zqTAUTLJzVhEz)5jybzj0rKfN5yKwh4-NGUlnYV<*S?FoJ8Ly_)s;uGF=Oe3?{S>|V
z%8s2eT3(FvpwmWlQeB=F1Eb#dBmU8B%>eFzyzPQ5d2Z&-Rcd>7i+tp+GBfmaZRfvx
z4mD8aX;m?sdk{K*@jV627~UU;RCqQ_aWYpooF@j(qc!yBI|<*2G<4@HhmhFE>~F{5
zxdh4H+*d9-UL5AeCm)uPzO5t@G;(jO-Qh*)_e693L5}UVdg=}EdU5MKv^noJML4M!
z@a~rUk8JJ=?nur@G90aQ-W*tfgZf2=k|TortiTs5VQV=slKNMnzt}fU3?T*ODBTn3
zO*}JD<Emp8$v7QMbUgEwGe+k%S)!|q6alS}U-(hoDSizG#>X>EK3z&4_HeDaAVh{K
z#5(9bv6sNKbH5YIOn&5AWsTO@nL=EL(P=QWW>G1B=7KZsCKL*1zI02+^?thP!eHSg
zmYRz7j+`xp@Cx<aSP3dz;+k`4`h~?JY!#5Jl8lbWu;>EHNNC7VJdIY;9OL~e6|E^D
zCNii*S;e=&UeiS<zx+eLpnjymsQi!Cc;I*tj@5^HGG=%H9UYpSNphwlLj9V`*EP-@
zwlsb*jb}aiqkf}(ib=jiqbgQkX2^$ScI-<C_VE&BfGHKBb7(JXZ{UNNylKj0cZtmN
zRkw5WfQjHAY;1_MnqjOUwFGuG+1M7Ij)y1=;^#F;!eVV^cH49hgY}126>2uVP+z)t
zj`ejry&2)B;X?>@oto57SwG;NX9t%!!1-cpFkB+;kEuV*L}J^&O58}gl|Qe;cy>t{
z%a^@uI8(&b%`LPy>pnRX*59!)yO$zbq}42o9?&xEb03B=c_CsHEZG<>=Kb4E*NUpi
zisdCdvIo0)>mbx((n=}}{G;PG0z9`{_qOy}ov^d88wleW7&qU2+0fAS$P@m!nj}jw
z=eDsU@jCePFk{YLrr%|{*j=vj(kr*R68o`1v&!PLy50vkK}h*`&m^`5g=SD=@r0iv
z#kz>|3jFDNB}p)-rSC`;NzX`I7d4^RTbtpWz*$T_ST?1>6}?JO)i&=pWg3DsA<6KV
zITm(DwAozx6Z(DI42SQ}jrv)hY-@@oBByK3_&}~ny0oA6r(*nI;UV&4cT~^|J{&@A
z4tdKmImdmn%16N)K0QS#DifA@>Ge!Y0)y)+-rv#2n@xJB2m?KdCfPD;#gOtSb*>00
zQsH<Fkf4x3S=__-RV}BJHW6BNfPMDxmFRaRBB12=*xRm)<y#ay;En|j>jU-oE{&#O
z8w6+Vb>cHItJ$|Lozq(p^`G7izN64xRXI*70|;B^b#s>U)_u|8Fu8Pb@xr#sxk9yk
zS4+<k;wJ6%^lyD&rlI#L$uLWK=7}{bES{ri1V4gl2gG$DulKTnG>h(TA5lTrJCl4X
zY3=+fB`fU|apBI5pvcA07<jcQxHN;wL{uuDNr7lVPAa~^;-e=G?|i8?Gl{b8Q;>-6
z<flVH6xFTsIZ#yZS<ToPS(Vw5Q`n(zhVD-Z39fw8@(X#qvwj#J23-q-`<s@Kc$Mxm
z;k(*yq`O$NuM!098?hm~^9AZ4RhAT?kI%&qsRy&F-Aj~r_scy!a^&6Xvu#y|e_8sl
zL68CoXEGjrDl0r5{%Cc-x-1U+^{sjJ6`d1ox|_a)3RHgQVM<uMU-Mpuj?VG(zP#7o
z{U=(f>-9_yREd0Qe0G2nRc#kqs8676+q^GuyJYpzZ(>wS)3&1KU-;c}CeQthiXij4
zE9UU<Wb0?~E7ahZ@F(yQ>Fa(T;<#)iidiT8UyZVoPDfU2%$lx>Ub_t`jWqi1(D^Zu
zuf%F75f2r|D+)^lX_4uQkKNBa%N=oO5L$Z<L~z-pCSRHDO_G@Lu!AuVaU6{QZx#Sa
z@l;1`h0R)Povy<SaF6-<p#>>p^<n*`eX<kZTtqEUJZ?IerX--YlpMXUpXI7or!?N$
z1rc3xRC$OoqR@9i$&@KL^o_KqN2ec}{?pc43ZaQJ;oR=9&|yR*;m}pg7Y#4R))?(V
za2Z@`&k6@kzd;vNf>g%{o&{O8)+$IuLx^fh@uHblxPC3nuLw}nWtim~nf(Z(lTl|e
z4Vf&PI%51#COk$3x7O>-Z?f#XnctMz7!(CmD@Ou{d))N`DA7`Hp+4%z_(J9-;37qh
zcxuNiFDS|^^A;9`op`I!Ab;(K)dh$#{5c>BkcXQ~k;OuSy*IE>iXs2<%VWCxO(zW#
z<r$~(%o4QuXsdXx-Szf%TuJMB?-bB5_#+SDges0Kb(mo|B4`ieVU*n>0J;jgNvzk^
z_>{h5MbM0B8E#B*e!;V`HEq82bk)lAdHJeMWkfXnN#N(A(Wbjp|B{R`$2upP*?D&n
zi0rFA5J95<ck0RjfdHDiKJFd_e7tTq%tc(YRh?!NFk5aqF1j>}9{d_yWMF!59mB%m
z_U%OB39(*lZF?I?Bv`rh*$H`|d+oDHUTxd>2zkKb(FK*o>shVPyoJBjUm(7w3bz&M
zwaBBBt_n^!I+aBXef%1u&(4?x6#$*!4gvWCE!rjD6(ZecwlH#PURIkIzrq+q)*F-n
zU1d1>!;S$`-#h&K#3UZc5`+9aRcA7s@v0fc)rSioY7D2-CCub;rvuMI!SgX;Lhln7
zS19y7%icQv@Oab?$9kV9IHjG^U|Yb4ib%hUm<5av)EX*fhP+MR5-NWe?k@3OF@c-@
zWNU3W=eiTuC@zi2wkw6oZ!Q;brr`1*Gq!+nDMk{M61gXy%qN6XMcxVfa#`Q_W6aAF
zlR`xt5t2Nr7tEww55|~k$_mfj_kBdv(C~{nEc&$w5}nlbqUW~0BCLF7)fQc)zqVO4
z7P$HhCfjIHP^~e$WEkU6wx@mw3*4}Ur=c~vz}b6GqWKj^c}~J^s>KTDj}XB3B~w(f
zO$p#<t#~mKV=l(gGrqe$4L~d$DGh&Yl4O5?9&k#B%!%>9%6RZVsgsAnd62y<<-y{{
zz-ueS#-(7~!36m5t;*&w$4zmy#Y$<9CMjG<sd!t=zh;VA_Qz0s71QSm<Q@K|5;1*;
zrK(BWqO2M|Fo$Y8=iM+_xGjTkt{2Pa3j|7!iGxNJ2)Qq;ImJnIPCNGeK<|@k31;{_
z`#BwZCV#APCAc-I*FA1yfUVP^v0n7Q2^%&KgxrC^E0Vj=l+t?-#x2ocJFx2}-_)~p
z_{I0hYQ@fPYIOzBav56IENjM`t6`9}dA*RaLgKr*x|;J*SM~kl?wqFybhMU3!<zcw
z<(ldy_Wfe_A5}R=&4#wg-~C(&D5pk562;rAisg!LhUbmhd)k;qI1vjP<BRgJ#I4_v
zG?xJ@sp+3AP0Amm{;$f}pJxVl3v2X!=JuJ@9neEUld_Ju91NN4SV?6`phX@#ul<W-
zmT-lAdo%A{D4Ef~4!GgVDEGl`x%5}uC2k|5uW@aYMsyzct<YNR@17{yMVb(=<(O^)
z3Fzs5`xeU8&1+<L-#r9z!z2;i3}uMXBBcuuy(MAd;X>qB1_vpQs841W>SRL3byaol
zkGc};VR%z(Gtz)nOAiS#QfXK%i(rlIoYQoW`7-wm=#JT^%^$yL+$tu~0~-^0Lwsf}
z{h1*sMv-lJH6#*0a(nX6U?#nLyVt^eWl9JN?UWCmMr7E1C-Nw`hFx7^FolsRcM25W
zJ-*I8O%G>?-z1&r@(c-oa3uG9jy}KvH*LPeJ0~higS}cB@WuDsV8oEU5S*1sW`})J
zCh_sU@H1@u0A;%IWrHrs7oCG_t8iF5cqGN+=EatjLFPw%@%|PbNN2611p9P-D0@m$
z`Yj3^>?W7FiAA?r%#`LULi)aH-&&nFx}K$`&5M03-oAeLA|k>e-jYKF7DpLoa=r^s
zFlMLG;^gAk(p=;%6+dSMvXuZ|Ou?me$^MoPfrh^jPzU$=2<?VjIlG|&i)L4$0X%v>
zDlbi&ugMQv06+5=oK5?0-yDF@C8SxAef>6|2Hy9itE)ih)mVS^{T|@0_Ki>6Y6`Jp
zv;Fs56^D0_=8m$@zmXJ9u|<IPS|J?!3o~zPn_R_rd&=%Zm$x>mxVXirsK%Y%sBBaM
zi9p#fL~XSOrA(7LY^JL7`sNDoL?y?@ryl*q-`rs^hAiIHZz#6l6Q-VVKZ;feT21v>
ze(0L9q7X1)pwb}cW}x|rOi9~jf`i3_6?$lB!4?xnCo?%CGK$IzA3^EWc*O&%(OCY#
zpN?#FO?gmQW?w-`y{`OL3jcoQ_RD9?M2uNMRbti)SL=Rx>ZYlRxx*F*RT%D#niKkT
zp$?|qJe-&zg1Fnl*icbtnmeP8xW{P1py~+%@eFPyo{C^-(H{;L;Vah^UmS8t48_w;
z`X4ks&LRloj5Z1Dnn(FEZp%cM4smlTNt{?j*mW{)SxOR+P*hL3k0X;%6OjjtXW&;$
zFRjA4pq0>hP2lu=PnJIu9TEh{p!N;VoAlm&#zhZ`cRt6C1FA<U76e$x5<D&`l4y2y
zTnh|+ABySr`K=+gbeP|812uIDaX`L(HV+-4+RwYsJJj5leWzBcgkw>g$+wuP*vH0S
zOY5{q<jW-(s<z3!({^uurAtw@rsH)9Ni!EEw^Kq~d#g>TiJBJvCSKkg`@9k1a8Ut`
zW(BSC@5v!OTIymK!pBaKQiJqDrzibl-Ih&<%>coQo%f<fJpdR`^?fsHkui%y{G-m$
z)-ie8@8uev|M6p5qe&n5;I)#q=(DU7UzYtrrN{R`K%y(~$EtkbKj37BQkNQZ1HP(m
zLR+fU`VaKZZ?)k>t6&)#QbtnH^+UL-+Q80<BP|(dJUDx|U)Lh64jiIX#W$29Y)4>0
zVJ!`Jm*9+TGsqlyK^d;Jw=V}i>!9oxu?W;#;d?cKgN;bd)UT$j^*jvVG}SW+T(^cx
zZY*>(7T7Gc*A?)T3u9vg=bteO#@W0AptK%(l-N$(tN2nz4m;z=dHu#?b@(B$PDRFa
zXx_`DXPl=Trt}A;MA2Ua&r9+<I^L2w><bXZx0PVh*i2F0tHDN*wOD-%k<OK0{b{V_
zsMYzXD^7!^ZGxkw^jFW15GySW3+-%(IcWO7o+g-R#&S6z?JWbOqEN^aMqTD`;0vWu
zPLsjYDssBeYnvDCRxO>%?G(n?r5a;U&VL+a+b%y*r3_-6;`~OwjrP9@>91a-$wuUe
zVelxN_^H&Av^~LLJ?cU!=$`RNY=P3%y#C5hi~&m8Xz4XLp_IqU(Gt9bI5I%ByM6X3
zmGJk~zBdHiPRXz?G=!VH#T;@a#j|w=omaAS#jA462w%OLmCr5e3b`nt;=-DK(kvVD
zNOamMAxOE^Nrx|tX_O&@1b^7Qo^k%qv>7y@Ol=}C6|OUsdqE*_|ITLYrc+Zjs6g5k
zLtx^2+xGGGW%OqWu;X|LYxx!7mSj!eZT08*W7i|YYFpR&h3o21oJl`rbJ{?^+s>&b
zbNyw9(Dt7h|E6=tqW%M9<uIGOhl@b^<lh!azfF4JbY?V3N7&@(EM{@Oa9hWjuu_Vw
z)=zq%7UI0JMLTmow-Hq7vq$p(lhngPt%X|a6V*n*)?FLBH<lo8suHDw{9Ge{Ji>!~
zCTpI7E0_3S`BT2#_xO0=<GDyWrw6~@wsHdD6o}D7iqNu!;ckfeBo{B}V=u#h)~bx}
z53PT7BEtWt+W_$d(l5T01$#;Ls}lYn;Bl*o8i_)U?3xo=+glvC!~Q-_W5zWf2Gg#*
zet(g1U&FxjJW3?-NHDcT%Z0;SM6)nTd4b1P&Ow<(9-42hxUa#~4DW}JywGc2rzI`}
z_#H#2J7K)tC(~sy7{?oN_(9a?dP|JyTJT5QZ=uwIu;8omkA{h!hBPjv>``hl5~rci
zLt(~HGv|rwHsGYFEiROqxt0KfapA~&Svg@I44&@PN+j&(gba?*3fSz;&x#Gk5?lY%
z1hMQ!R~D>UfJ9=*Q!87CnT6IyS>QgcTd*(W>kPFs#M{Xp7z>;X=i;Z0a0p@|jPCaf
z(Lw3Ao{`|G&puc@V|&pd;oIc~F8wa(f{io6vrF^*|6WPTlR?}%7J%%quq#SC4ID3K
zTWqB~dY+xO?@vN&P&W^)q6aQYd3No)zaC25SCIAPT!0~c9}+&g1CsRBPM7ZwU2oeM
zPi`8o#@6wb1-*Y0K~VB!A6)fS-Y?XyDRT$@h{@a<-2D77D{|%tR>AAm+2E*mVSGl{
z^+pC2?KS0?og_cMqP;vDF59FUVbdpB+0)dWnkTfbee1*!!sNcT&ZJ(F!Jx*)Y^C3E
zNI!ss&<HUF^Qogxlv!5#w?+Q|B(};24}oB_x!Yw>4^?{2KgpTg;Fb0eTkB#gcxPHG
z>E<&#?vq=`!<0<u*CarCNCjt#&#$@?X26JX6|DrxabP^V)2*A7UR%;>21eCRY(9lV
z{bML^O=Wl49Il}C-9WI=@r}O}mKY7+B%hQMiF?Sj;WYyEfq^QHl=YG#Ira+fSt4{r
zTtR0xFPPk9q!zyT@afbW6g?*OWEY&s4cny4i1g4LHW4f|8rXYb#8EnwkR+~vFkMNu
zsQNd5I9`pkZ<mnMOGYRU8|rgEjfI7%@3@qRPD<7*3wz$C>}196hy1&j<x(2kHftdg
z9NzD4;hr8GiX+&a97^bq%|-N??ccT^M^{iBbP+e(nL8Zz8*G5UG1MvRdX%AP_V;{%
zQ{odVgF`elx14_t*<J$%yG4A%GMLo`Jv_MZO6N@Mkkf$Gzj+X=5Zw3}$OmyZow8!i
zE&up&#`}0ibuQg`vGA}#@_I6u0r*8E03mn9!rhsC;sbb%d04k=D`VR4+A41LL98Nq
ziH;Q#7+@$b`cK8dhI%sf#WgcbZ^$oovnXc)iVkPUs85H=U|-N>v)58Wa_9B(MUNHh
z+mYeJiSuQQi?5y#u!Ir2b581!41-ddr(wV)s=B2o=|CfW*-hh!<*tcVdnFrbW0jLR
ziOOXjtKsC~1FwmMdbcItyR-qnf^mFd70^NZtD)BLsw<32Jptv<cOr36MHZsCwRJ+f
zW<IOug2=i2oor%)&+r*(JaO|s039<bF+aPu7($~ke5Oe3h6A(@l#I2k&xx9Ji3;Y{
zJ@n~)2hlH_--}LF#wn3brTeutz868np@U%}<L`&&Ni`bl3<OyZ#r~RY2<VS`a3oA?
zV9_Q7R<oSahwjakB8?$a6Zu7>QMnKa-!s|e?D6{HijXX#RsRGCaQfp$QtKxae${O3
zWQ5MqlohhJj0);4^y2U7XUEm}#tX+PyTUjww<qN>pb1aOH@KU<Ojvej!ZDIy3^<g+
zr}ci_Cp!PL{?c9US`N3?<OwYS-#D48lEbM73DvIf{VXpwt#q!O<b|qZx@j=?<x<eS
zbse$8)e`m{V<VB^`WM>{I*%<nitp1vm;S)sY5o!F91=T)$01QK$J(n`GV8M&3Y+W-
zVxOwq-+{9RztDJhc}Bgld4^wig67^vi>i7PjW0g(s%!zT4NZ-vhm2V#NY4B?ZZjU{
zO@2VP&87FJm(7iThSw!?f7(~yo9;gqdrXN!|G)h0e}wc#;{MQv@6|`!y_^V|geVMT
zQ-2C_?^!<BVjVW7MMM|7dchFrew9iXuEd1q_bjQTnm-;8?0l))TNUX??TMcRJXx@w
zkb{NE3#%U`-?%DC&V6bBfeXq-muY2iu#Nahn^H^s?QY~y-%{X_aU<|#LDy>qM}N->
z$LI}W$%?=tIOdcmn~)NlI_8Svz+yomqY&yHpP2swdcKm~ebGkFS@FXnswh^pB;qk=
ziT|khM|Bai5q6?6V)8biY%Q_`H!HmO%0nchEu~p_khT)r=#`E@ZcEvdZ7Ue_5%UCw
zGEY2}ongNS%M)Mi=OF-5!dpGr7C(;RFjKJQPq#607SkswIo;HA$uCP9sdpHiOYz($
z8P7=^17?N$_tK0}qC6cAiX_`pJiW_Pw87IwtQ7^1$_~Q-8>ndAl|(TjY#4|d^LjUs
z&zAo%N_|(J`gt$<<vNwXLJam1p?h3kP($e_Uv~c{D8M7qjnQKw)rt&W@RWD(PaZ;i
zs4Ml|G5;&_J08*q?20ajGe`~J*8etnnp2t9EoprBN=DtOb>Q#04ZTc-x^StrjoT7*
zcE)8>tjRrOo7gp%p#2}7!bxT?WtX(A-|u%4Tm1M_?l7ya@a=BX%lM_?vb}2E{`kgn
zh32+3d9|vw^zGu~rtv}ivfcE14s)Ld>)USt@UDGFXYxOc9Swpp^v+&4Zb9S4!9MQ?
zG&8C?SOBXhwsN7g4}3s5`6udx1{IuC>F0WeH&#*~trL~Kt)1H`o8Oh4{c6BT++Isw
zS7~Ai3hd{&i26Q1+zSUs;+nX@L3dr+Iz=g|bSg2}n6n7YU^P(s@*$g0yyOA)`Zk6Y
z&o&_5si?(wV}bP*7wQs&i6B7_@THwaRq9qkc0>lM!p`0ik@&q(A#Z(PghQV{A#!}0
z#%EY~?$X^dlH2opche6>1~aW(;gIRBw`7J@5A!2H>2%R+?6q3^nx9lC<fy&pFi^S!
z_4Fwb+e(Fu<7|X9y4Y|9i<Q$_VC1u3Qqg9A;|mSj*=VqJJSEIo!c-{V`<_<s;&&>o
z4k$9lx2w+{iG@lzlQaW_v2Uri8ZF7=3}22BNFFTtWr|m0ceb>zFcv~02i0k+PV1a?
zY<ft$*CyXg?86EUtz6>N8d{X1<B|DlRPt{-2cVP|n!7;3y{<#<?Z$r^&R=h@TFJ9Y
z1-InK5TLHHA)S$3u13tzVyXHJhqJ%>wCvrJrW@5CHm&*ksFrko3?pQ?Kq5M9U83FV
zaNW$?2|}?QpP>lh{8V=|t8zGQc|DWFS1?2QsqOGLt>p;l#sf($nn=7lg#y~vN@~_U
z)>9e)Z~xF9(9k8+PeO55?9fu-kWqKm_s*{Bd)KXyofW8wc^8y)@Lc!3lFL}p7i`(S
z<EK0Q_s(=K!2cH^vuEj7+z>S1xg2N{90@V|JKe5N%Vkr<?S*B-@~W7_mU2uJ(~3{|
zn-mTII1jiOTm_J^38%{d>h;EKvbERt2tAC!bpDkrZH99^1j;!)Ou@p4dl=L@!%mjE
zu4H}Ln4^GaPJP{a-qnKUk)cx#-%*-m_AQ=r29N&DQ;p9>N=p{f>}<tmD{P+q$vkvd
z76?fik&5!@s%-E{ax^<*cv2fpU-9<sOnQq`8B+g1@#A_={NlJ@p$a*E!lQ*S#Ep_R
zhAc@%-_;{6ZVw7Ay;sdC@2~2kDcEcFIA<ecfn&$3rQ1!l4&P<V5<cHl&*2E5Gcm<y
zJoHdjBtTbYroyxeWTE{m)p0w-w|fhuVHOJJJ|JVjC+dS}^HoH-V8iEPK?=T-jimTC
zuN2h*kZ$GDafjP7!{(vp1{B^&Fm$EI%B<00!?GQ(NH5{6*{)zzbbN#yzal5VWySo@
z;N^m6lQ0#1=@m>J(h8r=40#ymosBw8p?==fFa0jf2^fg5t|@s~q3d@~u<|oS;W4qS
zLzM(jYx~<aPD_k6uB81q;Sjv*2{hZ?4RWG9jvK;KOFQF@L7H09TupbM0FL7#Hmv>4
zPRu?9aJUwXAAm&qT`yx(N>t68?^rzbkDxqi8y<bv9`#B)GNuR~$~QE<fxG)A{g$|1
ze$$%Q9KUNV!5g0sx;l-}f9w5<F~PCIYWXokL>YgYe2ESmsI4E<$lBtA(vnvMG9p(i
zqbKMpK6~uS!?mtuC4?EZ<5%^@mlAcyM&^lcr?)x}PHFm03IYvF<i0CPN|U#qDFY&e
z_f(MsN^v%!DH4o-fg9`I-pR4o;iAM=_$(V9;v1vYcvp07vO<acSc&U%h)0}<l4_>X
z8zwm#rL#fkt|kMHTho_Jq=u>d(UN}VOvnGE<yi0vtpQB*H$L*hxv}fINBmetjkFg`
zv^wo3EA9_>d>dq{u)zcv$}>)vC^BN)1|*zbVy?{66cxnAwdy4F@pQzMj29t|I)@Cy
zp8bHr8wvA`mA!R9j631xjS>-yJ^ZNtz-@gy!tE?q6gqXRlHgQF)nyk%G@`T3SBHI-
zmcRNvApilouhyR3uL+drCpSSJiQME%M&M5?8xkfMG?yh78o{kAImT%`w^XuTiBI@L
z0{IU$CZ9AXA1<cMqiJ@sq@+SJzDXm9PngVZEs@Q!Z6G;ISxjj&KWdPBso5l%(qJ(<
zmuvn251#}VRa(m`_FjpCa}eHdSgv0dbVN9HLvBJHTvL4CYC_$fGvux*`|>Ym|2bS{
z4T0(VW!JN}m&?#bS#M&M_Osijqxnmk-&L_I^G2lJ`8cWljK^WU{&Qp7YOLSeuMxl4
z9K6SpKV#1mZ7Xdgo+tIsw7dU#lLUe>kUpvEhtT=P#_s%(^$FlJor*=6DpsAkYjz;N
zn{aUufrdN@l;O|eG!~{VKr`PRBrw(drYz`#%BC8}$tf8JQ-B&j*W*uBWS?pzIf`Nx
z$u6|mhugq>hu>Qhb~cY*r?hh~>GgOxhZNWX$3OVm@03wLiKwAzUsfY?-{K1<SF5!F
zopcy$UJqfqT@rsh3({63XUW|3PY|l3c*TrtBnhJ^46@DdPho8Q5OdvA>KK3}8MXfA
zFc4xhu#c1GK^=o!ZY-VhR>sytq2OS2(Ez=X`hz*&;p|B?S#ZLJ^lI0{A_3k<^(C$o
z2yY$!*6h-f>joHQoIiLjaZ3*(_gX26v31YezA9j}KqS*4kShNnt-TGcgICAT_q-)C
zq0?1L>aGaixKuK&6j5dr0v<b{K^qh1_y-#fVib^=acw_7VrU_6azi_r!8GJK+vB^X
zpc(J%+KC3ngJ6))X5D@X^!$ZLCb-}VT56(xftbJ5xy!`%M9(Bn5UGnA5|%xbC8?Rc
z)wOF4?-`fyIvc34fqn1OeVvd#rGd$O31u@1Y=`D<jgJx>*%I-*F@vZP#~FD$-Q*Jo
zZ1J4|#knH?)@OrMUQ1}a6mD*UExvLu6<kwHuI1D_U9Y`P_n2vR+CJw{9n|J9;J${B
zkeAIx9WOa+(T_7Z@iyLO*f1<z&!Fsmj*P|}HoyJSe{C!cYRI>GVLKNVAwp?1>_2Cp
zkdz0M)|eYodYs)#n_6HEh{LNg3jcb-f|+0TN1E$FTN!0wvBk-BYP=^a`KXb1SV>YO
zw8ZqYSZETq|DKKOh<`f*W-ar4Am_)(^4k!JoH?)?ZD_miWMQ+;&(CXT*_}PoNQW?N
zs@$vLd}WNl`$s-@Nqqhg#7}WY)@&L5=>)}B)vv9Y2sFEijt44f+^}u*U&lK3iI9el
z7oF4YM7cSGtM4sI_sd3~j?o|}T6YEA%|P8B>%0*WVrCDY2n*?8_-*RuwVcOYVR+2I
z<1J4+9AI>Z!i%sDdZI7aQ0#4wXtK*)T})05_rB%^VC4D{cWg`rk_FL7hOhx?<eH~Z
z(~Tsn{MmikXlaEdhbONFz(j>j8D5PAQ)`&cA#rmn#rk;Lr^9{R)rUd?Mb?!6Jcd5N
z)eZ{ZR47Zv65!lRuwTOA0O>~GL^^PdEA2uhV56&?Bd#H*Y?DX$K{2;(<sh4n-$o1B
z*{#*I32`QZKC&RmU=k(MD&FknMKhUw!FbAMQc~F^;JNo>YQxL_)J6TU*BDDKd7pZ@
zmTCE`LsPJcvGEp+0A&EC3`mb9g`R-w*ry<~9lDkgeE9C?`0-MbC?aN03l7l}Hw_VN
zknWWjvu8W@HKlyRtET*=^HSUD;XP)BPkNJ2KZDQwGEJ*)A-!QEt)t87A>Hl)nM`rs
z18K>n=QPNruMc}1^;4TIr&!Ic{BU)PG{*H-z&EMM(|zKQqGnxGS~yIoPY}=nfg+;0
zG^n`oSTG;3Rf*vrA#LD)7?^oEd@eNf(+=c?N(k1DElBj7CPI$Y>^k@q_&MIgFD=|m
zUVHkaXLI}t1oYJwfCXxTe}5n8v@aa(R-Qi@B&p~8Wec=CqyJeCMk)!)$1zBYD&j06
zDjA|FpB=RWo)UrWu42#sNSa)rSRft!*{MVcTNeRLo!tY^zxB)&atTX(Enlli%lt_q
zutA_t<&y}Ff^XdFQ$KdC!SLIUgVcDep}_qN<)iZXra<mCNcJpK@VxXZNILuJUc?4m
zrUO_P&FZ<K0GE-FLipvUIuyAR_%AVrM@AQOuy6PWoGR(<d7=y!<goEAZfM++cD4H6
zB}Lz=T8Are)5c#p`mFWx^_M^46TuBlSnw=o)-ps|$FK}rt1y_#hGha_=`F2MbSGI}
zMH6*~DR??WvA|2x+rRUc<&;(njj~<XVhoWeaQm%chu0ClQItTWU%oP$?`o=h4XYLW
z43b_S^SjZmvq*ye4ZLJWP57|B4WgPYCAVhFGWgM{$ykPjdgZWZU}+sEAUg;Yv>@e=
zUW~=oQ3b2V^_#VE0OK@ph`Ro>s@CeaUg?|*>4sqS$3GfRXvNKBL?eGtuJ7O}!^XPl
zx|$&&$da>7QCf2YQQlkuH^ofHu`_K6@~~Z5YB6HsO$8VB0=<v(lSe1B$ZYA4&|3Ft
z+j1fQg%KS@($vK<nnZ^4{|_ak%Kr<RtcdPvmZTQ1k>b0ph>EO;DHYdE=Vg!Mx~`iD
z&hs^nnC}e13#6=<2~Kle9p_lb3C#G30@)jJ#9DP7T>i;=2>%Pa|NTOKNN~SUk^p1L
zh}$5aLG%&-I5@|5&E+6UspK4!NKI~2#`>RBlw;cB_Rt9}`j&jpoOaL931~U1=-h1M
z*)I{o@sUWs@>#xfdCb{;t<iDv@01K)e?j9JbI#DBMF!Lg_DP$Nug2FKEW`X1IIgz{
zxeVOxG+Uiux-IYVBiIJ}wgjgLfi+7=l4L^~oFnT%yo7R1&gA&BR`5PIi=GR4fwY`#
z-tV=7f=z8Y$h9&`Ybkc^OvaLNqr#Cw@0u%NWIXr&AjKMf)#67A!BJtjZSra_%GGmI
zxRLGXw<|mxiEg7sDrKiuN7Ruy5%DY^IQ@|j?A^Ne=&<t7weUrr%oO>2^u=HB*JIWI
zpUe`E4b#dd87wuh9>PalM1$l=Q`*_Wm}h%6lEcFfl!ily4-;)UNEy+}GFQOBqvBPC
z%=wVH9nUml?N;-wo7{Hi@^LY-mdrEUf3F+g+<&nWxay5Ot~?w&O$mMjQ+jx&ih+i9
z@BLD88i7NKfKNLnDP5Bsg%cc~EbYgV#X&?(+*g|Jv#zcath;6`aTm9F*lEFd3)@_J
zQ7xhS_|@)|S{~*c;gHT@tNprNkGQ^*{Qtt@e^D~c28VbmhWFm{mZDqFG00%1`1#}R
zmZzw03U`$aa*ZKWkK0o0IYqydPeFQvsVt*1%N8$jM_M%Z|KSTd;6V&b=@uxWzm1~#
z6$O!vH{Tx)mmaxVu6v($iZp`iChSw(Kwok>3{^t3Y|*&5<41o8ySx^l=-SF9ajM52
zjRKXXuGVCU()4Yw4-8siKt%Fv=<YX1+?wOe>6`h-K;kR~Lz96TM9R<32LMxl4EsSM
z3X%fy$NH;96L|T{-Y$y{5maleMwSU)Qoc?Z7vE%w1qIHS;K}ay-9zKwzWx4*x1za~
z;&<tJGt}Ujg(#kS9>65$!DHh<L3Tc{(pB+N3!m{Bi?R1a0sN8leR!yrys1_3g(Z0o
z!_uQK;ezsKG^O)>?6DNy=TRvIx=^!A`iLfbMleFDMLhIae9ptR>iExY>%|-AG!r37
zk8=b%NP<bI`cxLR<zY~-X7LZdX9oS@ERG3|W%4wBI(`>|z{f_)j3#2*Azik|LweM2
z%P}rVO@$rr>8siAO`UW1yYa#t@UQ?!ay+Mp%r5;+bZ?im$byyyUXpPz0mULp6L`P3
zTOuk>P~Fg<**+XJPYygapJGk)+<IlyTkZ6?B#VZeS-dCfdebITfDGgxEpYbHTD|}^
z;o4=wb(CvCG>Y1j>)OOl0yo4GMa??@sxLUGxC<fd`aVWJp5NapE4#11q$nwpytfcs
zliE6KJk3Qb$_pKWVovp2`6g1=h$2m!F)-Xr=*#{>#6M+<)SeT<J9Vl$j7D%+BUgM1
z=nz3Ibla5M!t`B4{3>Dum(J=YIT8PwZ4i$9iOCj9d)Uel$hvjpJz12v^z&M~Nh@|F
z1JUJ|9TXk8dE_%nThz!tl?XN!7hQJ`Jy9d&%QzS+J>)#>@jYdZ>Pp9lfv4wrqo>@r
z6b<W*pN$uMaSn;!E;6=ZZ;yWkn6y;?dbxx%EB|goODrmqE^AEvH6~F;mB7Y2mgHNt
zL8@FOb(L*5-tCzBFN%?{n^gUIJY*BSY1@uxBMu`_*p>-7U>nNDFhL;|0JUOJ<21f`
zCEYS6P!JtmFR~zt)Gh|t@3osz#zAo|)2pMirf5#c`(kLN5ZijEc4Pto`g<z`IMl9V
zxUkD&w>(pU-yN}F@0B7;jUfSULO{WKLl?TRb!P$3?3K;n${Lgn?p+IA%Rl#nYW8gW
z!3Gr;Kgn;S-WX)<j=Uf`Ds~70lotbW8bINjf7Z6e@Dm#{BhByP>ZD5eS&Qr{=>;A>
zMSy2lb~tL|1CXnDX_7yWBTdj(#ZM<!$sv(@IMj=DreKc>zP=(y$4J3wavXByp2BcN
zLmU;bdbj$DDB~?c;Y>%(N?Mre(R5@4C$&x0Qu%O87VZ6~u;_;SzlRYbPw92P_-($N
zABKwG|2Xv?h>|^S%;{i@Ci#Xv62<!zbas_rH&mk3d4IrppLrdWWUb_SL7zy%U0)JD
z@auno^8de*zLLfne`VC9B0gu}RE?jpWx8haNhfH3Xex%*$V3SEY-j3^(1lnmLmixS
zYvRmByvqqJ=a{{_o#OvBFyG96*JImWEI3*}(JS~xz=O#naxC&7P^;5SfBILh?ooi4
z2EL%r7`K%1>S1JchWy(H^fT)2Vveu8d2mU<_y5DyUj@b0N6P~!4#5fT?(S|OxCer}
z6EwIE0fM_b1b25I+}+*XeegTqId$)?^MBYc`(am2%};uDuU_59f-)b_cfvyF6Zuzz
z5kUZpzPyi`x?D<DR$|yPTpa|8I2IR9<kz%&tIUZlUHU+BOT>RRTavrfyhCefdl#|s
z)k(WrfxIIdtKW|7`MQS-YIll~2_za6TP0J*A7y;1+tEy5qpT-+kbbpcVk+}nrD{K|
zG$!}>CMBeSrnA4JSre}0ng(h9h?XwH$L`MACm3dl@o==%6>3quulIZS>B=yCv{V@V
zI><b8kH-yTc?e`0>FGBjXlmq6b<6gTw1a3(3tf#F5hCF*9`^IBqa%lDvyIhm*}F#g
z?Fxc%Fp?z8;3Xb^VSxVLvvbCy-M;c$K^mLL!a#A`>H<+d``fMQ&HCdiW6@}y0iSTB
zW>ZeEO~H)-*(8AYM5j*}Q$nS?>ih*N?n(XT5lV;lHV!ibUTt6GJxXix|6Lkj%)y-a
zyqj+`H(Kc}NYb=|o0BC1s%KT)8$>2lCyr5V=LZgOkiG_G%z9!9d}3>_$DI&Pxl9JW
zxMd&>WdEOKp&tRUr-batMsB+B)%thMvH4*<c^H?CnjYh+)V<p+IC0GLnoN`dg0<B&
z=TK2}t1T!qO(AMg^rw!|#3@z6AoS6AHNnAzR%7kPIa~3*=mFJKf~m>qFpBSPo`C#j
zksKX_UC%8_&&(sgVY^vZFJ!A-OO44#KqytkF{LkR_X-sOe{BEhMUUxs2V@I&IkXU}
zLC*U_mgV5^f`X~Tgs7kqb3(=>3kt79dwL|{AH#C#BAzDc+y}Ng!g(X(r9QKXiLe%g
z?+fH_Gh&cap@D_z^-3eoYQ+EaoszoA)a$GgSYnwhGh(1td*KXNUEf9s?bM~{_0_a<
z9>htCY%v-4*<l&VEpJ!I;`&bb#|n-$InEbeIoIYH3}#DzybH!4s7H}HF~o(*wyGt?
z*}p8n>3}Fc{<~&)fCTcWX>NZ<OoQ=vFMM8b%x{016K?%uU(yOOILNdHiMM(QCye;|
zO0N72jz!_x;B!x|U-b<d{&gMd(Di)NHYdZ!I!Z+k>$|^yr01(G;9Fogf_vFS=I3#t
zluNpVPz`S&+7=w^oJxQ*9DDerc9<4(FJxbGZ<6;3Kr7wSbZW(Arx(Hf|J5$wJK`7;
z2E_Y&4s+`Su+}-2QRQBMt(3z>?t9P7TNJh@1?oJays*N3<UM-}JE3%FrO%pyZHMr^
z&NkQQ`!-Pce^v&pznVB%uQH+xJ<Cr?J_4xq)4%IxKd>OE6FI+d!L~EnESvdPAt-S+
z{}Y78CZkMft9Ys8Qm1A~G>#X7snFH_R|h*b!3FCAjNNmn8Z-muJ4mVFdX@wllRP>!
z#Z6*8rkP6}@Eed*d0LaleU<4Q^uT4g%3R8M?9o6WJx5zd*5{{QIxeUg-C~lS5OeH4
z5k+<G{#9PC!Vg3=-ak(X@F&$O$soel#j1~Ds#pBM1E^Ev(IH%0F_B%>-!y=f1dq1n
zzOB~J6Sg3nd_-!tQ(~l5gjeF&^7+U5FLcR;@1vq6Cs+EyOv4FTy;x~p)`T{j&T@O0
zN_B++H7bn#Ha%42e3hM~er>vHPBHwwR-)Q7jWS+w_`RLjXu^)E-LIh-@Lns)H5B|V
z!+Q3E3~ioWp2nf6W~HD|T_`EbmWubQV%-n^W+W&gOB6&n_$SH<(J0KJ9QeUVXSutc
z(Y!b%QlFzp@TfVfQOHj?&#f>x27dCc);ec{a_I=&Ec<j`BpL+IZWh>rjDfnwm4_h?
zYrb6qtfS#kLs9QGuZy5Lob%X*U#}vBu;bna(9h)EhVX4tZ(emAseIpucLnN}IDVgd
z31bAN4t*PjtmR)nJI4C_KNW`yBh<6Qv4sHWp_sSeK=+YL?I83_!21o4siB2kF`Li6
z|CNQ92j0JRZc^6oA;CE#zamJ7q)#y9%CM<02q&@X|9lM#W)%f;P>hN105XOR-G!M%
zbv)2t+V7av-(#7TW&~bxmpdy7^|7zdz%Mr(iY?JdA`tE?;cN#!ZV8FFmxfjmlb^zB
zC5*n^>=Z{sU{5)YH*}P1HmL7Au@1t&wvnLNUDti;^2An=+i)pQYly&;wKi`*oi=0m
z(~GR8i<O*YhgE7yXj<opuC%Gze_|jc+t~|86&U$#ET><A1T!mvrsknFCcau(mJHR$
z$KtMY^3H6Fr&}x2ECgi=G>Y`2tu&(^6Z@TT$>6eAhMCe2k#s_82dVy;aD2g-meFP=
z<oMEWFgsaI^K<1$U^#dTs4OtyYZ?1v2h;4!G{Ub(q196@-8J@W_q76la~<5kV6VlX
z<mss{8D7x|nprbeyOJ^K6+9Q7EY}H^GvDL3WSw33uJs^XF^Yg(@}~$3cHa9^ma)<x
z1jkIgOwH(?eQ4Q>mv00|5zPa9zx19@3z^hXj+QAG%mdLpKgMUF#Lv#MxdnTwM#M+r
zl>&f`no5d$4+G+4S>VVoS7g;Cu@(+)&vr8{68>kxLj}<=&5b#qtung>DE2nk4t}f-
z1F4-&TqH}Etgy+r?aqclB*ZhSBq}2aiG7h6!b5NI{}*+xq2nb7aWuPvQ1SAwd2e1f
zeSFi|pU3S9ud?rsUaLFVI`0-VT{^Zt9iL{SpK|&B|JdVyPL^pi7$JmlprJ6R_)m!F
zQL`iOi=@6wI?(bH(THF200AKuvZHE^A+MxF?eIydEID6J)4<0-;2p2Jwh-|lTJMq5
zBcHS^G@c-8iaSI5zBxxIAa^GR*0+L2<clYStXD~jNM0U`N8N_p_mHBlgNx6XIY~yI
zF@L<zbI}qW0MR=lZW{|piwz+D8`E#IHzq5bZL`cXFFg@xB^r`6^gYY>nv|L-b8T1v
zp?vr(+}@Y2;gOueEX(ubx!EXAJILh5n0<U>sW!RS1!jg?PPkKJ_x%<28-b>;#X>Fn
z?NQ>#rd2#Q+@}@y@gvk$v5`;{3Cs++xydG$DJ2QQE(g!hzKgs+^Qp2fi=U|Rud2I!
zj!}urmd+6YCzUfZ7%~>+H-Dd|A==!TT{YY~%qX=D5<AR(hj;jWmZhl4V@#XiZQ8Ve
zf|4D5;s%bQaQgq?hyxn=4X+uBGg0SEeMCa&l(Qb7eSg^Bbp=}Q1aHi1%|f8#aeBG5
z8BZcR-Uf?*XNHucT13&zf6?}t%rKWy0!(tJaHq~qBt3(E5Fa%LLm#Eyc}-7=_vXf3
zbL6;2z`N>G65))-fuk03h^h&aVKng`g?M29{GW?rBSLHrcXYkp%-PFiV=`Lm91XCY
zeqT0b7jYf!R)EI}1GrmADPA(*EX)T_wSQi`KTf|Dxrip|H}UyDm97Ql^e1H3E)fZr
z@pPc_lk6RY3`{=u^=yLQmzJMB>vWVWZ7fdSe^wLQLBH#2^Q8;<Q<QkrN<u7h%ZWtq
z>b#`e)8t6;YHF^#l2)WV^QGNq_XMcqQ&h-UUF#(7GLVQ$!hFkl>(YoT;s>RuoS3!R
zdKm0{3|gS7^=Hw>-P;df-q=1TXp^+ZKLXRBerh;x-FyX9C5egux$UQm$>M7HdQ$IJ
z-(#kZQ^Nt=->`^#jZi~IIhX6qH|h!<9lReMV^zRZa+o=bGgcnw$CqGyV6-tVAo>Si
zmm{<SXqsWgG)5Zj;)87frrgJd)twku28-GmLnpX9U_b5=mnzHVhPBpOw0aSD42u7*
zpXje7U4ci{7Zv&PO?iJ_xof(w&GvWOX%vLO7lJzUcV592JgpMHX8HFxX;`CBDDynl
zL)VOXS^h>MNVq-x3d<j9u+d4RwZNcitvdF7zE!Pglk9`Q=vsH}+cjvVNz$z#I4-(b
zA>-!#_r<bXy`UO2t_?0QLisA6CnLnH8L0nvvTvM?*I>~XC`oGYJXUO&Iw|Z8C3q2b
z&z6Jn^46^URfGT2(-%BaOluHtz?H(wen=~KJeM61t?xkF+e4+L-ZVv-pX~g9{^S5L
zUW)1aAP&+t2T$p}dx?5hIS1c^$mqvhysc1(Px`kzQa{<Y{dG<PLV=3M8F&qs)PaHZ
z|Fhm;{f#9dly9H=AVN%381j>I?kPdJQd#4PrzhQGEkP`%J;^#XQ~^dLY{N6qu4;SE
z+(V;PPfM&bh^vJDX$U@Q!5j7E$Cq4JF&si3Jf3qs+t$KliO>>8WxD2diEa!6Kf0?F
zooo?5^^JCJ6;B(pl$*|?o~hpmRPqjxV_}^tFo|AbzM}Rit0zTKqUUoRB`=gaZ%aR@
zf1{^Lj$`^$Mxm7C6Q%0nq~bR$**-|RU-J!8q?#>%qS;`hY>6dp;Wq7Z)bDQX5X<UP
zY*{&sZVd3OQ1<-wLk)W^D5sPP-s<1C;uA4wyb&8&1f2{_=x!?qJhaiDy>u3mX;lER
zDbLHb^L&JZXxH~z<@z}<Oa2!$wE6Crq`dX&V{YST<6m`3JYyy!^$JYmi#1{IX`0;T
zRmEnN(qj1m>~n!K(fKLZ{l%V2lsPvP@L;@zbDOSAbo|eFzEtfF;$C$-aw=9eo|icB
zb8^&<FNI45#y*o*zHfF|`8q9>Zmi{!PfCt}Ix4Dh;YxdQSJS%w!0svIIKTbQHw3kK
zfYAL_7-#vl5h!TXrR1Xk^5VRl1&1v%SBc(64fg19Y@n=!by8~dmM3G%`{MBz(Jrd!
z{68Q<0L#A!fMT<9xN!nDybEB3Cl(o#4}3n)tPtDyvA=;3GPwb^URQ)ig*hL`5+`iU
z8yCR)hhm<BsP@NVddsLQv9pMg|7s>y3Lt!u?jw=H+9Qda;L}>I=@ybT?kGH3cd7xO
z4;Aq@0K${`W`^`0W0C3a$ev4R)7ZP8#F#@-vX)V#36q1qZ%m0(!lt$Ye!DHEU4!|!
z>@5F-wmTysNVmP$p*<7rVE-=N#2QY$!(#T4Jx&|?q2!B;o3oRZAi#Dwkj@|?)-e2n
zQu3oz#g<eJ6lj9z9T_s(bm^jjZ!26pA8wjP<w|`|Qq0voR7F)_N{AU$B@8T_l_M4x
zG<lgIz169<(wNzwAQ%x#rr7Cbh}CZPRrUg$U2@sYerK?5cYzSEE6(03V$Z+823#xh
zrS<s~e)ZLyPL;Gj`lAOD9}dV!e5&jBA=-9D1;|1EY&EbN=73;JiD6VW=Ublqu4~m0
zIwRIsR%<?op&<6~W%2qfxtt|AJWg~#s6HFT*_pikTjqZBGfl=wRhhBH?4jsQp<nKp
zrAtt8Vvvu~Aj>+=FsXmd4w}g01kdy`@6FxhiWqEiew@Er6sxrm-8L5?VJs+|$YIRB
z$6)26qo}C}$!lJeu!R%hz#8u)zlIgzRh)w`i=?fCb>xq-?^mHxw$86bLa1{v0)W3^
z2$MsAj_O!?&j<{lhvRx`10UR;orLbpLh7^F5|tx;Cq)d$`tM*lTatc0XVpmhisQsY
zt~Ijx5aODr?GMjGEfx&A6Eg%o?}G-HLD1aX2Q>VJ*X#o_y@4(dU&XRH$+nG=2^$ax
znhgJEuq0sf{DjuI*k+Zq*PbKf{o_}&qM{=yWHh|vAjXsAVcU^SLrZF;1Ij+$%f?)P
zJ6uk#VDGamn{D>$5%zDU{}(9fVpyGiOH&r<Xfyq7;&|z>#f8?>QcHX$!T#2du<m%$
zdg9*oG<u)1<@4AdX}9_UMdGy8Wu)kJ^rSaago9I`?hk>BWV_|fb}`V`9?05JG5}L>
z(+F!SZHnQKgYI|(v|=WkhK{vx(R0Cq2@VBbLW8G+WIPoVX1EoDVSGW7?o-i&;L{%8
zzX9Kn8axX=FcC0VgjDP3II6ujtIp(-#qaqI4BZqCQnGo)mqs1$bzN}&r;c<J7u%6s
zH=z{kWy>FBo3>(F&IbtJ4uyHbl#cY{$+GFDA=9KdM__wg!`j&nJ5}TeO|*_1*x0Z)
zwslkl3+$6+C@TeuSIpr)geaJ6tkAR%V@?cC5ghaf^86}nefVxzFN9U&Hv^>7)dTd@
zMcK8h9NsMG&XT`X;zgh!GxAez-JfFc$%;<+C94MHS77que9QjHC|xC$Q2d8hRyV;^
z!RLlD8{F~XC`%j%-XeY9h4)dQorFUd_~OGvziyilB^G5nOQi!ov=e{H#%ElL`A(W0
zMMBc(ir+C=?Qounu-%7)vJO8~8-;>9mU~XqJIu7!J|VXKNlciNsv-zFyc0{dTq&vv
zDz<_lo1zQVe5UQ*Ri<53($1;<dAvS<JvmKN$rBK!1uPv<UGc2>Kq}*1wz`xMS@EH{
zu>YI~j@`aIunszblR_I3BqIsol=m+0#7)z1d#Cj>_zP@=d}~alV6$v9Q5VO*{CYwh
zZAZZVXX_%O*@^@nKfQ5~up%o<D{ge2I&%`tKQ{;MD^FzBod?UJ+)tENCF_u{H1o?f
zI@m0e2l<wFJi8~`n_|MSbK|o69w&CtH)Ri)JN%^1wKjb{X}1LV#Wp60vrR-PqP(7V
zTX0oYNqz?*QK&lg*He7BmfZi+O2z0#ktTQ9(AIaCf!42J;P~X<XN{H(f0uT>85r_#
zFx1!L&k@`cXb2z*VHLeaiXF#y7WUgcvA#{)n|-#-&SnAPl1bkn5m95qc;Wf$zZ%M%
zxn0WZI_leP`jXj6DmZTC;?DWwqVClYLwYi^)9}F=g2VNAx>RfpX+#JOX}W;bj8zVF
z*2f53cDcw7kvV>56VuGrwfogu9E|#YBdyF$c`l;s2p#Y37DWz1o#etVZ_zuKzqIcC
zyn0UWqjfE<n}du`{?QS;!GOd9kiKNE)G$^q*YQ@aH1SrhHnCQ2)Uw(QOcDy-K=M!b
zPjx$A`wyGW{+*i}IEozlEtMXei3D$<kRDsKw|)8FuK;g8_YhB$i|Bj4%iNgijndm-
zefacTuW5l6c@#7Q`FG4uzD(DTH0#L$%i<NGqU@}y_TzIG8n*)aQnKkP1V{H!yI0i0
z_u*DxWIM|Mbg&ka=i!N|%hXX{a6BRu&Mk26t>XpA-m^t;Ba`#VaOW=?sD_wMW?2$T
zv?vBAIzRZUaBg4G|Csy=iG}GH_Bo*-Vp5n>-(~V|1K`zQ?=K1Sek|0fw25V7K^cgt
zM&dOJN1V(w8Up;Xf#kG-?EAza<H28OYm)ESB&}*+WP8_nGhUMIWE@(olV{e>I~-vR
zQD6lp)LC^A(mr%#QUTDsumiT1XtP0x2W-(qa;NCgbm>au%q|R`UqWNllt8Ku%Mb*G
zPQ>P?$eGNv6SfVIzscRo|BB3`D92S8o)&&wwc6`&HBj-At~9Qc)Te1ZSAW$C&~Cj!
z!kkCZbQiy^>ZftELeC~(Hxv{5qIUGMjyjvDhjPKmj~?2}0--X`%U3TlXWBcfuval!
zzoxb#UrFU`P%B1LPd~P>AI17oOivdgoC|y{DRs_D(p#*Y1mc53xZ6Ld59?H`p1&5v
zH>3LF@#gUPNdDlaSKW8Crl>J;PBon<{YolmA+&lUFYrk_d8f-|Gj=;GK|iZ_)U0XC
zjAV{H44LJSE{KF=p6zUaf<78oKkP(>63T6vO>k9fr5Kyzv)Rw}kayP`S~Yc4y~EgV
zTeW7{p-+2*PxSP;%pd9c;TTh^+Jc(Od9%9Xhux+bF`GfVVQO;fWiJC^x^3~4y4hdk
z@n3_%Jt|bjwU7JFpuRQrE_OLywb)w#JF>{rhQH;{358<H>nT?LL0ppRu4{1dXnj<m
z4T3V$7MoKjh^TTC`(s0!t<%jZ9Huoo7yjk4Cnf5M0cRQc74(UYo2wtnifQOXViM32
zjfc7a`yeswM5rG{UVt4+F#^x`q_$B<)?@c_wf=!YA=!Q1%hQ`cv*H&6)Z4%JIZ|)g
zml?FpVls8Lq?kW-idxzrSEhT<_e>2tUJ)oIH*H)!ts0|)MK02-Z+<QlsP8=^NsALQ
zTK_78p!Ar4lDsE)8D2MNQ)yakjt7bLv)rJ3@ZiTuS?8OOBN!N6c?N_`WXLKDfFro(
zBO{>^ZSjeis89IPC@zSLD-`11bO-9qw=u`~v?_)U9{m5#0@%+>OPNYdv>&{wJ2^_y
z-(NMV@EU;u5N`7exp=tsT{e70Bi6WEZbL&OempbiLPIixOP>UuLA8{B#g(~T$epW^
zae{OE*#@2v$;gtLI?31Bp=9(=%T0jP-j4r9wF_22Y=Cu4x`pn$ITyi-hm5l<oD!b@
zd`xJbN7gMe`o6u&qrL(ePb!zgU}D2Z95xs7do3C-f?R=|@3IN%DK=pRf(;XIw-sgu
zqU=hlc`pmM+GhMgB4XAiYsQ9jWA6C(w?orhh4!MLG2S+7{4}_&{fI%*4}{z6vB9Zd
z>X4q2#XZ4O%I=070V>OU7V5BiT(mxBW`B>dzIY-rz%GtQO&#+Tk3(g*Uejpt8I`?&
z(#T#d1(-Ly9weoH&8)mtXV}2aN#D&5N^tL@3G$~Tsi>}e&m2CXt%_tO%*(;s7>4#E
z<?#RcfC17KZRi=4GfHPBFg1WrG}+>QZSjDE`KTeqKSQ3(vbLyKZ=p%_wGRcd<WFW+
zOk+Q0NK#S6MKi6+n55nH9wO=rw@wmB3Z2V`JyI;VhN&po`TvPR9ppwMJAz}h3iHgX
zrOH|#lmSH3L%)bnFA(fi9rPszhMq*@N9(y{c*loKde;(4%R)<U4|>*z3ZX`aM13hF
z)|ojqC(PWgu6o@`>;^Lv>GPOjpJyV$Mo0-NkFBJnMGvpedX#44Pm<U0Xw_eIxzwV{
zv7VM?bNKsVMSEq3S0kwh!N>G}9Xs|CMjO%Z);qr<ehNiRB@Q5LI!O8$7tD(L@Y~Y6
z(+;UBTkdKvW|kgpw_bRjv|D859Fj-(utq1TqaRbj@E8@phhWHiBEXvljMYGke(Dic
z{S`&Q+#3|i2HIG>7Dr5ygtU-#efOzlmTAA4y_=;vKUe<|BsND;n3LXJHoGcL4LA9L
zj6^%i>nvzBtu&)vmG>HT)g{#Vw6-kJ1dZvQM2}>?>b`9NM1{E1w3FY`?x5dzSTD-|
z*e0|ddk#4=fid^=Ik<5>&3+QDng`GuNI&YQ<m4FeJO9vTW1EGZD|)}lMRyN>KI9~v
zYsPVy?yy!<gSW%^v?_2A67l&&6gKOI6ddv0T2PyP(&v<nK!U4m0Jk$K{ef^t^hQ9M
zAQ?0n8LB~=n^pAw+WhJuwVRjjD~M{!TlZdyR*iO?1FD4Va*B7(<j0jub~w0~E=4*O
z$zmdlF$&Ax=iDC#{(F}GdUc_2b8Tpxlp1a{d_j%|VAhLOU&zi+K&Gfs^rGI+q@6sR
z;1l(Ik2@GNL;NR!L!gK{O{Tu{=b^VvsNwR2u(gMhCn=wpB%D|X4o-JMVgF~wFn~jw
zK}`im;Q?Z#Owq%r#oI5P>$i`7$$|MQ2*Ev(9Kmh2X4D}%;@I9|8@}SC@+~nj$?gq>
zbCfWKUL{JB1P`+;xg-6zQ`R{>8*YrUSoBX{fFwT}VuLxs3o1Y-IpM_wo<3@Tw3c_W
zg^ahcfs6?u3Sa9P4UsmZ*eV4YCL~%0rr0Mp7um_@F2{UM%wP`PlyL!T01YESZZ5q&
zG)Q?L#V0>gvEKib*5QRABbU$Js3}#KgDkRuoi2YvAddTC;7g)-xvg<_!s}q$TcFSd
z3X?=IPb`fg1sb(+&KW}Xx^kuPcezag7%o;m4Zd5gr9rwmbBf-lbX~KilR{l2P$MRa
zlM?>~+D%}HTK%BSlI%dfCuFes@{H^Aqpvj$4q=Lm6U_fg&Gxpde%oZJK@?6yTW4-t
zGk@kiN(4b#76c^E20C<i$8E&R$)rf<uD-_!WP1cTNNq89P&2WyagypD{w0yH9CSz1
z`7AWt5Bp)u2!fNEZ~aIAwlOPdjp>#ClaKWO0(IUtoTp|d{;RlWzX?g`goptIi-bW=
z?6|ns9-mqAH<DX3Cx<5qvrmTJ#$FeVYNL~Q7kpL&Mg1PzP=nAp8;M;}soPFWk=Dfx
zfFpnKQ{+Nxk`!lQvXqull#^k=ZtCQ<fPG{toxW+nH<uX1(RAwQZnUoY%-Z48*7R^n
zmmg7E90^=A<d$zow!)28TjZ%jY-xuMbWppwxX~!+emku9c-jXVqH%rYG4NOf-P?Hh
z>Xd&KMZVH+qyBBl3?RFG&%DUQEB5tzXCVgfPo1@&cMHzV+Y)hp9!?mFvYkO<q#vn!
zZ|{S1gpDsPxE)8Fy+I%z`j~E@?*)ggA}A0{Mv3yc|7t7KgC5F)>IU6BpJ<=Y&#|0T
zZ}H(qywfxX@xL*7;B8@nz}xX&yM{nKRfF0-W^6B_EB~u|&Q9;J?r)!`pSbsLqGFW<
zDLbc{{t|;C*L_%MAWF1!UdEadBd5d40o8wzwO=<Wla^f5y^bvf8IS#wAd5WbO#K77
zQq@`V$g-&F$0MVXD-hNybRP_DlBZ~ts})n2nf~!0ETrZBfYd32v$@av!=O!V?Em#N
zv)e`D#ou#oB9@1rb)|;#qQ8B|H+H-%GUw+6E0h$E$&Wkij-6&F2ik;x6b@{EJK!Ww
z|K?!D!bb=vmii^C8nc%tc8uKEc4(;Zr;}05lOLCv)vpw&DzJ9@AR6TJ@lff+FZps?
z_7(%van|0fJ7^~K?p8d->*BHdN&jg{e+>_!JVm3`H9~(W@gJ9=E1MTxGLllJQFc2k
zv#^OYx2MDf0r8$9g@6Tn(k}E)@26uy{Ss{z6wQg;dVZTk4O-}?&Wj+%Pa9{HcNkUy
z6%OJgKXF#iSF(TnV$OUG!+G|cFyHmwq<9gvUAIF1!~iNU6QsR{WepOFE#~^ZemzMa
zt{}?=;C@&zf+1aWLe@|;epXeEuzt1SY(Ofq@XSFA`CKC@N=rtr)snp*!KwJ{@%If^
ze!H66&IT2W9s;KLqfDNA6y>BZ6?@TA8#z7ZOXw>VS*C@r)Yvsy%(k}{KfGD_A;uB3
zjN-&Eg};Xn{Z~3!QUW)I@LIZ|9cWrCM-Ce*wkDagc8T2&EFE)(yt|wT0$vjCVQ-hF
zS`j{Rd8iwNjp#l=qS<f`K4QpPw#)d{{Z+GI+IlRwGiO$u63Pe8hZHCKU2vCvOG?Ob
z>EkgPo<}FAAvFZa>%(>D?w8ldeEnRdra`Ph1S_VqX0d_1Vrz!a2L^Qg9B}XPTla&o
zrc3(Dz$OhDka=zMPGYIZ@Y&1|vmvS@oR$3g;f&c!f7ueZ%6j$nE<K1yQ>e)#7rB|b
zgIMs*=ON6MvQ5~y-VsSyj}H?G?&DEV{{AI;$+!oIw$t*MS07lpuOAFVd}Hrl`g#MX
zgWF;{erQ09Ao#f1JP>Gk9V9$dT*ym9x&}<eGr%-m2rp|yl!7kzHCPgNBrE`P2X`?>
zx%fE44QHZKK9Z#F*n2(t1E81Mr9F~Q=p}virnQP9_n{=LJJWwu@Ip-2aikldKHr-g
ze^fv{a_0qYvC5bGw2nO5Sax#N6$q!Sz5q7pz{_;&gjp*EF_DI`Fmu;~(FN~@dd^}>
z({7C80#doU7+c%&+i6yOd^-)@<9SE2nD(VuPO<ofz~X8Y+)<`odydN#L3^bytX^<v
ze?#I<j6iZ!6QY)4iXe7N-OU54Yc7$-FCKmAoo4)WxF7TAeMfN1Z&=NdrRNCm;}c4c
z7`G&C39M3r0ZS~3g?Tu%yOszRpJ9X$|FVp`VOJxcWMDQBr~!O@^FXH!<sb9sFO?=B
zO{IF)#mp)|<tK6hX9!^S%se5)ZMyt5WTAYg`mFd{y&zG+m&9g&%S$Fyv13<LXG3)f
z4Ql<*VBJtPzXtD^MKsvsa*jQ|+q`Z#mNzlNceBy=at$TBz(0daQo7EDIq*cBM4*Fp
z^>UC5e99oM<n^IvWW0s{*pTaYg0W0#o|N;hL48iHtwQ<u6Q#HJM~$~m!&xAD%NH>7
z;JD*#1P6d4ALWs1+j>yLe8M-qshtmxIvk536{XQ<zv~6yxH!0eq}yE*)$HpvYv&Lz
zcJi0l(q^3grYKi+(T~+5#o>9f3il-BH!ex>#!B9G12g~A<Om(1-4P^OUDUo8BM&F2
z=U82?Xzl@L+5JZdda1xYzt5w&6KMmd-!CVGti*AO=#R!dle8o{E6GM(1QCRz-_qg;
z2`=$IS|x2<vZD6SMuXS-mV-X8u5{8_22#TCam{>cG;t;}Z@=Mh{`+`m_s$w}MAN8$
z=zitaOj?TKBAiE^ecAw_KO6Xzr!#&CFtfL8lm&5Igf8H`?YM6T;m)<Nd+se8b0(Ba
zJ`Wwz-V9;5qaF|TXC;d`K5-E1v0WFX7{qU~J?*+&{@vURs3aUr&so_5B;UCwebgSq
z<plSISk@q1rU~K{clRoBuA3hoIdm~?c&vE#&HSA43`e+Lkf@X)biYvc!QMT>5w2b$
zZR`!seGFR@+r4xV`T*G|7cc2K8-tnhyXrQdI;aSV1>Jp}KYs>9m_SDxjAEfi@Oz{f
zbjArXLI;1t0~onQEPsgoqappx9IJr#3abWFP(PZ0HJBqpqwjvQf6s^Gnd!Dlm!O$b
z#1~#vRL44Br~jKcOTC_E6&z?WZls86CXi2bGl~WqG)%|u(WB3$C%&(a3wmH)J6>I*
z)oscA>;^*8kV@-DKuWLs&Q4VFC6Jm=)d?Z&0%c8^3)M65sd133H~>W`v$5{5U6KA$
z=5HlRjz=4E;6o~{T>UGL^yg_h!*u*C*a#|fppk9m&?odILl&)1CDyQDx%%AFdwA%9
zGLti=%IKyIYL2c)#s2-T&~u&ee#CcyF(uB4;CvbT>OnP*eX>#8%E74t8|H0@nKw#C
z^KaonQl8vi?W6l>fmyt^YKAnZjYTOOwa1c*@B1jCvU0wOEul0|`N>Yd;9)#sYM=XV
z8a3%^3x4;FT#7H88QCf=9-AD#6(B383Kz)YIu}qj{Rx-6RdFXOhzelyBLP;$ziovg
z@yQGDL-9^V5&OWlAVCNcBG1dxCJ*Ni2N)i)Z2PF%M8650yK!3FAFHfBJbyc<>+_tF
zOl9|uvD%E0V})|wnBayJVuzg^e<qATw(GCp2M2a8O};VJPDyNXBTE>I{qZJDH^@L7
z*a{GnrU0^jbE1SM@fna~rMl$0OY7nm0FB-lwwqhLbd?uxPep^wY*`T7QHg$4_2yOP
zqi=sJW!;Mi`Q-5X`9FGVoiE*>Q^-At<0eUs<}~dlW(NuG$o04Fbt3Ml;~5KFsUY)2
zE9m?_fmh*-t3IMrQEJ=o4Y;t{7A2mqB)85IQ;vJ2bk=EC6V0o1b}dkb@6o*JSQw}k
zdgq|U{DStxB!8xZl}gOArBsjtz8hft=SAE1@Gpd}<*PGQ4>!UfZ02~nP|xQ;rk9v#
z)u*M;-er9^P$EPoxX~-1_8rZSF=V?NZIY54hPsJ_qjCgk4&msjO+PP3$(I<~McT<2
zCFKxQ(wSy5P!wesAo2;syXhz^0Da}pQu>ZzWE)P7ibS<*`}a-Eu*cF5SRL}=VO8LP
zvqv>)gBD*he4tuk!NV1bJF(piL~=SIPf1e6bKE^kAu()Sc@2qP4Xmj7LR>0(c&M*c
z0LK-qdaj|sH0+3EU!&EG!X1;Q+diVk_@Kt9cJJ_!J3)w3y_W8FC7MbT?T`c82X1HN
zlQI^zgcL$78K)*?^bAROW*#!B`kP?ZBOKKp)<V-4blO9xg+V!i?`nQfL-ulH!(Xzg
zuA8IL%&!3!aB8x$>Ird5B(vniN&L9<eK!Ac-32UOxzKeRF;-S?Bh4Rw`ATp%j%Yw3
z-$s_(utO{S%k+zSTeTU%cWfSPma;NT24^?Qyg&dbu;=CH5Wcg-YNa|R_Ku_P`9u^p
z{rTh`yhzfBl<Y{55aV>#!(wVA0+e;4y7KUb($F{OK2Jr$SI6;n&AnU-8#R)bxDX5W
zL^WM9AY)L1IhJ(DUg+~^5M$hJBt#{FXten_nxh;Eez?wkKmcoJBJu&BrU`DEDmyKe
z@w?OI`KlisvsOE0Cs9vQyizpKRZ~%ZZzwDKk#zgcFxU2*=Rc2_J?_y%h5O$$jM7Z|
z6z*R$_(vtc_5CBV^>NT#{6zZ&eusqBlX16!Pn;HtuecLT&ecM!M>&h&Y*>3#WRxoF
zKaYl(?*p;FnYa!8W!~QDI--*q6aTkygaM|-dz=^7L4nlIvCxg*K8g5Z`ig>y?7ce2
zAhD`td;H#}Iz5Q>RY@^Fy#u{X-#x}2{;ho_c%mJxAV#F-n_=Sl0{`{;;Ks@4>y#zf
zt7H26<->+mp*!+u@UHFpvslf5?k-4L@UBt*3atNl)yz(4I^A3pAdCz6nY@IZ6&pi-
zKbqKM-CYGk%rf%B-J~5Yx;|Kr=BG-fW@G>HVExWohW2lb48-5rk{B&L*F9ERwb<A=
zJK-1`Gu}fi7fk87q5UsB6eowSl`)$`)>n+LMs0&XC+2UcG$KDQk4WUN+MQ*+hL}@|
zNe5WuO|J>M<8>l{s~BE(TBMs7Zz3J((YhUOHoIp-ABG*yY1P}W&nbpQet<~=(I(xE
z3x;mIZKIl*-rh2^Pb`<&d_V8Es~rqj1*#A+2+!eO9$h-|-wRIH-*5blRH)tAOQ3K^
zeQpu9F6>6UO9fCjY{g92MB{HB%fs-5Nbum9Lxxuh+D0I0=A&}*6wU(+5JeLUd7Ei2
z8SXZg^eH|xa(FJ*GqSgu;4NAPcw#G7$)P78qbdn1WOqngbBJLef~Al1`E--!2LUj#
z;<S&kxh)f&3<&U^nq;=6!)K%Y%InP5k);Mb0{7H3n6dU)Bd@|sO(Q9KR>%_x&?L)}
z)IL1b>wsBCi$t<S_eFLR28!`SO%~XFERId>O8JMgP8DLnuW}m&fpVS!y$&;!WYy6s
z*n}f{ahlzL@secI19Kg+S9+U?B4i=AEhC%45aHPznIz=UpM@q+%-K`ls}Rf=txeRu
z8u%_?xCQQejGp$HOMTI&cd^s=_J%zN*+=*GUF+1j&H+87=q3zmfCfI>^9vFs`V{B)
z(<#Fl?D_=ohviO`I<o&UC6ZInDw_^PDlM5FCBwgF&xu_-)q5jJhihgSh8Ro|aL2Nz
zZ&NbDRiuH7fwZ{xv&Y`Ig*Dql=jgq>Z9R?KSCZ-=&^-dvdA6K^4P|*1Bpk-;NQlA+
zihpWr9IoE~I@8KAmIGP80o-;7gpUrY(5QbRFs(GuzHj1^D3f;id3rtBR4e&pe6OEn
z**5rbMONdrwt1iXn#^c1zHB5*e-n{xIFKUc*>V445YDn-$t2{~J`5#$_FEb>ShT-#
z*|xDU5HbDHb`X9y_QryM;P2k`g4iKqYq$IDkCL1_Saj(;3aM%g3CJ=7v8@OYwIibM
ziT0goYS?cGDrmSDt#<ufQ`;(&w5D;pey+S~{dUE8o{TmyNfeh~dB||&*Ws=WPCp-Z
z6da9D4}maLe7s~8$=>u^iodVAYG-(7+YHnFafa>zNOcE!VQncqmpcfjTwd+Ma=c4B
zymm>Pyt}h3@Fr|VJM6vfjuod6Tf79$)3^#ZX#VSmB8jBn*vussUYCM{!)H@fGFTL*
zGcmu-AWFv*->Jq{nR=tsHJZ5(D=Zp2r4^mZ@DKUPG3zmbbEf4x0>S&2Dg2PF`NP~4
zkDf>8?2Q~O3})IHHxoH$1OIL=wy|Lhhb+ck>9*zC;f&tjG|FF68WLio4s#O<fqFvt
zXW%Hpcj`SYt5&sYW<0)CH~z?0432B`CsX_tuNSF1X{<&@XG<SL>Mi-VxmK{$gpgY*
zz^qDa^wrX#S#N_Sr`dwiH?P)$lKH+h*^Y-4Mf6Tr=(6$+2Q?Atvyg)_{@p#RO@mV>
zn)(8Q`Rp(=6f3J_a#BUi@>~hjo{2mhol1I$v7+xjp;^ZHGwD;inIMvU?XT2}RvIPJ
zxb_j+TZtg;G8Sk?dg)&tRCnEkjt5PMfD^2n1LgufwGBTM9_pkXsvsIQAzyj@1|h|^
z!`SQ$nYIk5B}C{b)Jh~o)<G*4TEMUqp5==3MU;7?)5+$=m+23ywGqNPmxG3vBRS3}
zge)O#9>Fk&WiNuG0+*N&evAy(E|5nRfCcA0xo}Y%o3x4MuJb2|BM#=bdMu{t?%1m}
z)r8x$E9|w-_08;XENSU(lb3RKSkRss9X=le(sK6qLcaerlLic^b^5oQo5&~1r)n7=
z3$@uB46P1h_m?E)$3o4?%*2nSv+6!2PRy7oAz-q>hN9V@YZsI?oO80fkM`uEHQb8J
zaQ*%e8zY=u>?S19JDIk(H_kahVV;uDsgUwMjA@%sXVlt!?_~SSVTkp11Lp&DVHT5(
zP~g1t6gUrXuaNkNV2gibi8aX#p~B9j=3cwQ{+Rjm-qCC5B4U&x4O5}!zyto&;d3qj
z<l)oN@MWraajz#*)SBO4!J5DTre*Yb&x3jV>ElA*3#_eBar$`NWe{e5I=oB=o?tK)
z(Uo!|RBW}1{vKPgpk?WL+n5qg2YlUIlaDYv<#WO<`;YNDdEIn(f0@m<Ia4fLXF*`A
z^QVMbiHxOl43ZB(dt|=$4>zQ^+1~1RY%)wKCW!@mx4-e3?beIjCy1nS+1+ZNtcUHH
z=^lrQ)RC%KeVT>1J^3$ybfFKf+2-;Tqk}5eD-;qFs&bh4Dz?(?<0LkLx-~AQh4}-8
zl3H{UJ}%QZPr9aiB40#Xw*%35G)&*LIN!uj)`!dd;T8E$skHZH4q{V&9hqM%x*oG<
zWrk?P$I@d=VRYdH2q^lWnUTuGH#;4NwF=^FdHJnW(C<2gs0F&)H=h?%0%rz`?Msc#
zoTFyHiMP(!+C~z9)NdmhF*$dK)UCxN_l8twA-vi-R(7co`5p2BQS@u$jt50KFJ)%C
zhaqU4YglFFzn9*Sey^f($EQRw?MeIQi9d2uQii@&5>V5wseFxLH|v8EO}lz;Ngr23
zya<HAt+*yHSd?=d|Lw>SI+(eqoc-Z4@+ZZPj`7Az%$Ex;hif#|KEua{7}NF6D6isW
zPn{C!G{$sjnAihWO%_^Wd$5($CK6_5hPsIXNec_wwN7`X%BD#g5r1A;{3Cs=xaf;z
zBvPS%j_7z0$A@;AcU}!AGOXZf6CPX+A^|EPW)##~fD{yd|NgCh%;pI3{t;$^FDfEc
z66E|y5Yqp;{_x;n9!>KD{93%NUoM|E3t4|BNpe8LKl>l(&c%>f-0*GzScv%s^u<S`
zIiQC+G5Mc%<Olsii1)jn9Iljdd^~=F*BMY;ugz2WrP5E*_8R)B&_O70DbU?<^wSh|
zAVe_RJ_kM+y73cxS@EzC-2I{Q*t<lD&~X74*Jl&~?mIGKL&VB*95Cs%`Qy2LtfvbE
z4lNfS|G8UXS0O2Y?${r28gX6EeP~Goy3qZ$e9M>ps1Bka_oo8U=T%PYwar_gJ0F`!
zvQih)Q<Rp%4GUA*a^2B(trnsk9tK~eB*%mgGs&mtA!D+^YnOM;+IpLDLx;1vL%$9D
z*2DAAtg`QZ&;9s|ewL{RtJY}`s_^X5NcPblShy2m`t$XM0ZxI*O2#U3=r%Tm&m&K<
zQe^-bG4O-T@T!jwKI$HYla<Ri@u?*cmTEaBvu<6WGT^Xc43A&s23Uk(JB*hS=w*tW
z4|c>)3-c!j-d<+lORfQdEH`)a=@ZtUJ&()wtq#w@=k0@t_S2HOnPM5lOEp{XCftXi
z9>@^r6VllIW&-;iW+f0`?64CjCDI#rZHZN>&Qg$%hq*t0?Mc6r@eZpcb2W_!WrluE
zq$tZ;M%xr?_Ke8XCmc$}-!yEz6{4|s@0SDX+ytza3dm(d#b?lzWHyyRT1fg|mqu#7
z334A3Tz`x>L^a;1W;~c9j$qbdi9y9I&o?6!r9@h(kV+*pc^1a;Z2uFDUm7{R-#R8Y
ze2N?WmvlQj22hwpoR=T4`m2Pq;P2WS-l(P_+q+vVlUzIKWDgC)_g4oLq!*!^%6U9>
zQ|Ox=0e`Q`{`k)Z16gYr*IalSCI+WxexF<Unm5noc0oR!kw%`UKG2!HupRw7J^0Mm
zb6=pgeruWQsbCxr`cw4hu`Inq*s$D4B~8f8KHG;CJm?JENOJOgv3#NRB$=`GowKi+
zf6ANXH&5GTwFpfI>M#?aTV&q;93_9ZfMndnp5sWof#OP>FMRAjLUjy%duz(DA2%6f
z8{STIMb|!zY|R8}OYQDMB^Qkp6LF6u`L+5JM*|JJttsVovYv1bjV$|?*8``c_<U5f
z;KmE5w#V@Ep2ALOx^yb)!S6t&!w(Yjz=8R)BZ$MH+x$>votZ;F8pw6daM$O|a>~al
z_`7!1(Z9p(^+WB)d;co&2?}Y4XeU~^^s}_OY@qZXBsjn>DXGlW;Hwxfd5zBOFj)<1
zJN@sc{AS-W)=Gw}Vq*O1n3=JZnvwvdBoIS)ycCYV1yf?d0GhEs6?lyw0ZRdEW@Z?8
zX`7(cPSU<_v)%8L&Y(=Dt7BsDp|Ktj1j4#hfjC77AW_KME`Z=By?o8#p0t*e9qelc
z2OFSB!Sx1OP$y{qS*3EWN%v3Pekg)8U%GpcuS0rQ^<Sg#t%}v|WPX|9&Hd?*4m(2S
zxuy-#KT3BS7DX#xG&H#=<4Iqmr#P9EC6dSgc`Vm)TQXpM<u7rv()h;H$}BUSZFFuY
z)aTX<th?jS=g2y)m!N(khJKndQ#1gX2_RO<yVRyKy;_DJ3MG7>;=Xab95t|9e7kcP
zCj9b7mpVv!vS?EzS7oQfHU7?>F`hH(oqQZ-EJ5P_^PJpXZQRW6jiD<NFI6$QO+_OJ
z!7I&d0-g!(S}An^3iGCPX}S-wRETK=JDcp7K_NxUqm29C;K#Awwa%voZsnv>7n?%*
zG~G>X2&;(S1VKId8=BrSjm~kr-Mo3zFwF`*(l}3J&|-WuG}QXRz+rSu_f)<rUhOhQ
zaD*w}coW{Bd+g2c#Q*{pA8}u<qrYR$(UNI^Q}N=WfTzAG=~E*oeTG25((MF8^4tgp
zo}h(GI<I$|gzO)imf)5u)ppbisa`lz^`qKctLV3fCB>NRb`d{>YQ(WrK_5>MB=wBF
zZ=#3%PQE6N2p*O$Z5wjp6*sa~XR1Qkf&G)$<xaOG5pF`nK?eV0{!I^r*dhZSoqZA;
zYUzaNC&~-B!n81cfRDOdmo?$B>lxgqNB=YCY6Aj3jjZ0*-%9|W8*kN@1!ZQ12_H4d
zVvs12n-e(0`+eK@1Ux^xZV%@P#WQ!jkFnV`vwlh6MWgKjQ6!bQ^gmaygWp0$ewdxo
zPRLulV*A?qXdL^My2!N1BQX5F2u(D#<)yYjO?vw-MkvSN^}~DC@<k)3tuAk;1>qX)
zHAb}MM|t+x<&U8t>+ggu2~$^q%i4RM4x|)6#}qCOgo=%>t3-j-KzHHKoo>-M0BZI6
z-qA1FA?a;cn8Z!LquKktX5m<zJ?<dtMjS{aD|IwaCcFz*GaMrt+;3DLY%=((cx0vU
z-@&mF0}99Z(2U4sbp24`jcWe;*!HSv9f7jWwJL+QK~l{@x32GGW2mrB%jQjn*eiu1
z^+JV;h6fOY14$QL!vBuon&2@HR)Qt)m27Ws0}3m*Ms8-3GUkk#j&T6?O&poy>ICu`
z+`s<uH$sYD>-<%yDwW|eb$vQ$P+u9jM-)Bg=tL)5!B)`&)v|_VDEy@9CYbNO$#6SO
zP-KyVJ!kZ=Z*7~v8J%Xsw|u;_jcuEvs8^s7IB*#X?)ZwE*46G~QTf&!$2G2fSbZH{
zhlM|}^E0P6#*S>YqqsyMQAj6IkH|c+V#SFeK`6a|?{@H6n%XR1QQ?O_=4)AhYd-u!
zP0xPdnvZ`6!Xk_Cz9}UAdDVW@Bo1!F_(Q~GT_Oc*sU7;ZNwP=h4<@%6w%dN5=!_I@
zO%)^&=16g0tihy)RQd6}?M&kggS*4B0M=Do9WBbSr`;8c|C%jmIshXg5~1Yr5RG|R
z(4dw8O4=;*?hwb!G$mC43(n=eTzdDao9Hf4b5<IW#3D2dlR%UvXWVx$CV{y|mESsH
z4Bs{vRI4o2jL7u<eA^dBlKI^M_7>vF!hmc~mAf9f3?M(mynHSu<IdY?Uv}>*x`Af{
zem7v{sFTve^WlfPVqCRwE6Giw_ne=|!i%NQMv$%C8=$usjL2I&7GlBNLp!v4!ZR4e
zA2)ewzFAX1X&QQHc<~!dELewC>(bL39~^B%7Yf$e?Ns}D8NY0OJ}az97L6oWHA77)
z7d88w?=<{kY=UPgEk$tb`s^J#IwLLVG}i?dnXDn3yNfi-3MQ-vbrkGKZ+o7Xam~*+
z|H{}bd;wFd5<h1WoTiN8#$)yfm74c*a=<JLgbCpzdQKWc!w|G86N4l|1~W3hM5vPt
zjz`rfVh7#oS8?1!SI=+`1Z%{Cv3_`;Y~PdT^A%fvk}{5nQcPcMNudTc*J-ol<^uq=
z;R-`1R;*{TJWGYMr=^&b6uUP*auVd?KH|%&QSNx}0iiK7d*BpX2ETr+Zo!wH^EjV~
z8E4+_5jXjc$B2?nq<WC@)JN2h<x;yel*3W|0z<4+{Z@lv{ceuzb75c0_%=o%{g!=a
zS}|wG=`ACQnUxy!r4K^*jTX~3?t{WSx5)#ZZOP_G)vY=F4P{hwlI(oK(smXdFbGTb
zDFIb-8$*tNMR?MMCd!k<%@F}V#eS6}$ndxU4PC!O+1@y9Xw<2R2pMI(;8d3z<D+Ez
zcWcC1SjRaq$wIfA<)NdVy58w$!_YL9E|eXtJHdc6oGcL6UqJ)C_`;d@JV@u1K+-+=
zu}(!(lECN<^KX+fJp5djEEy*rzsBlt@N2|0uCFszBgRr<AO?Z6iq4k)>@2+y9G8&u
z<o!XFfeNXwg=CrOZ`8QTN@_ICGe;7ltQq=QQ^e*(<8NuIXMe3${W-}Fi(%BR<4YvX
zs5rvKNosyb(c=b8OYrA%w3HDQ<cmOiBgF6i0xM5!bqLqbJ7^S+Q>@=f-dp+Wnq0gc
zo><r70bi%;vp4vzS^biT&D%^h&Lxn83=`ibrbOaLQ&F!{?Kgc@9K^P(gu&JUqRq+l
zfBK(lsFG8i@`5(rvv)L+iq#p;H!6C}9KwHrQyV_oL!RDSv&_VncaC`DOJ=sj{fh2j
z?}ArQKX3L)hvqN}JfN@ePQ2sp-IcVZ8AdGwfo)6mwXM6GRT#on{+A(4lZktpVb3Io
zpWD!G|I*K7E*}JB^IWQ-ebyVr`2Q>p))VxXGMqlyjm?LFp!Ii3+v4Z7&qu3O@9UsP
zo+g`0iD_kV6z{s~x^U#bEpv40kYBwY4Pm31Lv8(S;Gpq-u=g+pGK>{XIl&5G1Mqdw
zU&e1t=N8mxON)uHq$kctgWH7{yOE7Bk#lo)+&~KL%xrQbFOzN5cLNV?9Xjf8DZJ*o
z#}#wxM#LxV=ZCYWul%^9L{Xv_)GUo+)Mqu?RmW=w_rN!bjT&}*hd3X<Olk^ha%KEv
z=9Sdi@sIg@^^$5ME=q+zZb3rhs;bw)9qQs4p-$TiOHE7m9O<-|Hr$pT@6&EN_Iw$A
zQk?FQ_I&sV{`hOoX#}BZ_o~!qMH9o5Vc|&?Q3quXP1aZnDYc<uf(wG}Mv`q3xC|9b
zE=OlW&kD=06#~CMqwF7b@(sRN!mKPA)gv4GSwz&1Ai0>{X}`DoEMuGS;8-|uSMNh$
zm-U?UwvV==pl>rSvSrs&!ja1S!o{CeIm{hkFFAr`Yi#-(#NI|GHrEj+pSS=cIWfjU
z37AoE&sNTKp7-w;xzaJnE=7oh%&cmEiRS{WpY(1ieMfKM#7ytUPSP7%Ai^zSvxG+N
zx$2Ne%;9an%ih8$%%%FMe9vS(c7nP1AaTxGe#vZVFG2Hf4ncM{aqy%zU0P6KI>Si=
z<|RXyyx}i1+vAA-NB%WQYy)%qPGUay=!Hd@s<jDPcXphCyn7c=(C-0zEFDm-1(Q-n
zux{v|H+~(Y4q|kMi9{oD`&Gi#(ze%KJch~~1wkBVH&GGP83HyoFaOCa1iTh9m(lwj
z^uCRF{$OVwb(Sc`=e@g%7v2`b4n<r%Tvz$HgooiKq3FGlPNUs*`!rT2d<^jaL(@Bk
z$H9Hy-*M8|Nn_i#(Ku<+G)CiOV%xUu#<p$SY;0rVXYTLs|Gb#@b6s=h?0wc=>$5+_
zvE>gfb+7hR=VwuOg_U$3CtVjEMsgDPApKFcS_t^*r-&EDx~Fd{y(_(Z;t+JXcCS}+
z2W);5FLs1)jKnH79ni<t$O%Wg3Zj_w{VaLi6mlV-^gn*Vw%ZHzZb5AO^RqILGPN(S
z7moNna?KLEwOC+7fjUqN&7io`UdqNFvQV;Q`x&gEZQ=Hn@|LaDlvHtD0`INLp(VT%
zJx_N!uw4hUVQy4)Wgz1P);`_x?kBpC?6z0EP-l%#^`&&&+1OImTm7gZR2Z@LSoS!C
zuVdEdoZ3J%SlW!g5wdasH9G6x#@q6^LnPrnW3a(@)Sl-d<SJKu`juY9a0gRqrpRfG
z)6sq~Gps2^02Ge*A0ycoJ$40mkQkSWH{2;=UUlQ}B@yuPkC^92J;v#g_E2v-d!a%a
zztX@XHRT0Dc_%>EAJt(N16ap6taN4(Soi7;-uY9+e!OnYob}VDab2xAJwM6>o>em4
z<L{3sn!-JS6n{ln9jQvpr%z4Hsq>?fx=`g+yw0SmGAMiwp1#-0iBt!AwAp@Kd{SR%
z3_4u;x%iE}WXqB7F}cHW2!PF-JWWCjDwl{`_rb}W_ca|v_lS+Hc_&ND0;Q{3dnHYp
z{gWI4d{F;84}k?ffl;GB?#`<gHmpiu-jW$OmqFq?aPfUkd*v7bw2^Wk6or2|DDGaH
zM5`^tPlXfAQZ@02&zx){Er=)lS6gu@Y-CKSDf@)|0}1BPA!u}C0EOyi`oZKC{_Ot{
z9;zNKVp|mX33nl6$J6zpk6p|x+}!NDpz^r&HsJ^?6geg79|(i~r1;~X-CUd>NFtP9
zwy7IZBk&e!+jyNCe)%<m(QfZ#)KT-vyS|s?b5txBE6LOtAOZ<tBpoZOqqZay9%+od
zw}eJ%pFcUK-*ZcOF?_qMG^xZtLCixABH(|H5Pycgfz-6-Sht<*?SE(1o(q|1VN4Y{
z{EiejyF^p2h|IJb?I;sq)muJyp1w|#J%+vqfzKt~zYc{k(>Oqha1=Km2<;W2pI6sY
zevZ3*s1LPa(t<alw?58y|73hkrGrsEvEW<_*dpjuh(r@h-Hot#p6zf?frPT{K1Z03
z%vdfUCQWhGPb&l}V`hDfxIo_SILLZT6metrGt%4-icnno9-Z1hFtKARm}=#eJ$b=3
z`dXbX+UxtiJ#2ZBzV+8Mqfb#1BnDn$b7`k4M7{}w=^OIM8PTyHr5A36l|G%mTuX;b
zrch&^O6oDU>{A?wMU}l+@y1SPc!93<oq%ObnZ}8+YN8BUX~vd<SWzI<^{E6T@hzl^
zQ}bL!#Ll<5-eI3RDYhnO-dP!m`m5PkC)L77i+%IKI5S2dB?GjmW3uyGQKUR4?B98Q
z1j<Ltz|ILVfof^3(VZVn1?~B6t5yO_9~0I7vH@PsCWb|0$&UOadD+GWVc3HPcG8A7
zFUs}Z9dex@Mv$t}c-Fw=7;3nQ?aWI0{oz}-@tH%Cq5j{OX%3ngG3NW<wsNo&$Z`Hx
zGLD`fKQG~}G)%zxMx+BvFdm?p`>vJ9Y0WS24#7b@1Tz?D4>CrL;4Nc+jN=vsW3})^
zswPnqOql_+!_l#PHXx(x9A1sZ=@xrQzG>;PSgIX5dbS5n=AQe1CKWHJR&AbKjA0wY
zsq1`m0yv1!Tj8_oJ%qAcZKpk5sc|b0m;h7HJFn09<GLS4KI<yoom+R{?U&n!r^muV
zJ&gK89H2Y)*=e;x&!+vN(`oJRH^yAJA-`#n-I1fFm8We;(aRjJopwtSA&x+^OBoj|
zAVagdC(b93*QxbAbpx?8v>-4t-3O+I@8!MVIFxKVkqu=RKgL$X;A_m8-*f)U#@>U|
zrFKtI{h7gH%LZyIz{FP=raf^iw60@D`^#N)ZL37sPoexCAHudzQP7q7UknaqG!F!t
z@z2>o&*NPF%D2QXI+j1auw_>g;emiYD|AN8fl<HEB1Z#B0fRB{FH#<U*APY@Wu--E
z$VnZ9-8jXK3X+~SQNFj|{Ns)O&6&}^GMhH$!Cs`i_bS|e{9)6}>AQAIM+6t`;0$j`
zMliu<8n8+=h>VV3!|EPt89!egHQ+&AokeOBBTk0kKP&jNVC@y{%qfd8Toxj{6t{!u
zxFLe7@S$d8B4HWvNvn})Cdrnd{$kR=B=7a>H-Az4S7Q=znCGA_zt7){104)R*$ej6
zXu}FZM1rHc9~{Pu5%M7wqdX&frj95i*tflFY>E+fUN!U%vNf&OMaky}284DgXE-ec
zRQ7S<9Vm=O*9?;fSUoD@IcIu}ey8pzO;U++Tp8Tju7&Y>|K=ViX9<$Xh&-Cm9oGwa
z1MT-T3IO}TStjV&RPE!#14B%r>_m4CLdw(|0Pq+(5@_aA0fzv^@#~T4Y!rJ^F&Rj)
zUhHu=zT=kj89F>LOmCJj9{Xk$5xsxR^+7PqzRQER7Dq)mKiY8=wP-N>>~V_VrSRdJ
z{7(c}k0J3Z-b&+OrF9VSh)*l%k;b;i9=}tVBkB+E)LvNwJj}2$l@K5XV!`we->CIH
zwK{@RxuFn+w;@mci6eWyhN$Ozm;^rmr0bfPJ^PESxd{9x1baN=5HZ*Fa^a=F(6<y3
z_kcv)hu5bFM<k2<$qz#8r0c<86l)Gw&*`N9auP>6UM|uvAHDT=iq^=h;Vwxp@;he_
zux{20GF8&MB-nIaZ;`R*#E_qX%AhgKZp4#*|FV+_?B9K(MHxmfljRzpa%OwiBG7oR
zHzzPu2{;8N<OS?v7D)Mgh#9)QXm!uVQ9y#b`Q_OYQf1ZiI@k{5QHID4;pIZ?k0$wT
zO<!PE+I$d_%rtl*`QM-ba;=m>!XO>ofE_!AI2~doiA)l;6{!V_#c)!?f`mR;UvE$V
zhH~#`g`a|vNa$<Dl`$A-xDbaEy&0DpZc2Tb(n(+tOh7dQlYsT)>bWXga6!sG9?P(?
z>)aG`Vlf9wb!#7)^-KyipizzMam#&5@>?|BVninWD7gs)4Da;jPnaK%75%DfS1a9m
zh~qv&kt9p^;<Sy@v$s>!q`nXrEEm4p^04Oj_4jvKaGEAvSNyU^|4zNE$qA;$CCodr
zW+_mP+jm>hR7MlbK-w1d>q35wK`Esa-*#2nG`ivMXdwjK8|l+~-ED2NrI7#k=`>~l
zg8S>V!{sY0Qn5(o#pppgEpNEPi@|l9_{l`)c4*^6rZl=)hoS9#s$iO3aE}LL36}^7
z_K3tZ>%5&ks!FX#vX$K$x}!Xxdv(%+N?+TLN|#F9{jweHj#JlCT-Q7A<I2Q@mH!{^
zkM^r_3y_S<>N2xXdiz@RSFtIJBT6(*)A=)Edbn?5hsCpfi~VmyhthlV+rCu`g7g+X
z<cx-!MN4N?;njiAaWLX|#&r_^aFYS~>=rKaAisBrdH1?mz7Ps4H#u=wOuy>?L@&jl
zUd0RZTXli`4`3j@(xCdfR!}c7naxAC88=bGv+f%TVWM8xWn^#3agFTP%<o)FeMrw$
zwqd)S_kW@D+_`JBTr`Lt`IC23p7YCH*^Wj$-VYY1&JE0#_`Q*&+5Wxwe1PBSCAZ^{
zJ_zM23%4#<yNYbSz-y%^J^|}v;V;(R4v}XfhU*uB<=ywkmYk)CC!B}!03&-$Eo!pf
z+xIG8Zwha4ZQ+zf0jL<)ubq0>j;!~Ef!?=+YXX}b&aSHPj^>Z29>EM?`9J~)${ChS
zbM==eOj<6jZ0<bbr+d07tNFZeyaeB&2J#`<_ql)-Y>wI^gR`28L1!3p;43ASWhv}Q
z;iQgBZT8awRf@eDxaacfv?<KwcLS_Y2b{982El_W28hExc|w5M&nuqv=_H_CVvUHU
zq*kj};0Db5VaazHek6Kc5{8qgkWGzFz-8lBfDl8v*&?ZYQR*v_DhpjOlR8^X>JXSE
z<Y%>XL>Mh*E8SS+w2=Jj%>1s_n73B@VaAg-O<|jqU)m;k;Ti$D`U8^nG&EPfdU$K6
z+me(iP-!zDxs7^XToOK-d!50R^t7pd>Ki-jIi{nvmkKpE3O;>`HG_CgcE*RX7=Ie)
zZw>4r2mG$xqWrmW^x)A+mkIZ2pKvIcbtRZ^Pw@0=-@8PV(~}q5f+Q4ua_(>k8yu)V
zD`~jmF|_(UgF3m)1mfH&>?%%&qSDo6=+2oqT+(wl1>WBR1*p!)@g4$>9yf8|pF!yW
z<mIu3I-`-lZ7T|}<Y<e2Y9=LX-elwqLcMzXX6`3<T@UL;BR8%iLPFOAU-9o~<q#N@
zhO!hYyYJj_keZB`kt*h`u5X;ZDlFfBQ;5ap5Onm$(ZK!gQ@J^v(^LVCQaO;og&JPV
zVg-_jbW~hwt?)_})CWhduiq~UPwS3d35wb-XdE#qtQB^zM6N-*Hz^jT0|$GockJH^
zFs=?b5}r^z^5u8Rmj!SM=xAE>>_LSx>S?{&y|f#%Ena*Vw&uKY=oezAQ1{ab%5A)O
zrJx7jatxcRPzUYDT<U}(+a;@*w6;CBp7?!A>=bj2SefVmtcLHyA@u_6m7aQG${%jh
zUf*sX6m8+{ZUblTwxyr?Il{vg&mhZ1B-R;9v7}XV%VM+MMb=DuTcpyOpaoD`IDlwL
z+}kg9C+iI5gPjmsQo+sP&rMy<1Op$lLp<egXqxlRwi$5jqkoTbro#QG$~Mc30V4I)
z@NklLqw-=Jm=K!NhbBHi34Sr=yK-hO<?b_X;A<O-`yCsCPRw6uIccBYwN2WtB}g03
zt*pHg`y^l@?7|FL7%mZg-qU}W%m$Q@2L?Bd^U|;IrWaL{!`*{94@e2_&Q3Le75?5G
z-f97Vh4TIQe!#oiQiMj}<akSgKyC!d8)paI^EQTL!h0FJ0u`F)od=0KN%_8UV|T``
zLQU-u&7o}ZZfpg|zdUbLg80bFnBz6dt0~J!OE4Y1l)+gqDqA)>{W#JpJ&&4h3w4$+
z`5MYh5jF}jk`+{o5-(*B?hA~SO$ohWeJEDnVY7Xe>(V)9hL|lSpDoAM!NGH-J2wWs
z=2soi??mR`$DL<crZYm*O;i1@G_oM`LXpo7`tf6gPUm0`hmIXXrT`7@CY_PoSF)Kd
z#9&Q-{YeaJ^vRY*eu{9*9-b#L;9+#w;tE4^K%}6EetKNcEYO$5)<8QFSaVZ(=;iKM
zVis_%tm)W3x0{3E^ClXsJUUO3Vk$9bhecd*=B!L~1&QA_fwWe0A&5xs3t8L#iq+hW
z#*UZq$l+qL-rbLluOepa^(7$pzv_=exo!n$58PzIs3G%fQJtc{Ptty086Pvug7G#T
zC6BDNqRdH}t?dl~NISll!I#415y94>y`a#ul4G&U@_8zD^;xBrBM(g1R}6$w^+x)3
zcsBl?`>Nf8rc(|dgihH96;wkk)`^hwWp<JO23;yNW;Um3$)8Kl?mOexJ0p{oXw^Oz
z<2UA{Mejp#6bhykT9BdPrs3JOwqf$Nx!OAV*VcN4@cQpTaZ+^0MM%=}#Xw~PZYE5c
zan&wCI^IzT>_&PT8J{r@IOVat1%V41a>h9nj#?rjHGli^>gVI9apcJ7AVf&V;y|b4
zL@BE{l%`$uwkl#AV<iSLj$H(A!=Zjk@*Y9JPJQ%U0GC?0RDsmHIYokrOR>J<6W4yt
zzHA#};yyYbKs3;z>by>|=w}JM*$A@Q9r3MjZ@Bvxq3ELG#+)h78(e?h$@$WX0=f7G
zWxB3-dklNk%Dey4hdmYD9TI%MG#_=ABulwHXn!3d$q2B%{8l{%L(&}8{YOPf$nA6Y
zk`0lvB3t^5_472{8*eP#C6o1s<$Jli6w^zr*l6UDi;UdEd1IQHWyLclVH<>1XxxdO
zQOc$5VDIb9&u#6obx3eTvJteJXv4kty&o1=HzB<Pi6;ziJS*qM4ee8XZmnZ=)VkR`
zO24Yt17_UTzAR3wi!v!R6{)3ni{07#pdRkr>^H-x9I%sl!2Io^O)<cN^2cJov(xE%
znt1w11*J|+9@h+3EC;iHnff24?dKpEa@Lk*><?e0KoPp%u*70EmOkq!GQ`*~)?M<-
zM-2Qoj#Wi<gU|<jYF;>W=I``J7Sk}xhCus+9F@g8FPnv{rBg!GFW8~=8*jBDTTVq$
zj32{h*vYq+eDwnrH_Y8HfY(f0VeZcCED6tdW_R@Fa`%5{(`zcG52Xw9&wAdXCSJ5y
zP%!=?C5-i2i-wV_Fszu6ria9Au==&IHs`KaO15vt#CqqddNP~_VA`Xj22V3y5BZIz
z+Sf2m`aJJs&2C;4-gTYTe}mcWjXRDSc$lKv&)e>lKh@&(wKr6bh%VF~9o<mpWNP3A
ze|>HI%0a<sb-vsmS#59DwZ0PQU;rCxz^2+A{uAryX1a^d$Z!`jFqlPgq}L2%i;>w<
zCYs@W48u%+FCsJ{F5ke+uiyNGz+;l)oF9S<Tc`@!c^A<FP~$e~mlsTwL3Ka)Rxl1o
z6nM~Ba~qX*&ypO`l!eZfujkWzO^JRNOAnqa0VJiH+PeHi#zhkFk9Ie6PB~3waVFxU
zjgsFy_|+_u*`zXlMoO$9{X7{cg|Q?YR?vX-9IzXn?G@G(U2~sKVmy_mU2Wa7!qmq$
zaIOJFE4l~-t%=ONiPO%5gfn8#nYE~w2nI_S?~c;6rVy!(A(oN%w;rX1ox54`gZe$a
zgc2+c7m1B`Q6dmc3Hw%c*cd3hXzLB+l%d<OV|jf3<&pQ}a^B*#Ebvv#cJ=R7+4y5T
zbfr29UO8ET4aIpbpja32aFGhmT%JWa#W4!7$=M7qRXb&VOGm}tRbWBn|3heBvWUx<
zllmh^+~Z-)9n0kC7LP`OV4AqI`RK~z(AHt#?cjLrw{F64wotK4z9Gj28I%GX#^KrP
z5{VU56p=1ecp<OI3-53Jh;C1_;~VJ}06AYq4-AfN$dAEDD0Rs@-tFFVuhBHI*d=)Z
zy<gak+mn~~QMr=0>4R*?6uz=_|HJr;i~2<x`~2I#UqJ5cZuvVcw%Z5t&b(dekQGkY
zuw}Elz@lQjgKWbZInb`dc7!H}J6la0AA&LJMWT#J{}jB(sCMq!eqo~&?4;_t=<&Js
zp9aR!R6_g1`>*H-7L_}RSQxrD1xZ;Dg6xcaeYS?w>GjSIu=1N3;hSiCW~iHbTY-@#
zK(M3d9-!On>jF8kR|S$NZ8I1+69v##<5CZsKL-y8^41W7=FAFoEpm(?=<Ke>JKgZ#
z3p_4?{XW0Y4>GgbK}bb-%)2=ssO;6hTv?VgZ;YtnQu8|4MOth4XVwX^22rh{PJGXk
zw;oL~5U90@x}!FjWFj>r%2NdFg>|+Fh8hL~{RGvp4i5TaR2Cuaor$C3gnfph6w<So
z1v-tkYi-O`t335J0=kU^zQjS`M-sm{VHfrO9m_-Hp>twI2YO?EJ}kXC+;09JT~WyA
znr?^^#4~!Q(Ler0ne<Z`V@8UFXG|FMXLh<jwC-dFOwsBZU778wzc4$Um?ys)K?nq4
zIPWPRx$k!xM;cZ^C@P|Tsu*dns`Pw{53|%bHUzt@XPGHbrcbNh1XvQ{^<`Ar@)+#v
z3unq3GDZCVEPyJ~z2W7DIylM){SR&Fe9<Z>sc<zV(TMhQ67d_{&q~6ftRmTbPB=R?
z4#f=iQj(U?gfwyUuo(*PX2W+j=8&HzcY|q}tz&I^x8|FqyKFAL4(>O!FAy0X_{UR$
z^v%FW%qU7>A_$|A7@7NrS90-ezmcw)YC%2UCjSMv_0~9>RBm_B+x;W!bcDU){6voH
zQdawE_YtL21Z+KieUEu7@wUf!(uj|lV@SI4gG6_dQ?D@|2lHc)Mp>G<1lSlpL$-(B
zs5e{${Lcu0NCeK5@Z!#pu%@ZE#_o2rF0lvc%B(;zq6<4NR>Ji?0VCsf+g30$N3Z8R
zNT8^$3(bw$CHZBxtI8c|ArC~Aj=>kFF4C^<ZNUVw6%f(<E0_f66?m;SQH!MY0OvV@
z;T@$99-#1po9ZQfO~Kh{SuHXMAWmuG?HU&2WE>~;RZ}CB-2JfHE-H)lWFu<uMXLQc
z{i6S$m`@hPY{*@_GgvJjihQ5%DRUVF=IZ6SQPMi0?GF06MuJ&UW1ZaWf&?{4pwb05
zaWG16kvzFh3|d*MaZr9_v~nI(F)#9;??|<g{%!O4NC=joW%8Vjg$B-jBg0fTy&@#3
z2@pg3(Zn^r;)@9tU@%=Yb0{8*M>_+t?luea&cgHaF_!P}cA>?o%T0Q((ec{9(O6Kg
zF5<R<0-<Zv!3E4@@mmtIRl*1Qzi%>A{+UMN?Qc?=iT1u_2xIK3NI&y_p^(I@uC^g-
zrW|%L8`Xb`DKS!ab5CU?fU+D5%$<u~<SB?k^QHrEyuM8x!G;pg)<Gvdx9Ekv!#C{i
z0dhVAEC*GL#+BsRLuUIT{Ko#z%#>1vVw{pO5jc*dD?=xUQpP>Si!PTOUjUmpn-!=-
zWk<cW8LsEwyzBkP@XYH-bnT-@fl3~OGh8j=uZswtfBM*EGQ4JK_8s=<OP)k~M?!u>
zNXNjAWOj7eX;|B=ZiW+@0D^drgsKl*HJjQLquWxewt(jX%dw}S@F!(XKYi}6AWy>|
z`PbAy7{^wND|tJ6Kv2D0o_xa3*^hRr+#jv<s|pA@fvG*8?ca;LVW+(-nsJS@HhSq$
zi_XbNXE;zVgyb^}71HhnYVPY1ea45KRig331XDq}(u|#PI&S3f5r{_vs`b5KXc~1U
zO0S2$dU4~56p&Dr3TwdYXJW6@@W!qSxW!^}we&|^m%7hUNP0RkT4x3b-=}D^g=1oO
zpTSGe#3x-*JN~h}UfFbzS`9=8)oq}1O_Gb`PWqLYd#asi)8Xna9j(pUj0Vnx?MDBZ
zzuNq4U?iTp{E0`O^JVgR#`IX-%Bc+1Y*4pfv(wE&NW-6xh*=NQU4ps@yw-PPAe{C;
z2s!#`0ST3}(aQg!Bw$Qa<_<^GD(dVNasAx_(MjU{TP#qf4(gAfADLe8kI{i<gUSQ?
z2n`2)OjT5=J0zXJ^2tnANv?E8w9db#g&b)pQ)@nv@1@N1bnfdW&21q76la*4hd_VJ
z0PMDd29~}`r?W@(+0_=~0~bF}ry;6Uni7uYf6Bq}+@@4`@~|S@_XFSq%o(~Wz$rzV
zp(MyOwJ8ZfbIj}8gFXp&{(5J0v$$$-Um6Tz#e8;$dD}MAjDz(@iu2&Q4JGk#VvBd@
zuSp-scpvEHkQzhO#yINSCRkt$E#Bya@m5ghk815MI4fD+4%Reqx{h?FuqbPSgDeT<
zD!uwoYlcA1jTu&#qjBF8r>4e~XY!*bqpc7DYorb_C2W_riyn?v=qux`6$X7*T0|yy
zE3{sbA*X#e5CDy4{?RWXP!nHyUSaqoOE?4nJ(JmG#g6E9*;sTs61<fC8OK{B)X6Gl
zjiA%h!u<o?9{)$X#WsTx#*q+HFWU~{q9xpOPtDq+wKdl^WNNPGmI)+l4ZYjZ8<Vsc
z-4U0Ac(qH_qIr7iJKsBA#8+Y#PE~tIJ4C6fbAZ9_>~ZL^v@cb*CADNX=y`Jj4aIfU
z&wi=IKvUy;n$YJ1&!6<Zm0jgYD4*KPb7AxTrLPb0RYdkwDhiAj@k3a=(r-4QXdm}R
zw9KDmCw!QSkOA?`&YAe=`L*Do{qsp~$|v}(eixXHKm9qSk4@F(4Pjf^P%vFP5<LA~
z_tx7Txb^XxG{fv6<h^^f44_@HG19w`S)F$@dkyquM)(%OhSM5}m<yS@xD5x2LqDDx
zp;VgNlJk%9!DKVq0Sv_vSl1GLI*62?LO&**<28(f6JEm+LXEA?m_enO7UbP267?yq
zoyvCiA6e+Hy$R=SV#76tncbtQK)XCZn;Pna$l%QSk<%W)eQ;V5u7yuuS>amd#9*-W
zj<%^{`j3raT=XlMbugHs1p_AJ?GFcaRJcI0QAqOo`Bbvg)rfaVH=K=@-*-?aZax1F
z=gblAG`hAK#fl;Mv2bM>RsJ+SNv=V|zQoyVg&i~RL*Z|*YP<tS<rAE{8cyk^!WG96
zAeD%%J&JXwHo<n7F0E-<1_^D5l&xk}z80@V!;lkVHC*ZJ4M-s?iO{{{D+{N#G%X*d
zYY`XH%9d<jyDFdZT?$O~(e9a?5^5n9d%f*0BWGd%G6`8zBK*Um*$@~oyL;DI)+dB6
zYe@@%hY~|yxjSDbV1vh(#o~rFHNKfodorg!!^`b3pCv-?s4szW*_lG|`+VRC=e6w7
z*H)7z@t=UE;nT0wzxE}u)}6B)1VU7J_$l7<f8z>wFI850OZOB;Qgb;!`kk)UFtXa-
z8iJAd#~1UC1YqQ|27;783+ha-(uM_>ZKI@b>$f!Xi-efAVyy#1kqfSHXxL*tr4Nwa
zi+=u3n2ho7&4FY#pib{@gyAPjT@-}d>@s$I=2<Ap0rXeLQ97eQFdNuE^V{erfoviU
znPQ+rhxmKeS=Qra60{__Se1dvrI{RpdnRo$#*}cS*bL;w9vLd;a3X?;tz64rDec7F
z@j4U0Q8d3Dpl>b~es|Esv=@u>sEx1LZTA*kGc1J-?Kac-T;Rt(7n~8?zp>*x14oAu
z-t+ef;=gbgp26m*hM+=|S4ouA(5QrlGH0XQk1EcJnx+c_l)|N(u>{jI2{A_#v*Wev
zCGnk2H2-XQ<$^4)#?>=PgjN~Cd{Whum5&X;zY?Le^-#!Z4~r)}_|~mK5^w~-$XM!j
znW%0wd?Qj16|~QfGTbYagFMARN!B#pQuqd|K^blRFL}Kll{YICusd-{!>2u3Ap?Q+
ziSDNZ=`(GPAW#5#4GS|wz*!M9`~?9eQqy{(^SIbd{Z9c!;GgZUf2m!CsmOUZGvo$w
z;IMZHbftB_IW@ZsE6en}oF61PUN$8ah|MWRfcZnOq*ExCv%T78^Fuyl@k*0qkA<*_
zRH;0o-@X*&_W%WoD{sDQo+u(6F=sk%fC^3Zx=o6Sv-+xSK-)jDP$)Pkh;M@6KZ*X{
zpeG2p(K5S#`$zdVLFz8C^Eu(2GzdzJ1oUA?jiA5*=%_KYm1!lIRA?rAEI)f&O>IAy
zXIFS$b(!U|lV=OETP<rfEOVb<cwTEdnno_X$RKwZ<bOh2?v~#)G)J0rrEPqg5nIPZ
zQJc`a8_^dnpc=?aylY!~SJkP@oyT%MIEQ@1oj(835Lphnm*H5Rj!nEJK^7YNQv~Z&
zDnS}M<GL}>vC!wxU+Bj><@|7>Vlh<UQ_^9_8f^Y}#F=MhYy58-s}!5`h%fqmoU$RX
z;rx6g2c$}F8}(SZ<=u6Mxj-=)VT)FJ(vYpi<0#!E?Ch8=FVE12Ejh)PoRbQ+1;$7k
zL}{7t5$)G`IGnQm-ywIi{aB0LN|&`Mh7$iDfEQN)LTMdjEA|C)c?NC8_t9I4icjF1
zAQOA@{louMp#42_af7mw0bv}w;eL8x|E;Dt>25+pRlR(5jSfOzr-!}k%kjBod>gkr
z^od}1S|ck!MOHLV7^3Gh-Sfh=n))qKu7_k63vM?P{Q7l-e<431$a2HOK|ItuBz6-$
zk@$TMJ>1B<l~@LCaTHqZg^u$38*X5yjAFL|6ck;uPoX)+H>wMA-rZ?lv)t^Ijr~+*
z1KLMc&3~%n?`hlN_37_%@qf|3Hzp#_s;_;kiwNP}?KwyW6U7fl1rjoeQZ1}jKZsnn
zZ~^u&#NBxvE>0gV89Ugn?glRQAjShN8K=;3l@L1_QzEc4W8dKVAYU>}WFVBQVX66`
zrre9P3<?Ot|MD$ka$~0j(VE~LC7h*kzlwad$??6_$qn+{aQ77S!U6~EmvIrxTfqIg
zYJK|U7%u#O4FFKt<K-pN(6o)l>xXNKI3hSn_DLG{aGllfdNtI;xm#q40%TFF10eP(
zJGkpZP6kw6{i2MUBhBkKEU3i3?gtZ$R|xV@44j2ko^N*!qk|<s-{e_F^pfSLsE(PJ
z;K=<nj_M8ab8eBCnte3fqWT0H?Q814))^qv2_CUx>jD%%+XVteUS|=z=8?WDihVpH
zaUC?&6}s$voE6^zDf1R(+dK|CeE?8f=1}eGfyf!li3kRQ`lyYaSz6txT;pCw_e^BP
zaY#B)80H1q<lFoqjMpu0!Umh9E1t++6lU2;u(T6aoh3Cfge-lLTX~NhEJH6KYsan=
z+<U%Y&T+%@6%V2Icjo`MLk}Bmwf;@ywJp|r!-x1@|BrSOKSpy3_c&nl#h4G9xH5Z8
zet^Mosgia;z$m?18?CimG_$%k9klTw=_l$571U=>jZmDB{YatiJ~~&<=Yh11k|zrC
zOw4LEcy%ZNuhYTl!@`%HnOHz0C>qR7Ry*gT|DwRJS)9~I?k9}<MiI|=-WSwXA=S?_
zA1NEQdlrs)qr9ESIGVTmm8EP?m=vm4WUK?@TRJ@PeWq566$Re}0P%T?kX?`VTd}Wg
zuI{H<gN-05l>V>RF}bLEJXRs@uGr40So@dn?TaWo;zY&Ogdx#vJMQMcJ<dvr#K(eI
z34&hyla~MkEYJGkMh{iEwxIp*PJZyKm$B~93AcH91vYvT{hL|Dzk0Y<1^vVzwl+g#
zPX-aNtej_Ib{Hc~?J6CaP0d{(tQ(S$%d>ZcMI#Me;Vwvh34Ew9?l4`~Jn&V|y^%Oi
zNNliRch4I>FE%LE!4QX0SW_~X%q7D$V(qKUmPKAFNgn*xlAnCP9+u!HSZS%7$2UlA
zo(_TeHd{UYR2lmBSa)vV>?cERyReBYN~87qOyn_O%W&rfNOb0wAC2l<N4dNXgx~3j
zJcm==TdU{A-A@Kz*+nq5v#9?B4CDz{xdw24$0??sP%V*pc|W|BgK8?8I6GXW35@oD
zVTha^@i5fA85jR!$H%9`v0OD?qs0dsAgHpuD)l`nDjNJdYb4&uSr-IV{x^v>2!CIN
z5Cl=aWU)H8stMM`q6bkD9~i!J?_t>zP<KcJy4-U+Ts>cVakwQ~7XK3?|1)V&K?%9;
z#60wAb2rIsj4d^GsuatVj71STyJ!hm?~Qdakwg1~lh`dy9s%0>O`D~OggL;j;jKjT
zzQc7M=U$&>NIQ3~C?d33op+mp=~oi4LR~fEzVzaZ6R(pugPH7+O7GV6yclRTn9Rh@
zENo$ru%5_JQT&*dT86@*5Kf~N#rVM6nkS2wP?<<%{F@NZS8tg`C5APhm<Y3MEsd^4
zJuZ@te^FepHz==TdDX68OS|zW>rRGk9MT_RCrZV*`J78UXJ4o*K}AY*=T@0bmd_5F
zFk=O<D%&LPuGwNCZT$hDYGL;(qGW+TSE%UUczrecFz(?`QK%k?a{8XzU@15YOL{`&
z7@!X9xC+Cm{~5ZkpBU=wo*o_39NFOAj8>g4%(=0Wj#Qwu0O9!W?9(9#3{H+mV<0a<
z{XJsAo|1Vmiq4q|&kCNie=;<zXJ+t`ni`lLTfdw_TFRkR`D<}^wC(@_w_9-kTDbh*
zYa6T(tW$wnd!^D(7r%Y`c)f?olndiganvVm$zT@EPbfk*Agn&6QoMWv(KpnRW*#IN
z$V0w)I0O5(ViqW?FnEp{(R)rasN3V%`*?1ycsUfM0#d@ec~agJf6JSdL&_0enG#go
zM~7`~?j+kF$Yu5Md1&_eRn}vmcFIX++LHO*pKCQ=VFXviRfO65fbJ@lqY*!6hn|F<
zoA>*9lZA{&HzzmuVw2015dkJhnb09jbRypxW#1UXFq)%%GXbR(|EK|3&2y`7z4*6E
z3SI7HhTq;D#xC&~QBtJ-R~3I)R2&-meD3csDgVD6puMQ1H*YD#ZTtVa#Q*AAYH!mt
zn7BF;bu^B0jYaX5n5=|g?iO^uz%gVCm8mESm`ndm23=fSP&xIg8rv^({OCDKs|R;w
zwzdksZ&jfg7h-kZxM;l=vd^}@e{jthRI#8!aQyY(4|ER?{)&oBQ`C9e#Nx^9|A^XU
z_nxoxKlpJ_!jo(wJxK4N$c0~Xpn!9bhY?X}N3?tq5ksnq_|lhR(!i|VJbc{njin50
zWoraHPSiDLR}!h-%$G*2+owDVnsP&d^CnQjF&qqyol1k%2GnSC{>_y^0qmqcT@kM%
zot<;pwD7X>GX^CC=BzU;tqup0m3E^a9bt741p8`@1~{uH3JD!w#^BdHV_s?}5|>z2
zwcAY+0m!W8$dC0)9yUamEZM6R?6;e&2A8{kq!yaOe4km20z`pQR93HUZcq+Unp1P~
z;x(V2svG1SH3i#+$~5i|x6Gz{39VOo{+UBZ1)V77Rdjgr-`sCPU-Cn(OdJitY)%A?
z8VUo&pi8@m0|*d948({Lm#KT2s+Ql^^Fo^ewXP7(7ED~H4a=gR9i69tV`rI2<<(?E
zN&K1y$}M=Co(kf{=nE<N`o-Sg(~k(;EpnG-SV;$P9rR-pk2i6nUgp#)f98O)OhlUv
z!h-EBUeyqP6@N>`5+$oMWLN`4lr?^aDCq2&?CWq`W7wsVUY{QRX3UgZ>ayd#S)6P1
z84()l77ctBan9xJ-4g_7nWgelY(XB#P+l3nIOhAbpFIpOiXZR#iH0ZuW<vXa*A^C3
zPV?JY$LDQK<F#^waD~m#%K!66NWp^>Am3JlnlxmgqJ@;u{+JZkM1YO*o%dX$sWkvM
zBAAhHBeXZ5Y>F?t6p&=GvyP*X9SoKu2b9b0>}ZI>^W)rjB9!fUC_7pg2K$X*m5It@
z*#q}kFy!^A+^FCYnGqmTLQ-pZ{P}RK@1CbRX^}-JpTTnI|5y4W(ED7%&_U9uGj$PI
zF!T2tkepq_xzlZ3?sZQuDKXJ!US8h0_)#)(q~tq}0*{`~0$qNfimCUko%kh@%!nw8
z9wPBd>JK0Swt3tRC!w;KV0oKpFtCgm->klKs=+jjxW{P((xo8kBapFwC$Xb8*Jt=_
z&W;pAs5Q5L3P`|QMNAk-|KSzWy@cFwP^I*Omu&!!FMq;3ZN24TpP}e73wDdbyPTJs
zY@_F-sasFJ5fqGUP7do`^FmT+B{G=ywgKxqJBs)IOaI1^tUT&bI&?A4S>^W4VH9o*
z+80!SBdAX*&yoCVrS2Dx95wIU1!RblEXn175}7A0NF7~X`u^G8lTi2~rC8)a!lb{u
zzBo`!Xv}clh~Gyl&XmfgR5J!*dNu9)V{3$$qgm0Ydn8!wbnp#OjYftNtYi8F!|ygR
z{dh!nRx<DKY7VT~X8SPqi$z#z`3~G)L!y1{Kb{OnO3E<wx*YVToFK7)4s&jGtcx*S
zFqRw~fivs4!r|}bMGbwk7@)gL_cTo~Bo$x7vJM%*rqxRe!sr<h;~j`XJgHX_mP~|^
zxHkt%LkhY<m%D*iltL1N9;`C<ZJ;{I!w5nAK3@DV<sf@yVNZwnzY}Lr3kUGI5P8-A
z^0_ux%2Mk-jAqlGj01J^t3}0?YU~$TEG}vp-hDt*Tq|{<))L<9rC~oU#7kT#Aq(pI
z_ks3M{^1;d+`Am#c6vdTZW9*NQTQ6t+N=DoQ*x`!@AoF)2oO~yH`Myuqx%us{}=^E
z$n#<!D+cS(1M;-~9lzz!G$JSXzn|=!jLVcU6LR{Hn?uo2;Dket$(5jg%d(hFJ#>7x
z=g%qa6JYouZ}+oCG|Ct`-L4LYkj<3pdM@>{*YB=0i}T=otN=*G`HlqMY+c<O!1=HY
z(BTilH{rT=6A*kl5S^}Zjx*6Q0*c8QA)VF&8mL02lE?Ac6033Zd6@ep;ZL<wu1iE1
zmO|2%<Ncw**`><HwAopvfw0<Az;vgD{?qjJvS@f=a7hh6aGhrbu)k;#bUp`405>s|
zJGXq$mI&3bO8ACy6fQPLl7gQUl+p^&d%vPil+B!s=!ROA<m^u%Qlmxm=(_aNL%<z)
zl^!T{$1)9?!Ks3*^Tdn04I|($=SNJ@^HHEb<yrtx-(u&{lA|-cR+aej&trQi#+{AP
z!c+}g$LkcmP-s3;JKX-FuL$fRQIY2{P?6v;&j*a0yAfdQk4re!v`7Jl%O>{Cq!hM8
zD*aOCe{)}{wdg2C_MRQyBT7L=4_PuXF@Zq;u@WWmDstn+SHg;dW$zJ`j)U`%D@~b~
zb#gS{e_Oqs%J@5z$P$U@?pJX|p9+d><F;gE_UGkJNEkkNw18#e33VYz-2NBzGRN|N
zyLdRhYkg3kqx1T|Q*{IP{`Hg;Vhh+7bd#i>WjTXt#7M|+DwyndD46`VQj0o6;7`w-
z9%q*!i6P6YQv9t#4M2eSzt`b>!;yYkv67H0i)F~KC)=J!gLdcrYQyY?1*%LTcb)e4
zy>HDn`}2|@MKAI;gNG}d|22!lWpT|bx(;vTRM2E6haV8T(NEq|+h)F!7`jmXp;YH%
zeBIY*vGUpY5k8Q8v=_H_zmeM^+~suI$YH;ELYi4f!vq(F3E@@)V(VNhUHcy1#k1w#
z-X8^hYao&RA-VTGT#9Nqx#A~2k?_D|*kjc&t`<{}fGCAVk}zptG<m#9WtKF@wMbvy
zctl(3U!|J0ANad7>^8)Y(tBhZXR3499mc9jb|}6UR2t&j7<Gp1L%dfw7-53|ke3P~
zD?lTodN$HM#G!DqUBT@FebbS6%BPw$Toh8hb_a0$8ZNEAzGU}c*e;>RX9wXvt)3eC
z)AJWb>7~8c5bp62#W7M;n~#+Ex0lxp%iii@zx^4CEC#2cB2}LGTbuxRME@Gp>Kp1c
z9WY(DdT;FRLW*r2X&hvH7c`^Uiev;Y4#i5pHmsHDkaa2y;0T_HeOC{f@N85zq+j-h
zp>WeU@gNd>_0k8W*BtI(<KHyhc9Onl+vPQGp`wWc+5{}+9Mg2sgMWj{5u~R$i7nzp
zh(~areXg%YMy<akzr7XepT$dx$XRA)mk-@>8b74jYpH65lbGab%~*!z87LG<bJXSm
zYlr9Na_C}6qd2N@#kD;uvEX&2PrimB`CnYec+KQ~MvOm7z%l5ukViK#rx706pJ>GK
znJ%r%a194kcW<|t=W6(DuFjo~yDunmc>kVX25ei<z$0w#FFI9j8V#3svG!ucB@X<_
zNcb#PL1EZURxGd0-D!3Gx$XP}fZjd(_Zzlo`%2R<V*kJ6O9(FDa4MMFO8*b@<mdnK
zbVLn+s6Z2b_b=7!R@P{tl0$X_Ew<{SXf`*lGaET&&Z5bVlg{EatqTAggd;wtYOIh?
zDQPJ-I|77RNN-|jY>_^2aO>2!$Ne##7T2`w@@cea=l%ID#C<tF+vX_Aap7!Db^c6K
zbv{9ky6ENUN3`Qe)`EIrj(}GZzUh_E!)@cTqOPXVNVX6l)4aXzLeP!E`(|0;^jNPo
z4}XhZ2n%X{#MYPNV-{p$=%KM|jPaYN<mK+yAx2jijU>4)c@N`UMYd5&iX(M^pb9()
z_nrQoTC>p4b{nDTI5?WVGGSeah?uPTfzHd-FM(Y0@K<qFmX;;Y@Gd!1v=I+FuXL#5
zwm(*f##WGGbqzZP6Jm@o8LBqGOZHi)lmAlqL5<ILGp({*H+ln5D8HJuf=;zAJgEff
z7SH(Ca5K$>*iOQq*_4rgcZdg8N9z)UYJSuVomA06RT6sExZN@ny?&2H;7r#40vVEn
zGfHtrSAgAvP!~ZMa+oDwJ4R)gIL*!*m6T2g!vLJs;B<cx3-^0;c^(hDDKy0?*~ct~
zq{3;5VKG8`9T9<}>HLo;d)3}UNF?iy0OAzo7`4?4#9dL0C;U*iK$wIvUaP3@$ywiX
z8T1n&Re{6-rCr8ZJk~6OrEg-Hc^DW!xX%sfoy|y5&ks4H2VU8pycE{N-&q98-3I#E
zuzPFln=T>-eP>bOi*xV#dWERRy?|}tL}g33?Qh70GA+W5{JMf~Voq|r&=7^7n8^gr
zl;TdiQK9`edj`gxSogY%X|W7lFm_R#n%8Zi@b|l+hWS#*^|$8(S{=N({KBWA_zvPN
zM*%%BROn~kET%v_%9AtutY?M7#G#=-&$tJB$8p#L5;sz#Uiqb=mO#a?%L3iJE3ZKl
z6hjpq#{XE}k-hrfMsn@<@gqtTD{_b+H$R_3CI^%5AN?=^-}e)avF?*lW~msV<H{g~
z5$V`p`VpIsyJd~fy=ZS@2mgYY#rGUP|KG8vhXXV+EBT&Op!{vwVe7$qN^#d(r)ek@
z-B}-2aIf!@rG;z6@OQ`IGP_?=<af^pi7YMTehZpHmazsNQ@_~xwwHE*EuITLJ{{Hj
z3W7$EBdf$hGa0Tb9{&pD<{fJ~S3g<sT!-7Wum+88Q}nsgYgTmGBmb->Jp{kG7W?*?
zdA`oT63Izlpw!vWejT4{&u3O=P6-Qi43+%1_dKT8*7xai7~ogAAvk|&wzttMAL6z*
zkRUIct@iElQ^xX8Jr4V~Si+cuQkI&lA|-4Rz`q37KKq2MWL}sT^7VUCFgufc;iXy~
zxpdAnJ~I#hgCh}5^h?Dz4q@CVDSyi-ey{-K5D)%Ub6;Q*HJh$7%vYxcQbZ!dB#X}^
z(V_iDG2v<P(B+r+L38wm^|S`DmJyS>m2AWs);H!fL{Af1sj35emkS8@#>3xDS97lJ
z3np26&RtB0cgD5N=*~K@cwj?&#*m5m%mbB#<5}UuVJ`Ot6JoW6R)$m<kDq~_-N+#O
z`h<03lW7vm*J1s3VLrWqUDY2>m4BAH<w;D9go6Lh_`t1?9AQ#P^p}R&!bmp=p9+VC
zktb|Ue=$g8F5}OHALNtb!;Q#?v7W-rQnO?6o!z0cQQg?9y}nKGL#Fk{JuXne1rD?3
z{_b+P#DqE<^R7;;!MPO2rytw>L&Jh8$W*avZ$@L8TG@X{YRpDN1YkcBMMc00tKxH^
zx;IMV%av)dS=HkjN3B}WSFcGs#y1(PcndQ+<-fZtW7W~Pc?nqL){))37Vo>0*J?9v
zj&<^NULp6K7k%q5-$v-1LWA4RN8;aBB^-Et3ot}*!<}^F&e#0#>W{nn+5^Sb7kz6V
z%)zN%zk_CN?2;N4*>jgFUJ)Dr-P9ztQ$nsF5>id9&CF6EU}0h&gdDFwP1M{!up`i7
zNyDgpULXEW(R0gFaSiY!tMar{YWE#U6b!YtZ~d=xLGl-VogaRPI9ynGxZikg3{T-2
z=QzEEal~)T&u=ne*2(r0NmNX7pH%dX5Dwu7@AxAwYEX*mmTN8I{uv-Q4}IM9rM-Ds
z|5DMMf2@DCzRcuISaPhz&w12TVdg+M-4IPru3Nm`f@6T9=VrWMcD<D&1emGmy!_Wi
zZoQs&JhE@`xr=bV@TOM>m~D>;_$O+xdD5^jc|Wqje6{bEo8fSk289<BI(WPyeX&sx
z8bc(<!)iYBNp5l89r$FOArAT!4j37PP6Z&vwQP7gA4c8tPu5R!!G_cL<i!F)lYs`r
z)_^|-M+1A&Ufx3cp?+w<9=tH9_5&*?hn1huOvpkfxPb41XIYI-%R}EX#l@T{eEaDY
zjDElQrhw+V&uAZo;K|lWD3sJ}hz^sxD*n!KsAV|k_M@jhn<CTuDn##GHkVEK&CTA0
zlULNp#jq}FJ3lYZWAH`rr{KXwVUyA@T?ON@@9LaIWQ@Yh8v^W%TSA0?nF74{$m#qs
zfmWYBKyi*eB}*^(g+E{9Hk=Rgd>e2pI&(?VmSEpV%EbuEZHU61|HZw83hhy%+sy``
zPyDVzBPGjCT1t`&2%qjHhCjQ0%K+!Jw+q@5YWTINlX(61`TPcZ%%SKD8H7EB@TQx4
zJBQ58ePZZ6>fe=9T6G^Es`0pjAg>cRaSROkU_sFcsjBpsqa}<KVYI1r_=UJZXGt1O
z)!wc(Lx;?Up}VL>507gkRrP0^&s}Kc^ur^HBql&9<LB#m2;pgukT2^oO{yxn-Q!)`
z@b0Lg`|RK!cnzmc;Ch{ze4T*~Fma+ri;Wu7$-B$6L9m|pPf8p~2BQlh6F5obUiVZk
z>u|59rD>xp_NsE2V-c7`QXlS^Mk*-(pBEa@F{641dej}BmXuuK3kFHq|7Z5)frjWY
zij@+cJ=71mxEA8-7nl+9?K!-QoTq#1EZ(9H$rbjIZ}-5~TQYKcUOP6{-|5`wMr<SZ
zzWvlK^J4A%Do^rRKy7vFUv+vJIxgaE_BDJxuzx4Jz>NHmIgXLtzqn`dQjgl%uzQ_1
ze5(Y0djq6VGq2^tX<G+wi+ds$NAu^(Yr7Diyo_{{O5b@l8{U4@Xl*yMnA27?HHcD=
zPqycIcZ98nIH)YTK`~kFUp3~#?5BA*xWy9cuZ(5FhTsKKosFF0@*r7NdS~KwG~Dw2
zu?m+s6Mcgw2v>VYr703K$h8gvTE9Uaa_iMKlDQ}&NK<{Cm0X{aM6Mf+yLkr9qv!~d
zcTIB@PBuu>Ia{Xl*r<6djZXmE%{ICpxOco*yf2=l@w~?;M=owTgl(h|rqkHERSmB-
zJM5A!*xuBqnjD#SrD7!)sEX6gS`|AkVoAIbkE*4gi=3Y{>TIpotVh0{hmX>Q-}gcu
zaBKm?Mvr`?tK`}7jwC<qR=-ALCGMakE;LIM$Z~$LQaeBlt;g3Q?{E|!L_L&G{S~G7
zehx2ogJ>n-a|$X9lgMsR*Gaqnx(`cZ|3WqT-^P84RW0;KJ4BPUSR&JgyyF(ugE=*_
z`W{uL!KosVYSv~yk?Pzo5=>Kc*^Z%rq29yGt{-vAfdKE10~bQ!=4fdqL+sXSUiMEi
zc=!STPB0p<T6<ZZ;*lDCT@VGbzyUt+^z23T60%@=3n)+i;h^)5z%`OvCcn>704}-R
zJMKZi=~0<HJT^WXpfj9%SD<!}z%kzN?V!pWd{OA?1Cs4R^w-k7M8SmpuR&AHp;(Dr
znWZt&i0_FZbQqy9Nr1$5sg=Bbv0Jzt0^b`<4P4=>Fd+C)_+Y3mwXyqGW~r%&!TK`=
zt))w(?9w+l7QNaSQ+X_uT12ZToQP;LA`Mc-C}Z!0P6e+0k^MiYZApK__TXa;?qu$C
zs?*!^sOW?CwB|7DZ>yzUHjduN17A~|6r6u^r!u8kP!D7$#t<~^XttSORo4`O+dvg)
zS9Hm!Z{gFfD}4g^L2tT#Q-Pe=YQ(#GWPB4pEXbt{w0AhG0T{F$VwtCC=17r1XFrck
z;&UV1T|!`GLy@pth6ppZU*K;+`L(5Dm7a+QGPa@P|7Hjwy}C~)&#^nV=F#(AQ|YSH
zhv7&&4HxqqJBrcYuh_;MD44Z4l50vXpWAI$kN<pm9T%Q1Jee%o2se4R)-l~T5rcb4
zb#5x^wi|9kTP#i9Uvj+PF9ch^7Cft_5D%ISyRPo<+_>NR3f1yoHV0yORJ|u|p=>`@
zwprwRe{RRj&r79WDiK(Y=nND<?ocrok<8jaG1U?Jw)Pof$YQ`J089$ykE<3h5Qa-A
zQx)sC3i(p(LO$l*%eESr?tpyRL!q$*avOmCy~0>xd6Y|`G_F;q`9~Mbf4bVl$48|L
zW3Kx^X<N^<lF(f=fo%29Z6c(mv6z^P6o(w^xV}PP&{yI8#-Ed^hOLm9wEcEhbb84>
zBS#F}8-7r773iOt$vl7r49(>A0N0Nmg!Qz#*VeyTg_<!6Kab1JyE5iv<Twrur5sq#
z6bj$6gV*36>q~IgUGZ2P?sPW@diQ^#_Kw+aX>~XHq*|kTY{uMNc6xeH8xirAdg#%v
z%NA?2uPFtFQG`2ykHgUTL*(ptsgbF=2for^td3IM{N8_h_v!p|rIrbip+ag{ycrFs
zy2E)ih8|(*dV^KL;>GChQ(glU9z5esa_lgn=BMdu7?Gs)odmxd%&oSxT*xb}U7H(S
ze80G)bIVfQV#+WYKX1x31T5Eo#bmL6rye_M$zY3y7wE$tM>IbX`%RNiPFgK2==?5`
z!Ze=7aC92#I|$1?rCg^%5*e<n@m8tMc~~dMCRCe1+%NpD3W};781E|Ko^-q+q5i-O
zA%#TWCq6r`>_sv131%3QnTHFtFRRjr7T}a4s9z+sstG469+OFSVbxuY5^2g}JxZ2e
z{efK92j)4(oQND*Chk#>^Zx-8LF~TPfDMkHOZqg>fDy_|BiWq`W9ZXR(PI9nDQQEc
zbPNKqG`hE#S+;&b(^^}fF@f4g@EMFX?c<nSD33nmSz9}5#su_AAkUShCC6@U{^(1g
zn4#*{_1n`+mbL%!dYYkou4BfG;^UFNb;m|?D&CjY<T7Y2NXL-ffPig&6chQemU$5?
zV2re2{`7R-_^}nO%UicUXd7kvoXVD?*>OYY3oq0xxK}$aL5o!ot~O}Agx`2250X9d
z(uOjR>*%=(AlM5bsMr(&_&tVU*>ivthYJD)ffFI{-p|~7B63&JuAh5Bddm%S(sS)I
zuCwdEe2t)&u;Yfg7xMKB-?fNeg8Zv*Y)P9RJ1~M)m8lB?eG$M3;`ogC#kRrg*;iaN
zWE<_r*q7{=Sso0pH(oo7ztdsU3^tMd>)qRU`b=NTy0eGh*M94>_ocslQ+vK_+0sUt
z9Su5BW?I=O)18q&!d~GAGB(TLn5!lf97lKM)b{MAYl6R>YU35QA?C*U7xIOp32~I*
zV9Hoaz+kftUN_9`s|m-ht+8FCU)lIr`s${~dS)jneklIxzkDbC<B#6Z7myL|9k6GL
zf51*g=!Sw1hTd?^tn|+3Uf#G3GX3$#Ni4th%4O-tZkn5Z@soGk&g(t>SvtOt4Ol<%
zoXgv`6TRad?mz?QTsS_x>Dt-pdsj^7Nnk^5A~x#c2wJ>;{)JbjFKyVB{_(a)(&y~a
zVh!29J0OVu_kV6qzw>=71|-v~%uI;jLwy2ZjbxAYvVTZ`qlfgeKJd5N`q<2XGz!xs
zkR#D)o)u7Xg#*+e`A8f<pOPy!$ca7W!tP)%I8g@K3D_Pb+Y7sGO!H&ZJ*3_0X4?V)
zBUdz+ulX+r9Eg~db#$Nry1`?$&&e7rK6cc;(R1hK9clFymx8G+W$nhT(qnVbZ?`+v
z6N6kov#G)emYHF7^L@R@(+@w=5EGmu6gvL;0(FHS=Fjpm0cf(LUPl5nY*b9a5AaG4
z#t0x(+5An~C_{hDX8TXPGyY8K8-4)~Tnm3=9Rh1C65CimEm^EXeGOyXut<F{ro3Yb
z1y-^h7|S-qgkD#ZPy$L}Bl+WfV>QI3R&%gTo#F)t<^!Wtsv&H^O=jv)+!zIO*+24x
zy*|Ha&?n6{%u?xgc_YX=jT^fa&6jz-Unvl8xpoKwS~RW_Rt_a%!h5VZR^vr8M~A{q
z>|A~hx;TQuB258-kYbE-(DjK?k%M;3FI9R^*607~hO$v6Fgsnndd0l-tvfaWR0o)-
z0%czd>*HR-HG(#@)mIIg@${2lv3NEHdZKLHvCkgKdf0sBU6wTv3QTYpti}R6Fj#Ti
zLQ+JKslmkhjyM&FLpa!bw1)qRQveW<^>3qJN<p9?(3=Q+Y5mSdeumh}YGFUjEA2$?
zx7r4o1s$GG!ioHEwl8tM(LSgCm%Fy6KmPJY`yz4od`Y#H^(s7_g#b<$f4#-ycQ2Wi
z5TC<c@OKX$VUO2)Uc4aPW?!_#iS!@0Q}w(3=@ehK&&St3xTnu!+s+jKmR4@}f#!U3
zwQQ6*(@-dKr$Yd-)s&4gz4C~L)2-P^j4zlvfkzYKctAXJaJWirp!vR4GZKy@Tw^~Z
zKJ%Sz=~Lg{ns(cxxVpJu*X+NUuP<Iatz+CoqIcJm&H34RGT?Ah&;NYc;;uX00mp{^
z?OU!*A71kicOO4__}IxH=r}rl$^E}y-;#dj`B!wDZQX7Vp34l}@$CR6U`D{<QTEK*
z=i9TP@p`~ctop+*ZAhQA`+WVw+z~LVty6y5z5(&>7cEQ|Pwbs<4i6SUB@dNYQnG`&
zkiIz&8^9QnCCDEtD<W>utc@{GmpFs#u;hV%qc+RaXljI3_6B>IHf2J(X!5yf*S>?a
z1Ab9ADw_(nDcNH!I^n~71859nbTJNj>40TYLV1@JuM1!Tu%K`*=@FrJrE>5XQxv1E
z*|4>38)Xh3IhyWyXt!xMgW^O5>&aeUqdwF7ni_w#elQO5s8s?#@&pDp6zd-~g>0PI
z5nDh4MZy?$b{|ua2V-s{I2}&upeSt<FE!votrtxgm#&<CfiX3sWZ%tPEQ4@^FM+?X
zqIYOo;c~%W14;Ocjw>+%-W+qf_g$Q-VlJZRMrd!2fR`T0bFGPPXh3Zmcru68P{v{n
zUq7-tJrC2TidR0;0_iZdv>W!&j7l==tLYRk^ofY8{C5A{FL2N-G^~)@F5zP(UC}fK
zCgUf5CJ486L8Fw8Kmd!v#q^cw`@Z4Ya^0|wzP`o}I@cIJXaFrPFT2*HEJ>yybXbG$
zhX^RV&ln%PJ3&L1W4orCa~knKH`)nbrL=^=)ywA^pMeh?V-d7^#J!?H9Tv&?us(H}
zdfRwbEu8~_o+xV`h<LLY^*Dy(5x2z5@zS_s7t9VTMT2H|=Jx@xHG1w#IcYrs@M)Zk
z$VH6;2>XPi$mLoPC<yc*0(eYxH<nv2zbL)ilIvGq(yT9B<4f(|vwV7b$<j;HN9_xR
zzxVm|U48m;H^yGmeR}Sd7pEV-ac;V9PUn1<f!`OenmL89pLyOD>2JUFVEXHCKG@ao
zjX(YJru3mVUOnh=YH6d)69?@XvkfWjde&D%+%0AfBLYo&l+pgf&3Vrb;}Q<pRCxTx
zE9{pu9Cy|UkBh)Bi`Uu@X}sR^;)Ut+_w7i3`JbC@hvnWO!(gNp|L%XRPcK+Fm5)dp
zX<ME+bTobFYnw;fj6uym%3|nUwjlt&@Zp!NZY=N%JHDT7$BDk<?`}(XZ{61zH~((U
zgXt%pJwM^N#ZrbIflDWkOYgK(eDDn$obJ*oWv*@9`CU7C<sG-oPrv=y`_t#_Uf&Jn
z$gyYA-`F2>KW%^1mC_0VNOtrsOlDgKJ?i*?pScq-GkapSpCD1mO9tbRiR1v1DJWG!
zn_c7So5T@yaMxAyrlrr`wn_U#%nsQsyTTs$g*KB#;!qO9D5A$WrTKFVGI9uV6c#U8
z%tQB=V_>DvY&aN@vjQk6ZkK3Vz1B_~Xjj3boQ@tlCUXsA;V+-~Ga2*K230U4N+goQ
zAFNT9H>TXC`5q&5#N8x-5mPp&DZ;<Nk9yIoxlgTbM9iP@gS3s&<Pb7Dq)%2qd&$f&
zzZtdpK~&)vaB$yIh5vkS&>P2}GQwZJJ1#~CzEPl0XnE!92N?{-T8NJ`LO>d<8}fMr
z4J6D!NyAuhTg`dUMlmAk)|3L#e8&1%_*&NyqR<3++UaxR(K(dil;*1|02k!QOF@`m
zcKZBiV>BLl5q{AZ>N5Vg{}^k<4~;a#m1F)s7IM5?Miv1)TFIB#46*g`b=RWlJ%Jd(
zlV=n^I^v3J30`|KLZF7?T6YE%#zf3cBCh&Jf%G~HbfQpcRER(6e`L@8gi}7|UouI2
z<$6*CmM)lSf1iv>PaXBoqX;6{xljT(fX-TM%-+wrpc#FaU$J;r9`5$Wy3Kg(65_b>
zROr+&BAm{X8S$<`5!U(M2R(TmqK*6Q(|WJCd`&x)^jrZ<87e_Y3^J|f5Gf891PTIm
z1a7rc3vPe-@j9vT?JF;tz?{L&wo#_Z1)tHqcJ<8k5<AV~*Z%dM^zl0$ZL&MX_68vE
z(xubVPuge1m)n<cJGmg2jT7mA<feJ)BWoW@ANazCwD(~5eVP7?cG}my+nz`Zrwwki
zUrQO5CmNJ=pM37<PL9uFTZ0iee|&RdYrj4ArIevZ0M8!$!Ru$I&%g88>3@CivX0m!
z)8FgScKQHLTKUM^u1_C+>$M3x#k;X=w38M7>5fM_!ZqyH#b%kNmYGw|wRpLXzewc|
z*qZ803wB?2zWjZ~^7cJ4qlIY)#e!3!{{5czC1nR?Ocx8|b7O24``l07oL+s^jE>qQ
zQ->XYoPF^1SEb*7^$L4DT62C^sr%5s{r77hPRF|Oxl;Wd9c&+o4<<ycLYsYk&=1v-
zJZ9#^;u#<IM`04u#X&<NF_MYmQ-WEE6J0}A;}w6<+flEWJJr<4^2q(juCQKXIU&kP
zWlPvkd1DX%D|%pHgK&xdL$Apr-W<P>3~uuIJhXzBkXL>Crfn9pNahZ{+_q8o_we5h
z)%y8Tn8o}TJn)fznr&eo#^e$m&Gg`a24F-@HF=Ih4;8?ldXgCuP+Yf4a|>t8ZX6*G
z?Zyi#ibkyglbn9@;>$49paL)CGw~;OMzLCc%N6<zKk2L^U={HTdF!1Be{f5;<Xsva
zmk+u|^7_{};&(XcCs$sFzS?LX7AfCzZ5%9N0DXx0IDuTsbfW<S`<Jb=X81`7s;tdR
z%w!j&0s~G<0hu7%+?1eGjY&A(n6|I~lBN034BtxW7zD8YfosBeV~4i!y~7wnSFurx
z7zkA{n&ZdT*YxDLiKRwtxW^oGe(HdB+^cK6V2yCs0OS?H;KRH0*7e(5W2sJsz?d<k
z(()y<fvDK@Mzl77j8(B7@kiep>WCj#@>TNFr%p+iTr{~F@AB5V%{%2G;}%VTg?m|`
zD$Ixwht!X8&^0DHA%+HHa8!wT{EVPt2;<em1>O@w11V~RBIr@~Fg(Tef<VtB&`qbw
z;GXvOo9CrZ{Fi5?rf-s|2j(Qp3BUhU%hLy6w<=vUVN89fv<m`lL15{nlbA30gV(I=
z_)Ri(hsWBNZ1K3V|9IyutUKuopmjS3)Be+2Hx5RevMp_t*>|WpJB`jS8)XI^6h*;_
z5t!1T*lN;aL{5x$XH)S5<!#TJlfL}Z&q=@Z-HQ@V8)!ngX7&Z?FW<B}{j>cte8b$%
zITo&6*!|I$Hja2=_3)8nJiVt~J`J~?o916=@o{5XVb8Vfr7~{xIq7%oWR7<}_wvTL
z{dcw-yp*9sV9tf(`3KOuU$h{NwfJa4dD*f{(`SDCri4fTHNEV7@^Jd{22T_;-I*b`
z-#)@;QpqMl2878DOlou{JxHu27%>KsDhUWVa8V610xX`0Mw_aXI5a9pa}trg_Fb0G
zyTm7i`BhSf-+ac1(O>}+JTMw<S}z}%fHe-mAe+G>y4TR_vPO|S0RX2J&OxWu=Zu_H
zdaRk{MNS9pizVNA=rQLXT(!<O%D@K}tHXZS8#dDg=aKn#1#gIjpP=Dr2G$sr&y=@b
ztR?EeFL>c6>;PSDmk(jrg(*SO>QF#3L4`cVt%g40X9TR{byqZJqbmG_mwn$qia=iJ
zA25Qi@)I@j1I;%F1m@t2cHjg3C>7>4dFaRdM3Y>!g#tzk{OfZ<9dtnim0cyB+nCvk
zR~RQFnQ4^^dVD}Gc&UTZPO1p9#TZQw=QlRZZ{oJ@;R94*^917yJX8l6O#?l#8G?Gq
z8wN-YBG5_wQjq1Tu3S0?0b3*;&SwkdYs?0^HpZ33Le@9ZfNldZ#@I0y6wtBQJf+4I
zl7}QJ<QQ0k4&JjFY#I-F1|IYnFWS0iLQ?&&tS=kX1;**GtK;!Y8TCLvRs!n@n&ZoM
zT`%atn|kW8OS_&pm>%B!Bm$6t;I57$1g&lV15MTeW1rZq?zt4s^418xpkW?+p$fer
z(`z;SC!CHE?nnBW6L=a(r4$4T0zHYq?GNuw>mKTk6WV9k7X?53mg~~HUT|f?7i+t*
z;I#RF?UK*Mx^aD)=o7w>_`X*xO&|ZU8`9OYrZmw7+KdB6e)%Pf(!adpMtd|<cjbLP
z^PTN!`@TbiTAP;ch)w!t{G@T+@lD!6t!fb-5(FkTD7MOpGD8vqRJz)JAb!#|n&6Q}
zO?n(x9g)Sh1NN_f=vtmWaPfq(^`S<$AKG&;{p;N$`)$9E-u6gipZoZIt1e3)dc)Oe
z;<%Qc%Gl^wIhpY|I`L=hkyA}5Uw>eC+Wgr5CYahd8((wPW$9CI#~%*O`vd4gyQlxj
zYgeWJ+MrEeCkv2&Sog@uh6=ro06w>m3<uiwDL)fX`SW}v{y+>N8vwtL+sFS^8*~(A
z3Th&j#FDt_0z3J{tG2}2aa}a)qBQF4v&|~E1?F&ayT}OIujGYnK^sFfi)`!}0sl1y
zVTo%jT!m1`Q-R4Sfrp9ZQC(6L$wXTd+vAtkZxv|}SJ-m<1D2459ia#K=Z^d74hSb;
zRc(?WtBtGXkhQI+|MWL}C61WPO5h-WMWY>*qc-XUAEpOI?FxU;An(%PVBZ=NkQC*>
zbd_z4nl!#yzl##Hz#nzA={45MP#``SGq<f9>&GFZWf{tXAk2tgW)h!dG2+E!XoGd@
z_=nDV0-Bm#R8KN73gt~0^qQ_L&jFvy98f$&AGLIdg0_#*7jIJzZ5uQP!g@w2_{i&i
zK;9VO3;4(jF^F4%v@j5@?sIO}<=K|3<g0dRjXVMo-iivv0%H@8fx)VK9C6J(4j2nY
z$3M2M<_DSJ^|-MX#}r4kSOT3oYrHN4S#H9D(xBBgvjMHRo2`9dr#(*S7)Yg@27#+r
z%o9i8j+Mg#^(jFh_%>olXC|$(gq}$&m(J-U+0C7AZmK;-6%hGLag8tqmWprUW#f)k
zh6R1Se;i}r>M~H6<^2>q{LH9yxJ=g8I}Lbl=+NP)(TU#)>nM?_TnYjOfu2F&ufDOR
zD}2SHi9Tfs^3}7O_c_-<7huZxG3k$gU?m?@K2X`hUl6DxaO1oSxv}ImcFK7CPEa3j
zVjHKDfB3D}rJu4Kh`zwkL@)Z<j1$Q}VkeUirnI(ErXi1B!orTRql8K+2n+=R=QiXU
z<?#K$Q{_XPcE<pAUH<0xF6HBkuADl!XWq5jwlmd-(+B?D9x3wX>$>Ne-+%U%O=k*6
zTjc)l*PHnV)H`mOZ}Gh>jq1{G6bxi>fyGZhuNj+V{?RsOmC`;0@S*a5w@r}md&RPZ
zQ-+#c@EqDdc+HB0BQ~2_K7ap?wD-VMO*Mbm>}E0qAMD$IB}S?b`uT@EhL|LQN#YZu
z`h63V_9k#1CJCa8(@4CShPrs$KP)(R%;>amhCL<{{vdINfB9qq7-0#bT2e<JR>-gL
zKYWM}GUPuj2Oknlk?v3%bU>7KWxf!ReqsVX`^N%ZS^##}ZW#PVnGIX_q$d#fFbD+|
zPlJ&S=yOV68`?rv_#$?SktPcLr%xDno81NIiuiypn%41fjTjH#Denk?H)G~@g*+6&
zHkhIL*+7SMP5(@~W$_G{+o+P)+$;$AKL7wg07*naRDBj`@(T#-e;$<o00#fzKb$A-
zT1yOJ5M;K+N}Mnx{G13rQ$QWm1x1kexR3y8ic_qmGl)&{XotKjpkHLQ3&zL;RUscW
zN1&jZ6hkm7=876zc>hvVnq-%BU`|rqmavsHut3z}Kh&Y7{~97j)&!H|#fdl?=7di2
zmCJ}Dz>O(b$atAWx>LY3(9y>**3=@Q_Lw9_jmt&cK#Ilb)P*oWUjf8nMV$0jb60Vm
z`N<7Y{oIS6KZlMSO=~vqiqTS^0)b@<W~MQtM{6Ounz(2A3UFP}wilL&7xZl{$f7pK
zy}xqtY%umjS!Wxv=r(aO6x4AV`nrP(JA(xG$LPfG3Lu+2L@Az;11;nu_D#CqdnL2?
zTElM{4td5eu*^938ZO1(f<TWWu=9z->C<<O>=!cIu>?PM(_DT&G+}J-d@&L~Gx0^p
z_rG$Pou1S8Ct^D=Ejqdz0mRIEUc4avpEs{gmrQKNlOzVRB6wQHPuUkNv0>&iJ87yL
z%17+v{iDaK&n^e*Q%j$oKXvrXCm5(fgW(@Ls@X}6!T4TehaQ1*N4NBL8G1unm;t-!
zN@*Vg%cf7_<BGm-Rrl;34RmmN$S=QSar*PuUzH|x!AT<bY<ptF9@p`;O^>Aq8hFCT
zkK8!7F{h|R+zxg9<IkR-zIVm+p`K>Y`aeCwCnyctfeun|-k5WEAAGAlR<B#iOP5}n
z{^|#>Nln`5TgU#$v1ig}@2!2awmw+e83E)sJnf-8A4c$NpnVpk<cHcI2Ok-j7{Lcd
zP~dfqMqc*oa#Y(_JQ6gRpiO1j+)HRuBqwr$E%ckoNLcXWE}E}2tFW*!(2SRlYYO)$
zYgGFW+0ZRL<aV0rqC9GASW%+NeRp|N!2#(3@tL%4<F+hA!85q%u}fhzCgDVVHIWP+
zki90&Nk-F>f(RNJy=|3|=^MxpH)e904-pEev;WjV@vNy2{zet1&;>cy34M_Dd920%
zm=yEDuA3G$Z=(#2mjez8oVF$$M{&O^`~ZNJ%6(!RxCCax4UED~u_c^9&BYishIzGS
z=!bk&JN{Wv4UA(2fIoR*m^ETP(6DH#UD#;ug%R9Om(vK8f(g1L&%TpFMmn4qs!b-x
zXSBVBJ(waVhM^WRu%0|>gN7;P6B_b6c#gY&K&+GzL%{DgT+9CJ8U0v87^(GB%xGM3
zfHq^p);rdkPB9o10}KrC*^3)=j6gXug3b}f3`po9k~F{dJeJ61?XTY7{6raD9OBEE
zF{9G*#j^;dE_#O&;kfYuX97-io!8r8pRr7wFfPrTjYl%}Mp^rS{oV^`6m$+B@I$eh
zn_;|deZ(+fWUR7fJfh>CD4FX0!27C=hlW1)*SLRkv|IuKu7!0R-5{5`AW#tK5d=QG
z#uBd$&aogF{_EefB>mhA7x?c~J+g2$H($B(vh>$Kcx~#XO+q#IOY;m6m^yK6`sfc|
zpWgPYxn2Lh!K0);VULvRhDR}Nw~dXTzklZ$thhv4%KRVoBN@+lmSGZX^0#J(38FI1
zAP^Yapl_G+58y%gT0|=ZUVqKZ^pUq+-!;1{E6@72p10tV^a*>U)Dn9vQ`5^|ef_}^
zap7;iIWqGO#@RPmeE+u>Ef|c7u9bbS{V}=xvdJCo-<I76(%oD4b+nC5FmA%wa}pj$
z_uMPH?}K>!(euIAtx97W`VE@Te5WL(PWc{Ut^=tMBu|tO(FghHOEM>0tc#5!DCEHf
zG8C)$$v|0N*bQa*_e+zNct1Jk9&EM39>-HJkpxwOry4<@|L6zot!!3HhGJOun*VH9
zbo7N^rPnDWFmD6}R$_kWVsShvZ-mG9A5J(CW-#S8+iW8@Xqc&@<x)vIWD5yAsY85W
z1L-vu^+lll(p0S<0f3HvLe>pG!*Ag&1+t3h8v*jpPc`cMRK$(^Il*VV#&Ur@D(=eZ
z4Ltgrz`)1qMf?M>z}SXW)X>QkMln95Hnkqm0WMEuqLt-9M_*%n|8h);(Lg^0xB<a+
zNoUAwZ9+cGjQOkBp<$Sj|8XHNbXtS_)(-hNaiijwsQNS{CJqHqQj@Rd>F+ai<hgxN
z_L?ecbNnz3U!ga)VUUf3b-AFHdIS+*Xjs(oeqifneI)A@U+9y8vBG#kzaI^K^84qM
z{7{oTE{m0SIYb275l^HeXnY(<C=u>DZ3w)X4{f{Gg7%d*JYLV#^h8;`e6D~L^5!#9
zwHj-LHcHUY44TuiK1N!(WOmmlDBv;5{=-Mo`fZORVimLIKia(G1CLzP7Gf1IVmfGQ
zLxE^|55%~TC$EiH?~nQ%d9t2qAOsnP2t9xUIPArH0qUMXqBvR*C<p`sc>n*yZG(H1
zN9Y=f8Yg1>+3T)KuU$QJB&P4EIq|ruzj))-jr|f?M}gVp#w?gRA^pRT+>n+|@9ca)
zJD1^ndsNw9+m|d~xUlc%aP6F9xO9DN5GU`qv{44%=xjn6T^<?Hgk@^1jiVd%(6(dt
z%UmggLjb$3f6E?4^FLp<q-P%KHgn4O^!K)7_T|ej9UOQgEd8YoJJTb34-Tsp+xHzx
z|8ZZlPCmddK)>?R#lwnIlP1IF$Um}Y+F}z$CzpS@Yw(Zy=w$zf+P3j_lFVPeX?4eM
ziW&?KH_pE(ec=07Cj4A$TER(OPaHbZG_!}*UaTsTDEijDf1t0l3Ob$uiM3aaUlW6Z
z1``4x;MYeVRAL6MfT%&zNYd|_xer<!%ZhnZU7O8g|CL0Qov<}0b&Ly^!H&BBqS-+B
z4;gBd#V3|0L)Hx)5E2~dK|@WR47|_=UW{eUAQP$UHrPfPQ!CAGL{xcB*-}j$mcdMn
zspOU`<2qx=c|^uF@Kd}<8XlVl`ybef6XMl|tI#3cknp4)l|VlFs$>0N`?HtM%DQ9D
zM%4UPsXXUOnNy4m;EI<I1%3g=iSf5l<M+ri@hXu?@QJ@r!8&C8B<53Teq(j~#V>i&
zXZmGUC110ibE|I_gAo*Gpgm|i9TQ3y<`5IF<N*!x?hg&B)?Wj@AtHpNLi0I4`Ru|e
zsZYwCH3n!mO|SvRsL3NXD?VugI;C_L0$7oFF=lMBxk_5}+8S4OW4!+UAisze-SxzF
zDaNtIMf5|SvZ2~!f{t+G7-G>nik+}6rTe$;O^-c!C?rdL3IwiRG0#T1K&*kWqiZz*
zwmL60Y2qQ(S<gzn_SmJT`KYlf<^UCcmKZFGO<~84myRh>MjR7Q3IIea(RO{H7@zpD
zaaipvPkj{Z$qTTF7>pV~5cgW$li(Dm3j#fgz&AGUPLDp`%%A)C=;!xey&^s5%8Pq+
z^^SKNUn2W6J57H~gC9?RyjO+NP6VDc@51zVZ@oTUI(b|>)iy#Gav*>F1FO;-ubtHh
zdrxL0a)OT^JhRV`PrFfOFwb>5nZZLvk3S%WN`KMY+X%#m#7#h<!KVUt1iaXF{fCxR
zeATMUI>MgeGy*4c{m!eFH+si8KI6ghn8R5<`R%Q#Nsm06chPz2{kG0cx~~oAdd6JS
zC!d>s-i|2k<npiO@k?jCJmc(<iXVEz)po>W15Y#<pw};0I5oYyfu~1dv;G$badhSY
zVQMUUe7KJf^OZ2J{whMEk3CO_FmVD3GAf=#VH<r@5Q)V~1K1+j;%%!~q$GN6t7l$3
zIZYlvR;HR(ZOo7j@HLVaF=2oI>$cN>+V9CB_>96zoX5#?D)A9L_g_eYZWg#aA*|`=
zMi)*n@XL<9`_tAvPr~79eV|6BNFnB8u{vy!5l#m>7=ypg<MxGE04P=B)r3IH<z1U(
zfRpjTZR!P&+L{XX+PIaBHwIWL)X=TV-@Le?zh~2Z;6{(&6g;z+4aBY#>k1_w3Ov9I
zR$Lr2)WnzQ39n!$C&rzZ__^XrT;m|)MfF*M=_8|4LkNn4(`X<|-mdJ!KF~tm{h_?Z
zDtW1b>(Bu?`C|=sRdbZ`Ac96lwb6$>6rjznT~NFa2I+M&<-sfeC12S~B@}l(5-Zmc
zMu4k`HMJ6Ptu+=+G357aU)ws(1$FX_>xme^=`>h(zE9Zn*Z|#lsfSH($HpofmyB#+
z+ht@UJJ6VJy{~LgClIH(EL$)$jTt@4xeV_BpA>ZP3cC8@2}ac`m(HO?FI?7b)|YPh
zbGR0hL?XWztY>2x&wd+M!K*PU$gnYvLyqqo|7q{<A<AX$-fRR0x)<o45V%F%o5&Qe
z3j)21z{l@+w6V^=_vK5|ix*F8to=h}I5tB3k>z_vHSkMhLxoAvD+FG&Xev(~nm8_g
zf9*YF&bIdjzEF*)Yc#dsGvqTpnf&w{Wg3)mZ;+`?jkRKTLlAhnycy1TrI0xN^VeUU
z@B^|}3zBSjhUwd$HK(x<e01HzX*f4}e&WvN`~h{IouKijuU%E1&)Q;izs`;+z1EKD
z?9}qmo`Y%AE=#PIGGGL-De&4k7YtagsRD1gVNN4|wtacSuBJjhY<5Rt1o;jml9?p2
z4r=uAA3xizVo?%CJ;;*5E9f8%!)UmWHdcBE1GVG|MB80(s!V-OrF482P9L_&O!`Wa
z`E6`Wbb!wtkS@u`IC{hiwf!T0jvK^;pUVUz=thd93EHAC{hQVf;zXHs_ShxC${D0L
ze9}amjTh3ZE{&qE7zpOj13qiJE=s{qHZ;TzCwIjhujCaom1g1#A^o??8W4pX#H^^&
zJbl*B$F93_I{BJ+abK<K>z4sQ4B<Jzh}znHf(dLfE>>_-wuU=trUy0S7iz#~MfgL9
zR8zgWnJ47Qld007`6<pz$XE9d>CNvS%Ex?)b?L=bbm&Bj>z^AMKF$`2)T{!Z$!9rh
zWX#aEabV$c@*gy5*02o*eyCku9N;abQxM?emAF>1f~v7rIX3fnUUUxw^%-r}gbZJ?
z(Dpe<Y?y(+L1z4*Z+a1^IfkeM?=$D*<{2Zhgf8p2Jzu@QY?N_uPFal^Gb$}zIKy~i
z1yK8YTnpgxq61&?R`oe{V))`K8@i#=y&Fiic9U(CVSFn7bYHeMv2k?RM!Z@P8*9C9
zfUjN{Cl9fXwh`_9A%w*THT5~!FPFXu2*?_UeTPf0AW#tKP6Q4eIhH=t&@ZLo3sSGS
z3O|EOIS~TSTX0Ew&x;qHXsFP76oH%OU&s@f%F}8T0Kf8*#p%13G<T!jC+|G7zt>K?
zQKm`Xkk5Zq_vo9txLptk1h50Jlz}5K#!iy?i#M)LH(!3yz=gX|0>3!^$c=Lw>DgoZ
zpGsfZ_}H-6e*caq(mmUr7?!!(G087}_o6ia;tB0g)e}09X!&LPC9#vstz{#@fS2d(
zH_z+IZ`l0i_byGdrkp!qyhc(0@m+q$Klus}8~e2nHGlhKk|g*sV4v;#IFlhZBpIXy
zmGEYQurBZ7)aRKP%H*UBlp0g$jp^E0ub8V1%Vrksgd=qyWxY|cIo$1lN#Msz1p_h_
zvLTN&5$^5u-{_Ka{5Vg@TU{F`22?4ffh**LZ^z^N(}O!7uPJD~dE3VAZoC|TemY<l
z(kEV=gdrM^hL!LcHTdfNV94@5NO`rhZ|LIXbB73YlE-y1X29wUz!Wm9Lr09Gj}hag
zT5Dfr8=-h=5QdsoGAQt0kD-G5EbeP|iG2Wr23~3hex#FRV+6-)@PgG!;0s#4EqTO+
z7z101x@^c(ttkv1C<pC?@y~f6>Baisg=~E;j0T+>QmE4?na|hS`d~z*i*!-gE~ZtQ
zvdz{YGiE@kjR)OGn486Es-O==SGB4(hstH-5r}m(OBh4`sF{c{(E6`0VuHMQzj+&*
zVRR3E+DO*1SHvjqomhI%QCx;|SbWsyh<WXYN#@u4OFAogz2CmR-X4!vN=pb_!^bZf
zE0+NmLzatc7dE)6q*sexsa~?+vNU$g=!($AEquY}I}g`CyHNZw7OhFxJf5p|b)OYY
zuo6|iA6zc(iGAb+W~D25yBOrtxrPu0)&$V8thEEi)2S)r1%ZM<cOY=<#$9RuQ^y+T
z%*&Twntr&UpW_cF*gtsv?DW#5&D{tvoLF`HTvyFJ-@Z&-9?4Z-XXKUf#ZsKW)Wq`5
zExXg+15XXu^wVyX8D+cpn^KP09~Y$*1cnNMr;au!zVP^*QU;E|`(CjuUDrdW3$%jJ
z&%f}>^pb||A|0+%UOr`yU200X(SFG(-z*sTYAu{LF@4wK&OK$SoVYQtHNC|)L;m#h
zt{9k5mvB!UH=2)KYmz(I_;~O3=4?hBZfqkV!eokdP~$^>(fxN<e((>9=v8e4{0{~W
zTTM+aZ*(gdV&Z^3UC6HC8nu1R74xSu=_#`+J1~RidN>*ELZVZAm96OKO$^rVV}G_=
z4JPYd0r3Mr<uPt8{O7ohf@CjteVEBvAGgtTrjK8`%}$ijbX2BIK$yddC~Ct1+T&vZ
zNHzftyy`$fG&6XoCeNWd3&!jfbTo21<bogeKxIFE;!+a~dC+fK+~jXLI{xq({yEO!
zAIZ36{A+*#B$PU{@l{=Gp@SFk>@xz%S_#~}4pEoQ_}3)R%x1YQi2s-cb-W}m;T-=|
z(BJ&Jf4KihL&(R5hPssIGdYN6{6y6qsG`dPzmmo5MIF;-{3<`lo#}lvluidApk>s?
z7PRXxE-Ydq#{~>#pyU&UFvj~f*VrS*SmF~9#2&Xz84Y#Sc2%gjF{YXuv#@Y#Y?@J*
zw)y~KdDoMNY(v9t4VLRE5m?QQGFXHPMF{}hc-fe*wP@hAG5f!2$sDow!1b<&9!m!f
zALDx=;{jZV4X{!Zv5feR`)kyhfg*7)bUveT1p;T64;q$-sK+>!_2LCM13>uhHQEHn
zYYdfA5GV-r1OlJEM~}m6lCyI!JTLv)%a%0B<Po*?H|+6L^DjDYL~SUOk1PUL*q4Yu
z^oFa`xpwk?DSZ(bW4Yx&v8NHtzF>Ucfd=&OUAix=ANa}S@wenjT^!w@lPL}~aHr=<
zfiD9chrr>Z&Dlvjra>FrI%T1M@;UR<D^@i12>78x5g&HH|CP(qipwSs-6TV#`E&Q~
zNC%!eIz-wlwV$<lno!OieNKAs%gY~4XS!BD_1w#k|D5+sm9<&!8=H5h!wuTZ*=F|#
zr0(JgW7F@wdPVBxlh>}EH6^`jWwVa5|LQQE5H_G->Ii0if**<HoIF7i#QuRE`6(m?
zBM}0cc&q$Xj3Ft*e@vKAgZlVu6UFF~XNRPrxGbJ^G40i4oR-N7m_?spXSNX*FnuJN
zk7fT&2E6i7^4Wh%QZ;!<hsVwzxZva(wt=fPFg0)j4JG)Dl-8WV<ChK`I+E_SL>Uxg
zZcboI<FpRaFcIxCU%`XPSY$<(<B(k_?`*+JIna#2wq(g1G}2uF!*y1WNeld~&mu;O
zngDD81ux7K_LFV^-rV4gGKees6YmLlY5(OTz>?2!ow14rXehY`UvwNP_MIMM7y=vm
zY`cv015>DV1B_Mjs81FDVymr@X@EXXWmm~VD;?ulBxrN|8^2r5n)ZZUR#V!Tsf~S3
z;_n~X849G+Z3nOObAGqVq@7-ie;5EIs6!4@$9x#3JR9ZG0SMsHM~D^1POKB^{L*rw
z&*<U37i*4{6b&sdz0ts<HI`n<L7jLQm*BE~&~SQ8#5QHK3N@r#8%^s!J1K80<?&0c
z1=6yGv(lK+b|MTGj|DZ+t=09W)`_fkQ&_ol4x7DjS@!@wPw*JF7B}U>@qjC12$G13
zQ)dZiJ?=vkyx`OO095gZ+CFPjn?szvb_FU#t5zGZ{8?{x&*Rf;@lgEjWd!hajW68a
zjL&&vosiW2!|z|2&Ko-_NTogv0>J8zZ6g4%E2Z}kIL|i6U?a$+b9>|T!ZTUn3HBcO
zfZbQ{1dgVa&!53P{IquhBogq!MH32se{IsKA5Flt*jOQOs6k1{a-vMNZmqO%+}=B%
z+nc8gw1UrA+hFnMufHl?I%!-h>>a~%(4Hy#A49x}<)J+X()t}wc8t%bGUOe%%u6$-
zG;b&HseBsFu`4f`n4W$4#ltye%X1w%@^rfGp*=10_cG(JzHD)tT>cPB|IMCTJ!xEX
zK15mjV2STjy>FQSWa2RXZj01NJvm~3(DA=r>~E$%f8P1$|NNUzY}}xM1f&v+B2oqy
z<BvjGblO-?96vT)aoGhqF%k1bT{!?7ZDLOj>F-wHAkIh*g&gbbKLnAO(=-s_3se^{
z?Z$zDnsZq`XtE(#*4{txljiPtV7ujFWDoSve%b|^O4tFLSghil0>DU=)EL@*<UO(M
zqm@5k0+AP2Skpt@P7YbH8Vk5kG`-{lH`+=LF$XW|3(gyt7GP}~S~C7-Ki(q_G<$Rj
z*f<alUyxZJcggD)BS6U!t1IBJ(=kV#$N7BJ$IP}moie6GOBIv#jenmx^vKuf`HUtn
zzomlrAJD+&bD&+_Kg1WYA(Nz&S0nI!ijD+p__?v1Q!;f2luWikDy0J2Ks^w3|7AhB
zb_xQq?uZwxQLL<LE}Tr|x*Ll<o(b_(#k}Oe6hEWl<RZo<RIo_5HoH{wXvmp-4%1ZW
zcWG?up^4nAqwoabEB6ij9`7o9sv0w9R9d#kP7mY4;zBe5wH3VYRXYP|aqq<Vip8_Q
z&=Y0trtN~syd_Ei2l%iiP`bxjKX2YbTpBa)p8~VOihNYSaiTB9;s+;Wq(D42Uca|j
z%6g?GymX(@{eW%a0`N*H2owZ*1p$2au;;*$M!EY7FJ72%5_TyAL*Q~dQRK&OnLjY0
z!rc`J{KhMmrn%)rq0_GAl1nG0cia7?X$2?m<I$!ADW}~i17*!RRc7c<4;To*g|~YU
zXwr#AWjAc=aW{SPI6GxyY3l+#%6Xxk!tnlAEo+?jpSy2pzXdSV8*$nNI8M>{q3h?g
zOI^?EdCgUwdkV+8hxYdDy0q#z9wYanMV<QytycYL2j7L~jY&WCoXgvxYFKovE%AMF
zoGsD81hn!f%zGfa694cazY>X_OfgNKI`L#oq^LrHawm|fawr;$gd}wPM4ad3#tm*{
z`P@sWfb*e6gHWTX1SZ<d^N<t1iVx%Hg#Q>0J*Y!IYATf|b-1`=q`Ab4G?L`0)90YP
z=TRQowJ+^_d_RU;E_hs%#HpS3L8sOhPFat0p}`Ip4%{-**Aa$;7Ih3-UCCqI*icYo
z;(8ylb@^zscA;n+YoGKH^zaorpb7*O)Muqzmd-T4`bcrK2^U&T|LBTfg5gH1#v(o<
zM&&>82NUqfm`IQ0ZIG-!P96$i2Fdi12cOaV+yKCbS3EjK>u!`v-Wc!?s$d6e3W){H
zFj3Qqhp}edl9s=UDRMy@1tn`-rYGbzRGSNfWCu+a@ahvYW*{#GRHV5eL_vtt7B=i6
zEjH*n$Or{KSMZg3q!I8n@-^cU*MpAh*LOX0i08OQ5P-*H)98%PtlDGEV<BQJ$A-rQ
z=4M1YUwxc1)MvVAq$@Yx+c)n>2Z!O*Qaai2%j)Hke8N2e>t<jAPrFn<2b`XLJuA(f
zadA3-Qtvzt;r7itdCNi!GL{9ABVvF7BHJ)V+=A+HNjNb=d_7LdX&37<excRIGcR>G
zFL>+)UQ|6>0t4WP|5%G!yJvAJju!-a5P>hPw|jDf%ChN`((A98*&vHM%hVseX>Pjw
z;@<h4qqEQ-8Ef8r-K_M|rI(J(<PDtl&DYINFIb?@=9*&u6NiqbJ0E>~K<2izQD#(w
z9^<n2V6%374rsLs^N1p_&yGZIN*On*JX^2TwR-=nmM0t~R?6uSxPI=1=`A<RK0RXx
zap0MRM+UfqbRgz`{{CkC0reBlo?o81Igo`t3Gegl7sDyzJM&n^+sa0QldPJ^I7Q}v
zykyZSd3tT|wX0_)Y?x_6*}ea%v}N~!CYU-RHs<69k|3T1L6T2R`?SAC3*H#T5Rx+J
zBOic1Uv@zqS(BhKX#gIMhZZolzbv0OjY?^UxAOe3&IiILI6(g~6hMRnCJ*-D)wt{z
z%|;>beDaTz<_l<*JoM>b$mcPWSUbSSFWt6jo3DeW3uLh-QNn)oVL$pT&cx)J``yR?
ztV5d|gamZd<f+{HsO{B9-q|>i4GLi!3|gKCI-o>?HBRE!<i{?Bi{T!91JN3MA7!7_
z2!4!W#SkN4=K48r;K#9$j|mXJm68f;Oc)F3I;jWpe7Pddt98H?j}Svq^oTj$|Bw%r
zoIX@gLTckA0}VloVha6uAAlnLF39;p-sd*~NlOl#A<q#SVwQ#PR)Z86oizSUD@vX}
zD<HXCIspN!CGD{FwRqIJ2hG<G>sDJk@tY9h!RYLh<plB=tjp*5Few$dykw7`AL8PJ
zgyuruit9ac?3wfp+uT^n=@D4HVy-a}Mc((c1|bJrHAZaN7WONbL9RE-hHZP(-u;Ko
zS-*GY7&j~!bJ!r`SYfjm3S$&CntD%w<Y=mfh!|L5tm2LPG?>W9I^?ZWN<p9?P!Q-@
z1it>j?ne3kt1nyPKVutZcE=by+VbAN{ypUj)E%=RLu?2(MZNn)%{w7wh+#Rc`d_ir
z*~i=Cs+v^3wRvDClee@{W?X}w)mApjG$~x0W$WV&+JrT+L61`y$tZa14Rg|UbDDef
z`$%B-v(LY>Q9G@lJbW~**)p&njIH8&*dE8$gvWv4)THmf+K#<0WzY!V52%-Q=&tj!
zQRdXk`}UjXC7g^^N`=5Ud#oOQ4mPEf7(dxcd9nl94Q4@<2tg8s$v!4dY{2_Ugxi=W
zMo>pmS>irsa>1npwe*4qqDaV)gEiXiFDvHZ1Sm|99dlbjvzO0J`)QbyVI2H*9&Y*&
zNgDwmM+d|sOpIpQNvX-B$?=-J4|6gmoqTTC5%OyWuu<lieM|gKd-M|JX&YR_gtAlX
z<F*<XN6iS>p^I@CnP#|gS>9N*=>_^AfI>m<qOBP;BtFFs+8lNf>uCC{g9DtH(iNTd
z)#Nd3-Lvh}hwB$i59Y?z;cC3Mh_iVCI-vnxbQ>3S0$s{DfM{oojmGt(0?60LOm74z
z)a6p?Cy9pH1#`wrhE!{2^oV8haB=be_kuyp?Q&sX$Utq_g0?XmdnFG)U7BOnxa$Qk
z=a7jSH&z>l4R~p8>9sB?5x<(N(&Z+=s4O48OOG@J{_2(Mh$Fz@LEId_ZnRCoWlfJ|
zL<Jr(!}vj*fL_H6;s*U#d)9Rthj7AD6{{FVC_-1nou*an(J~PDq5_xaY#^ksmB%ju
z#?zLi3umU$qw%q`VJDE)=Awl-7b7}=H?3MSyXtp&Yu%>m1l+i%MvaJz`!0b27KnFL
z#M5Hf!;*JkqoK<&Ho)l(mxBWF;C+Eny%*8WO9ZhVE5HP=z=z-j*tsS{xfBEn0$q#%
zP9gaA08b$pNjSd$su^kdWs^tJgszx-_3SC>l`EV3H)>Zb!Z11UYu~dZl_zlw9PDY6
z#-(>O>@j8E*gUXJk1cJKnK-U9zde1TCEeSwX9%9iheGR21e&x_2H%`7<<tnwIRD)A
zuIFEIYNj%J90Fr4vHHGOH0u#epTEEQ@aZ^*20^&v;k^l)WjejQ@!DAl8$3#B4+6Jb
zaZ!5|wOI=`3O}%GV2}D}vk4umZtBFb=}q=NDCKwrUVqK3)RfJ&_iWpDyaPQ?LDnM@
zAl9y*7GdO^TtVgo{W@Muh$vZtWB~s$!Qu%MY7X7d1z{<X9#1H$Q`g+K_S|Vx>{~1N
zofdwoXga%slruVXCim%qvB(o2V;?%y2yyFC7m`X!OqmpkkVXUYV5$m-jYwFngSwIz
z-@5g!pEhvc!;hr{_MvQ?)&UKeBa&ZO8`|P_Xnt@xowmq+P{VV|yKR&=LHPuujg9z0
zTgZZlzPQwe0w+f$5z&ptaa^)K5JOAQMO!&{YMOlBICUC&h5K~ZF7-_uiXS;e6tMt=
zFWzT?uH-Q$q=i3Ed!cfpN&zwLtCtMa3;A5|-ml{?1)3=HxhyO2yEJN(txlT}9k!7L
z7nAW`7Q%E@j6ucIVB-r+5k;EF=g#Buxsvz31S>r@2mL3bgtJ~VK(0Vmd?JV=>&}is
zc_|}}0M-m^%0<iVh$W-@dT|x4tC+Gi#y`kq8R%dK7vrO5>4hAF1M-r=e2gWC<@XEN
zO%^X1MlIk~iCXi>ZnbZ{?-A!I)u|8|Yd^!6EyVBOhGi@YdW5#NW_2aTx1y&u%4`=P
zh(pIg;i#BZ+!<hikmG1qyaE&NkQn!xAd6RT1D7EQZQ&37lCJ5juSbuxg@SNF8~6l9
zs(S>8;$}gh#}W9}mfi6ub;lzyVeC2SXJ2q-$J^Ny1Ap?_m#1^ij-SL$vAWnf5CmSe
z^0IWz?DB>AfnZkW#TPASUogH->ge{mhxVjn{uya2hPISBo7kYol03#ED_e2B@Dv0F
zhQMQX?0-{A*(h`RmBKHR*z{4#z!6wA^ZfLZC3+mfz@>*@VyKSI94fu-L<qa2@ePAg
z+J^v6p&Hepe>^qmn=kEKk<)9!qsPkq{=_(8lMMb?Y(lwb+Y?PNa|CRxrACzLGaHZv
zL4}J76D??xu6z|g1|o}60FfvRDac@i$piZXlPMJ+#0CLM1+&l7&gyKt*RGgvr^;Xs
zyvzXkVYbj%%;p1VW`~SMnQE-^Ut<s_yUU>+{!`X2ef%dDF+XJKJ?9Bsr~~r#k3N<j
zZ}IQab$o<JU=4$;z)YVrx2Yeiq4~*YJy}8zBxp3)k>x@4$2AZK=nbQ@ytMhmoP%wm
zL}g@<yfrahlxU;o#25$}AM_6>n*7)$_lmyd_~RPMN4ihs;5(qe+JHS`Ni-W$tl1z|
zQ_>)cmsBtTO9rLnFwS+X$j0OavQ%V)A<G7p+8~IieBAYtMme8~vSc*Yu+6OG`Wd6k
z>-`UX_A)Y5fX3lGmE=W>wkGKF{*j6-PbE-otTfYV%#S%>6EO>1eBNvbgvcRY2^p!B
z&OyM|i1#e4mA&*UHQHt($5iW{B|2F9$BYYbi7AT<<ryg!GdeAUjypYK2;rl9S=gdE
zA+I7`ee{|T<S{SmEj#w5t$WG_bwYBQ%jy;LoCElB75IMF8iFKbL(PR#&Py|=Ul`r4
zuh(tDMwxKa?r;`|+IXm#13E$o^k`chGN>8jsL_;;K$SHv<d;x2<^x8K2sh52EI@*2
zIF^B9U|OwFPr_52E(r7t0^c0q2?Qev%$u*9lP+k;qqIj7r&i~lWg7rqxuTpZ(<)v~
z<3q0W-7i|uG_yO(UgVg5;k%mi*s3QFA4~UcZ~b%RmNv>vXwYL^w(Y~ST}vqlv>$=(
zPc$dC&Mzm*oH78GPM?%sy0kePJx=M&2o1i=HX4mSCq7~sAtPFx7RlAU2im{UZJ<ZE
zwP<cz*<QZv(scg#F>Te^>&nKD8I`WG&0?Kc?%(lbCz>^)HsSQJ*Obj-r(A=kd>C+V
zIZ@`s>xi5OGAAfedrCz_U8@9$B~bXOzvya9o{2*vi&x1&aG@W0EN+{@JU$oYrD{m7
zE9OnB=5{x8(gHJr7JmD9*k6aC{u@gl|LKcNry6ET>|(kkqeN=RqZUq@jP1a}YK#*r
z8(z0zs}!H+3O?8jU6jWhl+mn#eO6<9u~wK`a%{Mi<j{d0*W$IPvKtJc%eXYD*$)Zt
zSS>t5r;lM`6dB-ct86%LA|cf+aPga$%oJ}!uk_wAQyWMLBR~Nf*D~W5c-ROAQCyxZ
zCKGZYlH=ETLrh6g^sr_aw1P%%i_{-~>C>35J}EkscLiaHrpV@kEo_iTB@f@jm>j?T
z<PlC0!yLkN?f~Se!)Ay$jtw`M3x%?M4InQEO^@V!Qqx2KT|dl=xyfHHBaeUwftEDZ
z4<TXiBO~TGGE2qdHP)2t$^eod){nRexv2F%fK2EKUK7P!&_GqTp#~l5uGJVfhOhzS
zp<D82rCaYQr^*nf(_B_BpO@jSSOGo?!DkJ8QC2RQ%eogXJNF$(kL<>VEA!nU0V4c4
z!kF_RYbzENhrq^KcCnWRDtRXnPhwi*L!UG#u-G>pm<zw#Wd+vi#ZpHA?I`(YYTz6L
zr7j2*1iBJ|J05ww5q^yygU4vkZiKB(GV;gmOLpZ{nI^4R>$d*XbLN+gWv#<?QqJcs
zxFju_Hu0q6oi=dS03M~+(ngskohY+y-=R)hk77@kAh7d^=ET;d20boqB*No8FJ7n*
zQAc9(E}0WYt>KX!O)1~pvZt-4K5gG@ZNljtuU=J7lWFV1yRxfhO&JQcgVuTf0Cq|b
zda{Nnc*8X_)2Q<3u~V+r(&>}a6&Fu9B~K>|?zYFP?rhMe<zd0hKFO~qft4WknJp25
z)CUtEktp#Y(a|v;DlkRV6<GjJ#8BMZHV!6KlHryap95n<Q<l%0>Ze)I0Gk9hS99fd
z*l)FxN8qXXpTz?g>B#<r+x<lni8?@lzDO#8%6TLU9>~YAQcW~~GHcgAD#p`XZ{vwF
z!j<!a-;~OFqwq8j&^W+3F^0bUmlS<*9?81~8fZKrPt8!EIjIQqiQTBi2;I)tKiMX_
zn1!E2BoA98?+%zQ^`}f2n-<P!_9+48iSRe~;Z_|pv@y<tG5bd}xPC_90w`vLp<@oN
ze!8Ft2%`x9(2cmrBeaDhSw8SLf|rmFzM!*D2b@9&Opc;ZXmoUGH+a~Q&lHg@WVbQC
z7svz7kcV%QqXv+n*Ix9QL|ufJwW$!g!P8eBLYN0*oPtbf$`&exVk?SGmnWrMIsyT%
z4_3_M0?XxVj#?YSs<Aj(hHDN7AkSCGfoKIf@WRE$5j%<@oGiluj~k7PU-U{A;*yIx
zpG;${d`DBa=K0FKWupv`I9*w`Xl5E?Uo>FhT3}`DMfh>yO6lHU?Uex8KqkLSn)4Zh
zoI3OC8vOkC_+;(vh+o~JBThXwftZuB4EnzD1=eKwz?tKIp9F)zCm(gV42OF40_^RD
zXpEYXnQ6TWO!2!Q&^rj=bKbkRHsf*6-)E-+moG)OKQ?Dh8J}Lh{L=O-E*eJ^foYS*
zrq{0So!>8q8tfmraqdu0(Kh`9xu>_ZQD#zuPNygvW!e@;MNtib-S*g}r;f#6qqTt!
zZB8jCNSt(K@Yoc*N*O!?Z@+n7V@{Q6_vtU29^0P|JaugFAP%91i!CA5sVAQdq1(fv
z`pQc>b5p^6<wTi2>p#X$y?S*yO{NbfC+oa&WiyVLeZcO$rBn#$1AI%Eu%X0=M(uZ2
zqajhk;^U~Qf-x|G;tau0@fdUcE*MG7NWwUI$f~7tF7bqSHHFMnG6QWTlH6Z+MJ~}f
zxfkLXhtoA*EB`S@2D`$4OR#2&S{jwO+GYnD)E7x8(9TM0?mzvBG7s<Cmv-4V%7JHK
z%G%`BH%l}D3{Zjj)(SGm=~7=zAdiyy*n=$SHC}-w7cfHQsG-yqn2*H7>YU&<ism2y
zRU0xWK0no4_t{Hlxi1`T<OQAr6RnNfz!!bEOC+ojH1xH85TrD50fvgP7>9g)O~?iz
ztzRXb@rS%*I4kGZ#4ZGbuogaTte;UtXAK4`<PB!8L9;?RDpHR`7lR_IN8jxit%?oF
zQ!4X9HRICA!V2Q9i#ko-b!YjQ)A-06e#!kzqi&ev8_b+d29)c_BY?FxEA-Oh)z=>D
zj#v^$!~{rBD}J)9jY(W<4>=Ub+J6k8%^Gbyim6seclkc|dWgFmRDp$T=%8KGxn|=w
zQ!6?L%jJX!j2$yNEnA4w#0<$;w7f{Z2!v7Xl}qQGP)|1!*KOJ%w-8sn8Cn-{r=Vjy
z$0LRihm1wl)z25ix(WFh=;SqJln<D*tk0v_IZwXV2CdXY0MM0E5GV-r1_Dj^oHthD
z&DYJ2W~tkZz#AI$rQkNZ-nHs}^rm@ftUWfUl=dL-k|op9^vUDeqil$^-2G_ln;%<>
zkP931yt{|?92jDFi~3$fph<Cs<M$>u=y7So9Zzq*uIwOf7v>k)MhyILY(ly7;r1S%
z`RxWA$N8c~m!z}LE+6d-MSNb-p&Ml$*?TbUKYVm3TyCV!mn^+Bm5rmVE#33&_{=7h
z2X~j>^XuzmX%Z_)f~dxocYX9VC<@v(pahHa7)gT8O#b+p5yDo3<Xt6QAn0UFtjR?j
zg6out=cZYgOp+NsSGJOUC4s_@7z>-(&+fop*ltt|Wcx+Wj#3yxp$%=YSBcgT4_0!-
z#p`<>+L;a<K0?`(U3i*|)GINT8J%boFv(@M)bWR{*a*OXjsr#AWQQud$^0Arb^nc7
z@gN=a!)W1g?uVmh4*j>f4}P2!gKW6GcSM39TQ@Ck{wa6i8DSCn*}se(ybVS)&40ld
z@gq*q^ZJqB=o|u7N+eg4w*j))MeB#Dj7S0F^IDkON=bpVA$~(%>Phc=sdQCzDCLA!
za{|^ezorj*u*hcXIU(ZL_)L||vbM)0^wL&vQx(Q2Oh3sV1d|SV*#O4A^SP+=Nl&?U
z3IbYN-oO04k>eEoSUkptud9MW!J*}5TwwjNr3w+jSjaJAzG4GQT^HP^PQ4E!b`VR9
zD4&n91kv1A8+_0QG8?A<@%@L>9b0ySP|9f#SiNGNaR5BLh^E&B)JUR@8#_8JnBP0!
z<X*RNJA*36Fkq;~9@-+Q4&;oBP%<8aC$M$-h-*Nr_lm=8kY_zy5?=!<qvoEx054)@
zI7SVSt9y&%)RX8GrwanTfxw*&_?#CjfiM5gz39B&Sd<a)a^;NkQ>T9BI|6Vte#*&q
z0LZIXUDkL%I>`Z?+WwXs=5&%tC$;5XJ9+<Di(hQ;^Dj@Tivv?9jy*L)2aP_s=ODk;
zQIdllv?yI*%0m`cO)1kRHDgELNZ^UxjxSg^btI<l+Btvl`Z?(ncb@(m(<2SQced^0
z55{&qYxLU#*pWI?zF%v9JU#a0;gOoYtLMcp+WOG3^W4OV8+epXDJi{fHNL%CN-GG=
zw?{8sYQH~h?eIsKZYdjOj$2NBm=7}2g^^%4swYN_)c1ZBbS9<rp+9sy!|Y@v0X&7m
zE-vUt;($$(&U(iAj5L&Gg(c0lY=1oRXiE6N4|`?*(Z@gJK+QK;#=bH2xnF}1NhR`w
zS32ZBgv{Tdd7Fej{tFXhQz1WOU7Q1FrDJv)<?VLjz_S*g{9cqFy99Bl&GSHsc>r_F
z*sz%#MkLMFEc-8R`p<8en>b{i>E}PShCJqSTald>UZ%*1K6^iTr&Au*sel^zF`;C_
zng_bvMk8Fmp!pwA$uHrCwmgliuNS-*qk&}Tunm$VVl5TFt|Rcb_|=*SUxV8ESby<J
zE#@~`h<c5Fg+InCd(fs)ra<$DPEz?^f|`mA<0fNvskl6O)OHJ8EfjOm`ZYg6Vi$4X
zMNYYDqvAinFnQP+I0bv)5%OU_xPupC^wD%=K2QSxP*&;@M<CWGYFnhrCN#B}*6{Vk
zIM&tZj7f6G%9G75IBkXuT7T-4e8^C(&!s6aUg?asG}~Cs7R5T=6Fv`Z07FV&v&S#3
zn%j)c#x!_@E~}T%OMjj|Z?gk8VFy#NBzlkI{hE04%&2qD!BE$i{kD;I{iAw3bi`@p
zG$IlOSXJ>J_rKs*ECWaJF>ujDiTk&WQ3HO?*cjT}_-erCpD!9Pp%A8q-nsyTz#(eD
z*jp$RZwmrFjX+Z#W&Wy$oCe(FVEO?&nflj1b8nMfD7HHSKXCPoG(H{yT2N^l0(kt}
z`#*bM$3KDP@X@CezHr&f=E9aX%3M6LIf?hD%y~S1Y5Mu+wr$~yqJlt^5;INO*f!je
z^~P&wrFLzU7;f7;b-v}7O-?IkOis7A`NWoX`->lp>+P{L3#UzNi|YG#Jkb_CL#gDQ
zAH8EJ)fe3zionBms!Z7^lTvx~*uVqyrpqr(A6@szzy*g<qDjAx4Hqm-4r4MTKRlxh
z8WO}&s)P!XBTi)l9nhR;B1)h@SxBQ1LZC@TByR`n5Y@{q3Hi@oyT`P<{b3uYSK<d#
z(|;SI|8T&@b8?mK@U@PE_-VR{C~0IGBEf`#8a`~t{3pPIO8|c7S+`;9sW-}O+)8_8
zD}>B8`={xZ+|o%lQdp_BIqW|evw4`@{!5+>_n%H=-^2uA$U-mLMn%&uh=(AbRoCyf
zL0)sh2H9K58;NA;1ReTT&7GRgpU|Ao6kzv>3cLbLDDVrI2@B!J_mAOO-$WMguZS)1
zxPpu~`t|q)s`#JrN83&blrBsP3GmeMH@e%y_yrI9WOiHCmd$<T!61JSLA4Sc1SQ(0
zvrh>CFc5%17npFNyj^0<i4cQ-bc3vzUp@*yjD>$;o77Z7h+)mc8ErnUi`&HMvwg-_
zN@pOT9gSElj}a^)imf7+-RKGz&~+aKFJlT6Y*>j{0hQw1C0Xh7Sm2=ZQok|dG@H=>
z|JZxeV7spDJnWDl01^ZV&NB!SU=JVxuv^`drEaog$&nOA<&>S2^dm#%k1I-5s^Uu7
z<s?6nR4Oi4#V%Wkt;7{MrBV_(WzDgy*5tNYQa9PHR&(g)&?GoCCx9Rc5+nwan8<v?
zTKk-f;o-f9d+$5vp1t3@XYalC8o#~need11-?_{iX1rXH0ghXn_Jztihh+F)iR1;}
z*Pq$n{1-p<SxjnM3W41_w>In7uWL@8<ee`CG<xqt&iQ)qnO%1;rDmbl9>d*aPU9oN
zl*`0IxG*0DjV}R}U)O*X?-tAS6Fpupncsm7$@=+Ciin^{1p2XHxwHiqJr`Q}+>1~*
zu8!alD?a~;DPI&sYR4kfOGTg}u&@ZcaIpU$^URMI|H*rYacB1-C(2*D|F-7u58x2M
zkTIT@?>~z}Pqj@SfvfQk#D4`hBl!KN4oqM9lr{1nikEXxW?3J-bQ3;aV@QpkDOg)Y
zV44WLc<|^n)y-5_y`#*z;pLkGJndN9^bz=(f&9|(<o@Y>{L+g9_ycPCJ6$yn0fE;B
zbO*Fq;`vuTeUskgoTZIL)BVNmH}pQF{qGIu4uA6k(}fimP57ohzf8;Co{;9Z{7Gly
zETu&diSnwXc-ed;Ns7d1Q9#J)Ec-n>Zt?Ru%+voYV9+ytqif?Ag9{xIwF^=9Qt@X2
zM3e9y`lXjN+Ul%P-bC_ZJQ06~Sw3>z;*adzPl0nber(6{_-}F+hO<yc!NfXy3ZLJ+
zxwvjjEU!EOQTv%X<(qYx4q1jp{dh1W>QJomiQaI5>*9!U5YdG=2_bL&k_izqJ-F*m
z@eKW<cySR<5j|;q2rFF0qrRmIKQjmd@0B@u6{j~K#>6q7vmOiJE-%MYLG3Q&QH5yQ
zN14)vH0`<e5+pLBNjyg&iEO1_G@Emx2vFHM<cWY4oY8}xI%!eji4&A^Sw2`kbIrZi
z923Xjp-7Pd%_>1_<;j(LBv3E?K_GJH%p774=oL%r-e>0pT}Sp7Ir5>gz|TH%!c@rd
z5KFX7yGxWC*6$_jOL_L0WAux!$*EU7oa2%FTuuL<e&t|u<bx9;)yw%2*tlVRv-?wb
z1Z)5^_fx3m{F3Ig_ia1Bk_B6N1b3V(IRyxqWI7nj9K_5yX>7~~BJ$g>b%ZdU1C-S`
z*FwHv3tiH0K0lX&7bM!%Lp;f-g>hX12L0^7n7x0FcswpJYP1!BaUw8;_j$j7_iQ$A
zT-JRL#<_`8iu#&O8}JcV*H1}XRkKzI{Lr>*o4c;BA5%6f{Qv6iVSHRF56Ud3E$g7n
zjRX4VB?kR0K6|RRioi4x*o!++45ihBGUwi@&)<1%bN#>${hd3K{!#gHR{ZCtzPIPz
zn$Gk1=<!p{n@8$L8ufj@$Lilc+)<{srulIkl&NiH2z++yH7gTX1Md3TI|({}^k$(%
zy$Gfi&M2gyB*F!dRf<&Km1mJeKg!7?K60X21Rd!luwkIZ7>hQ$%O`HXzS(%$rSic(
zyJ*28lI|=(j~J&GIqvupuBtGkg@_-_))jG=;2;q4Tu)_|$J{~@KPI6w`P{b(zVqT6
z&B;@zI}WwO@4R>b>~>K8r@2l5UbY?c(7!B#amdf=u^YyNWAcO@sdvn+4a#@or#!yU
z(F4neIBiM4=pY6!m&u<xDc8z#HJg2T;J)*J{8VxdrWgK*mRNM~Njv}r;ZTcZ5xN+6
z@h79!sE=PED<!gp5&G1Xyws8SyO#%|d3UJT`$AdyBpVo`7k8|C5p*(%IW^cB#YwvK
z;6h<?2XD$~-Iv0nfn=>qJ~3J40F<Dg(33jP(9gKdY|*Y9MY{QK_lQ~MB}8lM2Lfe(
zBR6@l1vHX#zx3)P8t>wgbR~*-N+3)L3${H-%mvbQw+s#QdC=yBbHuvICw|@Oh6*fS
z4&Bg(%%cY{Us2h}cIxzLef*LL^|B-apSypD_lC%6lLRzmIB%Lu@rJ_gor`g(a!DX!
zk$03~E3bdbDCZSvem(#gryDu-5Of%49v1oF6RMOKk1V88P5?bQ+>)27TWYS3tv9}o
zI>6MVJ02S*HQtKAco5(xRJ?lV{oV)s<2&p7T=UwbAN|yg^D<TK%m{&>-ZhMa95V`k
z=4v0@af9Bco4KKjH1Pemm-pJftb;N``zV)Z>Oq-{OrI*=Mc_2v3>m`h_tNLB*KW#T
z?K=qk<j$KrvpSq60@q);q2WQKzPA_NTHXoL3&k;n|1!8x40U~R5#UEJjlEsDVO{eh
zch~>^SZN;Ij*l(9`tpANchbS5^^oGZ_SH2!mpFK34TOCuo?xcJBiE?ljDiS@DQ$@)
zIa_(@q+B-1OH)#2+F!Qr(q`A)-CImY(Lx;$3($M}BVTc0-^P!56oyn-62D-O-;O^K
zNKGm4d8e)<8B@~_E0hQAMFmwJl6mr_H_wUBWB6Dmv4=m3f&>w5-$UBLNRup$StUu1
zznyFUvCzivBC$EnzP5bw;Eeo<_(M$pl`enfjC|_KlPy+?{cEqhy!rGU3;3Fx`-Tn3
zn1vQ6c<2yQ;MVQ9U)jjo7Bd5X&_wSrPUK&a_dxkefsmsQ%#!ZDSRQ^-B>YV^X(m+2
zHg%;u>6XVxOl`d6qdwA0>>}c#!v|u(MS{8FUJl?Qe_~z98*JgHXVQ)#qqV*4G9L0#
zOJe1?&S95~wXG=vzQc=i)LZVH^)5Rn=wR-idTyw;KJu6oZ!$QYlYE_5?&~lMHKdHk
zMGse`FXVz%_94>hZGE6B>0`$RdFr6fho0&8$5zVU9$Y@Z=kD-AAY;rP2*y5s0w2+L
z<)&fX&3v`6o;v+e^W@9;-5_#^c{FkGmx?3DFz|ChIoPFpOj*g34?LNdMIJQYQHG1H
zJY~oud0fu-GmC8FZbE9@5Y*@^0wX|xAMwmzUHjfxn67O;2>j?L>p_|MAUzu^{2Sxn
zxwqB=XM?V_UV-=Czqq~s@AJGg%{$6mj?a=E(qWmsZ%pT|0kce`s(UyH@X5rJ#Y1U#
z-%t<AoNKRsqW*9^7c5hw{p0}fePK!;q4x4Jf51)++^VP|umS|?N7FRT{dZi`Y{1<o
zYFjx1J8!vq<wA3mIC1*p=H2(}M;s*NEEuc65d;=T^u=08r}&Y@67q_UcMZfV9v1+#
z#tKU4zyRDR8)5+@@yiUxrjZzi=g{rhelxvMd@5#glre^V`6)VGYl}Y^nm1x;{WlNA
zQ#XU<7kg8!i?-CzcjX~ins~@YUOuo!z2831$1gqd-0MYF1IVNfiBm7)SM~q^KmbWZ
zK~%SlQJsBt&xPQX1*_qjA1yz{GaUOZw}LM)q8WTK?p6oa_(ff<KJsMz_C><MM@D{2
z01MXp;I6H@Q-BDIatW-BrXwX##3Xy+SMUdJS~7mg6Ouwj*b_S;82HDgCD!Vpv%4oW
z(e7VJL(L@J`n-RB{!<Bg*x1{5>y`o0+q|G=%TQhd5x%vk$STLUCSl}CSdc)-pbG_N
zN-rc!8(hYPqH>J<@wYhW%1ZR&!BRM(dFGyQ;!2noTfOuK0dH??uC_e)9sA5hG~MNe
zgOBok7}8@ccsSP<Q=B=03RmuZx2GEFO`7itW17~aS4zOw<{I_;t~6Zh_y%2Rm-63y
zwjPu*j!R$n+<RxUetjOu?mRhT_}N`|FRfsKR-b(Nt>(j%%r)mUd&0mRZ5v+g0vprY
zHIX?@68e-65zX^8cv5e?YDzW~q}|llIx)Uc!2)1#Tpr_tqDEX17!v|Rcpq)sb(@;o
z@Xt-P%?APgBl<lz){pI(58BhWa{ujH>L*J~AJ(%pesM?t56Zm$-iK%TeJ;YXs|a`E
z#ds)f@0&-O;{!U_Fce(J>gN;t4-YEzyRNH0#LwKYowr=o+_t6uGI=I0)13d<r}}^U
zN8V{;8b2Ej9A4fJ*lFThbyWnGi@-a_PA->qVfj9D$CibSZPtN(8b2@l-VPqC2W840
zWqpIiJQZ0~ns;$TSHHg>LsdpxK#3w>DpDPQG_2~|dbnH)ix{^Bn-q*<OFNy}=>F}#
zqfGiQL!QV-$8Uc~Zi_>cE=dIOW-O8@@l#OWt$g50EnyCzq)s7*2^Dl0<bZjI-y_fM
zPl3|&PBTx%oo48Jw$v#9NM?a1eB!`9wEQ$o8;&3A!CN>f-)WYIrW`;cS#qB(Pd#br
zj9j6vqB4}wK4_ZZ!k+T%srSJ}`q(AL&lYhSf6NI0h=z@^GkP@9jfrJ~?!{o?2AC&T
zQ=a&Rv^uQ^uY_^Y{)3Ne<+0@cP3ola+<)qn4jHoU7=rE*b4?DDmqX5_Qj+$QLy{wY
zib~!pC}f$UOA1WKRlFooUNR}jo(n<dOg7s1B^~ig-t3a8m$gTLIiurOk+a;R%%gnW
z;U1Gm`^N-`fAQw-=fi7Y;oW0EB!5|xESP1>JdB*{r73SPD?Qeefi!%xk(PPPyi6PA
zIRoDPZy#-5Jn*gs>h-(`Y}~NE+5M?I0$cyw@WjA>=Duy`m0h%jNBOZ!CHMRq%oyAJ
z^1&?SUhqx4E((xHQmmV=&2i54;B%+QQz+&X)uAvYxOlw_Qas)ZaM9~P^!b{Q{dj??
zkyiv3AA#i-Qr8lRAKcdeJGrkVKC`g>h3)kYKC{4e9y%Yqr|#W6a2~?l+}}8|ygJ^p
z56bY=;!xU2{I}`TuMg{R!%(mtqo3d1_ugQ_=EqIkc-1&=SG$r;`GKw1tc1Ptt^xs`
zw7zk3zjKf85$&JC$I`rhxPJ7<DpI71%n5-*^==!@XK+WE+E$0ajvM-a*!J*;^)OiO
zuHU$KaWIQ|D2-s5SAVha!D31F9PsBMSCckT0EjgeUBttK6x7-3p|l^QX=&z{U3cR^
zZW^PHc+h_iy8hc8L<jmrjAFGAdAgWL4|NF~>sHLv9lTt%K9zXQM2dLCWq*i`3`jsK
zAAkNpbGqCWfts4;Df~D22^^Lovx{F8Vv8Pnv&k1Np>SqX4BPhD2Td#zvtPwu`Dj}5
zOvF!lHSRS-3wg<T&gG(yK9Z3XbS@ohKvL-VNd2XkUef&V&fBGZVJ-~Xfh*oN|41h+
zv93wv3xg*;`oxkfJRHEemnchC;@9S%wpjA!wLJMEe^Q>BB=5eJqgwOIl60lKOqYZr
zAN5iZW78Nc=?|7Mu?%^0l!qNCXBn*70rBU@iin?LT+0@$R>#GB$N|skll0jaiIS8y
zNT3)l#XbaDAGucB+9SaIncRL6NX()>#@#~!N)^zwuhHwkP3R#V-)Jq5pO+xwxm0q5
zOyo1O(kXR!Wx1X*zN3s(Pui!sY|#1eGq2^W_Ddk}x%+p-=3|GwN#N$DT+)o|pv(cs
z&Ct=DhfGc6Fus`or1^m{`1~4b0r5++JGZRzx5z>VnDt7?ye2yymdCa8T#&kY(H;;6
zzpfZCz4j<iD?k;2iol{GFob`I+=Dx0)HYuPD(3UWdz#k1Fo2H)nr6IfrRx(nZEiMi
z%sWP|l^09f<y{<)EaQFTg*+%jXdZv9{zu2sDN<LLi@<mH4JViGy1xGfuI1uBO}@`=
zt*5T1iRbxreRco`lBV&;&op$L55r2Y2wW@zr%+^A&M$x$Yp2ih^`Ok^cjuO?FJHaz
zoFtEYcxq0ThGT|x4%eHaxCqx>WNAw&jD|G6*-xrUFc?rSY3fNU`Y6RB0r@TCVu8n|
zX?f<Co37p5+<N_0rGQcDYp|UDV<122jN=ct=qQ@v=iLW5cI8N87o}P~WnEIfWUM8A
z$mf@7iLH$l{{Y`H{89ewt8aI-;cwOFUzfay+CYwowdLKn*ozNq#XbqaTK1pu2aWNm
zAXW-H_SRStgYXk-Oly1UXbYHWIXnuCr9ZS-KH{e=T?<F{Y`dwscJm_M)zSC~9d1B2
z1&>K?1n~DhkfFPOt$gCgtxDXCZz^r`Aot4q7kf$@L|uCmE10$xJmy@5*05t~iHSG;
zAfNrfC_|GBdBYbhiH|M@Pd<-GvKQ?dX~abGp^Nfrl$R+km{jCbd0LbGh&OnplX!<7
zYS0qrAO;&Jwe<i2=NTQr2I*i-8~_A`dzE{qe9B3ogQ`5Iw8wb>EB9Hs!@ZAO#i(tf
zcMlvwNBm5nJF=h8-LR9w*5BGEKW$T<?V+dZ$1f#TOZA`Mb9ZN%*csVdZ`#t_bi;TG
zfZur$ca%|6B9*c8*E!YZxJG(i*6SU(^Ld~=kJm+xiKeCFDF7OIlb@Q%k#liNUP^JP
zEr0&7?~W@n6@iMtVk0nw_j&7~jrr%`?%VqP(d6?-WEd7N!$0fnzHL0eSFYu*@O^__
z19+YsSjIoSFZ-a(kUn~e$?@2$!#UirmdRXgFO9(QlcxuDccSeB_)qYq5ueTK5Dsw6
zCi3}e9RkkNxSi%3^`n>On^@J_iV=8w#2-y_!{!ambysd!F=kc32;4k?gEB{t*Ml-8
zUtRBrG83sNk*FZTu?r#W{YF2_Ba>rHqgdiXm1@nTS)7afW_??|3M*!nTv%Z@m$qm7
z%_SB&DMuM~IK__stLS6j>G(waDuQy2jQoyu$SO{$kP?qDY~%NM(gqpyEj!dlo7Rsp
z^<({!y^|lm^e7I>qz{&-^JKR?<@oIj*Gw<5<`VjlBqiC<%M;CY=plSk_+pcCXl49v
zVP(R1x<mPNtHpxylqN&U`yfP#9YPQ6+8WvytwY=dh4FV=0)@UTC%_;Ge~F}Ai@(%R
zjym=2-up*$t-N^1Nm{WF{NpR-EtC64dGc5njX*r)nOPxEvs}P~Uzu~ZWoup!K9ES|
zgJLv5IeznEdCo~l`Iw5<%1cI~x=_Ix@*?4q>)rTCCLIrPB#)-`(h~%f0kKEgNBX6G
z;|yOICr=(?;eizLI7=lU9`Y%_Bp-WEUa?>vhgf_d2fR+d(B(WKN%^dtg8eu+W0m&B
zhhaRnZ#E(OR?vL=`Tfnw(_!1o^?D8jcJI8ix$H9j*QxP0%O%ZccinXk?!_d%@cN<V
z&{5n1o=K*LB#L2qomIX&znQnb6HGFZ4iV)yo&j9XXGF;P`BJoNRW_i4vXal|idW4%
z;%kdbJnw;J?nm|`hNp&J5m<Nx-aOLpJBRbN{+=5*FZ@3AEkM@UHg8<lH@B)k7eIg?
zDS!F;x^EYNX==;6ZteepvbT<2_%(jn2W37nuovS$f9aj6ZFW^OUIZS)orzB1qr8UF
zcH##?ZIcLGyJ>whq>ql9^nO^g&ko?@PF|nFA(wYQs2}?=tjrvrw};*zZ;o!gW;h=e
z+Q-bSca-Vlu3W$;zO&DUUsqL3#5_q^V0Q(^WfU`mgZ%h1m7^>OoFN~&OMwyd)IaaS
z!JZwr*nN4x-_P@RbOob=1{pHa;<JiTpe8$+B+`kW1(7I_Dn48i8}b%i)E@v)t;W<<
z3Mz6yvcjLHu>R<CuVd7<$8b=FPDoYAi!pS|Hpf`=#S5`)rBs}a_RVaaU^Fjz@F?@p
z08)4Vy8Z_TL^u*-=%O)DQ-#$AOXd$2wNAcE7WbeGVs{HLh*K0T%ZiqWUsBi`A8lRk
zpVp0^@D27Z{uT>c?6}`}oGbr=i#uga<O`kbJZ|M_-usV_s0lvGX8tmOq_^^<(mqzg
z7w3}=kiYoWkr!PyWR+Gt3ms`lR9;BOmm22DFA~;D)0lJXBfanzraI^a0@^lQa{t)l
z+xenJxapkJu`aHW!lmG4t_1;o98M`rjFR$gE^y9=<h0B&^h!<I5{oZf!^j%E-nCiA
zRIBIx$IS=FPd7XOS=-VGT)uIAv-{Iqy?sJho6y{U-?pU%7ijfSd>o?zK!!1goZRZW
zSxb5z665Qja#lES?7XA`j!RB<aUz`t1hL1|Pd%2WeNEcFD?hR^kPu${xkj|ucgMnV
zWSG=&D*|IepzrVV-f_(pweY_N3CW)W_uX3WOtJ>hUzEvD-(2gX7X{!7g@<s6@4aQz
z@|Jy2h9~IOubcR_eT9(Cm!1Djz4y%{^EFznEgS+vJ8-r0mj0jMn|G4zxS^i7o)_jr
zxTDc>PLN&*ujTx3y^xN&UJ;lj0>@5__l_HPT)T)L@iEJ6nbdt94wtUOZA$yxKEU1n
zYU?1tclM*HFxZ)^0HUghiy~rUQ6vihCgAFlBCjr`SV<OQM1!1ESzJX;XG+(g6wd6j
zXFCqcg#TH>v@r+iD8fN?t#2weky%9{NV5PUVe&-$Q5=yJE-I7=6N;T3e~MEVm9Z=C
zQ95L(>5;wd$8voCz@g^7&VRH}&W#E@qHT@2Mj91Mza};&InLeM_%Rom7we=iYSB-|
zFE0BJ!PIE~^DZM9Kkw|&F;5FHL023<7Ny|h=ITuwntQk2XuFGc74{4&(ZW!)&RkfN
zNKAsRJ7X9t^QT<U$d{}d*wDoOfyet7PN3sRvtp{vKdR%dGC!;*<?UL^dL1-1%KPAq
z<Y^OhXNPr}&AMn^^7J`kaQ;ZB4IgXi3*|kQCSNEURwSgPB8hZ%c_;$jC}5F07xU2#
z>QM_>PwI8e5x{<E>$orY*6XLnoJXFaXa0~(Cid;?WJjLMe&La<^jQQl%oXcQdft_T
zxoYLiUk=H~Z|e3M<z>InsTd7J`+|j3tl`J__@&pxQZGv*@Hu?^lJ=eWu>sIA-BISz
z7jRI9Ve86^k}PH%Gxvy%vQc#Nny<)V;G8B4*0?}19^R6Ctb^p|83te|@*0N_<%$B1
z$JdC2(BN|%W4{IwOOMBgNR7B6FfIi8{yy*B*IzlV4e3#|^`EbMv>D5E<&!tngEGs6
zYAS3y25^Iw!}vJ#3$<k*l(}r(rGvZ0*Ehdk?<#Yl1gPsvBk<<~%b|Pg9c9ki(5<y-
zdKM<LBJkr*uH4Y?TiZ?JkDulIaGe##Rc}RLIS7pTAVGc2UrvUcpNDUxU(xehl%CRm
zZ=XMg(XFT$q6!B>k3Q-i!h$5sNy_G;pm|+O$+3$AF3q&X6wb3;W7Xh~B8-bUsd={j
z&O2|!M>gQkSGpVa)oYwVv+qW0pX7g#Sq!xtyUAEA&1*OQR%1Cz1Tk~5hznk+3H<Op
z!n8EnK6LEE=Ec|jx9MZJyNrAQowAZAx8i_aE4DoCaNccH9=tY00+>Wg=8gv-<^EC4
zWXvSA;*=IIbfqTF(+AQf<>^0&K__1p<i51)&gRmdAH1{YE4(t`5;Ox3`RI*xmzYQ<
zALT7)EKQe6vPJ@^02-Uq)gaAccJ9a*FgT}*xc3jtQYjPOQ@bxMl1VxC9rJWVezx+U
zKuq$%o4Ux8dCdK{b{U<>op3|EWgNOvDD%(j@QKPDje8rFZz*G5UBM}DJmJ*}+IbzV
zDFV)w+)-p5ex^I+nSaO~&be)RIFpo7x$pE|WN>s21c(Nbo>Q1mo;39uGsA<<K6o>4
znb_pb12**K6531|4SA6uHA+9_+;C9ld-b49fU#uv`TMtHJ#o9F*?i^3X2-Uhmy}tg
z#Ydj=M=>hbly{M(2?>G~g}Fr<JMnRdFOGvoN_mMRJyaR55T;B#ub8ja<zDj6d>NV$
zx03N(6D~%~akRvA)Yv2#wXX;)Fak%GQAAzK@NeC6`C6JEwe8!lsn`!K0q(w`-f4WE
zd&WCn^}QYW@WTI4yzGNA40dR@E&8+P4)%SIs((F3fKQ-DUwya79!|P;!{!am^_w?N
z66>s~cNds%67(JKi#;fF^!O>?VSB!?R%;c36(Ml^Wc<hVili2U`L-?n{{_A8NMF8w
zMSmX5J)Bdg$D7~h0y_^Slwt>pAbNwJeKw8Txn%f`KS(M7a7kkx+^~>)GqI?W3n;Cy
zd<Z6%E^};|SzkBc5ZAppEF+(b{~WM|FB1U-lW2?~__FGOsjx7)#e$KE7xEY|eobYC
zksE<Fey{p~0LOShhGwY8_%*nzjEiHB;bRx%wRp&+@9-riT%%v|1vC%UofW^9X)g>G
zUTVN1i^=TOf-h!<0&Dng#UtJzr2qCMG3)F<gG3`LnJd)rAP&kbV$1x`JucV=+(NH~
z8_PKX8XOvQO)`@bfAd0v_{`tMUwj}FZC|+$Xt)S}4)iU8cqpGPaLhcBt05yM2%RZ!
z3zTI<U`At4_lvv+UUB}535=~i`A>PNXywHd@>~)u!9!jyv7IfC6)jp9^@zXd@;sy?
z|2?bMwMRhv$(-U|D^I*%nqwm)qhsH}@)3UKlUI$r62vTebmdSP*Er{D=;i?d^O}$J
z^g+B_R--KK=QI|&hjF+h8{~~Kx=6?7*;n6g-Z}CCxoTSifjv9##G%*AVp~3L?!R}N
zUYC{-S(v4R@4esbfBR^gXGkRFqZ7*c#b}vlN>EKojxpCVNAs|Z^Vu>SJ0G3DdbLlQ
z^!fFlTFFO#+fR9}jI#j=eF+zg*e90i#|cY~x*{+d1P(8wh`N?w+<J9Atg%-3*Y_CL
z3i;LBy?p=&H&!2A|0G$H?)xVZE$20UMel@vdSG{}_{R6&Za%EHXYb#$XCNLu^y0y0
zNdL8bZ~fS%b7t1oYw9V|a{@CXV&Cz;*t@|T!$FzaDgu2+V9a-v*}M^Nyw`S72yDEp
z-**NZ((h&$wGqn{=G*#NVCJ|?NX$Zu-|BBQW`U3I{BxWY02Wh3bDzZ%(SpZa786pi
zRXyv`o*lQ8upDCg?1E2JHL?O^cN9R!Gu4lDIV1ky!IB~qeumVASd5Dp>;`aoz`zA6
z9u}dz4~<YyqVx#vB*XUD^ZU(XBL#EI(<R2laxslj6B%jG&D-j^_Q=c78IE^*iU_C>
zW~Uf*iE%0jUqcNl)FQO-*%NsP8|hE$D{0on{)czo(J{I>L+=am^Ii)iV$KJfm)bty
zH6a<!WJ-B3C$^z4ahC3!q^*({)HjL0@_{sLV2Wu3S>+muVaw`rPp!vsGP;yjqza>z
z2Mx=V7foK!CfTg7E6*6j8-Ae^FNvv>k)%%Yr}gZ~M}F$%FcicXbJ5syfR=)u)oTwB
z;Fjcr?(ohUxlP)(&fPCc{^-C9nJJOmN~y>l(5)lpm{C{SQQm}H+G3uiJV$KKFOOv`
znfZDs+jc-?UNH4tXl(1Dr(aFs+Mfr3%QvoXc7GZ_9|FYB?%H-9&c!4Dg$~RrX_z>G
zATk^zjnFI4BDb7hB(gcT{nCapzS`G!kEv61f*WHHj5X&xct!%h@t_%P119P*A8X_9
zclkSB_QqoD$c!~3YTRQ&;DZyVdtd*#q5fm^nx$ktbg^d8ui9`u+_Y-wRwc;aNBjO7
z#eaOiP+QSKnV}WpkDvOe;bEEDDgvuP;7|6xwHmQClezznEo)}G&xUWUx5%68_T1KU
zyq9x=bSc1Ufp#f$>RMkA81q4yD|-I%Z+*#;rTn;@A8pg)cJegd$*#=^5DGk4?C^jE
z4-z=VMept7Vuo^Ok@A+m=)o@`>WP-(oKhb<@@wqEP8O`aI-e^K%B24;e4_9ppX{X8
zJPe0A@fZKOL{2i|A^L<68QhW$vn+;kLF6Jw9i)-UHmS=+tETz(v-_I255M2M^5%O*
zzNny>#Bq_j)X6FJvFOXbEoONxQeKJ&5&>0>a&}m{(@O(qo#qd|l%tTAdBKo{Vzu0V
z5xHQ#SS9zKyKic)yJ|7-@KyFX_M;6xR}zotyZ8f}3H%8bK~~Rkwl2YAKi^`VTSIcN
zX{@D`%REU&u;!gLK%%E-<<Eq!)I$^01%Akj+L0GU#IzSakrpZ}k7+mwaW(71YVZ|g
zKrqcZb3OB~psWQ5vzLzaA?LxF3c_yf));}<H|9g;w027S$UW3%PV8$ZN#H#97qZM}
z^vtI`m?b`tNprqf7kORyPMem^+k{Tomq@I0tcQqUJ31YlvA<pUhn}hjWq{BH+voOd
z2ak5D`OJNHU66lK7#@4!fOAR#A^vzCIA@Vh9*8a$<+%Ac=W;$rl+#L8KM(wTFmJxz
zDt{@Y*MDXz*QqJ=6nQdPzXoHx5PWYy+XF0P$$pHW)R-#*<3QlV0Pa}3<%;#=*o+=V
zdDB(((;IrYE$4NKcQ&d2KzUwxX0~um&tIdK^E$txcfxnyzQw;c&J5D&EB?weZ%$un
z)wu8ooW>uLe>|{*GM~Hi+J)bj)dbl07wXl-)MuH#|MJcQlo!)Z4&V={i;1twE(QW8
zE;7%DjKU^-G)rw4hrs%EEBf=`;*70Iy1rB0g^i^Ry|=IONz_(4@Aa#2LMFn-9v8pq
zuL%hu8S|_JV2ZLpD~Dl-7bB`Cv}&Yl)|WloaYq?^rCF7IFj4Ub#<Mu&b^1@ogPR>6
z86%e5K+NI@W6nlC$tPFn3Hg{|5!OR5?WZqRmGh6AHxC_ae)_-t75gY*b5d4QnFGJ(
zqKH>q)EjiiuZiTyX-zET*njy$2k@=2$ug8Y8K#IfH~BF&q00)=2BhM^j6`G{*4fjX
z;%}M<7W8A6h*{a=z|7cd<qZ5u3*3ai1t#zh9ok6pB<3VYh8Hq1kD}!s=Nx^De@ir5
zM_vl3SBhJGAo0Jz*mD13A|Eoh$U?64pJT0wu9a4vQzb;o&_;|Eizj5u2l*4*a0heC
zQ58AKJK<wcTYkq&KI@~rXCl?hnj=t({K^Av6ZR}}(!F+Bezsc|`LDw<&KWXL7xN)=
zLixgl&{uMoG_>UNEUx6$YZ~l2ecY#iB8E-r?K~Hyv(*bvnm>QWAF(%KqfX93;PdzM
zpiI-OTeq&c?^BC;_p!5r2H}zC_LtYONHnoAH<`Cev2M<3%)~@vX9gb>3!<JeA!|Ka
zkR9=dj1;824tjwP!mWJRvH19|#3r%I-Vg~3;#6BjpdzrC2n^w)o%{Y?`(kE>quNye
z8E;VAd(9Oa2IcRfIJ@QYejnyr&g=Y&-U;VvtWVu^)u8t851)RcSq>0`3aUCga|FKi
z(mTyN$4<=L&a@2hO{CA<am}>UEtH<Cd;UVbP+%jNzT<tdKM(4i3>Gb8YdnjFz~%Lz
z%%W{+jb}*&SeE1a`s`i2Q(=+wtaspd#2o8Q{+v*6@MM98Bo!9PgYQxlz+9{(gp39p
zmpP?p-)_6%>h?}0u&v1DU&QZ{Pl;I`DV`{9(q9A7IveXGWMNf{;=_gOCC~_1U5?Rn
z2v*`RJPW@{xy7Gj$4_+3TVCeDPji;1Ij(_L^kGVh=o)cZRuTjHp8j`Twn!&_Y3f|a
zU{`#operm>Tn`2@(o-#j1F;S=4<(TG!391j<H#8^MhZQ~L|+?5xRS=_VlXCV+-Z^|
z%r1C^pUJ^MCbZN=ISIxx$6SlYguK#G(p*omb`UB;KJ$wC!+hemnoMR4G!=P7F7bFx
zG7-PVXUL~Q%H*9r(iifJPWfUVjO2wAiQmL?LHc#ky5-4ljJiH#Y!OlI)*J!sk?wAx
z%?)q3m)=iipb|nIF2S4on0b)7!JhoiP3zE@LmURsooA#wbJzSPncrjoGezpNTC6j<
z)S1_I<D;Ummi+P$EC&xCYo2-aAjNB20)ag{?`$r^9SraL<n7JI4gG#&eTgtuv~=vl
z6V3Dc-hqts#<|qyCFg$NdPdGEM|JTUp9gBpPc+M+$G~g(obZ|zwrhH=q)<G+_`FM%
zQaHv-fT-s(#_Tl)7TJ#xmKt+KU<?SH7{DECFJHf+67CqbZEi7MvA%x1``o~uf}txn
z)_<Oy0*otG(f0#o%lU+e6~D@UcI&W;@$Y_cvU#|E{L+e(d+Y*VehRll9Z=%|wt8E!
zb8pzjo_~bIxxF4PweNUe>>XiF>2tk@yFb<Qiol{Hu%W)@FZveO_|8Be3mfcN9A?c#
z1rW@&<{F9$CoyyprBhsy%3!9BU<eJCQsG5I&r8uD9gvdwTt0KpZKY_@#_9q;{WmRp
z`;-Sbh>}B%xVTILA0R1yP68hh$zv&vbSdjCiSm3TR#~j#!2sXYf(&f%74q`KGmP!O
z<Uu2+;s}w{39@;UP68U&sdFO!B4jLzb;asg`XD7BDSoYoQhI`K`!6nE6h9b?0t~1U
z^evk<Huv6nV~2UMh6-8n8}||&@yEma4r&er6Qu}HLKb5w(?!@xZL(%x@VAUKg#FZ3
zXr!kcjfKdW`zJqBoqS3m=L!X#f9#<$V)1i}u9{9`T9+v(6y3QznZMvldmPGtN<stZ
zR8LywFIj0*`N%P?NG5ngnKf{|Bd>}1K*dUJ{X&3yf=BLWIG1~=+`uw--!$~v#+Xx1
z;yL&JxAJj_<qW;DM?E2bwtXj#57s~@c%@T44YqTtPCq$j{^~(G4jw#QKYod@EZHvK
zxW3tiJIRddW0#&k(0qjah}2iEIB%I>eznu<AjYIKr!v<yAfn1&@aI@P$)SUKQeMxZ
zuqP3(Nrx3fnhPyYXr|WjHAB&P-p6wuNO+&d2T6^%A}|&NhVVY``b$?_!X3-@%{<O4
z>LI0>V?0d*8+w0->}f{ZA6<RFGx>7rfh&HM{egiUlwrF3?i0g0^w6IaAEzh0<HR35
zJ*=Gh+?~TZz&6j!y6n>WXYG9PUh$8WdAiq}jQ^Tit<g*Mj@4?Vt6W8(B2W>i2vh{7
zi$E4cSR=_|GD;pcqF;eL=DLd`(P520(j-T|5OK}Kla9C<rG%<k#L=qhB+aqy*?zOE
z+kg9#IMDwRKYa?HRYX#7D8DK^N6^ZFC>*H3W8;~D1zl6FlUV|0=wKr=A!fUnV7+TA
zf1R+e^eN(y_Z-6p#qd?!Dr3_o;}4hQCNC0?p4zlvo$|F4KMim_!wK0Wldd?rF=`QF
z`FmAo?Mu7wY%X2k-@kLu1ef+L@MA}$iJ#!ag%f?d4-R%{*q6kQOt}Gpr6C5r;GZm}
zQG<<%!HZOr4roY%1|Y{6)3ejpYi4QriU<b9S1c=gWY%}#$lNA>j&0U?O`rIsm@uY1
z$MQ*fKr_KKxj<!!ig8UD_S7I-l$UM}$R}Z%*W818?F|AY=k3wQ{0JD(v~RKI*EVb)
z=G{FfkM<m*<ux+*yt9^B!+cPaUgt-lNr!EaCi}rJo6IWA&u>4h<Xvk*2Yr9&=~r#H
zUYA7RfBHK=+x(|L`=up$7HRR(=U+!S@mi<XEJvr@!;rbAbzVF91XeVxs&8K_gHZC6
zEP22y^SP_j^3-H1wOX(9JZ+mk#w!6P7R1MWcbs^S5t$luMPShpI5mJf%JjVmy~sIo
z84h05+R|`#xIS0bX0I+=*Zuu+ZG2eD>T+J^S6tB?+8t#G&>!H?!}}*rErnHGs|XYX
zzWUr-%{%X(Eb}1@esJ4$LvV9Ve9ZrVTodCXG^#hbdmMMX|G8=p>o3QvhOP?E5rMux
z59bI)HB}K91p@ln7JrxW`Oi1@^)`PN9f3mr+3Ma!k^GrBQKX@KFi+5_L3CVDOE8P7
zHgm^#&po%=fiO=$?T^VN{s@q3Mx^zdwDjI_wj7Ediy6NIObZtDtY}EZvSfbpMZv`m
z6d&4kh*1UkLcM*r0Lrw5HI`!zItj5n5kKhs-Iw~(e-@M#9oIqw`_$g@c`<!SI;lw$
zhs=itb>WW|FKNEG;2*n0;EbaYOq@*1JnG<&1+NJp0dKBQ)X7tFq=wX6Hi`qAJVT%L
z$VRzv(B_r(@`@JrQWtygFD!Q*?c}ArDr@0{%z~IbRFfrj=l}%m$xsn4X1PKt<IGjY
zPWhyhhb`AVON#v{IBAjc)MCBKsik`92?DX-%)`t@?m6=)dgp}q*Lz4_=7D=owC|BS
znIGyY#=|%4yDe>jX1>r7JftcALbqO<CzNEK+6Nxu;g{tg46<?`{o^<g@nL=cF_1lf
zcl#~ZHP>#r^85@7w({uS{f-E^sQhK}D2LnEER)*jhz}@o&b-6JB5i(4US#Xky5*Ij
z6tS2F%<Fvah#oqnq3~%ozDr<5-25#B^&A&%e2CPDD*~fIKtI2G9_{Jw>vhk4T}1cB
z{v&cod-_p*uB^#+#aoYSVtYA8m-CNYD?TW5*Y#I6w_Lq(Ire+Q^Ztp`&6l4X)*V86
z1KtQ7`He^S4KC_0Y`=bRKF`bH<=k3pUcQXMPT%ofe~~9#d6K-gia<r6B2W>i2=oI1
z6hlx|$wD2AAADcHeXgnOB3fI3LCHmLAh?*~@?cv$i6w<d{hr6$Lzsvbg#=rlFB<pW
zbyLHi#w=W9>>SWfl{GM6d|D%&Uom&jl9PY}k2Ua`m{1R;*!0FJhb+9ZaSWn(%_6dJ
zprC}ar&cubS*d#&BN+?9Vm|vx-t;%hXEC?mqD}BfS|%u$59;>E*U+r-==Kz7<Ik8{
zAE-6sr<@u)=+!((B?j`~@gqC$Fz<q1mH&v7;cEMcGh#I+pnJmw6!A4I0Tw1H&xKY#
z@rNGDQz$fOAf2+JY1-06N?QA2FqT3l^jR-xoF~l&F#pJ;T#{bP=l(mkNg4#L0s8Da
zRXexZ{K3cJNN>bUPT6+`5<$-O6+Ks4dCO5m*2$x><$0wniPFPG$&+4N-w<$qz$@(`
zd+ogLAd_p|Ub`N9tF**eJFm^fg}oj*uujwBqnMZzdaxrOTjV3=r15YUmomrNp7{xG
zyaQb9YtdS$M-RS348{|uPB-6rc6je8Wi+*)J_09^6HmSTW>KtzSWFw`j>&oz6D`^N
z8kl)VUXGQ6DVO}{tuu0ze53_`oAaPaA=fCJ{2q7x!Q!}D0SjRVMudpZ{e*j5fYfL!
z0^>no2zQ|1ryta|HVB+J^-*)W@23FP2AW=4?R#CSm!M6-sgrn~^tmmk9=NKC-xs#u
z(C4_%>D#YA`r0|^l~@tzB5?5N@#ar(N0}kDUAJD{TzBP$A-OzPUyt|vg?g@F%)waS
z@xJULKi75r0)Ldi9MYqjm=gjQo9A<Kvzn<0R0O7kfJ%&bBR}8cXAwk&5a{m~N5ny<
zQWyybPzB&uTXME8abis(m}JkIK{gdyx`bSqVZUkP`sR~cZ!8uZzx`wkj^Ez0z>$7a
zAgB{GRzc2=Kjkxi>y=R|jV8=EO+#rcD^bvVRtr+H(sr_mhD$4M=H$&@k;Fs=UMS#H
z@w115axkH|(2;J3^4)m?qP`ic8AwmoAQfL3m)5AC4@`F95YzQnZ{pJ8wt{Dbh`F|(
zVBq17JAs|;;+O1a$(v7t;_2WoU@btsZSzc58s|d|6cW8G`>|kR(34etk*CjYOXuID
zKc4a0kxxw~dj-l<zrylGuKW3yu2YXWotNoUD_`^k#s%lF;k(LA;vZUSx5fx?@5*aa
z>|gFU^RE0%QDXV^$9E^<xU=(_^Vvg})+raV`NEWWWc_JFOn#1L-bjB@AaPp!_#+;+
zfqt2{4<+Yh-!!i~zK5TDWsQ-p=KF-eQ!l^O9LGO!`UMX;ro>{dVSpSeIckdML2uWP
zg%Bxs>r(DPz(j}ZRJ2Y$5kd#aTH-tod{K-v>E~qZjSvtMf#SYArv={F0jlv=1Qr*8
zA>29qL_H|8W(oM=N&Nh-Z7>M*y)HEvAXn&U-*I0~on%!9Wggryyt`LC`s%yQGY5{W
zaEmMZ*bw;DM_wJ=DbWY+8Q#IQdFIvO4~O+JTJywa7Pk71_r-o@;t>ARU>5sYbq))G
zE7q?Ymc!NCia<qR^$4K2;o_ky9xOP!Hp0S--{@CC4{!3Thb7Xrrg0SiKvTm)eo+y1
zOXr!q!B0J^v{-s>*WKH14zn4*P0Lrt5OHJ?$i01~&jky{sKszS<hXv$_{HWm4$UW`
z{mCU6Azi?mK|0eI8NF8a&s;Q1#h!`9W81P!`Y#`|l0z{INBrIz(qt7n$tu@)3Ee49
z9*R@9Z5dmSO&5=u<R8_z*~Wvr?@YqN_OTm^FJQztfDnF(As`00llT!q^74^urggEE
z3|K@XD;jA&2mnF=@BOx<_c7MEp3a4Q%BW8o{ZLZahW!dzt=VokV5VG_+unDDgNxO&
z=O||KHH0L?l{Oano$*KntC%N``__+kjWlDNwrG+x^AJB5mNUBA_W%Lz4>u~B(};I+
z@#eAPCT3oc?z}fWHkdn`Dst1UL-ejp+);*BaX>&{A)0sGP`=o3(dB0qxQj2eA3k8f
z^?amHZ44hb4?q2C>t7v>9)ZW6+wWWoB%EKII2kjam5Z5QU~-P(82~orV&P9nBbhbD
zSa&t@TH45;4a(0Xo<m$qU3JMbvR*hLX6;41E+lPaP}FcM0;54-2zL%Ydi>OA_M>ls
zeozm}^leL)^yl!0^|`VnKr>qGdp&T5ca$04K^f-2FMso;8Kpv1y9fyI)?EMmFNXJq
z#uo>8aBZIX^6m%qpv*k+ey`_vZ@9>hPTIWjvU$Q;ZB+zTg}@c-S9K10Rko^7MW7<E
zmI%0n!Z+|;;E3WNi!7*&OcXs({9v&pd0jY3TJ(}@3o~a%=AyH!D6)*pgpD%=W;ow-
z&#lld>m?=@Je_4&n{BYIixrAnad&sOKq<xD-K{u*;;sdXyF-!U?q1xrxLa^{LUQ=_
zwfFg*zfb0!S!>Nb*(AI*kdt=t3qF70tzuhN=W*e386!%4elHvTrU*o<+r*)t?0!s>
zLuPVl_O0PlpEh-hJ$G;`oNVph7UMZpH#;BLP@GIvh?4kkLx>Vf$ly=3z^)sRq&U6N
z->SP`4<yVVd#fFaSlr}U)^YA9e)XcyroG|wg@^F$S+l6;63JYP#jlRxyVUBCy<*^9
z=6U@JIBC`bu@7mSqaa!&cDkj{%~`OxB?hrBS5ZH1b=&07*-$U*ACvrKCZMQUOSR6;
zG}tX+jgoRgOIHD8-eBO&rGN+`Nzn!PS!>)`RhU8rE35F4JbQMKTkw3+fMoC{w?OIW
zTxEModWjiKeA;{+YW|5t@1z_?tZ#$E*P(AZQwDOvsY$gC#iAKQFnO)Qi03Mc5dKN|
z8uIVxoj@LVJN_T=WPK8ZT`W=%g?(%K(qyLz2Og01oeT#Q^=kKdzh8zS9{bqEUr+m`
zAQy!+kH+bE*6<n!dFK@*yJjaX?g0=|`QH0ED1m|_R5+sNQGyjxqbWottXgNxe5P%l
zhR7#2x0PLyP|Z~#m4A7^bO7FaEoWj}a@t%z>pUp`x7%#x+H*T_I=MEiRGIPiy>$x-
z3{gkS<c}k{Y)P{AY_aWcKLqO6h=%Bu8oozsL@GtkU&IEkiA9HB%`n;xf<Wt-j^ly@
z>t$lf7(wF+rA?afOt9wSy$I;|hfj3=$)Y;a5<4MbxTs^qV7AJqNr;Y`9}S96A{#Fb
z`X9Q3hJ3;sog#FDCHSNFZ8lYV9sm{~d#tr#8Wz#-UceJKFFGrfEm~!0C_3>|U})G-
zk&xr#B9jFRi#@8zlk^WURPvxE^qcu+m3>}9!Lu0IVG(mqVp~Reg!t0Ucy+{ZY@o-4
zRDe&8n9lmmd&*=+m5Yj<4SlOS1pc$dj7Njz{Lo={$7<X_p>*`}>Jc>r37Z3nx(bKn
zH%_|OFA@8J`u7jxdg;$D>Z5J!L(1QuMiQ4wOeCJv^~juOy`y~VzFU>i#x3lbz2cR+
zstI-B$$SUO{?(u$dj6deDtl6jnpGI~YG^Do<sLKtR?EtgKP@P|n3}c<-k767&oxMz
zOfE%fx`&0q%YUAHn}ai-GMC)r(448w`%ZovXc8-0WM2?A<!iM3k&Du`WFMO~yvi(3
z&qlggkMQ~+>&&Zn{F}Q+9Kj2tF4+DcBLYM8xxTY9EvnbUSU^SmXmd33Yxj`5aZieV
zLI(-RE+2>wEMh(u^E`4NRUrhbM$5VVioP(2plaB5lsDs#a0?~S9bUcj4%}Y3HLYag
zFrSPddKAEH_5I*s;s5xiH*HX1FEVp0G_1EXr`-Y8JW}!Np78V86kEJ}IenkoD})#%
zi+Xr^<9q~NPJSP1ci-Qv;}jE-e1z?}yOQsBZrr2Iiy&sxzn^dYZR-;8y7g+}5^`#4
z;WzwR&bW?scrt!!Xz88q{jV4u_C3@mWU^o?Ws-ok{rcdINbc8JFzqb6tBFX$^FRzP
zi4H^=_tgF1>>z~z!$E6YW{YSn_dSP6Q(Wf4AV<09{Ln75qI%i$8`!G_?FI9i`A!M!
zx+G=|{!4Ym%o!pj^@eRtoK_^^0Tae;zA|;Lg;)Ay^^3Jx)bgncS?7o<cV)mho06B8
zM*50QS7_IfyYGuj1&tGzYXS;*O7bItC0K7zs7D>%?i>bj7n!b{hSc!nWw*L&vRxh@
zdYOm~9AZa`DR^ABQ9kPlb-wlp{AK_oxY<Q{Cg_iG`N%9wXZMb!d4#NeY)$^KUb1eT
zlut$EUc1tqNNXQ8QKyrrtYJ{ABGdPkeosZBoZ%VA%xf4^X%xL;vDkfLDiP#@$T?<l
z!rS4elzD^SIUr*Dvu8r)kI_qT@36?zF3lC0T~aIX+)nP)Za?rTgWHDSR^EryyYq51
ztBnl2UxG?j|JUGmwni9P*5!U&>gbNd2vE&U_SeCD_l?Mz2zZ}4wQ5O~`sy#lBU4iP
zDmVG&@6Qs0h1yBQ;!K=TPbq&vZSH`#cAb6Uwj4o24UXZxzUpOmuL^2QiY(=~bPqX#
zK|91DFkvd-cLO=M2=7BwFBNrf``A$Kk`_7%#5tiQf2r==r)4q4yGsJ(><IO_e|AOJ
zvHB(y-0?&r>hEp!Qd;0S&h_1^!k_+h*ke-!jT}_H2YoplpG<hswFC}#U4De#wY6l@
z8w1uo_@{3!=@DN<M400Vv5f@pAgE2+|E#I5#tQtQuwMVT{5f(7=QD0A*o*_{%BGlM
zyT0U;B_IF$p<&Gg+aML_bk@K$=OwxI(eP;`_$LZQb{jT_s!B<vM!wA#PNsMP<UpNB
zBDokzqtBrx7W|;05aR9%RMdGdcQ?EczCg8o288@h?hm0~SZIDlJ`;%S#XglK?j5yh
z{T-&^{lrX+NUyn1B<(8aEx>}P{5KrlLOl48{>uK0SLq|RISET+s=u@5p<2tpb>3c~
z`iD+uE#TajL|h*^7+-axbrOA}CzGltW!sOj58D?GC&SL8>4eO`mjVvVDSTl+>{-L#
z?&-?xY3b#{lIV+PrzZ_XKk@7fJOtn5A6)3-XDnC7q3$vvbASjYBUI^tgY7msH~UGj
zaR<q=$UIB3?5uRi`>0+wD&ZK%yF%dv`eVTPfqZu(x~n9^iUOa3=^HE1^4t(w_RCl{
zpLW*+EKG5*v0bb3d$h4afJM{wTbpE!upmmN`lL^lg3Pnd{QHhsj3uR!YpmS_`A-{#
zdy`)N*E{+=?(@F1k+xb>m_tM2Xp2g5#9;_HL;JL9s||tLDel-jEtz17*E&OG)Xh{Z
zrS2x-)A17Tqk(4Pk0Nu!N=oHw5u{YA1@$58lj4Ud;LrJl7T=48CCMj(*91xR{`ZIq
z5A3{Wjc4kIE+dSw3G7W;_xHT7{NMTULBIRFLs0wsPR%*AujpXmM$1p-5VF?7!*3i0
z-6hr$j*;YoDtA>(^!{*oxPUkXy!<`;rLgk%#XXHD;5mt3QTKGW%m*@Q9*wV|OfBSj
z=Xtw{Nbhr!v!OSIF@%Cpdl2QVa`=Vd-+j}2PO1ghKp&&x{=dH48)<veXGwHsSp0KB
zHA3=@90J|SL`AUph|5Hxaa6~LW7z0{0<|W*OkAgvlhw?vCy0+@6C7axtSa)C9hAdk
z03vb=4mX3Do5H}@db`=ru38mkNBk6quNiE3qw=}-hAZMKTt+QyYxOY`ExQlWs09h#
z;{llek}^<9DeGJ}<d5M-@0MIQI8lbz0|Hbz6zi3}^pA(uV*@-vTII%1d~KjjK(Lff
zo5x?d*Dnk)wlL~Nc-%2jB*XR#3A?Le%m-16ZGS`;Cd_LZZwjIg)yG5$CFJ$zQYN^N
zFH-{fXrDQ<>`xrVV)3k#2)=!hIt}_X)kE?dqmGYmyi|7#v@DHH5EvH4#^Icen7ZGh
z01x5QuIUmF^ue!TUza|o<%=Kmb96g%XwUSy>}t+_$eh@|PnW!OCzE5gWoc?o>P${D
zT0J3~N*KGyX{*hxG9Q0FSW-Bq(qFc&`Dhq%-Ey6@6+q3q;9rC!QtuH!Uz5?06H?my
zwQaTJm#SGXQ(A5B<p}#j#_(B6w47Cxl#xDM*jF6&QDCBM9GQ9d-<zg?U_GMaK~F*@
z^U=C%N*aWtgkAOiY9tmuSZ#;1$Jfr9dvKl_zM$|)LG;e~1&Y%@y=@VX^RN(UkgcO;
zbE^8d{=EcYcM#V?$JiD1k61E56O&5C%r52CJw7^LT=&bxHtcIcM~%~q|85&bzS!ep
z!i|+UIat?%JlAhe=CZ#7GKPkKzK4nN)5vW`vo9aB^@n;7bc7r?{ohKI_%xRDrwjyi
z0ljio)vIlv7os5{^FHUoz{pQBdNTnlKFiM=qf&$hRVbnd&n8vHM~Sh|MxN4ygXDwx
zpIE4v+DN4+z0$-G`J!|`a8$GSa`9nL4lI8LFq}2mP{qPi+^yo$5yUGezMsruJvz2~
zFevfkj9L;<pCVVMGCift(>ZT&mk&7@WTMUWK(^2@5jK*0OuA^@X&RvRS9qdrN?y`#
zH_Lr(-dI?#wM_EFdz%|_VhM%crc6Zu1je_~W^VUO`7Vkp0HIP#C3N2F^f8Quab-KL
z)Z})F_+D!G2%~?sd|~s>lEX#>=SD9-3`pufRHe|TJ&9l%XT4v#Q`U>0q7caV+UzSJ
zPE5oUh|TV$78-&FsHinwoUC)jVG8Ak`-(FOH!>`(W?O9Z?yfmHdKK}n^Un#!t$)VQ
zedAP2;H4$c#;3IAJdNaBN}(-?*yl<sXtWkua>xUMNHL7uZ%ao?p^PdPRqEY|i01|L
zMq0+~ds5q+WXY<xr{BAqg?f@LX!*IF?n#1Ty!HfxeD~I$!=!#T*CfcvgF7z|^ICJF
z@ZyyWilS?ho1NrL_ubs8M5?g7MEu}F254VrCDh@nkz!zjDoULnuXSO0L%-ay>>0~%
zurEsD+kJwT+XEi8pcsyoOtrblqJR&b*4`@NuQxbwZ_8;OF2f<l;FoAo#j2A<5v*<U
zLg=>fWlrg6*$UCgt<(1pEb3SQ>dUZ@dzxdeopy%i%QK(L!hH~>$S+AYeq+C95rZne
z_lU~3?M!Aii1>2(FYUhns!}!5lY>vH6vv$KD?#*#n3g85U_UF{&s?d`<wv;YDt%fo
zQueCpwo<*T32=U_t9~9^_T-w|*2{z%cZ5B#ym&WB{T74bgWj`dBs@bSTFC00bibNm
zyJygmm){HFwscJJH^L@NF8@t}-|}83JQpXz4~%w>s4~O+pP~eJ@pEDfSR{hVVw!Ap
z(aSjQ{-pWMlOW==<~BFv#Xu7egEl;(JXq4!nRjq}v}{7jQCMnmr2E)SgpOzz^Fg`v
zC1t~+2a?s1+}f4eT&parK&2n|MFAHFq4P9eE9&fbs<E+Yx!+{ed76hg8B6v~e!fdG
zFFL4i*v$2PG^x9Ry5iKdiq=mwky8kS4=j0rBHIbFF9RV}+oif)cjz#V>KC7L`gPh2
z5__NHpB3@gj{BnLC;@u0Kv2XWw{@_RV5!tTHj>JA#|(T87$)yCL#ai{DfQq?dmBiw
z=B~%^bx6G?US~}P%ky-@d=e58k>ez%Xqep6xqnJ*?7<ECng(CCk8wpa7Gf^g5~KEc
zAUTa~OnRz5&JA7f=&QBv(R_M?1{vSC1<scN>Q}5VW63I{7_uNcZ^*P&r9-DGz~A=5
z%lqU`dh+q(m2w{tu>|xnPtds)M8=ltv*ptTg$nd!dvBLtGW4H=3mlgCmQe4g8T^ll
z*?AF9FwzITwW_Hyx1KLfC)H~(E(epyJMNMmH#?&uhZ%2jmcl)T^C>SD_TXB%=M~ij
z_Up6Bt0mWaPHxR8oDag?C&S0tPL?_bKNNmb#Uem~WeWW|3&;67>4zsIG#IN?x6k5T
zTQ)uxNXJl_%!|JIm%D(r0%f-E>u&biKW(E;`<{CtA=q(Gh1Ou%pY(Wc)+yXa27e_7
z|J(s=@SOH8hAN(DrL6d`?U=&CWF80dJU;eCfoh|qRviDyu)WC&)0DiiR5Ch_9YDrl
z42n?F7Rub`k|=OE>5ZSn>)lkWg%w2W$(cs9d=dNMI+|6zdCn)EIy}8DVHw;8M!^iu
z<prJSuq~1V-)H*tMJVH35~hXWRCQ-;(@tBX=va%I8nncoSf=3E53w`MeeHf(h;fXD
z&}$6$!y6#o5@jV+IQmb1AlGdOV-uii$G|tKd&&Vb4v3MDE}6EN#|pa%g!9;B?1FTo
zNxOxz0kCaN>uJlxAi`1ui%m-545z0<u~ZUJy=QG~E=IL__mxStmM7<LR8WR?b`;)?
z=NDHZe6H_?JYy#LzL_&vZf*yG!EWJDPXC)hxXD?JSb6KN7>pq4+ztGiM}^#n`vyNh
z?6&<h;L0-y6qcwYX!A^U+^J5~{Ji4FmZ0tx8<a;L6!51=jF(<0BiOWvG;hS8t3|Yc
zK#+xcGMwlE39Ho-`SSBLQ8VRYjn%Kw%6q`;;q2i+8JO5{aj_g?OL--70qeHkpER$g
zeQ)p~X!!BI=b}A(=_$Ir$l#r`aJaSMK8}!o0=kKy2YmO3#O0lq0{>OCQ8<J5j}OVw
zcn~2s^p5t`8yc4cwd-G>-}9KdY<36ww6LYuR);M{VvTag&p#FS;^p6wIpBv1Jv~9#
zrG-2+X2dEv4fu~SA5bWnVw?jH&5#%?V<LB5yh`)jSRw(Ty(L)B!gjjE9QeqbQsK<C
zDu;~?d=CFvZE-A)UkgeHLR!a3Y3H^?oVWiRv|jCw6nw%Yr3n-(`zWf|nr`u_EHvFZ
z*`$xD46`tsB|SoD-waa_#NiHv@_;mV%P??eo>D|oQFW0MXvM6o{vtA73Sbii5wli>
z1sf!J37Vwh;uE8-z?Nz|wRf(#KPqbQMU{XEV6D%5kx<Z)(jM5gkZ>Zujdd-zBbJDp
z_`60Uy$x)-lHHt)%KxgD*vM7qd(`)+`_x`+ijV4gKyo7`{k3qJ!AY{Y*oA2lOg&pg
zx>DGQr3$0NtvUd?`ac!`DFs?wBi3$`92ayTl6KOePkRn*)pRJ)*kibX)j-9bh(mX3
zQH8k;ESRqyrY)sz;YJ%M-PhD=YJ(&4(D70}jNkjA?=i#0X`<Bcd9M6=X32a|JU`&d
z&+!K~?S4#al_H1v7|=M|ot!w#RSJ0d^L(gYli6k@9nt&S=aG?}K;V|w7z$1>b71><
zVF4ci5-bFB+mGpA#}{`s#M~}>jrh8$trZRx{AR9MQk^rO^dB5J9P;>gOH6>2b_N%o
zAj#cr@dqmoD=4dab@{cnM>w#f^^t7gv(VGmLEo^*`-r#er59)!;+urseQxKEqqoED
zS)2-O4#d$(Vs^XgfxR#1h3I1{&pptLHFk^NEvG%t%iV{w+ONjT=d6Kn9%p&}=Yv*T
z9ptE7Q9WYYpbfnbkgI5?ny?ks6<;%`&*bc0sW<s;l|CE9JgM2pN9uB~5<M0_$PUrF
zwCDOl=T|3{Gbhp?nqVQMtr-u~K`EiRHII8kfr6Yr+b(}BjaZ1`KmRk-Dd?U_U`a&D
z+*ebb6UcK~YuHN#4&==)<L~uz?$T?O<C%`R4ApR^!ebqo^?wB_mYJAgQsu*#D(ZY3
z|3cv2;l(dPpEm*G@{YQZ5IUFFG-1CrL(=Zl;Q?~AK@&L?qs>0x&}&>M!Oc1+Oc~U6
z?v<hvEv87;@koR$<6o|=R(9MKZfV7@QW_wIT;m1~(QnTeBnQ(IKC}GQHa*7^m)PgO
z9t`njRTe}+az*xG600tEw`uHKXN)j2`!xu0^dE^s6&GnXJR6T*G+s>*x%a^?Dbwkn
zxpW~js8y(p=6pK%a74+B9&4@y6;=AEHz;tBs5)K*6!fnBac~=}Q&m~{Z=8rapHwO#
zPn2H;U|k!rk-d|fG3|D4Ig`kCEWq>mC+5pCqeLY)`tZ?S3WdGb+H_}{!<bIxy;Nf?
z+aIOZvw7vQ14`bwXsToHr_r=g!!;x<&$G5Stfs`THS<b5U3#eo>S73`{umNemP(e@
zjK+eqKuQ`RL`2zHCrZ5Z<TNozc08=hH?HyTHuIj1_;8U|b=pA%k5Q2WK<~!wg<fXZ
z=(yfVZ9}lnV8gfLYws4YuB2}&#9QE)MdqXApQhet`+t~@O3tTTmjpj{e)Hh>-f_PZ
zLDt>hN)C%#wt7BT1Ja-!0r&5x^8zkV&@N-u*E}&jkI&5~+5O)JB|c8X3Gb0we%BX+
zbs`hoyuz33Hu}d$3=cW-ahmvcjoWmyXOsHc@50#NVH|LvHr}Q4TK4K_3we2Kx&NDX
zGT@7K^lHsKjsNR=6;<sMlEr&G?nK%_C5#|e6BzsWU?@KP^-f>{#+39-1*SJs43aKN
zZhsUlQo?x{bq7@O<jr`soQ~Cx#$aIXt78#EXxr;qOd^3{fHW795fDZ5L0H(NJVOy~
znk_QzlsUqiSiXkQ2VuH5|LRVk`!1^h+>#STF)K?V+6P_(S_TohNP6U^=koNea&l+c
z23Lxh_mmN%EKVCblTq25X^C{mmb(<&zX2sCtj_@<7{0Ds>%(IBRD^F}oY98XV0<Ch
z0KGd2c`p||qnPxDl<X1CTNEzKJVth?8h}0-_FecwM+szgm(3lURfy))l;tdws`@r3
z*TJ<|Ew{OQ#e5*ie%!~@hHI`Ys&&FCYr)pW6wJO)eH-d)-G%NGBz{;VrYGxT-{N`{
z_eAweaH2{J_RlmZ$kV(+aB*>X=v0X~|Fa(vsG=u>7Orc7UHGg%p}NkeOKT4l^`gK=
za7PleBQ4v>)$--!<-MYLs@44dvb~H*p9wM^1lU{7Q4@0I$vgU(7^Jf#Tq^MPI8tHc
zZ0DA;$;Go}5CqS@NNBlAZ<hF4i@vM0$ZGVjoP4{uIw?j1G%o*}^!?AEN%q=L@yJRl
z1$<vzB05~hzO`c~$<C_!ZqttBOX~Az%%7K8Jy(9t0#8QNJNe16!^#VhlKmwgb=H*g
z0t9_}ZU`iT-C5y}<?8`Yre*b94|YUsJKnp<A%QV^UfqDZcO1~|XItNOLKH|-6>1PW
z;Mx1#K;#<H8hAUP$Eb`9U`=l^>|osFX%{%<x8Gd0-)u$qm=W}IiLzptjQjgir~C_K
zj%kHtscHB>St##lamK=YwL~eA2eGq6$J<B4%8FKObFB{=p{9y~gS|T1rp0e>0{79n
z7UL1EBWx!{RnL!WPCeBtjV^w-kO=xaZlmjQH1EqNl7LhFQgm7axf~`$)s#e$3{fP6
z^vF;nw|H6DJY;o*(jxN=->;djoXs8&>@HKeFIAllY^7^m^<<Y>=scl*F9i)s8m7Y*
z8oO!=gsfxuuZqw~$2GSFG6tu=*3m#SKoLSPAXs=TrkwI{y=PrxM`i{|tpkR1Bt2sl
zIq3XtZ^mo>9LMKE{KRtlo8awty&=PAkteDU(znzr$aui<-yGij#*_$uWG-C3RocAd
z1QVkN3)=%+QbLp(zm!PZ(Lx>jz#BfyuFvV=73cFX)xTH%_$VtR=jkP5^ie=*wBOls
zAAE|}x2H_?Q}j>H3%TLUuMV1-rdt)hU}rE>)x{wSGkDOMz#2`lU@#prR;PJ!CdOqJ
z2Bo&iZ$*hjPHE348VYgBX6?7E&P0!P^KaQ1i8=pS`7z!kRf&#A4zsCvNKHp_8!21t
z8`Uc1bQ?|$+!uPSCb_{d20z&SxKc%KuOt_u>e~?1P8pssu4wGEcBuaT6=P`KtR_%O
z?D6L}4-qT3?`NNbq@=eW>|O#uR~SmUivSqtcJnEopRhO9*1LhsQ`7Tk%I;gHR7nza
zIS5hQOG40c^drG8b#}g-&h@Ey?7L_+y{-4nmTP*e*#NA^8B@stpu^L=>|`SCEWJ$g
zS6EN@TmDUie{EZCDc`sO&cn~COn#r>{V@;)!H0|33huU=NLP-c?_wPH=9|6Vd3ElV
z=HdI3R$yKzFa6&Lo_BQM7@0Q?z;J>p2bqm&ATapncOK8%ttKKoWexp=bbkPqGD89S
zlt%KWRg2+5R0cjk8xM#OaAUpnkQIIVu#UBV=~pt#Ih^Cy&YoUd&V`*mI-Pn`LZkUE
zl`+&x{cJi@S(j@fv1NZ#NnjQjJ*cVn`;$y%pciHl-?t7>8c8EcR7$`FUcSi#xTf|$
znddweXW%!Yt~U6zY%H(W7DGK&8(P%cVS@)$iKdr9us`NL5yE2|Qy*nik4p2|Q}m4F
zynZ2Ow0oj|q<B>IHEHDWC>KD^<b5K#IkB<+8w^BhY;^K#4K4vizVP9+`yRthx*yj&
z?G;31b18g_=__z|{b>I2&=NV>ij2kimv5OqNfj8rO=hVtlN$4Nh-8P%o&1LNLF|$J
zbs}S;Ux9K=CL$Lw;(Kyl6m}pXLv#+rCLd2mkBqb5!EYO}_P(Qwq6bdDqzvv(uhZLA
zi-HHnDRn#QD6Wn+{S@dTh`my9$h&QTzr1!kC{2^OCRAf35rz*@gi>5`zVi8_CJlsO
zJXaYMb&^S39{rpC^eHJqf!ZmMg429j+ANnNj--IdU&_Q3Yx&2<-6nf3t&H);8np0T
zzQ`-RsJZY1<{p+;5mH=5C_h5;+O07k)-@o{@|Qdj01;k(p9<c_xA225W8RbyE)n<V
zVjoAHsv0{s5AcHY<QAlgo(O{I?-2c~w<2QG39?Bp#=T4dc;_EkEjj3aJca5BK|YH4
zzsF@r^yKmJ79TFfu3sq3vJQ7r4<PY|%qB+uptr*}oHwYYWz|*L4`$f^NE1{P@%LEW
zUU^RY4Mz#ax~^iUlm56kh8Qj|fjUK<C-k~su?(42#eZ_P8p<3EZz(-)0qw5yT-^6)
zKmL`3@nOj%q+J?{OW5Ctb~K^#pX(C`BU85(j(+?ckA~k2OMbv%jW3i`EC3UH&>6A`
z;L^_<thbTWBnXl5uC_dz``yF`i4pc0`XVS-+DTGmzLV`sivlv^>N(68t$k&~9{Lv;
za)m(ik@jVP^>wQL@iTnoC*YmMd?>sr@oP=fx<2bjt}a8IsA6s`@_eV}f<sd)p3%5$
z?h0JHSCp5K%#Q;+x-s@id6-n5Q%dTCl{xxxb(j!g$y^$dboJUoPTD<UUZc5I?3jhj
zPXsS&HCHsOaQU25v}3t&d|dlFq29XY@Sejc2V^bbKEKcBr0&**&pGjLMcdbn<xE*z
zD)D*Mu2pp#I@4_<zjWH9^9j^-hSHAn&UxBMcyuOYBk3*W*o@$W1C;~;LPDl-UW$tm
zd7ht^R)<1+&3zuFb16<nDq@Vie#`D#j4G1+kWlv?Ou0>*Hn=sb90KN#l(IZOjh#J*
zcH$;pWH+_ng+%Y!|7EKCl`BK@V<iqJhsa&Z<Ql8h_pSYKu_Ep9n#AtsP!GgNw3Cw^
zd|FWCMJXyQ-1Dd*%D^q`mUZZHeM~B8BkY*HCqW2%lLsyqjtCt0@&jLVKV}BJShHt)
zbg{jA!f{u0gImw%^eQ43m&3&dNC(iTh-Smc;%fGw-Wsu+RqP6PTV5pJNPPfg%Hy%-
zo7)A~I}YyqcfQN6gOjr3%#^}mBR>zV@`}y>P4pe1!LsP>{qlY4-~G9H1k$0#&k?;4
zKs{7K`+&k?(9CnZ9p>GJ>7e%UE9O)o?JddchOlE3bSKUkY4|AMC0*2z*&Gd<URW`4
zq?N%}QHbkH$H9H$;7?|R+g^4J9foRI929nnMg)#(wNmoYEN|eHrI(Y-%H&hS2=gZH
zNUm6N-5!4*-rQ8%BjEjP6<kM)>by-B1w6^YCmL9`wuFth=_YY`DY>6tVBA$=>_}bc
zO=j9?!hb0JxWMXZUGUq_OmR(R&BZCGU{*#yb^iEOFN)~Qp;8TzX#>%`x>O`hU+4}Y
zgu=AbM)yxK)Kq7omd%ThLi5HpY%X6F<8%Eq?So)y-TQ^McfwKN`F8Bnhfm)oa+awc
z*C&?s(VTZASb6FXB-;k7N_`84nYCw<@o^{m`L;EtRK?p8Zt^AZ&w*tlrW8N7Qv%Fv
z+{gT9xX{b<(Gz9&3bLegy{ZJgzPDaVGM0Gj^1-g3mKu3(*rq=B$84u#pye26Ta(Ry
zxD5whODPkPQ>6B5Gpc)<Z{)cDa2I~<xLFw8qU=wFm-fG2#I)LRn80~WF#pdJiun*^
zyBX%I<?IK2Z{Ih)5wCUo^zSYL9zzZ}*F}4pO{uRKPN?qHEvy<MDiI+Nadaw&JdiXy
z)<wW+E^(Id$15*?`Qjk}Bnd>k$@Qms@IBc4d~!Q!+$pjy9A(gA>vyaExKW>;#a;Tx
z6O;rOaSD=G!nxS;_BFnpdZjNdbG2&!p>*v$?$>e;&7a5<^KiD((HeX&SN}ysYaft~
z(0p+PG_w1%UhK;~{#YjrI9+%|GZw4-WfH$iC@BwQT$C{7ITZ`x|7Aj|HPDipwjuoV
z>hd&#6&{n(6`j1&BBfkiM@T#{-3Ied#U+T5lh0{aLPCut@9LBEtV=?W$|{jOWn^ja
z%<<>b6ajXc`p6VBONw{!>e*<Dzk2w3EV+v_df^Wt{1Cv~lulVcgs{eq;-Hv7sJh)u
zvLTDyhqt-JVMZ!lNVZkoh6cqLRtfe`c7x?K!aL;wUG8JtVsKi~r|az?mO(}-!3}rX
zw4#VpR^^K<ZyI<@+M3)OHEM^&k&VZ+)R7oE!j~kR*=%*172VreJOqNQAst0PWx>A;
z*Qf}K5#3y7Tg?4dZp5W3xb}lzoiwG#Z4*ZFBnykg@Qr{B{yKh%`OU-j7R~2vk;u;N
z>X6}AQoqrR)Zy!3&Wz$TkL<oEwp8~hj--)sCr)vIRxrPVY-~L!K2N9LTdXGgF^%Uk
zz`4*1+-3>}$~w7P_Qs05z42$_Fi>1+%C!|Wnty4U^x~B@pAtk2iBpiHyR#UizEyn_
zJlp^mrBY3G&?L+_B#hI{E^+erpDqPoq@*jHjiyVK*S)9uNP;@jp5IFrTECNDDMXC(
zRP^b7*l3)*w1H3aewrZ2xB|)pzzOY%=@=yu-Gm0fwjP)Nn!JCf6XAVSe?FqsYrmV5
z&x9UDg!^d|LVk~Kf$xX2TCT8In^^H+10KNolTfiM;dhEKsF{zLJ!@6{M;)JM#%%WA
zByd{K^9@%U^I90@wqfRfDz9N=75;k^TH$st@gi%tAWIkKu5|j}bal?>=68(4f9nwM
zjFn2yZB^tMmUI)b>%41H>9*8Pmi2vnln*4<PEDOkc#16K&=qB2(hWPo&WjVp$;GUz
z*`gTo*drKgdN7JTE&wkSM_?q0$K0F-oWPDlgv@T9B9d0z>vwYPJw_LPCc%uL6@wyw
z1s{FjWjP*P?=biAVD)I#kGkd9E2Z_1UXs@-5UloZNLfEfzalTuv1nARgd_mVBT3ie
zSOCgr3U2djD_vcsz&37K)>mwVp=&`+z%kgHBSdc8Sd>U;G<CUc_5y+GejuV+<B5(|
z#U;ry?|;g<pyqFM+#ndEouVO+&`hSMh{O}j^p8wK7IaoQ8C)9;xK5z;q-v5X5Lo-_
zf<52}L5yy-nJ8COd^UTx8H+KH_U$5lcS>ycsD>r((9J+8uN0nTk{-EC!o368R^z@)
z-0EDT);V+XLKdzH#>p*-rKz>#z9mIum{Q8*Whd)5`D3wns^8#z!E+wQH?2naWI@u8
z7m|1!fNXjNG7#w}q&z3F23yfvHm}t-&nlWH*0gErsWAfIXE0;4MgO-ubxNat?#qt0
zOmBM|Uv0A=)rbik)qDL#|KTc$Ve{H;y*()NXn#Pl5iON+9xee5joexrmG0mn^VcCF
z$lt*?ySVOfWqiRVXO}8>aVV&ty{ZXiyuJs-*Qwk(!S~Oj+5fPi1*1DY;OgiTX{w-Z
zz%~na*voV?MvMHbe$Vp;M7TXPB5j9aHQ*^kKW+Pu{J7M%vG1#9wr3@PA}xAJ&DYrL
zVe&tb#5k&$-!8BHnF$wI@`yM5PrK6TKUK|XS2&HDokV1@)7ysN703o<uKM5ZpDj=v
zS6PD~hTMg!?B?$aN;I`+NS<AJQhsDGXSnA;6RwsHl1Uk7kNw&pF7u24(~Rlr7~A%t
zNb2KRE9*or)LDts3arxvC2>~xar03uYEuM{AIBZZ7}#RYAsfNQR*0m3lJe(35PihH
zF~?KsWAk#4%6C#VTssS4^|ZwXj&HO!chPR>|5{Gl($qe`9QkK1vZ<5tjDEX(lC_3>
zWeW^Jv>aNAjK#zr8wmUZ&r^sc=|J%Eg?{$RHm}o>Xw)&?4o(k+HYgI1(OZbfj)zFe
z6Ii+rc<$w>3)l%v8_7!C4vyk*fYY%94tcjhZxmt{*c8>&_GQte=C;}wKS}9L;BO{P
zzA(gna^HJp0S7C0Ry~UchQ^8vC6>0YdIyP<*(7k+1+0VCoj@d|>(W~=lF#tiK;7eg
z_*sG5j(<Ua6B2*k_)lFqeq9)Tmj-=a)b2i$3n_(>g19Zzbh=H@wGu~CpVRpclu34H
zrKN*FS<0QL7pp^oz2?&wl8#r*H%hj}(SILLi9iV<Fvzzff>Leq?1747*3~OYhg4oB
zDYCjZns6&}nuFo3Wt7V)t)skCV!03gMu;&8Q!ZcBe!n}JAA@|H&Y?N|ZG=vwqMP7Z
zZl_o)<3rM;B_o05PKePPPq5dX7_-m&5TJ=6rbuW2F@FF$k>%7cuKZc^d%ZDUDt(Vi
zypzr|p&ueF!#BIb;y}-r0gq(`5vp&9u+mp%PyCQ)>dAs2U!I9)zVod$pgwRd)gS+0
z8sxKeBtmm5kI}Qh-|`WJp{xUi6{~h+I7e(Y;&#`zE&#47L99d(01h%0-T(7>ogs(U
z;8tyuOlfbnF~c`B>sxlm!TmCl)r5nG#rg5)c=GvJ()@bzc599oO&J48rZsB~_=)y&
z_)!^2(J+f@-}E16H*=E(TK~F6V3d3&T=>su^TXC?lw+ggU3#*3zdr*^YPn=(b@Lor
zh4E|u95+nfv8=U?HL}*^uhQKX#Ukd}HkLUfUnQn?w=GSl*3WoSeTnI{5ActSTQ8Nk
z<Ib2Or+TGq%rM}@?j0QAQ~$EDAC-l`9;I}&>8gD>Hh_;KI@J3e8R`lX`XrNKVHy{q
z8D0>dg3V&nQygSIwieLP=e^^!N7XUH6Ia9cZKC};q--DH8C{-SN;TIeZioX(2kcp+
z9;2)jqcl=Yn~s?OvGQq^^H8(fB8G1*^R1PdezQ`_I23&pbDhrkO(~g5;=v~X{COkR
zn>aURe2lI?_qkc5MCGU$wz8PVy-b1+Xy8$Id{h{aQIi;=aA+?x4zf1#|1IcN1i_K)
zvj{@m@*^XUE)bE5pCI+;WY4xCu=}e)9r8+%u<O={YN?)PtS#zM-DIaBD>2s<4r7aR
zlda|?!cH?4Jz_!TM6KnyZ};i-!n3n?VdTFZi|zR*#BoMQz=1;0YtQM%jdl2Cjqhnn
z#&LDj(#N2Hhx<$ZVdZ}v9EvD@yMoEhc2yS)l7Wy0_D#`$&{bpRnSi%hze(tI6aLbe
zZ<8<rywcvJkbm-`ic%}<=bLF!=SQ`1cZV<}@UA2~-+wX0FtwDf=nsIJ+haKC_9CM=
zK+pMV+3RD-T6I?Ylr1wD?F`2l#jfIg^@8br;6(VEuknQ^@~^>b=JalqwgE0BEK|`R
z%xzaR+x=NMe7R^u>W89+{M|xi3?rR@1az|6D%-X9n6&ClpyZFbCxu}*X&AItBm<Q<
z6Z?HlPI$@T%v~%1XMvmnHi~QVlO!AFK-7JMJRuXNeL=L9rtgk;yG&#RydwJ-t#4E*
zY6D=Z#yyrJ8&dq0mU*%yfIodclu$DBP~<VKl1D0sOVGtSA}%Xu&GEZq=b>Evulh2b
zQNIsUKdJj}AjFCF(4vnQV<Ek~LCkIWU5RqW0|tB)DdjDS$oXX`?E6WhZ2=rx7XbqK
zU<?<g?Rf2%8iL+5h&qkCrN6?ElTVRss`?m(RhB>tn{1GBdvAK%H(e%~S3x{Y|BNNz
z%Z6=ov4i0s0|U}<Ap;=%SThIU>c~;#p=BR6kNZjM=Ce?jG9g+D{~>41jgD6^fB!7=
zX9k>1*?FK+`!&{0Q4(Hf2G`WR2{K{Fcf+asST@34B4VxnfFXuAx}`W{Y_VuRFVn5H
z$&Lv6Ejw3;yDJPPgSMw?=Jj0c?S0(1_t!Z(LoRHUX39aTM+>u?`-Y}|DP~+&H+jji
zh$&SMUse<IxQN`-6UFMK_d;Rfg4Jr)-){Aa*8lW+_^vs8C+qv&d`xw<4|&Xd7dq_P
z<tmzarMK_<$@zL-Mk?$shN%RumMbp#wtSCsBtrih=e<i8H&7S<a*t%nFYjcwvge8p
zCsKGFeAgRtUrVRkn(k;Q{2zySq^oN2_jayN;$_qQh5pCp`dO<BFim3^|7Y)i9x4O`
z@P43biqX#Y`2G?BJ#3SJxoWGL2Qb{GU7SEAGT<WO_&aHh9R6>5$Va+*1K*VR4~<~U
z=+~NO6thM(I=v+65B4%-|CtGPVWbMjM9C-&Q@Xj1)|1pHGHQS%d2B9M_ShS0B@^fl
zoNb@=Y~UDle+BOsgj&jh>6jky0A^O}p<H#nlBcu8_{bGsk-g+wJm_lCU3-h}4$^P;
zF^Z4o+FR+u_EBX_W{ncV&KCMPiO<#$RFKIDdMyZJdHY?j<jC+Jup3ya6TM6t$vgI+
z!m?`bvRn7RVM`t+)_%;meWFWx8Z%-HV2BB)<vZ7mk!5+_wj9k`X`idMmxE!`sekVi
z_TsI_a0w4EK3x%-kBu0wqWRA=n2YY?u;Zy^ix@t~<S|voQS%X|Wm^|SGi!tod5Us*
zjFke4=xT-^#@CZexysb?ZY0V!h^V*yER4*3YLzqivz==iTk`G5>3u&p+|-YJWwRM)
zWY3v0JgKi5!5~BlBWWl|>$;NicIzD%;)*Mx_pIVwI!@A5#b?bq2&j&z+49{j#{9EU
zNOg6@U1K>%IWU5?FMX(V=p?9+@>4I7@g`3Y|AtZ0Z;UgzG`qp5!rAENvqsy5`(Ykg
zYq}2lM6P`9_u(~c7OLJl{qDqMhq16#?e|iG^0Qa&LacW21Nd)VXYC>zOZHm;_}|XD
zV8Bjqo{KmXJ}>I(oo-}hZ&~;6)?=pU^>vF_Azs)et(H~0(zv-t9l#(t4tVT+uoRy0
z80vXjQ|2-DI{ek$q5CEb7u55JIK5p&0TDM0e8>Z|3QU)ZISi-eiKr+W*SJdh?<nUv
zZ3^;b8?!YVwv@G9m;b7Wc>tSv>;cdmmpgaBsG_>h));<0^tl@R|5d&J{x9W7SMO4b
z@FP?33@i#`Rk51JEV9&yjTL@zU(DqwY!eD=(W|*`Wt{gYy{$rh;EbMr78b_nddr)Y
z5DFp{q*wAfsnX;KFx>8D_JgIE0b!G6LCt7)&ZHfszAXAh#yVla9Nj*sdzrpE5@X5W
zoiuMdygl6FDbT1kx{6hOB|&^KNp4;h8k{W6UQAP3Ku|ipkfI}K_C8Z+6giCoYORSs
z$JSdyP=H@>dy%wFG!EB^1GCay2f4nkE|<r>-8aLJm@H(^b3YpM=^HMLYPrcGu=Q#0
z58`_}82Q+`%&mc&WVut`BG6)^A9*N=7iUtmv$vD&uk(=4N(~#>u!_euru&s=4tf-v
z=MpfCW%gaoqA2x7fT10f{}##N$EI-{Gaop7h3(jaJ>Mf@>S8w2b+MPO9};k)o_0HT
zVqT|7CJYYJea}LcgjNOdq9QLp4mOR{QswPVm{-bY<7eFfX^o*$sVMjPF?Blor+Yq+
zQvNBGKb_Y^sQMUJMiZ3K1p)fc6Lr2R6Wv)J{#9=1LQsn7C3ns~id1kW=|y~zZ|V*~
zi=8>~XNFLDesA;D;suxg^oCX>U&z!kH0hvn=4BNHG0Um*>7jBVPKo`J?soXzkqf2c
zGGY_3a#cdmR7DoF<?kDCo8xy`Wz?5V8}M{-s%_yy_s&OknLj)n2KdcKksLE~1L}8s
zz)izhHG2`t4*lZW0dFV3Np`=zVp9Ph820zOA(9QC2Uh&;^6?6(?Ve|7uDzImi+9uR
z@8mt!MSm}oys(!+Qs&pb*2G_L37;V|hCeTCuOWlu5t%ity&Fv>VbcH9|Hrk$Lk{wV
zif!#!-_t}>)|LE~iTx}>w{M;C(T|bCPQhz_;0qhWNMVdOi}NG`{>{Vh^Oe4iOfEV@
z>Y=+Nu^%Fn+r8^*`YZ%>$TE7DLr!nBklrBR=aNnqd%1#g^B|+Z&vAI>D-pr_X?6>e
z-|pH1c7MZukCejiO%#Nm88yES?cCq};$7?7`}U9I?jl#n|NF_%7Ec!Y*38+azr6Nd
z>rimqYXqJSki7kMn0Jzsqr@;WQM*`q&v?J#Psa1o*eF@)ddD*0yyw>}1;0`BDyRP)
zir$K7)ee<cjVj$&8e`XA_6yQpF@refb2j`3fATtU{<|YON^r<9Zslc{BEq<XGFhVJ
zn*Ed9a!``^NzE7ML6I4<roK}3&b}A-A#xPhYR${(S6DYVtEgwY3Fpcn$5kXfOF@Xg
zPFF{AVWYe~DHk4;>Alg>zF^1`x@k1L3nR;@i}PO$->gmOUO^O6^h-CD$vnD4Ip6LW
z&g`n`EOY5%p@WSRebqbb0aD)fmLcd-3ioh1tuD2X<eA*r5w<-Ue0tVNs!SE)GGRot
z7}%<Q-hRIuO19eKuJq=sGa;(0r16z%hT+Y1pGo<@7pU`o*vPP7kU?@EI=to6F6iEV
z>$70yFl_MqIl`E5G3OS9JQYl^r}$ZCsodcJ>3t2L{Q33$hf*N;YJ>TeCNhI0nQ|fE
zRvO^4m_@|A73?pQE%4@Y3xr<&eu`;-L39B8*asVde+M+QJkz7NowoJdI|b~5`xaB8
zziq!dk=sEo$WcM5rud{+QcT;@Y;W2<*E!*cND&L&ZqsPoz{g=Rue}L3Wpz$t;{V_4
zNqx<K<tB8xwu_Qao8U~73qnZBM#09<rJpr59h5UDs_SIHOA{+UlfRW9^qzM??ye^&
zT4Wt!f~4~5(lGxRn*N5%vbCw(JBYhm*z^gaRXP9-c<6=`uc;8aU9Good!)mcO|=+r
zrZKliY?<*v;iEC3prm{!GsV+}(Xddi#7~A}{%9BtFQzF<2o*nyugBz*ue0w>HE`)O
zyl8#wHWmbR^HYz?{ALVj^EeE6+?>B?+Y-FY%Yb%z#i@_1pdsz9{ZORuTVym8Oxz3d
z5Q=v)u_RHGLuK^9>!bsT>NzTj9+`?i&=9@GbK%#g7|<_SPS+%9voWnrX4S=Ebv^}>
z|5V(i?8RP$uL1!~mA#TU`&+IN3vrPqe7+N{W@jYA@t$VRQ8K%;lHAfHlAFIla_`t`
z+A#No+BAWEC$F+(pv906-M4RFe$0{R?n_X<(f6E`q8-cQL})x1ycD?xE<T5~bxRtd
zvJE)hHlhz#p`pSeQ)4^_TYJ?d1xqUva&;MdT;|+Yv+t^ss;1U0lqz*QnNRpmdyTAA
zqg}mazf2@#<|R{SDek2!&+seTY5Q69STH!jWiw;gRzS|}ee9+>G8Y!VVFu}6w6?4}
zpKe5n-s%YswG@w_9P0D^%Al8te%HUeCVKA%E2o%O&w7@ahG`@#k_!+Hb#qb9j)(Vl
zpFq+#405Dv7vAwhby)3zH4niNp275$Ls;p}pzvN#O6O&<j^r*6GO%!jwMMtcso#Oq
zR5xE~RUQ>A)OS`f%;U(`;SS655ucr?y?v45>p>6n_2A(35q)}|wt4MGmh0a_m^Kxw
zL70xzw3^+3`(NLDj%J6)v!JF9&$|fxu|whC?D4^pHy$pZ&k?(S9E_Zu(K2H&^xxS}
zm?GqACqdQ*`Ozv~8%`cCeUA(4AybG}?vqu9F#rPVn`(At<8G%uY`879)}4!+=UXQN
zPe4`(T2Q#SWIko>0g@Xa{7zO3HJ^pb%(s=C92{N3c~Hpl8*9k62oF|AhjLyj^~{0q
z9+<<yHM~)7`Lw34)WAZs#2ZJ5R5!Y8?wC=y3fS3Q99Vw87_P)pM6HT>&q{aagTOGh
zBka%ss$iF7?eVnen?RS5PFBiGOq9#o_HlT0t++r-<O!3saS8s`PEayWVf1W#pqIzP
zgYQ*9J~a}6lvn>f0b%HFIWD1YP?Z3N6L$b1551Rg%L2<K@hKBC0P_#=Ns6=}6X!=m
zu-O1f(uXknt#HnrFE*(IfptoIbA1Iv#)U#fosU-SyIb0&-3whZ3Q`j!$Cs0*p*4->
z?&q`2%e>W!_{XXDj6a9k9`d@nD)plbOAJRF2vEOl#NGAhRyFUDP}fTEB*)?0(ga@$
zeCwWbcy|e)HZk)|{huN^rx!}&zg8He#U&yb`b%`pAKygUF_i9_$&Z#Oa+TE}sKq`!
zAyaj#&A%uo^WqzjhA#dhf(045k~W7fbASGAxyngDqHfM+JHpZ@Od86NcG;1f(5c~8
zt4Hu#<US{w;5(V?S^fQRzN&&QA-+?wKTEFO-ikIV+@I^O1?TOQGngM>=~;-XJI27H
z0kwf|D$yIbSgoKw=`kNj+Z)b3?hdg~)8(d{vRA;X^)74F8ngqfWf1Q%iFZ8F#IZH(
zVOYC?*T(r9iu~|k&CY%c`e)qLGEygj>yI0t(D|kgLNhc&rAnPYp6_psJk|#IL1YXi
zgfUV)asgG7=&+Yyu+%}j@I_wEV7Oa~a+RU6m<Qy_kik>LpW~vlKlROIJrVP~dk=iG
zAQ`?F_vaBj?lJE0h&KJNM;|+aZVl50i3fg&FsQorZ!ikv;cvtnQzi`q_$u%7XLk|L
z6N#9Rv#2J7_MTmX-a238Ig+7`#>}jWK8Vk`-qRf9IGrya8`nTi`nPtP`JJlh++a;#
z#VdYp9kc0iUBu#NJPPU23jJ7Sb(%$MgYu2<?{6j--yk&6f>i!?K6Y}n_N(9$0xZv_
zejJ%BIS-6kX;(cd3L>pabXR>^?iaRyHmCb35qbd%aZ$#Dp(fl+0~oW?m^^&hUl{g=
zKY1-a#(dx36W$&6M8L@TD+~Kvpv(rMWblg})vhB`sFD3LGHO*y4Ocj-(p3D2A!w;-
z6UwkpLj=iU;ai%zZwzqi6%p8ev*rACoM=BNM0^484c6Ic_-9V4K=Xx%a{<?1V)+yi
z%fKm5tqKhnHiP;o2O~ogt3ob-M0Fy2oJ{!m*v>fnbTGRBZEbXYT*M^L{KRteLBlsF
z<P2!JdcTM2D;+B!*}LS^^-_x?)I3g|$rfJA{?9soY1b?&g3Moo>YPIlsj}<N``}}6
zT>P-1L8H`EPQd=Zl1@(1Tj1YUfyg5Huot;)rCdQ4v9IY#M5SMrQ@PTLSUhP?-_(kf
zwxR<Cnt$~oh_BFxuf(zPIqabYU4PkLDCYLizR3{3*^D4#?zs<(w)%Ugvkd-SW1!{e
zfKT*GsuGw)4jIxs*kX53j+GV3t?aqajToybxDc~HZ)1MgGE1|*`;rmzxL(%z5IXxp
z@vDa%%2v$<&S7t|{^01+JQ)ZZ-CYB%a6+?$fsSrJ&6biG;9YjzCjI2u<e_q2f{RP$
zhnx!@Ouep2!4rqLPo}h_Kx3kRhSzm%kFY}fT8m$OFQVR>H7u3(FI%ArH^x^D6;1zB
z3TH40Na5?rc28t*9}Fz}=teEw2M}=4MEX=UaVlsfX-L!*hgkL4x8p0Lw?>ujC62UN
zh4b+e&oCZ-v$}7VC*w+CsCx~d@FWP*;;jI6I0xaYn3Yr`3mMN~nb2r{ksQy{5hFE;
zpO*PH%d_dZoY7X{9<W!a14zy~q_Q+?UdUhEf`yJwA-t)h0)b90<B3GK2A~niN7mQ#
zHu;e_Tl^=Kxj=Dq1dixf!T<@fwwaxjYoG{=x%<(qm}IegrTR2r?z$KZV|*$Xnuf>$
z*=VOyjv}{xGpO{ElHBR3Qd9j}t}{%X&=reRQo^u=q2F|QniR3PM9-+PFBt&Eo4RlE
z8d2Kpc~r<+|FS#e_g$WA4jnE;{G$|pz5pc`Vm4wzqbtGeB5nlDQro8qUpmY^wm0um
z)I+Jpg`Z{if8)jwMTaM-`KFReHZ4@Dw1Lax>Vt|uB$9pgu0+m7z$3p8gz;O3X=p5d
zFVu8cbZri&i{6Vy3Whli89!?Rm1TDgX5(F-#n@0dFDXO8=&UPqJ_4T#wp!T92%i?c
z=JfOMxR=zIL<RR&nc_c)2gWjEI&7f@)k$w>L|h8Hz56MN`eVj<?vpdmqD$yCZ70n)
zk6Sw%+j_+O@SfMhFAvJM%0xdP)lba5$3GU5!mAXEwfKMIk74$<i1^vAc)fb{haX_v
zJ(_Q|H+No}gK+Vd^|OfK**czcZ^kfc7(qrJpmhNb$%-3auQl$psrY-VAL>X3?rVMr
zAjr=8%?x#%gPaA+vB>l$__`^~V}Yo72t&cxd11ns;Wja)|2ADh%;O#iYLfncG`)pi
zlaKd4ObIBB$mr1xqdP?eL|UX91f;vWySqzNq#H)ZC;<UUX-4PB!GPc1pYQYh1AD!;
z`|LjFT;~cNJOggwAWAf35AOX&X!;l-eWq)uVFBSiVEn{qD;iT+kyc(o`|ttB!0bCi
znK~;4K6OgfZy$pM2&h8YbHz{FmW_-e>bZG`ktQbXrl>8sD3W*XiHYXMPhtt1@nZ{j
zeT!PWN_rhDN~!msO14~VA#kq_&rD+0lsU*x<0q9hOx{!S>IVGfa0PYj=7k_m#auvX
zeuFPBv&9-AmKhgHNQCJT6g8@FUN?E8P0#i%g6UL5Y*Ny{ne;OFepP=$TJM9&*V+ur
z6n_qR7W5YR&+ATbyM>>Lmgc|)h5*9z6T>hJ+v)LtB>Wz|-{nTE)-;C!Pwr>ZqvHW%
zw*tT$3$_*11irzzZe5emO`Om3Tf^FZwo!dDC?S@|izEXhco^JU``PRrxMBu9wHEvQ
z6ZzZFe<9rQV}a*Qkub*Nb2RFgBMlB(MK8<ZynF*U&rA-&YHty`QMr?j&a!#u#8Xwd
z_-Iyh5uC%;g<cspCp==<|NNfips*vy>qvSK-`{lUK&`i@JWjNcS$8SGoa_t&)FTC;
z>ph{ghb`une=Afp<t#=_*hbdZSj%5*;QEi|XA2PXy)I4k%L@klKerHz7^M|=In$3s
zBBre`pd;wcSu=EYLFYl-Y=AZAg=8N|uhTXE+c2nf01S)}bU3B5<(PVj<|Tb+10M=9
z)H(J0z6y2GLiIk&0&!Sx741b;Q%BsMh21Q=#H|)y;8@)(`#iu}thrXqA(65nF|dZ`
zM-{{GE)bqJaZRZ}*uQJ&RnjlEqceZg@|&UG;Om_6vA|Alvi1PHfyl}Bn@LzzQG2n3
zR5&1F0;5}a?b;M>Ye?vQmi#p1OhpqTK)E*)(g`B_X}#Eec?oEdF8s0;ZY%VEC7DmV
zfmSk3?I{C1Zp7D&18wS(%Icz+UNCYn{ZUSGC$Q;_{Zr8~B-cZ1ksx~d8DyY~Q=!MW
z<vF8rO}X-nk&`_6=%!{X^N<&<O!|p&E-S3NQUc9IMR)SmBhL}N&IkR(#sn@9+Ye=W
z@WHW9L9`>8^JaW$@+IHl&(CAp-4Wp?Z)P;1bJ}qdHV}~jl;kbkiK`Ri^XNhuQRYvt
z+J<OnCa`Yo)3<cpU*@J&GFlvDQW$m<Gu%4J`(W~Z_}1k23nKedzKAd7y8xA4y|SYT
z?;>H^0&yRm{B}J<>S0=y-kHGz0kyHr?2jng6BgdgUva_0F3)S?Ny|YQPGNm-gF`Hi
zVSfa0>nvEL8E(mjxUbZszfdsP2X;gLf>n_;L;uP(N#mEUKB`NRm$l2)M}PEGH4ELk
z5xU=UlVGIF_1!xIn2;7!Tik{GAS-B7pABu@X~R(lsg$W&v>PE+;BMZ?q|vE<d#Vx*
zJRLsJ{f#)nmat)qg~2}c&jB0^E1lG+UYRYipIO|~9gl~XV5MDPi6~LJ*(~2sqFF@r
zHb$P;@Fhvyyz^OMna}WZMwPKsOYAL6k~Cg>_3N%Yc77NTg5w14L`Xc_KK(%y1p3!!
zgD-pZ-8_)vzp-GB1j%JqwXq13v^C-_x$kvkB^UenE%k2$f%V|GFmmAIXZZ9&?XRHh
zk8H#$wvB`v+JQZ=AD3fJfv5ej4TiE?<`(y&f^kY!!zMjlfs!2A4HIWbLTiAlk&pL8
zkfL#d_%V35%sPwGCE~iS1yCztI&T;R|GC*-g&Tyr0}5StnldeDL3nR$db0eqMQ<ek
zikt*nP*6XcH4G9By(ZDTmroy!kbC2JRU+;FgFE0>TkQ?%%1r^VcZyxMXNvbPR(Z89
z*d(X5I&MeLvm(z3_2p1WM-U)(Urgib>17;AQyFKT?{jJ>4_<P0*p9ntigH}Lo6TF6
z`nN?wCu!F6Nn7Mu5P%?mV0s4A`Akr5Ixh6Q;W;sA?C1}{&Zv%}wzwf9?x0xyhj{eF
zKU6`JMXchW#qRS&$?BI5+d2$L#&lji-x@};d&Ab*=Mj-_iLdL+v6JRpRNJNy6(R8~
zRvmZZEH=euF4awPRRNvc<*}sQz77vEnj%%I#-l-JF-N^3_+;UMHkwUk8dN4*N>QAX
zTr(#<ua$PgALX(R8|Hf&vDoS$00$_C?UI)8tr|x)9wu5Wn}vc`KB+41u44DdwscK&
zdPASJr^<{@cgTBRitFrI#02Ygd%PQq)zT4%{dB8pk&tYO1O8GIzHLg`=y(xodQ1@b
zkG^&kCU{<ZVg0jX`y-93J|z*xyiurvFb^P^VuG%jD!5SYI!Am2Yc_|{CT(YAJ3@O*
z;qa5B(!w=QGW8e0`t4#lWn*qs$$WLrQD}+Tgq)*9=U}j;ar}=yay60V8f1mW_PXgz
zAXZ1M(U$Gbyo=vF38OFG2Eu1j{N4_IvG0tr#|RPBSv3xP2~fG%`Y|8ho5c;TN38W2
z^QAkVH-`L}-~R%An{%@3U=X-hp@7HKmbZOnamR2MVAB2z{8Za*RMY<W^jpK0PfNxW
z?{wobmB7Ez{}e%4Su9D#-t(ARHs+rXs{YM|_UwD{8>$i%q)NW9b;~5YS{7$n1Kbi9
z`d!qj;<LnQ42JcW5wNaU{5G={UVT)D=k$nI{#&I1w*95+ggxA5?mZtYjIRehELXSu
z#TCGyK(=hIw<txcM=WZOghl*44eFY40ew04MOIfYhf?m#;?dI$e>&)r*1q~EBuIGq
zVr2@%L3#qHpgZX$>+3uewWIke+eg#>dkkyJFQWU#NfoN|I7b)Bs7Y_Rl)@<HD@Zuh
zf~x#2`Z{fX2qajYcgUu|owdOMLRu7^1&wvAFHX3q;s>Nl-3LD9gIzT?*RQKZ0|#ZZ
z*@hEM(6TPZ@9enELcd}N4ylt3VD6E(f@KnrOtI94L)o!*%xP@|lP%?3C9XrI{S9rI
zStiC|rPi0v(n#1*(~vScMr=&64n&Ez6BFOwQ{kAKqnY6Gw#q>I78pO4r44LTSf)+7
zgU0=Pdg5A53=5GlZmOV-P&TTWXGD#1`726nF(hh3#ee4$`Q4hE4Jcw7_Q}w_>>p3X
zE~cA_(2p=#q{q>Vmw%Id>k}U7qK-6q0~pvI_pdmM_;&2L4zgg#*f!U><!IWvo0+2{
zRT4L3jgxt^#Jho9qsTR^s)R5{M1S&Sjp(fL$Xcl4zxWvIAv42gu9wu&pjMb&@asxe
zE$;`%3>gr%R~wl4O5ok-MglLp!x3^Vku$xP?aY$*I}u2ylzuo|xlk<eh`1h`7kU@$
zHFh^HxL`kGlU^^s!I8D58Ti-;XmZ0OKL=*5>Ia_8?h!a}=YuWJ8l-88gfoQfirgg~
zE{vhjDuoB>>ASG~p`aVQ)y(gb9aiiIZ7$Sne5Xd<pGK0NuUi$60nP140Gj3?QFvXK
z+w#5W`wdE+f1d-<M7{PNw>>Dk-D<kZ@-MQiw;dS<x}1z3d-haUru0c&xQsdZKc4hc
zo^m_g<vF#+S+&sH01Sfw-0TmTr{Jqokg=iEO|@tJ#OL0nPye-SHriq|>?+DTstG~D
zK-%G%B0nwBLc?rE6^~4I^8n&B*_Y~Eu^lS7_bo=FB^$j>8Er>Oq*1sk$8Q3wShXL$
zP)A=7jxeH-z0ygwq`lJq)gVury%^s}Qq8?$nXHB``lZP0%}dX7Fy@`v@2+bu;uf3N
z7zzs!{9Lkh_}=dpf-0WE3mafl1<I}Js^aFC$q!ivu49MtH1Q8aZP<<*&3`V{7F7xP
z%tJ&o3WQs<E-F8L7k#F(NEo6|v-0=v%N&5_XVO0s5d@Iv<rwG-`CHXgI&dk7f9S=Q
zj%g}!>oOQ<=<+Y-uxVgY_zuDX(*pkX6L0lhCRY^c6BQ`sEB}nu43~JIlw@7A*vnhM
z3;1<VvAXa$?kkRqi5C4Pw7gA0N@Snh4o2j6K?aF$o*S&XZ>PgrMWY%Bp5j>Ux3KYT
zvFTh6FV`DmKQJi3ABnf=ih(5wEwa)FP-Yn2&%=e6g7zw0Da5P`yfTiP2F@AIN6L~i
z;%a-PudKLL7l|9fMay=^@<*j$l=jfn*XsOxr2D+iYm3e>`ZoiCoT!vLmsG_y#?CT6
zz*j$<dhFeflZeD05d+x)L7cyaY05EW<V?Xxtr_RjCDfha0jliCcJ_{|x;X|Lu`RlQ
zcT&K|d|Lkd8zt&m_U7-lho?t^q_eVdDZe#5=w5K&fOq^#UCV5t>vXzNQ8SL!{-%>u
zko=NIpQo0mCm9J`F;4nUIxBNl3jEFgA$KLJ4;pfv5;PLvyLx@E_Lx-E7-;&Kj}P%S
zM$E@0=HqMq9u}al^L#9TLV*&E`B8yX6tzDCS4{%#7z?fMe_B*Ng}MB!4_tj2rK;^Q
zdBtr582?EApjO+}4W>w9>Fm1wxUvqP8G_bMb$l5U$}>lzjMIdR#2B9>%$gaV619mK
zce#mYD6+553;{W1{>TAS4{Vmun?V8i{7WZ8C7tlZlfzj^(c!uwVlfZr9Sp~SmNS%D
zNa^fSxy>w7(gU`IdVm!<C=M*y?$^@A`>3uNqwTwYwULHJSG8zMw~X6-K5Mz0_&MsF
zD1x$9Kc--P4oeTQKa1h_0KDINjauVG@yHX=u#>Du0Nv4!6Sc*|W}HN&4MG1z)Z05R
z^Rl-${%yYaWvGp$>IAXqCV&frJY_-y5|wVe#-5m13@h@j_m`@XgDXGZZ2|7NpC-{=
zexeI(4gX~wXAU5KM=}a7{)<q;VnqAWg%TC&cnuCYD0i%rAPX@0UFm*of^iRg&>;vz
z8dB}~Ru@@d&_|I_?aCF1569BldRs|NTO`Q3bqDv>!L|mvrJ$^=9{XC6cwN_Q_`YiI
zBb=k7r@CKh{^9b`ud2aP(^Z_pp{4sE`Y^@h_q1>g)xCxu<J7R90FTjj=+3zN<@pGt
z5YcJ0+tu#|htv6Kk>}C<uIbsAXMG=KcE3mP;ujP{&rp6a_}OZS0*(~%EY4Y#iFTPG
z_Vat}`4HbehM-@0eN(TO)fpXrh1ljfVaEf}8hx^m0MD(~-9+lM_u46^g?o?l6CTva
zq|Ju8*ilFUBcyiAk-c_EdSK0WN{*qS?DzVx!W6gz!G6lfKXC@|I~C{wV+)sNf6e=g
zTys`EqF@Ffs-!qn?v9I7S4`DkCJ0Eg)g$$Z%SVIUV&u}TAW99O?AqoBq#K-3KMJ!H
z0<^lg<}%G!c3N>mC{KOs!1tiZ_ZJU`Tv}B0wrkA?TtJhms-M6F0*A<vM?6ELwueyH
z!<hh~;wgd6Ho05Q@!J)vZ&^8>4`AKh|CT8KaCJLrvXBQF-zM;-8z9qbqa~hTduws@
zMDiK7+Hb;RV27MfDbV;6g1=H|ESfqn9dhV@9wLP4QvJa~P&Lx_4-*&Sun?QV!|%;C
zzCn}&QbdV5my5@WoBy_z;og45Z3`la+F)!g(sh1bWAPJylfqsgurgBG^GaC&<&Er7
z7*9#fInKA^aRD6lmlQ8E8&J7ZUeudvFkabo!`zPX%9HQwqIrkf`NFSHjM=HSm~JgT
z(w)WR9#rJ?PiBU%Z$=$!-K@La1wu9_zx9x}#;ciEil9iL74Vu;<x8XbvXD4h1@1J>
zX5c0B2R6v_avu-<T}YR~t>BW<a~<Zbr06Dr{=|PJzw=sTznY%o)4sdF2a_Lwj=9R0
z`!|mh&9-t6NXL4+YO=H#om3u;?ZYR}^`!{Ti0WNb`<rWMyYOb$NT5l-UDLK90}rob
ziuV}NF8=m06;69J+d;=&gsA!e`{oGF-Z#BfT{t%NfkGe=>X?Tp)c<nGaL08rV(}uB
zYHY7G;Mpy}stD2YJLV{jqXXCXr1LbO*w7!kFZJ1QBVp8Rwc@A%C}^E6KADwk@sYm8
zlx-mcK&L=@2{bvC^LE`*uN)HtJ<#h1!@o8*?3w*CxxPGGB#ehHCfL0ujzuLj<zy&q
z2I`nDcmHohZ3ysUQvkNDsGk)+dp})T1ZLr4o@?1HWSPh(@UG8CiGBYKCzs!NJ9lLg
zLor$*yv|>Cgd2OIK;s8S&>WKWJUeCK#wu&wY(oltotMm>z-&iThY@^NFn2`Ob0hw`
z*BCxQ-d2yXznjlmQD7JNAr6p1dFpSv_^-$#2u>DP^w{Y(mh=c;iWpjPv6<5CPMVB!
zSv;ycO#vi<_e{9|a4qi4ywdsqy#O5B_xj9Mhu=T$7StMFl<<%D1l;Z>z@k#_w$%(i
z?K-SH^jeb&U%Zxf-{-6m3Q#r*Sb4mYKmP@5K3f$Fc&{)$(|R5~l;yvX2YtOK1vi?!
z!`W5n_W6rVKix8i#Nu%&(76A#(^i_Bij1Hsd4(us+I1L5Py}cW7G;%yN`nGD6=1aH
zq2g8FN%?KOr;EaFSIUmtR}z<d#@;TX7$tJGCEY8KrbZj-WH;Dz#l98tuw55xqKZ&k
z@-@8Q(aP@ldxP2b;T6_0_Be)~3=3!>#%CQhwaLGd;B4Ueu-Tuss|QzmHi#QipQRe`
z>U_mSGqU$b6+o+5kan8xIFy+;H(NS$(Hp_P##8R?R%1Hvqc|v*wxz&q{CevH(c&25
zw};QuEFXbwnpTo+-`)$jwlv0@fW(1d&vKXJo*COec6$Y)iX{XwX^6umCKDB*Uehy1
z647^Zz>hHbkBOg~RFjW;hBSYkuIDA6c#@%D(68aPn@6BNOz_T1cyay+{zmnOb~vYp
z`-Xond)84(xLVC%(rYRC<w%9(jm_kOVcez7UrR8=4WIg_67%ssIL#imLm1(}>b}DS
zxhs9jIjRxpp^l;mY0mnkpeiyS_&ng5PC1DM4RMXsfS^b~alN=iy~fy*8Rgx~7_0^>
zeRq;nmI^K30*soKJN(B5nNHop@8@S_zHa-|zVZi+NChQYNlz{|bqQk(zX{<_l(yV&
zeG2l)!u==t^k8wGw4W9>R1_3+l`%e68`QF@XU!+%&~1xxr*`SDkNa#Z71XoRban3P
z?UuB#v5OKUh~_nHO@7{N056Zy>rs#VacN=_=)UAI2<)4amZ#}+>w0=xS;*K!j4rs3
zH`+Nld1W6U?GMgqFZqGu3Qlhks8ZYxS=22YJ+}=R+YPHF>nueVaDT(QVPN`zKL;P?
zmXHMZ?t+wC_o=_fZ|p9XlV}bFlLQXMy`Ke%)^w+h<2P>2h|ksI;y4!pcS$|IKAUfb
zsy(pJTjIbk=$_jQdvX9>-GC%B#Pq^S)H-#n!+%vffq}-IS?8V#0*D(LY-V%45+tN6
zcoVE&+!vl}gg(1YtC4L;Eb_(rOC<=_OZ}P4h}C4k??KY`x!s9W=F=j*N#0e6A5#(;
z%cRG1{|i@j9?~XKOdE`jYq^Z<C~dauIPL?5q1^X=Vy_{V#Y3)}^_8xScZ5pMY_maz
z=qDHSXw_9N*<}x(vR*!@k<Tng@S&8pu<{K5Qxv0}c)gi0d1n_J6I45vKP_)hj-o){
z4>}*<gTw}h$*_loI33tI64j~(f_X-+543heAItu7qh~zug&9^#QDV-7m~q9RLyx5)
z`MOQ|^jy>^Qw^D;^}gqf?4O<JJ=jzG3d8Dhyuc)&TcA{N*(amILmR`mtE;quc}&z0
zksy+FV`H;IpZ-Pb8j%V<z=m{Wq97)fb>ej4Zsr9;%Eu~q$09yKUNIAQ$KFU?!kGw@
zCZ!jL7*XA}@y4Q=EsSC*Sslke(vWZEg(e&;vWybPiLguOl8?h#?OJZ+;^X7o*;wDi
z$Nj@)ep4s04d{HRuN76+BRsSi;%PQYtu7HP2FbEG-9BMARFr2^+?b(35Eo+XghUJw
z+WJqmG6?{Bht~g&&}>NE1h@FdNBkFIp_>ulG?7=&dxn0H;x>WpK#98%3-1|72dwX=
z{W#u2G8X3?ABL!e|99fCntQS>ytr?Tb7BWQY~(;C{xzACEW7C(o8T{7K&qF=@(o!V
z*K2UboO<rQSG)R?W%%^rI{6IaeDPi&dD`m^+N2p?weOnpAAnDE9F5zy<-I`GGB7J?
z>IGlSTBmVaDOEINut`V)``^Q~#l7q(n0nZR2RQVCFzB}0Zv66G>MF-W7?<EY@8E#L
zWual!GruQiW)?JT0DjzwiK|>c=hU;L-U@o`;kd$@V-ik)IA9jxE<XSRn-JvZ-pH0v
z?SBL2k~Ue3dN#c4U8wPHV4|o+j><OJ_S9(J>%$N@x6^l?nK#Na&ZK`k`(NRp^bHkL
zhGF`0jVLm{3Th7SCyv9x{!&Tz8*{hL7ar1#GWgh5cYsE8u_bIkD(gD2%4CfC)7?n7
zf%qCOpGSe(D^+x)H-o-?(A<unbL}Lv1+pFcMq2Ij(d8)YJs%}Blq*9_mHKF}<I_aF
z$V}_6`O||m(2_s|eDbdALsH|`a`g*D2(WG^>us4S_DL*p1z%t)U6fdC2!xMS44=@o
z{&lYYThfVQ+&uT8cwU=nEzZ%sx2Ai(P1eBUO1CLb;lgKue&(_n6@zkLACOEy_aIA{
z-KKJPxaQ#(kQy7(A=KAWpZdrw)gRYJ&3`OS)~9-i9AZ8~kkC&mK6S*(R$U~AL4r4^
zW|pYSJ}f&PH%8Sw!gbHF1SuGgZinFt1THb#kXZ&An8dDeU4{$I)mJ)?#j)m2y=-Jl
zmKH9sD_CzKV1FFW@~u9L+Zw!e)sM0>$h7l#GI3tIcU?y>d;acn>}$D_anxgSN5%ym
z4>#gNCG+x>;Nh6Q1?7Kq1(}IltrU|~(+BXBtBFPJ@;{g37y300Wntr|GrbPjrSqHA
zGT<u_L&9kzG@!?i#9ZVS392jpa;C?Gi{#H62!<*^;$XK=Ry@CyZ_ATtYIBH&W7cN9
zsPz4wb;8{0rGlt&7`V08<RV~I?+G~7UL#TX4CWode@0n%?0D#dDHj>)tcAvn+!4%{
z&8#5?q8zS2x2$}{(GoakfN*k}bRB2HDsY@~OCvV5x7QiKcCSq0Zr6T*iM?pOMFaO8
zPqftrLPv(Y=U4V7drJH<<smCBI&m%zpdszi+S~zglX#BP+2*r27oxr@7r%zrS;yLI
zu&WAa`M^yf_bk8p9-X1m<7o53kqgg)(=Ijdm%$WrfiVcz<JD-KwR6O8{>P5j_<cxM
zaT4QCg1=5WN6u!Y{J+4LwD38ky$)0RrSTy@F3@t4f~*7J8^SgJ4vVy&AJ;((pxK_g
z39D@kvlTn!w#Y+?U3VHXb)tPFBbWr-tvc_Ug6SbP@=Xv_@vb$g-&xVLUwMeB6@Mab
znfO3c!AX5+);gPo2u8l%x3O~3-g;qftcmOyf7OWI?zN{pDY_%lQq)Eizo&0@P!)Sy
zF@_<13|J&?$wh`QiP9Rdcc{O$vzq7!_^^6B47dN5D-jEJ84E9oF4~7lKYCm>iQ&Tr
zO-E4bd6?5%K^gBwS@YemO8J61H=or$*n8{i_wU`I(uXWVQ{Up5OFob2xFPtS!#jDf
zUh{=g1^~W5zA*>?WvCM%`*2MDJi+S%?$&()0ALWL!jwBCfwp3gP^35N(*RnAnL2!&
z>0&IOt}=08s>#})HH6geD_M(s;JYWyo0nXJ9xWNbNmHBTL?<2_MbOE3qD_FP{PTd&
zkiRuibgK{{Hf!sCj9j$$E~8C;16vKb^g9aIv820TecN6UhBZfA&+N3o<YvNy37jTG
z$aiwKG5V!URE@UGa=Y5=ABFDmR;Bkzzsf*Yvfcfyg%U#<IYQ%X37bygnv+t`KVip}
z)jZYIQTV7*o-ryvLT=giDA1~Jn@7a0P0m?4GuC95`Wa=z@Z;I|Gxq7?xi9O!rhO~F
zy#F+X*zUE+xymJ+CeFY>7btp%3u)mE#;2#<m-!(b)=`|m^=_c!;`Q3YUo(#`_#--@
z$OH8hH-gIh+saYk{xr<XTXT@L39&cgRDAcWP+9xl$aPO+@$)MB+0%21ld<;G&a<&G
z%`hI0z8&^QEHtlw&x-J=qMq-Flcj~sXGG~6XgO1u5&FNyLikQ_kD*)hlj!|ZR8*^R
zbA8w2`Aopr&oIw*GP^h%H+}ehEyzUA`>0mK_ZUK2La<ro)Y$_D$_sQSO1rBWR`{Cw
zOvXOGI_u#7aPsqPy|PL~Ix5bByXSgMV|h9K#&y3?d@Ds+K~1TZ4UZJf+Q`Fi^N0*g
z_e@n|>_18L@Oun&m>1V?L?EFl6;fsVZ2tSNH_d^KNwy~icdvxyRDlp6mppC7qYnYo
zV&UQrx(5yfun?`iNbr)|)Sh)3kf*7YI^Aek1&g*5t;P?1Z7`EEKXB^tP=S-3cDUaP
z80$Sko^So2y#D7m%2WDMKBGcT|Jz%&-qYgHE_I4bOu;owQ8ivjlFEw=1Ek;YK;+>i
zJ+Y#+mKqAzs1t@P(S>UfTNnX<3C;1waCOD+>(JX?pJd%xUh+r_3~=7mqC(L4d0rFb
zw!2WIpe|-`z6`0tw)FcOsP<DrLpCcr!gx?oIg~l4Zp!0W(&{8QNuLj&7%fi{PraWr
zp$#9?W8#O6{)8`a35KmkcAONBM<q=o24Wr=zkA1{dKXhNy!2Wjq$tM*M_rbG9yjlg
zwW^Y?3$4Z;NS-Sg-*bzeI8xairbP?s5=qSxRpG5#K|iVl9`|JpT&+mC{hkbrFPZ#D
z0=gLquu&uYQ#d!9_;+ZESDxi~-Ol5I1t>sSJ#P2B+(uR0*ia^A1$Ek^%;>^ews#;T
zG@Wa(rE1^S6%FbzjMaTBo0L?=+xh|`bV>K?O9PvvrC~h6hfFht4SB$?_!TOOIo3K-
zRUU`=X0lP^-B|`Kp%9WUY0PFvZpW0+t}F)Znd4696&mn1^m-Xv8C|v}oI~&!XVpeG
z%?@hha$P!WCjCI(ts%a!INo$!a+Hpt?X=s%k=BmM+<l(qn-fwXX(+73bddI2u-Vs%
zvmbeKevzyIDN;cFtxM1QHLv@1QB2D`;UY2~dEIFN_G{qwuQb>xp+I)VsP(A^wk7@W
z9<Jo?`M3)-*7r2xsbt!+4cUD)N4b8HgY$k@gKrk?13zCiwqdCFo5wqofKCZObB~Gt
z?o5O0=DbNhZ#BN-AlVtswfw*D;a=lChb<36^t=ehVRNzN{BE4n&}a4MS)ktspT7$8
zO;rfO-)9?0chtvu0EnA|;9s@GYw2)!ST@V5y&;`A^~($)@7q|nn$AZ5>luTqxIqyU
z+>C5X>7X^FS~2-{;(7i(7OK?j89=I=6wrR6t_m?KHSU1*Hf#kO?OBF_KV0P@0duK1
zapA|Jvo*uJ>_!bI|0I9@(_?<tlS(z?QU$}+#Hz%ouGL8KpvRjmZtftT`M+(Q6J}6o
z8zm`_SCBmcNMGm!b$|l9-HhO4Os(k(*887h!OpSItLNiWqW+(X?H>N)q3x2P0%UbX
zHlrT&2<x?WFBqO#Gxx)SKEM5hY1+;d%et}f6-Al938TI4hq+4jWNDn0<zgJ(hRYsx
zD`*^lt<|ubCdu;zepWrqeeqOe;MJ#?cN-#4%>I_vr#;95yV24#RmetL#gb~`{Q`%k
zZ0}GVn6URoOSe!1MB!jpU<@L^kV`iD1x49qj1Mnrk~6xWpf@NO7kYf<<H2SyHd;k`
zP40k;ft|6f#wv);g~EvFV$SdT38M<DZ6B7EiE@IDW9}90Xm7ed)hYh;aIQ;zSnKd0
zy8s~`BW3Y@;2CMTGOj3m(b6Y!ExVu-bpTcYb*{xbQN_QHeq&|&(UPN2bl;lo<<6vy
z6;COI3t8%)39<Tc6h$MC`#&ovwI|NJcZ&|*B2{7)<}0GnA+_RWJ0((vCR^rEr{v|w
zCB<77ep`)~e%KJ#NuT&O8jHv3G};d@(IU6>0VF}_)>}q{Wq%EP%%J<|QG!ZUjQgWp
zZ7wC&2d4DlFZ1=3)ET>N%YZYz^Ql*@7QScw9=1<0cpu6_{I30v72)hC9+*2y#nM=-
z*K<*eq@6k7X19@lwjJ^}XCl3}!7v(wvLe(imDpS(`+j@1EvRU`>#|TLj(2UbX%)E}
zQ->5mq=#Xngk^KykPL#L?Vm8Uwg&`A+Rq2BA#rWGc$gFqDY%t3d?Ep$BB-A>UA{?8
zQCGCJcG*j!naFnK)xIV@`N2Xd@o@e)*3#(gRmF9Ike%C=b~(L=0j!OU-d~Rr4{lpZ
zY;KGE^S4C)Pjn8G`d`qU`Tj|PuC|GkH{*xkTzJ!WKLOVEz19Maea8Cy(zjh;7d8A~
z=P%PWSC(|w;A^jY^CG*`-h$B6uK;O}&3qnT$Yml~EC)V|$SQMQLN8Yv!w^<aV1==N
z6Gf12VTb*hYk!Gf2DJ<K)Td5pA=bFCv&+On&+Q`&n6)mZIYs`DYQi|AbCh%0?(~c~
ztsC~X&7?O1>84Vf#b~?lw}yHmEbV3Hvz`Uwvd(w5*<|<@2|u;Y+WO!ug?Wwx_^0bQ
z*yw9r7O-Kb^HcS@YRUmHK7qOBkPz7`LYrKH9~e6|1#6rxMSF>@M`<?oISz&Je3k8E
zV>b>nXSt<H@jO;J&^>;(8^ghOBe5zIsLS1h540Knvfr31Ga8T<8qmIchNd*4$N5Sv
z+vav7Ceaw682-y19q^{&-F8T^M+8TiLcr_@o<{<s$~Fw*3OD&JiwU|aQB)dDWtJ<B
zCUe;e$9=&uUrL43TdX9H6{kprwY@|CXEW;5=w{u{ksGmlidk>9`DYrM5<o%~^XuEN
z$+-oq5*0}hVZcJjBHiBomc+I~oY@ES7Q28;@Fy>BanVlb(O>SSo!*&{j`xC;I@sst
z>A98DB~u3pe@|F!Z4-q=Dz%q0CEm2FlG?Fwo>4UdK5cck`Tf+LKK!=i$wE!vj`tO^
z+$8^at`J&4)BfMhUhs=MmWyjTU+cT{vK)&^ZYmdE<r%80xyqqB{RAyiF#&Gw`52|&
zI@8S^>%>eP9%Gw%_Y{gnHO#AX>?_;Kl{K#q^Tz>Kf4+~u&pUGJE;IDoK7mf7-}2Q{
zM`4B-IQCTj^FA^v=;_FQH9J9FiTiSL`ei!`1+lDNNq%4Xp%-&b_9S4}4IC7S7`_^X
zoe7vp1oqV1@|hU`R9zF!Wj}NYLR|N6qfh%8%L=`)V`<}=_#9^cTg)}+GUYFv%60|6
zo_Nk(ts%*rpVXg!q>*OKCW_j;KLX4~071#{{am}T_51*p_S+JL%5nR~S;9qojJv>I
zz>x+R=XqQQ#EEq*)T$QKj>z;Iubi&|6&ZU4kVgcq1vZ$T-)^jXKM|=hH^_rUk&x3`
zH|dL3C%PZq(dOEAl=RNPLdqKzM60;T(tj(x>tJB#e&U<)+tmuH(ATiyRWHtm;|9qm
zRv74tP2A{l`_AI<iy=mD)pSkQ<CE(W*Dc8!68FzL<7!=dn0DvlpYb$qfT2jHT9peg
z3F#1RM7#hRxiuSu*&jw8y^!9SmIMWzQ5F-xa=$aM)gI>==|HKJQ<f+BWKWz^N9C`f
z)x7y_Fx5hf=~D~zr0qUnnj)y>5vsQk|1YBxAs^Rz{6NT!y`9ks_M5p({6_$Ao&+`#
zemsxza?^{#5kuO54EsY<^RD*?UZOP$Y(}^vKv4$>E5}Sjr}6*J3+U0ovHm|B>UjEl
zP<1tTq8_Ol+h#aOLd%z9Z%`|y)9}7(n(!6tQeW<*TCGqm;X9~1^Q;_N;fz=I_y)Ef
zsK9fJogW5eUX<1F$QQpO9?vEgryq#3!etnqW}-;NV^8@pq-XxpEaJyhQhigOJ8xzt
zr!sZTuCA+j;aop6la20Cm|4-gW;xPm-tzu0plLo19M{yN4A;X$CbM#n7!((};6$q^
z!FmgO?U7?>?l#t&3qD_Lnec}3q_6Ff3O5pE9($3vOIX7o$zo3G;w~_DqNZzl*94?A
z-}*>&AAyB#u5HL6($m~(>itYop)xj(`a2+tI>o}LmLp(HPabXc$(HfvyP&<+%|X)K
zTa7#Of8x)qGJ^LRQbTBm%Vs=;&hgH&p9l4Qen-p4A5>h%qn^#?gw<oc;z;_=7-2dn
zKpYR4=b==DrgRPQ7s+n&AsT`$S@T^zIjb=!`$wk&K6$|NZ``yUhs3VvA*ThGcJs!6
zxYOxG<mM;4?)3N25MNZk^R_Zn9zM`tq7mmfr60MDz_o4p!vC4wznhVxhD}zbTY$uN
z3T(^6fuF1=Bcxuq_hAK8^{elQhEDxxD*p?)|9*!pyq#sr<NI1h_5IO**ZKOl8-Swv
zCh(?HH-CfQmx2&sKJVuq6XQvdr)1bYP|STY?uimT?|LL@^OUzp@+{I-`s=r9L5y7r
zR4FAWL65(l&$<rxtO|Ewr@8dwHX=2^v(RPX?t$j$@;Ol-f;=!Z4)b^K2XmgkGRz*#
ztNW-!8I5DrmLq+alEJFK8YQ0cPtwSb(pDU-8&O-U+HtaSv*KVtdfHt~Rd*EH08za}
z63Qn*o!iY2)s?J*^Y8KS%a$QK%IkEiZ3{hJ{~(N3LyMsGfuu}vb?h#jsS7Z=jy24u
z5J{LRj=ek~{i3B-RpzqQefy^Bn`5ypz91Us%w9q1J>{lPn>5YBpqqzUekG23p<~$!
zwN$KI>fT<L2+P@^=;-AIsbTtjK--^Ifo=<_U7MQP#Z|KV_>)fKrcy8*IPVxQc_r{9
zaYu1#I=FCSviJ>g-ViX(pLwQL*y3H=K!W^(d2EB?vbM>a*E{a<C1Q^spueh|fZ5_)
zk8t5ycJSR*0mzVdfv5TUzvn=w5DZP=jJ*s<`iuYPJSOnwU+EYvdt#e&YL9Xot0`f*
znjWd<cVNJwb%~6a1~1vp*4R%dPFMXuWUydFB}Q;?eLX|bv#SxrQjslIWpFM*mk5ja
zdufQgN@Y$R12y$8<)wISn^=ttCZI`dk-e|Ij!Xm><bwttff*Xo$PSge;-msSdpz^=
z%kC!4YzmwDOgvM37+Vz}ZFU(0gG#z=UV#*a?-aGzZ}KH)d4v-#HLV`rm7VHh>7}wL
zT}FB;Q+5OIUg(6#Zn2JjC0edq?AP5W{!}H|X?N6DCn-hlho&EmKuji`k2(AK6}T1j
z)>cgR^$bbhggSy6#Gktu)7xqgl~R8bg0j&%8-WZOBeybNPk3mtS+8*MFmZ|U*5oWS
z4^kMUf@t6Q?8;I^Z+yvaRFk@lN)Rm(Qj!KjGtkBiMkF-uqCqVamWfUNxP!-VvxXmQ
zS;IN4f7jDxYCSZkM1$sibTV~%?!pVzDe=+Kj=0;oQ_<IMV6*Urt}|EABtwsKO08jr
z>M;q+TNS|ck)P2{X*r))IfM~i5JWX6;t(Olx7<^vPV#i#Pd1{xqK)Og`~yF{z~zXH
zrsY`D4)Q;)J}ri-UPscCUUHHn5IXEj``mXnu-?{?kJNT?lTV47uZuqJDN2oQ6@kF@
z{!a+XaeV~3r5#>~?7QHe_fVrRPb)n}vHE}Uf2L4{u?$hUO`lqcI{Mbn-WYg^4hOXP
z%~-YBO~4n{^(Kd;CyPA(o<!_H+M21=-TWSq#y3T~t=xRGWT>Q3((c1r;|D<Fvd+wp
zWe4?qL#1M8=6yPLVPPm|6fmSM^CcK5E&!qyHZH7Q1RDqVQaaI~f#c@$RP~T4d~USY
z;5QFh*4*nlNdviIP-_Pzsg;}c3xCz_#hcX^J-FOURa6jh*3DC;sO>CS<2q5T1fYO%
z+~t01-s!wiCU@Ht_v3~8R9KkbgUXu2A@ALzfj8p6?}_}8Oxdx&cZb}jaME;~_fOqk
z0K{_a5p_i`-rx(ohZI_Z@1AS}Vq&r)-fQcyO6ZwHeqRUakL}1%XLA0lIu7kDSbf+S
zSgyQHGQtT)VTCjowM8sSrKD2u?k)ycS8XX8ZzzXHi(km^@oGeA<diTa#?<=Ozl}Po
z(-7LK^ck56{dj%-TlvT^>`8eN-`#?ENi*30&(8N@anw!=UjU^BSdg|?mna&;TN6Q6
z{mN-D$wi=Fop|N$bzlXS!<g!aZ;u;VqnOh!y-Xd3>V+<op!(5>Smw;j{!F5@_hMN6
z)+@|$T$gFC#@%~8fMnybQNufkVCrOZS9-LmM*FrhzQ9dRk6GImL&VoK{<DfLLYMPq
zsru(D`LPooLTuXM3AlZ{=w;Yh-XxK0K1<uGyc3mA{@awFGnL$bOX2zb?;1{3%1HR*
z+h3ED#7;V^@m12kRZGt*y`V>*VScEUjAnu>F8hxaT$iMGI~EiCJu`IH_91z0YN39r
zuWsIyH9hgFbu;vn^_X-fn7E!0+#~uylF;N9IK+9WsTY->8h`9c@?oDf3-fDD?4+P8
zYHji%+%I~5;V0Eld-C2(`*=T`@)u71DNQbrZzny-vj7j;nfKZz^bJs-pVaw$p%4j)
zojBjtZ(r>Hu;yH;%x|-FS{(}Unk@P;9JJH35!pGG%i#oi4#~s#H6V}LWzzcP>(ZB2
zI-Y9mf9|((P`9?WxLSK~--{w17urJH0UTTJu(;KWb{xCmmCapJi7IdYxG0(cT;cj&
z>B;T>W`k3oX*F-z)iB{no9c8E?|aREoR$#V2y;{J^v#+Z2tQcrYPm9V!T;(QNfCw@
zQO$O&fB8knbA)Z5B;5WSFkJ7Ap1)!}_xpZ$B@{12Pi8~7u&4AGJRtMPx?;L;7I={Y
zosjNS@xodT7A?h)N=!=9I+oUSk^Uzd1DK#{*i((L=)QrhPTk1&aF~x&g}}E!QOEF*
z!ZXjCHpj|Q6MT3P-g=b7Q^Tlufsq&F(HEE8$xS4_M;IYU^Lt2q9C4$ZNb%>R&B}jd
z<1gr_0E*6xPlSCCgwm~oF6X?vJkhu=#5LK~sDji5VV3@^TWLv(^GU1EuW3w#{}!&>
zTEdpt2GNcJaoSDx43oOQ!0`-mB26mA)%If6Uo9VfJo#I1DY6}$FvOGe53Xn^_<?uw
z+yNm5XUEzVf~DviA#`Ow^Tr=VTdW#(535-&*Qu}ZhG?}MVq&iK9E{fIZmTT4P7-UH
zSg$eKDPmRGfBlJ=f09g$Al@>e$}*%1t#;%s{<~pRtTZ=O1_Aa)nhix_i&EnY<=7K#
zS;g@C^2_!6)U?2fD%s^kH+|AJ{j7!2Yq8Cj;zG-5iI2+H9nV*W?I#|y3LxO#OJ-lo
zwCH3%WP$(+byKj~JeZhtzLlyDL|87CHx+k2TJA|ji@_(&XYBraj{uY0Z$?BuI)Dl9
zo+@@EMsbObsl)BVSJTGYD4LXpoZCq%vM{4rxuIS8ERb2%u8xP;3pNVTYIz2hu=gwO
z9f#wWEMox#5{8oxr_Io|j5u*Q2Z8DTPmf{ec=I4Cq5Us~pxK()s+)ImB4k4xkDj1c
zljgJ3+C$6c3oUj0fIVZ1Mx$q{5|x2Z3s5+N`MyHppRBo(3TLIU1|T4GX}OK2vr&4=
z+(Np>yD2~zIZUS!>9#xH!csG^(m_VvvzGIUQ$7kVYwe++mV%Zdox7dz3>+^G<Sy~X
z>&<#QRqT{6eCpS|USp2U`Q>!pnUCVoGe>ujQ#(mga{Jw|pL@!L+#O_Vo&v2bvKP-w
zQupqw!yz{3z{Br6RU<wR^t@@RwnO@$4e(jhUuboaZtKe|%62*)Lu8(2(l2&3Nz18C
z*AAZ*LNYWARVn*3Sgb_{gdO+ic+}R1&DJfw7XQg3kye9q2!TmO{EfqhR)OK5IO`+W
zB;WH3z_|S=Fx{X-HSIq%_dNmX8~M6weKE5)gX<oRzcSPlxgh?v2R3ABQCY9NXQmp0
zar{eEU$ftrQ-8HDGf&h-_tEXb1&rl;H3w9!Y!#xjI!0R1xRn)#hCYTl@I~iFbJ7o$
zCmQ30ek&oki2b!YZ1nxJ-b8u&QKx{M@>7oKGl?Dit(fJ2wE}BaWyw`mnbMPj_Ag-2
zi^5W}5$2COvtFvAUt&Z~>WDD%NmPTQfpVp7vJy(FgzB=+-13IxL4P{1Uj^;+0e>aQ
zY_E7)EH=tTnk!KcgZ#%Toz->R&u8fY&GysuA<ZB64M-6`i|`}(Ek9L?Br@|ZMxhVO
zV!pj{c^JFK!{D;XC*6_0#S$}0$!E^5T%Vunx;Ip$)}M1dG^3ReTpjexx5FTD8hTyG
zBS5rmyz|kLxL7o6+KmQAp-{@Y-dp9P=5P*sTNQXZLhCSmuTMWeb(zE`^gBO}$xPk<
z&?Wz#<b7i$mA8N_vsD^PeO>iqZ-e(ry5Lcb4N@dFO=r8eS+9&seW`+%mWxAsM~#h6
z_&~L1y+bY%sP>KS8zEOTy+KY`>E1hH5yj8|vE+MOaW0LyBXzO7iIhZfG@B*Mc$cF3
z5w~o0cixlt7PuNKzQ|)23P#|gk3xE_v9F!aMhaw@Gzobj{v~DpjR5UTVjw8;Uzt}{
zJp2yJb&dCo$nXxvb4oIRV|qIHqTg%nSrJKtkMg*U?@kbML>31Hnp$M9pRuQVjx@U0
zCRH1b6?T7Iq7+IJy{^XEB`8aRoH=2Ekjxt+@bZNMt~_1PMu<JW5C$_{9n#Jg)(N66
z>~sbn%ne-WmU<kW<u;`WIbf$FMReV&UYVO9KXzO}_5T>~k-emJ8^rNa8w$`ej+{1|
zEds;?rcv^bqNlRAr8BrBdiG6HIANTG?})H0W>l07lt$qj*HuXBfY_Q(oRKrbl1O5w
z5hI(%Na>pcZE1H=9j%C${6TS_aIEXqC#&$w1u{8$$-35_8BpSxC>OG7z7<Cn<?lA=
z$!H-Mj5SpQ7Bxvqt}srbp400K)*=<vSH9(gzq-U52or_$R&7Ox49BCOT(#QbYEGIB
z`j`%di&-4|Jdb$85tmD>N3Ic?JKt@6rVinhFIzX^lw$Moajvv)p5kzcR&MXxKhA)8
z6+dgO5xhJwyUDQ=&58x!5V$4{Vj4n)%LhD9P2yq_&@&mtbt*0ed#VgFH#Di5g@UnJ
z3CPsm&uEwz-aW=6^DZbVs6IoCebe7w4Sc*IPWDA+ksWErN4Uj}8w`J%azh%NN}b|#
z@$t%+(UAJ1J-;jn)K+MtF){tL^z~EcRmY~YVuqkyEoaZ&{obchAu@Z6`7L}IaueS%
zmzIg8{D;Scvz-aNF|maVxrsLd){!UWQtPuBQ(LY!dP**2qB~}LeVF%_F?dS;dmT9g
z8?nQI_LQ>mmmN3sa6_Ph;qvf*>kuBHZW|;KT0Lc`|8zc{<<Lv%g{(L?hSN;s{Q7?r
zWCxM=?~AB(B>XA3`o+r06d3#v`5KVbD(SWqZ{I=gfIWeU27WQ8Rs^;b%KU1FRCD8p
znDkFY4f&l(2t4dI8u+vdNNIj=j_e&Kyt@-U8W)!iDv=UdC+qq1e|3b3UA(_Z55(#l
zibCA~0xASr@kSbs|HM~^%4=PO6Spv^kUHw}^k(63+p0x+7o*8zc2paBntG&m&xd}w
ztHpDE-WhsorgMnTV_;q@cP9LtpZCR>tk}GvK`qypm}_N|lq@ajeY$O$oyM>r&q(yg
zfO>J7oleQoV0#gLTk&g=B*8XhV9BT6vI557maF2fv5<#j<{ZyOY_#yH;2b3?!T#W4
zn0&nK6@1&)<ekM9H~FORp8lfqW_n9$Z)}Py!xr>T1CW3}I5Sxk-Q!PJ1qk|MujQlW
z;Sh`o#`N1G#T39R@J-BqSL&U9f|<V)H&7TqPrJ~a3~*F7Diztux+&r_*B8E4YIt6y
zDEE23k)As?7N3+v!zV<1&)K7jY2)pm#Ibxo_GQg<67<I^<g<ui_4?jJ>1F0h<QJ1f
z#-=$n8i#>pjC)J+i-7(?xcDETtUnLz4x4XIX9e_UWd8o!lk~|lbdhVnJbhrBTP8#*
z(K;7+;a~j^__hmpt^Zxe8WI)o>ilC%*e#Fd-9xCNS|=uvsC1loe&DU-Uch4T0CzJr
zf41)D%dWInOz=?fCYHMn@c};6V}5t&M$V<*u@Q4haTg;cMu%X$|2<aZ?(v04H=IQX
z{93po`-jl<o6c5AJ3V+zC0Fi`F5ynR5OTQ<Z3xY$wGe}g=6NLH<)btj1lVDXBno7H
z?Aau5X|qJi%W9>2x<C^=6LJnn8)jpZz->3c;VGY7SIVyce<T48?7E4OdmQOrog^4`
zoEvA5^-Df#jr#EXyXkc|62)?-A58-}ph885+XlnyF0>O|*A*n%G+v6{`7Cm9L-71M
zJdlo2h_O&;zKR{SC-gbh4JTlxf%A>$$k`QFHN(53dyk7~;EQ6XPYKf7tg~jJ?6E|<
zwkB0tNfQ3`ED4^A<0uo1T*@}1EUQwy!Zr)~l`RnNmt6@o;c2{oiQLfg7d2V_SR>-U
zb9r2};{WKE#1yO{SEMKopt8~8ogew^Gm-C%02^z!Hk|3c3wTum59kkM{_Qza^FT;I
zU32YCZI|R4jH%S(ka+kcx%r1{5(v%7ik1vX7&?5aiii=Kg*4Sde?)=q$J&iW>dx8+
z_3XlegHvPwApIJtM2FhVhZ#m=IjAuu&6UMW#?q4iP#cN;Ci`<UNOk_Z%^Pz_jBtZu
zRm?E1rM)}e&%WX0W=Ct`2%<liReoI&YqsDjS!+hwW`Kurx7A?1d7)g5Cxw=@;X(-r
zIK!-DL~F%!LwDp)Tp}^~$frYP!Gd9%s};xSk&|Z@^-{KTD-8?p&ti(A<oF<?r9_&_
zITH>q{PQ)!7$1G>HUQ_O{y2JI2M|K&X%VA{RM8@BNZ2s$775mge6@%Z){q79UoV@D
zKzT-ee5^Gz@>~wwrAZc;Mz&!^snCNrkd4eGBtRC45dxrU4}v4F_M(hyLc)}20};L4
z;u9P}?)N_%xB_MHEw+b>{F7{||NDHY=9_!iQBha15oaS?Fhv)#boU9>Q5h>W3UP8j
z2>mQ-YwK@+#jg=V3jC>OKiVJsm9<1+T${zALTVwV_R3WK;qN%Sl~Je|;6+gwH;7(W
zg_4XH2%C<}y1YNavU(T(mQ=&u;AV8oH~nDbwL=W>#?CfU>=Pc4XSBb{oM^bIX($vp
zZlgp#JwsK0UPUc8dTNJ>pH&*}0sV$6t)XfQ7{ks|p~g;2j*}tm9L&Vs{2CHX5JRqv
zU5T1pk%h_09GLwXu^+WvOJq%DL%kPuqRzZ9AAMt%H#4&*rY}eGXCKAke7wVUfUkuh
zm#P$XWhS5A=G&G{Mjbk)yQay6M2?MeM_(ms_q$!X`GINn!iD;b=0qFquGs-a&Cw`D
zJM=J=t(v>p5(;mHUtXK>Zixreanjn^fUY&y3CeNyJ<X^spAbNE8@G1{Ev8&`qNuHX
z(;FIi{Ri3ETt((uJ9495xwL7GmU^|v!#H@8n!~|(_QH*R3Bi4i-AV-CtHEF(_wZ^E
zjsHH;&@AtXs}k?YkT`q-RcI34y#qdh(O$F4{qaBh5U@wqZbLj7XT(=)pLVu0COo1(
zVOYy$1RGTfVzrFLvIr!Z?CrI->IPn2P`U;Gr-{Ww20>iJ-Po%Uf46CpMHbXFVYVp7
z+I@Tllnv{Q*A1r^)@#B#AZ@z-5UYJ+o<pt@_Qk)0(EH*KR{Ar%3_@ZIo!9^Gt@4PC
zxQR<*$2x1Dy`n6Xj|&PevIQF%A{(L#-FeQXBWurn&gHnp_2i4ZRQs#{V3vHMXw4{E
z^%DGW1YUXosotGSkk0#;<Rcx$)_89)J}vT=&JZ{=WkuQw@BeDPI$`#izSkZn{akb+
zo`|7_rCH}k8eeMj$?FYDELC3O<j)4Pv3CL4B%<X^d)Gg^D>E5@aU+-qB}d|v?x6+T
z3Yo$Q7P&I{vLo3ek9AzR;bD0&fh0_vdwMSj^=_60f%Hhc=RcH?!TjPq(Ju&bjWNcj
zcb{do?cXwQZRLb+-c{oIQ5=lAu!}P)GfmkI_r#eb^bp5{{$N6X?LR44KJ_?A+>`<!
zo_4ivbmG_<0Ire`s_IMi{&*<bi4q3n?L@*H`48mwZ#UxboMp`kkK86ezYn}(D;%a)
zT!IN@e&qvRm2c&=+kiU%#X9qC#MYNmTPTnTnI?#S&CB<^Z$Ao5RHK<qTz9e^R}s*)
zoE7s3$R+6Mb6eBE)clcH_)tP)aCk(O^3`M=F(>VJa{Fs85j~-IIj%M#n;&%Ybr;P#
zR+PWNf?YD(dSdqf@pKjrQMN(5r=+E&yO9Q^VUb2kKm?U;q+O&N1nKTrkW%UHU3zJd
zF6nL-SbF#D`+et}f8n|BXYRSKnfcAjoV&@C5|<27&GJF21(;9r9VSmD!%?E3VGqWx
zDF}93bx+b`R9>^Cf%9B~W?H4V{e~<*<?-!OX6RJYKUA1`M8jG|9PilN1)h*<0?<ka
z{x^%(S6M$8f%8DluS)di){Of@P_-$#4P~65A3dAp3DVxco7rtzjQaR44MPSa1hhP!
zXL$qehl)t3pq0?&7!N4Ug{N)^t{k_Sx5MnJ<(iIbO#G@d5qvZZF(3eP05$@Wsy5*h
zm11$0cEBB_#t?|Lw%|;E(;4=SgwkNaPM0%=^Cf<S<!k~*JOQVS?cZl?lt$*+0ZZ3!
z!_15}7;N@MbKn!5+oeU^mQ|i=JpSqn*5eHhUb)&ck<<p|W0PabHgS}?Zt9<gvk6TX
zwsWgjvl*kQHL1Q%VV(H;(A%ulMboIy^f0{O-g*M^?<WTD>XG;j%js$xxBePkbIJEU
zsbRtB)88W|?PYgQEiNP;<@u3;Y3|uqCXKY#l2I1k;>w#Wi=x;<TQi<M$}Vg*#I;bF
z^4Zk&LcE2DI)4_P*lGO%ixg{Rr@pGL?~l7kgHnG--=jJ8oLmD8{M3N9i7<M0u$<z=
z<vIBz`<D8h)vce5gq{~@XYb!>qGp}(F~<98HQLhL^+zSRqC#T5ljG5(cMF-Y{Z|Kn
z2sHhSePyE`p~P&`QM2>fIRlmp982Bs#$X^^eWN0B{><s6flvIMazLE>hT(8Ak!+2o
z4ii3V1mUT&dF`_R<lgop3O1qqpF?*fxwpXMC2vFj;xa#c^^$GeWL%1iN9BXHx6ayP
zU(wo{f&it6cxqZgch`j94#oAkrA7d|xvD6E>YiC%{88`%E`BcbF9DRAycFdP{3ng9
zv2I&WA9T!2nm`$6Eh!vJ9jI9J--A{f-PP@P{LA;jN#>)a{T~mkts9h5LnTEFQU=C3
zri$hRB<ow<w=ufyDWkD(RtOU$G0G`VBUCRKBt^I@B+LzEKf7Ct&4`Jx;rbu+<P0w(
z4?1A`Rz8j=G#^V*x%exr2nzHxC8NX;q@-=N`zfC03pEKY%&|t=K-h%v^~V*fjrer#
zUCVqzXva&9P-W)lU(OcI88aB#Tt5c47x%$8tZz|tWMqoimZgCc3F}h~Ye%mOa!%a9
zT)wi3Jd7{!d+J<j1@61d4!P^L9h;hgM5)b>UEmuiMKF3|gNz^H6iyCylH;FVQ>S<A
zFdf-lAzBA#ecYK`Vb5?qBb@j~W}16c-*_-}OH5oXZgdtWDQ`T_529!T!<mA5eR`h0
zS}EMmORAvd-)0jh=ifr*>{D~r0Zw1?u^UMKZCXl@5%}+Xx_SkSvx;_Z2|613oV9_@
zFE)9QBlDNjy3y{kMGUg&FJMkWrv?&h;JWb4WWzEJ{@}@-KFm~#zd1aKI%M(wChP@~
zVX?hjy%6680+ex*o>ybgtX68xU9Q}Nf5mvC9R5l^|B6K;C+o9hRVyRhL2Re;Ow!<Y
zFR1vj34AFBJ^t+&Fxnv34juV;QY8}ZW=?12iQt&$@$^Xc*=n&}`KKT6zBA&!OR_a6
zc~Qdj`I(Mq=-k0_?Anr34DDP(ZaQ8C2SFY{z0@h+jMh~XbXe&e92|s$B^g#9qQv%J
z-m?|orHd0kw>P`RIMIas?}-s2S^xec2%c><aw<IzdJ!41t3dO!+w9$19MlD+*E#Aj
zv_BWK)JWo<3s7}z>9(uc0w=$SphneI&8zcv6Sf(QOuc8&cNiw5{YTIpXsy0?0n9w~
z?@Dy?4H+G%Mq&+CmTl=Ua>9b&vR3)487ucx94mK!7tz_aTKS2qkoMSN_6!ZL^+)pI
z%hNX`95hr`<`ZMeG)F+0Tj$lL<@BNq#^8WS!v~P*JO-|J-}i^P5a3C?GCx`WJ0=e9
z1pO!&ZCO+owg;u$KZ$PcZHXlQsfC^o_Xj&R%&`h8_sMfD0|`yPn3{~`FC*$QRT#H;
zT1gt?sXygAf*O2v`6k=qSmZ~4@LhO33aJ^ZZ@&kQup?N*iiM<KF&g^daFjM_S!D(B
z?9I0Ruo^=5PLj*^xWI{d^V}p}G$@TAGfMe0?dLfk%SLcumHrKrWJc2_dkS%KNN9is
zvB>Tx2qY-^uSgJodf*h3o>E$__^^dpX{2%$jiYx!dDGSk+q(ZDOrh}GWF9Xyug9=~
zQr6wscGBg{-g|cXFW81ZZ`3*)QrhA4UdzlOI0x-iaZ1EphgV_mPj(ppfVIM_oVzsM
zwJPlfHVa<zK?x#%**nB*NM5eMZ#me6GoifI?TstnCNPI(q2iWf+#<w(7lhZYz?{*<
z_jiTjjuZL-VA>_UzJhCQ-GR(ogW+Exs(JK#_luv$#SR#+ox`NOwdR^jE@ZGNqW}*G
z?;drjeB4Hp{|r!_&@LJ-`cK-u0NrM>Y~169fcE7>dzGe>FYid#&&vJ3j{I#Qac^3B
zXTqIQ(6QyCx<8I_r>ZdZd5t8~8S9qs?dpNOm<#gNvY+G8Yb<PZbW_>;!g*BZ*X9Su
zqJ>qDLyH|~{t_dvM09Q5xa`xim#cv5Lth(eD%~S=$>}wg3L1J~@B}LklJhW{AKdc2
zKa;OTQ()$oSZFU+Ku-2MiiKi1kFk{Rh4RS>W_8UUKa5$X60o3`aQ=~eR{AowPYl>B
z;DDkv$NpB=h8QX9!DLGFX38p#^~?y}><0&|HKq>K7I1oHs)jNME!TG{nO;5R(ZgXq
zxL?ZN4$O_<a0)eEt#^*}1bCL&JLV__*J7v3R!B|&LxpFRxyaAYelm*?%|vFOgoCZr
zx0<-9Iufn8Lc>oOp8raUKo3x;2AXPr4;bo-JkR3uqX55Mw2Cf8Qk=<TkS=C`=U{Tb
zs-D7{E}o|2>He_?7rOZFYxIl{<?oUmUn!?R9E7ocK_j%~ps&_Ns~K=G(ZgePN9ady
z*i=*Z47Vf04uIbqVt;!$hgnRhjyM2^F?TuE!kkrUSlgNfr8h8nc<ibOr37oiPYr~3
zlJiCpTnE!oBk`3CqWMH3qW4z7gr@;zSylz7f6DWzD43hlv?sL;)|{z7f39V|NJLV<
z@P|xE6}J~a5)p{JN<}-A)h3@ij(KlwmO0IewutN#(pZ>(J5yQgolONXN99Srn0+jG
zvV!*h3M6)P-|dn|%G~ik$;|VRu{Z0ozla%XkpekvOV$yDA!a_~FqX@fR+N?Pg0*_x
z^J3>t^%PK^Lp@Aj^J5Z4gn2LLjVg)8UoO80sLvL57G|koV0;D#Pk%^jma%eBEdDPu
ze}lD(9G=}-P=sf8zt~-Uh0M%8VL|{QN4wqWtmoIfU^9WvyL^5FN7#QaDlS{_*T_ST
zk5|8#cI$DIm7b&4*ec|)xRcgKm-<t)$bROBy;C8?-B^B>0k-}^XbSnVsBky(+&jiS
z>8&|6U5tX$Y07uDV~(a@(!J(R0aHVRR5!<Z>Go4mjoeh;AEQ-YtvaY2l8IG*I-8bA
zM<1H$fo!7ZEBV1X3n{j_`hIe0p_J79`<kiWP`;$M(lU}51FjezufINds}W(@d|p4k
zy?bPXv*I994N=HOmk?Tj%||I_*@2qs`JCktHPE2=4^L&|!|UzWT2{G<%Rxw?iIImV
zsN<dw$FqZX8Q}XTHaKs#<i)AYwZs<)eI!9PFg656Wi=A4VjTY5Z+Tmo(?l?z`oFTJ
zgz4R7*u^&Q)`$^&?^`Zdb)|%<Ez6GQCq;5DPwGF;y<r?{v$N3e9lr)7)1>lrrkZuj
z=#?7eoPRn5a1`zpF9Iey!9RYM1r=-1aTO0(oE1hg@$VVCz4oqr&Wds5Q5btkku*nj
znQGP9oE&O&*h$MWPQUx?6PVg&vnu!5`{sDQFHT2uH+cuv<nVgkIbR-Lhvc4a>L2E0
zby|l9qRzz$4qkkISb|$AvhL?TfBqWvOo{!MXgkSa5WFjT#6|oCu9<i)_?K`>p5t~{
z@1XB|j64%R4~%aQ1I*{ki;D1^52R3TEhY5VgF%Iz?;_M>_kKJL13E4umxv2K>f~IQ
zx@9ozra`=)+;<wv=wGeM|JUPbOC90`Hdo%*ic;yksC5pCT^^FPx|1sc&o)7LSi6?}
z5&cIAg1l5sNEnDUT&t2r=qGNAbq_|Q1{*@&te^OU<K+8_C|2XN7!1NY^^u>!G19tb
zNpb@u!`U$d0Z&GeP1voKJq$K2A6e7=G=rkvoEj-}sV7CyhzWoX--=BB<wm{n`e3u*
zw=2v^_lX&?sZKg1U$wLTfTY+t$rT%ndqtOjiOgE4FLBQP{qm~;n?Rvde?IF<NwVa>
z@at4JaZ3u0W$^2HYucX%^yk^*E;j@qW2JZEAlI#KOYbCp55D?Q`dWh1gyDI3q9rdY
z(br&B+AneW{J)PT&4{JmTxP>ToIRftPYC<<QqP!*hu7Zj-47^A$Y;W4gNA(e<n*FH
z2&Gtz3XN5UuweE84AaMiG)+#-hN<=6;1GSHqrjq`KL0MOSo(<edV&cmu}$4GsV+5(
z%5`YX^RFlP4<&9I&|XZ6%{0ebbD-sj9IEfylqNmK3}GLY$@vXtn#B!$JkpS-vhl4v
zhm@%Xjekin6M2W}ZR~gPx{V-lt(xT1_rx<p`YwxI#-{-mn~vur+`MjuAH>EAt!8VI
zG_|xwzEuc1RAYXu&XY^DgILc*o@?>lxrVMZ$n8{oDRh?>!A6-<qUm226X-adA^%f^
zVC!lx4BGy7!t~Hg-GXkA32@DU@lHr~95-IVF%K<(F}Qr0kjl-ghJL@-@)7hQ^YmVB
zcVkra!d&gthp2Ex9isxeUnC#V#W|MZLTgfn5`>_f>-TbZqFibE=}n<<Z)Na_XA`M6
ziuRW#p5Ni!l#y3|-2`PTl{-JQ8i~?a68r|F3`aNd{%`Cx=$agw0VhBPWB+QMCCSvl
z+6?mxjGZ7Oh~au!Fj=;4gC4>D!Wq_!IOaI~8GNlPQISTr{C*?&74G{+zS6`*D}Is8
z$N>043EzsB7uA~<yEd9YI}7%k2`$tDjgJ&yjNoCYJUe~y1g3+Wz23!EcAX}()yi~P
z2;Y=8ybJbw{=iq5b}NR8`dx$y54qMcb4}Qf>rfqweZB>Yhp)$^C+>++!=?8`a+@mq
zB>V$RvEhJ=vWm-*ITTA@xGdxkahsqKTbu&<1Z4C%uy0rBSf6D}kahN-H6N!KJ>l@R
zDYd6#vB({ql&!Ml38;xd+mcrGl)}b|UKk(FH&RPj9NVA>%HT*;_anREBQ)Ztc1X3A
z(7qc&KmQ{(7mpbd%o^M7&^mE5`-4r^nvZmPdrvYYV2b0@PDQoqOZCp`wG#So;#DBY
zJ>%iVeN}NH>6$K84xgu*vfQ+-wETx(2?jr_jdSm*>%V!n2$-?^j)|f?r5^i9qUIG(
zuhxt7b_xx!nGK(4^M3UzlzJR5VdkrU8ph}|4TFFqNsu0P78=j=;6&>bPOPyROe)-|
zgBTv5A1K_-4Hekd5|>mrcI2azABvHy$9a#pXr%DJaZB?#`rJ$8HZ-2+7Bo1Ax<jbO
zhpDgorqVA01x6-LKGSJp5v@2w>zrkt!fL-bOOQ%1h?lJEQ$1!49-mpTkGq*x-VK2y
zYda1XJR=hpbqX&&UbMD^{k2>5`1%oS=C``dtooE0w!}b%Eh(3?wVdPD-huMU7k7We
zr;ZY}XzhU@r!Ag#SxM1{QyMEX|BnSQ=WOfxINt*egoUHvy6_b#MXh$!!%B^s?xr!O
z)NIimzw8YdU%ZlJh@vl|CQ&3;>y1%<L1k(Dn53odh7~nKylDQLE&`JY(pQR+^inG%
z=EsWZ@fyY|(5bDL)uZ@~H9d|D08x;Yf8mwPKSfx&IVH+mkB|LhQSrT4xpp5$-X?1R
z<2n)>TXR10`KPHPTlb1TJ#$g2OrLgL=NTQ1g%`J(Cx{7zG_N0j)kbGNTZuQbduK5<
zL&|Qph!eqcT1gcBtN>k#O0(TT?-dkMl@_{Qq7|(_b5T~Gt5l=~wk9yS+P?4oGkOzu
z20~m)&%>l<@~sIj`PQRp9uW`3&;Q0dC0Ey$oYXN?`f<dJkQ<SWH7Q6Fv16DJNmNrC
zf;p$lFMXzzbR_pe6;Ep?LT1##UlrBg#9ybrWKnBPtvu2hE*$OGhBSk(EZ#^i{NrIC
zEh;h-PFaJC&X4AkwN~?1x0am$jGe<K2muPV4M_emV#8sr%3_K&+f7`J9<1*6@&{(u
zRy|IZ@b8AN-f+`9ZZp`Z?GH4pbvB536i<XX{hUzod#}bY_4@gz;W#V73OvGWH&_kB
ze`aMw*L#xnB45P*)hr^w_a%1rcrIJlJ(*zYP}7YzN>)bo7hDr~nK5U0$90`;D8$S~
z5QiAj*VjCDCsf2vFzgN>lUm+IOxAYY;#3<MAn4GX4R6P^MB)zrJYwFQOqiM?ZfKzC
zCR7dOWH_?p{#HAeY@g}0u>xtml6;_-yStRAQUNu{;h|eJf9b0?NvJSXjy^s{1n^Xv
zvb2@HD?#LZ7tzJcRzP67+3Wwp_*{ecfiHE^>+j+XG;xUa?JOqf)eFu0XQ7E&?SI<e
zQ93%TWRxJ##AO5V5G5lY`z$8{T>X)&iP7eTzl4iTI>ggwxd{y(^cpv4$u%o=Y>}au
zD%|vVm;zl%jB<PT$2)ung?7RxY($iJ;2k}&`!5Rwxv*4@;s$sa30mne%|Jp_uc?Hp
z%qvg%!LvPs_(f@?0+9)9Dt%@Blku}h7k+6qpW)JcoZ=*wYRjJAgbVD@FO#v-R%$FQ
zV&-_&QNpnwAT6boV*%|tK~p-P))x@&+rm%Ni<lA6<6h2x?7}(L&Z<2Y)07H2b+tru
zYOTiNT8)81B=0KNs)lK{nH}y}$7+u@c^TOMVT&Zbs9dUX88G!7mDx2M7J)R#5v7&B
zNdpEd3ZCM!21M(>hUq(oJO8yLMhpr4Wi+^#3ptX+X=xu<3J}?G{s<i3Nws$lCNc85
zXv9q@Vv2fy8R3$wC{`Calw26=EgHqzv8_6i`?KcCokIL-h)ec0pYo1CHISaO+hW!3
zrLMmA(2r|%f*1?4nBoxO6HFeKEhB1<`2UNj`5fIfV%UD(UgE12cuKlJ3L1kT)5=FW
zR-HqZwla3*1ae^O%*M<qCne=Xd;n1u9qz#ZVjz+nMG#r_T6+2Kg}uOyun5&K`wzhV
zOxOru<5VQ_crlN8$T2|CT!VDMvFvU~&@{-{2i1lPM=-DI@oEJ&k*?1c;oJnOjl=Jq
zJAbpBqhR8_KwLvjEuZj)-HGL<#{@%0eN(?47Pf}+rZoOH>og-bJfN;KktZ{Fvl3)T
z3?)~sLea{Z?HdO}YX!_LYM)i(dEnRiPP;a$kcIJ0DXksUFHBFzeRKY!dbZ5ibs-$d
zv@P|H;`>*+e{xo;wI#*C?C(6+!7;C1^nB47{S8VsF)n86c=vt}HloxD{@&n9n_!aC
znmeHK70-{#v)r7B{wPqwKr2j@tJ*Y~@-q^XlsR8Q5SQzcXMK#`oG=p*)i&l++VaW7
zKSFTlS9Qjy)Q6c$e;SxC=}<Om8yjXvF6B6M`@7Ua@pg}cx-l<s;f85*D$80;f7k&n
zDz(V4c%J|2g_X&-RD$JNSj5BL@Z7xc%mHi8L`nlF*@G$6e&}y812!5u^!vpSFkE<;
zisAh+YhW&KMCOf^<Qpc!gG;o_sWQuknUQ?!?6Yc}?0THQ{%6)apw2k`skVkUrnY%O
z+QT*0n_qE;uD+EgELnY7PN!hHF>yd4C={#;h#cKjX*hLuiLA2jl6+D+Muqzs+Xwx#
zew}rc#*Hp(gO0A664Zyk)$MYbfYor=2Fr543dcsJM=WaKd9t4Q@y5?58ts2~o|juc
zfLe6Cb7=q1U~D1_F=X^}Ywzv<U@1X$h}<brP5ZCbZ*kDM#9+g@F07W)QKWg_7u1E=
zD0$d!xW<g_65NO)`>(n0mYyYuB~RK;^;CgI!Dl9<J#7N{$g`7nqN@qa_{dfR6U{jL
zjJV@3OD_{#_QLWMHT}Hs-zY2~=qj^L(Jyd&5)09Mp?JYRK@EZOJHM3oZ`wTrU+u%H
zsH*HI8vjwKvAx|hNt1hA+PWfKA^m5${NcZPju!4Y&<`Q`n+j25eNlwmWXie2;W|h?
zx{=hT3otP{T+I6gg;?mPw`Cqk>_@Uuo|!fE_W$Y&f9U0GP-V?ra1*RZ*iP>-jP_0F
zsW>bvwJKnx*;XJ^i3J$wn>peGs?a0QkLNPL6j*lY@0<-xqXqnCCgz$Cp7qz>jpye9
ztLy_RJG4I5xmDM>t-)`n!Y|eRzO%r>t->n2U$V!tm~v{gjMJ$0r?Nz8CeafS<xta#
z-gqEh`Zr~GiXx_-Qz;c03_i#|HRLF$r|6RW<tDCn62%o!_)<Xvl(uCUD#O&f1Un>d
z8ZYBaOP0jn5NA_X@&4fYV|*ECgDEw4Y3hN1+Z%ssa!Bx4+$lAu!<P7h3O@V&;+>ZM
z57n`?o3vDM?2r*Qd&LBPAAjOqL$inuFrm+k{=_kEkVH-1U|Ig^z_mr8V3_-1nZI3*
zqn^xM+-klqE&rAC^xjtUoCym9!GGK*x3eeW^s|j5qo?${{@a&lLT)Y=@o(bGSFmjM
z!i!0eqaiaEplWa+NQYsJEsIYz8}E*&y)e)S52Dy{_K!L~iZY2U@Klo1^KsMb|2Hsq
z?A|J#Pb8L)cy_j@X!dJQXE9*?hdoG}27R5xjydVn@VlDAE+E9U*f=G3&Fs#`KMK|0
zG&z37d@3d$jJ8G`gxujB`3#bjXzWVrco(rapt{{bm0)?#%$QMsGky|Rkr$Ga&=q>O
z{8K*Q{T*;c-4^tpJniR{e1}U9JZHQwTnHp)_*LVyPB_twygn>TWr8}-eOe|70m(SH
z-whsl|LyB|o)ssehkIO$+H_DjFK6IoaJaS#EEZc7n?@<$QCy6w>K3D9RQk2>gbpC!
zX;gMrdSw2COVUp{<=h*({XM!|ZYHGlZFvMvkWf&&&(2`)t@k)1qX(Vec81sLi_%dH
zS+Sg_zEC`;18I5G#${*T^D>rKITq+Mz`vG!*;oq0dHAmmToV-}w_g=uI#77j^(?;S
zjH;6==EJim`I2BFLXq>0@V1VG=UAQXsz=V3HU`c56gQB6s=sRwq^J)}3?*Ti5Khr!
z@bpx0343f*LOxbXZSX$#YCuR8*divQ?DHz{vcvfC7=KYx0wLr##4a!YDrLeK_bwCe
zDb%IO;ernzQfY`5_c7hbv_zVEd`dKG7*LksKnW8alzYp?boaTH29aQ}y6kCTwa((U
zFvLHjY{B7NgqS;n-(f1_&AuK4-wkbfxEg8|w|(Dfa+lkR<M6e)+gLq{jm|;m+>+_E
zpDTEuBBgQFL`Dic-+5W}Fw&o4B6#PS>4!DmYn4GgGMaZxV;sNuCBj^Qc96#L;+5Ap
ztTRElYz2#Eft24*==Vs5jUvc{<l)s1`EjvsQevEBjuGLV*@ei!;Y{Hw)lEyMdYzTU
z=KuOpP@xVxBx6HZ^RXiV6w)l#g4?mbk2x#|u)9-XaPmrB<mSVCBjc9vjPACUPR<H>
zBu;Nev=X4Wiro_@RE~Qb#ntjSMyJgU@LcJxZ#yOa<g~q(2Wq!O!US7*2QL#=kT@29
zZ=Z+Mec5o?A!wod>!>5qP$-Ond;Yj*BKZlWD&T7<F5ZQ>TxG{OH)Z9gWeSXmu78z?
z13MiQsrjFcK5LxZHZHx_KI0C7t6Yf;$|Q;K=NfzZuUa5Rx{iZlp+gM)rj41d>#14u
zNA8lJ@e^?KrCNuS@{V^iUc;0Fs#rQrb+RjRA`T`SW=E*n7?s>6$s!ks#7jGq#&tIh
zY+kQ-$65A(WYq%#{>rEEdzzrDgifl_OWs(0u`al`UsgXlDAN+yPuo&k^iJAC8~&I)
z4ZTti4E7!tj#MvY5W_)7%W47ICsuMFhiREo1wy~;CH}58Ul9!{wp|{55tx+bqnOu_
zala}23)b7|!7=JT6zZ^0D2pG%#Blxhr)ZMDf6NCPXPwvrcPH7A92~36!)K*RulKq=
zrW#IWbUxtv-Q*#S!yb~Qq%7DzEs<(CzNM-gh<hM5J;KIDhYp-_H^tfu;z$Md{Z$x8
zA7JWXx0%z+c(t2*Z)%#v_pz2^a=Mv#rZ#YX&p};kf{j*ZIUuL5CM3|;Ht4u~K7y32
zH>i3-a_Dr3SPI*5yi<njKUgEmWQ~(?lWC{+y7<~bqcQxxmD(EdUaFFZio$=fv<g^N
zp9Cr5FbS3F(yN&Ln_Kqu`q9*}oo8>2Q1~bvmELYGW8n}rp2b0_(wZ*+JFGU6b-VVU
zn-oBJ(69?~5@3?4U6w~qb!LFrZ!oxMUW`b;`66z)K$41Hs0l98Y*W5p+YF8MB#%?F
zK7C+=ua)@UYjLG9_1uZsJ8ui7vaI{FJ)kPk{6UX@tcJlSL@Z(HVVD~eD6lN=nSVF_
zi=vEU(F>Yz%nfs(<uL9!Wh4aA(OXurMq!5?Qbyn5cQ-nb_x^)`Q}f*#eR#$OD-DA6
zFC$Njr2X@R(MO-<RAl&`JK{-4w$fw=fn4-Ev+s=GmaB<U3zZCEmnUO>W0@%`NpG2K
z`-;|qeKEh*dWT*%-Kny}w5)NWD&gB>q}RNk*kZZK!-A^c+?yuDxmqWUxk-GsWO8a-
z>PXU2NpyBKR+0w-gMc%fVD=u&)tgnrCpT9wLqh{Y<e$?gM$>DrvmW=)A3x!0zQE9o
zu!e;PE!YM53*n`dxW=;RSZi}`XVsv~5|`oYE%^ckIEjGyAy)UFGpvdT2{J%9^L_pH
z&c>+&Mcvx+gD+5{V?}H%>9j=eOn$K0JPXy>oLCJu!+qy9DLXFeytz31OaC$wk;$6H
z?E2j5r0C%HuVo#~>4BmQLFaknS0-z_NwxX`Umr)5B%{VH1Ur()3#+H_tnsL5{6u+9
zD{wnXZTySvzo3ZUVQG!_McEA<;9l-U`8;3QOxrgJ{_l^O^-I9pHk01>av3Vjqf8FC
zJ`)_z3p3jyITv3*Qt`zOf3`eyAQKg69K9y&^r*M2hjd|FoWCRK#?_i%Vwl=wlel%<
z|1p<-%9xLOf-_R~<_+lt5yM(9`h@$0pH-*ES;~tjIuXKXbQz~RU-h-g9!{@z?$9_5
zl{rofIFPE_MP90hwEW#I|7m@!CI@W@o^yHfbxtKs)OTyUG?mfieB4>OVQ<>8o3;PO
z=wotYazUubCTm+J(+^|hRwv=kP(=_~m`?nz{qbTf=>pS)U2*9q;IK(AzeGP^DEXt(
zyr@4X=iOc5Cv%;9n~06yB~%#SyXcv_%GQu`f-B*W?;s(ZrMLaYEN;_xWrt&AycfvL
zo0pA?o1KYSLHAi)9XI+`b6eLM$YW@=YuP#vr@<~&2vZliHR3rKH*kEYe0M(aCNqFj
zY<PPKvInbQI(vdamvmNJs%DRj$kP4*fPW9+?U1Wl__TPX>vda9KMgV9=I`Zl_0w;S
zqc#xP4T7+$F({sEs6r0W8I+Vore8K3YgJR2P)+gpu&@!xw`4#mhGAGGTl>^**75s^
zY!&(uBulWIVH3b21i~eZBISA`QmBxVE05=UwdIPS?N#R?<IqSX9b_lmkr;S$05v*1
zk*VERt5$NoFCP)FN=q^=npaieRBdspTrL9#l93ZME>e|~CdxeMb<yTcc3a0}Gyh@m
zpGka7fz9!=oV&#;cN7NS1M%Za{56%vC<-VBdeGFB=&XcSTw0t0KG*uw%)VcA#Zr;O
zC{~*7&m{N)68fQ=)SJ<5Y39@7NG<sR5n_IyAPV=?#^ju~q@C1{2i8?OQVvK4I&i%9
znJf>EFj6eD%JhFMOB8J%+oq=A5~Oma`=KE8)sjYGmwUgg(t0FJud|rCQR9`(0#hT$
zC{HqFsc!!8`_HPzA_37~Mg=io&5zh1Wi7yx>w(?F9O8AH_Lu~R=(Sr;=+AgWDaPy@
zMJ}^NC0XlzSSZ?G+`1iLU|q?0?)<B0p}<H(Udil6MzEYZHetv1Ja5L#;9dV1sh)v<
zKrNG$Lc&#Hz|E6RgR^TV`HI096^69uvC~c9(QmoVE$r&;&}Hw0u{u9-d&c)pNvm7h
zF7rd~P{Zhu`OWyUSU^9o&2=v=hn1oSl<L4LKXs3f_6T0M@%y)$2v|VDl-&FmzPZ@D
zitk)C>YuvxaVb!j)0nxdSPoQ$=p5AlkQIa_7ufA^f}C=!T}7T9U!zLDH?v^Ab@J>F
z0)9w-BC0$e|A!0!(CV{(wZ#%ucx2uHJtdPl%rWRtq$BY3sk*+`W%NPw9HDz97fSGL
z|F$Tj5eaV>aDl|l9~lG3)d{}Q*xaE0ha9#w;ydD?{p@>AB>lSN$;-g?CEybBey{2a
z?>ScF%geB?C~v@a^;Y)7k+<(OtVHzYLe#Dx2=S-Uw`8637&Q!gPO>f?b%#~I_P9v5
zJTMCt?0A;i|6QI%=r!)S>z!tzP`~)&o3$ZI+TYt+kKI9!%Lz+Kq82$kAp$yYD6L(5
zVK`F0pA{2_u|hMc#t$l{wVld9+hwX`%}N?ac2h6YdzOZHH$~6gjYP2dw|CGjGVj?1
zov+3UMqn~UvHJ|jOpipW<zf>aKFF^k$zN<5%}m9$Pg#isJO(*cQh@$HtHfA8_ybfY
z>$PHc!p~FpH-d1`zI$uc+TsPm6*E@mp=`DioDs2n@NTT#S~I^#?^I24;PWqv6ZXD`
zOxr^a(qluyW4OK*;%Q0aO&;GBJRI_^C2_yzF08+!AX--o?fa*rci7~=!0&KWGWJhp
zUxYDiSfKm&A5CJ6jiU}T8~>rR;~H*?Hk?Kp7OT{-<maYP^hcLxq<ZnQ%u!;49g!Wo
z3{?(?DKh&NI6e(GjlwDyjIf#S$(Qx~K%eOA91`Cl`BNjX;{+Hu-+of=CC3^sHO7Q-
zBX{Mo3=UAeoe`d4n;EyUwp<A;Dmj*R*|*bwRYe^P9g0T3$n;|$D@^s$dO_(9f@CD6
z*W$h8(mn9EqMJ<rmL0UtFp&Zi{Wz^I0rRq(+Y-aJBTN6}!Gh8sj98g`2plzf<iE4d
zE6kL~NNDKPTP(StooskQB9%XcYlvO1tm2QMEeGZFd&s<F2D~B3m1JXWL(n*2C0}tC
z5Q}@BW{X6`<j^vii=5clil(iq!z}_tgHF_;n(d%ru%yy@OWMei8RF`@Z`))Upuv~u
zb;chD)%E&}+}~VTxdzf|2`%MvjTatHrwcQ32J<Vq<`8VqXC&d-;&#xV!vhf@#sR5r
zk8AAXo*AMg-@K`q(b-E$s9GIAq(3&8j4|of+)pjGfE<sKL$8j%4mml26}MyJb%*7|
zNB;FUgo`ZMD`Ymq)i|ti?~ojaODWghvM!uNn=|d})3z>5>4-Do8>v1=1#6b2kNPtc
z7Z!i*L{dZgbg>`VX46F_cO@R|t^~4NpexaIF4w~gW7KenwbDho7j_P6InRzy#h+s?
z3@aq5>%GbM*EmlJ4D`a8x2n!8v2jLyL*O55?uQzZv+<BgP4uzNOmqKtT_W3ss2-BW
zTj505L+qLW+w(a*yv2}O@UHuEMP@j%*gTW{_(n?Q0;h?u&T6I%pN7XkLLCrQ8_3_K
z>EwHF1Zk1bSqN<#_F$GLUi@~&@ED;z@S?ZR9Zf(ci>-%&ES}~rMJBbI7?~8rCy1h0
z)OgFAe-`GIve9I74OIDBw_UbrAxgNE$|V}(v6DMP$Ce6BlKE18qz()`WVDpMdh5bY
z%s8I1lfKL}YCC%w-eqzlCnpR{z<IYJ*{Ts}uncC`fz>u?Ojm!@2(jeyp_Y!8#2RBx
z_|T^`#Z0r+z`Vr5>myQ-XeMILYB%g9Rl<@{$X7@scd*odKTRBN@+#E0goGOlKLoX_
z0H<17wc7H}_7gqk1Nz}@){P;czV_)!(D?5M|E_5@J%dhPo}|pGd-;4apo+}cffb#0
zPL(pBg5!q`9uee|m)TU{7FRWB$&mj9?_ty>m4)O{haZxK1YJm#Lat4yX4{A1vGK}t
zX7OB6k9~1ROF?&O3C#fH#dH0qUuDYJPw)97&oG~yJ5IwB=E}d`l1iZKTx^zy20p1)
zRT}a$V7J_XUV`;%7#Dr0yYdv%{Kv?=sxIl+8g*TXLii0(v9F**9dVK$H0rg<HqDHt
zHk9CAXLGv+*Z{J2$rHg&_gcC1Ssi-sxK-PdafVvt4|xS-+LtS@RX4fk6;S;d?(~HR
zFD!+8qG*-}FnaBcR%yMpuW)3wL)EjkyHmpU%bnd9y0_C(J!Mf7U896AoPb1>1kYhb
zfqN{UM-w)#?ebRUs;bH=$U2_{`zBUkSW7*CA8?Kzn-)&RKr3uml=$I$N`yX|8s%d}
z;iS}}wyvlIQ9o8%sgE3#-LAO8{tpkJ&g_IGP}+4VRO*E3gK#obqHNY^d?H?J<BD-a
zYT!pswDrJ=IRzfqTw+q7hId}-79c)SplPUi{+_E?QQZI~G44gge{Dad1X@43hY=Dy
zEu8gj?2Yr90aGvRB(X8S)vxU+Y6E}%+j%{2UmwFxf0SH?=-6MF^WJYtDs`{&a<1OP
z;R1<;C5y1UwP0cmpVGz@U&^0`VX%d}wm%hCmC~XfCx#m*c;C7HfYJR-ZM4NbdHxM=
zC$(AFh$vi>uK%SX`l(@u`8(&SVr|kYADp`%chr0Axoes;J5Af_#p-`#xE}nD>Ij|7
z>%=jl4sNqoa>}_;r-urFG8ilDm!AR1Bsef<-0?b~Eez@&#BtY@gfgs??phNV7xWE|
zW;GsS&XabQ*SLm@4g8n80f@hj%T$0sbA-;_9~9Po!nom+tHNy(-S`P4C$MJb5jc9K
zk`)e;#tGUSxL%OIh=NlAeBJ~|142HszeZLMLZyYX9-GgXDpQVh9b)?GaUg^PXaEW%
z-7(|pT3#;>VEOnhf<~t2+$-q7YAI2ootqw!22_0|ZvJ=)0vDQisB{kRowI?FqPbp&
zwZ6oA5JW58^6YgR^a-a*f*?e}971A_uPm7~=u&ecgG0`fWFNvAC~E4FYjl}_kV)mM
z#Tetdo17)*NyQn1g{^A#Tw#U2;)_iIarb^#)*Fhs4BDKX`<eF(BgJqHM=7B}ue2=I
zD^#KX*3Rii)`J3$GuP~BuJ7Qcr)4JiHDZRVq|h)*cu4*&qIPxLB2OlpDPX*twcWBV
z!PF#(S>)wIP!n<xU@m}+tKtaFGvPpRJ*s|`HurxTH1qI)*~+_Zp*ct?2tp?_{LlCN
zXI#v3ctEU5fde8omVGeB#LjeJ#XF`4#mBcHo6)ct+lSw;o$yQ;$u$-fq95w&n<b;9
zO=I>75-W=dX+q=EZ=Hgj(q$s1O&yBVs-|LC3x_F~3ns{M(pPA!==-zct^#6Cp(!@>
z>~L;thLm``p>p-IoPoBHqss&<Qqt_4itX1XsM^}vNz`+pfU4Aqc>!JH&~%{cTg|l7
zhP2|z+{>SoF|Av<v_m6JfHIITJPm#Ytk!h<us6ukMSZ&)pmT*Ms-hN5^+McZSK`A0
zC+O7cFDKxEXTn!c1*<Mfz~`%?T#UnMiT`-N?~1nR#G5P@@S-PP`|Ht0EFa49o34ag
z@{w95Thjbb!2d3(Dc0>F2y)NB^aV*tXDf#~ecD~O!hol?l;yL>yD+~c*|m4I7-l`y
zA||->gXA_-U*$HQ3;~^_)&JVs+_yB-THlyI{Vrbu+|@cR1Nu}NzTaYlY=D@(o0DJp
zT)cxWmIdqq@D1RytN%_EoX#wuqqEH!x@ojRSY=7$;u~}r{rZGT&ewF=&G#u;IE>{S
ztg*}VF^I7}5+#3VPFx@1vL}uSkbCgs>lP%gvF{w-iEGg5rsqj3*BRuUJEI83e6@7R
zle7%~0kZw*01kQV?mfwxUz4-WD_LJBSr?3iJVp^*6@z^Hr@AeS^iaR%c{i>#YZhdh
zn=}-#Bh9mAO?b(0uTP{s9zYm$d_#>Re*{a-DgHfi@k$lHQb%i0u;J^)o!#rzoVAjZ
zPmq^<y|+RaX_>e>1>F8tC&ty@u~p;8XRFSSzvqPNl1}9D0$?!EE?QP#s(%xQ*uk{$
zDE!<0ck89L95XA`LV*K9MLob6SLC;kr=`4(MTElAHLS$=R>K_N&>XN5Tfk3UQvl3i
zU5DkOMkirG6mIGp6_RXK%t3c>yuQt>;Z9aGcK0`4-9ZD_fpe;Iv2ALuh#E3e8~KII
z1|J&w=Fg&vRrPg@GX=z&KzB~&p5A_2#M)pf+P7u*RZ^+;VwF2_Kjj+mDYs_0fw_PL
z-)hQCHlLb$W1A6wGwulg1kWdq<tsBAVxJexYMESh^i}8X#|9x=w3i(IOK{1IPCWYa
z^vvLmmYq*3kEB_zxTC#a5rbrUv11&Uu~#oJX0&5i)4PL4<{6#Vke%Y6I>uEgLR6W8
z3Y~q(k<}5d+gU&2R*4+%JKwu2z*_)yyKETTOjSEio_vC)gYJ*qSYrjeJ(y<hX%(S#
z?znQFwjQLR*faz4%x6B5NM_<NVZaFUM3b7RKPwuZ!rTMrn2=iIl|liD#3-(D)2>oy
z^Z{rU?)|WaJl}v1NVgMR#?}|+OZJ%E&3r%EyPIJQ<Ycmzyk1B<S>(TNhKGfp)%>WG
zJje{2dpH@yLzF?>^d52s305E1V{j_%=APX(ln4eMKf-(GLG1-hYVYW5;G{$TOIYHg
z=Sg-DpFRB(rnS0O*{7w?59@qWs12@HFpiT66QK6PqSn!Jh+Lo}?YzDigDKXy^vMDo
ziI{nQVz!ES{{RACnfsN9K^b1Hukhbf0a^<K5W|g;S225U=-!1o^8{`n!H(l|oZUh0
z4=E?e+alE^4Ob^Y>v$3j|DO{Rn_f6z{21^rz5h{t8%n|I$(o1m`I=w3eR=?JRRvqM
zg2W*QwADg2!?nefe8xm-9bN1@=J;g!fGKNJv}<ABjAnxQN*7|8``kBll?KcOI)o;f
z#<W}o@^Q<4n_L)+&t8`7Q+cI+=r@AqIB7;iL4jE1S=?2+uT{P_g=t=inoznBxR6C`
z8@Xg3T<T#1__PZcW^c#(9R;MI<g<5ZB#DMEs3?YyT(-MTbK(JyrQU@|W<?!qfJi=>
zm-V~yfd2I5#LX1GcO}Ssc0ZiNe%PpdNbY9lD-%d8YS{H&B*H_?2|cCdWpc{xpy>e3
z&R8;>Si-vadoJhTb;^LGQ^H;m<3#0RNi#235e1&$LJCaXt7jf&ku+<=Wncq2^il3|
z-<K#_rkSA8iI;lNL(H{Uaj=rH{`g$Myp`ZQU>Y73Pz`1fG==s3k%cU(v6{%dlmG1d
z#;l{Dgz#im#(Kn1;BVlUb+os}DxHES!kpN3QR=duSJj`{$-OtN={+CPmmP~VSv`x7
zq|f~PTacyIJ6>%=H`4t?OJKmmYPS0uD%g-|^mWq36<^-yc}<66_Xb8FF)tRM4#rl}
ztWcGsLW(z$iS%Z%52hA;l5$8t1Nw2<rOCDhjn2$vU{wCEq7uXRxR?K%OQ2(=3!41Q
z-QK%Mg(FFQqJskVrhPP|FzN-tJXNwc#c(h1o}SF_y-85l_;d6|UE4OxZS#C?QPb`N
zXtfgr!8YF%6ef!XfrA+BKd-JzGFHJKnB7)}OHS-urK+E1ROiIyf~^ZPzC%KL_d~2c
z@Hqnz8{bY;Yv>LEE0<1NF5|8;PxltNweOkmG3?d{;F$CI(}5Icfbtt`M*QS@t`Pv?
z<NCL~{TC(>#13=`(0W}9hsS%b-5z(lck!CWIiAHZtpy*Q9S@l8myZH4{7a|-5Cpy0
z*yApP4oj(cGnZNAb5P`nC}_#s_u+TWTyp{{|Jca>c}0nV`UEvPSRxmN9dva}h%hjs
zm^kpt@)9fOEm|UN6ggrCAX$GpL{0XK&*x*Le1`ZlO@eCUT6br^_1e9M`_@pSDhDG#
zI=zvy7H4qgk>S^uuM?}7EWf;h-Y?h|67#t}!SQ~$73_w^7bhjqnf^*>)34uFCg-sQ
z!eXuX8sslE%!WTJavoPfoJ>Bq{zRZG52AHz<f2`l<h!&4TnJfyf#+jykgPzTB9cq_
zWgC(P4c3|(_gFo&Z}$a0`e$)8j-T9WolVw=!En0-T^(Kkr4EaQkl>mezU@=k#k(^v
zk*noh6~e*Yrh`_0)SAEVLizbMDdo{T%7%1Y<JDrb5vHWyrt2A$)NP98J>%zGBcyZ)
zFb@2{pN2?vM(EExyyJO=Jcbw0=|X!*74cBOq|F3x7C)u%bU%tM43TI&U6rk98aPYR
zr3m$J@le5UaXX4yYx0G1JA?F6s`cLTo{9RJMfbAyxq#t$$c52ztE1uPCJ%??KXMAV
zD?T??nLN34a>um=M#Up+w$r87ssI-QT2dOWfYRPhnuk<7qhct--`KCvn}YO9iFA(2
zHBuQ!6~W;fb=gC#IHlz**!tEt^imW8z1|l?`v6*j*N>H6AFWl{j*@G;&qAi-KQAIy
zLEw(3h37|0NdHU4>%JJws~(>6^rLSd%v!!w29-(6-v!iyLL+(KX9-}9OI@skP`7&s
z)Omjl|4m%!f<&8NLIDR-?&F8OJC-mpM%$5CU$sZO1xpPLXUzt%nILvCYxqdhj-E&;
z=7h8JV>-e$V2efuQQ?`{$z`wVwvJMOjsTA*$&=}ptXc8f1m^P?x0hg77;1Aoi_6ts
zL(mm8p(*J5bL>Wo-A;yp)!)!2Q1>p{>DRsH<Z?|W4AoviK!lm;?QQ#7C-M#+gdDOu
z$!XjGYSVucK}=uUkvyzuwlmHvq@kWGZmf6-jN!<0;)nc?co~}ELT~7m@kkm(TSby^
z?UI%7h44wav@$b8^bVf~yjPCf+76*sxlFVxOdSz4UPjCYwdE0fV2$=9XG&)gPQ&9#
z;;Pv%NRhta3Qom&qc!<apGjlWRk`@xhhKG8G;Tvo0p#-cTpsQsI&40!Lby)>flJJ3
zmW@F?pf*XMV@}nId8rf=3C6|_nhynl9yfLezYV|P3cz)6a_V*8#|9QSVQUxMnvgxn
zK8NgVYl^+Fu$Z`QJMLz-8Weju>3se3<|wG|4d!d-syKZ1fd{NTD~nP6+LJwU1tYbg
z+mwZcz{BgGp;RmHwc0rTmdQP*8#!JG9(R4#(IW$R$o*V0!d`fIr^Ia4Q^nLT@lVHW
zYa92SkkWAo5ujAZYs*2v2cRKqoH9@tCS2^T#Ggh_KLTyJmBCu{eoa=^pYW~l*)z%N
zqCCQx!<TnTB!7i9+KZ!Lf16l6oeY}w1CCN>)Ko4Nx5u1an_X9Ll-hS;mxFpcxTtOG
zoe9fUo;kMZ8V8eZiT9o!2^ptmM9DgV1~@Xw3(yo>OQqx3En{vxYJI>a`H39PLUP*c
z13s`|`J#^`>nduEZq_%IiM~}3i%$>>NuSJmuCDQyMO3g7TEjK|I)~@G`3#vp@?13N
z>RjW}dFuVdgt(R)@UP&efe<jS4{+VG#4V~dZWbko2a49B0~*|>)5rz<2--vXK_28E
zU~GaDX@|;%cK>+Fmvpucgx9>Tmz1+`D&j<Ns7wFHygdUyB*W*yTZ-PpqrjzCKxj%e
zLqWPF;60W$v}$R>tg-ZoIiDFQATae3<qfme+}{>0uOU`APBKu6@gWF&uk;XCEn`}3
zO^fc|i%R%)tilMz%i@r?B9^~*Q(3yS!a?m<nhZBGmu;r6;md3OrJb#vt6qN0Rh!GH
za-Jx2`Qf5CU&q<FvtX^$hxtfg6?L^U^{`l{H?|oNi3v=cwCbKeC-^^|6>roKap@Mz
zN1@|Ou5Dq(CUY{TC*~CYWOhtcr`EDP43$FpD8sQDsLFy^qj2Wyy9R{Cogasmgn;Jc
zAXdy5+ktb}$A@HZ^%+9i^gu4lT<Us3iCuMmCTF*sRtZ6(!?&PMfW5v<oPVde#b+)k
z)7C8co0L3OIBzxitJ?(XBC(DQK(bm4tEtGkSwajwmToQO_>?zHS3KIohHeFZZItm`
zNP{*7yFfd6R@pppu=d?3zO$aQ-7B^De*4(o8iQhi_=uUl`@i`{LHK%9fo)GS9JG!N
znYg>&dx~Kv??KKYHF1hs2MOiM<ewE|qnow$L(z;CiA7f!C`mOeOa}(S8`rfGk_8~5
z*kL@5o;Rrz!cab69;j5Rl=UDU27E+~6HAm9hk#q@!#I;BtF`p}7Fay4R@O9xLerQn
z3j5Cg<d{Eb`Gk00g~lRaTU)T>%GVEmHy@E1ytbqMFLe^XnHStsL7y=%s@?NM1EOTI
z9E7!r6o0>28K3TroR5=6<Q6>I>lqC=7<o)Ygfia5@;+`qz9*Vi;rN!ICOXf~jQ4g<
z6i0!-1r?o2sY7_@@)r<82`2NWex5P;w1~5_z^0S!@{i@XAvU*R{2&h`m0WG1a5Yfk
zt*q{@TY>Mn)P&T<`E6G9!F?GZFrb}W5{Wz^W^xI-oji8Y-uiZ(6*FNE2p4zv-|o<V
zl-})bclyKOSTy>kEplxNiQ%bp;e9ykd;(Z`$Q2u32_r6l8Hs})=!Zm^xr^YvI??6l
zN7@b=nY9&qxb!MmEh(uvHi3cDJ|}?Hn+{Wp%e1j-`T?3U!`EK>F!%5NX%jwWv(uub
z^ZHCqi#CqhPBMslJHSjJ4xJp5L2Oj0iVJB3@AkEg+R6rMPjMEV_1G4PYD>sKeoT<%
zH_rWck?F9&Na^b7X$s_dIdM~lnV)F#%gH#iq|0XO)#WUw4Xykj6>^3X&2t+v8{=uI
zxUkk~b@~I^E5T8~c%}4Fs^`=Nv|+vpT!K7y%rmxJw=Td6hBgMSQx26_JPjC1XyglV
zaQ?y+{i{~`_R+(qG18h6`iob_#^>P0UzqSm&Q9MRzGTbcBHd?nn|Yc)lKQSe0R!sW
zf4&yrqIKPL1~x2WzI_(dNBB4#)IrJhsAF<P7r3p{w9lNb_Cqg8y;%B#1fPk&v(|0i
zWvfxX$$?1GE-|JM*=QoMa%um}oT3kLec>X9O3skQXU@$;<5avSa8(!I*NPWyd^%4q
zevC&)7df7ejN@Dc)+x4SpT=J_ma@m(s(2*-(R_}98UiD!(2M<P3wZj?DD~7PpX08L
ztr~LI$_;)SuME?TkzY;J*NP|Al97qR_952L5$DY6N@-}|i7W_y$(%s0Rmfr+k65VN
z{zr8Zw&U9R)D7<kJ&p=}Yb26Jt-+|L0S<Oo&8DXG&QmL+VJX9ZofHV=Z4nAl<jvaF
zWF}TK#Z(Rc*1wrbl5tiyu?$y%l?ppRnVI1IkJ&{y?PoLTkr5P4n~O4Bb05jSdmx=y
z^~wcM&M=QsuukS36WC4pfp-n8Yj*lHCJp)`*jXEZ&o3*&$<X|jM{F>6I&gE~oAVfb
zXT)A7-yW~se{~o~;(AE6ioiM(a{pVLnFPY_mP{7Nz3c_!NsWP^v}ZWHGXpe>c(q#7
zJKr^?@H<WfxV}@bV>%m%DB3&n-hAPjeV4Ex0d#fFmbCV``_Z5iu<zj%FIny^g?!(U
z;xoi@#P{rbR|sp@JpRQCUKugSqwPbpmu%&jdyd(vXTcvz3V@mNwS#dGsQ3B?Pe!?@
z3i50RO#I4oHrmEl1RoR+o1#18E)Cktx1$3kmqMBY3L^SPx)`Qc=r0#F#_iXlo@X~o
z+FsnZefWUOXN@J$X|A<(As*!J)qaWKS{iHqw}u#TCT`d>g$)F7UUAlm*BX_)&m@W3
z(ER1*t+pxAO2+zAA9c3m6w_2oS=DCuThfg2?Bo@y>1x%v(d^0RG0M-3|B7O?u3p%c
zUi9J`!Hc<E-o8b3mz|b1tyl^T0FpnNt4qI1kP+S$Rs33JdO5|;d71dWS4UQh^Fl6F
zD!Itb!;<f~$O_rsEBaLZQyv%prAd%2f$XXEV}If{L$-EmDI!)BCOKeShLkLO%tyHq
z8CzX!MDNwkD&_sRHy70C8ALBf7e#Xa5r3gop{&grKW$0mSFKPTT}2DAjeqV)SG{(d
zq@mZXV_M1dZ8%}C0{(v}d&{7@wq|RX0KpxCyAvR2aChHmaCdk2;O?%$gS&fh4ess^
z8~1nTJaW&yb?U47eyv)6yXWj4J$iJ{7c#~N@$3Xa;HS(cJP?==pK&dkDHEP@4zK~*
z6*Q6E)m|(EC8Zy0v<^mps=0n}04|5+F|phf56B=1v3)8;kr;MF0p9c9IC*-HHo9!X
z-iqvfnEJKSdVM~g4fZx|YPS09wNcCR>@D55C;(#+Q)AcqWEB8t#gOeJa*W5HWC~(3
zC<#G;LB|^?aJ{h4h{H3>b&GlAM27d>*cuSgHfIJsLjGw7Aq!YLrXgJn0hf*w{$0a@
zq}mQ;bA#rcnQio?(6%l2wMUp?d$a>kw*Hk2kj8niYq%V<Zdb`R%;;-R{wG9B<hH*%
zmUA8rdBr8^vb{a$bmhH{PwLFR-DUw!B5q>ZZ}#8tS_L>5dNQ0IZm?}))qRgoy(Qm<
zRz)!-7{`Lrybw_~{0Da)>1}tkO&E^W{C&T>rgz-UN4KOJD4cA*Z+-bSu?Wy|YmKJ8
zQfu*W7|U~c{H}@5PwjI%gXipZ@>mg-vF-EjkF^Y8#t`f<a7BA-k#^Ayy(7+^`K>Bh
z9YJm_=Bu6SWsI*|R<!E+YUL#ToA}A7+x=O&m&;XL)GErIz1Q2H?6GnszECCc443t%
z-*Pr*J@agxRCf%TA$C~#Fx43<KG(_C&)133MfEKWv@w@f*Yk*rz<1BIvu-WIX!&Bp
zgP(1J=XbUjCZP8h(Zj8{FNIJhi$Wfx$lLiiKG-DsFuH<U9?CnX;0!vaRK3V_@HTw4
zmd{rDov~HoaQulWT7+n`-m9dkufuEL;9*=2_Eiq~4`IW=$>V}a;5?b!u_~!ms;OJ7
z1o`{0#LF-7O3`}HTwR;+FeB&?rY5(UN;UYi(kSMEi_M8}$j7ASw#6wYfAJ`9m%DZ!
z>P*%v0m*^1_>(Oq3rSzOrwu_-T?;t?fc^nTQ_npJY8d;BYYrn0f|dAKu(J%VrQC|2
zHL4F9*UIm6jCu-@)(-Z9v;S46&r;8kp^t2Bh}YrD$rC!Ch)HaY8b4I4Mpfm+b)=sV
zl-#hJNjt61bco&%)8nVBSZ37E8XtBn=}dJ-YDLOxhBwiZP_cH>a|}_{@VAJOF^?Lf
zkaB-d*vt9_gY$f(X0?&->BhT`*hATENnAO1EAhTdP<812EDq)mdiNB+?zcH=2vhN$
zKq13S7rv5}dG`-A_TjaQrz2gGb6#b=6Ozym<3jEcntYHqyW~ueWJfTZVp)&YIp=Zu
zF|)fbnV}y)Nlw9kEVA^%ws#$vXuSsuC~tW2fj=r4$NucekOD~GZ#pf*0KbkB=rHRh
z+)UBvdd*{TYLDzXcw6^&JNfAF$;;uvV1=d5VF}GE?JDxl`=R$B>LljHQqQKZ%=B&I
zUW<9-N7urOC4kF>VOa}pf<u2#xiSNw$`!vy-tN9_dNmJAh|JUU36SdkZ4kYx)0uoc
zwXUVP=k!-|bf@*s(OCk=MCMgju_~t7gpj4J=<3oVXj_WrVB3Sywq@Ge!xq+xbxg-}
z=i|-E+4x!89cH@V$di(4gXJV|%UWRH?qF;|`C#g}9tz!WitDT(9-loWtm82JRg}ST
z`xoOI5Q(jWQpWWm<HJLamQDDU{nqggX5gCf?v}9M{ifG<H2${p0WzqryN9x~6P|20
z=P9d9^^m9v5Fg&69Wt47B0srpa}*Fp+UZj|vMsjhxVQ81VpIC60E5u?Xt1k;xBl_*
zj)KR0U%H9!H=+A>P_~;wXTLfIxkV}`4wZi_DGG!Gwal^;4V42-EE+vLkEz|`Km)L0
zfnR421zg9MJjr3I*GPg@QYV#|6Mhw;Y(nn4zKs0GqEjO!vcrR-4|Dhp%~yl%BlcmK
z5RLfXPfhc1ylRC5_H!%rP_r`4s<b-GwLOPhAe0)AP(I`>X&VvITEeBTCe)A*hAZvH
zA?zs$>lh~AO6d4SLq9W!DceIu57$`zFvp$HhcKUx;!B64nK!gHa<-CnD6nRN`C=FD
zjuK-Q(E^QG--^gtm(z@@VH`+LQne75s*tLjQoq5wEWo)@)(S<}y`ZfjV$&2=QFy|m
z4Rf<F-tBwGccF4w177R7pif2U)({hb_%1%|lX8GYQ#x5U>#vr}^UD-cFa~iP3&JQD
z8|hj~JtZZ6R*;h^jfxWKW66`zuNOxW-;=Wj1xjtZ=93EB&NagI7ksUZ&S0gb<k5Ut
z{s0q2*C&A;#%P}qX`L`crcB_pU-VavXzP9tF$$;Zi)I%@=e)v?_;^^O3E<Y)9t@8)
zV^)p+6?PN2)Q67HLzK;Ruw#9t{NIZ^uK!|wXF|GvK6suZYx`Cr`M?7AZ96s@F8QAv
zJiYyW{Cz*N4SL}}hF^3;rGa{gxVjcve74=!A7J^tMowS(pNy<f9`*ZFl0NuHT;6;l
z^eUanXXx<Hd>`e|vC#E?Nour~a(pW$Xv))WxGQjZeM)E$+}~ya{4QG!Q5knBV;jLI
zdB4ucR^2?J=6UO-(l7*G^<b@=x$@qo>p0p|={R3u>m1cC!n^vQL3B=U<C0UUUz=za
zqNp9hT!!KM;#Qe#_W*n6oA9bmxN|%@)<R?*yq*wTT_#PpJh>S-UYiGyd&?&Ierh{U
zgq`v5keELF8urpqbp()sznr$B)$|5%7d?uz^XXjLSlzW>w9tp1Rg^Y$b~xgWU$A9G
zt=+Dg`y8ncmUV*1$nrRgt=B9GmWY8GUSs6oSe5+wR}cL}FGil=fRsnHY}V^S&2!s(
zzL%R=gl?kuBqjarkNi?6ee7mwSn~0~UsIV|gc!e43f^Jrv>;@JHMe$BR`4$f;*)AK
zYq%aDvgwo3XAwirK?uy|2C7gtEH32UqVs{Cc;<Q<WJ=*9O1<kN3YJdUhpAAm=4P8T
za~W1w9np0?YO$u!=~CMxMve7!BW>m=hVVo*Ku8#D4$xi*C)nOfq>8CW{Hnt%4Cibq
zb4&e=^RqqGwb$Br>i0UAqbo9Qq2PoN|M-Axt&m_=6L<BwC<uDQ>1qUGIBK1#z>-7*
zNMJ9uyZk}=;^7rM<rS$<N4A#lWgy{5?IOkrrtFEIQF%zP^BU&QYgS?~)2on9`<e|L
zVV57?rS?~!;l|S^#IvTEQ0(^??RD_*cd4o{5?H=NSHM2bN+VTs=qQB(g)=mi3y2%+
z2<4e-_U1nL->!~ahcT-}BY<zTU)VpgfD3gh?^1VDS|7&6UIlLzv`k=!4T;Mfsl+5$
zFcLiwGinh{B{v(I13FGwrq!s=qYM;D)8(*##|Et2$g?J2ghLH4nWGZpck;IoVpjS8
z(5(22IKu=3e?cS9Y3~hiAF|!NXl~-Wb7&*b^)u=;7{z!eNCCX82O_$AZL{aRc%Pzr
zG)#lrS*CFW!DRX_+eZxoR+}rY=1h@0s~grnarQvRa_bZBWo5m*zsAvwbzS#h)E^1$
zeqhja{2o0wNR+0kLXf61@k)1#RogP?)WypDn94orbJ^-hUiXokyTyH<L`w#_<3X%`
zBlUjw$%ozyney%ZWHmvxA<B*GV@`8T9Q5)@&2@ZCV3{HeoeSi{)*mQ78)xbL80G4u
z&Hpyic-L3PA<uuita;X7adF7C5r#Y?Ow(LZuN0%b^Z>tyOE6g9;HJCdUQ7^yJg_6m
zkMT;O33G|uziQRLb3MM%NA-D<IJ#xY@p^P+d0$nFKS&&zF9gw0+yWi3i?e6py!;nK
zfEO16yMI=(E<#M!f-S!CEJ!N|VyWn1GCk*M&Wby$B?lcDI@WClluVlK_a~l-TuD*F
z60?OB41xD(6iSzE;lbbSi5~OJ)z6`q?N>{a&R!hl1`}L#W}mWtAAD91HuC<yeH){b
zBY?pC2pQH%;#aLdW=?aQ$CeYq>5!cW!N1EjS6ZkW|C1$kK(anll};PtK>UbGLgL`O
z@#y%PQ*kdAMS|yt2p>taSusQ%R1N!-l<wIczk^^58t`|5GrIz7?e15LityT8&KCBu
zqKAZc=sN1jZ1nf}?D|;({Aw>`{pF$eGBdbLwTON+3o2Qjd43C*XYc1riHWk?km+bZ
zs(>s`7cP8ZQ9m|9wGNBxvpAQPLe>HLhi5z_5xV8C?P9^y`c~moVj-x5{MHw&?Gpo2
z>}J2Q!y<1_&F(SG6U9ihtrrE@J|e;XPGJ7wE>v<N^O4N`VuJG&$j~0h-?$@bk5~Pn
zab!z$GrEo4@H+zT#K*3CY`gZ7Dv}r)Gy-MeNOiPr>F)FAmxnN^>Avon)Z*XDi4d}w
zda146*7DBBH3UdosECljyYGC;k^4kf4%xhqZJP*RVPfs>(w9`2`j*OC|B^R-3<JB4
z^-A~ZWQ)o{9pm+Z&^_-Us*2r8@w^Zh^6g}s%#3O8PkJ!mJ!^x-Gdd32fM*^XF(a<V
zSitSdo!AMfWG7!Oxw|>BZ4d{&zQP;zGF+#*ZZnzp4i5D9nntf`Ti95iTJKFTyGz2`
zy>K6m1MuVG>B)7YCq1~p%nj1nn&NlWURAJ_Pl|kreRSK09DLtk7(68c2lbua?YKy_
zsEC^%pP&hMdc_NwJ`BT$RXMstVNJ7CiALSwU5riAW;U}pTLT8TNxGmmmJitP({gz{
z9;dIEOSNIfc)-2B_lvuo?l2X;P~6U0bKiVuxy{O2)T$P)GTnjRozT8G4sf91!%5i@
zwtrNdl}=_oN8u$Til*9Hx8G)>oy~TSh4*t8^t<;V%wRhO?e^hzIZEMw1AhQ!TpVHv
z*kms~O(0q4ZIQvcZX&O7yOPY4t+zmAbZG%mGe+gFv3d<J(|X0NvfUzmNqA80C&!t*
zLiR0Qge0>6)r-Rbe-7zRE6!T<dGO>f-$`0nw_^9*^9POmP2Duu+6=t9rNn#74Kdme
zISEoiR%{p+d^vUC%>H~$%{EdUi7pCy0D(O&(f~YYxIoQ;vYI(fD3zHZX#*oCc4H79
zA8Qf+m;RcBo~S{}YbDl5KSbW`fETl##CO30__JU}-x8`<_ZgK21u<-b8=7`j4=2g{
ztPmj<ZtOVU-v#fr1lg2^R5c$7B?X(jA~l$aM||O+>aX%C59c|L-oFIwcDw>AGDkgd
zify33MiQ?rtLMuM9T*w)PAvemmy*7p$_pao4QFGo71@+ZKFjuxh>WIhV@hQVA2jxT
zl}c|m@aQ;}JPtz_FO`7@2L?mXd$Hd!kgar{E`YzX?_VJ3#C6b`26W?!4;f|N_+2R@
zWrFox2^Oj^A`Q$^lu5{a+;#Tk+fAprwEE930NZ{gV~czQZ#%b9kQ$H>J@RFu2=2Tv
z6nR63%Rbi_+h3I_4`t^SThxIjhC4G!;>0e8=v<rklujlpn(xKeosM=+@}x12;5(V;
z)62tq)2`v0{wHSRJGnlX5I$Z1>I*Tk44<F>YLpuY`Q5)ql|2^T?NBA(@}NCDIWTlC
zX6OR<HXL;OuzYoU?Ws8>-n>(WUmRnHx;AeNZ|mXbQ?`#bt{=GyVZln3VDO<Jn-!w$
zCk=f^koG^sk^NY8z%MVu_kpy?AKMyyh&9~|#vokSRuO&vywZ4KRi%HA<C<HupI}})
zNnpNmWk#i=$8||W)ua$G@s7^aQqsq5!FOi-G2yZ#E%4x+m10Yk-j<3Cb+WG@0XUk}
zy_&)+N(-W`AjJK;3id(V;WUK;j0^+#D(molGw@&ie^4a%hRJl8t~q<=^Q?%bw{4$$
zJG4YTmSkyKzm@QW&cI3gvRFo)w)rxuV+bE=*FVs3*3{{gf~Lxv;ro4P4(M7Z6Me&m
z-4{?0#{L80#cMhE!Ql19H`#$!N-fGcEgP0;9^KI*xIReW;M)h>P?t+3*NHe>9$)gE
zX`dw-hHYe4)9lp=-s{Bb>e2w=T<&=!u-cm-_<V?<B)0exb}n-;L%}?VCD@#4U-u5@
zs*vp8_^q5&9Ew_SgOW<oLE7jU+SC&`FEcCpF*`gr)6D6?L=y>DVxtW@HcnSU{X0#f
zOSxC-9D!M-PI(5b-_O;5BcdH(8!TOuf5AU_QAD{LlA|W(mReqgrCe}_L%HmT3GOa~
zp$z_jjA_lROiYaQ$<M_M7$;sc1SMsw^nsgBp;&Ip&i6baRyKe`_A^Qu09z>%T9TRU
z^_tq3mM`D;2A+suNn^Z4Om8qc>ulYYH|)W0zd6MMA{#uUuYIGHchwaGn~iEmVHH&_
z()x~Rz%z4?v{C>|7Jpx#<zfb55dQ)wFJNv{sggCL<Xmz~KqT{+)?+fpfY~Vdi)keY
z1_eHUD?OClx^e2~+@GNmE8<)#rOnY$_VSJUcdSQIiGBQyZY-~jz8()evzJ;jWIj%a
zNAEp^810(+ir$`8(ymoEVCkp24+a&rC>$b?nPvPU6cj$)FtcK55W!YYkl(flu6gD&
z-NtTkR53sO$@cPpq$2<RbblAv4BUw}-?+9`Jzl(<zq{L8A+qR-Szz&gyn|j@c@V}%
zXNdcyg`u7_TBAUDE#)K+z|j9;MXTK_S{D^$>OivsnLp~NmB|N7%PP7O#X!9&4kXit
zWOX*CH$QT47~sk$@L;6Vm3MC(Y@9BsPx@Ms`zCwKiqqq1R(zSpBW)GrRZ`#7E=f5+
z>eH7ZI3@a>$UB}?LrZ!!5>07_izSYVW(3MsE-Ax^neFsF5?1PV*4)<AYZ)Pin^HKM
znaTeq9d))kT}1r(5TBKvWzU=uX}TSMj?CWtu0fyBb{^*j`GlQTy!mqm^gFdVzR&dP
ze%Un!F&NceN3;TERkV3Hq8q-?4u@g?y{u<iPhN6nlteXn?Abd>^z@a1-I)2~{Lv;}
zlb{0o15ITZI17!5Cmo9o5$7ZK`O<3R&Zq<JC3`ohJ>l<3<ke~|zC8&7%3>QQq1aQi
zX54C1h8cYiulg_pq3SVAX3xQl4z$TQdGnvN?`gjGdJ?J}sQ2;c=WdXZ;8Q*<UygBE
zib$F9!!2i{k=2J~rG3%zUA6TSed>>enM)HZ#?<}eI55;rERxA`b_r!h&|f#HNbZ|y
z15B2N^k-0T_zM>Q&u3E}MJ=dU(q~YdVeSek-odMPY<!XR8d~@D+e*0Vk@5FE`TbPe
zrRzz)4*RTCi~_9%KA=cidtL?p#I7Wzr)W&UNGF=?H%{tv;H;>=Or~0RL@P(HAc{o9
zY&A@>=`x7(XO40INq10v{u}yDcHwk~AS_pv6AY3WHH~z=kd%K}7h>JWK9iiq;!%cd
zN{;GR3PRvzGevI~Co~z^cF>MR#1x;ZvIt|fL7Y@+SfjAXVy<4s4`~Gxntq~>X7ObT
z0c-_#u|ld$A<M99sj-gR#U}|9`0(Sig_K8Al)9L7ez-;mlV(rDNZCp!Wa5KCBRo~7
zuB!E1vasMcpJWvnmtU<Sf){!i&8W7&nf$s!B`xho1YZ<h3Y0^qiKSS<K`EEq*=oGl
z=wW9Q_*^8OoipgV`l{A;wG3~pp0!Wt&s~EvToczC!V#A7o3T^1+(<%*@>iriC7;<`
z2OI?N{8Y51f1Lt31DMXI)&Qx@drqXMB+1VAn5s{caYR@K{q5*@&@_bCC+7qn&NvSu
z(MxoYDT{Wu>UFYR_T^Il`zq#%=CX~@X)Mh5{*MR$=mY-x<8Xrfww-a3)4nCl<+7br
zsr$TdcqZMXKQpG<>3leHpa8LP@3r*Na#{6a{ZtFaN<W?)U0o{5h#5W%?Jh~iI3o;$
zTwaYOEdGr5L@RqYwSL4%eL@tf<j~<X@`(mHm=6%D7Ca_Zq=4|WL_y)mU5{+s4-&8~
zM@*`;$w9~Xy@=R9N__dScvH5rES$$w$c{${E^;3!<r17ag>F{h*N##&xo8Ueo+ih9
zge|TQH+Lquz(FI`7^>kYmZ><eB=RB@HC=<-BvmzY9lN1qKxpp3Zpfw!QD`YhIh9S3
z&OMIIv}9W`Pw`x@4)xuL5G;}tqFc?PSI9Fk2ovoDdTOQ(@UiW2B<r(MfZutBJ|eHM
z@6Vx_sU5$kl1+XcNjtVMNkWlXxHl_qxZjRm+$MDV-x#x}^KfW;xch-kn>4l!3Ps#r
z6L_xX`3Q3+bh{Xb2O)HlaND67q3Xjoo4CUf3%C^G_Lp%1g-xi{OVQQHgur+_XeV{Y
z5v5>O0I54P-&ds$srNDLVMl>hC^nYK)wCS30S-$+0X-1;_@99KC$7?2{J{WuF1GZQ
z|8d{Hz4+(TBCNMGO>su1YWw?&-Dj)qK<<jiymedHw5dvO2RQ=$U7^l)n|d4)Jf2b}
zgL<ovT$Q{*!nM|1Sv)UXlJ*mPaJ!_O%a64^UR%nM64w3-vVG*Wdc$h!8KXq|-Vo-@
zyVlY9hJ4j8DdAiMJm7ah1V3Fk<OyBChWvq?&%gw4eSUwXZ8@vpCfSn-q++FST)%W0
zY3Lr!T+?n`$3DGXgehEdnmpf3_tUf^4b>o)nx7xgmDkx3{EYbeyiT|C^)dYtDrGij
zlo4unSM3abw=eg$-`P$Q@6*)}24*fAp^RA*>x@O?wviiR#=GEUyB|BlY}02R1}M<K
zIvi@Zd(J7de44&EVOX5gq&CF$z@7Ru(PQ)~lc3)49u4{J4qNIy?Sd5f`YW0ToZ^1>
z=eDcwA`2&%q1Z#eho*ddd;FG-u$ypnI;rBmXeS~%?>r}5WNZ=yk0%4=GwvsSVrDY!
zyM$L76`@xYl!zXGH}QzsNBy48)V01bZ6gPY_@7Z*q2>MuzyI595t#lI5iZ6J)&D1e
z{_Fbv3I@v=>NO-u0ZDFXuah2BrI)BFk`|q7hZ}<J71tqS;1tdS(`YCPII7t()5s08
z`hgP4#@nCw67-do=pID}@Rw3}aQe`nme}(rWJ2P6XH;&NDRvypOxx55{YVX~aCsF~
zraztr^1GZ&G4Fmz=^Mrfd1$LwJ}l5j)Bv!CFg0dgVpQauI=a&DJ_xq~CLa=-<z7i(
zv&4=h+T-&FRZ^?mIh$@1R>ptBs~?!~R^H|>sY-gA3zQrh8fGmat^0&Yt~*M3p<vpp
z0jP_;RrU$|3B}jmQPG{0_;`cAnOOaPw@6^iySIm8&8q5=$MlZJu(zl-Y}{4pVB8~G
ze*ta3+oRV}$~ySBf_JGEk&8flT?UP5)>eSixKF#QP9ZqRsUEes^*|iqc}5cfwQTd`
zis0V*jzauvt|a2EI=QJl{d^*P1owC*&w=ycV@n04bIowg>`#9jJ_;mt^zb)j=BL8{
z8xlqSghZ*P`MiG!iPVsMF#e-#l`8QPH19Il#>pa*pPlk3Inm|DbSN^q*3JUM&C+Dx
zTVzCJhlH{AZUWMIhN&`^sE`jLTSqP9CDMZk^fp3x(DmdZtb3LGO7XeCeLl$?4`WxS
zfUt%SS0Eo00|7N)Sv>xnPej<7(~hU0@l!PpxRX=zEb3QqV)4A;PUvS&w?#mWR?v18
z@?v&<&exE3G3j&_)G^doBY^i<QY(!tLFW2&<s6dGU<|&7)JPdwL_Z6rkfwI+&8_KY
zHBt_hG)&0dGFZb+j;Bg)RK%w^R?N96qQ`{*3{r@g*-X~>_~1qZI4@EPUU#gb7i-s9
zCF8@TmM^ZW*NcRoafs+}7~a|m&CF=nh6g9@V-OBdNuZpG@R;uS)UNrc-GQ6BVph?6
z1H!<e&zJ6zj(Y=G8wrI13q!dX?VPW~RXC;(iEgM&+lSM(WJiJcCI4a-{&NfTriej0
zXUr&COOn6GxBo^v=<nUbROJ??Irg>jvNFfCyzw~XcWLr)L00!#l9#(2N6Rn*I?+KW
zgDQ+e$-4=`8o-hU^I#rM%=TmGsARQl+zM6TZoh!d;?zrlf6xx7Wm1y`;-N4~nW15*
zNg=ylP1{>g1J_BcQ~TZ^dxY~p)Qs@E7!S>ul1o6JaF9#pkrD^0BknMDa3qZGko!*~
z&LChA5^N4Vkfl+GaIx=Jq&TLFUSWaQHE*PFmVI96JuXbNC{#qvsxvCy#Nx=-X_SQ8
zTAixlmoR?w*5&X0LeAN^ke9pInEe6i9JkH>_jUBO?t8W$bq563X+;azj_TK~qMhu$
z;)XYX4bYB1R3=HlUDuwQ8)%SlSUE6scxqyv;mgd*&R<{fi8X;CJkOY_g>rqZeZqwo
zh8PP41+7s&(O2usM`qQgaN>&&rx`_GB=zsRa~u(Xn^{W+r=hT#<w3}HqaG$deg=W+
zf&ZfQ>LJvUDCUUOo`&}Sc;~;^;7?Y$7uyU+&N}14M%U9b^caDqq$*9!1kDlIP{KH;
z=_!&@?2_Al+*V}S6Ri2h%w5=T4^mFS7;>W<DF0p|Icy1rrDLG*Tm&4=^SPV_*~~tI
zCcno)^dK(^QtZav)`vl43VdBVHf~<?pGaoFGACK2<#!U{eOPz6%~&321&(ZwMN`jE
zuCJm%FlYs0h!&aJ(&Th|Iw83Cb~$MUWTqPDU1RQf9OV<4x7AG)lduq0*$cIUc4e!=
zq8i~=#MoNFsx+!9_~}v^dhqbC8d!1@CdDfb{U5w5<G*r8f1PpP$Brpz>q_10a#;A)
zirMn?*)aq(GiVje+z04_JsL2?-BBSqQ9G#%A5bE^AeqF=`ji_lA1acOG4y=`M>#%*
zpx!&j(6M=v65iZroaiF@SI2xf9ShCmql_w<wY0x_DeN}bE?q_YyvE<FqcJkj`8VN%
zg!B6cqnlv)k6zzOOEDV*hdI#;e~Gx_CWm26d!WG$vr?4jeZzai#B5N%m}+bv><>xU
zhvl1AYxP?GnofI`Uc#g#!%G6n0|#Zg#lqrXl>+|vA9z;E*YvD&YADs6R4}&v)xvPI
z&UK^IyRCXSNdWd3!x31m+r#+#2_)O0OZnq)z059?`n0#{@w}ArYH}mHmReY)OjvJ{
zat(t+!npKP8u_vrO4-)Mc2OT!w33X+sO+<N_>FmSXlau$mgA}DS>;t1sGf2wG_FDD
zj-3sH@A&QP%|T}IU@DrUraix(ubAi4Hmf19s-v@F?fsI)khRP^6o%)0`{Zm1OUISx
zNQW86Qn4z-M=sEQAZzh%*I4gdG9TYq?F7<(q2NYnIrIy?;HZm5kyoSob$04P;zf+U
z=%KX6M8o8xqaD<VBpsr_v<XVIhQr@sFpIx{bz&MU`vNIwzU+UU@W1}<g1|t-W4%<a
zyEB@ZJPzQ}lJP@{2pCOwQi@Dr!M;1YN}(Rd3cv_Ri(&hDcLZR7>`ot!v3egCTq@WJ
z5I>NfHW{??Lc2A(<Fn&;u=sc|&NVZB9M{qV?5x-~Y2TZlTXxg6b~`XMMaEfA0bDdF
z?LUWZyL&Os%yTKj736|y{1?h=(`K*0c4dpl^sqd)XX>-5#MO&sDmPBa`fL4_+3$7}
zkfMWMXH)K48edy@uA*d`=xa#KS?Nv7ncrb9o$Y|U1SO5UMY-_-N32MZnNo4o(I1=?
z0Wq9FC6~rs+gFbc9~wi$FUQ<htT-E<*y40>tW6${?LUx3YLM(*1%Nb_KY0Y#pJz`N
zhKJC8$A{cMwttTwSe_@>J?z&ovKVSR8x7`C)QQOIpMhCO8#KUt)wTSiclx(A)5Jh2
zb*%o`^6Gzr%YPYxF}6P~F=0HZMEqfLwD`)$k<4tjbO`}Y{vspvO)}&+(X+tBy{vKr
zO^-aeENwn#5!u#3p=_t=yTszGxZPSr2>!jQ4kK-Dk3EO_(y@==VP8A+-(wBB5(cm@
zhhZfKMaCkLRtq^80T$T9qoZ<G@hRF*DnSjqk^l{ausCbDRLtZw6WS@lY&N}04yBxE
z)Hb8V$riO0uoa`{!Vf$zB(^bUHt%iTYa0BVJtO*e{g@8(LRPN{Oj$h9z7uA2OCwC<
z1Z=65sBC32=-^JB9c;DX^*o#GS9WfFqeXDItsJ$JT*d*uV!O5-uUkwob9^Eo7SDva
z-VOQ6yU*!9gpG=ve$-Bvc`slWXLQqOvGj8NAie<#byD0|#t?{qVt_JrCjfMIzpQ~M
z+F}uj?CJS2i{h|!hR-rfTnQP8Emkp!v7Nm*iGtrS`}gx}0_zXb_)4!=HvBcj@L!An
zJD183S6dy$yBr07jnf-38m4Yxjg*)Oqc%-U9D+__-T$T>a+jSM-npANq$oXmB?d-m
zN)8aE_S`Rra!0i6g>{cDB->+JD6(XObX0Nl7XZ)|Ir`O@1GOIbUltN9g@MfdX7K14
zPYaJS&KQw9fXd`P6pSoOBLDa+mN65!+XPHrm3orL0jFBZEC=f09NAeDrkp}^)kE0F
z(|eDCWV+MiWq)jnaZ@cNC?hP!5Epo%bS@c|rrsP6!#9iy4-FK*rdHeCSH^NrHUc;g
zt5ebteCh0Xz)Pq5#(v{jg=?r>5~sjsr6;>Eyy4jWr!^yw@^qWi{5ALc$Q_8-khhL=
z70DTII?RCwQ+}NmaU2anPemQ?xbK*T1^3SuL+a}bsv}BkW0;$y8*rxe>N_Y!R;o1r
zbErX<R;&L*d5NAZYpDG_p8V&KcfS$aaq4sh&ce(XN^#Ygg!eU=kEJsHU?opY6-QN2
zavN*GJT|ZhYQb74kmpCHYA!HM^pjjuFV3`(40(~D8dS~|Z~fId+i`TfOr3x-E@=%t
z^7AG?>m+<)9y#j^QSm`$WMY*;WMc^q(oJgE4FPmg9z5k^g3w*9CfUayl)*F%m`8E5
zp$q6rD$GB4vGqKnZ@F$2zC17$T_)jD%UdJFOJXaq-c*y)7vt@Y3w?@y_e*w!q^}yB
zHRr|ML!i!;_Z>4{V;=eCoDB}SyA{g*^yP($eaZL6kTKUJGA@L|NIgOs$nAP&qx(FN
zA6z^U64*V@zF~vRf9KVQbIyiiXi<%BPxG{^j81~<1x;6VIS}?$N{;35&X51dkmuaF
z^GZ<JdxHhqz3It&>R8x!H6q}fioqcl(`f7E|1ovuA5-@qEoi9xqp5vJ|Hwn6@R<tx
z0=){7g_bcWrCBG&lHO^U_Qsis{VPeLMQdp>ei|T21GUQ%m<pJt_S8!PDu#RC7`p}s
z>iL3yB<i+3^)=dz|4axkbsd5Ud_K#+tYfpb0~5XSOJ}9xJ=45xOX>gIRadwOQyhXj
z8WEM?R)^RYj(+tSd<@majOWIz`<o(6Lc66WhHg)y11qtc^?M*%85+P8J}Va8a{jc)
zic-JAGgKiGT6#J(!ESt+<&I@AUiBnKJ&=1@U8&+Agj7z`7!DQYV8#;1m9)nZ=fyz>
zXb)@V>Py+N8@5rQ>uUz*-F_+a1eHxWBV3*VkRpui+0`585XmBE^LQx&N7}C;0{%?c
zo{m62h;;Yvu#*Yc6=J!l(^o(UQ_bs?GpcY>r!<zFzMv(`Qsl{}+V1Q1SjDW7`_Ga6
z7xs%l0|kyc>NSmse<1jBQ5JO}Z$#20K9fYvUdyLB5mDuPfWvvEN$3YSMd>Tn7YOhC
zV0Sb-EUi8Vnn^>M!a2w|dV~nrvZTx%2vDJq&Klyth94A_F$ffoXp!mhkIE{_V!`@y
zQoRV9<`(!)o8H9lewCuMLBbxk%nWd1-CGNkXOs*u^kYp-Oz95f*&*8thy#yR3RKU;
zw2Os!D|WVvy^`gEe;zjETbdmV2(p^Bxr!yBv5F%c$q;;c#t6_h*IfpTEjB7=Dil)R
zVyj9{!)Sc4a0pc{{wdh)b9F1|YogEXRfMg-^5()wr8|rcyX|^%hD=GVLIe)(x_<4p
zno?qf5rPv#E^bykb#c@xX%7}+kQ^C84dC_ua_qR3+`S&&(npEM!F%COT^=wnQc8@s
zeM(g|Fs3)h;v?5pOW0(JpRD2Y_oEwu`^U&7Ynl;%S}cE$+kgE%5JP^;aq!~4u1AlR
zcNhpAra1T^mR5*uSWA;2gFhY!XUwv(JI`liE<vt0MC+bF&OMZX8YCvt8?k0(;p60R
zUQdOn7k`?+Fl(6-x}=5qC~gxKNS;`XJk4G{2eq-t5FK;L_>#^<>MjaCC_^v+l_MQr
z43o|`*d{C#V|k0fN9!dK7)xVwf5ag~$!A@JWv9qFY;fbZrvx9Rmmy>C^w?t(<?1n{
zm^dh*!<L(~?D)D3!6eQa{aF9SsX)q`GPoj+XMYD@r3mQ*7Xv{V8-2kb2F7V_Aj?|V
z4{QeaTp>C?)M__iTFMH`WJkJ$igzr`Y7o`P!$6N(q?_{4qt6UeiqE?3&@Z7P4v};+
z_kk89Rg2Ms%rAWWTruu5q#B}x8!J-<-Vdf3vRG9^gx9=k@pbNSbU~<BLB505JNy~H
zXG{MM=+sXC3L)oP8oGb?!+(x`H#A85%s}%4QjRjJUe%A2yfHwGLN2#@&uKXp6aGko
zkEsQ)<UJy7I26z|Y1|Ndh5h+dAA6jk&0wXwL^gvX29l)iPd;5UJ`vVbaSQ_EE#FnA
z#z#x=_Z5kK!3wadyHU%ny8^V5B7GNrOPFLzM<wnG<{8E?MHR0v9QZzc!m&wgT2ncj
zkRc}_XAhk_t5JuE@~Y-{%ZRBI4!1awkC*E#p;3@Z?Jsdx9pR;eo~Au*vXtXc{u6G@
zT(C5cr*It3{Hg|Ek@Qojdm>nJtAKH$GEZD^W5zZfwkpMoG#N|oIiYGn$rwB_pw#7b
zaq0YZeuhAGgB`^dj&s#^fN9}^&j?z$t6#+HJEJL(DX3_HhZD!LNmh<)nA10vfp)Nq
z2;m-33V7o!!A&1lTK7~#AMG}ytSJ%wd)dfH_Q%dR_D=M5|3F0wZFZnGZ|Buzp`3)F
z9gwj2_pyTN$270WbX=NV`)iZwk&MsG-fT3syy<Wdv;9V?Zc18DRuBi(?e0s(<<@p+
zk&d)KLi{Zmv=^e)TQaRw_R;*KTWU;kW%-cBTgPFKHZG|=fR>9SIa7l_EMtFE?d|Q>
zpL2*q22Rpka28}f$y4?jYyQ?&4@NKh{bV-!#=FbJ+NfEci^wdG=Ae!YYdlHpSyBj~
z`N+h(!W6qxgFPvo_nV1-BAwfaD_R+OSwU~|ws_5RdFGRfVK&NPcG%;Yp@$3U9RUX!
z-f&7gd2x2Zc;k!p?NgybIlkNVI&_Z3N{u?@js+=Z;g=l*f;C@kU(^Kjn&ak7C3o2X
zrJQ(3xfrgYjw-Qop`G0xZS;_fD68iq{Z`-aIj(A&1RlcTDq^|5!7>jmzyD88l=<sG
z?~ZFo|KoucBmw2bx+FIPpW&ZmRH>Pn2eAn?V=1A~QQ1~#QDKxL1w0;J^wH(8Tt*MV
z8i1lW--6L=H>Bf9PBljx$_6?j#2cB>G>zxO1}w9Au5o(sUVePy_YV%w@FHB<K|WG3
zp_G(Jp;82N>czTy%GsBbK;?cuQ#<Qc#MDqC^ZN3ASiF!UUK1SV;wUuyr3}MLmb<Z!
zY1P_n1Nhm<1R0@R&ZegZ0zuBy!~j)HqZ);QRUYJPc>n4dbfxuX6lq<h_&9rvqL+sx
zMf?ipDi|YyX-6{tl_GK@J;eJ3`_-Wu3V#2Mg>#RBvt^YUpXwU#1KyZxlMnWUp<RTT
zI1xW_yyI)BjmT_}o4it!mFgy*b5_U~lY6}I%IbC>VVQq>`O4zV+9<9eV6G>KsgZ-j
zn#@a9{r_a6BD(wWCM?9O)Jn&}(im~3rQ(PTzreyK$nekzGT;uRJ&0?nkRL9&_)|8L
zj1rA{*7;FSOS(sM3d;B#u{Isf&K-zrI>rsKA&oYHv`C1jDruIGtyMo#J4p}oTIe_Y
z7I(xE0#I+1o{+b<)PL48NGf5=|6DOrbVzUl6m{M%f=vkq!Hh8SrwiKCx0PUN50WH(
z1|KIzoE4VcWu}^(kN6yH=8NFjVNeUy>cRoa5YS;JwznRdhHPgmW};gw477KCD?kQj
zuh(a8EW|*bup+_?N%CCzl*m1-vj&MlVO!MMjWHQgrMzT6bktaTwmth(74`grpAK$2
zU&Ci7OwVB_Vd!rv3hikJxKkT(1jk=_aqQJx&EIpYK51>PzP@2ur=55jufvZAk1N+e
zP5*LO>#B%&GHoI!RG<8U)&Tr_<}-o+Q?Y2iRnGlm#c~Sa|7kR(VdJ^@-TArgeePhT
z*rpu@G(t(Tx7UZ7AeAQMfb^LSi~5LFio>}>uEB2c<wDHo^Rz;)G!f4CkjT8-wA7i}
z^RUObSf!jXF3JPlx~GChpEL8dc#AP!@H=ROZlsJ<VzQuY!sn`IoYevl2ZTsb3Y9h1
z28Cxq$w43;j*fJgV~LA`I#?9`q^~!T(y}VZuv;+%JgtpkJ{*l=4`o5`VIGpbgjR=h
zOHbu@rd+#DZK6eTW*P_=o+U%$I@S#DOC^OhLRz80^bpIR%M^#)Mal-|^3VLptw}?|
zw2clL&nu)m0o5@!k59rM6jh+ZyA?)btnnB)iU}IX-$e*(`aLCM0KpmHP6d31Oh@WO
zg6?ZOj2Da)wqC5?STz#wdpc_d@-?8<t(`xkxI+O1{NFwiE9BFk1|A>?DN6sXE*H`h
zLs4TRJ1IB*M{{F<Uqp1<mwjuT^xlReu<fYJe;qzUZpg50>w3Kct=O>bY#?Z%rrf%6
z#7K~$OY`W3xnsU5CTFzlhfG9cfXI@>ioawS$&IKocOd){&zeaK(%{>=-;YUa?;h}9
z+qM>EQzjfvIA=XT9K|7j>_W4UG<GVeN-_+HIBfV5^B}3SEU^*CuruiGD4)_U!yXax
zLaH9J3UEi-=h+P7Y~>MQ<xef?Iw?SD^<1Vwf=qMg-6Fo=BWIeZb7t`q&97ZEQ~LTz
zv?hvp;Fe4$ieg4W3`$R4I8h&uU_L({)S$)0OT6oLBUc=M6cXr6Ak!C|XQf<GPv~>O
ztZ%zWpt1xSaa+bOPJF*3P3c0+QPF(~o80|gwhn;I_PT3u*)Xgk{+`^}a57<QDaYMt
zy$1B~yo@VeuFT^8Vcg8#l68+IhjKM<NZV_L?-8P%O*!C=dat!!<&9P3UC--bW`VHK
zf9|@`zo$>u0X`%!|B{%|FXR`gTJ-?}wIRn8HQ}!fIsfIo>;59C5cwBA{~go(x5tMB
z23ni&#dgDIhJp)}+UIOQ1=-oY=7pv2!<=n{YE%23ESOB*CQ*Xi7&i@Sf4dZhHG)8f
zWQjdE?FylIycBSlh5C2%O^D6$xIQ_k%cP9$9P2yt6|3)5Wt;{aa-~J|!B5V<fBEOo
zr7k(XWSuW>DSjrxce;1`I+(W_YI7QIVvg86=3i<l6n4pOku#Om;vNCR*P30+k#Ut^
zpL?m*Q-QeR;;({Tdz=-N0}m*^MiURCQ7q;$2vJ|%j}wVbq#}i=iu+%BInUxMSi=ML
z<ngqD7Dy=dg26m=!<#x;2Y4SiKZ!jo%dd`#2jn=J$06R>Yx23sC%*7qw%zv>FDF;n
zHlRoI2XCq@0U!yzUJ4uG@QhqRFvn$U{q>MV)9+lt`gYm*>~wChp=sSKpZDfB`dJnM
zcka~HsHSYA%C$}<Qzn^<cu$1<oi~l~z6>8j>=rQYti8aE>Xs*S-PKsz)@so2&}`J`
z%g^u{aN^fG``IsU{~NnwQvH!3+CMdXQ~4XI`(H|(Q5=R&x3k{+v=!d@both%?R9-{
zC8gSYAKAt;x8va$kGBQw=q;>IY1(LnP!fxxB(_AclNMcb?5BD60P?WWo#FMz!L-dh
zV)H~%$NpO5v!~JdfNy(t!~%;f{$i_+O2KBQBUr*`m_cTU2}yTfJ*WF|qh=*_xqFV5
zy>^}ABE9&s1At6lw11+r-dHE#w6-5MhQ?<q0sO-50<&f%DHFdiR6uCs)(cAD?<dA4
z)F#~7`>_a&$bO|G1h#_loGiWxQd{{WCGmam_?ZCljU<^%79Bh?qK0=#vh~@=LA5V1
zGlzLN<u(3*;xdVw$R_9UVGmYn?%BUPAJ8?O{6%*<nO=9v#h`Aic8^wm=gO|x-O9%$
z;#`3$%ie|iU<`dZ+O^SqW~{NP$+Ep{f!EZlPLSs_c|Z{x;4p%H`KLhr<|o9Rc-Zu1
zlDcA^=U-8?xc2ssm|XLceC^<9;hY1LnX4RRNz7y0bzYdWV8oUNOg59uYz=pRPcxXb
zf1Fg~(K!FlPC9_0({3$Tv+H-~dRw0Kt@PgUr@AUgdtSNNak~pz@B~k7p`{9_K_pp`
zksBtpNwv$9`dYFx4rerM4|h$)KBi_G>B@IA)ww_IxWXh}mFt%D%g^scqQfrl^;f!2
zBmbT-ry2sVi^qoYap(=;KEf%drc?}<wpf76HeikN6=A)z1`0zo%q2rDC=j$bEOwgD
znorVw;%JQKe#A}j%2P3e)y6dMYd@4b4kj*7m3<u7vc%UOR+$VSK03shKWm0(IX{M3
zw;C)gi{iI!4ans~5p)EVA~1g+Ktld0_n_c|+#B;6-BdCe0lGU;y<@^RVBbk{6~7vu
zF%shhk@xXj<8f;K%0yP%GH`G7#wcxX6pwB6w6`d&uoTB~mGzgImh-XtSssg=S(|I;
z452#$;A#nIvT90cIA?MikW92`(CyX*I16Ck<}+L)-5q64Moyn4##VFEb^W@kDL9z<
zvrsny-9^%)Q7$kt<T4^1Cer-$|B`D?f6%Sy!L)|_Kcd?>0YAx=vM+q9gH>-grYo;8
zJY`hlUPsB#EBnQ|>!XkdTA5+?e3lYw=Fh_9=E1UH@+7!`5nG!3ewS)7T}`58SykIj
z`6N)Iod<hZT&@S-6w{qr?v6-&cTS;_zRrg5#0QCYL?tf^c-P9_Hnf55$d`b)e<>s{
z=JtiHt^L!GTp9t8vl5qQx?<VUTw5@O@5psD<Gak|)4kRXBmXCE=9!7b)qESPU{vUZ
z?S>k{o#V9a$evYvqi~ERAApJI&+cF7pG#SMVK`iv_+Qbg%8me0`Y5Gli~|Y#t-BC0
z_uUtb+=SVX<sP6kta)*2R%PXlhi8<0yf{HMLiY>Z*roGCmbu0~7?7^s30`Ho+=>1a
zZ2{ovQn}AG9?Y7rY~Orobh@8tbiY<QOIiRPIUtyDg@wS|O1fVVHWf-xajM*(>$5Bh
zjwt1bD6QjR7Gfn>7Ci_IjoRF|d4*4uV-W|e<%w5m%}?mMinW0a247E@fvEaAZLh6#
zb?D;xzqDch4sG54pGqRo!V%Emi!Ddl)k7iCtCQ!=FxE<nO$z{!?rwH{4QedO=`aEI
z$dlNq7(M%a0C}R~lJt>wO24Wld#p#52DsR@9ed)m&d0_JWr+g0yhy+{1N-<B5`ggd
z<%#64xIA|`-i2&0QtP|djWp5F+U0^tgo7Urk|s9C9&bgk!vG^n8=c$YHBbZrJb@Ip
z@Y7##!ah{xAaG82kY9*TyYbA^*|3Pd?BkQS-aJ;|ij0xFM<)!)DTg~dMQcnCwr1s~
zP6AKArLjRCa)0a_V&r0%=E$Zya+)`i)EI_l>kI9F;C<h=Je#7@F+0d<Ti)gBVgLv5
z+;g!F(9P9AWEcQpnZ5GHka!eK+<GH(ScP@0(|b<RTsCiIM*W^=7}~cyH>ao?Xe%`~
zzTr2N@m}pZo&}}o^sK_E&Azl3(VX9p>kG>}qEI}pO27Gc5&>zrDj(gy9^W_ef&E@F
z^y=ec87F$~6<?7Ab;j}LbEYQvPB6T`jgM_x{`e2Q@yAbN9Dm~807P!`-(B}V$>|<6
zrZRDK_CoX^^2~`3;Jq)^nY<|kfe2Ur2i9xF&y{4Hm`k+YCwQ<d)>dGulnu(l(auF3
z?LFNY;1gVDeFL0jppAZkz8w8{tj*qme6YHrRIjG0RM2j}W!~#ZI8j`8VDWaPVjKh0
z>8t$X^zJyTc0#y}!xH^Q_-=#6j;3ggl=?#j^YnAhB-=^``O&0trX+AQZRqO7;(PFt
zT7E%IS=B2?JI7KOW(1(8hOG>lMQ3(Xrlzr&XQ#?AgZ2SK_DZf@+yKPH=LO?yLb)%e
z-}%CZHmk9{b%EV_-COfj{K|O7q%wRZ+hS>k(OX4WR`FYu*nEOew@7^IlsavFZ=iP_
zNch+7n$ZEN!{e-@m&-+_x_#)zj7QNFgN@Q(xXTFIY8ybS5i5DGd2+sBUD2x=m$!Z6
zpdEtdPY?EqM-n4s_U0OqCa5ZIt`ynv&PoB*KWjkK&g<4CZLf;<zcV5@;(yv=ZeX*(
ze};4n;HEk54$oJU8wEQ-DNXemww?ljjFvy+@|D^$N{B7G#TA*&BjilxHD3jWE6091
z<BEY$wN9IpTZaVElIjG5Snrg%n1{R$5DC#*eJa%Z#K<=#r7I+`@HCpdXqm%3Awn_r
z$5Mt0&x8n-J3c!N%Y4r8?+&Ymk<JByZ#$BV<OjeK0L?0KC{=t}x-_S%RBi!5?KFAz
zr;PZRg7V!E`=wGz2Lfdbwpbf@<pkH?2Q~6%kAiSYOSL75aZMmK77EkMtMsS<84h0m
z;jiIh>jzvW;UUF>U3AFVgoibc=YTWu&gw8~l}9khY|p*%HxV4`_@4dL4z}^r#{wMd
zq8@Y;7lVW`<^|~#2J^PdK%CCrunena_x&K%CK?&ndc&202-PNZReI}5{&z)Ba6HpH
zx+fkgrb-AXw=N`%6L0y2;kVIi54aBHXt`3yGt1BBp2rpci-!eW5JvbvsYd<Mp`zyR
z{`n7N(EE&{=6wu%2Ho`xMNrlLX7c>2%1b)Kq|;;4@ImakjW=$uJWcjnWkklu#Y6S4
zcI1e+MV1Vi;kWcAWQp>p4=gqORxUW5aTl^w13h>=i|em8<eyn+!z$VRB44OUM!5Zt
zRw-B3t(fJUq^J%S7O6?#UWxMqk)$Gm`Hp+!CPxLW1Ah<#gLjFufEO0^47%4E2cTUO
z5@d_{2pL+zBG$gL7;a`&){z<x#M3ZVx!p9Z!CsXL@DywYsJ_QFnHY2Cr6|~>V?L(x
z%l^^yjp9g2XIpmR(WLde{!oH=Tdsp^F%4&K)5GDPZVz;VCZ01v?MW3Lg@Ft$F+j3=
zl`P9>@}Xn;i^udBFliaT&frm@#I?TAjXr^WobIX!&nn&Bd35D$u7c6%p2_;x0CK~O
zoW_!l$JO55kRbs1$3*JF$L_^RW}BEQ|Eb%!@%wndr{BD9-Xylot%ldFQk|Kt$N#bQ
zzuWJi!nEX_b38QPGVSjH?7!8}Oi(us72yhKnaaZ9;?w@hSxo1EEX#e9=D8R4@Psz6
zZIPB5d0jn?KPL-af7o9BWyJ%<S@7s&MrA*}kY%E@hzs9!c)9x1jOOeBY~)=GdrmbK
zA(B4v-js^CHw{$};xi{(sGS@o*Q2mIOzJ468XtxoG+Zk42PvOu2{G$Ta>g-PaEcp-
zL4nczY7&W3b8@%_jZc=q5|im{+1X{{faqZS@>6?84F7<H+(5V4eaVbMd9UOF&r)iC
zvfZiE>jUhQ-?FbNMX(>90U12IgZ1<V0jyAg-vd|^?KPuszix3bN)H4c<0Sb$XJwsD
zc(OMk>v{`*SG?szadRKltf0R#rQ+vrkyNSD-bOdPTP6R+mqV9OWvUUqKpU0An!oTB
zaVKhLk+lvXBgy6PR>lp0zMXPry|Q5)!xyE^B3@y3!mzmbEyv5r^K5QmSvZDo&IPoD
zMr5r{Y(cQEHTn02X|pa_r?ccC2E6iET?ewej$9TJ(#Uo32||s$%KvS`G1C5ZgRYz^
z{*m+5vq0r|Tg+c^^#w|Sh=h&~kkUT{wDV(q<L%)Dxq*FS*`_T^H+zW0@uYhWE2qqM
z0C2XA8Kxhc6Ux$m=v0vD?{Y!=2$MZ_k-z{<4&ZsrYMg~areun%!N5yX3?7!b5mp&e
zYkFW>A5_flwMeivDjseWy<V!ECHAgq`G_va;4219{Gt7zfAumiXmMxEx7X9i%|2R^
ztfr?wp8o5&L;)e$lys4+-ltv?%QBlo!15(GBeqyaF+j7OSUHUl@=&ccq}YCC9g&+U
zIQ1xK{4~aR;4J>aZY_K=a+!K&YteM4f1$C#SURPCPr3%y1dDCZt}cS@m)*?R?~Q|^
zG=g76sgj&sKEr@?&ek(XBHhWpyeO8@mz%V`D~xp4YR-ci(?(K<lz;OH{EgP$-qIjG
zfokPT`?4fSRI_R-?b}Xg@P~EVamH3T31I(TO|uQ<Za-hVD!;Qr=l>Qlf<l_+ABQXf
zT`L;@F{F*4a_k>|I%CQBGS^eEV(rSi-05`U?IAmZ$>Uyh5E!>%@CND~&CftYCrUd8
zz#oj>7|hpxP?y}Jni?+7G{bkIWT&q>H5v*4hf=Z|*vGUuy!M;j<D_yQr}NHoCAQG9
z){3JXaom9oS`2R~VRtI@LGVuvQy-NG_j;&lKQnAPiT9C=kT(3G>0qI$S53QM)}jAN
zM3}LD#cJ8$)tu!q<dG8!>P<-~?R8i|u@v-9#{cW?z5klpwl7fSa8yL3N|7c-q+_Vk
z5vc+qy@L=49YiG5fLN#jL}~~S1p(=X-cd@Z0qMQ>-XRbOFW!6ZIY+;|_Xj+G%4hA!
z-g~V%#~gFcu~vpmy|sLIvzJE01?sOnn&y>=5$Rr4La>V#heV|yJ1fj~fu}BKCvP?k
z{5Pv9YIBYPzY!O@>^UZUHF~ERJHtWe9EJ}rJ29Y}&-yx<G?Qj1?PyVreRg20n^HDc
z;LqY`xI{)(8f9eWJXw;NAwJ=}X0?ms;6u8W^76^?Q6?Wu_F>l{<Hx<UX@IGaL&px4
z+R5|t!$rY>I`#G#*)ZMN{zByMYDWAyP9#5t*-n^BeWQyC0$(qY8wM(jwiHf%0p6=S
z_Qy`Zk@ky$;63M~aszYUKX@c^CSGbd$BI)FY|vZ0*VUyj?Tt~<KcUCP=!e9Is?W1l
zOAXiTyggQ3E0PlX^z~_O_h#-IU{Sbdqv{8e@3W4~T@=0673WEFioK(IT~AR&dPyDf
z()MsJ_Sf8fFV5z~DT&=3M~e8?@6uJ-AgEu7ik7x6ezWWKZzZDd|4>XYX4`D6wmi!@
zY@c>--uj2RjSpGHQM7?&XIPnK<GYQfxr9<%1mu%6_LS$N_@H6#kLVF<mgNbAc&ZG)
z_mnKO?-3l~Do|TgngVX$K^1rTvTUD}d2W{^+#O|4AL3s*cswlpCaZJ3iRXQZ>Xid?
zO=#%7?i(+iym~n3UA<-&Y2#Bmz}ZZx+H+axul2fxKC`G~8)r$2p11cpKi_nLE<haj
zgiK!knOx$Nk7o^DOZkV}Z1~?i`D!9oRU^YuaLa;b&hS0?Y=k4o54d=sAgE?;wK-7v
zn^Ppx{amHaiYn@VYw`YO#$Vr)8wL$2_+fHL@D%|8zyDMO_Df(SO9kew9me;|Brk_1
zvwnz}YzkawrHC6z{Zg6ZCGb>cUSapWo)7bj(P_cnxVc_7?^WAI)hDisZck#4wHBqa
z^U8tBp(k8Z&F(^%7aDLX9}h{m$N!80{%};wzNEM}9LY~Q*@ot4^A2m3yROp(F5(b(
zUStS0;q8NTbq#SxBRc0BNfjoppfThDYyD4mqmkWP(GuMP1Re1LftQs_m(0D>r4OXU
zD0rRe`76Gryc{n&Y#<gWyNB@Bn%fsSX0WwO<4Yn4hE(fY&hT^fT%V-FmpThKv8mmr
zeLdNzSKn?rz4D&hNQuyN>5H-6u58!%GI`pNRktmoK0f@K1Rp|nQ{kW{8qKtxwM2cp
z4|O}B(=dFy81GNJs)FN}ay5i~rHq1xS`AaLBfKIDZ5^Bk+HA|h`DKtBlgCedF+KOj
z2J^?B+bfJ8IkLF~)!f*vvefyrY2Mc2NEsk6doVA;z9{kg7BZp#Z^0h!{IKhDTLD}3
zmQiD_{H<#rzg<qQQazMX!|!qlZqR?wVEkbH`CGOcB+cmX&0qs_yYZ7pUIL}o0oTPo
zA&JVWR7-uQUxnTQy(X0Fl#7r#ci){y8|YnnkpAjO?c*dG?%mZ^Qx7Ml;5movYO&sz
zme%uMs}?xvPTx0`JTY<tYMF%c8zOxuy}smM7dy%2Z7E}d*NayKOyufLz^ig~FNtJ&
zqJo==zlRW1M{&dGIL21>u236CHx3t64KWNXHpJWyIX21dwm6(zOVyA1&_Lsv5RiiC
zRy`;{OR&{haV5tkt6}W(?9?7A1PqM3^8-xYg&Lx#p#9Wtck{kJ^m`(c%oJGk<{Ial
zp!$8$dz5I?8vQ4T1Upbw&35llyN+6Qd$U?XduYs3j(r`3?^?k3&HU0iVl@G27b}1W
zeZJ<1dgl43A@cU+4Oau18vp9`B!tJqcBsvJqdn+-q4<er1%MDoYIYjThA~<g2nO@_
zLKKBkRYB?3oz(4wI6jOl`im<F@uvbTh+f3ngV29UgaO?FJ<wSw?;LdfF^n&3lDMaH
z8Hqo)_ZU?tSc^1%lKjA|xMKwyJ#}?p&7h(F8n}?-{E64lb}jPe8T@hJCBi$GE?<+o
zL_qfH8#{#@r%<SCifz!pM*sRaJxBf(-NZGP==nX!JE!19IUkD5U*(%6*n_RNd}Ggi
zQ~yZg@xhQ6lWuw|TEmK=+3rYWG9KOi9NhA~UbHG|7F1I+=<gVn9=BRCDpx8?zXl*L
z#QME>kH#Hd69GQ#5@K>J4!}5Lp5=5%JccJtEJ~%6!!Q`?`ckp-cE$S+>aK0i=#tm|
zxcl{MMTgkw!RSi`bdKox21r<po2P?kVKxrFP)k~Xb@19$v*7BMsWdDwt<{dKb4N|)
ziXUtDC{tTdfpy0Gq?a2vC&~u<#>aB<%cZMu?VRabL<Avz$BhT0`Ei1_YnpV{v9-<p
z_xaV`dZ3XeMZ5wOXx=0{rI!06yEkUqJNpyul)&5+#dbY6XK5KntXXMjYMt0VM2CP_
zngFS_sOI~Y2|)C4vb->Gii8LpTZ^g_&=3Zu6$@=ylzTd<LHx5%r^PI|_xc<c=Cj=w
z8m>yr@(%jfoL4Qb)t}4QpSNzk9szkQf4=h9y8K*$z|cFdo`P7-g-D71HTu7W%VL?P
z?-qGg#$r}sB^g)O@<n3>IgM1EaMeFL06D@FX6jvVr31jxloP=M4-(vj;egV~dp?{f
zb8{E6!NM&_)T12~!gv7w{K3chM)QxDoWYh7P!tWMD#om-^|)i?Y@ZibL*5eMC>6o1
zzp~Zn<1})NS($2j;1E}PhL?4DD?ztkP|AQW1x7R1eztgYljloq;faefTSunl>sLHt
zE@Rz{G<{NMox}@j-RoRKg%*6uQ7K%GYwdC|sXkxxmh?1oY>`#eAz~RC<~A`0H94vW
zy!rvHrvmWh<qhCcdwD(1REBWqv79{2Y>(n&KQK=H$7~DFh(~%Dh?=htT2%9S+D8AV
z)(#{(*s{7BwBoetrn(!qoPl}{NNf^3kWmEmOfrtd;`oE;7IjD6H1=@PPn9yM&??rV
zl7cMVVd#m|F8DX~shok8miD8%b5o;a1h;Khs(@`b<S9a82z*x0C37}9I4G({h<BC0
z{Ub*C(x=Y(TK81ie~<q63xQFpcgmE|*_(U86>LXSYgaytGU~0fURI7b59uw>W;)M3
zfAY%ek7Mf0may-FPCAi>kCdXM&&Ds2*d9fxVlo2^O&ldgD(L#oHX4@<?<F+ECnB8C
zS`TYEjiDdM-uE=^JsF4<4jb8PtI9hqY~uIeZ`>nw3*O0JL|V=751COsKbJ_gEzvTK
zvbd^ZP(T{{Ezfzsdvx(UNAUUZ82yOSd6@IQ@<e1sHXUe`_~>1wR>71xmupIe;b13t
zC3~qoynBNOf4DBEa%76=%(pP?(wk$!f!_YruR~ibB|mC3nCl)`<qu(csL6d0b-4Q{
z^}EH5*KB4>BO&}mG#*NqE=g~xDm>JMPb)Ui(zI~CQNLBOJXu^ZtOo=2*R3nF8i8rb
z;bqu^v`!&oi6U6>RL&AWl|LyPX-YLGPfywX@^vzUW3F9hhYA}KZ92&#6wxFm;m;Lm
z`{Htxf>8T?AUL<<5b`2A0zEP+U|P45>*oPM`mIS$abdyT@q0@Gu`yW!n3Jfjrfsw3
zC}H|Pno0gayGw)|uP({Mh`idQSpPlx7aSr|zum#8c&kkKl$#y6tRYF}S#NGs3<+W!
zDJ_^51uSx_1M&^r9f|2Dluw73k5Wb%`aCX$0h)G=vTGw*wxC-<9<S{BG)_A1&3^IN
zj=CluP_9+)+g@~t)x%BBpVc|$f}`LY6jzR$&W>%q&;{vc9u64J2-H#R>7Mfs#*2^0
z(MjeVj}sBb9<vU)<4=K?7Z%F<%q1J&7FCQ?wH3Wti-`_(auFzuFV_%GFY;Yk*^OID
zP=Ds$p(s?oHJ+_zlB>O@5w3{$CB3_YKK%!DhmWx7Gv}*o%IyKTsPh+I>JAH$p&BK(
zsTVa8^_m4FShH>3!2p&WNj4tYbk>6D>P+3Ao$hn<>jUVI7o#=Yi|PHX`&#>-u|#IK
z@Tf`w0ax8R?=%sStB2W3_`;X+&>5jt>}ANUC<Tza9=ni`<WfWG1C;t%JL<C=drl8P
zqj+I8f8fQ+s5AD2H@*<EX0p&$>%Q>jNFW|~uM6Xj2@a6nfA~v4nkUBxB=4={dlv>I
ziszPL&S_&)ROL4eh~u#v`i!H_e`xztxK`A!J4hD}O!DVL@!=l3W_-2H5d9=|!aR#)
zA%4{W`AMGtV^|y&_YK^jcJcR$KuD|L*}!61&V0?7$cZ02Y2o)(p<v)ak48%PQMFMg
zI=Q8Z?|SC_BoB*nRB{5bt#M6A28IuguQ5pmeX|?%|I=PTK<rcmsb79bCz+&b1Pn?O
zW6a<WG?XfJw(-tjkm2g-)P;?mDI71B$FC0*#;3Pv#?cSC>W%I9pXkRFG9B#<EI1!V
zgOF9LXW+82(hc=<#QBMJrH5}N?|M-Q8`Zt%9dGrC<FB9;1u3dHt2;Tg)R`LRS@|d{
zIt_#9@<~hAHh4PJuUG8S-tAsy1%x;8iKwBAnz+jr4PHv=BxGbVcC$ZxQpUpuo%I3H
zmS(A-M(-x6N_HEj0~1~{#0a)2EYgKqy_6Lcfhh={n6FHAm2xNdn7T|F@wIeKBJU;g
z2tdy07rxDfFEpwpbdY1~oc1jw2e1FzaX3PLx|7d{6!*VJ|L0E7S6dlanM#K;Pi~|#
ziQd7z{D}c(@X{hAi?;;0l~B<R<_7Q77Sd#W{+21xj}EBct3C5iwu|ugp19%(2(=pe
zH2Su-O=k6vx2K;``Fq4$&kvTKmK(4RW2B0U>1ELnrZ6^hu+9<%?mF#VfX(=F==%L*
z275MAnx2<C)&lvO>Qmh0cV6!ue<SKlB1283PsY@KCd+RsNi$nx6YT69h6%DHX>q_F
zfR$WLTf~mZH5TKo2x^~)5=Ev|Ipvp`gO1|1&Zjd3#fr5e>)osTybgj$dj}&2EzqKU
zMO`JOvni!h6QeDSi-k_?vS-K0-Rkpo$qhP=+M*(twYDY>7*}Sf^(IRac`;Qzr%<On
zW&Sc}<`ZGI?;5wNdB=QAOP~U7Z)5T`ZSs)^vJBmyp#m(g+tw;*LB)9L!QCvO=>`k;
z$l+9Cr8{;!t5!kPQA#MaC$Co{wLwejhCo&pB8PpmO5kkDoNYnUnL6Io>UHX{ac2vm
zXHhF!T>pw$WQm*v*K9aeJ@&r-d-SiM=0`y=s^&=^ypd7fNmb^X+7Jm{v3l<l^ZHfQ
z8Pn}(4c5R#*A4DE+d?z%YR`HJC4r<B#Veb4r&ePi<SrAls!ks!To_N1zPO~+&l-kq
zl9hjM&3FP><YYsDNOYz9;~TDuG?&QJ&cstUA)52AWZvddgHTn+IS)Je;}T<dd2?{>
z6IZ{uep{o9UVFpwwc^=p8cK>UDh$(f0No>Ax<QvMO}Yo{qEC&uau4kVdZ^{<94&6(
zKFrxHNw{5pb?O(`6-#Mb=;h%9c0JzotT&C-#j~v9g*V{_Ru56~J8r9K*&Y^7G6{Bt
z;g20v2P6<~2>(wTyypfMvSCj%>x|6Uy^Iot0v~s<v(2b8d@4z?<A5Y-gjuhWYj7p0
zMypj1@`~V3F%~W+x4KQ)Q{j~f3Te9`;=dt;{!$<2%37?B7S^d3;-Zd0JRSv=(b;@}
zF>dT`jk;iQjKlSY=3d}zseoq|-aD0d3uO-;5YPp;T>ZyizA5~q;L(LC!51Umw)0+<
zB~w~7z+2fV(<Jyr`)eA&fq;0*aq7or-L0c?hjDKm?DIg4>Ri#qJ7eSVnt>5fI>4a!
zcbpo^!VAPj&dHcDbpercnU+(8GPm;_Nzd(W1U?SYMs==pyd!Zqx^z=c<Y-s_^81Qr
zMG^l95CpXs{4J|gLf2z3o}tB0DAK2YB0@W&Qp%2Cw4eDgBoz0gG$#$?+n4XL)=J+U
zW?An4Hp2NN7YNp;1L;F1tidGSLm|hvwtXRdlhQ|@4!D<HBvV&lMV@Y%$ujPB$@R~(
z%Q}j0I*1gmI>cd^zU1JHUofJs4OY0H1)TOZim!#%ubMrsuu`=Nnu~W*y_rVWG1?mn
zB6R8?*ND%^7;&ONf93?K@3TgS=VM!ifpm83c1SJBR4zh^bfb=wUdHZND>!~_Zour4
zR8R`G++3fHEG)!WIwyCbM9)$szv8tH^QpUct7V@Z_4Q3ifCXg!&WG?M@BRoOS-bin
zc4>v>e_7Js)Q=2vjj*IAr}r&m*%B?Ecd4Z0hCKZS>`Y<o19F53zPafN-vl~W87Lcw
zVr`^gB{I2L7F~RgKG$0GDW@FX$k!})$lVKthj5y`YRMbgkFFeBvkH)ZKPyyJ1)Y%R
zxZSQM<rt9_j|NQCJ~q+yjA1FmE*YkZa#)c7o1W2A%$4j&+Y!#WE0g^|1-RfG<ugf#
zWA#vP?;mQ<VKThpbaWgyss*JK>Z~1+Ia0bo!{Ae>U|(XQQapcAwvhwBX`NA*>HBF=
zo!`dV6fgD&+J1aADUj7|zI`wnY4xHTQ_!Tons~c4I2=%`X;yYvGU=Igo?T4SF>=J8
zu#`Gk2-AG28VDuiYhM)X9T4pJysdb|T`Y8>TqEhyHu!|{yDkEZXt*5;l8P@HXacCX
zt@kd@!Q_QwtY|~cq=-A$b!%5A7p<pALcg271S;P>9vze#b$c>%G~a9GeX=v5IApu-
zBPWaXtVYNZGXDZp-=vA|#8%#8lDJ?|UcD0i?(Qyi*#77eg>(Kro@@%hbAHZfIYlx@
zC{?P0g(Fi-a@k;&r4hj>hJw;iY6@Y1XwiQ;m0hlRaHHARI^7G>IZBNim^|@7un1B<
zDbB_f9g2>aNH<@f#2-LgO_KYbd#1o!#qguJ{TK81?+NIHL=h!N;v*5T@(+OE2StT@
zM@Aw#+>)=pc`3mpSOP+Qa*gjC@;uHBdT>WUhytMU{NsmNg2E^X7U)2NfxZ<9>UEKw
zNU8;Jp#SbNaCvTaz+~aH*^$~M)9+wyiZf~}MSw=O!f;aDyKD5tliTRkq^#gJaK43O
zuh)TEnuAoZ(qqX|qqEB=%sv|`$E4?r<s#9c0^uBkFWjJ!_`PrHVb-7}dG=z5^Da?x
zb;{;p<26DN6RZxqPSo>Jd=26IY^%kZm;!+1#g4j=L*bc_f5Z*pj$Dei{yA8l*`A-(
zmb{UVQ=L?)bweVA7$+p#Qh0FBU+a&b6@$NgVOocL`aB({i+O$lYCU7UHVi6v#UJ>k
zL|HK0SpB}SfTjQG-Lm%PyYCqC3q-LykOPOI)BMs%3RZHVec(IcOaj9dML~ayr5H#R
zH|ox90Ado<cPbJU;w+Q1ERN1BH5q#<26rgT4VzNC_Oa@i1n1E;m}MtYGSo0CJzIBz
zCCn>ETbLgXEx9gEPO{!;fK4Z?`(00pxbktGjG#U2)2w`A(#+kcs2y;38gfw-H$&Xv
z5U<DaxFEg42TrYhl%&jorvf+gW1~Z_=_fRb6`{XO6rqRlY6i7n)7s&r+Ls$oytiga
z>LD{c7Ug*idkc-Eg|X>d$)U}kJtm~mHXUnA+eGdG4f2;JvkI|qH|S8KGP3vxBME(q
z+U~+2AN6S@rY$6<(e-%Fs8{-y!KxX1N*25_&2?ka=!jlQv8i9H*WQmapg|j+Z<?hS
zJ&;{{od~I3;+yN?FBIq+OL#-@OBKLx5f4*-iz-Cdei{C6RVQ8fj%ceH#tJ^c<<jFG
zB*&g=h@F~TqMuoPNsRq*rRuB{8}gaM@oTR&*Q5bZta13ICv!0~C9<^=1P@gK&@fLq
znLX=8ZO=elAk|T>?jAxBg;lrnOT57z2hBppHabp@bSUBU?IBy#hknHaS~j}H2QK!^
z<X|!-;Q)6g1z|0NZ^`aZ-LWy9R`~8mB9$BW^>;I(L{J)%N^MT4CgDR9b)uUka`?Cl
z_#PgN^bI(w0|O`QZh0Z=zN{F0%ozcQO#&8_NArwt#8GXVR=Q=GmNGZ{<K7j<+9Jly
z3e50j9tKIXK@CE!tGnVQK9PEJL9?B%tM404lcK1-)kkkQo-a+G#|%ZqiNMPR<JEnL
zkWd97By}8G@op3Uo97_+G8jHaSaZO*U++NgH%33flQo-st$OBTVc@xSVM2=rVevDV
z^C53%T#NdhVo$dM-qE<<mcZ@j61e5j5&h#A|8tSR;P`KtD>Y4Y+5GmeNh!;PS%SA9
z@F>wOvDszzO6!%J&GbVS6ZNJZoHwRr7?62mjj3Al-LisS9EqfMQ`Vx`ZlCa65>nVT
z>MlKx_<5d~q~mr~!9kXce$&QSUO~kwsjv$j&ZCB^wY7g~z-)lS_Q+shqWX9Z*4)b{
z5m4G@czL7oxFzB?r{vpj4Gpc$_mZA~dY?`x0z?|sDO<k)-P`^wuAJD!rv%^C1oB`2
zw=QGps%$S*lAZ-~ar!aHf+kWi+tFZ0BtAcPtz5sor;j=QR)@O#bR|v4o~Eq6%S46w
zO=fAMsJFX{tBxXdn)o84Mfi1VMT;u}#_7jo5b42_rG<dA#gyIL@+Waqr<36xOr{^?
z1d@%41H|AJ86Bd<W|^?>U4lO_JE{_?s><RK;h@EJ^H3*h-s+y3d@Y-BBVjk_<{V^6
zy+Zzf^ZFlSCw+}iJFY%V0bCFayuMtkc;48>v77HgB)hu)DA7`El#tXT*fqXf+RbAG
zL6&ZaMq7zVn~jUImVcqMAj!ds)*TL+@RHEEwTPPK&QYIBl!s)zIxuqZLnICq?*k^3
zaiWv)RcK!0Gi2k02D-PUzc}aikl&(=e&j7`a;vW~?j#G`gcHVZ-nsG?Z3cn1Y*iE)
zWIV52?&gdUFbua-(^~owo%RRpU^c<ru&*3z+?R$->&BRK`$7d#cC`wQvO%M^N_}9e
zzN)d79u3RFVHgr5ENS9m5(EQgsS5*@B{yWZGj<nj598y;%GHJUTq=thTBPmQEg9;I
zqtK)&d#y{QTbl5(rMZCR1GZ~6yB^~UC((*R;N;#g+aTl(Ks+6Xq7ZRe<o@j?&{9Wh
zQHu(#knNDY?CuKxo8BA0Ac)jce5Sy}>3=2oAKLRb>RaA<dNd6gGoc@&@c)8tzCE})
z@~nipFhXk$f1v4*6DG43nlF1=cHOpS3}}pa)eqUH9vT-hM!lE`MLD{+iLV&{VTc%i
zITfx|qrst{R6o$Nv^epoo}*#}HP_-->*<7(3C%Rx*~yn(n%zsNKbx;u-S;K2rej*?
zcrdIJ9MGJ$cmj1vtKyjEPm0p3u?Xap417iSC%+Tmd+4O*^&9)CC+dQbGP;1Qq|>v>
zj9RR@un<Sl4qFntb<fV@J%0Zl3`aC_+gKPp-xYg+r9G3&QLZn%SFNKKVz1u;=nzg9
zsjS$4c|5s??KoLw#Pe3&O1HguY;#gsPRo5w3rtnvz%@n^K*hiS?VNu+F4!Mjae9RR
zeM|}J1T1+)mK_RbHfqDlZPCd88p&1znw>&on=_by!G~cZJZ!Az9esEDH|_k3YYu-R
zO#0YZrg}9nXkg8+HlGO+YcjdIPk3*XCjoIgFO`7YIWp@S``D>Wr&Q5w&f9u!K&I<7
zPkc*%a?Y~CQE7o={P622$<o!j=Jv#coG(nWofBPWjx`Kw6Xvc%Cs6R#j?G6aw?^63
z>ujmqfCLxZY6;@I1?t2EXd-=jEBC}Twf?m>_{QZ>%d+^^$=NkC0ZD3kA$kQNa9#wV
zz!*bMKe2UEI(0LoecW!axUp4R8Nj6jty5-`Otbg4X<jQ%C&7Q3V9UqYn&}!x)=lyP
z(d|m&{)b_=+t+6)7sTq2g~Jb}7M3NmItFsZ(77^+=WJv+<Lb$R17@*J=JTgtbf*3s
zQJ5%1G@K6wcgH#oxyDIH@o{o40LhjWg<inVY3mYy#REw=9uHE^{1X}fNvr-gUdgww
zM0+2N&-7Bk_3mA7lXjh&K6Kt5UOepNL@qFnO^!Y&$5wErZLSLe6<<*;l<ee<oHdTG
zy+CVtx(=eM!{{Qh%0Nu8hE$;F{6JM{lOJp3QIeT;IUVY$?_Re?07B*HbcEjo+qSgR
zQs9gulR3I=BAmBMz~FimI6itNVhaN!H~1e72KLL3a1q8XC^|amMiun<GN2<*Y3b2O
zni^p(b^hQ(GKVi~rMV!_ruSbJ8*X<{0Y@BlnXMc<xhr-&2F{FM!o9+(ErZ4Eo7@0s
zXsErsp^)Qi_`pD`O8SzdH!ewhB(WO7I~A~l6qKzte^TRHW?Exsm2}$6^EK@xy+3%r
zN@jg>NqfzO9*yV^n81;ow5=(3Y@KbA3f?yOZ!_rcDWlZ<h!toEwY_}lw+sKFe_#17
z)1(e}m2KH}G;r(CAI@M$Q>L55rqKnr7e(IPL)SNd;AB@6yv{(s0JC9x$LXT;#s*3~
zW?HlfUq74?WfJ3%T%4L!!B7GhA8^Uo1HX(t-rKTrC~z>a$NAHA_xa^%=#v)%4aYiA
zK0kic@TSUhr($_h#jeh=M2<cJQ8}?y`JJxJ=}*Qo%00836s*Y0$5x}`K8j`Z7RGoT
z11w9A(&!4SF^UawEAbn5o5f~4qBQeEX?wG#t!~?7Bi%rJK(qrBv5i%dp5qWEJl$+^
zGqUn#=^bhPJe1|xia%df9{8j-w@xW*@i1=7Z>tjjQvxr=Jx-jww;F=yDD9^rB`QzO
zgxPpx?4U!e*y)~HvoTRzu(|B(r<oLUzj6dQIlQxfy?KGBzxoEbX6I-OUnui*Ie}K>
zC>BL=thSP@R+aN%2eU{vM)`$kh`jPbX7DBBNiU-4kQ|CRFN>GMS)zT_{=|zrPFA&s
z&E?~cr;A$=*Jsjwo3=x}JdcjDt1TEO6$!7um*EF*ha6hY-C%3Yhnrm5zh88H1e&VV
zeVdZ%&ttFb&YDzbaDJ`FskJp~$KY}UqMPxdoROK|CkIiHT<GM09ZG?V;>R!|ej{UG
zZa5uzz;^Ce>a7QVZA52fXx1W<@ijBdVm#B)Gs{_q)%fK+CRI8Ulub8~4!0&}rSISd
zy3>vVZLlRysZ0EmA<uMt+r{c3AJ@mEO}bL6pLQifWJfo2)I@PafZJx$Od3MHe;iHe
zO|0E}ywx|f5U^~k+zaLT-J5AIzZK;D<06~fCckF4CoY!%X!Mh_1EsHFwQD&1q3NwW
z%HY;EToC3F{Uq1~*sSO;5`UC4;3Tg#=dtloY$xQT;iL1H4)8ET`4co((DeY3%7^2i
zgz!swT&HWv5R3~><T)mI$?R;*PW;@9TErDrFa0jcl~Pa22xL0qTU)hs+P_J<m=|oT
z{T6zQAHse#J9n8PQN#`EuW#_^w3j+-P@me@1_$Y_-X9?2u@aV~c}-(-k77J;d6+Ht
zNIud04&_XP*dyNxu#oNvZ6mn`#ke|Yi+tr*j<@&2V%!;+@3r)K3_2^Zp(P_<Cn0Ga
zDMp5c%&W>w@u|E$n0Ns`bqpRUe_%2haQ^1S?7X*QxCalwynoN3+<d>bv#P*5W@8MC
z2snQ%?zVci+naz`P04=r3zZ^EH2hf@JRI1Y{yVq(=L>$;1Z5T}^!+TY?^W1F{R*c<
z%f{kM6&R|Jj!y1l5#{d`DPQ)BPB=D7&Sp)gpDa}r<+I`I?<N>u!hqy;SNMs?tTRq#
zH1inl2CpA#nR5uExc9~SbY?Ywvb@sbX!nIlecKX!eGdNoho0G-Pr&}g@@x%v;$Cuj
znb;IXIeF?h(?$w(9=`j!)!!@t?vIX7AU1cPPNb@T8#QTP@akl(b5q(8?bSvrq22aa
zpvz55-FfRzL63J;5Z=KrtF#$tMSqDj)#j*xoCXA)Z)&OW5nGCSCuPQUr*%*UI>76L
zn!#2qae~X}2gbNU|2g<VC}eM8x48o!o@_hW5F`5KdUM$FO7vzi*S)PHt2oA)faOv=
z!J5t3{;etd3M!cQ3y=CwCWeR9=duJ8R`VS7t`EnUUS8MS&7rNcL|~6A-SWZM9ni5}
zHFY8JSLCM~RD5!|q5%kH=$t<Om#OdNSt)7Uf2L*iF(hu5(ltKiG%eR`U<ey!<3^V%
z2XYbXoPwvBAw<45I8Nq&sl9$%G%7|ExH0%(5uxOuY58{G4B)W->C7e7j4a23HgE*C
za*}4HQY(~?I5l5!KK3oiowPLZEd_!=1$hH(Nj^JjA$pyyenG$}hp$mZgq@i*XsAux
zN~GQD7=&agp`}SUsa!U=lw?hyxV_Wo3c~@Zvao&Uaq9=*M$%CZkz>K6DOW<l_-<Q0
z=D;=f54F98)wtY+R-}#%q!u*m#)Pwk&W?UF;>}v#(BBBl#I|?eOgP`)3dnNIxc=+3
zJ$pg)UJLYMN?P;guNQur<u_$kGFI3);n0=yui1r{N~av0&rS2|W)MSFp)S576qi+4
zKMFSK#x957oFILDndW9|!x=<Us*{~o3l15+yO^PB=sVt0!6Nlut%+e#fQM(3GQsYC
zz!*I0>7tO?l0YA=Vw`9|eKxXjD(k)FL?dDV*5AYDB@=Ms6?LVQ;`HRW#q@yJs7CQd
zO_Q(Pn@sYdt53cOwGTVeqlbG`9iyY~7JpsdSkO{7E`_No#dK9oV!~urb0crPN3@Sx
z$ukJ-<aTwgrj_=-6#=F0SdWf2?%)s}r{=<aEq#eF=S)L0Wd(b_RoJL!X2b_aeUhA|
z8jq5G<4QNjjO4F~xPge>bz;2ILH59~(tpE2+;2aY(IRfdq1V9R7W`u!aik((?XBZM
zsRhH4xN72~e>c|fmUw@K5F?PTYH?Xg_IG5(8!L05oD;bEwCO0_`Do<mb1B`1WM4t|
z(>qhSWtn!Rxd!vDkkXz~#tAo#BCsSWoPNzR8NR+*cscKF0Ao5TFM%wv$<Ae(h{LZy
z2D|D?A_MX{cHSsv60tQkbC#=r`_6TvWzY9fDM>I@MPkg=Y|94OXHBR1vRJ9G6DNkF
zLd$e%7y0${M8WXfaF9c1^7F%YGziJSZw>q%dD>ANRbzg$5lXIMYC*S5+M$e7^F}u7
z6FkJ7MMRHF{6TzUoepSG%7(ass=6Qx-zOUR%~kECtSv5b-Rg77+#2r^l{|_oP4Fo{
z;uG;Q{**Zjr%4|0e9YiNp6v&t(jOetbk1HJbR@!(whc)Ob_Pz0?OSI>Zj%0oIb$*h
zr{Nq)BW9c9Ra!Th#q~CM1*&a)X{8eqs1ipsAd6$3`W>>k)rsA)`h!x3#y@}I`mest
zQ_$kV-Codb&tHJAa??zNv9}t+oL^#7iwh20#CDv467Lx6#9Tm=38IUfh{3D~rvn?f
zp8SnxK{6@_<#1|cQNU%_7&DA=^E;Nw={InVl$fX<v`E#OpSx+Z{_~@vTr1_VVLe9i
z0;gI1e87gET(ztJ>Ygnc;D8v@t=}(qgtYo%t6>-t01$VbOc6rIGgcvENZ`M(ogxp4
z>TyO4WOR$BABs?hG`06T^!fA{Pu9&}zxl^mm3w+;L}q)DjYZFq9c^C|H;1W_$V^xm
zv8ArTrL!;N&hM8Gy7>U6*6B;gJVCz0_j(*=kPuNL5)mnrJR>4IC;xV(bnmE4K4Nu-
z>d%$jKy>v|VdYBQAKh^Rvc-jYla+dr4BqAAHINp_uH~uk{ljM&H76(JZv~G9e=7h`
zYTzXp3ibX>Oux?dPX-%^rTjv_Pw}juf`F_#nyg8z`cX}f=vZ!w)VRLrii^R@W2t0f
z)f~47O8N<h@L~Ej5pbky9hY2>7m~$gQO2-#5Lsj2N6P`&k#*@^6p(f7X<B!Ur}~Vc
z1p?Mi7vfRg8)x~RdwU%+ONBGL$li&ir>;r)o4GTw;)<OtPfmiCIkze{sR+_x_LAT>
z1?KqUj9d9JeBoIKD2WmrRbQ(v9Z=s)j_rF#$<P7|OJq@dYuixl&|}yhTSS@3Ezyc7
zi=nLid>LP;ZKnM!O$3{z23l0--TlF@*%*h3e(Lf!PqVtUDO=R};G9ZR0*s;TEb3kQ
zLzwbr!Fj{PFZaHZ$fm8#SLRNZW155(Of<K6bR6;VY)5BdwUpk2351a2ZQzlWG{v<9
z91o3kMJxTrr(zWFbIV@qoO+7@aXIk(L{cCQ-mz0(b8@2rvdH2;cML&zs}ua1fRX)h
zDRDJD!giJI<jvBL4Q>%ondMh@Q}wKtS>~U7z3kE@k~*tCLZ@1p#W+hdp~jv*`N?%y
zI3ecn`vB|t?XfP;7tD@_NY+;aHU-q4xrnMloQo?OyftTHS{@g$v6s``cJJ=ob{THD
zIA~KPn<FIq^85lo#0|#%QZ`@!Z{_1CnR&JcBb$@<3AiHUY_a~c_DGf=wy`~*eR?|Y
zyoe^l*BlQ*GR?2)&}f)lYXmNMI@(3s;Rp4EfqztfR9L$gX%jY=RK#B&o#Gn{^hq}S
z2+-$$;IZ#<6wVgcKM?N3I;I{1J~`BuncdQMI;BD@I7yZ{*fiJE2*q3CMR`&zc}Ce*
zOa}=6Zb5Wn<ND^kc31ziTpL~ejvdb@32Myd<@yJ)FEE(n7dyM9yG=N0|L%V^0smvT
zs(0~vFYPI*ZjwMvmFfDky+VI^&GFw-2dQMj%*?Fz-pS?{$EzClBBtY?AWILYExT{E
zN09j&W)X6gx~+e@JF7ycGT_;dy-z$7A_WBc#<(kZkcTLK2tC_RB5QU5Vo-IOybXzO
zy4x98t00lbS>>~Xk;$KxLjY#)=PsW%qL@)nMT~(e@JJdns&-*1T#ZbOLt+{Oos!eq
zSGk$Kp}zXanahv*=RWx$klt7-jMpnd_E%afe{wV1O4Qn+fO*Yss&gBC;QkM9?|bwN
zT$azX5%Z|Ew{MbQd~0x=0!x7jMn~M33(34COOQ?3V7Tmm%GZ5oXlDklDv%U97bpLq
zPpEAvTc^~%$#YQ2*)3k=t*l(94kEp$hPh|H+j90}F--w>u1n_7`uIDsK&;s!ZnN69
z;~gpg*p8ZVNBx;W{lKi&?-4K*pN*-{nk>OCs2pNi31dIap5wNo+Sls+%NeBhGtX;W
zjp%sY!d%0$IMQw2A|qZ*Etf_5KdO5y+u7!Gcl9~jgrZHmE_<aVGa&aGd#AL%y76IH
zW~4;C@oHw(QNOB<8T}fvl8M|;K(?X3=T^H?Auu}^6Rk--b=zi2bSYQkK~e&Jhth#%
zR}#!`7B<fe^!3%>7$sC*%ze5^`Ow77`Az+zaX7yJvfnLJfR~=&8%+6WFg(6akZVA&
z%HD~wF)E2MX)rgI_V>=HvAF1F>X^ZTu)=}t2Ta_93z)(u?vQF^Wd%l288FhI5gmSV
zGF&T%I@@im7X$ZEyKf{uzKnie68ln+M>G;;!LYH*mSin`J0cG<&=Lg|!IA8K+^L0g
z<8!$EZ@x?cRj1WcIDZ%dzUTW((-dF5QWw08Wqf>wZe-V)9=Tw8@!XC5qeaH)?nd9j
zg25!O0JBeeGGyz~*m~K-=IJdp7r&*{QnOm)F&pZ%D6073Z<_+uWXYJt%4CO?F_Ajg
z^&u7^eXpgu>MMI%XGxz-J9{H$@Q4Ag4(=8@0}|lu24nMGtULh%#XHAqPX?ArG_fV9
z(HIa@E6`}FY_D*q_o18K6>^PhwpZeN@W`S^_v$8Ygjs<NL7Myx)*-g$rUHn&C3`y?
zT0X#*U`?)kFJjEaeru!HCH(Pgiu_6$RL;SYXDgPiWNb0%g9iWhtq#MOSvs1u{?fXt
zB%U5r_6TTPU~WKNOlBxKW&#>v6s2|@PS*TovxW0Gp<%JW_Nhs&&&262{8l&ZR8A?<
z)`ZW!SZJa058sZ2o9jFR()IN<+v^|Br78o;Tju}xJ+iIKp0CreHU`W*{6&rUH>;Qi
zl6zq>;xy>f<v5E3N?U%>@1d{veMl#=zOogL!ZeYO&2{u=aUO3bo9`XcGfb_Qe|&Qe
z2Bd3LuX;>P6lnDc1ZvYxI}aw$E$XqpK{v#c6@M`UxOV{>sqV~=3yY5^y58#Rza*|?
zZbJzWAw~0T7WcFi!SlPP!r!II0=Xw~(!>kXi1SYkI(Shiv4Ja1DDVYi{yysm?xY^!
zN8=QZrRAzUYS*2vjCob?m3e5r`^fGz=)@qB`$f8r@(peC*eW61E@QM^`H%wbbpsp1
zmefz#5N{gbtnPawijsrFKU7^L-&yUvR#E8s98%<uvr?$$)l{!MDc7__+l`ZqtfP_c
z$=%Kv-&w47%ffGEm`emE_y+Pw`&;VY!rg!7Z#(o_jr$3N2F+07B;*Qvg*qI3bZj|9
z_1gN`MDAxVITUhaGyjm~4U61$?f%>kFJh^pTVFasUy|-o^SM0wmgaNqN<&4cSzc{(
zj+^#1G8~|KyF0#1x!Wqh%Ein{Lp#b^amR0ehP-9bFxbPSoYWCp!t{n3mvfUe)tx<E
zy|IjqJHeG7D}^HDThn{+LbC@t&i<56ft@Q!tGld$&V*^CPFxM@r>kA^;o#IIA|f5&
zI|GF}XJ@#EflTG*lpi!V=x5U9tL8T&ccfFBzaHD%cvn;X71YXI9MD~`n}?S@`1^T!
z<G3hMQnx%9cNBiZ#Y=%vn)s;SVjJ{ZO!{vIRv{2ryk*8O>$4E?V*3Ytfd;G_Bij0C
zyjM+!xAWtk-7J-mhqUUz2SYRX_M(~OO9ULd`XW%J?3e?xz7w3-lm{fO-%HROw)-5C
zLH;t~$n7A05kV~jC(FikTF2q$3><Gt(m$Q#MwdO|{gYoj3ulh<z-%m8C=x+B?+UgP
zE;H_WpB;vETQ?Vdv>xG2+9`ETIihTU9(#<~X)~l__<~5p24y(fL(`$c=~M;tdH@t&
zE2;#f?7yueWs$Y(IOm!Oxfak+K8Sw0VZ1m7`hYwfi2Ls9FKl*mm|XR$I@w--UGpx~
zCM@~^$lpcZ@)zmU&*sNA9bT5WM;FL`<AThItb#u9NC3aB9^YXsJX=*T8hYY8aHB#c
zgGecd^K-Tgl^a=saMnYK-X=bWFtlIp=YWb9lz-VuQpOXupmI0%E895XcuPhL#sacs
zf{Fk%127(5U2QoUYr5ap5bVV<R@Pa6^VVLk6gT2VjeY03#b|jo!)dYXa^4pi6#~y#
zw~To6iVqDibL%1p80ZD#!A7kGgD;h<Z}CT)y$5d+6=xG3#tO!7*yMM0%O4(cCHHXa
zNe?ABmEaFjbL<^l$+3a9`A5u6Ihq`t;ge8&>&fhJBURvPJaq>pmikn=A#sA(T^v&I
zH`M%9AleqkE0^}f8}15UG%3UC(~Y@e9Tw;Fe3EG^5xX(xN0H8DV^^x3tIE~+%hvcm
zy?Wf2i9z2{*m1#b1B3GfL>G!>-}239EAsQ}WP{<e9sKWzHdZ6ZWr3t%Ge3<VkN1in
zGf2+m=bNLiR4+#!n}i^j$}1hz<D?ogrESAy_u&JXO9IVN<|liy`?P*iUIdP295B4_
zI^05AIDO=B_5)Sd7ioxZ{X^m6wn7_U<Y7T7pbV;`D9po^)Z<mEkt?<IxfHe$Ms5W&
zJURnBE`p_|zIfPK*Ib!47qh>InKZN6nyoSYzt-{U+MQT$xoJA;-$DB)c{8o|u0Ea6
zB!$~8qIcUJl{+>T9;Bh#>^vv;?|gXcMBS+{cjsxe;b-HM0f>VM2A-(EO1T#CET@%?
zPy)E=AeP19g7l@?@dEY(^B8*4eM(;5-YY~3*PTfFLS^Mn;ErD9b;-q@evykyxT0ky
zk7_q6;!jY{_3@&V=b1Fc;~K(j^tA!CM}Y4gkhk}lTlD+!Vo1~|ufJ+ek!lZgvBY=i
zixfx}%LErti|13K10E*HaVVOvwA}jrZJ4C+1z1uX<NXV3uuokkjKD~!AR7O&QL*&p
zn{QQ`nphx2<f<WmY72k#FE5I!4pnAVLf2VELZ}nA#qg5%by^l}H)pkq6War|fRQ2d
z+$uMeo@Qt1?aIJKnw{X>)6+tOG_&ulV2|SJF9!{<&IfEJG}DQpr$=kccx1TN5h*Yh
z3KCwy64{n@2p5kC5DFTL(etrEoi^_!x2*%vW&AH};6}9QQF2whV%ooK9hv^&(C`d^
zg~3we#eYlIFA*{k{A_-{PH3XKfWaj467cZkf_eLPcG)P=ryH6#6FP=m6hL8BmRnZQ
z56ya~RoK&6FGoBM0p&bEz2mU_s43=_p;3!Oy)WBOiwU{qM%5EGwm{1wA7Zy+b~Yg_
zIQY53rqe>qBX!b+8BqO6e2H#IIMOZX*u)j`EYw<;to=by@a9}h7-;_^%o(+I(E8bF
z$U~i>?;xtw6J}pROQUfk9r3P5-KfL~K+)3Kbti;e^A~|GJ{=B*;hST@#~T383nCwr
zH-TfB_xWhyli?k8*VuZWgZ$m^3u_xNouo7;ZhoccU4`ouWAh2tO&<64+j1G^IBB;$
znCU?EM<VA>=C$WYaXBNj*MS*TUdc6TZW>UkJz^6hWuhaOWNxp}W7|cTgluJhWe{Fn
zB2y^Ai|S2%_hLIZfC}U%Q$koq*)=ZE2W1jf6f%C}N!g%))rFNUJaBddC4^dn(dgLY
zk%~yNCaIFOpwQon))mZnLp)+rxTuarHq1g83s|6WO%d$rgyX5^6z9@pb&(B-MLip>
z@txvGqlV&o9JiB}MVNu0iJHgDKQ$t3Xz&{wO+YSgmgwPEEUD3O1wMcrBQKo2YA4jL
zpTVH)5SW3OBg;A*X;yrQmK{AS?$UD~uOEsuKbc_$r|2?+3GimPug<LZfDHg`w*$5N
zXLjUqSud(Hyt#uyZeRYjlKCtRj~B2nL+Hle(&+z$8cuSry<wyOa*?fM+_z>92pGoK
z5U-n_2zsXe-s-7om(>QBLUvV5g+bxjL~?}E5k;-aYqA%#dxyiYyGk;E_=?<A#Y6ES
zr#_J}R`3=xV4@855-w3>k!SXZcf*MH>iKJ^VAm@7>Cc8WHEi(JU_#S<idIg?8_FEg
zuZaVdU;oTU|9R$ZB0P6GemnjA&9CGC{fcDhx1AW8_KslQKBx`c8;hyrc8GrV%=8)S
zpWoxl{N6t;6ef?bzQ_MIdiUGHRm%!96P<gBs^PBwmTQ+AjLt-#;PbtkBX$HkbT9Dx
z)lRF)_&_Ozk{*<XqmJ@)k>=NVDYvr^K2pdR5P3agBL5weWNzV20dbwV>-XsN^WmN?
zkw1Xow8ibxV9aNjh~3;qTc_6_;3}(D4GHF6RuMgTxmnexT)pwERf8fXx85%&Ch2bl
z@(U+RkexNh9pLAlJ<zH4dK6K3a<PxlVdinJS>S=XXeO~8?@6!BTfsE>--*=EoAThF
z$OQJ||1i*>Psl_^K*MkDoCXW<o15U^b%TUT+kjl!uDZ;|bC^cwH5=!?v3-s7+3OB^
zU*B4E-7@ES1|`}KDn)xQPkws#yQwr~0}ZKtyfc4}y<9Qf=`H7qZYq(Yv8bmU)4z8l
z{!=ff#^YJpollp4pSGXNE0sjZZ&9m-I~gcH^~wyuH18BuRc%^PNjoE)l?dX{&J@{R
z?zc*;YiWLb*@T((uN-t`Tn9N!FK@s1;9VmPSPRQ+)z+D_3qoh-8uTHL1vbn$ub$6F
zK{$WU4gb>%k;79=b<H;tznIovR+dOcFyu1MAAluw#aC&?+Ib3@c|79{R%5a#K$ig4
z;AmaN%4eM@T;|t|iB_9VGqGqAj-n<gb|1-}H{!R2bf_k%9#m?8NpuZkHW?vmSa8A{
z*;6(nt$&;GU-x?T?Pnhm_@Lr~wYx+RqdYyBvaow3D><tye0HWuny@CeRWaLC+?jY=
z(a1{Iv~?C{;e%A~j~i)5n-0$|zWJKLZK8dxcm`l2#Lyj!)VWVtdw|JcuwbPN7rAC#
zB-7Nxn{db*|0Y0H3FbxYcY(|Qpp@h6>-%^?zXQKxDf!!OyTfr?chuN_5E=;;GANjM
z$;$GC=sWZ_yOY+=x}bS@8E#UizT!2<pL_#9_-9gZmi@$b;|+R%nHX03`dllzf!rVz
zTt}|BWazCRL+!je=6{Nwe|mj#&yv5bWB5ET@+17Ts6TTNHo-N^o@Jir739Ya`5(J@
zk3`y@tQk!4Zdp|E+k~%Ke154K-JTfIJiSo+R-h=pMoHG+WDOkpKnJve+$YbUaXnn?
z$%KmP&_|o0`0@(0v}0ok=nlMlzEWKfn(llhxh&Z3^v64ni!NRYbR$?>Tl)#~hw4IC
zNo$R|Y6F^YXwT1Hr=hG_PUJXlhA64bs85!=P?o6l^%9DG3eP6V24}YjRO&d#JjyQ{
zcId?%+S)VI+1z&IzTl;yKQ|%;y`jJO?osm75$U#gO4qbPYPwDVGgBW<3%h0WdZk&2
z%b{lgd)MDBP|Xh5VDXw}Ytt&o1GVdded-q{Q$TEU$RGPA;P~F;zc&H=gR9GUs9mvt
zdV%-~zf_oE!pK_R?}|baX)CRgTkp<6<l)&CX<SWwf{z0`%Kp$VHkX>ycvIcsJgblR
z(B0%=UuYBikh7Ri|MvgoGcix_Dc|S9OR2xglm4;{zaYXVDb>0+lb$UuFxPAOdt=mZ
zwaj0FS%OY)huX;R_J-S@R5?E5?M!@*GL^zY&G0?o&7mH(Yc>=Tzw1eU&aMO=h$Lur
z|L<e`yeK0{umySEtRUp-QwBrSd*|d87W&R6L_8T5w0<4EthmIr;@WDi%{ga~k-j68
zAQ5%nkrLbko!FQvAA2hpFpfXV@Lz`fzmI|Juby(KK7aSm<o?U*G2_=v>t3Xv4&-tq
zA=QOs&lV(YJe;MAz7K~K1V$^N-U*!Cw}(!V!fb2s=UFPGhd!1|PZJ$zsE^H%j@McY
zI1fGA`7Nyf_r5;(7xlT_|Nru$o&-Y$Q``Z2q^`c1FoW|Q!^+~KQIvXM=X){K-TQ;g
zMTun1Hwa*hhCHh?%Nc0hiVF<k4&FpmDNHZEL)~+Nau<Pw`ty<K-E;&0jUkircs5BW
zn!Tur(*!!O?h1lx-K1me6Sg5)4QCJrco5mgAMzV2!DP=G5brKu@JEM_f9lmvJ)3S^
zaPieQ1v103PC>l-Mc-UzU+H)ZQDatMya*%fn4iOrsN;79PEvciL_Fo_xo9Dt5&w*p
z?|=LcntxrY?T<u1u?B&gP1!XFp5(o{u{0YjX1qw7(Bb6+dl1Q0jBY||G`|(B=1cEH
zNG&Tr^Si)X!FTZYklfC=fA@lmc!C`G?c~Jg_!xe#qoc59r2)3LWr3H)OGK%*2$0y@
zsy_g#+8l0~%r9qLW)`{NFZ_QRHdrRmf>|7xh7V~Svi|AGQl9wEKCe)_0NYGwFB4_^
zDMEJQn6%-dnZ-2xBwY>BLU%3-n6JLsDo!s~Z;AAD_IL_-4das`KYg#zKmH+Ye@&^^
z{LkEU-kgh+@c&%Hy8}nex3*#$GvPga3J`mKAR7=M$g<sk0UVCU;L`}-*-w-gF^P-3
z4}Z65EtdOdU!<;eU+5?oh322@ALxrmUUvbB{fGZ^<cl8sxBo3Ah&NIx!NLz$FO2FJ
zywJ~QuAsr2#!=cM<_o9zS7ZF2iGE{ygvU*`J#KHZ3#{R{$NGC>{{Q+?m~;9w*ULL^
T%+Z(d|5O#9C=|#)d;R|a0zt57

literal 0
HcmV?d00001

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/install_openpcdet.sh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/install_openpcdet.sh
new file mode 100644
index 000000000..53c0ac369
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/install_openpcdet.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+TARGET_DIR=${TARGET_DIR:-}
+
+PYTHON_PATH=$(which python3)
+PYTHON_DIST_PATH=${TARGET_DIR}/lib/python3/dist-packages
+
+PKG_DIR="build_pip"
+PKG_NAME="pcdet"
+
+if [[ ! -d ${PKG_DIR} ]]; then
+  echo "ERROR: Package directory ${PKG_DIR} doesn't exist"
+  exit 1
+fi
+
+latest_pkg="$(ls -t ${PKG_DIR} | grep ${PKG_NAME} | head -1)"
+if [[ "${latest_pkg}" == "" ]]; then
+  echo "ERROR: Cannot find latest ${PKG_NAME} package"
+  exit 1
+else
+  echo "INFO: Found latest package ${latest_pkg} in directory ${PKG_DIR}"
+fi
+
+if [[ "${TARGET_DIR}" != ""  ]]; then
+  ${PYTHON_PATH} -m pip install --upgrade --no-deps -t ${PYTHON_DIST_PATH} ${PKG_DIR}/${latest_pkg} || exit
+  echo "pcdet installed in ${PYTHON_DIST_PATH}; please add it to your PYTHONPATH."
+else
+  ${PYTHON_PATH} -m pip uninstall ${PKG_NAME} -y
+  ${PYTHON_PATH} -m pip install --no-deps ${PKG_DIR}/${latest_pkg} || exit
+fi
+
+# Return 0 status if all finished
+exit 0
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/__init__.py
new file mode 100644
index 000000000..9fdf7d2a8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/__init__.py
@@ -0,0 +1,24 @@
+import subprocess
+from pathlib import Path
+
+from .version import __version__
+
+__all__ = [
+    '__version__'
+]
+
+
+def get_git_commit_number():
+    if not (Path(__file__).parent / '../.git').exists():
+        return '0000000'
+
+    cmd_out = subprocess.run(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE)
+    git_commit_number = cmd_out.stdout.decode('utf-8')[:7]
+    return git_commit_number
+
+
+script_version = get_git_commit_number()
+
+
+if script_version not in __version__:
+    __version__ = __version__ + '+py%s' % script_version
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/config.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/config.py
new file mode 100644
index 000000000..02e5daf16
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/config.py
@@ -0,0 +1,85 @@
+from pathlib import Path
+
+import yaml
+from easydict import EasyDict
+
+
+def log_config_to_file(cfg, pre='cfg', logger=None):
+    for key, val in cfg.items():
+        if isinstance(cfg[key], EasyDict):
+            logger.info('----------- %s -----------' % (key))
+            log_config_to_file(cfg[key], pre=pre + '.' + key, logger=logger)
+            continue
+        logger.info('%s.%s: %s' % (pre, key, val))
+
+
+def cfg_from_list(cfg_list, config):
+    """Set config keys via list (e.g., from command line)."""
+    from ast import literal_eval
+    assert len(cfg_list) % 2 == 0
+    for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
+        key_list = k.split('.')
+        d = config
+        for subkey in key_list[:-1]:
+            assert subkey in d, 'NotFoundKey: %s' % subkey
+            d = d[subkey]
+        subkey = key_list[-1]
+        assert subkey in d, 'NotFoundKey: %s' % subkey
+        try:
+            value = literal_eval(v)
+        except:
+            value = v
+
+        if type(value) != type(d[subkey]) and isinstance(d[subkey], EasyDict):
+            key_val_list = value.split(',')
+            for src in key_val_list:
+                cur_key, cur_val = src.split(':')
+                val_type = type(d[subkey][cur_key])
+                cur_val = val_type(cur_val)
+                d[subkey][cur_key] = cur_val
+        elif type(value) != type(d[subkey]) and isinstance(d[subkey], list):
+            val_list = value.split(',')
+            for k, x in enumerate(val_list):
+                val_list[k] = type(d[subkey][0])(x)
+            d[subkey] = val_list
+        else:
+            assert type(value) == type(d[subkey]), \
+                'type {} does not match original type {}'.format(type(value), type(d[subkey]))
+            d[subkey] = value
+
+
+def merge_new_config(config, new_config):
+    if '_BASE_CONFIG_' in new_config:
+        with open(new_config['_BASE_CONFIG_'], 'r') as f:
+            try:
+                yaml_config = yaml.safe_load(f, Loader=yaml.FullLoader)
+            except:
+                yaml_config = yaml.safe_load(f)
+        config.update(EasyDict(yaml_config))
+
+    for key, val in new_config.items():
+        if not isinstance(val, dict):
+            config[key] = val
+            continue
+        if key not in config:
+            config[key] = EasyDict()
+        merge_new_config(config[key], val)
+
+    return config
+
+
+def cfg_from_yaml_file(cfg_file, config):
+    with open(cfg_file, 'r') as f:
+        try:
+            new_config = yaml.safe_load(f, Loader=yaml.FullLoader)
+        except:
+            new_config = yaml.safe_load(f)
+
+        merge_new_config(config=config, new_config=new_config)
+
+    return config
+
+
+cfg = EasyDict()
+cfg.ROOT_DIR = (Path(__file__).resolve().parent / '../').resolve()
+cfg.LOCAL_RANK = 0
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/__init__.py
new file mode 100644
index 000000000..d103bdb6b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/__init__.py
@@ -0,0 +1,82 @@
+import torch
+from functools import partial
+from torch.utils.data import DataLoader
+from torch.utils.data import DistributedSampler as _DistributedSampler
+
+from pcdet.utils import common_utils
+
+from .dataset import DatasetTemplate
+from .kitti.kitti_dataset import KittiDataset
+from .nuscenes.nuscenes_dataset import NuScenesDataset
+from .waymo.waymo_dataset import WaymoDataset
+from .pandaset.pandaset_dataset import PandasetDataset
+from .lyft.lyft_dataset import LyftDataset
+from .once.once_dataset import ONCEDataset
+# from .argo2.argo2_dataset import Argo2Dataset
+from .custom.custom_dataset import CustomDataset
+
+__all__ = {
+    'DatasetTemplate': DatasetTemplate,
+    'KittiDataset': KittiDataset,
+    'NuScenesDataset': NuScenesDataset,
+    'WaymoDataset': WaymoDataset,
+    'PandasetDataset': PandasetDataset,
+    'LyftDataset': LyftDataset,
+    'ONCEDataset': ONCEDataset,
+    'CustomDataset': CustomDataset
+}
+
+
+class DistributedSampler(_DistributedSampler):
+
+    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
+        super().__init__(dataset, num_replicas=num_replicas, rank=rank)
+        self.shuffle = shuffle
+
+    def __iter__(self):
+        if self.shuffle:
+            g = torch.Generator()
+            g.manual_seed(self.epoch)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = torch.arange(len(self.dataset)).tolist()
+
+        indices += indices[:(self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+
+def build_dataloader(dataset_cfg, class_names, batch_size, dist, root_path=None, workers=4, seed=None,
+                     logger=None, training=True, merge_all_iters_to_one_epoch=False, total_epochs=0):
+
+    dataset = __all__[dataset_cfg.DATASET](
+        dataset_cfg=dataset_cfg,
+        class_names=class_names,
+        root_path=root_path,
+        training=training,
+        logger=logger,
+    )
+
+    if merge_all_iters_to_one_epoch:
+        assert hasattr(dataset, 'merge_all_iters_to_one_epoch')
+        dataset.merge_all_iters_to_one_epoch(merge=True, epochs=total_epochs)
+
+    if dist:
+        if training:
+            sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+        else:
+            rank, world_size = common_utils.get_dist_info()
+            sampler = DistributedSampler(dataset, world_size, rank, shuffle=False)
+    else:
+        sampler = None
+    dataloader = DataLoader(
+        dataset, batch_size=batch_size, pin_memory=True, num_workers=workers,
+        shuffle=(sampler is None) and training, collate_fn=dataset.collate_batch,
+        drop_last=False, sampler=sampler, timeout=0, worker_init_fn=partial(common_utils.worker_init_fn, seed=seed)
+    )
+
+    return dataset, dataloader, sampler
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/__init__.py
new file mode 100644
index 000000000..139597f9c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/__init__.py
@@ -0,0 +1,2 @@
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_dataset.py
new file mode 100644
index 000000000..70e932e7f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_dataset.py
@@ -0,0 +1,537 @@
+import copy
+import pickle
+import argparse
+import os
+from os import path as osp
+import torch
+from av2.utils.io import read_feather
+import numpy as np
+import multiprocessing as mp
+import pickle as pkl
+from pathlib import Path
+import pandas as pd
+
+from ..dataset import DatasetTemplate
+from .argo2_utils.so3 import yaw_to_quat, quat_to_yaw
+from .argo2_utils.constants import LABEL_ATTR
+
+
+def process_single_segment(segment_path, split, info_list, ts2idx, output_dir, save_bin):
+    test_mode = 'test' in split
+    if not test_mode:
+        segment_anno = read_feather(Path(osp.join(segment_path, 'annotations.feather')))
+    segname = segment_path.split('/')[-1]
+
+    frame_path_list = os.listdir(osp.join(segment_path, 'sensors/lidar/'))
+
+    for frame_name in frame_path_list:
+        ts = int(osp.basename(frame_name).split('.')[0])
+
+        if not test_mode:
+            frame_anno = segment_anno[segment_anno['timestamp_ns'] == ts]
+        else:
+            frame_anno = None
+
+        frame_path = osp.join(segment_path, 'sensors/lidar/', frame_name)
+        frame_info = process_and_save_frame(frame_path, frame_anno, ts2idx, segname, output_dir, save_bin)
+        info_list.append(frame_info)
+
+
+def process_and_save_frame(frame_path, frame_anno, ts2idx, segname, output_dir, save_bin):
+    frame_info = {}
+    frame_info['uuid'] = segname + '/' + frame_path.split('/')[-1].split('.')[0]
+    frame_info['sample_idx'] = ts2idx[frame_info['uuid']]
+    frame_info['image'] = dict()
+    frame_info['point_cloud'] = dict(
+        num_features=4,
+        velodyne_path=None,
+    )
+    frame_info['calib'] = dict()  # not need for lidar-only
+    frame_info['pose'] = dict()  # not need for single frame
+    frame_info['annos'] = dict(
+        name=None,
+        truncated=None,
+        occluded=None,
+        alpha=None,
+        bbox=None,  # not need for lidar-only
+        dimensions=None,
+        location=None,
+        rotation_y=None,
+        index=None,
+        group_ids=None,
+        camera_id=None,
+        difficulty=None,
+        num_points_in_gt=None,
+    )
+    frame_info['sweeps'] = []  # not need for single frame
+    if frame_anno is not None:
+        frame_anno = frame_anno[frame_anno['num_interior_pts'] > 0]
+        cuboid_params = frame_anno.loc[:, list(LABEL_ATTR)].to_numpy()
+        cuboid_params = torch.from_numpy(cuboid_params)
+        yaw = quat_to_yaw(cuboid_params[:, -4:])
+        xyz = cuboid_params[:, :3]
+        lwh = cuboid_params[:, [3, 4, 5]]
+
+        cat = frame_anno['category'].to_numpy().tolist()
+        cat = [c.lower().capitalize() for c in cat]
+        cat = np.array(cat)
+
+        num_obj = len(cat)
+
+        annos = frame_info['annos']
+        annos['name'] = cat
+        annos['truncated'] = np.zeros(num_obj, dtype=np.float64)
+        annos['occluded'] = np.zeros(num_obj, dtype=np.int64)
+        annos['alpha'] = -10 * np.ones(num_obj, dtype=np.float64)
+        annos['dimensions'] = lwh.numpy().astype(np.float64)
+        annos['location'] = xyz.numpy().astype(np.float64)
+        annos['rotation_y'] = yaw.numpy().astype(np.float64)
+        annos['index'] = np.arange(num_obj, dtype=np.int32)
+        annos['num_points_in_gt'] = frame_anno['num_interior_pts'].to_numpy().astype(np.int32)
+    # frame_info['group_ids'] = np.arange(num_obj, dtype=np.int32)
+    prefix2split = {'0': 'training', '1': 'training', '2': 'testing'}
+    sample_idx = frame_info['sample_idx']
+    split = prefix2split[sample_idx[0]]
+    abs_save_path = osp.join(output_dir, split, 'velodyne', f'{sample_idx}.bin')
+    rel_save_path = osp.join(split, 'velodyne', f'{sample_idx}.bin')
+    frame_info['point_cloud']['velodyne_path'] = rel_save_path
+    if save_bin:
+        save_point_cloud(frame_path, abs_save_path)
+    return frame_info
+
+
+def save_point_cloud(frame_path, save_path):
+    lidar = read_feather(Path(frame_path))
+    lidar = lidar.loc[:, ['x', 'y', 'z', 'intensity']].to_numpy().astype(np.float32)
+    lidar.tofile(save_path)
+
+
+def prepare(root):
+    ts2idx = {}
+    ts_list = []
+    bin_idx_list = []
+    seg_path_list = []
+    seg_split_list = []
+    assert root.split('/')[-1] == 'sensor'
+    # include test if you need it
+    splits = ['train', 'val']  # , 'test']
+    num_train_samples = 0
+    num_val_samples = 0
+    num_test_samples = 0
+
+    # 0 for training, 1 for validation and 2 for testing.
+    prefixes = [0, 1, ]  # 2]
+
+    for i in range(len(splits)):
+        split = splits[i]
+        prefix = prefixes[i]
+        split_root = osp.join(root, split)
+        seg_file_list = os.listdir(split_root)
+        print(f'num of {split} segments:', len(seg_file_list))
+        for seg_idx, seg_name in enumerate(seg_file_list):
+            seg_path = osp.join(split_root, seg_name)
+            seg_path_list.append(seg_path)
+            seg_split_list.append(split)
+            assert seg_idx < 1000
+            frame_path_list = os.listdir(osp.join(seg_path, 'sensors/lidar/'))
+            for frame_idx, frame_path in enumerate(frame_path_list):
+                assert frame_idx < 1000
+                bin_idx = str(prefix) + str(seg_idx).zfill(3) + str(frame_idx).zfill(3)
+                ts = frame_path.split('/')[-1].split('.')[0]
+                ts = seg_name + '/' + ts  # ts is not unique, so add seg_name
+                ts2idx[ts] = bin_idx
+                ts_list.append(ts)
+                bin_idx_list.append(bin_idx)
+        if split == 'train':
+            num_train_samples = len(ts_list)
+        elif split == 'val':
+            num_val_samples = len(ts_list) - num_train_samples
+        else:
+            num_test_samples = len(ts_list) - num_train_samples - num_val_samples
+    # print three num samples
+    print('num of train samples:', num_train_samples)
+    print('num of val samples:', num_val_samples)
+    print('num of test samples:', num_test_samples)
+
+    assert len(ts_list) == len(set(ts_list))
+    assert len(bin_idx_list) == len(set(bin_idx_list))
+    return ts2idx, seg_path_list, seg_split_list
+
+def create_argo2_infos(seg_path_list, seg_split_list, info_list, ts2idx, output_dir, save_bin, token, num_process):
+    for seg_i, seg_path in enumerate(seg_path_list):
+        if seg_i % num_process != token:
+            continue
+        print(f'processing segment: {seg_i}/{len(seg_path_list)}')
+        split = seg_split_list[seg_i]
+        process_single_segment(seg_path, split, info_list, ts2idx, output_dir, save_bin)
+
+
+class Argo2Dataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
+        """
+        Args:
+            root_path:
+            dataset_cfg:
+            class_names:
+            training:
+            logger:
+        """
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
+        )
+        self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
+        self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
+
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
+
+        self.argo2_infos = []
+        self.include_argo2_data(self.mode)
+        self.evaluate_range = dataset_cfg.get("EVALUATE_RANGE", 200.0)
+
+    def include_argo2_data(self, mode):
+        if self.logger is not None:
+            self.logger.info('Loading Argoverse2 dataset')
+        argo2_infos = []
+
+        for info_path in self.dataset_cfg.INFO_PATH[mode]:
+            info_path = self.root_path / info_path
+            if not info_path.exists():
+                continue
+            with open(info_path, 'rb') as f:
+                infos = pickle.load(f)
+                argo2_infos.extend(infos)
+
+        self.argo2_infos.extend(argo2_infos)
+
+        if self.logger is not None:
+            self.logger.info('Total samples for Argo2 dataset: %d' % (len(argo2_infos)))
+
+    def set_split(self, split):
+        super().__init__(
+            dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger
+        )
+        self.split = split
+        self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
+
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
+
+    def get_lidar(self, idx):
+        lidar_file = self.root_split_path / 'velodyne' / ('%s.bin' % idx)
+        assert lidar_file.exists()
+        return np.fromfile(str(lidar_file), dtype=np.float32).reshape(-1, 4)
+
+    @staticmethod
+    def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
+        """
+        Args:
+            batch_dict:
+                frame_id:
+            pred_dicts: list of pred_dicts
+                pred_boxes: (N, 7), Tensor
+                pred_scores: (N), Tensor
+                pred_labels: (N), Tensor
+            class_names:
+            output_path:
+
+        Returns:
+
+        """
+        def get_template_prediction(num_samples):
+            ret_dict = {
+                'name': np.zeros(num_samples), 'truncated': np.zeros(num_samples),
+                'occluded': np.zeros(num_samples), 'alpha': np.zeros(num_samples),
+                'bbox': np.zeros([num_samples, 4]), 'dimensions': np.zeros([num_samples, 3]),
+                'location': np.zeros([num_samples, 3]), 'rotation_y': np.zeros(num_samples),
+                'score': np.zeros(num_samples), 'boxes_lidar': np.zeros([num_samples, 7])
+            }
+            return ret_dict
+
+        def generate_single_sample_dict(batch_index, box_dict):
+            pred_scores = box_dict['pred_scores'].cpu().numpy()
+            pred_boxes = box_dict['pred_boxes'].cpu().numpy()
+            pred_labels = box_dict['pred_labels'].cpu().numpy()
+            pred_dict = get_template_prediction(pred_scores.shape[0])
+            if pred_scores.shape[0] == 0:
+                return pred_dict
+
+            pred_boxes_img = pred_boxes
+            pred_boxes_camera = pred_boxes
+
+            pred_dict['name'] = np.array(class_names)[pred_labels - 1]
+            pred_dict['alpha'] = -np.arctan2(-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6]
+            pred_dict['bbox'] = pred_boxes_img
+            pred_dict['dimensions'] = pred_boxes_camera[:, 3:6]
+            pred_dict['location'] = pred_boxes_camera[:, 0:3]
+            pred_dict['rotation_y'] = pred_boxes_camera[:, 6]
+            pred_dict['score'] = pred_scores
+            pred_dict['boxes_lidar'] = pred_boxes
+
+            return pred_dict
+
+        annos = []
+        for index, box_dict in enumerate(pred_dicts):
+            frame_id = batch_dict['frame_id'][index]
+
+            single_pred_dict = generate_single_sample_dict(index, box_dict)
+            single_pred_dict['frame_id'] = frame_id
+            annos.append(single_pred_dict)
+
+            if output_path is not None:
+                cur_det_file = output_path / ('%s.txt' % frame_id)
+                with open(cur_det_file, 'w') as f:
+                    bbox = single_pred_dict['bbox']
+                    loc = single_pred_dict['location']
+                    dims = single_pred_dict['dimensions']  # lhw -> hwl
+
+                    for idx in range(len(bbox)):
+                        print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f'
+                              % (single_pred_dict['name'][idx], single_pred_dict['alpha'][idx],
+                                 bbox[idx][0], bbox[idx][1], bbox[idx][2], bbox[idx][3],
+                                 dims[idx][1], dims[idx][2], dims[idx][0], loc[idx][0],
+                                 loc[idx][1], loc[idx][2], single_pred_dict['rotation_y'][idx],
+                                 single_pred_dict['score'][idx]), file=f)
+
+        return annos
+
+    def __len__(self):
+        if self._merge_all_iters_to_one_epoch:
+            return len(self.argo2_infos) * self.total_epochs
+
+        return len(self.argo2_infos)
+
+    def __getitem__(self, index):
+        # index = 4
+        if self._merge_all_iters_to_one_epoch:
+            index = index % len(self.argo2_infos)
+
+        info = copy.deepcopy(self.argo2_infos[index])
+
+        sample_idx = info['point_cloud']['velodyne_path'].split('/')[-1].rstrip('.bin')
+        calib = None
+        get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points'])
+
+        input_dict = {
+            'frame_id': sample_idx,
+            'calib': calib,
+        }
+
+        if 'annos' in info:
+            annos = info['annos']
+            loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y']
+            gt_names = annos['name']
+            gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
+
+            input_dict.update({
+                'gt_names': gt_names,
+                'gt_boxes': gt_bboxes_3d
+            })
+
+        if "points" in get_item_list:
+            points = self.get_lidar(sample_idx)
+            input_dict['points'] = points
+
+        input_dict['calib'] = calib
+        data_dict = self.prepare_data(data_dict=input_dict)
+
+        return data_dict
+
+    def format_results(self,
+                       outputs,
+                       class_names,
+                       pklfile_prefix=None,
+                       submission_prefix=None,
+                       ):
+        """Format the results to .feather file with argo2 format.
+
+        Args:
+            outputs (list[dict]): Testing results of the dataset.
+            pklfile_prefix (str | None): The prefix of pkl files. It includes
+                the file path and the prefix of filename, e.g., "a/b/prefix".
+                If not specified, a temp file will be created. Default: None.
+            submission_prefix (str | None): The prefix of submitted files. It
+                includes the file path and the prefix of filename, e.g.,
+                "a/b/prefix". If not specified, a temp file will be created.
+                Default: None.
+
+        Returns:
+            tuple: (result_files, tmp_dir), result_files is a dict containing
+                the json filepaths, tmp_dir is the temporal directory created
+                for saving json files when jsonfile_prefix is not specified.
+        """
+        import pandas as pd
+
+        assert len(self.argo2_infos) == len(outputs)
+        num_samples = len(outputs)
+        print('\nGot {} samples'.format(num_samples))
+
+        serialized_dts_list = []
+
+        print('\nConvert predictions to Argoverse 2 format')
+        for i in range(num_samples):
+            out_i = outputs[i]
+            log_id, ts = self.argo2_infos[i]['uuid'].split('/')
+            track_uuid = None
+            #cat_id = out_i['labels_3d'].numpy().tolist()
+            #category = [class_names[i].upper() for i in cat_id]
+            category = [class_name.upper() for class_name in out_i['name']]
+            serialized_dts = pd.DataFrame(
+                self.lidar_box_to_argo2(out_i['bbox']).numpy(), columns=list(LABEL_ATTR)
+            )
+            serialized_dts["score"] = out_i['score']
+            serialized_dts["log_id"] = log_id
+            serialized_dts["timestamp_ns"] = int(ts)
+            serialized_dts["category"] = category
+            serialized_dts_list.append(serialized_dts)
+
+        dts = (
+            pd.concat(serialized_dts_list)
+            .set_index(["log_id", "timestamp_ns"])
+            .sort_index()
+        )
+
+        dts = dts.sort_values("score", ascending=False).reset_index()
+
+        if pklfile_prefix is not None:
+            if not pklfile_prefix.endswith(('.feather')):
+                pklfile_prefix = f'{pklfile_prefix}.feather'
+            dts.to_feather(pklfile_prefix)
+            print(f'Result is saved to {pklfile_prefix}.')
+
+        dts = dts.set_index(["log_id", "timestamp_ns"]).sort_index()
+
+        return dts
+
+    def lidar_box_to_argo2(self, boxes):
+        boxes = torch.Tensor(boxes)
+        cnt_xyz = boxes[:, :3]
+        lwh = boxes[:, [3, 4, 5]]
+        yaw = boxes[:, 6]
+
+        quat = yaw_to_quat(yaw)
+        argo_cuboid = torch.cat([cnt_xyz, lwh, quat], dim=1)
+        return argo_cuboid
+
+    def evaluation(self,
+                 results,
+                 class_names,
+                 eval_metric='waymo',
+                 logger=None,
+                 pklfile_prefix=None,
+                 submission_prefix=None,
+                 show=False,
+                 output_path=None,
+                 pipeline=None):
+        """Evaluation in Argo2 protocol.
+
+        Args:
+            results (list[dict]): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated.
+                Default: 'waymo'. Another supported metric is 'Argo2'.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+            pklfile_prefix (str | None): The prefix of pkl files. It includes
+                the file path and the prefix of filename, e.g., "a/b/prefix".
+                If not specified, a temp file will be created. Default: None.
+            submission_prefix (str | None): The prefix of submission datas.
+                If not specified, the submission data will not be generated.
+            show (bool): Whether to visualize.
+                Default: False.
+            out_dir (str): Path to save the visualization results.
+                Default: None.
+            pipeline (list[dict], optional): raw data loading for showing.
+                Default: None.
+
+        Returns:
+            dict[str: float]: results of each evaluation metric
+        """
+        from av2.evaluation.detection.constants import CompetitionCategories
+        from av2.evaluation.detection.utils import DetectionCfg
+        from av2.evaluation.detection.eval import evaluate
+        from av2.utils.io import read_feather
+
+        dts = self.format_results(results, class_names, pklfile_prefix, submission_prefix)
+        argo2_root = self.root_path
+        val_anno_path = osp.join(argo2_root, 'val_anno.feather')
+        gts = read_feather(Path(val_anno_path))
+        gts = gts.set_index(["log_id", "timestamp_ns"]).sort_values("category")
+
+        valid_uuids_gts = gts.index.tolist()
+        valid_uuids_dts = dts.index.tolist()
+        valid_uuids = set(valid_uuids_gts) & set(valid_uuids_dts)
+        gts = gts.loc[list(valid_uuids)].sort_index()
+
+        categories = set(x.value for x in CompetitionCategories)
+        categories &= set(gts["category"].unique().tolist())
+
+        dataset_dir = Path(argo2_root) / 'sensor' / 'val'
+        cfg = DetectionCfg(
+            dataset_dir=dataset_dir,
+            categories=tuple(sorted(categories)),
+            max_range_m=self.evaluate_range,
+            eval_only_roi_instances=True,
+        )
+
+        # Evaluate using Argoverse detection API.
+        eval_dts, eval_gts, metrics = evaluate(
+            dts.reset_index(), gts.reset_index(), cfg
+        )
+
+        valid_categories = sorted(categories) + ["AVERAGE_METRICS"]
+        ap_dict = {}
+        for index, row in metrics.iterrows():
+            ap_dict[index] = row.to_json()
+        return metrics.loc[valid_categories], ap_dict
+
+def parse_config():
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--root_path', type=str, default="/data/argo2/sensor")
+    parser.add_argument('--output_dir', type=str, default="/data/argo2/processed")
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_config()
+    root = args.root_path
+    output_dir = args.output_dir
+    save_bin = True
+    ts2idx, seg_path_list, seg_split_list = prepare(root)
+
+    velodyne_dir = Path(output_dir) / 'training' / 'velodyne'
+    if not velodyne_dir.exists():
+        velodyne_dir.mkdir(parents=True, exist_ok=True)
+
+    info_list = []
+    create_argo2_infos(seg_path_list, seg_split_list, info_list, ts2idx, output_dir, save_bin, 0, 1)
+
+    assert len(info_list) > 0
+
+    train_info = [e for e in info_list if e['sample_idx'][0] == '0']
+    val_info = [e for e in info_list if e['sample_idx'][0] == '1']
+    test_info = [e for e in info_list if e['sample_idx'][0] == '2']
+    trainval_info = train_info + val_info
+    assert len(train_info) + len(val_info) + len(test_info) == len(info_list)
+
+    # save info_list in under the output_dir as pickle file
+    with open(osp.join(output_dir, 'argo2_infos_train.pkl'), 'wb') as f:
+        pkl.dump(train_info, f)
+
+    with open(osp.join(output_dir, 'argo2_infos_val.pkl'), 'wb') as f:
+        pkl.dump(val_info, f)
+
+    # save validation anno feather
+    save_feather_path = os.path.join(output_dir, 'val_anno.feather')
+    val_seg_path_list = [seg_path for seg_path in seg_path_list if 'val' in seg_path]
+    assert len(val_seg_path_list) == len([i for i in seg_split_list if i == 'val'])
+
+    seg_anno_list = []
+    for seg_path in val_seg_path_list:
+        seg_anno = read_feather(osp.join(seg_path, 'annotations.feather'))
+        log_id = seg_path.split('/')[-1]
+        seg_anno["log_id"] = log_id
+        seg_anno_list.append(seg_anno)
+
+    gts = pd.concat(seg_anno_list).reset_index()
+    gts.to_feather(save_feather_path)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_utils/constants.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_utils/constants.py
new file mode 100644
index 000000000..e53a0c9d4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_utils/constants.py
@@ -0,0 +1,12 @@
+LABEL_ATTR = (
+    "tx_m",
+    "ty_m",
+    "tz_m",
+    "length_m",
+    "width_m",
+    "height_m",
+    "qw",
+    "qx",
+    "qy",
+    "qz",
+)
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_utils/so3.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_utils/so3.py
new file mode 100644
index 000000000..f5ec5e095
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/argo2/argo2_utils/so3.py
@@ -0,0 +1,141 @@
+"""SO(3) group transformations."""
+
+import kornia.geometry.conversions as C
+import torch
+from torch import Tensor
+from math import pi as PI
+
+
+@torch.jit.script
+def quat_to_mat(quat_wxyz: Tensor) -> Tensor:
+    """Convert scalar first quaternion to rotation matrix.
+
+    Args:
+        quat_wxyz: (...,4) Scalar first quaternions.
+
+    Returns:
+        (...,3,3) 3D rotation matrices.
+    """
+    return C.quaternion_to_rotation_matrix(
+        quat_wxyz, order=C.QuaternionCoeffOrder.WXYZ
+    )
+
+
+# @torch.jit.script
+def mat_to_quat(mat: Tensor) -> Tensor:
+    """Convert rotation matrix to scalar first quaternion.
+
+    Args:
+        mat: (...,3,3) 3D rotation matrices.
+
+    Returns:
+        (...,4) Scalar first quaternions.
+    """
+    return C.rotation_matrix_to_quaternion(
+        mat, order=C.QuaternionCoeffOrder.WXYZ
+    )
+
+
+@torch.jit.script
+def quat_to_xyz(
+    quat_wxyz: Tensor, singularity_value: float = PI / 2
+) -> Tensor:
+    """Convert scalar first quaternion to Tait-Bryan angles.
+
+    Reference:
+        https://en.wikipedia.org/wiki/Conversion_between_quaternions_and_Euler_angles#Source_code_2
+
+    Args:
+        quat_wxyz: (...,4) Scalar first quaternions.
+        singularity_value: Value that's set at the singularities.
+
+    Returns:
+        (...,3) The Tait-Bryan angles --- roll, pitch, and yaw.
+    """
+    qw = quat_wxyz[..., 0]
+    qx = quat_wxyz[..., 1]
+    qy = quat_wxyz[..., 2]
+    qz = quat_wxyz[..., 3]
+
+    # roll (x-axis rotation)
+    sinr_cosp = 2 * (qw * qx + qy * qz)
+    cosr_cosp = 1 - 2 * (qx * qx + qy * qy)
+    roll = torch.atan2(sinr_cosp, cosr_cosp)
+
+    # pitch (y-axis rotation)
+    pitch = 2 * (qw * qy - qz * qx)
+    is_out_of_range = torch.abs(pitch) >= 1
+    pitch[is_out_of_range] = torch.copysign(
+        torch.as_tensor(singularity_value), pitch[is_out_of_range]
+    )
+    pitch[~is_out_of_range] = torch.asin(pitch[~is_out_of_range])
+
+    # yaw (z-axis rotation)
+    siny_cosp = 2 * (qw * qz + qx * qy)
+    cosy_cosp = 1 - 2 * (qy * qy + qz * qz)
+    yaw = torch.atan2(siny_cosp, cosy_cosp)
+    xyz = torch.stack([roll, pitch, yaw], dim=-1)
+    return xyz
+
+
+@torch.jit.script
+def quat_to_yaw(quat_wxyz: Tensor) -> Tensor:
+    """Convert scalar first quaternion to yaw (rotation about vertical axis).
+
+    Reference:
+        https://en.wikipedia.org/wiki/Conversion_between_quaternions_and_Euler_angles#Source_code_2
+
+    Args:
+        quat_wxyz: (...,4) Scalar first quaternions.
+
+    Returns:
+        (...,) The rotation about the z-axis in radians.
+    """
+    xyz = quat_to_xyz(quat_wxyz)
+    yaw_rad: Tensor = xyz[..., -1]
+    return yaw_rad
+
+
+@torch.jit.script
+def xyz_to_quat(xyz_rad: Tensor) -> Tensor:
+    """Convert euler angles (xyz - pitch, roll, yaw) to scalar first quaternions.
+
+    Args:
+        xyz_rad: (...,3) Tensor of roll, pitch, and yaw in radians.
+
+    Returns:
+        (...,4) Scalar first quaternions (wxyz).
+    """
+    x_rad = xyz_rad[..., 0]
+    y_rad = xyz_rad[..., 1]
+    z_rad = xyz_rad[..., 2]
+
+    cy = torch.cos(z_rad * 0.5)
+    sy = torch.sin(z_rad * 0.5)
+    cp = torch.cos(y_rad * 0.5)
+    sp = torch.sin(y_rad * 0.5)
+    cr = torch.cos(x_rad * 0.5)
+    sr = torch.sin(x_rad * 0.5)
+
+    qw = cr * cp * cy + sr * sp * sy
+    qx = sr * cp * cy - cr * sp * sy
+    qy = cr * sp * cy + sr * cp * sy
+    qz = cr * cp * sy - sr * sp * cy
+    quat_wxyz = torch.stack([qw, qx, qy, qz], dim=-1)
+    return quat_wxyz
+
+
+@torch.jit.script
+def yaw_to_quat(yaw_rad: Tensor) -> Tensor:
+    """Convert yaw (rotation about the vertical axis) to scalar first quaternions.
+
+    Args:
+        yaw_rad: (...,1) Rotations about the z-axis.
+
+    Returns:
+        (...,4) scalar first quaternions (wxyz).
+    """
+    xyz_rad = torch.zeros_like(yaw_rad)[..., None].repeat_interleave(3, dim=-1)
+    xyz_rad[..., -1] = yaw_rad
+    quat_wxyz: Tensor = xyz_to_quat(xyz_rad)
+    return quat_wxyz
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/augmentor_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/augmentor_utils.py
new file mode 100644
index 000000000..3c088e33c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/augmentor_utils.py
@@ -0,0 +1,658 @@
+import numpy as np
+import math
+import copy
+from ...utils import common_utils
+from ...utils import box_utils
+
+
+def random_flip_along_x(gt_boxes, points, return_flip=False, enable=None):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C)
+    Returns:
+    """
+    if enable is None:
+        enable = np.random.choice([False, True], replace=False, p=[0.5, 0.5])
+    if enable:
+        gt_boxes[:, 1] = -gt_boxes[:, 1]
+        gt_boxes[:, 6] = -gt_boxes[:, 6]
+        points[:, 1] = -points[:, 1]
+        
+        if gt_boxes.shape[1] > 7:
+            gt_boxes[:, 8] = -gt_boxes[:, 8]
+    if return_flip:
+        return gt_boxes, points, enable
+    return gt_boxes, points
+
+
+def random_flip_along_y(gt_boxes, points, return_flip=False, enable=None):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C)
+    Returns:
+    """
+    if enable is None:
+        enable = np.random.choice([False, True], replace=False, p=[0.5, 0.5])
+    if enable:
+        gt_boxes[:, 0] = -gt_boxes[:, 0]
+        gt_boxes[:, 6] = -(gt_boxes[:, 6] + np.pi)
+        points[:, 0] = -points[:, 0]
+
+        if gt_boxes.shape[1] > 7:
+            gt_boxes[:, 7] = -gt_boxes[:, 7]
+    if return_flip:
+        return gt_boxes, points, enable
+    return gt_boxes, points
+
+
+def global_rotation(gt_boxes, points, rot_range, return_rot=False, noise_rotation=None):
+    """
+    Args:
+        gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        rot_range: [min, max]
+    Returns:
+    """
+    if noise_rotation is None: 
+        noise_rotation = np.random.uniform(rot_range[0], rot_range[1])
+    points = common_utils.rotate_points_along_z(points[np.newaxis, :, :], np.array([noise_rotation]))[0]
+    gt_boxes[:, 0:3] = common_utils.rotate_points_along_z(gt_boxes[np.newaxis, :, 0:3], np.array([noise_rotation]))[0]
+    gt_boxes[:, 6] += noise_rotation
+    if gt_boxes.shape[1] > 7:
+        gt_boxes[:, 7:9] = common_utils.rotate_points_along_z(
+            np.hstack((gt_boxes[:, 7:9], np.zeros((gt_boxes.shape[0], 1))))[np.newaxis, :, :],
+            np.array([noise_rotation])
+        )[0][:, 0:2]
+
+    if return_rot:
+        return gt_boxes, points, noise_rotation
+    return gt_boxes, points
+
+
+def global_scaling(gt_boxes, points, scale_range, return_scale=False):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading]
+        points: (M, 3 + C),
+        scale_range: [min, max]
+    Returns:
+    """
+    if scale_range[1] - scale_range[0] < 1e-3:
+        return gt_boxes, points
+    noise_scale = np.random.uniform(scale_range[0], scale_range[1])
+    points[:, :3] *= noise_scale
+    gt_boxes[:, :6] *= noise_scale
+    if gt_boxes.shape[1] > 7:
+        gt_boxes[:, 7:] *= noise_scale
+        
+    if return_scale:
+        return gt_boxes, points, noise_scale
+    return gt_boxes, points
+
+def global_scaling_with_roi_boxes(gt_boxes, roi_boxes, points, scale_range, return_scale=False):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading]
+        points: (M, 3 + C),
+        scale_range: [min, max]
+    Returns:
+    """
+    if scale_range[1] - scale_range[0] < 1e-3:
+        return gt_boxes, points
+    noise_scale = np.random.uniform(scale_range[0], scale_range[1])
+    points[:, :3] *= noise_scale
+    gt_boxes[:, :6] *= noise_scale
+    roi_boxes[:,:, [0,1,2,3,4,5,7,8]] *= noise_scale
+    if return_scale:
+        return gt_boxes,roi_boxes, points, noise_scale
+    return gt_boxes, roi_boxes, points
+
+
+def random_image_flip_horizontal(image, depth_map, gt_boxes, calib):
+    """
+    Performs random horizontal flip augmentation
+    Args:
+        image: (H_image, W_image, 3), Image
+        depth_map: (H_depth, W_depth), Depth map
+        gt_boxes: (N, 7), 3D box labels in LiDAR coordinates [x, y, z, w, l, h, ry]
+        calib: calibration.Calibration, Calibration object
+    Returns:
+        aug_image: (H_image, W_image, 3), Augmented image
+        aug_depth_map: (H_depth, W_depth), Augmented depth map
+        aug_gt_boxes: (N, 7), Augmented 3D box labels in LiDAR coordinates [x, y, z, w, l, h, ry]
+    """
+    # Randomly augment with 50% chance
+    enable = np.random.choice([False, True], replace=False, p=[0.5, 0.5])
+
+    if enable:
+        # Flip images
+        aug_image = np.fliplr(image)
+        aug_depth_map = np.fliplr(depth_map)
+        
+        # Flip 3D gt_boxes by flipping the centroids in image space
+        aug_gt_boxes = copy.copy(gt_boxes)
+        locations = aug_gt_boxes[:, :3]
+        img_pts, img_depth = calib.lidar_to_img(locations)
+        W = image.shape[1]
+        img_pts[:, 0] = W - img_pts[:, 0]
+        pts_rect = calib.img_to_rect(u=img_pts[:, 0], v=img_pts[:, 1], depth_rect=img_depth)
+        pts_lidar = calib.rect_to_lidar(pts_rect)
+        aug_gt_boxes[:, :3] = pts_lidar
+        aug_gt_boxes[:, 6] = -1 * aug_gt_boxes[:, 6]
+
+    else:
+        aug_image = image
+        aug_depth_map = depth_map
+        aug_gt_boxes = gt_boxes
+
+    return aug_image, aug_depth_map, aug_gt_boxes
+
+
+def random_local_translation_along_x(gt_boxes, points, offset_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        offset_range: [min max]]
+    Returns:
+    """
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        offset = np.random.uniform(offset_range[0], offset_range[1])
+        # augs[f'object_{idx}'] = offset
+        points_in_box, mask = get_points_in_box(points, box)
+        points[mask, 0] += offset
+        
+        gt_boxes[idx, 0] += offset
+    
+        # if gt_boxes.shape[1] > 7:
+        #     gt_boxes[idx, 7] += offset
+    
+    return gt_boxes, points
+
+
+def random_local_translation_along_y(gt_boxes, points, offset_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        offset_range: [min max]]
+    Returns:
+    """
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        offset = np.random.uniform(offset_range[0], offset_range[1])
+        # augs[f'object_{idx}'] = offset
+        points_in_box, mask = get_points_in_box(points, box)
+        points[mask, 1] += offset
+        
+        gt_boxes[idx, 1] += offset
+    
+        # if gt_boxes.shape[1] > 8:
+        #     gt_boxes[idx, 8] += offset
+    
+    return gt_boxes, points
+
+
+def random_local_translation_along_z(gt_boxes, points, offset_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        offset_range: [min max]]
+    Returns:
+    """
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        offset = np.random.uniform(offset_range[0], offset_range[1])
+        # augs[f'object_{idx}'] = offset
+        points_in_box, mask = get_points_in_box(points, box)
+        points[mask, 2] += offset
+        
+        gt_boxes[idx, 2] += offset
+    
+    return gt_boxes, points
+
+
+def global_frustum_dropout_top(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+    # threshold = max - length * uniform(0 ~ 0.2)
+    threshold = np.max(points[:, 2]) - intensity * (np.max(points[:, 2]) - np.min(points[:, 2]))
+    
+    points = points[points[:, 2] < threshold]
+    gt_boxes = gt_boxes[gt_boxes[:, 2] < threshold]
+    return gt_boxes, points
+
+
+def global_frustum_dropout_bottom(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+    
+    threshold = np.min(points[:, 2]) + intensity * (np.max(points[:, 2]) - np.min(points[:, 2]))
+    points = points[points[:, 2] > threshold]
+    gt_boxes = gt_boxes[gt_boxes[:, 2] > threshold]
+    
+    return gt_boxes, points
+
+
+def global_frustum_dropout_left(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+    
+    threshold = np.max(points[:, 1]) - intensity * (np.max(points[:, 1]) - np.min(points[:, 1]))
+    points = points[points[:, 1] < threshold]
+    gt_boxes = gt_boxes[gt_boxes[:, 1] < threshold]
+    
+    return gt_boxes, points
+
+
+def global_frustum_dropout_right(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+    
+    threshold = np.min(points[:, 1]) + intensity * (np.max(points[:, 1]) - np.min(points[:, 1]))
+    points = points[points[:, 1] > threshold]
+    gt_boxes = gt_boxes[gt_boxes[:, 1] > threshold]
+    
+    return gt_boxes, points
+
+
+def local_scaling(gt_boxes, points, scale_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading]
+        points: (M, 3 + C),
+        scale_range: [min, max]
+    Returns:
+    """
+    if scale_range[1] - scale_range[0] < 1e-3:
+        return gt_boxes, points
+    
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        noise_scale = np.random.uniform(scale_range[0], scale_range[1])
+        # augs[f'object_{idx}'] = noise_scale
+        points_in_box, mask = get_points_in_box(points, box)
+        
+        # tranlation to axis center
+        points[mask, 0] -= box[0]
+        points[mask, 1] -= box[1]
+        points[mask, 2] -= box[2]
+        
+        # apply scaling
+        points[mask, :3] *= noise_scale
+        
+        # tranlation back to original position
+        points[mask, 0] += box[0]
+        points[mask, 1] += box[1]
+        points[mask, 2] += box[2]
+        
+        gt_boxes[idx, 3:6] *= noise_scale
+    return gt_boxes, points
+
+
+def local_rotation(gt_boxes, points, rot_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]]
+        points: (M, 3 + C),
+        rot_range: [min, max]
+    Returns:
+    """
+    # augs = {}
+    for idx, box in enumerate(gt_boxes):
+        noise_rotation = np.random.uniform(rot_range[0], rot_range[1])
+        # augs[f'object_{idx}'] = noise_rotation
+        points_in_box, mask = get_points_in_box(points, box)
+        
+        centroid_x = box[0]
+        centroid_y = box[1]
+        centroid_z = box[2]
+        
+        # tranlation to axis center
+        points[mask, 0] -= centroid_x
+        points[mask, 1] -= centroid_y
+        points[mask, 2] -= centroid_z
+        box[0] -= centroid_x
+        box[1] -= centroid_y
+        box[2] -= centroid_z
+        
+        # apply rotation
+        points[mask, :] = common_utils.rotate_points_along_z(points[np.newaxis, mask, :], np.array([noise_rotation]))[0]
+        box[0:3] = common_utils.rotate_points_along_z(box[np.newaxis, np.newaxis, 0:3], np.array([noise_rotation]))[0][0]
+        
+        # tranlation back to original position
+        points[mask, 0] += centroid_x
+        points[mask, 1] += centroid_y
+        points[mask, 2] += centroid_z
+        box[0] += centroid_x
+        box[1] += centroid_y
+        box[2] += centroid_z
+        
+        gt_boxes[idx, 6] += noise_rotation
+        if gt_boxes.shape[1] > 8:
+            gt_boxes[idx, 7:9] = common_utils.rotate_points_along_z(
+                np.hstack((gt_boxes[idx, 7:9], np.zeros((gt_boxes.shape[0], 1))))[np.newaxis, :, :],
+                np.array([noise_rotation])
+            )[0][:, 0:2]
+    
+    return gt_boxes, points
+
+
+def local_frustum_dropout_top(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    for idx, box in enumerate(gt_boxes):
+        x, y, z, dx, dy, dz = box[0], box[1], box[2], box[3], box[4], box[5]
+        
+        intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+        points_in_box, mask = get_points_in_box(points, box)
+        threshold = (z + dz / 2) - intensity * dz
+        
+        points = points[np.logical_not(np.logical_and(mask, points[:, 2] >= threshold))]
+    
+    return gt_boxes, points
+
+
+def local_frustum_dropout_bottom(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    for idx, box in enumerate(gt_boxes):
+        x, y, z, dx, dy, dz = box[0], box[1], box[2], box[3], box[4], box[5]
+        
+        intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+        points_in_box, mask = get_points_in_box(points, box)
+        threshold = (z - dz / 2) + intensity * dz
+        
+        points = points[np.logical_not(np.logical_and(mask, points[:, 2] <= threshold))]
+    
+    return gt_boxes, points
+
+
+def local_frustum_dropout_left(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    for idx, box in enumerate(gt_boxes):
+        x, y, z, dx, dy, dz = box[0], box[1], box[2], box[3], box[4], box[5]
+        
+        intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+        points_in_box, mask = get_points_in_box(points, box)
+        threshold = (y + dy / 2) - intensity * dy
+        
+        points = points[np.logical_not(np.logical_and(mask, points[:, 1] >= threshold))]
+    
+    return gt_boxes, points
+
+
+def local_frustum_dropout_right(gt_boxes, points, intensity_range):
+    """
+    Args:
+        gt_boxes: (N, 7), [x, y, z, dx, dy, dz, heading, [vx], [vy]],
+        points: (M, 3 + C),
+        intensity: [min, max]
+    Returns:
+    """
+    for idx, box in enumerate(gt_boxes):
+        x, y, z, dx, dy, dz = box[0], box[1], box[2], box[3], box[4], box[5]
+        
+        intensity = np.random.uniform(intensity_range[0], intensity_range[1])
+        points_in_box, mask = get_points_in_box(points, box)
+        threshold = (y - dy / 2) + intensity * dy
+        
+        points = points[np.logical_not(np.logical_and(mask, points[:, 1] <= threshold))]
+    
+    return gt_boxes, points
+
+
+def get_points_in_box(points, gt_box):
+    x, y, z = points[:, 0], points[:, 1], points[:, 2]
+    cx, cy, cz = gt_box[0], gt_box[1], gt_box[2]
+    dx, dy, dz, rz = gt_box[3], gt_box[4], gt_box[5], gt_box[6]
+    shift_x, shift_y, shift_z = x - cx, y - cy, z - cz
+    
+    MARGIN = 1e-1
+    cosa, sina = math.cos(-rz), math.sin(-rz)
+    local_x = shift_x * cosa + shift_y * (-sina)
+    local_y = shift_x * sina + shift_y * cosa
+    
+    mask = np.logical_and(abs(shift_z) <= dz / 2.0, 
+                          np.logical_and(abs(local_x) <= dx / 2.0 + MARGIN, 
+                                         abs(local_y) <= dy / 2.0 + MARGIN))
+    
+    points = points[mask]
+    
+    return points, mask
+
+
+def get_pyramids(boxes):
+    pyramid_orders = np.array([
+        [0, 1, 5, 4],
+        [4, 5, 6, 7],
+        [7, 6, 2, 3],
+        [3, 2, 1, 0],
+        [1, 2, 6, 5],
+        [0, 4, 7, 3]
+    ])
+    boxes_corners = box_utils.boxes_to_corners_3d(boxes).reshape(-1, 24)
+    
+    pyramid_list = []
+    for order in pyramid_orders:
+        # frustum polygon: 5 corners, 5 surfaces
+        pyramid = np.concatenate((
+            boxes[:, 0:3],
+            boxes_corners[:, 3 * order[0]: 3 * order[0] + 3],
+            boxes_corners[:, 3 * order[1]: 3 * order[1] + 3],
+            boxes_corners[:, 3 * order[2]: 3 * order[2] + 3],
+            boxes_corners[:, 3 * order[3]: 3 * order[3] + 3]), axis=1)
+        pyramid_list.append(pyramid[:, None, :])
+    pyramids = np.concatenate(pyramid_list, axis=1)  # [N, 6, 15], 15=5*3
+    return pyramids
+
+
+def one_hot(x, num_class=1):
+    if num_class is None:
+        num_class = 1
+    ohx = np.zeros((len(x), num_class))
+    ohx[range(len(x)), x] = 1
+    return ohx
+
+
+def points_in_pyramids_mask(points, pyramids):
+    pyramids = pyramids.reshape(-1, 5, 3)
+    flags = np.zeros((points.shape[0], pyramids.shape[0]), dtype=np.bool)
+    for i, pyramid in enumerate(pyramids):
+        flags[:, i] = np.logical_or(flags[:, i], box_utils.in_hull(points[:, 0:3], pyramid))
+    return flags
+
+
+def local_pyramid_dropout(gt_boxes, points, dropout_prob, pyramids=None):
+    if pyramids is None:
+        pyramids = get_pyramids(gt_boxes).reshape([-1, 6, 5, 3])  # each six surface of boxes: [num_boxes, 6, 15=3*5]
+    drop_pyramid_indices = np.random.randint(0, 6, (pyramids.shape[0]))
+    drop_pyramid_one_hot = one_hot(drop_pyramid_indices, num_class=6)
+    drop_box_mask = np.random.uniform(0, 1, (pyramids.shape[0])) <= dropout_prob
+    if np.sum(drop_box_mask) != 0:
+        drop_pyramid_mask = (np.tile(drop_box_mask[:, None], [1, 6]) * drop_pyramid_one_hot) > 0
+        drop_pyramids = pyramids[drop_pyramid_mask]
+        point_masks = points_in_pyramids_mask(points, drop_pyramids)
+        points = points[np.logical_not(point_masks.any(-1))]
+    # print(drop_box_mask)
+    pyramids = pyramids[np.logical_not(drop_box_mask)]
+    return gt_boxes, points, pyramids
+
+
+def local_pyramid_sparsify(gt_boxes, points, prob, max_num_pts, pyramids=None):
+    if pyramids is None:
+        pyramids = get_pyramids(gt_boxes).reshape([-1, 6, 5, 3])  # each six surface of boxes: [num_boxes, 6, 15=3*5]
+    if pyramids.shape[0] > 0:
+        sparsity_prob, sparsity_num = prob, max_num_pts
+        sparsify_pyramid_indices = np.random.randint(0, 6, (pyramids.shape[0]))
+        sparsify_pyramid_one_hot = one_hot(sparsify_pyramid_indices, num_class=6)
+        sparsify_box_mask = np.random.uniform(0, 1, (pyramids.shape[0])) <= sparsity_prob
+        sparsify_pyramid_mask = (np.tile(sparsify_box_mask[:, None], [1, 6]) * sparsify_pyramid_one_hot) > 0
+        # print(sparsify_box_mask)
+        
+        pyramid_sampled = pyramids[sparsify_pyramid_mask]  # (-1,6,5,3)[(num_sample,6)]
+        # print(pyramid_sampled.shape)
+        pyramid_sampled_point_masks = points_in_pyramids_mask(points, pyramid_sampled)
+        pyramid_sampled_points_num = pyramid_sampled_point_masks.sum(0)  # the number of points in each surface pyramid
+        valid_pyramid_sampled_mask = pyramid_sampled_points_num > sparsity_num  # only much than sparsity_num should be sparse
+        
+        sparsify_pyramids = pyramid_sampled[valid_pyramid_sampled_mask]
+        if sparsify_pyramids.shape[0] > 0:
+            point_masks = pyramid_sampled_point_masks[:, valid_pyramid_sampled_mask]
+            remain_points = points[
+                np.logical_not(point_masks.any(-1))]  # points which outside the down sampling pyramid
+            to_sparsify_points = [points[point_masks[:, i]] for i in range(point_masks.shape[1])]
+            
+            sparsified_points = []
+            for sample in to_sparsify_points:
+                sampled_indices = np.random.choice(sample.shape[0], size=sparsity_num, replace=False)
+                sparsified_points.append(sample[sampled_indices])
+            sparsified_points = np.concatenate(sparsified_points, axis=0)
+            points = np.concatenate([remain_points, sparsified_points], axis=0)
+        pyramids = pyramids[np.logical_not(sparsify_box_mask)]
+    return gt_boxes, points, pyramids
+
+
+def local_pyramid_swap(gt_boxes, points, prob, max_num_pts, pyramids=None):
+    def get_points_ratio(points, pyramid):
+        surface_center = (pyramid[3:6] + pyramid[6:9] + pyramid[9:12] + pyramid[12:]) / 4.0
+        vector_0, vector_1, vector_2 = pyramid[6:9] - pyramid[3:6], pyramid[12:] - pyramid[3:6], pyramid[0:3] - surface_center
+        alphas = ((points[:, 0:3] - pyramid[3:6]) * vector_0).sum(-1) / np.power(vector_0, 2).sum()
+        betas = ((points[:, 0:3] - pyramid[3:6]) * vector_1).sum(-1) / np.power(vector_1, 2).sum()
+        gammas = ((points[:, 0:3] - surface_center) * vector_2).sum(-1) / np.power(vector_2, 2).sum()
+        return [alphas, betas, gammas]
+    
+    def recover_points_by_ratio(points_ratio, pyramid):
+        alphas, betas, gammas = points_ratio
+        surface_center = (pyramid[3:6] + pyramid[6:9] + pyramid[9:12] + pyramid[12:]) / 4.0
+        vector_0, vector_1, vector_2 = pyramid[6:9] - pyramid[3:6], pyramid[12:] - pyramid[3:6], pyramid[0:3] - surface_center
+        points = (alphas[:, None] * vector_0 + betas[:, None] * vector_1) + pyramid[3:6] + gammas[:, None] * vector_2
+        return points
+    
+    def recover_points_intensity_by_ratio(points_intensity_ratio, max_intensity, min_intensity):
+        return points_intensity_ratio * (max_intensity - min_intensity) + min_intensity
+    
+    # swap partition
+    if pyramids is None:
+        pyramids = get_pyramids(gt_boxes).reshape([-1, 6, 5, 3])  # each six surface of boxes: [num_boxes, 6, 15=3*5]
+    swap_prob, num_thres = prob, max_num_pts
+    swap_pyramid_mask = np.random.uniform(0, 1, (pyramids.shape[0])) <= swap_prob
+    
+    if swap_pyramid_mask.sum() > 0:
+        point_masks = points_in_pyramids_mask(points, pyramids)
+        point_nums = point_masks.sum(0).reshape(pyramids.shape[0], -1)  # [N, 6]
+        non_zero_pyramids_mask = point_nums > num_thres  # ingore dropout pyramids or highly occluded pyramids
+        selected_pyramids = non_zero_pyramids_mask * swap_pyramid_mask[:,
+                                                     None]  # selected boxes and all their valid pyramids
+        # print(selected_pyramids)
+        if selected_pyramids.sum() > 0:
+            # get to_swap pyramids
+            index_i, index_j = np.nonzero(selected_pyramids)
+            selected_pyramid_indices = [np.random.choice(index_j[index_i == i]) \
+                                            if e and (index_i == i).any() else 0 for i, e in
+                                        enumerate(swap_pyramid_mask)]
+            selected_pyramids_mask = selected_pyramids * one_hot(selected_pyramid_indices, num_class=6) == 1
+            to_swap_pyramids = pyramids[selected_pyramids_mask]
+            
+            # get swapped pyramids
+            index_i, index_j = np.nonzero(selected_pyramids_mask)
+            non_zero_pyramids_mask[selected_pyramids_mask] = False
+            swapped_index_i = np.array([np.random.choice(np.where(non_zero_pyramids_mask[:, j])[0]) if \
+                                            np.where(non_zero_pyramids_mask[:, j])[0].shape[0] > 0 else
+                                        index_i[i] for i, j in enumerate(index_j.tolist())])
+            swapped_indicies = np.concatenate([swapped_index_i[:, None], index_j[:, None]], axis=1)
+            swapped_pyramids = pyramids[
+                swapped_indicies[:, 0].astype(np.int32), swapped_indicies[:, 1].astype(np.int32)]
+            
+            # concat to_swap&swapped pyramids
+            swap_pyramids = np.concatenate([to_swap_pyramids, swapped_pyramids], axis=0)
+            swap_point_masks = points_in_pyramids_mask(points, swap_pyramids)
+            remain_points = points[np.logical_not(swap_point_masks.any(-1))]
+            
+            # swap pyramids
+            points_res = []
+            num_swapped_pyramids = swapped_pyramids.shape[0]
+            for i in range(num_swapped_pyramids):
+                to_swap_pyramid = to_swap_pyramids[i]
+                swapped_pyramid = swapped_pyramids[i]
+                
+                to_swap_points = points[swap_point_masks[:, i]]
+                swapped_points = points[swap_point_masks[:, i + num_swapped_pyramids]]
+                # for intensity transform
+                to_swap_points_intensity_ratio = (to_swap_points[:, -1:] - to_swap_points[:, -1:].min()) / \
+                                                 np.clip(
+                                                     (to_swap_points[:, -1:].max() - to_swap_points[:, -1:].min()),
+                                                     1e-6, 1)
+                swapped_points_intensity_ratio = (swapped_points[:, -1:] - swapped_points[:, -1:].min()) / \
+                                                 np.clip(
+                                                     (swapped_points[:, -1:].max() - swapped_points[:, -1:].min()),
+                                                     1e-6, 1)
+                
+                to_swap_points_ratio = get_points_ratio(to_swap_points, to_swap_pyramid.reshape(15))
+                swapped_points_ratio = get_points_ratio(swapped_points, swapped_pyramid.reshape(15))
+                new_to_swap_points = recover_points_by_ratio(swapped_points_ratio, to_swap_pyramid.reshape(15))
+                new_swapped_points = recover_points_by_ratio(to_swap_points_ratio, swapped_pyramid.reshape(15))
+                # for intensity transform
+                new_to_swap_points_intensity = recover_points_intensity_by_ratio(
+                    swapped_points_intensity_ratio, to_swap_points[:, -1:].max(),
+                    to_swap_points[:, -1:].min())
+                new_swapped_points_intensity = recover_points_intensity_by_ratio(
+                    to_swap_points_intensity_ratio, swapped_points[:, -1:].max(),
+                    swapped_points[:, -1:].min())
+                
+                # new_to_swap_points = np.concatenate([new_to_swap_points, swapped_points[:, -1:]], axis=1)
+                # new_swapped_points = np.concatenate([new_swapped_points, to_swap_points[:, -1:]], axis=1)
+                
+                new_to_swap_points = np.concatenate([new_to_swap_points, new_to_swap_points_intensity], axis=1)
+                new_swapped_points = np.concatenate([new_swapped_points, new_swapped_points_intensity], axis=1)
+                
+                points_res.append(new_to_swap_points)
+                points_res.append(new_swapped_points)
+            
+            points_res = np.concatenate(points_res, axis=0)
+            points = np.concatenate([remain_points, points_res], axis=0)
+    return gt_boxes, points
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/data_augmentor.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/data_augmentor.py
new file mode 100644
index 000000000..56acebc81
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/data_augmentor.py
@@ -0,0 +1,319 @@
+from functools import partial
+
+import numpy as np
+from PIL import Image
+
+from ...utils import common_utils
+from . import augmentor_utils, database_sampler
+
+
+class DataAugmentor(object):
+    def __init__(self, root_path, augmentor_configs, class_names, logger=None):
+        self.root_path = root_path
+        self.class_names = class_names
+        self.logger = logger
+
+        self.data_augmentor_queue = []
+        aug_config_list = augmentor_configs if isinstance(augmentor_configs, list) \
+            else augmentor_configs.AUG_CONFIG_LIST
+
+        for cur_cfg in aug_config_list:
+            if not isinstance(augmentor_configs, list):
+                if cur_cfg.NAME in augmentor_configs.DISABLE_AUG_LIST:
+                    continue
+            cur_augmentor = getattr(self, cur_cfg.NAME)(config=cur_cfg)
+            self.data_augmentor_queue.append(cur_augmentor)
+
+    def disable_augmentation(self, augmentor_configs):
+        self.data_augmentor_queue = []
+        aug_config_list = augmentor_configs if isinstance(augmentor_configs, list) \
+            else augmentor_configs.AUG_CONFIG_LIST
+
+        for cur_cfg in aug_config_list:
+            if not isinstance(augmentor_configs, list):
+                if cur_cfg.NAME in augmentor_configs.DISABLE_AUG_LIST:
+                    continue
+            cur_augmentor = getattr(self, cur_cfg.NAME)(config=cur_cfg)
+            self.data_augmentor_queue.append(cur_augmentor)
+             
+    def gt_sampling(self, config=None):
+        db_sampler = database_sampler.DataBaseSampler(
+            root_path=self.root_path,
+            sampler_cfg=config,
+            class_names=self.class_names,
+            logger=self.logger
+        )
+        return db_sampler
+
+    def __getstate__(self):
+        d = dict(self.__dict__)
+        del d['logger']
+        return d
+
+    def __setstate__(self, d):
+        self.__dict__.update(d)
+
+    def random_world_flip(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.random_world_flip, config=config)
+        gt_boxes, points = data_dict['gt_boxes'], data_dict['points']
+        for cur_axis in config['ALONG_AXIS_LIST']:
+            assert cur_axis in ['x', 'y']
+            gt_boxes, points, enable = getattr(augmentor_utils, 'random_flip_along_%s' % cur_axis)(
+                gt_boxes, points, return_flip=True
+            )
+            data_dict['flip_%s'%cur_axis] = enable
+            if 'roi_boxes' in data_dict.keys():
+                num_frame, num_rois,dim = data_dict['roi_boxes'].shape
+                roi_boxes, _, _ = getattr(augmentor_utils, 'random_flip_along_%s' % cur_axis)(
+                data_dict['roi_boxes'].reshape(-1,dim), np.zeros([1,3]), return_flip=True, enable=enable
+                )
+                data_dict['roi_boxes'] = roi_boxes.reshape(num_frame, num_rois,dim)
+
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        return data_dict
+
+    def random_world_rotation(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.random_world_rotation, config=config)
+        rot_range = config['WORLD_ROT_ANGLE']
+        if not isinstance(rot_range, list):
+            rot_range = [-rot_range, rot_range]
+        gt_boxes, points, noise_rot = augmentor_utils.global_rotation(
+            data_dict['gt_boxes'], data_dict['points'], rot_range=rot_range, return_rot=True
+        )
+        if 'roi_boxes' in data_dict.keys():
+            num_frame, num_rois,dim = data_dict['roi_boxes'].shape
+            roi_boxes, _, _ = augmentor_utils.global_rotation(
+            data_dict['roi_boxes'].reshape(-1, dim), np.zeros([1, 3]), rot_range=rot_range, return_rot=True, noise_rotation=noise_rot)
+            data_dict['roi_boxes'] = roi_boxes.reshape(num_frame, num_rois,dim)
+
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        data_dict['noise_rot'] = noise_rot
+        return data_dict
+
+    def random_world_scaling(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.random_world_scaling, config=config)
+        
+        if 'roi_boxes' in data_dict.keys():
+            gt_boxes, roi_boxes, points, noise_scale = augmentor_utils.global_scaling_with_roi_boxes(
+                data_dict['gt_boxes'], data_dict['roi_boxes'], data_dict['points'], config['WORLD_SCALE_RANGE'], return_scale=True
+            )
+            data_dict['roi_boxes'] = roi_boxes
+        else:
+            gt_boxes, points, noise_scale = augmentor_utils.global_scaling(
+                data_dict['gt_boxes'], data_dict['points'], config['WORLD_SCALE_RANGE'], return_scale=True
+            )
+
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        data_dict['noise_scale'] = noise_scale
+        return data_dict
+
+    def random_image_flip(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.random_image_flip, config=config)
+        images = data_dict["images"]
+        depth_maps = data_dict["depth_maps"]
+        gt_boxes = data_dict['gt_boxes']
+        gt_boxes2d = data_dict["gt_boxes2d"]
+        calib = data_dict["calib"]
+        for cur_axis in config['ALONG_AXIS_LIST']:
+            assert cur_axis in ['horizontal']
+            images, depth_maps, gt_boxes = getattr(augmentor_utils, 'random_image_flip_%s' % cur_axis)(
+                images, depth_maps, gt_boxes, calib,
+            )
+
+        data_dict['images'] = images
+        data_dict['depth_maps'] = depth_maps
+        data_dict['gt_boxes'] = gt_boxes
+        return data_dict
+
+    def random_world_translation(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.random_world_translation, config=config)
+        noise_translate_std = config['NOISE_TRANSLATE_STD']
+        assert len(noise_translate_std) == 3
+        noise_translate = np.array([
+            np.random.normal(0, noise_translate_std[0], 1),
+            np.random.normal(0, noise_translate_std[1], 1),
+            np.random.normal(0, noise_translate_std[2], 1),
+        ], dtype=np.float32).T
+
+        gt_boxes, points = data_dict['gt_boxes'], data_dict['points']
+        points[:, :3] += noise_translate
+        gt_boxes[:, :3] += noise_translate
+                
+        if 'roi_boxes' in data_dict.keys():
+            data_dict['roi_boxes'][:, :3] += noise_translate
+        
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        data_dict['noise_translate'] = noise_translate
+        return data_dict
+
+    def random_local_translation(self, data_dict=None, config=None):
+        """
+        Please check the correctness of it before using.
+        """
+        if data_dict is None:
+            return partial(self.random_local_translation, config=config)
+        offset_range = config['LOCAL_TRANSLATION_RANGE']
+        gt_boxes, points = data_dict['gt_boxes'], data_dict['points']
+        for cur_axis in config['ALONG_AXIS_LIST']:
+            assert cur_axis in ['x', 'y', 'z']
+            gt_boxes, points = getattr(augmentor_utils, 'random_local_translation_along_%s' % cur_axis)(
+                gt_boxes, points, offset_range,
+            )
+
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        return data_dict
+
+    def random_local_rotation(self, data_dict=None, config=None):
+        """
+        Please check the correctness of it before using.
+        """
+        if data_dict is None:
+            return partial(self.random_local_rotation, config=config)
+        rot_range = config['LOCAL_ROT_ANGLE']
+        if not isinstance(rot_range, list):
+            rot_range = [-rot_range, rot_range]
+        gt_boxes, points = augmentor_utils.local_rotation(
+            data_dict['gt_boxes'], data_dict['points'], rot_range=rot_range
+        )
+
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        return data_dict
+
+    def random_local_scaling(self, data_dict=None, config=None):
+        """
+        Please check the correctness of it before using.
+        """
+        if data_dict is None:
+            return partial(self.random_local_scaling, config=config)
+        gt_boxes, points = augmentor_utils.local_scaling(
+            data_dict['gt_boxes'], data_dict['points'], config['LOCAL_SCALE_RANGE']
+        )
+
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        return data_dict
+
+    def random_world_frustum_dropout(self, data_dict=None, config=None):
+        """
+        Please check the correctness of it before using.
+        """
+        if data_dict is None:
+            return partial(self.random_world_frustum_dropout, config=config)
+
+        intensity_range = config['INTENSITY_RANGE']
+        gt_boxes, points = data_dict['gt_boxes'], data_dict['points']
+        for direction in config['DIRECTION']:
+            assert direction in ['top', 'bottom', 'left', 'right']
+            gt_boxes, points = getattr(augmentor_utils, 'global_frustum_dropout_%s' % direction)(
+                gt_boxes, points, intensity_range,
+            )
+
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        return data_dict
+
+    def random_local_frustum_dropout(self, data_dict=None, config=None):
+        """
+        Please check the correctness of it before using.
+        """
+        if data_dict is None:
+            return partial(self.random_local_frustum_dropout, config=config)
+
+        intensity_range = config['INTENSITY_RANGE']
+        gt_boxes, points = data_dict['gt_boxes'], data_dict['points']
+        for direction in config['DIRECTION']:
+            assert direction in ['top', 'bottom', 'left', 'right']
+            gt_boxes, points = getattr(augmentor_utils, 'local_frustum_dropout_%s' % direction)(
+                gt_boxes, points, intensity_range,
+            )
+
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        return data_dict
+
+    def random_local_pyramid_aug(self, data_dict=None, config=None):
+        """
+        Refer to the paper:
+            SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud
+        """
+        if data_dict is None:
+            return partial(self.random_local_pyramid_aug, config=config)
+
+        gt_boxes, points = data_dict['gt_boxes'], data_dict['points']
+
+        gt_boxes, points, pyramids = augmentor_utils.local_pyramid_dropout(gt_boxes, points, config['DROP_PROB'])
+        gt_boxes, points, pyramids = augmentor_utils.local_pyramid_sparsify(gt_boxes, points,
+                                                                            config['SPARSIFY_PROB'],
+                                                                            config['SPARSIFY_MAX_NUM'],
+                                                                            pyramids)
+        gt_boxes, points = augmentor_utils.local_pyramid_swap(gt_boxes, points,
+                                                                 config['SWAP_PROB'],
+                                                                 config['SWAP_MAX_NUM'],
+                                                                 pyramids)
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['points'] = points
+        return data_dict
+
+    def imgaug(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.imgaug, config=config)
+        imgs = data_dict["camera_imgs"]
+        img_process_infos = data_dict['img_process_infos']
+        new_imgs = []
+        for img, img_process_info in zip(imgs, img_process_infos):
+            flip = False
+            if config.RAND_FLIP and np.random.choice([0, 1]):
+                flip = True
+            rotate = np.random.uniform(*config.ROT_LIM)
+            # aug images
+            if flip:
+                img = img.transpose(method=Image.FLIP_LEFT_RIGHT)
+            img = img.rotate(rotate)
+            img_process_info[2] = flip
+            img_process_info[3] = rotate
+            new_imgs.append(img)
+
+        data_dict["camera_imgs"] = new_imgs
+        return data_dict
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                points: (N, 3 + C_in)
+                gt_boxes: optional, (N, 7) [x, y, z, dx, dy, dz, heading]
+                gt_names: optional, (N), string
+                ...
+
+        Returns:
+        """
+        for cur_augmentor in self.data_augmentor_queue:
+            data_dict = cur_augmentor(data_dict=data_dict)
+
+        data_dict['gt_boxes'][:, 6] = common_utils.limit_period(
+            data_dict['gt_boxes'][:, 6], offset=0.5, period=2 * np.pi
+        )
+        # if 'calib' in data_dict:
+        #     data_dict.pop('calib')
+        if 'road_plane' in data_dict:
+            data_dict.pop('road_plane')
+        if 'gt_boxes_mask' in data_dict:
+            gt_boxes_mask = data_dict['gt_boxes_mask']
+            data_dict['gt_boxes'] = data_dict['gt_boxes'][gt_boxes_mask]
+            data_dict['gt_names'] = data_dict['gt_names'][gt_boxes_mask]
+            if 'gt_boxes2d' in data_dict:
+                data_dict['gt_boxes2d'] = data_dict['gt_boxes2d'][gt_boxes_mask]
+
+            data_dict.pop('gt_boxes_mask')
+        return data_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/database_sampler.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/database_sampler.py
new file mode 100644
index 000000000..105708a60
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/augmentor/database_sampler.py
@@ -0,0 +1,502 @@
+import pickle
+
+import os
+import copy
+import numpy as np
+from skimage import io
+import torch
+import SharedArray
+import torch.distributed as dist
+
+from ...ops.iou3d_nms import iou3d_nms_utils
+from ...utils import box_utils, common_utils, calibration_kitti
+from pcdet.datasets.kitti.kitti_object_eval_python import kitti_common
+
+class DataBaseSampler(object):
+    def __init__(self, root_path, sampler_cfg, class_names, logger=None):
+        self.root_path = root_path
+        self.class_names = class_names
+        self.sampler_cfg = sampler_cfg
+
+        self.img_aug_type = sampler_cfg.get('IMG_AUG_TYPE', None)
+        self.img_aug_iou_thresh = sampler_cfg.get('IMG_AUG_IOU_THRESH', 0.5)
+
+        self.logger = logger
+        self.db_infos = {}
+        for class_name in class_names:
+            self.db_infos[class_name] = []
+
+        self.use_shared_memory = sampler_cfg.get('USE_SHARED_MEMORY', False)
+
+        for db_info_path in sampler_cfg.DB_INFO_PATH:
+            db_info_path = self.root_path.resolve() / db_info_path
+            if not db_info_path.exists():
+                assert len(sampler_cfg.DB_INFO_PATH) == 1
+                sampler_cfg.DB_INFO_PATH[0] = sampler_cfg.BACKUP_DB_INFO['DB_INFO_PATH']
+                sampler_cfg.DB_DATA_PATH[0] = sampler_cfg.BACKUP_DB_INFO['DB_DATA_PATH']
+                db_info_path = self.root_path.resolve() / sampler_cfg.DB_INFO_PATH[0]
+                sampler_cfg.NUM_POINT_FEATURES = sampler_cfg.BACKUP_DB_INFO['NUM_POINT_FEATURES']
+
+            with open(str(db_info_path), 'rb') as f:
+                infos = pickle.load(f)
+                [self.db_infos[cur_class].extend(infos[cur_class]) for cur_class in class_names]
+
+        for func_name, val in sampler_cfg.PREPARE.items():
+            self.db_infos = getattr(self, func_name)(self.db_infos, val)
+
+        self.gt_database_data_key = self.load_db_to_shared_memory() if self.use_shared_memory else None
+
+        self.sample_groups = {}
+        self.sample_class_num = {}
+        self.limit_whole_scene = sampler_cfg.get('LIMIT_WHOLE_SCENE', False)
+
+        for x in sampler_cfg.SAMPLE_GROUPS:
+            class_name, sample_num = x.split(':')
+            if class_name not in class_names:
+                continue
+            self.sample_class_num[class_name] = sample_num
+            self.sample_groups[class_name] = {
+                'sample_num': sample_num,
+                'pointer': len(self.db_infos[class_name]),
+                'indices': np.arange(len(self.db_infos[class_name]))
+            }
+
+    def __getstate__(self):
+        d = dict(self.__dict__)
+        del d['logger']
+        return d
+
+    def __setstate__(self, d):
+        self.__dict__.update(d)
+
+    def __del__(self):
+        if self.use_shared_memory:
+            self.logger.info('Deleting GT database from shared memory')
+            cur_rank, num_gpus = common_utils.get_dist_info()
+            sa_key = self.sampler_cfg.DB_DATA_PATH[0]
+            if cur_rank % num_gpus == 0 and os.path.exists(f"/dev/shm/{sa_key}"):
+                SharedArray.delete(f"shm://{sa_key}")
+
+            if num_gpus > 1:
+                dist.barrier()
+            self.logger.info('GT database has been removed from shared memory')
+
+    def load_db_to_shared_memory(self):
+        self.logger.info('Loading GT database to shared memory')
+        cur_rank, world_size, num_gpus = common_utils.get_dist_info(return_gpu_per_machine=True)
+
+        assert self.sampler_cfg.DB_DATA_PATH.__len__() == 1, 'Current only support single DB_DATA'
+        db_data_path = self.root_path.resolve() / self.sampler_cfg.DB_DATA_PATH[0]
+        sa_key = self.sampler_cfg.DB_DATA_PATH[0]
+
+        if cur_rank % num_gpus == 0 and not os.path.exists(f"/dev/shm/{sa_key}"):
+            gt_database_data = np.load(db_data_path)
+            common_utils.sa_create(f"shm://{sa_key}", gt_database_data)
+
+        if num_gpus > 1:
+            dist.barrier()
+        self.logger.info('GT database has been saved to shared memory')
+        return sa_key
+
+    def filter_by_difficulty(self, db_infos, removed_difficulty):
+        new_db_infos = {}
+        for key, dinfos in db_infos.items():
+            pre_len = len(dinfos)
+            new_db_infos[key] = [
+                info for info in dinfos
+                if info['difficulty'] not in removed_difficulty
+            ]
+            if self.logger is not None:
+                self.logger.info('Database filter by difficulty %s: %d => %d' % (key, pre_len, len(new_db_infos[key])))
+        return new_db_infos
+
+    def filter_by_min_points(self, db_infos, min_gt_points_list):
+        for name_num in min_gt_points_list:
+            name, min_num = name_num.split(':')
+            min_num = int(min_num)
+            if min_num > 0 and name in db_infos.keys():
+                filtered_infos = []
+                for info in db_infos[name]:
+                    if info['num_points_in_gt'] >= min_num:
+                        filtered_infos.append(info)
+
+                if self.logger is not None:
+                    self.logger.info('Database filter by min points %s: %d => %d' %
+                                     (name, len(db_infos[name]), len(filtered_infos)))
+                db_infos[name] = filtered_infos
+
+        return db_infos
+
+    def sample_with_fixed_number(self, class_name, sample_group):
+        """
+        Args:
+            class_name:
+            sample_group:
+        Returns:
+
+        """
+        sample_num, pointer, indices = int(sample_group['sample_num']), sample_group['pointer'], sample_group['indices']
+        if pointer >= len(self.db_infos[class_name]):
+            indices = np.random.permutation(len(self.db_infos[class_name]))
+            pointer = 0
+
+        sampled_dict = [self.db_infos[class_name][idx] for idx in indices[pointer: pointer + sample_num]]
+        pointer += sample_num
+        sample_group['pointer'] = pointer
+        sample_group['indices'] = indices
+        return sampled_dict
+
+    @staticmethod
+    def put_boxes_on_road_planes(gt_boxes, road_planes, calib):
+        """
+        Only validate in KITTIDataset
+        Args:
+            gt_boxes: (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+            road_planes: [a, b, c, d]
+            calib:
+
+        Returns:
+        """
+        a, b, c, d = road_planes
+        center_cam = calib.lidar_to_rect(gt_boxes[:, 0:3])
+        cur_height_cam = (-d - a * center_cam[:, 0] - c * center_cam[:, 2]) / b
+        center_cam[:, 1] = cur_height_cam
+        cur_lidar_height = calib.rect_to_lidar(center_cam)[:, 2]
+        mv_height = gt_boxes[:, 2] - gt_boxes[:, 5] / 2 - cur_lidar_height
+        gt_boxes[:, 2] -= mv_height  # lidar view
+        return gt_boxes, mv_height
+
+    def copy_paste_to_image_kitti(self, data_dict, crop_feat, gt_number, point_idxes=None):
+        kitti_img_aug_type = 'by_depth'
+        kitti_img_aug_use_type = 'annotation'
+
+        image = data_dict['images']
+        boxes3d = data_dict['gt_boxes']
+        boxes2d = data_dict['gt_boxes2d']
+        corners_lidar = box_utils.boxes_to_corners_3d(boxes3d)
+        if 'depth' in kitti_img_aug_type:
+            paste_order = boxes3d[:,0].argsort()
+            paste_order = paste_order[::-1]
+        else:
+            paste_order = np.arange(len(boxes3d),dtype=np.int)
+
+        if 'reverse' in kitti_img_aug_type:
+            paste_order = paste_order[::-1]
+
+        paste_mask = -255 * np.ones(image.shape[:2], dtype=np.int)
+        fg_mask = np.zeros(image.shape[:2], dtype=np.int)
+        overlap_mask = np.zeros(image.shape[:2], dtype=np.int)
+        depth_mask = np.zeros((*image.shape[:2], 2), dtype=np.float)
+        points_2d, depth_2d = data_dict['calib'].lidar_to_img(data_dict['points'][:,:3])
+        points_2d[:,0] = np.clip(points_2d[:,0], a_min=0, a_max=image.shape[1]-1)
+        points_2d[:,1] = np.clip(points_2d[:,1], a_min=0, a_max=image.shape[0]-1)
+        points_2d = points_2d.astype(np.int)
+        for _order in paste_order:
+            _box2d = boxes2d[_order]
+            image[_box2d[1]:_box2d[3],_box2d[0]:_box2d[2]] = crop_feat[_order]
+            overlap_mask[_box2d[1]:_box2d[3],_box2d[0]:_box2d[2]] += \
+                (paste_mask[_box2d[1]:_box2d[3],_box2d[0]:_box2d[2]] > 0).astype(np.int)
+            paste_mask[_box2d[1]:_box2d[3],_box2d[0]:_box2d[2]] = _order
+
+            if 'cover' in kitti_img_aug_use_type:
+                # HxWx2 for min and max depth of each box region
+                depth_mask[_box2d[1]:_box2d[3],_box2d[0]:_box2d[2],0] = corners_lidar[_order,:,0].min()
+                depth_mask[_box2d[1]:_box2d[3],_box2d[0]:_box2d[2],1] = corners_lidar[_order,:,0].max()
+
+            # foreground area of original point cloud in image plane
+            if _order < gt_number:
+                fg_mask[_box2d[1]:_box2d[3],_box2d[0]:_box2d[2]] = 1
+
+        data_dict['images'] = image
+
+        # if not self.joint_sample:
+        #     return data_dict
+
+        new_mask = paste_mask[points_2d[:,1], points_2d[:,0]]==(point_idxes+gt_number)
+        if False:  # self.keep_raw:
+            raw_mask = (point_idxes == -1)
+        else:
+            raw_fg = (fg_mask == 1) & (paste_mask >= 0) & (paste_mask < gt_number)
+            raw_bg = (fg_mask == 0) & (paste_mask < 0)
+            raw_mask = raw_fg[points_2d[:,1], points_2d[:,0]] | raw_bg[points_2d[:,1], points_2d[:,0]]
+        keep_mask = new_mask | raw_mask
+        data_dict['points_2d'] = points_2d
+
+        if 'annotation' in kitti_img_aug_use_type:
+            data_dict['points'] = data_dict['points'][keep_mask]
+            data_dict['points_2d'] = data_dict['points_2d'][keep_mask]
+        elif 'projection' in kitti_img_aug_use_type:
+            overlap_mask[overlap_mask>=1] = 1
+            data_dict['overlap_mask'] = overlap_mask
+            if 'cover' in kitti_img_aug_use_type:
+                data_dict['depth_mask'] = depth_mask
+
+        return data_dict
+
+    def collect_image_crops_kitti(self, info, data_dict, obj_points, sampled_gt_boxes, sampled_gt_boxes2d, idx):
+        calib_file = kitti_common.get_calib_path(int(info['image_idx']), self.root_path, relative_path=False)
+        sampled_calib = calibration_kitti.Calibration(calib_file)
+        points_2d, depth_2d = sampled_calib.lidar_to_img(obj_points[:,:3])
+
+        if True:  # self.point_refine:
+            # align calibration metrics for points
+            points_ract = data_dict['calib'].img_to_rect(points_2d[:,0], points_2d[:,1], depth_2d)
+            points_lidar = data_dict['calib'].rect_to_lidar(points_ract)
+            obj_points[:, :3] = points_lidar
+            # align calibration metrics for boxes
+            box3d_raw = sampled_gt_boxes[idx].reshape(1,-1)
+            box3d_coords = box_utils.boxes_to_corners_3d(box3d_raw)[0]
+            box3d_box, box3d_depth = sampled_calib.lidar_to_img(box3d_coords)
+            box3d_coord_rect = data_dict['calib'].img_to_rect(box3d_box[:,0], box3d_box[:,1], box3d_depth)
+            box3d_rect = box_utils.corners_rect_to_camera(box3d_coord_rect).reshape(1,-1)
+            box3d_lidar = box_utils.boxes3d_kitti_camera_to_lidar(box3d_rect, data_dict['calib'])
+            box2d = box_utils.boxes3d_kitti_camera_to_imageboxes(box3d_rect, data_dict['calib'],
+                                                                    data_dict['images'].shape[:2])
+            sampled_gt_boxes[idx] = box3d_lidar[0]
+            sampled_gt_boxes2d[idx] = box2d[0]
+
+        obj_idx = idx * np.ones(len(obj_points), dtype=np.int)
+
+        # copy crops from images
+        img_path = self.root_path /  f'training/image_2/{info["image_idx"]}.png'
+        raw_image = io.imread(img_path)
+        raw_image = raw_image.astype(np.float32)
+        raw_center = info['bbox'].reshape(2,2).mean(0)
+        new_box = sampled_gt_boxes2d[idx].astype(np.int)
+        new_shape = np.array([new_box[2]-new_box[0], new_box[3]-new_box[1]])
+        raw_box = np.concatenate([raw_center-new_shape/2, raw_center+new_shape/2]).astype(np.int)
+        raw_box[0::2] = np.clip(raw_box[0::2], a_min=0, a_max=raw_image.shape[1])
+        raw_box[1::2] = np.clip(raw_box[1::2], a_min=0, a_max=raw_image.shape[0])
+        if (raw_box[2]-raw_box[0])!=new_shape[0] or (raw_box[3]-raw_box[1])!=new_shape[1]:
+            new_center = new_box.reshape(2,2).mean(0)
+            new_shape = np.array([raw_box[2]-raw_box[0], raw_box[3]-raw_box[1]])
+            new_box = np.concatenate([new_center-new_shape/2, new_center+new_shape/2]).astype(np.int)
+
+        img_crop2d = raw_image[raw_box[1]:raw_box[3],raw_box[0]:raw_box[2]] / 255
+
+        return new_box, img_crop2d, obj_points, obj_idx
+
+    def sample_gt_boxes_2d_kitti(self, data_dict, sampled_boxes, valid_mask):
+        mv_height = None
+        # filter out box2d iou > thres
+        if self.sampler_cfg.get('USE_ROAD_PLANE', False):
+            sampled_boxes, mv_height = self.put_boxes_on_road_planes(
+                sampled_boxes, data_dict['road_plane'], data_dict['calib']
+            )
+
+        # sampled_boxes2d = np.stack([x['bbox'] for x in sampled_dict], axis=0).astype(np.float32)
+        boxes3d_camera = box_utils.boxes3d_lidar_to_kitti_camera(sampled_boxes, data_dict['calib'])
+        sampled_boxes2d = box_utils.boxes3d_kitti_camera_to_imageboxes(boxes3d_camera, data_dict['calib'],
+                                                                        data_dict['images'].shape[:2])
+        sampled_boxes2d = torch.Tensor(sampled_boxes2d)
+        existed_boxes2d = torch.Tensor(data_dict['gt_boxes2d'])
+        iou2d1 = box_utils.pairwise_iou(sampled_boxes2d, existed_boxes2d).cpu().numpy()
+        iou2d2 = box_utils.pairwise_iou(sampled_boxes2d, sampled_boxes2d).cpu().numpy()
+        iou2d2[range(sampled_boxes2d.shape[0]), range(sampled_boxes2d.shape[0])] = 0
+        iou2d1 = iou2d1 if iou2d1.shape[1] > 0 else iou2d2
+
+        ret_valid_mask = ((iou2d1.max(axis=1)<self.img_aug_iou_thresh) &
+                         (iou2d2.max(axis=1)<self.img_aug_iou_thresh) &
+                         (valid_mask))
+
+        sampled_boxes2d = sampled_boxes2d[ret_valid_mask].cpu().numpy()
+        if mv_height is not None:
+            mv_height = mv_height[ret_valid_mask]
+        return sampled_boxes2d, mv_height, ret_valid_mask
+
+    def sample_gt_boxes_2d(self, data_dict, sampled_boxes, valid_mask):
+        mv_height = None
+
+        if self.img_aug_type == 'kitti':
+            sampled_boxes2d, mv_height, ret_valid_mask = self.sample_gt_boxes_2d_kitti(data_dict, sampled_boxes, valid_mask)
+        else:
+            raise NotImplementedError
+
+        return sampled_boxes2d, mv_height, ret_valid_mask
+
+    def initilize_image_aug_dict(self, data_dict, gt_boxes_mask):
+        img_aug_gt_dict = None
+        if self.img_aug_type is None:
+            pass
+        elif self.img_aug_type == 'kitti':
+            obj_index_list, crop_boxes2d = [], []
+            gt_number = gt_boxes_mask.sum().astype(np.int)
+            gt_boxes2d = data_dict['gt_boxes2d'][gt_boxes_mask].astype(np.int)
+            gt_crops2d = [data_dict['images'][_x[1]:_x[3],_x[0]:_x[2]] for _x in gt_boxes2d]
+
+            img_aug_gt_dict = {
+                'obj_index_list': obj_index_list,
+                'gt_crops2d': gt_crops2d,
+                'gt_boxes2d': gt_boxes2d,
+                'gt_number': gt_number,
+                'crop_boxes2d': crop_boxes2d
+            }
+        else:
+            raise NotImplementedError
+
+        return img_aug_gt_dict
+
+    def collect_image_crops(self, img_aug_gt_dict, info, data_dict, obj_points, sampled_gt_boxes, sampled_gt_boxes2d, idx):
+        if self.img_aug_type == 'kitti':
+            new_box, img_crop2d, obj_points, obj_idx = self.collect_image_crops_kitti(info, data_dict,
+                                                    obj_points, sampled_gt_boxes, sampled_gt_boxes2d, idx)
+            img_aug_gt_dict['crop_boxes2d'].append(new_box)
+            img_aug_gt_dict['gt_crops2d'].append(img_crop2d)
+            img_aug_gt_dict['obj_index_list'].append(obj_idx)
+        else:
+            raise NotImplementedError
+
+        return img_aug_gt_dict, obj_points
+
+    def copy_paste_to_image(self, img_aug_gt_dict, data_dict, points):
+        if self.img_aug_type == 'kitti':
+            obj_points_idx = np.concatenate(img_aug_gt_dict['obj_index_list'], axis=0)
+            point_idxes = -1 * np.ones(len(points), dtype=np.int)
+            point_idxes[:obj_points_idx.shape[0]] = obj_points_idx
+
+            data_dict['gt_boxes2d'] = np.concatenate([img_aug_gt_dict['gt_boxes2d'], np.array(img_aug_gt_dict['crop_boxes2d'])], axis=0)
+            data_dict = self.copy_paste_to_image_kitti(data_dict, img_aug_gt_dict['gt_crops2d'], img_aug_gt_dict['gt_number'], point_idxes)
+            if 'road_plane' in data_dict:
+                data_dict.pop('road_plane')
+        else:
+            raise NotImplementedError
+        return data_dict
+
+    def add_sampled_boxes_to_scene(self, data_dict, sampled_gt_boxes, total_valid_sampled_dict, mv_height=None, sampled_gt_boxes2d=None):
+        gt_boxes_mask = data_dict['gt_boxes_mask']
+        gt_boxes = data_dict['gt_boxes'][gt_boxes_mask]
+        gt_names = data_dict['gt_names'][gt_boxes_mask]
+        points = data_dict['points']
+        if self.sampler_cfg.get('USE_ROAD_PLANE', False) and mv_height is None:
+            sampled_gt_boxes, mv_height = self.put_boxes_on_road_planes(
+                sampled_gt_boxes, data_dict['road_plane'], data_dict['calib']
+            )
+            data_dict.pop('calib')
+            data_dict.pop('road_plane')
+
+        obj_points_list = []
+
+        # convert sampled 3D boxes to image plane
+        img_aug_gt_dict = self.initilize_image_aug_dict(data_dict, gt_boxes_mask)
+
+        if self.use_shared_memory:
+            gt_database_data = SharedArray.attach(f"shm://{self.gt_database_data_key}")
+            gt_database_data.setflags(write=0)
+        else:
+            gt_database_data = None
+
+        for idx, info in enumerate(total_valid_sampled_dict):
+            if self.use_shared_memory:
+                start_offset, end_offset = info['global_data_offset']
+                obj_points = copy.deepcopy(gt_database_data[start_offset:end_offset])
+            else:
+                file_path = self.root_path / info['path']
+
+                obj_points = np.fromfile(str(file_path), dtype=np.float32).reshape(
+                    [-1, self.sampler_cfg.NUM_POINT_FEATURES])
+                if obj_points.shape[0] != info['num_points_in_gt']:
+                    obj_points = np.fromfile(str(file_path), dtype=np.float64).reshape(-1, self.sampler_cfg.NUM_POINT_FEATURES)
+
+            assert obj_points.shape[0] == info['num_points_in_gt']
+            obj_points[:, :3] += info['box3d_lidar'][:3].astype(np.float32)
+
+            if self.sampler_cfg.get('USE_ROAD_PLANE', False):
+                # mv height
+                obj_points[:, 2] -= mv_height[idx]
+
+            if self.img_aug_type is not None:
+                img_aug_gt_dict, obj_points = self.collect_image_crops(
+                    img_aug_gt_dict, info, data_dict, obj_points, sampled_gt_boxes, sampled_gt_boxes2d, idx
+                )
+
+            obj_points_list.append(obj_points)
+
+        obj_points = np.concatenate(obj_points_list, axis=0)
+        sampled_gt_names = np.array([x['name'] for x in total_valid_sampled_dict])
+
+        if self.sampler_cfg.get('FILTER_OBJ_POINTS_BY_TIMESTAMP', False) or obj_points.shape[-1] != points.shape[-1]:
+            if self.sampler_cfg.get('FILTER_OBJ_POINTS_BY_TIMESTAMP', False):
+                min_time = min(self.sampler_cfg.TIME_RANGE[0], self.sampler_cfg.TIME_RANGE[1])
+                max_time = max(self.sampler_cfg.TIME_RANGE[0], self.sampler_cfg.TIME_RANGE[1])
+            else:
+                assert obj_points.shape[-1] == points.shape[-1] + 1
+                # transform multi-frame GT points to single-frame GT points
+                min_time = max_time = 0.0
+
+            time_mask = np.logical_and(obj_points[:, -1] < max_time + 1e-6, obj_points[:, -1] > min_time - 1e-6)
+            obj_points = obj_points[time_mask]
+
+        large_sampled_gt_boxes = box_utils.enlarge_box3d(
+            sampled_gt_boxes[:, 0:7], extra_width=self.sampler_cfg.REMOVE_EXTRA_WIDTH
+        )
+        points = box_utils.remove_points_in_boxes3d(points, large_sampled_gt_boxes)
+        points = np.concatenate([obj_points[:, :points.shape[-1]], points], axis=0)
+        gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0)
+        gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes], axis=0)
+        data_dict['gt_boxes'] = gt_boxes
+        data_dict['gt_names'] = gt_names
+        data_dict['points'] = points
+
+        if self.img_aug_type is not None:
+            data_dict = self.copy_paste_to_image(img_aug_gt_dict, data_dict, points)
+
+        return data_dict
+
+    def __call__(self, data_dict):
+        """
+        Args:
+            data_dict:
+                gt_boxes: (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+
+        Returns:
+
+        """
+        gt_boxes = data_dict['gt_boxes']
+        gt_names = data_dict['gt_names'].astype(str)
+        existed_boxes = gt_boxes
+        total_valid_sampled_dict = []
+        sampled_mv_height = []
+        sampled_gt_boxes2d = []
+
+        for class_name, sample_group in self.sample_groups.items():
+            if self.limit_whole_scene:
+                num_gt = np.sum(class_name == gt_names)
+                sample_group['sample_num'] = str(int(self.sample_class_num[class_name]) - num_gt)
+            if int(sample_group['sample_num']) > 0:
+                sampled_dict = self.sample_with_fixed_number(class_name, sample_group)
+
+                sampled_boxes = np.stack([x['box3d_lidar'] for x in sampled_dict], axis=0).astype(np.float32)
+
+                assert not self.sampler_cfg.get('DATABASE_WITH_FAKELIDAR', False), 'Please use latest codes to generate GT_DATABASE'
+
+                iou1 = iou3d_nms_utils.boxes_bev_iou_cpu(sampled_boxes[:, 0:7], existed_boxes[:, 0:7])
+                iou2 = iou3d_nms_utils.boxes_bev_iou_cpu(sampled_boxes[:, 0:7], sampled_boxes[:, 0:7])
+                iou2[range(sampled_boxes.shape[0]), range(sampled_boxes.shape[0])] = 0
+                iou1 = iou1 if iou1.shape[1] > 0 else iou2
+                valid_mask = ((iou1.max(axis=1) + iou2.max(axis=1)) == 0)
+
+                if self.img_aug_type is not None:
+                    sampled_boxes2d, mv_height, valid_mask = self.sample_gt_boxes_2d(data_dict, sampled_boxes, valid_mask)
+                    sampled_gt_boxes2d.append(sampled_boxes2d)
+                    if mv_height is not None:
+                        sampled_mv_height.append(mv_height)
+
+                valid_mask = valid_mask.nonzero()[0]
+                valid_sampled_dict = [sampled_dict[x] for x in valid_mask]
+                valid_sampled_boxes = sampled_boxes[valid_mask]
+
+                existed_boxes = np.concatenate((existed_boxes, valid_sampled_boxes[:, :existed_boxes.shape[-1]]), axis=0)
+                total_valid_sampled_dict.extend(valid_sampled_dict)
+
+        sampled_gt_boxes = existed_boxes[gt_boxes.shape[0]:, :]
+
+        if total_valid_sampled_dict.__len__() > 0:
+            sampled_gt_boxes2d = np.concatenate(sampled_gt_boxes2d, axis=0) if len(sampled_gt_boxes2d) > 0 else None
+            sampled_mv_height = np.concatenate(sampled_mv_height, axis=0) if len(sampled_mv_height) > 0 else None
+
+            data_dict = self.add_sampled_boxes_to_scene(
+                data_dict, sampled_gt_boxes, total_valid_sampled_dict, sampled_mv_height, sampled_gt_boxes2d
+            )
+
+        data_dict.pop('gt_boxes_mask')
+        return data_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/custom/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/custom/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/custom/custom_dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/custom/custom_dataset.py
new file mode 100644
index 000000000..3715210b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/custom/custom_dataset.py
@@ -0,0 +1,283 @@
+import copy
+import pickle
+import os
+
+import numpy as np
+
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+from ...utils import box_utils, common_utils
+from ..dataset import DatasetTemplate
+
+
+class CustomDataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
+        """
+        Args:
+            root_path:
+            dataset_cfg:
+            class_names:
+            training:
+            logger:
+        """
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
+        )
+        self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
+
+        split_dir = os.path.join(self.root_path, 'ImageSets', (self.split + '.txt'))
+        self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if os.path.exists(split_dir) else None
+
+        self.custom_infos = []
+        self.include_data(self.mode)
+        self.map_class_to_kitti = self.dataset_cfg.MAP_CLASS_TO_KITTI
+
+    def include_data(self, mode):
+        self.logger.info('Loading Custom dataset.')
+        custom_infos = []
+
+        for info_path in self.dataset_cfg.INFO_PATH[mode]:
+            info_path = self.root_path / info_path
+            if not info_path.exists():
+                continue
+            with open(info_path, 'rb') as f:
+                infos = pickle.load(f)
+                custom_infos.extend(infos)
+
+        self.custom_infos.extend(custom_infos)
+        self.logger.info('Total samples for CUSTOM dataset: %d' % (len(custom_infos)))
+
+    def get_label(self, idx):
+        label_file = self.root_path / 'labels' / ('%s.txt' % idx)
+        assert label_file.exists()
+        with open(label_file, 'r') as f:
+            lines = f.readlines()
+
+        # [N, 8]: (x y z dx dy dz heading_angle category_id)
+        gt_boxes = []
+        gt_names = []
+        for line in lines:
+            line_list = line.strip().split(' ')
+            gt_boxes.append(line_list[:-1])
+            gt_names.append(line_list[-1])
+
+        return np.array(gt_boxes, dtype=np.float32), np.array(gt_names)
+
+    def get_lidar(self, idx):
+        lidar_file = self.root_path / 'points' / ('%s.npy' % idx)
+        assert lidar_file.exists()
+        point_features = np.load(lidar_file)
+        return point_features
+
+    def set_split(self, split):
+        super().__init__(
+            dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training,
+            root_path=self.root_path, logger=self.logger
+        )
+        self.split = split
+
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
+
+    def __len__(self):
+        if self._merge_all_iters_to_one_epoch:
+            return len(self.sample_id_list) * self.total_epochs
+
+        return len(self.custom_infos)
+
+    def __getitem__(self, index):
+        if self._merge_all_iters_to_one_epoch:
+            index = index % len(self.custom_infos)
+
+        info = copy.deepcopy(self.custom_infos[index])
+        sample_idx = info['point_cloud']['lidar_idx']
+        points = self.get_lidar(sample_idx)
+        input_dict = {
+            'frame_id': self.sample_id_list[index],
+            'points': points
+        }
+
+        if 'annos' in info:
+            annos = info['annos']
+            annos = common_utils.drop_info_with_name(annos, name='DontCare')
+            gt_names = annos['name']
+            gt_boxes_lidar = annos['gt_boxes_lidar']
+            input_dict.update({
+                'gt_names': gt_names,
+                'gt_boxes': gt_boxes_lidar
+            })
+
+        data_dict = self.prepare_data(data_dict=input_dict)
+
+        return data_dict
+
+    def evaluation(self, det_annos, class_names, **kwargs):
+        if 'annos' not in self.custom_infos[0].keys():
+            return 'No ground-truth boxes for evaluation', {}
+
+        def kitti_eval(eval_det_annos, eval_gt_annos, map_name_to_kitti):
+            from ..kitti.kitti_object_eval_python import eval as kitti_eval
+            from ..kitti import kitti_utils
+
+            kitti_utils.transform_annotations_to_kitti_format(eval_det_annos, map_name_to_kitti=map_name_to_kitti)
+            kitti_utils.transform_annotations_to_kitti_format(
+                eval_gt_annos, map_name_to_kitti=map_name_to_kitti,
+                info_with_fakelidar=self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False)
+            )
+            kitti_class_names = [map_name_to_kitti[x] for x in class_names]
+            ap_result_str, ap_dict = kitti_eval.get_official_eval_result(
+                gt_annos=eval_gt_annos, dt_annos=eval_det_annos, current_classes=kitti_class_names
+            )
+            return ap_result_str, ap_dict
+
+        eval_det_annos = copy.deepcopy(det_annos)
+        eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.custom_infos]
+
+        if kwargs['eval_metric'] == 'kitti':
+            ap_result_str, ap_dict = kitti_eval(eval_det_annos, eval_gt_annos, self.map_class_to_kitti)
+        else:
+            raise NotImplementedError
+
+        return ap_result_str, ap_dict
+
+    def get_infos(self, class_names, num_workers=4, has_label=True, sample_id_list=None, num_features=4):
+        import concurrent.futures as futures
+
+        def process_single_scene(sample_idx):
+            print('%s sample_idx: %s' % (self.split, sample_idx))
+            info = {}
+            pc_info = {'num_features': num_features, 'lidar_idx': sample_idx}
+            info['point_cloud'] = pc_info
+
+            if has_label:
+                annotations = {}
+                gt_boxes_lidar, name = self.get_label(sample_idx)
+                annotations['name'] = name
+                annotations['gt_boxes_lidar'] = gt_boxes_lidar[:, :7]
+                info['annos'] = annotations
+
+            return info
+
+        sample_id_list = sample_id_list if sample_id_list is not None else self.sample_id_list
+
+        # create a thread pool to improve the velocity
+        with futures.ThreadPoolExecutor(num_workers) as executor:
+            infos = executor.map(process_single_scene, sample_id_list)
+        return list(infos)
+
+    def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'):
+        import torch
+
+        database_save_path = Path(self.root_path) / ('gt_database' if split == 'train' else ('gt_database_%s' % split))
+        db_info_save_path = Path(self.root_path) / ('custom_dbinfos_%s.pkl' % split)
+
+        database_save_path.mkdir(parents=True, exist_ok=True)
+        all_db_infos = {}
+
+        with open(info_path, 'rb') as f:
+            infos = pickle.load(f)
+
+        for k in range(len(infos)):
+            print('gt_database sample: %d/%d' % (k + 1, len(infos)))
+            info = infos[k]
+            sample_idx = info['point_cloud']['lidar_idx']
+            points = self.get_lidar(sample_idx)
+            annos = info['annos']
+            names = annos['name']
+            gt_boxes = annos['gt_boxes_lidar']
+
+            num_obj = gt_boxes.shape[0]
+            point_indices = roiaware_pool3d_utils.points_in_boxes_cpu(
+                torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes)
+            ).numpy()  # (nboxes, npoints)
+
+            for i in range(num_obj):
+                filename = '%s_%s_%d.bin' % (sample_idx, names[i], i)
+                filepath = database_save_path / filename
+                gt_points = points[point_indices[i] > 0]
+
+                gt_points[:, :3] -= gt_boxes[i, :3]
+                with open(filepath, 'w') as f:
+                    gt_points.tofile(f)
+
+                if (used_classes is None) or names[i] in used_classes:
+                    db_path = str(filepath.relative_to(self.root_path))  # gt_database/xxxxx.bin
+                    db_info = {'name': names[i], 'path': db_path, 'gt_idx': i,
+                               'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0]}
+                    if names[i] in all_db_infos:
+                        all_db_infos[names[i]].append(db_info)
+                    else:
+                        all_db_infos[names[i]] = [db_info]
+
+        # Output the num of all classes in database
+        for k, v in all_db_infos.items():
+            print('Database %s: %d' % (k, len(v)))
+
+        with open(db_info_save_path, 'wb') as f:
+            pickle.dump(all_db_infos, f)
+
+    @staticmethod
+    def create_label_file_with_name_and_box(class_names, gt_names, gt_boxes, save_label_path):
+        with open(save_label_path, 'w') as f:
+            for idx in range(gt_boxes.shape[0]):
+                boxes = gt_boxes[idx]
+                name = gt_names[idx]
+                if name not in class_names:
+                    continue
+                line = "{x} {y} {z} {l} {w} {h} {angle} {name}\n".format(
+                    x=boxes[0], y=boxes[1], z=(boxes[2]), l=boxes[3],
+                    w=boxes[4], h=boxes[5], angle=boxes[6], name=name
+                )
+                f.write(line)
+
+
+def create_custom_infos(dataset_cfg, class_names, data_path, save_path, workers=4):
+    dataset = CustomDataset(
+        dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path,
+        training=False, logger=common_utils.create_logger()
+    )
+    train_split, val_split = 'train', 'val'
+    num_features = len(dataset_cfg.POINT_FEATURE_ENCODING.src_feature_list)
+
+    train_filename = save_path / ('custom_infos_%s.pkl' % train_split)
+    val_filename = save_path / ('custom_infos_%s.pkl' % val_split)
+
+    print('------------------------Start to generate data infos------------------------')
+
+    dataset.set_split(train_split)
+    custom_infos_train = dataset.get_infos(
+        class_names, num_workers=workers, has_label=True, num_features=num_features
+    )
+    with open(train_filename, 'wb') as f:
+        pickle.dump(custom_infos_train, f)
+    print('Custom info train file is saved to %s' % train_filename)
+
+    dataset.set_split(val_split)
+    custom_infos_val = dataset.get_infos(
+        class_names, num_workers=workers, has_label=True, num_features=num_features
+    )
+    with open(val_filename, 'wb') as f:
+        pickle.dump(custom_infos_val, f)
+    print('Custom info train file is saved to %s' % val_filename)
+
+    print('------------------------Start create groundtruth database for data augmentation------------------------')
+    dataset.set_split(train_split)
+    dataset.create_groundtruth_database(train_filename, split=train_split)
+    print('------------------------Data preparation done------------------------')
+
+
+if __name__ == '__main__':
+    import sys
+
+    if sys.argv.__len__() > 1 and sys.argv[1] == 'create_custom_infos':
+        import yaml
+        from pathlib import Path
+        from easydict import EasyDict
+
+        dataset_cfg = EasyDict(yaml.safe_load(open(sys.argv[2])))
+        ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
+        create_custom_infos(
+            dataset_cfg=dataset_cfg,
+            class_names=['Vehicle', 'Pedestrian', 'Cyclist'],
+            data_path=ROOT_DIR / 'data' / 'custom',
+            save_path=ROOT_DIR / 'data' / 'custom',
+        )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/dataset.py
new file mode 100644
index 000000000..c1a7f6b03
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/dataset.py
@@ -0,0 +1,325 @@
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.utils.data as torch_data
+
+from ..utils import common_utils
+from .augmentor.data_augmentor import DataAugmentor
+from .processor.data_processor import DataProcessor
+from .processor.point_feature_encoder import PointFeatureEncoder
+
+
+class DatasetTemplate(torch_data.Dataset):
+    def __init__(self, dataset_cfg=None, class_names=None, training=True, root_path=None, logger=None):
+        super().__init__()
+        self.dataset_cfg = dataset_cfg
+        self.training = training
+        self.class_names = class_names
+        self.logger = logger
+        self.root_path = root_path if root_path is not None else Path(self.dataset_cfg.DATA_PATH)
+        self.logger = logger
+        if self.dataset_cfg is None or class_names is None:
+            return
+
+        self.point_cloud_range = np.array(self.dataset_cfg.POINT_CLOUD_RANGE, dtype=np.float32)
+        self.point_feature_encoder = PointFeatureEncoder(
+            self.dataset_cfg.POINT_FEATURE_ENCODING,
+            point_cloud_range=self.point_cloud_range
+        )
+        self.data_augmentor = DataAugmentor(
+            self.root_path, self.dataset_cfg.DATA_AUGMENTOR, self.class_names, logger=self.logger
+        ) if self.training else None
+        self.data_processor = DataProcessor(
+            self.dataset_cfg.DATA_PROCESSOR, point_cloud_range=self.point_cloud_range,
+            training=self.training, num_point_features=self.point_feature_encoder.num_point_features
+        )
+
+        self.grid_size = self.data_processor.grid_size
+        self.voxel_size = self.data_processor.voxel_size
+        self.total_epochs = 0
+        self._merge_all_iters_to_one_epoch = False
+
+        if hasattr(self.data_processor, "depth_downsample_factor"):
+            self.depth_downsample_factor = self.data_processor.depth_downsample_factor
+        else:
+            self.depth_downsample_factor = None
+            
+    @property
+    def mode(self):
+        return 'train' if self.training else 'test'
+
+    def __getstate__(self):
+        d = dict(self.__dict__)
+        del d['logger']
+        return d
+
+    def __setstate__(self, d):
+        self.__dict__.update(d)
+
+    def generate_prediction_dicts(self, batch_dict, pred_dicts, class_names, output_path=None):
+        """
+        Args:
+            batch_dict:
+                frame_id:
+            pred_dicts: list of pred_dicts
+                pred_boxes: (N, 7 or 9), Tensor
+                pred_scores: (N), Tensor
+                pred_labels: (N), Tensor
+            class_names:
+            output_path:
+
+        Returns:
+
+        """
+        
+        def get_template_prediction(num_samples):
+            box_dim = 9 if self.dataset_cfg.get('TRAIN_WITH_SPEED', False) else 7
+            ret_dict = {
+                'name': np.zeros(num_samples), 'score': np.zeros(num_samples),
+                'boxes_lidar': np.zeros([num_samples, box_dim]), 'pred_labels': np.zeros(num_samples)
+            }
+            return ret_dict
+
+        def generate_single_sample_dict(box_dict):
+            pred_scores = box_dict['pred_scores'].cpu().numpy()
+            pred_boxes = box_dict['pred_boxes'].cpu().numpy()
+            pred_labels = box_dict['pred_labels'].cpu().numpy()
+            pred_dict = get_template_prediction(pred_scores.shape[0])
+            if pred_scores.shape[0] == 0:
+                return pred_dict
+
+            pred_dict['name'] = np.array(class_names)[pred_labels - 1]
+            pred_dict['score'] = pred_scores
+            pred_dict['boxes_lidar'] = pred_boxes
+            pred_dict['pred_labels'] = pred_labels
+
+            return pred_dict
+
+        annos = []
+        for index, box_dict in enumerate(pred_dicts):
+            single_pred_dict = generate_single_sample_dict(box_dict)
+            single_pred_dict['frame_id'] = batch_dict['frame_id'][index]
+            if 'metadata' in batch_dict:
+                single_pred_dict['metadata'] = batch_dict['metadata'][index]
+            annos.append(single_pred_dict)
+
+        return annos
+
+    def merge_all_iters_to_one_epoch(self, merge=True, epochs=None):
+        if merge:
+            self._merge_all_iters_to_one_epoch = True
+            self.total_epochs = epochs
+        else:
+            self._merge_all_iters_to_one_epoch = False
+
+    def __len__(self):
+        raise NotImplementedError
+
+    def __getitem__(self, index):
+        """
+        To support a custom dataset, implement this function to load the raw data (and labels), then transform them to
+        the unified normative coordinate and call the function self.prepare_data() to process the data and send them
+        to the model.
+
+        Args:
+            index:
+
+        Returns:
+
+        """
+        raise NotImplementedError
+
+    def set_lidar_aug_matrix(self, data_dict):
+        """
+            Get lidar augment matrix (4 x 4), which are used to recover orig point coordinates.
+        """
+        lidar_aug_matrix = np.eye(4)
+        if 'flip_y' in data_dict.keys():
+            flip_x = data_dict['flip_x']
+            flip_y = data_dict['flip_y']
+            if flip_x:
+                lidar_aug_matrix[:3,:3] = np.array([[1, 0, 0], [0, -1, 0], [0, 0, 1]]) @ lidar_aug_matrix[:3,:3]
+            if flip_y:
+                lidar_aug_matrix[:3,:3] = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]]) @ lidar_aug_matrix[:3,:3]
+        if 'noise_rot' in data_dict.keys():
+            noise_rot = data_dict['noise_rot']
+            lidar_aug_matrix[:3,:3] = common_utils.angle2matrix(torch.tensor(noise_rot)) @ lidar_aug_matrix[:3,:3]
+        if 'noise_scale' in data_dict.keys():
+            noise_scale = data_dict['noise_scale']
+            lidar_aug_matrix[:3,:3] *= noise_scale
+        if 'noise_translate' in data_dict.keys():
+            noise_translate = data_dict['noise_translate']
+            lidar_aug_matrix[:3,3:4] = noise_translate.T
+        data_dict['lidar_aug_matrix'] = lidar_aug_matrix
+        return data_dict
+
+    def prepare_data(self, data_dict):
+        """
+        Args:
+            data_dict:
+                points: optional, (N, 3 + C_in)
+                gt_boxes: optional, (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+                gt_names: optional, (N), string
+                ...
+
+        Returns:
+            data_dict:
+                frame_id: string
+                points: (N, 3 + C_in)
+                gt_boxes: optional, (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+                gt_names: optional, (N), string
+                use_lead_xyz: bool
+                voxels: optional (num_voxels, max_points_per_voxel, 3 + C)
+                voxel_coords: optional (num_voxels, 3)
+                voxel_num_points: optional (num_voxels)
+                ...
+        """
+        if self.training:
+            assert 'gt_boxes' in data_dict, 'gt_boxes should be provided for training'
+            gt_boxes_mask = np.array([n in self.class_names for n in data_dict['gt_names']], dtype=np.bool_)
+            
+            if 'calib' in data_dict:
+                calib = data_dict['calib']
+            data_dict = self.data_augmentor.forward(
+                data_dict={
+                    **data_dict,
+                    'gt_boxes_mask': gt_boxes_mask
+                }
+            )
+            if 'calib' in data_dict:
+                data_dict['calib'] = calib
+        data_dict = self.set_lidar_aug_matrix(data_dict)
+        if data_dict.get('gt_boxes', None) is not None:
+            selected = common_utils.keep_arrays_by_name(data_dict['gt_names'], self.class_names)
+            data_dict['gt_boxes'] = data_dict['gt_boxes'][selected]
+            data_dict['gt_names'] = data_dict['gt_names'][selected]
+            gt_classes = np.array([self.class_names.index(n) + 1 for n in data_dict['gt_names']], dtype=np.int32)
+            gt_boxes = np.concatenate((data_dict['gt_boxes'], gt_classes.reshape(-1, 1).astype(np.float32)), axis=1)
+            data_dict['gt_boxes'] = gt_boxes
+
+            if data_dict.get('gt_boxes2d', None) is not None:
+                data_dict['gt_boxes2d'] = data_dict['gt_boxes2d'][selected]
+
+        if data_dict.get('points', None) is not None:
+            data_dict = self.point_feature_encoder.forward(data_dict)
+
+        data_dict = self.data_processor.forward(
+            data_dict=data_dict
+        )
+
+        if self.training and len(data_dict['gt_boxes']) == 0:
+            new_index = np.random.randint(self.__len__())
+            return self.__getitem__(new_index)
+
+        data_dict.pop('gt_names', None)
+
+        return data_dict
+
+    @staticmethod
+    def collate_batch(batch_list, _unused=False):
+        data_dict = defaultdict(list)
+        for cur_sample in batch_list:
+            for key, val in cur_sample.items():
+                data_dict[key].append(val)
+        batch_size = len(batch_list)
+        ret = {}
+        batch_size_ratio = 1
+
+        for key, val in data_dict.items():
+            try:
+                if key in ['voxels', 'voxel_num_points']:
+                    if isinstance(val[0], list):
+                        batch_size_ratio = len(val[0])
+                        val = [i for item in val for i in item]
+                    ret[key] = np.concatenate(val, axis=0)
+                elif key in ['points', 'voxel_coords']:
+                    coors = []
+                    if isinstance(val[0], list):
+                        val =  [i for item in val for i in item]
+                    for i, coor in enumerate(val):
+                        coor_pad = np.pad(coor, ((0, 0), (1, 0)), mode='constant', constant_values=i)
+                        coors.append(coor_pad)
+                    ret[key] = np.concatenate(coors, axis=0)
+                elif key in ['gt_boxes']:
+                    max_gt = max([len(x) for x in val])
+                    batch_gt_boxes3d = np.zeros((batch_size, max_gt, val[0].shape[-1]), dtype=np.float32)
+                    for k in range(batch_size):
+                        batch_gt_boxes3d[k, :val[k].__len__(), :] = val[k]
+                    ret[key] = batch_gt_boxes3d
+
+                elif key in ['roi_boxes']:
+                    max_gt = max([x.shape[1] for x in val])
+                    batch_gt_boxes3d = np.zeros((batch_size, val[0].shape[0], max_gt, val[0].shape[-1]), dtype=np.float32)
+                    for k in range(batch_size):
+                        batch_gt_boxes3d[k,:, :val[k].shape[1], :] = val[k]
+                    ret[key] = batch_gt_boxes3d
+
+                elif key in ['roi_scores', 'roi_labels']:
+                    max_gt = max([x.shape[1] for x in val])
+                    batch_gt_boxes3d = np.zeros((batch_size, val[0].shape[0], max_gt), dtype=np.float32)
+                    for k in range(batch_size):
+                        batch_gt_boxes3d[k,:, :val[k].shape[1]] = val[k]
+                    ret[key] = batch_gt_boxes3d
+
+                elif key in ['gt_boxes2d']:
+                    max_boxes = 0
+                    max_boxes = max([len(x) for x in val])
+                    batch_boxes2d = np.zeros((batch_size, max_boxes, val[0].shape[-1]), dtype=np.float32)
+                    for k in range(batch_size):
+                        if val[k].size > 0:
+                            batch_boxes2d[k, :val[k].__len__(), :] = val[k]
+                    ret[key] = batch_boxes2d
+                elif key in ["images", "depth_maps"]:
+                    # Get largest image size (H, W)
+                    max_h = 0
+                    max_w = 0
+                    for image in val:
+                        max_h = max(max_h, image.shape[0])
+                        max_w = max(max_w, image.shape[1])
+
+                    # Change size of images
+                    images = []
+                    for image in val:
+                        pad_h = common_utils.get_pad_params(desired_size=max_h, cur_size=image.shape[0])
+                        pad_w = common_utils.get_pad_params(desired_size=max_w, cur_size=image.shape[1])
+                        pad_width = (pad_h, pad_w)
+                        pad_value = 0
+
+                        if key == "images":
+                            pad_width = (pad_h, pad_w, (0, 0))
+                        elif key == "depth_maps":
+                            pad_width = (pad_h, pad_w)
+
+                        image_pad = np.pad(image,
+                                           pad_width=pad_width,
+                                           mode='constant',
+                                           constant_values=pad_value)
+
+                        images.append(image_pad)
+                    ret[key] = np.stack(images, axis=0)
+                elif key in ['calib']:
+                    ret[key] = val
+                elif key in ["points_2d"]:
+                    max_len = max([len(_val) for _val in val])
+                    pad_value = 0
+                    points = []
+                    for _points in val:
+                        pad_width = ((0, max_len-len(_points)), (0,0))
+                        points_pad = np.pad(_points,
+                                pad_width=pad_width,
+                                mode='constant',
+                                constant_values=pad_value)
+                        points.append(points_pad)
+                    ret[key] = np.stack(points, axis=0)
+                elif key in ['camera_imgs']:
+                    ret[key] = torch.stack([torch.stack(imgs,dim=0) for imgs in val],dim=0)
+                else:
+                    ret[key] = np.stack(val, axis=0)
+            except:
+                print('Error in collate_batch: key=%s' % key)
+                raise TypeError
+
+        ret['batch_size'] = batch_size * batch_size_ratio
+        return ret
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_dataset.py
new file mode 100644
index 000000000..411bd7509
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_dataset.py
@@ -0,0 +1,484 @@
+import copy
+import pickle
+
+import numpy as np
+from skimage import io
+
+from . import kitti_utils
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+from ...utils import box_utils, calibration_kitti, common_utils, object3d_kitti
+from ..dataset import DatasetTemplate
+
+
+class KittiDataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
+        """
+        Args:
+            root_path:
+            dataset_cfg:
+            class_names:
+            training:
+            logger:
+        """
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
+        )
+        self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
+        self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
+
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
+
+        self.kitti_infos = []
+        self.include_kitti_data(self.mode)
+
+    def include_kitti_data(self, mode):
+        if self.logger is not None:
+            self.logger.info('Loading KITTI dataset')
+        kitti_infos = []
+
+        for info_path in self.dataset_cfg.INFO_PATH[mode]:
+            info_path = self.root_path / info_path
+            if not info_path.exists():
+                continue
+            with open(info_path, 'rb') as f:
+                infos = pickle.load(f)
+                kitti_infos.extend(infos)
+
+        self.kitti_infos.extend(kitti_infos)
+
+        if self.logger is not None:
+            self.logger.info('Total samples for KITTI dataset: %d' % (len(kitti_infos)))
+
+    def set_split(self, split):
+        super().__init__(
+            dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger
+        )
+        self.split = split
+        self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
+
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
+
+    def get_lidar(self, idx):
+        lidar_file = self.root_split_path / 'velodyne' / ('%s.bin' % idx)
+        assert lidar_file.exists()
+        return np.fromfile(str(lidar_file), dtype=np.float32).reshape(-1, 4)
+
+    def get_image(self, idx):
+        """
+        Loads image for a sample
+        Args:
+            idx: int, Sample index
+        Returns:
+            image: (H, W, 3), RGB Image
+        """
+        img_file = self.root_split_path / 'image_2' / ('%s.png' % idx)
+        assert img_file.exists()
+        image = io.imread(img_file)
+        image = image.astype(np.float32)
+        image /= 255.0
+        return image
+
+    def get_image_shape(self, idx):
+        img_file = self.root_split_path / 'image_2' / ('%s.png' % idx)
+        assert img_file.exists()
+        return np.array(io.imread(img_file).shape[:2], dtype=np.int32)
+
+    def get_label(self, idx):
+        label_file = self.root_split_path / 'label_2' / ('%s.txt' % idx)
+        assert label_file.exists()
+        return object3d_kitti.get_objects_from_label(label_file)
+
+    def get_depth_map(self, idx):
+        """
+        Loads depth map for a sample
+        Args:
+            idx: str, Sample index
+        Returns:
+            depth: (H, W), Depth map
+        """
+        depth_file = self.root_split_path / 'depth_2' / ('%s.png' % idx)
+        assert depth_file.exists()
+        depth = io.imread(depth_file)
+        depth = depth.astype(np.float32)
+        depth /= 256.0
+        return depth
+
+    def get_calib(self, idx):
+        calib_file = self.root_split_path / 'calib' / ('%s.txt' % idx)
+        assert calib_file.exists()
+        return calibration_kitti.Calibration(calib_file)
+
+    def get_road_plane(self, idx):
+        plane_file = self.root_split_path / 'planes' / ('%s.txt' % idx)
+        if not plane_file.exists():
+            return None
+
+        with open(plane_file, 'r') as f:
+            lines = f.readlines()
+        lines = [float(i) for i in lines[3].split()]
+        plane = np.asarray(lines)
+
+        # Ensure normal is always facing up, this is in the rectified camera coordinate
+        if plane[1] > 0:
+            plane = -plane
+
+        norm = np.linalg.norm(plane[0:3])
+        plane = plane / norm
+        return plane
+
+    @staticmethod
+    def get_fov_flag(pts_rect, img_shape, calib):
+        """
+        Args:
+            pts_rect:
+            img_shape:
+            calib:
+
+        Returns:
+
+        """
+        pts_img, pts_rect_depth = calib.rect_to_img(pts_rect)
+        val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0] < img_shape[1])
+        val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1] < img_shape[0])
+        val_flag_merge = np.logical_and(val_flag_1, val_flag_2)
+        pts_valid_flag = np.logical_and(val_flag_merge, pts_rect_depth >= 0)
+
+        return pts_valid_flag
+
+    def get_infos(self, num_workers=4, has_label=True, count_inside_pts=True, sample_id_list=None):
+        import concurrent.futures as futures
+
+        def process_single_scene(sample_idx):
+            print('%s sample_idx: %s' % (self.split, sample_idx))
+            info = {}
+            pc_info = {'num_features': 4, 'lidar_idx': sample_idx}
+            info['point_cloud'] = pc_info
+
+            image_info = {'image_idx': sample_idx, 'image_shape': self.get_image_shape(sample_idx)}
+            info['image'] = image_info
+            calib = self.get_calib(sample_idx)
+
+            P2 = np.concatenate([calib.P2, np.array([[0., 0., 0., 1.]])], axis=0)
+            R0_4x4 = np.zeros([4, 4], dtype=calib.R0.dtype)
+            R0_4x4[3, 3] = 1.
+            R0_4x4[:3, :3] = calib.R0
+            V2C_4x4 = np.concatenate([calib.V2C, np.array([[0., 0., 0., 1.]])], axis=0)
+            calib_info = {'P2': P2, 'R0_rect': R0_4x4, 'Tr_velo_to_cam': V2C_4x4}
+
+            info['calib'] = calib_info
+
+            if has_label:
+                obj_list = self.get_label(sample_idx)
+                annotations = {}
+                annotations['name'] = np.array([obj.cls_type for obj in obj_list])
+                annotations['truncated'] = np.array([obj.truncation for obj in obj_list])
+                annotations['occluded'] = np.array([obj.occlusion for obj in obj_list])
+                annotations['alpha'] = np.array([obj.alpha for obj in obj_list])
+                annotations['bbox'] = np.concatenate([obj.box2d.reshape(1, 4) for obj in obj_list], axis=0)
+                annotations['dimensions'] = np.array([[obj.l, obj.h, obj.w] for obj in obj_list])  # lhw(camera) format
+                annotations['location'] = np.concatenate([obj.loc.reshape(1, 3) for obj in obj_list], axis=0)
+                annotations['rotation_y'] = np.array([obj.ry for obj in obj_list])
+                annotations['score'] = np.array([obj.score for obj in obj_list])
+                annotations['difficulty'] = np.array([obj.level for obj in obj_list], np.int32)
+
+                num_objects = len([obj.cls_type for obj in obj_list if obj.cls_type != 'DontCare'])
+                num_gt = len(annotations['name'])
+                index = list(range(num_objects)) + [-1] * (num_gt - num_objects)
+                annotations['index'] = np.array(index, dtype=np.int32)
+
+                loc = annotations['location'][:num_objects]
+                dims = annotations['dimensions'][:num_objects]
+                rots = annotations['rotation_y'][:num_objects]
+                loc_lidar = calib.rect_to_lidar(loc)
+                l, h, w = dims[:, 0:1], dims[:, 1:2], dims[:, 2:3]
+                loc_lidar[:, 2] += h[:, 0] / 2
+                gt_boxes_lidar = np.concatenate([loc_lidar, l, w, h, -(np.pi / 2 + rots[..., np.newaxis])], axis=1)
+                annotations['gt_boxes_lidar'] = gt_boxes_lidar
+
+                info['annos'] = annotations
+
+                if count_inside_pts:
+                    points = self.get_lidar(sample_idx)
+                    calib = self.get_calib(sample_idx)
+                    pts_rect = calib.lidar_to_rect(points[:, 0:3])
+
+                    fov_flag = self.get_fov_flag(pts_rect, info['image']['image_shape'], calib)
+                    pts_fov = points[fov_flag]
+                    corners_lidar = box_utils.boxes_to_corners_3d(gt_boxes_lidar)
+                    num_points_in_gt = -np.ones(num_gt, dtype=np.int32)
+
+                    for k in range(num_objects):
+                        flag = box_utils.in_hull(pts_fov[:, 0:3], corners_lidar[k])
+                        num_points_in_gt[k] = flag.sum()
+                    annotations['num_points_in_gt'] = num_points_in_gt
+
+            return info
+
+        sample_id_list = sample_id_list if sample_id_list is not None else self.sample_id_list
+        with futures.ThreadPoolExecutor(num_workers) as executor:
+            infos = executor.map(process_single_scene, sample_id_list)
+        return list(infos)
+
+    def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'):
+        import torch
+
+        database_save_path = Path(self.root_path) / ('gt_database' if split == 'train' else ('gt_database_%s' % split))
+        db_info_save_path = Path(self.root_path) / ('kitti_dbinfos_%s.pkl' % split)
+
+        database_save_path.mkdir(parents=True, exist_ok=True)
+        all_db_infos = {}
+
+        with open(info_path, 'rb') as f:
+            infos = pickle.load(f)
+
+        for k in range(len(infos)):
+            print('gt_database sample: %d/%d' % (k + 1, len(infos)))
+            info = infos[k]
+            sample_idx = info['point_cloud']['lidar_idx']
+            points = self.get_lidar(sample_idx)
+            annos = info['annos']
+            names = annos['name']
+            difficulty = annos['difficulty']
+            bbox = annos['bbox']
+            gt_boxes = annos['gt_boxes_lidar']
+
+            num_obj = gt_boxes.shape[0]
+            point_indices = roiaware_pool3d_utils.points_in_boxes_cpu(
+                torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes)
+            ).numpy()  # (nboxes, npoints)
+
+            for i in range(num_obj):
+                filename = '%s_%s_%d.bin' % (sample_idx, names[i], i)
+                filepath = database_save_path / filename
+                gt_points = points[point_indices[i] > 0]
+
+                gt_points[:, :3] -= gt_boxes[i, :3]
+                with open(filepath, 'w') as f:
+                    gt_points.tofile(f)
+
+                if (used_classes is None) or names[i] in used_classes:
+                    db_path = str(filepath.relative_to(self.root_path))  # gt_database/xxxxx.bin
+                    db_info = {'name': names[i], 'path': db_path, 'image_idx': sample_idx, 'gt_idx': i,
+                               'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0],
+                               'difficulty': difficulty[i], 'bbox': bbox[i], 'score': annos['score'][i]}
+                    if names[i] in all_db_infos:
+                        all_db_infos[names[i]].append(db_info)
+                    else:
+                        all_db_infos[names[i]] = [db_info]
+        for k, v in all_db_infos.items():
+            print('Database %s: %d' % (k, len(v)))
+
+        with open(db_info_save_path, 'wb') as f:
+            pickle.dump(all_db_infos, f)
+
+    @staticmethod
+    def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
+        """
+        Args:
+            batch_dict:
+                frame_id:
+            pred_dicts: list of pred_dicts
+                pred_boxes: (N, 7), Tensor
+                pred_scores: (N), Tensor
+                pred_labels: (N), Tensor
+            class_names:
+            output_path:
+
+        Returns:
+
+        """
+        def get_template_prediction(num_samples):
+            ret_dict = {
+                'name': np.zeros(num_samples), 'truncated': np.zeros(num_samples),
+                'occluded': np.zeros(num_samples), 'alpha': np.zeros(num_samples),
+                'bbox': np.zeros([num_samples, 4]), 'dimensions': np.zeros([num_samples, 3]),
+                'location': np.zeros([num_samples, 3]), 'rotation_y': np.zeros(num_samples),
+                'score': np.zeros(num_samples), 'boxes_lidar': np.zeros([num_samples, 7])
+            }
+            return ret_dict
+
+        def generate_single_sample_dict(batch_index, box_dict):
+            pred_scores = box_dict['pred_scores'].cpu().numpy()
+            pred_boxes = box_dict['pred_boxes'].cpu().numpy()
+            pred_labels = box_dict['pred_labels'].cpu().numpy()
+            pred_dict = get_template_prediction(pred_scores.shape[0])
+            if pred_scores.shape[0] == 0:
+                return pred_dict
+
+            calib = batch_dict['calib'][batch_index]
+            image_shape = batch_dict['image_shape'][batch_index].cpu().numpy()
+            pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(pred_boxes, calib)
+            pred_boxes_img = box_utils.boxes3d_kitti_camera_to_imageboxes(
+                pred_boxes_camera, calib, image_shape=image_shape
+            )
+
+            pred_dict['name'] = np.array(class_names)[pred_labels - 1]
+            pred_dict['alpha'] = -np.arctan2(-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6]
+            pred_dict['bbox'] = pred_boxes_img
+            pred_dict['dimensions'] = pred_boxes_camera[:, 3:6]
+            pred_dict['location'] = pred_boxes_camera[:, 0:3]
+            pred_dict['rotation_y'] = pred_boxes_camera[:, 6]
+            pred_dict['score'] = pred_scores
+            pred_dict['boxes_lidar'] = pred_boxes
+
+            return pred_dict
+
+        annos = []
+        for index, box_dict in enumerate(pred_dicts):
+            frame_id = batch_dict['frame_id'][index]
+
+            single_pred_dict = generate_single_sample_dict(index, box_dict)
+            single_pred_dict['frame_id'] = frame_id
+            annos.append(single_pred_dict)
+
+            if output_path is not None:
+                cur_det_file = output_path / ('%s.txt' % frame_id)
+                with open(cur_det_file, 'w') as f:
+                    bbox = single_pred_dict['bbox']
+                    loc = single_pred_dict['location']
+                    dims = single_pred_dict['dimensions']  # lhw -> hwl
+
+                    for idx in range(len(bbox)):
+                        print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f'
+                              % (single_pred_dict['name'][idx], single_pred_dict['alpha'][idx],
+                                 bbox[idx][0], bbox[idx][1], bbox[idx][2], bbox[idx][3],
+                                 dims[idx][1], dims[idx][2], dims[idx][0], loc[idx][0],
+                                 loc[idx][1], loc[idx][2], single_pred_dict['rotation_y'][idx],
+                                 single_pred_dict['score'][idx]), file=f)
+
+        return annos
+
+    def evaluation(self, det_annos, class_names, **kwargs):
+        if 'annos' not in self.kitti_infos[0].keys():
+            return None, {}
+
+        from .kitti_object_eval_python import eval as kitti_eval
+
+        eval_det_annos = copy.deepcopy(det_annos)
+        eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.kitti_infos]
+        ap_result_str, ap_dict = kitti_eval.get_official_eval_result(eval_gt_annos, eval_det_annos, class_names)
+
+        return ap_result_str, ap_dict
+
+    def __len__(self):
+        if self._merge_all_iters_to_one_epoch:
+            return len(self.kitti_infos) * self.total_epochs
+
+        return len(self.kitti_infos)
+
+    def __getitem__(self, index):
+        # index = 4
+        if self._merge_all_iters_to_one_epoch:
+            index = index % len(self.kitti_infos)
+
+        info = copy.deepcopy(self.kitti_infos[index])
+
+        sample_idx = info['point_cloud']['lidar_idx']
+        img_shape = info['image']['image_shape']
+        calib = self.get_calib(sample_idx)
+        get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points'])
+
+        input_dict = {
+            'frame_id': sample_idx,
+            'calib': calib,
+        }
+
+        if 'annos' in info:
+            annos = info['annos']
+            annos = common_utils.drop_info_with_name(annos, name='DontCare')
+            loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y']
+            gt_names = annos['name']
+            gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
+            gt_boxes_lidar = box_utils.boxes3d_kitti_camera_to_lidar(gt_boxes_camera, calib)
+
+            input_dict.update({
+                'gt_names': gt_names,
+                'gt_boxes': gt_boxes_lidar
+            })
+            if "gt_boxes2d" in get_item_list:
+                input_dict['gt_boxes2d'] = annos["bbox"]
+
+            road_plane = self.get_road_plane(sample_idx)
+            if road_plane is not None:
+                input_dict['road_plane'] = road_plane
+
+        if "points" in get_item_list:
+            points = self.get_lidar(sample_idx)
+            if self.dataset_cfg.FOV_POINTS_ONLY:
+                pts_rect = calib.lidar_to_rect(points[:, 0:3])
+                fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
+                points = points[fov_flag]
+            input_dict['points'] = points
+
+        if "images" in get_item_list:
+            input_dict['images'] = self.get_image(sample_idx)
+
+        if "depth_maps" in get_item_list:
+            input_dict['depth_maps'] = self.get_depth_map(sample_idx)
+
+        if "calib_matricies" in get_item_list:
+            input_dict["trans_lidar_to_cam"], input_dict["trans_cam_to_img"] = kitti_utils.calib_to_matricies(calib)
+
+        input_dict['calib'] = calib
+        data_dict = self.prepare_data(data_dict=input_dict)
+
+        data_dict['image_shape'] = img_shape
+        return data_dict
+
+
+def create_kitti_infos(dataset_cfg, class_names, data_path, save_path, workers=4):
+    dataset = KittiDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
+    train_split, val_split = 'train', 'val'
+
+    train_filename = save_path / ('kitti_infos_%s.pkl' % train_split)
+    val_filename = save_path / ('kitti_infos_%s.pkl' % val_split)
+    trainval_filename = save_path / 'kitti_infos_trainval.pkl'
+    test_filename = save_path / 'kitti_infos_test.pkl'
+
+    print('---------------Start to generate data infos---------------')
+
+    dataset.set_split(train_split)
+    kitti_infos_train = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True)
+    with open(train_filename, 'wb') as f:
+        pickle.dump(kitti_infos_train, f)
+    print('Kitti info train file is saved to %s' % train_filename)
+
+    dataset.set_split(val_split)
+    kitti_infos_val = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True)
+    with open(val_filename, 'wb') as f:
+        pickle.dump(kitti_infos_val, f)
+    print('Kitti info val file is saved to %s' % val_filename)
+
+    with open(trainval_filename, 'wb') as f:
+        pickle.dump(kitti_infos_train + kitti_infos_val, f)
+    print('Kitti info trainval file is saved to %s' % trainval_filename)
+
+    dataset.set_split('test')
+    kitti_infos_test = dataset.get_infos(num_workers=workers, has_label=False, count_inside_pts=False)
+    with open(test_filename, 'wb') as f:
+        pickle.dump(kitti_infos_test, f)
+    print('Kitti info test file is saved to %s' % test_filename)
+
+    print('---------------Start create groundtruth database for data augmentation---------------')
+    dataset.set_split(train_split)
+    dataset.create_groundtruth_database(train_filename, split=train_split)
+
+    print('---------------Data preparation Done---------------')
+
+
+if __name__ == '__main__':
+    import sys
+    if sys.argv.__len__() > 1 and sys.argv[1] == 'create_kitti_infos':
+        import yaml
+        from pathlib import Path
+        from easydict import EasyDict
+        dataset_cfg = EasyDict(yaml.safe_load(open(sys.argv[2])))
+        ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
+        create_kitti_infos(
+            dataset_cfg=dataset_cfg,
+            class_names=['Car', 'Pedestrian', 'Cyclist'],
+            data_path=ROOT_DIR / 'data' / 'kitti',
+            save_path=ROOT_DIR / 'data' / 'kitti'
+        )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/LICENSE b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/LICENSE
new file mode 100644
index 000000000..ab602974d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/README.md b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/README.md
new file mode 100644
index 000000000..913183edf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/README.md
@@ -0,0 +1,32 @@
+# kitti-object-eval-python
+**Note**: This is borrowed from [traveller59/kitti-object-eval-python](https://github.com/traveller59/kitti-object-eval-python)
+
+Fast kitti object detection eval in python(finish eval in less than 10 second), support 2d/bev/3d/aos. , support coco-style AP. If you use command line interface, numba need some time to compile jit functions.
+## Dependencies
+Only support python 3.6+, need `numpy`, `skimage`, `numba`, `fire`. If you have Anaconda, just install `cudatoolkit` in anaconda. Otherwise, please reference to this [page](https://github.com/numba/numba#custom-python-environments) to set up llvm and cuda for numba.
+* Install by conda:
+```
+conda install -c numba cudatoolkit=x.x  (8.0, 9.0, 9.1, depend on your environment) 
+```
+## Usage
+* commandline interface:
+```
+python evaluate.py evaluate --label_path=/path/to/your_gt_label_folder --result_path=/path/to/your_result_folder --label_split_file=/path/to/val.txt --current_class=0 --coco=False
+```
+* python interface:
+```Python
+import kitti_common as kitti
+from eval import get_official_eval_result, get_coco_eval_result
+def _read_imageset_file(path):
+    with open(path, 'r') as f:
+        lines = f.readlines()
+    return [int(line) for line in lines]
+det_path = "/path/to/your_result_folder"
+dt_annos = kitti.get_label_annos(det_path)
+gt_path = "/path/to/your_gt_label_folder"
+gt_split_file = "/path/to/val.txt" # from https://xiaozhichen.github.io/files/mv3d/imagesets.tar.gz
+val_image_ids = _read_imageset_file(gt_split_file)
+gt_annos = kitti.get_label_annos(gt_path, val_image_ids)
+print(get_official_eval_result(gt_annos, dt_annos, 0)) # 6s in my computer
+print(get_coco_eval_result(gt_annos, dt_annos, 0)) # 18s in my computer
+```
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/eval.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/eval.py
new file mode 100644
index 000000000..1d2a317af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/eval.py
@@ -0,0 +1,808 @@
+import io as sysio
+
+import numba
+import numpy as np
+
+from .rotate_iou import rotate_iou_gpu_eval
+
+
+@numba.jit
+def get_thresholds(scores: np.ndarray, num_gt, num_sample_pts=41):
+    scores.sort()
+    scores = scores[::-1]
+    current_recall = 0
+    thresholds = []
+    for i, score in enumerate(scores):
+        l_recall = (i + 1) / num_gt
+        if i < (len(scores) - 1):
+            r_recall = (i + 2) / num_gt
+        else:
+            r_recall = l_recall
+        if (((r_recall - current_recall) < (current_recall - l_recall))
+                and (i < (len(scores) - 1))):
+            continue
+        # recall = l_recall
+        thresholds.append(score)
+        current_recall += 1 / (num_sample_pts - 1.0)
+    return thresholds
+
+
+def clean_data(gt_anno, dt_anno, current_class, difficulty):
+    CLASS_NAMES = ['car', 'pedestrian', 'cyclist', 'van', 'person_sitting', 'truck']
+    MIN_HEIGHT = [40, 25, 25]
+    MAX_OCCLUSION = [0, 1, 2]
+    MAX_TRUNCATION = [0.15, 0.3, 0.5]
+    dc_bboxes, ignored_gt, ignored_dt = [], [], []
+    current_cls_name = CLASS_NAMES[current_class].lower()
+    num_gt = len(gt_anno["name"])
+    num_dt = len(dt_anno["name"])
+    num_valid_gt = 0
+    for i in range(num_gt):
+        bbox = gt_anno["bbox"][i]
+        gt_name = gt_anno["name"][i].lower()
+        height = bbox[3] - bbox[1]
+        valid_class = -1
+        if (gt_name == current_cls_name):
+            valid_class = 1
+        elif (current_cls_name == "Pedestrian".lower()
+              and "Person_sitting".lower() == gt_name):
+            valid_class = 0
+        elif (current_cls_name == "Car".lower() and "Van".lower() == gt_name):
+            valid_class = 0
+        else:
+            valid_class = -1
+        ignore = False
+        if ((gt_anno["occluded"][i] > MAX_OCCLUSION[difficulty])
+                or (gt_anno["truncated"][i] > MAX_TRUNCATION[difficulty])
+                or (height <= MIN_HEIGHT[difficulty])):
+            # if gt_anno["difficulty"][i] > difficulty or gt_anno["difficulty"][i] == -1:
+            ignore = True
+        if valid_class == 1 and not ignore:
+            ignored_gt.append(0)
+            num_valid_gt += 1
+        elif (valid_class == 0 or (ignore and (valid_class == 1))):
+            ignored_gt.append(1)
+        else:
+            ignored_gt.append(-1)
+    # for i in range(num_gt):
+        if gt_anno["name"][i] == "DontCare":
+            dc_bboxes.append(gt_anno["bbox"][i])
+    for i in range(num_dt):
+        if (dt_anno["name"][i].lower() == current_cls_name):
+            valid_class = 1
+        else:
+            valid_class = -1
+        height = abs(dt_anno["bbox"][i, 3] - dt_anno["bbox"][i, 1])
+        if height < MIN_HEIGHT[difficulty]:
+            ignored_dt.append(1)
+        elif valid_class == 1:
+            ignored_dt.append(0)
+        else:
+            ignored_dt.append(-1)
+
+    return num_valid_gt, ignored_gt, ignored_dt, dc_bboxes
+
+
+@numba.jit(nopython=True)
+def image_box_overlap(boxes, query_boxes, criterion=-1):
+    N = boxes.shape[0]
+    K = query_boxes.shape[0]
+    overlaps = np.zeros((N, K), dtype=boxes.dtype)
+    for k in range(K):
+        qbox_area = ((query_boxes[k, 2] - query_boxes[k, 0]) *
+                     (query_boxes[k, 3] - query_boxes[k, 1]))
+        for n in range(N):
+            iw = (min(boxes[n, 2], query_boxes[k, 2]) -
+                  max(boxes[n, 0], query_boxes[k, 0]))
+            if iw > 0:
+                ih = (min(boxes[n, 3], query_boxes[k, 3]) -
+                      max(boxes[n, 1], query_boxes[k, 1]))
+                if ih > 0:
+                    if criterion == -1:
+                        ua = (
+                            (boxes[n, 2] - boxes[n, 0]) *
+                            (boxes[n, 3] - boxes[n, 1]) + qbox_area - iw * ih)
+                    elif criterion == 0:
+                        ua = ((boxes[n, 2] - boxes[n, 0]) *
+                              (boxes[n, 3] - boxes[n, 1]))
+                    elif criterion == 1:
+                        ua = qbox_area
+                    else:
+                        ua = 1.0
+                    overlaps[n, k] = iw * ih / ua
+    return overlaps
+
+
+def bev_box_overlap(boxes, qboxes, criterion=-1):
+    riou = rotate_iou_gpu_eval(boxes, qboxes, criterion)
+    return riou
+
+
+@numba.jit(nopython=True, parallel=True)
+def d3_box_overlap_kernel(boxes, qboxes, rinc, criterion=-1):
+    # ONLY support overlap in CAMERA, not lider.
+    N, K = boxes.shape[0], qboxes.shape[0]
+    for i in range(N):
+        for j in range(K):
+            if rinc[i, j] > 0:
+                # iw = (min(boxes[i, 1] + boxes[i, 4], qboxes[j, 1] +
+                #         qboxes[j, 4]) - max(boxes[i, 1], qboxes[j, 1]))
+                iw = (min(boxes[i, 1], qboxes[j, 1]) - max(
+                    boxes[i, 1] - boxes[i, 4], qboxes[j, 1] - qboxes[j, 4]))
+
+                if iw > 0:
+                    area1 = boxes[i, 3] * boxes[i, 4] * boxes[i, 5]
+                    area2 = qboxes[j, 3] * qboxes[j, 4] * qboxes[j, 5]
+                    inc = iw * rinc[i, j]
+                    if criterion == -1:
+                        ua = (area1 + area2 - inc)
+                    elif criterion == 0:
+                        ua = area1
+                    elif criterion == 1:
+                        ua = area2
+                    else:
+                        ua = inc
+                    rinc[i, j] = inc / ua
+                else:
+                    rinc[i, j] = 0.0
+
+
+def d3_box_overlap(boxes, qboxes, criterion=-1):
+    rinc = rotate_iou_gpu_eval(boxes[:, [0, 2, 3, 5, 6]],
+                               qboxes[:, [0, 2, 3, 5, 6]], 2)
+    d3_box_overlap_kernel(boxes, qboxes, rinc, criterion)
+    return rinc
+
+
+@numba.jit(nopython=True)
+def compute_statistics_jit(overlaps,
+                           gt_datas,
+                           dt_datas,
+                           ignored_gt,
+                           ignored_det,
+                           dc_bboxes,
+                           metric,
+                           min_overlap,
+                           thresh=0,
+                           compute_fp=False,
+                           compute_aos=False):
+
+    det_size = dt_datas.shape[0]
+    gt_size = gt_datas.shape[0]
+    dt_scores = dt_datas[:, -1]
+    dt_alphas = dt_datas[:, 4]
+    gt_alphas = gt_datas[:, 4]
+    dt_bboxes = dt_datas[:, :4]
+    gt_bboxes = gt_datas[:, :4]
+
+    assigned_detection = [False] * det_size
+    ignored_threshold = [False] * det_size
+    if compute_fp:
+        for i in range(det_size):
+            if (dt_scores[i] < thresh):
+                ignored_threshold[i] = True
+    NO_DETECTION = -10000000
+    tp, fp, fn, similarity = 0, 0, 0, 0
+    # thresholds = [0.0]
+    # delta = [0.0]
+    thresholds = np.zeros((gt_size, ))
+    thresh_idx = 0
+    delta = np.zeros((gt_size, ))
+    delta_idx = 0
+    for i in range(gt_size):
+        if ignored_gt[i] == -1:
+            continue
+        det_idx = -1
+        valid_detection = NO_DETECTION
+        max_overlap = 0
+        assigned_ignored_det = False
+
+        for j in range(det_size):
+            if (ignored_det[j] == -1):
+                continue
+            if (assigned_detection[j]):
+                continue
+            if (ignored_threshold[j]):
+                continue
+            overlap = overlaps[j, i]
+            dt_score = dt_scores[j]
+            if (not compute_fp and (overlap > min_overlap)
+                    and dt_score > valid_detection):
+                det_idx = j
+                valid_detection = dt_score
+            elif (compute_fp and (overlap > min_overlap)
+                  and (overlap > max_overlap or assigned_ignored_det)
+                  and ignored_det[j] == 0):
+                max_overlap = overlap
+                det_idx = j
+                valid_detection = 1
+                assigned_ignored_det = False
+            elif (compute_fp and (overlap > min_overlap)
+                  and (valid_detection == NO_DETECTION)
+                  and ignored_det[j] == 1):
+                det_idx = j
+                valid_detection = 1
+                assigned_ignored_det = True
+
+        if (valid_detection == NO_DETECTION) and ignored_gt[i] == 0:
+            fn += 1
+        elif ((valid_detection != NO_DETECTION)
+              and (ignored_gt[i] == 1 or ignored_det[det_idx] == 1)):
+            assigned_detection[det_idx] = True
+        elif valid_detection != NO_DETECTION:
+            tp += 1
+            # thresholds.append(dt_scores[det_idx])
+            thresholds[thresh_idx] = dt_scores[det_idx]
+            thresh_idx += 1
+            if compute_aos:
+                # delta.append(gt_alphas[i] - dt_alphas[det_idx])
+                delta[delta_idx] = gt_alphas[i] - dt_alphas[det_idx]
+                delta_idx += 1
+
+            assigned_detection[det_idx] = True
+    if compute_fp:
+        for i in range(det_size):
+            if (not (assigned_detection[i] or ignored_det[i] == -1
+                     or ignored_det[i] == 1 or ignored_threshold[i])):
+                fp += 1
+        nstuff = 0
+        if metric == 0:
+            overlaps_dt_dc = image_box_overlap(dt_bboxes, dc_bboxes, 0)
+            for i in range(dc_bboxes.shape[0]):
+                for j in range(det_size):
+                    if (assigned_detection[j]):
+                        continue
+                    if (ignored_det[j] == -1 or ignored_det[j] == 1):
+                        continue
+                    if (ignored_threshold[j]):
+                        continue
+                    if overlaps_dt_dc[j, i] > min_overlap:
+                        assigned_detection[j] = True
+                        nstuff += 1
+        fp -= nstuff
+        if compute_aos:
+            tmp = np.zeros((fp + delta_idx, ))
+            # tmp = [0] * fp
+            for i in range(delta_idx):
+                tmp[i + fp] = (1.0 + np.cos(delta[i])) / 2.0
+                # tmp.append((1.0 + np.cos(delta[i])) / 2.0)
+            # assert len(tmp) == fp + tp
+            # assert len(delta) == tp
+            if tp > 0 or fp > 0:
+                similarity = np.sum(tmp)
+            else:
+                similarity = -1
+    return tp, fp, fn, similarity, thresholds[:thresh_idx]
+
+
+def get_split_parts(num, num_part):
+    same_part = num // num_part
+    remain_num = num % num_part
+    if same_part == 0:
+        return [num]
+
+    if remain_num == 0:
+        return [same_part] * num_part
+    else:
+        return [same_part] * num_part + [remain_num]
+
+
+@numba.jit(nopython=True)
+def fused_compute_statistics(overlaps,
+                             pr,
+                             gt_nums,
+                             dt_nums,
+                             dc_nums,
+                             gt_datas,
+                             dt_datas,
+                             dontcares,
+                             ignored_gts,
+                             ignored_dets,
+                             metric,
+                             min_overlap,
+                             thresholds,
+                             compute_aos=False):
+    gt_num = 0
+    dt_num = 0
+    dc_num = 0
+    for i in range(gt_nums.shape[0]):
+        for t, thresh in enumerate(thresholds):
+            overlap = overlaps[dt_num:dt_num + dt_nums[i], gt_num:
+                               gt_num + gt_nums[i]]
+
+            gt_data = gt_datas[gt_num:gt_num + gt_nums[i]]
+            dt_data = dt_datas[dt_num:dt_num + dt_nums[i]]
+            ignored_gt = ignored_gts[gt_num:gt_num + gt_nums[i]]
+            ignored_det = ignored_dets[dt_num:dt_num + dt_nums[i]]
+            dontcare = dontcares[dc_num:dc_num + dc_nums[i]]
+            tp, fp, fn, similarity, _ = compute_statistics_jit(
+                overlap,
+                gt_data,
+                dt_data,
+                ignored_gt,
+                ignored_det,
+                dontcare,
+                metric,
+                min_overlap=min_overlap,
+                thresh=thresh,
+                compute_fp=True,
+                compute_aos=compute_aos)
+            pr[t, 0] += tp
+            pr[t, 1] += fp
+            pr[t, 2] += fn
+            if similarity != -1:
+                pr[t, 3] += similarity
+        gt_num += gt_nums[i]
+        dt_num += dt_nums[i]
+        dc_num += dc_nums[i]
+
+
+def calculate_iou_partly(gt_annos, dt_annos, metric, num_parts=50):
+    """fast iou algorithm. this function can be used independently to
+    do result analysis. Must be used in CAMERA coordinate system.
+    Args:
+        gt_annos: dict, must from get_label_annos() in kitti_common.py
+        dt_annos: dict, must from get_label_annos() in kitti_common.py
+        metric: eval type. 0: bbox, 1: bev, 2: 3d
+        num_parts: int. a parameter for fast calculate algorithm
+    """
+    assert len(gt_annos) == len(dt_annos)
+    total_dt_num = np.stack([len(a["name"]) for a in dt_annos], 0)
+    total_gt_num = np.stack([len(a["name"]) for a in gt_annos], 0)
+    num_examples = len(gt_annos)
+    split_parts = get_split_parts(num_examples, num_parts)
+    parted_overlaps = []
+    example_idx = 0
+
+    for num_part in split_parts:
+        gt_annos_part = gt_annos[example_idx:example_idx + num_part]
+        dt_annos_part = dt_annos[example_idx:example_idx + num_part]
+        if metric == 0:
+            gt_boxes = np.concatenate([a["bbox"] for a in gt_annos_part], 0)
+            dt_boxes = np.concatenate([a["bbox"] for a in dt_annos_part], 0)
+            overlap_part = image_box_overlap(gt_boxes, dt_boxes)
+        elif metric == 1:
+            loc = np.concatenate(
+                [a["location"][:, [0, 2]] for a in gt_annos_part], 0)
+            dims = np.concatenate(
+                [a["dimensions"][:, [0, 2]] for a in gt_annos_part], 0)
+            rots = np.concatenate([a["rotation_y"] for a in gt_annos_part], 0)
+            gt_boxes = np.concatenate(
+                [loc, dims, rots[..., np.newaxis]], axis=1)
+            loc = np.concatenate(
+                [a["location"][:, [0, 2]] for a in dt_annos_part], 0)
+            dims = np.concatenate(
+                [a["dimensions"][:, [0, 2]] for a in dt_annos_part], 0)
+            rots = np.concatenate([a["rotation_y"] for a in dt_annos_part], 0)
+            dt_boxes = np.concatenate(
+                [loc, dims, rots[..., np.newaxis]], axis=1)
+            overlap_part = bev_box_overlap(gt_boxes, dt_boxes).astype(
+                np.float64)
+        elif metric == 2:
+            loc = np.concatenate([a["location"] for a in gt_annos_part], 0)
+            dims = np.concatenate([a["dimensions"] for a in gt_annos_part], 0)
+            rots = np.concatenate([a["rotation_y"] for a in gt_annos_part], 0)
+            gt_boxes = np.concatenate(
+                [loc, dims, rots[..., np.newaxis]], axis=1)
+            loc = np.concatenate([a["location"] for a in dt_annos_part], 0)
+            dims = np.concatenate([a["dimensions"] for a in dt_annos_part], 0)
+            rots = np.concatenate([a["rotation_y"] for a in dt_annos_part], 0)
+            dt_boxes = np.concatenate(
+                [loc, dims, rots[..., np.newaxis]], axis=1)
+            overlap_part = d3_box_overlap(gt_boxes, dt_boxes).astype(
+                np.float64)
+        else:
+            raise ValueError("unknown metric")
+        parted_overlaps.append(overlap_part)
+        example_idx += num_part
+    overlaps = []
+    example_idx = 0
+    for j, num_part in enumerate(split_parts):
+        gt_annos_part = gt_annos[example_idx:example_idx + num_part]
+        dt_annos_part = dt_annos[example_idx:example_idx + num_part]
+        gt_num_idx, dt_num_idx = 0, 0
+        for i in range(num_part):
+            gt_box_num = total_gt_num[example_idx + i]
+            dt_box_num = total_dt_num[example_idx + i]
+            overlaps.append(
+                parted_overlaps[j][gt_num_idx:gt_num_idx + gt_box_num,
+                                   dt_num_idx:dt_num_idx + dt_box_num])
+            gt_num_idx += gt_box_num
+            dt_num_idx += dt_box_num
+        example_idx += num_part
+
+    return overlaps, parted_overlaps, total_gt_num, total_dt_num
+
+
+def _prepare_data(gt_annos, dt_annos, current_class, difficulty):
+    gt_datas_list = []
+    dt_datas_list = []
+    total_dc_num = []
+    ignored_gts, ignored_dets, dontcares = [], [], []
+    total_num_valid_gt = 0
+    for i in range(len(gt_annos)):
+        rets = clean_data(gt_annos[i], dt_annos[i], current_class, difficulty)
+        num_valid_gt, ignored_gt, ignored_det, dc_bboxes = rets
+        ignored_gts.append(np.array(ignored_gt, dtype=np.int64))
+        ignored_dets.append(np.array(ignored_det, dtype=np.int64))
+        if len(dc_bboxes) == 0:
+            dc_bboxes = np.zeros((0, 4)).astype(np.float64)
+        else:
+            dc_bboxes = np.stack(dc_bboxes, 0).astype(np.float64)
+        total_dc_num.append(dc_bboxes.shape[0])
+        dontcares.append(dc_bboxes)
+        total_num_valid_gt += num_valid_gt
+        gt_datas = np.concatenate(
+            [gt_annos[i]["bbox"], gt_annos[i]["alpha"][..., np.newaxis]], 1)
+        dt_datas = np.concatenate([
+            dt_annos[i]["bbox"], dt_annos[i]["alpha"][..., np.newaxis],
+            dt_annos[i]["score"][..., np.newaxis]
+        ], 1)
+        gt_datas_list.append(gt_datas)
+        dt_datas_list.append(dt_datas)
+    total_dc_num = np.stack(total_dc_num, axis=0)
+    return (gt_datas_list, dt_datas_list, ignored_gts, ignored_dets, dontcares,
+            total_dc_num, total_num_valid_gt)
+
+
+def eval_class(gt_annos,
+               dt_annos,
+               current_classes,
+               difficultys,
+               metric,
+               min_overlaps,
+               compute_aos=False,
+               num_parts=100):
+    """Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP.
+    Args:
+        gt_annos: dict, must from get_label_annos() in kitti_common.py
+        dt_annos: dict, must from get_label_annos() in kitti_common.py
+        current_classes: list of int, 0: car, 1: pedestrian, 2: cyclist
+        difficultys: list of int. eval difficulty, 0: easy, 1: normal, 2: hard
+        metric: eval type. 0: bbox, 1: bev, 2: 3d
+        min_overlaps: float, min overlap. format: [num_overlap, metric, class].
+        num_parts: int. a parameter for fast calculate algorithm
+
+    Returns:
+        dict of recall, precision and aos
+    """
+    assert len(gt_annos) == len(dt_annos)
+    num_examples = len(gt_annos)
+    split_parts = get_split_parts(num_examples, num_parts)
+
+    rets = calculate_iou_partly(dt_annos, gt_annos, metric, num_parts)
+    overlaps, parted_overlaps, total_dt_num, total_gt_num = rets
+    N_SAMPLE_PTS = 41
+    num_minoverlap = len(min_overlaps)
+    num_class = len(current_classes)
+    num_difficulty = len(difficultys)
+    precision = np.zeros(
+        [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
+    recall = np.zeros(
+        [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
+    aos = np.zeros([num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
+    for m, current_class in enumerate(current_classes):
+        for l, difficulty in enumerate(difficultys):
+            rets = _prepare_data(gt_annos, dt_annos, current_class, difficulty)
+            (gt_datas_list, dt_datas_list, ignored_gts, ignored_dets,
+             dontcares, total_dc_num, total_num_valid_gt) = rets
+            for k, min_overlap in enumerate(min_overlaps[:, metric, m]):
+                thresholdss = []
+                for i in range(len(gt_annos)):
+                    rets = compute_statistics_jit(
+                        overlaps[i],
+                        gt_datas_list[i],
+                        dt_datas_list[i],
+                        ignored_gts[i],
+                        ignored_dets[i],
+                        dontcares[i],
+                        metric,
+                        min_overlap=min_overlap,
+                        thresh=0.0,
+                        compute_fp=False)
+                    tp, fp, fn, similarity, thresholds = rets
+                    thresholdss += thresholds.tolist()
+                thresholdss = np.array(thresholdss)
+                thresholds = get_thresholds(thresholdss, total_num_valid_gt)
+                thresholds = np.array(thresholds)
+                pr = np.zeros([len(thresholds), 4])
+                idx = 0
+                for j, num_part in enumerate(split_parts):
+                    gt_datas_part = np.concatenate(
+                        gt_datas_list[idx:idx + num_part], 0)
+                    dt_datas_part = np.concatenate(
+                        dt_datas_list[idx:idx + num_part], 0)
+                    dc_datas_part = np.concatenate(
+                        dontcares[idx:idx + num_part], 0)
+                    ignored_dets_part = np.concatenate(
+                        ignored_dets[idx:idx + num_part], 0)
+                    ignored_gts_part = np.concatenate(
+                        ignored_gts[idx:idx + num_part], 0)
+                    fused_compute_statistics(
+                        parted_overlaps[j],
+                        pr,
+                        total_gt_num[idx:idx + num_part],
+                        total_dt_num[idx:idx + num_part],
+                        total_dc_num[idx:idx + num_part],
+                        gt_datas_part,
+                        dt_datas_part,
+                        dc_datas_part,
+                        ignored_gts_part,
+                        ignored_dets_part,
+                        metric,
+                        min_overlap=min_overlap,
+                        thresholds=thresholds,
+                        compute_aos=compute_aos)
+                    idx += num_part
+                for i in range(len(thresholds)):
+                    recall[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 2])
+                    precision[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 1])
+                    if compute_aos:
+                        aos[m, l, k, i] = pr[i, 3] / (pr[i, 0] + pr[i, 1])
+                for i in range(len(thresholds)):
+                    precision[m, l, k, i] = np.max(
+                        precision[m, l, k, i:], axis=-1)
+                    recall[m, l, k, i] = np.max(recall[m, l, k, i:], axis=-1)
+                    if compute_aos:
+                        aos[m, l, k, i] = np.max(aos[m, l, k, i:], axis=-1)
+    ret_dict = {
+        "recall": recall,
+        "precision": precision,
+        "orientation": aos,
+    }
+    return ret_dict
+
+
+def get_mAP(prec):
+    sums = 0
+    for i in range(0, prec.shape[-1], 4):
+        sums = sums + prec[..., i]
+    return sums / 11 * 100
+
+
+def get_mAP_R40(prec):
+    sums = 0
+    for i in range(1, prec.shape[-1]):
+        sums = sums + prec[..., i]
+    return sums / 40 * 100
+
+
+def print_str(value, *arg, sstream=None):
+    if sstream is None:
+        sstream = sysio.StringIO()
+    sstream.truncate(0)
+    sstream.seek(0)
+    print(value, *arg, file=sstream)
+    return sstream.getvalue()
+
+
+def do_eval(gt_annos,
+            dt_annos,
+            current_classes,
+            min_overlaps,
+            compute_aos=False,
+            PR_detail_dict=None):
+    # min_overlaps: [num_minoverlap, metric, num_class]
+    difficultys = [0, 1, 2]
+    ret = eval_class(gt_annos, dt_annos, current_classes, difficultys, 0,
+                     min_overlaps, compute_aos)
+    # ret: [num_class, num_diff, num_minoverlap, num_sample_points]
+    mAP_bbox = get_mAP(ret["precision"])
+    mAP_bbox_R40 = get_mAP_R40(ret["precision"])
+
+    if PR_detail_dict is not None:
+        PR_detail_dict['bbox'] = ret['precision']
+
+    mAP_aos = mAP_aos_R40 = None
+    if compute_aos:
+        mAP_aos = get_mAP(ret["orientation"])
+        mAP_aos_R40 = get_mAP_R40(ret["orientation"])
+
+        if PR_detail_dict is not None:
+            PR_detail_dict['aos'] = ret['orientation']
+
+    ret = eval_class(gt_annos, dt_annos, current_classes, difficultys, 1,
+                     min_overlaps)
+    mAP_bev = get_mAP(ret["precision"])
+    mAP_bev_R40 = get_mAP_R40(ret["precision"])
+
+    if PR_detail_dict is not None:
+        PR_detail_dict['bev'] = ret['precision']
+
+    ret = eval_class(gt_annos, dt_annos, current_classes, difficultys, 2,
+                     min_overlaps)
+    mAP_3d = get_mAP(ret["precision"])
+    mAP_3d_R40 = get_mAP_R40(ret["precision"])
+    if PR_detail_dict is not None:
+        PR_detail_dict['3d'] = ret['precision']
+    return mAP_bbox, mAP_bev, mAP_3d, mAP_aos, mAP_bbox_R40, mAP_bev_R40, mAP_3d_R40, mAP_aos_R40
+
+
+def do_coco_style_eval(gt_annos, dt_annos, current_classes, overlap_ranges,
+                       compute_aos):
+    # overlap_ranges: [range, metric, num_class]
+    min_overlaps = np.zeros([10, *overlap_ranges.shape[1:]])
+    for i in range(overlap_ranges.shape[1]):
+        for j in range(overlap_ranges.shape[2]):
+            min_overlaps[:, i, j] = np.linspace(*overlap_ranges[:, i, j])
+    mAP_bbox, mAP_bev, mAP_3d, mAP_aos = do_eval(
+        gt_annos, dt_annos, current_classes, min_overlaps, compute_aos)
+    # ret: [num_class, num_diff, num_minoverlap]
+    mAP_bbox = mAP_bbox.mean(-1)
+    mAP_bev = mAP_bev.mean(-1)
+    mAP_3d = mAP_3d.mean(-1)
+    if mAP_aos is not None:
+        mAP_aos = mAP_aos.mean(-1)
+    return mAP_bbox, mAP_bev, mAP_3d, mAP_aos
+
+
+def get_official_eval_result(gt_annos, dt_annos, current_classes, PR_detail_dict=None):
+    overlap_0_7 = np.array([[0.7, 0.5, 0.5, 0.7,
+                             0.5, 0.7], [0.7, 0.5, 0.5, 0.7, 0.5, 0.7],
+                            [0.7, 0.5, 0.5, 0.7, 0.5, 0.7]])
+    overlap_0_5 = np.array([[0.7, 0.5, 0.5, 0.7,
+                             0.5, 0.5], [0.5, 0.25, 0.25, 0.5, 0.25, 0.5],
+                            [0.5, 0.25, 0.25, 0.5, 0.25, 0.5]])
+    min_overlaps = np.stack([overlap_0_7, overlap_0_5], axis=0)  # [2, 3, 5]
+    class_to_name = {
+        0: 'Car',
+        1: 'Pedestrian',
+        2: 'Cyclist',
+        3: 'Van',
+        4: 'Person_sitting',
+        5: 'Truck'
+    }
+    name_to_class = {v: n for n, v in class_to_name.items()}
+    if not isinstance(current_classes, (list, tuple)):
+        current_classes = [current_classes]
+    current_classes_int = []
+    for curcls in current_classes:
+        if isinstance(curcls, str):
+            current_classes_int.append(name_to_class[curcls])
+        else:
+            current_classes_int.append(curcls)
+    current_classes = current_classes_int
+    min_overlaps = min_overlaps[:, :, current_classes]
+    result = ''
+    # check whether alpha is valid
+    compute_aos = False
+    for anno in dt_annos:
+        if anno['alpha'].shape[0] != 0:
+            if anno['alpha'][0] != -10:
+                compute_aos = True
+            break
+    mAPbbox, mAPbev, mAP3d, mAPaos, mAPbbox_R40, mAPbev_R40, mAP3d_R40, mAPaos_R40 = do_eval(
+        gt_annos, dt_annos, current_classes, min_overlaps, compute_aos, PR_detail_dict=PR_detail_dict)
+
+    ret_dict = {}
+    for j, curcls in enumerate(current_classes):
+        # mAP threshold array: [num_minoverlap, metric, class]
+        # mAP result: [num_class, num_diff, num_minoverlap]
+        for i in range(min_overlaps.shape[0]):
+            result += print_str(
+                (f"{class_to_name[curcls]} "
+                 "AP@{:.2f}, {:.2f}, {:.2f}:".format(*min_overlaps[i, :, j])))
+            result += print_str((f"bbox AP:{mAPbbox[j, 0, i]:.4f}, "
+                                 f"{mAPbbox[j, 1, i]:.4f}, "
+                                 f"{mAPbbox[j, 2, i]:.4f}"))
+            result += print_str((f"bev  AP:{mAPbev[j, 0, i]:.4f}, "
+                                 f"{mAPbev[j, 1, i]:.4f}, "
+                                 f"{mAPbev[j, 2, i]:.4f}"))
+            result += print_str((f"3d   AP:{mAP3d[j, 0, i]:.4f}, "
+                                 f"{mAP3d[j, 1, i]:.4f}, "
+                                 f"{mAP3d[j, 2, i]:.4f}"))
+
+            if compute_aos:
+                result += print_str((f"aos  AP:{mAPaos[j, 0, i]:.2f}, "
+                                     f"{mAPaos[j, 1, i]:.2f}, "
+                                     f"{mAPaos[j, 2, i]:.2f}"))
+                # if i == 0:
+                   # ret_dict['%s_aos/easy' % class_to_name[curcls]] = mAPaos[j, 0, 0]
+                   # ret_dict['%s_aos/moderate' % class_to_name[curcls]] = mAPaos[j, 1, 0]
+                   # ret_dict['%s_aos/hard' % class_to_name[curcls]] = mAPaos[j, 2, 0]
+
+            result += print_str(
+                (f"{class_to_name[curcls]} "
+                 "AP_R40@{:.2f}, {:.2f}, {:.2f}:".format(*min_overlaps[i, :, j])))
+            result += print_str((f"bbox AP:{mAPbbox_R40[j, 0, i]:.4f}, "
+                                 f"{mAPbbox_R40[j, 1, i]:.4f}, "
+                                 f"{mAPbbox_R40[j, 2, i]:.4f}"))
+            result += print_str((f"bev  AP:{mAPbev_R40[j, 0, i]:.4f}, "
+                                 f"{mAPbev_R40[j, 1, i]:.4f}, "
+                                 f"{mAPbev_R40[j, 2, i]:.4f}"))
+            result += print_str((f"3d   AP:{mAP3d_R40[j, 0, i]:.4f}, "
+                                 f"{mAP3d_R40[j, 1, i]:.4f}, "
+                                 f"{mAP3d_R40[j, 2, i]:.4f}"))
+            if compute_aos:
+                result += print_str((f"aos  AP:{mAPaos_R40[j, 0, i]:.2f}, "
+                                     f"{mAPaos_R40[j, 1, i]:.2f}, "
+                                     f"{mAPaos_R40[j, 2, i]:.2f}"))
+                if i == 0:
+                   ret_dict['%s_aos/easy_R40' % class_to_name[curcls]] = mAPaos_R40[j, 0, 0]
+                   ret_dict['%s_aos/moderate_R40' % class_to_name[curcls]] = mAPaos_R40[j, 1, 0]
+                   ret_dict['%s_aos/hard_R40' % class_to_name[curcls]] = mAPaos_R40[j, 2, 0]
+
+            if i == 0:
+                # ret_dict['%s_3d/easy' % class_to_name[curcls]] = mAP3d[j, 0, 0]
+                # ret_dict['%s_3d/moderate' % class_to_name[curcls]] = mAP3d[j, 1, 0]
+                # ret_dict['%s_3d/hard' % class_to_name[curcls]] = mAP3d[j, 2, 0]
+                # ret_dict['%s_bev/easy' % class_to_name[curcls]] = mAPbev[j, 0, 0]
+                # ret_dict['%s_bev/moderate' % class_to_name[curcls]] = mAPbev[j, 1, 0]
+                # ret_dict['%s_bev/hard' % class_to_name[curcls]] = mAPbev[j, 2, 0]
+                # ret_dict['%s_image/easy' % class_to_name[curcls]] = mAPbbox[j, 0, 0]
+                # ret_dict['%s_image/moderate' % class_to_name[curcls]] = mAPbbox[j, 1, 0]
+                # ret_dict['%s_image/hard' % class_to_name[curcls]] = mAPbbox[j, 2, 0]
+
+                ret_dict['%s_3d/easy_R40' % class_to_name[curcls]] = mAP3d_R40[j, 0, 0]
+                ret_dict['%s_3d/moderate_R40' % class_to_name[curcls]] = mAP3d_R40[j, 1, 0]
+                ret_dict['%s_3d/hard_R40' % class_to_name[curcls]] = mAP3d_R40[j, 2, 0]
+                ret_dict['%s_bev/easy_R40' % class_to_name[curcls]] = mAPbev_R40[j, 0, 0]
+                ret_dict['%s_bev/moderate_R40' % class_to_name[curcls]] = mAPbev_R40[j, 1, 0]
+                ret_dict['%s_bev/hard_R40' % class_to_name[curcls]] = mAPbev_R40[j, 2, 0]
+                ret_dict['%s_image/easy_R40' % class_to_name[curcls]] = mAPbbox_R40[j, 0, 0]
+                ret_dict['%s_image/moderate_R40' % class_to_name[curcls]] = mAPbbox_R40[j, 1, 0]
+                ret_dict['%s_image/hard_R40' % class_to_name[curcls]] = mAPbbox_R40[j, 2, 0]
+
+    return result, ret_dict
+
+
+def get_coco_eval_result(gt_annos, dt_annos, current_classes):
+    class_to_name = {
+        0: 'Car',
+        1: 'Pedestrian',
+        2: 'Cyclist',
+        3: 'Van',
+        4: 'Person_sitting',
+    }
+    class_to_range = {
+        0: [0.5, 0.95, 10],
+        1: [0.25, 0.7, 10],
+        2: [0.25, 0.7, 10],
+        3: [0.5, 0.95, 10],
+        4: [0.25, 0.7, 10],
+    }
+    name_to_class = {v: n for n, v in class_to_name.items()}
+    if not isinstance(current_classes, (list, tuple)):
+        current_classes = [current_classes]
+    current_classes_int = []
+    for curcls in current_classes:
+        if isinstance(curcls, str):
+            current_classes_int.append(name_to_class[curcls])
+        else:
+            current_classes_int.append(curcls)
+    current_classes = current_classes_int
+    overlap_ranges = np.zeros([3, 3, len(current_classes)])
+    for i, curcls in enumerate(current_classes):
+        overlap_ranges[:, :, i] = np.array(
+            class_to_range[curcls])[:, np.newaxis]
+    result = ''
+    # check whether alpha is valid
+    compute_aos = False
+    for anno in dt_annos:
+        if anno['alpha'].shape[0] != 0:
+            if anno['alpha'][0] != -10:
+                compute_aos = True
+            break
+    mAPbbox, mAPbev, mAP3d, mAPaos = do_coco_style_eval(
+        gt_annos, dt_annos, current_classes, overlap_ranges, compute_aos)
+    for j, curcls in enumerate(current_classes):
+        # mAP threshold array: [num_minoverlap, metric, class]
+        # mAP result: [num_class, num_diff, num_minoverlap]
+        o_range = np.array(class_to_range[curcls])[[0, 2, 1]]
+        o_range[1] = (o_range[2] - o_range[0]) / (o_range[1] - 1)
+        result += print_str((f"{class_to_name[curcls]} "
+                             "coco AP@{:.2f}:{:.2f}:{:.2f}:".format(*o_range)))
+        result += print_str((f"bbox AP:{mAPbbox[j, 0]:.2f}, "
+                             f"{mAPbbox[j, 1]:.2f}, "
+                             f"{mAPbbox[j, 2]:.2f}"))
+        result += print_str((f"bev  AP:{mAPbev[j, 0]:.2f}, "
+                             f"{mAPbev[j, 1]:.2f}, "
+                             f"{mAPbev[j, 2]:.2f}"))
+        result += print_str((f"3d   AP:{mAP3d[j, 0]:.2f}, "
+                             f"{mAP3d[j, 1]:.2f}, "
+                             f"{mAP3d[j, 2]:.2f}"))
+        if compute_aos:
+            result += print_str((f"aos  AP:{mAPaos[j, 0]:.2f}, "
+                                 f"{mAPaos[j, 1]:.2f}, "
+                                 f"{mAPaos[j, 2]:.2f}"))
+    return result
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/evaluate.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/evaluate.py
new file mode 100644
index 000000000..7e1dab6f0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/evaluate.py
@@ -0,0 +1,33 @@
+import time
+
+import fire
+
+import .kitti_common as kitti
+from .eval import get_coco_eval_result, get_official_eval_result
+
+
+def _read_imageset_file(path):
+    with open(path, 'r') as f:
+        lines = f.readlines()
+    return [int(line) for line in lines]
+
+
+def evaluate(label_path,
+             result_path,
+             label_split_file,
+             current_class=0,
+             coco=False,
+             score_thresh=-1):
+    dt_annos = kitti.get_label_annos(result_path)
+    if score_thresh > 0:
+        dt_annos = kitti.filter_annos_low_score(dt_annos, score_thresh)
+    val_image_ids = _read_imageset_file(label_split_file)
+    gt_annos = kitti.get_label_annos(label_path, val_image_ids)
+    if coco:
+        return get_coco_eval_result(gt_annos, dt_annos, current_class)
+    else:
+        return get_official_eval_result(gt_annos, dt_annos, current_class)
+
+
+if __name__ == '__main__':
+    fire.Fire()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/kitti_common.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/kitti_common.py
new file mode 100644
index 000000000..27c91444d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/kitti_common.py
@@ -0,0 +1,412 @@
+import concurrent.futures as futures
+import os
+import pathlib
+import re
+from collections import OrderedDict
+
+import numpy as np
+from skimage import io
+
+
+def get_image_index_str(img_idx):
+    return "{:06d}".format(img_idx)
+
+
+def get_kitti_info_path(idx,
+                        prefix,
+                        info_type='image_2',
+                        file_tail='.png',
+                        training=True,
+                        relative_path=True):
+    img_idx_str = get_image_index_str(idx)
+    img_idx_str += file_tail
+    prefix = pathlib.Path(prefix)
+    if training:
+        file_path = pathlib.Path('training') / info_type / img_idx_str
+    else:
+        file_path = pathlib.Path('testing') / info_type / img_idx_str
+    if not (prefix / file_path).exists():
+        raise ValueError("file not exist: {}".format(file_path))
+    if relative_path:
+        return str(file_path)
+    else:
+        return str(prefix / file_path)
+
+
+def get_image_path(idx, prefix, training=True, relative_path=True):
+    return get_kitti_info_path(idx, prefix, 'image_2', '.png', training,
+                               relative_path)
+
+
+def get_label_path(idx, prefix, training=True, relative_path=True):
+    return get_kitti_info_path(idx, prefix, 'label_2', '.txt', training,
+                               relative_path)
+
+
+def get_velodyne_path(idx, prefix, training=True, relative_path=True):
+    return get_kitti_info_path(idx, prefix, 'velodyne', '.bin', training,
+                               relative_path)
+
+
+def get_calib_path(idx, prefix, training=True, relative_path=True):
+    return get_kitti_info_path(idx, prefix, 'calib', '.txt', training,
+                               relative_path)
+
+
+def _extend_matrix(mat):
+    mat = np.concatenate([mat, np.array([[0., 0., 0., 1.]])], axis=0)
+    return mat
+
+
+def get_kitti_image_info(path,
+                         training=True,
+                         label_info=True,
+                         velodyne=False,
+                         calib=False,
+                         image_ids=7481,
+                         extend_matrix=True,
+                         num_worker=8,
+                         relative_path=True,
+                         with_imageshape=True):
+    # image_infos = []
+    root_path = pathlib.Path(path)
+    if not isinstance(image_ids, list):
+        image_ids = list(range(image_ids))
+
+    def map_func(idx):
+        image_info = {'image_idx': idx}
+        annotations = None
+        if velodyne:
+            image_info['velodyne_path'] = get_velodyne_path(
+                idx, path, training, relative_path)
+        image_info['img_path'] = get_image_path(idx, path, training,
+                                                relative_path)
+        if with_imageshape:
+            img_path = image_info['img_path']
+            if relative_path:
+                img_path = str(root_path / img_path)
+            image_info['img_shape'] = np.array(
+                io.imread(img_path).shape[:2], dtype=np.int32)
+        if label_info:
+            label_path = get_label_path(idx, path, training, relative_path)
+            if relative_path:
+                label_path = str(root_path / label_path)
+            annotations = get_label_anno(label_path)
+        if calib:
+            calib_path = get_calib_path(
+                idx, path, training, relative_path=False)
+            with open(calib_path, 'r') as f:
+                lines = f.readlines()
+            P0 = np.array(
+                [float(info) for info in lines[0].split(' ')[1:13]]).reshape(
+                    [3, 4])
+            P1 = np.array(
+                [float(info) for info in lines[1].split(' ')[1:13]]).reshape(
+                    [3, 4])
+            P2 = np.array(
+                [float(info) for info in lines[2].split(' ')[1:13]]).reshape(
+                    [3, 4])
+            P3 = np.array(
+                [float(info) for info in lines[3].split(' ')[1:13]]).reshape(
+                    [3, 4])
+            if extend_matrix:
+                P0 = _extend_matrix(P0)
+                P1 = _extend_matrix(P1)
+                P2 = _extend_matrix(P2)
+                P3 = _extend_matrix(P3)
+            image_info['calib/P0'] = P0
+            image_info['calib/P1'] = P1
+            image_info['calib/P2'] = P2
+            image_info['calib/P3'] = P3
+            R0_rect = np.array([
+                float(info) for info in lines[4].split(' ')[1:10]
+            ]).reshape([3, 3])
+            if extend_matrix:
+                rect_4x4 = np.zeros([4, 4], dtype=R0_rect.dtype)
+                rect_4x4[3, 3] = 1.
+                rect_4x4[:3, :3] = R0_rect
+            else:
+                rect_4x4 = R0_rect
+            image_info['calib/R0_rect'] = rect_4x4
+            Tr_velo_to_cam = np.array([
+                float(info) for info in lines[5].split(' ')[1:13]
+            ]).reshape([3, 4])
+            Tr_imu_to_velo = np.array([
+                float(info) for info in lines[6].split(' ')[1:13]
+            ]).reshape([3, 4])
+            if extend_matrix:
+                Tr_velo_to_cam = _extend_matrix(Tr_velo_to_cam)
+                Tr_imu_to_velo = _extend_matrix(Tr_imu_to_velo)
+            image_info['calib/Tr_velo_to_cam'] = Tr_velo_to_cam
+            image_info['calib/Tr_imu_to_velo'] = Tr_imu_to_velo
+        if annotations is not None:
+            image_info['annos'] = annotations
+            add_difficulty_to_annos(image_info)
+        return image_info
+
+    with futures.ThreadPoolExecutor(num_worker) as executor:
+        image_infos = executor.map(map_func, image_ids)
+    return list(image_infos)
+
+
+def filter_kitti_anno(image_anno,
+                      used_classes,
+                      used_difficulty=None,
+                      dontcare_iou=None):
+    if not isinstance(used_classes, (list, tuple)):
+        used_classes = [used_classes]
+    img_filtered_annotations = {}
+    relevant_annotation_indices = [
+        i for i, x in enumerate(image_anno['name']) if x in used_classes
+    ]
+    for key in image_anno.keys():
+        img_filtered_annotations[key] = (
+            image_anno[key][relevant_annotation_indices])
+    if used_difficulty is not None:
+        relevant_annotation_indices = [
+            i for i, x in enumerate(img_filtered_annotations['difficulty'])
+            if x in used_difficulty
+        ]
+        for key in image_anno.keys():
+            img_filtered_annotations[key] = (
+                img_filtered_annotations[key][relevant_annotation_indices])
+
+    if 'DontCare' in used_classes and dontcare_iou is not None:
+        dont_care_indices = [
+            i for i, x in enumerate(img_filtered_annotations['name'])
+            if x == 'DontCare'
+        ]
+        # bounding box format [y_min, x_min, y_max, x_max]
+        all_boxes = img_filtered_annotations['bbox']
+        ious = iou(all_boxes, all_boxes[dont_care_indices])
+
+        # Remove all bounding boxes that overlap with a dontcare region.
+        if ious.size > 0:
+            boxes_to_remove = np.amax(ious, axis=1) > dontcare_iou
+            for key in image_anno.keys():
+                img_filtered_annotations[key] = (img_filtered_annotations[key][
+                    np.logical_not(boxes_to_remove)])
+    return img_filtered_annotations
+
+def filter_annos_low_score(image_annos, thresh):
+    new_image_annos = []
+    for anno in image_annos:
+        img_filtered_annotations = {}
+        relevant_annotation_indices = [
+            i for i, s in enumerate(anno['score']) if s >= thresh
+        ]
+        for key in anno.keys():
+            img_filtered_annotations[key] = (
+                anno[key][relevant_annotation_indices])
+        new_image_annos.append(img_filtered_annotations)
+    return new_image_annos
+
+def kitti_result_line(result_dict, precision=4):
+    prec_float = "{" + ":.{}f".format(precision) + "}"
+    res_line = []
+    all_field_default = OrderedDict([
+        ('name', None),
+        ('truncated', -1),
+        ('occluded', -1),
+        ('alpha', -10),
+        ('bbox', None),
+        ('dimensions', [-1, -1, -1]),
+        ('location', [-1000, -1000, -1000]),
+        ('rotation_y', -10),
+        ('score', None),
+    ])
+    res_dict = [(key, None) for key, val in all_field_default.items()]
+    res_dict = OrderedDict(res_dict)
+    for key, val in result_dict.items():
+        if all_field_default[key] is None and val is None:
+            raise ValueError("you must specify a value for {}".format(key))
+        res_dict[key] = val
+
+    for key, val in res_dict.items():
+        if key == 'name':
+            res_line.append(val)
+        elif key in ['truncated', 'alpha', 'rotation_y', 'score']:
+            if val is None:
+                res_line.append(str(all_field_default[key]))
+            else:
+                res_line.append(prec_float.format(val))
+        elif key == 'occluded':
+            if val is None:
+                res_line.append(str(all_field_default[key]))
+            else:
+                res_line.append('{}'.format(val))
+        elif key in ['bbox', 'dimensions', 'location']:
+            if val is None:
+                res_line += [str(v) for v in all_field_default[key]]
+            else:
+                res_line += [prec_float.format(v) for v in val]
+        else:
+            raise ValueError("unknown key. supported key:{}".format(
+                res_dict.keys()))
+    return ' '.join(res_line)
+
+
+def add_difficulty_to_annos(info):
+    min_height = [40, 25,
+                  25]  # minimum height for evaluated groundtruth/detections
+    max_occlusion = [
+        0, 1, 2
+    ]  # maximum occlusion level of the groundtruth used for eval_utils
+    max_trunc = [
+        0.15, 0.3, 0.5
+    ]  # maximum truncation level of the groundtruth used for eval_utils
+    annos = info['annos']
+    dims = annos['dimensions']  # lhw format
+    bbox = annos['bbox']
+    height = bbox[:, 3] - bbox[:, 1]
+    occlusion = annos['occluded']
+    truncation = annos['truncated']
+    diff = []
+    easy_mask = np.ones((len(dims), ), dtype=np.bool)
+    moderate_mask = np.ones((len(dims), ), dtype=np.bool)
+    hard_mask = np.ones((len(dims), ), dtype=np.bool)
+    i = 0
+    for h, o, t in zip(height, occlusion, truncation):
+        if o > max_occlusion[0] or h <= min_height[0] or t > max_trunc[0]:
+            easy_mask[i] = False
+        if o > max_occlusion[1] or h <= min_height[1] or t > max_trunc[1]:
+            moderate_mask[i] = False
+        if o > max_occlusion[2] or h <= min_height[2] or t > max_trunc[2]:
+            hard_mask[i] = False
+        i += 1
+    is_easy = easy_mask
+    is_moderate = np.logical_xor(easy_mask, moderate_mask)
+    is_hard = np.logical_xor(hard_mask, moderate_mask)
+
+    for i in range(len(dims)):
+        if is_easy[i]:
+            diff.append(0)
+        elif is_moderate[i]:
+            diff.append(1)
+        elif is_hard[i]:
+            diff.append(2)
+        else:
+            diff.append(-1)
+    annos["difficulty"] = np.array(diff, np.int32)
+    return diff
+
+
+def get_label_anno(label_path):
+    annotations = {}
+    annotations.update({
+        'name': [],
+        'truncated': [],
+        'occluded': [],
+        'alpha': [],
+        'bbox': [],
+        'dimensions': [],
+        'location': [],
+        'rotation_y': []
+    })
+    with open(label_path, 'r') as f:
+        lines = f.readlines()
+    # if len(lines) == 0 or len(lines[0]) < 15:
+    #     content = []
+    # else:
+    content = [line.strip().split(' ') for line in lines]
+    annotations['name'] = np.array([x[0] for x in content])
+    annotations['truncated'] = np.array([float(x[1]) for x in content])
+    annotations['occluded'] = np.array([int(x[2]) for x in content])
+    annotations['alpha'] = np.array([float(x[3]) for x in content])
+    annotations['bbox'] = np.array(
+        [[float(info) for info in x[4:8]] for x in content]).reshape(-1, 4)
+    # dimensions will convert hwl format to standard lhw(camera) format.
+    annotations['dimensions'] = np.array(
+        [[float(info) for info in x[8:11]] for x in content]).reshape(
+            -1, 3)[:, [2, 0, 1]]
+    annotations['location'] = np.array(
+        [[float(info) for info in x[11:14]] for x in content]).reshape(-1, 3)
+    annotations['rotation_y'] = np.array(
+        [float(x[14]) for x in content]).reshape(-1)
+    if len(content) != 0 and len(content[0]) == 16:  # have score
+        annotations['score'] = np.array([float(x[15]) for x in content])
+    else:
+        annotations['score'] = np.zeros([len(annotations['bbox'])])
+    return annotations
+
+def get_label_annos(label_folder, image_ids=None):
+    if image_ids is None:
+        filepaths = pathlib.Path(label_folder).glob('*.txt')
+        prog = re.compile(r'^\d{6}.txt$')
+        filepaths = filter(lambda f: prog.match(f.name), filepaths)
+        image_ids = [int(p.stem) for p in filepaths]
+        image_ids = sorted(image_ids)
+    if not isinstance(image_ids, list):
+        image_ids = list(range(image_ids))
+    annos = []
+    label_folder = pathlib.Path(label_folder)
+    for idx in image_ids:
+        image_idx = get_image_index_str(idx)
+        label_filename = label_folder / (image_idx + '.txt')
+        annos.append(get_label_anno(label_filename))
+    return annos
+
+def area(boxes, add1=False):
+    """Computes area of boxes.
+
+    Args:
+        boxes: Numpy array with shape [N, 4] holding N boxes
+
+    Returns:
+        a numpy array with shape [N*1] representing box areas
+    """
+    if add1:
+        return (boxes[:, 2] - boxes[:, 0] + 1.0) * (
+            boxes[:, 3] - boxes[:, 1] + 1.0)
+    else:
+        return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+
+
+def intersection(boxes1, boxes2, add1=False):
+    """Compute pairwise intersection areas between boxes.
+
+    Args:
+        boxes1: a numpy array with shape [N, 4] holding N boxes
+        boxes2: a numpy array with shape [M, 4] holding M boxes
+
+    Returns:
+        a numpy array with shape [N*M] representing pairwise intersection area
+    """
+    [y_min1, x_min1, y_max1, x_max1] = np.split(boxes1, 4, axis=1)
+    [y_min2, x_min2, y_max2, x_max2] = np.split(boxes2, 4, axis=1)
+
+    all_pairs_min_ymax = np.minimum(y_max1, np.transpose(y_max2))
+    all_pairs_max_ymin = np.maximum(y_min1, np.transpose(y_min2))
+    if add1:
+        all_pairs_min_ymax += 1.0
+    intersect_heights = np.maximum(
+        np.zeros(all_pairs_max_ymin.shape),
+        all_pairs_min_ymax - all_pairs_max_ymin)
+
+    all_pairs_min_xmax = np.minimum(x_max1, np.transpose(x_max2))
+    all_pairs_max_xmin = np.maximum(x_min1, np.transpose(x_min2))
+    if add1:
+        all_pairs_min_xmax += 1.0
+    intersect_widths = np.maximum(
+        np.zeros(all_pairs_max_xmin.shape),
+        all_pairs_min_xmax - all_pairs_max_xmin)
+    return intersect_heights * intersect_widths
+
+
+def iou(boxes1, boxes2, add1=False):
+    """Computes pairwise intersection-over-union between box collections.
+
+    Args:
+        boxes1: a numpy array with shape [N, 4] holding N boxes.
+        boxes2: a numpy array with shape [M, 4] holding N boxes.
+
+    Returns:
+        a numpy array with shape [N, M] representing pairwise iou scores.
+    """
+    intersect = intersection(boxes1, boxes2, add1)
+    area1 = area(boxes1, add1)
+    area2 = area(boxes2, add1)
+    union = np.expand_dims(
+        area1, axis=1) + np.expand_dims(
+            area2, axis=0) - intersect
+    return intersect / union
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/rotate_iou.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/rotate_iou.py
new file mode 100644
index 000000000..a377294e4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_object_eval_python/rotate_iou.py
@@ -0,0 +1,330 @@
+#####################
+# Based on https://github.com/hongzhenwang/RRPN-revise
+# Licensed under The MIT License
+# Author: yanyan, scrin@foxmail.com
+#####################
+import math
+
+import numba
+import numpy as np
+from numba import cuda
+
+
+@numba.jit(nopython=True)
+def div_up(m, n):
+    return m // n + (m % n > 0)
+
+@cuda.jit('(float32[:], float32[:], float32[:])', device=True, inline=True)
+def trangle_area(a, b, c):
+    return ((a[0] - c[0]) * (b[1] - c[1]) - (a[1] - c[1]) *
+            (b[0] - c[0])) / 2.0
+
+
+@cuda.jit('(float32[:], int32)', device=True, inline=True)
+def area(int_pts, num_of_inter):
+    area_val = 0.0
+    for i in range(num_of_inter - 2):
+        area_val += abs(
+            trangle_area(int_pts[:2], int_pts[2 * i + 2:2 * i + 4],
+                         int_pts[2 * i + 4:2 * i + 6]))
+    return area_val
+
+
+@cuda.jit('(float32[:], int32)', device=True, inline=True)
+def sort_vertex_in_convex_polygon(int_pts, num_of_inter):
+    if num_of_inter > 0:
+        center = cuda.local.array((2, ), dtype=numba.float32)
+        center[:] = 0.0
+        for i in range(num_of_inter):
+            center[0] += int_pts[2 * i]
+            center[1] += int_pts[2 * i + 1]
+        center[0] /= num_of_inter
+        center[1] /= num_of_inter
+        v = cuda.local.array((2, ), dtype=numba.float32)
+        vs = cuda.local.array((16, ), dtype=numba.float32)
+        for i in range(num_of_inter):
+            v[0] = int_pts[2 * i] - center[0]
+            v[1] = int_pts[2 * i + 1] - center[1]
+            d = math.sqrt(v[0] * v[0] + v[1] * v[1])
+            v[0] = v[0] / d
+            v[1] = v[1] / d
+            if v[1] < 0:
+                v[0] = -2 - v[0]
+            vs[i] = v[0]
+        j = 0
+        temp = 0
+        for i in range(1, num_of_inter):
+            if vs[i - 1] > vs[i]:
+                temp = vs[i]
+                tx = int_pts[2 * i]
+                ty = int_pts[2 * i + 1]
+                j = i
+                while j > 0 and vs[j - 1] > temp:
+                    vs[j] = vs[j - 1]
+                    int_pts[j * 2] = int_pts[j * 2 - 2]
+                    int_pts[j * 2 + 1] = int_pts[j * 2 - 1]
+                    j -= 1
+
+                vs[j] = temp
+                int_pts[j * 2] = tx
+                int_pts[j * 2 + 1] = ty
+
+
+@cuda.jit(
+    '(float32[:], float32[:], int32, int32, float32[:])',
+    device=True,
+    inline=True)
+def line_segment_intersection(pts1, pts2, i, j, temp_pts):
+    A = cuda.local.array((2, ), dtype=numba.float32)
+    B = cuda.local.array((2, ), dtype=numba.float32)
+    C = cuda.local.array((2, ), dtype=numba.float32)
+    D = cuda.local.array((2, ), dtype=numba.float32)
+
+    A[0] = pts1[2 * i]
+    A[1] = pts1[2 * i + 1]
+
+    B[0] = pts1[2 * ((i + 1) % 4)]
+    B[1] = pts1[2 * ((i + 1) % 4) + 1]
+
+    C[0] = pts2[2 * j]
+    C[1] = pts2[2 * j + 1]
+
+    D[0] = pts2[2 * ((j + 1) % 4)]
+    D[1] = pts2[2 * ((j + 1) % 4) + 1]
+    BA0 = B[0] - A[0]
+    BA1 = B[1] - A[1]
+    DA0 = D[0] - A[0]
+    CA0 = C[0] - A[0]
+    DA1 = D[1] - A[1]
+    CA1 = C[1] - A[1]
+    acd = DA1 * CA0 > CA1 * DA0
+    bcd = (D[1] - B[1]) * (C[0] - B[0]) > (C[1] - B[1]) * (D[0] - B[0])
+    if acd != bcd:
+        abc = CA1 * BA0 > BA1 * CA0
+        abd = DA1 * BA0 > BA1 * DA0
+        if abc != abd:
+            DC0 = D[0] - C[0]
+            DC1 = D[1] - C[1]
+            ABBA = A[0] * B[1] - B[0] * A[1]
+            CDDC = C[0] * D[1] - D[0] * C[1]
+            DH = BA1 * DC0 - BA0 * DC1
+            Dx = ABBA * DC0 - BA0 * CDDC
+            Dy = ABBA * DC1 - BA1 * CDDC
+            temp_pts[0] = Dx / DH
+            temp_pts[1] = Dy / DH
+            return True
+    return False
+
+
+@cuda.jit(
+    '(float32[:], float32[:], int32, int32, float32[:])',
+    device=True,
+    inline=True)
+def line_segment_intersection_v1(pts1, pts2, i, j, temp_pts):
+    a = cuda.local.array((2, ), dtype=numba.float32)
+    b = cuda.local.array((2, ), dtype=numba.float32)
+    c = cuda.local.array((2, ), dtype=numba.float32)
+    d = cuda.local.array((2, ), dtype=numba.float32)
+
+    a[0] = pts1[2 * i]
+    a[1] = pts1[2 * i + 1]
+
+    b[0] = pts1[2 * ((i + 1) % 4)]
+    b[1] = pts1[2 * ((i + 1) % 4) + 1]
+
+    c[0] = pts2[2 * j]
+    c[1] = pts2[2 * j + 1]
+
+    d[0] = pts2[2 * ((j + 1) % 4)]
+    d[1] = pts2[2 * ((j + 1) % 4) + 1]
+
+    area_abc = trangle_area(a, b, c)
+    area_abd = trangle_area(a, b, d)
+
+    if area_abc * area_abd >= 0:
+        return False
+
+    area_cda = trangle_area(c, d, a)
+    area_cdb = area_cda + area_abc - area_abd
+
+    if area_cda * area_cdb >= 0:
+        return False
+    t = area_cda / (area_abd - area_abc)
+
+    dx = t * (b[0] - a[0])
+    dy = t * (b[1] - a[1])
+    temp_pts[0] = a[0] + dx
+    temp_pts[1] = a[1] + dy
+    return True
+
+
+@cuda.jit('(float32, float32, float32[:])', device=True, inline=True)
+def point_in_quadrilateral(pt_x, pt_y, corners):
+    ab0 = corners[2] - corners[0]
+    ab1 = corners[3] - corners[1]
+
+    ad0 = corners[6] - corners[0]
+    ad1 = corners[7] - corners[1]
+
+    ap0 = pt_x - corners[0]
+    ap1 = pt_y - corners[1]
+
+    abab = ab0 * ab0 + ab1 * ab1
+    abap = ab0 * ap0 + ab1 * ap1
+    adad = ad0 * ad0 + ad1 * ad1
+    adap = ad0 * ap0 + ad1 * ap1
+
+    return abab >= abap and abap >= 0 and adad >= adap and adap >= 0
+
+
+@cuda.jit('(float32[:], float32[:], float32[:])', device=True, inline=True)
+def quadrilateral_intersection(pts1, pts2, int_pts):
+    num_of_inter = 0
+    for i in range(4):
+        if point_in_quadrilateral(pts1[2 * i], pts1[2 * i + 1], pts2):
+            int_pts[num_of_inter * 2] = pts1[2 * i]
+            int_pts[num_of_inter * 2 + 1] = pts1[2 * i + 1]
+            num_of_inter += 1
+        if point_in_quadrilateral(pts2[2 * i], pts2[2 * i + 1], pts1):
+            int_pts[num_of_inter * 2] = pts2[2 * i]
+            int_pts[num_of_inter * 2 + 1] = pts2[2 * i + 1]
+            num_of_inter += 1
+    temp_pts = cuda.local.array((2, ), dtype=numba.float32)
+    for i in range(4):
+        for j in range(4):
+            has_pts = line_segment_intersection(pts1, pts2, i, j, temp_pts)
+            if has_pts:
+                int_pts[num_of_inter * 2] = temp_pts[0]
+                int_pts[num_of_inter * 2 + 1] = temp_pts[1]
+                num_of_inter += 1
+
+    return num_of_inter
+
+
+@cuda.jit('(float32[:], float32[:])', device=True, inline=True)
+def rbbox_to_corners(corners, rbbox):
+    # generate clockwise corners and rotate it clockwise
+    angle = rbbox[4]
+    a_cos = math.cos(angle)
+    a_sin = math.sin(angle)
+    center_x = rbbox[0]
+    center_y = rbbox[1]
+    x_d = rbbox[2]
+    y_d = rbbox[3]
+    corners_x = cuda.local.array((4, ), dtype=numba.float32)
+    corners_y = cuda.local.array((4, ), dtype=numba.float32)
+    corners_x[0] = -x_d / 2
+    corners_x[1] = -x_d / 2
+    corners_x[2] = x_d / 2
+    corners_x[3] = x_d / 2
+    corners_y[0] = -y_d / 2
+    corners_y[1] = y_d / 2
+    corners_y[2] = y_d / 2
+    corners_y[3] = -y_d / 2
+    for i in range(4):
+        corners[2 *
+                i] = a_cos * corners_x[i] + a_sin * corners_y[i] + center_x
+        corners[2 * i
+                + 1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y
+
+
+@cuda.jit('(float32[:], float32[:])', device=True, inline=True)
+def inter(rbbox1, rbbox2):
+    corners1 = cuda.local.array((8, ), dtype=numba.float32)
+    corners2 = cuda.local.array((8, ), dtype=numba.float32)
+    intersection_corners = cuda.local.array((16, ), dtype=numba.float32)
+
+    rbbox_to_corners(corners1, rbbox1)
+    rbbox_to_corners(corners2, rbbox2)
+
+    num_intersection = quadrilateral_intersection(corners1, corners2,
+                                                  intersection_corners)
+    sort_vertex_in_convex_polygon(intersection_corners, num_intersection)
+    # print(intersection_corners.reshape([-1, 2])[:num_intersection])
+
+    return area(intersection_corners, num_intersection)
+
+
+@cuda.jit('(float32[:], float32[:], int32)', device=True, inline=True)
+def devRotateIoUEval(rbox1, rbox2, criterion=-1):
+    area1 = rbox1[2] * rbox1[3]
+    area2 = rbox2[2] * rbox2[3]
+    area_inter = inter(rbox1, rbox2)
+    if criterion == -1:
+        return area_inter / (area1 + area2 - area_inter)
+    elif criterion == 0:
+        return area_inter / area1
+    elif criterion == 1:
+        return area_inter / area2
+    else:
+        return area_inter
+
+@cuda.jit(fastmath=False)
+def rotate_iou_kernel_eval(N, K, dev_boxes, dev_query_boxes, dev_iou, criterion=-1):
+    threadsPerBlock = 8 * 8
+    row_start = cuda.blockIdx.x
+    col_start = cuda.blockIdx.y
+    tx = cuda.threadIdx.x
+    row_size = min(N - row_start * threadsPerBlock, threadsPerBlock)
+    col_size = min(K - col_start * threadsPerBlock, threadsPerBlock)
+    block_boxes = cuda.shared.array(shape=(64 * 5, ), dtype=numba.float32)
+    block_qboxes = cuda.shared.array(shape=(64 * 5, ), dtype=numba.float32)
+
+    dev_query_box_idx = threadsPerBlock * col_start + tx
+    dev_box_idx = threadsPerBlock * row_start + tx
+    if (tx < col_size):
+        block_qboxes[tx * 5 + 0] = dev_query_boxes[dev_query_box_idx * 5 + 0]
+        block_qboxes[tx * 5 + 1] = dev_query_boxes[dev_query_box_idx * 5 + 1]
+        block_qboxes[tx * 5 + 2] = dev_query_boxes[dev_query_box_idx * 5 + 2]
+        block_qboxes[tx * 5 + 3] = dev_query_boxes[dev_query_box_idx * 5 + 3]
+        block_qboxes[tx * 5 + 4] = dev_query_boxes[dev_query_box_idx * 5 + 4]
+    if (tx < row_size):
+        block_boxes[tx * 5 + 0] = dev_boxes[dev_box_idx * 5 + 0]
+        block_boxes[tx * 5 + 1] = dev_boxes[dev_box_idx * 5 + 1]
+        block_boxes[tx * 5 + 2] = dev_boxes[dev_box_idx * 5 + 2]
+        block_boxes[tx * 5 + 3] = dev_boxes[dev_box_idx * 5 + 3]
+        block_boxes[tx * 5 + 4] = dev_boxes[dev_box_idx * 5 + 4]
+    cuda.syncthreads()
+    if tx < row_size:
+        for i in range(col_size):
+            offset = row_start * threadsPerBlock * K + col_start * threadsPerBlock + tx * K + i
+            dev_iou[offset] = devRotateIoUEval(block_qboxes[i * 5:i * 5 + 5],
+                                           block_boxes[tx * 5:tx * 5 + 5], criterion)
+
+
+def rotate_iou_gpu_eval(boxes, query_boxes, criterion=-1, device_id=0):
+    """rotated box iou running in gpu. 500x faster than cpu version
+    (take 5ms in one example with numba.cuda code).
+    convert from [this project](
+        https://github.com/hongzhenwang/RRPN-revise/tree/master/pcdet/rotation).
+    
+    Args:
+        boxes (float tensor: [N, 5]): rbboxes. format: centers, dims, 
+            angles(clockwise when positive)
+        query_boxes (float tensor: [K, 5]): [description]
+        device_id (int, optional): Defaults to 0. [description]
+    
+    Returns:
+        [type]: [description]
+    """
+    box_dtype = boxes.dtype
+    boxes = boxes.astype(np.float32)
+    query_boxes = query_boxes.astype(np.float32)
+    N = boxes.shape[0]
+    K = query_boxes.shape[0]
+    iou = np.zeros((N, K), dtype=np.float32)
+    if N == 0 or K == 0:
+        return iou
+    threadsPerBlock = 8 * 8
+    cuda.select_device(device_id)
+    blockspergrid = (div_up(N, threadsPerBlock), div_up(K, threadsPerBlock))
+    
+    stream = cuda.stream()
+    with stream.auto_synchronize():
+        boxes_dev = cuda.to_device(boxes.reshape([-1]), stream)
+        query_boxes_dev = cuda.to_device(query_boxes.reshape([-1]), stream)
+        iou_dev = cuda.to_device(iou.reshape([-1]), stream)
+        rotate_iou_kernel_eval[blockspergrid, threadsPerBlock, stream](
+            N, K, boxes_dev, query_boxes_dev, iou_dev, criterion)
+        iou_dev.copy_to_host(iou.reshape([-1]), stream=stream)
+    return iou.astype(boxes.dtype)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_utils.py
new file mode 100644
index 000000000..62b487806
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/kitti/kitti_utils.py
@@ -0,0 +1,66 @@
+import numpy as np
+from ...utils import box_utils
+
+
+def transform_annotations_to_kitti_format(annos, map_name_to_kitti=None, info_with_fakelidar=False):
+    """
+    Args:
+        annos:
+        map_name_to_kitti: dict, map name to KITTI names (Car, Pedestrian, Cyclist)
+        info_with_fakelidar:
+    Returns:
+
+    """
+    for anno in annos:
+        # For lyft and nuscenes, different anno key in info
+        if 'name' not in anno:
+            anno['name'] = anno['gt_names']
+            anno.pop('gt_names')
+
+        for k in range(anno['name'].shape[0]):
+            anno['name'][k] = map_name_to_kitti[anno['name'][k]]
+
+        anno['bbox'] = np.zeros((len(anno['name']), 4))
+        anno['bbox'][:, 2:4] = 50  # [0, 0, 50, 50]
+        anno['truncated'] = np.zeros(len(anno['name']))
+        anno['occluded'] = np.zeros(len(anno['name']))
+        if 'boxes_lidar' in anno:
+            gt_boxes_lidar = anno['boxes_lidar'].copy()
+        else:
+            gt_boxes_lidar = anno['gt_boxes_lidar'].copy()
+
+        if len(gt_boxes_lidar) > 0:
+            if info_with_fakelidar:
+                gt_boxes_lidar = box_utils.boxes3d_kitti_fakelidar_to_lidar(gt_boxes_lidar)
+
+            gt_boxes_lidar[:, 2] -= gt_boxes_lidar[:, 5] / 2
+            anno['location'] = np.zeros((gt_boxes_lidar.shape[0], 3))
+            anno['location'][:, 0] = -gt_boxes_lidar[:, 1]  # x = -y_lidar
+            anno['location'][:, 1] = -gt_boxes_lidar[:, 2]  # y = -z_lidar
+            anno['location'][:, 2] = gt_boxes_lidar[:, 0]  # z = x_lidar
+            dxdydz = gt_boxes_lidar[:, 3:6]
+            anno['dimensions'] = dxdydz[:, [0, 2, 1]]  # lwh ==> lhw
+            anno['rotation_y'] = -gt_boxes_lidar[:, 6] - np.pi / 2.0
+            anno['alpha'] = -np.arctan2(-gt_boxes_lidar[:, 1], gt_boxes_lidar[:, 0]) + anno['rotation_y']
+        else:
+            anno['location'] = anno['dimensions'] = np.zeros((0, 3))
+            anno['rotation_y'] = anno['alpha'] = np.zeros(0)
+
+    return annos
+
+
+def calib_to_matricies(calib):
+    """
+    Converts calibration object to transformation matricies
+    Args:
+        calib: calibration.Calibration, Calibration object
+    Returns
+        V2R: (4, 4), Lidar to rectified camera transformation matrix
+        P2: (3, 4), Camera projection matrix
+    """
+    V2C = np.vstack((calib.V2C, np.array([0, 0, 0, 1], dtype=np.float32)))  # (4, 4)
+    R0 = np.hstack((calib.R0, np.zeros((3, 1), dtype=np.float32)))  # (3, 4)
+    R0 = np.vstack((R0, np.array([0, 0, 0, 1], dtype=np.float32)))  # (4, 4)
+    V2R = R0 @ V2C
+    P2 = calib.P2
+    return V2R, P2
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_dataset.py
new file mode 100644
index 000000000..4fd197acd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_dataset.py
@@ -0,0 +1,303 @@
+import copy
+import pickle
+from pathlib import Path
+
+import numpy as np
+from tqdm import tqdm
+
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+from ...utils import common_utils, box_utils
+from ..dataset import DatasetTemplate
+
+
+class LyftDataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
+        self.root_path = (root_path if root_path is not None else Path(dataset_cfg.DATA_PATH)) / dataset_cfg.VERSION
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=self.root_path, logger=logger
+        )
+        self.infos = []
+        self.include_lyft_data(self.mode)
+
+    def include_lyft_data(self, mode):
+        self.logger.info('Loading lyft dataset')
+        lyft_infos = []
+
+        for info_path in self.dataset_cfg.INFO_PATH[mode]:
+            info_path = self.root_path / info_path
+            if not info_path.exists():
+                continue
+            with open(info_path, 'rb') as f:
+                infos = pickle.load(f)
+                lyft_infos.extend(infos)
+
+        self.infos.extend(lyft_infos)
+        self.logger.info('Total samples for lyft dataset: %d' % (len(lyft_infos)))
+
+    @staticmethod
+    def remove_ego_points(points, center_radius=1.0):
+        mask = ~((np.abs(points[:, 0]) < center_radius*1.5) & (np.abs(points[:, 1]) < center_radius))
+        return points[mask]
+
+    def get_sweep(self, sweep_info):
+        lidar_path = self.root_path / sweep_info['lidar_path']
+        points_sweep = np.fromfile(str(lidar_path), dtype=np.float32, count=-1)
+        if points_sweep.shape[0] % 5 != 0:
+            points_sweep = points_sweep[: points_sweep.shape[0] - (points_sweep.shape[0] % 5)]
+        points_sweep = points_sweep.reshape([-1, 5])[:, :4]
+
+        points_sweep = self.remove_ego_points(points_sweep).T
+        if sweep_info['transform_matrix'] is not None:
+            num_points = points_sweep.shape[1]
+            points_sweep[:3, :] = sweep_info['transform_matrix'].dot(
+                np.vstack((points_sweep[:3, :], np.ones(num_points))))[:3, :]
+
+        cur_times = sweep_info['time_lag'] * np.ones((1, points_sweep.shape[1]))
+        return points_sweep.T, cur_times.T
+
+    def get_lidar_with_sweeps(self, index, max_sweeps=1):
+        info = self.infos[index]
+        lidar_path = self.root_path / info['lidar_path']
+        points = np.fromfile(str(lidar_path), dtype=np.float32, count=-1)
+        if points.shape[0] % 5 != 0:
+            points = points[: points.shape[0] - (points.shape[0] % 5)]
+        points = points.reshape([-1, 5])[:, :4]
+
+        sweep_points_list = [points]
+        sweep_times_list = [np.zeros((points.shape[0], 1))]
+
+        for k in np.random.choice(len(info['sweeps']), max_sweeps - 1, replace=False):
+            points_sweep, times_sweep = self.get_sweep(info['sweeps'][k])
+            sweep_points_list.append(points_sweep)
+            sweep_times_list.append(times_sweep)
+
+        points = np.concatenate(sweep_points_list, axis=0)
+        times = np.concatenate(sweep_times_list, axis=0).astype(points.dtype)
+
+        points = np.concatenate((points, times), axis=1)
+        return points
+
+    def __len__(self):
+        if self._merge_all_iters_to_one_epoch:
+            return len(self.infos) * self.total_epochs
+
+        return len(self.infos)
+
+    def __getitem__(self, index):
+        if self._merge_all_iters_to_one_epoch:
+            index = index % len(self.infos)
+
+        info = copy.deepcopy(self.infos[index])
+        points = self.get_lidar_with_sweeps(index, max_sweeps=self.dataset_cfg.MAX_SWEEPS)
+
+        input_dict = {
+            'points': points,
+            'frame_id': Path(info['lidar_path']).stem,
+            'metadata': {'token': info['token']}
+        }
+
+        if 'gt_boxes' in info:
+            input_dict.update({
+                'gt_boxes': info['gt_boxes'],
+                'gt_names': info['gt_names']
+            })
+
+        data_dict = self.prepare_data(data_dict=input_dict)
+
+        return data_dict
+
+    def kitti_eval(self, eval_det_annos, eval_gt_annos, class_names):
+        from ..kitti.kitti_object_eval_python import eval as kitti_eval
+        from ..kitti import kitti_utils
+
+        map_name_to_kitti = {
+            'car': 'Car',
+            'pedestrian': 'Pedestrian',
+            'truck': 'Truck',
+            'bicycle': 'Cyclist',
+            'motorcycle': 'Cyclist'
+        }
+
+        kitti_utils.transform_to_kitti_format(eval_det_annos, map_name_to_kitti=map_name_to_kitti)
+        kitti_utils.transform_to_kitti_format(
+            eval_gt_annos, map_name_to_kitti=map_name_to_kitti,
+            info_with_fakelidar=self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False)
+        )
+
+        kitti_class_names = [map_name_to_kitti[x] for x in class_names]
+
+        ap_result_str, ap_dict = kitti_eval.get_official_eval_result(
+            gt_annos=eval_gt_annos, dt_annos=eval_det_annos, current_classes=kitti_class_names
+        )
+        return ap_result_str, ap_dict
+
+    def evaluation(self, det_annos, class_names, **kwargs):
+        if kwargs['eval_metric'] == 'kitti':
+            eval_det_annos = copy.deepcopy(det_annos)
+            eval_gt_annos = copy.deepcopy(self.infos)
+            return self.kitti_eval(eval_det_annos, eval_gt_annos, class_names)
+        elif kwargs['eval_metric'] == 'lyft':
+            return self.lyft_eval(det_annos, class_names, 
+                                  iou_thresholds=self.dataset_cfg.EVAL_LYFT_IOU_LIST)
+        else:
+            raise NotImplementedError
+    
+    def lyft_eval(self, det_annos, class_names, iou_thresholds=[0.5]):
+        from lyft_dataset_sdk.lyftdataset import LyftDataset as Lyft
+        from . import lyft_utils
+        # from lyft_dataset_sdk.eval.detection.mAP_evaluation import get_average_precisions
+        from .lyft_mAP_eval.lyft_eval import get_average_precisions
+
+        lyft = Lyft(json_path=self.root_path / 'data', data_path=self.root_path, verbose=True)
+
+        det_lyft_boxes, sample_tokens = lyft_utils.convert_det_to_lyft_format(lyft, det_annos)
+        gt_lyft_boxes = lyft_utils.load_lyft_gt_by_tokens(lyft, sample_tokens)
+
+        average_precisions = get_average_precisions(gt_lyft_boxes, det_lyft_boxes, class_names, iou_thresholds)
+
+        ap_result_str, ap_dict = lyft_utils.format_lyft_results(average_precisions, class_names, iou_thresholds, version=self.dataset_cfg.VERSION)
+
+        return ap_result_str, ap_dict
+
+    def create_groundtruth_database(self, used_classes=None, max_sweeps=10):
+        import torch
+
+        database_save_path = self.root_path / f'gt_database'
+        db_info_save_path = self.root_path / f'lyft_dbinfos_{max_sweeps}sweeps.pkl'
+
+        database_save_path.mkdir(parents=True, exist_ok=True)
+        all_db_infos = {}
+
+        for idx in tqdm(range(len(self.infos))):
+            sample_idx = idx
+            info = self.infos[idx]
+            points = self.get_lidar_with_sweeps(idx, max_sweeps=max_sweeps)
+            gt_boxes = info['gt_boxes']
+            gt_names = info['gt_names']
+
+            box_idxs_of_pts = roiaware_pool3d_utils.points_in_boxes_gpu(
+                torch.from_numpy(points[:, 0:3]).unsqueeze(dim=0).float().cuda(),
+                torch.from_numpy(gt_boxes[:, 0:7]).unsqueeze(dim=0).float().cuda()
+            ).long().squeeze(dim=0).cpu().numpy()
+
+            for i in range(gt_boxes.shape[0]):
+                filename = '%s_%s_%d.bin' % (sample_idx, gt_names[i], i)
+                filepath = database_save_path / filename
+                gt_points = points[box_idxs_of_pts == i]
+
+                gt_points[:, :3] -= gt_boxes[i, :3]
+                with open(filepath, 'w') as f:
+                    gt_points.tofile(f)
+
+                if (used_classes is None) or gt_names[i] in used_classes:
+                    db_path = str(filepath.relative_to(self.root_path))  # gt_database/xxxxx.bin
+                    db_info = {'name': gt_names[i], 'path': db_path, 'image_idx': sample_idx, 'gt_idx': i,
+                               'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0]}
+                    if gt_names[i] in all_db_infos:
+                        all_db_infos[gt_names[i]].append(db_info)
+                    else:
+                        all_db_infos[gt_names[i]] = [db_info]
+        for k, v in all_db_infos.items():
+            print('Database %s: %d' % (k, len(v)))
+
+        with open(db_info_save_path, 'wb') as f:
+            pickle.dump(all_db_infos, f)
+
+
+def create_lyft_info(version, data_path, save_path, split, max_sweeps=10):
+    from lyft_dataset_sdk.lyftdataset import LyftDataset
+    from . import lyft_utils
+    data_path = data_path / version
+    save_path = save_path / version
+    split_path = data_path.parent / 'ImageSets'
+
+    if split is not None:
+        save_path = save_path / split
+        split_path = split_path / split
+
+    save_path.mkdir(exist_ok=True)
+
+    assert version in ['trainval', 'one_scene', 'test']
+
+    if version == 'trainval':
+        train_split_path = split_path / 'train.txt'
+        val_split_path = split_path / 'val.txt'
+    elif version == 'test':
+        train_split_path = split_path / 'test.txt'
+        val_split_path = None
+    elif version == 'one_scene':
+        train_split_path = split_path / 'one_scene.txt'
+        val_split_path = split_path / 'one_scene.txt'
+    else:
+        raise NotImplementedError
+
+    train_scenes = [x.strip() for x in open(train_split_path).readlines()] if train_split_path.exists() else []
+    val_scenes = [x.strip() for x in open(val_split_path).readlines()] if val_split_path is not None and val_split_path.exists() else []
+
+    lyft = LyftDataset(json_path=data_path / 'data', data_path=data_path, verbose=True)
+
+    available_scenes = lyft_utils.get_available_scenes(lyft)
+    available_scene_names = [s['name'] for s in available_scenes]
+    train_scenes = list(filter(lambda x: x in available_scene_names, train_scenes))
+    val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes))
+    train_scenes = set([available_scenes[available_scene_names.index(s)]['token'] for s in train_scenes])
+    val_scenes = set([available_scenes[available_scene_names.index(s)]['token'] for s in val_scenes])
+
+    print('%s: train scene(%d), val scene(%d)' % (version, len(train_scenes), len(val_scenes)))
+
+    train_lyft_infos, val_lyft_infos = lyft_utils.fill_trainval_infos(
+        data_path=data_path, lyft=lyft, train_scenes=train_scenes, val_scenes=val_scenes,
+        test='test' in version, max_sweeps=max_sweeps
+    )
+
+    if version == 'test':
+        print('test sample: %d' % len(train_lyft_infos))
+        with open(save_path / f'lyft_infos_test.pkl', 'wb') as f:
+            pickle.dump(train_lyft_infos, f)
+    else:
+        print('train sample: %d, val sample: %d' % (len(train_lyft_infos), len(val_lyft_infos)))
+        with open(save_path / f'lyft_infos_train.pkl', 'wb') as f:
+            pickle.dump(train_lyft_infos, f)
+        with open(save_path / f'lyft_infos_val.pkl', 'wb') as f:
+            pickle.dump(val_lyft_infos, f)
+
+
+if __name__ == '__main__':
+    import yaml
+    import argparse
+    from pathlib import Path
+    from easydict import EasyDict
+
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--cfg_file', type=str, default=None, help='specify the config of dataset')
+    parser.add_argument('--func', type=str, default='create_lyft_infos', help='')
+    parser.add_argument('--version', type=str, default='trainval', help='')
+    parser.add_argument('--split', type=str, default=None, help='')
+    parser.add_argument('--max_sweeps', type=int, default=10, help='')
+    args = parser.parse_args()
+
+    if args.func == 'create_lyft_infos':
+        try:
+            yaml_config = yaml.safe_load(open(args.cfg_file), Loader=yaml.FullLoader)
+        except:
+            yaml_config = yaml.safe_load(open(args.cfg_file))
+        dataset_cfg = EasyDict(yaml_config)
+        ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
+        dataset_cfg.VERSION = args.version
+        dataset_cfg.MAX_SWEEPS = args.max_sweeps 
+        create_lyft_info(
+            version=dataset_cfg.VERSION,
+            data_path=ROOT_DIR / 'data' / 'lyft',
+            save_path=ROOT_DIR / 'data' / 'lyft',
+            split=args.split,
+            max_sweeps=dataset_cfg.MAX_SWEEPS
+        )
+
+        lyft_dataset = LyftDataset(
+            dataset_cfg=dataset_cfg, class_names=None,
+            root_path=ROOT_DIR / 'data' / 'lyft',
+            logger=common_utils.create_logger(), training=True
+        )
+
+        if args.version != 'test':
+            lyft_dataset.create_groundtruth_database(max_sweeps=dataset_cfg.MAX_SWEEPS)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_mAP_eval/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_mAP_eval/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_mAP_eval/lyft_eval.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_mAP_eval/lyft_eval.py
new file mode 100644
index 000000000..872476ae3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_mAP_eval/lyft_eval.py
@@ -0,0 +1,435 @@
+"""
+modified from lyft toolkit https://github.com/lyft/nuscenes-devkit.git
+"""
+
+"""
+mAP 3D calculation for the data in nuScenes format.
+
+
+The intput files expected to have the format:
+
+Expected fields:
+
+
+gt = [{
+    'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac',
+    'translation': [974.2811881299899, 1714.6815014457964, -23.689857123368846],
+    'size': [1.796, 4.488, 1.664],
+    'rotation': [0.14882026466054782, 0, 0, 0.9888642620837121],
+    'name': 'car'
+}]
+
+prediction_result = {
+    'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac',
+    'translation': [971.8343488872263, 1713.6816097857359, -25.82534357061308],
+    'size': [2.519726579986132, 7.810161372666739, 3.483438286096803],
+    'rotation': [0.10913582721095375, 0.04099572636992043, 0.01927712319721745, 1.029328402625659],
+    'name': 'car',
+    'score': 0.3077029437237213
+}
+
+
+input arguments:
+
+--pred_file:  file with predictions
+--gt_file: ground truth file
+--iou_threshold: IOU threshold
+
+
+In general we would be interested in average of mAP at thresholds [0.5, 0.55, 0.6, 0.65,...0.95], similar to the
+standard COCO => one needs to run this file N times for every IOU threshold independently.
+
+"""
+
+import argparse
+import json
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+from pyquaternion import Quaternion
+from shapely.geometry import Polygon
+
+
+class Box3D:
+    """Data class used during detection evaluation. Can be a prediction or ground truth."""
+
+    def __init__(self, **kwargs):
+        sample_token = kwargs["sample_token"]
+        translation = kwargs["translation"]
+        size = kwargs["size"]
+        rotation = kwargs["rotation"]
+        name = kwargs["name"]
+        score = kwargs.get("score", -1)
+
+        if not isinstance(sample_token, str):
+            raise TypeError("Sample_token must be a string!")
+
+        if not len(translation) == 3:
+            raise ValueError("Translation must have 3 elements!")
+
+        if np.any(np.isnan(translation)):
+            raise ValueError("Translation may not be NaN!")
+
+        if not len(size) == 3:
+            raise ValueError("Size must have 3 elements!")
+
+        if np.any(np.isnan(size)):
+            raise ValueError("Size may not be NaN!")
+
+        if not len(rotation) == 4:
+            raise ValueError("Rotation must have 4 elements!")
+
+        if np.any(np.isnan(rotation)):
+            raise ValueError("Rotation may not be NaN!")
+
+        if name is None:
+            raise ValueError("Name cannot be empty!")
+
+        # Assign.
+        self.sample_token = sample_token
+        self.translation = translation
+        self.size = size
+        self.volume = np.prod(self.size)
+        self.score = score
+
+        assert np.all([x > 0 for x in size])
+        self.rotation = rotation
+        self.name = name
+        self.quaternion = Quaternion(self.rotation)
+
+        self.width, self.length, self.height = size
+
+        self.center_x, self.center_y, self.center_z = self.translation
+
+        self.min_z = self.center_z - self.height / 2
+        self.max_z = self.center_z + self.height / 2
+
+        self.ground_bbox_coords = None
+        self.ground_bbox_coords = self.get_ground_bbox_coords()
+
+    @staticmethod
+    def check_orthogonal(a, b, c):
+        """Check that vector (b - a) is orthogonal to the vector (c - a)."""
+        return np.isclose((b[0] - a[0]) * (c[0] - a[0]) + (b[1] - a[1]) * (c[1] - a[1]), 0)
+
+    def get_ground_bbox_coords(self):
+        if self.ground_bbox_coords is not None:
+            return self.ground_bbox_coords
+        return self.calculate_ground_bbox_coords()
+
+    def calculate_ground_bbox_coords(self):
+        """We assume that the 3D box has lower plane parallel to the ground.
+
+        Returns: Polygon with 4 points describing the base.
+
+        """
+        if self.ground_bbox_coords is not None:
+            return self.ground_bbox_coords
+
+        rotation_matrix = self.quaternion.rotation_matrix
+
+        cos_angle = rotation_matrix[0, 0]
+        sin_angle = rotation_matrix[1, 0]
+
+        point_0_x = self.center_x + self.length / 2 * cos_angle + self.width / 2 * sin_angle
+        point_0_y = self.center_y + self.length / 2 * sin_angle - self.width / 2 * cos_angle
+
+        point_1_x = self.center_x + self.length / 2 * cos_angle - self.width / 2 * sin_angle
+        point_1_y = self.center_y + self.length / 2 * sin_angle + self.width / 2 * cos_angle
+
+        point_2_x = self.center_x - self.length / 2 * cos_angle - self.width / 2 * sin_angle
+        point_2_y = self.center_y - self.length / 2 * sin_angle + self.width / 2 * cos_angle
+
+        point_3_x = self.center_x - self.length / 2 * cos_angle + self.width / 2 * sin_angle
+        point_3_y = self.center_y - self.length / 2 * sin_angle - self.width / 2 * cos_angle
+
+        point_0 = point_0_x, point_0_y
+        point_1 = point_1_x, point_1_y
+        point_2 = point_2_x, point_2_y
+        point_3 = point_3_x, point_3_y
+
+        assert self.check_orthogonal(point_0, point_1, point_3)
+        assert self.check_orthogonal(point_1, point_0, point_2)
+        assert self.check_orthogonal(point_2, point_1, point_3)
+        assert self.check_orthogonal(point_3, point_0, point_2)
+
+        self.ground_bbox_coords = Polygon(
+            [
+                (point_0_x, point_0_y),
+                (point_1_x, point_1_y),
+                (point_2_x, point_2_y),
+                (point_3_x, point_3_y),
+                (point_0_x, point_0_y),
+            ]
+        )
+
+        return self.ground_bbox_coords
+
+    def get_height_intersection(self, other):
+        min_z = max(other.min_z, self.min_z)
+        max_z = min(other.max_z, self.max_z)
+
+        return max(0, max_z - min_z)
+
+    def get_area_intersection(self, other) -> float:
+        result = self.ground_bbox_coords.intersection(other.ground_bbox_coords).area
+
+        assert result <= self.width * self.length
+
+        return result
+
+    def get_intersection(self, other) -> float:
+        height_intersection = self.get_height_intersection(other)
+
+        area_intersection = self.ground_bbox_coords.intersection(other.ground_bbox_coords).area
+
+        return height_intersection * area_intersection
+
+    def get_iou(self, other):
+        intersection = self.get_intersection(other)
+        union = self.volume + other.volume - intersection
+
+        iou = np.clip(intersection / union, 0, 1)
+
+        return iou
+
+    def __repr__(self):
+        return str(self.serialize())
+
+    def serialize(self) -> dict:
+        """Returns: Serialized instance as dict."""
+
+        return {
+            "sample_token": self.sample_token,
+            "translation": self.translation,
+            "size": self.size,
+            "rotation": self.rotation,
+            "name": self.name,
+            "volume": self.volume,
+            "score": self.score,
+        }
+
+
+def group_by_key(detections, key):
+    groups = defaultdict(list)
+    for detection in detections:
+        groups[detection[key]].append(detection)
+    return groups
+
+
+def wrap_in_box(input):
+    result = {}
+    for key, value in input.items():
+        result[key] = [Box3D(**x) for x in value]
+
+    return result
+
+
+def get_envelope(precisions):
+    """Compute the precision envelope.
+
+    Args:
+      precisions:
+
+    Returns:
+
+    """
+    for i in range(precisions.size - 1, 0, -1):
+        precisions[i - 1] = np.maximum(precisions[i - 1], precisions[i])
+    return precisions
+
+
+def get_ap(recalls, precisions):
+    """Calculate average precision.
+
+    Args:
+      recalls:
+      precisions: Returns (float): average precision.
+
+    Returns:
+
+    """
+    # correct AP calculation
+    # first append sentinel values at the end
+    recalls = np.concatenate(([0.0], recalls, [1.0]))
+    precisions = np.concatenate(([0.0], precisions, [0.0]))
+
+    precisions = get_envelope(precisions)
+
+    # to calculate area under PR curve, look for points where X axis (recall) changes value
+    i = np.where(recalls[1:] != recalls[:-1])[0]
+
+    # and sum (\Delta recall) * prec
+    ap = np.sum((recalls[i + 1] - recalls[i]) * precisions[i + 1])
+    return ap
+
+
+def get_ious(gt_boxes, predicted_box):
+    return [predicted_box.get_iou(x) for x in gt_boxes]
+
+
+def recall_precision(gt, predictions, iou_threshold_list):
+    num_gts = len(gt)
+
+    if num_gts == 0:
+        return -1, -1, -1
+
+    image_gts = group_by_key(gt, "sample_token")
+    image_gts = wrap_in_box(image_gts)
+
+    sample_gt_checked = {sample_token: np.zeros((len(boxes), len(iou_threshold_list))) for sample_token, boxes in image_gts.items()}
+
+    predictions = sorted(predictions, key=lambda x: x["score"], reverse=True)
+
+    # go down dets and mark TPs and FPs
+    num_predictions = len(predictions)
+    tp = np.zeros((num_predictions, len(iou_threshold_list)))
+    fp = np.zeros((num_predictions, len(iou_threshold_list)))
+
+    for prediction_index, prediction in enumerate(predictions):
+        predicted_box = Box3D(**prediction)
+
+        sample_token = prediction["sample_token"]
+
+        max_overlap = -np.inf
+        jmax = -1
+
+        try:
+            gt_boxes = image_gts[sample_token]  # gt_boxes per sample
+            gt_checked = sample_gt_checked[sample_token]  # gt flags per sample
+        except KeyError:
+            gt_boxes = []
+            gt_checked = None
+
+        if len(gt_boxes) > 0:
+            overlaps = get_ious(gt_boxes, predicted_box)
+
+            max_overlap = np.max(overlaps)
+
+            jmax = np.argmax(overlaps)
+
+        for i, iou_threshold in enumerate(iou_threshold_list):
+            if max_overlap > iou_threshold:
+                if gt_checked[jmax, i] == 0:
+                    tp[prediction_index, i] = 1.0
+                    gt_checked[jmax, i] = 1
+                else:
+                    fp[prediction_index, i] = 1.0
+            else:
+                fp[prediction_index, i] = 1.0
+
+    # compute precision recall
+    fp = np.cumsum(fp, axis=0)
+    tp = np.cumsum(tp, axis=0)
+
+    recalls = tp / float(num_gts)
+
+    assert np.all(0 <= recalls) & np.all(recalls <= 1)
+
+    # avoid divide by zero in case the first detection matches a difficult ground truth
+    precisions = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
+
+    assert np.all(0 <= precisions) & np.all(precisions <= 1)
+
+    ap_list = []
+    for i in range(len(iou_threshold_list)):
+        recall = recalls[:, i]
+        precision = precisions[:, i]
+        ap = get_ap(recall, precision)
+        ap_list.append(ap)
+
+    return recalls, precisions, ap_list
+
+
+def get_average_precisions(gt: list, predictions: list, class_names: list, iou_thresholds: list) -> np.array:
+    """Returns an array with an average precision per class.
+
+
+    Args:
+        gt: list of dictionaries in the format described below.
+        predictions: list of dictionaries in the format described below.
+        class_names: list of the class names.
+        iou_threshold: list of IOU thresholds used to calculate TP / FN
+
+    Returns an array with an average precision per class.
+
+
+    Ground truth and predictions should have schema:
+
+    gt = [{
+    'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac',
+    'translation': [974.2811881299899, 1714.6815014457964, -23.689857123368846],
+    'size': [1.796, 4.488, 1.664],
+    'rotation': [0.14882026466054782, 0, 0, 0.9888642620837121],
+    'name': 'car'
+    }]
+
+    predictions = [{
+        'sample_token': '0f0e3ce89d2324d8b45aa55a7b4f8207fbb039a550991a5149214f98cec136ac',
+        'translation': [971.8343488872263, 1713.6816097857359, -25.82534357061308],
+        'size': [2.519726579986132, 7.810161372666739, 3.483438286096803],
+        'rotation': [0.10913582721095375, 0.04099572636992043, 0.01927712319721745, 1.029328402625659],
+        'name': 'car',
+        'score': 0.3077029437237213
+    }]
+
+    """
+    assert all([0 <= iou_th <= 1 for iou_th in iou_thresholds])
+
+    gt_by_class_name = group_by_key(gt, "name")
+    pred_by_class_name = group_by_key(predictions, "name")
+
+    average_precisions = np.zeros(len(class_names))
+
+    for class_id, class_name in enumerate(class_names):
+        if class_name in pred_by_class_name:
+            recalls, precisions, ap_list = recall_precision(
+                gt_by_class_name[class_name], pred_by_class_name[class_name], iou_thresholds
+            )
+            aps = np.mean(ap_list)
+            average_precisions[class_id] = aps
+
+    return average_precisions
+
+
+def get_class_names(gt: dict) -> list:
+    """Get sorted list of class names.
+
+    Args:
+        gt:
+
+    Returns: Sorted list of class names.
+
+    """
+    return sorted(list(set([x["name"] for x in gt])))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    arg = parser.add_argument
+    arg("-p", "--pred_file", type=str, help="Path to the predictions file.", required=True)
+    arg("-g", "--gt_file", type=str, help="Path to the ground truth file.", required=True)
+    arg("-t", "--iou_threshold", type=float, help="iou threshold", default=0.5)
+
+    args = parser.parse_args()
+
+    gt_path = Path(args.gt_file)
+    pred_path = Path(args.pred_file)
+
+    with open(args.pred_file) as f:
+        predictions = json.load(f)
+
+    with open(args.gt_file) as f:
+        gt = json.load(f)
+
+    class_names = get_class_names(gt)
+    print("Class_names = ", class_names)
+
+    average_precisions = get_average_precisions(gt, predictions, class_names, args.iou_threshold)
+
+    mAP = np.mean(average_precisions)
+    print("Average per class mean average precision = ", mAP)
+
+    for class_id in sorted(list(zip(class_names, average_precisions.flatten().tolist()))):
+        print(class_id)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_utils.py
new file mode 100644
index 000000000..30e057ef9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/lyft/lyft_utils.py
@@ -0,0 +1,332 @@
+"""
+The Lyft data pre-processing and evaluation is modified from
+https://github.com/poodarchu/Det3D
+"""
+
+import operator
+from functools import reduce
+from pathlib import Path
+
+import numpy as np
+import tqdm
+from lyft_dataset_sdk.utils.data_classes import Box, Quaternion
+from lyft_dataset_sdk.lyftdataset import LyftDataset
+from lyft_dataset_sdk.utils.geometry_utils import transform_matrix
+from lyft_dataset_sdk.eval.detection.mAP_evaluation import Box3D
+
+
+def get_available_scenes(lyft):
+    available_scenes = []
+    print('total scene num:', len(lyft.scene))
+    for scene in lyft.scene:
+        scene_token = scene['token']
+        scene_rec = lyft.get('scene', scene_token)
+        sample_rec = lyft.get('sample', scene_rec['first_sample_token'])
+        sd_rec = lyft.get('sample_data', sample_rec['data']['LIDAR_TOP'])
+        has_more_frames = True
+        scene_not_exist = False
+        while has_more_frames:
+            lidar_path, boxes, _ = lyft.get_sample_data(sd_rec['token'])
+            if not Path(lidar_path).exists():
+                scene_not_exist = True
+                break
+            else:
+                break
+            # if not sd_rec['next'] == '':
+            #     sd_rec = nusc.get('sample_data', sd_rec['next'])
+            # else:
+            #     has_more_frames = False
+        if scene_not_exist:
+            continue
+        available_scenes.append(scene)
+    print('exist scene num:', len(available_scenes))
+    return available_scenes
+
+
+def get_sample_data(lyft, sample_data_token):
+    sd_rec = lyft.get("sample_data", sample_data_token)
+    cs_rec = lyft.get("calibrated_sensor", sd_rec["calibrated_sensor_token"])
+
+    sensor_rec = lyft.get("sensor", cs_rec["sensor_token"])
+    pose_rec = lyft.get("ego_pose", sd_rec["ego_pose_token"])
+
+    boxes = lyft.get_boxes(sample_data_token)
+
+    box_list = []
+    for box in boxes:
+        box.translate(-np.array(pose_rec["translation"]))
+        box.rotate(Quaternion(pose_rec["rotation"]).inverse)
+
+        box.translate(-np.array(cs_rec["translation"]))
+        box.rotate(Quaternion(cs_rec["rotation"]).inverse)
+
+        box_list.append(box)
+
+    return box_list, pose_rec
+
+
+def quaternion_yaw(q: Quaternion) -> float:
+    """
+    Calculate the yaw angle from a quaternion.
+    Note that this only works for a quaternion that represents a box in lidar or global coordinate frame.
+    It does not work for a box in the camera frame.
+    :param q: Quaternion of interest.
+    :return: Yaw angle in radians.
+    """
+
+    # Project into xy plane.
+    v = np.dot(q.rotation_matrix, np.array([1, 0, 0]))
+
+    # Measure yaw using arctan.
+    yaw = np.arctan2(v[1], v[0])
+
+    return yaw
+
+
+def fill_trainval_infos(data_path, lyft, train_scenes, val_scenes, test=False, max_sweeps=10):
+    train_lyft_infos = []
+    val_lyft_infos = []
+    progress_bar = tqdm.tqdm(total=len(lyft.sample), desc='create_info', dynamic_ncols=True)
+
+    # ref_chans = ["LIDAR_TOP", "LIDAR_FRONT_LEFT", "LIDAR_FRONT_RIGHT"]
+    ref_chan = "LIDAR_TOP"
+
+    for index, sample in enumerate(lyft.sample):
+        progress_bar.update()
+
+        ref_info = {}
+        ref_sd_token = sample["data"][ref_chan]
+        ref_sd_rec = lyft.get("sample_data", ref_sd_token)
+        ref_cs_token = ref_sd_rec["calibrated_sensor_token"]
+        ref_cs_rec = lyft.get("calibrated_sensor", ref_cs_token)
+
+        ref_to_car = transform_matrix(
+            ref_cs_rec["translation"],
+            Quaternion(ref_cs_rec["rotation"]),
+            inverse=False,
+        )
+
+        ref_from_car = transform_matrix(
+            ref_cs_rec["translation"],
+            Quaternion(ref_cs_rec["rotation"]),
+            inverse=True,
+        )
+
+        ref_lidar_path = lyft.get_sample_data_path(ref_sd_token)
+
+        ref_boxes, ref_pose_rec = get_sample_data(lyft, ref_sd_token)
+        ref_time = 1e-6 * ref_sd_rec["timestamp"]
+        car_from_global = transform_matrix(
+            ref_pose_rec["translation"],
+            Quaternion(ref_pose_rec["rotation"]),
+            inverse=True,
+        )
+
+        car_to_global = transform_matrix(
+            ref_pose_rec["translation"],
+            Quaternion(ref_pose_rec["rotation"]),
+            inverse=False,
+        )
+
+        info = {
+            "lidar_path": Path(ref_lidar_path).relative_to(data_path).__str__(),
+            "ref_from_car": ref_from_car,
+            "ref_to_car": ref_to_car,
+            'token': sample['token'],
+            'car_from_global': car_from_global,
+            'car_to_global': car_to_global,
+            'timestamp': ref_time,
+            'sweeps': []
+        }
+
+        sample_data_token = sample['data'][ref_chan]
+        curr_sd_rec = lyft.get('sample_data', sample_data_token)
+        sweeps = []
+
+        while len(sweeps) < max_sweeps - 1:
+            if curr_sd_rec['prev'] == '':
+                if len(sweeps) == 0:
+                    sweep = {
+                        'lidar_path': Path(ref_lidar_path).relative_to(data_path).__str__(),
+                        'sample_data_token': curr_sd_rec['token'],
+                        'transform_matrix': None,
+                        'time_lag': curr_sd_rec['timestamp'] * 0,
+                    }
+                    sweeps.append(sweep)
+                else:
+                    sweeps.append(sweeps[-1])
+            else:
+                curr_sd_rec = lyft.get('sample_data', curr_sd_rec['prev'])
+
+                # Get past pose
+                current_pose_rec = lyft.get('ego_pose', curr_sd_rec['ego_pose_token'])
+                global_from_car = transform_matrix(
+                    current_pose_rec['translation'], Quaternion(current_pose_rec['rotation']), inverse=False,
+                )
+
+                # Homogeneous transformation matrix from sensor coordinate frame to ego car frame.
+                current_cs_rec = lyft.get(
+                    'calibrated_sensor', curr_sd_rec['calibrated_sensor_token']
+                )
+                car_from_current = transform_matrix(
+                    current_cs_rec['translation'], Quaternion(current_cs_rec['rotation']), inverse=False,
+                )
+
+                tm = reduce(np.dot, [ref_from_car, car_from_global, global_from_car, car_from_current])
+
+                lidar_path = lyft.get_sample_data_path(curr_sd_rec['token'])
+
+                time_lag = ref_time - 1e-6 * curr_sd_rec['timestamp']
+
+                sweep = {
+                    'lidar_path': Path(lidar_path).relative_to(data_path).__str__(),
+                    'sample_data_token': curr_sd_rec['token'],
+                    'transform_matrix': tm,
+                    'global_from_car': global_from_car,
+                    'car_from_current': car_from_current,
+                    'time_lag': time_lag,
+                }
+                sweeps.append(sweep)
+
+        info['sweeps'] = sweeps
+
+        if not test:
+            annotations = [
+                lyft.get("sample_annotation", token) for token in sample["anns"]
+            ]
+
+            locs = np.array([b.center for b in ref_boxes]).reshape(-1, 3)
+            dims = np.array([b.wlh for b in ref_boxes]).reshape(-1, 3)[:, [1, 0, 2]]
+            rots = np.array([quaternion_yaw(b.orientation) for b in ref_boxes]).reshape(
+                -1, 1
+            )
+            velocity = np.array([b.velocity for b in ref_boxes]).reshape(-1, 3)
+            names = np.array([b.name for b in ref_boxes])
+            tokens = np.array([b.token for b in ref_boxes]).reshape(-1, 1)
+            gt_boxes = np.concatenate([locs, dims, rots], axis=1)
+
+            assert len(annotations) == len(gt_boxes)
+
+            info["gt_boxes"] = gt_boxes
+            info["gt_boxes_velocity"] = velocity
+            info["gt_names"] = names
+            info["gt_boxes_token"] = tokens
+
+        if sample["scene_token"] in train_scenes:
+            train_lyft_infos.append(info)
+        else:
+            val_lyft_infos.append(info)
+
+    progress_bar.close()
+    return train_lyft_infos, val_lyft_infos
+
+def boxes_lidar_to_lyft(boxes3d, scores=None, labels=None):
+    box_list = []
+    for k in range(boxes3d.shape[0]):
+        quat = Quaternion(axis=[0, 0, 1], radians=boxes3d[k, 6])
+        box = Box(
+            boxes3d[k, :3],
+            boxes3d[k, [4, 3, 5]],  # wlh
+            quat, label=labels[k] if labels is not None else np.nan,
+            score=scores[k] if scores is not None else np.nan,
+        )
+        box_list.append(box)
+    return box_list
+
+
+def lidar_lyft_box_to_global(lyft, boxes, sample_token):
+    s_record = lyft.get('sample', sample_token)
+    sample_data_token = s_record['data']['LIDAR_TOP']
+
+    sd_record = lyft.get('sample_data', sample_data_token)
+    cs_record = lyft.get('calibrated_sensor', sd_record['calibrated_sensor_token'])
+    sensor_record = lyft.get('sensor', cs_record['sensor_token'])
+    pose_record = lyft.get('ego_pose', sd_record['ego_pose_token'])
+
+    box_list = []
+    for box in boxes:
+        # Move box to ego vehicle coord system
+        box.rotate(Quaternion(cs_record['rotation']))
+        box.translate(np.array(cs_record['translation']))
+        # Move box to global coord system
+        box.rotate(Quaternion(pose_record['rotation']))
+        box.translate(np.array(pose_record['translation']))
+        box_list.append(box)
+    return box_list
+
+
+def convert_det_to_lyft_format(lyft, det_annos):
+    sample_tokens = []
+    det_lyft_box = [] 
+    for anno in det_annos:
+        sample_tokens.append(anno['metadata']['token'])
+
+        boxes_lyft_list = boxes_lidar_to_lyft(anno['boxes_lidar'], anno['score'], anno['pred_labels'])
+        boxes_list = lidar_lyft_box_to_global(lyft, boxes_lyft_list, anno['metadata']['token'])
+
+        for idx, box in enumerate(boxes_list):
+            name = anno['name'][idx]
+            box3d = {
+                'sample_token': anno['metadata']['token'],
+                'translation': box.center.tolist(),
+                'size': box.wlh.tolist(),
+                'rotation': box.orientation.elements.tolist(),
+                'name': name,
+                'score': box.score
+            }
+            det_lyft_box.append(box3d)
+    
+    return det_lyft_box, sample_tokens
+
+
+def load_lyft_gt_by_tokens(lyft, sample_tokens):
+    """
+    Modify from Lyft tutorial
+    """
+
+    gt_box3ds = []
+
+    # Load annotations and filter predictions and annotations.
+    for sample_token in sample_tokens:
+        
+        sample = lyft.get('sample', sample_token)
+
+        sample_annotation_tokens = sample['anns']
+
+        sample_lidar_token = sample["data"]["LIDAR_TOP"]
+        lidar_data = lyft.get("sample_data", sample_lidar_token)
+        ego_pose = lyft.get("ego_pose", lidar_data["ego_pose_token"])
+        ego_translation = np.array(ego_pose['translation'])
+        
+        for sample_annotation_token in sample_annotation_tokens:
+            sample_annotation = lyft.get('sample_annotation', sample_annotation_token)
+            sample_annotation_translation = sample_annotation['translation']
+            
+            class_name = sample_annotation['category_name']
+            
+            box3d = {
+                'sample_token': sample_token,
+                'translation': sample_annotation_translation,
+                'size': sample_annotation['size'],
+                'rotation': sample_annotation['rotation'],
+                'name': class_name
+            }
+            gt_box3ds.append(box3d)
+            
+    return gt_box3ds
+
+
+def format_lyft_results(classwise_ap, class_names, iou_threshold_list, version='trainval'):
+    ret_dict = {}
+    result = '----------------Lyft %s results-----------------\n' % version
+    result += 'Average precision over IoUs: {}\n'.format(str(iou_threshold_list))
+    for c_idx, class_name in enumerate(class_names):
+        result += '{:<20}: \t {:.4f}\n'.format(class_name, classwise_ap[c_idx])
+        ret_dict[class_name] = classwise_ap[c_idx]
+
+    result += '--------------average performance-------------\n'
+    mAP = np.mean(classwise_ap)
+    result += 'mAP:\t {:.4f}\n'.format(mAP)
+
+    ret_dict['mAP'] = mAP
+    return result, ret_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/nuscenes_dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/nuscenes_dataset.py
new file mode 100644
index 000000000..0f7000562
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/nuscenes_dataset.py
@@ -0,0 +1,434 @@
+import copy
+import pickle
+from pathlib import Path
+
+import numpy as np
+from tqdm import tqdm
+
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+from ...utils import common_utils
+from ..dataset import DatasetTemplate
+from pyquaternion import Quaternion
+from PIL import Image
+
+
+class NuScenesDataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
+        root_path = (root_path if root_path is not None else Path(dataset_cfg.DATA_PATH)) / dataset_cfg.VERSION
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
+        )
+        self.infos = []
+        self.camera_config = self.dataset_cfg.get('CAMERA_CONFIG', None)
+        if self.camera_config is not None:
+            self.use_camera = self.camera_config.get('USE_CAMERA', True)
+            self.camera_image_config = self.camera_config.IMAGE
+        else:
+            self.use_camera = False
+
+        self.include_nuscenes_data(self.mode)
+        if self.training and self.dataset_cfg.get('BALANCED_RESAMPLING', False):
+            self.infos = self.balanced_infos_resampling(self.infos)
+
+    def include_nuscenes_data(self, mode):
+        self.logger.info('Loading NuScenes dataset')
+        nuscenes_infos = []
+
+        for info_path in self.dataset_cfg.INFO_PATH[mode]:
+            info_path = self.root_path / info_path
+            if not info_path.exists():
+                continue
+            with open(info_path, 'rb') as f:
+                infos = pickle.load(f)
+                nuscenes_infos.extend(infos)
+
+        self.infos.extend(nuscenes_infos)
+        self.logger.info('Total samples for NuScenes dataset: %d' % (len(nuscenes_infos)))
+
+    def balanced_infos_resampling(self, infos):
+        """
+        Class-balanced sampling of nuScenes dataset from https://arxiv.org/abs/1908.09492
+        """
+        if self.class_names is None:
+            return infos
+
+        cls_infos = {name: [] for name in self.class_names}
+        for info in infos:
+            for name in set(info['gt_names']):
+                if name in self.class_names:
+                    cls_infos[name].append(info)
+
+        duplicated_samples = sum([len(v) for _, v in cls_infos.items()])
+        cls_dist = {k: len(v) / duplicated_samples for k, v in cls_infos.items()}
+
+        sampled_infos = []
+
+        frac = 1.0 / len(self.class_names)
+        ratios = [frac / v for v in cls_dist.values()]
+
+        for cur_cls_infos, ratio in zip(list(cls_infos.values()), ratios):
+            sampled_infos += np.random.choice(
+                cur_cls_infos, int(len(cur_cls_infos) * ratio)
+            ).tolist()
+        self.logger.info('Total samples after balanced resampling: %s' % (len(sampled_infos)))
+
+        cls_infos_new = {name: [] for name in self.class_names}
+        for info in sampled_infos:
+            for name in set(info['gt_names']):
+                if name in self.class_names:
+                    cls_infos_new[name].append(info)
+
+        cls_dist_new = {k: len(v) / len(sampled_infos) for k, v in cls_infos_new.items()}
+
+        return sampled_infos
+
+    def get_sweep(self, sweep_info):
+        def remove_ego_points(points, center_radius=1.0):
+            mask = ~((np.abs(points[:, 0]) < center_radius) & (np.abs(points[:, 1]) < center_radius))
+            return points[mask]
+
+        lidar_path = self.root_path / sweep_info['lidar_path']
+        points_sweep = np.fromfile(str(lidar_path), dtype=np.float32, count=-1).reshape([-1, 5])[:, :4]
+        points_sweep = remove_ego_points(points_sweep).T
+        if sweep_info['transform_matrix'] is not None:
+            num_points = points_sweep.shape[1]
+            points_sweep[:3, :] = sweep_info['transform_matrix'].dot(
+                np.vstack((points_sweep[:3, :], np.ones(num_points))))[:3, :]
+
+        cur_times = sweep_info['time_lag'] * np.ones((1, points_sweep.shape[1]))
+        return points_sweep.T, cur_times.T
+
+    def get_lidar_with_sweeps(self, index, max_sweeps=1):
+        info = self.infos[index]
+        lidar_path = self.root_path / info['lidar_path']
+        points = np.fromfile(str(lidar_path), dtype=np.float32, count=-1).reshape([-1, 5])[:, :4]
+
+        sweep_points_list = [points]
+        sweep_times_list = [np.zeros((points.shape[0], 1))]
+
+        for k in np.random.choice(len(info['sweeps']), max_sweeps - 1, replace=False):
+            points_sweep, times_sweep = self.get_sweep(info['sweeps'][k])
+            sweep_points_list.append(points_sweep)
+            sweep_times_list.append(times_sweep)
+
+        points = np.concatenate(sweep_points_list, axis=0)
+        times = np.concatenate(sweep_times_list, axis=0).astype(points.dtype)
+
+        points = np.concatenate((points, times), axis=1)
+        return points
+
+    def crop_image(self, input_dict):
+        W, H = input_dict["ori_shape"]
+        imgs = input_dict["camera_imgs"]
+        img_process_infos = []
+        crop_images = []
+        for img in imgs:
+            if self.training == True:
+                fH, fW = self.camera_image_config.FINAL_DIM
+                resize_lim = self.camera_image_config.RESIZE_LIM_TRAIN
+                resize = np.random.uniform(*resize_lim)
+                resize_dims = (int(W * resize), int(H * resize))
+                newW, newH = resize_dims
+                crop_h = newH - fH
+                crop_w = int(np.random.uniform(0, max(0, newW - fW)))
+                crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)
+            else:
+                fH, fW = self.camera_image_config.FINAL_DIM
+                resize_lim = self.camera_image_config.RESIZE_LIM_TEST
+                resize = np.mean(resize_lim)
+                resize_dims = (int(W * resize), int(H * resize))
+                newW, newH = resize_dims
+                crop_h = newH - fH
+                crop_w = int(max(0, newW - fW) / 2)
+                crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)
+            
+            # reisze and crop image
+            img = img.resize(resize_dims)
+            img = img.crop(crop)
+            crop_images.append(img)
+            img_process_infos.append([resize, crop, False, 0])
+        
+        input_dict['img_process_infos'] = img_process_infos
+        input_dict['camera_imgs'] = crop_images
+        return input_dict
+    
+    def load_camera_info(self, input_dict, info):
+        input_dict["image_paths"] = []
+        input_dict["lidar2camera"] = []
+        input_dict["lidar2image"] = []
+        input_dict["camera2ego"] = []
+        input_dict["camera_intrinsics"] = []
+        input_dict["camera2lidar"] = []
+
+        for _, camera_info in info["cams"].items():
+            input_dict["image_paths"].append(camera_info["data_path"])
+
+            # lidar to camera transform
+            lidar2camera_r = np.linalg.inv(camera_info["sensor2lidar_rotation"])
+            lidar2camera_t = (
+                camera_info["sensor2lidar_translation"] @ lidar2camera_r.T
+            )
+            lidar2camera_rt = np.eye(4).astype(np.float32)
+            lidar2camera_rt[:3, :3] = lidar2camera_r.T
+            lidar2camera_rt[3, :3] = -lidar2camera_t
+            input_dict["lidar2camera"].append(lidar2camera_rt.T)
+
+            # camera intrinsics
+            camera_intrinsics = np.eye(4).astype(np.float32)
+            camera_intrinsics[:3, :3] = camera_info["camera_intrinsics"]
+            input_dict["camera_intrinsics"].append(camera_intrinsics)
+
+            # lidar to image transform
+            lidar2image = camera_intrinsics @ lidar2camera_rt.T
+            input_dict["lidar2image"].append(lidar2image)
+
+            # camera to ego transform
+            camera2ego = np.eye(4).astype(np.float32)
+            camera2ego[:3, :3] = Quaternion(
+                camera_info["sensor2ego_rotation"]
+            ).rotation_matrix
+            camera2ego[:3, 3] = camera_info["sensor2ego_translation"]
+            input_dict["camera2ego"].append(camera2ego)
+
+            # camera to lidar transform
+            camera2lidar = np.eye(4).astype(np.float32)
+            camera2lidar[:3, :3] = camera_info["sensor2lidar_rotation"]
+            camera2lidar[:3, 3] = camera_info["sensor2lidar_translation"]
+            input_dict["camera2lidar"].append(camera2lidar)
+        # read image
+        filename = input_dict["image_paths"]
+        images = []
+        for name in filename:
+            images.append(Image.open(str(self.root_path / name)))
+        
+        input_dict["camera_imgs"] = images
+        input_dict["ori_shape"] = images[0].size
+        
+        # resize and crop image
+        input_dict = self.crop_image(input_dict)
+
+        return input_dict
+
+    def __len__(self):
+        if self._merge_all_iters_to_one_epoch:
+            return len(self.infos) * self.total_epochs
+
+        return len(self.infos)
+
+    def __getitem__(self, index):
+        if self._merge_all_iters_to_one_epoch:
+            index = index % len(self.infos)
+
+        info = copy.deepcopy(self.infos[index])
+        points = self.get_lidar_with_sweeps(index, max_sweeps=self.dataset_cfg.MAX_SWEEPS)
+
+        input_dict = {
+            'points': points,
+            'frame_id': Path(info['lidar_path']).stem,
+            'metadata': {'token': info['token']}
+        }
+
+        if 'gt_boxes' in info:
+            if self.dataset_cfg.get('FILTER_MIN_POINTS_IN_GT', False):
+                mask = (info['num_lidar_pts'] > self.dataset_cfg.FILTER_MIN_POINTS_IN_GT - 1)
+            else:
+                mask = None
+
+            input_dict.update({
+                'gt_names': info['gt_names'] if mask is None else info['gt_names'][mask],
+                'gt_boxes': info['gt_boxes'] if mask is None else info['gt_boxes'][mask]
+            })
+        if self.use_camera:
+            input_dict = self.load_camera_info(input_dict, info)
+
+        data_dict = self.prepare_data(data_dict=input_dict)
+
+        if self.dataset_cfg.get('SET_NAN_VELOCITY_TO_ZEROS', False) and 'gt_boxes' in info:
+            gt_boxes = data_dict['gt_boxes']
+            gt_boxes[np.isnan(gt_boxes)] = 0
+            data_dict['gt_boxes'] = gt_boxes
+
+        if not self.dataset_cfg.PRED_VELOCITY and 'gt_boxes' in data_dict:
+            data_dict['gt_boxes'] = data_dict['gt_boxes'][:, [0, 1, 2, 3, 4, 5, 6, -1]]
+
+        return data_dict
+
+    def evaluation(self, det_annos, class_names, **kwargs):
+        import json
+        from nuscenes.nuscenes import NuScenes
+        from . import nuscenes_utils
+        nusc = NuScenes(version=self.dataset_cfg.VERSION, dataroot=str(self.root_path), verbose=True)
+        nusc_annos = nuscenes_utils.transform_det_annos_to_nusc_annos(det_annos, nusc)
+        nusc_annos['meta'] = {
+            'use_camera': False,
+            'use_lidar': True,
+            'use_radar': False,
+            'use_map': False,
+            'use_external': False,
+        }
+
+        output_path = Path(kwargs['output_path'])
+        output_path.mkdir(exist_ok=True, parents=True)
+        res_path = str(output_path / 'results_nusc.json')
+        with open(res_path, 'w') as f:
+            json.dump(nusc_annos, f)
+
+        self.logger.info(f'The predictions of NuScenes have been saved to {res_path}')
+
+        if self.dataset_cfg.VERSION == 'v1.0-test':
+            return 'No ground-truth annotations for evaluation', {}
+
+        from nuscenes.eval.detection.config import config_factory
+        from nuscenes.eval.detection.evaluate import NuScenesEval
+
+        eval_set_map = {
+            'v1.0-mini': 'mini_val',
+            'v1.0-trainval': 'val',
+            'v1.0-test': 'test'
+        }
+        try:
+            eval_version = 'detection_cvpr_2019'
+            eval_config = config_factory(eval_version)
+        except:
+            eval_version = 'cvpr_2019'
+            eval_config = config_factory(eval_version)
+
+        nusc_eval = NuScenesEval(
+            nusc,
+            config=eval_config,
+            result_path=res_path,
+            eval_set=eval_set_map[self.dataset_cfg.VERSION],
+            output_dir=str(output_path),
+            verbose=True,
+        )
+        metrics_summary = nusc_eval.main(plot_examples=0, render_curves=False)
+
+        with open(output_path / 'metrics_summary.json', 'r') as f:
+            metrics = json.load(f)
+
+        result_str, result_dict = nuscenes_utils.format_nuscene_results(metrics, self.class_names, version=eval_version)
+        return result_str, result_dict
+
+    def create_groundtruth_database(self, used_classes=None, max_sweeps=10):
+        import torch
+
+        database_save_path = self.root_path / f'gt_database_{max_sweeps}sweeps_withvelo'
+        db_info_save_path = self.root_path / f'nuscenes_dbinfos_{max_sweeps}sweeps_withvelo.pkl'
+
+        database_save_path.mkdir(parents=True, exist_ok=True)
+        all_db_infos = {}
+
+        for idx in tqdm(range(len(self.infos))):
+            sample_idx = idx
+            info = self.infos[idx]
+            points = self.get_lidar_with_sweeps(idx, max_sweeps=max_sweeps)
+            gt_boxes = info['gt_boxes']
+            gt_names = info['gt_names']
+
+            box_idxs_of_pts = roiaware_pool3d_utils.points_in_boxes_gpu(
+                torch.from_numpy(points[:, 0:3]).unsqueeze(dim=0).float().cuda(),
+                torch.from_numpy(gt_boxes[:, 0:7]).unsqueeze(dim=0).float().cuda()
+            ).long().squeeze(dim=0).cpu().numpy()
+
+            for i in range(gt_boxes.shape[0]):
+                filename = '%s_%s_%d.bin' % (sample_idx, gt_names[i], i)
+                filepath = database_save_path / filename
+                gt_points = points[box_idxs_of_pts == i]
+
+                gt_points[:, :3] -= gt_boxes[i, :3]
+                with open(filepath, 'w') as f:
+                    gt_points.tofile(f)
+
+                if (used_classes is None) or gt_names[i] in used_classes:
+                    db_path = str(filepath.relative_to(self.root_path))  # gt_database/xxxxx.bin
+                    db_info = {'name': gt_names[i], 'path': db_path, 'image_idx': sample_idx, 'gt_idx': i,
+                               'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0]}
+                    if gt_names[i] in all_db_infos:
+                        all_db_infos[gt_names[i]].append(db_info)
+                    else:
+                        all_db_infos[gt_names[i]] = [db_info]
+        for k, v in all_db_infos.items():
+            print('Database %s: %d' % (k, len(v)))
+
+        with open(db_info_save_path, 'wb') as f:
+            pickle.dump(all_db_infos, f)
+
+
+def create_nuscenes_info(version, data_path, save_path, max_sweeps=10, with_cam=False):
+    from nuscenes.nuscenes import NuScenes
+    from nuscenes.utils import splits
+    from . import nuscenes_utils
+    data_path = data_path / version
+    save_path = save_path / version
+
+    assert version in ['v1.0-trainval', 'v1.0-test', 'v1.0-mini']
+    if version == 'v1.0-trainval':
+        train_scenes = splits.train
+        val_scenes = splits.val
+    elif version == 'v1.0-test':
+        train_scenes = splits.test
+        val_scenes = []
+    elif version == 'v1.0-mini':
+        train_scenes = splits.mini_train
+        val_scenes = splits.mini_val
+    else:
+        raise NotImplementedError
+
+    nusc = NuScenes(version=version, dataroot=data_path, verbose=True)
+    available_scenes = nuscenes_utils.get_available_scenes(nusc)
+    available_scene_names = [s['name'] for s in available_scenes]
+    train_scenes = list(filter(lambda x: x in available_scene_names, train_scenes))
+    val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes))
+    train_scenes = set([available_scenes[available_scene_names.index(s)]['token'] for s in train_scenes])
+    val_scenes = set([available_scenes[available_scene_names.index(s)]['token'] for s in val_scenes])
+
+    print('%s: train scene(%d), val scene(%d)' % (version, len(train_scenes), len(val_scenes)))
+
+    train_nusc_infos, val_nusc_infos = nuscenes_utils.fill_trainval_infos(
+        data_path=data_path, nusc=nusc, train_scenes=train_scenes, val_scenes=val_scenes,
+        test='test' in version, max_sweeps=max_sweeps, with_cam=with_cam
+    )
+
+    if version == 'v1.0-test':
+        print('test sample: %d' % len(train_nusc_infos))
+        with open(save_path / f'nuscenes_infos_{max_sweeps}sweeps_test.pkl', 'wb') as f:
+            pickle.dump(train_nusc_infos, f)
+    else:
+        print('train sample: %d, val sample: %d' % (len(train_nusc_infos), len(val_nusc_infos)))
+        with open(save_path / f'nuscenes_infos_{max_sweeps}sweeps_train.pkl', 'wb') as f:
+            pickle.dump(train_nusc_infos, f)
+        with open(save_path / f'nuscenes_infos_{max_sweeps}sweeps_val.pkl', 'wb') as f:
+            pickle.dump(val_nusc_infos, f)
+
+
+if __name__ == '__main__':
+    import yaml
+    import argparse
+    from pathlib import Path
+    from easydict import EasyDict
+
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--cfg_file', type=str, default=None, help='specify the config of dataset')
+    parser.add_argument('--func', type=str, default='create_nuscenes_infos', help='')
+    parser.add_argument('--version', type=str, default='v1.0-trainval', help='')
+    parser.add_argument('--with_cam', action='store_true', default=False, help='use camera or not')
+    args = parser.parse_args()
+
+    if args.func == 'create_nuscenes_infos':
+        dataset_cfg = EasyDict(yaml.safe_load(open(args.cfg_file)))
+        ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
+        dataset_cfg.VERSION = args.version
+        create_nuscenes_info(
+            version=dataset_cfg.VERSION,
+            data_path=ROOT_DIR / 'data' / 'nuscenes',
+            save_path=ROOT_DIR / 'data' / 'nuscenes',
+            max_sweeps=dataset_cfg.MAX_SWEEPS,
+            with_cam=args.with_cam
+        )
+
+        nuscenes_dataset = NuScenesDataset(
+            dataset_cfg=dataset_cfg, class_names=None,
+            root_path=ROOT_DIR / 'data' / 'nuscenes',
+            logger=common_utils.create_logger(), training=True
+        )
+        nuscenes_dataset.create_groundtruth_database(max_sweeps=dataset_cfg.MAX_SWEEPS)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/nuscenes_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/nuscenes_utils.py
new file mode 100644
index 000000000..4e2421d15
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/nuscenes/nuscenes_utils.py
@@ -0,0 +1,588 @@
+"""
+The NuScenes data pre-processing and evaluation is modified from
+https://github.com/traveller59/second.pytorch and https://github.com/poodarchu/Det3D
+"""
+
+import operator
+from functools import reduce
+from pathlib import Path
+
+import numpy as np
+import tqdm
+from nuscenes.utils.data_classes import Box
+from nuscenes.utils.geometry_utils import transform_matrix
+from pyquaternion import Quaternion
+
+map_name_from_general_to_detection = {
+    'human.pedestrian.adult': 'pedestrian',
+    'human.pedestrian.child': 'pedestrian',
+    'human.pedestrian.wheelchair': 'ignore',
+    'human.pedestrian.stroller': 'ignore',
+    'human.pedestrian.personal_mobility': 'ignore',
+    'human.pedestrian.police_officer': 'pedestrian',
+    'human.pedestrian.construction_worker': 'pedestrian',
+    'animal': 'ignore',
+    'vehicle.car': 'car',
+    'vehicle.motorcycle': 'motorcycle',
+    'vehicle.bicycle': 'bicycle',
+    'vehicle.bus.bendy': 'bus',
+    'vehicle.bus.rigid': 'bus',
+    'vehicle.truck': 'truck',
+    'vehicle.construction': 'construction_vehicle',
+    'vehicle.emergency.ambulance': 'ignore',
+    'vehicle.emergency.police': 'ignore',
+    'vehicle.trailer': 'trailer',
+    'movable_object.barrier': 'barrier',
+    'movable_object.trafficcone': 'traffic_cone',
+    'movable_object.pushable_pullable': 'ignore',
+    'movable_object.debris': 'ignore',
+    'static_object.bicycle_rack': 'ignore',
+}
+
+
+cls_attr_dist = {
+    'barrier': {
+        'cycle.with_rider': 0,
+        'cycle.without_rider': 0,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 0,
+        'vehicle.parked': 0,
+        'vehicle.stopped': 0,
+    },
+    'bicycle': {
+        'cycle.with_rider': 2791,
+        'cycle.without_rider': 8946,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 0,
+        'vehicle.parked': 0,
+        'vehicle.stopped': 0,
+    },
+    'bus': {
+        'cycle.with_rider': 0,
+        'cycle.without_rider': 0,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 9092,
+        'vehicle.parked': 3294,
+        'vehicle.stopped': 3881,
+    },
+    'car': {
+        'cycle.with_rider': 0,
+        'cycle.without_rider': 0,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 114304,
+        'vehicle.parked': 330133,
+        'vehicle.stopped': 46898,
+    },
+    'construction_vehicle': {
+        'cycle.with_rider': 0,
+        'cycle.without_rider': 0,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 882,
+        'vehicle.parked': 11549,
+        'vehicle.stopped': 2102,
+    },
+    'ignore': {
+        'cycle.with_rider': 307,
+        'cycle.without_rider': 73,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 165,
+        'vehicle.parked': 400,
+        'vehicle.stopped': 102,
+    },
+    'motorcycle': {
+        'cycle.with_rider': 4233,
+        'cycle.without_rider': 8326,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 0,
+        'vehicle.parked': 0,
+        'vehicle.stopped': 0,
+    },
+    'pedestrian': {
+        'cycle.with_rider': 0,
+        'cycle.without_rider': 0,
+        'pedestrian.moving': 157444,
+        'pedestrian.sitting_lying_down': 13939,
+        'pedestrian.standing': 46530,
+        'vehicle.moving': 0,
+        'vehicle.parked': 0,
+        'vehicle.stopped': 0,
+    },
+    'traffic_cone': {
+        'cycle.with_rider': 0,
+        'cycle.without_rider': 0,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 0,
+        'vehicle.parked': 0,
+        'vehicle.stopped': 0,
+    },
+    'trailer': {
+        'cycle.with_rider': 0,
+        'cycle.without_rider': 0,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 3421,
+        'vehicle.parked': 19224,
+        'vehicle.stopped': 1895,
+    },
+    'truck': {
+        'cycle.with_rider': 0,
+        'cycle.without_rider': 0,
+        'pedestrian.moving': 0,
+        'pedestrian.sitting_lying_down': 0,
+        'pedestrian.standing': 0,
+        'vehicle.moving': 21339,
+        'vehicle.parked': 55626,
+        'vehicle.stopped': 11097,
+    },
+}
+
+
+def get_available_scenes(nusc):
+    available_scenes = []
+    print('total scene num:', len(nusc.scene))
+    for scene in nusc.scene:
+        scene_token = scene['token']
+        scene_rec = nusc.get('scene', scene_token)
+        sample_rec = nusc.get('sample', scene_rec['first_sample_token'])
+        sd_rec = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP'])
+        has_more_frames = True
+        scene_not_exist = False
+        while has_more_frames:
+            lidar_path, boxes, _ = nusc.get_sample_data(sd_rec['token'])
+            if not Path(lidar_path).exists():
+                scene_not_exist = True
+                break
+            else:
+                break
+            # if not sd_rec['next'] == '':
+            #     sd_rec = nusc.get('sample_data', sd_rec['next'])
+            # else:
+            #     has_more_frames = False
+        if scene_not_exist:
+            continue
+        available_scenes.append(scene)
+    print('exist scene num:', len(available_scenes))
+    return available_scenes
+
+
+def get_sample_data(nusc, sample_data_token, selected_anntokens=None):
+    """
+    Returns the data path as well as all annotations related to that sample_data.
+    Note that the boxes are transformed into the current sensor's coordinate frame.
+    Args:
+        nusc:
+        sample_data_token: Sample_data token.
+        selected_anntokens: If provided only return the selected annotation.
+
+    Returns:
+
+    """
+    # Retrieve sensor & pose records
+    sd_record = nusc.get('sample_data', sample_data_token)
+    cs_record = nusc.get('calibrated_sensor', sd_record['calibrated_sensor_token'])
+    sensor_record = nusc.get('sensor', cs_record['sensor_token'])
+    pose_record = nusc.get('ego_pose', sd_record['ego_pose_token'])
+
+    data_path = nusc.get_sample_data_path(sample_data_token)
+
+    if sensor_record['modality'] == 'camera':
+        cam_intrinsic = np.array(cs_record['camera_intrinsic'])
+        imsize = (sd_record['width'], sd_record['height'])
+    else:
+        cam_intrinsic = imsize = None
+
+    # Retrieve all sample annotations and map to sensor coordinate system.
+    if selected_anntokens is not None:
+        boxes = list(map(nusc.get_box, selected_anntokens))
+    else:
+        boxes = nusc.get_boxes(sample_data_token)
+
+    # Make list of Box objects including coord system transforms.
+    box_list = []
+    for box in boxes:
+        box.velocity = nusc.box_velocity(box.token)
+        # Move box to ego vehicle coord system
+        box.translate(-np.array(pose_record['translation']))
+        box.rotate(Quaternion(pose_record['rotation']).inverse)
+
+        #  Move box to sensor coord system
+        box.translate(-np.array(cs_record['translation']))
+        box.rotate(Quaternion(cs_record['rotation']).inverse)
+
+        box_list.append(box)
+
+    return data_path, box_list, cam_intrinsic
+
+
+def quaternion_yaw(q: Quaternion) -> float:
+    """
+    Calculate the yaw angle from a quaternion.
+    Note that this only works for a quaternion that represents a box in lidar or global coordinate frame.
+    It does not work for a box in the camera frame.
+    :param q: Quaternion of interest.
+    :return: Yaw angle in radians.
+    """
+
+    # Project into xy plane.
+    v = np.dot(q.rotation_matrix, np.array([1, 0, 0]))
+
+    # Measure yaw using arctan.
+    yaw = np.arctan2(v[1], v[0])
+
+    return yaw
+    
+
+def obtain_sensor2top(
+    nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type="lidar"
+):
+    """Obtain the info with RT matric from general sensor to Top LiDAR.
+
+    Args:
+        nusc (class): Dataset class in the nuScenes dataset.
+        sensor_token (str): Sample data token corresponding to the
+            specific sensor type.
+        l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3).
+        l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego
+            in shape (3, 3).
+        e2g_t (np.ndarray): Translation from ego to global in shape (1, 3).
+        e2g_r_mat (np.ndarray): Rotation matrix from ego to global
+            in shape (3, 3).
+        sensor_type (str): Sensor to calibrate. Default: 'lidar'.
+
+    Returns:
+        sweep (dict): Sweep information after transformation.
+    """
+    sd_rec = nusc.get("sample_data", sensor_token)
+    cs_record = nusc.get("calibrated_sensor", sd_rec["calibrated_sensor_token"])
+    pose_record = nusc.get("ego_pose", sd_rec["ego_pose_token"])
+    data_path = str(nusc.get_sample_data_path(sd_rec["token"]))
+    # if os.getcwd() in data_path:  # path from lyftdataset is absolute path
+    #     data_path = data_path.split(f"{os.getcwd()}/")[-1]  # relative path
+    sweep = {
+        "data_path": data_path,
+        "type": sensor_type,
+        "sample_data_token": sd_rec["token"],
+        "sensor2ego_translation": cs_record["translation"],
+        "sensor2ego_rotation": cs_record["rotation"],
+        "ego2global_translation": pose_record["translation"],
+        "ego2global_rotation": pose_record["rotation"],
+        "timestamp": sd_rec["timestamp"],
+    }
+    l2e_r_s = sweep["sensor2ego_rotation"]
+    l2e_t_s = sweep["sensor2ego_translation"]
+    e2g_r_s = sweep["ego2global_rotation"]
+    e2g_t_s = sweep["ego2global_translation"]
+
+    # obtain the RT from sensor to Top LiDAR
+    # sweep->ego->global->ego'->lidar
+    l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
+    e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
+    R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
+        np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+    )
+    T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
+        np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+    )
+    T -= (
+        e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
+        + l2e_t @ np.linalg.inv(l2e_r_mat).T
+    ).squeeze(0)
+    sweep["sensor2lidar_rotation"] = R.T  # points @ R.T + T
+    sweep["sensor2lidar_translation"] = T
+    return sweep
+
+
+def fill_trainval_infos(data_path, nusc, train_scenes, val_scenes, test=False, max_sweeps=10, with_cam=False):
+    train_nusc_infos = []
+    val_nusc_infos = []
+    progress_bar = tqdm.tqdm(total=len(nusc.sample), desc='create_info', dynamic_ncols=True)
+
+    ref_chan = 'LIDAR_TOP'  # The radar channel from which we track back n sweeps to aggregate the point cloud.
+    chan = 'LIDAR_TOP'  # The reference channel of the current sample_rec that the point clouds are mapped to.
+
+    for index, sample in enumerate(nusc.sample):
+        progress_bar.update()
+
+        ref_sd_token = sample['data'][ref_chan]
+        ref_sd_rec = nusc.get('sample_data', ref_sd_token)
+        ref_cs_rec = nusc.get('calibrated_sensor', ref_sd_rec['calibrated_sensor_token'])
+        ref_pose_rec = nusc.get('ego_pose', ref_sd_rec['ego_pose_token'])
+        ref_time = 1e-6 * ref_sd_rec['timestamp']
+
+        ref_lidar_path, ref_boxes, _ = get_sample_data(nusc, ref_sd_token)
+
+        ref_cam_front_token = sample['data']['CAM_FRONT']
+        ref_cam_path, _, ref_cam_intrinsic = nusc.get_sample_data(ref_cam_front_token)
+
+        # Homogeneous transform from ego car frame to reference frame
+        ref_from_car = transform_matrix(
+            ref_cs_rec['translation'], Quaternion(ref_cs_rec['rotation']), inverse=True
+        )
+
+        # Homogeneous transformation matrix from global to _current_ ego car frame
+        car_from_global = transform_matrix(
+            ref_pose_rec['translation'], Quaternion(ref_pose_rec['rotation']), inverse=True,
+        )
+
+        info = {
+            'lidar_path': Path(ref_lidar_path).relative_to(data_path).__str__(),
+            'cam_front_path': Path(ref_cam_path).relative_to(data_path).__str__(),
+            'cam_intrinsic': ref_cam_intrinsic,
+            'token': sample['token'],
+            'sweeps': [],
+            'ref_from_car': ref_from_car,
+            'car_from_global': car_from_global,
+            'timestamp': ref_time,
+        }
+        if with_cam:
+            info['cams'] = dict()
+            l2e_r = ref_cs_rec["rotation"]
+            l2e_t = ref_cs_rec["translation"],
+            e2g_r = ref_pose_rec["rotation"]
+            e2g_t = ref_pose_rec["translation"]
+            l2e_r_mat = Quaternion(l2e_r).rotation_matrix
+            e2g_r_mat = Quaternion(e2g_r).rotation_matrix
+
+            # obtain 6 image's information per frame
+            camera_types = [
+                "CAM_FRONT",
+                "CAM_FRONT_RIGHT",
+                "CAM_FRONT_LEFT",
+                "CAM_BACK",
+                "CAM_BACK_LEFT",
+                "CAM_BACK_RIGHT",
+            ]
+            for cam in camera_types:
+                cam_token = sample["data"][cam]
+                cam_path, _, camera_intrinsics = nusc.get_sample_data(cam_token)
+                cam_info = obtain_sensor2top(
+                    nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam
+                )
+                cam_info['data_path'] = Path(cam_info['data_path']).relative_to(data_path).__str__()
+                cam_info.update(camera_intrinsics=camera_intrinsics)
+                info["cams"].update({cam: cam_info})
+        
+
+        sample_data_token = sample['data'][chan]
+        curr_sd_rec = nusc.get('sample_data', sample_data_token)
+        sweeps = []
+        while len(sweeps) < max_sweeps - 1:
+            if curr_sd_rec['prev'] == '':
+                if len(sweeps) == 0:
+                    sweep = {
+                        'lidar_path': Path(ref_lidar_path).relative_to(data_path).__str__(),
+                        'sample_data_token': curr_sd_rec['token'],
+                        'transform_matrix': None,
+                        'time_lag': curr_sd_rec['timestamp'] * 0,
+                    }
+                    sweeps.append(sweep)
+                else:
+                    sweeps.append(sweeps[-1])
+            else:
+                curr_sd_rec = nusc.get('sample_data', curr_sd_rec['prev'])
+
+                # Get past pose
+                current_pose_rec = nusc.get('ego_pose', curr_sd_rec['ego_pose_token'])
+                global_from_car = transform_matrix(
+                    current_pose_rec['translation'], Quaternion(current_pose_rec['rotation']), inverse=False,
+                )
+
+                # Homogeneous transformation matrix from sensor coordinate frame to ego car frame.
+                current_cs_rec = nusc.get(
+                    'calibrated_sensor', curr_sd_rec['calibrated_sensor_token']
+                )
+                car_from_current = transform_matrix(
+                    current_cs_rec['translation'], Quaternion(current_cs_rec['rotation']), inverse=False,
+                )
+
+                tm = reduce(np.dot, [ref_from_car, car_from_global, global_from_car, car_from_current])
+
+                lidar_path = nusc.get_sample_data_path(curr_sd_rec['token'])
+
+                time_lag = ref_time - 1e-6 * curr_sd_rec['timestamp']
+
+                sweep = {
+                    'lidar_path': Path(lidar_path).relative_to(data_path).__str__(),
+                    'sample_data_token': curr_sd_rec['token'],
+                    'transform_matrix': tm,
+                    'global_from_car': global_from_car,
+                    'car_from_current': car_from_current,
+                    'time_lag': time_lag,
+                }
+                sweeps.append(sweep)
+
+        info['sweeps'] = sweeps
+
+        assert len(info['sweeps']) == max_sweeps - 1, \
+            f"sweep {curr_sd_rec['token']} only has {len(info['sweeps'])} sweeps, " \
+            f"you should duplicate to sweep num {max_sweeps - 1}"
+
+        if not test:
+            annotations = [nusc.get('sample_annotation', token) for token in sample['anns']]
+
+            # the filtering gives 0.5~1 map improvement
+            num_lidar_pts = np.array([anno['num_lidar_pts'] for anno in annotations])
+            num_radar_pts = np.array([anno['num_radar_pts'] for anno in annotations])
+            mask = (num_lidar_pts + num_radar_pts > 0)
+
+            locs = np.array([b.center for b in ref_boxes]).reshape(-1, 3)
+            dims = np.array([b.wlh for b in ref_boxes]).reshape(-1, 3)[:, [1, 0, 2]]  # wlh == > dxdydz (lwh)
+            velocity = np.array([b.velocity for b in ref_boxes]).reshape(-1, 3)
+            rots = np.array([quaternion_yaw(b.orientation) for b in ref_boxes]).reshape(-1, 1)
+            names = np.array([b.name for b in ref_boxes])
+            tokens = np.array([b.token for b in ref_boxes])
+            gt_boxes = np.concatenate([locs, dims, rots, velocity[:, :2]], axis=1)
+
+            assert len(annotations) == len(gt_boxes) == len(velocity)
+
+            info['gt_boxes'] = gt_boxes[mask, :]
+            info['gt_boxes_velocity'] = velocity[mask, :]
+            info['gt_names'] = np.array([map_name_from_general_to_detection[name] for name in names])[mask]
+            info['gt_boxes_token'] = tokens[mask]
+            info['num_lidar_pts'] = num_lidar_pts[mask]
+            info['num_radar_pts'] = num_radar_pts[mask]
+
+        if sample['scene_token'] in train_scenes:
+            train_nusc_infos.append(info)
+        else:
+            val_nusc_infos.append(info)
+
+    progress_bar.close()
+    return train_nusc_infos, val_nusc_infos
+
+
+def boxes_lidar_to_nusenes(det_info):
+    boxes3d = det_info['boxes_lidar']
+    scores = det_info['score']
+    labels = det_info['pred_labels']
+
+    box_list = []
+    for k in range(boxes3d.shape[0]):
+        quat = Quaternion(axis=[0, 0, 1], radians=boxes3d[k, 6])
+        velocity = (*boxes3d[k, 7:9], 0.0) if boxes3d.shape[1] == 9 else (0.0, 0.0, 0.0)
+        box = Box(
+            boxes3d[k, :3],
+            boxes3d[k, [4, 3, 5]],  # wlh
+            quat, label=labels[k], score=scores[k], velocity=velocity,
+        )
+        box_list.append(box)
+    return box_list
+
+
+def lidar_nusc_box_to_global(nusc, boxes, sample_token):
+    s_record = nusc.get('sample', sample_token)
+    sample_data_token = s_record['data']['LIDAR_TOP']
+
+    sd_record = nusc.get('sample_data', sample_data_token)
+    cs_record = nusc.get('calibrated_sensor', sd_record['calibrated_sensor_token'])
+    sensor_record = nusc.get('sensor', cs_record['sensor_token'])
+    pose_record = nusc.get('ego_pose', sd_record['ego_pose_token'])
+
+    data_path = nusc.get_sample_data_path(sample_data_token)
+    box_list = []
+    for box in boxes:
+        # Move box to ego vehicle coord system
+        box.rotate(Quaternion(cs_record['rotation']))
+        box.translate(np.array(cs_record['translation']))
+        # Move box to global coord system
+        box.rotate(Quaternion(pose_record['rotation']))
+        box.translate(np.array(pose_record['translation']))
+        box_list.append(box)
+    return box_list
+
+
+def transform_det_annos_to_nusc_annos(det_annos, nusc):
+    nusc_annos = {
+        'results': {},
+        'meta': None,
+    }
+
+    for det in det_annos:
+        annos = []
+        box_list = boxes_lidar_to_nusenes(det)
+        box_list = lidar_nusc_box_to_global(
+            nusc=nusc, boxes=box_list, sample_token=det['metadata']['token']
+        )
+
+        for k, box in enumerate(box_list):
+            name = det['name'][k]
+            if np.sqrt(box.velocity[0] ** 2 + box.velocity[1] ** 2) > 0.2:
+                if name in ['car', 'construction_vehicle', 'bus', 'truck', 'trailer']:
+                    attr = 'vehicle.moving'
+                elif name in ['bicycle', 'motorcycle']:
+                    attr = 'cycle.with_rider'
+                else:
+                    attr = None
+            else:
+                if name in ['pedestrian']:
+                    attr = 'pedestrian.standing'
+                elif name in ['bus']:
+                    attr = 'vehicle.stopped'
+                else:
+                    attr = None
+            attr = attr if attr is not None else max(
+                cls_attr_dist[name].items(), key=operator.itemgetter(1))[0]
+            nusc_anno = {
+                'sample_token': det['metadata']['token'],
+                'translation': box.center.tolist(),
+                'size': box.wlh.tolist(),
+                'rotation': box.orientation.elements.tolist(),
+                'velocity': box.velocity[:2].tolist(),
+                'detection_name': name,
+                'detection_score': box.score,
+                'attribute_name': attr
+            }
+            annos.append(nusc_anno)
+
+        nusc_annos['results'].update({det["metadata"]["token"]: annos})
+
+    return nusc_annos
+
+
+def format_nuscene_results(metrics, class_names, version='default'):
+    result = '----------------Nuscene %s results-----------------\n' % version
+    for name in class_names:
+        threshs = ', '.join(list(metrics['label_aps'][name].keys()))
+        ap_list = list(metrics['label_aps'][name].values())
+
+        err_name =', '.join([x.split('_')[0] for x in list(metrics['label_tp_errors'][name].keys())])
+        error_list = list(metrics['label_tp_errors'][name].values())
+
+        result += f'***{name} error@{err_name} | AP@{threshs}\n'
+        result += ', '.join(['%.2f' % x for x in error_list]) + ' | '
+        result += ', '.join(['%.2f' % (x * 100) for x in ap_list])
+        result += f" | mean AP: {metrics['mean_dist_aps'][name]}"
+        result += '\n'
+
+    result += '--------------average performance-------------\n'
+    details = {}
+    for key, val in metrics['tp_errors'].items():
+        result += '%s:\t %.4f\n' % (key, val)
+        details[key] = val
+
+    result += 'mAP:\t %.4f\n' % metrics['mean_ap']
+    result += 'NDS:\t %.4f\n' % metrics['nd_score']
+
+    details.update({
+        'mAP': metrics['mean_ap'],
+        'NDS': metrics['nd_score'],
+    })
+
+    return result, details
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_dataset.py
new file mode 100644
index 000000000..0ff079843
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_dataset.py
@@ -0,0 +1,444 @@
+import copy
+import pickle
+import numpy as np
+
+from PIL import Image
+import torch
+import torch.nn.functional as F
+from pathlib import Path
+
+from ..dataset import DatasetTemplate
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+from ...utils import box_utils
+from .once_toolkits import Octopus
+
+class ONCEDataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
+        """
+        Args:
+            root_path:
+            dataset_cfg:
+            class_names:
+            training:
+            logger:
+        """
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
+        )
+        self.split = dataset_cfg.DATA_SPLIT['train'] if training else dataset_cfg.DATA_SPLIT['test']
+        assert self.split in ['train', 'val', 'test', 'raw_small', 'raw_medium', 'raw_large']
+
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_seq_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
+        self.cam_names = ['cam01', 'cam03', 'cam05', 'cam06', 'cam07', 'cam08', 'cam09']
+        self.cam_tags = ['top', 'top2', 'left_back', 'left_front', 'right_front', 'right_back', 'back']
+        self.toolkits = Octopus(self.root_path)
+
+        self.once_infos = []
+        self.include_once_data(self.split)
+
+    def include_once_data(self, split):
+        if self.logger is not None:
+            self.logger.info('Loading ONCE dataset')
+        once_infos = []
+
+        for info_path in self.dataset_cfg.INFO_PATH[split]:
+            info_path = self.root_path / info_path
+            if not info_path.exists():
+                continue
+            with open(info_path, 'rb') as f:
+                infos = pickle.load(f)
+                once_infos.extend(infos)
+
+        def check_annos(info):
+            return 'annos' in info
+
+        if self.split != 'raw':
+            once_infos = list(filter(check_annos,once_infos))
+
+        self.once_infos.extend(once_infos)
+
+        if self.logger is not None:
+            self.logger.info('Total samples for ONCE dataset: %d' % (len(once_infos)))
+
+    def set_split(self, split):
+        super().__init__(
+            dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger
+        )
+        self.split = split
+
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_seq_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
+
+    def get_lidar(self, sequence_id, frame_id):
+        return self.toolkits.load_point_cloud(sequence_id, frame_id)
+
+    def get_image(self, sequence_id, frame_id, cam_name):
+        return self.toolkits.load_image(sequence_id, frame_id, cam_name)
+
+    def project_lidar_to_image(self, sequence_id, frame_id):
+        return self.toolkits.project_lidar_to_image(sequence_id, frame_id)
+
+    def point_painting(self, points, info):
+        semseg_dir = './' # add your own seg directory
+        used_classes = [0,1,2,3,4,5]
+        num_classes = len(used_classes)
+        frame_id = str(info['frame_id'])
+        seq_id = str(info['sequence_id'])
+        painted = np.zeros((points.shape[0], num_classes)) # classes + bg
+        for cam_name in self.cam_names:
+            img_path = Path(semseg_dir) / Path(seq_id) / Path(cam_name) / Path(frame_id+'_label.png')
+            calib_info = info['calib'][cam_name]
+            cam_2_velo = calib_info['cam_to_velo']
+            cam_intri = np.hstack([calib_info['cam_intrinsic'], np.zeros((3, 1), dtype=np.float32)])
+            point_xyz = points[:, :3]
+            points_homo = np.hstack(
+                [point_xyz, np.ones(point_xyz.shape[0], dtype=np.float32).reshape((-1, 1))])
+            points_lidar = np.dot(points_homo, np.linalg.inv(cam_2_velo).T)
+            mask = points_lidar[:, 2] > 0
+            points_lidar = points_lidar[mask]
+            points_img = np.dot(points_lidar, cam_intri.T)
+            points_img = points_img / points_img[:, [2]]
+            uv = points_img[:, [0,1]]
+            #depth = points_img[:, [2]]
+            seg_map = np.array(Image.open(img_path)) # (H, W)
+            H, W = seg_map.shape
+            seg_feats = np.zeros((H*W, num_classes))
+            seg_map = seg_map.reshape(-1)
+            for cls_i in used_classes:
+                seg_feats[seg_map==cls_i, cls_i] = 1
+            seg_feats = seg_feats.reshape(H, W, num_classes).transpose(2, 0, 1)
+            uv[:, 0] = (uv[:, 0] - W / 2) / (W / 2)
+            uv[:, 1] = (uv[:, 1] - H / 2) / (H / 2)
+            uv_tensor = torch.from_numpy(uv).unsqueeze(0).unsqueeze(0)  # [1,1,N,2]
+            seg_feats = torch.from_numpy(seg_feats).unsqueeze(0) # [1,C,H,W]
+            proj_scores = F.grid_sample(seg_feats, uv_tensor, mode='bilinear', padding_mode='zeros')  # [1, C, 1, N]
+            proj_scores = proj_scores.squeeze(0).squeeze(1).transpose(0, 1).contiguous() # [N, C]
+            painted[mask] = proj_scores.numpy()
+        return np.concatenate([points, painted], axis=1)
+
+    def __len__(self):
+        if self._merge_all_iters_to_one_epoch:
+            return len(self.once_infos) * self.total_epochs
+
+        return len(self.once_infos)
+
+    def __getitem__(self, index):
+        if self._merge_all_iters_to_one_epoch:
+            index = index % len(self.once_infos)
+
+        info = copy.deepcopy(self.once_infos[index])
+        frame_id = info['frame_id']
+        seq_id = info['sequence_id']
+        points = self.get_lidar(seq_id, frame_id)
+
+        if self.dataset_cfg.get('POINT_PAINTING', False):
+            points = self.point_painting(points, info)
+
+        input_dict = {
+            'points': points,
+            'frame_id': frame_id,
+        }
+
+        if 'annos' in info:
+            annos = info['annos']
+            input_dict.update({
+                'gt_names': annos['name'],
+                'gt_boxes': annos['boxes_3d'],
+                'num_points_in_gt': annos.get('num_points_in_gt', None)
+            })
+
+        data_dict = self.prepare_data(data_dict=input_dict)
+        data_dict.pop('num_points_in_gt', None)
+        return data_dict
+
+    def get_infos(self, num_workers=4, sample_seq_list=None):
+        import concurrent.futures as futures
+        import json
+        root_path = self.root_path
+        cam_names = self.cam_names
+
+        """
+        # dataset json format
+        {
+            'meta_info': 
+            'calib': {
+                'cam01': {
+                    'cam_to_velo': list
+                    'cam_intrinsic': list
+                    'distortion': list
+                }
+                ...
+            }
+            'frames': [
+                {
+                    'frame_id': timestamp,
+                    'annos': {
+                        'names': list
+                        'boxes_3d': list of list
+                        'boxes_2d': {
+                            'cam01': list of list
+                            ...
+                        }
+                    }
+                    'pose': list
+                },
+                ...
+            ]
+        }
+        # open pcdet format
+        {
+            'meta_info':
+            'sequence_id': seq_idx
+            'frame_id': timestamp
+            'timestamp': timestamp
+            'lidar': path
+            'cam01': path
+            ...
+            'calib': {
+                'cam01': {
+                    'cam_to_velo': np.array
+                    'cam_intrinsic': np.array
+                    'distortion': np.array
+                }
+                ...
+            }
+            'pose': np.array
+            'annos': {
+                'name': np.array
+                'boxes_3d': np.array
+                'boxes_2d': {
+                    'cam01': np.array
+                    ....
+                }
+            }          
+        }
+        """
+        def process_single_sequence(seq_idx):
+            print('%s seq_idx: %s' % (self.split, seq_idx))
+            seq_infos = []
+            seq_path = Path(root_path) / 'data' / seq_idx
+            json_path = seq_path / ('%s.json' % seq_idx)
+            with open(json_path, 'r') as f:
+                info_this_seq = json.load(f)
+            meta_info = info_this_seq['meta_info']
+            calib = info_this_seq['calib']
+            for f_idx, frame in enumerate(info_this_seq['frames']):
+                frame_id = frame['frame_id']
+                if f_idx == 0:
+                    prev_id = None
+                else:
+                    prev_id = info_this_seq['frames'][f_idx-1]['frame_id']
+                if f_idx == len(info_this_seq['frames'])-1:
+                    next_id = None
+                else:
+                    next_id = info_this_seq['frames'][f_idx+1]['frame_id']
+                pc_path = str(seq_path / 'lidar_roof' / ('%s.bin' % frame_id))
+                pose = np.array(frame['pose'])
+                frame_dict = {
+                    'sequence_id': seq_idx,
+                    'frame_id': frame_id,
+                    'timestamp': int(frame_id),
+                    'prev_id': prev_id,
+                    'next_id': next_id,
+                    'meta_info': meta_info,
+                    'lidar': pc_path,
+                    'pose': pose
+                }
+                calib_dict = {}
+                for cam_name in cam_names:
+                    cam_path = str(seq_path / cam_name / ('%s.jpg' % frame_id))
+                    frame_dict.update({cam_name: cam_path})
+                    calib_dict[cam_name] = {}
+                    calib_dict[cam_name]['cam_to_velo'] = np.array(calib[cam_name]['cam_to_velo'])
+                    calib_dict[cam_name]['cam_intrinsic'] = np.array(calib[cam_name]['cam_intrinsic'])
+                    calib_dict[cam_name]['distortion'] = np.array(calib[cam_name]['distortion'])
+                frame_dict.update({'calib': calib_dict})
+
+                if 'annos' in frame:
+                    annos = frame['annos']
+                    boxes_3d = np.array(annos['boxes_3d'])
+                    if boxes_3d.shape[0] == 0:
+                        print(frame_id)
+                        continue
+                    boxes_2d_dict = {}
+                    for cam_name in cam_names:
+                        boxes_2d_dict[cam_name] = np.array(annos['boxes_2d'][cam_name])
+                    annos_dict = {
+                        'name': np.array(annos['names']),
+                        'boxes_3d': boxes_3d,
+                        'boxes_2d': boxes_2d_dict
+                    }
+
+                    points = self.get_lidar(seq_idx, frame_id)
+                    corners_lidar = box_utils.boxes_to_corners_3d(np.array(annos['boxes_3d']))
+                    num_gt = boxes_3d.shape[0]
+                    num_points_in_gt = -np.ones(num_gt, dtype=np.int32)
+                    for k in range(num_gt):
+                        flag = box_utils.in_hull(points[:, 0:3], corners_lidar[k])
+                        num_points_in_gt[k] = flag.sum()
+                    annos_dict['num_points_in_gt'] = num_points_in_gt
+
+                    frame_dict.update({'annos': annos_dict})
+                seq_infos.append(frame_dict)
+            return seq_infos
+
+        sample_seq_list = sample_seq_list if sample_seq_list is not None else self.sample_seq_list
+        with futures.ThreadPoolExecutor(num_workers) as executor:
+            infos = executor.map(process_single_sequence, sample_seq_list)
+        all_infos = []
+        for info in infos:
+            all_infos.extend(info)
+        return all_infos
+
+    def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'):
+        import torch
+
+        database_save_path = Path(self.root_path) / ('gt_database' if split == 'train' else ('gt_database_%s' % split))
+        db_info_save_path = Path(self.root_path) / ('once_dbinfos_%s.pkl' % split)
+
+        database_save_path.mkdir(parents=True, exist_ok=True)
+        all_db_infos = {}
+
+        with open(info_path, 'rb') as f:
+            infos = pickle.load(f)
+
+        for k in range(len(infos)):
+            if 'annos' not in infos[k]:
+                continue
+            print('gt_database sample: %d' % (k + 1))
+            info = infos[k]
+            frame_id = info['frame_id']
+            seq_id = info['sequence_id']
+            points = self.get_lidar(seq_id, frame_id)
+            annos = info['annos']
+            names = annos['name']
+            gt_boxes = annos['boxes_3d']
+
+            num_obj = gt_boxes.shape[0]
+            point_indices = roiaware_pool3d_utils.points_in_boxes_cpu(
+                torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes)
+            ).numpy()  # (nboxes, npoints)
+
+            for i in range(num_obj):
+                filename = '%s_%s_%d.bin' % (frame_id, names[i], i)
+                filepath = database_save_path / filename
+                gt_points = points[point_indices[i] > 0]
+
+                gt_points[:, :3] -= gt_boxes[i, :3]
+                with open(filepath, 'w') as f:
+                    gt_points.tofile(f)
+
+                db_path = str(filepath.relative_to(self.root_path))  # gt_database/xxxxx.bin
+                db_info = {'name': names[i], 'path': db_path, 'gt_idx': i,
+                            'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0]}
+                if names[i] in all_db_infos:
+                    all_db_infos[names[i]].append(db_info)
+                else:
+                    all_db_infos[names[i]] = [db_info]
+
+        for k, v in all_db_infos.items():
+            print('Database %s: %d' % (k, len(v)))
+
+        with open(db_info_save_path, 'wb') as f:
+            pickle.dump(all_db_infos, f)
+
+    @staticmethod
+    def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
+        def get_template_prediction(num_samples):
+            ret_dict = {
+                'name': np.zeros(num_samples), 'score': np.zeros(num_samples),
+                'boxes_3d': np.zeros((num_samples, 7))
+            }
+            return ret_dict
+
+        def generate_single_sample_dict(box_dict):
+            pred_scores = box_dict['pred_scores'].cpu().numpy()
+            pred_boxes = box_dict['pred_boxes'].cpu().numpy()
+            pred_labels = box_dict['pred_labels'].cpu().numpy()
+            pred_dict = get_template_prediction(pred_scores.shape[0])
+            if pred_scores.shape[0] == 0:
+                return pred_dict
+
+            pred_dict['name'] = np.array(class_names)[pred_labels - 1]
+            pred_dict['score'] = pred_scores
+            pred_dict['boxes_3d'] = pred_boxes
+            return pred_dict
+
+        annos = []
+        for index, box_dict in enumerate(pred_dicts):
+            frame_id = batch_dict['frame_id'][index]
+            single_pred_dict = generate_single_sample_dict(box_dict)
+            single_pred_dict['frame_id'] = frame_id
+            annos.append(single_pred_dict)
+
+            if output_path is not None:
+                raise NotImplementedError
+        return annos
+
+    def evaluation(self, det_annos, class_names, **kwargs):
+        from .once_eval.evaluation import get_evaluation_results
+
+        eval_det_annos = copy.deepcopy(det_annos)
+        eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.once_infos]
+        ap_result_str, ap_dict = get_evaluation_results(eval_gt_annos, eval_det_annos, class_names)
+
+        return ap_result_str, ap_dict
+
+def create_once_infos(dataset_cfg, class_names, data_path, save_path, workers=4):
+    dataset = ONCEDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
+
+    splits = ['train', 'val', 'test', 'raw_small', 'raw_medium', 'raw_large']
+    ignore = ['test']
+
+    print('---------------Start to generate data infos---------------')
+    for split in splits:
+        if split in ignore:
+            continue
+
+        filename = 'once_infos_%s.pkl' % split
+        filename = save_path / Path(filename)
+        dataset.set_split(split)
+        once_infos = dataset.get_infos(num_workers=workers)
+        with open(filename, 'wb') as f:
+            pickle.dump(once_infos, f)
+        print('ONCE info %s file is saved to %s' % (split, filename))
+
+    train_filename = save_path / 'once_infos_train.pkl'
+    print('---------------Start create groundtruth database for data augmentation---------------')
+    dataset.set_split('train')
+    dataset.create_groundtruth_database(train_filename, split='train')
+    print('---------------Data preparation Done---------------')
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--cfg_file', type=str, default=None, help='specify the config of dataset')
+    parser.add_argument('--func', type=str, default='create_waymo_infos', help='')
+    parser.add_argument('--runs_on', type=str, default='server', help='')
+    args = parser.parse_args()
+
+    if args.func == 'create_once_infos':
+        import yaml
+        from pathlib import Path
+        from easydict import EasyDict
+        dataset_cfg = EasyDict(yaml.load(open(args.cfg_file)))
+
+
+        ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
+        once_data_path = ROOT_DIR / 'data' / 'once'
+        once_save_path = ROOT_DIR / 'data' / 'once'
+
+        if args.runs_on == 'cloud':
+            once_data_path = Path('/cache/once/')
+            once_save_path = Path('/cache/once/')
+            dataset_cfg.DATA_PATH = dataset_cfg.CLOUD_DATA_PATH
+
+        create_once_infos(
+            dataset_cfg=dataset_cfg,
+            class_names=['Car', 'Bus', 'Truck', 'Pedestrian', 'Bicycle'],
+            data_path=once_data_path,
+            save_path=once_save_path
+        )
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/eval_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/eval_utils.py
new file mode 100644
index 000000000..45263923d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/eval_utils.py
@@ -0,0 +1,53 @@
+import numpy as np
+
+
+def compute_split_parts(num_samples, num_parts):
+    part_samples = num_samples // num_parts
+    remain_samples = num_samples % num_parts
+    if part_samples == 0:
+        return [num_samples]
+    if remain_samples == 0:
+        return [part_samples] * num_parts
+    else:
+        return [part_samples] * num_parts + [remain_samples]
+
+
+def overall_filter(boxes):
+    ignore = np.zeros(boxes.shape[0], dtype=bool)  # all false
+    return ignore
+
+
+def distance_filter(boxes, level):
+    ignore = np.ones(boxes.shape[0], dtype=bool)  # all true
+    dist = np.sqrt(np.sum(boxes[:, 0:3] * boxes[:, 0:3], axis=1))
+
+    if level == 0:  # 0-30m
+        flag = dist < 30
+    elif level == 1:  # 30-50m
+        flag = (dist >= 30) & (dist < 50)
+    elif level == 2:  # 50m-inf
+        flag = dist >= 50
+    else:
+        assert False, 'level < 3 for distance metric, found level %s' % (str(level))
+
+    ignore[flag] = False
+    return ignore
+
+
+def overall_distance_filter(boxes, level):
+    ignore = np.ones(boxes.shape[0], dtype=bool)  # all true
+    dist = np.sqrt(np.sum(boxes[:, 0:3] * boxes[:, 0:3], axis=1))
+
+    if level == 0:
+        flag = np.ones(boxes.shape[0], dtype=bool)
+    elif level == 1:  # 0-30m
+        flag = dist < 30
+    elif level == 2:  # 30-50m
+        flag = (dist >= 30) & (dist < 50)
+    elif level == 3:  # 50m-inf
+        flag = dist >= 50
+    else:
+        assert False, 'level < 4 for overall & distance metric, found level %s' % (str(level))
+
+    ignore[flag] = False
+    return ignore
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/evaluation.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/evaluation.py
new file mode 100644
index 000000000..d890e93d3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/evaluation.py
@@ -0,0 +1,420 @@
+"""
+Evaluation Server
+Written by Jiageng Mao
+"""
+
+import numpy as np
+import numba
+
+from .iou_utils import rotate_iou_gpu_eval
+from .eval_utils import compute_split_parts, overall_filter, distance_filter, overall_distance_filter
+
+iou_threshold_dict = {
+    'Car': 0.7,
+    'Bus': 0.7,
+    'Truck': 0.7,
+    'Pedestrian': 0.3,
+    'Cyclist': 0.5
+}
+
+superclass_iou_threshold_dict = {
+    'Vehicle': 0.7,
+    'Pedestrian': 0.3,
+    'Cyclist': 0.5
+}
+
+def get_evaluation_results(gt_annos, pred_annos, classes,
+                           use_superclass=True,
+                           iou_thresholds=None,
+                           num_pr_points=50,
+                           difficulty_mode='Overall&Distance',
+                           ap_with_heading=True,
+                           num_parts=100,
+                           print_ok=False
+                           ):
+
+    if iou_thresholds is None:
+        if use_superclass:
+            iou_thresholds = superclass_iou_threshold_dict
+        else:
+            iou_thresholds = iou_threshold_dict
+
+    assert len(gt_annos) == len(pred_annos), "the number of GT must match predictions"
+    assert difficulty_mode in ['Overall&Distance', 'Overall', 'Distance'], "difficulty mode is not supported"
+    if use_superclass:
+        if ('Car' in classes) or ('Bus' in classes) or ('Truck' in classes):
+            assert ('Car' in classes) and ('Bus' in classes) and ('Truck' in classes), "Car/Bus/Truck must all exist for vehicle detection"
+        classes = [cls_name for cls_name in classes if cls_name not in ['Car', 'Bus', 'Truck']]
+        classes.insert(0, 'Vehicle')
+
+    num_samples = len(gt_annos)
+    split_parts = compute_split_parts(num_samples, num_parts)
+    ious = compute_iou3d(gt_annos, pred_annos, split_parts, with_heading=ap_with_heading)
+
+    num_classes = len(classes)
+    if difficulty_mode == 'Distance':
+        num_difficulties = 3
+        difficulty_types = ['0-30m', '30-50m', '50m-inf']
+    elif difficulty_mode == 'Overall':
+        num_difficulties = 1
+        difficulty_types = ['overall']
+    elif difficulty_mode == 'Overall&Distance':
+        num_difficulties = 4
+        difficulty_types = ['overall', '0-30m', '30-50m', '50m-inf']
+    else:
+        raise NotImplementedError
+
+    precision = np.zeros([num_classes, num_difficulties, num_pr_points+1])
+    recall = np.zeros([num_classes, num_difficulties, num_pr_points+1])
+
+    for cls_idx, cur_class in enumerate(classes):
+        iou_threshold = iou_thresholds[cur_class]
+        for diff_idx in range(num_difficulties):
+            ### filter data & determine score thresholds on p-r curve ###
+            accum_all_scores, gt_flags, pred_flags = [], [], []
+            num_valid_gt = 0
+            for sample_idx in range(num_samples):
+                gt_anno = gt_annos[sample_idx]
+                pred_anno = pred_annos[sample_idx]
+                pred_score = pred_anno['score']
+                iou = ious[sample_idx]
+                gt_flag, pred_flag = filter_data(gt_anno, pred_anno, difficulty_mode,
+                                                    difficulty_level=diff_idx, class_name=cur_class, use_superclass=use_superclass)
+                gt_flags.append(gt_flag)
+                pred_flags.append(pred_flag)
+                num_valid_gt += sum(gt_flag == 0)
+                accum_scores = accumulate_scores(iou, pred_score, gt_flag, pred_flag,
+                                                 iou_threshold=iou_threshold)
+                accum_all_scores.append(accum_scores)
+            all_scores = np.concatenate(accum_all_scores, axis=0)
+            thresholds = get_thresholds(all_scores, num_valid_gt, num_pr_points=num_pr_points)
+
+            ### compute tp/fp/fn ###
+            confusion_matrix = np.zeros([len(thresholds), 3]) # only record tp/fp/fn
+            for sample_idx in range(num_samples):
+                pred_score = pred_annos[sample_idx]['score']
+                iou = ious[sample_idx]
+                gt_flag, pred_flag = gt_flags[sample_idx], pred_flags[sample_idx]
+                for th_idx, score_th in enumerate(thresholds):
+                    tp, fp, fn = compute_statistics(iou, pred_score, gt_flag, pred_flag,
+                                                    score_threshold=score_th, iou_threshold=iou_threshold)
+                    confusion_matrix[th_idx, 0] += tp
+                    confusion_matrix[th_idx, 1] += fp
+                    confusion_matrix[th_idx, 2] += fn
+
+            ### draw p-r curve ###
+            for th_idx in range(len(thresholds)):
+                recall[cls_idx, diff_idx, th_idx] = confusion_matrix[th_idx, 0] / \
+                                                    (confusion_matrix[th_idx, 0] + confusion_matrix[th_idx, 2])
+                precision[cls_idx, diff_idx, th_idx] = confusion_matrix[th_idx, 0] / \
+                                                       (confusion_matrix[th_idx, 0] + confusion_matrix[th_idx, 1])
+
+            for th_idx in range(len(thresholds)):
+                precision[cls_idx, diff_idx, th_idx] = np.max(
+                    precision[cls_idx, diff_idx, th_idx:], axis=-1)
+                recall[cls_idx, diff_idx, th_idx] = np.max(
+                    recall[cls_idx, diff_idx, th_idx:], axis=-1)
+
+    AP = 0
+    for i in range(1, precision.shape[-1]):
+        AP += precision[..., i]
+    AP = AP / num_pr_points * 100
+
+    ret_dict = {}
+
+    ret_str = "\n|AP@%-9s|" % (str(num_pr_points))
+    for diff_type in difficulty_types:
+        ret_str += '%-12s|' % diff_type
+    ret_str += '\n'
+    for cls_idx, cur_class in enumerate(classes):
+        ret_str += "|%-12s|" % cur_class
+        for diff_idx in range(num_difficulties):
+            diff_type = difficulty_types[diff_idx]
+            key = 'AP_' + cur_class + '/' + diff_type
+            ap_score = AP[cls_idx,diff_idx]
+            ret_dict[key] = ap_score
+            ret_str += "%-12.2f|" % ap_score
+        ret_str += "\n"
+    mAP = np.mean(AP, axis=0)
+    ret_str += "|%-12s|" % 'mAP'
+    for diff_idx in range(num_difficulties):
+        diff_type = difficulty_types[diff_idx]
+        key = 'AP_mean' + '/' + diff_type
+        ap_score = mAP[diff_idx]
+        ret_dict[key] = ap_score
+        ret_str += "%-12.2f|" % ap_score
+    ret_str += "\n"
+
+    if print_ok:
+        print(ret_str)
+    return ret_str, ret_dict
+
+@numba.jit(nopython=True)
+def get_thresholds(scores, num_gt, num_pr_points):
+    eps = 1e-6
+    scores.sort()
+    scores = scores[::-1]
+    recall_level = 0
+    thresholds = []
+    for i, score in enumerate(scores):
+        l_recall = (i + 1) / num_gt
+        if i < (len(scores) - 1):
+            r_recall = (i + 2) / num_gt
+        else:
+            r_recall = l_recall
+        if (r_recall + l_recall < 2 * recall_level) and i < (len(scores) - 1):
+            continue
+        thresholds.append(score)
+        recall_level += 1 / num_pr_points
+        # avoid numerical errors
+        # while r_recall + l_recall >= 2 * recall_level:
+        while r_recall + l_recall + eps > 2 * recall_level:
+            thresholds.append(score)
+            recall_level += 1 / num_pr_points
+    return thresholds
+
+@numba.jit(nopython=True)
+def accumulate_scores(iou, pred_scores, gt_flag, pred_flag, iou_threshold):
+    num_gt = iou.shape[0]
+    num_pred = iou.shape[1]
+    assigned = np.full(num_pred, False)
+    accum_scores = np.zeros(num_gt)
+    accum_idx = 0
+    for i in range(num_gt):
+        if gt_flag[i] == -1: # not the same class
+            continue
+        det_idx = -1
+        detected_score = -1
+        for j in range(num_pred):
+            if pred_flag[j] == -1: # not the same class
+                continue
+            if assigned[j]:
+                continue
+            iou_ij = iou[i, j]
+            pred_score = pred_scores[j]
+            if (iou_ij > iou_threshold) and (pred_score > detected_score):
+                det_idx = j
+                detected_score = pred_score
+
+        if (detected_score == -1) and (gt_flag[i] == 0): # false negative
+            pass
+        elif (detected_score != -1) and (gt_flag[i] == 1 or pred_flag[det_idx] == 1): # ignore
+            assigned[det_idx] = True
+        elif detected_score != -1: # true positive
+            accum_scores[accum_idx] = pred_scores[det_idx]
+            accum_idx += 1
+            assigned[det_idx] = True
+
+    return accum_scores[:accum_idx]
+
+@numba.jit(nopython=True)
+def compute_statistics(iou, pred_scores, gt_flag, pred_flag, score_threshold, iou_threshold):
+    num_gt = iou.shape[0]
+    num_pred = iou.shape[1]
+    assigned = np.full(num_pred, False)
+    under_threshold = pred_scores < score_threshold
+
+    tp, fp, fn = 0, 0, 0
+    for i in range(num_gt):
+        if gt_flag[i] == -1: # different classes
+            continue
+        det_idx = -1
+        detected = False
+        best_matched_iou = 0
+        gt_assigned_to_ignore = False
+
+        for j in range(num_pred):
+            if pred_flag[j] == -1: # different classes
+                continue
+            if assigned[j]: # already assigned to other GT
+                continue
+            if under_threshold[j]: # compute only boxes above threshold
+                continue
+            iou_ij = iou[i, j]
+            if (iou_ij > iou_threshold) and (iou_ij > best_matched_iou or gt_assigned_to_ignore) and pred_flag[j] == 0:
+                best_matched_iou = iou_ij
+                det_idx = j
+                detected = True
+                gt_assigned_to_ignore = False
+            elif (iou_ij > iou_threshold) and (not detected) and pred_flag[j] == 1:
+                det_idx = j
+                detected = True
+                gt_assigned_to_ignore = True
+
+        if (not detected) and gt_flag[i] == 0: # false negative
+            fn += 1
+        elif detected and (gt_flag[i] == 1 or pred_flag[det_idx] == 1): # ignore
+            assigned[det_idx] = True
+        elif detected: # true positive
+            tp += 1
+            assigned[det_idx] = True
+
+    for j in range(num_pred):
+        if not (assigned[j] or pred_flag[j] == -1 or pred_flag[j] == 1 or under_threshold[j]):
+            fp += 1
+
+    return tp, fp, fn
+
+def filter_data(gt_anno, pred_anno, difficulty_mode, difficulty_level, class_name, use_superclass):
+    """
+    Filter data by class name and difficulty
+
+    Args:
+        gt_anno:
+        pred_anno:
+        difficulty_mode:
+        difficulty_level:
+        class_name:
+
+    Returns:
+        gt_flags/pred_flags:
+            1 : same class but ignored with different difficulty levels
+            0 : accepted
+           -1 : rejected with different classes
+    """
+    num_gt = len(gt_anno['name'])
+    gt_flag = np.zeros(num_gt, dtype=np.int64)
+    if use_superclass:
+        if class_name == 'Vehicle':
+            reject = np.logical_or(gt_anno['name']=='Pedestrian', gt_anno['name']=='Cyclist')
+        else:
+            reject = gt_anno['name'] != class_name
+    else:
+        reject = gt_anno['name'] != class_name
+    gt_flag[reject] = -1
+    num_pred = len(pred_anno['name'])
+    pred_flag = np.zeros(num_pred, dtype=np.int64)
+    if use_superclass:
+        if class_name == 'Vehicle':
+            reject = np.logical_or(pred_anno['name']=='Pedestrian', pred_anno['name']=='Cyclist')
+        else:
+            reject = pred_anno['name'] != class_name
+    else:
+        reject = pred_anno['name'] != class_name
+    pred_flag[reject] = -1
+
+    if difficulty_mode == 'Overall':
+        ignore = overall_filter(gt_anno['boxes_3d'])
+        gt_flag[ignore] = 1
+        ignore = overall_filter(pred_anno['boxes_3d'])
+        pred_flag[ignore] = 1
+    elif difficulty_mode == 'Distance':
+        ignore = distance_filter(gt_anno['boxes_3d'], difficulty_level)
+        gt_flag[ignore] = 1
+        ignore = distance_filter(pred_anno['boxes_3d'], difficulty_level)
+        pred_flag[ignore] = 1
+    elif difficulty_mode == 'Overall&Distance':
+        ignore = overall_distance_filter(gt_anno['boxes_3d'], difficulty_level)
+        gt_flag[ignore] = 1
+        ignore = overall_distance_filter(pred_anno['boxes_3d'], difficulty_level)
+        pred_flag[ignore] = 1
+    else:
+        raise NotImplementedError
+
+    return gt_flag, pred_flag
+
+def iou3d_kernel(gt_boxes, pred_boxes):
+    """
+    Core iou3d computation (with cuda)
+
+    Args:
+        gt_boxes: [N, 7] (x, y, z, w, l, h, rot) in Lidar coordinates
+        pred_boxes: [M, 7]
+
+    Returns:
+        iou3d: [N, M]
+    """
+    intersection_2d = rotate_iou_gpu_eval(gt_boxes[:, [0, 1, 3, 4, 6]], pred_boxes[:, [0, 1, 3, 4, 6]], criterion=2)
+    gt_max_h = gt_boxes[:, [2]] + gt_boxes[:, [5]] * 0.5
+    gt_min_h = gt_boxes[:, [2]] - gt_boxes[:, [5]] * 0.5
+    pred_max_h = pred_boxes[:, [2]] + pred_boxes[:, [5]] * 0.5
+    pred_min_h = pred_boxes[:, [2]] - pred_boxes[:, [5]] * 0.5
+    max_of_min = np.maximum(gt_min_h, pred_min_h.T)
+    min_of_max = np.minimum(gt_max_h, pred_max_h.T)
+    inter_h = min_of_max - max_of_min
+    inter_h[inter_h <= 0] = 0
+    #inter_h[intersection_2d <= 0] = 0
+    intersection_3d = intersection_2d * inter_h
+    gt_vol = gt_boxes[:, [3]] * gt_boxes[:, [4]] * gt_boxes[:, [5]]
+    pred_vol = pred_boxes[:, [3]] * pred_boxes[:, [4]] * pred_boxes[:, [5]]
+    union_3d = gt_vol + pred_vol.T - intersection_3d
+    #eps = 1e-6
+    #union_3d[union_3d<eps] = eps
+    iou3d = intersection_3d / union_3d
+    return iou3d
+
+def iou3d_kernel_with_heading(gt_boxes, pred_boxes):
+    """
+    Core iou3d computation (with cuda)
+
+    Args:
+        gt_boxes: [N, 7] (x, y, z, w, l, h, rot) in Lidar coordinates
+        pred_boxes: [M, 7]
+
+    Returns:
+        iou3d: [N, M]
+    """
+    intersection_2d = rotate_iou_gpu_eval(gt_boxes[:, [0, 1, 3, 4, 6]], pred_boxes[:, [0, 1, 3, 4, 6]], criterion=2)
+    gt_max_h = gt_boxes[:, [2]] + gt_boxes[:, [5]] * 0.5
+    gt_min_h = gt_boxes[:, [2]] - gt_boxes[:, [5]] * 0.5
+    pred_max_h = pred_boxes[:, [2]] + pred_boxes[:, [5]] * 0.5
+    pred_min_h = pred_boxes[:, [2]] - pred_boxes[:, [5]] * 0.5
+    max_of_min = np.maximum(gt_min_h, pred_min_h.T)
+    min_of_max = np.minimum(gt_max_h, pred_max_h.T)
+    inter_h = min_of_max - max_of_min
+    inter_h[inter_h <= 0] = 0
+    #inter_h[intersection_2d <= 0] = 0
+    intersection_3d = intersection_2d * inter_h
+    gt_vol = gt_boxes[:, [3]] * gt_boxes[:, [4]] * gt_boxes[:, [5]]
+    pred_vol = pred_boxes[:, [3]] * pred_boxes[:, [4]] * pred_boxes[:, [5]]
+    union_3d = gt_vol + pred_vol.T - intersection_3d
+    #eps = 1e-6
+    #union_3d[union_3d<eps] = eps
+    iou3d = intersection_3d / union_3d
+
+    # rotation orientation filtering
+    diff_rot = gt_boxes[:, [6]] - pred_boxes[:, [6]].T
+    diff_rot = np.abs(diff_rot)
+    reverse_diff_rot = 2 * np.pi - diff_rot
+    diff_rot[diff_rot >= np.pi] = reverse_diff_rot[diff_rot >= np.pi] # constrain to [0-pi]
+    iou3d[diff_rot > np.pi/2] = 0 # unmatched if diff_rot > 90
+    return iou3d
+
+def compute_iou3d(gt_annos, pred_annos, split_parts, with_heading):
+    """
+    Compute iou3d of all samples by parts
+
+    Args:
+        with_heading: filter with heading
+        gt_annos: list of dicts for each sample
+        pred_annos:
+        split_parts: for part-based iou computation
+
+    Returns:
+        ious: list of iou arrays for each sample
+    """
+    gt_num_per_sample = np.stack([len(anno["name"]) for anno in gt_annos], 0)
+    pred_num_per_sample = np.stack([len(anno["name"]) for anno in pred_annos], 0)
+    ious = []
+    sample_idx = 0
+    for num_part_samples in split_parts:
+        gt_annos_part = gt_annos[sample_idx:sample_idx + num_part_samples]
+        pred_annos_part = pred_annos[sample_idx:sample_idx + num_part_samples]
+
+        gt_boxes = np.concatenate([anno["boxes_3d"] for anno in gt_annos_part], 0)
+        pred_boxes = np.concatenate([anno["boxes_3d"] for anno in pred_annos_part], 0)
+
+        if with_heading:
+            iou3d_part = iou3d_kernel_with_heading(gt_boxes, pred_boxes)
+        else:
+            iou3d_part = iou3d_kernel(gt_boxes, pred_boxes)
+
+        gt_num_idx, pred_num_idx = 0, 0
+        for idx in range(num_part_samples):
+            gt_box_num = gt_num_per_sample[sample_idx + idx]
+            pred_box_num = pred_num_per_sample[sample_idx + idx]
+            ious.append(iou3d_part[gt_num_idx: gt_num_idx + gt_box_num, pred_num_idx: pred_num_idx+pred_box_num])
+            gt_num_idx += gt_box_num
+            pred_num_idx += pred_box_num
+        sample_idx += num_part_samples
+    return ious
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/iou_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/iou_utils.py
new file mode 100644
index 000000000..927056e58
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_eval/iou_utils.py
@@ -0,0 +1,344 @@
+"""
+Rotate IoU computation is referred from https://github.com/hongzhenwang/RRPN-revise
+"""
+import math
+import numba
+import numpy as np
+from numba import cuda
+
+@numba.jit(nopython=True)
+def div_up(m, n):
+    return m // n + (m % n > 0)
+
+
+@cuda.jit('(float32[:], float32[:], float32[:])', device=True, inline=True)
+def trangle_area(a, b, c):
+    return ((a[0] - c[0]) * (b[1] - c[1]) - (a[1] - c[1]) *
+            (b[0] - c[0])) / 2.0
+
+
+@cuda.jit('(float32[:], int32)', device=True, inline=True)
+def area(int_pts, num_of_inter):
+    area_val = 0.0
+    for i in range(num_of_inter - 2):
+        area_val += abs(
+            trangle_area(int_pts[:2], int_pts[2 * i + 2:2 * i + 4],
+                         int_pts[2 * i + 4:2 * i + 6]))
+    return area_val
+
+
+@cuda.jit('(float32[:], int32)', device=True, inline=True)
+def sort_vertex_in_convex_polygon(int_pts, num_of_inter):
+    if num_of_inter > 0:
+        center = cuda.local.array((2,), dtype=numba.float32)
+        center[:] = 0.0
+        for i in range(num_of_inter):
+            center[0] += int_pts[2 * i]
+            center[1] += int_pts[2 * i + 1]
+        center[0] /= num_of_inter
+        center[1] /= num_of_inter
+        v = cuda.local.array((2,), dtype=numba.float32)
+        vs = cuda.local.array((16,), dtype=numba.float32)
+        for i in range(num_of_inter):
+            v[0] = int_pts[2 * i] - center[0]
+            v[1] = int_pts[2 * i + 1] - center[1]
+            d = math.sqrt(v[0] * v[0] + v[1] * v[1])
+            v[0] = v[0] / d
+            v[1] = v[1] / d
+            if v[1] < 0:
+                v[0] = -2 - v[0]
+            vs[i] = v[0]
+        j = 0
+        temp = 0
+        for i in range(1, num_of_inter):
+            if vs[i - 1] > vs[i]:
+                temp = vs[i]
+                tx = int_pts[2 * i]
+                ty = int_pts[2 * i + 1]
+                j = i
+                while j > 0 and vs[j - 1] > temp:
+                    vs[j] = vs[j - 1]
+                    int_pts[j * 2] = int_pts[j * 2 - 2]
+                    int_pts[j * 2 + 1] = int_pts[j * 2 - 1]
+                    j -= 1
+
+                vs[j] = temp
+                int_pts[j * 2] = tx
+                int_pts[j * 2 + 1] = ty
+
+
+@cuda.jit(
+    '(float32[:], float32[:], int32, int32, float32[:])',
+    device=True,
+    inline=True)
+def line_segment_intersection(pts1, pts2, i, j, temp_pts):
+    A = cuda.local.array((2,), dtype=numba.float32)
+    B = cuda.local.array((2,), dtype=numba.float32)
+    C = cuda.local.array((2,), dtype=numba.float32)
+    D = cuda.local.array((2,), dtype=numba.float32)
+
+    A[0] = pts1[2 * i]
+    A[1] = pts1[2 * i + 1]
+
+    B[0] = pts1[2 * ((i + 1) % 4)]
+    B[1] = pts1[2 * ((i + 1) % 4) + 1]
+
+    C[0] = pts2[2 * j]
+    C[1] = pts2[2 * j + 1]
+
+    D[0] = pts2[2 * ((j + 1) % 4)]
+    D[1] = pts2[2 * ((j + 1) % 4) + 1]
+    BA0 = B[0] - A[0]
+    BA1 = B[1] - A[1]
+    DA0 = D[0] - A[0]
+    CA0 = C[0] - A[0]
+    DA1 = D[1] - A[1]
+    CA1 = C[1] - A[1]
+    acd = DA1 * CA0 > CA1 * DA0
+    bcd = (D[1] - B[1]) * (C[0] - B[0]) > (C[1] - B[1]) * (D[0] - B[0])
+    if acd != bcd:
+        abc = CA1 * BA0 > BA1 * CA0
+        abd = DA1 * BA0 > BA1 * DA0
+        if abc != abd:
+            DC0 = D[0] - C[0]
+            DC1 = D[1] - C[1]
+            ABBA = A[0] * B[1] - B[0] * A[1]
+            CDDC = C[0] * D[1] - D[0] * C[1]
+            DH = BA1 * DC0 - BA0 * DC1
+            Dx = ABBA * DC0 - BA0 * CDDC
+            Dy = ABBA * DC1 - BA1 * CDDC
+            temp_pts[0] = Dx / DH
+            temp_pts[1] = Dy / DH
+            return True
+    return False
+
+
+@cuda.jit(
+    '(float32[:], float32[:], int32, int32, float32[:])',
+    device=True,
+    inline=True)
+def line_segment_intersection_v1(pts1, pts2, i, j, temp_pts):
+    a = cuda.local.array((2,), dtype=numba.float32)
+    b = cuda.local.array((2,), dtype=numba.float32)
+    c = cuda.local.array((2,), dtype=numba.float32)
+    d = cuda.local.array((2,), dtype=numba.float32)
+
+    a[0] = pts1[2 * i]
+    a[1] = pts1[2 * i + 1]
+
+    b[0] = pts1[2 * ((i + 1) % 4)]
+    b[1] = pts1[2 * ((i + 1) % 4) + 1]
+
+    c[0] = pts2[2 * j]
+    c[1] = pts2[2 * j + 1]
+
+    d[0] = pts2[2 * ((j + 1) % 4)]
+    d[1] = pts2[2 * ((j + 1) % 4) + 1]
+
+    area_abc = trangle_area(a, b, c)
+    area_abd = trangle_area(a, b, d)
+
+    if area_abc * area_abd >= 0:
+        return False
+
+    area_cda = trangle_area(c, d, a)
+    area_cdb = area_cda + area_abc - area_abd
+
+    if area_cda * area_cdb >= 0:
+        return False
+    t = area_cda / (area_abd - area_abc)
+
+    dx = t * (b[0] - a[0])
+    dy = t * (b[1] - a[1])
+    temp_pts[0] = a[0] + dx
+    temp_pts[1] = a[1] + dy
+    return True
+
+"""
+@cuda.jit('(float32, float32, float32[:])', device=True, inline=True)
+def point_in_quadrilateral(pt_x, pt_y, corners):
+    ab0 = corners[2] - corners[0]
+    ab1 = corners[3] - corners[1]
+
+    ad0 = corners[6] - corners[0]
+    ad1 = corners[7] - corners[1]
+
+    ap0 = pt_x - corners[0]
+    ap1 = pt_y - corners[1]
+
+    abab = ab0 * ab0 + ab1 * ab1
+    abap = ab0 * ap0 + ab1 * ap1
+    adad = ad0 * ad0 + ad1 * ad1
+    adap = ad0 * ap0 + ad1 * ap1
+
+    return abab >= abap and abap >= 0 and adad >= adap and adap >= 0
+"""
+
+@cuda.jit('(float32, float32, float32[:])', device=True, inline=True)
+def point_in_quadrilateral(pt_x, pt_y, corners):
+    PA0 = corners[0] - pt_x
+    PA1 = corners[1] - pt_y
+    PB0 = corners[2] - pt_x
+    PB1 = corners[3] - pt_y
+    PC0 = corners[4] - pt_x
+    PC1 = corners[5] - pt_y
+    PD0 = corners[6] - pt_x
+    PD1 = corners[7] - pt_y
+    PAB = PA0 * PB1 - PB0 * PA1
+    PBC = PB0 * PC1 - PC0 * PB1
+    PCD = PC0 * PD1 - PD0 * PC1
+    PDA = PD0 * PA1 - PA0 * PD1
+    return PAB >= 0 and PBC >= 0 and PCD >= 0 and PDA >= 0 or \
+           PAB <= 0 and PBC <= 0 and PCD <= 0 and PDA <= 0
+
+@cuda.jit('(float32[:], float32[:], float32[:])', device=True, inline=True)
+def quadrilateral_intersection(pts1, pts2, int_pts):
+    num_of_inter = 0
+    for i in range(4):
+        if point_in_quadrilateral(pts1[2 * i], pts1[2 * i + 1], pts2):
+            int_pts[num_of_inter * 2] = pts1[2 * i]
+            int_pts[num_of_inter * 2 + 1] = pts1[2 * i + 1]
+            num_of_inter += 1
+        if point_in_quadrilateral(pts2[2 * i], pts2[2 * i + 1], pts1):
+            int_pts[num_of_inter * 2] = pts2[2 * i]
+            int_pts[num_of_inter * 2 + 1] = pts2[2 * i + 1]
+            num_of_inter += 1
+    temp_pts = cuda.local.array((2,), dtype=numba.float32)
+    for i in range(4):
+        for j in range(4):
+            has_pts = line_segment_intersection(pts1, pts2, i, j, temp_pts)
+            if has_pts:
+                int_pts[num_of_inter * 2] = temp_pts[0]
+                int_pts[num_of_inter * 2 + 1] = temp_pts[1]
+                num_of_inter += 1
+
+    return num_of_inter
+
+@cuda.jit('(float32[:], float32[:])', device=True, inline=True)
+def rbbox_to_corners(corners, rbbox):
+    # generate clockwise corners and rotate it clockwise
+    angle = rbbox[4]
+    a_cos = math.cos(angle)
+    a_sin = math.sin(angle)
+    center_x = rbbox[0]
+    center_y = rbbox[1]
+    x_d = rbbox[2]
+    y_d = rbbox[3]
+    corners_x = cuda.local.array((4,), dtype=numba.float32)
+    corners_y = cuda.local.array((4,), dtype=numba.float32)
+    corners_x[0] = -x_d / 2
+    corners_x[1] = -x_d / 2
+    corners_x[2] = x_d / 2
+    corners_x[3] = x_d / 2
+    corners_y[0] = -y_d / 2
+    corners_y[1] = y_d / 2
+    corners_y[2] = y_d / 2
+    corners_y[3] = -y_d / 2
+    for i in range(4):
+        corners[2 *
+                i] = a_cos * corners_x[i] + a_sin * corners_y[i] + center_x
+        corners[2 * i
+                + 1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y
+
+
+@cuda.jit('(float32[:], float32[:])', device=True, inline=True)
+def inter(rbbox1, rbbox2):
+    corners1 = cuda.local.array((8,), dtype=numba.float32)
+    corners2 = cuda.local.array((8,), dtype=numba.float32)
+    intersection_corners = cuda.local.array((16,), dtype=numba.float32)
+
+    rbbox_to_corners(corners1, rbbox1)
+    rbbox_to_corners(corners2, rbbox2)
+
+    num_intersection = quadrilateral_intersection(corners1, corners2,
+                                                  intersection_corners)
+    sort_vertex_in_convex_polygon(intersection_corners, num_intersection)
+    # print(intersection_corners.reshape([-1, 2])[:num_intersection])
+
+    return area(intersection_corners, num_intersection)
+
+
+@cuda.jit('(float32[:], float32[:], int32)', device=True, inline=True)
+def devRotateIoUEval(rbox1, rbox2, criterion=-1):
+    area1 = rbox1[2] * rbox1[3]
+    area2 = rbox2[2] * rbox2[3]
+    area_inter = inter(rbox1, rbox2)
+    if criterion == -1:
+        return area_inter / (area1 + area2 - area_inter)
+    elif criterion == 0:
+        return area_inter / area1
+    elif criterion == 1:
+        return area_inter / area2
+    else:
+        return area_inter
+
+
+@cuda.jit('(int64, int64, float32[:], float32[:], float32[:], int32)', fastmath=False)
+def rotate_iou_kernel_eval(N, K, dev_boxes, dev_query_boxes, dev_iou, criterion=-1):
+    threadsPerBlock = 8 * 8
+    row_start = cuda.blockIdx.x
+    col_start = cuda.blockIdx.y
+    tx = cuda.threadIdx.x
+    row_size = min(N - row_start * threadsPerBlock, threadsPerBlock)
+    col_size = min(K - col_start * threadsPerBlock, threadsPerBlock)
+    block_boxes = cuda.shared.array(shape=(64 * 5,), dtype=numba.float32)
+    block_qboxes = cuda.shared.array(shape=(64 * 5,), dtype=numba.float32)
+
+    dev_query_box_idx = threadsPerBlock * col_start + tx
+    dev_box_idx = threadsPerBlock * row_start + tx
+    if (tx < col_size):
+        block_qboxes[tx * 5 + 0] = dev_query_boxes[dev_query_box_idx * 5 + 0]
+        block_qboxes[tx * 5 + 1] = dev_query_boxes[dev_query_box_idx * 5 + 1]
+        block_qboxes[tx * 5 + 2] = dev_query_boxes[dev_query_box_idx * 5 + 2]
+        block_qboxes[tx * 5 + 3] = dev_query_boxes[dev_query_box_idx * 5 + 3]
+        block_qboxes[tx * 5 + 4] = dev_query_boxes[dev_query_box_idx * 5 + 4]
+    if (tx < row_size):
+        block_boxes[tx * 5 + 0] = dev_boxes[dev_box_idx * 5 + 0]
+        block_boxes[tx * 5 + 1] = dev_boxes[dev_box_idx * 5 + 1]
+        block_boxes[tx * 5 + 2] = dev_boxes[dev_box_idx * 5 + 2]
+        block_boxes[tx * 5 + 3] = dev_boxes[dev_box_idx * 5 + 3]
+        block_boxes[tx * 5 + 4] = dev_boxes[dev_box_idx * 5 + 4]
+    cuda.syncthreads()
+    if tx < row_size:
+        for i in range(col_size):
+            offset = row_start * threadsPerBlock * K + col_start * threadsPerBlock + tx * K + i
+            dev_iou[offset] = devRotateIoUEval(block_qboxes[i * 5:i * 5 + 5],
+                                               block_boxes[tx * 5:tx * 5 + 5], criterion)
+
+
+def rotate_iou_gpu_eval(boxes, query_boxes, criterion=-1, device_id=0):
+    """rotated box iou running in gpu. 500x faster than cpu version
+    (take 5ms in one example with numba.cuda code).
+    convert from [this project](
+        https://github.com/hongzhenwang/RRPN-revise/tree/master/pcdet/rotation).
+
+    Args:
+        boxes (float tensor: [N, 5]): rbboxes. format: centers, dims,
+            angles(clockwise when positive)
+        query_boxes (float tensor: [K, 5]): [description]
+        device_id (int, optional): Defaults to 0. [description]
+
+    Returns:
+        [type]: [description]
+    """
+    box_dtype = boxes.dtype
+    boxes = boxes.astype(np.float32)
+    query_boxes = query_boxes.astype(np.float32)
+    N = boxes.shape[0]
+    K = query_boxes.shape[0]
+    iou = np.zeros((N, K), dtype=np.float32)
+    if N == 0 or K == 0:
+        return iou
+    threadsPerBlock = 8 * 8
+    cuda.select_device(device_id)
+    blockspergrid = (div_up(N, threadsPerBlock), div_up(K, threadsPerBlock))
+
+    stream = cuda.stream()
+    with stream.auto_synchronize():
+        boxes_dev = cuda.to_device(boxes.reshape([-1]), stream)
+        query_boxes_dev = cuda.to_device(query_boxes.reshape([-1]), stream)
+        iou_dev = cuda.to_device(iou.reshape([-1]), stream)
+        rotate_iou_kernel_eval[blockspergrid, threadsPerBlock, stream](
+            N, K, boxes_dev, query_boxes_dev, iou_dev, criterion)
+        iou_dev.copy_to_host(iou.reshape([-1]), stream=stream)
+    return iou.astype(boxes.dtype)
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_toolkits.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_toolkits.py
new file mode 100644
index 000000000..ee5666f10
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/once/once_toolkits.py
@@ -0,0 +1,125 @@
+import json
+import os.path as osp
+from collections import defaultdict
+import cv2
+import numpy as np
+
+class Octopus(object):
+    """
+    dataset structure:
+    - data_root
+        - train_split.txt
+        - val_split.txt
+        - test_split.txt
+        -
+    """
+    camera_names = ['cam01', 'cam03', 'cam05', 'cam06', 'cam07', 'cam08', 'cam09']
+    camera_tags = ['top', 'top2', 'left_back', 'left_front', 'right_front', 'right_back', 'back']
+
+    def __init__(self, dataset_root):
+        self.dataset_root = dataset_root
+        self.data_root = osp.join(self.dataset_root, 'data')
+        self._collect_basic_infos()
+
+    @property
+    def train_split_list(self):
+        if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'train_set.txt')):
+            train_split_list = None
+        else:
+            train_split_list = set(map(lambda x: x.strip(),
+                                       open(osp.join(self.data_root, 'train_set.txt')).readlines()))
+        return train_split_list
+
+    @property
+    def val_split_list(self):
+        if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'val_set.txt')):
+            val_split_list = None
+        else:
+            val_split_list = set(map(lambda x: x.strip(),
+                                     open(osp.join(self.data_root, 'val_set.txt')).readlines()))
+        return val_split_list
+
+    @property
+    def test_split_list(self):
+        if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'test_set.txt')):
+            test_split_list = None
+        else:
+            test_split_list = set(map(lambda x: x.strip(),
+                                       open(osp.join(self.data_root, 'test_set.txt')).readlines()))
+        return test_split_list
+
+    @property
+    def raw_split_list(self):
+        if not osp.isfile(osp.join(self.dataset_root, 'ImageSets', 'raw_set.txt')):
+            raw_split_list = None
+        else:
+            raw_split_list = set(map(lambda x: x.strip(),
+                                       open(osp.join(self.data_root, 'raw_set.txt')).readlines()))
+        return raw_split_list
+
+    def _find_split_name(self, seq_id):
+        if seq_id in self.raw_split_list:
+            return 'raw'
+        if seq_id in self.train_split_list:
+            return 'train'
+        if seq_id in self.test_split_list:
+            return 'test'
+        if seq_id in self.val_split_list:
+            return 'val'
+        print("sequence id {} corresponding to no split".format(seq_id))
+        raise NotImplementedError
+
+    def _collect_basic_infos(self):
+        self.train_info = defaultdict(dict)
+        if self.train_split_list is not None:
+            for train_seq in self.train_split_list:
+                anno_file_path = osp.join(self.data_root, train_seq, '{}.json'.format(train_seq))
+                if not osp.isfile(anno_file_path):
+                    print("no annotation file for sequence {}".format(train_seq))
+                    raise FileNotFoundError
+                anno_file = json.load(open(anno_file_path, 'r'))
+                for frame_anno in anno_file['frames']:
+                    self.train_info[train_seq][frame_anno['frame_id']] = {
+                        'pose': frame_anno['pose'],
+                        'calib': anno_file['calib'],
+                    }
+
+    def get_frame_anno(self, seq_id, frame_id):
+        split_name = self._find_split_name(seq_id)
+        frame_info = getattr(self, '{}_info'.format(split_name))[seq_id][frame_id]
+        if 'anno' in frame_info:
+            return frame_info['anno']
+        return None
+
+    def load_point_cloud(self, seq_id, frame_id):
+        bin_path = osp.join(self.data_root, seq_id, 'lidar_roof', '{}.bin'.format(frame_id))
+        points = np.fromfile(bin_path, dtype=np.float32).reshape(-1, 4)
+        return points
+
+    def load_image(self, seq_id, frame_id, cam_name):
+        cam_path = osp.join(self.data_root, seq_id, cam_name, '{}.jpg'.format(frame_id))
+        img_buf = cv2.cvtColor(cv2.imread(cam_path), cv2.COLOR_BGR2RGB)
+        return img_buf
+
+    def project_lidar_to_image(self, seq_id, frame_id):
+        points = self.load_point_cloud(seq_id, frame_id)
+
+        split_name = self._find_split_name(seq_id)
+        frame_info = getattr(self, '{}_info'.format(split_name))[seq_id][frame_id]
+        points_img_dict = dict()
+        for cam_name in self.__class__.camera_names:
+            calib_info = frame_info['calib'][cam_name]
+            cam_2_velo = calib_info['cam_to_velo']
+            cam_intri = calib_info['cam_intrinsic']
+            point_xyz = points[:, :3]
+            points_homo = np.hstack(
+                [point_xyz, np.ones(point_xyz.shape[0], dtype=np.float32).reshape((-1, 1))])
+            points_lidar = np.dot(points_homo, np.linalg.inv(cam_2_velo).T)
+            mask = points_lidar[:, 2] > 0
+            points_lidar = points_lidar[mask]
+            points_img = np.dot(points_lidar, cam_intri.T)
+            points_img_dict[cam_name] = points_img
+        return points_img_dict
+
+    def undistort_image(self, seq_id, frame_id):
+        pass
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/pandaset/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/pandaset/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/pandaset/pandaset_dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/pandaset/pandaset_dataset.py
new file mode 100644
index 000000000..b3c86d51e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/pandaset/pandaset_dataset.py
@@ -0,0 +1,489 @@
+"""
+    Dataset from Pandaset (Hesai)
+"""
+
+import pickle
+import os
+try:
+    import pandas as pd
+    import pandaset as ps
+except:
+    pass 
+import numpy as np
+
+from ..dataset import DatasetTemplate
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+
+import torch
+
+
+def pose_dict_to_numpy(pose):
+    """
+        Conert pandaset pose dict to a numpy vector in order to pass it through the network
+    """
+    pose_np = [pose["position"]["x"],
+               pose["position"]["y"],
+               pose["position"]["z"],
+               pose["heading"]["w"],
+               pose["heading"]["x"],
+               pose["heading"]["y"],
+               pose["heading"]["z"]]
+
+    return pose_np
+
+
+def pose_numpy_to_dict(pose):
+    """
+        Conert pandaset pose dict to a numpy vector in order to pass it through the network
+    """
+    pose_dict = {'position':
+                    {'x': pose[0],
+                     'y': pose[1],
+                     'z': pose[2]},
+                 'heading':
+                    {'w': pose[3],
+                     'x': pose[4],
+                     'y': pose[5],
+                     'z': pose[6]}}
+
+    return pose_dict
+
+
+class PandasetDataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
+        """
+        Args:
+            root_path:
+            dataset_cfg:
+            class_names:
+            training:
+            logger:
+        """
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
+        )
+        if root_path is None:
+            root_path = self.dataset_cfg.DATA_PATH
+        self.dataset = ps.DataSet(os.path.join(root_path, 'dataset'))
+        self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
+        self.pandaset_infos = []
+        self.include_pandaset_infos(self.mode)
+
+
+    def include_pandaset_infos(self, mode):
+        if self.logger is not None:
+            self.logger.info('Loading PandaSet dataset')
+        pandaset_infos = []
+
+        for info_path in self.dataset_cfg.INFO_PATH[mode]:
+            info_path = os.path.join(self.root_path, info_path)
+            if not os.path.exists(info_path):
+                continue
+            with open(info_path, 'rb') as f:
+                infos = pickle.load(f)
+                pandaset_infos.extend(infos)
+
+        self.pandaset_infos.extend(pandaset_infos)
+
+        if self.logger is not None:
+            self.logger.info('Total samples for PandaSet dataset ({}): {}'.format(self.mode, len(pandaset_infos)))
+
+
+    def set_split(self, split):
+        self.sequences = self.dataset_cfg.SEQUENCES[split]
+        self.split = split
+
+
+    def __len__(self):
+        return len(self.pandaset_infos)
+
+
+    def __getitem__(self, index):
+        """
+        To support a custom dataset, implement this function to load the raw data (and labels), then transform them to
+        the unified normative coordinate (x pointing forward, z pointing upwards) and call the function self.prepare_data() to process the data and send them
+        to the model.
+
+        Args:
+            index:
+
+        Returns:
+
+        """
+        info = self.pandaset_infos[index]
+        seq_idx = info['sequence']
+
+        pose = self._get_pose(info)
+        points = self._get_lidar_points(info, pose)
+        boxes, labels, zrot_world_to_ego = self._get_annotations(info, pose)
+        pose_np = pose_dict_to_numpy(pose)
+
+        input_dict = {'points': points,
+                      'gt_boxes': boxes,
+                      'gt_names': labels,
+                      'sequence': int(seq_idx),
+                      'frame_idx': info['frame_idx'],
+                      'zrot_world_to_ego': zrot_world_to_ego,
+                      'pose': pose_dict_to_numpy(pose)
+                     }
+        # seq_idx is converted to int because strings can't be passed to
+        # the gpu in pytorch
+        # zrot_world_to_ego is propagated in order to be able to transform the
+        # predicted yaws back to world coordinates
+
+        data_dict = self.prepare_data(data_dict=input_dict)
+
+        return data_dict
+
+
+    def _get_pose(self, info):
+        seq_idx = info['sequence']
+        # get pose for world to ego frame transformation
+        if self.dataset[seq_idx].lidar.poses is None:
+            self.dataset[seq_idx].lidar._load_poses()
+
+        pose = self.dataset[seq_idx].lidar.poses[info['frame_idx']]
+
+        return pose
+
+
+    def _get_lidar_points(self, info, pose):
+        """
+        Get lidar in the unified normative coordinate system for a given frame
+        The intensity is normalized to fit [0-1] range (pandaset intensity is in [0-255] range)
+        """
+        # get lidar points
+        lidar_frame = pd.read_pickle(info['lidar_path'])
+        # get points for the required lidar(s) only
+        device = self.dataset_cfg.get('LIDAR_DEVICE', 0)
+        if device != -1:
+            lidar_frame = lidar_frame[lidar_frame.d == device]
+        world_points = lidar_frame.to_numpy()
+        # There seems to be issues with the automatic deletion of pandas datasets sometimes
+        del lidar_frame
+
+        points_loc = world_points[:, :3]
+        points_int = world_points[:, 3]
+
+        # nromalize intensity
+        points_int = points_int / 255
+
+        ego_points = ps.geometry.lidar_points_to_ego(points_loc, pose)
+        # Pandaset ego coordinates are:
+        # - x pointing to the right
+        # - y pointing to the front
+        # - z pointing up
+        # Normative coordinates are:
+        # - x pointing foreward
+        # - y pointings to the left
+        # - z pointing to the top
+        # So a transformation is required to the match the normative coordinates
+        ego_points = ego_points[:, [1, 0, 2]] # switch x and y
+        ego_points[:, 1] = - ego_points[:, 1] # revert y axis
+
+        return np.append(ego_points, np.expand_dims(points_int, axis=1), axis=1).astype(np.float32)
+
+
+    def _get_annotations(self,info, pose):
+        """
+        Get box informations in the unified normative coordinate system for a given frame
+        """
+
+        # get boxes
+        cuboids = pd.read_pickle(info["cuboids_path"])
+        device = self.dataset_cfg.get('LIDAR_DEVICE', 0)
+        if device != -1:
+            # keep cuboids that are seen by a given device
+            cuboids = cuboids[cuboids["cuboids.sensor_id"] != 1 - device]
+
+        xs = cuboids['position.x'].to_numpy()
+        ys = cuboids['position.y'].to_numpy()
+        zs = cuboids['position.z'].to_numpy()
+        dxs = cuboids['dimensions.x'].to_numpy()
+        dys = cuboids['dimensions.y'].to_numpy()
+        dzs = cuboids['dimensions.z'].to_numpy()
+        yaws = cuboids['yaw'].to_numpy()
+        labels = cuboids['label'].to_numpy()
+
+        del cuboids  # There seem to be issues with the automatic deletion of pandas datasets sometimes
+
+        labels = np.array([self.dataset_cfg.TRAINING_CATEGORIES.get(lab, lab)
+                           for lab in labels] )
+
+        # Compute the center points coordinates in ego coordinates
+        centers = np.vstack([xs, ys, zs]).T
+        ego_centers = ps.geometry.lidar_points_to_ego(centers, pose)
+
+        # Compute the yaw in ego coordinates
+        # The following implementation supposes that the pitch of the car is
+        # negligible compared to its yaw, in order to be able to express the
+        # bbox coordinates in the ego coordinate system with an {axis aligned
+        # box + yaw} only representation
+        yaxis_points_from_pose = ps.geometry.lidar_points_to_ego(np.array([[0, 0, 0], [0, 1., 0]]), pose)
+        yaxis_from_pose = yaxis_points_from_pose[1, :] - yaxis_points_from_pose[0, :]
+
+        if yaxis_from_pose[-1] >= 10**-1:
+            if self.logger is not None:
+                self.logger.warning("The car's pitch is supposed to be negligible " +
+                                    "sin(pitch) is >= 10**-1 ({})".format(yaxis_from_pose[-1]))
+
+        # rotation angle in rads of the y axis around thz z axis
+        zrot_world_to_ego = np.arctan2(-yaxis_from_pose[0], yaxis_from_pose[1])
+        ego_yaws = yaws + zrot_world_to_ego
+
+        # Pandaset ego coordinates are:
+        # - x pointing to the right
+        # - y pointing to the front
+        # - z pointing up
+        # Normative coordinates are:
+        # - x pointing foreward
+        # - y pointings to the left
+        # - z pointing to the top
+        # So a transformation is required to the match the normative coordinates
+        ego_xs = ego_centers[:, 1]
+        ego_ys = -ego_centers[:, 0]
+        ego_zs = ego_centers[:, 2]
+        ego_dxs = dys
+        ego_dys = dxs  # stays >= 0
+        ego_dzs = dzs
+
+        ego_boxes = np.vstack([ego_xs, ego_ys, ego_zs, ego_dxs, ego_dys, ego_dzs, ego_yaws]).T
+
+        return ego_boxes.astype(np.float32), labels, zrot_world_to_ego
+
+
+    @staticmethod
+    def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
+        """
+        To support a custom dataset, implement this function to receive the predicted results from the model, and then
+        transform the unified normative coordinate to your required coordinate, and optionally save them to disk.
+
+        Args:
+            batch_dict: dict of original data from the dataloader
+            pred_dicts: dict of predicted results from the model
+                pred_boxes: (N, 7), Tensor
+                pred_scores: (N), Tensor
+                pred_labels: (N), Tensor
+            class_names:
+            output_path: if it is not None, save the results to this path
+        Returns:
+
+        """
+
+        def generate_single_sample_dataframe(batch_index, box_dict, zrot_world_to_ego, pose):
+            pred_boxes = box_dict["pred_boxes"].cpu().numpy()
+            pred_scores = box_dict["pred_scores"].cpu().numpy()
+            pred_labels = box_dict["pred_labels"].cpu().numpy()
+            zrot = zrot_world_to_ego.cpu().numpy()
+            pose_dict = pose_numpy_to_dict(pose.cpu().numpy())
+
+            xs = pred_boxes[:, 0]
+            ys = pred_boxes[:, 1]
+            zs = pred_boxes[:, 2]
+            dxs = pred_boxes[:, 3]
+            dys = pred_boxes[:, 4]
+            dzs = pred_boxes[:, 5]
+            yaws = pred_boxes[:, 6]
+            names = np.array(class_names)[pred_labels - 1]  # Predicted labels start on 1
+
+            # convert from normative coordinates to pandaset ego coordinates
+            ego_xs = - ys
+            ego_ys = xs
+            ego_zs = zs
+            ego_dxs = dys
+            ego_dys = dxs
+            ego_dzs = dzs
+            ego_yaws = yaws
+
+            # convert from pandaset ego coordinates to world coordinates
+            # for the moment, an simplified estimation of the ego yaw is computed in __getitem__
+            # which sets ego_yaw = world_yaw + zrot_world_to_ego
+            world_yaws = ego_yaws - zrot
+
+            ego_centers = np.vstack([ego_xs, ego_ys, ego_zs]).T
+            world_centers = ps.geometry.ego_to_lidar_points(ego_centers, pose_dict)
+            world_xs = world_centers[:, 0]
+            world_ys = world_centers[:, 1]
+            world_zs = world_centers[:, 2]
+            # dx, dy, dz remain unchanged as the bbox orientation is handled by
+            # the yaw information
+
+            data_dict = {'position.x': world_xs,
+                         'position.y': world_ys,
+                         'position.z': world_zs,
+                         'dimensions.x': ego_dxs,
+                         'dimensions.y': ego_dys,
+                         'dimensions.z': ego_dzs,
+                         'yaw': world_yaws % (2 * np.pi),
+                         'label': names,
+                         'score': pred_scores
+            }
+
+            return pd.DataFrame(data_dict)
+
+
+        annos = []
+        for index, box_dict in enumerate(pred_dicts):
+            frame_idx = batch_dict['frame_idx'][index]
+            seq_idx = batch_dict['sequence'][index]
+            zrot = batch_dict['zrot_world_to_ego'][index]
+            pose = batch_dict['pose'][index]
+
+            single_pred_df = generate_single_sample_dataframe(index, box_dict, zrot, pose)
+
+
+            single_pred_dict = {'preds' : single_pred_df,
+                                # 'name 'ensures testing the number of detections in a compatible format as kitti
+                                'name' : single_pred_df['label'].tolist(),
+                                'frame_idx': frame_idx,
+                                'sequence': str(seq_idx).zfill(3)}
+            # seq_idx was converted to int in self.__getitem__` because strings
+            # can't be passed to the gpu in pytorch.
+            # To convert it back to a string, we assume that the sequence is
+            # provided in pandaset format with 3 digits
+
+            if output_path is not None:
+                frame_id = str(int(frame_idx)).zfill(2)
+                seq_id = str(int(seq_idx)).zfill(3)
+                cur_det_file = os.path.join(output_path, seq_id, 'predictions',
+                                            'cuboids', ("{}.pkl.gz".format(frame_id)))
+                os.makedirs(os.path.dirname(cur_det_file), exist_ok=True)
+                single_pred_df.to_pickle(cur_det_file)
+
+            annos.append(single_pred_dict)
+
+        return annos
+
+
+    def get_infos(self):
+        """
+        Generate the dataset infos dict for each sample of the dataset.
+        For each sample, this dict contains:
+            - the sequence index
+            - the frame index
+            - the path to the lidar data
+            - the path to the bounding box annotations
+        """
+        infos = []
+        for seq in self.sequences:
+            s = self.dataset[seq]
+            s.load_lidar()
+            if len(s.lidar.data) > 100:
+                raise ValueError("The implementation for this dataset assumes that each sequence is " +
+                                 "no longer than 100 frames. The current sequence has {}".format(len(s.lidar.data)))
+            info = [{'sequence': seq,
+                     'frame_idx': ii,
+                     'lidar_path': os.path.join(self.root_path, 'dataset', seq, 'lidar', ("{:02d}.pkl.gz".format(ii))),
+                     'cuboids_path': os.path.join(self.root_path, 'dataset', seq,
+                                                  'annotations', 'cuboids', ("{:02d}.pkl.gz".format(ii)))
+                    } for ii in range(len(s.lidar.data))]
+            infos.extend(info)
+            del self.dataset._sequences[seq]
+
+        return infos
+
+
+    def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'):
+        database_save_path = os.path.join(self.root_path,
+                'gt_database' if split == 'train' else 'gt_database_{}'.format(split))
+        db_info_save_path = os.path.join(self.root_path,
+                'pandaset_dbinfos_{}.pkl'.format(split))
+
+        os.makedirs(database_save_path, exist_ok=True)
+        all_db_infos = {}
+
+        with open(info_path, 'rb') as f:
+            infos = pickle.load(f)
+
+        for k in range(len(infos)):
+            print('gt_database sample: %d/%d' % (k + 1, len(infos)))
+            info = infos[k]
+            sample_idx = info['frame_idx']
+            pose = self._get_pose(info)
+            points = self._get_lidar_points(info, pose)
+            gt_boxes, names, _ = self._get_annotations(info, pose)
+
+            num_obj = gt_boxes.shape[0]
+
+            point_indices = roiaware_pool3d_utils.points_in_boxes_cpu(
+                torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes)
+            ).numpy()  # (nboxes, npoints)
+
+            for i in range(num_obj):
+                tmp_name = names[i].replace("/", "").replace(" ", "")
+                filename = '%s_%s_%d.bin' % (sample_idx, tmp_name, i)
+                filepath = os.path.join(database_save_path, filename)
+                gt_points = points[point_indices[i] > 0]
+                gt_points[:, :3] -= gt_boxes[i, :3]
+                with open(filepath, 'wb') as f:
+                    gt_points.tofile(f)
+
+                if (used_classes is None) or names[i] in used_classes:
+                    db_path = os.path.relpath(filepath, self.root_path)  # gt_database/xxxxx.bin
+                    db_info = {'name': names[i], 'path': db_path, 'gt_idx': i,
+                               'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0],
+                               'difficulty': -1}
+                    if names[i] in all_db_infos:
+                        all_db_infos[names[i]].append(db_info)
+                    else:
+                        all_db_infos[names[i]] = [db_info]
+        for k, v in all_db_infos.items():
+            print('Database %s: %d' % (k, len(v)))
+
+        with open(db_info_save_path, 'wb') as f:
+            pickle.dump(all_db_infos, f)
+
+
+    def evaluation(self, det_annos, class_names, **kwargs):
+        self.logger.warning('Evaluation is not implemented for Pandaset as there is no official one. ' +
+                            'Returning an empty evaluation result.')
+        ap_result_str = ''
+        ap_dict = {}
+
+        return ap_result_str, ap_dict
+
+
+def create_pandaset_infos(dataset_cfg, class_names, data_path, save_path):
+    """
+    Create dataset_infos files in order not to have it in a preprocessed pickle
+    file with the info for each sample
+    See PandasetDataset.get_infos for further details.
+    """
+    dataset = PandasetDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
+    for split in ["train", "val", "test"]:
+        print("---------------- Start to generate {} data infos ---------------".format(split))
+        dataset.set_split(split)
+        infos = dataset.get_infos()
+        file_path = os.path.join(save_path, 'pandaset_infos_{}.pkl'.format(split))
+        with open(file_path, 'wb') as f:
+            pickle.dump(infos, f)
+        print("Pandaset info {} file is saved to {}".format(split, file_path))
+
+    print('------------Start create groundtruth database for data augmentation-----------')
+    dataset = PandasetDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
+    dataset.set_split("train")
+    dataset.create_groundtruth_database(
+        os.path.join(save_path, 'pandaset_infos_train.pkl'),
+        split="train"
+    )
+    print('---------------Data preparation Done---------------')
+
+
+if __name__ == '__main__':
+    import sys
+    if sys.argv.__len__() > 1 and sys.argv[1] == 'create_pandaset_infos':
+        import yaml
+        from pathlib import Path
+        from easydict import EasyDict
+        dataset_cfg = EasyDict(yaml.safe_load(open(sys.argv[2])))
+        ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
+        create_pandaset_infos(
+            dataset_cfg=dataset_cfg,
+            class_names=['Car', 'Pedestrian', 'Cyclist'],
+            data_path=ROOT_DIR / 'data' / 'pandaset',
+            save_path=ROOT_DIR / 'data' / 'pandaset'
+        )
+
+
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/data_processor.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/data_processor.py
new file mode 100644
index 000000000..4f72ab532
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/data_processor.py
@@ -0,0 +1,298 @@
+from functools import partial
+
+import numpy as np
+from skimage import transform
+import torch
+import torchvision
+from ...utils import box_utils, common_utils
+
+tv = None
+try:
+    import cumm.tensorview as tv
+except:
+    pass
+
+
+class VoxelGeneratorWrapper():
+    def __init__(self, vsize_xyz, coors_range_xyz, num_point_features, max_num_points_per_voxel, max_num_voxels):
+        try:
+            from spconv.utils import VoxelGeneratorV2 as VoxelGenerator
+            self.spconv_ver = 1
+        except:
+            try:
+                from spconv.utils import VoxelGenerator
+                self.spconv_ver = 1
+            except:
+                from spconv.utils import Point2VoxelCPU3d as VoxelGenerator
+                self.spconv_ver = 2
+
+        if self.spconv_ver == 1:
+            self._voxel_generator = VoxelGenerator(
+                voxel_size=vsize_xyz,
+                point_cloud_range=coors_range_xyz,
+                max_num_points=max_num_points_per_voxel,
+                max_voxels=max_num_voxels
+            )
+        else:
+            self._voxel_generator = VoxelGenerator(
+                vsize_xyz=vsize_xyz,
+                coors_range_xyz=coors_range_xyz,
+                num_point_features=num_point_features,
+                max_num_points_per_voxel=max_num_points_per_voxel,
+                max_num_voxels=max_num_voxels
+            )
+
+    def generate(self, points):
+        if self.spconv_ver == 1:
+            voxel_output = self._voxel_generator.generate(points)
+            if isinstance(voxel_output, dict):
+                voxels, coordinates, num_points = \
+                    voxel_output['voxels'], voxel_output['coordinates'], voxel_output['num_points_per_voxel']
+            else:
+                voxels, coordinates, num_points = voxel_output
+        else:
+            assert tv is not None, f"Unexpected error, library: 'cumm' wasn't imported properly."
+            voxel_output = self._voxel_generator.point_to_voxel(tv.from_numpy(points))
+            tv_voxels, tv_coordinates, tv_num_points = voxel_output
+            # make copy with numpy(), since numpy_view() will disappear as soon as the generator is deleted
+            voxels = tv_voxels.numpy()
+            coordinates = tv_coordinates.numpy()
+            num_points = tv_num_points.numpy()
+        return voxels, coordinates, num_points
+
+
+class DataProcessor(object):
+    def __init__(self, processor_configs, point_cloud_range, training, num_point_features):
+        self.point_cloud_range = point_cloud_range
+        self.training = training
+        self.num_point_features = num_point_features
+        self.mode = 'train' if training else 'test'
+        self.grid_size = self.voxel_size = None
+        self.data_processor_queue = []
+
+        self.voxel_generator = None
+
+        for cur_cfg in processor_configs:
+            cur_processor = getattr(self, cur_cfg.NAME)(config=cur_cfg)
+            self.data_processor_queue.append(cur_processor)
+
+    def mask_points_and_boxes_outside_range(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.mask_points_and_boxes_outside_range, config=config)
+
+        if data_dict.get('points', None) is not None:
+            mask = common_utils.mask_points_by_range(data_dict['points'], self.point_cloud_range)
+            data_dict['points'] = data_dict['points'][mask]
+
+        if data_dict.get('gt_boxes', None) is not None and config.REMOVE_OUTSIDE_BOXES and self.training:
+            mask = box_utils.mask_boxes_outside_range_numpy(
+                data_dict['gt_boxes'], self.point_cloud_range, min_num_corners=config.get('min_num_corners', 1), 
+                use_center_to_filter=config.get('USE_CENTER_TO_FILTER', True)
+            )
+            data_dict['gt_boxes'] = data_dict['gt_boxes'][mask]
+        return data_dict
+
+    def shuffle_points(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.shuffle_points, config=config)
+
+        if config.SHUFFLE_ENABLED[self.mode]:
+            points = data_dict['points']
+            shuffle_idx = np.random.permutation(points.shape[0])
+            points = points[shuffle_idx]
+            data_dict['points'] = points
+
+        return data_dict
+
+    def transform_points_to_voxels_placeholder(self, data_dict=None, config=None):
+        # just calculate grid size
+        if data_dict is None:
+            grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)
+            self.grid_size = np.round(grid_size).astype(np.int64)
+            self.voxel_size = config.VOXEL_SIZE
+            return partial(self.transform_points_to_voxels_placeholder, config=config)
+        
+        return data_dict
+
+    def double_flip(self, points):
+        # y flip
+        points_yflip = points.copy()
+        points_yflip[:, 1] = -points_yflip[:, 1]
+
+        # x flip
+        points_xflip = points.copy()
+        points_xflip[:, 0] = -points_xflip[:, 0]
+
+        # x y flip
+        points_xyflip = points.copy()
+        points_xyflip[:, 0] = -points_xyflip[:, 0]
+        points_xyflip[:, 1] = -points_xyflip[:, 1]
+
+        return points_yflip, points_xflip, points_xyflip
+
+    def transform_points_to_voxels(self, data_dict=None, config=None):
+        if data_dict is None:
+            grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)
+            self.grid_size = np.round(grid_size).astype(np.int64)
+            self.voxel_size = config.VOXEL_SIZE
+            # just bind the config, we will create the VoxelGeneratorWrapper later,
+            # to avoid pickling issues in multiprocess spawn
+            return partial(self.transform_points_to_voxels, config=config)
+
+        if self.voxel_generator is None:
+            self.voxel_generator = VoxelGeneratorWrapper(
+                vsize_xyz=config.VOXEL_SIZE,
+                coors_range_xyz=self.point_cloud_range,
+                num_point_features=self.num_point_features,
+                max_num_points_per_voxel=config.MAX_POINTS_PER_VOXEL,
+                max_num_voxels=config.MAX_NUMBER_OF_VOXELS[self.mode],
+            )
+
+        points = data_dict['points']
+        voxel_output = self.voxel_generator.generate(points)
+        voxels, coordinates, num_points = voxel_output
+
+        if not data_dict['use_lead_xyz']:
+            voxels = voxels[..., 3:]  # remove xyz in voxels(N, 3)
+
+        if config.get('DOUBLE_FLIP', False):
+            voxels_list, voxel_coords_list, voxel_num_points_list = [voxels], [coordinates], [num_points]
+            points_yflip, points_xflip, points_xyflip = self.double_flip(points)
+            points_list = [points_yflip, points_xflip, points_xyflip]
+            keys = ['yflip', 'xflip', 'xyflip']
+            for i, key in enumerate(keys):
+                voxel_output = self.voxel_generator.generate(points_list[i])
+                voxels, coordinates, num_points = voxel_output
+
+                if not data_dict['use_lead_xyz']:
+                    voxels = voxels[..., 3:]
+                voxels_list.append(voxels)
+                voxel_coords_list.append(coordinates)
+                voxel_num_points_list.append(num_points)
+
+            data_dict['voxels'] = voxels_list
+            data_dict['voxel_coords'] = voxel_coords_list
+            data_dict['voxel_num_points'] = voxel_num_points_list
+        else:
+            data_dict['voxels'] = voxels
+            data_dict['voxel_coords'] = coordinates
+            data_dict['voxel_num_points'] = num_points
+        return data_dict
+
+    def sample_points(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.sample_points, config=config)
+
+        num_points = config.NUM_POINTS[self.mode]
+        if num_points == -1:
+            return data_dict
+
+        points = data_dict['points']
+        if num_points < len(points):
+            pts_depth = np.linalg.norm(points[:, 0:3], axis=1)
+            pts_near_flag = pts_depth < 40.0
+            far_idxs_choice = np.where(pts_near_flag == 0)[0]
+            near_idxs = np.where(pts_near_flag == 1)[0]
+            choice = []
+            if num_points > len(far_idxs_choice):
+                near_idxs_choice = np.random.choice(near_idxs, num_points - len(far_idxs_choice), replace=False)
+                choice = np.concatenate((near_idxs_choice, far_idxs_choice), axis=0) \
+                    if len(far_idxs_choice) > 0 else near_idxs_choice
+            else: 
+                choice = np.arange(0, len(points), dtype=np.int32)
+                choice = np.random.choice(choice, num_points, replace=False)
+            np.random.shuffle(choice)
+        else:
+            choice = np.arange(0, len(points), dtype=np.int32)
+            if num_points > len(points):
+                extra_choice = np.random.choice(choice, num_points - len(points), replace=False)
+                choice = np.concatenate((choice, extra_choice), axis=0)
+            np.random.shuffle(choice)
+        data_dict['points'] = points[choice]
+        return data_dict
+
+    def calculate_grid_size(self, data_dict=None, config=None):
+        if data_dict is None:
+            grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)
+            self.grid_size = np.round(grid_size).astype(np.int64)
+            self.voxel_size = config.VOXEL_SIZE
+            return partial(self.calculate_grid_size, config=config)
+        return data_dict
+
+    def downsample_depth_map(self, data_dict=None, config=None):
+        if data_dict is None:
+            self.depth_downsample_factor = config.DOWNSAMPLE_FACTOR
+            return partial(self.downsample_depth_map, config=config)
+
+        data_dict['depth_maps'] = transform.downscale_local_mean(
+            image=data_dict['depth_maps'],
+            factors=(self.depth_downsample_factor, self.depth_downsample_factor)
+        )
+        return data_dict
+    
+    def image_normalize(self, data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.image_normalize, config=config)
+        mean = config.mean
+        std = config.std
+        compose = torchvision.transforms.Compose(
+            [
+                torchvision.transforms.ToTensor(),
+                torchvision.transforms.Normalize(mean=mean, std=std),
+            ]
+        )
+        data_dict["camera_imgs"] = [compose(img) for img in data_dict["camera_imgs"]]
+        return data_dict
+    
+    def image_calibrate(self,data_dict=None, config=None):
+        if data_dict is None:
+            return partial(self.image_calibrate, config=config)
+        img_process_infos = data_dict['img_process_infos']
+        transforms = []
+        for img_process_info in img_process_infos:
+            resize, crop, flip, rotate = img_process_info
+
+            rotation = torch.eye(2)
+            translation = torch.zeros(2)
+            # post-homography transformation
+            rotation *= resize
+            translation -= torch.Tensor(crop[:2])
+            if flip:
+                A = torch.Tensor([[-1, 0], [0, 1]])
+                b = torch.Tensor([crop[2] - crop[0], 0])
+                rotation = A.matmul(rotation)
+                translation = A.matmul(translation) + b
+            theta = rotate / 180 * np.pi
+            A = torch.Tensor(
+                [
+                    [np.cos(theta), np.sin(theta)],
+                    [-np.sin(theta), np.cos(theta)],
+                ]
+            )
+            b = torch.Tensor([crop[2] - crop[0], crop[3] - crop[1]]) / 2
+            b = A.matmul(-b) + b
+            rotation = A.matmul(rotation)
+            translation = A.matmul(translation) + b
+            transform = torch.eye(4)
+            transform[:2, :2] = rotation
+            transform[:2, 3] = translation
+            transforms.append(transform.numpy())
+        data_dict["img_aug_matrix"] = transforms
+        return data_dict
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                points: (N, 3 + C_in)
+                gt_boxes: optional, (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+                gt_names: optional, (N), string
+                ...
+
+        Returns:
+        """
+
+        for cur_processor in self.data_processor_queue:
+            data_dict = cur_processor(data_dict=data_dict)
+
+        return data_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/point_feature_encoder.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/point_feature_encoder.py
new file mode 100644
index 000000000..d22bce90f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/processor/point_feature_encoder.py
@@ -0,0 +1,57 @@
+import numpy as np
+
+
+class PointFeatureEncoder(object):
+    def __init__(self, config, point_cloud_range=None):
+        super().__init__()
+        self.point_encoding_config = config
+        assert list(self.point_encoding_config.src_feature_list[0:3]) == ['x', 'y', 'z']
+        self.used_feature_list = self.point_encoding_config.used_feature_list
+        self.src_feature_list = self.point_encoding_config.src_feature_list
+        self.point_cloud_range = point_cloud_range
+
+    @property
+    def num_point_features(self):
+        return getattr(self, self.point_encoding_config.encoding_type)(points=None)
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                points: (N, 3 + C_in)
+                ...
+        Returns:
+            data_dict:
+                points: (N, 3 + C_out),
+                use_lead_xyz: whether to use xyz as point-wise features
+                ...
+        """
+        data_dict['points'], use_lead_xyz = getattr(self, self.point_encoding_config.encoding_type)(
+            data_dict['points']
+        )
+        data_dict['use_lead_xyz'] = use_lead_xyz
+       
+        if self.point_encoding_config.get('filter_sweeps', False) and 'timestamp' in self.src_feature_list:
+            max_sweeps = self.point_encoding_config.max_sweeps
+            idx = self.src_feature_list.index('timestamp')
+            dt = np.round(data_dict['points'][:, idx], 2)
+            max_dt = sorted(np.unique(dt))[min(len(np.unique(dt))-1, max_sweeps-1)]
+            data_dict['points'] = data_dict['points'][dt <= max_dt]
+        
+        return data_dict
+
+    def absolute_coordinates_encoding(self, points=None):
+        if points is None:
+            num_output_features = len(self.used_feature_list)
+            return num_output_features
+
+        assert points.shape[-1] == len(self.src_feature_list)
+        point_feature_list = [points[:, 0:3]]
+        for x in self.used_feature_list:
+            if x in ['x', 'y', 'z']:
+                continue
+            idx = self.src_feature_list.index(x)
+            point_feature_list.append(points[:, idx:idx+1])
+        point_features = np.concatenate(point_feature_list, axis=1)
+        
+        return point_features, True
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_dataset.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_dataset.py
new file mode 100644
index 000000000..44632d53f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_dataset.py
@@ -0,0 +1,827 @@
+# OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
+# Reference https://github.com/open-mmlab/OpenPCDet
+# Written by Shaoshuai Shi, Chaoxu Guo
+# All Rights Reserved.
+
+import os
+import pickle
+import copy
+import numpy as np
+import torch
+import multiprocessing
+import SharedArray
+import torch.distributed as dist
+from tqdm import tqdm
+from pathlib import Path
+from functools import partial
+
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+from ...utils import box_utils, common_utils
+from ..dataset import DatasetTemplate
+
+
+class WaymoDataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
+        )
+        self.data_path = self.root_path / self.dataset_cfg.PROCESSED_DATA_TAG
+        self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_sequence_list = [x.strip() for x in open(split_dir).readlines()]
+
+        self.infos = []
+        self.seq_name_to_infos = self.include_waymo_data(self.mode)
+
+        self.use_shared_memory = self.dataset_cfg.get('USE_SHARED_MEMORY', False) and self.training
+        if self.use_shared_memory:
+            self.shared_memory_file_limit = self.dataset_cfg.get('SHARED_MEMORY_FILE_LIMIT', 0x7FFFFFFF)
+            self.load_data_to_shared_memory()
+
+        if self.dataset_cfg.get('USE_PREDBOX', False):
+            self.pred_boxes_dict = self.load_pred_boxes_to_dict(
+                pred_boxes_path=self.dataset_cfg.ROI_BOXES_PATH[self.mode]
+            )
+        else:
+            self.pred_boxes_dict = {}
+
+    def set_split(self, split):
+        super().__init__(
+            dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training,
+            root_path=self.root_path, logger=self.logger
+        )
+        self.split = split
+        split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
+        self.sample_sequence_list = [x.strip() for x in open(split_dir).readlines()]
+        self.infos = []
+        self.seq_name_to_infos = self.include_waymo_data(self.mode)
+
+    def include_waymo_data(self, mode):
+        self.logger.info('Loading Waymo dataset')
+        waymo_infos = []
+        seq_name_to_infos = {}
+
+        num_skipped_infos = 0
+        for k in range(len(self.sample_sequence_list)):
+            sequence_name = os.path.splitext(self.sample_sequence_list[k])[0]
+            info_path = self.data_path / sequence_name / ('%s.pkl' % sequence_name)
+            info_path = self.check_sequence_name_with_all_version(info_path)
+            if not info_path.exists():
+                num_skipped_infos += 1
+                continue
+            with open(info_path, 'rb') as f:
+                infos = pickle.load(f)
+                waymo_infos.extend(infos)
+
+            seq_name_to_infos[infos[0]['point_cloud']['lidar_sequence']] = infos
+
+        self.infos.extend(waymo_infos[:])
+        self.logger.info('Total skipped info %s' % num_skipped_infos)
+        self.logger.info('Total samples for Waymo dataset: %d' % (len(waymo_infos)))
+
+        if self.dataset_cfg.SAMPLED_INTERVAL[mode] > 1:
+            sampled_waymo_infos = []
+            for k in range(0, len(self.infos), self.dataset_cfg.SAMPLED_INTERVAL[mode]):
+                sampled_waymo_infos.append(self.infos[k])
+            self.infos = sampled_waymo_infos
+            self.logger.info('Total sampled samples for Waymo dataset: %d' % len(self.infos))
+            
+        use_sequence_data = self.dataset_cfg.get('SEQUENCE_CONFIG', None) is not None and self.dataset_cfg.SEQUENCE_CONFIG.ENABLED
+        if not use_sequence_data:
+            seq_name_to_infos = None 
+        return seq_name_to_infos
+
+    def load_pred_boxes_to_dict(self, pred_boxes_path):
+        self.logger.info(f'Loading and reorganizing pred_boxes to dict from path: {pred_boxes_path}')
+        with open(pred_boxes_path, 'rb') as f:
+            pred_dicts = pickle.load(f)
+
+        pred_boxes_dict = {}
+        for index, box_dict in enumerate(pred_dicts):
+            seq_name = box_dict['frame_id'][:-4].replace('training_', '').replace('validation_', '')
+            sample_idx = int(box_dict['frame_id'][-3:])
+
+            if seq_name not in pred_boxes_dict:
+                pred_boxes_dict[seq_name] = {}
+
+            pred_labels = np.array([self.class_names.index(box_dict['name'][k]) + 1 for k in range(box_dict['name'].shape[0])])
+            pred_boxes = np.concatenate((box_dict['boxes_lidar'], box_dict['score'][:, np.newaxis], pred_labels[:, np.newaxis]), axis=-1)
+            pred_boxes_dict[seq_name][sample_idx] = pred_boxes
+
+        self.logger.info(f'Predicted boxes has been loaded, total sequences: {len(pred_boxes_dict)}')
+        return pred_boxes_dict
+
+    def load_data_to_shared_memory(self):
+        self.logger.info(f'Loading training data to shared memory (file limit={self.shared_memory_file_limit})')
+
+        cur_rank, num_gpus = common_utils.get_dist_info()
+        all_infos = self.infos[:self.shared_memory_file_limit] \
+            if self.shared_memory_file_limit < len(self.infos) else self.infos
+        cur_infos = all_infos[cur_rank::num_gpus]
+        for info in cur_infos:
+            pc_info = info['point_cloud']
+            sequence_name = pc_info['lidar_sequence']
+            sample_idx = pc_info['sample_idx']
+
+            sa_key = f'{sequence_name}___{sample_idx}'
+            if os.path.exists(f"/dev/shm/{sa_key}"):
+                continue
+
+            points = self.get_lidar(sequence_name, sample_idx)
+            common_utils.sa_create(f"shm://{sa_key}", points)
+
+        dist.barrier()
+        self.logger.info('Training data has been saved to shared memory')
+
+    def clean_shared_memory(self):
+        self.logger.info(f'Clean training data from shared memory (file limit={self.shared_memory_file_limit})')
+
+        cur_rank, num_gpus = common_utils.get_dist_info()
+        all_infos = self.infos[:self.shared_memory_file_limit] \
+            if self.shared_memory_file_limit < len(self.infos) else self.infos
+        cur_infos = all_infos[cur_rank::num_gpus]
+        for info in cur_infos:
+            pc_info = info['point_cloud']
+            sequence_name = pc_info['lidar_sequence']
+            sample_idx = pc_info['sample_idx']
+
+            sa_key = f'{sequence_name}___{sample_idx}'
+            if not os.path.exists(f"/dev/shm/{sa_key}"):
+                continue
+
+            SharedArray.delete(f"shm://{sa_key}")
+
+        if num_gpus > 1:
+            dist.barrier()
+        self.logger.info('Training data has been deleted from shared memory')
+
+    @staticmethod
+    def check_sequence_name_with_all_version(sequence_file):
+        if not sequence_file.exists():
+            found_sequence_file = sequence_file
+            for pre_text in ['training', 'validation', 'testing']:
+                if not sequence_file.exists():
+                    temp_sequence_file = Path(str(sequence_file).replace('segment', pre_text + '_segment'))
+                    if temp_sequence_file.exists():
+                        found_sequence_file = temp_sequence_file
+                        break
+            if not found_sequence_file.exists():
+                found_sequence_file = Path(str(sequence_file).replace('_with_camera_labels', ''))
+            if found_sequence_file.exists():
+                sequence_file = found_sequence_file
+        return sequence_file
+
+    def get_infos(self, raw_data_path, save_path, num_workers=multiprocessing.cpu_count(), has_label=True, sampled_interval=1, update_info_only=False):
+        from . import waymo_utils
+        print('---------------The waymo sample interval is %d, total sequecnes is %d-----------------'
+              % (sampled_interval, len(self.sample_sequence_list)))
+
+        process_single_sequence = partial(
+            waymo_utils.process_single_sequence,
+            save_path=save_path, sampled_interval=sampled_interval, has_label=has_label, update_info_only=update_info_only
+        )
+        sample_sequence_file_list = [
+            self.check_sequence_name_with_all_version(raw_data_path / sequence_file)
+            for sequence_file in self.sample_sequence_list
+        ]
+
+        # process_single_sequence(sample_sequence_file_list[0])
+        with multiprocessing.Pool(num_workers) as p:
+            sequence_infos = list(tqdm(p.imap(process_single_sequence, sample_sequence_file_list),
+                                       total=len(sample_sequence_file_list)))
+
+        all_sequences_infos = [item for infos in sequence_infos for item in infos]
+        return all_sequences_infos
+
+    def get_lidar(self, sequence_name, sample_idx):
+        lidar_file = self.data_path / sequence_name / ('%04d.npy' % sample_idx)
+        point_features = np.load(lidar_file)  # (N, 7): [x, y, z, intensity, elongation, NLZ_flag]
+
+        points_all, NLZ_flag = point_features[:, 0:5], point_features[:, 5]
+        if not self.dataset_cfg.get('DISABLE_NLZ_FLAG_ON_POINTS', False):
+            points_all = points_all[NLZ_flag == -1]
+        if self.dataset_cfg.get('POINTS_TANH_DIM', None) is None:
+            points_all[:, 3] = np.tanh(points_all[:, 3])
+        else:
+            for dim_idx in self.dataset_cfg.POINTS_TANH_DIM:
+                points_all[:, dim_idx] = np.tanh(points_all[:, dim_idx])
+        return points_all
+
+    @staticmethod
+    def transform_prebox_to_current(pred_boxes3d, pose_pre, pose_cur):
+        """
+
+        Args:
+            pred_boxes3d (N, 9 or 11): [x, y, z, dx, dy, dz, raw, <vx, vy,> score, label]
+            pose_pre (4, 4):
+            pose_cur (4, 4):
+        Returns:
+
+        """
+        assert pred_boxes3d.shape[-1] in [9, 11]
+        pred_boxes3d = pred_boxes3d.copy()
+        expand_bboxes = np.concatenate([pred_boxes3d[:, :3], np.ones((pred_boxes3d.shape[0], 1))], axis=-1)
+
+        bboxes_global = np.dot(expand_bboxes, pose_pre.T)[:, :3]
+        expand_bboxes_global = np.concatenate([bboxes_global[:, :3],np.ones((bboxes_global.shape[0], 1))], axis=-1)
+        bboxes_pre2cur = np.dot(expand_bboxes_global, np.linalg.inv(pose_cur.T))[:, :3]
+        pred_boxes3d[:, 0:3] = bboxes_pre2cur
+
+        if pred_boxes3d.shape[-1] == 11:
+            expand_vels = np.concatenate([pred_boxes3d[:, 7:9], np.zeros((pred_boxes3d.shape[0], 1))], axis=-1)
+            vels_global = np.dot(expand_vels, pose_pre[:3, :3].T)
+            vels_pre2cur = np.dot(vels_global, np.linalg.inv(pose_cur[:3, :3].T))[:,:2]
+            pred_boxes3d[:, 7:9] = vels_pre2cur
+
+        pred_boxes3d[:, 6]  = pred_boxes3d[..., 6] + np.arctan2(pose_pre[..., 1, 0], pose_pre[..., 0, 0])
+        pred_boxes3d[:, 6]  = pred_boxes3d[..., 6] - np.arctan2(pose_cur[..., 1, 0], pose_cur[..., 0, 0])
+        return pred_boxes3d
+
+    @staticmethod
+    def reorder_rois_for_refining(pred_bboxes):
+        num_max_rois = max([len(bbox) for bbox in pred_bboxes])
+        num_max_rois = max(1, num_max_rois)  # at least one faked rois to avoid error
+        ordered_bboxes = np.zeros([len(pred_bboxes), num_max_rois, pred_bboxes[0].shape[-1]], dtype=np.float32)
+
+        for bs_idx in range(ordered_bboxes.shape[0]):
+            ordered_bboxes[bs_idx, :len(pred_bboxes[bs_idx])] = pred_bboxes[bs_idx]
+        return ordered_bboxes
+
+    def get_sequence_data(self, info, points, sequence_name, sample_idx, sequence_cfg, load_pred_boxes=False):
+        """
+        Args:
+            info:
+            points:
+            sequence_name:
+            sample_idx:
+            sequence_cfg:
+        Returns:
+        """
+
+        def remove_ego_points(points, center_radius=1.0):
+            mask = ~((np.abs(points[:, 0]) < center_radius) & (np.abs(points[:, 1]) < center_radius))
+            return points[mask]
+
+        def load_pred_boxes_from_dict(sequence_name, sample_idx):
+            """
+            boxes: (N, 11)  [x, y, z, dx, dy, dn, raw, vx, vy, score, label]
+            """
+            sequence_name = sequence_name.replace('training_', '').replace('validation_', '')
+            load_boxes = self.pred_boxes_dict[sequence_name][sample_idx]
+            assert load_boxes.shape[-1] == 11
+            load_boxes[:, 7:9] = -0.1 * load_boxes[:, 7:9]  # transfer speed to negtive motion from t to t-1
+            return load_boxes
+
+        pose_cur = info['pose'].reshape((4, 4))
+        num_pts_cur = points.shape[0]
+        sample_idx_pre_list = np.clip(sample_idx + np.arange(sequence_cfg.SAMPLE_OFFSET[0], sequence_cfg.SAMPLE_OFFSET[1]), 0, 0x7FFFFFFF)
+        sample_idx_pre_list = sample_idx_pre_list[::-1]
+
+        if sequence_cfg.get('ONEHOT_TIMESTAMP', False):
+            onehot_cur = np.zeros((points.shape[0], len(sample_idx_pre_list) + 1)).astype(points.dtype)
+            onehot_cur[:, 0] = 1
+            points = np.hstack([points, onehot_cur])
+        else:
+            points = np.hstack([points, np.zeros((points.shape[0], 1)).astype(points.dtype)])
+        points_pre_all = []
+        num_points_pre = []
+
+        pose_all = [pose_cur]
+        pred_boxes_all = []
+        if load_pred_boxes:
+            pred_boxes = load_pred_boxes_from_dict(sequence_name, sample_idx)
+            pred_boxes_all.append(pred_boxes)
+
+        sequence_info = self.seq_name_to_infos[sequence_name]
+
+        for idx, sample_idx_pre in enumerate(sample_idx_pre_list):
+
+            points_pre = self.get_lidar(sequence_name, sample_idx_pre)
+            pose_pre = sequence_info[sample_idx_pre]['pose'].reshape((4, 4))
+            expand_points_pre = np.concatenate([points_pre[:, :3], np.ones((points_pre.shape[0], 1))], axis=-1)
+            points_pre_global = np.dot(expand_points_pre, pose_pre.T)[:, :3]
+            expand_points_pre_global = np.concatenate([points_pre_global, np.ones((points_pre_global.shape[0], 1))], axis=-1)
+            points_pre2cur = np.dot(expand_points_pre_global, np.linalg.inv(pose_cur.T))[:, :3]
+            points_pre = np.concatenate([points_pre2cur, points_pre[:, 3:]], axis=-1)
+            if sequence_cfg.get('ONEHOT_TIMESTAMP', False):
+                onehot_vector = np.zeros((points_pre.shape[0], len(sample_idx_pre_list) + 1))
+                onehot_vector[:, idx + 1] = 1
+                points_pre = np.hstack([points_pre, onehot_vector])
+            else:
+                # add timestamp
+                points_pre = np.hstack([points_pre, 0.1 * (sample_idx - sample_idx_pre) * np.ones((points_pre.shape[0], 1)).astype(points_pre.dtype)])  # one frame 0.1s
+            points_pre = remove_ego_points(points_pre, 1.0)
+            points_pre_all.append(points_pre)
+            num_points_pre.append(points_pre.shape[0])
+            pose_all.append(pose_pre)
+
+            if load_pred_boxes:
+                pose_pre = sequence_info[sample_idx_pre]['pose'].reshape((4, 4))
+                pred_boxes = load_pred_boxes_from_dict(sequence_name, sample_idx_pre)
+                pred_boxes = self.transform_prebox_to_current(pred_boxes, pose_pre, pose_cur)
+                pred_boxes_all.append(pred_boxes)
+
+        points = np.concatenate([points] + points_pre_all, axis=0).astype(np.float32)
+        num_points_all = np.array([num_pts_cur] + num_points_pre).astype(np.int32)
+        poses = np.concatenate(pose_all, axis=0).astype(np.float32)
+
+        if load_pred_boxes:
+            temp_pred_boxes = self.reorder_rois_for_refining(pred_boxes_all)
+            pred_boxes = temp_pred_boxes[:, :, 0:9]
+            pred_scores = temp_pred_boxes[:, :, 9]
+            pred_labels = temp_pred_boxes[:, :, 10]
+        else:
+            pred_boxes = pred_scores = pred_labels = None
+
+        return points, num_points_all, sample_idx_pre_list, poses, pred_boxes, pred_scores, pred_labels
+
+    def __len__(self):
+        if self._merge_all_iters_to_one_epoch:
+            return len(self.infos) * self.total_epochs
+
+        return len(self.infos)
+
+    def __getitem__(self, index):
+        if self._merge_all_iters_to_one_epoch:
+            index = index % len(self.infos)
+
+        info = copy.deepcopy(self.infos[index])
+        pc_info = info['point_cloud']
+        sequence_name = pc_info['lidar_sequence']
+        sample_idx = pc_info['sample_idx']
+        input_dict = {
+            'sample_idx': sample_idx
+        }
+        if self.use_shared_memory and index < self.shared_memory_file_limit:
+            sa_key = f'{sequence_name}___{sample_idx}'
+            points = SharedArray.attach(f"shm://{sa_key}").copy()
+        else:
+            points = self.get_lidar(sequence_name, sample_idx)
+
+        if self.dataset_cfg.get('SEQUENCE_CONFIG', None) is not None and self.dataset_cfg.SEQUENCE_CONFIG.ENABLED:
+            points, num_points_all, sample_idx_pre_list, poses, pred_boxes, pred_scores, pred_labels = self.get_sequence_data(
+                info, points, sequence_name, sample_idx, self.dataset_cfg.SEQUENCE_CONFIG,
+                load_pred_boxes=self.dataset_cfg.get('USE_PREDBOX', False)
+            )
+            input_dict['poses'] = poses
+            if self.dataset_cfg.get('USE_PREDBOX', False):
+                input_dict.update({
+                    'roi_boxes': pred_boxes,
+                    'roi_scores': pred_scores,
+                    'roi_labels': pred_labels,
+                })
+
+        input_dict.update({
+            'points': points,
+            'frame_id': info['frame_id'],
+        })
+
+        if 'annos' in info:
+            annos = info['annos']
+            annos = common_utils.drop_info_with_name(annos, name='unknown')
+
+            if self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False):
+                gt_boxes_lidar = box_utils.boxes3d_kitti_fakelidar_to_lidar(annos['gt_boxes_lidar'])
+            else:
+                gt_boxes_lidar = annos['gt_boxes_lidar']
+
+            if self.dataset_cfg.get('TRAIN_WITH_SPEED', False):
+                assert gt_boxes_lidar.shape[-1] == 9
+            else:
+                gt_boxes_lidar = gt_boxes_lidar[:, 0:7]
+
+            if self.training and self.dataset_cfg.get('FILTER_EMPTY_BOXES_FOR_TRAIN', False):
+                mask = (annos['num_points_in_gt'] > 0)  # filter empty boxes
+                annos['name'] = annos['name'][mask]
+                gt_boxes_lidar = gt_boxes_lidar[mask]
+                annos['num_points_in_gt'] = annos['num_points_in_gt'][mask]
+
+            input_dict.update({
+                'gt_names': annos['name'],
+                'gt_boxes': gt_boxes_lidar,
+                'num_points_in_gt': annos.get('num_points_in_gt', None)
+            })
+
+        data_dict = self.prepare_data(data_dict=input_dict)
+        data_dict['metadata'] = info.get('metadata', info['frame_id'])
+        data_dict.pop('num_points_in_gt', None)
+        return data_dict
+
+    def evaluation(self, det_annos, class_names, **kwargs):
+        if 'annos' not in self.infos[0].keys():
+            return 'No ground-truth boxes for evaluation', {}
+
+        def kitti_eval(eval_det_annos, eval_gt_annos):
+            from ..kitti.kitti_object_eval_python import eval as kitti_eval
+            from ..kitti import kitti_utils
+
+            map_name_to_kitti = {
+                'Vehicle': 'Car',
+                'Pedestrian': 'Pedestrian',
+                'Cyclist': 'Cyclist',
+                'Sign': 'Sign',
+                'Car': 'Car'
+            }
+            kitti_utils.transform_annotations_to_kitti_format(eval_det_annos, map_name_to_kitti=map_name_to_kitti)
+            kitti_utils.transform_annotations_to_kitti_format(
+                eval_gt_annos, map_name_to_kitti=map_name_to_kitti,
+                info_with_fakelidar=self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False)
+            )
+            kitti_class_names = [map_name_to_kitti[x] for x in class_names]
+            ap_result_str, ap_dict = kitti_eval.get_official_eval_result(
+                gt_annos=eval_gt_annos, dt_annos=eval_det_annos, current_classes=kitti_class_names
+            )
+            return ap_result_str, ap_dict
+
+        def waymo_eval(eval_det_annos, eval_gt_annos):
+            from .waymo_eval import OpenPCDetWaymoDetectionMetricsEstimator
+            eval = OpenPCDetWaymoDetectionMetricsEstimator()
+
+            ap_dict = eval.waymo_evaluation(
+                eval_det_annos, eval_gt_annos, class_name=class_names,
+                distance_thresh=1000, fake_gt_infos=self.dataset_cfg.get('INFO_WITH_FAKELIDAR', False)
+            )
+            ap_result_str = '\n'
+            for key in ap_dict:
+                ap_dict[key] = ap_dict[key][0]
+                ap_result_str += '%s: %.4f \n' % (key, ap_dict[key])
+
+            return ap_result_str, ap_dict
+
+        eval_det_annos = copy.deepcopy(det_annos)
+        eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.infos]
+
+        if kwargs['eval_metric'] == 'kitti':
+            ap_result_str, ap_dict = kitti_eval(eval_det_annos, eval_gt_annos)
+        elif kwargs['eval_metric'] == 'waymo':
+            ap_result_str, ap_dict = waymo_eval(eval_det_annos, eval_gt_annos)
+        else:
+            raise NotImplementedError
+
+        return ap_result_str, ap_dict
+
+    def create_groundtruth_database(self, info_path, save_path, used_classes=None, split='train', sampled_interval=10,
+                                    processed_data_tag=None):
+
+        use_sequence_data = self.dataset_cfg.get('SEQUENCE_CONFIG', None) is not None and self.dataset_cfg.SEQUENCE_CONFIG.ENABLED
+
+        if use_sequence_data:
+            st_frame, ed_frame = self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[0], self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[1]
+            self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[0] = min(-4, st_frame)  # at least we use 5 frames for generating gt database to support various sequence configs (<= 5 frames)
+            st_frame = self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[0]
+            database_save_path = save_path / ('%s_gt_database_%s_sampled_%d_multiframe_%s_to_%s' % (processed_data_tag, split, sampled_interval, st_frame, ed_frame))
+            db_info_save_path = save_path / ('%s_waymo_dbinfos_%s_sampled_%d_multiframe_%s_to_%s.pkl' % (processed_data_tag, split, sampled_interval, st_frame, ed_frame))
+            db_data_save_path = save_path / ('%s_gt_database_%s_sampled_%d_multiframe_%s_to_%s_global.npy' % (processed_data_tag, split, sampled_interval, st_frame, ed_frame))
+        else:
+            database_save_path = save_path / ('%s_gt_database_%s_sampled_%d' % (processed_data_tag, split, sampled_interval))
+            db_info_save_path = save_path / ('%s_waymo_dbinfos_%s_sampled_%d.pkl' % (processed_data_tag, split, sampled_interval))
+            db_data_save_path = save_path / ('%s_gt_database_%s_sampled_%d_global.npy' % (processed_data_tag, split, sampled_interval))
+
+        database_save_path.mkdir(parents=True, exist_ok=True)
+        all_db_infos = {}
+        with open(info_path, 'rb') as f:
+            infos = pickle.load(f)
+
+        point_offset_cnt = 0
+        stacked_gt_points = []
+        for k in tqdm(range(0, len(infos), sampled_interval)):
+            # print('gt_database sample: %d/%d' % (k + 1, len(infos)))
+            info = infos[k]
+
+            pc_info = info['point_cloud']
+            sequence_name = pc_info['lidar_sequence']
+            sample_idx = pc_info['sample_idx']
+            points = self.get_lidar(sequence_name, sample_idx)
+
+            if use_sequence_data:
+                points, num_points_all, sample_idx_pre_list, _, _, _, _ = self.get_sequence_data(
+                    info, points, sequence_name, sample_idx, self.dataset_cfg.SEQUENCE_CONFIG
+                )
+
+            annos = info['annos']
+            names = annos['name']
+            difficulty = annos['difficulty']
+            gt_boxes = annos['gt_boxes_lidar']
+
+            if k % 4 != 0 and len(names) > 0:
+                mask = (names == 'Vehicle')
+                names = names[~mask]
+                difficulty = difficulty[~mask]
+                gt_boxes = gt_boxes[~mask]
+
+            if k % 2 != 0 and len(names) > 0:
+                mask = (names == 'Pedestrian')
+                names = names[~mask]
+                difficulty = difficulty[~mask]
+                gt_boxes = gt_boxes[~mask]
+
+            num_obj = gt_boxes.shape[0]
+            if num_obj == 0:
+                continue
+
+            box_idxs_of_pts = roiaware_pool3d_utils.points_in_boxes_gpu(
+                torch.from_numpy(points[:, 0:3]).unsqueeze(dim=0).float().cuda(),
+                torch.from_numpy(gt_boxes[:, 0:7]).unsqueeze(dim=0).float().cuda()
+            ).long().squeeze(dim=0).cpu().numpy()
+
+            for i in range(num_obj):
+                filename = '%s_%04d_%s_%d.bin' % (sequence_name, sample_idx, names[i], i)
+                filepath = database_save_path / filename
+                gt_points = points[box_idxs_of_pts == i]
+                gt_points[:, :3] -= gt_boxes[i, :3]
+
+                if (used_classes is None) or names[i] in used_classes:
+                    gt_points = gt_points.astype(np.float32)
+                    assert gt_points.dtype == np.float32
+                    with open(filepath, 'w') as f:
+                        gt_points.tofile(f)
+
+                    db_path = str(filepath.relative_to(self.root_path))  # gt_database/xxxxx.bin
+                    db_info = {'name': names[i], 'path': db_path, 'sequence_name': sequence_name,
+                               'sample_idx': sample_idx, 'gt_idx': i, 'box3d_lidar': gt_boxes[i],
+                               'num_points_in_gt': gt_points.shape[0], 'difficulty': difficulty[i]}
+
+                    # it will be used if you choose to use shared memory for gt sampling
+                    stacked_gt_points.append(gt_points)
+                    db_info['global_data_offset'] = [point_offset_cnt, point_offset_cnt + gt_points.shape[0]]
+                    point_offset_cnt += gt_points.shape[0]
+
+                    if names[i] in all_db_infos:
+                        all_db_infos[names[i]].append(db_info)
+                    else:
+                        all_db_infos[names[i]] = [db_info]
+        for k, v in all_db_infos.items():
+            print('Database %s: %d' % (k, len(v)))
+
+        with open(db_info_save_path, 'wb') as f:
+            pickle.dump(all_db_infos, f)
+
+        # it will be used if you choose to use shared memory for gt sampling
+        stacked_gt_points = np.concatenate(stacked_gt_points, axis=0)
+        np.save(db_data_save_path, stacked_gt_points)
+
+    def create_gt_database_of_single_scene(self, info_with_idx, database_save_path=None, use_sequence_data=False, used_classes=None,
+                                           total_samples=0, use_cuda=False, crop_gt_with_tail=False):
+        info, info_idx = info_with_idx
+        print('gt_database sample: %d/%d' % (info_idx, total_samples))
+
+        all_db_infos = {}
+        pc_info = info['point_cloud']
+        sequence_name = pc_info['lidar_sequence']
+        sample_idx = pc_info['sample_idx']
+        points = self.get_lidar(sequence_name, sample_idx)
+
+        if use_sequence_data:
+            points, num_points_all, sample_idx_pre_list, _, _, _, _ = self.get_sequence_data(
+                info, points, sequence_name, sample_idx, self.dataset_cfg.SEQUENCE_CONFIG
+            )
+
+        annos = info['annos']
+        names = annos['name']
+        difficulty = annos['difficulty']
+        gt_boxes = annos['gt_boxes_lidar']
+
+        if info_idx % 4 != 0 and len(names) > 0:
+            mask = (names == 'Vehicle')
+            names = names[~mask]
+            difficulty = difficulty[~mask]
+            gt_boxes = gt_boxes[~mask]
+
+        if info_idx % 2 != 0 and len(names) > 0:
+            mask = (names == 'Pedestrian')
+            names = names[~mask]
+            difficulty = difficulty[~mask]
+            gt_boxes = gt_boxes[~mask]
+
+        num_obj = gt_boxes.shape[0]
+        if num_obj == 0:
+            return {}
+
+        if use_sequence_data and crop_gt_with_tail:
+            assert gt_boxes.shape[1] == 9
+            speed = gt_boxes[:, 7:9]
+            sequence_cfg = self.dataset_cfg.SEQUENCE_CONFIG
+            assert sequence_cfg.SAMPLE_OFFSET[1] == 0
+            assert sequence_cfg.SAMPLE_OFFSET[0] < 0
+            num_frames = sequence_cfg.SAMPLE_OFFSET[1] - sequence_cfg.SAMPLE_OFFSET[0] + 1
+            assert num_frames > 1
+            latest_center = gt_boxes[:, 0:2]
+            oldest_center = latest_center - speed * (num_frames - 1) * 0.1
+            new_center = (latest_center + oldest_center) * 0.5
+            new_length = gt_boxes[:, 3] + np.linalg.norm(latest_center - oldest_center, axis=-1)
+            gt_boxes_crop = gt_boxes.copy()
+            gt_boxes_crop[:, 0:2] = new_center
+            gt_boxes_crop[:, 3] = new_length
+
+        else:
+            gt_boxes_crop = gt_boxes
+
+        if use_cuda:
+            box_idxs_of_pts = roiaware_pool3d_utils.points_in_boxes_gpu(
+                torch.from_numpy(points[:, 0:3]).unsqueeze(dim=0).float().cuda(),
+                torch.from_numpy(gt_boxes_crop[:, 0:7]).unsqueeze(dim=0).float().cuda()
+            ).long().squeeze(dim=0).cpu().numpy()
+        else:
+            box_point_mask = roiaware_pool3d_utils.points_in_boxes_cpu(
+                torch.from_numpy(points[:, 0:3]).float(),
+                torch.from_numpy(gt_boxes_crop[:, 0:7]).float()
+            ).long().numpy()  # (num_boxes, num_points)
+
+        for i in range(num_obj):
+            filename = '%s_%04d_%s_%d.bin' % (sequence_name, sample_idx, names[i], i)
+            filepath = database_save_path / filename
+            if use_cuda:
+                gt_points = points[box_idxs_of_pts == i]
+            else:
+                gt_points = points[box_point_mask[i] > 0]
+
+            gt_points[:, :3] -= gt_boxes[i, :3]
+
+            if (used_classes is None) or names[i] in used_classes:
+                gt_points = gt_points.astype(np.float32)
+                assert gt_points.dtype == np.float32
+                with open(filepath, 'w') as f:
+                    gt_points.tofile(f)
+
+                db_path = str(filepath.relative_to(self.root_path))  # gt_database/xxxxx.bin
+                db_info = {'name': names[i], 'path': db_path, 'sequence_name': sequence_name,
+                            'sample_idx': sample_idx, 'gt_idx': i, 'box3d_lidar': gt_boxes[i],
+                            'num_points_in_gt': gt_points.shape[0], 'difficulty': difficulty[i],
+                            'box3d_crop': gt_boxes_crop[i]}
+
+                if names[i] in all_db_infos:
+                    all_db_infos[names[i]].append(db_info)
+                else:
+                    all_db_infos[names[i]] = [db_info]
+        return all_db_infos
+
+    def create_groundtruth_database_parallel(self, info_path, save_path, used_classes=None, split='train', sampled_interval=10,
+                                             processed_data_tag=None, num_workers=16, crop_gt_with_tail=False):
+        use_sequence_data = self.dataset_cfg.get('SEQUENCE_CONFIG', None) is not None and self.dataset_cfg.SEQUENCE_CONFIG.ENABLED
+        if use_sequence_data:
+            st_frame, ed_frame = self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[0], self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[1]
+            self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[0] = min(-4, st_frame)  # at least we use 5 frames for generating gt database to support various sequence configs (<= 5 frames)
+            st_frame = self.dataset_cfg.SEQUENCE_CONFIG.SAMPLE_OFFSET[0]
+            database_save_path = save_path / ('%s_gt_database_%s_sampled_%d_multiframe_%s_to_%s_%sparallel' % (processed_data_tag, split, sampled_interval, st_frame, ed_frame, 'tail_' if crop_gt_with_tail else ''))
+            db_info_save_path = save_path / ('%s_waymo_dbinfos_%s_sampled_%d_multiframe_%s_to_%s_%sparallel.pkl' % (processed_data_tag, split, sampled_interval, st_frame, ed_frame, 'tail_' if crop_gt_with_tail else ''))
+        else:
+            database_save_path = save_path / ('%s_gt_database_%s_sampled_%d_parallel' % (processed_data_tag, split, sampled_interval))
+            db_info_save_path = save_path / ('%s_waymo_dbinfos_%s_sampled_%d_parallel.pkl' % (processed_data_tag, split, sampled_interval))
+            
+        database_save_path.mkdir(parents=True, exist_ok=True)
+
+        with open(info_path, 'rb') as f:
+            infos = pickle.load(f)
+
+        print(f'Number workers: {num_workers}')
+        create_gt_database_of_single_scene = partial(
+            self.create_gt_database_of_single_scene,
+            use_sequence_data=use_sequence_data, database_save_path=database_save_path,
+            used_classes=used_classes, total_samples=len(infos), use_cuda=False,
+            crop_gt_with_tail=crop_gt_with_tail
+        )
+        # create_gt_database_of_single_scene((infos[300], 0))
+        with multiprocessing.Pool(num_workers) as p:
+            all_db_infos_list = list(p.map(create_gt_database_of_single_scene, zip(infos, np.arange(len(infos)))))
+
+        all_db_infos = {}
+
+        for cur_db_infos in all_db_infos_list:
+            for key, val in cur_db_infos.items():
+                if key not in all_db_infos:
+                    all_db_infos[key] = val
+                else:
+                    all_db_infos[key].extend(val)
+
+        for k, v in all_db_infos.items():
+            print('Database %s: %d' % (k, len(v)))
+
+        with open(db_info_save_path, 'wb') as f:
+            pickle.dump(all_db_infos, f)
+
+def create_waymo_infos(dataset_cfg, class_names, data_path, save_path,
+                       raw_data_tag='raw_data', processed_data_tag='waymo_processed_data',
+                       workers=min(16, multiprocessing.cpu_count()), update_info_only=False):
+    dataset = WaymoDataset(
+        dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path,
+        training=False, logger=common_utils.create_logger()
+    )
+    train_split, val_split = 'train', 'val'
+
+    train_filename = save_path / ('%s_infos_%s.pkl' % (processed_data_tag, train_split))
+    val_filename = save_path / ('%s_infos_%s.pkl' % (processed_data_tag, val_split))
+
+    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+    print('---------------Start to generate data infos---------------')
+
+    dataset.set_split(train_split)
+    waymo_infos_train = dataset.get_infos(
+        raw_data_path=data_path / raw_data_tag,
+        save_path=save_path / processed_data_tag, num_workers=workers, has_label=True,
+        sampled_interval=1, update_info_only=update_info_only
+    )
+    with open(train_filename, 'wb') as f:
+        pickle.dump(waymo_infos_train, f)
+    print('----------------Waymo info train file is saved to %s----------------' % train_filename)
+
+    dataset.set_split(val_split)
+    waymo_infos_val = dataset.get_infos(
+        raw_data_path=data_path / raw_data_tag,
+        save_path=save_path / processed_data_tag, num_workers=workers, has_label=True,
+        sampled_interval=1, update_info_only=update_info_only
+    )
+    with open(val_filename, 'wb') as f:
+        pickle.dump(waymo_infos_val, f)
+    print('----------------Waymo info val file is saved to %s----------------' % val_filename)
+
+    if update_info_only:
+        return
+
+    print('---------------Start create groundtruth database for data augmentation---------------')
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    dataset.set_split(train_split)
+    dataset.create_groundtruth_database(
+        info_path=train_filename, save_path=save_path, split='train', sampled_interval=1,
+        used_classes=['Vehicle', 'Pedestrian', 'Cyclist'], processed_data_tag=processed_data_tag
+    )
+    print('---------------Data preparation Done---------------')
+
+
+def create_waymo_gt_database(
+    dataset_cfg, class_names, data_path, save_path, processed_data_tag='waymo_processed_data',
+    workers=min(16, multiprocessing.cpu_count()), use_parallel=False, crop_gt_with_tail=False):
+    dataset = WaymoDataset(
+        dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path,
+        training=False, logger=common_utils.create_logger()
+    )
+    train_split = 'train'
+    train_filename = save_path / ('%s_infos_%s.pkl' % (processed_data_tag, train_split))
+
+    print('---------------Start create groundtruth database for data augmentation---------------')
+    dataset.set_split(train_split)
+
+    if use_parallel:
+        dataset.create_groundtruth_database_parallel(
+            info_path=train_filename, save_path=save_path, split='train', sampled_interval=1,
+            used_classes=['Vehicle', 'Pedestrian', 'Cyclist'], processed_data_tag=processed_data_tag,
+            num_workers=workers, crop_gt_with_tail=crop_gt_with_tail
+        )
+    else:
+        dataset.create_groundtruth_database(
+            info_path=train_filename, save_path=save_path, split='train', sampled_interval=1,
+            used_classes=['Vehicle', 'Pedestrian', 'Cyclist'], processed_data_tag=processed_data_tag
+        )
+    print('---------------Data preparation Done---------------')
+
+
+if __name__ == '__main__':
+    import argparse
+    import yaml
+    from easydict import EasyDict
+
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--cfg_file', type=str, default=None, help='specify the config of dataset')
+    parser.add_argument('--func', type=str, default='create_waymo_infos', help='')
+    parser.add_argument('--processed_data_tag', type=str, default='waymo_processed_data_v0_5_0', help='')
+    parser.add_argument('--update_info_only', action='store_true', default=False, help='')
+    parser.add_argument('--use_parallel', action='store_true', default=False, help='')
+    parser.add_argument('--wo_crop_gt_with_tail', action='store_true', default=False, help='')
+
+    args = parser.parse_args()
+
+    ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
+
+    if args.func == 'create_waymo_infos':
+        try:
+            yaml_config = yaml.safe_load(open(args.cfg_file), Loader=yaml.FullLoader)
+        except:
+            yaml_config = yaml.safe_load(open(args.cfg_file))
+        dataset_cfg = EasyDict(yaml_config)
+        dataset_cfg.PROCESSED_DATA_TAG = args.processed_data_tag
+        create_waymo_infos(
+            dataset_cfg=dataset_cfg,
+            class_names=['Vehicle', 'Pedestrian', 'Cyclist'],
+            data_path=ROOT_DIR / 'data' / 'waymo',
+            save_path=ROOT_DIR / 'data' / 'waymo',
+            raw_data_tag='raw_data',
+            processed_data_tag=args.processed_data_tag,
+            update_info_only=args.update_info_only
+        )
+    elif args.func == 'create_waymo_gt_database':
+        try:
+            yaml_config = yaml.safe_load(open(args.cfg_file), Loader=yaml.FullLoader)
+        except:
+            yaml_config = yaml.safe_load(open(args.cfg_file))
+        dataset_cfg = EasyDict(yaml_config)
+        dataset_cfg.PROCESSED_DATA_TAG = args.processed_data_tag
+        create_waymo_gt_database(
+            dataset_cfg=dataset_cfg,
+            class_names=['Vehicle', 'Pedestrian', 'Cyclist'],
+            data_path=ROOT_DIR / 'data' / 'waymo',
+            save_path=ROOT_DIR / 'data' / 'waymo',
+            processed_data_tag=args.processed_data_tag,
+            use_parallel=args.use_parallel, 
+            crop_gt_with_tail=not args.wo_crop_gt_with_tail
+        )
+    else:
+        raise NotImplementedError
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_eval.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_eval.py
new file mode 100644
index 000000000..0cbf61af1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_eval.py
@@ -0,0 +1,251 @@
+# OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
+# Reference https://github.com/open-mmlab/OpenPCDet
+# Written by Shaoshuai Shi, Chaoxu Guo
+# All Rights Reserved 2019-2020.
+
+
+import numpy as np
+import pickle
+import tensorflow as tf
+from google.protobuf import text_format
+from waymo_open_dataset.metrics.python import detection_metrics
+from waymo_open_dataset.protos import metrics_pb2
+import argparse
+
+
+tf.get_logger().setLevel('INFO')
+
+
+def limit_period(val, offset=0.5, period=np.pi):
+    return val - np.floor(val / period + offset) * period
+
+
+class OpenPCDetWaymoDetectionMetricsEstimator(tf.test.TestCase):
+    WAYMO_CLASSES = ['unknown', 'Vehicle', 'Pedestrian', 'Sign', 'Cyclist']
+
+    def generate_waymo_type_results(self, infos, class_names, is_gt=False, fake_gt_infos=True):
+        def boxes3d_kitti_fakelidar_to_lidar(boxes3d_lidar):
+            """
+            Args:
+                boxes3d_fakelidar: (N, 7) [x, y, z, w, l, h, r] in old LiDAR coordinates, z is bottom center
+
+            Returns:
+                boxes3d_lidar: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+            """
+            w, l, h, r = boxes3d_lidar[:, 3:4], boxes3d_lidar[:, 4:5], boxes3d_lidar[:, 5:6], boxes3d_lidar[:, 6:7]
+            boxes3d_lidar[:, 2] += h[:, 0] / 2
+            return np.concatenate([boxes3d_lidar[:, 0:3], l, w, h, -(r + np.pi / 2)], axis=-1)
+
+        frame_id, boxes3d, obj_type, score, overlap_nlz, difficulty = [], [], [], [], [], []
+        for frame_index, info in enumerate(infos):
+            if is_gt:
+                box_mask = np.array([n in class_names for n in info['name']], dtype=np.bool_)
+                if 'num_points_in_gt' in info:
+                    zero_difficulty_mask = info['difficulty'] == 0
+                    info['difficulty'][(info['num_points_in_gt'] > 5) & zero_difficulty_mask] = 1
+                    info['difficulty'][(info['num_points_in_gt'] <= 5) & zero_difficulty_mask] = 2
+                    nonzero_mask = info['num_points_in_gt'] > 0
+                    box_mask = box_mask & nonzero_mask
+                else:
+                    print('Please provide the num_points_in_gt for evaluating on Waymo Dataset '
+                          '(If you create Waymo Infos before 20201126, please re-create the validation infos '
+                          'with version 1.2 Waymo dataset to get this attribute). SSS of OpenPCDet')
+                    raise NotImplementedError
+
+                num_boxes = box_mask.sum()
+                box_name = info['name'][box_mask]
+
+                difficulty.append(info['difficulty'][box_mask])
+                score.append(np.ones(num_boxes))
+                if fake_gt_infos:
+                    info['gt_boxes_lidar'] = boxes3d_kitti_fakelidar_to_lidar(info['gt_boxes_lidar'])
+
+                if info['gt_boxes_lidar'].shape[-1] == 9:
+                    boxes3d.append(info['gt_boxes_lidar'][box_mask][:, 0:7])
+                else:
+                    boxes3d.append(info['gt_boxes_lidar'][box_mask])
+            else:
+                num_boxes = len(info['boxes_lidar'])
+                difficulty.append([0] * num_boxes)
+                score.append(info['score'])
+                boxes3d.append(np.array(info['boxes_lidar'][:, :7]))
+                box_name = info['name']
+                if boxes3d[-1].shape[-1] == 9:
+                    boxes3d[-1] = boxes3d[-1][:, 0:7]
+
+            obj_type += [self.WAYMO_CLASSES.index(name) for i, name in enumerate(box_name)]
+            frame_id.append(np.array([frame_index] * num_boxes))
+            overlap_nlz.append(np.zeros(num_boxes))  # set zero currently
+
+        frame_id = np.concatenate(frame_id).reshape(-1).astype(np.int64)
+        boxes3d = np.concatenate(boxes3d, axis=0)
+        obj_type = np.array(obj_type).reshape(-1)
+        score = np.concatenate(score).reshape(-1)
+        overlap_nlz = np.concatenate(overlap_nlz).reshape(-1)
+        difficulty = np.concatenate(difficulty).reshape(-1).astype(np.int8)
+
+        boxes3d[:, -1] = limit_period(boxes3d[:, -1], offset=0.5, period=np.pi * 2)
+
+        return frame_id, boxes3d, obj_type, score, overlap_nlz, difficulty
+
+    def build_config(self):
+        config = metrics_pb2.Config()
+        config_text = """
+        breakdown_generator_ids: OBJECT_TYPE
+        difficulties {
+        levels:1
+        levels:2
+        }
+        matcher_type: TYPE_HUNGARIAN
+        iou_thresholds: 0.0
+        iou_thresholds: 0.7
+        iou_thresholds: 0.5
+        iou_thresholds: 0.5
+        iou_thresholds: 0.5
+        box_type: TYPE_3D
+        """
+
+        for x in range(0, 100):
+            config.score_cutoffs.append(x * 0.01)
+        config.score_cutoffs.append(1.0)
+
+        text_format.Merge(config_text, config)
+        return config
+
+    def build_graph(self, graph):
+        with graph.as_default():
+            self._pd_frame_id = tf.compat.v1.placeholder(dtype=tf.int64)
+            self._pd_bbox = tf.compat.v1.placeholder(dtype=tf.float32)
+            self._pd_type = tf.compat.v1.placeholder(dtype=tf.uint8)
+            self._pd_score = tf.compat.v1.placeholder(dtype=tf.float32)
+            self._pd_overlap_nlz = tf.compat.v1.placeholder(dtype=tf.bool)
+
+            self._gt_frame_id = tf.compat.v1.placeholder(dtype=tf.int64)
+            self._gt_bbox = tf.compat.v1.placeholder(dtype=tf.float32)
+            self._gt_type = tf.compat.v1.placeholder(dtype=tf.uint8)
+            self._gt_difficulty = tf.compat.v1.placeholder(dtype=tf.uint8)
+            metrics = detection_metrics.get_detection_metric_ops(
+                config=self.build_config(),
+                prediction_frame_id=self._pd_frame_id,
+                prediction_bbox=self._pd_bbox,
+                prediction_type=self._pd_type,
+                prediction_score=self._pd_score,
+                prediction_overlap_nlz=self._pd_overlap_nlz,
+                ground_truth_bbox=self._gt_bbox,
+                ground_truth_type=self._gt_type,
+                ground_truth_frame_id=self._gt_frame_id,
+                ground_truth_difficulty=self._gt_difficulty,
+            )
+            return metrics
+
+    def run_eval_ops(
+        self,
+        sess,
+        graph,
+        metrics,
+        prediction_frame_id,
+        prediction_bbox,
+        prediction_type,
+        prediction_score,
+        prediction_overlap_nlz,
+        ground_truth_frame_id,
+        ground_truth_bbox,
+        ground_truth_type,
+        ground_truth_difficulty,
+    ):
+        sess.run(
+            [tf.group([value[1] for value in metrics.values()])],
+            feed_dict={
+                self._pd_bbox: prediction_bbox,
+                self._pd_frame_id: prediction_frame_id,
+                self._pd_type: prediction_type,
+                self._pd_score: prediction_score,
+                self._pd_overlap_nlz: prediction_overlap_nlz,
+                self._gt_bbox: ground_truth_bbox,
+                self._gt_type: ground_truth_type,
+                self._gt_frame_id: ground_truth_frame_id,
+                self._gt_difficulty: ground_truth_difficulty,
+            },
+        )
+
+    def eval_value_ops(self, sess, graph, metrics):
+        return {item[0]: sess.run([item[1][0]]) for item in metrics.items()}
+
+    def mask_by_distance(self, distance_thresh, boxes_3d, *args):
+        mask = np.linalg.norm(boxes_3d[:, 0:2], axis=1) < distance_thresh + 0.5
+        boxes_3d = boxes_3d[mask]
+        ret_ans = [boxes_3d]
+        for arg in args:
+            ret_ans.append(arg[mask])
+
+        return tuple(ret_ans)
+
+    def waymo_evaluation(self, prediction_infos, gt_infos, class_name, distance_thresh=100, fake_gt_infos=True):
+        print('Start the waymo evaluation...')
+        assert len(prediction_infos) == len(gt_infos), '%d vs %d' % (prediction_infos.__len__(), gt_infos.__len__())
+
+        tf.compat.v1.disable_eager_execution()
+        pd_frameid, pd_boxes3d, pd_type, pd_score, pd_overlap_nlz, _ = self.generate_waymo_type_results(
+            prediction_infos, class_name, is_gt=False
+        )
+        gt_frameid, gt_boxes3d, gt_type, gt_score, gt_overlap_nlz, gt_difficulty = self.generate_waymo_type_results(
+            gt_infos, class_name, is_gt=True, fake_gt_infos=fake_gt_infos
+        )
+
+        pd_boxes3d, pd_frameid, pd_type, pd_score, pd_overlap_nlz = self.mask_by_distance(
+            distance_thresh, pd_boxes3d, pd_frameid, pd_type, pd_score, pd_overlap_nlz
+        )
+        gt_boxes3d, gt_frameid, gt_type, gt_score, gt_difficulty = self.mask_by_distance(
+            distance_thresh, gt_boxes3d, gt_frameid, gt_type, gt_score, gt_difficulty
+        )
+
+        print('Number: (pd, %d) VS. (gt, %d)' % (len(pd_boxes3d), len(gt_boxes3d)))
+        print('Level 1: %d, Level2: %d)' % ((gt_difficulty == 1).sum(), (gt_difficulty == 2).sum()))
+
+        if pd_score.max() > 1:
+            # assert pd_score.max() <= 1.0, 'Waymo evaluation only supports normalized scores'
+            pd_score = 1 / (1 + np.exp(-pd_score))
+            print('Warning: Waymo evaluation only supports normalized scores')
+
+        graph = tf.Graph()
+        metrics = self.build_graph(graph)
+        with self.test_session(graph=graph) as sess:
+            sess.run(tf.compat.v1.initializers.local_variables())
+            self.run_eval_ops(
+                sess, graph, metrics, pd_frameid, pd_boxes3d, pd_type, pd_score, pd_overlap_nlz,
+                gt_frameid, gt_boxes3d, gt_type, gt_difficulty,
+            )
+            with tf.compat.v1.variable_scope('detection_metrics', reuse=True):
+                aps = self.eval_value_ops(sess, graph, metrics)
+        return aps
+
+
+def main():
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--pred_infos', type=str, default=None, help='pickle file')
+    parser.add_argument('--gt_infos', type=str, default=None, help='pickle file')
+    parser.add_argument('--class_names', type=str, nargs='+', default=['Vehicle', 'Pedestrian', 'Cyclist'], help='')
+    parser.add_argument('--sampled_interval', type=int, default=5, help='sampled interval for GT sequences')
+    args = parser.parse_args()
+
+    pred_infos = pickle.load(open(args.pred_infos, 'rb'))
+    gt_infos = pickle.load(open(args.gt_infos, 'rb'))
+
+    print('Start to evaluate the waymo format results...')
+    eval = OpenPCDetWaymoDetectionMetricsEstimator()
+
+    gt_infos_dst = []
+    for idx in range(0, len(gt_infos), args.sampled_interval):
+        cur_info = gt_infos[idx]['annos']
+        cur_info['frame_id'] = gt_infos[idx]['frame_id']
+        gt_infos_dst.append(cur_info)
+
+    waymo_AP = eval.waymo_evaluation(
+        pred_infos, gt_infos_dst, class_name=args.class_names, distance_thresh=1000, fake_gt_infos=False
+    )
+
+    print(waymo_AP)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_utils.py
new file mode 100644
index 000000000..9bdcfe7b2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/datasets/waymo/waymo_utils.py
@@ -0,0 +1,268 @@
+# OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
+# Reference https://github.com/open-mmlab/OpenPCDet
+# Written by Shaoshuai Shi, Chaoxu Guo
+# All Rights Reserved 2019-2020.
+
+
+import os
+import pickle
+import numpy as np
+from ...utils import common_utils
+import tensorflow as tf
+from waymo_open_dataset.utils import frame_utils, transform_utils, range_image_utils
+from waymo_open_dataset import dataset_pb2
+
+try:
+    tf.enable_eager_execution()
+except:
+    pass
+
+WAYMO_CLASSES = ['unknown', 'Vehicle', 'Pedestrian', 'Sign', 'Cyclist']
+
+
+def generate_labels(frame, pose):
+    obj_name, difficulty, dimensions, locations, heading_angles = [], [], [], [], []
+    tracking_difficulty, speeds, accelerations, obj_ids = [], [], [], []
+    num_points_in_gt = []
+    laser_labels = frame.laser_labels
+
+    for i in range(len(laser_labels)):
+        box = laser_labels[i].box
+        class_ind = laser_labels[i].type
+        loc = [box.center_x, box.center_y, box.center_z]
+        heading_angles.append(box.heading)
+        obj_name.append(WAYMO_CLASSES[class_ind])
+        difficulty.append(laser_labels[i].detection_difficulty_level)
+        tracking_difficulty.append(laser_labels[i].tracking_difficulty_level)
+        dimensions.append([box.length, box.width, box.height])  # lwh in unified coordinate of OpenPCDet
+        locations.append(loc)
+        obj_ids.append(laser_labels[i].id)
+        num_points_in_gt.append(laser_labels[i].num_lidar_points_in_box)
+        speeds.append([laser_labels[i].metadata.speed_x, laser_labels[i].metadata.speed_y])
+        accelerations.append([laser_labels[i].metadata.accel_x, laser_labels[i].metadata.accel_y])
+
+    annotations = {}
+    annotations['name'] = np.array(obj_name)
+    annotations['difficulty'] = np.array(difficulty)
+    annotations['dimensions'] = np.array(dimensions)
+    annotations['location'] = np.array(locations)
+    annotations['heading_angles'] = np.array(heading_angles)
+
+    annotations['obj_ids'] = np.array(obj_ids)
+    annotations['tracking_difficulty'] = np.array(tracking_difficulty)
+    annotations['num_points_in_gt'] = np.array(num_points_in_gt)
+    annotations['speed_global'] = np.array(speeds)
+    annotations['accel_global'] = np.array(accelerations)
+
+    annotations = common_utils.drop_info_with_name(annotations, name='unknown')
+    if annotations['name'].__len__() > 0:
+        global_speed = np.pad(annotations['speed_global'], ((0, 0), (0, 1)), mode='constant', constant_values=0)  # (N, 3)
+        speed = np.dot(global_speed, np.linalg.inv(pose[:3, :3].T))
+        speed = speed[:, :2]
+        
+        gt_boxes_lidar = np.concatenate([
+            annotations['location'], annotations['dimensions'], annotations['heading_angles'][..., np.newaxis], speed],
+            axis=1
+        )
+    else:
+        gt_boxes_lidar = np.zeros((0, 9))
+    annotations['gt_boxes_lidar'] = gt_boxes_lidar
+    return annotations
+
+
+def convert_range_image_to_point_cloud(frame, range_images, camera_projections, range_image_top_pose, ri_index=(0, 1)):
+    """
+    Modified from the codes of Waymo Open Dataset.
+    Convert range images to point cloud.
+    Args:
+        frame: open dataset frame
+        range_images: A dict of {laser_name, [range_image_first_return, range_image_second_return]}.
+        camera_projections: A dict of {laser_name,
+            [camera_projection_from_first_return, camera_projection_from_second_return]}.
+        range_image_top_pose: range image pixel pose for top lidar.
+        ri_index: 0 for the first return, 1 for the second return.
+
+    Returns:
+        points: {[N, 3]} list of 3d lidar points of length 5 (number of lidars).
+        cp_points: {[N, 6]} list of camera projections of length 5 (number of lidars).
+    """
+    calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
+    points = []
+    cp_points = []
+    points_NLZ = []
+    points_intensity = []
+    points_elongation = []
+
+    frame_pose = tf.convert_to_tensor(np.reshape(np.array(frame.pose.transform), [4, 4]))
+    # [H, W, 6]
+    range_image_top_pose_tensor = tf.reshape(
+        tf.convert_to_tensor(range_image_top_pose.data), range_image_top_pose.shape.dims
+    )
+    # [H, W, 3, 3]
+    range_image_top_pose_tensor_rotation = transform_utils.get_rotation_matrix(
+        range_image_top_pose_tensor[..., 0], range_image_top_pose_tensor[..., 1],
+        range_image_top_pose_tensor[..., 2])
+    range_image_top_pose_tensor_translation = range_image_top_pose_tensor[..., 3:]
+    range_image_top_pose_tensor = transform_utils.get_transform(
+        range_image_top_pose_tensor_rotation,
+        range_image_top_pose_tensor_translation)
+
+    for c in calibrations:
+        points_single, cp_points_single, points_NLZ_single, points_intensity_single, points_elongation_single \
+            = [], [], [], [], []
+        for cur_ri_index in ri_index:
+            range_image = range_images[c.name][cur_ri_index]
+            if len(c.beam_inclinations) == 0:  # pylint: disable=g-explicit-length-test
+                beam_inclinations = range_image_utils.compute_inclination(
+                    tf.constant([c.beam_inclination_min, c.beam_inclination_max]),
+                    height=range_image.shape.dims[0])
+            else:
+                beam_inclinations = tf.constant(c.beam_inclinations)
+
+            beam_inclinations = tf.reverse(beam_inclinations, axis=[-1])
+            extrinsic = np.reshape(np.array(c.extrinsic.transform), [4, 4])
+
+            range_image_tensor = tf.reshape(
+                tf.convert_to_tensor(range_image.data), range_image.shape.dims)
+            pixel_pose_local = None
+            frame_pose_local = None
+            if c.name == dataset_pb2.LaserName.TOP:
+                pixel_pose_local = range_image_top_pose_tensor
+                pixel_pose_local = tf.expand_dims(pixel_pose_local, axis=0)
+                frame_pose_local = tf.expand_dims(frame_pose, axis=0)
+            range_image_mask = range_image_tensor[..., 0] > 0
+            range_image_NLZ = range_image_tensor[..., 3]
+            range_image_intensity = range_image_tensor[..., 1]
+            range_image_elongation = range_image_tensor[..., 2]
+            range_image_cartesian = range_image_utils.extract_point_cloud_from_range_image(
+                tf.expand_dims(range_image_tensor[..., 0], axis=0),
+                tf.expand_dims(extrinsic, axis=0),
+                tf.expand_dims(tf.convert_to_tensor(beam_inclinations), axis=0),
+                pixel_pose=pixel_pose_local,
+                frame_pose=frame_pose_local)
+
+            range_image_cartesian = tf.squeeze(range_image_cartesian, axis=0)
+            points_tensor = tf.gather_nd(range_image_cartesian,
+                                         tf.where(range_image_mask))
+            points_NLZ_tensor = tf.gather_nd(range_image_NLZ, tf.compat.v1.where(range_image_mask))
+            points_intensity_tensor = tf.gather_nd(range_image_intensity, tf.compat.v1.where(range_image_mask))
+            points_elongation_tensor = tf.gather_nd(range_image_elongation, tf.compat.v1.where(range_image_mask))
+            cp = camera_projections[c.name][0]
+            cp_tensor = tf.reshape(tf.convert_to_tensor(cp.data), cp.shape.dims)
+            cp_points_tensor = tf.gather_nd(cp_tensor, tf.where(range_image_mask))
+
+            points_single.append(points_tensor.numpy())
+            cp_points_single.append(cp_points_tensor.numpy())
+            points_NLZ_single.append(points_NLZ_tensor.numpy())
+            points_intensity_single.append(points_intensity_tensor.numpy())
+            points_elongation_single.append(points_elongation_tensor.numpy())
+
+        points.append(np.concatenate(points_single, axis=0))
+        cp_points.append(np.concatenate(cp_points_single, axis=0))
+        points_NLZ.append(np.concatenate(points_NLZ_single, axis=0))
+        points_intensity.append(np.concatenate(points_intensity_single, axis=0))
+        points_elongation.append(np.concatenate(points_elongation_single, axis=0))
+
+    return points, cp_points, points_NLZ, points_intensity, points_elongation
+
+
+def save_lidar_points(frame, cur_save_path, use_two_returns=True):
+    ret_outputs = frame_utils.parse_range_image_and_camera_projection(frame)
+    if len(ret_outputs) == 4:
+        range_images, camera_projections, seg_labels, range_image_top_pose = ret_outputs
+    else:
+        assert len(ret_outputs) == 3
+        range_images, camera_projections, range_image_top_pose = ret_outputs
+
+    points, cp_points, points_in_NLZ_flag, points_intensity, points_elongation = convert_range_image_to_point_cloud(
+        frame, range_images, camera_projections, range_image_top_pose, ri_index=(0, 1) if use_two_returns else (0,)
+    )
+
+    # 3d points in vehicle frame.
+    points_all = np.concatenate(points, axis=0)
+    points_in_NLZ_flag = np.concatenate(points_in_NLZ_flag, axis=0).reshape(-1, 1)
+    points_intensity = np.concatenate(points_intensity, axis=0).reshape(-1, 1)
+    points_elongation = np.concatenate(points_elongation, axis=0).reshape(-1, 1)
+
+    num_points_of_each_lidar = [point.shape[0] for point in points]
+    save_points = np.concatenate([
+        points_all, points_intensity, points_elongation, points_in_NLZ_flag
+    ], axis=-1).astype(np.float32)
+
+    np.save(cur_save_path, save_points)
+    # print('saving to ', cur_save_path)
+    return num_points_of_each_lidar
+
+
+def process_single_sequence(sequence_file, save_path, sampled_interval, has_label=True, use_two_returns=True, update_info_only=False):
+    sequence_name = os.path.splitext(os.path.basename(sequence_file))[0]
+
+    # print('Load record (sampled_interval=%d): %s' % (sampled_interval, sequence_name))
+    if not sequence_file.exists():
+        print('NotFoundError: %s' % sequence_file)
+        return []
+
+    dataset = tf.data.TFRecordDataset(str(sequence_file), compression_type='')
+    cur_save_dir = save_path / sequence_name
+    cur_save_dir.mkdir(parents=True, exist_ok=True)
+    pkl_file = cur_save_dir / ('%s.pkl' % sequence_name)
+
+    sequence_infos = []
+    if pkl_file.exists():
+        sequence_infos = pickle.load(open(pkl_file, 'rb'))
+        sequence_infos_old = None
+        if not update_info_only:
+            print('Skip sequence since it has been processed before: %s' % pkl_file)
+            return sequence_infos
+        else:
+            sequence_infos_old = sequence_infos
+            sequence_infos = []
+
+    for cnt, data in enumerate(dataset):
+        if cnt % sampled_interval != 0:
+            continue
+        # print(sequence_name, cnt)
+        frame = dataset_pb2.Frame()
+        frame.ParseFromString(bytearray(data.numpy()))
+
+        info = {}
+        pc_info = {'num_features': 5, 'lidar_sequence': sequence_name, 'sample_idx': cnt}
+        info['point_cloud'] = pc_info
+
+        info['frame_id'] = sequence_name + ('_%03d' % cnt)
+        info['metadata'] = {
+            'context_name': frame.context.name,
+            'timestamp_micros': frame.timestamp_micros
+        }
+        image_info = {}
+        for j in range(5):
+            width = frame.context.camera_calibrations[j].width
+            height = frame.context.camera_calibrations[j].height
+            image_info.update({'image_shape_%d' % j: (height, width)})
+        info['image'] = image_info
+
+        pose = np.array(frame.pose.transform, dtype=np.float32).reshape(4, 4)
+        info['pose'] = pose
+
+        if has_label:
+            annotations = generate_labels(frame, pose=pose)
+            info['annos'] = annotations
+
+        if update_info_only and sequence_infos_old is not None:
+            assert info['frame_id'] == sequence_infos_old[cnt]['frame_id']
+            num_points_of_each_lidar = sequence_infos_old[cnt]['num_points_of_each_lidar']
+        else:
+            num_points_of_each_lidar = save_lidar_points(
+                frame, cur_save_dir / ('%04d.npy' % cnt), use_two_returns=use_two_returns
+            )
+        info['num_points_of_each_lidar'] = num_points_of_each_lidar
+
+        sequence_infos.append(info)
+
+    with open(pkl_file, 'wb') as f:
+        pickle.dump(sequence_infos, f)
+
+    print('Infos are saved to (sampled_interval=%d): %s' % (sampled_interval, pkl_file))
+    return sequence_infos
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/__init__.py
new file mode 100644
index 000000000..7049bb4a0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/__init__.py
@@ -0,0 +1,54 @@
+from collections import namedtuple
+
+import numpy as np
+import torch
+
+from .detectors import build_detector
+
+try:
+    import kornia
+except:
+    pass 
+    # print('Warning: kornia is not installed. This package is only required by CaDDN')
+
+
+
+def build_network(model_cfg, num_class, dataset):
+    model = build_detector(
+        model_cfg=model_cfg, num_class=num_class, dataset=dataset
+    )
+    return model
+
+
+def load_data_to_gpu(batch_dict):
+    for key, val in batch_dict.items():
+        if key == 'camera_imgs':
+            batch_dict[key] = val.cuda()
+        elif not isinstance(val, np.ndarray):
+            continue
+        elif key in ['frame_id', 'metadata', 'calib', 'image_paths','ori_shape','img_process_infos']:
+            continue
+        elif key in ['images']:
+            batch_dict[key] = kornia.image_to_tensor(val).float().cuda().contiguous()
+        elif key in ['image_shape']:
+            batch_dict[key] = torch.from_numpy(val).int().cuda()
+        else:
+            batch_dict[key] = torch.from_numpy(val).float().cuda()
+
+
+def model_fn_decorator():
+    ModelReturn = namedtuple('ModelReturn', ['loss', 'tb_dict', 'disp_dict'])
+
+    def model_func(model, batch_dict):
+        load_data_to_gpu(batch_dict)
+        ret_dict, tb_dict, disp_dict = model(batch_dict)
+
+        loss = ret_dict['loss'].mean()
+        if hasattr(model, 'update_global_step'):
+            model.update_global_step()
+        else:
+            model.module.update_global_step()
+
+        return ModelReturn(loss, tb_dict, disp_dict)
+
+    return model_func
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/__init__.py
new file mode 100644
index 000000000..fea8d1653
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/__init__.py
@@ -0,0 +1,7 @@
+from .base_bev_backbone import BaseBEVBackbone, BaseBEVBackboneV1, BaseBEVResBackbone
+
+__all__ = {
+    'BaseBEVBackbone': BaseBEVBackbone,
+    'BaseBEVBackboneV1': BaseBEVBackboneV1,
+    'BaseBEVResBackbone': BaseBEVResBackbone,
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/base_bev_backbone.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/base_bev_backbone.py
new file mode 100644
index 000000000..4dc7dbb77
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/base_bev_backbone.py
@@ -0,0 +1,351 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class BaseBEVBackbone(nn.Module):
+    def __init__(self, model_cfg, input_channels):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        if self.model_cfg.get('LAYER_NUMS', None) is not None:
+            assert len(self.model_cfg.LAYER_NUMS) == len(self.model_cfg.LAYER_STRIDES) == len(self.model_cfg.NUM_FILTERS)
+            layer_nums = self.model_cfg.LAYER_NUMS
+            layer_strides = self.model_cfg.LAYER_STRIDES
+            num_filters = self.model_cfg.NUM_FILTERS
+        else:
+            layer_nums = layer_strides = num_filters = []
+
+        if self.model_cfg.get('UPSAMPLE_STRIDES', None) is not None:
+            assert len(self.model_cfg.UPSAMPLE_STRIDES) == len(self.model_cfg.NUM_UPSAMPLE_FILTERS)
+            num_upsample_filters = self.model_cfg.NUM_UPSAMPLE_FILTERS
+            upsample_strides = self.model_cfg.UPSAMPLE_STRIDES
+        else:
+            upsample_strides = num_upsample_filters = []
+
+        num_levels = len(layer_nums)
+        c_in_list = [input_channels, *num_filters[:-1]]
+        self.blocks = nn.ModuleList()
+        self.deblocks = nn.ModuleList()
+        for idx in range(num_levels):
+            cur_layers = [
+                nn.ZeroPad2d(1),
+                nn.Conv2d(
+                    c_in_list[idx], num_filters[idx], kernel_size=3,
+                    stride=layer_strides[idx], padding=0, bias=False
+                ),
+                nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                nn.ReLU()
+            ]
+            for k in range(layer_nums[idx]):
+                cur_layers.extend([
+                    nn.Conv2d(num_filters[idx], num_filters[idx], kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                    nn.ReLU()
+                ])
+            self.blocks.append(nn.Sequential(*cur_layers))
+            if len(upsample_strides) > 0:
+                stride = upsample_strides[idx]
+                if stride > 1 or (stride == 1 and not self.model_cfg.get('USE_CONV_FOR_NO_STRIDE', False)):
+                    self.deblocks.append(nn.Sequential(
+                        nn.ConvTranspose2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            upsample_strides[idx],
+                            stride=upsample_strides[idx], bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+                else:
+                    stride = np.round(1 / stride).astype(np.int)
+                    self.deblocks.append(nn.Sequential(
+                        nn.Conv2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            stride,
+                            stride=stride, bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+
+        c_in = sum(num_upsample_filters)
+        if len(upsample_strides) > num_levels:
+            self.deblocks.append(nn.Sequential(
+                nn.ConvTranspose2d(c_in, c_in, upsample_strides[-1], stride=upsample_strides[-1], bias=False),
+                nn.BatchNorm2d(c_in, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            ))
+
+        self.num_bev_features = c_in
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                spatial_features
+        Returns:
+        """
+        spatial_features = data_dict['spatial_features']
+        ups = []
+        ret_dict = {}
+        x = spatial_features
+        for i in range(len(self.blocks)):
+            x = self.blocks[i](x)
+
+            stride = int(spatial_features.shape[2] / x.shape[2])
+            ret_dict['spatial_features_%dx' % stride] = x
+            if len(self.deblocks) > 0:
+                ups.append(self.deblocks[i](x))
+            else:
+                ups.append(x)
+
+        if len(ups) > 1:
+            x = torch.cat(ups, dim=1)
+        elif len(ups) == 1:
+            x = ups[0]
+
+        if len(self.deblocks) > len(self.blocks):
+            x = self.deblocks[-1](x)
+
+        data_dict['spatial_features_2d'] = x
+
+        return data_dict
+
+
+class BaseBEVBackboneV1(nn.Module):
+    def __init__(self, model_cfg, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        layer_nums = self.model_cfg.LAYER_NUMS
+        num_filters = self.model_cfg.NUM_FILTERS
+        assert len(layer_nums) == len(num_filters) == 2
+
+        num_upsample_filters = self.model_cfg.NUM_UPSAMPLE_FILTERS
+        upsample_strides = self.model_cfg.UPSAMPLE_STRIDES
+        assert len(num_upsample_filters) == len(upsample_strides)
+
+        num_levels = len(layer_nums)
+        self.blocks = nn.ModuleList()
+        self.deblocks = nn.ModuleList()
+        for idx in range(num_levels):
+            cur_layers = [
+                nn.ZeroPad2d(1),
+                nn.Conv2d(
+                    num_filters[idx], num_filters[idx], kernel_size=3,
+                    stride=1, padding=0, bias=False
+                ),
+                nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                nn.ReLU()
+            ]
+            for k in range(layer_nums[idx]):
+                cur_layers.extend([
+                    nn.Conv2d(num_filters[idx], num_filters[idx], kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                    nn.ReLU()
+                ])
+            self.blocks.append(nn.Sequential(*cur_layers))
+            if len(upsample_strides) > 0:
+                stride = upsample_strides[idx]
+                if stride >= 1:
+                    self.deblocks.append(nn.Sequential(
+                        nn.ConvTranspose2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            upsample_strides[idx],
+                            stride=upsample_strides[idx], bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+                else:
+                    stride = np.round(1 / stride).astype(np.int)
+                    self.deblocks.append(nn.Sequential(
+                        nn.Conv2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            stride,
+                            stride=stride, bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+
+        c_in = sum(num_upsample_filters)
+        if len(upsample_strides) > num_levels:
+            self.deblocks.append(nn.Sequential(
+                nn.ConvTranspose2d(c_in, c_in, upsample_strides[-1], stride=upsample_strides[-1], bias=False),
+                nn.BatchNorm2d(c_in, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            ))
+
+        self.num_bev_features = c_in
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                spatial_features
+        Returns:
+        """
+        spatial_features = data_dict['multi_scale_2d_features']
+
+        x_conv4 = spatial_features['x_conv4']
+        x_conv5 = spatial_features['x_conv5']
+
+        ups = [self.deblocks[0](x_conv4)]
+
+        x = self.blocks[1](x_conv5)
+        ups.append(self.deblocks[1](x))
+
+        x = torch.cat(ups, dim=1)
+        x = self.blocks[0](x)
+
+        data_dict['spatial_features_2d'] = x
+
+        return data_dict
+
+
+class BasicBlock(nn.Module):
+    expansion: int = 1
+
+    def __init__(
+        self,
+        inplanes: int,
+        planes: int,
+        stride: int = 1,
+        padding: int = 1,
+        downsample: bool = False,
+    ) -> None:
+        super().__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=padding, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes, eps=1e-3, momentum=0.01)
+        self.relu1 = nn.ReLU()
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes, eps=1e-3, momentum=0.01)
+        self.relu2 = nn.ReLU()
+        self.downsample = downsample
+        if self.downsample:
+            self.downsample_layer = nn.Sequential(
+                nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False),
+                nn.BatchNorm2d(planes, eps=1e-3, momentum=0.01)
+            )
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu1(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample:
+            identity = self.downsample_layer(x)
+
+        out += identity
+        out = self.relu2(out)
+
+        return out
+
+
+class BaseBEVResBackbone(nn.Module):
+    def __init__(self, model_cfg, input_channels):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        if self.model_cfg.get('LAYER_NUMS', None) is not None:
+            assert len(self.model_cfg.LAYER_NUMS) == len(self.model_cfg.LAYER_STRIDES) == len(self.model_cfg.NUM_FILTERS)
+            layer_nums = self.model_cfg.LAYER_NUMS
+            layer_strides = self.model_cfg.LAYER_STRIDES
+            num_filters = self.model_cfg.NUM_FILTERS
+        else:
+            layer_nums = layer_strides = num_filters = []
+
+        if self.model_cfg.get('UPSAMPLE_STRIDES', None) is not None:
+            assert len(self.model_cfg.UPSAMPLE_STRIDES) == len(self.model_cfg.NUM_UPSAMPLE_FILTERS)
+            num_upsample_filters = self.model_cfg.NUM_UPSAMPLE_FILTERS
+            upsample_strides = self.model_cfg.UPSAMPLE_STRIDES
+        else:
+            upsample_strides = num_upsample_filters = []
+
+        num_levels = len(layer_nums)
+        c_in_list = [input_channels, *num_filters[:-1]]
+        self.blocks = nn.ModuleList()
+        self.deblocks = nn.ModuleList()
+        for idx in range(num_levels):
+            cur_layers = [
+                # nn.ZeroPad2d(1),
+                BasicBlock(c_in_list[idx], num_filters[idx], layer_strides[idx], 1, True)
+            ]
+            for k in range(layer_nums[idx]):
+                cur_layers.extend([
+                    BasicBlock(num_filters[idx], num_filters[idx])
+                ])
+            self.blocks.append(nn.Sequential(*cur_layers))
+            if len(upsample_strides) > 0:
+                stride = upsample_strides[idx]
+                if stride >= 1:
+                    self.deblocks.append(nn.Sequential(
+                        nn.ConvTranspose2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            upsample_strides[idx],
+                            stride=upsample_strides[idx], bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+                else:
+                    stride = np.round(1 / stride).astype(np.int)
+                    self.deblocks.append(nn.Sequential(
+                        nn.Conv2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            stride,
+                            stride=stride, bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+
+        c_in = sum(num_upsample_filters) if len(num_upsample_filters) > 0 else sum(num_filters)
+        if len(upsample_strides) > num_levels:
+            self.deblocks.append(nn.Sequential(
+                nn.ConvTranspose2d(c_in, c_in, upsample_strides[-1], stride=upsample_strides[-1], bias=False),
+                nn.BatchNorm2d(c_in, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            ))
+
+        self.num_bev_features = c_in
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                spatial_features
+        Returns:
+        """
+        spatial_features = data_dict['spatial_features']
+        ups = []
+        ret_dict = {}
+        x = spatial_features
+        for i in range(len(self.blocks)):
+            x = self.blocks[i](x)
+
+            stride = int(spatial_features.shape[2] / x.shape[2])
+            ret_dict['spatial_features_%dx' % stride] = x
+            if len(self.deblocks) > 0:
+                ups.append(self.deblocks[i](x))
+            else:
+                ups.append(x)
+
+        if len(ups) > 1:
+            x = torch.cat(ups, dim=1)
+        elif len(ups) == 1:
+            x = ups[0]
+
+        if len(self.deblocks) > len(self.blocks):
+            x = self.deblocks[-1](x)
+
+        data_dict['spatial_features_2d'] = x
+
+        return data_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/fuser/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/fuser/__init__.py
new file mode 100644
index 000000000..b65b9738c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/fuser/__init__.py
@@ -0,0 +1,4 @@
+from .convfuser import ConvFuser
+__all__ = {
+    'ConvFuser':ConvFuser
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/fuser/convfuser.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/fuser/convfuser.py
new file mode 100644
index 000000000..9107e75a7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/fuser/convfuser.py
@@ -0,0 +1,33 @@
+import torch
+from torch import nn
+
+
+class ConvFuser(nn.Module):
+    def __init__(self,model_cfg) -> None:
+        super().__init__()
+        self.model_cfg = model_cfg
+        in_channel = self.model_cfg.IN_CHANNEL
+        out_channel = self.model_cfg.OUT_CHANNEL
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channel, out_channel, 3, padding=1, bias=False),
+            nn.BatchNorm2d(out_channel),
+            nn.ReLU(True)
+            )
+        
+    def forward(self,batch_dict):
+        """
+        Args:
+            batch_dict:
+                spatial_features_img (tensor): Bev features from image modality
+                spatial_features (tensor): Bev features from lidar modality
+
+        Returns:
+            batch_dict:
+                spatial_features (tensor): Bev features after muli-modal fusion
+        """
+        img_bev = batch_dict['spatial_features_img']
+        lidar_bev = batch_dict['spatial_features']
+        cat_bev = torch.cat([img_bev,lidar_bev],dim=1)
+        mm_bev = self.conv(cat_bev)
+        batch_dict['spatial_features'] = mm_bev
+        return batch_dict
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/__init__.py
new file mode 100644
index 000000000..e7724d1ac
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/__init__.py
@@ -0,0 +1,10 @@
+from .height_compression import HeightCompression
+from .pointpillar_scatter import PointPillarScatter, PointPillarScatter3d
+from .conv2d_collapse import Conv2DCollapse
+
+__all__ = {
+    'HeightCompression': HeightCompression,
+    'PointPillarScatter': PointPillarScatter,
+    'Conv2DCollapse': Conv2DCollapse,
+    'PointPillarScatter3d': PointPillarScatter3d,
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/conv2d_collapse.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/conv2d_collapse.py
new file mode 100644
index 000000000..e7d73baf2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/conv2d_collapse.py
@@ -0,0 +1,38 @@
+import torch
+import torch.nn as nn
+
+from pcdet.models.model_utils.basic_block_2d import BasicBlock2D
+
+
+class Conv2DCollapse(nn.Module):
+
+    def __init__(self, model_cfg, grid_size):
+        """
+        Initializes 2D convolution collapse module
+        Args:
+            model_cfg: EasyDict, Model configuration
+            grid_size: (X, Y, Z) Voxel grid size
+        """
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_heights = grid_size[-1]
+        self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
+        self.block = BasicBlock2D(in_channels=self.num_bev_features * self.num_heights,
+                                  out_channels=self.num_bev_features,
+                                  **self.model_cfg.ARGS)
+
+    def forward(self, batch_dict):
+        """
+        Collapses voxel features to BEV via concatenation and channel reduction
+        Args:
+            batch_dict:
+                voxel_features: (B, C, Z, Y, X), Voxel feature representation
+        Returns:
+            batch_dict:
+                spatial_features: (B, C, Y, X), BEV feature representation
+        """
+        voxel_features = batch_dict["voxel_features"]
+        bev_features = voxel_features.flatten(start_dim=1, end_dim=2)  # (B, C, Z, Y, X) -> (B, C*Z, Y, X)
+        bev_features = self.block(bev_features)  # (B, C*Z, Y, X) -> (B, C, Y, X)
+        batch_dict["spatial_features"] = bev_features
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/height_compression.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/height_compression.py
new file mode 100644
index 000000000..98c8e573e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/height_compression.py
@@ -0,0 +1,26 @@
+import torch.nn as nn
+
+
+class HeightCompression(nn.Module):
+    def __init__(self, model_cfg, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        Returns:
+            batch_dict:
+                spatial_features:
+
+        """
+        encoded_spconv_tensor = batch_dict['encoded_spconv_tensor']
+        spatial_features = encoded_spconv_tensor.dense()
+        N, C, D, H, W = spatial_features.shape
+        spatial_features = spatial_features.view(N, C * D, H, W)
+        batch_dict['spatial_features'] = spatial_features
+        batch_dict['spatial_features_stride'] = batch_dict['encoded_spconv_tensor_stride']
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/pointpillar_scatter.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/pointpillar_scatter.py
new file mode 100644
index 000000000..c57cda867
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_2d/map_to_bev/pointpillar_scatter.py
@@ -0,0 +1,73 @@
+import torch
+import torch.nn as nn
+
+
+class PointPillarScatter(nn.Module):
+    def __init__(self, model_cfg, grid_size, **kwargs):
+        super().__init__()
+
+        self.model_cfg = model_cfg
+        self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
+        self.nx, self.ny, self.nz = grid_size
+        assert self.nz == 1
+
+    def forward(self, batch_dict, **kwargs):
+        pillar_features, coords = batch_dict['pillar_features'], batch_dict['voxel_coords']
+        batch_spatial_features = []
+        batch_size = coords[:, 0].max().int().item() + 1
+        for batch_idx in range(batch_size):
+            spatial_feature = torch.zeros(
+                self.num_bev_features,
+                self.nz * self.nx * self.ny,
+                dtype=pillar_features.dtype,
+                device=pillar_features.device)
+
+            batch_mask = coords[:, 0] == batch_idx
+            this_coords = coords[batch_mask, :]
+            indices = this_coords[:, 1] + this_coords[:, 2] * self.nx + this_coords[:, 3]
+            indices = indices.type(torch.long)
+            pillars = pillar_features[batch_mask, :]
+            pillars = pillars.t()
+            spatial_feature[:, indices] = pillars
+            batch_spatial_features.append(spatial_feature)
+
+        batch_spatial_features = torch.stack(batch_spatial_features, 0)
+        batch_spatial_features = batch_spatial_features.view(batch_size, self.num_bev_features * self.nz, self.ny, self.nx)
+        batch_dict['spatial_features'] = batch_spatial_features
+        return batch_dict
+
+
+class PointPillarScatter3d(nn.Module):
+    def __init__(self, model_cfg, grid_size, **kwargs):
+        super().__init__()
+        
+        self.model_cfg = model_cfg
+        self.nx, self.ny, self.nz = self.model_cfg.INPUT_SHAPE
+        self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
+        self.num_bev_features_before_compression = self.model_cfg.NUM_BEV_FEATURES // self.nz
+
+    def forward(self, batch_dict, **kwargs):
+        pillar_features, coords = batch_dict['pillar_features'], batch_dict['voxel_coords']
+        
+        batch_spatial_features = []
+        batch_size = coords[:, 0].max().int().item() + 1
+        for batch_idx in range(batch_size):
+            spatial_feature = torch.zeros(
+                self.num_bev_features_before_compression,
+                self.nz * self.nx * self.ny,
+                dtype=pillar_features.dtype,
+                device=pillar_features.device)
+
+            batch_mask = coords[:, 0] == batch_idx
+            this_coords = coords[batch_mask, :]
+            indices = this_coords[:, 1] * self.ny * self.nx + this_coords[:, 2] * self.nx + this_coords[:, 3]
+            indices = indices.type(torch.long)
+            pillars = pillar_features[batch_mask, :]
+            pillars = pillars.t()
+            spatial_feature[:, indices] = pillars
+            batch_spatial_features.append(spatial_feature)
+
+        batch_spatial_features = torch.stack(batch_spatial_features, 0)
+        batch_spatial_features = batch_spatial_features.view(batch_size, self.num_bev_features_before_compression * self.nz, self.ny, self.nx)
+        batch_dict['spatial_features'] = batch_spatial_features
+        return batch_dict
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/__init__.py
new file mode 100644
index 000000000..0a25c626a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/__init__.py
@@ -0,0 +1,22 @@
+from .pointnet2_backbone import PointNet2Backbone, PointNet2MSG
+from .spconv_backbone import VoxelBackBone8x, VoxelResBackBone8x
+from .spconv_backbone_2d import PillarBackBone8x, PillarRes18BackBone8x
+from .spconv_backbone_focal import VoxelBackBone8xFocal
+from .spconv_backbone_voxelnext import VoxelResBackBone8xVoxelNeXt
+from .spconv_backbone_voxelnext2d import VoxelResBackBone8xVoxelNeXt2D
+from .spconv_unet import UNetV2
+from .dsvt import DSVT
+
+__all__ = {
+    'VoxelBackBone8x': VoxelBackBone8x,
+    'UNetV2': UNetV2,
+    'PointNet2Backbone': PointNet2Backbone,
+    'PointNet2MSG': PointNet2MSG,
+    'VoxelResBackBone8x': VoxelResBackBone8x,
+    'VoxelBackBone8xFocal': VoxelBackBone8xFocal,
+    'VoxelResBackBone8xVoxelNeXt': VoxelResBackBone8xVoxelNeXt,
+    'VoxelResBackBone8xVoxelNeXt2D': VoxelResBackBone8xVoxelNeXt2D,
+    'PillarBackBone8x': PillarBackBone8x,
+    'PillarRes18BackBone8x': PillarRes18BackBone8x,
+    'DSVT': DSVT,
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/dsvt.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/dsvt.py
new file mode 100644
index 000000000..8c3e27936
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/dsvt.py
@@ -0,0 +1,616 @@
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+from math import ceil
+
+from pcdet.models.model_utils.dsvt_utils import get_window_coors, get_inner_win_inds_cuda, get_pooling_index, get_continous_inds
+from pcdet.models.model_utils.dsvt_utils import PositionEmbeddingLearned
+
+
+class DSVT(nn.Module):
+    '''Dynamic Sparse Voxel Transformer Backbone.
+    Args:
+        INPUT_LAYER: Config of input layer, which converts the output of vfe to dsvt input.
+        block_name (list[string]): Name of blocks for each stage. Length: stage_num.
+        set_info (list[list[int, int]]): A list of set config for each stage. Eelement i contains 
+            [set_size, block_num], where set_size is the number of voxel in a set and block_num is the
+            number of blocks for stage i. Length: stage_num.
+        d_model (list[int]): Number of input channels for each stage. Length: stage_num.
+        nhead (list[int]): Number of attention heads for each stage. Length: stage_num.
+        dim_feedforward (list[int]): Dimensions of the feedforward network in set attention for each stage. 
+            Length: stage num.
+        dropout (float): Drop rate of set attention. 
+        activation (string): Name of activation layer in set attention.
+        reduction_type (string): Pooling method between stages. One of: "attention", "maxpool", "linear".
+        output_shape (tuple[int, int]): Shape of output bev feature.
+        conv_out_channel (int): Number of output channels.
+
+    '''
+    def __init__(self, model_cfg, **kwargs):
+        super().__init__()
+
+        self.model_cfg = model_cfg
+        self.input_layer = DSVTInputLayer(self.model_cfg.INPUT_LAYER)
+        block_name = self.model_cfg.block_name
+        set_info = self.model_cfg.set_info
+        d_model = self.model_cfg.d_model
+        nhead = self.model_cfg.nhead
+        dim_feedforward = self.model_cfg.dim_feedforward
+        dropout = self.model_cfg.dropout
+        activation = self.model_cfg.activation
+        self.reduction_type = self.model_cfg.get('reduction_type', 'attention')
+        # save GPU memory
+        self.use_torch_ckpt = self.model_cfg.get('USE_CHECKPOINT', False)
+ 
+        # Sparse Regional Attention Blocks
+        stage_num = len(block_name)
+        for stage_id in range(stage_num):
+            num_blocks_this_stage = set_info[stage_id][-1]
+            dmodel_this_stage = d_model[stage_id]
+            dfeed_this_stage = dim_feedforward[stage_id]
+            num_head_this_stage = nhead[stage_id]
+            block_name_this_stage = block_name[stage_id]
+            block_module = _get_block_module(block_name_this_stage)
+            block_list=[]
+            norm_list=[]
+            for i in range(num_blocks_this_stage):
+                block_list.append(
+                    block_module(dmodel_this_stage, num_head_this_stage, dfeed_this_stage,
+                                 dropout, activation, batch_first=True)
+                )
+                norm_list.append(nn.LayerNorm(dmodel_this_stage))
+            self.__setattr__(f'stage_{stage_id}', nn.ModuleList(block_list))
+            self.__setattr__(f'residual_norm_stage_{stage_id}', nn.ModuleList(norm_list))
+
+            # apply pooling except the last stage
+            if stage_id < stage_num-1:
+                downsample_window = self.model_cfg.INPUT_LAYER.downsample_stride[stage_id]
+                dmodel_next_stage = d_model[stage_id+1]
+                pool_volume = torch.IntTensor(downsample_window).prod().item()
+                if self.reduction_type == 'linear':
+                    cat_feat_dim = dmodel_this_stage * torch.IntTensor(downsample_window).prod().item()
+                    self.__setattr__(f'stage_{stage_id}_reduction', Stage_Reduction_Block(cat_feat_dim, dmodel_next_stage))
+                elif self.reduction_type == 'maxpool':
+                    self.__setattr__(f'stage_{stage_id}_reduction', torch.nn.MaxPool1d(pool_volume))
+                elif self.reduction_type == 'attention':
+                    self.__setattr__(f'stage_{stage_id}_reduction', Stage_ReductionAtt_Block(dmodel_this_stage, pool_volume))
+                else:
+                    raise NotImplementedError
+
+        self.num_shifts = [2] * stage_num
+        self.output_shape = self.model_cfg.output_shape
+        self.stage_num = stage_num
+        self.set_info = set_info
+        self.num_point_features = self.model_cfg.conv_out_channel
+
+        self._reset_parameters()
+
+    def forward(self, batch_dict):
+        '''
+        Args:
+            bacth_dict (dict): 
+                The dict contains the following keys
+                - voxel_features (Tensor[float]): Voxel features after VFE. Shape of (N, d_model[0]), 
+                    where N is the number of input voxels.
+                - voxel_coords (Tensor[int]): Shape of (N, 4), corresponding voxel coordinates of each voxels.
+                    Each row is (batch_id, z, y, x). 
+                - ...
+        
+        Returns:
+            bacth_dict (dict):
+                The dict contains the following keys
+                - pillar_features (Tensor[float]):
+                - voxel_coords (Tensor[int]):
+                - ...
+        '''
+        voxel_info = self.input_layer(batch_dict)
+
+        voxel_feat = voxel_info['voxel_feats_stage0']
+        set_voxel_inds_list = [[voxel_info[f'set_voxel_inds_stage{s}_shift{i}'] for i in range(self.num_shifts[s])] for s in range(self.stage_num)]
+        set_voxel_masks_list = [[voxel_info[f'set_voxel_mask_stage{s}_shift{i}'] for i in range(self.num_shifts[s])] for s in range(self.stage_num)]
+        pos_embed_list = [[[voxel_info[f'pos_embed_stage{s}_block{b}_shift{i}'] for i in range(self.num_shifts[s])] for b in range(self.set_info[s][1])] for s in range(self.stage_num)]
+        pooling_mapping_index = [voxel_info[f'pooling_mapping_index_stage{s+1}'] for s in range(self.stage_num-1)]
+        pooling_index_in_pool = [voxel_info[f'pooling_index_in_pool_stage{s+1}'] for s in range(self.stage_num-1)]
+        pooling_preholder_feats = [voxel_info[f'pooling_preholder_feats_stage{s+1}'] for s in range(self.stage_num-1)]
+
+        output = voxel_feat
+        block_id = 0
+        for stage_id in range(self.stage_num):
+            block_layers = self.__getattr__(f'stage_{stage_id}')
+            residual_norm_layers = self.__getattr__(f'residual_norm_stage_{stage_id}')
+            for i in range(len(block_layers)):
+                block = block_layers[i]
+                residual = output.clone()
+                if self.use_torch_ckpt==False:
+                    output = block(output, set_voxel_inds_list[stage_id], set_voxel_masks_list[stage_id], pos_embed_list[stage_id][i], \
+                                block_id=block_id)
+                else:
+                    output = checkpoint(block, output, set_voxel_inds_list[stage_id], set_voxel_masks_list[stage_id], pos_embed_list[stage_id][i], block_id)
+                output = residual_norm_layers[i](output + residual)
+                block_id += 1
+            if stage_id < self.stage_num - 1:
+                # pooling
+                prepool_features = pooling_preholder_feats[stage_id].type_as(output)
+                pooled_voxel_num = prepool_features.shape[0]
+                pool_volume = prepool_features.shape[1]
+                prepool_features[pooling_mapping_index[stage_id], pooling_index_in_pool[stage_id]] = output
+                prepool_features = prepool_features.view(prepool_features.shape[0], -1)
+                
+                if self.reduction_type == 'linear':
+                    output = self.__getattr__(f'stage_{stage_id}_reduction')(prepool_features)
+                elif self.reduction_type == 'maxpool':
+                    prepool_features = prepool_features.view(pooled_voxel_num, pool_volume, -1).permute(0, 2, 1)
+                    output = self.__getattr__(f'stage_{stage_id}_reduction')(prepool_features).squeeze(-1)
+                elif self.reduction_type == 'attention':
+                    prepool_features = prepool_features.view(pooled_voxel_num, pool_volume, -1).permute(0, 2, 1)
+                    key_padding_mask = torch.zeros((pooled_voxel_num, pool_volume)).to(prepool_features.device).int()
+                    output = self.__getattr__(f'stage_{stage_id}_reduction')(prepool_features, key_padding_mask)
+                else:
+                    raise NotImplementedError
+
+        batch_dict['pillar_features'] = batch_dict['voxel_features'] = output
+        batch_dict['voxel_coords'] = voxel_info[f'voxel_coors_stage{self.stage_num - 1}']
+        return batch_dict
+
+    def _reset_parameters(self):
+        for name, p in self.named_parameters():
+            if p.dim() > 1 and 'scaler' not in name:
+                nn.init.xavier_uniform_(p)
+
+
+class DSVTBlock(nn.Module):
+    ''' Consist of two encoder layer, shift and shift back.
+    '''
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
+                 activation="relu", batch_first=True):
+        super().__init__()
+
+        encoder_1 = DSVT_EncoderLayer(d_model, nhead, dim_feedforward, dropout,
+                                        activation, batch_first)
+        encoder_2 = DSVT_EncoderLayer(d_model, nhead, dim_feedforward, dropout,
+                                        activation, batch_first)
+        self.encoder_list = nn.ModuleList([encoder_1, encoder_2])
+
+    def forward(
+            self,
+            src,
+            set_voxel_inds_list,
+            set_voxel_masks_list,
+            pos_embed_list,
+            block_id,
+    ):
+        num_shifts = 2
+        output = src
+        # TODO: bug to be fixed, mismatch of pos_embed
+        for i in range(num_shifts):
+            set_id = i
+            shift_id = block_id % 2
+            pos_embed_id = i
+            set_voxel_inds = set_voxel_inds_list[shift_id][set_id]
+            set_voxel_masks = set_voxel_masks_list[shift_id][set_id]
+            pos_embed = pos_embed_list[pos_embed_id]
+            layer = self.encoder_list[i]
+            output = layer(output, set_voxel_inds, set_voxel_masks, pos_embed)
+
+        return output
+
+
+class DSVT_EncoderLayer(nn.Module):
+
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
+                 activation="relu", batch_first=True, mlp_dropout=0):
+        super().__init__()
+        self.win_attn = SetAttention(d_model, nhead, dropout, dim_feedforward, activation, batch_first, mlp_dropout)
+        self.norm = nn.LayerNorm(d_model)
+        self.d_model = d_model
+
+    def forward(self,src,set_voxel_inds,set_voxel_masks,pos=None):
+        identity = src
+        src = self.win_attn(src, pos, set_voxel_masks, set_voxel_inds)
+        src = src + identity
+        src = self.norm(src)
+
+        return src
+
+class SetAttention(nn.Module):
+
+    def __init__(self, d_model, nhead, dropout, dim_feedforward=2048, activation="relu", batch_first=True, mlp_dropout=0):
+        super().__init__()
+        self.nhead = nhead
+        if batch_first:
+            self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
+        else:
+            self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(mlp_dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+        self.d_model = d_model
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Identity()
+        self.dropout2 = nn.Identity()
+
+        self.activation = _get_activation_fn(activation)
+
+    def forward(self, src, pos=None, key_padding_mask=None, voxel_inds=None):
+        '''
+        Args:
+            src (Tensor[float]): Voxel features with shape (N, C), where N is the number of voxels.
+            pos (Tensor[float]): Position embedding vectors with shape (N, C).
+            key_padding_mask (Tensor[bool]): Mask for redundant voxels within set. Shape of (set_num, set_size).
+            voxel_inds (Tensor[int]): Voxel indexs for each set. Shape of (set_num, set_size).
+        Returns:
+            src (Tensor[float]): Voxel features.
+        '''
+        set_features = src[voxel_inds]
+        if pos is not None:
+            set_pos = pos[voxel_inds]
+        else:
+            set_pos = None
+        if pos is not None:
+            query = set_features + set_pos
+            key = set_features + set_pos
+            value = set_features
+
+        if key_padding_mask is not None:
+            src2 = self.self_attn(query, key, value, key_padding_mask)[0]
+        else:
+            src2 = self.self_attn(query, key, value)[0]
+
+        # map voxel featurs from set space to voxel space: (set_num, set_size, C) --> (N, C)
+        flatten_inds = voxel_inds.reshape(-1)
+        unique_flatten_inds, inverse = torch.unique(flatten_inds, return_inverse=True)
+        perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)
+        inverse, perm = inverse.flip([0]), perm.flip([0])
+        perm = inverse.new_empty(unique_flatten_inds.size(0)).scatter_(0, inverse, perm)
+        src2 = src2.reshape(-1, self.d_model)[perm]
+
+        # FFN layer
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+
+        return src
+
+
+class Stage_Reduction_Block(nn.Module):
+    def __init__(self, input_channel, output_channel):
+        super().__init__()
+        self.linear1 = nn.Linear(input_channel, output_channel, bias=False)
+        self.norm = nn.LayerNorm(output_channel)
+
+    def forward(self, x):
+        src = x
+        src = self.norm(self.linear1(x))
+        return src
+
+
+class Stage_ReductionAtt_Block(nn.Module):
+    def __init__(self, input_channel, pool_volume):
+        super().__init__()
+        self.pool_volume = pool_volume
+        self.query_func = torch.nn.MaxPool1d(pool_volume)
+        self.norm = nn.LayerNorm(input_channel)
+        self.self_attn = nn.MultiheadAttention(input_channel, 8, batch_first=True)
+        self.pos_embedding = nn.Parameter(torch.randn(pool_volume, input_channel))
+        nn.init.normal_(self.pos_embedding, std=.01)
+
+    def forward(self, x, key_padding_mask):
+        # x: [voxel_num, c_dim, pool_volume]
+        src = self.query_func(x).permute(0, 2, 1)  # voxel_num, 1, c_dim
+        key = value = x.permute(0, 2, 1)
+        key = key + self.pos_embedding.unsqueeze(0).repeat(src.shape[0], 1, 1)
+        query = src.clone()
+        output = self.self_attn(query, key, value, key_padding_mask)[0]
+        src = self.norm(output + src).squeeze(1)
+        return src
+
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return torch.nn.functional.relu
+    if activation == "gelu":
+        return torch.nn.functional.gelu
+    if activation == "glu":
+        return torch.nn.functional.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+def _get_block_module(name):
+    """Return an block module given a string"""
+    if name == "DSVTBlock":
+        return DSVTBlock
+    raise RuntimeError(F"This Block not exist.")
+
+
+class DSVTInputLayer(nn.Module):
+    ''' 
+    This class converts the output of vfe to dsvt input.
+    We do in this class:
+    1. Window partition: partition voxels to non-overlapping windows.
+    2. Set partition: generate non-overlapped and size-equivalent local sets within each window.
+    3. Pre-compute the downsample infomation between two consecutive stages.
+    4. Pre-compute the position embedding vectors.
+
+    Args:
+        sparse_shape (tuple[int, int, int]): Shape of input space (xdim, ydim, zdim).
+        window_shape (list[list[int, int, int]]): Window shapes (winx, winy, winz) in different stages. Length: stage_num.
+        downsample_stride (list[list[int, int, int]]): Downsample strides between two consecutive stages. 
+            Element i is [ds_x, ds_y, ds_z], which is used between stage_i and stage_{i+1}. Length: stage_num - 1.
+        d_model (list[int]): Number of input channels for each stage. Length: stage_num.
+        set_info (list[list[int, int]]): A list of set config for each stage. Eelement i contains 
+            [set_size, block_num], where set_size is the number of voxel in a set and block_num is the
+            number of blocks for stage i. Length: stage_num.
+        hybrid_factor (list[int, int, int]): Control the window shape in different blocks. 
+            e.g. for block_{0} and block_{1} in stage_0, window shapes are [win_x, win_y, win_z] and 
+            [win_x * h[0], win_y * h[1], win_z * h[2]] respectively.
+        shift_list (list): Shift window. Length: stage_num.
+        normalize_pos (bool): Whether to normalize coordinates in position embedding.
+    '''
+    def __init__(self, model_cfg):
+        super().__init__()
+        
+        self.model_cfg = model_cfg 
+        self.sparse_shape = self.model_cfg.sparse_shape
+        self.window_shape = self.model_cfg.window_shape
+        self.downsample_stride = self.model_cfg.downsample_stride
+        self.d_model = self.model_cfg.d_model
+        self.set_info = self.model_cfg.set_info
+        self.stage_num = len(self.d_model)
+
+        self.hybrid_factor = self.model_cfg.hybrid_factor
+        self.window_shape = [[self.window_shape[s_id], [self.window_shape[s_id][coord_id] * self.hybrid_factor[coord_id] \
+                                                for coord_id in range(3)]] for s_id in range(self.stage_num)]
+        self.shift_list = self.model_cfg.shifts_list
+        self.normalize_pos = self.model_cfg.normalize_pos
+            
+        self.num_shifts = [2,] * len(self.window_shape)
+
+        self.sparse_shape_list = [self.sparse_shape]
+        # compute sparse shapes for each stage
+        for ds_stride in self.downsample_stride:
+            last_sparse_shape = self.sparse_shape_list[-1]
+            self.sparse_shape_list.append((ceil(last_sparse_shape[0]/ds_stride[0]), ceil(last_sparse_shape[1]/ds_stride[1]), ceil(last_sparse_shape[2]/ds_stride[2])))
+        
+        # position embedding layers
+        self.posembed_layers = nn.ModuleList()
+        for i in range(len(self.set_info)):
+            input_dim = 3 if self.sparse_shape_list[i][-1] > 1 else 2
+            stage_posembed_layers = nn.ModuleList()
+            for j in range(self.set_info[i][1]):
+                block_posembed_layers = nn.ModuleList()
+                for s in range(self.num_shifts[i]):
+                    block_posembed_layers.append(PositionEmbeddingLearned(input_dim, self.d_model[i]))
+                stage_posembed_layers.append(block_posembed_layers)
+            self.posembed_layers.append(stage_posembed_layers)
+       
+    def forward(self, batch_dict):
+        '''
+        Args:
+            bacth_dict (dict): 
+                The dict contains the following keys
+                - voxel_features (Tensor[float]): Voxel features after VFE with shape (N, d_model[0]), 
+                    where N is the number of input voxels.
+                - voxel_coords (Tensor[int]): Shape of (N, 4), corresponding voxel coordinates of each voxels.
+                    Each row is (batch_id, z, y, x). 
+                - ...
+        
+        Returns:
+            voxel_info (dict):
+                The dict contains the following keys
+                - voxel_coors_stage{i} (Tensor[int]): Shape of (N_i, 4). N is the number of voxels in stage_i.
+                    Each row is (batch_id, z, y, x).
+                - set_voxel_inds_stage{i}_shift{j} (Tensor[int]): Set partition index with shape (2, set_num, set_info[i][0]).
+                    2 indicates x-axis partition and y-axis partition. 
+                - set_voxel_mask_stage{i}_shift{i} (Tensor[bool]): Key mask used in set attention with shape (2, set_num, set_info[i][0]).
+                - pos_embed_stage{i}_block{i}_shift{i} (Tensor[float]): Position embedding vectors with shape (N_i, d_model[i]). N_i is the 
+                    number of remain voxels in stage_i;
+                - pooling_mapping_index_stage{i} (Tensor[int]): Pooling region index used in pooling operation between stage_{i-1} and stage_{i}
+                    with shape (N_{i-1}). 
+                - pooling_index_in_pool_stage{i} (Tensor[int]): Index inner region with shape (N_{i-1}). Combined with pooling_mapping_index_stage{i}, 
+                    we can map each voxel in satge_{i-1} to pooling_preholder_feats_stage{i}, which are input of downsample operation.
+                - pooling_preholder_feats_stage{i} (Tensor[int]): Preholder features initial with value 0. 
+                    Shape of (N_{i}, downsample_stride[i-1].prob(), d_moel[i-1]), where prob() returns the product of all elements.
+                - ...
+        '''
+        voxel_feats = batch_dict['voxel_features']
+        voxel_coors = batch_dict['voxel_coords'].long()
+
+        voxel_info = {}
+        voxel_info['voxel_feats_stage0'] = voxel_feats.clone()
+        voxel_info['voxel_coors_stage0'] = voxel_coors.clone()
+        
+        for stage_id in range(self.stage_num):
+            # window partition of corrsponding stage-map
+            voxel_info = self.window_partition(voxel_info, stage_id)
+            # generate set id of corrsponding stage-map
+            voxel_info = self.get_set(voxel_info, stage_id)
+            for block_id in range(self.set_info[stage_id][1]):
+                for shift_id in range(self.num_shifts[stage_id]):
+                    voxel_info[f'pos_embed_stage{stage_id}_block{block_id}_shift{shift_id}'] = \
+                    self.get_pos_embed(voxel_info[f'coors_in_win_stage{stage_id}_shift{shift_id}'], stage_id, block_id, shift_id)
+            
+            # compute pooling information
+            if stage_id < self.stage_num - 1:
+                voxel_info = self.subm_pooling(voxel_info, stage_id)
+        
+        return voxel_info
+    
+    @torch.no_grad()
+    def subm_pooling(self, voxel_info, stage_id):
+        # x,y,z stride
+        cur_stage_downsample = self.downsample_stride[stage_id]
+        # batch_win_coords is from 1 of x, y
+        batch_win_inds, _, index_in_win, batch_win_coors = get_pooling_index(voxel_info[f'voxel_coors_stage{stage_id}'], self.sparse_shape_list[stage_id], cur_stage_downsample)
+        # compute pooling mapping index
+        unique_batch_win_inds, contiguous_batch_win_inds = torch.unique(batch_win_inds, return_inverse=True)
+        voxel_info[f'pooling_mapping_index_stage{stage_id+1}'] = contiguous_batch_win_inds
+        
+        # generate empty placeholder features
+        placeholder_prepool_feats = voxel_info[f'voxel_feats_stage0'].new_zeros((len(unique_batch_win_inds), 
+                    torch.prod(torch.IntTensor(cur_stage_downsample)).item(), self.d_model[stage_id]))
+        voxel_info[f'pooling_index_in_pool_stage{stage_id+1}'] = index_in_win
+        voxel_info[f'pooling_preholder_feats_stage{stage_id+1}'] = placeholder_prepool_feats
+
+        # compute pooling coordinates
+        unique, inverse = unique_batch_win_inds.clone(), contiguous_batch_win_inds.clone()
+        perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)
+        inverse, perm = inverse.flip([0]), perm.flip([0])
+        perm = inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
+        pool_coors = batch_win_coors[perm]
+
+        voxel_info[f'voxel_coors_stage{stage_id+1}'] = pool_coors
+        
+        return voxel_info
+    
+    def get_set(self, voxel_info, stage_id):
+        '''
+        This is one of the core operation of DSVT. 
+        Given voxels' window ids and relative-coords inner window, we partition them into window-bounded and size-equivalent local sets.
+        To make it clear and easy to follow, we do not use loop to process two shifts.
+        Args:
+            voxel_info (dict): 
+                The dict contains the following keys
+                - batch_win_inds_s{i} (Tensor[float]): Windows indexs of each voxel with shape (N), computed by 'window_partition'.
+                - coors_in_win_shift{i} (Tensor[int]): Relative-coords inner window of each voxel with shape (N, 3), computed by 'window_partition'.
+                    Each row is (z, y, x). 
+                - ...
+        
+        Returns:
+            See from 'forward' function.
+        '''
+        batch_win_inds_shift0 = voxel_info[f'batch_win_inds_stage{stage_id}_shift0']
+        coors_in_win_shift0 = voxel_info[f'coors_in_win_stage{stage_id}_shift0']
+        set_voxel_inds_shift0 = self.get_set_single_shift(batch_win_inds_shift0, stage_id, shift_id=0, coors_in_win=coors_in_win_shift0)
+        voxel_info[f'set_voxel_inds_stage{stage_id}_shift0'] = set_voxel_inds_shift0  
+        # compute key masks, voxel duplication must happen continuously
+        prefix_set_voxel_inds_s0 = torch.roll(set_voxel_inds_shift0.clone(), shifts=1, dims=-1)
+        prefix_set_voxel_inds_s0[ :, :, 0] = -1
+        set_voxel_mask_s0 = (set_voxel_inds_shift0 == prefix_set_voxel_inds_s0)
+        voxel_info[f'set_voxel_mask_stage{stage_id}_shift0'] = set_voxel_mask_s0
+
+        batch_win_inds_shift1 = voxel_info[f'batch_win_inds_stage{stage_id}_shift1']
+        coors_in_win_shift1 = voxel_info[f'coors_in_win_stage{stage_id}_shift1']
+        set_voxel_inds_shift1 = self.get_set_single_shift(batch_win_inds_shift1, stage_id, shift_id=1, coors_in_win=coors_in_win_shift1)
+        voxel_info[f'set_voxel_inds_stage{stage_id}_shift1'] = set_voxel_inds_shift1  
+        # compute key masks, voxel duplication must happen continuously
+        prefix_set_voxel_inds_s1 = torch.roll(set_voxel_inds_shift1.clone(), shifts=1, dims=-1)
+        prefix_set_voxel_inds_s1[ :, :, 0] = -1
+        set_voxel_mask_s1 = (set_voxel_inds_shift1 == prefix_set_voxel_inds_s1)
+        voxel_info[f'set_voxel_mask_stage{stage_id}_shift1'] = set_voxel_mask_s1
+
+        return voxel_info
+    
+    def get_set_single_shift(self, batch_win_inds, stage_id, shift_id=None, coors_in_win=None):
+        device = batch_win_inds.device
+        # the number of voxels assigned to a set
+        voxel_num_set = self.set_info[stage_id][0]
+        # max number of voxels in a window
+        max_voxel = self.window_shape[stage_id][shift_id][0] * self.window_shape[stage_id][shift_id][1] * self.window_shape[stage_id][shift_id][2]
+        # get unique set indexs
+        contiguous_win_inds = torch.unique(batch_win_inds, return_inverse=True)[1]
+        voxelnum_per_win = torch.bincount(contiguous_win_inds)
+        win_num = voxelnum_per_win.shape[0]
+        setnum_per_win_float = voxelnum_per_win / voxel_num_set
+        setnum_per_win = torch.ceil(setnum_per_win_float).long()
+        set_win_inds, set_inds_in_win = get_continous_inds(setnum_per_win)
+        
+        # compution of Eq.3 in 'DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets' - https://arxiv.org/abs/2301.06051, 
+        # for each window, we can get voxel indexs belong to different sets.
+        offset_idx = set_inds_in_win[:,None].repeat(1, voxel_num_set) * voxel_num_set
+        base_idx = torch.arange(0, voxel_num_set, 1, device=device)
+        base_select_idx = offset_idx + base_idx
+        base_select_idx = base_select_idx * voxelnum_per_win[set_win_inds][:,None]
+        base_select_idx = base_select_idx.double() / (setnum_per_win[set_win_inds] * voxel_num_set)[:,None].double()
+        base_select_idx = torch.floor(base_select_idx)
+        # obtain unique indexs in whole space
+        select_idx = base_select_idx
+        select_idx = select_idx + set_win_inds.view(-1, 1) * max_voxel
+           
+        # this function will return unordered inner window indexs of each voxel
+        inner_voxel_inds = get_inner_win_inds_cuda(contiguous_win_inds)
+        global_voxel_inds = contiguous_win_inds * max_voxel + inner_voxel_inds
+        _, order1 = torch.sort(global_voxel_inds)
+
+        # get y-axis partition results
+        global_voxel_inds_sorty = contiguous_win_inds * max_voxel + \
+                coors_in_win[:,1] * self.window_shape[stage_id][shift_id][0] * self.window_shape[stage_id][shift_id][2] + \
+                coors_in_win[:,2] * self.window_shape[stage_id][shift_id][2] + \
+                coors_in_win[:,0]
+        _, order2 = torch.sort(global_voxel_inds_sorty)
+        inner_voxel_inds_sorty = -torch.ones_like(inner_voxel_inds)
+        inner_voxel_inds_sorty.scatter_(dim=0, index=order2, src=inner_voxel_inds[order1]) # get y-axis ordered inner window indexs of each voxel
+        voxel_inds_in_batch_sorty = inner_voxel_inds_sorty + max_voxel * contiguous_win_inds
+        voxel_inds_padding_sorty = -1 * torch.ones((win_num * max_voxel), dtype=torch.long, device=device)
+        voxel_inds_padding_sorty[voxel_inds_in_batch_sorty] = torch.arange(0, voxel_inds_in_batch_sorty.shape[0], dtype=torch.long, device=device)
+        set_voxel_inds_sorty = voxel_inds_padding_sorty[select_idx.long()]
+
+        # get x-axis partition results
+        global_voxel_inds_sortx = contiguous_win_inds * max_voxel + \
+                coors_in_win[:,2] * self.window_shape[stage_id][shift_id][1] * self.window_shape[stage_id][shift_id][2] + \
+                coors_in_win[:,1] * self.window_shape[stage_id][shift_id][2] + \
+                coors_in_win[:,0]
+        _, order2 = torch.sort(global_voxel_inds_sortx)
+        inner_voxel_inds_sortx = -torch.ones_like(inner_voxel_inds)
+        inner_voxel_inds_sortx.scatter_(dim=0,index=order2, src=inner_voxel_inds[order1]) # get x-axis ordered inner window indexs of each voxel
+        voxel_inds_in_batch_sortx = inner_voxel_inds_sortx + max_voxel * contiguous_win_inds
+        voxel_inds_padding_sortx = -1 * torch.ones((win_num * max_voxel), dtype=torch.long, device=device)
+        voxel_inds_padding_sortx[voxel_inds_in_batch_sortx] = torch.arange(0, voxel_inds_in_batch_sortx.shape[0], dtype=torch.long, device=device)
+        set_voxel_inds_sortx = voxel_inds_padding_sortx[select_idx.long()]
+
+        all_set_voxel_inds = torch.stack((set_voxel_inds_sorty, set_voxel_inds_sortx), dim=0)
+        return all_set_voxel_inds
+
+    @torch.no_grad()
+    def window_partition(self, voxel_info, stage_id):
+        for i in range(2):
+            batch_win_inds, coors_in_win = get_window_coors(voxel_info[f'voxel_coors_stage{stage_id}'], 
+                                                        self.sparse_shape_list[stage_id], self.window_shape[stage_id][i], i == 1, self.shift_list[stage_id][i])
+                                            
+            voxel_info[f'batch_win_inds_stage{stage_id}_shift{i}'] = batch_win_inds
+            voxel_info[f'coors_in_win_stage{stage_id}_shift{i}'] = coors_in_win
+        
+        return voxel_info
+
+    def get_pos_embed(self, coors_in_win, stage_id, block_id, shift_id):
+        '''
+        Args:
+        coors_in_win: shape=[N, 3], order: z, y, x
+        '''
+        # [N,]
+        window_shape = self.window_shape[stage_id][shift_id]
+       
+        embed_layer = self.posembed_layers[stage_id][block_id][shift_id]
+        if len(window_shape) == 2:
+            ndim = 2
+            win_x, win_y = window_shape
+            win_z = 0
+        elif  window_shape[-1] == 1:
+            ndim = 2
+            win_x, win_y = window_shape[:2]
+            win_z = 0
+        else:
+            win_x, win_y, win_z = window_shape
+            ndim = 3
+
+        assert coors_in_win.size(1) == 3
+        z, y, x = coors_in_win[:, 0] - win_z/2, coors_in_win[:, 1] - win_y/2, coors_in_win[:, 2] - win_x/2
+
+        if self.normalize_pos:
+            x = x / win_x * 2 * 3.1415 #[-pi, pi]
+            y = y / win_y * 2 * 3.1415 #[-pi, pi]
+            z = z / win_z * 2 * 3.1415 #[-pi, pi]
+        
+        if ndim==2:
+            location = torch.stack((x, y), dim=-1)
+        else:
+            location = torch.stack((x, y, z), dim=-1)
+        pos_embed = embed_layer(location)
+
+        return pos_embed
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/basic_blocks.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/basic_blocks.py
new file mode 100644
index 000000000..de29b4b99
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/basic_blocks.py
@@ -0,0 +1,65 @@
+import torch.nn as nn
+
+class BasicBlock1D(nn.Module):
+
+    def __init__(self, in_channels, out_channels, **kwargs):
+        """
+        Initializes convolutional block
+        Args:
+            in_channels: int, Number of input channels
+            out_channels: int, Number of output channels
+            **kwargs: Dict, Extra arguments for nn.Conv2d
+        """
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.conv = nn.Conv1d(in_channels=in_channels,
+                              out_channels=out_channels,
+                              **kwargs)
+        self.bn = nn.BatchNorm1d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, features):
+        """
+        Applies convolutional block
+        Args:
+            features: (B, C_in, H, W), Input features
+        Returns:
+            x: (B, C_out, H, W), Output features
+        """
+        x = self.conv(features)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+class BasicBlock2D(nn.Module):
+
+    def __init__(self, in_channels, out_channels, **kwargs):
+        """
+        Initializes convolutional block
+        Args:
+            in_channels: int, Number of input channels
+            out_channels: int, Number of output channels
+            **kwargs: Dict, Extra arguments for nn.Conv2d
+        """
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.conv = nn.Conv2d(in_channels=in_channels,
+                              out_channels=out_channels,
+                              **kwargs)
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, features):
+        """
+        Applies convolutional block
+        Args:
+            features: (B, C_in, H, W), Input features
+        Returns:
+            x: (B, C_out, H, W), Output features
+        """
+        x = self.conv(features)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/pyramid_ffn.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/pyramid_ffn.py
new file mode 100644
index 000000000..29773a027
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/pyramid_ffn.py
@@ -0,0 +1,77 @@
+import torch
+import torch.nn as nn
+from .basic_blocks import BasicBlock2D
+from .sem_deeplabv3 import SemDeepLabV3
+
+class PyramidFeat2D(nn.Module):
+
+    def __init__(self, optimize, model_cfg):
+        """
+        Initialize 2D feature network via pretrained model
+        Args:
+            model_cfg: EasyDict, Dense classification network config
+        """
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.is_optimize = optimize
+
+        # Create modules
+        self.ifn = SemDeepLabV3(
+            num_classes=model_cfg.num_class,
+            backbone_name=model_cfg.backbone,
+            **model_cfg.args
+        )
+        self.reduce_blocks = torch.nn.ModuleList()
+        self.out_channels = {}
+        for _idx, _channel in enumerate(model_cfg.channel_reduce["in_channels"]):
+            _channel_out = model_cfg.channel_reduce["out_channels"][_idx]
+            self.out_channels[model_cfg.args['feat_extract_layer'][_idx]] = _channel_out
+            block_cfg = {"in_channels": _channel,
+                         "out_channels": _channel_out,
+                         "kernel_size": model_cfg.channel_reduce["kernel_size"][_idx],
+                         "stride": model_cfg.channel_reduce["stride"][_idx],
+                         "bias": model_cfg.channel_reduce["bias"][_idx]}
+            self.reduce_blocks.append(BasicBlock2D(**block_cfg))
+
+    def get_output_feature_dim(self):
+        return self.out_channels
+
+    def forward(self, images):
+        """
+        Predicts depths and creates image depth feature volume using depth distributions
+        Args:
+            images: (N, 3, H_in, W_in), Input images
+        Returns:
+            batch_dict:
+                frustum_features: (N, C, D, H_out, W_out), Image depth features
+        """
+        # Pixel-wise depth classification
+        batch_dict = {}
+        ifn_result = self.ifn(images)
+
+        for _idx, _layer in enumerate(self.model_cfg.args['feat_extract_layer']):
+            image_features = ifn_result[_layer]
+            # Channel reduce
+            if self.reduce_blocks[_idx] is not None:
+                image_features = self.reduce_blocks[_idx](image_features)
+
+            batch_dict[_layer+"_feat2d"] = image_features
+        
+        if self.training:
+            # detach feature from graph if not optimize
+            if "logits" in ifn_result:
+                ifn_result["logits"].detach_()
+            if not self.is_optimize:
+                image_features.detach_()
+
+        return batch_dict
+
+    def get_loss(self):
+        """
+        Gets loss
+        Args:
+        Returns:
+            loss: (1), Network loss
+            tb_dict: dict[float], All losses to log in tensorboard
+        """
+        return None, None
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/sem_deeplabv3.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/sem_deeplabv3.py
new file mode 100644
index 000000000..9abf89818
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/sem_deeplabv3.py
@@ -0,0 +1,160 @@
+from collections import OrderedDict
+from pathlib import Path
+from torch import hub
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+
+class SegTemplate(nn.Module):
+    def __init__(self, constructor, feat_extract_layer, num_classes, pretrained_path=None, aux_loss=None):
+        """
+        Initializes depth distribution network.
+        Args:
+            constructor: function, Model constructor
+            feat_extract_layer: string, Layer to extract features from
+            num_classes: int, Number of classes
+            pretrained_path: string, (Optional) Path of the model to load weights from
+            aux_loss: bool, Flag to include auxillary loss
+        """
+        super().__init__()
+        self.num_classes = num_classes
+        self.pretrained_path = pretrained_path
+        self.pretrained = pretrained_path is not None
+        self.aux_loss = aux_loss
+
+        if self.pretrained:
+            # Preprocess Module
+            self.norm_mean = torch.Tensor([0.485, 0.456, 0.406])
+            self.norm_std = torch.Tensor([0.229, 0.224, 0.225])
+
+        # Model
+        self.model = self.get_model(constructor=constructor)
+        self.feat_extract_layer = feat_extract_layer
+
+        return_layers = {_layer:_layer for _layer in feat_extract_layer}
+        self.model.backbone.return_layers.update(return_layers)
+
+
+    def get_model(self, constructor):
+        """
+        Get model
+        Args:
+            constructor: function, Model constructor
+        Returns:
+            model: nn.Module, Model
+        """
+        # Get model
+        model = constructor(pretrained=False,
+                            pretrained_backbone=False,
+                            num_classes=self.num_classes,
+                            aux_loss=self.aux_loss)
+        # Update weights
+        if self.pretrained_path is not None:
+            model_dict = model.state_dict()
+
+            # Download pretrained model if not available yet
+            checkpoint_path = Path(self.pretrained_path)
+            if not checkpoint_path.exists():
+                checkpoint = checkpoint_path.name
+                save_dir = checkpoint_path.parent
+                save_dir.mkdir(parents=True, exist_ok=True)
+                url = f'https://download.pytorch.org/models/{checkpoint}'
+                hub.load_state_dict_from_url(url, save_dir)
+
+            # Get pretrained state dict
+            pretrained_dict = torch.load(self.pretrained_path)
+            #pretrained_dict = self.filter_pretrained_dict(model_dict=model_dict, pretrained_dict=pretrained_dict)
+
+            # Update current model state dict
+            model_dict.update(pretrained_dict)
+            model.load_state_dict(model_dict, strict=False)
+
+        return model.cuda()
+
+    def filter_pretrained_dict(self, model_dict, pretrained_dict):
+        """
+        Removes layers from pretrained state dict that are not used or changed in model
+        Args:
+            model_dict: dict, Default model state dictionary
+            pretrained_dict: dict, Pretrained model state dictionary
+        Returns:
+            pretrained_dict: dict, Pretrained model state dictionary with removed weights
+        """
+        # Removes aux classifier weights if not used
+        if "aux_classifier.0.weight" in pretrained_dict and "aux_classifier.0.weight" not in model_dict:
+            pretrained_dict = {key: value for key, value in pretrained_dict.items()
+                               if "aux_classifier" not in key}
+
+        # Removes final conv layer from weights if number of classes are different
+        model_num_classes = model_dict["classifier.4.weight"].shape[0]
+        pretrained_num_classes = pretrained_dict["classifier.4.weight"].shape[0]
+        if model_num_classes != pretrained_num_classes:
+            pretrained_dict.pop("classifier.4.weight")
+            pretrained_dict.pop("classifier.4.bias")
+
+        return pretrained_dict
+
+    def forward(self, images):
+        """
+        Forward pass
+        Args:
+            images: (N, 3, H_in, W_in), Input images
+        Returns
+            result: dict[torch.Tensor], Depth distribution result
+                features: (N, C, H_out, W_out), Image features
+                logits: (N, num_classes, H_out, W_out), Classification logits
+                aux: (N, num_classes, H_out, W_out), Auxillary classification logits
+        """
+
+        # Preprocess images
+        if self.pretrained:
+            images = (images - self.norm_mean[None, :, None, None].type_as(images)) / self.norm_std[None, :, None, None].type_as(images)
+        x = images.cuda()
+
+        # Extract features
+        result = OrderedDict()
+        features = self.model.backbone(x)
+        for _layer in self.feat_extract_layer:
+            result[_layer] = features[_layer]
+        return result
+
+        if 'features' in features.keys():
+            feat_shape = features['features'].shape[-2:]
+        else:
+            feat_shape = features['layer1'].shape[-2:]
+
+        # Prediction classification logits
+        x = features["out"] # comment the classifier to reduce memory
+        # x = self.model.classifier(x)
+        # x = F.interpolate(x, size=feat_shape, mode='bilinear', align_corners=False)
+        result["logits"] = x
+
+        # Prediction auxillary classification logits
+        if self.model.aux_classifier is not None:
+            x = features["aux"]
+            x = self.model.aux_classifier(x)
+            x = F.interpolate(x, size=feat_shape, mode='bilinear', align_corners=False)
+            result["aux"] = x
+
+        return result
+
+
+class SemDeepLabV3(SegTemplate):
+
+    def __init__(self, backbone_name, **kwargs):
+        """
+        Initializes SemDeepLabV3 model
+        Args:
+            backbone_name: string, ResNet Backbone Name [ResNet50/ResNet101]
+        """
+        if backbone_name == "ResNet50":
+            constructor = torchvision.models.segmentation.deeplabv3_resnet50
+        elif backbone_name == "ResNet101":
+            constructor = torchvision.models.segmentation.deeplabv3_resnet101
+        else:
+            raise NotImplementedError
+
+        super().__init__(constructor=constructor, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/focal_sparse_conv.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/focal_sparse_conv.py
new file mode 100644
index 000000000..79dfa26ba
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/focal_sparse_conv.py
@@ -0,0 +1,224 @@
+import torch
+import torch.nn as nn
+from pcdet.utils.spconv_utils import spconv
+from pcdet.ops.roiaware_pool3d.roiaware_pool3d_utils import points_in_boxes_gpu
+from pcdet.models.backbones_3d.focal_sparse_conv.focal_sparse_utils import split_voxels, check_repeat, FocalLoss
+from pcdet.utils import common_utils
+
+
+class FocalSparseConv(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, voxel_stride, norm_fn=None, indice_key=None,
+                image_channel=3, kernel_size=3, padding=1, mask_multi=False, use_img=False,
+                topk=False, threshold=0.5, skip_mask_kernel=False, enlarge_voxel_channels=-1, 
+                point_cloud_range=[-3, -40, 0, 1, 40, 70.4],
+                voxel_size = [0.1, 0.05, 0.05]):
+        super(FocalSparseConv, self).__init__()
+
+        self.conv = spconv.SubMConv3d(inplanes, planes, kernel_size=kernel_size, stride=1, bias=False, indice_key=indice_key)
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU(True)
+        offset_channels = kernel_size**3
+
+        self.topk = topk
+        self.threshold = threshold
+        self.voxel_stride = voxel_stride
+        self.focal_loss = FocalLoss()
+        self.mask_multi = mask_multi
+        self.skip_mask_kernel = skip_mask_kernel
+        self.use_img = use_img
+
+        voxel_channel = enlarge_voxel_channels if enlarge_voxel_channels>0 else inplanes
+        in_channels = image_channel + voxel_channel if use_img else voxel_channel
+
+        self.conv_enlarge = spconv.SparseSequential(spconv.SubMConv3d(inplanes, enlarge_voxel_channels, 
+            kernel_size=3, stride=1, padding=1, bias=False, indice_key=indice_key+'_enlarge'),
+            norm_fn(enlarge_voxel_channels),
+            nn.ReLU(True)) if enlarge_voxel_channels>0 else None
+
+        self.conv_imp = spconv.SubMConv3d(in_channels, offset_channels, kernel_size=3, stride=1, padding=1, bias=False, indice_key=indice_key+'_imp')
+
+        _step = int(kernel_size//2)
+        kernel_offsets = [[i, j, k] for i in range(-_step, _step+1) for j in range(-_step, _step+1) for k in range(-_step, _step+1)]
+        kernel_offsets.remove([0, 0, 0])
+        self.kernel_offsets = torch.Tensor(kernel_offsets).cuda()
+        self.inv_idx =  torch.Tensor([2, 1, 0]).long().cuda()
+        self.point_cloud_range = torch.Tensor(point_cloud_range).cuda()
+        self.voxel_size = torch.Tensor(voxel_size).cuda()
+
+    def construct_multimodal_features(self, x, x_rgb, batch_dict, fuse_sum=False):
+        """
+            Construct the multimodal features with both lidar sparse features and image features.
+            Args:
+                x: [N, C] lidar sparse features
+                x_rgb: [b, c, h, w] image features
+                batch_dict: input and output information during forward
+                fuse_sum: bool, manner for fusion, True - sum, False - concat
+
+            Return:
+                image_with_voxelfeatures: [N, C] fused multimodal features
+        """
+        batch_index = x.indices[:, 0]
+        spatial_indices = x.indices[:, 1:] * self.voxel_stride
+        voxels_3d = spatial_indices * self.voxel_size + self.point_cloud_range[:3]
+        calibs = batch_dict['calib']
+        batch_size = batch_dict['batch_size']
+        h, w = batch_dict['images'].shape[2:]
+
+        if not x_rgb.shape == batch_dict['images'].shape:
+            x_rgb = nn.functional.interpolate(x_rgb, (h, w), mode='bilinear')
+
+        image_with_voxelfeatures = []
+        voxels_2d_int_list = []
+        filter_idx_list = []
+        for b in range(batch_size):
+            x_rgb_batch = x_rgb[b]
+
+            calib = calibs[b]
+            voxels_3d_batch = voxels_3d[batch_index==b]
+            voxel_features_sparse = x.features[batch_index==b]
+
+            # Reverse the point cloud transformations to the original coords.
+            if 'noise_scale' in batch_dict:
+                voxels_3d_batch[:, :3] /= batch_dict['noise_scale'][b]
+            if 'noise_rot' in batch_dict:
+                voxels_3d_batch = common_utils.rotate_points_along_z(voxels_3d_batch[:, self.inv_idx].unsqueeze(0), -batch_dict['noise_rot'][b].unsqueeze(0))[0, :, self.inv_idx]
+            if 'flip_x' in batch_dict:
+                voxels_3d_batch[:, 1] *= -1 if batch_dict['flip_x'][b] else 1
+            if 'flip_y' in batch_dict:
+                voxels_3d_batch[:, 2] *= -1 if batch_dict['flip_y'][b] else 1
+
+            voxels_2d, _ = calib.lidar_to_img(voxels_3d_batch[:, self.inv_idx].cpu().numpy())
+
+            voxels_2d_int = torch.Tensor(voxels_2d).to(x_rgb_batch.device).long()
+
+            filter_idx = (0<=voxels_2d_int[:, 1]) * (voxels_2d_int[:, 1] < h) * (0<=voxels_2d_int[:, 0]) * (voxels_2d_int[:, 0] < w)
+
+            filter_idx_list.append(filter_idx)
+            voxels_2d_int = voxels_2d_int[filter_idx]
+            voxels_2d_int_list.append(voxels_2d_int)
+
+            image_features_batch = torch.zeros((voxel_features_sparse.shape[0], x_rgb_batch.shape[0]), device=x_rgb_batch.device)
+            image_features_batch[filter_idx] = x_rgb_batch[:, voxels_2d_int[:, 1], voxels_2d_int[:, 0]].permute(1, 0)
+
+            if fuse_sum:
+                image_with_voxelfeature = image_features_batch + voxel_features_sparse
+            else:
+                image_with_voxelfeature = torch.cat([image_features_batch, voxel_features_sparse], dim=1)
+
+            image_with_voxelfeatures.append(image_with_voxelfeature)
+
+        image_with_voxelfeatures = torch.cat(image_with_voxelfeatures)
+        return image_with_voxelfeatures
+
+    def _gen_sparse_features(self, x, imps_3d, batch_dict, voxels_3d):
+        """
+            Generate the output sparse features from the focal sparse conv.
+            Args:
+                x: [N, C], lidar sparse features
+                imps_3d: [N, kernelsize**3], the predicted importance values
+                batch_dict: input and output information during forward
+                voxels_3d: [N, 3], the 3d positions of voxel centers
+        """
+        batch_size = x.batch_size
+        voxel_features_fore = []
+        voxel_indices_fore = []
+        voxel_features_back = []
+        voxel_indices_back = []
+
+        box_of_pts_cls_targets = []
+        mask_voxels = []
+        mask_kernel_list = []
+
+        for b in range(batch_size):
+            if self.training:
+                index = x.indices[:, 0]
+                batch_index = index==b
+                mask_voxel = imps_3d[batch_index, -1].sigmoid()
+                voxels_3d_batch = voxels_3d[batch_index].unsqueeze(0)
+                mask_voxels.append(mask_voxel)
+                gt_boxes = batch_dict['gt_boxes'][b, :, :-1].unsqueeze(0)
+                box_of_pts_batch = points_in_boxes_gpu(voxels_3d_batch[:, :, self.inv_idx], gt_boxes).squeeze(0)
+                box_of_pts_cls_targets.append(box_of_pts_batch>=0)
+
+            features_fore, indices_fore, features_back, indices_back, mask_kernel = split_voxels(x, b, imps_3d, voxels_3d, self.kernel_offsets, mask_multi=self.mask_multi, topk=self.topk, threshold=self.threshold)
+
+            mask_kernel_list.append(mask_kernel)
+            voxel_features_fore.append(features_fore)
+            voxel_indices_fore.append(indices_fore)
+            voxel_features_back.append(features_back)
+            voxel_indices_back.append(indices_back)
+
+        voxel_features_fore = torch.cat(voxel_features_fore, dim=0)
+        voxel_indices_fore = torch.cat(voxel_indices_fore, dim=0)
+        voxel_features_back = torch.cat(voxel_features_back, dim=0)
+        voxel_indices_back = torch.cat(voxel_indices_back, dim=0)
+        mask_kernel = torch.cat(mask_kernel_list, dim=0)
+
+        x_fore = spconv.SparseConvTensor(voxel_features_fore, voxel_indices_fore, x.spatial_shape, x.batch_size)
+        x_back = spconv.SparseConvTensor(voxel_features_back, voxel_indices_back, x.spatial_shape, x.batch_size)
+
+        loss_box_of_pts = 0
+        if self.training:
+            mask_voxels = torch.cat(mask_voxels)
+            box_of_pts_cls_targets = torch.cat(box_of_pts_cls_targets)
+            mask_voxels_two_classes = torch.cat([1-mask_voxels.unsqueeze(-1), mask_voxels.unsqueeze(-1)], dim=1)
+            loss_box_of_pts = self.focal_loss(mask_voxels_two_classes, box_of_pts_cls_targets.long())
+
+        return x_fore, x_back, loss_box_of_pts, mask_kernel
+
+    def combine_out(self, x_fore, x_back, remove_repeat=False):
+        """
+            Combine the foreground and background sparse features together.
+            Args:
+                x_fore: [N1, C], foreground sparse features
+                x_back: [N2, C], background sparse features
+                remove_repeat: bool, whether to remove the spatial replicate features.
+        """
+        x_fore_features = torch.cat([x_fore.features, x_back.features], dim=0)
+        x_fore_indices = torch.cat([x_fore.indices, x_back.indices], dim=0)
+
+        if remove_repeat:
+            index = x_fore_indices[:, 0]
+            features_out_list = []
+            indices_coords_out_list = []
+            for b in range(x_fore.batch_size):
+                batch_index = index==b
+                features_out, indices_coords_out, _ = check_repeat(x_fore_features[batch_index], x_fore_indices[batch_index], flip_first=False)
+                features_out_list.append(features_out)
+                indices_coords_out_list.append(indices_coords_out)
+            x_fore_features = torch.cat(features_out_list, dim=0)
+            x_fore_indices = torch.cat(indices_coords_out_list, dim=0)
+
+        x_fore = x_fore.replace_feature(x_fore_features)
+        x_fore.indices = x_fore_indices
+
+        return x_fore
+        
+    def forward(self, x, batch_dict, x_rgb=None):
+        spatial_indices = x.indices[:, 1:] * self.voxel_stride
+        voxels_3d = spatial_indices * self.voxel_size + self.point_cloud_range[:3]
+
+        if self.use_img:
+            features_multimodal = self.construct_multimodal_features(x, x_rgb, batch_dict)
+            x_predict = spconv.SparseConvTensor(features_multimodal, x.indices, x.spatial_shape, x.batch_size)
+        else:
+            x_predict = self.conv_enlarge(x) if self.conv_enlarge else x
+
+        imps_3d = self.conv_imp(x_predict).features
+
+        x_fore, x_back, loss_box_of_pts, mask_kernel = self._gen_sparse_features(x, imps_3d, batch_dict, voxels_3d)
+
+        if not self.skip_mask_kernel:
+            x_fore = x_fore.replace_feature(x_fore.features * mask_kernel.unsqueeze(-1))
+        out = self.combine_out(x_fore, x_back, remove_repeat=True)
+        out = self.conv(out)
+
+        if self.use_img:
+            out = out.replace_feature(self.construct_multimodal_features(out, x_rgb, batch_dict, True))
+
+        out = out.replace_feature(self.bn1(out.features))
+        out = out.replace_feature(self.relu(out.features))
+
+        return out, batch_dict, loss_box_of_pts
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/focal_sparse_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/focal_sparse_utils.py
new file mode 100644
index 000000000..d2c234477
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/focal_sparse_conv/focal_sparse_utils.py
@@ -0,0 +1,147 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+
+class FocalLoss(nn.Module):
+
+    def __init__(self, gamma=2.0, eps=1e-7):
+        super(FocalLoss, self).__init__()
+        self.gamma = gamma
+        self.eps = eps
+
+    def one_hot(self, index, classes):
+        size = index.size() + (classes,)
+        view = index.size() + (1,)
+
+        mask = torch.Tensor(*size).fill_(0).to(index.device)
+
+        index = index.view(*view)
+        ones = 1.
+
+        if isinstance(index, Variable):
+            ones = Variable(torch.Tensor(index.size()).fill_(1).to(index.device))
+            mask = Variable(mask, volatile=index.volatile)
+
+        return mask.scatter_(1, index, ones)
+
+    def forward(self, input, target):
+        y = self.one_hot(target, input.size(-1))
+        logit = F.softmax(input, dim=-1)
+        logit = logit.clamp(self.eps, 1. - self.eps)
+
+        loss = -1 * y * torch.log(logit) # cross entropy
+        loss = loss * (1 - logit) ** self.gamma # focal loss
+
+        return loss.mean()
+
+def sort_by_indices(features, indices, features_add=None):
+    """
+        To sort the sparse features with its indices in a convenient manner.
+        Args:
+            features: [N, C], sparse features
+            indices: [N, 4], indices of sparse features
+            features_add: [N, C], additional features to sort
+    """
+    idx = indices[:, 1:]
+    idx_sum = idx.select(1, 0) * idx[:, 1].max() * idx[:, 2].max() + idx.select(1, 1) * idx[:, 2].max() + idx.select(1, 2)
+    _, ind = idx_sum.sort()
+    features = features[ind]
+    indices = indices[ind]
+    if not features_add is None:
+        features_add = features_add[ind]
+    return features, indices, features_add
+
+def check_repeat(features, indices, features_add=None, sort_first=True, flip_first=True):
+    """
+        Check that whether there are replicate indices in the sparse features, 
+        remove the replicate features if any.
+    """
+    if sort_first:
+        features, indices, features_add = sort_by_indices(features, indices, features_add)
+
+    if flip_first:
+        features, indices = features.flip([0]), indices.flip([0])
+
+    if not features_add is None:
+        features_add=features_add.flip([0])
+
+    idx = indices[:, 1:].int()
+    idx_sum = torch.add(torch.add(idx.select(1, 0) * idx[:, 1].max() * idx[:, 2].max(), idx.select(1, 1) * idx[:, 2].max()), idx.select(1, 2))
+    _unique, inverse, counts = torch.unique_consecutive(idx_sum, return_inverse=True, return_counts=True, dim=0)
+    
+    if _unique.shape[0] < indices.shape[0]:
+        perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)
+        features_new = torch.zeros((_unique.shape[0], features.shape[-1]), device=features.device)
+        features_new.index_add_(0, inverse.long(), features)
+        features = features_new
+        perm_ = inverse.new_empty(_unique.size(0)).scatter_(0, inverse, perm)
+        indices = indices[perm_].int()
+
+        if not features_add is None:
+            features_add_new = torch.zeros((_unique.shape[0],), device=features_add.device)
+            features_add_new.index_add_(0, inverse.long(), features_add)
+            features_add = features_add_new / counts
+    return features, indices, features_add
+
+
+def split_voxels(x, b, imps_3d, voxels_3d, kernel_offsets, mask_multi=True, topk=True, threshold=0.5):
+    """
+        Generate and split the voxels into foreground and background sparse features, based on the predicted importance values.
+        Args:
+            x: [N, C], input sparse features
+            b: int, batch size id
+            imps_3d: [N, kernelsize**3], the prediced importance values
+            voxels_3d: [N, 3], the 3d positions of voxel centers 
+            kernel_offsets: [kernelsize**3, 3], the offset coords in an kernel
+            mask_multi: bool, whether to multiply the predicted mask to features
+            topk: bool, whether to use topk or threshold for selection
+            threshold: float, threshold value
+    """
+    index = x.indices[:, 0]
+    batch_index = index==b
+    indices_ori = x.indices[batch_index]
+    features_ori = x.features[batch_index]
+    mask_voxel = imps_3d[batch_index, -1].sigmoid()
+    mask_kernel = imps_3d[batch_index, :-1].sigmoid()
+
+    if mask_multi:
+        features_ori *= mask_voxel.unsqueeze(-1)
+
+    if topk:
+        _, indices = mask_voxel.sort(descending=True)
+        indices_fore = indices[:int(mask_voxel.shape[0]*threshold)]
+        indices_back = indices[int(mask_voxel.shape[0]*threshold):]
+    else:
+        indices_fore = mask_voxel > threshold
+        indices_back = mask_voxel <= threshold
+
+    features_fore = features_ori[indices_fore]
+    coords_fore = indices_ori[indices_fore]
+
+    mask_kernel_fore = mask_kernel[indices_fore]
+    mask_kernel_bool = mask_kernel_fore>=threshold
+    voxel_kerels_imp = kernel_offsets.unsqueeze(0).repeat(mask_kernel_bool.shape[0],1, 1)
+    mask_kernel_fore = mask_kernel[indices_fore][mask_kernel_bool]
+    indices_fore_kernels = coords_fore[:, 1:].unsqueeze(1).repeat(1, kernel_offsets.shape[0], 1)
+    indices_with_imp = indices_fore_kernels + voxel_kerels_imp
+    selected_indices = indices_with_imp[mask_kernel_bool]
+    spatial_indices = (selected_indices[:, 0] >0) * (selected_indices[:, 1] >0) * (selected_indices[:, 2] >0)  * \
+                        (selected_indices[:, 0] < x.spatial_shape[0]) * (selected_indices[:, 1] < x.spatial_shape[1]) * (selected_indices[:, 2] < x.spatial_shape[2])
+    selected_indices = selected_indices[spatial_indices]
+    mask_kernel_fore = mask_kernel_fore[spatial_indices]
+    selected_indices = torch.cat([torch.ones((selected_indices.shape[0], 1), device=features_fore.device)*b, selected_indices], dim=1)
+
+    selected_features = torch.zeros((selected_indices.shape[0], features_ori.shape[1]), device=features_fore.device)
+
+    features_fore_cat = torch.cat([features_fore, selected_features], dim=0)
+    coords_fore = torch.cat([coords_fore, selected_indices], dim=0)
+    mask_kernel_fore = torch.cat([torch.ones(features_fore.shape[0], device=features_fore.device), mask_kernel_fore], dim=0)
+
+    features_fore, coords_fore, mask_kernel_fore = check_repeat(features_fore_cat, coords_fore, features_add=mask_kernel_fore)
+
+    features_back = features_ori[indices_back]
+    coords_back = indices_ori[indices_back]
+
+    return features_fore, coords_fore, features_back, coords_back, mask_kernel_fore
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pfe/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pfe/__init__.py
new file mode 100644
index 000000000..b65a3f59b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pfe/__init__.py
@@ -0,0 +1,5 @@
+from .voxel_set_abstraction import VoxelSetAbstraction
+
+__all__ = {
+    'VoxelSetAbstraction': VoxelSetAbstraction
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py
new file mode 100644
index 000000000..0f3b8ae93
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py
@@ -0,0 +1,411 @@
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ....ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
+from ....ops.pointnet2.pointnet2_stack import pointnet2_utils as pointnet2_stack_utils
+from ....utils import common_utils
+
+
+def bilinear_interpolate_torch(im, x, y):
+    """
+    Args:
+        im: (H, W, C) [y, x]
+        x: (N)
+        y: (N)
+
+    Returns:
+
+    """
+    x0 = torch.floor(x).long()
+    x1 = x0 + 1
+
+    y0 = torch.floor(y).long()
+    y1 = y0 + 1
+
+    x0 = torch.clamp(x0, 0, im.shape[1] - 1)
+    x1 = torch.clamp(x1, 0, im.shape[1] - 1)
+    y0 = torch.clamp(y0, 0, im.shape[0] - 1)
+    y1 = torch.clamp(y1, 0, im.shape[0] - 1)
+
+    Ia = im[y0, x0]
+    Ib = im[y1, x0]
+    Ic = im[y0, x1]
+    Id = im[y1, x1]
+
+    wa = (x1.type_as(x) - x) * (y1.type_as(y) - y)
+    wb = (x1.type_as(x) - x) * (y - y0.type_as(y))
+    wc = (x - x0.type_as(x)) * (y1.type_as(y) - y)
+    wd = (x - x0.type_as(x)) * (y - y0.type_as(y))
+    ans = torch.t((torch.t(Ia) * wa)) + torch.t(torch.t(Ib) * wb) + torch.t(torch.t(Ic) * wc) + torch.t(torch.t(Id) * wd)
+    return ans
+
+
+def sample_points_with_roi(rois, points, sample_radius_with_roi, num_max_points_of_part=200000):
+    """
+    Args:
+        rois: (M, 7 + C)
+        points: (N, 3)
+        sample_radius_with_roi:
+        num_max_points_of_part:
+
+    Returns:
+        sampled_points: (N_out, 3)
+    """
+    if points.shape[0] < num_max_points_of_part:
+        distance = (points[:, None, :] - rois[None, :, 0:3]).norm(dim=-1)
+        min_dis, min_dis_roi_idx = distance.min(dim=-1)
+        roi_max_dim = (rois[min_dis_roi_idx, 3:6] / 2).norm(dim=-1)
+        point_mask = min_dis < roi_max_dim + sample_radius_with_roi
+    else:
+        start_idx = 0
+        point_mask_list = []
+        while start_idx < points.shape[0]:
+            distance = (points[start_idx:start_idx + num_max_points_of_part, None, :] - rois[None, :, 0:3]).norm(dim=-1)
+            min_dis, min_dis_roi_idx = distance.min(dim=-1)
+            roi_max_dim = (rois[min_dis_roi_idx, 3:6] / 2).norm(dim=-1)
+            cur_point_mask = min_dis < roi_max_dim + sample_radius_with_roi
+            point_mask_list.append(cur_point_mask)
+            start_idx += num_max_points_of_part
+        point_mask = torch.cat(point_mask_list, dim=0)
+
+    sampled_points = points[:1] if point_mask.sum() == 0 else points[point_mask, :]
+
+    return sampled_points, point_mask
+
+
+def sector_fps(points, num_sampled_points, num_sectors):
+    """
+    Args:
+        points: (N, 3)
+        num_sampled_points: int
+        num_sectors: int
+
+    Returns:
+        sampled_points: (N_out, 3)
+    """
+    sector_size = np.pi * 2 / num_sectors
+    point_angles = torch.atan2(points[:, 1], points[:, 0]) + np.pi
+    sector_idx = (point_angles / sector_size).floor().clamp(min=0, max=num_sectors)
+    xyz_points_list = []
+    xyz_batch_cnt = []
+    num_sampled_points_list = []
+    for k in range(num_sectors):
+        mask = (sector_idx == k)
+        cur_num_points = mask.sum().item()
+        if cur_num_points > 0:
+            xyz_points_list.append(points[mask])
+            xyz_batch_cnt.append(cur_num_points)
+            ratio = cur_num_points / points.shape[0]
+            num_sampled_points_list.append(
+                min(cur_num_points, math.ceil(ratio * num_sampled_points))
+            )
+
+    if len(xyz_batch_cnt) == 0:
+        xyz_points_list.append(points)
+        xyz_batch_cnt.append(len(points))
+        num_sampled_points_list.append(num_sampled_points)
+        print(f'Warning: empty sector points detected in SectorFPS: points.shape={points.shape}')
+
+    xyz = torch.cat(xyz_points_list, dim=0)
+    xyz_batch_cnt = torch.tensor(xyz_batch_cnt, device=points.device).int()
+    sampled_points_batch_cnt = torch.tensor(num_sampled_points_list, device=points.device).int()
+
+    sampled_pt_idxs = pointnet2_stack_utils.stack_farthest_point_sample(
+        xyz.contiguous(), xyz_batch_cnt, sampled_points_batch_cnt
+    ).long()
+
+    sampled_points = xyz[sampled_pt_idxs]
+
+    return sampled_points
+
+
+class VoxelSetAbstraction(nn.Module):
+    def __init__(self, model_cfg, voxel_size, point_cloud_range, num_bev_features=None,
+                 num_rawpoint_features=None, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.voxel_size = voxel_size
+        self.point_cloud_range = point_cloud_range
+
+        SA_cfg = self.model_cfg.SA_LAYER
+
+        self.SA_layers = nn.ModuleList()
+        self.SA_layer_names = []
+        self.downsample_times_map = {}
+        c_in = 0
+        for src_name in self.model_cfg.FEATURES_SOURCE:
+            if src_name in ['bev', 'raw_points']:
+                continue
+            self.downsample_times_map[src_name] = SA_cfg[src_name].DOWNSAMPLE_FACTOR
+
+            if SA_cfg[src_name].get('INPUT_CHANNELS', None) is None:
+                input_channels = SA_cfg[src_name].MLPS[0][0] \
+                    if isinstance(SA_cfg[src_name].MLPS[0], list) else SA_cfg[src_name].MLPS[0]
+            else:
+                input_channels = SA_cfg[src_name]['INPUT_CHANNELS']
+
+            cur_layer, cur_num_c_out = pointnet2_stack_modules.build_local_aggregation_module(
+                input_channels=input_channels, config=SA_cfg[src_name]
+            )
+            self.SA_layers.append(cur_layer)
+            self.SA_layer_names.append(src_name)
+
+            c_in += cur_num_c_out
+
+        if 'bev' in self.model_cfg.FEATURES_SOURCE:
+            c_bev = num_bev_features
+            c_in += c_bev
+
+        if 'raw_points' in self.model_cfg.FEATURES_SOURCE:
+            self.SA_rawpoints, cur_num_c_out = pointnet2_stack_modules.build_local_aggregation_module(
+                input_channels=num_rawpoint_features - 3, config=SA_cfg['raw_points']
+            )
+
+            c_in += cur_num_c_out
+
+        self.vsa_point_feature_fusion = nn.Sequential(
+            nn.Linear(c_in, self.model_cfg.NUM_OUTPUT_FEATURES, bias=False),
+            nn.BatchNorm1d(self.model_cfg.NUM_OUTPUT_FEATURES),
+            nn.ReLU(),
+        )
+        self.num_point_features = self.model_cfg.NUM_OUTPUT_FEATURES
+        self.num_point_features_before_fusion = c_in
+
+    def interpolate_from_bev_features(self, keypoints, bev_features, batch_size, bev_stride):
+        """
+        Args:
+            keypoints: (N1 + N2 + ..., 4)
+            bev_features: (B, C, H, W)
+            batch_size:
+            bev_stride:
+
+        Returns:
+            point_bev_features: (N1 + N2 + ..., C)
+        """
+        x_idxs = (keypoints[:, 1] - self.point_cloud_range[0]) / self.voxel_size[0]
+        y_idxs = (keypoints[:, 2] - self.point_cloud_range[1]) / self.voxel_size[1]
+
+        x_idxs = x_idxs / bev_stride
+        y_idxs = y_idxs / bev_stride
+
+        point_bev_features_list = []
+        for k in range(batch_size):
+            bs_mask = (keypoints[:, 0] == k)
+
+            cur_x_idxs = x_idxs[bs_mask]
+            cur_y_idxs = y_idxs[bs_mask]
+            cur_bev_features = bev_features[k].permute(1, 2, 0)  # (H, W, C)
+            point_bev_features = bilinear_interpolate_torch(cur_bev_features, cur_x_idxs, cur_y_idxs)
+            point_bev_features_list.append(point_bev_features)
+
+        point_bev_features = torch.cat(point_bev_features_list, dim=0)  # (N1 + N2 + ..., C)
+        return point_bev_features
+
+    def sectorized_proposal_centric_sampling(self, roi_boxes, points):
+        """
+        Args:
+            roi_boxes: (M, 7 + C)
+            points: (N, 3)
+
+        Returns:
+            sampled_points: (N_out, 3)
+        """
+
+        sampled_points, _ = sample_points_with_roi(
+            rois=roi_boxes, points=points,
+            sample_radius_with_roi=self.model_cfg.SPC_SAMPLING.SAMPLE_RADIUS_WITH_ROI,
+            num_max_points_of_part=self.model_cfg.SPC_SAMPLING.get('NUM_POINTS_OF_EACH_SAMPLE_PART', 200000)
+        )
+        sampled_points = sector_fps(
+            points=sampled_points, num_sampled_points=self.model_cfg.NUM_KEYPOINTS,
+            num_sectors=self.model_cfg.SPC_SAMPLING.NUM_SECTORS
+        )
+        return sampled_points
+
+    def get_sampled_points(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+
+        Returns:
+            keypoints: (N1 + N2 + ..., 4), where 4 indicates [bs_idx, x, y, z]
+        """
+        batch_size = batch_dict['batch_size']
+        if self.model_cfg.POINT_SOURCE == 'raw_points':
+            src_points = batch_dict['points'][:, 1:4]
+            batch_indices = batch_dict['points'][:, 0].long()
+        elif self.model_cfg.POINT_SOURCE == 'voxel_centers':
+            src_points = common_utils.get_voxel_centers(
+                batch_dict['voxel_coords'][:, 1:4],
+                downsample_times=1,
+                voxel_size=self.voxel_size,
+                point_cloud_range=self.point_cloud_range
+            )
+            batch_indices = batch_dict['voxel_coords'][:, 0].long()
+        else:
+            raise NotImplementedError
+        keypoints_list = []
+        for bs_idx in range(batch_size):
+            bs_mask = (batch_indices == bs_idx)
+            sampled_points = src_points[bs_mask].unsqueeze(dim=0)  # (1, N, 3)
+            if self.model_cfg.SAMPLE_METHOD == 'FPS':
+                cur_pt_idxs = pointnet2_stack_utils.farthest_point_sample(
+                    sampled_points[:, :, 0:3].contiguous(), self.model_cfg.NUM_KEYPOINTS
+                ).long()
+
+                if sampled_points.shape[1] < self.model_cfg.NUM_KEYPOINTS:
+                    times = int(self.model_cfg.NUM_KEYPOINTS / sampled_points.shape[1]) + 1
+                    non_empty = cur_pt_idxs[0, :sampled_points.shape[1]]
+                    cur_pt_idxs[0] = non_empty.repeat(times)[:self.model_cfg.NUM_KEYPOINTS]
+
+                keypoints = sampled_points[0][cur_pt_idxs[0]].unsqueeze(dim=0)
+
+            elif self.model_cfg.SAMPLE_METHOD == 'SPC':
+                cur_keypoints = self.sectorized_proposal_centric_sampling(
+                    roi_boxes=batch_dict['rois'][bs_idx], points=sampled_points[0]
+                )
+                bs_idxs = cur_keypoints.new_ones(cur_keypoints.shape[0]) * bs_idx
+                keypoints = torch.cat((bs_idxs[:, None], cur_keypoints), dim=1)
+            else:
+                raise NotImplementedError
+
+            keypoints_list.append(keypoints)
+
+        keypoints = torch.cat(keypoints_list, dim=0)  # (B, M, 3) or (N1 + N2 + ..., 4)
+        if len(keypoints.shape) == 3:
+            batch_idx = torch.arange(batch_size, device=keypoints.device).view(-1, 1).repeat(1, keypoints.shape[1]).view(-1, 1)
+            keypoints = torch.cat((batch_idx.float(), keypoints.view(-1, 3)), dim=1)
+
+        return keypoints
+
+    @staticmethod
+    def aggregate_keypoint_features_from_one_source(
+            batch_size, aggregate_func, xyz, xyz_features, xyz_bs_idxs, new_xyz, new_xyz_batch_cnt,
+            filter_neighbors_with_roi=False, radius_of_neighbor=None, num_max_points_of_part=200000, rois=None
+    ):
+        """
+
+        Args:
+            aggregate_func:
+            xyz: (N, 3)
+            xyz_features: (N, C)
+            xyz_bs_idxs: (N)
+            new_xyz: (M, 3)
+            new_xyz_batch_cnt: (batch_size), [N1, N2, ...]
+
+            filter_neighbors_with_roi: True/False
+            radius_of_neighbor: float
+            num_max_points_of_part: int
+            rois: (batch_size, num_rois, 7 + C)
+        Returns:
+
+        """
+        xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+        if filter_neighbors_with_roi:
+            point_features = torch.cat((xyz, xyz_features), dim=-1) if xyz_features is not None else xyz
+            point_features_list = []
+            for bs_idx in range(batch_size):
+                bs_mask = (xyz_bs_idxs == bs_idx)
+                _, valid_mask = sample_points_with_roi(
+                    rois=rois[bs_idx], points=xyz[bs_mask],
+                    sample_radius_with_roi=radius_of_neighbor, num_max_points_of_part=num_max_points_of_part,
+                )
+                point_features_list.append(point_features[bs_mask][valid_mask])
+                xyz_batch_cnt[bs_idx] = valid_mask.sum()
+
+            valid_point_features = torch.cat(point_features_list, dim=0)
+            xyz = valid_point_features[:, 0:3]
+            xyz_features = valid_point_features[:, 3:] if xyz_features is not None else None
+        else:
+            for bs_idx in range(batch_size):
+                xyz_batch_cnt[bs_idx] = (xyz_bs_idxs == bs_idx).sum()
+
+        pooled_points, pooled_features = aggregate_func(
+            xyz=xyz.contiguous(),
+            xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz,
+            new_xyz_batch_cnt=new_xyz_batch_cnt,
+            features=xyz_features.contiguous(),
+        )
+        return pooled_features
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                keypoints: (B, num_keypoints, 3)
+                multi_scale_3d_features: {
+                        'x_conv4': ...
+                    }
+                points: optional (N, 1 + 3 + C) [bs_idx, x, y, z, ...]
+                spatial_features: optional
+                spatial_features_stride: optional
+
+        Returns:
+            point_features: (N, C)
+            point_coords: (N, 4)
+
+        """
+        keypoints = self.get_sampled_points(batch_dict)
+
+        point_features_list = []
+        if 'bev' in self.model_cfg.FEATURES_SOURCE:
+            point_bev_features = self.interpolate_from_bev_features(
+                keypoints, batch_dict['spatial_features'], batch_dict['batch_size'],
+                bev_stride=batch_dict['spatial_features_stride']
+            )
+            point_features_list.append(point_bev_features)
+
+        batch_size = batch_dict['batch_size']
+
+        new_xyz = keypoints[:, 1:4].contiguous()
+        new_xyz_batch_cnt = new_xyz.new_zeros(batch_size).int()
+        for k in range(batch_size):
+            new_xyz_batch_cnt[k] = (keypoints[:, 0] == k).sum()
+
+        if 'raw_points' in self.model_cfg.FEATURES_SOURCE:
+            raw_points = batch_dict['points']
+
+            pooled_features = self.aggregate_keypoint_features_from_one_source(
+                batch_size=batch_size, aggregate_func=self.SA_rawpoints,
+                xyz=raw_points[:, 1:4],
+                xyz_features=raw_points[:, 4:].contiguous() if raw_points.shape[1] > 4 else None,
+                xyz_bs_idxs=raw_points[:, 0],
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt,
+                filter_neighbors_with_roi=self.model_cfg.SA_LAYER['raw_points'].get('FILTER_NEIGHBOR_WITH_ROI', False),
+                radius_of_neighbor=self.model_cfg.SA_LAYER['raw_points'].get('RADIUS_OF_NEIGHBOR_WITH_ROI', None),
+                rois=batch_dict.get('rois', None)
+            )
+            point_features_list.append(pooled_features)
+
+        for k, src_name in enumerate(self.SA_layer_names):
+            cur_coords = batch_dict['multi_scale_3d_features'][src_name].indices
+            cur_features = batch_dict['multi_scale_3d_features'][src_name].features.contiguous()
+
+            xyz = common_utils.get_voxel_centers(
+                cur_coords[:, 1:4], downsample_times=self.downsample_times_map[src_name],
+                voxel_size=self.voxel_size, point_cloud_range=self.point_cloud_range
+            )
+
+            pooled_features = self.aggregate_keypoint_features_from_one_source(
+                batch_size=batch_size, aggregate_func=self.SA_layers[k],
+                xyz=xyz.contiguous(), xyz_features=cur_features, xyz_bs_idxs=cur_coords[:, 0],
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt,
+                filter_neighbors_with_roi=self.model_cfg.SA_LAYER[src_name].get('FILTER_NEIGHBOR_WITH_ROI', False),
+                radius_of_neighbor=self.model_cfg.SA_LAYER[src_name].get('RADIUS_OF_NEIGHBOR_WITH_ROI', None),
+                rois=batch_dict.get('rois', None)
+            )
+
+            point_features_list.append(pooled_features)
+
+        point_features = torch.cat(point_features_list, dim=-1)
+
+        batch_dict['point_features_before_fusion'] = point_features.view(-1, point_features.shape[-1])
+        point_features = self.vsa_point_feature_fusion(point_features.view(-1, point_features.shape[-1]))
+
+        batch_dict['point_features'] = point_features  # (BxN, C)
+        batch_dict['point_coords'] = keypoints  # (BxN, 4)
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pointnet2_backbone.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pointnet2_backbone.py
new file mode 100644
index 000000000..fdd0d021f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/pointnet2_backbone.py
@@ -0,0 +1,206 @@
+import torch
+import torch.nn as nn
+
+from ...ops.pointnet2.pointnet2_batch import pointnet2_modules
+from ...ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_modules_stack
+from ...ops.pointnet2.pointnet2_stack import pointnet2_utils as pointnet2_utils_stack
+
+
+class PointNet2MSG(nn.Module):
+    def __init__(self, model_cfg, input_channels, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        self.SA_modules = nn.ModuleList()
+        channel_in = input_channels - 3
+
+        self.num_points_each_layer = []
+        skip_channel_list = [input_channels - 3]
+        for k in range(self.model_cfg.SA_CONFIG.NPOINTS.__len__()):
+            mlps = self.model_cfg.SA_CONFIG.MLPS[k].copy()
+            channel_out = 0
+            for idx in range(mlps.__len__()):
+                mlps[idx] = [channel_in] + mlps[idx]
+                channel_out += mlps[idx][-1]
+
+            self.SA_modules.append(
+                pointnet2_modules.PointnetSAModuleMSG(
+                    npoint=self.model_cfg.SA_CONFIG.NPOINTS[k],
+                    radii=self.model_cfg.SA_CONFIG.RADIUS[k],
+                    nsamples=self.model_cfg.SA_CONFIG.NSAMPLE[k],
+                    mlps=mlps,
+                    use_xyz=self.model_cfg.SA_CONFIG.get('USE_XYZ', True),
+                )
+            )
+            skip_channel_list.append(channel_out)
+            channel_in = channel_out
+
+        self.FP_modules = nn.ModuleList()
+
+        for k in range(self.model_cfg.FP_MLPS.__len__()):
+            pre_channel = self.model_cfg.FP_MLPS[k + 1][-1] if k + 1 < len(self.model_cfg.FP_MLPS) else channel_out
+            self.FP_modules.append(
+                pointnet2_modules.PointnetFPModule(
+                    mlp=[pre_channel + skip_channel_list[k]] + self.model_cfg.FP_MLPS[k]
+                )
+            )
+
+        self.num_point_features = self.model_cfg.FP_MLPS[0][-1]
+
+    def break_up_pc(self, pc):
+        batch_idx = pc[:, 0]
+        xyz = pc[:, 1:4].contiguous()
+        features = (pc[:, 4:].contiguous() if pc.size(-1) > 4 else None)
+        return batch_idx, xyz, features
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                points: (num_points, 4 + C), [batch_idx, x, y, z, ...]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+                point_features: (N, C)
+        """
+        batch_size = batch_dict['batch_size']
+        points = batch_dict['points']
+        batch_idx, xyz, features = self.break_up_pc(points)
+
+        xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+        for bs_idx in range(batch_size):
+            xyz_batch_cnt[bs_idx] = (batch_idx == bs_idx).sum()
+
+        assert xyz_batch_cnt.min() == xyz_batch_cnt.max()
+        xyz = xyz.view(batch_size, -1, 3)
+        features = features.view(batch_size, -1, features.shape[-1]).permute(0, 2, 1).contiguous() if features is not None else None
+
+        l_xyz, l_features = [xyz], [features]
+        for i in range(len(self.SA_modules)):
+            li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i])
+            l_xyz.append(li_xyz)
+            l_features.append(li_features)
+
+        for i in range(-1, -(len(self.FP_modules) + 1), -1):
+            l_features[i - 1] = self.FP_modules[i](
+                l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i]
+            )  # (B, C, N)
+
+        point_features = l_features[0].permute(0, 2, 1).contiguous()  # (B, N, C)
+        batch_dict['point_features'] = point_features.view(-1, point_features.shape[-1])
+        batch_dict['point_coords'] = torch.cat((batch_idx[:, None].float(), l_xyz[0].view(-1, 3)), dim=1)
+        return batch_dict
+
+
+class PointNet2Backbone(nn.Module):
+    """
+    DO NOT USE THIS CURRENTLY SINCE IT MAY HAVE POTENTIAL BUGS, 20200723
+    """
+    def __init__(self, model_cfg, input_channels, **kwargs):
+        assert False, 'DO NOT USE THIS CURRENTLY SINCE IT MAY HAVE POTENTIAL BUGS, 20200723'
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        self.SA_modules = nn.ModuleList()
+        channel_in = input_channels - 3
+
+        self.num_points_each_layer = []
+        skip_channel_list = [input_channels]
+        for k in range(self.model_cfg.SA_CONFIG.NPOINTS.__len__()):
+            self.num_points_each_layer.append(self.model_cfg.SA_CONFIG.NPOINTS[k])
+            mlps = self.model_cfg.SA_CONFIG.MLPS[k].copy()
+            channel_out = 0
+            for idx in range(mlps.__len__()):
+                mlps[idx] = [channel_in] + mlps[idx]
+                channel_out += mlps[idx][-1]
+
+            self.SA_modules.append(
+                pointnet2_modules_stack.StackSAModuleMSG(
+                    radii=self.model_cfg.SA_CONFIG.RADIUS[k],
+                    nsamples=self.model_cfg.SA_CONFIG.NSAMPLE[k],
+                    mlps=mlps,
+                    use_xyz=self.model_cfg.SA_CONFIG.get('USE_XYZ', True),
+                )
+            )
+            skip_channel_list.append(channel_out)
+            channel_in = channel_out
+
+        self.FP_modules = nn.ModuleList()
+
+        for k in range(self.model_cfg.FP_MLPS.__len__()):
+            pre_channel = self.model_cfg.FP_MLPS[k + 1][-1] if k + 1 < len(self.model_cfg.FP_MLPS) else channel_out
+            self.FP_modules.append(
+                pointnet2_modules_stack.StackPointnetFPModule(
+                    mlp=[pre_channel + skip_channel_list[k]] + self.model_cfg.FP_MLPS[k]
+                )
+            )
+
+        self.num_point_features = self.model_cfg.FP_MLPS[0][-1]
+
+    def break_up_pc(self, pc):
+        batch_idx = pc[:, 0]
+        xyz = pc[:, 1:4].contiguous()
+        features = (pc[:, 4:].contiguous() if pc.size(-1) > 4 else None)
+        return batch_idx, xyz, features
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                points: (num_points, 4 + C), [batch_idx, x, y, z, ...]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+                point_features: (N, C)
+        """
+        batch_size = batch_dict['batch_size']
+        points = batch_dict['points']
+        batch_idx, xyz, features = self.break_up_pc(points)
+
+        xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+        for bs_idx in range(batch_size):
+            xyz_batch_cnt[bs_idx] = (batch_idx == bs_idx).sum()
+
+        l_xyz, l_features, l_batch_cnt = [xyz], [features], [xyz_batch_cnt]
+        for i in range(len(self.SA_modules)):
+            new_xyz_list = []
+            for k in range(batch_size):
+                if len(l_xyz) == 1:
+                    cur_xyz = l_xyz[0][batch_idx == k]
+                else:
+                    last_num_points = self.num_points_each_layer[i - 1]
+                    cur_xyz = l_xyz[-1][k * last_num_points: (k + 1) * last_num_points]
+                cur_pt_idxs = pointnet2_utils_stack.farthest_point_sample(
+                    cur_xyz[None, :, :].contiguous(), self.num_points_each_layer[i]
+                ).long()[0]
+                if cur_xyz.shape[0] < self.num_points_each_layer[i]:
+                    empty_num = self.num_points_each_layer[i] - cur_xyz.shape[1]
+                    cur_pt_idxs[0, -empty_num:] = cur_pt_idxs[0, :empty_num]
+                new_xyz_list.append(cur_xyz[cur_pt_idxs])
+            new_xyz = torch.cat(new_xyz_list, dim=0)
+
+            new_xyz_batch_cnt = xyz.new_zeros(batch_size).int().fill_(self.num_points_each_layer[i])
+            li_xyz, li_features = self.SA_modules[i](
+                xyz=l_xyz[i], features=l_features[i], xyz_batch_cnt=l_batch_cnt[i],
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt
+            )
+
+            l_xyz.append(li_xyz)
+            l_features.append(li_features)
+            l_batch_cnt.append(new_xyz_batch_cnt)
+
+        l_features[0] = points[:, 1:]
+        for i in range(-1, -(len(self.FP_modules) + 1), -1):
+            l_features[i - 1] = self.FP_modules[i](
+                unknown=l_xyz[i - 1], unknown_batch_cnt=l_batch_cnt[i - 1],
+                known=l_xyz[i], known_batch_cnt=l_batch_cnt[i],
+                unknown_feats=l_features[i - 1], known_feats=l_features[i]
+            )
+
+        batch_dict['point_features'] = l_features[0]
+        batch_dict['point_coords'] = torch.cat((batch_idx[:, None].float(), l_xyz[0]), dim=1)
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone.py
new file mode 100644
index 000000000..f0c231a66
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone.py
@@ -0,0 +1,295 @@
+from functools import partial
+
+import torch.nn as nn
+
+from ...utils.spconv_utils import replace_feature, spconv
+
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = spconv.SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, bias=None, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        if bias is None:
+            bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv3d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = spconv.SubMConv3d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = replace_feature(out, self.bn1(out.features))
+        out = replace_feature(out, self.relu(out.features))
+
+        out = self.conv2(out)
+        out = replace_feature(out, self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = replace_feature(out, out.features + identity.features)
+        out = replace_feature(out, self.relu(out.features))
+
+        return out
+
+
+class VoxelBackBone8x(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
+
+        self.conv_input = spconv.SparseSequential(
+            spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
+            norm_fn(16),
+            nn.ReLU(),
+        )
+        block = post_act_block
+
+        self.conv1 = spconv.SparseSequential(
+            block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408, 41] <- [800, 704, 21]
+            block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704, 21] <- [400, 352, 11]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352, 11] <- [200, 176, 5]
+            block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+        )
+
+        last_pad = 0
+        last_pad = self.model_cfg.get('last_pad', last_pad)
+        self.conv_out = spconv.SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
+                                bias=False, indice_key='spconv_down2'),
+            norm_fn(128),
+            nn.ReLU(),
+        )
+        self.num_point_features = 128
+        self.backbone_channels = {
+            'x_conv1': 16,
+            'x_conv2': 32,
+            'x_conv3': 64,
+            'x_conv4': 64
+        }
+
+
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        """
+        voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=voxel_features,
+            indices=voxel_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+
+        x = self.conv_input(input_sp_tensor)
+
+        x_conv1 = self.conv1(x)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+
+        # for detection head
+        # [200, 176, 5] -> [200, 176, 2]
+        out = self.conv_out(x_conv4)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_3d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_3d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+            }
+        })
+
+        return batch_dict
+
+
+class VoxelResBackBone8x(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        use_bias = self.model_cfg.get('USE_BIAS', None)
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
+
+        self.conv_input = spconv.SparseSequential(
+            spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
+            norm_fn(16),
+            nn.ReLU(),
+        )
+        block = post_act_block
+
+        self.conv1 = spconv.SparseSequential(
+            SparseBasicBlock(16, 16, bias=use_bias, norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(16, 16, bias=use_bias, norm_fn=norm_fn, indice_key='res1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408, 41] <- [800, 704, 21]
+            block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            SparseBasicBlock(32, 32, bias=use_bias, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(32, 32, bias=use_bias, norm_fn=norm_fn, indice_key='res2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704, 21] <- [400, 352, 11]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            SparseBasicBlock(64, 64, bias=use_bias, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(64, 64, bias=use_bias, norm_fn=norm_fn, indice_key='res3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352, 11] <- [200, 176, 5]
+            block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
+            SparseBasicBlock(128, 128, bias=use_bias, norm_fn=norm_fn, indice_key='res4'),
+            SparseBasicBlock(128, 128, bias=use_bias, norm_fn=norm_fn, indice_key='res4'),
+        )
+
+        last_pad = 0
+        last_pad = self.model_cfg.get('last_pad', last_pad)
+        self.conv_out = spconv.SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            spconv.SparseConv3d(128, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
+                                bias=False, indice_key='spconv_down2'),
+            norm_fn(128),
+            nn.ReLU(),
+        )
+        self.num_point_features = 128
+        self.backbone_channels = {
+            'x_conv1': 16,
+            'x_conv2': 32,
+            'x_conv3': 64,
+            'x_conv4': 128
+        }
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        """
+        voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=voxel_features,
+            indices=voxel_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        x = self.conv_input(input_sp_tensor)
+
+        x_conv1 = self.conv1(x)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+
+        # for detection head
+        # [200, 176, 5] -> [200, 176, 2]
+        out = self.conv_out(x_conv4)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_3d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+            }
+        })
+
+        batch_dict.update({
+            'multi_scale_3d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+            }
+        })
+        
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_2d.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_2d.py
new file mode 100644
index 000000000..3784ada1f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_2d.py
@@ -0,0 +1,300 @@
+from functools import partial
+
+import torch.nn as nn
+
+from ...utils.spconv_utils import replace_feature, spconv
+
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = spconv.SubMConv2d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = spconv.SparseConv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = spconv.SparseInverseConv2d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = spconv.SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+def post_act_block_dense(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, norm_fn=None):
+    m = nn.Sequential(
+        nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=padding, dilation=dilation, bias=False),
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv2d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = spconv.SubMConv2d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = replace_feature(out, self.bn1(out.features))
+        out = replace_feature(out, self.relu(out.features))
+
+        out = self.conv2(out)
+        out = replace_feature(out, self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = replace_feature(out, out.features + identity.features)
+        out = replace_feature(out, self.relu(out.features))
+
+        return out
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None):
+        super(BasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = nn.Conv2d(inplanes, planes, 3, stride=stride, padding=1, bias=bias)
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2d(planes, planes, 3, stride=stride, padding=1, bias=bias)
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = out + identity
+        out = self.relu(out)
+
+        return out
+    
+    
+class PillarBackBone8x(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+        self.sparse_shape = grid_size[[1, 0]]
+        
+        block = post_act_block
+        dense_block = post_act_block_dense
+
+        self.conv1 = spconv.SparseSequential(
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408] <- [800, 704]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704] <- [400, 352]
+            block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            block(128, 128, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            block(128, 128, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(128, 256, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv4', conv_type='spconv'),
+            block(256, 256, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+            block(256, 256, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+        )
+        
+        norm_fn = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)
+        self.conv5 = nn.Sequential(
+            # [200, 176] <- [100, 88]
+            dense_block(256, 256, 3, norm_fn=norm_fn, stride=2, padding=1),
+            dense_block(256, 256, 3, norm_fn=norm_fn, padding=1),
+            dense_block(256, 256, 3, norm_fn=norm_fn, padding=1),
+        )
+        
+        self.num_point_features = 256
+        self.backbone_channels = {
+            'x_conv1': 32,
+            'x_conv2': 64,
+            'x_conv3': 128,
+            'x_conv4': 256,
+            'x_conv5': 256
+        }
+
+
+    def forward(self, batch_dict):
+        pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=pillar_features,
+            indices=pillar_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        
+        x_conv1 = self.conv1(input_sp_tensor)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+        x_conv4 = x_conv4.dense()
+        x_conv5 = self.conv5(x_conv4)
+
+        batch_dict.update({
+            'multi_scale_2d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+                'x_conv5': x_conv5,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_2d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+                'x_conv5': 16,
+            }
+        })
+
+        return batch_dict
+
+
+class PillarRes18BackBone8x(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+        self.sparse_shape = grid_size[[1, 0]]
+        
+        block = post_act_block
+        dense_block = post_act_block_dense
+        
+        self.conv1 = spconv.SparseSequential(
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408] <- [800, 704]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704] <- [400, 352]
+            block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(128, 256, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv4', conv_type='spconv'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+        )
+        
+        norm_fn = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)
+        self.conv5 = nn.Sequential(
+            # [200, 176] <- [100, 88]
+            dense_block(256, 256, 3, norm_fn=norm_fn, stride=2, padding=1),
+            BasicBlock(256, 256, norm_fn=norm_fn),
+            BasicBlock(256, 256, norm_fn=norm_fn),
+        )
+
+        self.num_point_features = 256
+        self.backbone_channels = {
+            'x_conv1': 32,
+            'x_conv2': 64,
+            'x_conv3': 128,
+            'x_conv4': 256,
+            'x_conv5': 256
+        }
+
+    def forward(self, batch_dict):
+        pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=pillar_features,
+            indices=pillar_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        
+        x_conv1 = self.conv1(input_sp_tensor)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+        x_conv4 = x_conv4.dense()
+        x_conv5 = self.conv5(x_conv4)
+
+        # batch_dict.update({
+        #     'encoded_spconv_tensor': out,
+        #     'encoded_spconv_tensor_stride': 8
+        # })
+        batch_dict.update({
+            'multi_scale_2d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+                'x_conv5': x_conv5,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_2d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+                'x_conv5': 16,
+            }
+        })
+        
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_focal.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_focal.py
new file mode 100644
index 000000000..5869b8ba5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_focal.py
@@ -0,0 +1,269 @@
+from functools import partial
+
+import torch
+from pcdet.utils.spconv_utils import spconv
+import torch.nn as nn
+
+from .focal_sparse_conv.focal_sparse_conv import FocalSparseConv
+from .focal_sparse_conv.SemanticSeg.pyramid_ffn import PyramidFeat2D
+
+
+class objDict:
+    @staticmethod
+    def to_object(obj: object, **data):
+        obj.__dict__.update(data)
+
+class ConfigDict:
+    def __init__(self, name):
+        self.name = name
+    def __getitem__(self, item):
+        return getattr(self, item)
+
+
+class SparseSequentialBatchdict(spconv.SparseSequential):
+    def __init__(self, *args, **kwargs):
+        super(SparseSequentialBatchdict, self).__init__(*args, **kwargs)
+
+    def forward(self, input, batch_dict=None):
+        loss = 0
+        for k, module in self._modules.items():
+            if module is None:
+                continue
+            if isinstance(module, (FocalSparseConv,)):
+                input, batch_dict, _loss = module(input, batch_dict)
+                loss += _loss
+            else:
+                input = module(input)
+        return input, batch_dict, loss
+
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = spconv.SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(True),
+    )
+
+    return m
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv3d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU(True)
+        self.conv2 = spconv.SubMConv3d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = out.replace_feature(self.bn1(out.features))
+        out = out.replace_feature(self.relu(out.features))
+
+        out = self.conv2(out)
+        out = out.replace_feature(self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = out.replace_feature(out.features + identity.features)
+        out = out.replace_feature(self.relu(out.features))
+
+        return out
+
+
+class VoxelBackBone8xFocal(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
+
+        self.conv_input = spconv.SparseSequential(
+            spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
+            norm_fn(16),
+            nn.ReLU(True),
+        )
+
+        block = post_act_block
+
+        use_img = model_cfg.get('USE_IMG', False)
+        topk = model_cfg.get('TOPK', True)
+        threshold = model_cfg.get('THRESHOLD', 0.5)
+        kernel_size = model_cfg.get('KERNEL_SIZE', 3)
+        mask_multi = model_cfg.get('MASK_MULTI', False)
+        skip_mask_kernel = model_cfg.get('SKIP_MASK_KERNEL', False)
+        skip_mask_kernel_image =  model_cfg.get('SKIP_MASK_KERNEL_IMG', False)
+        enlarge_voxel_channels = model_cfg.get('ENLARGE_VOXEL_CHANNELS', -1)
+        img_pretrain = model_cfg.get('IMG_PRETRAIN', "../checkpoints/deeplabv3_resnet50_coco-cd0a2569.pth")
+
+        if use_img:
+            model_cfg_seg=dict(
+                name='SemDeepLabV3',
+                backbone='ResNet50',
+                num_class=21, # pretrained on COCO
+                args={"feat_extract_layer": ["layer1"],
+                    "pretrained_path": img_pretrain},
+                channel_reduce={
+                    "in_channels": [256],
+                    "out_channels": [16],
+                    "kernel_size": [1],
+                    "stride": [1],
+                    "bias": [False]
+                }
+            )
+            cfg_dict = ConfigDict('SemDeepLabV3')
+            objDict.to_object(cfg_dict, **model_cfg_seg)
+            self.semseg = PyramidFeat2D(optimize=True, model_cfg=cfg_dict)
+
+            self.conv_focal_multimodal = FocalSparseConv(16, 16, image_channel=model_cfg_seg['channel_reduce']['out_channels'][0],
+                                        topk=topk, threshold=threshold, use_img=True, skip_mask_kernel=skip_mask_kernel_image,
+                                        voxel_stride=1, norm_fn=norm_fn, indice_key='spconv_focal_multimodal')
+
+        special_spconv_fn = partial(FocalSparseConv, mask_multi=mask_multi, enlarge_voxel_channels=enlarge_voxel_channels, 
+                                    topk=topk, threshold=threshold, kernel_size=kernel_size, padding=kernel_size//2, 
+                                    skip_mask_kernel=skip_mask_kernel)
+        self.use_img = use_img
+
+        self.conv1 = SparseSequentialBatchdict(
+            block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+            special_spconv_fn(16, 16, voxel_stride=1, norm_fn=norm_fn, indice_key='focal1'),
+        )
+
+        self.conv2 =SparseSequentialBatchdict(
+            # [1600, 1408, 41] <- [800, 704, 21]
+            block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            special_spconv_fn(32, 32, voxel_stride=2, norm_fn=norm_fn, indice_key='focal2'),
+        )
+
+        self.conv3 = SparseSequentialBatchdict(
+            # [800, 704, 21] <- [400, 352, 11]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            special_spconv_fn(64, 64, voxel_stride=4, norm_fn=norm_fn, indice_key='focal3'),
+        )
+
+        self.conv4 = SparseSequentialBatchdict(
+            # [400, 352, 11] <- [200, 176, 5]
+            block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+        )
+
+        last_pad = 0
+        last_pad = self.model_cfg.get('last_pad', last_pad)
+        self.conv_out = spconv.SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
+                                bias=False, indice_key='spconv_down2'),
+            norm_fn(128),
+            nn.ReLU(True),
+        )
+        self.num_point_features = 128
+        self.backbone_channels = {
+            'x_conv1': 16,
+            'x_conv2': 32,
+            'x_conv3': 64,
+            'x_conv4': 64
+        }
+        
+        self.forward_ret_dict = {}
+        
+    def get_loss(self, tb_dict=None):
+        loss = self.forward_ret_dict['loss_box_of_pts']
+        if tb_dict is None:
+            tb_dict = {}
+        tb_dict['loss_box_of_pts'] = loss.item()
+        return loss, tb_dict
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        """
+        voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=voxel_features,
+            indices=voxel_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+
+        loss_img = 0
+
+        x = self.conv_input(input_sp_tensor)
+        x_conv1, batch_dict, loss1 = self.conv1(x, batch_dict)
+
+        if self.use_img:
+            x_image = self.semseg(batch_dict['images'])['layer1_feat2d']
+            x_conv1, batch_dict, loss_img = self.conv_focal_multimodal(x_conv1, batch_dict, x_image)
+
+        x_conv2, batch_dict, loss2 = self.conv2(x_conv1, batch_dict)
+        x_conv3, batch_dict, loss3 = self.conv3(x_conv2, batch_dict)
+        x_conv4, batch_dict, loss4 = self.conv4(x_conv3, batch_dict)
+
+        self.forward_ret_dict['loss_box_of_pts'] = loss1 + loss2 + loss3 + loss4 + loss_img
+        # for detection head
+        # [200, 176, 5] -> [200, 176, 2]
+        out = self.conv_out(x_conv4)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_3d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_3d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+            }
+        })
+
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_voxelnext.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_voxelnext.py
new file mode 100644
index 000000000..94ee73497
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_voxelnext.py
@@ -0,0 +1,225 @@
+from functools import partial
+import torch
+import torch.nn as nn
+
+from ...utils.spconv_utils import replace_feature, spconv
+
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = spconv.SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv3d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = spconv.SubMConv3d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = replace_feature(out, self.bn1(out.features))
+        out = replace_feature(out, self.relu(out.features))
+
+        out = self.conv2(out)
+        out = replace_feature(out, self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = replace_feature(out, out.features + identity.features)
+        out = replace_feature(out, self.relu(out.features))
+
+        return out
+
+
+class VoxelResBackBone8xVoxelNeXt(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+        spconv_kernel_sizes = model_cfg.get('SPCONV_KERNEL_SIZES', [3, 3, 3, 3])
+        channels = model_cfg.get('CHANNELS', [16, 32, 64, 128, 128])
+        out_channel = model_cfg.get('OUT_CHANNEL', 128)
+
+        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
+
+        self.conv_input = spconv.SparseSequential(
+            spconv.SubMConv3d(input_channels, channels[0], 3, padding=1, bias=False, indice_key='subm1'),
+            norm_fn(channels[0]),
+            nn.ReLU(),
+        )
+        block = post_act_block
+
+        self.conv1 = spconv.SparseSequential(
+            SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408, 41] <- [800, 704, 21]
+            block(channels[0], channels[1], spconv_kernel_sizes[0], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[0]//2), indice_key='spconv2', conv_type='spconv'),
+            SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704, 21] <- [400, 352, 11]
+            block(channels[1], channels[2], spconv_kernel_sizes[1], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[1]//2), indice_key='spconv3', conv_type='spconv'),
+            SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352, 11] <- [200, 176, 6]
+            block(channels[2], channels[3], spconv_kernel_sizes[2], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[2]//2), indice_key='spconv4', conv_type='spconv'),
+            SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'),
+            SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'),
+        )
+
+        self.conv5 = spconv.SparseSequential(
+            # [200, 176, 6] <- [100, 88, 3]
+            block(channels[3], channels[4], spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv5', conv_type='spconv'),
+            SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'),
+            SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'),
+        )
+        
+        self.conv6 = spconv.SparseSequential(
+            # [200, 176, 6] <- [100, 88, 3]
+            block(channels[4], channels[4], spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv6', conv_type='spconv'),
+            SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'),
+            SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'),
+        )
+        self.conv_out = spconv.SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            spconv.SparseConv2d(channels[3], out_channel, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2'),
+            norm_fn(out_channel),
+            nn.ReLU(),
+        )
+
+        self.shared_conv = spconv.SparseSequential(
+            spconv.SubMConv2d(out_channel, out_channel, 3, stride=1, padding=1, bias=True),
+            nn.BatchNorm1d(out_channel),
+            nn.ReLU(True),
+        )
+
+        self.forward_ret_dict = {}
+        self.num_point_features = out_channel
+        self.backbone_channels = {
+            'x_conv1': channels[0],
+            'x_conv2': channels[1],
+            'x_conv3': channels[2],
+            'x_conv4': channels[3]
+        }
+
+    def bev_out(self, x_conv):
+        features_cat = x_conv.features
+        indices_cat = x_conv.indices[:, [0, 2, 3]]
+        spatial_shape = x_conv.spatial_shape[1:]
+
+        indices_unique, _inv = torch.unique(indices_cat, dim=0, return_inverse=True)
+        features_unique = features_cat.new_zeros((indices_unique.shape[0], features_cat.shape[1]))
+        features_unique.index_add_(0, _inv, features_cat)
+
+        x_out = spconv.SparseConvTensor(
+            features=features_unique,
+            indices=indices_unique,
+            spatial_shape=spatial_shape,
+            batch_size=x_conv.batch_size
+        )
+        return x_out
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        """
+        voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=voxel_features,
+            indices=voxel_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        x = self.conv_input(input_sp_tensor)
+
+        x_conv1 = self.conv1(x)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+        x_conv5 = self.conv5(x_conv4)
+        x_conv6 = self.conv6(x_conv5)
+
+        x_conv5.indices[:, 1:] *= 2
+        x_conv6.indices[:, 1:] *= 4
+        x_conv4 = x_conv4.replace_feature(torch.cat([x_conv4.features, x_conv5.features, x_conv6.features]))
+        x_conv4.indices = torch.cat([x_conv4.indices, x_conv5.indices, x_conv6.indices])
+
+        out = self.bev_out(x_conv4)
+
+        out = self.conv_out(out)
+        out = self.shared_conv(out)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_3d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_3d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+            }
+        })
+        
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_voxelnext2d.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_voxelnext2d.py
new file mode 100644
index 000000000..bdfdf9984
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_backbone_voxelnext2d.py
@@ -0,0 +1,219 @@
+from functools import partial
+import torch
+import torch.nn as nn
+
+from ...utils.spconv_utils import replace_feature, spconv
+
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = spconv.SubMConv2d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = spconv.SparseConv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = spconv.SparseInverseConv2d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = spconv.SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv2d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = spconv.SubMConv2d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = replace_feature(out, self.bn1(out.features))
+        out = replace_feature(out, self.relu(out.features))
+
+        out = self.conv2(out)
+        out = replace_feature(out, self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = replace_feature(out, out.features + identity.features)
+        out = replace_feature(out, self.relu(out.features))
+
+        return out
+
+
+class VoxelResBackBone8xVoxelNeXt2D(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+        self.sparse_shape = grid_size[[1, 0]]
+        
+        block = post_act_block
+
+        spconv_kernel_sizes = model_cfg.get('SPCONV_KERNEL_SIZES', [3, 3, 3, 3])
+
+        self.conv1 = spconv.SparseSequential(
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408] <- [800, 704]
+            block(32, 64, spconv_kernel_sizes[0], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[0]//2), indice_key='spconv2', conv_type='spconv'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704] <- [400, 352]
+            block(64, 128, spconv_kernel_sizes[1], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[1]//2), indice_key='spconv3', conv_type='spconv'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(128, 256, spconv_kernel_sizes[2], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[2]//2), indice_key='spconv4', conv_type='spconv'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+        )
+
+        self.conv5 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(256, 256, spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv5', conv_type='spconv'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
+        )
+
+        self.conv6 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(256, 256, spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv6', conv_type='spconv'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
+        )
+
+        self.conv_out = spconv.SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            spconv.SparseConv2d(256, 256, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2'),
+            norm_fn(256),
+            nn.ReLU(),
+        )
+
+        self.shared_conv = spconv.SparseSequential(
+            spconv.SubMConv2d(256, 256, 3, stride=1, padding=1, bias=True),
+            nn.BatchNorm1d(256),
+            nn.ReLU(True),
+        )
+
+        self.num_point_features = 256
+        self.backbone_channels = {
+            'x_conv1': 32,
+            'x_conv2': 64,
+            'x_conv3': 128,
+            'x_conv4': 256,
+            'x_conv5': 256
+        }
+        self.forward_ret_dict = {}
+
+    def bev_out(self, x_conv):
+        features_cat = x_conv.features
+        indices_cat = x_conv.indices
+
+        indices_unique, _inv = torch.unique(indices_cat, dim=0, return_inverse=True)
+        features_unique = features_cat.new_zeros((indices_unique.shape[0], features_cat.shape[1]))
+        features_unique.index_add_(0, _inv, features_cat)
+
+        x_out = spconv.SparseConvTensor(
+            features=features_unique,
+            indices=indices_unique,
+            spatial_shape=x_conv.spatial_shape,
+            batch_size=x_conv.batch_size
+        )
+        return x_out
+
+    def forward(self, batch_dict):
+        pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=pillar_features,
+            indices=pillar_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        
+        x_conv1 = self.conv1(input_sp_tensor)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+        x_conv5 = self.conv5(x_conv4)
+        x_conv6 = self.conv6(x_conv5)
+
+        x_conv5.indices[:, 1:] *= 2
+        x_conv6.indices[:, 1:] *= 4
+        x_conv4 = x_conv4.replace_feature(torch.cat([x_conv4.features, x_conv5.features, x_conv6.features]))
+        x_conv4.indices = torch.cat([x_conv4.indices, x_conv5.indices, x_conv6.indices])
+
+        out = self.bev_out(x_conv4)
+
+        out = self.conv_out(out)
+        out = self.shared_conv(out)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_2d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+                'x_conv5': x_conv5,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_2d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+                'x_conv5': 16,
+            }
+        })
+        
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_unet.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_unet.py
new file mode 100644
index 000000000..a5e7c4b36
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/spconv_unet.py
@@ -0,0 +1,212 @@
+from functools import partial
+
+import torch
+import torch.nn as nn
+
+from ...utils.spconv_utils import replace_feature, spconv
+from ...utils import common_utils
+from .spconv_backbone import post_act_block
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, indice_key=None, norm_fn=None):
+        super(SparseBasicBlock, self).__init__()
+        self.conv1 = spconv.SubMConv3d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = spconv.SubMConv3d(
+            planes, planes, kernel_size=3, stride=1, padding=1, bias=False, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x.features
+
+        assert x.features.dim() == 2, 'x.features.dim()=%d' % x.features.dim()
+
+        out = self.conv1(x)
+        out = replace_feature(out, self.bn1(out.features))
+        out = replace_feature(out, self.relu(out.features))
+
+        out = self.conv2(out)
+        out = replace_feature(out, self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = replace_feature(out, out.features + identity)
+        out = replace_feature(out, self.relu(out.features))
+
+        return out
+
+
+class UNetV2(nn.Module):
+    """
+    Sparse Convolution based UNet for point-wise feature learning.
+    Reference Paper: https://arxiv.org/abs/1907.03670 (Shaoshuai Shi, et. al)
+    From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
+    """
+
+    def __init__(self, model_cfg, input_channels, grid_size, voxel_size, point_cloud_range, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
+        self.voxel_size = voxel_size
+        self.point_cloud_range = point_cloud_range
+
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+        self.conv_input = spconv.SparseSequential(
+            spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
+            norm_fn(16),
+            nn.ReLU(),
+        )
+        block = post_act_block
+
+        self.conv1 = spconv.SparseSequential(
+            block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408, 41] <- [800, 704, 21]
+            block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704, 21] <- [400, 352, 11]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352, 11] <- [200, 176, 5]
+            block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+        )
+
+        if self.model_cfg.get('RETURN_ENCODED_TENSOR', True):
+            last_pad = self.model_cfg.get('last_pad', 0)
+
+            self.conv_out = spconv.SparseSequential(
+                # [200, 150, 5] -> [200, 150, 2]
+                spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
+                                    bias=False, indice_key='spconv_down2'),
+                norm_fn(128),
+                nn.ReLU(),
+            )
+        else:
+            self.conv_out = None
+
+        # decoder
+        # [400, 352, 11] <- [200, 176, 5]
+        self.conv_up_t4 = SparseBasicBlock(64, 64, indice_key='subm4', norm_fn=norm_fn)
+        self.conv_up_m4 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4')
+        self.inv_conv4 = block(64, 64, 3, norm_fn=norm_fn, indice_key='spconv4', conv_type='inverseconv')
+
+        # [800, 704, 21] <- [400, 352, 11]
+        self.conv_up_t3 = SparseBasicBlock(64, 64, indice_key='subm3', norm_fn=norm_fn)
+        self.conv_up_m3 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3')
+        self.inv_conv3 = block(64, 32, 3, norm_fn=norm_fn, indice_key='spconv3', conv_type='inverseconv')
+
+        # [1600, 1408, 41] <- [800, 704, 21]
+        self.conv_up_t2 = SparseBasicBlock(32, 32, indice_key='subm2', norm_fn=norm_fn)
+        self.conv_up_m2 = block(64, 32, 3, norm_fn=norm_fn, indice_key='subm2')
+        self.inv_conv2 = block(32, 16, 3, norm_fn=norm_fn, indice_key='spconv2', conv_type='inverseconv')
+
+        # [1600, 1408, 41] <- [1600, 1408, 41]
+        self.conv_up_t1 = SparseBasicBlock(16, 16, indice_key='subm1', norm_fn=norm_fn)
+        self.conv_up_m1 = block(32, 16, 3, norm_fn=norm_fn, indice_key='subm1')
+
+        self.conv5 = spconv.SparseSequential(
+            block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1')
+        )
+        self.num_point_features = 16
+
+    def UR_block_forward(self, x_lateral, x_bottom, conv_t, conv_m, conv_inv):
+        x_trans = conv_t(x_lateral)
+        x = x_trans
+        x = replace_feature(x, torch.cat((x_bottom.features, x_trans.features), dim=1))
+        x_m = conv_m(x)
+        x = self.channel_reduction(x, x_m.features.shape[1])
+        x = replace_feature(x, x_m.features + x.features)
+        x = conv_inv(x)
+        return x
+
+    @staticmethod
+    def channel_reduction(x, out_channels):
+        """
+        Args:
+            x: x.features (N, C1)
+            out_channels: C2
+
+        Returns:
+
+        """
+        features = x.features
+        n, in_channels = features.shape
+        assert (in_channels % out_channels == 0) and (in_channels >= out_channels)
+
+        x = replace_feature(x, features.view(n, out_channels, -1).sum(dim=2))
+        return x
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+                point_features: (N, C)
+        """
+        voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=voxel_features,
+            indices=voxel_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        x = self.conv_input(input_sp_tensor)
+
+        x_conv1 = self.conv1(x)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+
+        if self.conv_out is not None:
+            # for detection head
+            # [200, 176, 5] -> [200, 176, 2]
+            out = self.conv_out(x_conv4)
+            batch_dict['encoded_spconv_tensor'] = out
+            batch_dict['encoded_spconv_tensor_stride'] = 8
+
+        # for segmentation head
+        # [400, 352, 11] <- [200, 176, 5]
+        x_up4 = self.UR_block_forward(x_conv4, x_conv4, self.conv_up_t4, self.conv_up_m4, self.inv_conv4)
+        # [800, 704, 21] <- [400, 352, 11]
+        x_up3 = self.UR_block_forward(x_conv3, x_up4, self.conv_up_t3, self.conv_up_m3, self.inv_conv3)
+        # [1600, 1408, 41] <- [800, 704, 21]
+        x_up2 = self.UR_block_forward(x_conv2, x_up3, self.conv_up_t2, self.conv_up_m2, self.inv_conv2)
+        # [1600, 1408, 41] <- [1600, 1408, 41]
+        x_up1 = self.UR_block_forward(x_conv1, x_up2, self.conv_up_t1, self.conv_up_m1, self.conv5)
+
+        batch_dict['point_features'] = x_up1.features
+        point_coords = common_utils.get_voxel_centers(
+            x_up1.indices[:, 1:], downsample_times=1, voxel_size=self.voxel_size,
+            point_cloud_range=self.point_cloud_range
+        )
+        batch_dict['point_coords'] = torch.cat((x_up1.indices[:, 0:1].float(), point_coords), dim=1)
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/__init__.py
new file mode 100644
index 000000000..e6660168c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/__init__.py
@@ -0,0 +1,18 @@
+from .mean_vfe import MeanVFE
+from .pillar_vfe import PillarVFE
+from .dynamic_mean_vfe import DynamicMeanVFE
+from .dynamic_pillar_vfe import DynamicPillarVFE, DynamicPillarVFESimple2D
+from .dynamic_voxel_vfe import DynamicVoxelVFE
+from .image_vfe import ImageVFE
+from .vfe_template import VFETemplate
+
+__all__ = {
+    'VFETemplate': VFETemplate,
+    'MeanVFE': MeanVFE,
+    'PillarVFE': PillarVFE,
+    'ImageVFE': ImageVFE,
+    'DynMeanVFE': DynamicMeanVFE,
+    'DynPillarVFE': DynamicPillarVFE,
+    'DynamicPillarVFESimple2D': DynamicPillarVFESimple2D,
+    'DynamicVoxelVFE': DynamicVoxelVFE,
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_mean_vfe.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_mean_vfe.py
new file mode 100644
index 000000000..b4c5b0667
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_mean_vfe.py
@@ -0,0 +1,76 @@
+import torch
+
+from .vfe_template import VFETemplate
+
+try:
+    import torch_scatter
+except Exception as e:
+    # Incase someone doesn't want to use dynamic pillar vfe and hasn't installed torch_scatter
+    pass
+
+from .vfe_template import VFETemplate
+
+
+class DynamicMeanVFE(VFETemplate):
+    def __init__(self, model_cfg, num_point_features, voxel_size, grid_size, point_cloud_range, **kwargs):
+        super().__init__(model_cfg=model_cfg)
+        self.num_point_features = num_point_features
+
+        self.grid_size = torch.tensor(grid_size).cuda()
+        self.voxel_size = torch.tensor(voxel_size).cuda()
+        self.point_cloud_range = torch.tensor(point_cloud_range).cuda()
+
+        self.voxel_x = voxel_size[0]
+        self.voxel_y = voxel_size[1]
+        self.voxel_z = voxel_size[2]
+        self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
+        self.y_offset = self.voxel_y / 2 + point_cloud_range[1]
+        self.z_offset = self.voxel_z / 2 + point_cloud_range[2]
+
+        self.scale_xyz = grid_size[0] * grid_size[1] * grid_size[2]
+        self.scale_yz = grid_size[1] * grid_size[2]
+        self.scale_z = grid_size[2]
+
+    def get_output_feature_dim(self):
+        return self.num_point_features
+
+    @torch.no_grad()
+    def forward(self, batch_dict, **kwargs):
+        """
+        Args:
+            batch_dict:
+                voxels: (num_voxels, max_points_per_voxel, C)
+                voxel_num_points: optional (num_voxels)
+            **kwargs:
+
+        Returns:
+            vfe_features: (num_voxels, C)
+        """
+        batch_size = batch_dict['batch_size']
+        points = batch_dict['points'] # (batch_idx, x, y, z, i, e)
+
+        # # debug
+        point_coords = torch.floor((points[:, 1:4] - self.point_cloud_range[0:3]) / self.voxel_size).int()
+        mask = ((point_coords >= 0) & (point_coords < self.grid_size)).all(dim=1)
+        points = points[mask]
+        point_coords = point_coords[mask]
+        merge_coords = points[:, 0].int() * self.scale_xyz + \
+                        point_coords[:, 0] * self.scale_yz + \
+                        point_coords[:, 1] * self.scale_z + \
+                        point_coords[:, 2]
+        points_data = points[:, 1:].contiguous()
+        
+        unq_coords, unq_inv, unq_cnt = torch.unique(merge_coords, return_inverse=True, return_counts=True)
+
+        points_mean = torch_scatter.scatter_mean(points_data, unq_inv, dim=0)
+        
+        unq_coords = unq_coords.int()
+        voxel_coords = torch.stack((unq_coords // self.scale_xyz,
+                                    (unq_coords % self.scale_xyz) // self.scale_yz,
+                                    (unq_coords % self.scale_yz) // self.scale_z,
+                                    unq_coords % self.scale_z), dim=1)
+        voxel_coords = voxel_coords[:, [0, 3, 2, 1]]
+        
+        batch_dict['voxel_features'] = points_mean.contiguous()
+        batch_dict['voxel_coords'] = voxel_coords.contiguous()
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_pillar_vfe.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_pillar_vfe.py
new file mode 100644
index 000000000..f5fb6b1ac
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_pillar_vfe.py
@@ -0,0 +1,240 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    import torch_scatter
+except Exception as e:
+    # Incase someone doesn't want to use dynamic pillar vfe and hasn't installed torch_scatter
+    pass
+
+from .vfe_template import VFETemplate
+
+
+class PFNLayerV2(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 use_norm=True,
+                 last_layer=False):
+        super().__init__()
+        
+        self.last_vfe = last_layer
+        self.use_norm = use_norm
+        if not self.last_vfe:
+            out_channels = out_channels // 2
+
+        if self.use_norm:
+            self.linear = nn.Linear(in_channels, out_channels, bias=False)
+            self.norm = nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01)
+        else:
+            self.linear = nn.Linear(in_channels, out_channels, bias=True)
+        
+        self.relu = nn.ReLU()
+
+    def forward(self, inputs, unq_inv):
+
+        x = self.linear(inputs)
+        x = self.norm(x) if self.use_norm else x
+        x = self.relu(x)
+        x_max = torch_scatter.scatter_max(x, unq_inv, dim=0)[0]
+
+        if self.last_vfe:
+            return x_max
+        else:
+            x_concatenated = torch.cat([x, x_max[unq_inv, :]], dim=1)
+            return x_concatenated
+
+
+class DynamicPillarVFE(VFETemplate):
+    def __init__(self, model_cfg, num_point_features, voxel_size, grid_size, point_cloud_range, **kwargs):
+        super().__init__(model_cfg=model_cfg)
+
+        self.use_norm = self.model_cfg.USE_NORM
+        self.with_distance = self.model_cfg.WITH_DISTANCE
+        self.use_absolute_xyz = self.model_cfg.USE_ABSLOTE_XYZ
+        num_point_features += 6 if self.use_absolute_xyz else 3
+        if self.with_distance:
+            num_point_features += 1
+
+        self.num_filters = self.model_cfg.NUM_FILTERS
+        assert len(self.num_filters) > 0
+        num_filters = [num_point_features] + list(self.num_filters)
+
+        pfn_layers = []
+        for i in range(len(num_filters) - 1):
+            in_filters = num_filters[i]
+            out_filters = num_filters[i + 1]
+            pfn_layers.append(
+                PFNLayerV2(in_filters, out_filters, self.use_norm, last_layer=(i >= len(num_filters) - 2))
+            )
+        self.pfn_layers = nn.ModuleList(pfn_layers)
+
+        self.voxel_x = voxel_size[0]
+        self.voxel_y = voxel_size[1]
+        self.voxel_z = voxel_size[2]
+        self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
+        self.y_offset = self.voxel_y / 2 + point_cloud_range[1]
+        self.z_offset = self.voxel_z / 2 + point_cloud_range[2]
+
+        self.scale_xy = grid_size[0] * grid_size[1]
+        self.scale_y = grid_size[1]
+        
+        self.grid_size = torch.tensor(grid_size).cuda()
+        self.voxel_size = torch.tensor(voxel_size).cuda()
+        self.point_cloud_range = torch.tensor(point_cloud_range).cuda()
+
+    def get_output_feature_dim(self):
+        return self.num_filters[-1]
+
+    def forward(self, batch_dict, **kwargs):
+        points = batch_dict['points'] # (batch_idx, x, y, z, i, e)
+
+        points_coords = torch.floor((points[:, [1,2]] - self.point_cloud_range[[0,1]]) / self.voxel_size[[0,1]]).int()
+        mask = ((points_coords >= 0) & (points_coords < self.grid_size[[0,1]])).all(dim=1)
+        points = points[mask]
+        points_coords = points_coords[mask]
+        points_xyz = points[:, [1, 2, 3]].contiguous()
+
+        merge_coords = points[:, 0].int() * self.scale_xy + \
+                       points_coords[:, 0] * self.scale_y + \
+                       points_coords[:, 1]
+        
+        unq_coords, unq_inv, unq_cnt = torch.unique(merge_coords, return_inverse=True, return_counts=True, dim=0)
+
+        points_mean = torch_scatter.scatter_mean(points_xyz, unq_inv, dim=0)
+        f_cluster = points_xyz - points_mean[unq_inv, :]
+
+        f_center = torch.zeros_like(points_xyz)
+        f_center[:, 0] = points_xyz[:, 0] - (points_coords[:, 0].to(points_xyz.dtype) * self.voxel_x + self.x_offset)
+        f_center[:, 1] = points_xyz[:, 1] - (points_coords[:, 1].to(points_xyz.dtype) * self.voxel_y + self.y_offset)
+        f_center[:, 2] = points_xyz[:, 2] - self.z_offset
+
+        if self.use_absolute_xyz:
+            features = [points[:, 1:], f_cluster, f_center]
+        else:
+            features = [points[:, 4:], f_cluster, f_center]
+        
+        if self.with_distance:
+            points_dist = torch.norm(points[:, 1:4], 2, dim=1, keepdim=True)
+            features.append(points_dist)
+        features = torch.cat(features, dim=-1)
+        
+        for pfn in self.pfn_layers:
+            features = pfn(features, unq_inv)
+        # features = self.linear1(features)
+        # features_max = torch_scatter.scatter_max(features, unq_inv, dim=0)[0]
+        # features = torch.cat([features, features_max[unq_inv, :]], dim=1)
+        # features = self.linear2(features)
+        # features = torch_scatter.scatter_max(features, unq_inv, dim=0)[0]
+        
+        # generate voxel coordinates
+        unq_coords = unq_coords.int()
+        voxel_coords = torch.stack((unq_coords // self.scale_xy,
+                                   (unq_coords % self.scale_xy) // self.scale_y,
+                                   unq_coords % self.scale_y,
+                                   torch.zeros(unq_coords.shape[0]).to(unq_coords.device).int()
+                                   ), dim=1)
+        voxel_coords = voxel_coords[:, [0, 3, 2, 1]]
+
+        batch_dict['voxel_features'] = batch_dict['pillar_features'] = features
+        batch_dict['voxel_coords'] = voxel_coords
+        return batch_dict
+
+
+class DynamicPillarVFESimple2D(VFETemplate):
+    def __init__(self, model_cfg, num_point_features, voxel_size, grid_size, point_cloud_range, **kwargs):
+        super().__init__(model_cfg=model_cfg)
+
+        self.use_norm = self.model_cfg.USE_NORM
+        self.with_distance = self.model_cfg.WITH_DISTANCE
+        self.use_absolute_xyz = self.model_cfg.USE_ABSLOTE_XYZ
+        # self.use_cluster_xyz = self.model_cfg.get('USE_CLUSTER_XYZ', True)
+        if self.use_absolute_xyz:
+            num_point_features += 3
+        # if self.use_cluster_xyz:
+        #     num_point_features += 3
+        if self.with_distance:
+            num_point_features += 1
+
+        self.num_filters = self.model_cfg.NUM_FILTERS
+        assert len(self.num_filters) > 0
+        num_filters = [num_point_features] + list(self.num_filters)
+
+        pfn_layers = []
+        for i in range(len(num_filters) - 1):
+            in_filters = num_filters[i]
+            out_filters = num_filters[i + 1]
+            pfn_layers.append(
+                PFNLayerV2(in_filters, out_filters, self.use_norm, last_layer=(i >= len(num_filters) - 2))
+            )
+        self.pfn_layers = nn.ModuleList(pfn_layers)
+
+        self.voxel_x = voxel_size[0]
+        self.voxel_y = voxel_size[1]
+        self.voxel_z = voxel_size[2]
+        self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
+        self.y_offset = self.voxel_y / 2 + point_cloud_range[1]
+        self.z_offset = self.voxel_z / 2 + point_cloud_range[2]
+
+        self.scale_xy = grid_size[0] * grid_size[1]
+        self.scale_y = grid_size[1]
+
+        self.grid_size = torch.tensor(grid_size[:2]).cuda()
+        self.voxel_size = torch.tensor(voxel_size).cuda()
+        self.point_cloud_range = torch.tensor(point_cloud_range).cuda()
+
+    def get_output_feature_dim(self):
+        return self.num_filters[-1]
+
+    def forward(self, batch_dict, **kwargs):
+        points = batch_dict['points']  # (batch_idx, x, y, z, i, e)
+
+        points_coords = torch.floor(
+            (points[:, [1, 2]] - self.point_cloud_range[[0, 1]]) / self.voxel_size[[0, 1]]).int()
+        mask = ((points_coords >= 0) & (points_coords < self.grid_size[[0, 1]])).all(dim=1)
+        points = points[mask]
+        points_coords = points_coords[mask]
+        points_xyz = points[:, [1, 2, 3]].contiguous()
+
+        merge_coords = points[:, 0].int() * self.scale_xy + \
+                       points_coords[:, 0] * self.scale_y + \
+                       points_coords[:, 1]
+
+        unq_coords, unq_inv, unq_cnt = torch.unique(merge_coords, return_inverse=True, return_counts=True, dim=0)
+
+        f_center = torch.zeros_like(points_xyz)
+        f_center[:, 0] = points_xyz[:, 0] - (points_coords[:, 0].to(points_xyz.dtype) * self.voxel_x + self.x_offset)
+        f_center[:, 1] = points_xyz[:, 1] - (points_coords[:, 1].to(points_xyz.dtype) * self.voxel_y + self.y_offset)
+        f_center[:, 2] = points_xyz[:, 2] - self.z_offset
+
+        features = [f_center]
+        if self.use_absolute_xyz:
+            features.append(points[:, 1:])
+        else:
+            features.append(points[:, 4:])
+
+        # if self.use_cluster_xyz:
+        #     points_mean = torch_scatter.scatter_mean(points_xyz, unq_inv, dim=0)
+        #     f_cluster = points_xyz - points_mean[unq_inv, :]
+        #     features.append(f_cluster)
+
+        if self.with_distance:
+            points_dist = torch.norm(points[:, 1:4], 2, dim=1, keepdim=True)
+            features.append(points_dist)
+        features = torch.cat(features, dim=-1)
+
+        for pfn in self.pfn_layers:
+            features = pfn(features, unq_inv)
+
+        # generate voxel coordinates
+        unq_coords = unq_coords.int()
+        pillar_coords = torch.stack((unq_coords // self.scale_xy,
+                                     (unq_coords % self.scale_xy) // self.scale_y,
+                                     unq_coords % self.scale_y,
+                                     ), dim=1)
+        pillar_coords = pillar_coords[:, [0, 2, 1]]
+
+        batch_dict['pillar_features'] = features
+        batch_dict['pillar_coords'] = pillar_coords
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_voxel_vfe.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_voxel_vfe.py
new file mode 100644
index 000000000..d878d491f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/dynamic_voxel_vfe.py
@@ -0,0 +1,106 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    import torch_scatter
+except Exception as e:
+    # Incase someone doesn't want to use dynamic pillar vfe and hasn't installed torch_scatter
+    pass
+
+from .vfe_template import VFETemplate
+from .dynamic_pillar_vfe import PFNLayerV2
+
+
+class DynamicVoxelVFE(VFETemplate):
+    def __init__(self, model_cfg, num_point_features, voxel_size, grid_size, point_cloud_range, **kwargs):
+        super().__init__(model_cfg=model_cfg)
+
+        self.use_norm = self.model_cfg.USE_NORM
+        self.with_distance = self.model_cfg.WITH_DISTANCE
+        self.use_absolute_xyz = self.model_cfg.USE_ABSLOTE_XYZ
+        num_point_features += 6 if self.use_absolute_xyz else 3
+        if self.with_distance:
+            num_point_features += 1
+
+        self.num_filters = self.model_cfg.NUM_FILTERS
+        assert len(self.num_filters) > 0
+        num_filters = [num_point_features] + list(self.num_filters)
+
+        pfn_layers = []
+        for i in range(len(num_filters) - 1):
+            in_filters = num_filters[i]
+            out_filters = num_filters[i + 1]
+            pfn_layers.append(
+                PFNLayerV2(in_filters, out_filters, self.use_norm, last_layer=(i >= len(num_filters) - 2))
+            )
+        self.pfn_layers = nn.ModuleList(pfn_layers)
+
+        self.voxel_x = voxel_size[0]
+        self.voxel_y = voxel_size[1]
+        self.voxel_z = voxel_size[2]
+        self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
+        self.y_offset = self.voxel_y / 2 + point_cloud_range[1]
+        self.z_offset = self.voxel_z / 2 + point_cloud_range[2]
+
+        self.scale_xyz = grid_size[0] * grid_size[1] * grid_size[2]
+        self.scale_yz = grid_size[1] * grid_size[2]
+        self.scale_z = grid_size[2]
+
+        self.grid_size = torch.tensor(grid_size).cuda()
+        self.voxel_size = torch.tensor(voxel_size).cuda()
+        self.point_cloud_range = torch.tensor(point_cloud_range).cuda()
+
+    def get_output_feature_dim(self):
+        return self.num_filters[-1]
+
+    def forward(self, batch_dict, **kwargs):
+        points = batch_dict['points'] # (batch_idx, x, y, z, i, e)
+
+        points_coords = torch.floor((points[:, [1,2,3]] - self.point_cloud_range[[0,1,2]]) / self.voxel_size[[0,1,2]]).int()
+        mask = ((points_coords >= 0) & (points_coords < self.grid_size[[0,1,2]])).all(dim=1)
+        points = points[mask]
+        points_coords = points_coords[mask]
+        points_xyz = points[:, [1, 2, 3]].contiguous()
+
+        merge_coords = points[:, 0].int() * self.scale_xyz + \
+                       points_coords[:, 0] * self.scale_yz + \
+                       points_coords[:, 1] * self.scale_z + \
+                       points_coords[:, 2]
+
+        unq_coords, unq_inv, unq_cnt = torch.unique(merge_coords, return_inverse=True, return_counts=True, dim=0)
+
+        points_mean = torch_scatter.scatter_mean(points_xyz, unq_inv, dim=0)
+        f_cluster = points_xyz - points_mean[unq_inv, :]
+
+        f_center = torch.zeros_like(points_xyz)
+        f_center[:, 0] = points_xyz[:, 0] - (points_coords[:, 0].to(points_xyz.dtype) * self.voxel_x + self.x_offset)
+        f_center[:, 1] = points_xyz[:, 1] - (points_coords[:, 1].to(points_xyz.dtype) * self.voxel_y + self.y_offset)
+        # f_center[:, 2] = points_xyz[:, 2] - self.z_offset
+        f_center[:, 2] = points_xyz[:, 2] - (points_coords[:, 2].to(points_xyz.dtype) * self.voxel_z + self.z_offset)
+
+        if self.use_absolute_xyz:
+            features = [points[:, 1:], f_cluster, f_center]
+        else:
+            features = [points[:, 4:], f_cluster, f_center]
+
+        if self.with_distance:
+            points_dist = torch.norm(points[:, 1:4], 2, dim=1, keepdim=True)
+            features.append(points_dist)
+        features = torch.cat(features, dim=-1)
+
+        for pfn in self.pfn_layers:
+            features = pfn(features, unq_inv)
+
+        # generate voxel coordinates
+        unq_coords = unq_coords.int()
+        voxel_coords = torch.stack((unq_coords // self.scale_xyz,
+                                    (unq_coords % self.scale_xyz) // self.scale_yz,
+                                    (unq_coords % self.scale_yz) // self.scale_z,
+                                    unq_coords % self.scale_z), dim=1)
+        voxel_coords = voxel_coords[:, [0, 3, 2, 1]]
+
+        batch_dict['pillar_features'] = batch_dict['voxel_features'] = features
+        batch_dict['voxel_coords'] = voxel_coords
+
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe.py
new file mode 100644
index 000000000..f6269869b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe.py
@@ -0,0 +1,85 @@
+import torch
+
+from .vfe_template import VFETemplate
+from .image_vfe_modules import ffn, f2v
+
+
+class ImageVFE(VFETemplate):
+    def __init__(self, model_cfg, grid_size, point_cloud_range, depth_downsample_factor, **kwargs):
+        super().__init__(model_cfg=model_cfg)
+        self.grid_size = grid_size
+        self.pc_range = point_cloud_range
+        self.downsample_factor = depth_downsample_factor
+        self.module_topology = [
+            'ffn', 'f2v'
+        ]
+        self.build_modules()
+
+    def build_modules(self):
+        """
+        Builds modules
+        """
+        for module_name in self.module_topology:
+            module = getattr(self, 'build_%s' % module_name)()
+            self.add_module(module_name, module)
+
+    def build_ffn(self):
+        """
+        Builds frustum feature network
+        Returns:
+            ffn_module: nn.Module, Frustum feature network
+        """
+        ffn_module = ffn.__all__[self.model_cfg.FFN.NAME](
+            model_cfg=self.model_cfg.FFN,
+            downsample_factor=self.downsample_factor
+        )
+        self.disc_cfg = ffn_module.disc_cfg
+        return ffn_module
+
+    def build_f2v(self):
+        """
+        Builds frustum to voxel transformation
+        Returns:
+            f2v_module: nn.Module, Frustum to voxel transformation
+        """
+        f2v_module = f2v.__all__[self.model_cfg.F2V.NAME](
+            model_cfg=self.model_cfg.F2V,
+            grid_size=self.grid_size,
+            pc_range=self.pc_range,
+            disc_cfg=self.disc_cfg
+        )
+        return f2v_module
+
+    def get_output_feature_dim(self):
+        """
+        Gets number of output channels
+        Returns:
+            out_feature_dim: int, Number of output channels
+        """
+        out_feature_dim = self.ffn.get_output_feature_dim()
+        return out_feature_dim
+
+    def forward(self, batch_dict, **kwargs):
+        """
+        Args:
+            batch_dict:
+                images: (N, 3, H_in, W_in), Input images
+            **kwargs:
+        Returns:
+            batch_dict:
+                voxel_features: (B, C, Z, Y, X), Image voxel features
+        """
+        batch_dict = self.ffn(batch_dict)
+        batch_dict = self.f2v(batch_dict)
+        return batch_dict
+
+    def get_loss(self):
+        """
+        Gets DDN loss
+        Returns:
+            loss: (1), Depth distribution network loss
+            tb_dict: dict[float], All losses to log in tensorboard
+        """
+
+        loss, tb_dict = self.ffn.get_loss()
+        return loss, tb_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/__init__.py
new file mode 100644
index 000000000..58b43bb65
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/__init__.py
@@ -0,0 +1,5 @@
+from .frustum_to_voxel import FrustumToVoxel
+
+__all__ = {
+    'FrustumToVoxel': FrustumToVoxel
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_grid_generator.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_grid_generator.py
new file mode 100644
index 000000000..c4d6d9822
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_grid_generator.py
@@ -0,0 +1,145 @@
+import torch
+import torch.nn as nn
+
+try:
+    from kornia.utils.grid import create_meshgrid3d
+    from kornia.geometry.linalg import transform_points
+except Exception as e:
+    # Note: Kornia team will fix this import issue to try to allow the usage of lower torch versions.
+    # print('Warning: kornia is not installed correctly, please ignore this warning if you do not use CaDDN. Otherwise, it is recommended to use torch version greater than 1.2 to use kornia properly.')
+    pass
+
+from pcdet.utils import transform_utils
+
+
+class FrustumGridGenerator(nn.Module):
+
+    def __init__(self, grid_size, pc_range, disc_cfg):
+        """
+        Initializes Grid Generator for frustum features
+        Args:
+            grid_size: [X, Y, Z], Voxel grid size
+            pc_range: [x_min, y_min, z_min, x_max, y_max, z_max], Voxelization point cloud range (m)
+            disc_cfg: EasyDict, Depth discretiziation configuration
+        """
+        super().__init__()
+        try:
+            import kornia
+        except Exception as e:
+            # Note: Kornia team will fix this import issue to try to allow the usage of lower torch versions.
+            print('Error: kornia is not installed correctly, please ignore this warning if you do not use CaDDN. '
+                  'Otherwise, it is recommended to use torch version greater than 1.2 to use kornia properly.')
+            exit(-1)
+
+        self.dtype = torch.float32
+        self.grid_size = torch.as_tensor(grid_size, dtype=self.dtype)
+        self.pc_range = pc_range
+        self.out_of_bounds_val = -2
+        self.disc_cfg = disc_cfg
+
+        # Calculate voxel size
+        pc_range = torch.as_tensor(pc_range).reshape(2, 3)
+        self.pc_min = pc_range[0]
+        self.pc_max = pc_range[1]
+        self.voxel_size = (self.pc_max - self.pc_min) / self.grid_size
+
+        # Create voxel grid
+        self.depth, self.width, self.height = self.grid_size.int()
+        self.voxel_grid = create_meshgrid3d(depth=self.depth,
+                                                         height=self.height,
+                                                         width=self.width,
+                                                         normalized_coordinates=False)
+
+        self.voxel_grid = self.voxel_grid.permute(0, 1, 3, 2, 4)  # XZY-> XYZ
+
+        # Add offsets to center of voxel
+        self.voxel_grid += 0.5
+        self.grid_to_lidar = self.grid_to_lidar_unproject(pc_min=self.pc_min,
+                                                          voxel_size=self.voxel_size)
+
+    def grid_to_lidar_unproject(self, pc_min, voxel_size):
+        """
+        Calculate grid to LiDAR unprojection for each plane
+        Args:
+            pc_min: [x_min, y_min, z_min], Minimum of point cloud range (m)
+            voxel_size: [x, y, z], Size of each voxel (m)
+        Returns:
+            unproject: (4, 4), Voxel grid to LiDAR unprojection matrix
+        """
+        x_size, y_size, z_size = voxel_size
+        x_min, y_min, z_min = pc_min
+        unproject = torch.tensor([[x_size, 0, 0, x_min],
+                                  [0, y_size, 0, y_min],
+                                  [0,  0, z_size, z_min],
+                                  [0,  0, 0, 1]],
+                                 dtype=self.dtype)  # (4, 4)
+
+        return unproject
+
+    def transform_grid(self, voxel_grid, grid_to_lidar, lidar_to_cam, cam_to_img):
+        """
+        Transforms voxel sampling grid into frustum sampling grid
+        Args:
+            grid: (B, X, Y, Z, 3), Voxel sampling grid
+            grid_to_lidar: (4, 4), Voxel grid to LiDAR unprojection matrix
+            lidar_to_cam: (B, 4, 4), LiDAR to camera frame transformation
+            cam_to_img: (B, 3, 4), Camera projection matrix
+        Returns:
+            frustum_grid: (B, X, Y, Z, 3), Frustum sampling grid
+        """
+        B = lidar_to_cam.shape[0]
+
+        # Create transformation matricies
+        V_G = grid_to_lidar  # Voxel Grid -> LiDAR (4, 4)
+        C_V = lidar_to_cam  # LiDAR -> Camera (B, 4, 4)
+        I_C = cam_to_img  # Camera -> Image (B, 3, 4)
+        trans = C_V @ V_G
+
+        # Reshape to match dimensions
+        trans = trans.reshape(B, 1, 1, 4, 4)
+        voxel_grid = voxel_grid.repeat_interleave(repeats=B, dim=0)
+
+        # Transform to camera frame
+        camera_grid = transform_points(trans_01=trans, points_1=voxel_grid)
+
+        # Project to image
+        I_C = I_C.reshape(B, 1, 1, 3, 4)
+        image_grid, image_depths = transform_utils.project_to_image(project=I_C, points=camera_grid)
+
+        # Convert depths to depth bins
+        image_depths = transform_utils.bin_depths(depth_map=image_depths, **self.disc_cfg)
+
+        # Stack to form frustum grid
+        image_depths = image_depths.unsqueeze(-1)
+        frustum_grid = torch.cat((image_grid, image_depths), dim=-1)
+        return frustum_grid
+
+    def forward(self, lidar_to_cam, cam_to_img, image_shape):
+        """
+        Generates sampling grid for frustum features
+        Args:
+            lidar_to_cam: (B, 4, 4), LiDAR to camera frame transformation
+            cam_to_img: (B, 3, 4), Camera projection matrix
+            image_shape: (B, 2), Image shape [H, W]
+        Returns:
+            frustum_grid (B, X, Y, Z, 3), Sampling grids for frustum features
+        """
+
+        frustum_grid = self.transform_grid(voxel_grid=self.voxel_grid.to(lidar_to_cam.device),
+                                           grid_to_lidar=self.grid_to_lidar.to(lidar_to_cam.device),
+                                           lidar_to_cam=lidar_to_cam,
+                                           cam_to_img=cam_to_img)
+
+        # Normalize grid
+        image_shape, _ = torch.max(image_shape, dim=0)
+        image_depth = torch.tensor([self.disc_cfg["num_bins"]],
+                                   device=image_shape.device,
+                                   dtype=image_shape.dtype)
+        frustum_shape = torch.cat((image_depth, image_shape))
+        frustum_grid = transform_utils.normalize_coords(coords=frustum_grid, shape=frustum_shape)
+
+        # Replace any NaNs or infinites with out of bounds
+        mask = ~torch.isfinite(frustum_grid)
+        frustum_grid[mask] = self.out_of_bounds_val
+
+        return frustum_grid
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_to_voxel.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_to_voxel.py
new file mode 100644
index 000000000..a1a66b5e5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/frustum_to_voxel.py
@@ -0,0 +1,54 @@
+import torch
+import torch.nn as nn
+
+from .frustum_grid_generator import FrustumGridGenerator
+from .sampler import Sampler
+
+
+class FrustumToVoxel(nn.Module):
+
+    def __init__(self, model_cfg, grid_size, pc_range, disc_cfg):
+        """
+        Initializes module to transform frustum features to voxel features via 3D transformation and sampling
+        Args:
+            model_cfg: EasyDict, Module configuration
+            grid_size: [X, Y, Z], Voxel grid size
+            pc_range: [x_min, y_min, z_min, x_max, y_max, z_max], Voxelization point cloud range (m)
+            disc_cfg: EasyDict, Depth discretiziation configuration
+        """
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.grid_size = grid_size
+        self.pc_range = pc_range
+        self.disc_cfg = disc_cfg
+        self.grid_generator = FrustumGridGenerator(grid_size=grid_size,
+                                                   pc_range=pc_range,
+                                                   disc_cfg=disc_cfg)
+        self.sampler = Sampler(**model_cfg.SAMPLER)
+
+    def forward(self, batch_dict):
+        """
+        Generates voxel features via 3D transformation and sampling
+        Args:
+            batch_dict:
+                frustum_features: (B, C, D, H_image, W_image), Image frustum features
+                lidar_to_cam: (B, 4, 4), LiDAR to camera frame transformation
+                cam_to_img: (B, 3, 4), Camera projection matrix
+                image_shape: (B, 2), Image shape [H, W]
+        Returns:
+            batch_dict:
+                voxel_features: (B, C, Z, Y, X), Image voxel features
+        """
+        # Generate sampling grid for frustum volume
+        grid = self.grid_generator(lidar_to_cam=batch_dict["trans_lidar_to_cam"],
+                                   cam_to_img=batch_dict["trans_cam_to_img"],
+                                   image_shape=batch_dict["image_shape"])  # (B, X, Y, Z, 3)
+
+        # Sample frustum volume to generate voxel volume
+        voxel_features = self.sampler(input_features=batch_dict["frustum_features"],
+                                      grid=grid)  # (B, C, X, Y, Z)
+
+        # (B, C, X, Y, Z) -> (B, C, Z, Y, X)
+        voxel_features = voxel_features.permute(0, 1, 4, 3, 2)
+        batch_dict["voxel_features"] = voxel_features
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/sampler.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/sampler.py
new file mode 100644
index 000000000..9ce51f450
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/f2v/sampler.py
@@ -0,0 +1,37 @@
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Sampler(nn.Module):
+
+    def __init__(self, mode="bilinear", padding_mode="zeros"):
+        """
+        Initializes module
+        Args:
+            mode: string, Sampling mode [bilinear/nearest]
+            padding_mode: string, Padding mode for outside grid values [zeros/border/reflection]
+        """
+        super().__init__()
+        self.mode = mode
+        self.padding_mode = padding_mode
+
+        if torch.__version__ >= '1.3':
+            self.grid_sample = partial(F.grid_sample, align_corners=True)
+        else:
+            self.grid_sample = F.grid_sample
+
+    def forward(self, input_features, grid):
+        """
+        Samples input using sampling grid
+        Args:
+            input_features: (B, C, D, H, W), Input frustum features
+            grid: (B, X, Y, Z, 3), Sampling grids for input features
+        Returns
+            output_features: (B, C, X, Y, Z) Output voxel features
+        """
+        # Sample from grid
+        output = self.grid_sample(input=input_features, grid=grid, mode=self.mode, padding_mode=self.padding_mode)
+        return output
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/__init__.py
new file mode 100644
index 000000000..576d5ebd6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/__init__.py
@@ -0,0 +1,5 @@
+from .depth_ffn import DepthFFN
+
+__all__ = {
+    'DepthFFN': DepthFFN
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/__init__.py
new file mode 100644
index 000000000..acbaf1060
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/__init__.py
@@ -0,0 +1,5 @@
+from .ddn_deeplabv3 import DDNDeepLabV3
+
+__all__ = {
+    'DDNDeepLabV3': DDNDeepLabV3
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_deeplabv3.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_deeplabv3.py
new file mode 100644
index 000000000..76be8ca62
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_deeplabv3.py
@@ -0,0 +1,24 @@
+from .ddn_template import DDNTemplate
+
+try:
+    import torchvision
+except:
+    pass
+
+
+class DDNDeepLabV3(DDNTemplate):
+
+    def __init__(self, backbone_name, **kwargs):
+        """
+        Initializes DDNDeepLabV3 model
+        Args:
+            backbone_name: string, ResNet Backbone Name [ResNet50/ResNet101]
+        """
+        if backbone_name == "ResNet50":
+            constructor = torchvision.models.segmentation.deeplabv3_resnet50
+        elif backbone_name == "ResNet101":
+            constructor = torchvision.models.segmentation.deeplabv3_resnet101
+        else:
+            raise NotImplementedError
+
+        super().__init__(constructor=constructor, **kwargs)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_template.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_template.py
new file mode 100644
index 000000000..be110d38d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn/ddn_template.py
@@ -0,0 +1,162 @@
+from collections import OrderedDict
+from pathlib import Path
+from torch import hub
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    from kornia.enhance.normalize import normalize
+except:
+    pass
+    # print('Warning: kornia is not installed. This package is only required by CaDDN')
+
+    
+class DDNTemplate(nn.Module):
+
+    def __init__(self, constructor, feat_extract_layer, num_classes, pretrained_path=None, aux_loss=None):
+        """
+        Initializes depth distribution network.
+        Args:
+            constructor: function, Model constructor
+            feat_extract_layer: string, Layer to extract features from
+            num_classes: int, Number of classes
+            pretrained_path: string, (Optional) Path of the model to load weights from
+            aux_loss: bool, Flag to include auxillary loss
+        """
+        super().__init__()
+        self.num_classes = num_classes
+        self.pretrained_path = pretrained_path
+        self.pretrained = pretrained_path is not None
+        self.aux_loss = aux_loss
+
+        if self.pretrained:
+            # Preprocess Module
+            self.norm_mean = torch.Tensor([0.485, 0.456, 0.406])
+            self.norm_std = torch.Tensor([0.229, 0.224, 0.225])
+
+        # Model
+        self.model = self.get_model(constructor=constructor)
+        self.feat_extract_layer = feat_extract_layer
+        self.model.backbone.return_layers = {
+            feat_extract_layer: 'features',
+            **self.model.backbone.return_layers
+        }
+
+    def get_model(self, constructor):
+        """
+        Get model
+        Args:
+            constructor: function, Model constructor
+        Returns:
+            model: nn.Module, Model
+        """
+        # Get model
+        model = constructor(pretrained=False,
+                            pretrained_backbone=False,
+                            num_classes=self.num_classes,
+                            aux_loss=self.aux_loss)
+
+        # Update weights
+        if self.pretrained_path is not None:
+            model_dict = model.state_dict()
+            
+            # Download pretrained model if not available yet
+            checkpoint_path = Path(self.pretrained_path)
+            if not checkpoint_path.exists():
+                checkpoint = checkpoint_path.name
+                save_dir = checkpoint_path.parent
+                save_dir.mkdir(parents=True)
+                url = f'https://download.pytorch.org/models/{checkpoint}'
+                hub.load_state_dict_from_url(url, save_dir)
+
+            # Get pretrained state dict
+            pretrained_dict = torch.load(self.pretrained_path)
+            pretrained_dict = self.filter_pretrained_dict(model_dict=model_dict,
+                                                          pretrained_dict=pretrained_dict)
+
+            # Update current model state dict
+            model_dict.update(pretrained_dict)
+            model.load_state_dict(model_dict)
+
+        return model
+
+    def filter_pretrained_dict(self, model_dict, pretrained_dict):
+        """
+        Removes layers from pretrained state dict that are not used or changed in model
+        Args:
+            model_dict: dict, Default model state dictionary
+            pretrained_dict: dict, Pretrained model state dictionary
+        Returns:
+            pretrained_dict: dict, Pretrained model state dictionary with removed weights
+        """
+        # Removes aux classifier weights if not used
+        if "aux_classifier.0.weight" in pretrained_dict and "aux_classifier.0.weight" not in model_dict:
+            pretrained_dict = {key: value for key, value in pretrained_dict.items()
+                               if "aux_classifier" not in key}
+
+        # Removes final conv layer from weights if number of classes are different
+        model_num_classes = model_dict["classifier.4.weight"].shape[0]
+        pretrained_num_classes = pretrained_dict["classifier.4.weight"].shape[0]
+        if model_num_classes != pretrained_num_classes:
+            pretrained_dict.pop("classifier.4.weight")
+            pretrained_dict.pop("classifier.4.bias")
+
+        return pretrained_dict
+
+    def forward(self, images):
+        """
+        Forward pass
+        Args:
+            images: (N, 3, H_in, W_in), Input images
+        Returns
+            result: dict[torch.Tensor], Depth distribution result
+                features: (N, C, H_out, W_out), Image features
+                logits: (N, num_classes, H_out, W_out), Classification logits
+                aux: (N, num_classes, H_out, W_out), Auxillary classification logits
+        """
+        # Preprocess images
+        x = self.preprocess(images)
+
+        # Extract features
+        result = OrderedDict()
+        features = self.model.backbone(x)
+        result['features'] = features['features']
+        feat_shape = features['features'].shape[-2:]
+
+        # Prediction classification logits
+        x = features["out"]
+        x = self.model.classifier(x)
+        x = F.interpolate(x, size=feat_shape, mode='bilinear', align_corners=False)
+        result["logits"] = x
+
+        # Prediction auxillary classification logits
+        if self.model.aux_classifier is not None:
+            x = features["aux"]
+            x = self.model.aux_classifier(x)
+            x = F.interpolate(x, size=feat_shape, mode='bilinear', align_corners=False)
+            result["aux"] = x
+
+        return result
+
+    def preprocess(self, images):
+        """
+        Preprocess images
+        Args:
+            images: (N, 3, H, W), Input images
+        Return
+            x: (N, 3, H, W), Preprocessed images
+        """
+        x = images
+        if self.pretrained:
+            # Create a mask for padded pixels
+            mask = (x == 0)
+
+            # Match ResNet pretrained preprocessing
+            x = normalize(x, mean=self.norm_mean, std=self.norm_std)
+
+            # Make padded pixels = 0
+            x[mask] = 0
+
+        return x
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/__init__.py
new file mode 100644
index 000000000..0ff8b2b5b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/__init__.py
@@ -0,0 +1,5 @@
+from .ddn_loss import DDNLoss
+
+__all__ = {
+    "DDNLoss": DDNLoss
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/balancer.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/balancer.py
new file mode 100644
index 000000000..47bf8d486
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/balancer.py
@@ -0,0 +1,50 @@
+import torch
+import torch.nn as nn
+
+from pcdet.utils import loss_utils
+
+
+class Balancer(nn.Module):
+    def __init__(self, fg_weight, bg_weight, downsample_factor=1):
+        """
+        Initialize fixed foreground/background loss balancer
+        Args:
+            fg_weight: float, Foreground loss weight
+            bg_weight: float, Background loss weight
+            downsample_factor: int, Depth map downsample factor
+        """
+        super().__init__()
+        self.fg_weight = fg_weight
+        self.bg_weight = bg_weight
+        self.downsample_factor = downsample_factor
+
+    def forward(self, loss, gt_boxes2d):
+        """
+        Forward pass
+        Args:
+            loss: (B, H, W), Pixel-wise loss
+            gt_boxes2d: (B, N, 4), 2D box labels for foreground/background balancing
+        Returns:
+            loss: (1), Total loss after foreground/background balancing
+            tb_dict: dict[float], All losses to log in tensorboard
+        """
+        # Compute masks
+        fg_mask = loss_utils.compute_fg_mask(gt_boxes2d=gt_boxes2d,
+                                             shape=loss.shape,
+                                             downsample_factor=self.downsample_factor,
+                                             device=loss.device)
+        bg_mask = ~fg_mask
+
+        # Compute balancing weights
+        weights = self.fg_weight * fg_mask + self.bg_weight * bg_mask
+        num_pixels = fg_mask.sum() + bg_mask.sum()
+
+        # Compute losses
+        loss *= weights
+        fg_loss = loss[fg_mask].sum() / num_pixels
+        bg_loss = loss[bg_mask].sum() / num_pixels
+
+        # Get total loss
+        loss = fg_loss + bg_loss
+        tb_dict = {"balancer_loss": loss.item(), "fg_loss": fg_loss.item(), "bg_loss": bg_loss.item()}
+        return loss, tb_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/ddn_loss.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/ddn_loss.py
new file mode 100644
index 000000000..f59c11f57
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/ddn_loss/ddn_loss.py
@@ -0,0 +1,75 @@
+import torch
+import torch.nn as nn
+
+
+from .balancer import Balancer
+from pcdet.utils import transform_utils
+
+try:
+    from kornia.losses.focal import FocalLoss
+except:
+    pass 
+    # print('Warning: kornia is not installed. This package is only required by CaDDN')
+
+    
+class DDNLoss(nn.Module):
+
+    def __init__(self,
+                 weight,
+                 alpha,
+                 gamma,
+                 disc_cfg,
+                 fg_weight,
+                 bg_weight,
+                 downsample_factor):
+        """
+        Initializes DDNLoss module
+        Args:
+            weight: float, Loss function weight
+            alpha: float, Alpha value for Focal Loss
+            gamma: float, Gamma value for Focal Loss
+            disc_cfg: dict, Depth discretiziation configuration
+            fg_weight: float, Foreground loss weight
+            bg_weight: float, Background loss weight
+            downsample_factor: int, Depth map downsample factor
+        """
+        super().__init__()
+        self.device = torch.cuda.current_device()
+        self.disc_cfg = disc_cfg
+        self.balancer = Balancer(downsample_factor=downsample_factor,
+                                 fg_weight=fg_weight,
+                                 bg_weight=bg_weight)
+
+        # Set loss function
+        self.alpha = alpha
+        self.gamma = gamma
+        self.loss_func = FocalLoss(alpha=self.alpha, gamma=self.gamma, reduction="none")
+        self.weight = weight
+
+    def forward(self, depth_logits, depth_maps, gt_boxes2d):
+        """
+        Gets DDN loss
+        Args:
+            depth_logits: (B, D+1, H, W), Predicted depth logits
+            depth_maps: (B, H, W), Depth map [m]
+            gt_boxes2d: torch.Tensor (B, N, 4), 2D box labels for foreground/background balancing
+        Returns:
+            loss: (1), Depth distribution network loss
+            tb_dict: dict[float], All losses to log in tensorboard
+        """
+        tb_dict = {}
+
+        # Bin depth map to create target
+        depth_target = transform_utils.bin_depths(depth_maps, **self.disc_cfg, target=True)
+
+        # Compute loss
+        loss = self.loss_func(depth_logits, depth_target)
+
+        # Compute foreground/background balancing
+        loss, tb_dict = self.balancer(loss=loss, gt_boxes2d=gt_boxes2d)
+
+        # Final loss
+        loss *= self.weight
+        tb_dict.update({"ddn_loss": loss.item()})
+
+        return loss, tb_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/depth_ffn.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/depth_ffn.py
new file mode 100644
index 000000000..c35e22927
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/image_vfe_modules/ffn/depth_ffn.py
@@ -0,0 +1,103 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+from . import ddn, ddn_loss
+from pcdet.models.model_utils.basic_block_2d import BasicBlock2D
+
+
+class DepthFFN(nn.Module):
+
+    def __init__(self, model_cfg, downsample_factor):
+        """
+        Initialize frustum feature network via depth distribution estimation
+        Args:
+            model_cfg: EasyDict, Depth classification network config
+            downsample_factor: int, Depth map downsample factor
+        """
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.disc_cfg = model_cfg.DISCRETIZE
+        self.downsample_factor = downsample_factor
+
+        # Create modules
+        self.ddn = ddn.__all__[model_cfg.DDN.NAME](
+            num_classes=self.disc_cfg["num_bins"] + 1,
+            backbone_name=model_cfg.DDN.BACKBONE_NAME,
+            **model_cfg.DDN.ARGS
+        )
+        self.channel_reduce = BasicBlock2D(**model_cfg.CHANNEL_REDUCE)
+        self.ddn_loss = ddn_loss.__all__[model_cfg.LOSS.NAME](
+            disc_cfg=self.disc_cfg,
+            downsample_factor=downsample_factor,
+            **model_cfg.LOSS.ARGS
+        )
+        self.forward_ret_dict = {}
+
+    def get_output_feature_dim(self):
+        return self.channel_reduce.out_channels
+
+    def forward(self, batch_dict):
+        """
+        Predicts depths and creates image depth feature volume using depth distributions
+        Args:
+            batch_dict:
+                images: (N, 3, H_in, W_in), Input images
+        Returns:
+            batch_dict:
+                frustum_features: (N, C, D, H_out, W_out), Image depth features
+        """
+        # Pixel-wise depth classification
+        images = batch_dict["images"]
+        ddn_result = self.ddn(images)
+        image_features = ddn_result["features"]
+        depth_logits = ddn_result["logits"]
+
+        # Channel reduce
+        if self.channel_reduce is not None:
+            image_features = self.channel_reduce(image_features)
+
+        # Create image feature plane-sweep volume
+        frustum_features = self.create_frustum_features(image_features=image_features,
+                                                        depth_logits=depth_logits)
+        batch_dict["frustum_features"] = frustum_features
+
+        if self.training:
+            self.forward_ret_dict["depth_maps"] = batch_dict["depth_maps"]
+            self.forward_ret_dict["gt_boxes2d"] = batch_dict["gt_boxes2d"]
+            self.forward_ret_dict["depth_logits"] = depth_logits
+        return batch_dict
+
+    def create_frustum_features(self, image_features, depth_logits):
+        """
+        Create image depth feature volume by multiplying image features with depth distributions
+        Args:
+            image_features: (N, C, H, W), Image features
+            depth_logits: (N, D+1, H, W), Depth classification logits
+        Returns:
+            frustum_features: (N, C, D, H, W), Image features
+        """
+        channel_dim = 1
+        depth_dim = 2
+
+        # Resize to match dimensions
+        image_features = image_features.unsqueeze(depth_dim)
+        depth_logits = depth_logits.unsqueeze(channel_dim)
+
+        # Apply softmax along depth axis and remove last depth category (> Max Range)
+        depth_probs = F.softmax(depth_logits, dim=depth_dim)
+        depth_probs = depth_probs[:, :, :-1]
+
+        # Multiply to form image depth feature volume
+        frustum_features = depth_probs * image_features
+        return frustum_features
+
+    def get_loss(self):
+        """
+        Gets DDN loss
+        Args:
+        Returns:
+            loss: (1), Depth distribution network loss
+            tb_dict: dict[float], All losses to log in tensorboard
+        """
+        loss, tb_dict = self.ddn_loss(**self.forward_ret_dict)
+        return loss, tb_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/mean_vfe.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/mean_vfe.py
new file mode 100644
index 000000000..42bd21ff3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/mean_vfe.py
@@ -0,0 +1,31 @@
+import torch
+
+from .vfe_template import VFETemplate
+
+
+class MeanVFE(VFETemplate):
+    def __init__(self, model_cfg, num_point_features, **kwargs):
+        super().__init__(model_cfg=model_cfg)
+        self.num_point_features = num_point_features
+
+    def get_output_feature_dim(self):
+        return self.num_point_features
+
+    def forward(self, batch_dict, **kwargs):
+        """
+        Args:
+            batch_dict:
+                voxels: (num_voxels, max_points_per_voxel, C)
+                voxel_num_points: optional (num_voxels)
+            **kwargs:
+
+        Returns:
+            vfe_features: (num_voxels, C)
+        """
+        voxel_features, voxel_num_points = batch_dict['voxels'], batch_dict['voxel_num_points']
+        points_mean = voxel_features[:, :, :].sum(dim=1, keepdim=False)
+        normalizer = torch.clamp_min(voxel_num_points.view(-1, 1), min=1.0).type_as(voxel_features)
+        points_mean = points_mean / normalizer
+        batch_dict['voxel_features'] = points_mean.contiguous()
+
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/pillar_vfe.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/pillar_vfe.py
new file mode 100644
index 000000000..a162a83e8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/pillar_vfe.py
@@ -0,0 +1,123 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .vfe_template import VFETemplate
+
+
+class PFNLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 use_norm=True,
+                 last_layer=False):
+        super().__init__()
+        
+        self.last_vfe = last_layer
+        self.use_norm = use_norm
+        if not self.last_vfe:
+            out_channels = out_channels // 2
+
+        if self.use_norm:
+            self.linear = nn.Linear(in_channels, out_channels, bias=False)
+            self.norm = nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01)
+        else:
+            self.linear = nn.Linear(in_channels, out_channels, bias=True)
+
+        self.part = 50000
+
+    def forward(self, inputs):
+        if inputs.shape[0] > self.part:
+            # nn.Linear performs randomly when batch size is too large
+            num_parts = inputs.shape[0] // self.part
+            part_linear_out = [self.linear(inputs[num_part*self.part:(num_part+1)*self.part])
+                               for num_part in range(num_parts+1)]
+            x = torch.cat(part_linear_out, dim=0)
+        else:
+            x = self.linear(inputs)
+        torch.backends.cudnn.enabled = False
+        x = self.norm(x.permute(0, 2, 1)).permute(0, 2, 1) if self.use_norm else x
+        torch.backends.cudnn.enabled = True
+        x = F.relu(x)
+        x_max = torch.max(x, dim=1, keepdim=True)[0]
+
+        if self.last_vfe:
+            return x_max
+        else:
+            x_repeat = x_max.repeat(1, inputs.shape[1], 1)
+            x_concatenated = torch.cat([x, x_repeat], dim=2)
+            return x_concatenated
+
+
+class PillarVFE(VFETemplate):
+    def __init__(self, model_cfg, num_point_features, voxel_size, point_cloud_range, **kwargs):
+        super().__init__(model_cfg=model_cfg)
+
+        self.use_norm = self.model_cfg.USE_NORM
+        self.with_distance = self.model_cfg.WITH_DISTANCE
+        self.use_absolute_xyz = self.model_cfg.USE_ABSLOTE_XYZ
+        num_point_features += 6 if self.use_absolute_xyz else 3
+        if self.with_distance:
+            num_point_features += 1
+
+        self.num_filters = self.model_cfg.NUM_FILTERS
+        assert len(self.num_filters) > 0
+        num_filters = [num_point_features] + list(self.num_filters)
+
+        pfn_layers = []
+        for i in range(len(num_filters) - 1):
+            in_filters = num_filters[i]
+            out_filters = num_filters[i + 1]
+            pfn_layers.append(
+                PFNLayer(in_filters, out_filters, self.use_norm, last_layer=(i >= len(num_filters) - 2))
+            )
+        self.pfn_layers = nn.ModuleList(pfn_layers)
+
+        self.voxel_x = voxel_size[0]
+        self.voxel_y = voxel_size[1]
+        self.voxel_z = voxel_size[2]
+        self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
+        self.y_offset = self.voxel_y / 2 + point_cloud_range[1]
+        self.z_offset = self.voxel_z / 2 + point_cloud_range[2]
+
+    def get_output_feature_dim(self):
+        return self.num_filters[-1]
+
+    def get_paddings_indicator(self, actual_num, max_num, axis=0):
+        actual_num = torch.unsqueeze(actual_num, axis + 1)
+        max_num_shape = [1] * len(actual_num.shape)
+        max_num_shape[axis + 1] = -1
+        max_num = torch.arange(max_num, dtype=torch.int, device=actual_num.device).view(max_num_shape)
+        paddings_indicator = actual_num.int() > max_num
+        return paddings_indicator
+
+    def forward(self, batch_dict, **kwargs):
+  
+        voxel_features, voxel_num_points, coords = batch_dict['voxels'], batch_dict['voxel_num_points'], batch_dict['voxel_coords']
+        points_mean = voxel_features[:, :, :3].sum(dim=1, keepdim=True) / voxel_num_points.type_as(voxel_features).view(-1, 1, 1)
+        f_cluster = voxel_features[:, :, :3] - points_mean
+
+        f_center = torch.zeros_like(voxel_features[:, :, :3])
+        f_center[:, :, 0] = voxel_features[:, :, 0] - (coords[:, 3].to(voxel_features.dtype).unsqueeze(1) * self.voxel_x + self.x_offset)
+        f_center[:, :, 1] = voxel_features[:, :, 1] - (coords[:, 2].to(voxel_features.dtype).unsqueeze(1) * self.voxel_y + self.y_offset)
+        f_center[:, :, 2] = voxel_features[:, :, 2] - (coords[:, 1].to(voxel_features.dtype).unsqueeze(1) * self.voxel_z + self.z_offset)
+
+        if self.use_absolute_xyz:
+            features = [voxel_features, f_cluster, f_center]
+        else:
+            features = [voxel_features[..., 3:], f_cluster, f_center]
+
+        if self.with_distance:
+            points_dist = torch.norm(voxel_features[:, :, :3], 2, 2, keepdim=True)
+            features.append(points_dist)
+        features = torch.cat(features, dim=-1)
+
+        voxel_count = features.shape[1]
+        mask = self.get_paddings_indicator(voxel_num_points, voxel_count, axis=0)
+        mask = torch.unsqueeze(mask, -1).type_as(voxel_features)
+        features *= mask
+        for pfn in self.pfn_layers:
+            features = pfn(features)
+        features = features.squeeze()
+        batch_dict['pillar_features'] = features
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/vfe_template.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/vfe_template.py
new file mode 100644
index 000000000..a862e3e54
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_3d/vfe/vfe_template.py
@@ -0,0 +1,22 @@
+import torch.nn as nn
+
+
+class VFETemplate(nn.Module):
+    def __init__(self, model_cfg, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+    def get_output_feature_dim(self):
+        raise NotImplementedError
+
+    def forward(self, **kwargs):
+        """
+        Args:
+            **kwargs:
+
+        Returns:
+            batch_dict:
+                ...
+                vfe_features: (num_voxels, C)
+        """
+        raise NotImplementedError
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/__init__.py
new file mode 100644
index 000000000..d9b0e68da
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/__init__.py
@@ -0,0 +1,4 @@
+from .swin import SwinTransformer
+__all__ = {
+    'SwinTransformer':SwinTransformer,
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/img_neck/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/img_neck/__init__.py
new file mode 100644
index 000000000..25e468068
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/img_neck/__init__.py
@@ -0,0 +1,4 @@
+from .generalized_lss import GeneralizedLSSFPN
+__all__ = {
+    'GeneralizedLSSFPN':GeneralizedLSSFPN,
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/img_neck/generalized_lss.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/img_neck/generalized_lss.py
new file mode 100644
index 000000000..399b0348b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/img_neck/generalized_lss.py
@@ -0,0 +1,76 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from ...model_utils.basic_block_2d import BasicBlock2D
+
+
+class GeneralizedLSSFPN(nn.Module):
+    """
+        This module implements FPN, which creates pyramid features built on top of some input feature maps.
+        This code is adapted from https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/necks/fpn.py with minimal modifications.
+    """
+    def __init__(self, model_cfg):
+        super().__init__()
+        self.model_cfg = model_cfg
+        in_channels =  self.model_cfg.IN_CHANNELS
+        out_channels = self.model_cfg.OUT_CHANNELS
+        num_ins = len(in_channels)
+        num_outs = self.model_cfg.NUM_OUTS
+        start_level = self.model_cfg.START_LEVEL
+        end_level = self.model_cfg.END_LEVEL
+
+        self.in_channels = in_channels
+
+        if end_level == -1:
+            self.backbone_end_level = num_ins - 1
+        else:
+            self.backbone_end_level = end_level
+            assert end_level <= len(in_channels)
+            assert num_outs == end_level - start_level
+        self.start_level = start_level
+        self.end_level = end_level
+
+        self.lateral_convs = nn.ModuleList()
+        self.fpn_convs = nn.ModuleList()
+
+        for i in range(self.start_level, self.backbone_end_level):
+            l_conv = BasicBlock2D(
+                in_channels[i] + (in_channels[i + 1] if i == self.backbone_end_level - 1 else out_channels),
+                out_channels, kernel_size=1, bias = False
+            )
+            fpn_conv = BasicBlock2D(out_channels,out_channels, kernel_size=3, padding=1, bias = False)
+            self.lateral_convs.append(l_conv)
+            self.fpn_convs.append(fpn_conv)
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                image_features (list[tensor]): Multi-stage features from image backbone.
+        Returns:
+            batch_dict:
+                image_fpn (list(tensor)): FPN features.
+        """
+        # upsample -> cat -> conv1x1 -> conv3x3
+        inputs = batch_dict['image_features']
+        assert len(inputs) == len(self.in_channels)
+
+        # build laterals
+        laterals = [inputs[i + self.start_level] for i in range(len(inputs))]
+
+        # build top-down path
+        used_backbone_levels = len(laterals) - 1
+        for i in range(used_backbone_levels - 1, -1, -1):
+            x = F.interpolate(
+                laterals[i + 1],
+                size=laterals[i].shape[2:],
+                mode='bilinear', align_corners=False,
+            )
+            laterals[i] = torch.cat([laterals[i], x], dim=1)
+            laterals[i] = self.lateral_convs[i](laterals[i])
+            laterals[i] = self.fpn_convs[i](laterals[i])
+
+        # build outputs
+        outs = [laterals[i] for i in range(used_backbone_levels)]
+        batch_dict['image_fpn'] = tuple(outs)
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/swin.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/swin.py
new file mode 100644
index 000000000..d428c2700
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/backbones_image/swin.py
@@ -0,0 +1,736 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""
+Mostly copy-paste from 
+    https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/backbones/swin.py 
+    
+"""
+
+import warnings
+from collections import OrderedDict
+from copy import deepcopy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+
+from ..model_utils.swin_utils import swin_converter
+from ..model_utils.swin_utils import PatchEmbed, PatchMerging
+from ..model_utils.swin_utils import FFN, DropPath, to_2tuple, trunc_normal_, trunc_normal_init, constant_init
+
+
+class WindowMSA(nn.Module):
+    """Window based multi-head self-attention (W-MSA) module with relative
+    position bias.
+
+    Args:
+        embed_dims (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (tuple[int]): The height and width of the window.
+        qkv_bias (bool, optional):  If True, add a learnable bias to q, k, v.
+            Default: True.
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Default: None.
+        attn_drop_rate (float, optional): Dropout ratio of attention weight.
+            Default: 0.0
+        proj_drop_rate (float, optional): Dropout ratio of output. Default: 0.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 window_size,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop_rate=0.,
+                 proj_drop_rate=0.):
+
+        super().__init__()
+        self._is_init = False
+
+        self.embed_dims = embed_dims
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_embed_dims = embed_dims // num_heads
+        self.scale = qk_scale or head_embed_dims**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # About 2x faster than original impl
+        Wh, Ww = self.window_size
+        rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
+        rel_position_index = rel_index_coords + rel_index_coords.T
+        rel_position_index = rel_position_index.flip(1).contiguous()
+        self.register_buffer('relative_position_index', rel_position_index)
+
+        self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop_rate)
+        self.proj = nn.Linear(embed_dims, embed_dims)
+        self.proj_drop = nn.Dropout(proj_drop_rate)
+
+        self.softmax = nn.Softmax(dim=-1)
+
+    def init_weights(self):
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+
+            x (tensor): input features with shape of (num_windows*B, N, C)
+            mask (tensor | None, Optional): mask with shape of (num_windows,
+                Wh*Ww, Wh*Ww), value should be between (-inf, 0].
+        """
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
+                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
+        # make torchscript happy (cannot use tensor as tuple)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1],
+                self.window_size[0] * self.window_size[1],
+                -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B // nW, nW, self.num_heads, N,
+                             N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+        attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    @staticmethod
+    def double_step_seq(step1, len1, step2, len2):
+        seq1 = torch.arange(0, step1 * len1, step1)
+        seq2 = torch.arange(0, step2 * len2, step2)
+        return (seq1[:, None] + seq2[None, :]).reshape(1, -1)
+
+
+class ShiftWindowMSA(nn.Module):
+    """Shifted Window Multihead Self-Attention Module.
+
+    Args:
+        embed_dims (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): The height and width of the window.
+        shift_size (int, optional): The shift step of each window towards
+            right-bottom. If zero, act as regular window-msa. Defaults to 0.
+        qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
+            Default: True
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Defaults: None.
+        attn_drop_rate (float, optional): Dropout ratio of attention weight.
+            Defaults: 0.
+        proj_drop_rate (float, optional): Dropout ratio of output.
+            Defaults: 0.
+        dropout_layer (dict, optional): The dropout_layer used before output.
+            Defaults: dict(type='DropPath', drop_prob=0.).
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 window_size,
+                 shift_size=0,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop_rate=0,
+                 proj_drop_rate=0,
+                 dropout_layer=dict(type='DropPath', drop_prob=0.)):
+        super().__init__()
+        self._is_init = False
+
+        self.window_size = window_size
+        self.shift_size = shift_size
+        assert 0 <= self.shift_size < self.window_size
+
+        self.w_msa = WindowMSA(
+            embed_dims=embed_dims,
+            num_heads=num_heads,
+            window_size=to_2tuple(window_size),
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop_rate=attn_drop_rate,
+            proj_drop_rate=proj_drop_rate,)
+        self.drop = DropPath(dropout_layer['drop_prob'])
+
+    def forward(self, query, hw_shape):
+        B, L, C = query.shape
+        H, W = hw_shape
+        assert L == H * W, 'input feature has wrong size'
+        query = query.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        query = F.pad(query, (0, 0, 0, pad_r, 0, pad_b))
+        H_pad, W_pad = query.shape[1], query.shape[2]
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_query = torch.roll(
+                query,
+                shifts=(-self.shift_size, -self.shift_size),
+                dims=(1, 2))
+
+            # calculate attention mask for SW-MSA
+            img_mask = torch.zeros((1, H_pad, W_pad, 1), device=query.device)
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size,
+                              -self.shift_size), slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size,
+                              -self.shift_size), slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            # nW, window_size, window_size, 1
+            mask_windows = self.window_partition(img_mask)
+            mask_windows = mask_windows.view(
+                -1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                              float(-100.0)).masked_fill(
+                                                  attn_mask == 0, float(0.0))
+        else:
+            shifted_query = query
+            attn_mask = None
+
+        # nW*B, window_size, window_size, C
+        query_windows = self.window_partition(shifted_query)
+        # nW*B, window_size*window_size, C
+        query_windows = query_windows.view(-1, self.window_size**2, C)
+
+        # W-MSA/SW-MSA (nW*B, window_size*window_size, C)
+        attn_windows = self.w_msa(query_windows, mask=attn_mask)
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size,
+                                         self.window_size, C)
+
+        # B H' W' C
+        shifted_x = self.window_reverse(attn_windows, H_pad, W_pad)
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(
+                shifted_x,
+                shifts=(self.shift_size, self.shift_size),
+                dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        x = self.drop(x)
+        return x
+
+    def window_reverse(self, windows, H, W):
+        """
+        Args:
+            windows: (num_windows*B, window_size, window_size, C)
+            H (int): Height of image
+            W (int): Width of image
+        Returns:
+            x: (B, H, W, C)
+        """
+        window_size = self.window_size
+        B = int(windows.shape[0] / (H * W / window_size / window_size))
+        x = windows.view(B, H // window_size, W // window_size, window_size,
+                         window_size, -1)
+        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+        return x
+
+    def window_partition(self, x):
+        """
+        Args:
+            x: (B, H, W, C)
+        Returns:
+            windows: (num_windows*B, window_size, window_size, C)
+        """
+        B, H, W, C = x.shape
+        window_size = self.window_size
+        x = x.view(B, H // window_size, window_size, W // window_size,
+                   window_size, C)
+        windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
+        windows = windows.view(-1, window_size, window_size, C)
+        return windows
+
+
+class SwinBlock(nn.Module):
+    """"
+    Args:
+        embed_dims (int): The feature dimension.
+        num_heads (int): Parallel attention heads.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        window_size (int, optional): The local window scale. Default: 7.
+        shift (bool, optional): whether to shift window or not. Default False.
+        qkv_bias (bool, optional): enable bias for qkv if True. Default: True.
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Default: None.
+        drop_rate (float, optional): Dropout rate. Default: 0.
+        attn_drop_rate (float, optional): Attention dropout rate. Default: 0.
+        drop_path_rate (float, optional): Stochastic depth rate. Default: 0.
+        act_cfg (dict, optional): The config dict of activation function.
+            Default: dict(type='GELU').
+        norm_cfg (dict, optional): The config dict of normalization.
+            Default: dict(type='LN').
+        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
+            will save some memory while slowing down the training speed.
+            Default: False.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 feedforward_channels,
+                 window_size=7,
+                 shift=False,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN'),
+                 with_cp=False,):
+        super(SwinBlock, self).__init__()
+        self._is_init = False
+
+        self.with_cp = with_cp
+
+        self.norm1 = nn.LayerNorm(embed_dims)
+        self.attn = ShiftWindowMSA(
+            embed_dims=embed_dims,
+            num_heads=num_heads,
+            window_size=window_size,
+            shift_size=window_size // 2 if shift else 0,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop_rate=attn_drop_rate,
+            proj_drop_rate=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),)
+
+        self.norm2 = nn.LayerNorm(embed_dims)
+        self.ffn = FFN(
+            embed_dims=embed_dims,
+            feedforward_channels=feedforward_channels,
+            num_fcs=2,
+            ffn_drop=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            act_cfg=act_cfg,
+            add_identity=True,)
+
+    def forward(self, x, hw_shape):
+
+        def _inner_forward(x):
+            identity = x
+            x = self.norm1(x)
+            x = self.attn(x, hw_shape)
+
+            x = x + identity
+
+            identity = x
+            x = self.norm2(x)
+            x = self.ffn(x, identity=identity)
+
+            return x
+
+        if self.with_cp and x.requires_grad:
+            x = cp.checkpoint(_inner_forward, x)
+        else:
+            x = _inner_forward(x)
+
+        return x
+
+
+class SwinBlockSequence(nn.Module):
+    """Implements one stage in Swin Transformer.
+
+    Args:
+        embed_dims (int): The feature dimension.
+        num_heads (int): Parallel attention heads.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        depth (int): The number of blocks in this stage.
+        window_size (int, optional): The local window scale. Default: 7.
+        qkv_bias (bool, optional): enable bias for qkv if True. Default: True.
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Default: None.
+        drop_rate (float, optional): Dropout rate. Default: 0.
+        attn_drop_rate (float, optional): Attention dropout rate. Default: 0.
+        drop_path_rate (float | list[float], optional): Stochastic depth
+            rate. Default: 0.
+        downsample (BaseModule | None, optional): The downsample operation
+            module. Default: None.
+        act_cfg (dict, optional): The config dict of activation function.
+            Default: dict(type='GELU').
+        norm_cfg (dict, optional): The config dict of normalization.
+            Default: dict(type='LN').
+        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
+            will save some memory while slowing down the training speed.
+            Default: False.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 feedforward_channels,
+                 depth,
+                 window_size=7,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 downsample=None,
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN'),
+                 with_cp=False):
+        super().__init__()
+        self._is_init = False
+
+        if isinstance(drop_path_rate, list):
+            drop_path_rates = drop_path_rate
+            assert len(drop_path_rates) == depth
+        else:
+            drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = SwinBlock(
+                embed_dims=embed_dims,
+                num_heads=num_heads,
+                feedforward_channels=feedforward_channels,
+                window_size=window_size,
+                shift=False if i % 2 == 0 else True,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop_rate=drop_rate,
+                attn_drop_rate=attn_drop_rate,
+                drop_path_rate=drop_path_rates[i],
+                act_cfg=act_cfg,
+                norm_cfg=norm_cfg,
+                with_cp=with_cp,)
+            self.blocks.append(block)
+
+        self.downsample = downsample
+
+    def forward(self, x, hw_shape):
+        for block in self.blocks:
+            x = block(x, hw_shape)
+
+        if self.downsample:
+            x_down, down_hw_shape = self.downsample(x, hw_shape)
+            return x_down, down_hw_shape, x, hw_shape
+        else:
+            return x, hw_shape, x, hw_shape
+
+
+class SwinTransformer(nn.Module):
+    """ Swin Transformer
+    A PyTorch implement of : `Swin Transformer:
+    Hierarchical Vision Transformer using Shifted Windows`  -
+        https://arxiv.org/abs/2103.14030
+
+    This code is adapted from https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/backbones/swin.py 
+    with minimal modifications.
+
+    Args:
+        pretrain_img_size (int | tuple[int]): The size of input image when
+            pretrain. Defaults: 224.
+        in_channels (int): The num of input channels.
+            Defaults: 3.
+        embed_dims (int): The feature dimension. Default: 96.
+        patch_size (int | tuple[int]): Patch size. Default: 4.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (int): Ratio of mlp hidden dim to embedding dim.
+            Default: 4.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+            Default: (2, 2, 6, 2).
+        num_heads (tuple[int]): Parallel attention heads of each Swin
+            Transformer stage. Default: (3, 6, 12, 24).
+        strides (tuple[int]): The patch merging or patch embedding stride of
+            each Swin Transformer stage. (In swin, we set kernel size equal to
+            stride.) Default: (4, 2, 2, 2).
+        out_indices (tuple[int]): Output from which stages.
+            Default: (0, 1, 2, 3).
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key,
+            value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Default: None.
+        patch_norm (bool): If add a norm layer for patch embed and patch
+            merging. Default: True.
+        drop_rate (float): Dropout rate. Defaults: 0.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Defaults: 0.1.
+        use_abs_pos_embed (bool): If True, add absolute position embedding to
+            the patch embedding. Defaults: False.
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='GELU').
+        norm_cfg (dict): Config dict for normalization layer at
+            output of backone. Defaults: dict(type='LN').
+        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
+            will save some memory while slowing down the training speed.
+            Default: False.
+        pretrained (str, optional): model pretrained path. Default: None.
+        convert_weights (bool): The flag indicates whether the
+            pre-trained model is from the original repo. We may need
+            to convert some keys to make it compatible.
+            Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            Default: -1 (-1 means not freezing any parameters).
+        init_cfg (dict, optional): The Config for initialization.
+            Defaults to None.
+    """
+
+    def __init__(self, model_cfg):
+        
+        self.model_cfg = model_cfg
+        pretrain_img_size = self.model_cfg.get('PRETRAIN_IMG_SIZE', 224)
+        init_cfg = self.model_cfg.get('INIT_CFG', None)
+        depths = self.model_cfg.DEPTHS
+        in_channels = self.model_cfg.get('IN_CHANNELS', 3)
+        strides = self.model_cfg.get('STRIDES', (4, 2, 2, 2))
+        patch_size = self.model_cfg.get('PATCH_SIZE', 4)
+        embed_dims = self.model_cfg.EMBED_DIMS
+        num_heads = self.model_cfg.NUM_HEADS
+        window_size = self.model_cfg.WINDOW_SIZE
+        mlp_ratio = self.model_cfg.MLP_RATIO
+        qkv_bias = self.model_cfg.get('QKV_BIAS', True)
+        qk_scale = self.model_cfg.get('QK_SCALE', None)
+        drop_rate = self.model_cfg.DROP_RATE
+        attn_drop_rate = self.model_cfg.ATTN_DROP_RATE
+        drop_path_rate = self.model_cfg.DROP_PATH_RATE
+        patch_norm = self.model_cfg.get('PATCH_NORM', True)
+        out_indices = self.model_cfg.get('OUT_INDICES', [0, 1, 2, 3])
+        with_cp = self.model_cfg.get('WITH_CP', False)
+        use_abs_pos_embed = self.model_cfg.get('USE_ABS_POS_EMBED', False)
+        act_cfg=dict(type='GELU')
+        norm_cfg=dict(type='LN')
+
+        self.convert_weights = self.model_cfg.get('CONVERT_WEIGHTS', False)
+        self.frozen_stages = self.model_cfg.get('FROZEN_STAGES', -1)
+
+        if isinstance(pretrain_img_size, int):
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+        elif isinstance(pretrain_img_size, tuple):
+            if len(pretrain_img_size) == 1:
+                pretrain_img_size = to_2tuple(pretrain_img_size[0])
+            assert len(pretrain_img_size) == 2, \
+                f'The size of image should have length 1 or 2, ' \
+                f'but got {len(pretrain_img_size)}'
+
+        super(SwinTransformer, self).__init__()
+        self.init_cfg = init_cfg
+
+        num_layers = len(depths)
+        self.out_indices = out_indices
+        self.use_abs_pos_embed = use_abs_pos_embed
+
+        assert strides[0] == patch_size, 'Use non-overlapping patch embed.'
+
+        self.patch_embed = PatchEmbed(
+            in_channels=in_channels,
+            embed_dims=embed_dims,
+            conv_type='Conv2d',
+            kernel_size=patch_size,
+            stride=strides[0],
+            norm_cfg=norm_cfg if patch_norm else None)
+
+        if self.use_abs_pos_embed:
+            patch_row = pretrain_img_size[0] // patch_size
+            patch_col = pretrain_img_size[1] // patch_size
+            num_patches = patch_row * patch_col
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros((1, num_patches, embed_dims)))
+
+        self.drop_after_pos = nn.Dropout(p=drop_rate)
+
+        # set stochastic depth decay rule
+        total_depth = sum(depths)
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
+        ]
+
+        self.stages = nn.ModuleList()
+        in_channels = embed_dims
+        for i in range(num_layers):
+            if i < num_layers - 1:
+                downsample = PatchMerging(
+                    in_channels=in_channels,
+                    out_channels=2 * in_channels,
+                    stride=strides[i + 1],
+                    norm_cfg=norm_cfg if patch_norm else None)
+            else:
+                downsample = None
+
+            stage = SwinBlockSequence(
+                embed_dims=in_channels,
+                num_heads=num_heads[i],
+                feedforward_channels=mlp_ratio * in_channels,
+                depth=depths[i],
+                window_size=window_size,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop_rate=drop_rate,
+                attn_drop_rate=attn_drop_rate,
+                drop_path_rate=dpr[sum(depths[:i]):sum(depths[:i + 1])],
+                downsample=downsample,
+                act_cfg=act_cfg,
+                norm_cfg=norm_cfg,
+                with_cp=with_cp)
+            self.stages.append(stage)
+            if downsample:
+                in_channels = downsample.out_channels
+
+        self.num_features = [int(embed_dims * 2**i) for i in range(num_layers)]
+        # Add a norm layer for each output
+        for i in out_indices:
+            layer =  nn.LayerNorm(self.num_features[i])
+            layer_name = f'norm{i}'
+            self.add_module(layer_name, layer)
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+            if self.use_abs_pos_embed:
+                self.absolute_pos_embed.requires_grad = False
+            self.drop_after_pos.eval()
+
+        for i in range(1, self.frozen_stages + 1):
+
+            if (i - 1) in self.out_indices:
+                norm_layer = getattr(self, f'norm{i-1}')
+                norm_layer.eval()
+                for param in norm_layer.parameters():
+                    param.requires_grad = False
+
+            m = self.stages[i - 1]
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+    def init_weights(self):
+        if self.init_cfg is None:
+            print(f'No pre-trained weights for '
+                        f'{self.__class__.__name__}, '
+                        f'training start from scratch')
+            if self.use_abs_pos_embed:
+                trunc_normal_(self.absolute_pos_embed, std=0.02)
+            for m in self.modules():
+                if isinstance(m, nn.Linear):
+                    trunc_normal_init(m, std=.02, bias=0.)
+                elif isinstance(m, nn.LayerNorm):
+                    constant_init(m, 1.0)
+        else:
+            assert 'checkpoint' in self.init_cfg, f'Only support ' \
+                                                  f'specify `Pretrained` in ' \
+                                                  f'`init_cfg` in ' \
+                                                  f'{self.__class__.__name__} '
+            ckpt = torch.load(self.init_cfg.checkpoint, map_location='cpu')
+            if 'state_dict' in ckpt:
+                _state_dict = ckpt['state_dict']
+            elif 'model' in ckpt:
+                _state_dict = ckpt['model']
+            else:
+                _state_dict = ckpt
+            if self.convert_weights:
+                # supported loading weight from original repo,
+                _state_dict = swin_converter(_state_dict)
+
+            state_dict = OrderedDict()
+            for k, v in _state_dict.items():
+                if k.startswith('backbone.'):
+                    state_dict[k[9:]] = v
+
+            # strip prefix of state_dict
+            if list(state_dict.keys())[0].startswith('module.'):
+                state_dict = {k[7:]: v for k, v in state_dict.items()}
+
+            # reshape absolute position embedding
+            if state_dict.get('absolute_pos_embed') is not None:
+                absolute_pos_embed = state_dict['absolute_pos_embed']
+                N1, L, C1 = absolute_pos_embed.size()
+                N2, C2, H, W = self.absolute_pos_embed.size()
+                if N1 != N2 or C1 != C2 or L != H * W:
+                    print('Error in loading absolute_pos_embed, pass')
+                else:
+                    state_dict['absolute_pos_embed'] = absolute_pos_embed.view(
+                        N2, H, W, C2).permute(0, 3, 1, 2).contiguous()
+
+            # interpolate position bias table if needed
+            relative_position_bias_table_keys = [
+                k for k in state_dict.keys()
+                if 'relative_position_bias_table' in k
+            ]
+            for table_key in relative_position_bias_table_keys:
+                table_pretrained = state_dict[table_key]
+                table_current = self.state_dict()[table_key]
+                L1, nH1 = table_pretrained.size()
+                L2, nH2 = table_current.size()
+                if nH1 != nH2:
+                    print(f'Error in loading {table_key}, pass')
+                elif L1 != L2:
+                    S1 = int(L1**0.5)
+                    S2 = int(L2**0.5)
+                    table_pretrained_resized = F.interpolate(
+                        table_pretrained.permute(1, 0).reshape(1, nH1, S1, S1),
+                        size=(S2, S2),
+                        mode='bicubic')
+                    state_dict[table_key] = table_pretrained_resized.view(
+                        nH2, L2).permute(1, 0).contiguous()
+
+            # load state_dict
+            self.load_state_dict(state_dict, False)
+
+    def forward(self, batch_dict):
+        x = batch_dict['camera_imgs']
+        B, N, C, H, W = x.size()
+        x = x.view(B * N, C, H, W)
+        x, hw_shape = self.patch_embed(x)
+
+        if self.use_abs_pos_embed:
+            x = x + self.absolute_pos_embed
+        x = self.drop_after_pos(x)
+
+        outs = []
+        for i, stage in enumerate(self.stages):
+            x, hw_shape, out, out_hw_shape = stage(x, hw_shape)
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                out = norm_layer(out)
+                out = out.view(-1, *out_hw_shape,
+                               self.num_features[i]).permute(0, 3, 1,
+                                                             2).contiguous()
+                outs.append(out)
+        batch_dict['image_features'] = outs
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/__init__.py
new file mode 100644
index 000000000..3bb33de36
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/__init__.py
@@ -0,0 +1,21 @@
+from .anchor_head_multi import AnchorHeadMulti
+from .anchor_head_single import AnchorHeadSingle
+from .anchor_head_template import AnchorHeadTemplate
+from .point_head_box import PointHeadBox
+from .point_head_simple import PointHeadSimple
+from .point_intra_part_head import PointIntraPartOffsetHead
+from .center_head import CenterHead
+from .voxelnext_head import VoxelNeXtHead
+from .transfusion_head import TransFusionHead
+
+__all__ = {
+    'AnchorHeadTemplate': AnchorHeadTemplate,
+    'AnchorHeadSingle': AnchorHeadSingle,
+    'PointIntraPartOffsetHead': PointIntraPartOffsetHead,
+    'PointHeadSimple': PointHeadSimple,
+    'PointHeadBox': PointHeadBox,
+    'AnchorHeadMulti': AnchorHeadMulti,
+    'CenterHead': CenterHead,
+    'VoxelNeXtHead': VoxelNeXtHead,
+    'TransFusionHead': TransFusionHead,
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_multi.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_multi.py
new file mode 100644
index 000000000..f562e4328
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_multi.py
@@ -0,0 +1,373 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ..backbones_2d import BaseBEVBackbone
+from .anchor_head_template import AnchorHeadTemplate
+
+
+class SingleHead(BaseBEVBackbone):
+    def __init__(self, model_cfg, input_channels, num_class, num_anchors_per_location, code_size, rpn_head_cfg=None,
+                 head_label_indices=None, separate_reg_config=None):
+        super().__init__(rpn_head_cfg, input_channels)
+
+        self.num_anchors_per_location = num_anchors_per_location
+        self.num_class = num_class
+        self.code_size = code_size
+        self.model_cfg = model_cfg
+        self.separate_reg_config = separate_reg_config
+        self.register_buffer('head_label_indices', head_label_indices)
+
+        if self.separate_reg_config is not None:
+            code_size_cnt = 0
+            self.conv_box = nn.ModuleDict()
+            self.conv_box_names = []
+            num_middle_conv = self.separate_reg_config.NUM_MIDDLE_CONV
+            num_middle_filter = self.separate_reg_config.NUM_MIDDLE_FILTER
+            conv_cls_list = []
+            c_in = input_channels
+            for k in range(num_middle_conv):
+                conv_cls_list.extend([
+                    nn.Conv2d(
+                        c_in, num_middle_filter,
+                        kernel_size=3, stride=1, padding=1, bias=False
+                    ),
+                    nn.BatchNorm2d(num_middle_filter),
+                    nn.ReLU()
+                ])
+                c_in = num_middle_filter
+            conv_cls_list.append(nn.Conv2d(
+                c_in, self.num_anchors_per_location * self.num_class,
+                kernel_size=3, stride=1, padding=1
+            ))
+            self.conv_cls = nn.Sequential(*conv_cls_list)
+
+            for reg_config in self.separate_reg_config.REG_LIST:
+                reg_name, reg_channel = reg_config.split(':')
+                reg_channel = int(reg_channel)
+                cur_conv_list = []
+                c_in = input_channels
+                for k in range(num_middle_conv):
+                    cur_conv_list.extend([
+                        nn.Conv2d(
+                            c_in, num_middle_filter,
+                            kernel_size=3, stride=1, padding=1, bias=False
+                        ),
+                        nn.BatchNorm2d(num_middle_filter),
+                        nn.ReLU()
+                    ])
+                    c_in = num_middle_filter
+
+                cur_conv_list.append(nn.Conv2d(
+                    c_in, self.num_anchors_per_location * int(reg_channel),
+                    kernel_size=3, stride=1, padding=1, bias=True
+                ))
+                code_size_cnt += reg_channel
+                self.conv_box[f'conv_{reg_name}'] = nn.Sequential(*cur_conv_list)
+                self.conv_box_names.append(f'conv_{reg_name}')
+
+            for m in self.conv_box.modules():
+                if isinstance(m, nn.Conv2d):
+                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                    if m.bias is not None:
+                        nn.init.constant_(m.bias, 0)
+
+            assert code_size_cnt == code_size, f'Code size does not match: {code_size_cnt}:{code_size}'
+        else:
+            self.conv_cls = nn.Conv2d(
+                input_channels, self.num_anchors_per_location * self.num_class,
+                kernel_size=1
+            )
+            self.conv_box = nn.Conv2d(
+                input_channels, self.num_anchors_per_location * self.code_size,
+                kernel_size=1
+            )
+
+        if self.model_cfg.get('USE_DIRECTION_CLASSIFIER', None) is not None:
+            self.conv_dir_cls = nn.Conv2d(
+                input_channels,
+                self.num_anchors_per_location * self.model_cfg.NUM_DIR_BINS,
+                kernel_size=1
+            )
+        else:
+            self.conv_dir_cls = None
+        self.use_multihead = self.model_cfg.get('USE_MULTIHEAD', False)
+        self.init_weights()
+
+    def init_weights(self):
+        pi = 0.01
+        if isinstance(self.conv_cls, nn.Conv2d):
+            nn.init.constant_(self.conv_cls.bias, -np.log((1 - pi) / pi))
+        else:
+            nn.init.constant_(self.conv_cls[-1].bias, -np.log((1 - pi) / pi))
+
+    def forward(self, spatial_features_2d):
+        ret_dict = {}
+        spatial_features_2d = super().forward({'spatial_features': spatial_features_2d})['spatial_features_2d']
+
+        cls_preds = self.conv_cls(spatial_features_2d)
+
+        if self.separate_reg_config is None:
+            box_preds = self.conv_box(spatial_features_2d)
+        else:
+            box_preds_list = []
+            for reg_name in self.conv_box_names:
+                box_preds_list.append(self.conv_box[reg_name](spatial_features_2d))
+            box_preds = torch.cat(box_preds_list, dim=1)
+
+        if not self.use_multihead:
+            box_preds = box_preds.permute(0, 2, 3, 1).contiguous()
+            cls_preds = cls_preds.permute(0, 2, 3, 1).contiguous()
+        else:
+            H, W = box_preds.shape[2:]
+            batch_size = box_preds.shape[0]
+            box_preds = box_preds.view(-1, self.num_anchors_per_location,
+                                       self.code_size, H, W).permute(0, 1, 3, 4, 2).contiguous()
+            cls_preds = cls_preds.view(-1, self.num_anchors_per_location,
+                                       self.num_class, H, W).permute(0, 1, 3, 4, 2).contiguous()
+            box_preds = box_preds.view(batch_size, -1, self.code_size)
+            cls_preds = cls_preds.view(batch_size, -1, self.num_class)
+
+        if self.conv_dir_cls is not None:
+            dir_cls_preds = self.conv_dir_cls(spatial_features_2d)
+            if self.use_multihead:
+                dir_cls_preds = dir_cls_preds.view(
+                    -1, self.num_anchors_per_location, self.model_cfg.NUM_DIR_BINS, H, W).permute(0, 1, 3, 4,
+                                                                                                  2).contiguous()
+                dir_cls_preds = dir_cls_preds.view(batch_size, -1, self.model_cfg.NUM_DIR_BINS)
+            else:
+                dir_cls_preds = dir_cls_preds.permute(0, 2, 3, 1).contiguous()
+
+        else:
+            dir_cls_preds = None
+
+        ret_dict['cls_preds'] = cls_preds
+        ret_dict['box_preds'] = box_preds
+        ret_dict['dir_cls_preds'] = dir_cls_preds
+
+        return ret_dict
+
+
+class AnchorHeadMulti(AnchorHeadTemplate):
+    def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range,
+                 predict_boxes_when_training=True, **kwargs):
+        super().__init__(
+            model_cfg=model_cfg, num_class=num_class, class_names=class_names, grid_size=grid_size,
+            point_cloud_range=point_cloud_range, predict_boxes_when_training=predict_boxes_when_training
+        )
+        self.model_cfg = model_cfg
+        self.separate_multihead = self.model_cfg.get('SEPARATE_MULTIHEAD', False)
+
+        if self.model_cfg.get('SHARED_CONV_NUM_FILTER', None) is not None:
+            shared_conv_num_filter = self.model_cfg.SHARED_CONV_NUM_FILTER
+            self.shared_conv = nn.Sequential(
+                nn.Conv2d(input_channels, shared_conv_num_filter, 3, stride=1, padding=1, bias=False),
+                nn.BatchNorm2d(shared_conv_num_filter, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            )
+        else:
+            self.shared_conv = None
+            shared_conv_num_filter = input_channels
+        self.rpn_heads = None
+        self.make_multihead(shared_conv_num_filter)
+
+    def make_multihead(self, input_channels):
+        rpn_head_cfgs = self.model_cfg.RPN_HEAD_CFGS
+        rpn_heads = []
+        class_names = []
+        for rpn_head_cfg in rpn_head_cfgs:
+            class_names.extend(rpn_head_cfg['HEAD_CLS_NAME'])
+
+        for rpn_head_cfg in rpn_head_cfgs:
+            num_anchors_per_location = sum([self.num_anchors_per_location[class_names.index(head_cls)]
+                                            for head_cls in rpn_head_cfg['HEAD_CLS_NAME']])
+            head_label_indices = torch.from_numpy(np.array([
+                self.class_names.index(cur_name) + 1 for cur_name in rpn_head_cfg['HEAD_CLS_NAME']
+            ]))
+
+            rpn_head = SingleHead(
+                self.model_cfg, input_channels,
+                len(rpn_head_cfg['HEAD_CLS_NAME']) if self.separate_multihead else self.num_class,
+                num_anchors_per_location, self.box_coder.code_size, rpn_head_cfg,
+                head_label_indices=head_label_indices,
+                separate_reg_config=self.model_cfg.get('SEPARATE_REG_CONFIG', None)
+            )
+            rpn_heads.append(rpn_head)
+        self.rpn_heads = nn.ModuleList(rpn_heads)
+
+    def forward(self, data_dict):
+        spatial_features_2d = data_dict['spatial_features_2d']
+        if self.shared_conv is not None:
+            spatial_features_2d = self.shared_conv(spatial_features_2d)
+
+        ret_dicts = []
+        for rpn_head in self.rpn_heads:
+            ret_dicts.append(rpn_head(spatial_features_2d))
+
+        cls_preds = [ret_dict['cls_preds'] for ret_dict in ret_dicts]
+        box_preds = [ret_dict['box_preds'] for ret_dict in ret_dicts]
+        ret = {
+            'cls_preds': cls_preds if self.separate_multihead else torch.cat(cls_preds, dim=1),
+            'box_preds': box_preds if self.separate_multihead else torch.cat(box_preds, dim=1),
+        }
+
+        if self.model_cfg.get('USE_DIRECTION_CLASSIFIER', False):
+            dir_cls_preds = [ret_dict['dir_cls_preds'] for ret_dict in ret_dicts]
+            ret['dir_cls_preds'] = dir_cls_preds if self.separate_multihead else torch.cat(dir_cls_preds, dim=1)
+
+        self.forward_ret_dict.update(ret)
+
+        if self.training:
+            targets_dict = self.assign_targets(
+                gt_boxes=data_dict['gt_boxes']
+            )
+            self.forward_ret_dict.update(targets_dict)
+
+        if not self.training or self.predict_boxes_when_training:
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=data_dict['batch_size'],
+                cls_preds=ret['cls_preds'], box_preds=ret['box_preds'], dir_cls_preds=ret.get('dir_cls_preds', None)
+            )
+
+            if isinstance(batch_cls_preds, list):
+                multihead_label_mapping = []
+                for idx in range(len(batch_cls_preds)):
+                    multihead_label_mapping.append(self.rpn_heads[idx].head_label_indices)
+
+                data_dict['multihead_label_mapping'] = multihead_label_mapping
+
+            data_dict['batch_cls_preds'] = batch_cls_preds
+            data_dict['batch_box_preds'] = batch_box_preds
+            data_dict['cls_preds_normalized'] = False
+
+        return data_dict
+
+    def get_cls_layer_loss(self):
+        loss_weights = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS
+        if 'pos_cls_weight' in loss_weights:
+            pos_cls_weight = loss_weights['pos_cls_weight']
+            neg_cls_weight = loss_weights['neg_cls_weight']
+        else:
+            pos_cls_weight = neg_cls_weight = 1.0
+
+        cls_preds = self.forward_ret_dict['cls_preds']
+        box_cls_labels = self.forward_ret_dict['box_cls_labels']
+        if not isinstance(cls_preds, list):
+            cls_preds = [cls_preds]
+        batch_size = int(cls_preds[0].shape[0])
+        cared = box_cls_labels >= 0  # [N, num_anchors]
+        positives = box_cls_labels > 0
+        negatives = box_cls_labels == 0
+        negative_cls_weights = negatives * 1.0 * neg_cls_weight
+
+        cls_weights = (negative_cls_weights + pos_cls_weight * positives).float()
+
+        reg_weights = positives.float()
+        if self.num_class == 1:
+            # class agnostic
+            box_cls_labels[positives] = 1
+        pos_normalizer = positives.sum(1, keepdim=True).float()
+
+        reg_weights /= torch.clamp(pos_normalizer, min=1.0)
+        cls_weights /= torch.clamp(pos_normalizer, min=1.0)
+        cls_targets = box_cls_labels * cared.type_as(box_cls_labels)
+        one_hot_targets = torch.zeros(
+            *list(cls_targets.shape), self.num_class + 1, dtype=cls_preds[0].dtype, device=cls_targets.device
+        )
+        one_hot_targets.scatter_(-1, cls_targets.unsqueeze(dim=-1).long(), 1.0)
+        one_hot_targets = one_hot_targets[..., 1:]
+        start_idx = c_idx = 0
+        cls_losses = 0
+
+        for idx, cls_pred in enumerate(cls_preds):
+            cur_num_class = self.rpn_heads[idx].num_class
+            cls_pred = cls_pred.view(batch_size, -1, cur_num_class)
+            if self.separate_multihead:
+                one_hot_target = one_hot_targets[:, start_idx:start_idx + cls_pred.shape[1],
+                                 c_idx:c_idx + cur_num_class]
+                c_idx += cur_num_class
+            else:
+                one_hot_target = one_hot_targets[:, start_idx:start_idx + cls_pred.shape[1]]
+            cls_weight = cls_weights[:, start_idx:start_idx + cls_pred.shape[1]]
+            cls_loss_src = self.cls_loss_func(cls_pred, one_hot_target, weights=cls_weight)  # [N, M]
+            cls_loss = cls_loss_src.sum() / batch_size
+            cls_loss = cls_loss * loss_weights['cls_weight']
+            cls_losses += cls_loss
+            start_idx += cls_pred.shape[1]
+        assert start_idx == one_hot_targets.shape[1]
+        tb_dict = {
+            'rpn_loss_cls': cls_losses.item()
+        }
+        return cls_losses, tb_dict
+
+    def get_box_reg_layer_loss(self):
+        box_preds = self.forward_ret_dict['box_preds']
+        box_dir_cls_preds = self.forward_ret_dict.get('dir_cls_preds', None)
+        box_reg_targets = self.forward_ret_dict['box_reg_targets']
+        box_cls_labels = self.forward_ret_dict['box_cls_labels']
+
+        positives = box_cls_labels > 0
+        reg_weights = positives.float()
+        pos_normalizer = positives.sum(1, keepdim=True).float()
+        reg_weights /= torch.clamp(pos_normalizer, min=1.0)
+
+        if not isinstance(box_preds, list):
+            box_preds = [box_preds]
+        batch_size = int(box_preds[0].shape[0])
+
+        if isinstance(self.anchors, list):
+            if self.use_multihead:
+                anchors = torch.cat(
+                    [anchor.permute(3, 4, 0, 1, 2, 5).contiguous().view(-1, anchor.shape[-1])
+                     for anchor in self.anchors], dim=0
+                )
+            else:
+                anchors = torch.cat(self.anchors, dim=-3)
+        else:
+            anchors = self.anchors
+        anchors = anchors.view(1, -1, anchors.shape[-1]).repeat(batch_size, 1, 1)
+
+        start_idx = 0
+        box_losses = 0
+        tb_dict = {}
+        for idx, box_pred in enumerate(box_preds):
+            box_pred = box_pred.view(
+                batch_size, -1,
+                box_pred.shape[-1] // self.num_anchors_per_location if not self.use_multihead else box_pred.shape[-1]
+            )
+            box_reg_target = box_reg_targets[:, start_idx:start_idx + box_pred.shape[1]]
+            reg_weight = reg_weights[:, start_idx:start_idx + box_pred.shape[1]]
+            # sin(a - b) = sinacosb-cosasinb
+            if box_dir_cls_preds is not None:
+                box_pred_sin, reg_target_sin = self.add_sin_difference(box_pred, box_reg_target)
+                loc_loss_src = self.reg_loss_func(box_pred_sin, reg_target_sin, weights=reg_weight)  # [N, M]
+            else:
+                loc_loss_src = self.reg_loss_func(box_pred, box_reg_target, weights=reg_weight)  # [N, M]
+            loc_loss = loc_loss_src.sum() / batch_size
+
+            loc_loss = loc_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['loc_weight']
+            box_losses += loc_loss
+            tb_dict['rpn_loss_loc'] = tb_dict.get('rpn_loss_loc', 0) + loc_loss.item()
+
+            if box_dir_cls_preds is not None:
+                if not isinstance(box_dir_cls_preds, list):
+                    box_dir_cls_preds = [box_dir_cls_preds]
+                dir_targets = self.get_direction_target(
+                    anchors, box_reg_targets,
+                    dir_offset=self.model_cfg.DIR_OFFSET,
+                    num_bins=self.model_cfg.NUM_DIR_BINS
+                )
+                box_dir_cls_pred = box_dir_cls_preds[idx]
+                dir_logit = box_dir_cls_pred.view(batch_size, -1, self.model_cfg.NUM_DIR_BINS)
+                weights = positives.type_as(dir_logit)
+                weights /= torch.clamp(weights.sum(-1, keepdim=True), min=1.0)
+
+                weight = weights[:, start_idx:start_idx + box_pred.shape[1]]
+                dir_target = dir_targets[:, start_idx:start_idx + box_pred.shape[1]]
+                dir_loss = self.dir_loss_func(dir_logit, dir_target, weights=weight)
+                dir_loss = dir_loss.sum() / batch_size
+                dir_loss = dir_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['dir_weight']
+                box_losses += dir_loss
+                tb_dict['rpn_loss_dir'] = tb_dict.get('rpn_loss_dir', 0) + dir_loss.item()
+            start_idx += box_pred.shape[1]
+        return box_losses, tb_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_single.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_single.py
new file mode 100644
index 000000000..83c62cce9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_single.py
@@ -0,0 +1,75 @@
+import numpy as np
+import torch.nn as nn
+
+from .anchor_head_template import AnchorHeadTemplate
+
+
+class AnchorHeadSingle(AnchorHeadTemplate):
+    def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range,
+                 predict_boxes_when_training=True, **kwargs):
+        super().__init__(
+            model_cfg=model_cfg, num_class=num_class, class_names=class_names, grid_size=grid_size, point_cloud_range=point_cloud_range,
+            predict_boxes_when_training=predict_boxes_when_training
+        )
+
+        self.num_anchors_per_location = sum(self.num_anchors_per_location)
+
+        self.conv_cls = nn.Conv2d(
+            input_channels, self.num_anchors_per_location * self.num_class,
+            kernel_size=1
+        )
+        self.conv_box = nn.Conv2d(
+            input_channels, self.num_anchors_per_location * self.box_coder.code_size,
+            kernel_size=1
+        )
+
+        if self.model_cfg.get('USE_DIRECTION_CLASSIFIER', None) is not None:
+            self.conv_dir_cls = nn.Conv2d(
+                input_channels,
+                self.num_anchors_per_location * self.model_cfg.NUM_DIR_BINS,
+                kernel_size=1
+            )
+        else:
+            self.conv_dir_cls = None
+        self.init_weights()
+
+    def init_weights(self):
+        pi = 0.01
+        nn.init.constant_(self.conv_cls.bias, -np.log((1 - pi) / pi))
+        nn.init.normal_(self.conv_box.weight, mean=0, std=0.001)
+
+    def forward(self, data_dict):
+        spatial_features_2d = data_dict['spatial_features_2d']
+
+        cls_preds = self.conv_cls(spatial_features_2d)
+        box_preds = self.conv_box(spatial_features_2d)
+
+        cls_preds = cls_preds.permute(0, 2, 3, 1).contiguous()  # [N, H, W, C]
+        box_preds = box_preds.permute(0, 2, 3, 1).contiguous()  # [N, H, W, C]
+
+        self.forward_ret_dict['cls_preds'] = cls_preds
+        self.forward_ret_dict['box_preds'] = box_preds
+
+        if self.conv_dir_cls is not None:
+            dir_cls_preds = self.conv_dir_cls(spatial_features_2d)
+            dir_cls_preds = dir_cls_preds.permute(0, 2, 3, 1).contiguous()
+            self.forward_ret_dict['dir_cls_preds'] = dir_cls_preds
+        else:
+            dir_cls_preds = None
+
+        if self.training:
+            targets_dict = self.assign_targets(
+                gt_boxes=data_dict['gt_boxes']
+            )
+            self.forward_ret_dict.update(targets_dict)
+
+        if not self.training or self.predict_boxes_when_training:
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=data_dict['batch_size'],
+                cls_preds=cls_preds, box_preds=box_preds, dir_cls_preds=dir_cls_preds
+            )
+            data_dict['batch_cls_preds'] = batch_cls_preds
+            data_dict['batch_box_preds'] = batch_box_preds
+            data_dict['cls_preds_normalized'] = False
+
+        return data_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_template.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_template.py
new file mode 100644
index 000000000..db8167a49
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/anchor_head_template.py
@@ -0,0 +1,275 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ...utils import box_coder_utils, common_utils, loss_utils
+from .target_assigner.anchor_generator import AnchorGenerator
+from .target_assigner.atss_target_assigner import ATSSTargetAssigner
+from .target_assigner.axis_aligned_target_assigner import AxisAlignedTargetAssigner
+
+
+class AnchorHeadTemplate(nn.Module):
+    def __init__(self, model_cfg, num_class, class_names, grid_size, point_cloud_range, predict_boxes_when_training):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_class = num_class
+        self.class_names = class_names
+        self.predict_boxes_when_training = predict_boxes_when_training
+        self.use_multihead = self.model_cfg.get('USE_MULTIHEAD', False)
+
+        anchor_target_cfg = self.model_cfg.TARGET_ASSIGNER_CONFIG
+        self.box_coder = getattr(box_coder_utils, anchor_target_cfg.BOX_CODER)(
+            num_dir_bins=anchor_target_cfg.get('NUM_DIR_BINS', 6),
+            **anchor_target_cfg.get('BOX_CODER_CONFIG', {})
+        )
+
+        anchor_generator_cfg = self.model_cfg.ANCHOR_GENERATOR_CONFIG
+        anchors, self.num_anchors_per_location = self.generate_anchors(
+            anchor_generator_cfg, grid_size=grid_size, point_cloud_range=point_cloud_range,
+            anchor_ndim=self.box_coder.code_size
+        )
+        self.anchors = [x.cuda() for x in anchors]
+        self.target_assigner = self.get_target_assigner(anchor_target_cfg)
+
+        self.forward_ret_dict = {}
+        self.build_losses(self.model_cfg.LOSS_CONFIG)
+
+    @staticmethod
+    def generate_anchors(anchor_generator_cfg, grid_size, point_cloud_range, anchor_ndim=7):
+        anchor_generator = AnchorGenerator(
+            anchor_range=point_cloud_range,
+            anchor_generator_config=anchor_generator_cfg
+        )
+        feature_map_size = [grid_size[:2] // config['feature_map_stride'] for config in anchor_generator_cfg]
+        anchors_list, num_anchors_per_location_list = anchor_generator.generate_anchors(feature_map_size)
+
+        if anchor_ndim != 7:
+            for idx, anchors in enumerate(anchors_list):
+                pad_zeros = anchors.new_zeros([*anchors.shape[0:-1], anchor_ndim - 7])
+                new_anchors = torch.cat((anchors, pad_zeros), dim=-1)
+                anchors_list[idx] = new_anchors
+
+        return anchors_list, num_anchors_per_location_list
+
+    def get_target_assigner(self, anchor_target_cfg):
+        if anchor_target_cfg.NAME == 'ATSS':
+            target_assigner = ATSSTargetAssigner(
+                topk=anchor_target_cfg.TOPK,
+                box_coder=self.box_coder,
+                use_multihead=self.use_multihead,
+                match_height=anchor_target_cfg.MATCH_HEIGHT
+            )
+        elif anchor_target_cfg.NAME == 'AxisAlignedTargetAssigner':
+            target_assigner = AxisAlignedTargetAssigner(
+                model_cfg=self.model_cfg,
+                class_names=self.class_names,
+                box_coder=self.box_coder,
+                match_height=anchor_target_cfg.MATCH_HEIGHT
+            )
+        else:
+            raise NotImplementedError
+        return target_assigner
+
+    def build_losses(self, losses_cfg):
+        self.add_module(
+            'cls_loss_func',
+            loss_utils.SigmoidFocalClassificationLoss(alpha=0.25, gamma=2.0)
+        )
+        reg_loss_name = 'WeightedSmoothL1Loss' if losses_cfg.get('REG_LOSS_TYPE', None) is None \
+            else losses_cfg.REG_LOSS_TYPE
+        self.add_module(
+            'reg_loss_func',
+            getattr(loss_utils, reg_loss_name)(code_weights=losses_cfg.LOSS_WEIGHTS['code_weights'])
+        )
+        self.add_module(
+            'dir_loss_func',
+            loss_utils.WeightedCrossEntropyLoss()
+        )
+
+    def assign_targets(self, gt_boxes):
+        """
+        Args:
+            gt_boxes: (B, M, 8)
+        Returns:
+
+        """
+        targets_dict = self.target_assigner.assign_targets(
+            self.anchors, gt_boxes
+        )
+        return targets_dict
+
+    def get_cls_layer_loss(self):
+        cls_preds = self.forward_ret_dict['cls_preds']
+        box_cls_labels = self.forward_ret_dict['box_cls_labels']
+        batch_size = int(cls_preds.shape[0])
+        cared = box_cls_labels >= 0  # [N, num_anchors]
+        positives = box_cls_labels > 0
+        negatives = box_cls_labels == 0
+        negative_cls_weights = negatives * 1.0
+        cls_weights = (negative_cls_weights + 1.0 * positives).float()
+        reg_weights = positives.float()
+        if self.num_class == 1:
+            # class agnostic
+            box_cls_labels[positives] = 1
+
+        pos_normalizer = positives.sum(1, keepdim=True).float()
+        reg_weights /= torch.clamp(pos_normalizer, min=1.0)
+        cls_weights /= torch.clamp(pos_normalizer, min=1.0)
+        cls_targets = box_cls_labels * cared.type_as(box_cls_labels)
+        cls_targets = cls_targets.unsqueeze(dim=-1)
+
+        cls_targets = cls_targets.squeeze(dim=-1)
+        one_hot_targets = torch.zeros(
+            *list(cls_targets.shape), self.num_class + 1, dtype=cls_preds.dtype, device=cls_targets.device
+        )
+        one_hot_targets.scatter_(-1, cls_targets.unsqueeze(dim=-1).long(), 1.0)
+        cls_preds = cls_preds.view(batch_size, -1, self.num_class)
+        one_hot_targets = one_hot_targets[..., 1:]
+        cls_loss_src = self.cls_loss_func(cls_preds, one_hot_targets, weights=cls_weights)  # [N, M]
+        cls_loss = cls_loss_src.sum() / batch_size
+
+        cls_loss = cls_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['cls_weight']
+        tb_dict = {
+            'rpn_loss_cls': cls_loss.item()
+        }
+        return cls_loss, tb_dict
+
+    @staticmethod
+    def add_sin_difference(boxes1, boxes2, dim=6):
+        assert dim != -1
+        rad_pred_encoding = torch.sin(boxes1[..., dim:dim + 1]) * torch.cos(boxes2[..., dim:dim + 1])
+        rad_tg_encoding = torch.cos(boxes1[..., dim:dim + 1]) * torch.sin(boxes2[..., dim:dim + 1])
+        boxes1 = torch.cat([boxes1[..., :dim], rad_pred_encoding, boxes1[..., dim + 1:]], dim=-1)
+        boxes2 = torch.cat([boxes2[..., :dim], rad_tg_encoding, boxes2[..., dim + 1:]], dim=-1)
+        return boxes1, boxes2
+
+    @staticmethod
+    def get_direction_target(anchors, reg_targets, one_hot=True, dir_offset=0, num_bins=2):
+        batch_size = reg_targets.shape[0]
+        anchors = anchors.view(batch_size, -1, anchors.shape[-1])
+        rot_gt = reg_targets[..., 6] + anchors[..., 6]
+        offset_rot = common_utils.limit_period(rot_gt - dir_offset, 0, 2 * np.pi)
+        dir_cls_targets = torch.floor(offset_rot / (2 * np.pi / num_bins)).long()
+        dir_cls_targets = torch.clamp(dir_cls_targets, min=0, max=num_bins - 1)
+
+        if one_hot:
+            dir_targets = torch.zeros(*list(dir_cls_targets.shape), num_bins, dtype=anchors.dtype,
+                                      device=dir_cls_targets.device)
+            dir_targets.scatter_(-1, dir_cls_targets.unsqueeze(dim=-1).long(), 1.0)
+            dir_cls_targets = dir_targets
+        return dir_cls_targets
+
+    def get_box_reg_layer_loss(self):
+        box_preds = self.forward_ret_dict['box_preds']
+        box_dir_cls_preds = self.forward_ret_dict.get('dir_cls_preds', None)
+        box_reg_targets = self.forward_ret_dict['box_reg_targets']
+        box_cls_labels = self.forward_ret_dict['box_cls_labels']
+        batch_size = int(box_preds.shape[0])
+
+        positives = box_cls_labels > 0
+        reg_weights = positives.float()
+        pos_normalizer = positives.sum(1, keepdim=True).float()
+        reg_weights /= torch.clamp(pos_normalizer, min=1.0)
+
+        if isinstance(self.anchors, list):
+            if self.use_multihead:
+                anchors = torch.cat(
+                    [anchor.permute(3, 4, 0, 1, 2, 5).contiguous().view(-1, anchor.shape[-1]) for anchor in
+                     self.anchors], dim=0)
+            else:
+                anchors = torch.cat(self.anchors, dim=-3)
+        else:
+            anchors = self.anchors
+        anchors = anchors.view(1, -1, anchors.shape[-1]).repeat(batch_size, 1, 1)
+        box_preds = box_preds.view(batch_size, -1,
+                                   box_preds.shape[-1] // self.num_anchors_per_location if not self.use_multihead else
+                                   box_preds.shape[-1])
+        # sin(a - b) = sinacosb-cosasinb
+        box_preds_sin, reg_targets_sin = self.add_sin_difference(box_preds, box_reg_targets)
+        loc_loss_src = self.reg_loss_func(box_preds_sin, reg_targets_sin, weights=reg_weights)  # [N, M]
+        loc_loss = loc_loss_src.sum() / batch_size
+
+        loc_loss = loc_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['loc_weight']
+        box_loss = loc_loss
+        tb_dict = {
+            'rpn_loss_loc': loc_loss.item()
+        }
+
+        if box_dir_cls_preds is not None:
+            dir_targets = self.get_direction_target(
+                anchors, box_reg_targets,
+                dir_offset=self.model_cfg.DIR_OFFSET,
+                num_bins=self.model_cfg.NUM_DIR_BINS
+            )
+
+            dir_logits = box_dir_cls_preds.view(batch_size, -1, self.model_cfg.NUM_DIR_BINS)
+            weights = positives.type_as(dir_logits)
+            weights /= torch.clamp(weights.sum(-1, keepdim=True), min=1.0)
+            dir_loss = self.dir_loss_func(dir_logits, dir_targets, weights=weights)
+            dir_loss = dir_loss.sum() / batch_size
+            dir_loss = dir_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['dir_weight']
+            box_loss += dir_loss
+            tb_dict['rpn_loss_dir'] = dir_loss.item()
+
+        return box_loss, tb_dict
+
+    def get_loss(self):
+        cls_loss, tb_dict = self.get_cls_layer_loss()
+        box_loss, tb_dict_box = self.get_box_reg_layer_loss()
+        tb_dict.update(tb_dict_box)
+        rpn_loss = cls_loss + box_loss
+
+        tb_dict['rpn_loss'] = rpn_loss.item()
+        return rpn_loss, tb_dict
+
+    def generate_predicted_boxes(self, batch_size, cls_preds, box_preds, dir_cls_preds=None):
+        """
+        Args:
+            batch_size:
+            cls_preds: (N, H, W, C1)
+            box_preds: (N, H, W, C2)
+            dir_cls_preds: (N, H, W, C3)
+
+        Returns:
+            batch_cls_preds: (B, num_boxes, num_classes)
+            batch_box_preds: (B, num_boxes, 7+C)
+
+        """
+        if isinstance(self.anchors, list):
+            if self.use_multihead:
+                anchors = torch.cat([anchor.permute(3, 4, 0, 1, 2, 5).contiguous().view(-1, anchor.shape[-1])
+                                     for anchor in self.anchors], dim=0)
+            else:
+                anchors = torch.cat(self.anchors, dim=-3)
+        else:
+            anchors = self.anchors
+        num_anchors = anchors.view(-1, anchors.shape[-1]).shape[0]
+        batch_anchors = anchors.view(1, -1, anchors.shape[-1]).repeat(batch_size, 1, 1)
+        batch_cls_preds = cls_preds.view(batch_size, num_anchors, -1).float() \
+            if not isinstance(cls_preds, list) else cls_preds
+        batch_box_preds = box_preds.view(batch_size, num_anchors, -1) if not isinstance(box_preds, list) \
+            else torch.cat(box_preds, dim=1).view(batch_size, num_anchors, -1)
+        batch_box_preds = self.box_coder.decode_torch(batch_box_preds, batch_anchors)
+
+        if dir_cls_preds is not None:
+            dir_offset = self.model_cfg.DIR_OFFSET
+            dir_limit_offset = self.model_cfg.DIR_LIMIT_OFFSET
+            dir_cls_preds = dir_cls_preds.view(batch_size, num_anchors, -1) if not isinstance(dir_cls_preds, list) \
+                else torch.cat(dir_cls_preds, dim=1).view(batch_size, num_anchors, -1)
+            dir_labels = torch.max(dir_cls_preds, dim=-1)[1]
+
+            period = (2 * np.pi / self.model_cfg.NUM_DIR_BINS)
+            dir_rot = common_utils.limit_period(
+                batch_box_preds[..., 6] - dir_offset, dir_limit_offset, period
+            )
+            batch_box_preds[..., 6] = dir_rot + dir_offset + period * dir_labels.to(batch_box_preds.dtype)
+
+        if isinstance(self.box_coder, box_coder_utils.PreviousResidualDecoder):
+            batch_box_preds[..., 6] = common_utils.limit_period(
+                -(batch_box_preds[..., 6] + np.pi / 2), offset=0.5, period=np.pi * 2
+            )
+
+        return batch_cls_preds, batch_box_preds
+
+    def forward(self, **kwargs):
+        raise NotImplementedError
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/center_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/center_head.py
new file mode 100644
index 000000000..38a6e3536
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/center_head.py
@@ -0,0 +1,416 @@
+import copy
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.nn.init import kaiming_normal_
+from ..model_utils import model_nms_utils
+from ..model_utils import centernet_utils
+from ...utils import loss_utils
+from functools import partial
+
+
+class SeparateHead(nn.Module):
+    def __init__(self, input_channels, sep_head_dict, init_bias=-2.19, use_bias=False, norm_func=None):
+        super().__init__()
+        self.sep_head_dict = sep_head_dict
+
+        for cur_name in self.sep_head_dict:
+            output_channels = self.sep_head_dict[cur_name]['out_channels']
+            num_conv = self.sep_head_dict[cur_name]['num_conv']
+
+            fc_list = []
+            for k in range(num_conv - 1):
+                fc_list.append(nn.Sequential(
+                    nn.Conv2d(input_channels, input_channels, kernel_size=3, stride=1, padding=1, bias=use_bias),
+                    nn.BatchNorm2d(input_channels) if norm_func is None else norm_func(input_channels),
+                    nn.ReLU()
+                ))
+            fc_list.append(nn.Conv2d(input_channels, output_channels, kernel_size=3, stride=1, padding=1, bias=True))
+            fc = nn.Sequential(*fc_list)
+            if 'hm' in cur_name:
+                fc[-1].bias.data.fill_(init_bias)
+            else:
+                for m in fc.modules():
+                    if isinstance(m, nn.Conv2d):
+                        kaiming_normal_(m.weight.data)
+                        if hasattr(m, "bias") and m.bias is not None:
+                            nn.init.constant_(m.bias, 0)
+
+            self.__setattr__(cur_name, fc)
+
+    def forward(self, x):
+        ret_dict = {}
+        for cur_name in self.sep_head_dict:
+            ret_dict[cur_name] = self.__getattr__(cur_name)(x)
+
+        return ret_dict
+
+
+class CenterHead(nn.Module):
+    def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range, voxel_size,
+                 predict_boxes_when_training=True):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_class = num_class
+        self.grid_size = grid_size
+        self.point_cloud_range = point_cloud_range
+        self.voxel_size = voxel_size
+        self.feature_map_stride = self.model_cfg.TARGET_ASSIGNER_CONFIG.get('FEATURE_MAP_STRIDE', None)
+
+        self.class_names = class_names
+        self.class_names_each_head = []
+        self.class_id_mapping_each_head = []
+
+        for cur_class_names in self.model_cfg.CLASS_NAMES_EACH_HEAD:
+            self.class_names_each_head.append([x for x in cur_class_names if x in class_names])
+            cur_class_id_mapping = torch.from_numpy(np.array(
+                [self.class_names.index(x) for x in cur_class_names if x in class_names]
+            )).cuda()
+            self.class_id_mapping_each_head.append(cur_class_id_mapping)
+
+        total_classes = sum([len(x) for x in self.class_names_each_head])
+        assert total_classes == len(self.class_names), f'class_names_each_head={self.class_names_each_head}'
+
+        norm_func = partial(nn.BatchNorm2d, eps=self.model_cfg.get('BN_EPS', 1e-5), momentum=self.model_cfg.get('BN_MOM', 0.1))
+        self.shared_conv = nn.Sequential(
+            nn.Conv2d(
+                input_channels, self.model_cfg.SHARED_CONV_CHANNEL, 3, stride=1, padding=1,
+                bias=self.model_cfg.get('USE_BIAS_BEFORE_NORM', False)
+            ),
+            norm_func(self.model_cfg.SHARED_CONV_CHANNEL),
+            nn.ReLU(),
+        )
+
+        self.heads_list = nn.ModuleList()
+        self.separate_head_cfg = self.model_cfg.SEPARATE_HEAD_CFG
+        for idx, cur_class_names in enumerate(self.class_names_each_head):
+            cur_head_dict = copy.deepcopy(self.separate_head_cfg.HEAD_DICT)
+            cur_head_dict['hm'] = dict(out_channels=len(cur_class_names), num_conv=self.model_cfg.NUM_HM_CONV)
+            self.heads_list.append(
+                SeparateHead(
+                    input_channels=self.model_cfg.SHARED_CONV_CHANNEL,
+                    sep_head_dict=cur_head_dict,
+                    init_bias=-2.19,
+                    use_bias=self.model_cfg.get('USE_BIAS_BEFORE_NORM', False),
+                    norm_func=norm_func
+                )
+            )
+        self.predict_boxes_when_training = predict_boxes_when_training
+        self.forward_ret_dict = {}
+        self.build_losses()
+
+    def build_losses(self):
+        self.add_module('hm_loss_func', loss_utils.FocalLossCenterNet())
+        self.add_module('reg_loss_func', loss_utils.RegLossCenterNet())
+
+    def assign_target_of_single_head(
+            self, num_classes, gt_boxes, feature_map_size, feature_map_stride, num_max_objs=500,
+            gaussian_overlap=0.1, min_radius=2
+    ):
+        """
+        Args:
+            gt_boxes: (N, 8)
+            feature_map_size: (2), [x, y]
+
+        Returns:
+
+        """
+        heatmap = gt_boxes.new_zeros(num_classes, feature_map_size[1], feature_map_size[0])
+        ret_boxes = gt_boxes.new_zeros((num_max_objs, gt_boxes.shape[-1] - 1 + 1))
+        inds = gt_boxes.new_zeros(num_max_objs).long()
+        mask = gt_boxes.new_zeros(num_max_objs).long()
+        ret_boxes_src = gt_boxes.new_zeros(num_max_objs, gt_boxes.shape[-1])
+        ret_boxes_src[:gt_boxes.shape[0]] = gt_boxes
+
+        x, y, z = gt_boxes[:, 0], gt_boxes[:, 1], gt_boxes[:, 2]
+        coord_x = (x - self.point_cloud_range[0]) / self.voxel_size[0] / feature_map_stride
+        coord_y = (y - self.point_cloud_range[1]) / self.voxel_size[1] / feature_map_stride
+        coord_x = torch.clamp(coord_x, min=0, max=feature_map_size[0] - 0.5)  # bugfixed: 1e-6 does not work for center.int()
+        coord_y = torch.clamp(coord_y, min=0, max=feature_map_size[1] - 0.5)  #
+        center = torch.cat((coord_x[:, None], coord_y[:, None]), dim=-1)
+        center_int = center.int()
+        center_int_float = center_int.float()
+
+        dx, dy, dz = gt_boxes[:, 3], gt_boxes[:, 4], gt_boxes[:, 5]
+        dx = dx / self.voxel_size[0] / feature_map_stride
+        dy = dy / self.voxel_size[1] / feature_map_stride
+
+        radius = centernet_utils.gaussian_radius(dx, dy, min_overlap=gaussian_overlap)
+        radius = torch.clamp_min(radius.int(), min=min_radius)
+
+        for k in range(min(num_max_objs, gt_boxes.shape[0])):
+            if dx[k] <= 0 or dy[k] <= 0:
+                continue
+
+            if not (0 <= center_int[k][0] <= feature_map_size[0] and 0 <= center_int[k][1] <= feature_map_size[1]):
+                continue
+
+            cur_class_id = (gt_boxes[k, -1] - 1).long()
+            centernet_utils.draw_gaussian_to_heatmap(heatmap[cur_class_id], center[k], radius[k].item())
+
+            inds[k] = center_int[k, 1] * feature_map_size[0] + center_int[k, 0]
+            mask[k] = 1
+
+            ret_boxes[k, 0:2] = center[k] - center_int_float[k].float()
+            ret_boxes[k, 2] = z[k]
+            ret_boxes[k, 3:6] = gt_boxes[k, 3:6].log()
+            ret_boxes[k, 6] = torch.cos(gt_boxes[k, 6])
+            ret_boxes[k, 7] = torch.sin(gt_boxes[k, 6])
+            if gt_boxes.shape[1] > 8:
+                ret_boxes[k, 8:] = gt_boxes[k, 7:-1]
+
+        return heatmap, ret_boxes, inds, mask, ret_boxes_src
+
+    def assign_targets(self, gt_boxes, feature_map_size=None, **kwargs):
+        """
+        Args:
+            gt_boxes: (B, M, 8)
+            range_image_polar: (B, 3, H, W)
+            feature_map_size: (2) [H, W]
+            spatial_cartesian: (B, 4, H, W)
+        Returns:
+
+        """
+        feature_map_size = feature_map_size[::-1]  # [H, W] ==> [x, y]
+        target_assigner_cfg = self.model_cfg.TARGET_ASSIGNER_CONFIG
+        # feature_map_size = self.grid_size[:2] // target_assigner_cfg.FEATURE_MAP_STRIDE
+
+        batch_size = gt_boxes.shape[0]
+        ret_dict = {
+            'heatmaps': [],
+            'target_boxes': [],
+            'inds': [],
+            'masks': [],
+            'heatmap_masks': [],
+            'target_boxes_src': [],
+        }
+
+        all_names = np.array(['bg', *self.class_names])
+        for idx, cur_class_names in enumerate(self.class_names_each_head):
+            heatmap_list, target_boxes_list, inds_list, masks_list, target_boxes_src_list = [], [], [], [], []
+            for bs_idx in range(batch_size):
+                cur_gt_boxes = gt_boxes[bs_idx]
+                gt_class_names = all_names[cur_gt_boxes[:, -1].cpu().long().numpy()]
+
+                gt_boxes_single_head = []
+
+                for idx, name in enumerate(gt_class_names):
+                    if name not in cur_class_names:
+                        continue
+                    temp_box = cur_gt_boxes[idx]
+                    temp_box[-1] = cur_class_names.index(name) + 1
+                    gt_boxes_single_head.append(temp_box[None, :])
+
+                if len(gt_boxes_single_head) == 0:
+                    gt_boxes_single_head = cur_gt_boxes[:0, :]
+                else:
+                    gt_boxes_single_head = torch.cat(gt_boxes_single_head, dim=0)
+
+                heatmap, ret_boxes, inds, mask, ret_boxes_src = self.assign_target_of_single_head(
+                    num_classes=len(cur_class_names), gt_boxes=gt_boxes_single_head.cpu(),
+                    feature_map_size=feature_map_size, feature_map_stride=target_assigner_cfg.FEATURE_MAP_STRIDE,
+                    num_max_objs=target_assigner_cfg.NUM_MAX_OBJS,
+                    gaussian_overlap=target_assigner_cfg.GAUSSIAN_OVERLAP,
+                    min_radius=target_assigner_cfg.MIN_RADIUS,
+                )
+                heatmap_list.append(heatmap.to(gt_boxes_single_head.device))
+                target_boxes_list.append(ret_boxes.to(gt_boxes_single_head.device))
+                inds_list.append(inds.to(gt_boxes_single_head.device))
+                masks_list.append(mask.to(gt_boxes_single_head.device))
+                target_boxes_src_list.append(ret_boxes_src.to(gt_boxes_single_head.device))
+
+            ret_dict['heatmaps'].append(torch.stack(heatmap_list, dim=0))
+            ret_dict['target_boxes'].append(torch.stack(target_boxes_list, dim=0))
+            ret_dict['inds'].append(torch.stack(inds_list, dim=0))
+            ret_dict['masks'].append(torch.stack(masks_list, dim=0))
+            ret_dict['target_boxes_src'].append(torch.stack(target_boxes_src_list, dim=0))
+        return ret_dict
+
+    def sigmoid(self, x):
+        y = torch.clamp(x.sigmoid(), min=1e-4, max=1 - 1e-4)
+        return y
+
+    def get_loss(self):
+        pred_dicts = self.forward_ret_dict['pred_dicts']
+        target_dicts = self.forward_ret_dict['target_dicts']
+
+        tb_dict = {}
+        loss = 0
+
+        for idx, pred_dict in enumerate(pred_dicts):
+            pred_dict['hm'] = self.sigmoid(pred_dict['hm'])
+            hm_loss = self.hm_loss_func(pred_dict['hm'], target_dicts['heatmaps'][idx])
+            hm_loss *= self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['cls_weight']
+
+            target_boxes = target_dicts['target_boxes'][idx]
+            pred_boxes = torch.cat([pred_dict[head_name] for head_name in self.separate_head_cfg.HEAD_ORDER], dim=1)
+
+            reg_loss = self.reg_loss_func(
+                pred_boxes, target_dicts['masks'][idx], target_dicts['inds'][idx], target_boxes
+            )
+            loc_loss = (reg_loss * reg_loss.new_tensor(self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['code_weights'])).sum()
+            loc_loss = loc_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['loc_weight']
+
+            loss += hm_loss + loc_loss
+            tb_dict['hm_loss_head_%d' % idx] = hm_loss.item()
+            tb_dict['loc_loss_head_%d' % idx] = loc_loss.item()
+
+            if 'iou' in pred_dict or self.model_cfg.get('IOU_REG_LOSS', False):
+
+                batch_box_preds = centernet_utils.decode_bbox_from_pred_dicts(
+                    pred_dict=pred_dict,
+                    point_cloud_range=self.point_cloud_range, voxel_size=self.voxel_size,
+                    feature_map_stride=self.feature_map_stride
+                )  # (B, H, W, 7 or 9)
+
+                if 'iou' in pred_dict:
+                    batch_box_preds_for_iou = batch_box_preds.permute(0, 3, 1, 2)  # (B, 7 or 9, H, W)
+
+                    iou_loss = loss_utils.calculate_iou_loss_centerhead(
+                        iou_preds=pred_dict['iou'],
+                        batch_box_preds=batch_box_preds_for_iou.clone().detach(),
+                        mask=target_dicts['masks'][idx],
+                        ind=target_dicts['inds'][idx], gt_boxes=target_dicts['target_boxes_src'][idx]
+                    )
+                    loss += iou_loss
+                    tb_dict['iou_loss_head_%d' % idx] = iou_loss.item()
+
+                if self.model_cfg.get('IOU_REG_LOSS', False):
+                    iou_reg_loss = loss_utils.calculate_iou_reg_loss_centerhead(
+                        batch_box_preds=batch_box_preds_for_iou,
+                        mask=target_dicts['masks'][idx],
+                        ind=target_dicts['inds'][idx], gt_boxes=target_dicts['target_boxes_src'][idx]
+                    )
+                    if target_dicts['masks'][idx].sum().item() != 0:
+                        iou_reg_loss = iou_reg_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['loc_weight']
+                        loss += iou_reg_loss
+                        tb_dict['iou_reg_loss_head_%d' % idx] = iou_reg_loss.item()
+                    else:
+                        loss += (batch_box_preds_for_iou * 0.).sum()
+                        tb_dict['iou_reg_loss_head_%d' % idx] = (batch_box_preds_for_iou * 0.).sum()
+
+
+
+        tb_dict['rpn_loss'] = loss.item()
+        return loss, tb_dict
+
+    def generate_predicted_boxes(self, batch_size, pred_dicts):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        post_center_limit_range = torch.tensor(post_process_cfg.POST_CENTER_LIMIT_RANGE).cuda().float()
+
+        ret_dict = [{
+            'pred_boxes': [],
+            'pred_scores': [],
+            'pred_labels': [],
+        } for k in range(batch_size)]
+        for idx, pred_dict in enumerate(pred_dicts):
+            batch_hm = pred_dict['hm'].sigmoid()
+            batch_center = pred_dict['center']
+            batch_center_z = pred_dict['center_z']
+            batch_dim = pred_dict['dim'].exp()
+            batch_rot_cos = pred_dict['rot'][:, 0].unsqueeze(dim=1)
+            batch_rot_sin = pred_dict['rot'][:, 1].unsqueeze(dim=1)
+            batch_vel = pred_dict['vel'] if 'vel' in self.separate_head_cfg.HEAD_ORDER else None
+
+            batch_iou = (pred_dict['iou'] + 1) * 0.5 if 'iou' in pred_dict else None
+
+            final_pred_dicts = centernet_utils.decode_bbox_from_heatmap(
+                heatmap=batch_hm, rot_cos=batch_rot_cos, rot_sin=batch_rot_sin,
+                center=batch_center, center_z=batch_center_z, dim=batch_dim, vel=batch_vel, iou=batch_iou,
+                point_cloud_range=self.point_cloud_range, voxel_size=self.voxel_size,
+                feature_map_stride=self.feature_map_stride,
+                K=post_process_cfg.MAX_OBJ_PER_SAMPLE,
+                circle_nms=(post_process_cfg.NMS_CONFIG.NMS_TYPE == 'circle_nms'),
+                score_thresh=post_process_cfg.SCORE_THRESH,
+                post_center_limit_range=post_center_limit_range
+            )
+
+            for k, final_dict in enumerate(final_pred_dicts):
+                final_dict['pred_labels'] = self.class_id_mapping_each_head[idx][final_dict['pred_labels'].long()]
+
+                if post_process_cfg.get('USE_IOU_TO_RECTIFY_SCORE', False) and 'pred_iou' in final_dict:
+                    pred_iou = torch.clamp(final_dict['pred_iou'], min=0, max=1.0)
+                    IOU_RECTIFIER = final_dict['pred_scores'].new_tensor(post_process_cfg.IOU_RECTIFIER)
+                    final_dict['pred_scores'] = torch.pow(final_dict['pred_scores'], 1 - IOU_RECTIFIER[final_dict['pred_labels']]) * torch.pow(pred_iou, IOU_RECTIFIER[final_dict['pred_labels']])
+
+                if post_process_cfg.NMS_CONFIG.NMS_TYPE not in  ['circle_nms', 'class_specific_nms']:
+                    selected, selected_scores = model_nms_utils.class_agnostic_nms(
+                        box_scores=final_dict['pred_scores'], box_preds=final_dict['pred_boxes'],
+                        nms_config=post_process_cfg.NMS_CONFIG,
+                        score_thresh=None
+                    )
+
+                elif post_process_cfg.NMS_CONFIG.NMS_TYPE == 'class_specific_nms':
+                    selected, selected_scores = model_nms_utils.class_specific_nms(
+                        box_scores=final_dict['pred_scores'], box_preds=final_dict['pred_boxes'],
+                        box_labels=final_dict['pred_labels'], nms_config=post_process_cfg.NMS_CONFIG,
+                        score_thresh=post_process_cfg.NMS_CONFIG.get('SCORE_THRESH', None)
+                    )
+                elif post_process_cfg.NMS_CONFIG.NMS_TYPE == 'circle_nms':
+                    raise NotImplementedError
+
+                final_dict['pred_boxes'] = final_dict['pred_boxes'][selected]
+                final_dict['pred_scores'] = selected_scores
+                final_dict['pred_labels'] = final_dict['pred_labels'][selected]
+
+                ret_dict[k]['pred_boxes'].append(final_dict['pred_boxes'])
+                ret_dict[k]['pred_scores'].append(final_dict['pred_scores'])
+                ret_dict[k]['pred_labels'].append(final_dict['pred_labels'])
+
+        for k in range(batch_size):
+            ret_dict[k]['pred_boxes'] = torch.cat(ret_dict[k]['pred_boxes'], dim=0)
+            ret_dict[k]['pred_scores'] = torch.cat(ret_dict[k]['pred_scores'], dim=0)
+            ret_dict[k]['pred_labels'] = torch.cat(ret_dict[k]['pred_labels'], dim=0) + 1
+
+        return ret_dict
+
+    @staticmethod
+    def reorder_rois_for_refining(batch_size, pred_dicts):
+        num_max_rois = max([len(cur_dict['pred_boxes']) for cur_dict in pred_dicts])
+        num_max_rois = max(1, num_max_rois)  # at least one faked rois to avoid error
+        pred_boxes = pred_dicts[0]['pred_boxes']
+
+        rois = pred_boxes.new_zeros((batch_size, num_max_rois, pred_boxes.shape[-1]))
+        roi_scores = pred_boxes.new_zeros((batch_size, num_max_rois))
+        roi_labels = pred_boxes.new_zeros((batch_size, num_max_rois)).long()
+
+        for bs_idx in range(batch_size):
+            num_boxes = len(pred_dicts[bs_idx]['pred_boxes'])
+
+            rois[bs_idx, :num_boxes, :] = pred_dicts[bs_idx]['pred_boxes']
+            roi_scores[bs_idx, :num_boxes] = pred_dicts[bs_idx]['pred_scores']
+            roi_labels[bs_idx, :num_boxes] = pred_dicts[bs_idx]['pred_labels']
+        return rois, roi_scores, roi_labels
+
+    def forward(self, data_dict):
+        spatial_features_2d = data_dict['spatial_features_2d']
+        x = self.shared_conv(spatial_features_2d)
+
+        pred_dicts = []
+        for head in self.heads_list:
+            pred_dicts.append(head(x))
+
+        if self.training:
+            target_dict = self.assign_targets(
+                data_dict['gt_boxes'], feature_map_size=spatial_features_2d.size()[2:],
+                feature_map_stride=data_dict.get('spatial_features_2d_strides', None)
+            )
+            self.forward_ret_dict['target_dicts'] = target_dict
+
+        self.forward_ret_dict['pred_dicts'] = pred_dicts
+
+        if not self.training or self.predict_boxes_when_training:
+            pred_dicts = self.generate_predicted_boxes(
+                data_dict['batch_size'], pred_dicts
+            )
+
+            if self.predict_boxes_when_training:
+                rois, roi_scores, roi_labels = self.reorder_rois_for_refining(data_dict['batch_size'], pred_dicts)
+                data_dict['rois'] = rois
+                data_dict['roi_scores'] = roi_scores
+                data_dict['roi_labels'] = roi_labels
+                data_dict['has_class_labels'] = True
+            else:
+                data_dict['final_box_dicts'] = pred_dicts
+
+        return data_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_box.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_box.py
new file mode 100644
index 000000000..06930b91b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_box.py
@@ -0,0 +1,115 @@
+import torch
+
+from ...utils import box_coder_utils, box_utils
+from .point_head_template import PointHeadTemplate
+
+
+class PointHeadBox(PointHeadTemplate):
+    """
+    A simple point-based segmentation head, which are used for PointRCNN.
+    Reference Paper: https://arxiv.org/abs/1812.04244
+    PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
+    """
+    def __init__(self, num_class, input_channels, model_cfg, predict_boxes_when_training=False, **kwargs):
+        super().__init__(model_cfg=model_cfg, num_class=num_class)
+        self.predict_boxes_when_training = predict_boxes_when_training
+        self.cls_layers = self.make_fc_layers(
+            fc_cfg=self.model_cfg.CLS_FC,
+            input_channels=input_channels,
+            output_channels=num_class
+        )
+
+        target_cfg = self.model_cfg.TARGET_CONFIG
+        self.box_coder = getattr(box_coder_utils, target_cfg.BOX_CODER)(
+            **target_cfg.BOX_CODER_CONFIG
+        )
+        self.box_layers = self.make_fc_layers(
+            fc_cfg=self.model_cfg.REG_FC,
+            input_channels=input_channels,
+            output_channels=self.box_coder.code_size
+        )
+
+    def assign_targets(self, input_dict):
+        """
+        Args:
+            input_dict:
+                point_features: (N1 + N2 + N3 + ..., C)
+                batch_size:
+                point_coords: (N1 + N2 + N3 + ..., 4) [bs_idx, x, y, z]
+                gt_boxes (optional): (B, M, 8)
+        Returns:
+            point_cls_labels: (N1 + N2 + N3 + ...), long type, 0:background, -1:ignored
+            point_part_labels: (N1 + N2 + N3 + ..., 3)
+        """
+        point_coords = input_dict['point_coords']
+        gt_boxes = input_dict['gt_boxes']
+        assert gt_boxes.shape.__len__() == 3, 'gt_boxes.shape=%s' % str(gt_boxes.shape)
+        assert point_coords.shape.__len__() in [2], 'points.shape=%s' % str(point_coords.shape)
+
+        batch_size = gt_boxes.shape[0]
+        extend_gt_boxes = box_utils.enlarge_box3d(
+            gt_boxes.view(-1, gt_boxes.shape[-1]), extra_width=self.model_cfg.TARGET_CONFIG.GT_EXTRA_WIDTH
+        ).view(batch_size, -1, gt_boxes.shape[-1])
+        targets_dict = self.assign_stack_targets(
+            points=point_coords, gt_boxes=gt_boxes, extend_gt_boxes=extend_gt_boxes,
+            set_ignore_flag=True, use_ball_constraint=False,
+            ret_part_labels=False, ret_box_labels=True
+        )
+
+        return targets_dict
+
+    def get_loss(self, tb_dict=None):
+        tb_dict = {} if tb_dict is None else tb_dict
+        point_loss_cls, tb_dict_1 = self.get_cls_layer_loss()
+        point_loss_box, tb_dict_2 = self.get_box_layer_loss()
+
+        point_loss = point_loss_cls + point_loss_box
+        tb_dict.update(tb_dict_1)
+        tb_dict.update(tb_dict_2)
+        return point_loss, tb_dict
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                point_features: (N1 + N2 + N3 + ..., C) or (B, N, C)
+                point_features_before_fusion: (N1 + N2 + N3 + ..., C)
+                point_coords: (N1 + N2 + N3 + ..., 4) [bs_idx, x, y, z]
+                point_labels (optional): (N1 + N2 + N3 + ...)
+                gt_boxes (optional): (B, M, 8)
+        Returns:
+            batch_dict:
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        """
+        if self.model_cfg.get('USE_POINT_FEATURES_BEFORE_FUSION', False):
+            point_features = batch_dict['point_features_before_fusion']
+        else:
+            point_features = batch_dict['point_features']
+        point_cls_preds = self.cls_layers(point_features)  # (total_points, num_class)
+        point_box_preds = self.box_layers(point_features)  # (total_points, box_code_size)
+
+        point_cls_preds_max, _ = point_cls_preds.max(dim=-1)
+        batch_dict['point_cls_scores'] = torch.sigmoid(point_cls_preds_max)
+
+        ret_dict = {'point_cls_preds': point_cls_preds,
+                    'point_box_preds': point_box_preds}
+        if self.training:
+            targets_dict = self.assign_targets(batch_dict)
+            ret_dict['point_cls_labels'] = targets_dict['point_cls_labels']
+            ret_dict['point_box_labels'] = targets_dict['point_box_labels']
+
+        if not self.training or self.predict_boxes_when_training:
+            point_cls_preds, point_box_preds = self.generate_predicted_boxes(
+                points=batch_dict['point_coords'][:, 1:4],
+                point_cls_preds=point_cls_preds, point_box_preds=point_box_preds
+            )
+            batch_dict['batch_cls_preds'] = point_cls_preds
+            batch_dict['batch_box_preds'] = point_box_preds
+            batch_dict['batch_index'] = batch_dict['point_coords'][:, 0]
+            batch_dict['cls_preds_normalized'] = False
+
+        self.forward_ret_dict = ret_dict
+
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_simple.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_simple.py
new file mode 100644
index 000000000..1be84153d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_simple.py
@@ -0,0 +1,91 @@
+import torch
+
+from ...utils import box_utils
+from .point_head_template import PointHeadTemplate
+
+
+class PointHeadSimple(PointHeadTemplate):
+    """
+    A simple point-based segmentation head, which are used for PV-RCNN keypoint segmentaion.
+    Reference Paper: https://arxiv.org/abs/1912.13192
+    PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
+    """
+    def __init__(self, num_class, input_channels, model_cfg, **kwargs):
+        super().__init__(model_cfg=model_cfg, num_class=num_class)
+        self.cls_layers = self.make_fc_layers(
+            fc_cfg=self.model_cfg.CLS_FC,
+            input_channels=input_channels,
+            output_channels=num_class
+        )
+
+    def assign_targets(self, input_dict):
+        """
+        Args:
+            input_dict:
+                point_features: (N1 + N2 + N3 + ..., C)
+                batch_size:
+                point_coords: (N1 + N2 + N3 + ..., 4) [bs_idx, x, y, z]
+                gt_boxes (optional): (B, M, 8)
+        Returns:
+            point_cls_labels: (N1 + N2 + N3 + ...), long type, 0:background, -1:ignored
+            point_part_labels: (N1 + N2 + N3 + ..., 3)
+        """
+        point_coords = input_dict['point_coords']
+        gt_boxes = input_dict['gt_boxes']
+        assert gt_boxes.shape.__len__() == 3, 'gt_boxes.shape=%s' % str(gt_boxes.shape)
+        assert point_coords.shape.__len__() in [2], 'points.shape=%s' % str(point_coords.shape)
+
+        batch_size = gt_boxes.shape[0]
+        extend_gt_boxes = box_utils.enlarge_box3d(
+            gt_boxes.view(-1, gt_boxes.shape[-1]), extra_width=self.model_cfg.TARGET_CONFIG.GT_EXTRA_WIDTH
+        ).view(batch_size, -1, gt_boxes.shape[-1])
+        targets_dict = self.assign_stack_targets(
+            points=point_coords, gt_boxes=gt_boxes, extend_gt_boxes=extend_gt_boxes,
+            set_ignore_flag=True, use_ball_constraint=False,
+            ret_part_labels=False
+        )
+
+        return targets_dict
+
+    def get_loss(self, tb_dict=None):
+        tb_dict = {} if tb_dict is None else tb_dict
+        point_loss_cls, tb_dict_1 = self.get_cls_layer_loss()
+
+        point_loss = point_loss_cls
+        tb_dict.update(tb_dict_1)
+        return point_loss, tb_dict
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                point_features: (N1 + N2 + N3 + ..., C) or (B, N, C)
+                point_features_before_fusion: (N1 + N2 + N3 + ..., C)
+                point_coords: (N1 + N2 + N3 + ..., 4) [bs_idx, x, y, z]
+                point_labels (optional): (N1 + N2 + N3 + ...)
+                gt_boxes (optional): (B, M, 8)
+        Returns:
+            batch_dict:
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        """
+        if self.model_cfg.get('USE_POINT_FEATURES_BEFORE_FUSION', False):
+            point_features = batch_dict['point_features_before_fusion']
+        else:
+            point_features = batch_dict['point_features']
+        point_cls_preds = self.cls_layers(point_features)  # (total_points, num_class)
+
+        ret_dict = {
+            'point_cls_preds': point_cls_preds,
+        }
+
+        point_cls_scores = torch.sigmoid(point_cls_preds)
+        batch_dict['point_cls_scores'], _ = point_cls_scores.max(dim=-1)
+
+        if self.training:
+            targets_dict = self.assign_targets(batch_dict)
+            ret_dict['point_cls_labels'] = targets_dict['point_cls_labels']
+        self.forward_ret_dict = ret_dict
+
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_template.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_template.py
new file mode 100644
index 000000000..9ea0af0db
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_head_template.py
@@ -0,0 +1,210 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+from ...utils import common_utils, loss_utils
+
+
+class PointHeadTemplate(nn.Module):
+    def __init__(self, model_cfg, num_class):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_class = num_class
+
+        self.build_losses(self.model_cfg.LOSS_CONFIG)
+        self.forward_ret_dict = None
+
+    def build_losses(self, losses_cfg):
+        self.add_module(
+            'cls_loss_func',
+            loss_utils.SigmoidFocalClassificationLoss(alpha=0.25, gamma=2.0)
+        )
+        reg_loss_type = losses_cfg.get('LOSS_REG', None)
+        if reg_loss_type == 'smooth-l1':
+            self.reg_loss_func = F.smooth_l1_loss
+        elif reg_loss_type == 'l1':
+            self.reg_loss_func = F.l1_loss
+        elif reg_loss_type == 'WeightedSmoothL1Loss':
+            self.reg_loss_func = loss_utils.WeightedSmoothL1Loss(
+                code_weights=losses_cfg.LOSS_WEIGHTS.get('code_weights', None)
+            )
+        else:
+            self.reg_loss_func = F.smooth_l1_loss
+
+    @staticmethod
+    def make_fc_layers(fc_cfg, input_channels, output_channels):
+        fc_layers = []
+        c_in = input_channels
+        for k in range(0, fc_cfg.__len__()):
+            fc_layers.extend([
+                nn.Linear(c_in, fc_cfg[k], bias=False),
+                nn.BatchNorm1d(fc_cfg[k]),
+                nn.ReLU(),
+            ])
+            c_in = fc_cfg[k]
+        fc_layers.append(nn.Linear(c_in, output_channels, bias=True))
+        return nn.Sequential(*fc_layers)
+
+    def assign_stack_targets(self, points, gt_boxes, extend_gt_boxes=None,
+                             ret_box_labels=False, ret_part_labels=False,
+                             set_ignore_flag=True, use_ball_constraint=False, central_radius=2.0):
+        """
+        Args:
+            points: (N1 + N2 + N3 + ..., 4) [bs_idx, x, y, z]
+            gt_boxes: (B, M, 8)
+            extend_gt_boxes: [B, M, 8]
+            ret_box_labels:
+            ret_part_labels:
+            set_ignore_flag:
+            use_ball_constraint:
+            central_radius:
+
+        Returns:
+            point_cls_labels: (N1 + N2 + N3 + ...), long type, 0:background, -1:ignored
+            point_box_labels: (N1 + N2 + N3 + ..., code_size)
+
+        """
+        assert len(points.shape) == 2 and points.shape[1] == 4, 'points.shape=%s' % str(points.shape)
+        assert len(gt_boxes.shape) == 3 and gt_boxes.shape[2] == 8, 'gt_boxes.shape=%s' % str(gt_boxes.shape)
+        assert extend_gt_boxes is None or len(extend_gt_boxes.shape) == 3 and extend_gt_boxes.shape[2] == 8, \
+            'extend_gt_boxes.shape=%s' % str(extend_gt_boxes.shape)
+        assert set_ignore_flag != use_ball_constraint, 'Choose one only!'
+        batch_size = gt_boxes.shape[0]
+        bs_idx = points[:, 0]
+        point_cls_labels = points.new_zeros(points.shape[0]).long()
+        point_box_labels = gt_boxes.new_zeros((points.shape[0], 8)) if ret_box_labels else None
+        point_part_labels = gt_boxes.new_zeros((points.shape[0], 3)) if ret_part_labels else None
+        for k in range(batch_size):
+            bs_mask = (bs_idx == k)
+            points_single = points[bs_mask][:, 1:4]
+            point_cls_labels_single = point_cls_labels.new_zeros(bs_mask.sum())
+            box_idxs_of_pts = roiaware_pool3d_utils.points_in_boxes_gpu(
+                points_single.unsqueeze(dim=0), gt_boxes[k:k + 1, :, 0:7].contiguous()
+            ).long().squeeze(dim=0)
+            box_fg_flag = (box_idxs_of_pts >= 0)
+            if set_ignore_flag:
+                extend_box_idxs_of_pts = roiaware_pool3d_utils.points_in_boxes_gpu(
+                    points_single.unsqueeze(dim=0), extend_gt_boxes[k:k+1, :, 0:7].contiguous()
+                ).long().squeeze(dim=0)
+                fg_flag = box_fg_flag
+                ignore_flag = fg_flag ^ (extend_box_idxs_of_pts >= 0)
+                point_cls_labels_single[ignore_flag] = -1
+            elif use_ball_constraint:
+                box_centers = gt_boxes[k][box_idxs_of_pts][:, 0:3].clone()
+                box_centers[:, 2] += gt_boxes[k][box_idxs_of_pts][:, 5] / 2
+                ball_flag = ((box_centers - points_single).norm(dim=1) < central_radius)
+                fg_flag = box_fg_flag & ball_flag
+            else:
+                raise NotImplementedError
+
+            gt_box_of_fg_points = gt_boxes[k][box_idxs_of_pts[fg_flag]]
+            point_cls_labels_single[fg_flag] = 1 if self.num_class == 1 else gt_box_of_fg_points[:, -1].long()
+            point_cls_labels[bs_mask] = point_cls_labels_single
+
+            if ret_box_labels and gt_box_of_fg_points.shape[0] > 0:
+                point_box_labels_single = point_box_labels.new_zeros((bs_mask.sum(), 8))
+                fg_point_box_labels = self.box_coder.encode_torch(
+                    gt_boxes=gt_box_of_fg_points[:, :-1], points=points_single[fg_flag],
+                    gt_classes=gt_box_of_fg_points[:, -1].long()
+                )
+                point_box_labels_single[fg_flag] = fg_point_box_labels
+                point_box_labels[bs_mask] = point_box_labels_single
+
+            if ret_part_labels:
+                point_part_labels_single = point_part_labels.new_zeros((bs_mask.sum(), 3))
+                transformed_points = points_single[fg_flag] - gt_box_of_fg_points[:, 0:3]
+                transformed_points = common_utils.rotate_points_along_z(
+                    transformed_points.view(-1, 1, 3), -gt_box_of_fg_points[:, 6]
+                ).view(-1, 3)
+                offset = torch.tensor([0.5, 0.5, 0.5]).view(1, 3).type_as(transformed_points)
+                point_part_labels_single[fg_flag] = (transformed_points / gt_box_of_fg_points[:, 3:6]) + offset
+                point_part_labels[bs_mask] = point_part_labels_single
+
+        targets_dict = {
+            'point_cls_labels': point_cls_labels,
+            'point_box_labels': point_box_labels,
+            'point_part_labels': point_part_labels
+        }
+        return targets_dict
+
+    def get_cls_layer_loss(self, tb_dict=None):
+        point_cls_labels = self.forward_ret_dict['point_cls_labels'].view(-1)
+        point_cls_preds = self.forward_ret_dict['point_cls_preds'].view(-1, self.num_class)
+
+        positives = (point_cls_labels > 0)
+        negative_cls_weights = (point_cls_labels == 0) * 1.0
+        cls_weights = (negative_cls_weights + 1.0 * positives).float()
+        pos_normalizer = positives.sum(dim=0).float()
+        cls_weights /= torch.clamp(pos_normalizer, min=1.0)
+
+        one_hot_targets = point_cls_preds.new_zeros(*list(point_cls_labels.shape), self.num_class + 1)
+        one_hot_targets.scatter_(-1, (point_cls_labels * (point_cls_labels >= 0).long()).unsqueeze(dim=-1).long(), 1.0)
+        one_hot_targets = one_hot_targets[..., 1:]
+        cls_loss_src = self.cls_loss_func(point_cls_preds, one_hot_targets, weights=cls_weights)
+        point_loss_cls = cls_loss_src.sum()
+
+        loss_weights_dict = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS
+        point_loss_cls = point_loss_cls * loss_weights_dict['point_cls_weight']
+        if tb_dict is None:
+            tb_dict = {}
+        tb_dict.update({
+            'point_loss_cls': point_loss_cls.item(),
+            'point_pos_num': pos_normalizer.item()
+        })
+        return point_loss_cls, tb_dict
+
+    def get_part_layer_loss(self, tb_dict=None):
+        pos_mask = self.forward_ret_dict['point_cls_labels'] > 0
+        pos_normalizer = max(1, (pos_mask > 0).sum().item())
+        point_part_labels = self.forward_ret_dict['point_part_labels']
+        point_part_preds = self.forward_ret_dict['point_part_preds']
+        point_loss_part = F.binary_cross_entropy(torch.sigmoid(point_part_preds), point_part_labels, reduction='none')
+        point_loss_part = (point_loss_part.sum(dim=-1) * pos_mask.float()).sum() / (3 * pos_normalizer)
+
+        loss_weights_dict = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS
+        point_loss_part = point_loss_part * loss_weights_dict['point_part_weight']
+        if tb_dict is None:
+            tb_dict = {}
+        tb_dict.update({'point_loss_part': point_loss_part.item()})
+        return point_loss_part, tb_dict
+
+    def get_box_layer_loss(self, tb_dict=None):
+        pos_mask = self.forward_ret_dict['point_cls_labels'] > 0
+        point_box_labels = self.forward_ret_dict['point_box_labels']
+        point_box_preds = self.forward_ret_dict['point_box_preds']
+
+        reg_weights = pos_mask.float()
+        pos_normalizer = pos_mask.sum().float()
+        reg_weights /= torch.clamp(pos_normalizer, min=1.0)
+
+        point_loss_box_src = self.reg_loss_func(
+            point_box_preds[None, ...], point_box_labels[None, ...], weights=reg_weights[None, ...]
+        )
+        point_loss_box = point_loss_box_src.sum()
+
+        loss_weights_dict = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS
+        point_loss_box = point_loss_box * loss_weights_dict['point_box_weight']
+        if tb_dict is None:
+            tb_dict = {}
+        tb_dict.update({'point_loss_box': point_loss_box.item()})
+        return point_loss_box, tb_dict
+
+    def generate_predicted_boxes(self, points, point_cls_preds, point_box_preds):
+        """
+        Args:
+            points: (N, 3)
+            point_cls_preds: (N, num_class)
+            point_box_preds: (N, box_code_size)
+        Returns:
+            point_cls_preds: (N, num_class)
+            point_box_preds: (N, box_code_size)
+
+        """
+        _, pred_classes = point_cls_preds.max(dim=-1)
+        point_box_preds = self.box_coder.decode_torch(point_box_preds, points, pred_classes + 1)
+
+        return point_cls_preds, point_box_preds
+
+    def forward(self, **kwargs):
+        raise NotImplementedError
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_intra_part_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_intra_part_head.py
new file mode 100644
index 000000000..794365e35
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/point_intra_part_head.py
@@ -0,0 +1,127 @@
+import torch
+
+from ...utils import box_coder_utils, box_utils
+from .point_head_template import PointHeadTemplate
+
+
+class PointIntraPartOffsetHead(PointHeadTemplate):
+    """
+    Point-based head for predicting the intra-object part locations.
+    Reference Paper: https://arxiv.org/abs/1907.03670
+    From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
+    """
+    def __init__(self, num_class, input_channels, model_cfg, predict_boxes_when_training=False, **kwargs):
+        super().__init__(model_cfg=model_cfg, num_class=num_class)
+        self.predict_boxes_when_training = predict_boxes_when_training
+        self.cls_layers = self.make_fc_layers(
+            fc_cfg=self.model_cfg.CLS_FC,
+            input_channels=input_channels,
+            output_channels=num_class
+        )
+        self.part_reg_layers = self.make_fc_layers(
+            fc_cfg=self.model_cfg.PART_FC,
+            input_channels=input_channels,
+            output_channels=3
+        )
+        target_cfg = self.model_cfg.TARGET_CONFIG
+        if target_cfg.get('BOX_CODER', None) is not None:
+            self.box_coder = getattr(box_coder_utils, target_cfg.BOX_CODER)(
+                **target_cfg.BOX_CODER_CONFIG
+            )
+            self.box_layers = self.make_fc_layers(
+                fc_cfg=self.model_cfg.REG_FC,
+                input_channels=input_channels,
+                output_channels=self.box_coder.code_size
+            )
+        else:
+            self.box_layers = None
+
+    def assign_targets(self, input_dict):
+        """
+        Args:
+            input_dict:
+                point_features: (N1 + N2 + N3 + ..., C)
+                batch_size:
+                point_coords: (N1 + N2 + N3 + ..., 4) [bs_idx, x, y, z]
+                gt_boxes (optional): (B, M, 8)
+        Returns:
+            point_cls_labels: (N1 + N2 + N3 + ...), long type, 0:background, -1:ignored
+            point_part_labels: (N1 + N2 + N3 + ..., 3)
+        """
+        point_coords = input_dict['point_coords']
+        gt_boxes = input_dict['gt_boxes']
+        assert gt_boxes.shape.__len__() == 3, 'gt_boxes.shape=%s' % str(gt_boxes.shape)
+        assert point_coords.shape.__len__() in [2], 'points.shape=%s' % str(point_coords.shape)
+
+        batch_size = gt_boxes.shape[0]
+        extend_gt_boxes = box_utils.enlarge_box3d(
+            gt_boxes.view(-1, gt_boxes.shape[-1]), extra_width=self.model_cfg.TARGET_CONFIG.GT_EXTRA_WIDTH
+        ).view(batch_size, -1, gt_boxes.shape[-1])
+        targets_dict = self.assign_stack_targets(
+            points=point_coords, gt_boxes=gt_boxes, extend_gt_boxes=extend_gt_boxes,
+            set_ignore_flag=True, use_ball_constraint=False,
+            ret_part_labels=True, ret_box_labels=(self.box_layers is not None)
+        )
+
+        return targets_dict
+
+    def get_loss(self, tb_dict=None):
+        tb_dict = {} if tb_dict is None else tb_dict
+        point_loss_cls, tb_dict = self.get_cls_layer_loss(tb_dict)
+        point_loss_part, tb_dict = self.get_part_layer_loss(tb_dict)
+        point_loss = point_loss_cls + point_loss_part
+
+        if self.box_layers is not None:
+            point_loss_box, tb_dict = self.get_box_layer_loss(tb_dict)
+            point_loss += point_loss_box
+        return point_loss, tb_dict
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                point_features: (N1 + N2 + N3 + ..., C) or (B, N, C)
+                point_coords: (N1 + N2 + N3 + ..., 4) [bs_idx, x, y, z]
+                point_labels (optional): (N1 + N2 + N3 + ...)
+                gt_boxes (optional): (B, M, 8)
+        Returns:
+            batch_dict:
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        """
+        point_features = batch_dict['point_features']
+        point_cls_preds = self.cls_layers(point_features)  # (total_points, num_class)
+        point_part_preds = self.part_reg_layers(point_features)
+
+        ret_dict = {
+            'point_cls_preds': point_cls_preds,
+            'point_part_preds': point_part_preds,
+        }
+        if self.box_layers is not None:
+            point_box_preds = self.box_layers(point_features)
+            ret_dict['point_box_preds'] = point_box_preds
+
+        point_cls_scores = torch.sigmoid(point_cls_preds)
+        point_part_offset = torch.sigmoid(point_part_preds)
+        batch_dict['point_cls_scores'], _ = point_cls_scores.max(dim=-1)
+        batch_dict['point_part_offset'] = point_part_offset
+
+        if self.training:
+            targets_dict = self.assign_targets(batch_dict)
+            ret_dict['point_cls_labels'] = targets_dict['point_cls_labels']
+            ret_dict['point_part_labels'] = targets_dict.get('point_part_labels')
+            ret_dict['point_box_labels'] = targets_dict.get('point_box_labels')
+
+        if self.box_layers is not None and (not self.training or self.predict_boxes_when_training):
+            point_cls_preds, point_box_preds = self.generate_predicted_boxes(
+                points=batch_dict['point_coords'][:, 1:4],
+                point_cls_preds=point_cls_preds, point_box_preds=ret_dict['point_box_preds']
+            )
+            batch_dict['batch_cls_preds'] = point_cls_preds
+            batch_dict['batch_box_preds'] = point_box_preds
+            batch_dict['batch_index'] = batch_dict['point_coords'][:, 0]
+            batch_dict['cls_preds_normalized'] = False
+
+        self.forward_ret_dict = ret_dict
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/anchor_generator.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/anchor_generator.py
new file mode 100644
index 000000000..0aa686110
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/anchor_generator.py
@@ -0,0 +1,79 @@
+import torch
+
+
+class AnchorGenerator(object):
+    def __init__(self, anchor_range, anchor_generator_config):
+        super().__init__()
+        self.anchor_generator_cfg = anchor_generator_config
+        self.anchor_range = anchor_range
+        self.anchor_sizes = [config['anchor_sizes'] for config in anchor_generator_config]
+        self.anchor_rotations = [config['anchor_rotations'] for config in anchor_generator_config]
+        self.anchor_heights = [config['anchor_bottom_heights'] for config in anchor_generator_config]
+        self.align_center = [config.get('align_center', False) for config in anchor_generator_config]
+
+        assert len(self.anchor_sizes) == len(self.anchor_rotations) == len(self.anchor_heights)
+        self.num_of_anchor_sets = len(self.anchor_sizes)
+
+    def generate_anchors(self, grid_sizes):
+        assert len(grid_sizes) == self.num_of_anchor_sets
+        all_anchors = []
+        num_anchors_per_location = []
+        for grid_size, anchor_size, anchor_rotation, anchor_height, align_center in zip(
+                grid_sizes, self.anchor_sizes, self.anchor_rotations, self.anchor_heights, self.align_center):
+
+            num_anchors_per_location.append(len(anchor_rotation) * len(anchor_size) * len(anchor_height))
+            if align_center:
+                x_stride = (self.anchor_range[3] - self.anchor_range[0]) / grid_size[0]
+                y_stride = (self.anchor_range[4] - self.anchor_range[1]) / grid_size[1]
+                x_offset, y_offset = x_stride / 2, y_stride / 2
+            else:
+                x_stride = (self.anchor_range[3] - self.anchor_range[0]) / (grid_size[0] - 1)
+                y_stride = (self.anchor_range[4] - self.anchor_range[1]) / (grid_size[1] - 1)
+                x_offset, y_offset = 0, 0
+
+            x_shifts = torch.arange(
+                self.anchor_range[0] + x_offset, self.anchor_range[3] + 1e-5, step=x_stride, dtype=torch.float32,
+            ).cuda()
+            y_shifts = torch.arange(
+                self.anchor_range[1] + y_offset, self.anchor_range[4] + 1e-5, step=y_stride, dtype=torch.float32,
+            ).cuda()
+            z_shifts = x_shifts.new_tensor(anchor_height)
+
+            num_anchor_size, num_anchor_rotation = anchor_size.__len__(), anchor_rotation.__len__()
+            anchor_rotation = x_shifts.new_tensor(anchor_rotation)
+            anchor_size = x_shifts.new_tensor(anchor_size)
+            x_shifts, y_shifts, z_shifts = torch.meshgrid([
+                x_shifts, y_shifts, z_shifts
+            ])  # [x_grid, y_grid, z_grid]
+            anchors = torch.stack((x_shifts, y_shifts, z_shifts), dim=-1)  # [x, y, z, 3]
+            anchors = anchors[:, :, :, None, :].repeat(1, 1, 1, anchor_size.shape[0], 1)
+            anchor_size = anchor_size.view(1, 1, 1, -1, 3).repeat([*anchors.shape[0:3], 1, 1])
+            anchors = torch.cat((anchors, anchor_size), dim=-1)
+            anchors = anchors[:, :, :, :, None, :].repeat(1, 1, 1, 1, num_anchor_rotation, 1)
+            anchor_rotation = anchor_rotation.view(1, 1, 1, 1, -1, 1).repeat([*anchors.shape[0:3], num_anchor_size, 1, 1])
+            anchors = torch.cat((anchors, anchor_rotation), dim=-1)  # [x, y, z, num_size, num_rot, 7]
+
+            anchors = anchors.permute(2, 1, 0, 3, 4, 5).contiguous()
+            #anchors = anchors.view(-1, anchors.shape[-1])
+            anchors[..., 2] += anchors[..., 5] / 2  # shift to box centers
+            all_anchors.append(anchors)
+        return all_anchors, num_anchors_per_location
+
+
+if __name__ == '__main__':
+    from easydict import EasyDict
+    config = [
+        EasyDict({
+            'anchor_sizes': [[2.1, 4.7, 1.7], [0.86, 0.91, 1.73], [0.84, 1.78, 1.78]],
+            'anchor_rotations': [0, 1.57],
+            'anchor_heights': [0, 0.5]
+        })
+    ]
+
+    A = AnchorGenerator(
+        anchor_range=[-75.2, -75.2, -2, 75.2, 75.2, 4],
+        anchor_generator_config=config
+    )
+    import pdb
+    pdb.set_trace()
+    A.generate_anchors([[188, 188]])
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/atss_target_assigner.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/atss_target_assigner.py
new file mode 100644
index 000000000..112af2cf7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/atss_target_assigner.py
@@ -0,0 +1,141 @@
+import torch
+
+from ....ops.iou3d_nms import iou3d_nms_utils
+from ....utils import common_utils
+
+
+class ATSSTargetAssigner(object):
+    """
+    Reference: https://arxiv.org/abs/1912.02424
+    """
+    def __init__(self, topk, box_coder, match_height=False):
+        self.topk = topk
+        self.box_coder = box_coder
+        self.match_height = match_height
+
+    def assign_targets(self, anchors_list, gt_boxes_with_classes, use_multihead=False):
+        """
+        Args:
+            anchors: [(N, 7), ...]
+            gt_boxes: (B, M, 8)
+        Returns:
+
+        """
+        if not isinstance(anchors_list, list):
+            anchors_list = [anchors_list]
+            single_set_of_anchor = True
+        else:
+            single_set_of_anchor = len(anchors_list) == 1
+        cls_labels_list, reg_targets_list, reg_weights_list = [], [], []
+        for anchors in anchors_list:
+            batch_size = gt_boxes_with_classes.shape[0]
+            gt_classes = gt_boxes_with_classes[:, :, -1]
+            gt_boxes = gt_boxes_with_classes[:, :, :-1]
+            if use_multihead:
+                anchors = anchors.permute(3, 4, 0, 1, 2, 5).contiguous().view(-1, anchors.shape[-1])
+            else:
+                anchors = anchors.view(-1, anchors.shape[-1])
+            cls_labels, reg_targets, reg_weights = [], [], []
+            for k in range(batch_size):
+                cur_gt = gt_boxes[k]
+                cnt = cur_gt.__len__() - 1
+                while cnt > 0 and cur_gt[cnt].sum() == 0:
+                    cnt -= 1
+                cur_gt = cur_gt[:cnt + 1]
+
+                cur_gt_classes = gt_classes[k][:cnt + 1]
+                cur_cls_labels, cur_reg_targets, cur_reg_weights = self.assign_targets_single(
+                    anchors, cur_gt, cur_gt_classes
+                )
+                cls_labels.append(cur_cls_labels)
+                reg_targets.append(cur_reg_targets)
+                reg_weights.append(cur_reg_weights)
+
+            cls_labels = torch.stack(cls_labels, dim=0)
+            reg_targets = torch.stack(reg_targets, dim=0)
+            reg_weights = torch.stack(reg_weights, dim=0)
+            cls_labels_list.append(cls_labels)
+            reg_targets_list.append(reg_targets)
+            reg_weights_list.append(reg_weights)
+
+        if single_set_of_anchor:
+            ret_dict = {
+                'box_cls_labels': cls_labels_list[0],
+                'box_reg_targets': reg_targets_list[0],
+                'reg_weights': reg_weights_list[0]
+            }
+        else:
+            ret_dict = {
+                'box_cls_labels': torch.cat(cls_labels_list, dim=1),
+                'box_reg_targets': torch.cat(reg_targets_list, dim=1),
+                'reg_weights': torch.cat(reg_weights_list, dim=1)
+            }
+        return ret_dict
+
+    def assign_targets_single(self, anchors, gt_boxes, gt_classes):
+        """
+        Args:
+            anchors: (N, 7) [x, y, z, dx, dy, dz, heading]
+            gt_boxes: (M, 7) [x, y, z, dx, dy, dz, heading]
+            gt_classes: (M)
+        Returns:
+
+        """
+        num_anchor = anchors.shape[0]
+        num_gt = gt_boxes.shape[0]
+
+        # select topk anchors for each gt_boxes
+        if self.match_height:
+            ious = iou3d_nms_utils.boxes_iou3d_gpu(anchors[:, 0:7], gt_boxes[:, 0:7])  # (N, M)
+        else:
+            ious = iou3d_nms_utils.boxes_iou_bev(anchors[:, 0:7], gt_boxes[:, 0:7])
+
+        distance = (anchors[:, None, 0:3] - gt_boxes[None, :, 0:3]).norm(dim=-1)  # (N, M)
+        _, topk_idxs = distance.topk(self.topk, dim=0, largest=False)  # (K, M)
+        candidate_ious = ious[topk_idxs, torch.arange(num_gt)]  # (K, M)
+        iou_mean_per_gt = candidate_ious.mean(dim=0)
+        iou_std_per_gt = candidate_ious.std(dim=0)
+        iou_thresh_per_gt = iou_mean_per_gt + iou_std_per_gt + 1e-6
+        is_pos = candidate_ious >= iou_thresh_per_gt[None, :]  # (K, M)
+
+        # check whether anchor_center in gt_boxes, only check BEV x-y axes
+        candidate_anchors = anchors[topk_idxs.view(-1)]  # (KxM, 7)
+        gt_boxes_of_each_anchor = gt_boxes[:, :].repeat(self.topk, 1)  # (KxM, 7)
+        xyz_local = candidate_anchors[:, 0:3] - gt_boxes_of_each_anchor[:, 0:3]
+        xyz_local = common_utils.rotate_points_along_z(
+            xyz_local[:, None, :], -gt_boxes_of_each_anchor[:, 6]
+        ).squeeze(dim=1)
+        xy_local = xyz_local[:, 0:2]
+        lw = gt_boxes_of_each_anchor[:, 3:5][:, [1, 0]]  # bugfixed: w ==> y, l ==> x in local coords
+        is_in_gt = ((xy_local <= lw / 2) & (xy_local >= -lw / 2)).all(dim=-1).view(-1, num_gt)  # (K, M)
+        is_pos = is_pos & is_in_gt  # (K, M)
+
+        for ng in range(num_gt):
+            topk_idxs[:, ng] += ng * num_anchor
+
+        # select the highest IoU if an anchor box is assigned with multiple gt_boxes
+        INF = -0x7FFFFFFF
+        ious_inf = torch.full_like(ious, INF).t().contiguous().view(-1)  # (MxN)
+        index = topk_idxs.view(-1)[is_pos.view(-1)]
+        ious_inf[index] = ious.t().contiguous().view(-1)[index]
+        ious_inf = ious_inf.view(num_gt, -1).t()  # (N, M)
+
+        anchors_to_gt_values, anchors_to_gt_indexs = ious_inf.max(dim=1)
+
+        # match the gt_boxes to the anchors which have maximum iou with them
+        max_iou_of_each_gt, argmax_iou_of_each_gt = ious.max(dim=0)
+        anchors_to_gt_indexs[argmax_iou_of_each_gt] = torch.arange(0, num_gt, device=ious.device)
+        anchors_to_gt_values[argmax_iou_of_each_gt] = max_iou_of_each_gt
+
+        cls_labels = gt_classes[anchors_to_gt_indexs]
+        cls_labels[anchors_to_gt_values == INF] = 0
+        matched_gts = gt_boxes[anchors_to_gt_indexs]
+
+        pos_mask = cls_labels > 0
+        reg_targets = matched_gts.new_zeros((num_anchor, self.box_coder.code_size))
+        reg_weights = matched_gts.new_zeros(num_anchor)
+        if pos_mask.sum() > 0:
+            reg_targets[pos_mask > 0] = self.box_coder.encode_torch(matched_gts[pos_mask > 0], anchors[pos_mask > 0])
+            reg_weights[pos_mask] = 1.0
+
+        return cls_labels, reg_targets, reg_weights
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/axis_aligned_target_assigner.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/axis_aligned_target_assigner.py
new file mode 100644
index 000000000..4dcd6e93c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/axis_aligned_target_assigner.py
@@ -0,0 +1,210 @@
+import numpy as np
+import torch
+
+from ....ops.iou3d_nms import iou3d_nms_utils
+from ....utils import box_utils
+
+
+class AxisAlignedTargetAssigner(object):
+    def __init__(self, model_cfg, class_names, box_coder, match_height=False):
+        super().__init__()
+
+        anchor_generator_cfg = model_cfg.ANCHOR_GENERATOR_CONFIG
+        anchor_target_cfg = model_cfg.TARGET_ASSIGNER_CONFIG
+        self.box_coder = box_coder
+        self.match_height = match_height
+        self.class_names = np.array(class_names)
+        self.anchor_class_names = [config['class_name'] for config in anchor_generator_cfg]
+        self.pos_fraction = anchor_target_cfg.POS_FRACTION if anchor_target_cfg.POS_FRACTION >= 0 else None
+        self.sample_size = anchor_target_cfg.SAMPLE_SIZE
+        self.norm_by_num_examples = anchor_target_cfg.NORM_BY_NUM_EXAMPLES
+        self.matched_thresholds = {}
+        self.unmatched_thresholds = {}
+        for config in anchor_generator_cfg:
+            self.matched_thresholds[config['class_name']] = config['matched_threshold']
+            self.unmatched_thresholds[config['class_name']] = config['unmatched_threshold']
+
+        self.use_multihead = model_cfg.get('USE_MULTIHEAD', False)
+        # self.separate_multihead = model_cfg.get('SEPARATE_MULTIHEAD', False)
+        # if self.seperate_multihead:
+        #     rpn_head_cfgs = model_cfg.RPN_HEAD_CFGS
+        #     self.gt_remapping = {}
+        #     for rpn_head_cfg in rpn_head_cfgs:
+        #         for idx, name in enumerate(rpn_head_cfg['HEAD_CLS_NAME']):
+        #             self.gt_remapping[name] = idx + 1
+
+    def assign_targets(self, all_anchors, gt_boxes_with_classes):
+        """
+        Args:
+            all_anchors: [(N, 7), ...]
+            gt_boxes: (B, M, 8)
+        Returns:
+
+        """
+
+        bbox_targets = []
+        cls_labels = []
+        reg_weights = []
+
+        batch_size = gt_boxes_with_classes.shape[0]
+        gt_classes = gt_boxes_with_classes[:, :, -1]
+        gt_boxes = gt_boxes_with_classes[:, :, :-1]
+        for k in range(batch_size):
+            cur_gt = gt_boxes[k]
+            cnt = cur_gt.__len__() - 1
+            while cnt > 0 and cur_gt[cnt].sum() == 0:
+                cnt -= 1
+            cur_gt = cur_gt[:cnt + 1]
+            cur_gt_classes = gt_classes[k][:cnt + 1].int()
+
+            target_list = []
+            for anchor_class_name, anchors in zip(self.anchor_class_names, all_anchors):
+                if cur_gt_classes.shape[0] > 1:
+                    mask = torch.from_numpy(self.class_names[cur_gt_classes.cpu() - 1] == anchor_class_name)
+                else:
+                    mask = torch.tensor([self.class_names[c - 1] == anchor_class_name
+                                         for c in cur_gt_classes], dtype=torch.bool)
+
+                if self.use_multihead:
+                    anchors = anchors.permute(3, 4, 0, 1, 2, 5).contiguous().view(-1, anchors.shape[-1])
+                    # if self.seperate_multihead:
+                    #     selected_classes = cur_gt_classes[mask].clone()
+                    #     if len(selected_classes) > 0:
+                    #         new_cls_id = self.gt_remapping[anchor_class_name]
+                    #         selected_classes[:] = new_cls_id
+                    # else:
+                    #     selected_classes = cur_gt_classes[mask]
+                    selected_classes = cur_gt_classes[mask]
+                else:
+                    feature_map_size = anchors.shape[:3]
+                    anchors = anchors.view(-1, anchors.shape[-1])
+                    selected_classes = cur_gt_classes[mask]
+
+                single_target = self.assign_targets_single(
+                    anchors,
+                    cur_gt[mask],
+                    gt_classes=selected_classes,
+                    matched_threshold=self.matched_thresholds[anchor_class_name],
+                    unmatched_threshold=self.unmatched_thresholds[anchor_class_name]
+                )
+                target_list.append(single_target)
+
+            if self.use_multihead:
+                target_dict = {
+                    'box_cls_labels': [t['box_cls_labels'].view(-1) for t in target_list],
+                    'box_reg_targets': [t['box_reg_targets'].view(-1, self.box_coder.code_size) for t in target_list],
+                    'reg_weights': [t['reg_weights'].view(-1) for t in target_list]
+                }
+
+                target_dict['box_reg_targets'] = torch.cat(target_dict['box_reg_targets'], dim=0)
+                target_dict['box_cls_labels'] = torch.cat(target_dict['box_cls_labels'], dim=0).view(-1)
+                target_dict['reg_weights'] = torch.cat(target_dict['reg_weights'], dim=0).view(-1)
+            else:
+                target_dict = {
+                    'box_cls_labels': [t['box_cls_labels'].view(*feature_map_size, -1) for t in target_list],
+                    'box_reg_targets': [t['box_reg_targets'].view(*feature_map_size, -1, self.box_coder.code_size)
+                                        for t in target_list],
+                    'reg_weights': [t['reg_weights'].view(*feature_map_size, -1) for t in target_list]
+                }
+                target_dict['box_reg_targets'] = torch.cat(
+                    target_dict['box_reg_targets'], dim=-2
+                ).view(-1, self.box_coder.code_size)
+
+                target_dict['box_cls_labels'] = torch.cat(target_dict['box_cls_labels'], dim=-1).view(-1)
+                target_dict['reg_weights'] = torch.cat(target_dict['reg_weights'], dim=-1).view(-1)
+
+            bbox_targets.append(target_dict['box_reg_targets'])
+            cls_labels.append(target_dict['box_cls_labels'])
+            reg_weights.append(target_dict['reg_weights'])
+
+        bbox_targets = torch.stack(bbox_targets, dim=0)
+
+        cls_labels = torch.stack(cls_labels, dim=0)
+        reg_weights = torch.stack(reg_weights, dim=0)
+        all_targets_dict = {
+            'box_cls_labels': cls_labels,
+            'box_reg_targets': bbox_targets,
+            'reg_weights': reg_weights
+
+        }
+        return all_targets_dict
+
+    def assign_targets_single(self, anchors, gt_boxes, gt_classes, matched_threshold=0.6, unmatched_threshold=0.45):
+
+        num_anchors = anchors.shape[0]
+        num_gt = gt_boxes.shape[0]
+
+        labels = torch.ones((num_anchors,), dtype=torch.int32, device=anchors.device) * -1
+        gt_ids = torch.ones((num_anchors,), dtype=torch.int32, device=anchors.device) * -1
+
+        if len(gt_boxes) > 0 and anchors.shape[0] > 0:
+            anchor_by_gt_overlap = iou3d_nms_utils.boxes_iou3d_gpu(anchors[:, 0:7], gt_boxes[:, 0:7]) \
+                if self.match_height else box_utils.boxes3d_nearest_bev_iou(anchors[:, 0:7], gt_boxes[:, 0:7])
+
+            # NOTE: The speed of these two versions depends the environment and the number of anchors
+            # anchor_to_gt_argmax = torch.from_numpy(anchor_by_gt_overlap.cpu().numpy().argmax(axis=1)).cuda()
+            anchor_to_gt_argmax = anchor_by_gt_overlap.argmax(dim=1)
+            anchor_to_gt_max = anchor_by_gt_overlap[torch.arange(num_anchors, device=anchors.device), anchor_to_gt_argmax]
+
+            # gt_to_anchor_argmax = torch.from_numpy(anchor_by_gt_overlap.cpu().numpy().argmax(axis=0)).cuda()
+            gt_to_anchor_argmax = anchor_by_gt_overlap.argmax(dim=0)
+            gt_to_anchor_max = anchor_by_gt_overlap[gt_to_anchor_argmax, torch.arange(num_gt, device=anchors.device)]
+            empty_gt_mask = gt_to_anchor_max == 0
+            gt_to_anchor_max[empty_gt_mask] = -1
+
+            anchors_with_max_overlap = (anchor_by_gt_overlap == gt_to_anchor_max).nonzero()[:, 0]
+            gt_inds_force = anchor_to_gt_argmax[anchors_with_max_overlap]
+            labels[anchors_with_max_overlap] = gt_classes[gt_inds_force]
+            gt_ids[anchors_with_max_overlap] = gt_inds_force.int()
+
+            pos_inds = anchor_to_gt_max >= matched_threshold
+            gt_inds_over_thresh = anchor_to_gt_argmax[pos_inds]
+            labels[pos_inds] = gt_classes[gt_inds_over_thresh]
+            gt_ids[pos_inds] = gt_inds_over_thresh.int()
+            bg_inds = (anchor_to_gt_max < unmatched_threshold).nonzero()[:, 0]
+        else:
+            bg_inds = torch.arange(num_anchors, device=anchors.device)
+
+        fg_inds = (labels > 0).nonzero()[:, 0]
+
+        if self.pos_fraction is not None:
+            num_fg = int(self.pos_fraction * self.sample_size)
+            if len(fg_inds) > num_fg:
+                num_disabled = len(fg_inds) - num_fg
+                disable_inds = torch.randperm(len(fg_inds))[:num_disabled]
+                labels[disable_inds] = -1
+                fg_inds = (labels > 0).nonzero()[:, 0]
+
+            num_bg = self.sample_size - (labels > 0).sum()
+            if len(bg_inds) > num_bg:
+                enable_inds = bg_inds[torch.randint(0, len(bg_inds), size=(num_bg,))]
+                labels[enable_inds] = 0
+            # bg_inds = torch.nonzero(labels == 0)[:, 0]
+        else:
+            if len(gt_boxes) == 0 or anchors.shape[0] == 0:
+                labels[:] = 0
+            else:
+                labels[bg_inds] = 0
+                labels[anchors_with_max_overlap] = gt_classes[gt_inds_force]
+
+        bbox_targets = anchors.new_zeros((num_anchors, self.box_coder.code_size))
+        if len(gt_boxes) > 0 and anchors.shape[0] > 0:
+            fg_gt_boxes = gt_boxes[anchor_to_gt_argmax[fg_inds], :]
+            fg_anchors = anchors[fg_inds, :]
+            bbox_targets[fg_inds, :] = self.box_coder.encode_torch(fg_gt_boxes, fg_anchors)
+
+        reg_weights = anchors.new_zeros((num_anchors,))
+
+        if self.norm_by_num_examples:
+            num_examples = (labels >= 0).sum()
+            num_examples = num_examples if num_examples > 1.0 else 1.0
+            reg_weights[labels > 0] = 1.0 / num_examples
+        else:
+            reg_weights[labels > 0] = 1.0
+
+        ret_dict = {
+            'box_cls_labels': labels,
+            'box_reg_targets': bbox_targets,
+            'reg_weights': reg_weights,
+        }
+        return ret_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/hungarian_assigner.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/hungarian_assigner.py
new file mode 100644
index 000000000..15da89af0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/target_assigner/hungarian_assigner.py
@@ -0,0 +1,131 @@
+import torch
+from scipy.optimize import linear_sum_assignment
+from pcdet.ops.iou3d_nms import iou3d_nms_cuda
+
+
+def height_overlaps(boxes1, boxes2):
+    """
+    Calculate height overlaps of two boxes.
+    """
+    boxes1_top_height = (boxes1[:,2]+ boxes1[:,5]).view(-1, 1)
+    boxes1_bottom_height = boxes1[:,2].view(-1, 1)
+    boxes2_top_height = (boxes2[:,2]+boxes2[:,5]).view(1, -1)
+    boxes2_bottom_height = boxes2[:,2].view(1, -1)
+
+    heighest_of_bottom = torch.max(boxes1_bottom_height, boxes2_bottom_height)
+    lowest_of_top = torch.min(boxes1_top_height, boxes2_top_height)
+    overlaps_h = torch.clamp(lowest_of_top - heighest_of_bottom, min=0)
+    return overlaps_h
+
+
+def overlaps(boxes1, boxes2):
+    """
+    Calculate 3D overlaps of two boxes.
+    """
+    rows = len(boxes1)
+    cols = len(boxes2)
+    if rows * cols == 0:
+        return boxes1.new(rows, cols)
+
+    # height overlap
+    overlaps_h = height_overlaps(boxes1, boxes2)
+    boxes1_bev = boxes1[:,:7]
+    boxes2_bev = boxes2[:,:7]
+
+    # bev overlap
+    overlaps_bev = boxes1_bev.new_zeros(
+        (boxes1_bev.shape[0], boxes2_bev.shape[0])
+    ).cuda()  # (N, M)
+    iou3d_nms_cuda.boxes_overlap_bev_gpu(
+        boxes1_bev.contiguous().cuda(), boxes2_bev.contiguous().cuda(), overlaps_bev
+    )
+
+    # 3d overlaps
+    overlaps_3d = overlaps_bev.to(boxes1.device) * overlaps_h
+
+    volume1 = (boxes1[:, 3] * boxes1[:, 4] * boxes1[:, 5]).view(-1, 1)
+    volume2 = (boxes2[:, 3] * boxes2[:, 4] * boxes2[:, 5]).view(1, -1)
+        
+    iou3d = overlaps_3d / torch.clamp(volume1 + volume2 - overlaps_3d, min=1e-8)
+
+    return iou3d
+
+
+
+class HungarianAssigner3D:
+    def __init__(self, cls_cost, reg_cost, iou_cost):
+        self.cls_cost = cls_cost
+        self.reg_cost = reg_cost
+        self.iou_cost = iou_cost
+
+    def focal_loss_cost(self, cls_pred, gt_labels):
+        weight = self.cls_cost.get('weight', 0.15)
+        alpha = self.cls_cost.get('alpha', 0.25)
+        gamma = self.cls_cost.get('gamma', 2.0)
+        eps = self.cls_cost.get('eps', 1e-12)
+
+        cls_pred = cls_pred.sigmoid()
+        neg_cost = -(1 - cls_pred + eps).log() * (
+            1 - alpha) * cls_pred.pow(gamma)
+        pos_cost = -(cls_pred + eps).log() * alpha * (
+            1 - cls_pred).pow(gamma)
+
+        cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels]
+        return cls_cost * weight
+    
+    def bevbox_cost(self, bboxes, gt_bboxes, point_cloud_range):
+        weight = self.reg_cost.get('weight', 0.25)
+
+        pc_start = bboxes.new(point_cloud_range[0:2])
+        pc_range = bboxes.new(point_cloud_range[3:5]) - bboxes.new(point_cloud_range[0:2])
+        # normalize the box center to [0, 1]
+        normalized_bboxes_xy = (bboxes[:, :2] - pc_start) / pc_range
+        normalized_gt_bboxes_xy = (gt_bboxes[:, :2] - pc_start) / pc_range
+        reg_cost = torch.cdist(normalized_bboxes_xy, normalized_gt_bboxes_xy, p=1)
+        return reg_cost * weight
+
+    def iou3d_cost(self, bboxes, gt_bboxes):
+        iou = overlaps(bboxes, gt_bboxes)
+        weight = self.iou_cost.get('weight', 0.25)
+        iou_cost = - iou
+        return iou_cost * weight, iou
+
+    def assign(self, bboxes, gt_bboxes, gt_labels, cls_pred, point_cloud_range):
+        num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
+
+        # 1. assign -1 by default
+        assigned_gt_inds = bboxes.new_full((num_bboxes,), -1, dtype=torch.long)
+        assigned_labels = bboxes.new_full((num_bboxes,), -1, dtype=torch.long)
+        if num_gts == 0 or num_bboxes == 0:
+            # No ground truth or boxes, return empty assignment
+            if num_gts == 0:
+                # No ground truth, assign all to background
+                assigned_gt_inds[:] = 0
+            return num_gts, assigned_gt_inds, max_overlaps, assigned_labels
+
+        # 2. compute the weighted costs
+        cls_cost = self.focal_loss_cost(cls_pred[0].T, gt_labels)
+        reg_cost = self.bevbox_cost(bboxes, gt_bboxes, point_cloud_range)
+        iou_cost, iou = self.iou3d_cost(bboxes, gt_bboxes)
+        
+
+        # weighted sum of above three costs
+        cost = cls_cost + reg_cost + iou_cost
+
+        # 3. do Hungarian matching on CPU using linear_sum_assignment
+        cost = cost.detach().cpu()
+        matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
+        matched_row_inds = torch.from_numpy(matched_row_inds).to(bboxes.device)
+        matched_col_inds = torch.from_numpy(matched_col_inds).to(bboxes.device)
+
+        # 4. assign backgrounds and foregrounds
+        # assign all indices to backgrounds first
+        assigned_gt_inds[:] = 0
+        # assign foregrounds based on matching results
+        assigned_gt_inds[matched_row_inds] = matched_col_inds + 1
+        assigned_labels[matched_row_inds] = gt_labels[matched_col_inds]
+
+        max_overlaps = torch.zeros_like(iou.max(1).values)
+        max_overlaps[matched_row_inds] = iou[matched_row_inds, matched_col_inds]
+
+        return assigned_gt_inds, max_overlaps
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/transfusion_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/transfusion_head.py
new file mode 100644
index 000000000..742211b86
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/transfusion_head.py
@@ -0,0 +1,479 @@
+import copy
+import numpy as np
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn.init import kaiming_normal_
+from ..model_utils.transfusion_utils import clip_sigmoid
+from ..model_utils.basic_block_2d import BasicBlock2D
+from ..model_utils.transfusion_utils import PositionEmbeddingLearned, TransformerDecoderLayer
+from .target_assigner.hungarian_assigner import HungarianAssigner3D
+from ...utils import loss_utils
+from ..model_utils import centernet_utils
+
+
+class SeparateHead_Transfusion(nn.Module):
+    def __init__(self, input_channels, head_channels, kernel_size, sep_head_dict, init_bias=-2.19, use_bias=False):
+        super().__init__()
+        self.sep_head_dict = sep_head_dict
+
+        for cur_name in self.sep_head_dict:
+            output_channels = self.sep_head_dict[cur_name]['out_channels']
+            num_conv = self.sep_head_dict[cur_name]['num_conv']
+
+            fc_list = []
+            for k in range(num_conv - 1):
+                fc_list.append(nn.Sequential(
+                    nn.Conv1d(input_channels, head_channels, kernel_size, stride=1, padding=kernel_size//2, bias=use_bias),
+                    nn.BatchNorm1d(head_channels),
+                    nn.ReLU()
+                ))
+            fc_list.append(nn.Conv1d(head_channels, output_channels, kernel_size, stride=1, padding=kernel_size//2, bias=True))
+            fc = nn.Sequential(*fc_list)
+            if 'hm' in cur_name:
+                fc[-1].bias.data.fill_(init_bias)
+            else:
+                for m in fc.modules():
+                    if isinstance(m, nn.Conv2d):
+                        kaiming_normal_(m.weight.data)
+                        if hasattr(m, "bias") and m.bias is not None:
+                            nn.init.constant_(m.bias, 0)
+
+            self.__setattr__(cur_name, fc)
+
+    def forward(self, x):
+        ret_dict = {}
+        for cur_name in self.sep_head_dict:
+            ret_dict[cur_name] = self.__getattr__(cur_name)(x)
+
+        return ret_dict
+
+
+
+class TransFusionHead(nn.Module):
+    """
+        This module implements TransFusionHead.
+        The code is adapted from https://github.com/mit-han-lab/bevfusion/ with minimal modifications.
+    """
+    def __init__(
+        self,
+        model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range, voxel_size, predict_boxes_when_training=True,
+    ):
+        super(TransFusionHead, self).__init__()
+
+        self.grid_size = grid_size
+        self.point_cloud_range = point_cloud_range
+        self.voxel_size = voxel_size
+        self.num_classes = num_class
+
+        self.model_cfg = model_cfg
+        self.feature_map_stride = self.model_cfg.TARGET_ASSIGNER_CONFIG.get('FEATURE_MAP_STRIDE', None)
+        self.dataset_name = self.model_cfg.TARGET_ASSIGNER_CONFIG.get('DATASET', 'nuScenes')
+
+        hidden_channel=self.model_cfg.HIDDEN_CHANNEL
+        self.num_proposals = self.model_cfg.NUM_PROPOSALS
+        self.bn_momentum = self.model_cfg.BN_MOMENTUM
+        self.nms_kernel_size = self.model_cfg.NMS_KERNEL_SIZE
+
+        num_heads = self.model_cfg.NUM_HEADS
+        dropout = self.model_cfg.DROPOUT
+        activation = self.model_cfg.ACTIVATION
+        ffn_channel = self.model_cfg.FFN_CHANNEL
+        bias = self.model_cfg.get('USE_BIAS_BEFORE_NORM', False)
+
+        loss_cls = self.model_cfg.LOSS_CONFIG.LOSS_CLS
+        self.use_sigmoid_cls = loss_cls.get("use_sigmoid", False)
+        if not self.use_sigmoid_cls:
+            self.num_classes += 1
+        self.loss_cls = loss_utils.SigmoidFocalClassificationLoss(gamma=loss_cls.gamma,alpha=loss_cls.alpha)
+        self.loss_cls_weight = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['cls_weight']
+        self.loss_bbox = loss_utils.L1Loss()
+        self.loss_bbox_weight = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['bbox_weight']
+        self.loss_heatmap = loss_utils.GaussianFocalLoss()
+        self.loss_heatmap_weight = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['hm_weight']
+
+        self.code_size = 10
+
+        # a shared convolution
+        self.shared_conv = nn.Conv2d(in_channels=input_channels,out_channels=hidden_channel,kernel_size=3,padding=1)
+        layers = []
+        layers.append(BasicBlock2D(hidden_channel,hidden_channel, kernel_size=3,padding=1,bias=bias))
+        layers.append(nn.Conv2d(in_channels=hidden_channel,out_channels=num_class,kernel_size=3,padding=1))
+        self.heatmap_head = nn.Sequential(*layers)
+        self.class_encoding = nn.Conv1d(num_class, hidden_channel, 1)
+
+        # transformer decoder layers for object query with LiDAR feature
+        self.decoder = TransformerDecoderLayer(hidden_channel, num_heads, ffn_channel, dropout, activation,
+                self_posembed=PositionEmbeddingLearned(2, hidden_channel),
+                cross_posembed=PositionEmbeddingLearned(2, hidden_channel),
+            )
+        # Prediction Head
+        heads = copy.deepcopy(self.model_cfg.SEPARATE_HEAD_CFG.HEAD_DICT)
+        heads['heatmap'] = dict(out_channels=self.num_classes, num_conv=self.model_cfg.NUM_HM_CONV)
+        self.prediction_head = SeparateHead_Transfusion(hidden_channel, 64, 1, heads, use_bias=bias)
+
+        self.init_weights()
+        self.bbox_assigner = HungarianAssigner3D(**self.model_cfg.TARGET_ASSIGNER_CONFIG.HUNGARIAN_ASSIGNER)
+
+        # Position Embedding for Cross-Attention, which is re-used during training
+        x_size = self.grid_size[0] // self.feature_map_stride
+        y_size = self.grid_size[1] // self.feature_map_stride
+        self.bev_pos = self.create_2D_grid(x_size, y_size)
+
+        self.forward_ret_dict = {}
+
+    def create_2D_grid(self, x_size, y_size):
+        meshgrid = [[0, x_size - 1, x_size], [0, y_size - 1, y_size]]
+        # NOTE: modified
+        batch_x, batch_y = torch.meshgrid(
+            *[torch.linspace(it[0], it[1], it[2]) for it in meshgrid]
+        )
+        batch_x = batch_x + 0.5
+        batch_y = batch_y + 0.5
+        coord_base = torch.cat([batch_x[None], batch_y[None]], dim=0)[None]
+        coord_base = coord_base.view(1, 2, -1).permute(0, 2, 1)
+        return coord_base
+
+    def init_weights(self):
+        # initialize transformer
+        for m in self.decoder.parameters():
+            if m.dim() > 1:
+                nn.init.xavier_uniform_(m)
+        if hasattr(self, "query"):
+            nn.init.xavier_normal_(self.query)
+        self.init_bn_momentum()
+
+    def init_bn_momentum(self):
+        for m in self.modules():
+            if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)):
+                m.momentum = self.bn_momentum
+
+    def predict(self, inputs):
+        batch_size = inputs.shape[0]
+        lidar_feat = self.shared_conv(inputs)
+
+        lidar_feat_flatten = lidar_feat.view(
+            batch_size, lidar_feat.shape[1], -1
+        )
+        bev_pos = self.bev_pos.repeat(batch_size, 1, 1).to(lidar_feat.device)
+
+        # query initialization
+        dense_heatmap = self.heatmap_head(lidar_feat)
+        heatmap = dense_heatmap.detach().sigmoid()
+        padding = self.nms_kernel_size // 2
+        local_max = torch.zeros_like(heatmap)
+        local_max_inner = F.max_pool2d(
+            heatmap, kernel_size=self.nms_kernel_size, stride=1, padding=0
+        )
+        local_max[:, :, padding:(-padding), padding:(-padding)] = local_max_inner
+        # for Pedestrian & Traffic_cone in nuScenes
+        if self.dataset_name == "nuScenes":
+            local_max[ :, 8, ] = F.max_pool2d(heatmap[:, 8], kernel_size=1, stride=1, padding=0)
+            local_max[ :, 9, ] = F.max_pool2d(heatmap[:, 9], kernel_size=1, stride=1, padding=0)
+        # for Pedestrian & Cyclist in Waymo
+        elif self.dataset_name == "Waymo":
+            local_max[ :, 1, ] = F.max_pool2d(heatmap[:, 1], kernel_size=1, stride=1, padding=0)
+            local_max[ :, 2, ] = F.max_pool2d(heatmap[:, 2], kernel_size=1, stride=1, padding=0)
+        heatmap = heatmap * (heatmap == local_max)
+        heatmap = heatmap.view(batch_size, heatmap.shape[1], -1)
+ 
+        # top num_proposals among all classes
+        top_proposals = heatmap.view(batch_size, -1).argsort(dim=-1, descending=True)[
+            ..., : self.num_proposals
+        ]
+        top_proposals_class = top_proposals // heatmap.shape[-1]
+        top_proposals_index = top_proposals % heatmap.shape[-1]
+        query_feat = lidar_feat_flatten.gather(
+            index=top_proposals_index[:, None, :].expand(-1, lidar_feat_flatten.shape[1], -1),
+            dim=-1,
+        )
+        self.query_labels = top_proposals_class
+
+        # add category embedding
+        one_hot = F.one_hot(top_proposals_class, num_classes=self.num_classes).permute(0, 2, 1)
+        
+        query_cat_encoding = self.class_encoding(one_hot.float())
+        query_feat += query_cat_encoding
+
+        query_pos = bev_pos.gather(
+            index=top_proposals_index[:, None, :].permute(0, 2, 1).expand(-1, -1, bev_pos.shape[-1]),
+            dim=1,
+        )
+        # convert to xy
+        query_pos = query_pos.flip(dims=[-1])
+        bev_pos = bev_pos.flip(dims=[-1])
+
+        query_feat = self.decoder(
+            query_feat, lidar_feat_flatten, query_pos, bev_pos
+        )
+        res_layer = self.prediction_head(query_feat)
+        res_layer["center"] = res_layer["center"] + query_pos.permute(0, 2, 1)
+
+        res_layer["query_heatmap_score"] = heatmap.gather(
+            index=top_proposals_index[:, None, :].expand(-1, self.num_classes, -1),
+            dim=-1,
+        )
+        res_layer["dense_heatmap"] = dense_heatmap
+
+        return res_layer
+
+    def forward(self, batch_dict):
+        feats = batch_dict['spatial_features_2d']
+        res = self.predict(feats)
+        if not self.training:
+            bboxes = self.get_bboxes(res)
+            batch_dict['final_box_dicts'] = bboxes
+        else:
+            gt_boxes = batch_dict['gt_boxes']
+            gt_bboxes_3d = gt_boxes[...,:-1]
+            gt_labels_3d =  gt_boxes[...,-1].long() - 1
+            loss, tb_dict = self.loss(gt_bboxes_3d, gt_labels_3d, res)
+            batch_dict['loss'] = loss
+            batch_dict['tb_dict'] = tb_dict
+        return batch_dict
+
+    def get_targets(self, gt_bboxes_3d, gt_labels_3d, pred_dicts):
+        assign_results = []
+        for batch_idx in range(len(gt_bboxes_3d)):
+            pred_dict = {}
+            for key in pred_dicts.keys():
+                pred_dict[key] = pred_dicts[key][batch_idx : batch_idx + 1]
+            gt_bboxes = gt_bboxes_3d[batch_idx]
+            valid_idx = []
+            # filter empty boxes
+            for i in range(len(gt_bboxes)):
+                if gt_bboxes[i][3] > 0 and gt_bboxes[i][4] > 0:
+                    valid_idx.append(i)
+            assign_result = self.get_targets_single(gt_bboxes[valid_idx], gt_labels_3d[batch_idx][valid_idx], pred_dict)
+            assign_results.append(assign_result)
+
+        res_tuple = tuple(map(list, zip(*assign_results)))
+        labels = torch.cat(res_tuple[0], dim=0)
+        label_weights = torch.cat(res_tuple[1], dim=0)
+        bbox_targets = torch.cat(res_tuple[2], dim=0)
+        bbox_weights = torch.cat(res_tuple[3], dim=0)
+        num_pos = np.sum(res_tuple[4])
+        matched_ious = np.mean(res_tuple[5])
+        heatmap = torch.cat(res_tuple[6], dim=0)
+        return labels, label_weights, bbox_targets, bbox_weights, num_pos, matched_ious, heatmap
+        
+
+    def get_targets_single(self, gt_bboxes_3d, gt_labels_3d, preds_dict):
+        
+        num_proposals = preds_dict["center"].shape[-1]
+        score = copy.deepcopy(preds_dict["heatmap"].detach())
+        center = copy.deepcopy(preds_dict["center"].detach())
+        height = copy.deepcopy(preds_dict["height"].detach())
+        dim = copy.deepcopy(preds_dict["dim"].detach())
+        rot = copy.deepcopy(preds_dict["rot"].detach())
+        if "vel" in preds_dict.keys():
+            vel = copy.deepcopy(preds_dict["vel"].detach())
+        else:
+            vel = None
+
+        boxes_dict = self.decode_bbox(score, rot, dim, center, height, vel)
+        bboxes_tensor = boxes_dict[0]["pred_boxes"]
+        gt_bboxes_tensor = gt_bboxes_3d.to(score.device)
+
+        assigned_gt_inds, ious = self.bbox_assigner.assign(
+            bboxes_tensor, gt_bboxes_tensor, gt_labels_3d,
+            score, self.point_cloud_range,
+        )
+        pos_inds = torch.nonzero(assigned_gt_inds > 0, as_tuple=False).squeeze(-1).unique()
+        neg_inds = torch.nonzero(assigned_gt_inds == 0, as_tuple=False).squeeze(-1).unique()
+        pos_assigned_gt_inds = assigned_gt_inds[pos_inds] - 1
+        if gt_bboxes_3d.numel() == 0:
+            assert pos_inds.numel() == 0
+            pos_gt_bboxes = torch.empty_like(gt_bboxes_3d).view(-1, 9)
+        else:
+            pos_gt_bboxes = gt_bboxes_3d[pos_assigned_gt_inds.long(), :]
+
+        # create target for loss computation
+        bbox_targets = torch.zeros([num_proposals, self.code_size]).to(center.device)
+        bbox_weights = torch.zeros([num_proposals, self.code_size]).to(center.device)
+        ious = torch.clamp(ious, min=0.0, max=1.0)
+        labels = bboxes_tensor.new_zeros(num_proposals, dtype=torch.long)
+        label_weights = bboxes_tensor.new_zeros(num_proposals, dtype=torch.long)
+
+        if gt_labels_3d is not None:  # default label is -1
+            labels += self.num_classes
+
+        # both pos and neg have classification loss, only pos has regression and iou loss
+        if len(pos_inds) > 0:
+            pos_bbox_targets = self.encode_bbox(pos_gt_bboxes)
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+            bbox_weights[pos_inds, :] = 1.0
+
+            if gt_labels_3d is None:
+                labels[pos_inds] = 1
+            else:
+                labels[pos_inds] = gt_labels_3d[pos_assigned_gt_inds]
+            label_weights[pos_inds] = 1.0
+
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # compute dense heatmap targets
+        device = labels.device
+        target_assigner_cfg = self.model_cfg.TARGET_ASSIGNER_CONFIG
+        feature_map_size = (self.grid_size[:2] // self.feature_map_stride) 
+        heatmap = gt_bboxes_3d.new_zeros(self.num_classes, feature_map_size[1], feature_map_size[0])
+        for idx in range(len(gt_bboxes_3d)):
+            width = gt_bboxes_3d[idx][3]
+            length = gt_bboxes_3d[idx][4]
+            width = width / self.voxel_size[0] / self.feature_map_stride
+            length = length / self.voxel_size[1] / self.feature_map_stride
+            if width > 0 and length > 0:
+                radius = centernet_utils.gaussian_radius(length.view(-1), width.view(-1), target_assigner_cfg.GAUSSIAN_OVERLAP)[0]
+                radius = max(target_assigner_cfg.MIN_RADIUS, int(radius))
+                x, y = gt_bboxes_3d[idx][0], gt_bboxes_3d[idx][1]
+
+                coor_x = (x - self.point_cloud_range[0]) / self.voxel_size[0] / self.feature_map_stride
+                coor_y = (y - self.point_cloud_range[1]) / self.voxel_size[1] / self.feature_map_stride
+
+                center = torch.tensor([coor_x, coor_y], dtype=torch.float32, device=device)
+                center_int = center.to(torch.int32)
+                centernet_utils.draw_gaussian_to_heatmap(heatmap[gt_labels_3d[idx]], center_int, radius)
+
+
+        mean_iou = ious[pos_inds].sum() / max(len(pos_inds), 1)
+        return (labels[None], label_weights[None], bbox_targets[None], bbox_weights[None], int(pos_inds.shape[0]), float(mean_iou), heatmap[None])
+
+    def loss(self, gt_bboxes_3d, gt_labels_3d, pred_dicts, **kwargs):
+
+        labels, label_weights, bbox_targets, bbox_weights, num_pos, matched_ious, heatmap = \
+            self.get_targets(gt_bboxes_3d, gt_labels_3d, pred_dicts)
+        loss_dict = dict()
+        loss_all = 0
+
+        # compute heatmap loss
+        loss_heatmap = self.loss_heatmap(
+            clip_sigmoid(pred_dicts["dense_heatmap"]),
+            heatmap,
+        ).sum() / max(heatmap.eq(1).float().sum().item(), 1)
+        loss_dict["loss_heatmap"] = loss_heatmap.item() * self.loss_heatmap_weight
+        loss_all += loss_heatmap * self.loss_heatmap_weight
+
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+        cls_score = pred_dicts["heatmap"].permute(0, 2, 1).reshape(-1, self.num_classes)
+
+        one_hot_targets = torch.zeros(*list(labels.shape), self.num_classes+1, dtype=cls_score.dtype, device=labels.device)
+        one_hot_targets.scatter_(-1, labels.unsqueeze(dim=-1).long(), 1.0)
+        one_hot_targets = one_hot_targets[..., :-1]
+        loss_cls = self.loss_cls(
+            cls_score, one_hot_targets, label_weights
+        ).sum() / max(num_pos, 1)
+
+        preds = torch.cat([pred_dicts[head_name] for head_name in self.model_cfg.SEPARATE_HEAD_CFG.HEAD_ORDER], dim=1).permute(0, 2, 1)
+        code_weights = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['code_weights']
+        reg_weights = bbox_weights * bbox_weights.new_tensor(code_weights)
+
+        loss_bbox = self.loss_bbox(preds, bbox_targets) 
+        loss_bbox = (loss_bbox * reg_weights).sum() / max(num_pos, 1)
+
+        loss_dict["loss_cls"] = loss_cls.item() * self.loss_cls_weight
+        loss_dict["loss_bbox"] = loss_bbox.item() * self.loss_bbox_weight
+        loss_all = loss_all + loss_cls * self.loss_cls_weight + loss_bbox * self.loss_bbox_weight
+
+        loss_dict[f"matched_ious"] = loss_cls.new_tensor(matched_ious)
+        loss_dict['loss_trans'] = loss_all
+
+        return loss_all,loss_dict
+
+    def encode_bbox(self, bboxes):
+        code_size = 10
+        targets = torch.zeros([bboxes.shape[0], code_size]).to(bboxes.device)
+        targets[:, 0] = (bboxes[:, 0] - self.point_cloud_range[0]) / (self.feature_map_stride * self.voxel_size[0])
+        targets[:, 1] = (bboxes[:, 1] - self.point_cloud_range[1]) / (self.feature_map_stride * self.voxel_size[1])
+        targets[:, 3:6] = bboxes[:, 3:6].log()
+        targets[:, 2] = bboxes[:, 2]
+        targets[:, 6] = torch.sin(bboxes[:, 6])
+        targets[:, 7] = torch.cos(bboxes[:, 6])
+        if code_size == 10:
+            targets[:, 8:10] = bboxes[:, 7:]
+        return targets
+
+    def decode_bbox(self, heatmap, rot, dim, center, height, vel, filter=False):
+        
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        score_thresh = post_process_cfg.SCORE_THRESH
+        post_center_range = post_process_cfg.POST_CENTER_RANGE
+        post_center_range = torch.tensor(post_center_range).cuda().float()
+        # class label
+        final_preds = heatmap.max(1, keepdims=False).indices
+        final_scores = heatmap.max(1, keepdims=False).values
+
+        center[:, 0, :] = center[:, 0, :] * self.feature_map_stride * self.voxel_size[0] + self.point_cloud_range[0]
+        center[:, 1, :] = center[:, 1, :] * self.feature_map_stride * self.voxel_size[1] + self.point_cloud_range[1]
+        dim = dim.exp()
+        rots, rotc = rot[:, 0:1, :], rot[:, 1:2, :]
+        rot = torch.atan2(rots, rotc)
+
+        if vel is None:
+            final_box_preds = torch.cat([center, height, dim, rot], dim=1).permute(0, 2, 1)
+        else:
+            final_box_preds = torch.cat([center, height, dim, rot, vel], dim=1).permute(0, 2, 1)
+
+        predictions_dicts = []
+        for i in range(heatmap.shape[0]):
+            boxes3d = final_box_preds[i]
+            scores = final_scores[i]
+            labels = final_preds[i]
+            predictions_dict = {
+                'pred_boxes': boxes3d,
+                'pred_scores': scores,
+                'pred_labels': labels
+            }
+            predictions_dicts.append(predictions_dict)
+
+        if filter is False:
+            return predictions_dicts
+
+        thresh_mask = final_scores > score_thresh        
+        mask = (final_box_preds[..., :3] >= post_center_range[:3]).all(2)
+        mask &= (final_box_preds[..., :3] <= post_center_range[3:]).all(2)
+
+        predictions_dicts = []
+        for i in range(heatmap.shape[0]):
+            cmask = mask[i, :]
+            cmask &= thresh_mask[i]
+
+            boxes3d = final_box_preds[i, cmask]
+            scores = final_scores[i, cmask]
+            labels = final_preds[i, cmask]
+            predictions_dict = {
+                'pred_boxes': boxes3d,
+                'pred_scores': scores,
+                'pred_labels': labels,
+            }
+
+            predictions_dicts.append(predictions_dict)
+
+        return predictions_dicts
+
+    def get_bboxes(self, preds_dicts):
+
+        batch_size = preds_dicts["heatmap"].shape[0]
+        batch_score = preds_dicts["heatmap"].sigmoid()
+        one_hot = F.one_hot(
+            self.query_labels, num_classes=self.num_classes
+        ).permute(0, 2, 1)
+        batch_score = batch_score * preds_dicts["query_heatmap_score"] * one_hot
+        batch_center = preds_dicts["center"]
+        batch_height = preds_dicts["height"]
+        batch_dim = preds_dicts["dim"]
+        batch_rot = preds_dicts["rot"]
+        batch_vel = None
+        if "vel" in preds_dicts:
+            batch_vel = preds_dicts["vel"]
+
+        ret_dict = self.decode_bbox(
+            batch_score, batch_rot, batch_dim,
+            batch_center, batch_height, batch_vel,
+            filter=True,
+        )
+        for k in range(batch_size):
+            ret_dict[k]['pred_labels'] = ret_dict[k]['pred_labels'].int() + 1
+
+        return ret_dict 
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/voxelnext_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/voxelnext_head.py
new file mode 100644
index 000000000..e2f234f73
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/dense_heads/voxelnext_head.py
@@ -0,0 +1,559 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.nn.init import kaiming_normal_
+from ..model_utils import centernet_utils
+from ..model_utils import model_nms_utils
+from ...utils import loss_utils
+from ...utils.spconv_utils import replace_feature, spconv
+import copy
+from easydict import EasyDict
+
+
+class SeparateHead(nn.Module):
+    def __init__(self, input_channels, sep_head_dict, kernel_size, init_bias=-2.19, use_bias=False):
+        super().__init__()
+        self.sep_head_dict = sep_head_dict
+
+        for cur_name in self.sep_head_dict:
+            output_channels = self.sep_head_dict[cur_name]['out_channels']
+            num_conv = self.sep_head_dict[cur_name]['num_conv']
+
+            fc_list = []
+            for k in range(num_conv - 1):
+                fc_list.append(spconv.SparseSequential(
+                    spconv.SubMConv2d(input_channels, input_channels, kernel_size, padding=int(kernel_size//2), bias=use_bias, indice_key=cur_name),
+                    nn.BatchNorm1d(input_channels),
+                    nn.ReLU()
+                ))
+            fc_list.append(spconv.SubMConv2d(input_channels, output_channels, 1, bias=True, indice_key=cur_name+'out'))
+            fc = nn.Sequential(*fc_list)
+            if 'hm' in cur_name:
+                fc[-1].bias.data.fill_(init_bias)
+            else:
+                for m in fc.modules():
+                    if isinstance(m, spconv.SubMConv2d):
+                        kaiming_normal_(m.weight.data)
+                        if hasattr(m, "bias") and m.bias is not None:
+                            nn.init.constant_(m.bias, 0)
+
+            self.__setattr__(cur_name, fc)
+
+    def forward(self, x):
+        ret_dict = {}
+        for cur_name in self.sep_head_dict:
+            ret_dict[cur_name] = self.__getattr__(cur_name)(x).features
+
+        return ret_dict
+
+
+class VoxelNeXtHead(nn.Module):
+    def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range, voxel_size,
+                 predict_boxes_when_training=False):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_class = num_class
+        self.grid_size = grid_size
+        self.point_cloud_range = torch.Tensor(point_cloud_range).cuda()
+        self.voxel_size = torch.Tensor(voxel_size).cuda()
+        self.feature_map_stride = self.model_cfg.TARGET_ASSIGNER_CONFIG.get('FEATURE_MAP_STRIDE', None)
+
+        self.class_names = class_names
+        self.class_names_each_head = []
+        self.class_id_mapping_each_head = []
+        self.gaussian_ratio = self.model_cfg.get('GAUSSIAN_RATIO', 1)
+        self.gaussian_type = self.model_cfg.get('GAUSSIAN_TYPE', ['nearst', 'gt_center'])
+        # The iou branch is only used for Waymo dataset
+        self.iou_branch = self.model_cfg.get('IOU_BRANCH', False)
+        if self.iou_branch:
+            self.rectifier = self.model_cfg.get('RECTIFIER')
+            nms_configs = self.model_cfg.POST_PROCESSING.NMS_CONFIG
+            self.nms_configs = [EasyDict(NMS_TYPE=nms_configs.NMS_TYPE, 
+                                    NMS_THRESH=nms_configs.NMS_THRESH[i],
+                                    NMS_PRE_MAXSIZE=nms_configs.NMS_PRE_MAXSIZE[i],
+                                    NMS_POST_MAXSIZE=nms_configs.NMS_POST_MAXSIZE[i]) for i in range(num_class)]
+
+        self.double_flip = self.model_cfg.get('DOUBLE_FLIP', False)
+        for cur_class_names in self.model_cfg.CLASS_NAMES_EACH_HEAD:
+            self.class_names_each_head.append([x for x in cur_class_names if x in class_names])
+            cur_class_id_mapping = torch.from_numpy(np.array(
+                [self.class_names.index(x) for x in cur_class_names if x in class_names]
+            )).cuda()
+            self.class_id_mapping_each_head.append(cur_class_id_mapping)
+
+        total_classes = sum([len(x) for x in self.class_names_each_head])
+        assert total_classes == len(self.class_names), f'class_names_each_head={self.class_names_each_head}'
+
+        kernel_size_head = self.model_cfg.get('KERNEL_SIZE_HEAD', 3)
+
+        self.heads_list = nn.ModuleList()
+        self.separate_head_cfg = self.model_cfg.SEPARATE_HEAD_CFG
+        for idx, cur_class_names in enumerate(self.class_names_each_head):
+            cur_head_dict = copy.deepcopy(self.separate_head_cfg.HEAD_DICT)
+            cur_head_dict['hm'] = dict(out_channels=len(cur_class_names), num_conv=self.model_cfg.NUM_HM_CONV)
+            self.heads_list.append(
+                SeparateHead(
+                    input_channels=self.model_cfg.get('SHARED_CONV_CHANNEL', 128),
+                    sep_head_dict=cur_head_dict,
+                    kernel_size=kernel_size_head,
+                    init_bias=-2.19,
+                    use_bias=self.model_cfg.get('USE_BIAS_BEFORE_NORM', False),
+                )
+            )
+        self.predict_boxes_when_training = predict_boxes_when_training
+        self.forward_ret_dict = {}
+        self.build_losses()
+
+    def build_losses(self):
+        self.add_module('hm_loss_func', loss_utils.FocalLossSparse())
+        self.add_module('reg_loss_func', loss_utils.RegLossSparse())
+        if self.iou_branch:
+            self.add_module('crit_iou', loss_utils.IouLossSparse())
+            self.add_module('crit_iou_reg', loss_utils.IouRegLossSparse())
+
+    def assign_targets(self, gt_boxes, num_voxels, spatial_indices, spatial_shape):
+        """
+        Args:
+            gt_boxes: (B, M, 8)
+        Returns:
+        """
+        target_assigner_cfg = self.model_cfg.TARGET_ASSIGNER_CONFIG
+
+        batch_size = gt_boxes.shape[0]
+        ret_dict = {
+            'heatmaps': [],
+            'target_boxes': [],
+            'inds': [],
+            'masks': [],
+            'heatmap_masks': [],
+            'gt_boxes': []
+        }
+
+        all_names = np.array(['bg', *self.class_names])
+        for idx, cur_class_names in enumerate(self.class_names_each_head):
+            heatmap_list, target_boxes_list, inds_list, masks_list, gt_boxes_list = [], [], [], [], []
+            for bs_idx in range(batch_size):
+                cur_gt_boxes = gt_boxes[bs_idx]
+                gt_class_names = all_names[cur_gt_boxes[:, -1].cpu().long().numpy()]
+
+                gt_boxes_single_head = []
+
+                for idx, name in enumerate(gt_class_names):
+                    if name not in cur_class_names:
+                        continue
+                    temp_box = cur_gt_boxes[idx]
+                    temp_box[-1] = cur_class_names.index(name) + 1
+                    gt_boxes_single_head.append(temp_box[None, :])
+
+                if len(gt_boxes_single_head) == 0:
+                    gt_boxes_single_head = cur_gt_boxes[:0, :]
+                else:
+                    gt_boxes_single_head = torch.cat(gt_boxes_single_head, dim=0)
+
+                heatmap, ret_boxes, inds, mask = self.assign_target_of_single_head(
+                    num_classes=len(cur_class_names), gt_boxes=gt_boxes_single_head,
+                    num_voxels=num_voxels[bs_idx], spatial_indices=spatial_indices[bs_idx], 
+                    spatial_shape=spatial_shape, 
+                    feature_map_stride=target_assigner_cfg.FEATURE_MAP_STRIDE,
+                    num_max_objs=target_assigner_cfg.NUM_MAX_OBJS,
+                    gaussian_overlap=target_assigner_cfg.GAUSSIAN_OVERLAP,
+                    min_radius=target_assigner_cfg.MIN_RADIUS,
+                )
+                heatmap_list.append(heatmap.to(gt_boxes_single_head.device))
+                target_boxes_list.append(ret_boxes.to(gt_boxes_single_head.device))
+                inds_list.append(inds.to(gt_boxes_single_head.device))
+                masks_list.append(mask.to(gt_boxes_single_head.device))
+                gt_boxes_list.append(gt_boxes_single_head[:, :-1])
+
+            ret_dict['heatmaps'].append(torch.cat(heatmap_list, dim=1).permute(1, 0))
+            ret_dict['target_boxes'].append(torch.stack(target_boxes_list, dim=0))
+            ret_dict['inds'].append(torch.stack(inds_list, dim=0))
+            ret_dict['masks'].append(torch.stack(masks_list, dim=0))
+            ret_dict['gt_boxes'].append(gt_boxes_list)
+
+        return ret_dict
+
+    def distance(self, voxel_indices, center):
+        distances = ((voxel_indices - center.unsqueeze(0))**2).sum(-1)
+        return distances
+
+    def assign_target_of_single_head(
+            self, num_classes, gt_boxes, num_voxels, spatial_indices, spatial_shape, feature_map_stride, num_max_objs=500,
+            gaussian_overlap=0.1, min_radius=2
+    ):
+        """
+        Args:
+            gt_boxes: (N, 8)
+            feature_map_size: (2), [x, y]
+
+        Returns:
+
+        """
+        heatmap = gt_boxes.new_zeros(num_classes, num_voxels)
+
+        ret_boxes = gt_boxes.new_zeros((num_max_objs, gt_boxes.shape[-1] - 1 + 1))
+        inds = gt_boxes.new_zeros(num_max_objs).long()
+        mask = gt_boxes.new_zeros(num_max_objs).long()
+
+        x, y, z = gt_boxes[:, 0], gt_boxes[:, 1], gt_boxes[:, 2]
+        coord_x = (x - self.point_cloud_range[0]) / self.voxel_size[0] / feature_map_stride
+        coord_y = (y - self.point_cloud_range[1]) / self.voxel_size[1] / feature_map_stride
+
+        coord_x = torch.clamp(coord_x, min=0, max=spatial_shape[1] - 0.5)  # bugfixed: 1e-6 does not work for center.int()
+        coord_y = torch.clamp(coord_y, min=0, max=spatial_shape[0] - 0.5)  #
+
+        center = torch.cat((coord_x[:, None], coord_y[:, None]), dim=-1)
+        center_int = center.int()
+        center_int_float = center_int.float()
+
+        dx, dy, dz = gt_boxes[:, 3], gt_boxes[:, 4], gt_boxes[:, 5]
+        dx = dx / self.voxel_size[0] / feature_map_stride
+        dy = dy / self.voxel_size[1] / feature_map_stride
+
+        radius = centernet_utils.gaussian_radius(dx, dy, min_overlap=gaussian_overlap)
+        radius = torch.clamp_min(radius.int(), min=min_radius)
+
+        for k in range(min(num_max_objs, gt_boxes.shape[0])):
+            if dx[k] <= 0 or dy[k] <= 0:
+                continue
+
+            if not (0 <= center_int[k][0] <= spatial_shape[1] and 0 <= center_int[k][1] <= spatial_shape[0]):
+                continue
+
+            cur_class_id = (gt_boxes[k, -1] - 1).long()
+            distance = self.distance(spatial_indices, center[k])
+            inds[k] = distance.argmin()
+            mask[k] = 1
+
+            if 'gt_center' in self.gaussian_type:
+                centernet_utils.draw_gaussian_to_heatmap_voxels(heatmap[cur_class_id], distance, radius[k].item() * self.gaussian_ratio)
+
+            if 'nearst' in self.gaussian_type:
+                centernet_utils.draw_gaussian_to_heatmap_voxels(heatmap[cur_class_id], self.distance(spatial_indices, spatial_indices[inds[k]]), radius[k].item() * self.gaussian_ratio)
+
+            ret_boxes[k, 0:2] = center[k] - spatial_indices[inds[k]][:2]
+            ret_boxes[k, 2] = z[k]
+            ret_boxes[k, 3:6] = gt_boxes[k, 3:6].log()
+            ret_boxes[k, 6] = torch.cos(gt_boxes[k, 6])
+            ret_boxes[k, 7] = torch.sin(gt_boxes[k, 6])
+            if gt_boxes.shape[1] > 8:
+                ret_boxes[k, 8:] = gt_boxes[k, 7:-1]
+
+        return heatmap, ret_boxes, inds, mask
+
+    def sigmoid(self, x):
+        y = torch.clamp(x.sigmoid(), min=1e-4, max=1 - 1e-4)
+        return y
+
+    def get_loss(self):
+        pred_dicts = self.forward_ret_dict['pred_dicts']
+        target_dicts = self.forward_ret_dict['target_dicts']
+        batch_index = self.forward_ret_dict['batch_index']
+
+        tb_dict = {}
+        loss = 0
+        batch_indices = self.forward_ret_dict['voxel_indices'][:, 0]
+        spatial_indices = self.forward_ret_dict['voxel_indices'][:, 1:]
+
+        for idx, pred_dict in enumerate(pred_dicts):
+            pred_dict['hm'] = self.sigmoid(pred_dict['hm'])
+            hm_loss = self.hm_loss_func(pred_dict['hm'], target_dicts['heatmaps'][idx])
+            hm_loss *= self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['cls_weight']
+
+            target_boxes = target_dicts['target_boxes'][idx]
+            pred_boxes = torch.cat([pred_dict[head_name] for head_name in self.separate_head_cfg.HEAD_ORDER], dim=1)
+
+            reg_loss = self.reg_loss_func(
+                pred_boxes, target_dicts['masks'][idx], target_dicts['inds'][idx], target_boxes, batch_index
+            )
+            loc_loss = (reg_loss * reg_loss.new_tensor(self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['code_weights'])).sum()
+            loc_loss = loc_loss * self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['loc_weight']
+            tb_dict['hm_loss_head_%d' % idx] = hm_loss.item()
+            tb_dict['loc_loss_head_%d' % idx] = loc_loss.item()
+            if self.iou_branch:
+                batch_box_preds = self._get_predicted_boxes(pred_dict, spatial_indices)
+                pred_boxes_for_iou = batch_box_preds.detach()
+                iou_loss = self.crit_iou(pred_dict['iou'], target_dicts['masks'][idx], target_dicts['inds'][idx],
+                                            pred_boxes_for_iou, target_dicts['gt_boxes'][idx], batch_indices)
+
+                iou_reg_loss = self.crit_iou_reg(batch_box_preds, target_dicts['masks'][idx], target_dicts['inds'][idx],
+                                                    target_dicts['gt_boxes'][idx], batch_indices)
+                iou_weight = self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['iou_weight'] if 'iou_weight' in self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS else self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['loc_weight']
+                iou_reg_loss = iou_reg_loss * iou_weight #self.model_cfg.LOSS_CONFIG.LOSS_WEIGHTS['loc_weight']
+
+                loss += (hm_loss + loc_loss + iou_loss + iou_reg_loss)
+                tb_dict['iou_loss_head_%d' % idx] = iou_loss.item()
+                tb_dict['iou_reg_loss_head_%d' % idx] = iou_reg_loss.item()
+            else:
+                loss += hm_loss + loc_loss
+
+        tb_dict['rpn_loss'] = loss.item()
+        return loss, tb_dict
+
+    def _get_predicted_boxes(self, pred_dict, spatial_indices):
+        center = pred_dict['center']
+        center_z = pred_dict['center_z']
+        #dim = pred_dict['dim'].exp()
+        dim = torch.exp(torch.clamp(pred_dict['dim'], min=-5, max=5))
+        rot_cos = pred_dict['rot'][:, 0].unsqueeze(dim=1)
+        rot_sin = pred_dict['rot'][:, 1].unsqueeze(dim=1)
+        angle = torch.atan2(rot_sin, rot_cos)
+        xs = (spatial_indices[:, 1:2] + center[:, 0:1]) * self.feature_map_stride * self.voxel_size[0] + self.point_cloud_range[0]
+        ys = (spatial_indices[:, 0:1] + center[:, 1:2]) * self.feature_map_stride * self.voxel_size[1] + self.point_cloud_range[1]
+
+        box_part_list = [xs, ys, center_z, dim, angle]
+        pred_box = torch.cat((box_part_list), dim=-1)
+        return pred_box
+
+    def rotate_class_specific_nms_iou(self, boxes, scores, iou_preds, labels, rectifier, nms_configs):
+        """
+        :param boxes: (N, 5) [x, y, z, l, w, h, theta]
+        :param scores: (N)
+        :param thresh:
+        :return:
+        """
+        assert isinstance(rectifier, list)
+
+        box_preds_list, scores_list, labels_list = [], [], []
+        for cls in range(self.num_class):
+            mask = labels == cls
+            boxes_cls = boxes[mask]
+            scores_cls = torch.pow(scores[mask], 1 - rectifier[cls]) * torch.pow(iou_preds[mask].squeeze(-1), rectifier[cls])
+            labels_cls = labels[mask]
+
+            selected, selected_scores = model_nms_utils.class_agnostic_nms(box_scores=scores_cls, box_preds=boxes_cls, 
+                                                        nms_config=nms_configs[cls], score_thresh=None)
+
+            box_preds_list.append(boxes_cls[selected])
+            scores_list.append(scores_cls[selected])
+            labels_list.append(labels_cls[selected])
+
+        return torch.cat(box_preds_list, dim=0), torch.cat(scores_list, dim=0), torch.cat(labels_list, dim=0)
+
+    def merge_double_flip(self, pred_dict, batch_size, voxel_indices, spatial_shape):
+        # spatial_shape (Z, Y, X)
+        pred_dict['hm'] = pred_dict['hm'].sigmoid()
+        pred_dict['dim'] = pred_dict['dim'].exp()
+
+        batch_indices = voxel_indices[:, 0]
+        spatial_indices = voxel_indices[:, 1:]
+
+        pred_dict_ = {k: [] for k in pred_dict.keys()}
+        counts = []
+        spatial_indices_ = []
+        for bs_idx in range(batch_size):
+            spatial_indices_batch = []
+            pred_dict_batch = {k: [] for k in pred_dict.keys()}
+            for i in range(4):
+                bs_indices = batch_indices == (bs_idx * 4 + i)
+                if i in [1, 3]:
+                    spatial_indices[bs_indices, 0] = spatial_shape[0] - spatial_indices[bs_indices, 0]
+                if i in [2, 3]:
+                    spatial_indices[bs_indices, 1] = spatial_shape[1] - spatial_indices[bs_indices, 1]
+
+                if i == 1:
+                    pred_dict['center'][bs_indices, 1] = - pred_dict['center'][bs_indices, 1]
+                    pred_dict['rot'][bs_indices, 1] *= -1
+                    pred_dict['vel'][bs_indices, 1] *= -1
+
+                if i == 2:
+                    pred_dict['center'][bs_indices, 0] = - pred_dict['center'][bs_indices, 0]
+                    pred_dict['rot'][bs_indices, 0] *= -1
+                    pred_dict['vel'][bs_indices, 0] *= -1
+
+                if i == 3:
+                    pred_dict['center'][bs_indices, 0] = - pred_dict['center'][bs_indices, 0]
+                    pred_dict['center'][bs_indices, 1] = - pred_dict['center'][bs_indices, 1]
+
+                    pred_dict['rot'][bs_indices, 1] *= -1
+                    pred_dict['rot'][bs_indices, 0] *= -1
+
+                    pred_dict['vel'][bs_indices] *= -1
+
+                spatial_indices_batch.append(spatial_indices[bs_indices])
+
+                for k in pred_dict.keys():
+                    pred_dict_batch[k].append(pred_dict[k][bs_indices])
+
+            spatial_indices_batch = torch.cat(spatial_indices_batch)
+
+            spatial_indices_unique, _inv, count = torch.unique(spatial_indices_batch, dim=0, return_inverse=True,
+                                                               return_counts=True)
+            spatial_indices_.append(spatial_indices_unique)
+            counts.append(count)
+            for k in pred_dict.keys():
+                pred_dict_batch[k] = torch.cat(pred_dict_batch[k])
+                features_unique = pred_dict_batch[k].new_zeros(
+                    (spatial_indices_unique.shape[0], pred_dict_batch[k].shape[1]))
+                features_unique.index_add_(0, _inv, pred_dict_batch[k])
+                pred_dict_[k].append(features_unique)
+
+        for k in pred_dict.keys():
+            pred_dict_[k] = torch.cat(pred_dict_[k])
+        counts = torch.cat(counts).unsqueeze(-1).float()
+        voxel_indices_ = torch.cat([torch.cat(
+            [torch.full((indices.shape[0], 1), i, device=indices.device, dtype=indices.dtype), indices], dim=1
+        ) for i, indices in enumerate(spatial_indices_)])
+
+        batch_hm = pred_dict_['hm']
+        batch_center = pred_dict_['center']
+        batch_center_z = pred_dict_['center_z']
+        batch_dim = pred_dict_['dim']
+        batch_rot_cos = pred_dict_['rot'][:, 0].unsqueeze(dim=1)
+        batch_rot_sin = pred_dict_['rot'][:, 1].unsqueeze(dim=1)
+        batch_vel = pred_dict_['vel'] if 'vel' in self.separate_head_cfg.HEAD_ORDER else None
+
+        batch_hm /= counts
+        batch_center /= counts
+        batch_center_z /= counts
+        batch_dim /= counts
+        batch_rot_cos /= counts
+        batch_rot_sin /= counts
+
+        if not batch_vel is None:
+            batch_vel /= counts
+
+        return batch_hm, batch_center, batch_center_z, batch_dim, batch_rot_cos, batch_rot_sin, batch_vel, None, voxel_indices_
+
+    def generate_predicted_boxes(self, batch_size, pred_dicts, voxel_indices, spatial_shape):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        post_center_limit_range = torch.tensor(post_process_cfg.POST_CENTER_LIMIT_RANGE).cuda().float()
+
+        ret_dict = [{
+            'pred_boxes': [],
+            'pred_scores': [],
+            'pred_labels': [],
+            'pred_ious': [],
+        } for k in range(batch_size)]
+        for idx, pred_dict in enumerate(pred_dicts):
+            if self.double_flip:
+                batch_hm, batch_center, batch_center_z, batch_dim, batch_rot_cos, batch_rot_sin, batch_vel, batch_iou, voxel_indices_ = \
+                self.merge_double_flip(pred_dict, batch_size, voxel_indices.clone(), spatial_shape)
+            else:
+                batch_hm = pred_dict['hm'].sigmoid()
+                batch_center = pred_dict['center']
+                batch_center_z = pred_dict['center_z']
+                batch_dim = pred_dict['dim'].exp()
+                batch_rot_cos = pred_dict['rot'][:, 0].unsqueeze(dim=1)
+                batch_rot_sin = pred_dict['rot'][:, 1].unsqueeze(dim=1)
+                batch_iou = (pred_dict['iou'] + 1) * 0.5 if self.iou_branch else None
+                batch_vel = pred_dict['vel'] if 'vel' in self.separate_head_cfg.HEAD_ORDER else None
+                voxel_indices_ = voxel_indices
+
+            final_pred_dicts = centernet_utils.decode_bbox_from_voxels_nuscenes(
+                batch_size=batch_size, indices=voxel_indices_,
+                obj=batch_hm, 
+                rot_cos=batch_rot_cos,
+                rot_sin=batch_rot_sin,
+                center=batch_center, center_z=batch_center_z,
+                dim=batch_dim, vel=batch_vel, iou=batch_iou,
+                point_cloud_range=self.point_cloud_range, voxel_size=self.voxel_size,
+                feature_map_stride=self.feature_map_stride,
+                K=post_process_cfg.MAX_OBJ_PER_SAMPLE,
+                #circle_nms=(post_process_cfg.NMS_CONFIG.NMS_TYPE == 'circle_nms'),
+                score_thresh=post_process_cfg.SCORE_THRESH,
+                post_center_limit_range=post_center_limit_range
+            )
+
+            for k, final_dict in enumerate(final_pred_dicts):
+                final_dict['pred_labels'] = self.class_id_mapping_each_head[idx][final_dict['pred_labels'].long()]
+                if not self.iou_branch:
+                    selected, selected_scores = model_nms_utils.class_agnostic_nms(
+                        box_scores=final_dict['pred_scores'], box_preds=final_dict['pred_boxes'],
+                        nms_config=post_process_cfg.NMS_CONFIG,
+                        score_thresh=None
+                    )
+
+                    final_dict['pred_boxes'] = final_dict['pred_boxes'][selected]
+                    final_dict['pred_scores'] = selected_scores
+                    final_dict['pred_labels'] = final_dict['pred_labels'][selected]
+
+                ret_dict[k]['pred_boxes'].append(final_dict['pred_boxes'])
+                ret_dict[k]['pred_scores'].append(final_dict['pred_scores'])
+                ret_dict[k]['pred_labels'].append(final_dict['pred_labels'])
+                ret_dict[k]['pred_ious'].append(final_dict['pred_ious'])
+
+        for k in range(batch_size):
+            pred_boxes = torch.cat(ret_dict[k]['pred_boxes'], dim=0)
+            pred_scores = torch.cat(ret_dict[k]['pred_scores'], dim=0)
+            pred_labels = torch.cat(ret_dict[k]['pred_labels'], dim=0)
+            if self.iou_branch:
+                pred_ious = torch.cat(ret_dict[k]['pred_ious'], dim=0)
+                pred_boxes, pred_scores, pred_labels = self.rotate_class_specific_nms_iou(pred_boxes, pred_scores, pred_ious, pred_labels, self.rectifier, self.nms_configs)
+
+            ret_dict[k]['pred_boxes'] = pred_boxes
+            ret_dict[k]['pred_scores'] = pred_scores
+            ret_dict[k]['pred_labels'] = pred_labels + 1
+
+        return ret_dict
+
+    @staticmethod
+    def reorder_rois_for_refining(batch_size, pred_dicts):
+        num_max_rois = max([len(cur_dict['pred_boxes']) for cur_dict in pred_dicts])
+        num_max_rois = max(1, num_max_rois)  # at least one faked rois to avoid error
+        pred_boxes = pred_dicts[0]['pred_boxes']
+
+        rois = pred_boxes.new_zeros((batch_size, num_max_rois, pred_boxes.shape[-1]))
+        roi_scores = pred_boxes.new_zeros((batch_size, num_max_rois))
+        roi_labels = pred_boxes.new_zeros((batch_size, num_max_rois)).long()
+
+        for bs_idx in range(batch_size):
+            num_boxes = len(pred_dicts[bs_idx]['pred_boxes'])
+
+            rois[bs_idx, :num_boxes, :] = pred_dicts[bs_idx]['pred_boxes']
+            roi_scores[bs_idx, :num_boxes] = pred_dicts[bs_idx]['pred_scores']
+            roi_labels[bs_idx, :num_boxes] = pred_dicts[bs_idx]['pred_labels']
+        return rois, roi_scores, roi_labels
+
+    def _get_voxel_infos(self, x):
+        spatial_shape = x.spatial_shape
+        voxel_indices = x.indices
+        spatial_indices = []
+        num_voxels = []
+        batch_size = x.batch_size
+        batch_index = voxel_indices[:, 0]
+
+        for bs_idx in range(batch_size):
+            batch_inds = batch_index==bs_idx
+            spatial_indices.append(voxel_indices[batch_inds][:, [2, 1]])
+            num_voxels.append(batch_inds.sum())
+
+        return spatial_shape, batch_index, voxel_indices, spatial_indices, num_voxels
+
+    def forward(self, data_dict):
+        x = data_dict['encoded_spconv_tensor']
+
+        spatial_shape, batch_index, voxel_indices, spatial_indices, num_voxels = self._get_voxel_infos(x)
+        self.forward_ret_dict['batch_index'] = batch_index
+        
+        pred_dicts = []
+        for head in self.heads_list:
+            pred_dicts.append(head(x))
+
+        if self.training:
+            target_dict = self.assign_targets(
+                data_dict['gt_boxes'], num_voxels, spatial_indices, spatial_shape
+            )
+            self.forward_ret_dict['target_dicts'] = target_dict
+
+        self.forward_ret_dict['pred_dicts'] = pred_dicts
+        self.forward_ret_dict['voxel_indices'] = voxel_indices
+
+        if not self.training or self.predict_boxes_when_training:
+            if self.double_flip:
+                data_dict['batch_size'] = data_dict['batch_size'] // 4
+            pred_dicts = self.generate_predicted_boxes(
+                data_dict['batch_size'], 
+                pred_dicts, voxel_indices, spatial_shape
+            )
+
+            if self.predict_boxes_when_training:
+                rois, roi_scores, roi_labels = self.reorder_rois_for_refining(data_dict['batch_size'], pred_dicts)
+                data_dict['rois'] = rois
+                data_dict['roi_scores'] = roi_scores
+                data_dict['roi_labels'] = roi_labels
+                data_dict['has_class_labels'] = True
+            else:
+                data_dict['final_box_dicts'] = pred_dicts
+
+        return data_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/PartA2_net.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/PartA2_net.py
new file mode 100644
index 000000000..890b59471
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/PartA2_net.py
@@ -0,0 +1,31 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class PartA2Net(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        loss_point, tb_dict = self.point_head.get_loss(tb_dict)
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss_rpn + loss_point + loss_rcnn
+        return loss, tb_dict, disp_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/__init__.py
new file mode 100644
index 000000000..1af193078
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/__init__.py
@@ -0,0 +1,46 @@
+from .detector3d_template import Detector3DTemplate
+from .PartA2_net import PartA2Net
+from .point_rcnn import PointRCNN
+from .pointpillar import PointPillar
+from .pv_rcnn import PVRCNN
+from .second_net import SECONDNet
+from .second_net_iou import SECONDNetIoU
+from .caddn import CaDDN
+from .voxel_rcnn import VoxelRCNN
+from .centerpoint import CenterPoint
+from .pv_rcnn_plusplus import PVRCNNPlusPlus
+from .mppnet import MPPNet
+from .mppnet_e2e import MPPNetE2E
+from .pillarnet import PillarNet
+from .voxelnext import VoxelNeXt
+from .transfusion import TransFusion
+from .bevfusion import BevFusion
+
+__all__ = {
+    'Detector3DTemplate': Detector3DTemplate,
+    'SECONDNet': SECONDNet,
+    'PartA2Net': PartA2Net,
+    'PVRCNN': PVRCNN,
+    'PointPillar': PointPillar,
+    'PointRCNN': PointRCNN,
+    'SECONDNetIoU': SECONDNetIoU,
+    'CaDDN': CaDDN,
+    'VoxelRCNN': VoxelRCNN,
+    'CenterPoint': CenterPoint,
+    'PillarNet': PillarNet,
+    'PVRCNNPlusPlus': PVRCNNPlusPlus,
+    'MPPNet': MPPNet,
+    'MPPNetE2E': MPPNetE2E,
+    'PillarNet': PillarNet,
+    'VoxelNeXt': VoxelNeXt,
+    'TransFusion': TransFusion,
+    'BevFusion': BevFusion,
+}
+
+
+def build_detector(model_cfg, num_class, dataset):
+    model = __all__[model_cfg.NAME](
+        model_cfg=model_cfg, num_class=num_class, dataset=dataset
+    )
+
+    return model
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/bevfusion.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/bevfusion.py
new file mode 100644
index 000000000..bb55fc094
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/bevfusion.py
@@ -0,0 +1,101 @@
+from .detector3d_template import Detector3DTemplate
+from .. import backbones_image, view_transforms
+from ..backbones_image import img_neck
+from ..backbones_2d import fuser
+
+class BevFusion(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_topology = [
+            'vfe', 'backbone_3d', 'map_to_bev_module', 'pfe',
+            'image_backbone','neck','vtransform','fuser',
+            'backbone_2d', 'dense_head',  'point_head', 'roi_head'
+        ]
+        self.module_list = self.build_networks()
+       
+    def build_neck(self,model_info_dict):
+        if self.model_cfg.get('NECK', None) is None:
+            return None, model_info_dict
+        neck_module = img_neck.__all__[self.model_cfg.NECK.NAME](
+            model_cfg=self.model_cfg.NECK
+        )
+        model_info_dict['module_list'].append(neck_module)
+
+        return neck_module, model_info_dict
+    
+    def build_vtransform(self,model_info_dict):
+        if self.model_cfg.get('VTRANSFORM', None) is None:
+            return None, model_info_dict
+        
+        vtransform_module = view_transforms.__all__[self.model_cfg.VTRANSFORM.NAME](
+            model_cfg=self.model_cfg.VTRANSFORM
+        )
+        model_info_dict['module_list'].append(vtransform_module)
+
+        return vtransform_module, model_info_dict
+
+    def build_image_backbone(self, model_info_dict):
+        if self.model_cfg.get('IMAGE_BACKBONE', None) is None:
+            return None, model_info_dict
+        image_backbone_module = backbones_image.__all__[self.model_cfg.IMAGE_BACKBONE.NAME](
+            model_cfg=self.model_cfg.IMAGE_BACKBONE
+        )
+        image_backbone_module.init_weights()
+        model_info_dict['module_list'].append(image_backbone_module)
+
+        return image_backbone_module, model_info_dict
+    
+    def build_fuser(self, model_info_dict):
+        if self.model_cfg.get('FUSER', None) is None:
+            return None, model_info_dict
+    
+        fuser_module = fuser.__all__[self.model_cfg.FUSER.NAME](
+            model_cfg=self.model_cfg.FUSER
+        )
+        model_info_dict['module_list'].append(fuser_module)
+        model_info_dict['num_bev_features'] = self.model_cfg.FUSER.OUT_CHANNEL
+        return fuser_module, model_info_dict
+
+    def forward(self, batch_dict):
+
+        for i,cur_module in enumerate(self.module_list):
+            batch_dict = cur_module(batch_dict)
+        
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss(batch_dict)
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self,batch_dict):
+        disp_dict = {}
+
+        loss_trans, tb_dict = batch_dict['loss'],batch_dict['tb_dict']
+        tb_dict = {
+            'loss_trans': loss_trans.item(),
+            **tb_dict
+        }
+
+        loss = loss_trans
+        return loss, tb_dict, disp_dict
+
+    def post_processing(self, batch_dict):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        final_pred_dict = batch_dict['final_box_dicts']
+        recall_dict = {}
+        for index in range(batch_size):
+            pred_boxes = final_pred_dict[index]['pred_boxes']
+
+            recall_dict = self.generate_recall_record(
+                box_preds=pred_boxes,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+        return final_pred_dict, recall_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/caddn.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/caddn.py
new file mode 100644
index 000000000..32f56a796
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/caddn.py
@@ -0,0 +1,38 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class CaDDN(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+
+        loss_rpn, tb_dict_rpn = self.dense_head.get_loss()
+        loss_depth, tb_dict_depth = self.vfe.get_loss()
+
+        tb_dict = {
+            'loss_rpn': loss_rpn.item(),
+            'loss_depth': loss_depth.item(),
+            **tb_dict_rpn,
+            **tb_dict_depth
+        }
+
+        loss = loss_rpn + loss_depth
+        return loss, tb_dict, disp_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/centerpoint.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/centerpoint.py
new file mode 100644
index 000000000..a5bc01163
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/centerpoint.py
@@ -0,0 +1,50 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class CenterPoint(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        tb_dict = {
+            'loss_rpn': loss_rpn.item(),
+            **tb_dict
+        }
+
+        loss = loss_rpn
+        return loss, tb_dict, disp_dict
+
+    def post_processing(self, batch_dict):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        final_pred_dict = batch_dict['final_box_dicts']
+        recall_dict = {}
+        for index in range(batch_size):
+            pred_boxes = final_pred_dict[index]['pred_boxes']
+
+            recall_dict = self.generate_recall_record(
+                box_preds=pred_boxes,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+        return final_pred_dict, recall_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/detector3d_template.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/detector3d_template.py
new file mode 100644
index 000000000..91e44bd46
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/detector3d_template.py
@@ -0,0 +1,415 @@
+import os
+
+import torch
+import torch.nn as nn
+import numpy as np
+from ...ops.iou3d_nms import iou3d_nms_utils
+from ...utils.spconv_utils import find_all_spconv_keys
+from .. import backbones_2d, backbones_3d, dense_heads, roi_heads
+from ..backbones_2d import map_to_bev
+from ..backbones_3d import pfe, vfe
+from ..model_utils import model_nms_utils
+
+
+class Detector3DTemplate(nn.Module):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_class = num_class
+        self.dataset = dataset
+        self.class_names = dataset.class_names
+        self.register_buffer('global_step', torch.LongTensor(1).zero_())
+
+        self.module_topology = [
+            'vfe', 'backbone_3d', 'map_to_bev_module', 'pfe',
+            'backbone_2d', 'dense_head',  'point_head', 'roi_head'
+        ]
+
+    @property
+    def mode(self):
+        return 'TRAIN' if self.training else 'TEST'
+
+    def update_global_step(self):
+        self.global_step += 1
+
+    def build_networks(self):
+        model_info_dict = {
+            'module_list': [],
+            'num_rawpoint_features': self.dataset.point_feature_encoder.num_point_features,
+            'num_point_features': self.dataset.point_feature_encoder.num_point_features,
+            'grid_size': self.dataset.grid_size,
+            'point_cloud_range': self.dataset.point_cloud_range,
+            'voxel_size': self.dataset.voxel_size,
+            'depth_downsample_factor': self.dataset.depth_downsample_factor
+        }
+        for module_name in self.module_topology:
+            module, model_info_dict = getattr(self, 'build_%s' % module_name)(
+                model_info_dict=model_info_dict
+            )
+            self.add_module(module_name, module)
+        return model_info_dict['module_list']
+
+    def build_vfe(self, model_info_dict):
+        if self.model_cfg.get('VFE', None) is None:
+            return None, model_info_dict
+
+        vfe_module = vfe.__all__[self.model_cfg.VFE.NAME](
+            model_cfg=self.model_cfg.VFE,
+            num_point_features=model_info_dict['num_rawpoint_features'],
+            point_cloud_range=model_info_dict['point_cloud_range'],
+            voxel_size=model_info_dict['voxel_size'],
+            grid_size=model_info_dict['grid_size'],
+            depth_downsample_factor=model_info_dict['depth_downsample_factor']
+        )
+        model_info_dict['num_point_features'] = vfe_module.get_output_feature_dim()
+        model_info_dict['module_list'].append(vfe_module)
+        return vfe_module, model_info_dict
+
+    def build_backbone_3d(self, model_info_dict):
+        if self.model_cfg.get('BACKBONE_3D', None) is None:
+            return None, model_info_dict
+
+        backbone_3d_module = backbones_3d.__all__[self.model_cfg.BACKBONE_3D.NAME](
+            model_cfg=self.model_cfg.BACKBONE_3D,
+            input_channels=model_info_dict['num_point_features'],
+            grid_size=model_info_dict['grid_size'],
+            voxel_size=model_info_dict['voxel_size'],
+            point_cloud_range=model_info_dict['point_cloud_range']
+        )
+        model_info_dict['module_list'].append(backbone_3d_module)
+        model_info_dict['num_point_features'] = backbone_3d_module.num_point_features
+        model_info_dict['backbone_channels'] = backbone_3d_module.backbone_channels \
+            if hasattr(backbone_3d_module, 'backbone_channels') else None
+        return backbone_3d_module, model_info_dict
+
+    def build_map_to_bev_module(self, model_info_dict):
+        if self.model_cfg.get('MAP_TO_BEV', None) is None:
+            return None, model_info_dict
+
+        map_to_bev_module = map_to_bev.__all__[self.model_cfg.MAP_TO_BEV.NAME](
+            model_cfg=self.model_cfg.MAP_TO_BEV,
+            grid_size=model_info_dict['grid_size']
+        )
+        model_info_dict['module_list'].append(map_to_bev_module)
+        model_info_dict['num_bev_features'] = map_to_bev_module.num_bev_features
+        return map_to_bev_module, model_info_dict
+
+    def build_backbone_2d(self, model_info_dict):
+        if self.model_cfg.get('BACKBONE_2D', None) is None:
+            return None, model_info_dict
+
+        backbone_2d_module = backbones_2d.__all__[self.model_cfg.BACKBONE_2D.NAME](
+            model_cfg=self.model_cfg.BACKBONE_2D,
+            input_channels=model_info_dict.get('num_bev_features', None)
+        )
+        model_info_dict['module_list'].append(backbone_2d_module)
+        model_info_dict['num_bev_features'] = backbone_2d_module.num_bev_features
+        return backbone_2d_module, model_info_dict
+
+    def build_pfe(self, model_info_dict):
+        if self.model_cfg.get('PFE', None) is None:
+            return None, model_info_dict
+
+        pfe_module = pfe.__all__[self.model_cfg.PFE.NAME](
+            model_cfg=self.model_cfg.PFE,
+            voxel_size=model_info_dict['voxel_size'],
+            point_cloud_range=model_info_dict['point_cloud_range'],
+            num_bev_features=model_info_dict['num_bev_features'],
+            num_rawpoint_features=model_info_dict['num_rawpoint_features']
+        )
+        model_info_dict['module_list'].append(pfe_module)
+        model_info_dict['num_point_features'] = pfe_module.num_point_features
+        model_info_dict['num_point_features_before_fusion'] = pfe_module.num_point_features_before_fusion
+        return pfe_module, model_info_dict
+
+    def build_dense_head(self, model_info_dict):
+        if self.model_cfg.get('DENSE_HEAD', None) is None:
+            return None, model_info_dict
+        dense_head_module = dense_heads.__all__[self.model_cfg.DENSE_HEAD.NAME](
+            model_cfg=self.model_cfg.DENSE_HEAD,
+            input_channels=model_info_dict['num_bev_features'] if 'num_bev_features' in model_info_dict else self.model_cfg.DENSE_HEAD.INPUT_FEATURES,
+            num_class=self.num_class if not self.model_cfg.DENSE_HEAD.CLASS_AGNOSTIC else 1,
+            class_names=self.class_names,
+            grid_size=model_info_dict['grid_size'],
+            point_cloud_range=model_info_dict['point_cloud_range'],
+            predict_boxes_when_training=self.model_cfg.get('ROI_HEAD', False),
+            voxel_size=model_info_dict.get('voxel_size', False)
+        )
+        model_info_dict['module_list'].append(dense_head_module)
+        return dense_head_module, model_info_dict
+
+    def build_point_head(self, model_info_dict):
+        if self.model_cfg.get('POINT_HEAD', None) is None:
+            return None, model_info_dict
+
+        if self.model_cfg.POINT_HEAD.get('USE_POINT_FEATURES_BEFORE_FUSION', False):
+            num_point_features = model_info_dict['num_point_features_before_fusion']
+        else:
+            num_point_features = model_info_dict['num_point_features']
+
+        point_head_module = dense_heads.__all__[self.model_cfg.POINT_HEAD.NAME](
+            model_cfg=self.model_cfg.POINT_HEAD,
+            input_channels=num_point_features,
+            num_class=self.num_class if not self.model_cfg.POINT_HEAD.CLASS_AGNOSTIC else 1,
+            predict_boxes_when_training=self.model_cfg.get('ROI_HEAD', False)
+        )
+
+        model_info_dict['module_list'].append(point_head_module)
+        return point_head_module, model_info_dict
+
+    def build_roi_head(self, model_info_dict):
+        if self.model_cfg.get('ROI_HEAD', None) is None:
+            return None, model_info_dict
+        point_head_module = roi_heads.__all__[self.model_cfg.ROI_HEAD.NAME](
+            model_cfg=self.model_cfg.ROI_HEAD,
+            input_channels=model_info_dict['num_point_features'],
+            backbone_channels= model_info_dict.get('backbone_channels', None),
+            point_cloud_range=model_info_dict['point_cloud_range'],
+            voxel_size=model_info_dict['voxel_size'],
+            num_class=self.num_class if not self.model_cfg.ROI_HEAD.CLASS_AGNOSTIC else 1,
+        )
+
+        model_info_dict['module_list'].append(point_head_module)
+        return point_head_module, model_info_dict
+
+    def forward(self, **kwargs):
+        raise NotImplementedError
+
+    def post_processing(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                batch_cls_preds: (B, num_boxes, num_classes | 1) or (N1+N2+..., num_classes | 1)
+                                or [(B, num_boxes, num_class1), (B, num_boxes, num_class2) ...]
+                multihead_label_mapping: [(num_class1), (num_class2), ...]
+                batch_box_preds: (B, num_boxes, 7+C) or (N1+N2+..., 7+C)
+                cls_preds_normalized: indicate whether batch_cls_preds is normalized
+                batch_index: optional (N1+N2+...)
+                has_class_labels: True/False
+                roi_labels: (B, num_rois)  1 .. num_classes
+                batch_pred_labels: (B, num_boxes, 1)
+        Returns:
+
+        """
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        recall_dict = {}
+        pred_dicts = []
+        for index in range(batch_size):
+            if batch_dict.get('batch_index', None) is not None:
+                assert batch_dict['batch_box_preds'].shape.__len__() == 2
+                batch_mask = (batch_dict['batch_index'] == index)
+            else:
+                assert batch_dict['batch_box_preds'].shape.__len__() == 3
+                batch_mask = index
+
+            box_preds = batch_dict['batch_box_preds'][batch_mask]
+            src_box_preds = box_preds
+            
+            if not isinstance(batch_dict['batch_cls_preds'], list):
+                cls_preds = batch_dict['batch_cls_preds'][batch_mask]
+
+                src_cls_preds = cls_preds
+                assert cls_preds.shape[1] in [1, self.num_class]
+
+                if not batch_dict['cls_preds_normalized']:
+                    cls_preds = torch.sigmoid(cls_preds)
+            else:
+                cls_preds = [x[batch_mask] for x in batch_dict['batch_cls_preds']]
+                src_cls_preds = cls_preds
+                if not batch_dict['cls_preds_normalized']:
+                    cls_preds = [torch.sigmoid(x) for x in cls_preds]
+
+            if post_process_cfg.NMS_CONFIG.MULTI_CLASSES_NMS:
+                if not isinstance(cls_preds, list):
+                    cls_preds = [cls_preds]
+                    multihead_label_mapping = [torch.arange(1, self.num_class, device=cls_preds[0].device)]
+                else:
+                    multihead_label_mapping = batch_dict['multihead_label_mapping']
+
+                cur_start_idx = 0
+                pred_scores, pred_labels, pred_boxes = [], [], []
+                for cur_cls_preds, cur_label_mapping in zip(cls_preds, multihead_label_mapping):
+                    assert cur_cls_preds.shape[1] == len(cur_label_mapping)
+                    cur_box_preds = box_preds[cur_start_idx: cur_start_idx + cur_cls_preds.shape[0]]
+                    cur_pred_scores, cur_pred_labels, cur_pred_boxes = model_nms_utils.multi_classes_nms(
+                        cls_scores=cur_cls_preds, box_preds=cur_box_preds,
+                        nms_config=post_process_cfg.NMS_CONFIG,
+                        score_thresh=post_process_cfg.SCORE_THRESH
+                    )
+                    cur_pred_labels = cur_label_mapping[cur_pred_labels]
+                    pred_scores.append(cur_pred_scores)
+                    pred_labels.append(cur_pred_labels)
+                    pred_boxes.append(cur_pred_boxes)
+                    cur_start_idx += cur_cls_preds.shape[0]
+
+                final_scores = torch.cat(pred_scores, dim=0)
+                final_labels = torch.cat(pred_labels, dim=0)
+                final_boxes = torch.cat(pred_boxes, dim=0)
+            else:
+                cls_preds, label_preds = torch.max(cls_preds, dim=-1)
+                if batch_dict.get('has_class_labels', False):
+                    label_key = 'roi_labels' if 'roi_labels' in batch_dict else 'batch_pred_labels'
+                    label_preds = batch_dict[label_key][index]
+                else:
+                    label_preds = label_preds + 1 
+                selected, selected_scores = model_nms_utils.class_agnostic_nms(
+                    box_scores=cls_preds, box_preds=box_preds,
+                    nms_config=post_process_cfg.NMS_CONFIG,
+                    score_thresh=post_process_cfg.SCORE_THRESH
+                )
+
+                if post_process_cfg.OUTPUT_RAW_SCORE:
+                    max_cls_preds, _ = torch.max(src_cls_preds, dim=-1)
+                    selected_scores = max_cls_preds[selected]
+
+                final_scores = selected_scores
+                final_labels = label_preds[selected]
+                final_boxes = box_preds[selected]
+                    
+            recall_dict = self.generate_recall_record(
+                box_preds=final_boxes if 'rois' not in batch_dict else src_box_preds,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )        
+
+            record_dict = {
+                'pred_boxes': final_boxes,
+                'pred_scores': final_scores,
+                'pred_labels': final_labels
+            }
+            pred_dicts.append(record_dict)
+
+        return pred_dicts, recall_dict
+
+    @staticmethod
+    def generate_recall_record(box_preds, recall_dict, batch_index, data_dict=None, thresh_list=None):
+        if 'gt_boxes' not in data_dict:
+            return recall_dict
+
+        rois = data_dict['rois'][batch_index] if 'rois' in data_dict else None
+        gt_boxes = data_dict['gt_boxes'][batch_index]
+
+        if recall_dict.__len__() == 0:
+            recall_dict = {'gt': 0}
+            for cur_thresh in thresh_list:
+                recall_dict['roi_%s' % (str(cur_thresh))] = 0
+                recall_dict['rcnn_%s' % (str(cur_thresh))] = 0
+
+        cur_gt = gt_boxes
+        k = cur_gt.__len__() - 1
+        while k >= 0 and cur_gt[k].sum() == 0:
+            k -= 1
+        cur_gt = cur_gt[:k + 1]
+
+        if cur_gt.shape[0] > 0:
+            if box_preds.shape[0] > 0:
+                iou3d_rcnn = iou3d_nms_utils.boxes_iou3d_gpu(box_preds[:, 0:7], cur_gt[:, 0:7])
+            else:
+                iou3d_rcnn = torch.zeros((0, cur_gt.shape[0]))
+
+            if rois is not None:
+                iou3d_roi = iou3d_nms_utils.boxes_iou3d_gpu(rois[:, 0:7], cur_gt[:, 0:7])
+
+            for cur_thresh in thresh_list:
+                if iou3d_rcnn.shape[0] == 0:
+                    recall_dict['rcnn_%s' % str(cur_thresh)] += 0
+                else:
+                    rcnn_recalled = (iou3d_rcnn.max(dim=0)[0] > cur_thresh).sum().item()
+                    recall_dict['rcnn_%s' % str(cur_thresh)] += rcnn_recalled
+                if rois is not None:
+                    roi_recalled = (iou3d_roi.max(dim=0)[0] > cur_thresh).sum().item()
+                    recall_dict['roi_%s' % str(cur_thresh)] += roi_recalled
+
+            recall_dict['gt'] += cur_gt.shape[0]
+        else:
+            gt_iou = box_preds.new_zeros(box_preds.shape[0])
+        return recall_dict
+
+    def _load_state_dict(self, model_state_disk, *, strict=True):
+        state_dict = self.state_dict()  # local cache of state_dict
+
+        spconv_keys = find_all_spconv_keys(self)
+
+        update_model_state = {}
+        for key, val in model_state_disk.items():
+            if key in spconv_keys and key in state_dict and state_dict[key].shape != val.shape:
+                # with different spconv versions, we need to adapt weight shapes for spconv blocks
+                # adapt spconv weights from version 1.x to version 2.x if you used weights from spconv 1.x
+
+                val_native = val.transpose(-1, -2)  # (k1, k2, k3, c_in, c_out) to (k1, k2, k3, c_out, c_in)
+                if val_native.shape == state_dict[key].shape:
+                    val = val_native.contiguous()
+                else:
+                    assert val.shape.__len__() == 5, 'currently only spconv 3D is supported'
+                    val_implicit = val.permute(4, 0, 1, 2, 3)  # (k1, k2, k3, c_in, c_out) to (c_out, k1, k2, k3, c_in)
+                    if val_implicit.shape == state_dict[key].shape:
+                        val = val_implicit.contiguous()
+
+            if key in state_dict and state_dict[key].shape == val.shape:
+                update_model_state[key] = val
+                # logger.info('Update weight %s: %s' % (key, str(val.shape)))
+
+        if strict:
+            self.load_state_dict(update_model_state)
+        else:
+            state_dict.update(update_model_state)
+            self.load_state_dict(state_dict)
+        return state_dict, update_model_state
+
+    def load_params_from_file(self, filename, logger, to_cpu=False, pre_trained_path=None):
+        if not os.path.isfile(filename):
+            raise FileNotFoundError
+
+        logger.info('==> Loading parameters from checkpoint %s to %s' % (filename, 'CPU' if to_cpu else 'GPU'))
+        loc_type = torch.device('cpu') if to_cpu else None
+        checkpoint = torch.load(filename, map_location=loc_type)
+        model_state_disk = checkpoint['model_state']
+        if not pre_trained_path is None:
+            pretrain_checkpoint = torch.load(pre_trained_path, map_location=loc_type)
+            pretrain_model_state_disk = pretrain_checkpoint['model_state']
+            model_state_disk.update(pretrain_model_state_disk)
+            
+        version = checkpoint.get("version", None)
+        if version is not None:
+            logger.info('==> Checkpoint trained from version: %s' % version)
+
+        state_dict, update_model_state = self._load_state_dict(model_state_disk, strict=False)
+
+        for key in state_dict:
+            if key not in update_model_state:
+                logger.info('Not updated weight %s: %s' % (key, str(state_dict[key].shape)))
+
+        logger.info('==> Done (loaded %d/%d)' % (len(update_model_state), len(state_dict)))
+
+    def load_params_with_optimizer(self, filename, to_cpu=False, optimizer=None, logger=None):
+        if not os.path.isfile(filename):
+            raise FileNotFoundError
+
+        logger.info('==> Loading parameters from checkpoint %s to %s' % (filename, 'CPU' if to_cpu else 'GPU'))
+        loc_type = torch.device('cpu') if to_cpu else None
+        checkpoint = torch.load(filename, map_location=loc_type)
+        epoch = checkpoint.get('epoch', -1)
+        it = checkpoint.get('it', 0.0)
+
+        self._load_state_dict(checkpoint['model_state'], strict=True)
+
+        if optimizer is not None:
+            if 'optimizer_state' in checkpoint and checkpoint['optimizer_state'] is not None:
+                logger.info('==> Loading optimizer parameters from checkpoint %s to %s'
+                            % (filename, 'CPU' if to_cpu else 'GPU'))
+                optimizer.load_state_dict(checkpoint['optimizer_state'])
+            else:
+                assert filename[-4] == '.', filename
+                src_file, ext = filename[:-4], filename[-3:]
+                optimizer_filename = '%s_optim.%s' % (src_file, ext)
+                if os.path.exists(optimizer_filename):
+                    optimizer_ckpt = torch.load(optimizer_filename, map_location=loc_type)
+                    optimizer.load_state_dict(optimizer_ckpt['optimizer_state'])
+
+        if 'version' in checkpoint:
+            print('==> Checkpoint trained from version: %s' % checkpoint['version'])
+        logger.info('==> Done')
+
+        return it, epoch
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/mppnet.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/mppnet.py
new file mode 100644
index 000000000..10eeb6873
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/mppnet.py
@@ -0,0 +1,181 @@
+import torch
+from .detector3d_template import Detector3DTemplate
+from pcdet.ops.iou3d_nms import iou3d_nms_utils
+import os
+import numpy as np
+import time
+from ...utils import common_utils
+from ..model_utils import model_nms_utils
+from pcdet.datasets.augmentor import augmentor_utils, database_sampler
+
+
+class MPPNet(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        batch_dict['proposals_list'] = batch_dict['roi_boxes']
+        for cur_module in self.module_list[:]:
+            batch_dict = cur_module(batch_dict)
+        
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+
+            return ret_dict, tb_dict, disp_dict
+        else:
+
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}  
+        tb_dict ={}
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+        loss = loss_rcnn
+
+        return loss, tb_dict, disp_dict
+
+    def post_processing(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                batch_cls_preds: (B, num_boxes, num_classes | 1) or (N1+N2+..., num_classes | 1)
+                                or [(B, num_boxes, num_class1), (B, num_boxes, num_class2) ...]
+                multihead_label_mapping: [(num_class1), (num_class2), ...]
+                batch_box_preds: (B, num_boxes, 7+C) or (N1+N2+..., 7+C)
+                cls_preds_normalized: indicate whether batch_cls_preds is normalized
+                batch_index: optional (N1+N2+...)
+                has_class_labels: True/False
+                roi_labels: (B, num_rois)  1 .. num_classes
+                batch_pred_labels: (B, num_boxes, 1)
+        Returns:
+
+        """
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        recall_dict = {}
+        pred_dicts = []
+        for index in range(batch_size):
+            if batch_dict.get('batch_index', None) is not None:
+                assert batch_dict['batch_box_preds'].shape.__len__() == 2
+                batch_mask = (batch_dict['batch_index'] == index)
+            else:
+                assert batch_dict['batch_box_preds'].shape.__len__() == 3
+                batch_mask = index
+
+            box_preds = batch_dict['batch_box_preds'][batch_mask]
+            src_box_preds = box_preds
+            if not isinstance(batch_dict['batch_cls_preds'], list):
+                cls_preds = batch_dict['batch_cls_preds'][batch_mask]
+
+                src_cls_preds = cls_preds
+                assert cls_preds.shape[1] in [1, self.num_class]
+
+                if not batch_dict['cls_preds_normalized']:
+                    cls_preds = torch.sigmoid(cls_preds)
+            else:
+                cls_preds = [x[batch_mask] for x in batch_dict['batch_cls_preds']]
+                src_cls_preds = cls_preds
+                if not batch_dict['cls_preds_normalized']:
+                    cls_preds = [torch.sigmoid(x) for x in cls_preds]
+
+            if post_process_cfg.NMS_CONFIG.MULTI_CLASSES_NMS:
+                if not isinstance(cls_preds, list):
+                    cls_preds = [cls_preds]
+                    multihead_label_mapping = [torch.arange(1, self.num_class, device=cls_preds[0].device)]
+                else:
+                    multihead_label_mapping = batch_dict['multihead_label_mapping']
+
+                cur_start_idx = 0
+                pred_scores, pred_labels, pred_boxes = [], [], []
+                for cur_cls_preds, cur_label_mapping in zip(cls_preds, multihead_label_mapping):
+                    assert cur_cls_preds.shape[1] == len(cur_label_mapping)
+                    cur_box_preds = box_preds[cur_start_idx: cur_start_idx + cur_cls_preds.shape[0]]
+                    cur_pred_scores, cur_pred_labels, cur_pred_boxes = model_nms_utils.multi_classes_nms(
+                        cls_scores=cur_cls_preds, box_preds=cur_box_preds,
+                        nms_config=post_process_cfg.NMS_CONFIG,
+                        score_thresh=post_process_cfg.SCORE_THRESH
+                    )
+                    cur_pred_labels = cur_label_mapping[cur_pred_labels]
+                    pred_scores.append(cur_pred_scores)
+                    pred_labels.append(cur_pred_labels)
+                    pred_boxes.append(cur_pred_boxes)
+                    cur_start_idx += cur_cls_preds.shape[0]
+
+                final_scores = torch.cat(pred_scores, dim=0)
+                final_labels = torch.cat(pred_labels, dim=0)
+                final_boxes = torch.cat(pred_boxes, dim=0)
+            else:
+                try:
+                    cls_preds, label_preds = torch.max(cls_preds, dim=-1)
+                except:
+                    record_dict = {
+                        'pred_boxes': torch.tensor([]),
+                        'pred_scores': torch.tensor([]),
+                        'pred_labels': torch.tensor([])
+                    }
+                    pred_dicts.append(record_dict)
+                    continue
+
+                if batch_dict.get('has_class_labels', False):
+                    label_key = 'roi_labels' if 'roi_labels' in batch_dict else 'batch_pred_labels'
+                    label_preds = batch_dict[label_key][index]
+                else:
+                    label_preds = label_preds + 1
+                
+                selected, selected_scores = model_nms_utils.class_agnostic_nms(
+                    box_scores=cls_preds, box_preds=box_preds,
+                    nms_config=post_process_cfg.NMS_CONFIG,
+                    score_thresh=post_process_cfg.SCORE_THRESH
+                )
+
+                if post_process_cfg.OUTPUT_RAW_SCORE:
+                    max_cls_preds, _ = torch.max(src_cls_preds, dim=-1)
+                    selected_scores = max_cls_preds[selected]
+
+                final_scores = selected_scores
+                final_labels = label_preds[selected]
+                final_boxes = box_preds[selected]
+
+                #########  Car DONOT Using NMS ###### 
+                if post_process_cfg.get('NOT_APPLY_NMS_FOR_VEL',False):
+                    
+                    pedcyc_mask = final_labels !=1 
+                    final_scores_pedcyc = final_scores[pedcyc_mask]
+                    final_labels_pedcyc = final_labels[pedcyc_mask]
+                    final_boxes_pedcyc = final_boxes[pedcyc_mask]
+
+                    car_mask = (label_preds==1) & (cls_preds > post_process_cfg.SCORE_THRESH)
+                    final_scores_car = cls_preds[car_mask]
+                    final_labels_car = label_preds[car_mask]
+                    final_boxes_car = box_preds[car_mask]
+
+                    final_scores  = torch.cat([final_scores_car,final_scores_pedcyc],0)
+                    final_labels  = torch.cat([final_labels_car,final_labels_pedcyc],0)
+                    final_boxes  = torch.cat([final_boxes_car,final_boxes_pedcyc],0)
+
+                #########  Car DONOT Using NMS ###### 
+
+            recall_dict = self.generate_recall_record(
+                box_preds=final_boxes if 'rois' not in batch_dict else src_box_preds,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+            
+
+            record_dict = {
+                'pred_boxes': final_boxes[:,:7],
+                'pred_scores': final_scores,
+                'pred_labels': final_labels
+            }
+            pred_dicts.append(record_dict)
+
+        return pred_dicts, recall_dict
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/mppnet_e2e.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/mppnet_e2e.py
new file mode 100644
index 000000000..7561c9b9a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/mppnet_e2e.py
@@ -0,0 +1,222 @@
+import torch
+import os
+import numpy as np
+import copy
+from ...utils import common_utils
+from ..model_utils import model_nms_utils
+from .detector3d_template import Detector3DTemplate
+from pcdet.ops.iou3d_nms import iou3d_nms_utils
+from pcdet.datasets.augmentor import augmentor_utils, database_sampler
+
+
+class MPPNetE2E(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+        self.module_topology = [
+            'vfe', 'backbone_3d', 'map_to_bev_module',
+            'backbone_2d', 'dense_head','roi_head'
+        ]
+        
+        self.num_frames = self.model_cfg.ROI_HEAD.Transformer.num_frames
+
+    def reset_memorybank(self):
+        self.memory_rois = None
+        self.memory_labels = None
+        self.memory_scores = None
+        self.memory_feature = None
+
+    def forward(self, batch_dict):
+
+        if batch_dict['sample_idx'][0] ==0:
+            self.reset_memorybank()
+            batch_dict['memory_bank'] = {}
+        else:
+            batch_dict['memory_bank'] = {'feature_bank':self.memory_feature}
+
+        if self.num_frames ==16:
+            batch_dict['points_backup'] = batch_dict['points'].clone()
+            time_mask = batch_dict['points'][:,-1] < 0.31 # centerpoint RPN only use 4frames
+            batch_dict['points'] = batch_dict['points'][time_mask]
+
+        for idx, cur_module in enumerate(self.module_list):
+            batch_dict = cur_module(batch_dict)
+            if self.module_topology[idx] == 'dense_head':
+
+                if self.memory_rois is None:
+                    self.memory_rois = [batch_dict['rois']]*self.num_frames
+                    self.memory_labels = [batch_dict['roi_labels'][:,:,None]]*self.num_frames
+                    self.memory_scores = [batch_dict['roi_scores'][:,:,None]]*self.num_frames
+                else:
+                    self.memory_rois.pop()
+                    self.memory_rois.insert(0,batch_dict['rois'])
+                    self.memory_labels.pop()
+                    self.memory_labels.insert(0,batch_dict['roi_labels'][:,:,None])
+                    self.memory_scores.pop()
+                    self.memory_scores.insert(0,batch_dict['roi_scores'][:,:,None])
+
+
+                batch_dict['memory_bank'].update({'rois': self.memory_rois,
+                                                'roi_labels': self.memory_labels,
+                                                'roi_scores': self.memory_scores})
+
+
+            if self.module_topology[idx] == 'roi_head':
+                if self.memory_feature is None:
+                    self.memory_feature = [batch_dict['geometory_feature_memory'][:,:64]]*self.num_frames
+
+                else:
+                    self.memory_feature.pop()
+                    self.memory_feature.insert(0,batch_dict['geometory_feature_memory'][:,:64])
+
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)       
+
+            return pred_dicts, recall_dicts
+
+
+    def get_training_loss(self):
+        disp_dict = {}
+
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        tb_dict = {
+            'loss_rpn': loss_rpn.item(),
+            **tb_dict
+        }
+
+        loss = loss_rpn
+        return loss, tb_dict, disp_dict
+
+
+    def post_processing(self, batch_dict):
+
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        recall_dict = {}
+        pred_dicts = []
+        for index in range(batch_size):
+            if batch_dict.get('batch_index', None) is not None:
+                assert batch_dict['batch_box_preds'].shape.__len__() == 2
+                batch_mask = (batch_dict['batch_index'] == index)
+            else:
+                assert batch_dict['batch_box_preds'].shape.__len__() == 3
+                batch_mask = index
+
+            box_preds = batch_dict['batch_box_preds'][batch_mask]
+            src_box_preds = box_preds
+            if not isinstance(batch_dict['batch_cls_preds'], list):
+                cls_preds = batch_dict['batch_cls_preds'][batch_mask]
+
+                src_cls_preds = cls_preds
+                assert cls_preds.shape[1] in [1, self.num_class]
+
+                if not batch_dict['cls_preds_normalized']:
+                    cls_preds = torch.sigmoid(cls_preds)
+            else:
+                cls_preds = [x[batch_mask] for x in batch_dict['batch_cls_preds']]
+                src_cls_preds = cls_preds
+                if not batch_dict['cls_preds_normalized']:
+                    cls_preds = [torch.sigmoid(x) for x in cls_preds]
+
+            if post_process_cfg.NMS_CONFIG.MULTI_CLASSES_NMS:
+                if not isinstance(cls_preds, list):
+                    cls_preds = [cls_preds]
+                    multihead_label_mapping = [torch.arange(1, self.num_class, device=cls_preds[0].device)]
+                else:
+                    multihead_label_mapping = batch_dict['multihead_label_mapping']
+
+                cur_start_idx = 0
+                pred_scores, pred_labels, pred_boxes = [], [], []
+                for cur_cls_preds, cur_label_mapping in zip(cls_preds, multihead_label_mapping):
+                    assert cur_cls_preds.shape[1] == len(cur_label_mapping)
+                    cur_box_preds = box_preds[cur_start_idx: cur_start_idx + cur_cls_preds.shape[0]]
+                    cur_pred_scores, cur_pred_labels, cur_pred_boxes = model_nms_utils.multi_classes_nms(
+                        cls_scores=cur_cls_preds, box_preds=cur_box_preds,
+                        nms_config=post_process_cfg.NMS_CONFIG,
+                        score_thresh=post_process_cfg.SCORE_THRESH
+                    )
+                    cur_pred_labels = cur_label_mapping[cur_pred_labels]
+                    pred_scores.append(cur_pred_scores)
+                    pred_labels.append(cur_pred_labels)
+                    pred_boxes.append(cur_pred_boxes)
+                    cur_start_idx += cur_cls_preds.shape[0]
+
+                final_scores = torch.cat(pred_scores, dim=0)
+                final_labels = torch.cat(pred_labels, dim=0)
+                final_boxes = torch.cat(pred_boxes, dim=0)
+            else:
+                try:
+                    cls_preds, label_preds = torch.max(cls_preds, dim=-1)
+                except:
+                    record_dict = {
+                        'pred_boxes': torch.tensor([]),
+                        'pred_scores': torch.tensor([]),
+                        'pred_labels': torch.tensor([])
+                    }
+                    pred_dicts.append(record_dict)
+                    continue
+
+                if batch_dict.get('has_class_labels', False):
+                    label_key = 'roi_labels' if 'roi_labels' in batch_dict else 'batch_pred_labels'
+                    label_preds = batch_dict[label_key][index]
+                else:
+                    label_preds = label_preds + 1
+                
+                selected, selected_scores = model_nms_utils.class_agnostic_nms(
+                    box_scores=cls_preds, box_preds=box_preds,
+                    nms_config=post_process_cfg.NMS_CONFIG,
+                    score_thresh=post_process_cfg.SCORE_THRESH
+                )
+
+                if post_process_cfg.OUTPUT_RAW_SCORE:
+                    max_cls_preds, _ = torch.max(src_cls_preds, dim=-1)
+                    selected_scores = max_cls_preds[selected]
+
+                final_scores = selected_scores
+                final_labels = label_preds[selected]
+                final_boxes = box_preds[selected]
+
+                #########  Car DONOT Using NMS ###### 
+                if post_process_cfg.get('NOT_APPLY_NMS_FOR_VEL',False):
+                    
+                    pedcyc_mask = final_labels !=1 
+                    final_scores_pedcyc = final_scores[pedcyc_mask]
+                    final_labels_pedcyc = final_labels[pedcyc_mask]
+                    final_boxes_pedcyc = final_boxes[pedcyc_mask]
+
+                    car_mask = (label_preds==1) & (cls_preds > post_process_cfg.SCORE_THRESH)
+                    final_scores_car = cls_preds[car_mask]
+                    final_labels_car = label_preds[car_mask]
+                    final_boxes_car = box_preds[car_mask]
+
+                    final_scores  = torch.cat([final_scores_car,final_scores_pedcyc],0)
+                    final_labels  = torch.cat([final_labels_car,final_labels_pedcyc],0)
+                    final_boxes  = torch.cat([final_boxes_car,final_boxes_pedcyc],0)
+
+                #########  Car DONOT Using NMS ###### 
+
+            recall_dict = self.generate_recall_record(
+                box_preds=final_boxes if 'rois' not in batch_dict else src_box_preds,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+            
+
+            record_dict = {
+                'pred_boxes': final_boxes[:,:7],
+                'pred_scores': final_scores,
+                'pred_labels': final_labels
+            }
+            pred_dicts.append(record_dict)
+
+        return pred_dicts, recall_dict
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pillarnet.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pillarnet.py
new file mode 100644
index 000000000..965a44163
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pillarnet.py
@@ -0,0 +1,50 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class PillarNet(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        tb_dict = {
+            'loss_rpn': loss_rpn.item(),
+            **tb_dict
+        }
+
+        loss = loss_rpn
+        return loss, tb_dict, disp_dict
+
+    def post_processing(self, batch_dict):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        final_pred_dict = batch_dict['final_box_dicts']
+        recall_dict = {}
+        for index in range(batch_size):
+            pred_boxes = final_pred_dict[index]['pred_boxes']
+
+            recall_dict = self.generate_recall_record(
+                box_preds=pred_boxes,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+        return final_pred_dict, recall_dict
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/point_rcnn.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/point_rcnn.py
new file mode 100644
index 000000000..5d26224fd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/point_rcnn.py
@@ -0,0 +1,30 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class PointRCNN(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+        loss_point, tb_dict = self.point_head.get_loss()
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss_point + loss_rcnn
+        return loss, tb_dict, disp_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pointpillar.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pointpillar.py
new file mode 100644
index 000000000..e21f8261a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pointpillar.py
@@ -0,0 +1,34 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class PointPillar(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        tb_dict = {
+            'loss_rpn': loss_rpn.item(),
+            **tb_dict
+        }
+
+        loss = loss_rpn
+        return loss, tb_dict, disp_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pv_rcnn.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pv_rcnn.py
new file mode 100644
index 000000000..4808513a2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pv_rcnn.py
@@ -0,0 +1,36 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class PVRCNN(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        loss_point, tb_dict = self.point_head.get_loss(tb_dict)
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss_rpn + loss_point + loss_rcnn
+        
+        if hasattr(self.backbone_3d, 'get_loss'):
+            loss_backbone3d, tb_dict = self.backbone_3d.get_loss(tb_dict)
+            loss += loss_backbone3d
+        
+        return loss, tb_dict, disp_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pv_rcnn_plusplus.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pv_rcnn_plusplus.py
new file mode 100644
index 000000000..2c64e6782
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/pv_rcnn_plusplus.py
@@ -0,0 +1,53 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class PVRCNNPlusPlus(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        batch_dict = self.vfe(batch_dict)
+        batch_dict = self.backbone_3d(batch_dict)
+        batch_dict = self.map_to_bev_module(batch_dict)
+        batch_dict = self.backbone_2d(batch_dict)
+        batch_dict = self.dense_head(batch_dict)
+
+        batch_dict = self.roi_head.proposal_layer(
+            batch_dict, nms_config=self.roi_head.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
+        )
+        if self.training:
+            targets_dict = self.roi_head.assign_targets(batch_dict)
+            batch_dict['rois'] = targets_dict['rois']
+            batch_dict['roi_labels'] = targets_dict['roi_labels']
+            batch_dict['roi_targets_dict'] = targets_dict
+            num_rois_per_scene = targets_dict['rois'].shape[1]
+            if 'roi_valid_num' in batch_dict:
+                batch_dict['roi_valid_num'] = [num_rois_per_scene for _ in range(batch_dict['batch_size'])]
+
+        batch_dict = self.pfe(batch_dict)
+        batch_dict = self.point_head(batch_dict)
+        batch_dict = self.roi_head(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        if self.point_head is not None:
+            loss_point, tb_dict = self.point_head.get_loss(tb_dict)
+        else:
+            loss_point = 0
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss_rpn + loss_point + loss_rcnn
+        return loss, tb_dict, disp_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/second_net.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/second_net.py
new file mode 100644
index 000000000..c6f2e3679
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/second_net.py
@@ -0,0 +1,34 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class SECONDNet(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        tb_dict = {
+            'loss_rpn': loss_rpn.item(),
+            **tb_dict
+        }
+
+        loss = loss_rpn
+        return loss, tb_dict, disp_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/second_net_iou.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/second_net_iou.py
new file mode 100644
index 000000000..1fc4e3bd9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/second_net_iou.py
@@ -0,0 +1,177 @@
+import torch
+from .detector3d_template import Detector3DTemplate
+from ..model_utils.model_nms_utils import class_agnostic_nms
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+
+
+class SECONDNetIoU(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        batch_dict['dataset_cfg'] = self.dataset.dataset_cfg
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss_rpn + loss_rcnn
+        return loss, tb_dict, disp_dict
+
+    @staticmethod
+    def cal_scores_by_npoints(cls_scores, iou_scores, num_points_in_gt, cls_thresh=10, iou_thresh=100):
+        """
+        Args:
+            cls_scores: (N)
+            iou_scores: (N)
+            num_points_in_gt: (N, 7+c)
+            cls_thresh: scalar
+            iou_thresh: scalar
+        """
+        assert iou_thresh >= cls_thresh
+        alpha = torch.zeros(cls_scores.shape, dtype=torch.float32).cuda()
+        alpha[num_points_in_gt <= cls_thresh] = 0
+        alpha[num_points_in_gt >= iou_thresh] = 1
+        
+        mask = ((num_points_in_gt > cls_thresh) & (num_points_in_gt < iou_thresh))
+        alpha[mask] = (num_points_in_gt[mask] - 10) / (iou_thresh - cls_thresh)
+        
+        scores = (1 - alpha) * cls_scores + alpha * iou_scores
+
+        return scores
+
+    def set_nms_score_by_class(self, iou_preds, cls_preds, label_preds, score_by_class):
+        n_classes = torch.unique(label_preds).shape[0]
+        nms_scores = torch.zeros(iou_preds.shape, dtype=torch.float32).cuda()
+        for i in range(n_classes):
+            mask = label_preds == (i + 1)
+            class_name = self.class_names[i]
+            score_type = score_by_class[class_name]
+            if score_type == 'iou':
+                nms_scores[mask] = iou_preds[mask]
+            elif score_type == 'cls':
+                nms_scores[mask] = cls_preds[mask]
+            else:
+                raise NotImplementedError
+
+        return nms_scores
+
+    def post_processing(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                batch_cls_preds: (B, num_boxes, num_classes | 1) or (N1+N2+..., num_classes | 1)
+                batch_box_preds: (B, num_boxes, 7+C) or (N1+N2+..., 7+C)
+                cls_preds_normalized: indicate whether batch_cls_preds is normalized
+                batch_index: optional (N1+N2+...)
+                roi_labels: (B, num_rois)  1 .. num_classes
+        Returns:
+
+        """
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        recall_dict = {}
+        pred_dicts = []
+        for index in range(batch_size):
+            if batch_dict.get('batch_index', None) is not None:
+                assert batch_dict['batch_cls_preds'].shape.__len__() == 2
+                batch_mask = (batch_dict['batch_index'] == index)
+            else:
+                assert batch_dict['batch_cls_preds'].shape.__len__() == 3
+                batch_mask = index
+
+            box_preds = batch_dict['batch_box_preds'][batch_mask]
+            iou_preds = batch_dict['batch_cls_preds'][batch_mask]
+            cls_preds = batch_dict['roi_scores'][batch_mask]
+
+            src_iou_preds = iou_preds
+            src_box_preds = box_preds
+            src_cls_preds = cls_preds
+            assert iou_preds.shape[1] in [1, self.num_class]
+
+            if not batch_dict['cls_preds_normalized']:
+                iou_preds = torch.sigmoid(iou_preds)
+                cls_preds = torch.sigmoid(cls_preds)
+
+            if post_process_cfg.NMS_CONFIG.MULTI_CLASSES_NMS:
+                raise NotImplementedError
+            else:
+                iou_preds, label_preds = torch.max(iou_preds, dim=-1)
+                label_preds = batch_dict['roi_labels'][index] if batch_dict.get('has_class_labels', False) else label_preds + 1
+
+                if post_process_cfg.NMS_CONFIG.get('SCORE_BY_CLASS', None) and \
+                        post_process_cfg.NMS_CONFIG.SCORE_TYPE == 'score_by_class':
+                    nms_scores = self.set_nms_score_by_class(
+                        iou_preds, cls_preds, label_preds, post_process_cfg.NMS_CONFIG.SCORE_BY_CLASS
+                    )
+                elif post_process_cfg.NMS_CONFIG.get('SCORE_TYPE', None) == 'iou' or \
+                        post_process_cfg.NMS_CONFIG.get('SCORE_TYPE', None) is None:
+                    nms_scores = iou_preds
+                elif post_process_cfg.NMS_CONFIG.SCORE_TYPE == 'cls':
+                    nms_scores = cls_preds
+                elif post_process_cfg.NMS_CONFIG.SCORE_TYPE == 'weighted_iou_cls':
+                    nms_scores = post_process_cfg.NMS_CONFIG.SCORE_WEIGHTS.iou * iou_preds + \
+                                 post_process_cfg.NMS_CONFIG.SCORE_WEIGHTS.cls * cls_preds
+                elif post_process_cfg.NMS_CONFIG.SCORE_TYPE == 'num_pts_iou_cls':
+                    point_mask = (batch_dict['points'][:, 0] == batch_mask)
+                    batch_points = batch_dict['points'][point_mask][:, 1:4]
+
+                    num_pts_in_gt = roiaware_pool3d_utils.points_in_boxes_cpu(
+                        batch_points.cpu(), box_preds[:, 0:7].cpu()
+                    ).sum(dim=1).float().cuda()
+                    
+                    score_thresh_cfg = post_process_cfg.NMS_CONFIG.SCORE_THRESH
+                    nms_scores = self.cal_scores_by_npoints(
+                        cls_preds, iou_preds, num_pts_in_gt, 
+                        score_thresh_cfg.cls, score_thresh_cfg.iou
+                    )
+                else:
+                    raise NotImplementedError
+
+                selected, selected_scores = class_agnostic_nms(
+                    box_scores=nms_scores, box_preds=box_preds,
+                    nms_config=post_process_cfg.NMS_CONFIG,
+                    score_thresh=post_process_cfg.SCORE_THRESH
+                )
+
+                if post_process_cfg.OUTPUT_RAW_SCORE:
+                    raise NotImplementedError
+
+                final_scores = selected_scores
+                final_labels = label_preds[selected]
+                final_boxes = box_preds[selected]
+
+            recall_dict = self.generate_recall_record(
+                box_preds=final_boxes if 'rois' not in batch_dict else src_box_preds,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+            record_dict = {
+                'pred_boxes': final_boxes,
+                'pred_scores': final_scores,
+                'pred_labels': final_labels,
+                'pred_cls_scores': cls_preds[selected],
+                'pred_iou_scores': iou_preds[selected]
+            }
+
+            pred_dicts.append(record_dict)
+
+        return pred_dicts, recall_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/transfusion.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/transfusion.py
new file mode 100644
index 000000000..16d81e849
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/transfusion.py
@@ -0,0 +1,50 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class TransFusion(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss(batch_dict)
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self,batch_dict):
+        disp_dict = {}
+
+        loss_trans, tb_dict = batch_dict['loss'],batch_dict['tb_dict']
+        tb_dict = {
+            'loss_trans': loss_trans.item(),
+            **tb_dict
+        }
+
+        loss = loss_trans
+        return loss, tb_dict, disp_dict
+
+    def post_processing(self, batch_dict):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        final_pred_dict = batch_dict['final_box_dicts']
+        recall_dict = {}
+        for index in range(batch_size):
+            pred_boxes = final_pred_dict[index]['pred_boxes']
+
+            recall_dict = self.generate_recall_record(
+                box_preds=pred_boxes,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+        return final_pred_dict, recall_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/voxel_rcnn.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/voxel_rcnn.py
new file mode 100644
index 000000000..469e868a3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/voxel_rcnn.py
@@ -0,0 +1,37 @@
+from .detector3d_template import Detector3DTemplate
+
+
+class VoxelRCNN(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+        loss = 0
+        
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss + loss_rpn + loss_rcnn
+        
+        if hasattr(self.backbone_3d, 'get_loss'):
+            loss_backbone3d, tb_dict = self.backbone_3d.get_loss(tb_dict)
+            loss += loss_backbone3d
+            
+        return loss, tb_dict, disp_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/voxelnext.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/voxelnext.py
new file mode 100644
index 000000000..f4524223a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/detectors/voxelnext.py
@@ -0,0 +1,44 @@
+from .detector3d_template import Detector3DTemplate
+
+class VoxelNeXt(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        
+        disp_dict = {}
+        loss, tb_dict = self.dense_head.get_loss()
+        
+        return loss, tb_dict, disp_dict
+
+    def post_processing(self, batch_dict):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        final_pred_dict = batch_dict['final_box_dicts']
+        recall_dict = {}
+        for index in range(batch_size):
+            pred_boxes = final_pred_dict[index]['pred_boxes']
+
+            recall_dict = self.generate_recall_record(
+                box_preds=pred_boxes,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+        return final_pred_dict, recall_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/basic_block_2d.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/basic_block_2d.py
new file mode 100644
index 000000000..f285eb53d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/basic_block_2d.py
@@ -0,0 +1,34 @@
+import torch.nn as nn
+
+
+class BasicBlock2D(nn.Module):
+
+    def __init__(self, in_channels, out_channels, **kwargs):
+        """
+        Initializes convolutional block
+        Args:
+            in_channels: int, Number of input channels
+            out_channels: int, Number of output channels
+            **kwargs: Dict, Extra arguments for nn.Conv2d
+        """
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.conv = nn.Conv2d(in_channels=in_channels,
+                              out_channels=out_channels,
+                              **kwargs)
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, features):
+        """
+        Applies convolutional block
+        Args:
+            features: (B, C_in, H, W), Input features
+        Returns:
+            x: (B, C_out, H, W), Output features
+        """
+        x = self.conv(features)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/centernet_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/centernet_utils.py
new file mode 100644
index 000000000..d24f1caf8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/centernet_utils.py
@@ -0,0 +1,385 @@
+# This file is modified from https://github.com/tianweiy/CenterPoint
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+import numba
+
+
+def gaussian_radius(height, width, min_overlap=0.5):
+    """
+    Args:
+        height: (N)
+        width: (N)
+        min_overlap:
+    Returns:
+    """
+    a1 = 1
+    b1 = (height + width)
+    c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
+    sq1 = (b1 ** 2 - 4 * a1 * c1).sqrt()
+    r1 = (b1 + sq1) / 2
+
+    a2 = 4
+    b2 = 2 * (height + width)
+    c2 = (1 - min_overlap) * width * height
+    sq2 = (b2 ** 2 - 4 * a2 * c2).sqrt()
+    r2 = (b2 + sq2) / 2
+
+    a3 = 4 * min_overlap
+    b3 = -2 * min_overlap * (height + width)
+    c3 = (min_overlap - 1) * width * height
+    sq3 = (b3 ** 2 - 4 * a3 * c3).sqrt()
+    r3 = (b3 + sq3) / 2
+    ret = torch.min(torch.min(r1, r2), r3)
+    return ret
+
+
+def gaussian2D(shape, sigma=1):
+    m, n = [(ss - 1.) / 2. for ss in shape]
+    y, x = np.ogrid[-m:m + 1, -n:n + 1]
+
+    h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
+    h[h < np.finfo(h.dtype).eps * h.max()] = 0
+    return h
+
+
+def draw_gaussian_to_heatmap(heatmap, center, radius, k=1, valid_mask=None):
+    diameter = 2 * radius + 1
+    gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
+
+    x, y = int(center[0]), int(center[1])
+
+    height, width = heatmap.shape[0:2]
+
+    left, right = min(x, radius), min(width - x, radius + 1)
+    top, bottom = min(y, radius), min(height - y, radius + 1)
+
+    masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
+    masked_gaussian = torch.from_numpy(
+        gaussian[radius - top:radius + bottom, radius - left:radius + right]
+    ).to(heatmap.device).float()
+
+    if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:  # TODO debug
+        if valid_mask is not None:
+            cur_valid_mask = valid_mask[y - top:y + bottom, x - left:x + right]
+            masked_gaussian = masked_gaussian * cur_valid_mask.float()
+
+        torch.max(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
+    return heatmap
+
+
+def _nms(heat, kernel=3):
+    pad = (kernel - 1) // 2
+
+    hmax = F.max_pool2d(heat, (kernel, kernel), stride=1, padding=pad)
+    keep = (hmax == heat).float()
+    return heat * keep
+
+
+def gaussian3D(shape, sigma=1):
+    m, n = [(ss - 1.) / 2. for ss in shape]
+    y, x = np.ogrid[-m:m + 1, -n:n + 1]
+
+    h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
+    h[h < np.finfo(h.dtype).eps * h.max()] = 0
+    return h
+
+
+def draw_gaussian_to_heatmap_voxels(heatmap, distances, radius, k=1):
+    diameter = 2 * radius + 1
+    sigma = diameter / 6
+    masked_gaussian = torch.exp(- distances / (2 * sigma * sigma))
+
+    torch.max(heatmap, masked_gaussian, out=heatmap)
+
+    return heatmap
+
+
+@numba.jit(nopython=True)
+def circle_nms(dets, thresh):
+    x1 = dets[:, 0]
+    y1 = dets[:, 1]
+    scores = dets[:, 2]
+    order = scores.argsort()[::-1].astype(np.int32)  # highest->lowest
+    ndets = dets.shape[0]
+    suppressed = np.zeros((ndets), dtype=np.int32)
+    keep = []
+    for _i in range(ndets):
+        i = order[_i]  # start with highest score box
+        if suppressed[i] == 1:  # if any box have enough iou with this, remove it
+            continue
+        keep.append(i)
+        for _j in range(_i + 1, ndets):
+            j = order[_j]
+            if suppressed[j] == 1:
+                continue
+            # calculate center distance between i and j box
+            dist = (x1[i] - x1[j]) ** 2 + (y1[i] - y1[j]) ** 2
+
+            # ovr = inter / areas[j]
+            if dist <= thresh:
+                suppressed[j] = 1
+    return keep
+
+
+def _circle_nms(boxes, min_radius, post_max_size=83):
+    """
+    NMS according to center distance
+    """
+    keep = np.array(circle_nms(boxes.cpu().numpy(), thresh=min_radius))[:post_max_size]
+
+    keep = torch.from_numpy(keep).long().to(boxes.device)
+
+    return keep
+
+
+def _gather_feat(feat, ind, mask=None):
+    dim = feat.size(2)
+    ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
+    feat = feat.gather(1, ind)
+    if mask is not None:
+        mask = mask.unsqueeze(2).expand_as(feat)
+        feat = feat[mask]
+        feat = feat.view(-1, dim)
+    return feat
+
+
+def _transpose_and_gather_feat(feat, ind):
+    feat = feat.permute(0, 2, 3, 1).contiguous()
+    feat = feat.view(feat.size(0), -1, feat.size(3))
+    feat = _gather_feat(feat, ind)
+    return feat
+
+
+def _topk(scores, K=40):
+    batch, num_class, height, width = scores.size()
+
+    topk_scores, topk_inds = torch.topk(scores.flatten(2, 3), K)
+
+    topk_inds = topk_inds % (height * width)
+    topk_ys = (topk_inds // width).float()
+    topk_xs = (topk_inds % width).int().float()
+
+    topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K)
+    topk_classes = (topk_ind // K).int()
+    topk_inds = _gather_feat(topk_inds.view(batch, -1, 1), topk_ind).view(batch, K)
+    topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K)
+    topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K)
+
+    return topk_score, topk_inds, topk_classes, topk_ys, topk_xs
+
+
+def decode_bbox_from_heatmap(heatmap, rot_cos, rot_sin, center, center_z, dim,
+                             point_cloud_range=None, voxel_size=None, feature_map_stride=None, vel=None, iou=None, K=100,
+                             circle_nms=False, score_thresh=None, post_center_limit_range=None):
+    batch_size, num_class, _, _ = heatmap.size()
+
+    if circle_nms:
+        # TODO: not checked yet
+        assert False, 'not checked yet'
+        heatmap = _nms(heatmap)
+
+    scores, inds, class_ids, ys, xs = _topk(heatmap, K=K)
+    center = _transpose_and_gather_feat(center, inds).view(batch_size, K, 2)
+    rot_sin = _transpose_and_gather_feat(rot_sin, inds).view(batch_size, K, 1)
+    rot_cos = _transpose_and_gather_feat(rot_cos, inds).view(batch_size, K, 1)
+    center_z = _transpose_and_gather_feat(center_z, inds).view(batch_size, K, 1)
+    dim = _transpose_and_gather_feat(dim, inds).view(batch_size, K, 3)
+
+    angle = torch.atan2(rot_sin, rot_cos)
+    xs = xs.view(batch_size, K, 1) + center[:, :, 0:1]
+    ys = ys.view(batch_size, K, 1) + center[:, :, 1:2]
+
+    xs = xs * feature_map_stride * voxel_size[0] + point_cloud_range[0]
+    ys = ys * feature_map_stride * voxel_size[1] + point_cloud_range[1]
+
+    box_part_list = [xs, ys, center_z, dim, angle]
+    if vel is not None:
+        vel = _transpose_and_gather_feat(vel, inds).view(batch_size, K, 2)
+        box_part_list.append(vel)
+
+    if iou is not None:
+        iou = _transpose_and_gather_feat(iou, inds).view(batch_size, K)
+
+    final_box_preds = torch.cat((box_part_list), dim=-1)
+    final_scores = scores.view(batch_size, K)
+    final_class_ids = class_ids.view(batch_size, K)
+
+    assert post_center_limit_range is not None
+    mask = (final_box_preds[..., :3] >= post_center_limit_range[:3]).all(2)
+    mask &= (final_box_preds[..., :3] <= post_center_limit_range[3:]).all(2)
+
+    if score_thresh is not None:
+        mask &= (final_scores > score_thresh)
+
+    ret_pred_dicts = []
+    for k in range(batch_size):
+        cur_mask = mask[k]
+        cur_boxes = final_box_preds[k, cur_mask]
+        cur_scores = final_scores[k, cur_mask]
+        cur_labels = final_class_ids[k, cur_mask]
+
+        if circle_nms:
+            assert False, 'not checked yet'
+            centers = cur_boxes[:, [0, 1]]
+            boxes = torch.cat((centers, scores.view(-1, 1)), dim=1)
+            keep = _circle_nms(boxes, min_radius=min_radius, post_max_size=nms_post_max_size)
+
+            cur_boxes = cur_boxes[keep]
+            cur_scores = cur_scores[keep]
+            cur_labels = cur_labels[keep]
+
+        ret_pred_dicts.append({
+            'pred_boxes': cur_boxes,
+            'pred_scores': cur_scores,
+            'pred_labels': cur_labels
+        })
+
+        if iou is not None:
+            ret_pred_dicts[-1]['pred_iou'] = iou[k, cur_mask]
+    return ret_pred_dicts
+
+def _topk_1d(scores, batch_size, batch_idx, obj, K=40, nuscenes=False):
+    # scores: (N, num_classes)
+    topk_score_list = []
+    topk_inds_list = []
+    topk_classes_list = []
+
+    for bs_idx in range(batch_size):
+        batch_inds = batch_idx==bs_idx
+        if obj.shape[-1] == 1 and not nuscenes:
+            score = scores[batch_inds].permute(1, 0)
+            topk_scores, topk_inds = torch.topk(score, K)
+            topk_score, topk_ind = torch.topk(obj[topk_inds.view(-1)].squeeze(-1), K) #torch.topk(topk_scores.view(-1), K)
+        else:
+            score = obj[batch_inds].permute(1, 0)
+            topk_scores, topk_inds = torch.topk(score, min(K, score.shape[-1]))
+            topk_score, topk_ind = torch.topk(topk_scores.view(-1), min(K, topk_scores.view(-1).shape[-1]))
+            #topk_score, topk_ind = torch.topk(score.reshape(-1), K)
+
+        topk_classes = (topk_ind // K).int()
+        topk_inds = topk_inds.view(-1).gather(0, topk_ind)
+        #print('topk_inds', topk_inds)
+
+        if not obj is None and obj.shape[-1] == 1:
+            topk_score_list.append(obj[batch_inds][topk_inds])
+        else:
+            topk_score_list.append(topk_score)
+        topk_inds_list.append(topk_inds)
+        topk_classes_list.append(topk_classes)
+
+    topk_score = torch.stack(topk_score_list)
+    topk_inds = torch.stack(topk_inds_list)
+    topk_classes = torch.stack(topk_classes_list)
+
+    return topk_score, topk_inds, topk_classes
+
+def gather_feat_idx(feats, inds, batch_size, batch_idx):
+    feats_list = []
+    dim = feats.size(-1)
+    _inds = inds.unsqueeze(-1).expand(inds.size(0), inds.size(1), dim)
+
+    for bs_idx in range(batch_size):
+        batch_inds = batch_idx==bs_idx
+        feat = feats[batch_inds]
+        feats_list.append(feat.gather(0, _inds[bs_idx]))
+    feats = torch.stack(feats_list)
+    return feats
+
+def decode_bbox_from_voxels_nuscenes(batch_size, indices, obj, rot_cos, rot_sin,
+                            center, center_z, dim, vel=None, iou=None, point_cloud_range=None, voxel_size=None, voxels_3d=None,
+                            feature_map_stride=None, K=100, score_thresh=None, post_center_limit_range=None, add_features=None):
+    batch_idx = indices[:, 0]
+    spatial_indices = indices[:, 1:]
+    scores, inds, class_ids = _topk_1d(None, batch_size, batch_idx, obj, K=K, nuscenes=True)
+
+    center = gather_feat_idx(center, inds, batch_size, batch_idx)
+    rot_sin = gather_feat_idx(rot_sin, inds, batch_size, batch_idx)
+    rot_cos = gather_feat_idx(rot_cos, inds, batch_size, batch_idx)
+    center_z = gather_feat_idx(center_z, inds, batch_size, batch_idx)
+    dim = gather_feat_idx(dim, inds, batch_size, batch_idx)
+    spatial_indices = gather_feat_idx(spatial_indices, inds, batch_size, batch_idx)
+
+    if not add_features is None:
+        add_features = [gather_feat_idx(add_feature, inds, batch_size, batch_idx) for add_feature in add_features]
+
+    if not isinstance(feature_map_stride, int):
+        feature_map_stride = gather_feat_idx(feature_map_stride.unsqueeze(-1), inds, batch_size, batch_idx)
+
+    angle = torch.atan2(rot_sin, rot_cos)
+    xs = (spatial_indices[:, :, -1:] + center[:, :, 0:1]) * feature_map_stride * voxel_size[0] + point_cloud_range[0]
+    ys = (spatial_indices[:, :, -2:-1] + center[:, :, 1:2]) * feature_map_stride * voxel_size[1] + point_cloud_range[1]
+    #zs = (spatial_indices[:, :, 0:1]) * feature_map_stride * voxel_size[2] + point_cloud_range[2] + center_z
+
+    box_part_list = [xs, ys, center_z, dim, angle]
+
+    if not vel is None:
+        vel = gather_feat_idx(vel, inds, batch_size, batch_idx)
+        box_part_list.append(vel)
+
+    if not iou is None:
+        iou = gather_feat_idx(iou, inds, batch_size, batch_idx)
+        iou = torch.clamp(iou, min=0, max=1.)
+
+    final_box_preds = torch.cat((box_part_list), dim=-1)
+    final_scores = scores.view(batch_size, K)
+    final_class_ids = class_ids.view(batch_size, K)
+    if not add_features is None:
+        add_features = [add_feature.view(batch_size, K, add_feature.shape[-1]) for add_feature in add_features]
+
+    assert post_center_limit_range is not None
+    mask = (final_box_preds[..., :3] >= post_center_limit_range[:3]).all(2)
+    mask &= (final_box_preds[..., :3] <= post_center_limit_range[3:]).all(2)
+
+    if score_thresh is not None:
+        mask &= (final_scores > score_thresh)
+
+    ret_pred_dicts = []
+    for k in range(batch_size):
+        cur_mask = mask[k]
+        cur_boxes = final_box_preds[k, cur_mask]
+        cur_scores = final_scores[k, cur_mask]
+        cur_labels = final_class_ids[k, cur_mask]
+        cur_add_features = [add_feature[k, cur_mask] for add_feature in add_features] if not add_features is None else None
+        cur_iou = iou[k, cur_mask] if not iou is None else None
+
+        ret_pred_dicts.append({
+            'pred_boxes': cur_boxes,
+            'pred_scores': cur_scores,
+            'pred_labels': cur_labels,
+            'pred_ious': cur_iou,
+            'add_features': cur_add_features,
+        })
+    return ret_pred_dicts
+
+
+def decode_bbox_from_pred_dicts(pred_dict, point_cloud_range=None, voxel_size=None, feature_map_stride=None):
+    batch_size, _, H, W = pred_dict['center'].shape
+
+    batch_center = pred_dict['center'].permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 2)  # (B, H, W, 2)
+    batch_center_z = pred_dict['center_z'].permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 1)  # (B, H, W, 1)
+    batch_dim = pred_dict['dim'].exp().permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 3)  # (B, H, W, 3)
+    batch_rot_cos = pred_dict['rot'][:, 0].unsqueeze(dim=1).permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 1)  # (B, H, W, 1)
+    batch_rot_sin = pred_dict['rot'][:, 1].unsqueeze(dim=1).permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 1)  # (B, H, W, 1)
+    batch_vel = pred_dict['vel'].permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 2) if 'vel' in pred_dict.keys() else None
+
+    angle = torch.atan2(batch_rot_sin, batch_rot_cos)  # (B, H*W, 1)
+
+    ys, xs = torch.meshgrid([torch.arange(0, H, device=batch_center.device, dtype=batch_center.dtype),
+                             torch.arange(0, W, device=batch_center.device, dtype=batch_center.dtype)])
+    ys = ys.view(1, H, W).repeat(batch_size, 1, 1)
+    xs = xs.view(1, H, W).repeat(batch_size, 1, 1)
+    xs = xs.view(batch_size, -1, 1) + batch_center[:, :, 0:1]
+    ys = ys.view(batch_size, -1, 1) + batch_center[:, :, 1:2]
+
+    xs = xs * feature_map_stride * voxel_size[0] + point_cloud_range[0]
+    ys = ys * feature_map_stride * voxel_size[1] + point_cloud_range[1]
+
+    box_part_list = [xs, ys, batch_center_z, batch_dim, angle]
+    if batch_vel is not None:
+        box_part_list.append(batch_vel)
+
+    box_preds = torch.cat((box_part_list), dim=-1).view(batch_size, H, W, -1)
+
+    return box_preds
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/dsvt_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/dsvt_utils.py
new file mode 100644
index 000000000..a3640528c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/dsvt_utils.py
@@ -0,0 +1,150 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from pcdet.ops.ingroup_inds.ingroup_inds_op import ingroup_inds
+
+
+get_inner_win_inds_cuda = ingroup_inds
+
+
+class PositionEmbeddingLearned(nn.Module):
+    """
+    Absolute pos embedding, learned.
+    """
+    def __init__(self, input_channel, num_pos_feats):
+        super().__init__()
+        self.position_embedding_head = nn.Sequential(
+            nn.Linear(input_channel, num_pos_feats),
+            nn.BatchNorm1d(num_pos_feats),
+            nn.ReLU(inplace=True),
+            nn.Linear(num_pos_feats, num_pos_feats))
+
+    def forward(self, xyz):
+        position_embedding = self.position_embedding_head(xyz)
+        return position_embedding
+
+
+@torch.no_grad()
+def get_window_coors(coors, sparse_shape, window_shape, do_shift, shift_list=None, return_win_coors=False):
+
+    if len(window_shape) == 2:
+        win_shape_x, win_shape_y = window_shape
+        win_shape_z = sparse_shape[-1]
+    else:
+        win_shape_x, win_shape_y, win_shape_z = window_shape
+
+    sparse_shape_x, sparse_shape_y, sparse_shape_z = sparse_shape
+    assert sparse_shape_z < sparse_shape_x, 'Usually holds... in case of wrong order'
+
+    max_num_win_x = int(np.ceil((sparse_shape_x / win_shape_x)) + 1) # plus one here to meet the needs of shift.
+    max_num_win_y = int(np.ceil((sparse_shape_y / win_shape_y)) + 1) # plus one here to meet the needs of shift.
+    max_num_win_z = int(np.ceil((sparse_shape_z / win_shape_z)) + 1) # plus one here to meet the needs of shift.
+    max_num_win_per_sample = max_num_win_x * max_num_win_y * max_num_win_z
+
+    if do_shift:
+        if shift_list is not None:
+            shift_x, shift_y, shift_z = shift_list[0], shift_list[1], shift_list[2]
+        else:
+            shift_x, shift_y, shift_z = win_shape_x // 2, win_shape_y // 2, win_shape_z // 2
+    else:
+        if shift_list is not None:
+            shift_x, shift_y, shift_z = shift_list[0], shift_list[1], shift_list[2]
+        else:
+            shift_x, shift_y, shift_z = win_shape_x, win_shape_y, win_shape_z
+    
+    # compatibility between 2D window and 3D window
+    if sparse_shape_z == win_shape_z:
+        shift_z = 0
+
+    shifted_coors_x = coors[:, 3] + shift_x
+    shifted_coors_y = coors[:, 2] + shift_y
+    shifted_coors_z = coors[:, 1] + shift_z
+
+    win_coors_x = shifted_coors_x // win_shape_x
+    win_coors_y = shifted_coors_y // win_shape_y
+    win_coors_z = shifted_coors_z // win_shape_z
+
+    if len(window_shape) == 2:
+        assert (win_coors_z == 0).all()
+
+    batch_win_inds = coors[:, 0] * max_num_win_per_sample + \
+                        win_coors_x * max_num_win_y * max_num_win_z + \
+                        win_coors_y * max_num_win_z + \
+                        win_coors_z
+
+    coors_in_win_x = shifted_coors_x % win_shape_x
+    coors_in_win_y = shifted_coors_y % win_shape_y
+    coors_in_win_z = shifted_coors_z % win_shape_z
+    coors_in_win = torch.stack([coors_in_win_z, coors_in_win_y, coors_in_win_x], dim=-1)
+    # coors_in_win = torch.stack([coors_in_win_x, coors_in_win_y], dim=-1)
+    if return_win_coors:
+        batch_win_coords = torch.stack([win_coors_z, win_coors_y, win_coors_x], dim=-1)
+        return batch_win_inds, coors_in_win, batch_win_coords
+    
+    return batch_win_inds, coors_in_win
+
+
+def get_pooling_index(coors, sparse_shape, window_shape):
+    win_shape_x, win_shape_y, win_shape_z = window_shape
+    sparse_shape_x, sparse_shape_y, sparse_shape_z = sparse_shape
+    
+    max_num_win_x = int(np.ceil((sparse_shape_x / win_shape_x)))
+    max_num_win_y = int(np.ceil((sparse_shape_y / win_shape_y))) 
+    max_num_win_z = int(np.ceil((sparse_shape_z / win_shape_z))) 
+    max_num_win_per_sample = max_num_win_x * max_num_win_y * max_num_win_z
+
+    coors_x = coors[:, 3] 
+    coors_y = coors[:, 2]
+    coors_z = coors[:, 1]
+
+    win_coors_x = coors_x // win_shape_x
+    win_coors_y = coors_y // win_shape_y
+    win_coors_z = coors_z // win_shape_z
+
+    batch_win_inds = coors[:, 0] * max_num_win_per_sample + \
+                        win_coors_x * max_num_win_y * max_num_win_z + \
+                        win_coors_y * max_num_win_z + \
+                        win_coors_z
+
+    coors_in_win_x = coors_x % win_shape_x
+    coors_in_win_y = coors_y % win_shape_y
+    coors_in_win_z = coors_z % win_shape_z
+    coors_in_win = torch.stack([coors_in_win_z, coors_in_win_y, coors_in_win_x], dim=-1)
+
+    index_in_win = coors_in_win_x * win_shape_y * win_shape_z + \
+                    coors_in_win_y * win_shape_z + \
+                    coors_in_win_z
+
+    batch_win_coords = torch.stack([coors[:, 0], win_coors_z, win_coors_y, win_coors_x], dim=-1)
+    return batch_win_inds, coors_in_win, index_in_win, batch_win_coords
+
+
+def get_continous_inds(setnum_per_win):
+    '''
+    Args:
+        setnum_per_win (Tensor[int]): Number of sets assigned to each window with shape (win_num).
+    Returns:
+        set_win_inds (Tensor[int]): Window indexs of each set with shape (set_num).
+        set_inds_in_win (Tensor[int]): Set indexs inner window with shape (set_num).
+
+    Examples:
+        setnum_per_win = torch.tensor([1, 2, 1, 3])
+        set_inds_in_win = get_continous_inds(setnum_per_win)
+        # we can get: set_inds_in_win = tensor([0, 0, 1, 0, 0, 1, 2])
+    '''
+    set_num = setnum_per_win.sum().item() # set_num = 7
+    setnum_per_win_cumsum = torch.cumsum(setnum_per_win, dim=0)[:-1] # [1, 3, 4]
+    set_win_inds = torch.full((set_num,), 0, device=setnum_per_win.device)
+    set_win_inds[setnum_per_win_cumsum] = 1 # [0, 1, 0, 1, 1, 0, 0]
+    set_win_inds = torch.cumsum(set_win_inds, dim=0) # [0, 1, 1, 2, 3, 3, 3]
+    
+    roll_set_win_inds_left = torch.roll(set_win_inds, -1)  # [1, 1, 2, 3, 3, 3, 0]
+    diff = set_win_inds - roll_set_win_inds_left # [-1, 0, -1, -1, 0, 0, 3]
+    end_pos_mask = diff != 0
+    template = torch.ones_like(set_win_inds)
+    template[end_pos_mask] = (setnum_per_win - 1) * -1  # [ 0, 1, -1, 0, 1, 1, -2]
+    set_inds_in_win = torch.cumsum(template,dim=0) # [0, 1, 0, 0, 1, 2, 0]
+    set_inds_in_win[end_pos_mask] = setnum_per_win # [1, 1, 2, 1, 1, 2, 3]
+    set_inds_in_win = set_inds_in_win - 1 # [0, 0, 1, 0, 0, 1, 2]
+    
+    return set_win_inds, set_inds_in_win
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/model_nms_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/model_nms_utils.py
new file mode 100644
index 000000000..8be1097e9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/model_nms_utils.py
@@ -0,0 +1,107 @@
+import torch
+
+from ...ops.iou3d_nms import iou3d_nms_utils
+
+
+def class_agnostic_nms(box_scores, box_preds, nms_config, score_thresh=None):
+    src_box_scores = box_scores
+    if score_thresh is not None:
+        scores_mask = (box_scores >= score_thresh)
+        box_scores = box_scores[scores_mask]
+        box_preds = box_preds[scores_mask]
+
+    selected = []
+    if box_scores.shape[0] > 0:
+        box_scores_nms, indices = torch.topk(box_scores, k=min(nms_config.NMS_PRE_MAXSIZE, box_scores.shape[0]))
+        boxes_for_nms = box_preds[indices]
+        keep_idx, selected_scores = getattr(iou3d_nms_utils, nms_config.NMS_TYPE)(
+                boxes_for_nms[:, 0:7], box_scores_nms, nms_config.NMS_THRESH, **nms_config
+        )
+        selected = indices[keep_idx[:nms_config.NMS_POST_MAXSIZE]]
+
+    if score_thresh is not None:
+        original_idxs = scores_mask.nonzero().view(-1)
+        selected = original_idxs[selected]
+    return selected, src_box_scores[selected]
+
+
+def multi_classes_nms(cls_scores, box_preds, nms_config, score_thresh=None):
+    """
+    Args:
+        cls_scores: (N, num_class)
+        box_preds: (N, 7 + C)
+        nms_config:
+        score_thresh:
+
+    Returns:
+
+    """
+    pred_scores, pred_labels, pred_boxes = [], [], []
+    for k in range(cls_scores.shape[1]):
+        if score_thresh is not None:
+            scores_mask = (cls_scores[:, k] >= score_thresh)
+            box_scores = cls_scores[scores_mask, k]
+            cur_box_preds = box_preds[scores_mask]
+        else:
+            box_scores = cls_scores[:, k]
+            cur_box_preds = box_preds
+
+        selected = []
+        if box_scores.shape[0] > 0:
+            box_scores_nms, indices = torch.topk(box_scores, k=min(nms_config.NMS_PRE_MAXSIZE, box_scores.shape[0]))
+            boxes_for_nms = cur_box_preds[indices]
+            keep_idx, selected_scores = getattr(iou3d_nms_utils, nms_config.NMS_TYPE)(
+                    boxes_for_nms[:, 0:7], box_scores_nms, nms_config.NMS_THRESH, **nms_config
+            )
+            selected = indices[keep_idx[:nms_config.NMS_POST_MAXSIZE]]
+
+        pred_scores.append(box_scores[selected])
+        pred_labels.append(box_scores.new_ones(len(selected)).long() * k)
+        pred_boxes.append(cur_box_preds[selected])
+
+    pred_scores = torch.cat(pred_scores, dim=0)
+    pred_labels = torch.cat(pred_labels, dim=0)
+    pred_boxes = torch.cat(pred_boxes, dim=0)
+
+    return pred_scores, pred_labels, pred_boxes
+
+
+def class_specific_nms(box_scores, box_preds, box_labels, nms_config, score_thresh=None):
+    """
+    Args:
+        cls_scores: (N,)
+        box_preds: (N, 7 + C)
+        box_labels: (N,)
+        nms_config:
+
+    Returns:
+
+    """
+    selected = []
+    for k in range(len(nms_config.NMS_THRESH)):
+        curr_mask = box_labels == k
+        if score_thresh is not None and isinstance(score_thresh, float):
+            curr_mask *= (box_scores > score_thresh)
+        elif score_thresh is not None and isinstance(score_thresh, list):
+            curr_mask *= (box_scores > score_thresh[k])
+        curr_idx = torch.nonzero(curr_mask)[:, 0]
+        curr_box_scores = box_scores[curr_mask]
+        cur_box_preds = box_preds[curr_mask]
+
+        if curr_box_scores.shape[0] > 0:
+            curr_box_scores_nms = curr_box_scores
+            curr_boxes_for_nms = cur_box_preds
+
+            keep_idx, _ = getattr(iou3d_nms_utils, 'nms_gpu')(
+                curr_boxes_for_nms, curr_box_scores_nms,
+                thresh=nms_config.NMS_THRESH[k],
+                pre_maxsize=nms_config.NMS_PRE_MAXSIZE[k],
+                post_max_size=nms_config.NMS_POST_MAXSIZE[k]
+            )
+            curr_selected = curr_idx[keep_idx]
+            selected.append(curr_selected)
+    if len(selected) != 0:
+        selected = torch.cat(selected)
+        
+
+    return selected, box_scores[selected]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/mppnet_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/mppnet_utils.py
new file mode 100644
index 000000000..10641ad3a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/mppnet_utils.py
@@ -0,0 +1,420 @@
+from os import getgrouplist
+import torch.nn as nn
+import torch
+import numpy as np
+import torch.nn.functional as F
+from typing import Optional, List
+from torch import Tensor
+from torch.nn.init import xavier_uniform_, zeros_, kaiming_normal_
+
+
+class PointNetfeat(nn.Module):
+    def __init__(self, input_dim, x=1,outchannel=512):
+        super(PointNetfeat, self).__init__()
+        if outchannel==256:
+            self.output_channel = 256
+        else:
+            self.output_channel = 512 * x
+        self.conv1 = torch.nn.Conv1d(input_dim, 64 * x, 1)
+        self.conv2 = torch.nn.Conv1d(64 * x, 128 * x, 1)
+        self.conv3 = torch.nn.Conv1d(128 * x, 256 * x, 1)
+        self.conv4 = torch.nn.Conv1d(256 * x,  self.output_channel, 1)
+        self.bn1 = nn.BatchNorm1d(64 * x)
+        self.bn2 = nn.BatchNorm1d(128 * x)
+        self.bn3 = nn.BatchNorm1d(256 * x)
+        self.bn4 = nn.BatchNorm1d(self.output_channel)
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.relu(self.bn3(self.conv3(x)))
+        x_ori = self.bn4(self.conv4(x)) 
+
+        x = torch.max(x_ori, 2, keepdim=True)[0]
+
+        x = x.view(-1, self.output_channel)
+        return x, x_ori
+
+class PointNet(nn.Module):
+    def __init__(self, input_dim, joint_feat=False,model_cfg=None):
+        super(PointNet, self).__init__()
+        self.joint_feat = joint_feat
+        channels = model_cfg.TRANS_INPUT
+
+        times=1
+        self.feat = PointNetfeat(input_dim, 1)
+
+        self.fc1 = nn.Linear(512, 256 )
+        self.fc2 = nn.Linear(256, channels)
+
+        self.pre_bn = nn.BatchNorm1d(input_dim)
+        self.bn1 = nn.BatchNorm1d(256)
+        self.bn2 = nn.BatchNorm1d(channels)
+        self.relu = nn.ReLU()
+
+        self.fc_s1 = nn.Linear(channels*times, 256)
+        self.fc_s2 = nn.Linear(256, 3, bias=False)
+        self.fc_ce1 = nn.Linear(channels*times, 256)
+        self.fc_ce2 = nn.Linear(256, 3, bias=False)
+        self.fc_hr1 = nn.Linear(channels*times, 256)
+        self.fc_hr2 = nn.Linear(256, 1, bias=False)
+
+    def forward(self, x, feat=None):
+
+        if self.joint_feat:
+            if len(feat.shape) > 2:
+                feat = torch.max(feat, 2, keepdim=True)[0]
+                x = feat.view(-1, self.output_channel)
+                x = F.relu(self.bn1(self.fc1(x)))
+                feat = F.relu(self.bn2(self.fc2(x)))
+            else:
+                feat = feat
+            feat_traj = None
+        else:
+            x, feat_traj = self.feat(self.pre_bn(x))
+            x = F.relu(self.bn1(self.fc1(x)))
+            feat = F.relu(self.bn2(self.fc2(x)))
+
+        x = F.relu(self.fc_ce1(feat))
+        centers = self.fc_ce2(x)
+
+        x = F.relu(self.fc_s1(feat))
+        sizes = self.fc_s2(x)
+
+        x = F.relu(self.fc_hr1(feat))
+        headings = self.fc_hr2(x)
+
+        return torch.cat([centers, sizes, headings],-1),feat,feat_traj
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear):
+                kaiming_normal_(m.weight.data)
+                if m.bias is not None:
+                    zeros_(m.bias)
+
+class MLP(nn.Module):
+
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+class SpatialMixerBlock(nn.Module):
+
+    def __init__(self,hidden_dim,grid_size,channels,config=None,dropout=0.0):
+        super().__init__()
+
+
+        self.mixer_x = MLP(input_dim = grid_size, hidden_dim = hidden_dim, output_dim = grid_size, num_layers = 3)
+        self.mixer_y = MLP(input_dim = grid_size, hidden_dim = hidden_dim, output_dim = grid_size, num_layers = 3)
+        self.mixer_z = MLP(input_dim = grid_size, hidden_dim = hidden_dim, output_dim = grid_size, num_layers = 3)
+        self.norm_x = nn.LayerNorm(channels)
+        self.norm_y = nn.LayerNorm(channels)
+        self.norm_z = nn.LayerNorm(channels)
+        self.norm_channel = nn.LayerNorm(channels)
+        self.ffn = nn.Sequential(
+                               nn.Linear(channels, 2*channels),
+                               nn.ReLU(),
+                               nn.Dropout(dropout),
+                               nn.Linear(2*channels, channels),
+                               )
+        self.config = config
+        self.grid_size = grid_size
+
+    def forward(self, src):
+
+        src_3d = src.permute(1,2,0).contiguous().view(src.shape[1],src.shape[2],
+                                   self.grid_size,self.grid_size,self.grid_size)
+        src_3d = src_3d.permute(0,1,4,3,2).contiguous() 
+        mixed_x = self.mixer_x(src_3d)
+        mixed_x = src_3d + mixed_x
+        mixed_x = self.norm_x(mixed_x.permute(0,2,3,4,1)).permute(0,4,1,2,3).contiguous()
+
+        mixed_y = self.mixer_y(mixed_x.permute(0,1,2,4,3)).permute(0,1,2,4,3).contiguous()
+        mixed_y =  mixed_x + mixed_y
+        mixed_y = self.norm_y(mixed_y.permute(0,2,3,4,1)).permute(0,4,1,2,3).contiguous()
+
+        mixed_z = self.mixer_z(mixed_y.permute(0,1,4,3,2)).permute(0,1,4,3,2).contiguous()
+
+        mixed_z =  mixed_y + mixed_z
+        mixed_z = self.norm_z(mixed_z.permute(0,2,3,4,1)).permute(0,4,1,2,3).contiguous()
+
+        src_mixer = mixed_z.view(src.shape[1],src.shape[2],-1).permute(2,0,1)
+        src_mixer = src_mixer + self.ffn(src_mixer)
+        src_mixer = self.norm_channel(src_mixer)
+
+        return src_mixer
+
+class Transformer(nn.Module):
+
+    def __init__(self, config, d_model=512, nhead=8, num_encoder_layers=6,
+                dim_feedforward=2048, dropout=0.1,activation="relu", normalize_before=False,
+                num_lidar_points=None,num_proxy_points=None, share_head=True,num_groups=None,
+                sequence_stride=None,num_frames=None):
+        super().__init__()
+
+        self.config = config
+        self.share_head = share_head
+        self.num_frames = num_frames
+        self.nhead = nhead
+        self.sequence_stride = sequence_stride
+        self.num_groups = num_groups
+        self.num_proxy_points = num_proxy_points
+        self.num_lidar_points = num_lidar_points
+        self.d_model = d_model
+        self.nhead = nhead
+        encoder_layer = [TransformerEncoderLayer(self.config, d_model, nhead, dim_feedforward,dropout, activation, 
+                      normalize_before, num_lidar_points,num_groups=num_groups) for i in range(num_encoder_layers)]
+
+        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+        self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm,self.config)
+
+        self.token = nn.Parameter(torch.zeros(self.num_groups, 1, d_model))
+
+        
+        if self.num_frames >4:
+  
+            self.group_length = self.num_frames // self.num_groups
+            self.fusion_all_group = MLP(input_dim = self.config.hidden_dim*self.group_length, 
+               hidden_dim = self.config.hidden_dim, output_dim = self.config.hidden_dim, num_layers = 4)
+
+            self.fusion_norm = FFN(d_model, dim_feedforward)
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def forward(self, src, pos=None):
+
+        BS, N, C = src.shape
+        if not pos is None:
+            pos = pos.permute(1, 0, 2)
+            
+        if self.num_frames == 16:
+            token_list = [self.token[i:(i+1)].repeat(BS,1,1) for i in range(self.num_groups)]
+            if self.sequence_stride ==1:
+                src_groups = src.view(src.shape[0],src.shape[1]//self.num_groups ,-1).chunk(4,dim=1)
+
+            elif self.sequence_stride ==4:
+                src_groups = []
+
+                for i in range(self.num_groups):
+                    groups = []
+                    for j in range(self.group_length):
+                        points_index_start = (i+j*self.sequence_stride)*self.num_proxy_points
+                        points_index_end = points_index_start + self.num_proxy_points
+                        groups.append(src[:,points_index_start:points_index_end])
+
+                    groups = torch.cat(groups,-1)
+                    src_groups.append(groups)
+
+            else:
+                raise NotImplementedError
+
+            src_merge = torch.cat(src_groups,1)
+            src = self.fusion_norm(src[:,:self.num_groups*self.num_proxy_points],self.fusion_all_group(src_merge))
+            src = [torch.cat([token_list[i],src[:,i*self.num_proxy_points:(i+1)*self.num_proxy_points]],dim=1) for i in range(self.num_groups)]
+            src = torch.cat(src,dim=0)
+
+        else:
+            token_list = [self.token[i:(i+1)].repeat(BS,1,1) for i in range(self.num_groups)]
+            src = [torch.cat([token_list[i],src[:,i*self.num_proxy_points:(i+1)*self.num_proxy_points]],dim=1) for i in range(self.num_groups)]
+            src = torch.cat(src,dim=0)
+
+        src = src.permute(1, 0, 2)
+        memory,tokens = self.encoder(src,pos=pos) 
+
+        memory = torch.cat(memory[0:1].chunk(4,dim=1),0)
+        return memory, tokens
+    
+
+class TransformerEncoder(nn.Module):
+
+    def __init__(self, encoder_layer, num_layers, norm=None,config=None):
+        super().__init__()
+        self.layers = nn.ModuleList(encoder_layer)
+        self.num_layers = num_layers
+        self.norm = norm
+        self.config = config
+
+    def forward(self, src,
+                pos: Optional[Tensor] = None):
+
+        token_list = []
+        output = src
+        for layer in self.layers:
+            output,tokens = layer(output,pos=pos)
+            token_list.append(tokens)
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output,token_list
+
+
+class TransformerEncoderLayer(nn.Module):
+    count = 0
+    def __init__(self, config, d_model, nhead, dim_feedforward=2048, dropout=0.1,
+                 activation="relu", normalize_before=False,num_points=None,num_groups=None):
+        super().__init__()
+        TransformerEncoderLayer.count += 1
+        self.layer_count = TransformerEncoderLayer.count
+        self.config = config
+        self.num_point = num_points
+        self.num_groups= num_groups
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        if self.layer_count <= self.config.enc_layers-1:
+            self.cross_attn_layers = nn.ModuleList()
+            for _ in range(self.num_groups):
+                self.cross_attn_layers.append(nn.MultiheadAttention(d_model, nhead, dropout=dropout))
+
+            self.ffn = FFN(d_model, dim_feedforward)
+            self.fusion_all_groups = MLP(input_dim = d_model*4, hidden_dim = d_model, output_dim = d_model, num_layers = 4)
+    
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+        self.mlp_mixer_3d = SpatialMixerBlock(self.config.use_mlp_mixer.hidden_dim,self.config.use_mlp_mixer.get('grid_size', 4),self.config.hidden_dim, self.config.use_mlp_mixer)
+
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(self,
+                     src,
+                     pos: Optional[Tensor] = None):
+
+        src_intra_group_fusion = self.mlp_mixer_3d(src[1:])
+        src = torch.cat([src[:1],src_intra_group_fusion],0)
+
+        token = src[:1]
+
+        if not pos is None:
+            key = self.with_pos_embed(src_intra_group_fusion, pos[1:])
+        else:
+            key = src_intra_group_fusion
+
+        src_summary = self.self_attn(token, key, value=src_intra_group_fusion)[0]
+        token = token + self.dropout1(src_summary)
+        token = self.norm1(token)
+        src_summary = self.linear2(self.dropout(self.activation(self.linear1(token))))
+        token = token + self.dropout2(src_summary)
+        token = self.norm2(token)
+        src = torch.cat([token,src[1:]],0)
+
+        if self.layer_count <= self.config.enc_layers-1:
+    
+            src_all_groups = src[1:].view((src.shape[0]-1)*4,-1,src.shape[-1])
+            src_groups_list = src_all_groups.chunk(self.num_groups,0)
+
+            src_all_groups = torch.cat(src_groups_list,-1)
+            src_all_groups_fusion = self.fusion_all_groups(src_all_groups)
+
+            key = self.with_pos_embed(src_all_groups_fusion, pos[1:])
+            query_list = [self.with_pos_embed(query, pos[1:]) for query in src_groups_list]
+
+            inter_group_fusion_list = []
+            for i in range(self.num_groups):
+                inter_group_fusion = self.cross_attn_layers[i](query_list[i], key, value=src_all_groups_fusion)[0]
+                inter_group_fusion = self.ffn(src_groups_list[i],inter_group_fusion)
+                inter_group_fusion_list.append(inter_group_fusion)
+
+            src_inter_group_fusion = torch.cat(inter_group_fusion_list,1)
+
+            src = torch.cat([src[:1],src_inter_group_fusion],0)
+
+        return src, torch.cat(src[:1].chunk(4,1),0)
+
+    def forward_pre(self, src,
+                    pos: Optional[Tensor] = None):
+        src2 = self.norm1(src)
+        q = k = self.with_pos_embed(src2, pos)
+        src2 = self.self_attn(q, k, value=src2)[0]
+        src = src + self.dropout1(src2)
+        src2 = self.norm2(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
+        src = src + self.dropout2(src2)
+        return src
+
+    def forward(self, src,
+                pos: Optional[Tensor] = None):
+
+        if self.normalize_before:
+            return self.forward_pre(src, pos)
+        return self.forward_post(src,  pos)
+
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+class FFN(nn.Module):
+    def __init__(self, d_model, dim_feedforward=2048, dropout=0.1,dout=None,
+                 activation="relu", normalize_before=False):
+        super().__init__()
+        
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+    def forward(self, tgt,tgt_input):
+        tgt = tgt + self.dropout2(tgt_input)
+        tgt = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+
+        return tgt
+
+def build_transformer(args):
+    return Transformer(
+        config = args,
+        d_model=args.hidden_dim,
+        dropout=args.dropout,
+        nhead=args.nheads,
+        dim_feedforward=args.dim_feedforward,
+        num_encoder_layers=args.enc_layers,
+        normalize_before=args.pre_norm,
+        num_lidar_points = args.num_lidar_points,
+        num_proxy_points = args.num_proxy_points,
+        num_frames = args.num_frames,
+        sequence_stride = args.get('sequence_stride',1),
+        num_groups=args.num_groups,
+    )
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/swin_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/swin_utils.py
new file mode 100644
index 000000000..3abd51fd1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/swin_utils.py
@@ -0,0 +1,659 @@
+"""
+Mostly copy-paste from 
+    https://github.com/open-mmlab/mmdetection/blob/ecac3a77becc63f23d9f6980b2a36f86acd00a8a/mmdet/models/layers/transformer/utils.py
+    
+"""
+
+import copy
+import math
+import warnings
+import collections.abc
+from collections import OrderedDict
+from itertools import repeat
+from typing import Sequence
+
+import torch
+from torch import Tensor
+import torch.nn as nn
+import torch.nn.functional as F
+
+# From PyTorch internals
+def _ntuple(n):
+
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+
+    return parse
+
+to_2tuple = _ntuple(2)
+
+def constant_init(module: nn.Module, val: float, bias: float = 0) -> None:
+    if hasattr(module, 'weight') and module.weight is not None:
+        nn.init.constant_(module.weight, val)
+    if hasattr(module, 'bias') and module.bias is not None:
+        nn.init.constant_(module.bias, bias)
+
+
+def trunc_normal_init(module: nn.Module,
+                      mean: float = 0,
+                      std: float = 1,
+                      a: float = -2,
+                      b: float = 2,
+                      bias: float = 0) -> None:
+    if hasattr(module, 'weight') and module.weight is not None:
+        trunc_normal_(module.weight, mean, std, a, b)  # type: ignore
+    if hasattr(module, 'bias') and module.bias is not None:
+        nn.init.constant_(module.bias, bias)  # type: ignore
+
+
+def _no_grad_trunc_normal_(tensor: Tensor, mean: float, std: float, a: float,
+                           b: float) -> Tensor:
+    # Method based on
+    # https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    # Modified from
+    # https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
+            'The distribution of values may be incorrect.',
+            stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        lower = norm_cdf((a - mean) / std)
+        upper = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [lower, upper], then translate
+        # to [2lower-1, 2upper-1].
+        tensor.uniform_(2 * lower - 1, 2 * upper - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor: Tensor,
+                  mean: float = 0.,
+                  std: float = 1.,
+                  a: float = -2.,
+                  b: float = 2.) -> Tensor:
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+
+    Modified from
+    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
+
+    Args:
+        tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.
+        mean (float): the mean of the normal distribution.
+        std (float): the standard deviation of the normal distribution.
+        a (float): the minimum cutoff value.
+        b (float): the maximum cutoff value.
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def drop_path(x: torch.Tensor,
+              drop_prob: float = 0.,
+              training: bool = False) -> torch.Tensor:
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of
+    residual blocks).
+
+    We follow the implementation
+    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    # handle tensors with different dimensions, not just 4D tensors.
+    shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)
+    random_tensor = keep_prob + torch.rand(
+        shape, dtype=x.dtype, device=x.device)
+    output = x.div(keep_prob) * random_tensor.floor()
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of
+    residual blocks).
+
+    We follow the implementation
+    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
+
+    Args:
+        drop_prob (float): Probability of the path to be zeroed. Default: 0.1
+    """
+
+    def __init__(self, drop_prob: float = 0.1):
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return drop_path(x, self.drop_prob, self.training)
+
+
+class FFN(nn.Module):
+    """Implements feed-forward networks (FFNs) with identity connection.
+
+    Args:
+        embed_dims (int): The feature dimension. Same as
+            `MultiheadAttention`. Defaults: 256.
+        feedforward_channels (int): The hidden dimension of FFNs.
+            Defaults: 1024.
+        num_fcs (int, optional): The number of fully-connected layers in
+            FFNs. Default: 2.
+        act_cfg (dict, optional): The activation config for FFNs.
+            Default: dict(type='ReLU')
+        ffn_drop (float, optional): Probability of an element to be
+            zeroed in FFN. Default 0.0.
+        add_identity (bool, optional): Whether to add the
+            identity connection. Default: `True`.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims=256,
+                 feedforward_channels=1024,
+                 num_fcs=2,
+                 act_cfg=dict(type='ReLU', inplace=True),
+                 ffn_drop=0.,
+                 dropout_layer=None,
+                 add_identity=True,
+                 init_cfg=None,
+                 **kwargs):
+        super().__init__()
+        self._is_init = False
+        self.init_cfg = copy.deepcopy(init_cfg)
+        assert num_fcs >= 2, 'num_fcs should be no less ' \
+            f'than 2. got {num_fcs}.'
+        self.embed_dims = embed_dims
+        self.feedforward_channels = feedforward_channels
+        self.num_fcs = num_fcs
+        self.act_cfg = act_cfg
+        # ignore act_cfg, default GELU
+        self.activate = nn.GELU()
+
+        layers = []
+        in_channels = embed_dims
+        for _ in range(num_fcs - 1):
+            layers.append(
+                nn.Sequential(
+                    nn.Linear(in_channels, feedforward_channels), self.activate,
+                    nn.Dropout(ffn_drop)))
+            in_channels = feedforward_channels
+        layers.append(nn.Linear(feedforward_channels, embed_dims))
+        layers.append(nn.Dropout(ffn_drop))
+        self.layers = nn.Sequential(*layers)
+        self.dropout_layer = DropPath(dropout_layer['drop_prob'])
+        self.add_identity = add_identity
+
+    def forward(self, x, identity=None):
+        """Forward function for `FFN`.
+
+        The function would add x to the output tensor if residue is None.
+        """
+        out = self.layers(x)
+        if not self.add_identity:
+            return self.dropout_layer(out)
+        if identity is None:
+            identity = x
+        return identity + self.dropout_layer(out)
+
+
+
+def nlc_to_nchw(x, hw_shape):
+    """Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor.
+
+    Args:
+        x (Tensor): The input tensor of shape [N, L, C] before conversion.
+        hw_shape (Sequence[int]): The height and width of output feature map.
+
+    Returns:
+        Tensor: The output tensor of shape [N, C, H, W] after conversion.
+    """
+    H, W = hw_shape
+    assert len(x.shape) == 3
+    B, L, C = x.shape
+    assert L == H * W, 'The seq_len does not match H, W'
+    return x.transpose(1, 2).reshape(B, C, H, W).contiguous()
+
+
+def nchw_to_nlc(x):
+    """Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor.
+
+    Args:
+        x (Tensor): The input tensor of shape [N, C, H, W] before conversion.
+
+    Returns:
+        Tensor: The output tensor of shape [N, L, C] after conversion.
+    """
+    assert len(x.shape) == 4
+    return x.flatten(2).transpose(1, 2).contiguous()
+
+
+class AdaptivePadding(nn.Module):
+    """Applies padding to input (if needed) so that input can get fully covered
+    by filter you specified. It support two modes "same" and "corner". The
+    "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around
+    input. The "corner"  mode would pad zero to bottom right.
+
+    Args:
+        kernel_size (int | tuple): Size of the kernel:
+        stride (int | tuple): Stride of the filter. Default: 1:
+        dilation (int | tuple): Spacing between kernel elements.
+            Default: 1
+        padding (str): Support "same" and "corner", "corner" mode
+            would pad zero to bottom right, and "same" mode would
+            pad zero around input. Default: "corner".
+    Example:
+        >>> kernel_size = 16
+        >>> stride = 16
+        >>> dilation = 1
+        >>> input = torch.rand(1, 1, 15, 17)
+        >>> adap_pad = AdaptivePadding(
+        >>>     kernel_size=kernel_size,
+        >>>     stride=stride,
+        >>>     dilation=dilation,
+        >>>     padding="corner")
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+        >>> input = torch.rand(1, 1, 16, 17)
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+    """
+
+    def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
+
+        super(AdaptivePadding, self).__init__()
+
+        assert padding in ('same', 'corner')
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        padding = to_2tuple(padding)
+        dilation = to_2tuple(dilation)
+
+        self.padding = padding
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.dilation = dilation
+
+    def get_pad_shape(self, input_shape):
+        input_h, input_w = input_shape
+        kernel_h, kernel_w = self.kernel_size
+        stride_h, stride_w = self.stride
+        output_h = math.ceil(input_h / stride_h)
+        output_w = math.ceil(input_w / stride_w)
+        pad_h = max((output_h - 1) * stride_h +
+                    (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
+        pad_w = max((output_w - 1) * stride_w +
+                    (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
+        return pad_h, pad_w
+
+    def forward(self, x):
+        pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
+        if pad_h > 0 or pad_w > 0:
+            if self.padding == 'corner':
+                x = F.pad(x, [0, pad_w, 0, pad_h])
+            elif self.padding == 'same':
+                x = F.pad(x, [
+                    pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
+                    pad_h - pad_h // 2
+                ])
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """Image to Patch Embedding.
+
+    We use a conv layer to implement PatchEmbed.
+
+    Args:
+        in_channels (int): The num of input channels. Default: 3
+        embed_dims (int): The dimensions of embedding. Default: 768
+        conv_type (str): The config dict for embedding
+            conv layer type selection. Default: "Conv2d.
+        kernel_size (int): The kernel_size of embedding conv. Default: 16.
+        stride (int): The slide stride of embedding conv.
+            Default: None (Would be set as `kernel_size`).
+        padding (int | tuple | string ): The padding length of
+            embedding conv. When it is a string, it means the mode
+            of adaptive padding, support "same" and "corner" now.
+            Default: "corner".
+        dilation (int): The dilation rate of embedding conv. Default: 1.
+        bias (bool): Bias of embed conv. Default: True.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: None.
+        input_size (int | tuple | None): The size of input, which will be
+            used to calculate the out size. Only work when `dynamic_size`
+            is False. Default: None.
+        init_cfg (`mmcv.ConfigDict`, optional): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(
+        self,
+        in_channels=3,
+        embed_dims=768,
+        conv_type='Conv2d',
+        kernel_size=16,
+        stride=16,
+        padding='corner',
+        dilation=1,
+        bias=True,
+        norm_cfg=None,
+        input_size=None,
+        init_cfg=None,
+    ):
+        super(PatchEmbed, self).__init__()
+        self._is_init = False
+
+        self.init_cfg = copy.deepcopy(init_cfg)
+        self.embed_dims = embed_dims
+        if stride is None:
+            stride = kernel_size
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        if isinstance(padding, str):
+            self.adap_padding = AdaptivePadding(
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding)
+            # disable the padding of conv
+            padding = 0
+        else:
+            self.adap_padding = None
+        padding = to_2tuple(padding)
+
+        self.projection = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=embed_dims,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        if norm_cfg is not None:
+            self.norm = nn.LayerNorm(embed_dims)
+        else:
+            self.norm = None
+
+        if input_size:
+            input_size = to_2tuple(input_size)
+            # `init_out_size` would be used outside to
+            # calculate the num_patches
+            # when `use_abs_pos_embed` outside
+            self.init_input_size = input_size
+            if self.adap_padding:
+                pad_h, pad_w = self.adap_padding.get_pad_shape(input_size)
+                input_h, input_w = input_size
+                input_h = input_h + pad_h
+                input_w = input_w + pad_w
+                input_size = (input_h, input_w)
+
+            # https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
+            h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
+                     (kernel_size[0] - 1) - 1) // stride[0] + 1
+            w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
+                     (kernel_size[1] - 1) - 1) // stride[1] + 1
+            self.init_out_size = (h_out, w_out)
+        else:
+            self.init_input_size = None
+            self.init_out_size = None
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Has shape (B, C, H, W). In most case, C is 3.
+
+        Returns:
+            tuple: Contains merged results and its spatial shape.
+
+                - x (Tensor): Has shape (B, out_h * out_w, embed_dims)
+                - out_size (tuple[int]): Spatial shape of x, arrange as
+                    (out_h, out_w).
+        """
+
+        if self.adap_padding:
+            x = self.adap_padding(x)
+
+        x = self.projection(x)
+        out_size = (x.shape[2], x.shape[3])
+        x = x.flatten(2).transpose(1, 2)
+        if self.norm is not None:
+            x = self.norm(x)
+        return x, out_size
+
+
+class PatchMerging(nn.Module):
+    """Merge patch feature map.
+
+    This layer groups feature map by kernel_size, and applies norm and linear
+    layers to the grouped feature map. Our implementation uses `nn.Unfold` to
+    merge patch, which is about 25% faster than original implementation.
+    Instead, we need to modify pretrained models for compatibility.
+
+    Args:
+        in_channels (int): The num of input channels.
+            to gets fully covered by filter and stride you specified..
+            Default: True.
+        out_channels (int): The num of output channels.
+        kernel_size (int | tuple, optional): the kernel size in the unfold
+            layer. Defaults to 2.
+        stride (int | tuple, optional): the stride of the sliding blocks in the
+            unfold layer. Default: None. (Would be set as `kernel_size`)
+        padding (int | tuple | string ): The padding length of
+            embedding conv. When it is a string, it means the mode
+            of adaptive padding, support "same" and "corner" now.
+            Default: "corner".
+        dilation (int | tuple, optional): dilation parameter in the unfold
+            layer. Default: 1.
+        bias (bool, optional): Whether to add bias in linear layer or not.
+            Defaults: False.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: dict(type='LN').
+        init_cfg (dict, optional): The extra config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=2,
+                 stride=None,
+                 padding='corner',
+                 dilation=1,
+                 bias=False,
+                 norm_cfg=dict(type='LN'),
+                 init_cfg=None):
+        super().__init__()
+        self._is_init = False
+        self.init_cfg = copy.deepcopy(init_cfg)
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        if stride:
+            stride = stride
+        else:
+            stride = kernel_size
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        if isinstance(padding, str):
+            self.adap_padding = AdaptivePadding(
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding)
+            # disable the padding of unfold
+            padding = 0
+        else:
+            self.adap_padding = None
+
+        padding = to_2tuple(padding)
+        self.sampler = nn.Unfold(
+            kernel_size=kernel_size,
+            dilation=dilation,
+            padding=padding,
+            stride=stride)
+
+        sample_dim = kernel_size[0] * kernel_size[1] * in_channels
+
+        if norm_cfg is not None:
+            self.norm = nn.LayerNorm(sample_dim)
+        else:
+            self.norm = None
+
+        self.reduction = nn.Linear(sample_dim, out_channels, bias=bias)
+
+    def forward(self, x, input_size):
+        """
+        Args:
+            x (Tensor): Has shape (B, H*W, C_in).
+            input_size (tuple[int]): The spatial shape of x, arrange as (H, W).
+                Default: None.
+
+        Returns:
+            tuple: Contains merged results and its spatial shape.
+
+                - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out)
+                - out_size (tuple[int]): Spatial shape of x, arrange as
+                    (Merged_H, Merged_W).
+        """
+        B, L, C = x.shape
+        assert isinstance(input_size, Sequence), f'Expect ' \
+                                                 f'input_size is ' \
+                                                 f'`Sequence` ' \
+                                                 f'but get {input_size}'
+
+        H, W = input_size
+        assert L == H * W, 'input feature has wrong size'
+
+        x = x.view(B, H, W, C).permute([0, 3, 1, 2])  # B, C, H, W
+        # Use nn.Unfold to merge patch. About 25% faster than original method,
+        # but need to modify pretrained model for compatibility
+
+        if self.adap_padding:
+            x = self.adap_padding(x)
+            H, W = x.shape[-2:]
+
+        x = self.sampler(x)
+        # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2)
+
+        out_h = (H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] *
+                 (self.sampler.kernel_size[0] - 1) -
+                 1) // self.sampler.stride[0] + 1
+        out_w = (W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] *
+                 (self.sampler.kernel_size[1] - 1) -
+                 1) // self.sampler.stride[1] + 1
+
+        output_size = (out_h, out_w)
+        x = x.transpose(1, 2)  # B, H/2*W/2, 4*C
+        x = self.norm(x) if self.norm else x
+        x = self.reduction(x)
+        return x, output_size
+
+
+def inverse_sigmoid(x, eps=1e-5):
+    """Inverse function of sigmoid.
+
+    Args:
+        x (Tensor): The tensor to do the
+            inverse.
+        eps (float): EPS avoid numerical
+            overflow. Defaults 1e-5.
+    Returns:
+        Tensor: The x has passed the inverse
+            function of sigmoid, has same
+            shape with input.
+    """
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+
+def swin_converter(ckpt):
+
+    new_ckpt = OrderedDict()
+
+    def correct_unfold_reduction_order(x):
+        out_channel, in_channel = x.shape
+        x = x.reshape(out_channel, 4, in_channel // 4)
+        x = x[:, [0, 2, 1, 3], :].transpose(1,
+                                            2).reshape(out_channel, in_channel)
+        return x
+
+    def correct_unfold_norm_order(x):
+        in_channel = x.shape[0]
+        x = x.reshape(4, in_channel // 4)
+        x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel)
+        return x
+
+    for k, v in ckpt.items():
+        if k.startswith('head'):
+            continue
+        elif k.startswith('layers'):
+            new_v = v
+            if 'attn.' in k:
+                new_k = k.replace('attn.', 'attn.w_msa.')
+            elif 'mlp.' in k:
+                if 'mlp.fc1.' in k:
+                    new_k = k.replace('mlp.fc1.', 'ffn.layers.0.0.')
+                elif 'mlp.fc2.' in k:
+                    new_k = k.replace('mlp.fc2.', 'ffn.layers.1.')
+                else:
+                    new_k = k.replace('mlp.', 'ffn.')
+            elif 'downsample' in k:
+                new_k = k
+                if 'reduction.' in k:
+                    new_v = correct_unfold_reduction_order(v)
+                elif 'norm.' in k:
+                    new_v = correct_unfold_norm_order(v)
+            else:
+                new_k = k
+            new_k = new_k.replace('layers', 'stages', 1)
+        elif k.startswith('patch_embed'):
+            new_v = v
+            if 'proj' in k:
+                new_k = k.replace('proj', 'projection')
+            else:
+                new_k = k
+        else:
+            new_v = v
+            new_k = k
+
+        new_ckpt['backbone.' + new_k] = new_v
+
+    return new_ckpt
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/transfusion_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/transfusion_utils.py
new file mode 100644
index 000000000..677827cba
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/model_utils/transfusion_utils.py
@@ -0,0 +1,102 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+def clip_sigmoid(x, eps=1e-4):
+    y = torch.clamp(x.sigmoid_(), min=eps, max=1 - eps)
+    return y
+
+
+class PositionEmbeddingLearned(nn.Module):
+    """
+    Absolute pos embedding, learned.
+    """
+
+    def __init__(self, input_channel, num_pos_feats=288):
+        super().__init__()
+        self.position_embedding_head = nn.Sequential(
+            nn.Conv1d(input_channel, num_pos_feats, kernel_size=1),
+            nn.BatchNorm1d(num_pos_feats),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(num_pos_feats, num_pos_feats, kernel_size=1))
+
+    def forward(self, xyz):
+        xyz = xyz.transpose(1, 2).contiguous()
+        position_embedding = self.position_embedding_head(xyz)
+        return position_embedding
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
+                 self_posembed=None, cross_posembed=None, cross_only=False):
+        super().__init__()
+        self.cross_only = cross_only
+        if not self.cross_only:
+            self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        def _get_activation_fn(activation):
+            """Return an activation function given a string"""
+            if activation == "relu":
+                return F.relu
+            if activation == "gelu":
+                return F.gelu
+            if activation == "glu":
+                return F.glu
+            raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+        self.activation = _get_activation_fn(activation)
+
+        self.self_posembed = self_posembed
+        self.cross_posembed = cross_posembed
+
+    def with_pos_embed(self, tensor, pos_embed):
+        return tensor if pos_embed is None else tensor + pos_embed
+
+    def forward(self, query, key, query_pos, key_pos, key_padding_mask=None, attn_mask=None):
+        # NxCxP to PxNxC
+        if self.self_posembed is not None:
+            query_pos_embed = self.self_posembed(query_pos).permute(2, 0, 1)
+        else:
+            query_pos_embed = None
+        if self.cross_posembed is not None:
+            key_pos_embed = self.cross_posembed(key_pos).permute(2, 0, 1)
+        else:
+            key_pos_embed = None
+
+        query = query.permute(2, 0, 1)
+        key = key.permute(2, 0, 1)
+
+        if not self.cross_only:
+            q = k = v = self.with_pos_embed(query, query_pos_embed)
+            query2 = self.self_attn(q, k, value=v)[0]
+            query = query + self.dropout1(query2)
+            query = self.norm1(query)
+        
+        query2 = self.multihead_attn(query=self.with_pos_embed(query, query_pos_embed),
+                                     key=self.with_pos_embed(key, key_pos_embed),
+                                     value=self.with_pos_embed(key, key_pos_embed), 
+                                     key_padding_mask=key_padding_mask, attn_mask=attn_mask)[0]
+
+        query = query + self.dropout2(query2)
+        query = self.norm2(query)
+
+        query2 = self.linear2(self.dropout(self.activation(self.linear1(query))))
+        query = query + self.dropout3(query2)
+        query = self.norm3(query)
+
+        # NxCxP to PxNxC
+        query = query.permute(1, 2, 0)
+        return query
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/__init__.py
new file mode 100644
index 000000000..693cec426
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/__init__.py
@@ -0,0 +1,19 @@
+from .partA2_head import PartA2FCHead
+from .pointrcnn_head import PointRCNNHead
+from .pvrcnn_head import PVRCNNHead
+from .second_head import SECONDHead
+from .voxelrcnn_head import VoxelRCNNHead
+from .roi_head_template import RoIHeadTemplate
+from .mppnet_head import MPPNetHead
+from .mppnet_memory_bank_e2e import MPPNetHeadE2E
+
+__all__ = {
+    'RoIHeadTemplate': RoIHeadTemplate,
+    'PartA2FCHead': PartA2FCHead,
+    'PVRCNNHead': PVRCNNHead,
+    'SECONDHead': SECONDHead,
+    'PointRCNNHead': PointRCNNHead,
+    'VoxelRCNNHead': VoxelRCNNHead,
+    'MPPNetHead': MPPNetHead,
+    'MPPNetHeadE2E': MPPNetHeadE2E,
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/mppnet_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/mppnet_head.py
new file mode 100644
index 000000000..909b9c732
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/mppnet_head.py
@@ -0,0 +1,992 @@
+from typing import ValuesView
+import torch.nn as nn
+import torch
+import numpy as np
+import copy
+import torch.nn.functional as F
+from pcdet.ops.iou3d_nms import iou3d_nms_utils
+from ...utils import common_utils, loss_utils
+from .roi_head_template import RoIHeadTemplate
+from ..model_utils.mppnet_utils import build_transformer, PointNet, MLP
+from .target_assigner.proposal_target_layer import ProposalTargetLayer
+from pcdet.ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
+
+
+class ProposalTargetLayerMPPNet(ProposalTargetLayer):
+    def __init__(self, roi_sampler_cfg):
+        super().__init__(roi_sampler_cfg = roi_sampler_cfg)
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                roi_scores: (B, num_rois)
+                gt_boxes: (B, N, 7 + C + 1)
+                roi_labels: (B, num_rois)
+        Returns:
+            batch_dict:
+                rois: (B, M, 7 + C)
+                gt_of_rois: (B, M, 7 + C)
+                gt_iou_of_rois: (B, M)
+                roi_scores: (B, M)
+                roi_labels: (B, M)
+                reg_valid_mask: (B, M)
+                rcnn_cls_labels: (B, M)
+        """
+
+        batch_rois, batch_gt_of_rois, batch_roi_ious, batch_roi_scores, batch_roi_labels, \
+        batch_trajectory_rois,batch_valid_length = self.sample_rois_for_mppnet(batch_dict=batch_dict)
+
+        # regression valid mask
+        reg_valid_mask = (batch_roi_ious > self.roi_sampler_cfg.REG_FG_THRESH).long()
+
+        # classification label
+        if self.roi_sampler_cfg.CLS_SCORE_TYPE == 'cls':
+            batch_cls_labels = (batch_roi_ious > self.roi_sampler_cfg.CLS_FG_THRESH).long()
+            ignore_mask = (batch_roi_ious > self.roi_sampler_cfg.CLS_BG_THRESH) & \
+                          (batch_roi_ious < self.roi_sampler_cfg.CLS_FG_THRESH)
+            batch_cls_labels[ignore_mask > 0] = -1
+        elif self.roi_sampler_cfg.CLS_SCORE_TYPE == 'roi_iou':
+            iou_bg_thresh = self.roi_sampler_cfg.CLS_BG_THRESH
+            iou_fg_thresh = self.roi_sampler_cfg.CLS_FG_THRESH
+            fg_mask = batch_roi_ious > iou_fg_thresh
+            bg_mask = batch_roi_ious < iou_bg_thresh
+            interval_mask = (fg_mask == 0) & (bg_mask == 0)
+
+            batch_cls_labels = (fg_mask > 0).float()
+            batch_cls_labels[interval_mask] = \
+                (batch_roi_ious[interval_mask] - iou_bg_thresh) / (iou_fg_thresh - iou_bg_thresh)
+        else:
+            raise NotImplementedError
+
+
+        targets_dict = {'rois': batch_rois, 'gt_of_rois': batch_gt_of_rois, 
+                        'gt_iou_of_rois': batch_roi_ious,'roi_scores': batch_roi_scores,
+                        'roi_labels': batch_roi_labels,'reg_valid_mask': reg_valid_mask, 
+                        'rcnn_cls_labels': batch_cls_labels,'trajectory_rois':batch_trajectory_rois,
+                        'valid_length': batch_valid_length,
+                        }
+
+        return targets_dict
+
+    def sample_rois_for_mppnet(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                roi_scores: (B, num_rois)
+                gt_boxes: (B, N, 7 + C + 1)
+                roi_labels: (B, num_rois)
+        Returns:
+        """
+        cur_frame_idx = 0
+        batch_size = batch_dict['batch_size']
+        rois = batch_dict['trajectory_rois'][:,cur_frame_idx,:,:]
+        roi_scores = batch_dict['roi_scores'][:,:,cur_frame_idx]
+        roi_labels = batch_dict['roi_labels']
+        gt_boxes = batch_dict['gt_boxes']
+
+        code_size = rois.shape[-1]
+        batch_rois = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE, code_size)
+        batch_gt_of_rois = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE, gt_boxes.shape[-1])
+        batch_roi_ious = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE)
+        batch_roi_scores = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE)
+        batch_roi_labels = rois.new_zeros((batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE), dtype=torch.long)
+
+
+
+        trajectory_rois = batch_dict['trajectory_rois']
+        batch_trajectory_rois = rois.new_zeros(batch_size, trajectory_rois.shape[1],self.roi_sampler_cfg.ROI_PER_IMAGE,trajectory_rois.shape[-1])
+
+        valid_length = batch_dict['valid_length']
+        batch_valid_length = rois.new_zeros((batch_size, batch_dict['trajectory_rois'].shape[1], self.roi_sampler_cfg.ROI_PER_IMAGE))
+
+        for index in range(batch_size):
+ 
+            cur_trajectory_rois = trajectory_rois[index]
+
+            cur_roi, cur_gt, cur_roi_labels, cur_roi_scores = rois[index],gt_boxes[index], roi_labels[index], roi_scores[index]
+
+            if 'valid_length' in batch_dict.keys():
+                cur_valid_length = valid_length[index]
+
+
+
+            k = cur_gt.__len__() - 1
+            while k > 0 and cur_gt[k].sum() == 0:
+                k -= 1
+
+            cur_gt = cur_gt[:k + 1]
+            cur_gt = cur_gt.new_zeros((1, cur_gt.shape[1])) if len(cur_gt) == 0 else cur_gt
+
+            if self.roi_sampler_cfg.get('SAMPLE_ROI_BY_EACH_CLASS', False):
+                max_overlaps, gt_assignment = self.get_max_iou_with_same_class(
+                    rois=cur_roi, roi_labels=cur_roi_labels,
+                    gt_boxes=cur_gt[:, 0:7], gt_labels=cur_gt[:, -1].long()
+                )
+
+            else:
+                iou3d = iou3d_nms_utils.boxes_iou3d_gpu(cur_roi, cur_gt[:, 0:7])  # (M, N)
+                max_overlaps, gt_assignment = torch.max(iou3d, dim=1)
+
+            sampled_inds,fg_inds, bg_inds = self.subsample_rois(max_overlaps=max_overlaps)
+
+            batch_roi_labels[index] = cur_roi_labels[sampled_inds.long()]
+
+
+            if self.roi_sampler_cfg.get('USE_ROI_AUG',False):
+
+                fg_rois, fg_iou3d = self.aug_roi_by_noise_torch(cur_roi[fg_inds], cur_gt[gt_assignment[fg_inds]],
+                                          max_overlaps[fg_inds], aug_times=self.roi_sampler_cfg.ROI_FG_AUG_TIMES)
+                bg_rois = cur_roi[bg_inds]
+                bg_iou3d = max_overlaps[bg_inds]
+
+                batch_rois[index] = torch.cat([fg_rois,bg_rois],0)
+                batch_roi_ious[index] = torch.cat([fg_iou3d,bg_iou3d],0)
+                batch_gt_of_rois[index] = cur_gt[gt_assignment[sampled_inds]]
+
+            else:
+                batch_rois[index] = cur_roi[sampled_inds]
+                batch_roi_ious[index] = max_overlaps[sampled_inds]
+                batch_gt_of_rois[index] = cur_gt[gt_assignment[sampled_inds]]
+
+
+            batch_roi_scores[index] = cur_roi_scores[sampled_inds]
+
+            if 'valid_length' in batch_dict.keys():
+                batch_valid_length[index] = cur_valid_length[:,sampled_inds]
+
+            if self.roi_sampler_cfg.USE_TRAJ_AUG.ENABLED:
+                batch_trajectory_rois_list = []
+                for idx in range(0,batch_dict['num_frames']):
+                    if idx== cur_frame_idx:
+                        batch_trajectory_rois_list.append(cur_trajectory_rois[cur_frame_idx:cur_frame_idx+1,sampled_inds])
+                        continue
+                    fg_trajs, _ = self.aug_roi_by_noise_torch(cur_trajectory_rois[idx,fg_inds], cur_trajectory_rois[idx,fg_inds][:,:8], max_overlaps[fg_inds], \
+                                        aug_times=self.roi_sampler_cfg.ROI_FG_AUG_TIMES,pos_thresh=self.roi_sampler_cfg.USE_TRAJ_AUG.THRESHOD)
+                    bg_trajs = cur_trajectory_rois[idx,bg_inds]
+                    batch_trajectory_rois_list.append(torch.cat([fg_trajs,bg_trajs],0)[None,:,:])
+                batch_trajectory_rois[index] = torch.cat(batch_trajectory_rois_list,0)
+            else:
+                batch_trajectory_rois[index] = cur_trajectory_rois[:,sampled_inds]
+                    
+        return batch_rois, batch_gt_of_rois, batch_roi_ious, batch_roi_scores, batch_roi_labels, batch_trajectory_rois,batch_valid_length
+
+    def subsample_rois(self, max_overlaps):
+        # sample fg, easy_bg, hard_bg
+        fg_rois_per_image = int(np.round(self.roi_sampler_cfg.FG_RATIO * self.roi_sampler_cfg.ROI_PER_IMAGE))
+        fg_thresh = min(self.roi_sampler_cfg.REG_FG_THRESH, self.roi_sampler_cfg.CLS_FG_THRESH)
+
+        fg_inds = ((max_overlaps >= fg_thresh)).nonzero().view(-1)
+        easy_bg_inds = ((max_overlaps < self.roi_sampler_cfg.CLS_BG_THRESH_LO)).nonzero().view(-1)
+        hard_bg_inds = ((max_overlaps < self.roi_sampler_cfg.REG_FG_THRESH) &
+                (max_overlaps >= self.roi_sampler_cfg.CLS_BG_THRESH_LO)).nonzero().view(-1)
+
+        fg_num_rois = fg_inds.numel()
+        bg_num_rois = hard_bg_inds.numel() + easy_bg_inds.numel()
+
+        if fg_num_rois > 0 and bg_num_rois > 0:
+            # sampling fg
+            fg_rois_per_this_image = min(fg_rois_per_image, fg_num_rois)
+
+            rand_num = torch.from_numpy(np.random.permutation(fg_num_rois)).type_as(max_overlaps).long()
+            fg_inds = fg_inds[rand_num[:fg_rois_per_this_image]]
+
+            # sampling bg
+            bg_rois_per_this_image = self.roi_sampler_cfg.ROI_PER_IMAGE - fg_rois_per_this_image
+            bg_inds = self.sample_bg_inds(
+                hard_bg_inds, easy_bg_inds, bg_rois_per_this_image, self.roi_sampler_cfg.HARD_BG_RATIO
+            )
+
+        elif fg_num_rois > 0 and bg_num_rois == 0:
+            # sampling fg
+            rand_num = np.floor(np.random.rand(self.roi_sampler_cfg.ROI_PER_IMAGE) * fg_num_rois)
+            rand_num = torch.from_numpy(rand_num).type_as(max_overlaps).long()
+            fg_inds = fg_inds[rand_num]
+            bg_inds = torch.tensor([]).type_as(fg_inds)
+
+        elif bg_num_rois > 0 and fg_num_rois == 0:
+            # sampling bg
+            bg_rois_per_this_image = self.roi_sampler_cfg.ROI_PER_IMAGE
+            bg_inds = self.sample_bg_inds(
+                hard_bg_inds, easy_bg_inds, bg_rois_per_this_image, self.roi_sampler_cfg.HARD_BG_RATIO
+            )
+        else:
+            print('maxoverlaps:(min=%f, max=%f)' % (max_overlaps.min().item(), max_overlaps.max().item()))
+            print('ERROR: FG=%d, BG=%d' % (fg_num_rois, bg_num_rois))
+            raise NotImplementedError
+
+        sampled_inds = torch.cat((fg_inds, bg_inds), dim=0)
+        return sampled_inds.long(), fg_inds.long(), bg_inds.long()
+
+    def aug_roi_by_noise_torch(self,roi_boxes3d, gt_boxes3d, iou3d_src, aug_times=10, pos_thresh=None):
+        iou_of_rois = torch.zeros(roi_boxes3d.shape[0]).type_as(gt_boxes3d)
+        if pos_thresh is None:
+            pos_thresh = min(self.roi_sampler_cfg.REG_FG_THRESH, self.roi_sampler_cfg.CLS_FG_THRESH)
+
+        for k in range(roi_boxes3d.shape[0]):
+            temp_iou = cnt = 0
+            roi_box3d = roi_boxes3d[k]
+
+            gt_box3d = gt_boxes3d[k].view(1, gt_boxes3d.shape[-1])
+            aug_box3d = roi_box3d
+            keep = True
+            while temp_iou < pos_thresh and cnt < aug_times:
+                if np.random.rand() <= self.roi_sampler_cfg.RATIO:
+                    aug_box3d = roi_box3d  # p=RATIO to keep the original roi box
+                    keep = True
+                else:
+                    aug_box3d = self.random_aug_box3d(roi_box3d)
+                    keep = False
+                aug_box3d = aug_box3d.view((1, aug_box3d.shape[-1]))
+                iou3d = iou3d_nms_utils.boxes_iou3d_gpu(aug_box3d[:,:7], gt_box3d[:,:7])
+                temp_iou = iou3d[0][0]
+                cnt += 1
+            roi_boxes3d[k] = aug_box3d.view(-1)
+            if cnt == 0 or keep:
+                iou_of_rois[k] = iou3d_src[k]
+            else:
+                iou_of_rois[k] = temp_iou
+        return roi_boxes3d, iou_of_rois
+
+    def random_aug_box3d(self,box3d):
+        """
+        :param box3d: (7) [x, y, z, h, w, l, ry]
+        random shift, scale, orientation
+        """
+
+        if self.roi_sampler_cfg.REG_AUG_METHOD == 'single':
+            pos_shift = (torch.rand(3, device=box3d.device) - 0.5)  # [-0.5 ~ 0.5]
+            hwl_scale = (torch.rand(3, device=box3d.device) - 0.5) / (0.5 / 0.15) + 1.0  #
+            angle_rot = (torch.rand(1, device=box3d.device) - 0.5) / (0.5 / (np.pi / 12))  # [-pi/12 ~ pi/12]
+            aug_box3d = torch.cat([box3d[0:3] + pos_shift, box3d[3:6] * hwl_scale, box3d[6:7] + angle_rot, box3d[7:]], dim=0)
+            return aug_box3d
+        elif self.roi_sampler_cfg.REG_AUG_METHOD == 'multiple':
+            # pos_range, hwl_range, angle_range, mean_iou
+            range_config = [[0.2, 0.1, np.pi / 12, 0.7],
+                            [0.3, 0.15, np.pi / 12, 0.6],
+                            [0.5, 0.15, np.pi / 9, 0.5],
+                            [0.8, 0.15, np.pi / 6, 0.3],
+                            [1.0, 0.15, np.pi / 3, 0.2]]
+            idx = torch.randint(low=0, high=len(range_config), size=(1,))[0].long()
+
+            pos_shift = ((torch.rand(3, device=box3d.device) - 0.5) / 0.5) * range_config[idx][0]
+            hwl_scale = ((torch.rand(3, device=box3d.device) - 0.5) / 0.5) * range_config[idx][1] + 1.0
+            angle_rot = ((torch.rand(1, device=box3d.device) - 0.5) / 0.5) * range_config[idx][2]
+
+            aug_box3d = torch.cat([box3d[0:3] + pos_shift, box3d[3:6] * hwl_scale, box3d[6:7] + angle_rot], dim=0)
+            return aug_box3d
+        elif self.roi_sampler_cfg.REG_AUG_METHOD == 'normal':
+            x_shift = np.random.normal(loc=0, scale=0.3)
+            y_shift = np.random.normal(loc=0, scale=0.2)
+            z_shift = np.random.normal(loc=0, scale=0.3)
+            h_shift = np.random.normal(loc=0, scale=0.25)
+            w_shift = np.random.normal(loc=0, scale=0.15)
+            l_shift = np.random.normal(loc=0, scale=0.5)
+            ry_shift = ((torch.rand() - 0.5) / 0.5) * np.pi / 12
+
+            aug_box3d = np.array([box3d[0] + x_shift, box3d[1] + y_shift, box3d[2] + z_shift, box3d[3] + h_shift,
+                                  box3d[4] + w_shift, box3d[5] + l_shift, box3d[6] + ry_shift], dtype=np.float32)
+            aug_box3d = torch.from_numpy(aug_box3d).type_as(box3d)
+            return aug_box3d
+        else:
+            raise NotImplementedError
+
+class MPPNetHead(RoIHeadTemplate):
+    def __init__(self,model_cfg, num_class=1,**kwargs):
+        super().__init__(num_class=num_class, model_cfg=model_cfg)
+        self.model_cfg = model_cfg
+        self.proposal_target_layer = ProposalTargetLayerMPPNet(roi_sampler_cfg=self.model_cfg.TARGET_CONFIG)
+        self.use_time_stamp = self.model_cfg.get('USE_TIMESTAMP',None)
+        self.num_lidar_points = self.model_cfg.Transformer.num_lidar_points
+        self.avg_stage1_score = self.model_cfg.get('AVG_STAGE1_SCORE', None)
+
+        self.nhead = model_cfg.Transformer.nheads
+        self.num_enc_layer = model_cfg.Transformer.enc_layers
+        hidden_dim = model_cfg.TRANS_INPUT
+        self.hidden_dim = model_cfg.TRANS_INPUT
+        self.num_groups = model_cfg.Transformer.num_groups
+
+        self.grid_size = model_cfg.ROI_GRID_POOL.GRID_SIZE
+        self.num_proxy_points = model_cfg.Transformer.num_proxy_points
+        self.seqboxembed = PointNet(8,model_cfg=self.model_cfg)
+        self.jointembed = MLP(self.hidden_dim*(self.num_groups+1), model_cfg.Transformer.hidden_dim, self.box_coder.code_size * self.num_class, 4)
+
+
+        num_radius = len(self.model_cfg.ROI_GRID_POOL.POOL_RADIUS)
+        self.up_dimension_geometry = MLP(input_dim = 29, hidden_dim = 64, output_dim =hidden_dim//num_radius, num_layers = 3)
+        self.up_dimension_motion = MLP(input_dim = 30, hidden_dim = 64, output_dim = hidden_dim, num_layers = 3)
+
+        self.transformer = build_transformer(model_cfg.Transformer)
+
+        self.roi_grid_pool_layer = pointnet2_stack_modules.StackSAModuleMSG(
+                radii=self.model_cfg.ROI_GRID_POOL.POOL_RADIUS,
+                nsamples=self.model_cfg.ROI_GRID_POOL.NSAMPLE,
+                mlps=self.model_cfg.ROI_GRID_POOL.MLPS,
+                use_xyz=True,
+                pool_method=self.model_cfg.ROI_GRID_POOL.POOL_METHOD,
+                )
+
+        self.class_embed = nn.ModuleList()
+        self.class_embed.append(nn.Linear(model_cfg.Transformer.hidden_dim, 1))
+
+        self.bbox_embed = nn.ModuleList()
+        for _ in range(self.num_groups):
+            self.bbox_embed.append(MLP(model_cfg.Transformer.hidden_dim, model_cfg.Transformer.hidden_dim, self.box_coder.code_size * self.num_class, 4))
+
+        if self.model_cfg.Transformer.use_grid_pos.enabled:
+            if self.model_cfg.Transformer.use_grid_pos.init_type == 'index':
+                self.grid_index = torch.cat([i.reshape(-1,1)for i in torch.meshgrid(torch.arange(self.grid_size), torch.arange(self.grid_size), torch.arange(self.grid_size))],1).float().cuda()
+                self.grid_pos_embeded = MLP(input_dim = 3, hidden_dim = 256, output_dim = hidden_dim, num_layers = 2)
+            else:
+                self.pos = nn.Parameter(torch.zeros(1, self.num_grid_points, 256))
+
+    def init_weights(self, weight_init='xavier'):
+        if weight_init == 'kaiming':
+            init_func = nn.init.kaiming_normal_
+        elif weight_init == 'xavier':
+            init_func = nn.init.xavier_normal_
+        elif weight_init == 'normal':
+            init_func = nn.init.normal_
+        else:
+            raise NotImplementedError
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                if weight_init == 'normal':
+                    init_func(m.weight, mean=0, std=0.001)
+                else:
+                    init_func(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        nn.init.normal_(self.bbox_embed.layers[-1].weight, mean=0, std=0.001)
+
+    def get_corner_points_of_roi(self, rois):
+        rois = rois.view(-1, rois.shape[-1])
+        batch_size_rcnn = rois.shape[0]
+
+        local_roi_grid_points = self.get_corner_points(rois, batch_size_rcnn)
+        local_roi_grid_points = common_utils.rotate_points_along_z(
+            local_roi_grid_points.clone(), rois[:, 6]
+        ).squeeze(dim=1)
+        global_center = rois[:, 0:3].clone()
+
+        global_roi_grid_points = local_roi_grid_points + global_center.unsqueeze(dim=1)
+        return global_roi_grid_points, local_roi_grid_points
+
+    @staticmethod
+    def get_dense_grid_points(rois, batch_size_rcnn, grid_size):
+        faked_features = rois.new_ones((grid_size, grid_size, grid_size))
+        dense_idx = faked_features.nonzero()  
+        dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float()  
+
+        local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6]
+        roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \
+                          - (local_roi_size.unsqueeze(dim=1) / 2)  
+        return roi_grid_points
+
+    @staticmethod
+    def get_corner_points(rois, batch_size_rcnn):
+        faked_features = rois.new_ones((2, 2, 2))
+
+        dense_idx = faked_features.nonzero()  
+        dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float()  
+
+        local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6]
+        roi_grid_points = dense_idx * local_roi_size.unsqueeze(dim=1) \
+                          - (local_roi_size.unsqueeze(dim=1) / 2)  
+        return roi_grid_points
+
+    def roi_grid_pool(self, batch_size, rois, point_coords, point_features,batch_dict=None,batch_cnt=None):
+
+        num_frames = batch_dict['num_frames']
+        num_rois = rois.shape[2]*rois.shape[1]
+
+        global_roi_proxy_points, local_roi_proxy_points = self.get_proxy_points_of_roi(
+            rois.permute(0,2,1,3).contiguous(), grid_size=self.grid_size
+        )  
+
+        global_roi_proxy_points = global_roi_proxy_points.view(batch_size, -1, 3)
+  
+
+        point_coords = point_coords.view(point_coords.shape[0]*num_frames,point_coords.shape[1]//num_frames,point_coords.shape[-1])
+        xyz = point_coords[:, :, 0:3].view(-1,3)
+
+        
+        num_points = point_coords.shape[1]
+        num_proxy_points = self.num_proxy_points
+
+        if batch_cnt is None:
+            xyz_batch_cnt = torch.tensor([num_points]*num_rois*batch_size).cuda().int()
+        else:
+            xyz_batch_cnt = torch.tensor(batch_cnt).cuda().int()
+
+        new_xyz_batch_cnt = torch.tensor([num_proxy_points]*num_rois*batch_size).cuda().int()
+        new_xyz = global_roi_proxy_points.view(-1, 3)
+
+        _, pooled_features = self.roi_grid_pool_layer(
+            xyz=xyz.contiguous(),
+            xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz,
+            new_xyz_batch_cnt=new_xyz_batch_cnt,
+            features=point_features.view(-1,point_features.shape[-1]).contiguous(),
+        )  
+
+        features = pooled_features.view(
+            point_features.shape[0], num_frames*self.num_proxy_points,
+            pooled_features.shape[-1]).contiguous()  
+
+        return features,global_roi_proxy_points.view(batch_size*rois.shape[2], num_frames*num_proxy_points,3).contiguous()
+
+    def get_proxy_points_of_roi(self, rois, grid_size):
+        rois = rois.view(-1, rois.shape[-1])
+        batch_size_rcnn = rois.shape[0]
+
+        local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size)  
+        local_roi_grid_points = common_utils.rotate_points_along_z(local_roi_grid_points.clone(), rois[:, 6]).squeeze(dim=1)
+        global_center = rois[:, 0:3].clone()
+        global_roi_grid_points = local_roi_grid_points + global_center.unsqueeze(dim=1)
+        return global_roi_grid_points, local_roi_grid_points
+
+    def spherical_coordinate(self, src, diag_dist):
+        assert (src.shape[-1] == 27)
+        device = src.device
+        indices_x = torch.LongTensor([0,3,6,9,12,15,18,21,24]).to(device)  #
+        indices_y = torch.LongTensor([1,4,7,10,13,16,19,22,25]).to(device) # 
+        indices_z = torch.LongTensor([2,5,8,11,14,17,20,23,26]).to(device) 
+        src_x = torch.index_select(src, -1, indices_x)
+        src_y = torch.index_select(src, -1, indices_y)
+        src_z = torch.index_select(src, -1, indices_z)
+        dis = (src_x ** 2 + src_y ** 2 + src_z ** 2) ** 0.5
+        phi = torch.atan(src_y / (src_x + 1e-5))
+        the = torch.acos(src_z / (dis + 1e-5))
+        dis = dis / (diag_dist + 1e-5)
+        src = torch.cat([dis, phi, the], dim = -1)
+        return src
+
+    def crop_current_frame_points(self, src, batch_size,trajectory_rois,num_rois,batch_dict):
+
+        for bs_idx in range(batch_size):
+            cur_batch_boxes = trajectory_rois[bs_idx,0,:,:7].view(-1,7)
+            cur_radiis = torch.sqrt((cur_batch_boxes[:,3]/2) ** 2 + (cur_batch_boxes[:,4]/2) ** 2) * 1.1
+            cur_points = batch_dict['points'][(batch_dict['points'][:, 0] == bs_idx)][:,1:]
+            dis = torch.norm((cur_points[:,:2].unsqueeze(0) - cur_batch_boxes[:,:2].unsqueeze(1).repeat(1,cur_points.shape[0],1)), dim = 2)
+            point_mask = (dis <= cur_radiis.unsqueeze(-1))
+            
+
+            sampled_idx = torch.topk(point_mask.float(),128)[1]
+            sampled_idx_buffer = sampled_idx[:, 0:1].repeat(1, 128)  
+            roi_idx = torch.arange(num_rois)[:, None].repeat(1, 128)
+            sampled_mask = point_mask[roi_idx, sampled_idx] 
+            sampled_idx_buffer[sampled_mask] = sampled_idx[sampled_mask]
+
+            src[bs_idx] = cur_points[sampled_idx_buffer][:,:,:5] 
+            empty_flag = sampled_mask.sum(-1)==0
+            src[bs_idx,empty_flag] = 0
+
+        src = src.repeat([1,1,trajectory_rois.shape[1],1])
+
+        return src
+
+    def crop_previous_frame_points(self,src,batch_size,trajectory_rois,num_rois,valid_length,batch_dict):
+        for bs_idx in range(batch_size):
+            
+            cur_points = batch_dict['points'][(batch_dict['points'][:, 0] == bs_idx)][:,1:]
+
+
+            for idx in range(1,trajectory_rois.shape[1]):
+            
+                time_mask = (cur_points[:,-1] - idx*0.1).abs() < 1e-3
+                cur_time_points = cur_points[time_mask]
+                cur_batch_boxes = trajectory_rois[bs_idx,idx,:,:7].view(-1,7)
+
+                cur_radiis = torch.sqrt((cur_batch_boxes[:,3]/2) ** 2 + (cur_batch_boxes[:,4]/2) ** 2) * 1.1
+                if not self.training and cur_batch_boxes.shape[0] > 32:
+                    length_iter= cur_batch_boxes.shape[0]//32
+                    dis_list = []
+                    for i in range(length_iter+1):
+                        dis = torch.norm((cur_time_points[:,:2].unsqueeze(0) - \
+                            cur_batch_boxes[32*i:32*(i+1),:2].unsqueeze(1).repeat(1,cur_time_points.shape[0],1)), dim = 2)
+                        dis_list.append(dis)
+                    dis = torch.cat(dis_list,0)
+                else:
+                    dis = torch.norm((cur_time_points[:,:2].unsqueeze(0) - \
+                        cur_batch_boxes[:,:2].unsqueeze(1).repeat(1,cur_time_points.shape[0],1)), dim = 2)
+                
+                point_mask = (dis <= cur_radiis.unsqueeze(-1)).view(trajectory_rois.shape[2],-1)
+
+                for roi_box_idx in range(0, num_rois):
+
+                    if not valid_length[bs_idx,idx,roi_box_idx]:
+                            continue
+
+                    cur_roi_points = cur_time_points[point_mask[roi_box_idx]]
+
+                    if cur_roi_points.shape[0] > self.num_lidar_points:
+                        np.random.seed(0)
+                        choice = np.random.choice(cur_roi_points.shape[0], self.num_lidar_points, replace=True)
+                        cur_roi_points_sample = cur_roi_points[choice]
+                        
+                    elif cur_roi_points.shape[0] == 0:
+                        cur_roi_points_sample = cur_roi_points.new_zeros(self.num_lidar_points, 6)
+
+                    else:
+                        empty_num = self.num_lidar_points - cur_roi_points.shape[0]
+                        add_zeros = cur_roi_points.new_zeros(empty_num, 6)
+                        add_zeros = cur_roi_points[0].repeat(empty_num, 1)
+                        cur_roi_points_sample = torch.cat([cur_roi_points, add_zeros], dim = 0)
+
+                    if not self.use_time_stamp:
+                        cur_roi_points_sample = cur_roi_points_sample[:,:-1]
+
+                    src[bs_idx, roi_box_idx, self.num_lidar_points*idx:self.num_lidar_points*(idx+1), :] = cur_roi_points_sample
+
+
+        return src
+    
+
+    def get_proposal_aware_geometry_feature(self,src, batch_size,trajectory_rois,num_rois,batch_dict):
+        proposal_aware_feat_list = []
+        for i in range(trajectory_rois.shape[1]):
+
+            corner_points, _ = self.get_corner_points_of_roi(trajectory_rois[:,i,:,:].contiguous()) 
+
+            corner_points = corner_points.view(batch_size, num_rois, -1, corner_points.shape[-1]) 
+            corner_points = corner_points.view(batch_size * num_rois, -1)
+            trajectory_roi_center = trajectory_rois[:,i,:,:].contiguous().reshape(batch_size * num_rois, -1)[:,:3]
+            corner_add_center_points = torch.cat([corner_points, trajectory_roi_center], dim = -1)
+            proposal_aware_feat = src[:,i*self.num_lidar_points:(i+1)*self.num_lidar_points,:3].repeat(1,1,9) - \
+                                  corner_add_center_points.unsqueeze(1).repeat(1,self.num_lidar_points,1) 
+
+            lwh = trajectory_rois[:,i,:,:].reshape(batch_size * num_rois, -1)[:,3:6].unsqueeze(1).repeat(1,proposal_aware_feat.shape[1],1)
+            diag_dist = (lwh[:,:,0]**2 + lwh[:,:,1]**2 + lwh[:,:,2]**2) ** 0.5
+            proposal_aware_feat = self.spherical_coordinate(proposal_aware_feat, diag_dist = diag_dist.unsqueeze(-1))
+            proposal_aware_feat_list.append(proposal_aware_feat)
+
+        proposal_aware_feat = torch.cat(proposal_aware_feat_list,dim=1)
+        proposal_aware_feat = torch.cat([proposal_aware_feat, src[:,:,3:]], dim = -1)
+        src_gemoetry = self.up_dimension_geometry(proposal_aware_feat) 
+        proxy_point_geometry, proxy_points = self.roi_grid_pool(batch_size,trajectory_rois,src,src_gemoetry,batch_dict,batch_cnt=None)
+        return proxy_point_geometry,proxy_points
+
+
+
+    def get_proposal_aware_motion_feature(self,proxy_point,batch_size,trajectory_rois,num_rois,batch_dict):
+
+
+        time_stamp   = torch.ones([proxy_point.shape[0],proxy_point.shape[1],1]).cuda()
+        padding_zero = torch.zeros([proxy_point.shape[0],proxy_point.shape[1],2]).cuda()
+        proxy_point_time_padding = torch.cat([padding_zero,time_stamp],-1)
+
+        num_frames = trajectory_rois.shape[1]
+
+        for i in range(num_frames):
+            proxy_point_time_padding[:,i*self.num_proxy_points:(i+1)*self.num_proxy_points,-1] = i*0.1
+
+
+        corner_points, _ = self.get_corner_points_of_roi(trajectory_rois[:,0,:,:].contiguous()) 
+        corner_points = corner_points.view(batch_size, num_rois, -1, corner_points.shape[-1]) 
+        corner_points = corner_points.view(batch_size * num_rois, -1)
+        trajectory_roi_center = trajectory_rois[:,0,:,:].reshape(batch_size * num_rois, -1)[:,:3]
+        corner_add_center_points = torch.cat([corner_points, trajectory_roi_center], dim = -1)
+
+        proposal_aware_feat = proxy_point[:,:,:3].repeat(1,1,9) - corner_add_center_points.unsqueeze(1) 
+
+        lwh = trajectory_rois[:,0,:,:].reshape(batch_size * num_rois, -1)[:,3:6].unsqueeze(1).repeat(1,proxy_point.shape[1],1)
+        diag_dist = (lwh[:,:,0]**2 + lwh[:,:,1]**2 + lwh[:,:,2]**2) ** 0.5
+        proposal_aware_feat = self.spherical_coordinate(proposal_aware_feat, diag_dist = diag_dist.unsqueeze(-1))
+
+
+        proposal_aware_feat = torch.cat([proposal_aware_feat,proxy_point_time_padding],-1)
+        proxy_point_motion_feat = self.up_dimension_motion(proposal_aware_feat)
+
+        return proxy_point_motion_feat
+
+    def trajectories_auxiliary_branch(self,trajectory_rois):
+
+        time_stamp = torch.ones([trajectory_rois.shape[0],trajectory_rois.shape[1],trajectory_rois.shape[2],1]).cuda()
+        for i in range(time_stamp.shape[1]):
+            time_stamp[:,i,:] = i*0.1 
+
+        box_seq = torch.cat([trajectory_rois[:,:,:,:7],time_stamp],-1)
+
+        box_seq[:, :, :,0:3] = box_seq[:, :, :,0:3] - box_seq[:, 0:1, :, 0:3]
+
+        roi_ry = box_seq[:,:,:,6] % (2 * np.pi)
+        roi_ry_t0 = roi_ry[:,0] 
+        roi_ry_t0 = roi_ry_t0.repeat(1,box_seq.shape[1])
+
+
+        box_seq = common_utils.rotate_points_along_z(
+            points=box_seq.view(-1, 1, box_seq.shape[-1]), angle=-roi_ry_t0.view(-1)
+        ).view(box_seq.shape[0],box_seq.shape[1], -1, box_seq.shape[-1])
+
+        box_seq[:, :, :, 6]  =  0
+
+        batch_rcnn = box_seq.shape[0]*box_seq.shape[2]
+
+        box_reg, box_feat, _ = self.seqboxembed(box_seq.permute(0,2,3,1).contiguous().view(batch_rcnn,box_seq.shape[-1],box_seq.shape[1]))
+        
+        return box_reg, box_feat
+
+    def generate_trajectory(self,cur_batch_boxes,proposals_list,batch_dict):
+ 
+        trajectory_rois = cur_batch_boxes[:,None,:,:].repeat(1,batch_dict['rois'].shape[-2],1,1)
+        trajectory_rois[:,0,:,:]= cur_batch_boxes
+        valid_length = torch.zeros([batch_dict['batch_size'],batch_dict['rois'].shape[-2],trajectory_rois.shape[2]])
+        valid_length[:,0] = 1
+        num_frames = batch_dict['rois'].shape[-2]
+        for i in range(1,num_frames):
+            frame = torch.zeros_like(cur_batch_boxes)
+            frame[:,:,0:2] = trajectory_rois[:,i-1,:,0:2] + trajectory_rois[:,i-1,:,7:9]
+            frame[:,:,2:] = trajectory_rois[:,i-1,:,2:]
+
+            for bs_idx in range( batch_dict['batch_size']):
+                iou3d = iou3d_nms_utils.boxes_iou3d_gpu(frame[bs_idx,:,:7], proposals_list[bs_idx,i,:,:7])
+                max_overlaps, traj_assignment = torch.max(iou3d, dim=1)
+                
+                fg_inds = ((max_overlaps >= 0.5)).nonzero().view(-1)
+                    
+                valid_length[bs_idx,i,fg_inds] = 1
+
+                trajectory_rois[bs_idx,i,fg_inds,:] = proposals_list[bs_idx,i,traj_assignment[fg_inds]]
+
+            batch_dict['valid_length'] = valid_length
+        
+        return trajectory_rois,valid_length
+
+    def forward(self, batch_dict):
+        """
+        :param input_data: input dict
+        :return:
+        """
+
+        batch_dict['rois'] = batch_dict['proposals_list'].permute(0,2,1,3)
+        num_rois = batch_dict['rois'].shape[1]
+        batch_dict['num_frames'] = batch_dict['rois'].shape[2]
+        batch_dict['roi_scores'] = batch_dict['roi_scores'].permute(0,2,1)
+        batch_dict['roi_labels'] = batch_dict['roi_labels'][:,0,:].long()
+        proposals_list = batch_dict['proposals_list']
+        batch_size = batch_dict['batch_size']
+        cur_batch_boxes = copy.deepcopy(batch_dict['rois'].detach())[:,:,0]
+        batch_dict['cur_frame_idx'] = 0
+
+        trajectory_rois,valid_length = self.generate_trajectory(cur_batch_boxes,proposals_list,batch_dict)
+
+        batch_dict['traj_memory'] = trajectory_rois
+        batch_dict['has_class_labels'] = True
+        batch_dict['trajectory_rois'] = trajectory_rois
+
+        if self.training:
+            targets_dict = self.assign_targets(batch_dict)
+            batch_dict['rois'] = targets_dict['rois']
+            batch_dict['roi_scores'] = targets_dict['roi_scores']
+            batch_dict['roi_labels'] = targets_dict['roi_labels']
+            targets_dict['trajectory_rois'][:,batch_dict['cur_frame_idx'],:,:] = batch_dict['rois']
+            trajectory_rois = targets_dict['trajectory_rois']
+            valid_length = targets_dict['valid_length']
+            empty_mask = batch_dict['rois'][:,:,:6].sum(-1)==0
+
+        else:
+            empty_mask = batch_dict['rois'][:,:,0,:6].sum(-1)==0
+            batch_dict['valid_traj_mask'] = ~empty_mask
+
+        rois = batch_dict['rois']
+        num_rois = batch_dict['rois'].shape[1]
+        num_sample = self.num_lidar_points 
+        src = rois.new_zeros(batch_size, num_rois, num_sample, 5) 
+
+        src = self.crop_current_frame_points(src, batch_size, trajectory_rois, num_rois,batch_dict)
+
+        src = self.crop_previous_frame_points(src, batch_size,trajectory_rois, num_rois,valid_length,batch_dict)
+
+        src = src.view(batch_size * num_rois, -1, src.shape[-1]) 
+
+        src_geometry_feature,proxy_points = self.get_proposal_aware_geometry_feature(src,batch_size,trajectory_rois,num_rois,batch_dict)
+
+        src_motion_feature = self.get_proposal_aware_motion_feature(proxy_points,batch_size,trajectory_rois,num_rois,batch_dict)
+
+        src = src_geometry_feature + src_motion_feature
+
+        box_reg, feat_box = self.trajectories_auxiliary_branch(trajectory_rois)
+        
+        if self.model_cfg.get('USE_TRAJ_EMPTY_MASK',None):
+            src[empty_mask.view(-1)] = 0
+
+        if self.model_cfg.Transformer.use_grid_pos.init_type == 'index':
+            pos = self.grid_pos_embeded(self.grid_index.cuda())[None,:,:]
+            pos = torch.cat([torch.zeros(1,1,self.hidden_dim).cuda(),pos],1)
+        else:
+            pos=None
+
+        hs, tokens = self.transformer(src,pos=pos)
+        point_cls_list = []
+        point_reg_list = []
+
+        for i in range(self.num_enc_layer):
+            point_cls_list.append(self.class_embed[0](tokens[i][0]))
+
+        for i in range(hs.shape[0]):
+            for j in range(self.num_enc_layer):
+                point_reg_list.append(self.bbox_embed[i](tokens[j][i]))
+
+        point_cls = torch.cat(point_cls_list,0)
+
+        point_reg = torch.cat(point_reg_list,0)
+        hs = hs.permute(1,0,2).reshape(hs.shape[1],-1)
+
+        joint_reg = self.jointembed(torch.cat([hs,feat_box],-1))
+
+        rcnn_cls = point_cls
+        rcnn_reg = joint_reg
+
+        if not self.training:
+            batch_dict['rois'] = batch_dict['rois'][:,:,0].contiguous()
+            rcnn_cls = rcnn_cls[-rcnn_cls.shape[0]//self.num_enc_layer:]
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
+            )
+
+            batch_dict['batch_box_preds'] = batch_box_preds
+
+            batch_dict['cls_preds_normalized'] = False
+            if self.avg_stage1_score:
+                stage1_score = batch_dict['roi_scores'][:,:,:1]
+                batch_cls_preds = F.sigmoid(batch_cls_preds)
+                if self.model_cfg.get('IOU_WEIGHT', None):
+                    batch_box_preds_list = []
+                    roi_labels_list = []
+                    batch_cls_preds_list = []
+                    for bs_idx in range(batch_size):
+                        car_mask = batch_dict['roi_labels'][bs_idx] ==1
+                        batch_cls_preds_car = batch_cls_preds[bs_idx].pow(self.model_cfg.IOU_WEIGHT[0])* \
+                                              stage1_score[bs_idx].pow(1-self.model_cfg.IOU_WEIGHT[0])
+                        batch_cls_preds_car = batch_cls_preds_car[car_mask][None]
+                        batch_cls_preds_pedcyc = batch_cls_preds[bs_idx].pow(self.model_cfg.IOU_WEIGHT[1])* \
+                                                 stage1_score[bs_idx].pow(1-self.model_cfg.IOU_WEIGHT[1])
+                        batch_cls_preds_pedcyc = batch_cls_preds_pedcyc[~car_mask][None]
+                        cls_preds = torch.cat([batch_cls_preds_car,batch_cls_preds_pedcyc],1)
+                        box_preds = torch.cat([batch_dict['batch_box_preds'][bs_idx][car_mask],
+                                                     batch_dict['batch_box_preds'][bs_idx][~car_mask]],0)[None]
+                        roi_labels = torch.cat([batch_dict['roi_labels'][bs_idx][car_mask],
+                                                batch_dict['roi_labels'][bs_idx][~car_mask]],0)[None]
+                        batch_box_preds_list.append(box_preds)
+                        roi_labels_list.append(roi_labels)
+                        batch_cls_preds_list.append(cls_preds)
+                    batch_dict['batch_box_preds'] = torch.cat(batch_box_preds_list,0)
+                    batch_dict['roi_labels'] = torch.cat(roi_labels_list,0)
+                    batch_cls_preds = torch.cat(batch_cls_preds_list,0)
+                    
+                else:
+                    batch_cls_preds = torch.sqrt(batch_cls_preds*stage1_score)
+                batch_dict['cls_preds_normalized']  = True
+
+            batch_dict['batch_cls_preds'] = batch_cls_preds
+                
+
+        else:
+            targets_dict['batch_size'] = batch_size
+            targets_dict['rcnn_cls'] = rcnn_cls
+            targets_dict['rcnn_reg'] = rcnn_reg
+            targets_dict['box_reg'] = box_reg
+            targets_dict['point_reg'] = point_reg
+            targets_dict['point_cls'] = point_cls
+            self.forward_ret_dict = targets_dict
+
+        return batch_dict
+
+    def get_loss(self, tb_dict=None):
+        tb_dict = {} if tb_dict is None else tb_dict
+        rcnn_loss = 0
+        rcnn_loss_cls, cls_tb_dict = self.get_box_cls_layer_loss(self.forward_ret_dict)
+        rcnn_loss += rcnn_loss_cls
+        tb_dict.update(cls_tb_dict)
+
+        rcnn_loss_reg, reg_tb_dict = self.get_box_reg_layer_loss(self.forward_ret_dict)
+        rcnn_loss += rcnn_loss_reg
+        tb_dict.update(reg_tb_dict)
+        tb_dict['rcnn_loss'] = rcnn_loss.item()
+        return rcnn_loss, tb_dict
+
+    def get_box_reg_layer_loss(self, forward_ret_dict):
+        loss_cfgs = self.model_cfg.LOSS_CONFIG
+        code_size = self.box_coder.code_size
+        reg_valid_mask = forward_ret_dict['reg_valid_mask'].view(-1)
+        batch_size = forward_ret_dict['batch_size']
+
+        gt_boxes3d_ct = forward_ret_dict['gt_of_rois'][..., 0:code_size]
+        gt_of_rois_src = forward_ret_dict['gt_of_rois_src'][..., 0:code_size].view(-1, code_size)
+
+        rcnn_reg = forward_ret_dict['rcnn_reg'] 
+
+        roi_boxes3d = forward_ret_dict['rois']
+
+        rcnn_batch_size = gt_boxes3d_ct.view(-1, code_size).shape[0]
+
+        fg_mask = (reg_valid_mask > 0)
+        fg_sum = fg_mask.long().sum().item()
+
+        tb_dict = {}
+
+        if loss_cfgs.REG_LOSS == 'smooth-l1':
+
+            rois_anchor = roi_boxes3d.clone().detach()[:,:,:7].contiguous().view(-1, code_size)
+            rois_anchor[:, 0:3] = 0
+            rois_anchor[:, 6] = 0
+            reg_targets = self.box_coder.encode_torch(
+                gt_boxes3d_ct.view(rcnn_batch_size, code_size), rois_anchor
+                )
+            rcnn_loss_reg = self.reg_loss_func(
+                rcnn_reg.view(rcnn_batch_size, -1).unsqueeze(dim=0),
+                reg_targets.unsqueeze(dim=0),
+            )  # [B, M, 7]
+            rcnn_loss_reg = (rcnn_loss_reg.view(rcnn_batch_size, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1)
+            rcnn_loss_reg = rcnn_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']*loss_cfgs.LOSS_WEIGHTS['traj_reg_weight'][0]
+
+            tb_dict['rcnn_loss_reg'] = rcnn_loss_reg.item()
+  
+            if self.model_cfg.USE_AUX_LOSS:
+                point_reg = forward_ret_dict['point_reg']
+
+                groups = point_reg.shape[0]//reg_targets.shape[0]
+                if groups != 1 :
+                    point_loss_regs = 0
+                    slice = reg_targets.shape[0]
+                    for i in range(groups):
+                        point_loss_reg = self.reg_loss_func(
+                        point_reg[i*slice:(i+1)*slice].view(slice, -1).unsqueeze(dim=0),reg_targets.unsqueeze(dim=0),) 
+                        point_loss_reg = (point_loss_reg.view(slice, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1)
+                        point_loss_reg = point_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']*loss_cfgs.LOSS_WEIGHTS['traj_reg_weight'][2]
+                        
+                        point_loss_regs += point_loss_reg
+                    point_loss_regs = point_loss_regs / groups
+                    tb_dict['point_loss_reg'] = point_loss_regs.item()
+                    rcnn_loss_reg += point_loss_regs 
+
+                else:
+                    point_loss_reg = self.reg_loss_func(point_reg.view(rcnn_batch_size, -1).unsqueeze(dim=0),reg_targets.unsqueeze(dim=0),)  
+                    point_loss_reg = (point_loss_reg.view(rcnn_batch_size, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1)
+                    point_loss_reg = point_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']*loss_cfgs.LOSS_WEIGHTS['traj_reg_weight'][2]
+                    tb_dict['point_loss_reg'] = point_loss_reg.item()
+                    rcnn_loss_reg += point_loss_reg
+
+                seqbox_reg = forward_ret_dict['box_reg']  
+                seqbox_loss_reg = self.reg_loss_func(seqbox_reg.view(rcnn_batch_size, -1).unsqueeze(dim=0),reg_targets.unsqueeze(dim=0),)
+                seqbox_loss_reg = (seqbox_loss_reg.view(rcnn_batch_size, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1)
+                seqbox_loss_reg = seqbox_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']*loss_cfgs.LOSS_WEIGHTS['traj_reg_weight'][1]
+                tb_dict['seqbox_loss_reg'] = seqbox_loss_reg.item()
+                rcnn_loss_reg += seqbox_loss_reg
+
+            if loss_cfgs.CORNER_LOSS_REGULARIZATION and fg_sum > 0:
+
+                fg_rcnn_reg = rcnn_reg.view(rcnn_batch_size, -1)[fg_mask]
+                fg_roi_boxes3d = roi_boxes3d[:,:,:7].contiguous().view(-1, code_size)[fg_mask]
+
+                fg_roi_boxes3d = fg_roi_boxes3d.view(1, -1, code_size)
+                batch_anchors = fg_roi_boxes3d.clone().detach()
+                roi_ry = fg_roi_boxes3d[:, :, 6].view(-1)
+                roi_xyz = fg_roi_boxes3d[:, :, 0:3].view(-1, 3)
+                batch_anchors[:, :, 0:3] = 0
+                rcnn_boxes3d = self.box_coder.decode_torch(
+                    fg_rcnn_reg.view(batch_anchors.shape[0], -1, code_size), batch_anchors
+                ).view(-1, code_size)
+
+                rcnn_boxes3d = common_utils.rotate_points_along_z(
+                    rcnn_boxes3d.unsqueeze(dim=1), roi_ry
+                ).squeeze(dim=1)
+                rcnn_boxes3d[:, 0:3] += roi_xyz
+
+                corner_loss_func = loss_utils.get_corner_loss_lidar
+
+                loss_corner = corner_loss_func(
+                    rcnn_boxes3d[:, 0:7],
+                    gt_of_rois_src[fg_mask][:, 0:7])
+
+                loss_corner = loss_corner.mean()
+                loss_corner = loss_corner * loss_cfgs.LOSS_WEIGHTS['rcnn_corner_weight']
+
+                rcnn_loss_reg += loss_corner
+                tb_dict['rcnn_loss_corner'] = loss_corner.item()
+
+        else:
+            raise NotImplementedError
+
+        return rcnn_loss_reg, tb_dict
+
+    def get_box_cls_layer_loss(self, forward_ret_dict):
+        loss_cfgs = self.model_cfg.LOSS_CONFIG
+        rcnn_cls = forward_ret_dict['rcnn_cls']
+        rcnn_cls_labels = forward_ret_dict['rcnn_cls_labels'].view(-1)
+
+        if loss_cfgs.CLS_LOSS == 'BinaryCrossEntropy':
+
+            rcnn_cls_flat = rcnn_cls.view(-1)
+
+            groups = rcnn_cls_flat.shape[0] // rcnn_cls_labels.shape[0]
+            if groups != 1:
+                rcnn_loss_cls = 0
+                slice = rcnn_cls_labels.shape[0]
+                for i in range(groups):
+                    batch_loss_cls = F.binary_cross_entropy(torch.sigmoid(rcnn_cls_flat[i*slice:(i+1)*slice]), 
+                                     rcnn_cls_labels.float(), reduction='none')
+
+                    cls_valid_mask = (rcnn_cls_labels >= 0).float() 
+                    rcnn_loss_cls = rcnn_loss_cls + (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0)
+
+                rcnn_loss_cls = rcnn_loss_cls / groups
+            
+            else:
+
+                batch_loss_cls = F.binary_cross_entropy(torch.sigmoid(rcnn_cls_flat), rcnn_cls_labels.float(), reduction='none')
+                cls_valid_mask = (rcnn_cls_labels >= 0).float() 
+                rcnn_loss_cls = (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0)
+
+
+        elif loss_cfgs.CLS_LOSS == 'CrossEntropy':
+            batch_loss_cls = F.cross_entropy(rcnn_cls, rcnn_cls_labels, reduction='none', ignore_index=-1)
+            cls_valid_mask = (rcnn_cls_labels >= 0).float()
+            rcnn_loss_cls = (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0)
+
+        else:
+            raise NotImplementedError
+
+        rcnn_loss_cls = rcnn_loss_cls * loss_cfgs.LOSS_WEIGHTS['rcnn_cls_weight'] 
+
+        tb_dict = {'rcnn_loss_cls': rcnn_loss_cls.item()}
+        return rcnn_loss_cls, tb_dict
+
+
+    def generate_predicted_boxes(self, batch_size, rois, cls_preds=None, box_preds=None):
+        """
+        Args:
+            batch_size:
+            rois: (B, N, 7)
+            cls_preds: (BN, num_class)
+            box_preds: (BN, code_size)
+        Returns:
+        """
+        code_size = self.box_coder.code_size
+        if cls_preds is not None:
+            batch_cls_preds = cls_preds.view(batch_size, -1, cls_preds.shape[-1])
+        else:
+            batch_cls_preds = None
+        batch_box_preds = box_preds.view(batch_size, -1, code_size)
+
+        roi_ry = rois[:, :, 6].view(-1)
+        roi_xyz = rois[:, :, 0:3].view(-1, 3)
+        local_rois = rois.clone().detach()
+        local_rois[:, :, 0:3] = 0
+
+        batch_box_preds = self.box_coder.decode_torch(batch_box_preds, local_rois).view(-1, code_size)
+
+        batch_box_preds = common_utils.rotate_points_along_z(
+            batch_box_preds.unsqueeze(dim=1), roi_ry
+        ).squeeze(dim=1)
+
+        batch_box_preds[:, 0:3] += roi_xyz
+        batch_box_preds = batch_box_preds.view(batch_size, -1, code_size)
+        batch_box_preds = torch.cat([batch_box_preds,rois[:,:,7:]],-1) 
+        return batch_cls_preds, batch_box_preds
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/mppnet_memory_bank_e2e.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/mppnet_memory_bank_e2e.py
new file mode 100644
index 000000000..b8af7f37a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/mppnet_memory_bank_e2e.py
@@ -0,0 +1,581 @@
+from typing import ValuesView
+import torch.nn as nn
+import torch
+import numpy as np
+import copy
+import torch.nn.functional as F
+from pcdet.ops.iou3d_nms import iou3d_nms_utils
+from ...utils import common_utils, loss_utils
+from .roi_head_template import RoIHeadTemplate
+from ..model_utils.mppnet_utils import build_transformer, PointNet, MLP
+from .target_assigner.proposal_target_layer import ProposalTargetLayer
+from pcdet.ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
+
+
+class MPPNetHeadE2E(RoIHeadTemplate):
+    def __init__(self,model_cfg, num_class=1,**kwargs):
+        super().__init__(num_class=num_class, model_cfg=model_cfg)
+        self.model_cfg = model_cfg
+        self.use_time_stamp = self.model_cfg.get('USE_TIMESTAMP',None)
+        self.num_lidar_points = self.model_cfg.Transformer.num_lidar_points
+        self.avg_stage1_score = self.model_cfg.get('AVG_STAGE1_SCORE', None)
+
+        self.nhead = model_cfg.Transformer.nheads
+        self.num_enc_layer = model_cfg.Transformer.enc_layers
+        hidden_dim = model_cfg.TRANS_INPUT
+        self.hidden_dim = model_cfg.TRANS_INPUT
+        self.num_groups = model_cfg.Transformer.num_groups
+
+        self.grid_size = model_cfg.ROI_GRID_POOL.GRID_SIZE
+        self.num_proxy_points = model_cfg.Transformer.num_proxy_points
+ 
+        self.seqboxembed = PointNet(8,model_cfg=self.model_cfg)
+        self.jointembed = MLP(self.hidden_dim*(self.num_groups+1), model_cfg.Transformer.hidden_dim, self.box_coder.code_size * self.num_class, 4)
+
+        num_radius = len(self.model_cfg.ROI_GRID_POOL.POOL_RADIUS)
+        self.up_dimension_geometry = MLP(input_dim = 29, hidden_dim = 64, output_dim =hidden_dim//num_radius, num_layers = 3)
+        self.up_dimension_motion = MLP(input_dim = 30, hidden_dim = 64, output_dim = hidden_dim, num_layers = 3)
+
+        self.transformer = build_transformer(model_cfg.Transformer)
+
+        self.roi_grid_pool_layer = pointnet2_stack_modules.StackSAModuleMSG(
+                radii=self.model_cfg.ROI_GRID_POOL.POOL_RADIUS,
+                nsamples=self.model_cfg.ROI_GRID_POOL.NSAMPLE,
+                mlps=self.model_cfg.ROI_GRID_POOL.MLPS,
+                use_xyz=True,
+                pool_method=self.model_cfg.ROI_GRID_POOL.POOL_METHOD,
+                )
+
+        self.class_embed = nn.ModuleList()
+        self.class_embed.append(nn.Linear(model_cfg.Transformer.hidden_dim, 1))
+
+        self.bbox_embed = nn.ModuleList()
+        for _ in range(self.num_groups):
+            self.bbox_embed.append(MLP(model_cfg.Transformer.hidden_dim, model_cfg.Transformer.hidden_dim, self.box_coder.code_size * self.num_class, 4))
+
+        if self.model_cfg.Transformer.use_grid_pos.enabled:
+            if self.model_cfg.Transformer.use_grid_pos.init_type == 'index':
+                self.grid_index = torch.cat([i.reshape(-1,1)for i in torch.meshgrid(torch.arange(self.grid_size), torch.arange(self.grid_size), torch.arange(self.grid_size))],1).float().cuda()
+                self.grid_pos_embeded = MLP(input_dim = 3, hidden_dim = 256, output_dim = hidden_dim, num_layers = 2)
+            else:
+                self.pos = nn.Parameter(torch.zeros(1, self.num_grid_points, 256))
+
+    def init_weights(self, weight_init='xavier'):
+        if weight_init == 'kaiming':
+            init_func = nn.init.kaiming_normal_
+        elif weight_init == 'xavier':
+            init_func = nn.init.xavier_normal_
+        elif weight_init == 'normal':
+            init_func = nn.init.normal_
+        else:
+            raise NotImplementedError
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                if weight_init == 'normal':
+                    init_func(m.weight, mean=0, std=0.001)
+                else:
+                    init_func(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        nn.init.normal_(self.bbox_embed.layers[-1].weight, mean=0, std=0.001)
+
+    def get_corner_points_of_roi(self, rois):
+        rois = rois.view(-1, rois.shape[-1])
+        batch_size_rcnn = rois.shape[0]
+
+        local_roi_grid_points = self.get_corner_points(rois, batch_size_rcnn)  
+        local_roi_grid_points = common_utils.rotate_points_along_z(
+            local_roi_grid_points.clone(), rois[:, 6]
+        ).squeeze(dim=1)
+        global_center = rois[:, 0:3].clone()
+
+
+        global_roi_grid_points = local_roi_grid_points + global_center.unsqueeze(dim=1)
+        return global_roi_grid_points, local_roi_grid_points
+
+    @staticmethod
+    def get_dense_grid_points(rois, batch_size_rcnn, grid_size):
+        if isinstance(grid_size,list):
+            faked_features = rois.new_ones((grid_size[0], grid_size[1], grid_size[2]))
+            grid_size = torch.tensor(grid_size).float().cuda()
+        else:
+            faked_features = rois.new_ones((grid_size, grid_size, grid_size))
+        dense_idx = faked_features.nonzero()  
+        dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float()  
+
+        local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6]
+        roi_grid_points = torch.div((dense_idx + 0.5), grid_size) * local_roi_size.unsqueeze(dim=1) - (local_roi_size.unsqueeze(dim=1) / 2) 
+        return roi_grid_points
+
+    @staticmethod
+    def get_corner_points(rois, batch_size_rcnn):
+        faked_features = rois.new_ones((2, 2, 2))
+
+        dense_idx = faked_features.nonzero() 
+        dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float()  
+
+        local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6]
+        roi_grid_points = dense_idx * local_roi_size.unsqueeze(dim=1) \
+                          - (local_roi_size.unsqueeze(dim=1) / 2) 
+        return roi_grid_points
+
+    def get_proxy_points_of_roi(self, rois, grid_size):
+        rois = rois.view(-1, rois.shape[-1])
+        batch_size_rcnn = rois.shape[0]
+
+        local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size)  
+        local_roi_grid_points = common_utils.rotate_points_along_z(local_roi_grid_points.clone(), rois[:, 6]).squeeze(dim=1)
+        global_center = rois[:, 0:3].clone()
+        global_roi_grid_points = local_roi_grid_points + global_center.unsqueeze(dim=1)
+        return global_roi_grid_points, local_roi_grid_points
+
+    def roi_grid_pool(self, batch_size, rois, point_coords, point_features,batch_dict=None,batch_cnt=None):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                point_coords: (num_points, 4)  [bs_idx, x, y, z]
+                point_features: (num_points, C)
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        Returns:
+        """
+
+        num_frames = batch_dict['num_frames']
+        num_rois = rois.shape[2]*rois.shape[1]
+
+        global_roi_proxy_points, local_roi_proxy_points = self.get_proxy_points_of_roi(
+            rois.permute(0,2,1,3).contiguous(), grid_size=self.grid_size
+        )  
+
+        num_points = point_coords.shape[1]
+        num_proxy_points = self.num_proxy_points
+
+        xyz = point_coords[:, :, 0:3].view(-1,3)
+        if batch_cnt is None:
+            xyz_batch_cnt = torch.tensor([num_points]*rois.shape[2]*batch_size).cuda().int() 
+        else:
+            xyz_batch_cnt = torch.tensor(batch_cnt).cuda().int()
+        new_xyz = torch.cat([i[0] for i in global_roi_proxy_points.chunk(rois.shape[2],0)],0)
+        new_xyz_batch_cnt = torch.tensor([self.num_proxy_points]*rois.shape[2]*batch_size).cuda().int()
+        
+        _, pooled_features = self.roi_grid_pool_layer(
+            xyz=xyz.contiguous(),
+            xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz,
+            new_xyz_batch_cnt=new_xyz_batch_cnt,
+            features=point_features.view(-1,point_features.shape[-1]).contiguous(),
+        )  
+
+        features = pooled_features.view(
+            point_features.shape[0], self.num_proxy_points,
+            pooled_features.shape[-1]
+        ).contiguous()  
+
+        return features,global_roi_proxy_points.view(batch_size*rois.shape[2], num_frames*num_proxy_points,3).contiguous()
+
+    def spherical_coordinate(self, src, diag_dist):
+
+        assert (src.shape[-1] == 27)
+        device = src.device
+        indices_x = torch.LongTensor([0,3,6,9,12,15,18,21,24]).to(device)  #
+        indices_y = torch.LongTensor([1,4,7,10,13,16,19,22,25]).to(device) # 
+        indices_z = torch.LongTensor([2,5,8,11,14,17,20,23,26]).to(device) 
+        src_x = torch.index_select(src, -1, indices_x)
+        src_y = torch.index_select(src, -1, indices_y)
+        src_z = torch.index_select(src, -1, indices_z)
+        dis = (src_x ** 2 + src_y ** 2 + src_z ** 2) ** 0.5
+        phi = torch.atan(src_y / (src_x + 1e-5))
+        the = torch.acos(src_z / (dis + 1e-5))
+        dis = dis / (diag_dist + 1e-5)
+        src = torch.cat([dis, phi, the], dim = -1)
+        return src
+
+    def crop_current_frame_points(self, src, batch_size,trajectory_rois,num_rois,num_sample, batch_dict):
+
+        for bs_idx in range(batch_size):
+
+            cur_batch_boxes = trajectory_rois[bs_idx,0,:,:7].view(-1,7)
+            cur_radiis = torch.sqrt((cur_batch_boxes[:,3]/2) ** 2 + (cur_batch_boxes[:,4]/2) ** 2) * 1.1
+            cur_points = batch_dict['points'][(batch_dict['points'][:, 0] == bs_idx)][:,1:]
+            time_mask = cur_points[:,-1].abs() < 1e-3
+            cur_points = cur_points[time_mask]
+            dis = torch.norm((cur_points[:,:2].unsqueeze(0) - cur_batch_boxes[:,:2].unsqueeze(1).repeat(1,cur_points.shape[0],1)), dim = 2)
+            point_mask = (dis <= cur_radiis.unsqueeze(-1))
+
+            mask = point_mask
+            sampled_idx = torch.topk(mask.float(),128)[1]
+            sampled_idx_buffer = sampled_idx[:, 0:1].repeat(1, 128)  
+            roi_idx = torch.arange(num_rois)[:, None].repeat(1, 128)
+            sampled_mask = mask[roi_idx, sampled_idx] 
+            sampled_idx_buffer[sampled_mask] = sampled_idx[sampled_mask]
+
+            src[bs_idx] = cur_points[sampled_idx_buffer][:,:,:5] 
+            empty_flag = sampled_mask.sum(-1)==0
+            src[bs_idx,empty_flag] = 0
+
+        return src
+
+    def trajectories_auxiliary_branch(self,trajectory_rois):
+
+        time_stamp = torch.ones([trajectory_rois.shape[0],trajectory_rois.shape[1],trajectory_rois.shape[2],1]).cuda()
+        for i in range(time_stamp.shape[1]):
+            time_stamp[:,i,:] = i*0.1 
+
+        box_seq = torch.cat([trajectory_rois[:,:,:,:7],time_stamp],-1)
+        box_seq[:, :, :,0:3]  = box_seq[:, :, :,0:3] - box_seq[:, 0:1, :, 0:3]
+
+
+        roi_ry = box_seq[:,:,:,6] % (2 * np.pi)
+        roi_ry_t0 = roi_ry[:,0] 
+        roi_ry_t0 = roi_ry_t0.repeat(1,box_seq.shape[1])
+
+        # transfer LiDAR coords to local coords
+        box_seq = common_utils.rotate_points_along_z(
+            points=box_seq.view(-1, 1, box_seq.shape[-1]), angle=-roi_ry_t0.view(-1)
+        ).view(box_seq.shape[0],box_seq.shape[1], -1, box_seq.shape[-1])
+
+        box_seq[:,:,:,6]  =  0
+
+        batch_rcnn = box_seq.shape[0]*box_seq.shape[2]
+
+        box_reg, box_feat, _ = self.seqboxembed(box_seq.permute(0,2,3,1).contiguous().view(batch_rcnn,box_seq.shape[-1],box_seq.shape[1]))
+        
+        return box_reg, box_feat
+
+    def get_proposal_aware_motion_feature(self,proxy_point,batch_size,trajectory_rois,num_rois,batch_dict):
+
+        time_stamp   = torch.ones([proxy_point.shape[0],proxy_point.shape[1],1]).cuda()
+        padding_zero = torch.zeros([proxy_point.shape[0],proxy_point.shape[1],2]).cuda()
+        proxy_point_padding = torch.cat([padding_zero,time_stamp],-1)
+
+        num_time_coding = trajectory_rois.shape[1]
+
+        for i in range(num_time_coding):
+            proxy_point_padding[:,i*self.num_proxy_points:(i+1)*self.num_proxy_points,-1] = i*0.1
+
+
+        ######### use T0 Norm ########
+        corner_points, _ = self.get_corner_points_of_roi(trajectory_rois[:,0,:,:].contiguous())  
+        corner_points = corner_points.view(batch_size, num_rois, -1, corner_points.shape[-1])  
+        corner_points = corner_points.view(batch_size * num_rois, -1)
+        corner_add_center_points = torch.cat([corner_points, trajectory_rois[:,0,:,:].reshape(batch_size * num_rois, -1)[:,:3]], dim = -1)
+
+        pos_fea = proxy_point[:,:,:3].repeat(1,1,9) - corner_add_center_points.unsqueeze(1) 
+
+        lwh = trajectory_rois[:,0,:,:].reshape(batch_size * num_rois, -1)[:,3:6].unsqueeze(1).repeat(1,proxy_point.shape[1],1)
+        diag_dist = (lwh[:,:,0]**2 + lwh[:,:,1]**2 + lwh[:,:,2]**2) ** 0.5
+        pos_fea = self.spherical_coordinate(pos_fea, diag_dist = diag_dist.unsqueeze(-1))
+        ######### use T0 Norm ########
+
+        proxy_point_padding = torch.cat([pos_fea,proxy_point_padding],-1)
+        proxy_point_motion_feat = self.up_dimension_motion(proxy_point_padding)
+
+        return proxy_point_motion_feat
+
+    def get_proposal_aware_geometry_feature(self,src, batch_size,trajectory_rois,num_rois,batch_dict):
+        
+        i = 0 # only current frame
+        corner_points, _ = self.get_corner_points_of_roi(trajectory_rois[:,i,:,:].contiguous()) 
+
+        corner_points = corner_points.view(batch_size, num_rois, -1, corner_points.shape[-1]) 
+        corner_points = corner_points.view(batch_size * num_rois, -1)
+        trajectory_roi_center = trajectory_rois[:,i,:,:].contiguous().reshape(batch_size * num_rois, -1)[:,:3]
+        corner_add_center_points = torch.cat([corner_points, trajectory_roi_center], dim = -1)
+        proposal_aware_feat = src[:,i*self.num_lidar_points:(i+1)*self.num_lidar_points,:3].repeat(1,1,9) - \
+                                corner_add_center_points.unsqueeze(1).repeat(1,self.num_lidar_points,1) 
+
+        lwh = trajectory_rois[:,i,:,:].reshape(batch_size * num_rois, -1)[:,3:6].unsqueeze(1).repeat(1,proposal_aware_feat.shape[1],1)
+        diag_dist = (lwh[:,:,0]**2 + lwh[:,:,1]**2 + lwh[:,:,2]**2) ** 0.5
+        proposal_aware_feat = self.spherical_coordinate(proposal_aware_feat, diag_dist = diag_dist.unsqueeze(-1))
+
+        proposal_aware_feat = torch.cat([proposal_aware_feat, src[:,:,3:]], dim = -1)
+        src_gemoetry = self.up_dimension_geometry(proposal_aware_feat) 
+        proxy_point_geometry, proxy_points = self.roi_grid_pool(batch_size,trajectory_rois,src,src_gemoetry,batch_dict,batch_cnt=None)
+        return proxy_point_geometry,proxy_points
+
+    @staticmethod
+    def reorder_rois_for_refining(pred_bboxes):
+
+        num_max_rois = max([len(bbox) for bbox in pred_bboxes])
+        num_max_rois = max(1, num_max_rois)  # at least one faked rois to avoid error
+        ordered_bboxes = torch.zeros([len(pred_bboxes),num_max_rois,pred_bboxes[0].shape[-1]]).cuda()
+
+        for bs_idx in range(ordered_bboxes.shape[0]):
+            ordered_bboxes[bs_idx,:len(pred_bboxes[bs_idx])] = pred_bboxes[bs_idx]
+        return ordered_bboxes
+
+    def transform_prebox_to_current_vel(self,pred_boxes3d,pose_pre,pose_cur):
+
+        expand_bboxes = np.concatenate([pred_boxes3d[:,:3], np.ones((pred_boxes3d.shape[0], 1))], axis=-1)
+        expand_vels = np.concatenate([pred_boxes3d[:,7:9], np.zeros((pred_boxes3d.shape[0], 1))], axis=-1)
+        bboxes_global = np.dot(expand_bboxes, pose_pre.T)[:, :3]
+        vels_global = np.dot(expand_vels, pose_pre[:3,:3].T)
+        moved_bboxes_global = copy.deepcopy(bboxes_global)
+        moved_bboxes_global[:,:2] = moved_bboxes_global[:,:2] - 0.1*vels_global[:,:2]
+
+        expand_bboxes_global = np.concatenate([bboxes_global[:,:3],np.ones((bboxes_global.shape[0], 1))], axis=-1)
+        expand_moved_bboxes_global = np.concatenate([moved_bboxes_global[:,:3],np.ones((bboxes_global.shape[0], 1))], axis=-1)
+        bboxes_pre2cur = np.dot(expand_bboxes_global, np.linalg.inv(pose_cur.T))[:, :3]
+
+        moved_bboxes_pre2cur = np.dot(expand_moved_bboxes_global, np.linalg.inv(pose_cur.T))[:, :3]
+        vels_pre2cur = np.dot(vels_global, np.linalg.inv(pose_cur[:3,:3].T))[:,:2]
+        bboxes_pre2cur = np.concatenate([bboxes_pre2cur, pred_boxes3d[:,3:7],vels_pre2cur],axis=-1)
+        bboxes_pre2cur[:,6]  = bboxes_pre2cur[..., 6] + np.arctan2(pose_pre[1, 0], pose_pre[0,0])
+        bboxes_pre2cur[:,6]  = bboxes_pre2cur[..., 6] - np.arctan2(pose_cur[1, 0], pose_cur[0,0])
+        bboxes_pre2cur[:,7:9] = moved_bboxes_pre2cur[:,:2] - bboxes_pre2cur[:,:2]
+        return bboxes_pre2cur[None,:,:]
+
+    def generate_trajectory(self,cur_batch_boxes,proposals_list,batch_dict):
+ 
+        trajectory_rois = cur_batch_boxes[:,None,:,:].repeat(1,batch_dict['rois'].shape[-2],1,1)
+        trajectory_rois[:,0,:,:]= cur_batch_boxes
+        valid_length = torch.zeros([batch_dict['batch_size'],batch_dict['rois'].shape[-2],trajectory_rois.shape[2]])
+        valid_length[:,0] = 1
+        num_frames = batch_dict['rois'].shape[-2]
+        matching_table = (trajectory_rois.new_ones([trajectory_rois.shape[1],trajectory_rois.shape[2]]) * -1).long()
+
+        for i in range(1,num_frames):
+            frame = torch.zeros_like(cur_batch_boxes)
+            frame[:,:,0:2] = trajectory_rois[:,i-1,:,0:2] + trajectory_rois[:,i-1,:,7:9]
+            frame[:,:,2:] = trajectory_rois[:,i-1,:,2:]
+
+            for bs_idx in range( batch_dict['batch_size']):
+                iou3d = iou3d_nms_utils.boxes_iou3d_gpu(frame[bs_idx,:,:7], proposals_list[bs_idx,i,:,:7])
+                max_overlaps, traj_assignment = torch.max(iou3d, dim=1)
+                
+                fg_inds = ((max_overlaps >= 0.5)).nonzero().view(-1)
+                    
+                valid_length[bs_idx,i,fg_inds] = 1
+                matching_table[i,fg_inds] = traj_assignment[fg_inds]
+
+                trajectory_rois[bs_idx,i,fg_inds,:] = proposals_list[bs_idx,i,traj_assignment[fg_inds]]
+
+            batch_dict['valid_length'] = valid_length
+        
+        return trajectory_rois,valid_length, matching_table
+
+    def forward(self, batch_dict):
+        """
+        :param input_data: input dict
+        :return:
+        """
+
+        if 'memory_bank' in batch_dict.keys():
+
+            rois_list = []
+            memory_list = copy.deepcopy(batch_dict['memory_bank'])
+
+            for idx in range(len(memory_list['rois'])):
+
+                rois = torch.cat([batch_dict['memory_bank']['rois'][idx][0],
+                                    batch_dict['memory_bank']['roi_scores'][idx][0],
+                                    batch_dict['memory_bank']['roi_labels'][idx][0]],-1)
+
+                rois_list.append(rois)
+
+            batch_rois = self.reorder_rois_for_refining(rois_list)
+            batch_dict['roi_scores'] = batch_rois[None,:,:,9]
+            batch_dict['roi_labels'] = batch_rois[None,:,:,10]
+           
+            proposals_list = []
+
+            for i in range(self.model_cfg.Transformer.num_frames):
+                pose_pre = batch_dict['poses'][0,i*4:(i+1)*4,:]
+                pred2cur = self.transform_prebox_to_current_vel(batch_rois[i,:,:9].cpu().numpy(),pose_pre=pose_pre.cpu().numpy(),
+                pose_cur=batch_dict['poses'][0,:4,:].cpu().numpy())
+                proposals_list.append(torch.from_numpy(pred2cur).cuda().float())
+            batch_rois = torch.cat(proposals_list,0)
+            batch_dict['proposals_list'] = batch_rois[None,:,:,:9]
+
+            batch_dict['rois'] = batch_rois.unsqueeze(0).permute(0,2,1,3)
+            num_rois = batch_dict['rois'].shape[1]
+            batch_dict['num_frames'] = batch_dict['rois'].shape[2]
+            roi_labels_list = copy.deepcopy(batch_dict['roi_labels'])
+
+            batch_dict['roi_scores'] = batch_dict['roi_scores'].permute(0,2,1)
+            batch_dict['roi_labels'] = batch_dict['roi_labels'][:,0,:].long()
+            proposals_list = batch_dict['proposals_list']
+            batch_size = batch_dict['batch_size']
+            cur_batch_boxes = copy.deepcopy(batch_dict['rois'].detach())[:,:,0]
+            batch_dict['cur_frame_idx'] = 0
+
+        else:
+ 
+            batch_dict['rois'] = batch_dict['proposals_list'].permute(0,2,1,3)
+            assert batch_dict['rois'].shape[0] ==1
+            num_rois = batch_dict['rois'].shape[1]
+            batch_dict['num_frames'] = batch_dict['rois'].shape[2]
+            roi_labels_list = copy.deepcopy(batch_dict['roi_labels'])
+
+            batch_dict['roi_scores'] = batch_dict['roi_scores'].permute(0,2,1)
+            batch_dict['roi_labels'] = batch_dict['roi_labels'][:,0,:].long()
+            proposals_list = batch_dict['proposals_list']
+            batch_size = batch_dict['batch_size']
+            cur_batch_boxes = copy.deepcopy(batch_dict['rois'].detach())[:,:,0]
+            batch_dict['cur_frame_idx'] = 0
+
+        trajectory_rois,effective_length,matching_table = self.generate_trajectory(cur_batch_boxes,proposals_list,batch_dict)
+
+
+        batch_dict['has_class_labels'] = True
+        batch_dict['trajectory_rois'] = trajectory_rois
+
+
+        rois = batch_dict['rois']
+        num_rois = batch_dict['rois'].shape[1]
+
+        if self.model_cfg.get('USE_TRAJ_EMPTY_MASK',None):
+            empty_mask = batch_dict['rois'][:,:,0,:6].sum(-1)==0
+            batch_dict['valid_traj_mask'] = ~empty_mask
+
+        num_sample = self.num_lidar_points 
+
+        src = rois.new_zeros(batch_size, num_rois, num_sample, 5)
+
+        src = self.crop_current_frame_points(src, batch_size, trajectory_rois, num_rois, num_sample, batch_dict)
+
+        src = src.view(batch_size * num_rois, -1, src.shape[-1])
+
+        src_geometry_feature,proxy_points = self.get_proposal_aware_geometry_feature(src,batch_size,trajectory_rois,num_rois,batch_dict)
+
+        src_motion_feature = self.get_proposal_aware_motion_feature(proxy_points,batch_size,trajectory_rois,num_rois,batch_dict)
+
+
+        if batch_dict['sample_idx'][0] >=1:
+
+            src_repeat = src_geometry_feature[:,None,:self.num_proxy_points,:].repeat([1,trajectory_rois.shape[1],1,1])
+            src_before = src_repeat[:,1:,:,:].clone() #[bs,traj,num_roi,C]
+            valid_length = batch_dict['num_frames'] -1 if batch_dict['sample_idx'][0] > batch_dict['num_frames'] -1 \
+                            else int(batch_dict['sample_idx'][0].item())
+            num_max_rois = max(trajectory_rois.shape[2], *[i.shape[0] for i in batch_dict['memory_bank']['feature_bank']])
+            feature_bank = self.reorder_memory(batch_dict['memory_bank']['feature_bank'][:valid_length],num_max_rois)
+            effective_length = effective_length[0,1:1+valid_length].bool() #rm dim of bs
+            for i in range(valid_length):
+                src_before[:,i][effective_length[i]] = feature_bank[i,matching_table[1+i][effective_length[i]]]
+
+            src_geometry_feature = torch.cat([src_repeat[:,:1],src_before],1).view(src_geometry_feature.shape[0],-1,
+                                              src_geometry_feature.shape[-1])
+
+        else:
+
+            src_geometry_feature = src_geometry_feature.repeat([1,trajectory_rois.shape[1],1])
+
+        batch_dict['geometory_feature_memory'] = src_geometry_feature[:,:self.num_proxy_points]
+
+
+        src = src_geometry_feature + src_motion_feature
+
+        
+        if self.model_cfg.get('USE_TRAJ_EMPTY_MASK',None):
+            src[empty_mask.view(-1)] = 0
+
+        if self.model_cfg.Transformer.use_grid_pos.init_type == 'index':
+            pos = self.grid_pos_embeded(self.grid_index.cuda())[None,:,:]
+            pos = torch.cat([torch.zeros(1,1,self.hidden_dim).cuda(),pos],1)
+        else:
+            pos=None
+
+        hs, tokens = self.transformer(src,pos=pos)
+        point_cls_list = []
+
+        for i in range(self.num_enc_layer):
+            point_cls_list.append(self.class_embed[0](tokens[i][0]))
+
+        point_cls = torch.cat(point_cls_list,0)
+
+        hs = hs.permute(1,0,2).reshape(hs.shape[1],-1)
+    
+        _, feat_box = self.trajectories_auxiliary_branch(trajectory_rois)
+
+        joint_reg = self.jointembed(torch.cat([hs,feat_box],-1))
+
+        rcnn_cls = point_cls
+        rcnn_reg = joint_reg
+
+        if not self.training:
+            batch_dict['rois'] = batch_dict['rois'][:,:,0].contiguous()
+            rcnn_cls = rcnn_cls[-rcnn_cls.shape[0]//self.num_enc_layer:]
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
+            )
+
+            batch_dict['batch_box_preds'] = batch_box_preds
+
+            batch_dict['cls_preds_normalized'] = False
+            if self.avg_stage1_score:
+                stage1_score = batch_dict['roi_scores'][:,:,:1]
+                batch_cls_preds = F.sigmoid(batch_cls_preds)
+                if self.model_cfg.get('IOU_WEIGHT', None):
+                    batch_box_preds_list = []
+                    roi_labels_list = []
+                    batch_cls_preds_list = []
+                    for bs_idx in range(batch_size):
+                        car_mask = batch_dict['roi_labels'][bs_idx] ==1
+                        batch_cls_preds_car = batch_cls_preds[bs_idx].pow(self.model_cfg.IOU_WEIGHT[0])* \
+                                              stage1_score[bs_idx].pow(1-self.model_cfg.IOU_WEIGHT[0])
+                        batch_cls_preds_car = batch_cls_preds_car[car_mask][None]
+                        batch_cls_preds_pedcyc = batch_cls_preds[bs_idx].pow(self.model_cfg.IOU_WEIGHT[1])* \
+                                                 stage1_score[bs_idx].pow(1-self.model_cfg.IOU_WEIGHT[1])
+                        batch_cls_preds_pedcyc = batch_cls_preds_pedcyc[~car_mask][None]
+                        cls_preds = torch.cat([batch_cls_preds_car,batch_cls_preds_pedcyc],1)
+                        box_preds = torch.cat([batch_dict['batch_box_preds'][bs_idx][car_mask],
+                                                     batch_dict['batch_box_preds'][bs_idx][~car_mask]],0)[None]
+                        roi_labels = torch.cat([batch_dict['roi_labels'][bs_idx][car_mask],
+                                                batch_dict['roi_labels'][bs_idx][~car_mask]],0)[None]
+                        batch_box_preds_list.append(box_preds)
+                        roi_labels_list.append(roi_labels)
+                        batch_cls_preds_list.append(cls_preds)
+                    batch_dict['batch_box_preds'] = torch.cat(batch_box_preds_list,0)
+                    batch_dict['roi_labels'] = torch.cat(roi_labels_list,0)
+                    batch_cls_preds = torch.cat(batch_cls_preds_list,0)
+                    
+                else:
+                    batch_cls_preds = torch.sqrt(batch_cls_preds*stage1_score)
+                batch_dict['cls_preds_normalized']  = True
+
+            batch_dict['batch_cls_preds'] = batch_cls_preds
+
+        return batch_dict
+
+    def reorder_memory(self, memory,num_max_rois):
+
+        ordered_memory = memory[0].new_zeros([len(memory),num_max_rois,memory[0].shape[1],memory[0].shape[2]])
+        for bs_idx in range(len(memory)):
+            ordered_memory[bs_idx,:len(memory[bs_idx])] = memory[bs_idx]
+        return ordered_memory
+
+    def generate_predicted_boxes(self, batch_size, rois, cls_preds=None, box_preds=None):
+        """
+        Args:
+            batch_size:
+            rois: (B, N, 7)
+            cls_preds: (BN, num_class)
+            box_preds: (BN, code_size)
+        Returns:
+        """
+        code_size = self.box_coder.code_size
+
+        if cls_preds is not None:
+            batch_cls_preds = cls_preds.view(batch_size, -1, cls_preds.shape[-1])
+        else:
+            batch_cls_preds = None
+        batch_box_preds = box_preds.view(batch_size, -1, code_size)
+
+        roi_ry = rois[:, :, 6].view(-1)
+        roi_xyz = rois[:, :, 0:3].view(-1, 3)
+        local_rois = rois.clone().detach()
+        local_rois[:, :, 0:3] = 0
+
+        batch_box_preds = self.box_coder.decode_torch(batch_box_preds, local_rois).view(-1, code_size)
+
+        batch_box_preds = common_utils.rotate_points_along_z(
+            batch_box_preds.unsqueeze(dim=1), roi_ry
+        ).squeeze(dim=1)
+
+        batch_box_preds[:, 0:3] += roi_xyz
+        batch_box_preds = batch_box_preds.view(batch_size, -1, code_size)
+        batch_box_preds = torch.cat([batch_box_preds,rois[:,:,7:]],-1)
+        return batch_cls_preds, batch_box_preds
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/partA2_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/partA2_head.py
new file mode 100644
index 000000000..8d7c07ce8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/partA2_head.py
@@ -0,0 +1,224 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ...ops.roiaware_pool3d import roiaware_pool3d_utils
+from ...utils.spconv_utils import spconv
+from .roi_head_template import RoIHeadTemplate
+
+
+class PartA2FCHead(RoIHeadTemplate):
+    def __init__(self, input_channels, model_cfg, num_class=1, **kwargs):
+        super().__init__(num_class=num_class, model_cfg=model_cfg)
+        self.model_cfg = model_cfg
+
+        self.SA_modules = nn.ModuleList()
+        block = self.post_act_block
+
+        c0 = self.model_cfg.ROI_AWARE_POOL.NUM_FEATURES // 2
+        self.conv_part = spconv.SparseSequential(
+            block(4, 64, 3, padding=1, indice_key='rcnn_subm1'),
+            block(64, c0, 3, padding=1, indice_key='rcnn_subm1_1'),
+        )
+        self.conv_rpn = spconv.SparseSequential(
+            block(input_channels, 64, 3, padding=1, indice_key='rcnn_subm2'),
+            block(64, c0, 3, padding=1, indice_key='rcnn_subm1_2'),
+        )
+
+        shared_fc_list = []
+        pool_size = self.model_cfg.ROI_AWARE_POOL.POOL_SIZE
+        pre_channel = self.model_cfg.ROI_AWARE_POOL.NUM_FEATURES * pool_size * pool_size * pool_size
+        for k in range(0, self.model_cfg.SHARED_FC.__len__()):
+            shared_fc_list.extend([
+                nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False),
+                nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]),
+                nn.ReLU()
+            ])
+            pre_channel = self.model_cfg.SHARED_FC[k]
+
+            if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
+                shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
+
+        self.shared_fc_layer = nn.Sequential(*shared_fc_list)
+
+        self.cls_layers = self.make_fc_layers(
+            input_channels=pre_channel, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC
+        )
+        self.reg_layers = self.make_fc_layers(
+            input_channels=pre_channel,
+            output_channels=self.box_coder.code_size * self.num_class,
+            fc_list=self.model_cfg.REG_FC
+        )
+
+        self.roiaware_pool3d_layer = roiaware_pool3d_utils.RoIAwarePool3d(
+            out_size=self.model_cfg.ROI_AWARE_POOL.POOL_SIZE,
+            max_pts_each_voxel=self.model_cfg.ROI_AWARE_POOL.MAX_POINTS_PER_VOXEL
+        )
+        self.init_weights(weight_init='xavier')
+
+    def init_weights(self, weight_init='xavier'):
+        if weight_init == 'kaiming':
+            init_func = nn.init.kaiming_normal_
+        elif weight_init == 'xavier':
+            init_func = nn.init.xavier_normal_
+        elif weight_init == 'normal':
+            init_func = nn.init.normal_
+        else:
+            raise NotImplementedError
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                if weight_init == 'normal':
+                    init_func(m.weight, mean=0, std=0.001)
+                else:
+                    init_func(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001)
+
+    def post_act_block(self, in_channels, out_channels, kernel_size, indice_key, stride=1, padding=0, conv_type='subm'):
+        if conv_type == 'subm':
+            m = spconv.SparseSequential(
+                spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key),
+                nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            )
+        elif conv_type == 'spconv':
+            m = spconv.SparseSequential(
+                spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                    bias=False, indice_key=indice_key),
+                nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            )
+        elif conv_type == 'inverseconv':
+            m = spconv.SparseSequential(
+                spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size,
+                                           indice_key=indice_key, bias=False),
+                nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            )
+        else:
+            raise NotImplementedError
+        return m
+
+    def roiaware_pool(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                point_coords: (num_points, 4)  [bs_idx, x, y, z]
+                point_features: (num_points, C)
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        Returns:
+
+        """
+        batch_size = batch_dict['batch_size']
+        batch_idx = batch_dict['point_coords'][:, 0]
+        point_coords = batch_dict['point_coords'][:, 1:4]
+        point_features = batch_dict['point_features']
+        part_features = torch.cat((
+            batch_dict['point_part_offset'] if not self.model_cfg.get('DISABLE_PART', False) else point_coords,
+            batch_dict['point_cls_scores'].view(-1, 1).detach()
+        ), dim=1)
+        part_features[part_features[:, -1] < self.model_cfg.SEG_MASK_SCORE_THRESH, 0:3] = 0
+
+        rois = batch_dict['rois']
+
+        pooled_part_features_list, pooled_rpn_features_list = [], []
+
+        for bs_idx in range(batch_size):
+            bs_mask = (batch_idx == bs_idx)
+            cur_point_coords = point_coords[bs_mask]
+            cur_part_features = part_features[bs_mask]
+            cur_rpn_features = point_features[bs_mask]
+            cur_roi = rois[bs_idx][:, 0:7].contiguous()  # (N, 7)
+
+            pooled_part_features = self.roiaware_pool3d_layer.forward(
+                cur_roi, cur_point_coords, cur_part_features, pool_method='avg'
+            )  # (N, out_x, out_y, out_z, 4)
+            pooled_rpn_features = self.roiaware_pool3d_layer.forward(
+                cur_roi, cur_point_coords, cur_rpn_features, pool_method='max'
+            )  # (N, out_x, out_y, out_z, C)
+
+            pooled_part_features_list.append(pooled_part_features)
+            pooled_rpn_features_list.append(pooled_rpn_features)
+
+        pooled_part_features = torch.cat(pooled_part_features_list, dim=0)  # (B * N, out_x, out_y, out_z, 4)
+        pooled_rpn_features = torch.cat(pooled_rpn_features_list, dim=0)  # (B * N, out_x, out_y, out_z, C)
+
+        return pooled_part_features, pooled_rpn_features
+
+    @staticmethod
+    def fake_sparse_idx(sparse_idx, batch_size_rcnn):
+        print('Warning: Sparse_Idx_Shape(%s) \r' % (str(sparse_idx.shape)), end='', flush=True)
+        # at most one sample is non-empty, then fake the first voxels of each sample(BN needs at least
+        # two values each channel) as non-empty for the below calculation
+        sparse_idx = sparse_idx.new_zeros((batch_size_rcnn, 3))
+        bs_idxs = torch.arange(batch_size_rcnn).type_as(sparse_idx).view(-1, 1)
+        sparse_idx = torch.cat((bs_idxs, sparse_idx), dim=1)
+        return sparse_idx
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+
+        Returns:
+
+        """
+        targets_dict = self.proposal_layer(
+            batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
+        )
+        if self.training:
+            targets_dict = self.assign_targets(batch_dict)
+            batch_dict['rois'] = targets_dict['rois']
+            batch_dict['roi_labels'] = targets_dict['roi_labels']
+
+        # RoI aware pooling
+        pooled_part_features, pooled_rpn_features = self.roiaware_pool(batch_dict)
+        batch_size_rcnn = pooled_part_features.shape[0]  # (B * N, out_x, out_y, out_z, 4)
+
+        # transform to sparse tensors
+        sparse_shape = np.array(pooled_part_features.shape[1:4], dtype=np.int32)
+        sparse_idx = pooled_part_features.sum(dim=-1).nonzero()  # (non_empty_num, 4) ==> [bs_idx, x_idx, y_idx, z_idx]
+        if sparse_idx.shape[0] < 3:
+            sparse_idx = self.fake_sparse_idx(sparse_idx, batch_size_rcnn)
+            if self.training:
+                # these are invalid samples
+                targets_dict['rcnn_cls_labels'].fill_(-1)
+                targets_dict['reg_valid_mask'].fill_(-1)
+
+        part_features = pooled_part_features[sparse_idx[:, 0], sparse_idx[:, 1], sparse_idx[:, 2], sparse_idx[:, 3]]
+        rpn_features = pooled_rpn_features[sparse_idx[:, 0], sparse_idx[:, 1], sparse_idx[:, 2], sparse_idx[:, 3]]
+        coords = sparse_idx.int().contiguous()
+        part_features = spconv.SparseConvTensor(part_features, coords, sparse_shape, batch_size_rcnn)
+        rpn_features = spconv.SparseConvTensor(rpn_features, coords, sparse_shape, batch_size_rcnn)
+
+        # forward rcnn network
+        x_part = self.conv_part(part_features)
+        x_rpn = self.conv_rpn(rpn_features)
+
+        merged_feature = torch.cat((x_rpn.features, x_part.features), dim=1)  # (N, C)
+        shared_feature = spconv.SparseConvTensor(merged_feature, coords, sparse_shape, batch_size_rcnn)
+        shared_feature = shared_feature.dense().view(batch_size_rcnn, -1, 1)
+
+        shared_feature = self.shared_fc_layer(shared_feature)
+
+        rcnn_cls = self.cls_layers(shared_feature).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, 1 or 2)
+        rcnn_reg = self.reg_layers(shared_feature).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, C)
+
+        if not self.training:
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
+            )
+            batch_dict['batch_cls_preds'] = batch_cls_preds
+            batch_dict['batch_box_preds'] = batch_box_preds
+            batch_dict['cls_preds_normalized'] = False
+        else:
+            targets_dict['rcnn_cls'] = rcnn_cls
+            targets_dict['rcnn_reg'] = rcnn_reg
+
+            self.forward_ret_dict = targets_dict
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/pointrcnn_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/pointrcnn_head.py
new file mode 100644
index 000000000..933f38475
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/pointrcnn_head.py
@@ -0,0 +1,179 @@
+import torch
+import torch.nn as nn
+
+from ...ops.pointnet2.pointnet2_batch import pointnet2_modules
+from ...ops.roipoint_pool3d import roipoint_pool3d_utils
+from ...utils import common_utils
+from .roi_head_template import RoIHeadTemplate
+
+
+class PointRCNNHead(RoIHeadTemplate):
+    def __init__(self, input_channels, model_cfg, num_class=1, **kwargs):
+        super().__init__(num_class=num_class, model_cfg=model_cfg)
+        self.model_cfg = model_cfg
+        use_bn = self.model_cfg.USE_BN
+        self.SA_modules = nn.ModuleList()
+        channel_in = input_channels
+
+        self.num_prefix_channels = 3 + 2  # xyz + point_scores + point_depth
+        xyz_mlps = [self.num_prefix_channels] + self.model_cfg.XYZ_UP_LAYER
+        shared_mlps = []
+        for k in range(len(xyz_mlps) - 1):
+            shared_mlps.append(nn.Conv2d(xyz_mlps[k], xyz_mlps[k + 1], kernel_size=1, bias=not use_bn))
+            if use_bn:
+                shared_mlps.append(nn.BatchNorm2d(xyz_mlps[k + 1]))
+            shared_mlps.append(nn.ReLU())
+        self.xyz_up_layer = nn.Sequential(*shared_mlps)
+
+        c_out = self.model_cfg.XYZ_UP_LAYER[-1]
+        self.merge_down_layer = nn.Sequential(
+            nn.Conv2d(c_out * 2, c_out, kernel_size=1, bias=not use_bn),
+            *[nn.BatchNorm2d(c_out), nn.ReLU()] if use_bn else [nn.ReLU()]
+        )
+
+        for k in range(self.model_cfg.SA_CONFIG.NPOINTS.__len__()):
+            mlps = [channel_in] + self.model_cfg.SA_CONFIG.MLPS[k]
+
+            npoint = self.model_cfg.SA_CONFIG.NPOINTS[k] if self.model_cfg.SA_CONFIG.NPOINTS[k] != -1 else None
+            self.SA_modules.append(
+                pointnet2_modules.PointnetSAModule(
+                    npoint=npoint,
+                    radius=self.model_cfg.SA_CONFIG.RADIUS[k],
+                    nsample=self.model_cfg.SA_CONFIG.NSAMPLE[k],
+                    mlp=mlps,
+                    use_xyz=True,
+                    bn=use_bn
+                )
+            )
+            channel_in = mlps[-1]
+
+        self.cls_layers = self.make_fc_layers(
+            input_channels=channel_in, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC
+        )
+        self.reg_layers = self.make_fc_layers(
+            input_channels=channel_in,
+            output_channels=self.box_coder.code_size * self.num_class,
+            fc_list=self.model_cfg.REG_FC
+        )
+
+        self.roipoint_pool3d_layer = roipoint_pool3d_utils.RoIPointPool3d(
+            num_sampled_points=self.model_cfg.ROI_POINT_POOL.NUM_SAMPLED_POINTS,
+            pool_extra_width=self.model_cfg.ROI_POINT_POOL.POOL_EXTRA_WIDTH
+        )
+        self.init_weights(weight_init='xavier')
+
+    def init_weights(self, weight_init='xavier'):
+        if weight_init == 'kaiming':
+            init_func = nn.init.kaiming_normal_
+        elif weight_init == 'xavier':
+            init_func = nn.init.xavier_normal_
+        elif weight_init == 'normal':
+            init_func = nn.init.normal_
+        else:
+            raise NotImplementedError
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                if weight_init == 'normal':
+                    init_func(m.weight, mean=0, std=0.001)
+                else:
+                    init_func(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001)
+
+    def roipool3d_gpu(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                point_coords: (num_points, 4)  [bs_idx, x, y, z]
+                point_features: (num_points, C)
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        Returns:
+
+        """
+        batch_size = batch_dict['batch_size']
+        batch_idx = batch_dict['point_coords'][:, 0]
+        point_coords = batch_dict['point_coords'][:, 1:4]
+        point_features = batch_dict['point_features']
+        rois = batch_dict['rois']  # (B, num_rois, 7 + C)
+        batch_cnt = point_coords.new_zeros(batch_size).int()
+        for bs_idx in range(batch_size):
+            batch_cnt[bs_idx] = (batch_idx == bs_idx).sum()
+
+        assert batch_cnt.min() == batch_cnt.max()
+
+        point_scores = batch_dict['point_cls_scores'].detach()
+        point_depths = point_coords.norm(dim=1) / self.model_cfg.ROI_POINT_POOL.DEPTH_NORMALIZER - 0.5
+        point_features_list = [point_scores[:, None], point_depths[:, None], point_features]
+        point_features_all = torch.cat(point_features_list, dim=1)
+        batch_points = point_coords.view(batch_size, -1, 3)
+        batch_point_features = point_features_all.view(batch_size, -1, point_features_all.shape[-1])
+
+        with torch.no_grad():
+            pooled_features, pooled_empty_flag = self.roipoint_pool3d_layer(
+                batch_points, batch_point_features, rois
+            )  # pooled_features: (B, num_rois, num_sampled_points, 3 + C), pooled_empty_flag: (B, num_rois)
+
+            # canonical transformation
+            roi_center = rois[:, :, 0:3]
+            pooled_features[:, :, :, 0:3] -= roi_center.unsqueeze(dim=2)
+
+            pooled_features = pooled_features.view(-1, pooled_features.shape[-2], pooled_features.shape[-1])
+            pooled_features[:, :, 0:3] = common_utils.rotate_points_along_z(
+                pooled_features[:, :, 0:3], -rois.view(-1, rois.shape[-1])[:, 6]
+            )
+            pooled_features[pooled_empty_flag.view(-1) > 0] = 0
+        return pooled_features
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+
+        Returns:
+
+        """
+        targets_dict = self.proposal_layer(
+            batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
+        )
+        if self.training:
+            targets_dict = self.assign_targets(batch_dict)
+            batch_dict['rois'] = targets_dict['rois']
+            batch_dict['roi_labels'] = targets_dict['roi_labels']
+
+        pooled_features = self.roipool3d_gpu(batch_dict)  # (total_rois, num_sampled_points, 3 + C)
+
+        xyz_input = pooled_features[..., 0:self.num_prefix_channels].transpose(1, 2).unsqueeze(dim=3).contiguous()
+        xyz_features = self.xyz_up_layer(xyz_input)
+        point_features = pooled_features[..., self.num_prefix_channels:].transpose(1, 2).unsqueeze(dim=3)
+        merged_features = torch.cat((xyz_features, point_features), dim=1)
+        merged_features = self.merge_down_layer(merged_features)
+
+        l_xyz, l_features = [pooled_features[..., 0:3].contiguous()], [merged_features.squeeze(dim=3).contiguous()]
+
+        for i in range(len(self.SA_modules)):
+            li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i])
+            l_xyz.append(li_xyz)
+            l_features.append(li_features)
+
+        shared_features = l_features[-1]  # (total_rois, num_features, 1)
+        rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, 1 or 2)
+        rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, C)
+
+        if not self.training:
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
+            )
+            batch_dict['batch_cls_preds'] = batch_cls_preds
+            batch_dict['batch_box_preds'] = batch_box_preds
+            batch_dict['cls_preds_normalized'] = False
+        else:
+            targets_dict['rcnn_cls'] = rcnn_cls
+            targets_dict['rcnn_reg'] = rcnn_reg
+
+            self.forward_ret_dict = targets_dict
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/pvrcnn_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/pvrcnn_head.py
new file mode 100644
index 000000000..6ec6b9806
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/pvrcnn_head.py
@@ -0,0 +1,175 @@
+import torch.nn as nn
+
+from ...ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
+from ...utils import common_utils
+from .roi_head_template import RoIHeadTemplate
+
+
+class PVRCNNHead(RoIHeadTemplate):
+    def __init__(self, input_channels, model_cfg, num_class=1, **kwargs):
+        super().__init__(num_class=num_class, model_cfg=model_cfg)
+        self.model_cfg = model_cfg
+
+        self.roi_grid_pool_layer, num_c_out = pointnet2_stack_modules.build_local_aggregation_module(
+            input_channels=input_channels, config=self.model_cfg.ROI_GRID_POOL
+        )
+
+        GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * num_c_out
+
+        shared_fc_list = []
+        for k in range(0, self.model_cfg.SHARED_FC.__len__()):
+            shared_fc_list.extend([
+                nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False),
+                nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]),
+                nn.ReLU()
+            ])
+            pre_channel = self.model_cfg.SHARED_FC[k]
+
+            if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
+                shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
+
+        self.shared_fc_layer = nn.Sequential(*shared_fc_list)
+
+        self.cls_layers = self.make_fc_layers(
+            input_channels=pre_channel, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC
+        )
+        self.reg_layers = self.make_fc_layers(
+            input_channels=pre_channel,
+            output_channels=self.box_coder.code_size * self.num_class,
+            fc_list=self.model_cfg.REG_FC
+        )
+        self.init_weights(weight_init='xavier')
+
+    def init_weights(self, weight_init='xavier'):
+        if weight_init == 'kaiming':
+            init_func = nn.init.kaiming_normal_
+        elif weight_init == 'xavier':
+            init_func = nn.init.xavier_normal_
+        elif weight_init == 'normal':
+            init_func = nn.init.normal_
+        else:
+            raise NotImplementedError
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                if weight_init == 'normal':
+                    init_func(m.weight, mean=0, std=0.001)
+                else:
+                    init_func(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001)
+
+    def roi_grid_pool(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                point_coords: (num_points, 4)  [bs_idx, x, y, z]
+                point_features: (num_points, C)
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        Returns:
+
+        """
+        batch_size = batch_dict['batch_size']
+        rois = batch_dict['rois']
+        point_coords = batch_dict['point_coords']
+        point_features = batch_dict['point_features']
+
+        point_features = point_features * batch_dict['point_cls_scores'].view(-1, 1)
+
+        global_roi_grid_points, local_roi_grid_points = self.get_global_grid_points_of_roi(
+            rois, grid_size=self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        )  # (BxN, 6x6x6, 3)
+        global_roi_grid_points = global_roi_grid_points.view(batch_size, -1, 3)  # (B, Nx6x6x6, 3)
+
+        xyz = point_coords[:, 1:4]
+        xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+        batch_idx = point_coords[:, 0]
+        for k in range(batch_size):
+            xyz_batch_cnt[k] = (batch_idx == k).sum()
+
+        new_xyz = global_roi_grid_points.view(-1, 3)
+        new_xyz_batch_cnt = xyz.new_zeros(batch_size).int().fill_(global_roi_grid_points.shape[1])
+        pooled_points, pooled_features = self.roi_grid_pool_layer(
+            xyz=xyz.contiguous(),
+            xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz,
+            new_xyz_batch_cnt=new_xyz_batch_cnt,
+            features=point_features.contiguous(),
+        )  # (M1 + M2 ..., C)
+
+        pooled_features = pooled_features.view(
+            -1, self.model_cfg.ROI_GRID_POOL.GRID_SIZE ** 3,
+            pooled_features.shape[-1]
+        )  # (BxN, 6x6x6, C)
+        return pooled_features
+
+    def get_global_grid_points_of_roi(self, rois, grid_size):
+        rois = rois.view(-1, rois.shape[-1])
+        batch_size_rcnn = rois.shape[0]
+
+        local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size)  # (B, 6x6x6, 3)
+        global_roi_grid_points = common_utils.rotate_points_along_z(
+            local_roi_grid_points.clone(), rois[:, 6]
+        ).squeeze(dim=1)
+        global_center = rois[:, 0:3].clone()
+        global_roi_grid_points += global_center.unsqueeze(dim=1)
+        return global_roi_grid_points, local_roi_grid_points
+
+    @staticmethod
+    def get_dense_grid_points(rois, batch_size_rcnn, grid_size):
+        faked_features = rois.new_ones((grid_size, grid_size, grid_size))
+        dense_idx = faked_features.nonzero()  # (N, 3) [x_idx, y_idx, z_idx]
+        dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float()  # (B, 6x6x6, 3)
+
+        local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6]
+        roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \
+                          - (local_roi_size.unsqueeze(dim=1) / 2)  # (B, 6x6x6, 3)
+        return roi_grid_points
+
+    def forward(self, batch_dict):
+        """
+        :param input_data: input dict
+        :return:
+        """
+
+        targets_dict = self.proposal_layer(
+            batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
+        )
+        if self.training:
+            targets_dict = batch_dict.get('roi_targets_dict', None)
+            if targets_dict is None:
+                targets_dict = self.assign_targets(batch_dict)
+                batch_dict['rois'] = targets_dict['rois']
+                batch_dict['roi_labels'] = targets_dict['roi_labels']
+
+        # RoI aware pooling
+        pooled_features = self.roi_grid_pool(batch_dict)  # (BxN, 6x6x6, C)
+
+        grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        batch_size_rcnn = pooled_features.shape[0]
+        pooled_features = pooled_features.permute(0, 2, 1).\
+            contiguous().view(batch_size_rcnn, -1, grid_size, grid_size, grid_size)  # (BxN, C, 6, 6, 6)
+
+        shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1))
+        rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, 1 or 2)
+        rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, C)
+
+        if not self.training:
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
+            )
+            batch_dict['batch_cls_preds'] = batch_cls_preds
+            batch_dict['batch_box_preds'] = batch_box_preds
+            batch_dict['cls_preds_normalized'] = False
+        else:
+            targets_dict['rcnn_cls'] = rcnn_cls
+            targets_dict['rcnn_reg'] = rcnn_reg
+
+            self.forward_ret_dict = targets_dict
+
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/roi_head_template.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/roi_head_template.py
new file mode 100644
index 000000000..5151614c6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/roi_head_template.py
@@ -0,0 +1,261 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...utils import box_coder_utils, common_utils, loss_utils
+from ..model_utils.model_nms_utils import class_agnostic_nms
+from .target_assigner.proposal_target_layer import ProposalTargetLayer
+
+
+class RoIHeadTemplate(nn.Module):
+    def __init__(self, num_class, model_cfg, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        self.num_class = num_class
+        self.box_coder = getattr(box_coder_utils, self.model_cfg.TARGET_CONFIG.BOX_CODER)(
+            **self.model_cfg.TARGET_CONFIG.get('BOX_CODER_CONFIG', {})
+        )
+        self.proposal_target_layer = ProposalTargetLayer(roi_sampler_cfg=self.model_cfg.TARGET_CONFIG)
+        self.build_losses(self.model_cfg.LOSS_CONFIG)
+        self.forward_ret_dict = None
+
+    def build_losses(self, losses_cfg):
+        self.add_module(
+            'reg_loss_func',
+            loss_utils.WeightedSmoothL1Loss(code_weights=losses_cfg.LOSS_WEIGHTS['code_weights'])
+        )
+
+    def make_fc_layers(self, input_channels, output_channels, fc_list):
+        fc_layers = []
+        pre_channel = input_channels
+        for k in range(0, fc_list.__len__()):
+            fc_layers.extend([
+                nn.Conv1d(pre_channel, fc_list[k], kernel_size=1, bias=False),
+                nn.BatchNorm1d(fc_list[k]),
+                nn.ReLU()
+            ])
+            pre_channel = fc_list[k]
+            if self.model_cfg.DP_RATIO >= 0 and k == 0:
+                fc_layers.append(nn.Dropout(self.model_cfg.DP_RATIO))
+        fc_layers.append(nn.Conv1d(pre_channel, output_channels, kernel_size=1, bias=True))
+        fc_layers = nn.Sequential(*fc_layers)
+        return fc_layers
+
+    @torch.no_grad()
+    def proposal_layer(self, batch_dict, nms_config):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                batch_cls_preds: (B, num_boxes, num_classes | 1) or (N1+N2+..., num_classes | 1)
+                batch_box_preds: (B, num_boxes, 7+C) or (N1+N2+..., 7+C)
+                cls_preds_normalized: indicate whether batch_cls_preds is normalized
+                batch_index: optional (N1+N2+...)
+            nms_config:
+
+        Returns:
+            batch_dict:
+                rois: (B, num_rois, 7+C)
+                roi_scores: (B, num_rois)
+                roi_labels: (B, num_rois)
+
+        """
+        if batch_dict.get('rois', None) is not None:
+            return batch_dict
+            
+        batch_size = batch_dict['batch_size']
+        batch_box_preds = batch_dict['batch_box_preds']
+        batch_cls_preds = batch_dict['batch_cls_preds']
+        rois = batch_box_preds.new_zeros((batch_size, nms_config.NMS_POST_MAXSIZE, batch_box_preds.shape[-1]))
+        roi_scores = batch_box_preds.new_zeros((batch_size, nms_config.NMS_POST_MAXSIZE))
+        roi_labels = batch_box_preds.new_zeros((batch_size, nms_config.NMS_POST_MAXSIZE), dtype=torch.long)
+
+        for index in range(batch_size):
+            if batch_dict.get('batch_index', None) is not None:
+                assert batch_cls_preds.shape.__len__() == 2
+                batch_mask = (batch_dict['batch_index'] == index)
+            else:
+                assert batch_dict['batch_cls_preds'].shape.__len__() == 3
+                batch_mask = index
+            box_preds = batch_box_preds[batch_mask]
+            cls_preds = batch_cls_preds[batch_mask]
+
+            cur_roi_scores, cur_roi_labels = torch.max(cls_preds, dim=1)
+
+            if nms_config.MULTI_CLASSES_NMS:
+                raise NotImplementedError
+            else:
+                selected, selected_scores = class_agnostic_nms(
+                    box_scores=cur_roi_scores, box_preds=box_preds, nms_config=nms_config
+                )
+
+            rois[index, :len(selected), :] = box_preds[selected]
+            roi_scores[index, :len(selected)] = cur_roi_scores[selected]
+            roi_labels[index, :len(selected)] = cur_roi_labels[selected]
+
+        batch_dict['rois'] = rois
+        batch_dict['roi_scores'] = roi_scores
+        batch_dict['roi_labels'] = roi_labels + 1
+        batch_dict['has_class_labels'] = True if batch_cls_preds.shape[-1] > 1 else False
+        batch_dict.pop('batch_index', None)
+        return batch_dict
+
+    def assign_targets(self, batch_dict):
+        batch_size = batch_dict['batch_size']
+        with torch.no_grad():
+            targets_dict = self.proposal_target_layer.forward(batch_dict)
+
+        rois = targets_dict['rois']  # (B, N, 7 + C)
+        gt_of_rois = targets_dict['gt_of_rois']  # (B, N, 7 + C + 1)
+        targets_dict['gt_of_rois_src'] = gt_of_rois.clone().detach()
+
+        # canonical transformation
+        roi_center = rois[:, :, 0:3]
+        roi_ry = rois[:, :, 6] % (2 * np.pi)
+        gt_of_rois[:, :, 0:3] = gt_of_rois[:, :, 0:3] - roi_center
+        gt_of_rois[:, :, 6] = gt_of_rois[:, :, 6] - roi_ry
+
+        # transfer LiDAR coords to local coords
+        gt_of_rois = common_utils.rotate_points_along_z(
+            points=gt_of_rois.view(-1, 1, gt_of_rois.shape[-1]), angle=-roi_ry.view(-1)
+        ).view(batch_size, -1, gt_of_rois.shape[-1])
+
+        # flip orientation if rois have opposite orientation
+        heading_label = gt_of_rois[:, :, 6] % (2 * np.pi)  # 0 ~ 2pi
+        opposite_flag = (heading_label > np.pi * 0.5) & (heading_label < np.pi * 1.5)
+        heading_label[opposite_flag] = (heading_label[opposite_flag] + np.pi) % (2 * np.pi)  # (0 ~ pi/2, 3pi/2 ~ 2pi)
+        flag = heading_label > np.pi
+        heading_label[flag] = heading_label[flag] - np.pi * 2  # (-pi/2, pi/2)
+        heading_label = torch.clamp(heading_label, min=-np.pi / 2, max=np.pi / 2)
+
+        gt_of_rois[:, :, 6] = heading_label
+        targets_dict['gt_of_rois'] = gt_of_rois
+        return targets_dict
+
+    def get_box_reg_layer_loss(self, forward_ret_dict):
+        loss_cfgs = self.model_cfg.LOSS_CONFIG
+        code_size = self.box_coder.code_size
+        reg_valid_mask = forward_ret_dict['reg_valid_mask'].view(-1)
+        gt_boxes3d_ct = forward_ret_dict['gt_of_rois'][..., 0:code_size]
+        gt_of_rois_src = forward_ret_dict['gt_of_rois_src'][..., 0:code_size].view(-1, code_size)
+        rcnn_reg = forward_ret_dict['rcnn_reg']  # (rcnn_batch_size, C)
+        roi_boxes3d = forward_ret_dict['rois']
+        rcnn_batch_size = gt_boxes3d_ct.view(-1, code_size).shape[0]
+
+        fg_mask = (reg_valid_mask > 0)
+        fg_sum = fg_mask.long().sum().item()
+
+        tb_dict = {}
+
+        if loss_cfgs.REG_LOSS == 'smooth-l1':
+            rois_anchor = roi_boxes3d.clone().detach().view(-1, code_size)
+            rois_anchor[:, 0:3] = 0
+            rois_anchor[:, 6] = 0
+            reg_targets = self.box_coder.encode_torch(
+                gt_boxes3d_ct.view(rcnn_batch_size, code_size), rois_anchor
+            )
+
+            rcnn_loss_reg = self.reg_loss_func(
+                rcnn_reg.view(rcnn_batch_size, -1).unsqueeze(dim=0),
+                reg_targets.unsqueeze(dim=0),
+            )  # [B, M, 7]
+            rcnn_loss_reg = (rcnn_loss_reg.view(rcnn_batch_size, -1) * fg_mask.unsqueeze(dim=-1).float()).sum() / max(fg_sum, 1)
+            rcnn_loss_reg = rcnn_loss_reg * loss_cfgs.LOSS_WEIGHTS['rcnn_reg_weight']
+            tb_dict['rcnn_loss_reg'] = rcnn_loss_reg.item()
+
+            if loss_cfgs.CORNER_LOSS_REGULARIZATION and fg_sum > 0:
+                # TODO: NEED to BE CHECK
+                fg_rcnn_reg = rcnn_reg.view(rcnn_batch_size, -1)[fg_mask]
+                fg_roi_boxes3d = roi_boxes3d.view(-1, code_size)[fg_mask]
+
+                fg_roi_boxes3d = fg_roi_boxes3d.view(1, -1, code_size)
+                batch_anchors = fg_roi_boxes3d.clone().detach()
+                roi_ry = fg_roi_boxes3d[:, :, 6].view(-1)
+                roi_xyz = fg_roi_boxes3d[:, :, 0:3].view(-1, 3)
+                batch_anchors[:, :, 0:3] = 0
+                rcnn_boxes3d = self.box_coder.decode_torch(
+                    fg_rcnn_reg.view(batch_anchors.shape[0], -1, code_size), batch_anchors
+                ).view(-1, code_size)
+
+                rcnn_boxes3d = common_utils.rotate_points_along_z(
+                    rcnn_boxes3d.unsqueeze(dim=1), roi_ry
+                ).squeeze(dim=1)
+                rcnn_boxes3d[:, 0:3] += roi_xyz
+
+                loss_corner = loss_utils.get_corner_loss_lidar(
+                    rcnn_boxes3d[:, 0:7],
+                    gt_of_rois_src[fg_mask][:, 0:7]
+                )
+                loss_corner = loss_corner.mean()
+                loss_corner = loss_corner * loss_cfgs.LOSS_WEIGHTS['rcnn_corner_weight']
+
+                rcnn_loss_reg += loss_corner
+                tb_dict['rcnn_loss_corner'] = loss_corner.item()
+        else:
+            raise NotImplementedError
+
+        return rcnn_loss_reg, tb_dict
+
+    def get_box_cls_layer_loss(self, forward_ret_dict):
+        loss_cfgs = self.model_cfg.LOSS_CONFIG
+        rcnn_cls = forward_ret_dict['rcnn_cls']
+        rcnn_cls_labels = forward_ret_dict['rcnn_cls_labels'].view(-1)
+        if loss_cfgs.CLS_LOSS == 'BinaryCrossEntropy':
+            rcnn_cls_flat = rcnn_cls.view(-1)
+            batch_loss_cls = F.binary_cross_entropy(torch.sigmoid(rcnn_cls_flat), rcnn_cls_labels.float(), reduction='none')
+            cls_valid_mask = (rcnn_cls_labels >= 0).float()
+            rcnn_loss_cls = (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0)
+        elif loss_cfgs.CLS_LOSS == 'CrossEntropy':
+            batch_loss_cls = F.cross_entropy(rcnn_cls, rcnn_cls_labels, reduction='none', ignore_index=-1)
+            cls_valid_mask = (rcnn_cls_labels >= 0).float()
+            rcnn_loss_cls = (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0)
+        else:
+            raise NotImplementedError
+
+        rcnn_loss_cls = rcnn_loss_cls * loss_cfgs.LOSS_WEIGHTS['rcnn_cls_weight']
+        tb_dict = {'rcnn_loss_cls': rcnn_loss_cls.item()}
+        return rcnn_loss_cls, tb_dict
+
+    def get_loss(self, tb_dict=None):
+        tb_dict = {} if tb_dict is None else tb_dict
+        rcnn_loss = 0
+        rcnn_loss_cls, cls_tb_dict = self.get_box_cls_layer_loss(self.forward_ret_dict)
+        rcnn_loss += rcnn_loss_cls
+        tb_dict.update(cls_tb_dict)
+
+        rcnn_loss_reg, reg_tb_dict = self.get_box_reg_layer_loss(self.forward_ret_dict)
+        rcnn_loss += rcnn_loss_reg
+        tb_dict.update(reg_tb_dict)
+        tb_dict['rcnn_loss'] = rcnn_loss.item()
+        return rcnn_loss, tb_dict
+
+    def generate_predicted_boxes(self, batch_size, rois, cls_preds, box_preds):
+        """
+        Args:
+            batch_size:
+            rois: (B, N, 7)
+            cls_preds: (BN, num_class)
+            box_preds: (BN, code_size)
+
+        Returns:
+
+        """
+        code_size = self.box_coder.code_size
+        # batch_cls_preds: (B, N, num_class or 1)
+        batch_cls_preds = cls_preds.view(batch_size, -1, cls_preds.shape[-1])
+        batch_box_preds = box_preds.view(batch_size, -1, code_size)
+
+        roi_ry = rois[:, :, 6].view(-1)
+        roi_xyz = rois[:, :, 0:3].view(-1, 3)
+        local_rois = rois.clone().detach()
+        local_rois[:, :, 0:3] = 0
+
+        batch_box_preds = self.box_coder.decode_torch(batch_box_preds, local_rois).view(-1, code_size)
+
+        batch_box_preds = common_utils.rotate_points_along_z(
+            batch_box_preds.unsqueeze(dim=1), roi_ry
+        ).squeeze(dim=1)
+        batch_box_preds[:, 0:3] += roi_xyz
+        batch_box_preds = batch_box_preds.view(batch_size, -1, code_size)
+        return batch_cls_preds, batch_box_preds
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/second_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/second_head.py
new file mode 100644
index 000000000..0f96c620b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/second_head.py
@@ -0,0 +1,188 @@
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .roi_head_template import RoIHeadTemplate
+from ...utils import common_utils, loss_utils
+
+
+class SECONDHead(RoIHeadTemplate):
+    def __init__(self, input_channels, model_cfg, num_class=1, **kwargs):
+        super().__init__(num_class=num_class, model_cfg=model_cfg)
+        self.model_cfg = model_cfg
+
+        GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        pre_channel = self.model_cfg.ROI_GRID_POOL.IN_CHANNEL * GRID_SIZE * GRID_SIZE
+
+        shared_fc_list = []
+        for k in range(0, self.model_cfg.SHARED_FC.__len__()):
+            shared_fc_list.extend([
+                nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False),
+                nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]),
+                nn.ReLU()
+            ])
+            pre_channel = self.model_cfg.SHARED_FC[k]
+
+            if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
+                shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
+
+        self.shared_fc_layer = nn.Sequential(*shared_fc_list)
+
+        self.iou_layers = self.make_fc_layers(
+            input_channels=pre_channel, output_channels=1, fc_list=self.model_cfg.IOU_FC
+        )
+        self.init_weights(weight_init='xavier')
+
+        if torch.__version__ >= '1.3':
+            self.affine_grid = partial(F.affine_grid, align_corners=True)
+            self.grid_sample = partial(F.grid_sample, align_corners=True)
+        else:
+            self.affine_grid = F.affine_grid
+            self.grid_sample = F.grid_sample
+
+    def init_weights(self, weight_init='xavier'):
+        if weight_init == 'kaiming':
+            init_func = nn.init.kaiming_normal_
+        elif weight_init == 'xavier':
+            init_func = nn.init.xavier_normal_
+        elif weight_init == 'normal':
+            init_func = nn.init.normal_
+        else:
+            raise NotImplementedError
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                if weight_init == 'normal':
+                    init_func(m.weight, mean=0, std=0.001)
+                else:
+                    init_func(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def roi_grid_pool(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                spatial_features_2d: (B, C, H, W)
+        Returns:
+
+        """
+        batch_size = batch_dict['batch_size']
+        rois = batch_dict['rois'].detach()
+        spatial_features_2d = batch_dict['spatial_features_2d'].detach()
+        height, width = spatial_features_2d.size(2), spatial_features_2d.size(3)
+
+        dataset_cfg = batch_dict['dataset_cfg']
+        min_x = dataset_cfg.POINT_CLOUD_RANGE[0]
+        min_y = dataset_cfg.POINT_CLOUD_RANGE[1]
+        voxel_size_x = dataset_cfg.DATA_PROCESSOR[-1].VOXEL_SIZE[0]
+        voxel_size_y = dataset_cfg.DATA_PROCESSOR[-1].VOXEL_SIZE[1]
+        down_sample_ratio = self.model_cfg.ROI_GRID_POOL.DOWNSAMPLE_RATIO
+
+        pooled_features_list = []
+        torch.backends.cudnn.enabled = False
+        for b_id in range(batch_size):
+            # Map global boxes coordinates to feature map coordinates
+            x1 = (rois[b_id, :, 0] - rois[b_id, :, 3] / 2 - min_x) / (voxel_size_x * down_sample_ratio)
+            x2 = (rois[b_id, :, 0] + rois[b_id, :, 3] / 2 - min_x) / (voxel_size_x * down_sample_ratio)
+            y1 = (rois[b_id, :, 1] - rois[b_id, :, 4] / 2 - min_y) / (voxel_size_y * down_sample_ratio)
+            y2 = (rois[b_id, :, 1] + rois[b_id, :, 4] / 2 - min_y) / (voxel_size_y * down_sample_ratio)
+
+            angle, _ = common_utils.check_numpy_to_torch(rois[b_id, :, 6])
+
+            cosa = torch.cos(angle)
+            sina = torch.sin(angle)
+
+            theta = torch.stack((
+                (x2 - x1) / (width - 1) * cosa, (x2 - x1) / (width - 1) * (-sina), (x1 + x2 - width + 1) / (width - 1),
+                (y2 - y1) / (height - 1) * sina, (y2 - y1) / (height - 1) * cosa, (y1 + y2 - height + 1) / (height - 1)
+            ), dim=1).view(-1, 2, 3).float()
+
+            grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+            grid = self.affine_grid(
+                theta,
+                torch.Size((rois.size(1), spatial_features_2d.size(1), grid_size, grid_size))
+            )
+
+            pooled_features = self.grid_sample(
+                spatial_features_2d[b_id].unsqueeze(0).expand(rois.size(1), spatial_features_2d.size(1), height, width),
+                grid
+            )
+
+            pooled_features_list.append(pooled_features)
+
+        torch.backends.cudnn.enabled = True
+        pooled_features = torch.cat(pooled_features_list, dim=0)
+
+        return pooled_features
+
+    def forward(self, batch_dict):
+        """
+        :param input_data: input dict
+        :return:
+        """
+        targets_dict = self.proposal_layer(
+            batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
+        )
+        if self.training:
+            targets_dict = self.assign_targets(batch_dict)
+            batch_dict['rois'] = targets_dict['rois']
+            batch_dict['roi_labels'] = targets_dict['roi_labels']
+
+        # RoI aware pooling
+        pooled_features = self.roi_grid_pool(batch_dict)  # (BxN, C, 7, 7)
+        batch_size_rcnn = pooled_features.shape[0]
+
+        shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1))
+        rcnn_iou = self.iou_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B*N, 1)
+
+        if not self.training:
+            batch_dict['batch_cls_preds'] = rcnn_iou.view(batch_dict['batch_size'], -1, rcnn_iou.shape[-1])
+            batch_dict['batch_box_preds'] = batch_dict['rois']
+            batch_dict['cls_preds_normalized'] = False
+        else:
+            targets_dict['rcnn_iou'] = rcnn_iou
+
+            self.forward_ret_dict = targets_dict
+
+        return batch_dict
+
+    def get_loss(self, tb_dict=None):
+        tb_dict = {} if tb_dict is None else tb_dict
+        rcnn_loss = 0
+        rcnn_loss_cls, cls_tb_dict = self.get_box_iou_layer_loss(self.forward_ret_dict)
+        rcnn_loss += rcnn_loss_cls
+        tb_dict.update(cls_tb_dict)
+
+        tb_dict['rcnn_loss'] = rcnn_loss.item()
+        return rcnn_loss, tb_dict
+
+    def get_box_iou_layer_loss(self, forward_ret_dict):
+        loss_cfgs = self.model_cfg.LOSS_CONFIG
+        rcnn_iou = forward_ret_dict['rcnn_iou']
+        rcnn_iou_labels = forward_ret_dict['rcnn_cls_labels'].view(-1)
+        rcnn_iou_flat = rcnn_iou.view(-1)
+        if loss_cfgs.IOU_LOSS == 'BinaryCrossEntropy':
+            batch_loss_iou = nn.functional.binary_cross_entropy_with_logits(
+                rcnn_iou_flat,
+                rcnn_iou_labels.float(), reduction='none'
+            )
+        elif loss_cfgs.IOU_LOSS == 'L2':
+            batch_loss_iou = nn.functional.mse_loss(rcnn_iou_flat, rcnn_iou_labels, reduction='none')
+        elif loss_cfgs.IOU_LOSS == 'smoothL1':
+            diff = rcnn_iou_flat - rcnn_iou_labels
+            batch_loss_iou = loss_utils.WeightedSmoothL1Loss.smooth_l1_loss(diff, 1.0 / 9.0)
+        elif loss_cfgs.IOU_LOSS == 'focalbce':
+            batch_loss_iou = loss_utils.sigmoid_focal_cls_loss(rcnn_iou_flat, rcnn_iou_labels)
+        else:
+            raise NotImplementedError
+
+        iou_valid_mask = (rcnn_iou_labels >= 0).float()
+        rcnn_loss_iou = (batch_loss_iou * iou_valid_mask).sum() / torch.clamp(iou_valid_mask.sum(), min=1.0)
+
+        rcnn_loss_iou = rcnn_loss_iou * loss_cfgs.LOSS_WEIGHTS['rcnn_iou_weight']
+        tb_dict = {'rcnn_loss_iou': rcnn_loss_iou.item()}
+        return rcnn_loss_iou, tb_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/target_assigner/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/target_assigner/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/target_assigner/proposal_target_layer.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/target_assigner/proposal_target_layer.py
new file mode 100644
index 000000000..49f5f0a04
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/target_assigner/proposal_target_layer.py
@@ -0,0 +1,228 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ....ops.iou3d_nms import iou3d_nms_utils
+
+
+class ProposalTargetLayer(nn.Module):
+    def __init__(self, roi_sampler_cfg):
+        super().__init__()
+        self.roi_sampler_cfg = roi_sampler_cfg
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                roi_scores: (B, num_rois)
+                gt_boxes: (B, N, 7 + C + 1)
+                roi_labels: (B, num_rois)
+        Returns:
+            batch_dict:
+                rois: (B, M, 7 + C)
+                gt_of_rois: (B, M, 7 + C)
+                gt_iou_of_rois: (B, M)
+                roi_scores: (B, M)
+                roi_labels: (B, M)
+                reg_valid_mask: (B, M)
+                rcnn_cls_labels: (B, M)
+        """
+        batch_rois, batch_gt_of_rois, batch_roi_ious, batch_roi_scores, batch_roi_labels = self.sample_rois_for_rcnn(
+            batch_dict=batch_dict
+        )
+        # regression valid mask
+        reg_valid_mask = (batch_roi_ious > self.roi_sampler_cfg.REG_FG_THRESH).long()
+
+        # classification label
+        if self.roi_sampler_cfg.CLS_SCORE_TYPE == 'cls':
+            batch_cls_labels = (batch_roi_ious > self.roi_sampler_cfg.CLS_FG_THRESH).long()
+            ignore_mask = (batch_roi_ious > self.roi_sampler_cfg.CLS_BG_THRESH) & \
+                          (batch_roi_ious < self.roi_sampler_cfg.CLS_FG_THRESH)
+            batch_cls_labels[ignore_mask > 0] = -1
+        elif self.roi_sampler_cfg.CLS_SCORE_TYPE == 'roi_iou':
+            iou_bg_thresh = self.roi_sampler_cfg.CLS_BG_THRESH
+            iou_fg_thresh = self.roi_sampler_cfg.CLS_FG_THRESH
+            fg_mask = batch_roi_ious > iou_fg_thresh
+            bg_mask = batch_roi_ious < iou_bg_thresh
+            interval_mask = (fg_mask == 0) & (bg_mask == 0)
+
+            batch_cls_labels = (fg_mask > 0).float()
+            batch_cls_labels[interval_mask] = \
+                (batch_roi_ious[interval_mask] - iou_bg_thresh) / (iou_fg_thresh - iou_bg_thresh)
+        else:
+            raise NotImplementedError
+
+        targets_dict = {'rois': batch_rois, 'gt_of_rois': batch_gt_of_rois, 'gt_iou_of_rois': batch_roi_ious,
+                        'roi_scores': batch_roi_scores, 'roi_labels': batch_roi_labels,
+                        'reg_valid_mask': reg_valid_mask,
+                        'rcnn_cls_labels': batch_cls_labels}
+
+        return targets_dict
+
+    def sample_rois_for_rcnn(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                roi_scores: (B, num_rois)
+                gt_boxes: (B, N, 7 + C + 1)
+                roi_labels: (B, num_rois)
+        Returns:
+        
+        """
+        batch_size = batch_dict['batch_size']
+        rois = batch_dict['rois']
+        roi_scores = batch_dict['roi_scores']
+        roi_labels = batch_dict['roi_labels']
+        gt_boxes = batch_dict['gt_boxes']
+
+        code_size = rois.shape[-1]
+        batch_rois = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE, code_size)
+        batch_gt_of_rois = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE, code_size + 1)
+        batch_roi_ious = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE)
+        batch_roi_scores = rois.new_zeros(batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE)
+        batch_roi_labels = rois.new_zeros((batch_size, self.roi_sampler_cfg.ROI_PER_IMAGE), dtype=torch.long)
+
+        for index in range(batch_size):
+            cur_roi, cur_gt, cur_roi_labels, cur_roi_scores = \
+                rois[index], gt_boxes[index], roi_labels[index], roi_scores[index]
+            k = cur_gt.__len__() - 1
+            while k >= 0 and cur_gt[k].sum() == 0:
+                k -= 1
+            cur_gt = cur_gt[:k + 1]
+            cur_gt = cur_gt.new_zeros((1, cur_gt.shape[1])) if len(cur_gt) == 0 else cur_gt
+
+            if self.roi_sampler_cfg.get('SAMPLE_ROI_BY_EACH_CLASS', False):
+                max_overlaps, gt_assignment = self.get_max_iou_with_same_class(
+                    rois=cur_roi, roi_labels=cur_roi_labels,
+                    gt_boxes=cur_gt[:, 0:7], gt_labels=cur_gt[:, -1].long()
+                )
+            else:
+                iou3d = iou3d_nms_utils.boxes_iou3d_gpu(cur_roi, cur_gt[:, 0:7])  # (M, N)
+                max_overlaps, gt_assignment = torch.max(iou3d, dim=1)
+
+            sampled_inds = self.subsample_rois(max_overlaps=max_overlaps)
+
+            batch_rois[index] = cur_roi[sampled_inds]
+            batch_roi_labels[index] = cur_roi_labels[sampled_inds]
+            batch_roi_ious[index] = max_overlaps[sampled_inds]
+            batch_roi_scores[index] = cur_roi_scores[sampled_inds]
+            batch_gt_of_rois[index] = cur_gt[gt_assignment[sampled_inds]]
+
+        return batch_rois, batch_gt_of_rois, batch_roi_ious, batch_roi_scores, batch_roi_labels
+
+    def subsample_rois(self, max_overlaps):
+        # sample fg, easy_bg, hard_bg
+        fg_rois_per_image = int(np.round(self.roi_sampler_cfg.FG_RATIO * self.roi_sampler_cfg.ROI_PER_IMAGE))
+        fg_thresh = min(self.roi_sampler_cfg.REG_FG_THRESH, self.roi_sampler_cfg.CLS_FG_THRESH)
+
+        fg_inds = ((max_overlaps >= fg_thresh)).nonzero().view(-1)
+        easy_bg_inds = ((max_overlaps < self.roi_sampler_cfg.CLS_BG_THRESH_LO)).nonzero().view(-1)
+        hard_bg_inds = ((max_overlaps < self.roi_sampler_cfg.REG_FG_THRESH) &
+                (max_overlaps >= self.roi_sampler_cfg.CLS_BG_THRESH_LO)).nonzero().view(-1)
+
+        fg_num_rois = fg_inds.numel()
+        bg_num_rois = hard_bg_inds.numel() + easy_bg_inds.numel()
+
+        if fg_num_rois > 0 and bg_num_rois > 0:
+            # sampling fg
+            fg_rois_per_this_image = min(fg_rois_per_image, fg_num_rois)
+
+            rand_num = torch.from_numpy(np.random.permutation(fg_num_rois)).type_as(max_overlaps).long()
+            fg_inds = fg_inds[rand_num[:fg_rois_per_this_image]]
+
+            # sampling bg
+            bg_rois_per_this_image = self.roi_sampler_cfg.ROI_PER_IMAGE - fg_rois_per_this_image
+            bg_inds = self.sample_bg_inds(
+                hard_bg_inds, easy_bg_inds, bg_rois_per_this_image, self.roi_sampler_cfg.HARD_BG_RATIO
+            )
+
+        elif fg_num_rois > 0 and bg_num_rois == 0:
+            # sampling fg
+            rand_num = np.floor(np.random.rand(self.roi_sampler_cfg.ROI_PER_IMAGE) * fg_num_rois)
+            rand_num = torch.from_numpy(rand_num).type_as(max_overlaps).long()
+            fg_inds = fg_inds[rand_num]
+            bg_inds = fg_inds[fg_inds < 0] # yield empty tensor
+
+        elif bg_num_rois > 0 and fg_num_rois == 0:
+            # sampling bg
+            bg_rois_per_this_image = self.roi_sampler_cfg.ROI_PER_IMAGE
+            bg_inds = self.sample_bg_inds(
+                hard_bg_inds, easy_bg_inds, bg_rois_per_this_image, self.roi_sampler_cfg.HARD_BG_RATIO
+            )
+        else:
+            print('maxoverlaps:(min=%f, max=%f)' % (max_overlaps.min().item(), max_overlaps.max().item()))
+            print('ERROR: FG=%d, BG=%d' % (fg_num_rois, bg_num_rois))
+            raise NotImplementedError
+
+        sampled_inds = torch.cat((fg_inds, bg_inds), dim=0)
+        return sampled_inds
+
+    @staticmethod
+    def sample_bg_inds(hard_bg_inds, easy_bg_inds, bg_rois_per_this_image, hard_bg_ratio):
+        if hard_bg_inds.numel() > 0 and easy_bg_inds.numel() > 0:
+            hard_bg_rois_num = min(int(bg_rois_per_this_image * hard_bg_ratio), len(hard_bg_inds))
+            easy_bg_rois_num = bg_rois_per_this_image - hard_bg_rois_num
+
+            # sampling hard bg
+            rand_idx = torch.randint(low=0, high=hard_bg_inds.numel(), size=(hard_bg_rois_num,)).long()
+            hard_bg_inds = hard_bg_inds[rand_idx]
+
+            # sampling easy bg
+            rand_idx = torch.randint(low=0, high=easy_bg_inds.numel(), size=(easy_bg_rois_num,)).long()
+            easy_bg_inds = easy_bg_inds[rand_idx]
+
+            bg_inds = torch.cat([hard_bg_inds, easy_bg_inds], dim=0)
+        elif hard_bg_inds.numel() > 0 and easy_bg_inds.numel() == 0:
+            hard_bg_rois_num = bg_rois_per_this_image
+            # sampling hard bg
+            rand_idx = torch.randint(low=0, high=hard_bg_inds.numel(), size=(hard_bg_rois_num,)).long()
+            bg_inds = hard_bg_inds[rand_idx]
+        elif hard_bg_inds.numel() == 0 and easy_bg_inds.numel() > 0:
+            easy_bg_rois_num = bg_rois_per_this_image
+            # sampling easy bg
+            rand_idx = torch.randint(low=0, high=easy_bg_inds.numel(), size=(easy_bg_rois_num,)).long()
+            bg_inds = easy_bg_inds[rand_idx]
+        else:
+            raise NotImplementedError
+
+        return bg_inds
+
+    @staticmethod
+    def get_max_iou_with_same_class(rois, roi_labels, gt_boxes, gt_labels):
+        """
+        Args:
+            rois: (N, 7)
+            roi_labels: (N)
+            gt_boxes: (N, )
+            gt_labels:
+            
+        Returns:
+        
+        """
+        """
+        :param rois: (N, 7)
+        :param roi_labels: (N)
+        :param gt_boxes: (N, 8)
+        :return:
+        """
+        max_overlaps = rois.new_zeros(rois.shape[0])
+        gt_assignment = roi_labels.new_zeros(roi_labels.shape[0])
+
+        for k in range(gt_labels.min().item(), gt_labels.max().item() + 1):
+            roi_mask = (roi_labels == k)
+            gt_mask = (gt_labels == k)
+            if roi_mask.sum() > 0 and gt_mask.sum() > 0:
+                cur_roi = rois[roi_mask]
+                cur_gt = gt_boxes[gt_mask]
+                original_gt_assignment = gt_mask.nonzero().view(-1)
+
+                iou3d = iou3d_nms_utils.boxes_iou3d_gpu(cur_roi[:, :7], cur_gt[:, :7])  # (M, N)
+                cur_max_overlaps, cur_gt_assignment = torch.max(iou3d, dim=1)
+                max_overlaps[roi_mask] = cur_max_overlaps
+                gt_assignment[roi_mask] = original_gt_assignment[cur_gt_assignment]
+
+        return max_overlaps, gt_assignment
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/voxelrcnn_head.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/voxelrcnn_head.py
new file mode 100644
index 000000000..df861d22b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/roi_heads/voxelrcnn_head.py
@@ -0,0 +1,262 @@
+import torch
+import torch.nn as nn
+from ...ops.pointnet2.pointnet2_stack import voxel_pool_modules as voxelpool_stack_modules
+from ...utils import common_utils
+from .roi_head_template import RoIHeadTemplate
+
+
+class VoxelRCNNHead(RoIHeadTemplate):
+    def __init__(self, backbone_channels, model_cfg, point_cloud_range, voxel_size, num_class=1, **kwargs):
+        super().__init__(num_class=num_class, model_cfg=model_cfg)
+        self.model_cfg = model_cfg
+        self.pool_cfg = model_cfg.ROI_GRID_POOL
+        LAYER_cfg = self.pool_cfg.POOL_LAYERS
+        self.point_cloud_range = point_cloud_range
+        self.voxel_size = voxel_size
+
+        c_out = 0
+        self.roi_grid_pool_layers = nn.ModuleList()
+        for src_name in self.pool_cfg.FEATURES_SOURCE:
+            mlps = LAYER_cfg[src_name].MLPS
+            for k in range(len(mlps)):
+                mlps[k] = [backbone_channels[src_name]] + mlps[k]
+            pool_layer = voxelpool_stack_modules.NeighborVoxelSAModuleMSG(
+                query_ranges=LAYER_cfg[src_name].QUERY_RANGES,
+                nsamples=LAYER_cfg[src_name].NSAMPLE,
+                radii=LAYER_cfg[src_name].POOL_RADIUS,
+                mlps=mlps,
+                pool_method=LAYER_cfg[src_name].POOL_METHOD,
+            )
+            
+            self.roi_grid_pool_layers.append(pool_layer)
+
+            c_out += sum([x[-1] for x in mlps])
+
+
+        GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        # c_out = sum([x[-1] for x in mlps])
+        pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * c_out
+
+        shared_fc_list = []
+        for k in range(0, self.model_cfg.SHARED_FC.__len__()):
+            shared_fc_list.extend([
+                nn.Linear(pre_channel, self.model_cfg.SHARED_FC[k], bias=False),
+                nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]),
+                nn.ReLU(inplace=True)
+            ])
+            pre_channel = self.model_cfg.SHARED_FC[k]
+
+            if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
+                shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
+        self.shared_fc_layer = nn.Sequential(*shared_fc_list)
+
+        cls_fc_list = []
+        for k in range(0, self.model_cfg.CLS_FC.__len__()):
+            cls_fc_list.extend([
+                nn.Linear(pre_channel, self.model_cfg.CLS_FC[k], bias=False),
+                nn.BatchNorm1d(self.model_cfg.CLS_FC[k]),
+                nn.ReLU()
+            ])
+            pre_channel = self.model_cfg.CLS_FC[k]
+
+            if k != self.model_cfg.CLS_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
+                cls_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
+        self.cls_fc_layers = nn.Sequential(*cls_fc_list)
+        self.cls_pred_layer = nn.Linear(pre_channel, self.num_class, bias=True)
+
+        reg_fc_list = []
+        for k in range(0, self.model_cfg.REG_FC.__len__()):
+            reg_fc_list.extend([
+                nn.Linear(pre_channel, self.model_cfg.REG_FC[k], bias=False),
+                nn.BatchNorm1d(self.model_cfg.REG_FC[k]),
+                nn.ReLU()
+            ])
+            pre_channel = self.model_cfg.REG_FC[k]
+
+            if k != self.model_cfg.REG_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
+                reg_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
+        self.reg_fc_layers = nn.Sequential(*reg_fc_list)
+        self.reg_pred_layer = nn.Linear(pre_channel, self.box_coder.code_size * self.num_class, bias=True)
+
+        self.init_weights()
+
+    def init_weights(self):
+        init_func = nn.init.xavier_normal_
+        for module_list in [self.shared_fc_layer, self.cls_fc_layers, self.reg_fc_layers]:
+            for m in module_list.modules():
+                if isinstance(m, nn.Linear):
+                    init_func(m.weight)
+                    if m.bias is not None:
+                        nn.init.constant_(m.bias, 0)
+                    
+        nn.init.normal_(self.cls_pred_layer.weight, 0, 0.01)
+        nn.init.constant_(self.cls_pred_layer.bias, 0)
+        nn.init.normal_(self.reg_pred_layer.weight, mean=0, std=0.001)
+        nn.init.constant_(self.reg_pred_layer.bias, 0)
+
+    # def _init_weights(self):
+    #     init_func = nn.init.xavier_normal_
+    #     for m in self.modules():
+    #         if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear):
+    #             init_func(m.weight)
+    #             if m.bias is not None:
+    #                 nn.init.constant_(m.bias, 0)
+    #     nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001)
+    
+    def roi_grid_pool(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                point_coords: (num_points, 4)  [bs_idx, x, y, z]
+                point_features: (num_points, C)
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        Returns:
+
+        """
+        rois = batch_dict['rois']
+        batch_size = batch_dict['batch_size']
+        with_vf_transform = batch_dict.get('with_voxel_feature_transform', False)
+        
+        roi_grid_xyz, _ = self.get_global_grid_points_of_roi(
+            rois, grid_size=self.pool_cfg.GRID_SIZE
+        )  # (BxN, 6x6x6, 3)
+        # roi_grid_xyz: (B, Nx6x6x6, 3)
+        roi_grid_xyz = roi_grid_xyz.view(batch_size, -1, 3)  
+
+        # compute the voxel coordinates of grid points
+        roi_grid_coords_x = (roi_grid_xyz[:, :, 0:1] - self.point_cloud_range[0]) // self.voxel_size[0]
+        roi_grid_coords_y = (roi_grid_xyz[:, :, 1:2] - self.point_cloud_range[1]) // self.voxel_size[1]
+        roi_grid_coords_z = (roi_grid_xyz[:, :, 2:3] - self.point_cloud_range[2]) // self.voxel_size[2]
+        # roi_grid_coords: (B, Nx6x6x6, 3)
+        roi_grid_coords = torch.cat([roi_grid_coords_x, roi_grid_coords_y, roi_grid_coords_z], dim=-1)
+
+        batch_idx = rois.new_zeros(batch_size, roi_grid_coords.shape[1], 1)
+        for bs_idx in range(batch_size):
+            batch_idx[bs_idx, :, 0] = bs_idx
+        # roi_grid_coords: (B, Nx6x6x6, 4)
+        # roi_grid_coords = torch.cat([batch_idx, roi_grid_coords], dim=-1)
+        # roi_grid_coords = roi_grid_coords.int()
+        roi_grid_batch_cnt = rois.new_zeros(batch_size).int().fill_(roi_grid_coords.shape[1])
+
+        pooled_features_list = []
+        for k, src_name in enumerate(self.pool_cfg.FEATURES_SOURCE):
+            pool_layer = self.roi_grid_pool_layers[k]
+            cur_stride = batch_dict['multi_scale_3d_strides'][src_name]
+            cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name]
+
+            if with_vf_transform:
+                cur_sp_tensors = batch_dict['multi_scale_3d_features_post'][src_name]
+            else:
+                cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name]
+
+            # compute voxel center xyz and batch_cnt
+            cur_coords = cur_sp_tensors.indices
+            cur_voxel_xyz = common_utils.get_voxel_centers(
+                cur_coords[:, 1:4],
+                downsample_times=cur_stride,
+                voxel_size=self.voxel_size,
+                point_cloud_range=self.point_cloud_range
+            )
+            cur_voxel_xyz_batch_cnt = cur_voxel_xyz.new_zeros(batch_size).int()
+            for bs_idx in range(batch_size):
+                cur_voxel_xyz_batch_cnt[bs_idx] = (cur_coords[:, 0] == bs_idx).sum()
+            # get voxel2point tensor
+            v2p_ind_tensor = common_utils.generate_voxel2pinds(cur_sp_tensors)
+            # compute the grid coordinates in this scale, in [batch_idx, x y z] order
+            cur_roi_grid_coords = roi_grid_coords // cur_stride
+            cur_roi_grid_coords = torch.cat([batch_idx, cur_roi_grid_coords], dim=-1)
+            cur_roi_grid_coords = cur_roi_grid_coords.int()
+            # voxel neighbor aggregation
+            pooled_features = pool_layer(
+                xyz=cur_voxel_xyz.contiguous(),
+                xyz_batch_cnt=cur_voxel_xyz_batch_cnt,
+                new_xyz=roi_grid_xyz.contiguous().view(-1, 3),
+                new_xyz_batch_cnt=roi_grid_batch_cnt,
+                new_coords=cur_roi_grid_coords.contiguous().view(-1, 4),
+                features=cur_sp_tensors.features.contiguous(),
+                voxel2point_indices=v2p_ind_tensor
+            )
+
+            pooled_features = pooled_features.view(
+                -1, self.pool_cfg.GRID_SIZE ** 3,
+                pooled_features.shape[-1]
+            )  # (BxN, 6x6x6, C)
+            pooled_features_list.append(pooled_features)
+        
+        ms_pooled_features = torch.cat(pooled_features_list, dim=-1)
+        
+        return ms_pooled_features
+
+
+    def get_global_grid_points_of_roi(self, rois, grid_size):
+        rois = rois.view(-1, rois.shape[-1])
+        batch_size_rcnn = rois.shape[0]
+
+        local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size)  # (B, 6x6x6, 3)
+        global_roi_grid_points = common_utils.rotate_points_along_z(
+            local_roi_grid_points.clone(), rois[:, 6]
+        ).squeeze(dim=1)
+        global_center = rois[:, 0:3].clone()
+        global_roi_grid_points += global_center.unsqueeze(dim=1)
+        return global_roi_grid_points, local_roi_grid_points
+
+    @staticmethod
+    def get_dense_grid_points(rois, batch_size_rcnn, grid_size):
+        faked_features = rois.new_ones((grid_size, grid_size, grid_size))
+        dense_idx = faked_features.nonzero()  # (N, 3) [x_idx, y_idx, z_idx]
+        dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float()  # (B, 6x6x6, 3)
+
+        local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6]
+        roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \
+                          - (local_roi_size.unsqueeze(dim=1) / 2)  # (B, 6x6x6, 3)
+        return roi_grid_points
+
+    def forward(self, batch_dict):
+        """
+        :param input_data: input dict
+        :return:
+        """
+
+        targets_dict = self.proposal_layer(
+            batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
+        )
+        if self.training:
+            targets_dict = self.assign_targets(batch_dict)
+            batch_dict['rois'] = targets_dict['rois']
+            batch_dict['roi_labels'] = targets_dict['roi_labels']
+
+        # RoI aware pooling
+        pooled_features = self.roi_grid_pool(batch_dict)  # (BxN, 6x6x6, C)
+
+        # Box Refinement
+        pooled_features = pooled_features.view(pooled_features.size(0), -1)
+        shared_features = self.shared_fc_layer(pooled_features)
+        rcnn_cls = self.cls_pred_layer(self.cls_fc_layers(shared_features))
+        rcnn_reg = self.reg_pred_layer(self.reg_fc_layers(shared_features))
+
+        # grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        # batch_size_rcnn = pooled_features.shape[0]
+        # pooled_features = pooled_features.permute(0, 2, 1).\
+        #     contiguous().view(batch_size_rcnn, -1, grid_size, grid_size, grid_size)  # (BxN, C, 6, 6, 6)
+
+        # shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1))
+        # rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, 1 or 2)
+        # rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, C)
+
+        if not self.training:
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
+            )
+            batch_dict['batch_cls_preds'] = batch_cls_preds
+            batch_dict['batch_box_preds'] = batch_box_preds
+            batch_dict['cls_preds_normalized'] = False
+        else:
+            targets_dict['rcnn_cls'] = rcnn_cls
+            targets_dict['rcnn_reg'] = rcnn_reg
+
+            self.forward_ret_dict = targets_dict
+
+        return batch_dict
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/view_transforms/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/view_transforms/__init__.py
new file mode 100644
index 000000000..e182aedf8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/view_transforms/__init__.py
@@ -0,0 +1,4 @@
+from .depth_lss import DepthLSSTransform
+__all__ = {
+    'DepthLSSTransform': DepthLSSTransform,
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/view_transforms/depth_lss.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/view_transforms/depth_lss.py
new file mode 100644
index 000000000..8fee40621
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/models/view_transforms/depth_lss.py
@@ -0,0 +1,258 @@
+import torch
+from torch import nn
+from pcdet.ops.bev_pool import bev_pool
+
+
+def gen_dx_bx(xbound, ybound, zbound):
+    dx = torch.Tensor([row[2] for row in [xbound, ybound, zbound]])
+    bx = torch.Tensor([row[0] + row[2] / 2.0 for row in [xbound, ybound, zbound]])
+    nx = torch.LongTensor(
+        [(row[1] - row[0]) / row[2] for row in [xbound, ybound, zbound]]
+    )
+    return dx, bx, nx
+
+
+class DepthLSSTransform(nn.Module):
+    """
+        This module implements LSS, which lists images into 3D and then splats onto bev features.
+        This code is adapted from https://github.com/mit-han-lab/bevfusion/ with minimal modifications.
+    """
+    def __init__(self, model_cfg):
+        super().__init__()
+        self.model_cfg = model_cfg
+        in_channel = self.model_cfg.IN_CHANNEL
+        out_channel = self.model_cfg.OUT_CHANNEL
+        self.image_size = self.model_cfg.IMAGE_SIZE
+        self.feature_size = self.model_cfg.FEATURE_SIZE
+        xbound = self.model_cfg.XBOUND
+        ybound = self.model_cfg.YBOUND
+        zbound = self.model_cfg.ZBOUND
+        self.dbound = self.model_cfg.DBOUND
+        downsample = self.model_cfg.DOWNSAMPLE
+
+        dx, bx, nx = gen_dx_bx(xbound, ybound, zbound)
+        self.dx = nn.Parameter(dx, requires_grad=False)
+        self.bx = nn.Parameter(bx, requires_grad=False)
+        self.nx = nn.Parameter(nx, requires_grad=False)
+
+        self.C = out_channel
+        self.frustum = self.create_frustum()
+        self.D = self.frustum.shape[0]
+
+        self.dtransform = nn.Sequential(
+            nn.Conv2d(1, 8, 1),
+            nn.BatchNorm2d(8),
+            nn.ReLU(True),
+            nn.Conv2d(8, 32, 5, stride=4, padding=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(True),
+            nn.Conv2d(32, 64, 5, stride=2, padding=2),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+        )
+        self.depthnet = nn.Sequential(
+            nn.Conv2d(in_channel + 64, in_channel, 3, padding=1),
+            nn.BatchNorm2d(in_channel),
+            nn.ReLU(True),
+            nn.Conv2d(in_channel, in_channel, 3, padding=1),
+            nn.BatchNorm2d(in_channel),
+            nn.ReLU(True),
+            nn.Conv2d(in_channel, self.D + self.C, 1),
+        )
+        if downsample > 1:
+            assert downsample == 2, downsample
+            self.downsample = nn.Sequential(
+                nn.Conv2d(out_channel, out_channel, 3, padding=1, bias=False),
+                nn.BatchNorm2d(out_channel),
+                nn.ReLU(True),
+                nn.Conv2d(out_channel, out_channel, 3, stride=downsample, padding=1, bias=False),
+                nn.BatchNorm2d(out_channel),
+                nn.ReLU(True),
+                nn.Conv2d(out_channel, out_channel, 3, padding=1, bias=False),
+                nn.BatchNorm2d(out_channel),
+                nn.ReLU(True),
+            )
+        else:
+            self.downsample = nn.Identity()
+    
+    def create_frustum(self):
+        iH, iW = self.image_size
+        fH, fW = self.feature_size
+
+        ds = torch.arange(*self.dbound, dtype=torch.float).view(-1, 1, 1).expand(-1, fH, fW)
+        D, _, _ = ds.shape
+        xs = torch.linspace(0, iW - 1, fW, dtype=torch.float).view(1, 1, fW).expand(D, fH, fW)
+        ys = torch.linspace(0, iH - 1, fH, dtype=torch.float).view(1, fH, 1).expand(D, fH, fW)
+        frustum = torch.stack((xs, ys, ds), -1)
+        
+        return nn.Parameter(frustum, requires_grad=False)
+
+    def get_geometry(self, camera2lidar_rots, camera2lidar_trans, intrins, post_rots, post_trans, **kwargs):
+
+        camera2lidar_rots = camera2lidar_rots.to(torch.float)
+        camera2lidar_trans = camera2lidar_trans.to(torch.float)
+        intrins = intrins.to(torch.float)
+        post_rots = post_rots.to(torch.float)
+        post_trans = post_trans.to(torch.float)
+
+        B, N, _ = camera2lidar_trans.shape
+
+        # undo post-transformation
+        # B x N x D x H x W x 3
+        points = self.frustum - post_trans.view(B, N, 1, 1, 1, 3)
+        points = torch.inverse(post_rots).view(B, N, 1, 1, 1, 3, 3).matmul(points.unsqueeze(-1))
+        
+        # cam_to_lidar
+        points = torch.cat((points[:, :, :, :, :, :2] * points[:, :, :, :, :, 2:3], points[:, :, :, :, :, 2:3]), 5)
+        combine = camera2lidar_rots.matmul(torch.inverse(intrins))
+        points = combine.view(B, N, 1, 1, 1, 3, 3).matmul(points).squeeze(-1)
+        points += camera2lidar_trans.view(B, N, 1, 1, 1, 3)
+
+        if "extra_rots" in kwargs:
+            extra_rots = kwargs["extra_rots"]
+            points = extra_rots.view(B, 1, 1, 1, 1, 3, 3).repeat(1, N, 1, 1, 1, 1, 1) \
+                .matmul(points.unsqueeze(-1)).squeeze(-1)
+            
+        if "extra_trans" in kwargs:
+            extra_trans = kwargs["extra_trans"]
+            points += extra_trans.view(B, 1, 1, 1, 1, 3).repeat(1, N, 1, 1, 1, 1)
+
+        return points
+
+    def bev_pool(self, geom_feats, x):
+        geom_feats = geom_feats.to(torch.float)
+        x = x.to(torch.float)
+
+        B, N, D, H, W, C = x.shape
+        Nprime = B * N * D * H * W
+
+        # flatten x
+        x = x.reshape(Nprime, C)
+
+        # flatten indices
+        geom_feats = ((geom_feats - (self.bx - self.dx / 2.0)) / self.dx).long()
+        geom_feats = geom_feats.view(Nprime, 3)
+        batch_ix = torch.cat([torch.full([Nprime // B, 1], ix, device=x.device, dtype=torch.long) for ix in range(B)])
+        geom_feats = torch.cat((geom_feats, batch_ix), 1)
+
+        # filter out points that are outside box
+        kept = (
+            (geom_feats[:, 0] >= 0)
+            & (geom_feats[:, 0] < self.nx[0])
+            & (geom_feats[:, 1] >= 0)
+            & (geom_feats[:, 1] < self.nx[1])
+            & (geom_feats[:, 2] >= 0)
+            & (geom_feats[:, 2] < self.nx[2])
+        )
+        x = x[kept]
+        geom_feats = geom_feats[kept]
+        x = bev_pool(x, geom_feats, B, self.nx[2], self.nx[0], self.nx[1])
+
+        # collapse Z
+        final = torch.cat(x.unbind(dim=2), 1)
+
+        return final
+
+    def get_cam_feats(self, x, d):
+        B, N, C, fH, fW = x.shape
+
+        d = d.view(B * N, *d.shape[2:])
+        x = x.view(B * N, C, fH, fW)
+
+        d = self.dtransform(d)
+        x = torch.cat([d, x], dim=1)
+        x = self.depthnet(x)
+
+        depth = x[:, : self.D].softmax(dim=1)
+        x = depth.unsqueeze(1) * x[:, self.D : (self.D + self.C)].unsqueeze(2)
+
+        x = x.view(B, N, self.C, self.D, fH, fW)
+        x = x.permute(0, 1, 3, 4, 5, 2)
+        return x
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                image_fpn (list[tensor]): image features after image neck
+
+        Returns:
+            batch_dict:
+                spatial_features_img (tensor): bev features from image modality
+        """
+        x = batch_dict['image_fpn'] 
+        x = x[0]
+        BN, C, H, W = x.size()
+        img = x.view(int(BN/6), 6, C, H, W)
+
+        camera_intrinsics = batch_dict['camera_intrinsics']
+        camera2lidar = batch_dict['camera2lidar']
+        img_aug_matrix = batch_dict['img_aug_matrix']
+        lidar_aug_matrix = batch_dict['lidar_aug_matrix']
+        lidar2image = batch_dict['lidar2image']
+
+        intrins = camera_intrinsics[..., :3, :3]
+        post_rots = img_aug_matrix[..., :3, :3]
+        post_trans = img_aug_matrix[..., :3, 3]
+        camera2lidar_rots = camera2lidar[..., :3, :3]
+        camera2lidar_trans = camera2lidar[..., :3, 3]
+
+        points = batch_dict['points']
+
+        batch_size = BN // 6
+        depth = torch.zeros(batch_size, img.shape[1], 1, *self.image_size).to(points[0].device)
+
+        for b in range(batch_size):
+            batch_mask = points[:,0] == b
+            cur_coords = points[batch_mask][:, 1:4]
+            cur_img_aug_matrix = img_aug_matrix[b]
+            cur_lidar_aug_matrix = lidar_aug_matrix[b]
+            cur_lidar2image = lidar2image[b]
+
+            # inverse aug
+            cur_coords -= cur_lidar_aug_matrix[:3, 3]
+            cur_coords = torch.inverse(cur_lidar_aug_matrix[:3, :3]).matmul(
+                cur_coords.transpose(1, 0)
+            )
+            # lidar2image
+            cur_coords = cur_lidar2image[:, :3, :3].matmul(cur_coords)
+            cur_coords += cur_lidar2image[:, :3, 3].reshape(-1, 3, 1)
+            # get 2d coords
+            dist = cur_coords[:, 2, :]
+            cur_coords[:, 2, :] = torch.clamp(cur_coords[:, 2, :], 1e-5, 1e5)
+            cur_coords[:, :2, :] /= cur_coords[:, 2:3, :]
+
+            # do image aug
+            cur_coords = cur_img_aug_matrix[:, :3, :3].matmul(cur_coords)
+            cur_coords += cur_img_aug_matrix[:, :3, 3].reshape(-1, 3, 1)
+            cur_coords = cur_coords[:, :2, :].transpose(1, 2)
+
+            # normalize coords for grid sample
+            cur_coords = cur_coords[..., [1, 0]]
+
+            # filter points outside of images
+            on_img = (
+                (cur_coords[..., 0] < self.image_size[0])
+                & (cur_coords[..., 0] >= 0)
+                & (cur_coords[..., 1] < self.image_size[1])
+                & (cur_coords[..., 1] >= 0)
+            )
+            for c in range(on_img.shape[0]):
+                masked_coords = cur_coords[c, on_img[c]].long()
+                masked_dist = dist[c, on_img[c]]
+                depth[b, c, 0, masked_coords[:, 0], masked_coords[:, 1]] = masked_dist
+
+        extra_rots = lidar_aug_matrix[..., :3, :3]
+        extra_trans = lidar_aug_matrix[..., :3, 3]
+        geom = self.get_geometry(
+            camera2lidar_rots, camera2lidar_trans, intrins, post_rots, 
+            post_trans, extra_rots=extra_rots, extra_trans=extra_trans,
+        )
+        # use points depth to assist the depth prediction in images
+        x = self.get_cam_feats(img, depth)
+        x = self.bev_pool(geom, x)
+        x = self.downsample(x)
+        # convert bev features from (b, c, x, y) to (b, c, y, x)
+        x = x.permute(0, 1, 3, 2)
+        batch_dict['spatial_features_img'] = x
+        return batch_dict
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/__init__.py
new file mode 100644
index 000000000..b60058a92
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/__init__.py
@@ -0,0 +1 @@
+from .bev_pool import bev_pool
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/bev_pool.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/bev_pool.py
new file mode 100644
index 000000000..5769a40a7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/bev_pool.py
@@ -0,0 +1,97 @@
+import torch
+
+from . import bev_pool_ext
+
+__all__ = ["bev_pool"]
+
+
+class QuickCumsum(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, geom_feats, ranks):
+        x = x.cumsum(0)
+        kept = torch.ones(x.shape[0], device=x.device, dtype=torch.bool)
+        kept[:-1] = ranks[1:] != ranks[:-1]
+
+        x, geom_feats = x[kept], geom_feats[kept]
+        x = torch.cat((x[:1], x[1:] - x[:-1]))
+
+        # save kept for backward
+        ctx.save_for_backward(kept)
+
+        # no gradient for geom_feats
+        ctx.mark_non_differentiable(geom_feats)
+
+        return x, geom_feats
+
+    @staticmethod
+    def backward(ctx, gradx, gradgeom):
+        (kept,) = ctx.saved_tensors
+        back = torch.cumsum(kept, 0)
+        back[kept] -= 1
+
+        val = gradx[back]
+
+        return val, None, None
+
+
+class QuickCumsumCuda(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, geom_feats, ranks, B, D, H, W):
+        kept = torch.ones(x.shape[0], device=x.device, dtype=torch.bool)
+        kept[1:] = ranks[1:] != ranks[:-1]
+        interval_starts = torch.where(kept)[0].int()
+        interval_lengths = torch.zeros_like(interval_starts)
+        interval_lengths[:-1] = interval_starts[1:] - interval_starts[:-1]
+        interval_lengths[-1] = x.shape[0] - interval_starts[-1]
+        geom_feats = geom_feats.int()
+
+        out = bev_pool_ext.bev_pool_forward(
+            x,
+            geom_feats,
+            interval_lengths,
+            interval_starts,
+            B,
+            D,
+            H,
+            W,
+        )
+
+        ctx.save_for_backward(interval_starts, interval_lengths, geom_feats)
+        ctx.saved_shapes = B, D, H, W
+        return out
+
+    @staticmethod
+    def backward(ctx, out_grad):
+        interval_starts, interval_lengths, geom_feats = ctx.saved_tensors
+        B, D, H, W = ctx.saved_shapes
+
+        out_grad = out_grad.contiguous()
+        x_grad = bev_pool_ext.bev_pool_backward(
+            out_grad,
+            geom_feats,
+            interval_lengths,
+            interval_starts,
+            B,
+            D,
+            H,
+            W,
+        )
+
+        return x_grad, None, None, None, None, None, None
+
+
+def bev_pool(feats, coords, B, D, H, W):
+    assert feats.shape[0] == coords.shape[0]
+
+    ranks = (
+        coords[:, 0] * (W * D * B)
+        + coords[:, 1] * (D * B)
+        + coords[:, 2] * B
+        + coords[:, 3]
+    )
+    indices = ranks.argsort()
+    feats, coords, ranks = feats[indices], coords[indices], ranks[indices]
+
+    x = QuickCumsumCuda.apply(feats, coords, ranks, B, D, H, W)
+    x = x.permute(0, 4, 1, 2, 3).contiguous()
+    return x
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/src/bev_pool.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/src/bev_pool.cpp
new file mode 100644
index 000000000..c1faf9bed
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/src/bev_pool.cpp
@@ -0,0 +1,94 @@
+#include <torch/torch.h>
+#include <c10/cuda/CUDAGuard.h>
+
+// CUDA function declarations
+void bev_pool(int b, int d, int h, int w, int n, int c, int n_intervals, const float* x,
+    const int* geom_feats, const int* interval_starts, const int* interval_lengths, float* out);
+
+void bev_pool_grad(int b, int d, int h, int w, int n, int c, int n_intervals, const float* out_grad,
+  const int* geom_feats, const int* interval_starts, const int* interval_lengths, float* x_grad);
+
+
+/*
+  Function: pillar pooling (forward, cuda)
+  Args:
+    x                : input features, FloatTensor[n, c]
+    geom_feats       : input coordinates, IntTensor[n, 4]
+    interval_lengths : starting position for pooled point, IntTensor[n_intervals]
+    interval_starts  : how many points in each pooled point, IntTensor[n_intervals]
+  Return:
+    out              : output features, FloatTensor[b, d, h, w, c]
+*/
+at::Tensor bev_pool_forward(
+  const at::Tensor _x,
+  const at::Tensor _geom_feats, 
+  const at::Tensor _interval_lengths, 
+  const at::Tensor _interval_starts,
+  int b, int d, int h, int w
+) {
+  int n = _x.size(0);
+  int c = _x.size(1);
+  int n_intervals = _interval_lengths.size(0);
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(_x));
+  const float* x = _x.data_ptr<float>();
+  const int* geom_feats = _geom_feats.data_ptr<int>();
+  const int* interval_lengths = _interval_lengths.data_ptr<int>();
+  const int* interval_starts = _interval_starts.data_ptr<int>();
+  
+  auto options =
+      torch::TensorOptions().dtype(_x.dtype()).device(_x.device());
+  at::Tensor _out = torch::zeros({b, d, h, w, c}, options);
+  float* out = _out.data_ptr<float>();
+  bev_pool(
+    b, d, h, w, n, c, n_intervals, x,
+    geom_feats, interval_starts, interval_lengths, out
+  );
+  return _out;
+}
+
+
+/*
+  Function: pillar pooling (backward, cuda)
+  Args:
+    out_grad         : input features, FloatTensor[b, d, h, w, c]
+    geom_feats       : input coordinates, IntTensor[n, 4]
+    interval_lengths : starting position for pooled point, IntTensor[n_intervals]
+    interval_starts  : how many points in each pooled point, IntTensor[n_intervals]
+  Return:
+    x_grad           : output features, FloatTensor[n, 4]
+*/
+at::Tensor bev_pool_backward(
+  const at::Tensor _out_grad,
+  const at::Tensor _geom_feats, 
+  const at::Tensor _interval_lengths, 
+  const at::Tensor _interval_starts,
+  int b, int d, int h, int w
+) {
+  int n = _geom_feats.size(0);
+  int c = _out_grad.size(4);
+  int n_intervals = _interval_lengths.size(0);
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(_out_grad));
+  const float* out_grad = _out_grad.data_ptr<float>();
+  const int* geom_feats = _geom_feats.data_ptr<int>();
+  const int* interval_lengths = _interval_lengths.data_ptr<int>();
+  const int* interval_starts = _interval_starts.data_ptr<int>();
+
+  auto options =
+      torch::TensorOptions().dtype(_out_grad.dtype()).device(_out_grad.device());
+  at::Tensor _x_grad = torch::zeros({n, c}, options);
+  float* x_grad = _x_grad.data_ptr<float>();
+  
+  bev_pool_grad(
+    b, d, h, w, n, c, n_intervals, out_grad,
+    geom_feats, interval_starts, interval_lengths, x_grad
+  );
+  
+  return _x_grad;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("bev_pool_forward", &bev_pool_forward,
+        "bev_pool_forward");
+  m.def("bev_pool_backward", &bev_pool_backward,
+        "bev_pool_backward");
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/src/bev_pool_cuda.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/src/bev_pool_cuda.cu
new file mode 100644
index 000000000..af3b85767
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/bev_pool/src/bev_pool_cuda.cu
@@ -0,0 +1,98 @@
+#include <stdio.h>
+#include <stdlib.h>
+
+/*
+  Function: pillar pooling
+  Args:
+    b                : batch size
+    d                : depth of the feature map
+    h                : height of pooled feature map
+    w                : width of pooled feature map
+    n                : number of input points
+    c                : number of channels
+    n_intervals      : number of unique points
+    x                : input features, FloatTensor[n, c]
+    geom_feats       : input coordinates, IntTensor[n, 4]
+    interval_lengths : starting position for pooled point, IntTensor[n_intervals]
+    interval_starts  : how many points in each pooled point, IntTensor[n_intervals]
+    out              : output features, FloatTensor[b, d, h, w, c]
+*/
+__global__ void bev_pool_kernel(int b, int d, int h, int w, int n, int c, int n_intervals,
+                                  const float *__restrict__ x,
+                                  const int *__restrict__ geom_feats,
+                                  const int *__restrict__ interval_starts,
+                                  const int *__restrict__ interval_lengths,
+                                  float* __restrict__ out) {
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  int index = idx / c;
+  int cur_c = idx % c;
+  if (index >= n_intervals) return;
+  int interval_start = interval_starts[index];
+  int interval_length = interval_lengths[index];
+  const int* cur_geom_feats = geom_feats + interval_start * 4;
+  const float* cur_x = x + interval_start * c + cur_c;
+  float* cur_out = out + cur_geom_feats[3] * d * h * w * c + 
+    cur_geom_feats[2] * h * w * c + cur_geom_feats[0] * w * c + 
+    cur_geom_feats[1] * c + cur_c;
+  float psum = 0;
+  for(int i = 0; i < interval_length; i++){
+    psum += cur_x[i * c];
+  }
+  *cur_out = psum;
+}
+
+
+/*
+  Function: pillar pooling backward
+  Args:
+    b                : batch size
+    d                : depth of the feature map
+    h                : height of pooled feature map
+    w                : width of pooled feature map
+    n                : number of input points
+    c                : number of channels
+    n_intervals      : number of unique points
+    out_grad         : gradient of the BEV fmap from top, FloatTensor[b, d, h, w, c]
+    geom_feats       : input coordinates, IntTensor[n, 4]
+    interval_lengths : starting position for pooled point, IntTensor[n_intervals]
+    interval_starts  : how many points in each pooled point, IntTensor[n_intervals]
+    x_grad           : gradient of the image fmap, FloatTensor
+*/
+__global__ void bev_pool_grad_kernel(int b, int d, int h, int w, int n, int c, int n_intervals,
+                                  const float *__restrict__ out_grad,
+                                  const int *__restrict__ geom_feats,
+                                  const int *__restrict__ interval_starts,
+                                  const int *__restrict__ interval_lengths,
+                                  float* __restrict__ x_grad) {
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  int index = idx / c;
+  int cur_c = idx % c;
+  if (index >= n_intervals) return;
+  int interval_start = interval_starts[index];
+  int interval_length = interval_lengths[index];
+  
+  const int* cur_geom_feats = geom_feats + interval_start * 4;
+  float* cur_x_grad = x_grad + interval_start * c + cur_c;
+  
+  const float* cur_out_grad = out_grad + cur_geom_feats[3] * d * h * w * c + 
+    cur_geom_feats[2] * h * w * c + cur_geom_feats[0] * w * c + 
+    cur_geom_feats[1] * c + cur_c;
+  for(int i = 0; i < interval_length; i++){
+    cur_x_grad[i * c] = *cur_out_grad;
+  }
+  
+}
+
+void bev_pool(int b, int d, int h, int w, int n, int c, int n_intervals, const float* x,
+  const int* geom_feats, const int* interval_starts, const int* interval_lengths, float* out) {
+  bev_pool_kernel<<<(int)ceil(((float)n_intervals * c / 256)), 256>>>(
+    b, d, h, w, n, c, n_intervals, x, geom_feats, interval_starts, interval_lengths, out
+  );
+}
+
+void bev_pool_grad(int b, int d, int h, int w, int n, int c, int n_intervals, const float* out_grad,
+  const int* geom_feats, const int* interval_starts, const int* interval_lengths, float* x_grad) {
+  bev_pool_grad_kernel<<<(int)ceil(((float)n_intervals * c / 256)), 256>>>(
+    b, d, h, w, n, c, n_intervals, out_grad, geom_feats, interval_starts, interval_lengths, x_grad
+  );
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/ingroup_inds_op.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/ingroup_inds_op.py
new file mode 100644
index 000000000..5c9b6e0e9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/ingroup_inds_op.py
@@ -0,0 +1,31 @@
+import torch
+
+try:
+    from . import ingroup_inds_cuda
+    # import ingroup_indices
+except ImportError:
+    ingroup_indices = None
+    print('Can not import ingroup indices')
+
+ingroup_indices = ingroup_inds_cuda
+
+from torch.autograd import Function
+class IngroupIndicesFunction(Function):
+
+    @staticmethod
+    def forward(ctx, group_inds):
+
+        out_inds = torch.zeros_like(group_inds) - 1
+
+        ingroup_indices.forward(group_inds, out_inds)
+
+        ctx.mark_non_differentiable(out_inds)
+
+        return out_inds
+
+    @staticmethod
+    def backward(ctx, g):
+
+        return None
+
+ingroup_inds = IngroupIndicesFunction.apply
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/error.cuh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/error.cuh
new file mode 100644
index 000000000..2dd5a8775
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/error.cuh
@@ -0,0 +1,18 @@
+#pragma once
+#include <stdio.h>
+
+#define CHECK_CALL(call)                                   \
+do                                                    \
+{                                                     \
+    const cudaError_t error_code = call;              \
+    if (error_code != cudaSuccess)                    \
+    {                                                 \
+        printf("CUDA Error:\n");                      \
+        printf("    File:       %s\n", __FILE__);     \
+        printf("    Line:       %d\n", __LINE__);     \
+        printf("    Error code: %d\n", error_code);   \
+        printf("    Error text: %s\n",                \
+            cudaGetErrorString(error_code));          \
+        exit(1);                                      \
+    }                                                 \
+} while (0)
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/ingroup_inds.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/ingroup_inds.cpp
new file mode 100644
index 000000000..8bd3389eb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/ingroup_inds.cpp
@@ -0,0 +1,54 @@
+#include <assert.h>
+#include <torch/extension.h>
+#include <torch/serialize/tensor.h>
+#include <vector>
+
+#define CHECK_CUDA(x) \
+  TORCH_CHECK(x.device().is_cuda(), #x, " must be a CUDAtensor ")
+#define CHECK_CONTIGUOUS(x) \
+  TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ")
+#define CHECK_INPUT(x) \
+  CHECK_CUDA(x);       \
+  CHECK_CONTIGUOUS(x)
+
+
+void ingroup_inds_launcher(
+    const long *group_inds_data,
+    long *out_inds_data,
+    int N,
+    int max_group_id
+);
+
+
+void ingroup_inds_gpu(
+  at::Tensor group_inds,
+  at::Tensor out_inds
+);
+
+void ingroup_inds_gpu(
+  at::Tensor group_inds,
+  at::Tensor out_inds
+) {
+
+  CHECK_INPUT(group_inds);
+  CHECK_INPUT(out_inds);
+  int N = group_inds.size(0);
+  int max_group_id = group_inds.max().item().toLong();
+
+
+  long *group_inds_data = group_inds.data_ptr<long>();
+  long *out_inds_data = out_inds.data_ptr<long>();
+
+  ingroup_inds_launcher(
+      group_inds_data,
+      out_inds_data,
+      N,
+      max_group_id
+  );
+
+}
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward", &ingroup_inds_gpu, "cuda version of get_inner_win_inds of SST");
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/ingroup_inds_kernel.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/ingroup_inds_kernel.cu
new file mode 100644
index 000000000..788284813
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/ingroup_inds/src/ingroup_inds_kernel.cu
@@ -0,0 +1,77 @@
+#include <assert.h>
+#include <vector>
+#include <math.h>
+#include <stdio.h>
+#include <torch/serialize/tensor.h>
+#include <torch/types.h>
+#include "cuda_fp16.h"
+
+#define CHECK_CALL(call)                                   \
+do                                                    \
+{                                                     \
+    const cudaError_t error_code = call;              \
+    if (error_code != cudaSuccess)                    \
+    {                                                 \
+        printf("CUDA Error:\n");                      \
+        printf("    File:       %s\n", __FILE__);     \
+        printf("    Line:       %d\n", __LINE__);     \
+        printf("    Error code: %d\n", error_code);   \
+        printf("    Error text: %s\n",                \
+            cudaGetErrorString(error_code));          \
+        exit(1);                                      \
+    }                                                 \
+} while (0)
+
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+
+// #define DEBUG
+// #define ASSERTION
+
+__global__ void ingroup_inds_kernel(
+    const long *group_inds,
+    long *out_inds,
+    int *ingroup_counter,
+    int N
+) {
+
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (idx >= N) return;
+  long this_group_id = group_inds[idx];
+
+  int cnt = atomicAdd(&ingroup_counter[this_group_id], 1);
+  out_inds[idx] = cnt;
+}
+
+
+ void ingroup_inds_launcher(
+  const long *group_inds,
+  long *out_inds,
+  int N,
+  int max_group_id
+  ) {
+
+  int *ingroup_counter = NULL;
+  CHECK_CALL(cudaMalloc(&ingroup_counter,   (max_group_id + 1) * sizeof(int)));
+  CHECK_CALL(cudaMemset(ingroup_counter, 0, (max_group_id + 1) * sizeof(int)));
+
+  dim3 blocks(DIVUP(N, THREADS_PER_BLOCK));
+  dim3 threads(THREADS_PER_BLOCK);
+
+  ingroup_inds_kernel<<<blocks, threads>>>(
+      group_inds,
+      out_inds,
+      ingroup_counter,
+      N
+  );
+
+  cudaFree(ingroup_counter);
+
+  #ifdef DEBUG
+  CHECK_CALL(cudaGetLastError());
+  CHECK_CALL(cudaDeviceSynchronize());
+  #endif
+
+  return;
+
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/iou3d_nms_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/iou3d_nms_utils.py
new file mode 100644
index 000000000..b63ca0d93
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/iou3d_nms_utils.py
@@ -0,0 +1,189 @@
+"""
+3D IoU Calculation and Rotated NMS
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+"""
+import torch
+
+from ...utils import common_utils
+from . import iou3d_nms_cuda
+
+
+def boxes_bev_iou_cpu(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+        ans_iou: (N, M)
+    """
+    boxes_a, is_numpy = common_utils.check_numpy_to_torch(boxes_a)
+    boxes_b, is_numpy = common_utils.check_numpy_to_torch(boxes_b)
+    assert not (boxes_a.is_cuda or boxes_b.is_cuda), 'Only support CPU tensors'
+    assert boxes_a.shape[1] == 7 and boxes_b.shape[1] == 7
+    ans_iou = boxes_a.new_zeros(torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
+    iou3d_nms_cuda.boxes_iou_bev_cpu(boxes_a.contiguous(), boxes_b.contiguous(), ans_iou)
+
+    return ans_iou.numpy() if is_numpy else ans_iou
+
+
+def boxes_iou_bev(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+        ans_iou: (N, M)
+    """
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 7
+    ans_iou = torch.cuda.FloatTensor(torch.Size((boxes_a.shape[0], boxes_b.shape[0]))).zero_()
+
+    iou3d_nms_cuda.boxes_iou_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), ans_iou)
+
+    return ans_iou
+
+
+def boxes_iou3d_gpu(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+        ans_iou: (N, M)
+    """
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 7
+
+    # height overlap
+    boxes_a_height_max = (boxes_a[:, 2] + boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_a_height_min = (boxes_a[:, 2] - boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_b_height_max = (boxes_b[:, 2] + boxes_b[:, 5] / 2).view(1, -1)
+    boxes_b_height_min = (boxes_b[:, 2] - boxes_b[:, 5] / 2).view(1, -1)
+
+    # bev overlap
+    overlaps_bev = torch.cuda.FloatTensor(torch.Size((boxes_a.shape[0], boxes_b.shape[0]))).zero_()  # (N, M)
+    iou3d_nms_cuda.boxes_overlap_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), overlaps_bev)
+
+    max_of_min = torch.max(boxes_a_height_min, boxes_b_height_min)
+    min_of_max = torch.min(boxes_a_height_max, boxes_b_height_max)
+    overlaps_h = torch.clamp(min_of_max - max_of_min, min=0)
+
+    # 3d iou
+    overlaps_3d = overlaps_bev * overlaps_h
+
+    vol_a = (boxes_a[:, 3] * boxes_a[:, 4] * boxes_a[:, 5]).view(-1, 1)
+    vol_b = (boxes_b[:, 3] * boxes_b[:, 4] * boxes_b[:, 5]).view(1, -1)
+
+    iou3d = overlaps_3d / torch.clamp(vol_a + vol_b - overlaps_3d, min=1e-6)
+
+    return iou3d
+
+def boxes_aligned_iou3d_gpu(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+        ans_iou: (N,)
+    """
+    assert boxes_a.shape[0] == boxes_b.shape[0]
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 7
+
+    # height overlap
+    boxes_a_height_max = (boxes_a[:, 2] + boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_a_height_min = (boxes_a[:, 2] - boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_b_height_max = (boxes_b[:, 2] + boxes_b[:, 5] / 2).view(-1, 1)
+    boxes_b_height_min = (boxes_b[:, 2] - boxes_b[:, 5] / 2).view(-1, 1)
+
+    # bev overlap
+    overlaps_bev = torch.cuda.FloatTensor(torch.Size((boxes_a.shape[0], 1))).zero_()  # (N, M)
+    iou3d_nms_cuda.boxes_aligned_overlap_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), overlaps_bev)
+
+    max_of_min = torch.max(boxes_a_height_min, boxes_b_height_min)
+    min_of_max = torch.min(boxes_a_height_max, boxes_b_height_max)
+    overlaps_h = torch.clamp(min_of_max - max_of_min, min=0)
+
+    # 3d iou
+    overlaps_3d = overlaps_bev * overlaps_h
+
+    vol_a = (boxes_a[:, 3] * boxes_a[:, 4] * boxes_a[:, 5]).view(-1, 1)
+    vol_b = (boxes_b[:, 3] * boxes_b[:, 4] * boxes_b[:, 5]).view(-1, 1)
+
+    iou3d = overlaps_3d / torch.clamp(vol_a + vol_b - overlaps_3d, min=1e-6)
+
+    return iou3d
+
+
+def nms_gpu(boxes, scores, thresh, pre_maxsize=None, **kwargs):
+    """
+    :param boxes: (N, 7) [x, y, z, dx, dy, dz, heading]
+    :param scores: (N)
+    :param thresh:
+    :return:
+    """
+    assert boxes.shape[1] == 7
+    order = scores.sort(0, descending=True)[1]
+    if pre_maxsize is not None:
+        order = order[:pre_maxsize]
+
+    boxes = boxes[order].contiguous()
+    keep = torch.LongTensor(boxes.size(0))
+    num_out = iou3d_nms_cuda.nms_gpu(boxes, keep, thresh)
+    return order[keep[:num_out].cuda()].contiguous(), None
+
+
+def nms_normal_gpu(boxes, scores, thresh, **kwargs):
+    """
+    :param boxes: (N, 7) [x, y, z, dx, dy, dz, heading]
+    :param scores: (N)
+    :param thresh:
+    :return:
+    """
+    assert boxes.shape[1] == 7
+    order = scores.sort(0, descending=True)[1]
+
+    boxes = boxes[order].contiguous()
+
+    keep = torch.LongTensor(boxes.size(0))
+    num_out = iou3d_nms_cuda.nms_normal_gpu(boxes, keep, thresh)
+    return order[keep[:num_out].cuda()].contiguous(), None
+
+
+def paired_boxes_iou3d_gpu(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+        ans_iou: (N)
+    """
+    assert boxes_a.shape[0] == boxes_b.shape[0]
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 7
+
+    # height overlap
+    boxes_a_height_max = (boxes_a[:, 2] + boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_a_height_min = (boxes_a[:, 2] - boxes_a[:, 5] / 2).view(-1, 1)
+    boxes_b_height_max = (boxes_b[:, 2] + boxes_b[:, 5] / 2).view(-1, 1)
+    boxes_b_height_min = (boxes_b[:, 2] - boxes_b[:, 5] / 2).view(-1, 1)
+
+    # bev overlap
+    overlaps_bev = torch.cuda.FloatTensor(torch.Size((boxes_a.shape[0], 1))).zero_()  # (N, ``)
+    iou3d_nms_cuda.paired_boxes_overlap_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(), overlaps_bev)
+
+    max_of_min = torch.max(boxes_a_height_min, boxes_b_height_min)
+    min_of_max = torch.min(boxes_a_height_max, boxes_b_height_max)
+    overlaps_h = torch.clamp(min_of_max - max_of_min, min=0)
+
+    # 3d iou
+    overlaps_3d = overlaps_bev * overlaps_h
+
+    vol_a = (boxes_a[:, 3] * boxes_a[:, 4] * boxes_a[:, 5]).view(-1, 1)
+    vol_b = (boxes_b[:, 3] * boxes_b[:, 4] * boxes_b[:, 5]).view(-1, 1)
+
+    iou3d = overlaps_3d / torch.clamp(vol_a + vol_b - overlaps_3d, min=1e-6)
+
+    return iou3d.view(-1)
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_cpu.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_cpu.cpp
new file mode 100644
index 000000000..c272a580b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_cpu.cpp
@@ -0,0 +1,273 @@
+/*
+3D Rotated IoU Calculation (CPU)
+Written by Shaoshuai Shi
+All Rights Reserved 2020.
+*/
+
+#include <stdio.h>
+#include <math.h>
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "iou3d_cpu.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+inline float min(float a, float b){
+    return a > b ? b : a;
+}
+
+inline float max(float a, float b){
+    return a > b ? a : b;
+}
+
+const float EPS = 1e-8;
+struct Point {
+    float x, y;
+    __device__ Point() {}
+    __device__ Point(float _x, float _y){
+        x = _x, y = _y;
+    }
+
+    __device__ void set(float _x, float _y){
+        x = _x; y = _y;
+    }
+
+    __device__ Point operator +(const Point &b)const{
+        return Point(x + b.x, y + b.y);
+    }
+
+    __device__ Point operator -(const Point &b)const{
+        return Point(x - b.x, y - b.y);
+    }
+};
+
+inline float cross(const Point &a, const Point &b){
+    return a.x * b.y - a.y * b.x;
+}
+
+inline float cross(const Point &p1, const Point &p2, const Point &p0){
+    return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y);
+}
+
+inline int check_rect_cross(const Point &p1, const Point &p2, const Point &q1, const Point &q2){
+    int ret = min(p1.x,p2.x) <= max(q1.x,q2.x)  &&
+              min(q1.x,q2.x) <= max(p1.x,p2.x) &&
+              min(p1.y,p2.y) <= max(q1.y,q2.y) &&
+              min(q1.y,q2.y) <= max(p1.y,p2.y);
+    return ret;
+}
+
+inline int check_in_box2d(const float *box, const Point &p){
+    //params: (7) [x, y, z, dx, dy, dz, heading]
+    const float MARGIN = 1e-2;
+
+    float center_x = box[0], center_y = box[1];
+    float angle_cos = cos(-box[6]), angle_sin = sin(-box[6]);  // rotate the point in the opposite direction of box
+    float rot_x = (p.x - center_x) * angle_cos + (p.y - center_y) * (-angle_sin);
+    float rot_y = (p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos;
+
+    return (fabs(rot_x) < box[3] / 2 + MARGIN && fabs(rot_y) < box[4] / 2 + MARGIN);
+}
+
+inline int intersection(const Point &p1, const Point &p0, const Point &q1, const Point &q0, Point &ans){
+    // fast exclusion
+    if (check_rect_cross(p0, p1, q0, q1) == 0) return 0;
+
+    // check cross standing
+    float s1 = cross(q0, p1, p0);
+    float s2 = cross(p1, q1, p0);
+    float s3 = cross(p0, q1, q0);
+    float s4 = cross(q1, p1, q0);
+
+    if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0;
+
+    // calculate intersection of two lines
+    float s5 = cross(q1, p1, p0);
+    if(fabs(s5 - s1) > EPS){
+        ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1);
+        ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1);
+
+    }
+    else{
+        float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y;
+        float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y;
+        float D = a0 * b1 - a1 * b0;
+
+        ans.x = (b0 * c1 - b1 * c0) / D;
+        ans.y = (a1 * c0 - a0 * c1) / D;
+    }
+
+    return 1;
+}
+
+inline void rotate_around_center(const Point &center, const float angle_cos, const float angle_sin, Point &p){
+    float new_x = (p.x - center.x) * angle_cos + (p.y - center.y) * (-angle_sin) + center.x;
+    float new_y = (p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y;
+    p.set(new_x, new_y);
+}
+
+inline int point_cmp(const Point &a, const Point &b, const Point &center){
+    return atan2(a.y - center.y, a.x - center.x) > atan2(b.y - center.y, b.x - center.x);
+}
+
+inline float box_overlap(const float *box_a, const float *box_b){
+    // params: box_a (7) [x, y, z, dx, dy, dz, heading]
+    // params: box_b (7) [x, y, z, dx, dy, dz, heading]
+
+//    float a_x1 = box_a[0], a_y1 = box_a[1], a_x2 = box_a[2], a_y2 = box_a[3], a_angle = box_a[4];
+//    float b_x1 = box_b[0], b_y1 = box_b[1], b_x2 = box_b[2], b_y2 = box_b[3], b_angle = box_b[4];
+    float a_angle = box_a[6], b_angle = box_b[6];
+    float a_dx_half = box_a[3] / 2, b_dx_half = box_b[3] / 2, a_dy_half = box_a[4] / 2, b_dy_half = box_b[4] / 2;
+    float a_x1 = box_a[0] - a_dx_half, a_y1 = box_a[1] - a_dy_half;
+    float a_x2 = box_a[0] + a_dx_half, a_y2 = box_a[1] + a_dy_half;
+    float b_x1 = box_b[0] - b_dx_half, b_y1 = box_b[1] - b_dy_half;
+    float b_x2 = box_b[0] + b_dx_half, b_y2 = box_b[1] + b_dy_half;
+
+    Point center_a(box_a[0], box_a[1]);
+    Point center_b(box_b[0], box_b[1]);
+
+    Point box_a_corners[5];
+    box_a_corners[0].set(a_x1, a_y1);
+    box_a_corners[1].set(a_x2, a_y1);
+    box_a_corners[2].set(a_x2, a_y2);
+    box_a_corners[3].set(a_x1, a_y2);
+
+    Point box_b_corners[5];
+    box_b_corners[0].set(b_x1, b_y1);
+    box_b_corners[1].set(b_x2, b_y1);
+    box_b_corners[2].set(b_x2, b_y2);
+    box_b_corners[3].set(b_x1, b_y2);
+
+    // get oriented corners
+    float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle);
+    float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle);
+
+    for (int k = 0; k < 4; k++){
+        rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]);
+        rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]);
+    }
+
+    box_a_corners[4] = box_a_corners[0];
+    box_b_corners[4] = box_b_corners[0];
+
+    // get intersection of lines
+    Point cross_points[16];
+    Point poly_center;
+    int cnt = 0, flag = 0;
+
+    poly_center.set(0, 0);
+    for (int i = 0; i < 4; i++){
+        for (int j = 0; j < 4; j++){
+            flag = intersection(box_a_corners[i + 1], box_a_corners[i], box_b_corners[j + 1], box_b_corners[j], cross_points[cnt]);
+            if (flag){
+                poly_center = poly_center + cross_points[cnt];
+                cnt++;
+            }
+        }
+    }
+
+    // check corners
+    for (int k = 0; k < 4; k++){
+        if (check_in_box2d(box_a, box_b_corners[k])){
+            poly_center = poly_center + box_b_corners[k];
+            cross_points[cnt] = box_b_corners[k];
+            cnt++;
+        }
+        if (check_in_box2d(box_b, box_a_corners[k])){
+            poly_center = poly_center + box_a_corners[k];
+            cross_points[cnt] = box_a_corners[k];
+            cnt++;
+        }
+    }
+
+    poly_center.x /= cnt;
+    poly_center.y /= cnt;
+
+    // sort the points of polygon
+    Point temp;
+    for (int j = 0; j < cnt - 1; j++){
+        for (int i = 0; i < cnt - j - 1; i++){
+            if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)){
+                temp = cross_points[i];
+                cross_points[i] = cross_points[i + 1];
+                cross_points[i + 1] = temp;
+            }
+        }
+    }
+
+    // get the overlap areas
+    float area = 0;
+    for (int k = 0; k < cnt - 1; k++){
+        area += cross(cross_points[k] - cross_points[0], cross_points[k + 1] - cross_points[0]);
+    }
+
+    return fabs(area) / 2.0;
+}
+
+inline float iou_bev(const float *box_a, const float *box_b){
+    // params: box_a (7) [x, y, z, dx, dy, dz, heading]
+    // params: box_b (7) [x, y, z, dx, dy, dz, heading]
+    float sa = box_a[3] * box_a[4];
+    float sb = box_b[3] * box_b[4];
+    float s_overlap = box_overlap(box_a, box_b);
+    return s_overlap / fmaxf(sa + sb - s_overlap, EPS);
+}
+
+
+int boxes_iou_bev_cpu(at::Tensor boxes_a_tensor, at::Tensor boxes_b_tensor, at::Tensor ans_iou_tensor){
+    // params boxes_a_tensor: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b_tensor: (M, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_iou_tensor: (N, M)
+
+    CHECK_CONTIGUOUS(boxes_a_tensor);
+    CHECK_CONTIGUOUS(boxes_b_tensor);
+
+    int num_boxes_a = boxes_a_tensor.size(0);
+    int num_boxes_b = boxes_b_tensor.size(0);
+    const float *boxes_a = boxes_a_tensor.data<float>();
+    const float *boxes_b = boxes_b_tensor.data<float>();
+    float *ans_iou = ans_iou_tensor.data<float>();
+
+    for (int i = 0; i < num_boxes_a; i++){
+        for (int j = 0; j < num_boxes_b; j++){
+            ans_iou[i * num_boxes_b + j] = iou_bev(boxes_a + i * 7, boxes_b + j * 7);
+        }
+    }
+    return 1;
+}
+
+int boxes_aligned_iou_bev_cpu(at::Tensor boxes_a_tensor, at::Tensor boxes_b_tensor, at::Tensor ans_iou_tensor){
+    // params boxes_a_tensor: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b_tensor: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_iou_tensor: (N, 1)
+
+    CHECK_CONTIGUOUS(boxes_a_tensor);
+    CHECK_CONTIGUOUS(boxes_b_tensor);
+
+    int num_boxes = boxes_a_tensor.size(0);
+    int num_boxes_b = boxes_b_tensor.size(0);
+    assert(num_boxes == num_boxes_b);
+    const float *boxes_a = boxes_a_tensor.data<float>();
+    const float *boxes_b = boxes_b_tensor.data<float>();
+    float *ans_iou = ans_iou_tensor.data<float>();
+
+    for (int i = 0; i < num_boxes; i++){
+        ans_iou[i] = iou_bev(boxes_a + i * 7, boxes_b + i * 7);
+    }
+    return 1;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_cpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_cpu.h
new file mode 100644
index 000000000..4d93bb6e3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_cpu.h
@@ -0,0 +1,11 @@
+#ifndef IOU3D_CPU_H
+#define IOU3D_CPU_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int boxes_iou_bev_cpu(at::Tensor boxes_a_tensor, at::Tensor boxes_b_tensor, at::Tensor ans_iou_tensor);
+int boxes_aligned_iou_bev_cpu(at::Tensor boxes_a_tensor, at::Tensor boxes_b_tensor, at::Tensor ans_iou_tensor);
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms.cpp
new file mode 100644
index 000000000..179a26cf6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms.cpp
@@ -0,0 +1,235 @@
+/*
+3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others)
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "iou3d_nms.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+
+#define CHECK_ERROR(ans) { gpuAssert((ans), __FILE__, __LINE__); }
+inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
+{
+   if (code != cudaSuccess)
+   {
+      fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
+      if (abort) exit(code);
+   }
+}
+
+const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
+
+void boxesalignedoverlapLauncher(const int num_box, const float *boxes_a, const float *boxes_b, float *ans_overlap);
+void boxesoverlapLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap);
+void PairedBoxesOverlapLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap);
+void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_iou);
+void nmsLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh);
+void nmsNormalLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh);
+
+
+int boxes_aligned_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_overlap){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_overlap: (N, 1)
+
+    CHECK_INPUT(boxes_a);
+    CHECK_INPUT(boxes_b);
+    CHECK_INPUT(ans_overlap);
+
+    int num_box = boxes_a.size(0);
+    int num_b = boxes_b.size(0);
+
+    assert(num_box == num_b);
+
+    const float * boxes_a_data = boxes_a.data<float>();
+    const float * boxes_b_data = boxes_b.data<float>();
+    float * ans_overlap_data = ans_overlap.data<float>();
+
+    boxesalignedoverlapLauncher(num_box, boxes_a_data, boxes_b_data, ans_overlap_data);
+
+    return 1;
+}
+
+int boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_overlap){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_overlap: (N, M)
+
+    CHECK_INPUT(boxes_a);
+    CHECK_INPUT(boxes_b);
+    CHECK_INPUT(ans_overlap);
+
+    int num_a = boxes_a.size(0);
+    int num_b = boxes_b.size(0);
+
+    const float * boxes_a_data = boxes_a.data<float>();
+    const float * boxes_b_data = boxes_b.data<float>();
+    float * ans_overlap_data = ans_overlap.data<float>();
+
+    boxesoverlapLauncher(num_a, boxes_a_data, num_b, boxes_b_data, ans_overlap_data);
+
+    return 1;
+}
+
+int paired_boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_overlap){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_overlap: (N, 1)
+
+    CHECK_INPUT(boxes_a);
+    CHECK_INPUT(boxes_b);
+    CHECK_INPUT(ans_overlap);
+
+    int num_a = boxes_a.size(0);
+    int num_b = boxes_b.size(0);
+
+    assert(num_a == num_b);
+
+    const float * boxes_a_data = boxes_a.data<float>();
+    const float * boxes_b_data = boxes_b.data<float>();
+    float * ans_overlap_data = ans_overlap.data<float>();
+
+    PairedBoxesOverlapLauncher(num_a, boxes_a_data, num_b, boxes_b_data, ans_overlap_data);
+
+    return 1;
+}
+
+int boxes_iou_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_iou){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+    // params ans_overlap: (N, M)
+    CHECK_INPUT(boxes_a);
+    CHECK_INPUT(boxes_b);
+    CHECK_INPUT(ans_iou);
+
+    int num_a = boxes_a.size(0);
+    int num_b = boxes_b.size(0);
+
+    const float * boxes_a_data = boxes_a.data<float>();
+    const float * boxes_b_data = boxes_b.data<float>();
+    float * ans_iou_data = ans_iou.data<float>();
+
+    boxesioubevLauncher(num_a, boxes_a_data, num_b, boxes_b_data, ans_iou_data);
+
+    return 1;
+}
+
+int nms_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh){
+    // params boxes: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params keep: (N)
+    CHECK_INPUT(boxes);
+    CHECK_CONTIGUOUS(keep);
+
+    int boxes_num = boxes.size(0);
+    const float * boxes_data = boxes.data<float>();
+    long * keep_data = keep.data<long>();
+
+    const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+
+    unsigned long long *mask_data = NULL;
+    CHECK_ERROR(cudaMalloc((void**)&mask_data, boxes_num * col_blocks * sizeof(unsigned long long)));
+    nmsLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh);
+
+    // unsigned long long mask_cpu[boxes_num * col_blocks];
+    // unsigned long long *mask_cpu = new unsigned long long [boxes_num * col_blocks];
+    std::vector<unsigned long long> mask_cpu(boxes_num * col_blocks);
+
+//    printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks);
+    CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data, boxes_num * col_blocks * sizeof(unsigned long long),
+                           cudaMemcpyDeviceToHost));
+
+    cudaFree(mask_data);
+
+    unsigned long long remv_cpu[col_blocks];
+    memset(remv_cpu, 0, col_blocks * sizeof(unsigned long long));
+
+    int num_to_keep = 0;
+
+    for (int i = 0; i < boxes_num; i++){
+        int nblock = i / THREADS_PER_BLOCK_NMS;
+        int inblock = i % THREADS_PER_BLOCK_NMS;
+
+        if (!(remv_cpu[nblock] & (1ULL << inblock))){
+            keep_data[num_to_keep++] = i;
+            unsigned long long *p = &mask_cpu[0] + i * col_blocks;
+            for (int j = nblock; j < col_blocks; j++){
+                remv_cpu[j] |= p[j];
+            }
+        }
+    }
+    if ( cudaSuccess != cudaGetLastError() ) printf( "Error!\n" );
+
+    return num_to_keep;
+}
+
+
+int nms_normal_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh){
+    // params boxes: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params keep: (N)
+
+    CHECK_INPUT(boxes);
+    CHECK_CONTIGUOUS(keep);
+
+    int boxes_num = boxes.size(0);
+    const float * boxes_data = boxes.data<float>();
+    long * keep_data = keep.data<long>();
+
+    const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+
+    unsigned long long *mask_data = NULL;
+    CHECK_ERROR(cudaMalloc((void**)&mask_data, boxes_num * col_blocks * sizeof(unsigned long long)));
+    nmsNormalLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh);
+
+    // unsigned long long mask_cpu[boxes_num * col_blocks];
+    // unsigned long long *mask_cpu = new unsigned long long [boxes_num * col_blocks];
+    std::vector<unsigned long long> mask_cpu(boxes_num * col_blocks);
+
+//    printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks);
+    CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data, boxes_num * col_blocks * sizeof(unsigned long long),
+                           cudaMemcpyDeviceToHost));
+
+    cudaFree(mask_data);
+
+    unsigned long long remv_cpu[col_blocks];
+    memset(remv_cpu, 0, col_blocks * sizeof(unsigned long long));
+
+    int num_to_keep = 0;
+
+    for (int i = 0; i < boxes_num; i++){
+        int nblock = i / THREADS_PER_BLOCK_NMS;
+        int inblock = i % THREADS_PER_BLOCK_NMS;
+
+        if (!(remv_cpu[nblock] & (1ULL << inblock))){
+            keep_data[num_to_keep++] = i;
+            unsigned long long *p = &mask_cpu[0] + i * col_blocks;
+            for (int j = nblock; j < col_blocks; j++){
+                remv_cpu[j] |= p[j];
+            }
+        }
+    }
+    if ( cudaSuccess != cudaGetLastError() ) printf( "Error!\n" );
+
+    return num_to_keep;
+}
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms.h
new file mode 100644
index 000000000..320202758
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms.h
@@ -0,0 +1,17 @@
+#ifndef IOU3D_NMS_H
+#define IOU3D_NMS_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <assert.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int boxes_aligned_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_overlap);
+int boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_overlap);
+int paired_boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_overlap);
+int boxes_iou_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b, at::Tensor ans_iou);
+int nms_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh);
+int nms_normal_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms_api.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms_api.cpp
new file mode 100644
index 000000000..972b55b5b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms_api.cpp
@@ -0,0 +1,20 @@
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+#include "iou3d_cpu.h"
+#include "iou3d_nms.h"
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("boxes_aligned_overlap_bev_gpu", &boxes_aligned_overlap_bev_gpu, "aligned oriented boxes overlap");
+	m.def("boxes_overlap_bev_gpu", &boxes_overlap_bev_gpu, "oriented boxes overlap");
+	m.def("paired_boxes_overlap_bev_gpu", &paired_boxes_overlap_bev_gpu, "oriented boxes overlap");
+	m.def("boxes_iou_bev_gpu", &boxes_iou_bev_gpu, "oriented boxes iou");
+	m.def("nms_gpu", &nms_gpu, "oriented nms gpu");
+	m.def("nms_normal_gpu", &nms_normal_gpu, "nms gpu");
+	m.def("boxes_aligned_iou_bev_cpu", &boxes_aligned_iou_bev_cpu, "aligned oriented boxes iou");
+	m.def("boxes_iou_bev_cpu", &boxes_iou_bev_cpu, "oriented boxes iou");
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu
new file mode 100644
index 000000000..23bdcbfa4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu
@@ -0,0 +1,464 @@
+/*
+3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others)
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <stdio.h>
+#define THREADS_PER_BLOCK 16
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+
+// #define DEBUG
+const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
+const float EPS = 1e-8;
+struct Point {
+    float x, y;
+    __device__ Point() {}
+    __device__ Point(float _x, float _y){
+        x = _x, y = _y;
+    }
+
+    __device__ void set(float _x, float _y){
+        x = _x; y = _y;
+    }
+
+    __device__ Point operator +(const Point &b)const{
+        return Point(x + b.x, y + b.y);
+    }
+
+    __device__ Point operator -(const Point &b)const{
+        return Point(x - b.x, y - b.y);
+    }
+};
+
+__device__ inline float cross(const Point &a, const Point &b){
+    return a.x * b.y - a.y * b.x;
+}
+
+__device__ inline float cross(const Point &p1, const Point &p2, const Point &p0){
+    return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y);
+}
+
+__device__ int check_rect_cross(const Point &p1, const Point &p2, const Point &q1, const Point &q2){
+    int ret = min(p1.x,p2.x) <= max(q1.x,q2.x)  &&
+              min(q1.x,q2.x) <= max(p1.x,p2.x) &&
+              min(p1.y,p2.y) <= max(q1.y,q2.y) &&
+              min(q1.y,q2.y) <= max(p1.y,p2.y);
+    return ret;
+}
+
+__device__ inline int check_in_box2d(const float *box, const Point &p){
+    //params: (7) [x, y, z, dx, dy, dz, heading]
+    const float MARGIN = 1e-2;
+
+    float center_x = box[0], center_y = box[1];
+    float angle_cos = cos(-box[6]), angle_sin = sin(-box[6]);  // rotate the point in the opposite direction of box
+    float rot_x = (p.x - center_x) * angle_cos + (p.y - center_y) * (-angle_sin);
+    float rot_y = (p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos;
+
+    return (fabs(rot_x) < box[3] / 2 + MARGIN && fabs(rot_y) < box[4] / 2 + MARGIN);
+}
+
+__device__ inline int intersection(const Point &p1, const Point &p0, const Point &q1, const Point &q0, Point &ans){
+    // fast exclusion
+    if (check_rect_cross(p0, p1, q0, q1) == 0) return 0;
+
+    // check cross standing
+    float s1 = cross(q0, p1, p0);
+    float s2 = cross(p1, q1, p0);
+    float s3 = cross(p0, q1, q0);
+    float s4 = cross(q1, p1, q0);
+
+    if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0;
+
+    // calculate intersection of two lines
+    float s5 = cross(q1, p1, p0);
+    if(fabs(s5 - s1) > EPS){
+        ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1);
+        ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1);
+
+    }
+    else{
+        float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y;
+        float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y;
+        float D = a0 * b1 - a1 * b0;
+
+        ans.x = (b0 * c1 - b1 * c0) / D;
+        ans.y = (a1 * c0 - a0 * c1) / D;
+    }
+
+    return 1;
+}
+
+__device__ inline void rotate_around_center(const Point &center, const float angle_cos, const float angle_sin, Point &p){
+    float new_x = (p.x - center.x) * angle_cos + (p.y - center.y) * (-angle_sin) + center.x;
+    float new_y = (p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y;
+    p.set(new_x, new_y);
+}
+
+__device__ inline int point_cmp(const Point &a, const Point &b, const Point &center){
+    return atan2(a.y - center.y, a.x - center.x) > atan2(b.y - center.y, b.x - center.x);
+}
+
+__device__ inline float box_overlap(const float *box_a, const float *box_b){
+    // params box_a: [x, y, z, dx, dy, dz, heading]
+    // params box_b: [x, y, z, dx, dy, dz, heading]
+
+    float a_angle = box_a[6], b_angle = box_b[6];
+    float a_dx_half = box_a[3] / 2, b_dx_half = box_b[3] / 2, a_dy_half = box_a[4] / 2, b_dy_half = box_b[4] / 2;
+    float a_x1 = box_a[0] - a_dx_half, a_y1 = box_a[1] - a_dy_half;
+    float a_x2 = box_a[0] + a_dx_half, a_y2 = box_a[1] + a_dy_half;
+    float b_x1 = box_b[0] - b_dx_half, b_y1 = box_b[1] - b_dy_half;
+    float b_x2 = box_b[0] + b_dx_half, b_y2 = box_b[1] + b_dy_half;
+
+    Point center_a(box_a[0], box_a[1]);
+    Point center_b(box_b[0], box_b[1]);
+
+#ifdef DEBUG
+    printf("a: (%.3f, %.3f, %.3f, %.3f, %.3f), b: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", a_x1, a_y1, a_x2, a_y2, a_angle,
+           b_x1, b_y1, b_x2, b_y2, b_angle);
+    printf("center a: (%.3f, %.3f), b: (%.3f, %.3f)\n", center_a.x, center_a.y, center_b.x, center_b.y);
+#endif
+
+    Point box_a_corners[5];
+    box_a_corners[0].set(a_x1, a_y1);
+    box_a_corners[1].set(a_x2, a_y1);
+    box_a_corners[2].set(a_x2, a_y2);
+    box_a_corners[3].set(a_x1, a_y2);
+
+    Point box_b_corners[5];
+    box_b_corners[0].set(b_x1, b_y1);
+    box_b_corners[1].set(b_x2, b_y1);
+    box_b_corners[2].set(b_x2, b_y2);
+    box_b_corners[3].set(b_x1, b_y2);
+
+    // get oriented corners
+    float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle);
+    float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle);
+
+    for (int k = 0; k < 4; k++){
+#ifdef DEBUG
+        printf("before corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
+#endif
+        rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]);
+        rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]);
+#ifdef DEBUG
+        printf("corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
+#endif
+    }
+
+    box_a_corners[4] = box_a_corners[0];
+    box_b_corners[4] = box_b_corners[0];
+
+    // get intersection of lines
+    Point cross_points[16];
+    Point poly_center;
+    int cnt = 0, flag = 0;
+
+    poly_center.set(0, 0);
+    for (int i = 0; i < 4; i++){
+        for (int j = 0; j < 4; j++){
+            flag = intersection(box_a_corners[i + 1], box_a_corners[i], box_b_corners[j + 1], box_b_corners[j], cross_points[cnt]);
+            if (flag){
+                poly_center = poly_center + cross_points[cnt];
+                cnt++;
+#ifdef DEBUG
+                printf("Cross points (%.3f, %.3f): a(%.3f, %.3f)->(%.3f, %.3f), b(%.3f, %.3f)->(%.3f, %.3f) \n",
+                    cross_points[cnt - 1].x, cross_points[cnt - 1].y,
+                    box_a_corners[i].x, box_a_corners[i].y, box_a_corners[i + 1].x, box_a_corners[i + 1].y,
+                    box_b_corners[i].x, box_b_corners[i].y, box_b_corners[i + 1].x, box_b_corners[i + 1].y);
+#endif
+            }
+        }
+    }
+
+    // check corners
+    for (int k = 0; k < 4; k++){
+        if (check_in_box2d(box_a, box_b_corners[k])){
+            poly_center = poly_center + box_b_corners[k];
+            cross_points[cnt] = box_b_corners[k];
+            cnt++;
+#ifdef DEBUG
+                printf("b corners in a: corner_b(%.3f, %.3f)", cross_points[cnt - 1].x, cross_points[cnt - 1].y);
+#endif
+        }
+        if (check_in_box2d(box_b, box_a_corners[k])){
+            poly_center = poly_center + box_a_corners[k];
+            cross_points[cnt] = box_a_corners[k];
+            cnt++;
+#ifdef DEBUG
+                printf("a corners in b: corner_a(%.3f, %.3f)", cross_points[cnt - 1].x, cross_points[cnt - 1].y);
+#endif
+        }
+    }
+
+    poly_center.x /= cnt;
+    poly_center.y /= cnt;
+
+    // sort the points of polygon
+    Point temp;
+    for (int j = 0; j < cnt - 1; j++){
+        for (int i = 0; i < cnt - j - 1; i++){
+            if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)){
+                temp = cross_points[i];
+                cross_points[i] = cross_points[i + 1];
+                cross_points[i + 1] = temp;
+            }
+        }
+    }
+
+#ifdef DEBUG
+    printf("cnt=%d\n", cnt);
+    for (int i = 0; i < cnt; i++){
+        printf("All cross point %d: (%.3f, %.3f)\n", i, cross_points[i].x, cross_points[i].y);
+    }
+#endif
+
+    // get the overlap areas
+    float area = 0;
+    for (int k = 0; k < cnt - 1; k++){
+        area += cross(cross_points[k] - cross_points[0], cross_points[k + 1] - cross_points[0]);
+    }
+
+    return fabs(area) / 2.0;
+}
+
+__device__ inline float iou_bev(const float *box_a, const float *box_b){
+    // params box_a: [x, y, z, dx, dy, dz, heading]
+    // params box_b: [x, y, z, dx, dy, dz, heading]
+    float sa = box_a[3] * box_a[4];
+    float sb = box_b[3] * box_b[4];
+    float s_overlap = box_overlap(box_a, box_b);
+    return s_overlap / fmaxf(sa + sb - s_overlap, EPS);
+}
+
+__global__ void boxes_overlap_kernel(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+    const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
+    const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+
+    if (a_idx >= num_a || b_idx >= num_b){
+        return;
+    }
+    const float * cur_box_a = boxes_a + a_idx * 7;
+    const float * cur_box_b = boxes_b + b_idx * 7;
+    float s_overlap = box_overlap(cur_box_a, cur_box_b);
+    ans_overlap[a_idx * num_b + b_idx] = s_overlap;
+}
+
+__global__ void paired_boxes_overlap_kernel(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+    const int idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+
+    if (idx >= num_a){
+        return;
+    }
+    const float * cur_box_a = boxes_a + idx * 7;
+    const float * cur_box_b = boxes_b + idx * 7;
+    float s_overlap = box_overlap(cur_box_a, cur_box_b);
+    // printf("idx=%d, box_a=(%.3f, %.3f, %.3f, ), box_b=(%.3f, %.3f, %.3f, ), overlap=%.5f\n", idx, cur_box_a[0], cur_box_a[1], cur_box_a[2], cur_box_b[0], cur_box_b[1], cur_box_b[2], s_overlap);
+    ans_overlap[idx] = s_overlap;
+}
+
+__global__ void boxes_aligned_overlap_kernel(const int num_box, const float *boxes_a, const float *boxes_b, float *ans_overlap){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+    const int idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+    if (idx >= num_box){
+        return;
+    }
+    const float * cur_box_a = boxes_a + idx * 7;
+    const float * cur_box_b = boxes_b + idx * 7;
+    float s_overlap = box_overlap(cur_box_a, cur_box_b);
+    ans_overlap[idx] = s_overlap;
+}
+
+__global__ void boxes_iou_bev_kernel(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_iou){
+    // params boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+    // params boxes_b: (M, 7) [x, y, z, dx, dy, dz, heading]
+    const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
+    const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+
+    if (a_idx >= num_a || b_idx >= num_b){
+        return;
+    }
+
+    const float * cur_box_a = boxes_a + a_idx * 7;
+    const float * cur_box_b = boxes_b + b_idx * 7;
+    float cur_iou_bev = iou_bev(cur_box_a, cur_box_b);
+    ans_iou[a_idx * num_b + b_idx] = cur_iou_bev;
+}
+
+__global__ void nms_kernel(const int boxes_num, const float nms_overlap_thresh,
+                           const float *boxes, unsigned long long *mask){
+    //params: boxes (N, 7) [x, y, z, dx, dy, dz, heading]
+    //params: mask (N, N/THREADS_PER_BLOCK_NMS)
+
+    const int row_start = blockIdx.y;
+    const int col_start = blockIdx.x;
+
+    // if (row_start > col_start) return;
+
+    const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
+    const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
+
+    __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 7];
+
+    if (threadIdx.x < col_size) {
+        block_boxes[threadIdx.x * 7 + 0] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 0];
+        block_boxes[threadIdx.x * 7 + 1] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 1];
+        block_boxes[threadIdx.x * 7 + 2] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 2];
+        block_boxes[threadIdx.x * 7 + 3] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 3];
+        block_boxes[threadIdx.x * 7 + 4] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 4];
+        block_boxes[threadIdx.x * 7 + 5] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 5];
+        block_boxes[threadIdx.x * 7 + 6] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 6];
+    }
+    __syncthreads();
+
+    if (threadIdx.x < row_size) {
+        const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
+        const float *cur_box = boxes + cur_box_idx * 7;
+
+        int i = 0;
+        unsigned long long t = 0;
+        int start = 0;
+        if (row_start == col_start) {
+          start = threadIdx.x + 1;
+        }
+        for (i = start; i < col_size; i++) {
+            if (iou_bev(cur_box, block_boxes + i * 7) > nms_overlap_thresh){
+                t |= 1ULL << i;
+            }
+        }
+        const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+        mask[cur_box_idx * col_blocks + col_start] = t;
+    }
+}
+
+
+__device__ inline float iou_normal(float const * const a, float const * const b) {
+    //params: a: [x, y, z, dx, dy, dz, heading]
+    //params: b: [x, y, z, dx, dy, dz, heading]
+
+    float left = fmaxf(a[0] - a[3] / 2, b[0] - b[3] / 2), right = fminf(a[0] + a[3] / 2, b[0] + b[3] / 2);
+    float top = fmaxf(a[1] - a[4] / 2, b[1] - b[4] / 2), bottom = fminf(a[1] + a[4] / 2, b[1] + b[4] / 2);
+    float width = fmaxf(right - left, 0.f), height = fmaxf(bottom - top, 0.f);
+    float interS = width * height;
+    float Sa = a[3] * a[4];
+    float Sb = b[3] * b[4];
+    return interS / fmaxf(Sa + Sb - interS, EPS);
+}
+
+
+__global__ void nms_normal_kernel(const int boxes_num, const float nms_overlap_thresh,
+                           const float *boxes, unsigned long long *mask){
+    //params: boxes (N, 7) [x, y, z, dx, dy, dz, heading]
+    //params: mask (N, N/THREADS_PER_BLOCK_NMS)
+
+    const int row_start = blockIdx.y;
+    const int col_start = blockIdx.x;
+
+    // if (row_start > col_start) return;
+
+    const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
+    const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
+
+    __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 7];
+
+    if (threadIdx.x < col_size) {
+        block_boxes[threadIdx.x * 7 + 0] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 0];
+        block_boxes[threadIdx.x * 7 + 1] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 1];
+        block_boxes[threadIdx.x * 7 + 2] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 2];
+        block_boxes[threadIdx.x * 7 + 3] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 3];
+        block_boxes[threadIdx.x * 7 + 4] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 4];
+        block_boxes[threadIdx.x * 7 + 5] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 5];
+        block_boxes[threadIdx.x * 7 + 6] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 7 + 6];
+    }
+    __syncthreads();
+
+    if (threadIdx.x < row_size) {
+        const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
+        const float *cur_box = boxes + cur_box_idx * 7;
+
+        int i = 0;
+        unsigned long long t = 0;
+        int start = 0;
+        if (row_start == col_start) {
+          start = threadIdx.x + 1;
+        }
+        for (i = start; i < col_size; i++) {
+            if (iou_normal(cur_box, block_boxes + i * 7) > nms_overlap_thresh){
+                t |= 1ULL << i;
+            }
+        }
+        const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+        mask[cur_box_idx * col_blocks + col_start] = t;
+    }
+}
+
+
+
+
+
+void boxesoverlapLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap){
+
+    dim3 blocks(DIVUP(num_b, THREADS_PER_BLOCK), DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
+
+    boxes_overlap_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b, ans_overlap);
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+void PairedBoxesOverlapLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap){
+
+    dim3 blocks(DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    paired_boxes_overlap_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b, ans_overlap);
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+void boxesalignedoverlapLauncher(const int num_box, const float *boxes_a, const float *boxes_b, float *ans_overlap){
+
+    dim3 blocks(DIVUP(num_box, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    boxes_aligned_overlap_kernel<<<blocks, threads>>>(num_box, boxes_a, boxes_b, ans_overlap);
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_iou){
+
+    dim3 blocks(DIVUP(num_b, THREADS_PER_BLOCK), DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
+
+    boxes_iou_bev_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b, ans_iou);
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+
+void nmsLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh){
+    dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
+                DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
+    dim3 threads(THREADS_PER_BLOCK_NMS);
+    nms_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
+}
+
+
+void nmsNormalLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh){
+    dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
+                DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
+    dim3 threads(THREADS_PER_BLOCK_NMS);
+    nms_normal_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_modules.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_modules.py
new file mode 100644
index 000000000..781a1726d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_modules.py
@@ -0,0 +1,174 @@
+from typing import List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from . import pointnet2_utils
+
+
+class _PointnetSAModuleBase(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.npoint = None
+        self.groupers = None
+        self.mlps = None
+        self.pool_method = 'max_pool'
+
+    def forward(self, xyz: torch.Tensor, features: torch.Tensor = None, new_xyz=None) -> (torch.Tensor, torch.Tensor):
+        """
+        :param xyz: (B, N, 3) tensor of the xyz coordinates of the features
+        :param features: (B, N, C) tensor of the descriptors of the the features
+        :param new_xyz:
+        :return:
+            new_xyz: (B, npoint, 3) tensor of the new features' xyz
+            new_features: (B, npoint, \sum_k(mlps[k][-1])) tensor of the new_features descriptors
+        """
+        new_features_list = []
+
+        xyz_flipped = xyz.transpose(1, 2).contiguous()
+        if new_xyz is None:
+            new_xyz = pointnet2_utils.gather_operation(
+                xyz_flipped,
+                pointnet2_utils.farthest_point_sample(xyz, self.npoint)
+            ).transpose(1, 2).contiguous() if self.npoint is not None else None
+
+        for i in range(len(self.groupers)):
+            new_features = self.groupers[i](xyz, new_xyz, features)  # (B, C, npoint, nsample)
+
+            new_features = self.mlps[i](new_features)  # (B, mlp[-1], npoint, nsample)
+            if self.pool_method == 'max_pool':
+                new_features = F.max_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                )  # (B, mlp[-1], npoint, 1)
+            elif self.pool_method == 'avg_pool':
+                new_features = F.avg_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                )  # (B, mlp[-1], npoint, 1)
+            else:
+                raise NotImplementedError
+
+            new_features = new_features.squeeze(-1)  # (B, mlp[-1], npoint)
+            new_features_list.append(new_features)
+
+        return new_xyz, torch.cat(new_features_list, dim=1)
+
+
+class PointnetSAModuleMSG(_PointnetSAModuleBase):
+    """Pointnet set abstraction layer with multiscale grouping"""
+
+    def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True,
+                 use_xyz: bool = True, pool_method='max_pool'):
+        """
+        :param npoint: int
+        :param radii: list of float, list of radii to group with
+        :param nsamples: list of int, number of samples in each ball query
+        :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale
+        :param bn: whether to use batchnorm
+        :param use_xyz:
+        :param pool_method: max_pool / avg_pool
+        """
+        super().__init__()
+
+        assert len(radii) == len(nsamples) == len(mlps)
+
+        self.npoint = npoint
+        self.groupers = nn.ModuleList()
+        self.mlps = nn.ModuleList()
+        for i in range(len(radii)):
+            radius = radii[i]
+            nsample = nsamples[i]
+            self.groupers.append(
+                pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
+                if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
+            )
+            mlp_spec = mlps[i]
+            if use_xyz:
+                mlp_spec[0] += 3
+
+            shared_mlps = []
+            for k in range(len(mlp_spec) - 1):
+                shared_mlps.extend([
+                    nn.Conv2d(mlp_spec[k], mlp_spec[k + 1], kernel_size=1, bias=False),
+                    nn.BatchNorm2d(mlp_spec[k + 1]),
+                    nn.ReLU()
+                ])
+            self.mlps.append(nn.Sequential(*shared_mlps))
+
+        self.pool_method = pool_method
+
+
+class PointnetSAModule(PointnetSAModuleMSG):
+    """Pointnet set abstraction layer"""
+
+    def __init__(self, *, mlp: List[int], npoint: int = None, radius: float = None, nsample: int = None,
+                 bn: bool = True, use_xyz: bool = True, pool_method='max_pool'):
+        """
+        :param mlp: list of int, spec of the pointnet before the global max_pool
+        :param npoint: int, number of features
+        :param radius: float, radius of ball
+        :param nsample: int, number of samples in the ball query
+        :param bn: whether to use batchnorm
+        :param use_xyz:
+        :param pool_method: max_pool / avg_pool
+        """
+        super().__init__(
+            mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], bn=bn, use_xyz=use_xyz,
+            pool_method=pool_method
+        )
+
+
+class PointnetFPModule(nn.Module):
+    r"""Propigates the features of one set to another"""
+
+    def __init__(self, *, mlp: List[int], bn: bool = True):
+        """
+        :param mlp: list of int
+        :param bn: whether to use batchnorm
+        """
+        super().__init__()
+
+        shared_mlps = []
+        for k in range(len(mlp) - 1):
+            shared_mlps.extend([
+                nn.Conv2d(mlp[k], mlp[k + 1], kernel_size=1, bias=False),
+                nn.BatchNorm2d(mlp[k + 1]),
+                nn.ReLU()
+            ])
+        self.mlp = nn.Sequential(*shared_mlps)
+
+    def forward(
+            self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        :param unknown: (B, n, 3) tensor of the xyz positions of the unknown features
+        :param known: (B, m, 3) tensor of the xyz positions of the known features
+        :param unknow_feats: (B, C1, n) tensor of the features to be propigated to
+        :param known_feats: (B, C2, m) tensor of features to be propigated
+        :return:
+            new_features: (B, mlp[-1], n) tensor of the features of the unknown features
+        """
+        if known is not None:
+            dist, idx = pointnet2_utils.three_nn(unknown, known)
+            dist_recip = 1.0 / (dist + 1e-8)
+            norm = torch.sum(dist_recip, dim=2, keepdim=True)
+            weight = dist_recip / norm
+
+            interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight)
+        else:
+            interpolated_feats = known_feats.expand(*known_feats.size()[0:2], unknown.size(1))
+
+        if unknow_feats is not None:
+            new_features = torch.cat([interpolated_feats, unknow_feats], dim=1)  # (B, C2 + C1, n)
+        else:
+            new_features = interpolated_feats
+
+        new_features = new_features.unsqueeze(-1)
+        new_features = self.mlp(new_features)
+
+        return new_features.squeeze(-1)
+
+
+if __name__ == "__main__":
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_utils.py
new file mode 100644
index 000000000..c57afe15c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_utils.py
@@ -0,0 +1,290 @@
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+from torch.autograd import Function, Variable
+
+from . import pointnet2_batch_cuda as pointnet2
+
+
+class FarthestPointSampling(Function):
+    @staticmethod
+    def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor:
+        """
+        Uses iterative farthest point sampling to select a set of npoint features that have the largest
+        minimum distance
+        :param ctx:
+        :param xyz: (B, N, 3) where N > npoint
+        :param npoint: int, number of features in the sampled set
+        :return:
+             output: (B, npoint) tensor containing the set
+        """
+        assert xyz.is_contiguous()
+
+        B, N, _ = xyz.size()
+        output = torch.cuda.IntTensor(B, npoint)
+        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
+
+        pointnet2.farthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
+        return output
+
+    @staticmethod
+    def backward(xyz, a=None):
+        return None, None
+
+
+farthest_point_sample = furthest_point_sample = FarthestPointSampling.apply
+
+
+class GatherOperation(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param features: (B, C, N)
+        :param idx: (B, npoint) index tensor of the features to gather
+        :return:
+            output: (B, C, npoint)
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+
+        B, npoint = idx.size()
+        _, C, N = features.size()
+        output = torch.cuda.FloatTensor(B, C, npoint)
+
+        pointnet2.gather_points_wrapper(B, C, N, npoint, features, idx, output)
+
+        ctx.for_backwards = (idx, C, N)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        idx, C, N = ctx.for_backwards
+        B, npoint = idx.size()
+
+        grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
+        grad_out_data = grad_out.data.contiguous()
+        pointnet2.gather_points_grad_wrapper(B, C, N, npoint, grad_out_data, idx, grad_features.data)
+        return grad_features, None
+
+
+gather_operation = GatherOperation.apply
+
+
+class ThreeNN(Function):
+
+    @staticmethod
+    def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Find the three nearest neighbors of unknown in known
+        :param ctx:
+        :param unknown: (B, N, 3)
+        :param known: (B, M, 3)
+        :return:
+            dist: (B, N, 3) l2 distance to the three nearest neighbors
+            idx: (B, N, 3) index of 3 nearest neighbors
+        """
+        assert unknown.is_contiguous()
+        assert known.is_contiguous()
+
+        B, N, _ = unknown.size()
+        m = known.size(1)
+        dist2 = torch.cuda.FloatTensor(B, N, 3)
+        idx = torch.cuda.IntTensor(B, N, 3)
+
+        pointnet2.three_nn_wrapper(B, N, m, unknown, known, dist2, idx)
+        return torch.sqrt(dist2), idx
+
+    @staticmethod
+    def backward(ctx, a=None, b=None):
+        return None, None
+
+
+three_nn = ThreeNN.apply
+
+
+class ThreeInterpolate(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
+        """
+        Performs weight linear interpolation on 3 features
+        :param ctx:
+        :param features: (B, C, M) Features descriptors to be interpolated from
+        :param idx: (B, n, 3) three nearest neighbors of the target features in features
+        :param weight: (B, n, 3) weights
+        :return:
+            output: (B, C, N) tensor of the interpolated features
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+        assert weight.is_contiguous()
+
+        B, c, m = features.size()
+        n = idx.size(1)
+        ctx.three_interpolate_for_backward = (idx, weight, m)
+        output = torch.cuda.FloatTensor(B, c, n)
+
+        pointnet2.three_interpolate_wrapper(B, c, m, n, features, idx, weight, output)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        :param ctx:
+        :param grad_out: (B, C, N) tensor with gradients of outputs
+        :return:
+            grad_features: (B, C, M) tensor with gradients of features
+            None:
+            None:
+        """
+        idx, weight, m = ctx.three_interpolate_for_backward
+        B, c, n = grad_out.size()
+
+        grad_features = Variable(torch.cuda.FloatTensor(B, c, m).zero_())
+        grad_out_data = grad_out.data.contiguous()
+
+        pointnet2.three_interpolate_grad_wrapper(B, c, n, m, grad_out_data, idx, weight, grad_features.data)
+        return grad_features, None, None
+
+
+three_interpolate = ThreeInterpolate.apply
+
+
+class GroupingOperation(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param features: (B, C, N) tensor of features to group
+        :param idx: (B, npoint, nsample) tensor containing the indicies of features to group with
+        :return:
+            output: (B, C, npoint, nsample) tensor
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+
+        B, nfeatures, nsample = idx.size()
+        _, C, N = features.size()
+        output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)
+
+        pointnet2.group_points_wrapper(B, C, N, nfeatures, nsample, features, idx, output)
+
+        ctx.for_backwards = (idx, N)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        :param ctx:
+        :param grad_out: (B, C, npoint, nsample) tensor of the gradients of the output from forward
+        :return:
+            grad_features: (B, C, N) gradient of the features
+        """
+        idx, N = ctx.for_backwards
+
+        B, C, npoint, nsample = grad_out.size()
+        grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
+
+        grad_out_data = grad_out.data.contiguous()
+        pointnet2.group_points_grad_wrapper(B, C, N, npoint, nsample, grad_out_data, idx, grad_features.data)
+        return grad_features, None
+
+
+grouping_operation = GroupingOperation.apply
+
+
+class BallQuery(Function):
+
+    @staticmethod
+    def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param radius: float, radius of the balls
+        :param nsample: int, maximum number of features in the balls
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: (B, npoint, 3) centers of the ball query
+        :return:
+            idx: (B, npoint, nsample) tensor with the indicies of the features that form the query balls
+        """
+        assert new_xyz.is_contiguous()
+        assert xyz.is_contiguous()
+
+        B, N, _ = xyz.size()
+        npoint = new_xyz.size(1)
+        idx = torch.cuda.IntTensor(B, npoint, nsample).zero_()
+
+        pointnet2.ball_query_wrapper(B, N, npoint, radius, nsample, new_xyz, xyz, idx)
+        return idx
+
+    @staticmethod
+    def backward(ctx, a=None):
+        return None, None, None, None
+
+
+ball_query = BallQuery.apply
+
+
+class QueryAndGroup(nn.Module):
+    def __init__(self, radius: float, nsample: int, use_xyz: bool = True):
+        """
+        :param radius: float, radius of ball
+        :param nsample: int, maximum number of features to gather in the ball
+        :param use_xyz:
+        """
+        super().__init__()
+        self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
+
+    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None) -> Tuple[torch.Tensor]:
+        """
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: (B, npoint, 3) centroids
+        :param features: (B, C, N) descriptors of the features
+        :return:
+            new_features: (B, 3 + C, npoint, nsample)
+        """
+        idx = ball_query(self.radius, self.nsample, xyz, new_xyz)
+        xyz_trans = xyz.transpose(1, 2).contiguous()
+        grouped_xyz = grouping_operation(xyz_trans, idx)  # (B, 3, npoint, nsample)
+        grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1)
+
+        if features is not None:
+            grouped_features = grouping_operation(features, idx)
+            if self.use_xyz:
+                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, C + 3, npoint, nsample)
+            else:
+                new_features = grouped_features
+        else:
+            assert self.use_xyz, "Cannot have not features and not use xyz as a feature!"
+            new_features = grouped_xyz
+
+        return new_features
+
+
+class GroupAll(nn.Module):
+    def __init__(self, use_xyz: bool = True):
+        super().__init__()
+        self.use_xyz = use_xyz
+
+    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None):
+        """
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: ignored
+        :param features: (B, C, N) descriptors of the features
+        :return:
+            new_features: (B, C + 3, 1, N)
+        """
+        grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
+        if features is not None:
+            grouped_features = features.unsqueeze(2)
+            if self.use_xyz:
+                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, 3 + C, 1, N)
+            else:
+                new_features = grouped_features
+        else:
+            new_features = grouped_xyz
+
+        return new_features
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query.cpp
new file mode 100644
index 000000000..c0e2d8fea
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query.cpp
@@ -0,0 +1,39 @@
+/*
+batch version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "ball_query_gpu.h"
+
+#define CHECK_CUDA(x) do { \
+	  if (!x.type().is_cuda()) { \
+		      fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+		      exit(-1); \
+		    } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+	  if (!x.is_contiguous()) { \
+		      fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+		      exit(-1); \
+		    } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int ball_query_wrapper_fast(int b, int n, int m, float radius, int nsample, 
+    at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor) {
+    CHECK_INPUT(new_xyz_tensor);
+    CHECK_INPUT(xyz_tensor);
+    const float *new_xyz = new_xyz_tensor.data<float>();
+    const float *xyz = xyz_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+    
+    ball_query_kernel_launcher_fast(b, n, m, radius, nsample, new_xyz, xyz, idx);
+    return 1;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query_gpu.cu
new file mode 100644
index 000000000..38c006369
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query_gpu.cu
@@ -0,0 +1,73 @@
+/*
+batch version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "ball_query_gpu.h"
+#include "cuda_utils.h"
+
+
+__global__ void ball_query_kernel_fast(int b, int n, int m, float radius, int nsample, 
+    const float *__restrict__ new_xyz, const float *__restrict__ xyz, int *__restrict__ idx) {
+    // new_xyz: (B, M, 3)
+    // xyz: (B, N, 3)
+    // output:
+    //      idx: (B, M, nsample)
+    int bs_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || pt_idx >= m) return;
+
+    new_xyz += bs_idx * m * 3 + pt_idx * 3;
+    xyz += bs_idx * n * 3;
+    idx += bs_idx * m * nsample + pt_idx * nsample;
+
+    float radius2 = radius * radius;
+    float new_x = new_xyz[0];
+    float new_y = new_xyz[1];
+    float new_z = new_xyz[2];
+
+    int cnt = 0;
+    for (int k = 0; k < n; ++k) {
+        float x = xyz[k * 3 + 0];
+        float y = xyz[k * 3 + 1];
+        float z = xyz[k * 3 + 2];
+        float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);
+        if (d2 < radius2){
+            if (cnt == 0){
+                for (int l = 0; l < nsample; ++l) {
+                    idx[l] = k;
+                }
+            }
+            idx[cnt] = k;
+            ++cnt;
+            if (cnt >= nsample) break;
+        }
+    }
+}
+
+
+void ball_query_kernel_launcher_fast(int b, int n, int m, float radius, int nsample, \
+    const float *new_xyz, const float *xyz, int *idx) {
+    // new_xyz: (B, M, 3)
+    // xyz: (B, N, 3)
+    // output:
+    //      idx: (B, M, nsample)
+
+    cudaError_t err;
+
+    dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    ball_query_kernel_fast<<<blocks, threads>>>(b, n, m, radius, nsample, new_xyz, xyz, idx);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query_gpu.h
new file mode 100644
index 000000000..1213dda79
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/ball_query_gpu.h
@@ -0,0 +1,15 @@
+#ifndef _BALL_QUERY_GPU_H
+#define _BALL_QUERY_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int ball_query_wrapper_fast(int b, int n, int m, float radius, int nsample, 
+	at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor);
+
+void ball_query_kernel_launcher_fast(int b, int n, int m, float radius, int nsample, 
+	const float *xyz, const float *new_xyz, int *idx);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/cuda_utils.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/cuda_utils.h
new file mode 100644
index 000000000..e2716b8e7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/cuda_utils.h
@@ -0,0 +1,16 @@
+#ifndef _CUDA_UTILS_H
+#define _CUDA_UTILS_H
+
+#include <cmath>
+#include <algorithm>
+
+#define TOTAL_THREADS 1024
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+
+inline int opt_n_threads(int work_size) {
+    const int pow_2 = std::log(static_cast<float>(work_size)) / std::log(2.0);
+
+    return std::max(std::min(1 << pow_2, TOTAL_THREADS), 1);
+}
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points.cpp
new file mode 100644
index 000000000..9735ae88d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points.cpp
@@ -0,0 +1,36 @@
+/*
+batch version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <vector>
+#include "group_points_gpu.h"
+
+
+int group_points_grad_wrapper_fast(int b, int c, int n, int npoints, int nsample, 
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) {
+
+    float *grad_points = grad_points_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    const float *grad_out = grad_out_tensor.data<float>();
+
+    group_points_grad_kernel_launcher_fast(b, c, n, npoints, nsample, grad_out, idx, grad_points);
+    return 1;
+}
+
+
+int group_points_wrapper_fast(int b, int c, int n, int npoints, int nsample, 
+    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor) {
+
+    const float *points = points_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *out = out_tensor.data<float>();
+
+    group_points_kernel_launcher_fast(b, c, n, npoints, nsample, points, idx, out);
+    return 1;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points_gpu.cu
new file mode 100644
index 000000000..d9038f69d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points_gpu.cu
@@ -0,0 +1,92 @@
+/*
+batch version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "group_points_gpu.h"
+
+
+__global__ void group_points_grad_kernel_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) {
+    // grad_out: (B, C, npoints, nsample)
+    // idx: (B, npoints, nsample)
+    // output:
+    //      grad_points: (B, C, N)
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int pt_idx = index / nsample;
+    if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
+
+    int sample_idx = index % nsample;
+    grad_out += bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx;
+    idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx; 
+    
+    atomicAdd(grad_points + bs_idx * c * n + c_idx * n + idx[0] , grad_out[0]);
+}
+
+void group_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *grad_out, const int *idx, float *grad_points) {
+    // grad_out: (B, C, npoints, nsample)
+    // idx: (B, npoints, nsample)
+    // output:
+    //      grad_points: (B, C, N)
+    cudaError_t err;
+    dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    group_points_grad_kernel_fast<<<blocks, threads>>>(b, c, n, npoints, nsample, grad_out, idx, grad_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void group_points_kernel_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) {
+    // points: (B, C, N)
+    // idx: (B, npoints, nsample)
+    // output:
+    //      out: (B, C, npoints, nsample)
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int pt_idx = index / nsample;
+    if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
+
+    int sample_idx = index % nsample;
+
+    idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx; 
+    int in_idx = bs_idx * c * n + c_idx * n + idx[0];
+    int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx;
+
+    out[out_idx] = points[in_idx];
+}
+
+
+void group_points_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *points, const int *idx, float *out) {
+    // points: (B, C, N)
+    // idx: (B, npoints, nsample)
+    // output:
+    //      out: (B, C, npoints, nsample)
+    cudaError_t err;
+    dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    group_points_kernel_fast<<<blocks, threads>>>(b, c, n, npoints, nsample, points, idx, out);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points_gpu.h
new file mode 100644
index 000000000..8a17c68bd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/group_points_gpu.h
@@ -0,0 +1,22 @@
+#ifndef _GROUP_POINTS_GPU_H
+#define _GROUP_POINTS_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <vector>
+
+
+int group_points_wrapper_fast(int b, int c, int n, int npoints, int nsample, 
+    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);
+
+void group_points_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *points, const int *idx, float *out);
+
+int group_points_grad_wrapper_fast(int b, int c, int n, int npoints, int nsample, 
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor);
+
+void group_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 
+    const float *grad_out, const int *idx, float *grad_points);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate.cpp
new file mode 100644
index 000000000..1c18e277d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate.cpp
@@ -0,0 +1,56 @@
+/*
+batch version of point interpolation, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "interpolate_gpu.h"
+
+
+void three_nn_wrapper_fast(int b, int n, int m, at::Tensor unknown_tensor, 
+    at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) {
+    const float *unknown = unknown_tensor.data<float>();
+    const float *known = known_tensor.data<float>();
+    float *dist2 = dist2_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+
+    three_nn_kernel_launcher_fast(b, n, m, unknown, known, dist2, idx);
+}
+
+
+void three_interpolate_wrapper_fast(int b, int c, int m, int n,
+                         at::Tensor points_tensor,
+                         at::Tensor idx_tensor,
+                         at::Tensor weight_tensor,
+                         at::Tensor out_tensor) {
+
+    const float *points = points_tensor.data<float>();
+    const float *weight = weight_tensor.data<float>();
+    float *out = out_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+
+    three_interpolate_kernel_launcher_fast(b, c, m, n, points, idx, weight, out);
+}
+
+
+void three_interpolate_grad_wrapper_fast(int b, int c, int n, int m,
+                            at::Tensor grad_out_tensor,
+                            at::Tensor idx_tensor,
+                            at::Tensor weight_tensor,
+                            at::Tensor grad_points_tensor) {
+
+    const float *grad_out = grad_out_tensor.data<float>();
+    const float *weight = weight_tensor.data<float>();
+    float *grad_points = grad_points_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+
+    three_interpolate_grad_kernel_launcher_fast(b, c, n, m, grad_out, idx, weight, grad_points);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate_gpu.cu
new file mode 100644
index 000000000..6922aeddc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate_gpu.cu
@@ -0,0 +1,169 @@
+/*
+batch version of point interpolation, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <float.h>
+
+#include "cuda_utils.h"
+#include "interpolate_gpu.h"
+
+
+__global__ void three_nn_kernel_fast(int b, int n, int m, const float *__restrict__ unknown, 
+    const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) {
+    // unknown: (B, N, 3)
+    // known: (B, M, 3)
+    // output: 
+    //      dist2: (B, N, 3)
+    //      idx: (B, N, 3)
+    
+    int bs_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || pt_idx >= n) return;
+
+    unknown += bs_idx * n * 3 + pt_idx * 3;
+    known += bs_idx * m * 3;
+    dist2 += bs_idx * n * 3 + pt_idx * 3;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+
+    float ux = unknown[0];
+    float uy = unknown[1];
+    float uz = unknown[2];
+
+    float best1 = FLT_MAX, best2 = FLT_MAX, best3 = FLT_MAX;
+    int besti1 = 0, besti2 = 0, besti3 = 0;
+    for (int k = 0; k < m; ++k) {
+        float x = known[k * 3 + 0];
+        float y = known[k * 3 + 1];
+        float z = known[k * 3 + 2];
+        float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
+        if (d < best1) {
+            best3 = best2; besti3 = besti2;
+            best2 = best1; besti2 = besti1;
+            best1 = d; besti1 = k;
+        } 
+        else if (d < best2) {
+            best3 = best2; besti3 = besti2;
+            best2 = d; besti2 = k;
+        } 
+        else if (d < best3) {
+            best3 = d; besti3 = k;
+        }
+    }
+    dist2[0] = best1; dist2[1] = best2; dist2[2] = best3;
+    idx[0] = besti1; idx[1] = besti2; idx[2] = besti3;
+}
+
+
+void three_nn_kernel_launcher_fast(int b, int n, int m, const float *unknown, 
+    const float *known, float *dist2, int *idx) {
+    // unknown: (B, N, 3)
+    // known: (B, M, 3)
+    // output: 
+    //      dist2: (B, N, 3)
+    //      idx: (B, N, 3)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    three_nn_kernel_fast<<<blocks, threads>>>(b, n, m, unknown, known, dist2, idx);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void three_interpolate_kernel_fast(int b, int c, int m, int n, const float *__restrict__ points, 
+    const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) {
+    // points: (B, C, M)
+    // idx: (B, N, 3)
+    // weight: (B, N, 3)
+    // output:
+    //      out: (B, C, N)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
+
+    weight += bs_idx * n * 3 + pt_idx * 3;
+    points += bs_idx * c * m + c_idx * m;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+    out += bs_idx * c * n + c_idx * n;
+
+    out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] + weight[2] * points[idx[2]];
+}
+
+void three_interpolate_kernel_launcher_fast(int b, int c, int m, int n, 
+    const float *points, const int *idx, const float *weight, float *out) {
+    // points: (B, C, M)
+    // idx: (B, N, 3)
+    // weight: (B, N, 3)
+    // output:
+    //      out: (B, C, N)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_kernel_fast<<<blocks, threads>>>(b, c, m, n, points, idx, weight, out);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void three_interpolate_grad_kernel_fast(int b, int c, int n, int m, const float *__restrict__ grad_out, 
+    const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) {
+    // grad_out: (B, C, N)
+    // weight: (B, N, 3)
+    // output:
+    //      grad_points: (B, C, M)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
+    
+    grad_out += bs_idx * c * n + c_idx * n + pt_idx;
+    weight += bs_idx * n * 3 + pt_idx * 3;
+    grad_points += bs_idx * c * m + c_idx * m;
+    idx += bs_idx * n * 3 + pt_idx * 3;
+
+
+    atomicAdd(grad_points + idx[0], grad_out[0] * weight[0]);
+    atomicAdd(grad_points + idx[1], grad_out[0] * weight[1]);
+    atomicAdd(grad_points + idx[2], grad_out[0] * weight[2]);
+}
+
+void three_interpolate_grad_kernel_launcher_fast(int b, int c, int n, int m, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_points) {
+    // grad_out: (B, C, N)
+    // weight: (B, N, 3)
+    // output:
+    //      grad_points: (B, C, M)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_grad_kernel_fast<<<blocks, threads>>>(b, c, n, m, grad_out, idx, weight, grad_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate_gpu.h
new file mode 100644
index 000000000..95ea1d799
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/interpolate_gpu.h
@@ -0,0 +1,30 @@
+#ifndef _INTERPOLATE_GPU_H
+#define _INTERPOLATE_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include<vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+
+void three_nn_wrapper_fast(int b, int n, int m, at::Tensor unknown_tensor, 
+  at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor);
+
+void three_nn_kernel_launcher_fast(int b, int n, int m, const float *unknown,
+	const float *known, float *dist2, int *idx);
+
+
+void three_interpolate_wrapper_fast(int b, int c, int m, int n, at::Tensor points_tensor, 
+    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor);
+
+void three_interpolate_kernel_launcher_fast(int b, int c, int m, int n, 
+    const float *points, const int *idx, const float *weight, float *out);
+
+
+void three_interpolate_grad_wrapper_fast(int b, int c, int n, int m, at::Tensor grad_out_tensor, 
+    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_points_tensor);
+
+void three_interpolate_grad_kernel_launcher_fast(int b, int c, int n, int m, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_points);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/pointnet2_api.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/pointnet2_api.cpp
new file mode 100644
index 000000000..284365061
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/pointnet2_api.cpp
@@ -0,0 +1,24 @@
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+
+#include "ball_query_gpu.h"
+#include "group_points_gpu.h"
+#include "sampling_gpu.h"
+#include "interpolate_gpu.h"
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("ball_query_wrapper", &ball_query_wrapper_fast, "ball_query_wrapper_fast");
+
+    m.def("group_points_wrapper", &group_points_wrapper_fast, "group_points_wrapper_fast");
+    m.def("group_points_grad_wrapper", &group_points_grad_wrapper_fast, "group_points_grad_wrapper_fast");
+
+    m.def("gather_points_wrapper", &gather_points_wrapper_fast, "gather_points_wrapper_fast");
+    m.def("gather_points_grad_wrapper", &gather_points_grad_wrapper_fast, "gather_points_grad_wrapper_fast");
+
+    m.def("farthest_point_sampling_wrapper", &farthest_point_sampling_wrapper, "farthest_point_sampling_wrapper");
+    
+    m.def("three_nn_wrapper", &three_nn_wrapper_fast, "three_nn_wrapper_fast");
+    m.def("three_interpolate_wrapper", &three_interpolate_wrapper_fast, "three_interpolate_wrapper_fast");
+    m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper_fast, "three_interpolate_grad_wrapper_fast");
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling.cpp
new file mode 100644
index 000000000..b00143036
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling.cpp
@@ -0,0 +1,46 @@
+/*
+batch version of point sampling and gathering, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <vector>
+#include "sampling_gpu.h"
+
+
+int gather_points_wrapper_fast(int b, int c, int n, int npoints, 
+    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor){
+    const float *points = points_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *out = out_tensor.data<float>();
+
+    gather_points_kernel_launcher_fast(b, c, n, npoints, points, idx, out);
+    return 1;
+}
+
+
+int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints, 
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) {
+
+    const float *grad_out = grad_out_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *grad_points = grad_points_tensor.data<float>();
+
+    gather_points_grad_kernel_launcher_fast(b, c, n, npoints, grad_out, idx, grad_points);
+    return 1;
+}
+
+
+int farthest_point_sampling_wrapper(int b, int n, int m,
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) {
+
+    const float *points = points_tensor.data<float>();
+    float *temp = temp_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+
+    farthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx);
+    return 1;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.cu
new file mode 100644
index 000000000..7aceca00e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.cu
@@ -0,0 +1,260 @@
+/*
+batch version of point sampling and gathering, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "sampling_gpu.h"
+
+
+__global__ void gather_points_kernel_fast(int b, int c, int n, int m, 
+    const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) {
+    // points: (B, C, N)
+    // idx: (B, M)
+    // output:
+    //      out: (B, C, M)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
+
+    out += bs_idx * c * m + c_idx * m + pt_idx;
+    idx += bs_idx * m + pt_idx;
+    points += bs_idx * c * n + c_idx * n;
+    out[0] = points[idx[0]];
+}
+
+void gather_points_kernel_launcher_fast(int b, int c, int n, int npoints, 
+    const float *points, const int *idx, float *out) {
+    // points: (B, C, N)
+    // idx: (B, npoints)
+    // output:
+    //      out: (B, C, npoints)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    gather_points_kernel_fast<<<blocks, threads>>>(b, c, n, npoints, points, idx, out);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+__global__ void gather_points_grad_kernel_fast(int b, int c, int n, int m, const float *__restrict__ grad_out, 
+    const int *__restrict__ idx, float *__restrict__ grad_points) {
+    // grad_out: (B, C, M)
+    // idx: (B, M)
+    // output:
+    //      grad_points: (B, C, N)
+
+    int bs_idx = blockIdx.z;
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
+
+    grad_out += bs_idx * c * m + c_idx * m + pt_idx;
+    idx += bs_idx * m + pt_idx;
+    grad_points += bs_idx * c * n + c_idx * n;
+
+    atomicAdd(grad_points + idx[0], grad_out[0]);
+}
+
+void gather_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, 
+    const float *grad_out, const int *idx, float *grad_points) {
+    // grad_out: (B, C, npoints)
+    // idx: (B, npoints)
+    // output:
+    //      grad_points: (B, C, N)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    gather_points_grad_kernel_fast<<<blocks, threads>>>(b, c, n, npoints, grad_out, idx, grad_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2){
+    const float v1 = dists[idx1], v2 = dists[idx2];
+    const int i1 = dists_i[idx1], i2 = dists_i[idx2];
+    dists[idx1] = max(v1, v2);
+    dists_i[idx1] = v2 > v1 ? i2 : i1;
+}
+
+template <unsigned int block_size>
+__global__ void farthest_point_sampling_kernel(int b, int n, int m,
+    const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) {
+    // dataset: (B, N, 3)
+    // tmp: (B, N)
+    // output:
+    //      idx: (B, M)
+
+    if (m <= 0) return;
+    __shared__ float dists[block_size];
+    __shared__ int dists_i[block_size];
+
+    int batch_index = blockIdx.x;
+    dataset += batch_index * n * 3;
+    temp += batch_index * n;
+    idxs += batch_index * m;
+
+    int tid = threadIdx.x;
+    const int stride = block_size;
+
+    int old = 0;
+    if (threadIdx.x == 0)
+    idxs[0] = old;
+
+    __syncthreads();
+    for (int j = 1; j < m; j++) {
+    int besti = 0;
+    float best = -1;
+    float x1 = dataset[old * 3 + 0];
+    float y1 = dataset[old * 3 + 1];
+    float z1 = dataset[old * 3 + 2];
+    for (int k = tid; k < n; k += stride) {
+        float x2, y2, z2;
+        x2 = dataset[k * 3 + 0];
+        y2 = dataset[k * 3 + 1];
+        z2 = dataset[k * 3 + 2];
+        // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
+        // if (mag <= 1e-3)
+        // continue;
+
+        float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
+        float d2 = min(d, temp[k]);
+        temp[k] = d2;
+        besti = d2 > best ? k : besti;
+        best = d2 > best ? d2 : best;
+    }
+    dists[tid] = best;
+    dists_i[tid] = besti;
+    __syncthreads();
+
+    if (block_size >= 1024) {
+        if (tid < 512) {
+            __update(dists, dists_i, tid, tid + 512);
+        }
+        __syncthreads();
+    }
+
+    if (block_size >= 512) {
+        if (tid < 256) {
+            __update(dists, dists_i, tid, tid + 256);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 256) {
+        if (tid < 128) {
+            __update(dists, dists_i, tid, tid + 128);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 128) {
+        if (tid < 64) {
+            __update(dists, dists_i, tid, tid + 64);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 64) {
+        if (tid < 32) {
+            __update(dists, dists_i, tid, tid + 32);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 32) {
+        if (tid < 16) {
+            __update(dists, dists_i, tid, tid + 16);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 16) {
+        if (tid < 8) {
+            __update(dists, dists_i, tid, tid + 8);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 8) {
+        if (tid < 4) {
+            __update(dists, dists_i, tid, tid + 4);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 4) {
+        if (tid < 2) {
+            __update(dists, dists_i, tid, tid + 2);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 2) {
+        if (tid < 1) {
+            __update(dists, dists_i, tid, tid + 1);
+        }
+        __syncthreads();
+    }
+
+    old = dists_i[0];
+    if (tid == 0)
+        idxs[j] = old;
+    }
+}
+
+void farthest_point_sampling_kernel_launcher(int b, int n, int m,
+    const float *dataset, float *temp, int *idxs) {
+    // dataset: (B, N, 3)
+    // tmp: (B, N)
+    // output:
+    //      idx: (B, M)
+
+    cudaError_t err;
+    unsigned int n_threads = opt_n_threads(n);
+
+    switch (n_threads) {
+        case 1024:
+        farthest_point_sampling_kernel<1024><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 512:
+        farthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 256:
+        farthest_point_sampling_kernel<256><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 128:
+        farthest_point_sampling_kernel<128><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 64:
+        farthest_point_sampling_kernel<64><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 32:
+        farthest_point_sampling_kernel<32><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 16:
+        farthest_point_sampling_kernel<16><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 8:
+        farthest_point_sampling_kernel<8><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 4:
+        farthest_point_sampling_kernel<4><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 2:
+        farthest_point_sampling_kernel<2><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 1:
+        farthest_point_sampling_kernel<1><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        default:
+        farthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs);
+    }
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.h
new file mode 100644
index 000000000..dc29476b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.h
@@ -0,0 +1,29 @@
+#ifndef _SAMPLING_GPU_H
+#define _SAMPLING_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <ATen/cuda/CUDAContext.h>
+#include<vector>
+
+
+int gather_points_wrapper_fast(int b, int c, int n, int npoints, 
+    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);
+
+void gather_points_kernel_launcher_fast(int b, int c, int n, int npoints, 
+    const float *points, const int *idx, float *out);
+
+
+int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints, 
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor);
+
+void gather_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, 
+    const float *grad_out, const int *idx, float *grad_points);
+
+
+int farthest_point_sampling_wrapper(int b, int n, int m,
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor);
+
+void farthest_point_sampling_kernel_launcher(int b, int n, int m,
+    const float *dataset, float *temp, int *idxs);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py
new file mode 100644
index 000000000..0210ab296
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py
@@ -0,0 +1,470 @@
+from typing import List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from . import pointnet2_utils
+
+
+def build_local_aggregation_module(input_channels, config):
+    local_aggregation_name = config.get('NAME', 'StackSAModuleMSG')
+
+    if local_aggregation_name == 'StackSAModuleMSG':
+        mlps = config.MLPS
+        for k in range(len(mlps)):
+            mlps[k] = [input_channels] + mlps[k]
+        cur_layer = StackSAModuleMSG(
+            radii=config.POOL_RADIUS, nsamples=config.NSAMPLE, mlps=mlps, use_xyz=True, pool_method='max_pool',
+        )
+        num_c_out = sum([x[-1] for x in mlps])
+    elif local_aggregation_name == 'VectorPoolAggregationModuleMSG':
+        cur_layer = VectorPoolAggregationModuleMSG(input_channels=input_channels, config=config)
+        num_c_out = config.MSG_POST_MLPS[-1]
+    else:
+        raise NotImplementedError
+
+    return cur_layer, num_c_out
+
+
+class StackSAModuleMSG(nn.Module):
+
+    def __init__(self, *, radii: List[float], nsamples: List[int], mlps: List[List[int]],
+                 use_xyz: bool = True, pool_method='max_pool'):
+        """
+        Args:
+            radii: list of float, list of radii to group with
+            nsamples: list of int, number of samples in each ball query
+            mlps: list of list of int, spec of the pointnet before the global pooling for each scale
+            use_xyz:
+            pool_method: max_pool / avg_pool
+        """
+        super().__init__()
+
+        assert len(radii) == len(nsamples) == len(mlps)
+
+        self.groupers = nn.ModuleList()
+        self.mlps = nn.ModuleList()
+        for i in range(len(radii)):
+            radius = radii[i]
+            nsample = nsamples[i]
+            self.groupers.append(pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz))
+            mlp_spec = mlps[i]
+            if use_xyz:
+                mlp_spec[0] += 3
+
+            shared_mlps = []
+            for k in range(len(mlp_spec) - 1):
+                shared_mlps.extend([
+                    nn.Conv2d(mlp_spec[k], mlp_spec[k + 1], kernel_size=1, bias=False),
+                    nn.BatchNorm2d(mlp_spec[k + 1]),
+                    nn.ReLU()
+                ])
+            self.mlps.append(nn.Sequential(*shared_mlps))
+        self.pool_method = pool_method
+
+        self.init_weights()
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            if isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1.0)
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt, features=None, empty_voxel_set_zeros=True):
+        """
+        :param xyz: (N1 + N2 ..., 3) tensor of the xyz coordinates of the features
+        :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+        :param new_xyz: (M1 + M2 ..., 3)
+        :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+        :param features: (N1 + N2 ..., C) tensor of the descriptors of the the features
+        :return:
+            new_xyz: (M1 + M2 ..., 3) tensor of the new features' xyz
+            new_features: (M1 + M2 ..., \sum_k(mlps[k][-1])) tensor of the new_features descriptors
+        """
+        new_features_list = []
+        for k in range(len(self.groupers)):
+            new_features, ball_idxs = self.groupers[k](
+                xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt, features
+            )  # (M1 + M2, C, nsample)
+            new_features = new_features.permute(1, 0, 2).unsqueeze(dim=0)  # (1, C, M1 + M2 ..., nsample)
+            new_features = self.mlps[k](new_features)  # (1, C, M1 + M2 ..., nsample)
+
+            if self.pool_method == 'max_pool':
+                new_features = F.max_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                ).squeeze(dim=-1)  # (1, C, M1 + M2 ...)
+            elif self.pool_method == 'avg_pool':
+                new_features = F.avg_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                ).squeeze(dim=-1)  # (1, C, M1 + M2 ...)
+            else:
+                raise NotImplementedError
+            new_features = new_features.squeeze(dim=0).permute(1, 0)  # (M1 + M2 ..., C)
+            new_features_list.append(new_features)
+
+        new_features = torch.cat(new_features_list, dim=1)  # (M1 + M2 ..., C)
+
+        return new_xyz, new_features
+
+
+class StackPointnetFPModule(nn.Module):
+    def __init__(self, *, mlp: List[int]):
+        """
+        Args:
+            mlp: list of int
+        """
+        super().__init__()
+        shared_mlps = []
+        for k in range(len(mlp) - 1):
+            shared_mlps.extend([
+                nn.Conv2d(mlp[k], mlp[k + 1], kernel_size=1, bias=False),
+                nn.BatchNorm2d(mlp[k + 1]),
+                nn.ReLU()
+            ])
+        self.mlp = nn.Sequential(*shared_mlps)
+
+    def forward(self, unknown, unknown_batch_cnt, known, known_batch_cnt, unknown_feats=None, known_feats=None):
+        """
+        Args:
+            unknown: (N1 + N2 ..., 3)
+            known: (M1 + M2 ..., 3)
+            unknow_feats: (N1 + N2 ..., C1)
+            known_feats: (M1 + M2 ..., C2)
+
+        Returns:
+            new_features: (N1 + N2 ..., C_out)
+        """
+        dist, idx = pointnet2_utils.three_nn(unknown, unknown_batch_cnt, known, known_batch_cnt)
+        dist_recip = 1.0 / (dist + 1e-8)
+        norm = torch.sum(dist_recip, dim=-1, keepdim=True)
+        weight = dist_recip / norm
+
+        interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight)
+
+        if unknown_feats is not None:
+            new_features = torch.cat([interpolated_feats, unknown_feats], dim=1)  # (N1 + N2 ..., C2 + C1)
+        else:
+            new_features = interpolated_feats
+        new_features = new_features.permute(1, 0)[None, :, :, None]  # (1, C, N1 + N2 ..., 1)
+        new_features = self.mlp(new_features)
+
+        new_features = new_features.squeeze(dim=0).squeeze(dim=-1).permute(1, 0)  # (N1 + N2 ..., C)
+        return new_features
+
+
+class VectorPoolLocalInterpolateModule(nn.Module):
+    def __init__(self, mlp, num_voxels, max_neighbour_distance, nsample, neighbor_type, use_xyz=True,
+                 neighbour_distance_multiplier=1.0, xyz_encoding_type='concat'):
+        """
+        Args:
+            mlp:
+            num_voxels:
+            max_neighbour_distance:
+            neighbor_type: 1: ball, others: cube
+            nsample: find all (-1), find limited number(>0)
+            use_xyz:
+            neighbour_distance_multiplier:
+            xyz_encoding_type:
+        """
+        super().__init__()
+        self.num_voxels = num_voxels  # [num_grid_x, num_grid_y, num_grid_z]: number of grids in each local area centered at new_xyz
+        self.num_total_grids = self.num_voxels[0] * self.num_voxels[1] * self.num_voxels[2]
+        self.max_neighbour_distance = max_neighbour_distance
+        self.neighbor_distance_multiplier = neighbour_distance_multiplier
+        self.nsample = nsample
+        self.neighbor_type = neighbor_type
+        self.use_xyz = use_xyz
+        self.xyz_encoding_type = xyz_encoding_type
+
+        if mlp is not None:
+            if self.use_xyz:
+                mlp[0] += 9 if self.xyz_encoding_type == 'concat' else 0
+            shared_mlps = []
+            for k in range(len(mlp) - 1):
+                shared_mlps.extend([
+                    nn.Conv2d(mlp[k], mlp[k + 1], kernel_size=1, bias=False),
+                    nn.BatchNorm2d(mlp[k + 1]),
+                    nn.ReLU()
+                ])
+            self.mlp = nn.Sequential(*shared_mlps)
+        else:
+            self.mlp = None
+
+        self.num_avg_length_of_neighbor_idxs = 1000
+
+    def forward(self, support_xyz, support_features, xyz_batch_cnt, new_xyz, new_xyz_grid_centers, new_xyz_batch_cnt):
+        """
+        Args:
+            support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            support_features: (N1 + N2 ..., C) point-wise features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+        Returns:
+            new_features: (N1 + N2 ..., C_out)
+        """
+        with torch.no_grad():
+            dist, idx, num_avg_length_of_neighbor_idxs = pointnet2_utils.three_nn_for_vector_pool_by_two_step(
+                support_xyz, xyz_batch_cnt, new_xyz, new_xyz_grid_centers, new_xyz_batch_cnt,
+                self.max_neighbour_distance, self.nsample, self.neighbor_type,
+                self.num_avg_length_of_neighbor_idxs, self.num_total_grids, self.neighbor_distance_multiplier
+            )
+        self.num_avg_length_of_neighbor_idxs = max(self.num_avg_length_of_neighbor_idxs, num_avg_length_of_neighbor_idxs.item())
+
+        dist_recip = 1.0 / (dist + 1e-8)
+        norm = torch.sum(dist_recip, dim=-1, keepdim=True)
+        weight = dist_recip / torch.clamp_min(norm, min=1e-8)
+
+        empty_mask = (idx.view(-1, 3)[:, 0] == -1)
+        idx.view(-1, 3)[empty_mask] = 0
+
+        interpolated_feats = pointnet2_utils.three_interpolate(support_features, idx.view(-1, 3), weight.view(-1, 3))
+        interpolated_feats = interpolated_feats.view(idx.shape[0], idx.shape[1], -1)  # (M1 + M2 ..., num_total_grids, C)
+        if self.use_xyz:
+            near_known_xyz = support_xyz[idx.view(-1, 3).long()].view(-1, 3, 3)  # ( (M1 + M2 ...)*num_total_grids, 3)
+            local_xyz = (new_xyz_grid_centers.view(-1, 1, 3) - near_known_xyz).view(-1, idx.shape[1], 9)
+            if self.xyz_encoding_type == 'concat':
+                interpolated_feats = torch.cat((interpolated_feats, local_xyz), dim=-1)  # ( M1 + M2 ..., num_total_grids, 9+C)
+            else:
+                raise NotImplementedError
+
+        new_features = interpolated_feats.view(-1, interpolated_feats.shape[-1])  # ((M1 + M2 ...) * num_total_grids, C)
+        new_features[empty_mask, :] = 0
+        if self.mlp is not None:
+            new_features = new_features.permute(1, 0)[None, :, :, None]  # (1, C, N1 + N2 ..., 1)
+            new_features = self.mlp(new_features)
+
+            new_features = new_features.squeeze(dim=0).squeeze(dim=-1).permute(1, 0)  # (N1 + N2 ..., C)
+        return new_features
+
+
+class VectorPoolAggregationModule(nn.Module):
+    def __init__(
+            self, input_channels, num_local_voxel=(3, 3, 3), local_aggregation_type='local_interpolation',
+            num_reduced_channels=30, num_channels_of_local_aggregation=32, post_mlps=(128,),
+            max_neighbor_distance=None, neighbor_nsample=-1, neighbor_type=0, neighbor_distance_multiplier=2.0):
+        super().__init__()
+        self.num_local_voxel = num_local_voxel
+        self.total_voxels = self.num_local_voxel[0] * self.num_local_voxel[1] * self.num_local_voxel[2]
+        self.local_aggregation_type = local_aggregation_type
+        assert self.local_aggregation_type in ['local_interpolation', 'voxel_avg_pool', 'voxel_random_choice']
+        self.input_channels = input_channels
+        self.num_reduced_channels = input_channels if num_reduced_channels is None else num_reduced_channels
+        self.num_channels_of_local_aggregation = num_channels_of_local_aggregation
+        self.max_neighbour_distance = max_neighbor_distance
+        self.neighbor_nsample = neighbor_nsample
+        self.neighbor_type = neighbor_type  # 1: ball, others: cube
+
+        if self.local_aggregation_type == 'local_interpolation':
+            self.local_interpolate_module = VectorPoolLocalInterpolateModule(
+                mlp=None, num_voxels=self.num_local_voxel,
+                max_neighbour_distance=self.max_neighbour_distance,
+                nsample=self.neighbor_nsample,
+                neighbor_type=self.neighbor_type,
+                neighbour_distance_multiplier=neighbor_distance_multiplier,
+            )
+            num_c_in = (self.num_reduced_channels + 9) * self.total_voxels
+        else:
+            self.local_interpolate_module = None
+            num_c_in = (self.num_reduced_channels + 3) * self.total_voxels
+
+        num_c_out = self.total_voxels * self.num_channels_of_local_aggregation
+
+        self.separate_local_aggregation_layer = nn.Sequential(
+            nn.Conv1d(num_c_in, num_c_out, kernel_size=1, groups=self.total_voxels, bias=False),
+            nn.BatchNorm1d(num_c_out),
+            nn.ReLU()
+        )
+
+        post_mlp_list = []
+        c_in = num_c_out
+        for cur_num_c in post_mlps:
+            post_mlp_list.extend([
+                nn.Conv1d(c_in, cur_num_c, kernel_size=1, bias=False),
+                nn.BatchNorm1d(cur_num_c),
+                nn.ReLU()
+            ])
+            c_in = cur_num_c
+        self.post_mlps = nn.Sequential(*post_mlp_list)
+
+        self.num_mean_points_per_grid = 20
+        self.init_weights()
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1.0)
+                nn.init.constant_(m.bias, 0)
+
+    def extra_repr(self) -> str:
+        ret = f'radius={self.max_neighbour_distance}, local_voxels=({self.num_local_voxel}, ' \
+              f'local_aggregation_type={self.local_aggregation_type}, ' \
+              f'num_c_reduction={self.input_channels}->{self.num_reduced_channels}, ' \
+              f'num_c_local_aggregation={self.num_channels_of_local_aggregation}'
+        return ret
+
+    def vector_pool_with_voxel_query(self, xyz, xyz_batch_cnt, features, new_xyz, new_xyz_batch_cnt):
+        use_xyz = 1
+        pooling_type = 0 if self.local_aggregation_type == 'voxel_avg_pool' else 1
+
+        new_features, new_local_xyz, num_mean_points_per_grid, point_cnt_of_grid = pointnet2_utils.vector_pool_with_voxel_query_op(
+            xyz, xyz_batch_cnt, features, new_xyz, new_xyz_batch_cnt,
+            self.num_local_voxel[0], self.num_local_voxel[1], self.num_local_voxel[2],
+            self.max_neighbour_distance, self.num_reduced_channels, use_xyz,
+            self.num_mean_points_per_grid, self.neighbor_nsample, self.neighbor_type,
+            pooling_type
+        )
+        self.num_mean_points_per_grid = max(self.num_mean_points_per_grid, num_mean_points_per_grid.item())
+
+        num_new_pts = new_features.shape[0]
+        new_local_xyz = new_local_xyz.view(num_new_pts, -1, 3)  # (N, num_voxel, 3)
+        new_features = new_features.view(num_new_pts, -1, self.num_reduced_channels)  # (N, num_voxel, C)
+        new_features = torch.cat((new_local_xyz, new_features), dim=-1).view(num_new_pts, -1)
+
+        return new_features, point_cnt_of_grid
+
+    @staticmethod
+    def get_dense_voxels_by_center(point_centers, max_neighbour_distance, num_voxels):
+        """
+        Args:
+            point_centers: (N, 3)
+            max_neighbour_distance: float
+            num_voxels: [num_x, num_y, num_z]
+
+        Returns:
+            voxel_centers: (N, total_voxels, 3)
+        """
+        R = max_neighbour_distance
+        device = point_centers.device
+        x_grids = torch.arange(-R + R / num_voxels[0], R - R / num_voxels[0] + 1e-5, 2 * R / num_voxels[0], device=device)
+        y_grids = torch.arange(-R + R / num_voxels[1], R - R / num_voxels[1] + 1e-5, 2 * R / num_voxels[1], device=device)
+        z_grids = torch.arange(-R + R / num_voxels[2], R - R / num_voxels[2] + 1e-5, 2 * R / num_voxels[2], device=device)
+        x_offset, y_offset, z_offset = torch.meshgrid(x_grids, y_grids, z_grids)  # shape: [num_x, num_y, num_z]
+        xyz_offset = torch.cat((
+            x_offset.contiguous().view(-1, 1),
+            y_offset.contiguous().view(-1, 1),
+            z_offset.contiguous().view(-1, 1)), dim=-1
+        )
+        voxel_centers = point_centers[:, None, :] + xyz_offset[None, :, :]
+        return voxel_centers
+
+    def vector_pool_with_local_interpolate(self, xyz, xyz_batch_cnt, features, new_xyz, new_xyz_batch_cnt):
+        """
+        Args:
+            xyz: (N, 3)
+            xyz_batch_cnt: (batch_size)
+            features: (N, C)
+            new_xyz: (M, 3)
+            new_xyz_batch_cnt: (batch_size)
+        Returns:
+            new_features: (M, total_voxels * C)
+        """
+        voxel_centers = self.get_dense_voxels_by_center(
+            point_centers=new_xyz, max_neighbour_distance=self.max_neighbour_distance, num_voxels=self.num_local_voxel
+        )  # (M1 + M2 + ..., total_voxels, 3)
+        voxel_features = self.local_interpolate_module.forward(
+            support_xyz=xyz, support_features=features, xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz, new_xyz_grid_centers=voxel_centers, new_xyz_batch_cnt=new_xyz_batch_cnt
+        )  # ((M1 + M2 ...) * total_voxels, C)
+
+        voxel_features = voxel_features.contiguous().view(-1, self.total_voxels * voxel_features.shape[-1])
+        return voxel_features
+
+    def forward(self, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt, features, **kwargs):
+        """
+        :param xyz: (N1 + N2 ..., 3) tensor of the xyz coordinates of the features
+        :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+        :param new_xyz: (M1 + M2 ..., 3)
+        :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+        :param features: (N1 + N2 ..., C) tensor of the descriptors of the the features
+        :return:
+            new_xyz: (M1 + M2 ..., 3) tensor of the new features' xyz
+            new_features: (M1 + M2 ..., \sum_k(mlps[k][-1])) tensor of the new_features descriptors
+        """
+        N, C = features.shape
+
+        assert C % self.num_reduced_channels == 0, \
+            f'the input channels ({C}) should be an integral multiple of num_reduced_channels({self.num_reduced_channels})'
+
+        features = features.view(N, -1, self.num_reduced_channels).sum(dim=1)
+
+        if self.local_aggregation_type in ['voxel_avg_pool', 'voxel_random_choice']:
+            vector_features, point_cnt_of_grid = self.vector_pool_with_voxel_query(
+                xyz=xyz, xyz_batch_cnt=xyz_batch_cnt, features=features,
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt
+            )
+        elif self.local_aggregation_type == 'local_interpolation':
+            vector_features = self.vector_pool_with_local_interpolate(
+                xyz=xyz, xyz_batch_cnt=xyz_batch_cnt, features=features,
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt
+            )  # (M1 + M2 + ..., total_voxels * C)
+        else:
+            raise NotImplementedError
+
+        vector_features = vector_features.permute(1, 0)[None, :, :]  # (1, num_voxels * C, M1 + M2 ...)
+
+        new_features = self.separate_local_aggregation_layer(vector_features)
+
+        new_features = self.post_mlps(new_features)
+        new_features = new_features.squeeze(dim=0).permute(1, 0)
+        return new_xyz, new_features
+
+
+class VectorPoolAggregationModuleMSG(nn.Module):
+    def __init__(self, input_channels, config):
+        super().__init__()
+        self.model_cfg = config
+        self.num_groups = self.model_cfg.NUM_GROUPS
+
+        self.layers = []
+        c_in = 0
+        for k in range(self.num_groups):
+            cur_config = self.model_cfg[f'GROUP_CFG_{k}']
+            cur_vector_pool_module = VectorPoolAggregationModule(
+                input_channels=input_channels, num_local_voxel=cur_config.NUM_LOCAL_VOXEL,
+                post_mlps=cur_config.POST_MLPS,
+                max_neighbor_distance=cur_config.MAX_NEIGHBOR_DISTANCE,
+                neighbor_nsample=cur_config.NEIGHBOR_NSAMPLE,
+                local_aggregation_type=self.model_cfg.LOCAL_AGGREGATION_TYPE,
+                num_reduced_channels=self.model_cfg.get('NUM_REDUCED_CHANNELS', None),
+                num_channels_of_local_aggregation=self.model_cfg.NUM_CHANNELS_OF_LOCAL_AGGREGATION,
+                neighbor_distance_multiplier=2.0
+            )
+            self.__setattr__(f'layer_{k}', cur_vector_pool_module)
+            c_in += cur_config.POST_MLPS[-1]
+
+        c_in += 3  # use_xyz
+
+        shared_mlps = []
+        for cur_num_c in self.model_cfg.MSG_POST_MLPS:
+            shared_mlps.extend([
+                nn.Conv1d(c_in, cur_num_c, kernel_size=1, bias=False),
+                nn.BatchNorm1d(cur_num_c),
+                nn.ReLU()
+            ])
+            c_in = cur_num_c
+        self.msg_post_mlps = nn.Sequential(*shared_mlps)
+
+    def forward(self, **kwargs):
+        features_list = []
+        for k in range(self.num_groups):
+            cur_xyz, cur_features = self.__getattr__(f'layer_{k}')(**kwargs)
+            features_list.append(cur_features)
+
+        features = torch.cat(features_list, dim=-1)
+        features = torch.cat((cur_xyz, features), dim=-1)
+        features = features.permute(1, 0)[None, :, :]  # (1, C, N)
+        new_features = self.msg_post_mlps(features)
+        new_features = new_features.squeeze(dim=0).permute(1, 0)  # (N, C)
+
+        return cur_xyz, new_features
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py
new file mode 100644
index 000000000..cd2c1f341
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py
@@ -0,0 +1,457 @@
+import torch
+import torch.nn as nn
+from torch.autograd import Function, Variable
+
+from . import pointnet2_stack_cuda as pointnet2
+
+
+class BallQuery(Function):
+
+    @staticmethod
+    def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
+                new_xyz: torch.Tensor, new_xyz_batch_cnt):
+        """
+        Args:
+            ctx:
+            radius: float, radius of the balls
+            nsample: int, maximum number of features in the balls
+            xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+
+        Returns:
+            idx: (M1 + M2, nsample) tensor with the indicies of the features that form the query balls
+        """
+        assert new_xyz.is_contiguous()
+        assert new_xyz_batch_cnt.is_contiguous()
+        assert xyz.is_contiguous()
+        assert xyz_batch_cnt.is_contiguous()
+
+        B = xyz_batch_cnt.shape[0]
+        M = new_xyz.shape[0]
+        idx = torch.cuda.IntTensor(M, nsample).zero_()
+
+        pointnet2.ball_query_wrapper(B, M, radius, nsample, new_xyz, new_xyz_batch_cnt, xyz, xyz_batch_cnt, idx)
+        empty_ball_mask = (idx[:, 0] == -1)
+        idx[empty_ball_mask] = 0
+
+        ctx.mark_non_differentiable(idx)
+        ctx.mark_non_differentiable(empty_ball_mask)
+
+        return idx, empty_ball_mask
+
+    @staticmethod
+    def backward(ctx, a=None, b=None):
+        return None, None, None, None, None, None
+
+
+ball_query = BallQuery.apply
+
+
+class GroupingOperation(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, features_batch_cnt: torch.Tensor,
+                idx: torch.Tensor, idx_batch_cnt: torch.Tensor):
+        """
+        Args:
+            ctx:
+            features: (N1 + N2 ..., C) tensor of features to group
+            features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+            idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+            idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+
+        Returns:
+            output: (M1 + M2, C, nsample) tensor
+        """
+        assert features.is_contiguous()
+        assert features_batch_cnt.is_contiguous()
+        assert idx.is_contiguous()
+        assert idx_batch_cnt.is_contiguous()
+
+        assert features.shape[0] == features_batch_cnt.sum(), \
+            'features: %s, features_batch_cnt: %s' % (str(features.shape), str(features_batch_cnt))
+        assert idx.shape[0] == idx_batch_cnt.sum(), \
+            'idx: %s, idx_batch_cnt: %s' % (str(idx.shape), str(idx_batch_cnt))
+
+        M, nsample = idx.size()
+        N, C = features.size()
+        B = idx_batch_cnt.shape[0]
+        output = torch.cuda.FloatTensor(M, C, nsample)
+
+        pointnet2.group_points_wrapper(B, M, C, nsample, features, features_batch_cnt, idx, idx_batch_cnt, output)
+
+        ctx.for_backwards = (B, N, idx, features_batch_cnt, idx_batch_cnt)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor):
+        """
+        Args:
+            ctx:
+            grad_out: (M1 + M2 ..., C, nsample) tensor of the gradients of the output from forward
+
+        Returns:
+            grad_features: (N1 + N2 ..., C) gradient of the features
+        """
+        B, N, idx, features_batch_cnt, idx_batch_cnt = ctx.for_backwards
+
+        M, C, nsample = grad_out.size()
+        grad_features = Variable(torch.cuda.FloatTensor(N, C).zero_())
+
+        grad_out_data = grad_out.data.contiguous()
+        pointnet2.group_points_grad_wrapper(B, M, C, N, nsample, grad_out_data, idx,
+                                            idx_batch_cnt, features_batch_cnt, grad_features.data)
+        return grad_features, None, None, None
+
+
+grouping_operation = GroupingOperation.apply
+
+
+class QueryAndGroup(nn.Module):
+    def __init__(self, radius: float, nsample: int, use_xyz: bool = True):
+        """
+        Args:
+            radius: float, radius of ball
+            nsample: int, maximum number of features to gather in the ball
+            use_xyz:
+        """
+        super().__init__()
+        self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
+
+    def forward(self, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
+                new_xyz: torch.Tensor, new_xyz_batch_cnt: torch.Tensor,
+                features: torch.Tensor = None):
+        """
+        Args:
+            xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+            features: (N1 + N2 ..., C) tensor of features to group
+
+        Returns:
+            new_features: (M1 + M2, C, nsample) tensor
+        """
+        assert xyz.shape[0] == xyz_batch_cnt.sum(), 'xyz: %s, xyz_batch_cnt: %s' % (str(xyz.shape), str(new_xyz_batch_cnt))
+        assert new_xyz.shape[0] == new_xyz_batch_cnt.sum(), \
+            'new_xyz: %s, new_xyz_batch_cnt: %s' % (str(new_xyz.shape), str(new_xyz_batch_cnt))
+
+        # idx: (M1 + M2 ..., nsample), empty_ball_mask: (M1 + M2 ...)
+        idx, empty_ball_mask = ball_query(self.radius, self.nsample, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt)
+        grouped_xyz = grouping_operation(xyz, xyz_batch_cnt, idx, new_xyz_batch_cnt)  # (M1 + M2, 3, nsample)
+        grouped_xyz -= new_xyz.unsqueeze(-1)
+
+        grouped_xyz[empty_ball_mask] = 0
+
+        if features is not None:
+            grouped_features = grouping_operation(features, xyz_batch_cnt, idx, new_xyz_batch_cnt)  # (M1 + M2, C, nsample)
+            grouped_features[empty_ball_mask] = 0
+            if self.use_xyz:
+                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (M1 + M2 ..., C + 3, nsample)
+            else:
+                new_features = grouped_features
+        else:
+            assert self.use_xyz, "Cannot have not features and not use xyz as a feature!"
+            new_features = grouped_xyz
+
+        return new_features, idx
+
+
+class FarthestPointSampling(Function):
+    @staticmethod
+    def forward(ctx, xyz: torch.Tensor, npoint: int):
+        """
+        Args:
+            ctx:
+            xyz: (B, N, 3) where N > npoint
+            npoint: int, number of features in the sampled set
+
+        Returns:
+            output: (B, npoint) tensor containing the set
+        """
+        assert xyz.is_contiguous()
+
+        B, N, _ = xyz.size()
+        output = torch.cuda.IntTensor(B, npoint)
+        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
+
+        pointnet2.farthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
+        return output
+
+    @staticmethod
+    def backward(xyz, a=None):
+        return None, None
+
+
+farthest_point_sample = furthest_point_sample = FarthestPointSampling.apply
+
+
+class StackFarthestPointSampling(Function):
+    @staticmethod
+    def forward(ctx, xyz, xyz_batch_cnt, npoint):
+        """
+        Args:
+            ctx:
+            xyz: (N1 + N2 + ..., 3) where N > npoint
+            xyz_batch_cnt: [N1, N2, ...]
+            npoint: int, number of features in the sampled set
+
+        Returns:
+            output: (npoint.sum()) tensor containing the set,
+            npoint: (M1, M2, ...)
+        """
+        assert xyz.is_contiguous() and xyz.shape[1] == 3
+
+        batch_size = xyz_batch_cnt.__len__()
+        if not isinstance(npoint, torch.Tensor):
+            if not isinstance(npoint, list):
+                npoint = [npoint for i in range(batch_size)]
+            npoint = torch.tensor(npoint, device=xyz.device).int()
+
+        N, _ = xyz.size()
+        temp = torch.cuda.FloatTensor(N).fill_(1e10)
+        output = torch.cuda.IntTensor(npoint.sum().item())
+
+        pointnet2.stack_farthest_point_sampling_wrapper(xyz, temp, xyz_batch_cnt, output, npoint)
+        return output
+
+    @staticmethod
+    def backward(xyz, a=None):
+        return None, None
+
+
+stack_farthest_point_sample = StackFarthestPointSampling.apply
+
+
+class ThreeNN(Function):
+    @staticmethod
+    def forward(ctx, unknown, unknown_batch_cnt, known, known_batch_cnt):
+        """
+        Args:
+            ctx:
+            unknown: (N1 + N2..., 3)
+            unknown_batch_cnt: (batch_size), [N1, N2, ...]
+            known: (M1 + M2..., 3)
+            known_batch_cnt: (batch_size), [M1, M2, ...]
+
+        Returns:
+            dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
+            idx: (N1 + N2 ..., 3)  index of the three nearest neighbors, range [0, M1+M2+...]
+        """
+        assert unknown.shape.__len__() == 2 and unknown.shape[1] == 3
+        assert known.shape.__len__() == 2 and known.shape[1] == 3
+        assert unknown_batch_cnt.__len__() == known_batch_cnt.__len__()
+
+        dist2 = unknown.new_zeros(unknown.shape)
+        idx = unknown_batch_cnt.new_zeros(unknown.shape).int()
+
+        pointnet2.three_nn_wrapper(
+            unknown.contiguous(), unknown_batch_cnt.contiguous(),
+            known.contiguous(), known_batch_cnt.contiguous(), dist2, idx
+        )
+        return torch.sqrt(dist2), idx
+
+    @staticmethod
+    def backward(ctx, a=None, b=None):
+        return None, None
+
+
+three_nn = ThreeNN.apply
+
+
+class ThreeInterpolate(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor):
+        """
+        Args:
+            ctx:
+            features: (M1 + M2 ..., C)
+            idx: [N1 + N2 ..., 3]
+            weight: [N1 + N2 ..., 3]
+
+        Returns:
+            out_tensor: (N1 + N2 ..., C)
+        """
+        assert idx.shape[0] == weight.shape[0] and idx.shape[1] == weight.shape[1] == 3
+
+        ctx.three_interpolate_for_backward = (idx, weight, features.shape[0])
+        output = features.new_zeros((idx.shape[0], features.shape[1]))
+        pointnet2.three_interpolate_wrapper(features.contiguous(), idx.contiguous(), weight.contiguous(), output)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor):
+        """
+        Args:
+            ctx:
+            grad_out: (N1 + N2 ..., C)
+
+        Returns:
+            grad_features: (M1 + M2 ..., C)
+        """
+        idx, weight, M = ctx.three_interpolate_for_backward
+        grad_features = grad_out.new_zeros((M, grad_out.shape[1]))
+        pointnet2.three_interpolate_grad_wrapper(
+            grad_out.contiguous(), idx.contiguous(), weight.contiguous(), grad_features
+        )
+        return grad_features, None, None
+
+
+three_interpolate = ThreeInterpolate.apply
+
+
+class ThreeNNForVectorPoolByTwoStep(Function):
+    @staticmethod
+    def forward(ctx, support_xyz, xyz_batch_cnt, new_xyz, new_xyz_grid_centers, new_xyz_batch_cnt,
+                max_neighbour_distance, nsample, neighbor_type, avg_length_of_neighbor_idxs, num_total_grids,
+                neighbor_distance_multiplier):
+        """
+        Args:
+            ctx:
+            // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            // xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            // new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
+            // new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+            // nsample: find all (-1), find limited number(>0)
+            // neighbor_type: 1: ball, others: cube
+            // neighbor_distance_multiplier: query_distance = neighbor_distance_multiplier * max_neighbour_distance
+
+        Returns:
+            // new_xyz_grid_idxs: (M1 + M2 ..., num_total_grids, 3) three-nn
+            // new_xyz_grid_dist2: (M1 + M2 ..., num_total_grids, 3) square of dist of three-nn
+        """
+        num_new_xyz = new_xyz.shape[0]
+        new_xyz_grid_dist2 = new_xyz_grid_centers.new_zeros(new_xyz_grid_centers.shape)
+        new_xyz_grid_idxs = new_xyz_grid_centers.new_zeros(new_xyz_grid_centers.shape).int().fill_(-1)
+
+        while True:
+            num_max_sum_points = avg_length_of_neighbor_idxs * num_new_xyz
+            stack_neighbor_idxs = new_xyz_grid_idxs.new_zeros(num_max_sum_points)
+            start_len = new_xyz_grid_idxs.new_zeros(num_new_xyz, 2).int()
+            cumsum = new_xyz_grid_idxs.new_zeros(1)
+
+            pointnet2.query_stacked_local_neighbor_idxs_wrapper_stack(
+                support_xyz.contiguous(), xyz_batch_cnt.contiguous(),
+                new_xyz.contiguous(), new_xyz_batch_cnt.contiguous(),
+                stack_neighbor_idxs.contiguous(), start_len.contiguous(), cumsum,
+                avg_length_of_neighbor_idxs, max_neighbour_distance * neighbor_distance_multiplier,
+                nsample, neighbor_type
+            )
+            avg_length_of_neighbor_idxs = cumsum[0].item() // num_new_xyz + int(cumsum[0].item() % num_new_xyz > 0)
+
+            if cumsum[0] <= num_max_sum_points:
+                break
+
+        stack_neighbor_idxs = stack_neighbor_idxs[:cumsum[0]]
+        pointnet2.query_three_nn_by_stacked_local_idxs_wrapper_stack(
+            support_xyz, new_xyz, new_xyz_grid_centers, new_xyz_grid_idxs, new_xyz_grid_dist2,
+            stack_neighbor_idxs, start_len, num_new_xyz, num_total_grids
+        )
+
+        return torch.sqrt(new_xyz_grid_dist2), new_xyz_grid_idxs, torch.tensor(avg_length_of_neighbor_idxs)
+
+
+three_nn_for_vector_pool_by_two_step = ThreeNNForVectorPoolByTwoStep.apply
+
+
+class VectorPoolWithVoxelQuery(Function):
+    @staticmethod
+    def forward(ctx, support_xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor, support_features: torch.Tensor,
+                new_xyz: torch.Tensor, new_xyz_batch_cnt: torch.Tensor, num_grid_x, num_grid_y, num_grid_z,
+                max_neighbour_distance, num_c_out_each_grid, use_xyz,
+                num_mean_points_per_grid=100, nsample=-1, neighbor_type=0, pooling_type=0):
+        """
+        Args:
+            ctx:
+            support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            support_features: (N1 + N2 ..., C)
+            new_xyz: (M1 + M2 ..., 3) centers of new positions
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+            num_grid_x: number of grids in each local area centered at new_xyz
+            num_grid_y:
+            num_grid_z:
+            max_neighbour_distance:
+            num_c_out_each_grid:
+            use_xyz:
+            neighbor_type: 1: ball, others: cube:
+            pooling_type: 0: avg_pool, 1: random choice
+        Returns:
+            new_features: (M1 + M2 ..., num_c_out)
+        """
+        assert support_xyz.is_contiguous()
+        assert support_features.is_contiguous()
+        assert xyz_batch_cnt.is_contiguous()
+        assert new_xyz.is_contiguous()
+        assert new_xyz_batch_cnt.is_contiguous()
+        num_total_grids = num_grid_x * num_grid_y * num_grid_z
+        num_c_out = num_c_out_each_grid * num_total_grids
+        N, num_c_in = support_features.shape
+        M = new_xyz.shape[0]
+
+        assert num_c_in % num_c_out_each_grid == 0, \
+            f'the input channels ({num_c_in}) should be an integral multiple of num_c_out_each_grid({num_c_out_each_grid})'
+
+        while True:
+            new_features = support_features.new_zeros((M, num_c_out))
+            new_local_xyz = support_features.new_zeros((M, 3 * num_total_grids))
+            point_cnt_of_grid = xyz_batch_cnt.new_zeros((M, num_total_grids))
+
+            num_max_sum_points = num_mean_points_per_grid * M
+            grouped_idxs = xyz_batch_cnt.new_zeros((num_max_sum_points, 3))
+
+            num_cum_sum = pointnet2.vector_pool_wrapper(
+                support_xyz, xyz_batch_cnt, support_features, new_xyz, new_xyz_batch_cnt,
+                new_features, new_local_xyz, point_cnt_of_grid, grouped_idxs,
+                num_grid_x, num_grid_y, num_grid_z, max_neighbour_distance, use_xyz,
+                num_max_sum_points, nsample, neighbor_type, pooling_type
+            )
+            num_mean_points_per_grid = num_cum_sum // M + int(num_cum_sum % M > 0)
+            if num_cum_sum <= num_max_sum_points:
+                break
+
+        grouped_idxs = grouped_idxs[:num_cum_sum]
+
+        normalizer = torch.clamp_min(point_cnt_of_grid[:, :, None].float(), min=1e-6)
+        new_features = (new_features.view(-1, num_total_grids, num_c_out_each_grid) / normalizer).view(-1, num_c_out)
+
+        if use_xyz:
+            new_local_xyz = (new_local_xyz.view(-1, num_total_grids, 3) / normalizer).view(-1, num_total_grids * 3)
+
+        num_mean_points_per_grid = torch.Tensor([num_mean_points_per_grid]).int()
+        nsample = torch.Tensor([nsample]).int()
+        ctx.vector_pool_for_backward = (point_cnt_of_grid, grouped_idxs, N, num_c_in)
+        ctx.mark_non_differentiable(new_local_xyz, num_mean_points_per_grid, nsample, point_cnt_of_grid)
+        return new_features, new_local_xyz, num_mean_points_per_grid, point_cnt_of_grid
+
+    @staticmethod
+    def backward(ctx, grad_new_features: torch.Tensor, grad_local_xyz: torch.Tensor, grad_num_cum_sum, grad_point_cnt_of_grid):
+        """
+        Args:
+            ctx:
+            grad_new_features: (M1 + M2 ..., num_c_out), num_c_out = num_c_out_each_grid * num_total_grids
+
+        Returns:
+            grad_support_features: (N1 + N2 ..., C_in)
+        """
+        point_cnt_of_grid, grouped_idxs, N, num_c_in = ctx.vector_pool_for_backward
+        grad_support_features = grad_new_features.new_zeros((N, num_c_in))
+
+        if grouped_idxs.shape[0] > 0:
+            pointnet2.vector_pool_grad_wrapper(
+                grad_new_features.contiguous(), point_cnt_of_grid, grouped_idxs,
+                grad_support_features
+            )
+
+        return None, None, grad_support_features, None, None, None, None, None, None, None, None, None, None, None, None
+
+
+vector_pool_with_voxel_query_op = VectorPoolWithVoxelQuery.apply
+
+
+if __name__ == '__main__':
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query.cpp
new file mode 100644
index 000000000..3376f75fa
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query.cpp
@@ -0,0 +1,45 @@
+/*
+Stacked-batch-data version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "ball_query_gpu.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int ball_query_wrapper_stack(int B, int M, float radius, int nsample,
+    at::Tensor new_xyz_tensor, at::Tensor new_xyz_batch_cnt_tensor,
+    at::Tensor xyz_tensor, at::Tensor xyz_batch_cnt_tensor, at::Tensor idx_tensor) {
+    CHECK_INPUT(new_xyz_tensor);
+    CHECK_INPUT(xyz_tensor);
+    CHECK_INPUT(new_xyz_batch_cnt_tensor);
+    CHECK_INPUT(xyz_batch_cnt_tensor);
+
+    const float *new_xyz = new_xyz_tensor.data<float>();
+    const float *xyz = xyz_tensor.data<float>();
+    const int *new_xyz_batch_cnt = new_xyz_batch_cnt_tensor.data<int>();
+    const int *xyz_batch_cnt = xyz_batch_cnt_tensor.data<int>();
+    int *idx = idx_tensor.data<int>();
+
+    ball_query_kernel_launcher_stack(B, M, radius, nsample, new_xyz, new_xyz_batch_cnt, xyz, xyz_batch_cnt, idx);
+    return 1;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query_gpu.cu
new file mode 100644
index 000000000..adaa6b1e8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query_gpu.cu
@@ -0,0 +1,90 @@
+/*
+Stacked-batch-data version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "ball_query_gpu.h"
+#include "cuda_utils.h"
+
+
+__global__ void ball_query_kernel_stack(int B, int M, float radius, int nsample, \
+    const float *new_xyz, const int *new_xyz_batch_cnt, const float *xyz, const int *xyz_batch_cnt, int *idx) {
+    // :param xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // :param new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // output:
+    //      idx: (M, nsample)
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= M) return;
+
+    int bs_idx = 0, pt_cnt = new_xyz_batch_cnt[0];
+    for (int k = 1; k < B; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += new_xyz_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int xyz_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) xyz_batch_start_idx += xyz_batch_cnt[k];
+    // for (int k = 0; k < bs_idx; k++) new_xyz_batch_start_idx += new_xyz_batch_cnt[k];
+
+    new_xyz += pt_idx * 3;
+    xyz += xyz_batch_start_idx * 3;
+    idx += pt_idx * nsample;
+
+    float radius2 = radius * radius;
+    float new_x = new_xyz[0];
+    float new_y = new_xyz[1];
+    float new_z = new_xyz[2];
+    int n = xyz_batch_cnt[bs_idx];
+
+    int cnt = 0;
+    for (int k = 0; k < n; ++k) {
+        float x = xyz[k * 3 + 0];
+        float y = xyz[k * 3 + 1];
+        float z = xyz[k * 3 + 2];
+        float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);
+        if (d2 < radius2){
+            if (cnt == 0){
+                for (int l = 0; l < nsample; ++l) {
+                    idx[l] = k;
+                }
+            }
+            idx[cnt] = k;
+            ++cnt;
+            if (cnt >= nsample) break;
+        }
+    }
+    if (cnt == 0) idx[0] = -1;
+}
+
+
+void ball_query_kernel_launcher_stack(int B, int M, float radius, int nsample,
+    const float *new_xyz, const int *new_xyz_batch_cnt, const float *xyz, const int *xyz_batch_cnt, int *idx){
+    // :param xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // :param new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // output:
+    //      idx: (M, nsample)
+
+    cudaError_t err;
+
+    dim3 blocks(DIVUP(M, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    ball_query_kernel_stack<<<blocks, threads>>>(B, M, radius, nsample, new_xyz, new_xyz_batch_cnt, xyz, xyz_batch_cnt, idx);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query_gpu.h
new file mode 100644
index 000000000..c74f12018
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/ball_query_gpu.h
@@ -0,0 +1,25 @@
+/*
+Stacked-batch-data version of ball query, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#ifndef _STACK_BALL_QUERY_GPU_H
+#define _STACK_BALL_QUERY_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int ball_query_wrapper_stack(int B, int M, float radius, int nsample,
+    at::Tensor new_xyz_tensor, at::Tensor new_xyz_batch_cnt_tensor,
+    at::Tensor xyz_tensor, at::Tensor xyz_batch_cnt_tensor, at::Tensor idx_tensor);
+
+
+void ball_query_kernel_launcher_stack(int B, int M, float radius, int nsample,
+    const float *new_xyz, const int *new_xyz_batch_cnt, const float *xyz, const int *xyz_batch_cnt, int *idx);
+
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/cuda_utils.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/cuda_utils.h
new file mode 100644
index 000000000..c1670f1c8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/cuda_utils.h
@@ -0,0 +1,9 @@
+#ifndef _STACK_CUDA_UTILS_H
+#define _STACK_CUDA_UTILS_H
+
+#include <cmath>
+
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points.cpp
new file mode 100644
index 000000000..d882c597f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points.cpp
@@ -0,0 +1,68 @@
+/*
+Stacked-batch-data version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <vector>
+#include "group_points_gpu.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int group_points_grad_wrapper_stack(int B, int M, int C, int N, int nsample,
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor,
+    at::Tensor features_batch_cnt_tensor, at::Tensor grad_features_tensor) {
+
+    CHECK_INPUT(grad_out_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(idx_batch_cnt_tensor);
+    CHECK_INPUT(features_batch_cnt_tensor);
+    CHECK_INPUT(grad_features_tensor);
+
+    const float *grad_out = grad_out_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    const int *idx_batch_cnt = idx_batch_cnt_tensor.data<int>();
+    const int *features_batch_cnt = features_batch_cnt_tensor.data<int>();
+    float *grad_features = grad_features_tensor.data<float>();
+
+    group_points_grad_kernel_launcher_stack(B, M, C, N, nsample, grad_out, idx, idx_batch_cnt, features_batch_cnt, grad_features);
+    return 1;
+}
+
+
+int group_points_wrapper_stack(int B, int M, int C, int nsample,
+    at::Tensor features_tensor, at::Tensor features_batch_cnt_tensor,
+    at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor, at::Tensor out_tensor) {
+
+    CHECK_INPUT(features_tensor);
+    CHECK_INPUT(features_batch_cnt_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(idx_batch_cnt_tensor);
+    CHECK_INPUT(out_tensor);
+
+    const float *features = features_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    const int *features_batch_cnt = features_batch_cnt_tensor.data<int>();
+    const int *idx_batch_cnt = idx_batch_cnt_tensor.data<int>();
+    float *out = out_tensor.data<float>();
+
+    group_points_kernel_launcher_stack(B, M, C, nsample, features, features_batch_cnt, idx, idx_batch_cnt, out);
+    return 1;
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points_gpu.cu
new file mode 100644
index 000000000..62e341e9c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points_gpu.cu
@@ -0,0 +1,125 @@
+/*
+Stacked-batch-data version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "cuda_utils.h"
+#include "group_points_gpu.h"
+
+
+__global__ void group_points_grad_kernel_stack(int B, int M, int C, int N, int nsample,
+    const float *grad_out, const int *idx, const int *idx_batch_cnt, const int *features_batch_cnt, float *grad_features) {
+    // :param grad_out: (M1 + M2 ..., C, nsample) tensor of the gradients of the output from forward
+    // :param idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+    // :param idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+    // :param features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+    // :return:
+    //     grad_features: (N1 + N2 ..., C) gradient of the features
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int sample_idx = index % nsample;
+    int C_idx = (index / nsample) % C;
+    int pt_idx = (index / nsample / C);
+
+    if (pt_idx >= M || C_idx >= C || sample_idx >= nsample) return;
+
+    int bs_idx = 0, pt_cnt = idx_batch_cnt[0];
+    for (int k = 1; k < B; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += idx_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int features_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) features_batch_start_idx += features_batch_cnt[k];
+
+    grad_out += pt_idx * C * nsample + C_idx * nsample + sample_idx;
+    idx += pt_idx * nsample + sample_idx;
+    grad_features += (features_batch_start_idx + idx[0]) * C + C_idx;
+
+    atomicAdd(grad_features, grad_out[0]);
+}
+
+void group_points_grad_kernel_launcher_stack(int B, int M, int C, int N, int nsample,
+    const float *grad_out, const int *idx, const int *idx_batch_cnt, const int *features_batch_cnt, float *grad_features) {
+    // :param grad_out: (M1 + M2 ..., C, nsample) tensor of the gradients of the output from forward
+    // :param idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+    // :param idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+    // :param features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+    // :return:
+    //     grad_features: (N1 + N2 ..., C) gradient of the features
+
+    cudaError_t err;
+    // dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 blocks(DIVUP(M * C * nsample, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    group_points_grad_kernel_stack<<<blocks, threads>>>(B, M, C, N, nsample, grad_out, idx, idx_batch_cnt, features_batch_cnt, grad_features);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void group_points_kernel_stack(int B, int M, int C, int nsample,
+    const float *features, const int *features_batch_cnt, const int *idx, const int *idx_batch_cnt, float *out) {
+    // :param features: (N1 + N2 ..., C) tensor of features to group
+    // :param features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+    // :param idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+    // :param idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+    // :return:
+    //     output: (M1 + M2, C, nsample) tensor
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int sample_idx = index % nsample;
+    int C_idx = (index / nsample) % C;
+    int pt_idx = (index / nsample / C);
+
+    if (pt_idx >= M || C_idx >= C || sample_idx >= nsample) return;
+
+    int bs_idx = 0, pt_cnt = idx_batch_cnt[0];
+    for (int k = 1; k < B; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += idx_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int features_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) features_batch_start_idx += features_batch_cnt[k];
+    features += features_batch_start_idx * C;
+
+    idx += pt_idx * nsample + sample_idx;
+    int in_idx = idx[0] * C + C_idx;
+    int out_idx = pt_idx * C * nsample + C_idx * nsample + sample_idx;
+
+    out[out_idx] = features[in_idx];
+}
+
+
+void group_points_kernel_launcher_stack(int B, int M, int C, int nsample,
+    const float *features, const int *features_batch_cnt, const int *idx, const int *idx_batch_cnt, float *out) {
+    // :param features: (N1 + N2 ..., C) tensor of features to group
+    // :param features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
+    // :param idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
+    // :param idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
+    // :return:
+    //     output: (M1 + M2, C, nsample) tensor
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(M * C * nsample, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    group_points_kernel_stack<<<blocks, threads>>>(B, M, C, nsample, features, features_batch_cnt, idx, idx_batch_cnt, out);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points_gpu.h
new file mode 100644
index 000000000..4a2662167
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/group_points_gpu.h
@@ -0,0 +1,31 @@
+/*
+Stacked-batch-data version of point grouping, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#ifndef _STACK_GROUP_POINTS_GPU_H
+#define _STACK_GROUP_POINTS_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <vector>
+
+
+int group_points_wrapper_stack(int B, int M, int C, int nsample,
+    at::Tensor features_tensor, at::Tensor features_batch_cnt_tensor,
+    at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor, at::Tensor out_tensor);
+
+void group_points_kernel_launcher_stack(int B, int M, int C, int nsample,
+    const float *features, const int *features_batch_cnt, const int *idx, const int *idx_batch_cnt, float *out);
+
+int group_points_grad_wrapper_stack(int B, int M, int C, int N, int nsample,
+    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor,
+    at::Tensor features_batch_cnt_tensor, at::Tensor grad_features_tensor);
+
+void group_points_grad_kernel_launcher_stack(int B, int M, int C, int N, int nsample,
+    const float *grad_out, const int *idx, const int *idx_batch_cnt, const int *features_batch_cnt, float *grad_features);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate.cpp
new file mode 100644
index 000000000..db9a41ae7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate.cpp
@@ -0,0 +1,107 @@
+/*
+Stacked-batch-data version of point interpolation, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "interpolate_gpu.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+void three_nn_wrapper_stack(at::Tensor unknown_tensor, 
+    at::Tensor unknown_batch_cnt_tensor, at::Tensor known_tensor, 
+    at::Tensor known_batch_cnt_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor){
+    // unknown: (N1 + N2 ..., 3)
+    // unknown_batch_cnt: (batch_size), [N1, N2, ...]
+    // known: (M1 + M2 ..., 3)
+    // known_batch_cnt: (batch_size), [M1, M2, ...]
+    // Return:
+    // dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
+    // idx: (N1 + N2 ..., 3)  index of the three nearest neighbors
+    CHECK_INPUT(unknown_tensor);
+    CHECK_INPUT(unknown_batch_cnt_tensor);
+    CHECK_INPUT(known_tensor);
+    CHECK_INPUT(known_batch_cnt_tensor);
+    CHECK_INPUT(dist2_tensor);
+    CHECK_INPUT(idx_tensor);
+
+    int batch_size = unknown_batch_cnt_tensor.size(0);
+    int N = unknown_tensor.size(0);
+    int M = known_tensor.size(0);
+    const float *unknown = unknown_tensor.data<float>();
+    const int *unknown_batch_cnt = unknown_batch_cnt_tensor.data<int>();
+    const float *known = known_tensor.data<float>();
+    const int *known_batch_cnt = known_batch_cnt_tensor.data<int>();
+    float *dist2 = dist2_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+
+    three_nn_kernel_launcher_stack(batch_size, N, M, unknown, unknown_batch_cnt, known, known_batch_cnt, dist2, idx);
+}
+
+
+void three_interpolate_wrapper_stack(at::Tensor features_tensor, 
+    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor) {
+    // features_tensor: (M1 + M2 ..., C)
+    // idx_tensor: [N1 + N2 ..., 3]
+    // weight_tensor: [N1 + N2 ..., 3]
+    // Return:
+    // out_tensor: (N1 + N2 ..., C)
+    CHECK_INPUT(features_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(weight_tensor);
+    CHECK_INPUT(out_tensor);
+
+    int N = out_tensor.size(0);
+    int channels = features_tensor.size(1);
+    const float *features = features_tensor.data<float>();
+    const float *weight = weight_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *out = out_tensor.data<float>();
+
+    three_interpolate_kernel_launcher_stack(N, channels, features, idx, weight, out);
+}
+
+
+void three_interpolate_grad_wrapper_stack(at::Tensor grad_out_tensor, at::Tensor idx_tensor,
+    at::Tensor weight_tensor, at::Tensor grad_features_tensor) {
+    // grad_out_tensor: (N1 + N2 ..., C)
+    // idx_tensor: [N1 + N2 ..., 3]
+    // weight_tensor: [N1 + N2 ..., 3]
+    // Return:
+    // grad_features_tensor: (M1 + M2 ..., C)
+    CHECK_INPUT(grad_out_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(weight_tensor);
+    CHECK_INPUT(grad_features_tensor);
+
+    int N = grad_out_tensor.size(0);
+    int channels = grad_out_tensor.size(1);
+    const float *grad_out = grad_out_tensor.data<float>();
+    const float *weight = weight_tensor.data<float>();
+    const int *idx = idx_tensor.data<int>();
+    float *grad_features = grad_features_tensor.data<float>();
+    
+    // printf("N=%d, channels=%d\n", N, channels);
+    three_interpolate_grad_kernel_launcher_stack(N, channels, grad_out, idx, weight, grad_features);
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate_gpu.cu
new file mode 100644
index 000000000..b87fa3460
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate_gpu.cu
@@ -0,0 +1,196 @@
+/*
+Stacked-batch-data version of point interpolation, modified from the original implementation of official PointNet++ codes.
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <float.h>
+
+#include "cuda_utils.h"
+#include "interpolate_gpu.h"
+
+
+__global__ void three_nn_kernel_stack(int batch_size, int N, int M, const float *unknown, 
+    const int *unknown_batch_cnt, const float *known, const int *known_batch_cnt,
+    float *dist2, int *idx) {
+    // unknown: (N1 + N2 ..., 3)
+    // unknown_batch_cnt: (batch_size), [N1, N2, ...]
+    // known: (M1 + M2 ..., 3)
+    // known_batch_cnt: (batch_size), [M1, M2, ...]
+    // Return:
+    // dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
+    // idx: (N1 + N2 ..., 3)  index of the three nearest neighbors
+
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= N) return;
+
+    int bs_idx = 0, pt_cnt = unknown_batch_cnt[0];
+    for (int k = 1; k < batch_size; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += unknown_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int cur_num_known_points = known_batch_cnt[bs_idx];
+
+    int known_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) known_batch_start_idx += known_batch_cnt[k];
+
+    known += known_batch_start_idx * 3;
+    unknown += pt_idx * 3;
+    dist2 += pt_idx * 3;
+    idx += pt_idx * 3;
+
+    float ux = unknown[0];
+    float uy = unknown[1];
+    float uz = unknown[2];
+
+    float best1 = FLT_MAX, best2 = FLT_MAX, best3 = FLT_MAX;
+    int besti1 = 0, besti2 = 0, besti3 = 0;
+    for (int k = 0; k < cur_num_known_points; ++k) {
+        float x = known[k * 3 + 0];
+        float y = known[k * 3 + 1];
+        float z = known[k * 3 + 2];
+        float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
+        if (d < best1) {
+            best3 = best2; besti3 = besti2;
+            best2 = best1; besti2 = besti1;
+            best1 = d; besti1 = k;
+        } 
+        else if (d < best2) {
+            best3 = best2; besti3 = besti2;
+            best2 = d; besti2 = k;
+        } 
+        else if (d < best3) {
+            best3 = d; besti3 = k;
+        }
+    }
+    dist2[0] = best1; dist2[1] = best2; dist2[2] = best3;
+    idx[0] = besti1 + known_batch_start_idx; 
+    idx[1] = besti2 + known_batch_start_idx; 
+    idx[2] = besti3 + known_batch_start_idx;
+}
+
+
+void three_nn_kernel_launcher_stack(int batch_size, int N, int M, const float *unknown, 
+    const int *unknown_batch_cnt, const float *known, const int *known_batch_cnt,
+    float *dist2, int *idx) {
+    // unknown: (N1 + N2 ..., 3)
+    // unknown_batch_cnt: (batch_size), [N1, N2, ...]
+    // known: (M1 + M2 ..., 3)
+    // known_batch_cnt: (batch_size), [M1, M2, ...]
+    // Return:
+    // dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
+    // idx: (N1 + N2 ..., 3)  index of the three nearest neighbors
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(N, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    three_nn_kernel_stack<<<blocks, threads>>>(
+        batch_size, N, M, unknown, unknown_batch_cnt, 
+        known, known_batch_cnt, dist2, idx
+    );
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+
+__global__ void three_interpolate_kernel_stack(int N, int channels, const float *features, 
+    const int *idx, const float *weight, float *out) {
+    // features: (M1 + M2 ..., C)
+    // idx: [N1 + N2 ..., 3]
+    // weight: [N1 + N2 ..., 3]
+    // Return:
+    // out: (N1 + N2 ..., C)
+
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= N || c_idx >= channels) return;
+
+    weight += pt_idx * 3;
+    idx += pt_idx * 3;
+    out += pt_idx * channels + c_idx;
+
+    out[0] = weight[0] * features[idx[0] * channels + c_idx] + 
+        weight[1] * features[idx[1] * channels + c_idx] + 
+        weight[2] * features[idx[2] * channels + c_idx];
+}
+
+
+
+void three_interpolate_kernel_launcher_stack(int N, int channels,
+    const float *features, const int *idx, const float *weight, float *out) {
+    // features: (M1 + M2 ..., C)
+    // idx: [N1 + N2 ..., 3]
+    // weight: [N1 + N2 ..., 3]
+    // Return:
+    // out: (N1 + N2 ..., C)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(N, THREADS_PER_BLOCK), channels);
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_kernel_stack<<<blocks, threads>>>(N, channels, features, idx, weight, out);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+__global__ void three_interpolate_grad_kernel_stack(int N, int channels, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_features) {
+    // grad_out_tensor: (N1 + N2 ..., C)
+    // idx_tensor: [N1 + N2 ..., 3]
+    // weight_tensor: [N1 + N2 ..., 3]
+    // Return:
+    // grad_features_tensor: (M1 + M2 ..., C)
+
+    int c_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= N || c_idx >= channels) return;
+
+    grad_out += pt_idx * channels + c_idx;
+    weight += pt_idx * 3;
+    idx += pt_idx * 3;
+    
+    // printf("pt_idx=%d, c_idx=%d, idx=(%d, %d, %d), grad_out=%f\n", pt_idx, c_idx, idx[0], idx[1], idx[2], grad_out[0]);
+
+    atomicAdd(grad_features + idx[0] * channels + c_idx, grad_out[0] * weight[0]);
+    atomicAdd(grad_features + idx[1] * channels + c_idx, grad_out[0] * weight[1]);
+    atomicAdd(grad_features + idx[2] * channels + c_idx, grad_out[0] * weight[2]);
+}
+
+
+void three_interpolate_grad_kernel_launcher_stack(int N, int channels, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_features) {
+    // grad_out_tensor: (N1 + N2 ..., C)
+    // idx_tensor: [N1 + N2 ..., 3]
+    // weight_tensor: [N1 + N2 ..., 3]
+    // Return:
+    // grad_features_tensor: (M1 + M2 ..., C)
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(N, THREADS_PER_BLOCK), channels);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    three_interpolate_grad_kernel_stack<<<blocks, threads>>>(
+        N, channels, grad_out, idx, weight, grad_features
+    );
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate_gpu.h
new file mode 100644
index 000000000..12775ec33
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/interpolate_gpu.h
@@ -0,0 +1,39 @@
+#ifndef _INTERPOLATE_GPU_H
+#define _INTERPOLATE_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include<vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+
+void three_nn_wrapper_stack(at::Tensor unknown_tensor, 
+    at::Tensor unknown_batch_cnt_tensor, at::Tensor known_tensor, 
+    at::Tensor known_batch_cnt_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor);
+
+
+void three_interpolate_wrapper_stack(at::Tensor features_tensor, 
+    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor);
+
+
+
+void three_interpolate_grad_wrapper_stack(at::Tensor grad_out_tensor, at::Tensor idx_tensor,
+    at::Tensor weight_tensor, at::Tensor grad_features_tensor);
+
+
+void three_nn_kernel_launcher_stack(int batch_size, int N, int M, const float *unknown, 
+    const int *unknown_batch_cnt, const float *known, const int *known_batch_cnt,
+    float *dist2, int *idx);
+
+
+void three_interpolate_kernel_launcher_stack(int N, int channels,
+    const float *features, const int *idx, const float *weight, float *out);
+
+
+
+void three_interpolate_grad_kernel_launcher_stack(int N, int channels, const float *grad_out, 
+    const int *idx, const float *weight, float *grad_features);
+
+
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/pointnet2_api.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/pointnet2_api.cpp
new file mode 100644
index 000000000..1b61e4158
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/pointnet2_api.cpp
@@ -0,0 +1,31 @@
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+
+#include "ball_query_gpu.h"
+#include "group_points_gpu.h"
+#include "sampling_gpu.h"
+#include "interpolate_gpu.h"
+#include "voxel_query_gpu.h"
+#include "vector_pool_gpu.h"
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("ball_query_wrapper", &ball_query_wrapper_stack, "ball_query_wrapper_stack");
+    m.def("voxel_query_wrapper", &voxel_query_wrapper_stack, "voxel_query_wrapper_stack");
+
+    m.def("farthest_point_sampling_wrapper", &farthest_point_sampling_wrapper, "farthest_point_sampling_wrapper");
+    m.def("stack_farthest_point_sampling_wrapper", &stack_farthest_point_sampling_wrapper, "stack_farthest_point_sampling_wrapper");
+
+    m.def("group_points_wrapper", &group_points_wrapper_stack, "group_points_wrapper_stack");
+    m.def("group_points_grad_wrapper", &group_points_grad_wrapper_stack, "group_points_grad_wrapper_stack");
+
+    m.def("three_nn_wrapper", &three_nn_wrapper_stack, "three_nn_wrapper_stack");
+    m.def("three_interpolate_wrapper", &three_interpolate_wrapper_stack, "three_interpolate_wrapper_stack");
+    m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper_stack, "three_interpolate_grad_wrapper_stack");
+
+    m.def("query_stacked_local_neighbor_idxs_wrapper_stack", &query_stacked_local_neighbor_idxs_wrapper_stack, "query_stacked_local_neighbor_idxs_wrapper_stack");
+    m.def("query_three_nn_by_stacked_local_idxs_wrapper_stack", &query_three_nn_by_stacked_local_idxs_wrapper_stack, "query_three_nn_by_stacked_local_idxs_wrapper_stack");
+
+    m.def("vector_pool_wrapper", &vector_pool_wrapper_stack, "vector_pool_grad_wrapper_stack");
+    m.def("vector_pool_grad_wrapper", &vector_pool_grad_wrapper_stack, "vector_pool_grad_wrapper_stack");
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling.cpp
new file mode 100644
index 000000000..6bc6b43b6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling.cpp
@@ -0,0 +1,57 @@
+#include <torch/serialize/tensor.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <vector>
+#include "sampling_gpu.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int farthest_point_sampling_wrapper(int b, int n, int m,
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) {
+
+    CHECK_INPUT(points_tensor);
+    CHECK_INPUT(temp_tensor);
+    CHECK_INPUT(idx_tensor);
+
+    const float *points = points_tensor.data<float>();
+    float *temp = temp_tensor.data<float>();
+    int *idx = idx_tensor.data<int>();
+
+    farthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx);
+    return 1;
+}
+
+
+int stack_farthest_point_sampling_wrapper(at::Tensor points_tensor,
+  at::Tensor temp_tensor, at::Tensor xyz_batch_cnt_tensor, at::Tensor idx_tensor,
+  at::Tensor num_sampled_points_tensor) {
+
+    CHECK_INPUT(points_tensor);
+    CHECK_INPUT(temp_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(xyz_batch_cnt_tensor);
+    CHECK_INPUT(num_sampled_points_tensor);
+
+    int batch_size = xyz_batch_cnt_tensor.size(0);
+    int N = points_tensor.size(0);
+    const float *points = points_tensor.data<float>();
+    float *temp = temp_tensor.data<float>();
+    int *xyz_batch_cnt = xyz_batch_cnt_tensor.data<int>();
+    int *idx = idx_tensor.data<int>();
+    int *num_sampled_points = num_sampled_points_tensor.data<int>();
+
+    stack_farthest_point_sampling_kernel_launcher(N, batch_size, points, temp, xyz_batch_cnt, idx, num_sampled_points);
+    return 1;
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.cu
new file mode 100644
index 000000000..1629276ca
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.cu
@@ -0,0 +1,350 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <algorithm>
+
+#include "cuda_utils.h"
+#include "sampling_gpu.h"
+#define TOTAL_THREADS 1024
+
+
+inline int opt_n_threads(int work_size) {
+    const int pow_2 = std::log(static_cast<float>(work_size)) / std::log(2.0);
+
+    return std::max(std::min(1 << pow_2, TOTAL_THREADS), 1);
+}
+
+
+__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2){
+    const float v1 = dists[idx1], v2 = dists[idx2];
+    const int i1 = dists_i[idx1], i2 = dists_i[idx2];
+    dists[idx1] = std::max(v1, v2);
+    dists_i[idx1] = v2 > v1 ? i2 : i1;
+}
+
+
+template <unsigned int block_size>
+__global__ void farthest_point_sampling_kernel(int b, int n, int m,
+    const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) {
+    // dataset: (B, N, 3)
+    // tmp: (B, N)
+    // output:
+    //      idx: (B, M)
+
+    if (m <= 0) return;
+    __shared__ float dists[block_size];
+    __shared__ int dists_i[block_size];
+
+    int batch_index = blockIdx.x;
+    dataset += batch_index * n * 3;
+    temp += batch_index * n;
+    idxs += batch_index * m;
+
+    int tid = threadIdx.x;
+    const int stride = block_size;
+
+    int old = 0;
+    if (threadIdx.x == 0)
+    idxs[0] = old;
+
+    __syncthreads();
+    for (int j = 1; j < m; j++) {
+    int besti = 0;
+    float best = -1;
+    float x1 = dataset[old * 3 + 0];
+    float y1 = dataset[old * 3 + 1];
+    float z1 = dataset[old * 3 + 2];
+    for (int k = tid; k < n; k += stride) {
+        float x2, y2, z2;
+        x2 = dataset[k * 3 + 0];
+        y2 = dataset[k * 3 + 1];
+        z2 = dataset[k * 3 + 2];
+        // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
+        // if (mag <= 1e-3)
+        // continue;
+
+        float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
+        float d2 = std::min(d, temp[k]);
+        temp[k] = d2;
+        besti = d2 > best ? k : besti;
+        best = d2 > best ? d2 : best;
+    }
+    dists[tid] = best;
+    dists_i[tid] = besti;
+    __syncthreads();
+
+    if (block_size >= 1024) {
+        if (tid < 512) {
+            __update(dists, dists_i, tid, tid + 512);
+        }
+        __syncthreads();
+    }
+
+    if (block_size >= 512) {
+        if (tid < 256) {
+            __update(dists, dists_i, tid, tid + 256);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 256) {
+        if (tid < 128) {
+            __update(dists, dists_i, tid, tid + 128);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 128) {
+        if (tid < 64) {
+            __update(dists, dists_i, tid, tid + 64);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 64) {
+        if (tid < 32) {
+            __update(dists, dists_i, tid, tid + 32);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 32) {
+        if (tid < 16) {
+            __update(dists, dists_i, tid, tid + 16);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 16) {
+        if (tid < 8) {
+            __update(dists, dists_i, tid, tid + 8);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 8) {
+        if (tid < 4) {
+            __update(dists, dists_i, tid, tid + 4);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 4) {
+        if (tid < 2) {
+            __update(dists, dists_i, tid, tid + 2);
+        }
+        __syncthreads();
+    }
+    if (block_size >= 2) {
+        if (tid < 1) {
+            __update(dists, dists_i, tid, tid + 1);
+        }
+        __syncthreads();
+    }
+
+    old = dists_i[0];
+    if (tid == 0)
+        idxs[j] = old;
+    }
+}
+
+void farthest_point_sampling_kernel_launcher(int b, int n, int m,
+    const float *dataset, float *temp, int *idxs) {
+    // dataset: (B, N, 3)
+    // tmp: (B, N)
+    // output:
+    //      idx: (B, M)
+
+    cudaError_t err;
+    unsigned int n_threads = opt_n_threads(n);
+
+    switch (n_threads) {
+        case 1024:
+        farthest_point_sampling_kernel<1024><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 512:
+        farthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 256:
+        farthest_point_sampling_kernel<256><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 128:
+        farthest_point_sampling_kernel<128><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 64:
+        farthest_point_sampling_kernel<64><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 32:
+        farthest_point_sampling_kernel<32><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 16:
+        farthest_point_sampling_kernel<16><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 8:
+        farthest_point_sampling_kernel<8><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 4:
+        farthest_point_sampling_kernel<4><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 2:
+        farthest_point_sampling_kernel<2><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        case 1:
+        farthest_point_sampling_kernel<1><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        default:
+        farthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs);
+    }
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
+
+
+template <unsigned int block_size>
+__global__ void stack_farthest_point_sampling_kernel(int batch_size, int N,
+    const float *dataset, float *temp, int *xyz_batch_cnt, int *idxs, int *num_sampled_points) {
+    // """
+    // Args:
+    //     ctx:
+    //     dataset: (N1 + N2 + ..., 3) where N > npoint
+    //     temp: (N1 + N2 + ...) where N > npoint
+    //     xyz_batch_cnt: [N1, N2, ...]
+    //     num_sampled_points: [M1, M2, ...] int, number of features in the sampled set
+
+    // Returns:
+    //     idxs: (npoint.sum()) tensor containing the set,
+    //     npoint: (M1, M2, ...)
+    // """
+
+    __shared__ float dists[block_size];
+    __shared__ int dists_i[block_size];
+
+    int bs_idx = blockIdx.x;
+
+    int xyz_batch_start_idx = 0, idxs_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++){
+        xyz_batch_start_idx += xyz_batch_cnt[k];
+        idxs_start_idx += num_sampled_points[k];
+    }
+
+    dataset += xyz_batch_start_idx * 3;
+    temp += xyz_batch_start_idx;
+    idxs += idxs_start_idx;
+
+    int n = xyz_batch_cnt[bs_idx];
+    int m = num_sampled_points[bs_idx];
+
+    int tid = threadIdx.x;
+    const int stride = block_size;
+
+    int old = 0;
+    if (threadIdx.x == 0) idxs[0] = xyz_batch_start_idx;
+
+    __syncthreads();
+    for (int j = 1; j < m; j++) {
+        int besti = 0;
+        float best = -1;
+        float x1 = dataset[old * 3 + 0];
+        float y1 = dataset[old * 3 + 1];
+        float z1 = dataset[old * 3 + 2];
+        for (int k = tid; k < n; k += stride) {
+            float x2, y2, z2;
+            x2 = dataset[k * 3 + 0];
+            y2 = dataset[k * 3 + 1];
+            z2 = dataset[k * 3 + 2];
+            // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
+            // if (mag <= 1e-3)
+            // continue;
+
+            float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
+            float d2 = std::min(d, temp[k]);
+            temp[k] = d2;
+            besti = d2 > best ? k : besti;
+            best = d2 > best ? d2 : best;
+        }
+        dists[tid] = best;
+        dists_i[tid] = besti;
+        __syncthreads();
+
+        if (block_size >= 1024) {
+            if (tid < 512) {
+                __update(dists, dists_i, tid, tid + 512);
+            }
+            __syncthreads();
+        }
+
+        if (block_size >= 512) {
+            if (tid < 256) {
+                __update(dists, dists_i, tid, tid + 256);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 256) {
+            if (tid < 128) {
+                __update(dists, dists_i, tid, tid + 128);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 128) {
+            if (tid < 64) {
+                __update(dists, dists_i, tid, tid + 64);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 64) {
+            if (tid < 32) {
+                __update(dists, dists_i, tid, tid + 32);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 32) {
+            if (tid < 16) {
+                __update(dists, dists_i, tid, tid + 16);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 16) {
+            if (tid < 8) {
+                __update(dists, dists_i, tid, tid + 8);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 8) {
+            if (tid < 4) {
+                __update(dists, dists_i, tid, tid + 4);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 4) {
+            if (tid < 2) {
+                __update(dists, dists_i, tid, tid + 2);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 2) {
+            if (tid < 1) {
+                __update(dists, dists_i, tid, tid + 1);
+            }
+            __syncthreads();
+        }
+
+        old = dists_i[0];
+        if (tid == 0)
+            idxs[j] = old + xyz_batch_start_idx;
+    }
+}
+
+
+void stack_farthest_point_sampling_kernel_launcher(int N, int batch_size,
+    const float *dataset, float *temp, int *xyz_batch_cnt, int *idxs, int *num_sampled_points) {
+    // """
+    // Args:
+    //     ctx:
+    //     dataset: (N1 + N2 + ..., 3) where N > npoint
+    //     temp: (N1 + N2 + ...) where N > npoint
+    //     xyz_batch_cnt: [N1, N2, ...]
+    //     npoint: int, number of features in the sampled set
+
+    // Returns:
+    //     idxs: (npoint.sum()) tensor containing the set,
+    //     npoint: (M1, M2, ...)
+    // """
+
+    cudaError_t err;
+    unsigned int n_threads = opt_n_threads(N);
+
+    stack_farthest_point_sampling_kernel<1024><<<batch_size, 1024>>>(
+        batch_size, N, dataset, temp, xyz_batch_cnt, idxs, num_sampled_points
+    );
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.h
new file mode 100644
index 000000000..c33996a40
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.h
@@ -0,0 +1,23 @@
+#ifndef _SAMPLING_GPU_H
+#define _SAMPLING_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <ATen/cuda/CUDAContext.h>
+#include<vector>
+
+
+int farthest_point_sampling_wrapper(int b, int n, int m,
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor);
+
+void farthest_point_sampling_kernel_launcher(int b, int n, int m,
+    const float *dataset, float *temp, int *idxs);
+
+int stack_farthest_point_sampling_wrapper(
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor xyz_batch_cnt_tensor,
+    at::Tensor idx_tensor, at::Tensor num_sampled_points_tensor);
+
+
+void stack_farthest_point_sampling_kernel_launcher(int N, int batch_size,
+    const float *dataset, float *temp, int *xyz_batch_cnt, int *idxs, int *num_sampled_points);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool.cpp
new file mode 100644
index 000000000..f3fed5ebd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool.cpp
@@ -0,0 +1,200 @@
+/*
+Vector-pool aggregation based local feature aggregation for point cloud.
+PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
+https://arxiv.org/abs/2102.00463
+
+Written by Shaoshuai Shi
+All Rights Reserved 2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "vector_pool_gpu.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int query_stacked_local_neighbor_idxs_wrapper_stack(at::Tensor support_xyz_tensor, at::Tensor xyz_batch_cnt_tensor,
+    at::Tensor new_xyz_tensor, at::Tensor new_xyz_batch_cnt_tensor,
+    at::Tensor stack_neighbor_idxs_tensor, at::Tensor start_len_tensor, at::Tensor cumsum_tensor,
+    int avg_length_of_neighbor_idxs, float max_neighbour_distance, int nsample, int neighbor_type){
+    // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
+    // new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // new_xyz_grid_idxs: (M1 + M2 ..., num_total_grids, 3) three-nn
+    // new_xyz_grid_dist2: (M1 + M2 ..., num_total_grids, 3) square of dist of three-nn
+    // num_grid_x, num_grid_y, num_grid_z: number of grids in each local area centered at new_xyz
+    // nsample: find all (-1), find limited number(>0)
+    // neighbor_type: 1: ball, others: cube
+
+    CHECK_INPUT(support_xyz_tensor);
+    CHECK_INPUT(xyz_batch_cnt_tensor);
+    CHECK_INPUT(new_xyz_tensor);
+    CHECK_INPUT(new_xyz_batch_cnt_tensor);
+    CHECK_INPUT(stack_neighbor_idxs_tensor);
+    CHECK_INPUT(start_len_tensor);
+    CHECK_INPUT(cumsum_tensor);
+
+    const float *support_xyz = support_xyz_tensor.data<float>();
+    const int *xyz_batch_cnt = xyz_batch_cnt_tensor.data<int>();
+    const float *new_xyz = new_xyz_tensor.data<float>();
+    const int *new_xyz_batch_cnt = new_xyz_batch_cnt_tensor.data<int>();
+    int *stack_neighbor_idxs = stack_neighbor_idxs_tensor.data<int>();
+    int *start_len = start_len_tensor.data<int>();
+    int *cumsum = cumsum_tensor.data<int>();
+
+    int batch_size = xyz_batch_cnt_tensor.size(0);
+    int M = new_xyz_tensor.size(0);
+
+    query_stacked_local_neighbor_idxs_kernel_launcher_stack(
+        support_xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt,
+        stack_neighbor_idxs, start_len, cumsum, avg_length_of_neighbor_idxs,
+        max_neighbour_distance, batch_size, M, nsample, neighbor_type
+    );
+    return 0;
+}
+
+
+int query_three_nn_by_stacked_local_idxs_wrapper_stack(at::Tensor support_xyz_tensor,
+    at::Tensor new_xyz_tensor, at::Tensor new_xyz_grid_centers_tensor,
+    at::Tensor new_xyz_grid_idxs_tensor, at::Tensor new_xyz_grid_dist2_tensor,
+    at::Tensor stack_neighbor_idxs_tensor, at::Tensor start_len_tensor,
+    int M, int num_total_grids){
+    // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
+    // new_xyz_grid_idxs: (M1 + M2 ..., num_total_grids, 3) three-nn
+    // new_xyz_grid_dist2: (M1 + M2 ..., num_total_grids, 3) square of dist of three-nn
+    // stack_neighbor_idxs: (max_length_of_neighbor_idxs)
+    // start_len: (M1 + M2, 2)  [start_offset, neighbor_length]
+
+    CHECK_INPUT(support_xyz_tensor);
+    CHECK_INPUT(new_xyz_tensor);
+    CHECK_INPUT(new_xyz_grid_centers_tensor);
+    CHECK_INPUT(new_xyz_grid_idxs_tensor);
+    CHECK_INPUT(new_xyz_grid_dist2_tensor);
+    CHECK_INPUT(stack_neighbor_idxs_tensor);
+    CHECK_INPUT(start_len_tensor);
+
+    const float *support_xyz = support_xyz_tensor.data<float>();
+    const float *new_xyz = new_xyz_tensor.data<float>();
+    const float *new_xyz_grid_centers = new_xyz_grid_centers_tensor.data<float>();
+    int *new_xyz_grid_idxs = new_xyz_grid_idxs_tensor.data<int>();
+    float *new_xyz_grid_dist2 = new_xyz_grid_dist2_tensor.data<float>();
+    int *stack_neighbor_idxs = stack_neighbor_idxs_tensor.data<int>();
+    int *start_len = start_len_tensor.data<int>();
+
+    query_three_nn_by_stacked_local_idxs_kernel_launcher_stack(
+        support_xyz, new_xyz, new_xyz_grid_centers,
+        new_xyz_grid_idxs, new_xyz_grid_dist2, stack_neighbor_idxs, start_len,
+        M, num_total_grids
+    );
+    return 0;
+}
+
+
+int vector_pool_wrapper_stack(at::Tensor support_xyz_tensor, at::Tensor xyz_batch_cnt_tensor,
+    at::Tensor support_features_tensor, at::Tensor new_xyz_tensor, at::Tensor new_xyz_batch_cnt_tensor,
+    at::Tensor new_features_tensor, at::Tensor new_local_xyz_tensor,
+    at::Tensor point_cnt_of_grid_tensor, at::Tensor grouped_idxs_tensor,
+    int num_grid_x, int num_grid_y, int num_grid_z, float max_neighbour_distance, int use_xyz,
+    int num_max_sum_points, int nsample, int neighbor_type, int pooling_type){
+    // support_xyz_tensor: (N1 + N2 ..., 3) xyz coordinates of the features
+    // support_features_tensor: (N1 + N2 ..., C)
+    // xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // new_xyz_tensor: (M1 + M2 ..., 3) centers of new positions
+    // new_features_tensor: (M1 + M2 ..., C)
+    // new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // point_cnt_of_grid: (M1 + M2 ..., num_total_grids)
+    // grouped_idxs_tensor: (num_max_sum_points, 3)
+    // num_grid_x, num_grid_y, num_grid_z: number of grids in each local area centered at new_xyz
+    // use_xyz: whether to calculate new_local_xyz
+    // neighbor_type: 1: ball, others: cube
+    // pooling_type: 0: avg_pool, 1: random choice
+
+    CHECK_INPUT(support_xyz_tensor);
+    CHECK_INPUT(support_features_tensor);
+    CHECK_INPUT(xyz_batch_cnt_tensor);
+    CHECK_INPUT(new_xyz_tensor);
+    CHECK_INPUT(new_xyz_batch_cnt_tensor);
+    CHECK_INPUT(new_features_tensor);
+    CHECK_INPUT(new_local_xyz_tensor);
+    CHECK_INPUT(point_cnt_of_grid_tensor);
+    CHECK_INPUT(grouped_idxs_tensor);
+
+    const float *support_xyz = support_xyz_tensor.data<float>();
+    const float *support_features = support_features_tensor.data<float>();
+    const int *xyz_batch_cnt = xyz_batch_cnt_tensor.data<int>();
+    const float *new_xyz = new_xyz_tensor.data<float>();
+    const int *new_xyz_batch_cnt = new_xyz_batch_cnt_tensor.data<int>();
+    float *new_features = new_features_tensor.data<float>();
+    float *new_local_xyz = new_local_xyz_tensor.data<float>();
+    int *point_cnt_of_grid = point_cnt_of_grid_tensor.data<int>();
+    int *grouped_idxs = grouped_idxs_tensor.data<int>();
+
+    int N = support_xyz_tensor.size(0);
+    int batch_size = xyz_batch_cnt_tensor.size(0);
+    int M = new_xyz_tensor.size(0);
+    int num_c_out = new_features_tensor.size(1);
+    int num_c_in = support_features_tensor.size(1);
+    int num_total_grids = point_cnt_of_grid_tensor.size(1);
+
+    int cum_sum = vector_pool_kernel_launcher_stack(
+        support_xyz, support_features, xyz_batch_cnt,
+        new_xyz, new_features, new_local_xyz, new_xyz_batch_cnt,
+        point_cnt_of_grid, grouped_idxs,
+        num_grid_x, num_grid_y, num_grid_z, max_neighbour_distance,
+        batch_size, N, M, num_c_in, num_c_out, num_total_grids, use_xyz, num_max_sum_points, nsample, neighbor_type, pooling_type
+    );
+    return cum_sum;
+}
+
+
+int vector_pool_grad_wrapper_stack(at::Tensor grad_new_features_tensor,
+    at::Tensor point_cnt_of_grid_tensor, at::Tensor grouped_idxs_tensor,
+    at::Tensor grad_support_features_tensor) {
+    // grad_new_features_tensor: (M1 + M2 ..., C_out)
+    // point_cnt_of_grid_tensor: (M1 + M2 ..., num_total_grids)
+    // grouped_idxs_tensor: (num_max_sum_points, 3) [idx of support_xyz, idx of new_xyz, idx of grid_idx in new_xyz]
+    // grad_support_features_tensor: (N1 + N2 ..., C_in)
+
+    CHECK_INPUT(grad_new_features_tensor);
+    CHECK_INPUT(point_cnt_of_grid_tensor);
+    CHECK_INPUT(grouped_idxs_tensor);
+    CHECK_INPUT(grad_support_features_tensor);
+
+    int M = grad_new_features_tensor.size(0);
+    int num_c_out = grad_new_features_tensor.size(1);
+    int N = grad_support_features_tensor.size(0);
+    int num_c_in = grad_support_features_tensor.size(1);
+    int num_total_grids = point_cnt_of_grid_tensor.size(1);
+    int num_max_sum_points = grouped_idxs_tensor.size(0);
+
+    const float *grad_new_features = grad_new_features_tensor.data<float>();
+    const int *point_cnt_of_grid = point_cnt_of_grid_tensor.data<int>();
+    const int *grouped_idxs = grouped_idxs_tensor.data<int>();
+    float *grad_support_features = grad_support_features_tensor.data<float>();
+
+    vector_pool_grad_kernel_launcher_stack(
+        grad_new_features, point_cnt_of_grid, grouped_idxs, grad_support_features,
+        N, M, num_c_out, num_c_in, num_total_grids, num_max_sum_points
+    );
+    return 1;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool_gpu.cu
new file mode 100644
index 000000000..5c33cf463
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool_gpu.cu
@@ -0,0 +1,487 @@
+/*
+Vector-pool aggregation based local feature aggregation for point cloud.
+PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
+https://arxiv.org/abs/2102.00463
+
+Written by Shaoshuai Shi
+All Rights Reserved 2020.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <float.h>
+
+#include "vector_pool_gpu.h"
+#include "cuda_utils.h"
+
+
+__global__ void query_three_nn_by_stacked_local_idxs_kernel(
+    const float *support_xyz, const float *new_xyz, const float *new_xyz_grid_centers,
+    int *new_xyz_grid_idxs, float *new_xyz_grid_dist2,
+    const int *stack_neighbor_idxs, const int *start_len,
+    int M, int num_total_grids){
+    // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
+    // new_xyz_grid_idxs: (M1 + M2 ..., num_total_grids, 3) three-nn
+    // new_xyz_grid_dist2: (M1 + M2 ..., num_total_grids, 3) square of dist of three-nn
+    // stack_neighbor_idxs: (max_length_of_neighbor_idxs)
+    // start_len: (M1 + M2, 2)  [start_offset, neighbor_length]
+
+    int grid_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (pt_idx >= M || grid_idx >= num_total_grids) return;
+
+    new_xyz += pt_idx * 3;
+    new_xyz_grid_centers += pt_idx * num_total_grids * 3 + grid_idx * 3;
+    new_xyz_grid_idxs += pt_idx * num_total_grids * 3 + grid_idx * 3;
+    new_xyz_grid_dist2 += pt_idx * num_total_grids * 3 + grid_idx * 3;
+
+    start_len += pt_idx * 2;
+    stack_neighbor_idxs += start_len[0];
+    int neighbor_length = start_len[1];
+
+    float center_x = new_xyz_grid_centers[0];
+    float center_y = new_xyz_grid_centers[1];
+    float center_z = new_xyz_grid_centers[2];
+
+    float best1 = FLT_MAX, best2 = FLT_MAX, best3 = FLT_MAX;
+    int besti1 = -1, besti2 = -1, besti3 = -1;
+    for (int k = 0; k < neighbor_length; k++){
+        int cur_neighbor_idx = stack_neighbor_idxs[k];
+
+        float x = support_xyz[cur_neighbor_idx * 3 + 0];
+        float y = support_xyz[cur_neighbor_idx * 3 + 1];
+        float z = support_xyz[cur_neighbor_idx * 3 + 2];
+
+        float d = (center_x - x) * (center_x - x) + (center_y - y) * (center_y - y) + (center_z - z) * (center_z - z);
+
+        if (d < best1) {
+            best3 = best2; besti3 = besti2;
+            best2 = best1; besti2 = besti1;
+            best1 = d; besti1 = cur_neighbor_idx;
+        }
+        else if (d < best2) {
+            best3 = best2; besti3 = besti2;
+            best2 = d; besti2 = cur_neighbor_idx;
+        }
+        else if (d < best3) {
+            best3 = d; besti3 = cur_neighbor_idx;
+        }
+    }
+    if (besti2 == -1){
+        besti2 = besti1; best2 = best1;
+    }
+    if (besti3 == -1){
+        besti3 = besti1; best3 = best1;
+    }
+    new_xyz_grid_dist2[0] = best1;
+    new_xyz_grid_dist2[1] = best2;
+    new_xyz_grid_dist2[2] = best3;
+    new_xyz_grid_idxs[0] = besti1;
+    new_xyz_grid_idxs[1] = besti2;
+    new_xyz_grid_idxs[2] = besti3;
+}
+
+
+int query_three_nn_by_stacked_local_idxs_kernel_launcher_stack(
+    const float *support_xyz, const float *new_xyz, const float *new_xyz_grid_centers,
+    int *new_xyz_grid_idxs, float *new_xyz_grid_dist2,
+    const int *stack_neighbor_idxs, const int *start_len,
+    int M, int num_total_grids){
+    // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
+    // new_xyz_grid_idxs: (M1 + M2 ..., num_total_grids, 3) three-nn
+    // new_xyz_grid_dist2: (M1 + M2 ..., num_total_grids, 3) square of dist of three-nn
+    // stack_neighbor_idxs: (max_length_of_neighbor_idxs)
+    // start_len: (M1 + M2, 2)  [start_offset, neighbor_length]
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(M, THREADS_PER_BLOCK), num_total_grids);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    query_three_nn_by_stacked_local_idxs_kernel<<<blocks, threads>>>(
+        support_xyz, new_xyz, new_xyz_grid_centers,
+        new_xyz_grid_idxs, new_xyz_grid_dist2, stack_neighbor_idxs, start_len,
+        M, num_total_grids
+    );
+
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+    return 0;
+}
+
+
+__global__ void query_stacked_local_neighbor_idxs_kernel(
+    const float *support_xyz, const int *xyz_batch_cnt, const float *new_xyz, const int *new_xyz_batch_cnt,
+    int *stack_neighbor_idxs, int *start_len, int *cumsum, int avg_length_of_neighbor_idxs,
+    float max_neighbour_distance, int batch_size, int M, int nsample, int neighbor_type){
+    // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // stack_neighbor_idxs: (max_length_of_neighbor_idxs)
+    // start_len: (M1 + M2, 2)  [start_offset, neighbor_length]
+    // cumsum: (1), max offset of current data in stack_neighbor_idxs
+    // max_neighbour_distance: float
+    // nsample: find all (-1), find limited number(>0)
+    // neighbor_type: 1: ball, others: cube
+
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= M) return;
+
+    int bs_idx = 0, pt_cnt = new_xyz_batch_cnt[0];
+    for (int k = 1; k < batch_size; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += new_xyz_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int xyz_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) xyz_batch_start_idx += xyz_batch_cnt[k];
+
+    support_xyz += xyz_batch_start_idx * 3;
+    new_xyz += pt_idx * 3;
+    start_len += pt_idx * 2;
+
+    float new_x = new_xyz[0];
+    float new_y = new_xyz[1];
+    float new_z = new_xyz[2];
+    int n = xyz_batch_cnt[bs_idx];
+
+    float local_x, local_y, local_z;
+    float radius2 = max_neighbour_distance * max_neighbour_distance;
+
+    int temp_idxs[1000];
+
+    int sample_cnt = 0;
+    for (int k = 0; k < n; ++k) {
+        local_x = support_xyz[k * 3 + 0] - new_x;
+        local_y = support_xyz[k * 3 + 1] - new_y;
+        local_z = support_xyz[k * 3 + 2] - new_z;
+
+        if (neighbor_type == 1){
+            // ball
+            if (local_x * local_x + local_y * local_y + local_z * local_z > radius2){
+                continue;
+            }
+        }
+        else{
+            // voxel
+            if ((fabs(local_x) > max_neighbour_distance) |
+                (fabs(local_y) > max_neighbour_distance) |
+                (fabs(local_z) > max_neighbour_distance)){
+                continue;
+            }
+        }
+        if (sample_cnt < 1000){
+            temp_idxs[sample_cnt] = k;
+        }
+        else{
+            break;
+        }
+        sample_cnt++;
+        if (nsample > 0 && sample_cnt >= nsample) break;
+    }
+    start_len[0] = atomicAdd(cumsum, sample_cnt);
+    start_len[1] = sample_cnt;
+
+    int max_thresh = avg_length_of_neighbor_idxs * M;
+    if (start_len[0] >= max_thresh) return;
+
+    stack_neighbor_idxs += start_len[0];
+    if (start_len[0] + sample_cnt >= max_thresh) sample_cnt = max_thresh - start_len[0];
+
+    for (int k = 0; k < sample_cnt; k++){
+        stack_neighbor_idxs[k] = temp_idxs[k] + xyz_batch_start_idx;
+    }
+}
+
+
+int query_stacked_local_neighbor_idxs_kernel_launcher_stack(
+    const float *support_xyz, const int *xyz_batch_cnt, const float *new_xyz, const int *new_xyz_batch_cnt,
+    int *stack_neighbor_idxs, int *start_len, int *cumsum, int avg_length_of_neighbor_idxs,
+    float max_neighbour_distance, int batch_size, int M, int nsample, int neighbor_type){
+    // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // stack_neighbor_idxs: (max_length_of_neighbor_idxs)
+    // start_len: (M1 + M2, 2)  [start_offset, neighbor_length]
+    // cumsum: (1), max offset of current data in stack_neighbor_idxs
+    // max_neighbour_distance: float
+    // nsample: find all (-1), find limited number(>0)
+    // neighbor_type: 1: ball, others: cube
+
+    cudaError_t err;
+    dim3 blocks(DIVUP(M, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    query_stacked_local_neighbor_idxs_kernel<<<blocks, threads>>>(
+        support_xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt,
+        stack_neighbor_idxs, start_len, cumsum, avg_length_of_neighbor_idxs,
+        max_neighbour_distance, batch_size, M, nsample, neighbor_type
+    );
+
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+    return 0;
+}
+
+
+__global__ void vector_pool_kernel_stack(
+    const float *support_xyz, const float *support_features, const int *xyz_batch_cnt,
+    const float *new_xyz, float *new_features, float *new_local_xyz, const int *new_xyz_batch_cnt,
+    int num_grid_x, int num_grid_y, int num_grid_z, float max_neighbour_distance,
+    int batch_size, int M, int num_c_in, int num_c_out,
+    int num_c_each_grid, int num_total_grids, int *point_cnt_of_grid, int *grouped_idxs,
+    int use_xyz, float grid_size_x, float grid_size_y,
+    float grid_size_z, int *cum_sum, int num_max_sum_points, int nsample, int neighbor_type, int pooling_type){
+    // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // support_features: (N1 + N2 ..., C)
+    // xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // new_features: (M1 + M2 ..., C), C = num_total_grids * num_c_each_grid
+    // new_local_xyz: (M1 + M2 ..., 3 * num_total_grids)
+    // new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // num_grid_x, num_grid_y, num_grid_z: number of grids in each local area centered at new_xyz
+    // point_cnt_of_grid: (M1 + M2 ..., num_total_grids)
+    // grouped_idxs: (num_max_sum_points, 3)[idx of support_xyz, idx of new_xyz, idx of grid_idx in new_xyz]
+    // use_xyz: whether to calculate new_local_xyz
+    // neighbor_type: 1: ball, others: cube
+    // pooling_type: 0: avg_pool, 1: random choice
+
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= M) return;
+
+    int bs_idx = 0, pt_cnt = new_xyz_batch_cnt[0];
+    for (int k = 1; k < batch_size; k++){
+        if (pt_idx < pt_cnt) break;
+        pt_cnt += new_xyz_batch_cnt[k];
+        bs_idx = k;
+    }
+
+    int xyz_batch_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++) xyz_batch_start_idx += xyz_batch_cnt[k];
+
+    support_xyz += xyz_batch_start_idx * 3;
+    support_features += xyz_batch_start_idx * num_c_in;
+
+    new_xyz += pt_idx * 3;
+    new_features += pt_idx * num_c_out;
+    point_cnt_of_grid += pt_idx * num_total_grids;
+    new_local_xyz += pt_idx * 3 * num_total_grids;
+
+    float new_x = new_xyz[0];
+    float new_y = new_xyz[1];
+    float new_z = new_xyz[2];
+    int n = xyz_batch_cnt[bs_idx], grid_idx_x, grid_idx_y, grid_idx_z, grid_idx;
+    float local_x, local_y, local_z;
+    float radius2 = max_neighbour_distance * max_neighbour_distance;
+
+    int sample_cnt = 0;
+    for (int k = 0; k < n; ++k) {
+        local_x = support_xyz[k * 3 + 0] - new_x;
+        local_y = support_xyz[k * 3 + 1] - new_y;
+        local_z = support_xyz[k * 3 + 2] - new_z;
+
+        if (neighbor_type == 1){
+            // ball
+            if (local_x * local_x + local_y * local_y + local_z * local_z > radius2){
+                continue;
+            }
+        }
+        else{
+            // voxel
+            if ((fabs(local_x) > max_neighbour_distance) |
+                (fabs(local_y) > max_neighbour_distance) |
+                (fabs(local_z) > max_neighbour_distance)){
+                continue;
+            }
+        }
+
+        grid_idx_x = floorf((local_x + max_neighbour_distance) / grid_size_x);
+        grid_idx_y = floorf((local_y + max_neighbour_distance) / grid_size_y);
+        grid_idx_z = floorf((local_z + max_neighbour_distance) / grid_size_z);
+        grid_idx = grid_idx_x * num_grid_y * num_grid_z + grid_idx_y * num_grid_z + grid_idx_z;
+        grid_idx = min(max(grid_idx, 0), num_total_grids - 1);
+
+        if (pooling_type == 0){
+            // avg pooling
+            point_cnt_of_grid[grid_idx] ++;
+
+            for (int i = 0; i < num_c_in; i++){
+                new_features[grid_idx * num_c_each_grid + i % num_c_each_grid] += support_features[k * num_c_in + i];
+            }
+            if (use_xyz){
+                new_local_xyz[grid_idx * 3 + 0] += local_x;
+                new_local_xyz[grid_idx * 3 + 1] += local_y;
+                new_local_xyz[grid_idx * 3 + 2] += local_z;
+            }
+
+            int cnt = atomicAdd(cum_sum, 1);
+            if (cnt >= num_max_sum_points) continue;  // continue to statistics the max number of points
+
+            grouped_idxs[cnt * 3 + 0] = xyz_batch_start_idx + k;
+            grouped_idxs[cnt * 3 + 1] = pt_idx;
+            grouped_idxs[cnt * 3 + 2] = grid_idx;
+
+            sample_cnt++;
+            if(nsample > 0 && sample_cnt >= nsample) break;
+        }
+        else if (pooling_type == 1){
+            // random choose one within sub-voxel
+            // printf("new_xyz=(%.2f, %.2f, %.2f, ), find neighbor k=%d: support_xyz=(%.2f, %.2f, %.2f), local_xyz=(%.2f, %.2f, %.2f), neighbor=%.2f, grid_idx=%d, point_cnt_of_grid_idx=%d\n",
+            // new_x, new_y, new_z, k, support_xyz[k * 3 + 0], support_xyz[k * 3 + 1], support_xyz[k * 3 + 2], local_x, local_y, local_z, max_neighbour_distance, grid_idx, point_cnt_of_grid[grid_idx]);
+
+            if (point_cnt_of_grid[grid_idx] == 0){
+                point_cnt_of_grid[grid_idx] ++;
+                for (int i = 0; i < num_c_in; i++){
+                    new_features[grid_idx * num_c_each_grid + i % num_c_each_grid] = support_features[k * num_c_in + i];
+                }
+                if (use_xyz){
+                    new_local_xyz[grid_idx * 3 + 0] = local_x;
+                    new_local_xyz[grid_idx * 3 + 1] = local_y;
+                    new_local_xyz[grid_idx * 3 + 2] = local_z;
+                }
+
+                int cnt = atomicAdd(cum_sum, 1);
+                if (cnt >= num_max_sum_points) continue;  // continue to statistics the max number of points
+
+                grouped_idxs[cnt * 3 + 0] = xyz_batch_start_idx + k;
+                grouped_idxs[cnt * 3 + 1] = pt_idx;
+                grouped_idxs[cnt * 3 + 2] = grid_idx;
+
+                sample_cnt++;
+                if(nsample > 0 && sample_cnt >= nsample || sample_cnt >= num_total_grids) break;
+            }
+
+        }
+
+    }
+}
+
+
+int vector_pool_kernel_launcher_stack(
+    const float *support_xyz, const float *support_features, const int *xyz_batch_cnt,
+    const float *new_xyz, float *new_features, float *new_local_xyz, const int *new_xyz_batch_cnt,
+    int *point_cnt_of_grid, int *grouped_idxs,
+    int num_grid_x, int num_grid_y, int num_grid_z, float max_neighbour_distance,
+    int batch_size, int N, int M, int num_c_in, int num_c_out, int num_total_grids,
+    int use_xyz, int num_max_sum_points, int nsample, int neighbor_type, int pooling_type){
+    // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+    // support_features: (N1 + N2 ..., C)
+    // xyz_batch_cnt: (batch_size), [N1, N2, ...]
+    // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+    // new_features: (M1 + M2 ..., C)
+    // new_local_xyz: (M1 + M2 ..., 3)
+    // new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+    // num_grid_x, num_grid_y, num_grid_z: number of grids in each local area centered at new_xyz
+    // use_xyz: whether to calculate new_local_xyz
+    // grouped_idxs: (num_max_sum_points, 3)[idx of support_xyz, idx of new_xyz, idx of grid_idx in new_xyz]
+    // neighbor_type: 1: ball, others: cube
+    // pooling_type: 0: avg_pool, 1: random choice
+
+
+    cudaError_t err;
+    int num_c_each_grid = num_c_out / num_total_grids;
+    float grid_size_x = max_neighbour_distance * 2 / num_grid_x;
+    float grid_size_y = max_neighbour_distance * 2 / num_grid_y;
+    float grid_size_z = max_neighbour_distance * 2 / num_grid_z;
+
+    dim3 blocks(DIVUP(M, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    int cum_sum = 0;
+    int *p_cum_sum;
+    cudaMalloc((void**)&p_cum_sum, sizeof(int));
+    cudaMemcpy(p_cum_sum, &cum_sum, sizeof(int), cudaMemcpyHostToDevice);
+
+    vector_pool_kernel_stack<<<blocks, threads>>>(
+        support_xyz, support_features, xyz_batch_cnt,
+        new_xyz, new_features, new_local_xyz, new_xyz_batch_cnt,
+        num_grid_x, num_grid_y, num_grid_z, max_neighbour_distance,
+        batch_size, M, num_c_in, num_c_out,
+        num_c_each_grid, num_total_grids, point_cnt_of_grid, grouped_idxs,
+        use_xyz, grid_size_x, grid_size_y, grid_size_z, p_cum_sum, num_max_sum_points,
+        nsample, neighbor_type, pooling_type
+    );
+
+    cudaMemcpy(&cum_sum, p_cum_sum, sizeof(int), cudaMemcpyDeviceToHost);
+
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+    return cum_sum;
+}
+
+
+__global__ void vector_pool_grad_kernel_stack(const float *grad_new_features,
+    const int *point_cnt_of_grid, const int *grouped_idxs,
+    float *grad_support_features, int N, int M, int num_c_out, int num_c_in,
+    int num_c_each_grid, int num_total_grids, int num_max_sum_points){
+    // grad_new_features: (M1 + M2 ..., C_out)
+    // point_cnt_of_grid: (M1 + M2 ..., num_total_grids)
+    // grouped_idxs: (num_max_sum_points, 3) [idx of support_xyz, idx of new_xyz, idx of grid_idx in new_xyz]
+    // grad_support_features: (N1 + N2 ..., C_in)
+
+    int channel_idx = blockIdx.y;
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (index >= num_max_sum_points || channel_idx >= num_c_in) return;
+
+    int idx_of_support_xyz = grouped_idxs[index * 3 + 0];
+    int idx_of_new_xyz = grouped_idxs[index * 3 + 1];
+    int idx_of_grid_idx = grouped_idxs[index * 3 + 2];
+
+    int num_total_pts = point_cnt_of_grid[idx_of_new_xyz * num_total_grids + idx_of_grid_idx];
+    grad_support_features += idx_of_support_xyz * num_c_in + channel_idx;
+
+    grad_new_features += idx_of_new_xyz * num_c_out + idx_of_grid_idx * num_c_each_grid;
+    int channel_idx_of_cin = channel_idx % num_c_each_grid;
+    float cur_grad = 1 / fmaxf(float(num_total_pts), 1.0);
+    atomicAdd(grad_support_features, grad_new_features[channel_idx_of_cin] * cur_grad);
+}
+
+
+void vector_pool_grad_kernel_launcher_stack(
+    const float *grad_new_features, const int *point_cnt_of_grid, const int *grouped_idxs,
+    float *grad_support_features, int N, int M, int num_c_out, int num_c_in, int num_total_grids,
+    int num_max_sum_points){
+    // grad_new_features: (M1 + M2 ..., C_out)
+    // point_cnt_of_grid: (M1 + M2 ..., num_total_grids)
+    // grouped_idxs: (num_max_sum_points, 3) [idx of support_xyz, idx of new_xyz, idx of grid_idx in new_xyz]
+    // grad_support_features: (N1 + N2 ..., C_in)
+    int num_c_each_grid = num_c_out / num_total_grids;
+
+    cudaError_t err;
+
+    dim3 blocks(DIVUP(num_max_sum_points, THREADS_PER_BLOCK), num_c_in);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    vector_pool_grad_kernel_stack<<<blocks, threads>>>(
+        grad_new_features, point_cnt_of_grid, grouped_idxs, grad_support_features,
+        N, M, num_c_out, num_c_in, num_c_each_grid, num_total_grids, num_max_sum_points
+    );
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool_gpu.h
new file mode 100644
index 000000000..febfb8553
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/vector_pool_gpu.h
@@ -0,0 +1,71 @@
+/*
+Vector-pool aggregation based local feature aggregation for point cloud.
+PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
+https://arxiv.org/abs/2102.00463
+
+Written by Shaoshuai Shi
+All Rights Reserved 2020.
+*/
+
+
+#ifndef _STACK_VECTOR_POOL_GPU_H
+#define _STACK_VECTOR_POOL_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+
+int query_stacked_local_neighbor_idxs_kernel_launcher_stack(
+    const float *support_xyz, const int *xyz_batch_cnt, const float *new_xyz, const int *new_xyz_batch_cnt,
+    int *stack_neighbor_idxs, int *start_len, int *cumsum, int avg_length_of_neighbor_idxs,
+    float max_neighbour_distance, int batch_size, int M, int nsample, int neighbor_type);
+
+int query_stacked_local_neighbor_idxs_wrapper_stack(at::Tensor support_xyz_tensor, at::Tensor xyz_batch_cnt_tensor,
+    at::Tensor new_xyz_tensor, at::Tensor new_xyz_batch_cnt_tensor,
+    at::Tensor stack_neighbor_idxs_tensor, at::Tensor start_len_tensor, at::Tensor cumsum_tensor,
+    int avg_length_of_neighbor_idxs, float max_neighbour_distance, int nsample, int neighbor_type);
+
+
+int query_three_nn_by_stacked_local_idxs_kernel_launcher_stack(
+    const float *support_xyz, const float *new_xyz, const float *new_xyz_grid_centers,
+    int *new_xyz_grid_idxs, float *new_xyz_grid_dist2,
+    const int *stack_neighbor_idxs, const int *start_len,
+    int M, int num_total_grids);
+
+int query_three_nn_by_stacked_local_idxs_wrapper_stack(at::Tensor support_xyz_tensor,
+    at::Tensor new_xyz_tensor, at::Tensor new_xyz_grid_centers_tensor,
+    at::Tensor new_xyz_grid_idxs_tensor, at::Tensor new_xyz_grid_dist2_tensor,
+    at::Tensor stack_neighbor_idxs_tensor, at::Tensor start_len_tensor,
+    int M, int num_total_grids);
+
+
+int vector_pool_wrapper_stack(at::Tensor support_xyz_tensor, at::Tensor xyz_batch_cnt_tensor,
+    at::Tensor support_features_tensor, at::Tensor new_xyz_tensor, at::Tensor new_xyz_batch_cnt_tensor,
+    at::Tensor new_features_tensor, at::Tensor new_local_xyz,
+    at::Tensor point_cnt_of_grid_tensor, at::Tensor grouped_idxs_tensor,
+    int num_grid_x, int num_grid_y, int num_grid_z, float max_neighbour_distance, int use_xyz,
+    int num_max_sum_points, int nsample, int neighbor_type, int pooling_type);
+
+
+int vector_pool_kernel_launcher_stack(
+    const float *support_xyz, const float *support_features, const int *xyz_batch_cnt,
+    const float *new_xyz, float *new_features, float * new_local_xyz, const int *new_xyz_batch_cnt,
+    int *point_cnt_of_grid, int *grouped_idxs,
+    int num_grid_x, int num_grid_y, int num_grid_z, float max_neighbour_distance,
+    int batch_size, int N, int M, int num_c_in, int num_c_out, int num_total_grids, int use_xyz,
+    int num_max_sum_points, int nsample, int neighbor_type, int pooling_type);
+
+
+int vector_pool_grad_wrapper_stack(at::Tensor grad_new_features_tensor,
+    at::Tensor point_cnt_of_grid_tensor, at::Tensor grouped_idxs_tensor,
+    at::Tensor grad_support_features_tensor);
+
+
+void vector_pool_grad_kernel_launcher_stack(
+    const float *grad_new_features, const int *point_cnt_of_grid, const int *grouped_idxs,
+    float *grad_support_features, int N, int M, int num_c_out, int num_c_in, int num_total_grids,
+    int num_max_sum_points);
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query.cpp
new file mode 100644
index 000000000..1bea75ed5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query.cpp
@@ -0,0 +1,41 @@
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include "voxel_query_gpu.h"
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+int voxel_query_wrapper_stack(int M, int R1, int R2, int R3, int nsample, float radius, 
+    int z_range, int y_range, int x_range, at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, 
+    at::Tensor new_coords_tensor, at::Tensor point_indices_tensor, at::Tensor idx_tensor) {
+    CHECK_INPUT(new_coords_tensor);
+    CHECK_INPUT(point_indices_tensor);
+    CHECK_INPUT(new_xyz_tensor);
+    CHECK_INPUT(xyz_tensor);
+    
+    const float *new_xyz = new_xyz_tensor.data<float>();
+    const float *xyz = xyz_tensor.data<float>();
+    const int *new_coords = new_coords_tensor.data<int>();
+    const int *point_indices = point_indices_tensor.data<int>();
+    int *idx = idx_tensor.data<int>();
+
+    voxel_query_kernel_launcher_stack(M, R1, R2, R3, nsample, radius, z_range, y_range, x_range, new_xyz, xyz, new_coords, point_indices, idx);
+    return 1;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query_gpu.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query_gpu.cu
new file mode 100644
index 000000000..a4953662f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query_gpu.cu
@@ -0,0 +1,113 @@
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <curand_kernel.h>
+
+#include "voxel_query_gpu.h"
+#include "cuda_utils.h"
+
+
+__global__ void voxel_query_kernel_stack(int M, int R1, int R2, int R3, int nsample, 
+            float radius, int z_range, int y_range, int x_range, const float *new_xyz, 
+            const float *xyz, const int *new_coords, const int *point_indices, int *idx) {
+    // :param new_coords: (M1 + M2 ..., 4) centers of the ball query
+    // :param point_indices: (B, Z, Y, X)
+    // output:
+    //      idx: (M1 + M2, nsample)
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pt_idx >= M) return;
+    
+    new_xyz += pt_idx * 3;
+    new_coords += pt_idx * 4;
+    idx += pt_idx * nsample;
+
+    curandState state;
+    curand_init(pt_idx, 0, 0, &state);
+    
+    float radius2 = radius * radius;
+    float new_x = new_xyz[0];
+    float new_y = new_xyz[1];
+    float new_z = new_xyz[2];
+
+    int batch_idx = new_coords[0];
+    int new_coords_z = new_coords[1];
+    int new_coords_y = new_coords[2];
+    int new_coords_x = new_coords[3];
+    
+    int cnt = 0;
+    int cnt2 = 0;
+    // for (int dz = -1*z_range; dz <= z_range; ++dz) {
+    for (int dz = -1*z_range; dz <= z_range; ++dz) {
+        int z_coord = new_coords_z + dz;
+        if (z_coord < 0 || z_coord >= R1) continue;
+
+        for (int dy = -1*y_range; dy <= y_range; ++dy) {
+            int y_coord = new_coords_y + dy;
+            if (y_coord < 0 || y_coord >= R2) continue;
+
+            for (int dx = -1*x_range; dx <= x_range; ++dx) {
+                int x_coord = new_coords_x + dx;
+                if (x_coord < 0 || x_coord >= R3) continue;
+
+                int index = batch_idx * R1 * R2 * R3 + \
+                            z_coord * R2 * R3 + \
+                            y_coord * R3 + \
+                            x_coord;
+                int neighbor_idx = point_indices[index];
+                if (neighbor_idx < 0) continue;
+                
+                float x_per = xyz[neighbor_idx*3 + 0];
+                float y_per = xyz[neighbor_idx*3 + 1];
+                float z_per = xyz[neighbor_idx*3 + 2];
+
+                float dist2 = (x_per - new_x) * (x_per - new_x) + (y_per - new_y) * (y_per - new_y) + (z_per - new_z) * (z_per - new_z);
+
+                if (dist2 > radius2) continue;
+                
+                ++cnt2;
+
+                if (cnt < nsample) {
+                    if (cnt == 0) {
+                        for (int l = 0; l < nsample; ++l) {
+                            idx[l] = neighbor_idx;
+                        }
+                    }
+                    idx[cnt] = neighbor_idx;
+                    ++cnt;
+                }
+                // else {
+                //     float rnd = curand_uniform(&state);
+                //     if (rnd < (float(nsample) / cnt2)) {
+                //         int insertidx = ceilf(curand_uniform(&state) * nsample) - 1;
+                //         idx[insertidx] = neighbor_idx;
+                //     }
+                // }
+            }
+        }
+    }
+   if (cnt == 0) idx[0] = -1;
+}
+
+
+void voxel_query_kernel_launcher_stack(int M, int R1, int R2, int R3, int nsample,
+    float radius, int z_range, int y_range, int x_range, const float *new_xyz, 
+    const float *xyz, const int *new_coords, const int *point_indices, int *idx) {
+    // :param new_coords: (M1 + M2 ..., 4) centers of the voxel query
+    // :param point_indices: (B, Z, Y, X) 
+    // output:
+    //      idx: (M1 + M2, nsample)
+
+    cudaError_t err;
+
+    dim3 blocks(DIVUP(M, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+
+    voxel_query_kernel_stack<<<blocks, threads>>>(M, R1, R2, R3, nsample, radius, z_range, y_range, x_range, new_xyz, xyz, new_coords, point_indices, idx);
+    // cudaDeviceSynchronize();  // for using printf in kernel function
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query_gpu.h
new file mode 100644
index 000000000..eddba654d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/src/voxel_query_gpu.h
@@ -0,0 +1,19 @@
+#ifndef _STACK_VOXEL_QUERY_GPU_H
+#define _STACK_VOXEL_QUERY_GPU_H
+
+#include <torch/serialize/tensor.h>
+#include <vector>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+
+int voxel_query_wrapper_stack(int M, int R1, int R2, int R3, int nsample, float radius, 
+    int z_range, int y_range, int x_range, at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, 
+    at::Tensor new_coords_tensor, at::Tensor point_indices_tensor, at::Tensor idx_tensor);
+
+
+void voxel_query_kernel_launcher_stack(int M, int R1, int R2, int R3, int nsample,
+    float radius, int z_range, int y_range, int x_range, const float *new_xyz, 
+    const float *xyz, const int *new_coords, const int *point_indices, int *idx);
+
+
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/voxel_pool_modules.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/voxel_pool_modules.py
new file mode 100644
index 000000000..033b5f1d1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/voxel_pool_modules.py
@@ -0,0 +1,131 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from . import voxel_query_utils
+from typing import List
+
+
+class NeighborVoxelSAModuleMSG(nn.Module):
+                 
+    def __init__(self, *, query_ranges: List[List[int]], radii: List[float], 
+        nsamples: List[int], mlps: List[List[int]], use_xyz: bool = True, pool_method='max_pool'):
+        """
+        Args:
+            query_ranges: list of int, list of neighbor ranges to group with
+            nsamples: list of int, number of samples in each ball query
+            mlps: list of list of int, spec of the pointnet before the global pooling for each scale
+            use_xyz:
+            pool_method: max_pool / avg_pool
+        """
+        super().__init__()
+
+        assert len(query_ranges) == len(nsamples) == len(mlps)
+        
+        self.groupers = nn.ModuleList()
+        self.mlps_in = nn.ModuleList()
+        self.mlps_pos = nn.ModuleList()
+        self.mlps_out = nn.ModuleList()
+        for i in range(len(query_ranges)):
+            max_range = query_ranges[i]
+            nsample = nsamples[i]
+            radius = radii[i]
+            self.groupers.append(voxel_query_utils.VoxelQueryAndGrouping(max_range, radius, nsample))
+            mlp_spec = mlps[i]
+
+            cur_mlp_in = nn.Sequential(
+                nn.Conv1d(mlp_spec[0], mlp_spec[1], kernel_size=1, bias=False),
+                nn.BatchNorm1d(mlp_spec[1])
+            )
+            
+            cur_mlp_pos = nn.Sequential(
+                nn.Conv2d(3, mlp_spec[1], kernel_size=1, bias=False),
+                nn.BatchNorm2d(mlp_spec[1])
+            )
+
+            cur_mlp_out = nn.Sequential(
+                nn.Conv1d(mlp_spec[1], mlp_spec[2], kernel_size=1, bias=False),
+                nn.BatchNorm1d(mlp_spec[2]),
+                nn.ReLU()
+            )
+
+            self.mlps_in.append(cur_mlp_in)
+            self.mlps_pos.append(cur_mlp_pos)
+            self.mlps_out.append(cur_mlp_out)
+
+        self.relu = nn.ReLU()
+        self.pool_method = pool_method
+
+        self.init_weights()
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1.0)
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt, \
+                                        new_coords, features, voxel2point_indices):
+        """
+        :param xyz: (N1 + N2 ..., 3) tensor of the xyz coordinates of the features
+        :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+        :param new_xyz: (M1 + M2 ..., 3)
+        :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+        :param features: (N1 + N2 ..., C) tensor of the descriptors of the the features
+        :param point_indices: (B, Z, Y, X) tensor of point indices
+        :return:
+            new_xyz: (M1 + M2 ..., 3) tensor of the new features' xyz
+            new_features: (M1 + M2 ..., \sum_k(mlps[k][-1])) tensor of the new_features descriptors
+        """
+        # change the order to [batch_idx, z, y, x]
+        new_coords = new_coords[:, [0, 3, 2, 1]].contiguous()
+        new_features_list = []
+        for k in range(len(self.groupers)):
+            # features_in: (1, C, M1+M2)
+            features_in = features.permute(1, 0).unsqueeze(0)
+            features_in = self.mlps_in[k](features_in)
+            # features_in: (1, M1+M2, C)
+            features_in = features_in.permute(0, 2, 1).contiguous()
+            # features_in: (M1+M2, C)
+            features_in = features_in.view(-1, features_in.shape[-1])
+            # grouped_features: (M1+M2, C, nsample)
+            # grouped_xyz: (M1+M2, 3, nsample)
+            grouped_features, grouped_xyz, empty_ball_mask = self.groupers[k](
+                new_coords, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt, features_in, voxel2point_indices
+            )
+            grouped_features[empty_ball_mask] = 0
+
+            # grouped_features: (1, C, M1+M2, nsample)
+            grouped_features = grouped_features.permute(1, 0, 2).unsqueeze(dim=0)
+            # grouped_xyz: (M1+M2, 3, nsample)
+            grouped_xyz = grouped_xyz - new_xyz.unsqueeze(-1)
+            grouped_xyz[empty_ball_mask] = 0
+            # grouped_xyz: (1, 3, M1+M2, nsample)
+            grouped_xyz = grouped_xyz.permute(1, 0, 2).unsqueeze(0)
+            # grouped_xyz: (1, C, M1+M2, nsample)
+            position_features = self.mlps_pos[k](grouped_xyz)
+            new_features = grouped_features + position_features
+            new_features = self.relu(new_features)
+            
+            if self.pool_method == 'max_pool':
+                new_features = F.max_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                ).squeeze(dim=-1)  # (1, C, M1 + M2 ...)
+            elif self.pool_method == 'avg_pool':
+                new_features = F.avg_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                ).squeeze(dim=-1)  # (1, C, M1 + M2 ...)
+            else:
+                raise NotImplementedError
+            
+            new_features = self.mlps_out[k](new_features)
+            new_features = new_features.squeeze(dim=0).permute(1, 0)  # (M1 + M2 ..., C)
+            new_features_list.append(new_features)
+        
+        # (M1 + M2 ..., C)
+        new_features = torch.cat(new_features_list, dim=1)
+        return new_features
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/voxel_query_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/voxel_query_utils.py
new file mode 100644
index 000000000..b22da2de1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/pointnet2/pointnet2_stack/voxel_query_utils.py
@@ -0,0 +1,100 @@
+import torch
+from torch.autograd import Variable
+from torch.autograd import Function
+import torch.nn as nn
+from typing import List
+
+from . import pointnet2_stack_cuda as pointnet2
+from . import pointnet2_utils
+
+class VoxelQuery(Function):
+
+    @staticmethod
+    def forward(ctx, max_range: int, radius: float, nsample: int, xyz: torch.Tensor, \
+                    new_xyz: torch.Tensor, new_coords: torch.Tensor, point_indices: torch.Tensor):
+        """
+        Args:
+            ctx:
+            max_range: int, max range of voxels to be grouped
+            nsample: int, maximum number of features in the balls
+            new_coords: (M1 + M2, 4), [batch_id, z, y, x] cooridnates of keypoints
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+            point_indices: (batch_size, Z, Y, X) 4-D tensor recording the point indices of voxels
+        Returns:
+            idx: (M1 + M2, nsample) tensor with the indicies of the features that form the query balls
+        """
+        assert new_xyz.is_contiguous()
+        assert xyz.is_contiguous()
+        assert new_coords.is_contiguous()
+        assert point_indices.is_contiguous()
+
+        M = new_coords.shape[0]
+        B, Z, Y, X = point_indices.shape
+        idx = torch.cuda.IntTensor(M, nsample).zero_()
+
+        z_range, y_range, x_range = max_range
+        pointnet2.voxel_query_wrapper(M, Z, Y, X, nsample, radius, z_range, y_range, x_range, \
+                    new_xyz, xyz, new_coords, point_indices, idx)
+
+        empty_ball_mask = (idx[:, 0] == -1)
+        idx[empty_ball_mask] = 0
+
+        return idx, empty_ball_mask
+
+    @staticmethod
+    def backward(ctx, a=None):
+        return None, None, None, None
+
+voxel_query = VoxelQuery.apply
+
+
+class VoxelQueryAndGrouping(nn.Module):
+    def __init__(self, max_range: int, radius: float, nsample: int):
+        """
+        Args:
+            radius: float, radius of ball
+            nsample: int, maximum number of features to gather in the ball
+        """
+        super().__init__()
+        self.max_range, self.radius, self.nsample = max_range, radius, nsample
+
+    def forward(self, new_coords: torch.Tensor, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
+                new_xyz: torch.Tensor, new_xyz_batch_cnt: torch.Tensor,
+                features: torch.Tensor, voxel2point_indices: torch.Tensor):
+        """
+        Args:
+            new_coords: (M1 + M2 ..., 3) centers voxel indices of the ball query
+            xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+            features: (N1 + N2 ..., C) tensor of features to group
+            voxel2point_indices: (B, Z, Y, X) tensor of points indices of voxels
+
+        Returns:
+            new_features: (M1 + M2, C, nsample) tensor
+        """
+        assert xyz.shape[0] == xyz_batch_cnt.sum(), 'xyz: %s, xyz_batch_cnt: %s' % (str(xyz.shape), str(new_xyz_batch_cnt))
+        assert new_coords.shape[0] == new_xyz_batch_cnt.sum(), \
+            'new_coords: %s, new_xyz_batch_cnt: %s' % (str(new_coords.shape), str(new_xyz_batch_cnt))
+        batch_size = xyz_batch_cnt.shape[0]
+        
+        # idx: (M1 + M2 ..., nsample), empty_ball_mask: (M1 + M2 ...)
+        idx1, empty_ball_mask1 = voxel_query(self.max_range, self.radius, self.nsample, xyz, new_xyz, new_coords, voxel2point_indices)
+
+        idx1 = idx1.view(batch_size, -1, self.nsample)
+        count = 0
+        for bs_idx in range(batch_size):
+            idx1[bs_idx] -= count
+            count += xyz_batch_cnt[bs_idx]
+        idx1 = idx1.view(-1, self.nsample)
+        idx1[empty_ball_mask1] = 0
+
+        idx = idx1
+        empty_ball_mask = empty_ball_mask1
+        
+        grouped_xyz = pointnet2_utils.grouping_operation(xyz, xyz_batch_cnt, idx, new_xyz_batch_cnt)
+        # grouped_features: (M1 + M2, C, nsample)
+        grouped_features = pointnet2_utils.grouping_operation(features, xyz_batch_cnt, idx, new_xyz_batch_cnt)  
+        
+        return grouped_features, grouped_xyz, empty_ball_mask
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/roiaware_pool3d_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/roiaware_pool3d_utils.py
new file mode 100644
index 000000000..d8ca924d3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/roiaware_pool3d_utils.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+from torch.autograd import Function
+
+from ...utils import common_utils
+from . import roiaware_pool3d_cuda
+
+
+def points_in_boxes_cpu(points, boxes):
+    """
+    Args:
+        points: (num_points, 3)
+        boxes: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO NOT overlaps
+    Returns:
+        point_indices: (N, num_points)
+    """
+    assert boxes.shape[1] == 7
+    assert points.shape[1] == 3
+    points, is_numpy = common_utils.check_numpy_to_torch(points)
+    boxes, is_numpy = common_utils.check_numpy_to_torch(boxes)
+
+    point_indices = points.new_zeros((boxes.shape[0], points.shape[0]), dtype=torch.int)
+    roiaware_pool3d_cuda.points_in_boxes_cpu(boxes.float().contiguous(), points.float().contiguous(), point_indices)
+
+    return point_indices.numpy() if is_numpy else point_indices
+
+
+def points_in_boxes_gpu(points, boxes):
+    """
+    :param points: (B, M, 3)
+    :param boxes: (B, T, 7), num_valid_boxes <= T
+    :return box_idxs_of_pts: (B, M), default background = -1
+    """
+    assert boxes.shape[0] == points.shape[0]
+    assert boxes.shape[2] == 7 and points.shape[2] == 3
+    batch_size, num_points, _ = points.shape
+
+    box_idxs_of_pts = points.new_zeros((batch_size, num_points), dtype=torch.int).fill_(-1)
+    roiaware_pool3d_cuda.points_in_boxes_gpu(boxes.contiguous(), points.contiguous(), box_idxs_of_pts)
+
+    return box_idxs_of_pts
+
+
+class RoIAwarePool3d(nn.Module):
+    def __init__(self, out_size, max_pts_each_voxel=128):
+        super().__init__()
+        self.out_size = out_size
+        self.max_pts_each_voxel = max_pts_each_voxel
+
+    def forward(self, rois, pts, pts_feature, pool_method='max'):
+        assert pool_method in ['max', 'avg']
+        return RoIAwarePool3dFunction.apply(rois, pts, pts_feature, self.out_size, self.max_pts_each_voxel, pool_method)
+
+
+class RoIAwarePool3dFunction(Function):
+    @staticmethod
+    def forward(ctx, rois, pts, pts_feature, out_size, max_pts_each_voxel, pool_method):
+        """
+        Args:
+            ctx:
+            rois: (N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+            pts: (npoints, 3)
+            pts_feature: (npoints, C)
+            out_size: int or tuple, like 7 or (7, 7, 7)
+            max_pts_each_voxel:
+            pool_method: 'max' or 'avg'
+
+        Returns:
+            pooled_features: (N, out_x, out_y, out_z, C)
+        """
+        assert rois.shape[1] == 7 and pts.shape[1] == 3
+        if isinstance(out_size, int):
+            out_x = out_y = out_z = out_size
+        else:
+            assert len(out_size) == 3
+            for k in range(3):
+                assert isinstance(out_size[k], int)
+            out_x, out_y, out_z = out_size
+
+        num_rois = rois.shape[0]
+        num_channels = pts_feature.shape[-1]
+        num_pts = pts.shape[0]
+
+        pooled_features = pts_feature.new_zeros((num_rois, out_x, out_y, out_z, num_channels))
+        argmax = pts_feature.new_zeros((num_rois, out_x, out_y, out_z, num_channels), dtype=torch.int)
+        pts_idx_of_voxels = pts_feature.new_zeros((num_rois, out_x, out_y, out_z, max_pts_each_voxel), dtype=torch.int)
+
+        pool_method_map = {'max': 0, 'avg': 1}
+        pool_method = pool_method_map[pool_method]
+        roiaware_pool3d_cuda.forward(rois, pts, pts_feature, argmax, pts_idx_of_voxels, pooled_features, pool_method)
+
+        ctx.roiaware_pool3d_for_backward = (pts_idx_of_voxels, argmax, pool_method, num_pts, num_channels)
+        return pooled_features
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        """
+        :param grad_out: (N, out_x, out_y, out_z, C)
+        :return:
+            grad_in: (npoints, C)
+        """
+        pts_idx_of_voxels, argmax, pool_method, num_pts, num_channels = ctx.roiaware_pool3d_for_backward
+
+        grad_in = grad_out.new_zeros((num_pts, num_channels))
+        roiaware_pool3d_cuda.backward(pts_idx_of_voxels, argmax, grad_out.contiguous(), grad_in, pool_method)
+
+        return None, None, grad_in, None, None, None
+
+
+if __name__ == '__main__':
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/src/roiaware_pool3d.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/src/roiaware_pool3d.cpp
new file mode 100644
index 000000000..00edfef81
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/src/roiaware_pool3d.cpp
@@ -0,0 +1,177 @@
+/*
+RoI-aware point cloud feature pooling
+Reference paper:  https://arxiv.org/abs/1907.03670
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+#include <assert.h>
+
+
+//#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ")
+//#define CHECK_CONTIGUOUS(x) AT_CHECK(x.is_contiguous(), #x, " must be contiguous ")
+//#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+void roiaware_pool3d_launcher(int boxes_num, int pts_num, int channels, int max_pts_each_voxel,
+    int out_x, int out_y, int out_z, const float *rois, const float *pts, const float *pts_feature,
+    int *argmax, int *pts_idx_of_voxels, float *pooled_features, int pool_method);
+
+void roiaware_pool3d_backward_launcher(int boxes_num, int out_x, int out_y, int out_z, int channels, int max_pts_each_voxel,
+    const int *pts_idx_of_voxels, const int *argmax, const float *grad_out, float *grad_in, int pool_method);
+
+void points_in_boxes_launcher(int batch_size, int boxes_num, int pts_num, const float *boxes,
+    const float *pts, int *box_idx_of_points);
+
+int roiaware_pool3d_gpu(at::Tensor rois, at::Tensor pts, at::Tensor pts_feature, at::Tensor argmax,
+    at::Tensor pts_idx_of_voxels, at::Tensor pooled_features, int pool_method){
+    // params rois: (N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (npoints, 3) [x, y, z]
+    // params pts_feature: (npoints, C)
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params pooled_features: (N, out_x, out_y, out_z, C)
+    // params pool_method: 0: max_pool 1: avg_pool
+
+//    CHECK_INPUT(rois);
+//    CHECK_INPUT(pts);
+//    CHECK_INPUT(pts_feature);
+//    CHECK_INPUT(argmax);
+//    CHECK_INPUT(pts_idx_of_voxels);
+//    CHECK_INPUT(pooled_features);
+
+    int boxes_num = rois.size(0);
+    int pts_num = pts.size(0);
+    int channels = pts_feature.size(1);
+    int max_pts_each_voxel = pts_idx_of_voxels.size(4);  // index 0 is the counter
+    int out_x = pts_idx_of_voxels.size(1);
+    int out_y = pts_idx_of_voxels.size(2);
+    int out_z = pts_idx_of_voxels.size(3);
+    assert ((out_x < 256) && (out_y < 256) && (out_z < 256));  // we encode index with 8bit
+
+    const float *rois_data = rois.data<float>();
+    const float *pts_data = pts.data<float>();
+    const float *pts_feature_data = pts_feature.data<float>();
+    int *argmax_data = argmax.data<int>();
+    int *pts_idx_of_voxels_data = pts_idx_of_voxels.data<int>();
+    float *pooled_features_data = pooled_features.data<float>();
+
+    roiaware_pool3d_launcher(boxes_num, pts_num, channels, max_pts_each_voxel, out_x, out_y, out_z,
+        rois_data, pts_data, pts_feature_data, argmax_data, pts_idx_of_voxels_data, pooled_features_data, pool_method);
+
+    return 1;
+}
+
+int roiaware_pool3d_gpu_backward(at::Tensor pts_idx_of_voxels, at::Tensor argmax, at::Tensor grad_out, at::Tensor grad_in, int pool_method){
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params grad_out: (N, out_x, out_y, out_z, C)
+    // params grad_in: (npoints, C), return value
+    // params pool_method: 0: max_pool 1: avg_pool
+
+//    CHECK_INPUT(pts_idx_of_voxels);
+//    CHECK_INPUT(argmax);
+//    CHECK_INPUT(grad_out);
+//    CHECK_INPUT(grad_in);
+
+    int boxes_num = pts_idx_of_voxels.size(0);
+    int out_x = pts_idx_of_voxels.size(1);
+    int out_y = pts_idx_of_voxels.size(2);
+    int out_z = pts_idx_of_voxels.size(3);
+    int max_pts_each_voxel = pts_idx_of_voxels.size(4);  // index 0 is the counter
+    int channels = grad_out.size(4);
+
+    const int *pts_idx_of_voxels_data = pts_idx_of_voxels.data<int>();
+    const int *argmax_data = argmax.data<int>();
+    const float *grad_out_data = grad_out.data<float>();
+    float *grad_in_data = grad_in.data<float>();
+
+    roiaware_pool3d_backward_launcher(boxes_num, out_x, out_y, out_z, channels, max_pts_each_voxel,
+        pts_idx_of_voxels_data, argmax_data, grad_out_data, grad_in_data, pool_method);
+
+    return 1;
+}
+
+int points_in_boxes_gpu(at::Tensor boxes_tensor, at::Tensor pts_tensor, at::Tensor box_idx_of_points_tensor){
+    // params boxes: (B, N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (B, npoints, 3) [x, y, z]
+    // params boxes_idx_of_points: (B, npoints), default -1
+
+//    CHECK_INPUT(boxes_tensor);
+//    CHECK_INPUT(pts_tensor);
+//    CHECK_INPUT(box_idx_of_points_tensor);
+
+    int batch_size = boxes_tensor.size(0);
+    int boxes_num = boxes_tensor.size(1);
+    int pts_num = pts_tensor.size(1);
+
+    const float *boxes = boxes_tensor.data<float>();
+    const float *pts = pts_tensor.data<float>();
+    int *box_idx_of_points = box_idx_of_points_tensor.data<int>();
+
+    points_in_boxes_launcher(batch_size, boxes_num, pts_num, boxes, pts, box_idx_of_points);
+
+    return 1;
+}
+
+
+inline void lidar_to_local_coords_cpu(float shift_x, float shift_y, float rot_angle, float &local_x, float &local_y){
+    float cosa = cos(-rot_angle), sina = sin(-rot_angle);
+    local_x = shift_x * cosa + shift_y * (-sina);
+    local_y = shift_x * sina + shift_y * cosa;
+}
+
+
+inline int check_pt_in_box3d_cpu(const float *pt, const float *box3d, float &local_x, float &local_y){
+    // param pt: (x, y, z)
+    // param box3d: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+    const float MARGIN = 1e-2;
+    float x = pt[0], y = pt[1], z = pt[2];
+    float cx = box3d[0], cy = box3d[1], cz = box3d[2];
+    float dx = box3d[3], dy = box3d[4], dz = box3d[5], rz = box3d[6];
+
+    if (fabsf(z - cz) > dz / 2.0) return 0;
+    lidar_to_local_coords_cpu(x - cx, y - cy, rz, local_x, local_y);
+    float in_flag = (fabs(local_x) < dx / 2.0 + MARGIN) & (fabs(local_y) < dy / 2.0 + MARGIN);
+    return in_flag;
+}
+
+
+int points_in_boxes_cpu(at::Tensor boxes_tensor, at::Tensor pts_tensor, at::Tensor pts_indices_tensor){
+    // params boxes: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO NOT overlaps
+    // params pts: (num_points, 3) [x, y, z]
+    // params pts_indices: (N, num_points)
+
+//    CHECK_CONTIGUOUS(boxes_tensor);
+//    CHECK_CONTIGUOUS(pts_tensor);
+//    CHECK_CONTIGUOUS(pts_indices_tensor);
+
+    int boxes_num = boxes_tensor.size(0);
+    int pts_num = pts_tensor.size(0);
+
+    const float *boxes = boxes_tensor.data<float>();
+    const float *pts = pts_tensor.data<float>();
+    int *pts_indices = pts_indices_tensor.data<int>();
+
+    float local_x = 0, local_y = 0;
+    for (int i = 0; i < boxes_num; i++){
+        for (int j = 0; j < pts_num; j++){
+            int cur_in_flag = check_pt_in_box3d_cpu(pts + j * 3, boxes + i * 7, local_x, local_y);
+            pts_indices[i * pts_num + j] = cur_in_flag;
+        }
+    }
+
+    return 1;
+}
+
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("forward", &roiaware_pool3d_gpu, "roiaware pool3d forward (CUDA)");
+    m.def("backward", &roiaware_pool3d_gpu_backward, "roiaware pool3d backward (CUDA)");
+    m.def("points_in_boxes_gpu", &points_in_boxes_gpu, "points_in_boxes_gpu forward (CUDA)");
+    m.def("points_in_boxes_cpu", &points_in_boxes_cpu, "points_in_boxes_cpu forward (CUDA)");
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/src/roiaware_pool3d_kernel.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/src/roiaware_pool3d_kernel.cu
new file mode 100644
index 000000000..5b52937f9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roiaware_pool3d/src/roiaware_pool3d_kernel.cu
@@ -0,0 +1,359 @@
+/*
+RoI-aware point cloud feature pooling
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+
+#include <math.h>
+#include <stdio.h>
+
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+// #define DEBUG
+
+
+__device__ inline void lidar_to_local_coords(float shift_x, float shift_y, float rot_angle, float &local_x, float &local_y){
+    float cosa = cos(-rot_angle), sina = sin(-rot_angle);
+    local_x = shift_x * cosa + shift_y * (-sina);
+    local_y = shift_x * sina + shift_y * cosa;
+}
+
+
+__device__ inline int check_pt_in_box3d(const float *pt, const float *box3d, float &local_x, float &local_y){
+    // param pt: (x, y, z)
+    // param box3d: [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+
+    const float MARGIN = 1e-5;
+    float x = pt[0], y = pt[1], z = pt[2];
+    float cx = box3d[0], cy = box3d[1], cz = box3d[2];
+    float dx = box3d[3], dy = box3d[4], dz = box3d[5], rz = box3d[6];
+
+    if (fabsf(z - cz) > dz / 2.0) return 0;
+    lidar_to_local_coords(x - cx, y - cy, rz, local_x, local_y);
+    float in_flag = (fabs(local_x) < dx / 2.0 + MARGIN) & (fabs(local_y) < dy / 2.0 + MARGIN);
+    return in_flag;
+}
+
+
+__global__ void generate_pts_mask_for_box3d(int boxes_num, int pts_num, int out_x, int out_y, int out_z,
+    const float *rois, const float *pts, int *pts_mask){
+    // params rois: [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (npoints, 3) [x, y, z]
+    // params pts_mask: (N, npoints): -1 means point doesnot in this box, otherwise: encode (x_idxs, y_idxs, z_idxs) by binary bit
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    int box_idx = blockIdx.y;
+    if (pt_idx >= pts_num || box_idx >= boxes_num) return;
+
+    pts += pt_idx * 3;
+    rois += box_idx * 7;
+    pts_mask += box_idx * pts_num + pt_idx;
+
+    float local_x = 0, local_y = 0;
+    int cur_in_flag = check_pt_in_box3d(pts, rois, local_x, local_y);
+
+    pts_mask[0] = -1;
+    if (cur_in_flag > 0){
+        float local_z = pts[2] - rois[2];
+        float dx = rois[3], dy = rois[4], dz = rois[5];
+
+        float x_res = dx / out_x;
+        float y_res = dy / out_y;
+        float z_res = dz / out_z;
+
+        unsigned int x_idx = int((local_x + dx / 2) / x_res);
+        unsigned int y_idx = int((local_y + dy / 2) / y_res);
+        unsigned int z_idx = int((local_z + dz / 2) / z_res);
+
+        x_idx = min(max(x_idx, 0), out_x - 1);
+        y_idx = min(max(y_idx, 0), out_y - 1);
+        z_idx = min(max(z_idx, 0), out_z - 1);
+
+        unsigned int idx_encoding = (x_idx << 16) + (y_idx << 8) + z_idx;
+        pts_mask[0] = idx_encoding;
+    }
+}
+
+
+__global__ void collect_inside_pts_for_box3d(int boxes_num, int pts_num, int max_pts_each_voxel,
+    int out_x, int out_y, int out_z, const int *pts_mask, int *pts_idx_of_voxels){
+    // params pts_mask: (N, npoints)  0 or 1
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+
+    int box_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (box_idx >= boxes_num) return;
+
+    int max_num_pts = max_pts_each_voxel - 1;  // index 0 is the counter
+    pts_idx_of_voxels += box_idx * out_x * out_y * out_z * max_pts_each_voxel;
+
+    for (int k = 0; k < pts_num; k++){
+        if (pts_mask[box_idx * pts_num + k] != -1){
+            unsigned int idx_encoding = pts_mask[box_idx * pts_num + k];
+            unsigned int x_idx = (idx_encoding >> 16) & 0xFF;
+            unsigned int y_idx = (idx_encoding >> 8) & 0xFF;
+            unsigned int z_idx = idx_encoding & 0xFF;
+            unsigned int base_offset = x_idx * out_y * out_z * max_pts_each_voxel + y_idx * out_z * max_pts_each_voxel + z_idx * max_pts_each_voxel;
+            unsigned int cnt = pts_idx_of_voxels[base_offset];
+            if (cnt < max_num_pts){
+                pts_idx_of_voxels[base_offset + cnt + 1] = k;
+                pts_idx_of_voxels[base_offset]++;
+            }
+#ifdef DEBUG
+        printf("collect: pts_%d, idx(%d, %d, %d), idx_encoding=%x\n",
+            k, x_idx, y_idx, z_idx, idx_encoding);
+#endif
+
+        }
+    }
+}
+
+
+__global__ void roiaware_maxpool3d(int boxes_num, int pts_num, int channels, int max_pts_each_voxel, int out_x,
+    int out_y, int out_z, const float *pts_feature, const int *pts_idx_of_voxels, float *pooled_features, int *argmax){
+    // params pts_feature: (npoints, C)
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel), index 0 is the counter
+    // params pooled_features: (N, out_x, out_y, out_z, C)
+    // params argmax: (N, out_x, out_y, out_z, C)
+
+    int box_idx = blockIdx.z;
+    int channel_idx = blockIdx.y;
+    int voxel_idx_flat = blockIdx.x * blockDim.x + threadIdx.x;
+
+    int x_idx = voxel_idx_flat / (out_y * out_z);
+    int y_idx = (voxel_idx_flat - x_idx * (out_y * out_z)) / out_z;
+    int z_idx = voxel_idx_flat % out_z;
+    if (box_idx >= boxes_num || channel_idx >= channels|| x_idx >= out_x || y_idx >= out_y || z_idx >= out_z) return;
+
+#ifdef DEBUG
+    printf("src pts_idx_of_voxels: (%p, ), argmax: %p\n", pts_idx_of_voxels, argmax);
+#endif
+
+    int offset_base = x_idx * out_y * out_z + y_idx * out_z + z_idx;
+    pts_idx_of_voxels += box_idx * out_x * out_y * out_z * max_pts_each_voxel + offset_base * max_pts_each_voxel;
+    pooled_features += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+    argmax += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+
+    int argmax_idx = -1;
+    float max_val = -1e50;
+
+    int total_pts = pts_idx_of_voxels[0];
+
+    for (int k = 1; k <= total_pts; k++){
+        if (pts_feature[pts_idx_of_voxels[k] * channels + channel_idx] > max_val){
+            max_val = pts_feature[pts_idx_of_voxels[k] * channels + channel_idx];
+            argmax_idx = pts_idx_of_voxels[k];
+        }
+    }
+
+    if (argmax_idx != -1){
+        pooled_features[0] = max_val;
+    }
+    argmax[0] = argmax_idx;
+
+#ifdef DEBUG
+    printf("channel_%d idx(%d, %d, %d), argmax_idx=(%d, %.3f), total=%d, after pts_idx: %p, argmax: (%p, %d)\n",
+        channel_idx, x_idx, y_idx, z_idx, argmax_idx, max_val, total_pts, pts_idx_of_voxels, argmax, argmax_idx);
+#endif
+}
+
+
+__global__ void roiaware_avgpool3d(int boxes_num, int pts_num, int channels, int max_pts_each_voxel, int out_x,
+    int out_y, int out_z, const float *pts_feature, const int *pts_idx_of_voxels, float *pooled_features){
+    // params pts_feature: (npoints, C)
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel), index 0 is the counter
+    // params pooled_features: (N, out_x, out_y, out_z, C)
+    // params argmax: (N, out_x, out_y, out_z, C)
+
+    int box_idx = blockIdx.z;
+    int channel_idx = blockIdx.y;
+    int voxel_idx_flat = blockIdx.x * blockDim.x + threadIdx.x;
+
+    int x_idx = voxel_idx_flat / (out_y * out_z);
+    int y_idx = (voxel_idx_flat - x_idx * (out_y * out_z)) / out_z;
+    int z_idx = voxel_idx_flat % out_z;
+    if (box_idx >= boxes_num || channel_idx >= channels|| x_idx >= out_x || y_idx >= out_y || z_idx >= out_z) return;
+
+    int offset_base = x_idx * out_y * out_z + y_idx * out_z + z_idx;
+    pts_idx_of_voxels += box_idx * out_x * out_y * out_z * max_pts_each_voxel + offset_base * max_pts_each_voxel;
+    pooled_features += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+
+    float sum_val = 0;
+    int total_pts = pts_idx_of_voxels[0];
+
+    for (int k = 1; k <= total_pts; k++){
+        sum_val += pts_feature[pts_idx_of_voxels[k] * channels + channel_idx];
+    }
+
+    if (total_pts > 0){
+        pooled_features[0] = sum_val / total_pts;
+    }
+}
+
+
+void roiaware_pool3d_launcher(int boxes_num, int pts_num, int channels, int max_pts_each_voxel, int out_x, int out_y, int out_z,
+    const float *rois, const float *pts, const float *pts_feature, int *argmax, int *pts_idx_of_voxels, float *pooled_features, int pool_method){
+    // params rois: (N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (npoints, 3) [x, y, z]
+    // params pts_feature: (npoints, C)
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params pooled_features: (N, out_x, out_y, out_z, C)
+    // params pool_method: 0: max_pool 1: avg_pool
+
+    int *pts_mask = NULL;
+    cudaMalloc(&pts_mask, boxes_num * pts_num * sizeof(int));  // (N, M)
+    cudaMemset(pts_mask, -1, boxes_num * pts_num * sizeof(int));
+
+    dim3 blocks_mask(DIVUP(pts_num, THREADS_PER_BLOCK), boxes_num);
+    dim3 threads(THREADS_PER_BLOCK);
+    generate_pts_mask_for_box3d<<<blocks_mask, threads>>>(boxes_num, pts_num, out_x, out_y, out_z, rois, pts, pts_mask);
+
+    // TODO: Merge the collect and pool functions, SS
+
+    dim3 blocks_collect(DIVUP(boxes_num, THREADS_PER_BLOCK));
+    collect_inside_pts_for_box3d<<<blocks_collect, threads>>>(boxes_num, pts_num, max_pts_each_voxel,
+        out_x, out_y, out_z, pts_mask, pts_idx_of_voxels);
+
+    dim3 blocks_pool(DIVUP(out_x * out_y * out_z, THREADS_PER_BLOCK), channels, boxes_num);
+    if (pool_method == 0){
+        roiaware_maxpool3d<<<blocks_pool, threads>>>(boxes_num, pts_num, channels, max_pts_each_voxel, out_x, out_y, out_z,
+            pts_feature, pts_idx_of_voxels, pooled_features, argmax);
+    }
+    else if (pool_method == 1){
+        roiaware_avgpool3d<<<blocks_pool, threads>>>(boxes_num, pts_num, channels, max_pts_each_voxel, out_x, out_y, out_z,
+            pts_feature, pts_idx_of_voxels, pooled_features);
+    }
+
+
+    cudaFree(pts_mask);
+
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+
+__global__ void roiaware_maxpool3d_backward(int boxes_num, int channels, int out_x, int out_y, int out_z,
+    const int *argmax, const float *grad_out, float *grad_in){
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params grad_out: (N, out_x, out_y, out_z, C)
+    // params grad_in: (npoints, C), return value
+
+    int box_idx = blockIdx.z;
+    int channel_idx = blockIdx.y;
+    int voxel_idx_flat = blockIdx.x * blockDim.x + threadIdx.x;
+
+    int x_idx = voxel_idx_flat / (out_y * out_z);
+    int y_idx = (voxel_idx_flat - x_idx * (out_y * out_z)) / out_z;
+    int z_idx = voxel_idx_flat % out_z;
+    if (box_idx >= boxes_num || channel_idx >= channels|| x_idx >= out_x || y_idx >= out_y || z_idx >= out_z) return;
+
+    int offset_base = x_idx * out_y * out_z + y_idx * out_z + z_idx;
+    argmax += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+    grad_out += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+
+    if (argmax[0] == -1) return;
+
+    atomicAdd(grad_in + argmax[0] * channels + channel_idx, grad_out[0] * 1);
+}
+
+
+__global__ void roiaware_avgpool3d_backward(int boxes_num, int channels, int out_x, int out_y, int out_z,
+    int max_pts_each_voxel, const int *pts_idx_of_voxels, const float *grad_out, float *grad_in){
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params grad_out: (N, out_x, out_y, out_z, C)
+    // params grad_in: (npoints, C), return value
+
+    int box_idx = blockIdx.z;
+    int channel_idx = blockIdx.y;
+    int voxel_idx_flat = blockIdx.x * blockDim.x + threadIdx.x;
+
+    int x_idx = voxel_idx_flat / (out_y * out_z);
+    int y_idx = (voxel_idx_flat - x_idx * (out_y * out_z)) / out_z;
+    int z_idx = voxel_idx_flat % out_z;
+    if (box_idx >= boxes_num || channel_idx >= channels|| x_idx >= out_x || y_idx >= out_y || z_idx >= out_z) return;
+
+    int offset_base = x_idx * out_y * out_z + y_idx * out_z + z_idx;
+    pts_idx_of_voxels += box_idx * out_x * out_y * out_z * max_pts_each_voxel + offset_base * max_pts_each_voxel;
+    grad_out += box_idx * out_x * out_y * out_z * channels + offset_base * channels + channel_idx;
+
+
+    int total_pts = pts_idx_of_voxels[0];
+    float cur_grad = 1 / fmaxf(float(total_pts), 1.0);
+    for (int k = 1; k <= total_pts; k++){
+        atomicAdd(grad_in + pts_idx_of_voxels[k] * channels + channel_idx, grad_out[0] * cur_grad);
+    }
+}
+
+
+void roiaware_pool3d_backward_launcher(int boxes_num, int out_x, int out_y, int out_z, int channels, int max_pts_each_voxel,
+    const int *pts_idx_of_voxels, const int *argmax, const float *grad_out, float *grad_in, int pool_method){
+    // params pts_idx_of_voxels: (N, out_x, out_y, out_z, max_pts_each_voxel)
+    // params argmax: (N, out_x, out_y, out_z, C)
+    // params grad_out: (N, out_x, out_y, out_z, C)
+    // params grad_in: (npoints, C), return value
+    // params pool_method: 0: max_pool, 1: avg_pool
+
+    dim3 blocks(DIVUP(out_x * out_y * out_z, THREADS_PER_BLOCK), channels, boxes_num);
+    dim3 threads(THREADS_PER_BLOCK);
+    if (pool_method == 0){
+        roiaware_maxpool3d_backward<<<blocks, threads>>>(
+            boxes_num, channels, out_x, out_y, out_z, argmax, grad_out, grad_in
+        );
+    }
+    else if (pool_method == 1){
+        roiaware_avgpool3d_backward<<<blocks, threads>>>(
+            boxes_num, channels, out_x, out_y, out_z, max_pts_each_voxel, pts_idx_of_voxels, grad_out, grad_in
+        );
+    }
+
+}
+
+
+__global__ void points_in_boxes_kernel(int batch_size, int boxes_num, int pts_num, const float *boxes,
+    const float *pts, int *box_idx_of_points){
+    // params boxes: (B, N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (B, npoints, 3) [x, y, z] in LiDAR coordinate
+    // params boxes_idx_of_points: (B, npoints), default -1
+
+    int bs_idx = blockIdx.y;
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (bs_idx >= batch_size || pt_idx >= pts_num) return;
+
+    boxes += bs_idx * boxes_num * 7;
+    pts += bs_idx * pts_num * 3 + pt_idx * 3;
+    box_idx_of_points += bs_idx * pts_num + pt_idx;
+
+    float local_x = 0, local_y = 0;
+    int cur_in_flag = 0;
+    for (int k = 0; k < boxes_num; k++){
+        cur_in_flag = check_pt_in_box3d(pts, boxes + k * 7, local_x, local_y);
+        if (cur_in_flag){
+            box_idx_of_points[0] = k;
+            break;
+        }
+    }
+}
+
+
+void points_in_boxes_launcher(int batch_size, int boxes_num, int pts_num, const float *boxes,
+    const float *pts, int *box_idx_of_points){
+    // params boxes: (B, N, 7) [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+    // params pts: (B, npoints, 3) [x, y, z]
+    // params boxes_idx_of_points: (B, npoints), default -1
+    cudaError_t err;
+
+    dim3 blocks(DIVUP(pts_num, THREADS_PER_BLOCK), batch_size);
+    dim3 threads(THREADS_PER_BLOCK);
+    points_in_boxes_kernel<<<blocks, threads>>>(batch_size, boxes_num, pts_num, boxes, pts, box_idx_of_points);
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/roipoint_pool3d_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/roipoint_pool3d_utils.py
new file mode 100644
index 000000000..1e13396cc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/roipoint_pool3d_utils.py
@@ -0,0 +1,67 @@
+import torch
+import torch.nn as nn
+from torch.autograd import Function
+
+from ...utils import box_utils
+from . import roipoint_pool3d_cuda
+
+
+class RoIPointPool3d(nn.Module):
+    def __init__(self, num_sampled_points=512, pool_extra_width=1.0):
+        super().__init__()
+        self.num_sampled_points = num_sampled_points
+        self.pool_extra_width = pool_extra_width
+
+    def forward(self, points, point_features, boxes3d):
+        """
+        Args:
+            points: (B, N, 3)
+            point_features: (B, N, C)
+            boxes3d: (B, M, 7), [x, y, z, dx, dy, dz, heading]
+
+        Returns:
+            pooled_features: (B, M, 512, 3 + C)
+            pooled_empty_flag: (B, M)
+        """
+        return RoIPointPool3dFunction.apply(
+            points, point_features, boxes3d, self.pool_extra_width, self.num_sampled_points
+        )
+
+
+class RoIPointPool3dFunction(Function):
+    @staticmethod
+    def forward(ctx, points, point_features, boxes3d, pool_extra_width, num_sampled_points=512):
+        """
+        Args:
+            ctx:
+            points: (B, N, 3)
+            point_features: (B, N, C)
+            boxes3d: (B, num_boxes, 7), [x, y, z, dx, dy, dz, heading]
+            pool_extra_width:
+            num_sampled_points:
+
+        Returns:
+            pooled_features: (B, num_boxes, 512, 3 + C)
+            pooled_empty_flag: (B, num_boxes)
+        """
+        assert points.shape.__len__() == 3 and points.shape[2] == 3
+        batch_size, boxes_num, feature_len = points.shape[0], boxes3d.shape[1], point_features.shape[2]
+        pooled_boxes3d = box_utils.enlarge_box3d(boxes3d.view(-1, 7), pool_extra_width).view(batch_size, -1, 7)
+
+        pooled_features = point_features.new_zeros((batch_size, boxes_num, num_sampled_points, 3 + feature_len))
+        pooled_empty_flag = point_features.new_zeros((batch_size, boxes_num)).int()
+
+        roipoint_pool3d_cuda.forward(
+            points.contiguous(), pooled_boxes3d.contiguous(),
+            point_features.contiguous(), pooled_features, pooled_empty_flag
+        )
+
+        return pooled_features, pooled_empty_flag
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        raise NotImplementedError
+
+
+if __name__ == '__main__':
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/src/roipoint_pool3d.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/src/roipoint_pool3d.cpp
new file mode 100644
index 000000000..e0f58ab00
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/src/roipoint_pool3d.cpp
@@ -0,0 +1,60 @@
+#include <torch/serialize/tensor.h>
+#include <torch/extension.h>
+
+#define CHECK_CUDA(x) do { \
+  if (!x.type().is_cuda()) { \
+    fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_CONTIGUOUS(x) do { \
+  if (!x.is_contiguous()) { \
+    fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \
+    exit(-1); \
+  } \
+} while (0)
+#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+
+
+void roipool3dLauncher(int batch_size, int pts_num, int boxes_num, int feature_in_len, int sampled_pts_num,
+                       const float *xyz, const float *boxes3d, const float *pts_feature, float *pooled_features, int *pooled_empty_flag);
+
+
+int roipool3d_gpu(at::Tensor xyz, at::Tensor boxes3d, at::Tensor pts_feature, at::Tensor pooled_features, at::Tensor pooled_empty_flag){
+    // params xyz: (B, N, 3)
+    // params boxes3d: (B, M, 7)
+    // params pts_feature: (B, N, C)
+    // params pooled_features: (B, M, 512, 3+C)
+    // params pooled_empty_flag: (B, M)
+    CHECK_INPUT(xyz);
+    CHECK_INPUT(boxes3d);
+    CHECK_INPUT(pts_feature);
+    CHECK_INPUT(pooled_features);
+    CHECK_INPUT(pooled_empty_flag);
+
+    int batch_size = xyz.size(0);
+    int pts_num = xyz.size(1);
+    int boxes_num = boxes3d.size(1);
+    int feature_in_len = pts_feature.size(2);
+    int sampled_pts_num = pooled_features.size(2);
+
+
+    const float * xyz_data = xyz.data<float>();
+    const float * boxes3d_data = boxes3d.data<float>();
+    const float * pts_feature_data = pts_feature.data<float>();
+    float * pooled_features_data = pooled_features.data<float>();
+    int * pooled_empty_flag_data = pooled_empty_flag.data<int>();
+
+    roipool3dLauncher(batch_size, pts_num, boxes_num, feature_in_len, sampled_pts_num,
+                       xyz_data, boxes3d_data, pts_feature_data, pooled_features_data, pooled_empty_flag_data);
+
+
+
+    return 1;
+}
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("forward", &roipool3d_gpu, "roipool3d forward (CUDA)");
+}
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/src/roipoint_pool3d_kernel.cu b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/src/roipoint_pool3d_kernel.cu
new file mode 100644
index 000000000..3fa034c12
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/ops/roipoint_pool3d/src/roipoint_pool3d_kernel.cu
@@ -0,0 +1,165 @@
+/*
+Point cloud feature pooling
+Written by Shaoshuai Shi
+All Rights Reserved 2018.
+*/
+
+#include <math.h>
+#include <stdio.h>
+
+#define THREADS_PER_BLOCK 256
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+// #define DEBUG
+
+
+__device__ inline void lidar_to_local_coords(float shift_x, float shift_y, float rot_angle, float &local_x, float &local_y){
+    float cosa = cos(-rot_angle), sina = sin(-rot_angle);
+    local_x = shift_x * cosa + shift_y * (-sina);
+    local_y = shift_x * sina + shift_y * cosa;
+}
+
+
+__device__ inline int check_pt_in_box3d(const float *pt, const float *box3d, float &local_x, float &local_y){
+    // param pt: (x, y, z)
+    // param box3d: [x, y, z, dx, dy, dz, heading] (x, y, z) is the box center
+
+    const float MARGIN = 1e-5;
+    float x = pt[0], y = pt[1], z = pt[2];
+    float cx = box3d[0], cy = box3d[1], cz = box3d[2];
+    float dx = box3d[3], dy = box3d[4], dz = box3d[5], rz = box3d[6];
+
+    if (fabsf(z - cz) > dz / 2.0) return 0;
+    lidar_to_local_coords(x - cx, y - cy, rz, local_x, local_y);
+    float in_flag = (fabs(local_x) < dx / 2.0 + MARGIN) & (fabs(local_y) < dy / 2.0 + MARGIN);
+    return in_flag;
+}
+
+
+__global__ void assign_pts_to_box3d(int batch_size, int pts_num, int boxes_num, const float *xyz, const float *boxes3d, int *pts_assign){
+    // params xyz: (B, N, 3)
+    // params boxes3d: (B, M, 7)
+    // params pts_assign: (B, N, M): idx of the corresponding box3d, -1 means background points
+    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    int box_idx = blockIdx.y;
+    int bs_idx = blockIdx.z;
+
+    if (pt_idx >= pts_num || box_idx >= boxes_num || bs_idx >= batch_size){
+        return;
+    }
+    int assign_idx = bs_idx * pts_num * boxes_num + pt_idx * boxes_num + box_idx;
+    pts_assign[assign_idx] = 0;
+
+    int box_offset = bs_idx * boxes_num * 7 + box_idx * 7;
+    int pt_offset = bs_idx * pts_num * 3 + pt_idx * 3;
+
+
+    float local_x = 0, local_y = 0;
+    int cur_in_flag = check_pt_in_box3d(xyz + pt_offset, boxes3d + box_offset, local_x, local_y);
+    pts_assign[assign_idx] = cur_in_flag;
+    // printf("bs=%d, pt=%d, in=%d\n", bs_idx, pt_idx, pts_assign[bs_idx * pts_num + pt_idx]);
+}
+
+
+__global__ void get_pooled_idx(int batch_size, int pts_num, int boxes_num, int sampled_pts_num,
+                               const int *pts_assign, int *pts_idx, int *pooled_empty_flag){
+    // params xyz: (B, N, 3)
+    // params pts_feature: (B, N, C)
+    // params pts_assign: (B, N)
+    // params pts_idx: (B, M, 512)
+    // params pooled_empty_flag: (B, M)
+
+    int boxes_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (boxes_idx >= boxes_num){
+        return;
+    }
+
+    int bs_idx = blockIdx.y;
+
+    int cnt = 0;
+    for (int k = 0; k < pts_num; k++){
+        if (pts_assign[bs_idx * pts_num * boxes_num + k * boxes_num + boxes_idx]){
+            if (cnt < sampled_pts_num){
+                pts_idx[bs_idx * boxes_num * sampled_pts_num + boxes_idx * sampled_pts_num + cnt] = k;
+                cnt++;
+            }
+            else break;
+        }
+    }
+
+    if (cnt == 0){
+        pooled_empty_flag[bs_idx * boxes_num + boxes_idx] = 1;
+    }
+    else if (cnt < sampled_pts_num){
+        // duplicate same points for sampling
+        for (int k = cnt; k < sampled_pts_num; k++){
+            int duplicate_idx = k % cnt;
+            int base_offset = bs_idx * boxes_num * sampled_pts_num + boxes_idx * sampled_pts_num;
+            pts_idx[base_offset + k] = pts_idx[base_offset + duplicate_idx];
+        }
+    }
+}
+
+
+__global__ void roipool3d_forward(int batch_size, int pts_num, int boxes_num, int feature_in_len, int sampled_pts_num,
+                                   const float *xyz, const int *pts_idx, const float *pts_feature,
+                                   float *pooled_features, int *pooled_empty_flag){
+    // params xyz: (B, N, 3)
+    // params pts_idx: (B, M, 512)
+    // params pts_feature: (B, N, C)
+    // params pooled_features: (B, M, 512, 3+C)
+    // params pooled_empty_flag: (B, M)
+
+    int sample_pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    int box_idx = blockIdx.y;
+    int bs_idx = blockIdx.z;
+
+    if (sample_pt_idx >= sampled_pts_num || box_idx >= boxes_num || bs_idx >= batch_size){
+        return;
+    }
+
+    if (pooled_empty_flag[bs_idx * boxes_num + box_idx]){
+        return;
+    }
+
+    int temp_idx = bs_idx * boxes_num * sampled_pts_num + box_idx * sampled_pts_num + sample_pt_idx;
+    int src_pt_idx = pts_idx[temp_idx];
+    int dst_feature_offset = temp_idx * (3 + feature_in_len);
+
+    for (int j = 0; j < 3; j++)
+        pooled_features[dst_feature_offset + j] = xyz[bs_idx * pts_num * 3 + src_pt_idx * 3 + j];
+
+    int src_feature_offset = bs_idx * pts_num * feature_in_len + src_pt_idx * feature_in_len;
+    for (int j = 0; j < feature_in_len; j++)
+        pooled_features[dst_feature_offset + 3 + j] = pts_feature[src_feature_offset + j];
+}
+
+
+void roipool3dLauncher(int batch_size, int pts_num, int boxes_num, int feature_in_len, int sampled_pts_num,
+                       const float *xyz, const float *boxes3d, const float *pts_feature, float *pooled_features, int *pooled_empty_flag){
+
+    // printf("batch_size=%d, pts_num=%d, boxes_num=%d\n", batch_size, pts_num, boxes_num);
+    int *pts_assign = NULL;
+    cudaMalloc(&pts_assign, batch_size * pts_num * boxes_num * sizeof(int));  // (batch_size, N, M)
+    // cudaMemset(&pts_assign, -1, batch_size * pts_num * boxes_num * sizeof(int));
+
+    dim3 blocks(DIVUP(pts_num, THREADS_PER_BLOCK), boxes_num, batch_size);  // blockIdx.x(col), blockIdx.y(row)
+    dim3 threads(THREADS_PER_BLOCK);
+    assign_pts_to_box3d<<<blocks, threads>>>(batch_size, pts_num, boxes_num, xyz, boxes3d, pts_assign);
+
+    int *pts_idx = NULL;
+    cudaMalloc(&pts_idx, batch_size * boxes_num * sampled_pts_num * sizeof(int));  // (batch_size, M, sampled_pts_num)
+
+    dim3 blocks2(DIVUP(boxes_num, THREADS_PER_BLOCK), batch_size);  // blockIdx.x(col), blockIdx.y(row)
+    get_pooled_idx<<<blocks2, threads>>>(batch_size, pts_num, boxes_num, sampled_pts_num, pts_assign, pts_idx, pooled_empty_flag);
+
+    dim3 blocks_pool(DIVUP(sampled_pts_num, THREADS_PER_BLOCK), boxes_num, batch_size);
+    roipool3d_forward<<<blocks_pool, threads>>>(batch_size, pts_num, boxes_num, feature_in_len, sampled_pts_num,
+                                                      xyz, pts_idx, pts_feature, pooled_features, pooled_empty_flag);
+
+    cudaFree(pts_assign);
+    cudaFree(pts_idx);
+
+#ifdef DEBUG
+    cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/box_coder_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/box_coder_utils.py
new file mode 100644
index 000000000..50ad143d6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/box_coder_utils.py
@@ -0,0 +1,222 @@
+import numpy as np
+import torch
+
+
+class ResidualCoder(object):
+    def __init__(self, code_size=7, encode_angle_by_sincos=False, **kwargs):
+        super().__init__()
+        self.code_size = code_size
+        self.encode_angle_by_sincos = encode_angle_by_sincos
+        if self.encode_angle_by_sincos:
+            self.code_size += 1
+
+    def encode_torch(self, boxes, anchors):
+        """
+        Args:
+            boxes: (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+            anchors: (N, 7 + C) [x, y, z, dx, dy, dz, heading or *[cos, sin], ...]
+
+        Returns:
+
+        """
+        anchors[:, 3:6] = torch.clamp_min(anchors[:, 3:6], min=1e-5)
+        boxes[:, 3:6] = torch.clamp_min(boxes[:, 3:6], min=1e-5)
+
+        xa, ya, za, dxa, dya, dza, ra, *cas = torch.split(anchors, 1, dim=-1)
+        xg, yg, zg, dxg, dyg, dzg, rg, *cgs = torch.split(boxes, 1, dim=-1)
+
+        diagonal = torch.sqrt(dxa ** 2 + dya ** 2)
+        xt = (xg - xa) / diagonal
+        yt = (yg - ya) / diagonal
+        zt = (zg - za) / dza
+        dxt = torch.log(dxg / dxa)
+        dyt = torch.log(dyg / dya)
+        dzt = torch.log(dzg / dza)
+        if self.encode_angle_by_sincos:
+            rt_cos = torch.cos(rg) - torch.cos(ra)
+            rt_sin = torch.sin(rg) - torch.sin(ra)
+            rts = [rt_cos, rt_sin]
+        else:
+            rts = [rg - ra]
+
+        cts = [g - a for g, a in zip(cgs, cas)]
+        return torch.cat([xt, yt, zt, dxt, dyt, dzt, *rts, *cts], dim=-1)
+
+    def decode_torch(self, box_encodings, anchors):
+        """
+        Args:
+            box_encodings: (B, N, 7 + C) or (N, 7 + C) [x, y, z, dx, dy, dz, heading or *[cos, sin], ...]
+            anchors: (B, N, 7 + C) or (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+
+        Returns:
+
+        """
+        xa, ya, za, dxa, dya, dza, ra, *cas = torch.split(anchors, 1, dim=-1)
+        if not self.encode_angle_by_sincos:
+            xt, yt, zt, dxt, dyt, dzt, rt, *cts = torch.split(box_encodings, 1, dim=-1)
+        else:
+            xt, yt, zt, dxt, dyt, dzt, cost, sint, *cts = torch.split(box_encodings, 1, dim=-1)
+
+        diagonal = torch.sqrt(dxa ** 2 + dya ** 2)
+        xg = xt * diagonal + xa
+        yg = yt * diagonal + ya
+        zg = zt * dza + za
+
+        dxg = torch.exp(dxt) * dxa
+        dyg = torch.exp(dyt) * dya
+        dzg = torch.exp(dzt) * dza
+
+        if self.encode_angle_by_sincos:
+            rg_cos = cost + torch.cos(ra)
+            rg_sin = sint + torch.sin(ra)
+            rg = torch.atan2(rg_sin, rg_cos)
+        else:
+            rg = rt + ra
+
+        cgs = [t + a for t, a in zip(cts, cas)]
+        return torch.cat([xg, yg, zg, dxg, dyg, dzg, rg, *cgs], dim=-1)
+
+
+class PreviousResidualDecoder(object):
+    def __init__(self, code_size=7, **kwargs):
+        super().__init__()
+        self.code_size = code_size
+
+    @staticmethod
+    def decode_torch(box_encodings, anchors):
+        """
+        Args:
+            box_encodings:  (B, N, 7 + ?) x, y, z, w, l, h, r, custom values
+            anchors: (B, N, 7 + C) or (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+
+        Returns:
+
+        """
+        xa, ya, za, dxa, dya, dza, ra, *cas = torch.split(anchors, 1, dim=-1)
+        xt, yt, zt, wt, lt, ht, rt, *cts = torch.split(box_encodings, 1, dim=-1)
+
+        diagonal = torch.sqrt(dxa ** 2 + dya ** 2)
+        xg = xt * diagonal + xa
+        yg = yt * diagonal + ya
+        zg = zt * dza + za
+
+        dxg = torch.exp(lt) * dxa
+        dyg = torch.exp(wt) * dya
+        dzg = torch.exp(ht) * dza
+        rg = rt + ra
+
+        cgs = [t + a for t, a in zip(cts, cas)]
+        return torch.cat([xg, yg, zg, dxg, dyg, dzg, rg, *cgs], dim=-1)
+
+
+class PreviousResidualRoIDecoder(object):
+    def __init__(self, code_size=7, **kwargs):
+        super().__init__()
+        self.code_size = code_size
+
+    @staticmethod
+    def decode_torch(box_encodings, anchors):
+        """
+        Args:
+            box_encodings:  (B, N, 7 + ?) x, y, z, w, l, h, r, custom values
+            anchors: (B, N, 7 + C) or (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+
+        Returns:
+
+        """
+        xa, ya, za, dxa, dya, dza, ra, *cas = torch.split(anchors, 1, dim=-1)
+        xt, yt, zt, wt, lt, ht, rt, *cts = torch.split(box_encodings, 1, dim=-1)
+
+        diagonal = torch.sqrt(dxa ** 2 + dya ** 2)
+        xg = xt * diagonal + xa
+        yg = yt * diagonal + ya
+        zg = zt * dza + za
+
+        dxg = torch.exp(lt) * dxa
+        dyg = torch.exp(wt) * dya
+        dzg = torch.exp(ht) * dza
+        rg = ra - rt
+
+        cgs = [t + a for t, a in zip(cts, cas)]
+        return torch.cat([xg, yg, zg, dxg, dyg, dzg, rg, *cgs], dim=-1)
+
+
+class PointResidualCoder(object):
+    def __init__(self, code_size=8, use_mean_size=True, **kwargs):
+        super().__init__()
+        self.code_size = code_size
+        self.use_mean_size = use_mean_size
+        if self.use_mean_size:
+            self.mean_size = torch.from_numpy(np.array(kwargs['mean_size'])).float().cuda()
+            assert self.mean_size.min() > 0
+
+    def encode_torch(self, gt_boxes, points, gt_classes=None):
+        """
+        Args:
+            gt_boxes: (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+            points: (N, 3) [x, y, z]
+            gt_classes: (N) [1, num_classes]
+        Returns:
+            box_coding: (N, 8 + C)
+        """
+        gt_boxes[:, 3:6] = torch.clamp_min(gt_boxes[:, 3:6], min=1e-5)
+
+        xg, yg, zg, dxg, dyg, dzg, rg, *cgs = torch.split(gt_boxes, 1, dim=-1)
+        xa, ya, za = torch.split(points, 1, dim=-1)
+
+        if self.use_mean_size:
+            assert gt_classes.max() <= self.mean_size.shape[0]
+            point_anchor_size = self.mean_size[gt_classes - 1]
+            dxa, dya, dza = torch.split(point_anchor_size, 1, dim=-1)
+            diagonal = torch.sqrt(dxa ** 2 + dya ** 2)
+            xt = (xg - xa) / diagonal
+            yt = (yg - ya) / diagonal
+            zt = (zg - za) / dza
+            dxt = torch.log(dxg / dxa)
+            dyt = torch.log(dyg / dya)
+            dzt = torch.log(dzg / dza)
+        else:
+            xt = (xg - xa)
+            yt = (yg - ya)
+            zt = (zg - za)
+            dxt = torch.log(dxg)
+            dyt = torch.log(dyg)
+            dzt = torch.log(dzg)
+
+        cts = [g for g in cgs]
+        return torch.cat([xt, yt, zt, dxt, dyt, dzt, torch.cos(rg), torch.sin(rg), *cts], dim=-1)
+
+    def decode_torch(self, box_encodings, points, pred_classes=None):
+        """
+        Args:
+            box_encodings: (N, 8 + C) [x, y, z, dx, dy, dz, cos, sin, ...]
+            points: [x, y, z]
+            pred_classes: (N) [1, num_classes]
+        Returns:
+
+        """
+        xt, yt, zt, dxt, dyt, dzt, cost, sint, *cts = torch.split(box_encodings, 1, dim=-1)
+        xa, ya, za = torch.split(points, 1, dim=-1)
+
+        if self.use_mean_size:
+            assert pred_classes.max() <= self.mean_size.shape[0]
+            point_anchor_size = self.mean_size[pred_classes - 1]
+            dxa, dya, dza = torch.split(point_anchor_size, 1, dim=-1)
+            diagonal = torch.sqrt(dxa ** 2 + dya ** 2)
+            xg = xt * diagonal + xa
+            yg = yt * diagonal + ya
+            zg = zt * dza + za
+
+            dxg = torch.exp(dxt) * dxa
+            dyg = torch.exp(dyt) * dya
+            dzg = torch.exp(dzt) * dza
+        else:
+            xg = xt + xa
+            yg = yt + ya
+            zg = zt + za
+            dxg, dyg, dzg = torch.split(torch.exp(box_encodings[..., 3:6]), 1, dim=-1)
+
+        rg = torch.atan2(sint, cost)
+
+        cgs = [t for t in cts]
+        return torch.cat([xg, yg, zg, dxg, dyg, dzg, rg, *cgs], dim=-1)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/box_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/box_utils.py
new file mode 100644
index 000000000..0e87de6f6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/box_utils.py
@@ -0,0 +1,440 @@
+import numpy as np
+import scipy
+import torch
+import copy
+from scipy.spatial import Delaunay
+
+from ..ops.roiaware_pool3d import roiaware_pool3d_utils
+from . import common_utils
+
+
+def in_hull(p, hull):
+    """
+    :param p: (N, K) test points
+    :param hull: (M, K) M corners of a box
+    :return (N) bool
+    """
+    try:
+        if not isinstance(hull, Delaunay):
+            hull = Delaunay(hull)
+        flag = hull.find_simplex(p) >= 0
+    except scipy.spatial.qhull.QhullError:
+        print('Warning: not a hull %s' % str(hull))
+        flag = np.zeros(p.shape[0], dtype=np.bool)
+
+    return flag
+
+
+def boxes_to_corners_3d(boxes3d):
+    """
+        7 -------- 4
+       /|         /|
+      6 -------- 5 .
+      | |        | |
+      . 3 -------- 0
+      |/         |/
+      2 -------- 1
+    Args:
+        boxes3d:  (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+
+    Returns:
+    """
+    boxes3d, is_numpy = common_utils.check_numpy_to_torch(boxes3d)
+
+    template = boxes3d.new_tensor((
+        [1, 1, -1], [1, -1, -1], [-1, -1, -1], [-1, 1, -1],
+        [1, 1, 1], [1, -1, 1], [-1, -1, 1], [-1, 1, 1],
+    )) / 2
+
+    corners3d = boxes3d[:, None, 3:6].repeat(1, 8, 1) * template[None, :, :]
+    corners3d = common_utils.rotate_points_along_z(corners3d.view(-1, 8, 3), boxes3d[:, 6]).view(-1, 8, 3)
+    corners3d += boxes3d[:, None, 0:3]
+
+    return corners3d.numpy() if is_numpy else corners3d
+
+def corners_rect_to_camera(corners):
+    """
+        7 -------- 4
+       /|         /|
+      6 -------- 5 .
+      | |        | |
+      . 3 -------- 0
+      |/         |/
+      2 -------- 1
+    Args:
+        corners:  (8, 3) [x0, y0, z0, ...], (x, y, z) is the point coordinate in image rect
+
+    Returns:
+        boxes_rect:  (7,) [x, y, z, l, h, w, r] in rect camera coords
+    """
+    height_group = [(0, 4), (1, 5), (2, 6), (3, 7)]
+    width_group = [(0, 1), (2, 3), (4, 5), (6, 7)]
+    length_group = [(0, 3), (1, 2), (4, 7), (5, 6)]
+    vector_group = [(0, 3), (1, 2), (4, 7), (5, 6)]
+    height, width, length = 0., 0., 0.
+    vector = np.zeros(2, dtype=np.float32)
+    for index_h, index_w, index_l, index_v in zip(height_group, width_group, length_group, vector_group):
+        height += np.linalg.norm(corners[index_h[0], :] - corners[index_h[1], :])
+        width += np.linalg.norm(corners[index_w[0], :] - corners[index_w[1], :])
+        length += np.linalg.norm(corners[index_l[0], :] - corners[index_l[1], :])
+        vector[0] += (corners[index_v[0], :] - corners[index_v[1], :])[0]
+        vector[1] += (corners[index_v[0], :] - corners[index_v[1], :])[2]
+
+    height, width, length = height*1.0/4, width*1.0/4, length*1.0/4
+    rotation_y = -np.arctan2(vector[1], vector[0])
+
+    center_point = corners.mean(axis=0)
+    center_point[1] += height/2
+    camera_rect = np.concatenate([center_point, np.array([length, height, width, rotation_y])])
+
+    return camera_rect
+
+
+def mask_boxes_outside_range_numpy(boxes, limit_range, min_num_corners=1, use_center_to_filter=True):
+    """
+    Args:
+        boxes: (N, 7) [x, y, z, dx, dy, dz, heading, ...], (x, y, z) is the box center
+        limit_range: [minx, miny, minz, maxx, maxy, maxz]
+        min_num_corners:
+
+    Returns:
+
+    """
+    if boxes.shape[1] > 7:
+        boxes = boxes[:, 0:7]
+    if use_center_to_filter:
+        box_centers = boxes[:, 0:3]
+        mask = ((box_centers >= limit_range[0:3]) & (box_centers <= limit_range[3:6])).all(axis=-1)
+    else:
+        corners = boxes_to_corners_3d(boxes)  # (N, 8, 3)
+        corners = corners[:, :, 0:2]
+        mask = ((corners >= limit_range[0:2]) & (corners <= limit_range[3:5])).all(axis=2)
+        mask = mask.sum(axis=1) >= min_num_corners  # (N)
+
+    return mask
+
+
+def remove_points_in_boxes3d(points, boxes3d):
+    """
+    Args:
+        points: (num_points, 3 + C)
+        boxes3d: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO NOT overlaps
+
+    Returns:
+
+    """
+    boxes3d, is_numpy = common_utils.check_numpy_to_torch(boxes3d)
+    points, is_numpy = common_utils.check_numpy_to_torch(points)
+    point_masks = roiaware_pool3d_utils.points_in_boxes_cpu(points[:, 0:3], boxes3d)
+    points = points[point_masks.sum(dim=0) == 0]
+
+    return points.numpy() if is_numpy else points
+
+
+def boxes3d_kitti_camera_to_lidar(boxes3d_camera, calib):
+    """
+    Args:
+        boxes3d_camera: (N, 7) [x, y, z, l, h, w, r] in rect camera coords
+        calib:
+
+    Returns:
+        boxes3d_lidar: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+
+    """
+    boxes3d_camera_copy = copy.deepcopy(boxes3d_camera)
+    xyz_camera, r = boxes3d_camera_copy[:, 0:3], boxes3d_camera_copy[:, 6:7]
+    l, h, w = boxes3d_camera_copy[:, 3:4], boxes3d_camera_copy[:, 4:5], boxes3d_camera_copy[:, 5:6]
+
+    xyz_lidar = calib.rect_to_lidar(xyz_camera)
+    xyz_lidar[:, 2] += h[:, 0] / 2
+    return np.concatenate([xyz_lidar, l, w, h, -(r + np.pi / 2)], axis=-1)
+
+
+def boxes3d_kitti_fakelidar_to_lidar(boxes3d_lidar):
+    """
+    Args:
+        boxes3d_fakelidar: (N, 7) [x, y, z, w, l, h, r] in old LiDAR coordinates, z is bottom center
+
+    Returns:
+        boxes3d_lidar: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+
+    """
+    boxes3d_lidar_copy = copy.deepcopy(boxes3d_lidar)
+    w, l, h = boxes3d_lidar_copy[:, 3:4], boxes3d_lidar_copy[:, 4:5], boxes3d_lidar_copy[:, 5:6]
+    r = boxes3d_lidar_copy[:, 6:7]
+
+    boxes3d_lidar_copy[:, 2] += h[:, 0] / 2
+    return np.concatenate([boxes3d_lidar_copy[:, 0:3], l, w, h, -(r + np.pi / 2)], axis=-1)
+
+
+def boxes3d_kitti_lidar_to_fakelidar(boxes3d_lidar):
+    """
+    Args:
+        boxes3d_lidar: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+
+    Returns:
+        boxes3d_fakelidar: [x, y, z, w, l, h, r] in old LiDAR coordinates, z is bottom center
+
+    """
+    boxes3d_lidar_copy = copy.deepcopy(boxes3d_lidar)
+    dx, dy, dz = boxes3d_lidar_copy[:, 3:4], boxes3d_lidar_copy[:, 4:5], boxes3d_lidar_copy[:, 5:6]
+    heading = boxes3d_lidar_copy[:, 6:7]
+
+    boxes3d_lidar_copy[:, 2] -= dz[:, 0] / 2
+    return np.concatenate([boxes3d_lidar_copy[:, 0:3], dy, dx, dz, -heading - np.pi / 2], axis=-1)
+
+
+def enlarge_box3d(boxes3d, extra_width=(0, 0, 0)):
+    """
+    Args:
+        boxes3d: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+        extra_width: [extra_x, extra_y, extra_z]
+
+    Returns:
+
+    """
+    boxes3d, is_numpy = common_utils.check_numpy_to_torch(boxes3d)
+    large_boxes3d = boxes3d.clone()
+
+    large_boxes3d[:, 3:6] += boxes3d.new_tensor(extra_width)[None, :]
+    return large_boxes3d
+
+
+def boxes3d_lidar_to_kitti_camera(boxes3d_lidar, calib):
+    """
+    :param boxes3d_lidar: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+    :param calib:
+    :return:
+        boxes3d_camera: (N, 7) [x, y, z, l, h, w, r] in rect camera coords
+    """
+    boxes3d_lidar_copy = copy.deepcopy(boxes3d_lidar)
+    xyz_lidar = boxes3d_lidar_copy[:, 0:3]
+    l, w, h = boxes3d_lidar_copy[:, 3:4], boxes3d_lidar_copy[:, 4:5], boxes3d_lidar_copy[:, 5:6]
+    r = boxes3d_lidar_copy[:, 6:7]
+
+    xyz_lidar[:, 2] -= h.reshape(-1) / 2
+    xyz_cam = calib.lidar_to_rect(xyz_lidar)
+    # xyz_cam[:, 1] += h.reshape(-1) / 2
+    r = -r - np.pi / 2
+    return np.concatenate([xyz_cam, l, h, w, r], axis=-1)
+
+
+def boxes3d_to_corners3d_kitti_camera(boxes3d, bottom_center=True):
+    """
+    :param boxes3d: (N, 7) [x, y, z, l, h, w, ry] in camera coords, see the definition of ry in KITTI dataset
+    :param bottom_center: whether y is on the bottom center of object
+    :return: corners3d: (N, 8, 3)
+        7 -------- 4
+       /|         /|
+      6 -------- 5 .
+      | |        | |
+      . 3 -------- 0
+      |/         |/
+      2 -------- 1
+    """
+    boxes_num = boxes3d.shape[0]
+    l, h, w = boxes3d[:, 3], boxes3d[:, 4], boxes3d[:, 5]
+    x_corners = np.array([l / 2., l / 2., -l / 2., -l / 2., l / 2., l / 2., -l / 2., -l / 2], dtype=np.float32).T
+    z_corners = np.array([w / 2., -w / 2., -w / 2., w / 2., w / 2., -w / 2., -w / 2., w / 2.], dtype=np.float32).T
+    if bottom_center:
+        y_corners = np.zeros((boxes_num, 8), dtype=np.float32)
+        y_corners[:, 4:8] = -h.reshape(boxes_num, 1).repeat(4, axis=1)  # (N, 8)
+    else:
+        y_corners = np.array([h / 2., h / 2., h / 2., h / 2., -h / 2., -h / 2., -h / 2., -h / 2.], dtype=np.float32).T
+
+    ry = boxes3d[:, 6]
+    zeros, ones = np.zeros(ry.size, dtype=np.float32), np.ones(ry.size, dtype=np.float32)
+    rot_list = np.array([[np.cos(ry), zeros, -np.sin(ry)],
+                         [zeros, ones, zeros],
+                         [np.sin(ry), zeros, np.cos(ry)]])  # (3, 3, N)
+    R_list = np.transpose(rot_list, (2, 0, 1))  # (N, 3, 3)
+
+    temp_corners = np.concatenate((x_corners.reshape(-1, 8, 1), y_corners.reshape(-1, 8, 1),
+                                   z_corners.reshape(-1, 8, 1)), axis=2)  # (N, 8, 3)
+    rotated_corners = np.matmul(temp_corners, R_list)  # (N, 8, 3)
+    x_corners, y_corners, z_corners = rotated_corners[:, :, 0], rotated_corners[:, :, 1], rotated_corners[:, :, 2]
+
+    x_loc, y_loc, z_loc = boxes3d[:, 0], boxes3d[:, 1], boxes3d[:, 2]
+
+    x = x_loc.reshape(-1, 1) + x_corners.reshape(-1, 8)
+    y = y_loc.reshape(-1, 1) + y_corners.reshape(-1, 8)
+    z = z_loc.reshape(-1, 1) + z_corners.reshape(-1, 8)
+
+    corners = np.concatenate((x.reshape(-1, 8, 1), y.reshape(-1, 8, 1), z.reshape(-1, 8, 1)), axis=2)
+
+    return corners.astype(np.float32)
+
+
+def boxes3d_kitti_camera_to_imageboxes(boxes3d, calib, image_shape=None):
+    """
+    :param boxes3d: (N, 7) [x, y, z, l, h, w, r] in rect camera coords
+    :param calib:
+    :return:
+        box_2d_preds: (N, 4) [x1, y1, x2, y2]
+    """
+    corners3d = boxes3d_to_corners3d_kitti_camera(boxes3d)
+    pts_img, _ = calib.rect_to_img(corners3d.reshape(-1, 3))
+    corners_in_image = pts_img.reshape(-1, 8, 2)
+
+    min_uv = np.min(corners_in_image, axis=1)  # (N, 2)
+    max_uv = np.max(corners_in_image, axis=1)  # (N, 2)
+    boxes2d_image = np.concatenate([min_uv, max_uv], axis=1)
+    if image_shape is not None:
+        boxes2d_image[:, 0] = np.clip(boxes2d_image[:, 0], a_min=0, a_max=image_shape[1] - 1)
+        boxes2d_image[:, 1] = np.clip(boxes2d_image[:, 1], a_min=0, a_max=image_shape[0] - 1)
+        boxes2d_image[:, 2] = np.clip(boxes2d_image[:, 2], a_min=0, a_max=image_shape[1] - 1)
+        boxes2d_image[:, 3] = np.clip(boxes2d_image[:, 3], a_min=0, a_max=image_shape[0] - 1)
+
+    return boxes2d_image
+
+
+def boxes_iou_normal(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 4) [x1, y1, x2, y2]
+        boxes_b: (M, 4) [x1, y1, x2, y2]
+
+    Returns:
+
+    """
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 4
+    x_min = torch.max(boxes_a[:, 0, None], boxes_b[None, :, 0])
+    x_max = torch.min(boxes_a[:, 2, None], boxes_b[None, :, 2])
+    y_min = torch.max(boxes_a[:, 1, None], boxes_b[None, :, 1])
+    y_max = torch.min(boxes_a[:, 3, None], boxes_b[None, :, 3])
+    x_len = torch.clamp_min(x_max - x_min, min=0)
+    y_len = torch.clamp_min(y_max - y_min, min=0)
+    area_a = (boxes_a[:, 2] - boxes_a[:, 0]) * (boxes_a[:, 3] - boxes_a[:, 1])
+    area_b = (boxes_b[:, 2] - boxes_b[:, 0]) * (boxes_b[:, 3] - boxes_b[:, 1])
+    a_intersect_b = x_len * y_len
+    iou = a_intersect_b / torch.clamp_min(area_a[:, None] + area_b[None, :] - a_intersect_b, min=1e-6)
+    return iou
+
+
+def boxes3d_lidar_to_aligned_bev_boxes(boxes3d):
+    """
+    Args:
+        boxes3d: (N, 7 + C) [x, y, z, dx, dy, dz, heading] in lidar coordinate
+
+    Returns:
+        aligned_bev_boxes: (N, 4) [x1, y1, x2, y2] in the above lidar coordinate
+    """
+    rot_angle = common_utils.limit_period(boxes3d[:, 6], offset=0.5, period=np.pi).abs()
+    choose_dims = torch.where(rot_angle[:, None] < np.pi / 4, boxes3d[:, [3, 4]], boxes3d[:, [4, 3]])
+    aligned_bev_boxes = torch.cat((boxes3d[:, 0:2] - choose_dims / 2, boxes3d[:, 0:2] + choose_dims / 2), dim=1)
+    return aligned_bev_boxes
+
+
+def boxes3d_nearest_bev_iou(boxes_a, boxes_b):
+    """
+    Args:
+        boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
+        boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading]
+
+    Returns:
+
+    """
+    boxes_bev_a = boxes3d_lidar_to_aligned_bev_boxes(boxes_a)
+    boxes_bev_b = boxes3d_lidar_to_aligned_bev_boxes(boxes_b)
+
+    return boxes_iou_normal(boxes_bev_a, boxes_bev_b)
+
+
+def area(box) -> torch.Tensor:
+    """
+    Computes the area of all the boxes.
+
+    Returns:
+        torch.Tensor: a vector with areas of each box.
+    """
+    area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])
+    return area
+
+
+# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
+# with slight modifications
+def pairwise_iou(boxes1, boxes2) -> torch.Tensor:
+    """
+    Given two lists of boxes of size N and M,
+    compute the IoU (intersection over union)
+    between __all__ N x M pairs of boxes.
+    The box order must be (xmin, ymin, xmax, ymax).
+
+    Args:
+        boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.
+
+    Returns:
+        Tensor: IoU, sized [N,M].
+    """
+    area1 = area(boxes1)
+    area2 = area(boxes2)
+
+    width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max(
+        boxes1[:, None, :2], boxes2[:, :2]
+    )  # [N,M,2]
+
+    width_height.clamp_(min=0)  # [N,M,2]
+    inter = width_height.prod(dim=2)  # [N,M]
+    del width_height
+
+    # handle empty boxes
+    iou = torch.where(
+        inter > 0,
+        inter / (area1[:, None] + area2 - inter),
+        torch.zeros(1, dtype=inter.dtype, device=inter.device),
+    )
+    return iou
+
+
+def center_to_corner2d(center, dim):
+    corners_norm = torch.tensor([[-0.5, -0.5], [-0.5, 0.5], [0.5, 0.5], [0.5, -0.5]], device=dim.device).type_as(center)  # (4, 2)
+    corners = dim.view([-1, 1, 2]) * corners_norm.view([1, 4, 2])  # (N, 4, 2)
+    corners = corners + center.view(-1, 1, 2)
+    return corners
+
+
+def bbox3d_overlaps_diou(pred_boxes, gt_boxes):
+    """
+    https://github.com/agent-sgs/PillarNet/blob/master/det3d/core/utils/center_utils.py
+    Args:
+        pred_boxes (N, 7): 
+        gt_boxes (N, 7): 
+
+    Returns:
+        _type_: _description_
+    """
+    assert pred_boxes.shape[0] == gt_boxes.shape[0]
+
+    qcorners = center_to_corner2d(pred_boxes[:, :2], pred_boxes[:, 3:5])  # (N, 4, 2)
+    gcorners = center_to_corner2d(gt_boxes[:, :2], gt_boxes[:, 3:5])  # (N, 4, 2)   
+
+    inter_max_xy = torch.minimum(qcorners[:, 2], gcorners[:, 2])
+    inter_min_xy = torch.maximum(qcorners[:, 0], gcorners[:, 0])
+    out_max_xy = torch.maximum(qcorners[:, 2], gcorners[:, 2])
+    out_min_xy = torch.minimum(qcorners[:, 0], gcorners[:, 0])
+
+    # calculate area
+    volume_pred_boxes = pred_boxes[:, 3] * pred_boxes[:, 4] * pred_boxes[:, 5]
+    volume_gt_boxes = gt_boxes[:, 3] * gt_boxes[:, 4] * gt_boxes[:, 5]
+
+    inter_h = torch.minimum(pred_boxes[:, 2] + 0.5 * pred_boxes[:, 5], gt_boxes[:, 2] + 0.5 * gt_boxes[:, 5]) - \
+              torch.maximum(pred_boxes[:, 2] - 0.5 * pred_boxes[:, 5], gt_boxes[:, 2] - 0.5 * gt_boxes[:, 5])
+    inter_h = torch.clamp(inter_h, min=0)
+
+    inter = torch.clamp((inter_max_xy - inter_min_xy), min=0)
+    volume_inter = inter[:, 0] * inter[:, 1] * inter_h
+    volume_union = volume_gt_boxes + volume_pred_boxes - volume_inter
+
+    # boxes_iou3d_gpu(pred_boxes, gt_boxes)
+    inter_diag = torch.pow(gt_boxes[:, 0:3] - pred_boxes[:, 0:3], 2).sum(-1)
+
+    outer_h = torch.maximum(gt_boxes[:, 2] + 0.5 * gt_boxes[:, 5], pred_boxes[:, 2] + 0.5 * pred_boxes[:, 5]) - \
+              torch.minimum(gt_boxes[:, 2] - 0.5 * gt_boxes[:, 5], pred_boxes[:, 2] - 0.5 * pred_boxes[:, 5])
+    outer_h = torch.clamp(outer_h, min=0)
+    outer = torch.clamp((out_max_xy - out_min_xy), min=0)
+    outer_diag = outer[:, 0] ** 2 + outer[:, 1] ** 2 + outer_h ** 2
+
+    dious = volume_inter / volume_union - inter_diag / outer_diag
+    dious = torch.clamp(dious, min=-1.0, max=1.0)
+
+    return dious
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/calibration_kitti.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/calibration_kitti.py
new file mode 100644
index 000000000..d7ff1f51b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/calibration_kitti.py
@@ -0,0 +1,125 @@
+import numpy as np
+
+
+def get_calib_from_file(calib_file):
+    with open(calib_file) as f:
+        lines = f.readlines()
+
+    obj = lines[2].strip().split(' ')[1:]
+    P2 = np.array(obj, dtype=np.float32)
+    obj = lines[3].strip().split(' ')[1:]
+    P3 = np.array(obj, dtype=np.float32)
+    obj = lines[4].strip().split(' ')[1:]
+    R0 = np.array(obj, dtype=np.float32)
+    obj = lines[5].strip().split(' ')[1:]
+    Tr_velo_to_cam = np.array(obj, dtype=np.float32)
+
+    return {'P2': P2.reshape(3, 4),
+            'P3': P3.reshape(3, 4),
+            'R0': R0.reshape(3, 3),
+            'Tr_velo2cam': Tr_velo_to_cam.reshape(3, 4)}
+
+
+class Calibration(object):
+    def __init__(self, calib_file):
+        if not isinstance(calib_file, dict):
+            calib = get_calib_from_file(calib_file)
+        else:
+            calib = calib_file
+
+        self.P2 = calib['P2']  # 3 x 4
+        self.R0 = calib['R0']  # 3 x 3
+        self.V2C = calib['Tr_velo2cam']  # 3 x 4
+
+        # Camera intrinsics and extrinsics
+        self.cu = self.P2[0, 2]
+        self.cv = self.P2[1, 2]
+        self.fu = self.P2[0, 0]
+        self.fv = self.P2[1, 1]
+        self.tx = self.P2[0, 3] / (-self.fu)
+        self.ty = self.P2[1, 3] / (-self.fv)
+
+    def cart_to_hom(self, pts):
+        """
+        :param pts: (N, 3 or 2)
+        :return pts_hom: (N, 4 or 3)
+        """
+        pts_hom = np.hstack((pts, np.ones((pts.shape[0], 1), dtype=np.float32)))
+        return pts_hom
+
+    def rect_to_lidar(self, pts_rect):
+        """
+        :param pts_lidar: (N, 3)
+        :return pts_rect: (N, 3)
+        """
+        pts_rect_hom = self.cart_to_hom(pts_rect)  # (N, 4)
+        R0_ext = np.hstack((self.R0, np.zeros((3, 1), dtype=np.float32)))  # (3, 4)
+        R0_ext = np.vstack((R0_ext, np.zeros((1, 4), dtype=np.float32)))  # (4, 4)
+        R0_ext[3, 3] = 1
+        V2C_ext = np.vstack((self.V2C, np.zeros((1, 4), dtype=np.float32)))  # (4, 4)
+        V2C_ext[3, 3] = 1
+
+        pts_lidar = np.dot(pts_rect_hom, np.linalg.inv(np.dot(R0_ext, V2C_ext).T))
+        return pts_lidar[:, 0:3]
+
+    def lidar_to_rect(self, pts_lidar):
+        """
+        :param pts_lidar: (N, 3)
+        :return pts_rect: (N, 3)
+        """
+        pts_lidar_hom = self.cart_to_hom(pts_lidar)
+        pts_rect = np.dot(pts_lidar_hom, np.dot(self.V2C.T, self.R0.T))
+        # pts_rect = reduce(np.dot, (pts_lidar_hom, self.V2C.T, self.R0.T))
+        return pts_rect
+
+    def rect_to_img(self, pts_rect):
+        """
+        :param pts_rect: (N, 3)
+        :return pts_img: (N, 2)
+        """
+        pts_rect_hom = self.cart_to_hom(pts_rect)
+        pts_2d_hom = np.dot(pts_rect_hom, self.P2.T)
+        pts_img = (pts_2d_hom[:, 0:2].T / pts_rect_hom[:, 2]).T  # (N, 2)
+        pts_rect_depth = pts_2d_hom[:, 2] - self.P2.T[3, 2]  # depth in rect camera coord
+        return pts_img, pts_rect_depth
+
+    def lidar_to_img(self, pts_lidar):
+        """
+        :param pts_lidar: (N, 3)
+        :return pts_img: (N, 2)
+        """
+        pts_rect = self.lidar_to_rect(pts_lidar)
+        pts_img, pts_depth = self.rect_to_img(pts_rect)
+        return pts_img, pts_depth
+
+    def img_to_rect(self, u, v, depth_rect):
+        """
+        :param u: (N)
+        :param v: (N)
+        :param depth_rect: (N)
+        :return:
+        """
+        x = ((u - self.cu) * depth_rect) / self.fu + self.tx
+        y = ((v - self.cv) * depth_rect) / self.fv + self.ty
+        pts_rect = np.concatenate((x.reshape(-1, 1), y.reshape(-1, 1), depth_rect.reshape(-1, 1)), axis=1)
+        return pts_rect
+
+    def corners3d_to_img_boxes(self, corners3d):
+        """
+        :param corners3d: (N, 8, 3) corners in rect coordinate
+        :return: boxes: (None, 4) [x1, y1, x2, y2] in rgb coordinate
+        :return: boxes_corner: (None, 8) [xi, yi] in rgb coordinate
+        """
+        sample_num = corners3d.shape[0]
+        corners3d_hom = np.concatenate((corners3d, np.ones((sample_num, 8, 1))), axis=2)  # (N, 8, 4)
+
+        img_pts = np.matmul(corners3d_hom, self.P2.T)  # (N, 8, 3)
+
+        x, y = img_pts[:, :, 0] / img_pts[:, :, 2], img_pts[:, :, 1] / img_pts[:, :, 2]
+        x1, y1 = np.min(x, axis=1), np.min(y, axis=1)
+        x2, y2 = np.max(x, axis=1), np.max(y, axis=1)
+
+        boxes = np.concatenate((x1.reshape(-1, 1), y1.reshape(-1, 1), x2.reshape(-1, 1), y2.reshape(-1, 1)), axis=1)
+        boxes_corner = np.concatenate((x.reshape(-1, 8, 1), y.reshape(-1, 8, 1)), axis=2)
+
+        return boxes, boxes_corner
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/common_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/common_utils.py
new file mode 100644
index 000000000..af70728db
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/common_utils.py
@@ -0,0 +1,295 @@
+import logging
+import os
+import pickle
+import random
+import shutil
+import subprocess
+import SharedArray
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+
+def check_numpy_to_torch(x):
+    if isinstance(x, np.ndarray):
+        return torch.from_numpy(x).float(), True
+    return x, False
+
+
+def limit_period(val, offset=0.5, period=np.pi):
+    val, is_numpy = check_numpy_to_torch(val)
+    ans = val - torch.floor(val / period + offset) * period
+    return ans.numpy() if is_numpy else ans
+
+
+def drop_info_with_name(info, name):
+    ret_info = {}
+    keep_indices = [i for i, x in enumerate(info['name']) if x != name]
+    for key in info.keys():
+        ret_info[key] = info[key][keep_indices]
+    return ret_info
+
+
+def rotate_points_along_z(points, angle):
+    """
+    Args:
+        points: (B, N, 3 + C)
+        angle: (B), angle along z-axis, angle increases x ==> y
+    Returns:
+
+    """
+    points, is_numpy = check_numpy_to_torch(points)
+    angle, _ = check_numpy_to_torch(angle)
+
+    cosa = torch.cos(angle)
+    sina = torch.sin(angle)
+    zeros = angle.new_zeros(points.shape[0])
+    ones = angle.new_ones(points.shape[0])
+    rot_matrix = torch.stack((
+        cosa,  sina, zeros,
+        -sina, cosa, zeros,
+        zeros, zeros, ones
+    ), dim=1).view(-1, 3, 3).float()
+    points_rot = torch.matmul(points[:, :, 0:3], rot_matrix)
+    points_rot = torch.cat((points_rot, points[:, :, 3:]), dim=-1)
+    return points_rot.numpy() if is_numpy else points_rot
+
+
+def angle2matrix(angle):
+    """
+    Args:
+        angle: angle along z-axis, angle increases x ==> y
+    Returns:
+        rot_matrix: (3x3 Tensor) rotation matrix
+    """
+
+    cosa = torch.cos(angle)
+    sina = torch.sin(angle)
+    rot_matrix = torch.tensor([
+        [cosa, -sina, 0],
+        [sina, cosa,  0],
+        [   0,    0,  1]
+    ])
+    return rot_matrix
+
+
+def mask_points_by_range(points, limit_range):
+    mask = (points[:, 0] >= limit_range[0]) & (points[:, 0] <= limit_range[3]) \
+           & (points[:, 1] >= limit_range[1]) & (points[:, 1] <= limit_range[4])
+    return mask
+
+
+def get_voxel_centers(voxel_coords, downsample_times, voxel_size, point_cloud_range):
+    """
+    Args:
+        voxel_coords: (N, 3)
+        downsample_times:
+        voxel_size:
+        point_cloud_range:
+
+    Returns:
+
+    """
+    assert voxel_coords.shape[1] == 3
+    voxel_centers = voxel_coords[:, [2, 1, 0]].float()  # (xyz)
+    voxel_size = torch.tensor(voxel_size, device=voxel_centers.device).float() * downsample_times
+    pc_range = torch.tensor(point_cloud_range[0:3], device=voxel_centers.device).float()
+    voxel_centers = (voxel_centers + 0.5) * voxel_size + pc_range
+    return voxel_centers
+
+
+def create_logger(log_file=None, rank=0, log_level=logging.INFO):
+    logger = logging.getLogger(__name__)
+    logger.setLevel(log_level if rank == 0 else 'ERROR')
+    formatter = logging.Formatter('%(asctime)s  %(levelname)5s  %(message)s')
+    console = logging.StreamHandler()
+    console.setLevel(log_level if rank == 0 else 'ERROR')
+    console.setFormatter(formatter)
+    logger.addHandler(console)
+    if log_file is not None:
+        file_handler = logging.FileHandler(filename=log_file)
+        file_handler.setLevel(log_level if rank == 0 else 'ERROR')
+        file_handler.setFormatter(formatter)
+        logger.addHandler(file_handler)
+    logger.propagate = False
+    return logger
+
+
+def set_random_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+def worker_init_fn(worker_id, seed=666):
+    if seed is not None:
+        random.seed(seed + worker_id)
+        np.random.seed(seed + worker_id)
+        torch.manual_seed(seed + worker_id)
+        torch.cuda.manual_seed(seed + worker_id)
+        torch.cuda.manual_seed_all(seed + worker_id)
+
+
+def get_pad_params(desired_size, cur_size):
+    """
+    Get padding parameters for np.pad function
+    Args:
+        desired_size: int, Desired padded output size
+        cur_size: int, Current size. Should always be less than or equal to cur_size
+    Returns:
+        pad_params: tuple(int), Number of values padded to the edges (before, after)
+    """
+    assert desired_size >= cur_size
+
+    # Calculate amount to pad
+    diff = desired_size - cur_size
+    pad_params = (0, diff)
+
+    return pad_params
+
+
+def keep_arrays_by_name(gt_names, used_classes):
+    inds = [i for i, x in enumerate(gt_names) if x in used_classes]
+    inds = np.array(inds, dtype=np.int64)
+    return inds
+
+
+def init_dist_slurm(tcp_port, local_rank, backend='nccl'):
+    """
+    modified from https://github.com/open-mmlab/mmdetection
+    Args:
+        tcp_port:
+        backend:
+
+    Returns:
+
+    """
+    proc_id = int(os.environ['SLURM_PROCID'])
+    ntasks = int(os.environ['SLURM_NTASKS'])
+    node_list = os.environ['SLURM_NODELIST']
+    num_gpus = torch.cuda.device_count()
+    torch.cuda.set_device(proc_id % num_gpus)
+    addr = subprocess.getoutput('scontrol show hostname {} | head -n1'.format(node_list))
+    os.environ['MASTER_PORT'] = str(tcp_port)
+    os.environ['MASTER_ADDR'] = addr
+    os.environ['WORLD_SIZE'] = str(ntasks)
+    os.environ['RANK'] = str(proc_id)
+    dist.init_process_group(backend=backend)
+
+    total_gpus = dist.get_world_size()
+    rank = dist.get_rank()
+    return total_gpus, rank
+
+
+def init_dist_pytorch(tcp_port, local_rank, backend='nccl'):
+    if mp.get_start_method(allow_none=True) is None:
+        mp.set_start_method('spawn')
+    # os.environ['MASTER_PORT'] = str(tcp_port)
+    # os.environ['MASTER_ADDR'] = 'localhost'
+    num_gpus = torch.cuda.device_count()
+    torch.cuda.set_device(local_rank % num_gpus)
+
+    dist.init_process_group(
+        backend=backend,
+        # init_method='tcp://127.0.0.1:%d' % tcp_port,
+        # rank=local_rank,
+        # world_size=num_gpus
+    )
+    rank = dist.get_rank()
+    return num_gpus, rank
+
+
+def get_dist_info(return_gpu_per_machine=False):
+    if torch.__version__ < '1.0':
+        initialized = dist._initialized
+    else:
+        if dist.is_available():
+            initialized = dist.is_initialized()
+        else:
+            initialized = False
+    if initialized:
+        rank = dist.get_rank()
+        world_size = dist.get_world_size()
+    else:
+        rank = 0
+        world_size = 1
+
+    if return_gpu_per_machine:
+        gpu_per_machine = torch.cuda.device_count()
+        return rank, world_size, gpu_per_machine
+
+    return rank, world_size
+
+
+def merge_results_dist(result_part, size, tmpdir):
+    rank, world_size = get_dist_info()
+    os.makedirs(tmpdir, exist_ok=True)
+
+    dist.barrier()
+    pickle.dump(result_part, open(os.path.join(tmpdir, 'result_part_{}.pkl'.format(rank)), 'wb'))
+    dist.barrier()
+
+    if rank != 0:
+        return None
+
+    part_list = []
+    for i in range(world_size):
+        part_file = os.path.join(tmpdir, 'result_part_{}.pkl'.format(i))
+        part_list.append(pickle.load(open(part_file, 'rb')))
+
+    ordered_results = []
+    for res in zip(*part_list):
+        ordered_results.extend(list(res))
+    ordered_results = ordered_results[:size]
+    shutil.rmtree(tmpdir)
+    return ordered_results
+
+
+def scatter_point_inds(indices, point_inds, shape):
+    ret = -1 * torch.ones(*shape, dtype=point_inds.dtype, device=point_inds.device)
+    ndim = indices.shape[-1]
+    flattened_indices = indices.view(-1, ndim)
+    slices = [flattened_indices[:, i] for i in range(ndim)]
+    ret[slices] = point_inds
+    return ret
+
+
+def generate_voxel2pinds(sparse_tensor):
+    device = sparse_tensor.indices.device
+    batch_size = sparse_tensor.batch_size
+    spatial_shape = sparse_tensor.spatial_shape
+    indices = sparse_tensor.indices.long()
+    point_indices = torch.arange(indices.shape[0], device=device, dtype=torch.int32)
+    output_shape = [batch_size] + list(spatial_shape)
+    v2pinds_tensor = scatter_point_inds(indices, point_indices, output_shape)
+    return v2pinds_tensor
+
+
+def sa_create(name, var):
+    x = SharedArray.create(name, var.shape, dtype=var.dtype)
+    x[...] = var[...]
+    x.flags.writeable = False
+    return x
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/commu_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/commu_utils.py
new file mode 100644
index 000000000..d9e866fba
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/commu_utils.py
@@ -0,0 +1,182 @@
+"""
+This file contains primitives for multi-gpu communication.
+This is useful when doing distributed training.
+
+deeply borrow from maskrcnn-benchmark and ST3D
+"""
+
+import pickle
+import time
+
+import torch
+import torch.distributed as dist
+
+
+def get_world_size():
+    if not dist.is_available():
+        return 1
+    if not dist.is_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not dist.is_available():
+        return 0
+    if not dist.is_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def synchronize():
+    """
+    Helper function to synchronize (barrier) among all processes when
+    using distributed training
+    """
+    if not dist.is_available():
+        return
+    if not dist.is_initialized():
+        return
+    world_size = dist.get_world_size()
+    if world_size == 1:
+        return
+    dist.barrier()
+
+
+def all_gather(data):
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of data gathered from each rank
+    """
+    world_size = get_world_size()
+    if world_size == 1:
+        return [data]
+
+    # serialized to a Tensor
+    origin_size = None
+    if not isinstance(data, torch.Tensor):
+        buffer = pickle.dumps(data)
+        storage = torch.ByteStorage.from_buffer(buffer)
+        tensor = torch.ByteTensor(storage).to("cuda")
+    else:
+        origin_size = data.size()
+        tensor = data.reshape(-1)
+
+    tensor_type = tensor.dtype
+
+    # obtain Tensor size of each rank
+    local_size = torch.LongTensor([tensor.numel()]).to("cuda")
+    size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)]
+    dist.all_gather(size_list, local_size)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.FloatTensor(size=(max_size,)).cuda().to(tensor_type))
+    if local_size != max_size:
+        padding = torch.FloatTensor(size=(max_size - local_size,)).cuda().to(tensor_type)
+        tensor = torch.cat((tensor, padding), dim=0)
+    dist.all_gather(tensor_list, tensor)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        if origin_size is None:
+            buffer = tensor.cpu().numpy().tobytes()[:size]
+            data_list.append(pickle.loads(buffer))
+        else:
+            buffer = tensor[:size]
+            data_list.append(buffer)
+
+    if origin_size is not None:
+        new_shape = [-1] + list(origin_size[1:])
+        resized_list = []
+        for data in data_list:
+            # suppose the difference of tensor size exist in first dimension
+            data = data.reshape(new_shape)
+            resized_list.append(data)
+
+        return resized_list
+    else:
+        return data_list
+
+
+def reduce_dict(input_dict, average=True):
+    """
+    Args:
+        input_dict (dict): all the values will be reduced
+        average (bool): whether to do average or sum
+    Reduce the values in the dictionary from all processes so that process with rank
+    0 has the averaged results. Returns a dict with the same fields as
+    input_dict, after reduction.
+    """
+    world_size = get_world_size()
+    if world_size < 2:
+        return input_dict
+    with torch.no_grad():
+        names = []
+        values = []
+        # sort the keys so that they are consistent across processes
+        for k in sorted(input_dict.keys()):
+            names.append(k)
+            values.append(input_dict[k])
+        values = torch.stack(values, dim=0)
+        dist.reduce(values, dst=0)
+        if dist.get_rank() == 0 and average:
+            # only main process gets accumulated, so only divide by
+            # world_size in this case
+            values /= world_size
+        reduced_dict = {k: v for k, v in zip(names, values)}
+    return reduced_dict
+
+
+def average_reduce_value(data):
+    data_list = all_gather(data)
+    return sum(data_list) / len(data_list)
+
+
+def all_reduce(data, op="sum", average=False):
+
+    def op_map(op):
+        op_dict = {
+            "SUM": dist.ReduceOp.SUM,
+            "MAX": dist.ReduceOp.MAX,
+            "MIN": dist.ReduceOp.MIN,
+            "PRODUCT": dist.ReduceOp.PRODUCT,
+        }
+        return op_dict[op]
+
+    world_size = get_world_size()
+    if world_size > 1:
+        reduced_data = data.clone()
+        dist.all_reduce(reduced_data, op=op_map(op.upper()))
+        if average:
+            assert op.upper() == 'SUM'
+            return reduced_data / world_size
+        else:
+            return reduced_data
+    return data
+
+
+@torch.no_grad()
+def concat_all_gather(tensor):
+    """
+    Performs all_gather operation on the provided tensors.
+    *** Warning ***: torch.distributed.all_gather has no gradient.
+    """
+    tensors_gather = [torch.ones_like(tensor)
+        for _ in range(torch.distributed.get_world_size())]
+    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
+
+    output = torch.cat(tensors_gather, dim=0)
+    return output
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/loss_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/loss_utils.py
new file mode 100644
index 000000000..bd114bae4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/loss_utils.py
@@ -0,0 +1,649 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from . import box_utils
+from pcdet.ops.iou3d_nms import iou3d_nms_utils
+
+
+class SigmoidFocalClassificationLoss(nn.Module):
+    """
+    Sigmoid focal cross entropy loss.
+    """
+
+    def __init__(self, gamma: float = 2.0, alpha: float = 0.25):
+        """
+        Args:
+            gamma: Weighting parameter to balance loss for hard and easy examples.
+            alpha: Weighting parameter to balance loss for positive and negative examples.
+        """
+        super(SigmoidFocalClassificationLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+
+    @staticmethod
+    def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
+        """ PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:
+            max(x, 0) - x * z + log(1 + exp(-abs(x))) in
+            https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
+
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predicted logits for each class
+            target: (B, #anchors, #classes) float tensor.
+                One-hot encoded classification targets
+
+        Returns:
+            loss: (B, #anchors, #classes) float tensor.
+                Sigmoid cross entropy loss without reduction
+        """
+        loss = torch.clamp(input, min=0) - input * target + \
+               torch.log1p(torch.exp(-torch.abs(input)))
+        return loss
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
+        """
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predicted logits for each class
+            target: (B, #anchors, #classes) float tensor.
+                One-hot encoded classification targets
+            weights: (B, #anchors) float tensor.
+                Anchor-wise weights.
+
+        Returns:
+            weighted_loss: (B, #anchors, #classes) float tensor after weighting.
+        """
+        pred_sigmoid = torch.sigmoid(input)
+        alpha_weight = target * self.alpha + (1 - target) * (1 - self.alpha)
+        pt = target * (1.0 - pred_sigmoid) + (1.0 - target) * pred_sigmoid
+        focal_weight = alpha_weight * torch.pow(pt, self.gamma)
+
+        bce_loss = self.sigmoid_cross_entropy_with_logits(input, target)
+
+        loss = focal_weight * bce_loss
+
+        if weights.shape.__len__() == 2 or \
+                (weights.shape.__len__() == 1 and target.shape.__len__() == 2):
+            weights = weights.unsqueeze(-1)
+
+        assert weights.shape.__len__() == loss.shape.__len__()
+
+        return loss * weights
+
+
+class WeightedSmoothL1Loss(nn.Module):
+    """
+    Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
+    https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
+                  | 0.5 * x ** 2 / beta   if abs(x) < beta
+    smoothl1(x) = |
+                  | abs(x) - 0.5 * beta   otherwise,
+    where x = input - target.
+    """
+    def __init__(self, beta: float = 1.0 / 9.0, code_weights: list = None):
+        """
+        Args:
+            beta: Scalar float.
+                L1 to L2 change point.
+                For beta values < 1e-5, L1 loss is computed.
+            code_weights: (#codes) float list if not None.
+                Code-wise weights.
+        """
+        super(WeightedSmoothL1Loss, self).__init__()
+        self.beta = beta
+        if code_weights is not None:
+            self.code_weights = np.array(code_weights, dtype=np.float32)
+            self.code_weights = torch.from_numpy(self.code_weights).cuda()
+
+    @staticmethod
+    def smooth_l1_loss(diff, beta):
+        if beta < 1e-5:
+            loss = torch.abs(diff)
+        else:
+            n = torch.abs(diff)
+            loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
+
+        return loss
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor = None):
+        """
+        Args:
+            input: (B, #anchors, #codes) float tensor.
+                Ecoded predicted locations of objects.
+            target: (B, #anchors, #codes) float tensor.
+                Regression targets.
+            weights: (B, #anchors) float tensor if not None.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted smooth l1 loss without reduction.
+        """
+        target = torch.where(torch.isnan(target), input, target)  # ignore nan targets
+
+        diff = input - target
+        # code-wise weighting
+        if self.code_weights is not None:
+            diff = diff * self.code_weights.view(1, 1, -1)
+
+        loss = self.smooth_l1_loss(diff, self.beta)
+
+        # anchor-wise weighting
+        if weights is not None:
+            assert weights.shape[0] == loss.shape[0] and weights.shape[1] == loss.shape[1]
+            loss = loss * weights.unsqueeze(-1)
+
+        return loss
+
+
+class WeightedL1Loss(nn.Module):
+    def __init__(self, code_weights: list = None):
+        """
+        Args:
+            code_weights: (#codes) float list if not None.
+                Code-wise weights.
+        """
+        super(WeightedL1Loss, self).__init__()
+        if code_weights is not None:
+            self.code_weights = np.array(code_weights, dtype=np.float32)
+            self.code_weights = torch.from_numpy(self.code_weights).cuda()
+
+    @torch.cuda.amp.custom_fwd(cast_inputs=torch.float16)
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor = None):
+        """
+        Args:
+            input: (B, #anchors, #codes) float tensor.
+                Ecoded predicted locations of objects.
+            target: (B, #anchors, #codes) float tensor.
+                Regression targets.
+            weights: (B, #anchors) float tensor if not None.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted smooth l1 loss without reduction.
+        """
+        target = torch.where(torch.isnan(target), input, target)  # ignore nan targets
+
+        diff = input - target
+        # code-wise weighting
+        if self.code_weights is not None:
+            diff = diff * self.code_weights.view(1, 1, -1)
+
+        loss = torch.abs(diff)
+
+        # anchor-wise weighting
+        if weights is not None:
+            assert weights.shape[0] == loss.shape[0] and weights.shape[1] == loss.shape[1]
+            loss = loss * weights.unsqueeze(-1)
+
+        return loss
+
+
+class WeightedCrossEntropyLoss(nn.Module):
+    """
+    Transform input to fit the fomation of PyTorch offical cross entropy loss
+    with anchor-wise weighting.
+    """
+    def __init__(self):
+        super(WeightedCrossEntropyLoss, self).__init__()
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
+        """
+        Args:
+            input: (B, #anchors, #classes) float tensor.
+                Predited logits for each class.
+            target: (B, #anchors, #classes) float tensor.
+                One-hot classification targets.
+            weights: (B, #anchors) float tensor.
+                Anchor-wise weights.
+
+        Returns:
+            loss: (B, #anchors) float tensor.
+                Weighted cross entropy loss without reduction
+        """
+        input = input.permute(0, 2, 1)
+        target = target.argmax(dim=-1)
+        loss = F.cross_entropy(input, target, reduction='none') * weights
+        return loss
+
+
+def get_corner_loss_lidar(pred_bbox3d: torch.Tensor, gt_bbox3d: torch.Tensor):
+    """
+    Args:
+        pred_bbox3d: (N, 7) float Tensor.
+        gt_bbox3d: (N, 7) float Tensor.
+
+    Returns:
+        corner_loss: (N) float Tensor.
+    """
+    assert pred_bbox3d.shape[0] == gt_bbox3d.shape[0]
+
+    pred_box_corners = box_utils.boxes_to_corners_3d(pred_bbox3d)
+    gt_box_corners = box_utils.boxes_to_corners_3d(gt_bbox3d)
+
+    gt_bbox3d_flip = gt_bbox3d.clone()
+    gt_bbox3d_flip[:, 6] += np.pi
+    gt_box_corners_flip = box_utils.boxes_to_corners_3d(gt_bbox3d_flip)
+    # (N, 8)
+    corner_dist = torch.min(torch.norm(pred_box_corners - gt_box_corners, dim=2),
+                            torch.norm(pred_box_corners - gt_box_corners_flip, dim=2))
+    # (N, 8)
+    corner_loss = WeightedSmoothL1Loss.smooth_l1_loss(corner_dist, beta=1.0)
+
+    return corner_loss.mean(dim=1)
+
+
+def compute_fg_mask(gt_boxes2d, shape, downsample_factor=1, device=torch.device("cpu")):
+    """
+    Compute foreground mask for images
+    Args:
+        gt_boxes2d: (B, N, 4), 2D box labels
+        shape: torch.Size or tuple, Foreground mask desired shape
+        downsample_factor: int, Downsample factor for image
+        device: torch.device, Foreground mask desired device
+    Returns:
+        fg_mask (shape), Foreground mask
+    """
+    fg_mask = torch.zeros(shape, dtype=torch.bool, device=device)
+
+    # Set box corners
+    gt_boxes2d /= downsample_factor
+    gt_boxes2d[:, :, :2] = torch.floor(gt_boxes2d[:, :, :2])
+    gt_boxes2d[:, :, 2:] = torch.ceil(gt_boxes2d[:, :, 2:])
+    gt_boxes2d = gt_boxes2d.long()
+
+    # Set all values within each box to True
+    B, N = gt_boxes2d.shape[:2]
+    for b in range(B):
+        for n in range(N):
+            u1, v1, u2, v2 = gt_boxes2d[b, n]
+            fg_mask[b, v1:v2, u1:u2] = True
+
+    return fg_mask
+
+
+def neg_loss_cornernet(pred, gt, mask=None):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint.
+    Modified focal loss. Exactly the same as CornerNet. Runs faster and costs a little bit more memory
+    Args:
+        pred: (batch x c x h x w)
+        gt: (batch x c x h x w)
+        mask: (batch x h x w)
+    Returns:
+    """
+    pos_inds = gt.eq(1).float()
+    neg_inds = gt.lt(1).float()
+
+    neg_weights = torch.pow(1 - gt, 4)
+
+    loss = 0
+
+    pos_loss = torch.log(pred) * torch.pow(1 - pred, 2) * pos_inds
+    neg_loss = torch.log(1 - pred) * torch.pow(pred, 2) * neg_weights * neg_inds
+
+    if mask is not None:
+        mask = mask[:, None, :, :].float()
+        pos_loss = pos_loss * mask
+        neg_loss = neg_loss * mask
+        num_pos = (pos_inds.float() * mask).sum()
+    else:
+        num_pos = pos_inds.float().sum()
+
+    pos_loss = pos_loss.sum()
+    neg_loss = neg_loss.sum()
+
+    if num_pos == 0:
+        loss = loss - neg_loss
+    else:
+        loss = loss - (pos_loss + neg_loss) / num_pos
+    return loss
+
+
+def neg_loss_sparse(pred, gt):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint.
+    Modified focal loss. Exactly the same as CornerNet. Runs faster and costs a little bit more memory
+    Args:
+        pred: (batch x c x n)
+        gt: (batch x c x n)
+    Returns:
+    """
+    pos_inds = gt.eq(1).float()
+    neg_inds = gt.lt(1).float()
+
+    neg_weights = torch.pow(1 - gt, 4)
+
+    loss = 0
+
+    pos_loss = torch.log(pred) * torch.pow(1 - pred, 2) * pos_inds
+    neg_loss = torch.log(1 - pred) * torch.pow(pred, 2) * neg_weights * neg_inds
+
+    num_pos = pos_inds.float().sum()
+
+    pos_loss = pos_loss.sum()
+    neg_loss = neg_loss.sum()
+
+    if num_pos == 0:
+        loss = loss - neg_loss
+    else:
+        loss = loss - (pos_loss + neg_loss) / num_pos
+    return loss
+
+
+class FocalLossCenterNet(nn.Module):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    """
+    def __init__(self):
+        super(FocalLossCenterNet, self).__init__()
+        self.neg_loss = neg_loss_cornernet
+
+    def forward(self, out, target, mask=None):
+        return self.neg_loss(out, target, mask=mask)
+
+
+def _reg_loss(regr, gt_regr, mask):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    L1 regression loss
+    Args:
+        regr (batch x max_objects x dim)
+        gt_regr (batch x max_objects x dim)
+        mask (batch x max_objects)
+    Returns:
+    """
+    num = mask.float().sum()
+    mask = mask.unsqueeze(2).expand_as(gt_regr).float()
+    isnotnan = (~ torch.isnan(gt_regr)).float()
+    mask *= isnotnan
+    regr = regr * mask
+    gt_regr = gt_regr * mask
+
+    loss = torch.abs(regr - gt_regr)
+    loss = loss.transpose(2, 0)
+
+    loss = torch.sum(loss, dim=2)
+    loss = torch.sum(loss, dim=1)
+    # else:
+    #  # D x M x B
+    #  loss = loss.reshape(loss.shape[0], -1)
+
+    # loss = loss / (num + 1e-4)
+    loss = loss / torch.clamp_min(num, min=1.0)
+    # import pdb; pdb.set_trace()
+    return loss
+
+
+def _gather_feat(feat, ind, mask=None):
+    dim  = feat.size(2)
+    ind  = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
+    feat = feat.gather(1, ind)
+    if mask is not None:
+        mask = mask.unsqueeze(2).expand_as(feat)
+        feat = feat[mask]
+        feat = feat.view(-1, dim)
+    return feat
+
+
+def _transpose_and_gather_feat(feat, ind):
+    feat = feat.permute(0, 2, 3, 1).contiguous()
+    feat = feat.view(feat.size(0), -1, feat.size(3))
+    feat = _gather_feat(feat, ind)
+    return feat
+
+
+class RegLossCenterNet(nn.Module):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    """
+
+    def __init__(self):
+        super(RegLossCenterNet, self).__init__()
+
+    def forward(self, output, mask, ind=None, target=None):
+        """
+        Args:
+            output: (batch x dim x h x w) or (batch x max_objects)
+            mask: (batch x max_objects)
+            ind: (batch x max_objects)
+            target: (batch x max_objects x dim)
+        Returns:
+        """
+        if ind is None:
+            pred = output
+        else:
+            pred = _transpose_and_gather_feat(output, ind)
+        loss = _reg_loss(pred, target, mask)
+        return loss
+
+
+class FocalLossSparse(nn.Module):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    """
+    def __init__(self):
+        super(FocalLossSparse, self).__init__()
+        self.neg_loss = neg_loss_sparse
+
+    def forward(self, out, target):
+        return self.neg_loss(out, target)
+
+
+class RegLossSparse(nn.Module):
+    """
+    Refer to https://github.com/tianweiy/CenterPoint
+    """
+
+    def __init__(self):
+        super(RegLossSparse, self).__init__()
+
+    def forward(self, output, mask, ind=None, target=None, batch_index=None):
+        """
+        Args:
+            output: (N x dim)
+            mask: (batch x max_objects)
+            ind: (batch x max_objects)
+            target: (batch x max_objects x dim)
+        Returns:
+        """
+
+        pred = []
+        batch_size = mask.shape[0]
+        for bs_idx in range(batch_size):
+            batch_inds = batch_index==bs_idx
+            pred.append(output[batch_inds][ind[bs_idx]])
+        pred = torch.stack(pred)
+
+        loss = _reg_loss(pred, target, mask)
+        return loss
+
+
+class IouLossSparse(nn.Module):
+    '''IouLoss loss for an output tensor
+    Arguments:
+        output (batch x dim x h x w)
+        mask (batch x max_objects)
+        ind (batch x max_objects)
+        target (batch x max_objects x dim)
+    '''
+
+    def __init__(self):
+        super(IouLossSparse, self).__init__()
+
+    def forward(self, iou_pred, mask, ind, box_pred, box_gt, batch_index):
+        if mask.sum() == 0:
+            return iou_pred.new_zeros((1))
+        batch_size = mask.shape[0]
+        mask = mask.bool()
+
+        loss = 0
+        for bs_idx in range(batch_size):
+            batch_inds = batch_index==bs_idx
+            pred = iou_pred[batch_inds][ind[bs_idx]][mask[bs_idx]]
+            pred_box = box_pred[batch_inds][ind[bs_idx]][mask[bs_idx]]
+            target = iou3d_nms_utils.boxes_aligned_iou3d_gpu(pred_box, box_gt[bs_idx])
+            target = 2 * target - 1
+            loss += F.l1_loss(pred, target, reduction='sum')
+
+        loss = loss / (mask.sum() + 1e-4)
+        return loss
+
+class IouRegLossSparse(nn.Module):
+    '''Distance IoU loss for output boxes
+        Arguments:
+            output (batch x dim x h x w)
+            mask (batch x max_objects)
+            ind (batch x max_objects)
+            target (batch x max_objects x dim)
+    '''
+
+    def __init__(self, type="DIoU"):
+        super(IouRegLossSparse, self).__init__()
+
+    def center_to_corner2d(self, center, dim):
+        corners_norm = torch.tensor([[-0.5, -0.5], [-0.5, 0.5], [0.5, 0.5], [0.5, -0.5]],
+                                    dtype=torch.float32, device=dim.device)
+        corners = dim.view([-1, 1, 2]) * corners_norm.view([1, 4, 2])
+        corners = corners + center.view(-1, 1, 2)
+        return corners
+
+    def bbox3d_iou_func(self, pred_boxes, gt_boxes):
+        assert pred_boxes.shape[0] == gt_boxes.shape[0]
+
+        qcorners = self.center_to_corner2d(pred_boxes[:, :2], pred_boxes[:, 3:5])
+        gcorners = self.center_to_corner2d(gt_boxes[:, :2], gt_boxes[:, 3:5])
+
+        inter_max_xy = torch.minimum(qcorners[:, 2], gcorners[:, 2])
+        inter_min_xy = torch.maximum(qcorners[:, 0], gcorners[:, 0])
+        out_max_xy = torch.maximum(qcorners[:, 2], gcorners[:, 2])
+        out_min_xy = torch.minimum(qcorners[:, 0], gcorners[:, 0])
+
+        # calculate area
+        volume_pred_boxes = pred_boxes[:, 3] * pred_boxes[:, 4] * pred_boxes[:, 5]
+        volume_gt_boxes = gt_boxes[:, 3] * gt_boxes[:, 4] * gt_boxes[:, 5]
+
+        inter_h = torch.minimum(pred_boxes[:, 2] + 0.5 * pred_boxes[:, 5], gt_boxes[:, 2] + 0.5 * gt_boxes[:, 5]) - \
+                torch.maximum(pred_boxes[:, 2] - 0.5 * pred_boxes[:, 5], gt_boxes[:, 2] - 0.5 * gt_boxes[:, 5])
+        inter_h = torch.clamp(inter_h, min=0)
+
+        inter = torch.clamp((inter_max_xy - inter_min_xy), min=0)
+        volume_inter = inter[:, 0] * inter[:, 1] * inter_h
+        volume_union = volume_gt_boxes + volume_pred_boxes - volume_inter
+
+        # boxes_iou3d_gpu(pred_boxes, gt_boxes)
+        inter_diag = torch.pow(gt_boxes[:, 0:3] - pred_boxes[:, 0:3], 2).sum(-1)
+
+        outer_h = torch.maximum(gt_boxes[:, 2] + 0.5 * gt_boxes[:, 5], pred_boxes[:, 2] + 0.5 * pred_boxes[:, 5]) - \
+                torch.minimum(gt_boxes[:, 2] - 0.5 * gt_boxes[:, 5], pred_boxes[:, 2] - 0.5 * pred_boxes[:, 5])
+        outer_h = torch.clamp(outer_h, min=0)
+        outer = torch.clamp((out_max_xy - out_min_xy), min=0)
+        outer_diag = outer[:, 0] ** 2 + outer[:, 1] ** 2 + outer_h ** 2
+
+        dious = volume_inter / volume_union - inter_diag / outer_diag
+        dious = torch.clamp(dious, min=-1.0, max=1.0)
+
+        return dious
+
+    def forward(self, box_pred, mask, ind, box_gt, batch_index):
+        if mask.sum() == 0:
+            return box_pred.new_zeros((1))
+        mask = mask.bool()
+        batch_size = mask.shape[0]
+
+        loss = 0
+        for bs_idx in range(batch_size):
+            batch_inds = batch_index==bs_idx
+            pred_box = box_pred[batch_inds][ind[bs_idx]]
+            iou = self.bbox3d_iou_func(pred_box[mask[bs_idx]], box_gt[bs_idx])
+            loss += (1. - iou).sum()
+
+        loss =  loss / (mask.sum() + 1e-4)
+        return loss
+
+class L1Loss(nn.Module):
+    def __init__(self):
+        super(L1Loss, self).__init__()
+       
+    def forward(self, pred, target):
+        if target.numel() == 0:
+            return pred.sum() * 0
+        assert pred.size() == target.size()
+        loss = torch.abs(pred - target)
+        return loss
+
+
+class GaussianFocalLoss(nn.Module):
+    """GaussianFocalLoss is a variant of focal loss.
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/1808.01244>`_
+    Code is modified from `kp_utils.py
+    <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_  # noqa: E501
+    Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
+    not 0/1 binary target.
+
+    Args:
+        alpha (float): Power of prediction.
+        gamma (float): Power of target for negative samples.
+        reduction (str): Options are "none", "mean" and "sum".
+        loss_weight (float): Loss weight of current loss.
+    """
+
+    def __init__(self,
+                 alpha=2.0,
+                 gamma=4.0):
+        super(GaussianFocalLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(self, pred, target):
+        eps = 1e-12
+        pos_weights = target.eq(1)
+        neg_weights = (1 - target).pow(self.gamma)
+        pos_loss = -(pred + eps).log() * (1 - pred).pow(self.alpha) * pos_weights
+        neg_loss = -(1 - pred + eps).log() * pred.pow(self.alpha) * neg_weights
+
+        return pos_loss + neg_loss
+
+
+def calculate_iou_loss_centerhead(iou_preds, batch_box_preds, mask, ind, gt_boxes):
+    """
+    Args:
+        iou_preds: (batch x 1 x h x w)
+        batch_box_preds: (batch x (7 or 9) x h x w)
+        mask: (batch x max_objects)
+        ind: (batch x max_objects)
+        gt_boxes: (batch x N, 7 or 9)
+    Returns:
+    """
+    if mask.sum() == 0:
+        return iou_preds.new_zeros((1))
+
+    mask = mask.bool()
+    selected_iou_preds = _transpose_and_gather_feat(iou_preds, ind)[mask]
+
+    selected_box_preds = _transpose_and_gather_feat(batch_box_preds, ind)[mask]
+    iou_target = iou3d_nms_utils.paired_boxes_iou3d_gpu(selected_box_preds[:, 0:7], gt_boxes[mask][:, 0:7])
+    # iou_target = iou3d_nms_utils.boxes_iou3d_gpu(selected_box_preds[:, 0:7].clone(), gt_boxes[mask][:, 0:7].clone()).diag()
+    iou_target = iou_target * 2 - 1  # [0, 1] ==> [-1, 1]
+
+    # print(selected_iou_preds.view(-1), iou_target)
+    loss = F.l1_loss(selected_iou_preds.view(-1), iou_target, reduction='sum')
+    loss = loss / torch.clamp(mask.sum(), min=1e-4)
+    return loss
+
+
+def calculate_iou_reg_loss_centerhead(batch_box_preds, mask, ind, gt_boxes):
+    if mask.sum() == 0:
+        return batch_box_preds.new_zeros((1))
+
+    mask = mask.bool()
+
+    selected_box_preds = _transpose_and_gather_feat(batch_box_preds, ind)
+
+    iou = box_utils.bbox3d_overlaps_diou(selected_box_preds[mask][:, 0:7], gt_boxes[mask][:, 0:7])
+
+    loss = (1.0 - iou).sum() / torch.clamp(mask.sum(), min=1e-4)
+    return loss
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/object3d_custom.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/object3d_custom.py
new file mode 100644
index 000000000..188d1bb0b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/object3d_custom.py
@@ -0,0 +1,83 @@
+import numpy as np
+
+
+def get_objects_from_label(label_file):
+    with open(label_file, 'r') as f:
+        lines = f.readlines()
+    objects = [Object3d(line) for line in lines]
+    return objects
+
+
+def cls_type_to_id(cls_type):
+    type_to_id = {'Car': 1, 'Pedestrian': 2, 'Cyclist': 3, 'Van': 4}
+    if cls_type not in type_to_id.keys():
+        return -1
+    return type_to_id[cls_type]
+
+
+class Object3d(object):
+    def __init__(self, line):
+        label = line.strip().split(' ')
+        self.src = line
+        self.cls_type = label[0]
+        self.cls_id = cls_type_to_id(self.cls_type)
+        self.truncation = float(label[1])
+        self.occlusion = float(label[2])  # 0:fully visible 1:partly occluded 2:largely occluded 3:unknown
+        self.alpha = float(label[3])
+        self.box2d = np.array((float(label[4]), float(label[5]), float(label[6]), float(label[7])), dtype=np.float32)
+        self.h = float(label[8])
+        self.w = float(label[9])
+        self.l = float(label[10])
+        self.loc = np.array((float(label[11]), float(label[12]), float(label[13])), dtype=np.float32)
+        self.dis_to_cam = np.linalg.norm(self.loc)
+        self.ry = float(label[14])
+        self.score = float(label[15]) if label.__len__() == 16 else -1.0
+        self.level_str = None
+        self.level = self.get_custom_obj_level()
+
+    def get_custom_obj_level(self):
+        height = float(self.box2d[3]) - float(self.box2d[1]) + 1
+
+        if height >= 40 and self.truncation <= 0.15 and self.occlusion <= 0:
+            self.level_str = 'Easy'
+            return 0  # Easy
+        elif height >= 25 and self.truncation <= 0.3 and self.occlusion <= 1:
+            self.level_str = 'Moderate'
+            return 1  # Moderate
+        elif height >= 25 and self.truncation <= 0.5 and self.occlusion <= 2:
+            self.level_str = 'Hard'
+            return 2  # Hard
+        else:
+            self.level_str = 'UnKnown'
+            return -1
+
+    def generate_corners3d(self):
+        """
+        generate corners3d representation for this object
+        :return corners_3d: (8, 3) corners of box3d in camera coord
+        """
+        l, h, w = self.l, self.h, self.w
+        x_corners = [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2]
+        y_corners = [0, 0, 0, 0, -h, -h, -h, -h]
+        z_corners = [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2]
+
+        R = np.array([[np.cos(self.ry), 0, np.sin(self.ry)],
+                      [0, 1, 0],
+                      [-np.sin(self.ry), 0, np.cos(self.ry)]])
+        corners3d = np.vstack([x_corners, y_corners, z_corners])  # (3, 8)
+        corners3d = np.dot(R, corners3d).T
+        corners3d = corners3d + self.loc
+        return corners3d
+
+    def to_str(self):
+        print_str = '%s %.3f %.3f %.3f box2d: %s hwl: [%.3f %.3f %.3f] pos: %s ry: %.3f' \
+                     % (self.cls_type, self.truncation, self.occlusion, self.alpha, self.box2d, self.h, self.w, self.l,
+                        self.loc, self.ry)
+        return print_str
+
+    def to_custom_format(self):
+        custom_str = '%s %.2f %d %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f' \
+                    % (self.cls_type, self.truncation, int(self.occlusion), self.alpha, self.box2d[0], self.box2d[1],
+                       self.box2d[2], self.box2d[3], self.h, self.w, self.l, self.loc[0], self.loc[1], self.loc[2],
+                       self.ry)
+        return custom_str
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/object3d_kitti.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/object3d_kitti.py
new file mode 100644
index 000000000..10528f604
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/object3d_kitti.py
@@ -0,0 +1,83 @@
+import numpy as np
+
+
+def get_objects_from_label(label_file):
+    with open(label_file, 'r') as f:
+        lines = f.readlines()
+    objects = [Object3d(line) for line in lines]
+    return objects
+
+
+def cls_type_to_id(cls_type):
+    type_to_id = {'Car': 1, 'Pedestrian': 2, 'Cyclist': 3, 'Van': 4}
+    if cls_type not in type_to_id.keys():
+        return -1
+    return type_to_id[cls_type]
+
+
+class Object3d(object):
+    def __init__(self, line):
+        label = line.strip().split(' ')
+        self.src = line
+        self.cls_type = label[0]
+        self.cls_id = cls_type_to_id(self.cls_type)
+        self.truncation = float(label[1])
+        self.occlusion = float(label[2])  # 0:fully visible 1:partly occluded 2:largely occluded 3:unknown
+        self.alpha = float(label[3])
+        self.box2d = np.array((float(label[4]), float(label[5]), float(label[6]), float(label[7])), dtype=np.float32)
+        self.h = float(label[8])
+        self.w = float(label[9])
+        self.l = float(label[10])
+        self.loc = np.array((float(label[11]), float(label[12]), float(label[13])), dtype=np.float32)
+        self.dis_to_cam = np.linalg.norm(self.loc)
+        self.ry = float(label[14])
+        self.score = float(label[15]) if label.__len__() == 16 else -1.0
+        self.level_str = None
+        self.level = self.get_kitti_obj_level()
+
+    def get_kitti_obj_level(self):
+        height = float(self.box2d[3]) - float(self.box2d[1]) + 1
+
+        if height >= 40 and self.truncation <= 0.15 and self.occlusion <= 0:
+            self.level_str = 'Easy'
+            return 0  # Easy
+        elif height >= 25 and self.truncation <= 0.3 and self.occlusion <= 1:
+            self.level_str = 'Moderate'
+            return 1  # Moderate
+        elif height >= 25 and self.truncation <= 0.5 and self.occlusion <= 2:
+            self.level_str = 'Hard'
+            return 2  # Hard
+        else:
+            self.level_str = 'UnKnown'
+            return -1
+
+    def generate_corners3d(self):
+        """
+        generate corners3d representation for this object
+        :return corners_3d: (8, 3) corners of box3d in camera coord
+        """
+        l, h, w = self.l, self.h, self.w
+        x_corners = [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2]
+        y_corners = [0, 0, 0, 0, -h, -h, -h, -h]
+        z_corners = [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2]
+
+        R = np.array([[np.cos(self.ry), 0, np.sin(self.ry)],
+                      [0, 1, 0],
+                      [-np.sin(self.ry), 0, np.cos(self.ry)]])
+        corners3d = np.vstack([x_corners, y_corners, z_corners])  # (3, 8)
+        corners3d = np.dot(R, corners3d).T
+        corners3d = corners3d + self.loc
+        return corners3d
+
+    def to_str(self):
+        print_str = '%s %.3f %.3f %.3f box2d: %s hwl: [%.3f %.3f %.3f] pos: %s ry: %.3f' \
+                     % (self.cls_type, self.truncation, self.occlusion, self.alpha, self.box2d, self.h, self.w, self.l,
+                        self.loc, self.ry)
+        return print_str
+
+    def to_kitti_format(self):
+        kitti_str = '%s %.2f %d %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f' \
+                    % (self.cls_type, self.truncation, int(self.occlusion), self.alpha, self.box2d[0], self.box2d[1],
+                       self.box2d[2], self.box2d[3], self.h, self.w, self.l, self.loc[0], self.loc[1], self.loc[2],
+                       self.ry)
+        return kitti_str
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/spconv_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/spconv_utils.py
new file mode 100644
index 000000000..1182a106f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/spconv_utils.py
@@ -0,0 +1,38 @@
+from typing import Set
+
+import spconv
+# if float(spconv.__version__[2:]) >= 2.2:
+#     spconv.constants.SPCONV_USE_DIRECT_TABLE = False
+    
+try:
+    import spconv.pytorch as spconv
+except:
+    import spconv as spconv
+
+import torch.nn as nn
+
+
+def find_all_spconv_keys(model: nn.Module, prefix="") -> Set[str]:
+    """
+    Finds all spconv keys that need to have weight's transposed
+    """
+    found_keys: Set[str] = set()
+    for name, child in model.named_children():
+        new_prefix = f"{prefix}.{name}" if prefix != "" else name
+
+        if isinstance(child, spconv.conv.SparseConvolution):
+            new_prefix = f"{new_prefix}.weight"
+            found_keys.add(new_prefix)
+
+        found_keys.update(find_all_spconv_keys(child, prefix=new_prefix))
+
+    return found_keys
+
+
+def replace_feature(out, new_features):
+    if "replace_feature" in out.__dir__():
+        # spconv 2.x behaviour
+        return out.replace_feature(new_features)
+    else:
+        out.features = new_features
+        return out
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/transform_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/transform_utils.py
new file mode 100644
index 000000000..b7fdcc213
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/pcdet/utils/transform_utils.py
@@ -0,0 +1,91 @@
+import math
+import torch
+
+try:
+    from kornia.geometry.conversions import (
+        convert_points_to_homogeneous,
+        convert_points_from_homogeneous,
+    )
+except:
+    pass 
+    # print('Warning: kornia is not installed. This package is only required by CaDDN')
+
+
+def project_to_image(project, points):
+    """
+    Project points to image
+    Args:
+        project [torch.tensor(..., 3, 4)]: Projection matrix
+        points [torch.Tensor(..., 3)]: 3D points
+    Returns:
+        points_img [torch.Tensor(..., 2)]: Points in image
+        points_depth [torch.Tensor(...)]: Depth of each point
+    """
+    # Reshape tensors to expected shape
+    points = convert_points_to_homogeneous(points)
+    points = points.unsqueeze(dim=-1)
+    project = project.unsqueeze(dim=1)
+
+    # Transform points to image and get depths
+    points_t = project @ points
+    points_t = points_t.squeeze(dim=-1)
+    points_img = convert_points_from_homogeneous(points_t)
+    points_depth = points_t[..., -1] - project[..., 2, 3]
+
+    return points_img, points_depth
+
+
+def normalize_coords(coords, shape):
+    """
+    Normalize coordinates of a grid between [-1, 1]
+    Args:
+        coords: (..., 3), Coordinates in grid
+        shape: (3), Grid shape
+    Returns:
+        norm_coords: (.., 3), Normalized coordinates in grid
+    """
+    min_n = -1
+    max_n = 1
+    shape = torch.flip(shape, dims=[0])  # Reverse ordering of shape
+
+    # Subtract 1 since pixel indexing from [0, shape - 1]
+    norm_coords = coords / (shape - 1) * (max_n - min_n) + min_n
+    return norm_coords
+
+
+def bin_depths(depth_map, mode, depth_min, depth_max, num_bins, target=False):
+    """
+    Converts depth map into bin indices
+    Args:
+        depth_map: (H, W), Depth Map
+        mode: string, Discretiziation mode (See https://arxiv.org/pdf/2005.13423.pdf for more details)
+            UD: Uniform discretiziation
+            LID: Linear increasing discretiziation
+            SID: Spacing increasing discretiziation
+        depth_min: float, Minimum depth value
+        depth_max: float, Maximum depth value
+        num_bins: int, Number of depth bins
+        target: bool, Whether the depth bins indices will be used for a target tensor in loss comparison
+    Returns:
+        indices: (H, W), Depth bin indices
+    """
+    if mode == "UD":
+        bin_size = (depth_max - depth_min) / num_bins
+        indices = ((depth_map - depth_min) / bin_size)
+    elif mode == "LID":
+        bin_size = 2 * (depth_max - depth_min) / (num_bins * (1 + num_bins))
+        indices = -0.5 + 0.5 * torch.sqrt(1 + 8 * (depth_map - depth_min) / bin_size)
+    elif mode == "SID":
+        indices = num_bins * (torch.log(1 + depth_map) - math.log(1 + depth_min)) / \
+            (math.log(1 + depth_max) - math.log(1 + depth_min))
+    else:
+        raise NotImplementedError
+
+    if target:
+        # Remove indicies outside of bounds
+        mask = (indices < 0) | (indices > num_bins) | (~torch.isfinite(indices))
+        indices[mask] = num_bins
+
+        # Convert to integer
+        indices = indices.type(torch.int64)
+    return indices
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/requirements.txt b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/requirements.txt
new file mode 100644
index 000000000..86b7dda67
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/requirements.txt
@@ -0,0 +1,4 @@
+scikit-image
+pyquaternion
+easydict
+tensorboardX
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/setup.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/setup.py
new file mode 100644
index 000000000..183fee9c3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/setup.py
@@ -0,0 +1,137 @@
+import os
+import subprocess
+
+from setuptools import find_packages, setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+
+def get_git_commit_number():
+    if not os.path.exists('.git'):
+        return '0000000'
+
+    cmd_out = subprocess.run(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE)
+    git_commit_number = cmd_out.stdout.decode('utf-8')[:7]
+    return git_commit_number
+
+
+def make_cuda_ext(name, module, sources):
+    cuda_ext = CUDAExtension(
+        name='%s.%s' % (module, name),
+        sources=[os.path.join(*module.split('.'), src) for src in sources]
+    )
+    return cuda_ext
+
+
+def write_version_to_file(version, target_file):
+    with open(target_file, 'w') as f:
+        print('__version__ = "%s"' % version, file=f)
+
+
+if __name__ == '__main__':
+    version = '0.6.0'
+    write_version_to_file(version, 'pcdet/version.py')
+
+    setup(
+        name='pcdet',
+        version=version,
+        description='OpenPCDet is a general codebase for 3D object detection from point cloud',
+        install_requires=[
+            'numpy',
+            'llvmlite',
+            'numba',
+            'tensorboardX',
+            'easydict',
+            'pyyaml',
+            'scikit-image',
+            'tqdm',
+            'SharedArray',
+            # 'spconv',  # spconv has different names depending on the cuda version
+        ],
+
+        author='Shaoshuai Shi',
+        author_email='shaoshuaics@gmail.com',
+        license='Apache License 2.0',
+        packages=find_packages(exclude=['tools', 'data', 'output']),
+        cmdclass={
+            'build_ext': BuildExtension,
+        },
+        ext_modules=[
+            make_cuda_ext(
+                name='iou3d_nms_cuda',
+                module='pcdet.ops.iou3d_nms',
+                sources=[
+                    'src/iou3d_cpu.cpp',
+                    'src/iou3d_nms_api.cpp',
+                    'src/iou3d_nms.cpp',
+                    'src/iou3d_nms_kernel.cu',
+                ]
+            ),
+            make_cuda_ext(
+                name='roiaware_pool3d_cuda',
+                module='pcdet.ops.roiaware_pool3d',
+                sources=[
+                    'src/roiaware_pool3d.cpp',
+                    'src/roiaware_pool3d_kernel.cu',
+                ]
+            ),
+            make_cuda_ext(
+                name='roipoint_pool3d_cuda',
+                module='pcdet.ops.roipoint_pool3d',
+                sources=[
+                    'src/roipoint_pool3d.cpp',
+                    'src/roipoint_pool3d_kernel.cu',
+                ]
+            ),
+            make_cuda_ext(
+                name='pointnet2_stack_cuda',
+                module='pcdet.ops.pointnet2.pointnet2_stack',
+                sources=[
+                    'src/pointnet2_api.cpp',
+                    'src/ball_query.cpp',
+                    'src/ball_query_gpu.cu',
+                    'src/group_points.cpp',
+                    'src/group_points_gpu.cu',
+                    'src/sampling.cpp',
+                    'src/sampling_gpu.cu', 
+                    'src/interpolate.cpp', 
+                    'src/interpolate_gpu.cu',
+                    'src/voxel_query.cpp', 
+                    'src/voxel_query_gpu.cu',
+                    'src/vector_pool.cpp',
+                    'src/vector_pool_gpu.cu'
+                ],
+            ),
+            make_cuda_ext(
+                name='pointnet2_batch_cuda',
+                module='pcdet.ops.pointnet2.pointnet2_batch',
+                sources=[
+                    'src/pointnet2_api.cpp',
+                    'src/ball_query.cpp',
+                    'src/ball_query_gpu.cu',
+                    'src/group_points.cpp',
+                    'src/group_points_gpu.cu',
+                    'src/interpolate.cpp',
+                    'src/interpolate_gpu.cu',
+                    'src/sampling.cpp',
+                    'src/sampling_gpu.cu',
+
+                ],
+            ),
+            make_cuda_ext(
+                name="bev_pool_ext",
+                module="pcdet.ops.bev_pool",
+                sources=[
+                    "src/bev_pool.cpp",
+                    "src/bev_pool_cuda.cu",
+                ],
+            ),
+            make_cuda_ext(
+                name='ingroup_inds_cuda',
+                module='pcdet.ops.ingroup_inds',
+                sources=[
+                    'src/ingroup_inds.cpp',
+                    'src/ingroup_inds_kernel.cu',
+                ]
+            ),
+        ],
+    )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/_init_path.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/_init_path.py
new file mode 100644
index 000000000..9fc2af401
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/_init_path.py
@@ -0,0 +1,2 @@
+import sys
+sys.path.insert(0, '../')
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/demo.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/demo.py
new file mode 100644
index 000000000..259d469f3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/demo.py
@@ -0,0 +1,112 @@
+import argparse
+import glob
+from pathlib import Path
+
+try:
+    import open3d
+    from visual_utils import open3d_vis_utils as V
+    OPEN3D_FLAG = True
+except:
+    import mayavi.mlab as mlab
+    from visual_utils import visualize_utils as V
+    OPEN3D_FLAG = False
+
+import numpy as np
+import torch
+
+from pcdet.config import cfg, cfg_from_yaml_file
+from pcdet.datasets import DatasetTemplate
+from pcdet.models import build_network, load_data_to_gpu
+from pcdet.utils import common_utils
+
+
+class DemoDataset(DatasetTemplate):
+    def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None, ext='.bin'):
+        """
+        Args:
+            root_path:
+            dataset_cfg:
+            class_names:
+            training:
+            logger:
+        """
+        super().__init__(
+            dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
+        )
+        self.root_path = root_path
+        self.ext = ext
+        data_file_list = glob.glob(str(root_path / f'*{self.ext}')) if self.root_path.is_dir() else [self.root_path]
+
+        data_file_list.sort()
+        self.sample_file_list = data_file_list
+
+    def __len__(self):
+        return len(self.sample_file_list)
+
+    def __getitem__(self, index):
+        if self.ext == '.bin':
+            points = np.fromfile(self.sample_file_list[index], dtype=np.float32).reshape(-1, 4)
+        elif self.ext == '.npy':
+            points = np.load(self.sample_file_list[index])
+        else:
+            raise NotImplementedError
+
+        input_dict = {
+            'points': points,
+            'frame_id': index,
+        }
+
+        data_dict = self.prepare_data(data_dict=input_dict)
+        return data_dict
+
+
+def parse_config():
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--cfg_file', type=str, default='cfgs/kitti_models/second.yaml',
+                        help='specify the config for demo')
+    parser.add_argument('--data_path', type=str, default='demo_data',
+                        help='specify the point cloud data file or directory')
+    parser.add_argument('--ckpt', type=str, default=None, help='specify the pretrained model')
+    parser.add_argument('--ext', type=str, default='.bin', help='specify the extension of your point cloud data file')
+
+    args = parser.parse_args()
+
+    cfg_from_yaml_file(args.cfg_file, cfg)
+
+    return args, cfg
+
+
+def main():
+    args, cfg = parse_config()
+    logger = common_utils.create_logger()
+    logger.info('-----------------Quick Demo of OpenPCDet-------------------------')
+    demo_dataset = DemoDataset(
+        dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, training=False,
+        root_path=Path(args.data_path), ext=args.ext, logger=logger
+    )
+    logger.info(f'Total number of samples: \t{len(demo_dataset)}')
+
+    model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=demo_dataset)
+    model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
+    model.cuda()
+    model.eval()
+    with torch.no_grad():
+        for idx, data_dict in enumerate(demo_dataset):
+            logger.info(f'Visualized sample index: \t{idx + 1}')
+            data_dict = demo_dataset.collate_batch([data_dict])
+            load_data_to_gpu(data_dict)
+            pred_dicts, _ = model.forward(data_dict)
+
+            V.draw_scenes(
+                points=data_dict['points'][:, 1:], ref_boxes=pred_dicts[0]['pred_boxes'],
+                ref_scores=pred_dicts[0]['pred_scores'], ref_labels=pred_dicts[0]['pred_labels']
+            )
+
+            if not OPEN3D_FLAG:
+                mlab.show(stop=True)
+
+    logger.info('Demo done.')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/eval_utils/eval_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/eval_utils/eval_utils.py
new file mode 100644
index 000000000..92abd2551
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/eval_utils/eval_utils.py
@@ -0,0 +1,140 @@
+import pickle
+import time
+
+import numpy as np
+import torch
+import tqdm
+
+from pcdet.models import load_data_to_gpu
+from pcdet.utils import common_utils
+
+
+def statistics_info(cfg, ret_dict, metric, disp_dict):
+    for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
+        metric['recall_roi_%s' % str(cur_thresh)] += ret_dict.get('roi_%s' % str(cur_thresh), 0)
+        metric['recall_rcnn_%s' % str(cur_thresh)] += ret_dict.get('rcnn_%s' % str(cur_thresh), 0)
+    metric['gt_num'] += ret_dict.get('gt', 0)
+    min_thresh = cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST[0]
+    disp_dict['recall_%s' % str(min_thresh)] = \
+        '(%d, %d) / %d' % (metric['recall_roi_%s' % str(min_thresh)], metric['recall_rcnn_%s' % str(min_thresh)], metric['gt_num'])
+
+
+def eval_one_epoch(cfg, args, model, dataloader, epoch_id, logger, dist_test=False, result_dir=None):
+    result_dir.mkdir(parents=True, exist_ok=True)
+
+    final_output_dir = result_dir / 'final_result' / 'data'
+    if args.save_to_file:
+        final_output_dir.mkdir(parents=True, exist_ok=True)
+
+    metric = {
+        'gt_num': 0,
+    }
+    for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
+        metric['recall_roi_%s' % str(cur_thresh)] = 0
+        metric['recall_rcnn_%s' % str(cur_thresh)] = 0
+
+    dataset = dataloader.dataset
+    class_names = dataset.class_names
+    det_annos = []
+
+    if getattr(args, 'infer_time', False):
+        start_iter = int(len(dataloader) * 0.1)
+        infer_time_meter = common_utils.AverageMeter()
+
+    logger.info('*************** EPOCH %s EVALUATION *****************' % epoch_id)
+    if dist_test:
+        num_gpus = torch.cuda.device_count()
+        local_rank = cfg.LOCAL_RANK % num_gpus
+        model = torch.nn.parallel.DistributedDataParallel(
+                model,
+                device_ids=[local_rank],
+                broadcast_buffers=False
+        )
+    model.eval()
+
+    if cfg.LOCAL_RANK == 0:
+        progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval', dynamic_ncols=True)
+    start_time = time.time()
+    for i, batch_dict in enumerate(dataloader):
+        load_data_to_gpu(batch_dict)
+
+        if getattr(args, 'infer_time', False):
+            start_time = time.time()
+
+        with torch.no_grad():
+            pred_dicts, ret_dict = model(batch_dict)
+
+        disp_dict = {}
+
+        if getattr(args, 'infer_time', False):
+            inference_time = time.time() - start_time
+            infer_time_meter.update(inference_time * 1000)
+            # use ms to measure inference time
+            disp_dict['infer_time'] = f'{infer_time_meter.val:.2f}({infer_time_meter.avg:.2f})'
+
+        statistics_info(cfg, ret_dict, metric, disp_dict)
+        annos = dataset.generate_prediction_dicts(
+            batch_dict, pred_dicts, class_names,
+            output_path=final_output_dir if args.save_to_file else None
+        )
+        det_annos += annos
+        if cfg.LOCAL_RANK == 0:
+            progress_bar.set_postfix(disp_dict)
+            progress_bar.update()
+
+    if cfg.LOCAL_RANK == 0:
+        progress_bar.close()
+
+    if dist_test:
+        rank, world_size = common_utils.get_dist_info()
+        det_annos = common_utils.merge_results_dist(det_annos, len(dataset), tmpdir=result_dir / 'tmpdir')
+        metric = common_utils.merge_results_dist([metric], world_size, tmpdir=result_dir / 'tmpdir')
+
+    logger.info('*************** Performance of EPOCH %s *****************' % epoch_id)
+    sec_per_example = (time.time() - start_time) / len(dataloader.dataset)
+    logger.info('Generate label finished(sec_per_example: %.4f second).' % sec_per_example)
+
+    if cfg.LOCAL_RANK != 0:
+        return {}
+
+    ret_dict = {}
+    if dist_test:
+        for key, val in metric[0].items():
+            for k in range(1, world_size):
+                metric[0][key] += metric[k][key]
+        metric = metric[0]
+
+    gt_num_cnt = metric['gt_num']
+    for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
+        cur_roi_recall = metric['recall_roi_%s' % str(cur_thresh)] / max(gt_num_cnt, 1)
+        cur_rcnn_recall = metric['recall_rcnn_%s' % str(cur_thresh)] / max(gt_num_cnt, 1)
+        logger.info('recall_roi_%s: %f' % (cur_thresh, cur_roi_recall))
+        logger.info('recall_rcnn_%s: %f' % (cur_thresh, cur_rcnn_recall))
+        ret_dict['recall/roi_%s' % str(cur_thresh)] = cur_roi_recall
+        ret_dict['recall/rcnn_%s' % str(cur_thresh)] = cur_rcnn_recall
+
+    total_pred_objects = 0
+    for anno in det_annos:
+        total_pred_objects += anno['name'].__len__()
+    logger.info('Average predicted number of objects(%d samples): %.3f'
+                % (len(det_annos), total_pred_objects / max(1, len(det_annos))))
+
+    with open(result_dir / 'result.pkl', 'wb') as f:
+        pickle.dump(det_annos, f)
+
+    result_str, result_dict = dataset.evaluation(
+        det_annos, class_names,
+        eval_metric=cfg.MODEL.POST_PROCESSING.EVAL_METRIC,
+        output_path=final_output_dir
+    )
+
+    logger.info(result_str)
+    ret_dict.update(result_dict)
+
+    logger.info('Result is saved to %s' % result_dir)
+    logger.info('****************Evaluation done.*****************')
+    return ret_dict
+
+
+if __name__ == '__main__':
+    pass
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/process_tools/create_integrated_database.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/process_tools/create_integrated_database.py
new file mode 100644
index 000000000..59f7b5381
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/process_tools/create_integrated_database.py
@@ -0,0 +1,86 @@
+import numpy as np
+import pickle as pkl
+from pathlib import Path
+import tqdm
+import copy
+import os 
+
+
+def create_integrated_db_with_infos(args, root_path):
+    """
+    Args:
+        args:
+    Returns:
+
+    """
+    # prepare
+    db_infos_path = args.src_db_info
+    db_info_global_path = db_infos_path
+    global_db_path = root_path / (args.new_db_name + '.npy')
+
+    db_infos = pkl.load(open(db_infos_path, 'rb'))
+    db_info_global = copy.deepcopy(db_infos)
+    start_idx = 0
+    global_db_list = []
+
+    for category, class_info in db_infos.items():
+        print('>>> Start processing %s' % category)
+        for idx, info in tqdm.tqdm(enumerate(class_info), total=len(class_info)):
+            obj_path = root_path / info['path']
+            obj_points = np.fromfile(str(obj_path), dtype=np.float32).reshape(
+                [-1, args.num_point_features])
+            num_points = obj_points.shape[0]
+            if num_points != info['num_points_in_gt']:
+                obj_points = np.fromfile(str(obj_path), dtype=np.float64).reshape([-1, args.num_point_features])
+                num_points = obj_points.shape[0]
+                obj_points = obj_points.astype(np.float32)
+            assert num_points == info['num_points_in_gt']
+                
+            db_info_global[category][idx]['global_data_offset'] = (start_idx, start_idx + num_points)
+            start_idx += num_points
+            global_db_list.append(obj_points)
+
+    global_db = np.concatenate(global_db_list)
+
+    with open(global_db_path, 'wb') as f:
+        np.save(f, global_db)
+
+    with open(db_info_global_path, 'wb') as f:
+        pkl.dump(db_info_global, f)
+
+    print(f"Successfully create integrated database at {global_db_path}")
+    print(f"Successfully create integrated database info at {db_info_global_path}")
+
+    return db_info_global, global_db
+
+
+def verify(info, whole_db, root_path, num_point_features):
+    obj_path = root_path / info['path']
+    obj_points = np.fromfile(str(obj_path), dtype=np.float32).reshape([-1, num_point_features])
+    mean_origin = obj_points.mean()
+
+    start_idx, end_idx = info['global_data_offset']
+    obj_points_new = whole_db[start_idx:end_idx]
+    mean_new = obj_points_new.mean()
+
+    assert mean_origin == mean_new
+
+    print("Verification pass!")
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--src_db_info', type=str, default='../../data/waymo/waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1_multiframe_-4_to_0_tail_parallel.pkl', help='')
+    parser.add_argument('--new_db_name', type=str, default='waymo_processed_data_v0_5_0_gt_database_train_sampled_1_multiframe_-4_to_0_tail_parallel_global', help='')
+    parser.add_argument('--num_point_features', type=int, default=6, help='number of feature channels for points')
+    parser.add_argument('--class_name', type=str, default='Vehicle', help='category name for verification')
+
+    args = parser.parse_args()
+
+    root_path = Path(os.path.dirname(args.src_db_info))
+
+    db_infos_global, whole_db = create_integrated_db_with_infos(args, root_path)
+    # simple verify
+    verify(db_infos_global[args.class_name][0], whole_db, root_path, args.num_point_features)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_test_mgpu.sh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_test_mgpu.sh
new file mode 100644
index 000000000..7e1a1d687
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_test_mgpu.sh
@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+GPUS=$2
+GPUS_PER_NODE=$GPUS
+PY_ARGS=${@:3}
+JOB_NAME=eval
+SRUN_ARGS=${SRUN_ARGS:-""}
+
+while true
+do
+    PORT=$(( ((RANDOM<<15)|RANDOM) % 49152 + 10000 ))
+    status="$(nc -z 127.0.0.1 $PORT < /dev/null &>/dev/null; echo $?)"
+    if [ "${status}" != "0" ]; then
+        break;
+    fi
+done
+echo $PORT
+
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --ntasks=${GPUS} \
+    --ntasks-per-node=${GPUS_PER_NODE} \
+    --kill-on-bad-exit=1 \
+    ${SRUN_ARGS} \
+    python -u test.py --launcher slurm --tcp_port $PORT ${PY_ARGS}
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_test_single.sh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_test_single.sh
new file mode 100644
index 000000000..2dea3ceaf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_test_single.sh
@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+GPUS=1
+GPUS_PER_NODE=1
+PY_ARGS=${@:2}
+JOB_NAME=eval
+SRUN_ARGS=${SRUN_ARGS:-""}
+
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --ntasks=${GPUS} \
+    --ntasks-per-node=${GPUS_PER_NODE} \
+    --kill-on-bad-exit=1 \
+    ${SRUN_ARGS} \
+    python -u test.py ${PY_ARGS}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_train.sh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_train.sh
new file mode 100644
index 000000000..7a4533f06
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_train.sh
@@ -0,0 +1,32 @@
+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+JOB_NAME=$2
+GPUS=$3
+PY_ARGS=${@:4}
+
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+SRUN_ARGS=${SRUN_ARGS:-""}
+
+while true
+do
+    PORT=$(( ((RANDOM<<15)|RANDOM) % 49152 + 10000 ))
+    status="$(nc -z 127.0.0.1 $PORT < /dev/null &>/dev/null; echo $?)"
+    if [ "${status}" != "0" ]; then
+        break;
+    fi
+done
+echo $PORT
+
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --ntasks=${GPUS} \
+    --ntasks-per-node=${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
+    --kill-on-bad-exit=1 \
+    ${SRUN_ARGS} \
+    python -u train.py --launcher slurm --tcp_port $PORT ${PY_ARGS}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_train_v2.sh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_train_v2.sh
new file mode 100644
index 000000000..d5ec0ed2f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/slurm_train_v2.sh
@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+JOB_NAME=$2
+GPUS=$3
+PY_ARGS=${@:4}
+
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-40}
+SRUN_ARGS=${SRUN_ARGS:-""}
+
+while true
+do
+    PORT=$(( ((RANDOM<<15)|RANDOM) % 49152 + 10000 ))
+    status="$(nc -z 127.0.0.1 $PORT < /dev/null &>/dev/null; echo $?)"
+    if [ "${status}" != "0" ]; then
+        break;
+    fi
+done
+echo $PORT
+
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
+    --kill-on-bad-exit=1 \
+    ${SRUN_ARGS} \
+    python -m torch.distributed.launch --nproc_per_node=${GPUS_PER_NODE} train.py --launcher pytorch --tcp_port ${PORT} ${PY_ARGS}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/torch_train.sh b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/torch_train.sh
new file mode 100644
index 000000000..59410491c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/scripts/torch_train.sh
@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+
+set -x
+NGPUS=$1
+PY_ARGS=${@:2}
+
+while true
+do
+    PORT=$(( ((RANDOM<<15)|RANDOM) % 49152 + 10000 ))
+    status="$(nc -z 127.0.0.1 $PORT < /dev/null &>/dev/null; echo $?)"
+    if [ "${status}" != "0" ]; then
+        break;
+    fi
+done
+echo $PORT
+
+torchrun --nproc_per_node=${NGPUS} --rdzv_endpoint=localhost:${PORT} train.py --launcher pytorch ${PY_ARGS}
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/test.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/test.py
new file mode 100644
index 000000000..51b7178c6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/test.py
@@ -0,0 +1,207 @@
+import _init_path
+import argparse
+import datetime
+import glob
+import os
+import re
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+from tensorboardX import SummaryWriter
+
+from eval_utils import eval_utils
+from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
+from pcdet.datasets import build_dataloader
+from pcdet.models import build_network
+from pcdet.utils import common_utils
+
+
+def parse_config():
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--cfg_file', type=str, default=None, help='specify the config for training')
+
+    parser.add_argument('--batch_size', type=int, default=None, required=False, help='batch size for training')
+    parser.add_argument('--workers', type=int, default=4, help='number of workers for dataloader')
+    parser.add_argument('--extra_tag', type=str, default='default', help='extra tag for this experiment')
+    parser.add_argument('--ckpt', type=str, default=None, help='checkpoint to start from')
+    parser.add_argument('--pretrained_model', type=str, default=None, help='pretrained_model')
+    parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none')
+    parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training')
+    parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
+    parser.add_argument('--set', dest='set_cfgs', default=None, nargs=argparse.REMAINDER,
+                        help='set extra config keys if needed')
+
+    parser.add_argument('--max_waiting_mins', type=int, default=30, help='max waiting minutes')
+    parser.add_argument('--start_epoch', type=int, default=0, help='')
+    parser.add_argument('--eval_tag', type=str, default='default', help='eval tag for this experiment')
+    parser.add_argument('--eval_all', action='store_true', default=False, help='whether to evaluate all checkpoints')
+    parser.add_argument('--ckpt_dir', type=str, default=None, help='specify a ckpt directory to be evaluated if needed')
+    parser.add_argument('--save_to_file', action='store_true', default=False, help='')
+    parser.add_argument('--infer_time', action='store_true', default=False, help='calculate inference latency')
+
+    args = parser.parse_args()
+
+    cfg_from_yaml_file(args.cfg_file, cfg)
+    cfg.TAG = Path(args.cfg_file).stem
+    cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1])  # remove 'cfgs' and 'xxxx.yaml'
+
+    np.random.seed(1024)
+
+    if args.set_cfgs is not None:
+        cfg_from_list(args.set_cfgs, cfg)
+
+    return args, cfg
+
+
+def eval_single_ckpt(model, test_loader, args, eval_output_dir, logger, epoch_id, dist_test=False):
+    # load checkpoint
+    model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=dist_test, 
+                                pre_trained_path=args.pretrained_model)
+    model.cuda()
+    
+    # start evaluation
+    eval_utils.eval_one_epoch(
+        cfg, args, model, test_loader, epoch_id, logger, dist_test=dist_test,
+        result_dir=eval_output_dir
+    )
+
+
+def get_no_evaluated_ckpt(ckpt_dir, ckpt_record_file, args):
+    ckpt_list = glob.glob(os.path.join(ckpt_dir, '*checkpoint_epoch_*.pth'))
+    ckpt_list.sort(key=os.path.getmtime)
+    evaluated_ckpt_list = [float(x.strip()) for x in open(ckpt_record_file, 'r').readlines()]
+
+    for cur_ckpt in ckpt_list:
+        num_list = re.findall('checkpoint_epoch_(.*).pth', cur_ckpt)
+        if num_list.__len__() == 0:
+            continue
+
+        epoch_id = num_list[-1]
+        if 'optim' in epoch_id:
+            continue
+        if float(epoch_id) not in evaluated_ckpt_list and int(float(epoch_id)) >= args.start_epoch:
+            return epoch_id, cur_ckpt
+    return -1, None
+
+
+def repeat_eval_ckpt(model, test_loader, args, eval_output_dir, logger, ckpt_dir, dist_test=False):
+    # evaluated ckpt record
+    ckpt_record_file = eval_output_dir / ('eval_list_%s.txt' % cfg.DATA_CONFIG.DATA_SPLIT['test'])
+    with open(ckpt_record_file, 'a'):
+        pass
+
+    # tensorboard log
+    if cfg.LOCAL_RANK == 0:
+        tb_log = SummaryWriter(log_dir=str(eval_output_dir / ('tensorboard_%s' % cfg.DATA_CONFIG.DATA_SPLIT['test'])))
+    total_time = 0
+    first_eval = True
+
+    while True:
+        # check whether there is checkpoint which is not evaluated
+        cur_epoch_id, cur_ckpt = get_no_evaluated_ckpt(ckpt_dir, ckpt_record_file, args)
+        if cur_epoch_id == -1 or int(float(cur_epoch_id)) < args.start_epoch:
+            wait_second = 30
+            if cfg.LOCAL_RANK == 0:
+                print('Wait %s seconds for next check (progress: %.1f / %d minutes): %s \r'
+                      % (wait_second, total_time * 1.0 / 60, args.max_waiting_mins, ckpt_dir), end='', flush=True)
+            time.sleep(wait_second)
+            total_time += 30
+            if total_time > args.max_waiting_mins * 60 and (first_eval is False):
+                break
+            continue
+
+        total_time = 0
+        first_eval = False
+
+        model.load_params_from_file(filename=cur_ckpt, logger=logger, to_cpu=dist_test)
+        model.cuda()
+
+        # start evaluation
+        cur_result_dir = eval_output_dir / ('epoch_%s' % cur_epoch_id) / cfg.DATA_CONFIG.DATA_SPLIT['test']
+        tb_dict = eval_utils.eval_one_epoch(
+            cfg, args, model, test_loader, cur_epoch_id, logger, dist_test=dist_test,
+            result_dir=cur_result_dir
+        )
+
+        if cfg.LOCAL_RANK == 0:
+            for key, val in tb_dict.items():
+                tb_log.add_scalar(key, val, cur_epoch_id)
+
+        # record this epoch which has been evaluated
+        with open(ckpt_record_file, 'a') as f:
+            print('%s' % cur_epoch_id, file=f)
+        logger.info('Epoch %s has been evaluated' % cur_epoch_id)
+
+
+def main():
+    args, cfg = parse_config()
+
+    if args.infer_time:
+        os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
+
+    if args.launcher == 'none':
+        dist_test = False
+        total_gpus = 1
+    else:
+        total_gpus, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)(
+            args.tcp_port, args.local_rank, backend='nccl'
+        )
+        dist_test = True
+
+    if args.batch_size is None:
+        args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU
+    else:
+        assert args.batch_size % total_gpus == 0, 'Batch size should match the number of gpus'
+        args.batch_size = args.batch_size // total_gpus
+
+    output_dir = cfg.ROOT_DIR / 'output' / cfg.EXP_GROUP_PATH / cfg.TAG / args.extra_tag
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    eval_output_dir = output_dir / 'eval'
+
+    if not args.eval_all:
+        num_list = re.findall(r'\d+', args.ckpt) if args.ckpt is not None else []
+        epoch_id = num_list[-1] if num_list.__len__() > 0 else 'no_number'
+        eval_output_dir = eval_output_dir / ('epoch_%s' % epoch_id) / cfg.DATA_CONFIG.DATA_SPLIT['test']
+    else:
+        eval_output_dir = eval_output_dir / 'eval_all_default'
+
+    if args.eval_tag is not None:
+        eval_output_dir = eval_output_dir / args.eval_tag
+
+    eval_output_dir.mkdir(parents=True, exist_ok=True)
+    log_file = eval_output_dir / ('log_eval_%s.txt' % datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
+    logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK)
+
+    # log to file
+    logger.info('**********************Start logging**********************')
+    gpu_list = os.environ['CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys() else 'ALL'
+    logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
+
+    if dist_test:
+        logger.info('total_batch_size: %d' % (total_gpus * args.batch_size))
+    for key, val in vars(args).items():
+        logger.info('{:16} {}'.format(key, val))
+    log_config_to_file(cfg, logger=logger)
+
+    ckpt_dir = args.ckpt_dir if args.ckpt_dir is not None else output_dir / 'ckpt'
+
+    test_set, test_loader, sampler = build_dataloader(
+        dataset_cfg=cfg.DATA_CONFIG,
+        class_names=cfg.CLASS_NAMES,
+        batch_size=args.batch_size,
+        dist=dist_test, workers=args.workers, logger=logger, training=False
+    )
+
+    model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=test_set)
+    with torch.no_grad():
+        if args.eval_all:
+            repeat_eval_ckpt(model, test_loader, args, eval_output_dir, logger, ckpt_dir, dist_test=dist_test)
+        else:
+            eval_single_ckpt(model, test_loader, args, eval_output_dir, logger, epoch_id, dist_test=dist_test)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train.py
new file mode 100644
index 000000000..54e524086
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train.py
@@ -0,0 +1,230 @@
+import _init_path
+import argparse
+import datetime
+import glob
+import os
+from pathlib import Path
+from test import repeat_eval_ckpt
+
+import torch
+import torch.nn as nn
+from tensorboardX import SummaryWriter
+
+from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
+from pcdet.datasets import build_dataloader
+from pcdet.models import build_network, model_fn_decorator
+from pcdet.utils import common_utils
+from train_utils.optimization import build_optimizer, build_scheduler
+from train_utils.train_utils import train_model
+
+
+def parse_config():
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--cfg_file', type=str, default=None, help='specify the config for training')
+
+    parser.add_argument('--batch_size', type=int, default=None, required=False, help='batch size for training')
+    parser.add_argument('--epochs', type=int, default=None, required=False, help='number of epochs to train for')
+    parser.add_argument('--workers', type=int, default=4, help='number of workers for dataloader')
+    parser.add_argument('--extra_tag', type=str, default='default', help='extra tag for this experiment')
+    parser.add_argument('--ckpt', type=str, default=None, help='checkpoint to start from')
+    parser.add_argument('--pretrained_model', type=str, default=None, help='pretrained_model')
+    parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none')
+    parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training')
+    parser.add_argument('--sync_bn', action='store_true', default=False, help='whether to use sync bn')
+    parser.add_argument('--fix_random_seed', action='store_true', default=False, help='')
+    parser.add_argument('--ckpt_save_interval', type=int, default=1, help='number of training epochs')
+    parser.add_argument('--local-rank', type=int, default=0, help='local rank for distributed training')
+    parser.add_argument('--max_ckpt_save_num', type=int, default=30, help='max number of saved checkpoint')
+    parser.add_argument('--merge_all_iters_to_one_epoch', action='store_true', default=False, help='')
+    parser.add_argument('--set', dest='set_cfgs', default=None, nargs=argparse.REMAINDER,
+                        help='set extra config keys if needed')
+
+    parser.add_argument('--max_waiting_mins', type=int, default=0, help='max waiting minutes')
+    parser.add_argument('--start_epoch', type=int, default=0, help='')
+    parser.add_argument('--num_epochs_to_eval', type=int, default=0, help='number of checkpoints to be evaluated')
+    parser.add_argument('--save_to_file', action='store_true', default=False, help='')
+    
+    parser.add_argument('--use_tqdm_to_record', action='store_true', default=False, help='if True, the intermediate losses will not be logged to file, only tqdm will be used')
+    parser.add_argument('--logger_iter_interval', type=int, default=50, help='')
+    parser.add_argument('--ckpt_save_time_interval', type=int, default=300, help='in terms of seconds')
+    parser.add_argument('--wo_gpu_stat', action='store_true', help='')
+    parser.add_argument('--use_amp', action='store_true', help='use mix precision training')
+    
+
+    args = parser.parse_args()
+
+    cfg_from_yaml_file(args.cfg_file, cfg)
+    cfg.TAG = Path(args.cfg_file).stem
+    cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1])  # remove 'cfgs' and 'xxxx.yaml'
+    
+    args.use_amp = args.use_amp or cfg.OPTIMIZATION.get('USE_AMP', False)
+
+    if args.set_cfgs is not None:
+        cfg_from_list(args.set_cfgs, cfg)
+
+    return args, cfg
+
+
+def main():
+    args, cfg = parse_config()
+    if args.launcher == 'none':
+        dist_train = False
+        total_gpus = 1
+    else:
+        total_gpus, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)(
+            args.tcp_port, args.local_rank, backend='nccl'
+        )
+        dist_train = True
+
+    if args.batch_size is None:
+        args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU
+    else:
+        assert args.batch_size % total_gpus == 0, 'Batch size should match the number of gpus'
+        args.batch_size = args.batch_size // total_gpus
+
+    args.epochs = cfg.OPTIMIZATION.NUM_EPOCHS if args.epochs is None else args.epochs
+
+    if args.fix_random_seed:
+        common_utils.set_random_seed(666 + cfg.LOCAL_RANK)
+
+    output_dir = cfg.ROOT_DIR / 'output' / cfg.EXP_GROUP_PATH / cfg.TAG / args.extra_tag
+    ckpt_dir = output_dir / 'ckpt'
+    output_dir.mkdir(parents=True, exist_ok=True)
+    ckpt_dir.mkdir(parents=True, exist_ok=True)
+
+    log_file = output_dir / ('train_%s.log' % datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
+    logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK)
+
+    # log to file
+    logger.info('**********************Start logging**********************')
+    gpu_list = os.environ['CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys() else 'ALL'
+    logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
+
+    if dist_train:
+        logger.info('Training in distributed mode : total_batch_size: %d' % (total_gpus * args.batch_size))
+    else:
+        logger.info('Training with a single process')
+        
+    for key, val in vars(args).items():
+        logger.info('{:16} {}'.format(key, val))
+    log_config_to_file(cfg, logger=logger)
+    if cfg.LOCAL_RANK == 0:
+        os.system('cp %s %s' % (args.cfg_file, output_dir))
+
+    tb_log = SummaryWriter(log_dir=str(output_dir / 'tensorboard')) if cfg.LOCAL_RANK == 0 else None
+
+    logger.info("----------- Create dataloader & network & optimizer -----------")
+    train_set, train_loader, train_sampler = build_dataloader(
+        dataset_cfg=cfg.DATA_CONFIG,
+        class_names=cfg.CLASS_NAMES,
+        batch_size=args.batch_size,
+        dist=dist_train, workers=args.workers,
+        logger=logger,
+        training=True,
+        merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch,
+        total_epochs=args.epochs,
+        seed=666 if args.fix_random_seed else None
+    )
+
+    model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=train_set)
+    if args.sync_bn:
+        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+    model.cuda()
+
+    optimizer = build_optimizer(model, cfg.OPTIMIZATION)
+
+    # load checkpoint if it is possible
+    start_epoch = it = 0
+    last_epoch = -1
+    if args.pretrained_model is not None:
+        model.load_params_from_file(filename=args.pretrained_model, to_cpu=dist_train, logger=logger)
+
+    if args.ckpt is not None:
+        it, start_epoch = model.load_params_with_optimizer(args.ckpt, to_cpu=dist_train, optimizer=optimizer, logger=logger)
+        last_epoch = start_epoch + 1
+    else:
+        ckpt_list = glob.glob(str(ckpt_dir / '*.pth'))
+              
+        if len(ckpt_list) > 0:
+            ckpt_list.sort(key=os.path.getmtime)
+            while len(ckpt_list) > 0:
+                try:
+                    it, start_epoch = model.load_params_with_optimizer(
+                        ckpt_list[-1], to_cpu=dist_train, optimizer=optimizer, logger=logger
+                    )
+                    last_epoch = start_epoch + 1
+                    break
+                except:
+                    ckpt_list = ckpt_list[:-1]
+
+    model.train()  # before wrap to DistributedDataParallel to support fixed some parameters
+    if dist_train:
+        model = nn.parallel.DistributedDataParallel(model, device_ids=[cfg.LOCAL_RANK % torch.cuda.device_count()])
+    logger.info(f'----------- Model {cfg.MODEL.NAME} created, param count: {sum([m.numel() for m in model.parameters()])} -----------')
+    logger.info(model)
+
+    lr_scheduler, lr_warmup_scheduler = build_scheduler(
+        optimizer, total_iters_each_epoch=len(train_loader), total_epochs=args.epochs,
+        last_epoch=last_epoch, optim_cfg=cfg.OPTIMIZATION
+    )
+
+    # -----------------------start training---------------------------
+    logger.info('**********************Start training %s/%s(%s)**********************'
+                % (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
+
+    train_model(
+        model,
+        optimizer,
+        train_loader,
+        model_func=model_fn_decorator(),
+        lr_scheduler=lr_scheduler,
+        optim_cfg=cfg.OPTIMIZATION,
+        start_epoch=start_epoch,
+        total_epochs=args.epochs,
+        start_iter=it,
+        rank=cfg.LOCAL_RANK,
+        tb_log=tb_log,
+        ckpt_save_dir=ckpt_dir,
+        train_sampler=train_sampler,
+        lr_warmup_scheduler=lr_warmup_scheduler,
+        ckpt_save_interval=args.ckpt_save_interval,
+        max_ckpt_save_num=args.max_ckpt_save_num,
+        merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch, 
+        logger=logger, 
+        logger_iter_interval=args.logger_iter_interval,
+        ckpt_save_time_interval=args.ckpt_save_time_interval,
+        use_logger_to_record=not args.use_tqdm_to_record, 
+        show_gpu_stat=not args.wo_gpu_stat,
+        use_amp=args.use_amp,
+        cfg=cfg
+    )
+
+    if hasattr(train_set, 'use_shared_memory') and train_set.use_shared_memory:
+        train_set.clean_shared_memory()
+
+    logger.info('**********************End training %s/%s(%s)**********************\n\n\n'
+                % (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
+
+    logger.info('**********************Start evaluation %s/%s(%s)**********************' %
+                (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
+    test_set, test_loader, sampler = build_dataloader(
+        dataset_cfg=cfg.DATA_CONFIG,
+        class_names=cfg.CLASS_NAMES,
+        batch_size=args.batch_size,
+        dist=dist_train, workers=args.workers, logger=logger, training=False
+    )
+    eval_output_dir = output_dir / 'eval' / 'eval_with_train'
+    eval_output_dir.mkdir(parents=True, exist_ok=True)
+    args.start_epoch = max(args.epochs - args.num_epochs_to_eval, 0)  # Only evaluate the last args.num_epochs_to_eval epochs
+
+    repeat_eval_ckpt(
+        model.module if dist_train else model,
+        test_loader, args, eval_output_dir, logger, ckpt_dir,
+        dist_test=dist_train
+    )
+    logger.info('**********************End evaluation %s/%s(%s)**********************' %
+                (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/__init__.py
new file mode 100644
index 000000000..888cfcf20
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/__init__.py
@@ -0,0 +1,68 @@
+from functools import partial
+
+import torch.nn as nn
+import torch.optim as optim
+import torch.optim.lr_scheduler as lr_sched
+
+from .fastai_optim import OptimWrapper
+from .learning_schedules_fastai import CosineWarmupLR, OneCycle, CosineAnnealing
+
+
+def build_optimizer(model, optim_cfg):
+    if optim_cfg.OPTIMIZER == 'adam':
+        optimizer = optim.Adam(model.parameters(), lr=optim_cfg.LR, weight_decay=optim_cfg.WEIGHT_DECAY)
+    elif optim_cfg.OPTIMIZER == 'sgd':
+        optimizer = optim.SGD(
+            model.parameters(), lr=optim_cfg.LR, weight_decay=optim_cfg.WEIGHT_DECAY,
+            momentum=optim_cfg.MOMENTUM
+        )
+    elif optim_cfg.OPTIMIZER in ['adam_onecycle','adam_cosineanneal']:
+        def children(m: nn.Module):
+            return list(m.children())
+
+        def num_children(m: nn.Module) -> int:
+            return len(children(m))
+
+        flatten_model = lambda m: sum(map(flatten_model, m.children()), []) if num_children(m) else [m]
+        get_layer_groups = lambda m: [nn.Sequential(*flatten_model(m))]
+        betas = optim_cfg.get('BETAS', (0.9, 0.99))
+        betas = tuple(betas)
+        optimizer_func = partial(optim.Adam, betas=betas)
+        optimizer = OptimWrapper.create(
+            optimizer_func, 3e-3, get_layer_groups(model), wd=optim_cfg.WEIGHT_DECAY, true_wd=True, bn_wd=True
+        )
+    else:
+        raise NotImplementedError
+
+    return optimizer
+
+
+def build_scheduler(optimizer, total_iters_each_epoch, total_epochs, last_epoch, optim_cfg):
+    decay_steps = [x * total_iters_each_epoch for x in optim_cfg.DECAY_STEP_LIST]
+    def lr_lbmd(cur_epoch):
+        cur_decay = 1
+        for decay_step in decay_steps:
+            if cur_epoch >= decay_step:
+                cur_decay = cur_decay * optim_cfg.LR_DECAY
+        return max(cur_decay, optim_cfg.LR_CLIP / optim_cfg.LR)
+
+    lr_warmup_scheduler = None
+    total_steps = total_iters_each_epoch * total_epochs
+    if optim_cfg.OPTIMIZER == 'adam_onecycle':
+        lr_scheduler = OneCycle(
+            optimizer, total_steps, optim_cfg.LR, list(optim_cfg.MOMS), optim_cfg.DIV_FACTOR, optim_cfg.PCT_START
+        )
+    elif optim_cfg.OPTIMIZER == 'adam_cosineanneal':
+        lr_scheduler = CosineAnnealing(
+            optimizer, total_steps, total_epochs, optim_cfg.LR, list(optim_cfg.MOMS), optim_cfg.PCT_START, optim_cfg.WARMUP_ITER
+        )
+    else:
+        lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd, last_epoch=last_epoch)
+
+        if optim_cfg.LR_WARMUP:
+            lr_warmup_scheduler = CosineWarmupLR(
+                optimizer, T_max=optim_cfg.WARMUP_EPOCH * len(total_iters_each_epoch),
+                eta_min=optim_cfg.LR / optim_cfg.DIV_FACTOR
+            )
+
+    return lr_scheduler, lr_warmup_scheduler
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/fastai_optim.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/fastai_optim.py
new file mode 100644
index 000000000..62909df40
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/fastai_optim.py
@@ -0,0 +1,264 @@
+# This file is modified from https://github.com/traveller59/second.pytorch
+
+try:
+    from collections.abc import Iterable
+except:
+    from collections import Iterable
+
+import torch
+from torch import nn
+from torch._utils import _unflatten_dense_tensors
+from torch.nn.utils import parameters_to_vector
+
+bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
+
+
+def split_bn_bias(layer_groups):
+    "Split the layers in `layer_groups` into batchnorm (`bn_types`) and non-batchnorm groups."
+    split_groups = []
+    for l in layer_groups:
+        l1, l2 = [], []
+        for c in l.children():
+            if isinstance(c, bn_types):
+                l2.append(c)
+            else:
+                l1.append(c)
+        split_groups += [nn.Sequential(*l1), nn.Sequential(*l2)]
+    return split_groups
+
+
+def get_master(layer_groups, flat_master: bool = False):
+    "Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32."
+    split_groups = split_bn_bias(layer_groups)
+    model_params = [[param for param in lg.parameters() if param.requires_grad] for lg in split_groups]
+    if flat_master:
+        master_params = []
+        for lg in model_params:
+            if len(lg) != 0:
+                mp = parameters_to_vector([param.data.float() for param in lg])
+                mp = torch.nn.Parameter(mp, requires_grad=True)
+                if mp.grad is None: mp.grad = mp.new(*mp.size())
+                master_params.append([mp])
+            else:
+                master_params.append([])
+        return model_params, master_params
+    else:
+        master_params = [[param.clone().float().detach() for param in lg] for lg in model_params]
+        for mp in master_params:
+            for param in mp: param.requires_grad = True
+        return model_params, master_params
+
+
+def model_g2master_g(model_params, master_params, flat_master: bool = False) -> None:
+    "Copy the `model_params` gradients to `master_params` for the optimizer step."
+    if flat_master:
+        for model_group, master_group in zip(model_params, master_params):
+            if len(master_group) != 0:
+                master_group[0].grad.data.copy_(parameters_to_vector([p.grad.data.float() for p in model_group]))
+    else:
+        for model_group, master_group in zip(model_params, master_params):
+            for model, master in zip(model_group, master_group):
+                if model.grad is not None:
+                    if master.grad is None: master.grad = master.data.new(*master.data.size())
+                    master.grad.data.copy_(model.grad.data)
+                else:
+                    master.grad = None
+
+
+def master2model(model_params, master_params, flat_master: bool = False) -> None:
+    "Copy `master_params` to `model_params`."
+    if flat_master:
+        for model_group, master_group in zip(model_params, master_params):
+            if len(model_group) != 0:
+                for model, master in zip(model_group, _unflatten_dense_tensors(master_group[0].data, model_group)):
+                    model.data.copy_(master)
+    else:
+        for model_group, master_group in zip(model_params, master_params):
+            for model, master in zip(model_group, master_group): model.data.copy_(master.data)
+
+
+def listify(p=None, q=None):
+    "Make `p` listy and the same length as `q`."
+    if p is None:
+        p = []
+    elif isinstance(p, str):
+        p = [p]
+    elif not isinstance(p, Iterable):
+        p = [p]
+    n = q if type(q) == int else len(p) if q is None else len(q)
+    if len(p) == 1: p = p * n
+    assert len(p) == n, f'List len mismatch ({len(p)} vs {n})'
+    return list(p)
+
+
+def trainable_params(m: nn.Module):
+    "Return list of trainable params in `m`."
+    res = filter(lambda p: p.requires_grad, m.parameters())
+    return res
+
+
+def is_tuple(x) -> bool: return isinstance(x, tuple)
+
+
+# copy from fastai.
+class OptimWrapper():
+    "Basic wrapper around `opt` to simplify hyper-parameters changes."
+
+    def __init__(self, opt, wd, true_wd: bool = False, bn_wd: bool = True):
+        self.opt, self.true_wd, self.bn_wd = opt, true_wd, bn_wd
+        self.opt_keys = list(self.opt.param_groups[0].keys())
+        self.opt_keys.remove('params')
+        self.read_defaults()
+        self.wd = wd
+
+    @classmethod
+    def create(cls, opt_func, lr,
+               layer_groups, **kwargs):
+        "Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`."
+        split_groups = split_bn_bias(layer_groups)
+        opt = opt_func([{'params': trainable_params(l), 'lr': 0} for l in split_groups])
+        opt = cls(opt, **kwargs)
+        opt.lr, opt.opt_func = listify(lr, layer_groups), opt_func
+        return opt
+
+    def new(self, layer_groups):
+        "Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters."
+        opt_func = getattr(self, 'opt_func', self.opt.__class__)
+        split_groups = split_bn_bias(layer_groups)
+        opt = opt_func([{'params': trainable_params(l), 'lr': 0} for l in split_groups])
+        return self.create(opt_func, self.lr, layer_groups, wd=self.wd, true_wd=self.true_wd, bn_wd=self.bn_wd)
+
+    def __repr__(self) -> str:
+        return f'OptimWrapper over {repr(self.opt)}.\nTrue weight decay: {self.true_wd}'
+
+    # Pytorch optimizer methods
+    def step(self) -> None:
+        "Set weight decay and step optimizer."
+        # weight decay outside of optimizer step (AdamW)
+        if self.true_wd:
+            for lr, wd, pg1, pg2 in zip(self._lr, self._wd, self.opt.param_groups[::2], self.opt.param_groups[1::2]):
+                for p in pg1['params']:
+                    # When some parameters are fixed:  Shaoshuai Shi
+                    if p.requires_grad is False:
+                        continue
+                    p.data.mul_(1 - wd * lr)
+                if self.bn_wd:
+                    for p in pg2['params']:
+                        # When some parameters are fixed:  Shaoshuai Shi
+                        if p.requires_grad is False:
+                            continue
+                        p.data.mul_(1 - wd * lr)
+            self.set_val('weight_decay', listify(0, self._wd))
+        self.opt.step()
+
+    def zero_grad(self) -> None:
+        "Clear optimizer gradients."
+        self.opt.zero_grad()
+
+    # Passthrough to the inner opt.
+    def __getattr__(self, k: str):
+        return getattr(self.opt, k, None)
+
+    def clear(self):
+        "Reset the state of the inner optimizer."
+        sd = self.state_dict()
+        sd['state'] = {}
+        self.load_state_dict(sd)
+
+    # Hyperparameters as properties
+    @property
+    def lr(self) -> float:
+        return self._lr[-1]
+
+    @lr.setter
+    def lr(self, val: float) -> None:
+        self._lr = self.set_val('lr', listify(val, self._lr))
+
+    @property
+    def mom(self) -> float:
+        return self._mom[-1]
+
+    @mom.setter
+    def mom(self, val: float) -> None:
+        if 'momentum' in self.opt_keys:
+            self.set_val('momentum', listify(val, self._mom))
+        elif 'betas' in self.opt_keys:
+            self.set_val('betas', (listify(val, self._mom), self._beta))
+        self._mom = listify(val, self._mom)
+
+    @property
+    def beta(self) -> float:
+        return None if self._beta is None else self._beta[-1]
+
+    @beta.setter
+    def beta(self, val: float) -> None:
+        "Set beta (or alpha as makes sense for given optimizer)."
+        if val is None: return
+        if 'betas' in self.opt_keys:
+            self.set_val('betas', (self._mom, listify(val, self._beta)))
+        elif 'alpha' in self.opt_keys:
+            self.set_val('alpha', listify(val, self._beta))
+        self._beta = listify(val, self._beta)
+
+    @property
+    def wd(self) -> float:
+        return self._wd[-1]
+
+    @wd.setter
+    def wd(self, val: float) -> None:
+        "Set weight decay."
+        if not self.true_wd: self.set_val('weight_decay', listify(val, self._wd), bn_groups=self.bn_wd)
+        self._wd = listify(val, self._wd)
+
+    # Helper functions
+    def read_defaults(self) -> None:
+        "Read the values inside the optimizer for the hyper-parameters."
+        self._beta = None
+        if 'lr' in self.opt_keys: self._lr = self.read_val('lr')
+        if 'momentum' in self.opt_keys: self._mom = self.read_val('momentum')
+        if 'alpha' in self.opt_keys: self._beta = self.read_val('alpha')
+        if 'betas' in self.opt_keys: self._mom, self._beta = self.read_val('betas')
+        if 'weight_decay' in self.opt_keys: self._wd = self.read_val('weight_decay')
+
+    def set_val(self, key: str, val, bn_groups: bool = True):
+        "Set `val` inside the optimizer dictionary at `key`."
+        if is_tuple(val): val = [(v1, v2) for v1, v2 in zip(*val)]
+        for v, pg1, pg2 in zip(val, self.opt.param_groups[::2], self.opt.param_groups[1::2]):
+            pg1[key] = v
+            if bn_groups: pg2[key] = v
+        return val
+
+    def read_val(self, key: str):
+        "Read a hyperparameter `key` in the optimizer dictionary."
+        val = [pg[key] for pg in self.opt.param_groups[::2]]
+        if is_tuple(val[0]): val = [o[0] for o in val], [o[1] for o in val]
+        return val
+
+
+class FastAIMixedOptim(OptimWrapper):
+    @classmethod
+    def create(cls, opt_func, lr,
+               layer_groups, model, flat_master=False, loss_scale=512.0, **kwargs):
+        "Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`."
+        opt = OptimWrapper.create(opt_func, lr, layer_groups, **kwargs)
+        opt.model_params, opt.master_params = get_master(layer_groups, flat_master)
+        opt.flat_master = flat_master
+        opt.loss_scale = loss_scale
+        opt.model = model
+        # Changes the optimizer so that the optimization step is done in FP32.
+        # opt = self.learn.opt
+        mom, wd, beta = opt.mom, opt.wd, opt.beta
+        lrs = [lr for lr in opt._lr for _ in range(2)]
+        opt_params = [{'params': mp, 'lr': lr} for mp, lr in zip(opt.master_params, lrs)]
+        opt.opt = opt_func(opt_params)
+        opt.mom, opt.wd, opt.beta = mom, wd, beta
+        return opt
+
+    def step(self):
+        model_g2master_g(self.model_params, self.master_params, self.flat_master)
+        for group in self.master_params:
+            for param in group: param.grad.div_(self.loss_scale)
+        super(FastAIMixedOptim, self).step()
+        self.model.zero_grad()
+        # Update the params from master to model.
+        master2model(self.model_params, self.master_params, self.flat_master)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/learning_schedules_fastai.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/learning_schedules_fastai.py
new file mode 100644
index 000000000..15f7d2349
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/optimization/learning_schedules_fastai.py
@@ -0,0 +1,162 @@
+# This file is modified from https://github.com/traveller59/second.pytorch
+
+import math
+from functools import partial
+
+import numpy as np
+import torch.optim.lr_scheduler as lr_sched
+
+from .fastai_optim import OptimWrapper
+
+
+class LRSchedulerStep(object):
+    def __init__(self, fai_optimizer: OptimWrapper, total_step, lr_phases,
+                 mom_phases):
+        # if not isinstance(fai_optimizer, OptimWrapper):
+        #     raise TypeError('{} is not a fastai OptimWrapper'.format(
+        #         type(fai_optimizer).__name__))
+        self.optimizer = fai_optimizer
+        self.total_step = total_step
+        self.lr_phases = []
+
+        for i, (start, lambda_func) in enumerate(lr_phases):
+            if len(self.lr_phases) != 0:
+                assert self.lr_phases[-1][0] < start
+            if isinstance(lambda_func, str):
+                lambda_func = eval(lambda_func)
+            if i < len(lr_phases) - 1:
+                self.lr_phases.append((int(start * total_step), int(lr_phases[i + 1][0] * total_step), lambda_func))
+            else:
+                self.lr_phases.append((int(start * total_step), total_step, lambda_func))
+        assert self.lr_phases[0][0] == 0
+        self.mom_phases = []
+        for i, (start, lambda_func) in enumerate(mom_phases):
+            if len(self.mom_phases) != 0:
+                assert self.mom_phases[-1][0] < start
+            if isinstance(lambda_func, str):
+                lambda_func = eval(lambda_func)
+            if i < len(mom_phases) - 1:
+                self.mom_phases.append((int(start * total_step), int(mom_phases[i + 1][0] * total_step), lambda_func))
+            else:
+                self.mom_phases.append((int(start * total_step), total_step, lambda_func))
+        assert self.mom_phases[0][0] == 0
+
+    def step(self, step, epoch=None):
+        for start, end, func in self.lr_phases:
+            if step >= start:
+                self.optimizer.lr = func((step - start) / (end - start))
+        for start, end, func in self.mom_phases:
+            if step >= start:
+                self.optimizer.mom = func((step - start) / (end - start))
+
+
+def annealing_cos(start, end, pct):
+    # print(pct, start, end)
+    "Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
+    cos_out = np.cos(np.pi * pct) + 1
+    return end + (start - end) / 2 * cos_out
+
+
+class OneCycle(LRSchedulerStep):
+    def __init__(self, fai_optimizer, total_step, lr_max, moms, div_factor,
+                 pct_start):
+        self.lr_max = lr_max
+        self.moms = moms
+        self.div_factor = div_factor
+        self.pct_start = pct_start
+        a1 = int(total_step * self.pct_start)
+        a2 = total_step - a1
+        low_lr = self.lr_max / self.div_factor
+        lr_phases = ((0, partial(annealing_cos, low_lr, self.lr_max)),
+                     (self.pct_start,
+                      partial(annealing_cos, self.lr_max, low_lr / 1e4)))
+        mom_phases = ((0, partial(annealing_cos, *self.moms)),
+                      (self.pct_start, partial(annealing_cos,
+                                               *self.moms[::-1])))
+        fai_optimizer.lr, fai_optimizer.mom = low_lr, self.moms[0]
+        super().__init__(fai_optimizer, total_step, lr_phases, mom_phases)
+
+
+class CosineWarmupLR(lr_sched._LRScheduler):
+    def __init__(self, optimizer, T_max, eta_min=0, last_epoch=-1):
+        self.T_max = T_max
+        self.eta_min = eta_min
+        super(CosineWarmupLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self, epoch=None):
+        return [self.eta_min + (base_lr - self.eta_min) *
+                (1 - math.cos(math.pi * self.last_epoch / self.T_max)) / 2
+                for base_lr in self.base_lrs]
+
+
+def linear_warmup(end, lr_max, pct):
+    k = (1 - pct / end) * (1 - 0.33333333)
+    warmup_lr = lr_max * (1 - k)
+    return warmup_lr
+
+
+class CosineAnnealing(LRSchedulerStep):
+    def __init__(self, fai_optimizer, total_step, total_epoch, lr_max, moms, pct_start, warmup_iter):
+        self.lr_max = lr_max
+        self.moms = moms
+        self.pct_start = pct_start
+
+        mom_phases = ((0, partial(annealing_cos, *self.moms)),
+                      (self.pct_start, partial(annealing_cos,
+                                               *self.moms[::-1])))
+        fai_optimizer.lr, fai_optimizer.mom = lr_max, self.moms[0]
+
+        self.optimizer = fai_optimizer
+        self.total_step = total_step 
+        self.warmup_iter = warmup_iter
+        self.total_epoch = total_epoch
+
+        self.mom_phases = []
+        for i, (start, lambda_func) in enumerate(mom_phases):
+            if len(self.mom_phases) != 0:
+                assert self.mom_phases[-1][0] < start
+            if isinstance(lambda_func, str):
+                lambda_func = eval(lambda_func)
+            if i < len(mom_phases) - 1:
+                self.mom_phases.append((int(start * total_step), int(mom_phases[i + 1][0] * total_step), lambda_func))
+            else:
+                self.mom_phases.append((int(start * total_step), total_step, lambda_func))
+        assert self.mom_phases[0][0] == 0
+    
+    def step(self, step, epoch):
+        # update lr
+        if step < self.warmup_iter:
+            self.optimizer.lr = linear_warmup(self.warmup_iter, self.lr_max, step)
+        else:
+            target_lr = self.lr_max * 0.001
+            cos_lr = annealing_cos(self.lr_max, target_lr, epoch / self.total_epoch)
+            self.optimizer.lr = cos_lr
+        # update mom
+        for start, end, func in self.mom_phases:
+            if step >= start:
+                self.optimizer.mom = func((step - start) / (end - start))
+
+
+class FakeOptim:
+    def __init__(self):
+        self.lr = 0
+        self.mom = 0
+
+
+if __name__ == "__main__":
+    import matplotlib.pyplot as plt
+
+    opt = FakeOptim()  # 3e-3, wd=0.4, div_factor=10
+    schd = OneCycle(opt, 100, 3e-3, (0.95, 0.85), 10.0, 0.1)
+
+    lrs = []
+    moms = []
+    for i in range(100):
+        schd.step(i)
+        lrs.append(opt.lr)
+        moms.append(opt.mom)
+    plt.plot(lrs)
+    # plt.plot(moms)
+    plt.show()
+    plt.plot(moms)
+    plt.show()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/train_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/train_utils.py
new file mode 100644
index 000000000..04071fb0e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/train_utils/train_utils.py
@@ -0,0 +1,272 @@
+import os
+
+import torch
+import tqdm
+import time
+import glob
+from torch.nn.utils import clip_grad_norm_
+from pcdet.utils import common_utils, commu_utils
+
+
+def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, accumulated_iter, optim_cfg,
+                    rank, tbar, total_it_each_epoch, dataloader_iter, tb_log=None, leave_pbar=False, 
+                    use_logger_to_record=False, logger=None, logger_iter_interval=50, cur_epoch=None, 
+                    total_epochs=None, ckpt_save_dir=None, ckpt_save_time_interval=300, show_gpu_stat=False, use_amp=False):
+    if total_it_each_epoch == len(train_loader):
+        dataloader_iter = iter(train_loader)
+
+    ckpt_save_cnt = 1
+    start_it = accumulated_iter % total_it_each_epoch
+
+    scaler = torch.cuda.amp.GradScaler(enabled=use_amp, init_scale=optim_cfg.get('LOSS_SCALE_FP16', 2.0**16))
+    
+    if rank == 0:
+        pbar = tqdm.tqdm(total=total_it_each_epoch, leave=leave_pbar, desc='train', dynamic_ncols=True)
+        data_time = common_utils.AverageMeter()
+        batch_time = common_utils.AverageMeter()
+        forward_time = common_utils.AverageMeter()
+        losses_m = common_utils.AverageMeter()
+
+    end = time.time()
+    for cur_it in range(start_it, total_it_each_epoch):
+        try:
+            batch = next(dataloader_iter)
+        except StopIteration:
+            dataloader_iter = iter(train_loader)
+            batch = next(dataloader_iter)
+            print('new iters')
+        
+        data_timer = time.time()
+        cur_data_time = data_timer - end
+
+        lr_scheduler.step(accumulated_iter, cur_epoch)
+
+        try:
+            cur_lr = float(optimizer.lr)
+        except:
+            cur_lr = optimizer.param_groups[0]['lr']
+
+        if tb_log is not None:
+            tb_log.add_scalar('meta_data/learning_rate', cur_lr, accumulated_iter)
+
+        model.train()
+        optimizer.zero_grad()
+
+        with torch.cuda.amp.autocast(enabled=use_amp):
+            loss, tb_dict, disp_dict = model_func(model, batch)
+
+        scaler.scale(loss).backward()
+        scaler.unscale_(optimizer)
+        clip_grad_norm_(model.parameters(), optim_cfg.GRAD_NORM_CLIP)
+        scaler.step(optimizer)
+        scaler.update()
+
+        accumulated_iter += 1
+ 
+        cur_forward_time = time.time() - data_timer
+        cur_batch_time = time.time() - end
+        end = time.time()
+
+        # average reduce
+        avg_data_time = commu_utils.average_reduce_value(cur_data_time)
+        avg_forward_time = commu_utils.average_reduce_value(cur_forward_time)
+        avg_batch_time = commu_utils.average_reduce_value(cur_batch_time)
+
+        # log to console and tensorboard
+        if rank == 0:
+            batch_size = batch.get('batch_size', None)
+            
+            data_time.update(avg_data_time)
+            forward_time.update(avg_forward_time)
+            batch_time.update(avg_batch_time)
+            losses_m.update(loss.item() , batch_size)
+            
+            disp_dict.update({
+                'loss': loss.item(), 'lr': cur_lr, 'd_time': f'{data_time.val:.2f}({data_time.avg:.2f})',
+                'f_time': f'{forward_time.val:.2f}({forward_time.avg:.2f})', 'b_time': f'{batch_time.val:.2f}({batch_time.avg:.2f})'
+            })
+            
+            if use_logger_to_record:
+                if accumulated_iter % logger_iter_interval == 0 or cur_it == start_it or cur_it + 1 == total_it_each_epoch:
+                    trained_time_past_all = tbar.format_dict['elapsed']
+                    second_each_iter = pbar.format_dict['elapsed'] / max(cur_it - start_it + 1, 1.0)
+
+                    trained_time_each_epoch = pbar.format_dict['elapsed']
+                    remaining_second_each_epoch = second_each_iter * (total_it_each_epoch - cur_it)
+                    remaining_second_all = second_each_iter * ((total_epochs - cur_epoch) * total_it_each_epoch - cur_it)
+                    
+                    logger.info(
+                        'Train: {:>4d}/{} ({:>3.0f}%) [{:>4d}/{} ({:>3.0f}%)]  '
+                        'Loss: {loss.val:#.4g} ({loss.avg:#.3g})  '
+                        'LR: {lr:.3e}  '
+                        f'Time cost: {tbar.format_interval(trained_time_each_epoch)}/{tbar.format_interval(remaining_second_each_epoch)} ' 
+                        f'[{tbar.format_interval(trained_time_past_all)}/{tbar.format_interval(remaining_second_all)}]  '
+                        'Acc_iter {acc_iter:<10d}  '
+                        'Data time: {data_time.val:.2f}({data_time.avg:.2f})  '
+                        'Forward time: {forward_time.val:.2f}({forward_time.avg:.2f})  '
+                        'Batch time: {batch_time.val:.2f}({batch_time.avg:.2f})'.format(
+                            cur_epoch+1,total_epochs, 100. * (cur_epoch+1) / total_epochs,
+                            cur_it,total_it_each_epoch, 100. * cur_it / total_it_each_epoch,
+                            loss=losses_m,
+                            lr=cur_lr,
+                            acc_iter=accumulated_iter,
+                            data_time=data_time,
+                            forward_time=forward_time,
+                            batch_time=batch_time
+                            )
+                    )
+                    
+                    if show_gpu_stat and accumulated_iter % (3 * logger_iter_interval) == 0:
+                        # To show the GPU utilization, please install gpustat through "pip install gpustat"
+                        gpu_info = os.popen('gpustat').read()
+                        logger.info(gpu_info)
+            else:                
+                pbar.update()
+                pbar.set_postfix(dict(total_it=accumulated_iter))
+                tbar.set_postfix(disp_dict)
+                # tbar.refresh()
+
+            if tb_log is not None:
+                tb_log.add_scalar('train/loss', loss, accumulated_iter)
+                tb_log.add_scalar('meta_data/learning_rate', cur_lr, accumulated_iter)
+                for key, val in tb_dict.items():
+                    tb_log.add_scalar('train/' + key, val, accumulated_iter)
+            
+            # save intermediate ckpt every {ckpt_save_time_interval} seconds         
+            time_past_this_epoch = pbar.format_dict['elapsed']
+            if time_past_this_epoch // ckpt_save_time_interval >= ckpt_save_cnt:
+                ckpt_name = ckpt_save_dir / 'latest_model'
+                save_checkpoint(
+                    checkpoint_state(model, optimizer, cur_epoch, accumulated_iter), filename=ckpt_name,
+                )
+                logger.info(f'Save latest model to {ckpt_name}')
+                ckpt_save_cnt += 1
+                
+    if rank == 0:
+        pbar.close()
+    return accumulated_iter
+
+
+def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_cfg,
+                start_epoch, total_epochs, start_iter, rank, tb_log, ckpt_save_dir, train_sampler=None,
+                lr_warmup_scheduler=None, ckpt_save_interval=1, max_ckpt_save_num=50,
+                merge_all_iters_to_one_epoch=False, use_amp=False,
+                use_logger_to_record=False, logger=None, logger_iter_interval=None, ckpt_save_time_interval=None, show_gpu_stat=False, cfg=None):
+    accumulated_iter = start_iter
+
+    # use for disable data augmentation hook
+    hook_config = cfg.get('HOOK', None) 
+    augment_disable_flag = False
+
+    with tqdm.trange(start_epoch, total_epochs, desc='epochs', dynamic_ncols=True, leave=(rank == 0)) as tbar:
+        total_it_each_epoch = len(train_loader)
+        if merge_all_iters_to_one_epoch:
+            assert hasattr(train_loader.dataset, 'merge_all_iters_to_one_epoch')
+            train_loader.dataset.merge_all_iters_to_one_epoch(merge=True, epochs=total_epochs)
+            total_it_each_epoch = len(train_loader) // max(total_epochs, 1)
+
+        dataloader_iter = iter(train_loader)
+        for cur_epoch in tbar:
+            if train_sampler is not None:
+                train_sampler.set_epoch(cur_epoch)
+
+            # train one epoch
+            if lr_warmup_scheduler is not None and cur_epoch < optim_cfg.WARMUP_EPOCH:
+                cur_scheduler = lr_warmup_scheduler
+            else:
+                cur_scheduler = lr_scheduler
+            
+            augment_disable_flag = disable_augmentation_hook(hook_config, dataloader_iter, total_epochs, cur_epoch, cfg, augment_disable_flag, logger)
+            accumulated_iter = train_one_epoch(
+                model, optimizer, train_loader, model_func,
+                lr_scheduler=cur_scheduler,
+                accumulated_iter=accumulated_iter, optim_cfg=optim_cfg,
+                rank=rank, tbar=tbar, tb_log=tb_log,
+                leave_pbar=(cur_epoch + 1 == total_epochs),
+                total_it_each_epoch=total_it_each_epoch,
+                dataloader_iter=dataloader_iter, 
+                
+                cur_epoch=cur_epoch, total_epochs=total_epochs,
+                use_logger_to_record=use_logger_to_record, 
+                logger=logger, logger_iter_interval=logger_iter_interval,
+                ckpt_save_dir=ckpt_save_dir, ckpt_save_time_interval=ckpt_save_time_interval, 
+                show_gpu_stat=show_gpu_stat,
+                use_amp=use_amp
+            )
+
+            # save trained model
+            trained_epoch = cur_epoch + 1
+            if trained_epoch % ckpt_save_interval == 0 and rank == 0:
+
+                ckpt_list = glob.glob(str(ckpt_save_dir / 'checkpoint_epoch_*.pth'))
+                ckpt_list.sort(key=os.path.getmtime)
+
+                if ckpt_list.__len__() >= max_ckpt_save_num:
+                    for cur_file_idx in range(0, len(ckpt_list) - max_ckpt_save_num + 1):
+                        os.remove(ckpt_list[cur_file_idx])
+
+                ckpt_name = ckpt_save_dir / ('checkpoint_epoch_%d' % trained_epoch)
+                save_checkpoint(
+                    checkpoint_state(model, optimizer, trained_epoch, accumulated_iter), filename=ckpt_name,
+                )
+
+
+def model_state_to_cpu(model_state):
+    model_state_cpu = type(model_state)()  # ordered dict
+    for key, val in model_state.items():
+        model_state_cpu[key] = val.cpu()
+    return model_state_cpu
+
+
+def checkpoint_state(model=None, optimizer=None, epoch=None, it=None):
+    optim_state = optimizer.state_dict() if optimizer is not None else None
+    if model is not None:
+        if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+            model_state = model_state_to_cpu(model.module.state_dict())
+        else:
+            model_state = model.state_dict()
+    else:
+        model_state = None
+
+    try:
+        import pcdet
+        version = 'pcdet+' + pcdet.__version__
+    except:
+        version = 'none'
+
+    return {'epoch': epoch, 'it': it, 'model_state': model_state, 'optimizer_state': optim_state, 'version': version}
+
+
+def save_checkpoint(state, filename='checkpoint'):
+    if False and 'optimizer_state' in state:
+        optimizer_state = state['optimizer_state']
+        state.pop('optimizer_state', None)
+        optimizer_filename = '{}_optim.pth'.format(filename)
+        if torch.__version__ >= '1.4':
+            torch.save({'optimizer_state': optimizer_state}, optimizer_filename, _use_new_zipfile_serialization=False)
+        else:
+            torch.save({'optimizer_state': optimizer_state}, optimizer_filename)
+
+    filename = '{}.pth'.format(filename)
+    if torch.__version__ >= '1.4':
+        torch.save(state, filename, _use_new_zipfile_serialization=False)
+    else:
+        torch.save(state, filename)
+
+
+def disable_augmentation_hook(hook_config, dataloader, total_epochs, cur_epoch, cfg, flag, logger):
+    """
+    This hook turns off the data augmentation during training.
+    """
+    if hook_config is not None:
+        DisableAugmentationHook = hook_config.get('DisableAugmentationHook', None)
+        if DisableAugmentationHook is not None:
+            num_last_epochs = DisableAugmentationHook.NUM_LAST_EPOCHS
+            if (total_epochs - num_last_epochs) <= cur_epoch and not flag:
+                DISABLE_AUG_LIST = DisableAugmentationHook.DISABLE_AUG_LIST
+                dataset_cfg=cfg.DATA_CONFIG
+                logger.info(f'Disable augmentations: {DISABLE_AUG_LIST}')
+                dataset_cfg.DATA_AUGMENTOR.DISABLE_AUG_LIST = DISABLE_AUG_LIST
+                dataloader._dataset.data_augmentor.disable_augmentation(dataset_cfg.DATA_AUGMENTOR)
+                flag = True
+    return flag
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/visual_utils/open3d_vis_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/visual_utils/open3d_vis_utils.py
new file mode 100644
index 000000000..706d037bd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/visual_utils/open3d_vis_utils.py
@@ -0,0 +1,116 @@
+"""
+Open3d visualization tool box
+Written by Jihan YANG
+All rights preserved from 2021 - present.
+"""
+import open3d
+import torch
+import matplotlib
+import numpy as np
+
+box_colormap = [
+    [1, 1, 1],
+    [0, 1, 0],
+    [0, 1, 1],
+    [1, 1, 0],
+]
+
+
+def get_coor_colors(obj_labels):
+    """
+    Args:
+        obj_labels: 1 is ground, labels > 1 indicates different instance cluster
+
+    Returns:
+        rgb: [N, 3]. color for each point.
+    """
+    colors = matplotlib.colors.XKCD_COLORS.values()
+    max_color_num = obj_labels.max()
+
+    color_list = list(colors)[:max_color_num+1]
+    colors_rgba = [matplotlib.colors.to_rgba_array(color) for color in color_list]
+    label_rgba = np.array(colors_rgba)[obj_labels]
+    label_rgba = label_rgba.squeeze()[:, :3]
+
+    return label_rgba
+
+
+def draw_scenes(points, gt_boxes=None, ref_boxes=None, ref_labels=None, ref_scores=None, point_colors=None, draw_origin=True):
+    if isinstance(points, torch.Tensor):
+        points = points.cpu().numpy()
+    if isinstance(gt_boxes, torch.Tensor):
+        gt_boxes = gt_boxes.cpu().numpy()
+    if isinstance(ref_boxes, torch.Tensor):
+        ref_boxes = ref_boxes.cpu().numpy()
+
+    vis = open3d.visualization.Visualizer()
+    vis.create_window()
+
+    vis.get_render_option().point_size = 1.0
+    vis.get_render_option().background_color = np.zeros(3)
+
+    # draw origin
+    if draw_origin:
+        axis_pcd = open3d.geometry.TriangleMesh.create_coordinate_frame(size=1.0, origin=[0, 0, 0])
+        vis.add_geometry(axis_pcd)
+
+    pts = open3d.geometry.PointCloud()
+    pts.points = open3d.utility.Vector3dVector(points[:, :3])
+
+    vis.add_geometry(pts)
+    if point_colors is None:
+        pts.colors = open3d.utility.Vector3dVector(np.ones((points.shape[0], 3)))
+    else:
+        pts.colors = open3d.utility.Vector3dVector(point_colors)
+
+    if gt_boxes is not None:
+        vis = draw_box(vis, gt_boxes, (0, 0, 1))
+
+    if ref_boxes is not None:
+        vis = draw_box(vis, ref_boxes, (0, 1, 0), ref_labels, ref_scores)
+
+    vis.run()
+    vis.destroy_window()
+
+
+def translate_boxes_to_open3d_instance(gt_boxes):
+    """
+             4-------- 6
+           /|         /|
+          5 -------- 3 .
+          | |        | |
+          . 7 -------- 1
+          |/         |/
+          2 -------- 0
+    """
+    center = gt_boxes[0:3]
+    lwh = gt_boxes[3:6]
+    axis_angles = np.array([0, 0, gt_boxes[6] + 1e-10])
+    rot = open3d.geometry.get_rotation_matrix_from_axis_angle(axis_angles)
+    box3d = open3d.geometry.OrientedBoundingBox(center, rot, lwh)
+
+    line_set = open3d.geometry.LineSet.create_from_oriented_bounding_box(box3d)
+
+    # import ipdb; ipdb.set_trace(context=20)
+    lines = np.asarray(line_set.lines)
+    lines = np.concatenate([lines, np.array([[1, 4], [7, 6]])], axis=0)
+
+    line_set.lines = open3d.utility.Vector2iVector(lines)
+
+    return line_set, box3d
+
+
+def draw_box(vis, gt_boxes, color=(0, 1, 0), ref_labels=None, score=None):
+    for i in range(gt_boxes.shape[0]):
+        line_set, box3d = translate_boxes_to_open3d_instance(gt_boxes[i])
+        if ref_labels is None:
+            line_set.paint_uniform_color(color)
+        else:
+            line_set.paint_uniform_color(box_colormap[ref_labels[i]])
+
+        vis.add_geometry(line_set)
+
+        # if score is not None:
+        #     corners = box3d.get_box_points()
+        #     vis.add_3d_label(corners[5], '%.2f' % score[i])
+    return vis
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/visual_utils/visualize_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/visual_utils/visualize_utils.py
new file mode 100644
index 000000000..41a142d02
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/openpcdet/tools/visual_utils/visualize_utils.py
@@ -0,0 +1,215 @@
+import mayavi.mlab as mlab
+import numpy as np
+import torch
+
+box_colormap = [
+    [1, 1, 1],
+    [0, 1, 0],
+    [0, 1, 1],
+    [1, 1, 0],
+]
+
+
+def check_numpy_to_torch(x):
+    if isinstance(x, np.ndarray):
+        return torch.from_numpy(x).float(), True
+    return x, False
+
+
+def rotate_points_along_z(points, angle):
+    """
+    Args:
+        points: (B, N, 3 + C)
+        angle: (B), angle along z-axis, angle increases x ==> y
+    Returns:
+
+    """
+    points, is_numpy = check_numpy_to_torch(points)
+    angle, _ = check_numpy_to_torch(angle)
+
+    cosa = torch.cos(angle)
+    sina = torch.sin(angle)
+    zeros = angle.new_zeros(points.shape[0])
+    ones = angle.new_ones(points.shape[0])
+    rot_matrix = torch.stack((
+        cosa,  sina, zeros,
+        -sina, cosa, zeros,
+        zeros, zeros, ones
+    ), dim=1).view(-1, 3, 3).float()
+    points_rot = torch.matmul(points[:, :, 0:3], rot_matrix)
+    points_rot = torch.cat((points_rot, points[:, :, 3:]), dim=-1)
+    return points_rot.numpy() if is_numpy else points_rot
+
+
+def boxes_to_corners_3d(boxes3d):
+    """
+        7 -------- 4
+       /|         /|
+      6 -------- 5 .
+      | |        | |
+      . 3 -------- 0
+      |/         |/
+      2 -------- 1
+    Args:
+        boxes3d:  (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center
+
+    Returns:
+    """
+    boxes3d, is_numpy = check_numpy_to_torch(boxes3d)
+
+    template = boxes3d.new_tensor((
+        [1, 1, -1], [1, -1, -1], [-1, -1, -1], [-1, 1, -1],
+        [1, 1, 1], [1, -1, 1], [-1, -1, 1], [-1, 1, 1],
+    )) / 2
+
+    corners3d = boxes3d[:, None, 3:6].repeat(1, 8, 1) * template[None, :, :]
+    corners3d = rotate_points_along_z(corners3d.view(-1, 8, 3), boxes3d[:, 6]).view(-1, 8, 3)
+    corners3d += boxes3d[:, None, 0:3]
+
+    return corners3d.numpy() if is_numpy else corners3d
+
+
+def visualize_pts(pts, fig=None, bgcolor=(0, 0, 0), fgcolor=(1.0, 1.0, 1.0),
+                  show_intensity=False, size=(600, 600), draw_origin=True):
+    if not isinstance(pts, np.ndarray):
+        pts = pts.cpu().numpy()
+    if fig is None:
+        fig = mlab.figure(figure=None, bgcolor=bgcolor, fgcolor=fgcolor, engine=None, size=size)
+
+    if show_intensity:
+        G = mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], pts[:, 3], mode='point',
+                          colormap='gnuplot', scale_factor=1, figure=fig)
+    else:
+        G = mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], mode='point',
+                          colormap='gnuplot', scale_factor=1, figure=fig)
+    if draw_origin:
+        mlab.points3d(0, 0, 0, color=(1, 1, 1), mode='cube', scale_factor=0.2)
+        mlab.plot3d([0, 3], [0, 0], [0, 0], color=(0, 0, 1), tube_radius=0.1)
+        mlab.plot3d([0, 0], [0, 3], [0, 0], color=(0, 1, 0), tube_radius=0.1)
+        mlab.plot3d([0, 0], [0, 0], [0, 3], color=(1, 0, 0), tube_radius=0.1)
+
+    return fig
+
+
+def draw_sphere_pts(pts, color=(0, 1, 0), fig=None, bgcolor=(0, 0, 0), scale_factor=0.2):
+    if not isinstance(pts, np.ndarray):
+        pts = pts.cpu().numpy()
+
+    if fig is None:
+        fig = mlab.figure(figure=None, bgcolor=bgcolor, fgcolor=None, engine=None, size=(600, 600))
+
+    if isinstance(color, np.ndarray) and color.shape[0] == 1:
+        color = color[0]
+        color = (color[0] / 255.0, color[1] / 255.0, color[2] / 255.0)
+
+    if isinstance(color, np.ndarray):
+        pts_color = np.zeros((pts.__len__(), 4), dtype=np.uint8)
+        pts_color[:, 0:3] = color
+        pts_color[:, 3] = 255
+        G = mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], np.arange(0, pts_color.__len__()), mode='sphere',
+                          scale_factor=scale_factor, figure=fig)
+        G.glyph.color_mode = 'color_by_scalar'
+        G.glyph.scale_mode = 'scale_by_vector'
+        G.module_manager.scalar_lut_manager.lut.table = pts_color
+    else:
+        mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], mode='sphere', color=color,
+                      colormap='gnuplot', scale_factor=scale_factor, figure=fig)
+
+    mlab.points3d(0, 0, 0, color=(1, 1, 1), mode='cube', scale_factor=0.2)
+    mlab.plot3d([0, 3], [0, 0], [0, 0], color=(0, 0, 1), line_width=3, tube_radius=None, figure=fig)
+    mlab.plot3d([0, 0], [0, 3], [0, 0], color=(0, 1, 0), line_width=3, tube_radius=None, figure=fig)
+    mlab.plot3d([0, 0], [0, 0], [0, 3], color=(1, 0, 0), line_width=3, tube_radius=None, figure=fig)
+
+    return fig
+
+
+def draw_grid(x1, y1, x2, y2, fig, tube_radius=None, color=(0.5, 0.5, 0.5)):
+    mlab.plot3d([x1, x1], [y1, y2], [0, 0], color=color, tube_radius=tube_radius, line_width=1, figure=fig)
+    mlab.plot3d([x2, x2], [y1, y2], [0, 0], color=color, tube_radius=tube_radius, line_width=1, figure=fig)
+    mlab.plot3d([x1, x2], [y1, y1], [0, 0], color=color, tube_radius=tube_radius, line_width=1, figure=fig)
+    mlab.plot3d([x1, x2], [y2, y2], [0, 0], color=color, tube_radius=tube_radius, line_width=1, figure=fig)
+    return fig
+
+
+def draw_multi_grid_range(fig, grid_size=20, bv_range=(-60, -60, 60, 60)):
+    for x in range(bv_range[0], bv_range[2], grid_size):
+        for y in range(bv_range[1], bv_range[3], grid_size):
+            fig = draw_grid(x, y, x + grid_size, y + grid_size, fig)
+
+    return fig
+
+
+def draw_scenes(points, gt_boxes=None, ref_boxes=None, ref_scores=None, ref_labels=None):
+    if not isinstance(points, np.ndarray):
+        points = points.cpu().numpy()
+    if ref_boxes is not None and not isinstance(ref_boxes, np.ndarray):
+        ref_boxes = ref_boxes.cpu().numpy()
+    if gt_boxes is not None and not isinstance(gt_boxes, np.ndarray):
+        gt_boxes = gt_boxes.cpu().numpy()
+    if ref_scores is not None and not isinstance(ref_scores, np.ndarray):
+        ref_scores = ref_scores.cpu().numpy()
+    if ref_labels is not None and not isinstance(ref_labels, np.ndarray):
+        ref_labels = ref_labels.cpu().numpy()
+
+    fig = visualize_pts(points)
+    fig = draw_multi_grid_range(fig, bv_range=(0, -40, 80, 40))
+    if gt_boxes is not None:
+        corners3d = boxes_to_corners_3d(gt_boxes)
+        fig = draw_corners3d(corners3d, fig=fig, color=(0, 0, 1), max_num=100)
+
+    if ref_boxes is not None and len(ref_boxes) > 0:
+        ref_corners3d = boxes_to_corners_3d(ref_boxes)
+        if ref_labels is None:
+            fig = draw_corners3d(ref_corners3d, fig=fig, color=(0, 1, 0), cls=ref_scores, max_num=100)
+        else:
+            for k in range(ref_labels.min(), ref_labels.max() + 1):
+                cur_color = tuple(box_colormap[k % len(box_colormap)])
+                mask = (ref_labels == k)
+                fig = draw_corners3d(ref_corners3d[mask], fig=fig, color=cur_color, cls=ref_scores[mask], max_num=100)
+    mlab.view(azimuth=-179, elevation=54.0, distance=104.0, roll=90.0)
+    return fig
+
+
+def draw_corners3d(corners3d, fig, color=(1, 1, 1), line_width=2, cls=None, tag='', max_num=500, tube_radius=None):
+    """
+    :param corners3d: (N, 8, 3)
+    :param fig:
+    :param color:
+    :param line_width:
+    :param cls:
+    :param tag:
+    :param max_num:
+    :return:
+    """
+    import mayavi.mlab as mlab
+    num = min(max_num, len(corners3d))
+    for n in range(num):
+        b = corners3d[n]  # (8, 3)
+
+        if cls is not None:
+            if isinstance(cls, np.ndarray):
+                mlab.text3d(b[6, 0], b[6, 1], b[6, 2], '%.2f' % cls[n], scale=(0.3, 0.3, 0.3), color=color, figure=fig)
+            else:
+                mlab.text3d(b[6, 0], b[6, 1], b[6, 2], '%s' % cls[n], scale=(0.3, 0.3, 0.3), color=color, figure=fig)
+
+        for k in range(0, 4):
+            i, j = k, (k + 1) % 4
+            mlab.plot3d([b[i, 0], b[j, 0]], [b[i, 1], b[j, 1]], [b[i, 2], b[j, 2]], color=color, tube_radius=tube_radius,
+                        line_width=line_width, figure=fig)
+
+            i, j = k + 4, (k + 1) % 4 + 4
+            mlab.plot3d([b[i, 0], b[j, 0]], [b[i, 1], b[j, 1]], [b[i, 2], b[j, 2]], color=color, tube_radius=tube_radius,
+                        line_width=line_width, figure=fig)
+
+            i, j = k, k + 4
+            mlab.plot3d([b[i, 0], b[j, 0]], [b[i, 1], b[j, 1]], [b[i, 2], b[j, 2]], color=color, tube_radius=tube_radius,
+                        line_width=line_width, figure=fig)
+
+        i, j = 0, 5
+        mlab.plot3d([b[i, 0], b[j, 0]], [b[i, 1], b[j, 1]], [b[i, 2], b[j, 2]], color=color, tube_radius=tube_radius,
+                    line_width=line_width, figure=fig)
+        i, j = 1, 4
+        mlab.plot3d([b[i, 0], b[j, 0]], [b[i, 1], b[j, 1]], [b[i, 2], b[j, 2]], color=color, tube_radius=tube_radius,
+                    line_width=line_width, figure=fig)
+
+    return fig
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/.gitignore b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/.gitignore
new file mode 100644
index 000000000..eedae348c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/.gitignore
@@ -0,0 +1,109 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+*.o
+*.out
+
+# Distribution / packaging
+.Python
+build/
+build_pip/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.vscode
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/.gitmodules b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/.gitmodules
new file mode 100644
index 000000000..2347f2a3f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "third_party/pybind11"]
+	path = third_party/pybind11
+	url = ssh://git@bitbucket.iluvatar.ai:7999/adapt/pybind11.git
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/CMakeLists.txt
new file mode 100644
index 000000000..a32e611af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/CMakeLists.txt
@@ -0,0 +1,50 @@
+cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
+project(SparseConv LANGUAGES CXX CUDA VERSION 1.0)
+
+option(SPCONV_BuildTests "Build the unit tests when BUILD_TESTING is enabled." ON)
+set(CMAKE_CXX_EXTENSIONS OFF) # avoid gnu++11 be added to CXX flags
+
+set(CUDA_TOOLKIT_ROOT_DIR "/usr/local/corex")
+MESSAGE( STATUS "this var key = ${CUDA_TOOLKIT_ROOT_DIR}.")
+# get_filename_component(CUDA_TOOLKIT_ROOT_DIR "${CUDA_TOOLKIT_ROOT_DIR}" DIRECTORY)
+# get_filename_component(CUDA_TOOLKIT_ROOT_DIR "${CUDA_TOOLKIT_ROOT_DIR}" DIRECTORY)
+if(WIN32) # true if windows (32 and 64 bit)
+    set(CUDA_LIB_PATH_HINTS "${CUDA_TOOLKIT_ROOT_DIR}/lib/x64")
+    add_compile_definitions(TV_WINDOWS)
+else()
+    set(CUDA_LIB_PATH_HINTS "${CUDA_TOOLKIT_ROOT_DIR}/lib64")
+    MESSAGE( STATUS "this var key = ${CUDA_LIB_PATH_HINTS}.")
+endif()
+
+find_library(CUDA_CUDART NAMES cudart HINTS ${CUDA_LIB_PATH_HINTS})
+find_library(CUDA_CUBLAS NAMES cublas HINTS ${CUDA_LIB_PATH_HINTS})
+if(CMAKE_BUILD_TYPE STREQUAL "Debug")
+    add_compile_definitions(TV_DEBUG)
+endif()
+
+find_package(Torch REQUIRED)
+
+# add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
+
+add_compile_definitions(SPCONV_CUDA)
+add_subdirectory(third_party/pybind11)
+
+set(CMAKE_C_COMPILER "/usr/bin/gcc")
+set(CMAKE_CXX_COMPILER "/usr/bin/g++")
+
+set(ALL_LIBS ${CUDA_CUDART} ${CUDA_CUBLAS} ${TORCH_LIBRARIES}) 
+
+set(ALL_INCLUDE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES} 
+    ${PROJECT_SOURCE_DIR}/include)
+
+
+add_subdirectory(src/spconv)
+add_subdirectory(src/utils)
+
+if (SPCONV_BuildTests)
+    include(CTest) #adds option BUILD_TESTING (default ON)
+    if(BUILD_TESTING)
+        enable_testing()
+        add_subdirectory(test)
+    endif()
+endif()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/LICENSE b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/LICENSE
new file mode 100644
index 000000000..081a04a3b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2019 Yan Yan
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/README-ILUVATAR.md b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/README-ILUVATAR.md
new file mode 100644
index 000000000..7cb737b51
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/README-ILUVATAR.md
@@ -0,0 +1,16 @@
+## git clone
+```shell
+git clone --recurse-submodules ssh://git@bitbucket.iluvatar.ai:7999/swapp/spconv.git
+```
+
+## build
+```shell
+bash build_spconv.sh
+```
+## install
+```shell
+bash install_spconv.sh
+```
+## clean
+```shell
+bash clean_spconv.sh
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/README.md b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/README.md
new file mode 100644
index 000000000..e8da50a4a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/README.md
@@ -0,0 +1,142 @@
+This is not what I realized. The original author is [yan yan](https://github.com/traveller59), this project provides convenience for everyone to download the spcov1.0 version.
+
+
+# SpConv: PyTorch Spatially Sparse Convolution Library
+
+This is a spatially sparse convolution library like [SparseConvNet](https://github.com/facebookresearch/SparseConvNet) but faster and easy to read. This library provide sparse convolution/transposed, submanifold convolution, inverse convolution and sparse maxpool.
+
+If you need more kinds of spatial layers such as avg pool, please implement it by yourself, I don't have time to do this.
+
+The GPU Indice Generation algorithm is a unofficial implementation of paper [SECOND](http://www.mdpi.com/1424-8220/18/10/3337). That algorithm (don't include GPU SubM indice generation algorithm) may be protected by patent.
+
+This project only support CUDA 9.0+. If you are using cuda 8.0, please update it to 9.0.
+
+## Install 
+
+0. Use ```git clone xxx.git --recursive``` to clone this repo.
+
+1. Install boost headers to your system include path, you can use either ```sudo apt-get install libboostall-dev``` or download compressed files from boost official website and copy headers to include path.
+
+2. Download cmake >= 3.13.2, then add cmake executables to PATH.
+
+3. Ensure you have install pytorch 1.0 in your environment, run ```python setup.py bdist_wheel``` (don't use ```python setup.py install```).
+
+4. Run ```cd ./dist```, use pip to install generated whl file.
+
+## Compare with SparseConvNet
+
+### Features
+
+* SparseConvNet's Sparse Convolution don't support padding and dilation, spconv support this.
+
+* spconv only contains sparse convolutions, the batchnorm and activations can directly use layers from torch.nn, SparseConvNet contains lots of their own implementation of layers such as batchnorm and activations.
+
+### Speed
+
+* spconv is faster than SparseConvNet due to gpu indice generation and gather-gemm-scatter algorithm. SparseConvNet use hand-written gemm which is slow.
+
+## Usage
+
+### SparseConvTensor
+
+```Python
+features = # your features with shape [N, numPlanes]
+indices = # your indices/coordinates with shape [N, ndim + 1], batch index must be put in indices[:, 0]
+spatial_shape = # spatial shape of your sparse tensor.
+batch_size = # batch size of your sparse tensor.
+x = spconv.SparseConvTensor(features, indices, spatial_shape, batch_size)
+x_dense_NCHW = x.dense() # convert sparse tensor to dense NCHW tensor.
+print(x.sparity) # helper function to check sparity. 
+```
+
+### Sparse Convolution
+
+```Python
+import spconv
+from torch import nn
+class ExampleNet(nn.Module):
+    def __init__(self, shape):
+        super().__init__()
+        self.net = spconv.SparseSequential(
+            spconv.SparseConv3d(32, 64, 3), # just like nn.Conv3d but don't support group and all([d > 1, s > 1])
+            nn.BatchNorm1d(64), # non-spatial layers can be used directly in SparseSequential.
+            nn.ReLU(),
+            spconv.SubMConv3d(64, 64, 3, indice_key="subm0"),
+            nn.BatchNorm1d(64),
+            nn.ReLU(),
+            # when use submanifold convolutions, their indices can be shared to save indices generation time.
+            spconv.SubMConv3d(64, 64, 3, indice_key="subm0"),
+            nn.BatchNorm1d(64),
+            nn.ReLU(),
+            spconv.SparseConvTranspose3d(64, 64, 3, 2),
+            nn.BatchNorm1d(64),
+            nn.ReLU(),
+            spconv.ToDense(), # convert spconv tensor to dense and convert it to NCHW format.
+            nn.Conv3d(64, 64, 3),
+            nn.BatchNorm1d(64),
+            nn.ReLU(),
+        )
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int() # unlike torch, this library only accept int coordinates.
+        x = spconv.SparseConvTensor(features, coors, self.shape, batch_size)
+        return self.net(x)# .dense()
+```
+
+### Inverse Convolution
+
+Inverse sparse convolution means "inv" of sparse convolution. the output of inverse convolution contains same indices as input of sparse convolution.
+
+Inverse convolution usually used in semantic segmentation.
+
+```Python
+class ExampleNet(nn.Module):
+    def __init__(self, shape):
+        super().__init__()
+        self.net = spconv.SparseSequential(
+            spconv.SparseConv3d(32, 64, 3, 2, indice_key="cp0"),
+            spconv.SparseInverseConv3d(64, 32, 3, indice_key="cp0"), # need provide kernel size to create weight
+        )
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors, self.shape, batch_size)
+        return self.net(x)
+```
+
+### Utility functions
+
+* convert point cloud to voxel
+
+```Python
+
+voxel_generator = spconv.utils.VoxelGenerator(
+    voxel_size=[0.1, 0.1, 0.1], 
+    point_cloud_range=[-50, -50, -3, 50, 50, 1],
+    max_num_points=30,
+    max_voxels=40000
+)
+
+points = # [N, 3+] tensor.
+voxels, coords, num_points_per_voxel = voxel_generator.generate(points)
+```
+
+## Implementation Details
+
+This implementation use gather-gemm-scatter framework to do sparse convolution.
+
+## Projects using spconv:
+
+* [second.pytorch](https://github.com/traveller59/second.pytorch): Point Cloud Object Detection in KITTI Dataset.
+
+## Authors
+
+* **Yan Yan** - *Initial work* - [traveller59](https://github.com/traveller59)
+
+* **Bo Li** - *gpu indice generation idea, owner of patent of the sparse conv gpu indice generation algorithm (don't include subm)* - [prclibo](https://github.com/prclibo)
+
+## License
+
+This project is licensed under the Apache license 2.0 License - see the [LICENSE.md](LICENSE.md) file for details
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/build_spconv.sh b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/build_spconv.sh
new file mode 100644
index 000000000..9189d4ce7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/build_spconv.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+COREX_VERSION=${COREX_VERSION:-latest}
+MAX_JOBS=${MAX_JOBS:-$(nproc --all)}
+PYTHON_PATH=$(which python3)
+${PYTHON_PATH} -m pip list | grep "^torch .*+corex" || {
+  echo "ERROR: building mmcv requries the corex torch has been installed."
+  exit 1
+}
+
+export MAX_JOBS=${MAX_JOBS}
+
+PY_VERSION=$(python3 -V 2>&1|awk '{print $2}'|awk -F '.' '{print $2}')
+
+if [ "$PY_VERSION" == "6" ] || [ "$PY_VERSION" == "7" ];
+then
+   export CPATH=$CPATH:/usr/local/include/python3.${PY_VERSION}m/
+else
+   export CPATH=$CPATH:/usr/local/include/python3.${PY_VERSION}/
+fi
+
+SIGN=${SIGN:-$(ixsmi -L)}
+if [[ $SIGN =~ MR || $SIGN =~ BI-V150 ]]
+then
+    export CMAKE_CUDA_ARCHITECTURES=ivcore11
+else
+    export CMAKE_CUDA_ARCHITECTURES=ivcore10
+fi
+
+${PYTHON_PATH} setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log || exit
+# Return 0 status if all finished
+exit 0
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/clean_spconv.sh b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/clean_spconv.sh
new file mode 100644
index 000000000..f43a0ef49
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/clean_spconv.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+rm -rf build
+rm -rf build_pip
+rm -rf spconv.egg-info
+pip3 uninstall spconv -y
+
+# Return 0 status if all finished
+exit 0
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/paramsgrid.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/paramsgrid.h
new file mode 100644
index 000000000..62d4a89c5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/paramsgrid.h
@@ -0,0 +1,62 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef PARAMS_GRID_H_
+#define PARAMS_GRID_H_
+#include <tuple>
+#include <vector>
+
+namespace detail {
+template <class T> int getTotalSize(std::vector<T> arg) { return arg.size(); }
+
+template <class T, class... TArgs>
+int getTotalSize(std::vector<T> arg, std::vector<TArgs>... args) {
+  return arg.size() * getTotalSize(args...);
+}
+template <typename T> int getSize(std::vector<T> arg) { return arg.size(); }
+
+template <int Idx, class TT, class T>
+void assigner(TT &src, std::vector<int> counter, std::vector<T> &arg) {
+  std::get<Idx>(src) = arg[counter[Idx]];
+}
+
+template <int Idx, class TT, class T, class... TArgs>
+void assigner(TT &src, std::vector<int> counter, std::vector<T> &arg,
+              std::vector<TArgs> &... args) {
+  std::get<Idx>(src) = arg[counter[Idx]];
+  assigner<Idx + 1>(src, counter, args...);
+}
+} // namespace detail
+template <class... TArgs>
+std::vector<std::tuple<TArgs...>> paramsGrid(std::vector<TArgs>... args) {
+  int length = detail::getTotalSize(args...);
+  std::vector<int> sizes = {detail::getSize(args)...};
+  int size = sizes.size();
+
+  std::vector<std::tuple<TArgs...>> params(length);
+  std::vector<int> counter(size);
+  for (int i = 0; i < length; ++i) {
+    detail::assigner<0>(params[i], counter, args...);
+    counter[size - 1] += 1;
+    for (int c = size - 1; c >= 0; --c) {
+      if (counter[c] == sizes[c] && c > 0) {
+        counter[c - 1] += 1;
+        counter[c] = 0;
+      }
+    }
+  }
+  return params;
+}
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/prettyprint.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/prettyprint.h
new file mode 100644
index 000000000..72cdb9ce9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/prettyprint.h
@@ -0,0 +1,445 @@
+//          Copyright Louis Delacroix 2010 - 2014.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+//
+// A pretty printing library for C++
+//
+// Usage:
+// Include this header, and operator<< will "just work".
+
+#ifndef H_PRETTY_PRINT
+#define H_PRETTY_PRINT
+
+#include <cstddef>
+#include <iterator>
+#include <memory>
+#include <ostream>
+#include <set>
+#include <tuple>
+#include <type_traits>
+#include <unordered_set>
+#include <utility>
+#include <valarray>
+
+namespace pretty_print
+{
+    namespace detail
+    {
+        // SFINAE type trait to detect whether T::const_iterator exists.
+
+        struct sfinae_base
+        {
+            using yes = char;
+            using no  = yes[2];
+        };
+
+        template <typename T>
+        struct has_const_iterator : private sfinae_base
+        {
+        private:
+            template <typename C> static yes & test(typename C::const_iterator*);
+            template <typename C> static no  & test(...);
+        public:
+            static const bool value = sizeof(test<T>(nullptr)) == sizeof(yes);
+            using type =  T;
+        };
+
+        template <typename T>
+        struct has_begin_end : private sfinae_base
+        {
+        private:
+            template <typename C>
+            static yes & f(typename std::enable_if<
+                std::is_same<decltype(static_cast<typename C::const_iterator(C::*)() const>(&C::begin)),
+                             typename C::const_iterator(C::*)() const>::value>::type *);
+
+            template <typename C> static no & f(...);
+
+            template <typename C>
+            static yes & g(typename std::enable_if<
+                std::is_same<decltype(static_cast<typename C::const_iterator(C::*)() const>(&C::end)),
+                             typename C::const_iterator(C::*)() const>::value, void>::type*);
+
+            template <typename C> static no & g(...);
+
+        public:
+            static bool const beg_value = sizeof(f<T>(nullptr)) == sizeof(yes);
+            static bool const end_value = sizeof(g<T>(nullptr)) == sizeof(yes);
+        };
+
+    }  // namespace detail
+
+
+    // Holds the delimiter values for a specific character type
+
+    template <typename TChar>
+    struct delimiters_values
+    {
+        using char_type = TChar;
+        const char_type * prefix;
+        const char_type * delimiter;
+        const char_type * postfix;
+    };
+
+
+    // Defines the delimiter values for a specific container and character type
+
+    template <typename T, typename TChar>
+    struct delimiters
+    {
+        using type = delimiters_values<TChar>;
+        static const type values; 
+    };
+
+
+    // Functor to print containers. You can use this directly if you want
+    // to specificy a non-default delimiters type. The printing logic can
+    // be customized by specializing the nested template.
+
+    template <typename T,
+              typename TChar = char,
+              typename TCharTraits = ::std::char_traits<TChar>,
+              typename TDelimiters = delimiters<T, TChar>>
+    struct print_container_helper
+    {
+        using delimiters_type = TDelimiters;
+        using ostream_type = std::basic_ostream<TChar, TCharTraits>;
+
+        template <typename U>
+        struct printer
+        {
+            static void print_body(const U & c, ostream_type & stream)
+            {
+                using std::begin;
+                using std::end;
+
+                auto it = begin(c);
+                const auto the_end = end(c);
+
+                if (it != the_end)
+                {
+                    for ( ; ; )
+                    {
+                        stream << *it;
+
+                    if (++it == the_end) break;
+
+                    if (delimiters_type::values.delimiter != NULL)
+                        stream << delimiters_type::values.delimiter;
+                    }
+                }
+            }
+        };
+
+        print_container_helper(const T & container)
+        : container_(container)
+        { }
+
+        inline void operator()(ostream_type & stream) const
+        {
+            if (delimiters_type::values.prefix != NULL)
+                stream << delimiters_type::values.prefix;
+
+            printer<T>::print_body(container_, stream);
+
+            if (delimiters_type::values.postfix != NULL)
+                stream << delimiters_type::values.postfix;
+        }
+
+    private:
+        const T & container_;
+    };
+
+    // Specialization for pairs
+
+    template <typename T, typename TChar, typename TCharTraits, typename TDelimiters>
+    template <typename T1, typename T2>
+    struct print_container_helper<T, TChar, TCharTraits, TDelimiters>::printer<std::pair<T1, T2>>
+    {
+        using ostream_type = typename print_container_helper<T, TChar, TCharTraits, TDelimiters>::ostream_type;
+
+        static void print_body(const std::pair<T1, T2> & c, ostream_type & stream)
+        {
+            stream << c.first;
+            if (print_container_helper<T, TChar, TCharTraits, TDelimiters>::delimiters_type::values.delimiter != NULL)
+                stream << print_container_helper<T, TChar, TCharTraits, TDelimiters>::delimiters_type::values.delimiter;
+            stream << c.second;
+        }
+    };
+
+    // Specialization for tuples
+
+    template <typename T, typename TChar, typename TCharTraits, typename TDelimiters>
+    template <typename ...Args>
+    struct print_container_helper<T, TChar, TCharTraits, TDelimiters>::printer<std::tuple<Args...>>
+    {
+        using ostream_type = typename print_container_helper<T, TChar, TCharTraits, TDelimiters>::ostream_type;
+        using element_type = std::tuple<Args...>;
+
+        template <std::size_t I> struct Int { };
+
+        static void print_body(const element_type & c, ostream_type & stream)
+        {
+            tuple_print(c, stream, Int<0>());
+        }
+
+        static void tuple_print(const element_type &, ostream_type &, Int<sizeof...(Args)>)
+        {
+        }
+
+        static void tuple_print(const element_type & c, ostream_type & stream,
+                                typename std::conditional<sizeof...(Args) != 0, Int<0>, std::nullptr_t>::type)
+        {
+            stream << std::get<0>(c);
+            tuple_print(c, stream, Int<1>());
+        }
+
+        template <std::size_t N>
+        static void tuple_print(const element_type & c, ostream_type & stream, Int<N>)
+        {
+            if (print_container_helper<T, TChar, TCharTraits, TDelimiters>::delimiters_type::values.delimiter != NULL)
+                stream << print_container_helper<T, TChar, TCharTraits, TDelimiters>::delimiters_type::values.delimiter;
+
+            stream << std::get<N>(c);
+
+            tuple_print(c, stream, Int<N + 1>());
+        }
+    };
+
+    // Prints a print_container_helper to the specified stream.
+
+    template<typename T, typename TChar, typename TCharTraits, typename TDelimiters>
+    inline std::basic_ostream<TChar, TCharTraits> & operator<<(
+        std::basic_ostream<TChar, TCharTraits> & stream,
+        const print_container_helper<T, TChar, TCharTraits, TDelimiters> & helper)
+    {
+        helper(stream);
+        return stream;
+    }
+
+
+    // Basic is_container template; specialize to derive from std::true_type for all desired container types
+
+    template <typename T>
+    struct is_container : public std::integral_constant<bool,
+                                                        detail::has_const_iterator<T>::value &&
+                                                        detail::has_begin_end<T>::beg_value  &&
+                                                        detail::has_begin_end<T>::end_value> { };
+
+    template <typename T, std::size_t N>
+    struct is_container<T[N]> : std::true_type { };
+
+    template <std::size_t N>
+    struct is_container<char[N]> : std::false_type { };
+
+    template <typename T>
+    struct is_container<std::valarray<T>> : std::true_type { };
+
+    template <typename T1, typename T2>
+    struct is_container<std::pair<T1, T2>> : std::true_type { };
+
+    template <typename ...Args>
+    struct is_container<std::tuple<Args...>> : std::true_type { };
+
+
+    // Default delimiters
+
+    template <typename T> struct delimiters<T, char> { static const delimiters_values<char> values; };
+    template <typename T> const delimiters_values<char> delimiters<T, char>::values = { "[", ", ", "]" };
+    template <typename T> struct delimiters<T, wchar_t> { static const delimiters_values<wchar_t> values; };
+    template <typename T> const delimiters_values<wchar_t> delimiters<T, wchar_t>::values = { L"[", L", ", L"]" };
+
+
+    // Delimiters for (multi)set and unordered_(multi)set
+
+    template <typename T, typename TComp, typename TAllocator>
+    struct delimiters< ::std::set<T, TComp, TAllocator>, char> { static const delimiters_values<char> values; };
+
+    template <typename T, typename TComp, typename TAllocator>
+    const delimiters_values<char> delimiters< ::std::set<T, TComp, TAllocator>, char>::values = { "{", ", ", "}" };
+
+    template <typename T, typename TComp, typename TAllocator>
+    struct delimiters< ::std::set<T, TComp, TAllocator>, wchar_t> { static const delimiters_values<wchar_t> values; };
+
+    template <typename T, typename TComp, typename TAllocator>
+    const delimiters_values<wchar_t> delimiters< ::std::set<T, TComp, TAllocator>, wchar_t>::values = { L"{", L", ", L"}" };
+
+    template <typename T, typename TComp, typename TAllocator>
+    struct delimiters< ::std::multiset<T, TComp, TAllocator>, char> { static const delimiters_values<char> values; };
+
+    template <typename T, typename TComp, typename TAllocator>
+    const delimiters_values<char> delimiters< ::std::multiset<T, TComp, TAllocator>, char>::values = { "{", ", ", "}" };
+
+    template <typename T, typename TComp, typename TAllocator>
+    struct delimiters< ::std::multiset<T, TComp, TAllocator>, wchar_t> { static const delimiters_values<wchar_t> values; };
+
+    template <typename T, typename TComp, typename TAllocator>
+    const delimiters_values<wchar_t> delimiters< ::std::multiset<T, TComp, TAllocator>, wchar_t>::values = { L"{", L", ", L"}" };
+
+    template <typename T, typename THash, typename TEqual, typename TAllocator>
+    struct delimiters< ::std::unordered_set<T, THash, TEqual, TAllocator>, char> { static const delimiters_values<char> values; };
+
+    template <typename T, typename THash, typename TEqual, typename TAllocator>
+    const delimiters_values<char> delimiters< ::std::unordered_set<T, THash, TEqual, TAllocator>, char>::values = { "{", ", ", "}" };
+
+    template <typename T, typename THash, typename TEqual, typename TAllocator>
+    struct delimiters< ::std::unordered_set<T, THash, TEqual, TAllocator>, wchar_t> { static const delimiters_values<wchar_t> values; };
+
+    template <typename T, typename THash, typename TEqual, typename TAllocator>
+    const delimiters_values<wchar_t> delimiters< ::std::unordered_set<T, THash, TEqual, TAllocator>, wchar_t>::values = { L"{", L", ", L"}" };
+
+    template <typename T, typename THash, typename TEqual, typename TAllocator>
+    struct delimiters< ::std::unordered_multiset<T, THash, TEqual, TAllocator>, char> { static const delimiters_values<char> values; };
+
+    template <typename T, typename THash, typename TEqual, typename TAllocator>
+    const delimiters_values<char> delimiters< ::std::unordered_multiset<T, THash, TEqual, TAllocator>, char>::values = { "{", ", ", "}" };
+
+    template <typename T, typename THash, typename TEqual, typename TAllocator>
+    struct delimiters< ::std::unordered_multiset<T, THash, TEqual, TAllocator>, wchar_t> { static const delimiters_values<wchar_t> values; };
+
+    template <typename T, typename THash, typename TEqual, typename TAllocator>
+    const delimiters_values<wchar_t> delimiters< ::std::unordered_multiset<T, THash, TEqual, TAllocator>, wchar_t>::values = { L"{", L", ", L"}" };
+
+
+    // Delimiters for pair and tuple
+
+    template <typename T1, typename T2> struct delimiters<std::pair<T1, T2>, char> { static const delimiters_values<char> values; };
+    template <typename T1, typename T2> const delimiters_values<char> delimiters<std::pair<T1, T2>, char>::values = { "(", ", ", ")" };
+    template <typename T1, typename T2> struct delimiters< ::std::pair<T1, T2>, wchar_t> { static const delimiters_values<wchar_t> values; };
+    template <typename T1, typename T2> const delimiters_values<wchar_t> delimiters< ::std::pair<T1, T2>, wchar_t>::values = { L"(", L", ", L")" };
+
+    template <typename ...Args> struct delimiters<std::tuple<Args...>, char> { static const delimiters_values<char> values; };
+    template <typename ...Args> const delimiters_values<char> delimiters<std::tuple<Args...>, char>::values = { "(", ", ", ")" };
+    template <typename ...Args> struct delimiters< ::std::tuple<Args...>, wchar_t> { static const delimiters_values<wchar_t> values; };
+    template <typename ...Args> const delimiters_values<wchar_t> delimiters< ::std::tuple<Args...>, wchar_t>::values = { L"(", L", ", L")" };
+
+
+    // Type-erasing helper class for easy use of custom delimiters.
+    // Requires TCharTraits = std::char_traits<TChar> and TChar = char or wchar_t, and MyDelims needs to be defined for TChar.
+    // Usage: "cout << pretty_print::custom_delims<MyDelims>(x)".
+
+    struct custom_delims_base
+    {
+        virtual ~custom_delims_base() { }
+        virtual std::ostream & stream(::std::ostream &) = 0;
+        virtual std::wostream & stream(::std::wostream &) = 0;
+    };
+
+    template <typename T, typename Delims>
+    struct custom_delims_wrapper : custom_delims_base
+    {
+        custom_delims_wrapper(const T & t_) : t(t_) { }
+
+        std::ostream & stream(std::ostream & s)
+        {
+            return s << print_container_helper<T, char, std::char_traits<char>, Delims>(t);
+        }
+
+        std::wostream & stream(std::wostream & s)
+        {
+            return s << print_container_helper<T, wchar_t, std::char_traits<wchar_t>, Delims>(t);
+        }
+
+    private:
+        const T & t;
+    };
+
+    template <typename Delims>
+    struct custom_delims
+    {
+        template <typename Container>
+        custom_delims(const Container & c) : base(new custom_delims_wrapper<Container, Delims>(c)) { }
+
+        std::unique_ptr<custom_delims_base> base;
+    };
+
+    template <typename TChar, typename TCharTraits, typename Delims>
+    inline std::basic_ostream<TChar, TCharTraits> & operator<<(std::basic_ostream<TChar, TCharTraits> & s, const custom_delims<Delims> & p)
+    {
+        return p.base->stream(s);
+    }
+
+
+    // A wrapper for a C-style array given as pointer-plus-size.
+    // Usage: std::cout << pretty_print_array(arr, n) << std::endl;
+
+    template<typename T>
+    struct array_wrapper_n
+    {
+        typedef const T * const_iterator;
+        typedef T value_type;
+
+        array_wrapper_n(const T * const a, size_t n) : _array(a), _n(n) { }
+        inline const_iterator begin() const { return _array; }
+        inline const_iterator end() const { return _array + _n; }
+
+    private:
+        const T * const _array;
+        size_t _n;
+    };
+
+
+    // A wrapper for hash-table based containers that offer local iterators to each bucket.
+    // Usage: std::cout << bucket_print(m, 4) << std::endl;  (Prints bucket 5 of container m.)
+
+    template <typename T>
+    struct bucket_print_wrapper
+    {
+        typedef typename T::const_local_iterator const_iterator;
+        typedef typename T::size_type size_type;
+
+        const_iterator begin() const
+        {
+            return m_map.cbegin(n);
+        }
+
+        const_iterator end() const
+        {
+            return m_map.cend(n);
+        }
+
+        bucket_print_wrapper(const T & m, size_type bucket) : m_map(m), n(bucket) { }
+
+    private:
+        const T & m_map;
+        const size_type n;
+    };
+
+}   // namespace pretty_print
+
+
+// Global accessor functions for the convenience wrappers
+
+template<typename T>
+inline pretty_print::array_wrapper_n<T> pretty_print_array(const T * const a, size_t n)
+{
+    return pretty_print::array_wrapper_n<T>(a, n);
+}
+
+template <typename T> pretty_print::bucket_print_wrapper<T>
+bucket_print(const T & m, typename T::size_type n)
+{
+    return pretty_print::bucket_print_wrapper<T>(m, n);
+}
+
+
+// Main magic entry point: An overload snuck into namespace std.
+// Can we do better?
+
+namespace std
+{
+    // Prints a container to the stream using default delimiters
+
+    template<typename T, typename TChar, typename TCharTraits>
+    inline typename enable_if< ::pretty_print::is_container<T>::value,
+                              basic_ostream<TChar, TCharTraits> &>::type
+    operator<<(basic_ostream<TChar, TCharTraits> & stream, const T & container)
+    {
+        return stream << ::pretty_print::print_container_helper<T, TChar, TCharTraits>(container);
+    }
+}
+
+
+
+#endif // H_PRETTY_PRINT
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/pybind11_utils.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/pybind11_utils.h
new file mode 100644
index 000000000..dfda11608
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/pybind11_utils.h
@@ -0,0 +1,61 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <algorithm>
+#include <iostream>
+#include <pybind11/embed.h> // everything needed for embedding
+#include <pybind11/functional.h>
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include <tensorview/tensorview.h>
+
+namespace py = pybind11;
+
+template <typename T, typename TPyObject>
+std::vector<T> array2Vector(TPyObject arr){
+    py::array arr_np = arr;
+    size_t size = arr.attr("size").template cast<size_t>();
+    py::array_t<T> arr_cc = arr_np;
+    std::vector<T> data(arr_cc.data(), arr_cc.data() + size);
+    return data;
+}
+
+template <typename T>
+std::vector<T> arrayT2Vector(py::array_t<T> arr)
+{
+  std::vector<T> data(arr.data(), arr.data() + arr.size());
+  return data;
+}
+
+template <typename T, typename TPyObject>
+tv::TensorView<T> array2TensorView(TPyObject arr){
+    py::array arr_np = arr;
+    py::array_t<T> arr_cc = arr_np;
+    tv::Shape shape;
+    for (int i = 0; i < arr_cc.ndim(); ++i){
+        shape.push_back(arr_cc.shape(i));
+    }
+    return tv::TensorView<T>(arr_cc.mutable_data(), shape);
+}
+template <typename T>
+tv::TensorView<T> arrayT2TensorView(py::array_t<T> arr){
+    tv::Shape shape;
+    for (int i = 0; i < arr.ndim(); ++i){
+        shape.push_back(arr.shape(i));
+    }
+    return tv::TensorView<T>(arr.mutable_data(), shape);
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/box_iou.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/box_iou.h
new file mode 100644
index 000000000..2d190f012
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/box_iou.h
@@ -0,0 +1,103 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+
+#ifndef BOX_IOU_H
+#define BOX_IOU_H
+
+#include <pybind11/pybind11.h>
+// must include pybind11/eigen.h if using eigen matrix as arguments.
+#include <algorithm>
+#include <boost/geometry.hpp>
+#include <boost/geometry/geometries/polygon.hpp>
+
+#include <pybind11/numpy.h>
+
+namespace spconv {
+// #include "voxelnet/core/cc/pybind11_helper.h"
+namespace py = pybind11;
+using namespace pybind11::literals;
+template <typename DType, typename ShapeContainer>
+inline py::array_t<DType> constant(ShapeContainer shape, DType value) {
+  // create ROWMAJOR array.
+  py::array_t<DType> array(shape);
+  std::fill(array.mutable_data(), array.mutable_data() + array.size(), value);
+  return array;
+}
+
+template <typename DType>
+inline py::array_t<DType> zeros(std::vector<long int> shape) {
+  return constant<DType, std::vector<long int>>(shape, 0);
+}
+
+template <typename DType>
+py::array_t<DType>
+rbbox_iou(py::array_t<DType> box_corners, py::array_t<DType> qbox_corners,
+          py::array_t<DType> standup_iou, DType standup_thresh) {
+  namespace bg = boost::geometry;
+  typedef bg::model::point<DType, 2, bg::cs::cartesian> point_t;
+  typedef bg::model::polygon<point_t> polygon_t;
+  polygon_t poly, qpoly;
+  std::vector<polygon_t> poly_inter, poly_union;
+  DType inter_area, union_area;
+  auto box_corners_r = box_corners.template unchecked<3>();
+  auto qbox_corners_r = qbox_corners.template unchecked<3>();
+  auto standup_iou_r = standup_iou.template unchecked<2>();
+  auto N = box_corners_r.shape(0);
+  auto K = qbox_corners_r.shape(0);
+  py::array_t<DType> overlaps = zeros<DType>({N, K});
+  auto overlaps_rw = overlaps.template mutable_unchecked<2>();
+  if (N == 0 || K == 0) {
+    return overlaps;
+  }
+  for (int k = 0; k < K; ++k) {
+    for (int n = 0; n < N; ++n) {
+      if (standup_iou_r(n, k) <= standup_thresh)
+        continue;
+      bg::append(poly, point_t(box_corners_r(n, 0, 0), box_corners_r(n, 0, 1)));
+      bg::append(poly, point_t(box_corners_r(n, 1, 0), box_corners_r(n, 1, 1)));
+      bg::append(poly, point_t(box_corners_r(n, 2, 0), box_corners_r(n, 2, 1)));
+      bg::append(poly, point_t(box_corners_r(n, 3, 0), box_corners_r(n, 3, 1)));
+      bg::append(poly, point_t(box_corners_r(n, 0, 0), box_corners_r(n, 0, 1)));
+      bg::append(qpoly,
+                 point_t(qbox_corners_r(k, 0, 0), qbox_corners_r(k, 0, 1)));
+      bg::append(qpoly,
+                 point_t(qbox_corners_r(k, 1, 0), qbox_corners_r(k, 1, 1)));
+      bg::append(qpoly,
+                 point_t(qbox_corners_r(k, 2, 0), qbox_corners_r(k, 2, 1)));
+      bg::append(qpoly,
+                 point_t(qbox_corners_r(k, 3, 0), qbox_corners_r(k, 3, 1)));
+      bg::append(qpoly,
+                 point_t(qbox_corners_r(k, 0, 0), qbox_corners_r(k, 0, 1)));
+
+      bg::intersection(poly, qpoly, poly_inter);
+
+      if (!poly_inter.empty()) {
+        inter_area = bg::area(poly_inter.front());
+        bg::union_(poly, qpoly, poly_union);
+        if (!poly_union.empty()) {
+          union_area = bg::area(poly_union.front());
+          overlaps_rw(n, k) = inter_area / union_area;
+        }
+        poly_union.clear();
+      }
+      poly.clear();
+      qpoly.clear();
+      poly_inter.clear();
+    }
+  }
+  return overlaps;
+}
+} // namespace spconv
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/geometry.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/geometry.h
new file mode 100644
index 000000000..b4c382e64
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/geometry.h
@@ -0,0 +1,297 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef SPCONV_GEOMETRY_H_
+#define SPCONV_GEOMETRY_H_
+
+#include <iostream>
+#include <limits>
+#include <tensorview/tensorview.h>
+
+namespace spconv {
+template <typename Index, unsigned NDim>
+TV_HOST_DEVICE Index getValidOutPos(const Index *input_pos,
+                                    const Index *kernelSize,
+                                    const Index *stride, const Index *padding,
+                                    const Index *dilation,
+                                    const Index *outSpatialShape, Index *out) {
+  Index lowers[NDim];
+  Index uppers[NDim];
+  Index counter[NDim];
+  Index counterSize[NDim];
+  Index pointCounter = 0;
+  Index val;
+  Index numPoints = 1;
+  Index m, offset;
+  bool valid = false;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    lowers[i] = (input_pos[i] - (kernelSize[i] - 1) * dilation[i] - 1 +
+                 stride[i] + padding[i]) /
+                stride[i];
+    uppers[i] = (input_pos[i] + padding[i]) / stride[i];
+  }
+
+#pragma unroll
+  for (unsigned i = 0; i < NDim; ++i) {
+    counterSize[i] = ((uppers[i] - lowers[i]) / dilation[i] + 1);
+    numPoints *= counterSize[i];
+  }
+
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    counter[i] = 0;
+  }
+  for (int i = 0; i < numPoints; ++i) {
+    valid = true;
+    m = 1;
+    offset = 0;
+#pragma unroll
+    for (int j = NDim - 1; j >= 0; --j) {
+      val = uppers[j] - counter[j] * dilation[j];
+      out[pointCounter * (NDim + 1) + j] = val;
+      if (val < 0 || (val > outSpatialShape[j] - 1)) {
+        valid = false;
+        // break;
+      }
+      offset += m * (input_pos[j] - val * stride[j] + padding[j]) / dilation[j];
+      m *= kernelSize[j];
+    }
+
+    out[pointCounter * (NDim + 1) + NDim] = offset;
+    if (valid)
+      ++pointCounter;
+    counter[NDim - 1] += 1;
+#pragma unroll
+    for (int c = NDim - 1; c >= 0; --c) {
+      if (counter[c] == counterSize[c] && c > 0) {
+        counter[c - 1] += 1;
+        counter[c] = 0;
+      }
+    }
+  }
+  return pointCounter;
+}
+
+template <typename Index, unsigned NDim>
+TV_HOST_DEVICE Index getValidOutPosTranspose(
+    const Index *input_pos, const Index *kernelSize, const Index *stride,
+    const Index *padding, const Index *dilation, const Index *outSpatialShape,
+    Index *out) {
+  Index lowers[NDim];
+  Index uppers[NDim];
+  Index counter[NDim];
+  Index counterSize[NDim];
+  Index pointCounter = 0;
+  Index val;
+  Index numPoints = 1;
+  Index m, offset;
+  bool valid = false;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    lowers[i] = input_pos[i] * stride[i] - padding[i];
+    uppers[i] = lowers[i] + (kernelSize[i] - 1) * dilation[i];
+  }
+#pragma unroll
+  for (unsigned i = 0; i < NDim; ++i) {
+    counterSize[i] = ((uppers[i] - lowers[i]) / dilation[i] + 1);
+    numPoints *= counterSize[i];
+  }
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    counter[i] = 0;
+  }
+  for (int i = 0; i < numPoints; ++i) {
+    valid = true;
+    m = 1;
+    offset = 0;
+#pragma unroll
+    for (int j = NDim - 1; j >= 0; --j) {
+      val = uppers[j] - counter[j] * dilation[j];
+      out[pointCounter * (NDim + 1) + j] = val;
+      if (val < 0 || (val > outSpatialShape[j] - 1)) {
+        valid = false;
+        // break;
+      }
+      offset += m * (val - lowers[j]) / dilation[j];
+      m *= kernelSize[j];
+    }
+    out[pointCounter * (NDim + 1) + NDim] = offset;
+    if (valid)
+      ++pointCounter;
+    counter[NDim - 1] += 1;
+#pragma unroll
+    for (int c = NDim - 1; c >= 0; --c) {
+      if (counter[c] == counterSize[c] && c > 0) {
+        counter[c - 1] += 1;
+        counter[c] = 0;
+      }
+    }
+  }
+  return pointCounter;
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+Index getIndicePairsConv(tv::TensorView<const Index> indicesIn,
+                         tv::TensorView<Index> indicesOut,
+                         tv::TensorView<IndexGrid> gridsOut,
+                         tv::TensorView<Index> indicePairs,
+                         tv::TensorView<Index> indiceNum,
+                         const Index *kernelSize, const Index *stride,
+                         const Index *padding, const Index *dilation,
+                         const Index *outSpatialShape) {
+  // indicesOut: num_active * kernelVolume * (NDim + 1)
+  Index numAct = 0;
+  auto numActIn = indicesIn.dim(0);
+  Index batchIdx = 0;
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index kernelVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[kernelVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  for (int j = 0; j < numActIn; ++j) {
+    batchIdx = indicesIn(j, 0);
+    numValidPoints = getValidOutPos<Index, NDim>(
+        indicesIn.data() + j * (NDim + 1) + 1, kernelSize, stride, padding,
+        dilation, outSpatialShape, validPoints);
+    for (Index i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      auto index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape) +
+                   spatialVolume * batchIdx;
+      if (gridsOut[index] == -1) {
+        for (unsigned k = 1; k < NDim + 1; ++k) {
+          indicesOut(numAct, k) = pointPtr[k - 1];
+        }
+        indicesOut(numAct, 0) = batchIdx;
+        gridsOut[index] = numAct++;
+      }
+      // indicePairs: [K, 2, L]
+      indicePairs(offset, 0, indiceNum[offset]) = j;
+      indicePairs(offset, 1, indiceNum[offset]++) = gridsOut[index];
+    }
+  }
+  return numAct;
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+Index getIndicePairsDeConv(tv::TensorView<const Index> indicesIn,
+                           tv::TensorView<Index> indicesOut,
+                           tv::TensorView<IndexGrid> gridsOut,
+                           tv::TensorView<Index> indicePairs,
+                           tv::TensorView<Index> indiceNum,
+                           const Index *kernelSize, const Index *stride,
+                           const Index *padding, const Index *dilation,
+                           const Index *outSpatialShape) {
+  Index numAct = 0;
+  auto numActIn = indicesIn.dim(0);
+  Index batchIdx = 0;
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index kernelVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[kernelVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  for (int j = 0; j < numActIn; ++j) {
+    batchIdx = indicesIn(j, 0);
+    numValidPoints = getValidOutPosTranspose<Index, NDim>(
+        indicesIn.data() + j * (NDim + 1) + 1, kernelSize, stride, padding,
+        dilation, outSpatialShape, validPoints);
+    for (Index i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      auto index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape) +
+                   spatialVolume * batchIdx;
+      if (gridsOut[index] == -1) {
+        for (unsigned k = 1; k < NDim + 1; ++k) {
+          indicesOut(numAct, k) = pointPtr[k - 1];
+        }
+        indicesOut(numAct, 0) = batchIdx;
+        gridsOut[index] = numAct++;
+      }
+      // indicePairs: [K, 2, L]
+      indicePairs(offset, 0, indiceNum[offset]) = j;
+      indicePairs(offset, 1, indiceNum[offset]++) = gridsOut[index];
+    }
+  }
+  return numAct;
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+Index getIndicePairsSubM(tv::TensorView<const Index> indicesIn,
+                         tv::TensorView<IndexGrid> gridsOut,
+                         tv::TensorView<Index> indicePairs,
+                         tv::TensorView<Index> indiceNum,
+                         const Index *const kernelSize,
+                         const Index *const stride, const Index *const padding,
+                         const Index *dilation, const Index *const outSpatialShape) {
+  Index numAct = 0;
+  auto numActIn = indicesIn.dim(0);
+  Index batchIdx = 0;
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index kernelVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[kernelVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  Index index = 0;
+  for (int j = 0; j < numActIn; ++j) {
+    index = tv::rowArrayIdx<Index, NDim>(indicesIn.data() + j * (NDim + 1) + 1,
+                                         outSpatialShape) +
+            spatialVolume * indicesIn(j, 0);
+    gridsOut[index] = j;
+  }
+  for (int j = 0; j < numActIn; ++j) {
+    numValidPoints = getValidOutPos<Index, NDim>(
+        indicesIn.data() + j * (NDim + 1) + 1, kernelSize, stride, padding,
+        dilation, outSpatialShape, validPoints);
+    for (Index i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape) +
+              spatialVolume * indicesIn(j, 0);
+      if (gridsOut[index] > -1) {
+        indicePairs(offset, 0, indiceNum[offset]) = j;
+        indicePairs(offset, 1, indiceNum[offset]++) = gridsOut[index];
+      }
+    }
+  }
+  return numActIn;
+}
+
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/indice.cu.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/indice.cu.h
new file mode 100644
index 000000000..276d3390c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/indice.cu.h
@@ -0,0 +1,244 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef INDICE_CU_H_
+#define INDICE_CU_H_
+#include <tensorview/tensorview.h>
+#include <tensorview/helper_kernel.cu.h>
+#include <spconv/geometry.h>
+
+namespace spconv {
+template <typename Index, typename IndexGrid, unsigned NDim,
+          int KernelMaxVolume = 256>
+__global__ void prepareIndicePairsKernel(
+    tv::TensorView<const Index> indicesIn, tv::TensorView<Index> indicesOut,
+    tv::TensorView<IndexGrid> gridsOut, tv::TensorView<Index> indicePairs,
+    tv::TensorView<Index> indiceNum, tv::TensorView<Index> indicePairUnique,
+    const tv::SimpleVector<Index, NDim> kernelSize,
+    const tv::SimpleVector<Index, NDim> stride,
+    const tv::SimpleVector<Index, NDim> padding,
+    const tv::SimpleVector<Index, NDim> dilation,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  auto numActIn = indicesIn.dim(0);
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index kernelVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[KernelMaxVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  auto indicePairsDim2 = indicePairs.dim(2);
+  Index index;
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    numValidPoints = getValidOutPos<Index, NDim>(
+        indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
+        stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
+        validPoints);
+    for (Index i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      auto oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
+      indicePairs(offset, 0, oldNum) = ix;
+      index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape.data()) +
+              spatialVolume * indicesIn(ix, 0);
+      indicePairs(offset, 1, oldNum) = index;
+      indicePairUnique[offset * indicePairsDim2 + oldNum] = index;
+    }
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim,
+          int KernelMaxVolume = 256>
+__global__ void prepareDeConvIndicePairsKernel(
+    tv::TensorView<const Index> indicesIn, tv::TensorView<Index> indicesOut,
+    tv::TensorView<IndexGrid> gridsOut, tv::TensorView<Index> indicePairs,
+    tv::TensorView<Index> indiceNum, tv::TensorView<Index> indicePairUnique,
+    const tv::SimpleVector<Index, NDim> kernelSize,
+    const tv::SimpleVector<Index, NDim> stride,
+    const tv::SimpleVector<Index, NDim> padding,
+    const tv::SimpleVector<Index, NDim> dilation,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  auto numActIn = indicesIn.dim(0);
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index kernelVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[KernelMaxVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  auto indicePairsDim2 = indicePairs.dim(2);
+  Index index;
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    numValidPoints = getValidOutPosTranspose<Index, NDim>(
+        indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
+        stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
+        validPoints);
+    for (Index i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      auto oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
+      indicePairs(offset, 0, oldNum) = ix;
+      index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape.data()) +
+              spatialVolume * indicesIn(ix, 0);
+      indicePairs(offset, 1, oldNum) = index;
+      indicePairUnique[offset * indicePairsDim2 + oldNum] = index;
+    }
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void assignGridAndIndiceOutKernel(
+    tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
+    int numAct, tv::TensorView<Index> indicePairs,
+    tv::TensorView<Index> indicePairUnique,
+    const tv::SimpleVector<Index, NDim> outSpatialShape, int batchSize) {
+
+  Index index;
+  auto indicesOutPtr = indicesOut.data();
+  for (int ix : tv::KernelLoopX<int>(numAct)) {
+    index = indicePairUnique[ix];
+    gridsOut[index] = ix;
+    index = tv::rowArrayIdxInv<Index, NDim>(
+        index, indicesOutPtr + ix * (NDim + 1) + 1, outSpatialShape.data());
+    indicesOut[ix * (NDim + 1)] = index % batchSize;
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void
+assignIndicePairsKernel(tv::TensorView<Index> indicesOut,
+                        tv::TensorView<IndexGrid> gridsOut, int numActIn,
+                        tv::TensorView<Index> indicePairs,
+                        tv::TensorView<Index> indicePairUnique,
+                        const tv::SimpleVector<Index, NDim> outSpatialShape) {
+
+  Index index;
+  int kernelVolume = indicePairs.dim(0);
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    for (int i = 0; i < kernelVolume; ++i) {
+      index = indicePairs(i, 1, ix);
+      if (index > -1) {
+        indicePairs(i, 1, ix) = gridsOut[index];
+      }
+    }
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim,
+          int KernelMaxVolume = 256>
+__global__ void
+prepareSubMGridKernel(tv::TensorView<const Index> indicesIn,
+                  tv::TensorView<IndexGrid> gridsOut,
+                  const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  auto numActIn = indicesIn.dim(0);
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index index = 0;
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    index = tv::rowArrayIdx<Index, NDim>(indicesIn.data() + ix * (NDim + 1) + 1,
+                                         outSpatialShape.data()) +
+            spatialVolume * indicesIn(ix, 0);
+    gridsOut[index] = ix;
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim,
+          int KernelMaxVolume = 256>
+__global__ void getSubMIndicePairsKernel(
+    tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
+    tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
+    const tv::SimpleVector<Index, NDim> kernelSize,
+    const tv::SimpleVector<Index, NDim> stride,
+    const tv::SimpleVector<Index, NDim> padding,
+    const tv::SimpleVector<Index, NDim> dilation,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
+  auto numActIn = indicesIn.dim(0);
+  Index spatialVolume = 1;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index numValidPoints = 0;
+  Index validPoints[KernelMaxVolume * (NDim + 1)];
+  Index *pointPtr = nullptr;
+  Index index = 0;
+  for (int ix : tv::KernelLoopX<int>(numActIn)) {
+    numValidPoints = getValidOutPos<Index, NDim>(
+        indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
+        stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
+        validPoints);
+    for (int i = 0; i < numValidPoints; ++i) {
+      pointPtr = validPoints + i * (NDim + 1);
+      auto offset = pointPtr[NDim];
+      index = tv::rowArrayIdx<Index, NDim>(pointPtr, outSpatialShape.data()) +
+              spatialVolume * indicesIn(ix, 0);
+      if (gridsOut[index] > -1) {
+        auto oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
+        indicePairs(offset, 1, oldNum) = gridsOut[index];
+        indicePairs(offset, 0, oldNum) = ix;
+      }
+    }
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void resetGridKernel(const Index *indicePairUnique,
+                                tv::TensorView<IndexGrid> gridsOut,
+                                int numAct) {
+  for (int ix : tv::KernelLoopX<int>(numAct)) {
+    gridsOut[indicePairUnique[ix]] = -1;
+  }
+}
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+__global__ void
+resetGridSubMKernel(const Index *indices, tv::TensorView<IndexGrid> gridsOut,
+                    const tv::SimpleVector<Index, NDim> outSpatialShape,
+                    int numAct) {
+  int outSpatialShapeReg[NDim];
+  for (int i = 0; i < NDim; ++i) {
+    outSpatialShapeReg[i] = outSpatialShape[i];
+  }
+  Index spatialVolume = 1;
+  auto indsPtr = indices;
+#pragma unroll
+  for (int i = 0; i < NDim; ++i) {
+    spatialVolume *= outSpatialShape[i];
+  }
+  Index index;
+  for (int ix : tv::KernelLoopX<int>(numAct)) {
+    indsPtr = indices + ix * (NDim + 1);
+    index = tv::rowArrayIdx<Index, NDim>(indsPtr + 1, outSpatialShapeReg);
+    gridsOut[index + spatialVolume * indsPtr[0]] = -1;
+  }
+}
+
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/indice.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/indice.h
new file mode 100644
index 000000000..10456783a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/indice.h
@@ -0,0 +1,79 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef SPARSE_CONV_INDICE_FUNCTOR_H_
+#define SPARSE_CONV_INDICE_FUNCTOR_H_
+#include <tensorview/tensorview.h>
+
+namespace spconv
+{
+namespace functor
+{
+template <typename Device, typename Index, typename IndexGrid, unsigned NDim>
+struct CreateConvIndicePairFunctorP1
+{
+    Index operator()(
+        const Device& d, tv::TensorView<const Index> indicesIn,
+        tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
+        tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
+        tv::TensorView<Index> indicePairUnique,
+        const tv::SimpleVector<Index, NDim> kernelSize,
+        const tv::SimpleVector<Index, NDim> stride,
+        const tv::SimpleVector<Index, NDim> padding,
+        const tv::SimpleVector<Index, NDim> dilation,
+        const tv::SimpleVector<Index, NDim> outSpatialShape, bool transpose);
+};
+
+template <typename Device, typename Index, typename IndexGrid, unsigned NDim>
+struct CreateConvIndicePairFunctorP2
+{
+    Index operator()(
+        const Device& d, tv::TensorView<const Index> indicesIn,
+        tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
+        tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
+        tv::TensorView<Index> indicePairUnique,
+        const tv::SimpleVector<Index, NDim> outSpatialShape, bool transpose,
+        bool resetGrid=false);
+};
+
+template <typename Device, typename Index, typename IndexGrid, unsigned NDim>
+struct CreateConvIndicePairFunctor
+{
+    Index operator()(
+        const Device& d, tv::TensorView<const Index> indicesIn,
+        tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
+        tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
+        const tv::SimpleVector<Index, NDim> kernelSize,
+        const tv::SimpleVector<Index, NDim> stride,
+        const tv::SimpleVector<Index, NDim> padding,
+        const tv::SimpleVector<Index, NDim> dilation,
+        const tv::SimpleVector<Index, NDim> outSpatialShape, bool transpose, bool resetGrid=false);
+};
+
+template <typename Device, typename Index, typename IndexGrid, unsigned NDim>
+struct CreateSubMIndicePairFunctor
+{
+    Index operator()(
+        const Device& d, tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
+        tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
+        const tv::SimpleVector<Index, NDim> kernelSize,
+        const tv::SimpleVector<Index, NDim> stride,
+        const tv::SimpleVector<Index, NDim> padding,
+        const tv::SimpleVector<Index, NDim> dilation,
+        const tv::SimpleVector<Index, NDim> outSpatialShape, bool transpose, bool resetGrid=false);
+};
+} // namespace functor
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/maxpool.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/maxpool.h
new file mode 100644
index 000000000..f743e1346
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/maxpool.h
@@ -0,0 +1,44 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef SPARSE_MAXPOOL_FUNCTOR_H_
+#define SPARSE_MAXPOOL_FUNCTOR_H_
+#include <tensorview/tensorview.h>
+
+namespace spconv
+{
+namespace functor
+{
+template <typename Device, typename T, typename Index>
+struct SparseMaxPoolForwardFunctor
+{
+    void operator()(const Device& d, tv::TensorView<T> outFeatures,
+                  tv::TensorView<const T> inFeatures,
+                  tv::TensorView<const Index> indices, int size);
+};
+
+template <typename Device, typename T, typename Index>
+struct SparseMaxPoolBackwardFunctor
+{
+    void operator()(const Device& d, tv::TensorView<const T> outFeatures,
+                  tv::TensorView<const T> inFeatures,
+                  tv::TensorView<const T> dout,
+                  tv::TensorView<T> din,
+                  tv::TensorView<const Index> indices, int size);
+};
+
+} // namespace functor
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/mp_helper.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/mp_helper.h
new file mode 100644
index 000000000..36681eebe
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/mp_helper.h
@@ -0,0 +1,47 @@
+#ifndef MP_HELPER_H_
+#define MP_HELPER_H_
+#include <type_traits>
+#include <utility>
+
+namespace spconv {
+template <class... T> struct mp_list {};
+
+template <class T, T... I>
+using mp_list_c = mp_list<std::integral_constant<T, I>...>;
+
+namespace detail {
+
+template <class... T, class F>
+constexpr F mp_for_each_impl(mp_list<T...>, F &&f) {
+  return std::initializer_list<int>{(f(T()), 0)...}, std::forward<F>(f);
+}
+
+template <class F> constexpr F mp_for_each_impl(mp_list<>, F &&f) {
+  return std::forward<F>(f);
+}
+
+} // namespace detail
+
+namespace detail {
+
+template <class A, template <class...> class B> struct mp_rename_impl {
+  // An error "no type named 'type'" here means that the first argument to
+  // mp_rename is not a list
+};
+
+template <template <class...> class A, class... T, template <class...> class B>
+struct mp_rename_impl<A<T...>, B> {
+  using type = B<T...>;
+};
+
+} // namespace detail
+
+template <class A, template <class...> class B>
+using mp_rename = typename detail::mp_rename_impl<A, B>::type;
+
+template <class L, class F> constexpr F mp_for_each(F &&f) {
+  return detail::mp_for_each_impl(mp_rename<L, mp_list>(), std::forward<F>(f));
+}
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/nms.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/nms.h
new file mode 100644
index 000000000..339f780f7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/nms.h
@@ -0,0 +1,201 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef NMS_CPU_H
+#define NMS_CPU_H
+#include <pybind11/pybind11.h>
+// must include pybind11/stl.h if using containers in STL in arguments.
+#include <algorithm>
+#include <boost/geometry.hpp>
+#include <pybind11/numpy.h>
+#include <pybind11/stl.h>
+#include <vector>
+#include "box_iou.h"
+#include "nms_gpu.h"
+namespace spconv {
+namespace py = pybind11;
+using namespace pybind11::literals;
+
+template <typename DType>
+std::vector<int> non_max_suppression_cpu(py::array_t<DType> boxes,
+                                         py::array_t<int> order, DType thresh,
+                                         DType eps = 0) {
+  auto ndets = boxes.shape(0);
+  auto boxes_r = boxes.template unchecked<2>();
+  auto order_r = order.template unchecked<1>();
+  auto suppressed = zeros<int>({ndets});
+  auto suppressed_rw = suppressed.template mutable_unchecked<1>();
+  auto area = zeros<DType>({ndets});
+  auto area_rw = area.template mutable_unchecked<1>();
+  // get areas
+  for (int i = 0; i < ndets; ++i) {
+    area_rw(i) = (boxes_r(i, 2) - boxes_r(i, 0) + eps) *
+                 (boxes_r(i, 3) - boxes_r(i, 1) + eps);
+  }
+  std::vector<int> keep;
+  int i, j;
+  DType xx1, xx2, w, h, inter, ovr;
+  for (int _i = 0; _i < ndets; ++_i) {
+    i = order_r(_i);
+    if (suppressed_rw(i) == 1)
+      continue;
+    keep.push_back(i);
+    for (int _j = _i + 1; _j < ndets; ++_j) {
+      j = order_r(_j);
+      if (suppressed_rw(j) == 1)
+        continue;
+      xx2 = std::min(boxes_r(i, 2), boxes_r(j, 2));
+      xx1 = std::max(boxes_r(i, 0), boxes_r(j, 0));
+      w = xx2 - xx1 + eps;
+      if (w > 0) {
+        xx2 = std::min(boxes_r(i, 3), boxes_r(j, 3));
+        xx1 = std::max(boxes_r(i, 1), boxes_r(j, 1));
+        h = xx2 - xx1 + eps;
+        if (h > 0) {
+          inter = w * h;
+          ovr = inter / (area_rw(i) + area_rw(j) - inter);
+          if (ovr >= thresh)
+            suppressed_rw(j) = 1;
+        }
+      }
+    }
+  }
+  return keep;
+}
+
+template <typename DType>
+std::vector<int> rotate_non_max_suppression_cpu(py::array_t<DType> box_corners,
+                                                py::array_t<int> order,
+                                                py::array_t<DType> standup_iou,
+                                                DType thresh) {
+  auto ndets = box_corners.shape(0);
+  auto box_corners_r = box_corners.template unchecked<3>();
+  auto order_r = order.template unchecked<1>();
+  auto suppressed = zeros<int>({ndets});
+  auto suppressed_rw = suppressed.template mutable_unchecked<1>();
+  auto standup_iou_r = standup_iou.template unchecked<2>();
+  std::vector<int> keep;
+  int i, j;
+
+  namespace bg = boost::geometry;
+  typedef bg::model::point<DType, 2, bg::cs::cartesian> point_t;
+  typedef bg::model::polygon<point_t> polygon_t;
+  polygon_t poly, qpoly;
+  std::vector<polygon_t> poly_inter, poly_union;
+  DType inter_area, union_area, overlap;
+
+  for (int _i = 0; _i < ndets; ++_i) {
+    i = order_r(_i);
+    if (suppressed_rw(i) == 1)
+      continue;
+    keep.push_back(i);
+    for (int _j = _i + 1; _j < ndets; ++_j) {
+      j = order_r(_j);
+      if (suppressed_rw(j) == 1)
+        continue;
+      if (standup_iou_r(i, j) <= 0.0)
+        continue;
+      // std::cout << "pre_poly" << std::endl;
+      try {
+        bg::append(poly,
+                   point_t(box_corners_r(i, 0, 0), box_corners_r(i, 0, 1)));
+        bg::append(poly,
+                   point_t(box_corners_r(i, 1, 0), box_corners_r(i, 1, 1)));
+        bg::append(poly,
+                   point_t(box_corners_r(i, 2, 0), box_corners_r(i, 2, 1)));
+        bg::append(poly,
+                   point_t(box_corners_r(i, 3, 0), box_corners_r(i, 3, 1)));
+        bg::append(poly,
+                   point_t(box_corners_r(i, 0, 0), box_corners_r(i, 0, 1)));
+        bg::append(qpoly,
+                   point_t(box_corners_r(j, 0, 0), box_corners_r(j, 0, 1)));
+        bg::append(qpoly,
+                   point_t(box_corners_r(j, 1, 0), box_corners_r(j, 1, 1)));
+        bg::append(qpoly,
+                   point_t(box_corners_r(j, 2, 0), box_corners_r(j, 2, 1)));
+        bg::append(qpoly,
+                   point_t(box_corners_r(j, 3, 0), box_corners_r(j, 3, 1)));
+        bg::append(qpoly,
+                   point_t(box_corners_r(j, 0, 0), box_corners_r(j, 0, 1)));
+        bg::intersection(poly, qpoly, poly_inter);
+      } catch (const std::exception &e) {
+        std::cout << "box i corners:" << std::endl;
+        for (int k = 0; k < 4; ++k) {
+          std::cout << box_corners_r(i, k, 0) << " " << box_corners_r(i, k, 1)
+                    << std::endl;
+        }
+        std::cout << "box j corners:" << std::endl;
+        for (int k = 0; k < 4; ++k) {
+          std::cout << box_corners_r(j, k, 0) << " " << box_corners_r(j, k, 1)
+                    << std::endl;
+        }
+        // throw e;
+        continue;
+      }
+      // std::cout << "post_poly" << std::endl;
+      // std::cout << "post_intsec" << std::endl;
+      if (!poly_inter.empty()) {
+        inter_area = bg::area(poly_inter.front());
+        // std::cout << "pre_union" << " " << inter_area << std::endl;
+        bg::union_(poly, qpoly, poly_union);
+        /*
+        if (poly_union.empty()){
+            std::cout << "intsec area:" << " " << inter_area << std::endl;
+            std::cout << "box i corners:" << std::endl;
+            for(int k = 0; k < 4; ++k){
+                std::cout << box_corners_r(i, k, 0) << " " << box_corners_r(i,
+        k, 1) << std::endl;
+            }
+            std::cout << "box j corners:" <<  std::endl;
+            for(int k = 0; k < 4; ++k){
+                std::cout << box_corners_r(j, k, 0) << " " << box_corners_r(j,
+        k, 1) << std::endl;
+            }
+        }*/
+        // std::cout << "post_union" << poly_union.empty() << std::endl;
+        if (!poly_union.empty()) { // ignore invalid box
+          union_area = bg::area(poly_union.front());
+          // std::cout << "post union area" << std::endl;
+          // std::cout << union_area << "debug" << std::endl;
+          overlap = inter_area / union_area;
+          if (overlap >= thresh)
+            suppressed_rw(j) = 1;
+          poly_union.clear();
+        }
+      }
+      poly.clear();
+      qpoly.clear();
+      poly_inter.clear();
+    }
+  }
+  return keep;
+}
+
+constexpr int const threadsPerBlock = sizeof(unsigned long long) * 8;
+
+template <typename DType>
+int non_max_suppression(py::array_t<DType> boxes, py::array_t<int> keep_out,
+                        DType nms_overlap_thresh, int device_id) {
+  py::buffer_info info = boxes.request();
+  auto boxes_ptr = static_cast<DType *>(info.ptr);
+  py::buffer_info info_k = keep_out.request();
+  auto keep_out_ptr = static_cast<int *>(info_k.ptr);
+
+  return _nms_gpu<DType, threadsPerBlock>(keep_out_ptr, boxes_ptr,
+                                          boxes.shape(0), boxes.shape(1),
+                                          nms_overlap_thresh, device_id);
+}
+
+} // namespace spconv
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/nms_gpu.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/nms_gpu.h
new file mode 100644
index 000000000..31270c482
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/nms_gpu.h
@@ -0,0 +1,18 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+template <typename DType, int BLOCK_THREADS>
+int _nms_gpu(int *keep_out, const DType *boxes_host, int boxes_num,
+             int boxes_dim, DType nms_overlap_thresh, int device_id);
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/point2voxel.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/point2voxel.h
new file mode 100644
index 000000000..f87d0b8a4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/point2voxel.h
@@ -0,0 +1,94 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <pybind11/pybind11.h>
+// must include pybind11/eigen.h if using eigen matrix as arguments.
+// must include pybind11/stl.h if using containers in STL in arguments.
+#include <algorithm>
+#include <pybind11/numpy.h>
+#include <pybind11/stl.h>
+// #include <vector>
+#include <iostream>
+#include <math.h>
+
+namespace spconv {
+namespace py = pybind11;
+using namespace pybind11::literals;
+
+template <typename DType, int NDim>
+int points_to_voxel_3d_np(py::array_t<DType> points, py::array_t<DType> voxels,
+                          py::array_t<int> coors,
+                          py::array_t<int> num_points_per_voxel,
+                          py::array_t<int> coor_to_voxelidx,
+                          std::vector<DType> voxel_size,
+                          std::vector<DType> coors_range, int max_points,
+                          int max_voxels) {
+  auto points_rw = points.template mutable_unchecked<2>();
+  auto voxels_rw = voxels.template mutable_unchecked<3>();
+  auto coors_rw = coors.mutable_unchecked<2>();
+  auto num_points_per_voxel_rw = num_points_per_voxel.mutable_unchecked<1>();
+  auto coor_to_voxelidx_rw = coor_to_voxelidx.mutable_unchecked<NDim>();
+  auto N = points_rw.shape(0);
+  auto num_features = points_rw.shape(1);
+  // auto ndim = points_rw.shape(1) - 1;
+  constexpr int ndim_minus_1 = NDim - 1;
+  int voxel_num = 0;
+  bool failed = false;
+  int coor[NDim];
+  int c;
+  int grid_size[NDim];
+  for (int i = 0; i < NDim; ++i) {
+    grid_size[i] =
+        round((coors_range[NDim + i] - coors_range[i]) / voxel_size[i]);
+  }
+  int voxelidx, num;
+  for (int i = 0; i < N; ++i) {
+    failed = false;
+    for (int j = 0; j < NDim; ++j) {
+      c = floor((points_rw(i, j) - coors_range[j]) / voxel_size[j]);
+      if ((c < 0 || c >= grid_size[j])) {
+        failed = true;
+        break;
+      }
+      coor[ndim_minus_1 - j] = c;
+    }
+    if (failed)
+      continue;
+    voxelidx = coor_to_voxelidx_rw(coor[0], coor[1], coor[2]);
+    if (voxelidx == -1) {
+      voxelidx = voxel_num;
+      if (voxel_num >= max_voxels)
+        break;
+      voxel_num += 1;
+      coor_to_voxelidx_rw(coor[0], coor[1], coor[2]) = voxelidx;
+      for (int k = 0; k < NDim; ++k) {
+        coors_rw(voxelidx, k) = coor[k];
+      }
+    }
+    num = num_points_per_voxel_rw(voxelidx);
+    if (num < max_points) {
+      for (int k = 0; k < num_features; ++k) {
+        voxels_rw(voxelidx, num, k) = points_rw(i, k);
+      }
+      num_points_per_voxel_rw(voxelidx) += 1;
+    }
+  }
+  for (int i = 0; i < voxel_num; ++i) {
+    coor_to_voxelidx_rw(coors_rw(i, 0), coors_rw(i, 1), coors_rw(i, 2)) = -1;
+  }
+  return voxel_num;
+}
+
+} // namespace spconv
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/pool_ops.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/pool_ops.h
new file mode 100644
index 000000000..3d4d188d1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/pool_ops.h
@@ -0,0 +1,97 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef SPARSE_POOL_OP_H_
+#define SPARSE_POOL_OP_H_
+
+#include <cuda_runtime_api.h>
+#include <spconv/maxpool.h>
+#include <torch/script.h>
+#include <torch_utils.h>
+#include <utility/timer.h>
+
+namespace spconv {
+template <typename T>
+torch::Tensor indiceMaxPool(torch::Tensor features, torch::Tensor indicePairs,
+                          torch::Tensor indiceNum, int64_t numAct) {
+  auto device = features.device().type();
+  auto kernelVolume = indicePairs.size(0);
+  auto numInPlanes = features.size(1);
+  auto indicePairNumCpu = indiceNum.to({torch::kCPU});
+  auto options =
+      torch::TensorOptions().dtype(features.dtype()).device(features.device());
+  torch::Tensor output = torch::zeros({numAct, numInPlanes}, options);
+  double totalTime = 0;
+  for (int i = 0; i < kernelVolume; ++i) {
+    auto nHot = indicePairNumCpu.data<int>()[i];
+    if (nHot <= 0) {
+      continue;
+    }
+    // auto timer = spconv::CudaContextTimer<>();
+    if (device == torch::kCPU) {
+      functor::SparseMaxPoolForwardFunctor<tv::CPU, T, int> forwardFtor;
+      forwardFtor(tv::CPU(), tv::torch2tv<T>(output),
+                  tv::torch2tv<const T>(features),
+                  tv::torch2tv<const int>(indicePairs).subview(i), nHot);
+    } else {
+      functor::SparseMaxPoolForwardFunctor<tv::GPU, T, int> forwardFtor;
+      forwardFtor(tv::TorchGPU(), tv::torch2tv<T>(output),
+                  tv::torch2tv<const T>(features),
+                  tv::torch2tv<const int>(indicePairs).subview(i), nHot);
+      TV_CHECK_CUDA_ERR();
+    }
+    // totalTime += timer.report() / 1000.0;
+  }
+  // std::cout << "maxpool forward time " << totalTime << std::endl;
+  return output;
+}
+
+template <typename T>
+torch::Tensor indiceMaxPoolBackward(torch::Tensor features,
+                                  torch::Tensor outFeatures,
+                                  torch::Tensor outGrad, torch::Tensor indicePairs,
+                                  torch::Tensor indiceNum) {
+  auto device = features.device().type();
+  auto numInPlanes = features.size(1);
+  auto indicePairNumCpu = indiceNum.to({torch::kCPU});
+  auto options =
+      torch::TensorOptions().dtype(features.dtype()).device(features.device());
+  torch::Tensor inputGrad = torch::zeros(features.sizes(), options);
+    auto kernelVolume = indicePairs.size(0);
+  for (int i = 0; i < kernelVolume; ++i) {
+    auto nHot = indicePairNumCpu.data<int>()[i];
+    if (nHot <= 0) {
+      continue;
+    }
+    if (device == torch::kCPU) {
+      functor::SparseMaxPoolBackwardFunctor<tv::CPU, T, int> backwardFtor;
+      backwardFtor(tv::CPU(), tv::torch2tv<const T>(outFeatures),
+                   tv::torch2tv<const T>(features),
+                   tv::torch2tv<const T>(outGrad), tv::torch2tv<T>(inputGrad),
+                   tv::torch2tv<const int>(indicePairs).subview(i), nHot);
+    } else {
+      functor::SparseMaxPoolBackwardFunctor<tv::GPU, T, int> backwardFtor;
+      backwardFtor(tv::TorchGPU(), tv::torch2tv<const T>(outFeatures),
+                   tv::torch2tv<const T>(features),
+                   tv::torch2tv<const T>(outGrad), tv::torch2tv<T>(inputGrad),
+                   tv::torch2tv<const int>(indicePairs).subview(i), nHot);
+      TV_CHECK_CUDA_ERR();
+    }
+  }
+  return inputGrad;
+}
+
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/reordering.cu.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/reordering.cu.h
new file mode 100644
index 000000000..01d6a69b5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/reordering.cu.h
@@ -0,0 +1,161 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef REORDERING_CU_H_
+#define REORDERING_CU_H_
+#include <tensorview/helper_kernel.cu.h>
+
+// see http://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf.
+namespace spconv {
+template <typename T, typename Index, int NumTLP, int NumILP>
+__global__ void gatherGenericKernel(T *buffer, const T *features,
+                                    const Index *indices, int size,
+                                    int numPlanes) {
+  int ILPStrideX[NumILP];
+  Index inds[NumILP];
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideX[ilp] = ilp * gridDim.x * blockDim.x;
+
+  for (int ix : tv::KernelLoopX<int, NumILP>(size)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ilp++) {
+      if (ix + ILPStrideX[ilp] < size)
+        inds[ilp] = indices[ix + ILPStrideX[ilp]] * numPlanes;
+    }
+    for (int iy : tv::KernelLoopY<int>(numPlanes)) {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        if (ix + ILPStrideX[ilp] < size)
+          buffer[(ix + ILPStrideX[ilp]) * numPlanes + iy] =
+              features[inds[ilp] + iy];
+      }
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP, typename VecType>
+__global__ void gatherVecKernel(T *buffer, const T *features,
+                                const Index *indices, int size, int numPlanes) {
+  int ILPStrideX[NumILP];
+  Index inds[NumILP];
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideX[ilp] = ilp * gridDim.x * blockDim.x;
+
+  for (int ix : tv::KernelLoopX<int, NumILP>(size)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ilp++) {
+      if (ix + ILPStrideX[ilp] < size)
+        inds[ilp] = indices[ix + ILPStrideX[ilp]] * numPlanes;
+    }
+    for (int iy : tv::KernelLoopY<int>(numPlanes)) {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        if (ix + ILPStrideX[ilp] < size)
+          reinterpret_cast<VecType *>(
+              buffer)[(ix + ILPStrideX[ilp]) * numPlanes + iy] =
+              reinterpret_cast<const VecType *>(features)[inds[ilp] + iy];
+      }
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP,
+          typename VecType = int4>
+__global__ void gatherVecBlockKernel(T *buffer, const T *features,
+                                     const Index *indices, int size,
+                                     int numPlanes) {
+  int ILPStrideY[NumILP];
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideY[ilp] = ilp * gridDim.y * blockDim.y;
+  features += blockIdx.x * NumTLP;
+  buffer += blockIdx.x * NumTLP;
+
+  for (int iy : tv::KernelLoopY<int, NumILP>(size)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ++ilp) {
+      reinterpret_cast<VecType *>(
+          buffer)[(iy + ILPStrideY[ilp]) * numPlanes + threadIdx.x] =
+          reinterpret_cast<const VecType *>(
+              features)[indices[iy + ILPStrideY[ilp]] * numPlanes +
+                        threadIdx.x];
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP>
+__global__ void scatterAddGenericKernel(T *outFeatures, const T *buffer,
+                                        const Index *indices, int size,
+                                        int numPlanes) {
+  int ILPStrideX[NumILP];
+  Index inds[NumILP];
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideX[ilp] = ilp * gridDim.x * blockDim.x;
+  for (int ix : tv::KernelLoopX<int, NumILP>(size)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ilp++) {
+      if (ix + ILPStrideX[ilp] < size)
+        inds[ilp] = indices[ix + ILPStrideX[ilp]] * numPlanes;
+    }
+    for (int iy : tv::KernelLoopY<int>(numPlanes)) {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        if (ix + ILPStrideX[ilp] < size) {
+          outFeatures[inds[ilp] + iy] +=
+              buffer[(ix + ILPStrideX[ilp]) * numPlanes + iy];
+        }
+      }
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP,
+          typename VecType = int4>
+__global__ void scatterAddVecBlockKernel(T *outFeatures, const T *buffer,
+                                         const Index *indices, int size,
+                                         int numPlanes) {
+  int ILPStrideY[NumILP];
+  constexpr int vecloadFactor = sizeof(VecType) / sizeof(T);
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideY[ilp] = ilp * gridDim.y * blockDim.y;
+  outFeatures += blockIdx.x * NumTLP;
+  buffer += blockIdx.x * NumTLP;
+  T buf[vecloadFactor];
+  T buf2[vecloadFactor];
+  Index idx;
+  for (int iy : tv::KernelLoopY<int, NumILP>(size)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ++ilp) {
+      idx = indices[iy + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+      reinterpret_cast<VecType *>(buf)[0] =
+          reinterpret_cast<VecType *>(outFeatures)[idx];
+      reinterpret_cast<VecType *>(buf2)[0] = reinterpret_cast<const VecType *>(
+          buffer)[(iy + ILPStrideY[ilp]) * numPlanes + threadIdx.x];
+#pragma unroll
+      for (int i = 0; i < vecloadFactor; i++) {
+        buf[i] += buf2[i];
+      }
+      reinterpret_cast<VecType *>(outFeatures)[idx] =
+          reinterpret_cast<VecType *>(buf)[0];
+    }
+  }
+}
+
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/reordering.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/reordering.h
new file mode 100644
index 000000000..0230e17c7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/reordering.h
@@ -0,0 +1,40 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef SPARSE_REORDERING_FUNCTOR_H_
+#define SPARSE_REORDERING_FUNCTOR_H_
+#include <tensorview/tensorview.h>
+
+namespace spconv
+{
+namespace functor
+{
+template <typename Device, typename T, typename Index>
+struct SparseGatherFunctor
+{
+    void operator()(const Device& d, tv::TensorView<T> buffer, tv::TensorView<const T> features,
+                    tv::TensorView<const Index> indices, int size);
+};
+
+template <typename Device, typename T, typename Index>
+struct SparseScatterAddFunctor
+{
+    void operator()(const Device& d, tv::TensorView<T> out_features,
+                    tv::TensorView<const T> buffer, tv::TensorView<const Index> indices,
+                    int size, bool stable=false);
+};
+} // namespace functor
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/spconv_ops.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/spconv_ops.h
new file mode 100644
index 000000000..dcbfa0b77
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/spconv/spconv_ops.h
@@ -0,0 +1,561 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef SPARSE_CONV_OP_H_
+#define SPARSE_CONV_OP_H_
+
+#include <cuda_runtime_api.h>
+#include <spconv/indice.h>
+#include <spconv/reordering.h>
+#include <torch/script.h>
+#include <torch_utils.h>
+#include <utility/timer.h>
+
+namespace spconv {
+// torch.jit's doc says only support int64, so we need to convert to int32.
+template <unsigned NDim>
+std::vector<torch::Tensor>
+getIndicePair(torch::Tensor indices, int64_t batchSize,
+        std::vector<int64_t> outSpatialShape, std::vector<int64_t> spatialShape,
+        std::vector<int64_t> kernelSize, std::vector<int64_t> stride,
+        std::vector<int64_t> padding, std::vector<int64_t> dilation,
+        std::vector<int64_t> outPadding, int64_t _subM, int64_t _transpose) {
+  // auto timer = spconv::CudaContextTimer<>();
+  bool subM = _subM != 0;
+  bool transpose = _transpose != 0;
+  auto numAct = indices.size(0);
+  auto coorDim = indices.size(1) - 1; // batchIdx + xyz
+  TV_ASSERT_RT_ERR(NDim == coorDim, "error");
+  TV_ASSERT_RT_ERR(kernelSize.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(outSpatialShape.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(stride.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(padding.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(outPadding.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(dilation.size() == coorDim, "error");
+  auto kernelVolume = kernelSize[0];
+  for (int i = 1; i < kernelSize.size(); ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  TV_ASSERT_RT_ERR(kernelVolume <= 256, "error");
+  auto outputVolume = outSpatialShape[0];
+  for (int i = 1; i < outSpatialShape.size(); ++i) {
+    outputVolume *= outSpatialShape[i];
+  }
+  torch::Tensor indicePairs =
+      torch::full({kernelVolume, 2, numAct}, -1,
+                   torch::dtype(torch::kInt32).device(indices.device()));
+  torch::Tensor indiceNum = torch::zeros(
+      {kernelVolume}, torch::dtype(torch::kInt32).device(indices.device()));
+  torch::Tensor gridOut =
+      torch::full({batchSize * outputVolume}, -1,
+                  torch::dtype(torch::kInt32).device(indices.device()));
+  // std::cout << "full time " << timer.report() / 1000.0 << std::endl;
+  int64_t numActOut = -1;
+  tv::SimpleVector<int, NDim> outSpatialShape32;
+  tv::SimpleVector<int, NDim> kernelSize32;
+  tv::SimpleVector<int, NDim> stride32;
+  tv::SimpleVector<int, NDim> padding32;
+  tv::SimpleVector<int, NDim> dilation32;
+  auto indicePairUnique =
+      torch::full({indicePairs.numel() / 2 + 1}, std::numeric_limits<int>::max(),
+                  torch::dtype(torch::kInt32).device(indices.device()));
+  for (int i = 0; i < NDim; ++i) {
+    outSpatialShape32.push_back(outSpatialShape[i]);
+    kernelSize32.push_back(kernelSize[i]);
+    if (subM) {
+      stride32.push_back(1);
+      padding32.push_back(kernelSize[i] / 2);
+      dilation32.push_back(dilation[i]);
+    } else {
+      stride32.push_back(stride[i]);
+      padding32.push_back(padding[i]);
+      dilation32.push_back(dilation[i]);
+    }
+  }
+  if (subM) {
+    if (indices.device().type() == torch::kCPU) {
+      auto getIndicePairFtor =
+          functor::CreateSubMIndicePairFunctor<tv::CPU, int, int, NDim>();
+      numActOut = getIndicePairFtor(
+          tv::CPU(), tv::torch2tv<const int>(indices), tv::torch2tv<int>(gridOut),
+          tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum), kernelSize32,
+          stride32, padding32, dilation32, outSpatialShape32, transpose);
+    } else {
+      auto getIndicePairFtor =
+          functor::CreateSubMIndicePairFunctor<tv::GPU, int, int, NDim>();
+      numActOut = getIndicePairFtor(
+          tv::TorchGPU(), tv::torch2tv<const int>(indices), tv::torch2tv<int>(gridOut),
+          tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum), kernelSize32,
+          stride32, padding32, dilation32, outSpatialShape32, transpose);
+    }
+    return {indices, indicePairs, indiceNum};
+  } else {
+    torch::Tensor outInds =
+        torch::zeros({numAct * kernelVolume, coorDim + 1},
+                    torch::dtype(torch::kInt32).device(indices.device()));
+    if (indices.device().type() == torch::kCPU) {
+      auto getIndicePairFtor = functor::CreateConvIndicePairFunctor<tv::CPU, int, int, NDim>();
+      numActOut = getIndicePairFtor(
+          tv::CPU(), tv::torch2tv<const int>(indices),
+          tv::torch2tv<int>(outInds), tv::torch2tv<int>(gridOut),
+          tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum), kernelSize32,
+          stride32, padding32, dilation32, outSpatialShape32, transpose);
+    } else {
+      auto getIndicePairFtorP1 =
+          functor::CreateConvIndicePairFunctorP1<tv::GPU, int, int, NDim>();
+      auto getIndicePairFtorP2 =
+          functor::CreateConvIndicePairFunctorP2<tv::GPU, int, int, NDim>();
+      numActOut =
+          getIndicePairFtorP1(tv::TorchGPU(), tv::torch2tv<const int>(indices),
+                        tv::torch2tv<int>(outInds), tv::torch2tv<int>(gridOut),
+                        tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum),
+                        tv::torch2tv<int>(indicePairUnique), kernelSize32, stride32,
+                        padding32, dilation32, outSpatialShape32, transpose);
+      if (numActOut > 0) {
+        auto res = torch::_unique(indicePairUnique);
+        indicePairUnique = std::get<0>(res);
+        numActOut = getIndicePairFtorP2(
+            tv::TorchGPU(), tv::torch2tv<const int>(indices),
+            tv::torch2tv<int>(outInds), tv::torch2tv<int>(gridOut),
+            tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum),
+            tv::torch2tv<int>(indicePairUnique), outSpatialShape32, transpose);
+      }
+    }
+    return {outInds.slice(0, 0, numActOut), indicePairs, indiceNum};
+  }
+}
+
+template <unsigned NDim>
+std::vector<torch::Tensor>
+getIndicePairPreGrid(torch::Tensor indices, torch::Tensor gridOut, int64_t batchSize,
+        std::vector<int64_t> outSpatialShape, std::vector<int64_t> spatialShape,
+        std::vector<int64_t> kernelSize, std::vector<int64_t> stride,
+        std::vector<int64_t> padding, std::vector<int64_t> dilation,
+        std::vector<int64_t> outPadding, int64_t _subM, int64_t _transpose) {
+  // auto timer = spconv::CudaContextTimer<>();
+  bool subM = _subM != 0;
+  bool transpose = _transpose != 0;
+  auto numAct = indices.size(0);
+  auto coorDim = indices.size(1) - 1; // batchIdx + xyz
+  TV_ASSERT_RT_ERR(NDim == coorDim, "error");
+  TV_ASSERT_RT_ERR(kernelSize.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(outSpatialShape.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(stride.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(padding.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(outPadding.size() == coorDim, "error");
+  TV_ASSERT_RT_ERR(dilation.size() == coorDim, "error");
+  auto kernelVolume = kernelSize[0];
+  for (int i = 1; i < kernelSize.size(); ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+  TV_ASSERT_RT_ERR(kernelVolume <= 256, "error");
+  auto outputVolume = outSpatialShape[0];
+  for (int i = 1; i < outSpatialShape.size(); ++i) {
+    outputVolume *= outSpatialShape[i];
+  }
+  TV_ASSERT_INVALID_ARG(gridOut.numel() >= outputVolume * batchSize, "error");
+  torch::Tensor indicePairs =
+      torch::full({kernelVolume, 2, numAct}, -1,
+                   torch::dtype(torch::kInt32).device(indices.device()));
+  torch::Tensor indiceNum = torch::zeros(
+      {kernelVolume}, torch::dtype(torch::kInt32).device(indices.device()));
+  // std::cout << "full time " << timer.report() / 1000.0 << std::endl;
+  int64_t numActOut = -1;
+  tv::SimpleVector<int, NDim> outSpatialShape32;
+  tv::SimpleVector<int, NDim> kernelSize32;
+  tv::SimpleVector<int, NDim> stride32;
+  tv::SimpleVector<int, NDim> padding32;
+  tv::SimpleVector<int, NDim> dilation32;
+  auto indicePairUnique =
+      torch::full({indicePairs.numel() / 2 + 1}, std::numeric_limits<int>::max(),
+                  torch::dtype(torch::kInt32).device(indices.device()));
+  for (int i = 0; i < NDim; ++i) {
+    outSpatialShape32.push_back(outSpatialShape[i]);
+    kernelSize32.push_back(kernelSize[i]);
+    if (subM) {
+      stride32.push_back(1);
+      padding32.push_back(kernelSize[i] / 2);
+      dilation32.push_back(dilation[i]);
+    } else {
+      stride32.push_back(stride[i]);
+      padding32.push_back(padding[i]);
+      dilation32.push_back(dilation[i]);
+    }
+  }
+  if (subM) {
+    if (indices.device().type() == torch::kCPU) {
+      auto getIndicePairFtor =
+          functor::CreateSubMIndicePairFunctor<tv::CPU, int, int, NDim>();
+      numActOut = getIndicePairFtor(
+          tv::CPU(), tv::torch2tv<const int>(indices), tv::torch2tv<int>(gridOut),
+          tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum), kernelSize32,
+          stride32, padding32, dilation32, outSpatialShape32, transpose);
+      gridOut.fill_(-1);
+    } else {
+      auto getIndicePairFtor =
+          functor::CreateSubMIndicePairFunctor<tv::GPU, int, int, NDim>();
+      numActOut = getIndicePairFtor(
+          tv::TorchGPU(), tv::torch2tv<const int>(indices), tv::torch2tv<int>(gridOut),
+          tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum), kernelSize32,
+          stride32, padding32, dilation32, outSpatialShape32, transpose, true);
+    }
+    return {indices, indicePairs, indiceNum};
+  } else {
+    torch::Tensor outInds =
+        torch::zeros({numAct * kernelVolume, coorDim + 1},
+                    torch::dtype(torch::kInt32).device(indices.device()));
+    if (indices.device().type() == torch::kCPU) {
+      auto getIndicePairFtor = functor::CreateConvIndicePairFunctor<tv::CPU, int, int, NDim>();
+      numActOut = getIndicePairFtor(
+          tv::CPU(), tv::torch2tv<const int>(indices),
+          tv::torch2tv<int>(outInds), tv::torch2tv<int>(gridOut),
+          tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum), kernelSize32,
+          stride32, padding32, dilation32, outSpatialShape32, transpose, true);
+      gridOut.fill_(-1);
+    } else {
+      auto getIndicePairFtorP1 =
+          functor::CreateConvIndicePairFunctorP1<tv::GPU, int, int, NDim>();
+      auto getIndicePairFtorP2 =
+          functor::CreateConvIndicePairFunctorP2<tv::GPU, int, int, NDim>();
+      numActOut =
+          getIndicePairFtorP1(tv::TorchGPU(), tv::torch2tv<const int>(indices),
+                        tv::torch2tv<int>(outInds), tv::torch2tv<int>(gridOut),
+                        tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum),
+                        tv::torch2tv<int>(indicePairUnique), kernelSize32, stride32,
+                        padding32, dilation32, outSpatialShape32, transpose);
+      if (numActOut > 0) {
+        auto res = torch::_unique(indicePairUnique);
+        indicePairUnique = std::get<0>(res);
+        numActOut = getIndicePairFtorP2(
+            tv::TorchGPU(), tv::torch2tv<const int>(indices),
+            tv::torch2tv<int>(outInds), tv::torch2tv<int>(gridOut),
+            tv::torch2tv<int>(indicePairs), tv::torch2tv<int>(indiceNum),
+            tv::torch2tv<int>(indicePairUnique), outSpatialShape32, transpose, true);
+      }
+    }
+    return {outInds.slice(0, 0, numActOut), indicePairs, indiceNum};
+  }
+  
+}
+
+
+template <typename T>
+torch::Tensor indiceConv(torch::Tensor features, torch::Tensor filters,
+                       torch::Tensor indicePairs, torch::Tensor indiceNum,
+                       int64_t numActOut, int64_t _inverse, int64_t _subM) {
+  bool subM = _subM != 0;
+  bool inverse = _inverse != 0;
+  auto device = features.device().type();
+  auto ndim = filters.dim() - 2;
+  auto kernelVolume = indicePairs.size(0);
+  auto numInPlanes = features.size(1);
+  auto numOutPlanes = filters.size(ndim + 1);
+  auto indicePairNumCpu = indiceNum.to({torch::kCPU});
+  auto indicePairMaxSizeIter = std::max_element(
+      indicePairNumCpu.data<int>(), indicePairNumCpu.data<int>() + kernelVolume);
+  int indicePairMaxOffset = indicePairMaxSizeIter - indicePairNumCpu.data<int>();
+  int indicePairMaxSize = *indicePairMaxSizeIter;
+  
+  /*if (_subM){
+    std::vector<int> indicePairNumVec(indicePairNumCpu.data<int>(), indicePairNumCpu.data<int>() + kernelVolume);
+    indicePairNumVec.erase(indicePairNumVec.begin() + indicePairMaxOffset);
+
+    auto indicePairVecMaxSizeIter = std::max_element(
+        indicePairNumVec.begin(), indicePairNumVec.end());
+    indicePairMaxSize = *indicePairVecMaxSizeIter;
+  }*/
+
+  auto options =
+      torch::TensorOptions().dtype(features.dtype()).device(features.device());
+  // auto indicePairOptions =
+  //     torch::TensorOptions().dtype(torch::kInt64).device(indicePairs.device());
+
+  torch::Tensor output = torch::zeros({numActOut, numOutPlanes}, options);
+  torch::Tensor inputBuffer = torch::zeros({indicePairMaxSize, numInPlanes}, options);
+  torch::Tensor outputBuffer =
+      torch::zeros({indicePairMaxSize, numOutPlanes}, options);
+  filters = filters.view({-1, numInPlanes, numOutPlanes});
+  if (subM) { // the center index of subm conv don't need gather and scatter
+              // add.
+    torch::mm_out(output, features, filters[indicePairMaxOffset]);
+  }
+  double totalGatherTime = 0;
+  double totalGEMMTime = 0;
+  double totalSAddTime = 0;
+  for (int i = 0; i < kernelVolume; ++i) {
+    auto nHot = indicePairNumCpu.data<int>()[i];
+    if (nHot <= 0 || (subM && i == indicePairMaxOffset)) {
+      continue;
+    }
+    // auto timer = spconv::CudaContextTimer<>();
+    auto outputBufferBlob =
+        torch::from_blob(outputBuffer.data<T>(), {nHot, numOutPlanes}, options);
+    auto inputBufferBlob =
+        torch::from_blob(inputBuffer.data<T>(), {nHot, numInPlanes}, options);
+
+    if (device == torch::kCPU) {
+      functor::SparseGatherFunctor<tv::CPU, T, int> gatherFtor;
+      gatherFtor(tv::CPU(), tv::torch2tv<T>(inputBuffer),
+                 tv::torch2tv<const T>(features),
+                 tv::torch2tv<const int>(indicePairs).subview(i, inverse), nHot);
+    } else {
+      functor::SparseGatherFunctor<tv::GPU, T, int> gatherFtor;
+      gatherFtor(tv::TorchGPU(), tv::torch2tv<T>(inputBuffer),
+                 tv::torch2tv<const T>(features),
+                 tv::torch2tv<const int>(indicePairs).subview(i, inverse), nHot);
+      TV_CHECK_CUDA_ERR();
+      /* slower than SparseGatherFunctor, may due to int->long conversion
+      auto indicePairLong = indicePairs[i][inverse].to(torch::kInt64);
+      auto indicePairBlob = torch::from_blob(indicePairLong.data<long>(), {nHot},
+      indicePairOptions); 
+      torch::index_select_out(inputBufferBlob, features, 0,
+      indicePairBlob);*/
+    }
+    // totalGatherTime += timer.report() / 1000.0;
+    torch::mm_out(outputBufferBlob, inputBufferBlob, filters[i]);
+    // totalGEMMTime += timer.report() / 1000.0;
+
+    if (device == torch::kCPU) {
+      functor::SparseScatterAddFunctor<tv::CPU, T, int> scatterFtor;
+      scatterFtor(tv::CPU(), tv::torch2tv<T>(output),
+                  tv::torch2tv<const T>(outputBuffer),
+                  tv::torch2tv<const int>(indicePairs).subview(i, !inverse), nHot,
+                  true);
+    } else {
+      functor::SparseScatterAddFunctor<tv::GPU, T, int> scatterFtor;
+      scatterFtor(tv::TorchGPU(), tv::torch2tv<T>(output),
+                  tv::torch2tv<const T>(outputBuffer),
+                  tv::torch2tv<const int>(indicePairs).subview(i, !inverse), nHot,
+                  true);
+      TV_CHECK_CUDA_ERR();
+    }
+    // totalSAddTime += timer.report() / 1000.0;
+  }
+  // std::cout << "gather time " << totalGatherTime << std::endl;
+  // std::cout << "gemm time " << totalGEMMTime << std::endl;
+  // std::cout << "scatteradd time " << totalSAddTime << std::endl;
+  return output;
+}
+
+template <typename T>
+std::vector<torch::Tensor>
+indiceConvBackward(torch::Tensor features, torch::Tensor filters,
+                 torch::Tensor outGrad, torch::Tensor indicePairs, torch::Tensor indiceNum,
+                 int64_t _inverse, int64_t _subM) {
+  bool subM = _subM != 0;
+  bool inverse = _inverse != 0;
+
+  auto device = features.device().type();
+  auto ndim = filters.dim() - 2;
+  auto kernelVolume = indicePairs.size(0);
+  auto numInPlanes = features.size(1);
+  auto numOutPlanes = filters.size(ndim + 1);
+  auto indicePairNumCpu = indiceNum.to({torch::kCPU});
+  auto indicePairMaxSizeIter = std::max_element(
+      indicePairNumCpu.data<int>(), indicePairNumCpu.data<int>() + kernelVolume);
+  int indicePairMaxOffset = indicePairMaxSizeIter - indicePairNumCpu.data<int>();
+  int indicePairMaxSize = *indicePairMaxSizeIter;
+  auto options =
+      torch::TensorOptions().dtype(features.dtype()).device(features.device());
+  auto filterShape = filters.sizes();
+  torch::Tensor inputGrad = torch::zeros(features.sizes(), options);
+  torch::Tensor filtersGrad = torch::zeros(filterShape, options);
+  torch::Tensor inputBuffer = torch::zeros({indicePairMaxSize, numInPlanes}, options);
+  torch::Tensor outputBuffer =
+      torch::zeros({indicePairMaxSize, numOutPlanes}, options);
+
+  filters = filters.view({-1, numInPlanes, numOutPlanes});
+  filtersGrad = filtersGrad.view({-1, numInPlanes, numOutPlanes});
+  if (subM) {
+    auto filterGradSub = filtersGrad[indicePairMaxOffset];
+    torch::mm_out(filterGradSub, features.t(), outGrad);
+    torch::mm_out(inputGrad, outGrad, filters[indicePairMaxOffset].t());
+  }
+  for (int i = 0; i < kernelVolume; ++i) {
+    auto nHot = indicePairNumCpu.data<int>()[i];
+    if (nHot <= 0 || (subM && i == indicePairMaxOffset)) {
+      continue;
+    }
+    if (device == torch::kCPU) {
+      functor::SparseGatherFunctor<tv::CPU, T, int> gatherFtor;
+      functor::SparseGatherFunctor<tv::CPU, T, int> gatherFtorOut;
+      gatherFtor(tv::CPU(), tv::torch2tv<T>(inputBuffer),
+                 tv::torch2tv<const T>(features),
+                 tv::torch2tv<const int>(indicePairs).subview(i, inverse), nHot);
+      gatherFtorOut(tv::CPU(), tv::torch2tv<T>(outputBuffer),
+                    tv::torch2tv<const T>(outGrad),
+                    tv::torch2tv<const int>(indicePairs).subview(i, !inverse), nHot);
+    } else {
+      functor::SparseGatherFunctor<tv::GPU, T, int> gatherFtor;
+      functor::SparseGatherFunctor<tv::GPU, T, int> gatherFtorOut;
+      gatherFtor(tv::TorchGPU(), tv::torch2tv<T>(inputBuffer),
+                 tv::torch2tv<const T>(features),
+                 tv::torch2tv<const int>(indicePairs).subview(i, inverse), nHot);
+      TV_CHECK_CUDA_ERR();
+      gatherFtorOut(tv::TorchGPU(), tv::torch2tv<T>(outputBuffer),
+                    tv::torch2tv<const T>(outGrad),
+                    tv::torch2tv<const int>(indicePairs).subview(i, !inverse), nHot);
+      TV_CHECK_CUDA_ERR();
+    }
+    auto filterGradSub = filtersGrad[i];
+    auto outputBufferBlob =
+        torch::from_blob(outputBuffer.data<T>(), {nHot, numOutPlanes}, options);
+    auto inputBufferBlob =
+        torch::from_blob(inputBuffer.data<T>(), {nHot, numInPlanes}, options);
+
+    torch::mm_out(filterGradSub, inputBufferBlob.t(), outputBufferBlob);
+    torch::mm_out(inputBufferBlob, outputBufferBlob, filters[i].t());
+    if (device == torch::kCPU) {
+      functor::SparseScatterAddFunctor<tv::CPU, T, int> scatterFtor;
+      scatterFtor(tv::CPU(), tv::torch2tv<T>(inputGrad),
+                  tv::torch2tv<const T>(inputBuffer),
+                  tv::torch2tv<const int>(indicePairs).subview(i, inverse), nHot);
+    } else {
+      functor::SparseScatterAddFunctor<tv::GPU, T, int> scatterFtor;
+      scatterFtor(tv::TorchGPU(), tv::torch2tv<T>(inputGrad),
+                  tv::torch2tv<const T>(inputBuffer),
+                  tv::torch2tv<const int>(indicePairs).subview(i, inverse), nHot);
+      TV_CHECK_CUDA_ERR();
+    }
+  }
+  return {inputGrad, filtersGrad.view(filterShape)};
+}
+
+template <typename T>
+torch::Tensor indiceConvDevelopDontUse(torch::Tensor features, torch::Tensor filters,
+                         torch::Tensor indicePairs, torch::Tensor indiceNum,
+                         int64_t numActOut, int64_t _inverse, int64_t _subM) {
+  bool subM = _subM != 0;
+  bool inverse = _inverse != 0;
+
+  auto device = features.device().type();
+  auto ndim = filters.dim() - 2;
+  auto kernelVolume = indicePairs.size(0);
+  auto numInPlanes = features.size(1);
+  auto numOutPlanes = filters.size(ndim + 1);
+  auto indicePairNumCpu = indiceNum.to({torch::kCPU});
+  auto totalActsTen = indicePairNumCpu.sum();
+  auto totalActs = indicePairNumCpu.data<int>()[0];
+  auto indicePairMaxSizeIter = std::max_element(
+      indicePairNumCpu.data<int>(), indicePairNumCpu.data<int>() + kernelVolume);
+  int indicePairMaxOffset = indicePairMaxSizeIter - indicePairNumCpu.data<int>();
+  int indicePairMaxSize = *indicePairMaxSizeIter;
+  std::vector<int> indicePairNumVec(indicePairNumCpu.data<int>(),
+                              indicePairNumCpu.data<int>() + kernelVolume);
+  indicePairNumVec.erase(indicePairNumVec.begin() + indicePairMaxOffset);
+  int subRuleMaxSize = *std::max_element(indicePairNumVec.begin(), indicePairNumVec.end());
+  if (subM) {
+    indicePairMaxSize = subRuleMaxSize;
+  }
+  auto timer = spconv::CudaContextTimer<>();
+  auto options =
+      torch::TensorOptions().dtype(features.dtype()).device(features.device());
+  // auto indicePairOptions =
+  //     torch::TensorOptions().dtype(torch::kInt64).device(indicePairs.device());
+
+  torch::Tensor output = torch::zeros({numActOut, numOutPlanes}, options);
+  torch::Tensor inputBuffer =
+      torch::zeros({kernelVolume, indicePairMaxSize, numInPlanes}, options);
+  torch::Tensor outputBuffer =
+      torch::zeros({kernelVolume, indicePairMaxSize, numOutPlanes}, options);
+  filters = filters.view({-1, numInPlanes, numOutPlanes});
+  std::cout << "create time " << timer.report()/1000.0 << std::endl;
+  if (subM) { // the center index of subm conv don't need gather and scatter
+              // add.
+    torch::mm_out(output, features, filters[indicePairMaxOffset]);
+  }
+  double totalGatherTime = 0;
+  double totalGEMMTime = 0;
+  double totalSAddTime = 0;
+  // auto timer = spconv::CudaContextTimer<>();
+  for (int i = 0; i < kernelVolume; ++i) {
+    auto nHot = indicePairNumCpu.data<int>()[i];
+    if (nHot <= 0 || (subM && i == indicePairMaxOffset)) {
+      continue;
+    }
+    // 
+    auto outputBufferBlob = torch::from_blob(outputBuffer[i].data<T>(),
+                                             {nHot, numOutPlanes}, options);
+    auto inputBufferBlob = torch::from_blob(inputBuffer[i].data<T>(),
+                                            {nHot, numInPlanes}, options);
+    if (device == torch::kCPU) {
+      functor::SparseGatherFunctor<tv::CPU, T, int> gatherFtor;
+      gatherFtor(tv::CPU(), tv::torch2tv<T>(inputBufferBlob),
+                 tv::torch2tv<const T>(features),
+                 tv::torch2tv<const int>(indicePairs).subview(i, inverse), nHot);
+    } else {
+      functor::SparseGatherFunctor<tv::GPU, T, int> gatherFtor;
+      gatherFtor(tv::TorchGPU(), tv::torch2tv<T>(inputBufferBlob),
+                 tv::torch2tv<const T>(features),
+                 tv::torch2tv<const int>(indicePairs).subview(i, inverse), nHot);
+      TV_CHECK_CUDA_ERR();
+    }
+    // }
+    // for (int i = 0; i < kernelVolume; ++i) {
+    // totalGatherTime += timer.report() / 1000.0;
+    // auto outputBufferBlob = torch::from_blob(outputBuffer[i].data<T>(),
+    // {nHot, numOutPlanes}, options);
+
+  }
+  // totalGatherTime += timer.report() / 1000.0;
+  for (int i = 0; i < kernelVolume; ++i) {
+    auto nHot = indicePairNumCpu.data<int>()[i];
+    if (nHot <= 0 || (subM && i == indicePairMaxOffset)) {
+      continue;
+    }
+    auto outputBufferBlob = torch::from_blob(outputBuffer[i].data<T>(),
+                                             {nHot, numOutPlanes}, options);
+    auto inputBufferBlob = torch::from_blob(inputBuffer[i].data<T>(),
+                                            {nHot, numInPlanes}, options);
+
+    torch::mm_out(outputBufferBlob, inputBufferBlob, filters[i]);
+  }
+  // totalGEMMTime += timer.report() / 1000.0;
+  // totalGEMMTime += timer.report() / 1000.0;
+  for (int i = 0; i < kernelVolume; ++i) {
+    auto nHot = indicePairNumCpu.data<int>()[i];
+    if (nHot <= 0 || (subM && i == indicePairMaxOffset)) {
+      continue;
+    }
+    auto outputBufferBlob = torch::from_blob(outputBuffer[i].data<T>(),
+                                             {nHot, numOutPlanes}, options);
+    auto inputBufferBlob = torch::from_blob(inputBuffer[i].data<T>(),
+                                            {nHot, numInPlanes}, options);
+
+    if (device == torch::kCPU) {
+      functor::SparseScatterAddFunctor<tv::CPU, T, int> scatterFtor;
+      scatterFtor(tv::CPU(), tv::torch2tv<T>(output),
+                  tv::torch2tv<const T>(outputBufferBlob),
+                  tv::torch2tv<const int>(indicePairs).subview(i, !inverse), nHot,
+                  true);
+    } else {
+      functor::SparseScatterAddFunctor<tv::GPU, T, int> scatterFtor;
+      scatterFtor(tv::TorchGPU(), tv::torch2tv<T>(output),
+                  tv::torch2tv<const T>(outputBufferBlob),
+                  tv::torch2tv<const int>(indicePairs).subview(i, !inverse), nHot,
+                  true);
+      TV_CHECK_CUDA_ERR();
+    }
+    // totalSAddTime += timer.report() / 1000.0;
+  }
+  // totalSAddTime += timer.report() / 1000.0;
+  // std::cout << "gather time " << totalGatherTime << std::endl;
+  // std::cout << "gemm time " << totalGEMMTime << std::endl;
+  // std::cout << "scatteradd time " << totalSAddTime << std::endl;
+  return output;
+}
+
+} // namespace spconv
+
+#endif
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/helper_kernel.cu.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/helper_kernel.cu.h
new file mode 100644
index 000000000..0cefea75e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/helper_kernel.cu.h
@@ -0,0 +1,81 @@
+#pragma once
+// from tensorflow
+namespace tv
+{
+namespace detail
+{
+
+template <typename T>
+class KernelLoop
+{
+  struct Iterator
+  {
+    __forceinline__ __device__ Iterator(T index, T delta) : index_(index), delta_(delta) {}
+    __forceinline__ __device__ T operator*() const { return index_; }
+    __forceinline__ __device__ Iterator &operator++()
+    {
+      index_ += delta_;
+      return *this;
+    }
+    __forceinline__ __device__ bool operator!=(const Iterator &other) const
+    {
+      bool greater = index_ > other.index_;
+      bool less = index_ < other.index_;
+      // Anything past an end iterator (delta_ == 0) is equal.
+      // In range-based for loops, this optimizes to 'return less'.
+      if (!other.delta_)
+      {
+        return less;
+      }
+      if (!delta_)
+      {
+        return greater;
+      }
+      return less || greater;
+    }
+
+  private:
+    T index_;
+    const T delta_;
+  };
+
+public:
+  __forceinline__ __device__ KernelLoop(T begin, T delta, T end)
+      : begin_(begin), delta_(delta), end_(end) {}
+
+  __forceinline__ __device__ Iterator begin() const { return Iterator{begin_, delta_}; }
+  __forceinline__ __device__ Iterator end() const { return Iterator{end_, 0}; }
+
+private:
+  T begin_;
+  T delta_;
+  T end_;
+};
+
+} // namespace detail
+template <typename T, int NumILP=1>
+__forceinline__ __device__ detail::KernelLoop<T> KernelLoopX(T count)
+{
+  return detail::KernelLoop<T>(blockIdx.x * blockDim.x + threadIdx.x,
+                                  gridDim.x * blockDim.x * NumILP, count);
+}
+
+// Helper to visit indices in the range 0 <= i < count using the y-coordinate.
+// Usage: for(int i : KernelLoopY(count)) { visit(i); }
+template <typename T, int NumILP=1>
+__forceinline__ __device__ detail::KernelLoop<T> KernelLoopY(T count)
+{
+  return detail::KernelLoop<T>(blockIdx.y * blockDim.y + threadIdx.y,
+                                  gridDim.y * blockDim.y * NumILP, count);
+}
+
+// Helper to visit indices in the range 0 <= i < count using the z-coordinate.
+// Usage: for(int i : KernelLoopZ(count)) { visit(i); }
+template <typename T, int NumILP=1>
+__forceinline__ __device__ detail::KernelLoop<T> KernelLoopZ(T count)
+{
+  return detail::KernelLoop<T>(blockIdx.z * blockDim.z + threadIdx.z,
+                                  gridDim.z * blockDim.z * NumILP, count);
+}
+
+} // namespace tv
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/helper_launch.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/helper_launch.h
new file mode 100644
index 000000000..2b35d415f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/helper_launch.h
@@ -0,0 +1,21 @@
+#pragma once
+// from pytorch.aten
+#include "tensorview.h"
+namespace tv
+{
+namespace launch
+{
+
+template <typename T1, typename T2>
+inline int DivUp(const T1 a, const T2 b) { return (a + b - 1) / b; }
+
+// Use 1024 threads per block, which requires cuda sm_2x or above
+constexpr int CUDA_NUM_THREADS = 1024;
+// CUDA: number of blocks for threads.
+inline int getBlocks(const int N)
+{
+    TV_ASSERT_RT_ERR(N > 0, "CUDA kernel launch blocks must be positive, but got N=", N);
+    return DivUp(N, CUDA_NUM_THREADS);
+}
+} // namespace launch
+} // namespace tv
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/tensorview.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/tensorview.h
new file mode 100644
index 000000000..677715429
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/tensorview/tensorview.h
@@ -0,0 +1,1146 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <algorithm>
+#include <cassert>
+#include <cstdlib>
+#include <cuda_runtime_api.h>
+#include <iostream>
+#include <memory>
+#include <prettyprint.h>
+#include <sstream>
+#include <type_traits>
+#include <vector>
+
+namespace tv {
+
+#ifdef __NVCC__
+#define TV_HOST_DEVICE_INLINE __forceinline__ __device__ __host__
+#define TV_DEVICE_INLINE __forceinline__ __device__
+#define TV_HOST_DEVICE __device__ __host__
+#define TV_ASSERT(expr) assert(expr)
+#elif defined(__CUDACC_RTC__)
+#define TV_ASSERT(expr) assert(expr)
+#define TV_HOST_DEVICE_INLINE __forceinline__ __device__
+#define TV_DEVICE_INLINE __forceinline__ __device__
+#define TV_HOST_DEVICE __device__ __host__
+#else
+#define TV_ASSERT(x) assert(x)
+#define TV_HOST_DEVICE_INLINE __forceinline__ __device__ __host__
+#define TV_DEVICE_INLINE __forceinline__ __device__
+#define TV_HOST_DEVICE __device__ __host__
+#define TV_ASSERT(expr) assert(expr)
+#endif
+
+#define TV_REQUIRE(expr, ...)                                                  \
+  {                                                                            \
+    if (!(expr)) {                                                             \
+      printf(__VA_ARGS__);                                                     \
+      assert(expr);                                                            \
+    }                                                                          \
+  }
+
+#define TV_DEVICE_REQUIRE(expr, ...)                                           \
+  {                                                                            \
+    if (!(expr) && threadIdx.x == 0)                                           \
+      printf(__VA_ARGS__);                                                     \
+    assert(expr);                                                              \
+  }
+
+template <class SStream, class T> void sstream_print(SStream &ss, T val) {
+  ss << val;
+}
+
+template <class SStream, class T, class... TArgs>
+void sstream_print(SStream &ss, T val, TArgs... args) {
+  ss << val << " ";
+  sstream_print(ss, args...);
+}
+
+#define TV_ASSERT_RT_ERR(expr, ...)                                            \
+  {                                                                            \
+    if (!(expr)) {                                                             \
+      std::stringstream __macro_s;                                             \
+      __macro_s << __FILE__ << " " << __LINE__ << "\n";                        \
+      __macro_s << #expr << " assert faild. ";                                 \
+      tv::sstream_print(__macro_s, __VA_ARGS__);                               \
+      throw std::runtime_error(__macro_s.str());                               \
+    }                                                                          \
+  }
+
+#define TV_ASSERT_INVALID_ARG(expr, ...)                                       \
+  {                                                                            \
+    if (!(expr)) {                                                             \
+      std::stringstream __macro_s;                                             \
+      __macro_s << __FILE__ << " " << __LINE__ << "\n";                        \
+      __macro_s << #expr << " assert faild. ";                                 \
+      tv::sstream_print(__macro_s, __VA_ARGS__);                               \
+      throw std::invalid_argument(__macro_s.str());                            \
+    }                                                                          \
+  }
+
+#define TV_CHECK_CUDA_ERR()                                                    \
+  {                                                                            \
+    auto err = cudaGetLastError();                                             \
+    if (err != cudaSuccess) {                                                  \
+      std::stringstream __macro_s;                                             \
+      __macro_s << __FILE__ << " " << __LINE__ << "\n";                        \
+      __macro_s << "cuda execution failed with error " << err;                 \
+      throw std::runtime_error(__macro_s.str());                               \
+    }                                                                          \
+  }
+
+struct GPU {
+  GPU(cudaStream_t s = 0) : mStream(s) {}
+  cudaStream_t stream() const { return mStream; }
+  cudaStream_t mStream = 0;
+};
+struct CPU {};
+
+#define TV_MAX_DIM 6
+/*
+template <typename T>
+constexpr size_t calc_align(size_t ndim)
+{
+  if (ndim * sizeof(T) == 1)
+    return 1;
+  else if (ndim * sizeof(T) == 2)
+    return 2;
+  else if (ndim * sizeof(T) <= 4 && ndim * sizeof(T) > 2)
+    return 4;
+  else if (ndim * sizeof(T) <= 8 && ndim * sizeof(T) > 4)
+    return 8;
+  else if (ndim * sizeof(T) <= 16 && ndim * sizeof(T) > 8)
+    return 16;
+  else if (ndim * sizeof(T) <= 32 && ndim * sizeof(T) > 16)
+    return 32;
+  else
+    return 64;
+}
+*/
+template <typename T, size_t MaxDim = TV_MAX_DIM>
+struct /*alignas(calc_align<T>(MaxDim))*/ SimpleVector {
+public:
+  TV_HOST_DEVICE_INLINE SimpleVector(){};
+  TV_HOST_DEVICE_INLINE SimpleVector(std::initializer_list<T> q) {
+    TV_ASSERT(q.size() <= MaxDim);
+    mSize = 0;
+    for (T s : q) {
+      mArray[mSize++] = s;
+    }
+    mSize = q.size();
+  }
+  SimpleVector(const std::vector<T> &arr) {
+    TV_ASSERT(arr.size() <= MaxDim);
+    for (size_t i = 0; i < arr.size(); ++i) {
+      mArray[i] = arr[i];
+    }
+    mSize = arr.size();
+  }
+  TV_HOST_DEVICE_INLINE SimpleVector(const SimpleVector<T, MaxDim> &arr) {
+    TV_ASSERT(arr.size() <= MaxDim);
+    for (size_t i = 0; i < arr.size(); ++i) {
+      mArray[i] = arr[i];
+    }
+    mSize = arr.size();
+  }
+  TV_HOST_DEVICE_INLINE T &operator[](int idx) {
+#ifdef TV_DEBUG
+    TV_ASSERT(idx >= 0 && idx < mSize);
+#endif
+    return mArray[idx];
+  }
+  TV_HOST_DEVICE_INLINE const T &operator[](int idx) const {
+#ifdef TV_DEBUG
+    TV_ASSERT(idx >= 0 && idx < mSize);
+#endif
+    return mArray[idx];
+  }
+  TV_HOST_DEVICE_INLINE void push_back(T s) {
+#ifdef TV_DEBUG
+    TV_ASSERT(mSize < MaxDim);
+#endif
+    mArray[mSize] = s;
+    mSize++;
+  }
+  TV_HOST_DEVICE_INLINE void pop_back() {
+#ifdef TV_DEBUG
+    TV_ASSERT(mSize > 0);
+#endif
+    mSize--;
+  }
+
+  TV_HOST_DEVICE_INLINE size_t size() const { return mSize; }
+  TV_HOST_DEVICE_INLINE const T *data() const { return mArray; }
+  TV_HOST_DEVICE_INLINE size_t empty() const { return mSize == 0; }
+
+  typedef size_t size_type;
+
+  class iterator {
+  public:
+    typedef iterator self_type;
+    typedef T value_type;
+    typedef T &reference;
+    typedef T *pointer;
+    typedef std::forward_iterator_tag iterator_category;
+    typedef std::ptrdiff_t difference_type;
+    TV_HOST_DEVICE_INLINE iterator(pointer ptr) : ptr_(ptr) {}
+    TV_HOST_DEVICE_INLINE self_type operator++(int junk) {
+      self_type i = *this;
+      ptr_++;
+      return i;
+    }
+    TV_HOST_DEVICE_INLINE self_type operator++() {
+      ptr_++;
+      return *this;
+    }
+    TV_HOST_DEVICE_INLINE reference operator*() { return *ptr_; }
+    TV_HOST_DEVICE_INLINE pointer operator->() { return ptr_; }
+    TV_HOST_DEVICE_INLINE bool operator==(const self_type &rhs) {
+      return ptr_ == rhs.ptr_;
+    }
+    TV_HOST_DEVICE_INLINE bool operator!=(const self_type &rhs) {
+      return ptr_ != rhs.ptr_;
+    }
+
+  private:
+    pointer ptr_;
+  };
+
+  class const_iterator {
+  public:
+    typedef const_iterator self_type;
+    typedef T value_type;
+    typedef const T &reference;
+    typedef const T *pointer;
+    typedef std::ptrdiff_t difference_type;
+    typedef std::forward_iterator_tag iterator_category;
+    TV_HOST_DEVICE_INLINE const_iterator(pointer ptr) : ptr_(ptr) {}
+    TV_HOST_DEVICE_INLINE self_type operator++(int junk) {
+      self_type i = *this;
+      ptr_++;
+      return i;
+    }
+    TV_HOST_DEVICE_INLINE self_type operator++() {
+      ptr_++;
+      return *this;
+    }
+    TV_HOST_DEVICE_INLINE reference operator*() { return *ptr_; }
+    TV_HOST_DEVICE_INLINE pointer operator->() { return ptr_; }
+    TV_HOST_DEVICE_INLINE bool operator==(const self_type &rhs) {
+      return ptr_ == rhs.ptr_;
+    }
+    TV_HOST_DEVICE_INLINE bool operator!=(const self_type &rhs) {
+      return ptr_ != rhs.ptr_;
+    }
+
+  private:
+    pointer ptr_;
+  };
+
+  TV_HOST_DEVICE_INLINE iterator begin() { return iterator(mArray); }
+
+  TV_HOST_DEVICE_INLINE iterator end() { return iterator(mArray + mSize); }
+
+  TV_HOST_DEVICE_INLINE const_iterator begin() const {
+    return const_iterator(mArray);
+  }
+
+  TV_HOST_DEVICE_INLINE const_iterator end() const {
+    return const_iterator(mArray + mSize);
+  }
+  TV_HOST_DEVICE_INLINE const_iterator cbegin() const {
+    return const_iterator(mArray);
+  }
+
+  TV_HOST_DEVICE_INLINE const_iterator cend() const {
+    return const_iterator(mArray + mSize);
+  }
+
+protected:
+  T mArray[MaxDim];
+  size_t mSize = 0;
+};
+
+template <typename T, size_t MaxDim>
+bool operator==(const SimpleVector<T, MaxDim> &lfs,
+                const SimpleVector<T, MaxDim> &rfs) {
+  if (lfs.size() != rfs.size())
+    return false;
+  for (size_t i = 0; i < lfs.size(); ++i) {
+    if (lfs[i] != rfs[i])
+      return false;
+  }
+  return true;
+}
+
+template <typename T, size_t MaxDim>
+bool operator!=(const SimpleVector<T, MaxDim> &lfs,
+                const SimpleVector<T, MaxDim> &rfs) {
+
+  return !(lfs == rfs);
+}
+
+struct Slice {
+  template <class... Integers> TV_HOST_DEVICE_INLINE Slice(Integers... ints) {
+    static_assert(sizeof...(ints) <= 3, "slice init must smaller than 3");
+    SimpleVector<int, 3> slices{int(ints)...};
+    mSlices[0] = -1;
+    mSlices[1] = -1;
+    mSlices[2] = -1;
+    for (size_t i = 0; i < slices.size(); ++i) {
+      mSlices[i] = slices[i];
+    }
+  }
+
+  TV_HOST_DEVICE_INLINE Slice() {
+    mSlices[0] = -1;
+    mSlices[1] = -1;
+    mSlices[2] = -1;
+  }
+  template <typename T>
+  TV_HOST_DEVICE_INLINE Slice(std::initializer_list<T> slice) {
+    mSlices[0] = -1;
+    mSlices[1] = -1;
+    mSlices[2] = -1;
+    TV_ASSERT(slice.size() <= 3);
+    int idx = 0;
+    for (T s : slice) {
+      mSlices[idx] = int(s);
+      ++idx;
+    }
+  }
+  TV_HOST_DEVICE_INLINE int &operator[](int idx) {
+#ifdef TV_DEBUG
+    TV_ASSERT(idx >= 0 && idx < 3);
+#endif
+    return mSlices[idx];
+  }
+  TV_HOST_DEVICE_INLINE const int &operator[](int idx) const {
+#ifdef TV_DEBUG
+    TV_ASSERT(idx >= 0 && idx < 3);
+#endif
+    return mSlices[idx];
+  }
+
+protected:
+  int mSlices[3];
+};
+
+template <size_t MaxDim = TV_MAX_DIM>
+struct ShapeBase : public SimpleVector<int, MaxDim> {
+  TV_HOST_DEVICE_INLINE ShapeBase() : SimpleVector<int, MaxDim>(){};
+  TV_HOST_DEVICE_INLINE ShapeBase(std::initializer_list<int> shape)
+      : SimpleVector<int, MaxDim>(shape) {}
+
+  template <typename T, template <class...> class Container>
+  ShapeBase(Container<T> shape) : SimpleVector<int, MaxDim>(shape) {}
+  TV_HOST_DEVICE_INLINE ShapeBase(const ShapeBase<MaxDim> &shape)
+      : SimpleVector<int, MaxDim>(shape) {}
+  ShapeBase(const std::vector<int> &arr) : SimpleVector<int, MaxDim>(arr) {}
+
+  ShapeBase<MaxDim> &operator=(const ShapeBase<MaxDim> &shape) = default;
+  TV_HOST_DEVICE_INLINE ShapeBase<MaxDim> subshape(int start, int end) const {
+#ifdef TV_DEBUG
+    TV_ASSERT(start >= 0 && end < this->mSize && end > start);
+#endif
+    ShapeBase<MaxDim> shape;
+    for (int i = start; i < end; ++i) {
+      shape.push_back(this->mArray[i]);
+    }
+    return shape;
+  }
+  TV_HOST_DEVICE_INLINE ShapeBase<MaxDim> subshape(int start) const {
+#ifdef TV_DEBUG
+    TV_ASSERT(start >= 0 && start <= this->mSize);
+#endif
+    ShapeBase<MaxDim> shape;
+    for (int i = start; i < this->mSize; ++i) {
+      shape.push_back(this->mArray[i]);
+    }
+    return shape;
+  }
+
+  TV_HOST_DEVICE_INLINE size_t size() const {
+    if (this->mSize == 0)
+      return 0;
+    size_t s = 1;
+    for (int i = 0; i < int(this->mSize); ++i) {
+      s *= this->mArray[i];
+    }
+    return s;
+  }
+  TV_HOST_DEVICE_INLINE size_t ndim() const { return this->mSize; }
+  TV_HOST_DEVICE_INLINE ShapeBase<MaxDim> squeeze() const {
+    ShapeBase<MaxDim> shape;
+    for (int i = 0; i < this->mSize; ++i) {
+      if (this->mArray[i] != 1)
+        shape.push_back(this->mArray[i]);
+    }
+    return shape;
+  }
+  TV_HOST_DEVICE_INLINE ShapeBase<MaxDim> squeeze(int dim) const {
+    ShapeBase<MaxDim> shape;
+    for (int i = 0; i < this->mSize; ++i) {
+      if (i != dim || this->mArray[i] != 1)
+        shape.push_back(this->mArray[i]);
+    }
+    return shape;
+  }
+};
+
+using Shape = ShapeBase<TV_MAX_DIM>;
+
+template <class... Inds>
+TV_HOST_DEVICE_INLINE unsigned rowArrayIdx(std::vector<int> &shape,
+                                           Inds... indexes) {
+  unsigned offset = 0;
+  unsigned m = 1;
+  int indexes_vec[sizeof...(indexes)] = {indexes...};
+#ifdef TV_DEBUG
+  TV_ASSERT(sizeof...(indexes) == shape.size());
+#endif
+#pragma unroll
+  for (int i = sizeof...(indexes) - 1; i >= 0; --i) {
+    offset += m * indexes_vec[i];
+    m *= shape[i];
+  }
+  return offset;
+}
+
+TV_HOST_DEVICE_INLINE unsigned rowArrayIdx(std::vector<int> &shape,
+                                           std::vector<int> &indexes_vec) {
+  unsigned offset = 0;
+  unsigned m = 1;
+  for (int i = shape.size() - 1; i >= 0; --i) {
+    offset += m * indexes_vec[i];
+    m *= shape[i];
+  }
+  return offset;
+}
+
+template <class... Inds>
+TV_HOST_DEVICE_INLINE unsigned rowArrayIdx(const Shape &shape,
+                                           Inds... indexes) {
+  unsigned offset = 0;
+  unsigned m = 1;
+  int indexes_vec[sizeof...(indexes)] = {indexes...};
+#pragma unroll
+  for (int i = sizeof...(indexes) - 1; i >= 0; --i) {
+    offset += m * indexes_vec[i];
+    m *= shape[i];
+  }
+  return offset;
+}
+
+TV_HOST_DEVICE_INLINE unsigned rowArrayIdx(const Shape &shape,
+                                           const Shape &indexes_vec) {
+  unsigned offset = 0;
+  unsigned m = 1;
+  for (int i = indexes_vec.ndim() - 1; i >= 0; --i) {
+    offset += m * indexes_vec[i];
+    m *= shape[i];
+  }
+  return offset;
+}
+
+template <typename Index, unsigned NDim>
+TV_HOST_DEVICE_INLINE unsigned rowArrayIdx(const Index *indexes,
+                                           const Index *shape) {
+  unsigned offset = 0;
+  unsigned m = 1;
+#pragma unroll
+  for (int i = NDim - 1; i >= 0; --i) {
+    offset += m * indexes[i];
+    m *= shape[i];
+  }
+  return offset;
+}
+
+template <typename Index, unsigned NDim>
+TV_HOST_DEVICE_INLINE Index rowArrayIdxInv(Index index, Index *output,
+                                           const Index *shape) {
+#pragma unroll
+  for (int i = NDim - 1; i >= 0; --i) {
+    output[i] = index % shape[i];
+    index -= output[i];
+    index /= shape[i];
+  }
+  return index;
+}
+
+template <int N> struct ArrayIndexRowMajor {
+  // mPtr[((i1 * mShape[1] + i2) * mShape[2] + i3) * mShape[3] + i4];
+  TV_HOST_DEVICE_INLINE static unsigned run(const Shape &shape,
+                                            const Shape &indexes) {
+    return indexes[N - 1] +
+           shape[N - 1] * ArrayIndexRowMajor<N - 1>::run(shape, indexes);
+  }
+};
+
+template <> struct ArrayIndexRowMajor<0> {
+  TV_HOST_DEVICE_INLINE static unsigned run(const Shape &shape,
+                                            const Shape &indexes) {
+    return 0;
+  }
+};
+
+namespace detail {
+template <typename T> constexpr const char *simpleTypeName(T val = T());
+template <> constexpr const char *simpleTypeName(float val) {
+  return "float32";
+}
+// template <> constexpr const char *simpleTypeName(double val) {
+//   return "float64";
+// }
+template <> constexpr const char *simpleTypeName(int val) { return "int32"; }
+template <> constexpr const char *simpleTypeName(unsigned val) {
+  return "uint32";
+}
+template <> constexpr const char *simpleTypeName(long val) { return "int64"; }
+template <> constexpr const char *simpleTypeName(unsigned long val) {
+  return "uint64";
+}
+}; // namespace detail
+
+template <typename T, int Rank = -1> struct TensorView {
+  TV_HOST_DEVICE_INLINE TensorView() {}
+  explicit TV_HOST_DEVICE_INLINE TensorView(T *ptr, Shape shape)
+      : mPtr(ptr), mShape(shape) {}
+  // explicit TV_HOST_DEVICE_INLINE TensorView(const
+  // TensorView<std::remove_const_t<T>> &tview) : mPtr(tview.data()),
+  // mShape(tview.shape()) {}
+  template <class... Integers>
+  explicit TV_HOST_DEVICE_INLINE TensorView(T *ptr, Integers... shapes)
+      : mPtr(ptr) {
+    mShape = {int(shapes)...};
+  }
+
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> &
+  assign(const TensorView<T, Rank> &tensor) {
+    TV_REQUIRE(tensor.shape() == shape(), "you must provide same input size%s",
+               "\n");
+    T *ptr = mPtr;
+    const T *other_ptr = tensor.data();
+    for (size_t i = 0; i < size(); ++i)
+      *(ptr++) = *(other_ptr++);
+    return *this;
+  }
+
+  template <typename T1>
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> &
+  assign(std::initializer_list<T1> seq) {
+    TV_REQUIRE(seq.size() == size(), "you must provide same input size%s",
+               "\n");
+    T *ptr = mPtr;
+    for (const T1 &s : seq)
+      *(ptr++) = T(s);
+    return *this;
+  }
+
+  template <class... Inds> TV_HOST_DEVICE_INLINE T &operator()(Inds... inds) {
+#ifdef TV_DEBUG
+    int idxes[sizeof...(Inds)]{int(inds)...};
+    TV_REQUIRE(sizeof...(inds) == mShape.ndim(),
+               "you provide %d indexes, but dim is %d\n", sizeof...(inds),
+               mShape.ndim());
+    for (int i = 0; i < sizeof...(inds); ++i) {
+      TV_REQUIRE(idxes[i] >= 0 && idxes[i] < mShape[i],
+                 "index-%d(%d) out-of-range: [0, %d)\n", i, idxes[i],
+                 mShape[i]);
+    }
+#endif
+    return mPtr[rowArrayIdx(mShape, int(inds)...)];
+  }
+  template <class... Inds>
+  TV_HOST_DEVICE_INLINE const T &operator()(Inds... inds) const {
+#ifdef TV_DEBUG
+    int idxes[sizeof...(Inds)]{int(inds)...};
+    TV_REQUIRE(sizeof...(inds) == mShape.ndim(),
+               "you provide %d indexes, but dim is %d\n", sizeof...(inds),
+               mShape.ndim());
+    for (int i = 0; i < sizeof...(inds); ++i) {
+      TV_REQUIRE(idxes[i] >= 0 && idxes[i] < mShape[i],
+                 "index-%d(%d) out-of-range: [0, %d)\n", i, idxes[i],
+                 mShape[i]);
+    }
+#endif
+    return mPtr[rowArrayIdx(mShape, int(inds)...)];
+  }
+  TV_HOST_DEVICE_INLINE T &operator()() {
+#if defined TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mPtr != nullptr,
+                      "you want get value but the view is empty.%s", "\n");
+    TV_DEVICE_REQUIRE(mShape.ndim() == 0,
+                      "you provide 0 indexes, but dim is %ld\n", mShape.ndim());
+#else
+    TV_REQUIRE(mPtr != nullptr, "you want get value but the view is empty.%s",
+               "\n");
+    TV_REQUIRE(mShape.ndim() == 0, "you provide 0 indexes, but dim is %ld\n",
+               mShape.ndim());
+#endif
+#endif
+    return mPtr[0];
+  }
+  TV_HOST_DEVICE_INLINE const T &operator()() const {
+#if defined TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mPtr != nullptr,
+                      "you want get value but the view is empty.%s", "\n");
+    TV_DEVICE_REQUIRE(mShape.ndim() == 0,
+                      "you provide 0 indexes, but dim is %ld\n", mShape.ndim());
+#else
+    TV_REQUIRE(mPtr != nullptr, "you want get value but the view is empty.%s",
+               "\n");
+    TV_REQUIRE(mShape.ndim() == 0, "you provide 0 indexes, but dim is %ld\n",
+               mShape.ndim());
+#endif
+#endif
+    return mPtr[0];
+  }
+
+  template <class T1> TV_HOST_DEVICE_INLINE T &operator()(T1 i1) {
+#if defined TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mShape.ndim() == 1,
+                      "you provide 1 indexes, but dim is %ld\n", mShape.ndim());
+    TV_DEVICE_REQUIRE(i1 >= 0 && i1 < mShape[0],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 0, i1, mShape[0]);
+#else
+    TV_REQUIRE(mShape.ndim() == 1, "you provide 1 indexes, but dim is %ld\n",
+               mShape.ndim());
+    TV_REQUIRE(i1 >= 0 && i1 < mShape[0],
+               "index-%d(%d) out-of-range: [0, %d)\n", 0, i1, mShape[0]);
+#endif
+#endif
+    return mPtr[i1];
+  }
+  template <class T1, class T2>
+  TV_HOST_DEVICE_INLINE T &operator()(T1 i1, T2 i2) {
+#ifdef TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mShape.ndim() == 2,
+                      "you provide 2 indexes, but dim is %ld\n", mShape.ndim());
+    TV_DEVICE_REQUIRE(i1 >= 0 && i1 < mShape[0],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1),
+                      mShape[0]);
+    TV_DEVICE_REQUIRE(i2 >= 0 && i2 < mShape[1],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2),
+                      mShape[1]);
+#else
+    TV_REQUIRE(mShape.ndim() == 2, "you provide 2 indexes, but dim is %ld\n",
+               mShape.ndim());
+    TV_REQUIRE(i1 >= 0 && i1 < mShape[0],
+               "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1), mShape[0]);
+    TV_REQUIRE(i2 >= 0 && i2 < mShape[1],
+               "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2), mShape[1]);
+#endif
+#endif
+    return mPtr[i1 * mShape[1] + i2];
+  }
+  template <class T1, class T2, class T3>
+  TV_HOST_DEVICE_INLINE T &operator()(T1 i1, T2 i2, T3 i3) {
+#ifdef TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mShape.ndim() == 3,
+                      "you provide 3 indexes, but dim is %ld\n", mShape.ndim());
+    TV_DEVICE_REQUIRE(i1 >= 0 && i1 < mShape[0],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1),
+                      mShape[0]);
+    TV_DEVICE_REQUIRE(i2 >= 0 && i2 < mShape[1],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2),
+                      mShape[1]);
+    TV_DEVICE_REQUIRE(i3 >= 0 && i3 < mShape[2],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 2, int(i3),
+                      mShape[2]);
+#else
+    TV_REQUIRE(mShape.ndim() == 3, "you provide 3 indexes, but dim is %ld\n",
+               mShape.ndim());
+    TV_REQUIRE(i1 >= 0 && i1 < mShape[0],
+               "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1), mShape[0]);
+    TV_REQUIRE(i2 >= 0 && i2 < mShape[1],
+               "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2), mShape[1]);
+    TV_REQUIRE(i3 >= 0 && i3 < mShape[2],
+               "index-%d(%d) out-of-range: [0, %d)\n", 2, int(i3), mShape[2]);
+#endif
+#endif
+    return mPtr[(i1 * mShape[1] + i2) * mShape[2] + i3];
+  }
+  template <class T1, class T2, class T3, class T4>
+  TV_HOST_DEVICE_INLINE T &operator()(T1 i1, T2 i2, T3 i3, T4 i4) {
+#ifdef TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mShape.ndim() == 4,
+                      "you provide 4 indexes, but dim is %ld\n", mShape.ndim());
+    TV_DEVICE_REQUIRE(i1 >= 0 && i1 < mShape[0],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1),
+                      mShape[0]);
+    TV_DEVICE_REQUIRE(i2 >= 0 && i2 < mShape[1],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2),
+                      mShape[1]);
+    TV_DEVICE_REQUIRE(i3 >= 0 && i3 < mShape[2],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 2, int(i3),
+                      mShape[2]);
+    TV_DEVICE_REQUIRE(i4 >= 0 && i4 < mShape[3],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 3, int(i4),
+                      mShape[3]);
+#else
+    TV_REQUIRE(mShape.ndim() == 4, "you provide 4 indexes, but dim is %ld\n",
+               mShape.ndim());
+    TV_REQUIRE(i1 >= 0 && i1 < mShape[0],
+               "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1), mShape[0]);
+    TV_REQUIRE(i2 >= 0 && i2 < mShape[1],
+               "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2), mShape[1]);
+    TV_REQUIRE(i3 >= 0 && i3 < mShape[2],
+               "index-%d(%d) out-of-range: [0, %d)\n", 2, int(i3), mShape[2]);
+    TV_REQUIRE(i4 >= 0 && i4 < mShape[3],
+               "index-%d(%d) out-of-range: [0, %d)\n", 3, int(i4), mShape[3]);
+#endif
+#endif
+    return mPtr[((i1 * mShape[1] + i2) * mShape[2] + i3) * mShape[3] + i4];
+  }
+
+  template <class T1> TV_HOST_DEVICE_INLINE const T &operator()(T1 i1) const {
+#ifdef TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mShape.ndim() == 1,
+                      "you provide 1 indexes, but dim is %ld\n", mShape.ndim());
+    TV_DEVICE_REQUIRE(i1 >= 0 && i1 < mShape[0],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1),
+                      mShape[0]);
+#else
+    TV_REQUIRE(mShape.ndim() == 1, "you provide 1 indexes, but dim is %ld\n",
+               mShape.ndim());
+    TV_REQUIRE(i1 >= 0 && i1 < mShape[0],
+               "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1), mShape[0]);
+#endif
+#endif
+    return mPtr[i1];
+  }
+  template <class T1, class T2>
+  TV_HOST_DEVICE_INLINE const T &operator()(T1 i1, T2 i2) const {
+#ifdef TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mShape.ndim() == 2,
+                      "you provide 2 indexes, but dim is %ld\n", mShape.ndim());
+    TV_DEVICE_REQUIRE(i1 >= 0 && i1 < mShape[0],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1),
+                      mShape[0]);
+    TV_DEVICE_REQUIRE(i2 >= 0 && i2 < mShape[1],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2),
+                      mShape[1]);
+#else
+    TV_REQUIRE(mShape.ndim() == 2, "you provide 2 indexes, but dim is %ld\n",
+               mShape.ndim());
+    TV_REQUIRE(i1 >= 0 && i1 < mShape[0],
+               "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1), mShape[0]);
+    TV_REQUIRE(i2 >= 0 && i2 < mShape[1],
+               "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2), mShape[1]);
+
+#endif
+#endif
+    return mPtr[i1 * mShape[1] + i2];
+  }
+  template <class T1, class T2, class T3>
+  TV_HOST_DEVICE_INLINE const T &operator()(T1 i1, T2 i2, T3 i3) const {
+#ifdef TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mShape.ndim() == 3,
+                      "you provide 3 indexes, but dim is %ld\n", mShape.ndim());
+    TV_DEVICE_REQUIRE(i1 >= 0 && i1 < mShape[0],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1),
+                      mShape[0]);
+    TV_DEVICE_REQUIRE(i2 >= 0 && i2 < mShape[1],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2),
+                      mShape[1]);
+    TV_DEVICE_REQUIRE(i3 >= 0 && i3 < mShape[2],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 2, int(i3),
+                      mShape[2]);
+#else
+    TV_REQUIRE(mShape.ndim() == 3, "you provide 3 indexes, but dim is %ld\n",
+               mShape.ndim());
+    TV_REQUIRE(i1 >= 0 && i1 < mShape[0],
+               "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1), mShape[0]);
+    TV_REQUIRE(i2 >= 0 && i2 < mShape[1],
+               "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2), mShape[1]);
+    TV_REQUIRE(i3 >= 0 && i3 < mShape[2],
+               "index-%d(%d) out-of-range: [0, %d)\n", 2, int(i3), mShape[2]);
+#endif
+#endif
+    return mPtr[(i1 * mShape[1] + i2) * mShape[2] + i3];
+  }
+  template <class T1, class T2, class T3, class T4>
+  TV_HOST_DEVICE_INLINE const T &operator()(T1 i1, T2 i2, T3 i3, T4 i4) const {
+#ifdef TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(mShape.ndim() == 4,
+                      "you provide 4 indexes, but dim is %ld\n", mShape.ndim());
+    TV_DEVICE_REQUIRE(i1 >= 0 && i1 < mShape[0],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1),
+                      mShape[0]);
+    TV_DEVICE_REQUIRE(i2 >= 0 && i2 < mShape[1],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2),
+                      mShape[1]);
+    TV_DEVICE_REQUIRE(i3 >= 0 && i3 < mShape[2],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 2, int(i3),
+                      mShape[2]);
+    TV_DEVICE_REQUIRE(i4 >= 0 && i4 < mShape[3],
+                      "index-%d(%d) out-of-range: [0, %d)\n", 3, int(i4),
+                      mShape[3]);
+#else
+    TV_REQUIRE(mShape.ndim() == 4, "you provide 4 indexes, but dim is %ld\n",
+               mShape.ndim());
+    TV_REQUIRE(i1 >= 0 && i1 < mShape[0],
+               "index-%d(%d) out-of-range: [0, %d)\n", 0, int(i1), mShape[0]);
+    TV_REQUIRE(i2 >= 0 && i2 < mShape[1],
+               "index-%d(%d) out-of-range: [0, %d)\n", 1, int(i2), mShape[1]);
+    TV_REQUIRE(i3 >= 0 && i3 < mShape[2],
+               "index-%d(%d) out-of-range: [0, %d)\n", 2, int(i3), mShape[2]);
+    TV_REQUIRE(i4 >= 0 && i4 < mShape[3],
+               "index-%d(%d) out-of-range: [0, %d)\n", 3, int(i4), mShape[3]);
+#endif
+#endif
+    return mPtr[((i1 * mShape[1] + i2) * mShape[2] + i3) * mShape[3] + i4];
+  }
+
+  TV_HOST_DEVICE_INLINE T &operator[](int idx) {
+#ifdef TV_DEBUG
+#if defined(__CUDA_ARCH__)
+    TV_DEVICE_REQUIRE(idx >= 0 && idx < size(),
+                      "index(%d) out-of-range: [0, %ld)\n", int(idx), size());
+#else
+    TV_REQUIRE(idx >= 0 && idx < size(), "index(%d) out-of-range: [0, %ld)\n",
+               int(idx), size());
+#endif
+#endif
+    return mPtr[idx];
+  }
+  // TODO: this is conflcit with operator[](SimpleVector<Slice> slice_vec).
+  /*TV_HOST_DEVICE_INLINE T &operator[](const Shape index) {
+    int idx = rowArrayIdx(mShape, index);
+#ifdef TV_DEBUG
+    TV_REQUIRE(idx >= 0 && idx < size(), "index(%d) out-of-range: [0, %ld)\n",
+                int(idx), size());
+#endif
+    return mPtr[idx];
+  }
+  TV_HOST_DEVICE_INLINE const T &operator[](const Shape index) const {
+    int idx = rowArrayIdx(mShape, index);
+#ifdef TV_DEBUG
+    TV_REQUIRE(idx >= 0 && idx < size(), "index(%d) out-of-range: [0, %ld)\n",
+                int(idx), size());
+#endif
+    return mPtr[idx];
+  }*/
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank>
+  operator[](SimpleVector<Slice> slice_vec) {
+    return _subview(slice_vec);
+  }
+  TV_HOST_DEVICE_INLINE const TensorView<T, Rank>
+  operator[](SimpleVector<Slice> slice_vec) const {
+    return _subview(slice_vec);
+  }
+  TV_HOST_DEVICE_INLINE bool empty() const { return mPtr == nullptr; }
+  TV_HOST_DEVICE_INLINE T *data() { return mPtr; }
+  TV_HOST_DEVICE_INLINE const T *data() const { return mPtr; }
+  TV_HOST_DEVICE_INLINE const Shape &shape() const { return mShape; }
+  TV_HOST_DEVICE_INLINE int dim(int idx) const { return mShape[idx]; }
+  TV_HOST_DEVICE_INLINE int ndim() const { return mShape.ndim(); }
+  template <class... Inds>
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> &reshape(Inds... newShapes) {
+    Shape shapes{int(newShapes)...};
+    TV_ASSERT(shapes.size() == size());
+    mShape = shapes;
+    return *this;
+  }
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> &reshape(Shape shapes) {
+    TV_ASSERT(shapes.size() == size());
+    mShape = shapes;
+    return *this;
+  }
+  template <class... Inds>
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> view(Inds... newShapes) const {
+    Shape shapes{int(newShapes)...};
+    for (size_t i = 0; i < shapes.ndim(); ++i) {
+      if (shapes[i] == -1) {
+        shapes[i] = 1;
+        shapes[i] = size() / shapes.size();
+        break;
+      }
+    }
+    TV_ASSERT(shapes.size() == size());
+    return TensorView<T, Rank>(mPtr, shapes);
+  }
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> view(Shape shapes) const {
+    TV_ASSERT(shapes.size() == size());
+    return TensorView<T, Rank>(mPtr, shapes);
+  }
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> squeeze() const {
+    return TensorView<T, Rank>(mPtr, mShape.squeeze());
+  }
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> squeeze(int dim) const {
+    return TensorView<T, Rank>(mPtr, mShape.squeeze(dim));
+  }
+  TV_HOST_DEVICE_INLINE size_t size() const { return mShape.size(); }
+
+  template <class... Slices>
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> subview(Slice slice,
+                                                    Slices... slices) const {
+    return subview<float, Slice, Slices...>(slice, slices...);
+  }
+  template <class T2 = float, class... Slices>
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> subview(Slices... slices) const {
+    Slice slice_vec[sizeof...(Slices)] = {to_slice(slices)...};
+    Shape new_shape{to_slice(slices)[0]...};
+    Shape start{to_slice(slices)[0]...};
+    TV_ASSERT(new_shape.ndim() <= mShape.ndim());
+    TV_ASSERT(new_shape.ndim() != 0);
+    size_t idxsize = new_shape.ndim();
+    for (size_t i = idxsize; i < mShape.ndim(); ++i) {
+      new_shape.push_back(0);
+      start.push_back(0);
+    }
+#pragma unroll
+    for (size_t i = 0; i < sizeof...(Slices); ++i) {
+      if (slice_vec[i][1] != -1) {
+        new_shape[i] = slice_vec[i][1] - slice_vec[i][0];
+        TV_ASSERT(new_shape[i] >= 0);
+      } else {
+        new_shape[i] = 1; // reduce dim
+      }
+    }
+    auto offset = rowArrayIdx(mShape, start);
+#pragma unroll
+    for (size_t i = sizeof...(Slices); i < mShape.ndim(); ++i) {
+      new_shape[i] = mShape[i];
+      TV_ASSERT(new_shape[i] >= 0);
+    }
+    Shape reduced_shape;
+#pragma unroll
+    for (size_t i = 0; i < sizeof...(Slices); ++i) {
+      if (slice_vec[i][1] != -1) {
+        reduced_shape.push_back(new_shape[i]);
+      }
+    }
+#pragma unroll
+    for (size_t i = sizeof...(Slices); i < mShape.ndim(); ++i) {
+      reduced_shape.push_back(new_shape[i]);
+    }
+    return TensorView<T, Rank>(mPtr + offset, reduced_shape);
+  }
+
+  template <class... Integers>
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank> subview(int id, Integers... ints) {
+    Shape start = {id, ints...};
+    for (int i = 1 + sizeof...(ints); i < ndim(); ++i) {
+      start.push_back(0);
+    }
+    return TensorView<T, Rank>(mPtr + rowArrayIdx(mShape, start),
+                               mShape.subshape(sizeof...(ints) + 1));
+  }
+
+  std::string repr() const {
+    std::ostringstream ss;
+    if (empty())
+      return "";
+    if (mShape.ndim() == 0) {
+      ss << *mPtr;
+      // ss << fmt::format("\nTensor: shape={}, dtype={}", mShape,
+      // detail::simpleTypeName<T>());
+      ss << "Tensor: dtype=" << detail::simpleTypeName<T>();
+      return ss.str();
+    }
+    Shape counter = mShape;
+    auto tensor_flat = this->view(-1);
+    for (int i = 0; i < counter.ndim(); ++i) {
+      counter[i] = 0;
+      ss << "[";
+    }
+    for (size_t i = 0; i < this->size(); ++i) {
+      ss << tensor_flat(rowArrayIdx(mShape, counter));
+      counter[counter.ndim() - 1] += 1;
+      int inc_count = 0;
+      bool print_comma = true;
+      for (int c = counter.ndim() - 1; c >= 0; --c) {
+        if (counter[c] == this->dim(c) && c > 0) {
+          ++inc_count;
+          counter[c - 1] += 1;
+          counter[c] = 0;
+          print_comma = false;
+        }
+      }
+      if (print_comma && i != this->size() - 1)
+        ss << ", ";
+      for (int j = 0; j < inc_count; ++j) {
+        ss << "]";
+      }
+      if (i != this->size() - 1) {
+        if (inc_count != 0)
+          ss << "\n";
+        for (int j = 0; j < inc_count; ++j) {
+          ss << "[";
+        }
+      }
+    }
+    ss << "]";
+    // ss << fmt::format("\nTensor: shape={}, dtype={}", mShape,
+    // detail::simpleTypeName<T>());
+    ss << "Tensor: dtype=" << detail::simpleTypeName<T>();
+    return ss.str();
+  }
+
+protected:
+  // TODO: make this function public.
+  // currently this function is called unexpectedly when using subview({0, 0}).
+  TV_HOST_DEVICE_INLINE TensorView<T, Rank>
+  _subview(SimpleVector<Slice> slice_vec) {
+    Shape new_shape;
+    for (int i = 0; i < slice_vec.size(); ++i) {
+      new_shape.push_back(slice_vec[i][0]);
+    }
+    Shape start = new_shape;
+    TV_ASSERT(new_shape.ndim() <= mShape.ndim());
+    TV_ASSERT(new_shape.ndim() != 0);
+    size_t idxsize = new_shape.ndim();
+    for (size_t i = idxsize; i < mShape.ndim(); ++i) {
+      new_shape.push_back(0);
+      start.push_back(0);
+    }
+    for (size_t i = 0; i < slice_vec.size(); ++i) {
+      if (slice_vec[i][1] != -1) {
+        new_shape[i] = slice_vec[i][1] - slice_vec[i][0];
+        TV_ASSERT(new_shape[i] >= 0);
+      } else {
+        new_shape[i] = 1; // reduce dim
+      }
+    }
+    auto offset = rowArrayIdx(mShape, start);
+    for (size_t i = slice_vec.size(); i < mShape.ndim(); ++i) {
+      new_shape[i] = mShape[i];
+      TV_ASSERT(new_shape[i] >= 0);
+    }
+    Shape reduced_shape;
+    for (size_t i = 0; i < slice_vec.size(); ++i) {
+      if (slice_vec[i][1] != -1) {
+        reduced_shape.push_back(new_shape[i]);
+      }
+    }
+    for (size_t i = slice_vec.size(); i < mShape.ndim(); ++i) {
+      reduced_shape.push_back(new_shape[i]);
+    }
+    return TensorView<T, Rank>(mPtr + offset, reduced_shape);
+  }
+  template <typename T1> TV_HOST_DEVICE_INLINE Slice to_slice(T1 s) const {
+    return Slice{int(s), -1, -1};
+  }
+
+  TV_HOST_DEVICE_INLINE Slice to_slice(Slice s) const { return Slice(s); }
+
+  T *mPtr = nullptr;
+  Shape mShape;
+};
+
+template <typename Os, typename T, int Rank>
+Os &operator<<(Os &os, const TensorView<T, Rank> &dt) {
+  os << dt.repr();
+  return os;
+}
+
+template <typename Os, typename T, int Rank>
+Os &operator<<(Os &os, const TensorView<const T, Rank> &dt) {
+  os << dt.repr();
+  return os;
+}
+
+namespace detail {
+template <typename T> constexpr const char *printfTypeFormat(T val = T());
+template <> constexpr const char *printfTypeFormat(float val) { return "%.2f"; }
+// template <> constexpr const char *printfTypeFormat(double val) {
+//   return "%.2f";
+// }
+template <> constexpr const char *printfTypeFormat(int val) { return "%d"; }
+template <> constexpr const char *printfTypeFormat(unsigned val) {
+  return "%u";
+}
+template <> constexpr const char *printfTypeFormat(long val) { return "%ld"; }
+template <> constexpr const char *printfTypeFormat(unsigned long val) {
+  return "%lu";
+}
+}; // namespace detail
+
+template <typename T>
+TV_HOST_DEVICE void printTensorView(const TensorView<T> tensor,
+                                    const char *format) {
+  if (tensor.empty())
+    return;
+  if (tensor.ndim() == 0) {
+    printf(format, tensor());
+    printf("\n");
+    return;
+  }
+  Shape counter = tensor.shape();
+  auto tensor_flat = tensor.view(-1);
+  for (int i = 0; i < counter.ndim(); ++i) {
+    counter[i] = 0;
+    printf("[");
+  }
+  for (size_t i = 0; i < tensor.size(); ++i) {
+    printf(format, tensor_flat(rowArrayIdx(tensor.shape(), counter)));
+    counter[counter.ndim() - 1] += 1;
+    int inc_count = 0;
+    bool print_comma = true;
+    for (int c = counter.ndim() - 1; c >= 0; --c) {
+      if (counter[c] == tensor.dim(c) && c > 0) {
+        ++inc_count;
+        counter[c - 1] += 1;
+        counter[c] = 0;
+        print_comma = false;
+      }
+    }
+    if (print_comma && i != tensor.size() - 1)
+      printf(", ");
+    for (int j = 0; j < inc_count; ++j) {
+      printf("]");
+    }
+    if (i != tensor.size() - 1) {
+      if (inc_count != 0)
+        printf("\n");
+      for (int j = 0; j < inc_count; ++j) {
+        printf("[");
+      }
+    }
+  }
+  printf("]\n");
+}
+
+template <typename T>
+TV_HOST_DEVICE void printTensorView(TensorView<T> tensor) {
+  using Traw = typename std::remove_const<T>::type;
+  return printTensorView(tensor, detail::printfTypeFormat<Traw>());
+}
+template <typename T>
+TV_HOST_DEVICE void printTensorView(const T *ptr, Shape shape) {
+  using Traw = typename std::remove_const<T>::type;
+  return printTensorView(TensorView<const T>(ptr, shape),
+                         detail::printfTypeFormat<Traw>());
+}
+template <typename T>
+TV_HOST_DEVICE void printTensorView(const T *ptr, Shape shape,
+                                    const char *format) {
+  return printTensorView(TensorView<const T>(ptr, shape), format);
+}
+
+} // namespace tv
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/torch_utils.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/torch_utils.h
new file mode 100644
index 000000000..28d58096c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/torch_utils.h
@@ -0,0 +1,66 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <tensorview/tensorview.h>
+#include <torch/script.h>
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+namespace tv {
+
+struct TorchGPU: public tv::GPU {
+  TorchGPU(){
+    mStream = at::cuda::getCurrentCUDAStream();
+  }
+};
+
+template <typename T> void check_torch_dtype(const torch::Tensor &tensor) {
+  switch (tensor.type().scalarType()) {
+  // case at::ScalarType::Double: {
+  //   auto val = std::is_same<std::remove_const_t<T>, double>::value;
+  //   TV_ASSERT_RT_ERR(val, "error");
+  //   break;
+  // }
+  case at::ScalarType::Float: {
+    auto val = std::is_same<std::remove_const_t<T>, float>::value;
+    TV_ASSERT_RT_ERR(val, "error");
+    break;
+  }
+  case at::ScalarType::Int: {
+    auto val = std::is_same<std::remove_const_t<T>, int>::value;
+    TV_ASSERT_RT_ERR(val, "error");
+    break;
+  }
+  case at::ScalarType::Half: {
+    auto val = std::is_same<std::remove_const_t<T>, at::Half>::value;
+    TV_ASSERT_RT_ERR(val, "error");
+    break;
+  }
+
+  default:
+    TV_ASSERT_RT_ERR(false, "error");
+  }
+}
+
+template <typename T>
+tv::TensorView<T> torch2tv(const torch::Tensor &tensor) {
+  check_torch_dtype<T>(tensor);
+  tv::Shape shape;
+  for (auto i : tensor.sizes()) {
+    shape.push_back(i);
+  }
+  return tv::TensorView<T>(tensor.data<std::remove_const_t<T>>(), shape);
+}
+} // namespace tv
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/utility/timer.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/utility/timer.h
new file mode 100644
index 000000000..43a41a602
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/include/utility/timer.h
@@ -0,0 +1,54 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <chrono>
+#include <cuda_runtime_api.h>
+#include <iostream>
+
+namespace spconv {
+
+template <typename TimeT = std::chrono::microseconds> struct CudaContextTimer {
+  CudaContextTimer() {
+    cudaDeviceSynchronize();
+    mCurTime = std::chrono::steady_clock::now();
+  }
+  typename TimeT::rep report() {
+    cudaDeviceSynchronize();
+    auto duration = std::chrono::duration_cast<TimeT>(
+        std::chrono::steady_clock::now() - mCurTime);
+    auto res = duration.count();
+    mCurTime = std::chrono::steady_clock::now();
+    return res;
+  }
+
+private:
+  std::chrono::time_point<std::chrono::steady_clock> mCurTime;
+};
+
+template <typename TimeT = std::chrono::microseconds> struct CPUTimer {
+  CPUTimer() { mCurTime = std::chrono::steady_clock::now(); }
+  typename TimeT::rep report() {
+    auto duration = std::chrono::duration_cast<TimeT>(
+        std::chrono::steady_clock::now() - mCurTime);
+    auto res = duration.count();
+    mCurTime = std::chrono::steady_clock::now();
+    return res;
+  }
+
+private:
+  std::chrono::time_point<std::chrono::steady_clock> mCurTime;
+};
+
+} // namespace spconv
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/install_spconv.sh b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/install_spconv.sh
new file mode 100644
index 000000000..391156034
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/install_spconv.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+
+TARGET_DIR=${TARGET_DIR:-}
+
+PYTHON_PATH=$(which python3)
+PYTHON_DIST_PATH=${TARGET_DIR}/lib/python3/dist-packages
+
+PKG_DIR="build_pip"
+PKG_NAME="spconv"
+
+if [[ ! -d ${PKG_DIR} ]]; then
+  echo "ERROR: Package directory ${PKG_DIR} doesn't exist"
+  exit 1
+fi
+
+cd ${PKG_DIR}
+
+latest_pkg="$(ls -t | grep ${PKG_NAME} | head -1)"
+if [[ "${latest_pkg}" == "" ]]; then
+  echo "ERROR: Cannot find latest ${PKG_NAME} package"
+  exit 1
+else
+  echo "INFO: Found latest package ${latest_pkg} in directory"
+fi
+
+if [[ "${TARGET_DIR}" != ""  ]]; then
+  ${PYTHON_PATH} -m pip install --upgrade --no-deps -t ${PYTHON_DIST_PATH} ${latest_pkg} || exit
+  echo "spconv installed in ${PYTHON_DIST_PATH}; please add it to your PYTHONPATH."
+else
+  ${PYTHON_PATH} -m pip uninstall ${PKG_NAME} -y
+  ${PYTHON_PATH} -m pip install --no-deps ${latest_pkg} || exit
+fi
+
+# Return 0 status if all finished
+exit 0
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/setup.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/setup.py
new file mode 100644
index 000000000..db9717d1b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/setup.py
@@ -0,0 +1,89 @@
+import os
+import re
+import sys
+import platform
+import subprocess
+import torch
+from setuptools import setup, Extension, find_packages
+from setuptools.command.build_ext import build_ext
+from distutils.version import LooseVersion
+
+from pathlib import Path
+
+# if 'LIBTORCH_ROOT' not in os.environ:
+#     raise ValueError("You must set LIBTORCH_ROOT to your torch c++ library.")
+
+LIBTORCH_ROOT = str(Path(torch.__file__).parent)
+
+PYTHON_VERSION = "{}.{}".format(sys.version_info.major, sys.version_info.minor)
+
+
+class CMakeExtension(Extension):
+    def __init__(self, name, sourcedir='', library_dirs=[]):
+        Extension.__init__(self, name, sources=[], library_dirs=library_dirs)
+        self.sourcedir = os.path.abspath(sourcedir)
+
+
+class CMakeBuild(build_ext):
+    def run(self):
+        try:
+            out = subprocess.check_output(['cmake', '--version'])
+        except OSError:
+            raise RuntimeError("CMake must be installed to build the following extensions: " +
+                               ", ".join(e.name for e in self.extensions))
+
+        if platform.system() == "Windows":
+            raise NotImplementedError
+
+        for ext in self.extensions:
+            self.build_extension(ext)
+
+    def build_extension(self, ext):
+        extdir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.name)))
+        print(extdir)
+        cmake_args = ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + extdir + "/spconv",
+                      '-DCMAKE_PREFIX_PATH=' + LIBTORCH_ROOT,
+                      '-DPYBIND11_PYTHON_VERSION={}'.format(PYTHON_VERSION),
+                      '-DSPCONV_BuildTests=OFF',
+                      '-DCMAKE_CUDA_FLAGS="--expt-relaxed-constexpr"']
+        print ("cmake_args",cmake_args)
+        
+        cfg = 'Debug' if self.debug else 'Release'
+        # cfg = 'Debug'
+        build_args = ['--config', cfg]
+        print(cfg)
+        if platform.system() == "Windows":
+            cmake_args += ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}'.format(cfg.upper(), extdir)]
+            if sys.maxsize > 2**32:
+                cmake_args += ['-A', 'x64']
+            build_args += ['--', '/m']
+        else:
+            cmake_args += ['-DCMAKE_BUILD_TYPE=' + cfg]
+            build_args += ['--', '-j4']
+
+        env = os.environ.copy()
+        env['CXXFLAGS'] = '{} -DVERSION_INFO=\\"{}\\"'.format(env.get('CXXFLAGS', ''),
+                                                              self.distribution.get_version())
+        if not os.path.exists(self.build_temp):
+            os.makedirs(self.build_temp)
+        
+        subprocess.check_call(['cmake', ext.sourcedir] + cmake_args, cwd=self.build_temp, env=env)
+        subprocess.check_call(['cmake', '--build', '.'] + build_args, cwd=self.build_temp)
+
+
+packages = find_packages(exclude=('tools', 'tools.*'))
+setup(
+    name='spconv',
+    version='1.0',
+    author='Yan Yan',
+    author_email='scrin@foxmail.com',
+    description='spatial sparse convolution for pytorch',
+    long_description='',
+    setup_requires = ['torch>=1.0.0'],
+    packages=packages,
+    package_dir = {'spconv': 'spconv'},
+    ext_modules=[CMakeExtension('spconv', library_dirs=[])],
+    cmdclass=dict(build_ext=CMakeBuild),
+    zip_safe=False,
+)
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/__init__.py
new file mode 100644
index 000000000..f94eac225
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/__init__.py
@@ -0,0 +1,97 @@
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+
+import numpy as np
+import torch
+from spconv import utils
+from spconv.conv import SparseConv2d, SparseConv3d, SubMConv2d, SubMConv3d
+from spconv.conv import SparseConvTranspose2d, SparseConvTranspose3d
+from spconv.conv import SparseInverseConv2d, SparseInverseConv3d
+from spconv.modules import SparseModule, SparseSequential
+from spconv.pool import SparseMaxPool2d, SparseMaxPool3d
+
+_LIB_PATH = str(Path(__file__).parent / "libspconv.so")
+torch.ops.load_library(_LIB_PATH)
+
+def scatter_nd(indices, updates, shape):
+    """pytorch edition of tensorflow scatter_nd.
+    this function don't contain except handle code. so use this carefully
+    when indice repeats, don't support repeat add which is supported
+    in tensorflow.
+    """
+    ret = torch.zeros(*shape, dtype=updates.dtype, device=updates.device)
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + shape[indices.shape[-1]:]
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    ret[slices] = updates.view(*output_shape)
+    return ret
+
+class SparseConvTensor(object):
+    def __init__(self, features, indices, spatial_shape, batch_size, grid=None):
+        """
+        Args:
+            grid: pre-allocated grid tensor. should be used when the volume of spatial shape
+                is very large.
+        """
+        self.features = features
+        self.indices = indices 
+        if self.indices.dtype != torch.int32:
+            self.indices.int()
+        self.spatial_shape = spatial_shape
+        self.batch_size = batch_size
+        self.indice_dict = {}
+        self.grid = grid
+
+    @property
+    def spatial_size(self):
+        return np.prod(self.spatial_shape)
+
+    def find_indice_pair(self, key):
+        if key is None:
+            return None 
+        if key in self.indice_dict:
+            return self.indice_dict[key]
+        return None
+
+    def dense(self, channels_first=True):
+        output_shape = [self.batch_size] + list(self.spatial_shape) + [self.features.shape[1]]
+        res = scatter_nd(self.indices.long(), self.features, output_shape)
+        if not channels_first:
+            return res
+        ndim = len(self.spatial_shape)
+        trans_params = list(range(0, ndim + 1))
+        trans_params.insert(1, ndim + 1)
+        return res.permute(*trans_params).contiguous()
+
+    @property
+    def sparity(self):
+        return self.indices.shape[0] / np.prod(self.spatial_shape) / self.batch_size
+
+
+class ToDense(SparseModule):
+    """convert SparseConvTensor to NCHW dense tensor.
+    """
+    def forward(self, x: SparseConvTensor):
+        return x.dense()
+
+class RemoveGrid(SparseModule):
+    """remove pre-allocated grid buffer.
+    """
+    def forward(self, x: SparseConvTensor):
+        x.grid = None
+        return x
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/conv.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/conv.py
new file mode 100644
index 000000000..18b6ac202
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/conv.py
@@ -0,0 +1,355 @@
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import time
+
+import numpy as np
+import spconv
+import spconv.functional as Fsp
+import torch
+from spconv import ops
+from spconv.modules import SparseModule
+from torch import nn
+from torch.nn import init
+from torch.nn.parameter import Parameter
+
+
+def _calculate_fan_in_and_fan_out_hwio(tensor):
+    dimensions = tensor.ndimension()
+    if dimensions < 2:
+        raise ValueError(
+            "Fan in and fan out can not be computed for tensor with fewer than 2 dimensions"
+        )
+
+    if dimensions == 2:  # Linear
+        fan_in = tensor.size(-2)
+        fan_out = tensor.size(-1)
+    else:
+        num_input_fmaps = tensor.size(-2)
+        num_output_fmaps = tensor.size(-1)
+        receptive_field_size = 1
+        if tensor.dim() > 2:
+            receptive_field_size = tensor[..., 0, 0].numel()
+        fan_in = num_input_fmaps * receptive_field_size
+        fan_out = num_output_fmaps * receptive_field_size
+
+    return fan_in, fan_out
+
+
+class SparseConvolution(SparseModule):
+    def __init__(self,
+                 ndim,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=True,
+                 subm=False,
+                 output_padding=0,
+                 transposed=False,
+                 inverse=False,
+                 indice_key=None):
+        super(SparseConvolution, self).__init__()
+        assert groups == 1
+        if not isinstance(kernel_size, (list, tuple)):
+            kernel_size = [kernel_size] * ndim
+        if not isinstance(stride, (list, tuple)):
+            stride = [stride] * ndim
+        if not isinstance(padding, (list, tuple)):
+            padding = [padding] * ndim
+        if not isinstance(dilation, (list, tuple)):
+            dilation = [dilation] * ndim
+        if not isinstance(output_padding, (list, tuple)):
+            output_padding = [output_padding] * ndim
+
+        for d, s in zip(dilation, stride):
+            assert any([s == 1, d == 1]), "don't support this."
+
+        self.ndim = ndim
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.conv1x1 = np.prod(kernel_size) == 1
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.transposed = transposed
+        self.inverse = inverse
+        self.output_padding = output_padding
+        self.groups = groups
+        self.subm = subm
+        self.indice_key = indice_key
+
+        self.weight = Parameter(
+            torch.Tensor(*kernel_size, in_channels, out_channels))
+        if bias:
+            self.bias = Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        n = self.in_channels
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        if self.bias is not None:
+            fan_in, _ = _calculate_fan_in_and_fan_out_hwio(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, input):
+        assert isinstance(input, spconv.SparseConvTensor)
+        features = input.features
+        device = features.device
+        indices = input.indices
+        spatial_shape = input.spatial_shape
+        batch_size = input.batch_size
+        if not self.subm:
+            if self.transposed:
+                out_spatial_shape = ops.get_deconv_output_size(
+                    spatial_shape, self.kernel_size, self.stride, self.padding, self.dilation, self.output_padding)
+            else:
+                out_spatial_shape = ops.get_conv_output_size(
+                    spatial_shape, self.kernel_size, self.stride, self.padding, self.dilation)
+
+        else:
+            out_spatial_shape = spatial_shape
+        # input.update_grid(out_spatial_shape)
+        # t = time.time()
+        if self.conv1x1:
+            input.features = torch.mm(
+                input.features,
+                self.weight.view(self.in_channels, self.out_channels))
+            if self.bias is not None:
+                input.features += self.bias
+            return input
+        datas = input.find_indice_pair(self.indice_key)
+        if self.inverse:
+            assert datas is not None and self.indice_key is not None
+            _, outids, indice_pairs, indice_pair_num, out_spatial_shape = datas
+            assert indice_pairs.shape[0] == np.prod(self.kernel_size), "inverse conv must have same kernel size as its couple conv"
+        else:
+            if self.indice_key is not None and datas is not None:
+                outids, _, indice_pairs, indice_pair_num, _ = datas
+            else:
+                outids, indice_pairs, indice_pair_num = ops.get_indice_pairs(
+                    indices, batch_size, spatial_shape, self.kernel_size,
+                    self.stride, self.padding, self.dilation, self.output_padding, self.subm, self.transposed, grid=input.grid)
+                input.indice_dict[self.indice_key] = (outids, indices, indice_pairs, indice_pair_num, spatial_shape)
+        if self.subm:
+            out_features = Fsp.indice_subm_conv(features, self.weight,
+                                              indice_pairs.to(device),
+                                              indice_pair_num,
+                                              outids.shape[0])
+        else:
+            if self.inverse:
+                out_features = Fsp.indice_inverse_conv(features,
+                                            self.weight, indice_pairs.to(device),
+                                            indice_pair_num, outids.shape[0])
+            else:
+                out_features = Fsp.indice_conv(features,
+                                            self.weight, indice_pairs.to(device),
+                                            indice_pair_num, outids.shape[0])
+
+        if self.bias is not None:
+            out_features += self.bias
+        out_tensor = spconv.SparseConvTensor(out_features, outids,
+                                             out_spatial_shape, batch_size)
+        out_tensor.indice_dict = input.indice_dict
+        out_tensor.grid = input.grid
+        return out_tensor
+
+
+class SparseConv2d(SparseConvolution):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=True,
+                 indice_key=None):
+        super(SparseConv2d, self).__init__(
+            2,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            indice_key=indice_key)
+
+
+class SparseConv3d(SparseConvolution):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=True,
+                 indice_key=None):
+        super(SparseConv3d, self).__init__(
+            3,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            indice_key=indice_key)
+
+class SparseConvTranspose2d(SparseConvolution):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=True,
+                 indice_key=None):
+        super(SparseConvTranspose2d, self).__init__(
+            2,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            transposed=True,
+            indice_key=indice_key)
+
+
+class SparseConvTranspose3d(SparseConvolution):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=True,
+                 
+                 indice_key=None):
+        super(SparseConvTranspose3d, self).__init__(
+            3,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            transposed=True,
+            indice_key=indice_key)
+
+class SparseInverseConv2d(SparseConvolution):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 indice_key,
+                 bias=True):
+        super(SparseInverseConv2d, self).__init__(
+            2,
+            in_channels,
+            out_channels,
+            kernel_size,
+            bias=bias,
+            inverse=True,
+            indice_key=indice_key)
+
+
+class SparseInverseConv3d(SparseConvolution):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 indice_key,
+                 bias=True):
+        super(SparseInverseConv3d, self).__init__(
+            3,
+            in_channels,
+            out_channels,
+            kernel_size,
+            bias=bias,
+            inverse=True,
+            indice_key=indice_key)
+
+
+class SubMConv2d(SparseConvolution):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=True,
+                 indice_key=None):
+        super(SubMConv2d, self).__init__(
+            2,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            True,
+            indice_key=indice_key)
+
+
+class SubMConv3d(SparseConvolution):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=True,
+                 indice_key=None):
+        super(SubMConv3d, self).__init__(
+            3,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            True,
+            indice_key=indice_key)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/functional.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/functional.py
new file mode 100644
index 000000000..b0a8f76ff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/functional.py
@@ -0,0 +1,118 @@
+
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import spconv.ops as ops
+import torch
+from torch import nn
+from torch.autograd import Function
+
+
+class SparseConvFunction(Function):
+    @staticmethod
+    def forward(
+            ctx,
+            features,
+            filters,
+            indice_pairs,
+            indice_pair_num,
+            num_activate_out):
+        ctx.save_for_backward(
+            indice_pairs,
+            indice_pair_num,
+            features,
+            filters)
+        return ops.indice_conv(features, filters, indice_pairs, indice_pair_num, num_activate_out, False)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        indice_pairs, indice_pair_num, features, filters = ctx.saved_tensors
+        input_bp, filters_bp = ops.indice_conv_backward(features, filters, grad_output, indice_pairs, indice_pair_num, False)
+        
+        return input_bp, filters_bp, None, None, None
+
+class SparseInverseConvFunction(Function):
+    @staticmethod
+    def forward(
+            ctx,
+            features,
+            filters,
+            indice_pairs,
+            indice_pair_num,
+            num_activate_out):
+        ctx.save_for_backward(
+            indice_pairs,
+            indice_pair_num,
+            features,
+            filters)
+        return ops.indice_conv(features, filters, indice_pairs, indice_pair_num, num_activate_out, True, False)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        indice_pairs, indice_pair_num, features, filters = ctx.saved_tensors
+        input_bp, filters_bp = ops.indice_conv_backward(features, filters, grad_output, indice_pairs, indice_pair_num, True, False)
+        
+        return input_bp, filters_bp, None, None, None
+
+
+class SubMConvFunction(Function):
+    @staticmethod
+    def forward(
+            ctx,
+            features,
+            filters,
+            indice_pairs,
+            indice_pair_num,
+            num_activate_out):
+        ctx.save_for_backward(
+            indice_pairs,
+            indice_pair_num,
+            features,
+            filters)
+        return ops.indice_conv(features, filters, indice_pairs, indice_pair_num, num_activate_out, False, True)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        indice_pairs, indice_pair_num, features, filters = ctx.saved_tensors
+        input_bp, filters_bp = ops.indice_conv_backward(features, filters, grad_output, indice_pairs, indice_pair_num, False, True)
+        
+        return input_bp, filters_bp, None, None, None
+
+
+class SparseMaxPoolFunction(Function):
+    @staticmethod
+    def forward(
+            ctx,
+            features,
+            indice_pairs,
+            indice_pair_num,
+            num_activate_out):
+        out = ops.indice_maxpool(features, indice_pairs, indice_pair_num, num_activate_out)
+        ctx.save_for_backward(
+            indice_pairs,
+            indice_pair_num,
+            features,
+            out)
+        return out
+    @staticmethod
+    def backward(ctx, grad_output):
+        indice_pairs, indice_pair_num, features, out = ctx.saved_tensors
+        input_bp = ops.indice_maxpool_backward(features, out, grad_output, indice_pairs, indice_pair_num)
+        return input_bp, None, None, None
+
+
+indice_conv = SparseConvFunction.apply
+indice_inverse_conv = SparseInverseConvFunction.apply
+indice_subm_conv = SubMConvFunction.apply
+indice_maxpool = SparseMaxPoolFunction.apply
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/modules.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/modules.py
new file mode 100644
index 000000000..01d45787c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/modules.py
@@ -0,0 +1,130 @@
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import OrderedDict
+
+import spconv
+import torch
+from torch import nn
+import time 
+
+def is_spconv_module(module):
+    spconv_modules = (SparseModule,)
+    return isinstance(module, spconv_modules)
+
+def _mean_update(vals, m_vals, t):
+    outputs = []
+    if not isinstance(vals, list):
+        vals = [vals]
+    if not isinstance(m_vals, list):
+        m_vals = [m_vals]
+    for val, m_val in zip(vals, m_vals):
+        output = t / float(t + 1) * m_val + 1 / float(t + 1) * val
+        outputs.append(output)
+    if len(outputs) == 1:
+        outputs = outputs[0]
+    return outputs
+
+
+class SparseModule(nn.Module):
+    """ place holder, all module subclass from this will take sptensor in SparseSequential.
+    """
+    pass
+
+
+class SparseSequential(SparseModule):
+    r"""A sequential container.
+    Modules will be added to it in the order they are passed in the constructor.
+    Alternatively, an ordered dict of modules can also be passed in.
+
+    To make it easier to understand, given is a small example::
+
+        # Example of using Sequential
+        model = SparseSequential(
+                  SparseConv2d(1,20,5),
+                  nn.ReLU(),
+                  SparseConv2d(20,64,5),
+                  nn.ReLU()
+                )
+
+        # Example of using Sequential with OrderedDict
+        model = SparseSequential(OrderedDict([
+                  ('conv1', SparseConv2d(1,20,5)),
+                  ('relu1', nn.ReLU()),
+                  ('conv2', SparseConv2d(20,64,5)),
+                  ('relu2', nn.ReLU())
+                ]))
+        
+        # Example of using Sequential with kwargs(python 3.6+)
+        model = SparseSequential(
+                  conv1=SparseConv2d(1,20,5),
+                  relu1=nn.ReLU(),
+                  conv2=SparseConv2d(20,64,5),
+                  relu2=nn.ReLU()
+                )
+    """
+
+    def __init__(self, *args, **kwargs):
+        super(SparseSequential, self).__init__()
+        if len(args) == 1 and isinstance(args[0], OrderedDict):
+            for key, module in args[0].items():
+                self.add_module(key, module)
+        else:
+            for idx, module in enumerate(args):
+                self.add_module(str(idx), module)
+        for name, module in kwargs.items():
+            if sys.version_info < (3, 6):
+                raise ValueError("kwargs only supported in py36+")
+            if name in self._modules:
+                raise ValueError("name exists.")
+            self.add_module(name, module)
+        self._sparity_dict = {}
+
+    def __getitem__(self, idx):
+        if not (-len(self) <= idx < len(self)):
+            raise IndexError('index {} is out of range'.format(idx))
+        if idx < 0:
+            idx += len(self)
+        it = iter(self._modules.values())
+        for i in range(idx):
+            next(it)
+        return next(it)
+
+    def __len__(self):
+        return len(self._modules)
+
+    @property 
+    def sparity_dict(self):
+        return self._sparity_dict
+
+    def add(self, module, name=None):
+        if name is None:
+            name = str(len(self._modules))
+            if name in self._modules:
+                raise KeyError("name exists")
+        self.add_module(name, module)
+
+    def forward(self, input):
+        for k, module in self._modules.items():
+            if is_spconv_module(module): # use SpConvTensor as input
+                assert isinstance(input, spconv.SparseConvTensor)
+                self._sparity_dict[k] = input.sparity
+                input = module(input)
+            else:
+                if isinstance(input, spconv.SparseConvTensor):
+                    if input.indices.shape[0] != 0:
+                        input.features = module(input.features)
+                else:
+                    input = module(input)
+        return input
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/ops.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/ops.py
new file mode 100644
index 000000000..2d9c2ac44
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/ops.py
@@ -0,0 +1,157 @@
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import spconv
+import torch
+
+
+def get_conv_output_size(input_size, kernel_size, stride, padding, dilation):
+    ndim = len(input_size)
+    output_size = []
+    for i in range(ndim):
+        size = (input_size[i] + 2 * padding[i] - dilation[i] *
+                (kernel_size[i] - 1) - 1) // stride[i] + 1
+        if kernel_size[i] == -1:
+            output_size.append(1)
+        else:
+            output_size.append(size)
+    return output_size
+
+
+def get_deconv_output_size(input_size, kernel_size, stride, padding, dilation,
+                            output_padding):
+    ndim = len(input_size)
+    output_size = []
+    for i in range(ndim):
+        if kernel_size[i] == -1:
+            raise ValueError("deconv don't support kernel_size < 0")
+        size = (input_size[i] - 1) * stride[i] - 2 * padding[i] + kernel_size[
+            i] + output_padding[i]
+        output_size.append(size)
+    return output_size
+
+
+def get_indice_pairs(indices,
+             batch_size,
+             spatial_shape,
+             ksize=3,
+             stride=1,
+             padding=0,
+             dilation=1,
+             out_padding=0,
+             subm=False,
+             transpose=False,
+             grid=None):
+    ndim = indices.shape[1] - 1
+    if not isinstance(ksize, (list, tuple)):
+        ksize = [ksize] * ndim
+    if not isinstance(stride, (list, tuple)):
+        stride = [stride] * ndim
+    if not isinstance(padding, (list, tuple)):
+        padding = [padding] * ndim
+    if not isinstance(dilation, (list, tuple)):
+        dilation = [dilation] * ndim
+    if not isinstance(out_padding, (list, tuple)):
+        out_padding = [out_padding] * ndim
+
+    for d, s in zip(dilation, stride):
+        assert any([s == 1, d == 1]), "don't support this."
+    
+    if not subm:
+        if transpose:
+            out_shape = get_deconv_output_size(spatial_shape, ksize, stride, padding,
+                                            dilation, out_padding)
+        else:
+            out_shape = get_conv_output_size(spatial_shape, ksize, stride, padding,
+                                            dilation)
+
+    else:
+        out_shape = spatial_shape
+    if grid is None:
+        if ndim == 2:
+            get_indice_pairs_func = torch.ops.spconv.get_indice_pairs_2d
+        elif ndim == 3:
+            get_indice_pairs_func = torch.ops.spconv.get_indice_pairs_3d
+        else:
+            raise NotImplementedError
+        return get_indice_pairs_func(indices, batch_size, out_shape, spatial_shape, ksize,
+                            stride, padding, dilation, out_padding, int(subm), int(transpose))
+    else:
+        if ndim == 2:
+            get_indice_pairs_func = torch.ops.spconv.get_indice_pairs_grid_2d
+        elif ndim == 3:
+            get_indice_pairs_func = torch.ops.spconv.get_indice_pairs_grid_3d
+        else:
+            raise NotImplementedError
+        return get_indice_pairs_func(indices, grid, batch_size, out_shape, spatial_shape, ksize,
+                            stride, padding, dilation, out_padding, int(subm), int(transpose))
+
+
+
+def indice_conv(features,
+              filters,
+              indice_pairs,
+              indice_pair_num,
+              num_activate_out,
+              inverse=False,
+              subm=False):
+    if filters.dtype == torch.float32:
+        return torch.ops.spconv.indice_conv_fp32(features, filters, indice_pairs,
+                                               indice_pair_num, num_activate_out,
+                                               int(inverse), int(subm))
+    elif filters.dtype == torch.half:
+        return torch.ops.spconv.indice_conv_half(features, filters, indice_pairs,
+                                               indice_pair_num, num_activate_out,
+                                               int(inverse), int(subm))
+    else:
+        raise NotImplementedError
+
+
+def indice_conv_backward(features,
+                       filters,
+                       out_bp,
+                       indice_pairs,
+                       indice_pair_num,
+                       inverse=False,
+                       subm=False):
+    if filters.dtype == torch.float32:
+        return torch.ops.spconv.indice_conv_backward_fp32(
+            features, filters, out_bp, indice_pairs, indice_pair_num, int(inverse), int(subm))
+    elif filters.dtype == torch.half:
+        return torch.ops.spconv.indice_conv_backward_half(
+            features, filters, out_bp, indice_pairs, indice_pair_num, int(inverse), int(subm))
+    else:
+        raise NotImplementedError
+
+
+def indice_maxpool(features, indice_pairs, indice_pair_num, num_activate_out):
+    if features.dtype == torch.float32:
+        return torch.ops.spconv.indice_maxpool_fp32(features, indice_pairs, indice_pair_num,
+                                                  num_activate_out)
+    elif features.dtype == torch.half:
+        return torch.ops.spconv.indice_maxpool_half(features, indice_pairs, indice_pair_num,
+                                                  num_activate_out)
+    else:
+        raise NotImplementedError
+
+
+def indice_maxpool_backward(features, out_features, out_bp, indice_pairs, indice_pair_num):
+    if features.dtype == torch.float32:
+        return torch.ops.spconv.indice_maxpool_backward_fp32(
+            features, out_features, out_bp, indice_pairs, indice_pair_num)
+    elif features.dtype == torch.half:
+        return torch.ops.spconv.indice_maxpool_backward_half(
+            features, out_features, out_bp, indice_pairs, indice_pair_num)
+    else:
+        raise NotImplementedError
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/pool.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/pool.py
new file mode 100644
index 000000000..b2e79868e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/pool.py
@@ -0,0 +1,105 @@
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import math
+import time
+
+import numpy as np
+import spconv
+import spconv.functional as Fsp
+import torch
+from spconv import ops
+from spconv.modules import SparseModule
+from torch import nn
+from torch.nn import init
+from torch.nn.parameter import Parameter
+
+
+class SparseMaxPool(SparseModule):
+    def __init__(self,
+                 ndim,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 subm=False):
+        super(SparseMaxPool, self).__init__()
+        if not isinstance(kernel_size, (list, tuple)):
+            kernel_size = [kernel_size] * ndim
+        if not isinstance(stride, (list, tuple)):
+            stride = [stride] * ndim
+        if not isinstance(padding, (list, tuple)):
+            padding = [padding] * ndim
+        if not isinstance(dilation, (list, tuple)):
+            dilation = [dilation] * ndim
+
+        self.ndim = ndim
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.subm = subm
+        self.dilation = dilation
+
+    def forward(self, input):
+        assert isinstance(input, spconv.SparseConvTensor)
+        features = input.features
+        device = features.device
+        indices = input.indices
+        spatial_shape = input.spatial_shape
+        batch_size = input.batch_size
+        if not self.subm:
+            out_spatial_shape = ops.get_conv_output_size(
+                spatial_shape, self.kernel_size, self.stride, self.padding, self.dilation)
+        else:
+            out_spatial_shape = spatial_shape
+        outids, indice_pairs, indice_pairs_num = ops.get_indice_pairs(
+            indices, batch_size, spatial_shape, self.kernel_size,
+            self.stride, self.padding, self.dilation, 0, self.subm)
+        
+        out_features = Fsp.indice_maxpool(features, indice_pairs.to(device),
+                                        indice_pairs_num.to(device), outids.shape[0])
+        out_tensor = spconv.SparseConvTensor(out_features, outids,
+                                             out_spatial_shape, batch_size)
+        out_tensor.indice_dict = input.indice_dict
+        out_tensor.grid = input.grid
+        return out_tensor
+
+
+class SparseMaxPool2d(SparseMaxPool):
+    def __init__(self,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1):
+        super(SparseMaxPool2d, self).__init__(
+            2,
+            kernel_size,
+            stride,
+            padding,
+            dilation)
+
+
+class SparseMaxPool3d(SparseMaxPool):
+    def __init__(self,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1):
+        super(SparseMaxPool3d, self).__init__(
+            3,
+            kernel_size,
+            stride,
+            padding,
+            dilation)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/test_utils.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/test_utils.py
new file mode 100644
index 000000000..c88af1b4e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/test_utils.py
@@ -0,0 +1,190 @@
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+
+class TestCase(unittest.TestCase):
+    def _GetNdArray(self, a):
+        if not isinstance(a, np.ndarray):
+            a = np.array(a)
+        return a
+
+    def assertAllEqual(self, a, b):
+        """Asserts that two numpy arrays have the same values.
+        Args:
+        a: the expected numpy ndarray or anything can be converted to one.
+        b: the actual numpy ndarray or anything can be converted to one.
+        """
+        a = self._GetNdArray(a)
+        b = self._GetNdArray(b)
+        self.assertEqual(a.shape, b.shape,
+                         "Shape mismatch: expected %s, got %s." % (a.shape,
+                                                                   b.shape))
+        same = (a == b)
+
+        if a.dtype == np.float32 or a.dtype == np.float64:
+            same = np.logical_or(same, np.logical_and(
+                np.isnan(a), np.isnan(b)))
+        if not np.all(same):
+            # Prints more details than np.testing.assert_array_equal.
+            diff = np.logical_not(same)
+            if a.ndim:
+                x = a[np.where(diff)]
+                y = b[np.where(diff)]
+                print("not equal where = ", np.where(diff))
+            else:
+                # np.where is broken for scalars
+                x, y = a, b
+            print("not equal lhs = ", x)
+            print("not equal rhs = ", y)
+            np.testing.assert_array_equal(a, b)
+
+    def assertAllClose(self, a, b, rtol=1e-6, atol=1e-6):
+        """Asserts that two numpy arrays, or dicts of same, have near values.
+        This does not support nested dicts.
+        Args:
+        a: The expected numpy ndarray (or anything can be converted to one), or
+            dict of same. Must be a dict iff `b` is a dict.
+        b: The actual numpy ndarray (or anything can be converted to one), or
+            dict of same. Must be a dict iff `a` is a dict.
+        rtol: relative tolerance.
+        atol: absolute tolerance.
+        Raises:
+        ValueError: if only one of `a` and `b` is a dict.
+        """
+        is_a_dict = isinstance(a, dict)
+        if is_a_dict != isinstance(b, dict):
+            raise ValueError("Can't compare dict to non-dict, %s vs %s." % (a,
+                                                                            b))
+        if is_a_dict:
+            self.assertCountEqual(
+                a.keys(),
+                b.keys(),
+                msg="mismatched keys, expected %s, got %s" % (a.keys(),
+                                                              b.keys()))
+            for k in a:
+                self._assertArrayLikeAllClose(
+                    a[k],
+                    b[k],
+                    rtol=rtol,
+                    atol=atol,
+                    msg="%s: expected %s, got %s." % (k, a, b))
+        else:
+            self._assertArrayLikeAllClose(a, b, rtol=rtol, atol=atol)
+
+    def _assertArrayLikeAllClose(self, a, b, rtol=1e-6, atol=1e-6, msg=None):
+        a = self._GetNdArray(a)
+        b = self._GetNdArray(b)
+        self.assertEqual(a.shape, b.shape,
+                         "Shape mismatch: expected %s, got %s." % (a.shape,
+                                                                   b.shape))
+        if not np.allclose(a, b, rtol=rtol, atol=atol):
+            # Prints more details than np.testing.assert_allclose.
+            #
+            # NOTE: numpy.allclose (and numpy.testing.assert_allclose)
+            # checks whether two arrays are element-wise equal within a
+            # tolerance. The relative difference (rtol * abs(b)) and the
+            # absolute difference atol are added together to compare against
+            # the absolute difference between a and b.  Here, we want to
+            # print out which elements violate such conditions.
+            cond = np.logical_or(
+                np.abs(a - b) > atol + rtol * np.abs(b),
+                np.isnan(a) != np.isnan(b))
+            if a.ndim:
+                x = a[np.where(cond)]
+                y = b[np.where(cond)]
+                print("not close where = ", np.where(cond))
+            else:
+                # np.where is broken for scalars
+                x, y = a, b
+            print("not close lhs = ", x)
+            print("not close rhs = ", y)
+            print("not close dif = ", np.abs(x - y))
+            print("not close tol = ", atol + rtol * np.abs(y))
+            print("dtype = %s, shape = %s" % (a.dtype, a.shape))
+            np.testing.assert_allclose(a, b, rtol=rtol, atol=atol, err_msg=msg)
+
+def params_grid(*params):
+    size = len(params)
+    length = 1
+    for p in params:
+        length *= len(p)
+    sizes = [len(p) for p in params]
+    counter = [0] * size
+    total = []
+    for i in range(length):
+        total.append([0]* size)
+    for i in range(length):
+        for j in range(size):
+            total[i][j] = params[j][counter[j]]
+        counter[size - 1] += 1
+        for c in range(size - 1, -1, -1):
+            if (counter[c] == sizes[c] and c > 0):
+                counter[c - 1] += 1
+                counter[c] = 0
+    return total
+
+def generate_sparse_data(shape,
+                    num_points,
+                    num_channels,
+                    integer=False,
+                    data_range=(-1, 1),
+                    with_dense=True,
+                    dtype=np.float32):
+    dense_shape = shape
+    ndim = len(dense_shape)
+    # num_points = np.random.randint(10, 100, size=[batch_size, ndim])
+    num_points = np.array(num_points)
+    # num_points = np.array([3, 2])
+    batch_size = len(num_points)
+    batch_indices = []
+    coors_total = np.stack(
+        np.meshgrid(*[np.arange(0, s) for s in shape]), axis=-1)
+    coors_total = coors_total.reshape(-1, ndim)
+    for i in range(batch_size):
+        np.random.shuffle(coors_total)
+        inds_total = coors_total[:num_points[i]]
+        inds_total = np.pad(
+            inds_total, ((0, 0), (0, 1)), mode="constant", constant_values=i)
+        batch_indices.append(inds_total)
+    if integer:
+        sparse_data = np.random.randint(
+            data_range[0], data_range[1], size=[num_points.sum(), num_channels]).astype(dtype)
+    else:
+        sparse_data = np.random.uniform(
+            data_range[0], data_range[1], size=[num_points.sum(), num_channels]).astype(dtype)
+
+    # sparse_data = np.arange(1, num_points.sum() + 1).astype(np.float32).reshape(5, 1)
+    
+        
+    res =  {
+        "features": sparse_data.astype(dtype),
+        
+    }
+    if with_dense:
+        dense_data = np.zeros(
+            [batch_size, num_channels, *dense_shape], dtype=sparse_data.dtype)
+        start = 0
+        for i, inds in enumerate(batch_indices):
+            for j, ind in enumerate(inds):
+                dense_slice = (i, slice(None), *ind[:-1])
+                dense_data[dense_slice] = sparse_data[start + j]
+            start += len(inds)
+        res["features_dense"] = dense_data.astype(dtype)
+    batch_indices = np.concatenate(batch_indices, axis=0)
+    res["indices"] = batch_indices.astype(np.int32)
+    return res 
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/utils/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/utils/__init__.py
new file mode 100644
index 000000000..31f8f4a00
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/spconv/utils/__init__.py
@@ -0,0 +1,112 @@
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+from spconv import spconv_utils
+# from spconv.spconv_utils import (non_max_suppression, non_max_suppression_cpu,
+#                                  points_to_voxel_3d_np, rbbox_iou,
+#                                  rotate_non_max_suppression_cpu)
+from spconv.spconv_utils import points_to_voxel_3d_np
+
+def points_to_voxel(points,
+                     voxel_size,
+                     coors_range,
+                     coor_to_voxelidx,
+                     max_points=35,
+                     max_voxels=20000):
+    """convert 3d points(N, >=3) to voxels. This version calculate
+    everything in one loop. now it takes only 0.8ms(~6k voxels) 
+    with c++ and 3.2ghz cpu.
+
+    Args:
+        points: [N, ndim] float tensor. points[:, :3] contain xyz points and
+            points[:, 3:] contain other information such as reflectivity.
+        voxel_size: [3] list/tuple or array, float. xyz, indicate voxel size
+        coors_range: [6] list/tuple or array, float. indicate voxel range.
+            format: xyzxyz, minmax
+        coor_to_voxelidx: int array. used as a dense map.
+        max_points: int. indicate maximum points contained in a voxel.
+        max_voxels: int. indicate maximum voxels this function create.
+            for voxelnet, 20000 is a good choice. you should shuffle points
+            before call this function because max_voxels may drop some points.
+
+    Returns:
+        voxels: [M, max_points, ndim] float tensor. only contain points.
+        coordinates: [M, 3] int32 tensor. zyx format.
+        num_points_per_voxel: [M] int32 tensor.
+    """
+    if not isinstance(voxel_size, np.ndarray):
+        voxel_size = np.array(voxel_size, dtype=points.dtype)
+    if not isinstance(coors_range, np.ndarray):
+        coors_range = np.array(coors_range, dtype=points.dtype)
+    voxelmap_shape = (coors_range[3:] - coors_range[:3]) / voxel_size
+    voxelmap_shape = tuple(np.round(voxelmap_shape).astype(np.int32).tolist())
+    voxelmap_shape = voxelmap_shape[::-1]
+    num_points_per_voxel = np.zeros(shape=(max_voxels, ), dtype=np.int32)
+    voxels = np.zeros(
+        shape=(max_voxels, max_points, points.shape[-1]), dtype=points.dtype)
+    coors = np.zeros(shape=(max_voxels, 3), dtype=np.int32)
+    voxel_num = points_to_voxel_3d_np(
+        points, voxels, coors, num_points_per_voxel, coor_to_voxelidx,
+        voxel_size.tolist(), coors_range.tolist(), max_points, max_voxels)
+    coors = coors[:voxel_num]
+    voxels = voxels[:voxel_num]
+    num_points_per_voxel = num_points_per_voxel[:voxel_num]
+    return voxels, coors, num_points_per_voxel
+
+class VoxelGenerator:
+    def __init__(self,
+                 voxel_size,
+                 point_cloud_range,
+                 max_num_points,
+                 max_voxels=20000):
+        point_cloud_range = np.array(point_cloud_range, dtype=np.float32)
+        # [0, -40, -3, 70.4, 40, 1]
+        voxel_size = np.array(voxel_size, dtype=np.float32)
+        grid_size = (
+            point_cloud_range[3:] - point_cloud_range[:3]) / voxel_size
+        grid_size = np.round(grid_size).astype(np.int64)
+        voxelmap_shape = tuple(np.round(grid_size).astype(np.int32).tolist())
+        voxelmap_shape = voxelmap_shape[::-1]
+
+        self._coor_to_voxelidx = np.full(voxelmap_shape, -1, dtype=np.int32)
+        self._voxel_size = voxel_size
+        self._point_cloud_range = point_cloud_range
+        self._max_num_points = max_num_points
+        self._max_voxels = max_voxels
+        self._grid_size = grid_size
+
+    def generate(self, points, max_voxels=None):
+        res = points_to_voxel(
+            points, self._voxel_size, self._point_cloud_range, self._coor_to_voxelidx,
+            self._max_num_points, max_voxels or self._max_voxels)
+        return res 
+
+
+    @property
+    def voxel_size(self):
+        return self._voxel_size
+
+    @property
+    def max_num_points_per_voxel(self):
+        return self._max_num_points
+
+
+    @property
+    def point_cloud_range(self):
+        return self._point_cloud_range
+
+    @property
+    def grid_size(self):
+        return self._grid_size
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/CMakeLists.txt
new file mode 100644
index 000000000..7e2c1434e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/CMakeLists.txt
@@ -0,0 +1,9 @@
+add_library(spconv SHARED all.cc indice.cc indice.cu 
+            reordering.cc reordering.cu maxpool.cc maxpool.cu)
+
+target_include_directories(spconv PRIVATE ${ALL_INCLUDE} )
+set_property(TARGET spconv PROPERTY CUDA_STANDARD 17)
+set_property(TARGET spconv PROPERTY CXX_STANDARD 17)
+set_target_properties(spconv PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
+target_link_libraries(spconv PRIVATE ${ALL_LIBS})
+install (TARGETS spconv DESTINATION lib)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/all.cc b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/all.cc
new file mode 100644
index 000000000..1af3c60df
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/all.cc
@@ -0,0 +1,34 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cuda_runtime_api.h>
+#include <spconv/pool_ops.h>
+#include <spconv/spconv_ops.h>
+
+static auto registry =
+    torch::RegisterOperators("spconv::get_indice_pairs_2d", &spconv::getIndicePair<2>)
+        .op("spconv::get_indice_pairs_3d", &spconv::getIndicePair<3>)
+        .op("spconv::get_indice_pairs_grid_2d", &spconv::getIndicePairPreGrid<2>)
+        .op("spconv::get_indice_pairs_grid_3d", &spconv::getIndicePairPreGrid<3>)
+        .op("spconv::indice_conv_fp32", &spconv::indiceConv<float>)
+        .op("spconv::indice_conv_backward_fp32", &spconv::indiceConvBackward<float>)
+        .op("spconv::indice_conv_half", &spconv::indiceConv<at::Half>)
+        .op("spconv::indice_conv_backward_half",
+            &spconv::indiceConvBackward<at::Half>)
+        .op("spconv::indice_maxpool_fp32", &spconv::indiceMaxPool<float>)
+        .op("spconv::indice_maxpool_backward_fp32",
+            &spconv::indiceMaxPoolBackward<float>)
+        .op("spconv::indice_maxpool_half", &spconv::indiceMaxPool<at::Half>)
+        .op("spconv::indice_maxpool_backward_half",
+            &spconv::indiceMaxPoolBackward<at::Half>);
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/indice.cc b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/indice.cc
new file mode 100644
index 000000000..68b652455
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/indice.cc
@@ -0,0 +1,90 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <spconv/geometry.h>
+#include <spconv/indice.h>
+#include <spconv/spconv_ops.h>
+#include <torch/script.h>
+
+namespace spconv {
+
+namespace functor {
+template <typename Index, typename IndexGrid, unsigned NDim>
+struct CreateConvIndicePairFunctor<tv::CPU, Index, IndexGrid, NDim> {
+  Index operator()(const tv::CPU& d, tv::TensorView<const Index> indicesIn,
+                     tv::TensorView<Index> indicesOut,
+                     tv::TensorView<IndexGrid> gridsOut,
+                     tv::TensorView<Index> indicePairs,
+                     tv::TensorView<Index> indiceNum,
+                     const tv::SimpleVector<Index, NDim> kernelSize,
+                     const tv::SimpleVector<Index, NDim> stride,
+                     const tv::SimpleVector<Index, NDim> padding,
+                     const tv::SimpleVector<Index, NDim> dilation,
+                     const tv::SimpleVector<Index, NDim> outSpatialShape,
+                     bool transpose, bool resetGrid) {
+    if (transpose)
+      return getIndicePairsDeConv<Index, IndexGrid, NDim>(
+          indicesIn, indicesOut,
+          gridsOut, indicePairs, indiceNum,
+          kernelSize.data(), stride.data(), padding.data(), dilation.data(),
+          outSpatialShape.data());
+    else
+      return getIndicePairsConv<Index, IndexGrid, NDim>(
+          indicesIn, indicesOut,
+          gridsOut, indicePairs, indiceNum,
+          kernelSize.data(), stride.data(), padding.data(), dilation.data(),
+          outSpatialShape.data());
+
+  }
+};
+template <typename Index, typename IndexGrid, unsigned NDim>
+struct CreateSubMIndicePairFunctor<tv::CPU, Index, IndexGrid, NDim> {
+  Index operator()(const tv::CPU& d, tv::TensorView<const Index> indicesIn,
+                     tv::TensorView<IndexGrid> gridsOut,
+                     tv::TensorView<Index> indicePairs,
+                     tv::TensorView<Index> indiceNum,
+                     const tv::SimpleVector<Index, NDim> kernelSize,
+                     const tv::SimpleVector<Index, NDim> stride,
+                     const tv::SimpleVector<Index, NDim> padding,
+                     const tv::SimpleVector<Index, NDim> dilation,
+                     const tv::SimpleVector<Index, NDim> outSpatialShape,
+                     bool transpose, bool resetGrid) {
+    return getIndicePairsSubM<Index, IndexGrid, NDim>(
+        indicesIn,
+        gridsOut, indicePairs, indiceNum,
+        kernelSize.data(), stride.data(), padding.data(), dilation.data(), outSpatialShape.data());
+  }
+};
+} // namespace functor
+
+#define DECLARE_CPU_SPECS_INDEX_NDIM(Index, NDIM)                              \
+  template struct functor::CreateConvIndicePairFunctor<tv::CPU, Index, int, NDIM>;      \
+  template struct functor::CreateSubMIndicePairFunctor<tv::CPU, Index, int,  \
+                                                         NDIM>;
+
+
+#define DECLARE_CPU_INDEX(Index)                                               \
+  DECLARE_CPU_SPECS_INDEX_NDIM(Index, 1);                                      \
+  DECLARE_CPU_SPECS_INDEX_NDIM(Index, 2);                                      \
+  DECLARE_CPU_SPECS_INDEX_NDIM(Index, 3);                                      \
+  DECLARE_CPU_SPECS_INDEX_NDIM(Index, 4);
+
+DECLARE_CPU_INDEX(int);
+DECLARE_CPU_INDEX(long);
+
+#undef DECLARE_CPU_INDEX
+#undef DECLARE_CPU_SPECS_INDEX_NDIM
+
+} // namespace spconv
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/indice.cu b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/indice.cu
new file mode 100644
index 000000000..f2a0889d8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/indice.cu
@@ -0,0 +1,158 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <ATen/ATen.h>
+#include <chrono>
+#include <limits>
+#include <spconv/mp_helper.h>
+#include <spconv/indice.h>
+#include <spconv/indice.cu.h>
+#include <tensorview/helper_launch.h>
+#include <tensorview/tensorview.h>
+#include <type_traits>
+#include <utility/timer.h>
+
+namespace spconv {
+namespace functor {
+template <typename Index, typename IndexGrid, unsigned NDim>
+struct CreateConvIndicePairFunctorP1<tv::GPU, Index, IndexGrid, NDim> {
+  Index operator()(const tv::GPU &d, tv::TensorView<const Index> indicesIn,
+                   tv::TensorView<Index> indicesOut,
+                   tv::TensorView<IndexGrid> gridsOut,
+                   tv::TensorView<Index> indicePairs,
+                   tv::TensorView<Index> indiceNum,
+                   tv::TensorView<Index> indicePairUnique,
+                   const tv::SimpleVector<Index, NDim> kernelSize,
+                   const tv::SimpleVector<Index, NDim> stride,
+                   const tv::SimpleVector<Index, NDim> padding,
+                   const tv::SimpleVector<Index, NDim> dilation,
+                   const tv::SimpleVector<Index, NDim> outSpatialShape,
+                   bool transpose) {
+    Index batchSize = gridsOut.dim(0);
+    auto numActIn = indicesIn.dim(0);
+    if (numActIn == 0)
+      return 0;
+    // auto timer = spconv::CudaContextTimer<>();
+    if (transpose)
+      prepareDeConvIndicePairsKernel<Index, IndexGrid, NDim, 256>
+          <<<tv::launch::getBlocks(numActIn), tv::launch::CUDA_NUM_THREADS, 0,
+             d.stream()>>>(indicesIn, indicesOut, gridsOut, indicePairs,
+                           indiceNum, indicePairUnique, kernelSize, stride,
+                           padding, dilation, outSpatialShape);
+    else
+      prepareIndicePairsKernel<Index, IndexGrid, NDim, 256>
+          <<<tv::launch::getBlocks(numActIn), tv::launch::CUDA_NUM_THREADS, 0,
+             d.stream()>>>(indicesIn, indicesOut, gridsOut, indicePairs,
+                           indiceNum, indicePairUnique, kernelSize, stride,
+                           padding, dilation, outSpatialShape);
+    TV_CHECK_CUDA_ERR();
+    // std::cout << "p1 gene time " << timer.report() / 1000.0 << std::endl;
+    return 1;
+  }
+};
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+struct CreateConvIndicePairFunctorP2<tv::GPU, Index, IndexGrid, NDim> {
+  Index operator()(const tv::GPU &d, tv::TensorView<const Index> indicesIn,
+                   tv::TensorView<Index> indicesOut,
+                   tv::TensorView<IndexGrid> gridsOut,
+                   tv::TensorView<Index> indicePairs,
+                   tv::TensorView<Index> indiceNum,
+                   tv::TensorView<Index> indicePairUnique,
+                   const tv::SimpleVector<Index, NDim> outSpatialShape,
+                   bool transpose, bool resetGrid) {
+    Index batchSize = gridsOut.dim(0);
+    auto kernelVolume = indicePairs.dim(0);
+    auto numActIn = indicesIn.dim(0);
+    if (numActIn == 0)
+      return 0;
+    Index numAct = indicePairUnique.dim(0) - 1;
+    assignGridAndIndiceOutKernel<Index, IndexGrid, NDim>
+        <<<tv::launch::getBlocks(numAct), tv::launch::CUDA_NUM_THREADS, 0,
+           d.stream()>>>(indicesOut, gridsOut, numAct, indicePairs,
+                         indicePairUnique, outSpatialShape, batchSize);
+    TV_CHECK_CUDA_ERR();
+    assignIndicePairsKernel<Index, IndexGrid, NDim>
+        <<<tv::launch::getBlocks(numActIn), tv::launch::CUDA_NUM_THREADS, 0,
+           d.stream()>>>(indicesOut, gridsOut, numActIn, indicePairs,
+                         indicePairUnique, outSpatialShape);
+    TV_CHECK_CUDA_ERR();
+    if (resetGrid) {
+      resetGridKernel<Index, IndexGrid, NDim>
+          <<<tv::launch::getBlocks(numAct), tv::launch::CUDA_NUM_THREADS, 0,
+             d.stream()>>>(indicePairUnique.data(), gridsOut, numAct);
+      TV_CHECK_CUDA_ERR();
+    }
+    return numAct;
+  }
+};
+
+template <typename Index, typename IndexGrid, unsigned NDim>
+struct CreateSubMIndicePairFunctor<tv::GPU, Index, IndexGrid, NDim> {
+  Index operator()(const tv::GPU &d, tv::TensorView<const Index> indicesIn,
+                   tv::TensorView<IndexGrid> gridsOut,
+                   tv::TensorView<Index> indicePairs,
+                   tv::TensorView<Index> indiceNum,
+                   const tv::SimpleVector<Index, NDim> kernelSize,
+                   const tv::SimpleVector<Index, NDim> stride,
+                   const tv::SimpleVector<Index, NDim> padding,
+                   const tv::SimpleVector<Index, NDim> dilation,
+                   const tv::SimpleVector<Index, NDim> outSpatialShape,
+                   bool transpose, bool resetGrid) {
+    auto numActIn = indicesIn.dim(0);
+    if (numActIn == 0)
+      return 0;
+    // auto timer = spconv::CudaContextTimer<>();
+    prepareSubMGridKernel<Index, IndexGrid, NDim>
+        <<<tv::launch::getBlocks(numActIn), tv::launch::CUDA_NUM_THREADS, 0,
+           d.stream()>>>(indicesIn, gridsOut, outSpatialShape);
+    TV_CHECK_CUDA_ERR();
+    getSubMIndicePairsKernel<Index, IndexGrid, NDim>
+        <<<tv::launch::getBlocks(numActIn), tv::launch::CUDA_NUM_THREADS, 0,
+           d.stream()>>>(indicesIn, gridsOut, indicePairs, indiceNum,
+                         kernelSize, stride, padding, dilation, outSpatialShape);
+    TV_CHECK_CUDA_ERR();
+    // std::cout << "subm gene time " << timer.report() / 1000.0 << std::endl;
+    if (resetGrid) {
+      resetGridSubMKernel<Index, IndexGrid, NDim>
+          <<<tv::launch::getBlocks(numActIn), tv::launch::CUDA_NUM_THREADS, 0,
+             d.stream()>>>(indicesIn.data(), gridsOut, outSpatialShape, numActIn);
+      TV_CHECK_CUDA_ERR();
+    }
+    return numActIn;
+  }
+};
+} // namespace functor
+
+#define DECLARE_GPU_SPECS_INDEX_NDIM(Index, NDIM)                              \
+  template struct functor::CreateConvIndicePairFunctor<tv::GPU, Index, int,    \
+                                                       NDIM>;                  \
+  template struct functor::CreateConvIndicePairFunctorP1<tv::GPU, Index, int,  \
+                                                         NDIM>;                \
+  template struct functor::CreateConvIndicePairFunctorP2<tv::GPU, Index, int,  \
+                                                         NDIM>;                \
+  template struct functor::CreateSubMIndicePairFunctor<tv::GPU, Index, int,    \
+                                                       NDIM>;
+
+#define DECLARE_GPU_INDEX(Index)                                               \
+  DECLARE_GPU_SPECS_INDEX_NDIM(Index, 1);                                      \
+  DECLARE_GPU_SPECS_INDEX_NDIM(Index, 2);                                      \
+  DECLARE_GPU_SPECS_INDEX_NDIM(Index, 3);                                      \
+  DECLARE_GPU_SPECS_INDEX_NDIM(Index, 4);
+
+DECLARE_GPU_INDEX(int);
+
+#undef DECLARE_GPU_INDEX
+#undef DECLARE_GPU_SPECS_INDEX_NDIM
+} // namespace spconv
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/maxpool.cc b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/maxpool.cc
new file mode 100644
index 000000000..bc903c30a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/maxpool.cc
@@ -0,0 +1,82 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <spconv/maxpool.h>
+#include <torch/script.h>
+
+namespace spconv {
+
+namespace functor {
+template <typename T, typename Index>
+struct SparseMaxPoolForwardFunctor<tv::CPU, T, Index> {
+  void operator()(const tv::CPU &d, tv::TensorView<T> outFeatures,
+                  tv::TensorView<const T> inFeatures,
+                  tv::TensorView<const Index> indices, int size) {
+    int stride = outFeatures.dim(1);
+    auto outFeaturesData = outFeatures.data();
+    auto inFeaturesData = inFeatures.data();
+    auto indicesIn = indices.subview(0).data();
+    auto indicesOut = indices.subview(1).data();
+    Index idxi, idxo;
+    for (int row = 0; row < size; row++) {
+      idxi = indicesIn[row] * stride;
+      idxo = indicesOut[row] * stride;
+      for (int plane = 0; plane < stride; ++plane)
+        if (outFeaturesData[idxo + plane] < inFeaturesData[idxi + plane])
+          outFeaturesData[idxo + plane] = inFeaturesData[idxi + plane];
+    }
+  }
+};
+
+template <typename T, typename Index>
+struct SparseMaxPoolBackwardFunctor<tv::CPU, T, Index> {
+  void operator()(const tv::CPU &d, tv::TensorView<const T> outFeatures,
+                  tv::TensorView<const T> inFeatures,
+                  tv::TensorView<const T> dout, tv::TensorView<T> din,
+                  tv::TensorView<const Index> indices, int size) {
+    int stride = outFeatures.dim(1);
+    auto outFeaturesData = outFeatures.data();
+    auto inFeaturesData = inFeatures.data();
+    auto doutData = dout.data();
+    auto dinData = din.data();
+    auto indicesIn = indices.subview(0).data();
+    auto indicesOut = indices.subview(1).data();
+    Index idxi, idxo;
+    for (int row = 0; row < size; row++) {
+      idxi = indicesIn[row] * stride;
+      idxo = indicesOut[row] * stride;
+      for (int plane = 0; plane < stride; ++plane)
+        if (outFeaturesData[idxo + plane] == inFeaturesData[idxi + plane])
+          dinData[idxi + plane] += doutData[idxo + plane];
+    }
+  }
+};
+} // namespace functor
+
+#define DECLARE_CPU_SPECS_T_INDEX(T, Index)                                    \
+  template struct functor::SparseMaxPoolForwardFunctor<tv::CPU, T, Index>;     \
+  template struct functor::SparseMaxPoolBackwardFunctor<tv::CPU, T, Index>;
+
+#define DECLARE_CPU_SPECS(T)                                                   \
+  DECLARE_CPU_SPECS_T_INDEX(T, int);                                           \
+  DECLARE_CPU_SPECS_T_INDEX(T, long);
+
+DECLARE_CPU_SPECS(float);
+// DECLARE_CPU_SPECS(double);
+DECLARE_CPU_SPECS(at::Half);
+
+#undef DECLARE_CPU_SPECS
+#undef DECLARE_CPU_SPECS_T_INDEX
+
+} // namespace spconv
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/maxpool.cu b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/maxpool.cu
new file mode 100644
index 000000000..94f8fe4bc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/maxpool.cu
@@ -0,0 +1,472 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cuda_fp16.h>
+#include <ATen/ATen.h>
+#include <chrono>
+#include <limits>
+#include <spconv/maxpool.h>
+#include <spconv/mp_helper.h>
+#include <tensorview/helper_kernel.cu.h>
+#include <tensorview/helper_launch.h>
+#include <tensorview/tensorview.h>
+#include <type_traits>
+
+namespace spconv {
+template <typename T, typename Index, int NumTLP, int NumILP>
+__global__ void maxPoolFwdBlockKernel(T *outFeatures, const T *inFeatures,
+                                      const Index *indicesIn,
+                                      const Index *indicesOut, int numHot,
+                                      int numPlanes) {
+  T in, out;
+  int ILPStrideY[NumILP];
+  Index idxo, idxi;
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideY[ilp] = threadIdx.y + ilp * blockDim.y;
+  outFeatures += blockIdx.y * NumTLP;
+  inFeatures += blockIdx.y * NumTLP;
+  for (int ix = blockIdx.x * blockDim.x; ix < numHot;
+       ix += blockDim.x * gridDim.x) {
+    {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        idxi = indicesIn[ix + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+        idxo = indicesOut[ix + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+        in = inFeatures[idxi];
+        out = outFeatures[idxo];
+        if (in > out) {
+          outFeatures[idxo] = in;
+        }
+      }
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP>
+__global__ void
+maxPoolFwdGenericBlockKernel(T *outFeatures, const T *inFeatures,
+                             const Index *indicesIn, const Index *indicesOut,
+                             int numHot, int numPlanes) {
+  // see http://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf.
+  int ILPStrideX[NumILP];
+  Index RI[NumILP];
+  Index RO[NumILP];
+  T in, out;
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideX[ilp] = ilp * gridDim.x * blockDim.x;
+  for (int ix : tv::KernelLoopX<int, NumILP>(numHot)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ilp++) {
+      RI[ilp] = indicesIn[ix + ILPStrideX[ilp]] * numPlanes;
+      RO[ilp] = indicesOut[ix + ILPStrideX[ilp]] * numPlanes;
+    }
+    for (int iy : tv::KernelLoopY<int>(numPlanes)) {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        in = inFeatures[RI[ilp] + iy];
+        out = outFeatures[RO[ilp] + iy];
+        if (static_cast<float>(in) > static_cast<float>(out)) {
+          outFeatures[RO[ilp] + iy] = in;
+        }
+      }
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP, typename VecType>
+__global__ void maxPoolFwdVecBlockKernel(T *outFeatures, const T *inFeatures,
+                                         const Index *indicesIn,
+                                         const Index *indicesOut, int numHot,
+                                         int numPlanes) {
+  // see http://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf.
+  int ILPStrideY[NumILP];
+  constexpr int vecloadFactor = sizeof(VecType) / sizeof(T);
+  T bufi[vecloadFactor];
+  T bufo[vecloadFactor];
+  Index idxi, idxo;
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideY[ilp] = threadIdx.y + ilp * blockDim.y;
+  outFeatures += blockIdx.y * NumTLP;
+  inFeatures += blockIdx.y * NumTLP;
+  for (int ix = blockIdx.x * blockDim.x * vecloadFactor; ix < numHot;
+       ix += blockDim.x * gridDim.x * vecloadFactor) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ++ilp) {
+      idxi = indicesIn[ix + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+      idxo = indicesOut[ix + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+      reinterpret_cast<VecType *>(bufo)[0] =
+          reinterpret_cast<VecType *>(outFeatures)[idxo];
+      reinterpret_cast<VecType *>(bufi)[0] =
+          reinterpret_cast<const VecType *>(inFeatures)[idxi];
+#pragma unroll
+      for (int i = 0; i < vecloadFactor; i++) {
+        if (static_cast<float>(bufi[i]) > static_cast<float>(bufo[i])) {
+          bufo[i] = bufi[i];
+        }
+      }
+      reinterpret_cast<VecType *>(outFeatures)[idxo] =
+          reinterpret_cast<VecType *>(bufo)[0];
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP>
+__global__ void maxPoolFwdGenericKernel(T *outFeatures, const T *inFeatures,
+                                        const Index *indicesIn,
+                                        const Index *indicesOut, int numHot,
+                                        int numPlanes) {
+  // see http://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf.
+  int ILPStrideX[NumILP];
+  Index RI[NumILP];
+  Index RO[NumILP];
+  T in, out;
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideX[ilp] = ilp * gridDim.x * blockDim.x;
+  for (int ix : tv::KernelLoopX<int, NumILP>(numHot)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ilp++) {
+      if (ix + ILPStrideX[ilp] < numHot) {
+        RI[ilp] = indicesIn[ix + ILPStrideX[ilp]] * numPlanes;
+        RO[ilp] = indicesOut[ix + ILPStrideX[ilp]] * numPlanes;
+      }
+    }
+    for (int iy : tv::KernelLoopY<int>(numPlanes)) {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        if (ix + ILPStrideX[ilp] < numHot) {
+          in = inFeatures[RI[ilp] + iy];
+          out = outFeatures[RO[ilp] + iy];
+          if (static_cast<float>(in) > static_cast<float>(out)) {
+            outFeatures[RO[ilp] + iy] = in;
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP>
+__global__ void
+maxPoolBwdBlockKernel(const T *outFeatures, const T *inFeatures, const T *dout,
+                      T *din, const Index *indicesIn, const Index *indicesOut,
+                      int numHot, int numPlanes) {
+  // see http://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf.
+  T in, out;
+  Index idxo, idxi;
+  int ILPStrideY[NumILP];
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideY[ilp] = threadIdx.y + ilp * blockDim.y;
+  outFeatures += blockIdx.y * NumTLP;
+  inFeatures += blockIdx.y * NumTLP;
+  dout += blockIdx.y * NumTLP;
+  din += blockIdx.y * NumTLP;
+  for (int ix = blockIdx.x * blockDim.x; ix < numHot;
+       ix += blockDim.x * gridDim.x) {
+    {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        idxi = indicesIn[ix + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+        idxo = indicesOut[ix + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+        in = inFeatures[idxi];
+        out = outFeatures[idxo];
+        if (static_cast<float>(in) == static_cast<float>(out)) {
+          din[idxi] += dout[idxo];
+        }
+      }
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP>
+__global__ void maxPoolBwdGenericBlockKernel(const T *outFeatures,
+                                             const T *inFeatures, const T *dout,
+                                             T *din, const Index *indicesIn,
+                                             const Index *indicesOut,
+                                             int numHot, int numPlanes) {
+  // see http://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf.
+  int ILPStrideX[NumILP];
+  Index RI[NumILP];
+  Index RO[NumILP];
+  T in, out;
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideX[ilp] = ilp * gridDim.x * blockDim.x;
+  for (int ix : tv::KernelLoopX<int, NumILP>(numHot)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ilp++) {
+      RI[ilp] = indicesIn[ix + ILPStrideX[ilp]] * numPlanes;
+      RO[ilp] = indicesOut[ix + ILPStrideX[ilp]] * numPlanes;
+    }
+    for (int iy : tv::KernelLoopY<int>(numPlanes)) {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        in = inFeatures[RI[ilp] + iy];
+        out = outFeatures[RO[ilp] + iy];
+        if (static_cast<float>(in) == static_cast<float>(out)) {
+          din[RI[ilp] + iy] += dout[RO[ilp] + iy];
+        }
+      }
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP, typename VecType>
+__global__ void
+maxPoolBwdVecBlockKernel(const T *outFeatures, const T *inFeatures,
+                         const T *dout, T *din, const Index *indicesIn,
+                         const Index *indicesOut, int numHot, int numPlanes) {
+  // see http://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf.
+  int ILPStrideY[NumILP];
+  constexpr int vecloadFactor = sizeof(VecType) / sizeof(T);
+  T bufi[vecloadFactor];
+  T bufo[vecloadFactor];
+  T bufdi[vecloadFactor];
+  T bufdo[vecloadFactor];
+  Index idxi, idxo;
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideY[ilp] = threadIdx.y + ilp * blockDim.y;
+  outFeatures += blockIdx.y * NumTLP;
+  inFeatures += blockIdx.y * NumTLP;
+  for (int ix = blockIdx.x * blockDim.x * vecloadFactor; ix < numHot;
+       ix += blockDim.x * gridDim.x * vecloadFactor) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ++ilp) {
+      idxi = indicesIn[ix + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+      idxo = indicesOut[ix + ILPStrideY[ilp]] * numPlanes + threadIdx.x;
+      reinterpret_cast<VecType *>(bufo)[0] =
+          reinterpret_cast<const VecType *>(outFeatures)[idxo];
+      reinterpret_cast<VecType *>(bufi)[0] =
+          reinterpret_cast<const VecType *>(inFeatures)[idxi];
+      reinterpret_cast<VecType *>(bufdo)[0] =
+          reinterpret_cast<const VecType *>(dout)[idxo];
+      reinterpret_cast<VecType *>(bufdi)[0] = reinterpret_cast<VecType *>(din)[idxi];
+
+#pragma unroll
+      for (int i = 0; i < vecloadFactor; i++) {
+        if (static_cast<float>(bufi[i]) == static_cast<float>(bufo[i])) {
+          bufdi[i] += bufdo[i];
+        }
+      }
+      reinterpret_cast<VecType *>(din)[idxi] = reinterpret_cast<VecType *>(bufdi)[0];
+    }
+  }
+}
+
+template <typename T, typename Index, int NumTLP, int NumILP>
+__global__ void
+maxPoolBwdGenericKernel(const T *outFeatures, const T *inFeatures,
+                        const T *dout, T *din, const Index *indicesIn,
+                        const Index *indicesOut, int numHot, int numPlanes) {
+  // see http://www.nvidia.com/content/GTC-2010/pdfs/2238_GTC2010.pdf.
+  int ILPStrideX[NumILP];
+  Index RI[NumILP];
+  Index RO[NumILP];
+  T in, out;
+#pragma unroll
+  for (int ilp = 0; ilp < NumILP; ilp++)
+    ILPStrideX[ilp] = ilp * gridDim.x * blockDim.x;
+  for (int ix : tv::KernelLoopX<int, NumILP>(numHot)) {
+#pragma unroll
+    for (int ilp = 0; ilp < NumILP; ilp++) {
+      if (ix + ILPStrideX[ilp] < numHot) {
+        RI[ilp] = indicesIn[ix + ILPStrideX[ilp]] * numPlanes;
+        RO[ilp] = indicesOut[ix + ILPStrideX[ilp]] * numPlanes;
+      }
+    }
+    for (int iy : tv::KernelLoopY<int>(numPlanes)) {
+#pragma unroll
+      for (int ilp = 0; ilp < NumILP; ++ilp) {
+        if (ix + ILPStrideX[ilp] < numHot) {
+          in = inFeatures[RI[ilp] + iy];
+          out = outFeatures[RO[ilp] + iy];
+          if (static_cast<float>(in) == static_cast<float>(out)) {
+            din[RI[ilp] + iy] += dout[RO[ilp] + iy];
+          }
+        }
+      }
+    }
+  }
+}
+
+namespace functor {
+template <typename T, typename Index>
+struct SparseMaxPoolForwardFunctor<tv::GPU, T, Index> {
+  using vecload_type_t =
+      std::conditional_t<std::is_same<T, at::Half>::value, int2, int4>;
+  using kernel_block_t = mp_list_c<int, 64, 32, 16>;
+  void operator()(const tv::GPU &d, tv::TensorView<T> outFeatures,
+                  tv::TensorView<const T> inFeatures,
+                  tv::TensorView<const Index> indices, int size) {
+    if (size <= 0)
+      return;
+    int numPlanes = inFeatures.dim(1);
+    bool notFound = true;
+    constexpr int vecloadFactor = sizeof(vecload_type_t) / sizeof(T);
+    mp_for_each<kernel_block_t>([=, &outFeatures, &inFeatures, &indices,
+                                 &notFound](auto NumTLP) {
+      constexpr int NumILP = NumTLP / 4;
+
+      int numHotBlock = (size / NumTLP) * NumTLP;
+      if (notFound) {
+        if (numPlanes % NumTLP == 0) {
+          if (numHotBlock >= NumTLP) {
+            maxPoolFwdVecBlockKernel<T, Index, int(NumTLP), NumILP, vecload_type_t>
+                <<<dim3(std::min(size / NumTLP, 512), numPlanes / NumTLP),
+                   dim3(NumTLP / vecloadFactor, NumTLP / NumILP), 0,
+                   d.stream()>>>(outFeatures.data(), inFeatures.data(),
+                                 indices.subview(0).data(),
+                                 indices.subview(1).data(), numHotBlock,
+                                 numPlanes / vecloadFactor);
+            TV_CHECK_CUDA_ERR();
+          }
+
+          if (size > numHotBlock) {
+            maxPoolFwdGenericKernel<T, Index, int(NumTLP), NumILP>
+                <<<dim3(1, numPlanes / NumTLP), dim3(NumTLP / NumILP, NumTLP),
+                   0, d.stream()>>>(outFeatures.data(), inFeatures.data(),
+                                    indices.subview(0).data() + numHotBlock,
+                                    indices.subview(1).data() + numHotBlock,
+                                    size - numHotBlock, numPlanes);
+            TV_CHECK_CUDA_ERR();
+          }
+          notFound = false;
+        }
+      }
+    });
+
+    if (notFound) {
+      constexpr int NumTLP = 64;
+      constexpr int NumILP = NumTLP / 4;
+      int numHotBlock = (size / NumTLP) * NumTLP;
+      if (numHotBlock >= NumTLP) {
+        maxPoolFwdGenericBlockKernel<T, Index, NumTLP, NumILP>
+            <<<dim3(size / NumTLP, tv::launch::DivUp(numPlanes, NumTLP)),
+               dim3(NumTLP / NumILP, NumTLP), 0, d.stream()>>>(
+                outFeatures.data(), inFeatures.data(),
+                indices.subview(0).data(), indices.subview(1).data(),
+                numHotBlock, numPlanes);
+        TV_CHECK_CUDA_ERR();
+      }
+
+      if (size > numHotBlock) {
+        maxPoolFwdGenericKernel<T, Index, NumTLP, NumILP>
+            <<<dim3(1, tv::launch::DivUp(numPlanes, NumTLP)),
+               dim3(NumTLP / NumILP, NumTLP), 0, d.stream()>>>(
+                outFeatures.data(), inFeatures.data(),
+                indices.subview(0).data() + numHotBlock,
+                indices.subview(1).data() + numHotBlock, size - numHotBlock,
+                numPlanes);
+        TV_CHECK_CUDA_ERR();
+      }
+    }
+  }
+};
+
+template <typename T, typename Index>
+struct SparseMaxPoolBackwardFunctor<tv::GPU, T, Index> {
+  using vecload_type_t =
+      std::conditional_t<std::is_same<T, at::Half>::value, int2, int4>;
+  using kernel_block_t = mp_list_c<int, 64, 32, 16>;
+  void operator()(const tv::GPU &d, tv::TensorView<const T> outFeatures,
+                  tv::TensorView<const T> inFeatures,
+                  tv::TensorView<const T> dout, tv::TensorView<T> din,
+                  tv::TensorView<const Index> indices, int size) {
+    if (size <= 0)
+      return;
+    int numPlanes = inFeatures.dim(1);
+    bool notFound = true;
+    constexpr int vecloadFactor = sizeof(vecload_type_t) / sizeof(T);
+    mp_for_each<kernel_block_t>([=, &outFeatures, &inFeatures, &dout, &din,
+                                 &indices, &notFound](auto NumTLP) {
+      constexpr int NumILP = NumTLP / 4;
+
+      int numHotBlock = (size / NumTLP) * NumTLP;
+      if (notFound) {
+        if (numPlanes % NumTLP == 0) {
+          if (numHotBlock >= NumTLP) {
+            maxPoolBwdVecBlockKernel<T, Index, int(NumTLP), NumILP, vecload_type_t>
+                <<<dim3(std::min(size / NumTLP, 512), numPlanes / NumTLP),
+                   dim3(NumTLP / vecloadFactor, NumTLP / NumILP), 0,
+                   d.stream()>>>(outFeatures.data(), inFeatures.data(),
+                                 dout.data(), din.data(),
+                                 indices.subview(0).data(),
+                                 indices.subview(1).data(), numHotBlock,
+                                 numPlanes / vecloadFactor);
+            TV_CHECK_CUDA_ERR();
+          }
+
+          if (size > numHotBlock) {
+            maxPoolBwdGenericKernel<T, Index, int(NumTLP), NumILP>
+                <<<dim3(1, numPlanes / NumTLP), dim3(NumTLP / NumILP, NumTLP),
+                   0, d.stream()>>>(outFeatures.data(), inFeatures.data(),
+                                    dout.data(), din.data(),
+                                    indices.subview(0).data() + numHotBlock,
+                                    indices.subview(1).data() + numHotBlock,
+                                    size - numHotBlock, numPlanes);
+            TV_CHECK_CUDA_ERR();
+          }
+          notFound = false;
+        }
+      }
+    });
+
+    if (notFound) {
+      constexpr int NumTLP = 64;
+      constexpr int NumILP = NumTLP / 4;
+      int numHotBlock = (size / NumTLP) * NumTLP;
+      if (numHotBlock >= NumTLP) {
+        maxPoolBwdGenericBlockKernel<T, Index, NumTLP, NumILP>
+            <<<dim3(size / NumTLP, tv::launch::DivUp(numPlanes, NumTLP)),
+               dim3(NumTLP / NumILP, NumTLP), 0, d.stream()>>>(
+                outFeatures.data(), inFeatures.data(), dout.data(), din.data(),
+                indices.subview(0).data(), indices.subview(1).data(),
+                numHotBlock, numPlanes);
+        TV_CHECK_CUDA_ERR();
+      }
+
+      if (size > numHotBlock) {
+        maxPoolBwdGenericKernel<T, Index, NumTLP, NumILP>
+            <<<dim3(1, tv::launch::DivUp(numPlanes, NumTLP)),
+               dim3(NumTLP / NumILP, NumTLP), 0, d.stream()>>>(
+                outFeatures.data(), inFeatures.data(), dout.data(), din.data(),
+                indices.subview(0).data() + numHotBlock,
+                indices.subview(1).data() + numHotBlock, size - numHotBlock,
+                numPlanes);
+        TV_CHECK_CUDA_ERR();
+      }
+    }
+  }
+};
+
+} // namespace functor
+
+#define DECLARE_GPU_SPECS_T_INDEX(T, Index)                                    \
+  template struct functor::SparseMaxPoolForwardFunctor<tv::GPU, T, Index>;     \
+  template struct functor::SparseMaxPoolBackwardFunctor<tv::GPU, T, Index>;
+
+#define DECLARE_GPU_SPECS(T) DECLARE_GPU_SPECS_T_INDEX(T, int);
+
+DECLARE_GPU_SPECS(float);
+// DECLARE_GPU_SPECS(double);
+DECLARE_GPU_SPECS(at::Half);
+
+#undef DECLARE_GPU_SPECS
+#undef DECLARE_GPU_SPECS_T_INDEX
+} // namespace spconv
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/reordering.cc b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/reordering.cc
new file mode 100644
index 000000000..8acc35b57
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/reordering.cc
@@ -0,0 +1,70 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <spconv/reordering.h>
+#include <torch/script.h>
+
+namespace spconv {
+namespace functor {
+template <typename T, typename Index>
+struct SparseGatherFunctor<tv::CPU, T, Index> {
+  void operator()(const tv::CPU& d, tv::TensorView<T> buffer, tv::TensorView<const T> features,
+                  tv::TensorView<const Index> indices, int size) {
+    int numPlanes = features.dim(1);
+    for (int i = 0; i < size; ++i) {
+      std::memcpy(buffer.data() + i * numPlanes,
+                  features.data() + indices[i] * numPlanes,
+                  sizeof(T) * numPlanes);
+    }
+  }
+};
+
+template <typename T, typename Index>
+struct SparseScatterAddFunctor<tv::CPU, T, Index> {
+  void operator()(const tv::CPU& d, tv::TensorView<T> outFeatures,
+                  tv::TensorView<const T> buffer, tv::TensorView<const Index> indices,
+                  int size, bool stable) {
+    int numPlanes = outFeatures.dim(1);
+    const T* buf = buffer.data();
+    T* out = outFeatures.data();
+    for (int i = 0; i < size; ++i) {
+      buf = buffer.data() + i * numPlanes;
+      out = outFeatures.data() + indices[i] * numPlanes;
+      for (int j = 0; j < numPlanes; ++j){
+        out[j] += buf[j];
+      }
+    }
+  }
+};
+
+} // namespace functor
+
+
+#define DECLARE_CPU_SPECS_T_INDEX(T, Index)               \
+  template struct functor::SparseGatherFunctor<tv::CPU, T, Index>;  \
+  template struct functor::SparseScatterAddFunctor<tv::CPU, T, Index>;
+
+#define DECLARE_CPU_SPECS(T)                                                   \
+  DECLARE_CPU_SPECS_T_INDEX(T, int);                                           \
+  DECLARE_CPU_SPECS_T_INDEX(T, long);
+
+DECLARE_CPU_SPECS(float);
+// DECLARE_CPU_SPECS(double);
+DECLARE_CPU_SPECS(at::Half);
+
+#undef DECLARE_CPU_SPECS
+#undef DECLARE_CPU_SPECS_T_INDEX
+
+} // namespace spconv
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/reordering.cu b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/reordering.cu
new file mode 100644
index 000000000..78bc5dad3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/spconv/reordering.cu
@@ -0,0 +1,155 @@
+// Copyright 2019 Yan Yan
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <ATen/ATen.h>
+#include <chrono>
+#include <limits>
+#include <spconv/mp_helper.h>
+#include <spconv/reordering.h>
+#include <spconv/reordering.cu.h>
+#include <tensorview/helper_kernel.cu.h>
+#include <tensorview/helper_launch.h>
+#include <tensorview/tensorview.h>
+#include <type_traits>
+#include <utility/timer.h>
+
+namespace spconv {
+namespace functor {
+template <typename T, typename Index>
+struct SparseGatherFunctor<tv::GPU, T, Index> {
+  using vecload_type_t =
+      std::conditional_t<std::is_same<T, at::Half>::value, int2, int4>;
+  using kernel_block_t = mp_list_c<int, 64, 32, 16>;
+  void operator()(const tv::GPU &d, tv::TensorView<T> buffer,
+                  tv::TensorView<const T> features,
+                  tv::TensorView<const Index> indices, int size) {
+    if (size <= 0)
+      return;
+    int numPlanes = features.dim(1);
+    bool notFound = true;
+    constexpr int vecloadFactor = sizeof(vecload_type_t) / sizeof(T);
+    mp_for_each<kernel_block_t>([=, &buffer, &features, &indices,
+                                 &notFound](auto NumTLP) {
+      constexpr int NumILP = NumTLP / 4;
+      // constexpr int NumILP = NumTLP / (64 / (NumTLP / vecloadFactor));
+      int nHotBlock = (size / NumTLP) * NumTLP;
+      if (notFound) {
+        if (numPlanes % NumTLP == 0) {
+          if (nHotBlock >= NumTLP) {
+            gatherVecBlockKernel<T, Index, int(NumTLP), NumILP, vecload_type_t>
+                <<<dim3(numPlanes / NumTLP, size / NumTLP),
+                   dim3(NumTLP / vecloadFactor, NumTLP / NumILP), 0,
+                   d.stream()>>>(buffer.data(), features.data(), indices.data(),
+                                 nHotBlock, numPlanes / vecloadFactor);
+
+            TV_CHECK_CUDA_ERR();
+          }
+          if (size - nHotBlock > 0) {
+            gatherVecKernel<T, Index, int(NumTLP), NumILP, vecload_type_t>
+                <<<dim3(1, numPlanes / NumTLP),
+                   dim3(NumTLP / NumILP, NumTLP / vecloadFactor), 0,
+                   d.stream()>>>(buffer.data() + nHotBlock * numPlanes,
+                                 features.data(), indices.data() + nHotBlock,
+                                 size - nHotBlock, numPlanes / vecloadFactor);
+            TV_CHECK_CUDA_ERR();
+          }
+          notFound = false;
+        }
+      }
+    });
+
+    if (notFound) {
+      constexpr int NumTLP = 64;
+      constexpr int NumILP = NumTLP / 4;
+      gatherGenericKernel<T, Index, NumTLP, NumILP>
+          <<<dim3(tv::launch::DivUp(size, NumTLP),
+                  tv::launch::DivUp(numPlanes, NumTLP)),
+             dim3(NumTLP / NumILP, NumTLP), 0, d.stream()>>>(
+              buffer.data(), features.data(), indices.data(), size, numPlanes);
+      TV_CHECK_CUDA_ERR();
+    }
+  }
+};
+template <typename T, typename Index>
+struct SparseScatterAddFunctor<tv::GPU, T, Index> {
+  using vecload_type_t =
+      std::conditional_t<std::is_same<T, at::Half>::value, int2, int4>;
+  using kernel_block_t = mp_list_c<int, 64, 32, 16>;
+  void operator()(const tv::GPU &d, tv::TensorView<T> outFeatures,
+                  tv::TensorView<const T> buffer,
+                  tv::TensorView<const Index> indices, int size, bool stable) {
+    if (size <= 0)
+      return;
+    int numPlanes = outFeatures.dim(1);
+    bool notFound = true;
+    constexpr int vecloadFactor =
+        sizeof(vecload_type_t) / sizeof(T); // important for half.
+    mp_for_each<kernel_block_t>([=, &d, &outFeatures, &buffer, &indices,
+                                 &notFound](auto NumTLP) {
+      // constexpr int NumILP = NumTLP / (64 / (NumTLP / vecloadFactor));
+      constexpr int NumILP = NumTLP / 4;
+      int nHotBlock = (size / NumTLP) * NumTLP;
+      if (notFound) {
+        if (numPlanes % NumTLP == 0) {
+          if (nHotBlock >= NumTLP) {
+            scatterAddVecBlockKernel<T, Index, int(NumTLP), NumILP,
+                                     vecload_type_t>
+                <<<dim3(numPlanes / NumTLP, size / NumTLP),
+                   dim3(NumTLP / vecloadFactor, NumTLP / NumILP), 0,
+                   d.stream()>>>(outFeatures.data(), buffer.data(),
+                                 indices.data(), nHotBlock,
+                                 numPlanes / vecloadFactor);
+            TV_CHECK_CUDA_ERR();
+          }
+          if (size - nHotBlock > 0) {
+            scatterAddGenericKernel<T, Index, int(NumTLP), NumILP>
+                <<<dim3(1, numPlanes / NumTLP), dim3(NumTLP / NumILP, NumTLP),
+                   0, d.stream()>>>(
+                    outFeatures.data(), buffer.data() + nHotBlock * numPlanes,
+                    indices.data() + nHotBlock, size - nHotBlock, numPlanes);
+            TV_CHECK_CUDA_ERR();
+          }
+          notFound = false;
+        }
+      }
+    });
+    if (notFound) {
+      constexpr int NumTLP = 64;
+      constexpr int NumILP = NumTLP / 4;
+      scatterAddGenericKernel<T, Index, NumTLP, NumILP>
+          <<<dim3(tv::launch::DivUp(size, NumTLP),
+                  tv::launch::DivUp(numPlanes, NumTLP)),
+             dim3(NumTLP / NumILP, NumTLP), 0, d.stream()>>>(
+              outFeatures.data(), buffer.data(), indices.data(), size,
+              numPlanes);
+      TV_CHECK_CUDA_ERR();
+    }
+  }
+};
+} // namespace functor
+
+
+#define DECLARE_GPU_SPECS_T_INDEX(T, Index)                                    \
+  template struct functor::SparseGatherFunctor<tv::GPU, T, Index>;             \
+  template struct functor::SparseScatterAddFunctor<tv::GPU, T, Index>;
+
+#define DECLARE_GPU_SPECS(T) DECLARE_GPU_SPECS_T_INDEX(T, int);
+
+DECLARE_GPU_SPECS(float);
+// DECLARE_GPU_SPECS(double);
+DECLARE_GPU_SPECS(at::Half);
+
+#undef DECLARE_GPU_SPECS
+#undef DECLARE_GPU_SPECS_T_INDEX
+} // namespace spconv
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/CMakeLists.txt
new file mode 100644
index 000000000..5dc8facee
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/CMakeLists.txt
@@ -0,0 +1,22 @@
+# add_library(spconv_nms SHARED nms.cu)
+# set_target_properties(spconv_nms PROPERTIES VERSION ${PROJECT_VERSION})
+# set_target_properties(spconv_nms PROPERTIES SOVERSION 1)
+# target_include_directories(spconv_nms PRIVATE ${ALL_INCLUDE})
+# set_property(TARGET spconv_nms PROPERTY CXX_STANDARD 14)
+# set_property(TARGET spconv_nms PROPERTY CUDA_STANDARD 14)
+# target_link_libraries(spconv_nms ${CUDA_CUDART})
+
+# install (TARGETS spconv_nms DESTINATION lib)
+
+add_library(spconv_utils SHARED all.cc)
+set_target_properties(spconv_utils PROPERTIES VERSION ${PROJECT_VERSION})
+set_target_properties(spconv_utils PROPERTIES SOVERSION 1)
+target_include_directories(spconv_utils PRIVATE ${ALL_INCLUDE}
+                    ${PROJECT_SOURCE_DIR}/third_party/pybind11/include)
+set_property(TARGET spconv_utils PROPERTY CXX_STANDARD 14)
+set_property(TARGET spconv_utils PROPERTY CUDA_STANDARD 14)
+set_target_properties(spconv_utils PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}"
+                                         SUFFIX "${PYTHON_MODULE_EXTENSION}")
+# target_link_libraries(spconv_utils ${CUDA_CUDART} pybind11::module -Wl,--no-as-needed spconv_nms)
+
+install (TARGETS spconv_utils DESTINATION lib)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/all.cc b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/all.cc
new file mode 100644
index 000000000..3e1e37151
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/all.cc
@@ -0,0 +1,54 @@
+// Copyright 2019 Yan Yan
+// 
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// 
+//     http://www.apache.org/licenses/LICENSE-2.0
+// 
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// #include <spconv/nms.h>
+#include <spconv/point2voxel.h>
+#include <spconv/box_iou.h>
+namespace py = pybind11;
+using namespace pybind11::literals;
+
+PYBIND11_MODULE(spconv_utils, m)
+{
+    m.doc() = "util pybind11 functions for spconv";
+//     m.def("non_max_suppression", &spconv::non_max_suppression<double>, py::return_value_policy::reference_internal, "bbox iou",
+//           "boxes"_a = 1, "keep_out"_a = 2, "nms_overlap_thresh"_a = 3, "device_id"_a = 4);
+//     m.def("non_max_suppression", &spconv::non_max_suppression<float>, py::return_value_policy::reference_internal, "bbox iou",
+//           "boxes"_a = 1, "keep_out"_a = 2, "nms_overlap_thresh"_a = 3, "device_id"_a = 4);
+//     m.def("non_max_suppression_cpu", &spconv::non_max_suppression_cpu<double>, py::return_value_policy::reference_internal, "bbox iou",
+//           "boxes"_a = 1, "order"_a = 2, "nms_overlap_thresh"_a = 3, "eps"_a = 4);
+//     m.def("non_max_suppression_cpu", &spconv::non_max_suppression_cpu<float>, py::return_value_policy::reference_internal, "bbox iou",
+//           "boxes"_a = 1, "order"_a = 2, "nms_overlap_thresh"_a = 3, "eps"_a = 4);
+//     m.def("rotate_non_max_suppression_cpu", &spconv::rotate_non_max_suppression_cpu<float>, py::return_value_policy::reference_internal, "bbox iou",
+//           "box_corners"_a = 1, "order"_a = 2, "standup_iou"_a = 3, "thresh"_a = 4);
+//     m.def("rotate_non_max_suppression_cpu", &spconv::rotate_non_max_suppression_cpu<double>, py::return_value_policy::reference_internal, "bbox iou",
+//           "box_corners"_a = 1, "order"_a = 2, "standup_iou"_a = 3, "thresh"_a = 4);
+//     m.def("rbbox_iou", &spconv::rbbox_iou<double>,
+//           py::return_value_policy::reference_internal, "rbbox iou",
+//           "box_corners"_a = 1, "qbox_corners"_a = 2, "standup_iou"_a = 3,
+//           "standup_thresh"_a = 4);
+    m.def("rbbox_iou", &spconv::rbbox_iou<float>,
+          py::return_value_policy::reference_internal, "rbbox iou",
+          "box_corners"_a = 1, "qbox_corners"_a = 2, "standup_iou"_a = 3,
+          "standup_thresh"_a = 4);
+    m.def("points_to_voxel_3d_np", &spconv::points_to_voxel_3d_np<float, 3>,
+          "matrix tensor_square", "points"_a = 1, "voxels"_a = 2, "coors"_a = 3,
+          "num_points_per_voxel"_a = 4, "coor_to_voxelidx"_a = 5,
+          "voxel_size"_a = 6, "coors_range"_a = 7, "max_points"_a = 8,
+          "max_voxels"_a = 9);
+//     m.def("points_to_voxel_3d_np", &spconv::points_to_voxel_3d_np<double, 3>,
+//           "matrix tensor_square", "points"_a = 1, "voxels"_a = 2, "coors"_a = 3,
+//           "num_points_per_voxel"_a = 4, "coor_to_voxelidx"_a = 5,
+//           "voxel_size"_a = 6, "coors_range"_a = 7, "max_points"_a = 8,
+//           "max_voxels"_a = 9);
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/nms.cu b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/nms.cu
new file mode 100644
index 000000000..b2cd49f8a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/src/utils/nms.cu
@@ -0,0 +1,165 @@
+// ------------------------------------------------------------------
+// Deformable Convolutional Networks
+// Copyright (c) 2015 Microsoft
+// Licensed under The MIT License
+// Modified from MATLAB Faster R-CNN (https://github.com/shaoqingren/faster_rcnn)
+// ------------------------------------------------------------------
+#include <vector>
+#include <iostream>
+#include <cuda_runtime.h>
+#include <spconv/nms_gpu.h>
+
+#define CUDA_CHECK(condition)                               \
+  /* Code block avoids redefinition of cudaError_t error */ \
+  do                                                        \
+  {                                                         \
+    cudaError_t error = condition;                          \
+    if (error != cudaSuccess)                               \
+    {                                                       \
+      std::cout << cudaGetErrorString(error) << std::endl;  \
+    }                                                       \
+  } while (0)
+
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+int const threadsPerBlock = sizeof(unsigned long long) * 8;
+
+template <typename DType>
+__device__ inline DType devIoU(DType const *const a, DType const *const b)
+{
+  DType left = max(a[0], b[0]), right = min(a[2], b[2]);
+  DType top = max(a[1], b[1]), bottom = min(a[3], b[3]);
+  DType width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
+  DType interS = width * height;
+  DType Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
+  DType Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
+  return interS / (Sa + Sb - interS);
+}
+
+template <typename DType, int BLOCK_THREADS>
+__global__ void nms_kernel(const int n_boxes, const DType nms_overlap_thresh,
+                           const DType *dev_boxes, unsigned long long *dev_mask)
+{
+  const int row_start = blockIdx.y;
+  const int col_start = blockIdx.x;
+
+  // if (row_start > col_start) return;
+
+  const int row_size =
+      min(n_boxes - row_start * BLOCK_THREADS, BLOCK_THREADS);
+  const int col_size =
+      min(n_boxes - col_start * BLOCK_THREADS, BLOCK_THREADS);
+
+  __shared__ DType block_boxes[BLOCK_THREADS * 5];
+  if (threadIdx.x < col_size)
+  {
+#pragma unroll
+    for (int i = 0; i < 5; ++i)
+    {
+      block_boxes[threadIdx.x * 5 + i] =
+          dev_boxes[(BLOCK_THREADS * col_start + threadIdx.x) * 5 + i];
+    }
+  }
+  __syncthreads();
+
+  if (threadIdx.x < row_size)
+  {
+    const int cur_box_idx = BLOCK_THREADS * row_start + threadIdx.x;
+    const DType *cur_box = dev_boxes + cur_box_idx * 5;
+    unsigned long long t = 0;
+    int start = 0;
+    if (row_start == col_start)
+    {
+      start = threadIdx.x + 1;
+    }
+    for (int i = start; i < col_size; i++)
+    {
+      if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh)
+      {
+        t |= 1ULL << i;
+      }
+    }
+    const int col_blocks = DIVUP(n_boxes, BLOCK_THREADS);
+    dev_mask[cur_box_idx * col_blocks + col_start] = t;
+  }
+}
+
+void _set_device(int device_id)
+{
+  int current_device;
+  CUDA_CHECK(cudaGetDevice(&current_device));
+  if (current_device == device_id)
+  {
+    return;
+  }
+  // The call to cudaSetDevice must come before any calls to Get, which
+  // may perform initialization using the GPU.
+  CUDA_CHECK(cudaSetDevice(device_id));
+}
+
+template <typename DType, int BLOCK_THREADS>
+int _nms_gpu(int *keep_out, const DType *boxes_host, int boxes_num,
+          int boxes_dim, DType nms_overlap_thresh, int device_id)
+{
+  _set_device(device_id);
+
+  DType *boxes_dev = NULL;
+  unsigned long long *mask_dev = NULL;
+
+  const int col_blocks = DIVUP(boxes_num, BLOCK_THREADS);
+
+  CUDA_CHECK(cudaMalloc(&boxes_dev,
+                        boxes_num * boxes_dim * sizeof(DType)));
+  CUDA_CHECK(cudaMemcpy(boxes_dev,
+                        boxes_host,
+                        boxes_num * boxes_dim * sizeof(DType),
+                        cudaMemcpyHostToDevice));
+
+  CUDA_CHECK(cudaMalloc(&mask_dev,
+                        boxes_num * col_blocks * sizeof(unsigned long long)));
+
+  dim3 blocks(DIVUP(boxes_num, BLOCK_THREADS),
+              DIVUP(boxes_num, BLOCK_THREADS));
+  dim3 threads(BLOCK_THREADS);
+  nms_kernel<DType, BLOCK_THREADS><<<blocks, threads>>>(boxes_num,
+                                  nms_overlap_thresh,
+                                  boxes_dev,
+                                  mask_dev);
+
+  std::vector<unsigned long long> mask_host(boxes_num * col_blocks);
+  CUDA_CHECK(cudaMemcpy(&mask_host[0],
+                        mask_dev,
+                        sizeof(unsigned long long) * boxes_num * col_blocks,
+                        cudaMemcpyDeviceToHost));
+
+  std::vector<unsigned long long> remv(col_blocks);
+  memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks);
+
+  int num_to_keep = 0;
+  for (int i = 0; i < boxes_num; i++)
+  {
+    int nblock = i / BLOCK_THREADS;
+    int inblock = i % BLOCK_THREADS;
+
+    if (!(remv[nblock] & (1ULL << inblock)))
+    {
+      keep_out[num_to_keep++] = i;
+      unsigned long long *p = &mask_host[0] + i * col_blocks;
+      for (int j = nblock; j < col_blocks; j++)
+      {
+        remv[j] |= p[j];
+      }
+    }
+  }
+
+  CUDA_CHECK(cudaFree(boxes_dev));
+  CUDA_CHECK(cudaFree(mask_dev));
+  return num_to_keep;
+}
+
+
+//template<>
+template int _nms_gpu<float, threadsPerBlock>(int *keep_out, const float *boxes_host, int boxes_num,
+          int boxes_dim, float nms_overlap_thresh, int device_id);
+//template<>
+// template int _nms_gpu<double, threadsPerBlock>(int *keep_out, const double *boxes_host, int boxes_num,
+//           int boxes_dim, double nms_overlap_thresh, int device_id);
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/CMakeLists.txt
new file mode 100644
index 000000000..028701ef1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/CMakeLists.txt
@@ -0,0 +1,27 @@
+set(CATCH_HEADER ${PROJECT_SOURCE_DIR}/third_party/catch2)
+add_library(catch_main OBJECT src/catch_main.cpp)
+# target_compile_features(catch_main PUBLIC cxx_std_2a)
+set_property(TARGET catch_main PROPERTY CXX_STANDARD 14)
+target_include_directories(catch_main PRIVATE ${CATCH_HEADER})
+
+file(GLOB files "src/test_*.cpp")
+foreach(file ${files})
+    get_filename_component(file_basename ${file} NAME_WE)
+    string(REGEX REPLACE "test_([^$]+)" "test-\\1" testcase ${file_basename})
+
+    add_executable(${testcase} ${file} $<TARGET_OBJECTS:catch_main>)
+    set_property(TARGET ${testcase} PROPERTY CXX_STANDARD 14)
+    # set_target_properties(${testcase} PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
+    # set_property(TARGET ${testcase} PROPERTY CUDA_STANDARD 14)
+
+    target_compile_definitions(${testcase} PRIVATE
+        CATCH_CONFIG_FAST_COMPILE
+    )
+    target_include_directories(${testcase} PRIVATE
+        ${CATCH_HEADER} ${ALL_INCLUDE}
+    )
+    target_link_libraries(${testcase} ${ALL_LIBS} pybind11::embed -Wl,--no-as-needed spconv)
+    add_test(NAME "${testcase}" 
+        COMMAND ${testcase} 
+        WORKING_DIRECTORY ${CMAKE_SOURCE_DIR})
+endforeach()
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/src/catch_main.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/src/catch_main.cpp
new file mode 100644
index 000000000..c8bf91e45
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/src/catch_main.cpp
@@ -0,0 +1,15 @@
+// 000-CatchMain.cpp
+
+// In a Catch project with multiple files, dedicate one file to compile the
+// source code of Catch itself and reuse the resulting object file for linking.
+
+// Let Catch provide main():
+#define CATCH_CONFIG_MAIN
+
+#include "catch.hpp"
+
+// That's it
+
+// Compile implementation of Catch for use with files that do contain tests:
+// - g++ -std=c++11 -Wall -I$(CATCH_SINGLE_INCLUDE) -c 000-CatchMain.cpp
+// - cl -EHsc -I%CATCH_SINGLE_INCLUDE% -c 000-CatchMain.cpp
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/src/test_conv_rule.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/src/test_conv_rule.cpp
new file mode 100644
index 000000000..fd00070b9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/src/test_conv_rule.cpp
@@ -0,0 +1,127 @@
+
+
+#include <algorithm>
+#include <iostream>
+#include <map>
+#include "catch.hpp"
+#include <prettyprint.h>
+#include <string>
+#include <vector>
+#include <exception>
+#include <fmt/format.h>
+#include <numeric>
+#include <pybind11/embed.h> // everything needed for embedding
+#include <pybind11/functional.h>
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <tuple>
+#include <pybind11_utils.h>
+#include <spconv/spconv_ops.h>
+
+namespace py = pybind11;
+
+TEST_CASE("GetConvIndPair", "[SpConvNet]")
+{
+    
+    using namespace py::literals;
+    py::scoped_interpreter guard{}; // start the interpreter and keep it alive
+    py::exec(R"(
+    from __future__ import print_function
+    import numpy as np
+    import math
+    # import spconv
+    # import torch
+
+    def get_convolution_output_size(input_size,
+                                    kernel_size,
+                                    stride,
+                                    padding=None,
+                                    rate=None):
+        ndim = len(input_size)
+        if padding is None:
+            padding = [0] * ndim
+
+        output_size = []
+        for i in range(ndim):
+            output_size.append((input_size[i] + 2 * padding[i] - (
+                (kernel_size[i] - 1) + 1)) // stride[i] + 1)
+        return output_size
+
+
+    def get_test_sparse_data(shape,
+                            num_points,
+                            num_channels,
+                            integer=False,
+                            dtype=np.float32):
+        dense_shape = shape
+        ndim = len(dense_shape)
+        # num_points = np.random.randint(10, 100, size=[batch_size, ndim])
+        num_points = np.array(num_points)
+        # num_points = np.array([3, 2])
+        batch_size = len(num_points)
+        batch_indices = []
+        coors_total = np.stack(
+            np.meshgrid(*[np.arange(0, s) for s in shape]), axis=-1)
+        coors_total = coors_total.reshape(-1, ndim)
+        for i in range(batch_size):
+            np.random.shuffle(coors_total)
+            inds_total = coors_total[:num_points[i]]
+            inds_total = np.pad(
+                inds_total, ((0, 0), (0, 1)), mode="constant", constant_values=i)
+            batch_indices.append(inds_total)
+        if integer:
+            sparse_data = np.random.randint(
+                20, 100, size=[num_points.sum(), num_channels]).astype(dtype)
+        else:
+            sparse_data = np.random.uniform(
+                -1, 1, size=[num_points.sum(), num_channels]).astype(dtype)
+
+        # sparse_data = np.arange(1, num_points.sum() + 1).astype(np.float32).reshape(5, 1)
+        dense_data = np.zeros(
+            [batch_size, num_channels, *dense_shape], dtype=sparse_data.dtype)
+        start = 0
+        for i, inds in enumerate(batch_indices):
+            for j, ind in enumerate(inds):
+                dense_slice = (i, slice(None), *ind[:-1])
+                dense_data[dense_slice] = sparse_data[start + j]
+            start += len(inds)
+        batch_indices = np.concatenate(batch_indices, axis=0)
+        return {
+            "features": sparse_data.astype(dtype),
+            "indices": batch_indices.astype(np.int32),
+            "features_dense": dense_data.astype(dtype),
+        }
+    shape = [50, 30, 30]
+    num_points = [5000] * 1
+    # np.random.seed(np.random.randint(1, 100000))
+    in_channels = 64
+    sparse_dict = get_test_sparse_data(shape, num_points, in_channels)
+    features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+    indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+    features_dense = sparse_dict["features_dense"]
+    # indices_t = torch.from_numpy(indices)
+    filters = np.random.uniform(0, 1, size=[3, 3, 3, 64, 64]).astype(np.float32)
+    # print(outids.shape)
+    )");
+    SECTION("DebugTest"){
+
+        auto inds = array2TensorView<int>(py::array(py::globals()["indices"]));
+        auto inds_tensor = torch::from_blob(inds.data(), {inds.dim(0), inds.dim(1)}, torch::dtype(torch::kInt32));
+        auto inds_gpu = inds_tensor.to(torch::Device(torch::kCPU));
+        
+        auto features = array2TensorView<float>(py::array(py::globals()["features"]));
+        auto features_tensor = torch::from_blob(features.data(), {features.dim(0), features.dim(1)}, torch::dtype(torch::kFloat));
+        auto features_gpu = features_tensor.to(torch::Device(torch::kCUDA, 0));
+        auto filters = array2TensorView<float>(py::array(py::globals()["filters"]));
+        auto filters_tensor = torch::from_blob(filters.data(), {filters.dim(0), filters.dim(1), filters.dim(2), filters.dim(3), filters.dim(4)}, torch::dtype(torch::kFloat));
+        auto filters_gpu = filters_tensor.to(torch::Device(torch::kCUDA, 0));
+        
+        auto outputs = spconv::getIndicePair<3>(inds_gpu, 1, {46, 26, 26}, {50, 30, 30}, {3, 3, 3},
+            {1, 1, 1}, {0, 0, 0}, {2, 2, 2}, {0, 0, 0}, false, false);
+        // std::cout << outputs[2] << std::endl;
+        /*
+        auto output = spconv::indiceConv<float>(features_gpu, filters_gpu, outputs[1], outputs[2], outputs[0].size(0), false);
+        std::cout << output << std::endl;*/
+    }
+}
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConv2d.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConv2d.py
new file mode 100644
index 000000000..961d3769c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConv2d.py
@@ -0,0 +1,125 @@
+from pathlib import Path
+import spconv 
+import torch
+from torch import nn 
+import numpy as np 
+import time 
+from spconv.test_utils import params_grid, generate_sparse_data, TestCase
+import unittest
+
+class SparseConv2dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [spconv.SparseConv2d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(spconv.SparseConv2d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = spconv.SparseSequential(
+            *layers,
+        )
+        # self.grid = torch.full([3, *shape], -1, dtype=torch.int32).cuda()
+        self.grid = None
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size, self.grid)
+        return self.net(x)# .dense()
+
+def gather_nd(params, indices):
+    # this function has a limit that MAX_ADVINDEX_CALC_DIMS=5
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + list(params.shape[indices.shape[-1]:])
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    return params[slices].view(*output_shape)
+
+def scatter_nd(indices, updates, shape):
+    """pytorch edition of tensorflow scatter_nd.
+    this function don't contain except handle code. so use this carefully
+    when indice repeats, don't support repeat add which is supported
+    in tensorflow.
+    """
+    ret = torch.zeros(*shape, dtype=updates.dtype, device=updates.device)
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + shape[indices.shape[-1]:]
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    ret[slices] = updates.view(*output_shape)
+    return ret
+
+class TestSpConv(TestCase):
+    
+    def testSpConv2d(self):
+        np.random.seed(484)
+        devices = ["cuda:0"]
+        shapes = [[18, 17]]
+        batchsizes = [1]
+
+        in_channels = [64]
+        out_channels = [32]
+        ksizes = [2]
+        strides = [1]
+        paddings = [0]
+        dilations = [1]
+
+        for dev, shape, bs, IC, OC, k, s, p, d in params_grid(
+            devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+            strides, paddings, dilations):
+            if all([s > 1, d > 1]):
+                continue # don't support this.
+            device = torch.device(dev)
+            num_points = [1000] * bs
+
+            sparse_dict = generate_sparse_data(shape, num_points, IC)
+            #print(sparse_dict)
+
+            features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+            indices = np.ascontiguousarray(sparse_dict["indices"][:, [2, 0, 1]]).astype(np.int32)
+            features_dense = sparse_dict["features_dense"].astype(np.float32)
+            filters = np.random.uniform(0, 1, size=[k, k, IC, OC]).astype(np.float32)
+            indices_t = torch.from_numpy(indices).int().to(device)
+            features_t = torch.from_numpy(features).to(device)
+            features_t.requires_grad = True
+            features_dense_t = torch.from_numpy(features_dense).to(device)
+            features_dense_t.requires_grad = True
+            net = SparseConv2dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device)
+            # print(net)
+
+            filters_t = torch.from_numpy(filters).to(device)
+            net.net[0].weight.data[:] = filters_t
+            out = net(features_t, indices_t, bs).dense()
+            dout = np.random.uniform(-0.2, 0.2, out.shape).astype(features.dtype)
+            dout_t = torch.from_numpy(dout).to(device)
+            out.backward(dout_t)
+            # print(features_t.grad.cpu())
+            print("backward end")
+            # din_dense = features_dense_t.grad.detach().permute(0, 2, 3, 4, 1).contiguous()
+            # din_sparse = gather_nd(din_dense, indices_t.long())
+            # din = features_t.grad.detach()
+            # din_np = din.cpu().numpy()
+            # din_sparse_np = din_sparse.cpu().numpy()
+            # self.assertAllClose(din_np, din_sparse_np, atol=1e-4)
+
+def main():
+    test = TestSpConv()
+    test.testSpConv2d()
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConv3d.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConv3d.py
new file mode 100644
index 000000000..4e8e58117
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConv3d.py
@@ -0,0 +1,160 @@
+from pathlib import Path
+import spconv 
+import torch
+from torch import nn 
+import numpy as np 
+import time 
+from spconv.test_utils import params_grid, generate_sparse_data, TestCase
+import unittest
+
+class SparseConv3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [spconv.SparseConv3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(spconv.SparseConv3d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = spconv.SparseSequential(
+            *layers,
+        )
+        # self.grid = torch.full([3, *shape], -1, dtype=torch.int32).cuda()
+        self.grid = None
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size, self.grid)
+        return self.net(x)# .dense()
+
+class Conv3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [nn.Conv3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(nn.Conv3d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = nn.Sequential(
+            *layers,
+        )
+        self.shape = shape
+
+    def forward(self, x):
+        return self.net(x)# .dense()
+
+def gather_nd(params, indices):
+    # this function has a limit that MAX_ADVINDEX_CALC_DIMS=5
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + list(params.shape[indices.shape[-1]:])
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    return params[slices].view(*output_shape)
+
+def scatter_nd(indices, updates, shape):
+    """pytorch edition of tensorflow scatter_nd.
+    this function don't contain except handle code. so use this carefully
+    when indice repeats, don't support repeat add which is supported
+    in tensorflow.
+    """
+    ret = torch.zeros(*shape, dtype=updates.dtype, device=updates.device)
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + shape[indices.shape[-1]:]
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    ret[slices] = updates.view(*output_shape)
+    return ret
+
+class TestSpConv(TestCase):
+    
+    def testSpConv3d(self):
+        np.random.seed(484)
+        devices = ["cuda:0"]
+        shapes = [[30, 20, 10]]
+        batchsizes = [1]
+
+        in_channels = [64]
+        out_channels = [32]
+        ksizes = [2]
+        strides = [1]
+        paddings = [0]
+        dilations = [1]
+
+        for dev, shape, bs, IC, OC, k, s, p, d in params_grid(
+            devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+            strides, paddings, dilations):
+            if all([s > 1, d > 1]):
+                continue # don't support this.
+            num_points = [1000] * bs
+            device = torch.device(dev)
+
+            sparse_dict = generate_sparse_data(shape, num_points, IC)
+            #print(sparse_dict)
+
+            features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+            indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+            features_dense = sparse_dict["features_dense"].astype(np.float32)
+            filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+            indices_t = torch.from_numpy(indices).int().to(device)
+            # print(features.shape)
+            features_t = torch.from_numpy(features).to(device)
+            features_t.requires_grad = True
+            # print(features_dense.shape)
+            features_dense_t = torch.from_numpy(features_dense).to(device)
+            # print(features_dense_t)
+            features_dense_t.requires_grad = True
+            net = SparseConv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device)
+            # print(net)
+            net_ref = Conv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device)
+            filters_t = torch.from_numpy(filters).to(device)
+            net_ref.net[0].weight.data[:] = filters_t.permute(4, 3, 0, 1, 2).contiguous()
+            net.net[0].weight.data[:] = filters_t
+            out_ref = net_ref(features_dense_t)
+            out = net(features_t, indices_t, bs).dense()
+            # print(out)
+            dout = np.random.uniform(-0.2, 0.2, out_ref.shape).astype(features.dtype)
+            dout_t = torch.from_numpy(dout).to(device)
+            out.backward(dout_t)
+            print("backward end")
+            print(np.linalg.norm(out.detach().cpu().numpy() - out_ref.detach().cpu().numpy()))
+            # print(features_t.grad)
+            # din_dense = features_dense_t.grad.detach().permute(0, 2, 3, 4, 1).contiguous()
+            # din_sparse = gather_nd(din_dense, indices_t.long())
+            # din = features_t.grad.detach()
+            # din_np = din.cpu().numpy()
+            # din_sparse_np = din_sparse.cpu().numpy()
+
+def main():
+    test = TestSpConv()
+    test.testSpConv3d()
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConvTensor.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConvTensor.py
new file mode 100644
index 000000000..3050666c2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseConvTensor.py
@@ -0,0 +1,20 @@
+import spconv
+from spconv.test_utils import params_grid, generate_sparse_data
+
+import numpy as np
+import torch
+
+np.random.seed(484)
+device = torch.device("cuda:0")
+
+batchsize = 1
+
+features = np.random.rand(1000,64).astype(np.float32)
+features = torch.from_numpy(features)
+indices = np.random.rand(1000,4).astype(np.int32)
+indices = torch.from_numpy(indices)
+shape = [19, 18, 17]
+
+x = spconv.SparseConvTensor(features, indices, shape, batchsize)
+x_dense_NCHW = x.dense()
+print(x.sparity)
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseInverseConv2d.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseInverseConv2d.py
new file mode 100644
index 000000000..0b5a5c099
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseInverseConv2d.py
@@ -0,0 +1,90 @@
+from pathlib import Path
+import spconv 
+import torch
+from torch import nn 
+import numpy as np 
+import time 
+from spconv.test_utils import params_grid, generate_sparse_data, TestCase
+import unittest
+
+class SparseCoupleDeConvTest(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride):
+        super().__init__()
+        self.net = spconv.SparseSequential(
+            spconv.SparseConv2d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                indice_key="cp0",
+                bias=False),
+            spconv.SparseInverseConv2d(
+                out_channels,
+                in_channels,
+                kernel_size,
+                indice_key="cp0",
+                bias=False),
+            
+        )
+        self.todense = spconv.ToDense()
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size )
+        return self.todense(self.net(x))# .dense()
+
+def main():
+    np.random.seed(484)
+    devices = ["cuda:0"]
+    shapes = [[20, 20]]
+    batchsizes = [1]
+
+    in_channels = [64]
+    out_channels = [32]
+    ksizes = [2]
+    strides = [2]
+    paddings = [0, 1]
+    dilations = [1, 2]
+
+    for dev, shape, bs, IC, OC, k, s in params_grid(
+        devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+        strides):
+        device = torch.device(dev)
+        num_points = [1000] * bs
+
+        sparse_dict = generate_sparse_data(shape, num_points, IC)
+
+        features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+        indices = np.ascontiguousarray(sparse_dict["indices"][:, [2, 0, 1]]).astype(np.int32)
+        features_dense = sparse_dict["features_dense"].astype(np.float32)
+        filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+        indices_t = torch.from_numpy(indices).int().to(device)
+        features_t = torch.from_numpy(features).to(device)
+        features_t.requires_grad = True
+        features_ref_t = torch.from_numpy(features).to(device)
+        features_ref_t.requires_grad = True
+        
+        filters_0 = np.random.uniform(0, 1, size=[k, k, IC, OC]).astype(np.float32)
+        filters_1 = np.random.uniform(0, 1, size=[k, k, OC, IC]).astype(np.float32)
+        
+        filters_t_0 = torch.from_numpy(filters_0).to(device)
+        filters_t_1 = torch.from_numpy(filters_1).to(device)
+
+        net = SparseCoupleDeConvTest(1, 2, shape, IC, OC, k, s).to(device)
+        net.net[0].weight.data[:] = filters_t_0
+        net.net[1].weight.data[:] = filters_t_1
+        # print(net.net[0].weight.shape)
+        out = net(features_t, indices_t, bs)
+        print(out.detach().cpu().numpy())
+        dout = np.random.uniform(-0.2, 0.2, out.shape).astype(features.dtype)
+        dout_t = torch.from_numpy(dout).to(device)
+        out.backward(dout_t)
+        din = features_t.grad.detach()
+        din_np = din.cpu().numpy()
+        print(din_np)
+
+if __name__ == '__main__':
+    main()
+    # unittest.main()
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseInverseConv3d.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseInverseConv3d.py
new file mode 100644
index 000000000..12d7ee43b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SparseInverseConv3d.py
@@ -0,0 +1,91 @@
+from pathlib import Path
+import spconv 
+import torch
+from torch import nn 
+import numpy as np 
+import time 
+from spconv.test_utils import params_grid, generate_sparse_data, TestCase
+import unittest
+
+class SparseCoupleDeConvTest(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride):
+        super().__init__()
+        self.net = spconv.SparseSequential(
+            spconv.SparseConv3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                indice_key="cp0",
+                bias=False),
+            spconv.SparseInverseConv3d(
+                out_channels,
+                in_channels,
+                kernel_size,
+                indice_key="cp0",
+                bias=False),
+            
+        )
+        self.todense = spconv.ToDense()
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size )
+        return self.todense(self.net(x))# .dense()
+
+def main():
+    np.random.seed(484)
+    devices = ["cuda:0"]
+    shapes = [[20, 20, 20]]
+    batchsizes = [1]
+
+    in_channels = [64]
+    out_channels = [32]
+    ksizes = [2]
+    strides = [2]
+    paddings = [0, 1, 2]
+    dilations = [1, 2, 3]
+
+    for dev, shape, bs, IC, OC, k, s in params_grid(
+        devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+        strides):
+        device = torch.device(dev)
+        num_points = [1000] * bs
+
+        sparse_dict = generate_sparse_data(shape, num_points, IC)
+
+        features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+        indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+        features_dense = sparse_dict["features_dense"].astype(np.float32)
+        filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+        indices_t = torch.from_numpy(indices).int().to(device)
+        indices_scn_t = torch.from_numpy(indices[:, [1, 2, 3, 0]]).int().to(device)
+        features_t = torch.from_numpy(features).to(device)
+        features_t.requires_grad = True
+        features_ref_t = torch.from_numpy(features).to(device)
+        features_ref_t.requires_grad = True
+        
+        filters_0 = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+        filters_1 = np.random.uniform(0, 1, size=[k, k, k, OC, IC]).astype(np.float32)
+        
+        filters_t_0 = torch.from_numpy(filters_0).to(device)
+        filters_t_1 = torch.from_numpy(filters_1).to(device)
+        
+        net = SparseCoupleDeConvTest(1, 3, shape, IC, OC, k, s).to(device)
+        net.net[0].weight.data[:] = filters_t_0
+        net.net[1].weight.data[:] = filters_t_1
+        # print(net.net[1].weight.shape)
+        out = net(features_t, indices_t, bs)
+        print(out.detach().cpu().numpy())
+        dout = np.random.uniform(-0.2, 0.2, out.shape).astype(features.dtype)
+        dout_t = torch.from_numpy(dout).to(device)
+        out.backward(dout_t)
+        din = features_t.grad.detach()
+        din_np = din.cpu().numpy()
+        print(din_np)
+
+if __name__ == '__main__':
+    main()
+    # unittest.main()
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SubMConv2d.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SubMConv2d.py
new file mode 100644
index 000000000..30cbb23be
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SubMConv2d.py
@@ -0,0 +1,126 @@
+from pathlib import Path
+import spconv 
+import torch
+from torch import nn 
+import numpy as np 
+import time 
+from spconv.test_utils import params_grid, generate_sparse_data, TestCase
+import unittest
+
+class SubMConv2dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [spconv.SubMConv2d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(spconv.SubMConv2d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = spconv.SparseSequential(
+            *layers,
+        )
+        self.grid = None
+        # self.grid = torch.full([3, *shape], -1, dtype=torch.int32).cuda()
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size, self.grid)
+        return self.net(x)# .dense()
+
+def gather_nd(params, indices):
+    # this function has a limit that MAX_ADVINDEX_CALC_DIMS=5
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + list(params.shape[indices.shape[-1]:])
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    return params[slices].view(*output_shape)
+
+def scatter_nd(indices, updates, shape):
+    """pytorch edition of tensorflow scatter_nd.
+    this function don't contain except handle code. so use this carefully
+    when indice repeats, don't support repeat add which is supported
+    in tensorflow.
+    """
+    ret = torch.zeros(*shape, dtype=updates.dtype, device=updates.device)
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + shape[indices.shape[-1]:]
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    ret[slices] = updates.view(*output_shape)
+    return ret
+
+class TestSpConv(TestCase):
+    
+    def testSpConv2d(self):
+        np.random.seed(484)
+        devices = ["cuda:0"]
+        shapes = [[18, 17]]
+        batchsizes = [1]
+
+        in_channels = [64]
+        out_channels = [32]
+        ksizes = [2]
+        strides = [1]
+        paddings = [0]
+        dilations = [1]
+
+        for dev, shape, bs, IC, OC, k, s, p, d in params_grid(
+            devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+            strides, paddings, dilations):
+            if all([s > 1, d > 1]):
+                continue # don't support this.
+            device = torch.device(dev)
+            num_points = [300] * bs
+
+            sparse_dict = generate_sparse_data(shape, num_points, IC)
+            #print(sparse_dict)
+
+            features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+            indices = np.ascontiguousarray(sparse_dict["indices"][:, [2, 0, 1]]).astype(np.int32)
+            features_dense = sparse_dict["features_dense"].astype(np.float32)
+            filters = np.random.uniform(0, 1, size=[k, k, IC, OC]).astype(np.float32)
+            indices_t = torch.from_numpy(indices).int().to(device)
+            features_t = torch.from_numpy(features).to(device)
+            features_t.requires_grad = True
+            features_dense_t = torch.from_numpy(features_dense).to(device)
+            features_dense_t.requires_grad = True
+            net = SubMConv2dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device)
+            # print(net)
+
+            filters_t = torch.from_numpy(filters).to(device)
+            net.net[0].weight.data[:] = filters_t
+            out = net(features_t, indices_t, bs).dense()
+            # print(out)
+            dout = np.random.uniform(-0.2, 0.2, out.shape).astype(features.dtype)
+            dout_t = torch.from_numpy(dout).to(device)
+            out.backward(dout_t)
+            # print(features_t.grad.cpu())
+            print("backward end")
+            # din_dense = features_dense_t.grad.detach().permute(0, 2, 3, 4, 1).contiguous()
+            # din_sparse = gather_nd(din_dense, indices_t.long())
+            # din = features_t.grad.detach()
+            # din_np = din.cpu().numpy()
+            # din_sparse_np = din_sparse.cpu().numpy()
+            # self.assertAllClose(din_np, din_sparse_np, atol=1e-4)
+
+def main():
+    test = TestSpConv()
+    test.testSpConv2d()
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SubMConv3d.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SubMConv3d.py
new file mode 100644
index 000000000..27ce94e3c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_SubMConv3d.py
@@ -0,0 +1,123 @@
+from pathlib import Path
+import spconv 
+import torch
+from torch import nn 
+import numpy as np 
+import time 
+from spconv.test_utils import params_grid, generate_sparse_data, TestCase
+import unittest
+
+class SubMConv3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [spconv.SubMConv3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(spconv.SubMConv3d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = spconv.SparseSequential(
+            *layers,
+        )
+        # self.grid = None
+        self.grid = torch.full([3, *shape], -1, dtype=torch.int32).cuda()
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size, self.grid)
+        return self.net(x)# .dense()
+
+def gather_nd(params, indices):
+    # this function has a limit that MAX_ADVINDEX_CALC_DIMS=5
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + list(params.shape[indices.shape[-1]:])
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    return params[slices].view(*output_shape)
+
+def scatter_nd(indices, updates, shape):
+    """pytorch edition of tensorflow scatter_nd.
+    this function don't contain except handle code. so use this carefully
+    when indice repeats, don't support repeat add which is supported
+    in tensorflow.
+    """
+    ret = torch.zeros(*shape, dtype=updates.dtype, device=updates.device)
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + shape[indices.shape[-1]:]
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    ret[slices] = updates.view(*output_shape)
+    return ret
+
+class TestSpConv(TestCase):
+    
+    def testSpConv3d(self):
+        np.random.seed(484)
+        devices = ["cuda:0"]
+        shapes = [[19, 18, 17]]
+        batchsizes = [1]
+
+        in_channels = [64]
+        out_channels = [32]
+        ksizes = [2]
+        strides = [1]
+        paddings = [0]
+        dilations = [1]
+
+        for dev, shape, bs, IC, OC, k, s, p, d in params_grid(
+            devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+            strides, paddings, dilations):
+            if all([s > 1, d > 1]):
+                continue # don't support this.
+            device = torch.device(dev)
+            num_points = [1000] * bs
+
+            sparse_dict = generate_sparse_data(shape, num_points, IC)
+
+            features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+            indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+            features_dense = sparse_dict["features_dense"].astype(np.float32)
+            filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+            indices_t = torch.from_numpy(indices).int().cuda()
+            features_t = torch.from_numpy(features).cuda()
+            features_t.requires_grad = True
+            features_dense_t = torch.from_numpy(features_dense).cuda()
+            features_dense_t.requires_grad = True
+            net = SubMConv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).cuda()
+
+            filters_t = torch.from_numpy(filters).cuda()
+            net.net[0].weight.data[:] = filters_t
+            out = net(features_t, indices_t, bs).dense()
+            dout = np.random.uniform(-0.2, 0.2, out.shape).astype(features.dtype)
+            dout_t = torch.from_numpy(dout).cuda()
+            out.backward(dout_t)
+            # print(features_t.grad.cpu())
+            print("backward end")
+            # din_dense = features_dense_t.grad.detach().permute(0, 2, 3, 4, 1).contiguous()
+            # din_sparse = gather_nd(din_dense, indices_t.long())
+            # din = features_t.grad.detach()
+            # din_np = din.cpu().numpy()
+            # din_sparse_np = din_sparse.cpu().numpy()
+            # self.assertAllClose(din_np, din_sparse_np, atol=1e-4)
+
+def main():
+    test = TestSpConv()
+    test.testSpConv3d()
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_conv.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_conv.py
new file mode 100644
index 000000000..4d7a5cd94
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/test/test_conv.py
@@ -0,0 +1,618 @@
+# Copyright 2019 Yan Yan
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+import spconv 
+import torch
+from torch import nn 
+import numpy as np 
+import time 
+from spconv.test_utils import params_grid, generate_sparse_data, TestCase
+import unittest
+import sparseconvnet as scn 
+
+class SparseConv3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [spconv.SparseConv3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(spconv.SparseConv3d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = spconv.SparseSequential(
+            *layers,
+        )
+        # self.grid = torch.full([3, *shape], -1, dtype=torch.int32).cuda()
+        self.grid = None
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size, self.grid)
+        return self.net(x)# .dense()
+
+class SubMConv3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [spconv.SubMConv3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(spconv.SubMConv3d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = spconv.SparseSequential(
+            *layers,
+        )
+        self.grid = torch.full([3, *shape], -1, dtype=torch.int32).cuda()
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size, self.grid)
+        return self.net(x)# .dense()
+
+
+class Conv3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [nn.Conv3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(nn.Conv3d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = nn.Sequential(
+            *layers,
+        )
+        self.shape = shape
+
+    def forward(self, x):
+        return self.net(x)# .dense()
+
+
+class SparseDeConv3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [spconv.SparseConvTranspose3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(spconv.SparseConvTranspose3d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = spconv.SparseSequential(
+            *layers,
+        )
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size)
+        return self.net(x)# .dense()
+
+class DeConv3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [nn.ConvTranspose3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False)]
+        for i in range(1, num_layers):
+            layers.append(nn.ConvTranspose3d(
+                out_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding=padding,
+                dilation=dilation,
+                bias=False))
+        self.net = nn.Sequential(
+            *layers,
+        )
+        self.shape = shape
+
+    def forward(self, x):
+        return self.net(x)# .dense()
+
+
+class SparseMaxPoolTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [spconv.SparseMaxPool3d(
+                kernel_size,
+                stride, padding, dilation)]
+        for i in range(1, num_layers):
+            layers.append(spconv.SparseMaxPool3d(
+                kernel_size,
+                stride, padding, dilation))
+        self.net = spconv.SparseSequential(
+            *layers,
+        )
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors, self.shape, batch_size )
+        return self.net(x)# .dense()
+
+class MaxPool3dTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, kernel_size,
+                 stride, padding, dilation):
+        super().__init__()
+        layers = [nn.MaxPool3d(
+                kernel_size,
+                stride, padding, dilation)]
+        for i in range(1, num_layers):
+            layers.append(nn.MaxPool3d(
+                kernel_size,
+                stride, padding, dilation))
+        self.net = nn.Sequential(
+            *layers,
+        )
+        self.shape = shape
+
+    def forward(self, x):
+        return self.net(x)# .dense()
+
+
+class SubmanifoldConvTestTorch(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size, stride):
+        super().__init__()
+        layers = [spconv.SubMConv3d(
+                in_channels, out_channels, kernel_size, bias=False, indice_key="subm0")]
+        for i in range(1, num_layers):
+            layers.append(spconv.SubMConv3d(
+                out_channels, out_channels, kernel_size, bias=False))
+        self.net = nn.Sequential(
+            *layers,
+        )
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors, self.shape, batch_size )
+        return self.net(x)
+
+class SCNCoupleDeConvTest(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride):
+        super().__init__()
+        self.scn_input = scn.InputLayer(ndim, shape, mode=0)
+        self.net = nn.Sequential(
+            scn.Convolution(
+                ndim,
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                bias=False),
+            scn.Deconvolution(
+                ndim,
+                out_channels,
+                in_channels,
+                kernel_size,
+                stride,
+                bias=False),
+            scn.SparseToDense(ndim, in_channels),
+        )
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.long().cpu()
+        x = self.scn_input((coors, features))
+        return self.net(x)
+
+class SparseCoupleDeConvTest(nn.Module):
+    def __init__(self, num_layers, ndim, shape, in_channels, out_channels, kernel_size,
+                 stride):
+        super().__init__()
+        self.net = spconv.SparseSequential(
+            spconv.SparseConv3d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                indice_key="cp0",
+                bias=False),
+            spconv.SparseInverseConv3d(
+                out_channels,
+                in_channels,
+                kernel_size,
+                indice_key="cp0",
+                bias=False),
+            
+        )
+        self.todense = spconv.ToDense()
+        self.shape = shape
+
+    def forward(self, features, coors, batch_size):
+        coors = coors.int()
+        x = spconv.SparseConvTensor(features, coors,self.shape, batch_size )
+        return self.todense(self.net(x))# .dense()
+
+
+def gather_nd(params, indices):
+    # this function has a limit that MAX_ADVINDEX_CALC_DIMS=5
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + list(params.shape[indices.shape[-1]:])
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    return params[slices].view(*output_shape)
+
+def scatter_nd(indices, updates, shape):
+    """pytorch edition of tensorflow scatter_nd.
+    this function don't contain except handle code. so use this carefully
+    when indice repeats, don't support repeat add which is supported
+    in tensorflow.
+    """
+    ret = torch.zeros(*shape, dtype=updates.dtype, device=updates.device)
+    ndim = indices.shape[-1]
+    output_shape = list(indices.shape[:-1]) + shape[indices.shape[-1]:]
+    flatted_indices = indices.view(-1, ndim)
+    slices = [flatted_indices[:, i] for i in range(ndim)]
+    slices += [Ellipsis]
+    ret[slices] = updates.view(*output_shape)
+    return ret
+
+
+class TestSpConv(TestCase):
+    
+    def testSpConv3d(self):
+        np.random.seed(484)
+        devices = ["cpu:0"]
+        shapes = [[19, 18, 17]]
+        batchsizes = [1, 2]
+
+        in_channels = [64]
+        out_channels = [32, 48, 64]
+        ksizes = [2, 3]
+        strides = [1, 2, 3]
+        paddings = [0, 1, 2]
+        dilations = [1, 2, 3]
+
+        for dev, shape, bs, IC, OC, k, s, p, d in params_grid(
+            devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+            strides, paddings, dilations):
+            if all([s > 1, d > 1]):
+                continue # don't support this.
+            device = torch.device(dev)
+            num_points = [1000] * bs
+
+            sparse_dict = generate_sparse_data(shape, num_points, IC)
+
+            features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+            indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+            features_dense = sparse_dict["features_dense"].astype(np.float32)
+            filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+            indices_t = torch.from_numpy(indices).int().to(device)
+            features_t = torch.from_numpy(features).to(device)
+            features_t.requires_grad = True
+            features_dense_t = torch.from_numpy(features_dense).to(device)
+            features_dense_t.requires_grad = True
+            net = SparseConv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device)
+            net_ref = Conv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device)
+            filters_t = torch.from_numpy(filters).to(device)
+            net_ref.net[0].weight.data[:] = filters_t.permute(4, 3, 0, 1, 2).contiguous()
+            net.net[0].weight.data[:] = filters_t
+            out_ref = net_ref(features_dense_t)
+            out = net(features_t, indices_t, bs).dense()
+            dout = np.random.uniform(-0.2, 0.2, out_ref.shape).astype(features.dtype)
+            dout_t = torch.from_numpy(dout).to(device)
+            out.backward(dout_t)
+            out_ref.backward(dout_t)
+            din_dense = features_dense_t.grad.detach().permute(0, 2, 3, 4, 1).contiguous()
+            din_sparse = gather_nd(din_dense, indices_t.long())
+            din = features_t.grad.detach()
+            din_np = din.cpu().numpy()
+            din_sparse_np = din_sparse.cpu().numpy()
+            self.assertAllClose(din_np, din_sparse_np, atol=1e-4)
+            for layer, layer_ref in zip(net.net, net_ref.net):
+                dw = layer.weight.grad.detach().cpu().numpy()
+                dw_ref = layer_ref.weight.grad.detach().cpu().numpy()
+                dw = dw.transpose(4, 3, 0, 1, 2)
+                self.assertAllClose(dw, dw_ref, atol=1e-4)
+
+            out_np = out.detach().cpu().numpy()
+            out_ref_np = out_ref.detach().cpu().numpy()
+            self.assertAllClose(out_np, out_ref_np, atol=1e-4)
+    
+    def testSpDeConv3d(self):
+        np.random.seed(484)
+        devices = ["cuda:0", "cpu:0"]
+        shapes = [[19, 18, 17]]
+        batchsizes = [1, 2]
+
+        in_channels = [64]
+        out_channels = [32, 48, 64]
+        ksizes = [2, 3]
+        strides = [2, 3]
+        paddings = [0, 1, 2]
+        dilations = [1, 2, 3]
+
+        for dev, shape, bs, IC, OC, k, s, p, d in params_grid(
+            devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+            strides, paddings, dilations):
+            if all([s > 1, d > 1]):
+                continue # don't support this.
+            device = torch.device(dev)
+            num_points = [1000] * bs
+
+            sparse_dict = generate_sparse_data(shape, num_points, IC)
+
+            features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+            indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+            features_dense = sparse_dict["features_dense"].astype(np.float32)
+            filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+            indices_t = torch.from_numpy(indices).int().to(device)
+            features_t = torch.from_numpy(features).to(device)
+            features_t.requires_grad = True
+            features_dense_t = torch.from_numpy(features_dense).to(device)
+            features_dense_t.requires_grad = True
+            net = SparseDeConv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device)
+            net_ref = DeConv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device)
+            filters_t = torch.from_numpy(filters).to(device)
+            net_ref.net[0].weight.data[:] = filters_t.permute(3, 4, 0, 1, 2).contiguous()
+            net.net[0].weight.data[:] = filters_t
+            out_ref = net_ref(features_dense_t)
+            out = net(features_t, indices_t, bs).dense()
+            dout = np.random.uniform(-0.2, 0.2, out_ref.shape).astype(features.dtype)
+            dout_t = torch.from_numpy(dout).to(device)
+            out.backward(dout_t)
+            out_ref.backward(dout_t)
+            din_dense = features_dense_t.grad.detach().permute(0, 2, 3, 4, 1).contiguous()
+            din_sparse = gather_nd(din_dense, indices_t.long())
+            din = features_t.grad.detach()
+            din_np = din.cpu().numpy()
+            din_sparse_np = din_sparse.cpu().numpy()
+            self.assertAllClose(din_np, din_sparse_np, atol=1e-4)
+            for layer, layer_ref in zip(net.net, net_ref.net):
+                dw = layer.weight.grad.detach().cpu().numpy()
+                dw_ref = layer_ref.weight.grad.detach().cpu().numpy()
+                dw = dw.transpose(3, 4, 0, 1, 2)
+                self.assertAllClose(dw, dw_ref, atol=1e-4)
+
+            out_np = out.detach().cpu().numpy()
+            out_ref_np = out_ref.detach().cpu().numpy()
+            self.assertAllClose(out_np, out_ref_np, atol=1e-4)
+    
+    def testSpCpConv3d(self):
+        np.random.seed(484)
+        devices = ["cuda:0", "cpu:0"]
+        shapes = [[20, 20, 20]]
+        batchsizes = [1, 2]
+
+        in_channels = [64]
+        out_channels = [32, 48, 64]
+        ksizes = [2]
+        strides = [2]
+        paddings = [0, 1, 2]
+        dilations = [1, 2, 3]
+
+        for dev, shape, bs, IC, OC, k, s in params_grid(
+            devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+            strides):
+            device = torch.device(dev)
+            num_points = [1000] * bs
+
+            sparse_dict = generate_sparse_data(shape, num_points, IC)
+
+            features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+            indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+            features_dense = sparse_dict["features_dense"].astype(np.float32)
+            filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+            indices_t = torch.from_numpy(indices).int().to(device)
+            indices_scn_t = torch.from_numpy(indices[:, [1, 2, 3, 0]]).int().to(device)
+            features_t = torch.from_numpy(features).to(device)
+            features_t.requires_grad = True
+            features_ref_t = torch.from_numpy(features).to(device)
+            features_ref_t.requires_grad = True
+
+            net_ref = SCNCoupleDeConvTest(1, 3, shape, IC, OC, k, s).to(device)
+            net = SparseCoupleDeConvTest(1, 3, shape, IC, OC, k, s).to(device)
+            net_ref.net[0].weight.data[:] = net.net[0].weight.data[:].view(*net_ref.net[0].weight.shape)
+            net_ref.net[1].weight.data[:] = net.net[1].weight.data[:].view(*net_ref.net[1].weight.shape)
+            out_ref = net_ref(features_ref_t, indices_scn_t, bs)
+            out = net(features_t, indices_t, bs)
+            dout = np.random.uniform(-0.2, 0.2, out_ref.shape).astype(features.dtype)
+            dout_t = torch.from_numpy(dout).to(device)
+            out.backward(dout_t)
+            out_ref.backward(dout_t)
+            din = features_t.grad.detach()
+            din_ref = features_ref_t.grad.detach()
+            din_np = din.cpu().numpy()
+            din_ref_np = din_ref.cpu().numpy()
+            self.assertAllClose(din_ref_np, din_np, atol=1e-4)
+            for layer, layer_ref in zip(net.net, net_ref.net):
+                dw = layer.weight.grad.detach().cpu().numpy()
+                dw_ref = layer_ref.weight.grad.detach().cpu().view(*dw.shape).numpy()
+                self.assertAllClose(dw, dw_ref, atol=1e-4)
+
+            out_np = out.detach().cpu().numpy()
+            out_ref_np = out_ref.detach().cpu().numpy()
+            self.assertAllClose(out_np, out_ref_np, atol=1e-4)
+
+
+    
+    def testSpMaxPool3d(self):
+        np.random.seed(485)
+        devices = ["cuda:0", "cpu:0"]
+        shapes = [[19, 18, 17]]
+        batchsizes = [1, 2]
+
+        in_channels = [64]
+        out_channels = [64]
+        ksizes = [2, 3]
+        strides = [1, 2, 3]
+        paddings = [0, 1]
+        dilations = [1, 2, 3]
+
+        for dev, shape, bs, IC, OC, k, s, p, d in params_grid(
+            devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+            strides, paddings, dilations):
+            if all([s > 1, d > 1]):
+                continue # don't support this.
+            device = torch.device(dev)
+            num_points = [1000] * bs
+            # when data contains negative, sparse maxpool is not equal to dense maxpool.
+            sparse_dict = generate_sparse_data(shape, num_points, IC, data_range=[0.1, 1])
+
+            features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+            indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+            features_dense = sparse_dict["features_dense"].astype(np.float32)
+            filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+            indices_t = torch.from_numpy(indices).int().to(device)
+            features_t = torch.from_numpy(features).to(device)
+            features_t.requires_grad = True
+            features_dense_t = torch.from_numpy(features_dense).to(device)
+            features_dense_t.requires_grad = True
+            net = SparseMaxPoolTestTorch(1, 3, shape, k, s, p, d).to(device)
+            net_ref = MaxPool3dTestTorch(1, 3, shape, k, s, p, d).to(device)
+
+            out_ref = net_ref(features_dense_t)
+            out = net(features_t, indices_t, bs)
+            outids = out.indices
+            outfeatures = out.features
+            out_dense = out.dense(channels_first=False)
+            out = out_dense.permute(0, 4, 1, 2, 3).contiguous()
+            
+            dout_sparse = np.random.uniform(-0.2, 0.2, outfeatures.shape).astype(features.dtype)
+            dout_sparse_t = torch.from_numpy(dout_sparse).to(device)
+            dout_t = scatter_nd(outids.long(), dout_sparse_t, list(out_dense.shape))
+            dout_t = dout_t.permute(0, 4, 1, 2, 3).contiguous()
+            out.backward(dout_t)
+            out_ref.backward(dout_t)
+            din_dense = features_dense_t.grad.detach().permute(0, 2, 3, 4, 1).contiguous()
+            din_sparse = gather_nd(din_dense, indices_t.long())
+            din = features_t.grad.detach()
+            din_np = din.cpu().numpy()
+            din_sparse_np = din_sparse.cpu().numpy()
+            self.assertAllClose(din_np, din_sparse_np, atol=1e-4)
+            
+            out_np = out.detach().cpu().numpy()
+            out_ref_np = out_ref.detach().cpu().numpy()
+            self.assertAllClose(out_np, out_ref_np, atol=1e-4)
+    
+
+def main():
+    # function for develop.
+    np.random.seed(484)
+    devices = ["cuda:0"]
+    shapes = [[50, 30, 30]]
+    batchsizes = [3]
+
+    
+    in_channels = [256]
+    out_channels = [256]
+    ksizes = [3]
+    strides = [1]
+    paddings = [0]
+    dilations = [1]
+
+    for dev, shape, bs, IC, OC, k, s, p, d in params_grid(
+        devices, shapes, batchsizes, in_channels, out_channels, ksizes, 
+        strides, paddings, dilations):
+        if all([s > 1, d > 1]):
+            continue
+        device = torch.device(dev)
+        num_points = [5000] * bs
+
+        sparse_dict = generate_sparse_data(shape, num_points, IC)
+
+        features = np.ascontiguousarray(sparse_dict["features"]).astype(np.float32)
+        indices = np.ascontiguousarray(sparse_dict["indices"][:, [3, 0, 1, 2]]).astype(np.int32)
+        features_dense = sparse_dict["features_dense"].astype(np.float32)
+        indices_t = torch.from_numpy(indices)
+        filters = np.random.uniform(0, 1, size=[k, k, k, IC, OC]).astype(np.float32)
+        indices_t = torch.from_numpy(indices).int().to(device).half()
+        features_t = torch.from_numpy(features).to(device).half()
+        
+        features_dense_t = torch.from_numpy(features_dense).to(device).half()
+        net = SparseConv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device).half()
+        net_ref = Conv3dTestTorch(1, 3, shape, IC, OC, k, s, p, d).to(device).half()
+        filters_t = torch.from_numpy(filters).to(device).half()
+        net_ref.net[0].weight[:] = filters_t.permute(4, 3, 0, 1, 2).contiguous()
+        net.net[0].weight[:] = filters_t
+        out_ref = net_ref(features_dense_t)
+        times = []
+        for i in range(30):
+            t = time.time()
+            out = net(features_t, indices_t, bs)
+            torch.cuda.synchronize()
+            times.append(time.time() - t)
+        # print((net.grid == -1).float().sum(), net.grid.numel())
+            # print("spconv time", time.time() - t)
+        print("spconv time", np.mean(times[2:]))
+        out = net(features_t, indices_t, bs).dense()
+        print(np.linalg.norm(out.detach().cpu().numpy() - out_ref.detach().cpu().numpy()))
+
+
+if __name__ == '__main__':
+    # main()
+    unittest.main()
+
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/catch2/catch.hpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/catch2/catch.hpp
new file mode 100644
index 000000000..4191607a7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/catch2/catch.hpp
@@ -0,0 +1,14020 @@
+/*
+ *  Catch v2.4.1
+ *  Generated: 2018-09-28 15:50:15.645795
+ *  ----------------------------------------------------------
+ *  This file has been merged from multiple headers. Please don't edit it directly
+ *  Copyright (c) 2018 Two Blue Cubes Ltd. All rights reserved.
+ *
+ *  Distributed under the Boost Software License, Version 1.0. (See accompanying
+ *  file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+ */
+#ifndef TWOBLUECUBES_SINGLE_INCLUDE_CATCH_HPP_INCLUDED
+#define TWOBLUECUBES_SINGLE_INCLUDE_CATCH_HPP_INCLUDED
+// start catch.hpp
+
+
+#define CATCH_VERSION_MAJOR 2
+#define CATCH_VERSION_MINOR 4
+#define CATCH_VERSION_PATCH 1
+
+#ifdef __clang__
+#    pragma clang system_header
+#elif defined __GNUC__
+#    pragma GCC system_header
+#endif
+
+// start catch_suppress_warnings.h
+
+#ifdef __clang__
+#   ifdef __ICC // icpc defines the __clang__ macro
+#       pragma warning(push)
+#       pragma warning(disable: 161 1682)
+#   else // __ICC
+#       pragma clang diagnostic push
+#       pragma clang diagnostic ignored "-Wpadded"
+#       pragma clang diagnostic ignored "-Wswitch-enum"
+#       pragma clang diagnostic ignored "-Wcovered-switch-default"
+#    endif
+#elif defined __GNUC__
+     // GCC likes to warn on REQUIREs, and we cannot suppress them
+     // locally because g++'s support for _Pragma is lacking in older,
+     // still supported, versions
+#    pragma GCC diagnostic ignored "-Wparentheses"
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wunused-variable"
+#    pragma GCC diagnostic ignored "-Wpadded"
+#endif
+// end catch_suppress_warnings.h
+#if defined(CATCH_CONFIG_MAIN) || defined(CATCH_CONFIG_RUNNER)
+#  define CATCH_IMPL
+#  define CATCH_CONFIG_ALL_PARTS
+#endif
+
+// In the impl file, we want to have access to all parts of the headers
+// Can also be used to sanely support PCHs
+#if defined(CATCH_CONFIG_ALL_PARTS)
+#  define CATCH_CONFIG_EXTERNAL_INTERFACES
+#  if defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#    undef CATCH_CONFIG_DISABLE_MATCHERS
+#  endif
+#  if !defined(CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER)
+#    define CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER
+#  endif
+#endif
+
+#if !defined(CATCH_CONFIG_IMPL_ONLY)
+// start catch_platform.h
+
+#ifdef __APPLE__
+# include <TargetConditionals.h>
+# if TARGET_OS_OSX == 1
+#  define CATCH_PLATFORM_MAC
+# elif TARGET_OS_IPHONE == 1
+#  define CATCH_PLATFORM_IPHONE
+# endif
+
+#elif defined(linux) || defined(__linux) || defined(__linux__)
+#  define CATCH_PLATFORM_LINUX
+
+#elif defined(WIN32) || defined(__WIN32__) || defined(_WIN32) || defined(_MSC_VER) || defined(__MINGW32__)
+#  define CATCH_PLATFORM_WINDOWS
+#endif
+
+// end catch_platform.h
+
+#ifdef CATCH_IMPL
+#  ifndef CLARA_CONFIG_MAIN
+#    define CLARA_CONFIG_MAIN_NOT_DEFINED
+#    define CLARA_CONFIG_MAIN
+#  endif
+#endif
+
+// start catch_user_interfaces.h
+
+namespace Catch {
+    unsigned int rngSeed();
+}
+
+// end catch_user_interfaces.h
+// start catch_tag_alias_autoregistrar.h
+
+// start catch_common.h
+
+// start catch_compiler_capabilities.h
+
+// Detect a number of compiler features - by compiler
+// The following features are defined:
+//
+// CATCH_CONFIG_COUNTER : is the __COUNTER__ macro supported?
+// CATCH_CONFIG_WINDOWS_SEH : is Windows SEH supported?
+// CATCH_CONFIG_POSIX_SIGNALS : are POSIX signals supported?
+// CATCH_CONFIG_DISABLE_EXCEPTIONS : Are exceptions enabled?
+// ****************
+// Note to maintainers: if new toggles are added please document them
+// in configuration.md, too
+// ****************
+
+// In general each macro has a _NO_<feature name> form
+// (e.g. CATCH_CONFIG_NO_POSIX_SIGNALS) which disables the feature.
+// Many features, at point of detection, define an _INTERNAL_ macro, so they
+// can be combined, en-mass, with the _NO_ forms later.
+
+#ifdef __cplusplus
+
+#  if (__cplusplus >= 201402L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 201402L)
+#    define CATCH_CPP14_OR_GREATER
+#  endif
+
+#  if (__cplusplus >= 201703L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 201703L)
+#    define CATCH_CPP17_OR_GREATER
+#  endif
+
+#endif
+
+#if defined(CATCH_CPP17_OR_GREATER)
+#  define CATCH_INTERNAL_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS
+#endif
+
+#ifdef __clang__
+
+#       define CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+            _Pragma( "clang diagnostic push" ) \
+            _Pragma( "clang diagnostic ignored \"-Wexit-time-destructors\"" ) \
+            _Pragma( "clang diagnostic ignored \"-Wglobal-constructors\"")
+#       define CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
+            _Pragma( "clang diagnostic pop" )
+
+#       define CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS \
+            _Pragma( "clang diagnostic push" ) \
+            _Pragma( "clang diagnostic ignored \"-Wparentheses\"" )
+#       define CATCH_INTERNAL_UNSUPPRESS_PARENTHESES_WARNINGS \
+            _Pragma( "clang diagnostic pop" )
+
+#       define CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS \
+            _Pragma( "clang diagnostic push" ) \
+            _Pragma( "clang diagnostic ignored \"-Wunused-variable\"" )
+#       define CATCH_INTERNAL_UNSUPPRESS_UNUSED_WARNINGS \
+            _Pragma( "clang diagnostic pop" )
+
+#endif // __clang__
+
+////////////////////////////////////////////////////////////////////////////////
+// Assume that non-Windows platforms support posix signals by default
+#if !defined(CATCH_PLATFORM_WINDOWS)
+    #define CATCH_INTERNAL_CONFIG_POSIX_SIGNALS
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// We know some environments not to support full POSIX signals
+#if defined(__CYGWIN__) || defined(__QNX__) || defined(__EMSCRIPTEN__) || defined(__DJGPP__)
+    #define CATCH_INTERNAL_CONFIG_NO_POSIX_SIGNALS
+#endif
+
+#ifdef __OS400__
+#       define CATCH_INTERNAL_CONFIG_NO_POSIX_SIGNALS
+#       define CATCH_CONFIG_COLOUR_NONE
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// Android somehow still does not support std::to_string
+#if defined(__ANDROID__)
+#    define CATCH_INTERNAL_CONFIG_NO_CPP11_TO_STRING
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// Not all Windows environments support SEH properly
+#if defined(__MINGW32__)
+#    define CATCH_INTERNAL_CONFIG_NO_WINDOWS_SEH
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// PS4
+#if defined(__ORBIS__)
+#    define CATCH_INTERNAL_CONFIG_NO_NEW_CAPTURE
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// Cygwin
+#ifdef __CYGWIN__
+
+// Required for some versions of Cygwin to declare gettimeofday
+// see: http://stackoverflow.com/questions/36901803/gettimeofday-not-declared-in-this-scope-cygwin
+#   define _BSD_SOURCE
+// some versions of cygwin (most) do not support std::to_string. Use the libstd check.
+// https://gcc.gnu.org/onlinedocs/gcc-4.8.2/libstdc++/api/a01053_source.html line 2812-2813
+# if !((__cplusplus >= 201103L) && defined(_GLIBCXX_USE_C99) \
+	       && !defined(_GLIBCXX_HAVE_BROKEN_VSWPRINTF))
+
+#	define CATCH_INTERNAL_CONFIG_NO_CPP11_TO_STRING
+
+# endif
+#endif // __CYGWIN__
+
+////////////////////////////////////////////////////////////////////////////////
+// Visual C++
+#ifdef _MSC_VER
+
+#  if _MSC_VER >= 1900 // Visual Studio 2015 or newer
+#    define CATCH_INTERNAL_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS
+#  endif
+
+// Universal Windows platform does not support SEH
+// Or console colours (or console at all...)
+#  if defined(WINAPI_FAMILY) && (WINAPI_FAMILY == WINAPI_FAMILY_APP)
+#    define CATCH_CONFIG_COLOUR_NONE
+#  else
+#    define CATCH_INTERNAL_CONFIG_WINDOWS_SEH
+#  endif
+
+#endif // _MSC_VER
+
+////////////////////////////////////////////////////////////////////////////////
+// Check if we are compiled with -fno-exceptions or equivalent
+#if defined(__EXCEPTIONS) || defined(__cpp_exceptions) || defined(_CPPUNWIND)
+#  define CATCH_INTERNAL_CONFIG_EXCEPTIONS_ENABLED
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// DJGPP
+#ifdef __DJGPP__
+#  define CATCH_INTERNAL_CONFIG_NO_WCHAR
+#endif // __DJGPP__
+
+////////////////////////////////////////////////////////////////////////////////
+
+// Use of __COUNTER__ is suppressed during code analysis in
+// CLion/AppCode 2017.2.x and former, because __COUNTER__ is not properly
+// handled by it.
+// Otherwise all supported compilers support COUNTER macro,
+// but user still might want to turn it off
+#if ( !defined(__JETBRAINS_IDE__) || __JETBRAINS_IDE__ >= 20170300L )
+    #define CATCH_INTERNAL_CONFIG_COUNTER
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// Check if string_view is available and usable
+// The check is split apart to work around v140 (VS2015) preprocessor issue...
+#if defined(__has_include)
+#if __has_include(<string_view>) && defined(CATCH_CPP17_OR_GREATER)
+#    define CATCH_INTERNAL_CONFIG_CPP17_STRING_VIEW
+#endif
+#endif
+
+////////////////////////////////////////////////////////////////////////////////
+// Check if variant is available and usable
+#if defined(__has_include)
+#  if __has_include(<variant>) && defined(CATCH_CPP17_OR_GREATER)
+#    if defined(__clang__) && (__clang_major__ < 8)
+       // work around clang bug with libstdc++ https://bugs.llvm.org/show_bug.cgi?id=31852
+       // fix should be in clang 8, workaround in libstdc++ 8.2
+#      include <ciso646>
+#      if defined(__GLIBCXX__) && defined(_GLIBCXX_RELEASE) && (_GLIBCXX_RELEASE < 9)
+#        define CATCH_CONFIG_NO_CPP17_VARIANT
+#     else
+#        define CATCH_INTERNAL_CONFIG_CPP17_VARIANT
+#      endif // defined(__GLIBCXX__) && defined(_GLIBCXX_RELEASE) && (_GLIBCXX_RELEASE < 9)
+#    endif // defined(__clang__) && (__clang_major__ < 8)
+#  endif // __has_include(<variant>) && defined(CATCH_CPP17_OR_GREATER)
+#endif // __has_include
+
+#if defined(CATCH_INTERNAL_CONFIG_COUNTER) && !defined(CATCH_CONFIG_NO_COUNTER) && !defined(CATCH_CONFIG_COUNTER)
+#   define CATCH_CONFIG_COUNTER
+#endif
+#if defined(CATCH_INTERNAL_CONFIG_WINDOWS_SEH) && !defined(CATCH_CONFIG_NO_WINDOWS_SEH) && !defined(CATCH_CONFIG_WINDOWS_SEH) && !defined(CATCH_INTERNAL_CONFIG_NO_WINDOWS_SEH)
+#   define CATCH_CONFIG_WINDOWS_SEH
+#endif
+// This is set by default, because we assume that unix compilers are posix-signal-compatible by default.
+#if defined(CATCH_INTERNAL_CONFIG_POSIX_SIGNALS) && !defined(CATCH_INTERNAL_CONFIG_NO_POSIX_SIGNALS) && !defined(CATCH_CONFIG_NO_POSIX_SIGNALS) && !defined(CATCH_CONFIG_POSIX_SIGNALS)
+#   define CATCH_CONFIG_POSIX_SIGNALS
+#endif
+// This is set by default, because we assume that compilers with no wchar_t support are just rare exceptions.
+#if !defined(CATCH_INTERNAL_CONFIG_NO_WCHAR) && !defined(CATCH_CONFIG_NO_WCHAR) && !defined(CATCH_CONFIG_WCHAR)
+#   define CATCH_CONFIG_WCHAR
+#endif
+
+#if !defined(CATCH_INTERNAL_CONFIG_NO_CPP11_TO_STRING) && !defined(CATCH_CONFIG_NO_CPP11_TO_STRING) && !defined(CATCH_CONFIG_CPP11_TO_STRING)
+#    define CATCH_CONFIG_CPP11_TO_STRING
+#endif
+
+#if defined(CATCH_INTERNAL_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS) && !defined(CATCH_CONFIG_NO_CPP17_UNCAUGHT_EXCEPTIONS) && !defined(CATCH_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS)
+#  define CATCH_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS
+#endif
+
+#if defined(CATCH_INTERNAL_CONFIG_CPP17_STRING_VIEW) && !defined(CATCH_CONFIG_NO_CPP17_STRING_VIEW) && !defined(CATCH_CONFIG_CPP17_STRING_VIEW)
+#  define CATCH_CONFIG_CPP17_STRING_VIEW
+#endif
+
+#if defined(CATCH_INTERNAL_CONFIG_CPP17_VARIANT) && !defined(CATCH_CONFIG_NO_CPP17_VARIANT) && !defined(CATCH_CONFIG_CPP17_VARIANT)
+#  define CATCH_CONFIG_CPP17_VARIANT
+#endif
+
+#if defined(CATCH_CONFIG_EXPERIMENTAL_REDIRECT)
+#  define CATCH_INTERNAL_CONFIG_NEW_CAPTURE
+#endif
+
+#if defined(CATCH_INTERNAL_CONFIG_NEW_CAPTURE) && !defined(CATCH_INTERNAL_CONFIG_NO_NEW_CAPTURE) && !defined(CATCH_CONFIG_NO_NEW_CAPTURE) && !defined(CATCH_CONFIG_NEW_CAPTURE)
+#  define CATCH_CONFIG_NEW_CAPTURE
+#endif
+
+#if !defined(CATCH_INTERNAL_CONFIG_EXCEPTIONS_ENABLED) && !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+#  define CATCH_CONFIG_DISABLE_EXCEPTIONS
+#endif
+
+#if !defined(CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS)
+#   define CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS
+#   define CATCH_INTERNAL_UNSUPPRESS_PARENTHESES_WARNINGS
+#endif
+#if !defined(CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS)
+#   define CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
+#   define CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
+#endif
+#if !defined(CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS)
+#   define CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS
+#   define CATCH_INTERNAL_UNSUPPRESS_UNUSED_WARNINGS
+#endif
+
+#if defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+#define CATCH_TRY if ((true))
+#define CATCH_CATCH_ALL if ((false))
+#define CATCH_CATCH_ANON(type) if ((false))
+#else
+#define CATCH_TRY try
+#define CATCH_CATCH_ALL catch (...)
+#define CATCH_CATCH_ANON(type) catch (type)
+#endif
+
+// end catch_compiler_capabilities.h
+#define INTERNAL_CATCH_UNIQUE_NAME_LINE2( name, line ) name##line
+#define INTERNAL_CATCH_UNIQUE_NAME_LINE( name, line ) INTERNAL_CATCH_UNIQUE_NAME_LINE2( name, line )
+#ifdef CATCH_CONFIG_COUNTER
+#  define INTERNAL_CATCH_UNIQUE_NAME( name ) INTERNAL_CATCH_UNIQUE_NAME_LINE( name, __COUNTER__ )
+#else
+#  define INTERNAL_CATCH_UNIQUE_NAME( name ) INTERNAL_CATCH_UNIQUE_NAME_LINE( name, __LINE__ )
+#endif
+
+#include <iosfwd>
+#include <string>
+#include <cstdint>
+
+namespace Catch {
+
+    struct CaseSensitive { enum Choice {
+        Yes,
+        No
+    }; };
+
+    class NonCopyable {
+        NonCopyable( NonCopyable const& )              = delete;
+        NonCopyable( NonCopyable && )                  = delete;
+        NonCopyable& operator = ( NonCopyable const& ) = delete;
+        NonCopyable& operator = ( NonCopyable && )     = delete;
+
+    protected:
+        NonCopyable();
+        virtual ~NonCopyable();
+    };
+
+    struct SourceLineInfo {
+
+        SourceLineInfo() = delete;
+        SourceLineInfo( char const* _file, std::size_t _line ) noexcept
+        :   file( _file ),
+            line( _line )
+        {}
+
+        SourceLineInfo( SourceLineInfo const& other )        = default;
+        SourceLineInfo( SourceLineInfo && )                  = default;
+        SourceLineInfo& operator = ( SourceLineInfo const& ) = default;
+        SourceLineInfo& operator = ( SourceLineInfo && )     = default;
+
+        bool empty() const noexcept;
+        bool operator == ( SourceLineInfo const& other ) const noexcept;
+        bool operator < ( SourceLineInfo const& other ) const noexcept;
+
+        char const* file;
+        std::size_t line;
+    };
+
+    std::ostream& operator << ( std::ostream& os, SourceLineInfo const& info );
+
+    // Use this in variadic streaming macros to allow
+    //    >> +StreamEndStop
+    // as well as
+    //    >> stuff +StreamEndStop
+    struct StreamEndStop {
+        std::string operator+() const;
+    };
+    template<typename T>
+    T const& operator + ( T const& value, StreamEndStop ) {
+        return value;
+    }
+}
+
+#define CATCH_INTERNAL_LINEINFO \
+    ::Catch::SourceLineInfo( __FILE__, static_cast<std::size_t>( __LINE__ ) )
+
+// end catch_common.h
+namespace Catch {
+
+    struct RegistrarForTagAliases {
+        RegistrarForTagAliases( char const* alias, char const* tag, SourceLineInfo const& lineInfo );
+    };
+
+} // end namespace Catch
+
+#define CATCH_REGISTER_TAG_ALIAS( alias, spec ) \
+    CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+    namespace{ Catch::RegistrarForTagAliases INTERNAL_CATCH_UNIQUE_NAME( AutoRegisterTagAlias )( alias, spec, CATCH_INTERNAL_LINEINFO ); } \
+    CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
+
+// end catch_tag_alias_autoregistrar.h
+// start catch_test_registry.h
+
+// start catch_interfaces_testcase.h
+
+#include <vector>
+#include <memory>
+
+namespace Catch {
+
+    class TestSpec;
+
+    struct ITestInvoker {
+        virtual void invoke () const = 0;
+        virtual ~ITestInvoker();
+    };
+
+    using ITestCasePtr = std::shared_ptr<ITestInvoker>;
+
+    class TestCase;
+    struct IConfig;
+
+    struct ITestCaseRegistry {
+        virtual ~ITestCaseRegistry();
+        virtual std::vector<TestCase> const& getAllTests() const = 0;
+        virtual std::vector<TestCase> const& getAllTestsSorted( IConfig const& config ) const = 0;
+    };
+
+    bool matchTest( TestCase const& testCase, TestSpec const& testSpec, IConfig const& config );
+    std::vector<TestCase> filterTests( std::vector<TestCase> const& testCases, TestSpec const& testSpec, IConfig const& config );
+    std::vector<TestCase> const& getAllTestCasesSorted( IConfig const& config );
+
+}
+
+// end catch_interfaces_testcase.h
+// start catch_stringref.h
+
+#include <cstddef>
+#include <string>
+#include <iosfwd>
+
+namespace Catch {
+
+    class StringData;
+
+    /// A non-owning string class (similar to the forthcoming std::string_view)
+    /// Note that, because a StringRef may be a substring of another string,
+    /// it may not be null terminated. c_str() must return a null terminated
+    /// string, however, and so the StringRef will internally take ownership
+    /// (taking a copy), if necessary. In theory this ownership is not externally
+    /// visible - but it does mean (substring) StringRefs should not be shared between
+    /// threads.
+    class StringRef {
+    public:
+        using size_type = std::size_t;
+
+    private:
+        friend struct StringRefTestAccess;
+
+        char const* m_start;
+        size_type m_size;
+
+        char* m_data = nullptr;
+
+        void takeOwnership();
+
+        static constexpr char const* const s_empty = "";
+
+    public: // construction/ assignment
+        StringRef() noexcept
+        :   StringRef( s_empty, 0 )
+        {}
+
+        StringRef( StringRef const& other ) noexcept
+        :   m_start( other.m_start ),
+            m_size( other.m_size )
+        {}
+
+        StringRef( StringRef&& other ) noexcept
+        :   m_start( other.m_start ),
+            m_size( other.m_size ),
+            m_data( other.m_data )
+        {
+            other.m_data = nullptr;
+        }
+
+        StringRef( char const* rawChars ) noexcept;
+
+        StringRef( char const* rawChars, size_type size ) noexcept
+        :   m_start( rawChars ),
+            m_size( size )
+        {}
+
+        StringRef( std::string const& stdString ) noexcept
+        :   m_start( stdString.c_str() ),
+            m_size( stdString.size() )
+        {}
+
+        ~StringRef() noexcept {
+            delete[] m_data;
+        }
+
+        auto operator = ( StringRef const &other ) noexcept -> StringRef& {
+            delete[] m_data;
+            m_data = nullptr;
+            m_start = other.m_start;
+            m_size = other.m_size;
+            return *this;
+        }
+
+        operator std::string() const;
+
+        void swap( StringRef& other ) noexcept;
+
+    public: // operators
+        auto operator == ( StringRef const& other ) const noexcept -> bool;
+        auto operator != ( StringRef const& other ) const noexcept -> bool;
+
+        auto operator[] ( size_type index ) const noexcept -> char;
+
+    public: // named queries
+        auto empty() const noexcept -> bool {
+            return m_size == 0;
+        }
+        auto size() const noexcept -> size_type {
+            return m_size;
+        }
+
+        auto numberOfCharacters() const noexcept -> size_type;
+        auto c_str() const -> char const*;
+
+    public: // substrings and searches
+        auto substr( size_type start, size_type size ) const noexcept -> StringRef;
+
+        // Returns the current start pointer.
+        // Note that the pointer can change when if the StringRef is a substring
+        auto currentData() const noexcept -> char const*;
+
+    private: // ownership queries - may not be consistent between calls
+        auto isOwned() const noexcept -> bool;
+        auto isSubstring() const noexcept -> bool;
+    };
+
+    auto operator + ( StringRef const& lhs, StringRef const& rhs ) -> std::string;
+    auto operator + ( StringRef const& lhs, char const* rhs ) -> std::string;
+    auto operator + ( char const* lhs, StringRef const& rhs ) -> std::string;
+
+    auto operator += ( std::string& lhs, StringRef const& sr ) -> std::string&;
+    auto operator << ( std::ostream& os, StringRef const& sr ) -> std::ostream&;
+
+    inline auto operator "" _sr( char const* rawChars, std::size_t size ) noexcept -> StringRef {
+        return StringRef( rawChars, size );
+    }
+
+} // namespace Catch
+
+inline auto operator "" _catch_sr( char const* rawChars, std::size_t size ) noexcept -> Catch::StringRef {
+    return Catch::StringRef( rawChars, size );
+}
+
+// end catch_stringref.h
+namespace Catch {
+
+template<typename C>
+class TestInvokerAsMethod : public ITestInvoker {
+    void (C::*m_testAsMethod)();
+public:
+    TestInvokerAsMethod( void (C::*testAsMethod)() ) noexcept : m_testAsMethod( testAsMethod ) {}
+
+    void invoke() const override {
+        C obj;
+        (obj.*m_testAsMethod)();
+    }
+};
+
+auto makeTestInvoker( void(*testAsFunction)() ) noexcept -> ITestInvoker*;
+
+template<typename C>
+auto makeTestInvoker( void (C::*testAsMethod)() ) noexcept -> ITestInvoker* {
+    return new(std::nothrow) TestInvokerAsMethod<C>( testAsMethod );
+}
+
+struct NameAndTags {
+    NameAndTags( StringRef const& name_ = StringRef(), StringRef const& tags_ = StringRef() ) noexcept;
+    StringRef name;
+    StringRef tags;
+};
+
+struct AutoReg : NonCopyable {
+    AutoReg( ITestInvoker* invoker, SourceLineInfo const& lineInfo, StringRef const& classOrMethod, NameAndTags const& nameAndTags ) noexcept;
+    ~AutoReg();
+};
+
+} // end namespace Catch
+
+#define INTERNAL_CATCH_EXPAND1(param) INTERNAL_CATCH_EXPAND2(param)
+#define INTERNAL_CATCH_EXPAND2(...) INTERNAL_CATCH_NO## __VA_ARGS__
+#define INTERNAL_CATCH_DEF(...) INTERNAL_CATCH_DEF __VA_ARGS__
+#define INTERNAL_CATCH_NOINTERNAL_CATCH_DEF
+
+#if defined(CATCH_CONFIG_DISABLE)
+    #define INTERNAL_CATCH_TESTCASE_NO_REGISTRATION( TestName, ... ) \
+        static void TestName()
+    #define INTERNAL_CATCH_TESTCASE_METHOD_NO_REGISTRATION( TestName, ClassName, ... ) \
+        namespace{                        \
+            struct TestName : INTERNAL_CATCH_EXPAND1(INTERNAL_CATCH_DEF ClassName) { \
+                void test();              \
+            };                            \
+        }                                 \
+        void TestName::test()
+
+#endif
+
+    ///////////////////////////////////////////////////////////////////////////////
+    #define INTERNAL_CATCH_TESTCASE2( TestName, ... ) \
+        static void TestName(); \
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        namespace{ Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( Catch::makeTestInvoker( &TestName ), CATCH_INTERNAL_LINEINFO, Catch::StringRef(), Catch::NameAndTags{ __VA_ARGS__ } ); } /* NOLINT */ \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
+        static void TestName()
+    #define INTERNAL_CATCH_TESTCASE( ... ) \
+        INTERNAL_CATCH_TESTCASE2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), __VA_ARGS__ )
+
+    ///////////////////////////////////////////////////////////////////////////////
+    #define INTERNAL_CATCH_METHOD_AS_TEST_CASE( QualifiedMethod, ... ) \
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        namespace{ Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( Catch::makeTestInvoker( &QualifiedMethod ), CATCH_INTERNAL_LINEINFO, "&" #QualifiedMethod, Catch::NameAndTags{ __VA_ARGS__ } ); } /* NOLINT */ \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
+
+    ///////////////////////////////////////////////////////////////////////////////
+    #define INTERNAL_CATCH_TEST_CASE_METHOD2( TestName, ClassName, ... )\
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        namespace{ \
+            struct TestName : INTERNAL_CATCH_EXPAND1(INTERNAL_CATCH_DEF ClassName) { \
+                void test(); \
+            }; \
+            Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar ) ( Catch::makeTestInvoker( &TestName::test ), CATCH_INTERNAL_LINEINFO, #ClassName, Catch::NameAndTags{ __VA_ARGS__ } ); /* NOLINT */ \
+        } \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
+        void TestName::test()
+    #define INTERNAL_CATCH_TEST_CASE_METHOD( ClassName, ... ) \
+        INTERNAL_CATCH_TEST_CASE_METHOD2( INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), ClassName, __VA_ARGS__ )
+
+    ///////////////////////////////////////////////////////////////////////////////
+    #define INTERNAL_CATCH_REGISTER_TESTCASE( Function, ... ) \
+        CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+        Catch::AutoReg INTERNAL_CATCH_UNIQUE_NAME( autoRegistrar )( Catch::makeTestInvoker( Function ), CATCH_INTERNAL_LINEINFO, Catch::StringRef(), Catch::NameAndTags{ __VA_ARGS__ } ); /* NOLINT */ \
+        CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
+
+// end catch_test_registry.h
+// start catch_capture.hpp
+
+// start catch_assertionhandler.h
+
+// start catch_assertioninfo.h
+
+// start catch_result_type.h
+
+namespace Catch {
+
+    // ResultWas::OfType enum
+    struct ResultWas { enum OfType {
+        Unknown = -1,
+        Ok = 0,
+        Info = 1,
+        Warning = 2,
+
+        FailureBit = 0x10,
+
+        ExpressionFailed = FailureBit | 1,
+        ExplicitFailure = FailureBit | 2,
+
+        Exception = 0x100 | FailureBit,
+
+        ThrewException = Exception | 1,
+        DidntThrowException = Exception | 2,
+
+        FatalErrorCondition = 0x200 | FailureBit
+
+    }; };
+
+    bool isOk( ResultWas::OfType resultType );
+    bool isJustInfo( int flags );
+
+    // ResultDisposition::Flags enum
+    struct ResultDisposition { enum Flags {
+        Normal = 0x01,
+
+        ContinueOnFailure = 0x02,   // Failures fail test, but execution continues
+        FalseTest = 0x04,           // Prefix expression with !
+        SuppressFail = 0x08         // Failures are reported but do not fail the test
+    }; };
+
+    ResultDisposition::Flags operator | ( ResultDisposition::Flags lhs, ResultDisposition::Flags rhs );
+
+    bool shouldContinueOnFailure( int flags );
+    inline bool isFalseTest( int flags ) { return ( flags & ResultDisposition::FalseTest ) != 0; }
+    bool shouldSuppressFailure( int flags );
+
+} // end namespace Catch
+
+// end catch_result_type.h
+namespace Catch {
+
+    struct AssertionInfo
+    {
+        StringRef macroName;
+        SourceLineInfo lineInfo;
+        StringRef capturedExpression;
+        ResultDisposition::Flags resultDisposition;
+
+        // We want to delete this constructor but a compiler bug in 4.8 means
+        // the struct is then treated as non-aggregate
+        //AssertionInfo() = delete;
+    };
+
+} // end namespace Catch
+
+// end catch_assertioninfo.h
+// start catch_decomposer.h
+
+// start catch_tostring.h
+
+#include <vector>
+#include <cstddef>
+#include <type_traits>
+#include <string>
+// start catch_stream.h
+
+#include <iosfwd>
+#include <cstddef>
+#include <ostream>
+
+namespace Catch {
+
+    std::ostream& cout();
+    std::ostream& cerr();
+    std::ostream& clog();
+
+    class StringRef;
+
+    struct IStream {
+        virtual ~IStream();
+        virtual std::ostream& stream() const = 0;
+    };
+
+    auto makeStream( StringRef const &filename ) -> IStream const*;
+
+    class ReusableStringStream {
+        std::size_t m_index;
+        std::ostream* m_oss;
+    public:
+        ReusableStringStream();
+        ~ReusableStringStream();
+
+        auto str() const -> std::string;
+
+        template<typename T>
+        auto operator << ( T const& value ) -> ReusableStringStream& {
+            *m_oss << value;
+            return *this;
+        }
+        auto get() -> std::ostream& { return *m_oss; }
+    };
+}
+
+// end catch_stream.h
+
+#ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+#include <string_view>
+#endif
+
+#ifdef __OBJC__
+// start catch_objc_arc.hpp
+
+#import <Foundation/Foundation.h>
+
+#ifdef __has_feature
+#define CATCH_ARC_ENABLED __has_feature(objc_arc)
+#else
+#define CATCH_ARC_ENABLED 0
+#endif
+
+void arcSafeRelease( NSObject* obj );
+id performOptionalSelector( id obj, SEL sel );
+
+#if !CATCH_ARC_ENABLED
+inline void arcSafeRelease( NSObject* obj ) {
+    [obj release];
+}
+inline id performOptionalSelector( id obj, SEL sel ) {
+    if( [obj respondsToSelector: sel] )
+        return [obj performSelector: sel];
+    return nil;
+}
+#define CATCH_UNSAFE_UNRETAINED
+#define CATCH_ARC_STRONG
+#else
+inline void arcSafeRelease( NSObject* ){}
+inline id performOptionalSelector( id obj, SEL sel ) {
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Warc-performSelector-leaks"
+#endif
+    if( [obj respondsToSelector: sel] )
+        return [obj performSelector: sel];
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+    return nil;
+}
+#define CATCH_UNSAFE_UNRETAINED __unsafe_unretained
+#define CATCH_ARC_STRONG __strong
+#endif
+
+// end catch_objc_arc.hpp
+#endif
+
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable:4180) // We attempt to stream a function (address) by const&, which MSVC complains about but is harmless
+#endif
+
+// We need a dummy global operator<< so we can bring it into Catch namespace later
+struct Catch_global_namespace_dummy {};
+std::ostream& operator<<(std::ostream&, Catch_global_namespace_dummy);
+
+namespace Catch {
+    // Bring in operator<< from global namespace into Catch namespace
+    using ::operator<<;
+
+    namespace Detail {
+
+        extern const std::string unprintableString;
+
+        std::string rawMemoryToString( const void *object, std::size_t size );
+
+        template<typename T>
+        std::string rawMemoryToString( const T& object ) {
+          return rawMemoryToString( &object, sizeof(object) );
+        }
+
+        template<typename T>
+        class IsStreamInsertable {
+            template<typename SS, typename TT>
+            static auto test(int)
+                -> decltype(std::declval<SS&>() << std::declval<TT>(), std::true_type());
+
+            template<typename, typename>
+            static auto test(...)->std::false_type;
+
+        public:
+            static const bool value = decltype(test<std::ostream, const T&>(0))::value;
+        };
+
+        template<typename E>
+        std::string convertUnknownEnumToString( E e );
+
+        template<typename T>
+        typename std::enable_if<
+            !std::is_enum<T>::value && !std::is_base_of<std::exception, T>::value,
+        std::string>::type convertUnstreamable( T const& ) {
+            return Detail::unprintableString;
+        }
+        template<typename T>
+        typename std::enable_if<
+            !std::is_enum<T>::value && std::is_base_of<std::exception, T>::value,
+         std::string>::type convertUnstreamable(T const& ex) {
+            return ex.what();
+        }
+
+        template<typename T>
+        typename std::enable_if<
+            std::is_enum<T>::value
+        , std::string>::type convertUnstreamable( T const& value ) {
+            return convertUnknownEnumToString( value );
+        }
+
+#if defined(_MANAGED)
+        //! Convert a CLR string to a utf8 std::string
+        template<typename T>
+        std::string clrReferenceToString( T^ ref ) {
+            if (ref == nullptr)
+                return std::string("null");
+            auto bytes = System::Text::Encoding::UTF8->GetBytes(ref->ToString());
+            cli::pin_ptr<System::Byte> p = &bytes[0];
+            return std::string(reinterpret_cast<char const *>(p), bytes->Length);
+        }
+#endif
+
+    } // namespace Detail
+
+    // If we decide for C++14, change these to enable_if_ts
+    template <typename T, typename = void>
+    struct StringMaker {
+        template <typename Fake = T>
+        static
+        typename std::enable_if<::Catch::Detail::IsStreamInsertable<Fake>::value, std::string>::type
+            convert(const Fake& value) {
+                ReusableStringStream rss;
+                // NB: call using the function-like syntax to avoid ambiguity with
+                // user-defined templated operator<< under clang.
+                rss.operator<<(value);
+                return rss.str();
+        }
+
+        template <typename Fake = T>
+        static
+        typename std::enable_if<!::Catch::Detail::IsStreamInsertable<Fake>::value, std::string>::type
+            convert( const Fake& value ) {
+#if !defined(CATCH_CONFIG_FALLBACK_STRINGIFIER)
+            return Detail::convertUnstreamable(value);
+#else
+            return CATCH_CONFIG_FALLBACK_STRINGIFIER(value);
+#endif
+        }
+    };
+
+    namespace Detail {
+
+        // This function dispatches all stringification requests inside of Catch.
+        // Should be preferably called fully qualified, like ::Catch::Detail::stringify
+        template <typename T>
+        std::string stringify(const T& e) {
+            return ::Catch::StringMaker<typename std::remove_cv<typename std::remove_reference<T>::type>::type>::convert(e);
+        }
+
+        template<typename E>
+        std::string convertUnknownEnumToString( E e ) {
+            return ::Catch::Detail::stringify(static_cast<typename std::underlying_type<E>::type>(e));
+        }
+
+#if defined(_MANAGED)
+        template <typename T>
+        std::string stringify( T^ e ) {
+            return ::Catch::StringMaker<T^>::convert(e);
+        }
+#endif
+
+    } // namespace Detail
+
+    // Some predefined specializations
+
+    template<>
+    struct StringMaker<std::string> {
+        static std::string convert(const std::string& str);
+    };
+
+#ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+    template<>
+    struct StringMaker<std::string_view> {
+        static std::string convert(std::string_view str);
+    };
+#endif
+
+    template<>
+    struct StringMaker<char const *> {
+        static std::string convert(char const * str);
+    };
+    template<>
+    struct StringMaker<char *> {
+        static std::string convert(char * str);
+    };
+
+#ifdef CATCH_CONFIG_WCHAR
+    template<>
+    struct StringMaker<std::wstring> {
+        static std::string convert(const std::wstring& wstr);
+    };
+
+# ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+    template<>
+    struct StringMaker<std::wstring_view> {
+        static std::string convert(std::wstring_view str);
+    };
+# endif
+
+    template<>
+    struct StringMaker<wchar_t const *> {
+        static std::string convert(wchar_t const * str);
+    };
+    template<>
+    struct StringMaker<wchar_t *> {
+        static std::string convert(wchar_t * str);
+    };
+#endif
+
+    // TBD: Should we use `strnlen` to ensure that we don't go out of the buffer,
+    //      while keeping string semantics?
+    template<int SZ>
+    struct StringMaker<char[SZ]> {
+        static std::string convert(char const* str) {
+            return ::Catch::Detail::stringify(std::string{ str });
+        }
+    };
+    template<int SZ>
+    struct StringMaker<signed char[SZ]> {
+        static std::string convert(signed char const* str) {
+            return ::Catch::Detail::stringify(std::string{ reinterpret_cast<char const *>(str) });
+        }
+    };
+    template<int SZ>
+    struct StringMaker<unsigned char[SZ]> {
+        static std::string convert(unsigned char const* str) {
+            return ::Catch::Detail::stringify(std::string{ reinterpret_cast<char const *>(str) });
+        }
+    };
+
+    template<>
+    struct StringMaker<int> {
+        static std::string convert(int value);
+    };
+    template<>
+    struct StringMaker<long> {
+        static std::string convert(long value);
+    };
+    template<>
+    struct StringMaker<long long> {
+        static std::string convert(long long value);
+    };
+    template<>
+    struct StringMaker<unsigned int> {
+        static std::string convert(unsigned int value);
+    };
+    template<>
+    struct StringMaker<unsigned long> {
+        static std::string convert(unsigned long value);
+    };
+    template<>
+    struct StringMaker<unsigned long long> {
+        static std::string convert(unsigned long long value);
+    };
+
+    template<>
+    struct StringMaker<bool> {
+        static std::string convert(bool b);
+    };
+
+    template<>
+    struct StringMaker<char> {
+        static std::string convert(char c);
+    };
+    template<>
+    struct StringMaker<signed char> {
+        static std::string convert(signed char c);
+    };
+    template<>
+    struct StringMaker<unsigned char> {
+        static std::string convert(unsigned char c);
+    };
+
+    template<>
+    struct StringMaker<std::nullptr_t> {
+        static std::string convert(std::nullptr_t);
+    };
+
+    template<>
+    struct StringMaker<float> {
+        static std::string convert(float value);
+    };
+    template<>
+    struct StringMaker<double> {
+        static std::string convert(double value);
+    };
+
+    template <typename T>
+    struct StringMaker<T*> {
+        template <typename U>
+        static std::string convert(U* p) {
+            if (p) {
+                return ::Catch::Detail::rawMemoryToString(p);
+            } else {
+                return "nullptr";
+            }
+        }
+    };
+
+    template <typename R, typename C>
+    struct StringMaker<R C::*> {
+        static std::string convert(R C::* p) {
+            if (p) {
+                return ::Catch::Detail::rawMemoryToString(p);
+            } else {
+                return "nullptr";
+            }
+        }
+    };
+
+#if defined(_MANAGED)
+    template <typename T>
+    struct StringMaker<T^> {
+        static std::string convert( T^ ref ) {
+            return ::Catch::Detail::clrReferenceToString(ref);
+        }
+    };
+#endif
+
+    namespace Detail {
+        template<typename InputIterator>
+        std::string rangeToString(InputIterator first, InputIterator last) {
+            ReusableStringStream rss;
+            rss << "{ ";
+            if (first != last) {
+                rss << ::Catch::Detail::stringify(*first);
+                for (++first; first != last; ++first)
+                    rss << ", " << ::Catch::Detail::stringify(*first);
+            }
+            rss << " }";
+            return rss.str();
+        }
+    }
+
+#ifdef __OBJC__
+    template<>
+    struct StringMaker<NSString*> {
+        static std::string convert(NSString * nsstring) {
+            if (!nsstring)
+                return "nil";
+            return std::string("@") + [nsstring UTF8String];
+        }
+    };
+    template<>
+    struct StringMaker<NSObject*> {
+        static std::string convert(NSObject* nsObject) {
+            return ::Catch::Detail::stringify([nsObject description]);
+        }
+
+    };
+    namespace Detail {
+        inline std::string stringify( NSString* nsstring ) {
+            return StringMaker<NSString*>::convert( nsstring );
+        }
+
+    } // namespace Detail
+#endif // __OBJC__
+
+} // namespace Catch
+
+//////////////////////////////////////////////////////
+// Separate std-lib types stringification, so it can be selectively enabled
+// This means that we do not bring in
+
+#if defined(CATCH_CONFIG_ENABLE_ALL_STRINGMAKERS)
+#  define CATCH_CONFIG_ENABLE_PAIR_STRINGMAKER
+#  define CATCH_CONFIG_ENABLE_TUPLE_STRINGMAKER
+#  define CATCH_CONFIG_ENABLE_VARIANT_STRINGMAKER
+#  define CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER
+#endif
+
+// Separate std::pair specialization
+#if defined(CATCH_CONFIG_ENABLE_PAIR_STRINGMAKER)
+#include <utility>
+namespace Catch {
+    template<typename T1, typename T2>
+    struct StringMaker<std::pair<T1, T2> > {
+        static std::string convert(const std::pair<T1, T2>& pair) {
+            ReusableStringStream rss;
+            rss << "{ "
+                << ::Catch::Detail::stringify(pair.first)
+                << ", "
+                << ::Catch::Detail::stringify(pair.second)
+                << " }";
+            return rss.str();
+        }
+    };
+}
+#endif // CATCH_CONFIG_ENABLE_PAIR_STRINGMAKER
+
+// Separate std::tuple specialization
+#if defined(CATCH_CONFIG_ENABLE_TUPLE_STRINGMAKER)
+#include <tuple>
+namespace Catch {
+    namespace Detail {
+        template<
+            typename Tuple,
+            std::size_t N = 0,
+            bool = (N < std::tuple_size<Tuple>::value)
+            >
+            struct TupleElementPrinter {
+            static void print(const Tuple& tuple, std::ostream& os) {
+                os << (N ? ", " : " ")
+                    << ::Catch::Detail::stringify(std::get<N>(tuple));
+                TupleElementPrinter<Tuple, N + 1>::print(tuple, os);
+            }
+        };
+
+        template<
+            typename Tuple,
+            std::size_t N
+        >
+            struct TupleElementPrinter<Tuple, N, false> {
+            static void print(const Tuple&, std::ostream&) {}
+        };
+
+    }
+
+    template<typename ...Types>
+    struct StringMaker<std::tuple<Types...>> {
+        static std::string convert(const std::tuple<Types...>& tuple) {
+            ReusableStringStream rss;
+            rss << '{';
+            Detail::TupleElementPrinter<std::tuple<Types...>>::print(tuple, rss.get());
+            rss << " }";
+            return rss.str();
+        }
+    };
+}
+#endif // CATCH_CONFIG_ENABLE_TUPLE_STRINGMAKER
+
+#if defined(CATCH_CONFIG_ENABLE_VARIANT_STRINGMAKER) && defined(CATCH_CONFIG_CPP17_VARIANT)
+#include <variant>
+namespace Catch {
+    template<>
+    struct StringMaker<std::monostate> {
+        static std::string convert(const std::monostate&) {
+            return "{ }";
+        }
+    };
+
+    template<typename... Elements>
+    struct StringMaker<std::variant<Elements...>> {
+        static std::string convert(const std::variant<Elements...>& variant) {
+            if (variant.valueless_by_exception()) {
+                return "{valueless variant}";
+            } else {
+                return std::visit(
+                    [](const auto& value) {
+                        return ::Catch::Detail::stringify(value);
+                    },
+                    variant
+                );
+            }
+        }
+    };
+}
+#endif // CATCH_CONFIG_ENABLE_VARIANT_STRINGMAKER
+
+namespace Catch {
+    struct not_this_one {}; // Tag type for detecting which begin/ end are being selected
+
+    // Import begin/ end from std here so they are considered alongside the fallback (...) overloads in this namespace
+    using std::begin;
+    using std::end;
+
+    not_this_one begin( ... );
+    not_this_one end( ... );
+
+    template <typename T>
+    struct is_range {
+        static const bool value =
+            !std::is_same<decltype(begin(std::declval<T>())), not_this_one>::value &&
+            !std::is_same<decltype(end(std::declval<T>())), not_this_one>::value;
+    };
+
+#if defined(_MANAGED) // Managed types are never ranges
+    template <typename T>
+    struct is_range<T^> {
+        static const bool value = false;
+    };
+#endif
+
+    template<typename Range>
+    std::string rangeToString( Range const& range ) {
+        return ::Catch::Detail::rangeToString( begin( range ), end( range ) );
+    }
+
+    // Handle vector<bool> specially
+    template<typename Allocator>
+    std::string rangeToString( std::vector<bool, Allocator> const& v ) {
+        ReusableStringStream rss;
+        rss << "{ ";
+        bool first = true;
+        for( bool b : v ) {
+            if( first )
+                first = false;
+            else
+                rss << ", ";
+            rss << ::Catch::Detail::stringify( b );
+        }
+        rss << " }";
+        return rss.str();
+    }
+
+    template<typename R>
+    struct StringMaker<R, typename std::enable_if<is_range<R>::value && !::Catch::Detail::IsStreamInsertable<R>::value>::type> {
+        static std::string convert( R const& range ) {
+            return rangeToString( range );
+        }
+    };
+
+    template <typename T, int SZ>
+    struct StringMaker<T[SZ]> {
+        static std::string convert(T const(&arr)[SZ]) {
+            return rangeToString(arr);
+        }
+    };
+
+} // namespace Catch
+
+// Separate std::chrono::duration specialization
+#if defined(CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER)
+#include <ctime>
+#include <ratio>
+#include <chrono>
+
+namespace Catch {
+
+template <class Ratio>
+struct ratio_string {
+    static std::string symbol();
+};
+
+template <class Ratio>
+std::string ratio_string<Ratio>::symbol() {
+    Catch::ReusableStringStream rss;
+    rss << '[' << Ratio::num << '/'
+        << Ratio::den << ']';
+    return rss.str();
+}
+template <>
+struct ratio_string<std::atto> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::femto> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::pico> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::nano> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::micro> {
+    static std::string symbol();
+};
+template <>
+struct ratio_string<std::milli> {
+    static std::string symbol();
+};
+
+    ////////////
+    // std::chrono::duration specializations
+    template<typename Value, typename Ratio>
+    struct StringMaker<std::chrono::duration<Value, Ratio>> {
+        static std::string convert(std::chrono::duration<Value, Ratio> const& duration) {
+            ReusableStringStream rss;
+            rss << duration.count() << ' ' << ratio_string<Ratio>::symbol() << 's';
+            return rss.str();
+        }
+    };
+    template<typename Value>
+    struct StringMaker<std::chrono::duration<Value, std::ratio<1>>> {
+        static std::string convert(std::chrono::duration<Value, std::ratio<1>> const& duration) {
+            ReusableStringStream rss;
+            rss << duration.count() << " s";
+            return rss.str();
+        }
+    };
+    template<typename Value>
+    struct StringMaker<std::chrono::duration<Value, std::ratio<60>>> {
+        static std::string convert(std::chrono::duration<Value, std::ratio<60>> const& duration) {
+            ReusableStringStream rss;
+            rss << duration.count() << " m";
+            return rss.str();
+        }
+    };
+    template<typename Value>
+    struct StringMaker<std::chrono::duration<Value, std::ratio<3600>>> {
+        static std::string convert(std::chrono::duration<Value, std::ratio<3600>> const& duration) {
+            ReusableStringStream rss;
+            rss << duration.count() << " h";
+            return rss.str();
+        }
+    };
+
+    ////////////
+    // std::chrono::time_point specialization
+    // Generic time_point cannot be specialized, only std::chrono::time_point<system_clock>
+    template<typename Clock, typename Duration>
+    struct StringMaker<std::chrono::time_point<Clock, Duration>> {
+        static std::string convert(std::chrono::time_point<Clock, Duration> const& time_point) {
+            return ::Catch::Detail::stringify(time_point.time_since_epoch()) + " since epoch";
+        }
+    };
+    // std::chrono::time_point<system_clock> specialization
+    template<typename Duration>
+    struct StringMaker<std::chrono::time_point<std::chrono::system_clock, Duration>> {
+        static std::string convert(std::chrono::time_point<std::chrono::system_clock, Duration> const& time_point) {
+            auto converted = std::chrono::system_clock::to_time_t(time_point);
+
+#ifdef _MSC_VER
+            std::tm timeInfo = {};
+            gmtime_s(&timeInfo, &converted);
+#else
+            std::tm* timeInfo = std::gmtime(&converted);
+#endif
+
+            auto const timeStampSize = sizeof("2017-01-16T17:06:45Z");
+            char timeStamp[timeStampSize];
+            const char * const fmt = "%Y-%m-%dT%H:%M:%SZ";
+
+#ifdef _MSC_VER
+            std::strftime(timeStamp, timeStampSize, fmt, &timeInfo);
+#else
+            std::strftime(timeStamp, timeStampSize, fmt, timeInfo);
+#endif
+            return std::string(timeStamp);
+        }
+    };
+}
+#endif // CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+// end catch_tostring.h
+#include <iosfwd>
+
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable:4389) // '==' : signed/unsigned mismatch
+#pragma warning(disable:4018) // more "signed/unsigned mismatch"
+#pragma warning(disable:4312) // Converting int to T* using reinterpret_cast (issue on x64 platform)
+#pragma warning(disable:4180) // qualifier applied to function type has no meaning
+#endif
+
+namespace Catch {
+
+    struct ITransientExpression {
+        auto isBinaryExpression() const -> bool { return m_isBinaryExpression; }
+        auto getResult() const -> bool { return m_result; }
+        virtual void streamReconstructedExpression( std::ostream &os ) const = 0;
+
+        ITransientExpression( bool isBinaryExpression, bool result )
+        :   m_isBinaryExpression( isBinaryExpression ),
+            m_result( result )
+        {}
+
+        // We don't actually need a virtual destructor, but many static analysers
+        // complain if it's not here :-(
+        virtual ~ITransientExpression();
+
+        bool m_isBinaryExpression;
+        bool m_result;
+
+    };
+
+    void formatReconstructedExpression( std::ostream &os, std::string const& lhs, StringRef op, std::string const& rhs );
+
+    template<typename LhsT, typename RhsT>
+    class BinaryExpr  : public ITransientExpression {
+        LhsT m_lhs;
+        StringRef m_op;
+        RhsT m_rhs;
+
+        void streamReconstructedExpression( std::ostream &os ) const override {
+            formatReconstructedExpression
+                    ( os, Catch::Detail::stringify( m_lhs ), m_op, Catch::Detail::stringify( m_rhs ) );
+        }
+
+    public:
+        BinaryExpr( bool comparisonResult, LhsT lhs, StringRef op, RhsT rhs )
+        :   ITransientExpression{ true, comparisonResult },
+            m_lhs( lhs ),
+            m_op( op ),
+            m_rhs( rhs )
+        {}
+    };
+
+    template<typename LhsT>
+    class UnaryExpr : public ITransientExpression {
+        LhsT m_lhs;
+
+        void streamReconstructedExpression( std::ostream &os ) const override {
+            os << Catch::Detail::stringify( m_lhs );
+        }
+
+    public:
+        explicit UnaryExpr( LhsT lhs )
+        :   ITransientExpression{ false, lhs ? true : false },
+            m_lhs( lhs )
+        {}
+    };
+
+    // Specialised comparison functions to handle equality comparisons between ints and pointers (NULL deduces as an int)
+    template<typename LhsT, typename RhsT>
+    auto compareEqual( LhsT const& lhs, RhsT const& rhs ) -> bool { return static_cast<bool>(lhs == rhs); }
+    template<typename T>
+    auto compareEqual( T* const& lhs, int rhs ) -> bool { return lhs == reinterpret_cast<void const*>( rhs ); }
+    template<typename T>
+    auto compareEqual( T* const& lhs, long rhs ) -> bool { return lhs == reinterpret_cast<void const*>( rhs ); }
+    template<typename T>
+    auto compareEqual( int lhs, T* const& rhs ) -> bool { return reinterpret_cast<void const*>( lhs ) == rhs; }
+    template<typename T>
+    auto compareEqual( long lhs, T* const& rhs ) -> bool { return reinterpret_cast<void const*>( lhs ) == rhs; }
+
+    template<typename LhsT, typename RhsT>
+    auto compareNotEqual( LhsT const& lhs, RhsT&& rhs ) -> bool { return static_cast<bool>(lhs != rhs); }
+    template<typename T>
+    auto compareNotEqual( T* const& lhs, int rhs ) -> bool { return lhs != reinterpret_cast<void const*>( rhs ); }
+    template<typename T>
+    auto compareNotEqual( T* const& lhs, long rhs ) -> bool { return lhs != reinterpret_cast<void const*>( rhs ); }
+    template<typename T>
+    auto compareNotEqual( int lhs, T* const& rhs ) -> bool { return reinterpret_cast<void const*>( lhs ) != rhs; }
+    template<typename T>
+    auto compareNotEqual( long lhs, T* const& rhs ) -> bool { return reinterpret_cast<void const*>( lhs ) != rhs; }
+
+    template<typename LhsT>
+    class ExprLhs {
+        LhsT m_lhs;
+    public:
+        explicit ExprLhs( LhsT lhs ) : m_lhs( lhs ) {}
+
+        template<typename RhsT>
+        auto operator == ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { compareEqual( m_lhs, rhs ), m_lhs, "==", rhs };
+        }
+        auto operator == ( bool rhs ) -> BinaryExpr<LhsT, bool> const {
+            return { m_lhs == rhs, m_lhs, "==", rhs };
+        }
+
+        template<typename RhsT>
+        auto operator != ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { compareNotEqual( m_lhs, rhs ), m_lhs, "!=", rhs };
+        }
+        auto operator != ( bool rhs ) -> BinaryExpr<LhsT, bool> const {
+            return { m_lhs != rhs, m_lhs, "!=", rhs };
+        }
+
+        template<typename RhsT>
+        auto operator > ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { static_cast<bool>(m_lhs > rhs), m_lhs, ">", rhs };
+        }
+        template<typename RhsT>
+        auto operator < ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { static_cast<bool>(m_lhs < rhs), m_lhs, "<", rhs };
+        }
+        template<typename RhsT>
+        auto operator >= ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { static_cast<bool>(m_lhs >= rhs), m_lhs, ">=", rhs };
+        }
+        template<typename RhsT>
+        auto operator <= ( RhsT const& rhs ) -> BinaryExpr<LhsT, RhsT const&> const {
+            return { static_cast<bool>(m_lhs <= rhs), m_lhs, "<=", rhs };
+        }
+
+        auto makeUnaryExpr() const -> UnaryExpr<LhsT> {
+            return UnaryExpr<LhsT>{ m_lhs };
+        }
+    };
+
+    void handleExpression( ITransientExpression const& expr );
+
+    template<typename T>
+    void handleExpression( ExprLhs<T> const& expr ) {
+        handleExpression( expr.makeUnaryExpr() );
+    }
+
+    struct Decomposer {
+        template<typename T>
+        auto operator <= ( T const& lhs ) -> ExprLhs<T const&> {
+            return ExprLhs<T const&>{ lhs };
+        }
+
+        auto operator <=( bool value ) -> ExprLhs<bool> {
+            return ExprLhs<bool>{ value };
+        }
+    };
+
+} // end namespace Catch
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+// end catch_decomposer.h
+// start catch_interfaces_capture.h
+
+#include <string>
+
+namespace Catch {
+
+    class AssertionResult;
+    struct AssertionInfo;
+    struct SectionInfo;
+    struct SectionEndInfo;
+    struct MessageInfo;
+    struct Counts;
+    struct BenchmarkInfo;
+    struct BenchmarkStats;
+    struct AssertionReaction;
+    struct SourceLineInfo;
+
+    struct ITransientExpression;
+    struct IGeneratorTracker;
+
+    struct IResultCapture {
+
+        virtual ~IResultCapture();
+
+        virtual bool sectionStarted(    SectionInfo const& sectionInfo,
+                                        Counts& assertions ) = 0;
+        virtual void sectionEnded( SectionEndInfo const& endInfo ) = 0;
+        virtual void sectionEndedEarly( SectionEndInfo const& endInfo ) = 0;
+
+        virtual auto acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker& = 0;
+
+        virtual void benchmarkStarting( BenchmarkInfo const& info ) = 0;
+        virtual void benchmarkEnded( BenchmarkStats const& stats ) = 0;
+
+        virtual void pushScopedMessage( MessageInfo const& message ) = 0;
+        virtual void popScopedMessage( MessageInfo const& message ) = 0;
+
+        virtual void handleFatalErrorCondition( StringRef message ) = 0;
+
+        virtual void handleExpr
+                (   AssertionInfo const& info,
+                    ITransientExpression const& expr,
+                    AssertionReaction& reaction ) = 0;
+        virtual void handleMessage
+                (   AssertionInfo const& info,
+                    ResultWas::OfType resultType,
+                    StringRef const& message,
+                    AssertionReaction& reaction ) = 0;
+        virtual void handleUnexpectedExceptionNotThrown
+                (   AssertionInfo const& info,
+                    AssertionReaction& reaction ) = 0;
+        virtual void handleUnexpectedInflightException
+                (   AssertionInfo const& info,
+                    std::string const& message,
+                    AssertionReaction& reaction ) = 0;
+        virtual void handleIncomplete
+                (   AssertionInfo const& info ) = 0;
+        virtual void handleNonExpr
+                (   AssertionInfo const &info,
+                    ResultWas::OfType resultType,
+                    AssertionReaction &reaction ) = 0;
+
+        virtual bool lastAssertionPassed() = 0;
+        virtual void assertionPassed() = 0;
+
+        // Deprecated, do not use:
+        virtual std::string getCurrentTestName() const = 0;
+        virtual const AssertionResult* getLastResult() const = 0;
+        virtual void exceptionEarlyReported() = 0;
+    };
+
+    IResultCapture& getResultCapture();
+}
+
+// end catch_interfaces_capture.h
+namespace Catch {
+
+    struct TestFailureException{};
+    struct AssertionResultData;
+    struct IResultCapture;
+    class RunContext;
+
+    class LazyExpression {
+        friend class AssertionHandler;
+        friend struct AssertionStats;
+        friend class RunContext;
+
+        ITransientExpression const* m_transientExpression = nullptr;
+        bool m_isNegated;
+    public:
+        LazyExpression( bool isNegated );
+        LazyExpression( LazyExpression const& other );
+        LazyExpression& operator = ( LazyExpression const& ) = delete;
+
+        explicit operator bool() const;
+
+        friend auto operator << ( std::ostream& os, LazyExpression const& lazyExpr ) -> std::ostream&;
+    };
+
+    struct AssertionReaction {
+        bool shouldDebugBreak = false;
+        bool shouldThrow = false;
+    };
+
+    class AssertionHandler {
+        AssertionInfo m_assertionInfo;
+        AssertionReaction m_reaction;
+        bool m_completed = false;
+        IResultCapture& m_resultCapture;
+
+    public:
+        AssertionHandler
+            (   StringRef const& macroName,
+                SourceLineInfo const& lineInfo,
+                StringRef capturedExpression,
+                ResultDisposition::Flags resultDisposition );
+        ~AssertionHandler() {
+            if ( !m_completed ) {
+                m_resultCapture.handleIncomplete( m_assertionInfo );
+            }
+        }
+
+        template<typename T>
+        void handleExpr( ExprLhs<T> const& expr ) {
+            handleExpr( expr.makeUnaryExpr() );
+        }
+        void handleExpr( ITransientExpression const& expr );
+
+        void handleMessage(ResultWas::OfType resultType, StringRef const& message);
+
+        void handleExceptionThrownAsExpected();
+        void handleUnexpectedExceptionNotThrown();
+        void handleExceptionNotThrownAsExpected();
+        void handleThrowingCallSkipped();
+        void handleUnexpectedInflightException();
+
+        void complete();
+        void setCompleted();
+
+        // query
+        auto allowThrows() const -> bool;
+    };
+
+    void handleExceptionMatchExpr( AssertionHandler& handler, std::string const& str, StringRef const& matcherString );
+
+} // namespace Catch
+
+// end catch_assertionhandler.h
+// start catch_message.h
+
+#include <string>
+#include <vector>
+
+namespace Catch {
+
+    struct MessageInfo {
+        MessageInfo(    StringRef const& _macroName,
+                        SourceLineInfo const& _lineInfo,
+                        ResultWas::OfType _type );
+
+        StringRef macroName;
+        std::string message;
+        SourceLineInfo lineInfo;
+        ResultWas::OfType type;
+        unsigned int sequence;
+
+        bool operator == ( MessageInfo const& other ) const;
+        bool operator < ( MessageInfo const& other ) const;
+    private:
+        static unsigned int globalCount;
+    };
+
+    struct MessageStream {
+
+        template<typename T>
+        MessageStream& operator << ( T const& value ) {
+            m_stream << value;
+            return *this;
+        }
+
+        ReusableStringStream m_stream;
+    };
+
+    struct MessageBuilder : MessageStream {
+        MessageBuilder( StringRef const& macroName,
+                        SourceLineInfo const& lineInfo,
+                        ResultWas::OfType type );
+
+        template<typename T>
+        MessageBuilder& operator << ( T const& value ) {
+            m_stream << value;
+            return *this;
+        }
+
+        MessageInfo m_info;
+    };
+
+    class ScopedMessage {
+    public:
+        explicit ScopedMessage( MessageBuilder const& builder );
+        ~ScopedMessage();
+
+        MessageInfo m_info;
+    };
+
+    class Capturer {
+        std::vector<MessageInfo> m_messages;
+        IResultCapture& m_resultCapture = getResultCapture();
+        size_t m_captured = 0;
+    public:
+        Capturer( StringRef macroName, SourceLineInfo const& lineInfo, ResultWas::OfType resultType, StringRef names );
+        ~Capturer();
+
+        void captureValue( size_t index, StringRef value );
+
+        template<typename T>
+        void captureValues( size_t index, T&& value ) {
+            captureValue( index, Catch::Detail::stringify( value ) );
+        }
+
+        template<typename T, typename... Ts>
+        void captureValues( size_t index, T&& value, Ts&&... values ) {
+            captureValues( index, value );
+            captureValues( index+1, values... );
+        }
+    };
+
+} // end namespace Catch
+
+// end catch_message.h
+#if !defined(CATCH_CONFIG_DISABLE)
+
+#if !defined(CATCH_CONFIG_DISABLE_STRINGIFICATION)
+  #define CATCH_INTERNAL_STRINGIFY(...) #__VA_ARGS__
+#else
+  #define CATCH_INTERNAL_STRINGIFY(...) "Disabled by CATCH_CONFIG_DISABLE_STRINGIFICATION"
+#endif
+
+#if defined(CATCH_CONFIG_FAST_COMPILE) || defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+
+///////////////////////////////////////////////////////////////////////////////
+// Another way to speed-up compilation is to omit local try-catch for REQUIRE*
+// macros.
+#define INTERNAL_CATCH_TRY
+#define INTERNAL_CATCH_CATCH( capturer )
+
+#else // CATCH_CONFIG_FAST_COMPILE
+
+#define INTERNAL_CATCH_TRY try
+#define INTERNAL_CATCH_CATCH( handler ) catch(...) { handler.handleUnexpectedInflightException(); }
+
+#endif
+
+#define INTERNAL_CATCH_REACT( handler ) handler.complete();
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_TEST( macroName, resultDisposition, ... ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__), resultDisposition ); \
+        INTERNAL_CATCH_TRY { \
+            CATCH_INTERNAL_SUPPRESS_PARENTHESES_WARNINGS \
+            catchAssertionHandler.handleExpr( Catch::Decomposer() <= __VA_ARGS__ ); \
+            CATCH_INTERNAL_UNSUPPRESS_PARENTHESES_WARNINGS \
+        } INTERNAL_CATCH_CATCH( catchAssertionHandler ) \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( (void)0, false && static_cast<bool>( !!(__VA_ARGS__) ) ) // the expression here is never evaluated at runtime but it forces the compiler to give it a look
+    // The double negation silences MSVC's C4800 warning, the static_cast forces short-circuit evaluation if the type has overloaded &&.
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_IF( macroName, resultDisposition, ... ) \
+    INTERNAL_CATCH_TEST( macroName, resultDisposition, __VA_ARGS__ ); \
+    if( Catch::getResultCapture().lastAssertionPassed() )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_ELSE( macroName, resultDisposition, ... ) \
+    INTERNAL_CATCH_TEST( macroName, resultDisposition, __VA_ARGS__ ); \
+    if( !Catch::getResultCapture().lastAssertionPassed() )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_NO_THROW( macroName, resultDisposition, ... ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__), resultDisposition ); \
+        try { \
+            static_cast<void>(__VA_ARGS__); \
+            catchAssertionHandler.handleExceptionNotThrownAsExpected(); \
+        } \
+        catch( ... ) { \
+            catchAssertionHandler.handleUnexpectedInflightException(); \
+        } \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_THROWS( macroName, resultDisposition, ... ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__), resultDisposition); \
+        if( catchAssertionHandler.allowThrows() ) \
+            try { \
+                static_cast<void>(__VA_ARGS__); \
+                catchAssertionHandler.handleUnexpectedExceptionNotThrown(); \
+            } \
+            catch( ... ) { \
+                catchAssertionHandler.handleExceptionThrownAsExpected(); \
+            } \
+        else \
+            catchAssertionHandler.handleThrowingCallSkipped(); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_THROWS_AS( macroName, exceptionType, resultDisposition, expr ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(expr) ", " CATCH_INTERNAL_STRINGIFY(exceptionType), resultDisposition ); \
+        if( catchAssertionHandler.allowThrows() ) \
+            try { \
+                static_cast<void>(expr); \
+                catchAssertionHandler.handleUnexpectedExceptionNotThrown(); \
+            } \
+            catch( exceptionType const& ) { \
+                catchAssertionHandler.handleExceptionThrownAsExpected(); \
+            } \
+            catch( ... ) { \
+                catchAssertionHandler.handleUnexpectedInflightException(); \
+            } \
+        else \
+            catchAssertionHandler.handleThrowingCallSkipped(); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_MSG( macroName, messageType, resultDisposition, ... ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, Catch::StringRef(), resultDisposition ); \
+        catchAssertionHandler.handleMessage( messageType, ( Catch::MessageStream() << __VA_ARGS__ + ::Catch::StreamEndStop() ).m_stream.str() ); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_CAPTURE( varName, macroName, ... ) \
+    auto varName = Catch::Capturer( macroName, CATCH_INTERNAL_LINEINFO, Catch::ResultWas::Info, #__VA_ARGS__ ); \
+    varName.captureValues( 0, __VA_ARGS__ )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_INFO( macroName, log ) \
+    Catch::ScopedMessage INTERNAL_CATCH_UNIQUE_NAME( scopedMessage )( Catch::MessageBuilder( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, Catch::ResultWas::Info ) << log );
+
+///////////////////////////////////////////////////////////////////////////////
+// Although this is matcher-based, it can be used with just a string
+#define INTERNAL_CATCH_THROWS_STR_MATCHES( macroName, resultDisposition, matcher, ... ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__) ", " CATCH_INTERNAL_STRINGIFY(matcher), resultDisposition ); \
+        if( catchAssertionHandler.allowThrows() ) \
+            try { \
+                static_cast<void>(__VA_ARGS__); \
+                catchAssertionHandler.handleUnexpectedExceptionNotThrown(); \
+            } \
+            catch( ... ) { \
+                Catch::handleExceptionMatchExpr( catchAssertionHandler, matcher, #matcher##_catch_sr ); \
+            } \
+        else \
+            catchAssertionHandler.handleThrowingCallSkipped(); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+#endif // CATCH_CONFIG_DISABLE
+
+// end catch_capture.hpp
+// start catch_section.h
+
+// start catch_section_info.h
+
+// start catch_totals.h
+
+#include <cstddef>
+
+namespace Catch {
+
+    struct Counts {
+        Counts operator - ( Counts const& other ) const;
+        Counts& operator += ( Counts const& other );
+
+        std::size_t total() const;
+        bool allPassed() const;
+        bool allOk() const;
+
+        std::size_t passed = 0;
+        std::size_t failed = 0;
+        std::size_t failedButOk = 0;
+    };
+
+    struct Totals {
+
+        Totals operator - ( Totals const& other ) const;
+        Totals& operator += ( Totals const& other );
+
+        Totals delta( Totals const& prevTotals ) const;
+
+        int error = 0;
+        Counts assertions;
+        Counts testCases;
+    };
+}
+
+// end catch_totals.h
+#include <string>
+
+namespace Catch {
+
+    struct SectionInfo {
+        SectionInfo
+            (   SourceLineInfo const& _lineInfo,
+                std::string const& _name );
+
+        // Deprecated
+        SectionInfo
+            (   SourceLineInfo const& _lineInfo,
+                std::string const& _name,
+                std::string const& ) : SectionInfo( _lineInfo, _name ) {}
+
+        std::string name;
+        std::string description; // !Deprecated: this will always be empty
+        SourceLineInfo lineInfo;
+    };
+
+    struct SectionEndInfo {
+        SectionInfo sectionInfo;
+        Counts prevAssertions;
+        double durationInSeconds;
+    };
+
+} // end namespace Catch
+
+// end catch_section_info.h
+// start catch_timer.h
+
+#include <cstdint>
+
+namespace Catch {
+
+    auto getCurrentNanosecondsSinceEpoch() -> uint64_t;
+    auto getEstimatedClockResolution() -> uint64_t;
+
+    class Timer {
+        uint64_t m_nanoseconds = 0;
+    public:
+        void start();
+        auto getElapsedNanoseconds() const -> uint64_t;
+        auto getElapsedMicroseconds() const -> uint64_t;
+        auto getElapsedMilliseconds() const -> unsigned int;
+        auto getElapsedSeconds() const -> double;
+    };
+
+} // namespace Catch
+
+// end catch_timer.h
+#include <string>
+
+namespace Catch {
+
+    class Section : NonCopyable {
+    public:
+        Section( SectionInfo const& info );
+        ~Section();
+
+        // This indicates whether the section should be executed or not
+        explicit operator bool() const;
+
+    private:
+        SectionInfo m_info;
+
+        std::string m_name;
+        Counts m_assertions;
+        bool m_sectionIncluded;
+        Timer m_timer;
+    };
+
+} // end namespace Catch
+
+#define INTERNAL_CATCH_SECTION( ... ) \
+    CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS \
+    if( Catch::Section const& INTERNAL_CATCH_UNIQUE_NAME( catch_internal_Section ) = Catch::SectionInfo( CATCH_INTERNAL_LINEINFO, __VA_ARGS__ ) ) \
+    CATCH_INTERNAL_UNSUPPRESS_UNUSED_WARNINGS
+
+#define INTERNAL_CATCH_DYNAMIC_SECTION( ... ) \
+    CATCH_INTERNAL_SUPPRESS_UNUSED_WARNINGS \
+    if( Catch::Section const& INTERNAL_CATCH_UNIQUE_NAME( catch_internal_Section ) = Catch::SectionInfo( CATCH_INTERNAL_LINEINFO, (Catch::ReusableStringStream() << __VA_ARGS__).str() ) ) \
+    CATCH_INTERNAL_UNSUPPRESS_UNUSED_WARNINGS
+
+// end catch_section.h
+// start catch_benchmark.h
+
+#include <cstdint>
+#include <string>
+
+namespace Catch {
+
+    class BenchmarkLooper {
+
+        std::string m_name;
+        std::size_t m_count = 0;
+        std::size_t m_iterationsToRun = 1;
+        uint64_t m_resolution;
+        Timer m_timer;
+
+        static auto getResolution() -> uint64_t;
+    public:
+        // Keep most of this inline as it's on the code path that is being timed
+        BenchmarkLooper( StringRef name )
+        :   m_name( name ),
+            m_resolution( getResolution() )
+        {
+            reportStart();
+            m_timer.start();
+        }
+
+        explicit operator bool() {
+            if( m_count < m_iterationsToRun )
+                return true;
+            return needsMoreIterations();
+        }
+
+        void increment() {
+            ++m_count;
+        }
+
+        void reportStart();
+        auto needsMoreIterations() -> bool;
+    };
+
+} // end namespace Catch
+
+#define BENCHMARK( name ) \
+    for( Catch::BenchmarkLooper looper( name ); looper; looper.increment() )
+
+// end catch_benchmark.h
+// start catch_interfaces_exception.h
+
+// start catch_interfaces_registry_hub.h
+
+#include <string>
+#include <memory>
+
+namespace Catch {
+
+    class TestCase;
+    struct ITestCaseRegistry;
+    struct IExceptionTranslatorRegistry;
+    struct IExceptionTranslator;
+    struct IReporterRegistry;
+    struct IReporterFactory;
+    struct ITagAliasRegistry;
+    class StartupExceptionRegistry;
+
+    using IReporterFactoryPtr = std::shared_ptr<IReporterFactory>;
+
+    struct IRegistryHub {
+        virtual ~IRegistryHub();
+
+        virtual IReporterRegistry const& getReporterRegistry() const = 0;
+        virtual ITestCaseRegistry const& getTestCaseRegistry() const = 0;
+        virtual ITagAliasRegistry const& getTagAliasRegistry() const = 0;
+
+        virtual IExceptionTranslatorRegistry const& getExceptionTranslatorRegistry() const = 0;
+
+        virtual StartupExceptionRegistry const& getStartupExceptionRegistry() const = 0;
+    };
+
+    struct IMutableRegistryHub {
+        virtual ~IMutableRegistryHub();
+        virtual void registerReporter( std::string const& name, IReporterFactoryPtr const& factory ) = 0;
+        virtual void registerListener( IReporterFactoryPtr const& factory ) = 0;
+        virtual void registerTest( TestCase const& testInfo ) = 0;
+        virtual void registerTranslator( const IExceptionTranslator* translator ) = 0;
+        virtual void registerTagAlias( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) = 0;
+        virtual void registerStartupException() noexcept = 0;
+    };
+
+    IRegistryHub const& getRegistryHub();
+    IMutableRegistryHub& getMutableRegistryHub();
+    void cleanUp();
+    std::string translateActiveException();
+
+}
+
+// end catch_interfaces_registry_hub.h
+#if defined(CATCH_CONFIG_DISABLE)
+    #define INTERNAL_CATCH_TRANSLATE_EXCEPTION_NO_REG( translatorName, signature) \
+        static std::string translatorName( signature )
+#endif
+
+#include <exception>
+#include <string>
+#include <vector>
+
+namespace Catch {
+    using exceptionTranslateFunction = std::string(*)();
+
+    struct IExceptionTranslator;
+    using ExceptionTranslators = std::vector<std::unique_ptr<IExceptionTranslator const>>;
+
+    struct IExceptionTranslator {
+        virtual ~IExceptionTranslator();
+        virtual std::string translate( ExceptionTranslators::const_iterator it, ExceptionTranslators::const_iterator itEnd ) const = 0;
+    };
+
+    struct IExceptionTranslatorRegistry {
+        virtual ~IExceptionTranslatorRegistry();
+
+        virtual std::string translateActiveException() const = 0;
+    };
+
+    class ExceptionTranslatorRegistrar {
+        template<typename T>
+        class ExceptionTranslator : public IExceptionTranslator {
+        public:
+
+            ExceptionTranslator( std::string(*translateFunction)( T& ) )
+            : m_translateFunction( translateFunction )
+            {}
+
+            std::string translate( ExceptionTranslators::const_iterator it, ExceptionTranslators::const_iterator itEnd ) const override {
+                try {
+                    if( it == itEnd )
+                        std::rethrow_exception(std::current_exception());
+                    else
+                        return (*it)->translate( it+1, itEnd );
+                }
+                catch( T& ex ) {
+                    return m_translateFunction( ex );
+                }
+            }
+
+        protected:
+            std::string(*m_translateFunction)( T& );
+        };
+
+    public:
+        template<typename T>
+        ExceptionTranslatorRegistrar( std::string(*translateFunction)( T& ) ) {
+            getMutableRegistryHub().registerTranslator
+                ( new ExceptionTranslator<T>( translateFunction ) );
+        }
+    };
+}
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_TRANSLATE_EXCEPTION2( translatorName, signature ) \
+    static std::string translatorName( signature ); \
+    CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS \
+    namespace{ Catch::ExceptionTranslatorRegistrar INTERNAL_CATCH_UNIQUE_NAME( catch_internal_ExceptionRegistrar )( &translatorName ); } \
+    CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS \
+    static std::string translatorName( signature )
+
+#define INTERNAL_CATCH_TRANSLATE_EXCEPTION( signature ) INTERNAL_CATCH_TRANSLATE_EXCEPTION2( INTERNAL_CATCH_UNIQUE_NAME( catch_internal_ExceptionTranslator ), signature )
+
+// end catch_interfaces_exception.h
+// start catch_approx.h
+
+#include <type_traits>
+
+namespace Catch {
+namespace Detail {
+
+    class Approx {
+    private:
+        bool equalityComparisonImpl(double other) const;
+        // Validates the new margin (margin >= 0)
+        // out-of-line to avoid including stdexcept in the header
+        void setMargin(double margin);
+        // Validates the new epsilon (0 < epsilon < 1)
+        // out-of-line to avoid including stdexcept in the header
+        void setEpsilon(double epsilon);
+
+    public:
+        explicit Approx ( double value );
+
+        static Approx custom();
+
+        Approx operator-() const;
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        Approx operator()( T const& value ) {
+            Approx approx( static_cast<double>(value) );
+            approx.m_epsilon = m_epsilon;
+            approx.m_margin = m_margin;
+            approx.m_scale = m_scale;
+            return approx;
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        explicit Approx( T const& value ): Approx(static_cast<double>(value))
+        {}
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        friend bool operator == ( const T& lhs, Approx const& rhs ) {
+            auto lhs_v = static_cast<double>(lhs);
+            return rhs.equalityComparisonImpl(lhs_v);
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        friend bool operator == ( Approx const& lhs, const T& rhs ) {
+            return operator==( rhs, lhs );
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        friend bool operator != ( T const& lhs, Approx const& rhs ) {
+            return !operator==( lhs, rhs );
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        friend bool operator != ( Approx const& lhs, T const& rhs ) {
+            return !operator==( rhs, lhs );
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        friend bool operator <= ( T const& lhs, Approx const& rhs ) {
+            return static_cast<double>(lhs) < rhs.m_value || lhs == rhs;
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        friend bool operator <= ( Approx const& lhs, T const& rhs ) {
+            return lhs.m_value < static_cast<double>(rhs) || lhs == rhs;
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        friend bool operator >= ( T const& lhs, Approx const& rhs ) {
+            return static_cast<double>(lhs) > rhs.m_value || lhs == rhs;
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        friend bool operator >= ( Approx const& lhs, T const& rhs ) {
+            return lhs.m_value > static_cast<double>(rhs) || lhs == rhs;
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        Approx& epsilon( T const& newEpsilon ) {
+            double epsilonAsDouble = static_cast<double>(newEpsilon);
+            setEpsilon(epsilonAsDouble);
+            return *this;
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        Approx& margin( T const& newMargin ) {
+            double marginAsDouble = static_cast<double>(newMargin);
+            setMargin(marginAsDouble);
+            return *this;
+        }
+
+        template <typename T, typename = typename std::enable_if<std::is_constructible<double, T>::value>::type>
+        Approx& scale( T const& newScale ) {
+            m_scale = static_cast<double>(newScale);
+            return *this;
+        }
+
+        std::string toString() const;
+
+    private:
+        double m_epsilon;
+        double m_margin;
+        double m_scale;
+        double m_value;
+    };
+} // end namespace Detail
+
+namespace literals {
+    Detail::Approx operator "" _a(long double val);
+    Detail::Approx operator "" _a(unsigned long long val);
+} // end namespace literals
+
+template<>
+struct StringMaker<Catch::Detail::Approx> {
+    static std::string convert(Catch::Detail::Approx const& value);
+};
+
+} // end namespace Catch
+
+// end catch_approx.h
+// start catch_string_manip.h
+
+#include <string>
+#include <iosfwd>
+
+namespace Catch {
+
+    bool startsWith( std::string const& s, std::string const& prefix );
+    bool startsWith( std::string const& s, char prefix );
+    bool endsWith( std::string const& s, std::string const& suffix );
+    bool endsWith( std::string const& s, char suffix );
+    bool contains( std::string const& s, std::string const& infix );
+    void toLowerInPlace( std::string& s );
+    std::string toLower( std::string const& s );
+    std::string trim( std::string const& str );
+    bool replaceInPlace( std::string& str, std::string const& replaceThis, std::string const& withThis );
+
+    struct pluralise {
+        pluralise( std::size_t count, std::string const& label );
+
+        friend std::ostream& operator << ( std::ostream& os, pluralise const& pluraliser );
+
+        std::size_t m_count;
+        std::string m_label;
+    };
+}
+
+// end catch_string_manip.h
+#ifndef CATCH_CONFIG_DISABLE_MATCHERS
+// start catch_capture_matchers.h
+
+// start catch_matchers.h
+
+#include <string>
+#include <vector>
+
+namespace Catch {
+namespace Matchers {
+    namespace Impl {
+
+        template<typename ArgT> struct MatchAllOf;
+        template<typename ArgT> struct MatchAnyOf;
+        template<typename ArgT> struct MatchNotOf;
+
+        class MatcherUntypedBase {
+        public:
+            MatcherUntypedBase() = default;
+            MatcherUntypedBase ( MatcherUntypedBase const& ) = default;
+            MatcherUntypedBase& operator = ( MatcherUntypedBase const& ) = delete;
+            std::string toString() const;
+
+        protected:
+            virtual ~MatcherUntypedBase();
+            virtual std::string describe() const = 0;
+            mutable std::string m_cachedToString;
+        };
+
+#ifdef __clang__
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wnon-virtual-dtor"
+#endif
+
+        template<typename ObjectT>
+        struct MatcherMethod {
+            virtual bool match( ObjectT const& arg ) const = 0;
+        };
+        template<typename PtrT>
+        struct MatcherMethod<PtrT*> {
+            virtual bool match( PtrT* arg ) const = 0;
+        };
+
+#ifdef __clang__
+#    pragma clang diagnostic pop
+#endif
+
+        template<typename T>
+        struct MatcherBase : MatcherUntypedBase, MatcherMethod<T> {
+
+            MatchAllOf<T> operator && ( MatcherBase const& other ) const;
+            MatchAnyOf<T> operator || ( MatcherBase const& other ) const;
+            MatchNotOf<T> operator ! () const;
+        };
+
+        template<typename ArgT>
+        struct MatchAllOf : MatcherBase<ArgT> {
+            bool match( ArgT const& arg ) const override {
+                for( auto matcher : m_matchers ) {
+                    if (!matcher->match(arg))
+                        return false;
+                }
+                return true;
+            }
+            std::string describe() const override {
+                std::string description;
+                description.reserve( 4 + m_matchers.size()*32 );
+                description += "( ";
+                bool first = true;
+                for( auto matcher : m_matchers ) {
+                    if( first )
+                        first = false;
+                    else
+                        description += " and ";
+                    description += matcher->toString();
+                }
+                description += " )";
+                return description;
+            }
+
+            MatchAllOf<ArgT>& operator && ( MatcherBase<ArgT> const& other ) {
+                m_matchers.push_back( &other );
+                return *this;
+            }
+
+            std::vector<MatcherBase<ArgT> const*> m_matchers;
+        };
+        template<typename ArgT>
+        struct MatchAnyOf : MatcherBase<ArgT> {
+
+            bool match( ArgT const& arg ) const override {
+                for( auto matcher : m_matchers ) {
+                    if (matcher->match(arg))
+                        return true;
+                }
+                return false;
+            }
+            std::string describe() const override {
+                std::string description;
+                description.reserve( 4 + m_matchers.size()*32 );
+                description += "( ";
+                bool first = true;
+                for( auto matcher : m_matchers ) {
+                    if( first )
+                        first = false;
+                    else
+                        description += " or ";
+                    description += matcher->toString();
+                }
+                description += " )";
+                return description;
+            }
+
+            MatchAnyOf<ArgT>& operator || ( MatcherBase<ArgT> const& other ) {
+                m_matchers.push_back( &other );
+                return *this;
+            }
+
+            std::vector<MatcherBase<ArgT> const*> m_matchers;
+        };
+
+        template<typename ArgT>
+        struct MatchNotOf : MatcherBase<ArgT> {
+
+            MatchNotOf( MatcherBase<ArgT> const& underlyingMatcher ) : m_underlyingMatcher( underlyingMatcher ) {}
+
+            bool match( ArgT const& arg ) const override {
+                return !m_underlyingMatcher.match( arg );
+            }
+
+            std::string describe() const override {
+                return "not " + m_underlyingMatcher.toString();
+            }
+            MatcherBase<ArgT> const& m_underlyingMatcher;
+        };
+
+        template<typename T>
+        MatchAllOf<T> MatcherBase<T>::operator && ( MatcherBase const& other ) const {
+            return MatchAllOf<T>() && *this && other;
+        }
+        template<typename T>
+        MatchAnyOf<T> MatcherBase<T>::operator || ( MatcherBase const& other ) const {
+            return MatchAnyOf<T>() || *this || other;
+        }
+        template<typename T>
+        MatchNotOf<T> MatcherBase<T>::operator ! () const {
+            return MatchNotOf<T>( *this );
+        }
+
+    } // namespace Impl
+
+} // namespace Matchers
+
+using namespace Matchers;
+using Matchers::Impl::MatcherBase;
+
+} // namespace Catch
+
+// end catch_matchers.h
+// start catch_matchers_floating.h
+
+#include <type_traits>
+#include <cmath>
+
+namespace Catch {
+namespace Matchers {
+
+    namespace Floating {
+
+        enum class FloatingPointKind : uint8_t;
+
+        struct WithinAbsMatcher : MatcherBase<double> {
+            WithinAbsMatcher(double target, double margin);
+            bool match(double const& matchee) const override;
+            std::string describe() const override;
+        private:
+            double m_target;
+            double m_margin;
+        };
+
+        struct WithinUlpsMatcher : MatcherBase<double> {
+            WithinUlpsMatcher(double target, int ulps, FloatingPointKind baseType);
+            bool match(double const& matchee) const override;
+            std::string describe() const override;
+        private:
+            double m_target;
+            int m_ulps;
+            FloatingPointKind m_type;
+        };
+
+    } // namespace Floating
+
+    // The following functions create the actual matcher objects.
+    // This allows the types to be inferred
+    Floating::WithinUlpsMatcher WithinULP(double target, int maxUlpDiff);
+    Floating::WithinUlpsMatcher WithinULP(float target, int maxUlpDiff);
+    Floating::WithinAbsMatcher WithinAbs(double target, double margin);
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_floating.h
+// start catch_matchers_generic.hpp
+
+#include <functional>
+#include <string>
+
+namespace Catch {
+namespace Matchers {
+namespace Generic {
+
+namespace Detail {
+    std::string finalizeDescription(const std::string& desc);
+}
+
+template <typename T>
+class PredicateMatcher : public MatcherBase<T> {
+    std::function<bool(T const&)> m_predicate;
+    std::string m_description;
+public:
+
+    PredicateMatcher(std::function<bool(T const&)> const& elem, std::string const& descr)
+        :m_predicate(std::move(elem)),
+        m_description(Detail::finalizeDescription(descr))
+    {}
+
+    bool match( T const& item ) const override {
+        return m_predicate(item);
+    }
+
+    std::string describe() const override {
+        return m_description;
+    }
+};
+
+} // namespace Generic
+
+    // The following functions create the actual matcher objects.
+    // The user has to explicitly specify type to the function, because
+    // infering std::function<bool(T const&)> is hard (but possible) and
+    // requires a lot of TMP.
+    template<typename T>
+    Generic::PredicateMatcher<T> Predicate(std::function<bool(T const&)> const& predicate, std::string const& description = "") {
+        return Generic::PredicateMatcher<T>(predicate, description);
+    }
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_generic.hpp
+// start catch_matchers_string.h
+
+#include <string>
+
+namespace Catch {
+namespace Matchers {
+
+    namespace StdString {
+
+        struct CasedString
+        {
+            CasedString( std::string const& str, CaseSensitive::Choice caseSensitivity );
+            std::string adjustString( std::string const& str ) const;
+            std::string caseSensitivitySuffix() const;
+
+            CaseSensitive::Choice m_caseSensitivity;
+            std::string m_str;
+        };
+
+        struct StringMatcherBase : MatcherBase<std::string> {
+            StringMatcherBase( std::string const& operation, CasedString const& comparator );
+            std::string describe() const override;
+
+            CasedString m_comparator;
+            std::string m_operation;
+        };
+
+        struct EqualsMatcher : StringMatcherBase {
+            EqualsMatcher( CasedString const& comparator );
+            bool match( std::string const& source ) const override;
+        };
+        struct ContainsMatcher : StringMatcherBase {
+            ContainsMatcher( CasedString const& comparator );
+            bool match( std::string const& source ) const override;
+        };
+        struct StartsWithMatcher : StringMatcherBase {
+            StartsWithMatcher( CasedString const& comparator );
+            bool match( std::string const& source ) const override;
+        };
+        struct EndsWithMatcher : StringMatcherBase {
+            EndsWithMatcher( CasedString const& comparator );
+            bool match( std::string const& source ) const override;
+        };
+
+        struct RegexMatcher : MatcherBase<std::string> {
+            RegexMatcher( std::string regex, CaseSensitive::Choice caseSensitivity );
+            bool match( std::string const& matchee ) const override;
+            std::string describe() const override;
+
+        private:
+            std::string m_regex;
+            CaseSensitive::Choice m_caseSensitivity;
+        };
+
+    } // namespace StdString
+
+    // The following functions create the actual matcher objects.
+    // This allows the types to be inferred
+
+    StdString::EqualsMatcher Equals( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+    StdString::ContainsMatcher Contains( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+    StdString::EndsWithMatcher EndsWith( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+    StdString::StartsWithMatcher StartsWith( std::string const& str, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+    StdString::RegexMatcher Matches( std::string const& regex, CaseSensitive::Choice caseSensitivity = CaseSensitive::Yes );
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_string.h
+// start catch_matchers_vector.h
+
+#include <algorithm>
+
+namespace Catch {
+namespace Matchers {
+
+    namespace Vector {
+        namespace Detail {
+            template <typename InputIterator, typename T>
+            size_t count(InputIterator first, InputIterator last, T const& item) {
+                size_t cnt = 0;
+                for (; first != last; ++first) {
+                    if (*first == item) {
+                        ++cnt;
+                    }
+                }
+                return cnt;
+            }
+            template <typename InputIterator, typename T>
+            bool contains(InputIterator first, InputIterator last, T const& item) {
+                for (; first != last; ++first) {
+                    if (*first == item) {
+                        return true;
+                    }
+                }
+                return false;
+            }
+        }
+
+        template<typename T>
+        struct ContainsElementMatcher : MatcherBase<std::vector<T>> {
+
+            ContainsElementMatcher(T const &comparator) : m_comparator( comparator) {}
+
+            bool match(std::vector<T> const &v) const override {
+                for (auto const& el : v) {
+                    if (el == m_comparator) {
+                        return true;
+                    }
+                }
+                return false;
+            }
+
+            std::string describe() const override {
+                return "Contains: " + ::Catch::Detail::stringify( m_comparator );
+            }
+
+            T const& m_comparator;
+        };
+
+        template<typename T>
+        struct ContainsMatcher : MatcherBase<std::vector<T>> {
+
+            ContainsMatcher(std::vector<T> const &comparator) : m_comparator( comparator ) {}
+
+            bool match(std::vector<T> const &v) const override {
+                // !TBD: see note in EqualsMatcher
+                if (m_comparator.size() > v.size())
+                    return false;
+                for (auto const& comparator : m_comparator) {
+                    auto present = false;
+                    for (const auto& el : v) {
+                        if (el == comparator) {
+                            present = true;
+                            break;
+                        }
+                    }
+                    if (!present) {
+                        return false;
+                    }
+                }
+                return true;
+            }
+            std::string describe() const override {
+                return "Contains: " + ::Catch::Detail::stringify( m_comparator );
+            }
+
+            std::vector<T> const& m_comparator;
+        };
+
+        template<typename T>
+        struct EqualsMatcher : MatcherBase<std::vector<T>> {
+
+            EqualsMatcher(std::vector<T> const &comparator) : m_comparator( comparator ) {}
+
+            bool match(std::vector<T> const &v) const override {
+                // !TBD: This currently works if all elements can be compared using !=
+                // - a more general approach would be via a compare template that defaults
+                // to using !=. but could be specialised for, e.g. std::vector<T> etc
+                // - then just call that directly
+                if (m_comparator.size() != v.size())
+                    return false;
+                for (std::size_t i = 0; i < v.size(); ++i)
+                    if (m_comparator[i] != v[i])
+                        return false;
+                return true;
+            }
+            std::string describe() const override {
+                return "Equals: " + ::Catch::Detail::stringify( m_comparator );
+            }
+            std::vector<T> const& m_comparator;
+        };
+
+        template<typename T>
+        struct UnorderedEqualsMatcher : MatcherBase<std::vector<T>> {
+            UnorderedEqualsMatcher(std::vector<T> const& target) : m_target(target) {}
+            bool match(std::vector<T> const& vec) const override {
+                // Note: This is a reimplementation of std::is_permutation,
+                //       because I don't want to include <algorithm> inside the common path
+                if (m_target.size() != vec.size()) {
+                    return false;
+                }
+                auto lfirst = m_target.begin(), llast = m_target.end();
+                auto rfirst = vec.begin(), rlast = vec.end();
+                // Cut common prefix to optimize checking of permuted parts
+                while (lfirst != llast && *lfirst == *rfirst) {
+                    ++lfirst; ++rfirst;
+                }
+                if (lfirst == llast) {
+                    return true;
+                }
+
+                for (auto mid = lfirst; mid != llast; ++mid) {
+                    // Skip already counted items
+                    if (Detail::contains(lfirst, mid, *mid)) {
+                        continue;
+                    }
+                    size_t num_vec = Detail::count(rfirst, rlast, *mid);
+                    if (num_vec == 0 || Detail::count(lfirst, llast, *mid) != num_vec) {
+                        return false;
+                    }
+                }
+
+                return true;
+            }
+
+            std::string describe() const override {
+                return "UnorderedEquals: " + ::Catch::Detail::stringify(m_target);
+            }
+        private:
+            std::vector<T> const& m_target;
+        };
+
+    } // namespace Vector
+
+    // The following functions create the actual matcher objects.
+    // This allows the types to be inferred
+
+    template<typename T>
+    Vector::ContainsMatcher<T> Contains( std::vector<T> const& comparator ) {
+        return Vector::ContainsMatcher<T>( comparator );
+    }
+
+    template<typename T>
+    Vector::ContainsElementMatcher<T> VectorContains( T const& comparator ) {
+        return Vector::ContainsElementMatcher<T>( comparator );
+    }
+
+    template<typename T>
+    Vector::EqualsMatcher<T> Equals( std::vector<T> const& comparator ) {
+        return Vector::EqualsMatcher<T>( comparator );
+    }
+
+    template<typename T>
+    Vector::UnorderedEqualsMatcher<T> UnorderedEquals(std::vector<T> const& target) {
+        return Vector::UnorderedEqualsMatcher<T>(target);
+    }
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_vector.h
+namespace Catch {
+
+    template<typename ArgT, typename MatcherT>
+    class MatchExpr : public ITransientExpression {
+        ArgT const& m_arg;
+        MatcherT m_matcher;
+        StringRef m_matcherString;
+    public:
+        MatchExpr( ArgT const& arg, MatcherT const& matcher, StringRef const& matcherString )
+        :   ITransientExpression{ true, matcher.match( arg ) },
+            m_arg( arg ),
+            m_matcher( matcher ),
+            m_matcherString( matcherString )
+        {}
+
+        void streamReconstructedExpression( std::ostream &os ) const override {
+            auto matcherAsString = m_matcher.toString();
+            os << Catch::Detail::stringify( m_arg ) << ' ';
+            if( matcherAsString == Detail::unprintableString )
+                os << m_matcherString;
+            else
+                os << matcherAsString;
+        }
+    };
+
+    using StringMatcher = Matchers::Impl::MatcherBase<std::string>;
+
+    void handleExceptionMatchExpr( AssertionHandler& handler, StringMatcher const& matcher, StringRef const& matcherString  );
+
+    template<typename ArgT, typename MatcherT>
+    auto makeMatchExpr( ArgT const& arg, MatcherT const& matcher, StringRef const& matcherString  ) -> MatchExpr<ArgT, MatcherT> {
+        return MatchExpr<ArgT, MatcherT>( arg, matcher, matcherString );
+    }
+
+} // namespace Catch
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CHECK_THAT( macroName, matcher, resultDisposition, arg ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(arg) ", " CATCH_INTERNAL_STRINGIFY(matcher), resultDisposition ); \
+        INTERNAL_CATCH_TRY { \
+            catchAssertionHandler.handleExpr( Catch::makeMatchExpr( arg, matcher, #matcher##_catch_sr ) ); \
+        } INTERNAL_CATCH_CATCH( catchAssertionHandler ) \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+///////////////////////////////////////////////////////////////////////////////
+#define INTERNAL_CATCH_THROWS_MATCHES( macroName, exceptionType, resultDisposition, matcher, ... ) \
+    do { \
+        Catch::AssertionHandler catchAssertionHandler( macroName##_catch_sr, CATCH_INTERNAL_LINEINFO, CATCH_INTERNAL_STRINGIFY(__VA_ARGS__) ", " CATCH_INTERNAL_STRINGIFY(exceptionType) ", " CATCH_INTERNAL_STRINGIFY(matcher), resultDisposition ); \
+        if( catchAssertionHandler.allowThrows() ) \
+            try { \
+                static_cast<void>(__VA_ARGS__ ); \
+                catchAssertionHandler.handleUnexpectedExceptionNotThrown(); \
+            } \
+            catch( exceptionType const& ex ) { \
+                catchAssertionHandler.handleExpr( Catch::makeMatchExpr( ex, matcher, #matcher##_catch_sr ) ); \
+            } \
+            catch( ... ) { \
+                catchAssertionHandler.handleUnexpectedInflightException(); \
+            } \
+        else \
+            catchAssertionHandler.handleThrowingCallSkipped(); \
+        INTERNAL_CATCH_REACT( catchAssertionHandler ) \
+    } while( false )
+
+// end catch_capture_matchers.h
+#endif
+// start catch_generators.hpp
+
+// start catch_interfaces_generatortracker.h
+
+
+#include <memory>
+
+namespace Catch {
+
+    namespace Generators {
+        class GeneratorBase {
+        protected:
+            size_t m_size = 0;
+
+        public:
+            GeneratorBase( size_t size ) : m_size( size ) {}
+            virtual ~GeneratorBase();
+            auto size() const -> size_t { return m_size; }
+        };
+        using GeneratorBasePtr = std::unique_ptr<GeneratorBase>;
+
+    } // namespace Generators
+
+    struct IGeneratorTracker {
+        virtual ~IGeneratorTracker();
+        virtual auto hasGenerator() const -> bool = 0;
+        virtual auto getGenerator() const -> Generators::GeneratorBasePtr const& = 0;
+        virtual void setGenerator( Generators::GeneratorBasePtr&& generator ) = 0;
+        virtual auto getIndex() const -> std::size_t = 0;
+    };
+
+} // namespace Catch
+
+// end catch_interfaces_generatortracker.h
+// start catch_enforce.h
+
+#include <stdexcept>
+
+namespace Catch {
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+    template <typename Ex>
+    [[noreturn]]
+    void throw_exception(Ex const& e) {
+        throw e;
+    }
+#else // ^^ Exceptions are enabled //  Exceptions are disabled vv
+    [[noreturn]]
+    void throw_exception(std::exception const& e);
+#endif
+} // namespace Catch;
+
+#define CATCH_PREPARE_EXCEPTION( type, msg ) \
+    type( ( Catch::ReusableStringStream() << msg ).str() )
+#define CATCH_INTERNAL_ERROR( msg ) \
+    Catch::throw_exception(CATCH_PREPARE_EXCEPTION( std::logic_error, CATCH_INTERNAL_LINEINFO << ": Internal Catch error: " << msg))
+#define CATCH_ERROR( msg ) \
+    Catch::throw_exception(CATCH_PREPARE_EXCEPTION( std::domain_error, msg ))
+#define CATCH_RUNTIME_ERROR( msg ) \
+    Catch::throw_exception(CATCH_PREPARE_EXCEPTION( std::runtime_error, msg ))
+#define CATCH_ENFORCE( condition, msg ) \
+    do{ if( !(condition) ) CATCH_ERROR( msg ); } while(false)
+
+// end catch_enforce.h
+#include <memory>
+#include <vector>
+#include <cassert>
+
+#include <utility>
+
+namespace Catch {
+namespace Generators {
+
+    // !TBD move this into its own location?
+    namespace pf{
+        template<typename T, typename... Args>
+        std::unique_ptr<T> make_unique( Args&&... args ) {
+            return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
+        }
+    }
+
+    template<typename T>
+    struct IGenerator {
+        virtual ~IGenerator() {}
+        virtual auto get( size_t index ) const -> T = 0;
+    };
+
+    template<typename T>
+    class SingleValueGenerator : public IGenerator<T> {
+        T m_value;
+    public:
+        SingleValueGenerator( T const& value ) : m_value( value ) {}
+
+        auto get( size_t ) const -> T override {
+            return m_value;
+        }
+    };
+
+    template<typename T>
+    class FixedValuesGenerator : public IGenerator<T> {
+        std::vector<T> m_values;
+
+    public:
+        FixedValuesGenerator( std::initializer_list<T> values ) : m_values( values ) {}
+
+        auto get( size_t index ) const -> T override {
+            return m_values[index];
+        }
+    };
+
+    template<typename T>
+    class RangeGenerator : public IGenerator<T> {
+        T const m_first;
+        T const m_last;
+
+    public:
+        RangeGenerator( T const& first, T const& last ) : m_first( first ), m_last( last ) {
+            assert( m_last > m_first );
+        }
+
+        auto get( size_t index ) const -> T override {
+            // ToDo:: introduce a safe cast to catch potential overflows
+            return static_cast<T>(m_first+index);
+        }
+    };
+
+    template<typename T>
+    struct NullGenerator : IGenerator<T> {
+        auto get( size_t ) const -> T override {
+            CATCH_INTERNAL_ERROR("A Null Generator is always empty");
+        }
+    };
+
+    template<typename T>
+    class Generator {
+        std::unique_ptr<IGenerator<T>> m_generator;
+        size_t m_size;
+
+    public:
+        Generator( size_t size, std::unique_ptr<IGenerator<T>> generator )
+        :   m_generator( std::move( generator ) ),
+            m_size( size )
+        {}
+
+        auto size() const -> size_t { return m_size; }
+        auto operator[]( size_t index ) const -> T {
+            assert( index < m_size );
+            return m_generator->get( index );
+        }
+    };
+
+    std::vector<size_t> randomiseIndices( size_t selectionSize, size_t sourceSize );
+
+    template<typename T>
+    class GeneratorRandomiser : public IGenerator<T> {
+        Generator<T> m_baseGenerator;
+
+        std::vector<size_t> m_indices;
+    public:
+        GeneratorRandomiser( Generator<T>&& baseGenerator, size_t numberOfItems )
+        :   m_baseGenerator( std::move( baseGenerator ) ),
+            m_indices( randomiseIndices( numberOfItems, m_baseGenerator.size() ) )
+        {}
+
+        auto get( size_t index ) const -> T override {
+            return m_baseGenerator[m_indices[index]];
+        }
+    };
+
+    template<typename T>
+    struct RequiresASpecialisationFor;
+
+    template<typename T>
+    auto all() -> Generator<T> { return RequiresASpecialisationFor<T>(); }
+
+    template<>
+    auto all<int>() -> Generator<int>;
+
+    template<typename T>
+    auto range( T const& first, T const& last ) -> Generator<T> {
+        return Generator<T>( (last-first), pf::make_unique<RangeGenerator<T>>( first, last ) );
+    }
+
+    template<typename T>
+    auto random( T const& first, T const& last ) -> Generator<T> {
+        auto gen = range( first, last );
+        auto size = gen.size();
+
+        return Generator<T>( size, pf::make_unique<GeneratorRandomiser<T>>( std::move( gen ), size ) );
+    }
+    template<typename T>
+    auto random( size_t size ) -> Generator<T> {
+        return Generator<T>( size, pf::make_unique<GeneratorRandomiser<T>>( all<T>(), size ) );
+    }
+
+    template<typename T>
+    auto values( std::initializer_list<T> values ) -> Generator<T> {
+        return Generator<T>( values.size(), pf::make_unique<FixedValuesGenerator<T>>( values ) );
+    }
+    template<typename T>
+    auto value( T const& val ) -> Generator<T> {
+        return Generator<T>( 1, pf::make_unique<SingleValueGenerator<T>>( val ) );
+    }
+
+    template<typename T>
+    auto as() -> Generator<T> {
+        return Generator<T>( 0, pf::make_unique<NullGenerator<T>>() );
+    }
+
+    template<typename... Ts>
+    auto table( std::initializer_list<std::tuple<Ts...>>&& tuples ) -> Generator<std::tuple<Ts...>> {
+        return values<std::tuple<Ts...>>( std::forward<std::initializer_list<std::tuple<Ts...>>>( tuples ) );
+    }
+
+    template<typename T>
+    struct Generators : GeneratorBase {
+        std::vector<Generator<T>> m_generators;
+
+        using type = T;
+
+        Generators() : GeneratorBase( 0 ) {}
+
+        void populate( T&& val ) {
+            m_size += 1;
+            m_generators.emplace_back( value( std::move( val ) ) );
+        }
+        template<typename U>
+        void populate( U&& val ) {
+            populate( T( std::move( val ) ) );
+        }
+        void populate( Generator<T>&& generator ) {
+            m_size += generator.size();
+            m_generators.emplace_back( std::move( generator ) );
+        }
+
+        template<typename U, typename... Gs>
+        void populate( U&& valueOrGenerator, Gs... moreGenerators ) {
+            populate( std::forward<U>( valueOrGenerator ) );
+            populate( std::forward<Gs>( moreGenerators )... );
+        }
+
+        auto operator[]( size_t index ) const -> T {
+            size_t sizes = 0;
+            for( auto const& gen : m_generators ) {
+                auto localIndex = index-sizes;
+                sizes += gen.size();
+                if( index < sizes )
+                    return gen[localIndex];
+            }
+            CATCH_INTERNAL_ERROR("Index '" << index << "' is out of range (" << sizes << ')');
+        }
+    };
+
+    template<typename T, typename... Gs>
+    auto makeGenerators( Generator<T>&& generator, Gs... moreGenerators ) -> Generators<T> {
+        Generators<T> generators;
+        generators.m_generators.reserve( 1+sizeof...(Gs) );
+        generators.populate( std::move( generator ), std::forward<Gs>( moreGenerators )... );
+        return generators;
+    }
+    template<typename T>
+    auto makeGenerators( Generator<T>&& generator ) -> Generators<T> {
+        Generators<T> generators;
+        generators.populate( std::move( generator ) );
+        return generators;
+    }
+    template<typename T, typename... Gs>
+    auto makeGenerators( T&& val, Gs... moreGenerators ) -> Generators<T> {
+        return makeGenerators( value( std::forward<T>( val ) ), std::forward<Gs>( moreGenerators )... );
+    }
+    template<typename T, typename U, typename... Gs>
+    auto makeGenerators( U&& val, Gs... moreGenerators ) -> Generators<T> {
+        return makeGenerators( value( T( std::forward<U>( val ) ) ), std::forward<Gs>( moreGenerators )... );
+    }
+
+    auto acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker&;
+
+    template<typename L>
+    // Note: The type after -> is weird, because VS2015 cannot parse
+    //       the expression used in the typedef inside, when it is in
+    //       return type. Yeah, ¯\_(ツ)_/¯
+    auto generate( SourceLineInfo const& lineInfo, L const& generatorExpression ) -> decltype(std::declval<decltype(generatorExpression())>()[0]) {
+        using UnderlyingType = typename decltype(generatorExpression())::type;
+
+        IGeneratorTracker& tracker = acquireGeneratorTracker( lineInfo );
+        if( !tracker.hasGenerator() )
+            tracker.setGenerator( pf::make_unique<Generators<UnderlyingType>>( generatorExpression() ) );
+
+        auto const& generator = static_cast<Generators<UnderlyingType> const&>( *tracker.getGenerator() );
+        return generator[tracker.getIndex()];
+    }
+
+} // namespace Generators
+} // namespace Catch
+
+#define GENERATE( ... ) \
+    Catch::Generators::generate( CATCH_INTERNAL_LINEINFO, []{ using namespace Catch::Generators; return makeGenerators( __VA_ARGS__ ); } )
+
+// end catch_generators.hpp
+
+// These files are included here so the single_include script doesn't put them
+// in the conditionally compiled sections
+// start catch_test_case_info.h
+
+#include <string>
+#include <vector>
+#include <memory>
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpadded"
+#endif
+
+namespace Catch {
+
+    struct ITestInvoker;
+
+    struct TestCaseInfo {
+        enum SpecialProperties{
+            None = 0,
+            IsHidden = 1 << 1,
+            ShouldFail = 1 << 2,
+            MayFail = 1 << 3,
+            Throws = 1 << 4,
+            NonPortable = 1 << 5,
+            Benchmark = 1 << 6
+        };
+
+        TestCaseInfo(   std::string const& _name,
+                        std::string const& _className,
+                        std::string const& _description,
+                        std::vector<std::string> const& _tags,
+                        SourceLineInfo const& _lineInfo );
+
+        friend void setTags( TestCaseInfo& testCaseInfo, std::vector<std::string> tags );
+
+        bool isHidden() const;
+        bool throws() const;
+        bool okToFail() const;
+        bool expectedToFail() const;
+
+        std::string tagsAsString() const;
+
+        std::string name;
+        std::string className;
+        std::string description;
+        std::vector<std::string> tags;
+        std::vector<std::string> lcaseTags;
+        SourceLineInfo lineInfo;
+        SpecialProperties properties;
+    };
+
+    class TestCase : public TestCaseInfo {
+    public:
+
+        TestCase( ITestInvoker* testCase, TestCaseInfo&& info );
+
+        TestCase withName( std::string const& _newName ) const;
+
+        void invoke() const;
+
+        TestCaseInfo const& getTestCaseInfo() const;
+
+        bool operator == ( TestCase const& other ) const;
+        bool operator < ( TestCase const& other ) const;
+
+    private:
+        std::shared_ptr<ITestInvoker> test;
+    };
+
+    TestCase makeTestCase(  ITestInvoker* testCase,
+                            std::string const& className,
+                            NameAndTags const& nameAndTags,
+                            SourceLineInfo const& lineInfo );
+}
+
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+// end catch_test_case_info.h
+// start catch_interfaces_runner.h
+
+namespace Catch {
+
+    struct IRunner {
+        virtual ~IRunner();
+        virtual bool aborting() const = 0;
+    };
+}
+
+// end catch_interfaces_runner.h
+
+#ifdef __OBJC__
+// start catch_objc.hpp
+
+#import <objc/runtime.h>
+
+#include <string>
+
+// NB. Any general catch headers included here must be included
+// in catch.hpp first to make sure they are included by the single
+// header for non obj-usage
+
+///////////////////////////////////////////////////////////////////////////////
+// This protocol is really only here for (self) documenting purposes, since
+// all its methods are optional.
+@protocol OcFixture
+
+@optional
+
+-(void) setUp;
+-(void) tearDown;
+
+@end
+
+namespace Catch {
+
+    class OcMethod : public ITestInvoker {
+
+    public:
+        OcMethod( Class cls, SEL sel ) : m_cls( cls ), m_sel( sel ) {}
+
+        virtual void invoke() const {
+            id obj = [[m_cls alloc] init];
+
+            performOptionalSelector( obj, @selector(setUp)  );
+            performOptionalSelector( obj, m_sel );
+            performOptionalSelector( obj, @selector(tearDown)  );
+
+            arcSafeRelease( obj );
+        }
+    private:
+        virtual ~OcMethod() {}
+
+        Class m_cls;
+        SEL m_sel;
+    };
+
+    namespace Detail{
+
+        inline std::string getAnnotation(   Class cls,
+                                            std::string const& annotationName,
+                                            std::string const& testCaseName ) {
+            NSString* selStr = [[NSString alloc] initWithFormat:@"Catch_%s_%s", annotationName.c_str(), testCaseName.c_str()];
+            SEL sel = NSSelectorFromString( selStr );
+            arcSafeRelease( selStr );
+            id value = performOptionalSelector( cls, sel );
+            if( value )
+                return [(NSString*)value UTF8String];
+            return "";
+        }
+    }
+
+    inline std::size_t registerTestMethods() {
+        std::size_t noTestMethods = 0;
+        int noClasses = objc_getClassList( nullptr, 0 );
+
+        Class* classes = (CATCH_UNSAFE_UNRETAINED Class *)malloc( sizeof(Class) * noClasses);
+        objc_getClassList( classes, noClasses );
+
+        for( int c = 0; c < noClasses; c++ ) {
+            Class cls = classes[c];
+            {
+                u_int count;
+                Method* methods = class_copyMethodList( cls, &count );
+                for( u_int m = 0; m < count ; m++ ) {
+                    SEL selector = method_getName(methods[m]);
+                    std::string methodName = sel_getName(selector);
+                    if( startsWith( methodName, "Catch_TestCase_" ) ) {
+                        std::string testCaseName = methodName.substr( 15 );
+                        std::string name = Detail::getAnnotation( cls, "Name", testCaseName );
+                        std::string desc = Detail::getAnnotation( cls, "Description", testCaseName );
+                        const char* className = class_getName( cls );
+
+                        getMutableRegistryHub().registerTest( makeTestCase( new OcMethod( cls, selector ), className, NameAndTags( name.c_str(), desc.c_str() ), SourceLineInfo("",0) ) );
+                        noTestMethods++;
+                    }
+                }
+                free(methods);
+            }
+        }
+        return noTestMethods;
+    }
+
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+
+    namespace Matchers {
+        namespace Impl {
+        namespace NSStringMatchers {
+
+            struct StringHolder : MatcherBase<NSString*>{
+                StringHolder( NSString* substr ) : m_substr( [substr copy] ){}
+                StringHolder( StringHolder const& other ) : m_substr( [other.m_substr copy] ){}
+                StringHolder() {
+                    arcSafeRelease( m_substr );
+                }
+
+                bool match( NSString* arg ) const override {
+                    return false;
+                }
+
+                NSString* CATCH_ARC_STRONG m_substr;
+            };
+
+            struct Equals : StringHolder {
+                Equals( NSString* substr ) : StringHolder( substr ){}
+
+                bool match( NSString* str ) const override {
+                    return  (str != nil || m_substr == nil ) &&
+                            [str isEqualToString:m_substr];
+                }
+
+                std::string describe() const override {
+                    return "equals string: " + Catch::Detail::stringify( m_substr );
+                }
+            };
+
+            struct Contains : StringHolder {
+                Contains( NSString* substr ) : StringHolder( substr ){}
+
+                bool match( NSString* str ) const {
+                    return  (str != nil || m_substr == nil ) &&
+                            [str rangeOfString:m_substr].location != NSNotFound;
+                }
+
+                std::string describe() const override {
+                    return "contains string: " + Catch::Detail::stringify( m_substr );
+                }
+            };
+
+            struct StartsWith : StringHolder {
+                StartsWith( NSString* substr ) : StringHolder( substr ){}
+
+                bool match( NSString* str ) const override {
+                    return  (str != nil || m_substr == nil ) &&
+                            [str rangeOfString:m_substr].location == 0;
+                }
+
+                std::string describe() const override {
+                    return "starts with: " + Catch::Detail::stringify( m_substr );
+                }
+            };
+            struct EndsWith : StringHolder {
+                EndsWith( NSString* substr ) : StringHolder( substr ){}
+
+                bool match( NSString* str ) const override {
+                    return  (str != nil || m_substr == nil ) &&
+                            [str rangeOfString:m_substr].location == [str length] - [m_substr length];
+                }
+
+                std::string describe() const override {
+                    return "ends with: " + Catch::Detail::stringify( m_substr );
+                }
+            };
+
+        } // namespace NSStringMatchers
+        } // namespace Impl
+
+        inline Impl::NSStringMatchers::Equals
+            Equals( NSString* substr ){ return Impl::NSStringMatchers::Equals( substr ); }
+
+        inline Impl::NSStringMatchers::Contains
+            Contains( NSString* substr ){ return Impl::NSStringMatchers::Contains( substr ); }
+
+        inline Impl::NSStringMatchers::StartsWith
+            StartsWith( NSString* substr ){ return Impl::NSStringMatchers::StartsWith( substr ); }
+
+        inline Impl::NSStringMatchers::EndsWith
+            EndsWith( NSString* substr ){ return Impl::NSStringMatchers::EndsWith( substr ); }
+
+    } // namespace Matchers
+
+    using namespace Matchers;
+
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+
+} // namespace Catch
+
+///////////////////////////////////////////////////////////////////////////////
+#define OC_MAKE_UNIQUE_NAME( root, uniqueSuffix ) root##uniqueSuffix
+#define OC_TEST_CASE2( name, desc, uniqueSuffix ) \
++(NSString*) OC_MAKE_UNIQUE_NAME( Catch_Name_test_, uniqueSuffix ) \
+{ \
+return @ name; \
+} \
++(NSString*) OC_MAKE_UNIQUE_NAME( Catch_Description_test_, uniqueSuffix ) \
+{ \
+return @ desc; \
+} \
+-(void) OC_MAKE_UNIQUE_NAME( Catch_TestCase_test_, uniqueSuffix )
+
+#define OC_TEST_CASE( name, desc ) OC_TEST_CASE2( name, desc, __LINE__ )
+
+// end catch_objc.hpp
+#endif
+
+#ifdef CATCH_CONFIG_EXTERNAL_INTERFACES
+// start catch_external_interfaces.h
+
+// start catch_reporter_bases.hpp
+
+// start catch_interfaces_reporter.h
+
+// start catch_config.hpp
+
+// start catch_test_spec_parser.h
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpadded"
+#endif
+
+// start catch_test_spec.h
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpadded"
+#endif
+
+// start catch_wildcard_pattern.h
+
+namespace Catch
+{
+    class WildcardPattern {
+        enum WildcardPosition {
+            NoWildcard = 0,
+            WildcardAtStart = 1,
+            WildcardAtEnd = 2,
+            WildcardAtBothEnds = WildcardAtStart | WildcardAtEnd
+        };
+
+    public:
+
+        WildcardPattern( std::string const& pattern, CaseSensitive::Choice caseSensitivity );
+        virtual ~WildcardPattern() = default;
+        virtual bool matches( std::string const& str ) const;
+
+    private:
+        std::string adjustCase( std::string const& str ) const;
+        CaseSensitive::Choice m_caseSensitivity;
+        WildcardPosition m_wildcard = NoWildcard;
+        std::string m_pattern;
+    };
+}
+
+// end catch_wildcard_pattern.h
+#include <string>
+#include <vector>
+#include <memory>
+
+namespace Catch {
+
+    class TestSpec {
+        struct Pattern {
+            virtual ~Pattern();
+            virtual bool matches( TestCaseInfo const& testCase ) const = 0;
+        };
+        using PatternPtr = std::shared_ptr<Pattern>;
+
+        class NamePattern : public Pattern {
+        public:
+            NamePattern( std::string const& name );
+            virtual ~NamePattern();
+            virtual bool matches( TestCaseInfo const& testCase ) const override;
+        private:
+            WildcardPattern m_wildcardPattern;
+        };
+
+        class TagPattern : public Pattern {
+        public:
+            TagPattern( std::string const& tag );
+            virtual ~TagPattern();
+            virtual bool matches( TestCaseInfo const& testCase ) const override;
+        private:
+            std::string m_tag;
+        };
+
+        class ExcludedPattern : public Pattern {
+        public:
+            ExcludedPattern( PatternPtr const& underlyingPattern );
+            virtual ~ExcludedPattern();
+            virtual bool matches( TestCaseInfo const& testCase ) const override;
+        private:
+            PatternPtr m_underlyingPattern;
+        };
+
+        struct Filter {
+            std::vector<PatternPtr> m_patterns;
+
+            bool matches( TestCaseInfo const& testCase ) const;
+        };
+
+    public:
+        bool hasFilters() const;
+        bool matches( TestCaseInfo const& testCase ) const;
+
+    private:
+        std::vector<Filter> m_filters;
+
+        friend class TestSpecParser;
+    };
+}
+
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+// end catch_test_spec.h
+// start catch_interfaces_tag_alias_registry.h
+
+#include <string>
+
+namespace Catch {
+
+    struct TagAlias;
+
+    struct ITagAliasRegistry {
+        virtual ~ITagAliasRegistry();
+        // Nullptr if not present
+        virtual TagAlias const* find( std::string const& alias ) const = 0;
+        virtual std::string expandAliases( std::string const& unexpandedTestSpec ) const = 0;
+
+        static ITagAliasRegistry const& get();
+    };
+
+} // end namespace Catch
+
+// end catch_interfaces_tag_alias_registry.h
+namespace Catch {
+
+    class TestSpecParser {
+        enum Mode{ None, Name, QuotedName, Tag, EscapedName };
+        Mode m_mode = None;
+        bool m_exclusion = false;
+        std::size_t m_start = std::string::npos, m_pos = 0;
+        std::string m_arg;
+        std::vector<std::size_t> m_escapeChars;
+        TestSpec::Filter m_currentFilter;
+        TestSpec m_testSpec;
+        ITagAliasRegistry const* m_tagAliases = nullptr;
+
+    public:
+        TestSpecParser( ITagAliasRegistry const& tagAliases );
+
+        TestSpecParser& parse( std::string const& arg );
+        TestSpec testSpec();
+
+    private:
+        void visitChar( char c );
+        void startNewMode( Mode mode, std::size_t start );
+        void escape();
+        std::string subString() const;
+
+        template<typename T>
+        void addPattern() {
+            std::string token = subString();
+            for( std::size_t i = 0; i < m_escapeChars.size(); ++i )
+                token = token.substr( 0, m_escapeChars[i]-m_start-i ) + token.substr( m_escapeChars[i]-m_start-i+1 );
+            m_escapeChars.clear();
+            if( startsWith( token, "exclude:" ) ) {
+                m_exclusion = true;
+                token = token.substr( 8 );
+            }
+            if( !token.empty() ) {
+                TestSpec::PatternPtr pattern = std::make_shared<T>( token );
+                if( m_exclusion )
+                    pattern = std::make_shared<TestSpec::ExcludedPattern>( pattern );
+                m_currentFilter.m_patterns.push_back( pattern );
+            }
+            m_exclusion = false;
+            m_mode = None;
+        }
+
+        void addFilter();
+    };
+    TestSpec parseTestSpec( std::string const& arg );
+
+} // namespace Catch
+
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+// end catch_test_spec_parser.h
+// start catch_interfaces_config.h
+
+#include <iosfwd>
+#include <string>
+#include <vector>
+#include <memory>
+
+namespace Catch {
+
+    enum class Verbosity {
+        Quiet = 0,
+        Normal,
+        High
+    };
+
+    struct WarnAbout { enum What {
+        Nothing = 0x00,
+        NoAssertions = 0x01,
+        NoTests = 0x02
+    }; };
+
+    struct ShowDurations { enum OrNot {
+        DefaultForReporter,
+        Always,
+        Never
+    }; };
+    struct RunTests { enum InWhatOrder {
+        InDeclarationOrder,
+        InLexicographicalOrder,
+        InRandomOrder
+    }; };
+    struct UseColour { enum YesOrNo {
+        Auto,
+        Yes,
+        No
+    }; };
+    struct WaitForKeypress { enum When {
+        Never,
+        BeforeStart = 1,
+        BeforeExit = 2,
+        BeforeStartAndExit = BeforeStart | BeforeExit
+    }; };
+
+    class TestSpec;
+
+    struct IConfig : NonCopyable {
+
+        virtual ~IConfig();
+
+        virtual bool allowThrows() const = 0;
+        virtual std::ostream& stream() const = 0;
+        virtual std::string name() const = 0;
+        virtual bool includeSuccessfulResults() const = 0;
+        virtual bool shouldDebugBreak() const = 0;
+        virtual bool warnAboutMissingAssertions() const = 0;
+        virtual bool warnAboutNoTests() const = 0;
+        virtual int abortAfter() const = 0;
+        virtual bool showInvisibles() const = 0;
+        virtual ShowDurations::OrNot showDurations() const = 0;
+        virtual TestSpec const& testSpec() const = 0;
+        virtual bool hasTestFilters() const = 0;
+        virtual RunTests::InWhatOrder runOrder() const = 0;
+        virtual unsigned int rngSeed() const = 0;
+        virtual int benchmarkResolutionMultiple() const = 0;
+        virtual UseColour::YesOrNo useColour() const = 0;
+        virtual std::vector<std::string> const& getSectionsToRun() const = 0;
+        virtual Verbosity verbosity() const = 0;
+    };
+
+    using IConfigPtr = std::shared_ptr<IConfig const>;
+}
+
+// end catch_interfaces_config.h
+// Libstdc++ doesn't like incomplete classes for unique_ptr
+
+#include <memory>
+#include <vector>
+#include <string>
+
+#ifndef CATCH_CONFIG_CONSOLE_WIDTH
+#define CATCH_CONFIG_CONSOLE_WIDTH 80
+#endif
+
+namespace Catch {
+
+    struct IStream;
+
+    struct ConfigData {
+        bool listTests = false;
+        bool listTags = false;
+        bool listReporters = false;
+        bool listTestNamesOnly = false;
+
+        bool showSuccessfulTests = false;
+        bool shouldDebugBreak = false;
+        bool noThrow = false;
+        bool showHelp = false;
+        bool showInvisibles = false;
+        bool filenamesAsTags = false;
+        bool libIdentify = false;
+
+        int abortAfter = -1;
+        unsigned int rngSeed = 0;
+        int benchmarkResolutionMultiple = 100;
+
+        Verbosity verbosity = Verbosity::Normal;
+        WarnAbout::What warnings = WarnAbout::Nothing;
+        ShowDurations::OrNot showDurations = ShowDurations::DefaultForReporter;
+        RunTests::InWhatOrder runOrder = RunTests::InDeclarationOrder;
+        UseColour::YesOrNo useColour = UseColour::Auto;
+        WaitForKeypress::When waitForKeypress = WaitForKeypress::Never;
+
+        std::string outputFilename;
+        std::string name;
+        std::string processName;
+#ifndef CATCH_CONFIG_DEFAULT_REPORTER
+#define CATCH_CONFIG_DEFAULT_REPORTER "console"
+#endif
+        std::string reporterName = CATCH_CONFIG_DEFAULT_REPORTER;
+#undef CATCH_CONFIG_DEFAULT_REPORTER
+
+        std::vector<std::string> testsOrTags;
+        std::vector<std::string> sectionsToRun;
+    };
+
+    class Config : public IConfig {
+    public:
+
+        Config() = default;
+        Config( ConfigData const& data );
+        virtual ~Config() = default;
+
+        std::string const& getFilename() const;
+
+        bool listTests() const;
+        bool listTestNamesOnly() const;
+        bool listTags() const;
+        bool listReporters() const;
+
+        std::string getProcessName() const;
+        std::string const& getReporterName() const;
+
+        std::vector<std::string> const& getTestsOrTags() const;
+        std::vector<std::string> const& getSectionsToRun() const override;
+
+        virtual TestSpec const& testSpec() const override;
+        bool hasTestFilters() const override;
+
+        bool showHelp() const;
+
+        // IConfig interface
+        bool allowThrows() const override;
+        std::ostream& stream() const override;
+        std::string name() const override;
+        bool includeSuccessfulResults() const override;
+        bool warnAboutMissingAssertions() const override;
+        bool warnAboutNoTests() const override;
+        ShowDurations::OrNot showDurations() const override;
+        RunTests::InWhatOrder runOrder() const override;
+        unsigned int rngSeed() const override;
+        int benchmarkResolutionMultiple() const override;
+        UseColour::YesOrNo useColour() const override;
+        bool shouldDebugBreak() const override;
+        int abortAfter() const override;
+        bool showInvisibles() const override;
+        Verbosity verbosity() const override;
+
+    private:
+
+        IStream const* openStream();
+        ConfigData m_data;
+
+        std::unique_ptr<IStream const> m_stream;
+        TestSpec m_testSpec;
+        bool m_hasTestFilters = false;
+    };
+
+} // end namespace Catch
+
+// end catch_config.hpp
+// start catch_assertionresult.h
+
+#include <string>
+
+namespace Catch {
+
+    struct AssertionResultData
+    {
+        AssertionResultData() = delete;
+
+        AssertionResultData( ResultWas::OfType _resultType, LazyExpression const& _lazyExpression );
+
+        std::string message;
+        mutable std::string reconstructedExpression;
+        LazyExpression lazyExpression;
+        ResultWas::OfType resultType;
+
+        std::string reconstructExpression() const;
+    };
+
+    class AssertionResult {
+    public:
+        AssertionResult() = delete;
+        AssertionResult( AssertionInfo const& info, AssertionResultData const& data );
+
+        bool isOk() const;
+        bool succeeded() const;
+        ResultWas::OfType getResultType() const;
+        bool hasExpression() const;
+        bool hasMessage() const;
+        std::string getExpression() const;
+        std::string getExpressionInMacro() const;
+        bool hasExpandedExpression() const;
+        std::string getExpandedExpression() const;
+        std::string getMessage() const;
+        SourceLineInfo getSourceInfo() const;
+        StringRef getTestMacroName() const;
+
+    //protected:
+        AssertionInfo m_info;
+        AssertionResultData m_resultData;
+    };
+
+} // end namespace Catch
+
+// end catch_assertionresult.h
+// start catch_option.hpp
+
+namespace Catch {
+
+    // An optional type
+    template<typename T>
+    class Option {
+    public:
+        Option() : nullableValue( nullptr ) {}
+        Option( T const& _value )
+        : nullableValue( new( storage ) T( _value ) )
+        {}
+        Option( Option const& _other )
+        : nullableValue( _other ? new( storage ) T( *_other ) : nullptr )
+        {}
+
+        ~Option() {
+            reset();
+        }
+
+        Option& operator= ( Option const& _other ) {
+            if( &_other != this ) {
+                reset();
+                if( _other )
+                    nullableValue = new( storage ) T( *_other );
+            }
+            return *this;
+        }
+        Option& operator = ( T const& _value ) {
+            reset();
+            nullableValue = new( storage ) T( _value );
+            return *this;
+        }
+
+        void reset() {
+            if( nullableValue )
+                nullableValue->~T();
+            nullableValue = nullptr;
+        }
+
+        T& operator*() { return *nullableValue; }
+        T const& operator*() const { return *nullableValue; }
+        T* operator->() { return nullableValue; }
+        const T* operator->() const { return nullableValue; }
+
+        T valueOr( T const& defaultValue ) const {
+            return nullableValue ? *nullableValue : defaultValue;
+        }
+
+        bool some() const { return nullableValue != nullptr; }
+        bool none() const { return nullableValue == nullptr; }
+
+        bool operator !() const { return nullableValue == nullptr; }
+        explicit operator bool() const {
+            return some();
+        }
+
+    private:
+        T *nullableValue;
+        alignas(alignof(T)) char storage[sizeof(T)];
+    };
+
+} // end namespace Catch
+
+// end catch_option.hpp
+#include <string>
+#include <iosfwd>
+#include <map>
+#include <set>
+#include <memory>
+
+namespace Catch {
+
+    struct ReporterConfig {
+        explicit ReporterConfig( IConfigPtr const& _fullConfig );
+
+        ReporterConfig( IConfigPtr const& _fullConfig, std::ostream& _stream );
+
+        std::ostream& stream() const;
+        IConfigPtr fullConfig() const;
+
+    private:
+        std::ostream* m_stream;
+        IConfigPtr m_fullConfig;
+    };
+
+    struct ReporterPreferences {
+        bool shouldRedirectStdOut = false;
+        bool shouldReportAllAssertions = false;
+    };
+
+    template<typename T>
+    struct LazyStat : Option<T> {
+        LazyStat& operator=( T const& _value ) {
+            Option<T>::operator=( _value );
+            used = false;
+            return *this;
+        }
+        void reset() {
+            Option<T>::reset();
+            used = false;
+        }
+        bool used = false;
+    };
+
+    struct TestRunInfo {
+        TestRunInfo( std::string const& _name );
+        std::string name;
+    };
+    struct GroupInfo {
+        GroupInfo(  std::string const& _name,
+                    std::size_t _groupIndex,
+                    std::size_t _groupsCount );
+
+        std::string name;
+        std::size_t groupIndex;
+        std::size_t groupsCounts;
+    };
+
+    struct AssertionStats {
+        AssertionStats( AssertionResult const& _assertionResult,
+                        std::vector<MessageInfo> const& _infoMessages,
+                        Totals const& _totals );
+
+        AssertionStats( AssertionStats const& )              = default;
+        AssertionStats( AssertionStats && )                  = default;
+        AssertionStats& operator = ( AssertionStats const& ) = default;
+        AssertionStats& operator = ( AssertionStats && )     = default;
+        virtual ~AssertionStats();
+
+        AssertionResult assertionResult;
+        std::vector<MessageInfo> infoMessages;
+        Totals totals;
+    };
+
+    struct SectionStats {
+        SectionStats(   SectionInfo const& _sectionInfo,
+                        Counts const& _assertions,
+                        double _durationInSeconds,
+                        bool _missingAssertions );
+        SectionStats( SectionStats const& )              = default;
+        SectionStats( SectionStats && )                  = default;
+        SectionStats& operator = ( SectionStats const& ) = default;
+        SectionStats& operator = ( SectionStats && )     = default;
+        virtual ~SectionStats();
+
+        SectionInfo sectionInfo;
+        Counts assertions;
+        double durationInSeconds;
+        bool missingAssertions;
+    };
+
+    struct TestCaseStats {
+        TestCaseStats(  TestCaseInfo const& _testInfo,
+                        Totals const& _totals,
+                        std::string const& _stdOut,
+                        std::string const& _stdErr,
+                        bool _aborting );
+
+        TestCaseStats( TestCaseStats const& )              = default;
+        TestCaseStats( TestCaseStats && )                  = default;
+        TestCaseStats& operator = ( TestCaseStats const& ) = default;
+        TestCaseStats& operator = ( TestCaseStats && )     = default;
+        virtual ~TestCaseStats();
+
+        TestCaseInfo testInfo;
+        Totals totals;
+        std::string stdOut;
+        std::string stdErr;
+        bool aborting;
+    };
+
+    struct TestGroupStats {
+        TestGroupStats( GroupInfo const& _groupInfo,
+                        Totals const& _totals,
+                        bool _aborting );
+        TestGroupStats( GroupInfo const& _groupInfo );
+
+        TestGroupStats( TestGroupStats const& )              = default;
+        TestGroupStats( TestGroupStats && )                  = default;
+        TestGroupStats& operator = ( TestGroupStats const& ) = default;
+        TestGroupStats& operator = ( TestGroupStats && )     = default;
+        virtual ~TestGroupStats();
+
+        GroupInfo groupInfo;
+        Totals totals;
+        bool aborting;
+    };
+
+    struct TestRunStats {
+        TestRunStats(   TestRunInfo const& _runInfo,
+                        Totals const& _totals,
+                        bool _aborting );
+
+        TestRunStats( TestRunStats const& )              = default;
+        TestRunStats( TestRunStats && )                  = default;
+        TestRunStats& operator = ( TestRunStats const& ) = default;
+        TestRunStats& operator = ( TestRunStats && )     = default;
+        virtual ~TestRunStats();
+
+        TestRunInfo runInfo;
+        Totals totals;
+        bool aborting;
+    };
+
+    struct BenchmarkInfo {
+        std::string name;
+    };
+    struct BenchmarkStats {
+        BenchmarkInfo info;
+        std::size_t iterations;
+        uint64_t elapsedTimeInNanoseconds;
+    };
+
+    struct IStreamingReporter {
+        virtual ~IStreamingReporter() = default;
+
+        // Implementing class must also provide the following static methods:
+        // static std::string getDescription();
+        // static std::set<Verbosity> getSupportedVerbosities()
+
+        virtual ReporterPreferences getPreferences() const = 0;
+
+        virtual void noMatchingTestCases( std::string const& spec ) = 0;
+
+        virtual void testRunStarting( TestRunInfo const& testRunInfo ) = 0;
+        virtual void testGroupStarting( GroupInfo const& groupInfo ) = 0;
+
+        virtual void testCaseStarting( TestCaseInfo const& testInfo ) = 0;
+        virtual void sectionStarting( SectionInfo const& sectionInfo ) = 0;
+
+        // *** experimental ***
+        virtual void benchmarkStarting( BenchmarkInfo const& ) {}
+
+        virtual void assertionStarting( AssertionInfo const& assertionInfo ) = 0;
+
+        // The return value indicates if the messages buffer should be cleared:
+        virtual bool assertionEnded( AssertionStats const& assertionStats ) = 0;
+
+        // *** experimental ***
+        virtual void benchmarkEnded( BenchmarkStats const& ) {}
+
+        virtual void sectionEnded( SectionStats const& sectionStats ) = 0;
+        virtual void testCaseEnded( TestCaseStats const& testCaseStats ) = 0;
+        virtual void testGroupEnded( TestGroupStats const& testGroupStats ) = 0;
+        virtual void testRunEnded( TestRunStats const& testRunStats ) = 0;
+
+        virtual void skipTest( TestCaseInfo const& testInfo ) = 0;
+
+        // Default empty implementation provided
+        virtual void fatalErrorEncountered( StringRef name );
+
+        virtual bool isMulti() const;
+    };
+    using IStreamingReporterPtr = std::unique_ptr<IStreamingReporter>;
+
+    struct IReporterFactory {
+        virtual ~IReporterFactory();
+        virtual IStreamingReporterPtr create( ReporterConfig const& config ) const = 0;
+        virtual std::string getDescription() const = 0;
+    };
+    using IReporterFactoryPtr = std::shared_ptr<IReporterFactory>;
+
+    struct IReporterRegistry {
+        using FactoryMap = std::map<std::string, IReporterFactoryPtr>;
+        using Listeners = std::vector<IReporterFactoryPtr>;
+
+        virtual ~IReporterRegistry();
+        virtual IStreamingReporterPtr create( std::string const& name, IConfigPtr const& config ) const = 0;
+        virtual FactoryMap const& getFactories() const = 0;
+        virtual Listeners const& getListeners() const = 0;
+    };
+
+} // end namespace Catch
+
+// end catch_interfaces_reporter.h
+#include <algorithm>
+#include <cstring>
+#include <cfloat>
+#include <cstdio>
+#include <cassert>
+#include <memory>
+#include <ostream>
+
+namespace Catch {
+    void prepareExpandedExpression(AssertionResult& result);
+
+    // Returns double formatted as %.3f (format expected on output)
+    std::string getFormattedDuration( double duration );
+
+    template<typename DerivedT>
+    struct StreamingReporterBase : IStreamingReporter {
+
+        StreamingReporterBase( ReporterConfig const& _config )
+        :   m_config( _config.fullConfig() ),
+            stream( _config.stream() )
+        {
+            m_reporterPrefs.shouldRedirectStdOut = false;
+            if( !DerivedT::getSupportedVerbosities().count( m_config->verbosity() ) )
+                CATCH_ERROR( "Verbosity level not supported by this reporter" );
+        }
+
+        ReporterPreferences getPreferences() const override {
+            return m_reporterPrefs;
+        }
+
+        static std::set<Verbosity> getSupportedVerbosities() {
+            return { Verbosity::Normal };
+        }
+
+        ~StreamingReporterBase() override = default;
+
+        void noMatchingTestCases(std::string const&) override {}
+
+        void testRunStarting(TestRunInfo const& _testRunInfo) override {
+            currentTestRunInfo = _testRunInfo;
+        }
+        void testGroupStarting(GroupInfo const& _groupInfo) override {
+            currentGroupInfo = _groupInfo;
+        }
+
+        void testCaseStarting(TestCaseInfo const& _testInfo) override  {
+            currentTestCaseInfo = _testInfo;
+        }
+        void sectionStarting(SectionInfo const& _sectionInfo) override {
+            m_sectionStack.push_back(_sectionInfo);
+        }
+
+        void sectionEnded(SectionStats const& /* _sectionStats */) override {
+            m_sectionStack.pop_back();
+        }
+        void testCaseEnded(TestCaseStats const& /* _testCaseStats */) override {
+            currentTestCaseInfo.reset();
+        }
+        void testGroupEnded(TestGroupStats const& /* _testGroupStats */) override {
+            currentGroupInfo.reset();
+        }
+        void testRunEnded(TestRunStats const& /* _testRunStats */) override {
+            currentTestCaseInfo.reset();
+            currentGroupInfo.reset();
+            currentTestRunInfo.reset();
+        }
+
+        void skipTest(TestCaseInfo const&) override {
+            // Don't do anything with this by default.
+            // It can optionally be overridden in the derived class.
+        }
+
+        IConfigPtr m_config;
+        std::ostream& stream;
+
+        LazyStat<TestRunInfo> currentTestRunInfo;
+        LazyStat<GroupInfo> currentGroupInfo;
+        LazyStat<TestCaseInfo> currentTestCaseInfo;
+
+        std::vector<SectionInfo> m_sectionStack;
+        ReporterPreferences m_reporterPrefs;
+    };
+
+    template<typename DerivedT>
+    struct CumulativeReporterBase : IStreamingReporter {
+        template<typename T, typename ChildNodeT>
+        struct Node {
+            explicit Node( T const& _value ) : value( _value ) {}
+            virtual ~Node() {}
+
+            using ChildNodes = std::vector<std::shared_ptr<ChildNodeT>>;
+            T value;
+            ChildNodes children;
+        };
+        struct SectionNode {
+            explicit SectionNode(SectionStats const& _stats) : stats(_stats) {}
+            virtual ~SectionNode() = default;
+
+            bool operator == (SectionNode const& other) const {
+                return stats.sectionInfo.lineInfo == other.stats.sectionInfo.lineInfo;
+            }
+            bool operator == (std::shared_ptr<SectionNode> const& other) const {
+                return operator==(*other);
+            }
+
+            SectionStats stats;
+            using ChildSections = std::vector<std::shared_ptr<SectionNode>>;
+            using Assertions = std::vector<AssertionStats>;
+            ChildSections childSections;
+            Assertions assertions;
+            std::string stdOut;
+            std::string stdErr;
+        };
+
+        struct BySectionInfo {
+            BySectionInfo( SectionInfo const& other ) : m_other( other ) {}
+            BySectionInfo( BySectionInfo const& other ) : m_other( other.m_other ) {}
+            bool operator() (std::shared_ptr<SectionNode> const& node) const {
+                return ((node->stats.sectionInfo.name == m_other.name) &&
+                        (node->stats.sectionInfo.lineInfo == m_other.lineInfo));
+            }
+            void operator=(BySectionInfo const&) = delete;
+
+        private:
+            SectionInfo const& m_other;
+        };
+
+        using TestCaseNode = Node<TestCaseStats, SectionNode>;
+        using TestGroupNode = Node<TestGroupStats, TestCaseNode>;
+        using TestRunNode = Node<TestRunStats, TestGroupNode>;
+
+        CumulativeReporterBase( ReporterConfig const& _config )
+        :   m_config( _config.fullConfig() ),
+            stream( _config.stream() )
+        {
+            m_reporterPrefs.shouldRedirectStdOut = false;
+            if( !DerivedT::getSupportedVerbosities().count( m_config->verbosity() ) )
+                CATCH_ERROR( "Verbosity level not supported by this reporter" );
+        }
+        ~CumulativeReporterBase() override = default;
+
+        ReporterPreferences getPreferences() const override {
+            return m_reporterPrefs;
+        }
+
+        static std::set<Verbosity> getSupportedVerbosities() {
+            return { Verbosity::Normal };
+        }
+
+        void testRunStarting( TestRunInfo const& ) override {}
+        void testGroupStarting( GroupInfo const& ) override {}
+
+        void testCaseStarting( TestCaseInfo const& ) override {}
+
+        void sectionStarting( SectionInfo const& sectionInfo ) override {
+            SectionStats incompleteStats( sectionInfo, Counts(), 0, false );
+            std::shared_ptr<SectionNode> node;
+            if( m_sectionStack.empty() ) {
+                if( !m_rootSection )
+                    m_rootSection = std::make_shared<SectionNode>( incompleteStats );
+                node = m_rootSection;
+            }
+            else {
+                SectionNode& parentNode = *m_sectionStack.back();
+                auto it =
+                    std::find_if(   parentNode.childSections.begin(),
+                                    parentNode.childSections.end(),
+                                    BySectionInfo( sectionInfo ) );
+                if( it == parentNode.childSections.end() ) {
+                    node = std::make_shared<SectionNode>( incompleteStats );
+                    parentNode.childSections.push_back( node );
+                }
+                else
+                    node = *it;
+            }
+            m_sectionStack.push_back( node );
+            m_deepestSection = std::move(node);
+        }
+
+        void assertionStarting(AssertionInfo const&) override {}
+
+        bool assertionEnded(AssertionStats const& assertionStats) override {
+            assert(!m_sectionStack.empty());
+            // AssertionResult holds a pointer to a temporary DecomposedExpression,
+            // which getExpandedExpression() calls to build the expression string.
+            // Our section stack copy of the assertionResult will likely outlive the
+            // temporary, so it must be expanded or discarded now to avoid calling
+            // a destroyed object later.
+            prepareExpandedExpression(const_cast<AssertionResult&>( assertionStats.assertionResult ) );
+            SectionNode& sectionNode = *m_sectionStack.back();
+            sectionNode.assertions.push_back(assertionStats);
+            return true;
+        }
+        void sectionEnded(SectionStats const& sectionStats) override {
+            assert(!m_sectionStack.empty());
+            SectionNode& node = *m_sectionStack.back();
+            node.stats = sectionStats;
+            m_sectionStack.pop_back();
+        }
+        void testCaseEnded(TestCaseStats const& testCaseStats) override {
+            auto node = std::make_shared<TestCaseNode>(testCaseStats);
+            assert(m_sectionStack.size() == 0);
+            node->children.push_back(m_rootSection);
+            m_testCases.push_back(node);
+            m_rootSection.reset();
+
+            assert(m_deepestSection);
+            m_deepestSection->stdOut = testCaseStats.stdOut;
+            m_deepestSection->stdErr = testCaseStats.stdErr;
+        }
+        void testGroupEnded(TestGroupStats const& testGroupStats) override {
+            auto node = std::make_shared<TestGroupNode>(testGroupStats);
+            node->children.swap(m_testCases);
+            m_testGroups.push_back(node);
+        }
+        void testRunEnded(TestRunStats const& testRunStats) override {
+            auto node = std::make_shared<TestRunNode>(testRunStats);
+            node->children.swap(m_testGroups);
+            m_testRuns.push_back(node);
+            testRunEndedCumulative();
+        }
+        virtual void testRunEndedCumulative() = 0;
+
+        void skipTest(TestCaseInfo const&) override {}
+
+        IConfigPtr m_config;
+        std::ostream& stream;
+        std::vector<AssertionStats> m_assertions;
+        std::vector<std::vector<std::shared_ptr<SectionNode>>> m_sections;
+        std::vector<std::shared_ptr<TestCaseNode>> m_testCases;
+        std::vector<std::shared_ptr<TestGroupNode>> m_testGroups;
+
+        std::vector<std::shared_ptr<TestRunNode>> m_testRuns;
+
+        std::shared_ptr<SectionNode> m_rootSection;
+        std::shared_ptr<SectionNode> m_deepestSection;
+        std::vector<std::shared_ptr<SectionNode>> m_sectionStack;
+        ReporterPreferences m_reporterPrefs;
+    };
+
+    template<char C>
+    char const* getLineOfChars() {
+        static char line[CATCH_CONFIG_CONSOLE_WIDTH] = {0};
+        if( !*line ) {
+            std::memset( line, C, CATCH_CONFIG_CONSOLE_WIDTH-1 );
+            line[CATCH_CONFIG_CONSOLE_WIDTH-1] = 0;
+        }
+        return line;
+    }
+
+    struct TestEventListenerBase : StreamingReporterBase<TestEventListenerBase> {
+        TestEventListenerBase( ReporterConfig const& _config );
+
+        void assertionStarting(AssertionInfo const&) override;
+        bool assertionEnded(AssertionStats const&) override;
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_bases.hpp
+// start catch_console_colour.h
+
+namespace Catch {
+
+    struct Colour {
+        enum Code {
+            None = 0,
+
+            White,
+            Red,
+            Green,
+            Blue,
+            Cyan,
+            Yellow,
+            Grey,
+
+            Bright = 0x10,
+
+            BrightRed = Bright | Red,
+            BrightGreen = Bright | Green,
+            LightGrey = Bright | Grey,
+            BrightWhite = Bright | White,
+            BrightYellow = Bright | Yellow,
+
+            // By intention
+            FileName = LightGrey,
+            Warning = BrightYellow,
+            ResultError = BrightRed,
+            ResultSuccess = BrightGreen,
+            ResultExpectedFailure = Warning,
+
+            Error = BrightRed,
+            Success = Green,
+
+            OriginalExpression = Cyan,
+            ReconstructedExpression = BrightYellow,
+
+            SecondaryText = LightGrey,
+            Headers = White
+        };
+
+        // Use constructed object for RAII guard
+        Colour( Code _colourCode );
+        Colour( Colour&& other ) noexcept;
+        Colour& operator=( Colour&& other ) noexcept;
+        ~Colour();
+
+        // Use static method for one-shot changes
+        static void use( Code _colourCode );
+
+    private:
+        bool m_moved = false;
+    };
+
+    std::ostream& operator << ( std::ostream& os, Colour const& );
+
+} // end namespace Catch
+
+// end catch_console_colour.h
+// start catch_reporter_registrars.hpp
+
+
+namespace Catch {
+
+    template<typename T>
+    class ReporterRegistrar {
+
+        class ReporterFactory : public IReporterFactory {
+
+            virtual IStreamingReporterPtr create( ReporterConfig const& config ) const override {
+                return std::unique_ptr<T>( new T( config ) );
+            }
+
+            virtual std::string getDescription() const override {
+                return T::getDescription();
+            }
+        };
+
+    public:
+
+        explicit ReporterRegistrar( std::string const& name ) {
+            getMutableRegistryHub().registerReporter( name, std::make_shared<ReporterFactory>() );
+        }
+    };
+
+    template<typename T>
+    class ListenerRegistrar {
+
+        class ListenerFactory : public IReporterFactory {
+
+            virtual IStreamingReporterPtr create( ReporterConfig const& config ) const override {
+                return std::unique_ptr<T>( new T( config ) );
+            }
+            virtual std::string getDescription() const override {
+                return std::string();
+            }
+        };
+
+    public:
+
+        ListenerRegistrar() {
+            getMutableRegistryHub().registerListener( std::make_shared<ListenerFactory>() );
+        }
+    };
+}
+
+#if !defined(CATCH_CONFIG_DISABLE)
+
+#define CATCH_REGISTER_REPORTER( name, reporterType ) \
+    CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS          \
+    namespace{ Catch::ReporterRegistrar<reporterType> catch_internal_RegistrarFor##reporterType( name ); } \
+    CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
+
+#define CATCH_REGISTER_LISTENER( listenerType ) \
+     CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS   \
+     namespace{ Catch::ListenerRegistrar<listenerType> catch_internal_RegistrarFor##listenerType; } \
+     CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
+#else // CATCH_CONFIG_DISABLE
+
+#define CATCH_REGISTER_REPORTER(name, reporterType)
+#define CATCH_REGISTER_LISTENER(listenerType)
+
+#endif // CATCH_CONFIG_DISABLE
+
+// end catch_reporter_registrars.hpp
+// Allow users to base their work off existing reporters
+// start catch_reporter_compact.h
+
+namespace Catch {
+
+    struct CompactReporter : StreamingReporterBase<CompactReporter> {
+
+        using StreamingReporterBase::StreamingReporterBase;
+
+        ~CompactReporter() override;
+
+        static std::string getDescription();
+
+        ReporterPreferences getPreferences() const override;
+
+        void noMatchingTestCases(std::string const& spec) override;
+
+        void assertionStarting(AssertionInfo const&) override;
+
+        bool assertionEnded(AssertionStats const& _assertionStats) override;
+
+        void sectionEnded(SectionStats const& _sectionStats) override;
+
+        void testRunEnded(TestRunStats const& _testRunStats) override;
+
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_compact.h
+// start catch_reporter_console.h
+
+#if defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable:4061) // Not all labels are EXPLICITLY handled in switch
+                              // Note that 4062 (not all labels are handled
+                              // and default is missing) is enabled
+#endif
+
+namespace Catch {
+    // Fwd decls
+    struct SummaryColumn;
+    class TablePrinter;
+
+    struct ConsoleReporter : StreamingReporterBase<ConsoleReporter> {
+        std::unique_ptr<TablePrinter> m_tablePrinter;
+
+        ConsoleReporter(ReporterConfig const& config);
+        ~ConsoleReporter() override;
+        static std::string getDescription();
+
+        void noMatchingTestCases(std::string const& spec) override;
+
+        void assertionStarting(AssertionInfo const&) override;
+
+        bool assertionEnded(AssertionStats const& _assertionStats) override;
+
+        void sectionStarting(SectionInfo const& _sectionInfo) override;
+        void sectionEnded(SectionStats const& _sectionStats) override;
+
+        void benchmarkStarting(BenchmarkInfo const& info) override;
+        void benchmarkEnded(BenchmarkStats const& stats) override;
+
+        void testCaseEnded(TestCaseStats const& _testCaseStats) override;
+        void testGroupEnded(TestGroupStats const& _testGroupStats) override;
+        void testRunEnded(TestRunStats const& _testRunStats) override;
+
+    private:
+
+        void lazyPrint();
+
+        void lazyPrintWithoutClosingBenchmarkTable();
+        void lazyPrintRunInfo();
+        void lazyPrintGroupInfo();
+        void printTestCaseAndSectionHeader();
+
+        void printClosedHeader(std::string const& _name);
+        void printOpenHeader(std::string const& _name);
+
+        // if string has a : in first line will set indent to follow it on
+        // subsequent lines
+        void printHeaderString(std::string const& _string, std::size_t indent = 0);
+
+        void printTotals(Totals const& totals);
+        void printSummaryRow(std::string const& label, std::vector<SummaryColumn> const& cols, std::size_t row);
+
+        void printTotalsDivider(Totals const& totals);
+        void printSummaryDivider();
+
+    private:
+        bool m_headerPrinted = false;
+    };
+
+} // end namespace Catch
+
+#if defined(_MSC_VER)
+#pragma warning(pop)
+#endif
+
+// end catch_reporter_console.h
+// start catch_reporter_junit.h
+
+// start catch_xmlwriter.h
+
+#include <vector>
+
+namespace Catch {
+
+    class XmlEncode {
+    public:
+        enum ForWhat { ForTextNodes, ForAttributes };
+
+        XmlEncode( std::string const& str, ForWhat forWhat = ForTextNodes );
+
+        void encodeTo( std::ostream& os ) const;
+
+        friend std::ostream& operator << ( std::ostream& os, XmlEncode const& xmlEncode );
+
+    private:
+        std::string m_str;
+        ForWhat m_forWhat;
+    };
+
+    class XmlWriter {
+    public:
+
+        class ScopedElement {
+        public:
+            ScopedElement( XmlWriter* writer );
+
+            ScopedElement( ScopedElement&& other ) noexcept;
+            ScopedElement& operator=( ScopedElement&& other ) noexcept;
+
+            ~ScopedElement();
+
+            ScopedElement& writeText( std::string const& text, bool indent = true );
+
+            template<typename T>
+            ScopedElement& writeAttribute( std::string const& name, T const& attribute ) {
+                m_writer->writeAttribute( name, attribute );
+                return *this;
+            }
+
+        private:
+            mutable XmlWriter* m_writer = nullptr;
+        };
+
+        XmlWriter( std::ostream& os = Catch::cout() );
+        ~XmlWriter();
+
+        XmlWriter( XmlWriter const& ) = delete;
+        XmlWriter& operator=( XmlWriter const& ) = delete;
+
+        XmlWriter& startElement( std::string const& name );
+
+        ScopedElement scopedElement( std::string const& name );
+
+        XmlWriter& endElement();
+
+        XmlWriter& writeAttribute( std::string const& name, std::string const& attribute );
+
+        XmlWriter& writeAttribute( std::string const& name, bool attribute );
+
+        template<typename T>
+        XmlWriter& writeAttribute( std::string const& name, T const& attribute ) {
+            ReusableStringStream rss;
+            rss << attribute;
+            return writeAttribute( name, rss.str() );
+        }
+
+        XmlWriter& writeText( std::string const& text, bool indent = true );
+
+        XmlWriter& writeComment( std::string const& text );
+
+        void writeStylesheetRef( std::string const& url );
+
+        XmlWriter& writeBlankLine();
+
+        void ensureTagClosed();
+
+    private:
+
+        void writeDeclaration();
+
+        void newlineIfNecessary();
+
+        bool m_tagIsOpen = false;
+        bool m_needsNewline = false;
+        std::vector<std::string> m_tags;
+        std::string m_indent;
+        std::ostream& m_os;
+    };
+
+}
+
+// end catch_xmlwriter.h
+namespace Catch {
+
+    class JunitReporter : public CumulativeReporterBase<JunitReporter> {
+    public:
+        JunitReporter(ReporterConfig const& _config);
+
+        ~JunitReporter() override;
+
+        static std::string getDescription();
+
+        void noMatchingTestCases(std::string const& /*spec*/) override;
+
+        void testRunStarting(TestRunInfo const& runInfo) override;
+
+        void testGroupStarting(GroupInfo const& groupInfo) override;
+
+        void testCaseStarting(TestCaseInfo const& testCaseInfo) override;
+        bool assertionEnded(AssertionStats const& assertionStats) override;
+
+        void testCaseEnded(TestCaseStats const& testCaseStats) override;
+
+        void testGroupEnded(TestGroupStats const& testGroupStats) override;
+
+        void testRunEndedCumulative() override;
+
+        void writeGroup(TestGroupNode const& groupNode, double suiteTime);
+
+        void writeTestCase(TestCaseNode const& testCaseNode);
+
+        void writeSection(std::string const& className,
+                          std::string const& rootName,
+                          SectionNode const& sectionNode);
+
+        void writeAssertions(SectionNode const& sectionNode);
+        void writeAssertion(AssertionStats const& stats);
+
+        XmlWriter xml;
+        Timer suiteTimer;
+        std::string stdOutForSuite;
+        std::string stdErrForSuite;
+        unsigned int unexpectedExceptions = 0;
+        bool m_okToFail = false;
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_junit.h
+// start catch_reporter_xml.h
+
+namespace Catch {
+    class XmlReporter : public StreamingReporterBase<XmlReporter> {
+    public:
+        XmlReporter(ReporterConfig const& _config);
+
+        ~XmlReporter() override;
+
+        static std::string getDescription();
+
+        virtual std::string getStylesheetRef() const;
+
+        void writeSourceInfo(SourceLineInfo const& sourceInfo);
+
+    public: // StreamingReporterBase
+
+        void noMatchingTestCases(std::string const& s) override;
+
+        void testRunStarting(TestRunInfo const& testInfo) override;
+
+        void testGroupStarting(GroupInfo const& groupInfo) override;
+
+        void testCaseStarting(TestCaseInfo const& testInfo) override;
+
+        void sectionStarting(SectionInfo const& sectionInfo) override;
+
+        void assertionStarting(AssertionInfo const&) override;
+
+        bool assertionEnded(AssertionStats const& assertionStats) override;
+
+        void sectionEnded(SectionStats const& sectionStats) override;
+
+        void testCaseEnded(TestCaseStats const& testCaseStats) override;
+
+        void testGroupEnded(TestGroupStats const& testGroupStats) override;
+
+        void testRunEnded(TestRunStats const& testRunStats) override;
+
+    private:
+        Timer m_testCaseTimer;
+        XmlWriter m_xml;
+        int m_sectionDepth = 0;
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_xml.h
+
+// end catch_external_interfaces.h
+#endif
+
+#endif // ! CATCH_CONFIG_IMPL_ONLY
+
+#ifdef CATCH_IMPL
+// start catch_impl.hpp
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wweak-vtables"
+#endif
+
+// Keep these here for external reporters
+// start catch_test_case_tracker.h
+
+#include <string>
+#include <vector>
+#include <memory>
+
+namespace Catch {
+namespace TestCaseTracking {
+
+    struct NameAndLocation {
+        std::string name;
+        SourceLineInfo location;
+
+        NameAndLocation( std::string const& _name, SourceLineInfo const& _location );
+    };
+
+    struct ITracker;
+
+    using ITrackerPtr = std::shared_ptr<ITracker>;
+
+    struct ITracker {
+        virtual ~ITracker();
+
+        // static queries
+        virtual NameAndLocation const& nameAndLocation() const = 0;
+
+        // dynamic queries
+        virtual bool isComplete() const = 0; // Successfully completed or failed
+        virtual bool isSuccessfullyCompleted() const = 0;
+        virtual bool isOpen() const = 0; // Started but not complete
+        virtual bool hasChildren() const = 0;
+
+        virtual ITracker& parent() = 0;
+
+        // actions
+        virtual void close() = 0; // Successfully complete
+        virtual void fail() = 0;
+        virtual void markAsNeedingAnotherRun() = 0;
+
+        virtual void addChild( ITrackerPtr const& child ) = 0;
+        virtual ITrackerPtr findChild( NameAndLocation const& nameAndLocation ) = 0;
+        virtual void openChild() = 0;
+
+        // Debug/ checking
+        virtual bool isSectionTracker() const = 0;
+        virtual bool isIndexTracker() const = 0;
+    };
+
+    class TrackerContext {
+
+        enum RunState {
+            NotStarted,
+            Executing,
+            CompletedCycle
+        };
+
+        ITrackerPtr m_rootTracker;
+        ITracker* m_currentTracker = nullptr;
+        RunState m_runState = NotStarted;
+
+    public:
+
+        static TrackerContext& instance();
+
+        ITracker& startRun();
+        void endRun();
+
+        void startCycle();
+        void completeCycle();
+
+        bool completedCycle() const;
+        ITracker& currentTracker();
+        void setCurrentTracker( ITracker* tracker );
+    };
+
+    class TrackerBase : public ITracker {
+    protected:
+        enum CycleState {
+            NotStarted,
+            Executing,
+            ExecutingChildren,
+            NeedsAnotherRun,
+            CompletedSuccessfully,
+            Failed
+        };
+
+        using Children = std::vector<ITrackerPtr>;
+        NameAndLocation m_nameAndLocation;
+        TrackerContext& m_ctx;
+        ITracker* m_parent;
+        Children m_children;
+        CycleState m_runState = NotStarted;
+
+    public:
+        TrackerBase( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent );
+
+        NameAndLocation const& nameAndLocation() const override;
+        bool isComplete() const override;
+        bool isSuccessfullyCompleted() const override;
+        bool isOpen() const override;
+        bool hasChildren() const override;
+
+        void addChild( ITrackerPtr const& child ) override;
+
+        ITrackerPtr findChild( NameAndLocation const& nameAndLocation ) override;
+        ITracker& parent() override;
+
+        void openChild() override;
+
+        bool isSectionTracker() const override;
+        bool isIndexTracker() const override;
+
+        void open();
+
+        void close() override;
+        void fail() override;
+        void markAsNeedingAnotherRun() override;
+
+    private:
+        void moveToParent();
+        void moveToThis();
+    };
+
+    class SectionTracker : public TrackerBase {
+        std::vector<std::string> m_filters;
+    public:
+        SectionTracker( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent );
+
+        bool isSectionTracker() const override;
+
+        static SectionTracker& acquire( TrackerContext& ctx, NameAndLocation const& nameAndLocation );
+
+        void tryOpen();
+
+        void addInitialFilters( std::vector<std::string> const& filters );
+        void addNextFilters( std::vector<std::string> const& filters );
+    };
+
+    class IndexTracker : public TrackerBase {
+        int m_size;
+        int m_index = -1;
+    public:
+        IndexTracker( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent, int size );
+
+        bool isIndexTracker() const override;
+        void close() override;
+
+        static IndexTracker& acquire( TrackerContext& ctx, NameAndLocation const& nameAndLocation, int size );
+
+        int index() const;
+
+        void moveNext();
+    };
+
+} // namespace TestCaseTracking
+
+using TestCaseTracking::ITracker;
+using TestCaseTracking::TrackerContext;
+using TestCaseTracking::SectionTracker;
+using TestCaseTracking::IndexTracker;
+
+} // namespace Catch
+
+// end catch_test_case_tracker.h
+
+// start catch_leak_detector.h
+
+namespace Catch {
+
+    struct LeakDetector {
+        LeakDetector();
+    };
+
+}
+// end catch_leak_detector.h
+// Cpp files will be included in the single-header file here
+// start catch_approx.cpp
+
+#include <cmath>
+#include <limits>
+
+namespace {
+
+// Performs equivalent check of std::fabs(lhs - rhs) <= margin
+// But without the subtraction to allow for INFINITY in comparison
+bool marginComparison(double lhs, double rhs, double margin) {
+    return (lhs + margin >= rhs) && (rhs + margin >= lhs);
+}
+
+}
+
+namespace Catch {
+namespace Detail {
+
+    Approx::Approx ( double value )
+    :   m_epsilon( std::numeric_limits<float>::epsilon()*100 ),
+        m_margin( 0.0 ),
+        m_scale( 0.0 ),
+        m_value( value )
+    {}
+
+    Approx Approx::custom() {
+        return Approx( 0 );
+    }
+
+    Approx Approx::operator-() const {
+        auto temp(*this);
+        temp.m_value = -temp.m_value;
+        return temp;
+    }
+
+    std::string Approx::toString() const {
+        ReusableStringStream rss;
+        rss << "Approx( " << ::Catch::Detail::stringify( m_value ) << " )";
+        return rss.str();
+    }
+
+    bool Approx::equalityComparisonImpl(const double other) const {
+        // First try with fixed margin, then compute margin based on epsilon, scale and Approx's value
+        // Thanks to Richard Harris for his help refining the scaled margin value
+        return marginComparison(m_value, other, m_margin) || marginComparison(m_value, other, m_epsilon * (m_scale + std::fabs(m_value)));
+    }
+
+    void Approx::setMargin(double margin) {
+        CATCH_ENFORCE(margin >= 0,
+            "Invalid Approx::margin: " << margin << '.'
+            << " Approx::Margin has to be non-negative.");
+        m_margin = margin;
+    }
+
+    void Approx::setEpsilon(double epsilon) {
+        CATCH_ENFORCE(epsilon >= 0 && epsilon <= 1.0,
+            "Invalid Approx::epsilon: " << epsilon << '.'
+            << " Approx::epsilon has to be in [0, 1]");
+        m_epsilon = epsilon;
+    }
+
+} // end namespace Detail
+
+namespace literals {
+    Detail::Approx operator "" _a(long double val) {
+        return Detail::Approx(val);
+    }
+    Detail::Approx operator "" _a(unsigned long long val) {
+        return Detail::Approx(val);
+    }
+} // end namespace literals
+
+std::string StringMaker<Catch::Detail::Approx>::convert(Catch::Detail::Approx const& value) {
+    return value.toString();
+}
+
+} // end namespace Catch
+// end catch_approx.cpp
+// start catch_assertionhandler.cpp
+
+// start catch_context.h
+
+#include <memory>
+
+namespace Catch {
+
+    struct IResultCapture;
+    struct IRunner;
+    struct IConfig;
+    struct IMutableContext;
+
+    using IConfigPtr = std::shared_ptr<IConfig const>;
+
+    struct IContext
+    {
+        virtual ~IContext();
+
+        virtual IResultCapture* getResultCapture() = 0;
+        virtual IRunner* getRunner() = 0;
+        virtual IConfigPtr const& getConfig() const = 0;
+    };
+
+    struct IMutableContext : IContext
+    {
+        virtual ~IMutableContext();
+        virtual void setResultCapture( IResultCapture* resultCapture ) = 0;
+        virtual void setRunner( IRunner* runner ) = 0;
+        virtual void setConfig( IConfigPtr const& config ) = 0;
+
+    private:
+        static IMutableContext *currentContext;
+        friend IMutableContext& getCurrentMutableContext();
+        friend void cleanUpContext();
+        static void createContext();
+    };
+
+    inline IMutableContext& getCurrentMutableContext()
+    {
+        if( !IMutableContext::currentContext )
+            IMutableContext::createContext();
+        return *IMutableContext::currentContext;
+    }
+
+    inline IContext& getCurrentContext()
+    {
+        return getCurrentMutableContext();
+    }
+
+    void cleanUpContext();
+}
+
+// end catch_context.h
+// start catch_debugger.h
+
+namespace Catch {
+    bool isDebuggerActive();
+}
+
+#ifdef CATCH_PLATFORM_MAC
+
+    #define CATCH_TRAP() __asm__("int $3\n" : : ) /* NOLINT */
+
+#elif defined(CATCH_PLATFORM_LINUX)
+    // If we can use inline assembler, do it because this allows us to break
+    // directly at the location of the failing check instead of breaking inside
+    // raise() called from it, i.e. one stack frame below.
+    #if defined(__GNUC__) && (defined(__i386) || defined(__x86_64))
+        #define CATCH_TRAP() asm volatile ("int $3") /* NOLINT */
+    #else // Fall back to the generic way.
+        #include <signal.h>
+
+        #define CATCH_TRAP() raise(SIGTRAP)
+    #endif
+#elif defined(_MSC_VER)
+    #define CATCH_TRAP() __debugbreak()
+#elif defined(__MINGW32__)
+    extern "C" __declspec(dllimport) void __stdcall DebugBreak();
+    #define CATCH_TRAP() DebugBreak()
+#endif
+
+#ifdef CATCH_TRAP
+    #define CATCH_BREAK_INTO_DEBUGGER() if( Catch::isDebuggerActive() ) { CATCH_TRAP(); }
+#else
+    namespace Catch {
+        inline void doNothing() {}
+    }
+    #define CATCH_BREAK_INTO_DEBUGGER() Catch::doNothing()
+#endif
+
+// end catch_debugger.h
+// start catch_run_context.h
+
+// start catch_fatal_condition.h
+
+// start catch_windows_h_proxy.h
+
+
+#if defined(CATCH_PLATFORM_WINDOWS)
+
+#if !defined(NOMINMAX) && !defined(CATCH_CONFIG_NO_NOMINMAX)
+#  define CATCH_DEFINED_NOMINMAX
+#  define NOMINMAX
+#endif
+#if !defined(WIN32_LEAN_AND_MEAN) && !defined(CATCH_CONFIG_NO_WIN32_LEAN_AND_MEAN)
+#  define CATCH_DEFINED_WIN32_LEAN_AND_MEAN
+#  define WIN32_LEAN_AND_MEAN
+#endif
+
+#ifdef __AFXDLL
+#include <AfxWin.h>
+#else
+#include <windows.h>
+#endif
+
+#ifdef CATCH_DEFINED_NOMINMAX
+#  undef NOMINMAX
+#endif
+#ifdef CATCH_DEFINED_WIN32_LEAN_AND_MEAN
+#  undef WIN32_LEAN_AND_MEAN
+#endif
+
+#endif // defined(CATCH_PLATFORM_WINDOWS)
+
+// end catch_windows_h_proxy.h
+#if defined( CATCH_CONFIG_WINDOWS_SEH )
+
+namespace Catch {
+
+    struct FatalConditionHandler {
+
+        static LONG CALLBACK handleVectoredException(PEXCEPTION_POINTERS ExceptionInfo);
+        FatalConditionHandler();
+        static void reset();
+        ~FatalConditionHandler();
+
+    private:
+        static bool isSet;
+        static ULONG guaranteeSize;
+        static PVOID exceptionHandlerHandle;
+    };
+
+} // namespace Catch
+
+#elif defined ( CATCH_CONFIG_POSIX_SIGNALS )
+
+#include <signal.h>
+
+namespace Catch {
+
+    struct FatalConditionHandler {
+
+        static bool isSet;
+        static struct sigaction oldSigActions[];
+        static stack_t oldSigStack;
+        static char altStackMem[];
+
+        static void handleSignal( int sig );
+
+        FatalConditionHandler();
+        ~FatalConditionHandler();
+        static void reset();
+    };
+
+} // namespace Catch
+
+#else
+
+namespace Catch {
+    struct FatalConditionHandler {
+        void reset();
+    };
+}
+
+#endif
+
+// end catch_fatal_condition.h
+#include <string>
+
+namespace Catch {
+
+    struct IMutableContext;
+
+    ///////////////////////////////////////////////////////////////////////////
+
+    class RunContext : public IResultCapture, public IRunner {
+
+    public:
+        RunContext( RunContext const& ) = delete;
+        RunContext& operator =( RunContext const& ) = delete;
+
+        explicit RunContext( IConfigPtr const& _config, IStreamingReporterPtr&& reporter );
+
+        ~RunContext() override;
+
+        void testGroupStarting( std::string const& testSpec, std::size_t groupIndex, std::size_t groupsCount );
+        void testGroupEnded( std::string const& testSpec, Totals const& totals, std::size_t groupIndex, std::size_t groupsCount );
+
+        Totals runTest(TestCase const& testCase);
+
+        IConfigPtr config() const;
+        IStreamingReporter& reporter() const;
+
+    public: // IResultCapture
+
+        // Assertion handlers
+        void handleExpr
+                (   AssertionInfo const& info,
+                    ITransientExpression const& expr,
+                    AssertionReaction& reaction ) override;
+        void handleMessage
+                (   AssertionInfo const& info,
+                    ResultWas::OfType resultType,
+                    StringRef const& message,
+                    AssertionReaction& reaction ) override;
+        void handleUnexpectedExceptionNotThrown
+                (   AssertionInfo const& info,
+                    AssertionReaction& reaction ) override;
+        void handleUnexpectedInflightException
+                (   AssertionInfo const& info,
+                    std::string const& message,
+                    AssertionReaction& reaction ) override;
+        void handleIncomplete
+                (   AssertionInfo const& info ) override;
+        void handleNonExpr
+                (   AssertionInfo const &info,
+                    ResultWas::OfType resultType,
+                    AssertionReaction &reaction ) override;
+
+        bool sectionStarted( SectionInfo const& sectionInfo, Counts& assertions ) override;
+
+        void sectionEnded( SectionEndInfo const& endInfo ) override;
+        void sectionEndedEarly( SectionEndInfo const& endInfo ) override;
+
+        auto acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker& override;
+
+        void benchmarkStarting( BenchmarkInfo const& info ) override;
+        void benchmarkEnded( BenchmarkStats const& stats ) override;
+
+        void pushScopedMessage( MessageInfo const& message ) override;
+        void popScopedMessage( MessageInfo const& message ) override;
+
+        std::string getCurrentTestName() const override;
+
+        const AssertionResult* getLastResult() const override;
+
+        void exceptionEarlyReported() override;
+
+        void handleFatalErrorCondition( StringRef message ) override;
+
+        bool lastAssertionPassed() override;
+
+        void assertionPassed() override;
+
+    public:
+        // !TBD We need to do this another way!
+        bool aborting() const final;
+
+    private:
+
+        void runCurrentTest( std::string& redirectedCout, std::string& redirectedCerr );
+        void invokeActiveTestCase();
+
+        void resetAssertionInfo();
+        bool testForMissingAssertions( Counts& assertions );
+
+        void assertionEnded( AssertionResult const& result );
+        void reportExpr
+                (   AssertionInfo const &info,
+                    ResultWas::OfType resultType,
+                    ITransientExpression const *expr,
+                    bool negated );
+
+        void populateReaction( AssertionReaction& reaction );
+
+    private:
+
+        void handleUnfinishedSections();
+
+        TestRunInfo m_runInfo;
+        IMutableContext& m_context;
+        TestCase const* m_activeTestCase = nullptr;
+        ITracker* m_testCaseTracker;
+        Option<AssertionResult> m_lastResult;
+
+        IConfigPtr m_config;
+        Totals m_totals;
+        IStreamingReporterPtr m_reporter;
+        std::vector<MessageInfo> m_messages;
+        AssertionInfo m_lastAssertionInfo;
+        std::vector<SectionEndInfo> m_unfinishedSections;
+        std::vector<ITracker*> m_activeSections;
+        TrackerContext m_trackerContext;
+        bool m_lastAssertionPassed = false;
+        bool m_shouldReportUnexpected = true;
+        bool m_includeSuccessfulResults;
+    };
+
+} // end namespace Catch
+
+// end catch_run_context.h
+namespace Catch {
+
+    namespace {
+        auto operator <<( std::ostream& os, ITransientExpression const& expr ) -> std::ostream& {
+            expr.streamReconstructedExpression( os );
+            return os;
+        }
+    }
+
+    LazyExpression::LazyExpression( bool isNegated )
+    :   m_isNegated( isNegated )
+    {}
+
+    LazyExpression::LazyExpression( LazyExpression const& other ) : m_isNegated( other.m_isNegated ) {}
+
+    LazyExpression::operator bool() const {
+        return m_transientExpression != nullptr;
+    }
+
+    auto operator << ( std::ostream& os, LazyExpression const& lazyExpr ) -> std::ostream& {
+        if( lazyExpr.m_isNegated )
+            os << "!";
+
+        if( lazyExpr ) {
+            if( lazyExpr.m_isNegated && lazyExpr.m_transientExpression->isBinaryExpression() )
+                os << "(" << *lazyExpr.m_transientExpression << ")";
+            else
+                os << *lazyExpr.m_transientExpression;
+        }
+        else {
+            os << "{** error - unchecked empty expression requested **}";
+        }
+        return os;
+    }
+
+    AssertionHandler::AssertionHandler
+        (   StringRef const& macroName,
+            SourceLineInfo const& lineInfo,
+            StringRef capturedExpression,
+            ResultDisposition::Flags resultDisposition )
+    :   m_assertionInfo{ macroName, lineInfo, capturedExpression, resultDisposition },
+        m_resultCapture( getResultCapture() )
+    {}
+
+    void AssertionHandler::handleExpr( ITransientExpression const& expr ) {
+        m_resultCapture.handleExpr( m_assertionInfo, expr, m_reaction );
+    }
+    void AssertionHandler::handleMessage(ResultWas::OfType resultType, StringRef const& message) {
+        m_resultCapture.handleMessage( m_assertionInfo, resultType, message, m_reaction );
+    }
+
+    auto AssertionHandler::allowThrows() const -> bool {
+        return getCurrentContext().getConfig()->allowThrows();
+    }
+
+    void AssertionHandler::complete() {
+        setCompleted();
+        if( m_reaction.shouldDebugBreak ) {
+
+            // If you find your debugger stopping you here then go one level up on the
+            // call-stack for the code that caused it (typically a failed assertion)
+
+            // (To go back to the test and change execution, jump over the throw, next)
+            CATCH_BREAK_INTO_DEBUGGER();
+        }
+        if (m_reaction.shouldThrow) {
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+            throw Catch::TestFailureException();
+#else
+            CATCH_ERROR( "Test failure requires aborting test!" );
+#endif
+        }
+    }
+    void AssertionHandler::setCompleted() {
+        m_completed = true;
+    }
+
+    void AssertionHandler::handleUnexpectedInflightException() {
+        m_resultCapture.handleUnexpectedInflightException( m_assertionInfo, Catch::translateActiveException(), m_reaction );
+    }
+
+    void AssertionHandler::handleExceptionThrownAsExpected() {
+        m_resultCapture.handleNonExpr(m_assertionInfo, ResultWas::Ok, m_reaction);
+    }
+    void AssertionHandler::handleExceptionNotThrownAsExpected() {
+        m_resultCapture.handleNonExpr(m_assertionInfo, ResultWas::Ok, m_reaction);
+    }
+
+    void AssertionHandler::handleUnexpectedExceptionNotThrown() {
+        m_resultCapture.handleUnexpectedExceptionNotThrown( m_assertionInfo, m_reaction );
+    }
+
+    void AssertionHandler::handleThrowingCallSkipped() {
+        m_resultCapture.handleNonExpr(m_assertionInfo, ResultWas::Ok, m_reaction);
+    }
+
+    // This is the overload that takes a string and infers the Equals matcher from it
+    // The more general overload, that takes any string matcher, is in catch_capture_matchers.cpp
+    void handleExceptionMatchExpr( AssertionHandler& handler, std::string const& str, StringRef const& matcherString  ) {
+        handleExceptionMatchExpr( handler, Matchers::Equals( str ), matcherString );
+    }
+
+} // namespace Catch
+// end catch_assertionhandler.cpp
+// start catch_assertionresult.cpp
+
+namespace Catch {
+    AssertionResultData::AssertionResultData(ResultWas::OfType _resultType, LazyExpression const & _lazyExpression):
+        lazyExpression(_lazyExpression),
+        resultType(_resultType) {}
+
+    std::string AssertionResultData::reconstructExpression() const {
+
+        if( reconstructedExpression.empty() ) {
+            if( lazyExpression ) {
+                ReusableStringStream rss;
+                rss << lazyExpression;
+                reconstructedExpression = rss.str();
+            }
+        }
+        return reconstructedExpression;
+    }
+
+    AssertionResult::AssertionResult( AssertionInfo const& info, AssertionResultData const& data )
+    :   m_info( info ),
+        m_resultData( data )
+    {}
+
+    // Result was a success
+    bool AssertionResult::succeeded() const {
+        return Catch::isOk( m_resultData.resultType );
+    }
+
+    // Result was a success, or failure is suppressed
+    bool AssertionResult::isOk() const {
+        return Catch::isOk( m_resultData.resultType ) || shouldSuppressFailure( m_info.resultDisposition );
+    }
+
+    ResultWas::OfType AssertionResult::getResultType() const {
+        return m_resultData.resultType;
+    }
+
+    bool AssertionResult::hasExpression() const {
+        return m_info.capturedExpression[0] != 0;
+    }
+
+    bool AssertionResult::hasMessage() const {
+        return !m_resultData.message.empty();
+    }
+
+    std::string AssertionResult::getExpression() const {
+        if( isFalseTest( m_info.resultDisposition ) )
+            return "!(" + m_info.capturedExpression + ")";
+        else
+            return m_info.capturedExpression;
+    }
+
+    std::string AssertionResult::getExpressionInMacro() const {
+        std::string expr;
+        if( m_info.macroName[0] == 0 )
+            expr = m_info.capturedExpression;
+        else {
+            expr.reserve( m_info.macroName.size() + m_info.capturedExpression.size() + 4 );
+            expr += m_info.macroName;
+            expr += "( ";
+            expr += m_info.capturedExpression;
+            expr += " )";
+        }
+        return expr;
+    }
+
+    bool AssertionResult::hasExpandedExpression() const {
+        return hasExpression() && getExpandedExpression() != getExpression();
+    }
+
+    std::string AssertionResult::getExpandedExpression() const {
+        std::string expr = m_resultData.reconstructExpression();
+        return expr.empty()
+                ? getExpression()
+                : expr;
+    }
+
+    std::string AssertionResult::getMessage() const {
+        return m_resultData.message;
+    }
+    SourceLineInfo AssertionResult::getSourceInfo() const {
+        return m_info.lineInfo;
+    }
+
+    StringRef AssertionResult::getTestMacroName() const {
+        return m_info.macroName;
+    }
+
+} // end namespace Catch
+// end catch_assertionresult.cpp
+// start catch_benchmark.cpp
+
+namespace Catch {
+
+    auto BenchmarkLooper::getResolution() -> uint64_t {
+        return getEstimatedClockResolution() * getCurrentContext().getConfig()->benchmarkResolutionMultiple();
+    }
+
+    void BenchmarkLooper::reportStart() {
+        getResultCapture().benchmarkStarting( { m_name } );
+    }
+    auto BenchmarkLooper::needsMoreIterations() -> bool {
+        auto elapsed = m_timer.getElapsedNanoseconds();
+
+        // Exponentially increasing iterations until we're confident in our timer resolution
+        if( elapsed < m_resolution ) {
+            m_iterationsToRun *= 10;
+            return true;
+        }
+
+        getResultCapture().benchmarkEnded( { { m_name }, m_count, elapsed } );
+        return false;
+    }
+
+} // end namespace Catch
+// end catch_benchmark.cpp
+// start catch_capture_matchers.cpp
+
+namespace Catch {
+
+    using StringMatcher = Matchers::Impl::MatcherBase<std::string>;
+
+    // This is the general overload that takes a any string matcher
+    // There is another overload, in catch_assertionhandler.h/.cpp, that only takes a string and infers
+    // the Equals matcher (so the header does not mention matchers)
+    void handleExceptionMatchExpr( AssertionHandler& handler, StringMatcher const& matcher, StringRef const& matcherString  ) {
+        std::string exceptionMessage = Catch::translateActiveException();
+        MatchExpr<std::string, StringMatcher const&> expr( exceptionMessage, matcher, matcherString );
+        handler.handleExpr( expr );
+    }
+
+} // namespace Catch
+// end catch_capture_matchers.cpp
+// start catch_commandline.cpp
+
+// start catch_commandline.h
+
+// start catch_clara.h
+
+// Use Catch's value for console width (store Clara's off to the side, if present)
+#ifdef CLARA_CONFIG_CONSOLE_WIDTH
+#define CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH
+#undef CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH
+#endif
+#define CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH CATCH_CONFIG_CONSOLE_WIDTH-1
+
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wweak-vtables"
+#pragma clang diagnostic ignored "-Wexit-time-destructors"
+#pragma clang diagnostic ignored "-Wshadow"
+#endif
+
+// start clara.hpp
+// Copyright 2017 Two Blue Cubes Ltd. All rights reserved.
+//
+// Distributed under the Boost Software License, Version 1.0. (See accompanying
+// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+//
+// See https://github.com/philsquared/Clara for more details
+
+// Clara v1.1.4
+
+
+#ifndef CATCH_CLARA_CONFIG_CONSOLE_WIDTH
+#define CATCH_CLARA_CONFIG_CONSOLE_WIDTH 80
+#endif
+
+#ifndef CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH
+#define CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH CATCH_CLARA_CONFIG_CONSOLE_WIDTH
+#endif
+
+#ifndef CLARA_CONFIG_OPTIONAL_TYPE
+#ifdef __has_include
+#if __has_include(<optional>) && __cplusplus >= 201703L
+#include <optional>
+#define CLARA_CONFIG_OPTIONAL_TYPE std::optional
+#endif
+#endif
+#endif
+
+// ----------- #included from clara_textflow.hpp -----------
+
+// TextFlowCpp
+//
+// A single-header library for wrapping and laying out basic text, by Phil Nash
+//
+// This work is licensed under the BSD 2-Clause license.
+// See the accompanying LICENSE file, or the one at https://opensource.org/licenses/BSD-2-Clause
+//
+// This project is hosted at https://github.com/philsquared/textflowcpp
+
+
+#include <cassert>
+#include <ostream>
+#include <sstream>
+#include <vector>
+
+#ifndef CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH
+#define CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH 80
+#endif
+
+namespace Catch { namespace clara { namespace TextFlow {
+
+    inline auto isWhitespace( char c ) -> bool {
+        static std::string chars = " \t\n\r";
+        return chars.find( c ) != std::string::npos;
+    }
+    inline auto isBreakableBefore( char c ) -> bool {
+        static std::string chars = "[({<|";
+        return chars.find( c ) != std::string::npos;
+    }
+    inline auto isBreakableAfter( char c ) -> bool {
+        static std::string chars = "])}>.,:;*+-=&/\\";
+        return chars.find( c ) != std::string::npos;
+    }
+
+    class Columns;
+
+    class Column {
+        std::vector<std::string> m_strings;
+        size_t m_width = CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH;
+        size_t m_indent = 0;
+        size_t m_initialIndent = std::string::npos;
+
+    public:
+        class iterator {
+            friend Column;
+
+            Column const& m_column;
+            size_t m_stringIndex = 0;
+            size_t m_pos = 0;
+
+            size_t m_len = 0;
+            size_t m_end = 0;
+            bool m_suffix = false;
+
+            iterator( Column const& column, size_t stringIndex )
+            :   m_column( column ),
+                m_stringIndex( stringIndex )
+            {}
+
+            auto line() const -> std::string const& { return m_column.m_strings[m_stringIndex]; }
+
+            auto isBoundary( size_t at ) const -> bool {
+                assert( at > 0 );
+                assert( at <= line().size() );
+
+                return at == line().size() ||
+                       ( isWhitespace( line()[at] ) && !isWhitespace( line()[at-1] ) ) ||
+                       isBreakableBefore( line()[at] ) ||
+                       isBreakableAfter( line()[at-1] );
+            }
+
+            void calcLength() {
+                assert( m_stringIndex < m_column.m_strings.size() );
+
+                m_suffix = false;
+                auto width = m_column.m_width-indent();
+                m_end = m_pos;
+                while( m_end < line().size() && line()[m_end] != '\n' )
+                    ++m_end;
+
+                if( m_end < m_pos + width ) {
+                    m_len = m_end - m_pos;
+                }
+                else {
+                    size_t len = width;
+                    while (len > 0 && !isBoundary(m_pos + len))
+                        --len;
+                    while (len > 0 && isWhitespace( line()[m_pos + len - 1] ))
+                        --len;
+
+                    if (len > 0) {
+                        m_len = len;
+                    } else {
+                        m_suffix = true;
+                        m_len = width - 1;
+                    }
+                }
+            }
+
+            auto indent() const -> size_t {
+                auto initial = m_pos == 0 && m_stringIndex == 0 ? m_column.m_initialIndent : std::string::npos;
+                return initial == std::string::npos ? m_column.m_indent : initial;
+            }
+
+            auto addIndentAndSuffix(std::string const &plain) const -> std::string {
+                return std::string( indent(), ' ' ) + (m_suffix ? plain + "-" : plain);
+            }
+
+        public:
+            explicit iterator( Column const& column ) : m_column( column ) {
+                assert( m_column.m_width > m_column.m_indent );
+                assert( m_column.m_initialIndent == std::string::npos || m_column.m_width > m_column.m_initialIndent );
+                calcLength();
+                if( m_len == 0 )
+                    m_stringIndex++; // Empty string
+            }
+
+            auto operator *() const -> std::string {
+                assert( m_stringIndex < m_column.m_strings.size() );
+                assert( m_pos <= m_end );
+                if( m_pos + m_column.m_width < m_end )
+                    return addIndentAndSuffix(line().substr(m_pos, m_len));
+                else
+                    return addIndentAndSuffix(line().substr(m_pos, m_end - m_pos));
+            }
+
+            auto operator ++() -> iterator& {
+                m_pos += m_len;
+                if( m_pos < line().size() && line()[m_pos] == '\n' )
+                    m_pos += 1;
+                else
+                    while( m_pos < line().size() && isWhitespace( line()[m_pos] ) )
+                        ++m_pos;
+
+                if( m_pos == line().size() ) {
+                    m_pos = 0;
+                    ++m_stringIndex;
+                }
+                if( m_stringIndex < m_column.m_strings.size() )
+                    calcLength();
+                return *this;
+            }
+            auto operator ++(int) -> iterator {
+                iterator prev( *this );
+                operator++();
+                return prev;
+            }
+
+            auto operator ==( iterator const& other ) const -> bool {
+                return
+                    m_pos == other.m_pos &&
+                    m_stringIndex == other.m_stringIndex &&
+                    &m_column == &other.m_column;
+            }
+            auto operator !=( iterator const& other ) const -> bool {
+                return !operator==( other );
+            }
+        };
+        using const_iterator = iterator;
+
+        explicit Column( std::string const& text ) { m_strings.push_back( text ); }
+
+        auto width( size_t newWidth ) -> Column& {
+            assert( newWidth > 0 );
+            m_width = newWidth;
+            return *this;
+        }
+        auto indent( size_t newIndent ) -> Column& {
+            m_indent = newIndent;
+            return *this;
+        }
+        auto initialIndent( size_t newIndent ) -> Column& {
+            m_initialIndent = newIndent;
+            return *this;
+        }
+
+        auto width() const -> size_t { return m_width; }
+        auto begin() const -> iterator { return iterator( *this ); }
+        auto end() const -> iterator { return { *this, m_strings.size() }; }
+
+        inline friend std::ostream& operator << ( std::ostream& os, Column const& col ) {
+            bool first = true;
+            for( auto line : col ) {
+                if( first )
+                    first = false;
+                else
+                    os << "\n";
+                os <<  line;
+            }
+            return os;
+        }
+
+        auto operator + ( Column const& other ) -> Columns;
+
+        auto toString() const -> std::string {
+            std::ostringstream oss;
+            oss << *this;
+            return oss.str();
+        }
+    };
+
+    class Spacer : public Column {
+
+    public:
+        explicit Spacer( size_t spaceWidth ) : Column( "" ) {
+            width( spaceWidth );
+        }
+    };
+
+    class Columns {
+        std::vector<Column> m_columns;
+
+    public:
+
+        class iterator {
+            friend Columns;
+            struct EndTag {};
+
+            std::vector<Column> const& m_columns;
+            std::vector<Column::iterator> m_iterators;
+            size_t m_activeIterators;
+
+            iterator( Columns const& columns, EndTag )
+            :   m_columns( columns.m_columns ),
+                m_activeIterators( 0 )
+            {
+                m_iterators.reserve( m_columns.size() );
+
+                for( auto const& col : m_columns )
+                    m_iterators.push_back( col.end() );
+            }
+
+        public:
+            explicit iterator( Columns const& columns )
+            :   m_columns( columns.m_columns ),
+                m_activeIterators( m_columns.size() )
+            {
+                m_iterators.reserve( m_columns.size() );
+
+                for( auto const& col : m_columns )
+                    m_iterators.push_back( col.begin() );
+            }
+
+            auto operator ==( iterator const& other ) const -> bool {
+                return m_iterators == other.m_iterators;
+            }
+            auto operator !=( iterator const& other ) const -> bool {
+                return m_iterators != other.m_iterators;
+            }
+            auto operator *() const -> std::string {
+                std::string row, padding;
+
+                for( size_t i = 0; i < m_columns.size(); ++i ) {
+                    auto width = m_columns[i].width();
+                    if( m_iterators[i] != m_columns[i].end() ) {
+                        std::string col = *m_iterators[i];
+                        row += padding + col;
+                        if( col.size() < width )
+                            padding = std::string( width - col.size(), ' ' );
+                        else
+                            padding = "";
+                    }
+                    else {
+                        padding += std::string( width, ' ' );
+                    }
+                }
+                return row;
+            }
+            auto operator ++() -> iterator& {
+                for( size_t i = 0; i < m_columns.size(); ++i ) {
+                    if (m_iterators[i] != m_columns[i].end())
+                        ++m_iterators[i];
+                }
+                return *this;
+            }
+            auto operator ++(int) -> iterator {
+                iterator prev( *this );
+                operator++();
+                return prev;
+            }
+        };
+        using const_iterator = iterator;
+
+        auto begin() const -> iterator { return iterator( *this ); }
+        auto end() const -> iterator { return { *this, iterator::EndTag() }; }
+
+        auto operator += ( Column const& col ) -> Columns& {
+            m_columns.push_back( col );
+            return *this;
+        }
+        auto operator + ( Column const& col ) -> Columns {
+            Columns combined = *this;
+            combined += col;
+            return combined;
+        }
+
+        inline friend std::ostream& operator << ( std::ostream& os, Columns const& cols ) {
+
+            bool first = true;
+            for( auto line : cols ) {
+                if( first )
+                    first = false;
+                else
+                    os << "\n";
+                os << line;
+            }
+            return os;
+        }
+
+        auto toString() const -> std::string {
+            std::ostringstream oss;
+            oss << *this;
+            return oss.str();
+        }
+    };
+
+    inline auto Column::operator + ( Column const& other ) -> Columns {
+        Columns cols;
+        cols += *this;
+        cols += other;
+        return cols;
+    }
+}}} // namespace Catch::clara::TextFlow
+
+// ----------- end of #include from clara_textflow.hpp -----------
+// ........... back in clara.hpp
+
+#include <memory>
+#include <set>
+#include <algorithm>
+
+#if !defined(CATCH_PLATFORM_WINDOWS) && ( defined(WIN32) || defined(__WIN32__) || defined(_WIN32) || defined(_MSC_VER) )
+#define CATCH_PLATFORM_WINDOWS
+#endif
+
+namespace Catch { namespace clara {
+namespace detail {
+
+    // Traits for extracting arg and return type of lambdas (for single argument lambdas)
+    template<typename L>
+    struct UnaryLambdaTraits : UnaryLambdaTraits<decltype( &L::operator() )> {};
+
+    template<typename ClassT, typename ReturnT, typename... Args>
+    struct UnaryLambdaTraits<ReturnT( ClassT::* )( Args... ) const> {
+        static const bool isValid = false;
+    };
+
+    template<typename ClassT, typename ReturnT, typename ArgT>
+    struct UnaryLambdaTraits<ReturnT( ClassT::* )( ArgT ) const> {
+        static const bool isValid = true;
+        using ArgType = typename std::remove_const<typename std::remove_reference<ArgT>::type>::type;
+        using ReturnType = ReturnT;
+    };
+
+    class TokenStream;
+
+    // Transport for raw args (copied from main args, or supplied via init list for testing)
+    class Args {
+        friend TokenStream;
+        std::string m_exeName;
+        std::vector<std::string> m_args;
+
+    public:
+        Args( int argc, char const* const* argv )
+            : m_exeName(argv[0]),
+              m_args(argv + 1, argv + argc) {}
+
+        Args( std::initializer_list<std::string> args )
+        :   m_exeName( *args.begin() ),
+            m_args( args.begin()+1, args.end() )
+        {}
+
+        auto exeName() const -> std::string {
+            return m_exeName;
+        }
+    };
+
+    // Wraps a token coming from a token stream. These may not directly correspond to strings as a single string
+    // may encode an option + its argument if the : or = form is used
+    enum class TokenType {
+        Option, Argument
+    };
+    struct Token {
+        TokenType type;
+        std::string token;
+    };
+
+    inline auto isOptPrefix( char c ) -> bool {
+        return c == '-'
+#ifdef CATCH_PLATFORM_WINDOWS
+            || c == '/'
+#endif
+        ;
+    }
+
+    // Abstracts iterators into args as a stream of tokens, with option arguments uniformly handled
+    class TokenStream {
+        using Iterator = std::vector<std::string>::const_iterator;
+        Iterator it;
+        Iterator itEnd;
+        std::vector<Token> m_tokenBuffer;
+
+        void loadBuffer() {
+            m_tokenBuffer.resize( 0 );
+
+            // Skip any empty strings
+            while( it != itEnd && it->empty() )
+                ++it;
+
+            if( it != itEnd ) {
+                auto const &next = *it;
+                if( isOptPrefix( next[0] ) ) {
+                    auto delimiterPos = next.find_first_of( " :=" );
+                    if( delimiterPos != std::string::npos ) {
+                        m_tokenBuffer.push_back( { TokenType::Option, next.substr( 0, delimiterPos ) } );
+                        m_tokenBuffer.push_back( { TokenType::Argument, next.substr( delimiterPos + 1 ) } );
+                    } else {
+                        if( next[1] != '-' && next.size() > 2 ) {
+                            std::string opt = "- ";
+                            for( size_t i = 1; i < next.size(); ++i ) {
+                                opt[1] = next[i];
+                                m_tokenBuffer.push_back( { TokenType::Option, opt } );
+                            }
+                        } else {
+                            m_tokenBuffer.push_back( { TokenType::Option, next } );
+                        }
+                    }
+                } else {
+                    m_tokenBuffer.push_back( { TokenType::Argument, next } );
+                }
+            }
+        }
+
+    public:
+        explicit TokenStream( Args const &args ) : TokenStream( args.m_args.begin(), args.m_args.end() ) {}
+
+        TokenStream( Iterator it, Iterator itEnd ) : it( it ), itEnd( itEnd ) {
+            loadBuffer();
+        }
+
+        explicit operator bool() const {
+            return !m_tokenBuffer.empty() || it != itEnd;
+        }
+
+        auto count() const -> size_t { return m_tokenBuffer.size() + (itEnd - it); }
+
+        auto operator*() const -> Token {
+            assert( !m_tokenBuffer.empty() );
+            return m_tokenBuffer.front();
+        }
+
+        auto operator->() const -> Token const * {
+            assert( !m_tokenBuffer.empty() );
+            return &m_tokenBuffer.front();
+        }
+
+        auto operator++() -> TokenStream & {
+            if( m_tokenBuffer.size() >= 2 ) {
+                m_tokenBuffer.erase( m_tokenBuffer.begin() );
+            } else {
+                if( it != itEnd )
+                    ++it;
+                loadBuffer();
+            }
+            return *this;
+        }
+    };
+
+    class ResultBase {
+    public:
+        enum Type {
+            Ok, LogicError, RuntimeError
+        };
+
+    protected:
+        ResultBase( Type type ) : m_type( type ) {}
+        virtual ~ResultBase() = default;
+
+        virtual void enforceOk() const = 0;
+
+        Type m_type;
+    };
+
+    template<typename T>
+    class ResultValueBase : public ResultBase {
+    public:
+        auto value() const -> T const & {
+            enforceOk();
+            return m_value;
+        }
+
+    protected:
+        ResultValueBase( Type type ) : ResultBase( type ) {}
+
+        ResultValueBase( ResultValueBase const &other ) : ResultBase( other ) {
+            if( m_type == ResultBase::Ok )
+                new( &m_value ) T( other.m_value );
+        }
+
+        ResultValueBase( Type, T const &value ) : ResultBase( Ok ) {
+            new( &m_value ) T( value );
+        }
+
+        auto operator=( ResultValueBase const &other ) -> ResultValueBase & {
+            if( m_type == ResultBase::Ok )
+                m_value.~T();
+            ResultBase::operator=(other);
+            if( m_type == ResultBase::Ok )
+                new( &m_value ) T( other.m_value );
+            return *this;
+        }
+
+        ~ResultValueBase() override {
+            if( m_type == Ok )
+                m_value.~T();
+        }
+
+        union {
+            T m_value;
+        };
+    };
+
+    template<>
+    class ResultValueBase<void> : public ResultBase {
+    protected:
+        using ResultBase::ResultBase;
+    };
+
+    template<typename T = void>
+    class BasicResult : public ResultValueBase<T> {
+    public:
+        template<typename U>
+        explicit BasicResult( BasicResult<U> const &other )
+        :   ResultValueBase<T>( other.type() ),
+            m_errorMessage( other.errorMessage() )
+        {
+            assert( type() != ResultBase::Ok );
+        }
+
+        template<typename U>
+        static auto ok( U const &value ) -> BasicResult { return { ResultBase::Ok, value }; }
+        static auto ok() -> BasicResult { return { ResultBase::Ok }; }
+        static auto logicError( std::string const &message ) -> BasicResult { return { ResultBase::LogicError, message }; }
+        static auto runtimeError( std::string const &message ) -> BasicResult { return { ResultBase::RuntimeError, message }; }
+
+        explicit operator bool() const { return m_type == ResultBase::Ok; }
+        auto type() const -> ResultBase::Type { return m_type; }
+        auto errorMessage() const -> std::string { return m_errorMessage; }
+
+    protected:
+        void enforceOk() const override {
+
+            // Errors shouldn't reach this point, but if they do
+            // the actual error message will be in m_errorMessage
+            assert( m_type != ResultBase::LogicError );
+            assert( m_type != ResultBase::RuntimeError );
+            if( m_type != ResultBase::Ok )
+                std::abort();
+        }
+
+        std::string m_errorMessage; // Only populated if resultType is an error
+
+        BasicResult( ResultBase::Type type, std::string const &message )
+        :   ResultValueBase<T>(type),
+            m_errorMessage(message)
+        {
+            assert( m_type != ResultBase::Ok );
+        }
+
+        using ResultValueBase<T>::ResultValueBase;
+        using ResultBase::m_type;
+    };
+
+    enum class ParseResultType {
+        Matched, NoMatch, ShortCircuitAll, ShortCircuitSame
+    };
+
+    class ParseState {
+    public:
+
+        ParseState( ParseResultType type, TokenStream const &remainingTokens )
+        : m_type(type),
+          m_remainingTokens( remainingTokens )
+        {}
+
+        auto type() const -> ParseResultType { return m_type; }
+        auto remainingTokens() const -> TokenStream { return m_remainingTokens; }
+
+    private:
+        ParseResultType m_type;
+        TokenStream m_remainingTokens;
+    };
+
+    using Result = BasicResult<void>;
+    using ParserResult = BasicResult<ParseResultType>;
+    using InternalParseResult = BasicResult<ParseState>;
+
+    struct HelpColumns {
+        std::string left;
+        std::string right;
+    };
+
+    template<typename T>
+    inline auto convertInto( std::string const &source, T& target ) -> ParserResult {
+        std::stringstream ss;
+        ss << source;
+        ss >> target;
+        if( ss.fail() )
+            return ParserResult::runtimeError( "Unable to convert '" + source + "' to destination type" );
+        else
+            return ParserResult::ok( ParseResultType::Matched );
+    }
+    inline auto convertInto( std::string const &source, std::string& target ) -> ParserResult {
+        target = source;
+        return ParserResult::ok( ParseResultType::Matched );
+    }
+    inline auto convertInto( std::string const &source, bool &target ) -> ParserResult {
+        std::string srcLC = source;
+        std::transform( srcLC.begin(), srcLC.end(), srcLC.begin(), []( char c ) { return static_cast<char>( ::tolower(c) ); } );
+        if (srcLC == "y" || srcLC == "1" || srcLC == "true" || srcLC == "yes" || srcLC == "on")
+            target = true;
+        else if (srcLC == "n" || srcLC == "0" || srcLC == "false" || srcLC == "no" || srcLC == "off")
+            target = false;
+        else
+            return ParserResult::runtimeError( "Expected a boolean value but did not recognise: '" + source + "'" );
+        return ParserResult::ok( ParseResultType::Matched );
+    }
+#ifdef CLARA_CONFIG_OPTIONAL_TYPE
+    template<typename T>
+    inline auto convertInto( std::string const &source, CLARA_CONFIG_OPTIONAL_TYPE<T>& target ) -> ParserResult {
+        T temp;
+        auto result = convertInto( source, temp );
+        if( result )
+            target = std::move(temp);
+        return result;
+    }
+#endif // CLARA_CONFIG_OPTIONAL_TYPE
+
+    struct NonCopyable {
+        NonCopyable() = default;
+        NonCopyable( NonCopyable const & ) = delete;
+        NonCopyable( NonCopyable && ) = delete;
+        NonCopyable &operator=( NonCopyable const & ) = delete;
+        NonCopyable &operator=( NonCopyable && ) = delete;
+    };
+
+    struct BoundRef : NonCopyable {
+        virtual ~BoundRef() = default;
+        virtual auto isContainer() const -> bool { return false; }
+        virtual auto isFlag() const -> bool { return false; }
+    };
+    struct BoundValueRefBase : BoundRef {
+        virtual auto setValue( std::string const &arg ) -> ParserResult = 0;
+    };
+    struct BoundFlagRefBase : BoundRef {
+        virtual auto setFlag( bool flag ) -> ParserResult = 0;
+        virtual auto isFlag() const -> bool { return true; }
+    };
+
+    template<typename T>
+    struct BoundValueRef : BoundValueRefBase {
+        T &m_ref;
+
+        explicit BoundValueRef( T &ref ) : m_ref( ref ) {}
+
+        auto setValue( std::string const &arg ) -> ParserResult override {
+            return convertInto( arg, m_ref );
+        }
+    };
+
+    template<typename T>
+    struct BoundValueRef<std::vector<T>> : BoundValueRefBase {
+        std::vector<T> &m_ref;
+
+        explicit BoundValueRef( std::vector<T> &ref ) : m_ref( ref ) {}
+
+        auto isContainer() const -> bool override { return true; }
+
+        auto setValue( std::string const &arg ) -> ParserResult override {
+            T temp;
+            auto result = convertInto( arg, temp );
+            if( result )
+                m_ref.push_back( temp );
+            return result;
+        }
+    };
+
+    struct BoundFlagRef : BoundFlagRefBase {
+        bool &m_ref;
+
+        explicit BoundFlagRef( bool &ref ) : m_ref( ref ) {}
+
+        auto setFlag( bool flag ) -> ParserResult override {
+            m_ref = flag;
+            return ParserResult::ok( ParseResultType::Matched );
+        }
+    };
+
+    template<typename ReturnType>
+    struct LambdaInvoker {
+        static_assert( std::is_same<ReturnType, ParserResult>::value, "Lambda must return void or clara::ParserResult" );
+
+        template<typename L, typename ArgType>
+        static auto invoke( L const &lambda, ArgType const &arg ) -> ParserResult {
+            return lambda( arg );
+        }
+    };
+
+    template<>
+    struct LambdaInvoker<void> {
+        template<typename L, typename ArgType>
+        static auto invoke( L const &lambda, ArgType const &arg ) -> ParserResult {
+            lambda( arg );
+            return ParserResult::ok( ParseResultType::Matched );
+        }
+    };
+
+    template<typename ArgType, typename L>
+    inline auto invokeLambda( L const &lambda, std::string const &arg ) -> ParserResult {
+        ArgType temp{};
+        auto result = convertInto( arg, temp );
+        return !result
+           ? result
+           : LambdaInvoker<typename UnaryLambdaTraits<L>::ReturnType>::invoke( lambda, temp );
+    }
+
+    template<typename L>
+    struct BoundLambda : BoundValueRefBase {
+        L m_lambda;
+
+        static_assert( UnaryLambdaTraits<L>::isValid, "Supplied lambda must take exactly one argument" );
+        explicit BoundLambda( L const &lambda ) : m_lambda( lambda ) {}
+
+        auto setValue( std::string const &arg ) -> ParserResult override {
+            return invokeLambda<typename UnaryLambdaTraits<L>::ArgType>( m_lambda, arg );
+        }
+    };
+
+    template<typename L>
+    struct BoundFlagLambda : BoundFlagRefBase {
+        L m_lambda;
+
+        static_assert( UnaryLambdaTraits<L>::isValid, "Supplied lambda must take exactly one argument" );
+        static_assert( std::is_same<typename UnaryLambdaTraits<L>::ArgType, bool>::value, "flags must be boolean" );
+
+        explicit BoundFlagLambda( L const &lambda ) : m_lambda( lambda ) {}
+
+        auto setFlag( bool flag ) -> ParserResult override {
+            return LambdaInvoker<typename UnaryLambdaTraits<L>::ReturnType>::invoke( m_lambda, flag );
+        }
+    };
+
+    enum class Optionality { Optional, Required };
+
+    struct Parser;
+
+    class ParserBase {
+    public:
+        virtual ~ParserBase() = default;
+        virtual auto validate() const -> Result { return Result::ok(); }
+        virtual auto parse( std::string const& exeName, TokenStream const &tokens) const -> InternalParseResult  = 0;
+        virtual auto cardinality() const -> size_t { return 1; }
+
+        auto parse( Args const &args ) const -> InternalParseResult {
+            return parse( args.exeName(), TokenStream( args ) );
+        }
+    };
+
+    template<typename DerivedT>
+    class ComposableParserImpl : public ParserBase {
+    public:
+        template<typename T>
+        auto operator|( T const &other ) const -> Parser;
+
+		template<typename T>
+        auto operator+( T const &other ) const -> Parser;
+    };
+
+    // Common code and state for Args and Opts
+    template<typename DerivedT>
+    class ParserRefImpl : public ComposableParserImpl<DerivedT> {
+    protected:
+        Optionality m_optionality = Optionality::Optional;
+        std::shared_ptr<BoundRef> m_ref;
+        std::string m_hint;
+        std::string m_description;
+
+        explicit ParserRefImpl( std::shared_ptr<BoundRef> const &ref ) : m_ref( ref ) {}
+
+    public:
+        template<typename T>
+        ParserRefImpl( T &ref, std::string const &hint )
+        :   m_ref( std::make_shared<BoundValueRef<T>>( ref ) ),
+            m_hint( hint )
+        {}
+
+        template<typename LambdaT>
+        ParserRefImpl( LambdaT const &ref, std::string const &hint )
+        :   m_ref( std::make_shared<BoundLambda<LambdaT>>( ref ) ),
+            m_hint(hint)
+        {}
+
+        auto operator()( std::string const &description ) -> DerivedT & {
+            m_description = description;
+            return static_cast<DerivedT &>( *this );
+        }
+
+        auto optional() -> DerivedT & {
+            m_optionality = Optionality::Optional;
+            return static_cast<DerivedT &>( *this );
+        };
+
+        auto required() -> DerivedT & {
+            m_optionality = Optionality::Required;
+            return static_cast<DerivedT &>( *this );
+        };
+
+        auto isOptional() const -> bool {
+            return m_optionality == Optionality::Optional;
+        }
+
+        auto cardinality() const -> size_t override {
+            if( m_ref->isContainer() )
+                return 0;
+            else
+                return 1;
+        }
+
+        auto hint() const -> std::string { return m_hint; }
+    };
+
+    class ExeName : public ComposableParserImpl<ExeName> {
+        std::shared_ptr<std::string> m_name;
+        std::shared_ptr<BoundValueRefBase> m_ref;
+
+        template<typename LambdaT>
+        static auto makeRef(LambdaT const &lambda) -> std::shared_ptr<BoundValueRefBase> {
+            return std::make_shared<BoundLambda<LambdaT>>( lambda) ;
+        }
+
+    public:
+        ExeName() : m_name( std::make_shared<std::string>( "<executable>" ) ) {}
+
+        explicit ExeName( std::string &ref ) : ExeName() {
+            m_ref = std::make_shared<BoundValueRef<std::string>>( ref );
+        }
+
+        template<typename LambdaT>
+        explicit ExeName( LambdaT const& lambda ) : ExeName() {
+            m_ref = std::make_shared<BoundLambda<LambdaT>>( lambda );
+        }
+
+        // The exe name is not parsed out of the normal tokens, but is handled specially
+        auto parse( std::string const&, TokenStream const &tokens ) const -> InternalParseResult override {
+            return InternalParseResult::ok( ParseState( ParseResultType::NoMatch, tokens ) );
+        }
+
+        auto name() const -> std::string { return *m_name; }
+        auto set( std::string const& newName ) -> ParserResult {
+
+            auto lastSlash = newName.find_last_of( "\\/" );
+            auto filename = ( lastSlash == std::string::npos )
+                    ? newName
+                    : newName.substr( lastSlash+1 );
+
+            *m_name = filename;
+            if( m_ref )
+                return m_ref->setValue( filename );
+            else
+                return ParserResult::ok( ParseResultType::Matched );
+        }
+    };
+
+    class Arg : public ParserRefImpl<Arg> {
+    public:
+        using ParserRefImpl::ParserRefImpl;
+
+        auto parse( std::string const &, TokenStream const &tokens ) const -> InternalParseResult override {
+            auto validationResult = validate();
+            if( !validationResult )
+                return InternalParseResult( validationResult );
+
+            auto remainingTokens = tokens;
+            auto const &token = *remainingTokens;
+            if( token.type != TokenType::Argument )
+                return InternalParseResult::ok( ParseState( ParseResultType::NoMatch, remainingTokens ) );
+
+            assert( !m_ref->isFlag() );
+            auto valueRef = static_cast<detail::BoundValueRefBase*>( m_ref.get() );
+
+            auto result = valueRef->setValue( remainingTokens->token );
+            if( !result )
+                return InternalParseResult( result );
+            else
+                return InternalParseResult::ok( ParseState( ParseResultType::Matched, ++remainingTokens ) );
+        }
+    };
+
+    inline auto normaliseOpt( std::string const &optName ) -> std::string {
+#ifdef CATCH_PLATFORM_WINDOWS
+        if( optName[0] == '/' )
+            return "-" + optName.substr( 1 );
+        else
+#endif
+            return optName;
+    }
+
+    class Opt : public ParserRefImpl<Opt> {
+    protected:
+        std::vector<std::string> m_optNames;
+
+    public:
+        template<typename LambdaT>
+        explicit Opt( LambdaT const &ref ) : ParserRefImpl( std::make_shared<BoundFlagLambda<LambdaT>>( ref ) ) {}
+
+        explicit Opt( bool &ref ) : ParserRefImpl( std::make_shared<BoundFlagRef>( ref ) ) {}
+
+        template<typename LambdaT>
+        Opt( LambdaT const &ref, std::string const &hint ) : ParserRefImpl( ref, hint ) {}
+
+        template<typename T>
+        Opt( T &ref, std::string const &hint ) : ParserRefImpl( ref, hint ) {}
+
+        auto operator[]( std::string const &optName ) -> Opt & {
+            m_optNames.push_back( optName );
+            return *this;
+        }
+
+        auto getHelpColumns() const -> std::vector<HelpColumns> {
+            std::ostringstream oss;
+            bool first = true;
+            for( auto const &opt : m_optNames ) {
+                if (first)
+                    first = false;
+                else
+                    oss << ", ";
+                oss << opt;
+            }
+            if( !m_hint.empty() )
+                oss << " <" << m_hint << ">";
+            return { { oss.str(), m_description } };
+        }
+
+        auto isMatch( std::string const &optToken ) const -> bool {
+            auto normalisedToken = normaliseOpt( optToken );
+            for( auto const &name : m_optNames ) {
+                if( normaliseOpt( name ) == normalisedToken )
+                    return true;
+            }
+            return false;
+        }
+
+        using ParserBase::parse;
+
+        auto parse( std::string const&, TokenStream const &tokens ) const -> InternalParseResult override {
+            auto validationResult = validate();
+            if( !validationResult )
+                return InternalParseResult( validationResult );
+
+            auto remainingTokens = tokens;
+            if( remainingTokens && remainingTokens->type == TokenType::Option ) {
+                auto const &token = *remainingTokens;
+                if( isMatch(token.token ) ) {
+                    if( m_ref->isFlag() ) {
+                        auto flagRef = static_cast<detail::BoundFlagRefBase*>( m_ref.get() );
+                        auto result = flagRef->setFlag( true );
+                        if( !result )
+                            return InternalParseResult( result );
+                        if( result.value() == ParseResultType::ShortCircuitAll )
+                            return InternalParseResult::ok( ParseState( result.value(), remainingTokens ) );
+                    } else {
+                        auto valueRef = static_cast<detail::BoundValueRefBase*>( m_ref.get() );
+                        ++remainingTokens;
+                        if( !remainingTokens )
+                            return InternalParseResult::runtimeError( "Expected argument following " + token.token );
+                        auto const &argToken = *remainingTokens;
+                        if( argToken.type != TokenType::Argument )
+                            return InternalParseResult::runtimeError( "Expected argument following " + token.token );
+                        auto result = valueRef->setValue( argToken.token );
+                        if( !result )
+                            return InternalParseResult( result );
+                        if( result.value() == ParseResultType::ShortCircuitAll )
+                            return InternalParseResult::ok( ParseState( result.value(), remainingTokens ) );
+                    }
+                    return InternalParseResult::ok( ParseState( ParseResultType::Matched, ++remainingTokens ) );
+                }
+            }
+            return InternalParseResult::ok( ParseState( ParseResultType::NoMatch, remainingTokens ) );
+        }
+
+        auto validate() const -> Result override {
+            if( m_optNames.empty() )
+                return Result::logicError( "No options supplied to Opt" );
+            for( auto const &name : m_optNames ) {
+                if( name.empty() )
+                    return Result::logicError( "Option name cannot be empty" );
+#ifdef CATCH_PLATFORM_WINDOWS
+                if( name[0] != '-' && name[0] != '/' )
+                    return Result::logicError( "Option name must begin with '-' or '/'" );
+#else
+                if( name[0] != '-' )
+                    return Result::logicError( "Option name must begin with '-'" );
+#endif
+            }
+            return ParserRefImpl::validate();
+        }
+    };
+
+    struct Help : Opt {
+        Help( bool &showHelpFlag )
+        :   Opt([&]( bool flag ) {
+                showHelpFlag = flag;
+                return ParserResult::ok( ParseResultType::ShortCircuitAll );
+            })
+        {
+            static_cast<Opt &>( *this )
+                    ("display usage information")
+                    ["-?"]["-h"]["--help"]
+                    .optional();
+        }
+    };
+
+    struct Parser : ParserBase {
+
+        mutable ExeName m_exeName;
+        std::vector<Opt> m_options;
+        std::vector<Arg> m_args;
+
+        auto operator|=( ExeName const &exeName ) -> Parser & {
+            m_exeName = exeName;
+            return *this;
+        }
+
+        auto operator|=( Arg const &arg ) -> Parser & {
+            m_args.push_back(arg);
+            return *this;
+        }
+
+        auto operator|=( Opt const &opt ) -> Parser & {
+            m_options.push_back(opt);
+            return *this;
+        }
+
+        auto operator|=( Parser const &other ) -> Parser & {
+            m_options.insert(m_options.end(), other.m_options.begin(), other.m_options.end());
+            m_args.insert(m_args.end(), other.m_args.begin(), other.m_args.end());
+            return *this;
+        }
+
+        template<typename T>
+        auto operator|( T const &other ) const -> Parser {
+            return Parser( *this ) |= other;
+        }
+
+        // Forward deprecated interface with '+' instead of '|'
+        template<typename T>
+        auto operator+=( T const &other ) -> Parser & { return operator|=( other ); }
+        template<typename T>
+        auto operator+( T const &other ) const -> Parser { return operator|( other ); }
+
+        auto getHelpColumns() const -> std::vector<HelpColumns> {
+            std::vector<HelpColumns> cols;
+            for (auto const &o : m_options) {
+                auto childCols = o.getHelpColumns();
+                cols.insert( cols.end(), childCols.begin(), childCols.end() );
+            }
+            return cols;
+        }
+
+        void writeToStream( std::ostream &os ) const {
+            if (!m_exeName.name().empty()) {
+                os << "usage:\n" << "  " << m_exeName.name() << " ";
+                bool required = true, first = true;
+                for( auto const &arg : m_args ) {
+                    if (first)
+                        first = false;
+                    else
+                        os << " ";
+                    if( arg.isOptional() && required ) {
+                        os << "[";
+                        required = false;
+                    }
+                    os << "<" << arg.hint() << ">";
+                    if( arg.cardinality() == 0 )
+                        os << " ... ";
+                }
+                if( !required )
+                    os << "]";
+                if( !m_options.empty() )
+                    os << " options";
+                os << "\n\nwhere options are:" << std::endl;
+            }
+
+            auto rows = getHelpColumns();
+            size_t consoleWidth = CATCH_CLARA_CONFIG_CONSOLE_WIDTH;
+            size_t optWidth = 0;
+            for( auto const &cols : rows )
+                optWidth = (std::max)(optWidth, cols.left.size() + 2);
+
+            optWidth = (std::min)(optWidth, consoleWidth/2);
+
+            for( auto const &cols : rows ) {
+                auto row =
+                        TextFlow::Column( cols.left ).width( optWidth ).indent( 2 ) +
+                        TextFlow::Spacer(4) +
+                        TextFlow::Column( cols.right ).width( consoleWidth - 7 - optWidth );
+                os << row << std::endl;
+            }
+        }
+
+        friend auto operator<<( std::ostream &os, Parser const &parser ) -> std::ostream& {
+            parser.writeToStream( os );
+            return os;
+        }
+
+        auto validate() const -> Result override {
+            for( auto const &opt : m_options ) {
+                auto result = opt.validate();
+                if( !result )
+                    return result;
+            }
+            for( auto const &arg : m_args ) {
+                auto result = arg.validate();
+                if( !result )
+                    return result;
+            }
+            return Result::ok();
+        }
+
+        using ParserBase::parse;
+
+        auto parse( std::string const& exeName, TokenStream const &tokens ) const -> InternalParseResult override {
+
+            struct ParserInfo {
+                ParserBase const* parser = nullptr;
+                size_t count = 0;
+            };
+            const size_t totalParsers = m_options.size() + m_args.size();
+            assert( totalParsers < 512 );
+            // ParserInfo parseInfos[totalParsers]; // <-- this is what we really want to do
+            ParserInfo parseInfos[512];
+
+            {
+                size_t i = 0;
+                for (auto const &opt : m_options) parseInfos[i++].parser = &opt;
+                for (auto const &arg : m_args) parseInfos[i++].parser = &arg;
+            }
+
+            m_exeName.set( exeName );
+
+            auto result = InternalParseResult::ok( ParseState( ParseResultType::NoMatch, tokens ) );
+            while( result.value().remainingTokens() ) {
+                bool tokenParsed = false;
+
+                for( size_t i = 0; i < totalParsers; ++i ) {
+                    auto&  parseInfo = parseInfos[i];
+                    if( parseInfo.parser->cardinality() == 0 || parseInfo.count < parseInfo.parser->cardinality() ) {
+                        result = parseInfo.parser->parse(exeName, result.value().remainingTokens());
+                        if (!result)
+                            return result;
+                        if (result.value().type() != ParseResultType::NoMatch) {
+                            tokenParsed = true;
+                            ++parseInfo.count;
+                            break;
+                        }
+                    }
+                }
+
+                if( result.value().type() == ParseResultType::ShortCircuitAll )
+                    return result;
+                if( !tokenParsed )
+                    return InternalParseResult::runtimeError( "Unrecognised token: " + result.value().remainingTokens()->token );
+            }
+            // !TBD Check missing required options
+            return result;
+        }
+    };
+
+    template<typename DerivedT>
+    template<typename T>
+    auto ComposableParserImpl<DerivedT>::operator|( T const &other ) const -> Parser {
+        return Parser() | static_cast<DerivedT const &>( *this ) | other;
+    }
+} // namespace detail
+
+// A Combined parser
+using detail::Parser;
+
+// A parser for options
+using detail::Opt;
+
+// A parser for arguments
+using detail::Arg;
+
+// Wrapper for argc, argv from main()
+using detail::Args;
+
+// Specifies the name of the executable
+using detail::ExeName;
+
+// Convenience wrapper for option parser that specifies the help option
+using detail::Help;
+
+// enum of result types from a parse
+using detail::ParseResultType;
+
+// Result type for parser operation
+using detail::ParserResult;
+
+}} // namespace Catch::clara
+
+// end clara.hpp
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+// Restore Clara's value for console width, if present
+#ifdef CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
+#define CATCH_CLARA_TEXTFLOW_CONFIG_CONSOLE_WIDTH CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
+#undef CATCH_TEMP_CLARA_CONFIG_CONSOLE_WIDTH
+#endif
+
+// end catch_clara.h
+namespace Catch {
+
+    clara::Parser makeCommandLineParser( ConfigData& config );
+
+} // end namespace Catch
+
+// end catch_commandline.h
+#include <fstream>
+#include <ctime>
+
+namespace Catch {
+
+    clara::Parser makeCommandLineParser( ConfigData& config ) {
+
+        using namespace clara;
+
+        auto const setWarning = [&]( std::string const& warning ) {
+                auto warningSet = [&]() {
+                    if( warning == "NoAssertions" )
+                        return WarnAbout::NoAssertions;
+
+                    if ( warning == "NoTests" )
+                        return WarnAbout::NoTests;
+
+                    return WarnAbout::Nothing;
+                }();
+
+                if (warningSet == WarnAbout::Nothing)
+                    return ParserResult::runtimeError( "Unrecognised warning: '" + warning + "'" );
+                config.warnings = static_cast<WarnAbout::What>( config.warnings | warningSet );
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const loadTestNamesFromFile = [&]( std::string const& filename ) {
+                std::ifstream f( filename.c_str() );
+                if( !f.is_open() )
+                    return ParserResult::runtimeError( "Unable to load input file: '" + filename + "'" );
+
+                std::string line;
+                while( std::getline( f, line ) ) {
+                    line = trim(line);
+                    if( !line.empty() && !startsWith( line, '#' ) ) {
+                        if( !startsWith( line, '"' ) )
+                            line = '"' + line + '"';
+                        config.testsOrTags.push_back( line + ',' );
+                    }
+                }
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setTestOrder = [&]( std::string const& order ) {
+                if( startsWith( "declared", order ) )
+                    config.runOrder = RunTests::InDeclarationOrder;
+                else if( startsWith( "lexical", order ) )
+                    config.runOrder = RunTests::InLexicographicalOrder;
+                else if( startsWith( "random", order ) )
+                    config.runOrder = RunTests::InRandomOrder;
+                else
+                    return clara::ParserResult::runtimeError( "Unrecognised ordering: '" + order + "'" );
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setRngSeed = [&]( std::string const& seed ) {
+                if( seed != "time" )
+                    return clara::detail::convertInto( seed, config.rngSeed );
+                config.rngSeed = static_cast<unsigned int>( std::time(nullptr) );
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setColourUsage = [&]( std::string const& useColour ) {
+                    auto mode = toLower( useColour );
+
+                    if( mode == "yes" )
+                        config.useColour = UseColour::Yes;
+                    else if( mode == "no" )
+                        config.useColour = UseColour::No;
+                    else if( mode == "auto" )
+                        config.useColour = UseColour::Auto;
+                    else
+                        return ParserResult::runtimeError( "colour mode must be one of: auto, yes or no. '" + useColour + "' not recognised" );
+                return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setWaitForKeypress = [&]( std::string const& keypress ) {
+                auto keypressLc = toLower( keypress );
+                if( keypressLc == "start" )
+                    config.waitForKeypress = WaitForKeypress::BeforeStart;
+                else if( keypressLc == "exit" )
+                    config.waitForKeypress = WaitForKeypress::BeforeExit;
+                else if( keypressLc == "both" )
+                    config.waitForKeypress = WaitForKeypress::BeforeStartAndExit;
+                else
+                    return ParserResult::runtimeError( "keypress argument must be one of: start, exit or both. '" + keypress + "' not recognised" );
+            return ParserResult::ok( ParseResultType::Matched );
+            };
+        auto const setVerbosity = [&]( std::string const& verbosity ) {
+            auto lcVerbosity = toLower( verbosity );
+            if( lcVerbosity == "quiet" )
+                config.verbosity = Verbosity::Quiet;
+            else if( lcVerbosity == "normal" )
+                config.verbosity = Verbosity::Normal;
+            else if( lcVerbosity == "high" )
+                config.verbosity = Verbosity::High;
+            else
+                return ParserResult::runtimeError( "Unrecognised verbosity, '" + verbosity + "'" );
+            return ParserResult::ok( ParseResultType::Matched );
+        };
+
+        auto cli
+            = ExeName( config.processName )
+            | Help( config.showHelp )
+            | Opt( config.listTests )
+                ["-l"]["--list-tests"]
+                ( "list all/matching test cases" )
+            | Opt( config.listTags )
+                ["-t"]["--list-tags"]
+                ( "list all/matching tags" )
+            | Opt( config.showSuccessfulTests )
+                ["-s"]["--success"]
+                ( "include successful tests in output" )
+            | Opt( config.shouldDebugBreak )
+                ["-b"]["--break"]
+                ( "break into debugger on failure" )
+            | Opt( config.noThrow )
+                ["-e"]["--nothrow"]
+                ( "skip exception tests" )
+            | Opt( config.showInvisibles )
+                ["-i"]["--invisibles"]
+                ( "show invisibles (tabs, newlines)" )
+            | Opt( config.outputFilename, "filename" )
+                ["-o"]["--out"]
+                ( "output filename" )
+            | Opt( config.reporterName, "name" )
+                ["-r"]["--reporter"]
+                ( "reporter to use (defaults to console)" )
+            | Opt( config.name, "name" )
+                ["-n"]["--name"]
+                ( "suite name" )
+            | Opt( [&]( bool ){ config.abortAfter = 1; } )
+                ["-a"]["--abort"]
+                ( "abort at first failure" )
+            | Opt( [&]( int x ){ config.abortAfter = x; }, "no. failures" )
+                ["-x"]["--abortx"]
+                ( "abort after x failures" )
+            | Opt( setWarning, "warning name" )
+                ["-w"]["--warn"]
+                ( "enable warnings" )
+            | Opt( [&]( bool flag ) { config.showDurations = flag ? ShowDurations::Always : ShowDurations::Never; }, "yes|no" )
+                ["-d"]["--durations"]
+                ( "show test durations" )
+            | Opt( loadTestNamesFromFile, "filename" )
+                ["-f"]["--input-file"]
+                ( "load test names to run from a file" )
+            | Opt( config.filenamesAsTags )
+                ["-#"]["--filenames-as-tags"]
+                ( "adds a tag for the filename" )
+            | Opt( config.sectionsToRun, "section name" )
+                ["-c"]["--section"]
+                ( "specify section to run" )
+            | Opt( setVerbosity, "quiet|normal|high" )
+                ["-v"]["--verbosity"]
+                ( "set output verbosity" )
+            | Opt( config.listTestNamesOnly )
+                ["--list-test-names-only"]
+                ( "list all/matching test cases names only" )
+            | Opt( config.listReporters )
+                ["--list-reporters"]
+                ( "list all reporters" )
+            | Opt( setTestOrder, "decl|lex|rand" )
+                ["--order"]
+                ( "test case order (defaults to decl)" )
+            | Opt( setRngSeed, "'time'|number" )
+                ["--rng-seed"]
+                ( "set a specific seed for random numbers" )
+            | Opt( setColourUsage, "yes|no" )
+                ["--use-colour"]
+                ( "should output be colourised" )
+            | Opt( config.libIdentify )
+                ["--libidentify"]
+                ( "report name and version according to libidentify standard" )
+            | Opt( setWaitForKeypress, "start|exit|both" )
+                ["--wait-for-keypress"]
+                ( "waits for a keypress before exiting" )
+            | Opt( config.benchmarkResolutionMultiple, "multiplier" )
+                ["--benchmark-resolution-multiple"]
+                ( "multiple of clock resolution to run benchmarks" )
+
+            | Arg( config.testsOrTags, "test name|pattern|tags" )
+                ( "which test or tests to use" );
+
+        return cli;
+    }
+
+} // end namespace Catch
+// end catch_commandline.cpp
+// start catch_common.cpp
+
+#include <cstring>
+#include <ostream>
+
+namespace Catch {
+
+    bool SourceLineInfo::empty() const noexcept {
+        return file[0] == '\0';
+    }
+    bool SourceLineInfo::operator == ( SourceLineInfo const& other ) const noexcept {
+        return line == other.line && (file == other.file || std::strcmp(file, other.file) == 0);
+    }
+    bool SourceLineInfo::operator < ( SourceLineInfo const& other ) const noexcept {
+        // We can assume that the same file will usually have the same pointer.
+        // Thus, if the pointers are the same, there is no point in calling the strcmp
+        return line < other.line || ( line == other.line && file != other.file && (std::strcmp(file, other.file) < 0));
+    }
+
+    std::ostream& operator << ( std::ostream& os, SourceLineInfo const& info ) {
+#ifndef __GNUG__
+        os << info.file << '(' << info.line << ')';
+#else
+        os << info.file << ':' << info.line;
+#endif
+        return os;
+    }
+
+    std::string StreamEndStop::operator+() const {
+        return std::string();
+    }
+
+    NonCopyable::NonCopyable() = default;
+    NonCopyable::~NonCopyable() = default;
+
+}
+// end catch_common.cpp
+// start catch_config.cpp
+
+namespace Catch {
+
+    Config::Config( ConfigData const& data )
+    :   m_data( data ),
+        m_stream( openStream() )
+    {
+        TestSpecParser parser(ITagAliasRegistry::get());
+        if (data.testsOrTags.empty()) {
+            parser.parse("~[.]"); // All not hidden tests
+        }
+        else {
+            m_hasTestFilters = true;
+            for( auto const& testOrTags : data.testsOrTags )
+                parser.parse( testOrTags );
+        }
+        m_testSpec = parser.testSpec();
+    }
+
+    std::string const& Config::getFilename() const {
+        return m_data.outputFilename ;
+    }
+
+    bool Config::listTests() const          { return m_data.listTests; }
+    bool Config::listTestNamesOnly() const  { return m_data.listTestNamesOnly; }
+    bool Config::listTags() const           { return m_data.listTags; }
+    bool Config::listReporters() const      { return m_data.listReporters; }
+
+    std::string Config::getProcessName() const { return m_data.processName; }
+    std::string const& Config::getReporterName() const { return m_data.reporterName; }
+
+    std::vector<std::string> const& Config::getTestsOrTags() const { return m_data.testsOrTags; }
+    std::vector<std::string> const& Config::getSectionsToRun() const { return m_data.sectionsToRun; }
+
+    TestSpec const& Config::testSpec() const { return m_testSpec; }
+    bool Config::hasTestFilters() const { return m_hasTestFilters; }
+
+    bool Config::showHelp() const { return m_data.showHelp; }
+
+    // IConfig interface
+    bool Config::allowThrows() const                   { return !m_data.noThrow; }
+    std::ostream& Config::stream() const               { return m_stream->stream(); }
+    std::string Config::name() const                   { return m_data.name.empty() ? m_data.processName : m_data.name; }
+    bool Config::includeSuccessfulResults() const      { return m_data.showSuccessfulTests; }
+    bool Config::warnAboutMissingAssertions() const    { return !!(m_data.warnings & WarnAbout::NoAssertions); }
+    bool Config::warnAboutNoTests() const              { return !!(m_data.warnings & WarnAbout::NoTests); }
+    ShowDurations::OrNot Config::showDurations() const { return m_data.showDurations; }
+    RunTests::InWhatOrder Config::runOrder() const     { return m_data.runOrder; }
+    unsigned int Config::rngSeed() const               { return m_data.rngSeed; }
+    int Config::benchmarkResolutionMultiple() const    { return m_data.benchmarkResolutionMultiple; }
+    UseColour::YesOrNo Config::useColour() const       { return m_data.useColour; }
+    bool Config::shouldDebugBreak() const              { return m_data.shouldDebugBreak; }
+    int Config::abortAfter() const                     { return m_data.abortAfter; }
+    bool Config::showInvisibles() const                { return m_data.showInvisibles; }
+    Verbosity Config::verbosity() const                { return m_data.verbosity; }
+
+    IStream const* Config::openStream() {
+        return Catch::makeStream(m_data.outputFilename);
+    }
+
+} // end namespace Catch
+// end catch_config.cpp
+// start catch_console_colour.cpp
+
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wexit-time-destructors"
+#endif
+
+// start catch_errno_guard.h
+
+namespace Catch {
+
+    class ErrnoGuard {
+    public:
+        ErrnoGuard();
+        ~ErrnoGuard();
+    private:
+        int m_oldErrno;
+    };
+
+}
+
+// end catch_errno_guard.h
+#include <sstream>
+
+namespace Catch {
+    namespace {
+
+        struct IColourImpl {
+            virtual ~IColourImpl() = default;
+            virtual void use( Colour::Code _colourCode ) = 0;
+        };
+
+        struct NoColourImpl : IColourImpl {
+            void use( Colour::Code ) {}
+
+            static IColourImpl* instance() {
+                static NoColourImpl s_instance;
+                return &s_instance;
+            }
+        };
+
+    } // anon namespace
+} // namespace Catch
+
+#if !defined( CATCH_CONFIG_COLOUR_NONE ) && !defined( CATCH_CONFIG_COLOUR_WINDOWS ) && !defined( CATCH_CONFIG_COLOUR_ANSI )
+#   ifdef CATCH_PLATFORM_WINDOWS
+#       define CATCH_CONFIG_COLOUR_WINDOWS
+#   else
+#       define CATCH_CONFIG_COLOUR_ANSI
+#   endif
+#endif
+
+#if defined ( CATCH_CONFIG_COLOUR_WINDOWS ) /////////////////////////////////////////
+
+namespace Catch {
+namespace {
+
+    class Win32ColourImpl : public IColourImpl {
+    public:
+        Win32ColourImpl() : stdoutHandle( GetStdHandle(STD_OUTPUT_HANDLE) )
+        {
+            CONSOLE_SCREEN_BUFFER_INFO csbiInfo;
+            GetConsoleScreenBufferInfo( stdoutHandle, &csbiInfo );
+            originalForegroundAttributes = csbiInfo.wAttributes & ~( BACKGROUND_GREEN | BACKGROUND_RED | BACKGROUND_BLUE | BACKGROUND_INTENSITY );
+            originalBackgroundAttributes = csbiInfo.wAttributes & ~( FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE | FOREGROUND_INTENSITY );
+        }
+
+        virtual void use( Colour::Code _colourCode ) override {
+            switch( _colourCode ) {
+                case Colour::None:      return setTextAttribute( originalForegroundAttributes );
+                case Colour::White:     return setTextAttribute( FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE );
+                case Colour::Red:       return setTextAttribute( FOREGROUND_RED );
+                case Colour::Green:     return setTextAttribute( FOREGROUND_GREEN );
+                case Colour::Blue:      return setTextAttribute( FOREGROUND_BLUE );
+                case Colour::Cyan:      return setTextAttribute( FOREGROUND_BLUE | FOREGROUND_GREEN );
+                case Colour::Yellow:    return setTextAttribute( FOREGROUND_RED | FOREGROUND_GREEN );
+                case Colour::Grey:      return setTextAttribute( 0 );
+
+                case Colour::LightGrey:     return setTextAttribute( FOREGROUND_INTENSITY );
+                case Colour::BrightRed:     return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_RED );
+                case Colour::BrightGreen:   return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_GREEN );
+                case Colour::BrightWhite:   return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_BLUE );
+                case Colour::BrightYellow:  return setTextAttribute( FOREGROUND_INTENSITY | FOREGROUND_RED | FOREGROUND_GREEN );
+
+                case Colour::Bright: CATCH_INTERNAL_ERROR( "not a colour" );
+
+                default:
+                    CATCH_ERROR( "Unknown colour requested" );
+            }
+        }
+
+    private:
+        void setTextAttribute( WORD _textAttribute ) {
+            SetConsoleTextAttribute( stdoutHandle, _textAttribute | originalBackgroundAttributes );
+        }
+        HANDLE stdoutHandle;
+        WORD originalForegroundAttributes;
+        WORD originalBackgroundAttributes;
+    };
+
+    IColourImpl* platformColourInstance() {
+        static Win32ColourImpl s_instance;
+
+        IConfigPtr config = getCurrentContext().getConfig();
+        UseColour::YesOrNo colourMode = config
+            ? config->useColour()
+            : UseColour::Auto;
+        if( colourMode == UseColour::Auto )
+            colourMode = UseColour::Yes;
+        return colourMode == UseColour::Yes
+            ? &s_instance
+            : NoColourImpl::instance();
+    }
+
+} // end anon namespace
+} // end namespace Catch
+
+#elif defined( CATCH_CONFIG_COLOUR_ANSI ) //////////////////////////////////////
+
+#include <unistd.h>
+
+namespace Catch {
+namespace {
+
+    // use POSIX/ ANSI console terminal codes
+    // Thanks to Adam Strzelecki for original contribution
+    // (http://github.com/nanoant)
+    // https://github.com/philsquared/Catch/pull/131
+    class PosixColourImpl : public IColourImpl {
+    public:
+        virtual void use( Colour::Code _colourCode ) override {
+            switch( _colourCode ) {
+                case Colour::None:
+                case Colour::White:     return setColour( "[0m" );
+                case Colour::Red:       return setColour( "[0;31m" );
+                case Colour::Green:     return setColour( "[0;32m" );
+                case Colour::Blue:      return setColour( "[0;34m" );
+                case Colour::Cyan:      return setColour( "[0;36m" );
+                case Colour::Yellow:    return setColour( "[0;33m" );
+                case Colour::Grey:      return setColour( "[1;30m" );
+
+                case Colour::LightGrey:     return setColour( "[0;37m" );
+                case Colour::BrightRed:     return setColour( "[1;31m" );
+                case Colour::BrightGreen:   return setColour( "[1;32m" );
+                case Colour::BrightWhite:   return setColour( "[1;37m" );
+                case Colour::BrightYellow:  return setColour( "[1;33m" );
+
+                case Colour::Bright: CATCH_INTERNAL_ERROR( "not a colour" );
+                default: CATCH_INTERNAL_ERROR( "Unknown colour requested" );
+            }
+        }
+        static IColourImpl* instance() {
+            static PosixColourImpl s_instance;
+            return &s_instance;
+        }
+
+    private:
+        void setColour( const char* _escapeCode ) {
+            Catch::cout() << '\033' << _escapeCode;
+        }
+    };
+
+    bool useColourOnPlatform() {
+        return
+#ifdef CATCH_PLATFORM_MAC
+            !isDebuggerActive() &&
+#endif
+#if !(defined(__DJGPP__) && defined(__STRICT_ANSI__))
+            isatty(STDOUT_FILENO)
+#else
+            false
+#endif
+            ;
+    }
+    IColourImpl* platformColourInstance() {
+        ErrnoGuard guard;
+        IConfigPtr config = getCurrentContext().getConfig();
+        UseColour::YesOrNo colourMode = config
+            ? config->useColour()
+            : UseColour::Auto;
+        if( colourMode == UseColour::Auto )
+            colourMode = useColourOnPlatform()
+                ? UseColour::Yes
+                : UseColour::No;
+        return colourMode == UseColour::Yes
+            ? PosixColourImpl::instance()
+            : NoColourImpl::instance();
+    }
+
+} // end anon namespace
+} // end namespace Catch
+
+#else  // not Windows or ANSI ///////////////////////////////////////////////
+
+namespace Catch {
+
+    static IColourImpl* platformColourInstance() { return NoColourImpl::instance(); }
+
+} // end namespace Catch
+
+#endif // Windows/ ANSI/ None
+
+namespace Catch {
+
+    Colour::Colour( Code _colourCode ) { use( _colourCode ); }
+    Colour::Colour( Colour&& rhs ) noexcept {
+        m_moved = rhs.m_moved;
+        rhs.m_moved = true;
+    }
+    Colour& Colour::operator=( Colour&& rhs ) noexcept {
+        m_moved = rhs.m_moved;
+        rhs.m_moved  = true;
+        return *this;
+    }
+
+    Colour::~Colour(){ if( !m_moved ) use( None ); }
+
+    void Colour::use( Code _colourCode ) {
+        static IColourImpl* impl = platformColourInstance();
+        impl->use( _colourCode );
+    }
+
+    std::ostream& operator << ( std::ostream& os, Colour const& ) {
+        return os;
+    }
+
+} // end namespace Catch
+
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
+
+// end catch_console_colour.cpp
+// start catch_context.cpp
+
+namespace Catch {
+
+    class Context : public IMutableContext, NonCopyable {
+
+    public: // IContext
+        virtual IResultCapture* getResultCapture() override {
+            return m_resultCapture;
+        }
+        virtual IRunner* getRunner() override {
+            return m_runner;
+        }
+
+        virtual IConfigPtr const& getConfig() const override {
+            return m_config;
+        }
+
+        virtual ~Context() override;
+
+    public: // IMutableContext
+        virtual void setResultCapture( IResultCapture* resultCapture ) override {
+            m_resultCapture = resultCapture;
+        }
+        virtual void setRunner( IRunner* runner ) override {
+            m_runner = runner;
+        }
+        virtual void setConfig( IConfigPtr const& config ) override {
+            m_config = config;
+        }
+
+        friend IMutableContext& getCurrentMutableContext();
+
+    private:
+        IConfigPtr m_config;
+        IRunner* m_runner = nullptr;
+        IResultCapture* m_resultCapture = nullptr;
+    };
+
+    IMutableContext *IMutableContext::currentContext = nullptr;
+
+    void IMutableContext::createContext()
+    {
+        currentContext = new Context();
+    }
+
+    void cleanUpContext() {
+        delete IMutableContext::currentContext;
+        IMutableContext::currentContext = nullptr;
+    }
+    IContext::~IContext() = default;
+    IMutableContext::~IMutableContext() = default;
+    Context::~Context() = default;
+}
+// end catch_context.cpp
+// start catch_debug_console.cpp
+
+// start catch_debug_console.h
+
+#include <string>
+
+namespace Catch {
+    void writeToDebugConsole( std::string const& text );
+}
+
+// end catch_debug_console.h
+#ifdef CATCH_PLATFORM_WINDOWS
+
+    namespace Catch {
+        void writeToDebugConsole( std::string const& text ) {
+            ::OutputDebugStringA( text.c_str() );
+        }
+    }
+
+#else
+
+    namespace Catch {
+        void writeToDebugConsole( std::string const& text ) {
+            // !TBD: Need a version for Mac/ XCode and other IDEs
+            Catch::cout() << text;
+        }
+    }
+
+#endif // Platform
+// end catch_debug_console.cpp
+// start catch_debugger.cpp
+
+#ifdef CATCH_PLATFORM_MAC
+
+#  include <assert.h>
+#  include <stdbool.h>
+#  include <sys/types.h>
+#  include <unistd.h>
+#  include <sys/sysctl.h>
+#  include <cstddef>
+#  include <ostream>
+
+namespace Catch {
+
+        // The following function is taken directly from the following technical note:
+        // http://developer.apple.com/library/mac/#qa/qa2004/qa1361.html
+
+        // Returns true if the current process is being debugged (either
+        // running under the debugger or has a debugger attached post facto).
+        bool isDebuggerActive(){
+
+            int                 mib[4];
+            struct kinfo_proc   info;
+            std::size_t         size;
+
+            // Initialize the flags so that, if sysctl fails for some bizarre
+            // reason, we get a predictable result.
+
+            info.kp_proc.p_flag = 0;
+
+            // Initialize mib, which tells sysctl the info we want, in this case
+            // we're looking for information about a specific process ID.
+
+            mib[0] = CTL_KERN;
+            mib[1] = KERN_PROC;
+            mib[2] = KERN_PROC_PID;
+            mib[3] = getpid();
+
+            // Call sysctl.
+
+            size = sizeof(info);
+            if( sysctl(mib, sizeof(mib) / sizeof(*mib), &info, &size, nullptr, 0) != 0 ) {
+                Catch::cerr() << "\n** Call to sysctl failed - unable to determine if debugger is active **\n" << std::endl;
+                return false;
+            }
+
+            // We're being debugged if the P_TRACED flag is set.
+
+            return ( (info.kp_proc.p_flag & P_TRACED) != 0 );
+        }
+    } // namespace Catch
+
+#elif defined(CATCH_PLATFORM_LINUX)
+    #include <fstream>
+    #include <string>
+
+    namespace Catch{
+        // The standard POSIX way of detecting a debugger is to attempt to
+        // ptrace() the process, but this needs to be done from a child and not
+        // this process itself to still allow attaching to this process later
+        // if wanted, so is rather heavy. Under Linux we have the PID of the
+        // "debugger" (which doesn't need to be gdb, of course, it could also
+        // be strace, for example) in /proc/$PID/status, so just get it from
+        // there instead.
+        bool isDebuggerActive(){
+            // Libstdc++ has a bug, where std::ifstream sets errno to 0
+            // This way our users can properly assert over errno values
+            ErrnoGuard guard;
+            std::ifstream in("/proc/self/status");
+            for( std::string line; std::getline(in, line); ) {
+                static const int PREFIX_LEN = 11;
+                if( line.compare(0, PREFIX_LEN, "TracerPid:\t") == 0 ) {
+                    // We're traced if the PID is not 0 and no other PID starts
+                    // with 0 digit, so it's enough to check for just a single
+                    // character.
+                    return line.length() > PREFIX_LEN && line[PREFIX_LEN] != '0';
+                }
+            }
+
+            return false;
+        }
+    } // namespace Catch
+#elif defined(_MSC_VER)
+    extern "C" __declspec(dllimport) int __stdcall IsDebuggerPresent();
+    namespace Catch {
+        bool isDebuggerActive() {
+            return IsDebuggerPresent() != 0;
+        }
+    }
+#elif defined(__MINGW32__)
+    extern "C" __declspec(dllimport) int __stdcall IsDebuggerPresent();
+    namespace Catch {
+        bool isDebuggerActive() {
+            return IsDebuggerPresent() != 0;
+        }
+    }
+#else
+    namespace Catch {
+       bool isDebuggerActive() { return false; }
+    }
+#endif // Platform
+// end catch_debugger.cpp
+// start catch_decomposer.cpp
+
+namespace Catch {
+
+    ITransientExpression::~ITransientExpression() = default;
+
+    void formatReconstructedExpression( std::ostream &os, std::string const& lhs, StringRef op, std::string const& rhs ) {
+        if( lhs.size() + rhs.size() < 40 &&
+                lhs.find('\n') == std::string::npos &&
+                rhs.find('\n') == std::string::npos )
+            os << lhs << " " << op << " " << rhs;
+        else
+            os << lhs << "\n" << op << "\n" << rhs;
+    }
+}
+// end catch_decomposer.cpp
+// start catch_enforce.cpp
+
+namespace Catch {
+#if defined(CATCH_CONFIG_DISABLE_EXCEPTIONS) && !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS_CUSTOM_HANDLER)
+    [[noreturn]]
+    void throw_exception(std::exception const& e) {
+        Catch::cerr() << "Catch will terminate because it needed to throw an exception.\n"
+                      << "The message was: " << e.what() << '\n';
+        std::terminate();
+    }
+#endif
+} // namespace Catch;
+// end catch_enforce.cpp
+// start catch_errno_guard.cpp
+
+#include <cerrno>
+
+namespace Catch {
+        ErrnoGuard::ErrnoGuard():m_oldErrno(errno){}
+        ErrnoGuard::~ErrnoGuard() { errno = m_oldErrno; }
+}
+// end catch_errno_guard.cpp
+// start catch_exception_translator_registry.cpp
+
+// start catch_exception_translator_registry.h
+
+#include <vector>
+#include <string>
+#include <memory>
+
+namespace Catch {
+
+    class ExceptionTranslatorRegistry : public IExceptionTranslatorRegistry {
+    public:
+        ~ExceptionTranslatorRegistry();
+        virtual void registerTranslator( const IExceptionTranslator* translator );
+        virtual std::string translateActiveException() const override;
+        std::string tryTranslators() const;
+
+    private:
+        std::vector<std::unique_ptr<IExceptionTranslator const>> m_translators;
+    };
+}
+
+// end catch_exception_translator_registry.h
+#ifdef __OBJC__
+#import "Foundation/Foundation.h"
+#endif
+
+namespace Catch {
+
+    ExceptionTranslatorRegistry::~ExceptionTranslatorRegistry() {
+    }
+
+    void ExceptionTranslatorRegistry::registerTranslator( const IExceptionTranslator* translator ) {
+        m_translators.push_back( std::unique_ptr<const IExceptionTranslator>( translator ) );
+    }
+
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+    std::string ExceptionTranslatorRegistry::translateActiveException() const {
+        try {
+#ifdef __OBJC__
+            // In Objective-C try objective-c exceptions first
+            @try {
+                return tryTranslators();
+            }
+            @catch (NSException *exception) {
+                return Catch::Detail::stringify( [exception description] );
+            }
+#else
+            // Compiling a mixed mode project with MSVC means that CLR
+            // exceptions will be caught in (...) as well. However, these
+            // do not fill-in std::current_exception and thus lead to crash
+            // when attempting rethrow.
+            // /EHa switch also causes structured exceptions to be caught
+            // here, but they fill-in current_exception properly, so
+            // at worst the output should be a little weird, instead of
+            // causing a crash.
+            if (std::current_exception() == nullptr) {
+                return "Non C++ exception. Possibly a CLR exception.";
+            }
+            return tryTranslators();
+#endif
+        }
+        catch( TestFailureException& ) {
+            std::rethrow_exception(std::current_exception());
+        }
+        catch( std::exception& ex ) {
+            return ex.what();
+        }
+        catch( std::string& msg ) {
+            return msg;
+        }
+        catch( const char* msg ) {
+            return msg;
+        }
+        catch(...) {
+            return "Unknown exception";
+        }
+    }
+
+#else // ^^ Exceptions are enabled // Exceptions are disabled vv
+    std::string ExceptionTranslatorRegistry::translateActiveException() const {
+        CATCH_INTERNAL_ERROR("Attempted to translate active exception under CATCH_CONFIG_DISABLE_EXCEPTIONS!");
+    }
+#endif
+
+    std::string ExceptionTranslatorRegistry::tryTranslators() const {
+        if( m_translators.empty() )
+            std::rethrow_exception(std::current_exception());
+        else
+            return m_translators[0]->translate( m_translators.begin()+1, m_translators.end() );
+    }
+}
+// end catch_exception_translator_registry.cpp
+// start catch_fatal_condition.cpp
+
+#if defined(__GNUC__)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wmissing-field-initializers"
+#endif
+
+#if defined( CATCH_CONFIG_WINDOWS_SEH ) || defined( CATCH_CONFIG_POSIX_SIGNALS )
+
+namespace {
+    // Report the error condition
+    void reportFatal( char const * const message ) {
+        Catch::getCurrentContext().getResultCapture()->handleFatalErrorCondition( message );
+    }
+}
+
+#endif // signals/SEH handling
+
+#if defined( CATCH_CONFIG_WINDOWS_SEH )
+
+namespace Catch {
+    struct SignalDefs { DWORD id; const char* name; };
+
+    // There is no 1-1 mapping between signals and windows exceptions.
+    // Windows can easily distinguish between SO and SigSegV,
+    // but SigInt, SigTerm, etc are handled differently.
+    static SignalDefs signalDefs[] = {
+        { EXCEPTION_ILLEGAL_INSTRUCTION,  "SIGILL - Illegal instruction signal" },
+        { EXCEPTION_STACK_OVERFLOW, "SIGSEGV - Stack overflow" },
+        { EXCEPTION_ACCESS_VIOLATION, "SIGSEGV - Segmentation violation signal" },
+        { EXCEPTION_INT_DIVIDE_BY_ZERO, "Divide by zero error" },
+    };
+
+    LONG CALLBACK FatalConditionHandler::handleVectoredException(PEXCEPTION_POINTERS ExceptionInfo) {
+        for (auto const& def : signalDefs) {
+            if (ExceptionInfo->ExceptionRecord->ExceptionCode == def.id) {
+                reportFatal(def.name);
+            }
+        }
+        // If its not an exception we care about, pass it along.
+        // This stops us from eating debugger breaks etc.
+        return EXCEPTION_CONTINUE_SEARCH;
+    }
+
+    FatalConditionHandler::FatalConditionHandler() {
+        isSet = true;
+        // 32k seems enough for Catch to handle stack overflow,
+        // but the value was found experimentally, so there is no strong guarantee
+        guaranteeSize = 32 * 1024;
+        exceptionHandlerHandle = nullptr;
+        // Register as first handler in current chain
+        exceptionHandlerHandle = AddVectoredExceptionHandler(1, handleVectoredException);
+        // Pass in guarantee size to be filled
+        SetThreadStackGuarantee(&guaranteeSize);
+    }
+
+    void FatalConditionHandler::reset() {
+        if (isSet) {
+            RemoveVectoredExceptionHandler(exceptionHandlerHandle);
+            SetThreadStackGuarantee(&guaranteeSize);
+            exceptionHandlerHandle = nullptr;
+            isSet = false;
+        }
+    }
+
+    FatalConditionHandler::~FatalConditionHandler() {
+        reset();
+    }
+
+bool FatalConditionHandler::isSet = false;
+ULONG FatalConditionHandler::guaranteeSize = 0;
+PVOID FatalConditionHandler::exceptionHandlerHandle = nullptr;
+
+} // namespace Catch
+
+#elif defined( CATCH_CONFIG_POSIX_SIGNALS )
+
+namespace Catch {
+
+    struct SignalDefs {
+        int id;
+        const char* name;
+    };
+
+    // 32kb for the alternate stack seems to be sufficient. However, this value
+    // is experimentally determined, so that's not guaranteed.
+    constexpr static std::size_t sigStackSize = 32768 >= MINSIGSTKSZ ? 32768 : MINSIGSTKSZ;
+
+    static SignalDefs signalDefs[] = {
+        { SIGINT,  "SIGINT - Terminal interrupt signal" },
+        { SIGILL,  "SIGILL - Illegal instruction signal" },
+        { SIGFPE,  "SIGFPE - Floating point error signal" },
+        { SIGSEGV, "SIGSEGV - Segmentation violation signal" },
+        { SIGTERM, "SIGTERM - Termination request signal" },
+        { SIGABRT, "SIGABRT - Abort (abnormal termination) signal" }
+    };
+
+    void FatalConditionHandler::handleSignal( int sig ) {
+        char const * name = "<unknown signal>";
+        for (auto const& def : signalDefs) {
+            if (sig == def.id) {
+                name = def.name;
+                break;
+            }
+        }
+        reset();
+        reportFatal(name);
+        raise( sig );
+    }
+
+    FatalConditionHandler::FatalConditionHandler() {
+        isSet = true;
+        stack_t sigStack;
+        sigStack.ss_sp = altStackMem;
+        sigStack.ss_size = sigStackSize;
+        sigStack.ss_flags = 0;
+        sigaltstack(&sigStack, &oldSigStack);
+        struct sigaction sa = { };
+
+        sa.sa_handler = handleSignal;
+        sa.sa_flags = SA_ONSTACK;
+        for (std::size_t i = 0; i < sizeof(signalDefs)/sizeof(SignalDefs); ++i) {
+            sigaction(signalDefs[i].id, &sa, &oldSigActions[i]);
+        }
+    }
+
+    FatalConditionHandler::~FatalConditionHandler() {
+        reset();
+    }
+
+    void FatalConditionHandler::reset() {
+        if( isSet ) {
+            // Set signals back to previous values -- hopefully nobody overwrote them in the meantime
+            for( std::size_t i = 0; i < sizeof(signalDefs)/sizeof(SignalDefs); ++i ) {
+                sigaction(signalDefs[i].id, &oldSigActions[i], nullptr);
+            }
+            // Return the old stack
+            sigaltstack(&oldSigStack, nullptr);
+            isSet = false;
+        }
+    }
+
+    bool FatalConditionHandler::isSet = false;
+    struct sigaction FatalConditionHandler::oldSigActions[sizeof(signalDefs)/sizeof(SignalDefs)] = {};
+    stack_t FatalConditionHandler::oldSigStack = {};
+    char FatalConditionHandler::altStackMem[sigStackSize] = {};
+
+} // namespace Catch
+
+#else
+
+namespace Catch {
+    void FatalConditionHandler::reset() {}
+}
+
+#endif // signals/SEH handling
+
+#if defined(__GNUC__)
+#    pragma GCC diagnostic pop
+#endif
+// end catch_fatal_condition.cpp
+// start catch_generators.cpp
+
+// start catch_random_number_generator.h
+
+#include <algorithm>
+#include <random>
+
+namespace Catch {
+
+    struct IConfig;
+
+    std::mt19937& rng();
+    void seedRng( IConfig const& config );
+    unsigned int rngSeed();
+
+}
+
+// end catch_random_number_generator.h
+#include <limits>
+#include <set>
+
+namespace Catch {
+
+IGeneratorTracker::~IGeneratorTracker() {}
+
+namespace Generators {
+
+    GeneratorBase::~GeneratorBase() {}
+
+    std::vector<size_t> randomiseIndices( size_t selectionSize, size_t sourceSize ) {
+
+        assert( selectionSize <= sourceSize );
+        std::vector<size_t> indices;
+        indices.reserve( selectionSize );
+        std::uniform_int_distribution<size_t> uid( 0, sourceSize-1 );
+
+        std::set<size_t> seen;
+        // !TBD: improve this algorithm
+        while( indices.size() < selectionSize ) {
+            auto index = uid( rng() );
+            if( seen.insert( index ).second )
+                indices.push_back( index );
+        }
+        return indices;
+    }
+
+    auto acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker& {
+        return getResultCapture().acquireGeneratorTracker( lineInfo );
+    }
+
+    template<>
+    auto all<int>() -> Generator<int> {
+        return range( std::numeric_limits<int>::min(), std::numeric_limits<int>::max() );
+    }
+
+} // namespace Generators
+} // namespace Catch
+// end catch_generators.cpp
+// start catch_interfaces_capture.cpp
+
+namespace Catch {
+    IResultCapture::~IResultCapture() = default;
+}
+// end catch_interfaces_capture.cpp
+// start catch_interfaces_config.cpp
+
+namespace Catch {
+    IConfig::~IConfig() = default;
+}
+// end catch_interfaces_config.cpp
+// start catch_interfaces_exception.cpp
+
+namespace Catch {
+    IExceptionTranslator::~IExceptionTranslator() = default;
+    IExceptionTranslatorRegistry::~IExceptionTranslatorRegistry() = default;
+}
+// end catch_interfaces_exception.cpp
+// start catch_interfaces_registry_hub.cpp
+
+namespace Catch {
+    IRegistryHub::~IRegistryHub() = default;
+    IMutableRegistryHub::~IMutableRegistryHub() = default;
+}
+// end catch_interfaces_registry_hub.cpp
+// start catch_interfaces_reporter.cpp
+
+// start catch_reporter_listening.h
+
+namespace Catch {
+
+    class ListeningReporter : public IStreamingReporter {
+        using Reporters = std::vector<IStreamingReporterPtr>;
+        Reporters m_listeners;
+        IStreamingReporterPtr m_reporter = nullptr;
+        ReporterPreferences m_preferences;
+
+    public:
+        ListeningReporter();
+
+        void addListener( IStreamingReporterPtr&& listener );
+        void addReporter( IStreamingReporterPtr&& reporter );
+
+    public: // IStreamingReporter
+
+        ReporterPreferences getPreferences() const override;
+
+        void noMatchingTestCases( std::string const& spec ) override;
+
+        static std::set<Verbosity> getSupportedVerbosities();
+
+        void benchmarkStarting( BenchmarkInfo const& benchmarkInfo ) override;
+        void benchmarkEnded( BenchmarkStats const& benchmarkStats ) override;
+
+        void testRunStarting( TestRunInfo const& testRunInfo ) override;
+        void testGroupStarting( GroupInfo const& groupInfo ) override;
+        void testCaseStarting( TestCaseInfo const& testInfo ) override;
+        void sectionStarting( SectionInfo const& sectionInfo ) override;
+        void assertionStarting( AssertionInfo const& assertionInfo ) override;
+
+        // The return value indicates if the messages buffer should be cleared:
+        bool assertionEnded( AssertionStats const& assertionStats ) override;
+        void sectionEnded( SectionStats const& sectionStats ) override;
+        void testCaseEnded( TestCaseStats const& testCaseStats ) override;
+        void testGroupEnded( TestGroupStats const& testGroupStats ) override;
+        void testRunEnded( TestRunStats const& testRunStats ) override;
+
+        void skipTest( TestCaseInfo const& testInfo ) override;
+        bool isMulti() const override;
+
+    };
+
+} // end namespace Catch
+
+// end catch_reporter_listening.h
+namespace Catch {
+
+    ReporterConfig::ReporterConfig( IConfigPtr const& _fullConfig )
+    :   m_stream( &_fullConfig->stream() ), m_fullConfig( _fullConfig ) {}
+
+    ReporterConfig::ReporterConfig( IConfigPtr const& _fullConfig, std::ostream& _stream )
+    :   m_stream( &_stream ), m_fullConfig( _fullConfig ) {}
+
+    std::ostream& ReporterConfig::stream() const { return *m_stream; }
+    IConfigPtr ReporterConfig::fullConfig() const { return m_fullConfig; }
+
+    TestRunInfo::TestRunInfo( std::string const& _name ) : name( _name ) {}
+
+    GroupInfo::GroupInfo(  std::string const& _name,
+                           std::size_t _groupIndex,
+                           std::size_t _groupsCount )
+    :   name( _name ),
+        groupIndex( _groupIndex ),
+        groupsCounts( _groupsCount )
+    {}
+
+     AssertionStats::AssertionStats( AssertionResult const& _assertionResult,
+                                     std::vector<MessageInfo> const& _infoMessages,
+                                     Totals const& _totals )
+    :   assertionResult( _assertionResult ),
+        infoMessages( _infoMessages ),
+        totals( _totals )
+    {
+        assertionResult.m_resultData.lazyExpression.m_transientExpression = _assertionResult.m_resultData.lazyExpression.m_transientExpression;
+
+        if( assertionResult.hasMessage() ) {
+            // Copy message into messages list.
+            // !TBD This should have been done earlier, somewhere
+            MessageBuilder builder( assertionResult.getTestMacroName(), assertionResult.getSourceInfo(), assertionResult.getResultType() );
+            builder << assertionResult.getMessage();
+            builder.m_info.message = builder.m_stream.str();
+
+            infoMessages.push_back( builder.m_info );
+        }
+    }
+
+     AssertionStats::~AssertionStats() = default;
+
+    SectionStats::SectionStats(  SectionInfo const& _sectionInfo,
+                                 Counts const& _assertions,
+                                 double _durationInSeconds,
+                                 bool _missingAssertions )
+    :   sectionInfo( _sectionInfo ),
+        assertions( _assertions ),
+        durationInSeconds( _durationInSeconds ),
+        missingAssertions( _missingAssertions )
+    {}
+
+    SectionStats::~SectionStats() = default;
+
+    TestCaseStats::TestCaseStats(  TestCaseInfo const& _testInfo,
+                                   Totals const& _totals,
+                                   std::string const& _stdOut,
+                                   std::string const& _stdErr,
+                                   bool _aborting )
+    : testInfo( _testInfo ),
+        totals( _totals ),
+        stdOut( _stdOut ),
+        stdErr( _stdErr ),
+        aborting( _aborting )
+    {}
+
+    TestCaseStats::~TestCaseStats() = default;
+
+    TestGroupStats::TestGroupStats( GroupInfo const& _groupInfo,
+                                    Totals const& _totals,
+                                    bool _aborting )
+    :   groupInfo( _groupInfo ),
+        totals( _totals ),
+        aborting( _aborting )
+    {}
+
+    TestGroupStats::TestGroupStats( GroupInfo const& _groupInfo )
+    :   groupInfo( _groupInfo ),
+        aborting( false )
+    {}
+
+    TestGroupStats::~TestGroupStats() = default;
+
+    TestRunStats::TestRunStats(   TestRunInfo const& _runInfo,
+                    Totals const& _totals,
+                    bool _aborting )
+    :   runInfo( _runInfo ),
+        totals( _totals ),
+        aborting( _aborting )
+    {}
+
+    TestRunStats::~TestRunStats() = default;
+
+    void IStreamingReporter::fatalErrorEncountered( StringRef ) {}
+    bool IStreamingReporter::isMulti() const { return false; }
+
+    IReporterFactory::~IReporterFactory() = default;
+    IReporterRegistry::~IReporterRegistry() = default;
+
+} // end namespace Catch
+// end catch_interfaces_reporter.cpp
+// start catch_interfaces_runner.cpp
+
+namespace Catch {
+    IRunner::~IRunner() = default;
+}
+// end catch_interfaces_runner.cpp
+// start catch_interfaces_testcase.cpp
+
+namespace Catch {
+    ITestInvoker::~ITestInvoker() = default;
+    ITestCaseRegistry::~ITestCaseRegistry() = default;
+}
+// end catch_interfaces_testcase.cpp
+// start catch_leak_detector.cpp
+
+#ifdef CATCH_CONFIG_WINDOWS_CRTDBG
+#include <crtdbg.h>
+
+namespace Catch {
+
+    LeakDetector::LeakDetector() {
+        int flag = _CrtSetDbgFlag(_CRTDBG_REPORT_FLAG);
+        flag |= _CRTDBG_LEAK_CHECK_DF;
+        flag |= _CRTDBG_ALLOC_MEM_DF;
+        _CrtSetDbgFlag(flag);
+        _CrtSetReportMode(_CRT_WARN, _CRTDBG_MODE_FILE | _CRTDBG_MODE_DEBUG);
+        _CrtSetReportFile(_CRT_WARN, _CRTDBG_FILE_STDERR);
+        // Change this to leaking allocation's number to break there
+        _CrtSetBreakAlloc(-1);
+    }
+}
+
+#else
+
+    Catch::LeakDetector::LeakDetector() {}
+
+#endif
+// end catch_leak_detector.cpp
+// start catch_list.cpp
+
+// start catch_list.h
+
+#include <set>
+
+namespace Catch {
+
+    std::size_t listTests( Config const& config );
+
+    std::size_t listTestsNamesOnly( Config const& config );
+
+    struct TagInfo {
+        void add( std::string const& spelling );
+        std::string all() const;
+
+        std::set<std::string> spellings;
+        std::size_t count = 0;
+    };
+
+    std::size_t listTags( Config const& config );
+
+    std::size_t listReporters( Config const& /*config*/ );
+
+    Option<std::size_t> list( Config const& config );
+
+} // end namespace Catch
+
+// end catch_list.h
+// start catch_text.h
+
+namespace Catch {
+    using namespace clara::TextFlow;
+}
+
+// end catch_text.h
+#include <limits>
+#include <algorithm>
+#include <iomanip>
+
+namespace Catch {
+
+    std::size_t listTests( Config const& config ) {
+        TestSpec testSpec = config.testSpec();
+        if( config.hasTestFilters() )
+            Catch::cout() << "Matching test cases:\n";
+        else {
+            Catch::cout() << "All available test cases:\n";
+        }
+
+        auto matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
+        for( auto const& testCaseInfo : matchedTestCases ) {
+            Colour::Code colour = testCaseInfo.isHidden()
+                ? Colour::SecondaryText
+                : Colour::None;
+            Colour colourGuard( colour );
+
+            Catch::cout() << Column( testCaseInfo.name ).initialIndent( 2 ).indent( 4 ) << "\n";
+            if( config.verbosity() >= Verbosity::High ) {
+                Catch::cout() << Column( Catch::Detail::stringify( testCaseInfo.lineInfo ) ).indent(4) << std::endl;
+                std::string description = testCaseInfo.description;
+                if( description.empty() )
+                    description = "(NO DESCRIPTION)";
+                Catch::cout() << Column( description ).indent(4) << std::endl;
+            }
+            if( !testCaseInfo.tags.empty() )
+                Catch::cout() << Column( testCaseInfo.tagsAsString() ).indent( 6 ) << "\n";
+        }
+
+        if( !config.hasTestFilters() )
+            Catch::cout() << pluralise( matchedTestCases.size(), "test case" ) << '\n' << std::endl;
+        else
+            Catch::cout() << pluralise( matchedTestCases.size(), "matching test case" ) << '\n' << std::endl;
+        return matchedTestCases.size();
+    }
+
+    std::size_t listTestsNamesOnly( Config const& config ) {
+        TestSpec testSpec = config.testSpec();
+        std::size_t matchedTests = 0;
+        std::vector<TestCase> matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
+        for( auto const& testCaseInfo : matchedTestCases ) {
+            matchedTests++;
+            if( startsWith( testCaseInfo.name, '#' ) )
+               Catch::cout() << '"' << testCaseInfo.name << '"';
+            else
+               Catch::cout() << testCaseInfo.name;
+            if ( config.verbosity() >= Verbosity::High )
+                Catch::cout() << "\t@" << testCaseInfo.lineInfo;
+            Catch::cout() << std::endl;
+        }
+        return matchedTests;
+    }
+
+    void TagInfo::add( std::string const& spelling ) {
+        ++count;
+        spellings.insert( spelling );
+    }
+
+    std::string TagInfo::all() const {
+        std::string out;
+        for( auto const& spelling : spellings )
+            out += "[" + spelling + "]";
+        return out;
+    }
+
+    std::size_t listTags( Config const& config ) {
+        TestSpec testSpec = config.testSpec();
+        if( config.hasTestFilters() )
+            Catch::cout() << "Tags for matching test cases:\n";
+        else {
+            Catch::cout() << "All available tags:\n";
+        }
+
+        std::map<std::string, TagInfo> tagCounts;
+
+        std::vector<TestCase> matchedTestCases = filterTests( getAllTestCasesSorted( config ), testSpec, config );
+        for( auto const& testCase : matchedTestCases ) {
+            for( auto const& tagName : testCase.getTestCaseInfo().tags ) {
+                std::string lcaseTagName = toLower( tagName );
+                auto countIt = tagCounts.find( lcaseTagName );
+                if( countIt == tagCounts.end() )
+                    countIt = tagCounts.insert( std::make_pair( lcaseTagName, TagInfo() ) ).first;
+                countIt->second.add( tagName );
+            }
+        }
+
+        for( auto const& tagCount : tagCounts ) {
+            ReusableStringStream rss;
+            rss << "  " << std::setw(2) << tagCount.second.count << "  ";
+            auto str = rss.str();
+            auto wrapper = Column( tagCount.second.all() )
+                                                    .initialIndent( 0 )
+                                                    .indent( str.size() )
+                                                    .width( CATCH_CONFIG_CONSOLE_WIDTH-10 );
+            Catch::cout() << str << wrapper << '\n';
+        }
+        Catch::cout() << pluralise( tagCounts.size(), "tag" ) << '\n' << std::endl;
+        return tagCounts.size();
+    }
+
+    std::size_t listReporters( Config const& /*config*/ ) {
+        Catch::cout() << "Available reporters:\n";
+        IReporterRegistry::FactoryMap const& factories = getRegistryHub().getReporterRegistry().getFactories();
+        std::size_t maxNameLen = 0;
+        for( auto const& factoryKvp : factories )
+            maxNameLen = (std::max)( maxNameLen, factoryKvp.first.size() );
+
+        for( auto const& factoryKvp : factories ) {
+            Catch::cout()
+                    << Column( factoryKvp.first + ":" )
+                            .indent(2)
+                            .width( 5+maxNameLen )
+                    +  Column( factoryKvp.second->getDescription() )
+                            .initialIndent(0)
+                            .indent(2)
+                            .width( CATCH_CONFIG_CONSOLE_WIDTH - maxNameLen-8 )
+                    << "\n";
+        }
+        Catch::cout() << std::endl;
+        return factories.size();
+    }
+
+    Option<std::size_t> list( Config const& config ) {
+        Option<std::size_t> listedCount;
+        if( config.listTests() )
+            listedCount = listedCount.valueOr(0) + listTests( config );
+        if( config.listTestNamesOnly() )
+            listedCount = listedCount.valueOr(0) + listTestsNamesOnly( config );
+        if( config.listTags() )
+            listedCount = listedCount.valueOr(0) + listTags( config );
+        if( config.listReporters() )
+            listedCount = listedCount.valueOr(0) + listReporters( config );
+        return listedCount;
+    }
+
+} // end namespace Catch
+// end catch_list.cpp
+// start catch_matchers.cpp
+
+namespace Catch {
+namespace Matchers {
+    namespace Impl {
+
+        std::string MatcherUntypedBase::toString() const {
+            if( m_cachedToString.empty() )
+                m_cachedToString = describe();
+            return m_cachedToString;
+        }
+
+        MatcherUntypedBase::~MatcherUntypedBase() = default;
+
+    } // namespace Impl
+} // namespace Matchers
+
+using namespace Matchers;
+using Matchers::Impl::MatcherBase;
+
+} // namespace Catch
+// end catch_matchers.cpp
+// start catch_matchers_floating.cpp
+
+// start catch_to_string.hpp
+
+#include <string>
+
+namespace Catch {
+    template <typename T>
+    std::string to_string(T const& t) {
+#if defined(CATCH_CONFIG_CPP11_TO_STRING)
+        return std::to_string(t);
+#else
+        ReusableStringStream rss;
+        rss << t;
+        return rss.str();
+#endif
+    }
+} // end namespace Catch
+
+// end catch_to_string.hpp
+#include <cstdlib>
+#include <cstdint>
+#include <cstring>
+
+namespace Catch {
+namespace Matchers {
+namespace Floating {
+enum class FloatingPointKind : uint8_t {
+    Float,
+    Double
+};
+}
+}
+}
+
+namespace {
+
+template <typename T>
+struct Converter;
+
+template <>
+struct Converter<float> {
+    static_assert(sizeof(float) == sizeof(int32_t), "Important ULP matcher assumption violated");
+    Converter(float f) {
+        std::memcpy(&i, &f, sizeof(f));
+    }
+    int32_t i;
+};
+
+template <>
+struct Converter<double> {
+    static_assert(sizeof(double) == sizeof(int64_t), "Important ULP matcher assumption violated");
+    Converter(double d) {
+        std::memcpy(&i, &d, sizeof(d));
+    }
+    int64_t i;
+};
+
+template <typename T>
+auto convert(T t) -> Converter<T> {
+    return Converter<T>(t);
+}
+
+template <typename FP>
+bool almostEqualUlps(FP lhs, FP rhs, int maxUlpDiff) {
+    // Comparison with NaN should always be false.
+    // This way we can rule it out before getting into the ugly details
+    if (std::isnan(lhs) || std::isnan(rhs)) {
+        return false;
+    }
+
+    auto lc = convert(lhs);
+    auto rc = convert(rhs);
+
+    if ((lc.i < 0) != (rc.i < 0)) {
+        // Potentially we can have +0 and -0
+        return lhs == rhs;
+    }
+
+    auto ulpDiff = std::abs(lc.i - rc.i);
+    return ulpDiff <= maxUlpDiff;
+}
+
+}
+
+namespace Catch {
+namespace Matchers {
+namespace Floating {
+    WithinAbsMatcher::WithinAbsMatcher(double target, double margin)
+        :m_target{ target }, m_margin{ margin } {
+        CATCH_ENFORCE(margin >= 0, "Invalid margin: " << margin << '.'
+            << " Margin has to be non-negative.");
+    }
+
+    // Performs equivalent check of std::fabs(lhs - rhs) <= margin
+    // But without the subtraction to allow for INFINITY in comparison
+    bool WithinAbsMatcher::match(double const& matchee) const {
+        return (matchee + m_margin >= m_target) && (m_target + m_margin >= matchee);
+    }
+
+    std::string WithinAbsMatcher::describe() const {
+        return "is within " + ::Catch::Detail::stringify(m_margin) + " of " + ::Catch::Detail::stringify(m_target);
+    }
+
+    WithinUlpsMatcher::WithinUlpsMatcher(double target, int ulps, FloatingPointKind baseType)
+        :m_target{ target }, m_ulps{ ulps }, m_type{ baseType } {
+        CATCH_ENFORCE(ulps >= 0, "Invalid ULP setting: " << ulps << '.'
+            << " ULPs have to be non-negative.");
+    }
+
+#if defined(__clang__)
+#pragma clang diagnostic push
+// Clang <3.5 reports on the default branch in the switch below
+#pragma clang diagnostic ignored "-Wunreachable-code"
+#endif
+
+    bool WithinUlpsMatcher::match(double const& matchee) const {
+        switch (m_type) {
+        case FloatingPointKind::Float:
+            return almostEqualUlps<float>(static_cast<float>(matchee), static_cast<float>(m_target), m_ulps);
+        case FloatingPointKind::Double:
+            return almostEqualUlps<double>(matchee, m_target, m_ulps);
+        default:
+            CATCH_INTERNAL_ERROR( "Unknown FloatingPointKind value" );
+        }
+    }
+
+#if defined(__clang__)
+#pragma clang diagnostic pop
+#endif
+
+    std::string WithinUlpsMatcher::describe() const {
+        return "is within " + Catch::to_string(m_ulps) + " ULPs of " + ::Catch::Detail::stringify(m_target) + ((m_type == FloatingPointKind::Float)? "f" : "");
+    }
+
+}// namespace Floating
+
+Floating::WithinUlpsMatcher WithinULP(double target, int maxUlpDiff) {
+    return Floating::WithinUlpsMatcher(target, maxUlpDiff, Floating::FloatingPointKind::Double);
+}
+
+Floating::WithinUlpsMatcher WithinULP(float target, int maxUlpDiff) {
+    return Floating::WithinUlpsMatcher(target, maxUlpDiff, Floating::FloatingPointKind::Float);
+}
+
+Floating::WithinAbsMatcher WithinAbs(double target, double margin) {
+    return Floating::WithinAbsMatcher(target, margin);
+}
+
+} // namespace Matchers
+} // namespace Catch
+
+// end catch_matchers_floating.cpp
+// start catch_matchers_generic.cpp
+
+std::string Catch::Matchers::Generic::Detail::finalizeDescription(const std::string& desc) {
+    if (desc.empty()) {
+        return "matches undescribed predicate";
+    } else {
+        return "matches predicate: \"" + desc + '"';
+    }
+}
+// end catch_matchers_generic.cpp
+// start catch_matchers_string.cpp
+
+#include <regex>
+
+namespace Catch {
+namespace Matchers {
+
+    namespace StdString {
+
+        CasedString::CasedString( std::string const& str, CaseSensitive::Choice caseSensitivity )
+        :   m_caseSensitivity( caseSensitivity ),
+            m_str( adjustString( str ) )
+        {}
+        std::string CasedString::adjustString( std::string const& str ) const {
+            return m_caseSensitivity == CaseSensitive::No
+                   ? toLower( str )
+                   : str;
+        }
+        std::string CasedString::caseSensitivitySuffix() const {
+            return m_caseSensitivity == CaseSensitive::No
+                   ? " (case insensitive)"
+                   : std::string();
+        }
+
+        StringMatcherBase::StringMatcherBase( std::string const& operation, CasedString const& comparator )
+        : m_comparator( comparator ),
+          m_operation( operation ) {
+        }
+
+        std::string StringMatcherBase::describe() const {
+            std::string description;
+            description.reserve(5 + m_operation.size() + m_comparator.m_str.size() +
+                                        m_comparator.caseSensitivitySuffix().size());
+            description += m_operation;
+            description += ": \"";
+            description += m_comparator.m_str;
+            description += "\"";
+            description += m_comparator.caseSensitivitySuffix();
+            return description;
+        }
+
+        EqualsMatcher::EqualsMatcher( CasedString const& comparator ) : StringMatcherBase( "equals", comparator ) {}
+
+        bool EqualsMatcher::match( std::string const& source ) const {
+            return m_comparator.adjustString( source ) == m_comparator.m_str;
+        }
+
+        ContainsMatcher::ContainsMatcher( CasedString const& comparator ) : StringMatcherBase( "contains", comparator ) {}
+
+        bool ContainsMatcher::match( std::string const& source ) const {
+            return contains( m_comparator.adjustString( source ), m_comparator.m_str );
+        }
+
+        StartsWithMatcher::StartsWithMatcher( CasedString const& comparator ) : StringMatcherBase( "starts with", comparator ) {}
+
+        bool StartsWithMatcher::match( std::string const& source ) const {
+            return startsWith( m_comparator.adjustString( source ), m_comparator.m_str );
+        }
+
+        EndsWithMatcher::EndsWithMatcher( CasedString const& comparator ) : StringMatcherBase( "ends with", comparator ) {}
+
+        bool EndsWithMatcher::match( std::string const& source ) const {
+            return endsWith( m_comparator.adjustString( source ), m_comparator.m_str );
+        }
+
+        RegexMatcher::RegexMatcher(std::string regex, CaseSensitive::Choice caseSensitivity): m_regex(std::move(regex)), m_caseSensitivity(caseSensitivity) {}
+
+        bool RegexMatcher::match(std::string const& matchee) const {
+            auto flags = std::regex::ECMAScript; // ECMAScript is the default syntax option anyway
+            if (m_caseSensitivity == CaseSensitive::Choice::No) {
+                flags |= std::regex::icase;
+            }
+            auto reg = std::regex(m_regex, flags);
+            return std::regex_match(matchee, reg);
+        }
+
+        std::string RegexMatcher::describe() const {
+            return "matches " + ::Catch::Detail::stringify(m_regex) + ((m_caseSensitivity == CaseSensitive::Choice::Yes)? " case sensitively" : " case insensitively");
+        }
+
+    } // namespace StdString
+
+    StdString::EqualsMatcher Equals( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
+        return StdString::EqualsMatcher( StdString::CasedString( str, caseSensitivity) );
+    }
+    StdString::ContainsMatcher Contains( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
+        return StdString::ContainsMatcher( StdString::CasedString( str, caseSensitivity) );
+    }
+    StdString::EndsWithMatcher EndsWith( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
+        return StdString::EndsWithMatcher( StdString::CasedString( str, caseSensitivity) );
+    }
+    StdString::StartsWithMatcher StartsWith( std::string const& str, CaseSensitive::Choice caseSensitivity ) {
+        return StdString::StartsWithMatcher( StdString::CasedString( str, caseSensitivity) );
+    }
+
+    StdString::RegexMatcher Matches(std::string const& regex, CaseSensitive::Choice caseSensitivity) {
+        return StdString::RegexMatcher(regex, caseSensitivity);
+    }
+
+} // namespace Matchers
+} // namespace Catch
+// end catch_matchers_string.cpp
+// start catch_message.cpp
+
+// start catch_uncaught_exceptions.h
+
+namespace Catch {
+    bool uncaught_exceptions();
+} // end namespace Catch
+
+// end catch_uncaught_exceptions.h
+#include <cassert>
+
+namespace Catch {
+
+    MessageInfo::MessageInfo(   StringRef const& _macroName,
+                                SourceLineInfo const& _lineInfo,
+                                ResultWas::OfType _type )
+    :   macroName( _macroName ),
+        lineInfo( _lineInfo ),
+        type( _type ),
+        sequence( ++globalCount )
+    {}
+
+    bool MessageInfo::operator==( MessageInfo const& other ) const {
+        return sequence == other.sequence;
+    }
+
+    bool MessageInfo::operator<( MessageInfo const& other ) const {
+        return sequence < other.sequence;
+    }
+
+    // This may need protecting if threading support is added
+    unsigned int MessageInfo::globalCount = 0;
+
+    ////////////////////////////////////////////////////////////////////////////
+
+    Catch::MessageBuilder::MessageBuilder( StringRef const& macroName,
+                                           SourceLineInfo const& lineInfo,
+                                           ResultWas::OfType type )
+        :m_info(macroName, lineInfo, type) {}
+
+    ////////////////////////////////////////////////////////////////////////////
+
+    ScopedMessage::ScopedMessage( MessageBuilder const& builder )
+    : m_info( builder.m_info )
+    {
+        m_info.message = builder.m_stream.str();
+        getResultCapture().pushScopedMessage( m_info );
+    }
+
+    ScopedMessage::~ScopedMessage() {
+        if ( !uncaught_exceptions() ){
+            getResultCapture().popScopedMessage(m_info);
+        }
+    }
+
+    Capturer::Capturer( StringRef macroName, SourceLineInfo const& lineInfo, ResultWas::OfType resultType, StringRef names ) {
+        auto start = std::string::npos;
+        for( size_t pos = 0; pos <= names.size(); ++pos ) {
+            char c = names[pos];
+            if( pos == names.size() || c == ' ' || c == '\t' || c == ',' || c == ']' ) {
+                if( start != std::string::npos ) {
+                    m_messages.push_back( MessageInfo( macroName, lineInfo, resultType ) );
+                    m_messages.back().message = names.substr( start, pos-start) + " := ";
+                    start = std::string::npos;
+                }
+            }
+            else if( c != '[' && c != ']' && start == std::string::npos )
+                start = pos;
+        }
+    }
+    Capturer::~Capturer() {
+        if ( !uncaught_exceptions() ){
+            assert( m_captured == m_messages.size() );
+            for( size_t i = 0; i < m_captured; ++i  )
+                m_resultCapture.popScopedMessage( m_messages[i] );
+        }
+    }
+
+    void Capturer::captureValue( size_t index, StringRef value ) {
+        assert( index < m_messages.size() );
+        m_messages[index].message += value;
+        m_resultCapture.pushScopedMessage( m_messages[index] );
+        m_captured++;
+    }
+
+} // end namespace Catch
+// end catch_message.cpp
+// start catch_output_redirect.cpp
+
+// start catch_output_redirect.h
+#ifndef TWOBLUECUBES_CATCH_OUTPUT_REDIRECT_H
+#define TWOBLUECUBES_CATCH_OUTPUT_REDIRECT_H
+
+#include <cstdio>
+#include <iosfwd>
+#include <string>
+
+namespace Catch {
+
+    class RedirectedStream {
+        std::ostream& m_originalStream;
+        std::ostream& m_redirectionStream;
+        std::streambuf* m_prevBuf;
+
+    public:
+        RedirectedStream( std::ostream& originalStream, std::ostream& redirectionStream );
+        ~RedirectedStream();
+    };
+
+    class RedirectedStdOut {
+        ReusableStringStream m_rss;
+        RedirectedStream m_cout;
+    public:
+        RedirectedStdOut();
+        auto str() const -> std::string;
+    };
+
+    // StdErr has two constituent streams in C++, std::cerr and std::clog
+    // This means that we need to redirect 2 streams into 1 to keep proper
+    // order of writes
+    class RedirectedStdErr {
+        ReusableStringStream m_rss;
+        RedirectedStream m_cerr;
+        RedirectedStream m_clog;
+    public:
+        RedirectedStdErr();
+        auto str() const -> std::string;
+    };
+
+#if defined(CATCH_CONFIG_NEW_CAPTURE)
+
+    // Windows's implementation of std::tmpfile is terrible (it tries
+    // to create a file inside system folder, thus requiring elevated
+    // privileges for the binary), so we have to use tmpnam(_s) and
+    // create the file ourselves there.
+    class TempFile {
+    public:
+        TempFile(TempFile const&) = delete;
+        TempFile& operator=(TempFile const&) = delete;
+        TempFile(TempFile&&) = delete;
+        TempFile& operator=(TempFile&&) = delete;
+
+        TempFile();
+        ~TempFile();
+
+        std::FILE* getFile();
+        std::string getContents();
+
+    private:
+        std::FILE* m_file = nullptr;
+    #if defined(_MSC_VER)
+        char m_buffer[L_tmpnam] = { 0 };
+    #endif
+    };
+
+    class OutputRedirect {
+    public:
+        OutputRedirect(OutputRedirect const&) = delete;
+        OutputRedirect& operator=(OutputRedirect const&) = delete;
+        OutputRedirect(OutputRedirect&&) = delete;
+        OutputRedirect& operator=(OutputRedirect&&) = delete;
+
+        OutputRedirect(std::string& stdout_dest, std::string& stderr_dest);
+        ~OutputRedirect();
+
+    private:
+        int m_originalStdout = -1;
+        int m_originalStderr = -1;
+        TempFile m_stdoutFile;
+        TempFile m_stderrFile;
+        std::string& m_stdoutDest;
+        std::string& m_stderrDest;
+    };
+
+#endif
+
+} // end namespace Catch
+
+#endif // TWOBLUECUBES_CATCH_OUTPUT_REDIRECT_H
+// end catch_output_redirect.h
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <sstream>
+#include <stdexcept>
+
+#if defined(CATCH_CONFIG_NEW_CAPTURE)
+    #if defined(_MSC_VER)
+    #include <io.h>      //_dup and _dup2
+    #define dup _dup
+    #define dup2 _dup2
+    #define fileno _fileno
+    #else
+    #include <unistd.h>  // dup and dup2
+    #endif
+#endif
+
+namespace Catch {
+
+    RedirectedStream::RedirectedStream( std::ostream& originalStream, std::ostream& redirectionStream )
+    :   m_originalStream( originalStream ),
+        m_redirectionStream( redirectionStream ),
+        m_prevBuf( m_originalStream.rdbuf() )
+    {
+        m_originalStream.rdbuf( m_redirectionStream.rdbuf() );
+    }
+
+    RedirectedStream::~RedirectedStream() {
+        m_originalStream.rdbuf( m_prevBuf );
+    }
+
+    RedirectedStdOut::RedirectedStdOut() : m_cout( Catch::cout(), m_rss.get() ) {}
+    auto RedirectedStdOut::str() const -> std::string { return m_rss.str(); }
+
+    RedirectedStdErr::RedirectedStdErr()
+    :   m_cerr( Catch::cerr(), m_rss.get() ),
+        m_clog( Catch::clog(), m_rss.get() )
+    {}
+    auto RedirectedStdErr::str() const -> std::string { return m_rss.str(); }
+
+#if defined(CATCH_CONFIG_NEW_CAPTURE)
+
+#if defined(_MSC_VER)
+    TempFile::TempFile() {
+        if (tmpnam_s(m_buffer)) {
+            CATCH_RUNTIME_ERROR("Could not get a temp filename");
+        }
+        if (fopen_s(&m_file, m_buffer, "w")) {
+            char buffer[100];
+            if (strerror_s(buffer, errno)) {
+                CATCH_RUNTIME_ERROR("Could not translate errno to a string");
+            }
+            CATCH_RUNTIME_ERROR("Coul dnot open the temp file: '" << m_buffer << "' because: " << buffer);
+        }
+    }
+#else
+    TempFile::TempFile() {
+        m_file = std::tmpfile();
+        if (!m_file) {
+            CATCH_RUNTIME_ERROR("Could not create a temp file.");
+        }
+    }
+
+#endif
+
+    TempFile::~TempFile() {
+         // TBD: What to do about errors here?
+         std::fclose(m_file);
+         // We manually create the file on Windows only, on Linux
+         // it will be autodeleted
+#if defined(_MSC_VER)
+         std::remove(m_buffer);
+#endif
+    }
+
+    FILE* TempFile::getFile() {
+        return m_file;
+    }
+
+    std::string TempFile::getContents() {
+        std::stringstream sstr;
+        char buffer[100] = {};
+        std::rewind(m_file);
+        while (std::fgets(buffer, sizeof(buffer), m_file)) {
+            sstr << buffer;
+        }
+        return sstr.str();
+    }
+
+    OutputRedirect::OutputRedirect(std::string& stdout_dest, std::string& stderr_dest) :
+        m_originalStdout(dup(1)),
+        m_originalStderr(dup(2)),
+        m_stdoutDest(stdout_dest),
+        m_stderrDest(stderr_dest) {
+        dup2(fileno(m_stdoutFile.getFile()), 1);
+        dup2(fileno(m_stderrFile.getFile()), 2);
+    }
+
+    OutputRedirect::~OutputRedirect() {
+        Catch::cout() << std::flush;
+        fflush(stdout);
+        // Since we support overriding these streams, we flush cerr
+        // even though std::cerr is unbuffered
+        Catch::cerr() << std::flush;
+        Catch::clog() << std::flush;
+        fflush(stderr);
+
+        dup2(m_originalStdout, 1);
+        dup2(m_originalStderr, 2);
+
+        m_stdoutDest += m_stdoutFile.getContents();
+        m_stderrDest += m_stderrFile.getContents();
+    }
+
+#endif // CATCH_CONFIG_NEW_CAPTURE
+
+} // namespace Catch
+
+#if defined(CATCH_CONFIG_NEW_CAPTURE)
+    #if defined(_MSC_VER)
+    #undef dup
+    #undef dup2
+    #undef fileno
+    #endif
+#endif
+// end catch_output_redirect.cpp
+// start catch_random_number_generator.cpp
+
+namespace Catch {
+
+    std::mt19937& rng() {
+        static std::mt19937 s_rng;
+        return s_rng;
+    }
+
+    void seedRng( IConfig const& config ) {
+        if( config.rngSeed() != 0 ) {
+            std::srand( config.rngSeed() );
+            rng().seed( config.rngSeed() );
+        }
+    }
+
+    unsigned int rngSeed() {
+        return getCurrentContext().getConfig()->rngSeed();
+    }
+}
+// end catch_random_number_generator.cpp
+// start catch_registry_hub.cpp
+
+// start catch_test_case_registry_impl.h
+
+#include <vector>
+#include <set>
+#include <algorithm>
+#include <ios>
+
+namespace Catch {
+
+    class TestCase;
+    struct IConfig;
+
+    std::vector<TestCase> sortTests( IConfig const& config, std::vector<TestCase> const& unsortedTestCases );
+    bool matchTest( TestCase const& testCase, TestSpec const& testSpec, IConfig const& config );
+
+    void enforceNoDuplicateTestCases( std::vector<TestCase> const& functions );
+
+    std::vector<TestCase> filterTests( std::vector<TestCase> const& testCases, TestSpec const& testSpec, IConfig const& config );
+    std::vector<TestCase> const& getAllTestCasesSorted( IConfig const& config );
+
+    class TestRegistry : public ITestCaseRegistry {
+    public:
+        virtual ~TestRegistry() = default;
+
+        virtual void registerTest( TestCase const& testCase );
+
+        std::vector<TestCase> const& getAllTests() const override;
+        std::vector<TestCase> const& getAllTestsSorted( IConfig const& config ) const override;
+
+    private:
+        std::vector<TestCase> m_functions;
+        mutable RunTests::InWhatOrder m_currentSortOrder = RunTests::InDeclarationOrder;
+        mutable std::vector<TestCase> m_sortedFunctions;
+        std::size_t m_unnamedCount = 0;
+        std::ios_base::Init m_ostreamInit; // Forces cout/ cerr to be initialised
+    };
+
+    ///////////////////////////////////////////////////////////////////////////
+
+    class TestInvokerAsFunction : public ITestInvoker {
+        void(*m_testAsFunction)();
+    public:
+        TestInvokerAsFunction( void(*testAsFunction)() ) noexcept;
+
+        void invoke() const override;
+    };
+
+    std::string extractClassName( StringRef const& classOrQualifiedMethodName );
+
+    ///////////////////////////////////////////////////////////////////////////
+
+} // end namespace Catch
+
+// end catch_test_case_registry_impl.h
+// start catch_reporter_registry.h
+
+#include <map>
+
+namespace Catch {
+
+    class ReporterRegistry : public IReporterRegistry {
+
+    public:
+
+        ~ReporterRegistry() override;
+
+        IStreamingReporterPtr create( std::string const& name, IConfigPtr const& config ) const override;
+
+        void registerReporter( std::string const& name, IReporterFactoryPtr const& factory );
+        void registerListener( IReporterFactoryPtr const& factory );
+
+        FactoryMap const& getFactories() const override;
+        Listeners const& getListeners() const override;
+
+    private:
+        FactoryMap m_factories;
+        Listeners m_listeners;
+    };
+}
+
+// end catch_reporter_registry.h
+// start catch_tag_alias_registry.h
+
+// start catch_tag_alias.h
+
+#include <string>
+
+namespace Catch {
+
+    struct TagAlias {
+        TagAlias(std::string const& _tag, SourceLineInfo _lineInfo);
+
+        std::string tag;
+        SourceLineInfo lineInfo;
+    };
+
+} // end namespace Catch
+
+// end catch_tag_alias.h
+#include <map>
+
+namespace Catch {
+
+    class TagAliasRegistry : public ITagAliasRegistry {
+    public:
+        ~TagAliasRegistry() override;
+        TagAlias const* find( std::string const& alias ) const override;
+        std::string expandAliases( std::string const& unexpandedTestSpec ) const override;
+        void add( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo );
+
+    private:
+        std::map<std::string, TagAlias> m_registry;
+    };
+
+} // end namespace Catch
+
+// end catch_tag_alias_registry.h
+// start catch_startup_exception_registry.h
+
+#include <vector>
+#include <exception>
+
+namespace Catch {
+
+    class StartupExceptionRegistry {
+    public:
+        void add(std::exception_ptr const& exception) noexcept;
+        std::vector<std::exception_ptr> const& getExceptions() const noexcept;
+    private:
+        std::vector<std::exception_ptr> m_exceptions;
+    };
+
+} // end namespace Catch
+
+// end catch_startup_exception_registry.h
+// start catch_singletons.hpp
+
+namespace Catch {
+
+    struct ISingleton {
+        virtual ~ISingleton();
+    };
+
+    void addSingleton( ISingleton* singleton );
+    void cleanupSingletons();
+
+    template<typename SingletonImplT, typename InterfaceT = SingletonImplT, typename MutableInterfaceT = InterfaceT>
+    class Singleton : SingletonImplT, public ISingleton {
+
+        static auto getInternal() -> Singleton* {
+            static Singleton* s_instance = nullptr;
+            if( !s_instance ) {
+                s_instance = new Singleton;
+                addSingleton( s_instance );
+            }
+            return s_instance;
+        }
+
+    public:
+        static auto get() -> InterfaceT const& {
+            return *getInternal();
+        }
+        static auto getMutable() -> MutableInterfaceT& {
+            return *getInternal();
+        }
+    };
+
+} // namespace Catch
+
+// end catch_singletons.hpp
+namespace Catch {
+
+    namespace {
+
+        class RegistryHub : public IRegistryHub, public IMutableRegistryHub,
+                            private NonCopyable {
+
+        public: // IRegistryHub
+            RegistryHub() = default;
+            IReporterRegistry const& getReporterRegistry() const override {
+                return m_reporterRegistry;
+            }
+            ITestCaseRegistry const& getTestCaseRegistry() const override {
+                return m_testCaseRegistry;
+            }
+            IExceptionTranslatorRegistry const& getExceptionTranslatorRegistry() const override {
+                return m_exceptionTranslatorRegistry;
+            }
+            ITagAliasRegistry const& getTagAliasRegistry() const override {
+                return m_tagAliasRegistry;
+            }
+            StartupExceptionRegistry const& getStartupExceptionRegistry() const override {
+                return m_exceptionRegistry;
+            }
+
+        public: // IMutableRegistryHub
+            void registerReporter( std::string const& name, IReporterFactoryPtr const& factory ) override {
+                m_reporterRegistry.registerReporter( name, factory );
+            }
+            void registerListener( IReporterFactoryPtr const& factory ) override {
+                m_reporterRegistry.registerListener( factory );
+            }
+            void registerTest( TestCase const& testInfo ) override {
+                m_testCaseRegistry.registerTest( testInfo );
+            }
+            void registerTranslator( const IExceptionTranslator* translator ) override {
+                m_exceptionTranslatorRegistry.registerTranslator( translator );
+            }
+            void registerTagAlias( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) override {
+                m_tagAliasRegistry.add( alias, tag, lineInfo );
+            }
+            void registerStartupException() noexcept override {
+                m_exceptionRegistry.add(std::current_exception());
+            }
+
+        private:
+            TestRegistry m_testCaseRegistry;
+            ReporterRegistry m_reporterRegistry;
+            ExceptionTranslatorRegistry m_exceptionTranslatorRegistry;
+            TagAliasRegistry m_tagAliasRegistry;
+            StartupExceptionRegistry m_exceptionRegistry;
+        };
+    }
+
+    using RegistryHubSingleton = Singleton<RegistryHub, IRegistryHub, IMutableRegistryHub>;
+
+    IRegistryHub const& getRegistryHub() {
+        return RegistryHubSingleton::get();
+    }
+    IMutableRegistryHub& getMutableRegistryHub() {
+        return RegistryHubSingleton::getMutable();
+    }
+    void cleanUp() {
+        cleanupSingletons();
+        cleanUpContext();
+    }
+    std::string translateActiveException() {
+        return getRegistryHub().getExceptionTranslatorRegistry().translateActiveException();
+    }
+
+} // end namespace Catch
+// end catch_registry_hub.cpp
+// start catch_reporter_registry.cpp
+
+namespace Catch {
+
+    ReporterRegistry::~ReporterRegistry() = default;
+
+    IStreamingReporterPtr ReporterRegistry::create( std::string const& name, IConfigPtr const& config ) const {
+        auto it =  m_factories.find( name );
+        if( it == m_factories.end() )
+            return nullptr;
+        return it->second->create( ReporterConfig( config ) );
+    }
+
+    void ReporterRegistry::registerReporter( std::string const& name, IReporterFactoryPtr const& factory ) {
+        m_factories.emplace(name, factory);
+    }
+    void ReporterRegistry::registerListener( IReporterFactoryPtr const& factory ) {
+        m_listeners.push_back( factory );
+    }
+
+    IReporterRegistry::FactoryMap const& ReporterRegistry::getFactories() const {
+        return m_factories;
+    }
+    IReporterRegistry::Listeners const& ReporterRegistry::getListeners() const {
+        return m_listeners;
+    }
+
+}
+// end catch_reporter_registry.cpp
+// start catch_result_type.cpp
+
+namespace Catch {
+
+    bool isOk( ResultWas::OfType resultType ) {
+        return ( resultType & ResultWas::FailureBit ) == 0;
+    }
+    bool isJustInfo( int flags ) {
+        return flags == ResultWas::Info;
+    }
+
+    ResultDisposition::Flags operator | ( ResultDisposition::Flags lhs, ResultDisposition::Flags rhs ) {
+        return static_cast<ResultDisposition::Flags>( static_cast<int>( lhs ) | static_cast<int>( rhs ) );
+    }
+
+    bool shouldContinueOnFailure( int flags )    { return ( flags & ResultDisposition::ContinueOnFailure ) != 0; }
+    bool shouldSuppressFailure( int flags )      { return ( flags & ResultDisposition::SuppressFail ) != 0; }
+
+} // end namespace Catch
+// end catch_result_type.cpp
+// start catch_run_context.cpp
+
+#include <cassert>
+#include <algorithm>
+#include <sstream>
+
+namespace Catch {
+
+    namespace Generators {
+        struct GeneratorTracker : TestCaseTracking::TrackerBase, IGeneratorTracker {
+            size_t m_index = static_cast<size_t>( -1 );
+            GeneratorBasePtr m_generator;
+
+            GeneratorTracker( TestCaseTracking::NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent )
+            :   TrackerBase( nameAndLocation, ctx, parent )
+            {}
+            ~GeneratorTracker();
+
+            static GeneratorTracker& acquire( TrackerContext& ctx, TestCaseTracking::NameAndLocation const& nameAndLocation ) {
+                std::shared_ptr<GeneratorTracker> tracker;
+
+                ITracker& currentTracker = ctx.currentTracker();
+                if( TestCaseTracking::ITrackerPtr childTracker = currentTracker.findChild( nameAndLocation ) ) {
+                    assert( childTracker );
+                    assert( childTracker->isIndexTracker() );
+                    tracker = std::static_pointer_cast<GeneratorTracker>( childTracker );
+                }
+                else {
+                    tracker = std::make_shared<GeneratorTracker>( nameAndLocation, ctx, &currentTracker );
+                    currentTracker.addChild( tracker );
+                }
+
+                if( !ctx.completedCycle() && !tracker->isComplete() ) {
+                    if( tracker->m_runState != ExecutingChildren && tracker->m_runState != NeedsAnotherRun )
+                        tracker->moveNext();
+                    tracker->open();
+                }
+
+                return *tracker;
+            }
+
+            void moveNext() {
+                m_index++;
+                m_children.clear();
+            }
+
+            // TrackerBase interface
+            bool isIndexTracker() const override { return true; }
+            auto hasGenerator() const -> bool override {
+                return !!m_generator;
+            }
+            void close() override {
+                TrackerBase::close();
+                if( m_runState == CompletedSuccessfully && m_index < m_generator->size()-1 )
+                    m_runState = Executing;
+            }
+
+            // IGeneratorTracker interface
+            auto getGenerator() const -> GeneratorBasePtr const& override {
+                return m_generator;
+            }
+            void setGenerator( GeneratorBasePtr&& generator ) override {
+                m_generator = std::move( generator );
+            }
+            auto getIndex() const -> size_t override {
+                return m_index;
+            }
+        };
+        GeneratorTracker::~GeneratorTracker() {}
+    }
+
+    RunContext::RunContext(IConfigPtr const& _config, IStreamingReporterPtr&& reporter)
+    :   m_runInfo(_config->name()),
+        m_context(getCurrentMutableContext()),
+        m_config(_config),
+        m_reporter(std::move(reporter)),
+        m_lastAssertionInfo{ StringRef(), SourceLineInfo("",0), StringRef(), ResultDisposition::Normal },
+        m_includeSuccessfulResults( m_config->includeSuccessfulResults() || m_reporter->getPreferences().shouldReportAllAssertions )
+    {
+        m_context.setRunner(this);
+        m_context.setConfig(m_config);
+        m_context.setResultCapture(this);
+        m_reporter->testRunStarting(m_runInfo);
+    }
+
+    RunContext::~RunContext() {
+        m_reporter->testRunEnded(TestRunStats(m_runInfo, m_totals, aborting()));
+    }
+
+    void RunContext::testGroupStarting(std::string const& testSpec, std::size_t groupIndex, std::size_t groupsCount) {
+        m_reporter->testGroupStarting(GroupInfo(testSpec, groupIndex, groupsCount));
+    }
+
+    void RunContext::testGroupEnded(std::string const& testSpec, Totals const& totals, std::size_t groupIndex, std::size_t groupsCount) {
+        m_reporter->testGroupEnded(TestGroupStats(GroupInfo(testSpec, groupIndex, groupsCount), totals, aborting()));
+    }
+
+    Totals RunContext::runTest(TestCase const& testCase) {
+        Totals prevTotals = m_totals;
+
+        std::string redirectedCout;
+        std::string redirectedCerr;
+
+        auto const& testInfo = testCase.getTestCaseInfo();
+
+        m_reporter->testCaseStarting(testInfo);
+
+        m_activeTestCase = &testCase;
+
+        ITracker& rootTracker = m_trackerContext.startRun();
+        assert(rootTracker.isSectionTracker());
+        static_cast<SectionTracker&>(rootTracker).addInitialFilters(m_config->getSectionsToRun());
+        do {
+            m_trackerContext.startCycle();
+            m_testCaseTracker = &SectionTracker::acquire(m_trackerContext, TestCaseTracking::NameAndLocation(testInfo.name, testInfo.lineInfo));
+            runCurrentTest(redirectedCout, redirectedCerr);
+        } while (!m_testCaseTracker->isSuccessfullyCompleted() && !aborting());
+
+        Totals deltaTotals = m_totals.delta(prevTotals);
+        if (testInfo.expectedToFail() && deltaTotals.testCases.passed > 0) {
+            deltaTotals.assertions.failed++;
+            deltaTotals.testCases.passed--;
+            deltaTotals.testCases.failed++;
+        }
+        m_totals.testCases += deltaTotals.testCases;
+        m_reporter->testCaseEnded(TestCaseStats(testInfo,
+                                  deltaTotals,
+                                  redirectedCout,
+                                  redirectedCerr,
+                                  aborting()));
+
+        m_activeTestCase = nullptr;
+        m_testCaseTracker = nullptr;
+
+        return deltaTotals;
+    }
+
+    IConfigPtr RunContext::config() const {
+        return m_config;
+    }
+
+    IStreamingReporter& RunContext::reporter() const {
+        return *m_reporter;
+    }
+
+    void RunContext::assertionEnded(AssertionResult const & result) {
+        if (result.getResultType() == ResultWas::Ok) {
+            m_totals.assertions.passed++;
+            m_lastAssertionPassed = true;
+        } else if (!result.isOk()) {
+            m_lastAssertionPassed = false;
+            if( m_activeTestCase->getTestCaseInfo().okToFail() )
+                m_totals.assertions.failedButOk++;
+            else
+                m_totals.assertions.failed++;
+        }
+        else {
+            m_lastAssertionPassed = true;
+        }
+
+        // We have no use for the return value (whether messages should be cleared), because messages were made scoped
+        // and should be let to clear themselves out.
+        static_cast<void>(m_reporter->assertionEnded(AssertionStats(result, m_messages, m_totals)));
+
+        // Reset working state
+        resetAssertionInfo();
+        m_lastResult = result;
+    }
+    void RunContext::resetAssertionInfo() {
+        m_lastAssertionInfo.macroName = StringRef();
+        m_lastAssertionInfo.capturedExpression = "{Unknown expression after the reported line}"_sr;
+    }
+
+    bool RunContext::sectionStarted(SectionInfo const & sectionInfo, Counts & assertions) {
+        ITracker& sectionTracker = SectionTracker::acquire(m_trackerContext, TestCaseTracking::NameAndLocation(sectionInfo.name, sectionInfo.lineInfo));
+        if (!sectionTracker.isOpen())
+            return false;
+        m_activeSections.push_back(&sectionTracker);
+
+        m_lastAssertionInfo.lineInfo = sectionInfo.lineInfo;
+
+        m_reporter->sectionStarting(sectionInfo);
+
+        assertions = m_totals.assertions;
+
+        return true;
+    }
+    auto RunContext::acquireGeneratorTracker( SourceLineInfo const& lineInfo ) -> IGeneratorTracker& {
+        using namespace Generators;
+        GeneratorTracker& tracker = GeneratorTracker::acquire( m_trackerContext, TestCaseTracking::NameAndLocation( "generator", lineInfo ) );
+        assert( tracker.isOpen() );
+        m_lastAssertionInfo.lineInfo = lineInfo;
+        return tracker;
+    }
+
+    bool RunContext::testForMissingAssertions(Counts& assertions) {
+        if (assertions.total() != 0)
+            return false;
+        if (!m_config->warnAboutMissingAssertions())
+            return false;
+        if (m_trackerContext.currentTracker().hasChildren())
+            return false;
+        m_totals.assertions.failed++;
+        assertions.failed++;
+        return true;
+    }
+
+    void RunContext::sectionEnded(SectionEndInfo const & endInfo) {
+        Counts assertions = m_totals.assertions - endInfo.prevAssertions;
+        bool missingAssertions = testForMissingAssertions(assertions);
+
+        if (!m_activeSections.empty()) {
+            m_activeSections.back()->close();
+            m_activeSections.pop_back();
+        }
+
+        m_reporter->sectionEnded(SectionStats(endInfo.sectionInfo, assertions, endInfo.durationInSeconds, missingAssertions));
+        m_messages.clear();
+    }
+
+    void RunContext::sectionEndedEarly(SectionEndInfo const & endInfo) {
+        if (m_unfinishedSections.empty())
+            m_activeSections.back()->fail();
+        else
+            m_activeSections.back()->close();
+        m_activeSections.pop_back();
+
+        m_unfinishedSections.push_back(endInfo);
+    }
+    void RunContext::benchmarkStarting( BenchmarkInfo const& info ) {
+        m_reporter->benchmarkStarting( info );
+    }
+    void RunContext::benchmarkEnded( BenchmarkStats const& stats ) {
+        m_reporter->benchmarkEnded( stats );
+    }
+
+    void RunContext::pushScopedMessage(MessageInfo const & message) {
+        m_messages.push_back(message);
+    }
+
+    void RunContext::popScopedMessage(MessageInfo const & message) {
+        m_messages.erase(std::remove(m_messages.begin(), m_messages.end(), message), m_messages.end());
+    }
+
+    std::string RunContext::getCurrentTestName() const {
+        return m_activeTestCase
+            ? m_activeTestCase->getTestCaseInfo().name
+            : std::string();
+    }
+
+    const AssertionResult * RunContext::getLastResult() const {
+        return &(*m_lastResult);
+    }
+
+    void RunContext::exceptionEarlyReported() {
+        m_shouldReportUnexpected = false;
+    }
+
+    void RunContext::handleFatalErrorCondition( StringRef message ) {
+        // First notify reporter that bad things happened
+        m_reporter->fatalErrorEncountered(message);
+
+        // Don't rebuild the result -- the stringification itself can cause more fatal errors
+        // Instead, fake a result data.
+        AssertionResultData tempResult( ResultWas::FatalErrorCondition, { false } );
+        tempResult.message = message;
+        AssertionResult result(m_lastAssertionInfo, tempResult);
+
+        assertionEnded(result);
+
+        handleUnfinishedSections();
+
+        // Recreate section for test case (as we will lose the one that was in scope)
+        auto const& testCaseInfo = m_activeTestCase->getTestCaseInfo();
+        SectionInfo testCaseSection(testCaseInfo.lineInfo, testCaseInfo.name);
+
+        Counts assertions;
+        assertions.failed = 1;
+        SectionStats testCaseSectionStats(testCaseSection, assertions, 0, false);
+        m_reporter->sectionEnded(testCaseSectionStats);
+
+        auto const& testInfo = m_activeTestCase->getTestCaseInfo();
+
+        Totals deltaTotals;
+        deltaTotals.testCases.failed = 1;
+        deltaTotals.assertions.failed = 1;
+        m_reporter->testCaseEnded(TestCaseStats(testInfo,
+                                  deltaTotals,
+                                  std::string(),
+                                  std::string(),
+                                  false));
+        m_totals.testCases.failed++;
+        testGroupEnded(std::string(), m_totals, 1, 1);
+        m_reporter->testRunEnded(TestRunStats(m_runInfo, m_totals, false));
+    }
+
+    bool RunContext::lastAssertionPassed() {
+         return m_lastAssertionPassed;
+    }
+
+    void RunContext::assertionPassed() {
+        m_lastAssertionPassed = true;
+        ++m_totals.assertions.passed;
+        resetAssertionInfo();
+    }
+
+    bool RunContext::aborting() const {
+        return m_totals.assertions.failed >= static_cast<std::size_t>(m_config->abortAfter());
+    }
+
+    void RunContext::runCurrentTest(std::string & redirectedCout, std::string & redirectedCerr) {
+        auto const& testCaseInfo = m_activeTestCase->getTestCaseInfo();
+        SectionInfo testCaseSection(testCaseInfo.lineInfo, testCaseInfo.name);
+        m_reporter->sectionStarting(testCaseSection);
+        Counts prevAssertions = m_totals.assertions;
+        double duration = 0;
+        m_shouldReportUnexpected = true;
+        m_lastAssertionInfo = { "TEST_CASE"_sr, testCaseInfo.lineInfo, StringRef(), ResultDisposition::Normal };
+
+        seedRng(*m_config);
+
+        Timer timer;
+        CATCH_TRY {
+            if (m_reporter->getPreferences().shouldRedirectStdOut) {
+#if !defined(CATCH_CONFIG_EXPERIMENTAL_REDIRECT)
+                RedirectedStdOut redirectedStdOut;
+                RedirectedStdErr redirectedStdErr;
+
+                timer.start();
+                invokeActiveTestCase();
+                redirectedCout += redirectedStdOut.str();
+                redirectedCerr += redirectedStdErr.str();
+#else
+                OutputRedirect r(redirectedCout, redirectedCerr);
+                timer.start();
+                invokeActiveTestCase();
+#endif
+            } else {
+                timer.start();
+                invokeActiveTestCase();
+            }
+            duration = timer.getElapsedSeconds();
+        } CATCH_CATCH_ANON (TestFailureException&) {
+            // This just means the test was aborted due to failure
+        } CATCH_CATCH_ALL {
+            // Under CATCH_CONFIG_FAST_COMPILE, unexpected exceptions under REQUIRE assertions
+            // are reported without translation at the point of origin.
+            if( m_shouldReportUnexpected ) {
+                AssertionReaction dummyReaction;
+                handleUnexpectedInflightException( m_lastAssertionInfo, translateActiveException(), dummyReaction );
+            }
+        }
+        Counts assertions = m_totals.assertions - prevAssertions;
+        bool missingAssertions = testForMissingAssertions(assertions);
+
+        m_testCaseTracker->close();
+        handleUnfinishedSections();
+        m_messages.clear();
+
+        SectionStats testCaseSectionStats(testCaseSection, assertions, duration, missingAssertions);
+        m_reporter->sectionEnded(testCaseSectionStats);
+    }
+
+    void RunContext::invokeActiveTestCase() {
+        FatalConditionHandler fatalConditionHandler; // Handle signals
+        m_activeTestCase->invoke();
+        fatalConditionHandler.reset();
+    }
+
+    void RunContext::handleUnfinishedSections() {
+        // If sections ended prematurely due to an exception we stored their
+        // infos here so we can tear them down outside the unwind process.
+        for (auto it = m_unfinishedSections.rbegin(),
+             itEnd = m_unfinishedSections.rend();
+             it != itEnd;
+             ++it)
+            sectionEnded(*it);
+        m_unfinishedSections.clear();
+    }
+
+    void RunContext::handleExpr(
+        AssertionInfo const& info,
+        ITransientExpression const& expr,
+        AssertionReaction& reaction
+    ) {
+        m_reporter->assertionStarting( info );
+
+        bool negated = isFalseTest( info.resultDisposition );
+        bool result = expr.getResult() != negated;
+
+        if( result ) {
+            if (!m_includeSuccessfulResults) {
+                assertionPassed();
+            }
+            else {
+                reportExpr(info, ResultWas::Ok, &expr, negated);
+            }
+        }
+        else {
+            reportExpr(info, ResultWas::ExpressionFailed, &expr, negated );
+            populateReaction( reaction );
+        }
+    }
+    void RunContext::reportExpr(
+            AssertionInfo const &info,
+            ResultWas::OfType resultType,
+            ITransientExpression const *expr,
+            bool negated ) {
+
+        m_lastAssertionInfo = info;
+        AssertionResultData data( resultType, LazyExpression( negated ) );
+
+        AssertionResult assertionResult{ info, data };
+        assertionResult.m_resultData.lazyExpression.m_transientExpression = expr;
+
+        assertionEnded( assertionResult );
+    }
+
+    void RunContext::handleMessage(
+            AssertionInfo const& info,
+            ResultWas::OfType resultType,
+            StringRef const& message,
+            AssertionReaction& reaction
+    ) {
+        m_reporter->assertionStarting( info );
+
+        m_lastAssertionInfo = info;
+
+        AssertionResultData data( resultType, LazyExpression( false ) );
+        data.message = message;
+        AssertionResult assertionResult{ m_lastAssertionInfo, data };
+        assertionEnded( assertionResult );
+        if( !assertionResult.isOk() )
+            populateReaction( reaction );
+    }
+    void RunContext::handleUnexpectedExceptionNotThrown(
+            AssertionInfo const& info,
+            AssertionReaction& reaction
+    ) {
+        handleNonExpr(info, Catch::ResultWas::DidntThrowException, reaction);
+    }
+
+    void RunContext::handleUnexpectedInflightException(
+            AssertionInfo const& info,
+            std::string const& message,
+            AssertionReaction& reaction
+    ) {
+        m_lastAssertionInfo = info;
+
+        AssertionResultData data( ResultWas::ThrewException, LazyExpression( false ) );
+        data.message = message;
+        AssertionResult assertionResult{ info, data };
+        assertionEnded( assertionResult );
+        populateReaction( reaction );
+    }
+
+    void RunContext::populateReaction( AssertionReaction& reaction ) {
+        reaction.shouldDebugBreak = m_config->shouldDebugBreak();
+        reaction.shouldThrow = aborting() || (m_lastAssertionInfo.resultDisposition & ResultDisposition::Normal);
+    }
+
+    void RunContext::handleIncomplete(
+            AssertionInfo const& info
+    ) {
+        m_lastAssertionInfo = info;
+
+        AssertionResultData data( ResultWas::ThrewException, LazyExpression( false ) );
+        data.message = "Exception translation was disabled by CATCH_CONFIG_FAST_COMPILE";
+        AssertionResult assertionResult{ info, data };
+        assertionEnded( assertionResult );
+    }
+    void RunContext::handleNonExpr(
+            AssertionInfo const &info,
+            ResultWas::OfType resultType,
+            AssertionReaction &reaction
+    ) {
+        m_lastAssertionInfo = info;
+
+        AssertionResultData data( resultType, LazyExpression( false ) );
+        AssertionResult assertionResult{ info, data };
+        assertionEnded( assertionResult );
+
+        if( !assertionResult.isOk() )
+            populateReaction( reaction );
+    }
+
+    IResultCapture& getResultCapture() {
+        if (auto* capture = getCurrentContext().getResultCapture())
+            return *capture;
+        else
+            CATCH_INTERNAL_ERROR("No result capture instance");
+    }
+}
+// end catch_run_context.cpp
+// start catch_section.cpp
+
+namespace Catch {
+
+    Section::Section( SectionInfo const& info )
+    :   m_info( info ),
+        m_sectionIncluded( getResultCapture().sectionStarted( m_info, m_assertions ) )
+    {
+        m_timer.start();
+    }
+
+    Section::~Section() {
+        if( m_sectionIncluded ) {
+            SectionEndInfo endInfo{ m_info, m_assertions, m_timer.getElapsedSeconds() };
+            if( uncaught_exceptions() )
+                getResultCapture().sectionEndedEarly( endInfo );
+            else
+                getResultCapture().sectionEnded( endInfo );
+        }
+    }
+
+    // This indicates whether the section should be executed or not
+    Section::operator bool() const {
+        return m_sectionIncluded;
+    }
+
+} // end namespace Catch
+// end catch_section.cpp
+// start catch_section_info.cpp
+
+namespace Catch {
+
+    SectionInfo::SectionInfo
+        (   SourceLineInfo const& _lineInfo,
+            std::string const& _name )
+    :   name( _name ),
+        lineInfo( _lineInfo )
+    {}
+
+} // end namespace Catch
+// end catch_section_info.cpp
+// start catch_session.cpp
+
+// start catch_session.h
+
+#include <memory>
+
+namespace Catch {
+
+    class Session : NonCopyable {
+    public:
+
+        Session();
+        ~Session() override;
+
+        void showHelp() const;
+        void libIdentify();
+
+        int applyCommandLine( int argc, char const * const * argv );
+
+        void useConfigData( ConfigData const& configData );
+
+        int run( int argc, char* argv[] );
+    #if defined(CATCH_CONFIG_WCHAR) && defined(WIN32) && defined(UNICODE)
+        int run( int argc, wchar_t* const argv[] );
+    #endif
+        int run();
+
+        clara::Parser const& cli() const;
+        void cli( clara::Parser const& newParser );
+        ConfigData& configData();
+        Config& config();
+    private:
+        int runInternal();
+
+        clara::Parser m_cli;
+        ConfigData m_configData;
+        std::shared_ptr<Config> m_config;
+        bool m_startupExceptions = false;
+    };
+
+} // end namespace Catch
+
+// end catch_session.h
+// start catch_version.h
+
+#include <iosfwd>
+
+namespace Catch {
+
+    // Versioning information
+    struct Version {
+        Version( Version const& ) = delete;
+        Version& operator=( Version const& ) = delete;
+        Version(    unsigned int _majorVersion,
+                    unsigned int _minorVersion,
+                    unsigned int _patchNumber,
+                    char const * const _branchName,
+                    unsigned int _buildNumber );
+
+        unsigned int const majorVersion;
+        unsigned int const minorVersion;
+        unsigned int const patchNumber;
+
+        // buildNumber is only used if branchName is not null
+        char const * const branchName;
+        unsigned int const buildNumber;
+
+        friend std::ostream& operator << ( std::ostream& os, Version const& version );
+    };
+
+    Version const& libraryVersion();
+}
+
+// end catch_version.h
+#include <cstdlib>
+#include <iomanip>
+
+namespace Catch {
+
+    namespace {
+        const int MaxExitCode = 255;
+
+        IStreamingReporterPtr createReporter(std::string const& reporterName, IConfigPtr const& config) {
+            auto reporter = Catch::getRegistryHub().getReporterRegistry().create(reporterName, config);
+            CATCH_ENFORCE(reporter, "No reporter registered with name: '" << reporterName << "'");
+
+            return reporter;
+        }
+
+        IStreamingReporterPtr makeReporter(std::shared_ptr<Config> const& config) {
+            if (Catch::getRegistryHub().getReporterRegistry().getListeners().empty()) {
+                return createReporter(config->getReporterName(), config);
+            }
+
+            auto multi = std::unique_ptr<ListeningReporter>(new ListeningReporter);
+
+            auto const& listeners = Catch::getRegistryHub().getReporterRegistry().getListeners();
+            for (auto const& listener : listeners) {
+                multi->addListener(listener->create(Catch::ReporterConfig(config)));
+            }
+            multi->addReporter(createReporter(config->getReporterName(), config));
+            return std::move(multi);
+        }
+
+        Catch::Totals runTests(std::shared_ptr<Config> const& config) {
+            // FixMe: Add listeners in order first, then add reporters.
+
+            auto reporter = makeReporter(config);
+
+            RunContext context(config, std::move(reporter));
+
+            Totals totals;
+
+            context.testGroupStarting(config->name(), 1, 1);
+
+            TestSpec testSpec = config->testSpec();
+
+            auto const& allTestCases = getAllTestCasesSorted(*config);
+            for (auto const& testCase : allTestCases) {
+                if (!context.aborting() && matchTest(testCase, testSpec, *config))
+                    totals += context.runTest(testCase);
+                else
+                    context.reporter().skipTest(testCase);
+            }
+
+            if (config->warnAboutNoTests() && totals.testCases.total() == 0) {
+                ReusableStringStream testConfig;
+
+                bool first = true;
+                for (const auto& input : config->getTestsOrTags()) {
+                    if (!first) { testConfig << ' '; }
+                    first = false;
+                    testConfig << input;
+                }
+
+                context.reporter().noMatchingTestCases(testConfig.str());
+                totals.error = -1;
+            }
+
+            context.testGroupEnded(config->name(), totals, 1, 1);
+            return totals;
+        }
+
+        void applyFilenamesAsTags(Catch::IConfig const& config) {
+            auto& tests = const_cast<std::vector<TestCase>&>(getAllTestCasesSorted(config));
+            for (auto& testCase : tests) {
+                auto tags = testCase.tags;
+
+                std::string filename = testCase.lineInfo.file;
+                auto lastSlash = filename.find_last_of("\\/");
+                if (lastSlash != std::string::npos) {
+                    filename.erase(0, lastSlash);
+                    filename[0] = '#';
+                }
+
+                auto lastDot = filename.find_last_of('.');
+                if (lastDot != std::string::npos) {
+                    filename.erase(lastDot);
+                }
+
+                tags.push_back(std::move(filename));
+                setTags(testCase, tags);
+            }
+        }
+
+    } // anon namespace
+
+    Session::Session() {
+        static bool alreadyInstantiated = false;
+        if( alreadyInstantiated ) {
+            CATCH_TRY { CATCH_INTERNAL_ERROR( "Only one instance of Catch::Session can ever be used" ); }
+            CATCH_CATCH_ALL { getMutableRegistryHub().registerStartupException(); }
+        }
+
+        // There cannot be exceptions at startup in no-exception mode.
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+        const auto& exceptions = getRegistryHub().getStartupExceptionRegistry().getExceptions();
+        if ( !exceptions.empty() ) {
+            m_startupExceptions = true;
+            Colour colourGuard( Colour::Red );
+            Catch::cerr() << "Errors occurred during startup!" << '\n';
+            // iterate over all exceptions and notify user
+            for ( const auto& ex_ptr : exceptions ) {
+                try {
+                    std::rethrow_exception(ex_ptr);
+                } catch ( std::exception const& ex ) {
+                    Catch::cerr() << Column( ex.what() ).indent(2) << '\n';
+                }
+            }
+        }
+#endif
+
+        alreadyInstantiated = true;
+        m_cli = makeCommandLineParser( m_configData );
+    }
+    Session::~Session() {
+        Catch::cleanUp();
+    }
+
+    void Session::showHelp() const {
+        Catch::cout()
+                << "\nCatch v" << libraryVersion() << "\n"
+                << m_cli << std::endl
+                << "For more detailed usage please see the project docs\n" << std::endl;
+    }
+    void Session::libIdentify() {
+        Catch::cout()
+                << std::left << std::setw(16) << "description: " << "A Catch test executable\n"
+                << std::left << std::setw(16) << "category: " << "testframework\n"
+                << std::left << std::setw(16) << "framework: " << "Catch Test\n"
+                << std::left << std::setw(16) << "version: " << libraryVersion() << std::endl;
+    }
+
+    int Session::applyCommandLine( int argc, char const * const * argv ) {
+        if( m_startupExceptions )
+            return 1;
+
+        auto result = m_cli.parse( clara::Args( argc, argv ) );
+        if( !result ) {
+            Catch::cerr()
+                << Colour( Colour::Red )
+                << "\nError(s) in input:\n"
+                << Column( result.errorMessage() ).indent( 2 )
+                << "\n\n";
+            Catch::cerr() << "Run with -? for usage\n" << std::endl;
+            return MaxExitCode;
+        }
+
+        if( m_configData.showHelp )
+            showHelp();
+        if( m_configData.libIdentify )
+            libIdentify();
+        m_config.reset();
+        return 0;
+    }
+
+    void Session::useConfigData( ConfigData const& configData ) {
+        m_configData = configData;
+        m_config.reset();
+    }
+
+    int Session::run( int argc, char* argv[] ) {
+        if( m_startupExceptions )
+            return 1;
+        int returnCode = applyCommandLine( argc, argv );
+        if( returnCode == 0 )
+            returnCode = run();
+        return returnCode;
+    }
+
+#if defined(CATCH_CONFIG_WCHAR) && defined(WIN32) && defined(UNICODE)
+    int Session::run( int argc, wchar_t* const argv[] ) {
+
+        char **utf8Argv = new char *[ argc ];
+
+        for ( int i = 0; i < argc; ++i ) {
+            int bufSize = WideCharToMultiByte( CP_UTF8, 0, argv[i], -1, NULL, 0, NULL, NULL );
+
+            utf8Argv[ i ] = new char[ bufSize ];
+
+            WideCharToMultiByte( CP_UTF8, 0, argv[i], -1, utf8Argv[i], bufSize, NULL, NULL );
+        }
+
+        int returnCode = run( argc, utf8Argv );
+
+        for ( int i = 0; i < argc; ++i )
+            delete [] utf8Argv[ i ];
+
+        delete [] utf8Argv;
+
+        return returnCode;
+    }
+#endif
+    int Session::run() {
+        if( ( m_configData.waitForKeypress & WaitForKeypress::BeforeStart ) != 0 ) {
+            Catch::cout() << "...waiting for enter/ return before starting" << std::endl;
+            static_cast<void>(std::getchar());
+        }
+        int exitCode = runInternal();
+        if( ( m_configData.waitForKeypress & WaitForKeypress::BeforeExit ) != 0 ) {
+            Catch::cout() << "...waiting for enter/ return before exiting, with code: " << exitCode << std::endl;
+            static_cast<void>(std::getchar());
+        }
+        return exitCode;
+    }
+
+    clara::Parser const& Session::cli() const {
+        return m_cli;
+    }
+    void Session::cli( clara::Parser const& newParser ) {
+        m_cli = newParser;
+    }
+    ConfigData& Session::configData() {
+        return m_configData;
+    }
+    Config& Session::config() {
+        if( !m_config )
+            m_config = std::make_shared<Config>( m_configData );
+        return *m_config;
+    }
+
+    int Session::runInternal() {
+        if( m_startupExceptions )
+            return 1;
+
+        if (m_configData.showHelp || m_configData.libIdentify) {
+            return 0;
+        }
+
+        CATCH_TRY {
+            config(); // Force config to be constructed
+
+            seedRng( *m_config );
+
+            if( m_configData.filenamesAsTags )
+                applyFilenamesAsTags( *m_config );
+
+            // Handle list request
+            if( Option<std::size_t> listed = list( config() ) )
+                return static_cast<int>( *listed );
+
+            auto totals = runTests( m_config );
+            // Note that on unices only the lower 8 bits are usually used, clamping
+            // the return value to 255 prevents false negative when some multiple
+            // of 256 tests has failed
+            return (std::min) (MaxExitCode, (std::max) (totals.error, static_cast<int>(totals.assertions.failed)));
+        }
+#if !defined(CATCH_CONFIG_DISABLE_EXCEPTIONS)
+        catch( std::exception& ex ) {
+            Catch::cerr() << ex.what() << std::endl;
+            return MaxExitCode;
+        }
+#endif
+    }
+
+} // end namespace Catch
+// end catch_session.cpp
+// start catch_singletons.cpp
+
+#include <vector>
+
+namespace Catch {
+
+    namespace {
+        static auto getSingletons() -> std::vector<ISingleton*>*& {
+            static std::vector<ISingleton*>* g_singletons = nullptr;
+            if( !g_singletons )
+                g_singletons = new std::vector<ISingleton*>();
+            return g_singletons;
+        }
+    }
+
+    ISingleton::~ISingleton() {}
+
+    void addSingleton(ISingleton* singleton ) {
+        getSingletons()->push_back( singleton );
+    }
+    void cleanupSingletons() {
+        auto& singletons = getSingletons();
+        for( auto singleton : *singletons )
+            delete singleton;
+        delete singletons;
+        singletons = nullptr;
+    }
+
+} // namespace Catch
+// end catch_singletons.cpp
+// start catch_startup_exception_registry.cpp
+
+namespace Catch {
+void StartupExceptionRegistry::add( std::exception_ptr const& exception ) noexcept {
+        CATCH_TRY {
+            m_exceptions.push_back(exception);
+        } CATCH_CATCH_ALL {
+            // If we run out of memory during start-up there's really not a lot more we can do about it
+            std::terminate();
+        }
+    }
+
+    std::vector<std::exception_ptr> const& StartupExceptionRegistry::getExceptions() const noexcept {
+        return m_exceptions;
+    }
+
+} // end namespace Catch
+// end catch_startup_exception_registry.cpp
+// start catch_stream.cpp
+
+#include <cstdio>
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <vector>
+#include <memory>
+
+namespace Catch {
+
+    Catch::IStream::~IStream() = default;
+
+    namespace detail { namespace {
+        template<typename WriterF, std::size_t bufferSize=256>
+        class StreamBufImpl : public std::streambuf {
+            char data[bufferSize];
+            WriterF m_writer;
+
+        public:
+            StreamBufImpl() {
+                setp( data, data + sizeof(data) );
+            }
+
+            ~StreamBufImpl() noexcept {
+                StreamBufImpl::sync();
+            }
+
+        private:
+            int overflow( int c ) override {
+                sync();
+
+                if( c != EOF ) {
+                    if( pbase() == epptr() )
+                        m_writer( std::string( 1, static_cast<char>( c ) ) );
+                    else
+                        sputc( static_cast<char>( c ) );
+                }
+                return 0;
+            }
+
+            int sync() override {
+                if( pbase() != pptr() ) {
+                    m_writer( std::string( pbase(), static_cast<std::string::size_type>( pptr() - pbase() ) ) );
+                    setp( pbase(), epptr() );
+                }
+                return 0;
+            }
+        };
+
+        ///////////////////////////////////////////////////////////////////////////
+
+        struct OutputDebugWriter {
+
+            void operator()( std::string const&str ) {
+                writeToDebugConsole( str );
+            }
+        };
+
+        ///////////////////////////////////////////////////////////////////////////
+
+        class FileStream : public IStream {
+            mutable std::ofstream m_ofs;
+        public:
+            FileStream( StringRef filename ) {
+                m_ofs.open( filename.c_str() );
+                CATCH_ENFORCE( !m_ofs.fail(), "Unable to open file: '" << filename << "'" );
+            }
+            ~FileStream() override = default;
+        public: // IStream
+            std::ostream& stream() const override {
+                return m_ofs;
+            }
+        };
+
+        ///////////////////////////////////////////////////////////////////////////
+
+        class CoutStream : public IStream {
+            mutable std::ostream m_os;
+        public:
+            // Store the streambuf from cout up-front because
+            // cout may get redirected when running tests
+            CoutStream() : m_os( Catch::cout().rdbuf() ) {}
+            ~CoutStream() override = default;
+
+        public: // IStream
+            std::ostream& stream() const override { return m_os; }
+        };
+
+        ///////////////////////////////////////////////////////////////////////////
+
+        class DebugOutStream : public IStream {
+            std::unique_ptr<StreamBufImpl<OutputDebugWriter>> m_streamBuf;
+            mutable std::ostream m_os;
+        public:
+            DebugOutStream()
+            :   m_streamBuf( new StreamBufImpl<OutputDebugWriter>() ),
+                m_os( m_streamBuf.get() )
+            {}
+
+            ~DebugOutStream() override = default;
+
+        public: // IStream
+            std::ostream& stream() const override { return m_os; }
+        };
+
+    }} // namespace anon::detail
+
+    ///////////////////////////////////////////////////////////////////////////
+
+    auto makeStream( StringRef const &filename ) -> IStream const* {
+        if( filename.empty() )
+            return new detail::CoutStream();
+        else if( filename[0] == '%' ) {
+            if( filename == "%debug" )
+                return new detail::DebugOutStream();
+            else
+                CATCH_ERROR( "Unrecognised stream: '" << filename << "'" );
+        }
+        else
+            return new detail::FileStream( filename );
+    }
+
+    // This class encapsulates the idea of a pool of ostringstreams that can be reused.
+    struct StringStreams {
+        std::vector<std::unique_ptr<std::ostringstream>> m_streams;
+        std::vector<std::size_t> m_unused;
+        std::ostringstream m_referenceStream; // Used for copy state/ flags from
+
+        auto add() -> std::size_t {
+            if( m_unused.empty() ) {
+                m_streams.push_back( std::unique_ptr<std::ostringstream>( new std::ostringstream ) );
+                return m_streams.size()-1;
+            }
+            else {
+                auto index = m_unused.back();
+                m_unused.pop_back();
+                return index;
+            }
+        }
+
+        void release( std::size_t index ) {
+            m_streams[index]->copyfmt( m_referenceStream ); // Restore initial flags and other state
+            m_unused.push_back(index);
+        }
+    };
+
+    ReusableStringStream::ReusableStringStream()
+    :   m_index( Singleton<StringStreams>::getMutable().add() ),
+        m_oss( Singleton<StringStreams>::getMutable().m_streams[m_index].get() )
+    {}
+
+    ReusableStringStream::~ReusableStringStream() {
+        static_cast<std::ostringstream*>( m_oss )->str("");
+        m_oss->clear();
+        Singleton<StringStreams>::getMutable().release( m_index );
+    }
+
+    auto ReusableStringStream::str() const -> std::string {
+        return static_cast<std::ostringstream*>( m_oss )->str();
+    }
+
+    ///////////////////////////////////////////////////////////////////////////
+
+#ifndef CATCH_CONFIG_NOSTDOUT // If you #define this you must implement these functions
+    std::ostream& cout() { return std::cout; }
+    std::ostream& cerr() { return std::cerr; }
+    std::ostream& clog() { return std::clog; }
+#endif
+}
+// end catch_stream.cpp
+// start catch_string_manip.cpp
+
+#include <algorithm>
+#include <ostream>
+#include <cstring>
+#include <cctype>
+
+namespace Catch {
+
+    namespace {
+        char toLowerCh(char c) {
+            return static_cast<char>( std::tolower( c ) );
+        }
+    }
+
+    bool startsWith( std::string const& s, std::string const& prefix ) {
+        return s.size() >= prefix.size() && std::equal(prefix.begin(), prefix.end(), s.begin());
+    }
+    bool startsWith( std::string const& s, char prefix ) {
+        return !s.empty() && s[0] == prefix;
+    }
+    bool endsWith( std::string const& s, std::string const& suffix ) {
+        return s.size() >= suffix.size() && std::equal(suffix.rbegin(), suffix.rend(), s.rbegin());
+    }
+    bool endsWith( std::string const& s, char suffix ) {
+        return !s.empty() && s[s.size()-1] == suffix;
+    }
+    bool contains( std::string const& s, std::string const& infix ) {
+        return s.find( infix ) != std::string::npos;
+    }
+    void toLowerInPlace( std::string& s ) {
+        std::transform( s.begin(), s.end(), s.begin(), toLowerCh );
+    }
+    std::string toLower( std::string const& s ) {
+        std::string lc = s;
+        toLowerInPlace( lc );
+        return lc;
+    }
+    std::string trim( std::string const& str ) {
+        static char const* whitespaceChars = "\n\r\t ";
+        std::string::size_type start = str.find_first_not_of( whitespaceChars );
+        std::string::size_type end = str.find_last_not_of( whitespaceChars );
+
+        return start != std::string::npos ? str.substr( start, 1+end-start ) : std::string();
+    }
+
+    bool replaceInPlace( std::string& str, std::string const& replaceThis, std::string const& withThis ) {
+        bool replaced = false;
+        std::size_t i = str.find( replaceThis );
+        while( i != std::string::npos ) {
+            replaced = true;
+            str = str.substr( 0, i ) + withThis + str.substr( i+replaceThis.size() );
+            if( i < str.size()-withThis.size() )
+                i = str.find( replaceThis, i+withThis.size() );
+            else
+                i = std::string::npos;
+        }
+        return replaced;
+    }
+
+    pluralise::pluralise( std::size_t count, std::string const& label )
+    :   m_count( count ),
+        m_label( label )
+    {}
+
+    std::ostream& operator << ( std::ostream& os, pluralise const& pluraliser ) {
+        os << pluraliser.m_count << ' ' << pluraliser.m_label;
+        if( pluraliser.m_count != 1 )
+            os << 's';
+        return os;
+    }
+
+}
+// end catch_string_manip.cpp
+// start catch_stringref.cpp
+
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wexit-time-destructors"
+#endif
+
+#include <ostream>
+#include <cstring>
+#include <cstdint>
+
+namespace {
+    const uint32_t byte_2_lead = 0xC0;
+    const uint32_t byte_3_lead = 0xE0;
+    const uint32_t byte_4_lead = 0xF0;
+}
+
+namespace Catch {
+    StringRef::StringRef( char const* rawChars ) noexcept
+    : StringRef( rawChars, static_cast<StringRef::size_type>(std::strlen(rawChars) ) )
+    {}
+
+    StringRef::operator std::string() const {
+        return std::string( m_start, m_size );
+    }
+
+    void StringRef::swap( StringRef& other ) noexcept {
+        std::swap( m_start, other.m_start );
+        std::swap( m_size, other.m_size );
+        std::swap( m_data, other.m_data );
+    }
+
+    auto StringRef::c_str() const -> char const* {
+        if( isSubstring() )
+           const_cast<StringRef*>( this )->takeOwnership();
+        return m_start;
+    }
+    auto StringRef::currentData() const noexcept -> char const* {
+        return m_start;
+    }
+
+    auto StringRef::isOwned() const noexcept -> bool {
+        return m_data != nullptr;
+    }
+    auto StringRef::isSubstring() const noexcept -> bool {
+        return m_start[m_size] != '\0';
+    }
+
+    void StringRef::takeOwnership() {
+        if( !isOwned() ) {
+            m_data = new char[m_size+1];
+            memcpy( m_data, m_start, m_size );
+            m_data[m_size] = '\0';
+            m_start = m_data;
+        }
+    }
+    auto StringRef::substr( size_type start, size_type size ) const noexcept -> StringRef {
+        if( start < m_size )
+            return StringRef( m_start+start, size );
+        else
+            return StringRef();
+    }
+    auto StringRef::operator == ( StringRef const& other ) const noexcept -> bool {
+        return
+            size() == other.size() &&
+            (std::strncmp( m_start, other.m_start, size() ) == 0);
+    }
+    auto StringRef::operator != ( StringRef const& other ) const noexcept -> bool {
+        return !operator==( other );
+    }
+
+    auto StringRef::operator[](size_type index) const noexcept -> char {
+        return m_start[index];
+    }
+
+    auto StringRef::numberOfCharacters() const noexcept -> size_type {
+        size_type noChars = m_size;
+        // Make adjustments for uft encodings
+        for( size_type i=0; i < m_size; ++i ) {
+            char c = m_start[i];
+            if( ( c & byte_2_lead ) == byte_2_lead ) {
+                noChars--;
+                if (( c & byte_3_lead ) == byte_3_lead )
+                    noChars--;
+                if( ( c & byte_4_lead ) == byte_4_lead )
+                    noChars--;
+            }
+        }
+        return noChars;
+    }
+
+    auto operator + ( StringRef const& lhs, StringRef const& rhs ) -> std::string {
+        std::string str;
+        str.reserve( lhs.size() + rhs.size() );
+        str += lhs;
+        str += rhs;
+        return str;
+    }
+    auto operator + ( StringRef const& lhs, const char* rhs ) -> std::string {
+        return std::string( lhs ) + std::string( rhs );
+    }
+    auto operator + ( char const* lhs, StringRef const& rhs ) -> std::string {
+        return std::string( lhs ) + std::string( rhs );
+    }
+
+    auto operator << ( std::ostream& os, StringRef const& str ) -> std::ostream& {
+        return os.write(str.currentData(), str.size());
+    }
+
+    auto operator+=( std::string& lhs, StringRef const& rhs ) -> std::string& {
+        lhs.append(rhs.currentData(), rhs.size());
+        return lhs;
+    }
+
+} // namespace Catch
+
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
+// end catch_stringref.cpp
+// start catch_tag_alias.cpp
+
+namespace Catch {
+    TagAlias::TagAlias(std::string const & _tag, SourceLineInfo _lineInfo): tag(_tag), lineInfo(_lineInfo) {}
+}
+// end catch_tag_alias.cpp
+// start catch_tag_alias_autoregistrar.cpp
+
+namespace Catch {
+
+    RegistrarForTagAliases::RegistrarForTagAliases(char const* alias, char const* tag, SourceLineInfo const& lineInfo) {
+        CATCH_TRY {
+            getMutableRegistryHub().registerTagAlias(alias, tag, lineInfo);
+        } CATCH_CATCH_ALL {
+            // Do not throw when constructing global objects, instead register the exception to be processed later
+            getMutableRegistryHub().registerStartupException();
+        }
+    }
+
+}
+// end catch_tag_alias_autoregistrar.cpp
+// start catch_tag_alias_registry.cpp
+
+#include <sstream>
+
+namespace Catch {
+
+    TagAliasRegistry::~TagAliasRegistry() {}
+
+    TagAlias const* TagAliasRegistry::find( std::string const& alias ) const {
+        auto it = m_registry.find( alias );
+        if( it != m_registry.end() )
+            return &(it->second);
+        else
+            return nullptr;
+    }
+
+    std::string TagAliasRegistry::expandAliases( std::string const& unexpandedTestSpec ) const {
+        std::string expandedTestSpec = unexpandedTestSpec;
+        for( auto const& registryKvp : m_registry ) {
+            std::size_t pos = expandedTestSpec.find( registryKvp.first );
+            if( pos != std::string::npos ) {
+                expandedTestSpec =  expandedTestSpec.substr( 0, pos ) +
+                                    registryKvp.second.tag +
+                                    expandedTestSpec.substr( pos + registryKvp.first.size() );
+            }
+        }
+        return expandedTestSpec;
+    }
+
+    void TagAliasRegistry::add( std::string const& alias, std::string const& tag, SourceLineInfo const& lineInfo ) {
+        CATCH_ENFORCE( startsWith(alias, "[@") && endsWith(alias, ']'),
+                      "error: tag alias, '" << alias << "' is not of the form [@alias name].\n" << lineInfo );
+
+        CATCH_ENFORCE( m_registry.insert(std::make_pair(alias, TagAlias(tag, lineInfo))).second,
+                      "error: tag alias, '" << alias << "' already registered.\n"
+                      << "\tFirst seen at: " << find(alias)->lineInfo << "\n"
+                      << "\tRedefined at: " << lineInfo );
+    }
+
+    ITagAliasRegistry::~ITagAliasRegistry() {}
+
+    ITagAliasRegistry const& ITagAliasRegistry::get() {
+        return getRegistryHub().getTagAliasRegistry();
+    }
+
+} // end namespace Catch
+// end catch_tag_alias_registry.cpp
+// start catch_test_case_info.cpp
+
+#include <cctype>
+#include <exception>
+#include <algorithm>
+#include <sstream>
+
+namespace Catch {
+
+    namespace {
+        TestCaseInfo::SpecialProperties parseSpecialTag( std::string const& tag ) {
+            if( startsWith( tag, '.' ) ||
+                tag == "!hide" )
+                return TestCaseInfo::IsHidden;
+            else if( tag == "!throws" )
+                return TestCaseInfo::Throws;
+            else if( tag == "!shouldfail" )
+                return TestCaseInfo::ShouldFail;
+            else if( tag == "!mayfail" )
+                return TestCaseInfo::MayFail;
+            else if( tag == "!nonportable" )
+                return TestCaseInfo::NonPortable;
+            else if( tag == "!benchmark" )
+                return static_cast<TestCaseInfo::SpecialProperties>( TestCaseInfo::Benchmark | TestCaseInfo::IsHidden );
+            else
+                return TestCaseInfo::None;
+        }
+        bool isReservedTag( std::string const& tag ) {
+            return parseSpecialTag( tag ) == TestCaseInfo::None && tag.size() > 0 && !std::isalnum( static_cast<unsigned char>(tag[0]) );
+        }
+        void enforceNotReservedTag( std::string const& tag, SourceLineInfo const& _lineInfo ) {
+            CATCH_ENFORCE( !isReservedTag(tag),
+                          "Tag name: [" << tag << "] is not allowed.\n"
+                          << "Tag names starting with non alpha-numeric characters are reserved\n"
+                          << _lineInfo );
+        }
+    }
+
+    TestCase makeTestCase(  ITestInvoker* _testCase,
+                            std::string const& _className,
+                            NameAndTags const& nameAndTags,
+                            SourceLineInfo const& _lineInfo )
+    {
+        bool isHidden = false;
+
+        // Parse out tags
+        std::vector<std::string> tags;
+        std::string desc, tag;
+        bool inTag = false;
+        std::string _descOrTags = nameAndTags.tags;
+        for (char c : _descOrTags) {
+            if( !inTag ) {
+                if( c == '[' )
+                    inTag = true;
+                else
+                    desc += c;
+            }
+            else {
+                if( c == ']' ) {
+                    TestCaseInfo::SpecialProperties prop = parseSpecialTag( tag );
+                    if( ( prop & TestCaseInfo::IsHidden ) != 0 )
+                        isHidden = true;
+                    else if( prop == TestCaseInfo::None )
+                        enforceNotReservedTag( tag, _lineInfo );
+
+                    tags.push_back( tag );
+                    tag.clear();
+                    inTag = false;
+                }
+                else
+                    tag += c;
+            }
+        }
+        if( isHidden ) {
+            tags.push_back( "." );
+        }
+
+        TestCaseInfo info( nameAndTags.name, _className, desc, tags, _lineInfo );
+        return TestCase( _testCase, std::move(info) );
+    }
+
+    void setTags( TestCaseInfo& testCaseInfo, std::vector<std::string> tags ) {
+        std::sort(begin(tags), end(tags));
+        tags.erase(std::unique(begin(tags), end(tags)), end(tags));
+        testCaseInfo.lcaseTags.clear();
+
+        for( auto const& tag : tags ) {
+            std::string lcaseTag = toLower( tag );
+            testCaseInfo.properties = static_cast<TestCaseInfo::SpecialProperties>( testCaseInfo.properties | parseSpecialTag( lcaseTag ) );
+            testCaseInfo.lcaseTags.push_back( lcaseTag );
+        }
+        testCaseInfo.tags = std::move(tags);
+    }
+
+    TestCaseInfo::TestCaseInfo( std::string const& _name,
+                                std::string const& _className,
+                                std::string const& _description,
+                                std::vector<std::string> const& _tags,
+                                SourceLineInfo const& _lineInfo )
+    :   name( _name ),
+        className( _className ),
+        description( _description ),
+        lineInfo( _lineInfo ),
+        properties( None )
+    {
+        setTags( *this, _tags );
+    }
+
+    bool TestCaseInfo::isHidden() const {
+        return ( properties & IsHidden ) != 0;
+    }
+    bool TestCaseInfo::throws() const {
+        return ( properties & Throws ) != 0;
+    }
+    bool TestCaseInfo::okToFail() const {
+        return ( properties & (ShouldFail | MayFail ) ) != 0;
+    }
+    bool TestCaseInfo::expectedToFail() const {
+        return ( properties & (ShouldFail ) ) != 0;
+    }
+
+    std::string TestCaseInfo::tagsAsString() const {
+        std::string ret;
+        // '[' and ']' per tag
+        std::size_t full_size = 2 * tags.size();
+        for (const auto& tag : tags) {
+            full_size += tag.size();
+        }
+        ret.reserve(full_size);
+        for (const auto& tag : tags) {
+            ret.push_back('[');
+            ret.append(tag);
+            ret.push_back(']');
+        }
+
+        return ret;
+    }
+
+    TestCase::TestCase( ITestInvoker* testCase, TestCaseInfo&& info ) : TestCaseInfo( std::move(info) ), test( testCase ) {}
+
+    TestCase TestCase::withName( std::string const& _newName ) const {
+        TestCase other( *this );
+        other.name = _newName;
+        return other;
+    }
+
+    void TestCase::invoke() const {
+        test->invoke();
+    }
+
+    bool TestCase::operator == ( TestCase const& other ) const {
+        return  test.get() == other.test.get() &&
+                name == other.name &&
+                className == other.className;
+    }
+
+    bool TestCase::operator < ( TestCase const& other ) const {
+        return name < other.name;
+    }
+
+    TestCaseInfo const& TestCase::getTestCaseInfo() const
+    {
+        return *this;
+    }
+
+} // end namespace Catch
+// end catch_test_case_info.cpp
+// start catch_test_case_registry_impl.cpp
+
+#include <sstream>
+
+namespace Catch {
+
+    std::vector<TestCase> sortTests( IConfig const& config, std::vector<TestCase> const& unsortedTestCases ) {
+
+        std::vector<TestCase> sorted = unsortedTestCases;
+
+        switch( config.runOrder() ) {
+            case RunTests::InLexicographicalOrder:
+                std::sort( sorted.begin(), sorted.end() );
+                break;
+            case RunTests::InRandomOrder:
+                seedRng( config );
+                std::shuffle( sorted.begin(), sorted.end(), rng() );
+                break;
+            case RunTests::InDeclarationOrder:
+                // already in declaration order
+                break;
+        }
+        return sorted;
+    }
+    bool matchTest( TestCase const& testCase, TestSpec const& testSpec, IConfig const& config ) {
+        return testSpec.matches( testCase ) && ( config.allowThrows() || !testCase.throws() );
+    }
+
+    void enforceNoDuplicateTestCases( std::vector<TestCase> const& functions ) {
+        std::set<TestCase> seenFunctions;
+        for( auto const& function : functions ) {
+            auto prev = seenFunctions.insert( function );
+            CATCH_ENFORCE( prev.second,
+                    "error: TEST_CASE( \"" << function.name << "\" ) already defined.\n"
+                    << "\tFirst seen at " << prev.first->getTestCaseInfo().lineInfo << "\n"
+                    << "\tRedefined at " << function.getTestCaseInfo().lineInfo );
+        }
+    }
+
+    std::vector<TestCase> filterTests( std::vector<TestCase> const& testCases, TestSpec const& testSpec, IConfig const& config ) {
+        std::vector<TestCase> filtered;
+        filtered.reserve( testCases.size() );
+        for( auto const& testCase : testCases )
+            if( matchTest( testCase, testSpec, config ) )
+                filtered.push_back( testCase );
+        return filtered;
+    }
+    std::vector<TestCase> const& getAllTestCasesSorted( IConfig const& config ) {
+        return getRegistryHub().getTestCaseRegistry().getAllTestsSorted( config );
+    }
+
+    void TestRegistry::registerTest( TestCase const& testCase ) {
+        std::string name = testCase.getTestCaseInfo().name;
+        if( name.empty() ) {
+            ReusableStringStream rss;
+            rss << "Anonymous test case " << ++m_unnamedCount;
+            return registerTest( testCase.withName( rss.str() ) );
+        }
+        m_functions.push_back( testCase );
+    }
+
+    std::vector<TestCase> const& TestRegistry::getAllTests() const {
+        return m_functions;
+    }
+    std::vector<TestCase> const& TestRegistry::getAllTestsSorted( IConfig const& config ) const {
+        if( m_sortedFunctions.empty() )
+            enforceNoDuplicateTestCases( m_functions );
+
+        if(  m_currentSortOrder != config.runOrder() || m_sortedFunctions.empty() ) {
+            m_sortedFunctions = sortTests( config, m_functions );
+            m_currentSortOrder = config.runOrder();
+        }
+        return m_sortedFunctions;
+    }
+
+    ///////////////////////////////////////////////////////////////////////////
+    TestInvokerAsFunction::TestInvokerAsFunction( void(*testAsFunction)() ) noexcept : m_testAsFunction( testAsFunction ) {}
+
+    void TestInvokerAsFunction::invoke() const {
+        m_testAsFunction();
+    }
+
+    std::string extractClassName( StringRef const& classOrQualifiedMethodName ) {
+        std::string className = classOrQualifiedMethodName;
+        if( startsWith( className, '&' ) )
+        {
+            std::size_t lastColons = className.rfind( "::" );
+            std::size_t penultimateColons = className.rfind( "::", lastColons-1 );
+            if( penultimateColons == std::string::npos )
+                penultimateColons = 1;
+            className = className.substr( penultimateColons, lastColons-penultimateColons );
+        }
+        return className;
+    }
+
+} // end namespace Catch
+// end catch_test_case_registry_impl.cpp
+// start catch_test_case_tracker.cpp
+
+#include <algorithm>
+#include <cassert>
+#include <stdexcept>
+#include <memory>
+#include <sstream>
+
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wexit-time-destructors"
+#endif
+
+namespace Catch {
+namespace TestCaseTracking {
+
+    NameAndLocation::NameAndLocation( std::string const& _name, SourceLineInfo const& _location )
+    :   name( _name ),
+        location( _location )
+    {}
+
+    ITracker::~ITracker() = default;
+
+    TrackerContext& TrackerContext::instance() {
+        static TrackerContext s_instance;
+        return s_instance;
+    }
+
+    ITracker& TrackerContext::startRun() {
+        m_rootTracker = std::make_shared<SectionTracker>( NameAndLocation( "{root}", CATCH_INTERNAL_LINEINFO ), *this, nullptr );
+        m_currentTracker = nullptr;
+        m_runState = Executing;
+        return *m_rootTracker;
+    }
+
+    void TrackerContext::endRun() {
+        m_rootTracker.reset();
+        m_currentTracker = nullptr;
+        m_runState = NotStarted;
+    }
+
+    void TrackerContext::startCycle() {
+        m_currentTracker = m_rootTracker.get();
+        m_runState = Executing;
+    }
+    void TrackerContext::completeCycle() {
+        m_runState = CompletedCycle;
+    }
+
+    bool TrackerContext::completedCycle() const {
+        return m_runState == CompletedCycle;
+    }
+    ITracker& TrackerContext::currentTracker() {
+        return *m_currentTracker;
+    }
+    void TrackerContext::setCurrentTracker( ITracker* tracker ) {
+        m_currentTracker = tracker;
+    }
+
+    TrackerBase::TrackerBase( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent )
+    :   m_nameAndLocation( nameAndLocation ),
+        m_ctx( ctx ),
+        m_parent( parent )
+    {}
+
+    NameAndLocation const& TrackerBase::nameAndLocation() const {
+        return m_nameAndLocation;
+    }
+    bool TrackerBase::isComplete() const {
+        return m_runState == CompletedSuccessfully || m_runState == Failed;
+    }
+    bool TrackerBase::isSuccessfullyCompleted() const {
+        return m_runState == CompletedSuccessfully;
+    }
+    bool TrackerBase::isOpen() const {
+        return m_runState != NotStarted && !isComplete();
+    }
+    bool TrackerBase::hasChildren() const {
+        return !m_children.empty();
+    }
+
+    void TrackerBase::addChild( ITrackerPtr const& child ) {
+        m_children.push_back( child );
+    }
+
+    ITrackerPtr TrackerBase::findChild( NameAndLocation const& nameAndLocation ) {
+        auto it = std::find_if( m_children.begin(), m_children.end(),
+            [&nameAndLocation]( ITrackerPtr const& tracker ){
+                return
+                    tracker->nameAndLocation().location == nameAndLocation.location &&
+                    tracker->nameAndLocation().name == nameAndLocation.name;
+            } );
+        return( it != m_children.end() )
+            ? *it
+            : nullptr;
+    }
+    ITracker& TrackerBase::parent() {
+        assert( m_parent ); // Should always be non-null except for root
+        return *m_parent;
+    }
+
+    void TrackerBase::openChild() {
+        if( m_runState != ExecutingChildren ) {
+            m_runState = ExecutingChildren;
+            if( m_parent )
+                m_parent->openChild();
+        }
+    }
+
+    bool TrackerBase::isSectionTracker() const { return false; }
+    bool TrackerBase::isIndexTracker() const { return false; }
+
+    void TrackerBase::open() {
+        m_runState = Executing;
+        moveToThis();
+        if( m_parent )
+            m_parent->openChild();
+    }
+
+    void TrackerBase::close() {
+
+        // Close any still open children (e.g. generators)
+        while( &m_ctx.currentTracker() != this )
+            m_ctx.currentTracker().close();
+
+        switch( m_runState ) {
+            case NeedsAnotherRun:
+                break;
+
+            case Executing:
+                m_runState = CompletedSuccessfully;
+                break;
+            case ExecutingChildren:
+                if( m_children.empty() || m_children.back()->isComplete() )
+                    m_runState = CompletedSuccessfully;
+                break;
+
+            case NotStarted:
+            case CompletedSuccessfully:
+            case Failed:
+                CATCH_INTERNAL_ERROR( "Illogical state: " << m_runState );
+
+            default:
+                CATCH_INTERNAL_ERROR( "Unknown state: " << m_runState );
+        }
+        moveToParent();
+        m_ctx.completeCycle();
+    }
+    void TrackerBase::fail() {
+        m_runState = Failed;
+        if( m_parent )
+            m_parent->markAsNeedingAnotherRun();
+        moveToParent();
+        m_ctx.completeCycle();
+    }
+    void TrackerBase::markAsNeedingAnotherRun() {
+        m_runState = NeedsAnotherRun;
+    }
+
+    void TrackerBase::moveToParent() {
+        assert( m_parent );
+        m_ctx.setCurrentTracker( m_parent );
+    }
+    void TrackerBase::moveToThis() {
+        m_ctx.setCurrentTracker( this );
+    }
+
+    SectionTracker::SectionTracker( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent )
+    :   TrackerBase( nameAndLocation, ctx, parent )
+    {
+        if( parent ) {
+            while( !parent->isSectionTracker() )
+                parent = &parent->parent();
+
+            SectionTracker& parentSection = static_cast<SectionTracker&>( *parent );
+            addNextFilters( parentSection.m_filters );
+        }
+    }
+
+    bool SectionTracker::isSectionTracker() const { return true; }
+
+    SectionTracker& SectionTracker::acquire( TrackerContext& ctx, NameAndLocation const& nameAndLocation ) {
+        std::shared_ptr<SectionTracker> section;
+
+        ITracker& currentTracker = ctx.currentTracker();
+        if( ITrackerPtr childTracker = currentTracker.findChild( nameAndLocation ) ) {
+            assert( childTracker );
+            assert( childTracker->isSectionTracker() );
+            section = std::static_pointer_cast<SectionTracker>( childTracker );
+        }
+        else {
+            section = std::make_shared<SectionTracker>( nameAndLocation, ctx, &currentTracker );
+            currentTracker.addChild( section );
+        }
+        if( !ctx.completedCycle() )
+            section->tryOpen();
+        return *section;
+    }
+
+    void SectionTracker::tryOpen() {
+        if( !isComplete() && (m_filters.empty() || m_filters[0].empty() ||  m_filters[0] == m_nameAndLocation.name ) )
+            open();
+    }
+
+    void SectionTracker::addInitialFilters( std::vector<std::string> const& filters ) {
+        if( !filters.empty() ) {
+            m_filters.push_back(""); // Root - should never be consulted
+            m_filters.push_back(""); // Test Case - not a section filter
+            m_filters.insert( m_filters.end(), filters.begin(), filters.end() );
+        }
+    }
+    void SectionTracker::addNextFilters( std::vector<std::string> const& filters ) {
+        if( filters.size() > 1 )
+            m_filters.insert( m_filters.end(), ++filters.begin(), filters.end() );
+    }
+
+    IndexTracker::IndexTracker( NameAndLocation const& nameAndLocation, TrackerContext& ctx, ITracker* parent, int size )
+    :   TrackerBase( nameAndLocation, ctx, parent ),
+        m_size( size )
+    {}
+
+    bool IndexTracker::isIndexTracker() const { return true; }
+
+    IndexTracker& IndexTracker::acquire( TrackerContext& ctx, NameAndLocation const& nameAndLocation, int size ) {
+        std::shared_ptr<IndexTracker> tracker;
+
+        ITracker& currentTracker = ctx.currentTracker();
+        if( ITrackerPtr childTracker = currentTracker.findChild( nameAndLocation ) ) {
+            assert( childTracker );
+            assert( childTracker->isIndexTracker() );
+            tracker = std::static_pointer_cast<IndexTracker>( childTracker );
+        }
+        else {
+            tracker = std::make_shared<IndexTracker>( nameAndLocation, ctx, &currentTracker, size );
+            currentTracker.addChild( tracker );
+        }
+
+        if( !ctx.completedCycle() && !tracker->isComplete() ) {
+            if( tracker->m_runState != ExecutingChildren && tracker->m_runState != NeedsAnotherRun )
+                tracker->moveNext();
+            tracker->open();
+        }
+
+        return *tracker;
+    }
+
+    int IndexTracker::index() const { return m_index; }
+
+    void IndexTracker::moveNext() {
+        m_index++;
+        m_children.clear();
+    }
+
+    void IndexTracker::close() {
+        TrackerBase::close();
+        if( m_runState == CompletedSuccessfully && m_index < m_size-1 )
+            m_runState = Executing;
+    }
+
+} // namespace TestCaseTracking
+
+using TestCaseTracking::ITracker;
+using TestCaseTracking::TrackerContext;
+using TestCaseTracking::SectionTracker;
+using TestCaseTracking::IndexTracker;
+
+} // namespace Catch
+
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
+// end catch_test_case_tracker.cpp
+// start catch_test_registry.cpp
+
+namespace Catch {
+
+    auto makeTestInvoker( void(*testAsFunction)() ) noexcept -> ITestInvoker* {
+        return new(std::nothrow) TestInvokerAsFunction( testAsFunction );
+    }
+
+    NameAndTags::NameAndTags( StringRef const& name_ , StringRef const& tags_ ) noexcept : name( name_ ), tags( tags_ ) {}
+
+    AutoReg::AutoReg( ITestInvoker* invoker, SourceLineInfo const& lineInfo, StringRef const& classOrMethod, NameAndTags const& nameAndTags ) noexcept {
+        CATCH_TRY {
+            getMutableRegistryHub()
+                    .registerTest(
+                        makeTestCase(
+                            invoker,
+                            extractClassName( classOrMethod ),
+                            nameAndTags,
+                            lineInfo));
+        } CATCH_CATCH_ALL {
+            // Do not throw when constructing global objects, instead register the exception to be processed later
+            getMutableRegistryHub().registerStartupException();
+        }
+    }
+
+    AutoReg::~AutoReg() = default;
+}
+// end catch_test_registry.cpp
+// start catch_test_spec.cpp
+
+#include <algorithm>
+#include <string>
+#include <vector>
+#include <memory>
+
+namespace Catch {
+
+    TestSpec::Pattern::~Pattern() = default;
+    TestSpec::NamePattern::~NamePattern() = default;
+    TestSpec::TagPattern::~TagPattern() = default;
+    TestSpec::ExcludedPattern::~ExcludedPattern() = default;
+
+    TestSpec::NamePattern::NamePattern( std::string const& name )
+    : m_wildcardPattern( toLower( name ), CaseSensitive::No )
+    {}
+    bool TestSpec::NamePattern::matches( TestCaseInfo const& testCase ) const {
+        return m_wildcardPattern.matches( toLower( testCase.name ) );
+    }
+
+    TestSpec::TagPattern::TagPattern( std::string const& tag ) : m_tag( toLower( tag ) ) {}
+    bool TestSpec::TagPattern::matches( TestCaseInfo const& testCase ) const {
+        return std::find(begin(testCase.lcaseTags),
+                         end(testCase.lcaseTags),
+                         m_tag) != end(testCase.lcaseTags);
+    }
+
+    TestSpec::ExcludedPattern::ExcludedPattern( PatternPtr const& underlyingPattern ) : m_underlyingPattern( underlyingPattern ) {}
+    bool TestSpec::ExcludedPattern::matches( TestCaseInfo const& testCase ) const { return !m_underlyingPattern->matches( testCase ); }
+
+    bool TestSpec::Filter::matches( TestCaseInfo const& testCase ) const {
+        // All patterns in a filter must match for the filter to be a match
+        for( auto const& pattern : m_patterns ) {
+            if( !pattern->matches( testCase ) )
+                return false;
+        }
+        return true;
+    }
+
+    bool TestSpec::hasFilters() const {
+        return !m_filters.empty();
+    }
+    bool TestSpec::matches( TestCaseInfo const& testCase ) const {
+        // A TestSpec matches if any filter matches
+        for( auto const& filter : m_filters )
+            if( filter.matches( testCase ) )
+                return true;
+        return false;
+    }
+}
+// end catch_test_spec.cpp
+// start catch_test_spec_parser.cpp
+
+namespace Catch {
+
+    TestSpecParser::TestSpecParser( ITagAliasRegistry const& tagAliases ) : m_tagAliases( &tagAliases ) {}
+
+    TestSpecParser& TestSpecParser::parse( std::string const& arg ) {
+        m_mode = None;
+        m_exclusion = false;
+        m_start = std::string::npos;
+        m_arg = m_tagAliases->expandAliases( arg );
+        m_escapeChars.clear();
+        for( m_pos = 0; m_pos < m_arg.size(); ++m_pos )
+            visitChar( m_arg[m_pos] );
+        if( m_mode == Name )
+            addPattern<TestSpec::NamePattern>();
+        return *this;
+    }
+    TestSpec TestSpecParser::testSpec() {
+        addFilter();
+        return m_testSpec;
+    }
+
+    void TestSpecParser::visitChar( char c ) {
+        if( m_mode == None ) {
+            switch( c ) {
+            case ' ': return;
+            case '~': m_exclusion = true; return;
+            case '[': return startNewMode( Tag, ++m_pos );
+            case '"': return startNewMode( QuotedName, ++m_pos );
+            case '\\': return escape();
+            default: startNewMode( Name, m_pos ); break;
+            }
+        }
+        if( m_mode == Name ) {
+            if( c == ',' ) {
+                addPattern<TestSpec::NamePattern>();
+                addFilter();
+            }
+            else if( c == '[' ) {
+                if( subString() == "exclude:" )
+                    m_exclusion = true;
+                else
+                    addPattern<TestSpec::NamePattern>();
+                startNewMode( Tag, ++m_pos );
+            }
+            else if( c == '\\' )
+                escape();
+        }
+        else if( m_mode == EscapedName )
+            m_mode = Name;
+        else if( m_mode == QuotedName && c == '"' )
+            addPattern<TestSpec::NamePattern>();
+        else if( m_mode == Tag && c == ']' )
+            addPattern<TestSpec::TagPattern>();
+    }
+    void TestSpecParser::startNewMode( Mode mode, std::size_t start ) {
+        m_mode = mode;
+        m_start = start;
+    }
+    void TestSpecParser::escape() {
+        if( m_mode == None )
+            m_start = m_pos;
+        m_mode = EscapedName;
+        m_escapeChars.push_back( m_pos );
+    }
+    std::string TestSpecParser::subString() const { return m_arg.substr( m_start, m_pos - m_start ); }
+
+    void TestSpecParser::addFilter() {
+        if( !m_currentFilter.m_patterns.empty() ) {
+            m_testSpec.m_filters.push_back( m_currentFilter );
+            m_currentFilter = TestSpec::Filter();
+        }
+    }
+
+    TestSpec parseTestSpec( std::string const& arg ) {
+        return TestSpecParser( ITagAliasRegistry::get() ).parse( arg ).testSpec();
+    }
+
+} // namespace Catch
+// end catch_test_spec_parser.cpp
+// start catch_timer.cpp
+
+#include <chrono>
+
+static const uint64_t nanosecondsInSecond = 1000000000;
+
+namespace Catch {
+
+    auto getCurrentNanosecondsSinceEpoch() -> uint64_t {
+        return std::chrono::duration_cast<std::chrono::nanoseconds>( std::chrono::high_resolution_clock::now().time_since_epoch() ).count();
+    }
+
+    namespace {
+        auto estimateClockResolution() -> uint64_t {
+            uint64_t sum = 0;
+            static const uint64_t iterations = 1000000;
+
+            auto startTime = getCurrentNanosecondsSinceEpoch();
+
+            for( std::size_t i = 0; i < iterations; ++i ) {
+
+                uint64_t ticks;
+                uint64_t baseTicks = getCurrentNanosecondsSinceEpoch();
+                do {
+                    ticks = getCurrentNanosecondsSinceEpoch();
+                } while( ticks == baseTicks );
+
+                auto delta = ticks - baseTicks;
+                sum += delta;
+
+                // If we have been calibrating for over 3 seconds -- the clock
+                // is terrible and we should move on.
+                // TBD: How to signal that the measured resolution is probably wrong?
+                if (ticks > startTime + 3 * nanosecondsInSecond) {
+                    return sum / i;
+                }
+            }
+
+            // We're just taking the mean, here. To do better we could take the std. dev and exclude outliers
+            // - and potentially do more iterations if there's a high variance.
+            return sum/iterations;
+        }
+    }
+    auto getEstimatedClockResolution() -> uint64_t {
+        static auto s_resolution = estimateClockResolution();
+        return s_resolution;
+    }
+
+    void Timer::start() {
+       m_nanoseconds = getCurrentNanosecondsSinceEpoch();
+    }
+    auto Timer::getElapsedNanoseconds() const -> uint64_t {
+        return getCurrentNanosecondsSinceEpoch() - m_nanoseconds;
+    }
+    auto Timer::getElapsedMicroseconds() const -> uint64_t {
+        return getElapsedNanoseconds()/1000;
+    }
+    auto Timer::getElapsedMilliseconds() const -> unsigned int {
+        return static_cast<unsigned int>(getElapsedMicroseconds()/1000);
+    }
+    auto Timer::getElapsedSeconds() const -> double {
+        return getElapsedMicroseconds()/1000000.0;
+    }
+
+} // namespace Catch
+// end catch_timer.cpp
+// start catch_tostring.cpp
+
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wexit-time-destructors"
+#    pragma clang diagnostic ignored "-Wglobal-constructors"
+#endif
+
+// Enable specific decls locally
+#if !defined(CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER)
+#define CATCH_CONFIG_ENABLE_CHRONO_STRINGMAKER
+#endif
+
+#include <cmath>
+#include <iomanip>
+
+namespace Catch {
+
+namespace Detail {
+
+    const std::string unprintableString = "{?}";
+
+    namespace {
+        const int hexThreshold = 255;
+
+        struct Endianness {
+            enum Arch { Big, Little };
+
+            static Arch which() {
+                union _{
+                    int asInt;
+                    char asChar[sizeof (int)];
+                } u;
+
+                u.asInt = 1;
+                return ( u.asChar[sizeof(int)-1] == 1 ) ? Big : Little;
+            }
+        };
+    }
+
+    std::string rawMemoryToString( const void *object, std::size_t size ) {
+        // Reverse order for little endian architectures
+        int i = 0, end = static_cast<int>( size ), inc = 1;
+        if( Endianness::which() == Endianness::Little ) {
+            i = end-1;
+            end = inc = -1;
+        }
+
+        unsigned char const *bytes = static_cast<unsigned char const *>(object);
+        ReusableStringStream rss;
+        rss << "0x" << std::setfill('0') << std::hex;
+        for( ; i != end; i += inc )
+             rss << std::setw(2) << static_cast<unsigned>(bytes[i]);
+       return rss.str();
+    }
+}
+
+template<typename T>
+std::string fpToString( T value, int precision ) {
+    if (std::isnan(value)) {
+        return "nan";
+    }
+
+    ReusableStringStream rss;
+    rss << std::setprecision( precision )
+        << std::fixed
+        << value;
+    std::string d = rss.str();
+    std::size_t i = d.find_last_not_of( '0' );
+    if( i != std::string::npos && i != d.size()-1 ) {
+        if( d[i] == '.' )
+            i++;
+        d = d.substr( 0, i+1 );
+    }
+    return d;
+}
+
+//// ======================================================= ////
+//
+//   Out-of-line defs for full specialization of StringMaker
+//
+//// ======================================================= ////
+
+std::string StringMaker<std::string>::convert(const std::string& str) {
+    if (!getCurrentContext().getConfig()->showInvisibles()) {
+        return '"' + str + '"';
+    }
+
+    std::string s("\"");
+    for (char c : str) {
+        switch (c) {
+        case '\n':
+            s.append("\\n");
+            break;
+        case '\t':
+            s.append("\\t");
+            break;
+        default:
+            s.push_back(c);
+            break;
+        }
+    }
+    s.append("\"");
+    return s;
+}
+
+#ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+std::string StringMaker<std::string_view>::convert(std::string_view str) {
+    return ::Catch::Detail::stringify(std::string{ str });
+}
+#endif
+
+std::string StringMaker<char const*>::convert(char const* str) {
+    if (str) {
+        return ::Catch::Detail::stringify(std::string{ str });
+    } else {
+        return{ "{null string}" };
+    }
+}
+std::string StringMaker<char*>::convert(char* str) {
+    if (str) {
+        return ::Catch::Detail::stringify(std::string{ str });
+    } else {
+        return{ "{null string}" };
+    }
+}
+
+#ifdef CATCH_CONFIG_WCHAR
+std::string StringMaker<std::wstring>::convert(const std::wstring& wstr) {
+    std::string s;
+    s.reserve(wstr.size());
+    for (auto c : wstr) {
+        s += (c <= 0xff) ? static_cast<char>(c) : '?';
+    }
+    return ::Catch::Detail::stringify(s);
+}
+
+# ifdef CATCH_CONFIG_CPP17_STRING_VIEW
+std::string StringMaker<std::wstring_view>::convert(std::wstring_view str) {
+    return StringMaker<std::wstring>::convert(std::wstring(str));
+}
+# endif
+
+std::string StringMaker<wchar_t const*>::convert(wchar_t const * str) {
+    if (str) {
+        return ::Catch::Detail::stringify(std::wstring{ str });
+    } else {
+        return{ "{null string}" };
+    }
+}
+std::string StringMaker<wchar_t *>::convert(wchar_t * str) {
+    if (str) {
+        return ::Catch::Detail::stringify(std::wstring{ str });
+    } else {
+        return{ "{null string}" };
+    }
+}
+#endif
+
+std::string StringMaker<int>::convert(int value) {
+    return ::Catch::Detail::stringify(static_cast<long long>(value));
+}
+std::string StringMaker<long>::convert(long value) {
+    return ::Catch::Detail::stringify(static_cast<long long>(value));
+}
+std::string StringMaker<long long>::convert(long long value) {
+    ReusableStringStream rss;
+    rss << value;
+    if (value > Detail::hexThreshold) {
+        rss << " (0x" << std::hex << value << ')';
+    }
+    return rss.str();
+}
+
+std::string StringMaker<unsigned int>::convert(unsigned int value) {
+    return ::Catch::Detail::stringify(static_cast<unsigned long long>(value));
+}
+std::string StringMaker<unsigned long>::convert(unsigned long value) {
+    return ::Catch::Detail::stringify(static_cast<unsigned long long>(value));
+}
+std::string StringMaker<unsigned long long>::convert(unsigned long long value) {
+    ReusableStringStream rss;
+    rss << value;
+    if (value > Detail::hexThreshold) {
+        rss << " (0x" << std::hex << value << ')';
+    }
+    return rss.str();
+}
+
+std::string StringMaker<bool>::convert(bool b) {
+    return b ? "true" : "false";
+}
+
+std::string StringMaker<char>::convert(char value) {
+    if (value == '\r') {
+        return "'\\r'";
+    } else if (value == '\f') {
+        return "'\\f'";
+    } else if (value == '\n') {
+        return "'\\n'";
+    } else if (value == '\t') {
+        return "'\\t'";
+    } else if ('\0' <= value && value < ' ') {
+        return ::Catch::Detail::stringify(static_cast<unsigned int>(value));
+    } else {
+        char chstr[] = "' '";
+        chstr[1] = value;
+        return chstr;
+    }
+}
+std::string StringMaker<signed char>::convert(signed char c) {
+    return ::Catch::Detail::stringify(static_cast<char>(c));
+}
+std::string StringMaker<unsigned char>::convert(unsigned char c) {
+    return ::Catch::Detail::stringify(static_cast<char>(c));
+}
+
+std::string StringMaker<std::nullptr_t>::convert(std::nullptr_t) {
+    return "nullptr";
+}
+
+std::string StringMaker<float>::convert(float value) {
+    return fpToString(value, 5) + 'f';
+}
+std::string StringMaker<double>::convert(double value) {
+    return fpToString(value, 10);
+}
+
+std::string ratio_string<std::atto>::symbol() { return "a"; }
+std::string ratio_string<std::femto>::symbol() { return "f"; }
+std::string ratio_string<std::pico>::symbol() { return "p"; }
+std::string ratio_string<std::nano>::symbol() { return "n"; }
+std::string ratio_string<std::micro>::symbol() { return "u"; }
+std::string ratio_string<std::milli>::symbol() { return "m"; }
+
+} // end namespace Catch
+
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
+
+// end catch_tostring.cpp
+// start catch_totals.cpp
+
+namespace Catch {
+
+    Counts Counts::operator - ( Counts const& other ) const {
+        Counts diff;
+        diff.passed = passed - other.passed;
+        diff.failed = failed - other.failed;
+        diff.failedButOk = failedButOk - other.failedButOk;
+        return diff;
+    }
+
+    Counts& Counts::operator += ( Counts const& other ) {
+        passed += other.passed;
+        failed += other.failed;
+        failedButOk += other.failedButOk;
+        return *this;
+    }
+
+    std::size_t Counts::total() const {
+        return passed + failed + failedButOk;
+    }
+    bool Counts::allPassed() const {
+        return failed == 0 && failedButOk == 0;
+    }
+    bool Counts::allOk() const {
+        return failed == 0;
+    }
+
+    Totals Totals::operator - ( Totals const& other ) const {
+        Totals diff;
+        diff.assertions = assertions - other.assertions;
+        diff.testCases = testCases - other.testCases;
+        return diff;
+    }
+
+    Totals& Totals::operator += ( Totals const& other ) {
+        assertions += other.assertions;
+        testCases += other.testCases;
+        return *this;
+    }
+
+    Totals Totals::delta( Totals const& prevTotals ) const {
+        Totals diff = *this - prevTotals;
+        if( diff.assertions.failed > 0 )
+            ++diff.testCases.failed;
+        else if( diff.assertions.failedButOk > 0 )
+            ++diff.testCases.failedButOk;
+        else
+            ++diff.testCases.passed;
+        return diff;
+    }
+
+}
+// end catch_totals.cpp
+// start catch_uncaught_exceptions.cpp
+
+#include <exception>
+
+namespace Catch {
+    bool uncaught_exceptions() {
+#if defined(CATCH_CONFIG_CPP17_UNCAUGHT_EXCEPTIONS)
+        return std::uncaught_exceptions() > 0;
+#else
+        return std::uncaught_exception();
+#endif
+  }
+} // end namespace Catch
+// end catch_uncaught_exceptions.cpp
+// start catch_version.cpp
+
+#include <ostream>
+
+namespace Catch {
+
+    Version::Version
+        (   unsigned int _majorVersion,
+            unsigned int _minorVersion,
+            unsigned int _patchNumber,
+            char const * const _branchName,
+            unsigned int _buildNumber )
+    :   majorVersion( _majorVersion ),
+        minorVersion( _minorVersion ),
+        patchNumber( _patchNumber ),
+        branchName( _branchName ),
+        buildNumber( _buildNumber )
+    {}
+
+    std::ostream& operator << ( std::ostream& os, Version const& version ) {
+        os  << version.majorVersion << '.'
+            << version.minorVersion << '.'
+            << version.patchNumber;
+        // branchName is never null -> 0th char is \0 if it is empty
+        if (version.branchName[0]) {
+            os << '-' << version.branchName
+               << '.' << version.buildNumber;
+        }
+        return os;
+    }
+
+    Version const& libraryVersion() {
+        static Version version( 2, 4, 1, "", 0 );
+        return version;
+    }
+
+}
+// end catch_version.cpp
+// start catch_wildcard_pattern.cpp
+
+#include <sstream>
+
+namespace Catch {
+
+    WildcardPattern::WildcardPattern( std::string const& pattern,
+                                      CaseSensitive::Choice caseSensitivity )
+    :   m_caseSensitivity( caseSensitivity ),
+        m_pattern( adjustCase( pattern ) )
+    {
+        if( startsWith( m_pattern, '*' ) ) {
+            m_pattern = m_pattern.substr( 1 );
+            m_wildcard = WildcardAtStart;
+        }
+        if( endsWith( m_pattern, '*' ) ) {
+            m_pattern = m_pattern.substr( 0, m_pattern.size()-1 );
+            m_wildcard = static_cast<WildcardPosition>( m_wildcard | WildcardAtEnd );
+        }
+    }
+
+    bool WildcardPattern::matches( std::string const& str ) const {
+        switch( m_wildcard ) {
+            case NoWildcard:
+                return m_pattern == adjustCase( str );
+            case WildcardAtStart:
+                return endsWith( adjustCase( str ), m_pattern );
+            case WildcardAtEnd:
+                return startsWith( adjustCase( str ), m_pattern );
+            case WildcardAtBothEnds:
+                return contains( adjustCase( str ), m_pattern );
+            default:
+                CATCH_INTERNAL_ERROR( "Unknown enum" );
+        }
+    }
+
+    std::string WildcardPattern::adjustCase( std::string const& str ) const {
+        return m_caseSensitivity == CaseSensitive::No ? toLower( str ) : str;
+    }
+}
+// end catch_wildcard_pattern.cpp
+// start catch_xmlwriter.cpp
+
+#include <iomanip>
+
+using uchar = unsigned char;
+
+namespace Catch {
+
+namespace {
+
+    size_t trailingBytes(unsigned char c) {
+        if ((c & 0xE0) == 0xC0) {
+            return 2;
+        }
+        if ((c & 0xF0) == 0xE0) {
+            return 3;
+        }
+        if ((c & 0xF8) == 0xF0) {
+            return 4;
+        }
+        CATCH_INTERNAL_ERROR("Invalid multibyte utf-8 start byte encountered");
+    }
+
+    uint32_t headerValue(unsigned char c) {
+        if ((c & 0xE0) == 0xC0) {
+            return c & 0x1F;
+        }
+        if ((c & 0xF0) == 0xE0) {
+            return c & 0x0F;
+        }
+        if ((c & 0xF8) == 0xF0) {
+            return c & 0x07;
+        }
+        CATCH_INTERNAL_ERROR("Invalid multibyte utf-8 start byte encountered");
+    }
+
+    void hexEscapeChar(std::ostream& os, unsigned char c) {
+        os << "\\x"
+            << std::uppercase << std::hex << std::setfill('0') << std::setw(2)
+            << static_cast<int>(c);
+    }
+
+} // anonymous namespace
+
+    XmlEncode::XmlEncode( std::string const& str, ForWhat forWhat )
+    :   m_str( str ),
+        m_forWhat( forWhat )
+    {}
+
+    void XmlEncode::encodeTo( std::ostream& os ) const {
+        // Apostrophe escaping not necessary if we always use " to write attributes
+        // (see: http://www.w3.org/TR/xml/#syntax)
+
+        for( std::size_t idx = 0; idx < m_str.size(); ++ idx ) {
+            uchar c = m_str[idx];
+            switch (c) {
+            case '<':   os << "&lt;"; break;
+            case '&':   os << "&amp;"; break;
+
+            case '>':
+                // See: http://www.w3.org/TR/xml/#syntax
+                if (idx > 2 && m_str[idx - 1] == ']' && m_str[idx - 2] == ']')
+                    os << "&gt;";
+                else
+                    os << c;
+                break;
+
+            case '\"':
+                if (m_forWhat == ForAttributes)
+                    os << "&quot;";
+                else
+                    os << c;
+                break;
+
+            default:
+                // Check for control characters and invalid utf-8
+
+                // Escape control characters in standard ascii
+                // see http://stackoverflow.com/questions/404107/why-are-control-characters-illegal-in-xml-1-0
+                if (c < 0x09 || (c > 0x0D && c < 0x20) || c == 0x7F) {
+                    hexEscapeChar(os, c);
+                    break;
+                }
+
+                // Plain ASCII: Write it to stream
+                if (c < 0x7F) {
+                    os << c;
+                    break;
+                }
+
+                // UTF-8 territory
+                // Check if the encoding is valid and if it is not, hex escape bytes.
+                // Important: We do not check the exact decoded values for validity, only the encoding format
+                // First check that this bytes is a valid lead byte:
+                // This means that it is not encoded as 1111 1XXX
+                // Or as 10XX XXXX
+                if (c <  0xC0 ||
+                    c >= 0xF8) {
+                    hexEscapeChar(os, c);
+                    break;
+                }
+
+                auto encBytes = trailingBytes(c);
+                // Are there enough bytes left to avoid accessing out-of-bounds memory?
+                if (idx + encBytes - 1 >= m_str.size()) {
+                    hexEscapeChar(os, c);
+                    break;
+                }
+                // The header is valid, check data
+                // The next encBytes bytes must together be a valid utf-8
+                // This means: bitpattern 10XX XXXX and the extracted value is sane (ish)
+                bool valid = true;
+                uint32_t value = headerValue(c);
+                for (std::size_t n = 1; n < encBytes; ++n) {
+                    uchar nc = m_str[idx + n];
+                    valid &= ((nc & 0xC0) == 0x80);
+                    value = (value << 6) | (nc & 0x3F);
+                }
+
+                if (
+                    // Wrong bit pattern of following bytes
+                    (!valid) ||
+                    // Overlong encodings
+                    (value < 0x80) ||
+                    (0x80 <= value && value < 0x800   && encBytes > 2) ||
+                    (0x800 < value && value < 0x10000 && encBytes > 3) ||
+                    // Encoded value out of range
+                    (value >= 0x110000)
+                    ) {
+                    hexEscapeChar(os, c);
+                    break;
+                }
+
+                // If we got here, this is in fact a valid(ish) utf-8 sequence
+                for (std::size_t n = 0; n < encBytes; ++n) {
+                    os << m_str[idx + n];
+                }
+                idx += encBytes - 1;
+                break;
+            }
+        }
+    }
+
+    std::ostream& operator << ( std::ostream& os, XmlEncode const& xmlEncode ) {
+        xmlEncode.encodeTo( os );
+        return os;
+    }
+
+    XmlWriter::ScopedElement::ScopedElement( XmlWriter* writer )
+    :   m_writer( writer )
+    {}
+
+    XmlWriter::ScopedElement::ScopedElement( ScopedElement&& other ) noexcept
+    :   m_writer( other.m_writer ){
+        other.m_writer = nullptr;
+    }
+    XmlWriter::ScopedElement& XmlWriter::ScopedElement::operator=( ScopedElement&& other ) noexcept {
+        if ( m_writer ) {
+            m_writer->endElement();
+        }
+        m_writer = other.m_writer;
+        other.m_writer = nullptr;
+        return *this;
+    }
+
+    XmlWriter::ScopedElement::~ScopedElement() {
+        if( m_writer )
+            m_writer->endElement();
+    }
+
+    XmlWriter::ScopedElement& XmlWriter::ScopedElement::writeText( std::string const& text, bool indent ) {
+        m_writer->writeText( text, indent );
+        return *this;
+    }
+
+    XmlWriter::XmlWriter( std::ostream& os ) : m_os( os )
+    {
+        writeDeclaration();
+    }
+
+    XmlWriter::~XmlWriter() {
+        while( !m_tags.empty() )
+            endElement();
+    }
+
+    XmlWriter& XmlWriter::startElement( std::string const& name ) {
+        ensureTagClosed();
+        newlineIfNecessary();
+        m_os << m_indent << '<' << name;
+        m_tags.push_back( name );
+        m_indent += "  ";
+        m_tagIsOpen = true;
+        return *this;
+    }
+
+    XmlWriter::ScopedElement XmlWriter::scopedElement( std::string const& name ) {
+        ScopedElement scoped( this );
+        startElement( name );
+        return scoped;
+    }
+
+    XmlWriter& XmlWriter::endElement() {
+        newlineIfNecessary();
+        m_indent = m_indent.substr( 0, m_indent.size()-2 );
+        if( m_tagIsOpen ) {
+            m_os << "/>";
+            m_tagIsOpen = false;
+        }
+        else {
+            m_os << m_indent << "</" << m_tags.back() << ">";
+        }
+        m_os << std::endl;
+        m_tags.pop_back();
+        return *this;
+    }
+
+    XmlWriter& XmlWriter::writeAttribute( std::string const& name, std::string const& attribute ) {
+        if( !name.empty() && !attribute.empty() )
+            m_os << ' ' << name << "=\"" << XmlEncode( attribute, XmlEncode::ForAttributes ) << '"';
+        return *this;
+    }
+
+    XmlWriter& XmlWriter::writeAttribute( std::string const& name, bool attribute ) {
+        m_os << ' ' << name << "=\"" << ( attribute ? "true" : "false" ) << '"';
+        return *this;
+    }
+
+    XmlWriter& XmlWriter::writeText( std::string const& text, bool indent ) {
+        if( !text.empty() ){
+            bool tagWasOpen = m_tagIsOpen;
+            ensureTagClosed();
+            if( tagWasOpen && indent )
+                m_os << m_indent;
+            m_os << XmlEncode( text );
+            m_needsNewline = true;
+        }
+        return *this;
+    }
+
+    XmlWriter& XmlWriter::writeComment( std::string const& text ) {
+        ensureTagClosed();
+        m_os << m_indent << "<!--" << text << "-->";
+        m_needsNewline = true;
+        return *this;
+    }
+
+    void XmlWriter::writeStylesheetRef( std::string const& url ) {
+        m_os << "<?xml-stylesheet type=\"text/xsl\" href=\"" << url << "\"?>\n";
+    }
+
+    XmlWriter& XmlWriter::writeBlankLine() {
+        ensureTagClosed();
+        m_os << '\n';
+        return *this;
+    }
+
+    void XmlWriter::ensureTagClosed() {
+        if( m_tagIsOpen ) {
+            m_os << ">" << std::endl;
+            m_tagIsOpen = false;
+        }
+    }
+
+    void XmlWriter::writeDeclaration() {
+        m_os << "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n";
+    }
+
+    void XmlWriter::newlineIfNecessary() {
+        if( m_needsNewline ) {
+            m_os << std::endl;
+            m_needsNewline = false;
+        }
+    }
+}
+// end catch_xmlwriter.cpp
+// start catch_reporter_bases.cpp
+
+#include <cstring>
+#include <cfloat>
+#include <cstdio>
+#include <cassert>
+#include <memory>
+
+namespace Catch {
+    void prepareExpandedExpression(AssertionResult& result) {
+        result.getExpandedExpression();
+    }
+
+    // Because formatting using c++ streams is stateful, drop down to C is required
+    // Alternatively we could use stringstream, but its performance is... not good.
+    std::string getFormattedDuration( double duration ) {
+        // Max exponent + 1 is required to represent the whole part
+        // + 1 for decimal point
+        // + 3 for the 3 decimal places
+        // + 1 for null terminator
+        const std::size_t maxDoubleSize = DBL_MAX_10_EXP + 1 + 1 + 3 + 1;
+        char buffer[maxDoubleSize];
+
+        // Save previous errno, to prevent sprintf from overwriting it
+        ErrnoGuard guard;
+#ifdef _MSC_VER
+        sprintf_s(buffer, "%.3f", duration);
+#else
+        sprintf(buffer, "%.3f", duration);
+#endif
+        return std::string(buffer);
+    }
+
+    TestEventListenerBase::TestEventListenerBase(ReporterConfig const & _config)
+        :StreamingReporterBase(_config) {}
+
+    void TestEventListenerBase::assertionStarting(AssertionInfo const &) {}
+
+    bool TestEventListenerBase::assertionEnded(AssertionStats const &) {
+        return false;
+    }
+
+} // end namespace Catch
+// end catch_reporter_bases.cpp
+// start catch_reporter_compact.cpp
+
+namespace {
+
+#ifdef CATCH_PLATFORM_MAC
+    const char* failedString() { return "FAILED"; }
+    const char* passedString() { return "PASSED"; }
+#else
+    const char* failedString() { return "failed"; }
+    const char* passedString() { return "passed"; }
+#endif
+
+    // Colour::LightGrey
+    Catch::Colour::Code dimColour() { return Catch::Colour::FileName; }
+
+    std::string bothOrAll( std::size_t count ) {
+        return count == 1 ? std::string() :
+               count == 2 ? "both " : "all " ;
+    }
+
+} // anon namespace
+
+namespace Catch {
+namespace {
+// Colour, message variants:
+// - white: No tests ran.
+// -   red: Failed [both/all] N test cases, failed [both/all] M assertions.
+// - white: Passed [both/all] N test cases (no assertions).
+// -   red: Failed N tests cases, failed M assertions.
+// - green: Passed [both/all] N tests cases with M assertions.
+void printTotals(std::ostream& out, const Totals& totals) {
+    if (totals.testCases.total() == 0) {
+        out << "No tests ran.";
+    } else if (totals.testCases.failed == totals.testCases.total()) {
+        Colour colour(Colour::ResultError);
+        const std::string qualify_assertions_failed =
+            totals.assertions.failed == totals.assertions.total() ?
+            bothOrAll(totals.assertions.failed) : std::string();
+        out <<
+            "Failed " << bothOrAll(totals.testCases.failed)
+            << pluralise(totals.testCases.failed, "test case") << ", "
+            "failed " << qualify_assertions_failed <<
+            pluralise(totals.assertions.failed, "assertion") << '.';
+    } else if (totals.assertions.total() == 0) {
+        out <<
+            "Passed " << bothOrAll(totals.testCases.total())
+            << pluralise(totals.testCases.total(), "test case")
+            << " (no assertions).";
+    } else if (totals.assertions.failed) {
+        Colour colour(Colour::ResultError);
+        out <<
+            "Failed " << pluralise(totals.testCases.failed, "test case") << ", "
+            "failed " << pluralise(totals.assertions.failed, "assertion") << '.';
+    } else {
+        Colour colour(Colour::ResultSuccess);
+        out <<
+            "Passed " << bothOrAll(totals.testCases.passed)
+            << pluralise(totals.testCases.passed, "test case") <<
+            " with " << pluralise(totals.assertions.passed, "assertion") << '.';
+    }
+}
+
+// Implementation of CompactReporter formatting
+class AssertionPrinter {
+public:
+    AssertionPrinter& operator= (AssertionPrinter const&) = delete;
+    AssertionPrinter(AssertionPrinter const&) = delete;
+    AssertionPrinter(std::ostream& _stream, AssertionStats const& _stats, bool _printInfoMessages)
+        : stream(_stream)
+        , result(_stats.assertionResult)
+        , messages(_stats.infoMessages)
+        , itMessage(_stats.infoMessages.begin())
+        , printInfoMessages(_printInfoMessages) {}
+
+    void print() {
+        printSourceInfo();
+
+        itMessage = messages.begin();
+
+        switch (result.getResultType()) {
+        case ResultWas::Ok:
+            printResultType(Colour::ResultSuccess, passedString());
+            printOriginalExpression();
+            printReconstructedExpression();
+            if (!result.hasExpression())
+                printRemainingMessages(Colour::None);
+            else
+                printRemainingMessages();
+            break;
+        case ResultWas::ExpressionFailed:
+            if (result.isOk())
+                printResultType(Colour::ResultSuccess, failedString() + std::string(" - but was ok"));
+            else
+                printResultType(Colour::Error, failedString());
+            printOriginalExpression();
+            printReconstructedExpression();
+            printRemainingMessages();
+            break;
+        case ResultWas::ThrewException:
+            printResultType(Colour::Error, failedString());
+            printIssue("unexpected exception with message:");
+            printMessage();
+            printExpressionWas();
+            printRemainingMessages();
+            break;
+        case ResultWas::FatalErrorCondition:
+            printResultType(Colour::Error, failedString());
+            printIssue("fatal error condition with message:");
+            printMessage();
+            printExpressionWas();
+            printRemainingMessages();
+            break;
+        case ResultWas::DidntThrowException:
+            printResultType(Colour::Error, failedString());
+            printIssue("expected exception, got none");
+            printExpressionWas();
+            printRemainingMessages();
+            break;
+        case ResultWas::Info:
+            printResultType(Colour::None, "info");
+            printMessage();
+            printRemainingMessages();
+            break;
+        case ResultWas::Warning:
+            printResultType(Colour::None, "warning");
+            printMessage();
+            printRemainingMessages();
+            break;
+        case ResultWas::ExplicitFailure:
+            printResultType(Colour::Error, failedString());
+            printIssue("explicitly");
+            printRemainingMessages(Colour::None);
+            break;
+            // These cases are here to prevent compiler warnings
+        case ResultWas::Unknown:
+        case ResultWas::FailureBit:
+        case ResultWas::Exception:
+            printResultType(Colour::Error, "** internal error **");
+            break;
+        }
+    }
+
+private:
+    void printSourceInfo() const {
+        Colour colourGuard(Colour::FileName);
+        stream << result.getSourceInfo() << ':';
+    }
+
+    void printResultType(Colour::Code colour, std::string const& passOrFail) const {
+        if (!passOrFail.empty()) {
+            {
+                Colour colourGuard(colour);
+                stream << ' ' << passOrFail;
+            }
+            stream << ':';
+        }
+    }
+
+    void printIssue(std::string const& issue) const {
+        stream << ' ' << issue;
+    }
+
+    void printExpressionWas() {
+        if (result.hasExpression()) {
+            stream << ';';
+            {
+                Colour colour(dimColour());
+                stream << " expression was:";
+            }
+            printOriginalExpression();
+        }
+    }
+
+    void printOriginalExpression() const {
+        if (result.hasExpression()) {
+            stream << ' ' << result.getExpression();
+        }
+    }
+
+    void printReconstructedExpression() const {
+        if (result.hasExpandedExpression()) {
+            {
+                Colour colour(dimColour());
+                stream << " for: ";
+            }
+            stream << result.getExpandedExpression();
+        }
+    }
+
+    void printMessage() {
+        if (itMessage != messages.end()) {
+            stream << " '" << itMessage->message << '\'';
+            ++itMessage;
+        }
+    }
+
+    void printRemainingMessages(Colour::Code colour = dimColour()) {
+        if (itMessage == messages.end())
+            return;
+
+        // using messages.end() directly yields (or auto) compilation error:
+        std::vector<MessageInfo>::const_iterator itEnd = messages.end();
+        const std::size_t N = static_cast<std::size_t>(std::distance(itMessage, itEnd));
+
+        {
+            Colour colourGuard(colour);
+            stream << " with " << pluralise(N, "message") << ':';
+        }
+
+        for (; itMessage != itEnd; ) {
+            // If this assertion is a warning ignore any INFO messages
+            if (printInfoMessages || itMessage->type != ResultWas::Info) {
+                stream << " '" << itMessage->message << '\'';
+                if (++itMessage != itEnd) {
+                    Colour colourGuard(dimColour());
+                    stream << " and";
+                }
+            }
+        }
+    }
+
+private:
+    std::ostream& stream;
+    AssertionResult const& result;
+    std::vector<MessageInfo> messages;
+    std::vector<MessageInfo>::const_iterator itMessage;
+    bool printInfoMessages;
+};
+
+} // anon namespace
+
+        std::string CompactReporter::getDescription() {
+            return "Reports test results on a single line, suitable for IDEs";
+        }
+
+        ReporterPreferences CompactReporter::getPreferences() const {
+            return m_reporterPrefs;
+        }
+
+        void CompactReporter::noMatchingTestCases( std::string const& spec ) {
+            stream << "No test cases matched '" << spec << '\'' << std::endl;
+        }
+
+        void CompactReporter::assertionStarting( AssertionInfo const& ) {}
+
+        bool CompactReporter::assertionEnded( AssertionStats const& _assertionStats ) {
+            AssertionResult const& result = _assertionStats.assertionResult;
+
+            bool printInfoMessages = true;
+
+            // Drop out if result was successful and we're not printing those
+            if( !m_config->includeSuccessfulResults() && result.isOk() ) {
+                if( result.getResultType() != ResultWas::Warning )
+                    return false;
+                printInfoMessages = false;
+            }
+
+            AssertionPrinter printer( stream, _assertionStats, printInfoMessages );
+            printer.print();
+
+            stream << std::endl;
+            return true;
+        }
+
+        void CompactReporter::sectionEnded(SectionStats const& _sectionStats) {
+            if (m_config->showDurations() == ShowDurations::Always) {
+                stream << getFormattedDuration(_sectionStats.durationInSeconds) << " s: " << _sectionStats.sectionInfo.name << std::endl;
+            }
+        }
+
+        void CompactReporter::testRunEnded( TestRunStats const& _testRunStats ) {
+            printTotals( stream, _testRunStats.totals );
+            stream << '\n' << std::endl;
+            StreamingReporterBase::testRunEnded( _testRunStats );
+        }
+
+        CompactReporter::~CompactReporter() {}
+
+    CATCH_REGISTER_REPORTER( "compact", CompactReporter )
+
+} // end namespace Catch
+// end catch_reporter_compact.cpp
+// start catch_reporter_console.cpp
+
+#include <cfloat>
+#include <cstdio>
+
+#if defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable:4061) // Not all labels are EXPLICITLY handled in switch
+ // Note that 4062 (not all labels are handled
+ // and default is missing) is enabled
+#endif
+
+namespace Catch {
+
+namespace {
+
+// Formatter impl for ConsoleReporter
+class ConsoleAssertionPrinter {
+public:
+    ConsoleAssertionPrinter& operator= (ConsoleAssertionPrinter const&) = delete;
+    ConsoleAssertionPrinter(ConsoleAssertionPrinter const&) = delete;
+    ConsoleAssertionPrinter(std::ostream& _stream, AssertionStats const& _stats, bool _printInfoMessages)
+        : stream(_stream),
+        stats(_stats),
+        result(_stats.assertionResult),
+        colour(Colour::None),
+        message(result.getMessage()),
+        messages(_stats.infoMessages),
+        printInfoMessages(_printInfoMessages) {
+        switch (result.getResultType()) {
+        case ResultWas::Ok:
+            colour = Colour::Success;
+            passOrFail = "PASSED";
+            //if( result.hasMessage() )
+            if (_stats.infoMessages.size() == 1)
+                messageLabel = "with message";
+            if (_stats.infoMessages.size() > 1)
+                messageLabel = "with messages";
+            break;
+        case ResultWas::ExpressionFailed:
+            if (result.isOk()) {
+                colour = Colour::Success;
+                passOrFail = "FAILED - but was ok";
+            } else {
+                colour = Colour::Error;
+                passOrFail = "FAILED";
+            }
+            if (_stats.infoMessages.size() == 1)
+                messageLabel = "with message";
+            if (_stats.infoMessages.size() > 1)
+                messageLabel = "with messages";
+            break;
+        case ResultWas::ThrewException:
+            colour = Colour::Error;
+            passOrFail = "FAILED";
+            messageLabel = "due to unexpected exception with ";
+            if (_stats.infoMessages.size() == 1)
+                messageLabel += "message";
+            if (_stats.infoMessages.size() > 1)
+                messageLabel += "messages";
+            break;
+        case ResultWas::FatalErrorCondition:
+            colour = Colour::Error;
+            passOrFail = "FAILED";
+            messageLabel = "due to a fatal error condition";
+            break;
+        case ResultWas::DidntThrowException:
+            colour = Colour::Error;
+            passOrFail = "FAILED";
+            messageLabel = "because no exception was thrown where one was expected";
+            break;
+        case ResultWas::Info:
+            messageLabel = "info";
+            break;
+        case ResultWas::Warning:
+            messageLabel = "warning";
+            break;
+        case ResultWas::ExplicitFailure:
+            passOrFail = "FAILED";
+            colour = Colour::Error;
+            if (_stats.infoMessages.size() == 1)
+                messageLabel = "explicitly with message";
+            if (_stats.infoMessages.size() > 1)
+                messageLabel = "explicitly with messages";
+            break;
+            // These cases are here to prevent compiler warnings
+        case ResultWas::Unknown:
+        case ResultWas::FailureBit:
+        case ResultWas::Exception:
+            passOrFail = "** internal error **";
+            colour = Colour::Error;
+            break;
+        }
+    }
+
+    void print() const {
+        printSourceInfo();
+        if (stats.totals.assertions.total() > 0) {
+            if (result.isOk())
+                stream << '\n';
+            printResultType();
+            printOriginalExpression();
+            printReconstructedExpression();
+        } else {
+            stream << '\n';
+        }
+        printMessage();
+    }
+
+private:
+    void printResultType() const {
+        if (!passOrFail.empty()) {
+            Colour colourGuard(colour);
+            stream << passOrFail << ":\n";
+        }
+    }
+    void printOriginalExpression() const {
+        if (result.hasExpression()) {
+            Colour colourGuard(Colour::OriginalExpression);
+            stream << "  ";
+            stream << result.getExpressionInMacro();
+            stream << '\n';
+        }
+    }
+    void printReconstructedExpression() const {
+        if (result.hasExpandedExpression()) {
+            stream << "with expansion:\n";
+            Colour colourGuard(Colour::ReconstructedExpression);
+            stream << Column(result.getExpandedExpression()).indent(2) << '\n';
+        }
+    }
+    void printMessage() const {
+        if (!messageLabel.empty())
+            stream << messageLabel << ':' << '\n';
+        for (auto const& msg : messages) {
+            // If this assertion is a warning ignore any INFO messages
+            if (printInfoMessages || msg.type != ResultWas::Info)
+                stream << Column(msg.message).indent(2) << '\n';
+        }
+    }
+    void printSourceInfo() const {
+        Colour colourGuard(Colour::FileName);
+        stream << result.getSourceInfo() << ": ";
+    }
+
+    std::ostream& stream;
+    AssertionStats const& stats;
+    AssertionResult const& result;
+    Colour::Code colour;
+    std::string passOrFail;
+    std::string messageLabel;
+    std::string message;
+    std::vector<MessageInfo> messages;
+    bool printInfoMessages;
+};
+
+std::size_t makeRatio(std::size_t number, std::size_t total) {
+    std::size_t ratio = total > 0 ? CATCH_CONFIG_CONSOLE_WIDTH * number / total : 0;
+    return (ratio == 0 && number > 0) ? 1 : ratio;
+}
+
+std::size_t& findMax(std::size_t& i, std::size_t& j, std::size_t& k) {
+    if (i > j && i > k)
+        return i;
+    else if (j > k)
+        return j;
+    else
+        return k;
+}
+
+struct ColumnInfo {
+    enum Justification { Left, Right };
+    std::string name;
+    int width;
+    Justification justification;
+};
+struct ColumnBreak {};
+struct RowBreak {};
+
+class Duration {
+    enum class Unit {
+        Auto,
+        Nanoseconds,
+        Microseconds,
+        Milliseconds,
+        Seconds,
+        Minutes
+    };
+    static const uint64_t s_nanosecondsInAMicrosecond = 1000;
+    static const uint64_t s_nanosecondsInAMillisecond = 1000 * s_nanosecondsInAMicrosecond;
+    static const uint64_t s_nanosecondsInASecond = 1000 * s_nanosecondsInAMillisecond;
+    static const uint64_t s_nanosecondsInAMinute = 60 * s_nanosecondsInASecond;
+
+    uint64_t m_inNanoseconds;
+    Unit m_units;
+
+public:
+    explicit Duration(uint64_t inNanoseconds, Unit units = Unit::Auto)
+        : m_inNanoseconds(inNanoseconds),
+        m_units(units) {
+        if (m_units == Unit::Auto) {
+            if (m_inNanoseconds < s_nanosecondsInAMicrosecond)
+                m_units = Unit::Nanoseconds;
+            else if (m_inNanoseconds < s_nanosecondsInAMillisecond)
+                m_units = Unit::Microseconds;
+            else if (m_inNanoseconds < s_nanosecondsInASecond)
+                m_units = Unit::Milliseconds;
+            else if (m_inNanoseconds < s_nanosecondsInAMinute)
+                m_units = Unit::Seconds;
+            else
+                m_units = Unit::Minutes;
+        }
+
+    }
+
+    auto value() const -> double {
+        switch (m_units) {
+        case Unit::Microseconds:
+            return m_inNanoseconds / static_cast<double>(s_nanosecondsInAMicrosecond);
+        case Unit::Milliseconds:
+            return m_inNanoseconds / static_cast<double>(s_nanosecondsInAMillisecond);
+        case Unit::Seconds:
+            return m_inNanoseconds / static_cast<double>(s_nanosecondsInASecond);
+        case Unit::Minutes:
+            return m_inNanoseconds / static_cast<double>(s_nanosecondsInAMinute);
+        default:
+            return static_cast<double>(m_inNanoseconds);
+        }
+    }
+    auto unitsAsString() const -> std::string {
+        switch (m_units) {
+        case Unit::Nanoseconds:
+            return "ns";
+        case Unit::Microseconds:
+            return "µs";
+        case Unit::Milliseconds:
+            return "ms";
+        case Unit::Seconds:
+            return "s";
+        case Unit::Minutes:
+            return "m";
+        default:
+            return "** internal error **";
+        }
+
+    }
+    friend auto operator << (std::ostream& os, Duration const& duration) -> std::ostream& {
+        return os << duration.value() << " " << duration.unitsAsString();
+    }
+};
+} // end anon namespace
+
+class TablePrinter {
+    std::ostream& m_os;
+    std::vector<ColumnInfo> m_columnInfos;
+    std::ostringstream m_oss;
+    int m_currentColumn = -1;
+    bool m_isOpen = false;
+
+public:
+    TablePrinter( std::ostream& os, std::vector<ColumnInfo> columnInfos )
+    :   m_os( os ),
+        m_columnInfos( std::move( columnInfos ) ) {}
+
+    auto columnInfos() const -> std::vector<ColumnInfo> const& {
+        return m_columnInfos;
+    }
+
+    void open() {
+        if (!m_isOpen) {
+            m_isOpen = true;
+            *this << RowBreak();
+            for (auto const& info : m_columnInfos)
+                *this << info.name << ColumnBreak();
+            *this << RowBreak();
+            m_os << Catch::getLineOfChars<'-'>() << "\n";
+        }
+    }
+    void close() {
+        if (m_isOpen) {
+            *this << RowBreak();
+            m_os << std::endl;
+            m_isOpen = false;
+        }
+    }
+
+    template<typename T>
+    friend TablePrinter& operator << (TablePrinter& tp, T const& value) {
+        tp.m_oss << value;
+        return tp;
+    }
+
+    friend TablePrinter& operator << (TablePrinter& tp, ColumnBreak) {
+        auto colStr = tp.m_oss.str();
+        // This takes account of utf8 encodings
+        auto strSize = Catch::StringRef(colStr).numberOfCharacters();
+        tp.m_oss.str("");
+        tp.open();
+        if (tp.m_currentColumn == static_cast<int>(tp.m_columnInfos.size() - 1)) {
+            tp.m_currentColumn = -1;
+            tp.m_os << "\n";
+        }
+        tp.m_currentColumn++;
+
+        auto colInfo = tp.m_columnInfos[tp.m_currentColumn];
+        auto padding = (strSize + 2 < static_cast<std::size_t>(colInfo.width))
+            ? std::string(colInfo.width - (strSize + 2), ' ')
+            : std::string();
+        if (colInfo.justification == ColumnInfo::Left)
+            tp.m_os << colStr << padding << " ";
+        else
+            tp.m_os << padding << colStr << " ";
+        return tp;
+    }
+
+    friend TablePrinter& operator << (TablePrinter& tp, RowBreak) {
+        if (tp.m_currentColumn > 0) {
+            tp.m_os << "\n";
+            tp.m_currentColumn = -1;
+        }
+        return tp;
+    }
+};
+
+ConsoleReporter::ConsoleReporter(ReporterConfig const& config)
+    : StreamingReporterBase(config),
+    m_tablePrinter(new TablePrinter(config.stream(),
+    {
+        { "benchmark name", CATCH_CONFIG_CONSOLE_WIDTH - 32, ColumnInfo::Left },
+        { "iters", 8, ColumnInfo::Right },
+        { "elapsed ns", 14, ColumnInfo::Right },
+        { "average", 14, ColumnInfo::Right }
+    })) {}
+ConsoleReporter::~ConsoleReporter() = default;
+
+std::string ConsoleReporter::getDescription() {
+    return "Reports test results as plain lines of text";
+}
+
+void ConsoleReporter::noMatchingTestCases(std::string const& spec) {
+    stream << "No test cases matched '" << spec << '\'' << std::endl;
+}
+
+void ConsoleReporter::assertionStarting(AssertionInfo const&) {}
+
+bool ConsoleReporter::assertionEnded(AssertionStats const& _assertionStats) {
+    AssertionResult const& result = _assertionStats.assertionResult;
+
+    bool includeResults = m_config->includeSuccessfulResults() || !result.isOk();
+
+    // Drop out if result was successful but we're not printing them.
+    if (!includeResults && result.getResultType() != ResultWas::Warning)
+        return false;
+
+    lazyPrint();
+
+    ConsoleAssertionPrinter printer(stream, _assertionStats, includeResults);
+    printer.print();
+    stream << std::endl;
+    return true;
+}
+
+void ConsoleReporter::sectionStarting(SectionInfo const& _sectionInfo) {
+    m_headerPrinted = false;
+    StreamingReporterBase::sectionStarting(_sectionInfo);
+}
+void ConsoleReporter::sectionEnded(SectionStats const& _sectionStats) {
+    m_tablePrinter->close();
+    if (_sectionStats.missingAssertions) {
+        lazyPrint();
+        Colour colour(Colour::ResultError);
+        if (m_sectionStack.size() > 1)
+            stream << "\nNo assertions in section";
+        else
+            stream << "\nNo assertions in test case";
+        stream << " '" << _sectionStats.sectionInfo.name << "'\n" << std::endl;
+    }
+    if (m_config->showDurations() == ShowDurations::Always) {
+        stream << getFormattedDuration(_sectionStats.durationInSeconds) << " s: " << _sectionStats.sectionInfo.name << std::endl;
+    }
+    if (m_headerPrinted) {
+        m_headerPrinted = false;
+    }
+    StreamingReporterBase::sectionEnded(_sectionStats);
+}
+
+void ConsoleReporter::benchmarkStarting(BenchmarkInfo const& info) {
+    lazyPrintWithoutClosingBenchmarkTable();
+
+    auto nameCol = Column( info.name ).width( static_cast<std::size_t>( m_tablePrinter->columnInfos()[0].width - 2 ) );
+
+    bool firstLine = true;
+    for (auto line : nameCol) {
+        if (!firstLine)
+            (*m_tablePrinter) << ColumnBreak() << ColumnBreak() << ColumnBreak();
+        else
+            firstLine = false;
+
+        (*m_tablePrinter) << line << ColumnBreak();
+    }
+}
+void ConsoleReporter::benchmarkEnded(BenchmarkStats const& stats) {
+    Duration average(stats.elapsedTimeInNanoseconds / stats.iterations);
+    (*m_tablePrinter)
+        << stats.iterations << ColumnBreak()
+        << stats.elapsedTimeInNanoseconds << ColumnBreak()
+        << average << ColumnBreak();
+}
+
+void ConsoleReporter::testCaseEnded(TestCaseStats const& _testCaseStats) {
+    m_tablePrinter->close();
+    StreamingReporterBase::testCaseEnded(_testCaseStats);
+    m_headerPrinted = false;
+}
+void ConsoleReporter::testGroupEnded(TestGroupStats const& _testGroupStats) {
+    if (currentGroupInfo.used) {
+        printSummaryDivider();
+        stream << "Summary for group '" << _testGroupStats.groupInfo.name << "':\n";
+        printTotals(_testGroupStats.totals);
+        stream << '\n' << std::endl;
+    }
+    StreamingReporterBase::testGroupEnded(_testGroupStats);
+}
+void ConsoleReporter::testRunEnded(TestRunStats const& _testRunStats) {
+    printTotalsDivider(_testRunStats.totals);
+    printTotals(_testRunStats.totals);
+    stream << std::endl;
+    StreamingReporterBase::testRunEnded(_testRunStats);
+}
+
+void ConsoleReporter::lazyPrint() {
+
+    m_tablePrinter->close();
+    lazyPrintWithoutClosingBenchmarkTable();
+}
+
+void ConsoleReporter::lazyPrintWithoutClosingBenchmarkTable() {
+
+    if (!currentTestRunInfo.used)
+        lazyPrintRunInfo();
+    if (!currentGroupInfo.used)
+        lazyPrintGroupInfo();
+
+    if (!m_headerPrinted) {
+        printTestCaseAndSectionHeader();
+        m_headerPrinted = true;
+    }
+}
+void ConsoleReporter::lazyPrintRunInfo() {
+    stream << '\n' << getLineOfChars<'~'>() << '\n';
+    Colour colour(Colour::SecondaryText);
+    stream << currentTestRunInfo->name
+        << " is a Catch v" << libraryVersion() << " host application.\n"
+        << "Run with -? for options\n\n";
+
+    if (m_config->rngSeed() != 0)
+        stream << "Randomness seeded to: " << m_config->rngSeed() << "\n\n";
+
+    currentTestRunInfo.used = true;
+}
+void ConsoleReporter::lazyPrintGroupInfo() {
+    if (!currentGroupInfo->name.empty() && currentGroupInfo->groupsCounts > 1) {
+        printClosedHeader("Group: " + currentGroupInfo->name);
+        currentGroupInfo.used = true;
+    }
+}
+void ConsoleReporter::printTestCaseAndSectionHeader() {
+    assert(!m_sectionStack.empty());
+    printOpenHeader(currentTestCaseInfo->name);
+
+    if (m_sectionStack.size() > 1) {
+        Colour colourGuard(Colour::Headers);
+
+        auto
+            it = m_sectionStack.begin() + 1, // Skip first section (test case)
+            itEnd = m_sectionStack.end();
+        for (; it != itEnd; ++it)
+            printHeaderString(it->name, 2);
+    }
+
+    SourceLineInfo lineInfo = m_sectionStack.back().lineInfo;
+
+    if (!lineInfo.empty()) {
+        stream << getLineOfChars<'-'>() << '\n';
+        Colour colourGuard(Colour::FileName);
+        stream << lineInfo << '\n';
+    }
+    stream << getLineOfChars<'.'>() << '\n' << std::endl;
+}
+
+void ConsoleReporter::printClosedHeader(std::string const& _name) {
+    printOpenHeader(_name);
+    stream << getLineOfChars<'.'>() << '\n';
+}
+void ConsoleReporter::printOpenHeader(std::string const& _name) {
+    stream << getLineOfChars<'-'>() << '\n';
+    {
+        Colour colourGuard(Colour::Headers);
+        printHeaderString(_name);
+    }
+}
+
+// if string has a : in first line will set indent to follow it on
+// subsequent lines
+void ConsoleReporter::printHeaderString(std::string const& _string, std::size_t indent) {
+    std::size_t i = _string.find(": ");
+    if (i != std::string::npos)
+        i += 2;
+    else
+        i = 0;
+    stream << Column(_string).indent(indent + i).initialIndent(indent) << '\n';
+}
+
+struct SummaryColumn {
+
+    SummaryColumn( std::string _label, Colour::Code _colour )
+    :   label( std::move( _label ) ),
+        colour( _colour ) {}
+    SummaryColumn addRow( std::size_t count ) {
+        ReusableStringStream rss;
+        rss << count;
+        std::string row = rss.str();
+        for (auto& oldRow : rows) {
+            while (oldRow.size() < row.size())
+                oldRow = ' ' + oldRow;
+            while (oldRow.size() > row.size())
+                row = ' ' + row;
+        }
+        rows.push_back(row);
+        return *this;
+    }
+
+    std::string label;
+    Colour::Code colour;
+    std::vector<std::string> rows;
+
+};
+
+void ConsoleReporter::printTotals( Totals const& totals ) {
+    if (totals.testCases.total() == 0) {
+        stream << Colour(Colour::Warning) << "No tests ran\n";
+    } else if (totals.assertions.total() > 0 && totals.testCases.allPassed()) {
+        stream << Colour(Colour::ResultSuccess) << "All tests passed";
+        stream << " ("
+            << pluralise(totals.assertions.passed, "assertion") << " in "
+            << pluralise(totals.testCases.passed, "test case") << ')'
+            << '\n';
+    } else {
+
+        std::vector<SummaryColumn> columns;
+        columns.push_back(SummaryColumn("", Colour::None)
+                          .addRow(totals.testCases.total())
+                          .addRow(totals.assertions.total()));
+        columns.push_back(SummaryColumn("passed", Colour::Success)
+                          .addRow(totals.testCases.passed)
+                          .addRow(totals.assertions.passed));
+        columns.push_back(SummaryColumn("failed", Colour::ResultError)
+                          .addRow(totals.testCases.failed)
+                          .addRow(totals.assertions.failed));
+        columns.push_back(SummaryColumn("failed as expected", Colour::ResultExpectedFailure)
+                          .addRow(totals.testCases.failedButOk)
+                          .addRow(totals.assertions.failedButOk));
+
+        printSummaryRow("test cases", columns, 0);
+        printSummaryRow("assertions", columns, 1);
+    }
+}
+void ConsoleReporter::printSummaryRow(std::string const& label, std::vector<SummaryColumn> const& cols, std::size_t row) {
+    for (auto col : cols) {
+        std::string value = col.rows[row];
+        if (col.label.empty()) {
+            stream << label << ": ";
+            if (value != "0")
+                stream << value;
+            else
+                stream << Colour(Colour::Warning) << "- none -";
+        } else if (value != "0") {
+            stream << Colour(Colour::LightGrey) << " | ";
+            stream << Colour(col.colour)
+                << value << ' ' << col.label;
+        }
+    }
+    stream << '\n';
+}
+
+void ConsoleReporter::printTotalsDivider(Totals const& totals) {
+    if (totals.testCases.total() > 0) {
+        std::size_t failedRatio = makeRatio(totals.testCases.failed, totals.testCases.total());
+        std::size_t failedButOkRatio = makeRatio(totals.testCases.failedButOk, totals.testCases.total());
+        std::size_t passedRatio = makeRatio(totals.testCases.passed, totals.testCases.total());
+        while (failedRatio + failedButOkRatio + passedRatio < CATCH_CONFIG_CONSOLE_WIDTH - 1)
+            findMax(failedRatio, failedButOkRatio, passedRatio)++;
+        while (failedRatio + failedButOkRatio + passedRatio > CATCH_CONFIG_CONSOLE_WIDTH - 1)
+            findMax(failedRatio, failedButOkRatio, passedRatio)--;
+
+        stream << Colour(Colour::Error) << std::string(failedRatio, '=');
+        stream << Colour(Colour::ResultExpectedFailure) << std::string(failedButOkRatio, '=');
+        if (totals.testCases.allPassed())
+            stream << Colour(Colour::ResultSuccess) << std::string(passedRatio, '=');
+        else
+            stream << Colour(Colour::Success) << std::string(passedRatio, '=');
+    } else {
+        stream << Colour(Colour::Warning) << std::string(CATCH_CONFIG_CONSOLE_WIDTH - 1, '=');
+    }
+    stream << '\n';
+}
+void ConsoleReporter::printSummaryDivider() {
+    stream << getLineOfChars<'-'>() << '\n';
+}
+
+CATCH_REGISTER_REPORTER("console", ConsoleReporter)
+
+} // end namespace Catch
+
+#if defined(_MSC_VER)
+#pragma warning(pop)
+#endif
+// end catch_reporter_console.cpp
+// start catch_reporter_junit.cpp
+
+#include <cassert>
+#include <sstream>
+#include <ctime>
+#include <algorithm>
+
+namespace Catch {
+
+    namespace {
+        std::string getCurrentTimestamp() {
+            // Beware, this is not reentrant because of backward compatibility issues
+            // Also, UTC only, again because of backward compatibility (%z is C++11)
+            time_t rawtime;
+            std::time(&rawtime);
+            auto const timeStampSize = sizeof("2017-01-16T17:06:45Z");
+
+#ifdef _MSC_VER
+            std::tm timeInfo = {};
+            gmtime_s(&timeInfo, &rawtime);
+#else
+            std::tm* timeInfo;
+            timeInfo = std::gmtime(&rawtime);
+#endif
+
+            char timeStamp[timeStampSize];
+            const char * const fmt = "%Y-%m-%dT%H:%M:%SZ";
+
+#ifdef _MSC_VER
+            std::strftime(timeStamp, timeStampSize, fmt, &timeInfo);
+#else
+            std::strftime(timeStamp, timeStampSize, fmt, timeInfo);
+#endif
+            return std::string(timeStamp);
+        }
+
+        std::string fileNameTag(const std::vector<std::string> &tags) {
+            auto it = std::find_if(begin(tags),
+                                   end(tags),
+                                   [] (std::string const& tag) {return tag.front() == '#'; });
+            if (it != tags.end())
+                return it->substr(1);
+            return std::string();
+        }
+    } // anonymous namespace
+
+    JunitReporter::JunitReporter( ReporterConfig const& _config )
+        :   CumulativeReporterBase( _config ),
+            xml( _config.stream() )
+        {
+            m_reporterPrefs.shouldRedirectStdOut = true;
+            m_reporterPrefs.shouldReportAllAssertions = true;
+        }
+
+    JunitReporter::~JunitReporter() {}
+
+    std::string JunitReporter::getDescription() {
+        return "Reports test results in an XML format that looks like Ant's junitreport target";
+    }
+
+    void JunitReporter::noMatchingTestCases( std::string const& /*spec*/ ) {}
+
+    void JunitReporter::testRunStarting( TestRunInfo const& runInfo )  {
+        CumulativeReporterBase::testRunStarting( runInfo );
+        xml.startElement( "testsuites" );
+    }
+
+    void JunitReporter::testGroupStarting( GroupInfo const& groupInfo ) {
+        suiteTimer.start();
+        stdOutForSuite.clear();
+        stdErrForSuite.clear();
+        unexpectedExceptions = 0;
+        CumulativeReporterBase::testGroupStarting( groupInfo );
+    }
+
+    void JunitReporter::testCaseStarting( TestCaseInfo const& testCaseInfo ) {
+        m_okToFail = testCaseInfo.okToFail();
+    }
+
+    bool JunitReporter::assertionEnded( AssertionStats const& assertionStats ) {
+        if( assertionStats.assertionResult.getResultType() == ResultWas::ThrewException && !m_okToFail )
+            unexpectedExceptions++;
+        return CumulativeReporterBase::assertionEnded( assertionStats );
+    }
+
+    void JunitReporter::testCaseEnded( TestCaseStats const& testCaseStats ) {
+        stdOutForSuite += testCaseStats.stdOut;
+        stdErrForSuite += testCaseStats.stdErr;
+        CumulativeReporterBase::testCaseEnded( testCaseStats );
+    }
+
+    void JunitReporter::testGroupEnded( TestGroupStats const& testGroupStats ) {
+        double suiteTime = suiteTimer.getElapsedSeconds();
+        CumulativeReporterBase::testGroupEnded( testGroupStats );
+        writeGroup( *m_testGroups.back(), suiteTime );
+    }
+
+    void JunitReporter::testRunEndedCumulative() {
+        xml.endElement();
+    }
+
+    void JunitReporter::writeGroup( TestGroupNode const& groupNode, double suiteTime ) {
+        XmlWriter::ScopedElement e = xml.scopedElement( "testsuite" );
+        TestGroupStats const& stats = groupNode.value;
+        xml.writeAttribute( "name", stats.groupInfo.name );
+        xml.writeAttribute( "errors", unexpectedExceptions );
+        xml.writeAttribute( "failures", stats.totals.assertions.failed-unexpectedExceptions );
+        xml.writeAttribute( "tests", stats.totals.assertions.total() );
+        xml.writeAttribute( "hostname", "tbd" ); // !TBD
+        if( m_config->showDurations() == ShowDurations::Never )
+            xml.writeAttribute( "time", "" );
+        else
+            xml.writeAttribute( "time", suiteTime );
+        xml.writeAttribute( "timestamp", getCurrentTimestamp() );
+
+        // Write test cases
+        for( auto const& child : groupNode.children )
+            writeTestCase( *child );
+
+        xml.scopedElement( "system-out" ).writeText( trim( stdOutForSuite ), false );
+        xml.scopedElement( "system-err" ).writeText( trim( stdErrForSuite ), false );
+    }
+
+    void JunitReporter::writeTestCase( TestCaseNode const& testCaseNode ) {
+        TestCaseStats const& stats = testCaseNode.value;
+
+        // All test cases have exactly one section - which represents the
+        // test case itself. That section may have 0-n nested sections
+        assert( testCaseNode.children.size() == 1 );
+        SectionNode const& rootSection = *testCaseNode.children.front();
+
+        std::string className = stats.testInfo.className;
+
+        if( className.empty() ) {
+            className = fileNameTag(stats.testInfo.tags);
+            if ( className.empty() )
+                className = "global";
+        }
+
+        if ( !m_config->name().empty() )
+            className = m_config->name() + "." + className;
+
+        writeSection( className, "", rootSection );
+    }
+
+    void JunitReporter::writeSection(  std::string const& className,
+                        std::string const& rootName,
+                        SectionNode const& sectionNode ) {
+        std::string name = trim( sectionNode.stats.sectionInfo.name );
+        if( !rootName.empty() )
+            name = rootName + '/' + name;
+
+        if( !sectionNode.assertions.empty() ||
+            !sectionNode.stdOut.empty() ||
+            !sectionNode.stdErr.empty() ) {
+            XmlWriter::ScopedElement e = xml.scopedElement( "testcase" );
+            if( className.empty() ) {
+                xml.writeAttribute( "classname", name );
+                xml.writeAttribute( "name", "root" );
+            }
+            else {
+                xml.writeAttribute( "classname", className );
+                xml.writeAttribute( "name", name );
+            }
+            xml.writeAttribute( "time", ::Catch::Detail::stringify( sectionNode.stats.durationInSeconds ) );
+
+            writeAssertions( sectionNode );
+
+            if( !sectionNode.stdOut.empty() )
+                xml.scopedElement( "system-out" ).writeText( trim( sectionNode.stdOut ), false );
+            if( !sectionNode.stdErr.empty() )
+                xml.scopedElement( "system-err" ).writeText( trim( sectionNode.stdErr ), false );
+        }
+        for( auto const& childNode : sectionNode.childSections )
+            if( className.empty() )
+                writeSection( name, "", *childNode );
+            else
+                writeSection( className, name, *childNode );
+    }
+
+    void JunitReporter::writeAssertions( SectionNode const& sectionNode ) {
+        for( auto const& assertion : sectionNode.assertions )
+            writeAssertion( assertion );
+    }
+
+    void JunitReporter::writeAssertion( AssertionStats const& stats ) {
+        AssertionResult const& result = stats.assertionResult;
+        if( !result.isOk() ) {
+            std::string elementName;
+            switch( result.getResultType() ) {
+                case ResultWas::ThrewException:
+                case ResultWas::FatalErrorCondition:
+                    elementName = "error";
+                    break;
+                case ResultWas::ExplicitFailure:
+                    elementName = "failure";
+                    break;
+                case ResultWas::ExpressionFailed:
+                    elementName = "failure";
+                    break;
+                case ResultWas::DidntThrowException:
+                    elementName = "failure";
+                    break;
+
+                // We should never see these here:
+                case ResultWas::Info:
+                case ResultWas::Warning:
+                case ResultWas::Ok:
+                case ResultWas::Unknown:
+                case ResultWas::FailureBit:
+                case ResultWas::Exception:
+                    elementName = "internalError";
+                    break;
+            }
+
+            XmlWriter::ScopedElement e = xml.scopedElement( elementName );
+
+            xml.writeAttribute( "message", result.getExpandedExpression() );
+            xml.writeAttribute( "type", result.getTestMacroName() );
+
+            ReusableStringStream rss;
+            if( !result.getMessage().empty() )
+                rss << result.getMessage() << '\n';
+            for( auto const& msg : stats.infoMessages )
+                if( msg.type == ResultWas::Info )
+                    rss << msg.message << '\n';
+
+            rss << "at " << result.getSourceInfo();
+            xml.writeText( rss.str(), false );
+        }
+    }
+
+    CATCH_REGISTER_REPORTER( "junit", JunitReporter )
+
+} // end namespace Catch
+// end catch_reporter_junit.cpp
+// start catch_reporter_listening.cpp
+
+#include <cassert>
+
+namespace Catch {
+
+    ListeningReporter::ListeningReporter() {
+        // We will assume that listeners will always want all assertions
+        m_preferences.shouldReportAllAssertions = true;
+    }
+
+    void ListeningReporter::addListener( IStreamingReporterPtr&& listener ) {
+        m_listeners.push_back( std::move( listener ) );
+    }
+
+    void ListeningReporter::addReporter(IStreamingReporterPtr&& reporter) {
+        assert(!m_reporter && "Listening reporter can wrap only 1 real reporter");
+        m_reporter = std::move( reporter );
+        m_preferences.shouldRedirectStdOut = m_reporter->getPreferences().shouldRedirectStdOut;
+    }
+
+    ReporterPreferences ListeningReporter::getPreferences() const {
+        return m_preferences;
+    }
+
+    std::set<Verbosity> ListeningReporter::getSupportedVerbosities() {
+        return std::set<Verbosity>{ };
+    }
+
+    void ListeningReporter::noMatchingTestCases( std::string const& spec ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->noMatchingTestCases( spec );
+        }
+        m_reporter->noMatchingTestCases( spec );
+    }
+
+    void ListeningReporter::benchmarkStarting( BenchmarkInfo const& benchmarkInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->benchmarkStarting( benchmarkInfo );
+        }
+        m_reporter->benchmarkStarting( benchmarkInfo );
+    }
+    void ListeningReporter::benchmarkEnded( BenchmarkStats const& benchmarkStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->benchmarkEnded( benchmarkStats );
+        }
+        m_reporter->benchmarkEnded( benchmarkStats );
+    }
+
+    void ListeningReporter::testRunStarting( TestRunInfo const& testRunInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testRunStarting( testRunInfo );
+        }
+        m_reporter->testRunStarting( testRunInfo );
+    }
+
+    void ListeningReporter::testGroupStarting( GroupInfo const& groupInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testGroupStarting( groupInfo );
+        }
+        m_reporter->testGroupStarting( groupInfo );
+    }
+
+    void ListeningReporter::testCaseStarting( TestCaseInfo const& testInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testCaseStarting( testInfo );
+        }
+        m_reporter->testCaseStarting( testInfo );
+    }
+
+    void ListeningReporter::sectionStarting( SectionInfo const& sectionInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->sectionStarting( sectionInfo );
+        }
+        m_reporter->sectionStarting( sectionInfo );
+    }
+
+    void ListeningReporter::assertionStarting( AssertionInfo const& assertionInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->assertionStarting( assertionInfo );
+        }
+        m_reporter->assertionStarting( assertionInfo );
+    }
+
+    // The return value indicates if the messages buffer should be cleared:
+    bool ListeningReporter::assertionEnded( AssertionStats const& assertionStats ) {
+        for( auto const& listener : m_listeners ) {
+            static_cast<void>( listener->assertionEnded( assertionStats ) );
+        }
+        return m_reporter->assertionEnded( assertionStats );
+    }
+
+    void ListeningReporter::sectionEnded( SectionStats const& sectionStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->sectionEnded( sectionStats );
+        }
+        m_reporter->sectionEnded( sectionStats );
+    }
+
+    void ListeningReporter::testCaseEnded( TestCaseStats const& testCaseStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testCaseEnded( testCaseStats );
+        }
+        m_reporter->testCaseEnded( testCaseStats );
+    }
+
+    void ListeningReporter::testGroupEnded( TestGroupStats const& testGroupStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testGroupEnded( testGroupStats );
+        }
+        m_reporter->testGroupEnded( testGroupStats );
+    }
+
+    void ListeningReporter::testRunEnded( TestRunStats const& testRunStats ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->testRunEnded( testRunStats );
+        }
+        m_reporter->testRunEnded( testRunStats );
+    }
+
+    void ListeningReporter::skipTest( TestCaseInfo const& testInfo ) {
+        for ( auto const& listener : m_listeners ) {
+            listener->skipTest( testInfo );
+        }
+        m_reporter->skipTest( testInfo );
+    }
+
+    bool ListeningReporter::isMulti() const {
+        return true;
+    }
+
+} // end namespace Catch
+// end catch_reporter_listening.cpp
+// start catch_reporter_xml.cpp
+
+#if defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable:4061) // Not all labels are EXPLICITLY handled in switch
+                              // Note that 4062 (not all labels are handled
+                              // and default is missing) is enabled
+#endif
+
+namespace Catch {
+    XmlReporter::XmlReporter( ReporterConfig const& _config )
+    :   StreamingReporterBase( _config ),
+        m_xml(_config.stream())
+    {
+        m_reporterPrefs.shouldRedirectStdOut = true;
+        m_reporterPrefs.shouldReportAllAssertions = true;
+    }
+
+    XmlReporter::~XmlReporter() = default;
+
+    std::string XmlReporter::getDescription() {
+        return "Reports test results as an XML document";
+    }
+
+    std::string XmlReporter::getStylesheetRef() const {
+        return std::string();
+    }
+
+    void XmlReporter::writeSourceInfo( SourceLineInfo const& sourceInfo ) {
+        m_xml
+            .writeAttribute( "filename", sourceInfo.file )
+            .writeAttribute( "line", sourceInfo.line );
+    }
+
+    void XmlReporter::noMatchingTestCases( std::string const& s ) {
+        StreamingReporterBase::noMatchingTestCases( s );
+    }
+
+    void XmlReporter::testRunStarting( TestRunInfo const& testInfo ) {
+        StreamingReporterBase::testRunStarting( testInfo );
+        std::string stylesheetRef = getStylesheetRef();
+        if( !stylesheetRef.empty() )
+            m_xml.writeStylesheetRef( stylesheetRef );
+        m_xml.startElement( "Catch" );
+        if( !m_config->name().empty() )
+            m_xml.writeAttribute( "name", m_config->name() );
+    }
+
+    void XmlReporter::testGroupStarting( GroupInfo const& groupInfo ) {
+        StreamingReporterBase::testGroupStarting( groupInfo );
+        m_xml.startElement( "Group" )
+            .writeAttribute( "name", groupInfo.name );
+    }
+
+    void XmlReporter::testCaseStarting( TestCaseInfo const& testInfo ) {
+        StreamingReporterBase::testCaseStarting(testInfo);
+        m_xml.startElement( "TestCase" )
+            .writeAttribute( "name", trim( testInfo.name ) )
+            .writeAttribute( "description", testInfo.description )
+            .writeAttribute( "tags", testInfo.tagsAsString() );
+
+        writeSourceInfo( testInfo.lineInfo );
+
+        if ( m_config->showDurations() == ShowDurations::Always )
+            m_testCaseTimer.start();
+        m_xml.ensureTagClosed();
+    }
+
+    void XmlReporter::sectionStarting( SectionInfo const& sectionInfo ) {
+        StreamingReporterBase::sectionStarting( sectionInfo );
+        if( m_sectionDepth++ > 0 ) {
+            m_xml.startElement( "Section" )
+                .writeAttribute( "name", trim( sectionInfo.name ) );
+            writeSourceInfo( sectionInfo.lineInfo );
+            m_xml.ensureTagClosed();
+        }
+    }
+
+    void XmlReporter::assertionStarting( AssertionInfo const& ) { }
+
+    bool XmlReporter::assertionEnded( AssertionStats const& assertionStats ) {
+
+        AssertionResult const& result = assertionStats.assertionResult;
+
+        bool includeResults = m_config->includeSuccessfulResults() || !result.isOk();
+
+        if( includeResults || result.getResultType() == ResultWas::Warning ) {
+            // Print any info messages in <Info> tags.
+            for( auto const& msg : assertionStats.infoMessages ) {
+                if( msg.type == ResultWas::Info && includeResults ) {
+                    m_xml.scopedElement( "Info" )
+                            .writeText( msg.message );
+                } else if ( msg.type == ResultWas::Warning ) {
+                    m_xml.scopedElement( "Warning" )
+                            .writeText( msg.message );
+                }
+            }
+        }
+
+        // Drop out if result was successful but we're not printing them.
+        if( !includeResults && result.getResultType() != ResultWas::Warning )
+            return true;
+
+        // Print the expression if there is one.
+        if( result.hasExpression() ) {
+            m_xml.startElement( "Expression" )
+                .writeAttribute( "success", result.succeeded() )
+                .writeAttribute( "type", result.getTestMacroName() );
+
+            writeSourceInfo( result.getSourceInfo() );
+
+            m_xml.scopedElement( "Original" )
+                .writeText( result.getExpression() );
+            m_xml.scopedElement( "Expanded" )
+                .writeText( result.getExpandedExpression() );
+        }
+
+        // And... Print a result applicable to each result type.
+        switch( result.getResultType() ) {
+            case ResultWas::ThrewException:
+                m_xml.startElement( "Exception" );
+                writeSourceInfo( result.getSourceInfo() );
+                m_xml.writeText( result.getMessage() );
+                m_xml.endElement();
+                break;
+            case ResultWas::FatalErrorCondition:
+                m_xml.startElement( "FatalErrorCondition" );
+                writeSourceInfo( result.getSourceInfo() );
+                m_xml.writeText( result.getMessage() );
+                m_xml.endElement();
+                break;
+            case ResultWas::Info:
+                m_xml.scopedElement( "Info" )
+                    .writeText( result.getMessage() );
+                break;
+            case ResultWas::Warning:
+                // Warning will already have been written
+                break;
+            case ResultWas::ExplicitFailure:
+                m_xml.startElement( "Failure" );
+                writeSourceInfo( result.getSourceInfo() );
+                m_xml.writeText( result.getMessage() );
+                m_xml.endElement();
+                break;
+            default:
+                break;
+        }
+
+        if( result.hasExpression() )
+            m_xml.endElement();
+
+        return true;
+    }
+
+    void XmlReporter::sectionEnded( SectionStats const& sectionStats ) {
+        StreamingReporterBase::sectionEnded( sectionStats );
+        if( --m_sectionDepth > 0 ) {
+            XmlWriter::ScopedElement e = m_xml.scopedElement( "OverallResults" );
+            e.writeAttribute( "successes", sectionStats.assertions.passed );
+            e.writeAttribute( "failures", sectionStats.assertions.failed );
+            e.writeAttribute( "expectedFailures", sectionStats.assertions.failedButOk );
+
+            if ( m_config->showDurations() == ShowDurations::Always )
+                e.writeAttribute( "durationInSeconds", sectionStats.durationInSeconds );
+
+            m_xml.endElement();
+        }
+    }
+
+    void XmlReporter::testCaseEnded( TestCaseStats const& testCaseStats ) {
+        StreamingReporterBase::testCaseEnded( testCaseStats );
+        XmlWriter::ScopedElement e = m_xml.scopedElement( "OverallResult" );
+        e.writeAttribute( "success", testCaseStats.totals.assertions.allOk() );
+
+        if ( m_config->showDurations() == ShowDurations::Always )
+            e.writeAttribute( "durationInSeconds", m_testCaseTimer.getElapsedSeconds() );
+
+        if( !testCaseStats.stdOut.empty() )
+            m_xml.scopedElement( "StdOut" ).writeText( trim( testCaseStats.stdOut ), false );
+        if( !testCaseStats.stdErr.empty() )
+            m_xml.scopedElement( "StdErr" ).writeText( trim( testCaseStats.stdErr ), false );
+
+        m_xml.endElement();
+    }
+
+    void XmlReporter::testGroupEnded( TestGroupStats const& testGroupStats ) {
+        StreamingReporterBase::testGroupEnded( testGroupStats );
+        // TODO: Check testGroupStats.aborting and act accordingly.
+        m_xml.scopedElement( "OverallResults" )
+            .writeAttribute( "successes", testGroupStats.totals.assertions.passed )
+            .writeAttribute( "failures", testGroupStats.totals.assertions.failed )
+            .writeAttribute( "expectedFailures", testGroupStats.totals.assertions.failedButOk );
+        m_xml.endElement();
+    }
+
+    void XmlReporter::testRunEnded( TestRunStats const& testRunStats ) {
+        StreamingReporterBase::testRunEnded( testRunStats );
+        m_xml.scopedElement( "OverallResults" )
+            .writeAttribute( "successes", testRunStats.totals.assertions.passed )
+            .writeAttribute( "failures", testRunStats.totals.assertions.failed )
+            .writeAttribute( "expectedFailures", testRunStats.totals.assertions.failedButOk );
+        m_xml.endElement();
+    }
+
+    CATCH_REGISTER_REPORTER( "xml", XmlReporter )
+
+} // end namespace Catch
+
+#if defined(_MSC_VER)
+#pragma warning(pop)
+#endif
+// end catch_reporter_xml.cpp
+
+namespace Catch {
+    LeakDetector leakDetector;
+}
+
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif
+
+// end catch_impl.hpp
+#endif
+
+#ifdef CATCH_CONFIG_MAIN
+// start catch_default_main.hpp
+
+#ifndef __OBJC__
+
+#if defined(CATCH_CONFIG_WCHAR) && defined(WIN32) && defined(_UNICODE) && !defined(DO_NOT_USE_WMAIN)
+// Standard C/C++ Win32 Unicode wmain entry point
+extern "C" int wmain (int argc, wchar_t * argv[], wchar_t * []) {
+#else
+// Standard C/C++ main entry point
+int main (int argc, char * argv[]) {
+#endif
+
+    return Catch::Session().run( argc, argv );
+}
+
+#else // __OBJC__
+
+// Objective-C entry point
+int main (int argc, char * const argv[]) {
+#if !CATCH_ARC_ENABLED
+    NSAutoreleasePool * pool = [[NSAutoreleasePool alloc] init];
+#endif
+
+    Catch::registerTestMethods();
+    int result = Catch::Session().run( argc, (char**)argv );
+
+#if !CATCH_ARC_ENABLED
+    [pool drain];
+#endif
+
+    return result;
+}
+
+#endif // __OBJC__
+
+// end catch_default_main.hpp
+#endif
+
+#if !defined(CATCH_CONFIG_IMPL_ONLY)
+
+#ifdef CLARA_CONFIG_MAIN_NOT_DEFINED
+#  undef CLARA_CONFIG_MAIN
+#endif
+
+#if !defined(CATCH_CONFIG_DISABLE)
+//////
+// If this config identifier is defined then all CATCH macros are prefixed with CATCH_
+#ifdef CATCH_CONFIG_PREFIX_ALL
+
+#define CATCH_REQUIRE( ... ) INTERNAL_CATCH_TEST( "CATCH_REQUIRE", Catch::ResultDisposition::Normal, __VA_ARGS__ )
+#define CATCH_REQUIRE_FALSE( ... ) INTERNAL_CATCH_TEST( "CATCH_REQUIRE_FALSE", Catch::ResultDisposition::Normal | Catch::ResultDisposition::FalseTest, __VA_ARGS__ )
+
+#define CATCH_REQUIRE_THROWS( ... ) INTERNAL_CATCH_THROWS( "CATCH_REQUIRE_THROWS", Catch::ResultDisposition::Normal, "", __VA_ARGS__ )
+#define CATCH_REQUIRE_THROWS_AS( expr, exceptionType ) INTERNAL_CATCH_THROWS_AS( "CATCH_REQUIRE_THROWS_AS", exceptionType, Catch::ResultDisposition::Normal, expr )
+#define CATCH_REQUIRE_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS_STR_MATCHES( "CATCH_REQUIRE_THROWS_WITH", Catch::ResultDisposition::Normal, matcher, expr )
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_REQUIRE_THROWS_MATCHES( expr, exceptionType, matcher ) INTERNAL_CATCH_THROWS_MATCHES( "CATCH_REQUIRE_THROWS_MATCHES", exceptionType, Catch::ResultDisposition::Normal, matcher, expr )
+#endif// CATCH_CONFIG_DISABLE_MATCHERS
+#define CATCH_REQUIRE_NOTHROW( ... ) INTERNAL_CATCH_NO_THROW( "CATCH_REQUIRE_NOTHROW", Catch::ResultDisposition::Normal, __VA_ARGS__ )
+
+#define CATCH_CHECK( ... ) INTERNAL_CATCH_TEST( "CATCH_CHECK", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_CHECK_FALSE( ... ) INTERNAL_CATCH_TEST( "CATCH_CHECK_FALSE", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::FalseTest, __VA_ARGS__ )
+#define CATCH_CHECKED_IF( ... ) INTERNAL_CATCH_IF( "CATCH_CHECKED_IF", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_CHECKED_ELSE( ... ) INTERNAL_CATCH_ELSE( "CATCH_CHECKED_ELSE", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_CHECK_NOFAIL( ... ) INTERNAL_CATCH_TEST( "CATCH_CHECK_NOFAIL", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::SuppressFail, __VA_ARGS__ )
+
+#define CATCH_CHECK_THROWS( ... )  INTERNAL_CATCH_THROWS( "CATCH_CHECK_THROWS", Catch::ResultDisposition::ContinueOnFailure, "", __VA_ARGS__ )
+#define CATCH_CHECK_THROWS_AS( expr, exceptionType ) INTERNAL_CATCH_THROWS_AS( "CATCH_CHECK_THROWS_AS", exceptionType, Catch::ResultDisposition::ContinueOnFailure, expr )
+#define CATCH_CHECK_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS_STR_MATCHES( "CATCH_CHECK_THROWS_WITH", Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_CHECK_THROWS_MATCHES( expr, exceptionType, matcher ) INTERNAL_CATCH_THROWS_MATCHES( "CATCH_CHECK_THROWS_MATCHES", exceptionType, Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define CATCH_CHECK_NOTHROW( ... ) INTERNAL_CATCH_NO_THROW( "CATCH_CHECK_NOTHROW", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_CHECK_THAT( arg, matcher ) INTERNAL_CHECK_THAT( "CATCH_CHECK_THAT", matcher, Catch::ResultDisposition::ContinueOnFailure, arg )
+
+#define CATCH_REQUIRE_THAT( arg, matcher ) INTERNAL_CHECK_THAT( "CATCH_REQUIRE_THAT", matcher, Catch::ResultDisposition::Normal, arg )
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+
+#define CATCH_INFO( msg ) INTERNAL_CATCH_INFO( "CATCH_INFO", msg )
+#define CATCH_WARN( msg ) INTERNAL_CATCH_MSG( "CATCH_WARN", Catch::ResultWas::Warning, Catch::ResultDisposition::ContinueOnFailure, msg )
+#define CATCH_CAPTURE( ... ) INTERNAL_CATCH_CAPTURE( INTERNAL_CATCH_UNIQUE_NAME(capturer), "CATCH_CAPTURE",__VA_ARGS__ )
+
+#define CATCH_TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE( __VA_ARGS__ )
+#define CATCH_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define CATCH_METHOD_AS_TEST_CASE( method, ... ) INTERNAL_CATCH_METHOD_AS_TEST_CASE( method, __VA_ARGS__ )
+#define CATCH_REGISTER_TEST_CASE( Function, ... ) INTERNAL_CATCH_REGISTER_TESTCASE( Function, __VA_ARGS__ )
+#define CATCH_SECTION( ... ) INTERNAL_CATCH_SECTION( __VA_ARGS__ )
+#define CATCH_DYNAMIC_SECTION( ... ) INTERNAL_CATCH_DYNAMIC_SECTION( __VA_ARGS__ )
+#define CATCH_FAIL( ... ) INTERNAL_CATCH_MSG( "CATCH_FAIL", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::Normal, __VA_ARGS__ )
+#define CATCH_FAIL_CHECK( ... ) INTERNAL_CATCH_MSG( "CATCH_FAIL_CHECK", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CATCH_SUCCEED( ... ) INTERNAL_CATCH_MSG( "CATCH_SUCCEED", Catch::ResultWas::Ok, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+
+#define CATCH_ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE()
+
+// "BDD-style" convenience wrappers
+#define CATCH_SCENARIO( ... ) CATCH_TEST_CASE( "Scenario: " __VA_ARGS__ )
+#define CATCH_SCENARIO_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, "Scenario: " __VA_ARGS__ )
+#define CATCH_GIVEN( desc )     INTERNAL_CATCH_DYNAMIC_SECTION( "    Given: " << desc )
+#define CATCH_AND_GIVEN( desc ) INTERNAL_CATCH_DYNAMIC_SECTION( "And given: " << desc )
+#define CATCH_WHEN( desc )      INTERNAL_CATCH_DYNAMIC_SECTION( "     When: " << desc )
+#define CATCH_AND_WHEN( desc )  INTERNAL_CATCH_DYNAMIC_SECTION( " And when: " << desc )
+#define CATCH_THEN( desc )      INTERNAL_CATCH_DYNAMIC_SECTION( "     Then: " << desc )
+#define CATCH_AND_THEN( desc )  INTERNAL_CATCH_DYNAMIC_SECTION( "      And: " << desc )
+
+// If CATCH_CONFIG_PREFIX_ALL is not defined then the CATCH_ prefix is not required
+#else
+
+#define REQUIRE( ... ) INTERNAL_CATCH_TEST( "REQUIRE", Catch::ResultDisposition::Normal, __VA_ARGS__  )
+#define REQUIRE_FALSE( ... ) INTERNAL_CATCH_TEST( "REQUIRE_FALSE", Catch::ResultDisposition::Normal | Catch::ResultDisposition::FalseTest, __VA_ARGS__ )
+
+#define REQUIRE_THROWS( ... ) INTERNAL_CATCH_THROWS( "REQUIRE_THROWS", Catch::ResultDisposition::Normal, __VA_ARGS__ )
+#define REQUIRE_THROWS_AS( expr, exceptionType ) INTERNAL_CATCH_THROWS_AS( "REQUIRE_THROWS_AS", exceptionType, Catch::ResultDisposition::Normal, expr )
+#define REQUIRE_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS_STR_MATCHES( "REQUIRE_THROWS_WITH", Catch::ResultDisposition::Normal, matcher, expr )
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define REQUIRE_THROWS_MATCHES( expr, exceptionType, matcher ) INTERNAL_CATCH_THROWS_MATCHES( "REQUIRE_THROWS_MATCHES", exceptionType, Catch::ResultDisposition::Normal, matcher, expr )
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define REQUIRE_NOTHROW( ... ) INTERNAL_CATCH_NO_THROW( "REQUIRE_NOTHROW", Catch::ResultDisposition::Normal, __VA_ARGS__ )
+
+#define CHECK( ... ) INTERNAL_CATCH_TEST( "CHECK", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CHECK_FALSE( ... ) INTERNAL_CATCH_TEST( "CHECK_FALSE", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::FalseTest, __VA_ARGS__ )
+#define CHECKED_IF( ... ) INTERNAL_CATCH_IF( "CHECKED_IF", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CHECKED_ELSE( ... ) INTERNAL_CATCH_ELSE( "CHECKED_ELSE", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CHECK_NOFAIL( ... ) INTERNAL_CATCH_TEST( "CHECK_NOFAIL", Catch::ResultDisposition::ContinueOnFailure | Catch::ResultDisposition::SuppressFail, __VA_ARGS__ )
+
+#define CHECK_THROWS( ... )  INTERNAL_CATCH_THROWS( "CHECK_THROWS", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define CHECK_THROWS_AS( expr, exceptionType ) INTERNAL_CATCH_THROWS_AS( "CHECK_THROWS_AS", exceptionType, Catch::ResultDisposition::ContinueOnFailure, expr )
+#define CHECK_THROWS_WITH( expr, matcher ) INTERNAL_CATCH_THROWS_STR_MATCHES( "CHECK_THROWS_WITH", Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CHECK_THROWS_MATCHES( expr, exceptionType, matcher ) INTERNAL_CATCH_THROWS_MATCHES( "CHECK_THROWS_MATCHES", exceptionType, Catch::ResultDisposition::ContinueOnFailure, matcher, expr )
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define CHECK_NOTHROW( ... ) INTERNAL_CATCH_NO_THROW( "CHECK_NOTHROW", Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CHECK_THAT( arg, matcher ) INTERNAL_CHECK_THAT( "CHECK_THAT", matcher, Catch::ResultDisposition::ContinueOnFailure, arg )
+
+#define REQUIRE_THAT( arg, matcher ) INTERNAL_CHECK_THAT( "REQUIRE_THAT", matcher, Catch::ResultDisposition::Normal, arg )
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+
+#define INFO( msg ) INTERNAL_CATCH_INFO( "INFO", msg )
+#define WARN( msg ) INTERNAL_CATCH_MSG( "WARN", Catch::ResultWas::Warning, Catch::ResultDisposition::ContinueOnFailure, msg )
+#define CAPTURE( ... ) INTERNAL_CATCH_CAPTURE( INTERNAL_CATCH_UNIQUE_NAME(capturer), "CAPTURE",__VA_ARGS__ )
+
+#define TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE( __VA_ARGS__ )
+#define TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, __VA_ARGS__ )
+#define METHOD_AS_TEST_CASE( method, ... ) INTERNAL_CATCH_METHOD_AS_TEST_CASE( method, __VA_ARGS__ )
+#define REGISTER_TEST_CASE( Function, ... ) INTERNAL_CATCH_REGISTER_TESTCASE( Function, __VA_ARGS__ )
+#define SECTION( ... ) INTERNAL_CATCH_SECTION( __VA_ARGS__ )
+#define DYNAMIC_SECTION( ... ) INTERNAL_CATCH_DYNAMIC_SECTION( __VA_ARGS__ )
+#define FAIL( ... ) INTERNAL_CATCH_MSG( "FAIL", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::Normal, __VA_ARGS__ )
+#define FAIL_CHECK( ... ) INTERNAL_CATCH_MSG( "FAIL_CHECK", Catch::ResultWas::ExplicitFailure, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define SUCCEED( ... ) INTERNAL_CATCH_MSG( "SUCCEED", Catch::ResultWas::Ok, Catch::ResultDisposition::ContinueOnFailure, __VA_ARGS__ )
+#define ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE()
+
+#endif
+
+#define CATCH_TRANSLATE_EXCEPTION( signature ) INTERNAL_CATCH_TRANSLATE_EXCEPTION( signature )
+
+// "BDD-style" convenience wrappers
+#define SCENARIO( ... ) TEST_CASE( "Scenario: " __VA_ARGS__ )
+#define SCENARIO_METHOD( className, ... ) INTERNAL_CATCH_TEST_CASE_METHOD( className, "Scenario: " __VA_ARGS__ )
+
+#define GIVEN( desc )     INTERNAL_CATCH_DYNAMIC_SECTION( "    Given: " << desc )
+#define AND_GIVEN( desc ) INTERNAL_CATCH_DYNAMIC_SECTION( "And given: " << desc )
+#define WHEN( desc )      INTERNAL_CATCH_DYNAMIC_SECTION( "     When: " << desc )
+#define AND_WHEN( desc )  INTERNAL_CATCH_DYNAMIC_SECTION( " And when: " << desc )
+#define THEN( desc )      INTERNAL_CATCH_DYNAMIC_SECTION( "     Then: " << desc )
+#define AND_THEN( desc )  INTERNAL_CATCH_DYNAMIC_SECTION( "      And: " << desc )
+
+using Catch::Detail::Approx;
+
+#else // CATCH_CONFIG_DISABLE
+
+//////
+// If this config identifier is defined then all CATCH macros are prefixed with CATCH_
+#ifdef CATCH_CONFIG_PREFIX_ALL
+
+#define CATCH_REQUIRE( ... )        (void)(0)
+#define CATCH_REQUIRE_FALSE( ... )  (void)(0)
+
+#define CATCH_REQUIRE_THROWS( ... ) (void)(0)
+#define CATCH_REQUIRE_THROWS_AS( expr, exceptionType ) (void)(0)
+#define CATCH_REQUIRE_THROWS_WITH( expr, matcher )     (void)(0)
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_REQUIRE_THROWS_MATCHES( expr, exceptionType, matcher ) (void)(0)
+#endif// CATCH_CONFIG_DISABLE_MATCHERS
+#define CATCH_REQUIRE_NOTHROW( ... ) (void)(0)
+
+#define CATCH_CHECK( ... )         (void)(0)
+#define CATCH_CHECK_FALSE( ... )   (void)(0)
+#define CATCH_CHECKED_IF( ... )    if (__VA_ARGS__)
+#define CATCH_CHECKED_ELSE( ... )  if (!(__VA_ARGS__))
+#define CATCH_CHECK_NOFAIL( ... )  (void)(0)
+
+#define CATCH_CHECK_THROWS( ... )  (void)(0)
+#define CATCH_CHECK_THROWS_AS( expr, exceptionType ) (void)(0)
+#define CATCH_CHECK_THROWS_WITH( expr, matcher )     (void)(0)
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_CHECK_THROWS_MATCHES( expr, exceptionType, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define CATCH_CHECK_NOTHROW( ... ) (void)(0)
+
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CATCH_CHECK_THAT( arg, matcher )   (void)(0)
+
+#define CATCH_REQUIRE_THAT( arg, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+
+#define CATCH_INFO( msg )    (void)(0)
+#define CATCH_WARN( msg )    (void)(0)
+#define CATCH_CAPTURE( msg ) (void)(0)
+
+#define CATCH_TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define CATCH_TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define CATCH_METHOD_AS_TEST_CASE( method, ... )
+#define CATCH_REGISTER_TEST_CASE( Function, ... ) (void)(0)
+#define CATCH_SECTION( ... )
+#define CATCH_DYNAMIC_SECTION( ... )
+#define CATCH_FAIL( ... ) (void)(0)
+#define CATCH_FAIL_CHECK( ... ) (void)(0)
+#define CATCH_SUCCEED( ... ) (void)(0)
+
+#define CATCH_ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+
+// "BDD-style" convenience wrappers
+#define CATCH_SCENARIO( ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define CATCH_SCENARIO_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_METHOD_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), className )
+#define CATCH_GIVEN( desc )
+#define CATCH_AND_GIVEN( desc )
+#define CATCH_WHEN( desc )
+#define CATCH_AND_WHEN( desc )
+#define CATCH_THEN( desc )
+#define CATCH_AND_THEN( desc )
+
+// If CATCH_CONFIG_PREFIX_ALL is not defined then the CATCH_ prefix is not required
+#else
+
+#define REQUIRE( ... )       (void)(0)
+#define REQUIRE_FALSE( ... ) (void)(0)
+
+#define REQUIRE_THROWS( ... ) (void)(0)
+#define REQUIRE_THROWS_AS( expr, exceptionType ) (void)(0)
+#define REQUIRE_THROWS_WITH( expr, matcher ) (void)(0)
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define REQUIRE_THROWS_MATCHES( expr, exceptionType, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define REQUIRE_NOTHROW( ... ) (void)(0)
+
+#define CHECK( ... ) (void)(0)
+#define CHECK_FALSE( ... ) (void)(0)
+#define CHECKED_IF( ... ) if (__VA_ARGS__)
+#define CHECKED_ELSE( ... ) if (!(__VA_ARGS__))
+#define CHECK_NOFAIL( ... ) (void)(0)
+
+#define CHECK_THROWS( ... )  (void)(0)
+#define CHECK_THROWS_AS( expr, exceptionType ) (void)(0)
+#define CHECK_THROWS_WITH( expr, matcher ) (void)(0)
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CHECK_THROWS_MATCHES( expr, exceptionType, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+#define CHECK_NOTHROW( ... ) (void)(0)
+
+#if !defined(CATCH_CONFIG_DISABLE_MATCHERS)
+#define CHECK_THAT( arg, matcher ) (void)(0)
+
+#define REQUIRE_THAT( arg, matcher ) (void)(0)
+#endif // CATCH_CONFIG_DISABLE_MATCHERS
+
+#define INFO( msg ) (void)(0)
+#define WARN( msg ) (void)(0)
+#define CAPTURE( msg ) (void)(0)
+
+#define TEST_CASE( ... )  INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+#define METHOD_AS_TEST_CASE( method, ... )
+#define REGISTER_TEST_CASE( Function, ... ) (void)(0)
+#define SECTION( ... )
+#define DYNAMIC_SECTION( ... )
+#define FAIL( ... ) (void)(0)
+#define FAIL_CHECK( ... ) (void)(0)
+#define SUCCEED( ... ) (void)(0)
+#define ANON_TEST_CASE() INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ))
+
+#endif
+
+#define CATCH_TRANSLATE_EXCEPTION( signature ) INTERNAL_CATCH_TRANSLATE_EXCEPTION_NO_REG( INTERNAL_CATCH_UNIQUE_NAME( catch_internal_ExceptionTranslator ), signature )
+
+// "BDD-style" convenience wrappers
+#define SCENARIO( ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ) )
+#define SCENARIO_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_METHOD_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( ____C_A_T_C_H____T_E_S_T____ ), className )
+
+#define GIVEN( desc )
+#define AND_GIVEN( desc )
+#define WHEN( desc )
+#define AND_WHEN( desc )
+#define THEN( desc )
+#define AND_THEN( desc )
+
+using Catch::Detail::Approx;
+
+#endif
+
+#endif // ! CATCH_CONFIG_IMPL_ONLY
+
+// start catch_reenable_warnings.h
+
+
+#ifdef __clang__
+#    ifdef __ICC // icpc defines the __clang__ macro
+#        pragma warning(pop)
+#    else
+#        pragma clang diagnostic pop
+#    endif
+#elif defined __GNUC__
+#    pragma GCC diagnostic pop
+#endif
+
+// end catch_reenable_warnings.h
+// end catch.hpp
+#endif // TWOBLUECUBES_SINGLE_INCLUDE_CATCH_HPP_INCLUDED
+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.appveyor.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.appveyor.yml
new file mode 100644
index 000000000..360760ac8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.appveyor.yml
@@ -0,0 +1,35 @@
+version: 1.0.{build}
+image:
+- Visual Studio 2017
+test: off
+skip_branch_with_pr: true
+build:
+  parallel: true
+platform:
+- x86
+environment:
+  matrix:
+  - PYTHON: 36
+    CONFIG: Debug
+install:
+- ps: |
+    $env:CMAKE_GENERATOR = "Visual Studio 15 2017"
+    if ($env:PLATFORM -eq "x64") { $env:PYTHON = "$env:PYTHON-x64" }
+    $env:PATH = "C:\Python$env:PYTHON\;C:\Python$env:PYTHON\Scripts\;$env:PATH"
+    python -W ignore -m pip install --upgrade pip wheel
+    python -W ignore -m pip install pytest numpy --no-warn-script-location pytest-timeout
+- ps: |
+    Start-FileDownload 'https://gitlab.com/libeigen/eigen/-/archive/3.3.7/eigen-3.3.7.zip'
+    7z x eigen-3.3.7.zip -y > $null
+    $env:CMAKE_INCLUDE_PATH = "eigen-3.3.7;$env:CMAKE_INCLUDE_PATH"
+build_script:
+- cmake -G "%CMAKE_GENERATOR%" -A "%CMAKE_ARCH%"
+    -DCMAKE_CXX_STANDARD=14
+    -DPYBIND11_WERROR=ON
+    -DDOWNLOAD_CATCH=ON
+    -DCMAKE_SUPPRESS_REGENERATION=1
+    .
+- set MSBuildLogger="C:\Program Files\AppVeyor\BuildAgent\Appveyor.MSBuildLogger.dll"
+- cmake --build . --config %CONFIG% --target pytest -- /m /v:m /logger:%MSBuildLogger%
+- cmake --build . --config %CONFIG% --target cpptest -- /m /v:m /logger:%MSBuildLogger%
+on_failure: if exist "tests\test_cmake_build" type tests\test_cmake_build\*.log*
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.clang-format b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.clang-format
new file mode 100644
index 000000000..b477a1603
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.clang-format
@@ -0,0 +1,38 @@
+---
+# See all possible options and defaults with:
+# clang-format --style=llvm --dump-config
+BasedOnStyle: LLVM
+AccessModifierOffset: -4
+AllowShortLambdasOnASingleLine: true
+AlwaysBreakTemplateDeclarations: Yes
+BinPackArguments: false
+BinPackParameters: false
+BreakBeforeBinaryOperators: All
+BreakConstructorInitializers: BeforeColon
+ColumnLimit: 99
+CommentPragmas: 'NOLINT:.*|^ IWYU pragma:'
+IncludeBlocks: Regroup
+IndentCaseLabels: true
+IndentPPDirectives: AfterHash
+IndentWidth: 4
+Language: Cpp
+SpaceAfterCStyleCast: true
+Standard: Cpp11
+StatementMacros: ['PyObject_HEAD']
+TabWidth: 4
+IncludeCategories:
+  - Regex:           '<pybind11/.*'
+    Priority:        -1
+  - Regex:           'pybind11.h"$'
+    Priority:        1
+  - Regex:           '^".*/?detail/'
+    Priority:        1
+    SortPriority:    2
+  - Regex:           '^"'
+    Priority:        1
+    SortPriority:    3
+  - Regex:           '<[[:alnum:]._]+>'
+    Priority:        4
+  - Regex:           '.*'
+    Priority:        5
+...
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.clang-tidy b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.clang-tidy
new file mode 100644
index 000000000..d945a4a27
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.clang-tidy
@@ -0,0 +1,75 @@
+FormatStyle: file
+
+Checks: |
+  *bugprone*,
+  *performance*,
+  clang-analyzer-optin.cplusplus.VirtualCall,
+  clang-analyzer-optin.performance.Padding,
+  cppcoreguidelines-init-variables,
+  cppcoreguidelines-prefer-member-initializer,
+  cppcoreguidelines-pro-type-static-cast-downcast,
+  cppcoreguidelines-slicing,
+  google-explicit-constructor,
+  llvm-namespace-comment,
+  misc-definitions-in-headers,
+  misc-misplaced-const,
+  misc-non-copyable-objects,
+  misc-static-assert,
+  misc-throw-by-value-catch-by-reference,
+  misc-uniqueptr-reset-release,
+  misc-unused-parameters,
+  modernize-avoid-bind,
+  modernize-loop-convert,
+  modernize-make-shared,
+  modernize-redundant-void-arg,
+  modernize-replace-auto-ptr,
+  modernize-replace-disallow-copy-and-assign-macro,
+  modernize-replace-random-shuffle,
+  modernize-shrink-to-fit,
+  modernize-use-auto,
+  modernize-use-bool-literals,
+  modernize-use-default-member-init,
+  modernize-use-emplace,
+  modernize-use-equals-default,
+  modernize-use-equals-delete,
+  modernize-use-noexcept,
+  modernize-use-nullptr,
+  modernize-use-override,
+  modernize-use-using,
+  readability-avoid-const-params-in-decls,
+  readability-braces-around-statements,
+  readability-const-return-type,
+  readability-container-size-empty,
+  readability-delete-null-pointer,
+  readability-else-after-return,
+  readability-implicit-bool-conversion,
+  readability-inconsistent-declaration-parameter-name,
+  readability-make-member-function-const,
+  readability-misplaced-array-index,
+  readability-non-const-parameter,
+  readability-qualified-auto,
+  readability-redundant-function-ptr-dereference,
+  readability-redundant-smartptr-get,
+  readability-redundant-string-cstr,
+  readability-simplify-subscript-expr,
+  readability-static-accessed-through-instance,
+  readability-static-definition-in-anonymous-namespace,
+  readability-string-compare,
+  readability-suspicious-call-argument,
+  readability-uniqueptr-delete-release,
+  -bugprone-easily-swappable-parameters,
+  -bugprone-exception-escape,
+  -bugprone-reserved-identifier,
+  -bugprone-unused-raii,
+
+CheckOptions:
+- key:             performance-for-range-copy.WarnOnAllAutoCopies
+  value:           true
+- key:             performance-inefficient-string-concatenation.StrictMode
+  value:           true
+- key:             performance-unnecessary-value-param.AllowedTypes
+  value:           'exception_ptr$;'
+- key:             readability-implicit-bool-conversion.AllowPointerConditions
+  value:           true
+
+HeaderFilterRegex: 'pybind11/.*h'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.cmake-format.yaml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.cmake-format.yaml
new file mode 100644
index 000000000..a2a69f3f8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.cmake-format.yaml
@@ -0,0 +1,73 @@
+parse:
+  additional_commands:
+    pybind11_add_module:
+      flags:
+        - THIN_LTO
+        - MODULE
+        - SHARED
+        - NO_EXTRAS
+        - EXCLUDE_FROM_ALL
+        - SYSTEM
+
+format:
+  line_width: 99
+  tab_size: 2
+
+  # If an argument group contains more than this many sub-groups
+  # (parg or kwarg groups) then force it to a vertical layout.
+  max_subgroups_hwrap: 2
+
+  # If a positional argument group contains more than this many
+  # arguments, then force it to a vertical layout.
+  max_pargs_hwrap: 6
+
+  # If a cmdline positional group consumes more than this many
+  # lines without nesting, then invalidate the layout (and nest)
+  max_rows_cmdline: 2
+  separate_ctrl_name_with_space: false
+  separate_fn_name_with_space: false
+  dangle_parens: false
+
+  # If the trailing parenthesis must be 'dangled' on its on
+  # 'line, then align it to this reference: `prefix`: the start'
+  # 'of the statement,  `prefix-indent`: the start of the'
+  # 'statement, plus one indentation  level, `child`: align to'
+  # the column of the arguments
+  dangle_align: prefix
+  # If the statement spelling length (including space and
+  # parenthesis) is smaller than this amount, then force reject
+  # nested layouts.
+  min_prefix_chars: 4
+
+  # If the statement spelling length (including space and
+  # parenthesis) is larger than the tab width by more than this
+  # amount, then force reject un-nested layouts.
+  max_prefix_chars: 10
+
+  # If a candidate layout is wrapped horizontally but it exceeds
+  # this many lines, then reject the layout.
+  max_lines_hwrap: 2
+
+  line_ending: unix
+
+  # Format command names consistently as 'lower' or 'upper' case
+  command_case: canonical
+
+  # Format keywords consistently as 'lower' or 'upper' case
+  # unchanged is valid too
+  keyword_case: 'upper'
+
+  # A list of command names which should always be wrapped
+  always_wrap: []
+
+  # If true, the argument lists which are known to be sortable
+  # will be sorted lexicographically
+  enable_sort: true
+
+  # If true, the parsers may infer whether or not an argument
+  # list is sortable (without annotation).
+  autosort: false
+
+# Causes a few issues - can be solved later, possibly.
+markup:
+  enable_markup: false
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.gitattributes b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.gitattributes
new file mode 100644
index 000000000..d611e1496
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.gitattributes
@@ -0,0 +1 @@
+docs/*.svg binary
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/CODEOWNERS b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/CODEOWNERS
new file mode 100644
index 000000000..4e2c66902
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/CODEOWNERS
@@ -0,0 +1,9 @@
+*.cmake @henryiii
+CMakeLists.txt @henryiii
+*.yml @henryiii
+*.yaml @henryiii
+/tools/ @henryiii
+/pybind11/ @henryiii
+noxfile.py @henryiii
+.clang-format @henryiii
+.clang-tidy @henryiii
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/CONTRIBUTING.md b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/CONTRIBUTING.md
new file mode 100644
index 000000000..00b1fea4c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/CONTRIBUTING.md
@@ -0,0 +1,388 @@
+Thank you for your interest in this project! Please refer to the following
+sections on how to contribute code and bug reports.
+
+### Reporting bugs
+
+Before submitting a question or bug report, please take a moment of your time
+and ensure that your issue isn't already discussed in the project documentation
+provided at [pybind11.readthedocs.org][] or in the [issue tracker][]. You can
+also check [gitter][] to see if it came up before.
+
+Assuming that you have identified a previously unknown problem or an important
+question, it's essential that you submit a self-contained and minimal piece of
+code that reproduces the problem. In other words: no external dependencies,
+isolate the function(s) that cause breakage, submit matched and complete C++
+and Python snippets that can be easily compiled and run in isolation; or
+ideally make a small PR with a failing test case that can be used as a starting
+point.
+
+## Pull requests
+
+Contributions are submitted, reviewed, and accepted using GitHub pull requests.
+Please refer to [this article][using pull requests] for details and adhere to
+the following rules to make the process as smooth as possible:
+
+* Make a new branch for every feature you're working on.
+* Make small and clean pull requests that are easy to review but make sure they
+  do add value by themselves.
+* Add tests for any new functionality and run the test suite (`cmake --build
+  build --target pytest`) to ensure that no existing features break.
+* Please run [`pre-commit`][pre-commit] to check your code matches the
+  project style. (Note that `gawk` is required.) Use `pre-commit run
+  --all-files` before committing (or use installed-mode, check pre-commit docs)
+  to verify your code passes before pushing to save time.
+* This project has a strong focus on providing general solutions using a
+  minimal amount of code, thus small pull requests are greatly preferred.
+
+### Licensing of contributions
+
+pybind11 is provided under a BSD-style license that can be found in the
+``LICENSE`` file. By using, distributing, or contributing to this project, you
+agree to the terms and conditions of this license.
+
+You are under no obligation whatsoever to provide any bug fixes, patches, or
+upgrades to the features, functionality or performance of the source code
+("Enhancements") to anyone; however, if you choose to make your Enhancements
+available either publicly, or directly to the author of this software, without
+imposing a separate written license agreement for such Enhancements, then you
+hereby grant the following license: a non-exclusive, royalty-free perpetual
+license to install, use, modify, prepare derivative works, incorporate into
+other computer software, distribute, and sublicense such enhancements or
+derivative works thereof, in binary and source code form.
+
+
+## Development of pybind11
+
+### Quick setup
+
+To setup a quick development environment, use [`nox`](https://nox.thea.codes).
+This will allow you to do some common tasks with minimal setup effort, but will
+take more time to run and be less flexible than a full development environment.
+If you use [`pipx run nox`](https://pipx.pypa.io), you don't even need to
+install `nox`. Examples:
+
+```bash
+# List all available sessions
+nox -l
+
+# Run linters
+nox -s lint
+
+# Run tests on Python 3.9
+nox -s tests-3.9
+
+# Build and preview docs
+nox -s docs -- serve
+
+# Build SDists and wheels
+nox -s build
+```
+
+### Full setup
+
+To setup an ideal development environment, run the following commands on a
+system with CMake 3.14+:
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+pip install -r tests/requirements.txt
+cmake -S . -B build -DDOWNLOAD_CATCH=ON -DDOWNLOAD_EIGEN=ON
+cmake --build build -j4
+```
+
+Tips:
+
+* You can use `virtualenv` (faster, from PyPI) instead of `venv`.
+* You can select any name for your environment folder; if it contains "env" it
+  will be ignored by git.
+* If you don't have CMake 3.14+, just add "cmake" to the pip install command.
+* You can use `-DPYBIND11_FINDPYTHON=ON` to use FindPython on CMake 3.12+
+* In classic mode, you may need to set `-DPYTHON_EXECUTABLE=/path/to/python`.
+  FindPython uses `-DPython_ROOT_DIR=/path/to` or
+  `-DPython_EXECUTABLE=/path/to/python`.
+
+### Configuration options
+
+In CMake, configuration options are given with "-D". Options are stored in the
+build directory, in the `CMakeCache.txt` file, so they are remembered for each
+build directory. Two selections are special - the generator, given with `-G`,
+and the compiler, which is selected based on environment variables `CXX` and
+similar, or `-DCMAKE_CXX_COMPILER=`. Unlike the others, these cannot be changed
+after the initial run.
+
+The valid options are:
+
+* `-DCMAKE_BUILD_TYPE`: Release, Debug, MinSizeRel, RelWithDebInfo
+* `-DPYBIND11_FINDPYTHON=ON`: Use CMake 3.12+'s FindPython instead of the
+  classic, deprecated, custom FindPythonLibs
+* `-DPYBIND11_NOPYTHON=ON`: Disable all Python searching (disables tests)
+* `-DBUILD_TESTING=ON`: Enable the tests
+* `-DDOWNLOAD_CATCH=ON`: Download catch to build the C++ tests
+* `-DDOWNLOAD_EIGEN=ON`: Download Eigen for the NumPy tests
+* `-DPYBIND11_INSTALL=ON/OFF`: Enable the install target (on by default for the
+  master project)
+* `-DUSE_PYTHON_INSTALL_DIR=ON`: Try to install into the python dir
+
+
+<details><summary>A few standard CMake tricks: (click to expand)</summary><p>
+
+* Use `cmake --build build -v` to see the commands used to build the files.
+* Use `cmake build -LH` to list the CMake options with help.
+* Use `ccmake` if available to see a curses (terminal) gui, or `cmake-gui` for
+  a completely graphical interface (not present in the PyPI package).
+* Use `cmake --build build -j12` to build with 12 cores (for example).
+* Use `-G` and the name of a generator to use something different. `cmake
+  --help` lists the generators available.
+      - On Unix, setting `CMAKE_GENERATER=Ninja` in your environment will give
+        you automatic mulithreading on all your CMake projects!
+* Open the `CMakeLists.txt` with QtCreator to generate for that IDE.
+* You can use `-DCMAKE_EXPORT_COMPILE_COMMANDS=ON` to generate the `.json` file
+  that some tools expect.
+
+</p></details>
+
+
+To run the tests, you can "build" the check target:
+
+```bash
+cmake --build build --target check
+```
+
+`--target` can be spelled `-t` in CMake 3.15+. You can also run individual
+tests with these targets:
+
+* `pytest`: Python tests only, using the
+[pytest](https://docs.pytest.org/en/stable/) framework
+* `cpptest`: C++ tests only
+* `test_cmake_build`: Install / subdirectory tests
+
+If you want to build just a subset of tests, use
+`-DPYBIND11_TEST_OVERRIDE="test_callbacks;test_pickling"`. If this is
+empty, all tests will be built. Tests are specified without an extension if they need both a .py and
+.cpp file.
+
+You may also pass flags to the `pytest` target by editing `tests/pytest.ini` or
+by using the `PYTEST_ADDOPTS` environment variable
+(see [`pytest` docs](https://docs.pytest.org/en/2.7.3/customize.html#adding-default-options)). As an example:
+
+```bash
+env PYTEST_ADDOPTS="--capture=no --exitfirst" \
+    cmake --build build --target pytest
+# Or using abbreviated flags
+env PYTEST_ADDOPTS="-s -x" cmake --build build --target pytest
+```
+
+### Formatting
+
+All formatting is handled by pre-commit.
+
+Install with brew (macOS) or pip (any OS):
+
+```bash
+# Any OS
+python3 -m pip install pre-commit
+
+# OR macOS with homebrew:
+brew install pre-commit
+```
+
+Then, you can run it on the items you've added to your staging area, or all
+files:
+
+```bash
+pre-commit run
+# OR
+pre-commit run --all-files
+```
+
+And, if you want to always use it, you can install it as a git hook (hence the
+name, pre-commit):
+
+```bash
+pre-commit install
+```
+
+### Clang-Format
+
+As of v2.6.2, pybind11 ships with a [`clang-format`][clang-format]
+configuration file at the top level of the repo (the filename is
+`.clang-format`). Currently, formatting is NOT applied automatically, but
+manually using `clang-format` for newly developed files is highly encouraged.
+To check if a file needs formatting:
+
+```bash
+clang-format -style=file --dry-run some.cpp
+```
+
+The output will show things to be fixed, if any. To actually format the file:
+
+```bash
+clang-format -style=file -i some.cpp
+```
+
+Note that the `-style-file` option searches the parent directories for the
+`.clang-format` file, i.e. the commands above can be run in any subdirectory
+of the pybind11 repo.
+
+### Clang-Tidy
+
+[`clang-tidy`][clang-tidy] performs deeper static code analyses and is
+more complex to run, compared to `clang-format`, but support for `clang-tidy`
+is built into the pybind11 CMake configuration. To run `clang-tidy`, the
+following recipe should work. Run the `docker` command from the top-level
+directory inside your pybind11 git clone. Files will be modified in place,
+so you can use git to monitor the changes.
+
+```bash
+docker run --rm -v $PWD:/mounted_pybind11 -it silkeh/clang:13
+apt-get update && apt-get install -y python3-dev python3-pytest
+cmake -S /mounted_pybind11/ -B build -DCMAKE_CXX_CLANG_TIDY="$(which clang-tidy);--use-color" -DDOWNLOAD_EIGEN=ON -DDOWNLOAD_CATCH=ON -DCMAKE_CXX_STANDARD=17
+cmake --build build -j 2
+```
+
+You can add `--fix` to the options list if you want.
+
+### Include what you use
+
+To run include what you use, install (`brew install include-what-you-use` on
+macOS), then run:
+
+```bash
+cmake -S . -B build-iwyu -DCMAKE_CXX_INCLUDE_WHAT_YOU_USE=$(which include-what-you-use)
+cmake --build build
+```
+
+The report is sent to stderr; you can pipe it into a file if you wish.
+
+### Build recipes
+
+This builds with the Intel compiler (assuming it is in your path, along with a
+recent CMake and Python):
+
+```bash
+python3 -m venv venv
+. venv/bin/activate
+pip install pytest
+cmake -S . -B build-intel -DCMAKE_CXX_COMPILER=$(which icpc) -DDOWNLOAD_CATCH=ON -DDOWNLOAD_EIGEN=ON -DPYBIND11_WERROR=ON
+```
+
+This will test the PGI compilers:
+
+```bash
+docker run --rm -it -v $PWD:/pybind11 nvcr.io/hpc/pgi-compilers:ce
+apt-get update && apt-get install -y python3-dev python3-pip python3-pytest
+wget -qO- "https://cmake.org/files/v3.18/cmake-3.18.2-Linux-x86_64.tar.gz" | tar --strip-components=1 -xz -C /usr/local
+cmake -S pybind11/ -B build
+cmake --build build
+```
+
+### Explanation of the SDist/wheel building design
+
+> These details below are _only_ for packaging the Python sources from git. The
+> SDists and wheels created do not have any extra requirements at all and are
+> completely normal.
+
+The main objective of the packaging system is to create SDists (Python's source
+distribution packages) and wheels (Python's binary distribution packages) that
+include everything that is needed to work with pybind11, and which can be
+installed without any additional dependencies. This is more complex than it
+appears: in order to support CMake as a first class language even when using
+the PyPI package, they must include the _generated_ CMake files (so as not to
+require CMake when installing the `pybind11` package itself). They should also
+provide the option to install to the "standard" location
+(`<ENVROOT>/include/pybind11` and `<ENVROOT>/share/cmake/pybind11`) so they are
+easy to find with CMake, but this can cause problems if you are not an
+environment or using ``pyproject.toml`` requirements. This was solved by having
+two packages; the "nice" pybind11 package that stores the includes and CMake
+files inside the package, that you get access to via functions in the package,
+and a `pybind11-global` package that can be included via `pybind11[global]` if
+you want the more invasive but discoverable file locations.
+
+If you want to install or package the GitHub source, it is best to have Pip 10
+or newer on Windows, macOS, or Linux (manylinux1 compatible, includes most
+distributions).  You can then build the SDists, or run any procedure that makes
+SDists internally, like making wheels or installing.
+
+
+```bash
+# Editable development install example
+python3 -m pip install -e .
+```
+
+Since Pip itself does not have an `sdist` command (it does have `wheel` and
+`install`), you may want to use the upcoming `build` package:
+
+```bash
+python3 -m pip install build
+
+# Normal package
+python3 -m build -s .
+
+# Global extra
+PYBIND11_GLOBAL_SDIST=1 python3 -m build -s .
+```
+
+If you want to use the classic "direct" usage of `python setup.py`, you will
+need CMake 3.15+ and either `make` or `ninja` preinstalled (possibly via `pip
+install cmake ninja`), since directly running Python on `setup.py` cannot pick
+up and install `pyproject.toml` requirements. As long as you have those two
+things, though, everything works the way you would expect:
+
+```bash
+# Normal package
+python3 setup.py sdist
+
+# Global extra
+PYBIND11_GLOBAL_SDIST=1 python3 setup.py sdist
+```
+
+A detailed explanation of the build procedure design for developers wanting to
+work on or maintain the packaging system is as follows:
+
+#### 1. Building from the source directory
+
+When you invoke any `setup.py` command from the source directory, including
+`pip wheel .` and `pip install .`, you will activate a full source build. This
+is made of the following steps:
+
+1. If the tool is PEP 518 compliant, like Pip 10+, it will create a temporary
+   virtual environment and install the build requirements (mostly CMake) into
+   it. (if you are not on Windows, macOS, or a manylinux compliant system, you
+   can disable this with `--no-build-isolation` as long as you have CMake 3.15+
+   installed)
+2. The environment variable `PYBIND11_GLOBAL_SDIST` is checked - if it is set
+   and truthy, this will be make the accessory `pybind11-global` package,
+   instead of the normal `pybind11` package. This package is used for
+   installing the files directly to your environment root directory, using
+   `pybind11[global]`.
+2. `setup.py` reads the version from `pybind11/_version.py` and verifies it
+   matches `includes/pybind11/detail/common.h`.
+3. CMake is run with `-DCMAKE_INSTALL_PREIFX=pybind11`. Since the CMake install
+   procedure uses only relative paths and is identical on all platforms, these
+   files are valid as long as they stay in the correct relative position to the
+   includes. `pybind11/share/cmake/pybind11` has the CMake files, and
+   `pybind11/include` has the includes. The build directory is discarded.
+4. Simpler files are placed in the SDist: `tools/setup_*.py.in`,
+   `tools/pyproject.toml` (`main` or `global`)
+5. The package is created by running the setup function in the
+   `tools/setup_*.py`.  `setup_main.py` fills in Python packages, and
+   `setup_global.py` fills in only the data/header slots.
+6. A context manager cleans up the temporary CMake install directory (even if
+   an error is thrown).
+
+### 2. Building from SDist
+
+Since the SDist has the rendered template files in `tools` along with the
+includes and CMake files in the correct locations, the builds are completely
+trivial and simple. No extra requirements are required. You can even use Pip 9
+if you really want to.
+
+
+[pre-commit]: https://pre-commit.com
+[clang-format]: https://clang.llvm.org/docs/ClangFormat.html
+[clang-tidy]: https://clang.llvm.org/extra/clang-tidy/
+[pybind11.readthedocs.org]: http://pybind11.readthedocs.org/en/latest
+[issue tracker]: https://github.com/pybind/pybind11/issues
+[gitter]: https://gitter.im/pybind/Lobby
+[using pull requests]: https://help.github.com/articles/using-pull-requests
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/ISSUE_TEMPLATE/bug-report.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/ISSUE_TEMPLATE/bug-report.yml
new file mode 100644
index 000000000..bd6a9a8e2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -0,0 +1,45 @@
+name: Bug Report
+description: File an issue about a bug
+title: "[BUG]: "
+labels: [triage]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Maintainers will only make a best effort to triage PRs. Please do your best to make the issue as easy to act on as possible, and only open if clearly a problem with pybind11 (ask first if unsure).
+  - type: checkboxes
+    id: steps
+    attributes:
+      label: Required prerequisites
+      description: Make sure you've completed the following steps before submitting your issue -- thank you!
+      options:
+        - label: Make sure you've read the [documentation](https://pybind11.readthedocs.io). Your issue may be addressed there.
+          required: true
+        - label: Search the [issue tracker](https://github.com/pybind/pybind11/issues) and [Discussions](https:/pybind/pybind11/discussions) to verify that this hasn't already been reported. +1 or comment there if it has.
+          required: true
+        - label: Consider asking first in the [Gitter chat room](https://gitter.im/pybind/Lobby) or in a [Discussion](https:/pybind/pybind11/discussions/new).
+          required: false
+
+  - type: textarea
+    id: description
+    attributes:
+      label: Problem description
+      placeholder: >-
+        Provide a short description, state the expected behavior and what
+        actually happens. Include relevant information like what version of
+        pybind11 you are using, what system you are on, and any useful commands
+        / output.
+    validations:
+      required: true
+
+  - type: textarea
+    id: code
+    attributes:
+      label: Reproducible example code
+      placeholder: >-
+        The code should be minimal, have no external dependencies, isolate the
+        function(s) that cause breakage. Submit matched and complete C++ and
+        Python snippets that can be easily compiled and run to diagnose the
+        issue. If possible, make a PR with a new, failing test to give us a
+        starting point to work on!
+      render: text
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/ISSUE_TEMPLATE/config.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 000000000..27f9a8044
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,8 @@
+blank_issues_enabled: false
+contact_links:
+  - name: Ask a question
+    url: https://github.com/pybind/pybind11/discussions/new
+    about: Please ask and answer questions here, or propose new ideas.
+  - name: Gitter room
+    url: https://gitter.im/pybind/Lobby
+    about: A room for discussing pybind11 with an active community
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/dependabot.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/dependabot.yml
new file mode 100644
index 000000000..2c7d17083
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/dependabot.yml
@@ -0,0 +1,7 @@
+version: 2
+updates:
+  # Maintain dependencies for GitHub Actions
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/labeler.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/labeler.yml
new file mode 100644
index 000000000..abb0d05aa
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/labeler.yml
@@ -0,0 +1,8 @@
+docs:
+- any:
+  - 'docs/**/*.rst'
+  - '!docs/changelog.rst'
+  - '!docs/upgrade.rst'
+
+ci:
+- '.github/workflows/*.yml'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/labeler_merged.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/labeler_merged.yml
new file mode 100644
index 000000000..2374ad42e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/labeler_merged.yml
@@ -0,0 +1,3 @@
+needs changelog:
+- all:
+  - '!docs/changelog.rst'
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/matchers/pylint.json b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/matchers/pylint.json
new file mode 100644
index 000000000..e3a6bd16b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/matchers/pylint.json
@@ -0,0 +1,32 @@
+{
+  "problemMatcher": [
+    {
+      "severity": "warning",
+      "pattern": [
+        {
+          "regexp": "^([^:]+):(\\d+):(\\d+): ([A-DF-Z]\\d+): \\033\\[[\\d;]+m([^\\033]+).*$",
+          "file": 1,
+          "line": 2,
+          "column": 3,
+          "code": 4,
+          "message": 5
+        }
+      ],
+      "owner": "pylint-warning"
+    },
+    {
+      "severity": "error",
+      "pattern": [
+        {
+          "regexp": "^([^:]+):(\\d+):(\\d+): (E\\d+): \\033\\[[\\d;]+m([^\\033]+).*$",
+          "file": 1,
+          "line": 2,
+          "column": 3,
+          "code": 4,
+          "message": 5
+        }
+      ],
+      "owner": "pylint-error"
+    }
+  ]
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/pull_request_template.md b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/pull_request_template.md
new file mode 100644
index 000000000..54b7f5100
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/pull_request_template.md
@@ -0,0 +1,19 @@
+<!--
+Title (above): please place [branch_name] at the beginning if you are targeting a branch other than master. *Do not target stable*.
+It is recommended to use conventional commit format, see conventionalcommits.org, but not required.
+-->
+## Description
+
+<!-- Include relevant issues or PRs here, describe what changed and why -->
+
+
+## Suggested changelog entry:
+
+<!-- Fill in the below block with the expected RestructuredText entry. Delete if no entry needed;
+     but do not delete header or rst block if an entry is needed! Will be collected via a script. -->
+
+```rst
+
+```
+
+<!-- If the upgrade guide needs updating, note that here too -->
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/ci.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/ci.yml
new file mode 100644
index 000000000..412282a4f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/ci.yml
@@ -0,0 +1,956 @@
+name: CI
+
+on:
+  workflow_dispatch:
+  pull_request:
+  push:
+    branches:
+      - master
+      - stable
+      - v*
+
+concurrency:
+  group: test-${{ github.ref }}
+  cancel-in-progress: true
+
+env:
+  PIP_ONLY_BINARY: numpy
+  FORCE_COLOR: 3
+  PYTEST_TIMEOUT: 300
+
+jobs:
+  # This is the "main" test suite, which tests a large number of different
+  # versions of default compilers and Python versions in GitHub Actions.
+  standard:
+    strategy:
+      fail-fast: false
+      matrix:
+        runs-on: [ubuntu-latest, windows-2022, macos-latest]
+        python:
+        - '3.6'
+        - '3.9'
+        - '3.10'
+        - '3.11-dev'
+        - 'pypy-3.7'
+        - 'pypy-3.8'
+        - 'pypy-3.9'
+
+        # Items in here will either be added to the build matrix (if not
+        # present), or add new keys to an existing matrix element if all the
+        # existing keys match.
+        #
+        # We support an optional key: args, for cmake args
+        include:
+          # Just add a key
+          - runs-on: ubuntu-latest
+            python: '3.6'
+            args: >
+              -DPYBIND11_FINDPYTHON=ON
+              -DCMAKE_CXX_FLAGS="-D_=1"
+          - runs-on: ubuntu-latest
+            python: 'pypy-3.8'
+            args: >
+              -DPYBIND11_FINDPYTHON=ON
+          - runs-on: windows-2019
+            python: '3.6'
+            args: >
+              -DPYBIND11_FINDPYTHON=ON
+          # Inject a couple Windows 2019 runs
+          - runs-on: windows-2019
+            python: '3.9'
+
+    name: "🐍 ${{ matrix.python }} • ${{ matrix.runs-on }} • x64 ${{ matrix.args }}"
+    runs-on: ${{ matrix.runs-on }}
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup Python ${{ matrix.python }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python }}
+
+    - name: Setup Boost (Linux)
+      # Can't use boost + define _
+      if: runner.os == 'Linux' && matrix.python != '3.6'
+      run: sudo apt-get install libboost-dev
+
+    - name: Setup Boost (macOS)
+      if: runner.os == 'macOS'
+      run: brew install boost
+
+    - name: Update CMake
+      uses: jwlawson/actions-setup-cmake@v1.12
+
+    - name: Cache wheels
+      if: runner.os == 'macOS'
+      uses: actions/cache@v3
+      with:
+        # This path is specific to macOS - we really only need it for PyPy NumPy wheels
+        # See https://github.com/actions/cache/blob/master/examples.md#python---pip
+        # for ways to do this more generally
+        path: ~/Library/Caches/pip
+        # Look to see if there is a cache hit for the corresponding requirements file
+        key: ${{ runner.os }}-pip-${{ matrix.python }}-x64-${{ hashFiles('tests/requirements.txt') }}
+
+    - name: Prepare env
+      run: |
+        python -m pip install -r tests/requirements.txt
+
+    - name: Setup annotations on Linux
+      if: runner.os == 'Linux'
+      run: python -m pip install pytest-github-actions-annotate-failures
+
+    # First build - C++11 mode and inplace
+    - name: Configure C++11 ${{ matrix.args }}
+      run: >
+        cmake -S . -B .
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=11
+        ${{ matrix.args }}
+
+    - name: Build C++11
+      run: cmake --build . -j 2
+
+    - name: Python tests C++11
+      run: cmake --build . --target pytest -j 2
+
+    - name: C++11 tests
+      # TODO: Figure out how to load the DLL on Python 3.8+
+      if: "!(runner.os == 'Windows' && (matrix.python == 3.8 || matrix.python == 3.9 || matrix.python == '3.10' || matrix.python == '3.11-dev' || matrix.python == 'pypy-3.8'))"
+      run: cmake --build .  --target cpptest -j 2
+
+    - name: Interface test C++11
+      run: cmake --build . --target test_cmake_build
+
+    - name: Clean directory
+      run: git clean -fdx
+
+    # Second build - C++17 mode and in a build directory
+    - name: Configure C++17
+      run: >
+        cmake -S . -B build2
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=17
+        ${{ matrix.args }}
+
+    - name: Build
+      run: cmake --build build2 -j 2
+
+    - name: Python tests
+      run: cmake --build build2 --target pytest
+
+    - name: C++ tests
+      # TODO: Figure out how to load the DLL on Python 3.8+
+      if: "!(runner.os == 'Windows' && (matrix.python == 3.8 || matrix.python == 3.9 || matrix.python == '3.10' || matrix.python == '3.11-dev' || matrix.python == 'pypy-3.8'))"
+      run: cmake --build build2 --target cpptest
+
+    # Third build - C++17 mode with unstable ABI
+    - name: Configure (unstable ABI)
+      run: >
+        cmake -S . -B build3
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=17
+        -DPYBIND11_INTERNALS_VERSION=10000000
+        "-DPYBIND11_TEST_OVERRIDE=test_call_policies.cpp;test_gil_scoped.cpp;test_thread.cpp"
+        ${{ matrix.args }}
+
+    - name: Build (unstable ABI)
+      run: cmake --build build3 -j 2
+
+    - name: Python tests (unstable ABI)
+      run: cmake --build build3 --target pytest
+
+    - name: Interface test
+      run: cmake --build build2 --target test_cmake_build
+
+    # This makes sure the setup_helpers module can build packages using
+    # setuptools
+    - name: Setuptools helpers test
+      run: pytest tests/extra_setuptools
+      if: "!(matrix.runs-on == 'windows-2022')"
+
+
+  deadsnakes:
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        # TODO: Fails on 3.10, investigate
+        - python-version: "3.9"
+          python-debug: true
+          valgrind: true
+        - python-version: "3.11-dev"
+          python-debug: false
+
+    name: "🐍 ${{ matrix.python-version }}${{ matrix.python-debug && '-dbg' || '' }} (deadsnakes)${{ matrix.valgrind && ' • Valgrind' || '' }} • x64"
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup Python ${{ matrix.python-version }} (deadsnakes)
+      uses: deadsnakes/action@v2.1.1
+      with:
+        python-version: ${{ matrix.python-version }}
+        debug: ${{ matrix.python-debug }}
+
+    - name: Update CMake
+      uses: jwlawson/actions-setup-cmake@v1.12
+
+    - name: Valgrind cache
+      if: matrix.valgrind
+      uses: actions/cache@v3
+      id: cache-valgrind
+      with:
+        path: valgrind
+        key: 3.16.1 # Valgrind version
+
+    - name: Compile Valgrind
+      if: matrix.valgrind && steps.cache-valgrind.outputs.cache-hit != 'true'
+      run: |
+        VALGRIND_VERSION=3.16.1
+        curl https://sourceware.org/pub/valgrind/valgrind-$VALGRIND_VERSION.tar.bz2 -o - | tar xj
+        mv valgrind-$VALGRIND_VERSION valgrind
+        cd valgrind
+        ./configure
+        make -j 2 > /dev/null
+
+    - name: Install Valgrind
+      if: matrix.valgrind
+      working-directory: valgrind
+      run: |
+        sudo make install
+        sudo apt-get update
+        sudo apt-get install libc6-dbg  # Needed by Valgrind
+
+    - name: Prepare env
+      run: |
+        python -m pip install -r tests/requirements.txt
+
+    - name: Configure
+      env:
+        SETUPTOOLS_USE_DISTUTILS: stdlib
+      run: >
+        cmake -S . -B build
+        -DCMAKE_BUILD_TYPE=Debug
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=17
+
+    - name: Build
+      run: cmake --build build -j 2
+
+    - name: Python tests
+      run: cmake --build build --target pytest
+
+    - name: C++ tests
+      run: cmake --build build --target cpptest
+
+    - name: Run Valgrind on Python tests
+      if: matrix.valgrind
+      run: cmake --build build --target memcheck
+
+
+  # Testing on clang using the excellent silkeh clang docker images
+  clang:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        clang:
+          - 3.6
+          - 3.7
+          - 3.9
+          - 7
+          - 9
+          - dev
+        std:
+          - 11
+        include:
+          - clang: 5
+            std: 14
+          - clang: 10
+            std: 20
+          - clang: 10
+            std: 17
+          - clang: 14
+            std: 20
+
+    name: "🐍 3 • Clang ${{ matrix.clang }} • C++${{ matrix.std }} • x64"
+    container: "silkeh/clang:${{ matrix.clang }}"
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Add wget and python3
+      run: apt-get update && apt-get install -y python3-dev python3-numpy python3-pytest libeigen3-dev
+
+    - name: Configure
+      shell: bash
+      run: >
+        cmake -S . -B build
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DCMAKE_CXX_STANDARD=${{ matrix.std }}
+        -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)")
+
+    - name: Build
+      run: cmake --build build -j 2
+
+    - name: Python tests
+      run: cmake --build build --target pytest
+
+    - name: C++ tests
+      run: cmake --build build --target cpptest
+
+    - name: Interface test
+      run: cmake --build build --target test_cmake_build
+
+
+  # Testing NVCC; forces sources to behave like .cu files
+  cuda:
+    runs-on: ubuntu-latest
+    name: "🐍 3.8 • CUDA 11.2 • Ubuntu 20.04"
+    container: nvidia/cuda:11.2.2-devel-ubuntu20.04
+
+    steps:
+    - uses: actions/checkout@v3
+
+    # tzdata will try to ask for the timezone, so set the DEBIAN_FRONTEND
+    - name: Install 🐍 3
+      run: apt-get update && DEBIAN_FRONTEND="noninteractive" apt-get install -y cmake git python3-dev python3-pytest python3-numpy
+
+    - name: Configure
+      run: cmake -S . -B build -DPYBIND11_CUDA_TESTS=ON -DPYBIND11_WERROR=ON -DDOWNLOAD_CATCH=ON
+
+    - name: Build
+      run: cmake --build build -j2 --verbose
+
+    - name: Python tests
+      run: cmake --build build --target pytest
+
+
+# TODO: Internal compiler error - report to NVidia
+#  # Testing CentOS 8 + PGI compilers
+#  centos-nvhpc8:
+#    runs-on: ubuntu-latest
+#    name: "🐍 3 • CentOS8 / PGI 20.11 • x64"
+#    container: centos:8
+#
+#    steps:
+#    - uses: actions/checkout@v3
+#
+#    - name: Add Python 3 and a few requirements
+#      run: yum update -y && yum install -y git python3-devel python3-numpy python3-pytest make environment-modules
+#
+#    - name: Install CMake with pip
+#      run: |
+#        python3 -m pip install --upgrade pip
+#        python3 -m pip install cmake --prefer-binary
+#
+#    - name: Install NVidia HPC SDK
+#      run: >
+#        yum -y install
+#        https://developer.download.nvidia.com/hpc-sdk/20.11/nvhpc-20-11-20.11-1.x86_64.rpm
+#        https://developer.download.nvidia.com/hpc-sdk/20.11/nvhpc-2020-20.11-1.x86_64.rpm
+#
+#    - name: Configure
+#      shell: bash
+#      run: |
+#        source /etc/profile.d/modules.sh
+#        module load /opt/nvidia/hpc_sdk/modulefiles/nvhpc/20.11
+#        cmake -S . -B build -DDOWNLOAD_CATCH=ON -DCMAKE_CXX_STANDARD=14 -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)")
+#
+#    - name: Build
+#      run: cmake --build build -j 2 --verbose
+#
+#    - name: Python tests
+#      run: cmake --build build --target pytest
+#
+#    - name: C++ tests
+#      run: cmake --build build --target cpptest
+#
+#    - name: Interface test
+#      run: cmake --build build --target test_cmake_build
+
+
+  # Testing on CentOS 7 + PGI compilers, which seems to require more workarounds
+  centos-nvhpc7:
+    runs-on: ubuntu-latest
+    name: "🐍 3 • CentOS7 / PGI 22.3 • x64"
+    container: centos:7
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Add Python 3 and a few requirements
+      run: yum update -y && yum install -y epel-release && yum install -y git python3-devel make environment-modules cmake3 yum-utils
+
+    - name: Install NVidia HPC SDK
+      run: yum-config-manager --add-repo https://developer.download.nvidia.com/hpc-sdk/rhel/nvhpc.repo && yum -y install nvhpc-22.3
+
+    # On CentOS 7, we have to filter a few tests (compiler internal error)
+    # and allow deeper template recursion (not needed on CentOS 8 with a newer
+    # standard library). On some systems, you many need further workarounds:
+    # https://github.com/pybind/pybind11/pull/2475
+    - name: Configure
+      shell: bash
+      run: |
+        source /etc/profile.d/modules.sh
+        module load /opt/nvidia/hpc_sdk/modulefiles/nvhpc/22.3
+        cmake3 -S . -B build -DDOWNLOAD_CATCH=ON \
+                            -DCMAKE_CXX_STANDARD=11 \
+                            -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") \
+                            -DCMAKE_CXX_FLAGS="-Wc,--pending_instantiations=0" \
+                            -DPYBIND11_TEST_FILTER="test_smart_ptr.cpp;test_virtual_functions.cpp"
+
+    # Building before installing Pip should produce a warning but not an error
+    - name: Build
+      run: cmake3 --build build -j 2 --verbose
+
+    - name: Install CMake with pip
+      run: |
+        python3 -m pip install --upgrade pip
+        python3 -m pip install pytest
+
+    - name: Python tests
+      run: cmake3 --build build --target pytest
+
+    - name: C++ tests
+      run: cmake3 --build build --target cpptest
+
+    - name: Interface test
+      run: cmake3 --build build --target test_cmake_build
+
+
+  # Testing on GCC using the GCC docker images (only recent images supported)
+  gcc:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        gcc:
+          - 7
+          - latest
+        std:
+          - 11
+        include:
+          - gcc: 10
+            std: 20
+
+    name: "🐍 3 • GCC ${{ matrix.gcc }} • C++${{ matrix.std }}• x64"
+    container: "gcc:${{ matrix.gcc }}"
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Add Python 3
+      run: apt-get update; apt-get install -y python3-dev python3-numpy python3-pytest python3-pip libeigen3-dev
+
+    - name: Update pip
+      run: python3 -m pip install --upgrade pip
+
+    - name: Update CMake
+      uses: jwlawson/actions-setup-cmake@v1.12
+
+    - name: Configure
+      shell: bash
+      run: >
+        cmake -S . -B build
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DCMAKE_CXX_STANDARD=${{ matrix.std }}
+        -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)")
+
+    - name: Build
+      run: cmake --build build -j 2
+
+    - name: Python tests
+      run: cmake --build build --target pytest
+
+    - name: C++ tests
+      run: cmake --build build --target cpptest
+
+    - name: Interface test
+      run: cmake --build build --target test_cmake_build
+
+
+  # Testing on ICC using the oneAPI apt repo
+  icc:
+    runs-on: ubuntu-20.04
+    strategy:
+      fail-fast: false
+
+    name: "🐍 3 • ICC latest • x64"
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Add apt repo
+      run: |
+        sudo apt-get update
+        sudo apt-get install -y wget build-essential pkg-config cmake ca-certificates gnupg
+        wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
+        sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
+        echo "deb https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
+
+    - name: Add ICC & Python 3
+      run: sudo apt-get update; sudo apt-get install -y intel-oneapi-compiler-dpcpp-cpp-and-cpp-classic cmake python3-dev python3-numpy python3-pytest python3-pip
+
+    - name: Update pip
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        python3 -m pip install --upgrade pip
+
+    - name: Install dependencies
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        python3 -m pip install -r tests/requirements.txt
+
+    - name: Configure C++11
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        cmake -S . -B build-11     \
+        -DPYBIND11_WERROR=ON    \
+        -DDOWNLOAD_CATCH=ON     \
+        -DDOWNLOAD_EIGEN=OFF    \
+        -DCMAKE_CXX_STANDARD=11             \
+        -DCMAKE_CXX_COMPILER=$(which icpc)  \
+        -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)")
+
+    - name: Build C++11
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        cmake --build build-11 -j 2 -v
+
+    - name: Python tests C++11
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        sudo service apport stop
+        cmake --build build-11 --target check
+
+    - name: C++ tests C++11
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        cmake --build build-11 --target cpptest
+
+    - name: Interface test C++11
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        cmake --build build-11 --target test_cmake_build
+
+    - name: Configure C++17
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        cmake -S . -B build-17     \
+        -DPYBIND11_WERROR=ON    \
+        -DDOWNLOAD_CATCH=ON     \
+        -DDOWNLOAD_EIGEN=OFF    \
+        -DCMAKE_CXX_STANDARD=17             \
+        -DCMAKE_CXX_COMPILER=$(which icpc)  \
+        -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)")
+
+    - name: Build C++17
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        cmake --build build-17 -j 2 -v
+
+    - name: Python tests C++17
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        sudo service apport stop
+        cmake --build build-17 --target check
+
+    - name: C++ tests C++17
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        cmake --build build-17 --target cpptest
+
+    - name: Interface test C++17
+      run: |
+        set +e; source /opt/intel/oneapi/setvars.sh; set -e
+        cmake --build build-17 --target test_cmake_build
+
+
+  # Testing on CentOS (manylinux uses a centos base, and this is an easy way
+  # to get GCC 4.8, which is the manylinux1 compiler).
+  centos:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        container:
+          - "centos:7"  # GCC 4.8
+          - "almalinux:8"
+          - "almalinux:9"
+
+    name: "🐍 3 • ${{ matrix.container }} • x64"
+    container: "${{ matrix.container }}"
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Add Python 3 (RHEL 7)
+      if: matrix.container == 'centos:7'
+      run: yum update -y && yum install -y python3-devel gcc-c++ make git
+
+    - name: Add Python 3 (RHEL 8+)
+      if: matrix.container != 'centos:7'
+      run: dnf update -y && dnf install -y python3-devel gcc-c++ make git
+
+    - name: Update pip
+      run: python3 -m pip install --upgrade pip
+
+    - name: Install dependencies
+      run: |
+        python3 -m pip install cmake -r tests/requirements.txt
+
+    - name: Configure
+      shell: bash
+      run: >
+        cmake -S . -B build
+        -DCMAKE_BUILD_TYPE=MinSizeRel
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=11
+        -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)")
+
+    - name: Build
+      run: cmake --build build -j 2
+
+    - name: Python tests
+      run: cmake --build build --target pytest
+
+    - name: C++ tests
+      run: cmake --build build --target cpptest
+
+    - name: Interface test
+      run: cmake --build build --target test_cmake_build
+
+
+  # This tests an "install" with the CMake tools
+  install-classic:
+    name: "🐍 3.7 • Debian • x86 •  Install"
+    runs-on: ubuntu-latest
+    container: i386/debian:buster
+
+    steps:
+    - uses: actions/checkout@v1  # Required to run inside docker
+
+    - name: Install requirements
+      run: |
+        apt-get update
+        apt-get install -y git make cmake g++ libeigen3-dev python3-dev python3-pip
+        pip3 install "pytest==6.*"
+
+    - name: Configure for install
+      run: >
+        cmake .
+        -DPYBIND11_INSTALL=1 -DPYBIND11_TEST=0
+        -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)")
+
+    - name: Make and install
+      run: make install
+
+    - name: Copy tests to new directory
+      run: cp -a tests /pybind11-tests
+
+    - name: Make a new test directory
+      run: mkdir /build-tests
+
+    - name: Configure tests
+      run: >
+        cmake ../pybind11-tests
+        -DDOWNLOAD_CATCH=ON
+        -DPYBIND11_WERROR=ON
+        -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)")
+      working-directory: /build-tests
+
+    - name: Python tests
+      run: make pytest -j 2
+      working-directory: /build-tests
+
+
+  # This verifies that the documentation is not horribly broken, and does a
+  # basic validation check on the SDist.
+  doxygen:
+    name: "Documentation build test"
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - uses: actions/setup-python@v4
+      with:
+        python-version: "3.x"
+
+    - name: Install Doxygen
+      run: sudo apt-get install -y doxygen librsvg2-bin # Changed to rsvg-convert in 20.04
+
+    - name: Build docs
+      run: pipx run nox -s docs
+
+    - name: Make SDist
+      run: pipx run nox -s build -- --sdist
+
+    - run: git status --ignored
+
+    - name: Check local include dir
+      run: >
+        ls pybind11;
+        python3 -c "import pybind11, pathlib; assert (a := pybind11.get_include()) == (b := str(pathlib.Path('include').resolve())), f'{a} != {b}'"
+
+    - name: Compare Dists (headers only)
+      working-directory: include
+      run: |
+        python3 -m pip install --user -U ../dist/*.tar.gz
+        installed=$(python3 -c "import pybind11; print(pybind11.get_include() + '/pybind11')")
+        diff -rq $installed ./pybind11
+
+  win32:
+    strategy:
+      fail-fast: false
+      matrix:
+        python:
+        - 3.6
+        - 3.7
+        - 3.8
+        - 3.9
+
+        include:
+          - python: 3.9
+            args: -DCMAKE_CXX_STANDARD=20
+          - python: 3.8
+            args: -DCMAKE_CXX_STANDARD=17
+
+    name: "🐍 ${{ matrix.python }} • MSVC 2019 • x86 ${{ matrix.args }}"
+    runs-on: windows-2019
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup Python ${{ matrix.python }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python }}
+        architecture: x86
+
+    - name: Update CMake
+      uses: jwlawson/actions-setup-cmake@v1.12
+
+    - name: Prepare MSVC
+      uses: ilammy/msvc-dev-cmd@v1.10.0
+      with:
+        arch: x86
+
+    - name: Prepare env
+      run: |
+        python -m pip install -r tests/requirements.txt
+
+    # First build - C++11 mode and inplace
+    - name: Configure ${{ matrix.args }}
+      run: >
+        cmake -S . -B build
+        -G "Visual Studio 16 2019" -A Win32
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        ${{ matrix.args }}
+    - name: Build C++11
+      run: cmake --build build -j 2
+
+    - name: Python tests
+      run: cmake --build build -t pytest
+
+  win32-debug:
+    strategy:
+      fail-fast: false
+      matrix:
+        python:
+        - 3.8
+        - 3.9
+
+        include:
+          - python: 3.9
+            args: -DCMAKE_CXX_STANDARD=20
+          - python: 3.8
+            args: -DCMAKE_CXX_STANDARD=17
+
+    name: "🐍 ${{ matrix.python }} • MSVC 2019 (Debug) • x86 ${{ matrix.args }}"
+    runs-on: windows-2019
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup Python ${{ matrix.python }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python }}
+        architecture: x86
+
+    - name: Update CMake
+      uses: jwlawson/actions-setup-cmake@v1.12
+
+    - name: Prepare MSVC
+      uses: ilammy/msvc-dev-cmd@v1.10.0
+      with:
+        arch: x86
+
+    - name: Prepare env
+      run: |
+        python -m pip install -r tests/requirements.txt
+
+    # First build - C++11 mode and inplace
+    - name: Configure ${{ matrix.args }}
+      run: >
+        cmake -S . -B build
+        -G "Visual Studio 16 2019" -A Win32
+        -DCMAKE_BUILD_TYPE=Debug
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        ${{ matrix.args }}
+    - name: Build C++11
+      run: cmake --build build --config Debug -j 2
+
+    - name: Python tests
+      run: cmake --build build --config Debug -t pytest
+
+
+  windows-2022:
+    strategy:
+      fail-fast: false
+      matrix:
+        python:
+        - 3.9
+
+    name: "🐍 ${{ matrix.python }} • MSVC 2022 C++20 • x64"
+    runs-on: windows-2022
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup Python ${{ matrix.python }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python }}
+
+    - name: Prepare env
+      run: |
+        python3 -m pip install -r tests/requirements.txt
+
+    - name: Update CMake
+      uses: jwlawson/actions-setup-cmake@v1.12
+
+    - name: Configure C++20
+      run: >
+        cmake -S . -B build
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=20
+
+    - name: Build C++20
+      run: cmake --build build -j 2
+
+    - name: Python tests
+      run: cmake --build build --target pytest
+
+    - name: C++20 tests
+      run: cmake --build build --target cpptest -j 2
+
+    - name: Interface test C++20
+      run: cmake --build build --target test_cmake_build
+
+  mingw:
+    name: "🐍 3 • windows-latest • ${{ matrix.sys }}"
+    runs-on: windows-latest
+    defaults:
+      run:
+        shell: msys2 {0}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - { sys: mingw64, env: x86_64 }
+          - { sys: mingw32, env: i686 }
+    steps:
+    - uses: msys2/setup-msys2@v2
+      with:
+        msystem: ${{matrix.sys}}
+        install: >-
+          git
+          mingw-w64-${{matrix.env}}-gcc
+          mingw-w64-${{matrix.env}}-python-pip
+          mingw-w64-${{matrix.env}}-python-numpy
+          mingw-w64-${{matrix.env}}-python-scipy
+          mingw-w64-${{matrix.env}}-cmake
+          mingw-w64-${{matrix.env}}-make
+          mingw-w64-${{matrix.env}}-python-pytest
+          mingw-w64-${{matrix.env}}-eigen3
+          mingw-w64-${{matrix.env}}-boost
+          mingw-w64-${{matrix.env}}-catch
+
+    - uses: actions/checkout@v3
+
+    - name: Configure C++11
+      # LTO leads to many undefined reference like
+      # `pybind11::detail::function_call::function_call(pybind11::detail::function_call&&)
+      run: cmake -G "MinGW Makefiles" -DCMAKE_CXX_STANDARD=11 -DDOWNLOAD_CATCH=ON -S . -B build
+
+    - name: Build C++11
+      run: cmake --build build -j 2
+
+    - name: Python tests C++11
+      run: cmake --build build --target pytest -j 2
+
+    - name: C++11 tests
+      run: PYTHONHOME=/${{matrix.sys}} PYTHONPATH=/${{matrix.sys}} cmake --build build --target cpptest -j 2
+
+    - name: Interface test C++11
+      run: PYTHONHOME=/${{matrix.sys}} PYTHONPATH=/${{matrix.sys}} cmake --build build --target test_cmake_build
+
+    - name: Clean directory
+      run: git clean -fdx
+
+    - name: Configure C++14
+      run: cmake -G "MinGW Makefiles" -DCMAKE_CXX_STANDARD=14 -DDOWNLOAD_CATCH=ON -S . -B build2
+
+    - name: Build C++14
+      run: cmake --build build2 -j 2
+
+    - name: Python tests C++14
+      run: cmake --build build2 --target pytest -j 2
+
+    - name: C++14 tests
+      run: PYTHONHOME=/${{matrix.sys}} PYTHONPATH=/${{matrix.sys}} cmake --build build2 --target cpptest -j 2
+
+    - name: Interface test C++14
+      run: PYTHONHOME=/${{matrix.sys}} PYTHONPATH=/${{matrix.sys}} cmake --build build2 --target test_cmake_build
+
+    - name: Clean directory
+      run: git clean -fdx
+
+    - name: Configure C++17
+      run: cmake -G "MinGW Makefiles" -DCMAKE_CXX_STANDARD=17 -DDOWNLOAD_CATCH=ON -S . -B build3
+
+    - name: Build C++17
+      run: cmake --build build3 -j 2
+
+    - name: Python tests C++17
+      run: cmake --build build3 --target pytest -j 2
+
+    - name: C++17 tests
+      run: PYTHONHOME=/${{matrix.sys}} PYTHONPATH=/${{matrix.sys}} cmake --build build3 --target cpptest -j 2
+
+    - name: Interface test C++17
+      run: PYTHONHOME=/${{matrix.sys}} PYTHONPATH=/${{matrix.sys}} cmake --build build3 --target test_cmake_build
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/configure.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/configure.yml
new file mode 100644
index 000000000..edcad4198
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/configure.yml
@@ -0,0 +1,80 @@
+name: Config
+
+on:
+  workflow_dispatch:
+  pull_request:
+  push:
+    branches:
+      - master
+      - stable
+      - v*
+
+jobs:
+  # This tests various versions of CMake in various combinations, to make sure
+  # the configure step passes.
+  cmake:
+    strategy:
+      fail-fast: false
+      matrix:
+        runs-on: [ubuntu-latest, macos-latest, windows-latest]
+        arch: [x64]
+        cmake: ["3.23"]
+
+        include:
+        - runs-on: ubuntu-latest
+          arch: x64
+          cmake: 3.4
+
+        - runs-on: macos-latest
+          arch: x64
+          cmake: 3.7
+
+        - runs-on: windows-2019
+          arch: x64 # x86 compilers seem to be missing on 2019 image
+          cmake: 3.18
+
+    name: 🐍 3.7 • CMake ${{ matrix.cmake }} • ${{ matrix.runs-on }}
+    runs-on: ${{ matrix.runs-on }}
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup Python 3.7
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.7
+        architecture: ${{ matrix.arch }}
+
+    - name: Prepare env
+      run: python -m pip install -r tests/requirements.txt
+
+    # An action for adding a specific version of CMake:
+    #   https://github.com/jwlawson/actions-setup-cmake
+    - name: Setup CMake ${{ matrix.cmake }}
+      uses: jwlawson/actions-setup-cmake@v1.12
+      with:
+        cmake-version: ${{ matrix.cmake }}
+
+    # These steps use a directory with a space in it intentionally
+    - name: Make build directories
+      run: mkdir "build dir"
+
+    - name: Configure
+      working-directory: build dir
+      shell: bash
+      run: >
+        cmake ..
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DPYTHON_EXECUTABLE=$(python -c "import sys; print(sys.executable)")
+
+    # Only build and test if this was manually triggered in the GitHub UI
+    - name: Build
+      working-directory: build dir
+      if: github.event_name == 'workflow_dispatch'
+      run: cmake --build . --config Release
+
+    - name: Test
+      working-directory: build dir
+      if: github.event_name == 'workflow_dispatch'
+      run: cmake --build . --config Release --target check
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/format.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/format.yml
new file mode 100644
index 000000000..31d893c47
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/format.yml
@@ -0,0 +1,55 @@
+# This is a format job. Pre-commit has a first-party GitHub action, so we use
+# that: https://github.com/pre-commit/action
+
+name: Format
+
+on:
+  workflow_dispatch:
+  pull_request:
+  push:
+    branches:
+    - master
+    - stable
+    - "v*"
+
+env:
+  FORCE_COLOR: 3
+
+jobs:
+  pre-commit:
+    name: Format
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v4
+      with:
+        python-version: "3.x"
+    - name: Add matchers
+      run: echo "::add-matcher::$GITHUB_WORKSPACE/.github/matchers/pylint.json"
+    - uses: pre-commit/action@v3.0.0
+      with:
+        # Slow hooks are marked with manual - slow is okay here, run them too
+        extra_args: --hook-stage manual --all-files
+
+  clang-tidy:
+    # When making changes here, please also review the "Clang-Tidy" section
+    # in .github/CONTRIBUTING.md and update as needed.
+    name: Clang-Tidy
+    runs-on: ubuntu-latest
+    container: silkeh/clang:13
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Install requirements
+      run: apt-get update && apt-get install -y python3-dev python3-pytest
+
+    - name: Configure
+      run: >
+        cmake -S . -B build
+        -DCMAKE_CXX_CLANG_TIDY="$(which clang-tidy);--use-color;--warnings-as-errors=*"
+        -DDOWNLOAD_EIGEN=ON
+        -DDOWNLOAD_CATCH=ON
+        -DCMAKE_CXX_STANDARD=17
+
+    - name: Build
+      run: cmake --build build -j 2 -- --keep-going
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/labeler.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/labeler.yml
new file mode 100644
index 000000000..d2b597968
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/labeler.yml
@@ -0,0 +1,16 @@
+name: Labeler
+on:
+  pull_request_target:
+    types: [closed]
+
+jobs:
+  label:
+    name: Labeler
+    runs-on: ubuntu-latest
+    steps:
+
+    - uses: actions/labeler@main
+      if: github.event.pull_request.merged == true
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        configuration-path: .github/labeler_merged.yml
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/pip.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/pip.yml
new file mode 100644
index 000000000..2c16735e1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/pip.yml
@@ -0,0 +1,110 @@
+name: Pip
+
+on:
+  workflow_dispatch:
+  pull_request:
+  push:
+    branches:
+    - master
+    - stable
+    - v*
+  release:
+    types:
+    - published
+
+env:
+  PIP_ONLY_BINARY: numpy
+
+jobs:
+  # This builds the sdists and wheels and makes sure the files are exactly as
+  # expected. Using Windows and Python 3.6, since that is often the most
+  # challenging matrix element.
+  test-packaging:
+    name: 🐍 3.6 • 📦 tests • windows-latest
+    runs-on: windows-latest
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup 🐍 3.6
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.6
+
+    - name: Prepare env
+      run: |
+        python -m pip install -r tests/requirements.txt
+
+    - name: Python Packaging tests
+      run: pytest tests/extra_python_package/
+
+
+  # This runs the packaging tests and also builds and saves the packages as
+  # artifacts.
+  packaging:
+    name: 🐍 3.8 • 📦 & 📦 tests • ubuntu-latest
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup 🐍 3.8
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.8
+
+    - name: Prepare env
+      run: |
+        python -m pip install -r tests/requirements.txt build twine
+
+    - name: Python Packaging tests
+      run: pytest tests/extra_python_package/
+
+    - name: Build SDist and wheels
+      run: |
+        python -m build
+        PYBIND11_GLOBAL_SDIST=1 python -m build
+
+    - name: Check metadata
+      run: twine check dist/*
+
+    - name: Save standard package
+      uses: actions/upload-artifact@v3
+      with:
+        name: standard
+        path: dist/pybind11-*
+
+    - name: Save global package
+      uses: actions/upload-artifact@v3
+      with:
+        name: global
+        path: dist/pybind11_global-*
+
+
+
+  # When a GitHub release is made, upload the artifacts to PyPI
+  upload:
+    name: Upload to PyPI
+    runs-on: ubuntu-latest
+    if: github.event_name == 'release' && github.event.action == 'published'
+    needs: [packaging]
+
+    steps:
+    - uses: actions/setup-python@v4
+      with:
+        python-version: "3.x"
+
+    # Downloads all to directories matching the artifact names
+    - uses: actions/download-artifact@v3
+
+    - name: Publish standard package
+      uses: pypa/gh-action-pypi-publish@v1.5.0
+      with:
+        password: ${{ secrets.pypi_password }}
+        packages_dir: standard/
+
+    - name: Publish global package
+      uses: pypa/gh-action-pypi-publish@v1.5.0
+      with:
+        password: ${{ secrets.pypi_password_global }}
+        packages_dir: global/
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/upstream.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/upstream.yml
new file mode 100644
index 000000000..a40a6c7d7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.github/workflows/upstream.yml
@@ -0,0 +1,112 @@
+
+name: Upstream
+
+on:
+  workflow_dispatch:
+  pull_request:
+
+concurrency:
+  group: upstream-${{ github.ref }}
+  cancel-in-progress: true
+
+env:
+  PIP_ONLY_BINARY: numpy
+
+jobs:
+  standard:
+    name: "🐍 3.11 latest internals • ubuntu-latest • x64"
+    runs-on: ubuntu-latest
+    if: "contains(github.event.pull_request.labels.*.name, 'python dev')"
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup Python 3.11
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.11-dev"
+
+    - name: Setup Boost (Linux)
+      if: runner.os == 'Linux'
+      run: sudo apt-get install libboost-dev
+
+    - name: Update CMake
+      uses: jwlawson/actions-setup-cmake@v1.12
+
+    - name: Prepare env
+      run: |
+        python -m pip install -r tests/requirements.txt
+
+    - name: Setup annotations on Linux
+      if: runner.os == 'Linux'
+      run: python -m pip install pytest-github-actions-annotate-failures
+
+    # First build - C++11 mode and inplace
+    - name: Configure C++11
+      run: >
+        cmake -S . -B .
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=11
+
+    - name: Build C++11
+      run: cmake --build . -j 2
+
+    - name: Python tests C++11
+      run: cmake --build . --target pytest -j 2
+
+    - name: C++11 tests
+      run: cmake --build .  --target cpptest -j 2
+
+    - name: Interface test C++11
+      run: cmake --build . --target test_cmake_build
+
+    - name: Clean directory
+      run: git clean -fdx
+
+    # Second build - C++17 mode and in a build directory
+    - name: Configure C++17
+      run: >
+        cmake -S . -B build2
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=17
+        ${{ matrix.args }}
+        ${{ matrix.args2 }}
+
+    - name: Build
+      run: cmake --build build2 -j 2
+
+    - name: Python tests
+      run: cmake --build build2 --target pytest
+
+    - name: C++ tests
+      run: cmake --build build2 --target cpptest
+
+    # Third build - C++17 mode with unstable ABI
+    - name: Configure (unstable ABI)
+      run: >
+        cmake -S . -B build3
+        -DPYBIND11_WERROR=ON
+        -DDOWNLOAD_CATCH=ON
+        -DDOWNLOAD_EIGEN=ON
+        -DCMAKE_CXX_STANDARD=17
+        -DPYBIND11_INTERNALS_VERSION=10000000
+        "-DPYBIND11_TEST_OVERRIDE=test_call_policies.cpp;test_gil_scoped.cpp;test_thread.cpp"
+        ${{ matrix.args }}
+
+    - name: Build (unstable ABI)
+      run: cmake --build build3 -j 2
+
+    - name: Python tests (unstable ABI)
+      run: cmake --build build3 --target pytest
+
+    - name: Interface test
+      run: cmake --build build2 --target test_cmake_build
+
+    # This makes sure the setup_helpers module can build packages using
+    # setuptools
+    - name: Setuptools helpers test
+      run: pytest tests/extra_setuptools
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.gitignore b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.gitignore
new file mode 100644
index 000000000..3cf4fbbda
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.gitignore
@@ -0,0 +1,45 @@
+CMakeCache.txt
+CMakeFiles
+Makefile
+cmake_install.cmake
+cmake_uninstall.cmake
+.DS_Store
+*.so
+*.pyd
+*.dll
+*.sln
+*.sdf
+*.opensdf
+*.vcxproj
+*.vcxproj.user
+*.filters
+example.dir
+Win32
+x64
+Release
+Debug
+.vs
+CTestTestfile.cmake
+Testing
+autogen
+MANIFEST
+/.ninja_*
+/*.ninja
+/docs/.build
+*.py[co]
+*.egg-info
+*~
+.*.swp
+.DS_Store
+/dist
+/*build*
+.cache/
+sosize-*.txt
+pybind11Config*.cmake
+pybind11Targets.cmake
+/*env*
+/.vscode
+/pybind11/include/*
+/pybind11/share/*
+/docs/_build/*
+.ipynb_checkpoints/
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.pre-commit-config.yaml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.pre-commit-config.yaml
new file mode 100644
index 000000000..ba9955a31
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.pre-commit-config.yaml
@@ -0,0 +1,170 @@
+# To use:
+#
+#     pre-commit run -a
+#
+# Or:
+#
+#     pre-commit install  # (runs every time you commit in git)
+#
+# To update this file:
+#
+#     pre-commit autoupdate
+#
+# See https://github.com/pre-commit/pre-commit
+
+repos:
+# Standard hooks
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: "v4.3.0"
+  hooks:
+  - id: check-added-large-files
+  - id: check-case-conflict
+  - id: check-docstring-first
+  - id: check-merge-conflict
+  - id: check-symlinks
+  - id: check-toml
+  - id: check-yaml
+  - id: debug-statements
+  - id: end-of-file-fixer
+  - id: mixed-line-ending
+  - id: requirements-txt-fixer
+  - id: trailing-whitespace
+
+# Upgrade old Python syntax
+- repo: https://github.com/asottile/pyupgrade
+  rev: "v2.37.1"
+  hooks:
+  - id: pyupgrade
+    args: [--py36-plus]
+
+# Nicely sort includes
+- repo: https://github.com/PyCQA/isort
+  rev: "5.10.1"
+  hooks:
+  - id: isort
+
+# Black, the code formatter, natively supports pre-commit
+- repo: https://github.com/psf/black
+  rev: "22.6.0" # Keep in sync with blacken-docs
+  hooks:
+  - id: black
+
+# Also code format the docs
+- repo: https://github.com/asottile/blacken-docs
+  rev: "v1.12.1"
+  hooks:
+  - id: blacken-docs
+    additional_dependencies:
+    - black==22.6.0 # keep in sync with black hook
+
+# Changes tabs to spaces
+- repo: https://github.com/Lucas-C/pre-commit-hooks
+  rev: "v1.3.0"
+  hooks:
+  - id: remove-tabs
+
+- repo: https://github.com/sirosen/texthooks
+  rev: "0.3.1"
+  hooks:
+  - id: fix-ligatures
+  - id: fix-smartquotes
+
+# Autoremoves unused imports
+- repo: https://github.com/hadialqattan/pycln
+  rev: "v2.0.1"
+  hooks:
+  - id: pycln
+    stages: [manual]
+
+# Checking for common mistakes
+- repo: https://github.com/pre-commit/pygrep-hooks
+  rev: "v1.9.0"
+  hooks:
+  - id: python-check-blanket-noqa
+  - id: python-check-blanket-type-ignore
+  - id: python-no-log-warn
+  - id: python-use-type-annotations
+  - id: rst-backticks
+  - id: rst-directive-colons
+  - id: rst-inline-touching-normal
+
+# Automatically remove noqa that are not used
+- repo: https://github.com/asottile/yesqa
+  rev: "v1.3.0"
+  hooks:
+  - id: yesqa
+    additional_dependencies: &flake8_dependencies
+      - flake8-bugbear
+      - pep8-naming
+
+# Flake8 also supports pre-commit natively (same author)
+- repo: https://github.com/PyCQA/flake8
+  rev: "4.0.1"
+  hooks:
+  - id: flake8
+    exclude: ^(docs/.*|tools/.*)$
+    additional_dependencies: *flake8_dependencies
+
+# PyLint has native support - not always usable, but works for us
+- repo: https://github.com/PyCQA/pylint
+  rev: "v2.14.4"
+  hooks:
+  - id: pylint
+    files: ^pybind11
+
+# CMake formatting
+- repo: https://github.com/cheshirekow/cmake-format-precommit
+  rev: "v0.6.13"
+  hooks:
+  - id: cmake-format
+    additional_dependencies: [pyyaml]
+    types: [file]
+    files: (\.cmake|CMakeLists.txt)(.in)?$
+
+# Check static types with mypy
+- repo: https://github.com/pre-commit/mirrors-mypy
+  rev: "v0.961"
+  hooks:
+  - id: mypy
+    args: []
+    exclude: ^(tests|docs)/
+    additional_dependencies: [nox, rich]
+
+# Checks the manifest for missing files (native support)
+- repo: https://github.com/mgedmin/check-manifest
+  rev: "0.48"
+  hooks:
+  - id: check-manifest
+    # This is a slow hook, so only run this if --hook-stage manual is passed
+    stages: [manual]
+    additional_dependencies: [cmake, ninja]
+
+# Check for spelling
+- repo: https://github.com/codespell-project/codespell
+  rev: "v2.1.0"
+  hooks:
+  - id: codespell
+    exclude: ".supp$"
+    args: ["-L", "nd,ot,thist"]
+
+# Check for common shell mistakes
+- repo: https://github.com/shellcheck-py/shellcheck-py
+  rev: "v0.8.0.4"
+  hooks:
+  - id: shellcheck
+
+# Disallow some common capitalization mistakes
+- repo: local
+  hooks:
+  - id: disallow-caps
+    name: Disallow improper capitalization
+    language: pygrep
+    entry: PyBind|Numpy|Cmake|CCache|PyTest
+    exclude: ^\.pre-commit-config.yaml$
+
+# Clang format the codebase automatically
+- repo: https://github.com/pre-commit/mirrors-clang-format
+  rev: "v14.0.6"
+  hooks:
+  - id: clang-format
+    types_or: [c++, c, cuda]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.readthedocs.yml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.readthedocs.yml
new file mode 100644
index 000000000..c9c61617c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/.readthedocs.yml
@@ -0,0 +1,3 @@
+python:
+  version: 3
+requirements_file: docs/requirements.txt
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/CMakeLists.txt
new file mode 100644
index 000000000..3787982cb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/CMakeLists.txt
@@ -0,0 +1,299 @@
+# CMakeLists.txt -- Build system for the pybind11 modules
+#
+# Copyright (c) 2015 Wenzel Jakob <wenzel@inf.ethz.ch>
+#
+# All rights reserved. Use of this source code is governed by a
+# BSD-style license that can be found in the LICENSE file.
+
+cmake_minimum_required(VERSION 3.4)
+
+# The `cmake_minimum_required(VERSION 3.4...3.22)` syntax does not work with
+# some versions of VS that have a patched CMake 3.11. This forces us to emulate
+# the behavior using the following workaround:
+if(${CMAKE_VERSION} VERSION_LESS 3.22)
+  cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION})
+else()
+  cmake_policy(VERSION 3.22)
+endif()
+
+# Avoid infinite recursion if tests include this as a subdirectory
+if(DEFINED PYBIND11_MASTER_PROJECT)
+  return()
+endif()
+
+# Extract project version from source
+file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/include/pybind11/detail/common.h"
+     pybind11_version_defines REGEX "#define PYBIND11_VERSION_(MAJOR|MINOR|PATCH) ")
+
+foreach(ver ${pybind11_version_defines})
+  if(ver MATCHES [[#define PYBIND11_VERSION_(MAJOR|MINOR|PATCH) +([^ ]+)$]])
+    set(PYBIND11_VERSION_${CMAKE_MATCH_1} "${CMAKE_MATCH_2}")
+  endif()
+endforeach()
+
+if(PYBIND11_VERSION_PATCH MATCHES [[\.([a-zA-Z0-9]+)$]])
+  set(pybind11_VERSION_TYPE "${CMAKE_MATCH_1}")
+endif()
+string(REGEX MATCH "^[0-9]+" PYBIND11_VERSION_PATCH "${PYBIND11_VERSION_PATCH}")
+
+project(
+  pybind11
+  LANGUAGES CXX
+  VERSION "${PYBIND11_VERSION_MAJOR}.${PYBIND11_VERSION_MINOR}.${PYBIND11_VERSION_PATCH}")
+
+# Standard includes
+include(GNUInstallDirs)
+include(CMakePackageConfigHelpers)
+include(CMakeDependentOption)
+
+if(NOT pybind11_FIND_QUIETLY)
+  message(STATUS "pybind11 v${pybind11_VERSION} ${pybind11_VERSION_TYPE}")
+endif()
+
+# Check if pybind11 is being used directly or via add_subdirectory
+if(CMAKE_SOURCE_DIR STREQUAL PROJECT_SOURCE_DIR)
+  ### Warn if not an out-of-source builds
+  if(CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_CURRENT_BINARY_DIR)
+    set(lines
+        "You are building in-place. If that is not what you intended to "
+        "do, you can clean the source directory with:\n"
+        "rm -r CMakeCache.txt CMakeFiles/ cmake_uninstall.cmake pybind11Config.cmake "
+        "pybind11ConfigVersion.cmake tests/CMakeFiles/\n")
+    message(AUTHOR_WARNING ${lines})
+  endif()
+
+  set(PYBIND11_MASTER_PROJECT ON)
+
+  if(OSX AND CMAKE_VERSION VERSION_LESS 3.7)
+    # Bug in macOS CMake < 3.7 is unable to download catch
+    message(WARNING "CMAKE 3.7+ needed on macOS to download catch, and newer HIGHLY recommended")
+  elseif(WINDOWS AND CMAKE_VERSION VERSION_LESS 3.8)
+    # Only tested with 3.8+ in CI.
+    message(WARNING "CMAKE 3.8+ tested on Windows, previous versions untested")
+  endif()
+
+  message(STATUS "CMake ${CMAKE_VERSION}")
+
+  if(CMAKE_CXX_STANDARD)
+    set(CMAKE_CXX_EXTENSIONS OFF)
+    set(CMAKE_CXX_STANDARD_REQUIRED ON)
+  endif()
+
+  set(pybind11_system "")
+
+  set_property(GLOBAL PROPERTY USE_FOLDERS ON)
+else()
+  set(PYBIND11_MASTER_PROJECT OFF)
+  set(pybind11_system SYSTEM)
+endif()
+
+# Options
+option(PYBIND11_INSTALL "Install pybind11 header files?" ${PYBIND11_MASTER_PROJECT})
+option(PYBIND11_TEST "Build pybind11 test suite?" ${PYBIND11_MASTER_PROJECT})
+option(PYBIND11_NOPYTHON "Disable search for Python" OFF)
+set(PYBIND11_INTERNALS_VERSION
+    ""
+    CACHE STRING "Override the ABI version, may be used to enable the unstable ABI.")
+
+cmake_dependent_option(
+  USE_PYTHON_INCLUDE_DIR
+  "Install pybind11 headers in Python include directory instead of default installation prefix"
+  OFF "PYBIND11_INSTALL" OFF)
+
+cmake_dependent_option(PYBIND11_FINDPYTHON "Force new FindPython" OFF
+                       "NOT CMAKE_VERSION VERSION_LESS 3.12" OFF)
+
+# NB: when adding a header don't forget to also add it to setup.py
+set(PYBIND11_HEADERS
+    include/pybind11/detail/class.h
+    include/pybind11/detail/common.h
+    include/pybind11/detail/descr.h
+    include/pybind11/detail/init.h
+    include/pybind11/detail/internals.h
+    include/pybind11/detail/type_caster_base.h
+    include/pybind11/detail/typeid.h
+    include/pybind11/attr.h
+    include/pybind11/buffer_info.h
+    include/pybind11/cast.h
+    include/pybind11/chrono.h
+    include/pybind11/common.h
+    include/pybind11/complex.h
+    include/pybind11/options.h
+    include/pybind11/eigen.h
+    include/pybind11/embed.h
+    include/pybind11/eval.h
+    include/pybind11/gil.h
+    include/pybind11/iostream.h
+    include/pybind11/functional.h
+    include/pybind11/numpy.h
+    include/pybind11/operators.h
+    include/pybind11/pybind11.h
+    include/pybind11/pytypes.h
+    include/pybind11/stl.h
+    include/pybind11/stl_bind.h
+    include/pybind11/stl/filesystem.h)
+
+# Compare with grep and warn if mismatched
+if(PYBIND11_MASTER_PROJECT AND NOT CMAKE_VERSION VERSION_LESS 3.12)
+  file(
+    GLOB_RECURSE _pybind11_header_check
+    LIST_DIRECTORIES false
+    RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}"
+    CONFIGURE_DEPENDS "include/pybind11/*.h")
+  set(_pybind11_here_only ${PYBIND11_HEADERS})
+  set(_pybind11_disk_only ${_pybind11_header_check})
+  list(REMOVE_ITEM _pybind11_here_only ${_pybind11_header_check})
+  list(REMOVE_ITEM _pybind11_disk_only ${PYBIND11_HEADERS})
+  if(_pybind11_here_only)
+    message(AUTHOR_WARNING "PYBIND11_HEADERS has extra files:" ${_pybind11_here_only})
+  endif()
+  if(_pybind11_disk_only)
+    message(AUTHOR_WARNING "PYBIND11_HEADERS is missing files:" ${_pybind11_disk_only})
+  endif()
+endif()
+
+# CMake 3.12 added list(TRANSFORM <list> PREPEND
+# But we can't use it yet
+string(REPLACE "include/" "${CMAKE_CURRENT_SOURCE_DIR}/include/" PYBIND11_HEADERS
+               "${PYBIND11_HEADERS}")
+
+# Cache variable so this can be used in parent projects
+set(pybind11_INCLUDE_DIR
+    "${CMAKE_CURRENT_LIST_DIR}/include"
+    CACHE INTERNAL "Directory where pybind11 headers are located")
+
+# Backward compatible variable for add_subdirectory mode
+if(NOT PYBIND11_MASTER_PROJECT)
+  set(PYBIND11_INCLUDE_DIR
+      "${pybind11_INCLUDE_DIR}"
+      CACHE INTERNAL "")
+endif()
+
+# Note: when creating targets, you cannot use if statements at configure time -
+# you need generator expressions, because those will be placed in the target file.
+# You can also place ifs *in* the Config.in, but not here.
+
+# This section builds targets, but does *not* touch Python
+# Non-IMPORT targets cannot be defined twice
+if(NOT TARGET pybind11_headers)
+  # Build the headers-only target (no Python included):
+  # (long name used here to keep this from clashing in subdirectory mode)
+  add_library(pybind11_headers INTERFACE)
+  add_library(pybind11::pybind11_headers ALIAS pybind11_headers) # to match exported target
+  add_library(pybind11::headers ALIAS pybind11_headers) # easier to use/remember
+
+  target_include_directories(
+    pybind11_headers ${pybind11_system} INTERFACE $<BUILD_INTERFACE:${pybind11_INCLUDE_DIR}>
+                                                  $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>)
+
+  target_compile_features(pybind11_headers INTERFACE cxx_inheriting_constructors cxx_user_literals
+                                                     cxx_right_angle_brackets)
+  if(NOT "${PYBIND11_INTERNALS_VERSION}" STREQUAL "")
+    target_compile_definitions(
+      pybind11_headers INTERFACE "PYBIND11_INTERNALS_VERSION=${PYBIND11_INTERNALS_VERSION}")
+  endif()
+else()
+  # It is invalid to install a target twice, too.
+  set(PYBIND11_INSTALL OFF)
+endif()
+
+include("${CMAKE_CURRENT_SOURCE_DIR}/tools/pybind11Common.cmake")
+
+# Relative directory setting
+if(USE_PYTHON_INCLUDE_DIR AND DEFINED Python_INCLUDE_DIRS)
+  file(RELATIVE_PATH CMAKE_INSTALL_INCLUDEDIR ${CMAKE_INSTALL_PREFIX} ${Python_INCLUDE_DIRS})
+elseif(USE_PYTHON_INCLUDE_DIR AND DEFINED PYTHON_INCLUDE_DIR)
+  file(RELATIVE_PATH CMAKE_INSTALL_INCLUDEDIR ${CMAKE_INSTALL_PREFIX} ${PYTHON_INCLUDE_DIRS})
+endif()
+
+if(PYBIND11_INSTALL)
+  install(DIRECTORY ${pybind11_INCLUDE_DIR}/pybind11 DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
+  set(PYBIND11_CMAKECONFIG_INSTALL_DIR
+      "${CMAKE_INSTALL_DATAROOTDIR}/cmake/${PROJECT_NAME}"
+      CACHE STRING "install path for pybind11Config.cmake")
+
+  if(IS_ABSOLUTE "${CMAKE_INSTALL_INCLUDEDIR}")
+    set(pybind11_INCLUDEDIR "${CMAKE_INSTALL_FULL_INCLUDEDIR}")
+  else()
+    set(pybind11_INCLUDEDIR "\$\{PACKAGE_PREFIX_DIR\}/${CMAKE_INSTALL_INCLUDEDIR}")
+  endif()
+
+  configure_package_config_file(
+    tools/${PROJECT_NAME}Config.cmake.in "${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake"
+    INSTALL_DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
+
+  if(CMAKE_VERSION VERSION_LESS 3.14)
+    # Remove CMAKE_SIZEOF_VOID_P from ConfigVersion.cmake since the library does
+    # not depend on architecture specific settings or libraries.
+    set(_PYBIND11_CMAKE_SIZEOF_VOID_P ${CMAKE_SIZEOF_VOID_P})
+    unset(CMAKE_SIZEOF_VOID_P)
+
+    write_basic_package_version_file(
+      ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
+      VERSION ${PROJECT_VERSION}
+      COMPATIBILITY AnyNewerVersion)
+
+    set(CMAKE_SIZEOF_VOID_P ${_PYBIND11_CMAKE_SIZEOF_VOID_P})
+  else()
+    # CMake 3.14+ natively supports header-only libraries
+    write_basic_package_version_file(
+      ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
+      VERSION ${PROJECT_VERSION}
+      COMPATIBILITY AnyNewerVersion ARCH_INDEPENDENT)
+  endif()
+
+  install(
+    FILES ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake
+          ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
+          tools/FindPythonLibsNew.cmake
+          tools/pybind11Common.cmake
+          tools/pybind11Tools.cmake
+          tools/pybind11NewTools.cmake
+    DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
+
+  if(NOT PYBIND11_EXPORT_NAME)
+    set(PYBIND11_EXPORT_NAME "${PROJECT_NAME}Targets")
+  endif()
+
+  install(TARGETS pybind11_headers EXPORT "${PYBIND11_EXPORT_NAME}")
+
+  install(
+    EXPORT "${PYBIND11_EXPORT_NAME}"
+    NAMESPACE "pybind11::"
+    DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
+
+  # Uninstall target
+  if(PYBIND11_MASTER_PROJECT)
+    configure_file("${CMAKE_CURRENT_SOURCE_DIR}/tools/cmake_uninstall.cmake.in"
+                   "${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake" IMMEDIATE @ONLY)
+
+    add_custom_target(uninstall COMMAND ${CMAKE_COMMAND} -P
+                                        ${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake)
+  endif()
+endif()
+
+# BUILD_TESTING takes priority, but only if this is the master project
+if(PYBIND11_MASTER_PROJECT AND DEFINED BUILD_TESTING)
+  if(BUILD_TESTING)
+    if(_pybind11_nopython)
+      message(FATAL_ERROR "Cannot activate tests in NOPYTHON mode")
+    else()
+      add_subdirectory(tests)
+    endif()
+  endif()
+else()
+  if(PYBIND11_TEST)
+    if(_pybind11_nopython)
+      message(FATAL_ERROR "Cannot activate tests in NOPYTHON mode")
+    else()
+      add_subdirectory(tests)
+    endif()
+  endif()
+endif()
+
+# Better symmetry with find_package(pybind11 CONFIG) mode.
+if(NOT PYBIND11_MASTER_PROJECT)
+  set(pybind11_FOUND
+      TRUE
+      CACHE INTERNAL "True if pybind11 and all required components found on the system")
+endif()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/LICENSE b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/LICENSE
new file mode 100644
index 000000000..e466b0dfd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/LICENSE
@@ -0,0 +1,29 @@
+Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>, All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Please also refer to the file .github/CONTRIBUTING.md, which clarifies licensing of
+external contributions to this project including patches, pull requests, etc.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/MANIFEST.in b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/MANIFEST.in
new file mode 100644
index 000000000..033303a74
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/MANIFEST.in
@@ -0,0 +1,5 @@
+recursive-include pybind11/include/pybind11 *.h
+recursive-include pybind11 *.py
+recursive-include pybind11 py.typed
+include pybind11/share/cmake/pybind11/*.cmake
+include LICENSE README.rst pyproject.toml setup.py setup.cfg
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/README.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/README.rst
new file mode 100644
index 000000000..3c75edb57
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/README.rst
@@ -0,0 +1,180 @@
+.. figure:: https://github.com/pybind/pybind11/raw/master/docs/pybind11-logo.png
+   :alt: pybind11 logo
+
+**pybind11 — Seamless operability between C++11 and Python**
+
+|Latest Documentation Status| |Stable Documentation Status| |Gitter chat| |GitHub Discussions| |CI| |Build status|
+
+|Repology| |PyPI package| |Conda-forge| |Python Versions|
+
+`Setuptools example <https://github.com/pybind/python_example>`_
+• `Scikit-build example <https://github.com/pybind/scikit_build_example>`_
+• `CMake example <https://github.com/pybind/cmake_example>`_
+
+.. start
+
+
+**pybind11** is a lightweight header-only library that exposes C++ types
+in Python and vice versa, mainly to create Python bindings of existing
+C++ code. Its goals and syntax are similar to the excellent
+`Boost.Python <http://www.boost.org/doc/libs/1_58_0/libs/python/doc/>`_
+library by David Abrahams: to minimize boilerplate code in traditional
+extension modules by inferring type information using compile-time
+introspection.
+
+The main issue with Boost.Python—and the reason for creating such a
+similar project—is Boost. Boost is an enormously large and complex suite
+of utility libraries that works with almost every C++ compiler in
+existence. This compatibility has its cost: arcane template tricks and
+workarounds are necessary to support the oldest and buggiest of compiler
+specimens. Now that C++11-compatible compilers are widely available,
+this heavy machinery has become an excessively large and unnecessary
+dependency.
+
+Think of this library as a tiny self-contained version of Boost.Python
+with everything stripped away that isn't relevant for binding
+generation. Without comments, the core header files only require ~4K
+lines of code and depend on Python (3.6+, or PyPy) and the C++
+standard library. This compact implementation was possible thanks to
+some of the new C++11 language features (specifically: tuples, lambda
+functions and variadic templates). Since its creation, this library has
+grown beyond Boost.Python in many ways, leading to dramatically simpler
+binding code in many common situations.
+
+Tutorial and reference documentation is provided at
+`pybind11.readthedocs.io <https://pybind11.readthedocs.io/en/latest>`_.
+A PDF version of the manual is available
+`here <https://pybind11.readthedocs.io/_/downloads/en/latest/pdf/>`_.
+And the source code is always available at
+`github.com/pybind/pybind11 <https://github.com/pybind/pybind11>`_.
+
+
+Core features
+-------------
+
+
+pybind11 can map the following core C++ features to Python:
+
+- Functions accepting and returning custom data structures per value,
+  reference, or pointer
+- Instance methods and static methods
+- Overloaded functions
+- Instance attributes and static attributes
+- Arbitrary exception types
+- Enumerations
+- Callbacks
+- Iterators and ranges
+- Custom operators
+- Single and multiple inheritance
+- STL data structures
+- Smart pointers with reference counting like ``std::shared_ptr``
+- Internal references with correct reference counting
+- C++ classes with virtual (and pure virtual) methods can be extended
+  in Python
+
+Goodies
+-------
+
+In addition to the core functionality, pybind11 provides some extra
+goodies:
+
+- Python 3.6+, and PyPy3 7.3 are supported with an implementation-agnostic
+  interface (pybind11 2.9 was the last version to support Python 2 and 3.5).
+
+- It is possible to bind C++11 lambda functions with captured
+  variables. The lambda capture data is stored inside the resulting
+  Python function object.
+
+- pybind11 uses C++11 move constructors and move assignment operators
+  whenever possible to efficiently transfer custom data types.
+
+- It's easy to expose the internal storage of custom data types through
+  Pythons' buffer protocols. This is handy e.g. for fast conversion
+  between C++ matrix classes like Eigen and NumPy without expensive
+  copy operations.
+
+- pybind11 can automatically vectorize functions so that they are
+  transparently applied to all entries of one or more NumPy array
+  arguments.
+
+- Python's slice-based access and assignment operations can be
+  supported with just a few lines of code.
+
+- Everything is contained in just a few header files; there is no need
+  to link against any additional libraries.
+
+- Binaries are generally smaller by a factor of at least 2 compared to
+  equivalent bindings generated by Boost.Python. A recent pybind11
+  conversion of PyRosetta, an enormous Boost.Python binding project,
+  `reported <https://graylab.jhu.edu/Sergey/2016.RosettaCon/PyRosetta-4.pdf>`_
+  a binary size reduction of **5.4x** and compile time reduction by
+  **5.8x**.
+
+- Function signatures are precomputed at compile time (using
+  ``constexpr``), leading to smaller binaries.
+
+- With little extra effort, C++ types can be pickled and unpickled
+  similar to regular Python objects.
+
+Supported compilers
+-------------------
+
+1. Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or
+   newer)
+2. GCC 4.8 or newer
+3. Microsoft Visual Studio 2017 or newer
+4. Intel classic C++ compiler 18 or newer (ICC 20.2 tested in CI)
+5. Cygwin/GCC (previously tested on 2.5.1)
+6. NVCC (CUDA 11.0 tested in CI)
+7. NVIDIA PGI (20.9 tested in CI)
+
+About
+-----
+
+This project was created by `Wenzel
+Jakob <http://rgl.epfl.ch/people/wjakob>`_. Significant features and/or
+improvements to the code were contributed by Jonas Adler, Lori A. Burns,
+Sylvain Corlay, Eric Cousineau, Aaron Gokaslan, Ralf Grosse-Kunstleve, Trent Houliston, Axel
+Huebl, @hulucc, Yannick Jadoul, Sergey Lyskov Johan Mabille, Tomasz Miąsko,
+Dean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Schäling, Pim
+Schellart, Henry Schreiner, Ivan Smirnov, Boris Staletic, and Patrick Stewart.
+
+We thank Google for a generous financial contribution to the continuous
+integration infrastructure used by this project.
+
+
+Contributing
+~~~~~~~~~~~~
+
+See the `contributing
+guide <https://github.com/pybind/pybind11/blob/master/.github/CONTRIBUTING.md>`_
+for information on building and contributing to pybind11.
+
+License
+~~~~~~~
+
+pybind11 is provided under a BSD-style license that can be found in the
+`LICENSE <https://github.com/pybind/pybind11/blob/master/LICENSE>`_
+file. By using, distributing, or contributing to this project, you agree
+to the terms and conditions of this license.
+
+.. |Latest Documentation Status| image:: https://readthedocs.org/projects/pybind11/badge?version=latest
+   :target: http://pybind11.readthedocs.org/en/latest
+.. |Stable Documentation Status| image:: https://img.shields.io/badge/docs-stable-blue.svg
+   :target: http://pybind11.readthedocs.org/en/stable
+.. |Gitter chat| image:: https://img.shields.io/gitter/room/gitterHQ/gitter.svg
+   :target: https://gitter.im/pybind/Lobby
+.. |CI| image:: https://github.com/pybind/pybind11/workflows/CI/badge.svg
+   :target: https://github.com/pybind/pybind11/actions
+.. |Build status| image:: https://ci.appveyor.com/api/projects/status/riaj54pn4h08xy40?svg=true
+   :target: https://ci.appveyor.com/project/wjakob/pybind11
+.. |PyPI package| image:: https://img.shields.io/pypi/v/pybind11.svg
+   :target: https://pypi.org/project/pybind11/
+.. |Conda-forge| image:: https://img.shields.io/conda/vn/conda-forge/pybind11.svg
+   :target: https://github.com/conda-forge/pybind11-feedstock
+.. |Repology| image:: https://repology.org/badge/latest-versions/python:pybind11.svg
+   :target: https://repology.org/project/python:pybind11/versions
+.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/pybind11.svg
+   :target: https://pypi.org/project/pybind11/
+.. |GitHub Discussions| image:: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github
+   :target: https://github.com/pybind/pybind11/discussions
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/Doxyfile b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/Doxyfile
new file mode 100644
index 000000000..09138db36
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/Doxyfile
@@ -0,0 +1,21 @@
+PROJECT_NAME           = pybind11
+INPUT                  = ../include/pybind11/
+RECURSIVE              = YES
+
+GENERATE_HTML          = NO
+GENERATE_LATEX         = NO
+GENERATE_XML           = YES
+XML_OUTPUT             = .build/doxygenxml
+XML_PROGRAMLISTING     = YES
+
+MACRO_EXPANSION        = YES
+EXPAND_ONLY_PREDEF     = YES
+EXPAND_AS_DEFINED      = PYBIND11_RUNTIME_EXCEPTION
+
+ALIASES                = "rst=\verbatim embed:rst"
+ALIASES               += "endrst=\endverbatim"
+
+QUIET                  = YES
+WARNINGS               = YES
+WARN_IF_UNDOCUMENTED   = NO
+PREDEFINED             = PYBIND11_NOINLINE
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/_static/css/custom.css b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/_static/css/custom.css
new file mode 100644
index 000000000..7a49a6ac4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/_static/css/custom.css
@@ -0,0 +1,3 @@
+.highlight .go {
+  color: #707070;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/chrono.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/chrono.rst
new file mode 100644
index 000000000..fbd46057a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/chrono.rst
@@ -0,0 +1,81 @@
+Chrono
+======
+
+When including the additional header file :file:`pybind11/chrono.h` conversions
+from C++11 chrono datatypes to python datetime objects are automatically enabled.
+This header also enables conversions of python floats (often from sources such
+as ``time.monotonic()``, ``time.perf_counter()`` and ``time.process_time()``)
+into durations.
+
+An overview of clocks in C++11
+------------------------------
+
+A point of confusion when using these conversions is the differences between
+clocks provided in C++11. There are three clock types defined by the C++11
+standard and users can define their own if needed. Each of these clocks have
+different properties and when converting to and from python will give different
+results.
+
+The first clock defined by the standard is ``std::chrono::system_clock``. This
+clock measures the current date and time. However, this clock changes with to
+updates to the operating system time. For example, if your time is synchronised
+with a time server this clock will change. This makes this clock a poor choice
+for timing purposes but good for measuring the wall time.
+
+The second clock defined in the standard is ``std::chrono::steady_clock``.
+This clock ticks at a steady rate and is never adjusted. This makes it excellent
+for timing purposes, however the value in this clock does not correspond to the
+current date and time. Often this clock will be the amount of time your system
+has been on, although it does not have to be. This clock will never be the same
+clock as the system clock as the system clock can change but steady clocks
+cannot.
+
+The third clock defined in the standard is ``std::chrono::high_resolution_clock``.
+This clock is the clock that has the highest resolution out of the clocks in the
+system. It is normally a typedef to either the system clock or the steady clock
+but can be its own independent clock. This is important as when using these
+conversions as the types you get in python for this clock might be different
+depending on the system.
+If it is a typedef of the system clock, python will get datetime objects, but if
+it is a different clock they will be timedelta objects.
+
+Provided conversions
+--------------------
+
+.. rubric:: C++ to Python
+
+- ``std::chrono::system_clock::time_point`` → ``datetime.datetime``
+    System clock times are converted to python datetime instances. They are
+    in the local timezone, but do not have any timezone information attached
+    to them (they are naive datetime objects).
+
+- ``std::chrono::duration`` → ``datetime.timedelta``
+    Durations are converted to timedeltas, any precision in the duration
+    greater than microseconds is lost by rounding towards zero.
+
+- ``std::chrono::[other_clocks]::time_point`` → ``datetime.timedelta``
+    Any clock time that is not the system clock is converted to a time delta.
+    This timedelta measures the time from the clocks epoch to now.
+
+.. rubric:: Python to C++
+
+- ``datetime.datetime`` or ``datetime.date`` or ``datetime.time`` → ``std::chrono::system_clock::time_point``
+    Date/time objects are converted into system clock timepoints. Any
+    timezone information is ignored and the type is treated as a naive
+    object.
+
+- ``datetime.timedelta`` → ``std::chrono::duration``
+    Time delta are converted into durations with microsecond precision.
+
+- ``datetime.timedelta`` → ``std::chrono::[other_clocks]::time_point``
+    Time deltas that are converted into clock timepoints are treated as
+    the amount of time from the start of the clocks epoch.
+
+- ``float`` → ``std::chrono::duration``
+    Floats that are passed to C++ as durations be interpreted as a number of
+    seconds. These will be converted to the duration using ``duration_cast``
+    from the float.
+
+- ``float`` → ``std::chrono::[other_clocks]::time_point``
+    Floats that are passed to C++ as time points will be interpreted as the
+    number of seconds from the start of the clocks epoch.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/custom.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/custom.rst
new file mode 100644
index 000000000..1df4d3e14
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/custom.rst
@@ -0,0 +1,93 @@
+Custom type casters
+===================
+
+In very rare cases, applications may require custom type casters that cannot be
+expressed using the abstractions provided by pybind11, thus requiring raw
+Python C API calls. This is fairly advanced usage and should only be pursued by
+experts who are familiar with the intricacies of Python reference counting.
+
+The following snippets demonstrate how this works for a very simple ``inty``
+type that that should be convertible from Python types that provide a
+``__int__(self)`` method.
+
+.. code-block:: cpp
+
+    struct inty { long long_value; };
+
+    void print(inty s) {
+        std::cout << s.long_value << std::endl;
+    }
+
+The following Python snippet demonstrates the intended usage from the Python side:
+
+.. code-block:: python
+
+    class A:
+        def __int__(self):
+            return 123
+
+
+    from example import print
+
+    print(A())
+
+To register the necessary conversion routines, it is necessary to add an
+instantiation of the ``pybind11::detail::type_caster<T>`` template.
+Although this is an implementation detail, adding an instantiation of this
+type is explicitly allowed.
+
+.. code-block:: cpp
+
+    namespace pybind11 { namespace detail {
+        template <> struct type_caster<inty> {
+        public:
+            /**
+             * This macro establishes the name 'inty' in
+             * function signatures and declares a local variable
+             * 'value' of type inty
+             */
+            PYBIND11_TYPE_CASTER(inty, const_name("inty"));
+
+            /**
+             * Conversion part 1 (Python->C++): convert a PyObject into a inty
+             * instance or return false upon failure. The second argument
+             * indicates whether implicit conversions should be applied.
+             */
+            bool load(handle src, bool) {
+                /* Extract PyObject from handle */
+                PyObject *source = src.ptr();
+                /* Try converting into a Python integer value */
+                PyObject *tmp = PyNumber_Long(source);
+                if (!tmp)
+                    return false;
+                /* Now try to convert into a C++ int */
+                value.long_value = PyLong_AsLong(tmp);
+                Py_DECREF(tmp);
+                /* Ensure return code was OK (to avoid out-of-range errors etc) */
+                return !(value.long_value == -1 && !PyErr_Occurred());
+            }
+
+            /**
+             * Conversion part 2 (C++ -> Python): convert an inty instance into
+             * a Python object. The second and third arguments are used to
+             * indicate the return value policy and parent object (for
+             * ``return_value_policy::reference_internal``) and are generally
+             * ignored by implicit casters.
+             */
+            static handle cast(inty src, return_value_policy /* policy */, handle /* parent */) {
+                return PyLong_FromLong(src.long_value);
+            }
+        };
+    }} // namespace pybind11::detail
+
+.. note::
+
+    A ``type_caster<T>`` defined with ``PYBIND11_TYPE_CASTER(T, ...)`` requires
+    that ``T`` is default-constructible (``value`` is first default constructed
+    and then ``load()`` assigns to it).
+
+.. warning::
+
+    When using custom type casters, it's important to declare them consistently
+    in every compilation unit of the Python extension module. Otherwise,
+    undefined behavior can ensue.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/eigen.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/eigen.rst
new file mode 100644
index 000000000..a5c11a3f1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/eigen.rst
@@ -0,0 +1,310 @@
+Eigen
+#####
+
+`Eigen <http://eigen.tuxfamily.org>`_ is C++ header-based library for dense and
+sparse linear algebra. Due to its popularity and widespread adoption, pybind11
+provides transparent conversion and limited mapping support between Eigen and
+Scientific Python linear algebra data types.
+
+To enable the built-in Eigen support you must include the optional header file
+:file:`pybind11/eigen.h`.
+
+Pass-by-value
+=============
+
+When binding a function with ordinary Eigen dense object arguments (for
+example, ``Eigen::MatrixXd``), pybind11 will accept any input value that is
+already (or convertible to) a ``numpy.ndarray`` with dimensions compatible with
+the Eigen type, copy its values into a temporary Eigen variable of the
+appropriate type, then call the function with this temporary variable.
+
+Sparse matrices are similarly copied to or from
+``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` objects.
+
+Pass-by-reference
+=================
+
+One major limitation of the above is that every data conversion implicitly
+involves a copy, which can be both expensive (for large matrices) and disallows
+binding functions that change their (Matrix) arguments.  Pybind11 allows you to
+work around this by using Eigen's ``Eigen::Ref<MatrixType>`` class much as you
+would when writing a function taking a generic type in Eigen itself (subject to
+some limitations discussed below).
+
+When calling a bound function accepting a ``Eigen::Ref<const MatrixType>``
+type, pybind11 will attempt to avoid copying by using an ``Eigen::Map`` object
+that maps into the source ``numpy.ndarray`` data: this requires both that the
+data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is
+``double``); and that the storage is layout compatible.  The latter limitation
+is discussed in detail in the section below, and requires careful
+consideration: by default, numpy matrices and Eigen matrices are *not* storage
+compatible.
+
+If the numpy matrix cannot be used as is (either because its types differ, e.g.
+passing an array of integers to an Eigen parameter requiring doubles, or
+because the storage is incompatible), pybind11 makes a temporary copy and
+passes the copy instead.
+
+When a bound function parameter is instead ``Eigen::Ref<MatrixType>`` (note the
+lack of ``const``), pybind11 will only allow the function to be called if it
+can be mapped *and* if the numpy array is writeable (that is
+``a.flags.writeable`` is true).  Any access (including modification) made to
+the passed variable will be transparently carried out directly on the
+``numpy.ndarray``.
+
+This means you can write code such as the following and have it work as
+expected:
+
+.. code-block:: cpp
+
+    void scale_by_2(Eigen::Ref<Eigen::VectorXd> v) {
+        v *= 2;
+    }
+
+Note, however, that you will likely run into limitations due to numpy and
+Eigen's difference default storage order for data; see the below section on
+:ref:`storage_orders` for details on how to bind code that won't run into such
+limitations.
+
+.. note::
+
+    Passing by reference is not supported for sparse types.
+
+Returning values to Python
+==========================
+
+When returning an ordinary dense Eigen matrix type to numpy (e.g.
+``Eigen::MatrixXd`` or ``Eigen::RowVectorXf``) pybind11 keeps the matrix and
+returns a numpy array that directly references the Eigen matrix: no copy of the
+data is performed.  The numpy array will have ``array.flags.owndata`` set to
+``False`` to indicate that it does not own the data, and the lifetime of the
+stored Eigen matrix will be tied to the returned ``array``.
+
+If you bind a function with a non-reference, ``const`` return type (e.g.
+``const Eigen::MatrixXd``), the same thing happens except that pybind11 also
+sets the numpy array's ``writeable`` flag to false.
+
+If you return an lvalue reference or pointer, the usual pybind11 rules apply,
+as dictated by the binding function's return value policy (see the
+documentation on :ref:`return_value_policies` for full details).  That means,
+without an explicit return value policy, lvalue references will be copied and
+pointers will be managed by pybind11.  In order to avoid copying, you should
+explicitly specify an appropriate return value policy, as in the following
+example:
+
+.. code-block:: cpp
+
+    class MyClass {
+        Eigen::MatrixXd big_mat = Eigen::MatrixXd::Zero(10000, 10000);
+    public:
+        Eigen::MatrixXd &getMatrix() { return big_mat; }
+        const Eigen::MatrixXd &viewMatrix() { return big_mat; }
+    };
+
+    // Later, in binding code:
+    py::class_<MyClass>(m, "MyClass")
+        .def(py::init<>())
+        .def("copy_matrix", &MyClass::getMatrix) // Makes a copy!
+        .def("get_matrix", &MyClass::getMatrix, py::return_value_policy::reference_internal)
+        .def("view_matrix", &MyClass::viewMatrix, py::return_value_policy::reference_internal)
+        ;
+
+.. code-block:: python
+
+    a = MyClass()
+    m = a.get_matrix()  # flags.writeable = True,  flags.owndata = False
+    v = a.view_matrix()  # flags.writeable = False, flags.owndata = False
+    c = a.copy_matrix()  # flags.writeable = True,  flags.owndata = True
+    # m[5,6] and v[5,6] refer to the same element, c[5,6] does not.
+
+Note in this example that ``py::return_value_policy::reference_internal`` is
+used to tie the life of the MyClass object to the life of the returned arrays.
+
+You may also return an ``Eigen::Ref``, ``Eigen::Map`` or other map-like Eigen
+object (for example, the return value of ``matrix.block()`` and related
+methods) that map into a dense Eigen type.  When doing so, the default
+behaviour of pybind11 is to simply reference the returned data: you must take
+care to ensure that this data remains valid!  You may ask pybind11 to
+explicitly *copy* such a return value by using the
+``py::return_value_policy::copy`` policy when binding the function.  You may
+also use ``py::return_value_policy::reference_internal`` or a
+``py::keep_alive`` to ensure the data stays valid as long as the returned numpy
+array does.
+
+When returning such a reference of map, pybind11 additionally respects the
+readonly-status of the returned value, marking the numpy array as non-writeable
+if the reference or map was itself read-only.
+
+.. note::
+
+    Sparse types are always copied when returned.
+
+.. _storage_orders:
+
+Storage orders
+==============
+
+Passing arguments via ``Eigen::Ref`` has some limitations that you must be
+aware of in order to effectively pass matrices by reference.  First and
+foremost is that the default ``Eigen::Ref<MatrixType>`` class requires
+contiguous storage along columns (for column-major types, the default in Eigen)
+or rows if ``MatrixType`` is specifically an ``Eigen::RowMajor`` storage type.
+The former, Eigen's default, is incompatible with ``numpy``'s default row-major
+storage, and so you will not be able to pass numpy arrays to Eigen by reference
+without making one of two changes.
+
+(Note that this does not apply to vectors (or column or row matrices): for such
+types the "row-major" and "column-major" distinction is meaningless).
+
+The first approach is to change the use of ``Eigen::Ref<MatrixType>`` to the
+more general ``Eigen::Ref<MatrixType, 0, Eigen::Stride<Eigen::Dynamic,
+Eigen::Dynamic>>`` (or similar type with a fully dynamic stride type in the
+third template argument).  Since this is a rather cumbersome type, pybind11
+provides a ``py::EigenDRef<MatrixType>`` type alias for your convenience (along
+with EigenDMap for the equivalent Map, and EigenDStride for just the stride
+type).
+
+This type allows Eigen to map into any arbitrary storage order.  This is not
+the default in Eigen for performance reasons: contiguous storage allows
+vectorization that cannot be done when storage is not known to be contiguous at
+compile time.  The default ``Eigen::Ref`` stride type allows non-contiguous
+storage along the outer dimension (that is, the rows of a column-major matrix
+or columns of a row-major matrix), but not along the inner dimension.
+
+This type, however, has the added benefit of also being able to map numpy array
+slices.  For example, the following (contrived) example uses Eigen with a numpy
+slice to multiply by 2 all coefficients that are both on even rows (0, 2, 4,
+...) and in columns 2, 5, or 8:
+
+.. code-block:: cpp
+
+    m.def("scale", [](py::EigenDRef<Eigen::MatrixXd> m, double c) { m *= c; });
+
+.. code-block:: python
+
+    # a = np.array(...)
+    scale_by_2(myarray[0::2, 2:9:3])
+
+The second approach to avoid copying is more intrusive: rearranging the
+underlying data types to not run into the non-contiguous storage problem in the
+first place.  In particular, that means using matrices with ``Eigen::RowMajor``
+storage, where appropriate, such as:
+
+.. code-block:: cpp
+
+    using RowMatrixXd = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+    // Use RowMatrixXd instead of MatrixXd
+
+Now bound functions accepting ``Eigen::Ref<RowMatrixXd>`` arguments will be
+callable with numpy's (default) arrays without involving a copying.
+
+You can, alternatively, change the storage order that numpy arrays use by
+adding the ``order='F'`` option when creating an array:
+
+.. code-block:: python
+
+    myarray = np.array(source, order="F")
+
+Such an object will be passable to a bound function accepting an
+``Eigen::Ref<MatrixXd>`` (or similar column-major Eigen type).
+
+One major caveat with this approach, however, is that it is not entirely as
+easy as simply flipping all Eigen or numpy usage from one to the other: some
+operations may alter the storage order of a numpy array.  For example, ``a2 =
+array.transpose()`` results in ``a2`` being a view of ``array`` that references
+the same data, but in the opposite storage order!
+
+While this approach allows fully optimized vectorized calculations in Eigen, it
+cannot be used with array slices, unlike the first approach.
+
+When *returning* a matrix to Python (either a regular matrix, a reference via
+``Eigen::Ref<>``, or a map/block into a matrix), no special storage
+consideration is required: the created numpy array will have the required
+stride that allows numpy to properly interpret the array, whatever its storage
+order.
+
+Failing rather than copying
+===========================
+
+The default behaviour when binding ``Eigen::Ref<const MatrixType>`` Eigen
+references is to copy matrix values when passed a numpy array that does not
+conform to the element type of ``MatrixType`` or does not have a compatible
+stride layout.  If you want to explicitly avoid copying in such a case, you
+should bind arguments using the ``py::arg().noconvert()`` annotation (as
+described in the :ref:`nonconverting_arguments` documentation).
+
+The following example shows an example of arguments that don't allow data
+copying to take place:
+
+.. code-block:: cpp
+
+    // The method and function to be bound:
+    class MyClass {
+        // ...
+        double some_method(const Eigen::Ref<const MatrixXd> &matrix) { /* ... */ }
+    };
+    float some_function(const Eigen::Ref<const MatrixXf> &big,
+                        const Eigen::Ref<const MatrixXf> &small) {
+        // ...
+    }
+
+    // The associated binding code:
+    using namespace pybind11::literals; // for "arg"_a
+    py::class_<MyClass>(m, "MyClass")
+        // ... other class definitions
+        .def("some_method", &MyClass::some_method, py::arg().noconvert());
+
+    m.def("some_function", &some_function,
+        "big"_a.noconvert(), // <- Don't allow copying for this arg
+        "small"_a            // <- This one can be copied if needed
+    );
+
+With the above binding code, attempting to call the the ``some_method(m)``
+method on a ``MyClass`` object, or attempting to call ``some_function(m, m2)``
+will raise a ``RuntimeError`` rather than making a temporary copy of the array.
+It will, however, allow the ``m2`` argument to be copied into a temporary if
+necessary.
+
+Note that explicitly specifying ``.noconvert()`` is not required for *mutable*
+Eigen references (e.g. ``Eigen::Ref<MatrixXd>`` without ``const`` on the
+``MatrixXd``): mutable references will never be called with a temporary copy.
+
+Vectors versus column/row matrices
+==================================
+
+Eigen and numpy have fundamentally different notions of a vector.  In Eigen, a
+vector is simply a matrix with the number of columns or rows set to 1 at
+compile time (for a column vector or row vector, respectively).  NumPy, in
+contrast, has comparable 2-dimensional 1xN and Nx1 arrays, but *also* has
+1-dimensional arrays of size N.
+
+When passing a 2-dimensional 1xN or Nx1 array to Eigen, the Eigen type must
+have matching dimensions: That is, you cannot pass a 2-dimensional Nx1 numpy
+array to an Eigen value expecting a row vector, or a 1xN numpy array as a
+column vector argument.
+
+On the other hand, pybind11 allows you to pass 1-dimensional arrays of length N
+as Eigen parameters.  If the Eigen type can hold a column vector of length N it
+will be passed as such a column vector.  If not, but the Eigen type constraints
+will accept a row vector, it will be passed as a row vector.  (The column
+vector takes precedence when both are supported, for example, when passing a
+1D numpy array to a MatrixXd argument).  Note that the type need not be
+explicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an
+Eigen ``Matrix<double, Dynamic, 5>``: you would end up with a 1x5 Eigen matrix.
+Passing the same to an ``Eigen::MatrixXd`` would result in a 5x1 Eigen matrix.
+
+When returning an Eigen vector to numpy, the conversion is ambiguous: a row
+vector of length 4 could be returned as either a 1D array of length 4, or as a
+2D array of size 1x4.  When encountering such a situation, pybind11 compromises
+by considering the returned Eigen type: if it is a compile-time vector--that
+is, the type has either the number of rows or columns set to 1 at compile
+time--pybind11 converts to a 1D numpy array when returning the value.  For
+instances that are a vector only at run-time (e.g. ``MatrixXd``,
+``Matrix<float, Dynamic, 4>``), pybind11 returns the vector as a 2D array to
+numpy.  If this isn't want you want, you can use ``array.reshape(...)`` to get
+a view of the same data in the desired dimensions.
+
+.. seealso::
+
+    The file :file:`tests/test_eigen.cpp` contains a complete example that
+    shows how to pass Eigen sparse and dense data types in more detail.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/functional.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/functional.rst
new file mode 100644
index 000000000..d9b460575
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/functional.rst
@@ -0,0 +1,109 @@
+Functional
+##########
+
+The following features must be enabled by including :file:`pybind11/functional.h`.
+
+
+Callbacks and passing anonymous functions
+=========================================
+
+The C++11 standard brought lambda functions and the generic polymorphic
+function wrapper ``std::function<>`` to the C++ programming language, which
+enable powerful new ways of working with functions. Lambda functions come in
+two flavors: stateless lambda function resemble classic function pointers that
+link to an anonymous piece of code, while stateful lambda functions
+additionally depend on captured variables that are stored in an anonymous
+*lambda closure object*.
+
+Here is a simple example of a C++ function that takes an arbitrary function
+(stateful or stateless) with signature ``int -> int`` as an argument and runs
+it with the value 10.
+
+.. code-block:: cpp
+
+    int func_arg(const std::function<int(int)> &f) {
+        return f(10);
+    }
+
+The example below is more involved: it takes a function of signature ``int -> int``
+and returns another function of the same kind. The return value is a stateful
+lambda function, which stores the value ``f`` in the capture object and adds 1 to
+its return value upon execution.
+
+.. code-block:: cpp
+
+    std::function<int(int)> func_ret(const std::function<int(int)> &f) {
+        return [f](int i) {
+            return f(i) + 1;
+        };
+    }
+
+This example demonstrates using python named parameters in C++ callbacks which
+requires using ``py::cpp_function`` as a wrapper. Usage is similar to defining
+methods of classes:
+
+.. code-block:: cpp
+
+    py::cpp_function func_cpp() {
+        return py::cpp_function([](int i) { return i+1; },
+           py::arg("number"));
+    }
+
+After including the extra header file :file:`pybind11/functional.h`, it is almost
+trivial to generate binding code for all of these functions.
+
+.. code-block:: cpp
+
+    #include <pybind11/functional.h>
+
+    PYBIND11_MODULE(example, m) {
+        m.def("func_arg", &func_arg);
+        m.def("func_ret", &func_ret);
+        m.def("func_cpp", &func_cpp);
+    }
+
+The following interactive session shows how to call them from Python.
+
+.. code-block:: pycon
+
+    $ python
+    >>> import example
+    >>> def square(i):
+    ...     return i * i
+    ...
+    >>> example.func_arg(square)
+    100L
+    >>> square_plus_1 = example.func_ret(square)
+    >>> square_plus_1(4)
+    17L
+    >>> plus_1 = func_cpp()
+    >>> plus_1(number=43)
+    44L
+
+.. warning::
+
+    Keep in mind that passing a function from C++ to Python (or vice versa)
+    will instantiate a piece of wrapper code that translates function
+    invocations between the two languages. Naturally, this translation
+    increases the computational cost of each function call somewhat. A
+    problematic situation can arise when a function is copied back and forth
+    between Python and C++ many times in a row, in which case the underlying
+    wrappers will accumulate correspondingly. The resulting long sequence of
+    C++ -> Python -> C++ -> ... roundtrips can significantly decrease
+    performance.
+
+    There is one exception: pybind11 detects case where a stateless function
+    (i.e. a function pointer or a lambda function without captured variables)
+    is passed as an argument to another C++ function exposed in Python. In this
+    case, there is no overhead. Pybind11 will extract the underlying C++
+    function pointer from the wrapped function to sidestep a potential C++ ->
+    Python -> C++ roundtrip. This is demonstrated in :file:`tests/test_callbacks.cpp`.
+
+.. note::
+
+    This functionality is very useful when generating bindings for callbacks in
+    C++ libraries (e.g. GUI libraries, asynchronous networking libraries, etc.).
+
+    The file :file:`tests/test_callbacks.cpp` contains a complete example
+    that demonstrates how to work with callbacks and anonymous functions in
+    more detail.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/index.rst
new file mode 100644
index 000000000..3ce9ea028
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/index.rst
@@ -0,0 +1,43 @@
+.. _type-conversions:
+
+Type conversions
+################
+
+Apart from enabling cross-language function calls, a fundamental problem
+that a binding tool like pybind11 must address is to provide access to
+native Python types in C++ and vice versa. There are three fundamentally
+different ways to do this—which approach is preferable for a particular type
+depends on the situation at hand.
+
+1. Use a native C++ type everywhere. In this case, the type must be wrapped
+   using pybind11-generated bindings so that Python can interact with it.
+
+2. Use a native Python type everywhere. It will need to be wrapped so that
+   C++ functions can interact with it.
+
+3. Use a native C++ type on the C++ side and a native Python type on the
+   Python side. pybind11 refers to this as a *type conversion*.
+
+   Type conversions are the most "natural" option in the sense that native
+   (non-wrapped) types are used everywhere. The main downside is that a copy
+   of the data must be made on every Python ↔ C++ transition: this is
+   needed since the C++ and Python versions of the same type generally won't
+   have the same memory layout.
+
+   pybind11 can perform many kinds of conversions automatically. An overview
+   is provided in the table ":ref:`conversion_table`".
+
+The following subsections discuss the differences between these options in more
+detail. The main focus in this section is on type conversions, which represent
+the last case of the above list.
+
+.. toctree::
+   :maxdepth: 1
+
+   overview
+   strings
+   stl
+   functional
+   chrono
+   eigen
+   custom
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/overview.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/overview.rst
new file mode 100644
index 000000000..011bd4c7a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/overview.rst
@@ -0,0 +1,170 @@
+Overview
+########
+
+.. rubric:: 1. Native type in C++, wrapper in Python
+
+Exposing a custom C++ type using :class:`py::class_` was covered in detail
+in the :doc:`/classes` section. There, the underlying data structure is
+always the original C++ class while the :class:`py::class_` wrapper provides
+a Python interface. Internally, when an object like this is sent from C++ to
+Python, pybind11 will just add the outer wrapper layer over the native C++
+object. Getting it back from Python is just a matter of peeling off the
+wrapper.
+
+.. rubric:: 2. Wrapper in C++, native type in Python
+
+This is the exact opposite situation. Now, we have a type which is native to
+Python, like a ``tuple`` or a ``list``. One way to get this data into C++ is
+with the :class:`py::object` family of wrappers. These are explained in more
+detail in the :doc:`/advanced/pycpp/object` section. We'll just give a quick
+example here:
+
+.. code-block:: cpp
+
+    void print_list(py::list my_list) {
+        for (auto item : my_list)
+            std::cout << item << " ";
+    }
+
+.. code-block:: pycon
+
+    >>> print_list([1, 2, 3])
+    1 2 3
+
+The Python ``list`` is not converted in any way -- it's just wrapped in a C++
+:class:`py::list` class. At its core it's still a Python object. Copying a
+:class:`py::list` will do the usual reference-counting like in Python.
+Returning the object to Python will just remove the thin wrapper.
+
+.. rubric:: 3. Converting between native C++ and Python types
+
+In the previous two cases we had a native type in one language and a wrapper in
+the other. Now, we have native types on both sides and we convert between them.
+
+.. code-block:: cpp
+
+    void print_vector(const std::vector<int> &v) {
+        for (auto item : v)
+            std::cout << item << "\n";
+    }
+
+.. code-block:: pycon
+
+    >>> print_vector([1, 2, 3])
+    1 2 3
+
+In this case, pybind11 will construct a new ``std::vector<int>`` and copy each
+element from the Python ``list``. The newly constructed object will be passed
+to ``print_vector``. The same thing happens in the other direction: a new
+``list`` is made to match the value returned from C++.
+
+Lots of these conversions are supported out of the box, as shown in the table
+below. They are very convenient, but keep in mind that these conversions are
+fundamentally based on copying data. This is perfectly fine for small immutable
+types but it may become quite expensive for large data structures. This can be
+avoided by overriding the automatic conversion with a custom wrapper (i.e. the
+above-mentioned approach 1). This requires some manual effort and more details
+are available in the :ref:`opaque` section.
+
+.. _conversion_table:
+
+List of all builtin conversions
+-------------------------------
+
+The following basic data types are supported out of the box (some may require
+an additional extension header to be included). To pass other data structures
+as arguments and return values, refer to the section on binding :ref:`classes`.
+
++------------------------------------+---------------------------+-----------------------------------+
+|  Data type                         |  Description              | Header file                       |
++====================================+===========================+===================================+
+| ``int8_t``, ``uint8_t``            | 8-bit integers            | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``int16_t``, ``uint16_t``          | 16-bit integers           | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``int32_t``, ``uint32_t``          | 32-bit integers           | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``int64_t``, ``uint64_t``          | 64-bit integers           | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``ssize_t``, ``size_t``            | Platform-dependent size   | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``float``, ``double``              | Floating point types      | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``bool``                           | Two-state Boolean type    | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``char``                           | Character literal         | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``char16_t``                       | UTF-16 character literal  | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``char32_t``                       | UTF-32 character literal  | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``wchar_t``                        | Wide character literal    | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``const char *``                   | UTF-8 string literal      | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``const char16_t *``               | UTF-16 string literal     | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``const char32_t *``               | UTF-32 string literal     | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``const wchar_t *``                | Wide string literal       | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::string``                    | STL dynamic UTF-8 string  | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::u16string``                 | STL dynamic UTF-16 string | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::u32string``                 | STL dynamic UTF-32 string | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::wstring``                   | STL dynamic wide string   | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::string_view``,              | STL C++17 string views    | :file:`pybind11/pybind11.h`       |
+| ``std::u16string_view``, etc.      |                           |                                   |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::pair<T1, T2>``              | Pair of two custom types  | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::tuple<...>``                | Arbitrary tuple of types  | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::reference_wrapper<...>``    | Reference type wrapper    | :file:`pybind11/pybind11.h`       |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::complex<T>``                | Complex numbers           | :file:`pybind11/complex.h`        |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::array<T, Size>``            | STL static array          | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::vector<T>``                 | STL dynamic array         | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::deque<T>``                  | STL double-ended queue    | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::valarray<T>``               | STL value array           | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::list<T>``                   | STL linked list           | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::map<T1, T2>``               | STL ordered map           | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::unordered_map<T1, T2>``     | STL unordered map         | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::set<T>``                    | STL ordered set           | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::unordered_set<T>``          | STL unordered set         | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::optional<T>``               | STL optional type (C++17) | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::experimental::optional<T>`` | STL optional type (exp.)  | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::variant<...>``              | Type-safe union (C++17)   | :file:`pybind11/stl.h`            |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::filesystem::path<T>``       | STL path (C++17) [#]_     | :file:`pybind11/stl/filesystem.h` |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::function<...>``             | STL polymorphic function  | :file:`pybind11/functional.h`     |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::chrono::duration<...>``     | STL time duration         | :file:`pybind11/chrono.h`         |
++------------------------------------+---------------------------+-----------------------------------+
+| ``std::chrono::time_point<...>``   | STL date/time             | :file:`pybind11/chrono.h`         |
++------------------------------------+---------------------------+-----------------------------------+
+| ``Eigen::Matrix<...>``             | Eigen: dense matrix       | :file:`pybind11/eigen.h`          |
++------------------------------------+---------------------------+-----------------------------------+
+| ``Eigen::Map<...>``                | Eigen: mapped memory      | :file:`pybind11/eigen.h`          |
++------------------------------------+---------------------------+-----------------------------------+
+| ``Eigen::SparseMatrix<...>``       | Eigen: sparse matrix      | :file:`pybind11/eigen.h`          |
++------------------------------------+---------------------------+-----------------------------------+
+
+.. [#] ``std::filesystem::path`` is converted to ``pathlib.Path`` and
+   ``os.PathLike`` is converted to ``std::filesystem::path``.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/stl.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/stl.rst
new file mode 100644
index 000000000..109763f7a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/stl.rst
@@ -0,0 +1,249 @@
+STL containers
+##############
+
+Automatic conversion
+====================
+
+When including the additional header file :file:`pybind11/stl.h`, conversions
+between ``std::vector<>``/``std::deque<>``/``std::list<>``/``std::array<>``/``std::valarray<>``,
+``std::set<>``/``std::unordered_set<>``, and
+``std::map<>``/``std::unordered_map<>`` and the Python ``list``, ``set`` and
+``dict`` data structures are automatically enabled. The types ``std::pair<>``
+and ``std::tuple<>`` are already supported out of the box with just the core
+:file:`pybind11/pybind11.h` header.
+
+The major downside of these implicit conversions is that containers must be
+converted (i.e. copied) on every Python->C++ and C++->Python transition, which
+can have implications on the program semantics and performance. Please read the
+next sections for more details and alternative approaches that avoid this.
+
+.. note::
+
+    Arbitrary nesting of any of these types is possible.
+
+.. seealso::
+
+    The file :file:`tests/test_stl.cpp` contains a complete
+    example that demonstrates how to pass STL data types in more detail.
+
+.. _cpp17_container_casters:
+
+C++17 library containers
+========================
+
+The :file:`pybind11/stl.h` header also includes support for ``std::optional<>``
+and ``std::variant<>``. These require a C++17 compiler and standard library.
+In C++14 mode, ``std::experimental::optional<>`` is supported if available.
+
+Various versions of these containers also exist for C++11 (e.g. in Boost).
+pybind11 provides an easy way to specialize the ``type_caster`` for such
+types:
+
+.. code-block:: cpp
+
+    // `boost::optional` as an example -- can be any `std::optional`-like container
+    namespace pybind11 { namespace detail {
+        template <typename T>
+        struct type_caster<boost::optional<T>> : optional_caster<boost::optional<T>> {};
+    }}
+
+The above should be placed in a header file and included in all translation units
+where automatic conversion is needed. Similarly, a specialization can be provided
+for custom variant types:
+
+.. code-block:: cpp
+
+    // `boost::variant` as an example -- can be any `std::variant`-like container
+    namespace pybind11 { namespace detail {
+        template <typename... Ts>
+        struct type_caster<boost::variant<Ts...>> : variant_caster<boost::variant<Ts...>> {};
+
+        // Specifies the function used to visit the variant -- `apply_visitor` instead of `visit`
+        template <>
+        struct visit_helper<boost::variant> {
+            template <typename... Args>
+            static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) {
+                return boost::apply_visitor(args...);
+            }
+        };
+    }} // namespace pybind11::detail
+
+The ``visit_helper`` specialization is not required if your ``name::variant`` provides
+a ``name::visit()`` function. For any other function name, the specialization must be
+included to tell pybind11 how to visit the variant.
+
+.. warning::
+
+    When converting a ``variant`` type, pybind11 follows the same rules as when
+    determining which function overload to call (:ref:`overload_resolution`), and
+    so the same caveats hold. In particular, the order in which the ``variant``'s
+    alternatives are listed is important, since pybind11 will try conversions in
+    this order. This means that, for example, when converting ``variant<int, bool>``,
+    the ``bool`` variant will never be selected, as any Python ``bool`` is already
+    an ``int`` and is convertible to a C++ ``int``. Changing the order of alternatives
+    (and using ``variant<bool, int>``, in this example) provides a solution.
+
+.. note::
+
+    pybind11 only supports the modern implementation of ``boost::variant``
+    which makes use of variadic templates. This requires Boost 1.56 or newer.
+
+.. _opaque:
+
+Making opaque types
+===================
+
+pybind11 heavily relies on a template matching mechanism to convert parameters
+and return values that are constructed from STL data types such as vectors,
+linked lists, hash tables, etc. This even works in a recursive manner, for
+instance to deal with lists of hash maps of pairs of elementary and custom
+types, etc.
+
+However, a fundamental limitation of this approach is that internal conversions
+between Python and C++ types involve a copy operation that prevents
+pass-by-reference semantics. What does this mean?
+
+Suppose we bind the following function
+
+.. code-block:: cpp
+
+    void append_1(std::vector<int> &v) {
+       v.push_back(1);
+    }
+
+and call it from Python, the following happens:
+
+.. code-block:: pycon
+
+   >>> v = [5, 6]
+   >>> append_1(v)
+   >>> print(v)
+   [5, 6]
+
+As you can see, when passing STL data structures by reference, modifications
+are not propagated back the Python side. A similar situation arises when
+exposing STL data structures using the ``def_readwrite`` or ``def_readonly``
+functions:
+
+.. code-block:: cpp
+
+    /* ... definition ... */
+
+    class MyClass {
+        std::vector<int> contents;
+    };
+
+    /* ... binding code ... */
+
+    py::class_<MyClass>(m, "MyClass")
+        .def(py::init<>())
+        .def_readwrite("contents", &MyClass::contents);
+
+In this case, properties can be read and written in their entirety. However, an
+``append`` operation involving such a list type has no effect:
+
+.. code-block:: pycon
+
+   >>> m = MyClass()
+   >>> m.contents = [5, 6]
+   >>> print(m.contents)
+   [5, 6]
+   >>> m.contents.append(7)
+   >>> print(m.contents)
+   [5, 6]
+
+Finally, the involved copy operations can be costly when dealing with very
+large lists. To deal with all of the above situations, pybind11 provides a
+macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based
+conversion machinery of types, thus rendering them *opaque*. The contents of
+opaque objects are never inspected or extracted, hence they *can* be passed by
+reference. For instance, to turn ``std::vector<int>`` into an opaque type, add
+the declaration
+
+.. code-block:: cpp
+
+    PYBIND11_MAKE_OPAQUE(std::vector<int>);
+
+before any binding code (e.g. invocations to ``class_::def()``, etc.). This
+macro must be specified at the top level (and outside of any namespaces), since
+it adds a template instantiation of ``type_caster``. If your binding code consists of
+multiple compilation units, it must be present in every file (typically via a
+common header) preceding any usage of ``std::vector<int>``. Opaque types must
+also have a corresponding ``class_`` declaration to associate them with a name
+in Python, and to define a set of available operations, e.g.:
+
+.. code-block:: cpp
+
+    py::class_<std::vector<int>>(m, "IntVector")
+        .def(py::init<>())
+        .def("clear", &std::vector<int>::clear)
+        .def("pop_back", &std::vector<int>::pop_back)
+        .def("__len__", [](const std::vector<int> &v) { return v.size(); })
+        .def("__iter__", [](std::vector<int> &v) {
+           return py::make_iterator(v.begin(), v.end());
+        }, py::keep_alive<0, 1>()) /* Keep vector alive while iterator is used */
+        // ....
+
+.. seealso::
+
+    The file :file:`tests/test_opaque_types.cpp` contains a complete
+    example that demonstrates how to create and expose opaque types using
+    pybind11 in more detail.
+
+.. _stl_bind:
+
+Binding STL containers
+======================
+
+The ability to expose STL containers as native Python objects is a fairly
+common request, hence pybind11 also provides an optional header file named
+:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try
+to match the behavior of their native Python counterparts as much as possible.
+
+The following example showcases usage of :file:`pybind11/stl_bind.h`:
+
+.. code-block:: cpp
+
+    // Don't forget this
+    #include <pybind11/stl_bind.h>
+
+    PYBIND11_MAKE_OPAQUE(std::vector<int>);
+    PYBIND11_MAKE_OPAQUE(std::map<std::string, double>);
+
+    // ...
+
+    // later in binding code:
+    py::bind_vector<std::vector<int>>(m, "VectorInt");
+    py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");
+
+When binding STL containers pybind11 considers the types of the container's
+elements to decide whether the container should be confined to the local module
+(via the :ref:`module_local` feature).  If the container element types are
+anything other than already-bound custom types bound without
+``py::module_local()`` the container binding will have ``py::module_local()``
+applied.  This includes converting types such as numeric types, strings, Eigen
+types; and types that have not yet been bound at the time of the stl container
+binding.  This module-local binding is designed to avoid potential conflicts
+between module bindings (for example, from two separate modules each attempting
+to bind ``std::vector<int>`` as a python type).
+
+It is possible to override this behavior to force a definition to be either
+module-local or global.  To do so, you can pass the attributes
+``py::module_local()`` (to make the binding module-local) or
+``py::module_local(false)`` (to make the binding global) into the
+``py::bind_vector`` or ``py::bind_map`` arguments:
+
+.. code-block:: cpp
+
+    py::bind_vector<std::vector<int>>(m, "VectorInt", py::module_local(false));
+
+Note, however, that such a global binding would make it impossible to load this
+module at the same time as any other pybind module that also attempts to bind
+the same container type (``std::vector<int>`` in the above example).
+
+See :ref:`module_local` for more details on module-local bindings.
+
+.. seealso::
+
+    The file :file:`tests/test_stl_binders.cpp` shows how to use the
+    convenience STL container wrappers.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/strings.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/strings.rst
new file mode 100644
index 000000000..e246c5219
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/cast/strings.rst
@@ -0,0 +1,292 @@
+Strings, bytes and Unicode conversions
+######################################
+
+Passing Python strings to C++
+=============================
+
+When a Python ``str`` is passed from Python to a C++ function that accepts
+``std::string`` or ``char *`` as arguments, pybind11 will encode the Python
+string to UTF-8. All Python ``str`` can be encoded in UTF-8, so this operation
+does not fail.
+
+The C++ language is encoding agnostic. It is the responsibility of the
+programmer to track encodings. It's often easiest to simply `use UTF-8
+everywhere <http://utf8everywhere.org/>`_.
+
+.. code-block:: c++
+
+    m.def("utf8_test",
+        [](const std::string &s) {
+            cout << "utf-8 is icing on the cake.\n";
+            cout << s;
+        }
+    );
+    m.def("utf8_charptr",
+        [](const char *s) {
+            cout << "My favorite food is\n";
+            cout << s;
+        }
+    );
+
+.. code-block:: pycon
+
+    >>> utf8_test("🎂")
+    utf-8 is icing on the cake.
+    🎂
+
+    >>> utf8_charptr("🍕")
+    My favorite food is
+    🍕
+
+.. note::
+
+    Some terminal emulators do not support UTF-8 or emoji fonts and may not
+    display the example above correctly.
+
+The results are the same whether the C++ function accepts arguments by value or
+reference, and whether or not ``const`` is used.
+
+Passing bytes to C++
+--------------------
+
+A Python ``bytes`` object will be passed to C++ functions that accept
+``std::string`` or ``char*`` *without* conversion.  In order to make a function
+*only* accept ``bytes`` (and not ``str``), declare it as taking a ``py::bytes``
+argument.
+
+
+Returning C++ strings to Python
+===============================
+
+When a C++ function returns a ``std::string`` or ``char*`` to a Python caller,
+**pybind11 will assume that the string is valid UTF-8** and will decode it to a
+native Python ``str``, using the same API as Python uses to perform
+``bytes.decode('utf-8')``. If this implicit conversion fails, pybind11 will
+raise a ``UnicodeDecodeError``.
+
+.. code-block:: c++
+
+    m.def("std_string_return",
+        []() {
+            return std::string("This string needs to be UTF-8 encoded");
+        }
+    );
+
+.. code-block:: pycon
+
+    >>> isinstance(example.std_string_return(), str)
+    True
+
+
+Because UTF-8 is inclusive of pure ASCII, there is never any issue with
+returning a pure ASCII string to Python. If there is any possibility that the
+string is not pure ASCII, it is necessary to ensure the encoding is valid
+UTF-8.
+
+.. warning::
+
+    Implicit conversion assumes that a returned ``char *`` is null-terminated.
+    If there is no null terminator a buffer overrun will occur.
+
+Explicit conversions
+--------------------
+
+If some C++ code constructs a ``std::string`` that is not a UTF-8 string, one
+can perform a explicit conversion and return a ``py::str`` object. Explicit
+conversion has the same overhead as implicit conversion.
+
+.. code-block:: c++
+
+    // This uses the Python C API to convert Latin-1 to Unicode
+    m.def("str_output",
+        []() {
+            std::string s = "Send your r\xe9sum\xe9 to Alice in HR"; // Latin-1
+            py::str py_s = PyUnicode_DecodeLatin1(s.data(), s.length());
+            return py_s;
+        }
+    );
+
+.. code-block:: pycon
+
+    >>> str_output()
+    'Send your résumé to Alice in HR'
+
+The `Python C API
+<https://docs.python.org/3/c-api/unicode.html#built-in-codecs>`_ provides
+several built-in codecs.
+
+
+One could also use a third party encoding library such as libiconv to transcode
+to UTF-8.
+
+Return C++ strings without conversion
+-------------------------------------
+
+If the data in a C++ ``std::string`` does not represent text and should be
+returned to Python as ``bytes``, then one can return the data as a
+``py::bytes`` object.
+
+.. code-block:: c++
+
+    m.def("return_bytes",
+        []() {
+            std::string s("\xba\xd0\xba\xd0");  // Not valid UTF-8
+            return py::bytes(s);  // Return the data without transcoding
+        }
+    );
+
+.. code-block:: pycon
+
+    >>> example.return_bytes()
+    b'\xba\xd0\xba\xd0'
+
+
+Note the asymmetry: pybind11 will convert ``bytes`` to ``std::string`` without
+encoding, but cannot convert ``std::string`` back to ``bytes`` implicitly.
+
+.. code-block:: c++
+
+    m.def("asymmetry",
+        [](std::string s) {  // Accepts str or bytes from Python
+            return s;  // Looks harmless, but implicitly converts to str
+        }
+    );
+
+.. code-block:: pycon
+
+    >>> isinstance(example.asymmetry(b"have some bytes"), str)
+    True
+
+    >>> example.asymmetry(b"\xba\xd0\xba\xd0")  # invalid utf-8 as bytes
+    UnicodeDecodeError: 'utf-8' codec can't decode byte 0xba in position 0: invalid start byte
+
+
+Wide character strings
+======================
+
+When a Python ``str`` is passed to a C++ function expecting ``std::wstring``,
+``wchar_t*``, ``std::u16string`` or ``std::u32string``, the ``str`` will be
+encoded to UTF-16 or UTF-32 depending on how the C++ compiler implements each
+type, in the platform's native endianness. When strings of these types are
+returned, they are assumed to contain valid UTF-16 or UTF-32, and will be
+decoded to Python ``str``.
+
+.. code-block:: c++
+
+    #define UNICODE
+    #include <windows.h>
+
+    m.def("set_window_text",
+        [](HWND hwnd, std::wstring s) {
+            // Call SetWindowText with null-terminated UTF-16 string
+            ::SetWindowText(hwnd, s.c_str());
+        }
+    );
+    m.def("get_window_text",
+        [](HWND hwnd) {
+            const int buffer_size = ::GetWindowTextLength(hwnd) + 1;
+            auto buffer = std::make_unique< wchar_t[] >(buffer_size);
+
+            ::GetWindowText(hwnd, buffer.data(), buffer_size);
+
+            std::wstring text(buffer.get());
+
+            // wstring will be converted to Python str
+            return text;
+        }
+    );
+
+Strings in multibyte encodings such as Shift-JIS must transcoded to a
+UTF-8/16/32 before being returned to Python.
+
+
+Character literals
+==================
+
+C++ functions that accept character literals as input will receive the first
+character of a Python ``str`` as their input. If the string is longer than one
+Unicode character, trailing characters will be ignored.
+
+When a character literal is returned from C++ (such as a ``char`` or a
+``wchar_t``), it will be converted to a ``str`` that represents the single
+character.
+
+.. code-block:: c++
+
+    m.def("pass_char", [](char c) { return c; });
+    m.def("pass_wchar", [](wchar_t w) { return w; });
+
+.. code-block:: pycon
+
+    >>> example.pass_char("A")
+    'A'
+
+While C++ will cast integers to character types (``char c = 0x65;``), pybind11
+does not convert Python integers to characters implicitly. The Python function
+``chr()`` can be used to convert integers to characters.
+
+.. code-block:: pycon
+
+    >>> example.pass_char(0x65)
+    TypeError
+
+    >>> example.pass_char(chr(0x65))
+    'A'
+
+If the desire is to work with an 8-bit integer, use ``int8_t`` or ``uint8_t``
+as the argument type.
+
+Grapheme clusters
+-----------------
+
+A single grapheme may be represented by two or more Unicode characters. For
+example 'é' is usually represented as U+00E9 but can also be expressed as the
+combining character sequence U+0065 U+0301 (that is, the letter 'e' followed by
+a combining acute accent). The combining character will be lost if the
+two-character sequence is passed as an argument, even though it renders as a
+single grapheme.
+
+.. code-block:: pycon
+
+    >>> example.pass_wchar("é")
+    'é'
+
+    >>> combining_e_acute = "e" + "\u0301"
+
+    >>> combining_e_acute
+    'é'
+
+    >>> combining_e_acute == "é"
+    False
+
+    >>> example.pass_wchar(combining_e_acute)
+    'e'
+
+Normalizing combining characters before passing the character literal to C++
+may resolve *some* of these issues:
+
+.. code-block:: pycon
+
+    >>> example.pass_wchar(unicodedata.normalize("NFC", combining_e_acute))
+    'é'
+
+In some languages (Thai for example), there are `graphemes that cannot be
+expressed as a single Unicode code point
+<http://unicode.org/reports/tr29/#Grapheme_Cluster_Boundaries>`_, so there is
+no way to capture them in a C++ character type.
+
+
+C++17 string views
+==================
+
+C++17 string views are automatically supported when compiling in C++17 mode.
+They follow the same rules for encoding and decoding as the corresponding STL
+string type (for example, a ``std::u16string_view`` argument will be passed
+UTF-16-encoded data, and a returned ``std::string_view`` will be decoded as
+UTF-8).
+
+References
+==========
+
+* `The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!) <https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/>`_
+* `C++ - Using STL Strings at Win32 API Boundaries <https://msdn.microsoft.com/en-ca/magazine/mt238407.aspx>`_
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/classes.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/classes.rst
new file mode 100644
index 000000000..49ddf5c0b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/classes.rst
@@ -0,0 +1,1335 @@
+Classes
+#######
+
+This section presents advanced binding code for classes and it is assumed
+that you are already familiar with the basics from :doc:`/classes`.
+
+.. _overriding_virtuals:
+
+Overriding virtual functions in Python
+======================================
+
+Suppose that a C++ class or interface has a virtual function that we'd like
+to override from within Python (we'll focus on the class ``Animal``; ``Dog`` is
+given as a specific example of how one would do this with traditional C++
+code).
+
+.. code-block:: cpp
+
+    class Animal {
+    public:
+        virtual ~Animal() { }
+        virtual std::string go(int n_times) = 0;
+    };
+
+    class Dog : public Animal {
+    public:
+        std::string go(int n_times) override {
+            std::string result;
+            for (int i=0; i<n_times; ++i)
+                result += "woof! ";
+            return result;
+        }
+    };
+
+Let's also suppose that we are given a plain function which calls the
+function ``go()`` on an arbitrary ``Animal`` instance.
+
+.. code-block:: cpp
+
+    std::string call_go(Animal *animal) {
+        return animal->go(3);
+    }
+
+Normally, the binding code for these classes would look as follows:
+
+.. code-block:: cpp
+
+    PYBIND11_MODULE(example, m) {
+        py::class_<Animal>(m, "Animal")
+            .def("go", &Animal::go);
+
+        py::class_<Dog, Animal>(m, "Dog")
+            .def(py::init<>());
+
+        m.def("call_go", &call_go);
+    }
+
+However, these bindings are impossible to extend: ``Animal`` is not
+constructible, and we clearly require some kind of "trampoline" that
+redirects virtual calls back to Python.
+
+Defining a new type of ``Animal`` from within Python is possible but requires a
+helper class that is defined as follows:
+
+.. code-block:: cpp
+
+    class PyAnimal : public Animal {
+    public:
+        /* Inherit the constructors */
+        using Animal::Animal;
+
+        /* Trampoline (need one for each virtual function) */
+        std::string go(int n_times) override {
+            PYBIND11_OVERRIDE_PURE(
+                std::string, /* Return type */
+                Animal,      /* Parent class */
+                go,          /* Name of function in C++ (must match Python name) */
+                n_times      /* Argument(s) */
+            );
+        }
+    };
+
+The macro :c:macro:`PYBIND11_OVERRIDE_PURE` should be used for pure virtual
+functions, and :c:macro:`PYBIND11_OVERRIDE` should be used for functions which have
+a default implementation.  There are also two alternate macros
+:c:macro:`PYBIND11_OVERRIDE_PURE_NAME` and :c:macro:`PYBIND11_OVERRIDE_NAME` which
+take a string-valued name argument between the *Parent class* and *Name of the
+function* slots, which defines the name of function in Python. This is required
+when the C++ and Python versions of the
+function have different names, e.g.  ``operator()`` vs ``__call__``.
+
+The binding code also needs a few minor adaptations (highlighted):
+
+.. code-block:: cpp
+    :emphasize-lines: 2,3
+
+    PYBIND11_MODULE(example, m) {
+        py::class_<Animal, PyAnimal /* <--- trampoline*/>(m, "Animal")
+            .def(py::init<>())
+            .def("go", &Animal::go);
+
+        py::class_<Dog, Animal>(m, "Dog")
+            .def(py::init<>());
+
+        m.def("call_go", &call_go);
+    }
+
+Importantly, pybind11 is made aware of the trampoline helper class by
+specifying it as an extra template argument to :class:`class_`. (This can also
+be combined with other template arguments such as a custom holder type; the
+order of template types does not matter).  Following this, we are able to
+define a constructor as usual.
+
+Bindings should be made against the actual class, not the trampoline helper class.
+
+.. code-block:: cpp
+    :emphasize-lines: 3
+
+    py::class_<Animal, PyAnimal /* <--- trampoline*/>(m, "Animal");
+        .def(py::init<>())
+        .def("go", &PyAnimal::go); /* <--- THIS IS WRONG, use &Animal::go */
+
+Note, however, that the above is sufficient for allowing python classes to
+extend ``Animal``, but not ``Dog``: see :ref:`virtual_and_inheritance` for the
+necessary steps required to providing proper overriding support for inherited
+classes.
+
+The Python session below shows how to override ``Animal::go`` and invoke it via
+a virtual method call.
+
+.. code-block:: pycon
+
+    >>> from example import *
+    >>> d = Dog()
+    >>> call_go(d)
+    'woof! woof! woof! '
+    >>> class Cat(Animal):
+    ...     def go(self, n_times):
+    ...         return "meow! " * n_times
+    ...
+    >>> c = Cat()
+    >>> call_go(c)
+    'meow! meow! meow! '
+
+If you are defining a custom constructor in a derived Python class, you *must*
+ensure that you explicitly call the bound C++ constructor using ``__init__``,
+*regardless* of whether it is a default constructor or not. Otherwise, the
+memory for the C++ portion of the instance will be left uninitialized, which
+will generally leave the C++ instance in an invalid state and cause undefined
+behavior if the C++ instance is subsequently used.
+
+.. versionchanged:: 2.6
+   The default pybind11 metaclass will throw a ``TypeError`` when it detects
+   that ``__init__`` was not called by a derived class.
+
+Here is an example:
+
+.. code-block:: python
+
+    class Dachshund(Dog):
+        def __init__(self, name):
+            Dog.__init__(self)  # Without this, a TypeError is raised.
+            self.name = name
+
+        def bark(self):
+            return "yap!"
+
+Note that a direct ``__init__`` constructor *should be called*, and ``super()``
+should not be used. For simple cases of linear inheritance, ``super()``
+may work, but once you begin mixing Python and C++ multiple inheritance,
+things will fall apart due to differences between Python's MRO and C++'s
+mechanisms.
+
+Please take a look at the :ref:`macro_notes` before using this feature.
+
+.. note::
+
+    When the overridden type returns a reference or pointer to a type that
+    pybind11 converts from Python (for example, numeric values, std::string,
+    and other built-in value-converting types), there are some limitations to
+    be aware of:
+
+    - because in these cases there is no C++ variable to reference (the value
+      is stored in the referenced Python variable), pybind11 provides one in
+      the PYBIND11_OVERRIDE macros (when needed) with static storage duration.
+      Note that this means that invoking the overridden method on *any*
+      instance will change the referenced value stored in *all* instances of
+      that type.
+
+    - Attempts to modify a non-const reference will not have the desired
+      effect: it will change only the static cache variable, but this change
+      will not propagate to underlying Python instance, and the change will be
+      replaced the next time the override is invoked.
+
+.. warning::
+
+    The :c:macro:`PYBIND11_OVERRIDE` and accompanying macros used to be called
+    ``PYBIND11_OVERLOAD`` up until pybind11 v2.5.0, and :func:`get_override`
+    used to be called ``get_overload``. This naming was corrected and the older
+    macro and function names may soon be deprecated, in order to reduce
+    confusion with overloaded functions and methods and ``py::overload_cast``
+    (see :ref:`classes`).
+
+.. seealso::
+
+    The file :file:`tests/test_virtual_functions.cpp` contains a complete
+    example that demonstrates how to override virtual functions using pybind11
+    in more detail.
+
+.. _virtual_and_inheritance:
+
+Combining virtual functions and inheritance
+===========================================
+
+When combining virtual methods with inheritance, you need to be sure to provide
+an override for each method for which you want to allow overrides from derived
+python classes.  For example, suppose we extend the above ``Animal``/``Dog``
+example as follows:
+
+.. code-block:: cpp
+
+    class Animal {
+    public:
+        virtual std::string go(int n_times) = 0;
+        virtual std::string name() { return "unknown"; }
+    };
+    class Dog : public Animal {
+    public:
+        std::string go(int n_times) override {
+            std::string result;
+            for (int i=0; i<n_times; ++i)
+                result += bark() + " ";
+            return result;
+        }
+        virtual std::string bark() { return "woof!"; }
+    };
+
+then the trampoline class for ``Animal`` must, as described in the previous
+section, override ``go()`` and ``name()``, but in order to allow python code to
+inherit properly from ``Dog``, we also need a trampoline class for ``Dog`` that
+overrides both the added ``bark()`` method *and* the ``go()`` and ``name()``
+methods inherited from ``Animal`` (even though ``Dog`` doesn't directly
+override the ``name()`` method):
+
+.. code-block:: cpp
+
+    class PyAnimal : public Animal {
+    public:
+        using Animal::Animal; // Inherit constructors
+        std::string go(int n_times) override { PYBIND11_OVERRIDE_PURE(std::string, Animal, go, n_times); }
+        std::string name() override { PYBIND11_OVERRIDE(std::string, Animal, name, ); }
+    };
+    class PyDog : public Dog {
+    public:
+        using Dog::Dog; // Inherit constructors
+        std::string go(int n_times) override { PYBIND11_OVERRIDE(std::string, Dog, go, n_times); }
+        std::string name() override { PYBIND11_OVERRIDE(std::string, Dog, name, ); }
+        std::string bark() override { PYBIND11_OVERRIDE(std::string, Dog, bark, ); }
+    };
+
+.. note::
+
+    Note the trailing commas in the ``PYBIND11_OVERRIDE`` calls to ``name()``
+    and ``bark()``. These are needed to portably implement a trampoline for a
+    function that does not take any arguments. For functions that take
+    a nonzero number of arguments, the trailing comma must be omitted.
+
+A registered class derived from a pybind11-registered class with virtual
+methods requires a similar trampoline class, *even if* it doesn't explicitly
+declare or override any virtual methods itself:
+
+.. code-block:: cpp
+
+    class Husky : public Dog {};
+    class PyHusky : public Husky {
+    public:
+        using Husky::Husky; // Inherit constructors
+        std::string go(int n_times) override { PYBIND11_OVERRIDE_PURE(std::string, Husky, go, n_times); }
+        std::string name() override { PYBIND11_OVERRIDE(std::string, Husky, name, ); }
+        std::string bark() override { PYBIND11_OVERRIDE(std::string, Husky, bark, ); }
+    };
+
+There is, however, a technique that can be used to avoid this duplication
+(which can be especially helpful for a base class with several virtual
+methods).  The technique involves using template trampoline classes, as
+follows:
+
+.. code-block:: cpp
+
+    template <class AnimalBase = Animal> class PyAnimal : public AnimalBase {
+    public:
+        using AnimalBase::AnimalBase; // Inherit constructors
+        std::string go(int n_times) override { PYBIND11_OVERRIDE_PURE(std::string, AnimalBase, go, n_times); }
+        std::string name() override { PYBIND11_OVERRIDE(std::string, AnimalBase, name, ); }
+    };
+    template <class DogBase = Dog> class PyDog : public PyAnimal<DogBase> {
+    public:
+        using PyAnimal<DogBase>::PyAnimal; // Inherit constructors
+        // Override PyAnimal's pure virtual go() with a non-pure one:
+        std::string go(int n_times) override { PYBIND11_OVERRIDE(std::string, DogBase, go, n_times); }
+        std::string bark() override { PYBIND11_OVERRIDE(std::string, DogBase, bark, ); }
+    };
+
+This technique has the advantage of requiring just one trampoline method to be
+declared per virtual method and pure virtual method override.  It does,
+however, require the compiler to generate at least as many methods (and
+possibly more, if both pure virtual and overridden pure virtual methods are
+exposed, as above).
+
+The classes are then registered with pybind11 using:
+
+.. code-block:: cpp
+
+    py::class_<Animal, PyAnimal<>> animal(m, "Animal");
+    py::class_<Dog, Animal, PyDog<>> dog(m, "Dog");
+    py::class_<Husky, Dog, PyDog<Husky>> husky(m, "Husky");
+    // ... add animal, dog, husky definitions
+
+Note that ``Husky`` did not require a dedicated trampoline template class at
+all, since it neither declares any new virtual methods nor provides any pure
+virtual method implementations.
+
+With either the repeated-virtuals or templated trampoline methods in place, you
+can now create a python class that inherits from ``Dog``:
+
+.. code-block:: python
+
+    class ShihTzu(Dog):
+        def bark(self):
+            return "yip!"
+
+.. seealso::
+
+    See the file :file:`tests/test_virtual_functions.cpp` for complete examples
+    using both the duplication and templated trampoline approaches.
+
+.. _extended_aliases:
+
+Extended trampoline class functionality
+=======================================
+
+.. _extended_class_functionality_forced_trampoline:
+
+Forced trampoline class initialisation
+--------------------------------------
+The trampoline classes described in the previous sections are, by default, only
+initialized when needed.  More specifically, they are initialized when a python
+class actually inherits from a registered type (instead of merely creating an
+instance of the registered type), or when a registered constructor is only
+valid for the trampoline class but not the registered class.  This is primarily
+for performance reasons: when the trampoline class is not needed for anything
+except virtual method dispatching, not initializing the trampoline class
+improves performance by avoiding needing to do a run-time check to see if the
+inheriting python instance has an overridden method.
+
+Sometimes, however, it is useful to always initialize a trampoline class as an
+intermediate class that does more than just handle virtual method dispatching.
+For example, such a class might perform extra class initialization, extra
+destruction operations, and might define new members and methods to enable a
+more python-like interface to a class.
+
+In order to tell pybind11 that it should *always* initialize the trampoline
+class when creating new instances of a type, the class constructors should be
+declared using ``py::init_alias<Args, ...>()`` instead of the usual
+``py::init<Args, ...>()``.  This forces construction via the trampoline class,
+ensuring member initialization and (eventual) destruction.
+
+.. seealso::
+
+    See the file :file:`tests/test_virtual_functions.cpp` for complete examples
+    showing both normal and forced trampoline instantiation.
+
+Different method signatures
+---------------------------
+The macro's introduced in :ref:`overriding_virtuals` cover most of the standard
+use cases when exposing C++ classes to Python. Sometimes it is hard or unwieldy
+to create a direct one-on-one mapping between the arguments and method return
+type.
+
+An example would be when the C++ signature contains output arguments using
+references (See also :ref:`faq_reference_arguments`). Another way of solving
+this is to use the method body of the trampoline class to do conversions to the
+input and return of the Python method.
+
+The main building block to do so is the :func:`get_override`, this function
+allows retrieving a method implemented in Python from within the trampoline's
+methods. Consider for example a C++ method which has the signature
+``bool myMethod(int32_t& value)``, where the return indicates whether
+something should be done with the ``value``. This can be made convenient on the
+Python side by allowing the Python function to return ``None`` or an ``int``:
+
+.. code-block:: cpp
+
+    bool MyClass::myMethod(int32_t& value)
+    {
+        pybind11::gil_scoped_acquire gil;  // Acquire the GIL while in this scope.
+        // Try to look up the overridden method on the Python side.
+        pybind11::function override = pybind11::get_override(this, "myMethod");
+        if (override) {  // method is found
+            auto obj = override(value);  // Call the Python function.
+            if (py::isinstance<py::int_>(obj)) {  // check if it returned a Python integer type
+                value = obj.cast<int32_t>();  // Cast it and assign it to the value.
+                return true;  // Return true; value should be used.
+            } else {
+                return false;  // Python returned none, return false.
+            }
+        }
+        return false;  // Alternatively return MyClass::myMethod(value);
+    }
+
+
+.. _custom_constructors:
+
+Custom constructors
+===================
+
+The syntax for binding constructors was previously introduced, but it only
+works when a constructor of the appropriate arguments actually exists on the
+C++ side.  To extend this to more general cases, pybind11 makes it possible
+to bind factory functions as constructors. For example, suppose you have a
+class like this:
+
+.. code-block:: cpp
+
+    class Example {
+    private:
+        Example(int); // private constructor
+    public:
+        // Factory function:
+        static Example create(int a) { return Example(a); }
+    };
+
+    py::class_<Example>(m, "Example")
+        .def(py::init(&Example::create));
+
+While it is possible to create a straightforward binding of the static
+``create`` method, it may sometimes be preferable to expose it as a constructor
+on the Python side. This can be accomplished by calling ``.def(py::init(...))``
+with the function reference returning the new instance passed as an argument.
+It is also possible to use this approach to bind a function returning a new
+instance by raw pointer or by the holder (e.g. ``std::unique_ptr``).
+
+The following example shows the different approaches:
+
+.. code-block:: cpp
+
+    class Example {
+    private:
+        Example(int); // private constructor
+    public:
+        // Factory function - returned by value:
+        static Example create(int a) { return Example(a); }
+
+        // These constructors are publicly callable:
+        Example(double);
+        Example(int, int);
+        Example(std::string);
+    };
+
+    py::class_<Example>(m, "Example")
+        // Bind the factory function as a constructor:
+        .def(py::init(&Example::create))
+        // Bind a lambda function returning a pointer wrapped in a holder:
+        .def(py::init([](std::string arg) {
+            return std::unique_ptr<Example>(new Example(arg));
+        }))
+        // Return a raw pointer:
+        .def(py::init([](int a, int b) { return new Example(a, b); }))
+        // You can mix the above with regular C++ constructor bindings as well:
+        .def(py::init<double>())
+        ;
+
+When the constructor is invoked from Python, pybind11 will call the factory
+function and store the resulting C++ instance in the Python instance.
+
+When combining factory functions constructors with :ref:`virtual function
+trampolines <overriding_virtuals>` there are two approaches.  The first is to
+add a constructor to the alias class that takes a base value by
+rvalue-reference.  If such a constructor is available, it will be used to
+construct an alias instance from the value returned by the factory function.
+The second option is to provide two factory functions to ``py::init()``: the
+first will be invoked when no alias class is required (i.e. when the class is
+being used but not inherited from in Python), and the second will be invoked
+when an alias is required.
+
+You can also specify a single factory function that always returns an alias
+instance: this will result in behaviour similar to ``py::init_alias<...>()``,
+as described in the :ref:`extended trampoline class documentation
+<extended_aliases>`.
+
+The following example shows the different factory approaches for a class with
+an alias:
+
+.. code-block:: cpp
+
+    #include <pybind11/factory.h>
+    class Example {
+    public:
+        // ...
+        virtual ~Example() = default;
+    };
+    class PyExample : public Example {
+    public:
+        using Example::Example;
+        PyExample(Example &&base) : Example(std::move(base)) {}
+    };
+    py::class_<Example, PyExample>(m, "Example")
+        // Returns an Example pointer.  If a PyExample is needed, the Example
+        // instance will be moved via the extra constructor in PyExample, above.
+        .def(py::init([]() { return new Example(); }))
+        // Two callbacks:
+        .def(py::init([]() { return new Example(); } /* no alias needed */,
+                      []() { return new PyExample(); } /* alias needed */))
+        // *Always* returns an alias instance (like py::init_alias<>())
+        .def(py::init([]() { return new PyExample(); }))
+        ;
+
+Brace initialization
+--------------------
+
+``pybind11::init<>`` internally uses C++11 brace initialization to call the
+constructor of the target class. This means that it can be used to bind
+*implicit* constructors as well:
+
+.. code-block:: cpp
+
+    struct Aggregate {
+        int a;
+        std::string b;
+    };
+
+    py::class_<Aggregate>(m, "Aggregate")
+        .def(py::init<int, const std::string &>());
+
+.. note::
+
+    Note that brace initialization preferentially invokes constructor overloads
+    taking a ``std::initializer_list``. In the rare event that this causes an
+    issue, you can work around it by using ``py::init(...)`` with a lambda
+    function that constructs the new object as desired.
+
+.. _classes_with_non_public_destructors:
+
+Non-public destructors
+======================
+
+If a class has a private or protected destructor (as might e.g. be the case in
+a singleton pattern), a compile error will occur when creating bindings via
+pybind11. The underlying issue is that the ``std::unique_ptr`` holder type that
+is responsible for managing the lifetime of instances will reference the
+destructor even if no deallocations ever take place. In order to expose classes
+with private or protected destructors, it is possible to override the holder
+type via a holder type argument to ``class_``. Pybind11 provides a helper class
+``py::nodelete`` that disables any destructor invocations. In this case, it is
+crucial that instances are deallocated on the C++ side to avoid memory leaks.
+
+.. code-block:: cpp
+
+    /* ... definition ... */
+
+    class MyClass {
+    private:
+        ~MyClass() { }
+    };
+
+    /* ... binding code ... */
+
+    py::class_<MyClass, std::unique_ptr<MyClass, py::nodelete>>(m, "MyClass")
+        .def(py::init<>())
+
+.. _destructors_that_call_python:
+
+Destructors that call Python
+============================
+
+If a Python function is invoked from a C++ destructor, an exception may be thrown
+of type :class:`error_already_set`. If this error is thrown out of a class destructor,
+``std::terminate()`` will be called, terminating the process. Class destructors
+must catch all exceptions of type :class:`error_already_set` to discard the Python
+exception using :func:`error_already_set::discard_as_unraisable`.
+
+Every Python function should be treated as *possibly throwing*. When a Python generator
+stops yielding items, Python will throw a ``StopIteration`` exception, which can pass
+though C++ destructors if the generator's stack frame holds the last reference to C++
+objects.
+
+For more information, see :ref:`the documentation on exceptions <unraisable_exceptions>`.
+
+.. code-block:: cpp
+
+    class MyClass {
+    public:
+        ~MyClass() {
+            try {
+                py::print("Even printing is dangerous in a destructor");
+                py::exec("raise ValueError('This is an unraisable exception')");
+            } catch (py::error_already_set &e) {
+                // error_context should be information about where/why the occurred,
+                // e.g. use __func__ to get the name of the current function
+                e.discard_as_unraisable(__func__);
+            }
+        }
+    };
+
+.. note::
+
+    pybind11 does not support C++ destructors marked ``noexcept(false)``.
+
+.. versionadded:: 2.6
+
+.. _implicit_conversions:
+
+Implicit conversions
+====================
+
+Suppose that instances of two types ``A`` and ``B`` are used in a project, and
+that an ``A`` can easily be converted into an instance of type ``B`` (examples of this
+could be a fixed and an arbitrary precision number type).
+
+.. code-block:: cpp
+
+    py::class_<A>(m, "A")
+        /// ... members ...
+
+    py::class_<B>(m, "B")
+        .def(py::init<A>())
+        /// ... members ...
+
+    m.def("func",
+        [](const B &) { /* .... */ }
+    );
+
+To invoke the function ``func`` using a variable ``a`` containing an ``A``
+instance, we'd have to write ``func(B(a))`` in Python. On the other hand, C++
+will automatically apply an implicit type conversion, which makes it possible
+to directly write ``func(a)``.
+
+In this situation (i.e. where ``B`` has a constructor that converts from
+``A``), the following statement enables similar implicit conversions on the
+Python side:
+
+.. code-block:: cpp
+
+    py::implicitly_convertible<A, B>();
+
+.. note::
+
+    Implicit conversions from ``A`` to ``B`` only work when ``B`` is a custom
+    data type that is exposed to Python via pybind11.
+
+    To prevent runaway recursion, implicit conversions are non-reentrant: an
+    implicit conversion invoked as part of another implicit conversion of the
+    same type (i.e. from ``A`` to ``B``) will fail.
+
+.. _static_properties:
+
+Static properties
+=================
+
+The section on :ref:`properties` discussed the creation of instance properties
+that are implemented in terms of C++ getters and setters.
+
+Static properties can also be created in a similar way to expose getters and
+setters of static class attributes. Note that the implicit ``self`` argument
+also exists in this case and is used to pass the Python ``type`` subclass
+instance. This parameter will often not be needed by the C++ side, and the
+following example illustrates how to instantiate a lambda getter function
+that ignores it:
+
+.. code-block:: cpp
+
+    py::class_<Foo>(m, "Foo")
+        .def_property_readonly_static("foo", [](py::object /* self */) { return Foo(); });
+
+Operator overloading
+====================
+
+Suppose that we're given the following ``Vector2`` class with a vector addition
+and scalar multiplication operation, all implemented using overloaded operators
+in C++.
+
+.. code-block:: cpp
+
+    class Vector2 {
+    public:
+        Vector2(float x, float y) : x(x), y(y) { }
+
+        Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); }
+        Vector2 operator*(float value) const { return Vector2(x * value, y * value); }
+        Vector2& operator+=(const Vector2 &v) { x += v.x; y += v.y; return *this; }
+        Vector2& operator*=(float v) { x *= v; y *= v; return *this; }
+
+        friend Vector2 operator*(float f, const Vector2 &v) {
+            return Vector2(f * v.x, f * v.y);
+        }
+
+        std::string toString() const {
+            return "[" + std::to_string(x) + ", " + std::to_string(y) + "]";
+        }
+    private:
+        float x, y;
+    };
+
+The following snippet shows how the above operators can be conveniently exposed
+to Python.
+
+.. code-block:: cpp
+
+    #include <pybind11/operators.h>
+
+    PYBIND11_MODULE(example, m) {
+        py::class_<Vector2>(m, "Vector2")
+            .def(py::init<float, float>())
+            .def(py::self + py::self)
+            .def(py::self += py::self)
+            .def(py::self *= float())
+            .def(float() * py::self)
+            .def(py::self * float())
+            .def(-py::self)
+            .def("__repr__", &Vector2::toString);
+    }
+
+Note that a line like
+
+.. code-block:: cpp
+
+            .def(py::self * float())
+
+is really just short hand notation for
+
+.. code-block:: cpp
+
+    .def("__mul__", [](const Vector2 &a, float b) {
+        return a * b;
+    }, py::is_operator())
+
+This can be useful for exposing additional operators that don't exist on the
+C++ side, or to perform other types of customization. The ``py::is_operator``
+flag marker is needed to inform pybind11 that this is an operator, which
+returns ``NotImplemented`` when invoked with incompatible arguments rather than
+throwing a type error.
+
+.. note::
+
+    To use the more convenient ``py::self`` notation, the additional
+    header file :file:`pybind11/operators.h` must be included.
+
+.. seealso::
+
+    The file :file:`tests/test_operator_overloading.cpp` contains a
+    complete example that demonstrates how to work with overloaded operators in
+    more detail.
+
+.. _pickling:
+
+Pickling support
+================
+
+Python's ``pickle`` module provides a powerful facility to serialize and
+de-serialize a Python object graph into a binary data stream. To pickle and
+unpickle C++ classes using pybind11, a ``py::pickle()`` definition must be
+provided. Suppose the class in question has the following signature:
+
+.. code-block:: cpp
+
+    class Pickleable {
+    public:
+        Pickleable(const std::string &value) : m_value(value) { }
+        const std::string &value() const { return m_value; }
+
+        void setExtra(int extra) { m_extra = extra; }
+        int extra() const { return m_extra; }
+    private:
+        std::string m_value;
+        int m_extra = 0;
+    };
+
+Pickling support in Python is enabled by defining the ``__setstate__`` and
+``__getstate__`` methods [#f3]_. For pybind11 classes, use ``py::pickle()``
+to bind these two functions:
+
+.. code-block:: cpp
+
+    py::class_<Pickleable>(m, "Pickleable")
+        .def(py::init<std::string>())
+        .def("value", &Pickleable::value)
+        .def("extra", &Pickleable::extra)
+        .def("setExtra", &Pickleable::setExtra)
+        .def(py::pickle(
+            [](const Pickleable &p) { // __getstate__
+                /* Return a tuple that fully encodes the state of the object */
+                return py::make_tuple(p.value(), p.extra());
+            },
+            [](py::tuple t) { // __setstate__
+                if (t.size() != 2)
+                    throw std::runtime_error("Invalid state!");
+
+                /* Create a new C++ instance */
+                Pickleable p(t[0].cast<std::string>());
+
+                /* Assign any additional state */
+                p.setExtra(t[1].cast<int>());
+
+                return p;
+            }
+        ));
+
+The ``__setstate__`` part of the ``py::pickle()`` definition follows the same
+rules as the single-argument version of ``py::init()``. The return type can be
+a value, pointer or holder type. See :ref:`custom_constructors` for details.
+
+An instance can now be pickled as follows:
+
+.. code-block:: python
+
+    import pickle
+
+    p = Pickleable("test_value")
+    p.setExtra(15)
+    data = pickle.dumps(p)
+
+
+.. note::
+    If given, the second argument to ``dumps`` must be 2 or larger - 0 and 1 are
+    not supported. Newer versions are also fine; for instance, specify ``-1`` to
+    always use the latest available version. Beware: failure to follow these
+    instructions will cause important pybind11 memory allocation routines to be
+    skipped during unpickling, which will likely lead to memory corruption
+    and/or segmentation faults. Python defaults to version 3 (Python 3-3.7) and
+    version 4 for Python 3.8+.
+
+.. seealso::
+
+    The file :file:`tests/test_pickling.cpp` contains a complete example
+    that demonstrates how to pickle and unpickle types using pybind11 in more
+    detail.
+
+.. [#f3] http://docs.python.org/3/library/pickle.html#pickling-class-instances
+
+Deepcopy support
+================
+
+Python normally uses references in assignments. Sometimes a real copy is needed
+to prevent changing all copies. The ``copy`` module [#f5]_ provides these
+capabilities.
+
+A class with pickle support is automatically also (deep)copy
+compatible. However, performance can be improved by adding custom
+``__copy__`` and ``__deepcopy__`` methods.
+
+For simple classes (deep)copy can be enabled by using the copy constructor,
+which should look as follows:
+
+.. code-block:: cpp
+
+    py::class_<Copyable>(m, "Copyable")
+        .def("__copy__",  [](const Copyable &self) {
+            return Copyable(self);
+        })
+        .def("__deepcopy__", [](const Copyable &self, py::dict) {
+            return Copyable(self);
+        }, "memo"_a);
+
+.. note::
+
+    Dynamic attributes will not be copied in this example.
+
+.. [#f5] https://docs.python.org/3/library/copy.html
+
+Multiple Inheritance
+====================
+
+pybind11 can create bindings for types that derive from multiple base types
+(aka. *multiple inheritance*). To do so, specify all bases in the template
+arguments of the ``class_`` declaration:
+
+.. code-block:: cpp
+
+    py::class_<MyType, BaseType1, BaseType2, BaseType3>(m, "MyType")
+       ...
+
+The base types can be specified in arbitrary order, and they can even be
+interspersed with alias types and holder types (discussed earlier in this
+document)---pybind11 will automatically find out which is which. The only
+requirement is that the first template argument is the type to be declared.
+
+It is also permitted to inherit multiply from exported C++ classes in Python,
+as well as inheriting from multiple Python and/or pybind11-exported classes.
+
+There is one caveat regarding the implementation of this feature:
+
+When only one base type is specified for a C++ type that actually has multiple
+bases, pybind11 will assume that it does not participate in multiple
+inheritance, which can lead to undefined behavior. In such cases, add the tag
+``multiple_inheritance`` to the class constructor:
+
+.. code-block:: cpp
+
+    py::class_<MyType, BaseType2>(m, "MyType", py::multiple_inheritance());
+
+The tag is redundant and does not need to be specified when multiple base types
+are listed.
+
+.. _module_local:
+
+Module-local class bindings
+===========================
+
+When creating a binding for a class, pybind11 by default makes that binding
+"global" across modules.  What this means is that a type defined in one module
+can be returned from any module resulting in the same Python type.  For
+example, this allows the following:
+
+.. code-block:: cpp
+
+    // In the module1.cpp binding code for module1:
+    py::class_<Pet>(m, "Pet")
+        .def(py::init<std::string>())
+        .def_readonly("name", &Pet::name);
+
+.. code-block:: cpp
+
+    // In the module2.cpp binding code for module2:
+    m.def("create_pet", [](std::string name) { return new Pet(name); });
+
+.. code-block:: pycon
+
+    >>> from module1 import Pet
+    >>> from module2 import create_pet
+    >>> pet1 = Pet("Kitty")
+    >>> pet2 = create_pet("Doggy")
+    >>> pet2.name()
+    'Doggy'
+
+When writing binding code for a library, this is usually desirable: this
+allows, for example, splitting up a complex library into multiple Python
+modules.
+
+In some cases, however, this can cause conflicts.  For example, suppose two
+unrelated modules make use of an external C++ library and each provide custom
+bindings for one of that library's classes.  This will result in an error when
+a Python program attempts to import both modules (directly or indirectly)
+because of conflicting definitions on the external type:
+
+.. code-block:: cpp
+
+    // dogs.cpp
+
+    // Binding for external library class:
+    py::class<pets::Pet>(m, "Pet")
+        .def("name", &pets::Pet::name);
+
+    // Binding for local extension class:
+    py::class<Dog, pets::Pet>(m, "Dog")
+        .def(py::init<std::string>());
+
+.. code-block:: cpp
+
+    // cats.cpp, in a completely separate project from the above dogs.cpp.
+
+    // Binding for external library class:
+    py::class<pets::Pet>(m, "Pet")
+        .def("get_name", &pets::Pet::name);
+
+    // Binding for local extending class:
+    py::class<Cat, pets::Pet>(m, "Cat")
+        .def(py::init<std::string>());
+
+.. code-block:: pycon
+
+    >>> import cats
+    >>> import dogs
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    ImportError: generic_type: type "Pet" is already registered!
+
+To get around this, you can tell pybind11 to keep the external class binding
+localized to the module by passing the ``py::module_local()`` attribute into
+the ``py::class_`` constructor:
+
+.. code-block:: cpp
+
+    // Pet binding in dogs.cpp:
+    py::class<pets::Pet>(m, "Pet", py::module_local())
+        .def("name", &pets::Pet::name);
+
+.. code-block:: cpp
+
+    // Pet binding in cats.cpp:
+    py::class<pets::Pet>(m, "Pet", py::module_local())
+        .def("get_name", &pets::Pet::name);
+
+This makes the Python-side ``dogs.Pet`` and ``cats.Pet`` into distinct classes,
+avoiding the conflict and allowing both modules to be loaded.  C++ code in the
+``dogs`` module that casts or returns a ``Pet`` instance will result in a
+``dogs.Pet`` Python instance, while C++ code in the ``cats`` module will result
+in a ``cats.Pet`` Python instance.
+
+This does come with two caveats, however: First, external modules cannot return
+or cast a ``Pet`` instance to Python (unless they also provide their own local
+bindings).  Second, from the Python point of view they are two distinct classes.
+
+Note that the locality only applies in the C++ -> Python direction.  When
+passing such a ``py::module_local`` type into a C++ function, the module-local
+classes are still considered.  This means that if the following function is
+added to any module (including but not limited to the ``cats`` and ``dogs``
+modules above) it will be callable with either a ``dogs.Pet`` or ``cats.Pet``
+argument:
+
+.. code-block:: cpp
+
+    m.def("pet_name", [](const pets::Pet &pet) { return pet.name(); });
+
+For example, suppose the above function is added to each of ``cats.cpp``,
+``dogs.cpp`` and ``frogs.cpp`` (where ``frogs.cpp`` is some other module that
+does *not* bind ``Pets`` at all).
+
+.. code-block:: pycon
+
+    >>> import cats, dogs, frogs  # No error because of the added py::module_local()
+    >>> mycat, mydog = cats.Cat("Fluffy"), dogs.Dog("Rover")
+    >>> (cats.pet_name(mycat), dogs.pet_name(mydog))
+    ('Fluffy', 'Rover')
+    >>> (cats.pet_name(mydog), dogs.pet_name(mycat), frogs.pet_name(mycat))
+    ('Rover', 'Fluffy', 'Fluffy')
+
+It is possible to use ``py::module_local()`` registrations in one module even
+if another module registers the same type globally: within the module with the
+module-local definition, all C++ instances will be cast to the associated bound
+Python type.  In other modules any such values are converted to the global
+Python type created elsewhere.
+
+.. note::
+
+    STL bindings (as provided via the optional :file:`pybind11/stl_bind.h`
+    header) apply ``py::module_local`` by default when the bound type might
+    conflict with other modules; see :ref:`stl_bind` for details.
+
+.. note::
+
+    The localization of the bound types is actually tied to the shared object
+    or binary generated by the compiler/linker.  For typical modules created
+    with ``PYBIND11_MODULE()``, this distinction is not significant.  It is
+    possible, however, when :ref:`embedding` to embed multiple modules in the
+    same binary (see :ref:`embedding_modules`).  In such a case, the
+    localization will apply across all embedded modules within the same binary.
+
+.. seealso::
+
+    The file :file:`tests/test_local_bindings.cpp` contains additional examples
+    that demonstrate how ``py::module_local()`` works.
+
+Binding protected member functions
+==================================
+
+It's normally not possible to expose ``protected`` member functions to Python:
+
+.. code-block:: cpp
+
+    class A {
+    protected:
+        int foo() const { return 42; }
+    };
+
+    py::class_<A>(m, "A")
+        .def("foo", &A::foo); // error: 'foo' is a protected member of 'A'
+
+On one hand, this is good because non-``public`` members aren't meant to be
+accessed from the outside. But we may want to make use of ``protected``
+functions in derived Python classes.
+
+The following pattern makes this possible:
+
+.. code-block:: cpp
+
+    class A {
+    protected:
+        int foo() const { return 42; }
+    };
+
+    class Publicist : public A { // helper type for exposing protected functions
+    public:
+        using A::foo; // inherited with different access modifier
+    };
+
+    py::class_<A>(m, "A") // bind the primary class
+        .def("foo", &Publicist::foo); // expose protected methods via the publicist
+
+This works because ``&Publicist::foo`` is exactly the same function as
+``&A::foo`` (same signature and address), just with a different access
+modifier. The only purpose of the ``Publicist`` helper class is to make
+the function name ``public``.
+
+If the intent is to expose ``protected`` ``virtual`` functions which can be
+overridden in Python, the publicist pattern can be combined with the previously
+described trampoline:
+
+.. code-block:: cpp
+
+    class A {
+    public:
+        virtual ~A() = default;
+
+    protected:
+        virtual int foo() const { return 42; }
+    };
+
+    class Trampoline : public A {
+    public:
+        int foo() const override { PYBIND11_OVERRIDE(int, A, foo, ); }
+    };
+
+    class Publicist : public A {
+    public:
+        using A::foo;
+    };
+
+    py::class_<A, Trampoline>(m, "A") // <-- `Trampoline` here
+        .def("foo", &Publicist::foo); // <-- `Publicist` here, not `Trampoline`!
+
+Binding final classes
+=====================
+
+Some classes may not be appropriate to inherit from. In C++11, classes can
+use the ``final`` specifier to ensure that a class cannot be inherited from.
+The ``py::is_final`` attribute can be used to ensure that Python classes
+cannot inherit from a specified type. The underlying C++ type does not need
+to be declared final.
+
+.. code-block:: cpp
+
+    class IsFinal final {};
+
+    py::class_<IsFinal>(m, "IsFinal", py::is_final());
+
+When you try to inherit from such a class in Python, you will now get this
+error:
+
+.. code-block:: pycon
+
+    >>> class PyFinalChild(IsFinal):
+    ...     pass
+    ...
+    TypeError: type 'IsFinal' is not an acceptable base type
+
+.. note:: This attribute is currently ignored on PyPy
+
+.. versionadded:: 2.6
+
+Binding classes with template parameters
+========================================
+
+pybind11 can also wrap classes that have template parameters. Consider these classes:
+
+.. code-block:: cpp
+
+    struct Cat {};
+    struct Dog {};
+
+    template <typename PetType>
+    struct Cage {
+        Cage(PetType& pet);
+        PetType& get();
+    };
+
+C++ templates may only be instantiated at compile time, so pybind11 can only
+wrap instantiated templated classes. You cannot wrap a non-instantiated template:
+
+.. code-block:: cpp
+
+    // BROKEN (this will not compile)
+    py::class_<Cage>(m, "Cage");
+        .def("get", &Cage::get);
+
+You must explicitly specify each template/type combination that you want to
+wrap separately.
+
+.. code-block:: cpp
+
+    // ok
+    py::class_<Cage<Cat>>(m, "CatCage")
+        .def("get", &Cage<Cat>::get);
+
+    // ok
+    py::class_<Cage<Dog>>(m, "DogCage")
+        .def("get", &Cage<Dog>::get);
+
+If your class methods have template parameters you can wrap those as well,
+but once again each instantiation must be explicitly specified:
+
+.. code-block:: cpp
+
+    typename <typename T>
+    struct MyClass {
+        template <typename V>
+        T fn(V v);
+    };
+
+    py::class<MyClass<int>>(m, "MyClassT")
+        .def("fn", &MyClass<int>::fn<std::string>);
+
+Custom automatic downcasters
+============================
+
+As explained in :ref:`inheritance`, pybind11 comes with built-in
+understanding of the dynamic type of polymorphic objects in C++; that
+is, returning a Pet to Python produces a Python object that knows it's
+wrapping a Dog, if Pet has virtual methods and pybind11 knows about
+Dog and this Pet is in fact a Dog. Sometimes, you might want to
+provide this automatic downcasting behavior when creating bindings for
+a class hierarchy that does not use standard C++ polymorphism, such as
+LLVM [#f4]_. As long as there's some way to determine at runtime
+whether a downcast is safe, you can proceed by specializing the
+``pybind11::polymorphic_type_hook`` template:
+
+.. code-block:: cpp
+
+    enum class PetKind { Cat, Dog, Zebra };
+    struct Pet {   // Not polymorphic: has no virtual methods
+        const PetKind kind;
+        int age = 0;
+      protected:
+        Pet(PetKind _kind) : kind(_kind) {}
+    };
+    struct Dog : Pet {
+        Dog() : Pet(PetKind::Dog) {}
+        std::string sound = "woof!";
+        std::string bark() const { return sound; }
+    };
+
+    namespace pybind11 {
+        template<> struct polymorphic_type_hook<Pet> {
+            static const void *get(const Pet *src, const std::type_info*& type) {
+                // note that src may be nullptr
+                if (src && src->kind == PetKind::Dog) {
+                    type = &typeid(Dog);
+                    return static_cast<const Dog*>(src);
+                }
+                return src;
+            }
+        };
+    } // namespace pybind11
+
+When pybind11 wants to convert a C++ pointer of type ``Base*`` to a
+Python object, it calls ``polymorphic_type_hook<Base>::get()`` to
+determine if a downcast is possible. The ``get()`` function should use
+whatever runtime information is available to determine if its ``src``
+parameter is in fact an instance of some class ``Derived`` that
+inherits from ``Base``. If it finds such a ``Derived``, it sets ``type
+= &typeid(Derived)`` and returns a pointer to the ``Derived`` object
+that contains ``src``. Otherwise, it just returns ``src``, leaving
+``type`` at its default value of nullptr. If you set ``type`` to a
+type that pybind11 doesn't know about, no downcasting will occur, and
+the original ``src`` pointer will be used with its static type
+``Base*``.
+
+It is critical that the returned pointer and ``type`` argument of
+``get()`` agree with each other: if ``type`` is set to something
+non-null, the returned pointer must point to the start of an object
+whose type is ``type``. If the hierarchy being exposed uses only
+single inheritance, a simple ``return src;`` will achieve this just
+fine, but in the general case, you must cast ``src`` to the
+appropriate derived-class pointer (e.g. using
+``static_cast<Derived>(src)``) before allowing it to be returned as a
+``void*``.
+
+.. [#f4] https://llvm.org/docs/HowToSetUpLLVMStyleRTTI.html
+
+.. note::
+
+    pybind11's standard support for downcasting objects whose types
+    have virtual methods is implemented using
+    ``polymorphic_type_hook`` too, using the standard C++ ability to
+    determine the most-derived type of a polymorphic object using
+    ``typeid()`` and to cast a base pointer to that most-derived type
+    (even if you don't know what it is) using ``dynamic_cast<void*>``.
+
+.. seealso::
+
+    The file :file:`tests/test_tagbased_polymorphic.cpp` contains a
+    more complete example, including a demonstration of how to provide
+    automatic downcasting for an entire class hierarchy without
+    writing one get() function for each class.
+
+Accessing the type object
+=========================
+
+You can get the type object from a C++ class that has already been registered using:
+
+.. code-block:: cpp
+
+    py::type T_py = py::type::of<T>();
+
+You can directly use ``py::type::of(ob)`` to get the type object from any python
+object, just like ``type(ob)`` in Python.
+
+.. note::
+
+    Other types, like ``py::type::of<int>()``, do not work, see :ref:`type-conversions`.
+
+.. versionadded:: 2.6
+
+Custom type setup
+=================
+
+For advanced use cases, such as enabling garbage collection support, you may
+wish to directly manipulate the ``PyHeapTypeObject`` corresponding to a
+``py::class_`` definition.
+
+You can do that using ``py::custom_type_setup``:
+
+.. code-block:: cpp
+
+   struct OwnsPythonObjects {
+       py::object value = py::none();
+   };
+   py::class_<OwnsPythonObjects> cls(
+       m, "OwnsPythonObjects", py::custom_type_setup([](PyHeapTypeObject *heap_type) {
+           auto *type = &heap_type->ht_type;
+           type->tp_flags |= Py_TPFLAGS_HAVE_GC;
+           type->tp_traverse = [](PyObject *self_base, visitproc visit, void *arg) {
+               auto &self = py::cast<OwnsPythonObjects&>(py::handle(self_base));
+               Py_VISIT(self.value.ptr());
+               return 0;
+           };
+           type->tp_clear = [](PyObject *self_base) {
+               auto &self = py::cast<OwnsPythonObjects&>(py::handle(self_base));
+               self.value = py::none();
+               return 0;
+           };
+       }));
+   cls.def(py::init<>());
+   cls.def_readwrite("value", &OwnsPythonObjects::value);
+
+.. versionadded:: 2.8
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/embedding.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/embedding.rst
new file mode 100644
index 000000000..dd980d483
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/embedding.rst
@@ -0,0 +1,262 @@
+.. _embedding:
+
+Embedding the interpreter
+#########################
+
+While pybind11 is mainly focused on extending Python using C++, it's also
+possible to do the reverse: embed the Python interpreter into a C++ program.
+All of the other documentation pages still apply here, so refer to them for
+general pybind11 usage. This section will cover a few extra things required
+for embedding.
+
+Getting started
+===============
+
+A basic executable with an embedded interpreter can be created with just a few
+lines of CMake and the ``pybind11::embed`` target, as shown below. For more
+information, see :doc:`/compiling`.
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.4)
+    project(example)
+
+    find_package(pybind11 REQUIRED)  # or `add_subdirectory(pybind11)`
+
+    add_executable(example main.cpp)
+    target_link_libraries(example PRIVATE pybind11::embed)
+
+The essential structure of the ``main.cpp`` file looks like this:
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h> // everything needed for embedding
+    namespace py = pybind11;
+
+    int main() {
+        py::scoped_interpreter guard{}; // start the interpreter and keep it alive
+
+        py::print("Hello, World!"); // use the Python API
+    }
+
+The interpreter must be initialized before using any Python API, which includes
+all the functions and classes in pybind11. The RAII guard class ``scoped_interpreter``
+takes care of the interpreter lifetime. After the guard is destroyed, the interpreter
+shuts down and clears its memory. No Python functions can be called after this.
+
+Executing Python code
+=====================
+
+There are a few different ways to run Python code. One option is to use ``eval``,
+``exec`` or ``eval_file``, as explained in :ref:`eval`. Here is a quick example in
+the context of an executable with an embedded interpreter:
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    namespace py = pybind11;
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        py::exec(R"(
+            kwargs = dict(name="World", number=42)
+            message = "Hello, {name}! The answer is {number}".format(**kwargs)
+            print(message)
+        )");
+    }
+
+Alternatively, similar results can be achieved using pybind11's API (see
+:doc:`/advanced/pycpp/index` for more details).
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    namespace py = pybind11;
+    using namespace py::literals;
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        auto kwargs = py::dict("name"_a="World", "number"_a=42);
+        auto message = "Hello, {name}! The answer is {number}"_s.format(**kwargs);
+        py::print(message);
+    }
+
+The two approaches can also be combined:
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    #include <iostream>
+
+    namespace py = pybind11;
+    using namespace py::literals;
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        auto locals = py::dict("name"_a="World", "number"_a=42);
+        py::exec(R"(
+            message = "Hello, {name}! The answer is {number}".format(**locals())
+        )", py::globals(), locals);
+
+        auto message = locals["message"].cast<std::string>();
+        std::cout << message;
+    }
+
+Importing modules
+=================
+
+Python modules can be imported using ``module_::import()``:
+
+.. code-block:: cpp
+
+    py::module_ sys = py::module_::import("sys");
+    py::print(sys.attr("path"));
+
+For convenience, the current working directory is included in ``sys.path`` when
+embedding the interpreter. This makes it easy to import local Python files:
+
+.. code-block:: python
+
+    """calc.py located in the working directory"""
+
+
+    def add(i, j):
+        return i + j
+
+
+.. code-block:: cpp
+
+    py::module_ calc = py::module_::import("calc");
+    py::object result = calc.attr("add")(1, 2);
+    int n = result.cast<int>();
+    assert(n == 3);
+
+Modules can be reloaded using ``module_::reload()`` if the source is modified e.g.
+by an external process. This can be useful in scenarios where the application
+imports a user defined data processing script which needs to be updated after
+changes by the user. Note that this function does not reload modules recursively.
+
+.. _embedding_modules:
+
+Adding embedded modules
+=======================
+
+Embedded binary modules can be added using the ``PYBIND11_EMBEDDED_MODULE`` macro.
+Note that the definition must be placed at global scope. They can be imported
+like any other module.
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    namespace py = pybind11;
+
+    PYBIND11_EMBEDDED_MODULE(fast_calc, m) {
+        // `m` is a `py::module_` which is used to bind functions and classes
+        m.def("add", [](int i, int j) {
+            return i + j;
+        });
+    }
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        auto fast_calc = py::module_::import("fast_calc");
+        auto result = fast_calc.attr("add")(1, 2).cast<int>();
+        assert(result == 3);
+    }
+
+Unlike extension modules where only a single binary module can be created, on
+the embedded side an unlimited number of modules can be added using multiple
+``PYBIND11_EMBEDDED_MODULE`` definitions (as long as they have unique names).
+
+These modules are added to Python's list of builtins, so they can also be
+imported in pure Python files loaded by the interpreter. Everything interacts
+naturally:
+
+.. code-block:: python
+
+    """py_module.py located in the working directory"""
+    import cpp_module
+
+    a = cpp_module.a
+    b = a + 1
+
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    namespace py = pybind11;
+
+    PYBIND11_EMBEDDED_MODULE(cpp_module, m) {
+        m.attr("a") = 1;
+    }
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        auto py_module = py::module_::import("py_module");
+
+        auto locals = py::dict("fmt"_a="{} + {} = {}", **py_module.attr("__dict__"));
+        assert(locals["a"].cast<int>() == 1);
+        assert(locals["b"].cast<int>() == 2);
+
+        py::exec(R"(
+            c = a + b
+            message = fmt.format(a, b, c)
+        )", py::globals(), locals);
+
+        assert(locals["c"].cast<int>() == 3);
+        assert(locals["message"].cast<std::string>() == "1 + 2 = 3");
+    }
+
+
+Interpreter lifetime
+====================
+
+The Python interpreter shuts down when ``scoped_interpreter`` is destroyed. After
+this, creating a new instance will restart the interpreter. Alternatively, the
+``initialize_interpreter`` / ``finalize_interpreter`` pair of functions can be used
+to directly set the state at any time.
+
+Modules created with pybind11 can be safely re-initialized after the interpreter
+has been restarted. However, this may not apply to third-party extension modules.
+The issue is that Python itself cannot completely unload extension modules and
+there are several caveats with regard to interpreter restarting. In short, not
+all memory may be freed, either due to Python reference cycles or user-created
+global data. All the details can be found in the CPython documentation.
+
+.. warning::
+
+    Creating two concurrent ``scoped_interpreter`` guards is a fatal error. So is
+    calling ``initialize_interpreter`` for a second time after the interpreter
+    has already been initialized.
+
+    Do not use the raw CPython API functions ``Py_Initialize`` and
+    ``Py_Finalize`` as these do not properly handle the lifetime of
+    pybind11's internal data.
+
+
+Sub-interpreter support
+=======================
+
+Creating multiple copies of ``scoped_interpreter`` is not possible because it
+represents the main Python interpreter. Sub-interpreters are something different
+and they do permit the existence of multiple interpreters. This is an advanced
+feature of the CPython API and should be handled with care. pybind11 does not
+currently offer a C++ interface for sub-interpreters, so refer to the CPython
+documentation for all the details regarding this feature.
+
+We'll just mention a couple of caveats the sub-interpreters support in pybind11:
+
+ 1. Sub-interpreters will not receive independent copies of embedded modules.
+    Instead, these are shared and modifications in one interpreter may be
+    reflected in another.
+
+ 2. Managing multiple threads, multiple interpreters and the GIL can be
+    challenging and there are several caveats here, even within the pure
+    CPython API (please refer to the Python docs for details). As for
+    pybind11, keep in mind that ``gil_scoped_release`` and ``gil_scoped_acquire``
+    do not take sub-interpreters into account.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/exceptions.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/exceptions.rst
new file mode 100644
index 000000000..2211caf5d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/exceptions.rst
@@ -0,0 +1,398 @@
+Exceptions
+##########
+
+Built-in C++ to Python exception translation
+============================================
+
+When Python calls C++ code through pybind11, pybind11 provides a C++ exception handler
+that will trap C++ exceptions, translate them to the corresponding Python exception,
+and raise them so that Python code can handle them.
+
+pybind11 defines translations for ``std::exception`` and its standard
+subclasses, and several special exception classes that translate to specific
+Python exceptions. Note that these are not actually Python exceptions, so they
+cannot be examined using the Python C API. Instead, they are pure C++ objects
+that pybind11 will translate the corresponding Python exception when they arrive
+at its exception handler.
+
+.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+
++--------------------------------------+--------------------------------------+
+|  Exception thrown by C++             |  Translated to Python exception type |
++======================================+======================================+
+| :class:`std::exception`              | ``RuntimeError``                     |
++--------------------------------------+--------------------------------------+
+| :class:`std::bad_alloc`              | ``MemoryError``                      |
++--------------------------------------+--------------------------------------+
+| :class:`std::domain_error`           | ``ValueError``                       |
++--------------------------------------+--------------------------------------+
+| :class:`std::invalid_argument`       | ``ValueError``                       |
++--------------------------------------+--------------------------------------+
+| :class:`std::length_error`           | ``ValueError``                       |
++--------------------------------------+--------------------------------------+
+| :class:`std::out_of_range`           | ``IndexError``                       |
++--------------------------------------+--------------------------------------+
+| :class:`std::range_error`            | ``ValueError``                       |
++--------------------------------------+--------------------------------------+
+| :class:`std::overflow_error`         | ``OverflowError``                    |
++--------------------------------------+--------------------------------------+
+| :class:`pybind11::stop_iteration`    | ``StopIteration`` (used to implement |
+|                                      | custom iterators)                    |
++--------------------------------------+--------------------------------------+
+| :class:`pybind11::index_error`       | ``IndexError`` (used to indicate out |
+|                                      | of bounds access in ``__getitem__``, |
+|                                      | ``__setitem__``, etc.)               |
++--------------------------------------+--------------------------------------+
+| :class:`pybind11::key_error`         | ``KeyError`` (used to indicate out   |
+|                                      | of bounds access in ``__getitem__``, |
+|                                      | ``__setitem__`` in dict-like         |
+|                                      | objects, etc.)                       |
++--------------------------------------+--------------------------------------+
+| :class:`pybind11::value_error`       | ``ValueError`` (used to indicate     |
+|                                      | wrong value passed in                |
+|                                      | ``container.remove(...)``)           |
++--------------------------------------+--------------------------------------+
+| :class:`pybind11::type_error`        | ``TypeError``                        |
++--------------------------------------+--------------------------------------+
+| :class:`pybind11::buffer_error`      | ``BufferError``                      |
++--------------------------------------+--------------------------------------+
+| :class:`pybind11::import_error`      | ``ImportError``                      |
++--------------------------------------+--------------------------------------+
+| :class:`pybind11::attribute_error`   | ``AttributeError``                   |
++--------------------------------------+--------------------------------------+
+| Any other exception                  | ``RuntimeError``                     |
++--------------------------------------+--------------------------------------+
+
+Exception translation is not bidirectional. That is, *catching* the C++
+exceptions defined above will not trap exceptions that originate from
+Python. For that, catch :class:`pybind11::error_already_set`. See :ref:`below
+<handling_python_exceptions_cpp>` for further details.
+
+There is also a special exception :class:`cast_error` that is thrown by
+:func:`handle::call` when the input arguments cannot be converted to Python
+objects.
+
+Registering custom translators
+==============================
+
+If the default exception conversion policy described above is insufficient,
+pybind11 also provides support for registering custom exception translators.
+Similar to pybind11 classes, exception translators can be local to the module
+they are defined in or global to the entire python session.  To register a simple
+exception conversion that translates a C++ exception into a new Python exception
+using the C++ exception's ``what()`` method, a helper function is available:
+
+.. code-block:: cpp
+
+    py::register_exception<CppExp>(module, "PyExp");
+
+This call creates a Python exception class with the name ``PyExp`` in the given
+module and automatically converts any encountered exceptions of type ``CppExp``
+into Python exceptions of type ``PyExp``.
+
+A matching function is available for registering a local exception translator:
+
+.. code-block:: cpp
+
+    py::register_local_exception<CppExp>(module, "PyExp");
+
+
+It is possible to specify base class for the exception using the third
+parameter, a ``handle``:
+
+.. code-block:: cpp
+
+    py::register_exception<CppExp>(module, "PyExp", PyExc_RuntimeError);
+    py::register_local_exception<CppExp>(module, "PyExp", PyExc_RuntimeError);
+
+Then ``PyExp`` can be caught both as ``PyExp`` and ``RuntimeError``.
+
+The class objects of the built-in Python exceptions are listed in the Python
+documentation on `Standard Exceptions <https://docs.python.org/3/c-api/exceptions.html#standard-exceptions>`_.
+The default base class is ``PyExc_Exception``.
+
+When more advanced exception translation is needed, the functions
+``py::register_exception_translator(translator)`` and
+``py::register_local_exception_translator(translator)`` can be used to register
+functions that can translate arbitrary exception types (and which may include
+additional logic to do so).  The functions takes a stateless callable (e.g. a
+function pointer or a lambda function without captured variables) with the call
+signature ``void(std::exception_ptr)``.
+
+When a C++ exception is thrown, the registered exception translators are tried
+in reverse order of registration (i.e. the last registered translator gets the
+first shot at handling the exception). All local translators will be tried
+before a global translator is tried.
+
+Inside the translator, ``std::rethrow_exception`` should be used within
+a try block to re-throw the exception.  One or more catch clauses to catch
+the appropriate exceptions should then be used with each clause using
+``PyErr_SetString`` to set a Python exception or ``ex(string)`` to set
+the python exception to a custom exception type (see below).
+
+To declare a custom Python exception type, declare a ``py::exception`` variable
+and use this in the associated exception translator (note: it is often useful
+to make this a static declaration when using it inside a lambda expression
+without requiring capturing).
+
+The following example demonstrates this for a hypothetical exception classes
+``MyCustomException`` and ``OtherException``: the first is translated to a
+custom python exception ``MyCustomError``, while the second is translated to a
+standard python RuntimeError:
+
+.. code-block:: cpp
+
+    static py::exception<MyCustomException> exc(m, "MyCustomError");
+    py::register_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) std::rethrow_exception(p);
+        } catch (const MyCustomException &e) {
+            exc(e.what());
+        } catch (const OtherException &e) {
+            PyErr_SetString(PyExc_RuntimeError, e.what());
+        }
+    });
+
+Multiple exceptions can be handled by a single translator, as shown in the
+example above. If the exception is not caught by the current translator, the
+previously registered one gets a chance.
+
+If none of the registered exception translators is able to handle the
+exception, it is handled by the default converter as described in the previous
+section.
+
+.. seealso::
+
+    The file :file:`tests/test_exceptions.cpp` contains examples
+    of various custom exception translators and custom exception types.
+
+.. note::
+
+    Call either ``PyErr_SetString`` or a custom exception's call
+    operator (``exc(string)``) for every exception caught in a custom exception
+    translator.  Failure to do so will cause Python to crash with ``SystemError:
+    error return without exception set``.
+
+    Exceptions that you do not plan to handle should simply not be caught, or
+    may be explicitly (re-)thrown to delegate it to the other,
+    previously-declared existing exception translators.
+
+    Note that ``libc++`` and ``libstdc++`` `behave differently <https://stackoverflow.com/questions/19496643/using-clang-fvisibility-hidden-and-typeinfo-and-type-erasure/28827430>`_
+    with ``-fvisibility=hidden``. Therefore exceptions that are used across ABI boundaries need to be explicitly exported, as exercised in ``tests/test_exceptions.h``.
+    See also: "Problems with C++ exceptions" under `GCC Wiki <https://gcc.gnu.org/wiki/Visibility>`_.
+
+
+Local vs Global Exception Translators
+=====================================
+
+When a global exception translator is registered, it will be applied across all
+modules in the reverse order of registration. This can create behavior where the
+order of module import influences how exceptions are translated.
+
+If module1 has the following translator:
+
+.. code-block:: cpp
+
+      py::register_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) std::rethrow_exception(p);
+        } catch (const std::invalid_argument &e) {
+            PyErr_SetString("module1 handled this")
+        }
+      }
+
+and module2 has the following similar translator:
+
+.. code-block:: cpp
+
+      py::register_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) std::rethrow_exception(p);
+        } catch (const std::invalid_argument &e) {
+            PyErr_SetString("module2 handled this")
+        }
+      }
+
+then which translator handles the invalid_argument will be determined by the
+order that module1 and module2 are imported. Since exception translators are
+applied in the reverse order of registration, which ever module was imported
+last will "win" and that translator will be applied.
+
+If there are multiple pybind11 modules that share exception types (either
+standard built-in or custom) loaded into a single python instance and
+consistent error handling behavior is needed, then local translators should be
+used.
+
+Changing the previous example to use ``register_local_exception_translator``
+would mean that when invalid_argument is thrown in the module2 code, the
+module2 translator will always handle it, while in module1, the module1
+translator will do the same.
+
+.. _handling_python_exceptions_cpp:
+
+Handling exceptions from Python in C++
+======================================
+
+When C++ calls Python functions, such as in a callback function or when
+manipulating Python objects, and Python raises an ``Exception``, pybind11
+converts the Python exception into a C++ exception of type
+:class:`pybind11::error_already_set` whose payload contains a C++ string textual
+summary and the actual Python exception. ``error_already_set`` is used to
+propagate Python exception back to Python (or possibly, handle them in C++).
+
+.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+
++--------------------------------------+--------------------------------------+
+|  Exception raised in Python          |  Thrown as C++ exception type        |
++======================================+======================================+
+| Any Python ``Exception``             | :class:`pybind11::error_already_set` |
++--------------------------------------+--------------------------------------+
+
+For example:
+
+.. code-block:: cpp
+
+    try {
+        // open("missing.txt", "r")
+        auto file = py::module_::import("io").attr("open")("missing.txt", "r");
+        auto text = file.attr("read")();
+        file.attr("close")();
+    } catch (py::error_already_set &e) {
+        if (e.matches(PyExc_FileNotFoundError)) {
+            py::print("missing.txt not found");
+        } else if (e.matches(PyExc_PermissionError)) {
+            py::print("missing.txt found but not accessible");
+        } else {
+            throw;
+        }
+    }
+
+Note that C++ to Python exception translation does not apply here, since that is
+a method for translating C++ exceptions to Python, not vice versa. The error raised
+from Python is always ``error_already_set``.
+
+This example illustrates this behavior:
+
+.. code-block:: cpp
+
+    try {
+        py::eval("raise ValueError('The Ring')");
+    } catch (py::value_error &boromir) {
+        // Boromir never gets the ring
+        assert(false);
+    } catch (py::error_already_set &frodo) {
+        // Frodo gets the ring
+        py::print("I will take the ring");
+    }
+
+    try {
+        // py::value_error is a request for pybind11 to raise a Python exception
+        throw py::value_error("The ball");
+    } catch (py::error_already_set &cat) {
+        // cat won't catch the ball since
+        // py::value_error is not a Python exception
+        assert(false);
+    } catch (py::value_error &dog) {
+        // dog will catch the ball
+        py::print("Run Spot run");
+        throw;  // Throw it again (pybind11 will raise ValueError)
+    }
+
+Handling errors from the Python C API
+=====================================
+
+Where possible, use :ref:`pybind11 wrappers <wrappers>` instead of calling
+the Python C API directly. When calling the Python C API directly, in
+addition to manually managing reference counts, one must follow the pybind11
+error protocol, which is outlined here.
+
+After calling the Python C API, if Python returns an error,
+``throw py::error_already_set();``, which allows pybind11 to deal with the
+exception and pass it back to the Python interpreter. This includes calls to
+the error setting functions such as ``PyErr_SetString``.
+
+.. code-block:: cpp
+
+    PyErr_SetString(PyExc_TypeError, "C API type error demo");
+    throw py::error_already_set();
+
+    // But it would be easier to simply...
+    throw py::type_error("pybind11 wrapper type error");
+
+Alternately, to ignore the error, call `PyErr_Clear
+<https://docs.python.org/3/c-api/exceptions.html#c.PyErr_Clear>`_.
+
+Any Python error must be thrown or cleared, or Python/pybind11 will be left in
+an invalid state.
+
+Chaining exceptions ('raise from')
+==================================
+
+Python has a mechanism for indicating that exceptions were caused by other
+exceptions:
+
+.. code-block:: py
+
+    try:
+        print(1 / 0)
+    except Exception as exc:
+        raise RuntimeError("could not divide by zero") from exc
+
+To do a similar thing in pybind11, you can use the ``py::raise_from`` function. It
+sets the current python error indicator, so to continue propagating the exception
+you should ``throw py::error_already_set()``.
+
+.. code-block:: cpp
+
+    try {
+        py::eval("print(1 / 0"));
+    } catch (py::error_already_set &e) {
+        py::raise_from(e, PyExc_RuntimeError, "could not divide by zero");
+        throw py::error_already_set();
+    }
+
+.. versionadded:: 2.8
+
+.. _unraisable_exceptions:
+
+Handling unraisable exceptions
+==============================
+
+If a Python function invoked from a C++ destructor or any function marked
+``noexcept(true)`` (collectively, "noexcept functions") throws an exception, there
+is no way to propagate the exception, as such functions may not throw.
+Should they throw or fail to catch any exceptions in their call graph,
+the C++ runtime calls ``std::terminate()`` to abort immediately.
+
+Similarly, Python exceptions raised in a class's ``__del__`` method do not
+propagate, but are logged by Python as an unraisable error. In Python 3.8+, a
+`system hook is triggered
+<https://docs.python.org/3/library/sys.html#sys.unraisablehook>`_
+and an auditing event is logged.
+
+Any noexcept function should have a try-catch block that traps
+class:`error_already_set` (or any other exception that can occur). Note that
+pybind11 wrappers around Python exceptions such as
+:class:`pybind11::value_error` are *not* Python exceptions; they are C++
+exceptions that pybind11 catches and converts to Python exceptions. Noexcept
+functions cannot propagate these exceptions either. A useful approach is to
+convert them to Python exceptions and then ``discard_as_unraisable`` as shown
+below.
+
+.. code-block:: cpp
+
+    void nonthrowing_func() noexcept(true) {
+        try {
+            // ...
+        } catch (py::error_already_set &eas) {
+            // Discard the Python error using Python APIs, using the C++ magic
+            // variable __func__. Python already knows the type and value and of the
+            // exception object.
+            eas.discard_as_unraisable(__func__);
+        } catch (const std::exception &e) {
+            // Log and discard C++ exceptions.
+            third_party::log(e);
+        }
+    }
+
+.. versionadded:: 2.6
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/functions.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/functions.rst
new file mode 100644
index 000000000..69e3d8a1d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/functions.rst
@@ -0,0 +1,614 @@
+Functions
+#########
+
+Before proceeding with this section, make sure that you are already familiar
+with the basics of binding functions and classes, as explained in :doc:`/basics`
+and :doc:`/classes`. The following guide is applicable to both free and member
+functions, i.e. *methods* in Python.
+
+.. _return_value_policies:
+
+Return value policies
+=====================
+
+Python and C++ use fundamentally different ways of managing the memory and
+lifetime of objects managed by them. This can lead to issues when creating
+bindings for functions that return a non-trivial type. Just by looking at the
+type information, it is not clear whether Python should take charge of the
+returned value and eventually free its resources, or if this is handled on the
+C++ side. For this reason, pybind11 provides a several *return value policy*
+annotations that can be passed to the :func:`module_::def` and
+:func:`class_::def` functions. The default policy is
+:enum:`return_value_policy::automatic`.
+
+Return value policies are tricky, and it's very important to get them right.
+Just to illustrate what can go wrong, consider the following simple example:
+
+.. code-block:: cpp
+
+    /* Function declaration */
+    Data *get_data() { return _data; /* (pointer to a static data structure) */ }
+    ...
+
+    /* Binding code */
+    m.def("get_data", &get_data); // <-- KABOOM, will cause crash when called from Python
+
+What's going on here? When ``get_data()`` is called from Python, the return
+value (a native C++ type) must be wrapped to turn it into a usable Python type.
+In this case, the default return value policy (:enum:`return_value_policy::automatic`)
+causes pybind11 to assume ownership of the static ``_data`` instance.
+
+When Python's garbage collector eventually deletes the Python
+wrapper, pybind11 will also attempt to delete the C++ instance (via ``operator
+delete()``) due to the implied ownership. At this point, the entire application
+will come crashing down, though errors could also be more subtle and involve
+silent data corruption.
+
+In the above example, the policy :enum:`return_value_policy::reference` should have
+been specified so that the global data instance is only *referenced* without any
+implied transfer of ownership, i.e.:
+
+.. code-block:: cpp
+
+    m.def("get_data", &get_data, py::return_value_policy::reference);
+
+On the other hand, this is not the right policy for many other situations,
+where ignoring ownership could lead to resource leaks.
+As a developer using pybind11, it's important to be familiar with the different
+return value policies, including which situation calls for which one of them.
+The following table provides an overview of available policies:
+
+.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+
++--------------------------------------------------+----------------------------------------------------------------------------+
+| Return value policy                              | Description                                                                |
++==================================================+============================================================================+
+| :enum:`return_value_policy::take_ownership`      | Reference an existing object (i.e. do not create a new copy) and take      |
+|                                                  | ownership. Python will call the destructor and delete operator when the    |
+|                                                  | object's reference count reaches zero. Undefined behavior ensues when the  |
+|                                                  | C++ side does the same, or when the data was not dynamically allocated.    |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::copy`                | Create a new copy of the returned object, which will be owned by Python.   |
+|                                                  | This policy is comparably safe because the lifetimes of the two instances  |
+|                                                  | are decoupled.                                                             |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::move`                | Use ``std::move`` to move the return value contents into a new instance    |
+|                                                  | that will be owned by Python. This policy is comparably safe because the   |
+|                                                  | lifetimes of the two instances (move source and destination) are decoupled.|
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::reference`           | Reference an existing object, but do not take ownership. The C++ side is   |
+|                                                  | responsible for managing the object's lifetime and deallocating it when    |
+|                                                  | it is no longer used. Warning: undefined behavior will ensue when the C++  |
+|                                                  | side deletes an object that is still referenced and used by Python.        |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::reference_internal`  | Indicates that the lifetime of the return value is tied to the lifetime    |
+|                                                  | of a parent object, namely the implicit ``this``, or ``self`` argument of  |
+|                                                  | the called method or property. Internally, this policy works just like     |
+|                                                  | :enum:`return_value_policy::reference` but additionally applies a          |
+|                                                  | ``keep_alive<0, 1>`` *call policy* (described in the next section) that    |
+|                                                  | prevents the parent object from being garbage collected as long as the     |
+|                                                  | return value is referenced by Python. This is the default policy for       |
+|                                                  | property getters created via ``def_property``, ``def_readwrite``, etc.     |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::automatic`           | This policy falls back to the policy                                       |
+|                                                  | :enum:`return_value_policy::take_ownership` when the return value is a     |
+|                                                  | pointer. Otherwise, it uses :enum:`return_value_policy::move` or           |
+|                                                  | :enum:`return_value_policy::copy` for rvalue and lvalue references,        |
+|                                                  | respectively. See above for a description of what all of these different   |
+|                                                  | policies do. This is the default policy for ``py::class_``-wrapped types.  |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::automatic_reference` | As above, but use policy :enum:`return_value_policy::reference` when the   |
+|                                                  | return value is a pointer. This is the default conversion policy for       |
+|                                                  | function arguments when calling Python functions manually from C++ code    |
+|                                                  | (i.e. via ``handle::operator()``) and the casters in ``pybind11/stl.h``.   |
+|                                                  | You probably won't need to use this explicitly.                            |
++--------------------------------------------------+----------------------------------------------------------------------------+
+
+Return value policies can also be applied to properties:
+
+.. code-block:: cpp
+
+    class_<MyClass>(m, "MyClass")
+        .def_property("data", &MyClass::getData, &MyClass::setData,
+                      py::return_value_policy::copy);
+
+Technically, the code above applies the policy to both the getter and the
+setter function, however, the setter doesn't really care about *return*
+value policies which makes this a convenient terse syntax. Alternatively,
+targeted arguments can be passed through the :class:`cpp_function` constructor:
+
+.. code-block:: cpp
+
+    class_<MyClass>(m, "MyClass")
+        .def_property("data",
+            py::cpp_function(&MyClass::getData, py::return_value_policy::copy),
+            py::cpp_function(&MyClass::setData)
+        );
+
+.. warning::
+
+    Code with invalid return value policies might access uninitialized memory or
+    free data structures multiple times, which can lead to hard-to-debug
+    non-determinism and segmentation faults, hence it is worth spending the
+    time to understand all the different options in the table above.
+
+.. note::
+
+    One important aspect of the above policies is that they only apply to
+    instances which pybind11 has *not* seen before, in which case the policy
+    clarifies essential questions about the return value's lifetime and
+    ownership.  When pybind11 knows the instance already (as identified by its
+    type and address in memory), it will return the existing Python object
+    wrapper rather than creating a new copy.
+
+.. note::
+
+    The next section on :ref:`call_policies` discusses *call policies* that can be
+    specified *in addition* to a return value policy from the list above. Call
+    policies indicate reference relationships that can involve both return values
+    and parameters of functions.
+
+.. note::
+
+   As an alternative to elaborate call policies and lifetime management logic,
+   consider using smart pointers (see the section on :ref:`smart_pointers` for
+   details). Smart pointers can tell whether an object is still referenced from
+   C++ or Python, which generally eliminates the kinds of inconsistencies that
+   can lead to crashes or undefined behavior. For functions returning smart
+   pointers, it is not necessary to specify a return value policy.
+
+.. _call_policies:
+
+Additional call policies
+========================
+
+In addition to the above return value policies, further *call policies* can be
+specified to indicate dependencies between parameters or ensure a certain state
+for the function call.
+
+Keep alive
+----------
+
+In general, this policy is required when the C++ object is any kind of container
+and another object is being added to the container. ``keep_alive<Nurse, Patient>``
+indicates that the argument with index ``Patient`` should be kept alive at least
+until the argument with index ``Nurse`` is freed by the garbage collector. Argument
+indices start at one, while zero refers to the return value. For methods, index
+``1`` refers to the implicit ``this`` pointer, while regular arguments begin at
+index ``2``. Arbitrarily many call policies can be specified. When a ``Nurse``
+with value ``None`` is detected at runtime, the call policy does nothing.
+
+When the nurse is not a pybind11-registered type, the implementation internally
+relies on the ability to create a *weak reference* to the nurse object. When
+the nurse object is not a pybind11-registered type and does not support weak
+references, an exception will be thrown.
+
+If you use an incorrect argument index, you will get a ``RuntimeError`` saying
+``Could not activate keep_alive!``. You should review the indices you're using.
+
+Consider the following example: here, the binding code for a list append
+operation ties the lifetime of the newly added element to the underlying
+container:
+
+.. code-block:: cpp
+
+    py::class_<List>(m, "List")
+        .def("append", &List::append, py::keep_alive<1, 2>());
+
+For consistency, the argument indexing is identical for constructors. Index
+``1`` still refers to the implicit ``this`` pointer, i.e. the object which is
+being constructed. Index ``0`` refers to the return type which is presumed to
+be ``void`` when a constructor is viewed like a function. The following example
+ties the lifetime of the constructor element to the constructed object:
+
+.. code-block:: cpp
+
+    py::class_<Nurse>(m, "Nurse")
+        .def(py::init<Patient &>(), py::keep_alive<1, 2>());
+
+.. note::
+
+    ``keep_alive`` is analogous to the ``with_custodian_and_ward`` (if Nurse,
+    Patient != 0) and ``with_custodian_and_ward_postcall`` (if Nurse/Patient ==
+    0) policies from Boost.Python.
+
+Call guard
+----------
+
+The ``call_guard<T>`` policy allows any scope guard type ``T`` to be placed
+around the function call. For example, this definition:
+
+.. code-block:: cpp
+
+    m.def("foo", foo, py::call_guard<T>());
+
+is equivalent to the following pseudocode:
+
+.. code-block:: cpp
+
+    m.def("foo", [](args...) {
+        T scope_guard;
+        return foo(args...); // forwarded arguments
+    });
+
+The only requirement is that ``T`` is default-constructible, but otherwise any
+scope guard will work. This is very useful in combination with ``gil_scoped_release``.
+See :ref:`gil`.
+
+Multiple guards can also be specified as ``py::call_guard<T1, T2, T3...>``. The
+constructor order is left to right and destruction happens in reverse.
+
+.. seealso::
+
+    The file :file:`tests/test_call_policies.cpp` contains a complete example
+    that demonstrates using `keep_alive` and `call_guard` in more detail.
+
+.. _python_objects_as_args:
+
+Python objects as arguments
+===========================
+
+pybind11 exposes all major Python types using thin C++ wrapper classes. These
+wrapper classes can also be used as parameters of functions in bindings, which
+makes it possible to directly work with native Python types on the C++ side.
+For instance, the following statement iterates over a Python ``dict``:
+
+.. code-block:: cpp
+
+    void print_dict(const py::dict& dict) {
+        /* Easily interact with Python types */
+        for (auto item : dict)
+            std::cout << "key=" << std::string(py::str(item.first)) << ", "
+                      << "value=" << std::string(py::str(item.second)) << std::endl;
+    }
+
+It can be exported:
+
+.. code-block:: cpp
+
+    m.def("print_dict", &print_dict);
+
+And used in Python as usual:
+
+.. code-block:: pycon
+
+    >>> print_dict({"foo": 123, "bar": "hello"})
+    key=foo, value=123
+    key=bar, value=hello
+
+For more information on using Python objects in C++, see :doc:`/advanced/pycpp/index`.
+
+Accepting \*args and \*\*kwargs
+===============================
+
+Python provides a useful mechanism to define functions that accept arbitrary
+numbers of arguments and keyword arguments:
+
+.. code-block:: python
+
+   def generic(*args, **kwargs):
+       ...  # do something with args and kwargs
+
+Such functions can also be created using pybind11:
+
+.. code-block:: cpp
+
+   void generic(py::args args, const py::kwargs& kwargs) {
+       /// .. do something with args
+       if (kwargs)
+           /// .. do something with kwargs
+   }
+
+   /// Binding code
+   m.def("generic", &generic);
+
+The class ``py::args`` derives from ``py::tuple`` and ``py::kwargs`` derives
+from ``py::dict``.
+
+You may also use just one or the other, and may combine these with other
+arguments.  Note, however, that ``py::kwargs`` must always be the last argument
+of the function, and ``py::args`` implies that any further arguments are
+keyword-only (see :ref:`keyword_only_arguments`).
+
+Please refer to the other examples for details on how to iterate over these,
+and on how to cast their entries into C++ objects. A demonstration is also
+available in ``tests/test_kwargs_and_defaults.cpp``.
+
+.. note::
+
+    When combining \*args or \*\*kwargs with :ref:`keyword_args` you should
+    *not* include ``py::arg`` tags for the ``py::args`` and ``py::kwargs``
+    arguments.
+
+Default arguments revisited
+===========================
+
+The section on :ref:`default_args` previously discussed basic usage of default
+arguments using pybind11. One noteworthy aspect of their implementation is that
+default arguments are converted to Python objects right at declaration time.
+Consider the following example:
+
+.. code-block:: cpp
+
+    py::class_<MyClass>("MyClass")
+        .def("myFunction", py::arg("arg") = SomeType(123));
+
+In this case, pybind11 must already be set up to deal with values of the type
+``SomeType`` (via a prior instantiation of ``py::class_<SomeType>``), or an
+exception will be thrown.
+
+Another aspect worth highlighting is that the "preview" of the default argument
+in the function signature is generated using the object's ``__repr__`` method.
+If not available, the signature may not be very helpful, e.g.:
+
+.. code-block:: pycon
+
+    FUNCTIONS
+    ...
+    |  myFunction(...)
+    |      Signature : (MyClass, arg : SomeType = <SomeType object at 0x101b7b080>) -> NoneType
+    ...
+
+The first way of addressing this is by defining ``SomeType.__repr__``.
+Alternatively, it is possible to specify the human-readable preview of the
+default argument manually using the ``arg_v`` notation:
+
+.. code-block:: cpp
+
+    py::class_<MyClass>("MyClass")
+        .def("myFunction", py::arg_v("arg", SomeType(123), "SomeType(123)"));
+
+Sometimes it may be necessary to pass a null pointer value as a default
+argument. In this case, remember to cast it to the underlying type in question,
+like so:
+
+.. code-block:: cpp
+
+    py::class_<MyClass>("MyClass")
+        .def("myFunction", py::arg("arg") = static_cast<SomeType *>(nullptr));
+
+.. _keyword_only_arguments:
+
+Keyword-only arguments
+======================
+
+Python implements keyword-only arguments by specifying an unnamed ``*``
+argument in a function definition:
+
+.. code-block:: python
+
+    def f(a, *, b):  # a can be positional or via keyword; b must be via keyword
+        pass
+
+
+    f(a=1, b=2)  # good
+    f(b=2, a=1)  # good
+    f(1, b=2)  # good
+    f(1, 2)  # TypeError: f() takes 1 positional argument but 2 were given
+
+Pybind11 provides a ``py::kw_only`` object that allows you to implement
+the same behaviour by specifying the object between positional and keyword-only
+argument annotations when registering the function:
+
+.. code-block:: cpp
+
+    m.def("f", [](int a, int b) { /* ... */ },
+          py::arg("a"), py::kw_only(), py::arg("b"));
+
+.. versionadded:: 2.6
+
+A ``py::args`` argument implies that any following arguments are keyword-only,
+as if ``py::kw_only()`` had been specified in the same relative location of the
+argument list as the ``py::args`` argument.  The ``py::kw_only()`` may be
+included to be explicit about this, but is not required.
+
+.. versionchanged:: 2.9
+   This can now be combined with ``py::args``. Before, ``py::args`` could only
+   occur at the end of the argument list, or immediately before a ``py::kwargs``
+   argument at the end.
+
+
+Positional-only arguments
+=========================
+
+Python 3.8 introduced a new positional-only argument syntax, using ``/`` in the
+function definition (note that this has been a convention for CPython
+positional arguments, such as in ``pow()``, since Python 2). You can
+do the same thing in any version of Python using ``py::pos_only()``:
+
+.. code-block:: cpp
+
+   m.def("f", [](int a, int b) { /* ... */ },
+          py::arg("a"), py::pos_only(), py::arg("b"));
+
+You now cannot give argument ``a`` by keyword. This can be combined with
+keyword-only arguments, as well.
+
+.. versionadded:: 2.6
+
+.. _nonconverting_arguments:
+
+Non-converting arguments
+========================
+
+Certain argument types may support conversion from one type to another.  Some
+examples of conversions are:
+
+* :ref:`implicit_conversions` declared using ``py::implicitly_convertible<A,B>()``
+* Calling a method accepting a double with an integer argument
+* Calling a ``std::complex<float>`` argument with a non-complex python type
+  (for example, with a float).  (Requires the optional ``pybind11/complex.h``
+  header).
+* Calling a function taking an Eigen matrix reference with a numpy array of the
+  wrong type or of an incompatible data layout.  (Requires the optional
+  ``pybind11/eigen.h`` header).
+
+This behaviour is sometimes undesirable: the binding code may prefer to raise
+an error rather than convert the argument.  This behaviour can be obtained
+through ``py::arg`` by calling the ``.noconvert()`` method of the ``py::arg``
+object, such as:
+
+.. code-block:: cpp
+
+    m.def("floats_only", [](double f) { return 0.5 * f; }, py::arg("f").noconvert());
+    m.def("floats_preferred", [](double f) { return 0.5 * f; }, py::arg("f"));
+
+Attempting the call the second function (the one without ``.noconvert()``) with
+an integer will succeed, but attempting to call the ``.noconvert()`` version
+will fail with a ``TypeError``:
+
+.. code-block:: pycon
+
+    >>> floats_preferred(4)
+    2.0
+    >>> floats_only(4)
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    TypeError: floats_only(): incompatible function arguments. The following argument types are supported:
+        1. (f: float) -> float
+
+    Invoked with: 4
+
+You may, of course, combine this with the :var:`_a` shorthand notation (see
+:ref:`keyword_args`) and/or :ref:`default_args`.  It is also permitted to omit
+the argument name by using the ``py::arg()`` constructor without an argument
+name, i.e. by specifying ``py::arg().noconvert()``.
+
+.. note::
+
+    When specifying ``py::arg`` options it is necessary to provide the same
+    number of options as the bound function has arguments.  Thus if you want to
+    enable no-convert behaviour for just one of several arguments, you will
+    need to specify a ``py::arg()`` annotation for each argument with the
+    no-convert argument modified to ``py::arg().noconvert()``.
+
+.. _none_arguments:
+
+Allow/Prohibiting None arguments
+================================
+
+When a C++ type registered with :class:`py::class_` is passed as an argument to
+a function taking the instance as pointer or shared holder (e.g. ``shared_ptr``
+or a custom, copyable holder as described in :ref:`smart_pointers`), pybind
+allows ``None`` to be passed from Python which results in calling the C++
+function with ``nullptr`` (or an empty holder) for the argument.
+
+To explicitly enable or disable this behaviour, using the
+``.none`` method of the :class:`py::arg` object:
+
+.. code-block:: cpp
+
+    py::class_<Dog>(m, "Dog").def(py::init<>());
+    py::class_<Cat>(m, "Cat").def(py::init<>());
+    m.def("bark", [](Dog *dog) -> std::string {
+        if (dog) return "woof!"; /* Called with a Dog instance */
+        else return "(no dog)"; /* Called with None, dog == nullptr */
+    }, py::arg("dog").none(true));
+    m.def("meow", [](Cat *cat) -> std::string {
+        // Can't be called with None argument
+        return "meow";
+    }, py::arg("cat").none(false));
+
+With the above, the Python call ``bark(None)`` will return the string ``"(no
+dog)"``, while attempting to call ``meow(None)`` will raise a ``TypeError``:
+
+.. code-block:: pycon
+
+    >>> from animals import Dog, Cat, bark, meow
+    >>> bark(Dog())
+    'woof!'
+    >>> meow(Cat())
+    'meow'
+    >>> bark(None)
+    '(no dog)'
+    >>> meow(None)
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    TypeError: meow(): incompatible function arguments. The following argument types are supported:
+        1. (cat: animals.Cat) -> str
+
+    Invoked with: None
+
+The default behaviour when the tag is unspecified is to allow ``None``.
+
+.. note::
+
+    Even when ``.none(true)`` is specified for an argument, ``None`` will be converted to a
+    ``nullptr`` *only* for custom and :ref:`opaque <opaque>` types. Pointers to built-in types
+    (``double *``, ``int *``, ...) and STL types (``std::vector<T> *``, ...; if ``pybind11/stl.h``
+    is included) are copied when converted to C++ (see :doc:`/advanced/cast/overview`) and will
+    not allow ``None`` as argument.  To pass optional argument of these copied types consider
+    using ``std::optional<T>``
+
+.. _overload_resolution:
+
+Overload resolution order
+=========================
+
+When a function or method with multiple overloads is called from Python,
+pybind11 determines which overload to call in two passes.  The first pass
+attempts to call each overload without allowing argument conversion (as if
+every argument had been specified as ``py::arg().noconvert()`` as described
+above).
+
+If no overload succeeds in the no-conversion first pass, a second pass is
+attempted in which argument conversion is allowed (except where prohibited via
+an explicit ``py::arg().noconvert()`` attribute in the function definition).
+
+If the second pass also fails a ``TypeError`` is raised.
+
+Within each pass, overloads are tried in the order they were registered with
+pybind11. If the ``py::prepend()`` tag is added to the definition, a function
+can be placed at the beginning of the overload sequence instead, allowing user
+overloads to proceed built in functions.
+
+What this means in practice is that pybind11 will prefer any overload that does
+not require conversion of arguments to an overload that does, but otherwise
+prefers earlier-defined overloads to later-defined ones.
+
+.. note::
+
+    pybind11 does *not* further prioritize based on the number/pattern of
+    overloaded arguments.  That is, pybind11 does not prioritize a function
+    requiring one conversion over one requiring three, but only prioritizes
+    overloads requiring no conversion at all to overloads that require
+    conversion of at least one argument.
+
+.. versionadded:: 2.6
+
+    The ``py::prepend()`` tag.
+
+Binding functions with template parameters
+==========================================
+
+You can bind functions that have template parameters. Here's a function:
+
+.. code-block:: cpp
+
+    template <typename T>
+    void set(T t);
+
+C++ templates cannot be instantiated at runtime, so you cannot bind the
+non-instantiated function:
+
+.. code-block:: cpp
+
+    // BROKEN (this will not compile)
+    m.def("set", &set);
+
+You must bind each instantiated function template separately. You may bind
+each instantiation with the same name, which will be treated the same as
+an overloaded function:
+
+.. code-block:: cpp
+
+    m.def("set", &set<int>);
+    m.def("set", &set<std::string>);
+
+Sometimes it's more clear to bind them with separate names, which is also
+an option:
+
+.. code-block:: cpp
+
+    m.def("setInt", &set<int>);
+    m.def("setString", &set<std::string>);
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/misc.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/misc.rst
new file mode 100644
index 000000000..edab15fcb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/misc.rst
@@ -0,0 +1,337 @@
+Miscellaneous
+#############
+
+.. _macro_notes:
+
+General notes regarding convenience macros
+==========================================
+
+pybind11 provides a few convenience macros such as
+:func:`PYBIND11_DECLARE_HOLDER_TYPE` and ``PYBIND11_OVERRIDE_*``. Since these
+are "just" macros that are evaluated in the preprocessor (which has no concept
+of types), they *will* get confused by commas in a template argument; for
+example, consider:
+
+.. code-block:: cpp
+
+    PYBIND11_OVERRIDE(MyReturnType<T1, T2>, Class<T3, T4>, func)
+
+The limitation of the C preprocessor interprets this as five arguments (with new
+arguments beginning after each comma) rather than three.  To get around this,
+there are two alternatives: you can use a type alias, or you can wrap the type
+using the ``PYBIND11_TYPE`` macro:
+
+.. code-block:: cpp
+
+    // Version 1: using a type alias
+    using ReturnType = MyReturnType<T1, T2>;
+    using ClassType = Class<T3, T4>;
+    PYBIND11_OVERRIDE(ReturnType, ClassType, func);
+
+    // Version 2: using the PYBIND11_TYPE macro:
+    PYBIND11_OVERRIDE(PYBIND11_TYPE(MyReturnType<T1, T2>),
+                      PYBIND11_TYPE(Class<T3, T4>), func)
+
+The ``PYBIND11_MAKE_OPAQUE`` macro does *not* require the above workarounds.
+
+.. _gil:
+
+Global Interpreter Lock (GIL)
+=============================
+
+When calling a C++ function from Python, the GIL is always held.
+The classes :class:`gil_scoped_release` and :class:`gil_scoped_acquire` can be
+used to acquire and release the global interpreter lock in the body of a C++
+function call. In this way, long-running C++ code can be parallelized using
+multiple Python threads. Taking :ref:`overriding_virtuals` as an example, this
+could be realized as follows (important changes highlighted):
+
+.. code-block:: cpp
+    :emphasize-lines: 8,9,31,32
+
+    class PyAnimal : public Animal {
+    public:
+        /* Inherit the constructors */
+        using Animal::Animal;
+
+        /* Trampoline (need one for each virtual function) */
+        std::string go(int n_times) {
+            /* Acquire GIL before calling Python code */
+            py::gil_scoped_acquire acquire;
+
+            PYBIND11_OVERRIDE_PURE(
+                std::string, /* Return type */
+                Animal,      /* Parent class */
+                go,          /* Name of function */
+                n_times      /* Argument(s) */
+            );
+        }
+    };
+
+    PYBIND11_MODULE(example, m) {
+        py::class_<Animal, PyAnimal> animal(m, "Animal");
+        animal
+            .def(py::init<>())
+            .def("go", &Animal::go);
+
+        py::class_<Dog>(m, "Dog", animal)
+            .def(py::init<>());
+
+        m.def("call_go", [](Animal *animal) -> std::string {
+            /* Release GIL before calling into (potentially long-running) C++ code */
+            py::gil_scoped_release release;
+            return call_go(animal);
+        });
+    }
+
+The ``call_go`` wrapper can also be simplified using the ``call_guard`` policy
+(see :ref:`call_policies`) which yields the same result:
+
+.. code-block:: cpp
+
+    m.def("call_go", &call_go, py::call_guard<py::gil_scoped_release>());
+
+
+Binding sequence data types, iterators, the slicing protocol, etc.
+==================================================================
+
+Please refer to the supplemental example for details.
+
+.. seealso::
+
+    The file :file:`tests/test_sequences_and_iterators.cpp` contains a
+    complete example that shows how to bind a sequence data type, including
+    length queries (``__len__``), iterators (``__iter__``), the slicing
+    protocol and other kinds of useful operations.
+
+
+Partitioning code over multiple extension modules
+=================================================
+
+It's straightforward to split binding code over multiple extension modules,
+while referencing types that are declared elsewhere. Everything "just" works
+without any special precautions. One exception to this rule occurs when
+extending a type declared in another extension module. Recall the basic example
+from Section :ref:`inheritance`.
+
+.. code-block:: cpp
+
+    py::class_<Pet> pet(m, "Pet");
+    pet.def(py::init<const std::string &>())
+       .def_readwrite("name", &Pet::name);
+
+    py::class_<Dog>(m, "Dog", pet /* <- specify parent */)
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Suppose now that ``Pet`` bindings are defined in a module named ``basic``,
+whereas the ``Dog`` bindings are defined somewhere else. The challenge is of
+course that the variable ``pet`` is not available anymore though it is needed
+to indicate the inheritance relationship to the constructor of ``class_<Dog>``.
+However, it can be acquired as follows:
+
+.. code-block:: cpp
+
+    py::object pet = (py::object) py::module_::import("basic").attr("Pet");
+
+    py::class_<Dog>(m, "Dog", pet)
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Alternatively, you can specify the base class as a template parameter option to
+``class_``, which performs an automated lookup of the corresponding Python
+type. Like the above code, however, this also requires invoking the ``import``
+function once to ensure that the pybind11 binding code of the module ``basic``
+has been executed:
+
+.. code-block:: cpp
+
+    py::module_::import("basic");
+
+    py::class_<Dog, Pet>(m, "Dog")
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Naturally, both methods will fail when there are cyclic dependencies.
+
+Note that pybind11 code compiled with hidden-by-default symbol visibility (e.g.
+via the command line flag ``-fvisibility=hidden`` on GCC/Clang), which is
+required for proper pybind11 functionality, can interfere with the ability to
+access types defined in another extension module.  Working around this requires
+manually exporting types that are accessed by multiple extension modules;
+pybind11 provides a macro to do just this:
+
+.. code-block:: cpp
+
+    class PYBIND11_EXPORT Dog : public Animal {
+        ...
+    };
+
+Note also that it is possible (although would rarely be required) to share arbitrary
+C++ objects between extension modules at runtime. Internal library data is shared
+between modules using capsule machinery [#f6]_ which can be also utilized for
+storing, modifying and accessing user-defined data. Note that an extension module
+will "see" other extensions' data if and only if they were built with the same
+pybind11 version. Consider the following example:
+
+.. code-block:: cpp
+
+    auto data = reinterpret_cast<MyData *>(py::get_shared_data("mydata"));
+    if (!data)
+        data = static_cast<MyData *>(py::set_shared_data("mydata", new MyData(42)));
+
+If the above snippet was used in several separately compiled extension modules,
+the first one to be imported would create a ``MyData`` instance and associate
+a ``"mydata"`` key with a pointer to it. Extensions that are imported later
+would be then able to access the data behind the same pointer.
+
+.. [#f6] https://docs.python.org/3/extending/extending.html#using-capsules
+
+Module Destructors
+==================
+
+pybind11 does not provide an explicit mechanism to invoke cleanup code at
+module destruction time. In rare cases where such functionality is required, it
+is possible to emulate it using Python capsules or weak references with a
+destruction callback.
+
+.. code-block:: cpp
+
+    auto cleanup_callback = []() {
+        // perform cleanup here -- this function is called with the GIL held
+    };
+
+    m.add_object("_cleanup", py::capsule(cleanup_callback));
+
+This approach has the potential downside that instances of classes exposed
+within the module may still be alive when the cleanup callback is invoked
+(whether this is acceptable will generally depend on the application).
+
+Alternatively, the capsule may also be stashed within a type object, which
+ensures that it not called before all instances of that type have been
+collected:
+
+.. code-block:: cpp
+
+    auto cleanup_callback = []() { /* ... */ };
+    m.attr("BaseClass").attr("_cleanup") = py::capsule(cleanup_callback);
+
+Both approaches also expose a potentially dangerous ``_cleanup`` attribute in
+Python, which may be undesirable from an API standpoint (a premature explicit
+call from Python might lead to undefined behavior). Yet another approach that
+avoids this issue involves weak reference with a cleanup callback:
+
+.. code-block:: cpp
+
+    // Register a callback function that is invoked when the BaseClass object is collected
+    py::cpp_function cleanup_callback(
+        [](py::handle weakref) {
+            // perform cleanup here -- this function is called with the GIL held
+
+            weakref.dec_ref(); // release weak reference
+        }
+    );
+
+    // Create a weak reference with a cleanup callback and initially leak it
+    (void) py::weakref(m.attr("BaseClass"), cleanup_callback).release();
+
+.. note::
+
+    PyPy does not garbage collect objects when the interpreter exits. An alternative
+    approach (which also works on CPython) is to use the :py:mod:`atexit` module [#f7]_,
+    for example:
+
+    .. code-block:: cpp
+
+        auto atexit = py::module_::import("atexit");
+        atexit.attr("register")(py::cpp_function([]() {
+            // perform cleanup here -- this function is called with the GIL held
+        }));
+
+    .. [#f7] https://docs.python.org/3/library/atexit.html
+
+
+Generating documentation using Sphinx
+=====================================
+
+Sphinx [#f4]_ has the ability to inspect the signatures and documentation
+strings in pybind11-based extension modules to automatically generate beautiful
+documentation in a variety formats. The python_example repository [#f5]_ contains a
+simple example repository which uses this approach.
+
+There are two potential gotchas when using this approach: first, make sure that
+the resulting strings do not contain any :kbd:`TAB` characters, which break the
+docstring parsing routines. You may want to use C++11 raw string literals,
+which are convenient for multi-line comments. Conveniently, any excess
+indentation will be automatically be removed by Sphinx. However, for this to
+work, it is important that all lines are indented consistently, i.e.:
+
+.. code-block:: cpp
+
+    // ok
+    m.def("foo", &foo, R"mydelimiter(
+        The foo function
+
+        Parameters
+        ----------
+    )mydelimiter");
+
+    // *not ok*
+    m.def("foo", &foo, R"mydelimiter(The foo function
+
+        Parameters
+        ----------
+    )mydelimiter");
+
+By default, pybind11 automatically generates and prepends a signature to the docstring of a function
+registered with ``module_::def()`` and ``class_::def()``. Sometimes this
+behavior is not desirable, because you want to provide your own signature or remove
+the docstring completely to exclude the function from the Sphinx documentation.
+The class ``options`` allows you to selectively suppress auto-generated signatures:
+
+.. code-block:: cpp
+
+    PYBIND11_MODULE(example, m) {
+        py::options options;
+        options.disable_function_signatures();
+
+        m.def("add", [](int a, int b) { return a + b; }, "A function which adds two numbers");
+    }
+
+Note that changes to the settings affect only function bindings created during the
+lifetime of the ``options`` instance. When it goes out of scope at the end of the module's init function,
+the default settings are restored to prevent unwanted side effects.
+
+.. [#f4] http://www.sphinx-doc.org
+.. [#f5] http://github.com/pybind/python_example
+
+.. _avoiding-cpp-types-in-docstrings:
+
+Avoiding C++ types in docstrings
+================================
+
+Docstrings are generated at the time of the declaration, e.g. when ``.def(...)`` is called.
+At this point parameter and return types should be known to pybind11.
+If a custom type is not exposed yet through a ``py::class_`` constructor or a custom type caster,
+its C++ type name will be used instead to generate the signature in the docstring:
+
+.. code-block:: text
+
+     |  __init__(...)
+     |      __init__(self: example.Foo, arg0: ns::Bar) -> None
+                                              ^^^^^^^
+
+
+This limitation can be circumvented by ensuring that C++ classes are registered with pybind11
+before they are used as a parameter or return type of a function:
+
+.. code-block:: cpp
+
+    PYBIND11_MODULE(example, m) {
+
+        auto pyFoo = py::class_<ns::Foo>(m, "Foo");
+        auto pyBar = py::class_<ns::Bar>(m, "Bar");
+
+        pyFoo.def(py::init<const ns::Bar&>());
+        pyBar.def(py::init<const ns::Foo&>());
+    }
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/index.rst
new file mode 100644
index 000000000..6885bdcff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/index.rst
@@ -0,0 +1,13 @@
+Python C++ interface
+####################
+
+pybind11 exposes Python types and functions using thin C++ wrappers, which
+makes it possible to conveniently call Python code from C++ without resorting
+to Python's C API.
+
+.. toctree::
+   :maxdepth: 2
+
+   object
+   numpy
+   utilities
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/numpy.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/numpy.rst
new file mode 100644
index 000000000..b6ef019ed
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/numpy.rst
@@ -0,0 +1,455 @@
+.. _numpy:
+
+NumPy
+#####
+
+Buffer protocol
+===============
+
+Python supports an extremely general and convenient approach for exchanging
+data between plugin libraries. Types can expose a buffer view [#f2]_, which
+provides fast direct access to the raw internal data representation. Suppose we
+want to bind the following simplistic Matrix class:
+
+.. code-block:: cpp
+
+    class Matrix {
+    public:
+        Matrix(size_t rows, size_t cols) : m_rows(rows), m_cols(cols) {
+            m_data = new float[rows*cols];
+        }
+        float *data() { return m_data; }
+        size_t rows() const { return m_rows; }
+        size_t cols() const { return m_cols; }
+    private:
+        size_t m_rows, m_cols;
+        float *m_data;
+    };
+
+The following binding code exposes the ``Matrix`` contents as a buffer object,
+making it possible to cast Matrices into NumPy arrays. It is even possible to
+completely avoid copy operations with Python expressions like
+``np.array(matrix_instance, copy = False)``.
+
+.. code-block:: cpp
+
+    py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
+       .def_buffer([](Matrix &m) -> py::buffer_info {
+            return py::buffer_info(
+                m.data(),                               /* Pointer to buffer */
+                sizeof(float),                          /* Size of one scalar */
+                py::format_descriptor<float>::format(), /* Python struct-style format descriptor */
+                2,                                      /* Number of dimensions */
+                { m.rows(), m.cols() },                 /* Buffer dimensions */
+                { sizeof(float) * m.cols(),             /* Strides (in bytes) for each index */
+                  sizeof(float) }
+            );
+        });
+
+Supporting the buffer protocol in a new type involves specifying the special
+``py::buffer_protocol()`` tag in the ``py::class_`` constructor and calling the
+``def_buffer()`` method with a lambda function that creates a
+``py::buffer_info`` description record on demand describing a given matrix
+instance. The contents of ``py::buffer_info`` mirror the Python buffer protocol
+specification.
+
+.. code-block:: cpp
+
+    struct buffer_info {
+        void *ptr;
+        py::ssize_t itemsize;
+        std::string format;
+        py::ssize_t ndim;
+        std::vector<py::ssize_t> shape;
+        std::vector<py::ssize_t> strides;
+    };
+
+To create a C++ function that can take a Python buffer object as an argument,
+simply use the type ``py::buffer`` as one of its arguments. Buffers can exist
+in a great variety of configurations, hence some safety checks are usually
+necessary in the function body. Below, you can see a basic example on how to
+define a custom constructor for the Eigen double precision matrix
+(``Eigen::MatrixXd``) type, which supports initialization from compatible
+buffer objects (e.g. a NumPy matrix).
+
+.. code-block:: cpp
+
+    /* Bind MatrixXd (or some other Eigen type) to Python */
+    typedef Eigen::MatrixXd Matrix;
+
+    typedef Matrix::Scalar Scalar;
+    constexpr bool rowMajor = Matrix::Flags & Eigen::RowMajorBit;
+
+    py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
+        .def(py::init([](py::buffer b) {
+            typedef Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic> Strides;
+
+            /* Request a buffer descriptor from Python */
+            py::buffer_info info = b.request();
+
+            /* Some basic validation checks ... */
+            if (info.format != py::format_descriptor<Scalar>::format())
+                throw std::runtime_error("Incompatible format: expected a double array!");
+
+            if (info.ndim != 2)
+                throw std::runtime_error("Incompatible buffer dimension!");
+
+            auto strides = Strides(
+                info.strides[rowMajor ? 0 : 1] / (py::ssize_t)sizeof(Scalar),
+                info.strides[rowMajor ? 1 : 0] / (py::ssize_t)sizeof(Scalar));
+
+            auto map = Eigen::Map<Matrix, 0, Strides>(
+                static_cast<Scalar *>(info.ptr), info.shape[0], info.shape[1], strides);
+
+            return Matrix(map);
+        }));
+
+For reference, the ``def_buffer()`` call for this Eigen data type should look
+as follows:
+
+.. code-block:: cpp
+
+    .def_buffer([](Matrix &m) -> py::buffer_info {
+        return py::buffer_info(
+            m.data(),                                /* Pointer to buffer */
+            sizeof(Scalar),                          /* Size of one scalar */
+            py::format_descriptor<Scalar>::format(), /* Python struct-style format descriptor */
+            2,                                       /* Number of dimensions */
+            { m.rows(), m.cols() },                  /* Buffer dimensions */
+            { sizeof(Scalar) * (rowMajor ? m.cols() : 1),
+              sizeof(Scalar) * (rowMajor ? 1 : m.rows()) }
+                                                     /* Strides (in bytes) for each index */
+        );
+     })
+
+For a much easier approach of binding Eigen types (although with some
+limitations), refer to the section on :doc:`/advanced/cast/eigen`.
+
+.. seealso::
+
+    The file :file:`tests/test_buffers.cpp` contains a complete example
+    that demonstrates using the buffer protocol with pybind11 in more detail.
+
+.. [#f2] http://docs.python.org/3/c-api/buffer.html
+
+Arrays
+======
+
+By exchanging ``py::buffer`` with ``py::array`` in the above snippet, we can
+restrict the function so that it only accepts NumPy arrays (rather than any
+type of Python object satisfying the buffer protocol).
+
+In many situations, we want to define a function which only accepts a NumPy
+array of a certain data type. This is possible via the ``py::array_t<T>``
+template. For instance, the following function requires the argument to be a
+NumPy array containing double precision values.
+
+.. code-block:: cpp
+
+    void f(py::array_t<double> array);
+
+When it is invoked with a different type (e.g. an integer or a list of
+integers), the binding code will attempt to cast the input into a NumPy array
+of the requested type. This feature requires the :file:`pybind11/numpy.h`
+header to be included. Note that :file:`pybind11/numpy.h` does not depend on
+the NumPy headers, and thus can be used without declaring a build-time
+dependency on NumPy; NumPy>=1.7.0 is a runtime dependency.
+
+Data in NumPy arrays is not guaranteed to packed in a dense manner;
+furthermore, entries can be separated by arbitrary column and row strides.
+Sometimes, it can be useful to require a function to only accept dense arrays
+using either the C (row-major) or Fortran (column-major) ordering. This can be
+accomplished via a second template argument with values ``py::array::c_style``
+or ``py::array::f_style``.
+
+.. code-block:: cpp
+
+    void f(py::array_t<double, py::array::c_style | py::array::forcecast> array);
+
+The ``py::array::forcecast`` argument is the default value of the second
+template parameter, and it ensures that non-conforming arguments are converted
+into an array satisfying the specified requirements instead of trying the next
+function overload.
+
+There are several methods on arrays; the methods listed below under references
+work, as well as the following functions based on the NumPy API:
+
+- ``.dtype()`` returns the type of the contained values.
+
+- ``.strides()`` returns a pointer to the strides of the array (optionally pass
+  an integer axis to get a number).
+
+- ``.flags()`` returns the flag settings. ``.writable()`` and ``.owndata()``
+  are directly available.
+
+- ``.offset_at()`` returns the offset (optionally pass indices).
+
+- ``.squeeze()`` returns a view with length-1 axes removed.
+
+- ``.view(dtype)`` returns a view of the array with a different dtype.
+
+- ``.reshape({i, j, ...})`` returns a view of the array with a different shape.
+  ``.resize({...})`` is also available.
+
+- ``.index_at(i, j, ...)`` gets the count from the beginning to a given index.
+
+
+There are also several methods for getting references (described below).
+
+Structured types
+================
+
+In order for ``py::array_t`` to work with structured (record) types, we first
+need to register the memory layout of the type. This can be done via
+``PYBIND11_NUMPY_DTYPE`` macro, called in the plugin definition code, which
+expects the type followed by field names:
+
+.. code-block:: cpp
+
+    struct A {
+        int x;
+        double y;
+    };
+
+    struct B {
+        int z;
+        A a;
+    };
+
+    // ...
+    PYBIND11_MODULE(test, m) {
+        // ...
+
+        PYBIND11_NUMPY_DTYPE(A, x, y);
+        PYBIND11_NUMPY_DTYPE(B, z, a);
+        /* now both A and B can be used as template arguments to py::array_t */
+    }
+
+The structure should consist of fundamental arithmetic types, ``std::complex``,
+previously registered substructures, and arrays of any of the above. Both C++
+arrays and ``std::array`` are supported. While there is a static assertion to
+prevent many types of unsupported structures, it is still the user's
+responsibility to use only "plain" structures that can be safely manipulated as
+raw memory without violating invariants.
+
+Vectorizing functions
+=====================
+
+Suppose we want to bind a function with the following signature to Python so
+that it can process arbitrary NumPy array arguments (vectors, matrices, general
+N-D arrays) in addition to its normal arguments:
+
+.. code-block:: cpp
+
+    double my_func(int x, float y, double z);
+
+After including the ``pybind11/numpy.h`` header, this is extremely simple:
+
+.. code-block:: cpp
+
+    m.def("vectorized_func", py::vectorize(my_func));
+
+Invoking the function like below causes 4 calls to be made to ``my_func`` with
+each of the array elements. The significant advantage of this compared to
+solutions like ``numpy.vectorize()`` is that the loop over the elements runs
+entirely on the C++ side and can be crunched down into a tight, optimized loop
+by the compiler. The result is returned as a NumPy array of type
+``numpy.dtype.float64``.
+
+.. code-block:: pycon
+
+    >>> x = np.array([[1, 3], [5, 7]])
+    >>> y = np.array([[2, 4], [6, 8]])
+    >>> z = 3
+    >>> result = vectorized_func(x, y, z)
+
+The scalar argument ``z`` is transparently replicated 4 times.  The input
+arrays ``x`` and ``y`` are automatically converted into the right types (they
+are of type  ``numpy.dtype.int64`` but need to be ``numpy.dtype.int32`` and
+``numpy.dtype.float32``, respectively).
+
+.. note::
+
+    Only arithmetic, complex, and POD types passed by value or by ``const &``
+    reference are vectorized; all other arguments are passed through as-is.
+    Functions taking rvalue reference arguments cannot be vectorized.
+
+In cases where the computation is too complicated to be reduced to
+``vectorize``, it will be necessary to create and access the buffer contents
+manually. The following snippet contains a complete example that shows how this
+works (the code is somewhat contrived, since it could have been done more
+simply using ``vectorize``).
+
+.. code-block:: cpp
+
+    #include <pybind11/pybind11.h>
+    #include <pybind11/numpy.h>
+
+    namespace py = pybind11;
+
+    py::array_t<double> add_arrays(py::array_t<double> input1, py::array_t<double> input2) {
+        py::buffer_info buf1 = input1.request(), buf2 = input2.request();
+
+        if (buf1.ndim != 1 || buf2.ndim != 1)
+            throw std::runtime_error("Number of dimensions must be one");
+
+        if (buf1.size != buf2.size)
+            throw std::runtime_error("Input shapes must match");
+
+        /* No pointer is passed, so NumPy will allocate the buffer */
+        auto result = py::array_t<double>(buf1.size);
+
+        py::buffer_info buf3 = result.request();
+
+        double *ptr1 = static_cast<double *>(buf1.ptr);
+        double *ptr2 = static_cast<double *>(buf2.ptr);
+        double *ptr3 = static_cast<double *>(buf3.ptr);
+
+        for (size_t idx = 0; idx < buf1.shape[0]; idx++)
+            ptr3[idx] = ptr1[idx] + ptr2[idx];
+
+        return result;
+    }
+
+    PYBIND11_MODULE(test, m) {
+        m.def("add_arrays", &add_arrays, "Add two NumPy arrays");
+    }
+
+.. seealso::
+
+    The file :file:`tests/test_numpy_vectorize.cpp` contains a complete
+    example that demonstrates using :func:`vectorize` in more detail.
+
+Direct access
+=============
+
+For performance reasons, particularly when dealing with very large arrays, it
+is often desirable to directly access array elements without internal checking
+of dimensions and bounds on every access when indices are known to be already
+valid.  To avoid such checks, the ``array`` class and ``array_t<T>`` template
+class offer an unchecked proxy object that can be used for this unchecked
+access through the ``unchecked<N>`` and ``mutable_unchecked<N>`` methods,
+where ``N`` gives the required dimensionality of the array:
+
+.. code-block:: cpp
+
+    m.def("sum_3d", [](py::array_t<double> x) {
+        auto r = x.unchecked<3>(); // x must have ndim = 3; can be non-writeable
+        double sum = 0;
+        for (py::ssize_t i = 0; i < r.shape(0); i++)
+            for (py::ssize_t j = 0; j < r.shape(1); j++)
+                for (py::ssize_t k = 0; k < r.shape(2); k++)
+                    sum += r(i, j, k);
+        return sum;
+    });
+    m.def("increment_3d", [](py::array_t<double> x) {
+        auto r = x.mutable_unchecked<3>(); // Will throw if ndim != 3 or flags.writeable is false
+        for (py::ssize_t i = 0; i < r.shape(0); i++)
+            for (py::ssize_t j = 0; j < r.shape(1); j++)
+                for (py::ssize_t k = 0; k < r.shape(2); k++)
+                    r(i, j, k) += 1.0;
+    }, py::arg().noconvert());
+
+To obtain the proxy from an ``array`` object, you must specify both the data
+type and number of dimensions as template arguments, such as ``auto r =
+myarray.mutable_unchecked<float, 2>()``.
+
+If the number of dimensions is not known at compile time, you can omit the
+dimensions template parameter (i.e. calling ``arr_t.unchecked()`` or
+``arr.unchecked<T>()``.  This will give you a proxy object that works in the
+same way, but results in less optimizable code and thus a small efficiency
+loss in tight loops.
+
+Note that the returned proxy object directly references the array's data, and
+only reads its shape, strides, and writeable flag when constructed.  You must
+take care to ensure that the referenced array is not destroyed or reshaped for
+the duration of the returned object, typically by limiting the scope of the
+returned instance.
+
+The returned proxy object supports some of the same methods as ``py::array`` so
+that it can be used as a drop-in replacement for some existing, index-checked
+uses of ``py::array``:
+
+- ``.ndim()`` returns the number of dimensions
+
+- ``.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to
+  the ``const T`` or ``T`` data, respectively, at the given indices.  The
+  latter is only available to proxies obtained via ``a.mutable_unchecked()``.
+
+- ``.itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``.
+
+- ``.ndim()`` returns the number of dimensions.
+
+- ``.shape(n)`` returns the size of dimension ``n``
+
+- ``.size()`` returns the total number of elements (i.e. the product of the shapes).
+
+- ``.nbytes()`` returns the number of bytes used by the referenced elements
+  (i.e. ``itemsize()`` times ``size()``).
+
+.. seealso::
+
+    The file :file:`tests/test_numpy_array.cpp` contains additional examples
+    demonstrating the use of this feature.
+
+Ellipsis
+========
+
+Python provides a convenient ``...`` ellipsis notation that is often used to
+slice multidimensional arrays. For instance, the following snippet extracts the
+middle dimensions of a tensor with the first and last index set to zero.
+
+.. code-block:: python
+
+   a = ...  # a NumPy array
+   b = a[0, ..., 0]
+
+The function ``py::ellipsis()`` function can be used to perform the same
+operation on the C++ side:
+
+.. code-block:: cpp
+
+   py::array a = /* A NumPy array */;
+   py::array b = a[py::make_tuple(0, py::ellipsis(), 0)];
+
+
+Memory view
+===========
+
+For a case when we simply want to provide a direct accessor to C/C++ buffer
+without a concrete class object, we can return a ``memoryview`` object. Suppose
+we wish to expose a ``memoryview`` for 2x4 uint8_t array, we can do the
+following:
+
+.. code-block:: cpp
+
+    const uint8_t buffer[] = {
+        0, 1, 2, 3,
+        4, 5, 6, 7
+    };
+    m.def("get_memoryview2d", []() {
+        return py::memoryview::from_buffer(
+            buffer,                                    // buffer pointer
+            { 2, 4 },                                  // shape (rows, cols)
+            { sizeof(uint8_t) * 4, sizeof(uint8_t) }   // strides in bytes
+        );
+    })
+
+This approach is meant for providing a ``memoryview`` for a C/C++ buffer not
+managed by Python. The user is responsible for managing the lifetime of the
+buffer. Using a ``memoryview`` created in this way after deleting the buffer in
+C++ side results in undefined behavior.
+
+We can also use ``memoryview::from_memory`` for a simple 1D contiguous buffer:
+
+.. code-block:: cpp
+
+    m.def("get_memoryview1d", []() {
+        return py::memoryview::from_memory(
+            buffer,               // buffer pointer
+            sizeof(uint8_t) * 8   // buffer size
+        );
+    })
+
+.. versionchanged:: 2.6
+    ``memoryview::from_memory`` added.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/object.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/object.rst
new file mode 100644
index 000000000..93e1a94d8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/object.rst
@@ -0,0 +1,286 @@
+Python types
+############
+
+.. _wrappers:
+
+Available wrappers
+==================
+
+All major Python types are available as thin C++ wrapper classes. These
+can also be used as function parameters -- see :ref:`python_objects_as_args`.
+
+Available types include :class:`handle`, :class:`object`, :class:`bool_`,
+:class:`int_`, :class:`float_`, :class:`str`, :class:`bytes`, :class:`tuple`,
+:class:`list`, :class:`dict`, :class:`slice`, :class:`none`, :class:`capsule`,
+:class:`iterable`, :class:`iterator`, :class:`function`, :class:`buffer`,
+:class:`array`, and :class:`array_t`.
+
+.. warning::
+
+    Be sure to review the :ref:`pytypes_gotchas` before using this heavily in
+    your C++ API.
+
+.. _instantiating_compound_types:
+
+Instantiating compound Python types from C++
+============================================
+
+Dictionaries can be initialized in the :class:`dict` constructor:
+
+.. code-block:: cpp
+
+    using namespace pybind11::literals; // to bring in the `_a` literal
+    py::dict d("spam"_a=py::none(), "eggs"_a=42);
+
+A tuple of python objects can be instantiated using :func:`py::make_tuple`:
+
+.. code-block:: cpp
+
+    py::tuple tup = py::make_tuple(42, py::none(), "spam");
+
+Each element is converted to a supported Python type.
+
+A `simple namespace`_ can be instantiated using
+
+.. code-block:: cpp
+
+    using namespace pybind11::literals;  // to bring in the `_a` literal
+    py::object SimpleNamespace = py::module_::import("types").attr("SimpleNamespace");
+    py::object ns = SimpleNamespace("spam"_a=py::none(), "eggs"_a=42);
+
+Attributes on a namespace can be modified with the :func:`py::delattr`,
+:func:`py::getattr`, and :func:`py::setattr` functions. Simple namespaces can
+be useful as lightweight stand-ins for class instances.
+
+.. _simple namespace: https://docs.python.org/3/library/types.html#types.SimpleNamespace
+
+.. _casting_back_and_forth:
+
+Casting back and forth
+======================
+
+In this kind of mixed code, it is often necessary to convert arbitrary C++
+types to Python, which can be done using :func:`py::cast`:
+
+.. code-block:: cpp
+
+    MyClass *cls = ...;
+    py::object obj = py::cast(cls);
+
+The reverse direction uses the following syntax:
+
+.. code-block:: cpp
+
+    py::object obj = ...;
+    MyClass *cls = obj.cast<MyClass *>();
+
+When conversion fails, both directions throw the exception :class:`cast_error`.
+
+.. _python_libs:
+
+Accessing Python libraries from C++
+===================================
+
+It is also possible to import objects defined in the Python standard
+library or available in the current Python environment (``sys.path``) and work
+with these in C++.
+
+This example obtains a reference to the Python ``Decimal`` class.
+
+.. code-block:: cpp
+
+    // Equivalent to "from decimal import Decimal"
+    py::object Decimal = py::module_::import("decimal").attr("Decimal");
+
+.. code-block:: cpp
+
+    // Try to import scipy
+    py::object scipy = py::module_::import("scipy");
+    return scipy.attr("__version__");
+
+
+.. _calling_python_functions:
+
+Calling Python functions
+========================
+
+It is also possible to call Python classes, functions and methods
+via ``operator()``.
+
+.. code-block:: cpp
+
+    // Construct a Python object of class Decimal
+    py::object pi = Decimal("3.14159");
+
+.. code-block:: cpp
+
+    // Use Python to make our directories
+    py::object os = py::module_::import("os");
+    py::object makedirs = os.attr("makedirs");
+    makedirs("/tmp/path/to/somewhere");
+
+One can convert the result obtained from Python to a pure C++ version
+if a ``py::class_`` or type conversion is defined.
+
+.. code-block:: cpp
+
+    py::function f = <...>;
+    py::object result_py = f(1234, "hello", some_instance);
+    MyClass &result = result_py.cast<MyClass>();
+
+.. _calling_python_methods:
+
+Calling Python methods
+========================
+
+To call an object's method, one can again use ``.attr`` to obtain access to the
+Python method.
+
+.. code-block:: cpp
+
+    // Calculate e^π in decimal
+    py::object exp_pi = pi.attr("exp")();
+    py::print(py::str(exp_pi));
+
+In the example above ``pi.attr("exp")`` is a *bound method*: it will always call
+the method for that same instance of the class. Alternately one can create an
+*unbound method* via the Python class (instead of instance) and pass the ``self``
+object explicitly, followed by other arguments.
+
+.. code-block:: cpp
+
+    py::object decimal_exp = Decimal.attr("exp");
+
+    // Compute the e^n for n=0..4
+    for (int n = 0; n < 5; n++) {
+        py::print(decimal_exp(Decimal(n));
+    }
+
+Keyword arguments
+=================
+
+Keyword arguments are also supported. In Python, there is the usual call syntax:
+
+.. code-block:: python
+
+    def f(number, say, to):
+        ...  # function code
+
+
+    f(1234, say="hello", to=some_instance)  # keyword call in Python
+
+In C++, the same call can be made using:
+
+.. code-block:: cpp
+
+    using namespace pybind11::literals; // to bring in the `_a` literal
+    f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++
+
+Unpacking arguments
+===================
+
+Unpacking of ``*args`` and ``**kwargs`` is also possible and can be mixed with
+other arguments:
+
+.. code-block:: cpp
+
+    // * unpacking
+    py::tuple args = py::make_tuple(1234, "hello", some_instance);
+    f(*args);
+
+    // ** unpacking
+    py::dict kwargs = py::dict("number"_a=1234, "say"_a="hello", "to"_a=some_instance);
+    f(**kwargs);
+
+    // mixed keywords, * and ** unpacking
+    py::tuple args = py::make_tuple(1234);
+    py::dict kwargs = py::dict("to"_a=some_instance);
+    f(*args, "say"_a="hello", **kwargs);
+
+Generalized unpacking according to PEP448_ is also supported:
+
+.. code-block:: cpp
+
+    py::dict kwargs1 = py::dict("number"_a=1234);
+    py::dict kwargs2 = py::dict("to"_a=some_instance);
+    f(**kwargs1, "say"_a="hello", **kwargs2);
+
+.. seealso::
+
+    The file :file:`tests/test_pytypes.cpp` contains a complete
+    example that demonstrates passing native Python types in more detail. The
+    file :file:`tests/test_callbacks.cpp` presents a few examples of calling
+    Python functions from C++, including keywords arguments and unpacking.
+
+.. _PEP448: https://www.python.org/dev/peps/pep-0448/
+
+.. _implicit_casting:
+
+Implicit casting
+================
+
+When using the C++ interface for Python types, or calling Python functions,
+objects of type :class:`object` are returned. It is possible to invoke implicit
+conversions to subclasses like :class:`dict`. The same holds for the proxy objects
+returned by ``operator[]`` or ``obj.attr()``.
+Casting to subtypes improves code readability and allows values to be passed to
+C++ functions that require a specific subtype rather than a generic :class:`object`.
+
+.. code-block:: cpp
+
+    #include <pybind11/numpy.h>
+    using namespace pybind11::literals;
+
+    py::module_ os = py::module_::import("os");
+    py::module_ path = py::module_::import("os.path");  // like 'import os.path as path'
+    py::module_ np = py::module_::import("numpy");  // like 'import numpy as np'
+
+    py::str curdir_abs = path.attr("abspath")(path.attr("curdir"));
+    py::print(py::str("Current directory: ") + curdir_abs);
+    py::dict environ = os.attr("environ");
+    py::print(environ["HOME"]);
+    py::array_t<float> arr = np.attr("ones")(3, "dtype"_a="float32");
+    py::print(py::repr(arr + py::int_(1)));
+
+These implicit conversions are available for subclasses of :class:`object`; there
+is no need to call ``obj.cast()`` explicitly as for custom classes, see
+:ref:`casting_back_and_forth`.
+
+.. note::
+    If a trivial conversion via move constructor is not possible, both implicit and
+    explicit casting (calling ``obj.cast()``) will attempt a "rich" conversion.
+    For instance, ``py::list env = os.attr("environ");`` will succeed and is
+    equivalent to the Python code ``env = list(os.environ)`` that produces a
+    list of the dict keys.
+
+..  TODO: Adapt text once PR #2349 has landed
+
+Handling exceptions
+===================
+
+Python exceptions from wrapper classes will be thrown as a ``py::error_already_set``.
+See :ref:`Handling exceptions from Python in C++
+<handling_python_exceptions_cpp>` for more information on handling exceptions
+raised when calling C++ wrapper classes.
+
+.. _pytypes_gotchas:
+
+Gotchas
+=======
+
+Default-Constructed Wrappers
+----------------------------
+
+When a wrapper type is default-constructed, it is **not** a valid Python object (i.e. it is not ``py::none()``). It is simply the same as
+``PyObject*`` null pointer. To check for this, use
+``static_cast<bool>(my_wrapper)``.
+
+Assigning py::none() to wrappers
+--------------------------------
+
+You may be tempted to use types like ``py::str`` and ``py::dict`` in C++
+signatures (either pure C++, or in bound signatures), and assign them default
+values of ``py::none()``. However, in a best case scenario, it will fail fast
+because ``None`` is not convertible to that type (e.g. ``py::dict``), or in a
+worse case scenario, it will silently work but corrupt the types you want to
+work with (e.g. ``py::str(py::none())`` will yield ``"None"`` in Python).
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/utilities.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/utilities.rst
new file mode 100644
index 000000000..af0f9cb2b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/pycpp/utilities.rst
@@ -0,0 +1,155 @@
+Utilities
+#########
+
+Using Python's print function in C++
+====================================
+
+The usual way to write output in C++ is using ``std::cout`` while in Python one
+would use ``print``. Since these methods use different buffers, mixing them can
+lead to output order issues. To resolve this, pybind11 modules can use the
+:func:`py::print` function which writes to Python's ``sys.stdout`` for consistency.
+
+Python's ``print`` function is replicated in the C++ API including optional
+keyword arguments ``sep``, ``end``, ``file``, ``flush``. Everything works as
+expected in Python:
+
+.. code-block:: cpp
+
+    py::print(1, 2.0, "three"); // 1 2.0 three
+    py::print(1, 2.0, "three", "sep"_a="-"); // 1-2.0-three
+
+    auto args = py::make_tuple("unpacked", true);
+    py::print("->", *args, "end"_a="<-"); // -> unpacked True <-
+
+.. _ostream_redirect:
+
+Capturing standard output from ostream
+======================================
+
+Often, a library will use the streams ``std::cout`` and ``std::cerr`` to print,
+but this does not play well with Python's standard ``sys.stdout`` and ``sys.stderr``
+redirection. Replacing a library's printing with ``py::print <print>`` may not
+be feasible. This can be fixed using a guard around the library function that
+redirects output to the corresponding Python streams:
+
+.. code-block:: cpp
+
+    #include <pybind11/iostream.h>
+
+    ...
+
+    // Add a scoped redirect for your noisy code
+    m.def("noisy_func", []() {
+        py::scoped_ostream_redirect stream(
+            std::cout,                               // std::ostream&
+            py::module_::import("sys").attr("stdout") // Python output
+        );
+        call_noisy_func();
+    });
+
+.. warning::
+
+    The implementation in ``pybind11/iostream.h`` is NOT thread safe. Multiple
+    threads writing to a redirected ostream concurrently cause data races
+    and potentially buffer overflows. Therefore it is currently a requirement
+    that all (possibly) concurrent redirected ostream writes are protected by
+    a mutex. #HelpAppreciated: Work on iostream.h thread safety. For more
+    background see the discussions under
+    `PR #2982 <https://github.com/pybind/pybind11/pull/2982>`_ and
+    `PR #2995 <https://github.com/pybind/pybind11/pull/2995>`_.
+
+This method respects flushes on the output streams and will flush if needed
+when the scoped guard is destroyed. This allows the output to be redirected in
+real time, such as to a Jupyter notebook. The two arguments, the C++ stream and
+the Python output, are optional, and default to standard output if not given. An
+extra type, ``py::scoped_estream_redirect <scoped_estream_redirect>``, is identical
+except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful with
+``py::call_guard``, which allows multiple items, but uses the default constructor:
+
+.. code-block:: cpp
+
+    // Alternative: Call single function using call guard
+    m.def("noisy_func", &call_noisy_function,
+          py::call_guard<py::scoped_ostream_redirect,
+                         py::scoped_estream_redirect>());
+
+The redirection can also be done in Python with the addition of a context
+manager, using the ``py::add_ostream_redirect() <add_ostream_redirect>`` function:
+
+.. code-block:: cpp
+
+    py::add_ostream_redirect(m, "ostream_redirect");
+
+The name in Python defaults to ``ostream_redirect`` if no name is passed.  This
+creates the following context manager in Python:
+
+.. code-block:: python
+
+    with ostream_redirect(stdout=True, stderr=True):
+        noisy_function()
+
+It defaults to redirecting both streams, though you can use the keyword
+arguments to disable one of the streams if needed.
+
+.. note::
+
+    The above methods will not redirect C-level output to file descriptors, such
+    as ``fprintf``. For those cases, you'll need to redirect the file
+    descriptors either directly in C or with Python's ``os.dup2`` function
+    in an operating-system dependent way.
+
+.. _eval:
+
+Evaluating Python expressions from strings and files
+====================================================
+
+pybind11 provides the ``eval``, ``exec`` and ``eval_file`` functions to evaluate
+Python expressions and statements. The following example illustrates how they
+can be used.
+
+.. code-block:: cpp
+
+    // At beginning of file
+    #include <pybind11/eval.h>
+
+    ...
+
+    // Evaluate in scope of main module
+    py::object scope = py::module_::import("__main__").attr("__dict__");
+
+    // Evaluate an isolated expression
+    int result = py::eval("my_variable + 10", scope).cast<int>();
+
+    // Evaluate a sequence of statements
+    py::exec(
+        "print('Hello')\n"
+        "print('world!');",
+        scope);
+
+    // Evaluate the statements in an separate Python file on disk
+    py::eval_file("script.py", scope);
+
+C++11 raw string literals are also supported and quite handy for this purpose.
+The only requirement is that the first statement must be on a new line following
+the raw string delimiter ``R"(``, ensuring all lines have common leading indent:
+
+.. code-block:: cpp
+
+    py::exec(R"(
+        x = get_answer()
+        if x == 42:
+            print('Hello World!')
+        else:
+            print('Bye!')
+        )", scope
+    );
+
+.. note::
+
+    `eval` and `eval_file` accept a template parameter that describes how the
+    string/file should be interpreted. Possible choices include ``eval_expr``
+    (isolated expression), ``eval_single_statement`` (a single statement, return
+    value is always ``none``), and ``eval_statements`` (sequence of statements,
+    return value is always ``none``). `eval` defaults to  ``eval_expr``,
+    `eval_file` defaults to ``eval_statements`` and `exec` is just a shortcut
+    for ``eval<eval_statements>``.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/smart_ptrs.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/smart_ptrs.rst
new file mode 100644
index 000000000..5a2220109
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/advanced/smart_ptrs.rst
@@ -0,0 +1,174 @@
+Smart pointers
+##############
+
+std::unique_ptr
+===============
+
+Given a class ``Example`` with Python bindings, it's possible to return
+instances wrapped in C++11 unique pointers, like so
+
+.. code-block:: cpp
+
+    std::unique_ptr<Example> create_example() { return std::unique_ptr<Example>(new Example()); }
+
+.. code-block:: cpp
+
+    m.def("create_example", &create_example);
+
+In other words, there is nothing special that needs to be done. While returning
+unique pointers in this way is allowed, it is *illegal* to use them as function
+arguments. For instance, the following function signature cannot be processed
+by pybind11.
+
+.. code-block:: cpp
+
+    void do_something_with_example(std::unique_ptr<Example> ex) { ... }
+
+The above signature would imply that Python needs to give up ownership of an
+object that is passed to this function, which is generally not possible (for
+instance, the object might be referenced elsewhere).
+
+std::shared_ptr
+===============
+
+The binding generator for classes, :class:`class_`, can be passed a template
+type that denotes a special *holder* type that is used to manage references to
+the object.  If no such holder type template argument is given, the default for
+a type named ``Type`` is ``std::unique_ptr<Type>``, which means that the object
+is deallocated when Python's reference count goes to zero.
+
+It is possible to switch to other types of reference counting wrappers or smart
+pointers, which is useful in codebases that rely on them. For instance, the
+following snippet causes ``std::shared_ptr`` to be used instead.
+
+.. code-block:: cpp
+
+    py::class_<Example, std::shared_ptr<Example> /* <- holder type */> obj(m, "Example");
+
+Note that any particular class can only be associated with a single holder type.
+
+One potential stumbling block when using holder types is that they need to be
+applied consistently. Can you guess what's broken about the following binding
+code?
+
+.. code-block:: cpp
+
+    class Child { };
+
+    class Parent {
+    public:
+       Parent() : child(std::make_shared<Child>()) { }
+       Child *get_child() { return child.get(); }  /* Hint: ** DON'T DO THIS ** */
+    private:
+        std::shared_ptr<Child> child;
+    };
+
+    PYBIND11_MODULE(example, m) {
+        py::class_<Child, std::shared_ptr<Child>>(m, "Child");
+
+        py::class_<Parent, std::shared_ptr<Parent>>(m, "Parent")
+           .def(py::init<>())
+           .def("get_child", &Parent::get_child);
+    }
+
+The following Python code will cause undefined behavior (and likely a
+segmentation fault).
+
+.. code-block:: python
+
+   from example import Parent
+
+   print(Parent().get_child())
+
+The problem is that ``Parent::get_child()`` returns a pointer to an instance of
+``Child``, but the fact that this instance is already managed by
+``std::shared_ptr<...>`` is lost when passing raw pointers. In this case,
+pybind11 will create a second independent ``std::shared_ptr<...>`` that also
+claims ownership of the pointer. In the end, the object will be freed **twice**
+since these shared pointers have no way of knowing about each other.
+
+There are two ways to resolve this issue:
+
+1. For types that are managed by a smart pointer class, never use raw pointers
+   in function arguments or return values. In other words: always consistently
+   wrap pointers into their designated holder types (such as
+   ``std::shared_ptr<...>``). In this case, the signature of ``get_child()``
+   should be modified as follows:
+
+.. code-block:: cpp
+
+    std::shared_ptr<Child> get_child() { return child; }
+
+2. Adjust the definition of ``Child`` by specifying
+   ``std::enable_shared_from_this<T>`` (see cppreference_ for details) as a
+   base class. This adds a small bit of information to ``Child`` that allows
+   pybind11 to realize that there is already an existing
+   ``std::shared_ptr<...>`` and communicate with it. In this case, the
+   declaration of ``Child`` should look as follows:
+
+.. _cppreference: http://en.cppreference.com/w/cpp/memory/enable_shared_from_this
+
+.. code-block:: cpp
+
+    class Child : public std::enable_shared_from_this<Child> { };
+
+.. _smart_pointers:
+
+Custom smart pointers
+=====================
+
+pybind11 supports ``std::unique_ptr`` and ``std::shared_ptr`` right out of the
+box. For any other custom smart pointer, transparent conversions can be enabled
+using a macro invocation similar to the following. It must be declared at the
+top namespace level before any binding code:
+
+.. code-block:: cpp
+
+    PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>);
+
+The first argument of :func:`PYBIND11_DECLARE_HOLDER_TYPE` should be a
+placeholder name that is used as a template parameter of the second argument.
+Thus, feel free to use any identifier, but use it consistently on both sides;
+also, don't use the name of a type that already exists in your codebase.
+
+The macro also accepts a third optional boolean parameter that is set to false
+by default. Specify
+
+.. code-block:: cpp
+
+    PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>, true);
+
+if ``SmartPtr<T>`` can always be initialized from a ``T*`` pointer without the
+risk of inconsistencies (such as multiple independent ``SmartPtr`` instances
+believing that they are the sole owner of the ``T*`` pointer). A common
+situation where ``true`` should be passed is when the ``T`` instances use
+*intrusive* reference counting.
+
+Please take a look at the :ref:`macro_notes` before using this feature.
+
+By default, pybind11 assumes that your custom smart pointer has a standard
+interface, i.e. provides a ``.get()`` member function to access the underlying
+raw pointer. If this is not the case, pybind11's ``holder_helper`` must be
+specialized:
+
+.. code-block:: cpp
+
+    // Always needed for custom holder types
+    PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>);
+
+    // Only needed if the type's `.get()` goes by another name
+    namespace pybind11 { namespace detail {
+        template <typename T>
+        struct holder_helper<SmartPtr<T>> { // <-- specialization
+            static const T *get(const SmartPtr<T> &p) { return p.getPointer(); }
+        };
+    }}
+
+The above specialization informs pybind11 that the custom ``SmartPtr`` class
+provides ``.get()`` functionality via ``.getPointer()``.
+
+.. seealso::
+
+    The file :file:`tests/test_smart_ptr.cpp` contains a complete example
+    that demonstrates how to work with custom reference-counting holder types
+    in more detail.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/basics.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/basics.rst
new file mode 100644
index 000000000..e9b24c7fa
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/basics.rst
@@ -0,0 +1,307 @@
+.. _basics:
+
+First steps
+###########
+
+This sections demonstrates the basic features of pybind11. Before getting
+started, make sure that development environment is set up to compile the
+included set of test cases.
+
+
+Compiling the test cases
+========================
+
+Linux/macOS
+-----------
+
+On Linux  you'll need to install the **python-dev** or **python3-dev** packages as
+well as **cmake**. On macOS, the included python version works out of the box,
+but **cmake** must still be installed.
+
+After installing the prerequisites, run
+
+.. code-block:: bash
+
+   mkdir build
+   cd build
+   cmake ..
+   make check -j 4
+
+The last line will both compile and run the tests.
+
+Windows
+-------
+
+On Windows, only **Visual Studio 2017** and newer are supported.
+
+.. Note::
+
+    To use the C++17 in Visual Studio 2017 (MSVC 14.1), pybind11 requires the flag
+    ``/permissive-`` to be passed to the compiler `to enforce standard conformance`_. When
+    building with Visual Studio 2019, this is not strictly necessary, but still advised.
+
+..  _`to enforce standard conformance`: https://docs.microsoft.com/en-us/cpp/build/reference/permissive-standards-conformance?view=vs-2017
+
+To compile and run the tests:
+
+.. code-block:: batch
+
+   mkdir build
+   cd build
+   cmake ..
+   cmake --build . --config Release --target check
+
+This will create a Visual Studio project, compile and run the target, all from the
+command line.
+
+.. Note::
+
+    If all tests fail, make sure that the Python binary and the testcases are compiled
+    for the same processor type and bitness (i.e. either **i386** or **x86_64**). You
+    can specify **x86_64** as the target architecture for the generated Visual Studio
+    project using ``cmake -A x64 ..``.
+
+.. seealso::
+
+    Advanced users who are already familiar with Boost.Python may want to skip
+    the tutorial and look at the test cases in the :file:`tests` directory,
+    which exercise all features of pybind11.
+
+Header and namespace conventions
+================================
+
+For brevity, all code examples assume that the following two lines are present:
+
+.. code-block:: cpp
+
+    #include <pybind11/pybind11.h>
+
+    namespace py = pybind11;
+
+Some features may require additional headers, but those will be specified as needed.
+
+.. _simple_example:
+
+Creating bindings for a simple function
+=======================================
+
+Let's start by creating Python bindings for an extremely simple function, which
+adds two numbers and returns their result:
+
+.. code-block:: cpp
+
+    int add(int i, int j) {
+        return i + j;
+    }
+
+For simplicity [#f1]_, we'll put both this function and the binding code into
+a file named :file:`example.cpp` with the following contents:
+
+.. code-block:: cpp
+
+    #include <pybind11/pybind11.h>
+
+    int add(int i, int j) {
+        return i + j;
+    }
+
+    PYBIND11_MODULE(example, m) {
+        m.doc() = "pybind11 example plugin"; // optional module docstring
+
+        m.def("add", &add, "A function that adds two numbers");
+    }
+
+.. [#f1] In practice, implementation and binding code will generally be located
+         in separate files.
+
+The :func:`PYBIND11_MODULE` macro creates a function that will be called when an
+``import`` statement is issued from within Python. The module name (``example``)
+is given as the first macro argument (it should not be in quotes). The second
+argument (``m``) defines a variable of type :class:`py::module_ <module>` which
+is the main interface for creating bindings. The method :func:`module_::def`
+generates binding code that exposes the ``add()`` function to Python.
+
+.. note::
+
+    Notice how little code was needed to expose our function to Python: all
+    details regarding the function's parameters and return value were
+    automatically inferred using template metaprogramming. This overall
+    approach and the used syntax are borrowed from Boost.Python, though the
+    underlying implementation is very different.
+
+pybind11 is a header-only library, hence it is not necessary to link against
+any special libraries and there are no intermediate (magic) translation steps.
+On Linux, the above example can be compiled using the following command:
+
+.. code-block:: bash
+
+    $ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)
+
+.. note::
+
+    If you used :ref:`include_as_a_submodule` to get the pybind11 source, then
+    use ``$(python3-config --includes) -Iextern/pybind11/include`` instead of
+    ``$(python3 -m pybind11 --includes)`` in the above compilation, as
+    explained in :ref:`building_manually`.
+
+For more details on the required compiler flags on Linux and macOS, see
+:ref:`building_manually`. For complete cross-platform compilation instructions,
+refer to the :ref:`compiling` page.
+
+The `python_example`_ and `cmake_example`_ repositories are also a good place
+to start. They are both complete project examples with cross-platform build
+systems. The only difference between the two is that `python_example`_ uses
+Python's ``setuptools`` to build the module, while `cmake_example`_ uses CMake
+(which may be preferable for existing C++ projects).
+
+.. _python_example: https://github.com/pybind/python_example
+.. _cmake_example: https://github.com/pybind/cmake_example
+
+Building the above C++ code will produce a binary module file that can be
+imported to Python. Assuming that the compiled module is located in the
+current directory, the following interactive Python session shows how to
+load and execute the example:
+
+.. code-block:: pycon
+
+    $ python
+    Python 3.9.10 (main, Jan 15 2022, 11:48:04)
+    [Clang 13.0.0 (clang-1300.0.29.3)] on darwin
+    Type "help", "copyright", "credits" or "license" for more information.
+    >>> import example
+    >>> example.add(1, 2)
+    3
+    >>>
+
+.. _keyword_args:
+
+Keyword arguments
+=================
+
+With a simple code modification, it is possible to inform Python about the
+names of the arguments ("i" and "j" in this case).
+
+.. code-block:: cpp
+
+    m.def("add", &add, "A function which adds two numbers",
+          py::arg("i"), py::arg("j"));
+
+:class:`arg` is one of several special tag classes which can be used to pass
+metadata into :func:`module_::def`. With this modified binding code, we can now
+call the function using keyword arguments, which is a more readable alternative
+particularly for functions taking many parameters:
+
+.. code-block:: pycon
+
+    >>> import example
+    >>> example.add(i=1, j=2)
+    3L
+
+The keyword names also appear in the function signatures within the documentation.
+
+.. code-block:: pycon
+
+    >>> help(example)
+
+    ....
+
+    FUNCTIONS
+        add(...)
+            Signature : (i: int, j: int) -> int
+
+            A function which adds two numbers
+
+A shorter notation for named arguments is also available:
+
+.. code-block:: cpp
+
+    // regular notation
+    m.def("add1", &add, py::arg("i"), py::arg("j"));
+    // shorthand
+    using namespace pybind11::literals;
+    m.def("add2", &add, "i"_a, "j"_a);
+
+The :var:`_a` suffix forms a C++11 literal which is equivalent to :class:`arg`.
+Note that the literal operator must first be made visible with the directive
+``using namespace pybind11::literals``. This does not bring in anything else
+from the ``pybind11`` namespace except for literals.
+
+.. _default_args:
+
+Default arguments
+=================
+
+Suppose now that the function to be bound has default arguments, e.g.:
+
+.. code-block:: cpp
+
+    int add(int i = 1, int j = 2) {
+        return i + j;
+    }
+
+Unfortunately, pybind11 cannot automatically extract these parameters, since they
+are not part of the function's type information. However, they are simple to specify
+using an extension of :class:`arg`:
+
+.. code-block:: cpp
+
+    m.def("add", &add, "A function which adds two numbers",
+          py::arg("i") = 1, py::arg("j") = 2);
+
+The default values also appear within the documentation.
+
+.. code-block:: pycon
+
+    >>> help(example)
+
+    ....
+
+    FUNCTIONS
+        add(...)
+            Signature : (i: int = 1, j: int = 2) -> int
+
+            A function which adds two numbers
+
+The shorthand notation is also available for default arguments:
+
+.. code-block:: cpp
+
+    // regular notation
+    m.def("add1", &add, py::arg("i") = 1, py::arg("j") = 2);
+    // shorthand
+    m.def("add2", &add, "i"_a=1, "j"_a=2);
+
+Exporting variables
+===================
+
+To expose a value from C++, use the ``attr`` function to register it in a
+module as shown below. Built-in types and general objects (more on that later)
+are automatically converted when assigned as attributes, and can be explicitly
+converted using the function ``py::cast``.
+
+.. code-block:: cpp
+
+    PYBIND11_MODULE(example, m) {
+        m.attr("the_answer") = 42;
+        py::object world = py::cast("World");
+        m.attr("what") = world;
+    }
+
+These are then accessible from Python:
+
+.. code-block:: pycon
+
+    >>> import example
+    >>> example.the_answer
+    42
+    >>> example.what
+    'World'
+
+.. _supported_types:
+
+Supported data types
+====================
+
+A large number of data types are supported out of the box and can be used
+seamlessly as functions arguments, return values or with ``py::cast`` in general.
+For a full overview, see the :doc:`advanced/cast/index` section.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/benchmark.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/benchmark.py
new file mode 100644
index 000000000..2150b6ca7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/benchmark.py
@@ -0,0 +1,87 @@
+import datetime as dt
+import os
+import random
+
+nfns = 4  # Functions per class
+nargs = 4  # Arguments per function
+
+
+def generate_dummy_code_pybind11(nclasses=10):
+    decl = ""
+    bindings = ""
+
+    for cl in range(nclasses):
+        decl += f"class cl{cl:03};\n"
+    decl += "\n"
+
+    for cl in range(nclasses):
+        decl += f"class {cl:03} {{\n"
+        decl += "public:\n"
+        bindings += f'    py::class_<cl{cl:03}>(m, "cl{cl:03}")\n'
+        for fn in range(nfns):
+            ret = random.randint(0, nclasses - 1)
+            params = [random.randint(0, nclasses - 1) for i in range(nargs)]
+            decl += f"    cl{ret:03} *fn_{fn:03}("
+            decl += ", ".join(f"cl{p:03} *" for p in params)
+            decl += ");\n"
+            bindings += f'        .def("fn_{fn:03}", &cl{cl:03}::fn_{fn:03})\n'
+        decl += "};\n\n"
+        bindings += "        ;\n"
+
+    result = "#include <pybind11/pybind11.h>\n\n"
+    result += "namespace py = pybind11;\n\n"
+    result += decl + "\n"
+    result += "PYBIND11_MODULE(example, m) {\n"
+    result += bindings
+    result += "}"
+    return result
+
+
+def generate_dummy_code_boost(nclasses=10):
+    decl = ""
+    bindings = ""
+
+    for cl in range(nclasses):
+        decl += f"class cl{cl:03};\n"
+    decl += "\n"
+
+    for cl in range(nclasses):
+        decl += "class cl%03i {\n" % cl
+        decl += "public:\n"
+        bindings += f'    py::class_<cl{cl:03}>("cl{cl:03}")\n'
+        for fn in range(nfns):
+            ret = random.randint(0, nclasses - 1)
+            params = [random.randint(0, nclasses - 1) for i in range(nargs)]
+            decl += f"    cl{ret:03} *fn_{fn:03}("
+            decl += ", ".join(f"cl{p:03} *" for p in params)
+            decl += ");\n"
+            bindings += f'        .def("fn_{fn:03}", &cl{cl:03}::fn_{fn:03}, py::return_value_policy<py::manage_new_object>())\n'
+        decl += "};\n\n"
+        bindings += "        ;\n"
+
+    result = "#include <boost/python.hpp>\n\n"
+    result += "namespace py = boost::python;\n\n"
+    result += decl + "\n"
+    result += "BOOST_PYTHON_MODULE(example) {\n"
+    result += bindings
+    result += "}"
+    return result
+
+
+for codegen in [generate_dummy_code_pybind11, generate_dummy_code_boost]:
+    print("{")
+    for i in range(0, 10):
+        nclasses = 2**i
+        with open("test.cpp", "w") as f:
+            f.write(codegen(nclasses))
+        n1 = dt.datetime.now()
+        os.system(
+            "g++ -Os -shared -rdynamic -undefined dynamic_lookup "
+            "-fvisibility=hidden -std=c++14 test.cpp -I include "
+            "-I /System/Library/Frameworks/Python.framework/Headers -o test.so"
+        )
+        n2 = dt.datetime.now()
+        elapsed = (n2 - n1).total_seconds()
+        size = os.stat("test.so").st_size
+        print("   {%i, %f, %i}," % (nclasses * nfns, elapsed, size))
+    print("}")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/benchmark.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/benchmark.rst
new file mode 100644
index 000000000..02c2ccde7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/benchmark.rst
@@ -0,0 +1,95 @@
+Benchmark
+=========
+
+The following is the result of a synthetic benchmark comparing both compilation
+time and module size of pybind11 against Boost.Python. A detailed report about a
+Boost.Python to pybind11 conversion of a real project is available here: [#f1]_.
+
+.. [#f1] http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
+
+Setup
+-----
+
+A python script (see the ``docs/benchmark.py`` file) was used to generate a set
+of files with dummy classes whose count increases for each successive benchmark
+(between 1 and 2048 classes in powers of two). Each class has four methods with
+a randomly generated signature with a return value and four arguments. (There
+was no particular reason for this setup other than the desire to generate many
+unique function signatures whose count could be controlled in a simple way.)
+
+Here is an example of the binding code for one class:
+
+.. code-block:: cpp
+
+    ...
+    class cl034 {
+    public:
+        cl279 *fn_000(cl084 *, cl057 *, cl065 *, cl042 *);
+        cl025 *fn_001(cl098 *, cl262 *, cl414 *, cl121 *);
+        cl085 *fn_002(cl445 *, cl297 *, cl145 *, cl421 *);
+        cl470 *fn_003(cl200 *, cl323 *, cl332 *, cl492 *);
+    };
+    ...
+
+    PYBIND11_MODULE(example, m) {
+        ...
+        py::class_<cl034>(m, "cl034")
+            .def("fn_000", &cl034::fn_000)
+            .def("fn_001", &cl034::fn_001)
+            .def("fn_002", &cl034::fn_002)
+            .def("fn_003", &cl034::fn_003)
+        ...
+    }
+
+The Boost.Python version looks almost identical except that a return value
+policy had to be specified as an argument to ``def()``. For both libraries,
+compilation was done with
+
+.. code-block:: bash
+
+    Apple LLVM version 7.0.2 (clang-700.1.81)
+
+and the following compilation flags
+
+.. code-block:: bash
+
+    g++ -Os -shared -rdynamic -undefined dynamic_lookup -fvisibility=hidden -std=c++14
+
+Compilation time
+----------------
+
+The following log-log plot shows how the compilation time grows for an
+increasing number of class and function declarations. pybind11 includes many
+fewer headers, which initially leads to shorter compilation times, but the
+performance is ultimately fairly similar (pybind11 is 19.8 seconds faster for
+the largest largest file with 2048 classes and a total of 8192 methods -- a
+modest **1.2x** speedup relative to Boost.Python, which required 116.35
+seconds).
+
+.. only:: not latex
+
+    .. image:: pybind11_vs_boost_python1.svg
+
+.. only:: latex
+
+    .. image:: pybind11_vs_boost_python1.png
+
+Module size
+-----------
+
+Differences between the two libraries become much more pronounced when
+considering the file size of the generated Python plugin: for the largest file,
+the binary generated by Boost.Python required 16.8 MiB, which was **2.17
+times** / **9.1 megabytes** larger than the output generated by pybind11. For
+very small inputs, Boost.Python has an edge in the plot below -- however, note
+that it stores many definitions in an external library, whose size was not
+included here, hence the comparison is slightly shifted in Boost.Python's
+favor.
+
+.. only:: not latex
+
+    .. image:: pybind11_vs_boost_python2.svg
+
+.. only:: latex
+
+    .. image:: pybind11_vs_boost_python2.png
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/changelog.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/changelog.rst
new file mode 100644
index 000000000..b926b27af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/changelog.rst
@@ -0,0 +1,2468 @@
+.. _changelog:
+
+Changelog
+#########
+
+Starting with version 1.8.0, pybind11 releases use a `semantic versioning
+<http://semver.org>`_ policy.
+
+Changes will be added here periodically from the "Suggested changelog entry"
+block in pull request descriptions.
+
+Version 2.10.0 (Jul 15, 2022)
+-----------------------------
+
+Removed support for Python 2.7, Python 3.5, and MSVC 2015. Support for MSVC
+2017 is limited due to availability of CI runners; we highly recommend MSVC
+2019 or 2022 be used. Initial support added for Python 3.11.
+
+New features:
+
+* ``py::anyset`` & ``py::frozenset`` were added, with copying (cast) to
+  ``std::set`` (similar to ``set``).
+  `#3901 <https://github.com/pybind/pybind11/pull/3901>`_
+
+* Support bytearray casting to string.
+  `#3707 <https://github.com/pybind/pybind11/pull/3707>`_
+
+* ``type_caster<std::monostate>`` was added. ``std::monostate`` is a tag type
+  that allows ``std::variant`` to act as an optional, or allows default
+  construction of a ``std::variant`` holding a non-default constructible type.
+  `#3818 <https://github.com/pybind/pybind11/pull/3818>`_
+
+* ``pybind11::capsule::set_name`` added to mutate the name of the capsule instance.
+  `#3866 <https://github.com/pybind/pybind11/pull/3866>`_
+
+* NumPy: dtype constructor from type number added, accessors corresponding to
+  Python API ``dtype.num``, ``dtype.byteorder``, ``dtype.flags`` and
+  ``dtype.alignment`` added.
+  `#3868 <https://github.com/pybind/pybind11/pull/3868>`_
+
+
+Changes:
+
+* Python 3.6 is now the minimum supported version.
+  `#3688 <https://github.com/pybind/pybind11/pull/3688>`_
+  `#3719 <https://github.com/pybind/pybind11/pull/3719>`_
+
+* The minimum version for MSVC is now 2017.
+  `#3722 <https://github.com/pybind/pybind11/pull/3722>`_
+
+* Fix issues with CPython 3.11 betas and add to supported test matrix.
+  `#3923 <https://github.com/pybind/pybind11/pull/3923>`_
+
+* ``error_already_set`` is now safer and more performant, especially for
+  exceptions with long tracebacks, by delaying computation.
+  `#1895 <https://github.com/pybind/pybind11/pull/1895>`_
+
+* Improve exception handling in python ``str`` bindings.
+  `#3826 <https://github.com/pybind/pybind11/pull/3826>`_
+
+* The bindings for capsules now have more consistent exception handling.
+  `#3825 <https://github.com/pybind/pybind11/pull/3825>`_
+
+* ``PYBIND11_OBJECT_CVT`` and ``PYBIND11_OBJECT_CVT_DEFAULT`` macro can now be
+  used to define classes in namespaces other than pybind11.
+  `#3797 <https://github.com/pybind/pybind11/pull/3797>`_
+
+* Error printing code now uses ``PYBIND11_DETAILED_ERROR_MESSAGES`` instead of
+  requiring ``NDEBUG``, allowing use with release builds if desired.
+  `#3913 <https://github.com/pybind/pybind11/pull/3913>`_
+
+* Implicit conversion of the literal ``0`` to ``pybind11::handle`` is now disabled.
+  `#4008 <https://github.com/pybind/pybind11/pull/4008>`_
+
+
+Bug fixes:
+
+* Fix exception handling when ``pybind11::weakref()`` fails.
+  `#3739 <https://github.com/pybind/pybind11/pull/3739>`_
+
+* ``module_::def_submodule`` was missing proper error handling. This is fixed now.
+  `#3973 <https://github.com/pybind/pybind11/pull/3973>`_
+
+* The behavior or ``error_already_set`` was made safer and the highly opaque
+  "Unknown internal error occurred" message was replaced with a more helpful
+  message.
+  `#3982 <https://github.com/pybind/pybind11/pull/3982>`_
+
+* ``error_already_set::what()`` now handles non-normalized exceptions correctly.
+  `#3971 <https://github.com/pybind/pybind11/pull/3971>`_
+
+* Support older C++ compilers where filesystem is not yet part of the standard
+  library and is instead included in ``std::experimental::filesystem``.
+  `#3840 <https://github.com/pybind/pybind11/pull/3840>`_
+
+* Fix ``-Wfree-nonheap-object`` warnings produced by GCC by avoiding returning
+  pointers to static objects with ``return_value_policy::take_ownership``.
+  `#3946 <https://github.com/pybind/pybind11/pull/3946>`_
+
+* Fix cast from pytype rvalue to another pytype.
+  `#3949 <https://github.com/pybind/pybind11/pull/3949>`_
+
+* Ensure proper behavior when garbage collecting classes with dynamic attributes in Python >=3.9.
+  `#4051 <https://github.com/pybind/pybind11/pull/4051>`_
+
+* A couple long-standing ``PYBIND11_NAMESPACE``
+  ``__attribute__((visibility("hidden")))`` inconsistencies are now fixed
+  (affects only unusual environments).
+  `#4043 <https://github.com/pybind/pybind11/pull/4043>`_
+
+* ``pybind11::detail::get_internals()`` is now resilient to in-flight Python
+  exceptions.
+  `#3981 <https://github.com/pybind/pybind11/pull/3981>`_
+
+* Arrays with a dimension of size 0 are now properly converted to dynamic Eigen
+  matrices (more common in NumPy 1.23).
+  `#4038 <https://github.com/pybind/pybind11/pull/4038>`_
+
+* Avoid catching unrelated errors when importing NumPy.
+  `#3974 <https://github.com/pybind/pybind11/pull/3974>`_
+
+Performance and style:
+
+* Added an accessor overload of ``(object &&key)`` to reference steal the
+  object when using python types as keys. This prevents unnecessary reference
+  count overhead for attr, dictionary, tuple, and sequence look ups. Added
+  additional regression tests. Fixed a performance bug the caused accessor
+  assignments to potentially perform unnecessary copies.
+  `#3970 <https://github.com/pybind/pybind11/pull/3970>`_
+
+* Perfect forward all args of ``make_iterator``.
+  `#3980 <https://github.com/pybind/pybind11/pull/3980>`_
+
+* Avoid potential bug in pycapsule destructor by adding an ``error_guard`` to
+  one of the dtors.
+  `#3958 <https://github.com/pybind/pybind11/pull/3958>`_
+
+* Optimize dictionary access in ``strip_padding`` for numpy.
+  `#3994 <https://github.com/pybind/pybind11/pull/3994>`_
+
+* ``stl_bind.h`` bindings now take slice args as a const-ref.
+  `#3852 <https://github.com/pybind/pybind11/pull/3852>`_
+
+* Made slice constructor more consistent, and improve performance of some
+  casters by allowing reference stealing.
+  `#3845 <https://github.com/pybind/pybind11/pull/3845>`_
+
+* Change numpy dtype from_args method to use const ref.
+  `#3878 <https://github.com/pybind/pybind11/pull/3878>`_
+
+* Follow rule of three to ensure ``PyErr_Restore`` is called only once.
+  `#3872 <https://github.com/pybind/pybind11/pull/3872>`_
+
+* Added missing perfect forwarding for ``make_iterator`` functions.
+  `#3860 <https://github.com/pybind/pybind11/pull/3860>`_
+
+* Optimize c++ to python function casting by using the rvalue caster.
+  `#3966 <https://github.com/pybind/pybind11/pull/3966>`_
+
+* Optimize Eigen sparse matrix casting by removing unnecessary temporary.
+  `#4064 <https://github.com/pybind/pybind11/pull/4064>`_
+
+* Avoid potential implicit copy/assignment constructors causing double free in
+  ``strdup_gaurd``.
+  `#3905 <https://github.com/pybind/pybind11/pull/3905>`_
+
+* Enable clang-tidy checks ``misc-definitions-in-headers``,
+  ``modernize-loop-convert``, and ``modernize-use-nullptr``.
+  `#3881 <https://github.com/pybind/pybind11/pull/3881>`_
+  `#3988 <https://github.com/pybind/pybind11/pull/3988>`_
+
+
+Build system improvements:
+
+* CMake: Fix file extension on Windows with cp36 and cp37 using FindPython.
+  `#3919 <https://github.com/pybind/pybind11/pull/3919>`_
+
+* CMake: Support multiple Python targets (such as on vcpkg).
+  `#3948 <https://github.com/pybind/pybind11/pull/3948>`_
+
+* CMake: Fix issue with NVCC on Windows.
+  `#3947 <https://github.com/pybind/pybind11/pull/3947>`_
+
+* CMake: Drop the bitness check on cross compiles (like targeting WebAssembly
+  via Emscripten).
+  `#3959 <https://github.com/pybind/pybind11/pull/3959>`_
+
+* Add MSVC builds in debug mode to CI.
+  `#3784 <https://github.com/pybind/pybind11/pull/3784>`_
+
+* MSVC 2022 C++20 coverage was added to GitHub Actions, including Eigen.
+  `#3732 <https://github.com/pybind/pybind11/pull/3732>`_,
+  `#3741 <https://github.com/pybind/pybind11/pull/3741>`_
+
+
+Backend and tidying up:
+
+* New theme for the documentation.
+  `#3109 <https://github.com/pybind/pybind11/pull/3109>`_
+
+* Remove idioms in code comments.  Use more inclusive language.
+  `#3809 <https://github.com/pybind/pybind11/pull/3809>`_
+
+* ``#include <iostream>`` was removed from the ``pybind11/stl.h`` header. Your
+  project may break if it has a transitive dependency on this include. The fix
+  is to "Include What You Use".
+  `#3928 <https://github.com/pybind/pybind11/pull/3928>`_
+
+* Avoid ``setup.py <command>`` usage in internal tests.
+  `#3734 <https://github.com/pybind/pybind11/pull/3734>`_
+
+
+Version 2.9.2 (Mar 29, 2022)
+----------------------------
+
+Changes:
+
+* Enum now has an ``__index__`` method on Python <3.8 too.
+  `#3700 <https://github.com/pybind/pybind11/pull/3700>`_
+
+* Local internals are now cleared after finalizing the interpreter.
+  `#3744 <https://github.com/pybind/pybind11/pull/3744>`_
+
+Bug fixes:
+
+* Better support for Python 3.11 alphas.
+  `#3694 <https://github.com/pybind/pybind11/pull/3694>`_
+
+* ``PYBIND11_TYPE_CASTER`` now uses fully qualified symbols, so it can be used
+  outside of ``pybind11::detail``.
+  `#3758 <https://github.com/pybind/pybind11/pull/3758>`_
+
+* Some fixes for PyPy 3.9.
+  `#3768 <https://github.com/pybind/pybind11/pull/3768>`_
+
+* Fixed a potential memleak in PyPy in ``get_type_override``.
+  `#3774 <https://github.com/pybind/pybind11/pull/3774>`_
+
+* Fix usage of ``VISIBILITY_INLINES_HIDDEN``.
+  `#3721 <https://github.com/pybind/pybind11/pull/3721>`_
+
+
+Build system improvements:
+
+* Uses ``sysconfig`` module to determine installation locations on Python >=
+  3.10, instead of ``distutils`` which has been deprecated.
+  `#3764 <https://github.com/pybind/pybind11/pull/3764>`_
+
+* Support Catch 2.13.5+ (supporting GLIBC 2.34+).
+  `#3679 <https://github.com/pybind/pybind11/pull/3679>`_
+
+* Fix test failures with numpy 1.22 by ignoring whitespace when comparing
+  ``str()`` of dtypes.
+  `#3682 <https://github.com/pybind/pybind11/pull/3682>`_
+
+
+Backend and tidying up:
+
+* clang-tidy: added ``readability-qualified-auto``,
+  ``readability-braces-around-statements``,
+  ``cppcoreguidelines-prefer-member-initializer``,
+  ``clang-analyzer-optin.performance.Padding``,
+  ``cppcoreguidelines-pro-type-static-cast-downcast``, and
+  ``readability-inconsistent-declaration-parameter-name``.
+  `#3702 <https://github.com/pybind/pybind11/pull/3702>`_,
+  `#3699 <https://github.com/pybind/pybind11/pull/3699>`_,
+  `#3716 <https://github.com/pybind/pybind11/pull/3716>`_,
+  `#3709 <https://github.com/pybind/pybind11/pull/3709>`_
+
+* clang-format was added to the pre-commit actions, and the entire code base
+  automatically reformatted (after several iterations preparing for this leap).
+  `#3713 <https://github.com/pybind/pybind11/pull/3713>`_
+
+
+Version 2.9.1 (Feb 2, 2022)
+---------------------------
+
+Changes:
+
+* If possible, attach Python exception with ``py::raise_from`` to ``TypeError``
+  when casting from C++ to Python. This will give additional info if Python
+  exceptions occur in the caster. Adds a test case of trying to convert a set
+  from C++ to Python when the hash function is not defined in Python.
+  `#3605 <https://github.com/pybind/pybind11/pull/3605>`_
+
+* Add a mapping of C++11 nested exceptions to their Python exception
+  equivalent using ``py::raise_from``. This attaches the nested exceptions in
+  Python using the ``__cause__`` field.
+  `#3608 <https://github.com/pybind/pybind11/pull/3608>`_
+
+* Propagate Python exception traceback using ``raise_from`` if a pybind11
+  function runs out of overloads.
+  `#3671 <https://github.com/pybind/pybind11/pull/3671>`_
+
+* ``py::multiple_inheritance`` is now only needed when C++ bases are hidden
+  from pybind11.
+  `#3650 <https://github.com/pybind/pybind11/pull/3650>`_ and
+  `#3659 <https://github.com/pybind/pybind11/pull/3659>`_
+
+
+Bug fixes:
+
+* Remove a boolean cast in ``numpy.h`` that causes MSVC C4800 warnings when
+  compiling against Python 3.10 or newer.
+  `#3669 <https://github.com/pybind/pybind11/pull/3669>`_
+
+* Render ``py::bool_`` and ``py::float_`` as ``bool`` and ``float``
+  respectively.
+  `#3622 <https://github.com/pybind/pybind11/pull/3622>`_
+
+Build system improvements:
+
+* Fix CMake extension suffix computation on Python 3.10+.
+  `#3663 <https://github.com/pybind/pybind11/pull/3663>`_
+
+* Allow ``CMAKE_ARGS`` to override CMake args in pybind11's own ``setup.py``.
+  `#3577 <https://github.com/pybind/pybind11/pull/3577>`_
+
+* Remove a few deprecated c-headers.
+  `#3610 <https://github.com/pybind/pybind11/pull/3610>`_
+
+* More uniform handling of test targets.
+  `#3590 <https://github.com/pybind/pybind11/pull/3590>`_
+
+* Add clang-tidy readability check to catch potentially swapped function args.
+  `#3611 <https://github.com/pybind/pybind11/pull/3611>`_
+
+
+Version 2.9.0 (Dec 28, 2021)
+----------------------------
+
+This is the last version to support Python 2.7 and 3.5.
+
+New Features:
+
+* Allow ``py::args`` to be followed by other arguments; the remaining arguments
+  are implicitly keyword-only, as if a ``py::kw_only{}`` annotation had been
+  used.
+  `#3402 <https://github.com/pybind/pybind11/pull/3402>`_
+
+Changes:
+
+* Make str/bytes/memoryview more interoperable with ``std::string_view``.
+  `#3521 <https://github.com/pybind/pybind11/pull/3521>`_
+
+* Replace ``_`` with ``const_name`` in internals, avoid defining ``pybind::_``
+  if ``_`` defined as macro (common gettext usage)
+  `#3423 <https://github.com/pybind/pybind11/pull/3423>`_
+
+
+Bug fixes:
+
+* Fix a rare warning about extra copy in an Eigen constructor.
+  `#3486 <https://github.com/pybind/pybind11/pull/3486>`_
+
+* Fix caching of the C++ overrides.
+  `#3465 <https://github.com/pybind/pybind11/pull/3465>`_
+
+* Add missing ``std::forward`` calls to some ``cpp_function`` overloads.
+  `#3443 <https://github.com/pybind/pybind11/pull/3443>`_
+
+* Support PyPy 7.3.7 and the PyPy3.8 beta. Test python-3.11 on PRs with the
+  ``python dev`` label.
+  `#3419 <https://github.com/pybind/pybind11/pull/3419>`_
+
+* Replace usage of deprecated ``Eigen::MappedSparseMatrix`` with
+  ``Eigen::Map<Eigen::SparseMatrix<...>>`` for Eigen 3.3+.
+  `#3499 <https://github.com/pybind/pybind11/pull/3499>`_
+
+* Tweaks to support Microsoft Visual Studio 2022.
+  `#3497 <https://github.com/pybind/pybind11/pull/3497>`_
+
+Build system improvements:
+
+* Nicer CMake printout and IDE organisation for pybind11's own tests.
+  `#3479 <https://github.com/pybind/pybind11/pull/3479>`_
+
+* CMake: report version type as part of the version string to avoid a spurious
+  space in the package status message.
+  `#3472 <https://github.com/pybind/pybind11/pull/3472>`_
+
+* Flags starting with ``-g`` in ``$CFLAGS`` and ``$CPPFLAGS`` are no longer
+  overridden by ``.Pybind11Extension``.
+  `#3436 <https://github.com/pybind/pybind11/pull/3436>`_
+
+* Ensure ThreadPool is closed in ``setup_helpers``.
+  `#3548 <https://github.com/pybind/pybind11/pull/3548>`_
+
+* Avoid LTS on ``mips64`` and ``ppc64le`` (reported broken).
+  `#3557 <https://github.com/pybind/pybind11/pull/3557>`_
+
+
+v2.8.1 (Oct 27, 2021)
+---------------------
+
+Changes and additions:
+
+* The simple namespace creation shortcut added in 2.8.0 was deprecated due to
+  usage of CPython internal API, and will be removed soon. Use
+  ``py::module_::import("types").attr("SimpleNamespace")``.
+  `#3374 <https://github.com/pybinyyd/pybind11/pull/3374>`_
+
+* Add C++ Exception type to throw and catch ``AttributeError``. Useful for
+  defining custom ``__setattr__`` and ``__getattr__`` methods.
+  `#3387 <https://github.com/pybind/pybind11/pull/3387>`_
+
+Fixes:
+
+* Fixed the potential for dangling references when using properties with
+  ``std::optional`` types.
+  `#3376 <https://github.com/pybind/pybind11/pull/3376>`_
+
+* Modernize usage of ``PyCodeObject`` on Python 3.9+ (moving toward support for
+  Python 3.11a1)
+  `#3368 <https://github.com/pybind/pybind11/pull/3368>`_
+
+* A long-standing bug in ``eigen.h`` was fixed (originally PR #3343). The bug
+  was unmasked by newly added ``static_assert``'s in the Eigen 3.4.0 release.
+  `#3352 <https://github.com/pybind/pybind11/pull/3352>`_
+
+* Support multiple raw inclusion of CMake helper files (Conan.io does this for
+  multi-config generators).
+  `#3420 <https://github.com/pybind/pybind11/pull/3420>`_
+
+* Fix harmless warning on upcoming CMake 3.22.
+  `#3368 <https://github.com/pybind/pybind11/pull/3368>`_
+
+* Fix 2.8.0 regression with MSVC 2017 + C++17 mode + Python 3.
+  `#3407 <https://github.com/pybind/pybind11/pull/3407>`_
+
+* Fix 2.8.0 regression that caused undefined behavior (typically
+  segfaults) in ``make_key_iterator``/``make_value_iterator`` if dereferencing
+  the iterator returned a temporary value instead of a reference.
+  `#3348 <https://github.com/pybind/pybind11/pull/3348>`_
+
+
+v2.8.0 (Oct 4, 2021)
+--------------------
+
+New features:
+
+* Added ``py::raise_from`` to enable chaining exceptions.
+  `#3215 <https://github.com/pybind/pybind11/pull/3215>`_
+
+* Allow exception translators to be optionally registered local to a module
+  instead of applying globally across all pybind11 modules. Use
+  ``register_local_exception_translator(ExceptionTranslator&& translator)``
+  instead of  ``register_exception_translator(ExceptionTranslator&&
+  translator)`` to keep your exception remapping code local to the module.
+  `#2650 <https://github.com/pybinyyd/pybind11/pull/2650>`_
+
+* Add ``make_simple_namespace`` function for instantiating Python
+  ``SimpleNamespace`` objects. **Deprecated in 2.8.1.**
+  `#2840 <https://github.com/pybind/pybind11/pull/2840>`_
+
+* ``pybind11::scoped_interpreter`` and ``initialize_interpreter`` have new
+  arguments to allow ``sys.argv`` initialization.
+  `#2341 <https://github.com/pybind/pybind11/pull/2341>`_
+
+* Allow Python builtins to be used as callbacks in CPython.
+  `#1413 <https://github.com/pybind/pybind11/pull/1413>`_
+
+* Added ``view`` to view arrays with a different datatype.
+  `#987 <https://github.com/pybind/pybind11/pull/987>`_
+
+* Implemented ``reshape`` on arrays.
+  `#984 <https://github.com/pybind/pybind11/pull/984>`_
+
+* Enable defining custom ``__new__`` methods on classes by fixing bug
+  preventing overriding methods if they have non-pybind11 siblings.
+  `#3265 <https://github.com/pybind/pybind11/pull/3265>`_
+
+* Add ``make_value_iterator()``, and fix ``make_key_iterator()`` to return
+  references instead of copies.
+  `#3293 <https://github.com/pybind/pybind11/pull/3293>`_
+
+* Improve the classes generated by ``bind_map``: `#3310 <https://github.com/pybind/pybind11/pull/3310>`_
+
+  * Change ``.items`` from an iterator to a dictionary view.
+  * Add ``.keys`` and ``.values`` (both dictionary views).
+  * Allow ``__contains__`` to take any object.
+
+* ``pybind11::custom_type_setup`` was added, for customizing the
+  ``PyHeapTypeObject`` corresponding to a class, which may be useful for
+  enabling garbage collection support, among other things.
+  `#3287 <https://github.com/pybind/pybind11/pull/3287>`_
+
+
+Changes:
+
+* Set ``__file__`` constant when running ``eval_file`` in an embedded interpreter.
+  `#3233 <https://github.com/pybind/pybind11/pull/3233>`_
+
+* Python objects and (C++17) ``std::optional`` now accepted in ``py::slice``
+  constructor.
+  `#1101 <https://github.com/pybind/pybind11/pull/1101>`_
+
+* The pybind11 proxy types ``str``, ``bytes``, ``bytearray``, ``tuple``,
+  ``list`` now consistently support passing ``ssize_t`` values for sizes and
+  indexes. Previously, only ``size_t`` was accepted in several interfaces.
+  `#3219 <https://github.com/pybind/pybind11/pull/3219>`_
+
+* Avoid evaluating ``PYBIND11_TLS_REPLACE_VALUE`` arguments more than once.
+  `#3290 <https://github.com/pybind/pybind11/pull/3290>`_
+
+Fixes:
+
+* Bug fix: enum value's ``__int__`` returning non-int when underlying type is
+  bool or of char type.
+  `#1334 <https://github.com/pybind/pybind11/pull/1334>`_
+
+* Fixes bug in setting error state in Capsule's pointer methods.
+  `#3261 <https://github.com/pybind/pybind11/pull/3261>`_
+
+* A long-standing memory leak in ``py::cpp_function::initialize`` was fixed.
+  `#3229 <https://github.com/pybind/pybind11/pull/3229>`_
+
+* Fixes thread safety for some ``pybind11::type_caster`` which require lifetime
+  extension, such as for ``std::string_view``.
+  `#3237 <https://github.com/pybind/pybind11/pull/3237>`_
+
+* Restore compatibility with gcc 4.8.4 as distributed by ubuntu-trusty, linuxmint-17.
+  `#3270 <https://github.com/pybind/pybind11/pull/3270>`_
+
+
+Build system improvements:
+
+* Fix regression in CMake Python package config: improper use of absolute path.
+  `#3144 <https://github.com/pybind/pybind11/pull/3144>`_
+
+* Cached Python version information could become stale when CMake was re-run
+  with a different Python version. The build system now detects this and
+  updates this information.
+  `#3299 <https://github.com/pybind/pybind11/pull/3299>`_
+
+* Specified UTF8-encoding in setup.py calls of open().
+  `#3137 <https://github.com/pybind/pybind11/pull/3137>`_
+
+* Fix a harmless warning from CMake 3.21 with the classic Python discovery.
+  `#3220 <https://github.com/pybind/pybind11/pull/3220>`_
+
+* Eigen repo and version can now be specified as cmake options.
+  `#3324 <https://github.com/pybind/pybind11/pull/3324>`_
+
+
+Backend and tidying up:
+
+* Reduced thread-local storage required for keeping alive temporary data for
+  type conversion to one key per ABI version, rather than one key per extension
+  module.  This makes the total thread-local storage required by pybind11 2
+  keys per ABI version.
+  `#3275 <https://github.com/pybind/pybind11/pull/3275>`_
+
+* Optimize NumPy array construction with additional moves.
+  `#3183 <https://github.com/pybind/pybind11/pull/3183>`_
+
+* Conversion to ``std::string`` and ``std::string_view`` now avoids making an
+  extra copy of the data on Python >= 3.3.
+  `#3257 <https://github.com/pybind/pybind11/pull/3257>`_
+
+* Remove const modifier from certain C++ methods on Python collections
+  (``list``, ``set``, ``dict``) such as (``clear()``, ``append()``,
+  ``insert()``, etc...) and annotated them with ``py-non-const``.
+
+* Enable readability ``clang-tidy-const-return`` and remove useless consts.
+  `#3254 <https://github.com/pybind/pybind11/pull/3254>`_
+  `#3194 <https://github.com/pybind/pybind11/pull/3194>`_
+
+* The clang-tidy ``google-explicit-constructor`` option was enabled.
+  `#3250 <https://github.com/pybind/pybind11/pull/3250>`_
+
+* Mark a pytype move constructor as noexcept (perf).
+  `#3236 <https://github.com/pybind/pybind11/pull/3236>`_
+
+* Enable clang-tidy check to guard against inheritance slicing.
+  `#3210 <https://github.com/pybind/pybind11/pull/3210>`_
+
+* Legacy warning suppression pragma were removed from eigen.h. On Unix
+  platforms, please use -isystem for Eigen include directories, to suppress
+  compiler warnings originating from Eigen headers. Note that CMake does this
+  by default. No adjustments are needed for Windows.
+  `#3198 <https://github.com/pybind/pybind11/pull/3198>`_
+
+* Format pybind11 with isort consistent ordering of imports
+  `#3195 <https://github.com/pybind/pybind11/pull/3195>`_
+
+* The warnings-suppression "pragma clamp" at the top/bottom of pybind11 was
+  removed, clearing the path to refactoring and IWYU cleanup.
+  `#3186 <https://github.com/pybind/pybind11/pull/3186>`_
+
+* Enable most bugprone checks in clang-tidy and fix the found potential bugs
+  and poor coding styles.
+  `#3166 <https://github.com/pybind/pybind11/pull/3166>`_
+
+* Add ``clang-tidy-readability`` rules to make boolean casts explicit improving
+  code readability. Also enabled other misc and readability clang-tidy checks.
+  `#3148 <https://github.com/pybind/pybind11/pull/3148>`_
+
+* Move object in ``.pop()`` for list.
+  `#3116 <https://github.com/pybind/pybind11/pull/3116>`_
+
+
+
+
+v2.7.1 (Aug 3, 2021)
+---------------------
+
+Minor missing functionality added:
+
+* Allow Python builtins to be used as callbacks in CPython.
+  `#1413 <https://github.com/pybind/pybind11/pull/1413>`_
+
+Bug fixes:
+
+* Fix regression in CMake Python package config: improper use of absolute path.
+  `#3144 <https://github.com/pybind/pybind11/pull/3144>`_
+
+* Fix Mingw64 and add to the CI testing matrix.
+  `#3132 <https://github.com/pybind/pybind11/pull/3132>`_
+
+* Specified UTF8-encoding in setup.py calls of open().
+  `#3137 <https://github.com/pybind/pybind11/pull/3137>`_
+
+* Add clang-tidy-readability rules to make boolean casts explicit improving
+  code readability. Also enabled other misc and readability clang-tidy checks.
+  `#3148 <https://github.com/pybind/pybind11/pull/3148>`_
+
+* Move object in ``.pop()`` for list.
+  `#3116 <https://github.com/pybind/pybind11/pull/3116>`_
+
+Backend and tidying up:
+
+* Removed and fixed warning suppressions.
+  `#3127 <https://github.com/pybind/pybind11/pull/3127>`_
+  `#3129 <https://github.com/pybind/pybind11/pull/3129>`_
+  `#3135 <https://github.com/pybind/pybind11/pull/3135>`_
+  `#3141 <https://github.com/pybind/pybind11/pull/3141>`_
+  `#3142 <https://github.com/pybind/pybind11/pull/3142>`_
+  `#3150 <https://github.com/pybind/pybind11/pull/3150>`_
+  `#3152 <https://github.com/pybind/pybind11/pull/3152>`_
+  `#3160 <https://github.com/pybind/pybind11/pull/3160>`_
+  `#3161 <https://github.com/pybind/pybind11/pull/3161>`_
+
+
+v2.7.0 (Jul 16, 2021)
+---------------------
+
+New features:
+
+* Enable ``py::implicitly_convertible<py::none, ...>`` for
+  ``py::class_``-wrapped types.
+  `#3059 <https://github.com/pybind/pybind11/pull/3059>`_
+
+* Allow function pointer extraction from overloaded functions.
+  `#2944 <https://github.com/pybind/pybind11/pull/2944>`_
+
+* NumPy: added ``.char_()`` to type which gives the NumPy public ``char``
+  result, which also distinguishes types by bit length (unlike ``.kind()``).
+  `#2864 <https://github.com/pybind/pybind11/pull/2864>`_
+
+* Add ``pybind11::bytearray`` to manipulate ``bytearray`` similar to ``bytes``.
+  `#2799 <https://github.com/pybind/pybind11/pull/2799>`_
+
+* ``pybind11/stl/filesystem.h`` registers a type caster that, on C++17/Python
+  3.6+, converts ``std::filesystem::path`` to ``pathlib.Path`` and any
+  ``os.PathLike`` to ``std::filesystem::path``.
+  `#2730 <https://github.com/pybind/pybind11/pull/2730>`_
+
+* A ``PYBIND11_VERSION_HEX`` define was added, similar to ``PY_VERSION_HEX``.
+  `#3120 <https://github.com/pybind/pybind11/pull/3120>`_
+
+
+
+Changes:
+
+* ``py::str`` changed to exclusively hold ``PyUnicodeObject``. Previously
+  ``py::str`` could also hold ``bytes``, which is probably surprising, was
+  never documented, and can mask bugs (e.g. accidental use of ``py::str``
+  instead of ``py::bytes``).
+  `#2409 <https://github.com/pybind/pybind11/pull/2409>`_
+
+* Add a safety guard to ensure that the Python GIL is held when C++ calls back
+  into Python via ``object_api<>::operator()`` (e.g. ``py::function``
+  ``__call__``).  (This feature is available for Python 3.6+ only.)
+  `#2919 <https://github.com/pybind/pybind11/pull/2919>`_
+
+* Catch a missing ``self`` argument in calls to ``__init__()``.
+  `#2914 <https://github.com/pybind/pybind11/pull/2914>`_
+
+* Use ``std::string_view`` if available to avoid a copy when passing an object
+  to a ``std::ostream``.
+  `#3042 <https://github.com/pybind/pybind11/pull/3042>`_
+
+* An important warning about thread safety was added to the ``iostream.h``
+  documentation; attempts to make ``py::scoped_ostream_redirect`` thread safe
+  have been removed, as it was only partially effective.
+  `#2995 <https://github.com/pybind/pybind11/pull/2995>`_
+
+
+Fixes:
+
+* Performance: avoid unnecessary strlen calls.
+  `#3058 <https://github.com/pybind/pybind11/pull/3058>`_
+
+* Fix auto-generated documentation string when using ``const T`` in
+  ``pyarray_t``.
+  `#3020 <https://github.com/pybind/pybind11/pull/3020>`_
+
+* Unify error messages thrown by ``simple_collector``/``unpacking_collector``.
+  `#3013 <https://github.com/pybind/pybind11/pull/3013>`_
+
+* ``pybind11::builtin_exception`` is now explicitly exported, which means the
+  types included/defined in different modules are identical, and exceptions
+  raised in different modules can be caught correctly. The documentation was
+  updated to explain that custom exceptions that are used across module
+  boundaries need to be explicitly exported as well.
+  `#2999 <https://github.com/pybind/pybind11/pull/2999>`_
+
+* Fixed exception when printing UTF-8 to a ``scoped_ostream_redirect``.
+  `#2982 <https://github.com/pybind/pybind11/pull/2982>`_
+
+* Pickle support enhancement: ``setstate`` implementation will attempt to
+  ``setattr`` ``__dict__`` only if the unpickled ``dict`` object is not empty,
+  to not force use of ``py::dynamic_attr()`` unnecessarily.
+  `#2972 <https://github.com/pybind/pybind11/pull/2972>`_
+
+* Allow negative timedelta values to roundtrip.
+  `#2870 <https://github.com/pybind/pybind11/pull/2870>`_
+
+* Fix unchecked errors could potentially swallow signals/other exceptions.
+  `#2863 <https://github.com/pybind/pybind11/pull/2863>`_
+
+* Add null pointer check with ``std::localtime``.
+  `#2846 <https://github.com/pybind/pybind11/pull/2846>`_
+
+* Fix the ``weakref`` constructor from ``py::object`` to create a new
+  ``weakref`` on conversion.
+  `#2832 <https://github.com/pybind/pybind11/pull/2832>`_
+
+* Avoid relying on exceptions in C++17 when getting a ``shared_ptr`` holder
+  from a ``shared_from_this`` class.
+  `#2819 <https://github.com/pybind/pybind11/pull/2819>`_
+
+* Allow the codec's exception to be raised instead of :code:`RuntimeError` when
+  casting from :code:`py::str` to :code:`std::string`.
+  `#2903 <https://github.com/pybind/pybind11/pull/2903>`_
+
+
+Build system improvements:
+
+* In ``setup_helpers.py``, test for platforms that have some multiprocessing
+  features but lack semaphores, which ``ParallelCompile`` requires.
+  `#3043 <https://github.com/pybind/pybind11/pull/3043>`_
+
+* Fix ``pybind11_INCLUDE_DIR`` in case ``CMAKE_INSTALL_INCLUDEDIR`` is
+  absolute.
+  `#3005 <https://github.com/pybind/pybind11/pull/3005>`_
+
+* Fix bug not respecting ``WITH_SOABI`` or ``WITHOUT_SOABI`` to CMake.
+  `#2938 <https://github.com/pybind/pybind11/pull/2938>`_
+
+* Fix the default ``Pybind11Extension`` compilation flags with a Mingw64 python.
+  `#2921 <https://github.com/pybind/pybind11/pull/2921>`_
+
+* Clang on Windows: do not pass ``/MP`` (ignored flag).
+  `#2824 <https://github.com/pybind/pybind11/pull/2824>`_
+
+* ``pybind11.setup_helpers.intree_extensions`` can be used to generate
+  ``Pybind11Extension`` instances from cpp files placed in the Python package
+  source tree.
+  `#2831 <https://github.com/pybind/pybind11/pull/2831>`_
+
+Backend and tidying up:
+
+* Enable clang-tidy performance, readability, and modernization checks
+  throughout the codebase to enforce best coding practices.
+  `#3046 <https://github.com/pybind/pybind11/pull/3046>`_,
+  `#3049 <https://github.com/pybind/pybind11/pull/3049>`_,
+  `#3051 <https://github.com/pybind/pybind11/pull/3051>`_,
+  `#3052 <https://github.com/pybind/pybind11/pull/3052>`_,
+  `#3080 <https://github.com/pybind/pybind11/pull/3080>`_, and
+  `#3094 <https://github.com/pybind/pybind11/pull/3094>`_
+
+
+* Checks for common misspellings were added to the pre-commit hooks.
+  `#3076 <https://github.com/pybind/pybind11/pull/3076>`_
+
+* Changed ``Werror`` to stricter ``Werror-all`` for Intel compiler and fixed
+  minor issues.
+  `#2948 <https://github.com/pybind/pybind11/pull/2948>`_
+
+* Fixed compilation with GCC < 5 when the user defines ``_GLIBCXX_USE_CXX11_ABI``.
+  `#2956 <https://github.com/pybind/pybind11/pull/2956>`_
+
+* Added nox support for easier local testing and linting of contributions.
+  `#3101 <https://github.com/pybind/pybind11/pull/3101>`_ and
+  `#3121 <https://github.com/pybind/pybind11/pull/3121>`_
+
+* Avoid RTD style issue with docutils 0.17+.
+  `#3119 <https://github.com/pybind/pybind11/pull/3119>`_
+
+* Support pipx run, such as ``pipx run pybind11 --include`` for a quick compile.
+  `#3117 <https://github.com/pybind/pybind11/pull/3117>`_
+
+
+
+v2.6.2 (Jan 26, 2021)
+---------------------
+
+Minor missing functionality added:
+
+* enum: add missing Enum.value property.
+  `#2739 <https://github.com/pybind/pybind11/pull/2739>`_
+
+* Allow thread termination to be avoided during shutdown for CPython 3.7+ via
+  ``.disarm`` for ``gil_scoped_acquire``/``gil_scoped_release``.
+  `#2657 <https://github.com/pybind/pybind11/pull/2657>`_
+
+Fixed or improved behavior in a few special cases:
+
+* Fix bug where the constructor of ``object`` subclasses would not throw on
+  being passed a Python object of the wrong type.
+  `#2701 <https://github.com/pybind/pybind11/pull/2701>`_
+
+* The ``type_caster`` for integers does not convert Python objects with
+  ``__int__`` anymore with ``noconvert`` or during the first round of trying
+  overloads.
+  `#2698 <https://github.com/pybind/pybind11/pull/2698>`_
+
+* When casting to a C++ integer, ``__index__`` is always called and not
+  considered as conversion, consistent with Python 3.8+.
+  `#2801 <https://github.com/pybind/pybind11/pull/2801>`_
+
+Build improvements:
+
+* Setup helpers: ``extra_compile_args`` and ``extra_link_args`` automatically set by
+  Pybind11Extension are now prepended, which allows them to be overridden
+  by user-set ``extra_compile_args`` and ``extra_link_args``.
+  `#2808 <https://github.com/pybind/pybind11/pull/2808>`_
+
+* Setup helpers: Don't trigger unused parameter warning.
+  `#2735 <https://github.com/pybind/pybind11/pull/2735>`_
+
+* CMake: Support running with ``--warn-uninitialized`` active.
+  `#2806 <https://github.com/pybind/pybind11/pull/2806>`_
+
+* CMake: Avoid error if included from two submodule directories.
+  `#2804 <https://github.com/pybind/pybind11/pull/2804>`_
+
+* CMake: Fix ``STATIC`` / ``SHARED`` being ignored in FindPython mode.
+  `#2796 <https://github.com/pybind/pybind11/pull/2796>`_
+
+* CMake: Respect the setting for ``CMAKE_CXX_VISIBILITY_PRESET`` if defined.
+  `#2793 <https://github.com/pybind/pybind11/pull/2793>`_
+
+* CMake: Fix issue with FindPython2/FindPython3 not working with ``pybind11::embed``.
+  `#2662 <https://github.com/pybind/pybind11/pull/2662>`_
+
+* CMake: mixing local and installed pybind11's would prioritize the installed
+  one over the local one (regression in 2.6.0).
+  `#2716 <https://github.com/pybind/pybind11/pull/2716>`_
+
+
+Bug fixes:
+
+* Fixed segfault in multithreaded environments when using
+  ``scoped_ostream_redirect``.
+  `#2675 <https://github.com/pybind/pybind11/pull/2675>`_
+
+* Leave docstring unset when all docstring-related options are disabled, rather
+  than set an empty string.
+  `#2745 <https://github.com/pybind/pybind11/pull/2745>`_
+
+* The module key in builtins that pybind11 uses to store its internals changed
+  from std::string to a python str type (more natural on Python 2, no change on
+  Python 3).
+  `#2814 <https://github.com/pybind/pybind11/pull/2814>`_
+
+* Fixed assertion error related to unhandled (later overwritten) exception in
+  CPython 3.8 and 3.9 debug builds.
+  `#2685 <https://github.com/pybind/pybind11/pull/2685>`_
+
+* Fix ``py::gil_scoped_acquire`` assert with CPython 3.9 debug build.
+  `#2683 <https://github.com/pybind/pybind11/pull/2683>`_
+
+* Fix issue with a test failing on pytest 6.2.
+  `#2741 <https://github.com/pybind/pybind11/pull/2741>`_
+
+Warning fixes:
+
+* Fix warning modifying constructor parameter 'flag' that shadows a field of
+  'set_flag' ``[-Wshadow-field-in-constructor-modified]``.
+  `#2780 <https://github.com/pybind/pybind11/pull/2780>`_
+
+* Suppressed some deprecation warnings about old-style
+  ``__init__``/``__setstate__`` in the tests.
+  `#2759 <https://github.com/pybind/pybind11/pull/2759>`_
+
+Valgrind work:
+
+* Fix invalid access when calling a pybind11 ``__init__`` on a non-pybind11
+  class instance.
+  `#2755 <https://github.com/pybind/pybind11/pull/2755>`_
+
+* Fixed various minor memory leaks in pybind11's test suite.
+  `#2758 <https://github.com/pybind/pybind11/pull/2758>`_
+
+* Resolved memory leak in cpp_function initialization when exceptions occurred.
+  `#2756 <https://github.com/pybind/pybind11/pull/2756>`_
+
+* Added a Valgrind build, checking for leaks and memory-related UB, to CI.
+  `#2746 <https://github.com/pybind/pybind11/pull/2746>`_
+
+Compiler support:
+
+* Intel compiler was not activating C++14 support due to a broken define.
+  `#2679 <https://github.com/pybind/pybind11/pull/2679>`_
+
+* Support ICC and NVIDIA HPC SDK in C++17 mode.
+  `#2729 <https://github.com/pybind/pybind11/pull/2729>`_
+
+* Support Intel OneAPI compiler (ICC 20.2) and add to CI.
+  `#2573 <https://github.com/pybind/pybind11/pull/2573>`_
+
+
+
+v2.6.1 (Nov 11, 2020)
+---------------------
+
+* ``py::exec``, ``py::eval``, and ``py::eval_file`` now add the builtins module
+  as ``"__builtins__"`` to their ``globals`` argument, better matching ``exec``
+  and ``eval`` in pure Python.
+  `#2616 <https://github.com/pybind/pybind11/pull/2616>`_
+
+* ``setup_helpers`` will no longer set a minimum macOS version higher than the
+  current version.
+  `#2622 <https://github.com/pybind/pybind11/pull/2622>`_
+
+* Allow deleting static properties.
+  `#2629 <https://github.com/pybind/pybind11/pull/2629>`_
+
+* Seal a leak in ``def_buffer``, cleaning up the ``capture`` object after the
+  ``class_`` object goes out of scope.
+  `#2634 <https://github.com/pybind/pybind11/pull/2634>`_
+
+* ``pybind11_INCLUDE_DIRS`` was incorrect, potentially causing a regression if
+  it was expected to include ``PYTHON_INCLUDE_DIRS`` (please use targets
+  instead).
+  `#2636 <https://github.com/pybind/pybind11/pull/2636>`_
+
+* Added parameter names to the ``py::enum_`` constructor and methods, avoiding
+  ``arg0`` in the generated docstrings.
+  `#2637 <https://github.com/pybind/pybind11/pull/2637>`_
+
+* Added ``needs_recompile`` optional function to the ``ParallelCompiler``
+  helper, to allow a recompile to be skipped based on a user-defined function.
+  `#2643 <https://github.com/pybind/pybind11/pull/2643>`_
+
+
+v2.6.0 (Oct 21, 2020)
+---------------------
+
+See :ref:`upgrade-guide-2.6` for help upgrading to the new version.
+
+New features:
+
+* Keyword-only arguments supported in Python 2 or 3 with ``py::kw_only()``.
+  `#2100 <https://github.com/pybind/pybind11/pull/2100>`_
+
+* Positional-only arguments supported in Python 2 or 3 with ``py::pos_only()``.
+  `#2459 <https://github.com/pybind/pybind11/pull/2459>`_
+
+* ``py::is_final()`` class modifier to block subclassing (CPython only).
+  `#2151 <https://github.com/pybind/pybind11/pull/2151>`_
+
+* Added ``py::prepend()``, allowing a function to be placed at the beginning of
+  the overload chain.
+  `#1131 <https://github.com/pybind/pybind11/pull/1131>`_
+
+* Access to the type object now provided with ``py::type::of<T>()`` and
+  ``py::type::of(h)``.
+  `#2364 <https://github.com/pybind/pybind11/pull/2364>`_
+
+* Perfect forwarding support for methods.
+  `#2048 <https://github.com/pybind/pybind11/pull/2048>`_
+
+* Added ``py::error_already_set::discard_as_unraisable()``.
+  `#2372 <https://github.com/pybind/pybind11/pull/2372>`_
+
+* ``py::hash`` is now public.
+  `#2217 <https://github.com/pybind/pybind11/pull/2217>`_
+
+* ``py::class_<union_type>`` is now supported. Note that writing to one data
+  member of the union and reading another (type punning) is UB in C++. Thus
+  pybind11-bound enums should never be used for such conversions.
+  `#2320 <https://github.com/pybind/pybind11/pull/2320>`_.
+
+* Classes now check local scope when registering members, allowing a subclass
+  to have a member with the same name as a parent (such as an enum).
+  `#2335 <https://github.com/pybind/pybind11/pull/2335>`_
+
+Code correctness features:
+
+* Error now thrown when ``__init__`` is forgotten on subclasses.
+  `#2152 <https://github.com/pybind/pybind11/pull/2152>`_
+
+* Throw error if conversion to a pybind11 type if the Python object isn't a
+  valid instance of that type, such as ``py::bytes(o)`` when ``py::object o``
+  isn't a bytes instance.
+  `#2349 <https://github.com/pybind/pybind11/pull/2349>`_
+
+* Throw if conversion to ``str`` fails.
+  `#2477 <https://github.com/pybind/pybind11/pull/2477>`_
+
+
+API changes:
+
+* ``py::module`` was renamed ``py::module_`` to avoid issues with C++20 when
+  used unqualified, but an alias ``py::module`` is provided for backward
+  compatibility.
+  `#2489 <https://github.com/pybind/pybind11/pull/2489>`_
+
+* Public constructors for ``py::module_`` have been deprecated; please use
+  ``pybind11::module_::create_extension_module`` if you were using the public
+  constructor (fairly rare after ``PYBIND11_MODULE`` was introduced).
+  `#2552 <https://github.com/pybind/pybind11/pull/2552>`_
+
+* ``PYBIND11_OVERLOAD*`` macros and ``get_overload`` function replaced by
+  correctly-named ``PYBIND11_OVERRIDE*`` and ``get_override``, fixing
+  inconsistencies in the presence of a closing ``;`` in these macros.
+  ``get_type_overload`` is deprecated.
+  `#2325 <https://github.com/pybind/pybind11/pull/2325>`_
+
+Packaging / building improvements:
+
+* The Python package was reworked to be more powerful and useful.
+  `#2433 <https://github.com/pybind/pybind11/pull/2433>`_
+
+  * :ref:`build-setuptools` is easier thanks to a new
+    ``pybind11.setup_helpers`` module, which provides utilities to use
+    setuptools with pybind11. It can be used via PEP 518, ``setup_requires``,
+    or by directly importing or copying ``setup_helpers.py`` into your project.
+
+  * CMake configuration files are now included in the Python package. Use
+    ``pybind11.get_cmake_dir()`` or ``python -m pybind11 --cmakedir`` to get
+    the directory with the CMake configuration files, or include the
+    site-packages location in your ``CMAKE_MODULE_PATH``. Or you can use the
+    new ``pybind11[global]`` extra when you install ``pybind11``, which
+    installs the CMake files and headers into your base environment in the
+    standard location.
+
+  * ``pybind11-config`` is another way to write ``python -m pybind11`` if you
+    have your PATH set up.
+
+  * Added external typing support to the helper module, code from
+    ``import pybind11`` can now be type checked.
+    `#2588 <https://github.com/pybind/pybind11/pull/2588>`_
+
+* Minimum CMake required increased to 3.4.
+  `#2338 <https://github.com/pybind/pybind11/pull/2338>`_ and
+  `#2370 <https://github.com/pybind/pybind11/pull/2370>`_
+
+  * Full integration with CMake's C++ standard system and compile features
+    replaces ``PYBIND11_CPP_STANDARD``.
+
+  * Generated config file is now portable to different Python/compiler/CMake
+    versions.
+
+  * Virtual environments prioritized if ``PYTHON_EXECUTABLE`` is not set
+    (``venv``, ``virtualenv``, and ``conda``) (similar to the new FindPython
+    mode).
+
+  * Other CMake features now natively supported, like
+    ``CMAKE_INTERPROCEDURAL_OPTIMIZATION``, ``set(CMAKE_CXX_VISIBILITY_PRESET
+    hidden)``.
+
+  * ``CUDA`` as a language is now supported.
+
+  * Helper functions ``pybind11_strip``, ``pybind11_extension``,
+    ``pybind11_find_import`` added, see :doc:`cmake/index`.
+
+  * Optional :ref:`find-python-mode` and :ref:`nopython-mode` with CMake.
+    `#2370 <https://github.com/pybind/pybind11/pull/2370>`_
+
+* Uninstall target added.
+  `#2265 <https://github.com/pybind/pybind11/pull/2265>`_ and
+  `#2346 <https://github.com/pybind/pybind11/pull/2346>`_
+
+* ``pybind11_add_module()`` now accepts an optional ``OPT_SIZE`` flag that
+  switches the binding target to size-based optimization if the global build
+  type can not always be fixed to ``MinSizeRel`` (except in debug mode, where
+  optimizations remain disabled).  ``MinSizeRel`` or this flag reduces binary
+  size quite substantially (~25% on some platforms).
+  `#2463 <https://github.com/pybind/pybind11/pull/2463>`_
+
+Smaller or developer focused features and fixes:
+
+* Moved ``mkdoc.py`` to a new repo, `pybind11-mkdoc`_. There are no longer
+  submodules in the main repo.
+
+* ``py::memoryview`` segfault fix and update, with new
+  ``py::memoryview::from_memory`` in Python 3, and documentation.
+  `#2223 <https://github.com/pybind/pybind11/pull/2223>`_
+
+* Fix for ``buffer_info`` on Python 2.
+  `#2503 <https://github.com/pybind/pybind11/pull/2503>`_
+
+* If ``__eq__`` defined but not ``__hash__``, ``__hash__`` is now set to
+  ``None``.
+  `#2291 <https://github.com/pybind/pybind11/pull/2291>`_
+
+* ``py::ellipsis`` now also works on Python 2.
+  `#2360 <https://github.com/pybind/pybind11/pull/2360>`_
+
+* Pointer to ``std::tuple`` & ``std::pair`` supported in cast.
+  `#2334 <https://github.com/pybind/pybind11/pull/2334>`_
+
+* Small fixes in NumPy support. ``py::array`` now uses ``py::ssize_t`` as first
+  argument type.
+  `#2293 <https://github.com/pybind/pybind11/pull/2293>`_
+
+* Added missing signature for ``py::array``.
+  `#2363 <https://github.com/pybind/pybind11/pull/2363>`_
+
+* ``unchecked_mutable_reference`` has access to operator ``()`` and ``[]`` when
+  const.
+  `#2514 <https://github.com/pybind/pybind11/pull/2514>`_
+
+* ``py::vectorize`` is now supported on functions that return void.
+  `#1969 <https://github.com/pybind/pybind11/pull/1969>`_
+
+* ``py::capsule`` supports ``get_pointer`` and ``set_pointer``.
+  `#1131 <https://github.com/pybind/pybind11/pull/1131>`_
+
+* Fix crash when different instances share the same pointer of the same type.
+  `#2252 <https://github.com/pybind/pybind11/pull/2252>`_
+
+* Fix for ``py::len`` not clearing Python's error state when it fails and throws.
+  `#2575 <https://github.com/pybind/pybind11/pull/2575>`_
+
+* Bugfixes related to more extensive testing, new GitHub Actions CI.
+  `#2321 <https://github.com/pybind/pybind11/pull/2321>`_
+
+* Bug in timezone issue in Eastern hemisphere midnight fixed.
+  `#2438 <https://github.com/pybind/pybind11/pull/2438>`_
+
+* ``std::chrono::time_point`` now works when the resolution is not the same as
+  the system.
+  `#2481 <https://github.com/pybind/pybind11/pull/2481>`_
+
+* Bug fixed where ``py::array_t`` could accept arrays that did not match the
+  requested ordering.
+  `#2484 <https://github.com/pybind/pybind11/pull/2484>`_
+
+* Avoid a segfault on some compilers when types are removed in Python.
+  `#2564 <https://github.com/pybind/pybind11/pull/2564>`_
+
+* ``py::arg::none()`` is now also respected when passing keyword arguments.
+  `#2611 <https://github.com/pybind/pybind11/pull/2611>`_
+
+* PyPy fixes, PyPy 7.3.x now supported, including PyPy3. (Known issue with
+  PyPy2 and Windows `#2596 <https://github.com/pybind/pybind11/issues/2596>`_).
+  `#2146 <https://github.com/pybind/pybind11/pull/2146>`_
+
+* CPython 3.9.0 workaround for undefined behavior (macOS segfault).
+  `#2576 <https://github.com/pybind/pybind11/pull/2576>`_
+
+* CPython 3.9 warning fixes.
+  `#2253 <https://github.com/pybind/pybind11/pull/2253>`_
+
+* Improved C++20 support, now tested in CI.
+  `#2489 <https://github.com/pybind/pybind11/pull/2489>`_
+  `#2599 <https://github.com/pybind/pybind11/pull/2599>`_
+
+* Improved but still incomplete debug Python interpreter support.
+  `#2025 <https://github.com/pybind/pybind11/pull/2025>`_
+
+* NVCC (CUDA 11) now supported and tested in CI.
+  `#2461 <https://github.com/pybind/pybind11/pull/2461>`_
+
+* NVIDIA PGI compilers now supported and tested in CI.
+  `#2475 <https://github.com/pybind/pybind11/pull/2475>`_
+
+* At least Intel 18 now explicitly required when compiling with Intel.
+  `#2577 <https://github.com/pybind/pybind11/pull/2577>`_
+
+* Extensive style checking in CI, with `pre-commit`_ support. Code
+  modernization, checked by clang-tidy.
+
+* Expanded docs, including new main page, new installing section, and CMake
+  helpers page, along with over a dozen new sections on existing pages.
+
+* In GitHub, new docs for contributing and new issue templates.
+
+.. _pre-commit: https://pre-commit.com
+
+.. _pybind11-mkdoc: https://github.com/pybind/pybind11-mkdoc
+
+v2.5.0 (Mar 31, 2020)
+-----------------------------------------------------
+
+* Use C++17 fold expressions in type casters, if available. This can
+  improve performance during overload resolution when functions have
+  multiple arguments.
+  `#2043 <https://github.com/pybind/pybind11/pull/2043>`_.
+
+* Changed include directory resolution in ``pybind11/__init__.py``
+  and installation in ``setup.py``. This fixes a number of open issues
+  where pybind11 headers could not be found in certain environments.
+  `#1995 <https://github.com/pybind/pybind11/pull/1995>`_.
+
+* C++20 ``char8_t`` and ``u8string`` support. `#2026
+  <https://github.com/pybind/pybind11/pull/2026>`_.
+
+* CMake: search for Python 3.9. `bb9c91
+  <https://github.com/pybind/pybind11/commit/bb9c91>`_.
+
+* Fixes for MSYS-based build environments.
+  `#2087 <https://github.com/pybind/pybind11/pull/2087>`_,
+  `#2053 <https://github.com/pybind/pybind11/pull/2053>`_.
+
+* STL bindings for ``std::vector<...>::clear``. `#2074
+  <https://github.com/pybind/pybind11/pull/2074>`_.
+
+* Read-only flag for ``py::buffer``. `#1466
+  <https://github.com/pybind/pybind11/pull/1466>`_.
+
+* Exception handling during module initialization.
+  `bf2b031 <https://github.com/pybind/pybind11/commit/bf2b031>`_.
+
+* Support linking against a CPython debug build.
+  `#2025 <https://github.com/pybind/pybind11/pull/2025>`_.
+
+* Fixed issues involving the availability and use of aligned ``new`` and
+  ``delete``. `#1988 <https://github.com/pybind/pybind11/pull/1988>`_,
+  `759221 <https://github.com/pybind/pybind11/commit/759221>`_.
+
+* Fixed a resource leak upon interpreter shutdown.
+  `#2020 <https://github.com/pybind/pybind11/pull/2020>`_.
+
+* Fixed error handling in the boolean caster.
+  `#1976 <https://github.com/pybind/pybind11/pull/1976>`_.
+
+v2.4.3 (Oct 15, 2019)
+-----------------------------------------------------
+
+* Adapt pybind11 to a C API convention change in Python 3.8. `#1950
+  <https://github.com/pybind/pybind11/pull/1950>`_.
+
+v2.4.2 (Sep 21, 2019)
+-----------------------------------------------------
+
+* Replaced usage of a C++14 only construct. `#1929
+  <https://github.com/pybind/pybind11/pull/1929>`_.
+
+* Made an ifdef future-proof for Python >= 4. `f3109d
+  <https://github.com/pybind/pybind11/commit/f3109d>`_.
+
+v2.4.1 (Sep 20, 2019)
+-----------------------------------------------------
+
+* Fixed a problem involving implicit conversion from enumerations to integers
+  on Python 3.8. `#1780 <https://github.com/pybind/pybind11/pull/1780>`_.
+
+v2.4.0 (Sep 19, 2019)
+-----------------------------------------------------
+
+* Try harder to keep pybind11-internal data structures separate when there
+  are potential ABI incompatibilities. Fixes crashes that occurred when loading
+  multiple pybind11 extensions that were e.g. compiled by GCC (libstdc++)
+  and Clang (libc++).
+  `#1588 <https://github.com/pybind/pybind11/pull/1588>`_ and
+  `c9f5a <https://github.com/pybind/pybind11/commit/c9f5a>`_.
+
+* Added support for ``__await__``, ``__aiter__``, and ``__anext__`` protocols.
+  `#1842 <https://github.com/pybind/pybind11/pull/1842>`_.
+
+* ``pybind11_add_module()``: don't strip symbols when compiling in
+  ``RelWithDebInfo`` mode. `#1980
+  <https://github.com/pybind/pybind11/pull/1980>`_.
+
+* ``enum_``: Reproduce Python behavior when comparing against invalid values
+  (e.g. ``None``, strings, etc.). Add back support for ``__invert__()``.
+  `#1912 <https://github.com/pybind/pybind11/pull/1912>`_,
+  `#1907 <https://github.com/pybind/pybind11/pull/1907>`_.
+
+* List insertion operation for ``py::list``.
+  Added ``.empty()`` to all collection types.
+  Added ``py::set::contains()`` and ``py::dict::contains()``.
+  `#1887 <https://github.com/pybind/pybind11/pull/1887>`_,
+  `#1884 <https://github.com/pybind/pybind11/pull/1884>`_,
+  `#1888 <https://github.com/pybind/pybind11/pull/1888>`_.
+
+* ``py::details::overload_cast_impl`` is available in C++11 mode, can be used
+  like ``overload_cast`` with an additional set of parentheses.
+  `#1581 <https://github.com/pybind/pybind11/pull/1581>`_.
+
+* Fixed ``get_include()`` on Conda.
+  `#1877 <https://github.com/pybind/pybind11/pull/1877>`_.
+
+* ``stl_bind.h``: negative indexing support.
+  `#1882 <https://github.com/pybind/pybind11/pull/1882>`_.
+
+* Minor CMake fix to add MinGW compatibility.
+  `#1851 <https://github.com/pybind/pybind11/pull/1851>`_.
+
+* GIL-related fixes.
+  `#1836 <https://github.com/pybind/pybind11/pull/1836>`_,
+  `8b90b <https://github.com/pybind/pybind11/commit/8b90b>`_.
+
+* Other very minor/subtle fixes and improvements.
+  `#1329 <https://github.com/pybind/pybind11/pull/1329>`_,
+  `#1910 <https://github.com/pybind/pybind11/pull/1910>`_,
+  `#1863 <https://github.com/pybind/pybind11/pull/1863>`_,
+  `#1847 <https://github.com/pybind/pybind11/pull/1847>`_,
+  `#1890 <https://github.com/pybind/pybind11/pull/1890>`_,
+  `#1860 <https://github.com/pybind/pybind11/pull/1860>`_,
+  `#1848 <https://github.com/pybind/pybind11/pull/1848>`_,
+  `#1821 <https://github.com/pybind/pybind11/pull/1821>`_,
+  `#1837 <https://github.com/pybind/pybind11/pull/1837>`_,
+  `#1833 <https://github.com/pybind/pybind11/pull/1833>`_,
+  `#1748 <https://github.com/pybind/pybind11/pull/1748>`_,
+  `#1852 <https://github.com/pybind/pybind11/pull/1852>`_.
+
+v2.3.0 (June 11, 2019)
+-----------------------------------------------------
+
+* Significantly reduced module binary size (10-20%) when compiled in C++11 mode
+  with GCC/Clang, or in any mode with MSVC. Function signatures are now always
+  precomputed at compile time (this was previously only available in C++14 mode
+  for non-MSVC compilers).
+  `#934 <https://github.com/pybind/pybind11/pull/934>`_.
+
+* Add basic support for tag-based static polymorphism, where classes
+  provide a method to returns the desired type of an instance.
+  `#1326 <https://github.com/pybind/pybind11/pull/1326>`_.
+
+* Python type wrappers (``py::handle``, ``py::object``, etc.)
+  now support map Python's number protocol onto C++ arithmetic
+  operators such as ``operator+``, ``operator/=``, etc.
+  `#1511 <https://github.com/pybind/pybind11/pull/1511>`_.
+
+* A number of improvements related to enumerations:
+
+   1. The ``enum_`` implementation was rewritten from scratch to reduce
+      code bloat. Rather than instantiating a full implementation for each
+      enumeration, most code is now contained in a generic base class.
+      `#1511 <https://github.com/pybind/pybind11/pull/1511>`_.
+
+   2. The ``value()``  method of ``py::enum_`` now accepts an optional
+      docstring that will be shown in the documentation of the associated
+      enumeration. `#1160 <https://github.com/pybind/pybind11/pull/1160>`_.
+
+   3. check for already existing enum value and throw an error if present.
+      `#1453 <https://github.com/pybind/pybind11/pull/1453>`_.
+
+* Support for over-aligned type allocation via C++17's aligned ``new``
+  statement. `#1582 <https://github.com/pybind/pybind11/pull/1582>`_.
+
+* Added ``py::ellipsis()`` method for slicing of multidimensional NumPy arrays
+  `#1502 <https://github.com/pybind/pybind11/pull/1502>`_.
+
+* Numerous Improvements to the ``mkdoc.py`` script for extracting documentation
+  from C++ header files.
+  `#1788 <https://github.com/pybind/pybind11/pull/1788>`_.
+
+* ``pybind11_add_module()``: allow including Python as a ``SYSTEM`` include path.
+  `#1416 <https://github.com/pybind/pybind11/pull/1416>`_.
+
+* ``pybind11/stl.h`` does not convert strings to ``vector<string>`` anymore.
+  `#1258 <https://github.com/pybind/pybind11/issues/1258>`_.
+
+* Mark static methods as such to fix auto-generated Sphinx documentation.
+  `#1732 <https://github.com/pybind/pybind11/pull/1732>`_.
+
+* Re-throw forced unwind exceptions (e.g. during pthread termination).
+  `#1208 <https://github.com/pybind/pybind11/pull/1208>`_.
+
+* Added ``__contains__`` method to the bindings of maps (``std::map``,
+  ``std::unordered_map``).
+  `#1767 <https://github.com/pybind/pybind11/pull/1767>`_.
+
+* Improvements to ``gil_scoped_acquire``.
+  `#1211 <https://github.com/pybind/pybind11/pull/1211>`_.
+
+* Type caster support for ``std::deque<T>``.
+  `#1609 <https://github.com/pybind/pybind11/pull/1609>`_.
+
+* Support for ``std::unique_ptr`` holders, whose deleters differ between a base and derived
+  class. `#1353 <https://github.com/pybind/pybind11/pull/1353>`_.
+
+* Construction of STL array/vector-like data structures from
+  iterators. Added an ``extend()`` operation.
+  `#1709 <https://github.com/pybind/pybind11/pull/1709>`_,
+
+* CMake build system improvements for projects that include non-C++
+  files (e.g. plain C, CUDA) in ``pybind11_add_module`` et al.
+  `#1678 <https://github.com/pybind/pybind11/pull/1678>`_.
+
+* Fixed asynchronous invocation and deallocation of Python functions
+  wrapped in ``std::function``.
+  `#1595 <https://github.com/pybind/pybind11/pull/1595>`_.
+
+* Fixes regarding return value policy propagation in STL type casters.
+  `#1603 <https://github.com/pybind/pybind11/pull/1603>`_.
+
+* Fixed scoped enum comparisons.
+  `#1571 <https://github.com/pybind/pybind11/pull/1571>`_.
+
+* Fixed iostream redirection for code that releases the GIL.
+  `#1368 <https://github.com/pybind/pybind11/pull/1368>`_,
+
+* A number of CI-related fixes.
+  `#1757 <https://github.com/pybind/pybind11/pull/1757>`_,
+  `#1744 <https://github.com/pybind/pybind11/pull/1744>`_,
+  `#1670 <https://github.com/pybind/pybind11/pull/1670>`_.
+
+v2.2.4 (September 11, 2018)
+-----------------------------------------------------
+
+* Use new Python 3.7 Thread Specific Storage (TSS) implementation if available.
+  `#1454 <https://github.com/pybind/pybind11/pull/1454>`_,
+  `#1517 <https://github.com/pybind/pybind11/pull/1517>`_.
+
+* Fixes for newer MSVC versions and C++17 mode.
+  `#1347 <https://github.com/pybind/pybind11/pull/1347>`_,
+  `#1462 <https://github.com/pybind/pybind11/pull/1462>`_.
+
+* Propagate return value policies to type-specific casters
+  when casting STL containers.
+  `#1455 <https://github.com/pybind/pybind11/pull/1455>`_.
+
+* Allow ostream-redirection of more than 1024 characters.
+  `#1479 <https://github.com/pybind/pybind11/pull/1479>`_.
+
+* Set ``Py_DEBUG`` define when compiling against a debug Python build.
+  `#1438 <https://github.com/pybind/pybind11/pull/1438>`_.
+
+* Untangle integer logic in number type caster to work for custom
+  types that may only be castable to a restricted set of builtin types.
+  `#1442 <https://github.com/pybind/pybind11/pull/1442>`_.
+
+* CMake build system: Remember Python version in cache file.
+  `#1434 <https://github.com/pybind/pybind11/pull/1434>`_.
+
+* Fix for custom smart pointers: use ``std::addressof`` to obtain holder
+  address instead of ``operator&``.
+  `#1435 <https://github.com/pybind/pybind11/pull/1435>`_.
+
+* Properly report exceptions thrown during module initialization.
+  `#1362 <https://github.com/pybind/pybind11/pull/1362>`_.
+
+* Fixed a segmentation fault when creating empty-shaped NumPy array.
+  `#1371 <https://github.com/pybind/pybind11/pull/1371>`_.
+
+* The version of Intel C++ compiler must be >= 2017, and this is now checked by
+  the header files. `#1363 <https://github.com/pybind/pybind11/pull/1363>`_.
+
+* A few minor typo fixes and improvements to the test suite, and
+  patches that silence compiler warnings.
+
+* Vectors now support construction from generators, as well as ``extend()`` from a
+  list or generator.
+  `#1496 <https://github.com/pybind/pybind11/pull/1496>`_.
+
+
+v2.2.3 (April 29, 2018)
+-----------------------------------------------------
+
+* The pybind11 header location detection was replaced by a new implementation
+  that no longer depends on ``pip`` internals (the recently released ``pip``
+  10 has restricted access to this API).
+  `#1190 <https://github.com/pybind/pybind11/pull/1190>`_.
+
+* Small adjustment to an implementation detail to work around a compiler segmentation fault in Clang 3.3/3.4.
+  `#1350 <https://github.com/pybind/pybind11/pull/1350>`_.
+
+* The minimal supported version of the Intel compiler was >= 17.0 since
+  pybind11 v2.1. This check is now explicit, and a compile-time error is raised
+  if the compiler meet the requirement.
+  `#1363 <https://github.com/pybind/pybind11/pull/1363>`_.
+
+* Fixed an endianness-related fault in the test suite.
+  `#1287 <https://github.com/pybind/pybind11/pull/1287>`_.
+
+v2.2.2 (February 7, 2018)
+-----------------------------------------------------
+
+* Fixed a segfault when combining embedded interpreter
+  shutdown/reinitialization with external loaded pybind11 modules.
+  `#1092 <https://github.com/pybind/pybind11/pull/1092>`_.
+
+* Eigen support: fixed a bug where Nx1/1xN numpy inputs couldn't be passed as
+  arguments to Eigen vectors (which for Eigen are simply compile-time fixed
+  Nx1/1xN matrices).
+  `#1106 <https://github.com/pybind/pybind11/pull/1106>`_.
+
+* Clarified to license by moving the licensing of contributions from
+  ``LICENSE`` into ``CONTRIBUTING.md``: the licensing of contributions is not
+  actually part of the software license as distributed.  This isn't meant to be
+  a substantial change in the licensing of the project, but addresses concerns
+  that the clause made the license non-standard.
+  `#1109 <https://github.com/pybind/pybind11/issues/1109>`_.
+
+* Fixed a regression introduced in 2.1 that broke binding functions with lvalue
+  character literal arguments.
+  `#1128 <https://github.com/pybind/pybind11/pull/1128>`_.
+
+* MSVC: fix for compilation failures under /permissive-, and added the flag to
+  the appveyor test suite.
+  `#1155 <https://github.com/pybind/pybind11/pull/1155>`_.
+
+* Fixed ``__qualname__`` generation, and in turn, fixes how class names
+  (especially nested class names) are shown in generated docstrings.
+  `#1171 <https://github.com/pybind/pybind11/pull/1171>`_.
+
+* Updated the FAQ with a suggested project citation reference.
+  `#1189 <https://github.com/pybind/pybind11/pull/1189>`_.
+
+* Added fixes for deprecation warnings when compiled under C++17 with
+  ``-Wdeprecated`` turned on, and add ``-Wdeprecated`` to the test suite
+  compilation flags.
+  `#1191 <https://github.com/pybind/pybind11/pull/1191>`_.
+
+* Fixed outdated PyPI URLs in ``setup.py``.
+  `#1213 <https://github.com/pybind/pybind11/pull/1213>`_.
+
+* Fixed a refcount leak for arguments that end up in a ``py::args`` argument
+  for functions with both fixed positional and ``py::args`` arguments.
+  `#1216 <https://github.com/pybind/pybind11/pull/1216>`_.
+
+* Fixed a potential segfault resulting from possible premature destruction of
+  ``py::args``/``py::kwargs`` arguments with overloaded functions.
+  `#1223 <https://github.com/pybind/pybind11/pull/1223>`_.
+
+* Fixed ``del map[item]`` for a ``stl_bind.h`` bound stl map.
+  `#1229 <https://github.com/pybind/pybind11/pull/1229>`_.
+
+* Fixed a regression from v2.1.x where the aggregate initialization could
+  unintentionally end up at a constructor taking a templated
+  ``std::initializer_list<T>`` argument.
+  `#1249 <https://github.com/pybind/pybind11/pull/1249>`_.
+
+* Fixed an issue where calling a function with a keep_alive policy on the same
+  nurse/patient pair would cause the internal patient storage to needlessly
+  grow (unboundedly, if the nurse is long-lived).
+  `#1251 <https://github.com/pybind/pybind11/issues/1251>`_.
+
+* Various other minor fixes.
+
+v2.2.1 (September 14, 2017)
+-----------------------------------------------------
+
+* Added ``py::module_::reload()`` member function for reloading a module.
+  `#1040 <https://github.com/pybind/pybind11/pull/1040>`_.
+
+* Fixed a reference leak in the number converter.
+  `#1078 <https://github.com/pybind/pybind11/pull/1078>`_.
+
+* Fixed compilation with Clang on host GCC < 5 (old libstdc++ which isn't fully
+  C++11 compliant). `#1062 <https://github.com/pybind/pybind11/pull/1062>`_.
+
+* Fixed a regression where the automatic ``std::vector<bool>`` caster would
+  fail to compile. The same fix also applies to any container which returns
+  element proxies instead of references.
+  `#1053 <https://github.com/pybind/pybind11/pull/1053>`_.
+
+* Fixed a regression where the ``py::keep_alive`` policy could not be applied
+  to constructors. `#1065 <https://github.com/pybind/pybind11/pull/1065>`_.
+
+* Fixed a nullptr dereference when loading a ``py::module_local`` type
+  that's only registered in an external module.
+  `#1058 <https://github.com/pybind/pybind11/pull/1058>`_.
+
+* Fixed implicit conversion of accessors to types derived from ``py::object``.
+  `#1076 <https://github.com/pybind/pybind11/pull/1076>`_.
+
+* The ``name`` in ``PYBIND11_MODULE(name, variable)`` can now be a macro.
+  `#1082 <https://github.com/pybind/pybind11/pull/1082>`_.
+
+* Relaxed overly strict ``py::pickle()`` check for matching get and set types.
+  `#1064 <https://github.com/pybind/pybind11/pull/1064>`_.
+
+* Conversion errors now try to be more informative when it's likely that
+  a missing header is the cause (e.g. forgetting ``<pybind11/stl.h>``).
+  `#1077 <https://github.com/pybind/pybind11/pull/1077>`_.
+
+v2.2.0 (August 31, 2017)
+-----------------------------------------------------
+
+* Support for embedding the Python interpreter. See the
+  :doc:`documentation page </advanced/embedding>` for a
+  full overview of the new features.
+  `#774 <https://github.com/pybind/pybind11/pull/774>`_,
+  `#889 <https://github.com/pybind/pybind11/pull/889>`_,
+  `#892 <https://github.com/pybind/pybind11/pull/892>`_,
+  `#920 <https://github.com/pybind/pybind11/pull/920>`_.
+
+  .. code-block:: cpp
+
+      #include <pybind11/embed.h>
+      namespace py = pybind11;
+
+      int main() {
+          py::scoped_interpreter guard{}; // start the interpreter and keep it alive
+
+          py::print("Hello, World!"); // use the Python API
+      }
+
+* Support for inheriting from multiple C++ bases in Python.
+  `#693 <https://github.com/pybind/pybind11/pull/693>`_.
+
+  .. code-block:: python
+
+      from cpp_module import CppBase1, CppBase2
+
+
+      class PyDerived(CppBase1, CppBase2):
+          def __init__(self):
+              CppBase1.__init__(self)  # C++ bases must be initialized explicitly
+              CppBase2.__init__(self)
+
+* ``PYBIND11_MODULE`` is now the preferred way to create module entry points.
+  ``PYBIND11_PLUGIN`` is deprecated. See :ref:`macros` for details.
+  `#879 <https://github.com/pybind/pybind11/pull/879>`_.
+
+  .. code-block:: cpp
+
+      // new
+      PYBIND11_MODULE(example, m) {
+          m.def("add", [](int a, int b) { return a + b; });
+      }
+
+      // old
+      PYBIND11_PLUGIN(example) {
+          py::module m("example");
+          m.def("add", [](int a, int b) { return a + b; });
+          return m.ptr();
+      }
+
+* pybind11's headers and build system now more strictly enforce hidden symbol
+  visibility for extension modules. This should be seamless for most users,
+  but see the :doc:`upgrade` if you use a custom build system.
+  `#995 <https://github.com/pybind/pybind11/pull/995>`_.
+
+* Support for ``py::module_local`` types which allow multiple modules to
+  export the same C++ types without conflicts. This is useful for opaque
+  types like ``std::vector<int>``. ``py::bind_vector`` and ``py::bind_map``
+  now default to ``py::module_local`` if their elements are builtins or
+  local types. See :ref:`module_local` for details.
+  `#949 <https://github.com/pybind/pybind11/pull/949>`_,
+  `#981 <https://github.com/pybind/pybind11/pull/981>`_,
+  `#995 <https://github.com/pybind/pybind11/pull/995>`_,
+  `#997 <https://github.com/pybind/pybind11/pull/997>`_.
+
+* Custom constructors can now be added very easily using lambdas or factory
+  functions which return a class instance by value, pointer or holder. This
+  supersedes the old placement-new ``__init__`` technique.
+  See :ref:`custom_constructors` for details.
+  `#805 <https://github.com/pybind/pybind11/pull/805>`_,
+  `#1014 <https://github.com/pybind/pybind11/pull/1014>`_.
+
+  .. code-block:: cpp
+
+      struct Example {
+          Example(std::string);
+      };
+
+      py::class_<Example>(m, "Example")
+          .def(py::init<std::string>()) // existing constructor
+          .def(py::init([](int n) { // custom constructor
+              return std::make_unique<Example>(std::to_string(n));
+          }));
+
+* Similarly to custom constructors, pickling support functions are now bound
+  using the ``py::pickle()`` adaptor which improves type safety. See the
+  :doc:`upgrade` and :ref:`pickling` for details.
+  `#1038 <https://github.com/pybind/pybind11/pull/1038>`_.
+
+* Builtin support for converting C++17 standard library types and general
+  conversion improvements:
+
+  1. C++17 ``std::variant`` is supported right out of the box. C++11/14
+     equivalents (e.g. ``boost::variant``) can also be added with a simple
+     user-defined specialization. See :ref:`cpp17_container_casters` for details.
+     `#811 <https://github.com/pybind/pybind11/pull/811>`_,
+     `#845 <https://github.com/pybind/pybind11/pull/845>`_,
+     `#989 <https://github.com/pybind/pybind11/pull/989>`_.
+
+  2. Out-of-the-box support for C++17 ``std::string_view``.
+     `#906 <https://github.com/pybind/pybind11/pull/906>`_.
+
+  3. Improved compatibility of the builtin ``optional`` converter.
+     `#874 <https://github.com/pybind/pybind11/pull/874>`_.
+
+  4. The ``bool`` converter now accepts ``numpy.bool_`` and types which
+     define ``__bool__`` (Python 3.x) or ``__nonzero__`` (Python 2.7).
+     `#925 <https://github.com/pybind/pybind11/pull/925>`_.
+
+  5. C++-to-Python casters are now more efficient and move elements out
+     of rvalue containers whenever possible.
+     `#851 <https://github.com/pybind/pybind11/pull/851>`_,
+     `#936 <https://github.com/pybind/pybind11/pull/936>`_,
+     `#938 <https://github.com/pybind/pybind11/pull/938>`_.
+
+  6. Fixed ``bytes`` to ``std::string/char*`` conversion on Python 3.
+     `#817 <https://github.com/pybind/pybind11/pull/817>`_.
+
+  7. Fixed lifetime of temporary C++ objects created in Python-to-C++ conversions.
+     `#924 <https://github.com/pybind/pybind11/pull/924>`_.
+
+* Scope guard call policy for RAII types, e.g. ``py::call_guard<py::gil_scoped_release>()``,
+  ``py::call_guard<py::scoped_ostream_redirect>()``. See :ref:`call_policies` for details.
+  `#740 <https://github.com/pybind/pybind11/pull/740>`_.
+
+* Utility for redirecting C++ streams to Python (e.g. ``std::cout`` ->
+  ``sys.stdout``). Scope guard ``py::scoped_ostream_redirect`` in C++ and
+  a context manager in Python. See :ref:`ostream_redirect`.
+  `#1009 <https://github.com/pybind/pybind11/pull/1009>`_.
+
+* Improved handling of types and exceptions across module boundaries.
+  `#915 <https://github.com/pybind/pybind11/pull/915>`_,
+  `#951 <https://github.com/pybind/pybind11/pull/951>`_,
+  `#995 <https://github.com/pybind/pybind11/pull/995>`_.
+
+* Fixed destruction order of ``py::keep_alive`` nurse/patient objects
+  in reference cycles.
+  `#856 <https://github.com/pybind/pybind11/pull/856>`_.
+
+* NumPy and buffer protocol related improvements:
+
+  1. Support for negative strides in Python buffer objects/numpy arrays. This
+     required changing integers from unsigned to signed for the related C++ APIs.
+     Note: If you have compiler warnings enabled, you may notice some new conversion
+     warnings after upgrading. These can be resolved with ``static_cast``.
+     `#782 <https://github.com/pybind/pybind11/pull/782>`_.
+
+  2. Support ``std::complex`` and arrays inside ``PYBIND11_NUMPY_DTYPE``.
+     `#831 <https://github.com/pybind/pybind11/pull/831>`_,
+     `#832 <https://github.com/pybind/pybind11/pull/832>`_.
+
+  3. Support for constructing ``py::buffer_info`` and ``py::arrays`` using
+     arbitrary containers or iterators instead of requiring a ``std::vector``.
+     `#788 <https://github.com/pybind/pybind11/pull/788>`_,
+     `#822 <https://github.com/pybind/pybind11/pull/822>`_,
+     `#860 <https://github.com/pybind/pybind11/pull/860>`_.
+
+  4. Explicitly check numpy version and require >= 1.7.0.
+     `#819 <https://github.com/pybind/pybind11/pull/819>`_.
+
+* Support for allowing/prohibiting ``None`` for specific arguments and improved
+  ``None`` overload resolution order. See :ref:`none_arguments` for details.
+  `#843 <https://github.com/pybind/pybind11/pull/843>`_.
+  `#859 <https://github.com/pybind/pybind11/pull/859>`_.
+
+* Added ``py::exec()`` as a shortcut for ``py::eval<py::eval_statements>()``
+  and support for C++11 raw string literals as input. See :ref:`eval`.
+  `#766 <https://github.com/pybind/pybind11/pull/766>`_,
+  `#827 <https://github.com/pybind/pybind11/pull/827>`_.
+
+* ``py::vectorize()`` ignores non-vectorizable arguments and supports
+  member functions.
+  `#762 <https://github.com/pybind/pybind11/pull/762>`_.
+
+* Support for bound methods as callbacks (``pybind11/functional.h``).
+  `#815 <https://github.com/pybind/pybind11/pull/815>`_.
+
+* Allow aliasing pybind11 methods: ``cls.attr("foo") = cls.attr("bar")``.
+  `#802 <https://github.com/pybind/pybind11/pull/802>`_.
+
+* Don't allow mixed static/non-static overloads.
+  `#804 <https://github.com/pybind/pybind11/pull/804>`_.
+
+* Fixed overriding static properties in derived classes.
+  `#784 <https://github.com/pybind/pybind11/pull/784>`_.
+
+* Added support for write only properties.
+  `#1144 <https://github.com/pybind/pybind11/pull/1144>`_.
+
+* Improved deduction of member functions of a derived class when its bases
+  aren't registered with pybind11.
+  `#855 <https://github.com/pybind/pybind11/pull/855>`_.
+
+  .. code-block:: cpp
+
+      struct Base {
+          int foo() { return 42; }
+      }
+
+      struct Derived : Base {}
+
+      // Now works, but previously required also binding `Base`
+      py::class_<Derived>(m, "Derived")
+          .def("foo", &Derived::foo); // function is actually from `Base`
+
+* The implementation of ``py::init<>`` now uses C++11 brace initialization
+  syntax to construct instances, which permits binding implicit constructors of
+  aggregate types. `#1015 <https://github.com/pybind/pybind11/pull/1015>`_.
+
+    .. code-block:: cpp
+
+        struct Aggregate {
+            int a;
+            std::string b;
+        };
+
+        py::class_<Aggregate>(m, "Aggregate")
+            .def(py::init<int, const std::string &>());
+
+* Fixed issues with multiple inheritance with offset base/derived pointers.
+  `#812 <https://github.com/pybind/pybind11/pull/812>`_,
+  `#866 <https://github.com/pybind/pybind11/pull/866>`_,
+  `#960 <https://github.com/pybind/pybind11/pull/960>`_.
+
+* Fixed reference leak of type objects.
+  `#1030 <https://github.com/pybind/pybind11/pull/1030>`_.
+
+* Improved support for the ``/std:c++14`` and ``/std:c++latest`` modes
+  on MSVC 2017.
+  `#841 <https://github.com/pybind/pybind11/pull/841>`_,
+  `#999 <https://github.com/pybind/pybind11/pull/999>`_.
+
+* Fixed detection of private operator new on MSVC.
+  `#893 <https://github.com/pybind/pybind11/pull/893>`_,
+  `#918 <https://github.com/pybind/pybind11/pull/918>`_.
+
+* Intel C++ compiler compatibility fixes.
+  `#937 <https://github.com/pybind/pybind11/pull/937>`_.
+
+* Fixed implicit conversion of ``py::enum_`` to integer types on Python 2.7.
+  `#821 <https://github.com/pybind/pybind11/pull/821>`_.
+
+* Added ``py::hash`` to fetch the hash value of Python objects, and
+  ``.def(hash(py::self))`` to provide the C++ ``std::hash`` as the Python
+  ``__hash__`` method.
+  `#1034 <https://github.com/pybind/pybind11/pull/1034>`_.
+
+* Fixed ``__truediv__`` on Python 2 and ``__itruediv__`` on Python 3.
+  `#867 <https://github.com/pybind/pybind11/pull/867>`_.
+
+* ``py::capsule`` objects now support the ``name`` attribute. This is useful
+  for interfacing with ``scipy.LowLevelCallable``.
+  `#902 <https://github.com/pybind/pybind11/pull/902>`_.
+
+* Fixed ``py::make_iterator``'s ``__next__()`` for past-the-end calls.
+  `#897 <https://github.com/pybind/pybind11/pull/897>`_.
+
+* Added ``error_already_set::matches()`` for checking Python exceptions.
+  `#772 <https://github.com/pybind/pybind11/pull/772>`_.
+
+* Deprecated ``py::error_already_set::clear()``. It's no longer needed
+  following a simplification of the ``py::error_already_set`` class.
+  `#954 <https://github.com/pybind/pybind11/pull/954>`_.
+
+* Deprecated ``py::handle::operator==()`` in favor of ``py::handle::is()``
+  `#825 <https://github.com/pybind/pybind11/pull/825>`_.
+
+* Deprecated ``py::object::borrowed``/``py::object::stolen``.
+  Use ``py::object::borrowed_t{}``/``py::object::stolen_t{}`` instead.
+  `#771 <https://github.com/pybind/pybind11/pull/771>`_.
+
+* Changed internal data structure versioning to avoid conflicts between
+  modules compiled with different revisions of pybind11.
+  `#1012 <https://github.com/pybind/pybind11/pull/1012>`_.
+
+* Additional compile-time and run-time error checking and more informative messages.
+  `#786 <https://github.com/pybind/pybind11/pull/786>`_,
+  `#794 <https://github.com/pybind/pybind11/pull/794>`_,
+  `#803 <https://github.com/pybind/pybind11/pull/803>`_.
+
+* Various minor improvements and fixes.
+  `#764 <https://github.com/pybind/pybind11/pull/764>`_,
+  `#791 <https://github.com/pybind/pybind11/pull/791>`_,
+  `#795 <https://github.com/pybind/pybind11/pull/795>`_,
+  `#840 <https://github.com/pybind/pybind11/pull/840>`_,
+  `#844 <https://github.com/pybind/pybind11/pull/844>`_,
+  `#846 <https://github.com/pybind/pybind11/pull/846>`_,
+  `#849 <https://github.com/pybind/pybind11/pull/849>`_,
+  `#858 <https://github.com/pybind/pybind11/pull/858>`_,
+  `#862 <https://github.com/pybind/pybind11/pull/862>`_,
+  `#871 <https://github.com/pybind/pybind11/pull/871>`_,
+  `#872 <https://github.com/pybind/pybind11/pull/872>`_,
+  `#881 <https://github.com/pybind/pybind11/pull/881>`_,
+  `#888 <https://github.com/pybind/pybind11/pull/888>`_,
+  `#899 <https://github.com/pybind/pybind11/pull/899>`_,
+  `#928 <https://github.com/pybind/pybind11/pull/928>`_,
+  `#931 <https://github.com/pybind/pybind11/pull/931>`_,
+  `#944 <https://github.com/pybind/pybind11/pull/944>`_,
+  `#950 <https://github.com/pybind/pybind11/pull/950>`_,
+  `#952 <https://github.com/pybind/pybind11/pull/952>`_,
+  `#962 <https://github.com/pybind/pybind11/pull/962>`_,
+  `#965 <https://github.com/pybind/pybind11/pull/965>`_,
+  `#970 <https://github.com/pybind/pybind11/pull/970>`_,
+  `#978 <https://github.com/pybind/pybind11/pull/978>`_,
+  `#979 <https://github.com/pybind/pybind11/pull/979>`_,
+  `#986 <https://github.com/pybind/pybind11/pull/986>`_,
+  `#1020 <https://github.com/pybind/pybind11/pull/1020>`_,
+  `#1027 <https://github.com/pybind/pybind11/pull/1027>`_,
+  `#1037 <https://github.com/pybind/pybind11/pull/1037>`_.
+
+* Testing improvements.
+  `#798 <https://github.com/pybind/pybind11/pull/798>`_,
+  `#882 <https://github.com/pybind/pybind11/pull/882>`_,
+  `#898 <https://github.com/pybind/pybind11/pull/898>`_,
+  `#900 <https://github.com/pybind/pybind11/pull/900>`_,
+  `#921 <https://github.com/pybind/pybind11/pull/921>`_,
+  `#923 <https://github.com/pybind/pybind11/pull/923>`_,
+  `#963 <https://github.com/pybind/pybind11/pull/963>`_.
+
+v2.1.1 (April 7, 2017)
+-----------------------------------------------------
+
+* Fixed minimum version requirement for MSVC 2015u3
+  `#773 <https://github.com/pybind/pybind11/pull/773>`_.
+
+v2.1.0 (March 22, 2017)
+-----------------------------------------------------
+
+* pybind11 now performs function overload resolution in two phases. The first
+  phase only considers exact type matches, while the second allows for implicit
+  conversions to take place. A special ``noconvert()`` syntax can be used to
+  completely disable implicit conversions for specific arguments.
+  `#643 <https://github.com/pybind/pybind11/pull/643>`_,
+  `#634 <https://github.com/pybind/pybind11/pull/634>`_,
+  `#650 <https://github.com/pybind/pybind11/pull/650>`_.
+
+* Fixed a regression where static properties no longer worked with classes
+  using multiple inheritance. The ``py::metaclass`` attribute is no longer
+  necessary (and deprecated as of this release) when binding classes with
+  static properties.
+  `#679 <https://github.com/pybind/pybind11/pull/679>`_,
+
+* Classes bound using ``pybind11`` can now use custom metaclasses.
+  `#679 <https://github.com/pybind/pybind11/pull/679>`_,
+
+* ``py::args`` and ``py::kwargs`` can now be mixed with other positional
+  arguments when binding functions using pybind11.
+  `#611 <https://github.com/pybind/pybind11/pull/611>`_.
+
+* Improved support for C++11 unicode string and character types; added
+  extensive documentation regarding pybind11's string conversion behavior.
+  `#624 <https://github.com/pybind/pybind11/pull/624>`_,
+  `#636 <https://github.com/pybind/pybind11/pull/636>`_,
+  `#715 <https://github.com/pybind/pybind11/pull/715>`_.
+
+* pybind11 can now avoid expensive copies when converting Eigen arrays to NumPy
+  arrays (and vice versa). `#610 <https://github.com/pybind/pybind11/pull/610>`_.
+
+* The "fast path" in ``py::vectorize`` now works for any full-size group of C or
+  F-contiguous arrays. The non-fast path is also faster since it no longer performs
+  copies of the input arguments (except when type conversions are necessary).
+  `#610 <https://github.com/pybind/pybind11/pull/610>`_.
+
+* Added fast, unchecked access to NumPy arrays via a proxy object.
+  `#746 <https://github.com/pybind/pybind11/pull/746>`_.
+
+* Transparent support for class-specific ``operator new`` and
+  ``operator delete`` implementations.
+  `#755 <https://github.com/pybind/pybind11/pull/755>`_.
+
+* Slimmer and more efficient STL-compatible iterator interface for sequence types.
+  `#662 <https://github.com/pybind/pybind11/pull/662>`_.
+
+* Improved custom holder type support.
+  `#607 <https://github.com/pybind/pybind11/pull/607>`_.
+
+* ``nullptr`` to ``None`` conversion fixed in various builtin type casters.
+  `#732 <https://github.com/pybind/pybind11/pull/732>`_.
+
+* ``enum_`` now exposes its members via a special ``__members__`` attribute.
+  `#666 <https://github.com/pybind/pybind11/pull/666>`_.
+
+* ``std::vector`` bindings created using ``stl_bind.h`` can now optionally
+  implement the buffer protocol. `#488 <https://github.com/pybind/pybind11/pull/488>`_.
+
+* Automated C++ reference documentation using doxygen and breathe.
+  `#598 <https://github.com/pybind/pybind11/pull/598>`_.
+
+* Added minimum compiler version assertions.
+  `#727 <https://github.com/pybind/pybind11/pull/727>`_.
+
+* Improved compatibility with C++1z.
+  `#677 <https://github.com/pybind/pybind11/pull/677>`_.
+
+* Improved ``py::capsule`` API. Can be used to implement cleanup
+  callbacks that are involved at module destruction time.
+  `#752 <https://github.com/pybind/pybind11/pull/752>`_.
+
+* Various minor improvements and fixes.
+  `#595 <https://github.com/pybind/pybind11/pull/595>`_,
+  `#588 <https://github.com/pybind/pybind11/pull/588>`_,
+  `#589 <https://github.com/pybind/pybind11/pull/589>`_,
+  `#603 <https://github.com/pybind/pybind11/pull/603>`_,
+  `#619 <https://github.com/pybind/pybind11/pull/619>`_,
+  `#648 <https://github.com/pybind/pybind11/pull/648>`_,
+  `#695 <https://github.com/pybind/pybind11/pull/695>`_,
+  `#720 <https://github.com/pybind/pybind11/pull/720>`_,
+  `#723 <https://github.com/pybind/pybind11/pull/723>`_,
+  `#729 <https://github.com/pybind/pybind11/pull/729>`_,
+  `#724 <https://github.com/pybind/pybind11/pull/724>`_,
+  `#742 <https://github.com/pybind/pybind11/pull/742>`_,
+  `#753 <https://github.com/pybind/pybind11/pull/753>`_.
+
+v2.0.1 (Jan 4, 2017)
+-----------------------------------------------------
+
+* Fix pointer to reference error in type_caster on MSVC
+  `#583 <https://github.com/pybind/pybind11/pull/583>`_.
+
+* Fixed a segmentation in the test suite due to a typo
+  `cd7eac <https://github.com/pybind/pybind11/commit/cd7eac>`_.
+
+v2.0.0 (Jan 1, 2017)
+-----------------------------------------------------
+
+* Fixed a reference counting regression affecting types with custom metaclasses
+  (introduced in v2.0.0-rc1).
+  `#571 <https://github.com/pybind/pybind11/pull/571>`_.
+
+* Quenched a CMake policy warning.
+  `#570 <https://github.com/pybind/pybind11/pull/570>`_.
+
+v2.0.0-rc1 (Dec 23, 2016)
+-----------------------------------------------------
+
+The pybind11 developers are excited to issue a release candidate of pybind11
+with a subsequent v2.0.0 release planned in early January next year.
+
+An incredible amount of effort by went into pybind11 over the last ~5 months,
+leading to a release that is jam-packed with exciting new features and numerous
+usability improvements. The following list links PRs or individual commits
+whenever applicable.
+
+Happy Christmas!
+
+* Support for binding C++ class hierarchies that make use of multiple
+  inheritance. `#410 <https://github.com/pybind/pybind11/pull/410>`_.
+
+* PyPy support: pybind11 now supports nightly builds of PyPy and will
+  interoperate with the future 5.7 release. No code changes are necessary,
+  everything "just" works as usual. Note that we only target the Python 2.7
+  branch for now; support for 3.x will be added once its ``cpyext`` extension
+  support catches up. A few minor features remain unsupported for the time
+  being (notably dynamic attributes in custom types).
+  `#527 <https://github.com/pybind/pybind11/pull/527>`_.
+
+* Significant work on the documentation -- in particular, the monolithic
+  ``advanced.rst`` file was restructured into a easier to read hierarchical
+  organization. `#448 <https://github.com/pybind/pybind11/pull/448>`_.
+
+* Many NumPy-related improvements:
+
+  1. Object-oriented API to access and modify NumPy ``ndarray`` instances,
+     replicating much of the corresponding NumPy C API functionality.
+     `#402 <https://github.com/pybind/pybind11/pull/402>`_.
+
+  2. NumPy array ``dtype`` array descriptors are now first-class citizens and
+     are exposed via a new class ``py::dtype``.
+
+  3. Structured dtypes can be registered using the ``PYBIND11_NUMPY_DTYPE()``
+     macro. Special ``array`` constructors accepting dtype objects were also
+     added.
+
+     One potential caveat involving this change: format descriptor strings
+     should now be accessed via ``format_descriptor::format()`` (however, for
+     compatibility purposes, the old syntax ``format_descriptor::value`` will
+     still work for non-structured data types). `#308
+     <https://github.com/pybind/pybind11/pull/308>`_.
+
+  4. Further improvements to support structured dtypes throughout the system.
+     `#472 <https://github.com/pybind/pybind11/pull/472>`_,
+     `#474 <https://github.com/pybind/pybind11/pull/474>`_,
+     `#459 <https://github.com/pybind/pybind11/pull/459>`_,
+     `#453 <https://github.com/pybind/pybind11/pull/453>`_,
+     `#452 <https://github.com/pybind/pybind11/pull/452>`_, and
+     `#505 <https://github.com/pybind/pybind11/pull/505>`_.
+
+  5. Fast access operators. `#497 <https://github.com/pybind/pybind11/pull/497>`_.
+
+  6. Constructors for arrays whose storage is owned by another object.
+     `#440 <https://github.com/pybind/pybind11/pull/440>`_.
+
+  7. Added constructors for ``array`` and ``array_t`` explicitly accepting shape
+     and strides; if strides are not provided, they are deduced assuming
+     C-contiguity. Also added simplified constructors for 1-dimensional case.
+
+  8. Added buffer/NumPy support for ``char[N]`` and ``std::array<char, N>`` types.
+
+  9. Added ``memoryview`` wrapper type which is constructible from ``buffer_info``.
+
+* Eigen: many additional conversions and support for non-contiguous
+  arrays/slices.
+  `#427 <https://github.com/pybind/pybind11/pull/427>`_,
+  `#315 <https://github.com/pybind/pybind11/pull/315>`_,
+  `#316 <https://github.com/pybind/pybind11/pull/316>`_,
+  `#312 <https://github.com/pybind/pybind11/pull/312>`_, and
+  `#267 <https://github.com/pybind/pybind11/pull/267>`_
+
+* Incompatible changes in ``class_<...>::class_()``:
+
+    1. Declarations of types that provide access via the buffer protocol must
+       now include the ``py::buffer_protocol()`` annotation as an argument to
+       the ``class_`` constructor.
+
+    2. Declarations of types that require a custom metaclass (i.e. all classes
+       which include static properties via commands such as
+       ``def_readwrite_static()``) must now include the ``py::metaclass()``
+       annotation as an argument to the ``class_`` constructor.
+
+       These two changes were necessary to make type definitions in pybind11
+       future-proof, and to support PyPy via its cpyext mechanism. `#527
+       <https://github.com/pybind/pybind11/pull/527>`_.
+
+
+    3. This version of pybind11 uses a redesigned mechanism for instantiating
+       trampoline classes that are used to override virtual methods from within
+       Python. This led to the following user-visible syntax change: instead of
+
+       .. code-block:: cpp
+
+           py::class_<TrampolineClass>("MyClass")
+             .alias<MyClass>()
+             ....
+
+       write
+
+       .. code-block:: cpp
+
+           py::class_<MyClass, TrampolineClass>("MyClass")
+             ....
+
+       Importantly, both the original and the trampoline class are now
+       specified as an arguments (in arbitrary order) to the ``py::class_``
+       template, and the ``alias<..>()`` call is gone. The new scheme has zero
+       overhead in cases when Python doesn't override any functions of the
+       underlying C++ class. `rev. 86d825
+       <https://github.com/pybind/pybind11/commit/86d825>`_.
+
+* Added ``eval`` and ``eval_file`` functions for evaluating expressions and
+  statements from a string or file. `rev. 0d3fc3
+  <https://github.com/pybind/pybind11/commit/0d3fc3>`_.
+
+* pybind11 can now create types with a modifiable dictionary.
+  `#437 <https://github.com/pybind/pybind11/pull/437>`_ and
+  `#444 <https://github.com/pybind/pybind11/pull/444>`_.
+
+* Support for translation of arbitrary C++ exceptions to Python counterparts.
+  `#296 <https://github.com/pybind/pybind11/pull/296>`_ and
+  `#273 <https://github.com/pybind/pybind11/pull/273>`_.
+
+* Report full backtraces through mixed C++/Python code, better reporting for
+  import errors, fixed GIL management in exception processing.
+  `#537 <https://github.com/pybind/pybind11/pull/537>`_,
+  `#494 <https://github.com/pybind/pybind11/pull/494>`_,
+  `rev. e72d95 <https://github.com/pybind/pybind11/commit/e72d95>`_, and
+  `rev. 099d6e <https://github.com/pybind/pybind11/commit/099d6e>`_.
+
+* Support for bit-level operations, comparisons, and serialization of C++
+  enumerations. `#503 <https://github.com/pybind/pybind11/pull/503>`_,
+  `#508 <https://github.com/pybind/pybind11/pull/508>`_,
+  `#380 <https://github.com/pybind/pybind11/pull/380>`_,
+  `#309 <https://github.com/pybind/pybind11/pull/309>`_.
+  `#311 <https://github.com/pybind/pybind11/pull/311>`_.
+
+* The ``class_`` constructor now accepts its template arguments in any order.
+  `#385 <https://github.com/pybind/pybind11/pull/385>`_.
+
+* Attribute and item accessors now have a more complete interface which makes
+  it possible to chain attributes as in
+  ``obj.attr("a")[key].attr("b").attr("method")(1, 2, 3)``. `#425
+  <https://github.com/pybind/pybind11/pull/425>`_.
+
+* Major redesign of the default and conversion constructors in ``pytypes.h``.
+  `#464 <https://github.com/pybind/pybind11/pull/464>`_.
+
+* Added built-in support for ``std::shared_ptr`` holder type. It is no longer
+  necessary to to include a declaration of the form
+  ``PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>)`` (though continuing to
+  do so won't cause an error).
+  `#454 <https://github.com/pybind/pybind11/pull/454>`_.
+
+* New ``py::overload_cast`` casting operator to select among multiple possible
+  overloads of a function. An example:
+
+    .. code-block:: cpp
+
+        py::class_<Pet>(m, "Pet")
+            .def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
+            .def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");
+
+  This feature only works on C++14-capable compilers.
+  `#541 <https://github.com/pybind/pybind11/pull/541>`_.
+
+* C++ types are automatically cast to Python types, e.g. when assigning
+  them as an attribute. For instance, the following is now legal:
+
+    .. code-block:: cpp
+
+        py::module m = /* ... */
+        m.attr("constant") = 123;
+
+  (Previously, a ``py::cast`` call was necessary to avoid a compilation error.)
+  `#551 <https://github.com/pybind/pybind11/pull/551>`_.
+
+* Redesigned ``pytest``-based test suite. `#321 <https://github.com/pybind/pybind11/pull/321>`_.
+
+* Instance tracking to detect reference leaks in test suite. `#324 <https://github.com/pybind/pybind11/pull/324>`_
+
+* pybind11 can now distinguish between multiple different instances that are
+  located at the same memory address, but which have different types.
+  `#329 <https://github.com/pybind/pybind11/pull/329>`_.
+
+* Improved logic in ``move`` return value policy.
+  `#510 <https://github.com/pybind/pybind11/pull/510>`_,
+  `#297 <https://github.com/pybind/pybind11/pull/297>`_.
+
+* Generalized unpacking API to permit calling Python functions from C++ using
+  notation such as ``foo(a1, a2, *args, "ka"_a=1, "kb"_a=2, **kwargs)``. `#372 <https://github.com/pybind/pybind11/pull/372>`_.
+
+* ``py::print()`` function whose behavior matches that of the native Python
+  ``print()`` function. `#372 <https://github.com/pybind/pybind11/pull/372>`_.
+
+* Added ``py::dict`` keyword constructor:``auto d = dict("number"_a=42,
+  "name"_a="World");``. `#372 <https://github.com/pybind/pybind11/pull/372>`_.
+
+* Added ``py::str::format()`` method and ``_s`` literal: ``py::str s = "1 + 2
+  = {}"_s.format(3);``. `#372 <https://github.com/pybind/pybind11/pull/372>`_.
+
+* Added ``py::repr()`` function which is equivalent to Python's builtin
+  ``repr()``. `#333 <https://github.com/pybind/pybind11/pull/333>`_.
+
+* Improved construction and destruction logic for holder types. It is now
+  possible to reference instances with smart pointer holder types without
+  constructing the holder if desired. The ``PYBIND11_DECLARE_HOLDER_TYPE``
+  macro now accepts an optional second parameter to indicate whether the holder
+  type uses intrusive reference counting.
+  `#533 <https://github.com/pybind/pybind11/pull/533>`_ and
+  `#561 <https://github.com/pybind/pybind11/pull/561>`_.
+
+* Mapping a stateless C++ function to Python and back is now "for free" (i.e.
+  no extra indirections or argument conversion overheads). `rev. 954b79
+  <https://github.com/pybind/pybind11/commit/954b79>`_.
+
+* Bindings for ``std::valarray<T>``.
+  `#545 <https://github.com/pybind/pybind11/pull/545>`_.
+
+* Improved support for C++17 capable compilers.
+  `#562 <https://github.com/pybind/pybind11/pull/562>`_.
+
+* Bindings for ``std::optional<t>``.
+  `#475 <https://github.com/pybind/pybind11/pull/475>`_,
+  `#476 <https://github.com/pybind/pybind11/pull/476>`_,
+  `#479 <https://github.com/pybind/pybind11/pull/479>`_,
+  `#499 <https://github.com/pybind/pybind11/pull/499>`_, and
+  `#501 <https://github.com/pybind/pybind11/pull/501>`_.
+
+* ``stl_bind.h``: general improvements and support for ``std::map`` and
+  ``std::unordered_map``.
+  `#490 <https://github.com/pybind/pybind11/pull/490>`_,
+  `#282 <https://github.com/pybind/pybind11/pull/282>`_,
+  `#235 <https://github.com/pybind/pybind11/pull/235>`_.
+
+* The ``std::tuple``, ``std::pair``, ``std::list``, and ``std::vector`` type
+  casters now accept any Python sequence type as input. `rev. 107285
+  <https://github.com/pybind/pybind11/commit/107285>`_.
+
+* Improved CMake Python detection on multi-architecture Linux.
+  `#532 <https://github.com/pybind/pybind11/pull/532>`_.
+
+* Infrastructure to selectively disable or enable parts of the automatically
+  generated docstrings. `#486 <https://github.com/pybind/pybind11/pull/486>`_.
+
+* ``reference`` and ``reference_internal`` are now the default return value
+  properties for static and non-static properties, respectively. `#473
+  <https://github.com/pybind/pybind11/pull/473>`_. (the previous defaults
+  were ``automatic``). `#473 <https://github.com/pybind/pybind11/pull/473>`_.
+
+* Support for ``std::unique_ptr`` with non-default deleters or no deleter at
+  all (``py::nodelete``). `#384 <https://github.com/pybind/pybind11/pull/384>`_.
+
+* Deprecated ``handle::call()`` method. The new syntax to call Python
+  functions is simply ``handle()``. It can also be invoked explicitly via
+  ``handle::operator<X>()``, where ``X`` is an optional return value policy.
+
+* Print more informative error messages when ``make_tuple()`` or ``cast()``
+  fail. `#262 <https://github.com/pybind/pybind11/pull/262>`_.
+
+* Creation of holder types for classes deriving from
+  ``std::enable_shared_from_this<>`` now also works for ``const`` values.
+  `#260 <https://github.com/pybind/pybind11/pull/260>`_.
+
+* ``make_iterator()`` improvements for better compatibility with various
+  types (now uses prefix increment operator); it now also accepts iterators
+  with different begin/end types as long as they are equality comparable.
+  `#247 <https://github.com/pybind/pybind11/pull/247>`_.
+
+* ``arg()`` now accepts a wider range of argument types for default values.
+  `#244 <https://github.com/pybind/pybind11/pull/244>`_.
+
+* Support ``keep_alive`` where the nurse object may be ``None``. `#341
+  <https://github.com/pybind/pybind11/pull/341>`_.
+
+* Added constructors for ``str`` and ``bytes`` from zero-terminated char
+  pointers, and from char pointers and length. Added constructors for ``str``
+  from ``bytes`` and for ``bytes`` from ``str``, which will perform UTF-8
+  decoding/encoding as required.
+
+* Many other improvements of library internals without user-visible changes
+
+
+1.8.1 (July 12, 2016)
+----------------------
+* Fixed a rare but potentially very severe issue when the garbage collector ran
+  during pybind11 type creation.
+
+1.8.0 (June 14, 2016)
+----------------------
+* Redesigned CMake build system which exports a convenient
+  ``pybind11_add_module`` function to parent projects.
+* ``std::vector<>`` type bindings analogous to Boost.Python's ``indexing_suite``
+* Transparent conversion of sparse and dense Eigen matrices and vectors (``eigen.h``)
+* Added an ``ExtraFlags`` template argument to the NumPy ``array_t<>`` wrapper
+  to disable an enforced cast that may lose precision, e.g. to create overloads
+  for different precisions and complex vs real-valued matrices.
+* Prevent implicit conversion of floating point values to integral types in
+  function arguments
+* Fixed incorrect default return value policy for functions returning a shared
+  pointer
+* Don't allow registering a type via ``class_`` twice
+* Don't allow casting a ``None`` value into a C++ lvalue reference
+* Fixed a crash in ``enum_::operator==`` that was triggered by the ``help()`` command
+* Improved detection of whether or not custom C++ types can be copy/move-constructed
+* Extended ``str`` type to also work with ``bytes`` instances
+* Added a ``"name"_a`` user defined string literal that is equivalent to ``py::arg("name")``.
+* When specifying function arguments via ``py::arg``, the test that verifies
+  the number of arguments now runs at compile time.
+* Added ``[[noreturn]]`` attribute to ``pybind11_fail()`` to quench some
+  compiler warnings
+* List function arguments in exception text when the dispatch code cannot find
+  a matching overload
+* Added ``PYBIND11_OVERLOAD_NAME`` and ``PYBIND11_OVERLOAD_PURE_NAME`` macros which
+  can be used to override virtual methods whose name differs in C++ and Python
+  (e.g. ``__call__`` and ``operator()``)
+* Various minor ``iterator`` and ``make_iterator()`` improvements
+* Transparently support ``__bool__`` on Python 2.x and Python 3.x
+* Fixed issue with destructor of unpickled object not being called
+* Minor CMake build system improvements on Windows
+* New ``pybind11::args`` and ``pybind11::kwargs`` types to create functions which
+  take an arbitrary number of arguments and keyword arguments
+* New syntax to call a Python function from C++ using ``*args`` and ``*kwargs``
+* The functions ``def_property_*`` now correctly process docstring arguments (these
+  formerly caused a segmentation fault)
+* Many ``mkdoc.py`` improvements (enumerations, template arguments, ``DOC()``
+  macro accepts more arguments)
+* Cygwin support
+* Documentation improvements (pickling support, ``keep_alive``, macro usage)
+
+1.7 (April 30, 2016)
+----------------------
+* Added a new ``move`` return value policy that triggers C++11 move semantics.
+  The automatic return value policy falls back to this case whenever a rvalue
+  reference is encountered
+* Significantly more general GIL state routines that are used instead of
+  Python's troublesome ``PyGILState_Ensure`` and ``PyGILState_Release`` API
+* Redesign of opaque types that drastically simplifies their usage
+* Extended ability to pass values of type ``[const] void *``
+* ``keep_alive`` fix: don't fail when there is no patient
+* ``functional.h``: acquire the GIL before calling a Python function
+* Added Python RAII type wrappers ``none`` and ``iterable``
+* Added ``*args`` and ``*kwargs`` pass-through parameters to
+  ``pybind11.get_include()`` function
+* Iterator improvements and fixes
+* Documentation on return value policies and opaque types improved
+
+1.6 (April 30, 2016)
+----------------------
+* Skipped due to upload to PyPI gone wrong and inability to recover
+  (https://github.com/pypa/packaging-problems/issues/74)
+
+1.5 (April 21, 2016)
+----------------------
+* For polymorphic types, use RTTI to try to return the closest type registered with pybind11
+* Pickling support for serializing and unserializing C++ instances to a byte stream in Python
+* Added a convenience routine ``make_iterator()`` which turns a range indicated
+  by a pair of C++ iterators into a iterable Python object
+* Added ``len()`` and a variadic ``make_tuple()`` function
+* Addressed a rare issue that could confuse the current virtual function
+  dispatcher and another that could lead to crashes in multi-threaded
+  applications
+* Added a ``get_include()`` function to the Python module that returns the path
+  of the directory containing the installed pybind11 header files
+* Documentation improvements: import issues, symbol visibility, pickling, limitations
+* Added casting support for ``std::reference_wrapper<>``
+
+1.4 (April 7, 2016)
+--------------------------
+* Transparent type conversion for ``std::wstring`` and ``wchar_t``
+* Allow passing ``nullptr``-valued strings
+* Transparent passing of ``void *`` pointers using capsules
+* Transparent support for returning values wrapped in ``std::unique_ptr<>``
+* Improved docstring generation for compatibility with Sphinx
+* Nicer debug error message when default parameter construction fails
+* Support for "opaque" types that bypass the transparent conversion layer for STL containers
+* Redesigned type casting interface to avoid ambiguities that could occasionally cause compiler errors
+* Redesigned property implementation; fixes crashes due to an unfortunate default return value policy
+* Anaconda package generation support
+
+1.3 (March 8, 2016)
+--------------------------
+
+* Added support for the Intel C++ compiler (v15+)
+* Added support for the STL unordered set/map data structures
+* Added support for the STL linked list data structure
+* NumPy-style broadcasting support in ``pybind11::vectorize``
+* pybind11 now displays more verbose error messages when ``arg::operator=()`` fails
+* pybind11 internal data structures now live in a version-dependent namespace to avoid ABI issues
+* Many, many bugfixes involving corner cases and advanced usage
+
+1.2 (February 7, 2016)
+--------------------------
+
+* Optional: efficient generation of function signatures at compile time using C++14
+* Switched to a simpler and more general way of dealing with function default
+  arguments. Unused keyword arguments in function calls are now detected and
+  cause errors as expected
+* New ``keep_alive`` call policy analogous to Boost.Python's ``with_custodian_and_ward``
+* New ``pybind11::base<>`` attribute to indicate a subclass relationship
+* Improved interface for RAII type wrappers in ``pytypes.h``
+* Use RAII type wrappers consistently within pybind11 itself. This
+  fixes various potential refcount leaks when exceptions occur
+* Added new ``bytes`` RAII type wrapper (maps to ``string`` in Python 2.7)
+* Made handle and related RAII classes const correct, using them more
+  consistently everywhere now
+* Got rid of the ugly ``__pybind11__`` attributes on the Python side---they are
+  now stored in a C++ hash table that is not visible in Python
+* Fixed refcount leaks involving NumPy arrays and bound functions
+* Vastly improved handling of shared/smart pointers
+* Removed an unnecessary copy operation in ``pybind11::vectorize``
+* Fixed naming clashes when both pybind11 and NumPy headers are included
+* Added conversions for additional exception types
+* Documentation improvements (using multiple extension modules, smart pointers,
+  other minor clarifications)
+* unified infrastructure for parsing variadic arguments in ``class_`` and cpp_function
+* Fixed license text (was: ZLIB, should have been: 3-clause BSD)
+* Python 3.2 compatibility
+* Fixed remaining issues when accessing types in another plugin module
+* Added enum comparison and casting methods
+* Improved SFINAE-based detection of whether types are copy-constructible
+* Eliminated many warnings about unused variables and the use of ``offsetof()``
+* Support for ``std::array<>`` conversions
+
+1.1 (December 7, 2015)
+--------------------------
+
+* Documentation improvements (GIL, wrapping functions, casting, fixed many typos)
+* Generalized conversion of integer types
+* Improved support for casting function objects
+* Improved support for ``std::shared_ptr<>`` conversions
+* Initial support for ``std::set<>`` conversions
+* Fixed type resolution issue for types defined in a separate plugin module
+* CMake build system improvements
+* Factored out generic functionality to non-templated code (smaller code size)
+* Added a code size / compile time benchmark vs Boost.Python
+* Added an appveyor CI script
+
+1.0 (October 15, 2015)
+------------------------
+* Initial release
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/classes.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/classes.rst
new file mode 100644
index 000000000..c0c53135b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/classes.rst
@@ -0,0 +1,541 @@
+.. _classes:
+
+Object-oriented code
+####################
+
+Creating bindings for a custom type
+===================================
+
+Let's now look at a more complex example where we'll create bindings for a
+custom C++ data structure named ``Pet``. Its definition is given below:
+
+.. code-block:: cpp
+
+    struct Pet {
+        Pet(const std::string &name) : name(name) { }
+        void setName(const std::string &name_) { name = name_; }
+        const std::string &getName() const { return name; }
+
+        std::string name;
+    };
+
+The binding code for ``Pet`` looks as follows:
+
+.. code-block:: cpp
+
+    #include <pybind11/pybind11.h>
+
+    namespace py = pybind11;
+
+    PYBIND11_MODULE(example, m) {
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def("setName", &Pet::setName)
+            .def("getName", &Pet::getName);
+    }
+
+:class:`class_` creates bindings for a C++ *class* or *struct*-style data
+structure. :func:`init` is a convenience function that takes the types of a
+constructor's parameters as template arguments and wraps the corresponding
+constructor (see the :ref:`custom_constructors` section for details). An
+interactive Python session demonstrating this example is shown below:
+
+.. code-block:: pycon
+
+    % python
+    >>> import example
+    >>> p = example.Pet("Molly")
+    >>> print(p)
+    <example.Pet object at 0x10cd98060>
+    >>> p.getName()
+    'Molly'
+    >>> p.setName("Charly")
+    >>> p.getName()
+    'Charly'
+
+.. seealso::
+
+    Static member functions can be bound in the same way using
+    :func:`class_::def_static`.
+
+Keyword and default arguments
+=============================
+It is possible to specify keyword and default arguments using the syntax
+discussed in the previous chapter. Refer to the sections :ref:`keyword_args`
+and :ref:`default_args` for details.
+
+Binding lambda functions
+========================
+
+Note how ``print(p)`` produced a rather useless summary of our data structure in the example above:
+
+.. code-block:: pycon
+
+    >>> print(p)
+    <example.Pet object at 0x10cd98060>
+
+To address this, we could bind a utility function that returns a human-readable
+summary to the special method slot named ``__repr__``. Unfortunately, there is no
+suitable functionality in the ``Pet`` data structure, and it would be nice if
+we did not have to change it. This can easily be accomplished by binding a
+Lambda function instead:
+
+.. code-block:: cpp
+
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def("setName", &Pet::setName)
+            .def("getName", &Pet::getName)
+            .def("__repr__",
+                [](const Pet &a) {
+                    return "<example.Pet named '" + a.name + "'>";
+                }
+            );
+
+Both stateless [#f1]_ and stateful lambda closures are supported by pybind11.
+With the above change, the same Python code now produces the following output:
+
+.. code-block:: pycon
+
+    >>> print(p)
+    <example.Pet named 'Molly'>
+
+.. [#f1] Stateless closures are those with an empty pair of brackets ``[]`` as the capture object.
+
+.. _properties:
+
+Instance and static fields
+==========================
+
+We can also directly expose the ``name`` field using the
+:func:`class_::def_readwrite` method. A similar :func:`class_::def_readonly`
+method also exists for ``const`` fields.
+
+.. code-block:: cpp
+
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def_readwrite("name", &Pet::name)
+            // ... remainder ...
+
+This makes it possible to write
+
+.. code-block:: pycon
+
+    >>> p = example.Pet("Molly")
+    >>> p.name
+    'Molly'
+    >>> p.name = "Charly"
+    >>> p.name
+    'Charly'
+
+Now suppose that ``Pet::name`` was a private internal variable
+that can only be accessed via setters and getters.
+
+.. code-block:: cpp
+
+    class Pet {
+    public:
+        Pet(const std::string &name) : name(name) { }
+        void setName(const std::string &name_) { name = name_; }
+        const std::string &getName() const { return name; }
+    private:
+        std::string name;
+    };
+
+In this case, the method :func:`class_::def_property`
+(:func:`class_::def_property_readonly` for read-only data) can be used to
+provide a field-like interface within Python that will transparently call
+the setter and getter functions:
+
+.. code-block:: cpp
+
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def_property("name", &Pet::getName, &Pet::setName)
+            // ... remainder ...
+
+Write only properties can be defined by passing ``nullptr`` as the
+input for the read function.
+
+.. seealso::
+
+    Similar functions :func:`class_::def_readwrite_static`,
+    :func:`class_::def_readonly_static` :func:`class_::def_property_static`,
+    and :func:`class_::def_property_readonly_static` are provided for binding
+    static variables and properties. Please also see the section on
+    :ref:`static_properties` in the advanced part of the documentation.
+
+Dynamic attributes
+==================
+
+Native Python classes can pick up new attributes dynamically:
+
+.. code-block:: pycon
+
+    >>> class Pet:
+    ...     name = "Molly"
+    ...
+    >>> p = Pet()
+    >>> p.name = "Charly"  # overwrite existing
+    >>> p.age = 2  # dynamically add a new attribute
+
+By default, classes exported from C++ do not support this and the only writable
+attributes are the ones explicitly defined using :func:`class_::def_readwrite`
+or :func:`class_::def_property`.
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet")
+        .def(py::init<>())
+        .def_readwrite("name", &Pet::name);
+
+Trying to set any other attribute results in an error:
+
+.. code-block:: pycon
+
+    >>> p = example.Pet()
+    >>> p.name = "Charly"  # OK, attribute defined in C++
+    >>> p.age = 2  # fail
+    AttributeError: 'Pet' object has no attribute 'age'
+
+To enable dynamic attributes for C++ classes, the :class:`py::dynamic_attr` tag
+must be added to the :class:`py::class_` constructor:
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet", py::dynamic_attr())
+        .def(py::init<>())
+        .def_readwrite("name", &Pet::name);
+
+Now everything works as expected:
+
+.. code-block:: pycon
+
+    >>> p = example.Pet()
+    >>> p.name = "Charly"  # OK, overwrite value in C++
+    >>> p.age = 2  # OK, dynamically add a new attribute
+    >>> p.__dict__  # just like a native Python class
+    {'age': 2}
+
+Note that there is a small runtime cost for a class with dynamic attributes.
+Not only because of the addition of a ``__dict__``, but also because of more
+expensive garbage collection tracking which must be activated to resolve
+possible circular references. Native Python classes incur this same cost by
+default, so this is not anything to worry about. By default, pybind11 classes
+are more efficient than native Python classes. Enabling dynamic attributes
+just brings them on par.
+
+.. _inheritance:
+
+Inheritance and automatic downcasting
+=====================================
+
+Suppose now that the example consists of two data structures with an
+inheritance relationship:
+
+.. code-block:: cpp
+
+    struct Pet {
+        Pet(const std::string &name) : name(name) { }
+        std::string name;
+    };
+
+    struct Dog : Pet {
+        Dog(const std::string &name) : Pet(name) { }
+        std::string bark() const { return "woof!"; }
+    };
+
+There are two different ways of indicating a hierarchical relationship to
+pybind11: the first specifies the C++ base class as an extra template
+parameter of the :class:`class_`:
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet")
+       .def(py::init<const std::string &>())
+       .def_readwrite("name", &Pet::name);
+
+    // Method 1: template parameter:
+    py::class_<Dog, Pet /* <- specify C++ parent type */>(m, "Dog")
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Alternatively, we can also assign a name to the previously bound ``Pet``
+:class:`class_` object and reference it when binding the ``Dog`` class:
+
+.. code-block:: cpp
+
+    py::class_<Pet> pet(m, "Pet");
+    pet.def(py::init<const std::string &>())
+       .def_readwrite("name", &Pet::name);
+
+    // Method 2: pass parent class_ object:
+    py::class_<Dog>(m, "Dog", pet /* <- specify Python parent type */)
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Functionality-wise, both approaches are equivalent. Afterwards, instances will
+expose fields and methods of both types:
+
+.. code-block:: pycon
+
+    >>> p = example.Dog("Molly")
+    >>> p.name
+    'Molly'
+    >>> p.bark()
+    'woof!'
+
+The C++ classes defined above are regular non-polymorphic types with an
+inheritance relationship. This is reflected in Python:
+
+.. code-block:: cpp
+
+    // Return a base pointer to a derived instance
+    m.def("pet_store", []() { return std::unique_ptr<Pet>(new Dog("Molly")); });
+
+.. code-block:: pycon
+
+    >>> p = example.pet_store()
+    >>> type(p)  # `Dog` instance behind `Pet` pointer
+    Pet          # no pointer downcasting for regular non-polymorphic types
+    >>> p.bark()
+    AttributeError: 'Pet' object has no attribute 'bark'
+
+The function returned a ``Dog`` instance, but because it's a non-polymorphic
+type behind a base pointer, Python only sees a ``Pet``. In C++, a type is only
+considered polymorphic if it has at least one virtual function and pybind11
+will automatically recognize this:
+
+.. code-block:: cpp
+
+    struct PolymorphicPet {
+        virtual ~PolymorphicPet() = default;
+    };
+
+    struct PolymorphicDog : PolymorphicPet {
+        std::string bark() const { return "woof!"; }
+    };
+
+    // Same binding code
+    py::class_<PolymorphicPet>(m, "PolymorphicPet");
+    py::class_<PolymorphicDog, PolymorphicPet>(m, "PolymorphicDog")
+        .def(py::init<>())
+        .def("bark", &PolymorphicDog::bark);
+
+    // Again, return a base pointer to a derived instance
+    m.def("pet_store2", []() { return std::unique_ptr<PolymorphicPet>(new PolymorphicDog); });
+
+.. code-block:: pycon
+
+    >>> p = example.pet_store2()
+    >>> type(p)
+    PolymorphicDog  # automatically downcast
+    >>> p.bark()
+    'woof!'
+
+Given a pointer to a polymorphic base, pybind11 performs automatic downcasting
+to the actual derived type. Note that this goes beyond the usual situation in
+C++: we don't just get access to the virtual functions of the base, we get the
+concrete derived type including functions and attributes that the base type may
+not even be aware of.
+
+.. seealso::
+
+    For more information about polymorphic behavior see :ref:`overriding_virtuals`.
+
+
+Overloaded methods
+==================
+
+Sometimes there are several overloaded C++ methods with the same name taking
+different kinds of input arguments:
+
+.. code-block:: cpp
+
+    struct Pet {
+        Pet(const std::string &name, int age) : name(name), age(age) { }
+
+        void set(int age_) { age = age_; }
+        void set(const std::string &name_) { name = name_; }
+
+        std::string name;
+        int age;
+    };
+
+Attempting to bind ``Pet::set`` will cause an error since the compiler does not
+know which method the user intended to select. We can disambiguate by casting
+them to function pointers. Binding multiple functions to the same Python name
+automatically creates a chain of function overloads that will be tried in
+sequence.
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet")
+       .def(py::init<const std::string &, int>())
+       .def("set", static_cast<void (Pet::*)(int)>(&Pet::set), "Set the pet's age")
+       .def("set", static_cast<void (Pet::*)(const std::string &)>(&Pet::set), "Set the pet's name");
+
+The overload signatures are also visible in the method's docstring:
+
+.. code-block:: pycon
+
+    >>> help(example.Pet)
+
+    class Pet(__builtin__.object)
+     |  Methods defined here:
+     |
+     |  __init__(...)
+     |      Signature : (Pet, str, int) -> NoneType
+     |
+     |  set(...)
+     |      1. Signature : (Pet, int) -> NoneType
+     |
+     |      Set the pet's age
+     |
+     |      2. Signature : (Pet, str) -> NoneType
+     |
+     |      Set the pet's name
+
+If you have a C++14 compatible compiler [#cpp14]_, you can use an alternative
+syntax to cast the overloaded function:
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet")
+        .def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
+        .def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");
+
+Here, ``py::overload_cast`` only requires the parameter types to be specified.
+The return type and class are deduced. This avoids the additional noise of
+``void (Pet::*)()`` as seen in the raw cast. If a function is overloaded based
+on constness, the ``py::const_`` tag should be used:
+
+.. code-block:: cpp
+
+    struct Widget {
+        int foo(int x, float y);
+        int foo(int x, float y) const;
+    };
+
+    py::class_<Widget>(m, "Widget")
+       .def("foo_mutable", py::overload_cast<int, float>(&Widget::foo))
+       .def("foo_const",   py::overload_cast<int, float>(&Widget::foo, py::const_));
+
+If you prefer the ``py::overload_cast`` syntax but have a C++11 compatible compiler only,
+you can use ``py::detail::overload_cast_impl`` with an additional set of parentheses:
+
+.. code-block:: cpp
+
+    template <typename... Args>
+    using overload_cast_ = pybind11::detail::overload_cast_impl<Args...>;
+
+    py::class_<Pet>(m, "Pet")
+        .def("set", overload_cast_<int>()(&Pet::set), "Set the pet's age")
+        .def("set", overload_cast_<const std::string &>()(&Pet::set), "Set the pet's name");
+
+.. [#cpp14] A compiler which supports the ``-std=c++14`` flag.
+
+.. note::
+
+    To define multiple overloaded constructors, simply declare one after the
+    other using the ``.def(py::init<...>())`` syntax. The existing machinery
+    for specifying keyword and default arguments also works.
+
+Enumerations and internal types
+===============================
+
+Let's now suppose that the example class contains internal types like enumerations, e.g.:
+
+.. code-block:: cpp
+
+    struct Pet {
+        enum Kind {
+            Dog = 0,
+            Cat
+        };
+
+        struct Attributes {
+            float age = 0;
+        };
+
+        Pet(const std::string &name, Kind type) : name(name), type(type) { }
+
+        std::string name;
+        Kind type;
+        Attributes attr;
+    };
+
+The binding code for this example looks as follows:
+
+.. code-block:: cpp
+
+    py::class_<Pet> pet(m, "Pet");
+
+    pet.def(py::init<const std::string &, Pet::Kind>())
+        .def_readwrite("name", &Pet::name)
+        .def_readwrite("type", &Pet::type)
+        .def_readwrite("attr", &Pet::attr);
+
+    py::enum_<Pet::Kind>(pet, "Kind")
+        .value("Dog", Pet::Kind::Dog)
+        .value("Cat", Pet::Kind::Cat)
+        .export_values();
+
+    py::class_<Pet::Attributes>(pet, "Attributes")
+        .def(py::init<>())
+        .def_readwrite("age", &Pet::Attributes::age);
+
+
+To ensure that the nested types ``Kind`` and ``Attributes`` are created within the scope of ``Pet``, the
+``pet`` :class:`class_` instance must be supplied to the :class:`enum_` and :class:`class_`
+constructor. The :func:`enum_::export_values` function exports the enum entries
+into the parent scope, which should be skipped for newer C++11-style strongly
+typed enums.
+
+.. code-block:: pycon
+
+    >>> p = Pet("Lucy", Pet.Cat)
+    >>> p.type
+    Kind.Cat
+    >>> int(p.type)
+    1L
+
+The entries defined by the enumeration type are exposed in the ``__members__`` property:
+
+.. code-block:: pycon
+
+    >>> Pet.Kind.__members__
+    {'Dog': Kind.Dog, 'Cat': Kind.Cat}
+
+The ``name`` property returns the name of the enum value as a unicode string.
+
+.. note::
+
+    It is also possible to use ``str(enum)``, however these accomplish different
+    goals. The following shows how these two approaches differ.
+
+    .. code-block:: pycon
+
+        >>> p = Pet("Lucy", Pet.Cat)
+        >>> pet_type = p.type
+        >>> pet_type
+        Pet.Cat
+        >>> str(pet_type)
+        'Pet.Cat'
+        >>> pet_type.name
+        'Cat'
+
+.. note::
+
+    When the special tag ``py::arithmetic()`` is specified to the ``enum_``
+    constructor, pybind11 creates an enumeration that also supports rudimentary
+    arithmetic and bit-level operations like comparisons, and, or, xor, negation,
+    etc.
+
+    .. code-block:: cpp
+
+        py::enum_<Pet::Kind>(pet, "Kind", py::arithmetic())
+           ...
+
+    By default, these are omitted to conserve space.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/cmake/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/cmake/index.rst
new file mode 100644
index 000000000..eaf66d70f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/cmake/index.rst
@@ -0,0 +1,8 @@
+CMake helpers
+-------------
+
+Pybind11 can be used with ``add_subdirectory(extern/pybind11)``, or from an
+install with ``find_package(pybind11 CONFIG)``. The interface provided in
+either case is functionally identical.
+
+.. cmake-module:: ../../tools/pybind11Config.cmake.in
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/compiling.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/compiling.rst
new file mode 100644
index 000000000..2b543be0b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/compiling.rst
@@ -0,0 +1,638 @@
+.. _compiling:
+
+Build systems
+#############
+
+.. _build-setuptools:
+
+Building with setuptools
+========================
+
+For projects on PyPI, building with setuptools is the way to go. Sylvain Corlay
+has kindly provided an example project which shows how to set up everything,
+including automatic generation of documentation using Sphinx. Please refer to
+the [python_example]_ repository.
+
+.. [python_example] https://github.com/pybind/python_example
+
+A helper file is provided with pybind11 that can simplify usage with setuptools.
+
+To use pybind11 inside your ``setup.py``, you have to have some system to
+ensure that ``pybind11`` is installed when you build your package. There are
+four possible ways to do this, and pybind11 supports all four: You can ask all
+users to install pybind11 beforehand (bad), you can use
+:ref:`setup_helpers-pep518` (good, but very new and requires Pip 10),
+:ref:`setup_helpers-setup_requires` (discouraged by Python packagers now that
+PEP 518 is available, but it still works everywhere), or you can
+:ref:`setup_helpers-copy-manually` (always works but you have to manually sync
+your copy to get updates).
+
+An example of a ``setup.py`` using pybind11's helpers:
+
+.. code-block:: python
+
+    from glob import glob
+    from setuptools import setup
+    from pybind11.setup_helpers import Pybind11Extension
+
+    ext_modules = [
+        Pybind11Extension(
+            "python_example",
+            sorted(glob("src/*.cpp")),  # Sort source files for reproducibility
+        ),
+    ]
+
+    setup(..., ext_modules=ext_modules)
+
+If you want to do an automatic search for the highest supported C++ standard,
+that is supported via a ``build_ext`` command override; it will only affect
+``Pybind11Extensions``:
+
+.. code-block:: python
+
+    from glob import glob
+    from setuptools import setup
+    from pybind11.setup_helpers import Pybind11Extension, build_ext
+
+    ext_modules = [
+        Pybind11Extension(
+            "python_example",
+            sorted(glob("src/*.cpp")),
+        ),
+    ]
+
+    setup(..., cmdclass={"build_ext": build_ext}, ext_modules=ext_modules)
+
+If you have single-file extension modules that are directly stored in the
+Python source tree (``foo.cpp`` in the same directory as where a ``foo.py``
+would be located), you can also generate ``Pybind11Extensions`` using
+``setup_helpers.intree_extensions``: ``intree_extensions(["path/to/foo.cpp",
+...])`` returns a list of ``Pybind11Extensions`` which can be passed to
+``ext_modules``, possibly after further customizing their attributes
+(``libraries``, ``include_dirs``, etc.).  By doing so, a ``foo.*.so`` extension
+module will be generated and made available upon installation.
+
+``intree_extension`` will automatically detect if you are using a ``src``-style
+layout (as long as no namespace packages are involved), but you can also
+explicitly pass ``package_dir`` to it (as in ``setuptools.setup``).
+
+Since pybind11 does not require NumPy when building, a light-weight replacement
+for NumPy's parallel compilation distutils tool is included. Use it like this:
+
+.. code-block:: python
+
+    from pybind11.setup_helpers import ParallelCompile
+
+    # Optional multithreaded build
+    ParallelCompile("NPY_NUM_BUILD_JOBS").install()
+
+    setup(...)
+
+The argument is the name of an environment variable to control the number of
+threads, such as ``NPY_NUM_BUILD_JOBS`` (as used by NumPy), though you can set
+something different if you want; ``CMAKE_BUILD_PARALLEL_LEVEL`` is another choice
+a user might expect. You can also pass ``default=N`` to set the default number
+of threads (0 will take the number of threads available) and ``max=N``, the
+maximum number of threads; if you have a large extension you may want set this
+to a memory dependent number.
+
+If you are developing rapidly and have a lot of C++ files, you may want to
+avoid rebuilding files that have not changed. For simple cases were you are
+using ``pip install -e .`` and do not have local headers, you can skip the
+rebuild if an object file is newer than its source (headers are not checked!)
+with the following:
+
+.. code-block:: python
+
+    from pybind11.setup_helpers import ParallelCompile, naive_recompile
+
+    ParallelCompile("NPY_NUM_BUILD_JOBS", needs_recompile=naive_recompile).install()
+
+
+If you have a more complex build, you can implement a smarter function and pass
+it to ``needs_recompile``, or you can use [Ccache]_ instead. ``CXX="cache g++"
+pip install -e .`` would be the way to use it with GCC, for example. Unlike the
+simple solution, this even works even when not compiling in editable mode, but
+it does require Ccache to be installed.
+
+Keep in mind that Pip will not even attempt to rebuild if it thinks it has
+already built a copy of your code, which it deduces from the version number.
+One way to avoid this is to use [setuptools_scm]_, which will generate a
+version number that includes the number of commits since your last tag and a
+hash for a dirty directory. Another way to force a rebuild is purge your cache
+or use Pip's ``--no-cache-dir`` option.
+
+.. [Ccache] https://ccache.dev
+
+.. [setuptools_scm] https://github.com/pypa/setuptools_scm
+
+.. _setup_helpers-pep518:
+
+PEP 518 requirements (Pip 10+ required)
+---------------------------------------
+
+If you use `PEP 518's <https://www.python.org/dev/peps/pep-0518/>`_
+``pyproject.toml`` file, you can ensure that ``pybind11`` is available during
+the compilation of your project.  When this file exists, Pip will make a new
+virtual environment, download just the packages listed here in ``requires=``,
+and build a wheel (binary Python package). It will then throw away the
+environment, and install your wheel.
+
+Your ``pyproject.toml`` file will likely look something like this:
+
+.. code-block:: toml
+
+    [build-system]
+    requires = ["setuptools>=42", "wheel", "pybind11~=2.6.1"]
+    build-backend = "setuptools.build_meta"
+
+.. note::
+
+    The main drawback to this method is that a `PEP 517`_ compliant build tool,
+    such as Pip 10+, is required for this approach to work; older versions of
+    Pip completely ignore this file. If you distribute binaries (called wheels
+    in Python) using something like `cibuildwheel`_, remember that ``setup.py``
+    and ``pyproject.toml`` are not even contained in the wheel, so this high
+    Pip requirement is only for source builds, and will not affect users of
+    your binary wheels. If you are building SDists and wheels, then
+    `pypa-build`_ is the recommended official tool.
+
+.. _PEP 517: https://www.python.org/dev/peps/pep-0517/
+.. _cibuildwheel: https://cibuildwheel.readthedocs.io
+.. _pypa-build: https://pypa-build.readthedocs.io/en/latest/
+
+.. _setup_helpers-setup_requires:
+
+Classic ``setup_requires``
+--------------------------
+
+If you want to support old versions of Pip with the classic
+``setup_requires=["pybind11"]`` keyword argument to setup, which triggers a
+two-phase ``setup.py`` run, then you will need to use something like this to
+ensure the first pass works (which has not yet installed the ``setup_requires``
+packages, since it can't install something it does not know about):
+
+.. code-block:: python
+
+    try:
+        from pybind11.setup_helpers import Pybind11Extension
+    except ImportError:
+        from setuptools import Extension as Pybind11Extension
+
+
+It doesn't matter that the Extension class is not the enhanced subclass for the
+first pass run; and the second pass will have the ``setup_requires``
+requirements.
+
+This is obviously more of a hack than the PEP 518 method, but it supports
+ancient versions of Pip.
+
+.. _setup_helpers-copy-manually:
+
+Copy manually
+-------------
+
+You can also copy ``setup_helpers.py`` directly to your project; it was
+designed to be usable standalone, like the old example ``setup.py``. You can
+set ``include_pybind11=False`` to skip including the pybind11 package headers,
+so you can use it with git submodules and a specific git version. If you use
+this, you will need to import from a local file in ``setup.py`` and ensure the
+helper file is part of your MANIFEST.
+
+
+Closely related, if you include pybind11 as a subproject, you can run the
+``setup_helpers.py`` inplace. If loaded correctly, this should even pick up
+the correct include for pybind11, though you can turn it off as shown above if
+you want to input it manually.
+
+Suggested usage if you have pybind11 as a submodule in ``extern/pybind11``:
+
+.. code-block:: python
+
+    DIR = os.path.abspath(os.path.dirname(__file__))
+
+    sys.path.append(os.path.join(DIR, "extern", "pybind11"))
+    from pybind11.setup_helpers import Pybind11Extension  # noqa: E402
+
+    del sys.path[-1]
+
+
+.. versionchanged:: 2.6
+
+    Added ``setup_helpers`` file.
+
+Building with cppimport
+========================
+
+[cppimport]_ is a small Python import hook that determines whether there is a C++
+source file whose name matches the requested module. If there is, the file is
+compiled as a Python extension using pybind11 and placed in the same folder as
+the C++ source file. Python is then able to find the module and load it.
+
+.. [cppimport] https://github.com/tbenthompson/cppimport
+
+.. _cmake:
+
+Building with CMake
+===================
+
+For C++ codebases that have an existing CMake-based build system, a Python
+extension module can be created with just a few lines of code:
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.4...3.18)
+    project(example LANGUAGES CXX)
+
+    add_subdirectory(pybind11)
+    pybind11_add_module(example example.cpp)
+
+This assumes that the pybind11 repository is located in a subdirectory named
+:file:`pybind11` and that the code is located in a file named :file:`example.cpp`.
+The CMake command ``add_subdirectory`` will import the pybind11 project which
+provides the ``pybind11_add_module`` function. It will take care of all the
+details needed to build a Python extension module on any platform.
+
+A working sample project, including a way to invoke CMake from :file:`setup.py` for
+PyPI integration, can be found in the [cmake_example]_  repository.
+
+.. [cmake_example] https://github.com/pybind/cmake_example
+
+.. versionchanged:: 2.6
+   CMake 3.4+ is required.
+
+Further information can be found at :doc:`cmake/index`.
+
+pybind11_add_module
+-------------------
+
+To ease the creation of Python extension modules, pybind11 provides a CMake
+function with the following signature:
+
+.. code-block:: cmake
+
+    pybind11_add_module(<name> [MODULE | SHARED] [EXCLUDE_FROM_ALL]
+                        [NO_EXTRAS] [THIN_LTO] [OPT_SIZE] source1 [source2 ...])
+
+This function behaves very much like CMake's builtin ``add_library`` (in fact,
+it's a wrapper function around that command). It will add a library target
+called ``<name>`` to be built from the listed source files. In addition, it
+will take care of all the Python-specific compiler and linker flags as well
+as the OS- and Python-version-specific file extension. The produced target
+``<name>`` can be further manipulated with regular CMake commands.
+
+``MODULE`` or ``SHARED`` may be given to specify the type of library. If no
+type is given, ``MODULE`` is used by default which ensures the creation of a
+Python-exclusive module. Specifying ``SHARED`` will create a more traditional
+dynamic library which can also be linked from elsewhere. ``EXCLUDE_FROM_ALL``
+removes this target from the default build (see CMake docs for details).
+
+Since pybind11 is a template library, ``pybind11_add_module`` adds compiler
+flags to ensure high quality code generation without bloat arising from long
+symbol names and duplication of code in different translation units. It
+sets default visibility to *hidden*, which is required for some pybind11
+features and functionality when attempting to load multiple pybind11 modules
+compiled under different pybind11 versions.  It also adds additional flags
+enabling LTO (Link Time Optimization) and strip unneeded symbols. See the
+:ref:`FAQ entry <faq:symhidden>` for a more detailed explanation. These
+latter optimizations are never applied in ``Debug`` mode.  If ``NO_EXTRAS`` is
+given, they will always be disabled, even in ``Release`` mode. However, this
+will result in code bloat and is generally not recommended.
+
+As stated above, LTO is enabled by default. Some newer compilers also support
+different flavors of LTO such as `ThinLTO`_. Setting ``THIN_LTO`` will cause
+the function to prefer this flavor if available. The function falls back to
+regular LTO if ``-flto=thin`` is not available. If
+``CMAKE_INTERPROCEDURAL_OPTIMIZATION`` is set (either ``ON`` or ``OFF``), then
+that will be respected instead of the built-in flag search.
+
+.. note::
+
+   If you want to set the property form on targets or the
+   ``CMAKE_INTERPROCEDURAL_OPTIMIZATION_<CONFIG>`` versions of this, you should
+   still use ``set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF)`` (otherwise a
+   no-op) to disable pybind11's ipo flags.
+
+The ``OPT_SIZE`` flag enables size-based optimization equivalent to the
+standard ``/Os`` or ``-Os`` compiler flags and the ``MinSizeRel`` build type,
+which avoid optimizations that that can substantially increase the size of the
+resulting binary. This flag is particularly useful in projects that are split
+into performance-critical parts and associated bindings. In this case, we can
+compile the project in release mode (and hence, optimize performance globally),
+and specify ``OPT_SIZE`` for the binding target, where size might be the main
+concern as performance is often less critical here. A ~25% size reduction has
+been observed in practice. This flag only changes the optimization behavior at
+a per-target level and takes precedence over the global CMake build type
+(``Release``, ``RelWithDebInfo``) except for ``Debug`` builds, where
+optimizations remain disabled.
+
+.. _ThinLTO: http://clang.llvm.org/docs/ThinLTO.html
+
+Configuration variables
+-----------------------
+
+By default, pybind11 will compile modules with the compiler default or the
+minimum standard required by pybind11, whichever is higher.  You can set the
+standard explicitly with
+`CMAKE_CXX_STANDARD <https://cmake.org/cmake/help/latest/variable/CMAKE_CXX_STANDARD.html>`_:
+
+.. code-block:: cmake
+
+    set(CMAKE_CXX_STANDARD 14 CACHE STRING "C++ version selection")  # or 11, 14, 17, 20
+    set(CMAKE_CXX_STANDARD_REQUIRED ON)  # optional, ensure standard is supported
+    set(CMAKE_CXX_EXTENSIONS OFF)  # optional, keep compiler extensions off
+
+The variables can also be set when calling CMake from the command line using
+the ``-D<variable>=<value>`` flag. You can also manually set ``CXX_STANDARD``
+on a target or use ``target_compile_features`` on your targets - anything that
+CMake supports.
+
+Classic Python support: The target Python version can be selected by setting
+``PYBIND11_PYTHON_VERSION`` or an exact Python installation can be specified
+with ``PYTHON_EXECUTABLE``.  For example:
+
+.. code-block:: bash
+
+    cmake -DPYBIND11_PYTHON_VERSION=3.6 ..
+
+    # Another method:
+    cmake -DPYTHON_EXECUTABLE=/path/to/python ..
+
+    # This often is a good way to get the current Python, works in environments:
+    cmake -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") ..
+
+
+find_package vs. add_subdirectory
+---------------------------------
+
+For CMake-based projects that don't include the pybind11 repository internally,
+an external installation can be detected through ``find_package(pybind11)``.
+See the `Config file`_ docstring for details of relevant CMake variables.
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.4...3.18)
+    project(example LANGUAGES CXX)
+
+    find_package(pybind11 REQUIRED)
+    pybind11_add_module(example example.cpp)
+
+Note that ``find_package(pybind11)`` will only work correctly if pybind11
+has been correctly installed on the system, e. g. after downloading or cloning
+the pybind11 repository  :
+
+.. code-block:: bash
+
+    # Classic CMake
+    cd pybind11
+    mkdir build
+    cd build
+    cmake ..
+    make install
+
+    # CMake 3.15+
+    cd pybind11
+    cmake -S . -B build
+    cmake --build build -j 2  # Build on 2 cores
+    cmake --install build
+
+Once detected, the aforementioned ``pybind11_add_module`` can be employed as
+before. The function usage and configuration variables are identical no matter
+if pybind11 is added as a subdirectory or found as an installed package. You
+can refer to the same [cmake_example]_ repository for a full sample project
+-- just swap out ``add_subdirectory`` for ``find_package``.
+
+.. _Config file: https://github.com/pybind/pybind11/blob/master/tools/pybind11Config.cmake.in
+
+
+.. _find-python-mode:
+
+FindPython mode
+---------------
+
+CMake 3.12+ (3.15+ recommended, 3.18.2+ ideal) added a new module called
+FindPython that had a highly improved search algorithm and modern targets
+and tools. If you use FindPython, pybind11 will detect this and use the
+existing targets instead:
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.15...3.22)
+    project(example LANGUAGES CXX)
+
+    find_package(Python 3.6 COMPONENTS Interpreter Development REQUIRED)
+    find_package(pybind11 CONFIG REQUIRED)
+    # or add_subdirectory(pybind11)
+
+    pybind11_add_module(example example.cpp)
+
+You can also use the targets (as listed below) with FindPython. If you define
+``PYBIND11_FINDPYTHON``, pybind11 will perform the FindPython step for you
+(mostly useful when building pybind11's own tests, or as a way to change search
+algorithms from the CMake invocation, with ``-DPYBIND11_FINDPYTHON=ON``.
+
+.. warning::
+
+    If you use FindPython to multi-target Python versions, use the individual
+    targets listed below, and avoid targets that directly include Python parts.
+
+There are `many ways to hint or force a discovery of a specific Python
+installation <https://cmake.org/cmake/help/latest/module/FindPython.html>`_),
+setting ``Python_ROOT_DIR`` may be the most common one (though with
+virtualenv/venv support, and Conda support, this tends to find the correct
+Python version more often than the old system did).
+
+.. warning::
+
+    When the Python libraries (i.e. ``libpythonXX.a`` and ``libpythonXX.so``
+    on Unix) are not available, as is the case on a manylinux image, the
+    ``Development`` component will not be resolved by ``FindPython``. When not
+    using the embedding functionality, CMake 3.18+ allows you to specify
+    ``Development.Module`` instead of ``Development`` to resolve this issue.
+
+.. versionadded:: 2.6
+
+Advanced: interface library targets
+-----------------------------------
+
+Pybind11 supports modern CMake usage patterns with a set of interface targets,
+available in all modes. The targets provided are:
+
+   ``pybind11::headers``
+     Just the pybind11 headers and minimum compile requirements
+
+   ``pybind11::pybind11``
+     Python headers + ``pybind11::headers``
+
+   ``pybind11::python_link_helper``
+     Just the "linking" part of pybind11:module
+
+   ``pybind11::module``
+     Everything for extension modules - ``pybind11::pybind11`` + ``Python::Module`` (FindPython CMake 3.15+) or ``pybind11::python_link_helper``
+
+   ``pybind11::embed``
+     Everything for embedding the Python interpreter - ``pybind11::pybind11`` + ``Python::Python`` (FindPython) or Python libs
+
+   ``pybind11::lto`` / ``pybind11::thin_lto``
+     An alternative to `INTERPROCEDURAL_OPTIMIZATION` for adding link-time optimization.
+
+   ``pybind11::windows_extras``
+     ``/bigobj`` and ``/mp`` for MSVC.
+
+   ``pybind11::opt_size``
+     ``/Os`` for MSVC, ``-Os`` for other compilers. Does nothing for debug builds.
+
+Two helper functions are also provided:
+
+    ``pybind11_strip(target)``
+      Strips a target (uses ``CMAKE_STRIP`` after the target is built)
+
+    ``pybind11_extension(target)``
+      Sets the correct extension (with SOABI) for a target.
+
+You can use these targets to build complex applications. For example, the
+``add_python_module`` function is identical to:
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.4)
+    project(example LANGUAGES CXX)
+
+    find_package(pybind11 REQUIRED)  # or add_subdirectory(pybind11)
+
+    add_library(example MODULE main.cpp)
+
+    target_link_libraries(example PRIVATE pybind11::module pybind11::lto pybind11::windows_extras)
+
+    pybind11_extension(example)
+    if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo)
+        # Strip unnecessary sections of the binary on Linux/macOS
+        pybind11_strip(example)
+    endif()
+
+    set_target_properties(example PROPERTIES CXX_VISIBILITY_PRESET "hidden"
+                                             CUDA_VISIBILITY_PRESET "hidden")
+
+Instead of setting properties, you can set ``CMAKE_*`` variables to initialize these correctly.
+
+.. warning::
+
+    Since pybind11 is a metatemplate library, it is crucial that certain
+    compiler flags are provided to ensure high quality code generation. In
+    contrast to the ``pybind11_add_module()`` command, the CMake interface
+    provides a *composable* set of targets to ensure that you retain flexibility.
+    It can be especially important to provide or set these properties; the
+    :ref:`FAQ <faq:symhidden>` contains an explanation on why these are needed.
+
+.. versionadded:: 2.6
+
+.. _nopython-mode:
+
+Advanced: NOPYTHON mode
+-----------------------
+
+If you want complete control, you can set ``PYBIND11_NOPYTHON`` to completely
+disable Python integration (this also happens if you run ``FindPython2`` and
+``FindPython3`` without running ``FindPython``). This gives you complete
+freedom to integrate into an existing system (like `Scikit-Build's
+<https://scikit-build.readthedocs.io>`_ ``PythonExtensions``).
+``pybind11_add_module`` and ``pybind11_extension`` will be unavailable, and the
+targets will be missing any Python specific behavior.
+
+.. versionadded:: 2.6
+
+Embedding the Python interpreter
+--------------------------------
+
+In addition to extension modules, pybind11 also supports embedding Python into
+a C++ executable or library. In CMake, simply link with the ``pybind11::embed``
+target. It provides everything needed to get the interpreter running. The Python
+headers and libraries are attached to the target. Unlike ``pybind11::module``,
+there is no need to manually set any additional properties here. For more
+information about usage in C++, see :doc:`/advanced/embedding`.
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.4...3.18)
+    project(example LANGUAGES CXX)
+
+    find_package(pybind11 REQUIRED)  # or add_subdirectory(pybind11)
+
+    add_executable(example main.cpp)
+    target_link_libraries(example PRIVATE pybind11::embed)
+
+.. _building_manually:
+
+Building manually
+=================
+
+pybind11 is a header-only library, hence it is not necessary to link against
+any special libraries and there are no intermediate (magic) translation steps.
+
+On Linux, you can compile an example such as the one given in
+:ref:`simple_example` using the following command:
+
+.. code-block:: bash
+
+    $ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)
+
+The ``python3 -m pybind11 --includes`` command fetches the include paths for
+both pybind11 and Python headers. This assumes that pybind11 has been installed
+using ``pip`` or ``conda``. If it hasn't, you can also manually specify
+``-I <path-to-pybind11>/include`` together with the Python includes path
+``python3-config --includes``.
+
+On macOS: the build command is almost the same but it also requires passing
+the ``-undefined dynamic_lookup`` flag so as to ignore missing symbols when
+building the module:
+
+.. code-block:: bash
+
+    $ c++ -O3 -Wall -shared -std=c++11 -undefined dynamic_lookup $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)
+
+In general, it is advisable to include several additional build parameters
+that can considerably reduce the size of the created binary. Refer to section
+:ref:`cmake` for a detailed example of a suitable cross-platform CMake-based
+build system that works on all platforms including Windows.
+
+.. note::
+
+    On Linux and macOS, it's better to (intentionally) not link against
+    ``libpython``. The symbols will be resolved when the extension library
+    is loaded into a Python binary. This is preferable because you might
+    have several different installations of a given Python version (e.g. the
+    system-provided Python, and one that ships with a piece of commercial
+    software). In this way, the plugin will work with both versions, instead
+    of possibly importing a second Python library into a process that already
+    contains one (which will lead to a segfault).
+
+
+Building with Bazel
+===================
+
+You can build with the Bazel build system using the `pybind11_bazel
+<https://github.com/pybind/pybind11_bazel>`_ repository.
+
+Generating binding code automatically
+=====================================
+
+The ``Binder`` project is a tool for automatic generation of pybind11 binding
+code by introspecting existing C++ codebases using LLVM/Clang. See the
+[binder]_ documentation for details.
+
+.. [binder] http://cppbinder.readthedocs.io/en/latest/about.html
+
+[AutoWIG]_ is a Python library that wraps automatically compiled libraries into
+high-level languages. It parses C++ code using LLVM/Clang technologies and
+generates the wrappers using the Mako templating engine. The approach is automatic,
+extensible, and applies to very complex C++ libraries, composed of thousands of
+classes or incorporating modern meta-programming constructs.
+
+.. [AutoWIG] https://github.com/StatisKit/AutoWIG
+
+[robotpy-build]_ is a is a pure python, cross platform build tool that aims to
+simplify creation of python wheels for pybind11 projects, and provide
+cross-project dependency management. Additionally, it is able to autogenerate
+customizable pybind11-based wrappers by parsing C++ header files.
+
+.. [robotpy-build] https://robotpy-build.readthedocs.io
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/conf.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/conf.py
new file mode 100644
index 000000000..2da6773f4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/conf.py
@@ -0,0 +1,369 @@
+#!/usr/bin/env python3
+#
+# pybind11 documentation build configuration file, created by
+# sphinx-quickstart on Sun Oct 11 19:23:48 2015.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import os
+import re
+import subprocess
+import sys
+from pathlib import Path
+
+DIR = Path(__file__).parent.resolve()
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+# sys.path.insert(0, os.path.abspath('.'))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "breathe",
+    "sphinx_copybutton",
+    "sphinxcontrib.rsvgconverter",
+    "sphinxcontrib.moderncmakedomain",
+]
+
+breathe_projects = {"pybind11": ".build/doxygenxml/"}
+breathe_default_project = "pybind11"
+breathe_domain_by_extension = {"h": "cpp"}
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = [".templates"]
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+# source_suffix = ['.rst', '.md']
+source_suffix = ".rst"
+
+# The encoding of source files.
+# source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = "index"
+
+# General information about the project.
+project = "pybind11"
+copyright = "2017, Wenzel Jakob"
+author = "Wenzel Jakob"
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+
+# Read the listed version
+with open("../pybind11/_version.py") as f:
+    code = compile(f.read(), "../pybind11/_version.py", "exec")
+loc = {}
+exec(code, loc)
+
+# The full version, including alpha/beta/rc tags.
+version = loc["__version__"]
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+# today = ''
+# Else, today_fmt is used as the format for a strftime call.
+# today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = [".build", "release.rst"]
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+default_role = "any"
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+# add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+# add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+# show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+# pygments_style = 'monokai'
+
+# A list of ignored prefixes for module index sorting.
+# modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+# keep_warnings = False
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+
+html_theme = "furo"
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+# html_theme_options = {}
+
+# Add any paths that contain custom themes here, relative to this directory.
+# html_theme_path = []
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<version> documentation".
+# html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+# html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+# html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+# html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+html_css_files = [
+    "css/custom.css",
+]
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+# html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+# html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+# html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+# html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+# html_additional_pages = {}
+
+# If false, no module index is generated.
+# html_domain_indices = True
+
+# If false, no index is generated.
+# html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+# html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+# html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+# html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+# html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+# html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+# html_file_suffix = None
+
+# Language to be used for generating the HTML full-text search index.
+# Sphinx supports the following languages:
+#   'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja'
+#   'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr'
+# html_search_language = 'en'
+
+# A dictionary with options for the search language support, empty by default.
+# Now only 'ja' uses this config value
+# html_search_options = {'type': 'default'}
+
+# The name of a javascript file (relative to the configuration directory) that
+# implements a search results scorer. If empty, the default will be used.
+# html_search_scorer = 'scorer.js'
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "pybind11doc"
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_engine = "pdflatex"
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    # 'papersize': 'letterpaper',
+    #
+    # The font size ('10pt', '11pt' or '12pt').
+    # 'pointsize': '10pt',
+    #
+    # Additional stuff for the LaTeX preamble.
+    # remove blank pages (between the title page and the TOC, etc.)
+    "classoptions": ",openany,oneside",
+    "preamble": r"""
+\usepackage{fontawesome}
+\usepackage{textgreek}
+\DeclareUnicodeCharacter{00A0}{}
+\DeclareUnicodeCharacter{2194}{\faArrowsH}
+\DeclareUnicodeCharacter{1F382}{\faBirthdayCake}
+\DeclareUnicodeCharacter{1F355}{\faAdjust}
+\DeclareUnicodeCharacter{0301}{'}
+\DeclareUnicodeCharacter{03C0}{\textpi}
+
+""",
+    # Latex figure (float) alignment
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (master_doc, "pybind11.tex", "pybind11 Documentation", "Wenzel Jakob", "manual"),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+# latex_logo = 'pybind11-logo.png'
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+# latex_use_parts = False
+
+# If true, show page references after internal links.
+# latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+# latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+# latex_appendices = []
+
+# If false, no module index is generated.
+# latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [(master_doc, "pybind11", "pybind11 Documentation", [author], 1)]
+
+# If true, show URL addresses after external links.
+# man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (
+        master_doc,
+        "pybind11",
+        "pybind11 Documentation",
+        author,
+        "pybind11",
+        "One line description of project.",
+        "Miscellaneous",
+    ),
+]
+
+# Documents to append as an appendix to all manuals.
+# texinfo_appendices = []
+
+# If false, no module index is generated.
+# texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+# texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+# texinfo_no_detailmenu = False
+
+primary_domain = "cpp"
+highlight_language = "cpp"
+
+
+def generate_doxygen_xml(app):
+    build_dir = os.path.join(app.confdir, ".build")
+    if not os.path.exists(build_dir):
+        os.mkdir(build_dir)
+
+    try:
+        subprocess.call(["doxygen", "--version"])
+        retcode = subprocess.call(["doxygen"], cwd=app.confdir)
+        if retcode < 0:
+            sys.stderr.write(f"doxygen error code: {-retcode}\n")
+    except OSError as e:
+        sys.stderr.write(f"doxygen execution failed: {e}\n")
+
+
+def prepare(app):
+    with open(DIR.parent / "README.rst") as f:
+        contents = f.read()
+
+    if app.builder.name == "latex":
+        # Remove badges and stuff from start
+        contents = contents[contents.find(r".. start") :]
+
+        # Filter out section titles for index.rst for LaTeX
+        contents = re.sub(r"^(.*)\n[-~]{3,}$", r"**\1**", contents, flags=re.MULTILINE)
+
+    with open(DIR / "readme.rst", "w") as f:
+        f.write(contents)
+
+
+def clean_up(app, exception):
+    (DIR / "readme.rst").unlink()
+
+
+def setup(app):
+
+    # Add hook for building doxygen xml when needed
+    app.connect("builder-inited", generate_doxygen_xml)
+
+    # Copy the readme in
+    app.connect("builder-inited", prepare)
+
+    # Clean up the generated readme
+    app.connect("build-finished", clean_up)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/faq.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/faq.rst
new file mode 100644
index 000000000..28498e7df
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/faq.rst
@@ -0,0 +1,307 @@
+Frequently asked questions
+##########################
+
+"ImportError: dynamic module does not define init function"
+===========================================================
+
+1. Make sure that the name specified in PYBIND11_MODULE is identical to the
+filename of the extension library (without suffixes such as ``.so``).
+
+2. If the above did not fix the issue, you are likely using an incompatible
+version of Python that does not match what you compiled with.
+
+"Symbol not found: ``__Py_ZeroStruct`` / ``_PyInstanceMethod_Type``"
+========================================================================
+
+See the first answer.
+
+"SystemError: dynamic module not initialized properly"
+======================================================
+
+See the first answer.
+
+The Python interpreter immediately crashes when importing my module
+===================================================================
+
+See the first answer.
+
+.. _faq_reference_arguments:
+
+Limitations involving reference arguments
+=========================================
+
+In C++, it's fairly common to pass arguments using mutable references or
+mutable pointers, which allows both read and write access to the value
+supplied by the caller. This is sometimes done for efficiency reasons, or to
+realize functions that have multiple return values. Here are two very basic
+examples:
+
+.. code-block:: cpp
+
+    void increment(int &i) { i++; }
+    void increment_ptr(int *i) { (*i)++; }
+
+In Python, all arguments are passed by reference, so there is no general
+issue in binding such code from Python.
+
+However, certain basic Python types (like ``str``, ``int``, ``bool``,
+``float``, etc.) are **immutable**. This means that the following attempt
+to port the function to Python doesn't have the same effect on the value
+provided by the caller -- in fact, it does nothing at all.
+
+.. code-block:: python
+
+    def increment(i):
+        i += 1  # nope..
+
+pybind11 is also affected by such language-level conventions, which means that
+binding ``increment`` or ``increment_ptr`` will also create Python functions
+that don't modify their arguments.
+
+Although inconvenient, one workaround is to encapsulate the immutable types in
+a custom type that does allow modifications.
+
+An other alternative involves binding a small wrapper lambda function that
+returns a tuple with all output arguments (see the remainder of the
+documentation for examples on binding lambda functions). An example:
+
+.. code-block:: cpp
+
+    int foo(int &i) { i++; return 123; }
+
+and the binding code
+
+.. code-block:: cpp
+
+   m.def("foo", [](int i) { int rv = foo(i); return std::make_tuple(rv, i); });
+
+
+How can I reduce the build time?
+================================
+
+It's good practice to split binding code over multiple files, as in the
+following example:
+
+:file:`example.cpp`:
+
+.. code-block:: cpp
+
+    void init_ex1(py::module_ &);
+    void init_ex2(py::module_ &);
+    /* ... */
+
+    PYBIND11_MODULE(example, m) {
+        init_ex1(m);
+        init_ex2(m);
+        /* ... */
+    }
+
+:file:`ex1.cpp`:
+
+.. code-block:: cpp
+
+    void init_ex1(py::module_ &m) {
+        m.def("add", [](int a, int b) { return a + b; });
+    }
+
+:file:`ex2.cpp`:
+
+.. code-block:: cpp
+
+    void init_ex2(py::module_ &m) {
+        m.def("sub", [](int a, int b) { return a - b; });
+    }
+
+:command:`python`:
+
+.. code-block:: pycon
+
+    >>> import example
+    >>> example.add(1, 2)
+    3
+    >>> example.sub(1, 1)
+    0
+
+As shown above, the various ``init_ex`` functions should be contained in
+separate files that can be compiled independently from one another, and then
+linked together into the same final shared object.  Following this approach
+will:
+
+1. reduce memory requirements per compilation unit.
+
+2. enable parallel builds (if desired).
+
+3. allow for faster incremental builds. For instance, when a single class
+   definition is changed, only a subset of the binding code will generally need
+   to be recompiled.
+
+"recursive template instantiation exceeded maximum depth of 256"
+================================================================
+
+If you receive an error about excessive recursive template evaluation, try
+specifying a larger value, e.g. ``-ftemplate-depth=1024`` on GCC/Clang. The
+culprit is generally the generation of function signatures at compile time
+using C++14 template metaprogramming.
+
+.. _`faq:hidden_visibility`:
+
+"'SomeClass' declared with greater visibility than the type of its field 'SomeClass::member' [-Wattributes]"
+============================================================================================================
+
+This error typically indicates that you are compiling without the required
+``-fvisibility`` flag.  pybind11 code internally forces hidden visibility on
+all internal code, but if non-hidden (and thus *exported*) code attempts to
+include a pybind type (for example, ``py::object`` or ``py::list``) you can run
+into this warning.
+
+To avoid it, make sure you are specifying ``-fvisibility=hidden`` when
+compiling pybind code.
+
+As to why ``-fvisibility=hidden`` is necessary, because pybind modules could
+have been compiled under different versions of pybind itself, it is also
+important that the symbols defined in one module do not clash with the
+potentially-incompatible symbols defined in another.  While Python extension
+modules are usually loaded with localized symbols (under POSIX systems
+typically using ``dlopen`` with the ``RTLD_LOCAL`` flag), this Python default
+can be changed, but even if it isn't it is not always enough to guarantee
+complete independence of the symbols involved when not using
+``-fvisibility=hidden``.
+
+Additionally, ``-fvisibility=hidden`` can deliver considerably binary size
+savings. (See the following section for more details.)
+
+
+.. _`faq:symhidden`:
+
+How can I create smaller binaries?
+==================================
+
+To do its job, pybind11 extensively relies on a programming technique known as
+*template metaprogramming*, which is a way of performing computation at compile
+time using type information. Template metaprogramming usually instantiates code
+involving significant numbers of deeply nested types that are either completely
+removed or reduced to just a few instructions during the compiler's optimization
+phase. However, due to the nested nature of these types, the resulting symbol
+names in the compiled extension library can be extremely long. For instance,
+the included test suite contains the following symbol:
+
+.. only:: html
+
+    .. code-block:: none
+
+        _​_​Z​N​8​p​y​b​i​n​d​1​1​1​2​c​p​p​_​f​u​n​c​t​i​o​n​C​1​I​v​8​E​x​a​m​p​l​e​2​J​R​N​S​t​3​_​_​1​6​v​e​c​t​o​r​I​N​S​3​_​1​2​b​a​s​i​c​_​s​t​r​i​n​g​I​w​N​S​3​_​1​1​c​h​a​r​_​t​r​a​i​t​s​I​w​E​E​N​S​3​_​9​a​l​l​o​c​a​t​o​r​I​w​E​E​E​E​N​S​8​_​I​S​A​_​E​E​E​E​E​J​N​S​_​4​n​a​m​e​E​N​S​_​7​s​i​b​l​i​n​g​E​N​S​_​9​i​s​_​m​e​t​h​o​d​E​A​2​8​_​c​E​E​E​M​T​0​_​F​T​_​D​p​T​1​_​E​D​p​R​K​T​2​_
+
+.. only:: not html
+
+    .. code-block:: cpp
+
+        __ZN8pybind1112cpp_functionC1Iv8Example2JRNSt3__16vectorINS3_12basic_stringIwNS3_11char_traitsIwEENS3_9allocatorIwEEEENS8_ISA_EEEEEJNS_4nameENS_7siblingENS_9is_methodEA28_cEEEMT0_FT_DpT1_EDpRKT2_
+
+which is the mangled form of the following function type:
+
+.. code-block:: cpp
+
+    pybind11::cpp_function::cpp_function<void, Example2, std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&, pybind11::name, pybind11::sibling, pybind11::is_method, char [28]>(void (Example2::*)(std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&), pybind11::name const&, pybind11::sibling const&, pybind11::is_method const&, char const (&) [28])
+
+The memory needed to store just the mangled name of this function (196 bytes)
+is larger than the actual piece of code (111 bytes) it represents! On the other
+hand, it's silly to even give this function a name -- after all, it's just a
+tiny cog in a bigger piece of machinery that is not exposed to the outside
+world. So we'll generally only want to export symbols for those functions which
+are actually called from the outside.
+
+This can be achieved by specifying the parameter ``-fvisibility=hidden`` to GCC
+and Clang, which sets the default symbol visibility to *hidden*, which has a
+tremendous impact on the final binary size of the resulting extension library.
+(On Visual Studio, symbols are already hidden by default, so nothing needs to
+be done there.)
+
+In addition to decreasing binary size, ``-fvisibility=hidden`` also avoids
+potential serious issues when loading multiple modules and is required for
+proper pybind operation.  See the previous FAQ entry for more details.
+
+How can I properly handle Ctrl-C in long-running functions?
+===========================================================
+
+Ctrl-C is received by the Python interpreter, and holds it until the GIL
+is released, so a long-running function won't be interrupted.
+
+To interrupt from inside your function, you can use the ``PyErr_CheckSignals()``
+function, that will tell if a signal has been raised on the Python side.  This
+function merely checks a flag, so its impact is negligible. When a signal has
+been received, you must either explicitly interrupt execution by throwing
+``py::error_already_set`` (which will propagate the existing
+``KeyboardInterrupt``), or clear the error (which you usually will not want):
+
+.. code-block:: cpp
+
+    PYBIND11_MODULE(example, m)
+    {
+        m.def("long running_func", []()
+        {
+            for (;;) {
+                if (PyErr_CheckSignals() != 0)
+                    throw py::error_already_set();
+                // Long running iteration
+            }
+        });
+    }
+
+CMake doesn't detect the right Python version
+=============================================
+
+The CMake-based build system will try to automatically detect the installed
+version of Python and link against that. When this fails, or when there are
+multiple versions of Python and it finds the wrong one, delete
+``CMakeCache.txt`` and then add ``-DPYTHON_EXECUTABLE=$(which python)`` to your
+CMake configure line. (Replace ``$(which python)`` with a path to python if
+your prefer.)
+
+You can alternatively try ``-DPYBIND11_FINDPYTHON=ON``, which will activate the
+new CMake FindPython support instead of pybind11's custom search. Requires
+CMake 3.12+, and 3.15+ or 3.18.2+ are even better. You can set this in your
+``CMakeLists.txt`` before adding or finding pybind11, as well.
+
+Inconsistent detection of Python version in CMake and pybind11
+==============================================================
+
+The functions ``find_package(PythonInterp)`` and ``find_package(PythonLibs)``
+provided by CMake for Python version detection are modified by pybind11 due to
+unreliability and limitations that make them unsuitable for pybind11's needs.
+Instead pybind11 provides its own, more reliable Python detection CMake code.
+Conflicts can arise, however, when using pybind11 in a project that *also* uses
+the CMake Python detection in a system with several Python versions installed.
+
+This difference may cause inconsistencies and errors if *both* mechanisms are
+used in the same project.
+
+There are three possible solutions:
+
+1. Avoid using ``find_package(PythonInterp)`` and ``find_package(PythonLibs)``
+   from CMake and rely on pybind11 in detecting Python version. If this is not
+   possible, the CMake machinery should be called *before* including pybind11.
+2. Set ``PYBIND11_FINDPYTHON`` to ``True`` or use ``find_package(Python
+   COMPONENTS Interpreter Development)`` on modern CMake (3.12+, 3.15+ better,
+   3.18.2+ best). Pybind11 in these cases uses the new CMake FindPython instead
+   of the old, deprecated search tools, and these modules are much better at
+   finding the correct Python.
+3. Set ``PYBIND11_NOPYTHON`` to ``TRUE``. Pybind11 will not search for Python.
+   However, you will have to use the target-based system, and do more setup
+   yourself, because it does not know about or include things that depend on
+   Python, like ``pybind11_add_module``. This might be ideal for integrating
+   into an existing system, like scikit-build's Python helpers.
+
+How to cite this project?
+=========================
+
+We suggest the following BibTeX template to cite pybind11 in scientific
+discourse:
+
+.. code-block:: bash
+
+    @misc{pybind11,
+       author = {Wenzel Jakob and Jason Rhinelander and Dean Moldovan},
+       year = {2017},
+       note = {https://github.com/pybind/pybind11},
+       title = {pybind11 -- Seamless operability between C++11 and Python}
+    }
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/index.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/index.rst
new file mode 100644
index 000000000..4e2e8ca3a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/index.rst
@@ -0,0 +1,48 @@
+.. only:: latex
+
+   Intro
+   =====
+
+.. include:: readme.rst
+
+.. only:: not latex
+
+    Contents:
+
+.. toctree::
+   :maxdepth: 1
+
+   changelog
+   upgrade
+
+.. toctree::
+   :caption: The Basics
+   :maxdepth: 2
+
+   installing
+   basics
+   classes
+   compiling
+
+.. toctree::
+   :caption: Advanced Topics
+   :maxdepth: 2
+
+   advanced/functions
+   advanced/classes
+   advanced/exceptions
+   advanced/smart_ptrs
+   advanced/cast/index
+   advanced/pycpp/index
+   advanced/embedding
+   advanced/misc
+
+.. toctree::
+   :caption: Extra Information
+   :maxdepth: 1
+
+   faq
+   benchmark
+   limitations
+   reference
+   cmake/index
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/installing.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/installing.rst
new file mode 100644
index 000000000..30b9f1853
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/installing.rst
@@ -0,0 +1,105 @@
+.. _installing:
+
+Installing the library
+######################
+
+There are several ways to get the pybind11 source, which lives at
+`pybind/pybind11 on GitHub <https://github.com/pybind/pybind11>`_. The pybind11
+developers recommend one of the first three ways listed here, submodule, PyPI,
+or conda-forge, for obtaining pybind11.
+
+.. _include_as_a_submodule:
+
+Include as a submodule
+======================
+
+When you are working on a project in Git, you can use the pybind11 repository
+as a submodule. From your git repository, use:
+
+.. code-block:: bash
+
+    git submodule add -b stable ../../pybind/pybind11 extern/pybind11
+    git submodule update --init
+
+This assumes you are placing your dependencies in ``extern/``, and that you are
+using GitHub; if you are not using GitHub, use the full https or ssh URL
+instead of the relative URL ``../../pybind/pybind11`` above. Some other servers
+also require the ``.git`` extension (GitHub does not).
+
+From here, you can now include ``extern/pybind11/include``, or you can use
+the various integration tools (see :ref:`compiling`) pybind11 provides directly
+from the local folder.
+
+Include with PyPI
+=================
+
+You can download the sources and CMake files as a Python package from PyPI
+using Pip. Just use:
+
+.. code-block:: bash
+
+    pip install pybind11
+
+This will provide pybind11 in a standard Python package format. If you want
+pybind11 available directly in your environment root, you can use:
+
+.. code-block:: bash
+
+    pip install "pybind11[global]"
+
+This is not recommended if you are installing with your system Python, as it
+will add files to ``/usr/local/include/pybind11`` and
+``/usr/local/share/cmake/pybind11``, so unless that is what you want, it is
+recommended only for use in virtual environments or your ``pyproject.toml``
+file (see :ref:`compiling`).
+
+Include with conda-forge
+========================
+
+You can use pybind11 with conda packaging via `conda-forge
+<https://github.com/conda-forge/pybind11-feedstock>`_:
+
+.. code-block:: bash
+
+    conda install -c conda-forge pybind11
+
+
+Include with vcpkg
+==================
+You can download and install pybind11 using the Microsoft `vcpkg
+<https://github.com/Microsoft/vcpkg/>`_ dependency manager:
+
+.. code-block:: bash
+
+    git clone https://github.com/Microsoft/vcpkg.git
+    cd vcpkg
+    ./bootstrap-vcpkg.sh
+    ./vcpkg integrate install
+    vcpkg install pybind11
+
+The pybind11 port in vcpkg is kept up to date by Microsoft team members and
+community contributors. If the version is out of date, please `create an issue
+or pull request <https://github.com/Microsoft/vcpkg/>`_ on the vcpkg
+repository.
+
+Global install with brew
+========================
+
+The brew package manager (Homebrew on macOS, or Linuxbrew on Linux) has a
+`pybind11 package
+<https://github.com/Homebrew/homebrew-core/blob/master/Formula/pybind11.rb>`_.
+To install:
+
+.. code-block:: bash
+
+    brew install pybind11
+
+.. We should list Conan, and possibly a few other C++ package managers (hunter,
+.. perhaps). Conan has a very clean CMake integration that would be good to show.
+
+Other options
+=============
+
+Other locations you can find pybind11 are `listed here
+<https://repology.org/project/python:pybind11/versions>`_; these are maintained
+by various packagers and the community.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/limitations.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/limitations.rst
new file mode 100644
index 000000000..def5ad659
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/limitations.rst
@@ -0,0 +1,72 @@
+Limitations
+###########
+
+Design choices
+^^^^^^^^^^^^^^
+
+pybind11 strives to be a general solution to binding generation, but it also has
+certain limitations:
+
+- pybind11 casts away ``const``-ness in function arguments and return values.
+  This is in line with the Python language, which has no concept of ``const``
+  values. This means that some additional care is needed to avoid bugs that
+  would be caught by the type checker in a traditional C++ program.
+
+- The NumPy interface ``pybind11::array`` greatly simplifies accessing
+  numerical data from C++ (and vice versa), but it's not a full-blown array
+  class like ``Eigen::Array`` or ``boost.multi_array``. ``Eigen`` objects are
+  directly supported, however, with ``pybind11/eigen.h``.
+
+Large but useful features could be implemented in pybind11 but would lead to a
+significant increase in complexity. Pybind11 strives to be simple and compact.
+Users who require large new features are encouraged to write an extension to
+pybind11; see `pybind11_json <https://github.com/pybind/pybind11_json>`_ for an
+example.
+
+
+Known bugs
+^^^^^^^^^^
+
+These are issues that hopefully will one day be fixed, but currently are
+unsolved. If you know how to help with one of these issues, contributions
+are welcome!
+
+- Intel 20.2 is currently having an issue with the test suite.
+  `#2573 <https://github.com/pybind/pybind11/pull/2573>`_
+
+- Debug mode Python does not support 1-5 tests in the test suite currently.
+  `#2422 <https://github.com/pybind/pybind11/pull/2422>`_
+
+- PyPy3 7.3.1 and 7.3.2 have issues with several tests on 32-bit Windows.
+
+Known limitations
+^^^^^^^^^^^^^^^^^
+
+These are issues that are probably solvable, but have not been fixed yet. A
+clean, well written patch would likely be accepted to solve them.
+
+- Type casters are not kept alive recursively.
+  `#2527 <https://github.com/pybind/pybind11/issues/2527>`_
+  One consequence is that containers of ``char *`` are currently not supported.
+  `#2245 <https://github.com/pybind/pybind11/issues/2245>`_
+
+- The ``cpptest`` does not run on Windows with Python 3.8 or newer, due to DLL
+  loader changes. User code that is correctly installed should not be affected.
+  `#2560 <https://github.com/pybind/pybind11/issue/2560>`_
+
+Python 3.9.0 warning
+^^^^^^^^^^^^^^^^^^^^
+
+Combining older versions of pybind11 (< 2.6.0) with Python on exactly 3.9.0
+will trigger undefined behavior that typically manifests as crashes during
+interpreter shutdown (but could also destroy your data. **You have been
+warned**).
+
+This issue was `fixed in Python <https://github.com/python/cpython/pull/22670>`_.
+As a mitigation for this bug, pybind11 2.6.0 or newer includes a workaround
+specifically when Python 3.9.0 is detected at runtime, leaking about 50 bytes
+of memory when a callback function is garbage collected.  For reference, the
+pybind11 test suite has about 2,000 such callbacks, but only 49 are garbage
+collected before the end-of-process. Wheels (even if built with Python 3.9.0)
+will correctly avoid the leak when run in Python 3.9.1, and this does not
+affect other 3.X versions.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11-logo.png b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11-logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..2d633a4d0c129d6bdd94b4134c9516fdc92bb192
GIT binary patch
literal 61034
zcmeGE<9BA=6D@$ow$(w$9XlPnW81cE8=Z7)vt!#G+jcs(-FN5xopbJga6jEKo+lsn
zSkFS$teRDG?U4w1*&m2-cyJ&fAczv;B8ng&5LO@{pz<(Kz!R8?TV)Us%rBnG>duM=
zZp8MEcBU5ACdAGj_9nz8?iQvXAnvP`>1K6g-yMVh9a6hPl=^eZ3Zz{_@$m6a{l+3q
zZZ{hAoqJbSp+MQ$0sB7nob#`1%I~@)nlRh2a!J8zTMpNR>@bRu&u{S64&m?n<Ga$~
zYgX64{wbo@Ym;541E-I-{O3a_XJ6ay&8s~^hmY$+xqlxN_m?4E-A@j^72K3v%iRp|
zIVDq5M$fwDnV0cD?7A6Til+|meRkRtFSph=rT%q<clr7L6SPpaz^@*q&LVJf|3{aD
zxj&`1H1*8!w{+=x$2*6JFX_#w8e<=M?e+S5@D}`C*w=qgC(lmzV=o^saszT7KB6B=
zbf?W?-q+3D;SY}keo}91F}G8gMf~r(f9_Jwz5i{tzjhwhb2B7GE;8iz-4<|pA~R%K
z(|6mId$WGv=`}wZUCjiAi8{He8{E|HhDV%iQ6{$(4B=0`dJGJpNpY23e=ibsx~tFD
z!n2qmYAcO(R~jR|Tt8luJRC_QD#@{mxbzwF>c-@D+x(SMJUp<wcIJ0|{ifIbMoBc)
zdEyrRjiNF(2Z|Ci)1=htB}Hu}&b`~GzPX2=uj?GS8x+2DR?ur~eI+;Sx%E%O6TH^*
zxzmBm;2{GxkBuKgPhd-PZEEVVKq-?}1ye(!X7qgR9I@G7>J-*v0amFD(c*X~896E5
z<IRqefyc%XMVS|-^}_2BHV#jgy15EPgtvy--Z-r$L(_%y`=L;eiALq}#-?xax5-)-
zi<VZE%`+W`x^4HXT9-b~L>9)J(_&w2?wCToJTvjXFFER7z8M#%nWeFxTGvl8`GDrA
zBv-bro><XtSsWtDoVBf8cv<sbe`g$^9+Krfzhn8SSL1q1w`ALS{=i-J?`*ZX-0%4b
zS|@6&s;F7XQ|EPs&G)6@VGm8g+3*DSixm0nbukjiQDJrnp_If)rYDw|Ar)<{xRPD=
zmjxzGbsVquYqLvVmka)rHKTX_6Rj7wEU)>tf3cIi4HwluuNhS~$LpTl4{1URtj5K$
zTR9hUC-e%pevi-AE%ocVK2s*=vpEZ+X(eHfR*}fw8gdg6dK7MU4ou8$IjlC#O?b=K
zi|6$B{bn0GbtLSYL+Kv(il(E7%jjwoC5s&l5O~Oqwd#d?&i!sbUDg`8oYLZUXkE_a
zk$APsvN;dwqVU+bt!)lr)9S8T)oMEo5rbwkVhNy9qUxRfji}{}Gen#89$)UCpM4Fu
zH<B*JgnH00i{CRcJiET1&kc4mV}axJ(ePG6(ji4^dktSs*F}3cc4onobE76#m1lN!
z7V@4ie{*YU+<xnd6hFRe)MaVdVv|3i5oj8e1&KziHn?aq)vG_qARyQ9^}g;qPx>d1
z2I4go-M=U(U=NuuM9+Txqki2K_GuA+ark+EJcPE)Kon8`sK<M4rTX%BWtW?Htv#DH
zbp7&@kL&S825M8YDTyije$&~$LH&I|z9s>6lg0C<1m?{O+j$?Fr%jrH3FyZ<vn@l+
zE<&7AYS`R2hIo(Tv)s%C6xEh}`Wn-eqV-fkipw-*doOUx_lVnzzJ|dDccNRARm6@g
zTnE!g!)hBJX5Xfk9&L>6ngjOZ11Qz2-kWv8U(;YiW&TI#ZSy;4tg?pq&2D2R6D>^o
z_b;>pqfSJiw8*sBc$41yJQsT;_~Hw%@c{u@n$+!-3I0JZsPNGkX}b+#!w6sIbS!mO
zXxy|R>A_)NSMaj`qz<5~<J`I!c4w)vq{RJN)3^2HUoTdLF?!InHDwgzuV>Q!d*+(f
z_-sjRy}Njg)8vyR7SO@KP{L@NYKK$N&XiJ7?sa%t%PmH2>klOrLnkolPJfWB3mUDQ
zMg=w1!<8ElPSbJNWb{|l3#PVr=y%f27<Ti#Y<*+Snj05mC?-2EZOoV@cT#IJ35fuL
zSQhy5eA1YCgJotdWGR<qK6Ki}ny*;P;k>-5$t%Lb3i^-_a;C!&)J%C_X;CHfvF`y`
z4Et<dz5HN=I7kNLX}=Y2v;*~ushWb)B#!_TG&@X(H#nWYIX8p+AKiE!Lv#Ni7oqtS
zI->CNho&#e1r$`nhF9#uTdYbO$4hY2n>cF0Slme2FZfM`mGIRK+3DLdT<NGqoEW#$
z?h*2MAO}5QJtT!5J=_@G{@7#Etv@A<{UveRu}subo;?X%F-#nwkGO>KSZN>It@X?Y
z0x--GX>r`$kIb07SBf9gdxXn=&F{YRxiH}TJF6Pe!}Jcf=3#esRCS!(-i4)st0XpW
zfMLw+g$c>>u8pv$`Qt5csNDZ5ZX-2I3g(ojoB51ZAo@)jtKD8m{K!(riX;N0dZTbr
z?`Z!C+)98;1Hs0G)~JT^i}CCb$i160)tfZ6bQ`A5M8;NCF@upWCM=lLGj$x68;t2R
zYyqb_ecR7MZT4wLog*yyQP@-Ynf+{vrAum0pS*m>*<mw8__#Z~5!EVd^dQ1{uhj+U
z0FZb78G}5TW9nee2TKi~Cb!MZ#@PEirK|ArreRmzpP`Q&1o$`1(PHF;@=I}5t=?#g
z&4(@<#2`4HrD`I658us*QcG7Mm&e*Q?4qxX!&2~w6d_o*@!-N8^w`0ZOR>R{MC6HC
zzC|kT)=Y`~<nX#5zTBo7KIo~)B{=`m+Sk}S*Og0j$H|!~q58EurUgwL%6&JP8BI2x
ziDCM2V%#)95fzj<G>L+~PgDS2#TX*2Pkm@i{vJ2z$5EI8e1-amGmNZo3H3T(O;20L
zumOQ(I6P7Z9c%DaeYCZe<)gHqdy`mYS!#>$a7;8?DhjePdOhg#a3EPYcC`^B#jbs$
zJsNB|LYKMP$vzteqodjf_>jr0ur&gf|AmntHO&nsd$3rf1#Ei?)vZY^)OVsyP1Z}C
z2cePEaI$+Zc}8pMGxS%!+JO6Ze0J#;q;$wLi6LtG4)p$(Oi+kj>q>saqupxypQ&Y`
z@7Ez2jNmnFly~(8?3RxeS8xyCwjMVQ{A2J+FYfMTjTRYMN+~cFrwN8Tv1Js8)|#c-
zORUIrBl_g*4z6kn(LhtjcsLLYF67UN1<VeNcFspMnFPJeHv@fY^Dq?A$>DZm7+JB{
zZ?p!7D*X$K>nhlC3PZ=dYxTBqh;;_FSN%2n`U>q_&++*~y*PTX0x}_#p(vR)Q#)E#
zl^pHjjosC-4eiKt?~m`RO2Oe^D<3#An|laGs`fh-zGx2wA4h+u-}@ynp-~Gp43-h@
zSORmYxmQ7cPtw=GjV-!lk)nwdrj`zZtD1qnNyWm)DO(eUWth<NN9;Vzea&u-a%>7x
zVnfymGLxKT3wv?iU>%82Uf($#l1Ga9mpym~IH*b7pF9XwH2C=uMR-S0pV{8ZkzVXb
zVI*8w6Z~<4Ko|UPH@4wcIm2J5>~59Q!*~xY)!CA8x!j6nf|g`P;ymnk*`UbAOc<+U
z>4p8jQtrKmTR&;hv*E5+RPK;t59Vyw;?0YLUJqh?9eM0S_5Jzcd|7al9ZM>bi2B%p
zebs5Ganh;}K~RiK@pgJ0VR-fRUnt45AO=3mo&2}K-QE2<YwD+xaIPZ3Y+}E0t-(X1
zUo9l!VH6Zto9fDImu4kkHRA~;LSBPNyB?ZDFTWt)Kpd3w$LM#W^b*!dbi%k~Bitbo
zj!7n~Cfs*P#z;p<0umRM>59o~sKXOypoI7F4nPgG-H$mP=&bQ}`XBUTbN?|h{(#X8
z5K{*o+kky%35|x4NUFgdpYL5vp(3Qn*)4EQnj*&TUUjm6$Cgy{SYn~#LIQm$k}#;t
z>A?MAN%h@8WSSaJZhY=Es*l0g8OvW#Gj&@&qACPpWMYbVQ0ETbC|tr4$tn}p&K@!v
zgVAug<yobTc)i)7RBlJq7*P=`ZJWmhjxSm^vlLzsRk>Ep5T}3<mHq&iVO=Cm>lX%T
z=x}fLO{=|W;8ge<a$3MFCfEy=TaO*4<&v?2zv=S;*L&}Q;J);Cl_1MhY&xGHv5>26
z5XWCesno86h=cmXI1#wLp}EfruYUykLE<pH1qK7k!V*2e6t#Op5+!~-O>KDRP0jPD
zNe-HWRtFf_g1;Y`Q`|-FaGkiGi@R+x(v!xy(S*}IaJr@lD86&pnC1_Qdi0QSWJKo9
z66!jkN12{Ez|5!1lg4JOfETsX@k+uXruV9o_&M^Cw<YVnRpH7{Ao)Wb{7}J02`^8y
zVP=}u#}m}prHD*7q-FP44|fS#g}Rf5Rm-w#5-W=k;d&%qBIe6-#9x~;;Ds82DopL)
z!>(SGczJ?*Gord-K~TZnj6#Q*IUm`sBZNY*jmG{H&GDm7!Cv$#ije3<=-am|s*9h)
z7xeYJT)UKHmdCkSeop;Ci4&b={sErW(mj{Nl-ilwW~qY8zu?dV4B(+0#$M@;N%4hw
ze?@~IBU4eF+UtK!7cNms6@}AQetX;A=+}a^fDpF13!SCmrZ(<PY)LLpXS<;!#FYEp
z(|&ds4;!Z9kY$OUFi9f@1)je|W~!-25uw5oz~M5mFitlb@-)0Y%`orjsUt26`%V&$
zCO@R27y#O`JpWw<N@J5!qBJ2*0G`?&rsKHYmp#^SM$a>9VCA87tc!h<LjNQ0tBAYZ
zFXGcBqm+3oXh!rVd(31J*vkvLrJqdwV5nDpp7!??5OJ60;5&&c5|aO5r6ef9T<tK|
z6-d8ViVJ-$n_d?BC`b9eh_Fj|pZ7hxFv1-()$xaCKZJ(3e@xL8q$#AkS)`DE(0cXy
zq>D<1L73y!)4c&NY7iS&C^NRAK$t8jS6~E!3`OlOA@6k?JFd$YKF+T}LcOt2BflWz
z=%={(b|Ly;D<Z9?;Yr#FYNGrxGa~QFG=yPO3H$}F1}A%azRNscI}WQMH`zFMHBnS8
z!<tF=jArXyIz)h}l{h%;)jYLfyGHSw7Q?<J!J7q&AU&5cLomN<P_lvKc%<$hnI0i7
z2P>p($BYF?97n>6jejo&Ck%(dcSI*)|H?>nOAJLDy^BQJoky&s$XOR4Kny=Htq;+6
zQWMops<}DPwf&pISm_E@+PM%E4EM_{0iCT~MZe7=q`u)PSC5fDO!gHH2#L{k=L7MQ
z?p%Fd8i`))d_FORnn>0LdOb)6q_$l_zI7o6@3{ikmwO0lG!CI}@byJhcxeVb#dE?l
zY<t=k=%!RmlqhUFK}^o~V3$xahLub%(2vWoa^>G#a%8X8XcOmUROH7cQK5gKU#=xK
z#o!eB8>4a6<$y)9kX_S3MNfl>l4viK9O6i78lq;gK==wKXPkwPB|JOJ3(lKdCQdqc
z2~B^CCizYvB8!<hyG<hThVzALdB%!Y1pmp_SvA`>91Cp6@Yo%EyQ$z#ns8v2aOekg
zCUhS0R+#j`dfIcxM(=N3q>M!JdRP0kgln~rXz8y(ow}P4l+&aR#ST83o)1CXAfj{k
z#<I|e8E|4{9+OnOZp5fZBKX~qaL$B0#xU?PM5vEK)p4)$M9Etwhh>c_HKU0JaE(-F
zNPFT;lAGwr0pKi@sY+-}!hK<eEIpw?y#g3T*$aUZ^8q9<4KOna(6t;|Lv&_hOH6E&
zaSZ`jT1TFf>$k)l98l4&eB)e%yRJf7edo-WsiIWqNwJYk1-a`8R2y0X5Ibl+YCS3E
zm4}N6Rv~HG`9qrNdp$jBa>FfJmM{?N!La3{)Qo|J!pm+!jm%zUTo(S>BrXG0VTASG
zF>LT(<OvD`M74)^`hJjJjoW7@V4n`7IG<!*BaWy)$rVyH{)ILj-0xl-oB3#?%sW5%
z^Nop<tMU&!B;!vTPVf*5D)eq*S!QNsL^J}DheKgSa;mR5cQCy|lQ`teva2<Je2GM<
zVSfh43)=3p@B42zVi}f!dq>Y1Jw2#|uO2lXCD~2?OE(M6-uzavl%P-N>>EB&P%?0R
zOFE?m38v^*3!R-MpeR9^V8VdNr-{V2fs!bD=WH9kP>K(|K&2_}&YuUiu1{<qS#$<%
z9lszDC<spli}-ugAF(|gj<i5RB&JgumKfO*Cx-191q<i;o5w^`j))pXHDX<r(qv^4
zI6I>NF~J7O1pObdAwiM(h}mD*Q_S>EP~VL(xW4Pmjo^khpPa=UxzVfo6Z9gE<i6P}
zK(sOv!_Jch`d;@R<*bYCVExLy>j9$@;rH|&+_h0LC5}wApT*H4fDJB(>lzu~LrGRv
zzH6b%kf7eSE~W0ALaHJ>#^hxYkU``ck`-_Fnw?|Fb-gA)fT90^91?Xy(Ad@pMou$D
z!^<U!1#;^hyW&8R@Ju*hAKQ%T&&1S*_O+OECinZCMr6@XYDLeJSU1=~)v}Xb{k`Rw
zQff>8ZdAxK>MCzzutyhK$z&o?J$rl%+H4tiqx4c#^0|8AaZ+vpTm4Xur@rlda%;E5
zG>LVYuxsxPXTrLJDE<>x*ql@t4@_adgSxV4h{1Q>oBm5>buz<z2R3)2RVbQ*=w%dj
zLj&v)VJ=*n1_&)uk6>~5Kf=U)=pd_z)PXEkj^Q$4KF-Vc;{LT_eUCy?CZx=?uY`Q4
zOA&ap_-sG9X?e?tAw_t}(?vc&Z3t<&vPtGs9yy^lBhIu#g@&{jPX9!IZ5w6Epx!o$
zEWRcD%i9o3&sD;o7~=4QbL(FKScTEL>|D^#3>O3In9gjP=86RAc-q~gI#o4wXq5R$
zH`A~U)74<rXg#lEDEy#cYpnHqg!meEwLv|**-YANG5<I{yao6^Fdb$jL1GeQW5}M0
zlJD}2m-#zE`dE3tlreSkprhfvSt=k+eO|n>YsFm{5!NN?RXg*99qEzyG_Qj3F1jqa
zv9iZ8(thgOFD^&WZF!=pcB4BlH)dy3C*&ElY$3`_JVF$TQu8w)27M`cLFk*>jMEtC
zp@lXR*ft#7PNA+BMKYex_?cH$J{ed{h!B2k0Lfai12X(RLm)t<qu(ddr<)Q;GHB8j
z3kUa0@0n&t$Z_Fc!LO1XEu9EvpPWipIplqR9#WWP>3s?WAqK%(Raz?5`WZUj2ko2N
zBbylo_n`}A4gCp%^aa$gA*jnI`Im)bl=MmbL^fyRzI|g+-Q>>VY(WERyT$8{w82M7
ztwwdfXn_cHQ*tAtKKK>LC(JL;Go^yXc^MVeK7<do5ejH<f6Gq`VWO@s#)%@I-C~RX
zT^}A+cl(=N5KjFA%X_oS*`2W~1XR-i3t?e-31Q*?E0hBAXr|9E87dJ4oKT`5abtsc
zV+ZX1NVIrzVTqV@sK0hQo7~-OkLed*R@A?(QN#1&2ZhA2X~An1CFaB@6=BxSIQy8H
zT2kPa$Q~Z^9=^YHJ^C}`u+udgpwM!kiWCQ}?#|XN-bun|J^A}?IJ2Pd*oUW|o@R5o
zY$BFwHg3JQuI4i>1#`)`ua9$T?6B?3OjwIuchzi3pD64&EyR){!tsCGpt{c375Swy
z_LTKJTv(&0E~Q+nFkm`c8~h^}TwyHvrE37~2c{i$(=5Jes~nqw)wph|Qrj6}ahgFg
zBOVlfp?HFcfUFiqO87do)l+-aM@<WYLlNTu(*PHi=S=KN(AG-Z^-i8*(IPQxm;2@q
z2G(;Sl0%QJp=>qIfETltI+~Aro@k%Q>~iw}QuD3QsMAQ(v);c*&b%hhKNuwM>wK;`
zJ90)i=mdx88Y&VUX||kRKyKqzj-Zf`@)$@O)vU%hw6oT_?M9#9LFiB&qf$HPrvdRA
z%24Wu2nZ4gA_$5SK`1rw1lC?$!wCcg9{uwR3X-0Q13ZLwmXHyJ-iN~i1E)i8<U|4i
zAqJ5U5ma_xJ?r#v*SgPo@R2$+*E_E;?xmm!_Rj*NBeS7f#gWviIfe^al+<jSaNeD~
zv)NA9MK?3eF3GbiVPd&u)hQOqmRL(OJIa+deQ`~1QjkP9Cs{^{qkss{j9J#8{l2*`
z>iKVa=6WW=TB6CNVI$|1dHAQb#9_1V<?Z_>zdwo)3`y|+|IhzVZD5N3g#q+)Mb=zY
z0Xh(55DtI5fo>O27_g(bt~U^&4&(o?N9v=X{gEAZ$dmu35c#L1#Qi^486V7p!4P`|
zP%NmSTvrf$x-qUUz(oG@r)e^pAl6F%YJUHQBaDRxJ_`#ASMEGF-H>3Q!9pYuV4%Rq
z+10i9r5ze|ssZ)}4gLSYpl~RrWlLJoR9Z(z=WN9+C&onB%gd{xtc+SESqA2M_S^p(
ziNXxI1jDAHqEZVEjr7N~uS}`L5D^ivOaz~S;rr+DByp1kQH8{x<!K-no{3?Ss}sN3
z{&~09pUX;5PoG|0g=_Y5ZOfQC6n1iA>D#Hf8OlK6V4w(&)7~A~vvTvN+(Z3bE0G6u
z{O8W8bC;(3vs7|nbb1!I+n}Q$u&MFCj%BYZbPW6?WMM70Ew+O_b#ni+191+%Z1#at
zdc(IhkH?FRTJ3nD8dws)6~o}T*`b8P#sn0_!SD`<U=&5HVO$vc6!^x*RylD__p98S
zH6(UH2`DBlv@KRPw%*hGgC_K)#jhwZ`WtO7j&5Qd7?Fm=BxGv01pZGEu7F*?`_#%)
z74jffC#2+jI;ZgNTF8M*uFAsBPR)!zZdRDy?{`^QgvH2Y7PrPX0hk#uen;)yGUK%-
zn^BL6p)omekAg#TV!(L<vPIYKjzmkY&d#Mlo{F08d5jnj&kZru=@}UcYHB5K3h)`h
zDDh*BN>S0uRawC(<QP_feefr>?Kr_Z-pIo^u5-)EBog?Fii?UwpKZRFvrEII>8ykN
zM%%g)gZ>ZVBan`k_kg1uvqyE*)P6Ya%ob^$ek2<3TbazE2$`ZHv(nz5ou3!AGLn$N
znl7RUi618z(`Q-*iTEG%k^iLa0#0l(n?oUNVlJ%^hr*oUzAvi$8m*iTk1j;@IUF}`
z3aymUpEx78Fi!N>;NaT{>9^WqINFe}!5GJ@xG)g$$%?wZLNEh(LNL??h?G_rBee1*
zCVrByrtvYM)W8&`KIes>%M;0009&@@bZXr}xVlNfxG4OolHq3~LXM+S9|1#B&$=|D
zor)5#_TRF{N+l-t6NqLKq;AA2V(<WJ_O;cNloD>S;X{OuPEHg*sCp=fgFmN?V_vbh
zG@-9RvkJ5Zi=RWvrWNpX7X>EtH;ex+7Uhy~&=-sX@@d+_eIpGzj?7+tlpT9c3*=D>
z;#M*YIC7sc8es2Djh_SKl>hIP2YCk{#0>l+Y`yw%f=DD}W>%DAVX!36frf}A!S?+l
zh?FCV+hjIb9Li~I!6?j1=Je7q)G+^T-lCwal~JixDqDY7kdr{wtZ^Um;k(c<t+11U
zkhq{gZ77P+sD6JMrIP{FWKo2*ZyA~-D<uY8UVVLn&(Ht)An|iAJVKvCC9%`t6SPsp
zd9GgHJ$y)iFI_^&5s?OCtgMcl2?z}v|8K*~_Osfl<6pb}1a*2n9xSl@2bszo8DW^T
ziN*w7yWlwQ|7L61*$+)HUYG|l4hmUZT<qw8&6z5B@VKhAfFWTC{xlVUd9uSlXnG~G
zS&@I!+uGVvciB1tIL2_yEYhb)C43YWHaByqs;Cs5+3~T%k{DxMksTzZr{l>SF#pw@
z7?Xoh=o|k;gA6MNwrUmvhB0~~tFQP9+m1cX#Kc5;m%Efy6jyn1hzyX}AbD=SN`9Ip
zJ5OC0hNLK>m|UL6v*8Q|G!8QzIEkey(%;+Xt9d0kYQKgr7|^gJ^&PI34hcd3XQzLE
z&=fYD*sL}E<w<(&2>>5uwYB9OgYM4XFlu+ZoV4J#va^eA)uw-76B4h2p*TnZQro`w
zxwW~u{G{1L$J>9$Es`Uy1@(SdF>~tLS#Pqze-|D_Z)R>0M1ssP%kfdAn-9)yftLjk
z#h5eVByDGBH@PI6^q3qbBk;+!iZAAt^yBS8CNgxpeG%)>rra4)<WP#GB*FV&a)v+4
ze*ew~;QzVMh{P3b&U^u62lp@^bdtW3k`g+wy&MORtdm40I5r?r&cvsh;o;$_^GScs
zEM)q{&JbK(O>L+>NQB0+$e*-~A4p;*=kNC0Jpt-KIJD^vpfI%N%L~JdK=PR%-4EUX
zrsNXQ1p~}Kl8Xzn4J4osLmtmWvNo_?4HY_&CNkgU#l_I3CU#{NmAu+o)W*if(yA)i
zlZ*fX5FjwVeE9;v>?C0TjfedG9EE|6r0~z8>}TQtHk1eoI<1|$j~!b_G<ih>lDvp2
z<cK^G0$(Wz2hjwv#-Mk!67v~lrBXbll@mS#FqcU27-SI1?Vx8_2V2WD?_ED$ynu{H
zV_aJZP?>ErcL@U)T*y3baOR#O#EUnBb+@A(|9>1~K~l~Fr18&u>?at>W()^5o1z65
z%A-i+0$!@XQ;mLoQ;allS+gF2lK;G#7M4LkOGF>o!-W+!;LMfvlTJC)M!n+v$7lSs
z>|~<;ZV$q+68|Oy@-~YNoa6p8akbcDNyN!5`#uMMMs(s)s1kgT$U<m6{CFsFFiKye
zlVs|zbe-I-xw)W%AF-A%zN%H)5x;+bV;`q&&?7_Zb(-QWIeyh>bEa0U)}09bM1nj-
zZeHsZKnzJx5##B=EQ*P>6z24q&BT7+R@c^y?(gsG4ODX$)EIFJXRd&07;(m>UeC9>
zbIyMNhO_6$1cGk#IkX-~`>mW!S2qBSQ#12KxKse^9-f-o*;RAZ80Y2$?(FO=XY+co
zBHy6v_4oH9JeX0<pKW#X+W|+80kU?ULWX*wHhl`k5Xs{F{M7|9kR}a_zt{d`V`bg<
z-r%v{_IKQ%T|+A^t5R@tbF;oZnDBtV<FMNtC+#qs_RW*0YBC!7+0jTODQ;b0_GzAE
zb#%YnU#<3LX0s$%744M&SqN@)Lz`1)+WYCsz<Rlcr26vuC-=p6X{zaK7N-N4-t(wh
zjo-hIj?STp432(d=A=LOi}hnb1#xV{X#u25l3<L@RgKAmzGG?g9PgWv4tO?@8pDA|
zE8jt*_POS*%}y_ey%>%Il~PbiDQhFs&wXr%Dn+#i@k|5)r-YU7Z)EWk<O6YbaoMRI
zA{_II%h5lcEh50wXfTkIL+-lGy4)V2%%Kf`l>E;uf>lMi|HWcFHQ}^2=b@l)Ui!4`
z114O^ky?Vm>)+D`dH~?S%&3AuP6F~E_l*TT0qvmxKL*@DlkJA4y^xHL5AQ?>i+T5_
z3#xS5;i}%{4Jkcp5h8(1{i7m+|9=Z8+FO~mrov+f!JZp;{CmGVEERaEE1}GG2b1G}
zckb=HRAk16`j0F|Y&dWA<8YuRLSO-2IUhGYr<JIs$bvm*nc@lhLFCh*zxB_idBb&L
zYINk1ENl9qT<;wY021WuhtxS4LpBsZb>vMug@RuJ1jeI2vtlD9D*cCAAS+@v$b%;~
zx7t&bxM0@Fga7Yv0adlr)7tK1$#ksP6aYrY-Q)C}4BV(8pOqYrrt<<>@*sf|+IH(R
zs<WQ|_|Fg_p$7+jE-o&k1GT?CvtHqFZma!I@OL5h|Hh(OzWMsiG!RLSYvbtSVgPQ6
z{2yGddV`>ow(35O1G6+qO(p8IQwFwERYmXpJf=68(sfsq|IG8(l%7ZBHKgBw=i_P5
z+?qD~)uG6g9${J^@Pwr0Cr|DWXM8p`OOqq8PzlMS`B+ww8^Ny3qXPi?UR|7>w@>x{
zc?xDPvSEJykg@)gH7)xk{^vtdb9$b!0N>iwX<4!FsqiIM-9-PMhSZyFb!K4MK>jm3
z#?dPLXT<|UlC~QVdeEonuRRZ-l5gvJZzyUo7|X=+bB8W&ZbP3Sm_JvGJm{kh`WhU!
zE7tYp$gx#kM<?qR+xQcpaf%w;x7%uSki^Q?f*;ENy4)@|^KaNCL;>7r)2m6iJ~*Gd
z{V!hfj`$DzLg1yU&ecDixNdI>*!_PX$TaL4VN&_@qLMR3LxRF@U^k=LU5}lYNx||;
zdU|?}2YnzcpCyekd!`2?Hq>YJK@x0b#%^V%5YAR)Da4Hnm?E?49pLY)Wuw?6>DQ#+
z%VM>m;}3l$6}?&3_wc1)yii`j<9+`H)bBZ3q?`ZJjSyh22?2hzi0w49?B0S{K260h
z`HABpQuhs2WmG8%9WWyh$o{4vd=|a11_#CRg2#4^U{~M3<%Ndlt(aL^u{WD+G91^N
z{x@5n5A8%q3CvvybKXXWhm%6abp9XUfn~Qm6~<bv(GQ8HPa8|k5~QcGt)dRkow|zm
zjY^k7PE*m@x_tM{QJGD-xeO~hJxJ*i5?%j@sh>ugBPZ+h{k{h-pLO^c7v(y=uB~no
zUn=^o8GmHg=9~<F!~eu5L;z+)C6{~UNtzAnJE2sLG)sAs4@vl&iRzrTdpxn<Z$Ac1
z^i}1=z*hUV2X2R5h_0KY>O|?4gmgvL&$u*itt}gyvw}9QiSsDhuKQLnNp@tUTF;rk
z)L$5cq*@P?p%JWG9Yl3Rd~}Z!8Ev5Nj=%*{U^J6=u}Lz9;WrDJEBLY+<319CJV3o3
ze6tMA`Qcv-=1#O0<<k$l-*C~T`NGqvn9-Uit~BLuL+!g55>e5KbWAf$A}B8ABvr{u
zU&2DDG*gBX20ERY>9f1)q51qz+Yf7EankUrTte+(eswi+h{&(QnA@%PHLK97TkmZb
zo7@157q`ee9(w@q9IM3}b@=94_YaEzg^s_DEBYB0Do14}=Hp^Fa#V_T7`JVCFAM)G
z!^WkbTMsLNFFuq4gCDLaF9Ou@zkI^{+%VvHy3kVjiC4~U{4Y>qC>KP%#}}ZP$H#K_
zvo5o0G`smSR;ZA=&%QafiI-Uu$zblt2K&Xv;z~h(^clnLIeD})WSbjy<c!Z%{%+Hi
zj4q!FX<s3V|D!x!g0Rch^<rlhZp>wqJN4GbI^-~cvZ34MkTT7{J1&=CVxSl_s5P5P
zKG|er`f>PMPMS>)8snWbF=(oIWmRL?P^L8vh-D_=+}xF?+*{8`*#LRO+jc9m{m*yX
zpdhxossUQbDirT~5mhM3^fmp)D7j1ETu1&EvdF)FIq<x$={H3MYOSJhlxRlFh=DFm
zH5>g^r2HIdjbh)l6)MN~f=ipqkBy0G3**Vl_dfi?;nk*jH^+@XW;jWS5f1`KlvhLn
zSz)@DID$1$7dlH8`Y(q2J6QW0=P!Mawss=QsZfU(A%6PM&?LJNNd2h01&Yo~p{;##
zw$Y%=%Fq!i<GZK=BL#xX6Val6gn9}85vo4(Y2oQp;v*30x@6!LJp%f$up}Qw`B0;&
zf^`tztEwXV)oALRr@<9I+HA8BK@8%f0PCfr=>?ccJ-om%(;ju!$V0tzg%78y7jwQ#
zB-thHWg43geCD55mX_PDLo8<-wvTK%Y&Zy`gF|`pFj-ky$6!H!-cXalK=smo##Ri)
z4Pr=Lb&<<ebI<Xul|GGfqE!F>E%~jrqOvk+sN`+1ur8K$Pa%u99UFb6p3HA=BZ?N%
z*(C=kAiIibIe=EGk^~x=P?|%ILqf0h2%i;O%md(8F{X6*lr;+-C56$0J`5{XEGygA
zysr|e;xL$gPkW*jx|RV8*5cHr#-k%F5^kN`EX!x?q`qbs=`pLL<n$FfssTw-3IEF8
zD+PJZ?)xkodyJWT%~42TJdd2Y);mnu(%Jp6_}^1D1pcM9sFNNM!+0J$4Gfe@^R3;(
zpWQUVAxCc)?O|V^WORl{|D?%TB(q`Fl7BKS$%;S>9HG*lLsmq^#T~kgz1Lqc#I7iI
z*GO9->oG?mE<UDIO$Z-D-)b9kK5HC?zxL){{2i=F{vK4I&a(tnpx{q+lsjbzlFj*L
z@M@UzcXfTgA=>p#gw#zpu5=3zUI4?7oND@5{7LHzBvmP=7`NZKHO0kH|C4V@D=TGi
zAxsJWN(BVm(1=bSDMCQ#DEeIo6l!<h2tZd)RYb8h&Z=iB<~p=$1A~)>j0~k^-FPS<
z76gd>fBr-Q6d#vlLX1Adq5LLgqpv|#lI46PZo8jVynJ>EC8|TB59u!*ULB^T0*o}%
zmk*ZJH}Yy;NTzBT%%iY=1Ml06E!t;m1oD9cxA>|v3qVzQnoa3aW<OVxr1Vgz7(uQJ
z<nQiFzk$8%u*fD`=G+U60U=KA)SsK<PCO3W{)N?E7_wrFVgGv4s@~p#R)v8J!_e)3
zc~A6#Q=v*zfyFXziY^ia>e)%?FN<Z(w|wRBHs$w3EYe>mU$7+TDf@suZNjqq-b@F)
z!K)Rmw$&CW@VbUWK&S+z&5fB6Beby%jqa0Yr%zVU<h^H2bkH)+h<nF~yZhHTU-2n@
zS97wOm1b<jQo+}ahF6lwsJ{}vxMPvMp5zoeJUV*sNJ|YR1{A3rl%l}D_L@pII}1ig
zjPyyfD(DXwJ9braU{zwaARu*_TU!SY?WNsoS~jmC#ttPU?h?X<puj+EKfdlfzAp3{
zV(z-xYvE88>vkg%aK_zY-+Fce@<VzA&M%JIV!)e~m93p-_KzEGavVoe6|pV}kh>Ar
zyVvffI&U%%kE7SiGc%w$Q_$U8)z(fwMQudWB@WF%%+1Y*HFDyxPcTfxM?JVix0WL!
z@SW*caO?k?(`#Q)G?Y=XcPBWT$jR|sLuC8D9M`3fOD<@q8pgPSl7jSDu=H7{cijEW
z;+PN~8j)(L{SC&Ig%rpaYJHj~>xN8LiCSieW3iW@9H`F>)AByp3Vc1N$DF>B?zuNw
zp=2ZT_&>vA_1HG|8P@g~vkGBA9NoQ;?dZQ}HShenM(6Z91MyclR+5%0Rj7B>ARiY)
z<?R%H>L>DIWX4m0!UUBjpwTsVCadyl@iUeL-Bwp)2n`$BX}|?mW5nNEVTE!KDwSB`
z7>5$vxcD2|5uwGk#b-h>m(&Hbd4^=*&<$>7TPpq<)(=kV4tO?{)Tl-Sff4Vn21hhC
z2F&+<_WQF37Pl_`$&`j3t+CUNL=Z^I7-aPleDzZK%Bw0IXG}~?9pgUWq1)#gpuAEJ
zFwAFV^}i9-?f(7yH|w>lJfx{;l12gwAw2ETt)x-yw*__S=rC^_IY98pZ*0V@tgM8A
z0K0njHn+0cQ=b<rv=|^ODwUCu@i;}71RdJD&IJYmov^u$jSBo3{U4dXowI8CTt-dn
z;~{e<Mm)FUEbezRKgMhYFrv0##}*ZpfW!updzfCdA!h3v^8Gx4Cd>3r8&`-GPnZ?T
z*h{EmG_{#@V6a`dQ^2mp1hi|}9i4r>jj>4H2_OnP9jvQ>!7wJj4<Swh*JbPLaXjIC
zh;JeGH6yRo(HdA|IgqKHdl#=^Vzu58=s6!fk`-EP92WnmZ7$#xDqv-`=K`!gYD?T;
ze#mO~MGY~4P4`-G^jSxz@TMVvMMLq5_^dxtCMecDV<soQ*F*=7N7q?`=o`~UuBp-b
z50Gu&vlw)E-=HCNdV*3;1v>ycDrtXppk0od<fz&XS@N_$Q)X}mAz&vR7X!so>d5kZ
z{p$WYoBQS#_HtdKIZg*@O27W~T`OK5&@+NI+TBP2C9APf#VL;h6L1uuCUCl(U^H2;
z<m;YUfe^$6raRJ+?q4@SI>*!@yx<kFnKf&cT9}*n)KBq6$x}h(OsSzz2D|eRK6&%q
zJv<l!x++0bD~Ut>MWQE>x{68&utY&~bF=<Su%q^~Hy=b$ud+{p<O%)<?TeU3I(*64
z;Bu3VmIfS~fWuQ(A^PfPaZrb0=|9VkU94Wvs%|9lx_^qj%!Gp54-lp~Yp6OFSlDlf
zZxxytnuU-un+<eXcKz7JC>EZcVOOii2FkQLVQpT7{&UCvV}FCkj{P@GLsqN<dx=Li
z*vbwXUEJ0~fDDsgi(@#4l0#A8rjNsr(-Dqd(tlOaj+Ep5kGavoS}8fIx!6#~oH%e#
zhs3$d0r%5amp^p{uFlJ+1xI_B;c8ML)F;4b#ejsBs6zT@Y=l`uh?$L%UO^KLd6B+6
zlewINhcWQ_8poMS|C$G1mBD4_3(eu~gUanR2WQ}Rh;L>$Yxqa6LiEp=e&gwWQ>Gqk
z$%bDr<r2XIWcly=!BTM}k+S;_vNgp4&V4;<`A+eVa{*5qvK}0OZX5=vVz~tc;JVe#
z#N1LYE^L}wTBrz6wI*Y1y0uz`Qz}}ad*MTC#U-*Uv8)6^Uz8d&Kziy&?>GNC=9QJf
z0-{CW)+NI-srH5~ho+_`3If#3{CuJ%E!3zp?PQ{<1#-Nz0{X2JBTn$r>B66G6ZV(E
zCY!bSf9r7rJUXrieM*ooVaP;-pUsBq2dgFA#-;J^htrdjzW2v1z1gAPB?XBSA=HcJ
z9V-)m<gq}kO;LBsNj2aJO4^l77h1??gmhOB^1~a;tNOWZGa0@lZ7}#{@t_alV-H@b
zR%2e^Z-zXszoM?UJYXreWR+Gp8_5>o%a(j$FUN%XWT-g6xB-Wqr>KqeSqo?B*_$NV
zhfO<xX&vV7Z96=wkW;FW_6G5_OP$jVe-CiW3k1e=80vk&83-8*!Xd={L5meMVHj7>
zLQ4Uvs-z9Vh(7?EX9}l4iwPe;(A&n0=R|1yOXYU4m9OQ?V2eh5K0`({n5`Bs@@1Qa
zuJ-uM=`Oj2;@a{!)ba$B`ovdYmP2}^?e&3(=(1t`$yn~id2P-ZJsB2%*S{2;4khd;
zFy@Yrj!l69i;Joa&CLZ>^cKU)5>64=e<5R=C$3)1nUGjo_~>RirB6Dvnydu@vriq)
z{aLeY8q5bSXS}?)EiEnQ16;rV95_~_)2Ysq4?kjKW8<{n7EqX&vYa`XFH<TzIDTOD
z=`YYO7+uiD2{$wHzVzDa4@JZu9nGP@95`{SbGy`4R#Qvyc}=QoWgNh>DNQ4D&rSpa
z%b|<`z^S_G3L9^y#E=%HNF)fxan)Fg%Fd9@KmUL0uLxbw^ZYv(gF}pNf4v;uEAQ1^
z*PtE7+VK_Iclg%3HiLejnAo>xr7CFzcsL#E%bGy0+fov^a!Z?%B0w?e#<PU@Z##1_
zy7lfBo~5Tb7Mp=!)x&fmF{VLOsPF!1!*p_3l7X+WUC`d(X%_W)haXVq2zDPt2hkru
z!_J?<5$Pl}74AqJG-Vgp`$`*;kRt+`pPR$E$SXX`Szk<kt$$uOFSdeiWC~-nSK9*G
zq>J4>*k;T@oS@JXs)gNS!S)p|rq>cppC!r>UnBafG!*EROBe4oH`W{NPzLkFL<l7;
z2D-&P_d>4$iP>ynXG>!vz&6XREXPLVlC)Z*4)5O%_iR{j!+z&7QI^V%9Lsmcil}Bt
z3k!qGW^sLr@`017;v87qTwL>M7!kG0<8%j!tdj5fvaPyjo1ORoF^N*n0?J)g@}Y%_
z#k{SwFBg>*H!6eI%S}qRc3cUhh0dr_v@hi^afn#{4|hRRaof!fjoT$!6DNa6w$>lI
zuvzRzs=f78M~s9?e_{~ot$-^G5-M3DFpw#)%RYYZ!|K|D;-u+gXM3)!mVd(d*C|^6
zFp`pNqeaB@8#T9`R~2?#N4`#Uv?isp48_1Tqa(E=n%2XL9Yd5p&CDpl!@y(F4LrFc
z)S7F%rbp<bC(9~G5<pj_=;=|K2#pVvoTLboFVE-M^H}dAy0T*R>sVn6R4*Cu;AK&j
zG08p6;{->{QoghPbW%$S^%ylCH%U}M2P*UoMb#L=>9HXf%9n)ng{5|Vrl{~}F$3m7
znVbnRP^ch6O4+a(4o0r)<}gQBFlAl#%!F_@SSEGZa-fUp3jl|X<l<DGU0;uWcyPTJ
zv&A3XbH<CU(;0h4awdruUU-5x%CA_YiRs6?=e&D;7fp~z_?euHMZjq<z>DP;NGU2K
zMzdRFe*<Y{ZXP$5B^hyWU<wrB$|~d<EH-I{b~O#?Ju?MAOze4>{_nd0@HY*_-CMDC
zKBrpgU8YPgSpZoD(rT?WT48s6+*ez@q!-!JOV^6^$7mO8u)#w3XjeoGGLMx<L`Rp$
z2FXf#biQ_8_T|`lAOLM>cpxz>9lD&Y=iS~@53fs$`!Vykb^7rvt%&DaG$9#CZF<Tx
zB<^w3dgYJAG<k54C@h9S_1${1`4WqpRjp3@wV24gp6UrUCR|ACm+5b`rP6H&GE~v+
z_7RBelj6Ipl{DWBSYdGRV|&e~hq?=`@kk^DhO^7+BqbA*{{%T}urYM|!rF$xiZSV$
zQe<$*?Z7rzmcawfbo5X@3d9$PP9!6so%L<>%KtY|ZyBgLL8rO?1u;bEM9H%XH3%OM
zPjc7xoDI9J2@B{@bRuVzp#;BP3Sd{M&*i}kyU{GYh^T6n#q|HtUYeOv_-Vw1kM_0A
zy6dlF>(yEPRNv)eN^m&e3Qv~77FGM~q_QvNkDmn8r4QxR)kA=SG&4P&yJ@wbX?Vx|
z-RVHOx3fez3<-Fd`|T^eURTBOX>!|Vl0xKgOv{8Q`eTj3NS}gKgw3g}!8U?ELpN5l
z@=Bd5Ly;N{1(Oex#h1;Sn;C1XTaTBs^+pZRKG}2b+IQ~S`!~{TPMr=57!{tMnj}xk
zfaOV=KjW@C;arps@O_PjNO&M26t%o8nP8zOjm%?ZpLU7{o&MjoRC%P`updJSBK{XN
zK!@*);v5;DiA(5qQJ11(8Uu}My1?_#k^`VMF$LrVLF6Dow86V$_z7DL-H7H%9T_wq
zG@&x$uV}b^hmv%0vJPtexd<rDHfyT00sd>}V#Fxom@w|ti;1%GO2&p0Y@i<0CXF&;
z`FG?4>c$fD!Td2DNWd)sK=1|nh(&DaB}qO(MOh%ae((2z7R_I{UG=xReAGKV*+90R
zuJ_Wre1U!)JAS#z<JQ#K&CTfj<sQUesYKS|<>mjPb$u3LA|y$~NPt#2K-dK>aXkI+
z?bR|iVbFqQz<^<BuepEjlANaEo-<d?PSx7=D9w;TZR+QTIZdAthZsgkGd5khLZ=>%
zDTt*!p>jU+;$mo_7gfCog!^JAKOwd@JNn0k259?0?wdlFTzEY8X*P`np>U~&LIlTT
zXi+&v5K@nH34Y3iyzJft<zuTvon;?5>66XS+Gh_T<8W(gl)p|(@?o+K?`)KW574Q;
zyD`F#pL+3r^x@_q?6{zSOzuh@lKxps78Mm~g9mJczX~PZy#NBW1h|n}_MddR6Mr5Y
zgk)oO87w0Q%F#7v&K)f>y~N^T3TJ2ML}}`EgjX?A^;KBn3pYh7a{1TC%b~?eE#QRv
zITk*@6@XyJh+J+mn<DgnY~LDqd%pdf(|I#Wlin4k$N?0h30`&V*s5qzCbsn6ZC=}&
zd8R8oJTGY!%Qo!GHmd5hrZ_QT;rWb3%Z|6{3+i?5B=p)x0kWev)5k83>@rP(f6Iq-
ze>mq-UUag=M7i%e<6zSx_t`T2@zF*=H<W2}Gh~Ky-`<dL8CMyu7Az$gE{9}KQ1G8H
z?9n%7rJtYZQ#kxRp03DuF%Lh$)Ob>nK0M61DyQ35s}DopuJ8cCj0++0mVxrYe7s4Y
zfePMl#Ob(;_Ej-7k=W4K7>Bz7As=+VUmHg<uXz3qxfm7Zsj#Qhc!?n$UXB;3kr6xF
z8i4}rMsc!u#E`ZrpC|TSmSBlKDvaCh0w_gj#$p~ZZk-E~oiy{3U3FXRNiHw*y1EE~
z>qmSJTj>`MIl0(_%L1H<kXyQ@g{7r}&dx8fJU8O;(@+9V3|ap(pB7I9^h!GXm|@IY
z&RyoQ1bvWZy&(yq=RPW}b_<GZ*caD!On{R@5yn#4E4snMvi)H9jEus?jOrbBAMV&o
zDH%vu8Nu2YKoXd~w_wdaMUkm9Z-wHQ$FtXI-L&K-xUHj#;wlHKNa-ATw)-vq<Wj>$
zNV&1?BEJ?$Q~7M${!h2{6${e(;Auq0w160v-7`F&5D+{(bzIY=0qwz$(^cU^mo;Cs
zU?R0xaMw7%uoaeVRG{bbk%q&a9%C}XslLm1Mw|1UbKGSc8vzOxDXjYWIhjstl$}@&
z>hJH`Bbw_*`bznFuSZ)}jubZ)_1z;km96FcB6X$Gl|V|l?D5mn%hg8s*N1Z@d?9d<
zP4_)SnJp;*d;40}Yi%wx4`(Y0jtCa#9Lh-lNpGveGL+>9C)?(OQz~ZW746SZ)~jzC
zwCXh|NMT<df)Ard64}eeM#A`?d^@fTxd#UaQKCePq+`zpf|m67BYt7@{ZY;Y<SwTy
zY?K%<B&j!d<9Y@E5gd-71v7;@Fc9R9c1b$zhJ#wW;d+g4)g<LBj8;FgG7?1l6g&5h
zLcW#{0-6udQ(m<fU{7yw!(@?Fq(+0$4n9R_0X1Ab$z}?~2ocOMz0sNblE{@Y<Bp>-
z$`)-SF{-!)y>^q3OyJ3h<<q;wi!`F-skj}`><twxu%%1W+Su5LwkDv+Nx9R?e#b0A
zvmEuXl}-O+X+??2GS=FlWF;sf(HE?m74ysAm-2X|WclFnwbtkLp|FmU{BSaB?(RJ?
z=#wB~*xLb6f%h!zFlBnbew)<66MtQAo|H??+H7-WBYYn(u;=<>-<oOPo@vi%?a+;<
z5Uk5!tUcP`Jn|I*3BkzO8DVd4#=>hd_@%1PrT+_#3w{5JFTU8|K;o2?u^_;X`0qia
z_A4>hJ*yb}qpQ{!#@%=%QSnI-9J)Eq^_57^EbE9Agr%}qU?8-aM!iMTR}};<eEb?z
zv?|XUo2k`0^=M89de8->VH<}2D=3%TstKQW5fg^tkJJSq1qLrZ)IggEsA*sQ`0+-b
z6*fHJ3l{>u1_eY+d;q{o<X;V)r4Gw)wFlK9{n>kG<na005|NGIG5$$A>eNwzJPRby
zw4Zoz>D_7)6XozHeLs(4=qi1FmCu-#g?g8rO+c=d@z7i{7Qz>(rBe&f9ChmcHMXeD
z$;|wdt+@j|$NkB`*|}kFSjw+wK?*xJN{o?@9jSc+_GoviepQFl=Q|uoi^s$_?~nS}
zKGXWXotpl?&OOHs{icn5=Bz>}!4>bO(iGrdNl6`y3<Z&4R+5~uzIzd3Vq&(q?QNv8
zlz;)XCNvVE*kSn>_uD5TCW4c}?JJ-u{u5-@NyzT>_J<O%jlwq#Ch}ms@8uM)O7V;9
z+k<N<(Dub<0=WB!M#9)}f4&C4R%2Revy%d9nZ7SA6bV)W8ecL0BaR5K*iinc?}~!Z
z-Z;Fd>ckxX6A}<q7$2|vIRgvX&BT$OI1s!DyMI!sj`ZVrVuLLnigJjuIoW572hxUf
zi+>DRTGCgn+q1K=G2zHh&(4AY!rwU0O_F$=IB@k@77AQ0j*b`5X*Jm+$TDGJ{&g<8
z(Ax{^Q;QUmIk<IhO>wn6cdN5nqPJdcuoxxg2Yr`~r-rDtIbE!D-;JPT{>G6HT;dfk
zr#7`-rAhU>Kh+`&U_h=rO^NX=KY<`Xfxo=IUXR!Ri;+M2CQSjfOQ>Q6sQmxVt<*c*
zNxJTqOPJg)2$s)Pmu(g!ZYZ7JTIarx9B-Un_FN)$hP&CdfGYriCd%Ppe#Z|80{$}s
zq6&&AXduetXlwIk(CbAfzS&e~4AEN+qM4klzPB|_pe`l!ER+Irfgo}L&XhkRm7M>q
z%w=NXGbR?T-kD+~n~Mh;0|rHZKXr~i`%!`{SVomHARq?=eckpO<ce0&yydeN8ZzJY
z46@O2yr_DcG3vooP889rZ}803G1)IMX*7#sH`bTqix0Bc(qUr0gjixttJ>nKs;Y{2
zCdFS@qAjl%?MY6N&;tf>88e^sT{e`Le9#Aod9*(1U^!D^E~kssuQk}y%Hm6rs#GFg
zaBRni$3W-I^Ryk5K4B4}xms3KA`aZn0>Z_t>D>NaC;6rxkU`C<o1Wi&Sy@?)-@GGj
zT7fq)TYD4S8NWCg^l~9n6rcjiK|Zl~XwS7#Bmb!nPR`SE7!xBWO#KK%L9hSL4$Q|(
z^#_r%3SIA;N78323)c+)*(N!?eNRh;E9pCS-ad!t#lgq_!HPHW8|8QP<+m4TC?#Jk
zVqMdFj<uYgSH3&gmP6X`*N9XR^}mjoLEe^f`P!oVF;j63=G@WhtV_S&6!^5C_J@t#
z$r@9phEw-7z@^uWJ|l6u6dWFlA}d;1=l|e%sp|#undq~kjdkaTAIr3qxSJaroHrD@
z$_NEog|MHlW3e)-&Xxq)2LswI#l>Rh@PcRwokIn-QOSxL-M}j>z61wcSHQb>Fk$_G
zdY!ADUZr4Z1*kmAl15CJG4eQnnb(;5qZXH`rsn=?ua+g1i1!7vE9HreJtpVkyvsJA
zH6)tIg*<E~Ue*$fe`+~3zsA_h4;rgL-PwIXC=vjmf(B9vl`Mg#qEIK(YXn#NU7G~I
zzA&-!MB9Dbt19bs!#Eu(=jZn!Hjlw($Z)+|KmmLp8il2A?agCLpZWhG>MMilY=UOd
z;Bau)5Zv7@NN@-q+}+)SySrPE;1Vpjy9Jlv8sy+E_u>7%dv6s#Ql#qS?9T2?Pj}BI
zBw$qd;T4-cRYUPX3u1FN&Ib#P(--pWSiRdO(tA!b`Xb3I`U7NPSs0mIjgEvo15O{~
z7=4PcRp9M1RZl<k4Xxf~_uSF$v7qiortyKKBN&==AaZZMm~LU1@IqWYCNXQVauAN>
zp2wvm(nB&;>?F%hpU;!3a<Y8p+eyU&-Irv3YZiEbzI2m<HV>i;pDZO+S4Kbw90u$N
zj{xIBGiUO}+PcCCT0f+vOrK8ma{_I@UxM!j#Aw?u6(q$3LGN2ROEIJ$x7Y<6(q6wn
zzuZoi0)QH2c=K;wtC4h|X|0E?jL6=tffe`ch*)gCoM2qPmMj<Dm_uaxxESJU<=|~O
z&!$)S?6RQA3<cmzD%IJ?sigDKw};TF>*37U-ujJLuS1^BSFEWGSR#46&!8rAz4?!L
zBQ7x^H2n{3m+r3g4PT#IYc<F5eN%7T*FzJAS^OHnr7Y25J5Vu&I<V=jO3?jDs@3`i
zVSQFgxh?bXwkpLzqxJy!zEqyA*DQi7tUHR}0D3|qe(u;da0jVd77A5S0oDKkGu24B
z@?8bykmBYueU}JBt^)f<Ki}Q<=cRw$ojnzqX*L!(lvG^VLV1ephGiBX%l=sbch3O4
z-Vs5{S>z#G00<)@i!@k><REw@h6zyfY7EeS+iU>LOA$`80&Ox{qDJD_o>7x=Q;_;-
zJDs`nmX25C+~VSn_Qs?7%5lZ)k;}KPW>u`B4e4sws+Xrb<DJ16%T;Dlu)I9r)KRIN
zG5DDFv(>@Ggvhs!x2(-tuu@%CfL;=7HWTXYD6Ree4DA&^=57P2s0Tv%F((+IX3n$W
z6{73j6rzTNKLB`nMs#Rqk%faJ`)}yTUjGDl%^<&hO;6aFx4&fGfJ!rDse<d)nsrxz
z9ycFjT+Xj34Gt!N3pf!!7#VxOu!h(q1M;L*otXz^q$b1DRkMQooiW+|ARK{EIFLG+
zN}<8Ojfrocub01KS_7Q$SJQc#heN5ep-FDN5D+geSY_4)((9&*Ub@sBMQ<Q4#fwZ=
zTU)k(>NDUrPBD@X;#deAX!o;pg!`7F1sGA^xp^WTt?e5x;4iUd`lQP75%UOud<h^d
zF1J*RlnB+b;+)n?tYrX3zCKUA2^`xHFs`qfU=_7GRKrv~K6*W$hY5=V8k*$0>C8x;
zHa?}*K@kk{lCo*3xq#$3aa*dyNuMwa{{VeWI~qDC@HicGkM_nq?eHd8&%>TmU+V8|
zM(TGV4Eb#IHh#xZ;6RE>?1vtP+7+JT|E9Qf-+8xL5VsNcbmqH}47N_XQ@P*pUk$Oi
z=ecHgT*wI?wrJ0;FdHB$)|~zBL7SX<bVqLbq+f$^Q;iw}QEU1<1e{79Z{Embjmkb^
z?sb?n7nim^!?0x|bvOcvE?lV(0OOLa09UPFhjS2{8=^p(By&;E{qr^FPsLzho<t}I
zT0bWWMk56tG6i0={wN^128)gK)iyQZ*fkT{HMivpHw^8F%FD+%C7nq)dYJ*1Dl3k=
zm_L!h4kbiMqpv9a4a11Klz=!c2^c+BR#uXY>(U1@t@d>OOiwFuQ0UQMB@TI%`>8Qp
zJ-G}fh>v6;nHz~^DU<=%-)vWGdZo@74v1Z;KoxybG<KvdmUSdT91*3N0h7<;Hppc`
zqUB)y@m_fq)qBl;&?HzCD_;fgQUN=<T1PpC-5e<1^>pxs5V~>@AX)mQ>l@txwA@z&
zzx$%Re6ZD9@|1472}vav`pyh1gFdG)?eWgUqBjc{!RHotkndKHwcnjamnUl1<Ak%O
z^DN!-L{p6+AP>s>L2|DLZ3uW1JT8H*>yBU-R3Zpu4TActzR#M8Y>2GO6!1|dz@w*(
zoR6>@R<_@8W%K)!Bp_+93^5rQ7Gs;KIO+cbl9vZfXhIo6lzj^VxYHK9QXPrNjS%3k
zbeQ0XPzl%DwsJ=GE9e4PsqDYK6+T=6BtPX+%S;d{u{Vj>FErK;CTo=Q=@}#^^%$Ce
zk{>_UO|gE-ULX7mz5^%@Xn-FDjQ<9=GGg*50wibxB#J$-41n=?>6fJnYZT|@V{u5U
z0}a+!`#+XUmGe+@GYK3xv#_#}a@d}zGSNL^?WlS+8ng=><hdCbbbt1(LJFUMD*0Mr
zeDV$uF_66SwzaKVYmTgYuF)9}IX*tVKzln!Q}N0&Avl}`Qwp|%exVZcB`?76CEXVN
zht-CMb$9?w&dn6PNenLke1kH!>BhU6%0^zP9%X{eKsH<J)c450xq1a|@jEgKFQj>z
zm(%!>!M_;uX{@T2u@mBeh$ut9_=(uz6D6Yn`KC;Lyff~14>y_9X*I+7y=G(ZiC<ss
z^Vc*4+{v4fDgN7`#fzCRp|~L~;piGpe@KPWoy<i&Aiu*u|4R!Gjr&+WD>^-)5IF%v
z=+Kp~-H$I?li@@2-yc}k0-hk?5wFrVxLVN*G_xTcpUWLI!}T?p799nE`#rz&f4k%H
z+s9(D>(Ay*nLc(c)8Rwo9@mFZ#xmr}0;pkw=H*Q%zD<XvEr%t8hHr)(1oJgjt~fK5
zXFZsKNwbDS2i*R2Mf%?Wm{*A%#o|R9ZNc#&=X>;!I*@gliMuHMYjBM>dNlQ_^_4$F
z02URYJT#yn<PZok^6~S}EH6it=6WFlzF;7ESy)=qhPS-{f0C&yk+Eusr6ACMxUMJw
ze2EuNFP9oTM7c8?d>-dys3Zd6r>o6Hfa6%N1{XL8u$VgD0wjR{nKmfgCz*dr-9Kp-
z2XW#=ki7fp#m^M~F#6yQFh~OsTw3T<CMt3KE3P$@UOSJqzxDs(@Dk+oh6}X!GHs?Z
zz0&T<-uW<LKUrqOr~zig9326KdihxHHY?|?^KN)Av5(XEM70{~JGoERhRv&6ZoEp$
zTcLoI_}30FPy||l8g6Yegxyhro`(ew1q35#<V9{OIl6@#YcQs7(>6%CWW;KyR)rm9
zkJ2S%e>AV#JukA#UAWA|QNN=*yAj(OP&bHFdM&SeYBqI|M$KmDcYm$uc>nNm>7}nr
zHUqz|>6ZBLd7|zfL8xw-6_<nre+VUts5WDVTA}rX3ZUM)%%H31Nu-vUljCr8x;_+=
z^>>81@U`fm{`&Qd?ZQWV`*K&~aGheT+2SA+EeQ>D89-~nfWv;mMF?Em6E{K|u6Msj
zwB$!|<$hhT7hLY!%h;$80nML_`9zQNC3E4FuP0e9K03`<rQWfk(U9Q-6D90N8bH9q
zhb;u2q(QD9KlarTBV){B0?Of^KZWgWf~F^>c(|jyLsjW2)&(@s(BB<+qJg0NYHS1)
zM_O=U%<8E+A&NKTgd#FML7k);6KIojRG#H8Fol#il{WHss@CY)m1g0mm0a?)Uedzi
z{Co&-2#f)y<Xs)^3FiV1j>o^BrIEUomhu8+6&3X9Hog7NxdrEoKM!RC<PO{+E}!)P
zY4qJQfae@y#pLsRf1&lT%FgIrxG{2_&RA!=Sgi{rmD%XA++-!2B2ATFMiWYMaRxQ`
zfiwmta$%#q&Pv{6h`ilR%asniJNTTA{^*1ZI7mQO*2@vQ>Ua0Zf4)|LdvSKt+((8u
zeS8x>K-Hhy*;#S#c6@iySL&a6?VXb1R_S%Ox%a7J@O5PW>`4!Ze9v+V$aVt{O*-qN
z{_KWixkmM(|I_dk3aQ|mIt;}53aS3k;1kK9|MM!E!&T-k$Ea&#^XA^hZ}g;5_3b?4
zNAM@0MJO1?+y6HKy5>7{?XH)Q4)DYN#R-#w>riZYx=Kz=;^iPH&dRiKcs=5~`HE&M
z{?~gwL|6D87iefiO+yHAqVUxLMllPUt3rY=V2mM)@A&4?+Gv!5SX)o-BcP_vnDl#a
zgjz6!YKD4pZUrR#s3e%dv&JVKTmAc12#>)Ivg9j%C5?ybwHeIT#;77Fe$$}kzVYiK
z1-u#OULBq&AZ(+U(&54MXTPxrk5$?-$%K1JQx?>|NT9EdV9ywrXaw}l$dxAh4W_8i
z4>&mOdNp4wX`AZOHLpz2eu~lfBZi1s*xBWl>${Ts&Dv32c^k#19oz-Mp&0>#U%(k4
z1o}P1-!q8wI`<btq<J%7b8E3Ilxu!zDh;Dmn`A*{n^_XU#uP0nJ<Y7H-`Mlq9nkdl
ze$)#c&VuUezi<>vq3xP60a0u`M<s$c1>2!bw_eGZBYax6l**L<fHwyf85z~c<ed+?
zm<`6r21pFjhuwpuV1kPI;70I#2>9))K%V*8+~tgYgT%l6lyHX^oqna0m1ONMZYFNt
zo}{f{-|lZ#y^>=i>H5$sl&`Nw65Mv81k2LCfdEdDkd%#NjX&VhXtk5oDNBF>HAr_q
z?|$X1a$mnZEdwO%cTWAEHu(FDMQ{&jD)!IuwNl!5;A`2utfV;}{lQ+cQEw$xYteO_
z`dEQYRb*I5qySgh;R%pU2R7uuV{a4Xkl+6irW*SQ5a3#~ov6I`h+3Q<Fqx!2T*!Zj
zP~lLLMY3zz3RNY1V1m$xbqJb!%PdCmZMu^G@+es2B2UsUu8!Kf#lmH~lB`iD`N?T6
z-=kl-nT=<=?r%~g76jrVHyz*<DK3TuMo@kZ8sdp$aoDYW6<MQzU04pECJx^Sw}erG
zG)UqY+;aqab+mm~BC5yQLb*P*VC%g0eL%6^>zHc*oI1deNYVs&wfLdK3uph%><GM8
zmAw(rf_He~QO#P&j0J?arL@#sva8E@2=!#OnVDJ)<wb$bn967sVC-l9{uL}%DoRS9
z+$epo*Xi-@N3uBjVUKrRaQZK!2b(VZpcsiQCq88@bX?2*sjR40XF4E*2hLiCsgche
zmXgm6qm<7AtQXeKjJOhFyW0s2*ZLic-qX(U<rU6`L5vp(vG2e6jlnhG(%xCR2D}IP
zL;$4iSRvIce9_QwupW9>*VVG$%9MsVWAV&BE?&<2_b*z90-W;n0BpRtCw{zrog}IP
zQ%ESQQF>gK{m%eEXBja`2A05`{yNL->A1V6VbHAo$v^X+*2u9y>I+@bnEDcpNPD`d
zx_qDogP)H#l`q|Y#9-#R89~gZH8KV@+HY%X>l#+_6s{F_SwgV-xZi%Jex^HVu-LPW
zwe^^GzNW5j)%vtoq)GIMFN5NKZ-CKn$q<_K4^WVvMCCFnzbQqFzh%n4G%P14I;h07
zkY#b80-tzApjpvBYlI&qZJj%|CkFgxef<j!mYA{e$Ii$38io0qZ%!;-a6h=(2rr_?
z?+cyI4SEo{Lg+;Dg#G<iy4w8T{A_?5sy=3xJPqI}cJx(%#0~;-_f)2E48eae7{&9>
z?J8645{>l1<fC1uB`1^K?`U3Uy6TbO+VqLUsqyh(0tnzj;^E^D+%Mr7&EV<ryFvYT
z{p5ldZKhrApKVMYru;z@S!um9A}6^i%}0L8_pc8dq&>~rUH<Y0#JJYm39dE-k@5gA
zG57jl(12$Ex_0c5>>T84tWjF;&lJuj?iD2Uf^cg?QIEo>N<t_E$cwDsM3YZPaa)UB
zyfC87BFX(Z;`{NJcMDW$8J>|gI;{m|t9c6QC=l6R$MQfk-p`i&>UZ_0e}B+q0DpH8
zoNo{$-nD@iH&mqXa+b)oL=qF|(1pd~LI*vtzyB=fSPoIAdA9HU>%_P`za(kV!i<BP
zd*`eTtZtv3((?<gwQFpHt|K)Y1prDJi{0Pm6$XCYHqxwe>63^ckn5R}5UJl(=wQb0
z&oqf0X$(*;$WiB4*VIU{;Pns25SAien*YNV>q@$old=I+RVlzHLgKlbkd0sqF~Akc
zDZ#K%HmXqjI=SjF8$<8-;KU3-^%FPxN-laiDir}tBLWi6pWy>Wc*Mj5OZPl_zNauk
zkLS3F!I|2Ezh8z>2XOYw*Jue9NFsdgw%GpKV{W{iO9}U6G(;lMQXz27j#mPz_s-pr
z7t4@k;7op4PkvaLhRMQV^+@H<g?o(QwrEeJG(BgRHkv^}>k<k_o2O;x1N0b4Ocndl
z18mlbc$x2JiE756km#qGqcgv30e(j~R<`gdmM`r%KwAq(>Z9b_d_%iC)Xzt$OYQmj
z4VWqH0pD3~XbyOCcey3r<z*exx9a2<EmbQQME3z6_LY@y#oV$K>Fd>oEP{JHCnqNi
zSX;k-e|gNnjjYV{(QOL`)gWHq?f=yOR6SNAcBp|L$r<3&9=3eWzuai8p)i_j?hA$W
zf4u;Q?rehf*sH(*N#r}z)2bjZWg$E7hqX$xfK{d00PJhc!_YhFLa!l)M?w%Pie(6r
z2v0;mw}IzGs57&%Au^xrj0qFEqHdu>;KYeS2e(MQ?Y^I0vEK%`>yezAHjkoYXZlyC
zIU9<P48Z)^{dIh+2_^|%&%3p`dKDSa|3=8VX$IM?uOAVdwbBHSG>-{lgge{g!=4^X
zd|jSGZ`|`cb~vMBV#o<%O2&z_e(&<_HM>f1l&mRbm$3h>B!J*?dv?9Pm0<HaD=7MV
zX`H0S)o&VKw#Mn*F=||n3Az3EtM}fqoB0>77lpm2xyQ4L@|eg^4ODqUZouG0<|5EN
ztLMzSDOruBLV<`(dP=IQ7@3O&%H7Io6YG`=v%4-6)Yv0HT?meOg+3(>_n<Cvvbzc&
z_{${avi;sJz%#+_cUJH@?0kUPkF|6ePv+1)Q4cgCX>;}T!prFW_47LsQAchMvp_eR
zpDb`40q1ak$E=uqN7lB}I_r4k#Qga9RJX_6G$Muqv38wXN3H*AdVK-aZbse%dhyxL
zdV6YkOKh<I-RAwHKIJ4hv&dej=b{?4|9gUk@ZFs%2N+EpDJrNEe7EcIRU)9jq00&I
z_&z>I$J*rq;zSzLoso+TQJuwgW@4S{P!TRdL!#R3qC577B;v7kNAAkSjgX2#E1g4b
zayGWy>vo_{6Y;IYunS@uQ?sSt6{R6DeUWn<m7_*H_;MeQr4$^{vix(wU0=<1S8Quz
zMv)D7Us~yB7UxAi$ft;pj}Msdoa?!E>u>lL2<tS_9&T<ld?m?JcB<{%_SeckNDNm|
zaI`KxZX6xJ#4_RfVZFSM?f8*j+xGYO18KtORGooA=xBfoEA?I9pU85y63aq-%rTLq
zGSUVJ@mNyf*wD}q6TwYiD8=44R+$`Zm&VX(H`?~rT%{eg(KQ!)ap5R#eDOYaIWd~m
z;_`sl`P9MH?sv)fU`}s8`%)$nLI5G+4=;=p(~^Gwx{<pa22c|_E_a?fEo_t{bVmoN
zfN{d3`U+}ru*g9Z1=wUwIjHeLfNmuLhAN0%q)R#`O?o89VzS$<iSdYFKv}mlr=-u_
zPv?Keq*%H50RN`npM>9vj9Narqp#mKA$KcH=AUof{YCzIpY=<T+_SXjdE)s$q$zGH
zcHN>ZrFFp#)$p9J#x`H*9KgPN`=k_xb<x$~ZVSHGTOpEvxqk4m`nWX-g#^MhmObMK
zgvCs_2u@CE?h7Q{%AYyAz6UUwNK?O+lFmVvwB5)1DNu{;@TIgX71N65NvkZR)FQw<
zTG=WM!vvTZcB!T};=6F(hw)MC(&r{6rnwL#+&@C*K;LkG=H6AI#U%;#nDc`g^FM*-
z9%!%KEA=|OMsKrDxyFCkPf!7FaJf1vDMH?qjAPFCQHKw3p{#s-p@$wE#(lFc@1U~l
zb)x2COn-!nMe~<f^WAR3(gEVjc1+CXalPgh_-T5^6B%&sg<bS0`az~tY=ngya|Zgl
z0pL*akq_rY;%P+o{_hOZAt_P7m|gHZL$6UL$O^Eu6_#<V%ln;TEpvXOI2g}G%*~mZ
zIV~hJaIpaZ?GEig7sYsWLW30%`!E0#xfNY|D2XGO5HJ4p70cpmDR!P~J$EU-GX4X<
z@$55d29NooBh%O0eyG`=!lR_4_$MObJH$yohqcXHE<|(I+4ReTaHw1cH*(9a)66$|
zzqf&D&O?bu3(ZRGs{sncQk#1XA9R4RW1tnyXmTS?Q((`giEjmneIrnbNI;~{EOKdc
zJcBk!ziKoW$OnX?{op%bdX-ou60h|!8c@mSuD>N00rGAp5v(frMmybU5+e>Klm5}b
zlE=ULl%w+~eUk1OjrDX8`To}01GiC(gwxAz;SK=I2BW0-YOCEC*IlI;Nj&d&mVVz@
z-ygr-bk&;mLF2LMggN8FIlTTMdkh-{#Zcr*8)F!<0#;seO?k&2D)KpaFXHROpANr$
zbxFADFA!SW%q69oqY6R72y%`#-u0T!x~#BqBU+qUq@jI=c?+bEBV7-5Mp~Ree+@L+
zFv`i%S+19L`}(ijW&+no@`wLO68)!4dZv`{mMLJ-DL({>a&S?}Q|rPq%nf^c?1f5>
zsd}o>Di>@_EUJQjv^r?P%u36nhB*AYdiZ7AvOQ9nu-Fa^>Q6jr85<Y<uB@JZQm4n-
ztNwOZ!C@p%MhYfRA#>UtPDq}TPoK8Zy8RIfh~*h_fO}a`e!!+tzr6EikkH=3i*Xcq
zsR5ukxSW2Dhs^tIlA!7FCDLd|P^c}-H2J*c-5T()Awsa=x$bO5X=;lvGQ&vNK0P#h
z8*mIZkim42@Ermy(fLw|)I}Bb8&2+%P-J8)k|EX&3*k0Y%mhQPS;X6h5rzTVq2U({
ztFLk9DI>KDZ4REI!tSrV?}?V*UExpN=|7=Hm!m=>M3SU>E1?5l53i`T<tscQqEG;s
zFWW>$ae8&Q<fsv5#%ch%95Mt*JX$>Tyj3hq`;vCSqeIU%KHa`(MDF~va$thg$5AUp
z^rR1*9JrGv0mvb9JuY=X)dgsFfVM?E8{UKJp=bV1U_Srr*RQe(M{aD<4Mfod<y8Bk
zEh9GE^#=2}5@2Q{AK+~RztGj$0%vcj**<UIRMAS$=R!ihMv*rY5EwFa|70TRf5d1Z
z!ow%-yZ3Mz6r0$12CHZDZ+!H(uOG~eB#X~fZQ;QdfG+AG!$19(1QRVz)$fZ!I@DWC
zZ@`66n<)<XgDcIT$Eki^OMijqR6!zR$|BFp<4yNCe@2orv*F0$X%ft&&>l-Ux%)bt
z!6^-#E#WH!o}6X#`F9F9*WVXMHkBcoG|Y~xhb$8GgN^;h*&pD9hc}QY+;E2PN4(?H
zmxf6NJd_@a_9+lCULKalD^|*XcMlOxgv@f7Z;N*N6alRCe2)QZOi%ygx*9})Yr%S%
zh1~cJ@Hj-yK>BJZ0?eF{iDH4n*uS}9urr|Gcw$0E0kR@s@i{#`_1I02;$SbxK?Vv<
z=r^0yE`MPF6J_4rMu}5+JRX&DcHsP%_RMbM-J6fbLsF1YQGw>_vOdKo@Q?q)$kZ<Q
z6#M=&P*a$Ck7;V$hF<Ua<Vrjo0LWhL>(uvhx6PjSBA|l=$bNG7vxkOQu<(H-f$=Lv
zCEf4}zl`6a-<Fn^TEw1Z&y={Sy=k$%=vt5<01~S`@1g(KVE^00tME?AJUu{V0)*xd
zl_unAdYOET=gp(Md__J%d5%L#$<VqW(ZV^=AkiDi&Z=;(OX)06#tybi$F(!LC&0Bd
zt6$^;U*gxL@B6rNOF<ZPDK!$NS!C59)}g$WqLwu%DSv>bhNDOwe}@TbD1b&pSF#qS
ze2~W~gQ&u)!7x=+nK1ljVoV7fC$HrH3NbKiz-70l3Hl$Mr7oeawHBjvmeq7wro_kV
zO2eVip9S3__U3LoNy3Xb38!UD!;3t;uov%s<>6qTvqJf;=ZReNpdXgNSLX-%b<6%-
zso2|i&CA6N{tMu{stA|FB>A#<`%%NmO{Tz36Sz(!o@Ia+NsylWYJB<XehA!XxOZs<
z2s%7OT<@BWoFtw&BwxzC`~JxTK<spY=#wim+u<Ta+MlIGH4<!hF^%PJIhAb75rD@b
z&Io=6G0x)&g!c;E(9+k1_8xH_>-AA?ODjk#zwVlLWnQ$avqZ^>fuz%?_bW~!{HSO1
z-U99|bpfjGGoUW%t=N}$7Gv*OF(E5eX54(I!*n(r*yRKkPvvtpQabH<-SXTqgYhIx
zQ~A-QL87{`#p8laoSZ~Qu{ElC_4}uab#z6_gwpatkX1eo>}0l}@Tu^R*`w{D?y(p8
zhYht1&Ja39fY|`ZtT>;`o=U%mp|^iS>%Lw6fcWdcbK=lS<gj#F`;C!>AZGd)_E2@G
z+u!@(Zm*Ic2q8@>RQb!$+5zJiIh%9UJw-h%ea}z%!|T&l>@|p>W5oNFX`PntYUMw{
zOKX+4KWPUI9y1;BP{YuXajw)&)f239-v^eCjR8ZfZ{#00EDcND^?i5F5Cf*{$g?v$
zd3lS506*#s1d4At3n^53#Y?GDGz;UGl1!M+TQC42#LC4LYr&CPZ}aId_*M6q>3M6Q
zFFd+mJ_J@77ypEQh2L&599K$D2vKxeA5*7vyFk$oc_-HNPouc{Vm(BB(H6V;M(!=C
z=iU-4OfG-$>no%sM%yj#<Su4PInoIqrRls*my3`>=YyG~@z!$38)+^8t<WRHYaMz7
zTC!19TXY?GiEEcDSu*565~8DjGFh^OoNonQ!BelYui8cEJ~5(3PWfd9%#lSVpZ4^=
z;pbcYacFEDaIyu4%sJ<Wlk8Q=KuT%CjrlG1gOpY7Sk@-(mqEJ><=KA#78nbvEuTlQ
zM)Sa3Ej8W@tGxC6W1K*uJ21Bm5CiPMjYwad{aq4hl;sw#^U*w{av}tC#>Ix9Sh0qf
zAM(xr6<0EJUGj+@GmBU>x-mAcU@1=X@Ov_9CGNG;h|KTS;UgD=zr^lahdm9`LxcHo
zM^xAhi1B1U_`zZN9tg+acE)4jX#l#Xc~AD7hHpzLc16G<;T9Acp{g_JC%Bwex7h_`
zOHja)hy2pc9WJlSJU0b<pYKO(Y^B6Pw>Y`NEb2DH$fhw2&A*ZZJ9P*q?M|U57nhV&
z(%KsQQylefrLP@gx6)AnL@)k#0{sKJ*NCR3r{JsA)5b^~3Y88?3|2Cl_<Eo?nO}SL
zsP>Q6;e!-aBhl8+&Rf^J6Wry%vrvpqzfwi=kZ7L*aeNg%A!4tP{3M7R=dbF|G+;@W
zfoE8FF;)^BvS2KyJETiJPM@lT0gS2cPs;CnwaqYpjLmom<mSM~)Ay;E^)+7nC=NK(
zS5(2jy0v&xj%Ux<;uOZ=NQ8KFWGZu7TmR+*fZ;I=f%7fznb%%X_yp(he}I`;+ErPi
zZszc~jju}B;k@;r&MJdA#g0E46^X$NTHKMS%m3)$e&_hWfHR9M+$*>*Z9c1C43WT;
zb#uMVK0vQ#D04o_un~_u5{gOhY{)+EjngJp0`D*nFhU3S84eK@L7FuHd^0{^CO{2C
zL2V;;%fbd2Xu#SONG4MAZ4tH00j6t7xNZbswu}L03k9pWs0Dl&Muo68N}P#g?ilk6
z6+-P(IOC^32fKu9-*VQ92(Ch?KwinlwZFe*nQC!%xfk%DR3tTt#)~soDF)L~LVhcq
zJu<R9gTNI#f`3sc+3*XQ4*joJKQ-efbl&P?CJIm{?Va*}Wq;o1#@1}I!B?-=3^-0>
z>b;T0g$_@%$mV_G3e*+N=5s~-*k>5t3r~cD4t(jbR~FV~iJ=R|{KE#T#Na4ne;>i3
zN`g;O)%vkWLsP&UgeWOJ!3Ke+qyJ6gQ3i6?qZKffjztzZQflzm^9gaybqMkKEM#%U
z^%f(Qsk}erOo=>_6S0N>^UPz2?Wo~!&n6xAD*Il%kJw{x2|wH8*mbS_7FDy#3~;V%
zR$VY>O(14nFTIaYe<7N$Qe<$rMDlyjXG2u<d>?~gae4NuU}RF%FeNWGX3|#GKj9?v
zlQ6!QwNhd^Qt^vavc#VWFIJc7YO^{rxT@BoozwNOrHJ=e=)1GvA1$lQ*#FCDB*nCz
z1LF{Nm!Y0zE~^I3S%zFOTbIRY=Rt40F`eh|HeBr2k9aP1hCq2Pr0KT8F<{FuWF>Sg
zpbl07&<C})?5@`bHB1%tu6BbNjBIbl&~(seIa2O5MyMXi*YR0ayw?lGH)y*jn&=Nm
z$b}H1HDL;QIv;M&_%#go210lqSMc|0a3VF6lGS#!`~f`exZO||tKVfh@s*(>a`T|e
z50bQ{1|8t<xU2=D293CSOxSuYqRn>>vGF-aJ6-NWK6<>~N$rAxO=;Ddkd05LP_mJ*
zgzqtlyouWH4RMUYSd>mtn;ozImPnZcRV*Q`8TrtG6git5nU4A;gVPho8Y%?i9M{Qs
z2K>WgCC*6uwV>ym!ETq06Zh=6EpjYvvd;iSHoOeo9q6gxv)ieCe(|(uM2R73;{&2f
zg5;_|P-w$vI(VB}&ys9*%smQ3v)WLCJ*yYbWa;=)A}5_-tF=zy6~)+wIdin@c2M#Z
znQ^4RTzc@0JEt{AxVbMk!`bdpuH<uG>tFWM++TYx{IQt)C3m_dHd0ZhRz+V{6XgO5
zxtO-0FepWhGDy{Ea75x^MCz4bed6L`k;oR%^nEv;gl^iW=nKSW--!9x+zY1}a_PrS
z=$@|5ANhohq60MroM-m~5{`cLM@)!*fY|IEI6)3=``y*OCu>dykHr%U*+)S2+j6j`
zTx<aB|KfJ6>lgMsT~aQY0K3+DDhV%ulxi$8i$yaUG<T*~KfW^qw_v>SRP$M-V_Frq
zTqA%%76b#yFh-RkR9t<e_<R#LVXP;{pY1u%$2^+Jp~qPj(p0}hp46-z2@d6z91md^
zhGSepb9Xy*ME?4v_j`~~=BaMJq?FXmLTdlSFRan}+!}7T^43}v(PxD<G_j^-B(Vhs
zQmrf`;irm?{6Z`Pe1hnN=zk+WhUCP%o~qhvOGd-KP$Je)beBrgT#TGsvT3FcUZ@;Q
zEJjUk(_XS4E@^g=D93%*M3%O##O=G5n?nf5&LAe`nM;_#4D4}izkNK8zh{BTg`Hv@
z^pOD(U6CVJ-jqy13Tq{n?{|9Jhtkw;dg^L@p2>xsR|7Vn$NTvFUqu(}SluQM4#QZW
zA)eBl&V%Z|t33a$dCIR00ukT@O2;1Y3w$h8^mc#9Ki#NCy%C>;`*l1lm(B<|`S>y?
z?^6u+Kn~O9tct&ZVTIL&yJS=)?~&wTVmnUUN$T*8h&It-t|!sqOzag4c1~O&hcf7$
zBHW(3=;}(=>eo`)FW}Z4DvmXsn1TXFnn;Km3Xud)BB2#yp~25v6kiy<F}4T&?NG?r
zS@BUn3|%x(zaa>)e~{iKhtICAEU-)}lOA&9n4_+n%_2VU(8l}`{14ml;FMbgi|1Sb
zX^<i4+x<xWc#&$3#K8z=ICW<OT$C5_C(rqC5A;cn$%&{|2Qm<is>-a7@!W`WbUG5V
zprq&5`Rl|N--j>c2stiHnaDA^Ci2$SiAqk)8N~E_&Ee%#(4a0%_n>&&+n{((daPkn
zBc?U?EsWb!Z$dr%-%j7>Q$Rm+iPYyF=pAk4@vBi4zaQY83q-wiJ=w&jhi{ecntusU
ze%WRFZ8mAr@)RXO>anQ=_w5brAFPr2o#G<?6KY{Mf!S}>L>YSMT@~FOxLSV4ot_%I
zV2TTn1Zw-<+84$E8HvxnD13Z=VHp|t;zgl9tRSC%-Q>xuq4ar~^{L#GOJm?7@80$O
zCC-P(kt)i0Uz&L+R((l|-0WUoA*JJ2u@2b55JdS|8<nPFP>J`*iRO%JOg!h$EuIU0
zS{_jSgdeEhECi#xcs%5ZNvF3<=*3>CpG7=iHU$)FF3+bJJ7;qJZy1KE)Qp7(OMLS=
z{$|P+maF8oKOj|t@EXR+()TYn%U1Bjf?Q-9&LKQDa_r8}yf?yie=zp24}2tph%(F<
z4733A9+rs2-vb+mzgHrEIrE@dNE6+2Al$7t3LS%ua{Z6TX4YRJ&CJcwqXt0Sf>DV>
zV12*WvDSgO*tejd5%|@levSwVgxxe}G5#(I9#&kpB|KQD0M)msCMW^U@qk}N@~)X|
z*rnacf0j%cvc(3wki`a>2^D~G96sP^dW#HmnSXZ)`c{x{%<8-a9maRQ?rq;gFaJ5A
z-{cdFgz4PH;nowu`D5D<I%3EH6@7@xg=UX!li78uyn7Xlf@_|njS~dODR<=|@k<u}
zt0v->3lPg#68_MFo-qRfOdyuPQq{z1>LHZpWb-XT^%C!)Atd&zxP3jJbL7<~CgHda
zu5~fW%9R{<`#s0UCj(8jW*Y`0OJ8r&;A4;bi6%UH*=H~E;wfhmv6~vTYDD4+&W8!K
z54;eyX7x_~i>n*zH-|!zB+Dy-UqO}9yl6QbxlL&z#p5*{WSU&ztqyKve$;1h<f|ue
z<kqQS5)&aoK&n`}<zvczL#_vnX(1$tL{ROdaK~jd@31~+Kf{b1o;)Lycj6dhexyk<
zQ=(vgUJ6K{y+#erxty6GyWHGDeIA?26Xg+zh{9zKG&UDiafD|;5e4Oc5x|o8PJt9p
zi3&9oSU@|CpGfH1m~wd1X&}FJ>3GvLki95`f9KyQR0=kdFmWSd-@-o^K+7!Hs{3WD
zs5U<>*Um}cSc#s0Ds7Z0(g~_L%sOt=)u<=VWdS;n0mC+X)db4f{gy|cim5!lWR9EV
z4w7Q>omG)q29WdC$$OSX5o_9m3$7d?-apH<`tMq<uilY+L7S_KBP4X+EUInw7H^3u
zmOYPUPBR1vzrEfm>8VGaO-*e!OmzZv1ecZbPuSrJa&H4Vd_5XFI$F6kp@Xj)45^P5
zUC|wXKqcgNc6}F-M4n+N6d=Zq95A^dl>bOL6Sqz&91#A*je3xbjErXbybP9<mmJM#
zAB1NzbVXsiHtz8_Uh<r+`B85(ASpTa@BE%Q_kU{3%Pc2Q=pG*D@Doq$m%x<37_MR9
zMR)i|^l`seHZDPzF-2v(1L=b3y2p%SrNFjr;=Zn_X#pn$U28+bH-@@A6ItZ{FIp==
zr23b5UsRVbY~RO^fEqd2-_K$(#Q_tmh5kt#DlC?g(J6#3fvYHWKxXXER(Anva#~to
zp@jzx;%c+)fCjh~8cSlUk$YXreDOdD3!kW>*>m5Mq;%?<=co+~R|^fngciNMPq-+S
z=rODL4NDU~P*05dymICxqW;&<$>g~*IYN$=@YH@$t)GE4f8Av#HwGfh5wVGNaUHxl
ztnL@4&pli&uNRp+&4ldC2!zR`x1U9XigV}@_BF^J+zb+V!0pugy_0xF92!SRj{eOl
zlrTivA515!1X4t>w;%0}A2Q8!KbpuRW^cOJ8}@~I-l02ht%(jdJ%0xhG024sM{KB+
z^t&NVEiy_(X^&%XBrQ1x53j~=D6DF6K6Bmc3ryXmIfsVzOWtDoKZ~T^ApWxs=(or5
zt9%%e|LQ*;35n|;Gi$%}-`4b?m;n}kLJ8AWL}h~>LKu`OHUm6Fj?V|GOh0xtw06QI
z)UM#<-M@3Inzj0*sfG)HVg`|&w|F2FzgF0#4$Uvu?o1J{22Cc5bauoq9s|st4`oCw
zF`5z6$uKKjzcY)a+^JF$kLHYN^~DbuE9}58d4jhl{W?6}OQ-Hei+=jyWWZiP9(`zp
z5*<3#|JgS#2UD1z#&1~;13dfCPyKn8(xZc7mohJNZ4Nq2NKhM@u&+=^1Zk@;4y~xN
z5=LMuz*LKc!#4VA%~*9lO7Lwg&)_tZZhTx)>D%^a1`=7>)Ln%ANnuv}L1Q%A9^lil
z_5vuK;>C5<`kcHhRjYR;O(hb4ID}1X)O~VPQr;i<eD}A$5}at}EGmG*&M!&~z>Xi~
z87Mp2l+tpf<J$H(M$dNUiSXS=rtbj3@!rt6RUv`W?;+b=LD^;bOe0AQ28T|0r}+sb
z0!T{3VH+bWjn*UGHDo}J@)H370vR?*XBQ;e$x?VvfulzUpO#9Y^t391p2T;Cf5i;y
z15!=WM?D{%#bbtoI-&mJB{GGT;WwR~wK*URDY_N4S7Jd;1s!NkBSXhYLr|jKf(0KT
zIOe0a5|m+}d=*w~pd*4N?OvQueH<|P+8#yN`9%AsIV>VaM~`W+)v&FEf@oFLuEM&y
ze3v&qVPj&d@EJmrk-)vyEbywKFmDATC6WQ<<`#G(q~)KLH5u4J4TTkVGElX4jGJzt
zri3^og3*(bHk%DR&fE#ivTCc6No;GVWDT`S07&usUq5OHB`q!K4}KvmTwDNnyn+G1
zpmReNg9iDSKslYN!0S>KkMAuaRz?`Zm1LCNUpbG~e*xl`sDcQ8H4X#T2D8{z=3#<%
z?(YutdR-ei<0&BvX}<69>S{H>)7~ykJ~_I2zyN}vHhA*R%-YQ31zPlKQi^0L7&<ST
zb)F*PvU)F`FlEnb<)M8PsItjvo{n#VH}sdfmFMNf`xiZZfQ|LO%#&XrLXc9LVMS!!
z-kdB?Y~jTHf0L0i2^N_#ja(N&w^KqzIJRz#Ta5&hz?+la#wpZA#R!SpQ_k})wJ>>5
zXCQEJAE1F+gFig~aG<HJwePsmadZECVp@xA{9Gh5JEnn_eR4*BJ;7z!HzBTu319=#
zw+CEJ|JfTQHCR1~0Lq9>&4q2P2A0=D{*C^WS5fMiiicO|&pkgp)&3=+6Xl6<a2*V>
zb!BomtE!bh+}i?`bn&bWc1Dw^4*Q-7;Tq-xC}OfhY3%I+ZNF}J0Q@DfrTYgxY7Me>
zkY^aZHNjQ-dqvrne;#KA9AJ$S%3{Nro+i(JSS3*XIvc0IhXK4gAUYm`gy!q(+tf+Q
zikl0~il=8Lrgl*CMIn%0Rp;o=$ijo@cp(|0<o8GSLMkl89i5t@FCl6h4&dKc3;zS^
zZv9>0kL@di6tBco)I2&6#YG{;>T&e*L*P0po{)|wSi_=w@S@q$bGyGwe^_rkm2AJg
zb>vuYfV^hoqxzKpw4hTvc`%==!Wa=0`e+3Jql7P59H)?b1sdX7&S%eL3U|5U=8M21
zEaKBI%WG=cBaYAL(ijK8Tp)*u>`uV{^#U}Wb#5-E3eZ?*bCvY#EEyV$KU=Ar+d7vO
z2H($D0*MtDxPiAo)Xqft>Z^*1`WB`{#zPK7k~zna`MBn%xP@1I#v1RpPq4+uaeBwR
z8?e;m3z|+82sCUVP31R2WbZAHg?Ci%aQT#XRFuDN2bFjh&|rAvLQeo0KQ(v77ng~j
zHG)N0%7QKX8ZNXLNUM!y)=pB9_7hh6+t+=C<qv1gXJQdl##|T@3<UbO?$TsDtTAEW
zzri^%gburh(pP?Z{ZUMO=9d{?a0gZr!{{H21_XwHZ!H>|u)3e4f~6a?ApR$|v1}<h
z$qi%x0bER+hjV`N{a*U0yAA)1g*%|2ras3Ziz=|xM9<BaaMzS;X$Ou$8<%yKU~yoF
z4MF$sjNMg;8nWM;?bg@%b$FWhirkMeL!T>=sH!K6Dr4Pq)F{lA>3MXp!2nTY3%!Jq
zO%=_NQD5LOZ9wThoOY>iLqow~J1_aoc}R*}Xr@bxml>!hl)Re#l~TwS)FA_0I*vJw
zqJqv3FF~T$xum`Fi#4pHFwSpG5YxhV!R7GzG>G=#<NnR*md@_F+~srC=P)Kj9$R^g
zsC-j*sHWKC!^1KaXip=iY}lXiz$>MJ)YjpxwY~>>k)A$1Q&j>W1E6|9gMhbHjcsg@
z!JSXlRbc7&I1Xo;+B6|JZ@E~M)$|;LNro~JQ8-Qm%<+p!1UsMyK*Nx!Tz{nET(@Uq
zG1D_qgUorq)I-Rt^jr~{Xa$ydZOy7$WFppRgwnuoYJR|yXV!X67SUKdQcdhffY(HT
zr~mhy;&Bk9qS-bWrpbUBDK_am`mZr~UTxt%p|BhhcvsBaos?n*Z>i->tAWUI2aeQr
zXaB|;JvU;rxyILOU2*2Zzq|5DmD97l&7S$1bCpTUMdp&&VVkI-PiLukA2_ofuE2q`
zrLv9`S4+a`1AWS7H*DTOV8zwb0)XFhU_9p-{BA0n<-BUgT#%_n|4$w##>V55P9<c*
zYGGO<VxbsKoq-iAVV+pnvHDF<RDb}gpi$~TY}bR0q!7iDNGdB}qqKt1t=s8FX(sND
zgETO3=!`*?@C3#DGgZ3qICGAaVZzj&I4t?(7_T%>NhT{`1U$er_RKWI@2#v?5@?M7
z%YQ9q<R?A^#Qy^J7DrohKhBDVru!E^ZTQH!HX;t37*9DX;LKi7`R%9Uw6;13jp-n{
z`ob~bJ`VF{`SbLEgVT{}7~ZR)sDO29-7?o4vz}t9CoD2@G~kdS#~G_SmX|?LWk$R!
z&)UeG1N&^^toa0ezY4IkD3APuK{`4nqpYPIds&Z>oNNJP(yy`NC=f2bZYvRl)wHFj
z2*{I9MU;TEuwk5=cg9!9vb=iRo=;INNrZ;O_8z?Z`hGC8&sw46u>qsg#n~&<l|2Wh
zeMsBxNE20r<+GI}lCLefZCiwXOy5wM5~h_AQ)OJh2Z+oP^m;Sw1$Q#$T$2#KYI?-B
z7X@GuXYcssy$qncFzjluOYzMaIC!ja1W$aTJw?EhM@Y_)ILe8<{+a^^?m*2WvXj!%
zLU><+`nN!gybqi+4-v+q2O@JsJjuV#d4BGDhz4!7hG1!;YF|CehAAnRh;1&zLg!-l
zuD3cDc#*WyN<$gdwfw{wy_F3SGi<+km9DI!3+QgJ@ZO;*K~_VdHZTD&Ck2B14BVx(
zbrkVvX0jtnJnw$_%h)?6d-Oa|l1Mu>Hn^yu-EB2x`sbE{29$`Cc(g7|Bc}HH_nHPv
zZe2f=-CFB!c~u?};qVoZpLw*1J}F5Pp4~J$pH7=*4E4ouZwC3=iz!pmM8Zg8^jryO
zrZ)bnn~ZgkrwwrWCT1!LMLSw<x}(u02s2MFmf%_0Rzi7tUt<txBdPGSoB$$xppZ`s
zGmD>@O;ExM3fg1u)VQpQHi*(O9d&ARGofLB!@T4I8h7%|hbls%u4%Ubc~sh3wsRun
zFAI<l&*|c<(X){3M|N79NkgI}`4`=RbRh_&i8i2`U7?9B?6%MjW8|&8@-RvciH*@|
z(^8mDWE;_jz$BTHyt)zzJ?y&Y(-I)};SmQ5@S<}<OsA|Nj9N+uw?33+BEt=pI(hZH
z_K^8qJcBn=V2A4=q_;(sbAKV6A{GEwLqQGtaI`>^!OmmB<hA!bnaxb<RsNjIABMux
z>gFfSW_z2wY(b1FAF{p5ql}<O)kcWKsviWx@vwKwhU>PK^1|pw<p<QLR7cnBRu*N2
zYMN(H0rQA=&IQP8A4yFJS#cFUkXt)ZVbQU9^=8U29@M8O(L@In7}VLcL^XY`xzZ>+
z7*lEC69@)gwOGsYe%SuWB=tBy{*?2?NYw|NaO5~S6Tz@`k2{_f|2urT(RL*)$jI@}
z2_AFynrU2{S_Szo%4+g7Sd#hn!@&DVFr=h>G5VbzpoRwJ<TSc-Iw)0%{(-TNJm(|$
zSB?#aK4TNsvLsLsVz<3)n3R7I=xT~-p8j&_e?gf0mmMTw!(=na<EgZO#rAWPdgmVz
zkgs~7$yYDY{-Dqe8MxCrZ|xQOvxMsd|Ix*<E_I}#j%kE`E%77tUG<Q4O=mRN7p&OL
z!XbbKs~8(R0f01xb%)tZ<h<8QHj4BfS$mRz5}B5x-_{NtU5JsrQ(63i0g%XX3I--F
z7?~R45mcfe9vU{Z8Va#gSQd^PV0Dnc#&f8T)HsLuyzA`b5fohRC}Mex5_lGLRU-NP
zu@~@k7x>zqSa7m?m+mPgC8@X6V1QLW-o<+ad0?DB@;eKYGH;1{{7LyiC5@rM8&92+
zKTI@c#!OKhenzAcmWh;gkgFT4mf-RT&43$vnO{k}>!`~L1n=&9yzL|m5IJPNR5n}%
z$}FNGvzreEN_bCbKA{mxOE#i9atQNZBR{2P47zxH;OP5aNYhVCu$<dV>&K}eTbAf&
zT~Tjl;Q@8GrjC|JC7S3lJ}G&=9+t!74~!9w%0NPFWx#7@29i1DTO#fiBYMP!kk!Uh
z=Iq9sLKb!#UNLfdT+DyG@(aR-m%&w=hv?p%ceGinMg5csqlMG$i2Pf)>pS<ID;Wn#
zNI4=1_OC#u>EW-9&xsx|lW1EFv(8=S9vI$V#8he{uIm?YwlRVG#GX$7jpP6cC_@jd
zf})chfwyX`a4NGL+QS6Ets@3*9l(1wJp(M^GC95TR!_wa&w?+`0n_#86jP4%4oE!-
zxu>PcGa=w#FtYI>$kYf{Ok!c<(_+XH8;mRtMX?5+v-iGV`OM$LhD;-K$3IeT>V9WQ
zO|2^3b<?^o8uSZ6Obtc};j<}3;;`}J<m%?d;U_V3uSaEdx}X9}7G#vvV)Aojn1CRf
zS`aL1;jrkYmID!Vrc8OD&QDoiM&~Y5FE#l$15o66P{B`LFgXOu&7#rlp!Ll1->{^=
zzMGynL}TA~@Zj4;=fZXV+}+=8JWu-Q;#OECCW<>r_)8d?tx24ji-d=d4}RG=v^~Fu
z1eS~<9<nnYf`H$}cfdg2lZ^o6Ry$BVKrL3I@YJ*7`aN%*mSAjG-SM;X3c`9o<ZgA>
zBbSt#v#?_ZP+nbO!C)2~87(_Zz*m<WIjYcV?X&tckv1g2w^(3t$eY@-_|PruEJf__
zWy;vKH@{)owYIL1aqqi#|2BV;znA&UsONc1Q%=YEg#s?uocxl(BOs)^0a%E@7(fe5
zBgafM>sb3{P0(NSGyN?0w11<uSyKXt9MPG%_NHT$8_u$ds(x!QQCK1T`6vZtLJ*}B
zD7Y1TvDL#2;t@6xVit&?YOZ1YaZ}coL<v_RZIffn#EEM+ZY(B09ugH?NiP1=d+`n^
zEZ+^J|Cc15gkZ&+G*lVx*J){JL)$a8O2pVcMzim6C+Q+=>vSX33?0;Tzz`V9{~;bg
zfdHfz4z=JZ>Vkgi9j7;o8QT`uM%g%94z`$#3@vfvx=MZo$nJn3VmmyItHk4gzEWki
zE$B3|Z-?TzmAll#Y;oY{*Bl`ELFSt0dr6d)!>gee;1w$p4ShGXm`;YXM9xgBsJ6`b
zsPm`7N=V3uKn<IKM_uEUg`;uP{UK-w+&y1`X@HNY*N3N->Jc7Tjr{1kYIt?g9*(O^
zB!C|A{I44>3tJY|ZWUN>j-Vv4sM;aUyuY<8QRHrvew+_*0DCO(KjHq1#m0&&^s<L-
zQJ$`j05rLsb<Xr>>uw*#lGv3f^{6dXbADV{Vg&9a4J0Z(R3WHQ?U`OIRVplqQ-lv)
zt7~5{;1PypAx+6%14&9$MIvc4pB9m%=+x~u<Kb#sQvo%LMKUVB6;|v_o^>)YB%i30
zE*o!CHcdUcY?&2MI`P{1P~}thg%m$*?2UCpXr_J`Ix<ye_`K8T9!u>igQU0>77{;=
z#amEoW!*$<%81IzH8J)d@mBQZ8~rL@i6xfqsRG~Kr3sT<GUq@?^&WOVxL=UWgpk}C
z(Mm;gROjBqoS8#S?<<1xK}~;kj<(~)CA?}4ynIsrPYjNl@PM8RBk8hnk&=Q=z9MQG
zwxH!@E%8F-extg|WKP|YPfErPkXk`J<7`x^hSz_XeG#<JN8;AH5P;My>M4*<larr8
z&z+eSCF68ZwI`myxUj*RGJS%8yqQ!aSVnWRf33?G3l}Xxn4X>4<IGIL@ivKY@12(S
z@dvd07CIJnq-SjFw9XP-cl4$7PZ`llTk2sv1LTmZ>|Zz8pZ0r~1MYKLNP}&hKfr4x
z@w#bBr-n>p;&mV8(&N*Un`8{Vujf`r_(!l~dAFg5OQ9x4c_p9CCs2eqIzV(u$nV=s
z{4{Ykq_%I`Hzx$PTKbQm1>PW;UfDo|nby9twr=Qpr)bi&%(&n&ksSV&&c_i+hQe~*
zh2^L|Deu}68359>`le;FAtS@0M-5$EW=WxEB11!13}WVHgl4%W`Ry+1r0cjc`UD1!
z*D>rz>hAYSB;N1MRuuoWG>(VcGg0;+UU8f(sRW0dur?aKbKD=uSF@qT7ED@~nar4o
z5JT#~MN_p+<&^X{-x;o!TKtG7Z%_kEXjnKxs;8K#PP+zJnlh5pAiF(i0yP;TJs48e
z9xx$@_<ZIfdUW{GHTnhvlj1*-K$WIJ6qg(0hyjWV(@h*XMg%!d6!24;<4ylPwZG?f
z)sDq~2mnR7zFQDpPt`{9#UCVa@`uGHH}=NUG~P=oy*)8IxHQ9qe$97p+Rb^3f=$|n
z5+>y3oxV@%pMo-CvN_Te$I`B7s78lc$@qV30WiXtydk;sR&F;0s-G7Q_ng}P50bJg
zjcwpSQ;E$QXyHQ#^#F&A?JI*8c_hX51+SodiO&RmO-GRXhP!-)D*Hbod=ybEo?4<J
zcf~?B<_#VH7lLcFXrsR?U#B@>|8AJa5=kpB>|#iBee>@FgFDdx0Fiz8uW#-eS%w2Y
zv0pSHgtBNyr&BgUaq#WV{)yGH%!&uym|f*wx}cKL4JI3yfwq(T;Vd6-W2<($Nx#ys
zlMZlx-#!0ex3<={3h(#8G}9z=N>r{mA%|~BZv_A-08)9u1b?_##lO9y22+aPyUtC1
zq<#(>jdMM)h#ERvyij`oCu}=wI=_u+Yo;>43&j1l3n?nm<J=rABWHCD;4gSR^M3>L
zc=`xeqqfm0bN-c?y48XFtYU^+D%YIH=l_XGhoGlYnJcxtS~<0*(o&je&E<vQ<f#^k
zm>LRTaRGRMB>^O$8h8nG7mOrhr&lJpxNKJYDLF&5HT}4a^dZU%7)0D3#J}11@mO)0
z*87<^_IrnNI6h>S>;+XKVErGe-U28K=ldR}8|jc1>5`Q0mKG3@?k?$WkS^&4sfTXq
zR6syLx;vzi?sxI``Of^`8OL!P6n3B8yXT&B?!CQ4N<W7(k=NbU$$(TgNAWYAi5tEQ
zdkp||9yec12<2Y*pLZpQoG)`o4rMc{2`GlC@(qp6sFeZUi620dLTCjurn3CQU?q_)
z+K#QOvnK+Up`=1VOBV-tPMYqTE(3Px>HR<v8mL|odj<j2i2PbgZtUEbD1ZQBOJ<vt
zain_pmw}OUU)*i;<SuP=u4N8mijgQQgAj<^Kpd4Lp$eaQNc|Q(&vbYE_W%`f*!Y=}
zX1z@CPlWrcNI^%k{36T!is^+Bi;lZ>2H;P8`TL>#6ZwBvoG<={%7Uviwz)c9(MQ`k
zi1-&Lgm1@VZG42q8ky0+1w-I?MT)Mte(00sX&Et)*QORg;h;=K7Li|Om0Upf&3YmF
zddK_{Pgl&v$5v8GX_v?G1v;^{@}4SqlRm>lI`9O>^j57MH2Wfvpr+>mNuDqiW|V!E
z7eBCTeR79jv2l(}X}G=X=G9{RA<bqFn-dd=opkx$X!1-il*uL>cvQJ2U@X}DI`<*U
zT9rhHJuaOzDr4GVkua!0QGB`6r^9$Ryi!8T>VI|Rmeyq6dz}cA`s6!nbN?}FJ5e&*
zdqL<qS^5o<I!TV>zp|Qme?vOF-DVj!zj;eOD=N_e0niI8iE*~5L0?2oYv>)Ag8ux`
z8XlHdouDd-KmvGL^}<iDVAz;<4;qTFg-?YEIY~AU%x&@ZFhFAxr)`kXz$J5Aal}dQ
zbb6zv-ep6D5@7yHE^r)MS0Y@gXKd=L>+T^7A^F+feU|}da|ASATE7GxC{K@Z7Z4iV
zD6Z8}$k;TCL(Aw~A@{pe3>KR>@r1X41Y;MVw`*WgM7AmFvDe!*9u7Wcy6+K?r}no6
zgXz8~mbQb5%?_`sm~S}W+P;~7_9)b4GukP;x15p)=tVg(hD5zF5$h(=$Pad+d6wou
zWhV81>x|>lf@|F}MUe=Y8p>j#@>fb)y@+U0LKl{v+)Vfa({<hFJRjVy%&o1p&cROL
z>!nW%siKGP-_sNM^D$reb`0O9^TJbJF$5$G77(Ny2GO&3JPQ-YlfhQq*Bp+ltenYb
zk(`q=tG~Cf3PRkx^$;r{KQkw}D#C;Ajw||Y^_$7GiL4w=4bVU?mVEJ-R%O*$5?$Q9
zf?X~b;HIo4CrHT&)g|uyyitlohs@91;fBJ-#>QC%L0S5Uy8h2#_|fIJ^hgW<#<Ok!
z<9>#*?)KhYhQ3(G1+&)H5wn&iQlv6p9^?ed>R3VwfNb2~gRc5UxfcK@1`y{gAyI`%
z&--0uGztL@RLQlA&749}kk~Sh@82iQwhYF0=jGa6mm?7pk@as*MlHakb&(i4zS<!`
z4(XwhO{{eM5Mi%CGxv^zNkgIY?yz;(amAsR3+Ws^z?5(!M=wEYOJ)*kP>(AVQq(^7
z3%Ye6o$?QysWpW#AK#uy4wCi`2s1RuE<!&hyCW*SJzs&T<#sos0!1Xx+yvRz*9$^V
z_fFcvAaleADsC+jU&rHs1}hKei{v~7Y!Lv}U|MZ1{P9vMslqK$)U_tQ@O(SFBu=Ix
zjv?7+6I5}Jyc!hS9&vemx9C0bQRkZgT8!zKo8|1Sd6Uj5`qvn7mfb(3A|oyD+wbYo
zwG-F169=isz}xn4HC+G^%dmz&s_NS`nYF1xPplt4%yhT+(||yV!l^i$Un$&3=H-w7
z5)m@hQF^9RzS4kDc^E>*jaQRjHhv`W3G`<pl!15BIb<NG0kFf+z`#EhwUq=U`C1ev
zBfn=2si8XBt%-k~>p+jPt@0vdj%Rx{??E#b_11-Suw5Idm@yTVgKc(Sn^JNZNynC<
z#CWChL41NnrEbi9zpopOhPHS2_C8-5)`a6`|If(CiM0`Of^__Kz33&3MI#M2vU)V0
zerpx9G(UJ-3O@I5du^Honfn>5Ji9<boEpY3GNScWVQ!#MaD?{L0k8$~RMA(M=ab~f
zyzVsCc48=vtVZ-cPkHA0hrlmpklR83^R)m7j0mD3p~C>SJp(3LvmZ_jiEuFvyno@Q
z<&A`bk^zN_F`Y!}-shj6T)JZ<z{@IdwIB}YoG?mi0cA}GCgbGv!eo)c^}HY24sB%^
zx;fNISPiWf<-=XMeyphrJ^Z+R<B2=0yu68*$Mn9Ua`d1T4<d#)mzKJ*WH`-h!c5Te
zpF`kNt+~ze!b%9VT<gUNuCL~2>*tg-OJtd)6Xc;bOlp7@UT9vg$3JES*;po`Qf-J)
zm{>HqEb<qQ|1@)6jIS<kp&7PWew7XNOlKt(EaD(9oU)d~)bHj-QwWV@$a|*&0|uAX
zJaz35=A`}rN_slA`TK+br|rH}N=HIxeOkJGl394DhoELhx4L*bwW_Eq;l043cX1Lg
zX39^~I5pnQEg7kpaxg*CpROxf@x51d7wq~vFj{L4r}<P}k}U|b$2~AnHcHL6`9x$H
zvNO@})lMwED01@4>D@Oz@xyo+EKeO=8f6*(Ll~ty6l?@<UmVazqc#k?prSS87Fm9^
z8HWbgDFjihkrxA)09fPXn5QfO=S_T^-Ie|hos$%HE3XDyPCUXhzvSNP7BT4h>jqOG
z&IH}K3DA)|cbFxEDN!*c+u<V2cFW)WGBX3vh7y|<IpWvRf%Yp`*Q>l*o$n3%KdWu6
zNvYytd&hX(H<@5|5W9hnNF!@d%>4HgkZQiGaUqX!hRARtyu}x1+SVG=rz4$TK41-(
z3%wYYN7!HvNgvxR#hE-6)7J)*Y!Ap(i)A-$|E()C=!Q@gk+J38wd(ZGdZ?K0$|2=O
zcrA?=NG6{Nlq0BD*6e6}dsaJg^;XkK>p!~)Av4K<DH$W9Ak){QR|6}<<PI%@d)4KH
zCiR2n8CJ9Lz6=e^r;sB7R2L@Pi-<mP6*%BWkzJ>7U?NQaIp23<!ag{DRC~%ER2fc!
z7OLdu0S|!Y%e`cY6n2cGjt!c{x(AwPs8-M|>p&8emiC!zKpy=v9jL#}UW2FRT14@4
zbVf|ZC8O(_5F7L{b<n!mak5Qe{4~d~arLuYez{iG=D>$KO~e$Dx#x%1VLDu&9|cEZ
zJc<2<Yf0q$U!5WWznHZL-9cb5M+Kk?c#lRrD6+XCUk~c=;DNM!x77hJUIwj3(?&}Q
zvMrWU@Ftv?d%<Pqi;;l?_*)Xas?YCp;DC4RB4SMKAxEPIlPgM*$7CPfd3QjI8*#wQ
z!J*QPwTu>ggrcg{4otA20Ci-0FmI6q!MVx4`31EQFV`OhNxAjafGWGb^mi_US62)$
zX0KHwjM@5(5p)x~d7ojFJm8UyY|My4isH|E%{DZ0>7o=y8cacj-HlA#e56MVmXqP9
z7ez7F@m>+L9Y3@&>AF0&!oH)1r=s%Xfb4OJ!X$$OXp3ktFUp!v+MvG*rbbI(MgG9j
zhi=?HPthpnMm^nYN>diRZ5T;K3hA!nGzkyWz*<wKzUF)>q!2p8D5m(gG;cc|T@K{G
zaeJ_n;BzAi`EJ4Y-QQC*Sj;f<_5ctFaQ(jxw1_w*yAz9oK4f)bn^M2CQZ|v~NZy4?
za)qrJ1EHT#Wn*z*^BhQ3>s#DAYFr$uv#S0(0f*Z?sKaGq;&oX80zh&Z8IA)V3BLva
zOM6jefY&)}u_8f&i=c5`Z1bpN783ai$MAeMpWSfJ<R1I1O~rOksR1ET9S4>~0b8Gu
z=S+qUZw4T_Hn>n+omk~^O~U(u=_&7q|H{aS9WC3~g)5pSErLjEv)c)cuz<_6Q9{8;
zp4ok_aKr3Z5bnCAXxsU1afzNb08rg$OoY8V{kc}#N|&M572K)?x!{e0<$uc}qf?X*
zOrMjRQF(krQN!MsA<Z9_m}1MIF7f})8_nR&02c;k>+ns5Y`(597k;48u;D%zy`<wM
zA#jlNkZCL|xE7b6hVXQzj|6R-dBMB^lt117(v|0RA#J`N38@%sTh}R-765W@Ua-M#
z;G^s?npNv}&1+%Kr2sh%DG?R20c;Gd^;alNnlaPi=}dxF<viD=WpcU0JWP%b?68Sz
zJNkUVKT*&FyFgBMLIOKY(Z<esb5RT@;{-{!UrIU+J3>YShxpbber;oAWyN$k8+}6w
zMKEs=M@w~#TG>_C#pRoRbj5SxNN_;N>nXovz+!`Fs1A41YsTL3`PUpC{3*X9Xk@w#
z5)+!B+}Byn)z3F)++uq#z}fvU%UH9ik+Up@mn8_J>!>d}%+*HzcB84ok)N;Cr8dIQ
zqFoM4(sb&A9uH(HSxW@vZVk=O2Qbse^_U=B&fYCbE4O2}p@2n3z4+L|R@d=ZNIs-6
z82puM!d8~#V7{5lSRFmL%wJ7?IJL`rkHD#-#RHjE=iZB#A&(xLGXwFht_vU^P-Ec1
z6UBFG{buYej;`hXrE=|$DkK})4QTK{F@why5T|d=fv$&Ytm0(IILsbPva)-V@*+<n
z@fAv@VXpS<pc=>xKUAFW24y(jPkJPte2mf=fTL&lqW5iv<QLk3m*<3i=(7Ux2%BSk
zNJOqywY(>gr@TH}uErHEL35fh1>-sQ@hwnX08bQCTACI`wlKGpB2gYfTT?7D3M>5A
zT0pDB*y>JwCPm+Ou5_^J@OjhUpc}EsZ<D^lPbL@ME`^H#w*v|UEWmJ%G*eWxrXca)
za}Xe$yZ%h*+fjSOMF|<dG(!Z5v|bgI);fxomNCtWLad#_9hBBKpiwY^lObGbTJS^i
z_zS^8xSt~o=o3uUw5&5&5TFUfYzQL7D2XS#f4MP{!A6vPJD(Lv;vNH^B}!0%`<E%c
zy`Ji5w!w+fByDt0JT^*U^}JaQ)17RqQ1774Pj$Zk0t4YRR@iT}?Q1HZ)&INzBXT@g
z*-ZD0qaK_DEVB)!pm^|xF5pwSJ}|TB0V6oL^cuJTFkqZn*QDl&T*|+P*@F4s4F>{P
zGLkC+SYaGT^QQxoH!VV){(srufFMP68Yj`AJeW?SNwtG<^v$#zQE1W~_0nfH+O}!>
z&r+m7Go!W6axK4YE@nE~#&?Za12Y;J3`x;so-fxXpJ7l{HswdjWj8617Ig?p4*DIG
z*0~d@$Xf9!X7R=7a1plhPGvUmEu`otrNcV9Zsf#2OBK+aS)RE2m-XU&bN7JrAqX4U
z38fuZL$gLznJOntAA!xNY-wzucI>qMXBA+Ty?>7!LCH;^2y<Gl^;VwU7ewvGBoPn*
zz5^75f&r*779_1Y35urFW>%=o+cpf2crBZ!p9<eR9_FcWW{tDjpT7}_TtTf^2%Up6
zt~j#Fx1U5#bv_3DKdAADIO37a%nO`IqdsYR@weU$|B@xc__5(QbC+{YGmyv#Yy7RT
z1?~R`^?MSP*C^$sC5DMVbCPM=E6Xann)Oth&+7WRxyc?;OvD9~|0q3^&dNJgtjjrp
zbk)~;S;V0F-eIp!2v}!=s7_1h&Yns1k)2aYJv1e}`o(YKzo=@E#?T5jr6)KSDqFCf
z3}<k#g3BFmQd`u<CF4lFtFNbKC4Uf%^S0(cGfdF078c=w2MNft|JX8>y|Z;j^ah@Z
ziP)5tU4M$&I4XRB0!A`$?*S?g(g*^$Pbyp~v?$_VqQ3VIDJ={*l0DugrOv`!aok2%
zUq6|wdwo(}0t6F+N?qmTZVcXN3+`U7q&iOQMiV?MEi7^^4JWTLHb;1jQn+!pc~>2p
zMd;7@TrFAgd|Aq{6)$(W4-!Cn`!VTE$Rr*F82fr!*;iA_`oR7|)ONaz?Cj@@yn&;-
z?F2l|hBbhH(tX9`kUH)KW>Yh3)W8OaK%Qh&7=bO~8De+^WJgMjb0Io8jwaUr2<xE>
zu?iLaF*7|qFZBZ!9C7VuQ1$$9!Lt!6#zqZ?#Afl?Ll7HnD|$C#mqM%jY#E8(UOSfh
z;YurA8o8Um1ZnhlvvNWhI01YD;_ZaQmuo7EbU5|#2T9Es*~6czNVjvq{V49^BDE9@
zOc6YOYYdd}ghn-s^LgX~?^%7h&S37r1gJR~Gzidol5$Ha+w@e9AN%$rh(e3%j=m<v
zq<U`#D8xyyl(CpswYlI<S-Wgu3=x`Yh8hjuYPl{pLnFojo+=V0Tgouw`o@sWx8b!c
zlLP0+nLRK&M0=UD5N<EHHzEHkuaw&B$V8N9=fsbsRMgS<09SfCRqPzGc-wTyfdJ&$
zhk)=uo)I0K0&E8+Qx@;jzYZTSMASnfguT5%I~D^R;P38Y=cI~%7DT|@c<<W0>iAW4
zIFN(IOyDfwnno`SrhheI$gmUBlN4bBa5v0#_gR8OgP*`AWeH8gl%iCj!;W3O=A=@w
zA7%c_^jxH|Td36R{QDiV(uM~<#>z^ZdP-d^Mh-Yz2&4PdAJMG8)Ivig2+J>IkRpYd
zLql<HAS)@XB7U!JoK2!)Nf;x58QxuDF*Y>FPB5ubea@Wqob_v&<3V=W7-c=qNRFah
zI)@f?{+kos>vDF6BH@dODPRNs1?f4Ea#D658EyC2x4B_Y&3s{jkOA}c_<fca8y6RB
zwsEe9qy^+rN&WotXMR_j>|03>Wv#rz2PPJP4RiB4JY}EA8P`vF@@q5|A?c0Wu^;V)
zR4KY^zMHrc0lw!opp215rMiDR&({{)Fbb<CaV<1XgSXz~=d`!?HD_pO;E5&nzXA;d
zw9$06H$_0ZPFYF4%d((Z{@5iu+;%o+*O~t1iV^Oxl)TH%DX&IWodc9v%-3dsPcZ<z
z`u6KH7A!0v2QmoR=m$M%wTA`)I!id^1<DKSKQa=mFkn$^Q%uPKYV8CKH$?xvAQaTo
z+9|(-sRJ74Rsa()Xp`la+(kOj(^OkCOx5Rn53vXZcGia{XZ;UKG2hpvkk!P1C}8bW
za`G-0DiW=!?hXd3UE+_)j7Q%(8%0d!B<BZBL2Hw6>4#gFm%>*Yy~X3gm=czy1PKG;
zGI4QcydT&-&Y$rztVO!0yBX2<>QCJBX;dHMX~tYyt*W>jt3hw9g?B&SzsvPp6YyxO
za2hV$LlB$(<}Du%7Gv#QFx08Y!9;*}pf-j3zkmhIX@{%bL5;o$VvVLC@n*ty{(5$T
z6w>o7#p|eUX}=dV_}<i6gT7S!Cdja)`8WLr?=%&E2*q!mbhVrE!O3T&xc5-k^(jA*
ztLB?&fYz-RpnbFUVI&P<;AN(vM4oc>w$*`h!3ZlVVeHt4_MVY|3)`PceWp^nsi#$9
zrn2wm1gwhH*#*Y5kR3OCrrik+Xrxq0CR)OGL--e}oPom9Yh_k8N*2jTc8l_G+%U6A
z-wabA2oByuc487Hjoi<8Gu`b@G>-4cj%Fiqy&nwe?zzMUfD;JD0Qa0ecfGucl15Gj
zsou7+=s)gX&*zS?vVwk8MldcS3&W--PpNZK9aDzJvV63)vXPRD&5`)Bhqd_&HJM*>
zAp`+REYVO`7iToa`})v2C%ylaP>-p)*-{D<Ht1IUI|!wj&-Q_MIwRiXXzI8V^8{)#
z>zOZ1douv9oc?I-{|~bWDFka^<<Yf#l3&$gl@E-P_dTk6L&6%XMW(SPuo64vPww%S
zKoQw}Nq)LMHL@WZlFmu3?3E?Ze;|{tn>q=c4(enYU|q$DoetAig5fUmKpG#s=5&hu
zA8MM_g@MPcW!8?HUP6T<DFqZepV#S4+R(S6cSB&UFF@taB<*V+wh-(b8=Bfu+fqQr
z@yzgB{19?T;roi5T(-ROFr9T4WzErjB12c!e+3ZFcpjOvpX0cvn{~j5lB&;d970xg
zP3rWhIT>6g@z;vqYe=XKO`9VSiMfrOnSzxqh;S7E#LC<!?b|#@3k{<Mn5<_&6RA)L
z9?w({2?kN}H+p^hbNPm>e0thSJmf!8`IwuS<@&?Yw8Z>073fGF9X-7{U(tO}ripYh
z{ozjWwB759<~#Qbd2CxQ0!3M?#RSd_`DaQ=1#F9V&i&xV(e#B6sy}%~<U_V#W0MfA
zMDAIl&$?S}Ehs3@KRlKlXgmPMJ@~+-;ARKF#T*|B!)swz7rnP{&kFZ6c(wTK7JWQ&
zJosiEWcThM?P-zUzO_4j>}~h&jxl)T(zJ14HZ-0oXfk`C1czMmd-}yLA2IBDLEN?0
zqKnr>TmJ<dg2UQh!ceKAwTll%4(><xWGDn1HGvBjvAq;MG*R#0K+U$De$3qEiya}Z
z;<}GrKSS!0f0jDM`R%&9Uz@WD3I<^a9U=7B#ku2`i^{We0_Oy}K))EKrc8q3G*w$F
zTnxOpYVJh+3%`FPb$(!FX?VW!)ND4D8Rjr|ivyZZb%~Yh@v`l{W>FlMB30o3xZFg?
z25X<0sOF=_8q0=X)UE1VmfIir)3gSk>76jAonLdezQH!HO-3T%KczFQf!Y<2Oh#cV
zOyj)QO5I<t|Lkc%Zvlx)n=8>0DipRq8qC<UrjYaQ@hY4~3eD`Wyv}9xAO2hX^p}9&
z$un<`E;5X)4FhkhaEuc#NL<75d{@>vdoPqq0$7b<hEPr*L~VQMC5)yNNCLj6i`GuW
z@6d#qyKXF{u(FnXwzF!7jXgc_<1NshC8zzyi-)DFybTE`uKjl^AM-_2B}s%`$Ou@_
zd-qMd**SS)0=hUb;dFIrP(1G(UX2Sao;bV{qy?rMc_$O7)buWWyp8#UH?=f{0(tm+
z%|s64gz5W{MV5^aE6^b+`{S>5i3Ve1vhdHYpUCi;O95U_NriQ-xZ!s_sD{uI>%QIj
zXrQ7{6t{6hX^n)H`pi^%#l_g{9~{(QaDdC^g*ggN)eDu))jR>ZH|n``e6j_eIN2nT
zWr)c=(Jtz{g2KTvoDr#pxz>U{KnK2Du&)*>oP#%>L5ak@?Y#f~I?^*6TpYS68F?u2
ziCjyfFa1H*Wi#tn<bG&=S4~fNQ!{oVrs~XAl@;QqTSO%g0%~d^n05kIfy}S0E86~N
z6Ze4+u-zVhQBnn={Yu_i_Uy#rfu`s<Ht9V}fD#M>r|U!p3Ap}?B0veGcRHNHvlJ00
zsmHxwW>LrFz*a@1*Mp2Zqh8jQ$R(8XrU9;OP;#S*mMgAFl`JQL=OBzjmPJg0$q@zc
zNd)t4PLTYed!2%UOZ5&eE^6jK(JUF_W;YW!N&@i&`rQ8y=LAeDc0p-qYWk|61`}lA
z0Pz4b#>~4()<09QX}N%4m|Am`$Oo(jTVi$1dLAip1-D!8y2qNx0hiEgs8wSAruAPY
zwe-f`?%xobmOaDb&nHe&muj88uQ15_xiegiEG!7qA^G)vVSj7idshp~hTfo|Ob~aW
zT_T1i3=#na?~MNL!_J4c_G2oBq4rgtA532PbnS)r+~lq30UfUrznwF{fC7P3)_JpC
z_`#td>0dOZRy~Q86coA{R4l=u&z2fYAG!tHNnKyGZmVk{4Ru+>`1rV5tyz_LA}NT)
zubvN#hPlq6=E7(FkscW>0yka(yZWQp!)XZ?@-3xj=QR9(L=``XKb+8rqiiKw8dfEU
zCKl;=WHNtCWvj>r+*j#Q;<a?Oj4iAa9+&^&bE{%nr0`KwY_6*n?bzd;u?OTaK?|xv
zY?6LZhmu8ivKyR=$2DbrO0e=|oO#@eq?HV;1dfn>r+x8{sP5S@TK$XxngTHvRLPTA
z4B7!BXKzTwF6MtXC(tA-vX6eiC2J*{ix?f%K^W!uVN6&Z3S(aLsjE0U?F_Oe*`{eC
z+6aoqLc6{5`+om1o-sq;JWTFyYG}|JV)?PEQ;Q&?HypB*Fq!I<1uV97He3X*8TpLS
zSXU6xldt%QZG`2O73aREbyI1zHs}%dUr|*vY;d_O&cO%uWN;Xyn!^CWXrczVx3&m>
z@S2QkqMdRUDnp$t*)N&4H}@J6SG`FpIi2~kq4cC`*Q^iHnDEh16~8kYnFOTloi6rp
zMeQ^4Du~|wls;#{h6Qc*!<5|i16pJ+e8*t=N`;?;qApH4+H0c7S42}uTyvOQfPxwA
zbVj01FE8tlYCLC>%b=M>1|FZ=3v0)sVtrvmv@O*hH~J;}i{-~kDr#kShy|a>7PYJn
zL+VPqSJOo$ei-6kFT>N)L1~Jd_2>;X%G56+@oYc$=gm@@tL1>bB^;K2C>N?dDB+^0
zCZ?x*!=zSDN%+B8?@9&`U_Ad3U?7KN)~W*~Ey3n5W%Bjbfvrk2Mz4PO^}ZyA2vx27
zrzLOz4x5(Z(&>2es0&tR2Zqb5%yeIV^gUOQp$xN;a}dj8)$=JPZd&riz?BT8LRs}7
zL+kC@ki|6qgLt2Zc+|ab-(kNTGn_xB0mG5L`zK3vTA2(ZzvkfK*~sI*RZ;L<4($mK
z*fmg#b%v%Hs<`l{{Fi?C=_$PgrIW;lQRiV@CpZdNcmmPuiTFE9RW#vQvdO8({v7Sr
z-lYNFU=qgR#?10^_Q#*KDZ^~wCiFg2e@W!38c}6gKZ!)%xHPznSvb(V61Mm81G8`5
z4>_MbXk(cw{L7M_b7fGx{S}$7quH8P(~vZuZI`A84L&`;_UN|0Pd3PcQ>C%iA>kpY
z(;Y5p3*xe2EA5M%L1T==!952`H|}Si?g^;^>byIL^~wRYqp|$w$7L&-f01XNC*D9U
zT)<8jYm51a(Y4>x*2;)+x}<RCT%jp7n-wX4qH_%<cUsJyT$S6ZLw^b#qdJ8gaMRLS
z0|CPPv`m(J=Xlu_hfgt6oGRgDQ1(g4*4{N0LX>_QTugHkWTdB^DKh<4W#%BHuCl{U
zR@eA>zIPkkU;4gj4hNQTi)o>)Zl08Xh0lj3w%Vs`E}vT6&#oG^wGz#iJYjw@xG7+A
zZKpSYnPA$jcg%4A!>NFua|2a(mVryr7u;*Hop-i6yT!oB6Tl<C5|gz46e=ywCmZzL
zg$|UNcRn}}n1m}WCQ;ZNbPyeJFJDn>?nSyCE=G7YRfTocNzvvH>z}xVjjj)bmwh|?
zb+<Orc4El@-1!MqpdMBk7aaqi?4$!V<z$~=XWm8H{Iq~Fwb%@khqdI+;$N+g^vQGw
zj{Dt1BG1JQe6ZMBJ={otnBsFy>w5cx?y4pc_)HAHRwQyI3<QjJiD)N-Qd=oj4&rgS
zo8R{*;GX@TV!?6x0k_gV$T*=-ehhTtQkZx@gG-C~R^f+v=?J&V1UNoKZO1O2J>`}}
z_rAiiR`@SpgL}fa@eZk)3J#w~!$#D}$!Sl(-f7@gDE2eBgKo?7&TrV8{-0!f5jGS}
z^ivo3*eBKY6D=o-Dt0@s=^2JBluvmqFfe1Q&<r{Fh<;wz0Il|Z>**ZoarEgll&jej
zxhrvuy0R<{>Mr_9rUmy+IEn8`3jZ@4L#-n1X_(qNVGxsg<9;%rb&e6@Sn*_5ewNZ(
z?D4N0r&Jji8PvuZzN~{Ye-*N*<aashBmouqxN9TYxUa{G1WaWxP(u7Z$Zkt5WQ9`n
zs+>f<cjT^XpuihfOT?W9CKeI&DhSLa=GOQ^kLivNtfa~Asw#;G>UGzM&82qefFml=
z(zc=-U6<XFe$Lw!LecY|om??bUsGq2y)#g?|8IJ_p1|Yv#&$o)z-P0~Fy*tC`@=ZX
z{AZGrP-m5j$8HXvQk7g_fx{aX=$1o^PVCV5G7pQcWeitGOOaqYTrjU^V4VH%hR!0a
zoQ5zh78gr~b__Hiyt)4o-lhugs#~9HcXo6a>?NPnfm?>Nbm-Toabd!~)d501pC;H&
zerKW}@r_7q;?CQ{S3n34wD&P4=3xPBQ4bQz$Ld;UA|I*W(=S$a7*y&W8i}i~{JhEC
zmVIG6cqU@Kj(l;yI0(XMz_mS`n%0x=O758ZJQw?boB${sAi-x^Uh+e*wa<cPo-`7F
zI4mhCk+2L8{f(WI2Z?_bk-yBI{&BHvyDKk7O?@PXU{HrEsB6rG5H?5@97mVH@5;Rc
z*s;2?cHf`wrTMFm(y@VrQt;3Qr?oyZy^v1RT|8(;Y&Xa{t_P`8g#iad=bJlTyoF8n
z@q3UVbb1?BX7SPX`T4J_{)5;-MMX3MrxMuQwL?5R53YW=uX(_Xh9b-7?$&>$Ccmmo
z)qgX5qNG-qi^Y0{_rcc|)JXjiK7u*x&PMJum9w%OH!~h?+%(K4QKGg2VZ*W?lgrY#
zz%>D%I_NqJm+XIgPT@9_)kO1<*D@bFCj<2_$r`ET9_l7AXEE&0x}dwDS8k_Nbj|AT
zh;7O9lw9hX-4LbYj0UAbD(e(O0-B)+qczu$M|Z$o-tSoHHa2Vxbw7LEMBcJ^;hvlk
zJxUlPwV^-1Ts1TGVRh7XB@866XC2}67)0Fm5|WZHIj2zdRzZVc;bq@r*!!aiA@_D{
zlV+66K9Om{@(rHP`A}NPxpeH{%c?X2@?|h3u>c91|3@;Mh(b%f_a7bZUPZ*HNEko>
z%)^9@oI0q<dEJCma``40P)BpVI32&0BOAH-sxOIh6bDYqD<N2#ATNAoFXa`N<!hNJ
zh@vU+%`s#9M^+Bz2)5-zK!OZnuhRF9MxN+$=<`n{V8X1x*)T^bv=8cs&NG#QQz6;?
z5zqm3e0thUj(1edAs#}rA|b)gixHVz{wku9TIiFJ?GU0CY!_8)=T-YL^$`T#0j+E0
z<^G+Oo``H~2Ou%?D=J1D?_L2b-7~iZ)E<Cd;qjaojZ~(gx-^dGVpi^3h`IHoGa`cA
zNro6gS_D`_0aXv2RtjF#;AA_DUS?pTjI8zs<rsP!5k)WohcGJ|eDYK{k?0uC!P7+*
znPh)BRJPYC1t0;--?YCrH9ly7<2<BanK9>`#=mPrznGeBIQy{Zafd!g>*+>5ZEpcJ
zRAT@#YRPU`3@gaYx>DzrG!p3AG1>Wu%?X++P$R)$$YQ@5N|2qVs|DpTuTBIsgG%y}
zDXg;n*3?M^77dqQC>bfmk7#goweYb+$Cx^sj=&hx0`D;J)SP5!2zB4?w=1{jD~_Eo
z@gyS*QEk6%us}U>PE8A^z*a~_+kYYd5VYa$=a+JmI_EInu;+@d1-tvn19W7j&GBY;
zOhI^H*d=>CYp30vyNsbLTZf3RPb8u-aWGd$n?zSb%Az{e)v}F4Ny!XC_F6obU8>t5
z>trCkspAce4b;QBUY+Rdfo}K3lDXE(&eVg@X&(+43{oQ*lJxu!{?}*$No(URRT`>x
z9z?HC5F0SSnlz!B!^U(NpL+YllnQRs-wSq|q1j_@T;PWE7hu?mg4_EnRk_S02W=3l
zB6pufo%#W0bjJTjEBETasgwIwbit{}igF8vx;659H<L5oA-VR`*5hyEXR9&MC1=G|
zY-l=}J+Q=(?way7HgRe!c*x#&jH~;XdR`lXI;3Qxvf&sJqQ3#sP{E}@5zySYkxLSS
z7GdRy>3jf92%3_x+XUI(J10zC_8tF$K7YFa6mrEV{aad}JK}|tj1=K+jpKPJpupDh
zy|hrDfSSXK&mVY2B<svo01fqYqGbQ;L05EUQbNTYnU_*_7!MTHTG-e~fY-2RgQCV)
zz!vV@_D36kwkgHFbgk8!3dhl-rB*O{BSC{p!Rk}u=e0HL=W1#_&!~S&Gvli+{Kevt
z8%!j)zl|MS+}y@P$;=j3R<$&94P&x5IneHN*KLQq<;T9pw)P(_d{)5hqL13zq+Z>D
zfLi*G>jn1#lpK_A_k3_D_m?rjt|2sbwu^Y3^1cM*uFD;-o}K?O#$BxSdW|qt?RtRp
zUU_O?IQel$Tm5)zz5N9<GyV%P^&4PGw2p3CwT?#5c4}|o9v(bG<yoF1&Ui6uwk7Rs
z>!Nl3V}E9BX66H6hY?&Je@b?)wjP2(9e-Fr<jLOYW{;9gi;r6IIu~tw+`dB!p{cQ3
zB67!jnNI<Vx2w?#7{#LgW)<Q(D&4!F25#WoZkh_#;k-T-X6oU!M!*J=!z&~bo{2iM
zQ|uyX^2MkJM4{exH@mTg-&G=Vw_xIhg__26=D}WX=_pI8Uk0EL;v2qe0fiOJEkO<t
zfnsK6{*Z*8%V_KRdajirz9zN^?kc=|q)B=8NG$<jOt$O`MbR9yQ`DDyrq5FR{UxCD
z)*+9dZb|vekV2E@_I5#gGM(_|9L*JNZS9J$0=aidhqa4BhG|WQe|}M$I6yl96>S!D
zBrSNO_RZODi}e3;M_`fH$xybHrw@3STb@r+$FT#tDRlL%i^LQvziD3%*sm({@2W%4
zDoDxxs=7D8p7S+$)dL|jFFV)bC!JeBn#s%!<3Cfqv^_i&nA=cL>;~m;cR6og@6iD?
zxTGzH=@kcb_m}|4yKl?-$gzmdMJolsCa9M~URbylCxHdJADE>|BB%7b3i;2h%!kRP
z+>Sd8Avg#i#+Zs`JaTF#lz8lCxwc&#=`4!WosvtNMgFdzmX8DcJtjPUres<@p-Gzb
zNXxT5z2+!AVc72+o|T<QOuf*o@#t|Y(y6E?G)379tBM(vMlwQ!_yT4;f+&IrPCjTL
zY;UEU!=k*Bq#oMj!2$Z0jxR1A(D8ax#@DRM62xHv@hOsxa*L0#vk^;jCf`CSjtg{t
z_Mo=L2GK<J;|jsIo(^8(_cTkl%afCvgxEquPK~L?$@Tx4o3LtsG?*xW#!{wvVw~*7
zWCI~39*>3MC6GyZag7*bw49Xha!T9uuXcyueK1Mmj`k;Jx*|Wi#s<Y6fX|@%WRQCB
z`vO?XZ!0An7i_u3k|lMudL2RFCbM#68#Rtyj5W{kLT<9r!H7Mb+j(Rb%I#j|$-dqv
z&j5kKo0l!{N$;!EbQBYd9vLXGL^w@J;{!Dg2=*Or;9Ecn&zYJq*s;<T)3raSE4cqY
zg#-t_!`8t5ziBHLV012M^$*%%IDi2+lfm?_4`yp%wHs~NOs^Hs8KL4n!c3#?cuj3)
ze72z7F&t0e0yd$SmpvyH9yePlpd0nx=Zn#uv*$q`;Ar#s-t&O}>bHlBsr)aSx!UJx
zvLNs{ql>Br1M6}iC~-i1<_Q%<;YpjAa?4+mk%jb7&*Stbwk$J+fH<JBB7~rTmp355
zqD$7DvV(AP@_?SZyZK8HHJ$~P{?(_n?YzP2M^Y%0h4A|<j25UCIP?s=iID(sf9ZBB
z8ZDP}-08g^Y7kG27KpF6E3*s;5pc7#M77Rx9vieC`%aymji_!7utw*oxW+9UumR0(
zqW)`hf+`BWSoxz^29=UPHmM~JunD-CqD|zS8|^bsUEkC>1(3a2|9+x2jwo13ZCx${
zw}uP{0}Xz041jTC3X~q$-7Ochf52WUD5za_k!~>pp)b7f1^8Wk=57A&$NK_tT3>7E
zrD2~@GV=>@8O%Rs0&IL%F>ZOq+T0BP^8%C~%K=5n9P$4gSI_KMZ{T(ha$mk-AC@%H
zr%x&%w{j@iko$6L^3T`ecwhoW?tTA~=(zc1!oXZ*@Ic7G0Xdq{inO`IL}*oRc{xe?
zUI@r(2NDc1hr5igr<g8oA$yXN9=t`p90r70n_!NMe+Y7Q=fjF``s9Ob#pj2uv5<Uj
z|Am~?*4|zmkV8b+c$*)!Js<$HK5>(M1+0SC>gkx)XerJT!HI_7V~&cJR%nj!nKjyr
z=nOwpB|H~II?$CPxP0k_W{`8S7nVsN=a$kNac(tNg&z5LD#>L3WoWp%az<~?kL1k4
zOAEMJaz=G#vZ_0pnwqNic7O|f4glJ3#J~YSUPAfwDEB;*nU<86O#u66>h7~es&Izi
z;Tzb9Bsx$K5<w}F?4h$q{(W;2P0#%XM^!y>aOgu7bN5yN8(;_PBtHpcKM)8lPlaK;
z$nH&gJf+5C1{D1F*1OTwiC-$5HXS&ordxEUrT@PiHw$3EfU4y>^gCg`;JG=Z{Bgqd
zNDJ?0V01s!DP+FPqPuakG!}BLx;dGJN~h@Ki`7gBr^*=usHhr?O9?8dD1;3IUVPOF
z!?v4gp)+5V?g^^Z8g5xIU8Q%q-D^-Q9u~L_%eRz_{jGmq$<Y7&ZBg$O6#;r{)3qe`
z=~*Xy8GXBK*PAWq^}(l223hU0df|A}{&CxWAh(JFV0|In)><=Jr?sYKOX*%_ZAZQn
z-e0;r=s7+4`HPYJ)@8YL0#NNFUyI$kIR6$}66vLdK}tZsvRY7D3lAjVj>ujpe{kJG
zi~8*()kBiU5p97p^V$DdBnEm=IfBIXejF6hx9s0v*A^29rc~wcENUrF^6<nWCeT5X
zVT4K-vvHuRRg{W;CxQ>`5>KYKfDi~Ngp3x0YHK6``}%`l!xTKw>KB2ggu&)!U7wgN
z(qVRQmU(rT^bJNaw1*cy(i4+(u-|!Y)+^9hizTjQe))hVZs<xASfcVnO64?4E)THq
z)xP4eJTc#^{LUD_oEI3V+?Pu3R+Nvhf6r!9sA6Jb@|&6nK;((J40fahkzF+jbA;tx
zwJiNRd}9fjkR<(u&-siS?YA!pm(Ib+CnUo}=u42i0`!SZ?tK!FwZn8{_<j|xGch#u
zxqne-;-a50uoMjt%_MN4rlH10MXgO&aT@IOMxR&`QOS1w;Sh8`ni(~;Fu=^uO~eKK
zMG<|89!OzCa5b|^uslE$HQ9AmHn!-zGN(dH;XA+Oo|UI-j9R(M*DBR$(LBI`@s(a+
zD8EkwCfT&wXW@fP0#vA>!^w$Cy`MX#b0*^G-;$P4=zd4g0T8q|O@MR9dv1j|QzoK9
z^*O)9Ybw=LYt9EhW(V`ds0glmABb;wN=#ieDrY0Y*Bv+R$puCY8I&1{jRdAw#r15V
z%_l^<owXs`cx=SslthuHrkSKDA&y5qxMYjU(P<}Wj}IDe()<e2+^spqd|&e?_DNLF
zEi6QWv1gMBWPy?i0p{5rKRjm=9i(p(aXx=`WtWnao4!8dDY9c$wN!}Xdh+vfBO~wE
zDn`C9ZG{}$>BiX3{XM+?yhRd+%9I$~3!Tr(3TNhKREL@RlH#o)g6oOA&D&Pi)|b^`
zFDa;~@ZmC{WKBL9U}JB_^~D&|x_bAyQOVUX4>o?C-s*r{#_qAWG*Ru>$4s9+c;7oV
zwM<nUL0G2lcCQ|nZ5JAK|CS1FJZ_qel9hjKu#)ie+rBn;)r@xTOnk{<cA`Ko^``y&
z_h%vU3{(u#Vo~agNgW;%J4sb14qWw`yRv~x_6by$oUo^MU%_c#b7mTaY>ZA}BwdX)
zb~SfFK5so(p&V4`PGX@P=ql;3A!QY}J?|eI$9}~xw6hnMj=_9ilU8g>!yZn=w|ILJ
zF0h<}V}}h;Fm~ULth{%$^?Rq}s=7jC;K004-%bLLJPMS*;&e;{(!P3_-MQpKIfyL1
zFTP5c&m9K%Z3jXNTkR{pB2(((9El27F3qKxQr4WH!qvC)SwCbb3nkYIbL#oHf=PBB
z)VEP2h&QtLq*fx_=8QR`dnHO38bjM;BdM+J#a@C%IgV-&EcDJ0-QY?>#vVylunYqE
zga9kMV8D$>rZlT_z0Q6s#WqItXiUu5a{I)d&`$_ukh|P}sd%U{QLNOtnUrtCBEVWO
zer)pd3%&P7m@E}`cQ1ypK>q09b`P3$=%GndF&M_;vHo&6hnpAz5s6n-r^;9LQ5JAv
z?|6vS?^oCG!-Q-H%oOw<JG~-wc(v=+y~71+p;^)JAPgHOOU-tpr)4X){o#9$>Qe5D
zL?0L4n5c=J&jb|gINk&@dipR!opfX9M8+=HqM46oNz)OE`1&Hj-*^n5!egObubp`s
z?0SCMOMr8X6Gey8@jFzvu;7Vwr!)+M8ze|1orLQ8GBolgYt~w@KbFx`pwxng|GN5z
z{Gr@K+fx~Ttl?!Q^_UBH&)Qpoa~TccS6U6zu;q|$B;Z6N9|j%%qiztAjSX3q_4KkG
zACYst+g_yd;uRiWSx_PPKxLrDifF2G#jG?<{mG%eB;2oTZhpzAQR(dYIT|#e%0Rv9
zwK(TrGw{34<aILZ%Sjvwc{z<Xge<Kz5~9CQL!7`QI>BiHNk15&yd+fgUZ2L;fF;)n
z_-5IaQeJ-Q>HJNX!C4^yA`?fAe79V>f7yOWigmwAt3AF<_U8;yuPvAGILnI2fx7QZ
z9g)>-`L}O-atP|S;gsJK+6z)%yQ<sGtDc>Uu0vPR87{gA_T5sYIVR0&`3VnF#1~|L
z@F$0x-(BB?3r>sQms}3q;PiI{H3Tgnl#W%AxBfo!6M~Xs>e?JsU2`RNcRow}Wg8Ld
z6#v3IHiPPHmK}Q}q)w4XMMb4M8;QW9>gUg&wZteX#`hsnHY|%xwejAuG|EnfADq$u
z7Iq!!8q;!@Y1E;xGnUXZG7cdL@WNX;M~C>*g0s~YLng?#{@-cZGU0A*cCcNdZ>#ab
zpiyE7G~5NEZ$u^vkEd_-epNe4-8PeCKRj|eL_`&@Bmzw`nL^g}+o_#Gfs@}zh@a~V
zU#{ruZl%T-1`dM$?>^7Oc5kIcD6>u;&#a$$IBvLTYz&O!fZi(*v#YB3ua$A;JVnGG
z&1S4`Mvk+)^_Rv4OL$urZYhO}+CL%4+dd%O)p9jBpYQt~9%2dTH50=p;v0AlGPwgy
zuEA}!xgIaqC?2^}?WzPFyY4X^qsuOHC_p4f6x{%kYvYtEOH~Eogp5s%@bT&ko^DVr
zk?U+Y@PU=<G$$aMos&#P_0>d=gdo~|z00a@KEeE<DH!}bErNJFCtxHb46&EU<SoIA
zFzwUb?P6v2c0#gPV!IS*g$arwA4EKIeMeL#`W3yGvbevG7<1Qb2oFhhVfSPZJ2ccN
z1+rT@jAv?uU^8&0&`)lnd^N<{Zg1K8LgF~O$Of#nz8R2#^B&x5TUucJ={qf&oU*F6
z8%pR96x4mcpJ0@|_kOFQa=|_d{xaSDXHkWf9im?yY@ca*$7Ra(@5buhkbTc*tdypE
zg8|)%;hRJ7bc`tMcvL`CA~pZ_D<|KBRYH(Iepj}}ujjoOfjD{x{wk&OVSHICx*~yQ
ztvSy1O$X*O!U@WsS-a7oHxwPs$`i>-YVC@a(fXE^=s|H-$KAdC^at_lTW=ed;cBy4
zMLuBf#vl=hA{Rc98_wWI_0^w0Cszmo9fA8guBw!h{9ukrS`C!c-^r=f-u||M^}n^+
zkLmdnct*VU-4>CBXtv%>V)csX!PC*t{STB8lHu+7T#zrb+x-RY>0idG*JP31@~PFK
zrz2Gr{Luzov0P8*x?n3@Nbk86dej-QugKiLgC@t>K9HmNP9ntnkwemP69YsFTen%a
zYW&fTYVsuQSTRS1emuEl#GKJcWmbRxl`!M}veRvC(Ah+qza}EX$QglCFw>?|jvT4l
z4?QzU11$P&^hU~eus7Q55yvX(mE;SA{^%d6e;0{YXw{kZC~CKBD)(;p7>|M=V(7HC
z2L8M;Bca!_z&n>EWQ)Q;_CT7Rq0Q>{3k~jG5AGgNt7PAF5O5^}=-y$Dxpl3bd9WEz
z!_>QF`!mVKNuUOfF_-_bdZBHc6dLh0^z0%Wrt5_b(8c|(#_eK#DoaUO#M^hzj`=ks
z3ywoo*v~!%i`^h0c!Q0CQjR0<nR8M|Ra1kK#WoDbhO!=*;!;u<3R@AnVWJ$b+>5!V
z>}AOGS#zoqvK0IkYxeZZv^6jV754UrhQ2TYU5NW*5|O*}%Nq|NXT8tjADlOL2n>JL
zV_QH_bZ1swTUSrN9gdCeeDCetofk+)^?FUpM6SqTH4>5t+lM{~J(UGj?YJTJB{^Af
zmLUV3_^rW6I@g>&*|8{kasGLQ4He?QSUh&htWmDmks=3`+tx+`#znmTn;nyum|{|J
zRX!E+5$amn=*h@wEr2-MmzLxJgZwQ4StpH^Y4n-4$PAajU04bQl8ECeE*y*yToCm2
zgOIxb%WitI%>d)M7s^<_#$wdf9?s`}oWZ0F|0WLNF0=dORL^FQXkmS4sf9Oo{QgzG
z`vgnrFLLay4r{R20R+%n_Qmb=@f7~Z2cucNON5JyONo2_n(*W`CgHuc*6BFL+KAvC
zXr9}uj*2eDvl!v`aP;7QUh^|5*X*Y}@29PZf!d%d_L7%SJDWx;-7U~Q#;VpZ<Q+HS
z*Tr0J8|l_|5=?8Nr{_0x^l3rL1KCHoDDZJPIbrwrn19d2kq{nco2@Zi4}BLrGG!1o
zTgHcKOmWjuk7bFGcwYCbmwiz9mLv^8vI=<HNGI!|G-FPMO=DG6e$0CHZTGzP^d1Tq
z$36I?toW2(TU%>4vhJjAYD)ba>4VavtIJ}fOrF+pOCpI;|L@AyG_&@mJ9^c6&3zS<
z&(?wKTnSSiJC?sV@myj>gN>lXu)26HdGQ)wsY`xV!Fd7<IDay7Z~X~L>ljEEwyrsv
z{L`sD@mXEp3v*$m8YAW++K>uPSa04Jd$hv=Nxdq&FS06AVr%*}&s0edL3*rYWM%gk
z`tL~aumA|e6QEFHp7d_KoVQlNgO9hE)H7S)M0_Z+_vt|4_Bn8JKUo`jsjq)8YWOsw
zUri(U^!bLsmkRN#4eQGJ{I3nowrig;7jy_@eLB_$>FFeie;f#my<?bYxA7?iVxppC
zD5zosqo}XG=(Ien96i;X@vywfM#%Vk(V`$_#5y7OUEJyK&H$1P7$M62>aED$n@PZ#
z43deYYz;jAETuDMDhKx$OR!@rLU7RdcQDHH7ltj^ceWQ>mX=NU`PDX`%yRtxw~r^Y
zI;n;VidoJ&LVhv2qHzSDdZN{o3mDiXWQivCq=CCioXJX~Ak)%f(1K-gLbiWF{liFf
zPIS`nM~1eP+mBw-a90m^_|Gt*#z%w8?qRk<^Q@t2ZSTqlQ)ME^;L&+FvjOuaB+;uP
z_~b^w0sXYWB0WNeDndfw1xG_*;?xsL1}q}RhH6ebYS~c>qRVlXYX=#UtmdP~X@Jig
z?MTLHns3e{U2t*JIh&2Kx^q?b5PP(O260KcFBmC?A`@ydwNWBnVfNk+asY`Ticf$K
zhXNOEHc88ue9?{|+xJMx{a8)orKvRItcC?HVmKJty43N-wOQMs&iCOqQ;!+~P*j;l
z)sMehLv>F^t<=rBmk#6^tU*KwADbIiw)Qxa>;gu97s@%+V%N5`5ET{{YIgeiT)MWC
zx;7a9z%}|X9mNcg#p8b`@^{V2-{${=+SUCI;v}2wQ<3<?l1phw*hO>3z{06kuE0s|
zH<bR-Gw~Wy$@ptVf6*1H)7^&n@w(?NT|{84-+}n`p2m@cwlzPRcpamqXK662Gid}w
zgs8`wKHKx9VisSJy8!gWfnUMejO)RVrK`or3=MCx1^tyQ$Av)R;M;|cfI`2=h8sLz
zG(=OEy}loZzfZVxIyy$}E$m}gN2@ZselxCMG%Zx8e9}-KNPrWq$AH&=Xib`Z`eg{^
z-+vf5WIBt-lOBOCeA|4cnI1sf#k*X<mpjNeckt)iZ|3UKl9GYJ&Sks#dSq&9>e+9O
z#+M4#25&~Q<dE?|hy*y}&00+~^lTxj4OA;OdrII1h@!dm^_;C$6U&?XmBY)O$xK>3
z;ITmipw|B1yQ`CPpx5lo_E$_4E{tsv>OiU&c|wfRP|q1fEw-~+pVR)ni!|IX^Sx23
zE_pp2nWqVd(8b&v!y%40UF0W*BQu@BKYpCCI#=|+L(-kerTdv%Bvhyku!mxfWp%Nr
z7XFyD`p8D77g5gbf$nt~|51;9wmZ#6V<`t``8T;m_Y-yN_uuE?{6t?UQoNjog0j<g
zq+YbE7|q?j8YS<$;9fT2Mssa7By17nQpDyUQhe%7Iqs|EUJ0d$h@(PIpfc)>CHn-)
zH<A1-#n){*<+aRG0A~L-jlO57cHD+LTxundOJNxaw^RWS1sy$oH$=Uxc4+SkMiZrV
z?UUj^n6F{_hc*(Nr29KE*lIcaSsv;DJUl!)W@geEM#;jMQS0gQK_MY~`Hp2lK_9z0
z9rpup%}>E*znqR*z<JTM5aQuv?#gT}D8D8ZLDg!Yg?~v7AwCITM9mn!vwt*lo=({6
zaa1_|SigUshTy%=|AdH|vK;L7G}E~uZaQQ5#4in{>0qudCR2;%n5q{EkD6?G$@lQ5
ztGoAl=6E^QBQnMCf_uiL>}%Rb5|O!`J`MX@^3#{255p#^^Lbj)n-R!%DWQL3bzR2!
zb4u*G&K6?U9**Ax&1}dX9V0j^gyaukO4Mlg{;{rXfoWawCN8qfFD&FL7>%RT5|%i6
zJ4TXhJR}3=pz(!w&N>#pbaj!Hdbqj0*jIV~o}ESRNHvL5P~O8TKvyAAZX)}~Pye)$
zkh@0`5X}nbu4sD5T`#GwbZJQ|N=v)JT?X&W@<ja%8|m*aNl6#Z-jxAMn?5cWIf>(t
zcU$D>b)#2vIy^ho|2-qbf4y=cC~68J5Qey~YpC2%6ab~wvaW*8fDsMhd+mOWW-GT>
z)50mqf3O5?=Z>bQK?XNqw%E3Q<_`J9*x_{i^!=#mk~ax6pN@txh?S}8`@0KBfUf2<
z){T8SvRw2TZBK1-e`%+8>LGA_Viv5uz}`{i-a<}(Yrnpn(XrUlW>x25{C!@6OW0NX
zJ<RIzWK!#q&a$<-f|nPZpZh6Pj9P!S`=HB-r{Kw5`!Dt*2;{OQ>aGX=%;9p;1C*vd
z|2*|HO=VQ5@w@QV)|#U~r5sr&az>|GHp4$D+!`26LDV-W>q{#uf3&#lXTD1>7lndS
zqY@@3{^U-+q4tFhe;OnPg>0h5rC%9d%~05WoJ?lcW;OnS26Bi`@=AsZ6@Tl!Z~h(}
zBF;{67Vfscv#Re&XUJ}OzVH1QZwY=0{*`!P9ms+chC$ks@cVri^4n0_#cE+ztV}@y
zQE78$8Cp`MM~c&S{^MRHGyQ0L@&(Isq1vHIzTZUn<nB92PzF0Jt+;pdSIplpW^|(^
zL2~)=)PzZg)n2A*qdUwm`WGtJ{du0W<9(kjXwAb^pyKehcZ~H81)X}PUJxsN@-hd{
zTH%qML}>p#0m_-rH;=g<J2xJcC4aY=w-k0(sl3+oi!88uRfygG<RF!>YeG#g5=}0Q
zWZCFAfSoZ~7~(&1kespFtF@|7>eEu^n5WGc85x-~-FkS4Qm2NEc9AI?dl&L{f4V~4
z!-Mxjm7X9aZ0#c)XHrtqY`vxGrCQ=aoEa&N6g_`&KH93FQEn2>-I7FRQ7yFVn-m3^
zG7CsaiHM{mJlNOJBu0(=wl;5xqMM6`3NWcW7(ktD80W4^+>dkpZ%HCmKhuYUeeH~Y
zu~4L#HGgQ0wm>+Iw)fQ-i2MwGcZB#XI!Iax2at3_&xH9F)iGSGW0H!Y$3iZ;!Ks7}
zpB<m^|F697{-^r=|38S#vd4KLjy*zgBC?OnV|MIWw#djzW+6)UI+2litV6c4C1h`o
z5G7mL`*W4v-+$u!yg7b4=XqV%<9b|=>;AZ3kL&T+6h0KuXZ^}o>ReJMxY_)5zkbHM
zv#$pGV9A8ZV%t}mDGT{)q(|En2hR{;cIh}HP`+83uaJ2LlEsPssMir`sw=m4JL!~r
zs@Gxs9(}Fc3WYz{H<B2d+~2$>?g){WQ&zr2iF$7G?sJbodxC>{<|@<_NH^L{XJMbK
z$dUY`MbR@lt@KxAPj>^et4dfPnf4M4%gy_>Oh+=+klLJ&L=~F1_OE<nALF`=zdE#E
z2<k&V#MM&$C+^Z7$E6NPkk<eSQZen|dl3Vx`Te9pMQ%9m<WO7#8;q-1+h5UzHs5O~
zx9(DDRHiAZb5&I(*>8x|p=jvN6ZB2<k~yBPc5bV7p2j+k^5eXwo%`IL<lygNKz?m~
zeRIJ~)%4BM)g`;u$)-H5jDW6vVF1t>!Dp7SIx3Ln#&`Gbwno!{+s0Gb2Rq%Pyk^Fv
zNOeszrmz)scmDZ{{OS`@ie2Z4_x*=9)l?2qR^cms$2k>!`ZyHG9dp${>y{?7^vl<K
zWE{!NU;4tuK{ox9>IJ<e(tgN9dtOWO9y6b3tJnH*S$ll+DS7`12wbh&hmihO(55BC
z@GI)j+M|h*)x$g2!^T&`#utLYrfl_78}%pMLx0Gcvjy-SL-%J6B6fsJ)UL!7Hb8>%
zbe?RGP6XC5?<n8)H8qZ~&P7J^wN%*j1!d;sSbxUr18ja;%VSSZIH+pwQSQoZBV2#B
za%YhLgBsokcx1mU!2?-F0fMb1ou_A(uQ!rW%T_JKCef9VLnOuIMp?+}<>CBym6fyr
z05Ns0v^cuBd;)L8+V!*EGceev%Ovo6VIC1|AX%z7wX;Dr5_d9#Tz-~Q>>w6+G@Rku
zbG_!(PXB;^b9eVEdFLi@`ao5dP+D5LF~)|Tj<)Iz((-~Uwax^7B!*~TBblFeI;RWE
zQq*5$FTqdzz1k^I?lb?i`0U3=37?-6T-VmV`exiC$}G<MbmViuT)JXUK6m0kL$T?Z
zDe3_o3#yypH#MipQlM%qbUXO%_H@N`K5>!6l<s)o)d|~TP170QAD)yrB*?Xv?y5`$
zQ#>iz+^>48pOWN?;XO8h!D?B*Ip@waAVz_KDaLUUhfLV<)~%@TTdtC!Kvv(-DV584
zZlbTc<iA15wK9?bSZV(baar9Iziu)H_aeT8Ka;0weV$kuZccPcVt<S4G9JyL;d|<4
zj(0n_1sm=6(B`Yk%XLncmoskrp2<e``%Bd)dFPe7eq?sg3m5vdxo^ju{9ZBb9&fzN
zK)p(bhE_Fa&7h^?PIvH0AJg6Gm@%i<PgvJ#0AF};^S5-#rH&Svnpp&VFi-w=Qwiy_
zu<Z`RPG3a!;9}eij7?SXNgNpG_4!*bvSV0BAjw!n{e9<b{=<V+$XwsCv08l7h&o<j
zPA=7U+mfK!;2QNW(M>PeeNr=SxlugP;3EXH9cg6ft(hn@xooCpR<^+}XQJ<H%~o2g
zHccW|%hrwYx|g=Uy=~ZR{n05v71b_c^P98QY_HogtW<t@q|_0;7`M|!U{|KKH}!f(
zQ=0%1d*$T1xO@iB!1Ueay6{!e$ux4MTkN8>d~HhnE){LbfT<zhU7B#Z_DDN4!bpC2
zeD1#3tK`To+eYQvo)3Gz?|Wh~R^jlX_@&Cig~w&;l+~4$*39{rsZ#8UR9Q2^R~gNu
z)s82)H#Gf0aqg|-(|1Rru9>opHP<NI&ND2_ya|FD@aDoglxpWARY9h!U1!6?(ZH5h
zseqehsSu=aP%5xETOpxk(7Nm7B(-sL+3xUmlF5)|B7qOTr_JhcQ#;*Dp-s1Q+{v5k
zDdKkAq-5JxjFMu=XRzr}F)z{i#%EB-!HC_ady>Vt>BN7_a(v=#&-KwK2KZ7R_~d2J
zF@99nyMhfpe_Nn_OSUn=vV#TGrDT#Veez1zk*pc}Ij2QGOwt>k7$faRE*FtI)t+*T
z=#N2C^cP!7#l5n9jBjWRKPPk;;L``(Wu>>50t$6+oLQ|YB9JA!#XYa~WOQ8sYd?Qt
zzIjmB)JP$FI4P`i))g%mYr|WSMt-d$VE_IT=S~-%0=yuV&aGXeY&Rz@;ZE#GCfh0F
z8x}PKyL6nvwspn}`w3Lml3LH&g1eD$=fRKS&TJAD%1c&)6bH8lmiFHK<Tf^3I7!SB
zv9`v=h=7Vp)Lwc5Jr(0ytk0FN`H_P9gU?HyBz|qEZZTY=7*xwai9FrjXkgo+;yUYP
z`R?Q1qn{A?jiZa(xituFJL|7ykRhAQpygfZKAUTf;t=(jZ&W~tRa;+PzoR)^%edu{
z*}tw#BT(#yaTHSunJux!*`w(NhugLH)+x>pJ-)oX&B9vqpHOlzV8Vp=dg2O%@p5)C
zhpp)uO1~=k{E38RG8jS$eI>jbN#Y|(*}AANspDJjGELk$YslBx|LU;xL|LG*uU8I%
zK!}wSojhfzJ+<G7p!r#~CqdY4WrP(ZOY(PXzIC$HuVWY!ZOH~H>!hgZX$C%kW)oQ(
z6k0H<qCW~qG2C!82Ax26?Ud3y;Jau0eYX@68hu%Y?b`G3s}jj@0<EB7)Qi8*UEK5H
z;#B=b)@l$cA{A;Os~^!LB%si|8QQ-Z+_`YKw`+fBbF5Wfm_i%!bA@iZ1Le&|xkX_w
zsV5)sHAw$<Ltq<$CMv3!xLhTFSILD4_UFlt6=!dQHt~3nj&vQ}dTKqHh9_+Z*q;1=
zCSU{fHB#@806~C10E1kTgK2mihc^2#&?cDJ7YKy@P!%D6@6S+#lGk>hxQ;W($j;Gq
z+a_oz0ag2wE=`aps7SlMf_}@&0U!e?np5RrmQZ|+?GwuP-<aAEq)zpq@*(n>qvOlW
zZ^?8MAMHu#K_zYzSuc;WtL}4iR%;W7^nDqRf@SBF0?diQqy;qnA$QHo{tL@m*oviD
zp4IO#iz3Rr6g2$Z`?Lv1&@?MPm%GRyloH+<aP0R*Fm-H+-%i%=V4S$7dh#-%(Tlsn
zhO<kyY<~p+Wuf6J=0i5cv4HHM+-eFpQg;2YWxrVNow|`dJyfEkqa!@j%Piz)BbLyc
z1<WZ~n~2C^g~HEzqMB*KvT=f@ezJ^yf|?#e6v}ODU~nhj(uRVfy?!LLP*13hCFAfC
z(KOGO&F#IqZX@TJ!dDomYSmcBLNSK@qV?-ELdASSJJvYYibe{V<ojATf+48L2N+?z
z8~V?Ze(-uVy}uf7XZqSf)YfVxp$B*!z;I+axz?F(`Iv6inr->uT$zZ(%z(EvQNG=~
zlW%3HczS`n{Pbr-v&0_P6gL{&Zup~3DER*lNrL}M-+o7<7MmxMV~2D7VagM!K!m`f
zL^N!%!Nv9(PzN*3UBky)`VXXCjDES*l7qZAZQpOsc`*ULOh_@j)CnMuNZ3NUV&_ms
zNN=bE+pSz7)BQ66w@y2m+m*`iT?bKp;GA$?T<gw)X|&Q7(gpwTh_eUbI!PN|t?2AZ
zKJ8IE<M|<*&vAgbWtXHd6%}C0qw^K%osL}M4}7N08x-<~EL0#SA|O8ScWp4!G~|ec
zUHRa_c{*)`0>cce{Hiu<x}B(g_WZp5Y&N|Ij8BE^<%Me5Kh{XV=9zfRhvjLe-5w`N
z7LrdwsAOA)!#~~$%2P{8Om3!$J|%UjnNB^+S%m^;j<W|u_=d2}S=t)vb<DgvasZo_
z=VlZ2xP{+n%rz0**x0Ib+4@9OiSXm~x8=k#y~sbSAFTf)ZUsZVxPV*{dk{)GH{oY>
z=_5a_(R0C<e=4mI%l$0BEC7+hQcXGi$}j0xNV5K1L2i`97Ey>{_A^=>uin%=zokE>
z9axMoF7WAJ5x#ne*EX-u>%J+<vUxsFV0K(h=GS5wdogDkDa2ODyMITuCgV<z7fp(c
z&E%)PH(6S&3TJ9eY0V5~nSQJ6-^P@ZmwJLZl`g!CO(Rkwpp`R5_1ejJu43<veyDyR
z{-ZB-a=|(9$uHrFvnK`xjLXfA|1$~YRxmL}uU4oI?bgP!{-rzok5OTHY6G-g8%7*?
z{11)-ntSs7e*Mf--in?*vjdT#^0etOOI0jPC^+2G!)j&P5i*=EU~!!N{>iYmeu(7L
z1CaooUm{87zHrjt+nJwl0ysA~pdD>-x_7njREz-wWBGsULEw)llj`TjY<$c}LAzP1
z11J&<OhFUQaQFQ3r{3MoZwj0%JD73=NeF~l!bjrNr5Kadm;f<DT?aQ{nmQJHA}x(h
z$*%bQaiw{pH`*Qtk{??HQuUTl*<)rj8qS{bNJ_rz^XFhO?D%WF!Tfn?6gX%f;$7z`
zC@*qEyeQ<;CSYXNB5{~F`3aJ~-4{WU1%c`h*mDT5+c8MU2_%#Mrp$h-R;w5?zrq&#
z_UZ5a7geK*l$XAwu5`ka8Ja5uiD7?aw-oS)4O<bu(EJ-*<8A1;5jjQ(cc^Kqd{mk-
zq2nBi9Na=(OO1cgv7o|9-8XmCEMIy_=~Vx9WnSyJQ0kSLfmjiwVNyiP5BMM5qY3|7
zvF|+Sl2%ekU21ag#XeMWfGL6iVog}C!qeFGzY$e0J~3PX7vPB~80!Hm=r?Lj1b_DC
z(GaT($or<)DrRL&FZi-Iv~U(5TjU6t=q+2)>{q_SstkKedR~lYFflTg-WuOD`O4?`
zwqLF&UmfR1Ha2RwPP#sdL+t?15T$sb_&>nOR32MFTW%L{C?myoGKnC?hHqHL+83}I
zWYgYjYE5-Sa-!ZhR+PBnP!?#IRmX0J$aOaQmmF5ymgun{0jJh^{gZ>vtJziam+mQ5
zwn1;ed#_IT*HXnA;zQWFfTf;0#*zPR4Dmp#RGd*r-7JOHT^^;&-rMXqvcnDX>AqEI
z7DKMde#Ssok||W)oIM^Mji2`}DsX-cW7Q&rd)ZJNP3{~4TFPh3?rPS@UnyqNNv3h;
zNo$4LgZB6hSJFK<gu4!`V0|M0mY87oF7{JlacaU+h+xFtZye)WFs^UEE>V*-unQW5
zWCs|M%x#P$Dv2r@iiuu=5dWZSfwqt2XUP5*_)xb^hiJ3QEUK7^S53_PYz8u#ee|6w
z#OQd6DGAR_<7oelMNcRk&ZXicWJ)^{E?s~J8P;f4EBzWjT9y~tz+huUG8a3N2$u4j
zQuG?G_I$w2wA!D+<+xq>=xHWE=O%B@IQG{4$(FRPg<IaenVgfn&b7>?BUdXOcFNNy
zHT&PkphvEHlvID7D6=}|F<|?Z*a-;IP(#9QyjVerM9Ryvx6uKj0#3X$L^-GtpeRo_
zK)$(qBoNoiYC`$m)>UED{ms(x8-5$62Pg7h(#1QMi)gRZWG|a?Xr}ovcvivfrA)|-
z=(D<EV!fpWsus(FRh*|8-Wl!z#7b>-hB-y$H~zLy6(vmrD;~T@LNXk}MjZ2eUj5;e
zFsJJY0{(kD#K(WhWLX?=;kgMdR~(aa8e{$hMTFYp%V!YP2(=BMDW$fF`bN+P+a7NQ
zr&z;b(=9J~Nn;Rr6`XSF`U3@#iyHpAHa8yRzMl%a#a!a#_LhgfCBIJfVhi|GZF<G2
z8<0CEl9I3+KIC-SHkaXTH|Q{MTqMse*$@Ns049nhli^4AQN8W-5bZ0L@F4|gvw8Nh
zUXR+%5OmTWZ?+qa?#&le@yB_2YKD?L*JADQk7u0~1m6bnt7~&oMZ&nBL8&l;o+W3T
zlXURH#6ENXhOfS?y`djtuW0sjTx{~I+z6ZAZ$|8ClG72BdbBBR_sl%NcF$fG_92Ma
zAe;UXt88L`XdC+1^_a$U^0_mvuJh!b$NB%!Gt7qxaC;sf1$(~{;ajYD1}%5Wy~%#P
zvd+<5=1Rsx`C4iGGWBu;KixG9)QCEhQxVFye!QO{5e=lV<O^dl>_Vzvtkk{lj)r<6
zm7pB3Jb|4F>;;cT!%8#sN&LL%lB5pp%{5XYD*J~U8clVP4P&+XM017nPq0A2fy)Cs
zm~pI`2CW4v8-V+RoP|!id2hY?eTPr%?N<<|hnfd77(FCMM3*D9yeQa<6W#o>p&lRc
z7n|Qia9&8@syiGYaabf7$S0x$p(Y_qvTmU!G#s1DS4b~@^en5V4<bb`J~;7|{ZxpV
zcIoq(sNo^r4BB9gfxee?mvIOpr>MtcJoS#)7tR}Jzo``x^N<&}-DkuoHh`m7s^y5x
zjv<TVRfyBo%dc1XY_ON^mb8*Y*Dh)Ns|HtmvE&vM`@-R&>@1MEA)9t2S)R>gkvP}o
z8UnfqZtd}b*V!@if>F4M1ga50YaSRR)l>d<NXk<II6frhmChwRF!9;EDHR~HjhAgs
z2iO;0awso3(^kXi%MDWsWV*zjIU{?St-)trnDvd4iXX69>6(e?kDYz*cvBk5yb0kv
ztJB!y0i&g}xDdaE8yoTedZknv2Jf?M;I)^q5Cgp;@vCztFLmmDqud8u9A2|6?Lv94
zK&OW`bvtGpaUQLWT3;p1cr~WOb+71)zY{5V8OE)shsZb32>jr`c1QA^8`!QZ7j>sG
z4-Lj%WDk^`x}S;3zW&JYshCejB|B(G`Y5+Dx1Qpvrf2oMA1>!#C&E)!pRMl%SKAn2
zEh&!VYc|WmO_I|;l}2)cu)Z|sdD;SI-H_q{Wod`a7gr8~O{U%*^n4wD!KLwcllDr&
z=`WUI&}Z#0(ERMN*tNhso69luTPxFc0|O(YvW4#{YTV<g|5}46;W|U3;RQ=6YUD%@
z*Hu_i1cGM5^QR!Lp1dIrW<<jNl9CXQ0!i-Vu2K`AJPD>;VjQZp)X+=pbMFiKTc~=n
z-;B5BSPeUlwJj86Z%m$HG&HbbL|-yPqDDE3JO<8xjBn>1x>X9jn$+{hBJiS+={}wo
z<GM3v_;&_|KA--x@gKT6uzCG0dva;Tj}&`TZyn$YzsJR&TQ_v72)e*<Z*^>RI%fFA
z_cHD^y?fZX+Nw=Q6=IpSu0Y-pofgWk9&@Q#rF3m%idCr<{~HNSJ-<9Hp;SRENC3n#
z^s|dkyFw2oibZvcp+a~khL0sV-0tkUS9!dd=1FsMsBCQQRsAb?v9ra;hVe2JGSlvO
zo9XM#IEMh*0^N+O73b}~m0s;^s@d-~DoWJ`;3R06B*EJIrJG4>Uwh0PaRTaQyerP6
zBcT%HDEB8{>?4WE@W0)l$?vb?_&Lg^2D8Q!5hID?m7`Vyb#{>D1W@EUrY{cR2hVRQ
z*5p=&QRjt%tA>5V6cpE79$oc#@NaIE>lnu#|MyTio21gSJ@YdREczCb8|2ACiaMMJ
zGo+BrZ3TRK!AkE>gG^D1dP+ah<Vw_;+?OZnx#bPk&S0QH+TeJK_2vFvKAwfyijmN7
z*&B&Q;ZDYxPB1Z}FwTgWOMbTLgk(|aH#A7=*ocF8LAexQ_v3op1>!!g#Ph%OdgiFA
z@%8Gdi)LEy0Otq2nODz7k$vMQgg$c*({~wM)Gm<JFTNT9r#P$LhJ2GO2Wnc?=e!Y=
zc$^w!5h~18yCt8mGGddhi4gKZo!V>>;KuWF%!|Gq9^^9AI{)aUScL^rb3+OZYuVHn
zb$NxLvA5rldutJ%Hzog;ml$iQr6ehQckULgoorl=wP8H{#OM3t7_A|;JQ*7&$Edj~
zf4j8RuXjc&#X^QN{%temrescP1Bw8Y|In<36N0+Ev`TDs+zB>#k%9^aoN|zMYo+H2
z%-YjdQPO5y{@rRvYz>Wk51_Qb<d<}!+I7E5jJya5!c$CK+Y+9%oHCE-0*poLs&0Kq
z(_$lx`&?CF+(y1R2u-rjNsY{0=^cLY33=RB3>$n>x(dJ8<_8ZFoUD*FKU)1>zYXoz
zw)#oEXiZ~C7GyIO8`Y^jchi~}Tf~mg!3wZ(N~P&=;<;C(VpGzN5}?*#y1{A8;HjN)
ztkgo-`tj<#w}6+2Wv{vW^c5!vF}_Am2&6f;D`)_fn~46#nR9*>(ndM*@NC!(ai3(#
zDOkbka8j};_07MP^|WX5k5l<BtB@u(CE`woi*Ri5MDR-+BgoqMwB!SaQOdnJV>L|=
zHRJ=;P>JlA+n@U|7;rRcIu$r{#PZR1Q4nMeF70*@2#Elti%A}U;^NcVnfx$_#QZA{
zrqts;ZRxraD22oE?1*n`(<qB=2J}KHOk;omj8+sr7tPz7F%kyMxR4jC4Oo+8Coo?(
z@v@03Rddb9u(Qcg>VdT^nwVR|Cs4KPylepC{Z2g4WDH^CVq%`(z)x8f`wUxDW8<am
z*D9cXO_V`FS{-#;ga9M)q~VD?#6xy_OgrgJ4^(Z^Y0{V%NImp_^CtSQD)5RgKD6mg
z-A+$&OJzW#tM9qH@1{RaBC&T+_mvt(#XVSmxl89@=`o*%^9Hq`VyEca?{k0Xa%3_?
zZYV|)bB$U`KB#lXlaCI2S9KlWT(h`qBEX~Un90Wild$9zZ%5Xt#qwU}ORw@@1Ck)+
z&R5JQZ|pI@9Dm0vkZRPtLp5<IzhuJS3DP{krJ`#96^)4`2Gh%DQ%Gi>jBiA3RiGQ)
zQ=N|~(LGkJ-(kJk#y9$o+kZvxW<hE%^50ucz+2t;Iewo&AEUf5?79SNJ3pEWt-Qo+
z6`0%a(1F+8?mMH|aqP<c@vWzNzj7Lgrm|AG;rg*OE!YZu+)X?`oqfs-yO4=@M4iOM
z6<<%rE*ZF^EYU>l&*ma_P20U9FzL)wf!s?CfCH#~Oc=&3MR%(&fOAI%UMgZgXYbsD
zJ=Jcw0>8ga*2y$-q2)-nI@+;>Tp`*^yNf$KBo(60xl4F^Y4-!YwuXc$((n_Vl5&JJ
zy1d(*d0id$c6fLSVue^o$UQUE#)nut^rkVthBow=^orCTQCrw(UwAPI)9&uj5gT72
z@Z~sl7mJ5M(kU_=V7neJyIIUM3-IF|Aws;esilf_h6UhPlsr=8M1Qt7pRoX)`!-s$
zD!QkxM2Fp>pKtP#aSBL$b3s7x)_hmHfL!t0W-;*svJqXQ=|Z-B_hk0~Km*w7G@!M(
z8_mC=wt%MDp(7%<j~oF__#26dJ~s=8miyQi%m(+K(JcMyo(AQrV?;cYXf51ddLF-*
zg=fs?{ndF`w|^b=n8dSnKEjQFU3%60Lh7e3bYKbqIPw45lq-!ki)!meGfxHrcE?)v
zr^n>pS?!Ym`HWyBz-fc^Nvw)cB`Vk5lGeUDg9Q#cr(RnZ?u*qF!M+4Ini#8ndZsx!
zKjYX=2l8nH%VAfUcNp9Myx+N%33^&RiOrv7w+J+qZh{<ZrG@vG+y~z~JQ|#$XD%UI
zuy<CE9^Rt25LUJ`wF#Ktm%SNS^)c?h3!7;V>q^9aVtU9x7$t9j_`<ihFMtL5kVXv=
zk@mEi;_S!Dwq&QW->G>|By}Z2ehuHX`cR?FKh9Pt;r{j`Y=EgquW_g2zm*@s2f{PM
zbm?MA9||F8m=Y<MCrl;sC5~VBaP9f=oAw`ve#F1WiN3&)59}RYW>?RD6Hu%(qqa>V
z9rPVHk$rr5%Rfx)0u9la2tEJno86fR_Nu?#5ipYG(qXZe-byFB4R9Y*&HD=Oe4vAm
zAE!+0;9{wKb=$bI3lPo4MJ3?G?wd?A2Kd01Di_Lypmd*uClL6MLd2AyN!U-7g3qNm
zu!|Cwk(5Z1NKSba;&XP286Uf=ICPvJ59d{wfzS^QxsRu3OU5hM5*u=Vo(IAYqM9&u
zBo~6?eL$W{P}Y+Ai8_MoD9!z*tL<-#;{5+WbVB%EM%Hg1&RQHyV-O*KBlC#{ttQRN
zOhDF@MNkR02#Yb4|JNsmcrq*GB(IEkzFbrwm-knmMEnVn=bZ(bX9SkF^7mdwR_W7O
z{$ly333y$he0Oqz>Dguwrnwcp7=LT*+w9k;Ur!vy+0rjM_5as@_b=y{<Hsokev68D
PL%^e|q=_h1unhS>A&ROf

literal 0
HcmV?d00001

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python1.png b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python1.png
new file mode 100644
index 0000000000000000000000000000000000000000..833231f240809884fb6eb4079db528b9b3c0a9ac
GIT binary patch
literal 44653
zcmcG$2UJsC+by~S0ty0#-ob!0=^(vo5G*tyf`Ficf<(IX7WG9sm=~2^M5TnLV(3kf
zqLhFLNUs_YA@oSUEBJlqd;fFpx%d9}j=LSh0TGhD*LupF^O?&iQ)2^0dLDWRf*3Ct
zBCkLYH35RC7?09|SD;a&cJMdttxE<-=z#JkyDl#gf`p(8NIeVx^j~8^!M3+Ebe0L^
zluJApUr4)3+^xi8Mvqunu(97i?d46A)QB_va{ou?doHD#>gxSkJ`bPb!V!I?Pw5v%
z+Vw4+*q_BrvXAM*3&`}ZM~5FjMT=o`@BXbuAd}lVpWc-*`XZ{9*iGCETyaq#su8z3
z9gW-lGKrdWLWuvym0BV)2O%v&Q&UrML4kqsjNxY`5d<X_71D8aR<o+8mRkmoo2Rx~
zlR4AU(z2+t^_;+)=X`3OPKkt|!mGvV>Pa2N(3|6pE2q+eA!gWb95G<xqn?$JYP`1=
zCIR-lfEdtmQGleoPo53WB<=+~+*ViPWAb|%Dm10q-3C*ls_a{<okgxI_Oe8M94fH*
zIrXJ&dgZqYa#j<{B5RV+F#R;IPmW$#{H8BNWEW(`Cgfcy=!dnYo>=j()kE$JV(bGl
z{kk3q5Iob{pw9OL`{!(IMjp{+K8cJJSDnhHK4G@Z5`}b-NwQ(gu3qkeFs=|2%^RpO
z4XXX*nI86QaWQ0~xMF;m)%5FylZc1{7e-9LBV}nS2<^JAyW??PWprq0>l$?MwBvg`
zwW)e)m@0+`r8TX)lj1yh-ER!Ox3{NK_%lGfR^ZeRSUyY+{WFoq05v%MtZD1mtj>)s
z2ahPx_((pc8}gVu(YYsQpgYlq{YUr{!0pLNWkyNbBCx-caiK8CVgm-bSy~Pf6jNfE
zXAOodHdt}h^o=!zg@p}FlhKf{i7I+A3puL{g(Vkhm){#w5Mzl74*sjFf!ZT2a`(==
z?FsuT+ocV}sy3AIs3NM}1k%jIg{5pee#kE@jKTe!fMIZUNyd3;kGhfIGJn*fXhsv$
zHdAWZPvETIYt7M*De<|kVmX|P$8T^$gQr-cI8hsY(EdrRVXelqi(_ivABbI0&)A1y
z1dsGL5CgcOlx*;!nZywJx#Q-p-RWu(FP0CwJye6;8$BZ<Bc18r`itUjv?dd9IM^FJ
z4u`wxF=gx07SFHJouWwOf!YdxMnK1HGzSL<37PT^trzM^5hx$Djg3v4)fxz)r#J!k
z*!khN4sE&9bAAtsY^zpZJ$Udy4TVCbHEiNb>y(C9YzNPbRJiL5S!>uJGgmd;cdW`{
zh1cYvc!k!#it)1jU%zVps4mI)E0hW5@ec2sE1wG@POP=s-N9kzCfGc&t$M+dl9K&}
zE|rPhim2`i*|%1|4B^+PvV(ea^wVe?2p}FWv?dE%_>>U?WaE8n9Tlo{QAzo@MG|S^
z>-hM%Pd2w@k(KAeNIrI^2*1tsbxq|jua6B3Kar<}CU-(f`<q17ZWff50w0sk$d@mx
zj`QW!)zx!T5rUx#7(*qVF{Q^eJC5H!KNGu#PB_vtHaJ*OP*)d1cIM{c;kl##vX|AX
zDm~>4dcc0L=vvtXfv78z_=%><*6pqQ`ExIN9HgbChlIRNz~OO^86Lw?`HvqzPW4b7
zh3Q&sG|%7P&h6cdYMUXkV1<kD4Scq2Vu_#nit+YGJYX2pPq{kK?^nbCzoc?MK4Kgc
znvZK>!!Drk`be3k(+x?P<pU+oX!K6XjH)|b57i{<RK)Ms_)Y59Y9L*`P<sj(U-aVj
z)?}sCL3fV5&(=GHN1QT}m#_Q5Mi&Cczn|h}hl=n{6?nX>xc2zBZ&v+-gJd{#3^HI2
zOtGU{?gw=z>@-JI{LCoR<SnR-!L=G@1+P*Eu{NXH9S3PkA|@`pzG9gqAjJ{W&*N}a
zSO-<VKD;1?+VGygA2^mh=p%q>gEZ4RzK?T7A?u59gHW=WvjQZ<8igzoT%<nQ=BZ4j
zO1p)ga6PFKD_`p7>KY=7H5@I#UBVu0g;qTwA9v{7!)?Ebw6*zNRnLo&;443eWC3+3
z(@`Oy1g}gp`TkZGVl|tzLvlx>dA+(^H3h;HF|yI^X)B+1K5}9Oc^tIAeEIV3u06qt
zizT7W0kK;8EeKmXhFImFAnVu#hk<7%M4o?{5l-~<e{tP!%4h2_F+h;7fVIu@D%J0F
z+<sEzD4h_^War<a4K&NzgtbgV5tevdD909_7HY)(bd3-QboTP_w6V10Ox5sPim=$o
z!Zwsw=F>I0Vhv{x7|DtyrKMTdQK$`l)OFfye`zlj3_<u{|F_93C&9*gR19mVS%iy0
z`RJ=>)H@^-=cp<L8u>Gc#o__MVwe<MU?|9G{dsK=_vxnM>-wk=hKr$@xWFu2=&R!5
zMeLNF2`Kv6$-QYOdOwJ>(JyX2!Y|Y9<$sx94*|LF;4$?}wR@x(&kU<P+8BciWLTwX
z5{9~|Jo08#1*ON@xL9fB43P13%MFNC8Hil?Qww?r#VfY%3SkXx87B>(c*sK*V~?<Z
zQ`^P0)};q8ggRj!nesmbW@FMzBirNJIal13TX&_fhPz|gdx&<<B!_EX4Y#+q>**#Z
zAU>K&JCrM`-<|54b^BIIcXu}->DSs3jLxDOracR~_hx{@;Ic4u#|lmUslySIaVvI<
zA48`8#yHskd!PUF9K;dRZr$d2<@HYT?wdJ2Xsu~#`8tk=Zux!Ws9F07*z&%uj33?k
z18BnpQs%4|fZo6wAJ^B{heG1B8D^@YkPshJ#FK+60mjLs@TRQ;Xf$q@jscI`9`o1S
zhoKh<5Fg6_V2^L>><XVl=fUyr=Y}FBC$KyEv$l}UlZNw}jM?W<vKt`>9~>379-C#F
zNO<zKvBH_RnzmHh!*tgspl#F!b!+lO>#euu(2aqjYu5hzK^(NF_xVs8^Jd$rX8uOw
z^pl~G7~42eho*;lasvvT1l<*Nh9=t*)sV2e;Er^>W@17hUSyuDcJtr-H8dd+Y-K?`
z$r-*op|^jq7zK)I#I3hhuJfTA8yjUWd*@{7NilpF_mg2I+RQi3tQOWYzraPnk{x2X
z@2=Fk%ue_z8U#o~(2D=AmqVwl#YQny9^JkOX+k5&*;@{sk0`P9mM;eM0G!9LvRSx7
z>sOp4v>uqLG7hiN`nj;+MEzTE_P~GN&fT5V-y%_3vXJVO8So<!;YE#+{dnsw-N^9p
zzF*JDZs^UJ_0Dj4dASa(F`E4}1)_w}%3VUa(=2Czj`Dc={tOb~=`*!b@o{D)5yYg!
zqxdcB*_isZXmk9+&!2ATicZEnjb12Mw2O<nPgrh2>&%Ke#7Q;xb|BMI9dR9`RfeKd
zw>4x*9Vr~Pb9zIQ)Yp+Dn<W^9%%p=*buRBEW_^5oPC9h@L7kTU5ocfY)PN3^CF^D)
z!qQ>k!w-oGBpj$`7*{Z>e+-5qa6j2W)zxUB<8RxN!<@lw5BO__`hpZ&H00JK1lc37
zm8Ph$qm!oyYvXFUoDw8QNY)xXVHzAqU&g<G{D#}j2jw-OuAgbSTTNMC_?8T25bS+o
zjBo;2R)3T$-3hb)0dB=R!M}f`xi6irHSVqQCgxh;$E|mYTU%QbV@5VlVGX|)x*Xra
zcBA{|*B4t{0a&w6?)biR#G_nZ)ZEh2@6J>*DHh;69*0bt)=h6d#{!%-z&JA38$$OO
z!il>w@G8#60%)JV(Wz|c7h}sM4!*XkV_WbsX6Sd`^aHS0DGAZ-LC|`1J3FObPM2Fr
z`PRz+u!F>++KZuayHSp~^E!`q)hZYpM`HR8_}ii&XWA3IZEHiar>_>NUPab32}vU6
zK_)H4^mShH=5nY9&HBEVY2T-)$Vl3mc15#r$<fqfkljbvXiXIkCkeYDgX#X##0}XF
z;=vNPHi@Unx9%=@w4K13uroM8RyDqh9H_d&<;8AWnTpzV^)U-c9}P^y$*>;Fn_M_+
zd+Tp((O70Gf2KxHEiJ7i=86et<m`HryxVE%bcT?1YAf^KzMD8yVXG3oN;+86db&ml
zXd8iTXMY{82)(*eV1t}348NSkH6>>+DV+{$T&qo^-?|yAb~|h>uGi_3{Y#&P0Uw-;
z=Vy0&5tgd*_3eSC`;$Z{+apeiUQH)<H}VIGL)!YQl{KG}LdyO*CN)QPj1$B+I582{
zZ}qUi#q;}G=~IqwsAqI^w}`Z3DWoGAY(3>9f70TNomBdR-Fr;4PNKR<W&cd_{1^0I
zF}^rlmQzVdsgr8;WjA6K+oY{>`0E1$n@@3eG`jk;yP%KBKuTxMpoGH;V8~Z**Q)~v
zYqal%e)+UWC4S=a)>^Gl*?EbxvkVL=Ck9XhL<H(Bx$W0z)mlhmMg+*<A}dW+lsHOS
zW|(c*skt}7oG)&7(=bMgJ$I0*afFwbH{x>KW=L53dr!E8F=N>Mv1%V#<|U4;96^YQ
zJGa9KY?b9B!F5gdq{!Vj%ZuU7xB26U0a=@x?k|V|pQg$SUFH`8=JwakRJ&=yWy8LV
zj@G(XDh~`#F9hbys9vIVmmLkf*WL_e%G}er2NpUU$8;^OdX~67dvws2Q%6L+j<|U$
zJfuNuG{-j_`)N98?%{_Kx=W&oZ7|hiP^|pIV|1DC;;=Mg^<`7V&W0U#>*g2csEhX@
z-J9zBcA$oA20P!1O`(JM`T3oM4<0<*B$JubbTW(&V2#ZH!R?1bZ@h`!6(@Dhy&~nm
zjIRz_)(&S56AkJ2gIB%!_8P3(qYBRGc|nfh`0X_j1R_;3z1+y+1#-5kDQRtU$m9Mc
zNEdaWdW9OjD9CV{jWD;kSPV0*=6dSu=f|wFf&Xo=VOZ#sP^qAbnMip5!aoFulMyKB
zvQmv_g=5Q#@p&=rg-^0_cU@*VzfnE0PuYU1#W|XV>q}xdqyt}-m6Zu@g@1g0qbL6b
z&W-=(;PvoD#q-~^PX<p2u6na$({X1}<Sp+rtw*0^J&&^-cOd!kIn5v4;kS48{^DF^
zf&V?sBxPH<Tq=w&8`#%F%Cwx;iJ?Xy&MiH=-f8yRuN|!*Cnq->Vy7V4P<Nj#DA_m6
zw105s*=|di#(C>&_Q*_0+irP@pFe+|g^Z<wgPzT(5)*pXE1Mc#?rVZf&ZUoW%AtRa
zGl@J3At^w)X&8?d6m-o(6<+11ztNqI?V0}E{Kz1JEZ6#LNEGqNAbN*(>~9y3?hy7*
z%`8}WS!BC6jvk((btSEr?W)PXB}%J$nTJD57%_3R|1_EWO&=F%HT6wcn{&{oct%yL
zos-ZU*O*XAvhd&fp5zzz-U!*c?kzoPSv40c|I6#QGh-3HWw6}IzJmR9fFL7zcqsvH
zpKvxzIFs(tFuQ$X$aoV#vR+dG0$0A3{>}^weHCJ{k@D8@n>}a~>EV`l>guYOC+cJW
zx!0gZ5T4k7;BHNpH;Uuq;;Ph4>TvbpBd$0qT&sQ7Z48~j!t=yI@6e(#6=C7lMENyE
zR2AVF&JSNzKm~l=ImVp##>HQ8wIYA}6o!|+;u~&7v-B!b`|3S_Z8vv(F|EnBZYoVL
z{H!jKaH|-I51d#i@EyLJXY`TaYx1E0M*?lpo4jF<LiH~gXx#XO)+k%%iJx6Q+rYXy
z{PPIoJnm;cDehr2Oc!Y<xm)vc*?&VjzK+2BtO(y6I#+@d7AUhYF)^8^J8BufxrPi~
z(E*U}T_YCkTtCQ1MF72!C|P#w0)N{DX1~2Dp{S;YnoAsS0s*Y?`Un?6yL#G8JfvaR
zEOh2)hO7AsrkTj@YAmt0ULR!i^Y!K6Ox=D5P%rmrt96j$$^vx?M=>se+MjN7rP^(x
ze$TPiD=i<Jt7n4FBrbaKIccm`ytf|x@<m}*pjmf+f1t{?r1bUc+h2Qq9XeU-Nz}Tp
zmU);od~A^2;$LeL)p=?Jqx;qps+X!4;=i@+RmYGroIuKDNd+I^0M&Ay;vv>WwtEWm
z6_&mU`8GYw<vnYq&D-v%uopjbkJ{t@6fuRrWXuQf`M>1vWWRp>-M!gW41u7hG5`cU
z8NYccT-N>+BCAb%X=g#Tn~s6}EgZ)|euUevT>g#<?+=bJ?cZ)n(&3r6?l|V#3Q&qM
z*Y1s}a%+-|W3_iJUC8E;Qvx#dg(#Q963>gF3C@P2;b(*Fm>oJZC93L43HM7!wUDoV
zSKIaU^q4B1E&CoGCg_Sr2f6&EOovMj`r4WJ`nLcpmK-N{MG6XKVTw3V{#`%VeE06%
z0G4nn3sfn8iIGBzt%dDz6XX(iiIFLIW&zqa)R<YXD(q^O45E|{LN97Trhp<aKw}V&
z*qP$pB3E+^bJLQ86DtMf|7`j&LIGv@KSx?u*g|bp2X^QOb(!a7k~#n#9UNR#?H=~z
zO9IW{$|0o(C7lyDOb_9<u>j;Qf*uoiH;w)D)lMHir!$x|`o+*%=iVHf=n?NKTV9L|
zga_?*0erwE0~>%$c3CkUPa0giyAt=(=`XAD)#FX2sM`-72*#Zsq)uj>WCnGO=N-$s
zGp?}EFI$evskqL6*|*J82Iwz5bwulH6J+vb%QLO9z8PZi>L9RBhL!&0p7Qu@bK0^b
z-Mm^0;IT)z(EQ~^%hol`)^o_{ssuyP#2C@SEVClm6Z8H-A79_l7N4;%*UbMm6w#)e
zygVxl<MQ2@zqxBLHIa)%tc-tQ1Ry0DUA*TGp&(^|iW#plY8$3p&b3Fy5NG1zXKL&2
z2Z2R*EZ(Dq;8+S=4W)*{ZG!hKUkrt{+<NPM(KivSjRYiY6com`MJ*#6B1mb&1}}S+
zFemjl3}fZdPwYh&JBP}g!tfsML@hM{u=G&&bh-xCj2S>4I!fsxQxv>V8M3o^4Yv)0
zS2bA&h0mx0+K|yZ1sR{a>GCFWR~A$KMgRFgsr|uOO@W5<$bGs-o%1hyz4_a!7`7G)
z2;g}UVO4p6kSEZQbuQRl?N>p*>@@)Jv;_A9IXl)~I=Pb*`ISUC`aAUuI*>K$qc!R}
z&E(4{<TM-9VAh`>nO=%>hhrxST%<9=`i=zr;Ns$94S!nzed7vZmAcI{lM@>TczVV0
z!K8)iDi96N@S9@L`1H=cL;TFHF4C8cKIYbk`-Q}S;%0sagcVxlq|P?qy8-R*s@+WR
z087bdJf7^!QO}L@4klHNSyu&FYzK#i0$$9hl5sfc+NBm|VYhWnl8S>`E-}DPQ$WWA
zIi81`@Z=*Bp-{RTOsg+T4IqC#WQk-QvuD)_Y#5z~%xcAt?I`dDu-^=rmqmDz-oEx{
zn~;exor?qP`8eD*d4GT3FaM`aoIF`0evKa*L2VQxR%sz#tPYWUtJswTi-t*oTbx$y
zu7mCY_M`!In)ka$w<j-s8XX?qP6Di>4i{>K{2P)1gq>`S4uPvNmr5NcZw(9#Ou1E@
zM<Usd#slc2I_TpK5V8xrfO;7{d(flJ(HH=ApRM-@rP7^==hqlt3a}$jI=t$euR+dA
z;{tUs*3fB~(#eKzgl1geH9*=uT{7X&WN%aSApMQkkxEpm=aEY+SQSCTsS1a-cu~ln
zx~CMEg!}0P3%-c5r<r8c@crdqh8w2?3CjF>2lw+LL^!FVwP}c6OxgKa2;@C7G%kej
z6Vkago{baK1GjP+#ZOp!ev^gc`sr0ol0I~BB%5xF+=Ruczp6~V2WUPDC9Aiu?05;X
zwFF8D&z4PAe^4ede&$K4ah?t|c@8QUK1khH#Lz;Du+clQ0bLfMhH?Vo8^>hWu_mx~
zDxeJ*z-;4ocRFH_h*Eydi~|aJSy))8fx>%(Arjp(0payLa`eb#V)F)2DPt-Y1KGBk
zYMB~QP#~;!kZY}YW!B{Nm10PS1<MF=6#m3D5!Q}FCWSxg32TqL&VuEBx@ULnc_foD
zg~SSD+Y2BjPV87~a-KDyYXS@V6k*SyGity(>ikO+=IY}9R~JQCE}&dt%Qk?Efyn#F
zwU%ltV@Wgl7Vu5td5u=9>)u^w6bo^<=WOSbDrBwS&tXHxHuKwk#+J(n+BPw}T8}Fd
zZVOI6%eo}a7R_}0`2C{mHJisS-|E%0a&U0yFS%Y5&w))6TlE<gd}~#t%tkML6$Lta
zrfr4Wm?7Cj;Iu?1LiICwuLQs+AV3DMy}y=^D=Cpt0!YIgwE=FI0arb=RvR+T!2nU@
z!0@v#5>Y<qL7%rLzg_-j$JeHI4TVN+Yz+?&H^gpeY5oo5N%5o(Y2{YuDY8+ObPtHb
zBbXLNI6sn>lb}aDNm1}0(dD_a!Vj;whv451uU>Hg^m{+NV%EKiv|~~~`sdaEzyI!i
z^>;S)(=>n0dNZUK<ZG<wcM=Ls5hUxz;~RG3{TklB=R_5Az-fAF1XGw?5VtJ)X)_$`
zsXn)R-@moViw~J1+DIeRP>Ul8m+8tr1qvF!5>kkcY!}(HOU@^HXLILG^YNowPhhwq
zT4<eQko#!pLA|{(s$V6yZ@x`b@&w!{-lGThIWBS*Im-)$s?dbgT|}uoTk~X|wZa8n
zQRN%|wNr0=C(GfkECPiahHeP4(HHZ(=RQh_w1BD7H}=5{xUsnNDEw7qX8Pw)=1uSW
zVdRWhZ?;BFXfXe0AHu86Uy*<IxfQyC%fU;JZYV-Xp_?5mhark>H?sP~^ElkGK^Fq9
zP|zu#xz~#IjKb|}VRWL~39V`KH+-{6oTxfsHu@FnV~EsHzP11$n$>zsZeJ8lbQdSj
z2(-VY3{HT0@@dIiY7wN96VT0H&ZuU6%eqXb*yhP}K{PRs4y+iZy#rlEF}Hf+(H`H}
zM2Of7-*vo8e-r3--~Bc18gx>a?f|`^hys{;)HY8=#!@tcIn{3YM9*JQ>IR9QW*>sm
zK59YaB7{10amBP4Ul3ujHx^+b4S~-pqYi0MjqmHHKp3-tNaxizk=fBb<Ky)jZ1i~Q
zW!pjNh51j&XZclCTi4)QSJgAznAI8JjQxPBMn(poes(cLXHE)<)VsODCxl)kb?jBr
zc}b!6X7<@|GmGBu?Ak6)1=x|A!Wje^)!XXdS5%Nnii`IaEXYA`vR>}g$P#)B@Bni(
zI4Y#P%lyKPY^7n~(9G((la&(dVDdG{cd98|wtoG~rIB3qHvdhHp4g8Qo=A%ixn9G{
zM#axgTgv$IIob87MLPQ@o-xXd6s0Bfe)@2C1ldOiKp+@qfb0B+=@iMc=H}*j(vBx2
z_~YSC<aaS3Ok1N%W7}o~=_${{p3p{+On)hO6ntZ5>R%{{6RGu+G7A{tz7HXW#WG(a
zEqbjq&GoIy?ijgWzdq&yEo&wey0Sh?0SVk4$E#A3spc(8`e1DF7;FTPgIJ+|ughh2
z;lxktv>6W~A6r_bWD&*9Qa(E>X#2;rn1#>avnVqiaJ{^-a{13!O7zAfxzRlo*?nXr
zh#mSmFfe{K8F3ML`_|U#_cb6f=_6LXKzk9M(?q^kY4ZdzN&q~90ECV#T0s59k4j}0
zApUB;=@5I3?>-ZP$4;CLr_d^Owf|nvwMb`iRL1StYxj!3gLSZYOL?d{aAj!)`O<zX
zlY_&b!5uo8P+eRO+mE`>BF`}%Kd#9ig;W@@9xriuvNo0#k)T{t!qM^!GNwL3wOk2J
zR7Ct$XQQ)Sl8F&!={<6D<udz=Hy7txv%OFBE^87-Xu#s#F08AoW6-tiQTuH4C}?mx
z{Z}=0v?8sm^{d8!cc;mOjU_G1_cfe4T!y-cR8w2ISjxURr<Zt=Y@V=gsmZ<akioY{
z@2x11BX(TQA^VQRP$rs)98Z={A;%i!9h?KiKbC~!E(eMuQY`@fV*lVX=HcW-RD&i1
z#;GBtNkgPX&Wjf}ud4ECn<IU}EHdIz_c>$`P#YME@L~ln${EI%yQerhbixS1VY(AK
z@z=ZSH_LQ_*9ITZiAuA<jnv~Q3$?Tq4_VRQC|Ic8olVN`<Z@u5d-K&g$n9`lC&=h0
z2M2T4^qeH<szP_J&T`W7(uM52`s-wlL^IY2cN7Q}`=eFfDiCI&n$Jg6640|(ZPqn<
zNMD+a@W{uXzkA7ou@NJb$*g|qn%uh^wcg*p{=_D-K0YMlxT65Z3N~iw5ruN5@bBL{
ze(-*pf3MiSA(R*6sz($T9HxnNN2E3WN`57WRMJ;<O*>pfzK=gk<@Pts{p%EamD<1r
zn6k2bZk`i$?2UB}6Xyg=)m=Ui#H2w*Jw^#UQ_8$T#eMuO)I%A1^?;h-4jQu}0QKnL
z2|_?-#TiQ6Re*8TH89Xy3z;BoXu1dW1blvQCij!o&K)Qs;mg;GCn&q=Uk?h}^^u7Y
z!UPMr3mW~ho+P^#5|*-19XtUN$3s<<K;FBJ3k;yFUcRaL`&S<EAgHCjPnVlqe}BwH
zhGw}*J9Xpin_uMw+trzZ&y@GlKZ5$}o^l~B74;{Y^PQDaPp4;%={cRud0~M!L6x;8
zXMi0LtElAMvlmC?!G1GCugmV({goqK6u2#&RX@uMYY|4I+Q!dlJ~0#-=LQ)9u2efd
za^wm<`@g*a-RF3&hJ_7OZhEGdP$qMqJ|mH=5rdkC?Uz5hxPbL}{%)THf~Wdq$ga6T
zTh_LDPfDcoEx@xVZ#QgWGt6<of1^ITaR_r3CVuwY7vCmU`mJ@(w_PAU??0J5LpV(L
zotu|Nq6Dcr6QFdh&L5|!>Ow9BUWk!&#FfE&KLRlo^;d3g?$6B3%mkROkx^G%$-;29
zBE0uBko}A0MRRdO_titp4>t)k0V#OZWt2)@-~7m<ZgZwgyP8=<ehKWnM6CYbBLkVs
zT+6>x3oe{TCWg1HnVy6PpcGPm(E^G|(jv@+Y*7N^D3nb<RDHGnuU>lMt)NLolVTmW
zy_JrvB`OS%i7%@1Wzs<PByUmiUZoGy0pj*3mDeDZ9x|O}b?QnzQwQ|+LZGf{#;gC=
z!k9}-iEn_FwiPi(&~A3w(?`;16V*!?^R~AgQ@5Ivph|uS6jA{RHxIsuu%r`4te}M5
z0zF|oTM<+d`lKt%e5WMMQ!cmC5qh;^+f#5eJ4GgJrJ1i(DWPuk*Pp2VIy@Kp>x{#6
z;)!01TYu`Q64R~O`4!Ub%+zHeq|$4U6_RHG#6yAk1qw2?u?ga}91)e2J)9iShXah7
z1cY~ffVXn$)Z)uNg4`Bo3lx2JaZhPZQ*X^^DXEp&k6JZ2wkH4B7WwiWQABDmUqEj3
z38siR`8#jr?|A+zm@QDMItj-pt>rbYELJ|I7|-yi`wCOM{r<XJc<|k#MVj9~dONa4
zHB&&;tiJUJ6~DK)w-m>X>hk!SlE2I4;G6H#%GHeJ&QVa3lUsg|yHkWe;Jp9b%ky4a
zj;!v6*vpEqM#B>aA3m8Nk%@7A<pl_~tH;K;C<Q;07*NwlsSfyx3P0E!y}E%KoV-8!
zqcq*)ZvW-rYwX94uleEgM{nj#`Mds#x(C6doFD!>a+$^K4*Uf%%x*>*Z(1LJugg(o
z>^Z?^V)cx~PrA2%u7^9ydHT(tC|8HtL<GX98pDocJc<<Mi~%*SWZ|)fh6Vu!5!M8t
zY?uKehlP=mu?!67QV{Qe8$pZjz5rs_22k)D#ab6oW%Kt_ldMjd=}$krX#uaUsS$>r
zQ&|DI><DONn|ET>_!_UE7po4lJ9wfLOub$@QHrIwIAztrl1&jZH2kQc%90K<ryK<^
zKMpLy^CBHB54XWqhI|DK>Y>T4?6MPZn(!8oPM1adm?ygro0GM*^}9*6ix%a8gf}oY
zmV+1JG*Nx!|G*8DxB~T(jnY$IPyoLBc&!7BqBVWWUOUk{PJw3@!2R*NK5I?W9etRH
zl;;KV^pBnT2|(UA!Oy{I9{e|JAO%su_1I6Qb>VP#P-l4Wb0BymdE$Q16P^4hk0y><
zH198xzvBeHiG0{}cf<ldJTl@(T`4ighg7Ql@#BZnVSB*i@mf@q*gsDu{HNsnSFpkH
zKVAeS$pZc_OOZA$bmq(%mFf~S#dT5|*>Vftnw*Tg(>JeF8Ui}k|5R+wA=!=q&isdT
z#66@uEEst9f!TKj52Q($$yYQ>DF8$1(Mr#mpDBt?E{EF#{2Myd-;Oj=5T%(ZKq4(b
z+pE)a0{x9$<~3z!autB7h628({?*PXdjae>f41d30D2a19$wzUz@VT~Dh#L~Bb8um
z8E~yWcx%^G{603aHr+UnbX1)gzS!lNerjOY8^9+;!0PiTjEbI$60uVr-?$t&=s*NN
z=A;kDfn5F9dS662aXWC>psQ|h!BJ8i;F;9b)wi7pnHjK6DFD`Pf8;7Vhf-`!-Urdu
zYf4Q`ZDN4H#^QbdwJ8+TdGlFM++o88eUm}cAnNngpr9a5Ov#@O0ltraFWG<C9Hm4(
zT&SJ`6LD_{jH>7$=SFN~KJwhT<|5$H$&LJF{Q3$r9VI`WUl!%GLYrLazo(w@;}i$d
z0;{?T&{s{32GtD-%<Gbci-!T(n%oSN!VhE~MiF4GJhKNnfmi=T&VLh^{A;81DIzSi
zbX=Vd&IlCVQw$W0hu+)X7=i9FfEUf408R=XtvCPYXqI4L<dDx>>snfbB5dr=5psFh
z_TM^m8F7)$l7H$*PDu5S$2C`v`iAJSuL<dTju{pHo_@!>RIgE_=9ICM*{yqy1#YB3
z*#XM9V(mwl1_uO^H}g1UV~fE||2*hCaikqJ!df{60o%R(^|;3E$gKH}v)2XVCT8h_
zik<QI`-!CKuWkr+XQQ-fwUo=C*5jti3x0M3-RC5O6&OR8LA5*(s8rNasgjniuCDL%
z@|?IqX$g9{P^~<78Mz+)Z_JWP<3VY<;-%wtJ7*b!y^b4kZiOzTnTDK3R?3ON`Jd(*
zt=uaHg{PhJ3DQuBbZCQ1Kki(}#6d33&N{f09OA>5SCyOlBap-RnOItviwMgn+QSfx
zJMRr5=^Av1Xe_NBFx)7r@Yf`#@{;N&uTIuqhy{X4@?A5{Qfs`+O_?MOmc|(<RN22q
z{>O(eBsyAJH&*N=*sEB1`>yX^sMh3CaQ|gxYidwd#@~|`%9JAU=;bv4$SH)5(o|1d
zhAoZ1e6ltnml<-zpP8a1=($+45$Z_|<;&k)Gl^qwO3Q(63g5CH^$W~QlF|<CIx}c%
zdj0yI7<2$+HcOD-bF`GrK6<wPdXa;mPdDo|g$uw*M&<O7Nsa>-cy(%m@g<%edz${B
zL^@=5z{Y(M=D~nDcPY>NFi1=}QhCY7(GM;}o<&-4<hHbzMWz?vPGA}1XQbd*bCi!5
zX2L(S0&TjcLZvH{w9_mXvF$MBUrOz<Gn-3+TdDQCk=h(fNqntq<ebmKi9qodx9ZFB
zmx<LRabP!a+cN!HF{*cfL~3AO@OAj_a{QsF{$+9|vOWGRJam-pW3IxNuU{?3`ucze
zEx({(%4Gz3TlCPqbPJbil-F0gnUa4zc*b6~OQvR}L{RVh^xLJEJ?=&T0ptih+k3xw
zI2M~c0x4SfJdad@Dz6~j&W6(a+Gq-t$`0LD#?*tFobf2(c5%ot$in9b8xl+^5UD>p
z#bE0RuV3fyl8cv4F`IY^A#xg@hODG_e7{qGqnqp)y0__CVBu0n3`nt%+_(wENC>`l
z>-zQU@84WHqC4TyrqIe3wiO|&qxLKjc+J-DlnwO{-Sa(TkP-FS1jMkDa@ut*Ik_hk
zmRmRd+{Au2D6#}PU-x|Lh3N)Hksa`XhgUZH%C!h1OgFxJj9e-BN(K_M7G|(BLxb!C
zg)-jY#B71CetmEZ{eoeFoFLb#i`*|ykP*ghcLM<;!#FR@wE84Rj5IX{^xMUz;sgka
zp8@UtI&h^?X8X^lXMiSsd4fz(YP|<kN|ly2Ab4;BhbjZ|RZPg`>w;iRtM!Szr^w%y
zGC+6~@+RTY+l2V|c!oQKOs&N%x}6(>bhia<8Y1e$Iqk)<xPGXcsRKfA>6Qvmg>TY#
zTE<?jS<v3x9J{@z`T0GwhiQcKu=vh&z;R`mKO=@FoRmfL=0?YJX>X|3bl4R61H#~a
z+qC03VII|yLMfNEZK^+52hn0O>Pe)HOU5X4tb7}z9iuJs2j~ki?J=(t2+8sekMX{5
zaoReS-VU9&pc{g}z-=;be$f6#w<DgUJ<jb`N3Km3Yf!{2k4DaNQ0sO-_Sy^mJs5GG
zwm#_oueW1;QvpZ%BO*aO?*lc)@zYG{nbumSz8j+A@bAySejAE(U+u5J{d5Obn9|30
zy+j}@v^@l&#>5d$f1Q2dAot#1yJVR}BKO|TiX6RNyr+*wFSLGPd~4N7hdFiW`L8q5
zqvSn%UWa|YD5Ux&K@y1+*2%T@PX0%@UP6OoY!#rr4kyry8UMKP8-m}zw5c&v2<(;X
zo9xOWjiV=@T?n~36Vcon7C|ErtR6D$6d&Whe@{&(L1Av@)AQ%g@2#u=HpJx6=^iI<
z$*9c%f0nm0_FEf@Lf)#vm9SzpK>P$<syL&h^!2aisW#~NI+-lkPmz<bhEnGJAZ4`(
zH80-R0fir`G({Q%WjKt#M5oPD-~?O+pltsynQTT?zzu@~4$VR+yS#i;*^YeK!tS}O
zy|xY;{dEz9EZ{d94*`1*7#TKBL-&$xpcn&JsldIrr!y-9R?Ycs`P$|Im(DA(sVMPY
zA3(CbdY2`dgPWjPeu%TXT8;As;PV81xEr0EIjX^`wp@gbK7_MT5z^*~X&E=WCz#Nt
zb7b-?YH!LgT#>)FY3hx9tCWm!0U~FW1>ggEB2k_KM=gKP?TEBqQKiOwZqUK+SB577
zzAdhL+g>v$bI`ST&j6B*X%7Ss2FA&j1H)qav%&DYBtU^~y$$j9UNeSQabSQArrk50
zj<0R&W#ld=rnsKeMjfQUmJDJ)6xEgQ^7v&p@_XCAP)5*0AjbqS=h<<>vqSl6I-lCk
zQ71kFa*y-vur5XDJs!VzuOElX+o^B3f|`J`c^df<t6dh7XJ(m;LFwoNVp%;H<JRFa
zVUyhOPx(K)76ytJGFQcPA9Mgc3KREIStLgn)Y(Iv@)>)lO@iZv+RTsva$M<J04Nj6
z($P+kvzyyE?If#%>jZF59&7ZW)Y{0%IUx@Et`EP7E?pUai2!8u;@2Ocy7y}bV=}R|
zCLvl|Bk(jLXa4SRtR^e}(LtZ)jEoE-gdqknLp>N_xLFiMPXM#P%kZI$i?0QkKxc=w
zsU^4$5BoMLo{-{M(5BiAR#1ckfKEY||6vMlLlZf1`CtAi#fY06p~sfShR|tF#4LxI
z+!8_L=>CggO~tg+!iOfSWhnLWvIxsO9Lw06+;9X?3cHnuVc&S52EKFV4E9&kYj5>+
zBxXbbra{5}CxL`VU8#5ou0a||0rl=#Mj0zFZMl-5cM9@l#c7Y;qh0&7Ezu4cU4BM)
zRS`K7t$)Op^Y?$@imySO?kmQ%Y&nCv%REpS+c!jGh}ol3f&S&+40=QnMC!disMgsc
z9D6%nhSjVE@YnxujDz8^nSOq8aol!&LnqK#N)}c_Of2pIc%wN%PAd5M{g6`uSKQxC
zYTNOD_|gAEaRne}dXk?;<cumakAoZa@(EC@!bkz*x{YuD1Opt1%*P^{%b-T`Ue@pW
zu+?gx1@%YW`pBJDbY{dV%Ew&tS)llw5JdsGzkC<W%wPS+&!{T5OCgnf!E#exM<SKz
z|G`b(yW)--85#A1iL@X!2Cx*~>x19fBytp3Vp?3{<mFLaU@(wIDGNcs8}Ag=ej8V<
zmSH?00O)3pkjE_ObCk=Ojlvt>-04ak2Rdy(kOu`0BLQ?uVrv|)B_UnS{807;fO3z!
zQ4|XO1Cs%kKQEd%&!|of0I!Dx4Z$o&;n^q0V1S@<=-Yta{O9_=R&iVV%cDMbtbXqx
z0!K*<a`rpWZVV0)IsiJr*2qgAo(bl$I*cIr%}!91*`uHobCszchMlRn1<-Aaa6^Y|
z1E=1>RrbxggQht$ABawW<O7|c&0ojgINd}bB!yd(`EjZ8t?P6Y>4k@j%U_)(%CQKq
zL3uhhHVT%eFZ|2x5Wy4HYOf#iV1N#5p!+lchl(5iON9MDAzZeH-jGacD3hoK=~Bld
zk*5wvT+LI8`i_2JXH^dcLNL&3v<~|FkTc4@70QYJNZ}e|V`FgW5=_eHy5-?!DYk+$
z76tgt(Zi>zCk+Z69Q2k^N*x>9q2xL@w@mELsDT0Z;CBj*j6x!`28J0C?@oicgOY_z
zQji2soEx+p{69A8EZ8VI52wSS05I7fqFkGW!O{uZ8#ds@$cG>QH~S?ZDS{{k6PhvT
zM)4j%T-16IIn=;(`TtH2&8_*TWF{BmoO)wL^JytA4YbH}dUP+M%x!FV7w6}%(LyB)
z<8##|jOwiLIs_jRfg+OP?!ZRb4y6)tHfu1E2jR@!xTr@=Svu6oLKjt;4n$9;ic+Ga
z{Z1KA6!P66&jbSUXk();hb~1X7(a0L6XbojElSOIN7}AaFKo+*JSIz<aoP|!Oechn
z9pVPMLQeJ@O89^t&CAz?khJOQ>LNTOiqjO0*R45DB%hg{IjfruAu&fk+WR(ZRfHpc
z&uyF7?w;*Ydt}Nb5#spuF%n59Z|9%~Qh=TGla~}&$iBvFW~<uy<3#r0Hud%hExDw{
zTp&I&@-raZ0QKUW2P?_aa*`7?9^b@`61t*-$-!h|dX)jv;=v(rG}CAOEl4Bh9@|CG
zJRhhnmjc>PzUE4z`G2heJSD(sapxaOXcs!`u1l2NnflF2mc~IbvC9F`|ELAtcvl3@
zkS_o*hMegBH`_a95T8~_hJ|rPx)Ks;(M{pbH~;TsOv#u<{K@u8zrxCftp7UcDX6sH
zx2RG`x$NG<4aQO7mn8h-zdS|z$&##no5y5t{TkFfr!SE8(eSoJ;-}vr-;nI_hgws9
z&avgamBpKFLz$70OtgoMAU|h|XPXK$=5TNP!B4|L1)w3B1x!<lU|d`r3Jg;$VY;ra
zi>|h=Y5BEM_VvM`;%wE1NIT=aK0hFxNp`p{g(t|?tSqscj#3FJd0iU2`}He6Z2h{H
zIY-H5)&E)91ai|PMQip@y$AG<IQf{v8VH8gxTdXu%RSff6~<QQFV&xv<fQh$^_M7f
z!L-pLuy+{WF7bNtA9?d1Is3nxCp}wOSZdK3eyZ)TWvsE57-2|4H@A2F>ccH*$L)Lh
z`fQqCDhXMCo2^KD`^JsQc|(!E-wpto;oq`@co9prJf#|H64rblfFO0AsW6EV*!DW;
z>bc$>+f$;IVDPpkki+8?t;g2$a#o#yd-v8jsbjg5H#awBpz9z-5cDjE*u<<wW8FQ(
z)zoLRSA=Trr?jise#g^rihZu>uy0Ea58%aiwJ_+`0c#zy#m`rXVrRf%ooA;gLz2mA
zw@SdO`M?yA1cP`yL=cj_Jw0-#`IxdK5H)~fHtP{ze9n;^OqdB{xXHSj2x)6ZVLu!H
zud*j))t8o*j$&Rz&#OyL9}3Uh{~0UgzniOoQ?0uK7$!tCxsv|w`%Wn1>VcpZrTQWD
zQl7VXQ*@!X<vx63)74AwgEN&pfl3t&9}+_F{-a_d-hJR&OUf=Yy*-qvb(aa#?!Rf4
zJ?%CKtmD8~7`DE02mo#mUmo%0BeFn(bRJBYH#4dluw7G>PKt3}#Hcz#Ai-V&kwxX<
z<>Vv=CfYq*Yb!uHq03cOfA-*iRaIegAB&c@=E}JyFir8FKAV^PQ)Ins9ow!R&w6e@
z0XG=g9*EkDjT~jpHbx2nXGb2BOT!gpJ<X&kd`lL-#e*3H=t2O@XVbQwbU}^zKKJY6
zV;D`SM)%lnJx#b7jpPoN*u=JYaO4d`Bjqe&@3eB8r}ojwJCuV?G0+Xl)D#wctkJwR
z*_&c1p~C=!mn(d$AX2HOWXEODXYr{HHz-Sz1_BtB__xJ8$?J}fPXf4au_;Udkdy><
zvS8hTciVYhnXpz*E5tBKiXJ=@(L9BQ@lj27v;b>6g|%61_(FLUTcor(`FK9x*Z%(S
zt0lHo{@{q%pM0VmDYB~a`hKg;GaifvP?-+QLAwi7qdL#a>E8?R2mN|+A>P85(o5O9
zdtV;*z^C)3n(n6kh1dfNf7O<Sr^lhl_9`eFM`m_&j;(5Vv$&XuefF6mR(%B+4-D95
zsJy^*``=B(PT)QVPSfjDuwK7TtetMhU=ULwF5kw29i;ZDaBnc^Z~|cN;*iOR+aa#s
zoGMp~Uo(abzyX_*g&*|HS@ck^^ZL+-#bP`m`l!VQDv=}k^A+W^evp}r6!lLQh|;vQ
zwC|v5P%@s)&wXp<-*%N68wi<JIGuW8a-%&=Zi0C-6cyCxxgth7rh4aw1)f=VfU8XL
zFn}chO&5uu>7-unZ>q>TJ#=3c(g@q%JsJLY<S+5N=h$C?6Ll&Wfa~;-&1g5bP!3H4
zi5uW~Zmb1D3;kL;qh>ozH}U+uH6Qg`N)x{TSHe+@0p+LJUfsQRctDZXgBimL3qAvU
z;n|LBFCaSsH$Axt@&N~ZxDcY>ob4Gvg}Em^NrV?v?icitNZ~Z!7VkrcU>$E`3RHj9
z+<fhYp3yeMN2?1t&p$R4QRuK{fX{Q!{|2s>jc>p{nf?a&)Y~pqw9KsZK{tRrnYPq$
zus^Nj2@d?t1W#nCIkd*QQd~k47X|ErF&$**6c`Ll&kWjncXfgjA$(57iw^&k6mAHz
zk40c2bHneVG=a^{1-%#o#ZxWcC9G*{TrQJ=yn%h+>Dm56Dj>P2v~f}Ynn_hhWxyuu
zUYO3k0_Tx*UXK*vEvdc*toIrm&|$P1*g__B?2+*!bwPf#jjexQH)hAK>Cc{of<jlo
z`A_e9e0ew5nkJ?$bS2u5ANsel@ki!Jc=VDPy5vh5uagI2$+cixl>KQ$sd@k14YI1)
zsWfFVv_eYoBm8DWyM1Zx=e64B@KiUdH<hKO?%J5TXeanJ2*Ond2wrD|e#hYU?SXD#
zSCIoI3S!i{;%uvQ{1tuHXp~Q>zLLcmbaBWRuC*)kGc|ssaek(VEH-S(hYTBZmZKo+
zgaNQ{YyfLk$(8<agxAFV5CNcN@O=!qAJuL%v*It3VN9yp4B*kG$TCcywR3vs?gAaE
z2$x1VO4GLyaAdTt6nuFoN97Q#YdW~r5*{V!BfsCZzRU{zHbf43q8x@Clu53`4y(tn
zNkA&NpYH%hU&wZ~r}4<Z*(HNv0`JWOI^;(q0Mj$uiMk(LTwHKV^YdF=(ATeDM^#79
ztRUIat)#A^W1|XO%HR8o=B%CowTlJY5#8e56Tl=IKXd=PSC#o-vCSYrUpWVa@w&D)
zRDY1*L6KJCtGg0OSKLm%??F&W3VntP&gS|DS4!kdj9U*s`SsVWDGU9t!tY>Obn!$}
z{IIHG-Ctg2QJ-Iao8G|C=NoSDWqV0KwWC5j;o$iqT1F1sw_;VO5_1&|`WuYSEbSCi
z>NbleL{dZ=>ISx@xvhFhenIoEla@I3d-)sL96BTDE~dkp<aPy~oN2Y4-`VFy;X%~!
zBr|9R&Ao`&&wXQd>Fu?5ju&h$UFwJfz*<GplS4xL*|TR|ly7|R-+wh7ua68IDR&(a
zro7@dQeG^vWQ9adUs>-fZ<ecN%ZG-G@e?Z{4Fq3cMI|}kyEj^Xt0g)QjYc1IaCx&Y
z?*K^UZeaFa#p9G*i?xP|yNPp(E+Biu)A4%9?dLW5ugUp_Sg_{wy+Ia8(aIc?%m*`=
zZml%(La7-7>+is}*K>|}@5>rNr=i(;ha2tsxf1c%gxW`|9MAG+)WU7iD@FjPP`~N3
zc?ombGd`Sr+?A#xm})3u4T9qm%~3E;1=khh^_+iu0g$2?@IhpxTi~YmBju-_?%;&+
zZBTMKVHQ`fj^lg?h?d(Q9@5Z>utb%`$(P<%NYR*yH$YYc9Pi7fp9fwpF0i<WcR)Ix
z<r(}`W|-%wFdvYqa(YP>`N};CnbWyedu?F&`3IYrcIUDIV_>Ja%BDXxS*(w26+1;x
zIC*dA-RqEv5VGq-mE4UOL->j&i5=~lNDNr?Y(pUGeD2@BCnX_qH#*0;?LWUXi~Fl2
zL4yfCZ=;?O%3o)oAAJ=ZomReKGYXXXj|Fq&0FFNk5;gL~AXBz#_V2sEI_*!B4Nh#y
zyAHn(7#N<xhyj1})d{Z->0+9gD~IcX6gmG$l#pcW^{Eqy=soh3+MPOC+12_HzP!B5
zC?Fu9j<l2ax^I3loB`!XrqO$H&&^GnTL=(w=5NX8Sg@M?{r%>^lC}a|8lY9bsGNag
zm%tE}IW}ib9PJAHIK3<k@RTRC8RZ$5MJ=hsU|kUS750JQOBmOIVVXvkNmYkV6iu9k
zBlEq9`nn^k463J(QnC7JBEDo019l)MeY5))N8d&oy5HowI=LU8dBRT$f$-$^m{m&>
z&$?TD9s9}A1-f|gqPBDK2BsfaYT!VmU@gK6($-rnN9y}FKbN*?y4#mqOnvmBE;yZ~
zpZn_7t25C#KCQ`vGnDx5xKk#Z^!#}VwOy<R2OL{@JXsT*%~qkN{T)hl7#L=TA<fpC
zYjeY3#xh6@Lij~NK>>f|pS=c?Vd*(7$t&-HLl4jWjn9t{+!~L^FgV+H1X!i0faLB+
z?;*NO{`~p#j>gC}G@0i8__aUl-ufDrNCc)oksB|?19ci-T}YIn`i#jsqIcrKX`!33
zfNR&hnCy){R5>C)-e|l#0u=9{59Ga?Soy5d!a|KP>mVwO{10Vlg1nF;Ud{7uVyzbS
z@<eI7TLBJt@p6`Sh^Q{Wl8T;f!icf1ES<0`U_14>9s`F-oy3yA=-*X?2kS>2SV?8y
z=qFLUE0b8w^|tPqS;VB|bkehD{3jmYq=cIGl0V3JIcK~pVa=r&?`MOapm|Rn128cO
z94Z%s(OIDzo?d)?l|%I^cI@i4Yu9)lPu0pDC(`K2%@OJ%V@n(r)U=K_gYaXRiNBqb
zELf!Gzl2AJI_G?(mEv~j!ndZL%9JPTpp63^$MHHEic4It{XvQiRgvd{C>taq^Jr;p
zcZ?KD)}I{RS|ggcfHI>`-t#;V1_zf@POl@bya&U>dlxQ5g&ONeTY&Qz<u6~pr0FMc
zv9CR<VNSTb9z^{tgulW=D%svHcyV}3#ooKy^(B86n;?2oCc9;=mY<S$ES!seBacWs
zWL}4dKwmAabO8CpeOK><KOs3$;K;cAUq5QjqK#{%Rf+E5@s}KPRzt#Ul+(0)0Uqq4
zyaDxs@@nwV?W9FucSQTHRVf264Jl6dGJv92gm^nObZ$L<u1fv%RO}x8^4qhm;Dnk2
z$Nfv0ev89C*~9>qUtr9JCeb|A1jlvR<OzX-<*iKPZ#n`{ANXGzeMsLt+w$+<=;-LE
zSOTZFj1!b2sO_nhsU*;gz$r1Uk6zRp7-oIY@qnmB*h%U=3TiddH|xx1SF`5r&lVv4
zh6@1V7JcvbY3<AFOZGIWs{#r@7z-;O&`#;PGg0UD{qta8dKiG8i_CHIVX-;R0?R{Z
z>7?FiaLC%(jt>t9vg_*WxA+V{)PGe8`lCf)rlmu%>nBHriCB%ymChSQx2=Q;_eFB1
zGK6M~!Mhx5O$L5EliLd1>{wRpvAd1ZL7+I8ycwu(whnR-j8cSBF5l7e$dmsuLlb2R
z?)H5A_fI!7d#e>ktMEb;^)dD&wHxYoaRx0g{*~(u<9Qu6c|J8F&wu&*)TLqI%5Xbk
znAgYS!y%zSX<R@RYViF0RDx$pN{&J278gxp>>0nib!GQP$`|32$Atcq;Ve;NWkW?>
zMxXY%*E5NDkEy0+a2PWJb0BRRixAEMNiMy_1<HBVYqr3I_=ndtf6q9n&YzsjIOkli
zco8|st}ZGO3IxoPf#CS<5tSROwV<kZ)sqf@dEIP+?2aD@&3pA~Hszl3ju!%}DwWo?
zZ?2qHC-7~8YfbPaTN!w?U%5g@bGAN=p|DY*bbQLXeMKL>7S(T$ww*t%42NTHDNm+4
zVdRd>z8%m$8r;O?9j2TXBH+y2?D0tcM^NMC$<N?et>ueH?BHj$2D#Gf-U_E^eY%l4
z=WvyKIseR$qO?*qgYXw%QN3vS0%)z?-fB6mccL~3ObNU(K<JI9*`%^NBJgIQ<TE=X
ztAsT;?IQ^qcg@l4ckfoOqDD9*WEeQ4RDsbs-8D8qI`Pvex1T2K2lD>LxiGpPoy<$x
zno3hUa(pgDLrd4z@|pvdKTPuMWj>XuClTUv=AI%EZ_j`Ukm!=XW77`TauqDTfp(YL
z|LAY|<#p#HXQCw^NH=Q|7j^GoW;7nSHu_G=Pa0>RP_wgtYYDT2)nLr&dx(+TN&$~m
z2694l$R`zkh$LPB(&jbKwv&jEOCl_{Gl^aA2FgG3cOd(~<HN1setcI(i}2=Jgfw=2
zEdApP@~S8N+fEFlVD>arlN0pZJ>n8=r&2hn4UVsk793wh56oK&(bzIMMhscni&AH`
zg_w+gnXw|0%jOm&k4pq_a4&%W-{ng^seVWK125+z<f}-)8l&;$pVl-{J_7o@SIX{)
zu>MexBei+t<L@ax*Rdg#sP6vFNd5?l3uWRNck0wBBQv(eNDU6Gz&^GADbiYju7n)#
zZ>Q1Np;DMEblO0x8tcRD!MKF~6=n0lAV^c0`<tLDmnvOSzI`-rOM?sZ4s|69=gwoe
zf62GwshXCSmL^7`iS{(zw7JwfRJl$X=*7B}%Rf4?%w~>+@3_BQ0v;G*FXr`?z9i4(
z2g)dpv?aU4ZbMdb9zu^_#-7O<t=u)5kY%)yEyzcQYK<Gd=2B8tc5618XOmEtm`n+c
zM^TZf6+MIt3b|9vzE&Q$E_I1q`e@`tB1lbERF+OSVNU%DXc|8q#ur0tlnF-g(v1;b
zS>;rO&A#<5Ed50gZ}d(x(ZaGH{^rS#`w&_rY^L2$8{Bimxivm=eKo4IBqil&fucul
zIJH)%GWsQ*5cV2>`ku$=%GAKXKIgKLEyAB|xxz}Ahc^>e|B$!9go+63aEQw{1dVy8
zUWfX(&@z(iF|il~bggtbCmfz)s+=3I_VHW1J5w=U&2R0ddn%pD84e|JIJdV<y<69J
z<yoEfE<1Tl%R_mwv+q4X_o=TMGTz^<`;g+9u>ON6Yrb>W0Yjcy%@p?Rt5<;?Fz!N@
z?Jh!j)SOiAtsz?F)92mX>1b2yyhS}=9!jPUPkU|V*=E_S4f9iVc5G0~g_jh3)p;dn
z{({s|6Xw6PIa6u2%lQCBoxZ~~DV5Ey(zGW(sa55$^M!qf>|syLyhD`)iA~pMrg;&{
zp4a<D&fO0#DV_1?>$Y#|(yWyBYsedFG<d)v#K?C<3L1|~Tbf8;SYs0U>w`gNLhGK~
zULK1RcgI5^LuD0}tm97riE)}#$&8GBH_Gy2CQe=h^yYJ(ZHgtQ-X%@$ukidIe7$!d
z)qndxe(aJxvO-akJu)&IRv{7DE3=N7-LWeqBMmbYl|mHZ$j*+OB>UKM5{~WQ7{BXy
z-}m=>-=E*__c?#P-&yDNI<M#Tyq=GBu^<=N&pR|sCP}}lt=+2^j!l47<C?;rhFTcH
zSRnU(!iBOTnq~Ed&)V$?Kj_Wwnlvt+%GV(nKh(WrurM{yT<?dzKc5(KOdWxy+C0zS
zs`gx&a2<V8#g@xsWLcZ2=*i#_zfo7uBYU5L!lMl-E4R41&L$KzdU`R`MfFKqecjaC
z+A;n8>y^c1T{NMTJB%X^W9o~mG_#p17^>PwmiID?xi`bmkq)YoZO<si8g>iUU+~#H
zAgTp(6KcK%_(~+_d2yKI+nQ4ruh+BJ?^Bi2%X@|@hnJQA5zT%`=SbXGIE5CqbAgsa
zqR%QO?libpDPFK^8X9^UO7n3#L|bN0-^V3?A1FTF(3Z*^Wlx1){Bsz#?B#Th!gJs3
zJUOdRh1UdncoEJ$h<81oM`qhLVoO~;G_|=`U+n$RN7>^pf#{QM`+(<g)<fOUvC%1P
zO}Oni3r<X;Ta`!Q>$W7?u;WsAc8{|3zNyt@HLEH8PPTV$rccB^l@J*{I>|IV{yeYs
z+%wznAvQE@x9{%U3L@~gj}*1U*9%2F+CI04u*x~?CA&l^(Utw1k4R(vNxgdtdl~(x
ztazgDoo$S$MV~o#o*dgRZ%eLVREJ1W+?VRdkDRB+N4Aev4oKop&&;BW7fy}~S$(yl
z*f|lfV~t+ysTahsv$M~oLuOdKG;S)YO0(s)xMlhMp11mzG)$BcTA7-$dY3O>MzU$n
z#Yu}?HE~U*h(=v`mg>voVUeMC4Wuf+AydZ>J)17`$+|(xe_mQ>`<TaH<SVh}UZ-!S
zQ5m5jZunKpaLEmCyd3&{pZU0zmB7fX8dkoBFr}5p<IRz%w4Yebl5))w>$;p7gd5FP
zCJ^c~ye>~0`o^brVMeIy<AZR!%qfm)Q3ZEHq?b6bM2Yx}l^uJm@ZB$o{jx@T=QwyO
zYs1p2#$Hr~$uC{THb-TtMPx+o3E;n8(3a?<#SYDy&Z_(0<DpW^#ODqvOw|g>dfU$J
zm^1RX#^1SDb?xWs+?Fa<c<UCrd$tTssWui`*}uW}rJwMHD_~;!7k19#>^Ng66XhWA
zFt;5#GeKdgV21R>IG1MJVdv0ni37d1|9BC4iYciUWDuWU68;E(5^og0d%K3=R2M52
zaw_C0GNuwP#fDFWG^$Mf(j2p}EMHskp|(`0uA=4{93?+{q4;a`Z*;e&S8hw)+X{X3
z?Vf<~zV0kC>^-<@^P=wyz6LLpcUAjmQqZbt7z|QDJYd*Vy2bsxxJS@;!H{G8hu7D#
zZfDh>n%AD{BM-39BRpa)@M>SZTv?t-2+XRi9Jcg+xazelT9bFLVnQN3m@9x;U@Xt4
zBCN&Tvae?jZ8&?{+qOsdk6L_nPuR~YDS!F?#rJ4R)Jk~!DtLJaw2e#1z9HV0)Pgu^
z)91;_lf=;C@}Ybv=8j@gj6VoNwR>)<CdB!%Bkb`pap%|@-LkQJR|1(rIHI@@BWG1t
z=E<TbSw^pn=b@9vAI)0Sp=2Z?s1=k3v-hKQ>xHIhgGcU(>h)I2`@i%Fi|2_@l?w21
zS=nLo@Li|2y17e%b@juwtzm=<35>74i=vOvR1gApPNOF+d%SZ$*|sbDixru1qHq1I
zn#v003zG<_Qjq9o^dXF;p>|MfwmpBiux#^$@mj`^Q<%Cr99BGtK;ZlN?1)3B5cPgU
z*d1ZaDQLo%FKcvv`?0Fl!IWoh;XsZ3c|}9$h3RbFZNF?G{q)Z@=RYCDa`r>A2rco#
zeUq`FstK05N37Xjze4J}+bVe>eZ~@WeWQJfn%UQsq!z8b=H4$hoLWqoQ7b1ydbMw3
zB6ulO7PIcAj_OMJPyV%bZ;qcVdb4<CVgc>AFJncug#2^G0{7cvKhm;F)#`-Atwmj2
zC)Fj>6|1x=M*N)0V{PyDRp#Aei=LOS?8QASA=~$ouq=0@u^tTW1_j%DaNb7u4FNXS
zDY<=nin!;7T)>4_a5)r~ZR~*wwkM#DX#Jr#EgYI+mYzI=?89JV|1l<M#{95-XKVHJ
z^OO|R`}V0IeS5&2$@KDP*i5^Ahs&6qv@PGo)jEq}hg%Kzp5&NXTdS587S2Q8ISd&1
zDjXh{rK8j>J6<&^{$5_=7`1Ji4mzIspllQhq53`19k!n}>1)VKfqZ|4-rE^;QC0Bv
zDtkS|MSDHS-)R0IxOY+_spb@i=ZPnOkh3Cc<;piEp5W5z_Z#BVQRpOBFXV#&ZRN&c
zW*37x#;vHc&A}gU4@2%o{uR2>e5s=nR_GK+MMEq?pqz!RJK#iDsrcktj43!BzJXpz
z({QaH;-?z5rLE4eK>Pp3p0n#nqn8a6W!1Wmq`-rxdO~^8_KFw_ls)#79%Z!qpcnl>
z!^Yjvw$!E|ZLtBgk8$La9LRxk#*OAK#w#CvXzoZeG*-cZ?EE;y5&ovYZgA8$o*QSd
zj;|CqGRPEMkW8$v%;al~kK&Ih5ZSeUx%&0jNv!MEhkGgkR(;(jzll#Phh@DjZ<om~
z8E+&#+Yb?5ta#otMzA-FdZq!oas$Yh;vOUP6Kk_aJnME#K$JO8+$#ndY124HN(QiH
zFbyp8Sj{f0kxuMSa6#(zAyZdD&$9?Zf<?-s<k9!-&Dy^C8oh=iZ(&(^vqeFUf`I0`
z&!5j`pu$?@l%!v!hPyg{j|zJz=5lkBE4fSjjCgXLSw~*LytV(gINdDQ1i5Qxxci2z
z`-YBUR%y>YH*N{fxVIV_%e9h@)egXy<qkL(`!bNvaMYt#5euqT&2C<r^-(?&=p2h5
z`&n(p6K`+Z#%4qXD|t9_W8e9P^~6o}rB>}q;gM@eSRC??_4hB=%74lkg{+;Y=h`7k
zoxE2R07qq|CCe{YYCJI^$LOjV18w1Oh>}gy(^oh6)EYb{36iXmJ{$MA!6T+Yc=wPp
zaZynsx@?2Vq0{fZasDbzqOZ~jAnk)KsjCnsl>l&Un5c2JQ80+b1AYda3t>J(PJ&0s
zYP7XSn9B^tj$n^`4BE1MTk}Ohc~r(FgcyFh{r>0Rt(8_UK_`LxNn4IzO9O7s1_(t)
z)N##aj;*DgbKfoFI>qPl=%m-f+b$FD)hFNnO&-?@?O*rVQ}2<xJ!szfw-{}RIfGVx
z5Y~gKJl$hrw=jxbQ1+%ONa*6lNb0ap(3)C5@87%jCI5WD4V*-j`Ll|N@&`(X2uey7
zkN$do^LKfo{Nz&|-41==5j^o@VYWXFS>7~$tm-r`!>P^1Nfz8^T+nmCGRJ(CR7-Pm
za<Hq_0c2tFEmlTgNf^@_M0Ildz+I^e4+E9BLJPLr))K7bl|FrI-huSz*)+X9`Xm5s
z@MS14-;ZT+Vdjb*j`Xn^Vbk9W_Zy~E?cIHMi@GRe<Z7s?gV#n2=hu!31&Ne%YJ@Gq
ztO%lvzTyFUecd?A$J(y_yZF7M*l>YL+#fxbkn!wo#foEQPpZZuhmoAvU-PnFmzVQU
z+0^xxJ{8O#Pb@BX9bqPRD<_IlIZvx?yjrq3nPNH7cg=KC>jiPRr%HBXmw2wWO38)2
zj{lD+|9ZIRM6DVx`ld^jdwmiP;NW{+rZ+>uz&_V*S<g#(fl>u+*AVHWd*upWLe9HT
zgL|_EfCY=wH9lfIH~o0{-to4+-d_68qcfD@aJ;s{P_(4t3d=Yh5*9+z5AZU&W@zor
z$Cc{?^6zCE-?y23+@?{C1=>*IW~W!I+QR!cYoqzMZLKCz$E=a|3R4@0y8_rHW|a^M
zlSFo_i)hvgy|n`Lg{DLA-XcZMcs18{wSHLLW%uIs*j{I_lKwas!Rq54@<qhdvzeVD
zgE5#-;cg8}$_oRUE~-eYVg4Uw4BTHtE~X&V$@w~)iBfp-hI^G${WGfCC*&rzf-&Dh
zLV0#MM*qfzcw*<aZLxjUD5cy}RKc_ri)-TO;>Hh^dkIvv8vR*)WFwMVbi?88K5+d7
zWqq<OSUvUU7(g4>8OI2V@-4xx2IO2i9!g`^2(-ieAG2`lB0e5*L8nqw_@#2l4!}db
zYohlU<tU1@zV_yh?%K&-5%>5NaH6I{a41rDCp1#@<i-M4yVkv)+$!?L;+txp#1{95
z>)EP9^YtH`_Usc|ixCM;?wM=1w)rAzX-v;l;~a_+JzkgRsL)M&_LHBeE21{Xu{a8o
z!98}5p&etptN1CC4V9qL2UX)sg5$>Hk17*`&9M)XYBvJ-l20rwb82?mnI?+iX?3Ji
zX{#Y?0%g74CVEuP%tVFD<-cL3$~0C1qmy?mnflC-;4q5oQ+vcl^5?R~NrO!y8(g;)
z&Af?vfr&rmtScmyy&n!<M>GG9r}!Mgsc^9pQ6Yc#sD$=ih7{j1y?v^WSxxc2U5)Gn
zB^CwMKvoHJD@{Vu)Yan2w;s0lMbYa=v4__DvD~A|UY3{FI~#1iy3_jwNzCy2+I;rn
zpf;)`GOl;yR((UhXDv)^;@im8DQ<`Fw}-5`h<cpezcVfjov(Sgdx%7NMERq;u|=Dx
zJ^p=0t5;=-2ov<J#p_-i+IWf^KafFW<Fy8vm_hh(Gra@KgWNwR;noM;`XIkDf#=5J
z@1MGoBa(EDjN`T4&Bwn!4_E}+<V-+SLGjc%P>%IOIP8Ya7vI@}EgFfMwV%5P2BkkQ
z7q;3>Jr*0MbtP+l4gLDH)^hg~Y@sW&&;`&odYJ-<JH-PX-DQEK0ns$QsJ{+t+c7!9
zX+Zf()^1KZu55JjAd4@Byh~gjh%WQze;LQuGBkIM|F1*!1{{_u)}K49`?1~Pr{*f!
z?+S@~CU5oW{T}UA`q#W>SwzkRCbTL+`k2A^h8ceL+{@7L)vO$X&PJAuFZ(3-w*;*@
z;=>6WYRT#KR;+fIn8*D(obdj6zS+}R!3NV6Tv9<c0I?jYT^yq_-l`3|&KYHE4L}u7
z&nr@yr*D*cV7bIcC-~#yUHXAm!fyLk-8@8z5zUdWkWc4r;-sJE2wRX+!B_#gs>==&
z1EBf*a1=dH?bXqtSU}g4r05vO0>(lzywuqc^T6URLaPg5qxYu8vmn>_2LRQOtH&|B
zEJWY9M2lFLE(sAcFM0g|bjk#K&Mev&@;7Xwx3BR}p%c&b4RL@J!zjo)uDSn}+OJmh
zd@e+Iyago+kN5sk;$T0s1Ai%G3}ySl8NvXk8VbC+YCEUw>$H~wnty!9Ij#0g3Q<Oh
z?=FlLX5qwvAu+S_7DzIK4qz2sGA>(KhQg)qzRbzF>2!jEH;Vqe6PsqwAL$)F&F;Mx
z&N%4|qFS$9X&)ZY(`saYQT!Iow~h`P?oY`ADLN<pH4UHfR=<gflXr+K>+_#3f6kgD
zFcoAo@aq>FA^DPnIh$s89|@7lWYo)2*^+YTl;3_YdeZ21LX$pon5>7=S@c)?;%4?A
zuG~M*Ch1C4I$A(NBCSGVus?o6>|>WjMwg>Z0cW#9D7fWQK9u$IIH)CEc~hGb24d8V
z@aFm;*thf(9r`D}aduwL>(O5Ws)86VeL>{;UvoB+&O^gdW2?4KJmWiG72wP>z>YQH
z)xNyFeNXh=hWwXBQ&kW8Tj@F;`5PwnCYeZ8kWIv{TuGCD+ip=cU{S@S@VzC{(lgSs
zv#tjEQx5MIVQA?ALZ^1m5e@QaG5Maw^iY;gI;g0i0F_s^K|79wbR_SWP7pYNaw;9v
zj^K~7-}Kx<zM2BIVh?%=$jb$@a~(UnfwR<<O>+$4Om4a^wsd@I3>MipC^Hh|?+Od6
zO?t~UV>=*Iivi+gTzDL8pcxA}`i?DWbF)@mIX34y1V8ivUywnfz6PwwvG+R!_ajA9
z<?RFS$wA_?a;pUqH4a)82W5UwPfvBk0OO_`giSY+mNK4jS!>M7>eiM4g3%JmFV*q_
zfdxFLj?dGTCHfy$vEu;o1X>Vspi8hsAFTs2tZ(Q&pr5fhf5u5HqhepwFzsBT-p3{`
z>dRR^!_mP!0}zJ@0{j|j?vK#*xQL2~Y$&t@lPTT?@ujVZ@5fA#IIJ&d2uOpFAKTl%
z45W~1=GE+}pwZztX`c^e{LpekI>erUred!ZttT{B3w=XEz*3S>gG5|Vu^BQ$1!Jik
zh-Bt+2O~VshcW;<M>t6AoaTF2KH{~D!@&MVY|u4=k729UR*a~!$PBv{a95}>5@{I|
z)3%8t?v;ZFES7Y<ZlEup*<yl|NA|JTBZ9ugU*wayuxi~sWFjIWHr+aJV%i4e)X^?R
zu0u22jX`E8$|a>_ZK;io-u@Q>2H53?ZB1}0QagVc-*aLiR8U}<M=U6m4=aSP4<q!q
zar#u3ZSV_nnOVh2Q>TJGV}shPE;K~9^TflLHji`;?7BfxP)&P%oW~=7x<&HqvWs8J
z)Yo&YITWZlJRXVbFsFF|Kk%%iq~tQf@U=8|(x;mrMXg&1yR8wIa)gz9K$J9hSAHpE
zUuJx`CDjSI(2_`vbFrT>tD9_;7fQgL<rogq19c0cgpo0L@AHH}OY$$QPH_3fYLO+;
z->RYG%)PU_PhC!DcXLAU<;iV!uEhT$Q_cAURHhzo05Yvi%@b7Kk}6tQroO7OXaEJ&
zB`)ZvMHRgwXQ1-Z4Lc;P-df-f=ie?2SEL|Z$S5x?ooAxFlBF3N^DkcYS7gCtrFK3Z
zE!g1DEpA7x`+{tgJ*~eEL5UhFZVP0OvVR~wBQ}2Ke0yn(p3XfFljm37gnGoC;|U~J
z9I>!GV)byC;jeh#P#F};5@0_#Xpa@Nc?_30>AJ?5yTqC2HJQKOeD>_wXKshSpe({4
z9hI@yb^5!A)C#((XC1r0E}gokz?zl{x3#0PF4v%y_fd?F6<ikeafJt4;wd(FXEPx3
zG$T$LMKKKjVJp|H(Gp2QV_srDeAhS`N@8E*rcu8%T<BNhc89XVmU+=3qMEOx!Mf#6
zE#Jx;9i#Ii>X}NwZCBSoas_g6XpKM9(V>P_g`Xw;TMLl#SjVy%9z~rx_)b$3ljrz~
zvCt#oiR7)E4kYn*9_&h$(n97I7TcL`bW`vmZttM#TynDI#p5NZZN#rhk%|xlAoIA5
zS466%qOR7$GRfZ_22^Taph%Z;NZRZ)fS>0xyO>duDn!3MPgM)q)(Z!Fgv%sJRFI(_
zw)c8vWhGXM4$RYcd-&8}Ni*Jj&)dRv;>|+X$W*9i%X?TM@12~SXztq&K*N-ssLaQl
zq)2BWMYky-;k}n2JtI7xmr3)aecA**je7GBA3pd|#d`nZa`*-(O4wc;KYQ<@ul*M{
z`z#y9%%usD{&zW}Rtw8G5}H<>bmtSpUoDQf&1V+Wtxcq8=XfS93n%GxcGYN)@dSuX
zH9*N)+3#mdlD=7gG6&3#$L$!rP*5a%K_pHa=x~}qx+(_MwI7TVeHSpIuCH^OIH$;>
z1G5xJRqO)O$;pJv8&<lykIMSWp`|;Jmxzjsf1er69@d`R^4OmY60hfo8mK<S<=v~Z
zCMsiNjtnqIT0-gwGyMGvkoT*J+fQ%dr2iEY$4qf`7*cP5X}jAu#J|=rbSM(CTI&!q
ziN0RmAN#cGTfi$LF#JZxo1{xQ94vbv&!}8r&|XvhvYqa8eYMEi=g<=ScDCl4k>h-{
zsf|xovQG>hNne;qme|bO8u*D+I+YiRwZ-CRHCtlHw6lC3p3K70`)j&!zf`CNp7uEu
zA0EyQF3!-yzP`NDl02JNpXnV7m^1AnhPaU$@U~QGvxbk_;Y?`G&C9m*u<}L2ed44U
z6TRHv!9mT>jAO^h>I%NhCajz(PR7h1|CGBsz>87%p2ph3OG^BR<E0P+earT#F+v!~
z*frqb{Jn38r!D>l<Y2vXgL3Wyk-cPkk8*_NxXFM!o=<p->AXe3M-qzwft^7;lAm8w
zvs(gCt^$|C43~q_qe#E`p|UwS2ffSq)!#rKZG3%jrsRJ#-xruP?qv!J<mSPO`?*@p
zeIVvA3?WYv0G&H3QhJ6Qxc~tUGv%=FF+Xg4X}Lk-kwisBmD$ncfrvnM;RX|p-8a&2
zMKZ7U=g48;YT+ewm0oFu?xXMQPwV=MJNyC;UH>bO`-Mr==sxI=HV%0pje2RBk`5zv
z+I<Y*Nm6hh@*R)klC`0WJg)rn!b3JvAdn?iI!0&gj_#Q?KK3(4|A(FJ$9-OahFAkS
z7tPwmyqT^{<u|L##}8G*CO-%0xFxKdKCpB42annQCGJgkANfb4;rQzr1<xIR9KK~M
zk(?)jN|3YXT47_e%CAs$hHCO)4~1=|(*gMPDsOyhH*$KAup1Jd4F_|YR2iplbrc^E
zpuAV$bSP@l9}MDrN2%uiAq2@pxyW-`GnJ(!RRQuGJ!xWc{uIypesTEtl++<<M3Gii
zDkvt{kJB|?Kr&sPr;`#52rC{@40Fa;*Jz97ydqSof5b2i@ln7Y&lvE@PLY8|rYZ=Z
z`cpKhyl>Pl?i}xChV;x^;aD5WrI0Y{kDRVFqmbja*b%R||AW(Kqwj+yjx0Wx6ZPso
zxY)qEaL=1YK+xsCBv49MCDvl%c>V|dd+rIQ=7bzlOU7P}VkUT8>SbenBlAXiLcjiu
zeu{bx@+6U!D-j9~uV(id@DCzIo^R`8m*e~(izBRXwRVxXo$a_Y9Il%Mr#2n+1gcOf
zz2SHtm)~6<IJ;wFjVna0fr1_y=Dbet*6v(-8bW<Y3EqAXXcBb(rv-5rwom@^aF|hz
z&FMfO<F<ai)0ULcXm|VxNe#)kpi*j2ae-X%B%No!N3&(MR)Q47vv)te#;G#Q&|>*F
zS?o$4Jf(NOHS4%na>j3ZK572Cqe{uY)mb#K`?PWTl6#%>^1()|O+2m79;lfnT&ln1
z8~-g4HKLUjfX4>KO27TZSqk0j^Wxw9vMn#3RuOx4T2mx#_n`9dgiq1(<hS8T8z8Zk
zh##i(WD{aZ5||7c6@}IbvpS^x_nd-6SWo3q6pwSgJ6B@a;n1?*Gr0!&)tk#H+WAmz
z-{$5zH~=McolR32s$*=ctZ0Ig8kgpy!U;V)B@fYIn!>W#Mx??{K<7kzeFDWEsHF&7
zN>(h@V@zEkvP*vBJ6K#;M`l3Vp;j~BFQ+hBI9|68R0?I|^s6{bHsy=Mj>?dEke<IP
z<8ie{$MBoUVQYR+E&Z&(isdn*64k2yE?pM-Ouu%Wvou<;gm-W*;(=gqU0Yz!zyLaa
zGV_i|Zl^`(40~yDsr>enh0usQp5p=zothY@glh3C&d3C_kKQh17gH%-9NIa$Np+3h
zc;ZO~hqvJSG4j39sp<}I!9pG{q%YA&-b-nTWhomtMb3&B>u0gP^LgRQ5yP`6<%+rR
z%k>7~#8tk!qrw!)ld<l-2pm~7c`<nyf;&8qCT87rya(+VXp0^DNhdYX{74dTGbTdW
zc0YEG^UBJOB)VyEW_#O8qVM5zm8V2!ZA0VaeY;z?W^dIyjz?f6F<A_h7PKs{pfpRJ
zDmler;lRjq8|9F;|Ml-wkxJlb<A(=vx*Cou1JyS-{En{vCgb5!o7HTWL8dv-`QN{!
z$dMYYt5b|n`!Tgiypw23761o5CNK7ntY``aqtTQwngp8K?$%GN5sTxr-hOvQy-s`-
z<``DL`=XRH#nJN(YNeVpl@?2bMO43O4O*m@XtWmFJX2#$hSeY(Y4)U0OGeRCJPa2a
z=N|ubVQ9lKB75S=esmNp@V<puXkob!4xN^({FscA-Ht2+ul7?Z#||I6kl1}2R3K=L
zmRc9H)12RwON#4C&riVS!IL4YeauDLmfOgoLtEj4N=bhB#3s6Fs}<ekBjY9FLA%0+
z)mdB)THx`1ICgTpaaRRrx!N_0j9<a|V0M374Nrv^lkwJHMl=7JxN*%kp}@a}d{fw?
zWzjUk8l5!DUa7VkLY{&})q8qz&?QhxAoe&EN>r|qeN?hCQo}ZJVf)8}h0G@DRJF3;
z2(mW-f$EHQp`f)?dfHxSi}sqZ2Xi|ao@RkYwMzjcOk;Nli(0RB{!3^m7)oWVv2otd
zZ~Yfot*ap<MdbO=lzPisVod$x`+a(2J<uZ+01-fuE3EX*V)#kB<7c|t<fSJiTmsuC
z*wKcg7lmm;@9UB)BZsijx@!AzmKi8kiBFUY;#*|nY*w!dO+r*~JJ<!m`u#O;EIB_}
z-;C<Up(V<XN(3ywQ(*Ddc`KPOLW`eJ5ia4+(d*MLlUk^TQ@<2u@cXnCt#{-JThvxE
zA^anf$JRcdUOyt_ws6lwee(j7W4yF2^@e<lim|07G0#oDif>@p0;LNj>=?FO9Y6w?
z{@&hQHU-|vMY#0gT>jd&9|t1HilV8)<A?OH%P~4IOzMEk?mi>99NgDC$(elrlVrTc
zn81`FXB0cSF86d3@1v#;?k>*J!!N40624tr*v)9xe~ve&+mJinHloh@u2Z=@KfATz
zw-$fLi0z<3wt5a~5<7eJrBA^y6$RBu^jMFoH{O@Vx>&sK=Ps9T^WM2HEV!%6Ub&4j
zU+-GWU3p`43%TG_4<>PSvfnMKEj1fM1+}I60B&G8OXVv<dXE^gWe)%N@lp<B^cm0`
z7U=i69Qvp(yfk!yZo$at=j}LiT+nDPTtQY@9Z%ok<Z_MHxiG$mwVExtB!Lvr@#h~r
za=fYNnz~T4k^GZ_mgOOjW@v4;L%@{QLwajL%((MU{7Y00b~f9)`AbPXffzK(&&Xup
zLLN-}nDeEzLD(PM@(t_*gje%`lHZNCQCbx&rn0U}*;Gph6KRit<SC~MOs`?D7Y$zk
zLSgvkHmK@@IitpC_8xzCHeJW|U3onKxxi%<0nKo3SQ8H*^KTl>wU^m>$RVUaaY=;R
zS>?u!U-EToM!S#V6d;muh|VoRnc)nrIC|3blDwSd-j#@(T&{;3{H@C7DgNwL*wILk
zWkqLv$+Xo+dEou&moBPfh)swPe;cdxSW8cAsh3$8b^6w4(E!VIf*J1J>UoY*f$_`Z
zyqDrGM7_jJ1EuL<cipx%S;Bf-WNG;#6;PNZr%44C*V0*d*P*(KGuVB!)J7)gyKB=a
zID)7ktj_*wq243Zcl2@Gj-R)q2z0LmgMdmcZHh{Ec)`_Ooucnll~W<?v~y>F^jU5x
z%hS2!-^JYOzc;85Ff4B;S~2oG?q%px++i{MxFDa9kX`?9;n+RE<&`HHfC`SnEe*N!
zd3@{KyD#T_s)!tE%O`h&!?;gHZC>uDfIwBjfAlUBH=Gm9pOJaCwfi{IU=4x{=#yXK
zpH?lLBj2&Kk}fhw=l9w+nT%z{@lfHByWV*b8yB+D1L__Va{~6h|22=O%DYSzXMfRX
z1#9X0Aw&RjrW6CObBX>Zx4HDs`XKFV#x^!KfK)>0AK~2Gt$@yVBTM(h(*ze8CrUZH
zD4fX`>i0R(yJa;U$#qU$+Z#!L4IHZ>-ZjhO#tr}ToKf!3fz`>qmC+z|Dh{aQV+7RE
zDv{f=$gHjJK52rUjZ5tfL)@EF>Nz1lspxQRWC<oeo~sac#xRpwDaFkuEqJ{wGLOdH
z%s-e{6vvc>0*K@fc-I$pl!MNC=m&(Y2Rm~}MIFA}UaEJ9#8SfXY!wlC94&DuQhGi5
zieojXq<b2rUbuX4C#!`f%i5f{cGS)re@Vmtl0Z@_Z`1@;-%y@c`vX0@Q8i^`ZoklT
z`z_Znce)*EWaE)h^NOO!vWf<c`W1Ll!y2i8;e_F?_jwr+NUy`2%CjOO@;{!rhco^p
z$Q$!nAKt9Fr_!2baZh)Xv3>zLVw%+X)XUPY{Sdc94@gA~{yCaW?@4pO!nLqS|6ZeB
z!8l~5p_N(Yp8{;-YW3l&x&q&GZe7XcT)hUE1~29)nzmt)Z#VhMlMU)P@jgR^LoWL^
z<JaCI_IpiZ>U%4RV|k0y+vmo!u)CfUZ{5`CcKjZzsnhOU=T8h{P++0^==K;la6Js6
z6p*(mG?Mq|-Z0JfTk=+^6Y*6^I6$<0Z*WHK-gsW{?j2gzlulphF@#BE#x1!f$HcvG
z>PW5$AI%le6jk`X`RM}BxBK>VN3&`v&<?A-&wuk4Cm&O+^!=+#gF&JFemvE_L0?p|
z9^lpUW}neRY2yOAtjm<a%R|tkX=|hsL&M{TUz@YyuWLEAbR=B&POA($B;&{#@?h-H
z0&6(YXIUkR=Q7B9BI}N%=yV+73TXFX)09Ksq6-=Ne5+^F6(h^cl{aIvU+NH3El9>9
zADl0(xW9~cU|~*LUif&<a>z@tGI!www(mOsTjV$2yu9$$a1I*9eeHT9tM$KPUPPwt
zowG}RUp&2gZ&7&-GG#fO+=(bUGwn$cIxw}V;B~#ifa^`5(Ea696@JgS22P)I*2J8e
z6KQ%3@bqgq>ZJ!Pq%%hBawi4px{N$-3+$}&|DM}26jpOiaWPLi8qiXipZ*A!iSo?t
zLmu=?K4`gyF^BQ5w&Ue@ztB{uZF_8w4Odd2r33aQ#yRQ*#vQHH))hV2s*1~65enm4
z;rqX)mQPi-@^c?=d4moq+aGBClue<HSVuDxX6v==<nTzZGhSoIG1;D69`swgm)x>F
zsXRW{{m8|C;tpspm7dYCgOmUZZW2J4Ywz)K=I(e$(YUjuXIaY*N%&nAx-N$cae8(M
zB81L-W~%U9%^v~QQBgHuzl!4X9HREe)F%vMZg%nd>ZaT!MMA;7V_`wzI8V;6t2knX
zeL1GWk+F+cw#8({%GNf_j!lz>LOnqlM68cEBqcoE-D_w8S-;p@?u1Q6Eu>dc*Qx~S
z`m8ti7llmDAiK_d6RCuNwC7p4-v2+YNplOO;i%Br@F8V1oTr(c>j(NuR-;sKl{EFZ
zB>ZSA%s-^X(=V%o=Bn=6D=B6!gQFox_rZ;$9)H46t$48=Z4)oBJV|L2xgW)}X{>vU
zFV-vn5iuS-Z`16Rv=>(}4FS_&TtN6hq)Jxxxi~mJ&H)P|q4&nfp!w*_Cg=7ir`#c;
zQ-S<-&#m1%&%Es|b$z26jOljiETz+Gx)7n}?^69u0VrDCUZ3-P0jJO(MU*jZ?}kVY
zo_bdFnGykq!7E?NpOFVXnK1B7K78Bi6b1z2N{hfgrGIzkzbf=pHbNA@-;aHJ!FX(C
z>j+pya;-1g#-9Jz={in$iZ_whxoB3i)$o41kH?t@q~(|Y{{0z^MZ+_E`-m*Etg(2X
z>{p^ep@$Uwi0jO;x-FI7&s8WYERb+y64QNSB>9^U#l7hXcm_>%UUhXa+0w%1MK)$G
zF1~VKbmQlw-q1vNSZ6{c2%nUpdQUhTx*dBT?s${awB1K3)8?LN$LP~${no(5vJGGA
zt691q$~b@z<^@>a<GVU30+OoU2DR|qhGcCTo#H1GQN>thsEb#__T=c$3HAv_NSr~f
z@D-nJ32s`gPU$5PJbxVmh%cvlO)ulWd!4iy3nsa-*_+pte*N=B0YZZ!F)WzwBbwY^
zpiw?)v@ipBsC&D4X_O2VefW39#rt_X6ozcP)boMh!K&uN>B+Usb7&ggsyKZ`=(MKc
z=WOY>)vZ(Q!m%5Va^B^-;96355x-j7Ha<aGatUxqj9P}idO-8uEv#i&f2Fp<FF9Pf
zRhx<8<%zt!HlNGCaOwlb4Mwxr>J<z2x4wdw%M2!Lrnfud#jf!??4gR(era2URB!(+
zD1`}IS0M|<5_F)REq~`?3K;F~vS}{Dn;a`%y?|VRp@<bfF>3b&jDlPASE@n#UIQR3
zeK^1b92?z^T6q7C^B4z66VUAb3+-73;HkTU`)1I99}R~w2?3U?us7T_1vG9DSzOci
zWIqsvA}Wo84Pekc-tY3|_Ep&8ZXw-^ZL0^}<Ugos)g}U$v4E{)#VV!Hz&nE5Ex5m2
zl9|!J_+sa6#`2W{)JAt=lo4@z!F|xpuqNTjlP5URC<hSzYxWIsZMC-iBpeWwSZ$d!
z70y)9b}A;@YGXsD`)%JB6@}7m-jYds(X(2q@meuHL)02f8?bu5*I%gj1_&W(X9|S*
z+=1C;8;x7J+^PWS)=3NLyfLa1@oJNaB{da9eAQw%$&3&JO?ModIvTlhb!zu$AnyEv
z*iI1cS6@$tnrxo%kv$-N1}#wc!K*dIe&O|IC#ZDB;N4!z6&?cKG8Q+P;G(kpKaC+g
z3hlDz0eu2^mM46@Z|D(-xVEv9#4v@HUzW&4^|c?D3_48w7M?x<%?2_*615?sP%KHI
zrnxvamsE!R^0Ylgj>-ieb3z3Gz3?Y4NuM0Bdc)L;(~mFJj$*MYtmt2ytM3+x#R%fW
zURrhW=g(`A5upD_`Xrpfh9NqB+@Qv2aBz2Kd}!vzsMzcK;>1m}31(Pnl0qQPZ%QPn
z>r~4Tcx-M2W(DwFBXfhPfE$l5F>F<mL?jrGBVAiOOBKM;kO0L*D6!aEB)%iu52X0b
zf`=j<Apofz0e3nSD^CTK{r})M4jf2emgWcU-w<R+y$sAr5A$8lgd}<2B58N)&FcXi
zjw>kJ6PSwl-%MXAf*1i+_ra1=FXY{al){f6I{R15$Pvt67P-O}z<;QMJMnb|=|lnN
zeWmecl7;8tqbDwDOeD`?MAc-%C5`Pqki|uksumK`l`<m0x^qH+<+T3rzwC2d_u)f0
zcqLtM9V*v51QquoQ0W*-SD+EV&7kqRisV8Pk(4Yx)F_p4HBSLV2r<6@AIdoa7MggK
zj{E-wyuZvwB?<hOFb%A2fR_2=Rkug~^?nbfG5uTJHOvD0<xflSG|5BYD<A29GXC9)
z{mZqaKb(B{)u&J1r<cATh*Ux1J5NOdKrKk^EM1@&;NBXOuao}oRZUk`+?SsD4k^&X
zWI4jyNTp1CjIuE4nz{HvW0wHl+3R2&{biTj#`y}q`G*Bj0}y^G{CN#WyHKzGY3r<W
zQEhq;Ste#Ii}uD#y<6ETW_}s=4C@E7oqw;dVs1#7*7a@$yVA(-w+$39e3i{bOhCr>
zN|w__v+6+aAg~OZAYh;DhclwOvBSX5IKELZXD)zSE%MDV3ZW}fi$ijW_iF6dO}nh)
zrRDL8+C4s^&NZ!Oi`xu2J#;M;e~!myS%yw~HrS|^Iydfmw<<n{PxuNr)C$YQ)AY2E
z*&uEHPybQcFo|qA$&e0VAr9Y~8`66OSa+!!^TJp7q7$im0+-7I#zM8cF!w7JH?089
zbf`gzzqmjco8$8mN*z;63F`U1e30-bqo$V{CgF6`lC-<P_=k9S>@9M;9DW^K1MA4)
zd4+4r$oW?1pI_KFv41p<X6vU3syoK-Qrl6xCf5&ms#NT>xw!PHg!H>M%OCqs(-Q0L
zmhd*SPwvCz+To|er4F37In(AsBn5Ig+TpP(E3-AD9?uoaN0m2iJ_^++DH7VrquF_Q
zUL;{1%f;-+n<}4@)!N^a#mhI;43bC7yB(O9V%grqFJJ=4sAFW$XLn{U)|t><j_Xpv
zj+}WXrGs<q$k-!r48y@9UQJS0)5?~Ah%cToRw04)Q)#4n#A8v~`U4feS|1mLvX4B&
zk{m!scy_<_;e$iquGes^hToaS1mkgxAw0sL`Mi_U?+^LqsvBmQWFF;bgY8?VHLugM
z>L-1)IoBvA!e=g9dA@r-38n%ZgS750`IhQr*uvs~(VFcd><+@V2BUz=-x|FiYl2m!
zbL<%`pvQVbs+d)0IGE3?Ipl~BG<ji$j33*@-p73!WGJmpJ~3pK>be1&r!WZ#E-ODp
zDgz401!MRjO~cob_5w2Yw@6DF<g~Xc`sB@#8E5~h$dBAvr^%+`<L^4xvb0M6;5wV$
z&z;x_lUn-t%X|6Pmgf&+R&74@$wShs{6SqIsVDk|09nUBIxvd5!e?{e>iLg#s353s
zqlmZOG4a`f66rfr7qwU`8YM#S%=v)AmfU&o+7J&qsO6NwSq{a*ktbL4Y_%5+lX7|i
zQ>7m`dSDv;2^pDjTm^M^@V6bl6QX~7mMiH#ji>Jt`y!sEW%29~heMQ<fM;N}V4RvP
zEl}1_wsnEab)1)KQPbYCy0<I&vaV_KVVhRo2=|Nqlx{k436j9|!!NJ&H~VM)QsX7s
z@8)$Cp`3}XEu@4^nXH4_21E*AA_mmpp(9W<ij|&h7EnmvsSIjJKO1Ghso2f^?US2)
zRJJ;Y{@$_%r*~I?&U(*2qHFZ?j((nbhj#oYZE`b*Y#GDJXxZD@J0YDX7+}!X-P^E7
zk3u2@oZ8t)MqX=x5+a~CayqaITHHJ#!1{z#l*>KGY{*|wF;O;9TwoFo;+wibgHt<9
z>D_9m=ln}$U3omfyY-VYkbg9aqOmzgfrr*fI^6gXtZ+w{*zygI3$Q*r_%#4IUCoep
z=M86ZNtz_#P28H@N)DZK2X1Q6ex&H?>h_aKwj2=5qzCE;co!M4><7R%P?kpzs^tNu
z!}eQk8O|q~@TZsyXz{}03aA>!oB)UKdz`D0(mRCjrrEoQ7FSAZ5?kZ=3|ecQq;%8*
zMop?+FNkj5Ldw*^?9hf1<U9Bj1Ln8Y{~-uSsegocY}`RmsY!P1knOcJ1kl%*CSHu4
zM6LhAY4Hu`<K6w<6sI-&)T9=_RP6;p?b~k@jy^3jIfKboe_vf)EkM@@xxl0U+)@%6
z3StC`uTpNhc|DlcgT-=oMU3?#^#8Yj)BP&-f`jsc;XW2qw^yWKK7VKX@{6j}nvXU;
z*y8=0-`-IQ8J+*X^e^e&to?@)8o*@N|Br2gvz;N|<4bPvN&zbKwyJWY-H^CS$+jqN
z$L}PO6RF0Bl~a9q?)z#gUqgXV31cBc7yqa3yyWDgmhhp-7-39x3|(u(?SC?5@c*sJ
zT8}vOAnx+(A6ImBOF=;*UcUkRA9U7bRpFpa_6J{I;I-@5ee|HSqXjdvq3th@Bncrr
zA{LT?|83G`4#R_vz&$K%;!#UFh8%_Cmt9hD*m865!eg|*jNO!1*ldn?y!%Bu_{pf?
zhXC!Yip{<POsGJ@z@*ILP?==J-j&JNe`^6o!Z`DG{NWgyW%v$E&l<12g2%>^zdI*d
zSKe{*0o_eoz3&_3GqLTyp_0t7eWC@pm)CS&kcl1R;p!uux7nrdb3FPx?ot}pzBkA@
z(@kLeU0Nf&-BUe|C&c-?d8hGXD;K3$UBU;c4#PYkc#XcC4tnP1`7qh;9P+U<pH~mo
zZUJp;;H%|jAcO48tdF~pj=%RXw0eB3k57u*yIK5&Oi)1bp}0RK<Oe;<tbY5d@TT$7
zs$j$BB{#hNJUtctyF;C4i!oFQJU3S<zA~VkPE4IST4A*nal;Octl7U2LvjVf#XX{p
ziYLc7s*^iE{?=|y_u?#JJDbnDqnAIp{IPfQ8JSLLf3v(!GPSy@U2&UgbTw$*M8acZ
zW9K4|$M_Q}wHg@1PuPk{wO|eF0U*-+zVEp>IyII5ttMhWs|@$^+LfN4<n*^9j(%&-
z4Ubd^PUI8glgJ5=>R{GQzq;}IgCQy~@i?EoXf-WO*_Ai#^OuKpE<9eogJ4#TRnSnq
zV$u}(gIrvnLcob-=&Zi;M(C)zOv70)TcBiCiJ@x2>G<9Gvd}8$bLQqmmPaPs4iM`L
z$5-1UkhRDA4)jN7nJaZnDnMmZMardPqbyRvMdZJb8FGm8)C%b|7KrW68k08Z!Z2h$
zlcH*&?f8J$Q~SE2zMy<dGAxEPok<|w@r4TR-k%uLOw^gz1I4tQ>vipgw+0RQdytSS
zmI>`{GEkDb5yeUlQ+~L8-rEulOU3hpuUe{3`pv$vQM<#e{}zn@eWY&^dU~0ZNl6W3
zGf)DMo&d=<$)pHh!)vi2kvEv)(p~(X^$R~T8x}fq^;O`}`tIC!`&Fogb5nQsfk*v&
z470oi)|1iVqUz;W6<lMs6yw#QRiy}dbNfo?DL;PBSz0;63mv;g$Zv6mIYv*fyixIr
zV=;U<HGPgzC6K_CI;ZX3*5_#zhEKkH#27)j=}EO0)I=kU&uCJlIztGZTTK~R%iYSt
z)m(4Np52;pVjbLX<L$RDQT`Kr@U*<S{}iW!?x|d2)Kw>phal7xpWG$$NS)S+SY<q1
zSt5y&(<<M_az%!Em&mf`$M0_yD|fFlNt14)0PUgEm@O8(MS|X%BjOR`4WZZ;Pel%F
zW}b1x;2Qt_km174cw_CRI`y<2Bl+s_7<cr<E+#&&4j3I*nEB#_fCf6q<<%I(#@>c7
z2l@Vz!c`4N1DUiP8wgwcbH3bNawTM6tfRyqBytCHVAv?tq{IyHX#A5A`?Ap!A_N5I
zJoO)cMD|V#J+8RZ=X3p8W!!#WO!f7MF-DtL!@=X#%0+eK8B+_QY_wbsofjQCzb=<+
zYOrET3SB*dB}h|*=wIi{#z`N61Qn5kq@#&%K71bnU^r?$yKW|p=e1m1W6G4qYu21F
z`A`C5dgi0y*!{=_3OU=b2G(mK!zK!9YgcV|37l;z@8&m}_x%P9wReBTXD6k_ghI3b
z1w8%N>*p9DeVY7cT#$(g2nO+_2*vYoCPgV8y9&}t1=8O&KJGRgdX_ISoAsYLCnBNc
z?iDbw*_br4A*!|G-@^`~8rmUTmGtVC3dG1hr)>4U2&ZyS8brSzfAct_d+wRn&DQ7v
z*%?8^Rhnt0`mr;y{I&BbVT8ENnZuQ)St}s?aMluLQ!>%mIL-2~m9&az;9#fxm9X1<
zu+}X9y|AudcJ=gRq%b45gGqlZzI@!uexs~IqrN+)Dh^qcUaH|o$U-EK_ln%XSQUhH
zBuwX2P-Z^WYUPw=t!IH0>4Sky(?K>=ch$5FjvOos#h|xOU|h}Q!!~E;!Ffdn)66_!
zleUC!))|y$^!401^OP|=cZg9_V=pjnXVoUW(3dpiAu5@LG*&@4<2$}9B*EjK-xN3~
z|NOqONH6_(#7mzaoAX$&uO{R*!IW>J>sv<>PmQh^RNqH|K+IL=p2M?ucJ46I;B+`?
zCXWEEQTNp>I1`I}o$z|FNF3A?TI~ms$nFIfft+Q-^<pY5|K1wJqeLn96vSrV^jp%S
zo?~>)_o>Tp`IwkMMZw=;6Lx_}os%V)@`WhZv)gH9$bOg%loX@oiCSrzLM%3QAIpvO
z`PT=fAD)x0(hBp%$#$$vIuOCuCj8PZ&kFvEAWQ@N{7lF>+fQX1I|5cwc+O|$7{x*R
zK$RJ-g*h*(9aj1!P5rQ;D+|eD1QMKrB^AlJFL+3zJ&{8J4@YN=RxY55bH3Ju=7aGS
zuio)-I>%L#)zcwFt@=M02t6eTu^g_bsv^20;1e^4*^Yt(&)Dum`2w||E&ynM6|WkG
zjHS>%sPLrIUdY@$d6o;ZW{<(J!u-F@r0CfQu?~x8Jh?8Ud{SZIX8DJTH1&I+D*d$B
zg5B*RAj0!&Rb%Yf3VoG6<fq^0_5ES)>eks=beb&ApL{pEz3n2aBm|oJOqHs_V}AB=
zzW@8%L!6DaS6B?&Me;k!#XFRG;C%4j_}g5afd<2MlXoJEK@t}#ZC?h9|2cNne^t_b
z7TfK+6MzWzy&vru|9ea|EzNbZ(6R!53Q8e+A)JBo5Gw4jYI2UV_fDS+kemxAnwu6`
z1-Z&+>>ffuE(6SF5X?C|(LgMunj=S)Mi;QRnc^f-9!S2Mq=jU7Sh&2SoW|=|t7GY)
z5MmW^_B6}kJt_UOn&NS)b(elwQI7ZC>wW)cWYscZ&o}GeKl8WU2ULISlaD0j$<cK6
zi!e?_ozGWDfIFM245RX7eW&LYnj*F<v?`nY<63IEi@HD;ru4$JAn9?MG&W9gCpZmn
zoIv1j^uow@&9mIQFl~yrk@vK~34~`t>l6ws_@3MNf)5|8$q_)T*8yBSQZwxEX~0s$
zF*#qY8w>#zqYID)WS08fnIl%`(cG7?h0>54kS6g+`t7}t>Ab|=0@D;o9l{opvL6*-
zwV%$L4EmUurB<|ic~}x)L=)x34~45X8U8=B_v9DFF^g%vmA{`0hC;^vnnKgt7Wn3{
zHAv#=DifQlsa;f0(i5ta=L%u#sAGq)_X}%eFRe8suJwLscU&oYf0ysU08MPL^rLsF
zY`^VDa(^-AAkKOk<MmodPW0(7vL+7cB$)KZ<kwo7NRss2$gzEmX)ETSeqdU@h9oj4
zDa5u_X*gc3D?d72E;(Nj@;0WrgRt8}BxD!3@IU=Q6P|`R3VxuBZ@31vz3$c>T}!3J
zcz5E|y<vlIYU_UuPwVxr+VLx0*XUXc3A15-L5if;9E`>Vy$T)9n}%d}n(%-}ZFOtA
zl|Ko)1PC8S6hgwFp}okL=Qv1#eT@s+rx@2qq$j9t|6xy<irZY#XP!NpGzJfbwpIVG
z^;ifDRM9~QOG++|jmpBH98kS4aleMk<7k{%ayA(m45)H4M;ZH-E)r4HvthEt)J-+g
zgcbAa*GpSU3a3;tDEs^@(%WhtijLU{-rw6*H|n+3`wee<KG%#0lZz7p#a1W#6O!Bh
zOeOK<HbBFm(9d|<?ucA$0Wf64OR-wl1pfX}W_F>&O7$8rU!r#c1bh96tB9p@{eJ2S
zL-Mef7;C%$pTyKd10B*vzs3mL2^^WO%exKyPzsh~rh5NQVi<mL1%(;ixTr&#BG5CW
z7yVmTiQ$Cio2_K%vxNQv_u<;RF+b<5`)i~P4nm_L;PnvO_i3*BBm^H{k&a1r$EBkp
zQUE!1!yJ?j1Sw<WYOD7SY4(-67@W(CoXl|#+p>U_uYn5H9pN@?GfmtxPj*^>2+791
zYE2E}xLD^EEIe?Q2pnv7wAY2%WLi=mG@7y)GKqD>ayuN&zkPS}w!k+9lT7<K^FPE|
z1$h64-XiJkvfT}zKY#X3NlD2=gco#aJZR-y&Ch=uCq2?=%4%2(0bdIfUW&hod!q-j
znGc@2h#ZDWL-FwxDVr1q#}Xn)n}eK!LIs)YKl4yx|NfuQ;inN#T}Z_r)~o$eiMz<*
zgsJ<GOF6~>IlyYP2Z72VaNz8O&nz#t4ZOO`W(R!>B=2Z%*4CMs^t3xZseqwLmnXl;
zKEDrKfK>P?M6zOn;6eT7a&RU$gT!E%LkCq>1ygVygZp#qAn1|SH0%9IxU#@f7tAOf
zOli<dGr`prse5a2m2g~IzWS~+vO7g%xiqC_g$5Jj*+rR|=$ZpL{tROTg-30*2mJ-;
zZvsxT4APDN{?BXA=c%W-fa8o`&_BJy^NT2Q+=mQ7ex8apIJl^{*1dK6K}Ul59=14A
z|MFRBaBBQay$LtyncIKto-C3uhwJ=s#?cVHmjQ>R0%D#_y_%We=;Pn7t)?awHvtjD
z7b>jmshK<+wT8!$TGzM@n-#Lh0^PP=N0YuRAmUx^mT~1G6i+!A!|H%|`Rdg~<;&G$
zad&4d;)31yJgR@tB5YqY>1aGH0@T3*^vw@&LFWB_wxj_;yzfZ;%USNsxgeXoxi65C
zOq!%<1&sx?lU&Oxp6_8}<83`Nu!u5=A?Ju&`PkjZ%KqY-@Y&YY>Noj!4+ei7?<hI?
zyXDW|!QH;na8)2i$Ve&khKYW`ZKXeAG^-aIBkv$&wSxw{pC&yJ&K5|5NDj4OQrIrV
z``an`Z{wv=|KxMql<S{*RaqIN9$rk9>7eMGP&XFl{wvz=>P5DkY73Y`tD8y=chDG!
zL{+Ns_y3F&62)jxIqtm?&qS2R&u~&@rNVSH((74cyR6~F){JYc)3f!1e>r>i;8qsm
z!ZgE~)!fZO%j~wwJq<h#?xVn@D#n=QMzM``Uw5}RJ;K=7IC#|inED^ROXh50MY}<S
z`;6SVjAV*`kG~09{-F2#&{^BT7$IG;qXlPMnvCTjxGiW=)-VDFKB?+!NErQ7#t-_4
z2+~{pqllWz)*E<1X2%+i53M4cU+*g^`lFm+S-$%QF?moeo*yexxqg{6Y|{lWQ*i|3
z?KTuC5#;vsUHr++zN~jmsUOAvlBJa4$&vJ5eA;?-bF&n}eFf8X03iJU<)PAPdu_J*
zN#r{O$fi`u#&02P{>cY#mwg@wI`31qv%X)S58F^I^}xN;e~bOVcKuEq%X3Kb`VDJ0
z=E^a69RIl++|;N4JtC`d+-caD1soQ+Fufp!jD%fgKG^x}o@4YG{0x=JPMN0#P#_K_
z95lZr?x>_iqKkQQnP`%Z3Tf^c1ktbPojP%FX7~z!w|o5Gg$SMwQWMi9)grbQlFdjb
znDeB$U6!hd)N+?PvIlQ*a2`86ZeqAIbp2#v!N|t&;o00bZ>p-RRj8`>?BLly-jZr+
zBIgu0yMJ3=K!n6h{pTs*!o+kMd0Fo4CQ@ZwNcbWXLRIqpxr-4`<Lm~Zy>1#!>rq>d
z(yfS_qxp-yez8LDyrV!mWzig-qE*<i;WlK$Qv5i2&<QO^*t4bb!t@d<FWIQQQwir^
z<9}=vIr5RR&)`L04{tgL-ZW<<kG8Rx*_WEx`$vS%{S&>|{TP~Ss;_W4zSCo2<hzvN
zG_6C~Q;M$u2(F-jE+sLCK<+IdatI!3S!fpD3zVqCjXMfAP6@^hjW9#JEi{5MY185`
z>gox4MXzD$x2;xCQ&le)k${B3TAcMP2;F0?NuSCuX*_NG-_HwnhY~n{GbdTMW}ZbB
zj=((4K!Fl-4SD5+MiZADa#n_DWerDAAxIunQJ_2VSlplTe@b48`*kVGTfzy)nwdwt
zG5oy@JAQg25Z>m2V2qo!6G-<3sP5K56A6Q1@suA)V<R*!K(VH}$X3Vq`$Qcp4mWuD
z@TGs2BHh)jGfX#|nuYm>P6^;owj5vy0!qSA0wPjvOV`5!&Whh5dzkog$7R*UZRBj$
z-hVgs@p<9`>46ZE*aO|Shzh3TOA+zn!G=M)kC1|Dg;`ay?hlcMRDOv0hIiq-Xy2p8
z(9GNTY`63HwtXerXyA5!VPPE1{`dv8EMaPinDwq6rp}xL+o4JCL3l9*JjTKbS{R0d
z^A>vLSa>GkM)GXdt^HP0F{W-0lc33gfpT@omLD7SnoagVn7Vr^O^jkuFUIJPlh<>M
z2QXdk%>Lxp6L`7{0hTqnV+?t%!wfLS8EmEB9c6AI77p&TaoLgcL!6}tXV(i8L=WO8
zccLM)H<;!UPk&ixW$`?WDH~F!AHR)=ULW3l9bu!YzA?{u`pek+i@d4H-xQvimUo84
zGt$70kz-^x>`zMEb83vD|3Lcf_olx2WVzKWY`Qjm4&i@%>e90plb87581S`@pLKY<
ztU5}RX|kTCyqf!;!?HlmUf`lj)_ukX?zYFQI2-C8#cl=?le-5WoQ4J!3FH_U{QM`7
zY@RupM|x)FGX1~4l>4=#e6>D0_)xgqB!w{so23HM9%OcC=!((v<bSo#Rum*oD?8v=
zD<dTT?_T<k`l-9p$DVWX251w~W2|21TS8(clsD4EI@7Gx3*O(}B{GMtwUX#nIIq7R
z|K<}K7u<WU_1#KX#^KK}+@cE}5lck9pLLNE?3g4_30OX?Ovtt`JqenCk-{64;dv3u
zb!tyyT=OR>#rY{Q2^&l%ZW(dHZ^WKo@gt2ZrcSAP9toSw`8P&q7>~gazDdO(zBZO5
zRuGvCkwI#v`j?^%E`xiwy|Opap={A^sx71CPAVa!)oy4X3jx0{=7NhfV#71{WV!(9
zhp+eM!SQ2JD8Io0S-xwp-Lp&}+Z@;VtNA<X;opOKwY0j|ulHQ9u^le|KHMJj_R!U=
z)h62LWEa`Nr&MOaKQ8vRP?ZhiyRH{LF|@ViUzne-rbCE`iY`t=ls;`oiBrdnMY3O3
z%sg<CV+Yt?TloT*II^A0`--5G@`8mC6ItJ(vGRH)zYbr{6cT+APbM`$;XOpQH61xn
zD!Q8af$jdq6EHg&TxpynfS~Pv2X($Dx(5NMBaZ*dqtfw$@sIblS>v|s-fuhOXs6*}
zrXxyD-nW5XmXMS*AS+f+jftU+k)AOz(dTj?MP_SI{J*<N-2YSEwT45vw(Wb2&8rQK
z5+&56&61X+jhL~iM`?q^B+GWCu*!sFZ_KQAhQzYkAW@;>QKO<!m@%yiEoxXT+rhM6
zLWH78jLmmFtKRoJzT-Q-Ki~K3dw%%AVLYDazOVbd&g;6)6S=gB>Et&V*F!J<3h{V+
z-liOdw`D_?f5#Ny6gDB|S0(%Z`H)&ttqPK?LS3-UU@bUhRdSxIu+Q1q6fDWJ45@N>
z3BG@WMF<<Zf61p0GVE7f*x7l|23wJ^1`dBP0(puqqXYjmOD2ps)ea?PsQM(pVMcgw
zDv!{L6zJD-Z+|u7Bp?#Oy}Zn($Cx6s|BRnNh8t1kgp4^EGK7v)yF;d&w6E~S0dOeT
zRYSleXej08`ubm#iKC_s{2?8X#VJC-+W-RK*WI)MLgY8g$Fomr`2_|p42D~1Pu&(}
z1XwcrD~0>NMs$CT(Vc!)J+n0w##3=zi{K-a9~d2VI+m2J`?oztu!hZwWmyh|_$|gp
z;ya61|99fyUjd822Oyv|M*i{x;Xe`#;)yY;uy>RS3ku$ShaA6os}LR(#PwgW#lJmj
zv@3cucKz1hSXc}5<2({UqKhq(yHvo9Xc~;x{!Lz2jAi7DU@vE{q-quPH$k(z>(}Ld
z$=`4h6}I6TA|1(laGgA_!6pRw-JM<C@WEF$9yKSmzl-uZNu)Nnp(@lkHZ|<OJ_7@Y
z6abc6{8h2inK)?I`NmwrFP$~<t2FUr6aUjvNW=E7{_|4I-6a&wbihlXCC>xK?AyZ{
z0cT9&B3>F9_@%L?tw9kqCTOFZE~#E58*2%WU%kVw-uEMr6p+!(EI5&T3R-fq7lQfL
z#o@SftfXYwf#ils9MI!kD1K?c&sC{izh7FD0)Xntp%S{2w3tYRKwn=sb9hPamSYu*
z5@z&wxa=ZWz`YO^WVGRf(lKM5+S6bN-%P}W2_YMX2nN3rPhV5Tamcs@f8+E-McwQ5
zEqp<%_UnJV*?gfR4Hr6L*{i$5t6yKnh2WcT5l`+XdFBp=8px;`G8X$G9`1fw*4W@@
z=!cuX)OIewB`064zZkel2j~oKO4Q10Q<p{By07nFFxk$K*)cqC;1Z+Sve1%@#0)&!
zfGSp~QOr8LnsoSTF8(7WDkMiIJKqGyi`Uj(>5<1BwG2(_d6eqs(s;o|cAPRU;L^<6
z_B;=)kQzf*lClVF9aujq_);1S-o#|9m;=2kCe>7LJ6dO8|Id{VIzE;hripcG@5Ak4
zGJLTT81Z(Rp50^na+F5C?!&&}lk_w0&=k2Pp!vDw_Ug#@r&F9L6y!vS${d!bB`<Ss
z*vD8-RkE=BS+qWpD;i^Eo#wAPl$6LlRP2Vdk5=u}&&{m~Kp3ikMe{{_y=HL;t1|z{
zGwskmO!@1g{y%=rq5TRA)?Qh{??sPxV#h$a(@||dF-5fv8XbXOamEbRP_5wGQkCoD
z_Pbpw0wqE_tP>Qxc@}mrbx6Rg1t1Ghv~7a6#?ra_2h1Gup%}Ff1?_L-as#tro>p+Q
z>CU^WS5221E<PyvY)>622SqHYk5Y11I)G6~ZRT4q;#33kHr!eAlXJC?Y>g|AW_aO+
z$4^OHpzVNu`7ph{rR7WCUedw)Eg&*fOsZ4<kYv)63GOV$j-tmD%3GyXVwV%gtvuLT
zqBD5(F{nN`&0LA3#U<j?iXs|{A)O{+B-(=W+l{@-!<nve&hs@1Lm4)dCR|aQM>9SA
z-W8ZPbUasMkLGYX`o?dRxnu9)Hg`zd-qxmx`9=e)fQheHT1tMi7db-8Up92^Lc}J_
zIg=@AR`^2Y%=DHGu|G?_A?!bWa3>z?Hu{uw*8#<oTEJKNHgo{RNGITic=)Y)Q)LNh
zkIFoS;LL%tYklsSnHSk)(IqtLF$KRfehw(A#%I}UDGlB0Y*dzBu_J&bFcy_cZ+^RR
z1fT{LpFo^eJlfVg>w9&Gq%PnC`wCD(nMu&k35hO73uDTM<yOk;Li33yAmql9L|s+s
zT^`L@xp)w{{uXWzoAIJh2JQtq$U!Sr93Byomg}FxTF!BHk?CBBG{>AXTKH3okhj?Z
zr_~FN)Y)I1ISLt`DM+>lf|H(z3PJ%k*Ob!W3Fd~*@1263Qan)h7A-_y=$sS@pT&^!
zA)r|)z1c+>&pzxH3V(a<n|+`5JgW4B=i^ItOy>qXfb7h6In+wPh)a(gtjckX$1}lH
zQxh#z_;P%GM#ZX2yP_bXOJcxQ!5dukkbYrNk(H5EK|*3J1Ss`~O!;V4y0?OvqIG!w
z4~(ffNZpz-jPHk+FW|;1(p^tY?ts=}=HO5GL>yzv24?OHAjz*BC|lGVR;2Plm$KzH
zlp^b1&^G53fct1xpyabUsa<g?-K7I;!%|d&GjPlPvLU#qJMVzE^@0yjvNq)q&J)Ek
z0}R=PKDUr*3=q_E^h^0jSL`Q`m$C`w;zHM=@5#X%Ia-7sQp$!68}dSB-%-ZraF&A|
z@<;+bl$!Gb$_koiV8NL(WXsT~OIQKENk4CrhH9poCc~1u4v}JYtXrq2<l?N7dzdXq
z))%2KcesJD3{N1rN?{?GW2<~nV|2VP8!BdvL1pclLZlnRirao9je9jyKH53WOsJtF
zpQOi;TOH2^B$`&ylzmh=?xa6;^M+W+$hWRkxs~NOB;FmpIln&&m60YRq+5ZUacYbw
z8S(DkWs%W)N$Kz&6pCo|Gp&js-CoK}_M~3XfViLIlk`scgEyqTPU|z;H|!bDK*3;I
zU`ekL`Xg)1A?pnnTLB6q{#f@&)M!suV}1RH_Pt@?ZL$_&SoWerr3x*|K27kDwYvFP
z3E5=jKfwoL4$AM(tYRr(wiu)GE*^B;bIiH#aZ1!)>T%_I)42A&Ex#i)w`V72<02mh
zC$n`z%ZY{*2bJLUoP|IF&i!NmjJUz{I9n|shDbq>ISPKS(Qt9zEOXR=fjmeGIWqS2
z+Z_sh+V9_0P)BflD`vQI!&0XNJE%-A-kF8#WynW{w_SX%%mMc|l21IY_Z<~|*fsMT
zBu%gUimk=3%Mw<v%Gl$zF_s~YTtLb1=X}&ui}xPv?^oc=LR^d*iqfAP3H5sU(5*od
zeoVesR!%ay$CUr_WMvnIojDC)Zy!L#8@-lw9j6AhQajbB-i_DUx}1#GH`vs702@*X
zmX6&fbGUViFi6PJ7mdAdeJxKSC%fm|&rn4-_6CR|J;3AjXrJ3dOCO`e8XKz}4Wn-I
zXbB!&f4}o~HaFiVWMt3HfEw45aiO$aT<g3r=hr-3Ft%QOqn<tQm3Yb%T`w<>v7UnA
zY_nqtRW~A*fQRjqs@(4BQ6zeB=ajIgBqC79S53RTD*aS)n0Rz@+2{EbO@J2#dEI)m
zn$Bdqw$E8gEk5ooyj;2$TT!E3tc75#cw@_S=luY;{lf>^Gv%T8?pw@8E|<D42Lyfw
z8GJucWC>Hg?o)fu*kS}Md~!RFxbq+y-rp&d#N*JWYguhf(WJp&Sum&=8~|ONc{9L+
zs*73UrIXvKhHF6u9Ki?G>x&p+o6v#FveI^GL{GoUBpQn93`FxN@Qh6QbiD;#JY$#;
zDxI2()GXfL^Xzjwqt~zH9p5`W={_W0CV7=H*C%2q4J-te&I6Hb%U0$*Rkw-_nbD;l
zr@{jC9apn;*+mnBdOzOJ3F$LSCf(B(?Iz-oDx!|~BT#`cvkP5CT2hMnHd+Xyt;q60
zi|@wX`l4TAT6*@QYNE<F;9Z2P#3oM&XL{RtBwrTHB#)BzXi@&Pfqq?19V2mu<*uJP
zj!D9`tu;Ax;QrHW4lOs-)Y*J&vK*gYEOKW`y#9=kf(Oeh8z?xi>&xw7g&>BYg6%Hw
zZPgw<H7y70v#NA?!)W58+J@oBOR2||&=;XJBDO<)DVWrK|IS_K-1B6`4_=5J<~g<n
zK_z%bGg@y}cmsiVCz?QAX$`VwYza>Y*!2d0rvoV22~f6D@Oh@hq&g&tDKRCr&-S5k
z9zwr1^XIb$hNP-=<LkX%zpKDpbsP<Wk9P#OW+jrZt(_?9Y-=Cr(^CxonJMwci;7bA
zGZ?mklIa|7>^#x71ngv?E9MIa`6FFX2kJa534co51ndWhr!$HloEWSUfbF*hGU0HM
zt(f)4Tt2f>9f9H8;UTR9@#qnSej$WNV;EB!oT!+wAwdwd2*;CufkV!g$9q^@9Lmyj
zOydUB5y<p)dwYd9!zw9ztac?FMc%b67KJms-^HbUUVwFuMyj1b91@X&Pabv?x9~eI
zyPwbI;jW+{OY11@9GiOE7j;eE2)@tEV!xM1RD#!Xtj;|BE2YQ6%E{!u(SR>?#2tNv
zj>8ksD|-}=Ue39V<>5h07{fS+$0>4@-t2%vjgD_PGSd~k<PF3X2f>M?Dxy=5Ll;cc
z3;tlUi!2khA&*DkeW5mwVoy?Gb)W_p&6H|KM&olhnN!0CngkmT*aP;CGSGNDNAw-~
zgF0ddBM4@dI};M!34BfcW3X)K)V4-=3C<u=xs<boI&#%yCDFNf^xh8={!@Hn86|2>
zboSMm_!H(+9WI8?n_VcngwjF-Xj{J49(D2c?ebPG1WSqspjKt9ks5Km_l8dj+3qnf
zbBkSOT4BEl@+vVJhzF#_USbHOiG}3BofY^GE2&@`kWs!lu;rdV^;HUVJpO;YR-RSV
z<#4%Y{Ul&l7aCs(T0IwK1I<CT+wv&IOgR%C5>e8kp*tXFexo;vtrQICL0`>Gk6?oG
zPoGo+=`0mZ(mdr4+9)WWN@=}TiY@n%d|pM0ZGCVjf)uHTXyW;wuI@Ig2_(`qU!t9$
zVZprf+ocf3+nP_lGMTlCypFXQSpWAek3)O!gDdvz)kGDqAsr$)h8007n*nN3d9XDO
zqiEu_`fty5(tVfL8FJN#qq}Xe2wol`no(K^wb-c%SyA$aVW`C3(hqq?F@{7|o#Fa-
zmKFpx<&MF!V}1rq;`3$CV<63+mClr~Ro1OqeE!vv7Y2>|L9s&qrYa+5U|?*CR0yEs
zI@crJ&LF#vDbK}^ZK}HC+E_#~Gj$ct7HL5_Q4pl_6~s59e$tW)k_u<+n7x69x5{-s
zb4FDZg4D?MvwYYa7SjcAcS}UZw%u%vgwq`ozi;bWRa8kzza*KYag57(<!Y>zANkB6
z<qwaB<vf!fzGalVZFdh?74U<=-lA={ZMPMvf<#~QR~9~Dg-^UAx(3e5$Doy++JE@0
zF}TPTY{k@6?$wW0Jg^BDE8W|R<*@j=YpBEf8}-RGi8We`FgoWcDOLcuZ$vn^gwO9(
zdb1mu+jfs4OC_HoymMhnb(TjfdwIk)Q%*c%qvotL>-!!zk83mN4@w0@CXkF#Qxp!|
z#2xYeFg$v8G%sl)nx^OY$s*pDG^y`f=ZHO<pK7cSps>!J5d@1!8a*4zuXdR5Ot!qL
zXovXpj2GoGxcQv}*`N{1<OPDiBSEDD!<@H5^kwpy>d}uwppl>jJVeT?8YVxYV-UL+
zckhp4N~XW#lw;ky&=|6YCJ=9iu~zDi&x4YP&Rh1G=Eom?&#7j}u7F2c?86&3p4?}~
z=y9BF^PYrtNW1%$PXxL(^`fg(>HsKq^~V?syrATK7ml539I^{{xd5uWd!+!3ZUS>x
z&<P!d57p1rbReGMhnF6nb6}uq7?wqQV@xnjFUYyk5zB2DMeX!t_GWnehRZyFWx2AL
zw^e*7Sj2{g(bSKdhUC*}4ZR})zIDa`7lga+;I4!RwZrhUML?7>T^m!#Un&7aK66`A
zZPNGHrQBC4WL@iz5swU(N`>wikBlg8O*02-AE?!=p^`@uxjnB2|LNh4I4t>R)=H?_
zLr<((2(UXjl&;^2jkT2OAD!K7(jtTo_Bmy&1?Es{pKgkH>a;^wcJS!YLWT5B|Iq><
zNjv9H=>Y5-OX0r;DXX7IoK`g560yFs5WrEW*^)Sc)bjIP=uWvQBnW^Pa1sT1fiiGH
z%{XcT$|kOfKL{z_G~CBj0D#Sy?U{r#_bwfxc;#$upb_P#;mv4lVxQ_ViyK-$AXLA6
zb*@MXeA}A&4L<=%UZ+iFutM%l!C@c!oIhy9*%n`WE}IcBn7J8T-xj1)OK;=)<PSv%
z?AHo`Q8tG)q+=}@D2rthlQ8H1L2!|V4u9G3b=SYzWP#FQ3hbqxzXjqzV}TaDs~u|m
zN*?dn_t9}L%46=Zz_KSE_AA(EhVr;BH+rX;>eh3k^<s%W*9}zS-5FBxHWI2zf2!On
z2&OU5)huLqzK`nMiPR$J?x*R^L@=gGxX++6z*_#(@FiETRuf2znq9&^5{Ed^9MKw4
ziDzGtkG^a03tLhR5=Mn>+NShWyE`vE7fv&gl+TnKAV*T<aDW7c<#YacehFH&VCe|P
z6lOxDgT!G^9@f>n8${yZUIS*Y1}bCk0#kJhH$lZlwG&bg`{CD>{&NFw?J&GFa=hr*
zEBC>*PG@4s9XuXy;Qigx(c*wp=E+^>>;!n+3dk$}h`7f66!%%Mp@y(o!A?O*h~h*6
z@tQIB%VC8$5A3?>3q<;;f+E^MqN%+pxzDEubyoZdfa|hCBpZ%)<F1ciI)uY9R1GpD
z8JRKq-7BbT!4I-j!k?1wSy_DK-X@039PP%s7eiN>0d<U=4o*F3Br_*$2==Un?~6iw
zbAsJ22HQ^(ehRmoeSIIp6@9QhokoP<25!jP3~v6to1{FDeL2yx3lX--%cxokkw0LU
z6Kh;;yQlC86NbHloqw@gk$4#swZ-?lGrZY$8C_=65aYim1+O7;_Dz(>x*{6I?XR+-
zFu_CEQOh~rB(sI0c&JAm^i5J(x|UjG^{y?f!xajB7NmC7H*~!1I=-1~hB0P*J1e4G
zKh>V7uRvbnOlS?t#W$kQq(?T`(7aS*6yVx;4hU@Wwi2R(wt+Lpdn+t)Cjvvj_~1S_
zx5uwTuD-|kgTVXTym>edT;AW$wB-xGRkTyXJU5$qWD>0<&|jm3mo<lV*jy9Q_Y<rJ
q*rPk)SHe9bwq`GMAl}ipi#5p(7Dz3AkanvgxOUn)V7#q9C;tQXNV01H

literal 0
HcmV?d00001

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python1.svg b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python1.svg
new file mode 100644
index 000000000..5bf950e6f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python1.svg
@@ -0,0 +1,427 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="468pt" height="252pt" viewBox="0 0 468 252" version="1.1">
+<defs>
+<g>
+<symbol overflow="visible" id="glyph0-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph0-1">
+<path style="stroke:none;" d="M 3.726562 0 L 2.847656 0 L 2.847656 -5.601562 C 2.636719 -5.398438 2.359375 -5.195312 2.015625 -4.996094 C 1.671875 -4.792969 1.363281 -4.640625 1.089844 -4.539062 L 1.089844 -5.390625 C 1.582031 -5.621094 2.011719 -5.902344 2.378906 -6.230469 C 2.746094 -6.558594 3.007812 -6.878906 3.160156 -7.1875 L 3.726562 -7.1875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-2">
+<path style="stroke:none;" d="M 0.414062 -3.53125 C 0.414062 -4.375 0.503906 -5.058594 0.675781 -5.574219 C 0.851562 -6.089844 1.109375 -6.488281 1.453125 -6.765625 C 1.796875 -7.046875 2.226562 -7.1875 2.75 -7.1875 C 3.132812 -7.1875 3.46875 -7.109375 3.757812 -6.957031 C 4.046875 -6.800781 4.289062 -6.578125 4.476562 -6.285156 C 4.664062 -5.996094 4.8125 -5.640625 4.921875 -5.222656 C 5.03125 -4.804688 5.082031 -4.238281 5.082031 -3.53125 C 5.082031 -2.691406 4.996094 -2.011719 4.824219 -1.496094 C 4.652344 -0.980469 4.394531 -0.582031 4.050781 -0.300781 C 3.707031 -0.0195312 3.273438 0.121094 2.75 0.121094 C 2.058594 0.121094 1.515625 -0.125 1.125 -0.621094 C 0.652344 -1.214844 0.414062 -2.1875 0.414062 -3.53125 Z M 1.320312 -3.53125 C 1.320312 -2.355469 1.457031 -1.574219 1.730469 -1.183594 C 2.007812 -0.796875 2.34375 -0.601562 2.75 -0.601562 C 3.152344 -0.601562 3.492188 -0.796875 3.765625 -1.1875 C 4.042969 -1.578125 4.179688 -2.359375 4.179688 -3.53125 C 4.179688 -4.710938 4.042969 -5.492188 3.765625 -5.878906 C 3.492188 -6.265625 3.148438 -6.460938 2.738281 -6.460938 C 2.335938 -6.460938 2.011719 -6.289062 1.773438 -5.945312 C 1.46875 -5.511719 1.320312 -4.707031 1.320312 -3.53125 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-3">
+<path style="stroke:none;" d="M 5.035156 -0.84375 L 5.035156 0 L 0.304688 0 C 0.296875 -0.210938 0.332031 -0.414062 0.40625 -0.609375 C 0.527344 -0.933594 0.71875 -1.25 0.984375 -1.5625 C 1.25 -1.875 1.632812 -2.234375 2.132812 -2.648438 C 2.910156 -3.285156 3.4375 -3.789062 3.710938 -4.164062 C 3.984375 -4.535156 4.121094 -4.886719 4.121094 -5.21875 C 4.121094 -5.566406 3.996094 -5.863281 3.746094 -6.101562 C 3.5 -6.339844 3.171875 -6.460938 2.773438 -6.460938 C 2.351562 -6.460938 2.011719 -6.332031 1.757812 -6.078125 C 1.503906 -5.824219 1.375 -5.472656 1.371094 -5.023438 L 0.46875 -5.117188 C 0.53125 -5.789062 0.761719 -6.304688 1.167969 -6.65625 C 1.570312 -7.011719 2.113281 -7.1875 2.792969 -7.1875 C 3.480469 -7.1875 4.023438 -6.996094 4.421875 -6.617188 C 4.824219 -6.234375 5.023438 -5.761719 5.023438 -5.199219 C 5.023438 -4.914062 4.964844 -4.632812 4.847656 -4.355469 C 4.730469 -4.078125 4.535156 -3.789062 4.265625 -3.480469 C 3.992188 -3.175781 3.542969 -2.753906 2.910156 -2.222656 C 2.382812 -1.78125 2.042969 -1.480469 1.894531 -1.320312 C 1.746094 -1.164062 1.621094 -1.003906 1.523438 -0.84375 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-4">
+<path style="stroke:none;" d="M 0.414062 -1.875 L 1.335938 -1.953125 C 1.40625 -1.503906 1.566406 -1.167969 1.8125 -0.941406 C 2.0625 -0.714844 2.363281 -0.601562 2.714844 -0.601562 C 3.136719 -0.601562 3.496094 -0.761719 3.789062 -1.078125 C 4.082031 -1.398438 4.226562 -1.820312 4.226562 -2.347656 C 4.226562 -2.851562 4.085938 -3.246094 3.804688 -3.535156 C 3.523438 -3.824219 3.15625 -3.96875 2.699219 -3.96875 C 2.417969 -3.96875 2.160156 -3.90625 1.933594 -3.777344 C 1.707031 -3.648438 1.527344 -3.480469 1.398438 -3.277344 L 0.570312 -3.382812 L 1.265625 -7.0625 L 4.824219 -7.0625 L 4.824219 -6.21875 L 1.96875 -6.21875 L 1.582031 -4.296875 C 2.011719 -4.597656 2.460938 -4.746094 2.933594 -4.746094 C 3.558594 -4.746094 4.085938 -4.53125 4.515625 -4.097656 C 4.945312 -3.664062 5.160156 -3.105469 5.160156 -2.425781 C 5.160156 -1.777344 4.972656 -1.21875 4.59375 -0.746094 C 4.136719 -0.167969 3.507812 0.121094 2.714844 0.121094 C 2.0625 0.121094 1.53125 -0.0585938 1.121094 -0.425781 C 0.710938 -0.789062 0.472656 -1.273438 0.414062 -1.875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-5">
+<path style="stroke:none;" d="M 0.820312 0 L 0.820312 -7.15625 L 5.648438 -7.15625 L 5.648438 -6.3125 L 1.765625 -6.3125 L 1.765625 -4.097656 L 5.125 -4.097656 L 5.125 -3.25 L 1.765625 -3.25 L 1.765625 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-6">
+<path style="stroke:none;" d="M 4.058594 0 L 4.058594 -0.761719 C 3.65625 -0.175781 3.105469 0.117188 2.414062 0.117188 C 2.105469 0.117188 1.820312 0.0585938 1.554688 -0.0585938 C 1.289062 -0.175781 1.09375 -0.324219 0.964844 -0.5 C 0.835938 -0.679688 0.746094 -0.894531 0.695312 -1.152344 C 0.65625 -1.324219 0.640625 -1.597656 0.640625 -1.972656 L 0.640625 -5.1875 L 1.519531 -5.1875 L 1.519531 -2.308594 C 1.519531 -1.851562 1.535156 -1.542969 1.570312 -1.382812 C 1.625 -1.152344 1.746094 -0.96875 1.921875 -0.835938 C 2.101562 -0.703125 2.324219 -0.640625 2.585938 -0.640625 C 2.851562 -0.640625 3.097656 -0.707031 3.328125 -0.84375 C 3.5625 -0.976562 3.726562 -1.160156 3.820312 -1.394531 C 3.917969 -1.625 3.964844 -1.964844 3.964844 -2.40625 L 3.964844 -5.1875 L 4.84375 -5.1875 L 4.84375 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-7">
+<path style="stroke:none;" d="M 0.660156 0 L 0.660156 -5.1875 L 1.449219 -5.1875 L 1.449219 -4.449219 C 1.832031 -5.019531 2.382812 -5.304688 3.101562 -5.304688 C 3.414062 -5.304688 3.699219 -5.246094 3.960938 -5.132812 C 4.222656 -5.023438 4.421875 -4.875 4.550781 -4.691406 C 4.679688 -4.507812 4.773438 -4.292969 4.824219 -4.042969 C 4.855469 -3.878906 4.875 -3.59375 4.875 -3.1875 L 4.875 0 L 3.992188 0 L 3.992188 -3.15625 C 3.992188 -3.511719 3.960938 -3.78125 3.890625 -3.957031 C 3.824219 -4.132812 3.703125 -4.277344 3.527344 -4.382812 C 3.351562 -4.488281 3.148438 -4.539062 2.914062 -4.539062 C 2.539062 -4.539062 2.21875 -4.421875 1.945312 -4.183594 C 1.671875 -3.945312 1.539062 -3.496094 1.539062 -2.832031 L 1.539062 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-8">
+<path style="stroke:none;" d="M 4.042969 -1.898438 L 4.90625 -1.789062 C 4.8125 -1.191406 4.570312 -0.726562 4.183594 -0.386719 C 3.792969 -0.0507812 3.316406 0.117188 2.75 0.117188 C 2.039062 0.117188 1.46875 -0.113281 1.039062 -0.578125 C 0.605469 -1.042969 0.390625 -1.707031 0.390625 -2.574219 C 0.390625 -3.132812 0.484375 -3.625 0.667969 -4.042969 C 0.855469 -4.460938 1.136719 -4.777344 1.515625 -4.988281 C 1.894531 -5.199219 2.308594 -5.304688 2.753906 -5.304688 C 3.316406 -5.304688 3.777344 -5.160156 4.136719 -4.875 C 4.492188 -4.589844 4.722656 -4.1875 4.824219 -3.664062 L 3.96875 -3.53125 C 3.886719 -3.878906 3.746094 -4.140625 3.539062 -4.316406 C 3.332031 -4.492188 3.082031 -4.578125 2.789062 -4.578125 C 2.34375 -4.578125 1.984375 -4.421875 1.710938 -4.105469 C 1.433594 -3.789062 1.292969 -3.285156 1.292969 -2.597656 C 1.292969 -1.902344 1.425781 -1.394531 1.695312 -1.078125 C 1.960938 -0.761719 2.308594 -0.605469 2.738281 -0.605469 C 3.085938 -0.605469 3.371094 -0.710938 3.601562 -0.921875 C 3.835938 -1.132812 3.980469 -1.460938 4.042969 -1.898438 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-9">
+<path style="stroke:none;" d="M 2.578125 -0.785156 L 2.703125 -0.0078125 C 2.457031 0.0429688 2.234375 0.0703125 2.039062 0.0703125 C 1.722656 0.0703125 1.476562 0.0195312 1.296875 -0.0820312 C 1.121094 -0.183594 1 -0.316406 0.929688 -0.480469 C 0.855469 -0.644531 0.820312 -0.992188 0.820312 -1.519531 L 0.820312 -4.5 L 0.175781 -4.5 L 0.175781 -5.1875 L 0.820312 -5.1875 L 0.820312 -6.46875 L 1.695312 -6.996094 L 1.695312 -5.1875 L 2.578125 -5.1875 L 2.578125 -4.5 L 1.695312 -4.5 L 1.695312 -1.46875 C 1.695312 -1.21875 1.710938 -1.058594 1.742188 -0.984375 C 1.773438 -0.914062 1.820312 -0.859375 1.890625 -0.816406 C 1.960938 -0.773438 2.0625 -0.75 2.191406 -0.75 C 2.289062 -0.75 2.417969 -0.761719 2.578125 -0.785156 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-10">
+<path style="stroke:none;" d="M 0.664062 -6.148438 L 0.664062 -7.15625 L 1.542969 -7.15625 L 1.542969 -6.148438 Z M 0.664062 0 L 0.664062 -5.1875 L 1.542969 -5.1875 L 1.542969 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-11">
+<path style="stroke:none;" d="M 0.332031 -2.59375 C 0.332031 -3.554688 0.597656 -4.265625 1.132812 -4.726562 C 1.578125 -5.109375 2.121094 -5.304688 2.765625 -5.304688 C 3.476562 -5.304688 4.058594 -5.070312 4.511719 -4.601562 C 4.964844 -4.132812 5.191406 -3.488281 5.191406 -2.664062 C 5.191406 -2 5.089844 -1.472656 4.890625 -1.089844 C 4.691406 -0.707031 4.398438 -0.410156 4.015625 -0.199219 C 3.632812 0.0117188 3.214844 0.117188 2.765625 0.117188 C 2.039062 0.117188 1.449219 -0.117188 1.003906 -0.582031 C 0.554688 -1.046875 0.332031 -1.71875 0.332031 -2.59375 Z M 1.234375 -2.59375 C 1.234375 -1.929688 1.378906 -1.429688 1.671875 -1.101562 C 1.960938 -0.769531 2.324219 -0.605469 2.765625 -0.605469 C 3.199219 -0.605469 3.5625 -0.773438 3.851562 -1.101562 C 4.140625 -1.433594 4.289062 -1.941406 4.289062 -2.621094 C 4.289062 -3.261719 4.140625 -3.75 3.851562 -4.078125 C 3.558594 -4.410156 3.195312 -4.574219 2.765625 -4.574219 C 2.324219 -4.574219 1.960938 -4.410156 1.671875 -4.082031 C 1.382812 -3.753906 1.234375 -3.257812 1.234375 -2.59375 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-12">
+<path style="stroke:none;" d="M 0.308594 -1.546875 L 1.175781 -1.683594 C 1.226562 -1.335938 1.363281 -1.070312 1.585938 -0.882812 C 1.808594 -0.699219 2.117188 -0.605469 2.519531 -0.605469 C 2.921875 -0.605469 3.222656 -0.6875 3.417969 -0.851562 C 3.613281 -1.015625 3.710938 -1.210938 3.710938 -1.429688 C 3.710938 -1.628906 3.625 -1.785156 3.453125 -1.898438 C 3.332031 -1.976562 3.03125 -2.078125 2.554688 -2.195312 C 1.910156 -2.359375 1.460938 -2.5 1.214844 -2.621094 C 0.964844 -2.738281 0.777344 -2.902344 0.648438 -3.113281 C 0.519531 -3.324219 0.453125 -3.554688 0.453125 -3.808594 C 0.453125 -4.039062 0.507812 -4.253906 0.613281 -4.449219 C 0.71875 -4.648438 0.863281 -4.8125 1.046875 -4.941406 C 1.183594 -5.042969 1.367188 -5.128906 1.605469 -5.199219 C 1.839844 -5.269531 2.09375 -5.304688 2.363281 -5.304688 C 2.769531 -5.304688 3.128906 -5.242188 3.433594 -5.125 C 3.742188 -5.007812 3.96875 -4.851562 4.117188 -4.652344 C 4.261719 -4.453125 4.363281 -4.183594 4.417969 -3.847656 L 3.558594 -3.730469 C 3.519531 -3.996094 3.40625 -4.207031 3.21875 -4.355469 C 3.03125 -4.503906 2.769531 -4.578125 2.425781 -4.578125 C 2.023438 -4.578125 1.734375 -4.511719 1.5625 -4.378906 C 1.390625 -4.246094 1.304688 -4.089844 1.304688 -3.910156 C 1.304688 -3.796875 1.339844 -3.695312 1.410156 -3.601562 C 1.484375 -3.507812 1.59375 -3.429688 1.75 -3.367188 C 1.835938 -3.335938 2.09375 -3.261719 2.523438 -3.144531 C 3.144531 -2.976562 3.578125 -2.84375 3.824219 -2.738281 C 4.070312 -2.632812 4.265625 -2.476562 4.40625 -2.273438 C 4.546875 -2.074219 4.613281 -1.824219 4.613281 -1.523438 C 4.613281 -1.230469 4.527344 -0.953125 4.359375 -0.695312 C 4.1875 -0.4375 3.941406 -0.238281 3.617188 -0.09375 C 3.296875 0.046875 2.929688 0.117188 2.523438 0.117188 C 1.851562 0.117188 1.335938 -0.0234375 0.984375 -0.304688 C 0.632812 -0.582031 0.40625 -0.996094 0.308594 -1.546875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph1-1">
+<path style="stroke:none;" d="M -2.300781 -0.449219 L -2.378906 -1.34375 C -2.019531 -1.386719 -1.726562 -1.484375 -1.496094 -1.636719 C -1.265625 -1.792969 -1.082031 -2.03125 -0.941406 -2.359375 C -0.796875 -2.683594 -0.726562 -3.050781 -0.726562 -3.457031 C -0.726562 -3.820312 -0.78125 -4.136719 -0.890625 -4.414062 C -0.996094 -4.691406 -1.144531 -4.898438 -1.332031 -5.03125 C -1.519531 -5.167969 -1.722656 -5.234375 -1.945312 -5.234375 C -2.167969 -5.234375 -2.363281 -5.167969 -2.53125 -5.039062 C -2.699219 -4.910156 -2.839844 -4.695312 -2.953125 -4.394531 C -3.027344 -4.203125 -3.144531 -3.777344 -3.304688 -3.121094 C -3.460938 -2.460938 -3.609375 -2 -3.75 -1.738281 C -3.929688 -1.398438 -4.152344 -1.140625 -4.417969 -0.972656 C -4.683594 -0.804688 -4.980469 -0.722656 -5.308594 -0.722656 C -5.667969 -0.722656 -6.007812 -0.824219 -6.320312 -1.03125 C -6.632812 -1.234375 -6.875 -1.535156 -7.035156 -1.929688 C -7.199219 -2.324219 -7.28125 -2.761719 -7.28125 -3.242188 C -7.28125 -3.773438 -7.195312 -4.242188 -7.023438 -4.644531 C -6.851562 -5.050781 -6.601562 -5.363281 -6.269531 -5.582031 C -5.9375 -5.800781 -5.5625 -5.917969 -5.140625 -5.933594 L -5.074219 -5.023438 C -5.527344 -4.976562 -5.867188 -4.808594 -6.097656 -4.527344 C -6.328125 -4.246094 -6.445312 -3.832031 -6.445312 -3.28125 C -6.445312 -2.707031 -6.339844 -2.289062 -6.128906 -2.027344 C -5.921875 -1.765625 -5.667969 -1.636719 -5.371094 -1.636719 C -5.113281 -1.636719 -4.902344 -1.726562 -4.734375 -1.914062 C -4.570312 -2.097656 -4.398438 -2.574219 -4.226562 -3.34375 C -4.050781 -4.113281 -3.898438 -4.640625 -3.769531 -4.925781 C -3.578125 -5.34375 -3.335938 -5.652344 -3.039062 -5.851562 C -2.746094 -6.046875 -2.40625 -6.148438 -2.023438 -6.148438 C -1.640625 -6.148438 -1.28125 -6.039062 -0.945312 -5.820312 C -0.609375 -5.601562 -0.347656 -5.289062 -0.160156 -4.878906 C 0.0273438 -4.472656 0.121094 -4.011719 0.121094 -3.5 C 0.121094 -2.851562 0.0273438 -2.308594 -0.160156 -1.871094 C -0.351562 -1.433594 -0.632812 -1.089844 -1.011719 -0.84375 C -1.390625 -0.59375 -1.820312 -0.460938 -2.300781 -0.449219 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-2">
+<path style="stroke:none;" d="M -1.671875 -4.210938 L -1.558594 -5.117188 C -1.027344 -4.972656 -0.617188 -4.707031 -0.320312 -4.320312 C -0.0273438 -3.933594 0.117188 -3.4375 0.117188 -2.835938 C 0.117188 -2.078125 -0.117188 -1.476562 -0.582031 -1.03125 C -1.050781 -0.585938 -1.707031 -0.367188 -2.546875 -0.367188 C -3.421875 -0.367188 -4.097656 -0.589844 -4.578125 -1.039062 C -5.0625 -1.488281 -5.304688 -2.070312 -5.304688 -2.789062 C -5.304688 -3.480469 -5.066406 -4.046875 -4.59375 -4.488281 C -4.121094 -4.925781 -3.457031 -5.148438 -2.601562 -5.148438 C -2.550781 -5.148438 -2.472656 -5.144531 -2.367188 -5.140625 L -2.367188 -1.273438 C -1.796875 -1.304688 -1.363281 -1.46875 -1.058594 -1.757812 C -0.757812 -2.046875 -0.605469 -2.410156 -0.605469 -2.84375 C -0.605469 -3.164062 -0.691406 -3.4375 -0.859375 -3.667969 C -1.027344 -3.894531 -1.296875 -4.074219 -1.671875 -4.210938 Z M -3.089844 -1.324219 L -3.089844 -4.21875 C -3.527344 -4.179688 -3.855469 -4.070312 -4.070312 -3.886719 C -4.410156 -3.605469 -4.578125 -3.242188 -4.578125 -2.796875 C -4.578125 -2.394531 -4.445312 -2.054688 -4.175781 -1.78125 C -3.90625 -1.503906 -3.542969 -1.351562 -3.089844 -1.324219 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-3">
+<path style="stroke:none;" d="M -1.898438 -4.042969 L -1.789062 -4.90625 C -1.191406 -4.8125 -0.726562 -4.570312 -0.386719 -4.183594 C -0.0507812 -3.792969 0.117188 -3.316406 0.117188 -2.75 C 0.117188 -2.039062 -0.113281 -1.46875 -0.578125 -1.039062 C -1.042969 -0.605469 -1.707031 -0.390625 -2.574219 -0.390625 C -3.132812 -0.390625 -3.625 -0.484375 -4.042969 -0.667969 C -4.460938 -0.855469 -4.777344 -1.136719 -4.988281 -1.515625 C -5.199219 -1.894531 -5.304688 -2.308594 -5.304688 -2.753906 C -5.304688 -3.316406 -5.160156 -3.777344 -4.875 -4.136719 C -4.589844 -4.492188 -4.1875 -4.722656 -3.664062 -4.824219 L -3.53125 -3.96875 C -3.878906 -3.886719 -4.140625 -3.746094 -4.316406 -3.539062 C -4.492188 -3.332031 -4.578125 -3.082031 -4.578125 -2.789062 C -4.578125 -2.34375 -4.421875 -1.984375 -4.105469 -1.710938 C -3.789062 -1.433594 -3.285156 -1.292969 -2.597656 -1.292969 C -1.902344 -1.292969 -1.394531 -1.425781 -1.078125 -1.695312 C -0.761719 -1.960938 -0.605469 -2.308594 -0.605469 -2.738281 C -0.605469 -3.085938 -0.710938 -3.371094 -0.921875 -3.601562 C -1.132812 -3.835938 -1.460938 -3.980469 -1.898438 -4.042969 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-4">
+<path style="stroke:none;" d="M -2.59375 -0.332031 C -3.554688 -0.332031 -4.265625 -0.597656 -4.726562 -1.132812 C -5.109375 -1.578125 -5.304688 -2.121094 -5.304688 -2.765625 C -5.304688 -3.476562 -5.070312 -4.058594 -4.601562 -4.511719 C -4.132812 -4.964844 -3.488281 -5.191406 -2.664062 -5.191406 C -2 -5.191406 -1.472656 -5.089844 -1.089844 -4.890625 C -0.707031 -4.691406 -0.410156 -4.398438 -0.199219 -4.015625 C 0.0117188 -3.632812 0.117188 -3.214844 0.117188 -2.765625 C 0.117188 -2.039062 -0.117188 -1.449219 -0.582031 -1.003906 C -1.046875 -0.554688 -1.71875 -0.332031 -2.59375 -0.332031 Z M -2.59375 -1.234375 C -1.929688 -1.234375 -1.429688 -1.378906 -1.101562 -1.671875 C -0.769531 -1.960938 -0.605469 -2.324219 -0.605469 -2.765625 C -0.605469 -3.199219 -0.773438 -3.5625 -1.101562 -3.851562 C -1.433594 -4.140625 -1.941406 -4.289062 -2.621094 -4.289062 C -3.261719 -4.289062 -3.75 -4.140625 -4.078125 -3.851562 C -4.410156 -3.558594 -4.574219 -3.195312 -4.574219 -2.765625 C -4.574219 -2.324219 -4.410156 -1.960938 -4.082031 -1.671875 C -3.753906 -1.382812 -3.257812 -1.234375 -2.59375 -1.234375 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-5">
+<path style="stroke:none;" d="M 0 -0.660156 L -5.1875 -0.660156 L -5.1875 -1.449219 L -4.449219 -1.449219 C -5.019531 -1.832031 -5.304688 -2.382812 -5.304688 -3.101562 C -5.304688 -3.414062 -5.246094 -3.699219 -5.132812 -3.960938 C -5.023438 -4.222656 -4.875 -4.421875 -4.691406 -4.550781 C -4.507812 -4.679688 -4.292969 -4.773438 -4.042969 -4.824219 C -3.878906 -4.855469 -3.59375 -4.875 -3.1875 -4.875 L 0 -4.875 L 0 -3.992188 L -3.15625 -3.992188 C -3.511719 -3.992188 -3.78125 -3.960938 -3.957031 -3.890625 C -4.132812 -3.824219 -4.277344 -3.703125 -4.382812 -3.527344 C -4.488281 -3.351562 -4.539062 -3.148438 -4.539062 -2.914062 C -4.539062 -2.539062 -4.421875 -2.21875 -4.183594 -1.945312 C -3.945312 -1.671875 -3.496094 -1.539062 -2.832031 -1.539062 L 0 -1.539062 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-6">
+<path style="stroke:none;" d="M 0 -4.023438 L -0.65625 -4.023438 C -0.140625 -3.695312 0.117188 -3.210938 0.117188 -2.574219 C 0.117188 -2.160156 0.00390625 -1.78125 -0.226562 -1.433594 C -0.453125 -1.085938 -0.769531 -0.816406 -1.179688 -0.628906 C -1.585938 -0.4375 -2.058594 -0.34375 -2.585938 -0.34375 C -3.105469 -0.34375 -3.574219 -0.429688 -3.996094 -0.601562 C -4.417969 -0.773438 -4.742188 -1.03125 -4.964844 -1.375 C -5.191406 -1.722656 -5.304688 -2.109375 -5.304688 -2.535156 C -5.304688 -2.847656 -5.238281 -3.125 -5.105469 -3.367188 C -4.972656 -3.613281 -4.800781 -3.8125 -4.589844 -3.964844 L -7.15625 -3.964844 L -7.15625 -4.839844 L 0 -4.839844 Z M -2.585938 -1.246094 C -1.921875 -1.246094 -1.425781 -1.386719 -1.097656 -1.664062 C -0.769531 -1.945312 -0.605469 -2.273438 -0.605469 -2.65625 C -0.605469 -3.039062 -0.761719 -3.367188 -1.078125 -3.636719 C -1.390625 -3.90625 -1.871094 -4.039062 -2.515625 -4.039062 C -3.226562 -4.039062 -3.746094 -3.902344 -4.078125 -3.628906 C -4.410156 -3.355469 -4.574219 -3.015625 -4.574219 -2.617188 C -4.574219 -2.226562 -4.414062 -1.898438 -4.097656 -1.636719 C -3.777344 -1.375 -3.273438 -1.246094 -2.585938 -1.246094 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-7">
+<path style="stroke:none;" d="M -1.546875 -0.308594 L -1.683594 -1.175781 C -1.335938 -1.226562 -1.070312 -1.363281 -0.882812 -1.585938 C -0.699219 -1.808594 -0.605469 -2.117188 -0.605469 -2.519531 C -0.605469 -2.921875 -0.6875 -3.222656 -0.851562 -3.417969 C -1.015625 -3.613281 -1.210938 -3.710938 -1.429688 -3.710938 C -1.628906 -3.710938 -1.785156 -3.625 -1.898438 -3.453125 C -1.976562 -3.332031 -2.078125 -3.03125 -2.195312 -2.554688 C -2.359375 -1.910156 -2.5 -1.460938 -2.621094 -1.214844 C -2.738281 -0.964844 -2.902344 -0.777344 -3.113281 -0.648438 C -3.324219 -0.519531 -3.554688 -0.453125 -3.808594 -0.453125 C -4.039062 -0.453125 -4.253906 -0.507812 -4.449219 -0.613281 C -4.648438 -0.71875 -4.8125 -0.863281 -4.941406 -1.046875 C -5.042969 -1.183594 -5.128906 -1.367188 -5.199219 -1.605469 C -5.269531 -1.839844 -5.304688 -2.09375 -5.304688 -2.363281 C -5.304688 -2.769531 -5.242188 -3.128906 -5.125 -3.433594 C -5.007812 -3.742188 -4.851562 -3.96875 -4.652344 -4.117188 C -4.453125 -4.261719 -4.183594 -4.363281 -3.847656 -4.417969 L -3.730469 -3.558594 C -3.996094 -3.519531 -4.207031 -3.40625 -4.355469 -3.21875 C -4.503906 -3.03125 -4.578125 -2.769531 -4.578125 -2.425781 C -4.578125 -2.023438 -4.511719 -1.734375 -4.378906 -1.5625 C -4.246094 -1.390625 -4.089844 -1.304688 -3.910156 -1.304688 C -3.796875 -1.304688 -3.695312 -1.339844 -3.601562 -1.410156 C -3.507812 -1.484375 -3.429688 -1.59375 -3.367188 -1.75 C -3.335938 -1.835938 -3.261719 -2.09375 -3.144531 -2.523438 C -2.976562 -3.144531 -2.84375 -3.578125 -2.738281 -3.824219 C -2.632812 -4.070312 -2.476562 -4.265625 -2.273438 -4.40625 C -2.074219 -4.546875 -1.824219 -4.613281 -1.523438 -4.613281 C -1.230469 -4.613281 -0.953125 -4.527344 -0.695312 -4.359375 C -0.4375 -4.1875 -0.238281 -3.941406 -0.09375 -3.617188 C 0.046875 -3.296875 0.117188 -2.929688 0.117188 -2.523438 C 0.117188 -1.851562 -0.0234375 -1.335938 -0.304688 -0.984375 C -0.582031 -0.632812 -0.996094 -0.40625 -1.546875 -0.308594 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph2-1">
+<path style="stroke:none;" d="M 7.054688 -3.011719 L 8.191406 -2.726562 C 7.953125 -1.792969 7.523438 -1.078125 6.90625 -0.589844 C 6.285156 -0.0976562 5.53125 0.148438 4.632812 0.148438 C 3.707031 0.148438 2.957031 -0.0429688 2.375 -0.417969 C 1.796875 -0.796875 1.355469 -1.34375 1.050781 -2.054688 C 0.75 -2.769531 0.597656 -3.539062 0.597656 -4.359375 C 0.597656 -5.253906 0.769531 -6.035156 1.109375 -6.699219 C 1.453125 -7.367188 1.9375 -7.871094 2.570312 -8.21875 C 3.199219 -8.5625 3.894531 -8.734375 4.652344 -8.734375 C 5.511719 -8.734375 6.234375 -8.515625 6.820312 -8.078125 C 7.40625 -7.640625 7.8125 -7.027344 8.046875 -6.234375 L 6.925781 -5.96875 C 6.726562 -6.59375 6.4375 -7.050781 6.058594 -7.335938 C 5.679688 -7.621094 5.203125 -7.765625 4.628906 -7.765625 C 3.96875 -7.765625 3.417969 -7.605469 2.972656 -7.289062 C 2.53125 -6.972656 2.21875 -6.546875 2.039062 -6.015625 C 1.859375 -5.480469 1.769531 -4.929688 1.769531 -4.367188 C 1.769531 -3.636719 1.875 -2.996094 2.089844 -2.453125 C 2.300781 -1.90625 2.632812 -1.5 3.082031 -1.230469 C 3.53125 -0.960938 4.015625 -0.828125 4.539062 -0.828125 C 5.175781 -0.828125 5.71875 -1.007812 6.15625 -1.375 C 6.597656 -1.742188 6.898438 -2.289062 7.054688 -3.011719 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-2">
+<path style="stroke:none;" d="M 0.398438 -3.109375 C 0.398438 -4.261719 0.71875 -5.117188 1.359375 -5.671875 C 1.894531 -6.132812 2.546875 -6.363281 3.316406 -6.363281 C 4.171875 -6.363281 4.871094 -6.082031 5.414062 -5.523438 C 5.957031 -4.960938 6.226562 -4.1875 6.226562 -3.199219 C 6.226562 -2.398438 6.109375 -1.769531 5.867188 -1.308594 C 5.628906 -0.851562 5.277344 -0.492188 4.820312 -0.242188 C 4.359375 0.0117188 3.859375 0.140625 3.316406 0.140625 C 2.445312 0.140625 1.742188 -0.140625 1.203125 -0.695312 C 0.667969 -1.253906 0.398438 -2.0625 0.398438 -3.109375 Z M 1.484375 -3.109375 C 1.484375 -2.3125 1.65625 -1.71875 2.003906 -1.320312 C 2.351562 -0.925781 2.789062 -0.726562 3.316406 -0.726562 C 3.839844 -0.726562 4.273438 -0.925781 4.625 -1.324219 C 4.972656 -1.722656 5.144531 -2.328125 5.144531 -3.148438 C 5.144531 -3.917969 4.96875 -4.5 4.621094 -4.894531 C 4.269531 -5.292969 3.835938 -5.492188 3.316406 -5.492188 C 2.789062 -5.492188 2.351562 -5.292969 2.003906 -4.898438 C 1.65625 -4.503906 1.484375 -3.90625 1.484375 -3.109375 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-3">
+<path style="stroke:none;" d="M 0.789062 0 L 0.789062 -6.222656 L 1.734375 -6.222656 L 1.734375 -5.351562 C 1.929688 -5.65625 2.1875 -5.898438 2.515625 -6.085938 C 2.839844 -6.269531 3.207031 -6.363281 3.621094 -6.363281 C 4.082031 -6.363281 4.460938 -6.265625 4.753906 -6.078125 C 5.050781 -5.886719 5.257812 -5.617188 5.378906 -5.273438 C 5.871094 -6 6.511719 -6.363281 7.300781 -6.363281 C 7.917969 -6.363281 8.390625 -6.191406 8.726562 -5.851562 C 9.058594 -5.507812 9.222656 -4.984375 9.222656 -4.273438 L 9.222656 0 L 8.171875 0 L 8.171875 -3.921875 C 8.171875 -4.34375 8.140625 -4.644531 8.070312 -4.832031 C 8.003906 -5.015625 7.878906 -5.164062 7.699219 -5.28125 C 7.519531 -5.394531 7.308594 -5.449219 7.066406 -5.449219 C 6.628906 -5.449219 6.265625 -5.304688 5.976562 -5.011719 C 5.6875 -4.722656 5.542969 -4.257812 5.542969 -3.617188 L 5.542969 0 L 4.488281 0 L 4.488281 -4.042969 C 4.488281 -4.511719 4.402344 -4.863281 4.230469 -5.097656 C 4.058594 -5.332031 3.777344 -5.449219 3.386719 -5.449219 C 3.089844 -5.449219 2.816406 -5.371094 2.5625 -5.214844 C 2.3125 -5.058594 2.128906 -4.828125 2.015625 -4.53125 C 1.902344 -4.230469 1.84375 -3.796875 1.84375 -3.226562 L 1.84375 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-4">
+<path style="stroke:none;" d="M 0.789062 2.382812 L 0.789062 -6.222656 L 1.75 -6.222656 L 1.75 -5.414062 C 1.976562 -5.730469 2.234375 -5.96875 2.519531 -6.125 C 2.804688 -6.285156 3.148438 -6.363281 3.554688 -6.363281 C 4.085938 -6.363281 4.554688 -6.226562 4.960938 -5.953125 C 5.367188 -5.679688 5.675781 -5.292969 5.882812 -4.796875 C 6.089844 -4.296875 6.195312 -3.75 6.195312 -3.15625 C 6.195312 -2.519531 6.078125 -1.949219 5.851562 -1.4375 C 5.621094 -0.929688 5.289062 -0.539062 4.855469 -0.265625 C 4.417969 0.00390625 3.960938 0.140625 3.480469 0.140625 C 3.128906 0.140625 2.8125 0.0664062 2.535156 -0.0820312 C 2.253906 -0.230469 2.023438 -0.417969 1.84375 -0.644531 L 1.84375 2.382812 Z M 1.746094 -3.078125 C 1.746094 -2.277344 1.90625 -1.683594 2.234375 -1.300781 C 2.558594 -0.917969 2.949219 -0.726562 3.410156 -0.726562 C 3.878906 -0.726562 4.28125 -0.925781 4.613281 -1.320312 C 4.949219 -1.71875 5.117188 -2.332031 5.117188 -3.164062 C 5.117188 -3.957031 4.953125 -4.550781 4.625 -4.945312 C 4.300781 -5.339844 3.910156 -5.539062 3.457031 -5.539062 C 3.007812 -5.539062 2.609375 -5.328125 2.265625 -4.90625 C 1.917969 -4.488281 1.746094 -3.875 1.746094 -3.078125 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-5">
+<path style="stroke:none;" d="M 0.796875 -7.375 L 0.796875 -8.589844 L 1.851562 -8.589844 L 1.851562 -7.375 Z M 0.796875 0 L 0.796875 -6.222656 L 1.851562 -6.222656 L 1.851562 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-6">
+<path style="stroke:none;" d="M 0.765625 0 L 0.765625 -8.589844 L 1.820312 -8.589844 L 1.820312 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-7">
+<path style="stroke:none;" d="M 4.851562 -0.765625 C 4.460938 -0.433594 4.085938 -0.203125 3.722656 -0.0625 C 3.363281 0.0742188 2.976562 0.140625 2.5625 0.140625 C 1.878906 0.140625 1.351562 -0.0273438 0.984375 -0.359375 C 0.617188 -0.695312 0.433594 -1.121094 0.433594 -1.640625 C 0.433594 -1.945312 0.503906 -2.222656 0.640625 -2.476562 C 0.78125 -2.726562 0.960938 -2.929688 1.1875 -3.082031 C 1.410156 -3.234375 1.664062 -3.351562 1.945312 -3.429688 C 2.152344 -3.484375 2.464844 -3.535156 2.882812 -3.585938 C 3.734375 -3.6875 4.359375 -3.808594 4.765625 -3.949219 C 4.769531 -4.09375 4.769531 -4.1875 4.769531 -4.226562 C 4.769531 -4.65625 4.671875 -4.957031 4.46875 -5.132812 C 4.199219 -5.371094 3.800781 -5.492188 3.269531 -5.492188 C 2.773438 -5.492188 2.40625 -5.402344 2.171875 -5.230469 C 1.933594 -5.054688 1.757812 -4.75 1.648438 -4.304688 L 0.617188 -4.445312 C 0.710938 -4.886719 0.863281 -5.246094 1.078125 -5.515625 C 1.292969 -5.789062 1.601562 -5.996094 2.007812 -6.144531 C 2.414062 -6.289062 2.886719 -6.363281 3.421875 -6.363281 C 3.953125 -6.363281 4.382812 -6.300781 4.71875 -6.175781 C 5.050781 -6.050781 5.292969 -5.894531 5.449219 -5.703125 C 5.605469 -5.515625 5.714844 -5.273438 5.777344 -4.984375 C 5.8125 -4.804688 5.828125 -4.484375 5.828125 -4.015625 L 5.828125 -2.609375 C 5.828125 -1.628906 5.851562 -1.007812 5.898438 -0.746094 C 5.941406 -0.488281 6.03125 -0.238281 6.164062 0 L 5.0625 0 C 4.953125 -0.21875 4.882812 -0.476562 4.851562 -0.765625 Z M 4.765625 -3.125 C 4.382812 -2.96875 3.804688 -2.835938 3.039062 -2.726562 C 2.605469 -2.664062 2.300781 -2.59375 2.121094 -2.515625 C 1.941406 -2.4375 1.804688 -2.320312 1.703125 -2.171875 C 1.605469 -2.019531 1.558594 -1.851562 1.558594 -1.671875 C 1.558594 -1.390625 1.664062 -1.15625 1.878906 -0.96875 C 2.089844 -0.78125 2.402344 -0.6875 2.8125 -0.6875 C 3.21875 -0.6875 3.578125 -0.773438 3.898438 -0.953125 C 4.214844 -1.128906 4.445312 -1.375 4.59375 -1.679688 C 4.707031 -1.917969 4.765625 -2.273438 4.765625 -2.734375 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-8">
+<path style="stroke:none;" d="M 3.09375 -0.945312 L 3.246094 -0.0117188 C 2.949219 0.0507812 2.683594 0.0820312 2.449219 0.0820312 C 2.066406 0.0820312 1.769531 0.0234375 1.558594 -0.101562 C 1.347656 -0.222656 1.199219 -0.378906 1.113281 -0.578125 C 1.027344 -0.773438 0.984375 -1.1875 0.984375 -1.820312 L 0.984375 -5.402344 L 0.210938 -5.402344 L 0.210938 -6.222656 L 0.984375 -6.222656 L 0.984375 -7.765625 L 2.03125 -8.398438 L 2.03125 -6.222656 L 3.09375 -6.222656 L 3.09375 -5.402344 L 2.03125 -5.402344 L 2.03125 -1.765625 C 2.03125 -1.464844 2.050781 -1.269531 2.089844 -1.183594 C 2.125 -1.097656 2.1875 -1.03125 2.269531 -0.976562 C 2.355469 -0.925781 2.476562 -0.902344 2.632812 -0.902344 C 2.75 -0.902344 2.902344 -0.914062 3.09375 -0.945312 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-9">
+<path style="stroke:none;" d="M 0.789062 0 L 0.789062 -6.222656 L 1.742188 -6.222656 L 1.742188 -5.335938 C 2.199219 -6.019531 2.859375 -6.363281 3.71875 -6.363281 C 4.09375 -6.363281 4.441406 -6.296875 4.753906 -6.160156 C 5.070312 -6.027344 5.304688 -5.851562 5.460938 -5.632812 C 5.617188 -5.414062 5.726562 -5.152344 5.789062 -4.851562 C 5.828125 -4.65625 5.847656 -4.3125 5.847656 -3.828125 L 5.847656 0 L 4.792969 0 L 4.792969 -3.785156 C 4.792969 -4.214844 4.75 -4.535156 4.671875 -4.75 C 4.589844 -4.960938 4.441406 -5.132812 4.234375 -5.257812 C 4.023438 -5.386719 3.78125 -5.449219 3.5 -5.449219 C 3.050781 -5.449219 2.660156 -5.304688 2.335938 -5.023438 C 2.007812 -4.738281 1.84375 -4.195312 1.84375 -3.398438 L 1.84375 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-10">
+<path style="stroke:none;" d="M 5.050781 -2.003906 L 6.140625 -1.867188 C 5.96875 -1.230469 5.648438 -0.738281 5.1875 -0.386719 C 4.722656 -0.0351562 4.125 0.140625 3.40625 0.140625 C 2.496094 0.140625 1.773438 -0.140625 1.238281 -0.699219 C 0.707031 -1.261719 0.4375 -2.046875 0.4375 -3.058594 C 0.4375 -4.105469 0.710938 -4.917969 1.25 -5.496094 C 1.789062 -6.074219 2.484375 -6.363281 3.34375 -6.363281 C 4.175781 -6.363281 4.859375 -6.078125 5.382812 -5.515625 C 5.910156 -4.949219 6.175781 -4.148438 6.175781 -3.125 C 6.175781 -3.0625 6.171875 -2.96875 6.171875 -2.84375 L 1.53125 -2.84375 C 1.570312 -2.160156 1.761719 -1.632812 2.109375 -1.273438 C 2.457031 -0.910156 2.890625 -0.726562 3.410156 -0.726562 C 3.796875 -0.726562 4.125 -0.828125 4.398438 -1.03125 C 4.671875 -1.234375 4.890625 -1.558594 5.050781 -2.003906 Z M 1.585938 -3.710938 L 5.0625 -3.710938 C 5.015625 -4.234375 4.882812 -4.625 4.664062 -4.886719 C 4.328125 -5.292969 3.890625 -5.496094 3.359375 -5.496094 C 2.875 -5.496094 2.464844 -5.335938 2.136719 -5.007812 C 1.804688 -4.683594 1.625 -4.25 1.585938 -3.710938 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-11">
+<path style="stroke:none;" d="M 1.042969 0 L 1.042969 -5.402344 L 0.109375 -5.402344 L 0.109375 -6.222656 L 1.042969 -6.222656 L 1.042969 -6.882812 C 1.042969 -7.300781 1.078125 -7.613281 1.15625 -7.816406 C 1.257812 -8.089844 1.433594 -8.3125 1.691406 -8.480469 C 1.945312 -8.652344 2.304688 -8.734375 2.765625 -8.734375 C 3.0625 -8.734375 3.390625 -8.703125 3.75 -8.632812 L 3.59375 -7.710938 C 3.375 -7.75 3.164062 -7.769531 2.96875 -7.769531 C 2.648438 -7.769531 2.421875 -7.703125 2.289062 -7.5625 C 2.15625 -7.425781 2.09375 -7.171875 2.09375 -6.796875 L 2.09375 -6.222656 L 3.304688 -6.222656 L 3.304688 -5.402344 L 2.09375 -5.402344 L 2.09375 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-12">
+<path style="stroke:none;" d="M 4.828125 0 L 4.828125 -0.785156 C 4.433594 -0.167969 3.851562 0.140625 3.085938 0.140625 C 2.589844 0.140625 2.136719 0.00390625 1.71875 -0.269531 C 1.304688 -0.542969 0.980469 -0.925781 0.753906 -1.414062 C 0.523438 -1.90625 0.410156 -2.46875 0.410156 -3.105469 C 0.410156 -3.726562 0.515625 -4.289062 0.71875 -4.796875 C 0.925781 -5.300781 1.238281 -5.6875 1.652344 -5.960938 C 2.066406 -6.230469 2.53125 -6.363281 3.039062 -6.363281 C 3.414062 -6.363281 3.75 -6.285156 4.042969 -6.125 C 4.335938 -5.96875 4.574219 -5.761719 4.757812 -5.507812 L 4.757812 -8.589844 L 5.804688 -8.589844 L 5.804688 0 Z M 1.492188 -3.105469 C 1.492188 -2.308594 1.664062 -1.710938 2 -1.320312 C 2.335938 -0.925781 2.730469 -0.726562 3.1875 -0.726562 C 3.648438 -0.726562 4.039062 -0.914062 4.363281 -1.292969 C 4.683594 -1.667969 4.84375 -2.242188 4.84375 -3.015625 C 4.84375 -3.867188 4.679688 -4.492188 4.351562 -4.890625 C 4.023438 -5.289062 3.621094 -5.492188 3.140625 -5.492188 C 2.671875 -5.492188 2.28125 -5.296875 1.964844 -4.914062 C 1.652344 -4.53125 1.492188 -3.929688 1.492188 -3.105469 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-13">
+<path style="stroke:none;" d="M 4.867188 0 L 4.867188 -0.914062 C 4.382812 -0.210938 3.726562 0.140625 2.894531 0.140625 C 2.527344 0.140625 2.183594 0.0703125 1.867188 -0.0703125 C 1.546875 -0.210938 1.3125 -0.386719 1.15625 -0.601562 C 1.003906 -0.8125 0.894531 -1.074219 0.832031 -1.382812 C 0.789062 -1.589844 0.765625 -1.917969 0.765625 -2.367188 L 0.765625 -6.222656 L 1.820312 -6.222656 L 1.820312 -2.773438 C 1.820312 -2.222656 1.84375 -1.851562 1.886719 -1.65625 C 1.953125 -1.378906 2.09375 -1.164062 2.308594 -1.003906 C 2.523438 -0.847656 2.789062 -0.765625 3.105469 -0.765625 C 3.421875 -0.765625 3.71875 -0.847656 3.996094 -1.011719 C 4.273438 -1.171875 4.46875 -1.394531 4.585938 -1.671875 C 4.699219 -1.953125 4.757812 -2.359375 4.757812 -2.890625 L 4.757812 -6.222656 L 5.8125 -6.222656 L 5.8125 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph3-1">
+<path style="stroke:none;" d="M 0.953125 0 L 0.953125 -9.304688 L 4.445312 -9.304688 C 5.15625 -9.304688 5.722656 -9.210938 6.152344 -9.023438 C 6.582031 -8.835938 6.921875 -8.546875 7.164062 -8.152344 C 7.40625 -7.761719 7.527344 -7.351562 7.527344 -6.925781 C 7.527344 -6.527344 7.421875 -6.152344 7.203125 -5.800781 C 6.988281 -5.449219 6.664062 -5.167969 6.226562 -4.953125 C 6.789062 -4.785156 7.222656 -4.503906 7.523438 -4.105469 C 7.828125 -3.707031 7.980469 -3.238281 7.980469 -2.699219 C 7.980469 -2.261719 7.886719 -1.855469 7.703125 -1.480469 C 7.519531 -1.105469 7.292969 -0.820312 7.019531 -0.617188 C 6.75 -0.414062 6.410156 -0.257812 6 -0.15625 C 5.59375 -0.0507812 5.09375 0 4.5 0 Z M 2.183594 -5.394531 L 4.195312 -5.394531 C 4.742188 -5.394531 5.132812 -5.429688 5.371094 -5.503906 C 5.683594 -5.597656 5.917969 -5.75 6.078125 -5.96875 C 6.238281 -6.183594 6.316406 -6.453125 6.316406 -6.78125 C 6.316406 -7.089844 6.242188 -7.359375 6.09375 -7.59375 C 5.945312 -7.828125 5.734375 -7.992188 5.460938 -8.078125 C 5.183594 -8.164062 4.710938 -8.207031 4.042969 -8.207031 L 2.183594 -8.207031 Z M 2.183594 -1.097656 L 4.5 -1.097656 C 4.898438 -1.097656 5.175781 -1.113281 5.339844 -1.140625 C 5.621094 -1.191406 5.859375 -1.277344 6.050781 -1.398438 C 6.242188 -1.515625 6.394531 -1.6875 6.519531 -1.914062 C 6.640625 -2.140625 6.703125 -2.402344 6.703125 -2.699219 C 6.703125 -3.046875 6.613281 -3.347656 6.4375 -3.601562 C 6.257812 -3.859375 6.011719 -4.039062 5.695312 -4.140625 C 5.382812 -4.246094 4.929688 -4.296875 4.335938 -4.296875 L 2.183594 -4.296875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-2">
+<path style="stroke:none;" d="M 0.429688 -3.371094 C 0.429688 -4.617188 0.777344 -5.542969 1.472656 -6.144531 C 2.050781 -6.644531 2.757812 -6.894531 3.59375 -6.894531 C 4.519531 -6.894531 5.277344 -6.589844 5.867188 -5.984375 C 6.453125 -5.375 6.746094 -4.535156 6.746094 -3.464844 C 6.746094 -2.597656 6.617188 -1.914062 6.355469 -1.417969 C 6.097656 -0.921875 5.71875 -0.535156 5.222656 -0.261719 C 4.722656 0.015625 4.179688 0.152344 3.59375 0.152344 C 2.648438 0.152344 1.886719 -0.148438 1.304688 -0.753906 C 0.722656 -1.359375 0.429688 -2.230469 0.429688 -3.371094 Z M 1.605469 -3.371094 C 1.605469 -2.507812 1.792969 -1.859375 2.171875 -1.429688 C 2.546875 -1 3.023438 -0.789062 3.59375 -0.789062 C 4.160156 -0.789062 4.632812 -1.003906 5.007812 -1.433594 C 5.382812 -1.867188 5.574219 -2.523438 5.574219 -3.410156 C 5.574219 -4.242188 5.382812 -4.875 5.003906 -5.304688 C 4.625 -5.734375 4.15625 -5.949219 3.59375 -5.949219 C 3.023438 -5.949219 2.546875 -5.734375 2.171875 -5.304688 C 1.792969 -4.878906 1.605469 -4.234375 1.605469 -3.371094 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-3">
+<path style="stroke:none;" d="M 0.398438 -2.011719 L 1.53125 -2.191406 C 1.59375 -1.738281 1.769531 -1.390625 2.058594 -1.148438 C 2.347656 -0.90625 2.753906 -0.789062 3.273438 -0.789062 C 3.800781 -0.789062 4.1875 -0.894531 4.445312 -1.109375 C 4.699219 -1.320312 4.824219 -1.570312 4.824219 -1.859375 C 4.824219 -2.117188 4.710938 -2.320312 4.488281 -2.46875 C 4.332031 -2.570312 3.941406 -2.699219 3.320312 -2.855469 C 2.480469 -3.066406 1.902344 -3.25 1.578125 -3.40625 C 1.253906 -3.558594 1.007812 -3.773438 0.839844 -4.046875 C 0.671875 -4.320312 0.589844 -4.621094 0.589844 -4.953125 C 0.589844 -5.253906 0.660156 -5.53125 0.796875 -5.785156 C 0.933594 -6.042969 1.121094 -6.253906 1.359375 -6.421875 C 1.535156 -6.554688 1.777344 -6.667969 2.085938 -6.757812 C 2.390625 -6.847656 2.722656 -6.894531 3.070312 -6.894531 C 3.601562 -6.894531 4.066406 -6.816406 4.464844 -6.664062 C 4.867188 -6.511719 5.160156 -6.304688 5.351562 -6.046875 C 5.542969 -5.785156 5.671875 -5.4375 5.746094 -5 L 4.628906 -4.851562 C 4.578125 -5.195312 4.429688 -5.46875 4.1875 -5.664062 C 3.945312 -5.859375 3.597656 -5.953125 3.15625 -5.953125 C 2.628906 -5.953125 2.253906 -5.867188 2.03125 -5.695312 C 1.808594 -5.519531 1.695312 -5.316406 1.695312 -5.085938 C 1.695312 -4.9375 1.742188 -4.804688 1.835938 -4.683594 C 1.929688 -4.5625 2.074219 -4.460938 2.273438 -4.378906 C 2.386719 -4.335938 2.722656 -4.242188 3.28125 -4.085938 C 4.089844 -3.871094 4.652344 -3.695312 4.972656 -3.558594 C 5.292969 -3.421875 5.542969 -3.21875 5.726562 -2.957031 C 5.90625 -2.695312 6 -2.371094 6 -1.980469 C 6 -1.601562 5.886719 -1.242188 5.664062 -0.90625 C 5.441406 -0.570312 5.121094 -0.308594 4.703125 -0.125 C 4.285156 0.0585938 3.8125 0.152344 3.28125 0.152344 C 2.40625 0.152344 1.738281 -0.03125 1.277344 -0.394531 C 0.820312 -0.757812 0.527344 -1.296875 0.398438 -2.011719 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-4">
+<path style="stroke:none;" d="M 3.351562 -1.023438 L 3.515625 -0.0117188 C 3.195312 0.0546875 2.90625 0.0898438 2.652344 0.0898438 C 2.238281 0.0898438 1.917969 0.0234375 1.6875 -0.109375 C 1.460938 -0.238281 1.300781 -0.410156 1.207031 -0.625 C 1.113281 -0.839844 1.066406 -1.289062 1.066406 -1.972656 L 1.066406 -5.851562 L 0.226562 -5.851562 L 0.226562 -6.742188 L 1.066406 -6.742188 L 1.066406 -8.410156 L 2.203125 -9.097656 L 2.203125 -6.742188 L 3.351562 -6.742188 L 3.351562 -5.851562 L 2.203125 -5.851562 L 2.203125 -1.910156 C 2.203125 -1.585938 2.222656 -1.375 2.261719 -1.28125 C 2.304688 -1.1875 2.367188 -1.113281 2.460938 -1.058594 C 2.550781 -1.003906 2.679688 -0.976562 2.851562 -0.976562 C 2.976562 -0.976562 3.144531 -0.992188 3.351562 -1.023438 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-5">
+<path style="stroke:none;" d="M 1.179688 0 L 1.179688 -1.300781 L 2.480469 -1.300781 L 2.480469 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-6">
+<path style="stroke:none;" d="M 1.003906 0 L 1.003906 -9.304688 L 4.511719 -9.304688 C 5.128906 -9.304688 5.601562 -9.277344 5.929688 -9.21875 C 6.386719 -9.140625 6.769531 -8.996094 7.078125 -8.78125 C 7.386719 -8.566406 7.636719 -8.269531 7.824219 -7.882812 C 8.011719 -7.5 8.105469 -7.074219 8.105469 -6.613281 C 8.105469 -5.824219 7.855469 -5.152344 7.351562 -4.605469 C 6.847656 -4.058594 5.9375 -3.78125 4.621094 -3.78125 L 2.234375 -3.78125 L 2.234375 0 Z M 2.234375 -4.882812 L 4.640625 -4.882812 C 5.4375 -4.882812 6 -5.03125 6.335938 -5.324219 C 6.667969 -5.621094 6.835938 -6.039062 6.835938 -6.578125 C 6.835938 -6.964844 6.738281 -7.296875 6.542969 -7.574219 C 6.34375 -7.851562 6.085938 -8.035156 5.765625 -8.125 C 5.558594 -8.179688 5.171875 -8.207031 4.613281 -8.207031 L 2.234375 -8.207031 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-7">
+<path style="stroke:none;" d="M 0.804688 2.597656 L 0.679688 1.523438 C 0.929688 1.589844 1.148438 1.625 1.332031 1.625 C 1.585938 1.625 1.789062 1.582031 1.941406 1.5 C 2.09375 1.414062 2.21875 1.296875 2.316406 1.140625 C 2.390625 1.027344 2.503906 0.746094 2.664062 0.292969 C 2.6875 0.230469 2.722656 0.136719 2.765625 0.0117188 L 0.210938 -6.742188 L 1.441406 -6.742188 L 2.84375 -2.835938 C 3.027344 -2.34375 3.1875 -1.820312 3.332031 -1.277344 C 3.464844 -1.800781 3.621094 -2.3125 3.800781 -2.8125 L 5.242188 -6.742188 L 6.386719 -6.742188 L 3.820312 0.113281 C 3.546875 0.855469 3.332031 1.363281 3.179688 1.644531 C 2.976562 2.019531 2.746094 2.296875 2.480469 2.472656 C 2.21875 2.648438 1.90625 2.734375 1.542969 2.734375 C 1.324219 2.734375 1.078125 2.6875 0.804688 2.597656 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-8">
+<path style="stroke:none;" d="M 0.855469 0 L 0.855469 -9.304688 L 2 -9.304688 L 2 -5.96875 C 2.53125 -6.585938 3.207031 -6.894531 4.019531 -6.894531 C 4.519531 -6.894531 4.953125 -6.796875 5.320312 -6.597656 C 5.6875 -6.402344 5.949219 -6.128906 6.109375 -5.78125 C 6.269531 -5.433594 6.347656 -4.933594 6.347656 -4.273438 L 6.347656 0 L 5.203125 0 L 5.203125 -4.273438 C 5.203125 -4.84375 5.082031 -5.257812 4.832031 -5.519531 C 4.585938 -5.78125 4.234375 -5.910156 3.78125 -5.910156 C 3.445312 -5.910156 3.125 -5.820312 2.828125 -5.644531 C 2.53125 -5.46875 2.316406 -5.234375 2.191406 -4.933594 C 2.0625 -4.632812 2 -4.21875 2 -3.6875 L 2 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-9">
+<path style="stroke:none;" d="M 0.855469 0 L 0.855469 -6.742188 L 1.886719 -6.742188 L 1.886719 -5.78125 C 2.382812 -6.523438 3.09375 -6.894531 4.03125 -6.894531 C 4.4375 -6.894531 4.808594 -6.820312 5.152344 -6.675781 C 5.492188 -6.527344 5.746094 -6.335938 5.914062 -6.101562 C 6.085938 -5.863281 6.203125 -5.582031 6.273438 -5.257812 C 6.3125 -5.046875 6.335938 -4.675781 6.335938 -4.144531 L 6.335938 0 L 5.191406 0 L 5.191406 -4.101562 C 5.191406 -4.566406 5.148438 -4.914062 5.058594 -5.144531 C 4.96875 -5.375 4.8125 -5.558594 4.585938 -5.695312 C 4.359375 -5.835938 4.09375 -5.902344 3.789062 -5.902344 C 3.304688 -5.902344 2.882812 -5.75 2.53125 -5.441406 C 2.175781 -5.132812 2 -4.546875 2 -3.679688 L 2 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-10">
+<path style="stroke:none;" d="M 0.855469 2.582031 L 0.855469 -6.742188 L 1.898438 -6.742188 L 1.898438 -5.867188 C 2.144531 -6.207031 2.421875 -6.464844 2.730469 -6.636719 C 3.039062 -6.808594 3.414062 -6.894531 3.851562 -6.894531 C 4.429688 -6.894531 4.9375 -6.746094 5.375 -6.449219 C 5.816406 -6.152344 6.148438 -5.734375 6.375 -5.195312 C 6.597656 -4.65625 6.710938 -4.066406 6.710938 -3.421875 C 6.710938 -2.730469 6.585938 -2.109375 6.339844 -1.558594 C 6.089844 -1.007812 5.730469 -0.582031 5.257812 -0.289062 C 4.785156 0.00390625 4.289062 0.152344 3.769531 0.152344 C 3.390625 0.152344 3.046875 0.0703125 2.746094 -0.0898438 C 2.441406 -0.25 2.195312 -0.453125 2 -0.699219 L 2 2.582031 Z M 1.890625 -3.332031 C 1.890625 -2.464844 2.066406 -1.824219 2.417969 -1.410156 C 2.769531 -0.996094 3.195312 -0.789062 3.695312 -0.789062 C 4.203125 -0.789062 4.636719 -1 5 -1.429688 C 5.359375 -1.859375 5.542969 -2.527344 5.542969 -3.429688 C 5.542969 -4.289062 5.363281 -4.929688 5.011719 -5.359375 C 4.65625 -5.785156 4.234375 -6 3.746094 -6 C 3.257812 -6 2.828125 -5.769531 2.453125 -5.316406 C 2.078125 -4.859375 1.890625 -4.199219 1.890625 -3.332031 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-11">
+<path style="stroke:none;" d="M 1.910156 0 L 0.851562 0 L 0.851562 -9.304688 L 1.992188 -9.304688 L 1.992188 -5.984375 C 2.476562 -6.589844 3.089844 -6.894531 3.839844 -6.894531 C 4.253906 -6.894531 4.648438 -6.808594 5.019531 -6.644531 C 5.390625 -6.476562 5.691406 -6.242188 5.933594 -5.9375 C 6.171875 -5.636719 6.359375 -5.269531 6.492188 -4.84375 C 6.628906 -4.414062 6.695312 -3.957031 6.695312 -3.472656 C 6.695312 -2.316406 6.410156 -1.425781 5.839844 -0.792969 C 5.269531 -0.164062 4.582031 0.152344 3.78125 0.152344 C 2.988281 0.152344 2.363281 -0.179688 1.910156 -0.84375 Z M 1.898438 -3.421875 C 1.898438 -2.613281 2.007812 -2.027344 2.226562 -1.667969 C 2.585938 -1.082031 3.074219 -0.789062 3.6875 -0.789062 C 4.1875 -0.789062 4.617188 -1.003906 4.984375 -1.4375 C 5.347656 -1.871094 5.527344 -2.519531 5.527344 -3.375 C 5.527344 -4.257812 5.355469 -4.90625 5.003906 -5.324219 C 4.65625 -5.742188 4.234375 -5.953125 3.738281 -5.953125 C 3.238281 -5.953125 2.808594 -5.738281 2.445312 -5.304688 C 2.082031 -4.871094 1.898438 -4.242188 1.898438 -3.421875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-12">
+<path style="stroke:none;" d="M 0.863281 -7.992188 L 0.863281 -9.304688 L 2.007812 -9.304688 L 2.007812 -7.992188 Z M 0.863281 0 L 0.863281 -6.742188 L 2.007812 -6.742188 L 2.007812 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-13">
+<path style="stroke:none;" d="M 5.230469 0 L 5.230469 -0.851562 C 4.804688 -0.183594 4.175781 0.152344 3.34375 0.152344 C 2.808594 0.152344 2.3125 0.00390625 1.863281 -0.292969 C 1.414062 -0.589844 1.0625 -1 0.816406 -1.53125 C 0.570312 -2.0625 0.445312 -2.675781 0.445312 -3.363281 C 0.445312 -4.035156 0.554688 -4.648438 0.78125 -5.195312 C 1.003906 -5.742188 1.339844 -6.164062 1.789062 -6.457031 C 2.238281 -6.75 2.738281 -6.894531 3.292969 -6.894531 C 3.699219 -6.894531 4.0625 -6.808594 4.378906 -6.636719 C 4.695312 -6.464844 4.957031 -6.242188 5.15625 -5.96875 L 5.15625 -9.304688 L 6.289062 -9.304688 L 6.289062 0 Z M 1.617188 -3.363281 C 1.617188 -2.5 1.800781 -1.855469 2.164062 -1.429688 C 2.527344 -1 2.957031 -0.789062 3.453125 -0.789062 C 3.953125 -0.789062 4.375 -0.992188 4.726562 -1.398438 C 5.074219 -1.808594 5.25 -2.429688 5.25 -3.269531 C 5.25 -4.191406 5.070312 -4.867188 4.714844 -5.300781 C 4.359375 -5.730469 3.921875 -5.949219 3.402344 -5.949219 C 2.894531 -5.949219 2.46875 -5.742188 2.128906 -5.324219 C 1.789062 -4.910156 1.617188 -4.257812 1.617188 -3.363281 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-14">
+<path style="stroke:none;" d="M 4.84375 0 L 3.699219 0 L 3.699219 -7.28125 C 3.425781 -7.019531 3.066406 -6.757812 2.617188 -6.492188 C 2.171875 -6.230469 1.769531 -6.035156 1.414062 -5.902344 L 1.414062 -7.007812 C 2.054688 -7.308594 2.613281 -7.671875 3.089844 -8.101562 C 3.570312 -8.527344 3.90625 -8.941406 4.105469 -9.34375 L 4.84375 -9.34375 Z "/>
+</symbol>
+</g>
+<clipPath id="clip1">
+  <path d="M 89 21 L 91 21 L 91 221 L 89 221 Z "/>
+</clipPath>
+<clipPath id="clip2">
+  <path d="M 201 21 L 203 21 L 203 221 L 201 221 Z "/>
+</clipPath>
+<clipPath id="clip3">
+  <path d="M 313 21 L 315 21 L 315 221 L 313 221 Z "/>
+</clipPath>
+<clipPath id="clip4">
+  <path d="M 33 180 L 355 180 L 355 182 L 33 182 Z "/>
+</clipPath>
+<clipPath id="clip5">
+  <path d="M 33 146 L 355 146 L 355 148 L 33 148 Z "/>
+</clipPath>
+<clipPath id="clip6">
+  <path d="M 33 120 L 355 120 L 355 122 L 33 122 Z "/>
+</clipPath>
+<clipPath id="clip7">
+  <path d="M 33 95 L 355 95 L 355 97 L 33 97 Z "/>
+</clipPath>
+<clipPath id="clip8">
+  <path d="M 33 61 L 355 61 L 355 63 L 33 63 Z "/>
+</clipPath>
+</defs>
+<g id="surface11">
+<g clip-path="url(#clip1)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 90.226562 220.007812 L 90.226562 21 "/>
+</g>
+<g clip-path="url(#clip2)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 201.996094 220.007812 L 201.996094 21 "/>
+</g>
+<g clip-path="url(#clip3)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 313.761719 220.007812 L 313.761719 21 "/>
+</g>
+<g clip-path="url(#clip4)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 33 180.734375 L 355 180.734375 "/>
+</g>
+<g clip-path="url(#clip5)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 33 146.960938 L 355 146.960938 "/>
+</g>
+<g clip-path="url(#clip6)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 33 121.410156 L 355 121.410156 "/>
+</g>
+<g clip-path="url(#clip7)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 33 95.859375 L 355 95.859375 "/>
+</g>
+<g clip-path="url(#clip8)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 33 62.082031 L 355 62.082031 "/>
+</g>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 49.128906 169 C 49.128906 168.167969 48.800781 167.375 48.214844 166.785156 C 47.625 166.199219 46.832031 165.871094 46 165.871094 C 45.167969 165.871094 44.375 166.199219 43.785156 166.785156 C 43.199219 167.375 42.871094 168.167969 42.871094 169 C 42.871094 169.832031 43.199219 170.625 43.785156 171.214844 C 44.375 171.800781 45.167969 172.128906 46 172.128906 C 46.832031 172.128906 47.625 171.800781 48.214844 171.214844 C 48.800781 170.625 49.128906 169.832031 49.128906 169 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 82.128906 169 C 82.128906 168.167969 81.800781 167.375 81.214844 166.785156 C 80.625 166.199219 79.832031 165.871094 79 165.871094 C 78.167969 165.871094 77.375 166.199219 76.785156 166.785156 C 76.199219 167.375 75.871094 168.167969 75.871094 169 C 75.871094 169.832031 76.199219 170.625 76.785156 171.214844 C 77.375 171.800781 78.167969 172.128906 79 172.128906 C 79.832031 172.128906 80.625 171.800781 81.214844 171.214844 C 81.800781 170.625 82.128906 169.832031 82.128906 169 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 116.128906 167 C 116.128906 166.167969 115.800781 165.375 115.214844 164.785156 C 114.625 164.199219 113.832031 163.871094 113 163.871094 C 112.167969 163.871094 111.375 164.199219 110.785156 164.785156 C 110.199219 165.375 109.871094 166.167969 109.871094 167 C 109.871094 167.832031 110.199219 168.625 110.785156 169.214844 C 111.375 169.800781 112.167969 170.128906 113 170.128906 C 113.832031 170.128906 114.625 169.800781 115.214844 169.214844 C 115.800781 168.625 116.128906 167.832031 116.128906 167 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 150.128906 159 C 150.128906 158.167969 149.800781 157.375 149.214844 156.785156 C 148.625 156.199219 147.832031 155.871094 147 155.871094 C 146.167969 155.871094 145.375 156.199219 144.785156 156.785156 C 144.199219 157.375 143.871094 158.167969 143.871094 159 C 143.871094 159.832031 144.199219 160.625 144.785156 161.214844 C 145.375 161.800781 146.167969 162.128906 147 162.128906 C 147.832031 162.128906 148.625 161.800781 149.214844 161.214844 C 149.800781 160.625 150.128906 159.832031 150.128906 159 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 183.128906 146 C 183.128906 145.167969 182.800781 144.375 182.214844 143.785156 C 181.625 143.199219 180.832031 142.871094 180 142.871094 C 179.167969 142.871094 178.375 143.199219 177.785156 143.785156 C 177.199219 144.375 176.871094 145.167969 176.871094 146 C 176.871094 146.832031 177.199219 147.625 177.785156 148.214844 C 178.375 148.800781 179.167969 149.128906 180 149.128906 C 180.832031 149.128906 181.625 148.800781 182.214844 148.214844 C 182.800781 147.625 183.128906 146.832031 183.128906 146 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 217.128906 128 C 217.128906 127.167969 216.800781 126.375 216.214844 125.785156 C 215.625 125.199219 214.832031 124.871094 214 124.871094 C 213.167969 124.871094 212.375 125.199219 211.785156 125.785156 C 211.199219 126.375 210.871094 127.167969 210.871094 128 C 210.871094 128.832031 211.199219 129.625 211.785156 130.214844 C 212.375 130.800781 213.167969 131.128906 214 131.128906 C 214.832031 131.128906 215.625 130.800781 216.214844 130.214844 C 216.800781 129.625 217.128906 128.832031 217.128906 128 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 251.128906 108 C 251.128906 107.167969 250.800781 106.375 250.214844 105.785156 C 249.625 105.199219 248.832031 104.871094 248 104.871094 C 247.167969 104.871094 246.375 105.199219 245.785156 105.785156 C 245.199219 106.375 244.871094 107.167969 244.871094 108 C 244.871094 108.832031 245.199219 109.625 245.785156 110.214844 C 246.375 110.800781 247.167969 111.128906 248 111.128906 C 248.832031 111.128906 249.625 110.800781 250.214844 110.214844 C 250.800781 109.625 251.128906 108.832031 251.128906 108 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 284.128906 85 C 284.128906 84.167969 283.800781 83.375 283.214844 82.785156 C 282.625 82.199219 281.832031 81.871094 281 81.871094 C 280.167969 81.871094 279.375 82.199219 278.785156 82.785156 C 278.199219 83.375 277.871094 84.167969 277.871094 85 C 277.871094 85.832031 278.199219 86.625 278.785156 87.214844 C 279.375 87.800781 280.167969 88.128906 281 88.128906 C 281.832031 88.128906 282.625 87.800781 283.214844 87.214844 C 283.800781 86.625 284.128906 85.832031 284.128906 85 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 318.128906 59 C 318.128906 58.167969 317.800781 57.375 317.214844 56.785156 C 316.625 56.199219 315.832031 55.871094 315 55.871094 C 314.167969 55.871094 313.375 56.199219 312.785156 56.785156 C 312.199219 57.375 311.871094 58.167969 311.871094 59 C 311.871094 59.832031 312.199219 60.625 312.785156 61.214844 C 313.375 61.800781 314.167969 62.128906 315 62.128906 C 315.832031 62.128906 316.625 61.800781 317.214844 61.214844 C 317.800781 60.625 318.128906 59.832031 318.128906 59 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 352.128906 31 C 352.128906 30.167969 351.800781 29.375 351.214844 28.785156 C 350.625 28.199219 349.832031 27.871094 349 27.871094 C 348.167969 27.871094 347.375 28.199219 346.785156 28.785156 C 346.199219 29.375 345.871094 30.167969 345.871094 31 C 345.871094 31.832031 346.199219 32.625 346.785156 33.214844 C 347.375 33.800781 348.167969 34.128906 349 34.128906 C 349.832031 34.128906 350.625 33.800781 351.214844 33.214844 C 351.800781 32.625 352.128906 31.832031 352.128906 31 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 49.128906 205 C 49.128906 204.167969 48.800781 203.375 48.214844 202.785156 C 47.625 202.199219 46.832031 201.871094 46 201.871094 C 45.167969 201.871094 44.375 202.199219 43.785156 202.785156 C 43.199219 203.375 42.871094 204.167969 42.871094 205 C 42.871094 205.832031 43.199219 206.625 43.785156 207.214844 C 44.375 207.800781 45.167969 208.128906 46 208.128906 C 46.832031 208.128906 47.625 207.800781 48.214844 207.214844 C 48.800781 206.625 49.128906 205.832031 49.128906 205 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 82.128906 199 C 82.128906 198.167969 81.800781 197.375 81.214844 196.785156 C 80.625 196.199219 79.832031 195.871094 79 195.871094 C 78.167969 195.871094 77.375 196.199219 76.785156 196.785156 C 76.199219 197.375 75.871094 198.167969 75.871094 199 C 75.871094 199.832031 76.199219 200.625 76.785156 201.214844 C 77.375 201.800781 78.167969 202.128906 79 202.128906 C 79.832031 202.128906 80.625 201.800781 81.214844 201.214844 C 81.800781 200.625 82.128906 199.832031 82.128906 199 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 116.128906 190 C 116.128906 189.167969 115.800781 188.375 115.214844 187.785156 C 114.625 187.199219 113.832031 186.871094 113 186.871094 C 112.167969 186.871094 111.375 187.199219 110.785156 187.785156 C 110.199219 188.375 109.871094 189.167969 109.871094 190 C 109.871094 190.832031 110.199219 191.625 110.785156 192.214844 C 111.375 192.800781 112.167969 193.128906 113 193.128906 C 113.832031 193.128906 114.625 192.800781 115.214844 192.214844 C 115.800781 191.625 116.128906 190.832031 116.128906 190 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 150.128906 177 C 150.128906 176.167969 149.800781 175.375 149.214844 174.785156 C 148.625 174.199219 147.832031 173.871094 147 173.871094 C 146.167969 173.871094 145.375 174.199219 144.785156 174.785156 C 144.199219 175.375 143.871094 176.167969 143.871094 177 C 143.871094 177.832031 144.199219 178.625 144.785156 179.214844 C 145.375 179.800781 146.167969 180.128906 147 180.128906 C 147.832031 180.128906 148.625 179.800781 149.214844 179.214844 C 149.800781 178.625 150.128906 177.832031 150.128906 177 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 183.128906 159 C 183.128906 158.167969 182.800781 157.375 182.214844 156.785156 C 181.625 156.199219 180.832031 155.871094 180 155.871094 C 179.167969 155.871094 178.375 156.199219 177.785156 156.785156 C 177.199219 157.375 176.871094 158.167969 176.871094 159 C 176.871094 159.832031 177.199219 160.625 177.785156 161.214844 C 178.375 161.800781 179.167969 162.128906 180 162.128906 C 180.832031 162.128906 181.625 161.800781 182.214844 161.214844 C 182.800781 160.625 183.128906 159.832031 183.128906 159 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 217.128906 138 C 217.128906 137.167969 216.800781 136.375 216.214844 135.785156 C 215.625 135.199219 214.832031 134.871094 214 134.871094 C 213.167969 134.871094 212.375 135.199219 211.785156 135.785156 C 211.199219 136.375 210.871094 137.167969 210.871094 138 C 210.871094 138.832031 211.199219 139.625 211.785156 140.214844 C 212.375 140.800781 213.167969 141.128906 214 141.128906 C 214.832031 141.128906 215.625 140.800781 216.214844 140.214844 C 216.800781 139.625 217.128906 138.832031 217.128906 138 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 251.128906 115 C 251.128906 114.167969 250.800781 113.375 250.214844 112.785156 C 249.625 112.199219 248.832031 111.871094 248 111.871094 C 247.167969 111.871094 246.375 112.199219 245.785156 112.785156 C 245.199219 113.375 244.871094 114.167969 244.871094 115 C 244.871094 115.832031 245.199219 116.625 245.785156 117.214844 C 246.375 117.800781 247.167969 118.128906 248 118.128906 C 248.832031 118.128906 249.625 117.800781 250.214844 117.214844 C 250.800781 116.625 251.128906 115.832031 251.128906 115 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 284.128906 91 C 284.128906 90.167969 283.800781 89.375 283.214844 88.785156 C 282.625 88.199219 281.832031 87.871094 281 87.871094 C 280.167969 87.871094 279.375 88.199219 278.785156 88.785156 C 278.199219 89.375 277.871094 90.167969 277.871094 91 C 277.871094 91.832031 278.199219 92.625 278.785156 93.214844 C 279.375 93.800781 280.167969 94.128906 281 94.128906 C 281.832031 94.128906 282.625 93.800781 283.214844 93.214844 C 283.800781 92.625 284.128906 91.832031 284.128906 91 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 318.128906 65 C 318.128906 64.167969 317.800781 63.375 317.214844 62.785156 C 316.625 62.199219 315.832031 61.871094 315 61.871094 C 314.167969 61.871094 313.375 62.199219 312.785156 62.785156 C 312.199219 63.375 311.871094 64.167969 311.871094 65 C 311.871094 65.832031 312.199219 66.625 312.785156 67.214844 C 313.375 67.800781 314.167969 68.128906 315 68.128906 C 315.832031 68.128906 316.625 67.800781 317.214844 67.214844 C 317.800781 66.625 318.128906 65.832031 318.128906 65 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 352.128906 38 C 352.128906 37.167969 351.800781 36.375 351.214844 35.785156 C 350.625 35.199219 349.832031 34.871094 349 34.871094 C 348.167969 34.871094 347.375 35.199219 346.785156 35.785156 C 346.199219 36.375 345.871094 37.167969 345.871094 38 C 345.871094 38.832031 346.199219 39.625 346.785156 40.214844 C 347.375 40.800781 348.167969 41.128906 349 41.128906 C 349.832031 41.128906 350.625 40.800781 351.214844 40.214844 C 351.800781 39.625 352.128906 38.832031 352.128906 38 Z "/>
+<path style="fill:none;stroke-width:0.5;stroke-linecap:square;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 220.007812 L 33 220.007812 "/>
+<path style="fill:none;stroke-width:0.5;stroke-linecap:square;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 220.007812 L 33 21 "/>
+<path style="fill:none;stroke-width:0.5;stroke-linecap:square;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 21 L 355 21 "/>
+<path style="fill:none;stroke-width:0.5;stroke-linecap:square;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 21 L 355 220.007812 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 90.226562 220.007812 L 90.226562 216.785156 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="84.725786" y="232.006944"/>
+  <use xlink:href="#glyph0-2" x="90.28731" y="232.006944"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 201.996094 220.007812 L 201.996094 216.785156 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="193.49443" y="232.006944"/>
+  <use xlink:href="#glyph0-2" x="199.055953" y="232.006944"/>
+  <use xlink:href="#glyph0-2" x="204.617477" y="232.006944"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 313.761719 220.007812 L 313.761719 216.785156 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="302.763074" y="232.006944"/>
+  <use xlink:href="#glyph0-2" x="308.324597" y="232.006944"/>
+  <use xlink:href="#glyph0-2" x="313.88612" y="232.006944"/>
+  <use xlink:href="#glyph0-2" x="319.447644" y="232.006944"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 45.75 220.007812 L 45.75 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 56.578125 220.007812 L 56.578125 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 65.429688 220.007812 L 65.429688 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 72.914062 220.007812 L 72.914062 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 79.394531 220.007812 L 79.394531 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 85.113281 220.007812 L 85.113281 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 123.871094 220.007812 L 123.871094 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 143.554688 220.007812 L 143.554688 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 157.515625 220.007812 L 157.515625 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 168.347656 220.007812 L 168.347656 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 177.199219 220.007812 L 177.199219 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 184.679688 220.007812 L 184.679688 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 191.164062 220.007812 L 191.164062 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 196.878906 220.007812 L 196.878906 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 235.640625 220.007812 L 235.640625 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 255.320312 220.007812 L 255.320312 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 269.285156 220.007812 L 269.285156 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 280.117188 220.007812 L 280.117188 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 288.96875 220.007812 L 288.96875 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 296.449219 220.007812 L 296.449219 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 302.929688 220.007812 L 302.929688 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 308.648438 220.007812 L 308.648438 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 347.410156 220.007812 L 347.410156 218.398438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 180.734375 L 36.21875 180.734375 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-3" x="24" y="183.235493"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 146.960938 L 36.21875 146.960938 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-4" x="24" y="149.45964"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 121.410156 L 36.21875 121.410156 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="19" y="123.909193"/>
+  <use xlink:href="#glyph0-2" x="24.561523" y="123.909193"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 95.859375 L 36.21875 95.859375 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-3" x="19" y="98.358747"/>
+  <use xlink:href="#glyph0-2" x="24.561523" y="98.358747"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 62.082031 L 36.21875 62.082031 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-4" x="19" y="64.582894"/>
+  <use xlink:href="#glyph0-2" x="24.561523" y="64.582894"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 206.285156 L 34.609375 206.285156 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 165.789062 L 34.609375 165.789062 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 155.183594 L 34.609375 155.183594 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 140.238281 L 34.609375 140.238281 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 134.558594 L 34.609375 134.558594 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 129.632812 L 34.609375 129.632812 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 125.292969 L 34.609375 125.292969 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 80.914062 L 34.609375 80.914062 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 70.308594 L 34.609375 70.308594 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 33 36.53125 L 34.609375 36.53125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 90.226562 21 L 90.226562 24.21875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 201.996094 21 L 201.996094 24.21875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 313.761719 21 L 313.761719 24.21875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 45.75 21 L 45.75 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 56.578125 21 L 56.578125 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 65.429688 21 L 65.429688 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 72.914062 21 L 72.914062 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 79.394531 21 L 79.394531 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 85.113281 21 L 85.113281 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 123.871094 21 L 123.871094 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 143.554688 21 L 143.554688 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 157.515625 21 L 157.515625 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 168.347656 21 L 168.347656 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 177.199219 21 L 177.199219 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 184.679688 21 L 184.679688 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 191.164062 21 L 191.164062 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 196.878906 21 L 196.878906 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 235.640625 21 L 235.640625 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 255.320312 21 L 255.320312 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 269.285156 21 L 269.285156 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 280.117188 21 L 280.117188 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 288.96875 21 L 288.96875 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 296.449219 21 L 296.449219 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 302.929688 21 L 302.929688 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 308.648438 21 L 308.648438 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 347.410156 21 L 347.410156 22.609375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 180.734375 L 351.78125 180.734375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 146.960938 L 351.78125 146.960938 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 121.410156 L 351.78125 121.410156 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 95.859375 L 351.78125 95.859375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 62.082031 L 351.78125 62.082031 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 206.285156 L 353.390625 206.285156 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 165.789062 L 353.390625 165.789062 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 155.183594 L 353.390625 155.183594 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 140.238281 L 353.390625 140.238281 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 134.558594 L 353.390625 134.558594 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 129.632812 L 353.390625 129.632812 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 125.292969 L 353.390625 125.292969 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 80.914062 L 353.390625 80.914062 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 70.308594 L 353.390625 70.308594 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 36.53125 L 353.390625 36.53125 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-5" x="172.5" y="251.006944"/>
+  <use xlink:href="#glyph0-6" x="178.608398" y="251.006944"/>
+  <use xlink:href="#glyph0-7" x="184.169922" y="251.006944"/>
+  <use xlink:href="#glyph0-8" x="189.731445" y="251.006944"/>
+  <use xlink:href="#glyph0-9" x="194.731445" y="251.006944"/>
+  <use xlink:href="#glyph0-10" x="197.509766" y="251.006944"/>
+  <use xlink:href="#glyph0-11" x="199.731445" y="251.006944"/>
+  <use xlink:href="#glyph0-7" x="205.292969" y="251.006944"/>
+  <use xlink:href="#glyph0-12" x="210.854492" y="251.006944"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph1-1" x="9" y="140.003472"/>
+  <use xlink:href="#glyph1-2" x="9" y="133.33355"/>
+  <use xlink:href="#glyph1-3" x="9" y="127.772027"/>
+  <use xlink:href="#glyph1-4" x="9" y="122.772027"/>
+  <use xlink:href="#glyph1-5" x="9" y="117.210503"/>
+  <use xlink:href="#glyph1-6" x="9" y="111.64898"/>
+  <use xlink:href="#glyph1-7" x="9" y="106.087457"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph2-1" x="122" y="12"/>
+  <use xlink:href="#glyph2-2" x="130.666016" y="12"/>
+  <use xlink:href="#glyph2-3" x="137.339844" y="12"/>
+  <use xlink:href="#glyph2-4" x="147.335938" y="12"/>
+  <use xlink:href="#glyph2-5" x="154.009766" y="12"/>
+  <use xlink:href="#glyph2-6" x="156.675781" y="12"/>
+  <use xlink:href="#glyph2-7" x="159.341797" y="12"/>
+  <use xlink:href="#glyph2-8" x="166.015625" y="12"/>
+  <use xlink:href="#glyph2-5" x="169.349609" y="12"/>
+  <use xlink:href="#glyph2-2" x="172.015625" y="12"/>
+  <use xlink:href="#glyph2-9" x="178.689453" y="12"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph2-8" x="188" y="12"/>
+  <use xlink:href="#glyph2-5" x="191.333984" y="12"/>
+  <use xlink:href="#glyph2-3" x="194" y="12"/>
+  <use xlink:href="#glyph2-10" x="203.996094" y="12"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph2-2" x="214" y="12"/>
+  <use xlink:href="#glyph2-11" x="220.673828" y="12"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph2-3" x="227" y="12"/>
+  <use xlink:href="#glyph2-2" x="236.996094" y="12"/>
+  <use xlink:href="#glyph2-12" x="243.669922" y="12"/>
+  <use xlink:href="#glyph2-13" x="250.34375" y="12"/>
+  <use xlink:href="#glyph2-6" x="257.017578" y="12"/>
+  <use xlink:href="#glyph2-10" x="259.683594" y="12"/>
+</g>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 382.148438 116 C 382.148438 115.164062 381.816406 114.363281 381.226562 113.773438 C 380.636719 113.183594 379.835938 112.851562 379 112.851562 C 378.164062 112.851562 377.363281 113.183594 376.773438 113.773438 C 376.183594 114.363281 375.851562 115.164062 375.851562 116 C 375.851562 116.835938 376.183594 117.636719 376.773438 118.226562 C 377.363281 118.816406 378.164062 119.148438 379 119.148438 C 379.835938 119.148438 380.636719 118.816406 381.226562 118.226562 C 381.816406 117.636719 382.148438 116.835938 382.148438 116 Z "/>
+<g style="fill:rgb(0%,0%,0%);fill-opacity:1;">
+  <use xlink:href="#glyph3-1" x="391" y="120.28418"/>
+  <use xlink:href="#glyph3-2" x="399.670898" y="120.28418"/>
+  <use xlink:href="#glyph3-2" x="406.901367" y="120.28418"/>
+  <use xlink:href="#glyph3-3" x="414.131836" y="120.28418"/>
+  <use xlink:href="#glyph3-4" x="420.631836" y="120.28418"/>
+</g>
+<g style="fill:rgb(0%,0%,0%);fill-opacity:1;">
+  <use xlink:href="#glyph3-5" x="424" y="120.28418"/>
+</g>
+<g style="fill:rgb(0%,0%,0%);fill-opacity:1;">
+  <use xlink:href="#glyph3-6" x="428" y="120.28418"/>
+  <use xlink:href="#glyph3-7" x="436.670898" y="120.28418"/>
+  <use xlink:href="#glyph3-4" x="443.170898" y="120.28418"/>
+  <use xlink:href="#glyph3-8" x="446.783203" y="120.28418"/>
+  <use xlink:href="#glyph3-2" x="454.013672" y="120.28418"/>
+  <use xlink:href="#glyph3-9" x="461.244141" y="120.28418"/>
+</g>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 382.148438 139 C 382.148438 138.164062 381.816406 137.363281 381.226562 136.773438 C 380.636719 136.183594 379.835938 135.851562 379 135.851562 C 378.164062 135.851562 377.363281 136.183594 376.773438 136.773438 C 376.183594 137.363281 375.851562 138.164062 375.851562 139 C 375.851562 139.835938 376.183594 140.636719 376.773438 141.226562 C 377.363281 141.816406 378.164062 142.148438 379 142.148438 C 379.835938 142.148438 380.636719 141.816406 381.226562 141.226562 C 381.816406 140.636719 382.148438 139.835938 382.148438 139 Z "/>
+<g style="fill:rgb(0%,0%,0%);fill-opacity:1;">
+  <use xlink:href="#glyph3-10" x="391" y="143.28418"/>
+  <use xlink:href="#glyph3-7" x="398.230469" y="143.28418"/>
+  <use xlink:href="#glyph3-11" x="404.730469" y="143.28418"/>
+  <use xlink:href="#glyph3-12" x="411.960938" y="143.28418"/>
+  <use xlink:href="#glyph3-9" x="414.849609" y="143.28418"/>
+  <use xlink:href="#glyph3-13" x="422.080078" y="143.28418"/>
+  <use xlink:href="#glyph3-14" x="429.310547" y="143.28418"/>
+  <use xlink:href="#glyph3-14" x="436.541016" y="143.28418"/>
+</g>
+</g>
+</svg>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python2.png b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python2.png
new file mode 100644
index 0000000000000000000000000000000000000000..9f17272c50663957d6ae6d8e23fdd5a15757e71f
GIT binary patch
literal 41121
zcmcG$2{_bm-#7dlYuP7j)*2;A*6eFFmdcVCl08D%$sVSK#-6`~tdTIu8nP>8iAb_9
zL)2s)`(O;uIlBJW^<4LJy~lk&@A1CVag-dh{C?;8T|UeAoXD#NI?N2b3=jk{U(!Wh
zgCOc22%>@=q65G27mTd{|IppMqJxC?DSxsX@)IHG7<37F-XtJ>VLbSrrEiAD-XO_G
z<Wic5zVVCj2UoL`5XH$JoMYj|IX`G;fAoxt-iSJxp(Ojpv><t-oIyCi{4V#^6CFya
z&pZn)%j-lH62d>x!q19+z#eMQJgxu2;<uW<2tJ)ih)*rGYi~&1K9M@Ow$h&4r<B#B
zR3fXd9N2^RQKvtK_`i5OFZk#OArLu@rp1=#>=9Gah;QabMrGu=*Ck6;8Ic$aM$}OS
zg7;p(a^lFo7y=)c-B}%i^PqzUeLd((3kW_d0lVX^DPi=bI3k(7O?~C}yW)A6q6IQ^
zCUD|s)fzwcuCiN|2QD;_jV6m)N(+_n#W9S2GNY<O0Go8iOb|}<z6E;n#gV(JvT{-V
zDS2aCuxWru_K3Np3+{rKBiae^{4VmPU~@YBZsFqER_Q&QSr&T&cGrj0c4BfUklMRu
z&XxWs<7kzeg$Tmt^Zv#yNX2ABL0&dC$yJ_Dww0e1iknWkIBt_gSULK*R+93rf?Pew
zbE#_XF_)P8+j-XR5$xgHhNy9tNkc7tc{=1w6KW$HtM0DSd7UjG{+G}M{iHaK80`A%
zIC$|~^e0)kz9Z?zjT-~;FI$k$_Z9*t`m1DPPNFhSBiIs1sy+5odPpR4ngLvUj`Ux8
zBKXI40W@ee<9vgVptp=xHN_SJTD_|9LNEUO+`PP>HD&tB?|ybB_pm1jdtvY}D2u${
zg!|gpCu@j|!K&j)dz<%1qA)PDXjCoB&wydqO=O$brm2aEt7uo_%}-I&xuVY(6}v0+
z_4OrLDw_gS)knS3r(2{<?7OsaM1Pk4QyB(rRc@nxs^}IIUt^KPz}s)}KBA^2)@qd%
z6>Iu-JO(8N1w#X4WA($@EFcy-9=vvA?|nI4yL1EMh6274$;6nK9*Bf@SCC*S<{Jq%
zsbNcMh}A1ITuiUeE=<?D^EYe2QbzH!WJu;bn`OP<;z&q~lAo%eMhDND6%<CFP4F$V
zfOeaSWD#ft|85?-XNhbi1WugXE@H*Bk47jz!>BF=PLN3I$$BEmuje#iip7(v19az?
zW2Z88_AI%0?Iq|vwQ)X1{Tr<*CBd2gVOeYM4mUr3QrE3Ym(8xJfHuGp;{|{76weDr
zj;-%FwMePTaz&GMQ5uo^B-?<;7)ub3`W+6dZrJDVUATpUuc0)?M@DvPPwy0(%rzKs
zgm5?85CfH|mf6rHTaEjpdom}FL?$6teIcEVO{4yOXjuRr$Payxy4Uht&c(VTt3?4;
zWZJKC!@@$N&oIs(X>?Zu-A*L8?4$=1h+A@vgs|dCUnM<c<7?~{$b>k&9)qz2p>mdd
z>l9ey_X4XqCy~g+OQBw+ry;S3z6sxP5$H`jXLmf+GHfiyG9lPVoGaL~AB*aHJYooz
za)f8?3?xKdYK|O%&QWijMcc!66;t*0jL%mO-r`K)MmqR}*<fp7njw2GhVKJ+6>Tg9
z%{&F+=$^KlWsfZf1qMDiL?VB<+IN+}>V=v&cWbsF`Kb2O*dPy?)O#B@?xMyIP5N&&
z(GFKpZCQDgDY4ndbYyk9ws|cX_ZvpH+e7a3%SX`W*dTkER8>Qi5Y@5>L@b;Oqxub7
zHig%;_>K?mTAVr(d29Q@MhQ&y4B`zmKs7`+sRjw&wW@xRwYFyluc2!;!tPv|*(2{=
zLGD>2_w3PqDOB0k$X6TH(o8Kpdjy=TZftC<52TudJq(E<{G)W)(ff4EBB+L8-|;IE
z6_f-HI%_a0lG9NnAs8>oTXf#dZPDJ$I`HTF-UZbJU2)n{D$PTa_i)6pZ*_0V%DfE3
zx$5YfN9*e9N~pdn4mI+(nHOSZQ5#FSamshRlr!Rjs%wf#z7Wo+Kg`wHc`GzHIM^?n
zyw%wBwh7-i`*!yCZ)bW>2K1L<SNUPQ>N99d9DNn}EHKmGKFeO{@T4H*LA`tt>ZDVN
z-#we^PIvV*VjlbYHG4ye2Y7QgTq8LcyK@rNpqzA>Tm|xdWnf^y<5&C!4%#zCrCC^5
zgeeYrG4O|vMNJcksstExz46|*UYl1ay=^ge;uwNeDQ+!tqHqKRuLo)aHlr@CNZs79
z&BXYGB=r&6f}<rjtiu}a^kcD0OG}P3+V@Ig+ot1h$)q~LjwM~r;L_^7Gs7^j7J>`g
zkkb=M#24Hl9qM-aezR=I7y5Nv0TC954@^5)18Jz5>fH%&nf_O<bV^=H0OjAge6pQV
z?>uB%RTYQK%*+O*bH{5i>v1$Tz15y{=t`GCzk<2dU7O@zIyCezv1_ZOU3=VzqtGk;
zG{2K*CUltkLTSs6zzhD*n~S3*(6Sd)q8R5hP&sH#Y9gn%p+Xp)pyiOTU=FQ@@cq9)
z@tQn$2WrRV2J)iylai7;Nug2+2Km%IYkNjWK`OTBoZn}1m6G=g=1`3Ujjtr4oXbEx
z0-><6H_lU0M3C#w#H$^e?17FULI~qztr_*}p?AB}m0pvIw=$YSl~V}{U%b*8gR8ZS
z?jH9}zA$dG#T%4KltrE0`Qc7!Wso{_EGFl7Un9X|u5O%2&O#_qYvzdVwB5vm>i^vJ
zx+;6G!vO7M&HKinpddQ_PXaOXwWlCOID~Yt3UdS1KuF6S>(S+vP9zS#O2()rg>3~6
z`ZlG4Hb8W*Tm5{7b*C|FIb^@?%a<>8+6fy7R0Er)A%_+-_`f01J-ThW4W@V)ijgK~
z>WCz63wRqB+P9{h@1bs{U&B|pqr+P{YFMeD$%nIdgY+gIBWJk7d$f$!FYHZq?{Re=
zNf3atvBo4-^7~yMw{W4GkY;0wduR2w)DymbdQZpDto;cabrkthSWo;(`!4hC$6;L#
zGKMTqrt`NS6D%t4pipJ9a5!9FB)v)>nK-<YAi*PVi4FP-v##Fw&3D`r{i+YrME+te
z71&-JESQsl+MstPC`n%{e`<6VuYIWBN4SpaHyde%r$~gCJUXmHXxb-L?^n}~yFWkv
zME@ACgB_3cRIR&Ib8!&&WxjP)DE-O)TIBhWb@$hV-@j81+qQESJ^rC+pflyunsO}6
zU9!HBJ{t>zboYeHU%Ys6q-IbkZkp-T{tD#K`RuGmK|w)E0uJv(_60u;@kdaHM90Lc
z;}Ughi%D73=jN?6i?DTdBxfj%&RGX^Wm>R-@K^)fr;2VKJJR6o+qZ|OYL_lRHuRo?
z=$3>tCQbZWy~!%d%F0I~KXQZQ^bmA!c97XNKs6ji=VEc2AvhuNWK2ErY7ez0;fFJI
zahF@n@Sufm>&>!4>Po)D@L7I`HBF&I^{qwb6?cMzyer;5JjNr{gjwk1{>jPA%*+X*
zS~f@nE!DanY=Qmt>z5ViSu|22cop<SSekjeK2Fd*n1WL1K#@c6(!!OVgbj@YG|=gl
zp87BZwl#8<v>p<cPaxS0f$U3wCYUEJprCwQH$1^N5_$8w@GJ;qMN|WOO#)#P)Hp1T
z^I5LwoLYR5wBx2!w|&9<Q`Q-xh3wUb#}2(B2JWtUb~}j<yP7_Bms7OYBizBUCBW8n
zjMnGqX%-h3%~2t~VhvbR;@~L_t)S1x9uxe1{ZRr%hA#|`DclMz2-9iva$FjzbSdri
z1y?QBoB&Xd5hlmkXUX7F0TzpWK0Y?K+qO3%CokVUT<wADes9{|oe1!b5P+Lk+}zx(
z+Q)O|s-bV7zJ0;&_{_ZGkkneLx=4M4QU>k2dSG32%RJ~?)cV_g{7ya`TF}*_H1FZm
zR%cYk*4Pqav9)Z?Hn4YYXveUmeV=3k>0vBS^7no-oAJfkB}OQYE1`*Md46vsvfW~h
zwHk&dkR<Dh>-d^uM589CY%khn6&DxV2j@Q~=3&PXRH#LwpJQu^u(l^d+A2x)cDBoO
z@>vV{yz=t$2_kt>ct{dt$9>@*Br?}~y;o-+u1OCejo!X&lJ88G)R5<jR<p3N36o0D
z-6d<1F6Z~P`i^r8v;`LD<ow7DA64ri$)ZV+Z8ix;yiw&k{Peo`rtu7yZ14cjTJL*?
z@6Vn+(>QKJ!)xC?UlKcC+qSM0Z^-1gyQ}u}IN}NeA#1FYr$QX}Yw7*wtxJT;4_W8k
zHx>q+ot-_OkRWR8NSY05!w+IhSR;)VV(t|+;bW!iR!2!^|I(W2nxa!^U3Ux%MkFZK
zG|7V)<c9tBv?7Mp*z&;Pz3rs|AlAa~DhoumH~re*IXURdx8rV8<9#c3T2X<jfTpzJ
z)hoy53(^{5V;1~j_y>#<iuHs|6ER;7bOoe|65>7a%*52R3Ok2@R0P_#bwLA(J8qzr
zU~`qJc@E@H$A>k@^WvVW_1xTCtxIXfr<-LWGq2p6E}_y$Twk|Y4@(&J6Oa3QEVx9p
zn|d2@SkY$FH`@R-LD;ZjRs{fdZhpSxg8R&_EkH#Zyx#U*ob6T?Wa&X)jZy!s;`vO5
z$*iyOqESI+<~CQi)Sl{|cvdi%O{6DuuGQNdt4>LUCs+WAv)%fDOxDlhS70gSRR%lB
zjP9$7#Awg93&+=Eb93$C=y=#Wxl}!M4z1o?XzszWEtMHA@*Gv^TLL+b#IDt=5F;%_
z=Xq00>r<<POyFazAEYQ2mD5Chm3p%G`Sa&4^dmZmga8fi=vbI0Tg24a;`srV@9I%G
z6|$*a#y-EgwvF~w)r+wTb;TqllvS2S{HOM_n*ApcS3KTLMNm^OvO?fpszeeyeqw_j
zV#Mh^Bj#&0=)d;$_O67^F!UU1PFCv)b|?)Qe$wHPsdwTCcVv6K*320J0fA_%!3INA
zUT*GmOZ(X$1G(6a#_HWwRYD{Zu_6cGR!0lrH6Crf!3su?5!-y)t=24ui2-%~gwX89
z#=XM;!+2gnBwRm@uz>2Rn=N2(@g$WNObOIYyL<&YMSp(T1P}nwW0{+&P-NXujd*}6
zJ>vFIl$<x@4+PPZ2cl<jq;g)!WTO+ZHtE+wtcKs~4NyeWYdItkNUThiC3+%jMHul5
z?liTgsAOnM25m@<#+<uVG0e(h`iRedhs#mtnAsBnjjzemib)~e)9g?qjjam0kCfgf
z4iD5t?GLQglOC_t24wn2OaEe;DL$$$*z5$ed}KMbdk7uB)fo2EbMw|k7Z;aAY$!*w
z5LAD(8L#EQi4H`p+8|al0rKM8nI%eMcWy&zDBlEZ=wVH2G)~)S{rGytoIe15JT&jk
zu{*NRbDNc2*>=uZ4N$^vL|GcCiZ>GWPjW>!TtRtIT|JJ-y4EjQI5*rL@?>oq6gi^2
zoZO<&O*7D8OeTE?eLzYT=&B0ZK$E&q@PlJR-v<V&g3qbksJzp^o|&0x|3OOi0@At=
zi`_9koIlhUB#1Zy2%YNq_wVaJoc#xeh7{X)d>%+^KRh->VN-+S@qwxqpdOaBrqsku
z(?Ie}_PnaRyu9f<J3HG5Zxc2eiviGYycFA_+NLW6+LWZDa*i>z?3>s5EuyPCdE@e~
zBpakD`_5V9h&~rVNG?n?kZSD2Eh#PW|NjSj_z;U!Z%uJ994!PXUR|({#1vrT2FZe-
zL$yc!Pms004$L%Nfb92z3~e8H?FMno<6Azgw66LJZZvB9gbFXpc5PBM!`OZ{9m?Ae
z6*|qgy(JjvOs4$BvO;<};Fa!wPC-FK>D3F2bJr(V`jp?unWHH3MK&5d2kAN|C#MJN
zgGttD-=@dqS-qPAmzt)kI1^G^Q~V3&N^(T!1aH6U5RJ;21x4{O?fKGSzi0Qs736+z
z1L^@g&3l-AS0{Mkb#ZyQs2uxT(Vb4`o%mn7+9OVii?Pxugm;jkVUG~Y1}Gbs$z=6w
z@~bOqt6z6k)V$Y`FK?i5hbCux*W*9k44a*u4ZIa&4~NHUfL^CJ=qn`gsq9+e_tVpX
z|GcuBm1pSzu1QPKJu%^+(q?;^>|t6o{b_n-TH@X0gBDg_jpBLJ{A-2PF;)A{-5xJP
zB1SVc;!6}JY}o&~DoR0Dk|i>p>7FnUbPF5w^;ZC$_h_dc(uBX&58_ovo&9{O-o6rd
zak$5s6!+4m%(}_jHs&y@vdu5<{nH&>HKuJ|y7kfH+C(6rgxjSExb63*<8(>OPTuRS
zBo`-PA)GMl8(hGCCT|am%0g*7kL=vOM<nL6_Gz8W!n<3A%Dr3fvvhJ+!Zi}@qd}`=
zr`O4hWcWC}qGiN!mJ*+CPRGUk3r+5ra%e^W)iHw`>Arx}6iZS*8<nGV#;jQNgC7?n
z=bHVBo{1)s3%Y&`;agR~mZXAuLtT1oUkmvx+%5otrX6;znmBmk^qo;8ZH)9h%$23Z
zGk;MW`Lmm+LN+QVEm!o%<x%7`8msOYGkgt&s%-h^8#k}jpK^6NmXl5aV9-)^#zTy^
zOezyWOAmP~n$i;v9udtyQvdwqd<XzylVgJMSv1)rJQck}vRc&|TaxUc?>xvT`fS<6
z4%J&KtVT5!Wj8-Xs;Q~Too92s<Ki;vrosw0I_))+T7br+yT_OpVe3(x3AYe3pp_4K
zrE7sZgR{}RzulVhsqFfK?x;Bib2(PH$6gcp>eaPEW2l*@qM@+x_UlExb3v^sZgxC=
z)W6?mgcpoO>N@d6q{T{Gx}`buOSRs8WXHo}&ob04lW;PzV;gae6|&>;mWsxFz6j`g
zpYQl%H#|id%!zFC9UrGqY*+lToEPB2C^zW*l=oY;sexR-mywd${Y1!wDnOe?<2AOZ
zeq?_x)lwte`LD@yaPV?~?VR;+fx9M!duk@dMz(Nt3#3QCY=E*H=+7W`ZYNKy3_i0z
z(#tc|E^BoE<dtW7S?VFR0O^HN6g2#CF((keigCsj{)7q7@mk+tH}E@ua5<GIKs_>4
z`~{soJbFgD?fbTI<ah-dHE$vGG~I1Kn(t#3>*XrAkQRlWUjoz^5yd!FcFuz0;ynkR
z2yX10tw#jXgbE>}z|_L%a_3HWWkxs#YV+d#AXQ&lT)adxYF^Z7Y8CaK5pUgh@-LSt
zfybDr_)oG%FnC8)`+4}`gOF@Zxr$`bHZmCywnR>cOGgkAAl~A4zG5_;+FyUh+?rM4
z;yA+!Pom3rW9I}EPI>ewMl`>sCQugL2^Kepzh}+;ZHAp{N^RKOD9j}OrJ<-dFS?22
z6dayJ1yX>r(At{0*U7php7$6MTkqC~KjGy}6LZ*x_qGtNwYWAHLQDY2laz;W>B;At
zG{+?4v!g%!rq*IaB2T_|0hx5-NTd&}%d~$KV2TP#6jqps$$=a9G4TbIv8KaMFvlBT
z>;4c67=cO%4(Ff}f3`_43ohQU-YpdQxjLfH+QhwPi7&dEQ-vGO$qg>ey2KPYr}grK
zA6t%hK9&=4ji6}o24oUV!JRjl?>a^%w^gFuWF1pFVSx@j`dRUlme<!cktTP=!R27u
z-OjUZfspSR?Y>~LR9jb9O?Q6MsbYB4)#*4w6Y0+SpqceS_JWh66?wa&_Ts0<u%iq6
z`M3+YS_&^U-sU>I#}qg#x}_BWXs+T$$;qm!DhWuS%7@K}1t)CdVzpD$0I7#g<oQd;
zB&0LGw}2GUt~Bda^OEZGewc#AbmTdC3atYVfd_n&>ZIckYlC~s$-JHm$WJY|NB_~_
zE&?UP=zf4|W4l-SQ9GXE9tRoW#&}_PYzA1qD=0(S(zdmF`h$1`lHfU1#;js@E)Z4g
zJ*44DoRn~5OQ6NpUPaD<`x5iV-pI!?!o*8KprNIh3{CpO|5cE{2Z62;gnAR3gNe>z
zGY948Xv9>A5yxURB`#4RddN)B{C~{#B2E0jrE@N;5)S_zP26ak;qLQDB|w`mf!|?V
zr{20zG7se5aSBX7c<@qDf)LX-eF|0}-|7koVf>;h@@JRtc&%vEFW7Gis~_&ZXtHEl
zblvNwp`oEZ8%^5C$cW!)_eG@1WdPX3jv?~-?73dgxq_PWIRn!r(<%-KRsv<$Y0i5-
zmv;0L=TiChr^5cZp(-h+I@n!MtxcAYFJ+lpzO<$!gTz5J^Yp<Z>}+F{Y*iTHMulYo
zB4U$)6XR8~Y(@rv2*^W&qm&!U#E;vnP{R|>QnZDG$2ppRggO#=QFQ+HXz_d~y!r8C
z=S@~iubO&s^Kyrs_1V4*DJCMR5oZs|tCEjZ^#`x?=MBNci1~pxqb`(^>*DHK^ahKK
zS{$oc(*zi9z)ujg53%_l<|tO~glW<+2el^?I;8BnxVV>ic~NccPNE%;U>;Tjwj4uQ
zG(F_62)lPLU%h%ojrO3tp$~ulai$h~Kvmpf&7*B_BlFYqKWZu~H(o=HH@5D{r0(9w
zh8iJx)1E)yw7_U{Me3R&S*RxIPa&Tjwf`U*1!~AgxI~UuGfyI|=T+pE;3ed({Gw?5
z9?0DIf;n!O<znDO6WE?SAnyAfER&DVnj@6__cA#jER&^FQ9SCkDJrs^6;^*}^5$qD
zrGkKBeQ&beM+o=;2xtwp@nB%URN{#Cp9O^!S9h_L*A&i$Lsgh|<$u0rH|2?N4-f{N
z?Yo)^=T5$P^QP%Hg=3y7m`jFslK>!7Ur_AkJbU)6%8m=JzrPZRjH6jrn#e4u2SWJ&
zWxb)aJpFeGc;N61@YuW4dSay=9mCvytZX5mePKkW)<;ac34Hr9hgLkmGBwl-JC=|d
zz-bEHr<L{jD-qWu5u8;I4_ZE3LQtF69ZHmGqFl6I0xEU+phvo0h*hmC4%TC%siso&
z&6cNtrkgP}v=l9k(CHx41VbYcW!I3Q{1Go$Cm)88imsRCs57+?sooKvyA%i3VDdHD
z(U>tVn)A0fv`U8lE?vC#_fS7$j^%iP!i5h$0Z)2;*hn?MZ}z&5PMa8nqq)jB84pc-
zvl)$wit>T+pGG8aa&<#rL2Av^E<M(A@B(6b1wJrrX`Bmm^=+fpV4XxmBo?Si9mcQp
zhJ!Zx%?OJj>PeTsFtjXt!UI16RiU9r(yI3E$kWrtzqF0MH#mNI9vebT0{D#%&CDmS
zO767TllfgyQUjctY3ZmiCgAEAM669p2gG!E#(?W!4~p+4Zb$kFs?x<vo5%H%1XC~P
z{SrA6c{o^bdVz-u@tjTM<J-l>(P|<3Nd`zIR+_+>U_p2141&<PA84kLMxX-bQuiF`
zrSvbWZH=%#!>r?lb5jU!BcS)1gJSq}LK41RfH{Y0SJ|n`l6M=KLVUB0*|wc(TwUaw
z*lJZU-3XzqZItds@CrW0+WOQsaCuCE5C8s$hu9YIg%F9|@q*XrqE3CZ<$~)!NpJ#2
z7C_vtK%t%gHhcGpo=6YR%k5EEGce_*=-|USShfTOZdc{wVd=dR_j*qw;+5kn8bQbP
zAtbH@sNUtzzknicx}|?{tN_s?0QqesmQh2swY52oJ$&}obXP$Mf8W?cf4;QGE1l(E
zS3_C4YXa1Lahiq;rRFKh^TPQ29;MaE_3p=`P0NuF*%wtW-;3D`HH-n$ugO#+NV(@v
z`j7ytDwu4@z_;Jx153<9`hCaUpg5YVsCRe#0D3(cm4l0F&&b4|U8-negt~y1KcCy~
z{R;ak1gGKlH2>nX9$<tsirq0%OoL7`smTX$2*BWH*h~i7czhu12&G_4Z2cf=aO$f}
zMbGJNhXpJW66m4rvfjeks`aB*Rjc~Q;`t?(2a{i128$Od&4*6mxOS#a0atV5p1VRG
zwjt|?DKTVXt={DdJVt~LZk~<RiEQtKK$kIpiS@<SN@?V@{77xjS^MU{r;m1y#mH7>
zed&Y9mrrC6-0F9ooQlbxl}MKbpI{2wMLx>)2y?&+<~$x^4LbIRt-c}8z2LDQpnfMA
zm6LH%UL+?+rgZ_ig!gKo!0rX(SrrypgoZ%2=hFQ5W!rF@zpet%B7amTlL#vSrF(=(
z?r!8mzO+WcY(1LPBB6jx;-f_-<kLQ2J~B3!07xq4&-vrt;jb-^`%JYo0<CxAgvL^=
zcS$$#yoT2GdvE&6hpF3Wfj;a%xYkyDZ_js7i@o=w{LyH<J`@+1<1B(6<mfTtXgH5N
zhOlT`MI&V7fCoYawWchBR&HM#M(Lhu93xT>!o86ncd60kqL~UMN-Id6md@IWjA&Tu
z4`&(^(+G<jh5?$fma6{StvU>(6aPfi2Cb>t$i#0B;+bKrlWMIg)v$mUgouez=7PBt
zgMH)vhnaY<3oz#Wzh?n19|x#JUzvH<;tZ{#`-u>m^6cw@t%-P<H4QP|38w5<wu#gU
zte77$c$rg{=M)-e+d>qAGOJ$@%jRq080QNqcRy8N_tG%HYD|Kb=(3@&LqpdYC+|VQ
z(3p!fB9`L6pMd&XQ(_wlgtCms1?j+E<_7jMlmYO=X;A(@8<7**Tn3{hPiuQ7bA=o1
zJXqfDI_zQe`;F*zOch%AiICe0Uc<krZmRp*E#aIWDR=vjz{Ut;o@9Y5ok`W3cyc*!
z;z)cO4tUHVP#k9hq3l@BdrbQ*U@TbWQ9rI6?^B4180lz%Hgg9RMx&b5M8X1Wc|msv
zGuNjTCF?HQ^^>RGbLpg1P~HSX{!l8W;GKxoN_sU%a}Ub*5{K5leM}${Iq?FU9GpoE
z8J%*zC)?^8o#R}A^<x=-j)}U2R9@4Fr+`T@k5vz0=OXwzE}H8W|4^gf#1uNzna1ju
zS`+b+KlfQSc+$s5=~d(7<K*c=<NQ4&(r>+5O{4stsj6Vu>uE&~+EPQLpv&#sQ<664
zPV}sS35WX_@6~+|8xIwFqni2NWgD@NmXPg$)N`Ebu-V9xX-KrD_PM{rpMY(Cr<;H&
zpb>Fc83S4lU-Qz75n!>Mf~lJC9<<)XLH*fJDm_<l+xtfo>Yy&3m$M!>jUPA^dJo^c
zQ20He2UjF^fde7-;;^}DyaM2Wl(_Iyc-6DFQQo<vcrvOh3F`jf!o}7{zPj6PJ95jV
z$y+=5O-$Yq#crRt2;qgi5^IHS3lv;mPdu{?@<7Se{7><bY|q5zkMCG)-f(KZA)zpR
z#ZS}eP&&7=3z1w)X3SD_R<SAMZ`z&B&+6<7)<!EZS9IPth#bd}xF@ht&NpuY9-jR_
z22C@0^zJq4iQB1*s`5vZ-bU>?D7tB_N6~g>O_(hJ4Z<>A<N~1e<p}SqNNr`IXiRI0
zv<OGSLnDq`f1qRwvAy2@2%r0r_i`=HL4-Kl)>~SvNTgO<f>RC^m_2X;*ytyadpzho
zYZm#DPp1PXV!`;usWx%kR?XSeZ9YhUdV{ghmwKmVpV{f&bVdhmMMf&;5*o~HfJd*W
z03U9-g;}AyuBx0*Nc)jObOE(oTE+RhgY}$gI}LBd4p#Vptg*b`U6Ws;^?VIJ$HcP!
z*8x=q`0EgVpdfX9&)Upix+l76r7Tb$fJ^kS4By8@iGaq=+_KD%8c*nOa4!c5IaX6V
znvL~A&VUI8h2d^lqZM@^iLgiGrJG5rhRT)O3U3)k&jSRGHO1&QNWXGj61s9Ov+$q1
zs}tEuQe~&?`ah_a36LwJDCM@et}ghsbgLPv*yPGN7EJr%z4#M3-k*K|so_5|smOx#
z6<)?a{NX=QiD>?|RCvVH4p3?TqTph2a4*;TjbRHbs)X~DN@4}NoIc7)Id09_*tjnm
zP)~T$5sJ2hX{SP2iT+cjG42nCxB#L;YVt&!!p?ngmACLXU<L;=h_qt^b^st`EhCJ$
zZ0iD3%W|V=)F$ALNFdaRAbLztsDs8<@cpA7I#ME4FZ1xEH~bO|E$tW#KGw9e>;MF(
zUlvJR(|RcgMl$LpnObN;Km3_Ni)mGgQ;mnA)P4s}1dS>kJaczV)#D<;e$ZDq%^c&r
z74YRC5I`ufDX!}Mf1~OAk+l9hQHP>Zjed|?p!<58QYhIIx}}&D08Xi*kV1Ps9iE8$
z(!bDPYms@alwHIJ`j2*NB9#CrAB8MIje`FK%6B|yn8+&65qU~DQ|{s)+!weR_knW7
z@INgJ|Cu)3Tv4S%I@|#}O$$uuQ9yC3M!J!?W>!|@4k@PiYyDYJKr2OI5JC8~NLgM3
zI|6I2ro_2YTpw^eq<wvTtD(uoY3<XgdfnU!LC~R=C$i@wMGkQRQ47Tin%jgQ3ojqy
zFV2LA(1io;DJ=plcUulE{wc&6b#jBs{F(A{Jz9eCCs+8wwcjn$#$Jn+xUH+hA=lkw
zI;a#y5%=O%X)^I{ze5&k-5q2Z;mlbSff?vtvM9Ml(%x-~s0QyHQj!cIw#EFu0d0_7
zm#+1ejC-yOn<Xi8>r4Ds4ApiUL8;=jtKn-+x%-sy1<dCpyiL%c*_G623}(S@g)-9h
zy{&xRO*ZB?FB6`4;*)=O-FjR#rv|9x!>vG<0kO7owXhy)?y{3t(EeEgjUlvo&HOBV
zy!D+>n*?Nzu~r`4SC-~oyz~k)z<Sz9lY^xG@8ujZR<Qt~yC>9ULOy+#nb=(j&bO(o
z{t=dJrs<OXS6C_yI&^xws~&gg?kwMQn*50VB_Q<zj=diAADPq(nVb~44}v-zC)*U)
z?#<V8dT8u^>M=_xebuzcP_Ob}_ZRK{mO_#;A0Tm_2fF2dBuR^98R4HXw#0VrOzByi
zWYyN1f$95rsr8H-6KuRb6|YtdE<LQaMdVxxr^sx8vW!qdnd0|C0VFQ5)8XpkPgvu!
zTP|wF$g1<)qixx5SP`&@%iM?Cwpe|N0Y=K5KRUtp$2?ZllarpKe8@N8xz_g_PQ=Fk
z#*DDT^=Uv+q%8ZpJs@Tk{vLchc*A1!TXdNDcg<&R4I94;Ar`(%LMVRYmx@T_07#kt
zqW?%zB$93*!Hc8RIGY7~)&ltW#Zvhs)JfPi$p#k&4jkR&I_|Os>m5A)GH=&|-2L4P
z#5J9sd6?8f_&L<wFt{)rZvOs9AQPtjJ1|2TARhZF1vxpl%8W>DLnK$hoDpL690WuT
zuEIMRSP6~<7Dzm^@MUq+7I>@FD}<Vha_^-4Rm3uw+a-(w70(&<#d2q<ZI}4c9lMRR
z7-$#D-YK^8k6NxnXg^;D^y419kRGT_O6*UaAJ~FSHQC6<2hO|Uv&-ya%uBG9MjT#K
zQi#qNX_XdG6{8SwHxoDqt#+q0V4iOHQhk$A4j%WFG0ul$*S>4Ar{e?pf`sadJ5e16
z>4~IO?Fs(iM~C=)+uePFcG3(ea7lF`eXnKYNadXovOD4rI^*Iz?!Dls0U~u|AM&8O
zUOg4|!_b`hCSG%ebe>PFILLmwm5?OL1vk0`gdosua*lt|SFcoyqRl_O-icNpcQS_B
z$yc{0%OYc6Yh@2yI0EN{{}08i#-=^tw$1f*RW_RQztY0U*;ECxNxgB?5&^}Xe)5Q%
zn}0k51MY-elZ{gcoae*=HeGw|RuLdlE`I1iUyO9y$D3sq2D<K{4EM55<j4RKwgB`N
z3!aE^NclhqMtlQmog=V)D0Rd(X7*5vlvFD}g=`gk|A$)VexTOn<=x^#gXKTaewWGs
znr!Ei;Q5CZ^S_H*lW*N}sIEK{PgM`uxqFJKh1^IW4pCyya`25Of4(DOqG77mcYgDL
z0rK+kxxB_=Z>sn&seYZ9$gIqGj460Z=~e%LVb%rMw&4R6&cPk1-vi&jr@Mgu3X7XQ
z_sA~5&dO^1Fy+dVcn^B>fjD*O;Ko{0q@r>F)be^1P4NsIVEi2LMx7%@!=S#Ifa%Qn
zzb%n3ki>x{qS{l}3jzXR0rXkQZd1U|pPU0fMl|0Z{SQ6`rI4Oe|2GrjzXmICxBi8j
zDECas!7(YO9;1E%KwCs8U?XF`mKm$8gIogIX5ykMd(uCmm2bG~0<xv;N9ym5J8M~V
zW<xAMjx?#ffSik!rvE>6qSynGzvk?r2cJcwtU=8x81wb__mFGd!j9Ne77gL8f)kD`
zusl#LNqYtd8w=zyM$N|*c-cbKMTj|BW}^@8{=eQnl9r-rfeDyLcJI^^JL(-M8s(L|
zYZWFNCQvX)r>~S&nS43Vz_>p(Zd!E&7`!<ETOsp4Qs6A2Toc6|rJF%Pa|Wn+6W`-#
z+5<x<Vk#0a^|UVo6j)?(ha%V7{+-}P9REu295UtU+1U8Vc3EJcs8AGlF*#6g{?Al?
znr4_sP%QeO=)sXvghZu{d~>=x+>M;R2nK?`9qjQR905(r&emxZ<Rn7@3MR3LN0;&U
zdtMUmm?4ubv#fXP7eQr4RxLkw0-60`8&`YgO%4&-;;Y{zKD;e<oz5#XEOZB-ZxIEY
zWd{LD`~lw&_(^>B@;i-9ZIspcRqJlWp|u9xb(BfXcg3d`sjt1Mlejwh*L#auzGFKT
zjE&^zY8z54tJ_(98D3z2{$C5&iT&S%c5gnXN@lIt?vi3^M19ws9*Q1p@_oL@R7Lw=
zv~5bs045vo9=~8m{>EjyJ=5}|<|1#X)r6=#x0Y3$Td+doR;ahC(_I6VYZDbGWeH9e
zGHaW@PAWu?j;{HUa>og>#SJ|9q1Z>i#%r>zcfnB5Fpp33ANib;{XqS8qfnQ(Z{NC7
zdp0ccC#GWReIKdau@0m?1#S5lQN^-9j~Mn|uMdkv6iE!3(0(Ev3&1^-TMHeT@|8$U
zTo6rTwNUG+Yx|Rss{oOhbaMy`YH>X>j)LD(9o}hW|Mp;zv%BPYb6e2O{=p<M;E4<a
zK7hd#q#w@Yvx_fa-~?v+3)hCtt!?aTrJbDVZi~;Gwk*_)F34Gyg9iCO^6XM$_)q!y
z^U%Q}wpH#!tR<JiwvrHS<zft9XnEcfbm<KEHO(zo`+!zqfkJVP*7)G>Z*FcD{VVeO
z`}?y1^1pym>;9u405Dl!`s&p$OR(&(U7A*2yup_HrF`L(N(`%X>-Uc>sQF#^DN1HS
z@c?Cp1C)7+!Z1wk9PlDajf>KCrl4xs)<+0$DbV^nIs4Ax8KD8zem$+eL95jS(v>Ie
z5(%@y3)0^<*`HDDJ)}w1Ka3~K-Eil0XVCUyrf?5av}YqE!+xXF8mD6<q|Kn~aSw2;
zGy#VZ`vK%nLT96sdN_ac467A<#+%*&+xkbX2do^8(bLbnmJj{SR7NR;QTv0T<Py!3
z8-F#DMy1&^^5Ajdy7JcobAosFY~F@Vv4ff=&YAnpj8=%xk6w<*CC#FLp~Jzu5%ZlI
z(5Ljnm(QIq4G^Tp27O(tKb~I)5Pz)y{5Pit2%zqkSD&$S^K}*UjfdM4TAVY(h0QqC
zIP}H-4I2w-%u+bt+%#q_OEvq!NhqRmz}mOJEVjp0B2)~4C<k^kk`@TmHIU=$xkXTa
z%_;TQTrBB1U&NFWwpKW?V+<6wr1+Xn1-0dSj(e#L4T8;O<a2>q$6x;vk>7jyec098
zs`@+%k7>^Ule2*Kf`$dkNrSc9p7P6or?<t`c`MT?TM_gMyUr7loc*grx}DJQZn_yx
zO~JVIOpCa>$r@lCJ$sHFh|&$=&`SE#%AzUd(%XWKCJCr}Hz^xEzqB-QCuW5*x4<Q4
zxTeum#@B4PYIDo*AHw|^N*TO|!`(i}y2?sl*zZz`%}=poe#S6H9Iz(}Y5F4!P?!`Y
z7&QMUA}wfFx3S87Ug<i3AX&ftXM+aw3I%f);Dr^GK63#1Xi6zHfC>!!7@@cwD~5v(
z+>3)H2O|rpO8F>+MAQX`RB1<G=M%Mz-r^`vH`$Pe2fo5$?>zqB*`VPN1wvB>hoRli
zek_R39l+(Iz-k9Z0HFTd&wscbjl-Ii(vG<V(%%OBfR@oYR!t{R_&-w$ay2+a!X6wH
zbmt#~N}~t<*$;~62Uh|kU)NDi2hJ|i;hD5!*|cIbrOO;UfKt)+WkU)7!WsbmhT9D>
zXZQWd=?ps_clp+OS4x|0O^K6YqPFjX{keZiSN>ao14n79VDA7cM0^HHgW!MTd0sDf
z=m4f*zKR{s5;5eTEhT(fQ7)Lc#60-MqI!^WSA+lWiVx-xz+=GuW1OFxGx!7fD9GzT
zv0~HwyTTZ82*l)IC}RR>X%WWBY|OISUzpD<lpy_ol&IL?!yo=-M%x}RN(xk>q0IT_
zl>6>g6BHR<+~aS)bhYrRFkiZ8eZ$#?Ey=113dskf0!lr;;|%cB66`n_>Iy1uXvko+
zds+y_tR>LH*q~sro51+Y@qPxZp9s5vIP6lpgN#CKe<+`OLx1Y(_qUyFKNuk_IKze!
zLrCK+o;S|7N|=OIsfD!el_`t#I!ylC$iTtW$_TJ-ea)zcUSPwhO3NA>LoI;!`E=3J
zK_5&7MC*nx9;4uvGsu?{Ug;y!V}&OBHNUiBjS&+cwku-}b!UlX5EU%F=D$m;Nc5h_
z#sWGjq-B(M5YncbpM1cZQK8R3yBClXClNwWDGKEQ9PMcFHep8OOx3YNyI@ofe*j#8
zq-*i;*mA}2f}4G158!5_sNShDg`KB`q*oyxMR8Zo9R>Oq7(+PE=IO`<Ps%9ItZD#Z
zy(7gWN$p8USMpKMFvu5%z{D#7a`oarb$Igl>*Iwp|6U$=z@an-i3XC3@b82hnT_~w
za<i;WGLA=EYxE~pd=0v`(MD<_4>mEsh2jdwcQN;<>eC|+%w4zdpDN~SeYag6Wu+DD
zCjfy?8tTXHj86b10enOzz<-<xYFyJ`BY7-#-qaaHw3iA0zLu0UNrpzElWx}sbXi>Y
zN*PY%darf$jVU;0XtKOJ5tvrB<=tDh6rtjVKLP6Wt@a;E83t)_adADc>yxCt%aE=}
zqL5ktJj$0B?Ftstsb@{%wBJzKD=dNP$vE)Hg(=fiIr;f`J$NBk!efwVg4G%E7)<Lk
zY9(dfT636>9y}-JOjzy4%xB=KFVT4Zg7gj0y`x#3>C4+SEBYD|x8wnXTe;c(qXtZt
zsz8klw)E&EirJcjeFMFMy<=@wMKvfU=&JUQj0E|3O~;*uD_Ns#ic3l~G!8v@t>wS~
z6<}+NgDVLnZrIgM>B5iy4PsutO3c^9>yi%L{Z{hMs;-1or|da3c7?{f-^b+_*?nm>
zHvZ`eo>-NCo6(R2Bm#^zJe1(63fh<}gw$Cl!ARc(GX!S(2=r3y%>s>ttfSGN_86ME
z;O0i_Z$sV{TMT~vDm8OC9?aw?&EBoWu^!G5#lobhn>i8Q$I&fXGY;???q+;q#|*wz
zUQVCpKY?}ovL@{5Btxh@?qxhiU7o8=uq3m=ZwZdQF4y%dPLzT?&lQ7lwH&NOmt)1o
zL8TnY{<PK@LYkn<=54MfjS)8O!I6z>NE0Y&q3{~UW{c>fZ%YDH_uL`Lo^+)(0{xvt
zK$m>2h=v;*8}#60LM`akC3M%-g2m9Csy*Of4{kPaA`297UTC)jYkj@QCwm1<mkBn5
zffN09m+eRqcKa@anNY#O_)mMgC7Crk$29oPC-2uRr3sPA*ZG6%|B~|KLMM#*{f+^F
zzUx`0S2$-6EddTXVt1h1GAp}*Gle%$THusUpEphOFjS8C=HL575Hd)RY1EcUO)tUj
zOAoDp3%g7?<K+Vt(|{pa;Pq-GT@EP)hn|wbiJV}Hue;Vr9xS%1lLQm;H;~=t4M7{<
z&y>`!+Lm=cZWZcT%Pgf>GSAb}{9#-D*O&Twf4@GzRQ2d$Y&xx2IRhe*v?}oF@Dun3
z=BQU3_44B%G&w8cVDygC;}gz4j8lC*X9@=3x1q_}rPELx<k>_ZHJmYu#!66KT@U^N
z=BfcRgF`wj-zh@<kpv-V$Tdc(pGXb^EYp`m%Zchxj_4gQx8Hy#C*R)`uCaOc0NBZy
z(rSl60^V`kS@?n|=V`XBxKc}_xE~SSvqQM{vs{bjJs9Z{#qoi;)&(Zz)q0>sGlR|I
zL(W(f&l}J_fgn@^bAT_$mJ76(h1o5=ew+_*eEYKNY1mNy8d3-lTXM;jz&8N;j%k0G
z+wI$dzL$-_@gFdtF%NwNrzS#`!5H<7HOfjLV(KOow%}GriF0}uenrmaX>b%svB~5+
z5E5An=JwaEZ0~%E;%uWo?^%zj2X(?`N7Y7%=KTfGe0$ccD}G-_By2$tWd!&=0Oe*S
zo#4<Bqt?sJzrA|Jb$d(y6?4`sVn*=9od>^NzS^Gtn3~{GYxcswz)K>n{3ovS$Npv<
zIi8SdT*23pCpI4MuQ$56U*V5m@k06TH*4U0PF+eqkta@j;|9aoryy+3pA{_-wfLmt
zcK-8o5r2_mn~&Aqv)MH7(Quobm>tu*a-HGwJtP|&2Q6GdQwVNe2U%S!IBVscD3Ti7
z)TGhPlxmQ8N#x6nqf8btI`Eb^L5X<J=`We>iHci-e1}UOx|PP>bfYHUbu=mwv(10D
zd8^Np_8JlaVZGDC7~A(9=h!aot8z9sYdpE|WK9Unhrec>Tq|XKM5bv5Sn5_+iZnPG
zg0-ffL_mgMfZv6Y)4|+)I27up@l-+kN4DFL#Lgfpj1B!~&$1<GKxk|x4dl_d{mTQU
zdKQs9*x#Q4gNo<lMjJzd`J4B!I}c$I?;c~)Pa;l%gAVq6tdI~_!nD@RWY-6*@_A$&
z7)Ks&_!v-+;n#5NN+~az*2(K$%<z44+tuEAwBXGfwfd^6&6MxfzqCloLv*7&C*AIu
zAb**fnl{=OjJ}(WG9DQB>%VQov$pe4Lo9gOA9rT{y1#DQ)F`o@jFs(fm`=-YG<ihm
z&mMWVpLB8XSiFYmK&6=^=~Gm$-&~*|Wx<#X`9OqLT<e(h`n0Qj;f{TN6Z^wsTkkOG
z;dak=6h9+j^{LZS)LX1*dRRK^jFQqV2X-inAKAc)AO;UGr)ielA2*u4IrP#`^ys=^
zu$x#!)p2)^_Ki9e5NetV-@M8Aso32ChG56)1J^Dd_3sNkJn28(7T@&d$=U;yZv%#3
zFupArrg@I{3u^ZeBq}M1yG43*?2t7#ykJhed4eDF=_VMm4~Ff2_8nKS#W!m~mdR}c
z7HOyrC+yBbj#jh`LDc<f|3ka5!#ELyg~`TY`>suE>v?vl)V?ilsc5=~>Uu6#g;|y(
z0ad8%YL6Mw4QbXl>Q_m4?qVKu5Hh`A>CoJ$jobv?88S3s{mw<#<}pFTZnwHL@Kej{
z8%=$4+%IF{yK7s9IzM?ec_Fghrd$PWRNTHr6OO3;W8RjprH@kl++tF}uul`L-m<7f
z_@%txwu*GBo`m7aujk(G4@`jU|MR^7Oy~w!`tzVJdMWzAr79^Z0fua)jg3Ys%O;tP
z6spPG(%d)=Slz9aCFLWURL$z5dJ8Q%qJ$5~++L4@xk`k$fvRBBnYHx!+7jxa;MJLq
zpP+M{AE*KHOC45y2%NIoEF}P@tsJ~mgV%xh^RS@l?fb3#_|!?y)7gO?0W*0EdsmF5
zG1|HL+l@^MXdF0SBS$tpmh&L;(@kR_FYClg^MUgv#})##D^Sr+dq~dY8PRepxfF#4
z_n!}2xHw`Jd!5%gaKuwb?FH9VOD5;XpHdC_Dx6B}5Pl|ZF7qRJT>FV6q-%IoO{lrH
zXzeHDNTtZ#OuE+ANI%)xa>Q!t9&PgmWUUh?m%X!md_AVCfww`jwMJ*ImeE#+pSrn3
z>RslND>GC~RZ6w#lsJ22$f#SOU5#JkE_qWmzkgt$(FXY|s(lJF4bJ3_)y~agd0^`T
zX6J-~vu2Y7Royz(ecA#XP;7M+)MGZ>cbhwZ5*!j<W4)LIzuB9k<pLYy^5f659eMCG
z%O)vh<q;J#y6dyzPwD_RI-4}>iq-8WA9CRc!FUZ0Kc;i5@0go-2gBP>$X2;b|5cK?
zJs_p9e+oK7QXHg7lM012ndI-!yFE27+~!{Ry3lw6+7)aj*#$4e(=3lkeU$sLk*2C}
z?i_O9jm2OQmfMj(lnyf4kOlh)1X_JC47ME>a-yi^4ZEhCYsrqmXf%&K_Te!!W?6_C
z4mWysyYfsZ!({$zc3DTNltQV+S6zWiXh!QX9zR#)xeo=Ow+DBF1kvFt*=?}<hy9SA
zd;P4cjLtrftJ*C1eub;))$+;T--BG~N2j1n7P*`sUt!>w;5Q5QFebfB7?Z7U6PU7{
zH`%y=3Snxl|853q+62`kExhI~n93Hu)!6C?C0vngxSQNRW8&NMz3=QW9*7z3&u`S-
z-2Oho$*TX_YEzNj3GrE;Nbeu7MtDwd0VAKz7HaNR`ecOO4Je8U+w~b7uk*Mr?bvR~
zR#xJ+xk<b1Hbhg#?$<wyg1A~XYk!rWTJW3+qZ)##+;C!ExX?!EF!Ep>%3OA`#owea
z^@%1>FW+8JZSdn-I5a#GBj1<nh{GA)dzhs3&AI9Opo_6v)w(fp!K5Y&rl`EJTmQYL
z<98(4s$qcc7gYei-&0NQg{uTU+rd|Jo!5t^cEb$%jfaS9f{+j)x7*9S1aA+8GLux;
zq3g`YSSFkAhNr9bBwGjF_H}Z?Ux30=WNv-#04a89(6{Fj{RGIb_6M|#ao`;5_OU|}
zFi*4MdFI4qh<|KiLj7>_K#@-(I0%=_eCr`XM!vFE9<X~L<eRbn+_)X^B}=0Tz=)BR
zkgzm-;8^EVQ82~zOexN|#&_N{Z1!8;@V?=x96M8EV`G$&5q!ARu4S`pF-24ZWE&ei
z^$^rZ;2t+c*;mIIYA&aSMhTcbiy2d?o?2oJAEPEGd>{0MtbhO9y#y^qSlKd_UNSq<
zYy!za640_1q_LE@<W<^X{%dzyVu-f7JKU;ZL2T%yY~NmKhuyC+oIq*x?Jt+qo_!EW
zXRxJmy_sy9xf1fCiZS!~ifRb;O<J_aOEBwn)Pn4|jaaZ;OS4;R?HX!xyw~Jpss@`p
zUa96C@z4(cI*hBi43c-q&^T0;yo-skw<;yz^RLC9EWJpS-LJx_aCVk|-zaJ8RyfN7
zO;T+!q1mB&(2D2d1XL!Nvo9c@?~D)vK&|q?&Z!|(zQi-cVs?)L2U@6IJs&%()v)(h
zh`WEmoi<STGcDgb_a^G4N8{dkO?R(`guTEDa`=fO^9+!$NR3UU<>jjjrS@Pf*(<%K
zksz5aW%nVb)c&;NO29qy9S<G&(ZhX$^b5NYBn`UvO4Q<^W7mymxz1%b>Qi~1L|-w+
zg)!F^SB0EF2iqkU3_`oyXz9|9SubesP+ihcJle{7?K~ui)_*hz+v0b<c^x)64hcr+
ze=LSw%|a1_hCT*zx~`fgU{yHP8nV}VE3OX8LXI!zV19hXM+@742qR24TXD3fZ<MC`
zgS!*$(BNp=#oh|t(Km&)vR{4H>^OsN_DqBZFuDHN4Terp4Kex^kbivNehA%@eQm(G
z5FA3|8oze3RDn<In%mfOF*EAPx{!|m+xXk!@P;2mzQ`K?l;dpkM~6nZ@!5YiHuPcf
zk#AKj)mZ)tESW4S-KcgmiTdRaYTw!b3}St36-!9!NIb<ommH%FgU=pGV1ciYB=wQu
z4lyfQFNF_LZF<6Ep;n$y?PrScL>rl+8^33^rH2H2*Qb4l#(pvnq4KI*Sp1jVRyqjH
zA~t+>!CYaHnKYzJ(5V&)T{=jkxqG|2zw}bS{1B3mU)s=y7WfdN?0PNP-6=k%*2%$g
z+W)9q@qMdVWwj>73|0RR5>cCubHN%L<jc@vLw)hM7qq~(*^}QG#<eLzWsB>~%FoP-
zp77~lyk?U{zs#|)JDUW_=f(Nwp4yzKQV!+uOW6>zwIM{89^S|@#v68sHL*1xFKsF6
zq4|o*>}@*(vrJK@8?qbMu{E=Yb_F&gI!d6TaI)1716oC9L{90k8|Y@q0&VxPnJn3Z
z{hX4GTR%R76Rd|JNqp^qr+IzAiZ?jWPe1AM^XJcaFoG<*Ky}lGPRan)^wmsK84hL|
z{ayC+FwTnS<mBEUdouhG4CbJ7=k8xS_x$uZoKlrezh#&Q3T8HyIT8lvXG#0*SUgqA
zjqbbF)5NIKBxi97y&oY^Su5#!Vb^EqM36hS%kXQk;0*OUCV6;q#A1iOLz0qUmD9O3
zN0TRaoah&r(HGvqntz(NiNW+*x)lypy%)(!%@uZsc^Y9CR!UoX`e@YD)*q6CcSeV{
z$&f37Kn<xMN*i;!+>_VHy{V1#Xk;0Uym6Q>*u&!4_@fCTEKIatTM3N?^)Q-SGW2Yv
z=FlfliK0O<R5>}yZF6tsyMxRy1L@b<FTQVHyu=DX@*Dl^Q{W4Kpkj2!lg2}v+z#LX
zW^GN)_TI!C7{vhnEbQE1j()>szm)p<nJEUSx2Wv`v`;<iV#z#trSy{Zudx}{oP7Mb
zLHWEetsef-b&jvZ51Xb`@`dxAjHPNRvzy<(W&Fhr+tL`C`bp60xi>m^_1(v#r7fxP
z9YI|e41W}rsx=enm*b0S!yZ}LGn8KJl+B>ARd!ts#vijM1q-$Hk+S12_S9M>+CGk?
zt_fqY=uMmnD~*>KE|ER!6Y7g<vOq=Zo<*NB*&u--g+YYxsb=4CC&2#g!1p7qwymzN
zX07|bdGR9<tj|3S9r%VSVH7NgLXk~V=ze#1_abGfWOV{5T03GcxgIw9P~mE*j&EBS
z11Ix>=WOF<Af4+2f0YH#aYDA!^_9@0w@e+(K9^JZS>LTSa2&UE%b)N5*d0mYw!>=)
z(M=Y7pT09>H%k1#A{55tH&uS&9d(_==1-7oHq2?d#f1F5%F+gc4SMkfeNfPSp@@*P
z+jH7DlBd(AcD_fDLt{Rf%#=*LcB=1}dYsc96M`(#lZk%=1TZyh&>KuwctbEXL_lX4
ze;peh-d+q)ofsyfJ|ysFexa>DusS0i{4hGSYA&@@^2R28aCB^7s+_~m>vf+U`>g1~
zliw>xZ~3iXlQdTDxr8^UGv+Q;`d(-E7@5U5+A`nFa;fpeMu4DSCpKAXeF?T4JzjvL
zs{1m493pOTm#Tf3<s70{aZtgf(N4=`5|g7hH+|aAW&85)=x4&f$vj`^4!%2sZ``G%
z;T3QVbpRF9E*5w5dX4SP1Bba>Arf{j1bmlGQt9+@v0G)=U26#>C+yq~5+Qx0>f#Bp
z+r{(UsQwOwcN0F4ys>^&C@S!^nEmp!rqRPtF2T^W&#a7my=c|LX){Wt7oTOdbTc9d
z_cK4e3rd{0c6yvI&2YS-eD2jEI>WkF)R&xSm-w^KE^$rRHt}0->00`9f-UpH?vMDE
zZm&Jsg_~BWQpShl3VL1+`~2Rs^Y|yp=^bmglC9+ZNSliq90|{Wl#>k3Xb3gG9{y=L
zXhmcD03R4~)?6K1ct;1!rOGTQI=7qTsek?af#p%b_rg1=7`=0?kA_{&BTr**oJ0Ut
zCQ4{Jw{a4D+07LVe=Gl&C$*I-EdzHd@HYR8wzm$8vTfJJA3{KpQt47ckPwiTREClg
zVL(7il#p&|6e%SHq@<K)kPxJ6041bBngMj^8l`jC_wau2TJN{kKK5_@_Hpd_Ltz+(
zdG6=FuQ;#sIxoLd_rNEP^j~-fGG;vHZBE~Ww6qULcb~kf%4xK`x#=!)IK62jz2<B7
z!HV0(1JxMfplqhRYB{38Do5fGV2->#Bc~?Q)^hW%9zlvc>-oB0J1Rr?Dbg7iCJByB
z3((1hQM40<rEh%WTd_?3X<2J^I;O<JsmJz`7&@S9)4&wSNO&j1N1`uHYV<{eg3Htf
z;a!mjVI4A^PX<AC#)KN@l#9#DUEOabZ-e+}<Gd88ZNTOlpWIsJj8l#3<}t4__Bw=T
zx{$f-g-tuW{Ihrm7Rp;Jt1jR7eayCBIA_adyW6!vQ2FFwY5BgYz`Y&mH4mke;S)E+
zNp~$*bMLAjJyOc2U<j`+)7^04cyqh|S4@AiD}|lFmc$smOD^X2^yu65ZOr%kZPQWd
z{GWOiXP8@+v$(u^D@q?WdDwga+iA5esSiZIw*f+>ub4H^XO*ZCKTho7i)HoTqRfnG
z*{UZyNo33X**`c~Mj*p$cS*ZrpI#M$s2ZrNS6XaY_PsuM-w2F@k_l5Hu$|Vk6(ZeU
zd{bAo$!A+j-@MY$WUix^QGLX<hHXl0y>8M(^j1b@iF>Rl4yrRAZhFv1jf^()+)gIA
zN+flBdq^D;&rO#jo(=!8m1pi_*@gFrSZYf0=sL`M<G085+QG%`wPk54k0h4`HhrIF
z4{Lm+aDno*^}zKT(aptnCS-M7TQi5;8Bu<h>>T|~NrUEBmB#!kqXSR8e9!ML##PeV
z2}nZ6#K_YPO^Z5?zDCRp51VNyb9uU$F+6Xvx>v2>HjcjmdMOQyrB4hXhYs^&g1YM&
zX!E!s^=N4S&d6t5^H7#1S`YKjTCKb49xYJ_ck@u~rttLm>>-C&>&`M%@|W#?c~jWM
z?z4<#mtF8k+LxpkYCM<nHh>sOU$E~Ov`($Xg<L+#Ya*!Yd0^=MHS~o>lqunFg|V-v
zX4~6`CXp>%eSsSHEK0>M49@IvG9S^6nJ;yjna*U@(YN|R2}p9+ri;6al~+986twF6
zStqWeGaz&6)5472?A(gwRGYX7Ty`hIaAbHG4oK^T2ya!6OXE*J(oiis*x5}b_@$m1
zIBPofrNv|3($4j0v&DAH3B1`XNE+ILe8qM;wz<aAm-Wk|xXk0GGLYmbS$6Q;o-T^E
zJZc7-)H$MtFfGMKzdf3F7fce}j|YB;M&k+c@9gus^zHCxH#J{xSyt?ESzkY<T-$vJ
ztSrWlaxK%ME=8I^o_USCLZEd6o!XGN#an9h?!?)XamD@7^V`>CcYf$h=4qxe5d6mR
zYjaWd5&3jLIUs#jqL&4z9VM%9T&TGtL$$+B^G4&rFdDK82SQJw%oN%MZ@xV)hr4UF
zJEuRhv-6c%d!GN~gaKL0v`8?<(nbv(8ns&T29}%qeAEhES9CXvn>TGGFW*oj4m<}H
zhiar!oVy=`I5OJqZtuy3iXqBS=1M=hHNmw=$RrT8T2pmHEe(q5%iq#dV&pZen#1=%
zwbB=?_NvKXY^`_9j;Y1Guw*WdRQ1f2`ujSim?4^Nj&h2;$6Xc1KhR6zaz1K>5K`Pc
zv^U_AM@+@Dv5b?92csB!f2`cC;_*@0zSCart&G${d#$wF?ibgj1A~Cr2hbbI#fsqx
zsIonN_Ust~GCSV?V=Rtx8w#7;dQYSYBi9+e!M1q`y50x7_F(ut!aE}DOS8YzCG9ZZ
znG8<0Az*G2S~uWoK~CdO6C+V&PrCvuBXUgm75eTCE6hu-NjFKb3RsL@`$_Qm0#d_k
zM72+}clY|*y-~jdiog-+$rUY0J2r~31N4cFGNxguODFn~iJFXy0jbn?vkxO5Pxqeq
zSth5RTC>N5WvE{-RGcdWif=%oOc}nTM|EJ@w>9B9%rEq`Er|g70C?K$9<HZ?7ZQaQ
zRN+MzUIv1u(BN*m^5(r0<(F4yuUyNdl80x|#3b_a@a$LhIy_%3s@lEPmOV-vdq3zJ
zL%pjRE#yOu4Eh;zOZxDn-FrDFQP0iY-R}{+FKFFTAc#5BsR`i?3d2pUy}a+^-&u=z
zmm$QSr;AIiJ;d$x{OaoBI?zzc^7%>Ur42s$Z?kq|*TzDdZ>o#YAoa}`ouj=^$Z1u{
z?qI0{P5B>|n)Ru0Y>r<g0sP$>wp*j_s9}V+700EYr4DD&P3Ax$4Y-kC0toh<RX$&=
zt6wnWGpXO#7#m)y2Y)&Y;eP@7GQu3yq&=LHr=GlNrl6outTA-PsRy6s_#E<cX;ut|
z*y;S5^jfT7e((hvzK4Q3@Wqc-6(UH@O<5C|rj4zwPbH{0I)Ia)ra;X--~=#5878EJ
zMYl`eR^=<O>u<Okd)}oa6vq1ka4g!EsL=c$!=s~pD;pcdK<A7XRJ#rh(-OjzN*wX4
zFLB<sAHDcxR*}~DOY~}OMzm5^fHRX7eV}9W&H2U-I#)o3Ea&0j$<1Gxho*lXVFef&
z7>IoRGGp$-qQu*}wTJy$qk{AA_a1GuqFbZPufm?h3Ry;>CAeNOgX%UQY*Y$aY63GO
z2UeV0gfm%!;A9&I|0Ds8re5K;Zv9~`h+AhELOQ~-bAaq(I?(#5s#|ufUCu3nu%IVj
z2l55Tuf6HqbWo)ASE05~JAO@<JtX}ywaqtBo>FKNx`4)sntK=ppR%>%Le{a1-yE;(
zZ>*E$El#xG5@8$M00lvd?zXeUmu_g`$bXQoTn2ahLyUUoY5<|_9&F<Q9C~<@t?G7^
ztF48FMKM_^UW(fnjdbY)xw91p=7jU}583!ASL`A-Jcf2IfLh|l>JJvm?#r{~i^hJZ
z@l~eoZSEfllUhL<K7wD(za|mZ0Tdl5f|&`Zv>y_c^=<Lrp_~G35$Q;%Zb{sJ!p#z7
znWJxE4+&w}E$*mRs1CfOYN{oL2qwbT-Eic<wfXW`z(XTdnEnOeB7^JlJ%Z=WNI&`<
zyk1im%~vupVNlp#jwi_^FBzCf?NWj{67LBso_kLW)1|Das34}Z;L(|J$*{^~ixKw1
znRlWO*Js`&JouG5XsoS0Km>utz%+AW1=za;21FXKvww<wldDN<Yf+WB?_<A@=nIp!
zYUDC>Mp77^*^W<5Ml-zqdrC~MorQ0#{Jjrf&Ig?5Rbpdk02(i1pn6mU^^cDmlx-=8
zzt)&8v>ye4<PaSiv4RKbg{!<-^g891w^LMr2mSDMWH`6bmtksbY`hMX1bepXH$bpD
z!F-{Bale-3fsOj6UBrhI2IEnF&}{4iTA8tfIclMEr{%T|GIjz@QU@e~K`vJ&wXRS!
zO!#&j<&E;Ad1hcYL`6m6;DTa6Td18tb8qZ5S9GMf2ebr%I=w<vNEi`fbWV~X(q9)|
zg7@12vW3kN1JpxXgh56J9zcGTZF4v6P%U#{DG~i=pCaUjW#&r;afc~`MvN8!#_{Tz
zJfvYkxjfYi&3g-QPNZ}21uH?=6A)c*@$&LcLeCYPd!Bf1GIXX%<iX!8^+2+>uB=~*
zB#J_J@Aw5wygGl`pD0i~AuK<8tt?&_{80m(O5`1gJdhrNYmZITtvD}F8@LLaV}j%e
zXA4(wMY8e`QI|}7s+gNp%al~O)8o-T#c0$Had#EQ-f=h2q-U~-5MX1zLwQk^VsxdB
zf{qs%?4>KY9KZiDluU6Fmi0m?Bm*$H$~>06KwqR0+a)}wKR|u39^5kL7?rOCzm1_x
zM24+{1J(wyZw!cItBP}zjhH?Hr**+LO<hXB;9~H&2vhjD^JeRIZ`0um4d^;~b?jsN
zpKRYvf%UlujZzHn99Ok5@gu8!S5!EX$Y#HNeElLn3COZYwM4xL<K~mvkp@}uH=q}4
z0-gSrE-9#}7&Zk&6(b<@qE^a_aC4ZhKcG!^e|fc9cxOBR=Xt;kBU1k}S^v5an|uFb
z1j!x2-;C#N5C@|`WiV6QDsQf)u+TQ+$7j*x_C(OoL5?JcPCHGQ9#2&}m*k~yMw7B-
zQYo$He>4mHX8I`d&mQ%y^wRTlk@c(j_0s`2fR$!gFIxnSFc7?FI`1K#9e!z-n7Yq;
zg}x_3bDG!lGL^PDYTD^==t6@Fq*$dem)g#td5=aqpHjm}YKI<T+{(|)osu@pe~$AW
z&$r+Iey!pdAWimV@nwEK;O?c7nVG^HKoPh864)|ztGk80)f>3e%h!*A9*nES&|Y)t
zwbxJI4QOR~BnPA>-DdXsbcc69(pHRWNEeY#@Z}fUYKMwhRJ?+Z?N-hSMCW_W!pvQa
zJ&O^VG%-gqeMVH^onfsuiNlrQByhG{+OSQdoFr?)x)a2ZqaJ_CwU@ycj4#aMLC)}#
zZXRaOA1dmpc=GTr*pp1O1YE(rr`Y2W#jL<d{ADwEnbboGUEEInJE5WgiAd(pk<t6B
zSy=;1`oa|X&!_{1#@i<+CI~q-VQoOo&jM7d%Zq`2sQZ}Ez-%}!sVS2tc_|LufHgO2
z`giya<j>^6bg;Cx_Q*csk8Xueez8{vZGz{`I*WxH)XamKnWw`>^yjb%+d*E!tNO*P
z2#;_j0;-zmjF>tBhDa7Rw##_PJwFdiD(djGOEaK@yfsp&k7D62;_x9TX{ah(*88Kf
z{sx6B+u*Ev=JBUzvfHoZUzVSHP7D#hozL{{`!O-U7cSo#6&<D|_n&o`K^l;Fy_?jO
z>5EZ$0(f6R6Iw%b<u2BDar=U0XvGA}9w@irlIgGy-j*#h#y)S(3sgr|@qGES&*BAe
z)0H?J1Qg%pfI1{0Xwn91C!H|1n7#B;n1~y=og8o(awIOZeLnVwGQJjQBN@T>+?W;%
z+S-&hUTDHNgGsMYKBbCj)!m_^N~SymXW>9<KnNly#pQq?M$198gAhjJ;@O>OOSj78
zYfIvSSMZR$Br|WntGf~*#OON{z(MlT9(;>y4dZ7M(AU*<;`C<YkyK9)F9XD4E8OY(
z1R_NZD4j%#oUIHH5SqYq+=TeEAAW4@K${I8rN}(KVw509GV@0#l9YjQ&pf-J?}-Ew
z7vvcO-MuR3AqfT~BQ!E+P-X2j_E%4p_Dmc8!x-;W*|5VtyyxfSd+rm7LII(X2bp*Y
zO&5?u&@<wJ5n3Jh)0wL)G-MzGJ$vM=mqU%WI%+c@r*+D<TaTT`#IzG|cNH%}xQ%a5
z+VQHwqp*DWN26&#??)Y42WKG|2Vl74j37`=;RRUJYY-Lmn71qFayXn5BtkZ|l4nya
z5~!Ft)%?uEo5K6>ScRS8W0Le`u(W8!C$ACH;V*h9uHnq5YFm;d+aOV3r?7ShsN?~e
z-!CAB6NW(d98~Jj8Bw6h8prXrM`x5jK#_s+74s0u)<G%3QY#DO0Noc4tBo&h=pi*<
ze3eKDuE!uk$kuNLL)g!9cIRLdelw;v5#w~=J3=~(3Evql28+s2X42r*<$a;2M=-JW
z4`qV%W<-Sdi66QnBisvV*axCIJSmKR4?5k9;)HeyOV86)&iV1#MQ`R_tG7IKw<9iP
zkM^F2+RSIgD|+#NUxs}08XaG*D^7bt?u$?u!5Bb**MS<V*@Ao!zn*$}Xkb`G<hRG|
z_<&q%Y+-?3DYeLK2E+x55D<D8yrJPYB%Q4+MPFs({i}MHbqDw#80<Pe=!I0QA{r=@
zveZk&jBTprr+!&A7&oP!VIn;(FPU-~vmrL#{#AI6<ma=o;5r8aC-Fc`OYb-{!3R>L
z4%A36i~Zz%evJBK27%L^sMUaTNNGp_FKMRhAR1J#SW*uCg#%3?YpCCd<u`ZWAE=RH
z){4HJHB{qyAHfHxSgoP>^^d(%;@QyBP8&z2F~trVd<njt6UNf;^(xnTS!n-?-LLu!
z$Q)>c;CHA0N_HAiibM&)Z%)Y5e2!d|^#Sh65guT#bG1eV;bM8EMj&CscQWOU%4GcY
zs8In!=&hFO=~DeCZm;Sa7ps)ygxHU)gIod$BIFwg%ZSt+gE@%WU6N!(mO#FwNO9p6
z83HrBz&&I!-^nw9QL)m_IXuXJy6eEz{PLq0sLJoi>mNlSS%SLfBQs?y^|T&ITmA;^
z(M0#p&03UpVyk~RYZ#x(l!}v(hcuYKEx<<(>ZU;b=j*=xzyj8f6TZu$cL|bww2^E#
zL_u~0+Q(n(35$|mrK!lQ8&W(wC_f)oJYV3#qhIpSQ1*~Q-{d!w*uqnPfnu@Y2}GO4
zDGRt`86hlh*71We4jm{J)h;Rr7KONAbG$cDqgE@B`By!Ko!0hk=rZ)B<@(oyeOWuq
z&H=RhlKJI{5LA-WYZ_&I8B@}Fz4yW_!`R2^p$GFc1QIu>wM=h@YWQzyq~c8~_+P%q
z=S(!VrT*p3!PX+sHP)8`#AQ&@mRfrrb}xCg_GuJa*8x>KY+CEKJaTqS{~3EZ+S@aC
zv!Zn8bhoApt7(us!C{>9&mg()`;E+?EF2@vIlL=V;Of}2s}extk-zQfT{G>aK3(wb
zZ!SPVNz)ySLTMGA1ii=U+?Sb_>%##-8oxk+I;q{31oGuxecu#>$~f37?c5F>5T5hV
zf=o4KPKE3QyeHapk1m^;a*<uz1%`nBulP+scQ~j8Bq%?^JAkXD1yqsUVFh_uE6_Un
zM-A*_f4xB8rm+53ir!&@TB%BC-$bCD+Jo8#k|0D%3HMfHH?rVK+1(y`K30PaM42GO
z)k<AsQ+6q!Wp~%jlWy<QXj?3md@Z}x<F-FKW}(lCmyX(CyMm4k_&AFkrji)u78b^e
zGa9{g=r=0Ll!R+P*%<I5gpMP!eM31c)RT2-*(k^6%1RD?1So0!l9WR4x0bpFy)dRF
zNFkLH_aa@xH*7etk9v-MdwA$DHvQ|xp+0MS95l$AVAY6O9xcD<QT^NzOIG)sj_exw
z<n`~ryi80z8N#iT!0jo5nTH-xX|n)^A+veAy!nuryyd+1$2z~1PXhM^d0kUi2}|)j
z$hWcd;2k#tjkS-MAX9?3jr8wW%1(5))7~|Wv^zr&Qw|M@q&#a%zKR(Y+o*Q_(C0!w
z^p+|RJ$bZ8ebH|92E$db@VKJJKwwx0`vDxuN^#J#*^3`MG$Y%woL$Ygy8n3WBPtFr
zHR-n4IS&(|{=K*M*(*DoTkZ>SR`Yeg4#$3sKIwhHS^Yu(qY#kJ1dmKSs>{bDGOZtv
zp6WIkA)Q=$a>Pm-nvlgZNCAR<+m!=JdQ;}HhP2aIbgj>#M52S1IV~6OyKSena7i`1
zV1}EfWxy@`^`X=otmki0`P!T1R6KpQ{yOvmU9YxeZ_iyVignk9R7X`mnadtLwgIHU
zIX&IZ8!`nYUH8XKW)dHmM#E-Y3)xFCcx2aly{><Mps(*K7U&1xX#^6Y_i&EB3^;lq
ziY*Lx_XBF6yttzR+O%BZ?qy+97%vmYLIR!hNtI>?6edQ}S&Tg55Z#bkj69*Zrm@V9
zZG!-t%#&#fGa_%txt1TJ8W0O+{#eVrDsF;Q%vqM)W8W=c>##2b#VTn;z4p=q`n~F3
z{JC!91<KM^8i>qJO{*nqwcPYqQV8o59w<h@p0IhOu_`Do9NV(^R`JNPA!YGV_}?<f
zTXeKUL`3GKs;qUN-}YA@m$h6C=aJC-5P#n$C@Y%jVfpbvsm1CWL_&B6%;MsrJt%Mf
zD=h4=;xuOnCVy1=E0_9D5XL<s{B?8q{WW5<%dpcY5!Z7~zHAzpd3|gZgdBoUwaY~j
zXjbA<!JT8`CBLKty)h0qS-VT?A21DR!hToeWdd^}EapqM^7me(h5G5p3OuGX2{9%+
zj>EYK40KDbE018`{DlR#>e<oxJHEWz3;&3Zu&F+sb+DjO*nz1s>p+xdoYQ-2!DJM*
z{A772qV1eYX`}BcB<ag?&LbnXeekufLMCe6^#V?@13@>vcXlWh*5H7{@XR724?&P_
zstgNeXWRQD96M+U(=Wz%QVhH1MzDW1(8@g2sD$`8-q^G`vB$<HncqmILe(OH#MPY>
zd5=d1q#vk|HW15;^^-J;I>#^g`vKfeo<83>p=r{(rE#6oBP}_Kx)OJ?wYNJv&~-8e
zKF%Nze9-i5iM?VbAe;6Yoep?@>utMYMaemts0%PwDa^`IAC8~K!ZI>@w~R*!Z5KTK
zBCV>XV_OxiJl&A%wLnafZ!|qqN!yA5ot4DiyJ(Kgx8QRtCCRmtcQ!I1<LTS|#$fx8
zA!EsvQ*sZfydX25%mB$iF#*VlIZ*gP^cRiN22=yt$D6N20eZagvf;(=g9z72C^hAE
zXy+E?W6(5})fP?`t)$+Mb?s@mbRE(qnJexSJznXrV_1k-kt5XqX4QLtmS!8IxEec(
z7{cEl%}GE9xHUJo-JmbyTK&AA*`B#SNzGnh@268HyUkQv$3;zuD=+<&8fiE7@mG5b
z`M5s|@U?auS=i-LuhA}p_P$pqzP+;PHV%l$={97^y>Bkkc5DseYdwKJX8Lpf=a4KX
zkK~3HAzd>knML=~hTk>2%{RTlwy^$@WI3@|@GA_DCWG#U0m*DiaKsI}mD$22KcPgE
zj`Jm)<k+Pb6}$oZ{U1PR5HjKd%`n0m{QBdrw7b6BG7q)3^1ix57dB19M-Zs&ndOkn
z^&r|#i{gVruH1rwtWNQIgA#~e6)e<&s<Jo);_(I?LP##e_ZVO+ToO^`p_n5rAS5Mz
zSaOFwnDCaeBC&~yAaRzo7m*-W)2~~^9(0polyAOWFIaJgDnXR;;u!A^?A(<%-#{x-
zI%s$t22!@aMpU_JK#)sZxIzfD{xgi)Ll!|zw@3@xSMK<HpUP}gTI4q19*RTiDPlbo
zQgjnCY%WO%O{$p0{_^uO)Ogfs=~nSU7pU4*{(R>jYDc#F9}LG*?WE_g5W;1YU=xC+
zbr_hgJjm%U#n=GEr3+8cizORiB=us>zhDFq+k;ps`BzJ~#twQgA!(P%AUSdYCZ6jc
zv<mS~Z;v-q2GqeggdsIybACFh6hlD=`GB-{`U~xblY#9&B_1Ucw}+HZx6*_o^Y^~~
z4cd<3axB99l_9oG7oY}xh$EN)kWF^&zFBoKql*aVqA#Ru<r^Rum=St~l$stFRRg~y
z4E$2im8GVu5<<}or#!mc>@T{5PM_UPzq}+x{76}8#Aoowk1yvN`p|5dlz`AnwR1|s
zW1!XS@NL^S;GEB&tAR3jgr%xm;xQ8gvH5bT7I$D-Ar{@t9cThUy2f>myI1D680z5N
zTBI}=fP?DaKOg7?F%W6*U4V#NG*O4Q0+|(PX(Bz&HkWnH=8rt61~`g&0V(nse=BqL
z3mG~npl3--q-W?TUAi#;!fyQzt{mhdVVA-U)Y{j7LT&UG2|npnoNLdy<(T1<EA0-9
zRTR3KUD|6IcfR`Ox=+_@Hz8+!c-h|817}H_m;x_pTwfAzBem9Yg*s?*;<M8~QlQ6y
z;&@^C_W|H<1=@1;0mO!rpz~X|QG<!_d|piEeiO#z7?GTXI0|7VoiD*bG`GoZPqQzq
z;PFi!eENdtGEYZlIph58#_HYkf`mhvUHXKYn|^`QklAAJ?<;+X*{K53gbZG?XwKH~
zQb)F1opi$O&Z(C{tP~db<d)<loiG58*Ki>VtTj^R-%}qEi2{~+MTo|DyC^h|=*}KT
z*tqX1aAxN_E!dZ|=RUkKL0z0J^cKHqT6Zu@->3qo{4>hpHc;qMn0)SM+bAuS&7NOC
zj*z9r*k@cHxy_cJ9LS<@ITcLz2|(dGi&wEX0E7x2`TGE&uK^nCE@RG=+NWT(anFM1
zS;3w9>&wGtN$~OhJE+L>^xwH;My+Q^_>UjDa9v%PnB7=u131h$XB`%D*$0Ga5scy+
zV4x9DO}%rLX`dl{qyXUqa=sup2P!bDN7$D0m#=q|{AY5`Hpt1w3@M$kAYtrjLTy0M
z^FO#jNkFWVt)&n2NuTBAIR49+5M|6T*vOZVJLc_oU_gJViG7VOEUcx){BJqV6Ej=~
zEAH`w;_nn1b~2j^fB=BeSrLrs!!cm!RLL3A59(V(Kt)XlJ__zu-5JzhQ!J$q=X%p|
z0$2?sQb6(X=hwMpYkhzeQOO&Oy`YT04d=fc6At>$aNvnNyb$)}F(u(V4onc4`YVO@
ziS2v>=2AI=t)*SC`Ztb)tC^61K~A8^3(I-^pDvBc04I(JeF<hJrZPB=;K+Q?cCPiW
zTN8HJoW6MNDw_pphx-hHPHoV9R(*Rd3HMX$C$E*VLbFHoUto^by4iRx{aZ#Cq6E&=
z`6%=nC^^IdL?H5SN<%Dyh1g6D@B^fAQI;_uO-d2M2Pp68g4}o2yj|xLFeYW&uA}Zj
zf)>{@Us!Cazf=52QA|;tQ8VIGdhN;*R7P<%B@MxRntI!jOyB+{8(^Trqp4!V05lMO
zci{-jN0kgJr4>w2oE00-4e(?cokhCtFybmnHTS(3@A-sf3w6qa6&~ZQbNS;s588u@
zEGG2%)4kq=!-{XuzN8foVc;MO6S54#97Pk}Ddop4-~Hh{#kqMPO$cuP-|G#k3w>vi
z0WK9!VSY}6B=(k+(WFk?M|J9$8o4ZW5|7l^yHumIjsbJZ9{)EC9EBJlgNUUD@GQ*y
zC;mB@2;v<EX}=`n-0LRza&~%gL$=IU;_+Dk{i_{F87oB&t6BQfA(ILCuu+Jr+GiK&
zDR4n?Bnwle#u=LB1fe5@x@7t$0dOhebmv0`LH6lOnfX32%*=^uU;_Y<|L2mD7^t^W
z4LCjS1ECI;<TRbIGJKCrDxtZ$^yh#O-~;6xIv$1jUU)Oh9DLzbNJyCat+sAwro~sd
zO?VY3;)u0%14aHL0G@z%vB15HMYrM1o)q4fQ06XqW2f~XVSH9ossSw`>lsJRY1-6)
z9fH2sOU&!>>*7`Z&L%c?eH_2!w-7(Ca9?6z`&ZfLqP{X0F3YlS#BfClO#+BA%(@z6
z7T<wx8|y`>5Z_eKo)atE4^?4i>TTu(v&M+c;?IteB{j1X#EVmhJwtl0sL`d`pP=8(
zc!{(Idevsu+<b;#toZ{cTL9|t2>^Zf!I7p4&e09^xD<I|(c}c#ndw4Ys`D##-HGgF
z=uNw7tLUGvdHHy}wPfed9d_OQb{BWFvq<AWWoEuD<EC)G(naf~&)wVF*qxWe)MW(v
zCDpXZI0D|H)!V$k<6aWsUJlD53bnudG+8ddAJ~8j^buC9Z>zR7Do-G7btHo@CYgX=
zfQ?ks?)moXGram%v7jo!OX@#n4#^hSeB>7<#VSOH6}hD6!H79&6wx<#)U9@Bo+<~_
z3J}<QCnu2k78s@fky(t58?e|a{NSUZaMT=p-f}L9%D9H70Q(v8C)zIaO`6?%;TV<D
zXm(&4A|vrVB}^R{dh@4G=f`o|JrFJ73IH<+^Ojfie{Fw&Gl0`s3(F=51fHgb5D)QW
z)`)=sx{`lWU4*@>*}bG5Z+1<zNBDG-SW}n%NWtCWYHtR)$?rim^LAwdtB5~ASC>d$
z2Fg5wu?Gs}0%?k{h$UcfGw%W;`tI9SJ+S$pB-x+f$jt9LLc^92vl3W4ZwpOPc`%7Q
z2f~Md^~mbM1bgxg?TnR+54%SLmYMp7>3o%!p4V;^ucm?iTeCA$rX&e$vEnnER+~Q3
z--Ry{Xi0o9XFnhc2bPd(i(T^yas~VShTC$He!BOf;H$ISCc@(kgv}B_&Al9Ntq`#X
z6l3*UqPP;JiY1<+jzfs$mQ<W*ueR?!$Tp%DFK*lCy;`>VeF(<>2$(fcw@gOV%EqWn
zQ3Jp~r`H`-gSc$~+W>&6BFH(Z$hg2KszY3g35Nhf>dDmvvD8W;<aydhC#%ssFD3Dr
zKD{QV^SXYCP|kSJsqK5I1qTD){}0QrChO3!@?!>X-xydMvK&C<8*Zc1g4f~B5JXi|
zLMO)_9u|TRwlN>*MJ16UVk-lMWe@1Uu}cXS+>G;jPag?JoUPHndu!n741fZ>mJ^c|
z#mrs?7}nwVw=av+-I0KdfCWe6u-+8_sN2C&pqo4oFvd+O{&HqGL>w0ev#_(_q7}dl
z!$rG9Nb|EThB*R_Y)N2rP556s>|W#IK?|t>)8%kj)Eau55ex$ADJ6n^1Y^4j5OB~H
zt9>Q&Exm;p7;UFQNeE8$nGCfd%%nI!|KVTJPtjc5B2G*eOi%Pn#mWYrvh+UZ_67Qq
z$)y%_pDOo1`glq<>z?nYe#fcL1fm>J`U<!9{NI}D8wYceBriR~0gz=3^Oz{_-}CdA
zzpstM{o1Lk5k^6Kq-JIiTDh_*A!*?K_6~x6Ail6ADh9WT`APA<pg1aN2(Q9brbEC3
z^rM7r?ig-$6Tu&ymoL+dB%W&tV|O(%^8K9$vh~;o&$C?^q`TQ!j$k31#i*y=C<2v>
zf7<#QP&}Dqqi#TCJodTM(_ov}E#ZBy=k;*wCAtAh=l`MA=UJXRTV(<>nm{WMz<I)V
zU=Uyt3tb97*0Qi*zZ`jqgRT8d#}zPr2^pr5ZrZe-hW2!;FwH=rrb1=v;KfAubwH{X
zl62-5{&vC%>esCpEpipvVlwb>Q4wcOb`=7VpZ^?idtbTg?yy7Hb4PdX1D?t0M(xci
zr5ktJk}B>{e$pM@Ew$);s%+KHML1^lVO~+lb3EEg@5-&~nIEsStrrAD+n#M>3w5BI
z!22JLf!e;$6||smDb8i2FYeS(J^-gM8xeZYSaOH**tIj3)A1<~;%u65PHie&L4oq<
zTiK1;jdPG8B!a)M^3jAKQXTAyX0R(rUNj2#@x;%^C*jQ=-2JMcOY+}=c;LBjd{18l
zwt<1FMjvLm=K)3?0!~T5CBkMy_(_n^!XHqms9mi!?d3~U;qYe#Qllw=J^|G4s$O@P
z;-yxbHNTW0-G2~9)OQtQ@}y$u(%E6-)>2Fa3eZP4sBFH&m0%7;PUizd;qFlge;OnM
zBo2}=W)&&Kp)>^Ct<SngKP>pe=CgEBe6IYCDso&*K=m;!#NO0n{Hev{<YZF;!c;LT
zTMpFaJ|ajV>CZMpxx!%lmmdU38GbEvTNeWzjlpZHP|wkk%zzBd2eGcKt<`5HQnT+7
z&n6}&x&};L5)Qx~1J&?n?RzKo>5J~6%vz>w*Pjd0t6U<$dVUIcZ+JA7a+1i-Ao=Lu
z>i$(BI1X1~K|uky>0uyXH}?Uo3H>wcmzSxM4x-9zAl@uTX2uh!4Am1(me-TQWrzc*
zVluKXTR|$ovQ!uQkOr1iR8?hMn5Df#sZJg9rtAB6?D-Fp#(+hGHJjPf!v9^D;!s=Q
z3SHZvk5n?Y=w`<`h9P3`r=mfGLI=mG23PHk6%`aDKho3l2thb(N|%Uk-5s$0aV}7N
z#&>HM%+HC6K)XU~ePS18+Wysj0jf~o{j``0O3*7!D_i<ZdrOz{YnL))Yc1P`To)Uy
z)>M3$?Sky*o+-c@83>!5K^I^E39{$g;aP7Zzr941@aW!&jnCTW_Csrt_k1IxLa`Gh
zFF$Z&S-3LURMX+^_>d_abwekY&jk4LdBlYNpS`>lX;hbcIt}O+bcXytBC6hW=)>lG
zidPX~$9PC8(D`;1jx8j(=$wjZ6i8Ng0JS5ZJc`#HF@Wnp6C;`&+yx#%SChbaxcXDX
zZF;CJg1%e_<4sBE6W%cgGDESg=Ny*`09VWYqB-MP_EXyA%6{ms`!4e2v6cSO02I~J
z=#aZOGqpz%;E>Yj-klUhb%491j)68=sBOP7gKu-RS!}E=-@27<=p4wGUOs~}>zuU+
zM)e#^X~So-5ba?dkY^f4W+}De<|7l6Mg*V+^@Eut4p5kqtEN#pIgY<_+9~@}qPOiT
z31mj}+rx2N8IKRIKQ-oU6E|B1m>iG+uCf6w0fg08L0fTE4#mjmsDLYzRVGjYB@+dV
z0W_lzreV)s%42QpsY)qYq6!ZQ+HO3au<ZGrii7vJfIabBaHjHVb54cZSu9O}_Z-=-
z0i%VePHyi?otjQg{)*FD@o2+BZ_kbG%xp7Ko_zN!soy%^PG57@NXcXmS~!6$__kq8
z6M1E`n35tRun^?4>nvlLe$3_-F(;mEfiwG!aso$&Fp*o-^J&G_B8Oz~20d6WNDDll
zrUNP$LEy8#Egop?hm5h2(UGP_w=mnL6BvBTxixjh+CobR!{&Ox+9Ym;%|C(L1DeHX
zd{cp7uWj`yGg}gIlBfJ^7*UsQn(6e2)5_(OQI@n3kv?f;XnO8jf^Q$xbTeXBdPDPi
zjfMsSa03-C1g+MlfSM~6&}Fm1Ri(cdaeJq4i4e(4m0hxe?ho(&7%E$Ht(tP_3V_xI
zjWpQ1g2|$z&iDIYCKQD(ekeF4d^@YQXnKMysS}krEe?@nY>9HOX1-tLO9l!2mDYn+
z%gjl{GiU-&w+E1<J7u-OkTfnRpGV)0|1Ou9G{gC9bRy-COw0^Qr|YY<zj>>S&qXV{
zzk;gT$hP!FGH5qh#Ac8M60=MPb22l-fD%bnP9~cm{5B2#VL09unMXOGQRo@*5U#Co
z9NL8sAAu@3Z%_>p6<OrJ;q`^53@}}le{kb@K_U(MR3h8v4!odHPft%RB|V($y;{LN
zR<1Dq;jfBo?!-e}I_u3{q9w1O5M*%v1oI+6fA-}T154xaZiv<=Ow;yF931aWKdl98
zRXd|#2!*)rg)2euA`rLTp?nw0tS0mZmj+`;Ks`3gnS*;EUt}fLipmQHmm`=(cifAG
z&umoq`f^X|4o87>WA465oX0ND^qwYkG_TG1nBzbj^4in=Pa4Knks3^)5OB*YgxYk5
zjrA%yGk<d7P;Qq!K+Ac$yjZ+SWZrfcMj*ZtyQso3H~~bV?IBYjqaY4OgpxwQgqP#S
zfCWUgqwCi2G}w)<mIVmdBIk<<lBJU>rTchmzO{uZAwWT%8LGzTA0YWF*2}u-1^T{I
z#6ikz%*UpNciicYGR4b(Fgyg=oquU|f6MH~6CTGqn`-HJPUaH0%#@Ac(zn~~zr3I#
zwlYLj9AIh6!H1)v=NFNo5Ys0Fe_N6TxM8HNbX;UH<Pis4fy@U#oMESiADqtoT>jT9
zPy1Y@Sx(Odn$Gb6q4Z;7D1a_KEWh87QFForw0&YP_LCAloOk5cPnXRP{`p^G&Wj5S
zFcFb|a5iMKSK*Y}*#BM7sryN$PxmF;gr8)f`!c4mTaQWi^F`tI`mpd`&DDf8$^kW^
z$*3@<T%6f@g(67|3kgJM-XFGrn9k5paKP2z(aS3<?{Q24SZfitPr5RL16))(K_qqD
z!@speOS1IwK`U#azgZIjub%uZ8Y-U#pIiP-J73lX+?jU96AF9xkIW{8{s&PjUANxb
zcP&MTu-9>~nB_l%-NjY-EC{eTTcdKW)Ym>=@YM!ZaugeT;hQM&2)`p;TPbcmaY<nh
zU}c2?oiisZyiBSn&)R(JPnt~Lc5>mu5DB4v!+VLj)Ss^b=no!Ba@YHGMXogObfif%
zX?z%!W0uSPgv5!ugPx{vfL8HbC~2dZF&#MhDUDz%^?b7R2V&`|D*7kyL#=Tq+#1Y9
zm%2vsG8vkv8NRB$|6c&l6oA=*lL!FM*Doc~l&{W69=>KL9!}`^185zHT8!Ds<$?ac
zNFGt>R2-j%5NOhmgC7Eh&nXUDU~pLT%FVp`m}wZ>wf;z%2y`(;7_1)oa|P};|KQ5}
z$j-Ljh$#_iv%?W?@c#=`;9Rkex&nMSkyAt1SI7Yl=Pu!w+%_X@e(k%$Jr}WD(Z#~8
zdxq8p#Z<tD*&8veZGe`gB)cn3fd^j)qCHw6%<a}5W<6cJ`ka=sS;#t~hfiH=nq)8s
zy*OhQOaq$zb293R8DG%@xn8D4X`O+7`9?f5`JgIlZ`7PMW2o_8)yubLeZtg{1gNKb
zO(W7<3?#)Xc}sodkIH`$2iaR;j}HL(Y&Z&YG%4M;%ChDjX1xdW?iVC9=RueC2^ezc
zKqkeK`9S}ljkF+WDF&z-8-pfpg<Q-~n?%9g#HG23Ln+jU^ES2#8SG?q+m!jv=(e=^
zBP@`lv;G}(Y2ZF(%rfX6{q*UR^cf-u(zs&G5h<7Y{^>d@AoY>EALh^AqK~C#+}pQ&
zA-~yQtK9#ApuRjX@~dL^@txNRD~9jb%!Pd{FGapHY49$rGOl>cPgB@&-PHr1l!P0J
z_xs0xZ~^4*Db~P^R-^9~d}Y0I-=FXxhEOn;ld}_><SM%totCqDJ>`k`xJ;c?$s#(Y
zUA^}p6;p2&=cp;`+M8PLWH4<2nIW9#=!JX3za2UJ?j1#K_q(!4qOub^6r7f^3vQ!L
zOi?-&J$A_MU6p$j3Xh=BtFPW#8dP$1-HLL;8<Hn6mmg#i2D7Xox{L<}Zc0q;GksFT
z5CmDKOGNpA=r|gD8%TiuVh^5!0G)Gz9`}DB!K_qY=!@hY{=}|4)dwWzMqu~{yO$L{
z1R=L|dE(O@EsJ8`<=!AJ52Sy&a3JWkClh~fez)Fr_V?1#l7!Zv(1yQjg`uAyMp~Yo
zH?fN=*XGdDGa%EjXgbN0<-@=?=c$E__LOn`I1aC?7-?a6&Vw4qeJ`f=%Z&;ofdz_N
z7uW3XoD9w8)(1h6y{MG#Pol3%xiEYXho`BTWsyOG=wjMAD3_r}SnzeSUyWX&sFlI)
zlWBuMrO}z0Qx%eH&U?-E<<Q&iHOpNv>5IB&>*Aj`E1h}o*F^Ly<t-=6_)Zp7nJHw`
zJ&VV=Rb$t^tRTVcyu3WE!I64bh^|HUl018sSNZ~rrlxmJXg{Y-@Z7EBxd%_Q51n@A
zY>9@}`V!*B<l;Rwaf_AIQ(ZeFvipXtt9O&Kk=^E-te*1C3fj!yK}GSjeCaE8xEJ-S
zNNh@L$hh>7CRhfIFLL%s$}hhmVsBC!9M_>Nyavi@Ls{I0?{b&dho8mkpkFY>cg=Hd
zV0l!v;5_k|xin&Hw|SQX(G7oHFjiq;czEaGv=?N?<2>i}{`<4#(*(D<X7~|fKn&l}
zx+>WZ`R=vgjmD{NnuWA;)@~b({isAWZ*#DlRBYa-|JvvxCL%K2>cxk2fLh&9{A3OK
z2F0~h-sPF=H#8K^Uz^(4AT8rVXL8H;-X)nF5;?I<c&Y^!JaBG9PNdw&$j~soLy8nq
z7k$5Iv((s^I>oj;jvjBD@5{)RAOll>jyW(Fp^#5TRSu!qnE5mU<lAQzheP$kki3a?
zIt77niqXwPt+;H;mP&wfD3d>}kIV4&IQ(6&Y#bYDQT{@~O9L#JdgOzGZC_#A_-ql4
z0|rh(g??#PqK!uI_EY}VZRNd=qbw6nn+==JzU_VPdq2JXaK8iOxLjs_Z-MHjbU7sP
zTm)R)s*)<JY{!avjKlkGiOB4Se2prD^^`3qs<BQK_E(jinFPAa)}E-=6%Mb+crD^F
zpjL(pwdk3cQcVkoS(d<H#~vvPNS#yja&j)w!V-o<#?5JX`ebVNNOIFRDQO(@in7;v
z&jN5nL~aWkUAim5^*VD1EUi3Z_*u#MYpFWiSE9Q9UaXaa0GHF=+2s_DnaZZyi4M$T
z_Ox2?VkOGEW3f-iEj?$_JZU}*B#&<#`cB1q(m>$g*(N+qQ6Q-U?+<R)P-WidMK|gF
zp-}PEvX+Kjq0Zho5v#8&MYoK1r_Sw}nk(jbJxz_|iT^&j*$bI<9PX@Esnm*Ln4l1F
zXpOddA;{|^*w&g4ceaXRZ+fpyBsSCqn2mM?eUXoG^x9QO!IWt_zc`QEuu1qLeZpTq
zV~&?^s{YGnKq74Kt<2tS^~s%rsk+j)?hS1(bEpz+^%97bNkpZ9X^Bm)i@n>2t0gnZ
zxKj>tRsm}pn;UjBjeetTk&MKfy#$a>IB5u8R(FaxCVEJuVGNa#_}4Xu+q^fC21)i7
zxK#Pqnfd%0LvLtrKC0#er(SSaV)J6eV@m?dO66a7?c}HLlGr3X^#Oa7gjSI36(teJ
zx{xsLppyfGa4(i)bS@1K`PO}P#6Dp=9Cx4}6zIw(dJ;h$q&-{aW|qKjj66EEuXRQ%
z$wr<7Hci&u!-Hm{cJ*eX?D=j%5n%f2ssKv9^ey&@WwhEvf-47Zcj-jS9xk~8KH`^B
ztkHXGvycy7veJiu-x(ln5LD%k?K-Y8-%F7mDpZ%9d1@529cg>J58L~s;X3%`>&n0!
zI^j&5LAn9NBn^(v#>;5m9qBDpM$D?Uu<EU3jzrn*UWyqPqlJNU8LQG;*i?&N9x(oQ
zQT28k%8{kUl|XRk`}`(YsK#+rM!sI5_yW!>I5Sn%9R0D}9p+GF>G*rEw%t7bJ*t;7
zNfsN$k~KHnTsAp9?$t$_drP0-AuynpBkw#ZKMd6P#3ffKX@9x;Ps_?$%>Y@_g3q_o
z5<PXuc=I%ZV=^E|PA#|3)5Bxa$_s4zMB56(TM`ocPXA$Xi}ciph=>|f!KRH(Mm;JV
zjx{W5OEv&^B+p(;XJh%u{da(Do~$)*Z>(Dvhte}T)mXbeDT2UjV7Q=PY~=zl5|p?b
zNUzMBS5n@l?|}Y<^HLBKANU~o>zgSH_Y9?bdo+_G_;61{nv>}X{uYa<la%%Npjh(5
zm++f1GT|`{_dY+AmI1RyZQf6t6zQ?{Nm2B7fHk}184#bH#}j`6kYHT&ZYG+dH<T5%
zw^;$BAbLKH7|I2+F7iGCgt$A$86`gP)KgtAUCF{yz-@2dvQiMkC!giI2Mp??K~mh&
z2K)Ysr>U%<VE=n?y-7gBIX$<#UfH2Ux?{7kdnXuYfbWu2)g8U$>#F9y@V1<?WmiO_
zDfubA)hIL+Sa;gry@3+~V>{d0*6Dc#GUFR7Oj3_?REZrv^vHUjm^S_yxDhr?2~M)s
z*+~Wp;vOo8CexD8#R9!TxgIG3Xf7C-gM!F3N5<cGw=LuBCrs07PEi%7d!M4U;5TuL
zWDNrbaBNZb`GVM1O^MLfp6cO27+kcoDPGmFI(LiWW=!&8JQePh40zx+xyqz9O>!;A
z#mds3?ugUu?(Fbie18h_le~WC^F!a5Y_Dstj)1@B!TIzz=;!7MPr1}4jEnRzonV!h
z^h!+pzR%9i9vc?z%7dwdj%WU9K)Ua0CvorddzX5qIE2e*gvxU^{Dbzr2x@MXb6$Jp
z+HF_J@!smHRfXZY=(SguF9f&HatuflSaajf;C~B0TD}HTR%Y8w2-tYycAk}G-H-^1
z>Fz!p=h?2kDzbS$au;VSZ^gym%UMh@DCEeo`98{xCld01ihbhF7VF<*A0+o;MgTTO
znZ@e~@X14}u@`Y??TIs!I1pG304?mxJENve!eA+~Y#kj<K?CuFF`)cc9LXq74xIJb
zhI*mB(37&)4(!_6Mj!A2{Y%|zUqv(o(&YlMUS3vsR~5B?B;+oCBL!wI;a?kxNJ*$1
zPk563Mi1`w|MzAt&z=BxU!5S4KHUEh6VxMo_60H06VP9P)L?I%TSVQ=BXCH%|8+?I
zzYPnp{*I&^{9@&Q$7LM+U@(^!ZuK`B<p+Q9b-n*9s#u_>`_|oPXPLLNw<iaT!iNFe
z7lVU?n?WyLyg0>051ri(#Ta%;VN&_lpHh8D5QW)^m#^c@0azLaW3YoVlI@!gR|*4?
z!P=~`<B5p9w>rB75Ttq$g`@X#hP<W}=&dp*qpa#W4-Z6N?gAa6LcJnyhiS=OE5xZU
z*DB6l4h|RUSFx-}0Kv@E@@K<{IIh|g*!0ZvDsxTEDYw({`Iu<GxsUeZs8dZ1tHehx
zol_D~iMXg`4n%-=?vl9XZB>Wf>V;J?_jwQ-8S&9*^_8t{KmPo@8+NNl$sj@T+N*?m
z*R{SUJ<$dJ4TeP{Q=_Av9iv%5a+(~#u&&qt_2(p$4qa;MIl9V*)N%%G+f%H>QdXKU
z=y+w>-I&lyRP#te*Zk-c1CPV^qP9_KD!Jp<KxLpA#dVC~4@_>|Bo`b4n_8IDPvVu+
z!jmRFlAqKD3HI-c(JbF3E~7l8Cz<W+i}ZY~!1J-iB-fl6$yIT2AFbA6M*H6%ZFj1<
zx!D-_Xp}!U8m28G-c@!Pb&?)#Cl|9PK5b8)E8A7}G<Kx+C_0n7)>7Kbvb+F$Bx~X|
zXTj=ajr#-da~D&_c~K3#uQPqxQ*6Xs_PjRy?~?qqx;!qcF=V>Ev(vCYVOxT7sV>Kp
zsIQ*b{pb^3SE4es{khZsBVSiH%C9!cha0W#Hu=ZSv$Vp}GInlbF~{civ`YDe8`evG
z<Tz<%l=NCYkQaFyFk<umd$-6y*k!K(G|Dp7b#vY3z5nC?`l%wbJob^KM;%n%Yxl7z
zhi5i6l7^T5wb{?M%m4Tv!OI5Ic!BlboR1w;DXy!!bUhRWA@>))E)#y67s)3})##|B
zJm4cHe&i5ZCIX8B!s0wPPtO4FPw!o7d>_BhwMrWQyn!VZu9H-a&1>VX0(!19aoaA!
zq?R5=IS&qsIWpdfL*4h!_9%(@4zQM{d)wRF8->@S&|VwZ>%vBsmQuDA6QkmGV@{_J
zEok0+qXlkCZtBF^^Ry<}PgFG)b%e0DwS5zHdnI4I_Q)}5JTMyrVmnrr+$v%y?)3sa
zSrJfkl@_iWKnY+rHn65yqrh#`RN7CDpf?ty4J%X!Svge8?4QNrmn#?OdN<ac)@6qR
zvwlD)YKuC#rOMAvb2^Bc1X0bY`;+yF3ft&44CYi>wS-x;dz7=m{nP1Jf=Em71Aae0
zzpD4+tAipk&aEM26p_U@Xx?-wkeJ47uTj0xQ4NUU=*-DlMN43lviH<pEAjcO41#r;
zk}RuiXgJxw`|-ECssLln6slx>ecc`iK3UxU;|Z=oV2&B;P-}2*v<wiPth7IR-rzh<
zr#Mgs42PcZQFgYzO0Dy~m-yu>Ozi^hdycBlm<w~kCG&Zne#!3H@SmVkW)jjGJYcxJ
zh3=v?b{iOAUp+cHDjB+uKCX(561Z}qJ$YOswl{XWK-EVWdGW#N+vPWwyS1O60S|^^
zxLd6_sJ(&Z0uD*VQw~0O5-Y366OXli;>Xmks2Xr{d6%+9jg#5j5;Jfg?YG65_~@1=
ztcLnRaKPK`2f{}<sn=hWEnD4d89i%hpk#J9Ywq6#IgL?ol0)hh5>d1%<2f7C*9sgz
z)&6A-JXLG0+M}2qS^tFZAKl9tbl&jy2kzFd!tiAA#(DB?GvIS-ju=!ESCFhz9`_Kc
zxAW@ij$Ry=G2Y90@TSfb&>q}AXH%$x5Z6#!u3@_nB;pON1YasD8nVD#+nQLtBEyQ6
zL4eg0Feu{QI3VEkwyHpXzrSN|<I13jG5a!|D%dF-jg5`_?cW~%2?(LwaNW~XHH{7F
z?VbREM<|C3wc@}yZYZyINfTQXS}|6(3<z?Qauy|KgCy;`r|Hj))i{m=Si`P9PL{Y1
z9A6e;pBZ4tZemteyuB~^6GBc8o#yBxm)dvQ4&H3|4?I-dz!Ista-t*-b<6m=yg53%
zC$0-~3mR4X40mHS2ZJ`SMi#^0oPAT1)2=Vi#Bz9<&DDBsE%=!zoc;-z40E30P&G&}
z7B8!mwJM3bqNwe*@zaM3**!Qg@F#(?rHX^u%96JF2rK*BuRL4ibta?I%};uT(f1BR
zvJ=~!kMF8Z0NsFs*mPS<Q|18)wb%>6ccVFKjyh@%EmdD<F1fPX`gnUEa`>4H9*b!7
zrOr+g2Hv={6%k@#d1_;6#ihEbrz+Kk=qg+L;(cO#M29XuL>I)-TuD;AOCrV5PzOlP
z2cYfu)b&%XH@d#hYp+1d?^$#<F=raklxsA5(vaP-qCn!Q`rzb?&IfI<pgM)aCJ#ZD
zBCa<8&YTg)Np7}%R*Jc*%U)g1=`@C{uYs>?_q`7;U{#s8VKX<ySUh8krReiq;0aug
z8mb&{0<mJVf0<t@$5Vsd!U0^5d^$L}iP?)xZPv(7@B&?|+rAgzj$?Oa+ErugFrt_h
zf6mUkBz*~P(RHj`s=!zI439pm;8GyDCA@HhLC@23Pb1dh_I(tvQ;$`97~Wg>2$gfK
zYH8)mv2dwgd-Zu?B`#Le4un5W!*Kl*{f5Q_j?P}8XPmnQP%fC&Mqpv|Fknj7`x~|X
zqX7Y%#B48}_asRtZK<B_t*4zV={}^K%ZW|L^Kr!5q-Gbvf$KONI_Z`1%rFX&&OfAV
z`F$+)Kq2zLq$*l%=v~bPGo=q+L~Ul4!H4LFRlO?T)~5qozoSAOJ<I#N#HCrYxEa!Y
zVzTW_cK{4uahTMtGYj1%X+5f+XdJ2YDQcWviEg!eyXMGRu&`h`)Zch(us;~uyQDbq
z+XXD4O(Y0ef|A!`IXvZ_ZP$WqmV^K8_7B}fd`n@^8F)bLXnkQxKmy0`vx@1%OjK+O
z5F??9Ebb|@b3VDTbwvUU*=?Iz96p)wWtdie7E!~qY!LeeoDmZ;X8UR3`ZakA0;C&&
zwIZ5PRrg|(*WKkNDDM`A$X;VzbrXdyn^_vIYPHu=xD|4Uii@A#JH&WRjE+|6`xWUi
znzTuuBfqLK)Gp#i(=MCxlj^8teK4R;W<8*I17WIR6kB=keYwm0cR$qiH-E2^M;=G=
zxN4Su^-<a3kjB}vZ@lC1oUxrA77+2s%qCWTM7FO9HoA3*x3EPzKJg9USz=k<e)-}*
zzdn!vw#R8cR$}2%B>MU1Y(>l6?kVANDNazuwi#bKq_Pm!$5Xcz=|j?i8M^N|Jog$l
zs8%-|3*`JQz6arzKaAy&i`x!NZt$P~9^d4_vzx$g)Z5=b%Bj>_XS5O>AmaUrvc+gc
zt4WgH;Dx~wjjAiCM@~z)n4gR|Ille!6j(z~wL!4y_C(yI5UwkxPra4=nryOmW2#gD
zBC;(zJarPyL6$V$#4_SZ0EN<nq(9`ZWdEsZ6Jz@yQ;^LQsq)0%b4=W3QQh0w-Srph
zi$L1VZ6vB0f>3?)+s`A(#IE_oBm?*$7*3Z+F$zLQf1N=s4r&maAr2qELGSmWPAE7~
zxY`oBz@GxC4$^@5*u!o+V=yhci_@m(z33KcaEnH)A?l<IME$w3KZb^etSt4PEselO
z$ytkT(!9w_8Gjn<Atoy7P8Q9qI6%uGv$DYoMlUX^JOKQIEA4-%8ERR#D?sF%@_mmK
zLgQdh<%FlU94P+fAb_6viA14~P$xvHS~W*Ct*7CT)Bg7Et{#WqG|JSJ`%2|kXJ4y?
zr*g4hzIML`*7@kbX&s;}I-YW-gC3qGZO-+QQRq-kKlQ<eYp+OMy%~`a<>sv+M<~CF
zY^&sP3baIHxn7}tj9YHqOtstcAg|I$AAn`DVjDS^>#<m@y))Ac_1%4aRsIcZB~HmZ
z*+<^f-CZW_>9`aJS?y7eGt+537`{=M?WuaWclV=H=Z^**C@E<P$%$Osd97d1MtaQ-
zSjfo~F?(;P?L&<ev32itTT`DMBoaA7c~@hQMz{`4{f-A7>n9T%f$;RI$=+?5lkFiN
z;65=R+-iurFyps}@^5GM^mPWp*K!_v?~_jw>jHYZ@2ST6+Pa+pH30vE6@8E=@O62p
z-0N90@x-t&G4a6*;l<OW5v5>Iz#ZW_3owOxrr~6F@<_R4=8MfxbfSPMmYU4CQ&zZ~
zwfaaI1Uqurb;p3C(TxG$N&Bi{&D+b_tBc*qu8t(;_Z9X$ur43(SLZ({Slc<72bUq^
zyHb#6Vr3~M>gHUQQF}zKmU#1IsOx6kCr3A#|8cze|N4bV-U*%n+u8>Kt%0f)YkqIH
z?EM1WM!|D2<c2;zNB^s)YmaK;O5?*)AE#7hPZhL_U}Yj;u(72ef(aI(FhD^l26=|+
z5*BFXVIYP`aIN5WD_BM#faN7qk8G3_qP!yE_$UE^#Rdfe36a-g5Q9J@ZbHa@^ql>7
z&YYQh?tJ(AJ-)f$y$GADudN#l`Fyw|2U`XN@3*NlLy#*@K*5lMJPq#(@gb?7A3ic5
zOnSR;zAc7!@3`Ztb8pk3!l=xP`QzK*@pr?!JrN{+Qjv240HdEX>wYq_w8LMvSjWFE
zZ`0V$Sc0ASqUPxlb0j08KAcwuHXL%nSj*XXVWXvVaiKz<ppkrx;hA+q#@h0nZY3oI
zpU&4eg#=`u2n_6%8;wT060I8+h<=x)6J&F_CgxOEfx0^#q+Jrci6K+tg~i$nga8na
z#s#p^vRSOvJH%O1Pv1D0=i{fC3$dL%V+$;8zQXtq6yxQFH@jgAMR#MuP&EyU2-FAR
z==YB@e+g56#x}i%y0B=DGluTAPZALV_Wa1=4@I0Gf16zP-fV|!xp<X*l9+&TGX}fO
zkqbHHbXA@?5(LfrrZv6F9O2hDNe8lYGan_pT|RK_?y30r7bdLzsEkcgqUw__VjyO3
zg<ozx4g;%5kh5J`e-@<%>TeTfShda0ovn1dTt0<Y-AY{|#Hqo9tO%-#OzOy<E_=%~
z?87J-x&jTaglNNQPU;k3h6U$x9k8fzl`1!dx}Z}SE#B(1jo@wuUQVI3zr0a*Y_r!-
z+O3$1t?N}7qj9jR7qVEa`>Qp)TL@B93zQ6DHZM=Q(=r)kg|Q20Aa~n0^M^IG#I&?G
z#R8#3&&R`^tr%9`BWOLUHu=k*l#s344B42GxuT!2Z-Iu2p(!b^4k?V^&17(t;5eH=
z%=nRF83QYejd88v`zt3jx%<>r!1oP*uou||>zK61ing<<#jHX?hqbdFGY{bZ$+L`W
z<r(!3s8it-KVTGfD%F}{agN)^Yin<|+hh3Lo33;lhGV_7k3OujgTQkEPZ)72EANO6
zo0ahLEQ1d}TT~jCCb}Z=e?6ZIAyLtKSCCcsGa$&#L?RaDwNyO2o~fBDUtrZ=4YJ~Y
zro<MbF5HE*DQ9drmaS}X?)5;oB-?FiD37Hn+ylda4Fu@(2;Y>U+lOz&xwJ_1zf@LM
z4$N<Y5(v%C`<yQXsG+NT2ZDT#0N;TkoHa9ROhRupT-}<P#_mkje&#YZsc}e%Y(6SG
z;}RXpkPN?qkhJsHKX@;WowrUWI>w{H89q`t)sF{*fRcs_o=g31kCTPO0X(?jd9npc
zt-PsL`Gsn8Ngauh=5u#S7l-@jnV2dHqWWV~AI!zP&+__Qh)pPlrv?qZ3*Bx^wi8BP
zBY^7&C{fNBv-$NwiFPk024(qXT0$TC%e${DDk`k7?3<i_H+?I-k0;~s<{%t4m89i1
zo?Pv)e8>pM?jUe6UvDs&$c0lB;FW8kTMk3o0(a@?_erDDrqW}(o+=F_r`{%Cut*%a
zX#Z}S>v=mNhh<Ec`fL=^1uY~jqcp!^4^Ki+l3tq6SkyV{pFG|R3rBv%egJv8Gb<1<
z{Q+&LGR@Kax_sbO2)`gMVt)yEW{eD7$_o4Mzvi_1$z-6yQFUPv`IEM3ybVst@xa0s
z8l|@4ocrrbbII~yJ^TaJ>IS+SRqj#epAgV@gJ`MLtj)Qm4B3o9!EEH56f%#<+ONv2
z#>^*FdkN@%J)R%ZLbs7xkSf`Zr^N;@|0n1UO6!Y@_Fvv12Y8S!{pySNeOt5UZbBZd
z<xq8-po_?jB68O2(aawR`@VTHgoFIta=zs$Ot~)h9u@VnHH!Zh=hOb`Poh%C)6L-9
zbQL8+7Xhg)cfN8R)cs|tkZ(7zO<|iS{>EkqYMJQ|0e}_?rrr(WpOnMVklR28I*6RP
zF~@Z0d3-_)A0QP%(T-Lj-d{M?hD&}zdpZ|Rj|k9?LFGU!v`jt(2NUj1Y7tkH)!x`u
zv7tHy6_#hV88$G7l)wMp=zQtTF>G1U5i9G#8#HrsH#jg8o%!qYk9c@`ra&6?l?|Zx
z%H;)`wIs}|<nG*Ysn^=ODHT~wL8${u;isbL{&^+t7T{3_Wl~`r`#KTr%Br3T?3=%m
zQ@}@i;_uKs2bG7)<S7c{Mz-mngjbRFYM%E*35Ex~{8IuzyaRKEj)jzi{9Oxq+o;;3
zegcZ#^}}7(2-0`P2H;@Vt(F$uDkLupF2K{atn115vM&?Xkj?Lp^p$RZKhGd;Z?}c+
zVLw_?;?6&-qH<S07`A#p&qC3mE<0VwN!Gf9i0Fr{JbCa3$PrNkPp|chXo%s+;gF!X
z(uByfAkL`~ahw@ptg&4r675sp1`YoCSA+IYyRa=KU_7%e&CR3Tqk@L^xlVKk9A{!k
z1e=5Mspue-JC=)dm9|AXZ#Lx&RJ3MkOI2gw`?|wCRrf>#vJT1RoXOKysaf7&#1#?`
z*49q88u!3@`3Up*B7DtLDAu&cZZa&t_kZNKU(M~6-=Pf)Klpk566PE1)8HL{@qbes
BtT6xp

literal 0
HcmV?d00001

diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python2.svg b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python2.svg
new file mode 100644
index 000000000..5ed6530ca
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/pybind11_vs_boost_python2.svg
@@ -0,0 +1,427 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="468pt" height="246pt" viewBox="0 0 468 246" version="1.1">
+<defs>
+<g>
+<symbol overflow="visible" id="glyph0-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph0-1">
+<path style="stroke:none;" d="M 3.726562 0 L 2.847656 0 L 2.847656 -5.601562 C 2.636719 -5.398438 2.359375 -5.195312 2.015625 -4.996094 C 1.671875 -4.792969 1.363281 -4.640625 1.089844 -4.539062 L 1.089844 -5.390625 C 1.582031 -5.621094 2.011719 -5.902344 2.378906 -6.230469 C 2.746094 -6.558594 3.007812 -6.878906 3.160156 -7.1875 L 3.726562 -7.1875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-2">
+<path style="stroke:none;" d="M 0.414062 -3.53125 C 0.414062 -4.375 0.503906 -5.058594 0.675781 -5.574219 C 0.851562 -6.089844 1.109375 -6.488281 1.453125 -6.765625 C 1.796875 -7.046875 2.226562 -7.1875 2.75 -7.1875 C 3.132812 -7.1875 3.46875 -7.109375 3.757812 -6.957031 C 4.046875 -6.800781 4.289062 -6.578125 4.476562 -6.285156 C 4.664062 -5.996094 4.8125 -5.640625 4.921875 -5.222656 C 5.03125 -4.804688 5.082031 -4.238281 5.082031 -3.53125 C 5.082031 -2.691406 4.996094 -2.011719 4.824219 -1.496094 C 4.652344 -0.980469 4.394531 -0.582031 4.050781 -0.300781 C 3.707031 -0.0195312 3.273438 0.121094 2.75 0.121094 C 2.058594 0.121094 1.515625 -0.125 1.125 -0.621094 C 0.652344 -1.214844 0.414062 -2.1875 0.414062 -3.53125 Z M 1.320312 -3.53125 C 1.320312 -2.355469 1.457031 -1.574219 1.730469 -1.183594 C 2.007812 -0.796875 2.34375 -0.601562 2.75 -0.601562 C 3.152344 -0.601562 3.492188 -0.796875 3.765625 -1.1875 C 4.042969 -1.578125 4.179688 -2.359375 4.179688 -3.53125 C 4.179688 -4.710938 4.042969 -5.492188 3.765625 -5.878906 C 3.492188 -6.265625 3.148438 -6.460938 2.738281 -6.460938 C 2.335938 -6.460938 2.011719 -6.289062 1.773438 -5.945312 C 1.46875 -5.511719 1.320312 -4.707031 1.320312 -3.53125 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-3">
+<path style="stroke:none;" d="M 0.820312 0 L 0.820312 -7.15625 L 5.648438 -7.15625 L 5.648438 -6.3125 L 1.765625 -6.3125 L 1.765625 -4.097656 L 5.125 -4.097656 L 5.125 -3.25 L 1.765625 -3.25 L 1.765625 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-4">
+<path style="stroke:none;" d="M 4.058594 0 L 4.058594 -0.761719 C 3.65625 -0.175781 3.105469 0.117188 2.414062 0.117188 C 2.105469 0.117188 1.820312 0.0585938 1.554688 -0.0585938 C 1.289062 -0.175781 1.09375 -0.324219 0.964844 -0.5 C 0.835938 -0.679688 0.746094 -0.894531 0.695312 -1.152344 C 0.65625 -1.324219 0.640625 -1.597656 0.640625 -1.972656 L 0.640625 -5.1875 L 1.519531 -5.1875 L 1.519531 -2.308594 C 1.519531 -1.851562 1.535156 -1.542969 1.570312 -1.382812 C 1.625 -1.152344 1.746094 -0.96875 1.921875 -0.835938 C 2.101562 -0.703125 2.324219 -0.640625 2.585938 -0.640625 C 2.851562 -0.640625 3.097656 -0.707031 3.328125 -0.84375 C 3.5625 -0.976562 3.726562 -1.160156 3.820312 -1.394531 C 3.917969 -1.625 3.964844 -1.964844 3.964844 -2.40625 L 3.964844 -5.1875 L 4.84375 -5.1875 L 4.84375 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-5">
+<path style="stroke:none;" d="M 0.660156 0 L 0.660156 -5.1875 L 1.449219 -5.1875 L 1.449219 -4.449219 C 1.832031 -5.019531 2.382812 -5.304688 3.101562 -5.304688 C 3.414062 -5.304688 3.699219 -5.246094 3.960938 -5.132812 C 4.222656 -5.023438 4.421875 -4.875 4.550781 -4.691406 C 4.679688 -4.507812 4.773438 -4.292969 4.824219 -4.042969 C 4.855469 -3.878906 4.875 -3.59375 4.875 -3.1875 L 4.875 0 L 3.992188 0 L 3.992188 -3.15625 C 3.992188 -3.511719 3.960938 -3.78125 3.890625 -3.957031 C 3.824219 -4.132812 3.703125 -4.277344 3.527344 -4.382812 C 3.351562 -4.488281 3.148438 -4.539062 2.914062 -4.539062 C 2.539062 -4.539062 2.21875 -4.421875 1.945312 -4.183594 C 1.671875 -3.945312 1.539062 -3.496094 1.539062 -2.832031 L 1.539062 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-6">
+<path style="stroke:none;" d="M 4.042969 -1.898438 L 4.90625 -1.789062 C 4.8125 -1.191406 4.570312 -0.726562 4.183594 -0.386719 C 3.792969 -0.0507812 3.316406 0.117188 2.75 0.117188 C 2.039062 0.117188 1.46875 -0.113281 1.039062 -0.578125 C 0.605469 -1.042969 0.390625 -1.707031 0.390625 -2.574219 C 0.390625 -3.132812 0.484375 -3.625 0.667969 -4.042969 C 0.855469 -4.460938 1.136719 -4.777344 1.515625 -4.988281 C 1.894531 -5.199219 2.308594 -5.304688 2.753906 -5.304688 C 3.316406 -5.304688 3.777344 -5.160156 4.136719 -4.875 C 4.492188 -4.589844 4.722656 -4.1875 4.824219 -3.664062 L 3.96875 -3.53125 C 3.886719 -3.878906 3.746094 -4.140625 3.539062 -4.316406 C 3.332031 -4.492188 3.082031 -4.578125 2.789062 -4.578125 C 2.34375 -4.578125 1.984375 -4.421875 1.710938 -4.105469 C 1.433594 -3.789062 1.292969 -3.285156 1.292969 -2.597656 C 1.292969 -1.902344 1.425781 -1.394531 1.695312 -1.078125 C 1.960938 -0.761719 2.308594 -0.605469 2.738281 -0.605469 C 3.085938 -0.605469 3.371094 -0.710938 3.601562 -0.921875 C 3.835938 -1.132812 3.980469 -1.460938 4.042969 -1.898438 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-7">
+<path style="stroke:none;" d="M 2.578125 -0.785156 L 2.703125 -0.0078125 C 2.457031 0.0429688 2.234375 0.0703125 2.039062 0.0703125 C 1.722656 0.0703125 1.476562 0.0195312 1.296875 -0.0820312 C 1.121094 -0.183594 1 -0.316406 0.929688 -0.480469 C 0.855469 -0.644531 0.820312 -0.992188 0.820312 -1.519531 L 0.820312 -4.5 L 0.175781 -4.5 L 0.175781 -5.1875 L 0.820312 -5.1875 L 0.820312 -6.46875 L 1.695312 -6.996094 L 1.695312 -5.1875 L 2.578125 -5.1875 L 2.578125 -4.5 L 1.695312 -4.5 L 1.695312 -1.46875 C 1.695312 -1.21875 1.710938 -1.058594 1.742188 -0.984375 C 1.773438 -0.914062 1.820312 -0.859375 1.890625 -0.816406 C 1.960938 -0.773438 2.0625 -0.75 2.191406 -0.75 C 2.289062 -0.75 2.417969 -0.761719 2.578125 -0.785156 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-8">
+<path style="stroke:none;" d="M 0.664062 -6.148438 L 0.664062 -7.15625 L 1.542969 -7.15625 L 1.542969 -6.148438 Z M 0.664062 0 L 0.664062 -5.1875 L 1.542969 -5.1875 L 1.542969 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-9">
+<path style="stroke:none;" d="M 0.332031 -2.59375 C 0.332031 -3.554688 0.597656 -4.265625 1.132812 -4.726562 C 1.578125 -5.109375 2.121094 -5.304688 2.765625 -5.304688 C 3.476562 -5.304688 4.058594 -5.070312 4.511719 -4.601562 C 4.964844 -4.132812 5.191406 -3.488281 5.191406 -2.664062 C 5.191406 -2 5.089844 -1.472656 4.890625 -1.089844 C 4.691406 -0.707031 4.398438 -0.410156 4.015625 -0.199219 C 3.632812 0.0117188 3.214844 0.117188 2.765625 0.117188 C 2.039062 0.117188 1.449219 -0.117188 1.003906 -0.582031 C 0.554688 -1.046875 0.332031 -1.71875 0.332031 -2.59375 Z M 1.234375 -2.59375 C 1.234375 -1.929688 1.378906 -1.429688 1.671875 -1.101562 C 1.960938 -0.769531 2.324219 -0.605469 2.765625 -0.605469 C 3.199219 -0.605469 3.5625 -0.773438 3.851562 -1.101562 C 4.140625 -1.433594 4.289062 -1.941406 4.289062 -2.621094 C 4.289062 -3.261719 4.140625 -3.75 3.851562 -4.078125 C 3.558594 -4.410156 3.195312 -4.574219 2.765625 -4.574219 C 2.324219 -4.574219 1.960938 -4.410156 1.671875 -4.082031 C 1.382812 -3.753906 1.234375 -3.257812 1.234375 -2.59375 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph0-10">
+<path style="stroke:none;" d="M 0.308594 -1.546875 L 1.175781 -1.683594 C 1.226562 -1.335938 1.363281 -1.070312 1.585938 -0.882812 C 1.808594 -0.699219 2.117188 -0.605469 2.519531 -0.605469 C 2.921875 -0.605469 3.222656 -0.6875 3.417969 -0.851562 C 3.613281 -1.015625 3.710938 -1.210938 3.710938 -1.429688 C 3.710938 -1.628906 3.625 -1.785156 3.453125 -1.898438 C 3.332031 -1.976562 3.03125 -2.078125 2.554688 -2.195312 C 1.910156 -2.359375 1.460938 -2.5 1.214844 -2.621094 C 0.964844 -2.738281 0.777344 -2.902344 0.648438 -3.113281 C 0.519531 -3.324219 0.453125 -3.554688 0.453125 -3.808594 C 0.453125 -4.039062 0.507812 -4.253906 0.613281 -4.449219 C 0.71875 -4.648438 0.863281 -4.8125 1.046875 -4.941406 C 1.183594 -5.042969 1.367188 -5.128906 1.605469 -5.199219 C 1.839844 -5.269531 2.09375 -5.304688 2.363281 -5.304688 C 2.769531 -5.304688 3.128906 -5.242188 3.433594 -5.125 C 3.742188 -5.007812 3.96875 -4.851562 4.117188 -4.652344 C 4.261719 -4.453125 4.363281 -4.183594 4.417969 -3.847656 L 3.558594 -3.730469 C 3.519531 -3.996094 3.40625 -4.207031 3.21875 -4.355469 C 3.03125 -4.503906 2.769531 -4.578125 2.425781 -4.578125 C 2.023438 -4.578125 1.734375 -4.511719 1.5625 -4.378906 C 1.390625 -4.246094 1.304688 -4.089844 1.304688 -3.910156 C 1.304688 -3.796875 1.339844 -3.695312 1.410156 -3.601562 C 1.484375 -3.507812 1.59375 -3.429688 1.75 -3.367188 C 1.835938 -3.335938 2.09375 -3.261719 2.523438 -3.144531 C 3.144531 -2.976562 3.578125 -2.84375 3.824219 -2.738281 C 4.070312 -2.632812 4.265625 -2.476562 4.40625 -2.273438 C 4.546875 -2.074219 4.613281 -1.824219 4.613281 -1.523438 C 4.613281 -1.230469 4.527344 -0.953125 4.359375 -0.695312 C 4.1875 -0.4375 3.941406 -0.238281 3.617188 -0.09375 C 3.296875 0.046875 2.929688 0.117188 2.523438 0.117188 C 1.851562 0.117188 1.335938 -0.0234375 0.984375 -0.304688 C 0.632812 -0.582031 0.40625 -0.996094 0.308594 -1.546875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph1-1">
+<path style="stroke:none;" d="M 0.375 -1.6875 L 1.203125 -1.757812 C 1.265625 -1.351562 1.410156 -1.050781 1.632812 -0.847656 C 1.855469 -0.644531 2.125 -0.539062 2.445312 -0.539062 C 2.824219 -0.539062 3.148438 -0.683594 3.410156 -0.972656 C 3.671875 -1.257812 3.804688 -1.640625 3.804688 -2.113281 C 3.804688 -2.566406 3.679688 -2.921875 3.425781 -3.179688 C 3.171875 -3.441406 2.839844 -3.574219 2.429688 -3.574219 C 2.175781 -3.574219 1.945312 -3.515625 1.742188 -3.398438 C 1.535156 -3.28125 1.375 -3.132812 1.257812 -2.949219 L 0.515625 -3.046875 L 1.136719 -6.355469 L 4.34375 -6.355469 L 4.34375 -5.597656 L 1.769531 -5.597656 L 1.421875 -3.867188 C 1.808594 -4.136719 2.214844 -4.273438 2.640625 -4.273438 C 3.203125 -4.273438 3.679688 -4.078125 4.066406 -3.6875 C 4.453125 -3.296875 4.644531 -2.796875 4.644531 -2.183594 C 4.644531 -1.601562 4.476562 -1.097656 4.136719 -0.671875 C 3.722656 -0.152344 3.15625 0.109375 2.445312 0.109375 C 1.859375 0.109375 1.378906 -0.0546875 1.007812 -0.382812 C 0.636719 -0.710938 0.425781 -1.144531 0.375 -1.6875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-2">
+<path style="stroke:none;" d="M 4.476562 -4.863281 L 3.691406 -4.804688 C 3.621094 -5.113281 3.523438 -5.339844 3.390625 -5.480469 C 3.179688 -5.707031 2.914062 -5.820312 2.601562 -5.820312 C 2.351562 -5.820312 2.128906 -5.75 1.9375 -5.609375 C 1.6875 -5.425781 1.492188 -5.160156 1.347656 -4.8125 C 1.203125 -4.464844 1.132812 -3.96875 1.125 -3.320312 C 1.316406 -3.613281 1.546875 -3.828125 1.824219 -3.96875 C 2.097656 -4.109375 2.386719 -4.179688 2.6875 -4.179688 C 3.214844 -4.179688 3.664062 -3.984375 4.035156 -3.597656 C 4.40625 -3.210938 4.59375 -2.707031 4.59375 -2.09375 C 4.59375 -1.6875 4.503906 -1.3125 4.332031 -0.964844 C 4.15625 -0.617188 3.917969 -0.351562 3.613281 -0.167969 C 3.308594 0.015625 2.960938 0.109375 2.574219 0.109375 C 1.914062 0.109375 1.378906 -0.132812 0.960938 -0.617188 C 0.546875 -1.101562 0.339844 -1.902344 0.339844 -3.015625 C 0.339844 -4.261719 0.570312 -5.164062 1.027344 -5.730469 C 1.429688 -6.222656 1.96875 -6.46875 2.648438 -6.46875 C 3.15625 -6.46875 3.570312 -6.328125 3.894531 -6.042969 C 4.21875 -5.757812 4.414062 -5.367188 4.476562 -4.863281 Z M 1.25 -2.085938 C 1.25 -1.8125 1.304688 -1.554688 1.421875 -1.304688 C 1.539062 -1.054688 1.699219 -0.867188 1.90625 -0.734375 C 2.113281 -0.605469 2.332031 -0.539062 2.5625 -0.539062 C 2.894531 -0.539062 3.183594 -0.675781 3.421875 -0.945312 C 3.664062 -1.214844 3.785156 -1.582031 3.785156 -2.042969 C 3.785156 -2.488281 3.664062 -2.839844 3.429688 -3.097656 C 3.191406 -3.351562 2.890625 -3.480469 2.53125 -3.480469 C 2.171875 -3.480469 1.871094 -3.351562 1.621094 -3.097656 C 1.371094 -2.839844 1.25 -2.503906 1.25 -2.085938 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph1-3">
+<path style="stroke:none;" d="M 0.425781 -5.597656 L 0.425781 -6.359375 L 4.597656 -6.359375 L 4.597656 -5.742188 C 4.1875 -5.304688 3.78125 -4.726562 3.378906 -4.003906 C 2.976562 -3.28125 2.664062 -2.535156 2.445312 -1.769531 C 2.285156 -1.230469 2.183594 -0.640625 2.140625 0 L 1.328125 0 C 1.335938 -0.507812 1.4375 -1.117188 1.625 -1.835938 C 1.816406 -2.554688 2.089844 -3.246094 2.445312 -3.914062 C 2.800781 -4.578125 3.179688 -5.140625 3.582031 -5.597656 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph2-1">
+<path style="stroke:none;" d="M 0 -0.734375 L -7.15625 -0.734375 L -7.15625 -3.417969 C -7.15625 -3.964844 -7.085938 -4.402344 -6.941406 -4.734375 C -6.796875 -5.0625 -6.574219 -5.324219 -6.273438 -5.511719 C -5.972656 -5.699219 -5.65625 -5.789062 -5.328125 -5.789062 C -5.023438 -5.789062 -4.734375 -5.707031 -4.460938 -5.542969 C -4.191406 -5.375 -3.976562 -5.125 -3.808594 -4.789062 C -3.679688 -5.222656 -3.464844 -5.554688 -3.160156 -5.789062 C -2.851562 -6.023438 -2.492188 -6.136719 -2.074219 -6.136719 C -1.738281 -6.136719 -1.429688 -6.066406 -1.140625 -5.925781 C -0.851562 -5.785156 -0.628906 -5.609375 -0.472656 -5.398438 C -0.316406 -5.191406 -0.199219 -4.929688 -0.121094 -4.617188 C -0.0390625 -4.304688 0 -3.917969 0 -3.460938 Z M -4.148438 -1.679688 L -4.148438 -3.226562 C -4.148438 -3.648438 -4.179688 -3.949219 -4.234375 -4.132812 C -4.304688 -4.371094 -4.421875 -4.554688 -4.589844 -4.675781 C -4.757812 -4.796875 -4.964844 -4.859375 -5.214844 -4.859375 C -5.453125 -4.859375 -5.660156 -4.800781 -5.84375 -4.6875 C -6.023438 -4.574219 -6.148438 -4.410156 -6.214844 -4.199219 C -6.28125 -3.988281 -6.3125 -3.625 -6.3125 -3.109375 L -6.3125 -1.679688 Z M -0.84375 -1.679688 L -0.84375 -3.460938 C -0.84375 -3.765625 -0.855469 -3.984375 -0.878906 -4.105469 C -0.917969 -4.324219 -0.984375 -4.507812 -1.074219 -4.652344 C -1.164062 -4.800781 -1.296875 -4.921875 -1.472656 -5.015625 C -1.648438 -5.109375 -1.847656 -5.15625 -2.074219 -5.15625 C -2.34375 -5.15625 -2.574219 -5.085938 -2.769531 -4.953125 C -2.96875 -4.816406 -3.105469 -4.625 -3.1875 -4.382812 C -3.265625 -4.140625 -3.304688 -3.789062 -3.304688 -3.335938 L -3.304688 -1.679688 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-2">
+<path style="stroke:none;" d="M 1.996094 -0.621094 L 1.171875 -0.523438 C 1.222656 -0.714844 1.25 -0.882812 1.25 -1.023438 C 1.25 -1.21875 1.21875 -1.375 1.152344 -1.492188 C 1.085938 -1.609375 0.996094 -1.707031 0.878906 -1.78125 C 0.789062 -1.835938 0.574219 -1.925781 0.226562 -2.050781 C 0.175781 -2.066406 0.105469 -2.09375 0.0078125 -2.128906 L -5.1875 -0.160156 L -5.1875 -1.109375 L -2.183594 -2.1875 C -1.800781 -2.328125 -1.402344 -2.453125 -0.980469 -2.5625 C -1.382812 -2.664062 -1.777344 -2.785156 -2.164062 -2.925781 L -5.1875 -4.03125 L -5.1875 -4.914062 L 0.0859375 -2.9375 C 0.65625 -2.726562 1.050781 -2.5625 1.265625 -2.445312 C 1.554688 -2.289062 1.765625 -2.109375 1.902344 -1.910156 C 2.039062 -1.707031 2.105469 -1.464844 2.105469 -1.1875 C 2.105469 -1.015625 2.070312 -0.828125 1.996094 -0.621094 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-3">
+<path style="stroke:none;" d="M -0.785156 -2.578125 L -0.0078125 -2.703125 C 0.0429688 -2.457031 0.0703125 -2.234375 0.0703125 -2.039062 C 0.0703125 -1.722656 0.0195312 -1.476562 -0.0820312 -1.296875 C -0.183594 -1.121094 -0.316406 -1 -0.480469 -0.929688 C -0.644531 -0.855469 -0.992188 -0.820312 -1.519531 -0.820312 L -4.5 -0.820312 L -4.5 -0.175781 L -5.1875 -0.175781 L -5.1875 -0.820312 L -6.46875 -0.820312 L -6.996094 -1.695312 L -5.1875 -1.695312 L -5.1875 -2.578125 L -4.5 -2.578125 L -4.5 -1.695312 L -1.46875 -1.695312 C -1.21875 -1.695312 -1.058594 -1.710938 -0.984375 -1.742188 C -0.914062 -1.773438 -0.859375 -1.820312 -0.816406 -1.890625 C -0.773438 -1.960938 -0.75 -2.0625 -0.75 -2.191406 C -0.75 -2.289062 -0.761719 -2.417969 -0.785156 -2.578125 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-4">
+<path style="stroke:none;" d="M -1.671875 -4.210938 L -1.558594 -5.117188 C -1.027344 -4.972656 -0.617188 -4.707031 -0.320312 -4.320312 C -0.0273438 -3.933594 0.117188 -3.4375 0.117188 -2.835938 C 0.117188 -2.078125 -0.117188 -1.476562 -0.582031 -1.03125 C -1.050781 -0.585938 -1.707031 -0.367188 -2.546875 -0.367188 C -3.421875 -0.367188 -4.097656 -0.589844 -4.578125 -1.039062 C -5.0625 -1.488281 -5.304688 -2.070312 -5.304688 -2.789062 C -5.304688 -3.480469 -5.066406 -4.046875 -4.59375 -4.488281 C -4.121094 -4.925781 -3.457031 -5.148438 -2.601562 -5.148438 C -2.550781 -5.148438 -2.472656 -5.144531 -2.367188 -5.140625 L -2.367188 -1.273438 C -1.796875 -1.304688 -1.363281 -1.46875 -1.058594 -1.757812 C -0.757812 -2.046875 -0.605469 -2.410156 -0.605469 -2.84375 C -0.605469 -3.164062 -0.691406 -3.4375 -0.859375 -3.667969 C -1.027344 -3.894531 -1.296875 -4.074219 -1.671875 -4.210938 Z M -3.089844 -1.324219 L -3.089844 -4.21875 C -3.527344 -4.179688 -3.855469 -4.070312 -4.070312 -3.886719 C -4.410156 -3.605469 -4.578125 -3.242188 -4.578125 -2.796875 C -4.578125 -2.394531 -4.445312 -2.054688 -4.175781 -1.78125 C -3.90625 -1.503906 -3.542969 -1.351562 -3.089844 -1.324219 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph2-5">
+<path style="stroke:none;" d="M -1.546875 -0.308594 L -1.683594 -1.175781 C -1.335938 -1.226562 -1.070312 -1.363281 -0.882812 -1.585938 C -0.699219 -1.808594 -0.605469 -2.117188 -0.605469 -2.519531 C -0.605469 -2.921875 -0.6875 -3.222656 -0.851562 -3.417969 C -1.015625 -3.613281 -1.210938 -3.710938 -1.429688 -3.710938 C -1.628906 -3.710938 -1.785156 -3.625 -1.898438 -3.453125 C -1.976562 -3.332031 -2.078125 -3.03125 -2.195312 -2.554688 C -2.359375 -1.910156 -2.5 -1.460938 -2.621094 -1.214844 C -2.738281 -0.964844 -2.902344 -0.777344 -3.113281 -0.648438 C -3.324219 -0.519531 -3.554688 -0.453125 -3.808594 -0.453125 C -4.039062 -0.453125 -4.253906 -0.507812 -4.449219 -0.613281 C -4.648438 -0.71875 -4.8125 -0.863281 -4.941406 -1.046875 C -5.042969 -1.183594 -5.128906 -1.367188 -5.199219 -1.605469 C -5.269531 -1.839844 -5.304688 -2.09375 -5.304688 -2.363281 C -5.304688 -2.769531 -5.242188 -3.128906 -5.125 -3.433594 C -5.007812 -3.742188 -4.851562 -3.96875 -4.652344 -4.117188 C -4.453125 -4.261719 -4.183594 -4.363281 -3.847656 -4.417969 L -3.730469 -3.558594 C -3.996094 -3.519531 -4.207031 -3.40625 -4.355469 -3.21875 C -4.503906 -3.03125 -4.578125 -2.769531 -4.578125 -2.425781 C -4.578125 -2.023438 -4.511719 -1.734375 -4.378906 -1.5625 C -4.246094 -1.390625 -4.089844 -1.304688 -3.910156 -1.304688 C -3.796875 -1.304688 -3.695312 -1.339844 -3.601562 -1.410156 C -3.507812 -1.484375 -3.429688 -1.59375 -3.367188 -1.75 C -3.335938 -1.835938 -3.261719 -2.09375 -3.144531 -2.523438 C -2.976562 -3.144531 -2.84375 -3.578125 -2.738281 -3.824219 C -2.632812 -4.070312 -2.476562 -4.265625 -2.273438 -4.40625 C -2.074219 -4.546875 -1.824219 -4.613281 -1.523438 -4.613281 C -1.230469 -4.613281 -0.953125 -4.527344 -0.695312 -4.359375 C -0.4375 -4.1875 -0.238281 -3.941406 -0.09375 -3.617188 C 0.046875 -3.296875 0.117188 -2.929688 0.117188 -2.523438 C 0.117188 -1.851562 -0.0234375 -1.335938 -0.304688 -0.984375 C -0.582031 -0.632812 -0.996094 -0.40625 -1.546875 -0.308594 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph3-1">
+<path style="stroke:none;" d="M 0.984375 0 L 0.984375 -8.589844 L 6.78125 -8.589844 L 6.78125 -7.578125 L 2.121094 -7.578125 L 2.121094 -4.914062 L 6.152344 -4.914062 L 6.152344 -3.902344 L 2.121094 -3.902344 L 2.121094 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-2">
+<path style="stroke:none;" d="M 0.796875 -7.375 L 0.796875 -8.589844 L 1.851562 -8.589844 L 1.851562 -7.375 Z M 0.796875 0 L 0.796875 -6.222656 L 1.851562 -6.222656 L 1.851562 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-3">
+<path style="stroke:none;" d="M 0.765625 0 L 0.765625 -8.589844 L 1.820312 -8.589844 L 1.820312 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-4">
+<path style="stroke:none;" d="M 5.050781 -2.003906 L 6.140625 -1.867188 C 5.96875 -1.230469 5.648438 -0.738281 5.1875 -0.386719 C 4.722656 -0.0351562 4.125 0.140625 3.40625 0.140625 C 2.496094 0.140625 1.773438 -0.140625 1.238281 -0.699219 C 0.707031 -1.261719 0.4375 -2.046875 0.4375 -3.058594 C 0.4375 -4.105469 0.710938 -4.917969 1.25 -5.496094 C 1.789062 -6.074219 2.484375 -6.363281 3.34375 -6.363281 C 4.175781 -6.363281 4.859375 -6.078125 5.382812 -5.515625 C 5.910156 -4.949219 6.175781 -4.148438 6.175781 -3.125 C 6.175781 -3.0625 6.171875 -2.96875 6.171875 -2.84375 L 1.53125 -2.84375 C 1.570312 -2.160156 1.761719 -1.632812 2.109375 -1.273438 C 2.457031 -0.910156 2.890625 -0.726562 3.410156 -0.726562 C 3.796875 -0.726562 4.125 -0.828125 4.398438 -1.03125 C 4.671875 -1.234375 4.890625 -1.558594 5.050781 -2.003906 Z M 1.585938 -3.710938 L 5.0625 -3.710938 C 5.015625 -4.234375 4.882812 -4.625 4.664062 -4.886719 C 4.328125 -5.292969 3.890625 -5.496094 3.359375 -5.496094 C 2.875 -5.496094 2.464844 -5.335938 2.136719 -5.007812 C 1.804688 -4.683594 1.625 -4.25 1.585938 -3.710938 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-5">
+<path style="stroke:none;" d="M 0.367188 -1.859375 L 1.414062 -2.023438 C 1.472656 -1.605469 1.632812 -1.28125 1.902344 -1.0625 C 2.167969 -0.839844 2.542969 -0.726562 3.023438 -0.726562 C 3.507812 -0.726562 3.867188 -0.824219 4.101562 -1.023438 C 4.335938 -1.21875 4.453125 -1.453125 4.453125 -1.71875 C 4.453125 -1.957031 4.351562 -2.140625 4.140625 -2.28125 C 3.996094 -2.375 3.640625 -2.492188 3.0625 -2.636719 C 2.289062 -2.832031 1.753906 -3 1.457031 -3.144531 C 1.15625 -3.285156 0.929688 -3.484375 0.777344 -3.734375 C 0.621094 -3.988281 0.546875 -4.265625 0.546875 -4.570312 C 0.546875 -4.847656 0.609375 -5.105469 0.734375 -5.339844 C 0.863281 -5.578125 1.035156 -5.773438 1.253906 -5.929688 C 1.417969 -6.050781 1.640625 -6.152344 1.925781 -6.238281 C 2.207031 -6.320312 2.511719 -6.363281 2.835938 -6.363281 C 3.324219 -6.363281 3.753906 -6.292969 4.121094 -6.152344 C 4.492188 -6.011719 4.765625 -5.820312 4.9375 -5.582031 C 5.113281 -5.339844 5.234375 -5.019531 5.304688 -4.617188 L 4.273438 -4.476562 C 4.226562 -4.796875 4.089844 -5.046875 3.863281 -5.226562 C 3.640625 -5.40625 3.320312 -5.496094 2.914062 -5.496094 C 2.429688 -5.496094 2.082031 -5.414062 1.875 -5.257812 C 1.667969 -5.097656 1.5625 -4.90625 1.5625 -4.695312 C 1.5625 -4.558594 1.609375 -4.433594 1.695312 -4.324219 C 1.78125 -4.210938 1.914062 -4.117188 2.097656 -4.042969 C 2.203125 -4.003906 2.515625 -3.914062 3.03125 -3.773438 C 3.777344 -3.574219 4.296875 -3.410156 4.589844 -3.285156 C 4.886719 -3.15625 5.117188 -2.972656 5.285156 -2.730469 C 5.453125 -2.488281 5.539062 -2.1875 5.539062 -1.828125 C 5.539062 -1.476562 5.433594 -1.144531 5.230469 -0.835938 C 5.023438 -0.523438 4.726562 -0.285156 4.34375 -0.113281 C 3.957031 0.0546875 3.515625 0.140625 3.03125 0.140625 C 2.222656 0.140625 1.605469 -0.0273438 1.179688 -0.363281 C 0.757812 -0.699219 0.484375 -1.195312 0.367188 -1.859375 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-6">
+<path style="stroke:none;" d="M 0.234375 0 L 0.234375 -0.855469 L 4.195312 -5.402344 C 3.746094 -5.378906 3.351562 -5.367188 3.007812 -5.367188 L 0.46875 -5.367188 L 0.46875 -6.222656 L 5.554688 -6.222656 L 5.554688 -5.523438 L 2.1875 -1.578125 L 1.535156 -0.855469 C 2.007812 -0.890625 2.453125 -0.90625 2.867188 -0.90625 L 5.742188 -0.90625 L 5.742188 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-7">
+<path style="stroke:none;" d="M 0.398438 -3.109375 C 0.398438 -4.261719 0.71875 -5.117188 1.359375 -5.671875 C 1.894531 -6.132812 2.546875 -6.363281 3.316406 -6.363281 C 4.171875 -6.363281 4.871094 -6.082031 5.414062 -5.523438 C 5.957031 -4.960938 6.226562 -4.1875 6.226562 -3.199219 C 6.226562 -2.398438 6.109375 -1.769531 5.867188 -1.308594 C 5.628906 -0.851562 5.277344 -0.492188 4.820312 -0.242188 C 4.359375 0.0117188 3.859375 0.140625 3.316406 0.140625 C 2.445312 0.140625 1.742188 -0.140625 1.203125 -0.695312 C 0.667969 -1.253906 0.398438 -2.0625 0.398438 -3.109375 Z M 1.484375 -3.109375 C 1.484375 -2.3125 1.65625 -1.71875 2.003906 -1.320312 C 2.351562 -0.925781 2.789062 -0.726562 3.316406 -0.726562 C 3.839844 -0.726562 4.273438 -0.925781 4.625 -1.324219 C 4.972656 -1.722656 5.144531 -2.328125 5.144531 -3.148438 C 5.144531 -3.917969 4.96875 -4.5 4.621094 -4.894531 C 4.269531 -5.292969 3.835938 -5.492188 3.316406 -5.492188 C 2.789062 -5.492188 2.351562 -5.292969 2.003906 -4.898438 C 1.65625 -4.503906 1.484375 -3.90625 1.484375 -3.109375 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-8">
+<path style="stroke:none;" d="M 1.042969 0 L 1.042969 -5.402344 L 0.109375 -5.402344 L 0.109375 -6.222656 L 1.042969 -6.222656 L 1.042969 -6.882812 C 1.042969 -7.300781 1.078125 -7.613281 1.15625 -7.816406 C 1.257812 -8.089844 1.433594 -8.3125 1.691406 -8.480469 C 1.945312 -8.652344 2.304688 -8.734375 2.765625 -8.734375 C 3.0625 -8.734375 3.390625 -8.703125 3.75 -8.632812 L 3.59375 -7.710938 C 3.375 -7.75 3.164062 -7.769531 2.96875 -7.769531 C 2.648438 -7.769531 2.421875 -7.703125 2.289062 -7.5625 C 2.15625 -7.425781 2.09375 -7.171875 2.09375 -6.796875 L 2.09375 -6.222656 L 3.304688 -6.222656 L 3.304688 -5.402344 L 2.09375 -5.402344 L 2.09375 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-9">
+<path style="stroke:none;" d="M 0.789062 0 L 0.789062 -6.222656 L 1.734375 -6.222656 L 1.734375 -5.351562 C 1.929688 -5.65625 2.1875 -5.898438 2.515625 -6.085938 C 2.839844 -6.269531 3.207031 -6.363281 3.621094 -6.363281 C 4.082031 -6.363281 4.460938 -6.265625 4.753906 -6.078125 C 5.050781 -5.886719 5.257812 -5.617188 5.378906 -5.273438 C 5.871094 -6 6.511719 -6.363281 7.300781 -6.363281 C 7.917969 -6.363281 8.390625 -6.191406 8.726562 -5.851562 C 9.058594 -5.507812 9.222656 -4.984375 9.222656 -4.273438 L 9.222656 0 L 8.171875 0 L 8.171875 -3.921875 C 8.171875 -4.34375 8.140625 -4.644531 8.070312 -4.832031 C 8.003906 -5.015625 7.878906 -5.164062 7.699219 -5.28125 C 7.519531 -5.394531 7.308594 -5.449219 7.066406 -5.449219 C 6.628906 -5.449219 6.265625 -5.304688 5.976562 -5.011719 C 5.6875 -4.722656 5.542969 -4.257812 5.542969 -3.617188 L 5.542969 0 L 4.488281 0 L 4.488281 -4.042969 C 4.488281 -4.511719 4.402344 -4.863281 4.230469 -5.097656 C 4.058594 -5.332031 3.777344 -5.449219 3.386719 -5.449219 C 3.089844 -5.449219 2.816406 -5.371094 2.5625 -5.214844 C 2.3125 -5.058594 2.128906 -4.828125 2.015625 -4.53125 C 1.902344 -4.230469 1.84375 -3.796875 1.84375 -3.226562 L 1.84375 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-10">
+<path style="stroke:none;" d="M 4.828125 0 L 4.828125 -0.785156 C 4.433594 -0.167969 3.851562 0.140625 3.085938 0.140625 C 2.589844 0.140625 2.136719 0.00390625 1.71875 -0.269531 C 1.304688 -0.542969 0.980469 -0.925781 0.753906 -1.414062 C 0.523438 -1.90625 0.410156 -2.46875 0.410156 -3.105469 C 0.410156 -3.726562 0.515625 -4.289062 0.71875 -4.796875 C 0.925781 -5.300781 1.238281 -5.6875 1.652344 -5.960938 C 2.066406 -6.230469 2.53125 -6.363281 3.039062 -6.363281 C 3.414062 -6.363281 3.75 -6.285156 4.042969 -6.125 C 4.335938 -5.96875 4.574219 -5.761719 4.757812 -5.507812 L 4.757812 -8.589844 L 5.804688 -8.589844 L 5.804688 0 Z M 1.492188 -3.105469 C 1.492188 -2.308594 1.664062 -1.710938 2 -1.320312 C 2.335938 -0.925781 2.730469 -0.726562 3.1875 -0.726562 C 3.648438 -0.726562 4.039062 -0.914062 4.363281 -1.292969 C 4.683594 -1.667969 4.84375 -2.242188 4.84375 -3.015625 C 4.84375 -3.867188 4.679688 -4.492188 4.351562 -4.890625 C 4.023438 -5.289062 3.621094 -5.492188 3.140625 -5.492188 C 2.671875 -5.492188 2.28125 -5.296875 1.964844 -4.914062 C 1.652344 -4.53125 1.492188 -3.929688 1.492188 -3.105469 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph3-11">
+<path style="stroke:none;" d="M 4.867188 0 L 4.867188 -0.914062 C 4.382812 -0.210938 3.726562 0.140625 2.894531 0.140625 C 2.527344 0.140625 2.183594 0.0703125 1.867188 -0.0703125 C 1.546875 -0.210938 1.3125 -0.386719 1.15625 -0.601562 C 1.003906 -0.8125 0.894531 -1.074219 0.832031 -1.382812 C 0.789062 -1.589844 0.765625 -1.917969 0.765625 -2.367188 L 0.765625 -6.222656 L 1.820312 -6.222656 L 1.820312 -2.773438 C 1.820312 -2.222656 1.84375 -1.851562 1.886719 -1.65625 C 1.953125 -1.378906 2.09375 -1.164062 2.308594 -1.003906 C 2.523438 -0.847656 2.789062 -0.765625 3.105469 -0.765625 C 3.421875 -0.765625 3.71875 -0.847656 3.996094 -1.011719 C 4.273438 -1.171875 4.46875 -1.394531 4.585938 -1.671875 C 4.699219 -1.953125 4.757812 -2.359375 4.757812 -2.890625 L 4.757812 -6.222656 L 5.8125 -6.222656 L 5.8125 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-0">
+<path style="stroke:none;" d=""/>
+</symbol>
+<symbol overflow="visible" id="glyph4-1">
+<path style="stroke:none;" d="M 0.953125 0 L 0.953125 -9.304688 L 4.445312 -9.304688 C 5.15625 -9.304688 5.722656 -9.210938 6.152344 -9.023438 C 6.582031 -8.835938 6.921875 -8.546875 7.164062 -8.152344 C 7.40625 -7.761719 7.527344 -7.351562 7.527344 -6.925781 C 7.527344 -6.527344 7.421875 -6.152344 7.203125 -5.800781 C 6.988281 -5.449219 6.664062 -5.167969 6.226562 -4.953125 C 6.789062 -4.785156 7.222656 -4.503906 7.523438 -4.105469 C 7.828125 -3.707031 7.980469 -3.238281 7.980469 -2.699219 C 7.980469 -2.261719 7.886719 -1.855469 7.703125 -1.480469 C 7.519531 -1.105469 7.292969 -0.820312 7.019531 -0.617188 C 6.75 -0.414062 6.410156 -0.257812 6 -0.15625 C 5.59375 -0.0507812 5.09375 0 4.5 0 Z M 2.183594 -5.394531 L 4.195312 -5.394531 C 4.742188 -5.394531 5.132812 -5.429688 5.371094 -5.503906 C 5.683594 -5.597656 5.917969 -5.75 6.078125 -5.96875 C 6.238281 -6.183594 6.316406 -6.453125 6.316406 -6.78125 C 6.316406 -7.089844 6.242188 -7.359375 6.09375 -7.59375 C 5.945312 -7.828125 5.734375 -7.992188 5.460938 -8.078125 C 5.183594 -8.164062 4.710938 -8.207031 4.042969 -8.207031 L 2.183594 -8.207031 Z M 2.183594 -1.097656 L 4.5 -1.097656 C 4.898438 -1.097656 5.175781 -1.113281 5.339844 -1.140625 C 5.621094 -1.191406 5.859375 -1.277344 6.050781 -1.398438 C 6.242188 -1.515625 6.394531 -1.6875 6.519531 -1.914062 C 6.640625 -2.140625 6.703125 -2.402344 6.703125 -2.699219 C 6.703125 -3.046875 6.613281 -3.347656 6.4375 -3.601562 C 6.257812 -3.859375 6.011719 -4.039062 5.695312 -4.140625 C 5.382812 -4.246094 4.929688 -4.296875 4.335938 -4.296875 L 2.183594 -4.296875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-2">
+<path style="stroke:none;" d="M 0.429688 -3.371094 C 0.429688 -4.617188 0.777344 -5.542969 1.472656 -6.144531 C 2.050781 -6.644531 2.757812 -6.894531 3.59375 -6.894531 C 4.519531 -6.894531 5.277344 -6.589844 5.867188 -5.984375 C 6.453125 -5.375 6.746094 -4.535156 6.746094 -3.464844 C 6.746094 -2.597656 6.617188 -1.914062 6.355469 -1.417969 C 6.097656 -0.921875 5.71875 -0.535156 5.222656 -0.261719 C 4.722656 0.015625 4.179688 0.152344 3.59375 0.152344 C 2.648438 0.152344 1.886719 -0.148438 1.304688 -0.753906 C 0.722656 -1.359375 0.429688 -2.230469 0.429688 -3.371094 Z M 1.605469 -3.371094 C 1.605469 -2.507812 1.792969 -1.859375 2.171875 -1.429688 C 2.546875 -1 3.023438 -0.789062 3.59375 -0.789062 C 4.160156 -0.789062 4.632812 -1.003906 5.007812 -1.433594 C 5.382812 -1.867188 5.574219 -2.523438 5.574219 -3.410156 C 5.574219 -4.242188 5.382812 -4.875 5.003906 -5.304688 C 4.625 -5.734375 4.15625 -5.949219 3.59375 -5.949219 C 3.023438 -5.949219 2.546875 -5.734375 2.171875 -5.304688 C 1.792969 -4.878906 1.605469 -4.234375 1.605469 -3.371094 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-3">
+<path style="stroke:none;" d="M 0.398438 -2.011719 L 1.53125 -2.191406 C 1.59375 -1.738281 1.769531 -1.390625 2.058594 -1.148438 C 2.347656 -0.90625 2.753906 -0.789062 3.273438 -0.789062 C 3.800781 -0.789062 4.1875 -0.894531 4.445312 -1.109375 C 4.699219 -1.320312 4.824219 -1.570312 4.824219 -1.859375 C 4.824219 -2.117188 4.710938 -2.320312 4.488281 -2.46875 C 4.332031 -2.570312 3.941406 -2.699219 3.320312 -2.855469 C 2.480469 -3.066406 1.902344 -3.25 1.578125 -3.40625 C 1.253906 -3.558594 1.007812 -3.773438 0.839844 -4.046875 C 0.671875 -4.320312 0.589844 -4.621094 0.589844 -4.953125 C 0.589844 -5.253906 0.660156 -5.53125 0.796875 -5.785156 C 0.933594 -6.042969 1.121094 -6.253906 1.359375 -6.421875 C 1.535156 -6.554688 1.777344 -6.667969 2.085938 -6.757812 C 2.390625 -6.847656 2.722656 -6.894531 3.070312 -6.894531 C 3.601562 -6.894531 4.066406 -6.816406 4.464844 -6.664062 C 4.867188 -6.511719 5.160156 -6.304688 5.351562 -6.046875 C 5.542969 -5.785156 5.671875 -5.4375 5.746094 -5 L 4.628906 -4.851562 C 4.578125 -5.195312 4.429688 -5.46875 4.1875 -5.664062 C 3.945312 -5.859375 3.597656 -5.953125 3.15625 -5.953125 C 2.628906 -5.953125 2.253906 -5.867188 2.03125 -5.695312 C 1.808594 -5.519531 1.695312 -5.316406 1.695312 -5.085938 C 1.695312 -4.9375 1.742188 -4.804688 1.835938 -4.683594 C 1.929688 -4.5625 2.074219 -4.460938 2.273438 -4.378906 C 2.386719 -4.335938 2.722656 -4.242188 3.28125 -4.085938 C 4.089844 -3.871094 4.652344 -3.695312 4.972656 -3.558594 C 5.292969 -3.421875 5.542969 -3.21875 5.726562 -2.957031 C 5.90625 -2.695312 6 -2.371094 6 -1.980469 C 6 -1.601562 5.886719 -1.242188 5.664062 -0.90625 C 5.441406 -0.570312 5.121094 -0.308594 4.703125 -0.125 C 4.285156 0.0585938 3.8125 0.152344 3.28125 0.152344 C 2.40625 0.152344 1.738281 -0.03125 1.277344 -0.394531 C 0.820312 -0.757812 0.527344 -1.296875 0.398438 -2.011719 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-4">
+<path style="stroke:none;" d="M 3.351562 -1.023438 L 3.515625 -0.0117188 C 3.195312 0.0546875 2.90625 0.0898438 2.652344 0.0898438 C 2.238281 0.0898438 1.917969 0.0234375 1.6875 -0.109375 C 1.460938 -0.238281 1.300781 -0.410156 1.207031 -0.625 C 1.113281 -0.839844 1.066406 -1.289062 1.066406 -1.972656 L 1.066406 -5.851562 L 0.226562 -5.851562 L 0.226562 -6.742188 L 1.066406 -6.742188 L 1.066406 -8.410156 L 2.203125 -9.097656 L 2.203125 -6.742188 L 3.351562 -6.742188 L 3.351562 -5.851562 L 2.203125 -5.851562 L 2.203125 -1.910156 C 2.203125 -1.585938 2.222656 -1.375 2.261719 -1.28125 C 2.304688 -1.1875 2.367188 -1.113281 2.460938 -1.058594 C 2.550781 -1.003906 2.679688 -0.976562 2.851562 -0.976562 C 2.976562 -0.976562 3.144531 -0.992188 3.351562 -1.023438 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-5">
+<path style="stroke:none;" d="M 1.179688 0 L 1.179688 -1.300781 L 2.480469 -1.300781 L 2.480469 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-6">
+<path style="stroke:none;" d="M 1.003906 0 L 1.003906 -9.304688 L 4.511719 -9.304688 C 5.128906 -9.304688 5.601562 -9.277344 5.929688 -9.21875 C 6.386719 -9.140625 6.769531 -8.996094 7.078125 -8.78125 C 7.386719 -8.566406 7.636719 -8.269531 7.824219 -7.882812 C 8.011719 -7.5 8.105469 -7.074219 8.105469 -6.613281 C 8.105469 -5.824219 7.855469 -5.152344 7.351562 -4.605469 C 6.847656 -4.058594 5.9375 -3.78125 4.621094 -3.78125 L 2.234375 -3.78125 L 2.234375 0 Z M 2.234375 -4.882812 L 4.640625 -4.882812 C 5.4375 -4.882812 6 -5.03125 6.335938 -5.324219 C 6.667969 -5.621094 6.835938 -6.039062 6.835938 -6.578125 C 6.835938 -6.964844 6.738281 -7.296875 6.542969 -7.574219 C 6.34375 -7.851562 6.085938 -8.035156 5.765625 -8.125 C 5.558594 -8.179688 5.171875 -8.207031 4.613281 -8.207031 L 2.234375 -8.207031 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-7">
+<path style="stroke:none;" d="M 0.804688 2.597656 L 0.679688 1.523438 C 0.929688 1.589844 1.148438 1.625 1.332031 1.625 C 1.585938 1.625 1.789062 1.582031 1.941406 1.5 C 2.09375 1.414062 2.21875 1.296875 2.316406 1.140625 C 2.390625 1.027344 2.503906 0.746094 2.664062 0.292969 C 2.6875 0.230469 2.722656 0.136719 2.765625 0.0117188 L 0.210938 -6.742188 L 1.441406 -6.742188 L 2.84375 -2.835938 C 3.027344 -2.34375 3.1875 -1.820312 3.332031 -1.277344 C 3.464844 -1.800781 3.621094 -2.3125 3.800781 -2.8125 L 5.242188 -6.742188 L 6.386719 -6.742188 L 3.820312 0.113281 C 3.546875 0.855469 3.332031 1.363281 3.179688 1.644531 C 2.976562 2.019531 2.746094 2.296875 2.480469 2.472656 C 2.21875 2.648438 1.90625 2.734375 1.542969 2.734375 C 1.324219 2.734375 1.078125 2.6875 0.804688 2.597656 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-8">
+<path style="stroke:none;" d="M 0.855469 0 L 0.855469 -9.304688 L 2 -9.304688 L 2 -5.96875 C 2.53125 -6.585938 3.207031 -6.894531 4.019531 -6.894531 C 4.519531 -6.894531 4.953125 -6.796875 5.320312 -6.597656 C 5.6875 -6.402344 5.949219 -6.128906 6.109375 -5.78125 C 6.269531 -5.433594 6.347656 -4.933594 6.347656 -4.273438 L 6.347656 0 L 5.203125 0 L 5.203125 -4.273438 C 5.203125 -4.84375 5.082031 -5.257812 4.832031 -5.519531 C 4.585938 -5.78125 4.234375 -5.910156 3.78125 -5.910156 C 3.445312 -5.910156 3.125 -5.820312 2.828125 -5.644531 C 2.53125 -5.46875 2.316406 -5.234375 2.191406 -4.933594 C 2.0625 -4.632812 2 -4.21875 2 -3.6875 L 2 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-9">
+<path style="stroke:none;" d="M 0.855469 0 L 0.855469 -6.742188 L 1.886719 -6.742188 L 1.886719 -5.78125 C 2.382812 -6.523438 3.09375 -6.894531 4.03125 -6.894531 C 4.4375 -6.894531 4.808594 -6.820312 5.152344 -6.675781 C 5.492188 -6.527344 5.746094 -6.335938 5.914062 -6.101562 C 6.085938 -5.863281 6.203125 -5.582031 6.273438 -5.257812 C 6.3125 -5.046875 6.335938 -4.675781 6.335938 -4.144531 L 6.335938 0 L 5.191406 0 L 5.191406 -4.101562 C 5.191406 -4.566406 5.148438 -4.914062 5.058594 -5.144531 C 4.96875 -5.375 4.8125 -5.558594 4.585938 -5.695312 C 4.359375 -5.835938 4.09375 -5.902344 3.789062 -5.902344 C 3.304688 -5.902344 2.882812 -5.75 2.53125 -5.441406 C 2.175781 -5.132812 2 -4.546875 2 -3.679688 L 2 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-10">
+<path style="stroke:none;" d="M 0.855469 2.582031 L 0.855469 -6.742188 L 1.898438 -6.742188 L 1.898438 -5.867188 C 2.144531 -6.207031 2.421875 -6.464844 2.730469 -6.636719 C 3.039062 -6.808594 3.414062 -6.894531 3.851562 -6.894531 C 4.429688 -6.894531 4.9375 -6.746094 5.375 -6.449219 C 5.816406 -6.152344 6.148438 -5.734375 6.375 -5.195312 C 6.597656 -4.65625 6.710938 -4.066406 6.710938 -3.421875 C 6.710938 -2.730469 6.585938 -2.109375 6.339844 -1.558594 C 6.089844 -1.007812 5.730469 -0.582031 5.257812 -0.289062 C 4.785156 0.00390625 4.289062 0.152344 3.769531 0.152344 C 3.390625 0.152344 3.046875 0.0703125 2.746094 -0.0898438 C 2.441406 -0.25 2.195312 -0.453125 2 -0.699219 L 2 2.582031 Z M 1.890625 -3.332031 C 1.890625 -2.464844 2.066406 -1.824219 2.417969 -1.410156 C 2.769531 -0.996094 3.195312 -0.789062 3.695312 -0.789062 C 4.203125 -0.789062 4.636719 -1 5 -1.429688 C 5.359375 -1.859375 5.542969 -2.527344 5.542969 -3.429688 C 5.542969 -4.289062 5.363281 -4.929688 5.011719 -5.359375 C 4.65625 -5.785156 4.234375 -6 3.746094 -6 C 3.257812 -6 2.828125 -5.769531 2.453125 -5.316406 C 2.078125 -4.859375 1.890625 -4.199219 1.890625 -3.332031 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-11">
+<path style="stroke:none;" d="M 1.910156 0 L 0.851562 0 L 0.851562 -9.304688 L 1.992188 -9.304688 L 1.992188 -5.984375 C 2.476562 -6.589844 3.089844 -6.894531 3.839844 -6.894531 C 4.253906 -6.894531 4.648438 -6.808594 5.019531 -6.644531 C 5.390625 -6.476562 5.691406 -6.242188 5.933594 -5.9375 C 6.171875 -5.636719 6.359375 -5.269531 6.492188 -4.84375 C 6.628906 -4.414062 6.695312 -3.957031 6.695312 -3.472656 C 6.695312 -2.316406 6.410156 -1.425781 5.839844 -0.792969 C 5.269531 -0.164062 4.582031 0.152344 3.78125 0.152344 C 2.988281 0.152344 2.363281 -0.179688 1.910156 -0.84375 Z M 1.898438 -3.421875 C 1.898438 -2.613281 2.007812 -2.027344 2.226562 -1.667969 C 2.585938 -1.082031 3.074219 -0.789062 3.6875 -0.789062 C 4.1875 -0.789062 4.617188 -1.003906 4.984375 -1.4375 C 5.347656 -1.871094 5.527344 -2.519531 5.527344 -3.375 C 5.527344 -4.257812 5.355469 -4.90625 5.003906 -5.324219 C 4.65625 -5.742188 4.234375 -5.953125 3.738281 -5.953125 C 3.238281 -5.953125 2.808594 -5.738281 2.445312 -5.304688 C 2.082031 -4.871094 1.898438 -4.242188 1.898438 -3.421875 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-12">
+<path style="stroke:none;" d="M 0.863281 -7.992188 L 0.863281 -9.304688 L 2.007812 -9.304688 L 2.007812 -7.992188 Z M 0.863281 0 L 0.863281 -6.742188 L 2.007812 -6.742188 L 2.007812 0 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-13">
+<path style="stroke:none;" d="M 5.230469 0 L 5.230469 -0.851562 C 4.804688 -0.183594 4.175781 0.152344 3.34375 0.152344 C 2.808594 0.152344 2.3125 0.00390625 1.863281 -0.292969 C 1.414062 -0.589844 1.0625 -1 0.816406 -1.53125 C 0.570312 -2.0625 0.445312 -2.675781 0.445312 -3.363281 C 0.445312 -4.035156 0.554688 -4.648438 0.78125 -5.195312 C 1.003906 -5.742188 1.339844 -6.164062 1.789062 -6.457031 C 2.238281 -6.75 2.738281 -6.894531 3.292969 -6.894531 C 3.699219 -6.894531 4.0625 -6.808594 4.378906 -6.636719 C 4.695312 -6.464844 4.957031 -6.242188 5.15625 -5.96875 L 5.15625 -9.304688 L 6.289062 -9.304688 L 6.289062 0 Z M 1.617188 -3.363281 C 1.617188 -2.5 1.800781 -1.855469 2.164062 -1.429688 C 2.527344 -1 2.957031 -0.789062 3.453125 -0.789062 C 3.953125 -0.789062 4.375 -0.992188 4.726562 -1.398438 C 5.074219 -1.808594 5.25 -2.429688 5.25 -3.269531 C 5.25 -4.191406 5.070312 -4.867188 4.714844 -5.300781 C 4.359375 -5.730469 3.921875 -5.949219 3.402344 -5.949219 C 2.894531 -5.949219 2.46875 -5.742188 2.128906 -5.324219 C 1.789062 -4.910156 1.617188 -4.257812 1.617188 -3.363281 Z "/>
+</symbol>
+<symbol overflow="visible" id="glyph4-14">
+<path style="stroke:none;" d="M 4.84375 0 L 3.699219 0 L 3.699219 -7.28125 C 3.425781 -7.019531 3.066406 -6.757812 2.617188 -6.492188 C 2.171875 -6.230469 1.769531 -6.035156 1.414062 -5.902344 L 1.414062 -7.007812 C 2.054688 -7.308594 2.613281 -7.671875 3.089844 -8.101562 C 3.570312 -8.527344 3.90625 -8.941406 4.105469 -9.34375 L 4.84375 -9.34375 Z "/>
+</symbol>
+</g>
+<clipPath id="clip1">
+  <path d="M 94 19 L 96 19 L 96 215 L 94 215 Z "/>
+</clipPath>
+<clipPath id="clip2">
+  <path d="M 204 19 L 206 19 L 206 215 L 204 215 Z "/>
+</clipPath>
+<clipPath id="clip3">
+  <path d="M 314 19 L 316 19 L 316 215 L 314 215 Z "/>
+</clipPath>
+<clipPath id="clip4">
+  <path d="M 39 171 L 355 171 L 355 173 L 39 173 Z "/>
+</clipPath>
+<clipPath id="clip5">
+  <path d="M 39 107 L 355 107 L 355 109 L 39 109 Z "/>
+</clipPath>
+<clipPath id="clip6">
+  <path d="M 39 44 L 355 44 L 355 46 L 39 46 Z "/>
+</clipPath>
+</defs>
+<g id="surface18">
+<g clip-path="url(#clip1)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 95.160156 214.296875 L 95.160156 19 "/>
+</g>
+<g clip-path="url(#clip2)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 204.84375 214.296875 L 204.84375 19 "/>
+</g>
+<g clip-path="url(#clip3)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 314.53125 214.296875 L 314.53125 19 "/>
+</g>
+<g clip-path="url(#clip4)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 39 171.960938 L 355 171.960938 "/>
+</g>
+<g clip-path="url(#clip5)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 39 108.234375 L 355 108.234375 "/>
+</g>
+<g clip-path="url(#clip6)" clip-rule="nonzero">
+<path style="fill:none;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:0.5;stroke-dasharray:1,2;stroke-miterlimit:3.25;" d="M 39 44.511719 L 355 44.511719 "/>
+</g>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 55.070312 201 C 55.070312 200.183594 54.75 199.402344 54.171875 198.828125 C 53.597656 198.25 52.816406 197.929688 52 197.929688 C 51.183594 197.929688 50.402344 198.25 49.828125 198.828125 C 49.25 199.402344 48.929688 200.183594 48.929688 201 C 48.929688 201.816406 49.25 202.597656 49.828125 203.171875 C 50.402344 203.75 51.183594 204.070312 52 204.070312 C 52.816406 204.070312 53.597656 203.75 54.171875 203.171875 C 54.75 202.597656 55.070312 201.816406 55.070312 201 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 88.070312 176 C 88.070312 175.183594 87.75 174.402344 87.171875 173.828125 C 86.597656 173.25 85.816406 172.929688 85 172.929688 C 84.183594 172.929688 83.402344 173.25 82.828125 173.828125 C 82.25 174.402344 81.929688 175.183594 81.929688 176 C 81.929688 176.816406 82.25 177.597656 82.828125 178.171875 C 83.402344 178.75 84.183594 179.070312 85 179.070312 C 85.816406 179.070312 86.597656 178.75 87.171875 178.171875 C 87.75 177.597656 88.070312 176.816406 88.070312 176 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 121.070312 160 C 121.070312 159.183594 120.75 158.402344 120.171875 157.828125 C 119.597656 157.25 118.816406 156.929688 118 156.929688 C 117.183594 156.929688 116.402344 157.25 115.828125 157.828125 C 115.25 158.402344 114.929688 159.183594 114.929688 160 C 114.929688 160.816406 115.25 161.597656 115.828125 162.171875 C 116.402344 162.75 117.183594 163.070312 118 163.070312 C 118.816406 163.070312 119.597656 162.75 120.171875 162.171875 C 120.75 161.597656 121.070312 160.816406 121.070312 160 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 154.070312 142 C 154.070312 141.183594 153.75 140.402344 153.171875 139.828125 C 152.597656 139.25 151.816406 138.929688 151 138.929688 C 150.183594 138.929688 149.402344 139.25 148.828125 139.828125 C 148.25 140.402344 147.929688 141.183594 147.929688 142 C 147.929688 142.816406 148.25 143.597656 148.828125 144.171875 C 149.402344 144.75 150.183594 145.070312 151 145.070312 C 151.816406 145.070312 152.597656 144.75 153.171875 144.171875 C 153.75 143.597656 154.070312 142.816406 154.070312 142 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 187.070312 124 C 187.070312 123.183594 186.75 122.402344 186.171875 121.828125 C 185.597656 121.25 184.816406 120.929688 184 120.929688 C 183.183594 120.929688 182.402344 121.25 181.828125 121.828125 C 181.25 122.402344 180.929688 123.183594 180.929688 124 C 180.929688 124.816406 181.25 125.597656 181.828125 126.171875 C 182.402344 126.75 183.183594 127.070312 184 127.070312 C 184.816406 127.070312 185.597656 126.75 186.171875 126.171875 C 186.75 125.597656 187.070312 124.816406 187.070312 124 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 220.070312 105 C 220.070312 104.183594 219.75 103.402344 219.171875 102.828125 C 218.597656 102.25 217.816406 101.929688 217 101.929688 C 216.183594 101.929688 215.402344 102.25 214.828125 102.828125 C 214.25 103.402344 213.929688 104.183594 213.929688 105 C 213.929688 105.816406 214.25 106.597656 214.828125 107.171875 C 215.402344 107.75 216.183594 108.070312 217 108.070312 C 217.816406 108.070312 218.597656 107.75 219.171875 107.171875 C 219.75 106.597656 220.070312 105.816406 220.070312 105 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 253.070312 86 C 253.070312 85.183594 252.75 84.402344 252.171875 83.828125 C 251.597656 83.25 250.816406 82.929688 250 82.929688 C 249.183594 82.929688 248.402344 83.25 247.828125 83.828125 C 247.25 84.402344 246.929688 85.183594 246.929688 86 C 246.929688 86.816406 247.25 87.597656 247.828125 88.171875 C 248.402344 88.75 249.183594 89.070312 250 89.070312 C 250.816406 89.070312 251.597656 88.75 252.171875 88.171875 C 252.75 87.597656 253.070312 86.816406 253.070312 86 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 286.070312 67 C 286.070312 66.183594 285.75 65.402344 285.171875 64.828125 C 284.597656 64.25 283.816406 63.929688 283 63.929688 C 282.183594 63.929688 281.402344 64.25 280.828125 64.828125 C 280.25 65.402344 279.929688 66.183594 279.929688 67 C 279.929688 67.816406 280.25 68.597656 280.828125 69.171875 C 281.402344 69.75 282.183594 70.070312 283 70.070312 C 283.816406 70.070312 284.597656 69.75 285.171875 69.171875 C 285.75 68.597656 286.070312 67.816406 286.070312 67 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 319.070312 48 C 319.070312 47.183594 318.75 46.402344 318.171875 45.828125 C 317.597656 45.25 316.816406 44.929688 316 44.929688 C 315.183594 44.929688 314.402344 45.25 313.828125 45.828125 C 313.25 46.402344 312.929688 47.183594 312.929688 48 C 312.929688 48.816406 313.25 49.597656 313.828125 50.171875 C 314.402344 50.75 315.183594 51.070312 316 51.070312 C 316.816406 51.070312 317.597656 50.75 318.171875 50.171875 C 318.75 49.597656 319.070312 48.816406 319.070312 48 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 352.070312 29 C 352.070312 28.183594 351.75 27.402344 351.171875 26.828125 C 350.597656 26.25 349.816406 25.929688 349 25.929688 C 348.183594 25.929688 347.402344 26.25 346.828125 26.828125 C 346.25 27.402344 345.929688 28.183594 345.929688 29 C 345.929688 29.816406 346.25 30.597656 346.828125 31.171875 C 347.402344 31.75 348.183594 32.070312 349 32.070312 C 349.816406 32.070312 350.597656 31.75 351.171875 31.171875 C 351.75 30.597656 352.070312 29.816406 352.070312 29 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 55.070312 191 C 55.070312 190.183594 54.75 189.402344 54.171875 188.828125 C 53.597656 188.25 52.816406 187.929688 52 187.929688 C 51.183594 187.929688 50.402344 188.25 49.828125 188.828125 C 49.25 189.402344 48.929688 190.183594 48.929688 191 C 48.929688 191.816406 49.25 192.597656 49.828125 193.171875 C 50.402344 193.75 51.183594 194.070312 52 194.070312 C 52.816406 194.070312 53.597656 193.75 54.171875 193.171875 C 54.75 192.597656 55.070312 191.816406 55.070312 191 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 88.070312 181 C 88.070312 180.183594 87.75 179.402344 87.171875 178.828125 C 86.597656 178.25 85.816406 177.929688 85 177.929688 C 84.183594 177.929688 83.402344 178.25 82.828125 178.828125 C 82.25 179.402344 81.929688 180.183594 81.929688 181 C 81.929688 181.816406 82.25 182.597656 82.828125 183.171875 C 83.402344 183.75 84.183594 184.070312 85 184.070312 C 85.816406 184.070312 86.597656 183.75 87.171875 183.171875 C 87.75 182.597656 88.070312 181.816406 88.070312 181 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 121.070312 170 C 121.070312 169.183594 120.75 168.402344 120.171875 167.828125 C 119.597656 167.25 118.816406 166.929688 118 166.929688 C 117.183594 166.929688 116.402344 167.25 115.828125 167.828125 C 115.25 168.402344 114.929688 169.183594 114.929688 170 C 114.929688 170.816406 115.25 171.597656 115.828125 172.171875 C 116.402344 172.75 117.183594 173.070312 118 173.070312 C 118.816406 173.070312 119.597656 172.75 120.171875 172.171875 C 120.75 171.597656 121.070312 170.816406 121.070312 170 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 154.070312 157 C 154.070312 156.183594 153.75 155.402344 153.171875 154.828125 C 152.597656 154.25 151.816406 153.929688 151 153.929688 C 150.183594 153.929688 149.402344 154.25 148.828125 154.828125 C 148.25 155.402344 147.929688 156.183594 147.929688 157 C 147.929688 157.816406 148.25 158.597656 148.828125 159.171875 C 149.402344 159.75 150.183594 160.070312 151 160.070312 C 151.816406 160.070312 152.597656 159.75 153.171875 159.171875 C 153.75 158.597656 154.070312 157.816406 154.070312 157 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 187.070312 142 C 187.070312 141.183594 186.75 140.402344 186.171875 139.828125 C 185.597656 139.25 184.816406 138.929688 184 138.929688 C 183.183594 138.929688 182.402344 139.25 181.828125 139.828125 C 181.25 140.402344 180.929688 141.183594 180.929688 142 C 180.929688 142.816406 181.25 143.597656 181.828125 144.171875 C 182.402344 144.75 183.183594 145.070312 184 145.070312 C 184.816406 145.070312 185.597656 144.75 186.171875 144.171875 C 186.75 143.597656 187.070312 142.816406 187.070312 142 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 220.070312 125 C 220.070312 124.183594 219.75 123.402344 219.171875 122.828125 C 218.597656 122.25 217.816406 121.929688 217 121.929688 C 216.183594 121.929688 215.402344 122.25 214.828125 122.828125 C 214.25 123.402344 213.929688 124.183594 213.929688 125 C 213.929688 125.816406 214.25 126.597656 214.828125 127.171875 C 215.402344 127.75 216.183594 128.070312 217 128.070312 C 217.816406 128.070312 218.597656 127.75 219.171875 127.171875 C 219.75 126.597656 220.070312 125.816406 220.070312 125 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 253.070312 107 C 253.070312 106.183594 252.75 105.402344 252.171875 104.828125 C 251.597656 104.25 250.816406 103.929688 250 103.929688 C 249.183594 103.929688 248.402344 104.25 247.828125 104.828125 C 247.25 105.402344 246.929688 106.183594 246.929688 107 C 246.929688 107.816406 247.25 108.597656 247.828125 109.171875 C 248.402344 109.75 249.183594 110.070312 250 110.070312 C 250.816406 110.070312 251.597656 109.75 252.171875 109.171875 C 252.75 108.597656 253.070312 107.816406 253.070312 107 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 286.070312 88 C 286.070312 87.183594 285.75 86.402344 285.171875 85.828125 C 284.597656 85.25 283.816406 84.929688 283 84.929688 C 282.183594 84.929688 281.402344 85.25 280.828125 85.828125 C 280.25 86.402344 279.929688 87.183594 279.929688 88 C 279.929688 88.816406 280.25 89.597656 280.828125 90.171875 C 281.402344 90.75 282.183594 91.070312 283 91.070312 C 283.816406 91.070312 284.597656 90.75 285.171875 90.171875 C 285.75 89.597656 286.070312 88.816406 286.070312 88 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 319.070312 69 C 319.070312 68.183594 318.75 67.402344 318.171875 66.828125 C 317.597656 66.25 316.816406 65.929688 316 65.929688 C 315.183594 65.929688 314.402344 66.25 313.828125 66.828125 C 313.25 67.402344 312.929688 68.183594 312.929688 69 C 312.929688 69.816406 313.25 70.597656 313.828125 71.171875 C 314.402344 71.75 315.183594 72.070312 316 72.070312 C 316.816406 72.070312 317.597656 71.75 318.171875 71.171875 C 318.75 70.597656 319.070312 69.816406 319.070312 69 Z "/>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 352.070312 50 C 352.070312 49.183594 351.75 48.402344 351.171875 47.828125 C 350.597656 47.25 349.816406 46.929688 349 46.929688 C 348.183594 46.929688 347.402344 47.25 346.828125 47.828125 C 346.25 48.402344 345.929688 49.183594 345.929688 50 C 345.929688 50.816406 346.25 51.597656 346.828125 52.171875 C 347.402344 52.75 348.183594 53.070312 349 53.070312 C 349.816406 53.070312 350.597656 52.75 351.171875 52.171875 C 351.75 51.597656 352.070312 50.816406 352.070312 50 Z "/>
+<path style="fill:none;stroke-width:0.5;stroke-linecap:square;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 214.296875 L 39 214.296875 "/>
+<path style="fill:none;stroke-width:0.5;stroke-linecap:square;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 214.296875 L 39 19 "/>
+<path style="fill:none;stroke-width:0.5;stroke-linecap:square;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 19 L 355 19 "/>
+<path style="fill:none;stroke-width:0.5;stroke-linecap:square;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 19 L 355 214.296875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 95.160156 214.296875 L 95.160156 211.140625 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="89.659467" y="226.29874"/>
+  <use xlink:href="#glyph0-2" x="95.220991" y="226.29874"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 204.84375 214.296875 L 204.84375 211.140625 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="196.345465" y="226.29874"/>
+  <use xlink:href="#glyph0-2" x="201.906989" y="226.29874"/>
+  <use xlink:href="#glyph0-2" x="207.468512" y="226.29874"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 314.53125 214.296875 L 314.53125 211.140625 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="303.531463" y="226.29874"/>
+  <use xlink:href="#glyph0-2" x="309.092987" y="226.29874"/>
+  <use xlink:href="#glyph0-2" x="314.65451" y="226.29874"/>
+  <use xlink:href="#glyph0-2" x="320.216034" y="226.29874"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 51.511719 214.296875 L 51.511719 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 62.140625 214.296875 L 62.140625 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 70.824219 214.296875 L 70.824219 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 78.167969 214.296875 L 78.167969 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 84.53125 214.296875 L 84.53125 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 90.140625 214.296875 L 90.140625 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 128.179688 214.296875 L 128.179688 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 147.492188 214.296875 L 147.492188 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 161.195312 214.296875 L 161.195312 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 171.828125 214.296875 L 171.828125 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 180.511719 214.296875 L 180.511719 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 187.855469 214.296875 L 187.855469 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 194.214844 214.296875 L 194.214844 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 199.828125 214.296875 L 199.828125 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 237.863281 214.296875 L 237.863281 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 257.179688 214.296875 L 257.179688 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 270.882812 214.296875 L 270.882812 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 281.511719 214.296875 L 281.511719 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 290.199219 214.296875 L 290.199219 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 297.539062 214.296875 L 297.539062 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 303.902344 214.296875 L 303.902344 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 309.511719 214.296875 L 309.511719 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 347.550781 214.296875 L 347.550781 212.71875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 171.960938 L 42.160156 171.960938 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="19" y="176.961688"/>
+  <use xlink:href="#glyph0-2" x="24.561523" y="176.961688"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph1-1" x="30" y="172.81325"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 108.234375 L 42.160156 108.234375 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="19" y="113.23603"/>
+  <use xlink:href="#glyph0-2" x="24.561523" y="113.23603"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph1-2" x="30" y="109.087593"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 44.511719 L 42.160156 44.511719 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-1" x="19" y="49.510373"/>
+  <use xlink:href="#glyph0-2" x="24.561523" y="49.510373"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph1-3" x="30" y="45.361936"/>
+</g>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 205.28125 L 40.578125 205.28125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 197.320312 L 40.578125 197.320312 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 191.144531 L 40.578125 191.144531 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 186.097656 L 40.578125 186.097656 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 181.832031 L 40.578125 181.832031 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 178.136719 L 40.578125 178.136719 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 174.878906 L 40.578125 174.878906 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 152.777344 L 40.578125 152.777344 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 141.558594 L 40.578125 141.558594 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 133.59375 L 40.578125 133.59375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 127.417969 L 40.578125 127.417969 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 122.375 L 40.578125 122.375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 118.105469 L 40.578125 118.105469 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 114.410156 L 40.578125 114.410156 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 111.152344 L 40.578125 111.152344 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 89.050781 L 40.578125 89.050781 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 77.832031 L 40.578125 77.832031 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 69.871094 L 40.578125 69.871094 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 63.695312 L 40.578125 63.695312 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 58.648438 L 40.578125 58.648438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 54.382812 L 40.578125 54.382812 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 50.6875 L 40.578125 50.6875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 47.425781 L 40.578125 47.425781 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 39 25.328125 L 40.578125 25.328125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 95.160156 19 L 95.160156 22.160156 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 204.84375 19 L 204.84375 22.160156 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 314.53125 19 L 314.53125 22.160156 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 51.511719 19 L 51.511719 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 62.140625 19 L 62.140625 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 70.824219 19 L 70.824219 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 78.167969 19 L 78.167969 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 84.53125 19 L 84.53125 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 90.140625 19 L 90.140625 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 128.179688 19 L 128.179688 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 147.492188 19 L 147.492188 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 161.195312 19 L 161.195312 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 171.828125 19 L 171.828125 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 180.511719 19 L 180.511719 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 187.855469 19 L 187.855469 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 194.214844 19 L 194.214844 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 199.828125 19 L 199.828125 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 237.863281 19 L 237.863281 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 257.179688 19 L 257.179688 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 270.882812 19 L 270.882812 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 281.511719 19 L 281.511719 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 290.199219 19 L 290.199219 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 297.539062 19 L 297.539062 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 303.902344 19 L 303.902344 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 309.511719 19 L 309.511719 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 347.550781 19 L 347.550781 20.578125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 171.960938 L 351.839844 171.960938 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 108.234375 L 351.839844 108.234375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 44.511719 L 351.839844 44.511719 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 205.28125 L 353.421875 205.28125 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 197.320312 L 353.421875 197.320312 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 191.144531 L 353.421875 191.144531 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 186.097656 L 353.421875 186.097656 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 181.832031 L 353.421875 181.832031 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 178.136719 L 353.421875 178.136719 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 174.878906 L 353.421875 174.878906 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 152.777344 L 353.421875 152.777344 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 141.558594 L 353.421875 141.558594 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 133.59375 L 353.421875 133.59375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 127.417969 L 353.421875 127.417969 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 122.375 L 353.421875 122.375 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 118.105469 L 353.421875 118.105469 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 114.410156 L 353.421875 114.410156 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 111.152344 L 353.421875 111.152344 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 89.050781 L 353.421875 89.050781 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 77.832031 L 353.421875 77.832031 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 69.871094 L 353.421875 69.871094 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 63.695312 L 353.421875 63.695312 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 58.648438 L 353.421875 58.648438 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 54.382812 L 353.421875 54.382812 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 50.6875 L 353.421875 50.6875 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 47.425781 L 353.421875 47.425781 "/>
+<path style="fill:none;stroke-width:0.1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(39.99939%,39.99939%,39.99939%);stroke-opacity:1;stroke-miterlimit:3.25;" d="M 355 25.328125 L 353.421875 25.328125 "/>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph0-3" x="175.5" y="245.29874"/>
+  <use xlink:href="#glyph0-4" x="181.608398" y="245.29874"/>
+  <use xlink:href="#glyph0-5" x="187.169922" y="245.29874"/>
+  <use xlink:href="#glyph0-6" x="192.731445" y="245.29874"/>
+  <use xlink:href="#glyph0-7" x="197.731445" y="245.29874"/>
+  <use xlink:href="#glyph0-8" x="200.509766" y="245.29874"/>
+  <use xlink:href="#glyph0-9" x="202.731445" y="245.29874"/>
+  <use xlink:href="#glyph0-5" x="208.292969" y="245.29874"/>
+  <use xlink:href="#glyph0-10" x="213.854492" y="245.29874"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph2-1" x="9" y="129.14937"/>
+  <use xlink:href="#glyph2-2" x="9" y="122.479448"/>
+  <use xlink:href="#glyph2-3" x="9" y="117.479448"/>
+  <use xlink:href="#glyph2-4" x="9" y="114.701128"/>
+  <use xlink:href="#glyph2-5" x="9" y="109.139605"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph3-1" x="148" y="12"/>
+  <use xlink:href="#glyph3-2" x="155.330078" y="12"/>
+  <use xlink:href="#glyph3-3" x="157.996094" y="12"/>
+  <use xlink:href="#glyph3-4" x="160.662109" y="12"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph3-5" x="170" y="12"/>
+  <use xlink:href="#glyph3-2" x="176" y="12"/>
+  <use xlink:href="#glyph3-6" x="178.666016" y="12"/>
+  <use xlink:href="#glyph3-4" x="184.666016" y="12"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph3-7" x="194" y="12"/>
+  <use xlink:href="#glyph3-8" x="200.673828" y="12"/>
+</g>
+<g style="fill:rgb(39.99939%,39.99939%,39.99939%);fill-opacity:1;">
+  <use xlink:href="#glyph3-9" x="207" y="12"/>
+  <use xlink:href="#glyph3-7" x="216.996094" y="12"/>
+  <use xlink:href="#glyph3-10" x="223.669922" y="12"/>
+  <use xlink:href="#glyph3-11" x="230.34375" y="12"/>
+  <use xlink:href="#glyph3-3" x="237.017578" y="12"/>
+  <use xlink:href="#glyph3-4" x="239.683594" y="12"/>
+</g>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(36.84082%,50.67749%,70.979309%);fill-opacity:1;" d="M 382.148438 113 C 382.148438 112.164062 381.816406 111.363281 381.226562 110.773438 C 380.636719 110.183594 379.835938 109.851562 379 109.851562 C 378.164062 109.851562 377.363281 110.183594 376.773438 110.773438 C 376.183594 111.363281 375.851562 112.164062 375.851562 113 C 375.851562 113.835938 376.183594 114.636719 376.773438 115.226562 C 377.363281 115.816406 378.164062 116.148438 379 116.148438 C 379.835938 116.148438 380.636719 115.816406 381.226562 115.226562 C 381.816406 114.636719 382.148438 113.835938 382.148438 113 Z "/>
+<g style="fill:rgb(0%,0%,0%);fill-opacity:1;">
+  <use xlink:href="#glyph4-1" x="391" y="117.28418"/>
+  <use xlink:href="#glyph4-2" x="399.670898" y="117.28418"/>
+  <use xlink:href="#glyph4-2" x="406.901367" y="117.28418"/>
+  <use xlink:href="#glyph4-3" x="414.131836" y="117.28418"/>
+  <use xlink:href="#glyph4-4" x="420.631836" y="117.28418"/>
+</g>
+<g style="fill:rgb(0%,0%,0%);fill-opacity:1;">
+  <use xlink:href="#glyph4-5" x="424" y="117.28418"/>
+</g>
+<g style="fill:rgb(0%,0%,0%);fill-opacity:1;">
+  <use xlink:href="#glyph4-6" x="428" y="117.28418"/>
+  <use xlink:href="#glyph4-7" x="436.670898" y="117.28418"/>
+  <use xlink:href="#glyph4-4" x="443.170898" y="117.28418"/>
+  <use xlink:href="#glyph4-8" x="446.783203" y="117.28418"/>
+  <use xlink:href="#glyph4-2" x="454.013672" y="117.28418"/>
+  <use xlink:href="#glyph4-9" x="461.244141" y="117.28418"/>
+</g>
+<path style=" stroke:none;fill-rule:evenodd;fill:rgb(88.070679%,61.103821%,14.204407%);fill-opacity:1;" d="M 382.148438 136 C 382.148438 135.164062 381.816406 134.363281 381.226562 133.773438 C 380.636719 133.183594 379.835938 132.851562 379 132.851562 C 378.164062 132.851562 377.363281 133.183594 376.773438 133.773438 C 376.183594 134.363281 375.851562 135.164062 375.851562 136 C 375.851562 136.835938 376.183594 137.636719 376.773438 138.226562 C 377.363281 138.816406 378.164062 139.148438 379 139.148438 C 379.835938 139.148438 380.636719 138.816406 381.226562 138.226562 C 381.816406 137.636719 382.148438 136.835938 382.148438 136 Z "/>
+<g style="fill:rgb(0%,0%,0%);fill-opacity:1;">
+  <use xlink:href="#glyph4-10" x="391" y="140.28418"/>
+  <use xlink:href="#glyph4-7" x="398.230469" y="140.28418"/>
+  <use xlink:href="#glyph4-11" x="404.730469" y="140.28418"/>
+  <use xlink:href="#glyph4-12" x="411.960938" y="140.28418"/>
+  <use xlink:href="#glyph4-9" x="414.849609" y="140.28418"/>
+  <use xlink:href="#glyph4-13" x="422.080078" y="140.28418"/>
+  <use xlink:href="#glyph4-14" x="429.310547" y="140.28418"/>
+  <use xlink:href="#glyph4-14" x="436.541016" y="140.28418"/>
+</g>
+</g>
+</svg>
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/reference.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/reference.rst
new file mode 100644
index 000000000..e64a03519
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/reference.rst
@@ -0,0 +1,130 @@
+.. _reference:
+
+.. warning::
+
+    Please be advised that the reference documentation discussing pybind11
+    internals is currently incomplete. Please refer to the previous sections
+    and the pybind11 header files for the nitty gritty details.
+
+Reference
+#########
+
+.. _macros:
+
+Macros
+======
+
+.. doxygendefine:: PYBIND11_MODULE
+
+.. _core_types:
+
+Convenience classes for arbitrary Python types
+==============================================
+
+Common member functions
+-----------------------
+
+.. doxygenclass:: object_api
+    :members:
+
+Without reference counting
+--------------------------
+
+.. doxygenclass:: handle
+    :members:
+
+With reference counting
+-----------------------
+
+.. doxygenclass:: object
+    :members:
+
+.. doxygenfunction:: reinterpret_borrow
+
+.. doxygenfunction:: reinterpret_steal
+
+Convenience classes for specific Python types
+=============================================
+
+.. doxygenclass:: module_
+    :members:
+
+.. doxygengroup:: pytypes
+    :members:
+
+Convenience functions converting to Python types
+================================================
+
+.. doxygenfunction:: make_tuple(Args&&...)
+
+.. doxygenfunction:: make_iterator(Iterator, Sentinel, Extra &&...)
+.. doxygenfunction:: make_iterator(Type &, Extra&&...)
+
+.. doxygenfunction:: make_key_iterator(Iterator, Sentinel, Extra &&...)
+.. doxygenfunction:: make_key_iterator(Type &, Extra&&...)
+
+.. doxygenfunction:: make_value_iterator(Iterator, Sentinel, Extra &&...)
+.. doxygenfunction:: make_value_iterator(Type &, Extra&&...)
+
+.. _extras:
+
+Passing extra arguments to ``def`` or ``class_``
+================================================
+
+.. doxygengroup:: annotations
+    :members:
+
+Embedding the interpreter
+=========================
+
+.. doxygendefine:: PYBIND11_EMBEDDED_MODULE
+
+.. doxygenfunction:: initialize_interpreter
+
+.. doxygenfunction:: finalize_interpreter
+
+.. doxygenclass:: scoped_interpreter
+
+Redirecting C++ streams
+=======================
+
+.. doxygenclass:: scoped_ostream_redirect
+
+.. doxygenclass:: scoped_estream_redirect
+
+.. doxygenfunction:: add_ostream_redirect
+
+Python built-in functions
+=========================
+
+.. doxygengroup:: python_builtins
+    :members:
+
+Inheritance
+===========
+
+See :doc:`/classes` and :doc:`/advanced/classes` for more detail.
+
+.. doxygendefine:: PYBIND11_OVERRIDE
+
+.. doxygendefine:: PYBIND11_OVERRIDE_PURE
+
+.. doxygendefine:: PYBIND11_OVERRIDE_NAME
+
+.. doxygendefine:: PYBIND11_OVERRIDE_PURE_NAME
+
+.. doxygenfunction:: get_override
+
+Exceptions
+==========
+
+.. doxygenclass:: error_already_set
+    :members:
+
+.. doxygenclass:: builtin_exception
+    :members:
+
+Literals
+========
+
+.. doxygennamespace:: literals
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/release.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/release.rst
new file mode 100644
index 000000000..e761cdf7a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/release.rst
@@ -0,0 +1,97 @@
+On version numbers
+^^^^^^^^^^^^^^^^^^
+
+The two version numbers (C++ and Python) must match when combined (checked when
+you build the PyPI package), and must be a valid `PEP 440
+<https://www.python.org/dev/peps/pep-0440>`_ version when combined.
+
+For example:
+
+.. code-block:: C++
+
+    #define PYBIND11_VERSION_MAJOR X
+    #define PYBIND11_VERSION_MINOR Y
+    #define PYBIND11_VERSION_PATCH Z.dev1
+
+For beta, ``PYBIND11_VERSION_PATCH`` should be ``Z.b1``. RC's can be ``Z.rc1``.
+Always include the dot (even though PEP 440 allows it to be dropped). For a
+final release, this must be a simple integer. There is also a HEX version of
+the version just below.
+
+
+To release a new version of pybind11:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you don't have nox, you should either use ``pipx run nox`` instead, or use
+``pipx install nox`` or ``brew install nox`` (Unix).
+
+- Update the version number
+    - Update ``PYBIND11_VERSION_MAJOR`` etc. in
+      ``include/pybind11/detail/common.h``. PATCH should be a simple integer.
+    - Update the version HEX just below, as well.
+    - Update ``pybind11/_version.py`` (match above)
+    - Run ``nox -s tests_packaging`` to ensure this was done correctly.
+    - Ensure that all the information in ``setup.cfg`` is up-to-date, like
+      supported Python versions.
+    - Add release date in ``docs/changelog.rst``.
+          - Check to make sure
+            `needs-changelog <https://github.com/pybind/pybind11/pulls?q=is%3Apr+is%3Aclosed+label%3A%22needs+changelog%22>`_
+            issues are entered in the changelog (clear the label when done).
+    - ``git add`` and ``git commit``, ``git push``. **Ensure CI passes**. (If it
+      fails due to a known flake issue, either ignore or restart CI.)
+- Add a release branch if this is a new minor version, or update the existing release branch if it is a patch version
+    - New branch: ``git checkout -b vX.Y``, ``git push -u origin vX.Y``
+    - Update branch: ``git checkout vX.Y``, ``git merge <release branch>``, ``git push``
+- Update tags (optional; if you skip this, the GitHub release makes a
+    non-annotated tag for you)
+    - ``git tag -a vX.Y.Z -m 'vX.Y.Z release'``.
+    - ``git push --tags``.
+- Update stable
+    - ``git checkout stable``
+    - ``git merge master``
+    - ``git push``
+- Make a GitHub release (this shows up in the UI, sends new release
+  notifications to users watching releases, and also uploads PyPI packages).
+  (Note: if you do not use an existing tag, this creates a new lightweight tag
+  for you, so you could skip the above step.)
+    - GUI method: Under `releases <https://github.com/pybind/pybind11/releases>`_
+      click "Draft a new release" on the far right, fill in the tag name
+      (if you didn't tag above, it will be made here), fill in a release name
+      like "Version X.Y.Z", and copy-and-paste the markdown-formatted (!) changelog
+      into the description (usually ``cat docs/changelog.rst | pandoc -f rst -t gfm``).
+      Check "pre-release" if this is a beta/RC.
+    - CLI method: with ``gh`` installed, run ``gh release create vX.Y.Z -t "Version X.Y.Z"``
+      If this is a pre-release, add ``-p``.
+
+- Get back to work
+    - Make sure you are on master, not somewhere else: ``git checkout master``
+    - Update version macros in ``include/pybind11/detail/common.h`` (set PATCH to
+      ``0.dev1`` and increment MINOR).
+    - Update ``_version.py`` to match
+    - Run ``nox -s tests_packaging`` to ensure this was done correctly.
+    - Add a spot for in-development updates in ``docs/changelog.rst``.
+    - ``git add``, ``git commit``, ``git push``
+
+If a version branch is updated, remember to set PATCH to ``1.dev1``.
+
+If you'd like to bump homebrew, run:
+
+.. code-block:: console
+
+    brew bump-formula-pr --url https://github.com/pybind/pybind11/archive/vX.Y.Z.tar.gz
+
+Conda-forge should automatically make a PR in a few hours, and automatically
+merge it if there are no issues.
+
+
+Manual packaging
+^^^^^^^^^^^^^^^^
+
+If you need to manually upload releases, you can download the releases from the job artifacts and upload them with twine. You can also make the files locally (not recommended in general, as your local directory is more likely to be "dirty" and SDists love picking up random unrelated/hidden files); this is the procedure:
+
+.. code-block:: bash
+
+    nox -s build
+    twine upload dist/*
+
+This makes SDists and wheels, and the final line uploads them.
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/requirements.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/requirements.txt
new file mode 100644
index 000000000..d2a9ae164
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/requirements.txt
@@ -0,0 +1,6 @@
+breathe==4.34.0
+furo==2022.6.21
+sphinx==5.0.2
+sphinx-copybutton==0.5.0
+sphinxcontrib-moderncmakedomain==3.21.4
+sphinxcontrib-svg2pdfconverter==1.2.0
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/upgrade.rst b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/upgrade.rst
new file mode 100644
index 000000000..6a9db2d08
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/docs/upgrade.rst
@@ -0,0 +1,552 @@
+Upgrade guide
+#############
+
+This is a companion guide to the :doc:`changelog`. While the changelog briefly
+lists all of the new features, improvements and bug fixes, this upgrade guide
+focuses only the subset which directly impacts your experience when upgrading
+to a new version. But it goes into more detail. This includes things like
+deprecated APIs and their replacements, build system changes, general code
+modernization and other useful information.
+
+.. _upgrade-guide-2.9:
+
+v2.9
+====
+
+* Any usage of the recently added ``py::make_simple_namespace`` should be
+  converted to using ``py::module_::import("types").attr("SimpleNamespace")``
+  instead.
+
+* The use of ``_`` in custom type casters can now be replaced with the more
+  readable ``const_name`` instead. The old ``_`` shortcut has been retained
+  unless it is being used as a macro (like for gettext).
+
+
+.. _upgrade-guide-2.7:
+
+v2.7
+====
+
+*Before* v2.7, ``py::str`` can hold ``PyUnicodeObject`` or ``PyBytesObject``,
+and ``py::isinstance<str>()`` is ``true`` for both ``py::str`` and
+``py::bytes``. Starting with v2.7, ``py::str`` exclusively holds
+``PyUnicodeObject`` (`#2409 <https://github.com/pybind/pybind11/pull/2409>`_),
+and ``py::isinstance<str>()`` is ``true`` only for ``py::str``. To help in
+the transition of user code, the ``PYBIND11_STR_LEGACY_PERMISSIVE`` macro
+is provided as an escape hatch to go back to the legacy behavior. This macro
+will be removed in future releases. Two types of required fixes are expected
+to be common:
+
+* Accidental use of ``py::str`` instead of ``py::bytes``, masked by the legacy
+  behavior. These are probably very easy to fix, by changing from
+  ``py::str`` to ``py::bytes``.
+
+* Reliance on py::isinstance<str>(obj) being ``true`` for
+  ``py::bytes``. This is likely to be easy to fix in most cases by adding
+  ``|| py::isinstance<bytes>(obj)``, but a fix may be more involved, e.g. if
+  ``py::isinstance<T>`` appears in a template. Such situations will require
+  careful review and custom fixes.
+
+
+.. _upgrade-guide-2.6:
+
+v2.6
+====
+
+Usage of the ``PYBIND11_OVERLOAD*`` macros and ``get_overload`` function should
+be replaced by ``PYBIND11_OVERRIDE*`` and ``get_override``. In the future, the
+old macros may be deprecated and removed.
+
+``py::module`` has been renamed ``py::module_``, but a backward compatible
+typedef has been included. This change was to avoid a language change in C++20
+that requires unqualified ``module`` not be placed at the start of a logical
+line. Qualified usage is unaffected and the typedef will remain unless the
+C++ language rules change again.
+
+The public constructors of ``py::module_`` have been deprecated. Use
+``PYBIND11_MODULE`` or ``module_::create_extension_module`` instead.
+
+An error is now thrown when ``__init__`` is forgotten on subclasses. This was
+incorrect before, but was not checked. Add a call to ``__init__`` if it is
+missing.
+
+A ``py::type_error`` is now thrown when casting to a subclass (like
+``py::bytes`` from ``py::object``) if the conversion is not valid. Make a valid
+conversion instead.
+
+The undocumented ``h.get_type()`` method has been deprecated and replaced by
+``py::type::of(h)``.
+
+Enums now have a ``__str__`` method pre-defined; if you want to override it,
+the simplest fix is to add the new ``py::prepend()`` tag when defining
+``"__str__"``.
+
+If ``__eq__`` defined but not ``__hash__``, ``__hash__`` is now set to
+``None``, as in normal CPython. You should add ``__hash__`` if you intended the
+class to be hashable, possibly using the new ``py::hash`` shortcut.
+
+The constructors for ``py::array`` now always take signed integers for size,
+for consistency. This may lead to compiler warnings on some systems. Cast to
+``py::ssize_t`` instead of ``std::size_t``.
+
+The ``tools/clang`` submodule and ``tools/mkdoc.py`` have been moved to a
+standalone package, `pybind11-mkdoc`_. If you were using those tools, please
+use them via a pip install from the new location.
+
+The ``pybind11`` package on PyPI no longer fills the wheel "headers" slot - if
+you were using the headers from this slot, they are available by requesting the
+``global`` extra, that is, ``pip install "pybind11[global]"``. (Most users will
+be unaffected, as the ``pybind11/include`` location is reported by ``python -m
+pybind11 --includes`` and ``pybind11.get_include()`` is still correct and has
+not changed since 2.5).
+
+.. _pybind11-mkdoc: https://github.com/pybind/pybind11-mkdoc
+
+CMake support:
+--------------
+
+The minimum required version of CMake is now 3.4.  Several details of the CMake
+support have been deprecated; warnings will be shown if you need to change
+something. The changes are:
+
+* ``PYBIND11_CPP_STANDARD=<platform-flag>`` is deprecated, please use
+  ``CMAKE_CXX_STANDARD=<number>`` instead, or any other valid CMake CXX or CUDA
+  standard selection method, like ``target_compile_features``.
+
+* If you do not request a standard, pybind11 targets will compile with the
+  compiler default, but not less than C++11, instead of forcing C++14 always.
+  If you depend on the old behavior, please use ``set(CMAKE_CXX_STANDARD 14 CACHE STRING "")``
+  instead.
+
+* Direct ``pybind11::module`` usage should always be accompanied by at least
+  ``set(CMAKE_CXX_VISIBILITY_PRESET hidden)`` or similar - it used to try to
+  manually force this compiler flag (but not correctly on all compilers or with
+  CUDA).
+
+* ``pybind11_add_module``'s ``SYSTEM`` argument is deprecated and does nothing;
+  linking now behaves like other imported libraries consistently in both
+  config and submodule mode, and behaves like a ``SYSTEM`` library by
+  default.
+
+* If ``PYTHON_EXECUTABLE`` is not set, virtual environments (``venv``,
+  ``virtualenv``, and ``conda``) are prioritized over the standard search
+  (similar to the new FindPython mode).
+
+In addition, the following changes may be of interest:
+
+* ``CMAKE_INTERPROCEDURAL_OPTIMIZATION`` will be respected by
+  ``pybind11_add_module`` if set instead of linking to ``pybind11::lto`` or
+  ``pybind11::thin_lto``.
+
+* Using ``find_package(Python COMPONENTS Interpreter Development)`` before
+  pybind11 will cause pybind11 to use the new Python mechanisms instead of its
+  own custom search, based on a patched version of classic ``FindPythonInterp``
+  / ``FindPythonLibs``. In the future, this may become the default. A recent
+  (3.15+ or 3.18.2+) version of CMake is recommended.
+
+
+
+v2.5
+====
+
+The Python package now includes the headers as data in the package itself, as
+well as in the "headers" wheel slot. ``pybind11 --includes`` and
+``pybind11.get_include()`` report the new location, which is always correct
+regardless of how pybind11 was installed, making the old ``user=`` argument
+meaningless. If you are not using the function to get the location already, you
+are encouraged to switch to the package location.
+
+
+v2.2
+====
+
+Deprecation of the ``PYBIND11_PLUGIN`` macro
+--------------------------------------------
+
+``PYBIND11_MODULE`` is now the preferred way to create module entry points.
+The old macro emits a compile-time deprecation warning.
+
+.. code-block:: cpp
+
+    // old
+    PYBIND11_PLUGIN(example) {
+        py::module m("example", "documentation string");
+
+        m.def("add", [](int a, int b) { return a + b; });
+
+        return m.ptr();
+    }
+
+    // new
+    PYBIND11_MODULE(example, m) {
+        m.doc() = "documentation string"; // optional
+
+        m.def("add", [](int a, int b) { return a + b; });
+    }
+
+
+New API for defining custom constructors and pickling functions
+---------------------------------------------------------------
+
+The old placement-new custom constructors have been deprecated. The new approach
+uses ``py::init()`` and factory functions to greatly improve type safety.
+
+Placement-new can be called accidentally with an incompatible type (without any
+compiler errors or warnings), or it can initialize the same object multiple times
+if not careful with the Python-side ``__init__`` calls. The new-style custom
+constructors prevent such mistakes. See :ref:`custom_constructors` for details.
+
+.. code-block:: cpp
+
+    // old -- deprecated (runtime warning shown only in debug mode)
+    py::class<Foo>(m, "Foo")
+        .def("__init__", [](Foo &self, ...) {
+            new (&self) Foo(...); // uses placement-new
+        });
+
+    // new
+    py::class<Foo>(m, "Foo")
+        .def(py::init([](...) { // Note: no `self` argument
+            return new Foo(...); // return by raw pointer
+            // or: return std::make_unique<Foo>(...); // return by holder
+            // or: return Foo(...); // return by value (move constructor)
+        }));
+
+Mirroring the custom constructor changes, ``py::pickle()`` is now the preferred
+way to get and set object state. See :ref:`pickling` for details.
+
+.. code-block:: cpp
+
+    // old -- deprecated (runtime warning shown only in debug mode)
+    py::class<Foo>(m, "Foo")
+        ...
+        .def("__getstate__", [](const Foo &self) {
+            return py::make_tuple(self.value1(), self.value2(), ...);
+        })
+        .def("__setstate__", [](Foo &self, py::tuple t) {
+            new (&self) Foo(t[0].cast<std::string>(), ...);
+        });
+
+    // new
+    py::class<Foo>(m, "Foo")
+        ...
+        .def(py::pickle(
+            [](const Foo &self) { // __getstate__
+                return py::make_tuple(self.value1(), self.value2(), ...); // unchanged
+            },
+            [](py::tuple t) { // __setstate__, note: no `self` argument
+                return new Foo(t[0].cast<std::string>(), ...);
+                // or: return std::make_unique<Foo>(...); // return by holder
+                // or: return Foo(...); // return by value (move constructor)
+            }
+        ));
+
+For both the constructors and pickling, warnings are shown at module
+initialization time (on import, not when the functions are called).
+They're only visible when compiled in debug mode. Sample warning:
+
+.. code-block:: none
+
+    pybind11-bound class 'mymodule.Foo' is using an old-style placement-new '__init__'
+    which has been deprecated. See the upgrade guide in pybind11's docs.
+
+
+Stricter enforcement of hidden symbol visibility for pybind11 modules
+---------------------------------------------------------------------
+
+pybind11 now tries to actively enforce hidden symbol visibility for modules.
+If you're using either one of pybind11's :doc:`CMake or Python build systems
+<compiling>` (the two example repositories) and you haven't been exporting any
+symbols, there's nothing to be concerned about. All the changes have been done
+transparently in the background. If you were building manually or relied on
+specific default visibility, read on.
+
+Setting default symbol visibility to *hidden* has always been recommended for
+pybind11 (see :ref:`faq:symhidden`). On Linux and macOS, hidden symbol
+visibility (in conjunction with the ``strip`` utility) yields much smaller
+module binaries. `CPython's extension docs`_ also recommend hiding symbols
+by default, with the goal of avoiding symbol name clashes between modules.
+Starting with v2.2, pybind11 enforces this more strictly: (1) by declaring
+all symbols inside the ``pybind11`` namespace as hidden and (2) by including
+the ``-fvisibility=hidden`` flag on Linux and macOS (only for extension
+modules, not for embedding the interpreter).
+
+.. _CPython's extension docs: https://docs.python.org/3/extending/extending.html#providing-a-c-api-for-an-extension-module
+
+The namespace-scope hidden visibility is done automatically in pybind11's
+headers and it's generally transparent to users. It ensures that:
+
+* Modules compiled with different pybind11 versions don't clash with each other.
+
+* Some new features, like ``py::module_local`` bindings, can work as intended.
+
+The ``-fvisibility=hidden`` flag applies the same visibility to user bindings
+outside of the ``pybind11`` namespace. It's now set automatic by pybind11's
+CMake and Python build systems, but this needs to be done manually by users
+of other build systems. Adding this flag:
+
+* Minimizes the chances of symbol conflicts between modules. E.g. if two
+  unrelated modules were statically linked to different (ABI-incompatible)
+  versions of the same third-party library, a symbol clash would be likely
+  (and would end with unpredictable results).
+
+* Produces smaller binaries on Linux and macOS, as pointed out previously.
+
+Within pybind11's CMake build system, ``pybind11_add_module`` has always been
+setting the ``-fvisibility=hidden`` flag in release mode. From now on, it's
+being applied unconditionally, even in debug mode and it can no longer be opted
+out of with the ``NO_EXTRAS`` option. The ``pybind11::module`` target now also
+adds this flag to its interface. The ``pybind11::embed`` target is unchanged.
+
+The most significant change here is for the ``pybind11::module`` target. If you
+were previously relying on default visibility, i.e. if your Python module was
+doubling as a shared library with dependents, you'll need to either export
+symbols manually (recommended for cross-platform libraries) or factor out the
+shared library (and have the Python module link to it like the other
+dependents). As a temporary workaround, you can also restore default visibility
+using the CMake code below, but this is not recommended in the long run:
+
+.. code-block:: cmake
+
+    target_link_libraries(mymodule PRIVATE pybind11::module)
+
+    add_library(restore_default_visibility INTERFACE)
+    target_compile_options(restore_default_visibility INTERFACE -fvisibility=default)
+    target_link_libraries(mymodule PRIVATE restore_default_visibility)
+
+
+Local STL container bindings
+----------------------------
+
+Previous pybind11 versions could only bind types globally -- all pybind11
+modules, even unrelated ones, would have access to the same exported types.
+However, this would also result in a conflict if two modules exported the
+same C++ type, which is especially problematic for very common types, e.g.
+``std::vector<int>``. :ref:`module_local` were added to resolve this (see
+that section for a complete usage guide).
+
+``py::class_`` still defaults to global bindings (because these types are
+usually unique across modules), however in order to avoid clashes of opaque
+types, ``py::bind_vector`` and ``py::bind_map`` will now bind STL containers
+as ``py::module_local`` if their elements are: builtins (``int``, ``float``,
+etc.), not bound using ``py::class_``, or bound as ``py::module_local``. For
+example, this change allows multiple modules to bind ``std::vector<int>``
+without causing conflicts. See :ref:`stl_bind` for more details.
+
+When upgrading to this version, if you have multiple modules which depend on
+a single global binding of an STL container, note that all modules can still
+accept foreign  ``py::module_local`` types in the direction of Python-to-C++.
+The locality only affects the C++-to-Python direction. If this is needed in
+multiple modules, you'll need to either:
+
+* Add a copy of the same STL binding to all of the modules which need it.
+
+* Restore the global status of that single binding by marking it
+  ``py::module_local(false)``.
+
+The latter is an easy workaround, but in the long run it would be best to
+localize all common type bindings in order to avoid conflicts with
+third-party modules.
+
+
+Negative strides for Python buffer objects and numpy arrays
+-----------------------------------------------------------
+
+Support for negative strides required changing the integer type from unsigned
+to signed in the interfaces of ``py::buffer_info`` and ``py::array``. If you
+have compiler warnings enabled, you may notice some new conversion warnings
+after upgrading. These can be resolved using ``static_cast``.
+
+
+Deprecation of some ``py::object`` APIs
+---------------------------------------
+
+To compare ``py::object`` instances by pointer, you should now use
+``obj1.is(obj2)`` which is equivalent to ``obj1 is obj2`` in Python.
+Previously, pybind11 used ``operator==`` for this (``obj1 == obj2``), but
+that could be confusing and is now deprecated (so that it can eventually
+be replaced with proper rich object comparison in a future release).
+
+For classes which inherit from ``py::object``, ``borrowed`` and ``stolen``
+were previously available as protected constructor tags. Now the types
+should be used directly instead: ``borrowed_t{}`` and ``stolen_t{}``
+(`#771 <https://github.com/pybind/pybind11/pull/771>`_).
+
+
+Stricter compile-time error checking
+------------------------------------
+
+Some error checks have been moved from run time to compile time. Notably,
+automatic conversion of ``std::shared_ptr<T>`` is not possible when ``T`` is
+not directly registered with ``py::class_<T>`` (e.g. ``std::shared_ptr<int>``
+or ``std::shared_ptr<std::vector<T>>`` are not automatically convertible).
+Attempting to bind a function with such arguments now results in a compile-time
+error instead of waiting to fail at run time.
+
+``py::init<...>()`` constructor definitions are also stricter and now prevent
+bindings which could cause unexpected behavior:
+
+.. code-block:: cpp
+
+    struct Example {
+        Example(int &);
+    };
+
+    py::class_<Example>(m, "Example")
+        .def(py::init<int &>()); // OK, exact match
+        // .def(py::init<int>()); // compile-time error, mismatch
+
+A non-``const`` lvalue reference is not allowed to bind to an rvalue. However,
+note that a constructor taking ``const T &`` can still be registered using
+``py::init<T>()`` because a ``const`` lvalue reference can bind to an rvalue.
+
+v2.1
+====
+
+Minimum compiler versions are enforced at compile time
+------------------------------------------------------
+
+The minimums also apply to v2.0 but the check is now explicit and a compile-time
+error is raised if the compiler does not meet the requirements:
+
+* GCC >= 4.8
+* clang >= 3.3 (appleclang >= 5.0)
+* MSVC >= 2015u3
+* Intel C++ >= 15.0
+
+
+The ``py::metaclass`` attribute is not required for static properties
+---------------------------------------------------------------------
+
+Binding classes with static properties is now possible by default. The
+zero-parameter version of ``py::metaclass()`` is deprecated. However, a new
+one-parameter ``py::metaclass(python_type)`` version was added for rare
+cases when a custom metaclass is needed to override pybind11's default.
+
+.. code-block:: cpp
+
+    // old -- emits a deprecation warning
+    py::class_<Foo>(m, "Foo", py::metaclass())
+        .def_property_readonly_static("foo", ...);
+
+    // new -- static properties work without the attribute
+    py::class_<Foo>(m, "Foo")
+        .def_property_readonly_static("foo", ...);
+
+    // new -- advanced feature, override pybind11's default metaclass
+    py::class_<Bar>(m, "Bar", py::metaclass(custom_python_type))
+        ...
+
+
+v2.0
+====
+
+Breaking changes in ``py::class_``
+----------------------------------
+
+These changes were necessary to make type definitions in pybind11
+future-proof, to support PyPy via its ``cpyext`` mechanism (`#527
+<https://github.com/pybind/pybind11/pull/527>`_), and to improve efficiency
+(`rev. 86d825 <https://github.com/pybind/pybind11/commit/86d825>`_).
+
+1. Declarations of types that provide access via the buffer protocol must
+   now include the ``py::buffer_protocol()`` annotation as an argument to
+   the ``py::class_`` constructor.
+
+   .. code-block:: cpp
+
+       py::class_<Matrix>("Matrix", py::buffer_protocol())
+           .def(py::init<...>())
+           .def_buffer(...);
+
+2. Classes which include static properties (e.g. ``def_readwrite_static()``)
+   must now include the ``py::metaclass()`` attribute. Note: this requirement
+   has since been removed in v2.1. If you're upgrading from 1.x, it's
+   recommended to skip directly to v2.1 or newer.
+
+3. This version of pybind11 uses a redesigned mechanism for instantiating
+   trampoline classes that are used to override virtual methods from within
+   Python. This led to the following user-visible syntax change:
+
+   .. code-block:: cpp
+
+       // old v1.x syntax
+       py::class_<TrampolineClass>("MyClass")
+           .alias<MyClass>()
+           ...
+
+       // new v2.x syntax
+       py::class_<MyClass, TrampolineClass>("MyClass")
+           ...
+
+   Importantly, both the original and the trampoline class are now specified
+   as arguments to the ``py::class_`` template, and the ``alias<..>()`` call
+   is gone. The new scheme has zero overhead in cases when Python doesn't
+   override any functions of the underlying C++ class.
+   `rev. 86d825 <https://github.com/pybind/pybind11/commit/86d825>`_.
+
+   The class type must be the first template argument given to ``py::class_``
+   while the trampoline can be mixed in arbitrary order with other arguments
+   (see the following section).
+
+
+Deprecation of the ``py::base<T>()`` attribute
+----------------------------------------------
+
+``py::base<T>()`` was deprecated in favor of specifying ``T`` as a template
+argument to ``py::class_``. This new syntax also supports multiple inheritance.
+Note that, while the type being exported must be the first argument in the
+``py::class_<Class, ...>`` template, the order of the following types (bases,
+holder and/or trampoline) is not important.
+
+.. code-block:: cpp
+
+    // old v1.x
+    py::class_<Derived>("Derived", py::base<Base>());
+
+    // new v2.x
+    py::class_<Derived, Base>("Derived");
+
+    // new -- multiple inheritance
+    py::class_<Derived, Base1, Base2>("Derived");
+
+    // new -- apart from `Derived` the argument order can be arbitrary
+    py::class_<Derived, Base1, Holder, Base2, Trampoline>("Derived");
+
+
+Out-of-the-box support for ``std::shared_ptr``
+----------------------------------------------
+
+The relevant type caster is now built in, so it's no longer necessary to
+include a declaration of the form:
+
+.. code-block:: cpp
+
+    PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>)
+
+Continuing to do so won't cause an error or even a deprecation warning,
+but it's completely redundant.
+
+
+Deprecation of a few ``py::object`` APIs
+----------------------------------------
+
+All of the old-style calls emit deprecation warnings.
+
++---------------------------------------+---------------------------------------------+
+|  Old syntax                           |  New syntax                                 |
++=======================================+=============================================+
+| ``obj.call(args...)``                 | ``obj(args...)``                            |
++---------------------------------------+---------------------------------------------+
+| ``obj.str()``                         | ``py::str(obj)``                            |
++---------------------------------------+---------------------------------------------+
+| ``auto l = py::list(obj); l.check()`` | ``py::isinstance<py::list>(obj)``           |
++---------------------------------------+---------------------------------------------+
+| ``py::object(ptr, true)``             | ``py::reinterpret_borrow<py::object>(ptr)`` |
++---------------------------------------+---------------------------------------------+
+| ``py::object(ptr, false)``            | ``py::reinterpret_steal<py::object>(ptr)``  |
++---------------------------------------+---------------------------------------------+
+| ``if (obj.attr("foo"))``              | ``if (py::hasattr(obj, "foo"))``            |
++---------------------------------------+---------------------------------------------+
+| ``if (obj["bar"])``                   | ``if (obj.contains("bar"))``                |
++---------------------------------------+---------------------------------------------+
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/attr.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/attr.h
new file mode 100644
index 000000000..db7cd8eff
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/attr.h
@@ -0,0 +1,678 @@
+/*
+    pybind11/attr.h: Infrastructure for processing custom
+    type and function attributes
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "cast.h"
+
+#include <functional>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+/// \addtogroup annotations
+/// @{
+
+/// Annotation for methods
+struct is_method {
+    handle class_;
+    explicit is_method(const handle &c) : class_(c) {}
+};
+
+/// Annotation for operators
+struct is_operator {};
+
+/// Annotation for classes that cannot be subclassed
+struct is_final {};
+
+/// Annotation for parent scope
+struct scope {
+    handle value;
+    explicit scope(const handle &s) : value(s) {}
+};
+
+/// Annotation for documentation
+struct doc {
+    const char *value;
+    explicit doc(const char *value) : value(value) {}
+};
+
+/// Annotation for function names
+struct name {
+    const char *value;
+    explicit name(const char *value) : value(value) {}
+};
+
+/// Annotation indicating that a function is an overload associated with a given "sibling"
+struct sibling {
+    handle value;
+    explicit sibling(const handle &value) : value(value.ptr()) {}
+};
+
+/// Annotation indicating that a class derives from another given type
+template <typename T>
+struct base {
+
+    PYBIND11_DEPRECATED(
+        "base<T>() was deprecated in favor of specifying 'T' as a template argument to class_")
+    base() = default;
+};
+
+/// Keep patient alive while nurse lives
+template <size_t Nurse, size_t Patient>
+struct keep_alive {};
+
+/// Annotation indicating that a class is involved in a multiple inheritance relationship
+struct multiple_inheritance {};
+
+/// Annotation which enables dynamic attributes, i.e. adds `__dict__` to a class
+struct dynamic_attr {};
+
+/// Annotation which enables the buffer protocol for a type
+struct buffer_protocol {};
+
+/// Annotation which requests that a special metaclass is created for a type
+struct metaclass {
+    handle value;
+
+    PYBIND11_DEPRECATED("py::metaclass() is no longer required. It's turned on by default now.")
+    metaclass() = default;
+
+    /// Override pybind11's default metaclass
+    explicit metaclass(handle value) : value(value) {}
+};
+
+/// Specifies a custom callback with signature `void (PyHeapTypeObject*)` that
+/// may be used to customize the Python type.
+///
+/// The callback is invoked immediately before `PyType_Ready`.
+///
+/// Note: This is an advanced interface, and uses of it may require changes to
+/// work with later versions of pybind11.  You may wish to consult the
+/// implementation of `make_new_python_type` in `detail/classes.h` to understand
+/// the context in which the callback will be run.
+struct custom_type_setup {
+    using callback = std::function<void(PyHeapTypeObject *heap_type)>;
+
+    explicit custom_type_setup(callback value) : value(std::move(value)) {}
+
+    callback value;
+};
+
+/// Annotation that marks a class as local to the module:
+struct module_local {
+    const bool value;
+    constexpr explicit module_local(bool v = true) : value(v) {}
+};
+
+/// Annotation to mark enums as an arithmetic type
+struct arithmetic {};
+
+/// Mark a function for addition at the beginning of the existing overload chain instead of the end
+struct prepend {};
+
+/** \rst
+    A call policy which places one or more guard variables (``Ts...``) around the function call.
+
+    For example, this definition:
+
+    .. code-block:: cpp
+
+        m.def("foo", foo, py::call_guard<T>());
+
+    is equivalent to the following pseudocode:
+
+    .. code-block:: cpp
+
+        m.def("foo", [](args...) {
+            T scope_guard;
+            return foo(args...); // forwarded arguments
+        });
+ \endrst */
+template <typename... Ts>
+struct call_guard;
+
+template <>
+struct call_guard<> {
+    using type = detail::void_type;
+};
+
+template <typename T>
+struct call_guard<T> {
+    static_assert(std::is_default_constructible<T>::value,
+                  "The guard type must be default constructible");
+
+    using type = T;
+};
+
+template <typename T, typename... Ts>
+struct call_guard<T, Ts...> {
+    struct type {
+        T guard{}; // Compose multiple guard types with left-to-right default-constructor order
+        typename call_guard<Ts...>::type next{};
+    };
+};
+
+/// @} annotations
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+/* Forward declarations */
+enum op_id : int;
+enum op_type : int;
+struct undefined_t;
+template <op_id id, op_type ot, typename L = undefined_t, typename R = undefined_t>
+struct op_;
+void keep_alive_impl(size_t Nurse, size_t Patient, function_call &call, handle ret);
+
+/// Internal data structure which holds metadata about a keyword argument
+struct argument_record {
+    const char *name;  ///< Argument name
+    const char *descr; ///< Human-readable version of the argument value
+    handle value;      ///< Associated Python object
+    bool convert : 1;  ///< True if the argument is allowed to convert when loading
+    bool none : 1;     ///< True if None is allowed when loading
+
+    argument_record(const char *name, const char *descr, handle value, bool convert, bool none)
+        : name(name), descr(descr), value(value), convert(convert), none(none) {}
+};
+
+/// Internal data structure which holds metadata about a bound function (signature, overloads,
+/// etc.)
+struct function_record {
+    function_record()
+        : is_constructor(false), is_new_style_constructor(false), is_stateless(false),
+          is_operator(false), is_method(false), has_args(false), has_kwargs(false),
+          prepend(false) {}
+
+    /// Function name
+    char *name = nullptr; /* why no C++ strings? They generate heavier code.. */
+
+    // User-specified documentation string
+    char *doc = nullptr;
+
+    /// Human-readable version of the function signature
+    char *signature = nullptr;
+
+    /// List of registered keyword arguments
+    std::vector<argument_record> args;
+
+    /// Pointer to lambda function which converts arguments and performs the actual call
+    handle (*impl)(function_call &) = nullptr;
+
+    /// Storage for the wrapped function pointer and captured data, if any
+    void *data[3] = {};
+
+    /// Pointer to custom destructor for 'data' (if needed)
+    void (*free_data)(function_record *ptr) = nullptr;
+
+    /// Return value policy associated with this function
+    return_value_policy policy = return_value_policy::automatic;
+
+    /// True if name == '__init__'
+    bool is_constructor : 1;
+
+    /// True if this is a new-style `__init__` defined in `detail/init.h`
+    bool is_new_style_constructor : 1;
+
+    /// True if this is a stateless function pointer
+    bool is_stateless : 1;
+
+    /// True if this is an operator (__add__), etc.
+    bool is_operator : 1;
+
+    /// True if this is a method
+    bool is_method : 1;
+
+    /// True if the function has a '*args' argument
+    bool has_args : 1;
+
+    /// True if the function has a '**kwargs' argument
+    bool has_kwargs : 1;
+
+    /// True if this function is to be inserted at the beginning of the overload resolution chain
+    bool prepend : 1;
+
+    /// Number of arguments (including py::args and/or py::kwargs, if present)
+    std::uint16_t nargs;
+
+    /// Number of leading positional arguments, which are terminated by a py::args or py::kwargs
+    /// argument or by a py::kw_only annotation.
+    std::uint16_t nargs_pos = 0;
+
+    /// Number of leading arguments (counted in `nargs`) that are positional-only
+    std::uint16_t nargs_pos_only = 0;
+
+    /// Python method object
+    PyMethodDef *def = nullptr;
+
+    /// Python handle to the parent scope (a class or a module)
+    handle scope;
+
+    /// Python handle to the sibling function representing an overload chain
+    handle sibling;
+
+    /// Pointer to next overload
+    function_record *next = nullptr;
+};
+
+/// Special data structure which (temporarily) holds metadata about a bound class
+struct type_record {
+    PYBIND11_NOINLINE type_record()
+        : multiple_inheritance(false), dynamic_attr(false), buffer_protocol(false),
+          default_holder(true), module_local(false), is_final(false) {}
+
+    /// Handle to the parent scope
+    handle scope;
+
+    /// Name of the class
+    const char *name = nullptr;
+
+    // Pointer to RTTI type_info data structure
+    const std::type_info *type = nullptr;
+
+    /// How large is the underlying C++ type?
+    size_t type_size = 0;
+
+    /// What is the alignment of the underlying C++ type?
+    size_t type_align = 0;
+
+    /// How large is the type's holder?
+    size_t holder_size = 0;
+
+    /// The global operator new can be overridden with a class-specific variant
+    void *(*operator_new)(size_t) = nullptr;
+
+    /// Function pointer to class_<..>::init_instance
+    void (*init_instance)(instance *, const void *) = nullptr;
+
+    /// Function pointer to class_<..>::dealloc
+    void (*dealloc)(detail::value_and_holder &) = nullptr;
+
+    /// List of base classes of the newly created type
+    list bases;
+
+    /// Optional docstring
+    const char *doc = nullptr;
+
+    /// Custom metaclass (optional)
+    handle metaclass;
+
+    /// Custom type setup.
+    custom_type_setup::callback custom_type_setup_callback;
+
+    /// Multiple inheritance marker
+    bool multiple_inheritance : 1;
+
+    /// Does the class manage a __dict__?
+    bool dynamic_attr : 1;
+
+    /// Does the class implement the buffer protocol?
+    bool buffer_protocol : 1;
+
+    /// Is the default (unique_ptr) holder type used?
+    bool default_holder : 1;
+
+    /// Is the class definition local to the module shared object?
+    bool module_local : 1;
+
+    /// Is the class inheritable from python classes?
+    bool is_final : 1;
+
+    PYBIND11_NOINLINE void add_base(const std::type_info &base, void *(*caster)(void *) ) {
+        auto *base_info = detail::get_type_info(base, false);
+        if (!base_info) {
+            std::string tname(base.name());
+            detail::clean_type_id(tname);
+            pybind11_fail("generic_type: type \"" + std::string(name)
+                          + "\" referenced unknown base type \"" + tname + "\"");
+        }
+
+        if (default_holder != base_info->default_holder) {
+            std::string tname(base.name());
+            detail::clean_type_id(tname);
+            pybind11_fail("generic_type: type \"" + std::string(name) + "\" "
+                          + (default_holder ? "does not have" : "has")
+                          + " a non-default holder type while its base \"" + tname + "\" "
+                          + (base_info->default_holder ? "does not" : "does"));
+        }
+
+        bases.append((PyObject *) base_info->type);
+
+#if PY_VERSION_HEX < 0x030B0000
+        dynamic_attr |= base_info->type->tp_dictoffset != 0;
+#else
+        dynamic_attr |= (base_info->type->tp_flags & Py_TPFLAGS_MANAGED_DICT) != 0;
+#endif
+
+        if (caster) {
+            base_info->implicit_casts.emplace_back(type, caster);
+        }
+    }
+};
+
+inline function_call::function_call(const function_record &f, handle p) : func(f), parent(p) {
+    args.reserve(f.nargs);
+    args_convert.reserve(f.nargs);
+}
+
+/// Tag for a new-style `__init__` defined in `detail/init.h`
+struct is_new_style_constructor {};
+
+/**
+ * Partial template specializations to process custom attributes provided to
+ * cpp_function_ and class_. These are either used to initialize the respective
+ * fields in the type_record and function_record data structures or executed at
+ * runtime to deal with custom call policies (e.g. keep_alive).
+ */
+template <typename T, typename SFINAE = void>
+struct process_attribute;
+
+template <typename T>
+struct process_attribute_default {
+    /// Default implementation: do nothing
+    static void init(const T &, function_record *) {}
+    static void init(const T &, type_record *) {}
+    static void precall(function_call &) {}
+    static void postcall(function_call &, handle) {}
+};
+
+/// Process an attribute specifying the function's name
+template <>
+struct process_attribute<name> : process_attribute_default<name> {
+    static void init(const name &n, function_record *r) { r->name = const_cast<char *>(n.value); }
+};
+
+/// Process an attribute specifying the function's docstring
+template <>
+struct process_attribute<doc> : process_attribute_default<doc> {
+    static void init(const doc &n, function_record *r) { r->doc = const_cast<char *>(n.value); }
+};
+
+/// Process an attribute specifying the function's docstring (provided as a C-style string)
+template <>
+struct process_attribute<const char *> : process_attribute_default<const char *> {
+    static void init(const char *d, function_record *r) { r->doc = const_cast<char *>(d); }
+    static void init(const char *d, type_record *r) { r->doc = const_cast<char *>(d); }
+};
+template <>
+struct process_attribute<char *> : process_attribute<const char *> {};
+
+/// Process an attribute indicating the function's return value policy
+template <>
+struct process_attribute<return_value_policy> : process_attribute_default<return_value_policy> {
+    static void init(const return_value_policy &p, function_record *r) { r->policy = p; }
+};
+
+/// Process an attribute which indicates that this is an overloaded function associated with a
+/// given sibling
+template <>
+struct process_attribute<sibling> : process_attribute_default<sibling> {
+    static void init(const sibling &s, function_record *r) { r->sibling = s.value; }
+};
+
+/// Process an attribute which indicates that this function is a method
+template <>
+struct process_attribute<is_method> : process_attribute_default<is_method> {
+    static void init(const is_method &s, function_record *r) {
+        r->is_method = true;
+        r->scope = s.class_;
+    }
+};
+
+/// Process an attribute which indicates the parent scope of a method
+template <>
+struct process_attribute<scope> : process_attribute_default<scope> {
+    static void init(const scope &s, function_record *r) { r->scope = s.value; }
+};
+
+/// Process an attribute which indicates that this function is an operator
+template <>
+struct process_attribute<is_operator> : process_attribute_default<is_operator> {
+    static void init(const is_operator &, function_record *r) { r->is_operator = true; }
+};
+
+template <>
+struct process_attribute<is_new_style_constructor>
+    : process_attribute_default<is_new_style_constructor> {
+    static void init(const is_new_style_constructor &, function_record *r) {
+        r->is_new_style_constructor = true;
+    }
+};
+
+inline void check_kw_only_arg(const arg &a, function_record *r) {
+    if (r->args.size() > r->nargs_pos && (!a.name || a.name[0] == '\0')) {
+        pybind11_fail("arg(): cannot specify an unnamed argument after a kw_only() annotation or "
+                      "args() argument");
+    }
+}
+
+inline void append_self_arg_if_needed(function_record *r) {
+    if (r->is_method && r->args.empty()) {
+        r->args.emplace_back("self", nullptr, handle(), /*convert=*/true, /*none=*/false);
+    }
+}
+
+/// Process a keyword argument attribute (*without* a default value)
+template <>
+struct process_attribute<arg> : process_attribute_default<arg> {
+    static void init(const arg &a, function_record *r) {
+        append_self_arg_if_needed(r);
+        r->args.emplace_back(a.name, nullptr, handle(), !a.flag_noconvert, a.flag_none);
+
+        check_kw_only_arg(a, r);
+    }
+};
+
+/// Process a keyword argument attribute (*with* a default value)
+template <>
+struct process_attribute<arg_v> : process_attribute_default<arg_v> {
+    static void init(const arg_v &a, function_record *r) {
+        if (r->is_method && r->args.empty()) {
+            r->args.emplace_back(
+                "self", /*descr=*/nullptr, /*parent=*/handle(), /*convert=*/true, /*none=*/false);
+        }
+
+        if (!a.value) {
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            std::string descr("'");
+            if (a.name) {
+                descr += std::string(a.name) + ": ";
+            }
+            descr += a.type + "'";
+            if (r->is_method) {
+                if (r->name) {
+                    descr += " in method '" + (std::string) str(r->scope) + "."
+                             + (std::string) r->name + "'";
+                } else {
+                    descr += " in method of '" + (std::string) str(r->scope) + "'";
+                }
+            } else if (r->name) {
+                descr += " in function '" + (std::string) r->name + "'";
+            }
+            pybind11_fail("arg(): could not convert default argument " + descr
+                          + " into a Python object (type not registered yet?)");
+#else
+            pybind11_fail("arg(): could not convert default argument "
+                          "into a Python object (type not registered yet?). "
+                          "#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for "
+                          "more information.");
+#endif
+        }
+        r->args.emplace_back(a.name, a.descr, a.value.inc_ref(), !a.flag_noconvert, a.flag_none);
+
+        check_kw_only_arg(a, r);
+    }
+};
+
+/// Process a keyword-only-arguments-follow pseudo argument
+template <>
+struct process_attribute<kw_only> : process_attribute_default<kw_only> {
+    static void init(const kw_only &, function_record *r) {
+        append_self_arg_if_needed(r);
+        if (r->has_args && r->nargs_pos != static_cast<std::uint16_t>(r->args.size())) {
+            pybind11_fail("Mismatched args() and kw_only(): they must occur at the same relative "
+                          "argument location (or omit kw_only() entirely)");
+        }
+        r->nargs_pos = static_cast<std::uint16_t>(r->args.size());
+    }
+};
+
+/// Process a positional-only-argument maker
+template <>
+struct process_attribute<pos_only> : process_attribute_default<pos_only> {
+    static void init(const pos_only &, function_record *r) {
+        append_self_arg_if_needed(r);
+        r->nargs_pos_only = static_cast<std::uint16_t>(r->args.size());
+        if (r->nargs_pos_only > r->nargs_pos) {
+            pybind11_fail("pos_only(): cannot follow a py::args() argument");
+        }
+        // It also can't follow a kw_only, but a static_assert in pybind11.h checks that
+    }
+};
+
+/// Process a parent class attribute.  Single inheritance only (class_ itself already guarantees
+/// that)
+template <typename T>
+struct process_attribute<T, enable_if_t<is_pyobject<T>::value>>
+    : process_attribute_default<handle> {
+    static void init(const handle &h, type_record *r) { r->bases.append(h); }
+};
+
+/// Process a parent class attribute (deprecated, does not support multiple inheritance)
+template <typename T>
+struct process_attribute<base<T>> : process_attribute_default<base<T>> {
+    static void init(const base<T> &, type_record *r) { r->add_base(typeid(T), nullptr); }
+};
+
+/// Process a multiple inheritance attribute
+template <>
+struct process_attribute<multiple_inheritance> : process_attribute_default<multiple_inheritance> {
+    static void init(const multiple_inheritance &, type_record *r) {
+        r->multiple_inheritance = true;
+    }
+};
+
+template <>
+struct process_attribute<dynamic_attr> : process_attribute_default<dynamic_attr> {
+    static void init(const dynamic_attr &, type_record *r) { r->dynamic_attr = true; }
+};
+
+template <>
+struct process_attribute<custom_type_setup> {
+    static void init(const custom_type_setup &value, type_record *r) {
+        r->custom_type_setup_callback = value.value;
+    }
+};
+
+template <>
+struct process_attribute<is_final> : process_attribute_default<is_final> {
+    static void init(const is_final &, type_record *r) { r->is_final = true; }
+};
+
+template <>
+struct process_attribute<buffer_protocol> : process_attribute_default<buffer_protocol> {
+    static void init(const buffer_protocol &, type_record *r) { r->buffer_protocol = true; }
+};
+
+template <>
+struct process_attribute<metaclass> : process_attribute_default<metaclass> {
+    static void init(const metaclass &m, type_record *r) { r->metaclass = m.value; }
+};
+
+template <>
+struct process_attribute<module_local> : process_attribute_default<module_local> {
+    static void init(const module_local &l, type_record *r) { r->module_local = l.value; }
+};
+
+/// Process a 'prepend' attribute, putting this at the beginning of the overload chain
+template <>
+struct process_attribute<prepend> : process_attribute_default<prepend> {
+    static void init(const prepend &, function_record *r) { r->prepend = true; }
+};
+
+/// Process an 'arithmetic' attribute for enums (does nothing here)
+template <>
+struct process_attribute<arithmetic> : process_attribute_default<arithmetic> {};
+
+template <typename... Ts>
+struct process_attribute<call_guard<Ts...>> : process_attribute_default<call_guard<Ts...>> {};
+
+/**
+ * Process a keep_alive call policy -- invokes keep_alive_impl during the
+ * pre-call handler if both Nurse, Patient != 0 and use the post-call handler
+ * otherwise
+ */
+template <size_t Nurse, size_t Patient>
+struct process_attribute<keep_alive<Nurse, Patient>>
+    : public process_attribute_default<keep_alive<Nurse, Patient>> {
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N != 0 && P != 0, int> = 0>
+    static void precall(function_call &call) {
+        keep_alive_impl(Nurse, Patient, call, handle());
+    }
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N != 0 && P != 0, int> = 0>
+    static void postcall(function_call &, handle) {}
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N == 0 || P == 0, int> = 0>
+    static void precall(function_call &) {}
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N == 0 || P == 0, int> = 0>
+    static void postcall(function_call &call, handle ret) {
+        keep_alive_impl(Nurse, Patient, call, ret);
+    }
+};
+
+/// Recursively iterate over variadic template arguments
+template <typename... Args>
+struct process_attributes {
+    static void init(const Args &...args, function_record *r) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r);
+        using expander = int[];
+        (void) expander{
+            0, ((void) process_attribute<typename std::decay<Args>::type>::init(args, r), 0)...};
+    }
+    static void init(const Args &...args, type_record *r) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r);
+        using expander = int[];
+        (void) expander{0,
+                        (process_attribute<typename std::decay<Args>::type>::init(args, r), 0)...};
+    }
+    static void precall(function_call &call) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call);
+        using expander = int[];
+        (void) expander{0,
+                        (process_attribute<typename std::decay<Args>::type>::precall(call), 0)...};
+    }
+    static void postcall(function_call &call, handle fn_ret) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call, fn_ret);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(fn_ret);
+        using expander = int[];
+        (void) expander{
+            0, (process_attribute<typename std::decay<Args>::type>::postcall(call, fn_ret), 0)...};
+    }
+};
+
+template <typename T>
+using is_call_guard = is_instantiation<call_guard, T>;
+
+/// Extract the ``type`` from the first `call_guard` in `Extras...` (or `void_type` if none found)
+template <typename... Extra>
+using extract_guard_t = typename exactly_one_t<is_call_guard, call_guard<>, Extra...>::type;
+
+/// Check the number of named arguments at compile time
+template <typename... Extra,
+          size_t named = constexpr_sum(std::is_base_of<arg, Extra>::value...),
+          size_t self = constexpr_sum(std::is_same<is_method, Extra>::value...)>
+constexpr bool expected_num_args(size_t nargs, bool has_args, bool has_kwargs) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(nargs, has_args, has_kwargs);
+    return named == 0 || (self + named + size_t(has_args) + size_t(has_kwargs)) == nargs;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/buffer_info.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/buffer_info.h
new file mode 100644
index 000000000..06120d556
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/buffer_info.h
@@ -0,0 +1,193 @@
+/*
+    pybind11/buffer_info.h: Python buffer object interface
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Default, C-style strides
+inline std::vector<ssize_t> c_strides(const std::vector<ssize_t> &shape, ssize_t itemsize) {
+    auto ndim = shape.size();
+    std::vector<ssize_t> strides(ndim, itemsize);
+    if (ndim > 0) {
+        for (size_t i = ndim - 1; i > 0; --i) {
+            strides[i - 1] = strides[i] * shape[i];
+        }
+    }
+    return strides;
+}
+
+// F-style strides; default when constructing an array_t with `ExtraFlags & f_style`
+inline std::vector<ssize_t> f_strides(const std::vector<ssize_t> &shape, ssize_t itemsize) {
+    auto ndim = shape.size();
+    std::vector<ssize_t> strides(ndim, itemsize);
+    for (size_t i = 1; i < ndim; ++i) {
+        strides[i] = strides[i - 1] * shape[i - 1];
+    }
+    return strides;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Information record describing a Python buffer object
+struct buffer_info {
+    void *ptr = nullptr;          // Pointer to the underlying storage
+    ssize_t itemsize = 0;         // Size of individual items in bytes
+    ssize_t size = 0;             // Total number of entries
+    std::string format;           // For homogeneous buffers, this should be set to
+                                  // format_descriptor<T>::format()
+    ssize_t ndim = 0;             // Number of dimensions
+    std::vector<ssize_t> shape;   // Shape of the tensor (1 entry per dimension)
+    std::vector<ssize_t> strides; // Number of bytes between adjacent entries
+                                  // (for each per dimension)
+    bool readonly = false;        // flag to indicate if the underlying storage may be written to
+
+    buffer_info() = default;
+
+    buffer_info(void *ptr,
+                ssize_t itemsize,
+                const std::string &format,
+                ssize_t ndim,
+                detail::any_container<ssize_t> shape_in,
+                detail::any_container<ssize_t> strides_in,
+                bool readonly = false)
+        : ptr(ptr), itemsize(itemsize), size(1), format(format), ndim(ndim),
+          shape(std::move(shape_in)), strides(std::move(strides_in)), readonly(readonly) {
+        if (ndim != (ssize_t) shape.size() || ndim != (ssize_t) strides.size()) {
+            pybind11_fail("buffer_info: ndim doesn't match shape and/or strides length");
+        }
+        for (size_t i = 0; i < (size_t) ndim; ++i) {
+            size *= shape[i];
+        }
+    }
+
+    template <typename T>
+    buffer_info(T *ptr,
+                detail::any_container<ssize_t> shape_in,
+                detail::any_container<ssize_t> strides_in,
+                bool readonly = false)
+        : buffer_info(private_ctr_tag(),
+                      ptr,
+                      sizeof(T),
+                      format_descriptor<T>::format(),
+                      static_cast<ssize_t>(shape_in->size()),
+                      std::move(shape_in),
+                      std::move(strides_in),
+                      readonly) {}
+
+    buffer_info(void *ptr,
+                ssize_t itemsize,
+                const std::string &format,
+                ssize_t size,
+                bool readonly = false)
+        : buffer_info(ptr, itemsize, format, 1, {size}, {itemsize}, readonly) {}
+
+    template <typename T>
+    buffer_info(T *ptr, ssize_t size, bool readonly = false)
+        : buffer_info(ptr, sizeof(T), format_descriptor<T>::format(), size, readonly) {}
+
+    template <typename T>
+    buffer_info(const T *ptr, ssize_t size, bool readonly = true)
+        : buffer_info(
+            const_cast<T *>(ptr), sizeof(T), format_descriptor<T>::format(), size, readonly) {}
+
+    explicit buffer_info(Py_buffer *view, bool ownview = true)
+        : buffer_info(
+            view->buf,
+            view->itemsize,
+            view->format,
+            view->ndim,
+            {view->shape, view->shape + view->ndim},
+            /* Though buffer::request() requests PyBUF_STRIDES, ctypes objects
+             * ignore this flag and return a view with NULL strides.
+             * When strides are NULL, build them manually.  */
+            view->strides
+                ? std::vector<ssize_t>(view->strides, view->strides + view->ndim)
+                : detail::c_strides({view->shape, view->shape + view->ndim}, view->itemsize),
+            (view->readonly != 0)) {
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        this->m_view = view;
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        this->ownview = ownview;
+    }
+
+    buffer_info(const buffer_info &) = delete;
+    buffer_info &operator=(const buffer_info &) = delete;
+
+    buffer_info(buffer_info &&other) noexcept { (*this) = std::move(other); }
+
+    buffer_info &operator=(buffer_info &&rhs) noexcept {
+        ptr = rhs.ptr;
+        itemsize = rhs.itemsize;
+        size = rhs.size;
+        format = std::move(rhs.format);
+        ndim = rhs.ndim;
+        shape = std::move(rhs.shape);
+        strides = std::move(rhs.strides);
+        std::swap(m_view, rhs.m_view);
+        std::swap(ownview, rhs.ownview);
+        readonly = rhs.readonly;
+        return *this;
+    }
+
+    ~buffer_info() {
+        if (m_view && ownview) {
+            PyBuffer_Release(m_view);
+            delete m_view;
+        }
+    }
+
+    Py_buffer *view() const { return m_view; }
+    Py_buffer *&view() { return m_view; }
+
+private:
+    struct private_ctr_tag {};
+
+    buffer_info(private_ctr_tag,
+                void *ptr,
+                ssize_t itemsize,
+                const std::string &format,
+                ssize_t ndim,
+                detail::any_container<ssize_t> &&shape_in,
+                detail::any_container<ssize_t> &&strides_in,
+                bool readonly)
+        : buffer_info(
+            ptr, itemsize, format, ndim, std::move(shape_in), std::move(strides_in), readonly) {}
+
+    Py_buffer *m_view = nullptr;
+    bool ownview = false;
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename T, typename SFINAE = void>
+struct compare_buffer_info {
+    static bool compare(const buffer_info &b) {
+        return b.format == format_descriptor<T>::format() && b.itemsize == (ssize_t) sizeof(T);
+    }
+};
+
+template <typename T>
+struct compare_buffer_info<T, detail::enable_if_t<std::is_integral<T>::value>> {
+    static bool compare(const buffer_info &b) {
+        return (size_t) b.itemsize == sizeof(T)
+               && (b.format == format_descriptor<T>::value
+                   || ((sizeof(T) == sizeof(long))
+                       && b.format == (std::is_unsigned<T>::value ? "L" : "l"))
+                   || ((sizeof(T) == sizeof(size_t))
+                       && b.format == (std::is_unsigned<T>::value ? "N" : "n")));
+    }
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/cast.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/cast.h
new file mode 100644
index 000000000..a0e32281b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/cast.h
@@ -0,0 +1,1665 @@
+/*
+    pybind11/cast.h: Partial template specializations to cast between
+    C++ and Python types
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "detail/descr.h"
+#include "detail/type_caster_base.h"
+#include "detail/typeid.h"
+#include "pytypes.h"
+
+#include <array>
+#include <cstring>
+#include <functional>
+#include <iosfwd>
+#include <iterator>
+#include <memory>
+#include <string>
+#include <tuple>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename type, typename SFINAE = void>
+class type_caster : public type_caster_base<type> {};
+template <typename type>
+using make_caster = type_caster<intrinsic_t<type>>;
+
+// Shortcut for calling a caster's `cast_op_type` cast operator for casting a type_caster to a T
+template <typename T>
+typename make_caster<T>::template cast_op_type<T> cast_op(make_caster<T> &caster) {
+    return caster.operator typename make_caster<T>::template cast_op_type<T>();
+}
+template <typename T>
+typename make_caster<T>::template cast_op_type<typename std::add_rvalue_reference<T>::type>
+cast_op(make_caster<T> &&caster) {
+    return std::move(caster).operator typename make_caster<T>::
+        template cast_op_type<typename std::add_rvalue_reference<T>::type>();
+}
+
+template <typename type>
+class type_caster<std::reference_wrapper<type>> {
+private:
+    using caster_t = make_caster<type>;
+    caster_t subcaster;
+    using reference_t = type &;
+    using subcaster_cast_op_type = typename caster_t::template cast_op_type<reference_t>;
+
+    static_assert(
+        std::is_same<typename std::remove_const<type>::type &, subcaster_cast_op_type>::value
+            || std::is_same<reference_t, subcaster_cast_op_type>::value,
+        "std::reference_wrapper<T> caster requires T to have a caster with an "
+        "`operator T &()` or `operator const T &()`");
+
+public:
+    bool load(handle src, bool convert) { return subcaster.load(src, convert); }
+    static constexpr auto name = caster_t::name;
+    static handle
+    cast(const std::reference_wrapper<type> &src, return_value_policy policy, handle parent) {
+        // It is definitely wrong to take ownership of this pointer, so mask that rvp
+        if (policy == return_value_policy::take_ownership
+            || policy == return_value_policy::automatic) {
+            policy = return_value_policy::automatic_reference;
+        }
+        return caster_t::cast(&src.get(), policy, parent);
+    }
+    template <typename T>
+    using cast_op_type = std::reference_wrapper<type>;
+    explicit operator std::reference_wrapper<type>() { return cast_op<type &>(subcaster); }
+};
+
+#define PYBIND11_TYPE_CASTER(type, py_name)                                                       \
+protected:                                                                                        \
+    type value;                                                                                   \
+                                                                                                  \
+public:                                                                                           \
+    static constexpr auto name = py_name;                                                         \
+    template <typename T_,                                                                        \
+              ::pybind11::detail::enable_if_t<                                                    \
+                  std::is_same<type, ::pybind11::detail::remove_cv_t<T_>>::value,                 \
+                  int> = 0>                                                                       \
+    static ::pybind11::handle cast(                                                               \
+        T_ *src, ::pybind11::return_value_policy policy, ::pybind11::handle parent) {             \
+        if (!src)                                                                                 \
+            return ::pybind11::none().release();                                                  \
+        if (policy == ::pybind11::return_value_policy::take_ownership) {                          \
+            auto h = cast(std::move(*src), policy, parent);                                       \
+            delete src;                                                                           \
+            return h;                                                                             \
+        }                                                                                         \
+        return cast(*src, policy, parent);                                                        \
+    }                                                                                             \
+    operator type *() { return &value; }               /* NOLINT(bugprone-macro-parentheses) */   \
+    operator type &() { return value; }                /* NOLINT(bugprone-macro-parentheses) */   \
+    operator type &&() && { return std::move(value); } /* NOLINT(bugprone-macro-parentheses) */   \
+    template <typename T_>                                                                        \
+    using cast_op_type = ::pybind11::detail::movable_cast_op_type<T_>
+
+template <typename CharT>
+using is_std_char_type = any_of<std::is_same<CharT, char>, /* std::string */
+#if defined(PYBIND11_HAS_U8STRING)
+                                std::is_same<CharT, char8_t>, /* std::u8string */
+#endif
+                                std::is_same<CharT, char16_t>, /* std::u16string */
+                                std::is_same<CharT, char32_t>, /* std::u32string */
+                                std::is_same<CharT, wchar_t>   /* std::wstring */
+                                >;
+
+template <typename T>
+struct type_caster<T, enable_if_t<std::is_arithmetic<T>::value && !is_std_char_type<T>::value>> {
+    using _py_type_0 = conditional_t<sizeof(T) <= sizeof(long), long, long long>;
+    using _py_type_1 = conditional_t<std::is_signed<T>::value,
+                                     _py_type_0,
+                                     typename std::make_unsigned<_py_type_0>::type>;
+    using py_type = conditional_t<std::is_floating_point<T>::value, double, _py_type_1>;
+
+public:
+    bool load(handle src, bool convert) {
+        py_type py_value;
+
+        if (!src) {
+            return false;
+        }
+
+#if !defined(PYPY_VERSION)
+        auto index_check = [](PyObject *o) { return PyIndex_Check(o); };
+#else
+        // In PyPy 7.3.3, `PyIndex_Check` is implemented by calling `__index__`,
+        // while CPython only considers the existence of `nb_index`/`__index__`.
+        auto index_check = [](PyObject *o) { return hasattr(o, "__index__"); };
+#endif
+
+        if (std::is_floating_point<T>::value) {
+            if (convert || PyFloat_Check(src.ptr())) {
+                py_value = (py_type) PyFloat_AsDouble(src.ptr());
+            } else {
+                return false;
+            }
+        } else if (PyFloat_Check(src.ptr())
+                   || (!convert && !PYBIND11_LONG_CHECK(src.ptr()) && !index_check(src.ptr()))) {
+            return false;
+        } else {
+            handle src_or_index = src;
+            // PyPy: 7.3.7's 3.8 does not implement PyLong_*'s __index__ calls.
+#if PY_VERSION_HEX < 0x03080000 || defined(PYPY_VERSION)
+            object index;
+            if (!PYBIND11_LONG_CHECK(src.ptr())) { // So: index_check(src.ptr())
+                index = reinterpret_steal<object>(PyNumber_Index(src.ptr()));
+                if (!index) {
+                    PyErr_Clear();
+                    if (!convert)
+                        return false;
+                } else {
+                    src_or_index = index;
+                }
+            }
+#endif
+            if (std::is_unsigned<py_type>::value) {
+                py_value = as_unsigned<py_type>(src_or_index.ptr());
+            } else { // signed integer:
+                py_value = sizeof(T) <= sizeof(long)
+                               ? (py_type) PyLong_AsLong(src_or_index.ptr())
+                               : (py_type) PYBIND11_LONG_AS_LONGLONG(src_or_index.ptr());
+            }
+        }
+
+        // Python API reported an error
+        bool py_err = py_value == (py_type) -1 && PyErr_Occurred();
+
+        // Check to see if the conversion is valid (integers should match exactly)
+        // Signed/unsigned checks happen elsewhere
+        if (py_err
+            || (std::is_integral<T>::value && sizeof(py_type) != sizeof(T)
+                && py_value != (py_type) (T) py_value)) {
+            PyErr_Clear();
+            if (py_err && convert && (PyNumber_Check(src.ptr()) != 0)) {
+                auto tmp = reinterpret_steal<object>(std::is_floating_point<T>::value
+                                                         ? PyNumber_Float(src.ptr())
+                                                         : PyNumber_Long(src.ptr()));
+                PyErr_Clear();
+                return load(tmp, false);
+            }
+            return false;
+        }
+
+        value = (T) py_value;
+        return true;
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<std::is_floating_point<U>::value, handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PyFloat_FromDouble((double) src);
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<!std::is_floating_point<U>::value && std::is_signed<U>::value
+                                       && (sizeof(U) <= sizeof(long)),
+                                   handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PYBIND11_LONG_FROM_SIGNED((long) src);
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<!std::is_floating_point<U>::value && std::is_unsigned<U>::value
+                                       && (sizeof(U) <= sizeof(unsigned long)),
+                                   handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PYBIND11_LONG_FROM_UNSIGNED((unsigned long) src);
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<!std::is_floating_point<U>::value && std::is_signed<U>::value
+                                       && (sizeof(U) > sizeof(long)),
+                                   handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PyLong_FromLongLong((long long) src);
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<!std::is_floating_point<U>::value && std::is_unsigned<U>::value
+                                       && (sizeof(U) > sizeof(unsigned long)),
+                                   handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PyLong_FromUnsignedLongLong((unsigned long long) src);
+    }
+
+    PYBIND11_TYPE_CASTER(T, const_name<std::is_integral<T>::value>("int", "float"));
+};
+
+template <typename T>
+struct void_caster {
+public:
+    bool load(handle src, bool) {
+        if (src && src.is_none()) {
+            return true;
+        }
+        return false;
+    }
+    static handle cast(T, return_value_policy /* policy */, handle /* parent */) {
+        return none().inc_ref();
+    }
+    PYBIND11_TYPE_CASTER(T, const_name("None"));
+};
+
+template <>
+class type_caster<void_type> : public void_caster<void_type> {};
+
+template <>
+class type_caster<void> : public type_caster<void_type> {
+public:
+    using type_caster<void_type>::cast;
+
+    bool load(handle h, bool) {
+        if (!h) {
+            return false;
+        }
+        if (h.is_none()) {
+            value = nullptr;
+            return true;
+        }
+
+        /* Check if this is a capsule */
+        if (isinstance<capsule>(h)) {
+            value = reinterpret_borrow<capsule>(h);
+            return true;
+        }
+
+        /* Check if this is a C++ type */
+        const auto &bases = all_type_info((PyTypeObject *) type::handle_of(h).ptr());
+        if (bases.size() == 1) { // Only allowing loading from a single-value type
+            value = values_and_holders(reinterpret_cast<instance *>(h.ptr())).begin()->value_ptr();
+            return true;
+        }
+
+        /* Fail */
+        return false;
+    }
+
+    static handle cast(const void *ptr, return_value_policy /* policy */, handle /* parent */) {
+        if (ptr) {
+            return capsule(ptr).release();
+        }
+        return none().inc_ref();
+    }
+
+    template <typename T>
+    using cast_op_type = void *&;
+    explicit operator void *&() { return value; }
+    static constexpr auto name = const_name("capsule");
+
+private:
+    void *value = nullptr;
+};
+
+template <>
+class type_caster<std::nullptr_t> : public void_caster<std::nullptr_t> {};
+
+template <>
+class type_caster<bool> {
+public:
+    bool load(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (src.ptr() == Py_True) {
+            value = true;
+            return true;
+        }
+        if (src.ptr() == Py_False) {
+            value = false;
+            return true;
+        }
+        if (convert || (std::strcmp("numpy.bool_", Py_TYPE(src.ptr())->tp_name) == 0)) {
+            // (allow non-implicit conversion for numpy booleans)
+
+            Py_ssize_t res = -1;
+            if (src.is_none()) {
+                res = 0; // None is implicitly converted to False
+            }
+#if defined(PYPY_VERSION)
+            // On PyPy, check that "__bool__" attr exists
+            else if (hasattr(src, PYBIND11_BOOL_ATTR)) {
+                res = PyObject_IsTrue(src.ptr());
+            }
+#else
+            // Alternate approach for CPython: this does the same as the above, but optimized
+            // using the CPython API so as to avoid an unneeded attribute lookup.
+            else if (auto *tp_as_number = src.ptr()->ob_type->tp_as_number) {
+                if (PYBIND11_NB_BOOL(tp_as_number)) {
+                    res = (*PYBIND11_NB_BOOL(tp_as_number))(src.ptr());
+                }
+            }
+#endif
+            if (res == 0 || res == 1) {
+                value = (res != 0);
+                return true;
+            }
+            PyErr_Clear();
+        }
+        return false;
+    }
+    static handle cast(bool src, return_value_policy /* policy */, handle /* parent */) {
+        return handle(src ? Py_True : Py_False).inc_ref();
+    }
+    PYBIND11_TYPE_CASTER(bool, const_name("bool"));
+};
+
+// Helper class for UTF-{8,16,32} C++ stl strings:
+template <typename StringType, bool IsView = false>
+struct string_caster {
+    using CharT = typename StringType::value_type;
+
+    // Simplify life by being able to assume standard char sizes (the standard only guarantees
+    // minimums, but Python requires exact sizes)
+    static_assert(!std::is_same<CharT, char>::value || sizeof(CharT) == 1,
+                  "Unsupported char size != 1");
+#if defined(PYBIND11_HAS_U8STRING)
+    static_assert(!std::is_same<CharT, char8_t>::value || sizeof(CharT) == 1,
+                  "Unsupported char8_t size != 1");
+#endif
+    static_assert(!std::is_same<CharT, char16_t>::value || sizeof(CharT) == 2,
+                  "Unsupported char16_t size != 2");
+    static_assert(!std::is_same<CharT, char32_t>::value || sizeof(CharT) == 4,
+                  "Unsupported char32_t size != 4");
+    // wchar_t can be either 16 bits (Windows) or 32 (everywhere else)
+    static_assert(!std::is_same<CharT, wchar_t>::value || sizeof(CharT) == 2 || sizeof(CharT) == 4,
+                  "Unsupported wchar_t size != 2/4");
+    static constexpr size_t UTF_N = 8 * sizeof(CharT);
+
+    bool load(handle src, bool) {
+        handle load_src = src;
+        if (!src) {
+            return false;
+        }
+        if (!PyUnicode_Check(load_src.ptr())) {
+            return load_raw(load_src);
+        }
+
+        // For UTF-8 we avoid the need for a temporary `bytes` object by using
+        // `PyUnicode_AsUTF8AndSize`.
+        if (PYBIND11_SILENCE_MSVC_C4127(UTF_N == 8)) {
+            Py_ssize_t size = -1;
+            const auto *buffer
+                = reinterpret_cast<const CharT *>(PyUnicode_AsUTF8AndSize(load_src.ptr(), &size));
+            if (!buffer) {
+                PyErr_Clear();
+                return false;
+            }
+            value = StringType(buffer, static_cast<size_t>(size));
+            return true;
+        }
+
+        auto utfNbytes
+            = reinterpret_steal<object>(PyUnicode_AsEncodedString(load_src.ptr(),
+                                                                  UTF_N == 8    ? "utf-8"
+                                                                  : UTF_N == 16 ? "utf-16"
+                                                                                : "utf-32",
+                                                                  nullptr));
+        if (!utfNbytes) {
+            PyErr_Clear();
+            return false;
+        }
+
+        const auto *buffer
+            = reinterpret_cast<const CharT *>(PYBIND11_BYTES_AS_STRING(utfNbytes.ptr()));
+        size_t length = (size_t) PYBIND11_BYTES_SIZE(utfNbytes.ptr()) / sizeof(CharT);
+        // Skip BOM for UTF-16/32
+        if (PYBIND11_SILENCE_MSVC_C4127(UTF_N > 8)) {
+            buffer++;
+            length--;
+        }
+        value = StringType(buffer, length);
+
+        // If we're loading a string_view we need to keep the encoded Python object alive:
+        if (IsView) {
+            loader_life_support::add_patient(utfNbytes);
+        }
+
+        return true;
+    }
+
+    static handle
+    cast(const StringType &src, return_value_policy /* policy */, handle /* parent */) {
+        const char *buffer = reinterpret_cast<const char *>(src.data());
+        auto nbytes = ssize_t(src.size() * sizeof(CharT));
+        handle s = decode_utfN(buffer, nbytes);
+        if (!s) {
+            throw error_already_set();
+        }
+        return s;
+    }
+
+    PYBIND11_TYPE_CASTER(StringType, const_name(PYBIND11_STRING_NAME));
+
+private:
+    static handle decode_utfN(const char *buffer, ssize_t nbytes) {
+#if !defined(PYPY_VERSION)
+        return UTF_N == 8    ? PyUnicode_DecodeUTF8(buffer, nbytes, nullptr)
+               : UTF_N == 16 ? PyUnicode_DecodeUTF16(buffer, nbytes, nullptr, nullptr)
+                             : PyUnicode_DecodeUTF32(buffer, nbytes, nullptr, nullptr);
+#else
+        // PyPy segfaults when on PyUnicode_DecodeUTF16 (and possibly on PyUnicode_DecodeUTF32 as
+        // well), so bypass the whole thing by just passing the encoding as a string value, which
+        // works properly:
+        return PyUnicode_Decode(buffer,
+                                nbytes,
+                                UTF_N == 8    ? "utf-8"
+                                : UTF_N == 16 ? "utf-16"
+                                              : "utf-32",
+                                nullptr);
+#endif
+    }
+
+    // When loading into a std::string or char*, accept a bytes/bytearray object as-is (i.e.
+    // without any encoding/decoding attempt).  For other C++ char sizes this is a no-op.
+    // which supports loading a unicode from a str, doesn't take this path.
+    template <typename C = CharT>
+    bool load_raw(enable_if_t<std::is_same<C, char>::value, handle> src) {
+        if (PYBIND11_BYTES_CHECK(src.ptr())) {
+            // We were passed raw bytes; accept it into a std::string or char*
+            // without any encoding attempt.
+            const char *bytes = PYBIND11_BYTES_AS_STRING(src.ptr());
+            if (!bytes) {
+                pybind11_fail("Unexpected PYBIND11_BYTES_AS_STRING() failure.");
+            }
+            value = StringType(bytes, (size_t) PYBIND11_BYTES_SIZE(src.ptr()));
+            return true;
+        }
+        if (PyByteArray_Check(src.ptr())) {
+            // We were passed a bytearray; accept it into a std::string or char*
+            // without any encoding attempt.
+            const char *bytearray = PyByteArray_AsString(src.ptr());
+            if (!bytearray) {
+                pybind11_fail("Unexpected PyByteArray_AsString() failure.");
+            }
+            value = StringType(bytearray, (size_t) PyByteArray_Size(src.ptr()));
+            return true;
+        }
+
+        return false;
+    }
+
+    template <typename C = CharT>
+    bool load_raw(enable_if_t<!std::is_same<C, char>::value, handle>) {
+        return false;
+    }
+};
+
+template <typename CharT, class Traits, class Allocator>
+struct type_caster<std::basic_string<CharT, Traits, Allocator>,
+                   enable_if_t<is_std_char_type<CharT>::value>>
+    : string_caster<std::basic_string<CharT, Traits, Allocator>> {};
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+template <typename CharT, class Traits>
+struct type_caster<std::basic_string_view<CharT, Traits>,
+                   enable_if_t<is_std_char_type<CharT>::value>>
+    : string_caster<std::basic_string_view<CharT, Traits>, true> {};
+#endif
+
+// Type caster for C-style strings.  We basically use a std::string type caster, but also add the
+// ability to use None as a nullptr char* (which the string caster doesn't allow).
+template <typename CharT>
+struct type_caster<CharT, enable_if_t<is_std_char_type<CharT>::value>> {
+    using StringType = std::basic_string<CharT>;
+    using StringCaster = make_caster<StringType>;
+    StringCaster str_caster;
+    bool none = false;
+    CharT one_char = 0;
+
+public:
+    bool load(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (src.is_none()) {
+            // Defer accepting None to other overloads (if we aren't in convert mode):
+            if (!convert) {
+                return false;
+            }
+            none = true;
+            return true;
+        }
+        return str_caster.load(src, convert);
+    }
+
+    static handle cast(const CharT *src, return_value_policy policy, handle parent) {
+        if (src == nullptr) {
+            return pybind11::none().inc_ref();
+        }
+        return StringCaster::cast(StringType(src), policy, parent);
+    }
+
+    static handle cast(CharT src, return_value_policy policy, handle parent) {
+        if (std::is_same<char, CharT>::value) {
+            handle s = PyUnicode_DecodeLatin1((const char *) &src, 1, nullptr);
+            if (!s) {
+                throw error_already_set();
+            }
+            return s;
+        }
+        return StringCaster::cast(StringType(1, src), policy, parent);
+    }
+
+    explicit operator CharT *() {
+        return none ? nullptr : const_cast<CharT *>(static_cast<StringType &>(str_caster).c_str());
+    }
+    explicit operator CharT &() {
+        if (none) {
+            throw value_error("Cannot convert None to a character");
+        }
+
+        auto &value = static_cast<StringType &>(str_caster);
+        size_t str_len = value.size();
+        if (str_len == 0) {
+            throw value_error("Cannot convert empty string to a character");
+        }
+
+        // If we're in UTF-8 mode, we have two possible failures: one for a unicode character that
+        // is too high, and one for multiple unicode characters (caught later), so we need to
+        // figure out how long the first encoded character is in bytes to distinguish between these
+        // two errors.  We also allow want to allow unicode characters U+0080 through U+00FF, as
+        // those can fit into a single char value.
+        if (PYBIND11_SILENCE_MSVC_C4127(StringCaster::UTF_N == 8) && str_len > 1 && str_len <= 4) {
+            auto v0 = static_cast<unsigned char>(value[0]);
+            // low bits only: 0-127
+            // 0b110xxxxx - start of 2-byte sequence
+            // 0b1110xxxx - start of 3-byte sequence
+            // 0b11110xxx - start of 4-byte sequence
+            size_t char0_bytes = (v0 & 0x80) == 0      ? 1
+                                 : (v0 & 0xE0) == 0xC0 ? 2
+                                 : (v0 & 0xF0) == 0xE0 ? 3
+                                                       : 4;
+
+            if (char0_bytes == str_len) {
+                // If we have a 128-255 value, we can decode it into a single char:
+                if (char0_bytes == 2 && (v0 & 0xFC) == 0xC0) { // 0x110000xx 0x10xxxxxx
+                    one_char = static_cast<CharT>(((v0 & 3) << 6)
+                                                  + (static_cast<unsigned char>(value[1]) & 0x3F));
+                    return one_char;
+                }
+                // Otherwise we have a single character, but it's > U+00FF
+                throw value_error("Character code point not in range(0x100)");
+            }
+        }
+
+        // UTF-16 is much easier: we can only have a surrogate pair for values above U+FFFF, thus a
+        // surrogate pair with total length 2 instantly indicates a range error (but not a "your
+        // string was too long" error).
+        else if (PYBIND11_SILENCE_MSVC_C4127(StringCaster::UTF_N == 16) && str_len == 2) {
+            one_char = static_cast<CharT>(value[0]);
+            if (one_char >= 0xD800 && one_char < 0xE000) {
+                throw value_error("Character code point not in range(0x10000)");
+            }
+        }
+
+        if (str_len != 1) {
+            throw value_error("Expected a character, but multi-character string found");
+        }
+
+        one_char = value[0];
+        return one_char;
+    }
+
+    static constexpr auto name = const_name(PYBIND11_STRING_NAME);
+    template <typename _T>
+    using cast_op_type = pybind11::detail::cast_op_type<_T>;
+};
+
+// Base implementation for std::tuple and std::pair
+template <template <typename...> class Tuple, typename... Ts>
+class tuple_caster {
+    using type = Tuple<Ts...>;
+    static constexpr auto size = sizeof...(Ts);
+    using indices = make_index_sequence<size>;
+
+public:
+    bool load(handle src, bool convert) {
+        if (!isinstance<sequence>(src)) {
+            return false;
+        }
+        const auto seq = reinterpret_borrow<sequence>(src);
+        if (seq.size() != size) {
+            return false;
+        }
+        return load_impl(seq, convert, indices{});
+    }
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        return cast_impl(std::forward<T>(src), policy, parent, indices{});
+    }
+
+    // copied from the PYBIND11_TYPE_CASTER macro
+    template <typename T>
+    static handle cast(T *src, return_value_policy policy, handle parent) {
+        if (!src) {
+            return none().release();
+        }
+        if (policy == return_value_policy::take_ownership) {
+            auto h = cast(std::move(*src), policy, parent);
+            delete src;
+            return h;
+        }
+        return cast(*src, policy, parent);
+    }
+
+    static constexpr auto name
+        = const_name("Tuple[") + concat(make_caster<Ts>::name...) + const_name("]");
+
+    template <typename T>
+    using cast_op_type = type;
+
+    explicit operator type() & { return implicit_cast(indices{}); }
+    explicit operator type() && { return std::move(*this).implicit_cast(indices{}); }
+
+protected:
+    template <size_t... Is>
+    type implicit_cast(index_sequence<Is...>) & {
+        return type(cast_op<Ts>(std::get<Is>(subcasters))...);
+    }
+    template <size_t... Is>
+    type implicit_cast(index_sequence<Is...>) && {
+        return type(cast_op<Ts>(std::move(std::get<Is>(subcasters)))...);
+    }
+
+    static constexpr bool load_impl(const sequence &, bool, index_sequence<>) { return true; }
+
+    template <size_t... Is>
+    bool load_impl(const sequence &seq, bool convert, index_sequence<Is...>) {
+#ifdef __cpp_fold_expressions
+        if ((... || !std::get<Is>(subcasters).load(seq[Is], convert))) {
+            return false;
+        }
+#else
+        for (bool r : {std::get<Is>(subcasters).load(seq[Is], convert)...}) {
+            if (!r) {
+                return false;
+            }
+        }
+#endif
+        return true;
+    }
+
+    /* Implementation: Convert a C++ tuple into a Python tuple */
+    template <typename T, size_t... Is>
+    static handle
+    cast_impl(T &&src, return_value_policy policy, handle parent, index_sequence<Is...>) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(src, policy, parent);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(policy, parent);
+        std::array<object, size> entries{{reinterpret_steal<object>(
+            make_caster<Ts>::cast(std::get<Is>(std::forward<T>(src)), policy, parent))...}};
+        for (const auto &entry : entries) {
+            if (!entry) {
+                return handle();
+            }
+        }
+        tuple result(size);
+        int counter = 0;
+        for (auto &entry : entries) {
+            PyTuple_SET_ITEM(result.ptr(), counter++, entry.release().ptr());
+        }
+        return result.release();
+    }
+
+    Tuple<make_caster<Ts>...> subcasters;
+};
+
+template <typename T1, typename T2>
+class type_caster<std::pair<T1, T2>> : public tuple_caster<std::pair, T1, T2> {};
+
+template <typename... Ts>
+class type_caster<std::tuple<Ts...>> : public tuple_caster<std::tuple, Ts...> {};
+
+/// Helper class which abstracts away certain actions. Users can provide specializations for
+/// custom holders, but it's only necessary if the type has a non-standard interface.
+template <typename T>
+struct holder_helper {
+    static auto get(const T &p) -> decltype(p.get()) { return p.get(); }
+};
+
+/// Type caster for holder types like std::shared_ptr, etc.
+/// The SFINAE hook is provided to help work around the current lack of support
+/// for smart-pointer interoperability. Please consider it an implementation
+/// detail that may change in the future, as formal support for smart-pointer
+/// interoperability is added into pybind11.
+template <typename type, typename holder_type, typename SFINAE = void>
+struct copyable_holder_caster : public type_caster_base<type> {
+public:
+    using base = type_caster_base<type>;
+    static_assert(std::is_base_of<base, type_caster<type>>::value,
+                  "Holder classes are only supported for custom types");
+    using base::base;
+    using base::cast;
+    using base::typeinfo;
+    using base::value;
+
+    bool load(handle src, bool convert) {
+        return base::template load_impl<copyable_holder_caster<type, holder_type>>(src, convert);
+    }
+
+    explicit operator type *() { return this->value; }
+    // static_cast works around compiler error with MSVC 17 and CUDA 10.2
+    // see issue #2180
+    explicit operator type &() { return *(static_cast<type *>(this->value)); }
+    explicit operator holder_type *() { return std::addressof(holder); }
+    explicit operator holder_type &() { return holder; }
+
+    static handle cast(const holder_type &src, return_value_policy, handle) {
+        const auto *ptr = holder_helper<holder_type>::get(src);
+        return type_caster_base<type>::cast_holder(ptr, &src);
+    }
+
+protected:
+    friend class type_caster_generic;
+    void check_holder_compat() {
+        if (typeinfo->default_holder) {
+            throw cast_error("Unable to load a custom holder type from a default-holder instance");
+        }
+    }
+
+    bool load_value(value_and_holder &&v_h) {
+        if (v_h.holder_constructed()) {
+            value = v_h.value_ptr();
+            holder = v_h.template holder<holder_type>();
+            return true;
+        }
+        throw cast_error("Unable to cast from non-held to held instance (T& to Holder<T>) "
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                         "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for "
+                         "type information)");
+#else
+                         "of type '"
+                         + type_id<holder_type>() + "''");
+#endif
+    }
+
+    template <typename T = holder_type,
+              detail::enable_if_t<!std::is_constructible<T, const T &, type *>::value, int> = 0>
+    bool try_implicit_casts(handle, bool) {
+        return false;
+    }
+
+    template <typename T = holder_type,
+              detail::enable_if_t<std::is_constructible<T, const T &, type *>::value, int> = 0>
+    bool try_implicit_casts(handle src, bool convert) {
+        for (auto &cast : typeinfo->implicit_casts) {
+            copyable_holder_caster sub_caster(*cast.first);
+            if (sub_caster.load(src, convert)) {
+                value = cast.second(sub_caster.value);
+                holder = holder_type(sub_caster.holder, (type *) value);
+                return true;
+            }
+        }
+        return false;
+    }
+
+    static bool try_direct_conversions(handle) { return false; }
+
+    holder_type holder;
+};
+
+/// Specialize for the common std::shared_ptr, so users don't need to
+template <typename T>
+class type_caster<std::shared_ptr<T>> : public copyable_holder_caster<T, std::shared_ptr<T>> {};
+
+/// Type caster for holder types like std::unique_ptr.
+/// Please consider the SFINAE hook an implementation detail, as explained
+/// in the comment for the copyable_holder_caster.
+template <typename type, typename holder_type, typename SFINAE = void>
+struct move_only_holder_caster {
+    static_assert(std::is_base_of<type_caster_base<type>, type_caster<type>>::value,
+                  "Holder classes are only supported for custom types");
+
+    static handle cast(holder_type &&src, return_value_policy, handle) {
+        auto *ptr = holder_helper<holder_type>::get(src);
+        return type_caster_base<type>::cast_holder(ptr, std::addressof(src));
+    }
+    static constexpr auto name = type_caster_base<type>::name;
+};
+
+template <typename type, typename deleter>
+class type_caster<std::unique_ptr<type, deleter>>
+    : public move_only_holder_caster<type, std::unique_ptr<type, deleter>> {};
+
+template <typename type, typename holder_type>
+using type_caster_holder = conditional_t<is_copy_constructible<holder_type>::value,
+                                         copyable_holder_caster<type, holder_type>,
+                                         move_only_holder_caster<type, holder_type>>;
+
+template <typename T, bool Value = false>
+struct always_construct_holder {
+    static constexpr bool value = Value;
+};
+
+/// Create a specialization for custom holder types (silently ignores std::shared_ptr)
+#define PYBIND11_DECLARE_HOLDER_TYPE(type, holder_type, ...)                                      \
+    PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)                                                  \
+    namespace detail {                                                                            \
+    template <typename type>                                                                      \
+    struct always_construct_holder<holder_type> : always_construct_holder<void, ##__VA_ARGS__> {  \
+    };                                                                                            \
+    template <typename type>                                                                      \
+    class type_caster<holder_type, enable_if_t<!is_shared_ptr<holder_type>::value>>               \
+        : public type_caster_holder<type, holder_type> {};                                        \
+    }                                                                                             \
+    PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
+
+// PYBIND11_DECLARE_HOLDER_TYPE holder types:
+template <typename base, typename holder>
+struct is_holder_type
+    : std::is_base_of<detail::type_caster_holder<base, holder>, detail::type_caster<holder>> {};
+// Specialization for always-supported unique_ptr holders:
+template <typename base, typename deleter>
+struct is_holder_type<base, std::unique_ptr<base, deleter>> : std::true_type {};
+
+template <typename T>
+struct handle_type_name {
+    static constexpr auto name = const_name<T>();
+};
+template <>
+struct handle_type_name<bool_> {
+    static constexpr auto name = const_name("bool");
+};
+template <>
+struct handle_type_name<bytes> {
+    static constexpr auto name = const_name(PYBIND11_BYTES_NAME);
+};
+template <>
+struct handle_type_name<int_> {
+    static constexpr auto name = const_name("int");
+};
+template <>
+struct handle_type_name<iterable> {
+    static constexpr auto name = const_name("Iterable");
+};
+template <>
+struct handle_type_name<iterator> {
+    static constexpr auto name = const_name("Iterator");
+};
+template <>
+struct handle_type_name<float_> {
+    static constexpr auto name = const_name("float");
+};
+template <>
+struct handle_type_name<none> {
+    static constexpr auto name = const_name("None");
+};
+template <>
+struct handle_type_name<args> {
+    static constexpr auto name = const_name("*args");
+};
+template <>
+struct handle_type_name<kwargs> {
+    static constexpr auto name = const_name("**kwargs");
+};
+
+template <typename type>
+struct pyobject_caster {
+    template <typename T = type, enable_if_t<std::is_same<T, handle>::value, int> = 0>
+    pyobject_caster() : value() {}
+
+    // `type` may not be default constructible (e.g. frozenset, anyset).  Initializing `value`
+    // to a nil handle is safe since it will only be accessed if `load` succeeds.
+    template <typename T = type, enable_if_t<std::is_base_of<object, T>::value, int> = 0>
+    pyobject_caster() : value(reinterpret_steal<type>(handle())) {}
+
+    template <typename T = type, enable_if_t<std::is_same<T, handle>::value, int> = 0>
+    bool load(handle src, bool /* convert */) {
+        value = src;
+        return static_cast<bool>(value);
+    }
+
+    template <typename T = type, enable_if_t<std::is_base_of<object, T>::value, int> = 0>
+    bool load(handle src, bool /* convert */) {
+        if (!isinstance<type>(src)) {
+            return false;
+        }
+        value = reinterpret_borrow<type>(src);
+        return true;
+    }
+
+    static handle cast(const handle &src, return_value_policy /* policy */, handle /* parent */) {
+        return src.inc_ref();
+    }
+    PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name);
+};
+
+template <typename T>
+class type_caster<T, enable_if_t<is_pyobject<T>::value>> : public pyobject_caster<T> {};
+
+// Our conditions for enabling moving are quite restrictive:
+// At compile time:
+// - T needs to be a non-const, non-pointer, non-reference type
+// - type_caster<T>::operator T&() must exist
+// - the type must be move constructible (obviously)
+// At run-time:
+// - if the type is non-copy-constructible, the object must be the sole owner of the type (i.e. it
+//   must have ref_count() == 1)h
+// If any of the above are not satisfied, we fall back to copying.
+template <typename T>
+using move_is_plain_type
+    = satisfies_none_of<T, std::is_void, std::is_pointer, std::is_reference, std::is_const>;
+template <typename T, typename SFINAE = void>
+struct move_always : std::false_type {};
+template <typename T>
+struct move_always<
+    T,
+    enable_if_t<
+        all_of<move_is_plain_type<T>,
+               negation<is_copy_constructible<T>>,
+               std::is_move_constructible<T>,
+               std::is_same<decltype(std::declval<make_caster<T>>().operator T &()), T &>>::value>>
+    : std::true_type {};
+template <typename T, typename SFINAE = void>
+struct move_if_unreferenced : std::false_type {};
+template <typename T>
+struct move_if_unreferenced<
+    T,
+    enable_if_t<
+        all_of<move_is_plain_type<T>,
+               negation<move_always<T>>,
+               std::is_move_constructible<T>,
+               std::is_same<decltype(std::declval<make_caster<T>>().operator T &()), T &>>::value>>
+    : std::true_type {};
+template <typename T>
+using move_never = none_of<move_always<T>, move_if_unreferenced<T>>;
+
+// Detect whether returning a `type` from a cast on type's type_caster is going to result in a
+// reference or pointer to a local variable of the type_caster.  Basically, only
+// non-reference/pointer `type`s and reference/pointers from a type_caster_generic are safe;
+// everything else returns a reference/pointer to a local variable.
+template <typename type>
+using cast_is_temporary_value_reference
+    = bool_constant<(std::is_reference<type>::value || std::is_pointer<type>::value)
+                    && !std::is_base_of<type_caster_generic, make_caster<type>>::value
+                    && !std::is_same<intrinsic_t<type>, void>::value>;
+
+// When a value returned from a C++ function is being cast back to Python, we almost always want to
+// force `policy = move`, regardless of the return value policy the function/method was declared
+// with.
+template <typename Return, typename SFINAE = void>
+struct return_value_policy_override {
+    static return_value_policy policy(return_value_policy p) { return p; }
+};
+
+template <typename Return>
+struct return_value_policy_override<
+    Return,
+    detail::enable_if_t<std::is_base_of<type_caster_generic, make_caster<Return>>::value, void>> {
+    static return_value_policy policy(return_value_policy p) {
+        return !std::is_lvalue_reference<Return>::value && !std::is_pointer<Return>::value
+                   ? return_value_policy::move
+                   : p;
+    }
+};
+
+// Basic python -> C++ casting; throws if casting fails
+template <typename T, typename SFINAE>
+type_caster<T, SFINAE> &load_type(type_caster<T, SFINAE> &conv, const handle &handle) {
+    static_assert(!detail::is_pyobject<T>::value,
+                  "Internal error: type_caster should only be used for C++ types");
+    if (!conv.load(handle, true)) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+        throw cast_error("Unable to cast Python instance to C++ type (#define "
+                         "PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+#else
+        throw cast_error("Unable to cast Python instance of type "
+                         + (std::string) str(type::handle_of(handle)) + " to C++ type '"
+                         + type_id<T>() + "'");
+#endif
+    }
+    return conv;
+}
+// Wrapper around the above that also constructs and returns a type_caster
+template <typename T>
+make_caster<T> load_type(const handle &handle) {
+    make_caster<T> conv;
+    load_type(conv, handle);
+    return conv;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+// pytype -> C++ type
+template <typename T, detail::enable_if_t<!detail::is_pyobject<T>::value, int> = 0>
+T cast(const handle &handle) {
+    using namespace detail;
+    static_assert(!cast_is_temporary_value_reference<T>::value,
+                  "Unable to cast type to reference: value is local to type caster");
+    return cast_op<T>(load_type<T>(handle));
+}
+
+// pytype -> pytype (calls converting constructor)
+template <typename T, detail::enable_if_t<detail::is_pyobject<T>::value, int> = 0>
+T cast(const handle &handle) {
+    return T(reinterpret_borrow<object>(handle));
+}
+
+// C++ type -> py::object
+template <typename T, detail::enable_if_t<!detail::is_pyobject<T>::value, int> = 0>
+object cast(T &&value,
+            return_value_policy policy = return_value_policy::automatic_reference,
+            handle parent = handle()) {
+    using no_ref_T = typename std::remove_reference<T>::type;
+    if (policy == return_value_policy::automatic) {
+        policy = std::is_pointer<no_ref_T>::value     ? return_value_policy::take_ownership
+                 : std::is_lvalue_reference<T>::value ? return_value_policy::copy
+                                                      : return_value_policy::move;
+    } else if (policy == return_value_policy::automatic_reference) {
+        policy = std::is_pointer<no_ref_T>::value     ? return_value_policy::reference
+                 : std::is_lvalue_reference<T>::value ? return_value_policy::copy
+                                                      : return_value_policy::move;
+    }
+    return reinterpret_steal<object>(
+        detail::make_caster<T>::cast(std::forward<T>(value), policy, parent));
+}
+
+template <typename T>
+T handle::cast() const {
+    return pybind11::cast<T>(*this);
+}
+template <>
+inline void handle::cast() const {
+    return;
+}
+
+template <typename T>
+detail::enable_if_t<!detail::move_never<T>::value, T> move(object &&obj) {
+    if (obj.ref_count() > 1) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+        throw cast_error(
+            "Unable to cast Python instance to C++ rvalue: instance has multiple references"
+            " (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+#else
+        throw cast_error("Unable to move from Python " + (std::string) str(type::handle_of(obj))
+                         + " instance to C++ " + type_id<T>()
+                         + " instance: instance has multiple references");
+#endif
+    }
+
+    // Move into a temporary and return that, because the reference may be a local value of `conv`
+    T ret = std::move(detail::load_type<T>(obj).operator T &());
+    return ret;
+}
+
+// Calling cast() on an rvalue calls pybind11::cast with the object rvalue, which does:
+// - If we have to move (because T has no copy constructor), do it.  This will fail if the moved
+//   object has multiple references, but trying to copy will fail to compile.
+// - If both movable and copyable, check ref count: if 1, move; otherwise copy
+// - Otherwise (not movable), copy.
+template <typename T>
+detail::enable_if_t<!detail::is_pyobject<T>::value && detail::move_always<T>::value, T>
+cast(object &&object) {
+    return move<T>(std::move(object));
+}
+template <typename T>
+detail::enable_if_t<!detail::is_pyobject<T>::value && detail::move_if_unreferenced<T>::value, T>
+cast(object &&object) {
+    if (object.ref_count() > 1) {
+        return cast<T>(object);
+    }
+    return move<T>(std::move(object));
+}
+template <typename T>
+detail::enable_if_t<!detail::is_pyobject<T>::value && detail::move_never<T>::value, T>
+cast(object &&object) {
+    return cast<T>(object);
+}
+
+// pytype rvalue -> pytype (calls converting constructor)
+template <typename T>
+detail::enable_if_t<detail::is_pyobject<T>::value, T> cast(object &&object) {
+    return T(std::move(object));
+}
+
+template <typename T>
+T object::cast() const & {
+    return pybind11::cast<T>(*this);
+}
+template <typename T>
+T object::cast() && {
+    return pybind11::cast<T>(std::move(*this));
+}
+template <>
+inline void object::cast() const & {
+    return;
+}
+template <>
+inline void object::cast() && {
+    return;
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Declared in pytypes.h:
+template <typename T, enable_if_t<!is_pyobject<T>::value, int>>
+object object_or_cast(T &&o) {
+    return pybind11::cast(std::forward<T>(o));
+}
+
+// Placeholder type for the unneeded (and dead code) static variable in the
+// PYBIND11_OVERRIDE_OVERRIDE macro
+struct override_unused {};
+template <typename ret_type>
+using override_caster_t = conditional_t<cast_is_temporary_value_reference<ret_type>::value,
+                                        make_caster<ret_type>,
+                                        override_unused>;
+
+// Trampoline use: for reference/pointer types to value-converted values, we do a value cast, then
+// store the result in the given variable.  For other types, this is a no-op.
+template <typename T>
+enable_if_t<cast_is_temporary_value_reference<T>::value, T> cast_ref(object &&o,
+                                                                     make_caster<T> &caster) {
+    return cast_op<T>(load_type(caster, o));
+}
+template <typename T>
+enable_if_t<!cast_is_temporary_value_reference<T>::value, T> cast_ref(object &&,
+                                                                      override_unused &) {
+    pybind11_fail("Internal error: cast_ref fallback invoked");
+}
+
+// Trampoline use: Having a pybind11::cast with an invalid reference type is going to
+// static_assert, even though if it's in dead code, so we provide a "trampoline" to pybind11::cast
+// that only does anything in cases where pybind11::cast is valid.
+template <typename T>
+enable_if_t<cast_is_temporary_value_reference<T>::value, T> cast_safe(object &&) {
+    pybind11_fail("Internal error: cast_safe fallback invoked");
+}
+template <typename T>
+enable_if_t<std::is_same<void, intrinsic_t<T>>::value, void> cast_safe(object &&) {}
+template <typename T>
+enable_if_t<detail::none_of<cast_is_temporary_value_reference<T>,
+                            std::is_same<void, intrinsic_t<T>>>::value,
+            T>
+cast_safe(object &&o) {
+    return pybind11::cast<T>(std::move(o));
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+// The overloads could coexist, i.e. the #if is not strictly speaking needed,
+// but it is an easy minor optimization.
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+inline cast_error cast_error_unable_to_convert_call_arg() {
+    return cast_error("Unable to convert call argument to Python object (#define "
+                      "PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+}
+#else
+inline cast_error cast_error_unable_to_convert_call_arg(const std::string &name,
+                                                        const std::string &type) {
+    return cast_error("Unable to convert call argument '" + name + "' of type '" + type
+                      + "' to Python object");
+}
+#endif
+
+template <return_value_policy policy = return_value_policy::automatic_reference>
+tuple make_tuple() {
+    return tuple(0);
+}
+
+template <return_value_policy policy = return_value_policy::automatic_reference, typename... Args>
+tuple make_tuple(Args &&...args_) {
+    constexpr size_t size = sizeof...(Args);
+    std::array<object, size> args{{reinterpret_steal<object>(
+        detail::make_caster<Args>::cast(std::forward<Args>(args_), policy, nullptr))...}};
+    for (size_t i = 0; i < args.size(); i++) {
+        if (!args[i]) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            throw cast_error_unable_to_convert_call_arg();
+#else
+            std::array<std::string, size> argtypes{{type_id<Args>()...}};
+            throw cast_error_unable_to_convert_call_arg(std::to_string(i), argtypes[i]);
+#endif
+        }
+    }
+    tuple result(size);
+    int counter = 0;
+    for (auto &arg_value : args) {
+        PyTuple_SET_ITEM(result.ptr(), counter++, arg_value.release().ptr());
+    }
+    return result;
+}
+
+/// \ingroup annotations
+/// Annotation for arguments
+struct arg {
+    /// Constructs an argument with the name of the argument; if null or omitted, this is a
+    /// positional argument.
+    constexpr explicit arg(const char *name = nullptr)
+        : name(name), flag_noconvert(false), flag_none(true) {}
+    /// Assign a value to this argument
+    template <typename T>
+    arg_v operator=(T &&value) const;
+    /// Indicate that the type should not be converted in the type caster
+    arg &noconvert(bool flag = true) {
+        flag_noconvert = flag;
+        return *this;
+    }
+    /// Indicates that the argument should/shouldn't allow None (e.g. for nullable pointer args)
+    arg &none(bool flag = true) {
+        flag_none = flag;
+        return *this;
+    }
+
+    const char *name;        ///< If non-null, this is a named kwargs argument
+    bool flag_noconvert : 1; ///< If set, do not allow conversion (requires a supporting type
+                             ///< caster!)
+    bool flag_none : 1;      ///< If set (the default), allow None to be passed to this argument
+};
+
+/// \ingroup annotations
+/// Annotation for arguments with values
+struct arg_v : arg {
+private:
+    template <typename T>
+    arg_v(arg &&base, T &&x, const char *descr = nullptr)
+        : arg(base), value(reinterpret_steal<object>(detail::make_caster<T>::cast(
+                         std::forward<T>(x), return_value_policy::automatic, {}))),
+          descr(descr)
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+          ,
+          type(type_id<T>())
+#endif
+    {
+        // Workaround! See:
+        // https://github.com/pybind/pybind11/issues/2336
+        // https://github.com/pybind/pybind11/pull/2685#issuecomment-731286700
+        if (PyErr_Occurred()) {
+            PyErr_Clear();
+        }
+    }
+
+public:
+    /// Direct construction with name, default, and description
+    template <typename T>
+    arg_v(const char *name, T &&x, const char *descr = nullptr)
+        : arg_v(arg(name), std::forward<T>(x), descr) {}
+
+    /// Called internally when invoking `py::arg("a") = value`
+    template <typename T>
+    arg_v(const arg &base, T &&x, const char *descr = nullptr)
+        : arg_v(arg(base), std::forward<T>(x), descr) {}
+
+    /// Same as `arg::noconvert()`, but returns *this as arg_v&, not arg&
+    arg_v &noconvert(bool flag = true) {
+        arg::noconvert(flag);
+        return *this;
+    }
+
+    /// Same as `arg::nonone()`, but returns *this as arg_v&, not arg&
+    arg_v &none(bool flag = true) {
+        arg::none(flag);
+        return *this;
+    }
+
+    /// The default value
+    object value;
+    /// The (optional) description of the default value
+    const char *descr;
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+    /// The C++ type name of the default value (only available when compiled in debug mode)
+    std::string type;
+#endif
+};
+
+/// \ingroup annotations
+/// Annotation indicating that all following arguments are keyword-only; the is the equivalent of
+/// an unnamed '*' argument
+struct kw_only {};
+
+/// \ingroup annotations
+/// Annotation indicating that all previous arguments are positional-only; the is the equivalent of
+/// an unnamed '/' argument (in Python 3.8)
+struct pos_only {};
+
+template <typename T>
+arg_v arg::operator=(T &&value) const {
+    return {*this, std::forward<T>(value)};
+}
+
+/// Alias for backward compatibility -- to be removed in version 2.0
+template <typename /*unused*/>
+using arg_t = arg_v;
+
+inline namespace literals {
+/** \rst
+    String literal version of `arg`
+ \endrst */
+constexpr arg operator"" _a(const char *name, size_t) { return arg(name); }
+} // namespace literals
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename T>
+using is_kw_only = std::is_same<intrinsic_t<T>, kw_only>;
+template <typename T>
+using is_pos_only = std::is_same<intrinsic_t<T>, pos_only>;
+
+// forward declaration (definition in attr.h)
+struct function_record;
+
+/// Internal data associated with a single function call
+struct function_call {
+    function_call(const function_record &f, handle p); // Implementation in attr.h
+
+    /// The function data:
+    const function_record &func;
+
+    /// Arguments passed to the function:
+    std::vector<handle> args;
+
+    /// The `convert` value the arguments should be loaded with
+    std::vector<bool> args_convert;
+
+    /// Extra references for the optional `py::args` and/or `py::kwargs` arguments (which, if
+    /// present, are also in `args` but without a reference).
+    object args_ref, kwargs_ref;
+
+    /// The parent, if any
+    handle parent;
+
+    /// If this is a call to an initializer, this argument contains `self`
+    handle init_self;
+};
+
+/// Helper class which loads arguments for C++ functions called from Python
+template <typename... Args>
+class argument_loader {
+    using indices = make_index_sequence<sizeof...(Args)>;
+
+    template <typename Arg>
+    using argument_is_args = std::is_same<intrinsic_t<Arg>, args>;
+    template <typename Arg>
+    using argument_is_kwargs = std::is_same<intrinsic_t<Arg>, kwargs>;
+    // Get kwargs argument position, or -1 if not present:
+    static constexpr auto kwargs_pos = constexpr_last<argument_is_kwargs, Args...>();
+
+    static_assert(kwargs_pos == -1 || kwargs_pos == (int) sizeof...(Args) - 1,
+                  "py::kwargs is only permitted as the last argument of a function");
+
+public:
+    static constexpr bool has_kwargs = kwargs_pos != -1;
+
+    // py::args argument position; -1 if not present.
+    static constexpr int args_pos = constexpr_last<argument_is_args, Args...>();
+
+    static_assert(args_pos == -1 || args_pos == constexpr_first<argument_is_args, Args...>(),
+                  "py::args cannot be specified more than once");
+
+    static constexpr auto arg_names = concat(type_descr(make_caster<Args>::name)...);
+
+    bool load_args(function_call &call) { return load_impl_sequence(call, indices{}); }
+
+    template <typename Return, typename Guard, typename Func>
+    // NOLINTNEXTLINE(readability-const-return-type)
+    enable_if_t<!std::is_void<Return>::value, Return> call(Func &&f) && {
+        return std::move(*this).template call_impl<remove_cv_t<Return>>(
+            std::forward<Func>(f), indices{}, Guard{});
+    }
+
+    template <typename Return, typename Guard, typename Func>
+    enable_if_t<std::is_void<Return>::value, void_type> call(Func &&f) && {
+        std::move(*this).template call_impl<remove_cv_t<Return>>(
+            std::forward<Func>(f), indices{}, Guard{});
+        return void_type();
+    }
+
+private:
+    static bool load_impl_sequence(function_call &, index_sequence<>) { return true; }
+
+    template <size_t... Is>
+    bool load_impl_sequence(function_call &call, index_sequence<Is...>) {
+#ifdef __cpp_fold_expressions
+        if ((... || !std::get<Is>(argcasters).load(call.args[Is], call.args_convert[Is]))) {
+            return false;
+        }
+#else
+        for (bool r : {std::get<Is>(argcasters).load(call.args[Is], call.args_convert[Is])...}) {
+            if (!r) {
+                return false;
+            }
+        }
+#endif
+        return true;
+    }
+
+    template <typename Return, typename Func, size_t... Is, typename Guard>
+    Return call_impl(Func &&f, index_sequence<Is...>, Guard &&) && {
+        return std::forward<Func>(f)(cast_op<Args>(std::move(std::get<Is>(argcasters)))...);
+    }
+
+    std::tuple<make_caster<Args>...> argcasters;
+};
+
+/// Helper class which collects only positional arguments for a Python function call.
+/// A fancier version below can collect any argument, but this one is optimal for simple calls.
+template <return_value_policy policy>
+class simple_collector {
+public:
+    template <typename... Ts>
+    explicit simple_collector(Ts &&...values)
+        : m_args(pybind11::make_tuple<policy>(std::forward<Ts>(values)...)) {}
+
+    const tuple &args() const & { return m_args; }
+    dict kwargs() const { return {}; }
+
+    tuple args() && { return std::move(m_args); }
+
+    /// Call a Python function and pass the collected arguments
+    object call(PyObject *ptr) const {
+        PyObject *result = PyObject_CallObject(ptr, m_args.ptr());
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<object>(result);
+    }
+
+private:
+    tuple m_args;
+};
+
+/// Helper class which collects positional, keyword, * and ** arguments for a Python function call
+template <return_value_policy policy>
+class unpacking_collector {
+public:
+    template <typename... Ts>
+    explicit unpacking_collector(Ts &&...values) {
+        // Tuples aren't (easily) resizable so a list is needed for collection,
+        // but the actual function call strictly requires a tuple.
+        auto args_list = list();
+        using expander = int[];
+        (void) expander{0, (process(args_list, std::forward<Ts>(values)), 0)...};
+
+        m_args = std::move(args_list);
+    }
+
+    const tuple &args() const & { return m_args; }
+    const dict &kwargs() const & { return m_kwargs; }
+
+    tuple args() && { return std::move(m_args); }
+    dict kwargs() && { return std::move(m_kwargs); }
+
+    /// Call a Python function and pass the collected arguments
+    object call(PyObject *ptr) const {
+        PyObject *result = PyObject_Call(ptr, m_args.ptr(), m_kwargs.ptr());
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<object>(result);
+    }
+
+private:
+    template <typename T>
+    void process(list &args_list, T &&x) {
+        auto o = reinterpret_steal<object>(
+            detail::make_caster<T>::cast(std::forward<T>(x), policy, {}));
+        if (!o) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            throw cast_error_unable_to_convert_call_arg();
+#else
+            throw cast_error_unable_to_convert_call_arg(std::to_string(args_list.size()),
+                                                        type_id<T>());
+#endif
+        }
+        args_list.append(std::move(o));
+    }
+
+    void process(list &args_list, detail::args_proxy ap) {
+        for (auto a : ap) {
+            args_list.append(a);
+        }
+    }
+
+    void process(list & /*args_list*/, arg_v a) {
+        if (!a.name) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            nameless_argument_error();
+#else
+            nameless_argument_error(a.type);
+#endif
+        }
+        if (m_kwargs.contains(a.name)) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            multiple_values_error();
+#else
+            multiple_values_error(a.name);
+#endif
+        }
+        if (!a.value) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            throw cast_error_unable_to_convert_call_arg();
+#else
+            throw cast_error_unable_to_convert_call_arg(a.name, a.type);
+#endif
+        }
+        m_kwargs[a.name] = a.value;
+    }
+
+    void process(list & /*args_list*/, detail::kwargs_proxy kp) {
+        if (!kp) {
+            return;
+        }
+        for (auto k : reinterpret_borrow<dict>(kp)) {
+            if (m_kwargs.contains(k.first)) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                multiple_values_error();
+#else
+                multiple_values_error(str(k.first));
+#endif
+            }
+            m_kwargs[k.first] = k.second;
+        }
+    }
+
+    [[noreturn]] static void nameless_argument_error() {
+        throw type_error(
+            "Got kwargs without a name; only named arguments "
+            "may be passed via py::arg() to a python function call. "
+            "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+    }
+    [[noreturn]] static void nameless_argument_error(const std::string &type) {
+        throw type_error("Got kwargs without a name of type '" + type
+                         + "'; only named "
+                           "arguments may be passed via py::arg() to a python function call. ");
+    }
+    [[noreturn]] static void multiple_values_error() {
+        throw type_error(
+            "Got multiple values for keyword argument "
+            "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+    }
+
+    [[noreturn]] static void multiple_values_error(const std::string &name) {
+        throw type_error("Got multiple values for keyword argument '" + name + "'");
+    }
+
+private:
+    tuple m_args;
+    dict m_kwargs;
+};
+
+// [workaround(intel)] Separate function required here
+// We need to put this into a separate function because the Intel compiler
+// fails to compile enable_if_t<!all_of<is_positional<Args>...>::value>
+// (tested with ICC 2021.1 Beta 20200827).
+template <typename... Args>
+constexpr bool args_are_all_positional() {
+    return all_of<is_positional<Args>...>::value;
+}
+
+/// Collect only positional arguments for a Python function call
+template <return_value_policy policy,
+          typename... Args,
+          typename = enable_if_t<args_are_all_positional<Args...>()>>
+simple_collector<policy> collect_arguments(Args &&...args) {
+    return simple_collector<policy>(std::forward<Args>(args)...);
+}
+
+/// Collect all arguments, including keywords and unpacking (only instantiated when needed)
+template <return_value_policy policy,
+          typename... Args,
+          typename = enable_if_t<!args_are_all_positional<Args...>()>>
+unpacking_collector<policy> collect_arguments(Args &&...args) {
+    // Following argument order rules for generalized unpacking according to PEP 448
+    static_assert(constexpr_last<is_positional, Args...>()
+                          < constexpr_first<is_keyword_or_ds, Args...>()
+                      && constexpr_last<is_s_unpacking, Args...>()
+                             < constexpr_first<is_ds_unpacking, Args...>(),
+                  "Invalid function call: positional args must precede keywords and ** unpacking; "
+                  "* unpacking must precede ** unpacking");
+    return unpacking_collector<policy>(std::forward<Args>(args)...);
+}
+
+template <typename Derived>
+template <return_value_policy policy, typename... Args>
+object object_api<Derived>::operator()(Args &&...args) const {
+#ifndef NDEBUG
+    if (!PyGILState_Check()) {
+        pybind11_fail("pybind11::object_api<>::operator() PyGILState_Check() failure.");
+    }
+#endif
+    return detail::collect_arguments<policy>(std::forward<Args>(args)...).call(derived().ptr());
+}
+
+template <typename Derived>
+template <return_value_policy policy, typename... Args>
+object object_api<Derived>::call(Args &&...args) const {
+    return operator()<policy>(std::forward<Args>(args)...);
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+template <typename T>
+handle type::handle_of() {
+    static_assert(std::is_base_of<detail::type_caster_generic, detail::make_caster<T>>::value,
+                  "py::type::of<T> only supports the case where T is a registered C++ types.");
+
+    return detail::get_type_handle(typeid(T), true);
+}
+
+#define PYBIND11_MAKE_OPAQUE(...)                                                                 \
+    PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)                                                  \
+    namespace detail {                                                                            \
+    template <>                                                                                   \
+    class type_caster<__VA_ARGS__> : public type_caster_base<__VA_ARGS__> {};                     \
+    }                                                                                             \
+    PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
+
+/// Lets you pass a type containing a `,` through a macro parameter without needing a separate
+/// typedef, e.g.:
+/// `PYBIND11_OVERRIDE(PYBIND11_TYPE(ReturnType<A, B>), PYBIND11_TYPE(Parent<C, D>), f, arg)`
+#define PYBIND11_TYPE(...) __VA_ARGS__
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/chrono.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/chrono.h
new file mode 100644
index 000000000..167ea0e3d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/chrono.h
@@ -0,0 +1,225 @@
+/*
+    pybind11/chrono.h: Transparent conversion between std::chrono and python's datetime
+
+    Copyright (c) 2016 Trent Houliston <trent@houliston.me> and
+                       Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <chrono>
+#include <cmath>
+#include <ctime>
+#include <datetime.h>
+#include <mutex>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename type>
+class duration_caster {
+public:
+    using rep = typename type::rep;
+    using period = typename type::period;
+
+    // signed 25 bits required by the standard.
+    using days = std::chrono::duration<int_least32_t, std::ratio<86400>>;
+
+    bool load(handle src, bool) {
+        using namespace std::chrono;
+
+        // Lazy initialise the PyDateTime import
+        if (!PyDateTimeAPI) {
+            PyDateTime_IMPORT;
+        }
+
+        if (!src) {
+            return false;
+        }
+        // If invoked with datetime.delta object
+        if (PyDelta_Check(src.ptr())) {
+            value = type(duration_cast<duration<rep, period>>(
+                days(PyDateTime_DELTA_GET_DAYS(src.ptr()))
+                + seconds(PyDateTime_DELTA_GET_SECONDS(src.ptr()))
+                + microseconds(PyDateTime_DELTA_GET_MICROSECONDS(src.ptr()))));
+            return true;
+        }
+        // If invoked with a float we assume it is seconds and convert
+        if (PyFloat_Check(src.ptr())) {
+            value = type(duration_cast<duration<rep, period>>(
+                duration<double>(PyFloat_AsDouble(src.ptr()))));
+            return true;
+        }
+        return false;
+    }
+
+    // If this is a duration just return it back
+    static const std::chrono::duration<rep, period> &
+    get_duration(const std::chrono::duration<rep, period> &src) {
+        return src;
+    }
+
+    // If this is a time_point get the time_since_epoch
+    template <typename Clock>
+    static std::chrono::duration<rep, period>
+    get_duration(const std::chrono::time_point<Clock, std::chrono::duration<rep, period>> &src) {
+        return src.time_since_epoch();
+    }
+
+    static handle cast(const type &src, return_value_policy /* policy */, handle /* parent */) {
+        using namespace std::chrono;
+
+        // Use overloaded function to get our duration from our source
+        // Works out if it is a duration or time_point and get the duration
+        auto d = get_duration(src);
+
+        // Lazy initialise the PyDateTime import
+        if (!PyDateTimeAPI) {
+            PyDateTime_IMPORT;
+        }
+
+        // Declare these special duration types so the conversions happen with the correct
+        // primitive types (int)
+        using dd_t = duration<int, std::ratio<86400>>;
+        using ss_t = duration<int, std::ratio<1>>;
+        using us_t = duration<int, std::micro>;
+
+        auto dd = duration_cast<dd_t>(d);
+        auto subd = d - dd;
+        auto ss = duration_cast<ss_t>(subd);
+        auto us = duration_cast<us_t>(subd - ss);
+        return PyDelta_FromDSU(dd.count(), ss.count(), us.count());
+    }
+
+    PYBIND11_TYPE_CASTER(type, const_name("datetime.timedelta"));
+};
+
+inline std::tm *localtime_thread_safe(const std::time_t *time, std::tm *buf) {
+#if (defined(__STDC_LIB_EXT1__) && defined(__STDC_WANT_LIB_EXT1__)) || defined(_MSC_VER)
+    if (localtime_s(buf, time))
+        return nullptr;
+    return buf;
+#else
+    static std::mutex mtx;
+    std::lock_guard<std::mutex> lock(mtx);
+    std::tm *tm_ptr = std::localtime(time);
+    if (tm_ptr != nullptr) {
+        *buf = *tm_ptr;
+    }
+    return tm_ptr;
+#endif
+}
+
+// This is for casting times on the system clock into datetime.datetime instances
+template <typename Duration>
+class type_caster<std::chrono::time_point<std::chrono::system_clock, Duration>> {
+public:
+    using type = std::chrono::time_point<std::chrono::system_clock, Duration>;
+    bool load(handle src, bool) {
+        using namespace std::chrono;
+
+        // Lazy initialise the PyDateTime import
+        if (!PyDateTimeAPI) {
+            PyDateTime_IMPORT;
+        }
+
+        if (!src) {
+            return false;
+        }
+
+        std::tm cal;
+        microseconds msecs;
+
+        if (PyDateTime_Check(src.ptr())) {
+            cal.tm_sec = PyDateTime_DATE_GET_SECOND(src.ptr());
+            cal.tm_min = PyDateTime_DATE_GET_MINUTE(src.ptr());
+            cal.tm_hour = PyDateTime_DATE_GET_HOUR(src.ptr());
+            cal.tm_mday = PyDateTime_GET_DAY(src.ptr());
+            cal.tm_mon = PyDateTime_GET_MONTH(src.ptr()) - 1;
+            cal.tm_year = PyDateTime_GET_YEAR(src.ptr()) - 1900;
+            cal.tm_isdst = -1;
+            msecs = microseconds(PyDateTime_DATE_GET_MICROSECOND(src.ptr()));
+        } else if (PyDate_Check(src.ptr())) {
+            cal.tm_sec = 0;
+            cal.tm_min = 0;
+            cal.tm_hour = 0;
+            cal.tm_mday = PyDateTime_GET_DAY(src.ptr());
+            cal.tm_mon = PyDateTime_GET_MONTH(src.ptr()) - 1;
+            cal.tm_year = PyDateTime_GET_YEAR(src.ptr()) - 1900;
+            cal.tm_isdst = -1;
+            msecs = microseconds(0);
+        } else if (PyTime_Check(src.ptr())) {
+            cal.tm_sec = PyDateTime_TIME_GET_SECOND(src.ptr());
+            cal.tm_min = PyDateTime_TIME_GET_MINUTE(src.ptr());
+            cal.tm_hour = PyDateTime_TIME_GET_HOUR(src.ptr());
+            cal.tm_mday = 1;  // This date (day, month, year) = (1, 0, 70)
+            cal.tm_mon = 0;   // represents 1-Jan-1970, which is the first
+            cal.tm_year = 70; // earliest available date for Python's datetime
+            cal.tm_isdst = -1;
+            msecs = microseconds(PyDateTime_TIME_GET_MICROSECOND(src.ptr()));
+        } else {
+            return false;
+        }
+
+        value = time_point_cast<Duration>(system_clock::from_time_t(std::mktime(&cal)) + msecs);
+        return true;
+    }
+
+    static handle cast(const std::chrono::time_point<std::chrono::system_clock, Duration> &src,
+                       return_value_policy /* policy */,
+                       handle /* parent */) {
+        using namespace std::chrono;
+
+        // Lazy initialise the PyDateTime import
+        if (!PyDateTimeAPI) {
+            PyDateTime_IMPORT;
+        }
+
+        // Get out microseconds, and make sure they are positive, to avoid bug in eastern
+        // hemisphere time zones (cfr. https://github.com/pybind/pybind11/issues/2417)
+        using us_t = duration<int, std::micro>;
+        auto us = duration_cast<us_t>(src.time_since_epoch() % seconds(1));
+        if (us.count() < 0) {
+            us += seconds(1);
+        }
+
+        // Subtract microseconds BEFORE `system_clock::to_time_t`, because:
+        // > If std::time_t has lower precision, it is implementation-defined whether the value is
+        // rounded or truncated. (https://en.cppreference.com/w/cpp/chrono/system_clock/to_time_t)
+        std::time_t tt
+            = system_clock::to_time_t(time_point_cast<system_clock::duration>(src - us));
+
+        std::tm localtime;
+        std::tm *localtime_ptr = localtime_thread_safe(&tt, &localtime);
+        if (!localtime_ptr) {
+            throw cast_error("Unable to represent system_clock in local time");
+        }
+        return PyDateTime_FromDateAndTime(localtime.tm_year + 1900,
+                                          localtime.tm_mon + 1,
+                                          localtime.tm_mday,
+                                          localtime.tm_hour,
+                                          localtime.tm_min,
+                                          localtime.tm_sec,
+                                          us.count());
+    }
+    PYBIND11_TYPE_CASTER(type, const_name("datetime.datetime"));
+};
+
+// Other clocks that are not the system clock are not measured as datetime.datetime objects
+// since they are not measured on calendar time. So instead we just make them timedeltas
+// Or if they have passed us a time as a float we convert that
+template <typename Clock, typename Duration>
+class type_caster<std::chrono::time_point<Clock, Duration>>
+    : public duration_caster<std::chrono::time_point<Clock, Duration>> {};
+
+template <typename Rep, typename Period>
+class type_caster<std::chrono::duration<Rep, Period>>
+    : public duration_caster<std::chrono::duration<Rep, Period>> {};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/common.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/common.h
new file mode 100644
index 000000000..6c8a4f1e8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/common.h
@@ -0,0 +1,2 @@
+#include "detail/common.h"
+#warning "Including 'common.h' is deprecated. It will be removed in v3.0. Use 'pybind11.h'."
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/complex.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/complex.h
new file mode 100644
index 000000000..8a831c12c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/complex.h
@@ -0,0 +1,74 @@
+/*
+    pybind11/complex.h: Complex number support
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <complex>
+
+/// glibc defines I as a macro which breaks things, e.g., boost template names
+#ifdef I
+#    undef I
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+template <typename T>
+struct format_descriptor<std::complex<T>, detail::enable_if_t<std::is_floating_point<T>::value>> {
+    static constexpr const char c = format_descriptor<T>::c;
+    static constexpr const char value[3] = {'Z', c, '\0'};
+    static std::string format() { return std::string(value); }
+};
+
+#ifndef PYBIND11_CPP17
+
+template <typename T>
+constexpr const char
+    format_descriptor<std::complex<T>,
+                      detail::enable_if_t<std::is_floating_point<T>::value>>::value[3];
+
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename T>
+struct is_fmt_numeric<std::complex<T>, detail::enable_if_t<std::is_floating_point<T>::value>> {
+    static constexpr bool value = true;
+    static constexpr int index = is_fmt_numeric<T>::index + 3;
+};
+
+template <typename T>
+class type_caster<std::complex<T>> {
+public:
+    bool load(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (!convert && !PyComplex_Check(src.ptr())) {
+            return false;
+        }
+        Py_complex result = PyComplex_AsCComplex(src.ptr());
+        if (result.real == -1.0 && PyErr_Occurred()) {
+            PyErr_Clear();
+            return false;
+        }
+        value = std::complex<T>((T) result.real, (T) result.imag);
+        return true;
+    }
+
+    static handle
+    cast(const std::complex<T> &src, return_value_policy /* policy */, handle /* parent */) {
+        return PyComplex_FromDoubles((double) src.real(), (double) src.imag());
+    }
+
+    PYBIND11_TYPE_CASTER(std::complex<T>, const_name("complex"));
+};
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/class.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/class.h
new file mode 100644
index 000000000..42720f844
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/class.h
@@ -0,0 +1,742 @@
+/*
+    pybind11/detail/class.h: Python C API implementation details for py::class_
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "../attr.h"
+#include "../options.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if !defined(PYPY_VERSION)
+#    define PYBIND11_BUILTIN_QUALNAME
+#    define PYBIND11_SET_OLDPY_QUALNAME(obj, nameobj)
+#else
+// In PyPy, we still set __qualname__ so that we can produce reliable function type
+// signatures; in CPython this macro expands to nothing:
+#    define PYBIND11_SET_OLDPY_QUALNAME(obj, nameobj)                                             \
+        setattr((PyObject *) obj, "__qualname__", nameobj)
+#endif
+
+inline std::string get_fully_qualified_tp_name(PyTypeObject *type) {
+#if !defined(PYPY_VERSION)
+    return type->tp_name;
+#else
+    auto module_name = handle((PyObject *) type).attr("__module__").cast<std::string>();
+    if (module_name == PYBIND11_BUILTINS_MODULE)
+        return type->tp_name;
+    else
+        return std::move(module_name) + "." + type->tp_name;
+#endif
+}
+
+inline PyTypeObject *type_incref(PyTypeObject *type) {
+    Py_INCREF(type);
+    return type;
+}
+
+#if !defined(PYPY_VERSION)
+
+/// `pybind11_static_property.__get__()`: Always pass the class instead of the instance.
+extern "C" inline PyObject *pybind11_static_get(PyObject *self, PyObject * /*ob*/, PyObject *cls) {
+    return PyProperty_Type.tp_descr_get(self, cls, cls);
+}
+
+/// `pybind11_static_property.__set__()`: Just like the above `__get__()`.
+extern "C" inline int pybind11_static_set(PyObject *self, PyObject *obj, PyObject *value) {
+    PyObject *cls = PyType_Check(obj) ? obj : (PyObject *) Py_TYPE(obj);
+    return PyProperty_Type.tp_descr_set(self, cls, value);
+}
+
+/** A `static_property` is the same as a `property` but the `__get__()` and `__set__()`
+    methods are modified to always use the object type instead of a concrete instance.
+    Return value: New reference. */
+inline PyTypeObject *make_static_property_type() {
+    constexpr auto *name = "pybind11_static_property";
+    auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
+
+    /* Danger zone: from now (and until PyType_Ready), make sure to
+       issue no Python C API calls which could potentially invoke the
+       garbage collector (the GC will call type_traverse(), which will in
+       turn find the newly constructed type in an invalid state) */
+    auto *heap_type = (PyHeapTypeObject *) PyType_Type.tp_alloc(&PyType_Type, 0);
+    if (!heap_type) {
+        pybind11_fail("make_static_property_type(): error allocating type!");
+    }
+
+    heap_type->ht_name = name_obj.inc_ref().ptr();
+#    ifdef PYBIND11_BUILTIN_QUALNAME
+    heap_type->ht_qualname = name_obj.inc_ref().ptr();
+#    endif
+
+    auto *type = &heap_type->ht_type;
+    type->tp_name = name;
+    type->tp_base = type_incref(&PyProperty_Type);
+    type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
+    type->tp_descr_get = pybind11_static_get;
+    type->tp_descr_set = pybind11_static_set;
+
+    if (PyType_Ready(type) < 0) {
+        pybind11_fail("make_static_property_type(): failure in PyType_Ready()!");
+    }
+
+    setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
+    PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
+
+    return type;
+}
+
+#else // PYPY
+
+/** PyPy has some issues with the above C API, so we evaluate Python code instead.
+    This function will only be called once so performance isn't really a concern.
+    Return value: New reference. */
+inline PyTypeObject *make_static_property_type() {
+    auto d = dict();
+    PyObject *result = PyRun_String(R"(\
+class pybind11_static_property(property):
+    def __get__(self, obj, cls):
+        return property.__get__(self, cls, cls)
+
+    def __set__(self, obj, value):
+        cls = obj if isinstance(obj, type) else type(obj)
+        property.__set__(self, cls, value)
+)",
+                                    Py_file_input,
+                                    d.ptr(),
+                                    d.ptr());
+    if (result == nullptr)
+        throw error_already_set();
+    Py_DECREF(result);
+    return (PyTypeObject *) d["pybind11_static_property"].cast<object>().release().ptr();
+}
+
+#endif // PYPY
+
+/** Types with static properties need to handle `Type.static_prop = x` in a specific way.
+    By default, Python replaces the `static_property` itself, but for wrapped C++ types
+    we need to call `static_property.__set__()` in order to propagate the new value to
+    the underlying C++ data structure. */
+extern "C" inline int pybind11_meta_setattro(PyObject *obj, PyObject *name, PyObject *value) {
+    // Use `_PyType_Lookup()` instead of `PyObject_GetAttr()` in order to get the raw
+    // descriptor (`property`) instead of calling `tp_descr_get` (`property.__get__()`).
+    PyObject *descr = _PyType_Lookup((PyTypeObject *) obj, name);
+
+    // The following assignment combinations are possible:
+    //   1. `Type.static_prop = value`             --> descr_set: `Type.static_prop.__set__(value)`
+    //   2. `Type.static_prop = other_static_prop` --> setattro:  replace existing `static_prop`
+    //   3. `Type.regular_attribute = value`       --> setattro:  regular attribute assignment
+    auto *const static_prop = (PyObject *) get_internals().static_property_type;
+    const auto call_descr_set = (descr != nullptr) && (value != nullptr)
+                                && (PyObject_IsInstance(descr, static_prop) != 0)
+                                && (PyObject_IsInstance(value, static_prop) == 0);
+    if (call_descr_set) {
+        // Call `static_property.__set__()` instead of replacing the `static_property`.
+#if !defined(PYPY_VERSION)
+        return Py_TYPE(descr)->tp_descr_set(descr, obj, value);
+#else
+        if (PyObject *result = PyObject_CallMethod(descr, "__set__", "OO", obj, value)) {
+            Py_DECREF(result);
+            return 0;
+        } else {
+            return -1;
+        }
+#endif
+    } else {
+        // Replace existing attribute.
+        return PyType_Type.tp_setattro(obj, name, value);
+    }
+}
+
+/**
+ * Python 3's PyInstanceMethod_Type hides itself via its tp_descr_get, which prevents aliasing
+ * methods via cls.attr("m2") = cls.attr("m1"): instead the tp_descr_get returns a plain function,
+ * when called on a class, or a PyMethod, when called on an instance.  Override that behaviour here
+ * to do a special case bypass for PyInstanceMethod_Types.
+ */
+extern "C" inline PyObject *pybind11_meta_getattro(PyObject *obj, PyObject *name) {
+    PyObject *descr = _PyType_Lookup((PyTypeObject *) obj, name);
+    if (descr && PyInstanceMethod_Check(descr)) {
+        Py_INCREF(descr);
+        return descr;
+    }
+    return PyType_Type.tp_getattro(obj, name);
+}
+
+/// metaclass `__call__` function that is used to create all pybind11 objects.
+extern "C" inline PyObject *pybind11_meta_call(PyObject *type, PyObject *args, PyObject *kwargs) {
+
+    // use the default metaclass call to create/initialize the object
+    PyObject *self = PyType_Type.tp_call(type, args, kwargs);
+    if (self == nullptr) {
+        return nullptr;
+    }
+
+    // This must be a pybind11 instance
+    auto *instance = reinterpret_cast<detail::instance *>(self);
+
+    // Ensure that the base __init__ function(s) were called
+    for (const auto &vh : values_and_holders(instance)) {
+        if (!vh.holder_constructed()) {
+            PyErr_Format(PyExc_TypeError,
+                         "%.200s.__init__() must be called when overriding __init__",
+                         get_fully_qualified_tp_name(vh.type->type).c_str());
+            Py_DECREF(self);
+            return nullptr;
+        }
+    }
+
+    return self;
+}
+
+/// Cleanup the type-info for a pybind11-registered type.
+extern "C" inline void pybind11_meta_dealloc(PyObject *obj) {
+    auto *type = (PyTypeObject *) obj;
+    auto &internals = get_internals();
+
+    // A pybind11-registered type will:
+    // 1) be found in internals.registered_types_py
+    // 2) have exactly one associated `detail::type_info`
+    auto found_type = internals.registered_types_py.find(type);
+    if (found_type != internals.registered_types_py.end() && found_type->second.size() == 1
+        && found_type->second[0]->type == type) {
+
+        auto *tinfo = found_type->second[0];
+        auto tindex = std::type_index(*tinfo->cpptype);
+        internals.direct_conversions.erase(tindex);
+
+        if (tinfo->module_local) {
+            get_local_internals().registered_types_cpp.erase(tindex);
+        } else {
+            internals.registered_types_cpp.erase(tindex);
+        }
+        internals.registered_types_py.erase(tinfo->type);
+
+        // Actually just `std::erase_if`, but that's only available in C++20
+        auto &cache = internals.inactive_override_cache;
+        for (auto it = cache.begin(), last = cache.end(); it != last;) {
+            if (it->first == (PyObject *) tinfo->type) {
+                it = cache.erase(it);
+            } else {
+                ++it;
+            }
+        }
+
+        delete tinfo;
+    }
+
+    PyType_Type.tp_dealloc(obj);
+}
+
+/** This metaclass is assigned by default to all pybind11 types and is required in order
+    for static properties to function correctly. Users may override this using `py::metaclass`.
+    Return value: New reference. */
+inline PyTypeObject *make_default_metaclass() {
+    constexpr auto *name = "pybind11_type";
+    auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
+
+    /* Danger zone: from now (and until PyType_Ready), make sure to
+       issue no Python C API calls which could potentially invoke the
+       garbage collector (the GC will call type_traverse(), which will in
+       turn find the newly constructed type in an invalid state) */
+    auto *heap_type = (PyHeapTypeObject *) PyType_Type.tp_alloc(&PyType_Type, 0);
+    if (!heap_type) {
+        pybind11_fail("make_default_metaclass(): error allocating metaclass!");
+    }
+
+    heap_type->ht_name = name_obj.inc_ref().ptr();
+#ifdef PYBIND11_BUILTIN_QUALNAME
+    heap_type->ht_qualname = name_obj.inc_ref().ptr();
+#endif
+
+    auto *type = &heap_type->ht_type;
+    type->tp_name = name;
+    type->tp_base = type_incref(&PyType_Type);
+    type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
+
+    type->tp_call = pybind11_meta_call;
+
+    type->tp_setattro = pybind11_meta_setattro;
+    type->tp_getattro = pybind11_meta_getattro;
+
+    type->tp_dealloc = pybind11_meta_dealloc;
+
+    if (PyType_Ready(type) < 0) {
+        pybind11_fail("make_default_metaclass(): failure in PyType_Ready()!");
+    }
+
+    setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
+    PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
+
+    return type;
+}
+
+/// For multiple inheritance types we need to recursively register/deregister base pointers for any
+/// base classes with pointers that are difference from the instance value pointer so that we can
+/// correctly recognize an offset base class pointer. This calls a function with any offset base
+/// ptrs.
+inline void traverse_offset_bases(void *valueptr,
+                                  const detail::type_info *tinfo,
+                                  instance *self,
+                                  bool (*f)(void * /*parentptr*/, instance * /*self*/)) {
+    for (handle h : reinterpret_borrow<tuple>(tinfo->type->tp_bases)) {
+        if (auto *parent_tinfo = get_type_info((PyTypeObject *) h.ptr())) {
+            for (auto &c : parent_tinfo->implicit_casts) {
+                if (c.first == tinfo->cpptype) {
+                    auto *parentptr = c.second(valueptr);
+                    if (parentptr != valueptr) {
+                        f(parentptr, self);
+                    }
+                    traverse_offset_bases(parentptr, parent_tinfo, self, f);
+                    break;
+                }
+            }
+        }
+    }
+}
+
+inline bool register_instance_impl(void *ptr, instance *self) {
+    get_internals().registered_instances.emplace(ptr, self);
+    return true; // unused, but gives the same signature as the deregister func
+}
+inline bool deregister_instance_impl(void *ptr, instance *self) {
+    auto &registered_instances = get_internals().registered_instances;
+    auto range = registered_instances.equal_range(ptr);
+    for (auto it = range.first; it != range.second; ++it) {
+        if (self == it->second) {
+            registered_instances.erase(it);
+            return true;
+        }
+    }
+    return false;
+}
+
+inline void register_instance(instance *self, void *valptr, const type_info *tinfo) {
+    register_instance_impl(valptr, self);
+    if (!tinfo->simple_ancestors) {
+        traverse_offset_bases(valptr, tinfo, self, register_instance_impl);
+    }
+}
+
+inline bool deregister_instance(instance *self, void *valptr, const type_info *tinfo) {
+    bool ret = deregister_instance_impl(valptr, self);
+    if (!tinfo->simple_ancestors) {
+        traverse_offset_bases(valptr, tinfo, self, deregister_instance_impl);
+    }
+    return ret;
+}
+
+/// Instance creation function for all pybind11 types. It allocates the internal instance layout
+/// for holding C++ objects and holders.  Allocation is done lazily (the first time the instance is
+/// cast to a reference or pointer), and initialization is done by an `__init__` function.
+inline PyObject *make_new_instance(PyTypeObject *type) {
+#if defined(PYPY_VERSION)
+    // PyPy gets tp_basicsize wrong (issue 2482) under multiple inheritance when the first
+    // inherited object is a plain Python type (i.e. not derived from an extension type).  Fix it.
+    ssize_t instance_size = static_cast<ssize_t>(sizeof(instance));
+    if (type->tp_basicsize < instance_size) {
+        type->tp_basicsize = instance_size;
+    }
+#endif
+    PyObject *self = type->tp_alloc(type, 0);
+    auto *inst = reinterpret_cast<instance *>(self);
+    // Allocate the value/holder internals:
+    inst->allocate_layout();
+
+    return self;
+}
+
+/// Instance creation function for all pybind11 types. It only allocates space for the
+/// C++ object, but doesn't call the constructor -- an `__init__` function must do that.
+extern "C" inline PyObject *pybind11_object_new(PyTypeObject *type, PyObject *, PyObject *) {
+    return make_new_instance(type);
+}
+
+/// An `__init__` function constructs the C++ object. Users should provide at least one
+/// of these using `py::init` or directly with `.def(__init__, ...)`. Otherwise, the
+/// following default function will be used which simply throws an exception.
+extern "C" inline int pybind11_object_init(PyObject *self, PyObject *, PyObject *) {
+    PyTypeObject *type = Py_TYPE(self);
+    std::string msg = get_fully_qualified_tp_name(type) + ": No constructor defined!";
+    PyErr_SetString(PyExc_TypeError, msg.c_str());
+    return -1;
+}
+
+inline void add_patient(PyObject *nurse, PyObject *patient) {
+    auto &internals = get_internals();
+    auto *instance = reinterpret_cast<detail::instance *>(nurse);
+    instance->has_patients = true;
+    Py_INCREF(patient);
+    internals.patients[nurse].push_back(patient);
+}
+
+inline void clear_patients(PyObject *self) {
+    auto *instance = reinterpret_cast<detail::instance *>(self);
+    auto &internals = get_internals();
+    auto pos = internals.patients.find(self);
+    assert(pos != internals.patients.end());
+    // Clearing the patients can cause more Python code to run, which
+    // can invalidate the iterator. Extract the vector of patients
+    // from the unordered_map first.
+    auto patients = std::move(pos->second);
+    internals.patients.erase(pos);
+    instance->has_patients = false;
+    for (PyObject *&patient : patients) {
+        Py_CLEAR(patient);
+    }
+}
+
+/// Clears all internal data from the instance and removes it from registered instances in
+/// preparation for deallocation.
+inline void clear_instance(PyObject *self) {
+    auto *instance = reinterpret_cast<detail::instance *>(self);
+
+    // Deallocate any values/holders, if present:
+    for (auto &v_h : values_and_holders(instance)) {
+        if (v_h) {
+
+            // We have to deregister before we call dealloc because, for virtual MI types, we still
+            // need to be able to get the parent pointers.
+            if (v_h.instance_registered()
+                && !deregister_instance(instance, v_h.value_ptr(), v_h.type)) {
+                pybind11_fail(
+                    "pybind11_object_dealloc(): Tried to deallocate unregistered instance!");
+            }
+
+            if (instance->owned || v_h.holder_constructed()) {
+                v_h.type->dealloc(v_h);
+            }
+        }
+    }
+    // Deallocate the value/holder layout internals:
+    instance->deallocate_layout();
+
+    if (instance->weakrefs) {
+        PyObject_ClearWeakRefs(self);
+    }
+
+    PyObject **dict_ptr = _PyObject_GetDictPtr(self);
+    if (dict_ptr) {
+        Py_CLEAR(*dict_ptr);
+    }
+
+    if (instance->has_patients) {
+        clear_patients(self);
+    }
+}
+
+/// Instance destructor function for all pybind11 types. It calls `type_info.dealloc`
+/// to destroy the C++ object itself, while the rest is Python bookkeeping.
+extern "C" inline void pybind11_object_dealloc(PyObject *self) {
+    clear_instance(self);
+
+    auto *type = Py_TYPE(self);
+    type->tp_free(self);
+
+#if PY_VERSION_HEX < 0x03080000
+    // `type->tp_dealloc != pybind11_object_dealloc` means that we're being called
+    // as part of a derived type's dealloc, in which case we're not allowed to decref
+    // the type here. For cross-module compatibility, we shouldn't compare directly
+    // with `pybind11_object_dealloc`, but with the common one stashed in internals.
+    auto pybind11_object_type = (PyTypeObject *) get_internals().instance_base;
+    if (type->tp_dealloc == pybind11_object_type->tp_dealloc)
+        Py_DECREF(type);
+#else
+    // This was not needed before Python 3.8 (Python issue 35810)
+    // https://github.com/pybind/pybind11/issues/1946
+    Py_DECREF(type);
+#endif
+}
+
+std::string error_string();
+
+/** Create the type which can be used as a common base for all classes.  This is
+    needed in order to satisfy Python's requirements for multiple inheritance.
+    Return value: New reference. */
+inline PyObject *make_object_base_type(PyTypeObject *metaclass) {
+    constexpr auto *name = "pybind11_object";
+    auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
+
+    /* Danger zone: from now (and until PyType_Ready), make sure to
+       issue no Python C API calls which could potentially invoke the
+       garbage collector (the GC will call type_traverse(), which will in
+       turn find the newly constructed type in an invalid state) */
+    auto *heap_type = (PyHeapTypeObject *) metaclass->tp_alloc(metaclass, 0);
+    if (!heap_type) {
+        pybind11_fail("make_object_base_type(): error allocating type!");
+    }
+
+    heap_type->ht_name = name_obj.inc_ref().ptr();
+#ifdef PYBIND11_BUILTIN_QUALNAME
+    heap_type->ht_qualname = name_obj.inc_ref().ptr();
+#endif
+
+    auto *type = &heap_type->ht_type;
+    type->tp_name = name;
+    type->tp_base = type_incref(&PyBaseObject_Type);
+    type->tp_basicsize = static_cast<ssize_t>(sizeof(instance));
+    type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
+
+    type->tp_new = pybind11_object_new;
+    type->tp_init = pybind11_object_init;
+    type->tp_dealloc = pybind11_object_dealloc;
+
+    /* Support weak references (needed for the keep_alive feature) */
+    type->tp_weaklistoffset = offsetof(instance, weakrefs);
+
+    if (PyType_Ready(type) < 0) {
+        pybind11_fail("PyType_Ready failed in make_object_base_type(): " + error_string());
+    }
+
+    setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
+    PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
+
+    assert(!PyType_HasFeature(type, Py_TPFLAGS_HAVE_GC));
+    return (PyObject *) heap_type;
+}
+
+/// dynamic_attr: Support for `d = instance.__dict__`.
+extern "C" inline PyObject *pybind11_get_dict(PyObject *self, void *) {
+    PyObject *&dict = *_PyObject_GetDictPtr(self);
+    if (!dict) {
+        dict = PyDict_New();
+    }
+    Py_XINCREF(dict);
+    return dict;
+}
+
+/// dynamic_attr: Support for `instance.__dict__ = dict()`.
+extern "C" inline int pybind11_set_dict(PyObject *self, PyObject *new_dict, void *) {
+    if (!PyDict_Check(new_dict)) {
+        PyErr_Format(PyExc_TypeError,
+                     "__dict__ must be set to a dictionary, not a '%.200s'",
+                     get_fully_qualified_tp_name(Py_TYPE(new_dict)).c_str());
+        return -1;
+    }
+    PyObject *&dict = *_PyObject_GetDictPtr(self);
+    Py_INCREF(new_dict);
+    Py_CLEAR(dict);
+    dict = new_dict;
+    return 0;
+}
+
+/// dynamic_attr: Allow the garbage collector to traverse the internal instance `__dict__`.
+extern "C" inline int pybind11_traverse(PyObject *self, visitproc visit, void *arg) {
+    PyObject *&dict = *_PyObject_GetDictPtr(self);
+    Py_VISIT(dict);
+// https://docs.python.org/3/c-api/typeobj.html#c.PyTypeObject.tp_traverse
+#if PY_VERSION_HEX >= 0x03090000
+    Py_VISIT(Py_TYPE(self));
+#endif
+    return 0;
+}
+
+/// dynamic_attr: Allow the GC to clear the dictionary.
+extern "C" inline int pybind11_clear(PyObject *self) {
+    PyObject *&dict = *_PyObject_GetDictPtr(self);
+    Py_CLEAR(dict);
+    return 0;
+}
+
+/// Give instances of this type a `__dict__` and opt into garbage collection.
+inline void enable_dynamic_attributes(PyHeapTypeObject *heap_type) {
+    auto *type = &heap_type->ht_type;
+    type->tp_flags |= Py_TPFLAGS_HAVE_GC;
+#if PY_VERSION_HEX < 0x030B0000
+    type->tp_dictoffset = type->tp_basicsize;           // place dict at the end
+    type->tp_basicsize += (ssize_t) sizeof(PyObject *); // and allocate enough space for it
+#else
+    type->tp_flags |= Py_TPFLAGS_MANAGED_DICT;
+#endif
+    type->tp_traverse = pybind11_traverse;
+    type->tp_clear = pybind11_clear;
+
+    static PyGetSetDef getset[] = {
+        {const_cast<char *>("__dict__"), pybind11_get_dict, pybind11_set_dict, nullptr, nullptr},
+        {nullptr, nullptr, nullptr, nullptr, nullptr}};
+    type->tp_getset = getset;
+}
+
+/// buffer_protocol: Fill in the view as specified by flags.
+extern "C" inline int pybind11_getbuffer(PyObject *obj, Py_buffer *view, int flags) {
+    // Look for a `get_buffer` implementation in this type's info or any bases (following MRO).
+    type_info *tinfo = nullptr;
+    for (auto type : reinterpret_borrow<tuple>(Py_TYPE(obj)->tp_mro)) {
+        tinfo = get_type_info((PyTypeObject *) type.ptr());
+        if (tinfo && tinfo->get_buffer) {
+            break;
+        }
+    }
+    if (view == nullptr || !tinfo || !tinfo->get_buffer) {
+        if (view) {
+            view->obj = nullptr;
+        }
+        PyErr_SetString(PyExc_BufferError, "pybind11_getbuffer(): Internal error");
+        return -1;
+    }
+    std::memset(view, 0, sizeof(Py_buffer));
+    buffer_info *info = tinfo->get_buffer(obj, tinfo->get_buffer_data);
+    if ((flags & PyBUF_WRITABLE) == PyBUF_WRITABLE && info->readonly) {
+        delete info;
+        // view->obj = nullptr;  // Was just memset to 0, so not necessary
+        PyErr_SetString(PyExc_BufferError, "Writable buffer requested for readonly storage");
+        return -1;
+    }
+    view->obj = obj;
+    view->ndim = 1;
+    view->internal = info;
+    view->buf = info->ptr;
+    view->itemsize = info->itemsize;
+    view->len = view->itemsize;
+    for (auto s : info->shape) {
+        view->len *= s;
+    }
+    view->readonly = static_cast<int>(info->readonly);
+    if ((flags & PyBUF_FORMAT) == PyBUF_FORMAT) {
+        view->format = const_cast<char *>(info->format.c_str());
+    }
+    if ((flags & PyBUF_STRIDES) == PyBUF_STRIDES) {
+        view->ndim = (int) info->ndim;
+        view->strides = info->strides.data();
+        view->shape = info->shape.data();
+    }
+    Py_INCREF(view->obj);
+    return 0;
+}
+
+/// buffer_protocol: Release the resources of the buffer.
+extern "C" inline void pybind11_releasebuffer(PyObject *, Py_buffer *view) {
+    delete (buffer_info *) view->internal;
+}
+
+/// Give this type a buffer interface.
+inline void enable_buffer_protocol(PyHeapTypeObject *heap_type) {
+    heap_type->ht_type.tp_as_buffer = &heap_type->as_buffer;
+
+    heap_type->as_buffer.bf_getbuffer = pybind11_getbuffer;
+    heap_type->as_buffer.bf_releasebuffer = pybind11_releasebuffer;
+}
+
+/** Create a brand new Python type according to the `type_record` specification.
+    Return value: New reference. */
+inline PyObject *make_new_python_type(const type_record &rec) {
+    auto name = reinterpret_steal<object>(PYBIND11_FROM_STRING(rec.name));
+
+    auto qualname = name;
+    if (rec.scope && !PyModule_Check(rec.scope.ptr()) && hasattr(rec.scope, "__qualname__")) {
+        qualname = reinterpret_steal<object>(
+            PyUnicode_FromFormat("%U.%U", rec.scope.attr("__qualname__").ptr(), name.ptr()));
+    }
+
+    object module_;
+    if (rec.scope) {
+        if (hasattr(rec.scope, "__module__")) {
+            module_ = rec.scope.attr("__module__");
+        } else if (hasattr(rec.scope, "__name__")) {
+            module_ = rec.scope.attr("__name__");
+        }
+    }
+
+    const auto *full_name = c_str(
+#if !defined(PYPY_VERSION)
+        module_ ? str(module_).cast<std::string>() + "." + rec.name :
+#endif
+                rec.name);
+
+    char *tp_doc = nullptr;
+    if (rec.doc && options::show_user_defined_docstrings()) {
+        /* Allocate memory for docstring (using PyObject_MALLOC, since
+           Python will free this later on) */
+        size_t size = std::strlen(rec.doc) + 1;
+        tp_doc = (char *) PyObject_MALLOC(size);
+        std::memcpy((void *) tp_doc, rec.doc, size);
+    }
+
+    auto &internals = get_internals();
+    auto bases = tuple(rec.bases);
+    auto *base = (bases.empty()) ? internals.instance_base : bases[0].ptr();
+
+    /* Danger zone: from now (and until PyType_Ready), make sure to
+       issue no Python C API calls which could potentially invoke the
+       garbage collector (the GC will call type_traverse(), which will in
+       turn find the newly constructed type in an invalid state) */
+    auto *metaclass
+        = rec.metaclass.ptr() ? (PyTypeObject *) rec.metaclass.ptr() : internals.default_metaclass;
+
+    auto *heap_type = (PyHeapTypeObject *) metaclass->tp_alloc(metaclass, 0);
+    if (!heap_type) {
+        pybind11_fail(std::string(rec.name) + ": Unable to create type object!");
+    }
+
+    heap_type->ht_name = name.release().ptr();
+#ifdef PYBIND11_BUILTIN_QUALNAME
+    heap_type->ht_qualname = qualname.inc_ref().ptr();
+#endif
+
+    auto *type = &heap_type->ht_type;
+    type->tp_name = full_name;
+    type->tp_doc = tp_doc;
+    type->tp_base = type_incref((PyTypeObject *) base);
+    type->tp_basicsize = static_cast<ssize_t>(sizeof(instance));
+    if (!bases.empty()) {
+        type->tp_bases = bases.release().ptr();
+    }
+
+    /* Don't inherit base __init__ */
+    type->tp_init = pybind11_object_init;
+
+    /* Supported protocols */
+    type->tp_as_number = &heap_type->as_number;
+    type->tp_as_sequence = &heap_type->as_sequence;
+    type->tp_as_mapping = &heap_type->as_mapping;
+    type->tp_as_async = &heap_type->as_async;
+
+    /* Flags */
+    type->tp_flags |= Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HEAPTYPE;
+    if (!rec.is_final) {
+        type->tp_flags |= Py_TPFLAGS_BASETYPE;
+    }
+
+    if (rec.dynamic_attr) {
+        enable_dynamic_attributes(heap_type);
+    }
+
+    if (rec.buffer_protocol) {
+        enable_buffer_protocol(heap_type);
+    }
+
+    if (rec.custom_type_setup_callback) {
+        rec.custom_type_setup_callback(heap_type);
+    }
+
+    if (PyType_Ready(type) < 0) {
+        pybind11_fail(std::string(rec.name) + ": PyType_Ready failed: " + error_string());
+    }
+
+    assert(!rec.dynamic_attr || PyType_HasFeature(type, Py_TPFLAGS_HAVE_GC));
+
+    /* Register type with the parent scope */
+    if (rec.scope) {
+        setattr(rec.scope, rec.name, (PyObject *) type);
+    } else {
+        Py_INCREF(type); // Keep it alive forever (reference leak)
+    }
+
+    if (module_) { // Needed by pydoc
+        setattr((PyObject *) type, "__module__", module_);
+    }
+
+    PYBIND11_SET_OLDPY_QUALNAME(type, qualname);
+
+    return (PyObject *) type;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/common.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/common.h
new file mode 100644
index 000000000..1da323f31
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/common.h
@@ -0,0 +1,1169 @@
+/*
+    pybind11/detail/common.h -- Basic macros
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#define PYBIND11_VERSION_MAJOR 2
+#define PYBIND11_VERSION_MINOR 10
+#define PYBIND11_VERSION_PATCH 0
+
+// Similar to Python's convention: https://docs.python.org/3/c-api/apiabiversion.html
+// Additional convention: 0xD = dev
+#define PYBIND11_VERSION_HEX 0x020A0000
+
+#define PYBIND11_NAMESPACE_BEGIN(name) namespace name {
+#define PYBIND11_NAMESPACE_END(name) }
+
+// Robust support for some features and loading modules compiled against different pybind versions
+// requires forcing hidden visibility on pybind code, so we enforce this by setting the attribute
+// on the main `pybind11` namespace.
+#if !defined(PYBIND11_NAMESPACE)
+#    ifdef __GNUG__
+#        define PYBIND11_NAMESPACE pybind11 __attribute__((visibility("hidden")))
+#    else
+#        define PYBIND11_NAMESPACE pybind11
+#    endif
+#endif
+
+#if !(defined(_MSC_VER) && __cplusplus == 199711L)
+#    if __cplusplus >= 201402L
+#        define PYBIND11_CPP14
+#        if __cplusplus >= 201703L
+#            define PYBIND11_CPP17
+#            if __cplusplus >= 202002L
+#                define PYBIND11_CPP20
+// Please update tests/pybind11_tests.cpp `cpp_std()` when adding a macro here.
+#            endif
+#        endif
+#    endif
+#elif defined(_MSC_VER) && __cplusplus == 199711L
+// MSVC sets _MSVC_LANG rather than __cplusplus (supposedly until the standard is fully
+// implemented). Unless you use the /Zc:__cplusplus flag on Visual Studio 2017 15.7 Preview 3
+// or newer.
+#    if _MSVC_LANG >= 201402L
+#        define PYBIND11_CPP14
+#        if _MSVC_LANG > 201402L
+#            define PYBIND11_CPP17
+#            if _MSVC_LANG >= 202002L
+#                define PYBIND11_CPP20
+#            endif
+#        endif
+#    endif
+#endif
+
+// Compiler version assertions
+#if defined(__INTEL_COMPILER)
+#    if __INTEL_COMPILER < 1800
+#        error pybind11 requires Intel C++ compiler v18 or newer
+#    elif __INTEL_COMPILER < 1900 && defined(PYBIND11_CPP14)
+#        error pybind11 supports only C++11 with Intel C++ compiler v18. Use v19 or newer for C++14.
+#    endif
+/* The following pragma cannot be pop'ed:
+   https://community.intel.com/t5/Intel-C-Compiler/Inline-and-no-inline-warning/td-p/1216764 */
+#    pragma warning disable 2196 // warning #2196: routine is both "inline" and "noinline"
+#elif defined(__clang__) && !defined(__apple_build_version__)
+#    if __clang_major__ < 3 || (__clang_major__ == 3 && __clang_minor__ < 3)
+#        error pybind11 requires clang 3.3 or newer
+#    endif
+#elif defined(__clang__)
+// Apple changes clang version macros to its Xcode version; the first Xcode release based on
+// (upstream) clang 3.3 was Xcode 5:
+#    if __clang_major__ < 5
+#        error pybind11 requires Xcode/clang 5.0 or newer
+#    endif
+#elif defined(__GNUG__)
+#    if __GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 8)
+#        error pybind11 requires gcc 4.8 or newer
+#    endif
+#elif defined(_MSC_VER)
+#    if _MSC_VER < 1910
+#        error pybind11 2.10+ requires MSVC 2017 or newer
+#    endif
+#endif
+
+#if !defined(PYBIND11_EXPORT)
+#    if defined(WIN32) || defined(_WIN32)
+#        define PYBIND11_EXPORT __declspec(dllexport)
+#    else
+#        define PYBIND11_EXPORT __attribute__((visibility("default")))
+#    endif
+#endif
+
+#if !defined(PYBIND11_EXPORT_EXCEPTION)
+#    ifdef __MINGW32__
+// workaround for:
+// error: 'dllexport' implies default visibility, but xxx has already been declared with a
+// different visibility
+#        define PYBIND11_EXPORT_EXCEPTION
+#    else
+#        define PYBIND11_EXPORT_EXCEPTION PYBIND11_EXPORT
+#    endif
+#endif
+
+// For CUDA, GCC7, GCC8:
+// PYBIND11_NOINLINE_FORCED is incompatible with `-Wattributes -Werror`.
+// When defining PYBIND11_NOINLINE_FORCED, it is best to also use `-Wno-attributes`.
+// However, the measured shared-library size saving when using noinline are only
+// 1.7% for CUDA, -0.2% for GCC7, and 0.0% for GCC8 (using -DCMAKE_BUILD_TYPE=MinSizeRel,
+// the default under pybind11/tests).
+#if !defined(PYBIND11_NOINLINE_FORCED)                                                            \
+    && (defined(__CUDACC__) || (defined(__GNUC__) && (__GNUC__ == 7 || __GNUC__ == 8)))
+#    define PYBIND11_NOINLINE_DISABLED
+#endif
+
+// The PYBIND11_NOINLINE macro is for function DEFINITIONS.
+// In contrast, FORWARD DECLARATIONS should never use this macro:
+// https://stackoverflow.com/questions/9317473/forward-declaration-of-inline-functions
+#if defined(PYBIND11_NOINLINE_DISABLED) // Option for maximum portability and experimentation.
+#    define PYBIND11_NOINLINE inline
+#elif defined(_MSC_VER)
+#    define PYBIND11_NOINLINE __declspec(noinline) inline
+#else
+#    define PYBIND11_NOINLINE __attribute__((noinline)) inline
+#endif
+
+#if defined(__MINGW32__)
+// For unknown reasons all PYBIND11_DEPRECATED member trigger a warning when declared
+// whether it is used or not
+#    define PYBIND11_DEPRECATED(reason)
+#elif defined(PYBIND11_CPP14)
+#    define PYBIND11_DEPRECATED(reason) [[deprecated(reason)]]
+#else
+#    define PYBIND11_DEPRECATED(reason) __attribute__((deprecated(reason)))
+#endif
+
+#if defined(PYBIND11_CPP17)
+#    define PYBIND11_MAYBE_UNUSED [[maybe_unused]]
+#elif defined(_MSC_VER) && !defined(__clang__)
+#    define PYBIND11_MAYBE_UNUSED
+#else
+#    define PYBIND11_MAYBE_UNUSED __attribute__((__unused__))
+#endif
+
+/* Don't let Python.h #define (v)snprintf as macro because they are implemented
+   properly in Visual Studio since 2015. */
+#if defined(_MSC_VER)
+#    define HAVE_SNPRINTF 1
+#endif
+
+/// Include Python header, disable linking to pythonX_d.lib on Windows in debug mode
+#if defined(_MSC_VER)
+#    pragma warning(push)
+// C4505: 'PySlice_GetIndicesEx': unreferenced local function has been removed (PyPy only)
+#    pragma warning(disable : 4505)
+#    if defined(_DEBUG) && !defined(Py_DEBUG)
+// Workaround for a VS 2022 issue.
+// NOTE: This workaround knowingly violates the Python.h include order requirement:
+// https://docs.python.org/3/c-api/intro.html#include-files
+// See https://github.com/pybind/pybind11/pull/3497 for full context.
+#        include <yvals.h>
+#        if _MSVC_STL_VERSION >= 143
+#            include <crtdefs.h>
+#        endif
+#        define PYBIND11_DEBUG_MARKER
+#        undef _DEBUG
+#    endif
+#endif
+
+// https://en.cppreference.com/w/c/chrono/localtime
+#if defined(__STDC_LIB_EXT1__) && !defined(__STDC_WANT_LIB_EXT1__)
+#    define __STDC_WANT_LIB_EXT1__
+#endif
+
+#ifdef __has_include
+// std::optional (but including it in c++14 mode isn't allowed)
+#    if defined(PYBIND11_CPP17) && __has_include(<optional>)
+#        define PYBIND11_HAS_OPTIONAL 1
+#    endif
+// std::experimental::optional (but not allowed in c++11 mode)
+#    if defined(PYBIND11_CPP14) && (__has_include(<experimental/optional>) && \
+                                 !__has_include(<optional>))
+#        define PYBIND11_HAS_EXP_OPTIONAL 1
+#    endif
+// std::variant
+#    if defined(PYBIND11_CPP17) && __has_include(<variant>)
+#        define PYBIND11_HAS_VARIANT 1
+#    endif
+#elif defined(_MSC_VER) && defined(PYBIND11_CPP17)
+#    define PYBIND11_HAS_OPTIONAL 1
+#    define PYBIND11_HAS_VARIANT 1
+#endif
+
+#if defined(PYBIND11_CPP17)
+#    if defined(__has_include)
+#        if __has_include(<string_view>)
+#            define PYBIND11_HAS_STRING_VIEW
+#        endif
+#    elif defined(_MSC_VER)
+#        define PYBIND11_HAS_STRING_VIEW
+#    endif
+#endif
+
+#if defined(__cpp_lib_char8_t) && __cpp_lib_char8_t >= 201811L
+#    define PYBIND11_HAS_U8STRING
+#endif
+
+#include <Python.h>
+#if PY_VERSION_HEX < 0x03060000
+#    error "PYTHON < 3.6 IS UNSUPPORTED. pybind11 v2.9 was the last to support Python 2 and 3.5."
+#endif
+#include <frameobject.h>
+#include <pythread.h>
+
+/* Python #defines overrides on all sorts of core functions, which
+   tends to weak havok in C++ codebases that expect these to work
+   like regular functions (potentially with several overloads) */
+#if defined(isalnum)
+#    undef isalnum
+#    undef isalpha
+#    undef islower
+#    undef isspace
+#    undef isupper
+#    undef tolower
+#    undef toupper
+#endif
+
+#if defined(copysign)
+#    undef copysign
+#endif
+
+#if defined(_MSC_VER)
+#    if defined(PYBIND11_DEBUG_MARKER)
+#        define _DEBUG
+#        undef PYBIND11_DEBUG_MARKER
+#    endif
+#    pragma warning(pop)
+#endif
+
+#include <cstddef>
+#include <cstring>
+#include <exception>
+#include <forward_list>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <typeindex>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+#if defined(__has_include)
+#    if __has_include(<version>)
+#        include <version>
+#    endif
+#endif
+
+// #define PYBIND11_STR_LEGACY_PERMISSIVE
+// If DEFINED, pybind11::str can hold PyUnicodeObject or PyBytesObject
+//             (probably surprising and never documented, but this was the
+//             legacy behavior until and including v2.6.x). As a side-effect,
+//             pybind11::isinstance<str>() is true for both pybind11::str and
+//             pybind11::bytes.
+// If UNDEFINED, pybind11::str can only hold PyUnicodeObject, and
+//               pybind11::isinstance<str>() is true only for pybind11::str.
+//               However, for Python 2 only (!), the pybind11::str caster
+//               implicitly decoded bytes to PyUnicodeObject. This was to ease
+//               the transition from the legacy behavior to the non-permissive
+//               behavior.
+
+/// Compatibility macros for Python 2 / Python 3 versions TODO: remove
+#define PYBIND11_INSTANCE_METHOD_NEW(ptr, class_) PyInstanceMethod_New(ptr)
+#define PYBIND11_INSTANCE_METHOD_CHECK PyInstanceMethod_Check
+#define PYBIND11_INSTANCE_METHOD_GET_FUNCTION PyInstanceMethod_GET_FUNCTION
+#define PYBIND11_BYTES_CHECK PyBytes_Check
+#define PYBIND11_BYTES_FROM_STRING PyBytes_FromString
+#define PYBIND11_BYTES_FROM_STRING_AND_SIZE PyBytes_FromStringAndSize
+#define PYBIND11_BYTES_AS_STRING_AND_SIZE PyBytes_AsStringAndSize
+#define PYBIND11_BYTES_AS_STRING PyBytes_AsString
+#define PYBIND11_BYTES_SIZE PyBytes_Size
+#define PYBIND11_LONG_CHECK(o) PyLong_Check(o)
+#define PYBIND11_LONG_AS_LONGLONG(o) PyLong_AsLongLong(o)
+#define PYBIND11_LONG_FROM_SIGNED(o) PyLong_FromSsize_t((ssize_t) (o))
+#define PYBIND11_LONG_FROM_UNSIGNED(o) PyLong_FromSize_t((size_t) (o))
+#define PYBIND11_BYTES_NAME "bytes"
+#define PYBIND11_STRING_NAME "str"
+#define PYBIND11_SLICE_OBJECT PyObject
+#define PYBIND11_FROM_STRING PyUnicode_FromString
+#define PYBIND11_STR_TYPE ::pybind11::str
+#define PYBIND11_BOOL_ATTR "__bool__"
+#define PYBIND11_NB_BOOL(ptr) ((ptr)->nb_bool)
+#define PYBIND11_BUILTINS_MODULE "builtins"
+// Providing a separate declaration to make Clang's -Wmissing-prototypes happy.
+// See comment for PYBIND11_MODULE below for why this is marked "maybe unused".
+#define PYBIND11_PLUGIN_IMPL(name)                                                                \
+    extern "C" PYBIND11_MAYBE_UNUSED PYBIND11_EXPORT PyObject *PyInit_##name();                   \
+    extern "C" PYBIND11_EXPORT PyObject *PyInit_##name()
+
+#define PYBIND11_TRY_NEXT_OVERLOAD ((PyObject *) 1) // special failure return code
+#define PYBIND11_STRINGIFY(x) #x
+#define PYBIND11_TOSTRING(x) PYBIND11_STRINGIFY(x)
+#define PYBIND11_CONCAT(first, second) first##second
+#define PYBIND11_ENSURE_INTERNALS_READY pybind11::detail::get_internals();
+
+#define PYBIND11_CHECK_PYTHON_VERSION                                                             \
+    {                                                                                             \
+        const char *compiled_ver                                                                  \
+            = PYBIND11_TOSTRING(PY_MAJOR_VERSION) "." PYBIND11_TOSTRING(PY_MINOR_VERSION);        \
+        const char *runtime_ver = Py_GetVersion();                                                \
+        size_t len = std::strlen(compiled_ver);                                                   \
+        if (std::strncmp(runtime_ver, compiled_ver, len) != 0                                     \
+            || (runtime_ver[len] >= '0' && runtime_ver[len] <= '9')) {                            \
+            PyErr_Format(PyExc_ImportError,                                                       \
+                         "Python version mismatch: module was compiled for Python %s, "           \
+                         "but the interpreter version is incompatible: %s.",                      \
+                         compiled_ver,                                                            \
+                         runtime_ver);                                                            \
+            return nullptr;                                                                       \
+        }                                                                                         \
+    }
+
+#define PYBIND11_CATCH_INIT_EXCEPTIONS                                                            \
+    catch (pybind11::error_already_set & e) {                                                     \
+        pybind11::raise_from(e, PyExc_ImportError, "initialization failed");                      \
+        return nullptr;                                                                           \
+    }                                                                                             \
+    catch (const std::exception &e) {                                                             \
+        PyErr_SetString(PyExc_ImportError, e.what());                                             \
+        return nullptr;                                                                           \
+    }
+
+/** \rst
+    ***Deprecated in favor of PYBIND11_MODULE***
+
+    This macro creates the entry point that will be invoked when the Python interpreter
+    imports a plugin library. Please create a `module_` in the function body and return
+    the pointer to its underlying Python object at the end.
+
+    .. code-block:: cpp
+
+        PYBIND11_PLUGIN(example) {
+            pybind11::module_ m("example", "pybind11 example plugin");
+            /// Set up bindings here
+            return m.ptr();
+        }
+\endrst */
+#define PYBIND11_PLUGIN(name)                                                                     \
+    PYBIND11_DEPRECATED("PYBIND11_PLUGIN is deprecated, use PYBIND11_MODULE")                     \
+    static PyObject *pybind11_init();                                                             \
+    PYBIND11_PLUGIN_IMPL(name) {                                                                  \
+        PYBIND11_CHECK_PYTHON_VERSION                                                             \
+        PYBIND11_ENSURE_INTERNALS_READY                                                           \
+        try {                                                                                     \
+            return pybind11_init();                                                               \
+        }                                                                                         \
+        PYBIND11_CATCH_INIT_EXCEPTIONS                                                            \
+    }                                                                                             \
+    PyObject *pybind11_init()
+
+/** \rst
+    This macro creates the entry point that will be invoked when the Python interpreter
+    imports an extension module. The module name is given as the fist argument and it
+    should not be in quotes. The second macro argument defines a variable of type
+    `py::module_` which can be used to initialize the module.
+
+    The entry point is marked as "maybe unused" to aid dead-code detection analysis:
+    since the entry point is typically only looked up at runtime and not referenced
+    during translation, it would otherwise appear as unused ("dead") code.
+
+    .. code-block:: cpp
+
+        PYBIND11_MODULE(example, m) {
+            m.doc() = "pybind11 example module";
+
+            // Add bindings here
+            m.def("foo", []() {
+                return "Hello, World!";
+            });
+        }
+\endrst */
+#define PYBIND11_MODULE(name, variable)                                                           \
+    static ::pybind11::module_::module_def PYBIND11_CONCAT(pybind11_module_def_, name)            \
+        PYBIND11_MAYBE_UNUSED;                                                                    \
+    PYBIND11_MAYBE_UNUSED                                                                         \
+    static void PYBIND11_CONCAT(pybind11_init_, name)(::pybind11::module_ &);                     \
+    PYBIND11_PLUGIN_IMPL(name) {                                                                  \
+        PYBIND11_CHECK_PYTHON_VERSION                                                             \
+        PYBIND11_ENSURE_INTERNALS_READY                                                           \
+        auto m = ::pybind11::module_::create_extension_module(                                    \
+            PYBIND11_TOSTRING(name), nullptr, &PYBIND11_CONCAT(pybind11_module_def_, name));      \
+        try {                                                                                     \
+            PYBIND11_CONCAT(pybind11_init_, name)(m);                                             \
+            return m.ptr();                                                                       \
+        }                                                                                         \
+        PYBIND11_CATCH_INIT_EXCEPTIONS                                                            \
+    }                                                                                             \
+    void PYBIND11_CONCAT(pybind11_init_, name)(::pybind11::module_ & (variable))
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+using ssize_t = Py_ssize_t;
+using size_t = std::size_t;
+
+template <typename IntType>
+inline ssize_t ssize_t_cast(const IntType &val) {
+    static_assert(sizeof(IntType) <= sizeof(ssize_t), "Implicit narrowing is not permitted.");
+    return static_cast<ssize_t>(val);
+}
+
+/// Approach used to cast a previously unknown C++ instance into a Python object
+enum class return_value_policy : uint8_t {
+    /** This is the default return value policy, which falls back to the policy
+        return_value_policy::take_ownership when the return value is a pointer.
+        Otherwise, it uses return_value::move or return_value::copy for rvalue
+        and lvalue references, respectively. See below for a description of what
+        all of these different policies do. */
+    automatic = 0,
+
+    /** As above, but use policy return_value_policy::reference when the return
+        value is a pointer. This is the default conversion policy for function
+        arguments when calling Python functions manually from C++ code (i.e. via
+        handle::operator()). You probably won't need to use this. */
+    automatic_reference,
+
+    /** Reference an existing object (i.e. do not create a new copy) and take
+        ownership. Python will call the destructor and delete operator when the
+        object's reference count reaches zero. Undefined behavior ensues when
+        the C++ side does the same.. */
+    take_ownership,
+
+    /** Create a new copy of the returned object, which will be owned by
+        Python. This policy is comparably safe because the lifetimes of the two
+        instances are decoupled. */
+    copy,
+
+    /** Use std::move to move the return value contents into a new instance
+        that will be owned by Python. This policy is comparably safe because the
+        lifetimes of the two instances (move source and destination) are
+        decoupled. */
+    move,
+
+    /** Reference an existing object, but do not take ownership. The C++ side
+        is responsible for managing the object's lifetime and deallocating it
+        when it is no longer used. Warning: undefined behavior will ensue when
+        the C++ side deletes an object that is still referenced and used by
+        Python. */
+    reference,
+
+    /** This policy only applies to methods and properties. It references the
+        object without taking ownership similar to the above
+        return_value_policy::reference policy. In contrast to that policy, the
+        function or property's implicit this argument (called the parent) is
+        considered to be the the owner of the return value (the child).
+        pybind11 then couples the lifetime of the parent to the child via a
+        reference relationship that ensures that the parent cannot be garbage
+        collected while Python is still using the child. More advanced
+        variations of this scheme are also possible using combinations of
+        return_value_policy::reference and the keep_alive call policy */
+    reference_internal
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline static constexpr int log2(size_t n, int k = 0) {
+    return (n <= 1) ? k : log2(n >> 1, k + 1);
+}
+
+// Returns the size as a multiple of sizeof(void *), rounded up.
+inline static constexpr size_t size_in_ptrs(size_t s) {
+    return 1 + ((s - 1) >> log2(sizeof(void *)));
+}
+
+/**
+ * The space to allocate for simple layout instance holders (see below) in multiple of the size of
+ * a pointer (e.g.  2 means 16 bytes on 64-bit architectures).  The default is the minimum required
+ * to holder either a std::unique_ptr or std::shared_ptr (which is almost always
+ * sizeof(std::shared_ptr<T>)).
+ */
+constexpr size_t instance_simple_holder_in_ptrs() {
+    static_assert(sizeof(std::shared_ptr<int>) >= sizeof(std::unique_ptr<int>),
+                  "pybind assumes std::shared_ptrs are at least as big as std::unique_ptrs");
+    return size_in_ptrs(sizeof(std::shared_ptr<int>));
+}
+
+// Forward declarations
+struct type_info;
+struct value_and_holder;
+
+struct nonsimple_values_and_holders {
+    void **values_and_holders;
+    uint8_t *status;
+};
+
+/// The 'instance' type which needs to be standard layout (need to be able to use 'offsetof')
+struct instance {
+    PyObject_HEAD
+    /// Storage for pointers and holder; see simple_layout, below, for a description
+    union {
+        void *simple_value_holder[1 + instance_simple_holder_in_ptrs()];
+        nonsimple_values_and_holders nonsimple;
+    };
+    /// Weak references
+    PyObject *weakrefs;
+    /// If true, the pointer is owned which means we're free to manage it with a holder.
+    bool owned : 1;
+    /**
+     * An instance has two possible value/holder layouts.
+     *
+     * Simple layout (when this flag is true), means the `simple_value_holder` is set with a
+     * pointer and the holder object governing that pointer, i.e. [val1*][holder].  This layout is
+     * applied whenever there is no python-side multiple inheritance of bound C++ types *and* the
+     * type's holder will fit in the default space (which is large enough to hold either a
+     * std::unique_ptr or std::shared_ptr).
+     *
+     * Non-simple layout applies when using custom holders that require more space than
+     * `shared_ptr` (which is typically the size of two pointers), or when multiple inheritance is
+     * used on the python side.  Non-simple layout allocates the required amount of memory to have
+     * multiple bound C++ classes as parents.  Under this layout, `nonsimple.values_and_holders` is
+     * set to a pointer to allocated space of the required space to hold a sequence of value
+     * pointers and holders followed `status`, a set of bit flags (1 byte each), i.e.
+     * [val1*][holder1][val2*][holder2]...[bb...]  where each [block] is rounded up to a multiple
+     * of `sizeof(void *)`.  `nonsimple.status` is, for convenience, a pointer to the beginning of
+     * the [bb...] block (but not independently allocated).
+     *
+     * Status bits indicate whether the associated holder is constructed (&
+     * status_holder_constructed) and whether the value pointer is registered (&
+     * status_instance_registered) in `registered_instances`.
+     */
+    bool simple_layout : 1;
+    /// For simple layout, tracks whether the holder has been constructed
+    bool simple_holder_constructed : 1;
+    /// For simple layout, tracks whether the instance is registered in `registered_instances`
+    bool simple_instance_registered : 1;
+    /// If true, get_internals().patients has an entry for this object
+    bool has_patients : 1;
+
+    /// Initializes all of the above type/values/holders data (but not the instance values
+    /// themselves)
+    void allocate_layout();
+
+    /// Destroys/deallocates all of the above
+    void deallocate_layout();
+
+    /// Returns the value_and_holder wrapper for the given type (or the first, if `find_type`
+    /// omitted).  Returns a default-constructed (with `.inst = nullptr`) object on failure if
+    /// `throw_if_missing` is false.
+    value_and_holder get_value_and_holder(const type_info *find_type = nullptr,
+                                          bool throw_if_missing = true);
+
+    /// Bit values for the non-simple status flags
+    static constexpr uint8_t status_holder_constructed = 1;
+    static constexpr uint8_t status_instance_registered = 2;
+};
+
+static_assert(std::is_standard_layout<instance>::value,
+              "Internal error: `pybind11::detail::instance` is not standard layout!");
+
+/// from __cpp_future__ import (convenient aliases from C++14/17)
+#if defined(PYBIND11_CPP14)
+using std::conditional_t;
+using std::enable_if_t;
+using std::remove_cv_t;
+using std::remove_reference_t;
+#else
+template <bool B, typename T = void>
+using enable_if_t = typename std::enable_if<B, T>::type;
+template <bool B, typename T, typename F>
+using conditional_t = typename std::conditional<B, T, F>::type;
+template <typename T>
+using remove_cv_t = typename std::remove_cv<T>::type;
+template <typename T>
+using remove_reference_t = typename std::remove_reference<T>::type;
+#endif
+
+#if defined(PYBIND11_CPP20)
+using std::remove_cvref;
+using std::remove_cvref_t;
+#else
+template <class T>
+struct remove_cvref {
+    using type = remove_cv_t<remove_reference_t<T>>;
+};
+template <class T>
+using remove_cvref_t = typename remove_cvref<T>::type;
+#endif
+
+/// Index sequences
+#if defined(PYBIND11_CPP14)
+using std::index_sequence;
+using std::make_index_sequence;
+#else
+template <size_t...>
+struct index_sequence {};
+template <size_t N, size_t... S>
+struct make_index_sequence_impl : make_index_sequence_impl<N - 1, N - 1, S...> {};
+template <size_t... S>
+struct make_index_sequence_impl<0, S...> {
+    using type = index_sequence<S...>;
+};
+template <size_t N>
+using make_index_sequence = typename make_index_sequence_impl<N>::type;
+#endif
+
+/// Make an index sequence of the indices of true arguments
+template <typename ISeq, size_t, bool...>
+struct select_indices_impl {
+    using type = ISeq;
+};
+template <size_t... IPrev, size_t I, bool B, bool... Bs>
+struct select_indices_impl<index_sequence<IPrev...>, I, B, Bs...>
+    : select_indices_impl<conditional_t<B, index_sequence<IPrev..., I>, index_sequence<IPrev...>>,
+                          I + 1,
+                          Bs...> {};
+template <bool... Bs>
+using select_indices = typename select_indices_impl<index_sequence<>, 0, Bs...>::type;
+
+/// Backports of std::bool_constant and std::negation to accommodate older compilers
+template <bool B>
+using bool_constant = std::integral_constant<bool, B>;
+template <typename T>
+struct negation : bool_constant<!T::value> {};
+
+// PGI/Intel cannot detect operator delete with the "compatible" void_t impl, so
+// using the new one (C++14 defect, so generally works on newer compilers, even
+// if not in C++17 mode)
+#if defined(__PGIC__) || defined(__INTEL_COMPILER)
+template <typename...>
+using void_t = void;
+#else
+template <typename...>
+struct void_t_impl {
+    using type = void;
+};
+template <typename... Ts>
+using void_t = typename void_t_impl<Ts...>::type;
+#endif
+
+/// Compile-time all/any/none of that check the boolean value of all template types
+#if defined(__cpp_fold_expressions) && !(defined(_MSC_VER) && (_MSC_VER < 1916))
+template <class... Ts>
+using all_of = bool_constant<(Ts::value && ...)>;
+template <class... Ts>
+using any_of = bool_constant<(Ts::value || ...)>;
+#elif !defined(_MSC_VER)
+template <bool...>
+struct bools {};
+template <class... Ts>
+using all_of = std::is_same<bools<Ts::value..., true>, bools<true, Ts::value...>>;
+template <class... Ts>
+using any_of = negation<all_of<negation<Ts>...>>;
+#else
+// MSVC has trouble with the above, but supports std::conjunction, which we can use instead (albeit
+// at a slight loss of compilation efficiency).
+template <class... Ts>
+using all_of = std::conjunction<Ts...>;
+template <class... Ts>
+using any_of = std::disjunction<Ts...>;
+#endif
+template <class... Ts>
+using none_of = negation<any_of<Ts...>>;
+
+template <class T, template <class> class... Predicates>
+using satisfies_all_of = all_of<Predicates<T>...>;
+template <class T, template <class> class... Predicates>
+using satisfies_any_of = any_of<Predicates<T>...>;
+template <class T, template <class> class... Predicates>
+using satisfies_none_of = none_of<Predicates<T>...>;
+
+/// Strip the class from a method type
+template <typename T>
+struct remove_class {};
+template <typename C, typename R, typename... A>
+struct remove_class<R (C::*)(A...)> {
+    using type = R(A...);
+};
+template <typename C, typename R, typename... A>
+struct remove_class<R (C::*)(A...) const> {
+    using type = R(A...);
+};
+
+/// Helper template to strip away type modifiers
+template <typename T>
+struct intrinsic_type {
+    using type = T;
+};
+template <typename T>
+struct intrinsic_type<const T> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T>
+struct intrinsic_type<T *> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T>
+struct intrinsic_type<T &> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T>
+struct intrinsic_type<T &&> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T, size_t N>
+struct intrinsic_type<const T[N]> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T, size_t N>
+struct intrinsic_type<T[N]> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T>
+using intrinsic_t = typename intrinsic_type<T>::type;
+
+/// Helper type to replace 'void' in some expressions
+struct void_type {};
+
+/// Helper template which holds a list of types
+template <typename...>
+struct type_list {};
+
+/// Compile-time integer sum
+#ifdef __cpp_fold_expressions
+template <typename... Ts>
+constexpr size_t constexpr_sum(Ts... ns) {
+    return (0 + ... + size_t{ns});
+}
+#else
+constexpr size_t constexpr_sum() { return 0; }
+template <typename T, typename... Ts>
+constexpr size_t constexpr_sum(T n, Ts... ns) {
+    return size_t{n} + constexpr_sum(ns...);
+}
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(constexpr_impl)
+/// Implementation details for constexpr functions
+constexpr int first(int i) { return i; }
+template <typename T, typename... Ts>
+constexpr int first(int i, T v, Ts... vs) {
+    return v ? i : first(i + 1, vs...);
+}
+
+constexpr int last(int /*i*/, int result) { return result; }
+template <typename T, typename... Ts>
+constexpr int last(int i, int result, T v, Ts... vs) {
+    return last(i + 1, v ? i : result, vs...);
+}
+PYBIND11_NAMESPACE_END(constexpr_impl)
+
+/// Return the index of the first type in Ts which satisfies Predicate<T>.
+/// Returns sizeof...(Ts) if none match.
+template <template <typename> class Predicate, typename... Ts>
+constexpr int constexpr_first() {
+    return constexpr_impl::first(0, Predicate<Ts>::value...);
+}
+
+/// Return the index of the last type in Ts which satisfies Predicate<T>, or -1 if none match.
+template <template <typename> class Predicate, typename... Ts>
+constexpr int constexpr_last() {
+    return constexpr_impl::last(0, -1, Predicate<Ts>::value...);
+}
+
+/// Return the Nth element from the parameter pack
+template <size_t N, typename T, typename... Ts>
+struct pack_element {
+    using type = typename pack_element<N - 1, Ts...>::type;
+};
+template <typename T, typename... Ts>
+struct pack_element<0, T, Ts...> {
+    using type = T;
+};
+
+/// Return the one and only type which matches the predicate, or Default if none match.
+/// If more than one type matches the predicate, fail at compile-time.
+template <template <typename> class Predicate, typename Default, typename... Ts>
+struct exactly_one {
+    static constexpr auto found = constexpr_sum(Predicate<Ts>::value...);
+    static_assert(found <= 1, "Found more than one type matching the predicate");
+
+    static constexpr auto index = found ? constexpr_first<Predicate, Ts...>() : 0;
+    using type = conditional_t<found, typename pack_element<index, Ts...>::type, Default>;
+};
+template <template <typename> class P, typename Default>
+struct exactly_one<P, Default> {
+    using type = Default;
+};
+
+template <template <typename> class Predicate, typename Default, typename... Ts>
+using exactly_one_t = typename exactly_one<Predicate, Default, Ts...>::type;
+
+/// Defer the evaluation of type T until types Us are instantiated
+template <typename T, typename... /*Us*/>
+struct deferred_type {
+    using type = T;
+};
+template <typename T, typename... Us>
+using deferred_t = typename deferred_type<T, Us...>::type;
+
+/// Like is_base_of, but requires a strict base (i.e. `is_strict_base_of<T, T>::value == false`,
+/// unlike `std::is_base_of`)
+template <typename Base, typename Derived>
+using is_strict_base_of
+    = bool_constant<std::is_base_of<Base, Derived>::value && !std::is_same<Base, Derived>::value>;
+
+/// Like is_base_of, but also requires that the base type is accessible (i.e. that a Derived
+/// pointer can be converted to a Base pointer) For unions, `is_base_of<T, T>::value` is False, so
+/// we need to check `is_same` as well.
+template <typename Base, typename Derived>
+using is_accessible_base_of
+    = bool_constant<(std::is_same<Base, Derived>::value || std::is_base_of<Base, Derived>::value)
+                    && std::is_convertible<Derived *, Base *>::value>;
+
+template <template <typename...> class Base>
+struct is_template_base_of_impl {
+    template <typename... Us>
+    static std::true_type check(Base<Us...> *);
+    static std::false_type check(...);
+};
+
+/// Check if a template is the base of a type. For example:
+/// `is_template_base_of<Base, T>` is true if `struct T : Base<U> {}` where U can be anything
+template <template <typename...> class Base, typename T>
+// Sadly, all MSVC versions incl. 2022 need the workaround, even in C++20 mode.
+// See also: https://github.com/pybind/pybind11/pull/3741
+#if !defined(_MSC_VER)
+using is_template_base_of
+    = decltype(is_template_base_of_impl<Base>::check((intrinsic_t<T> *) nullptr));
+#else
+struct is_template_base_of
+    : decltype(is_template_base_of_impl<Base>::check((intrinsic_t<T> *) nullptr)) {
+};
+#endif
+
+/// Check if T is an instantiation of the template `Class`. For example:
+/// `is_instantiation<shared_ptr, T>` is true if `T == shared_ptr<U>` where U can be anything.
+template <template <typename...> class Class, typename T>
+struct is_instantiation : std::false_type {};
+template <template <typename...> class Class, typename... Us>
+struct is_instantiation<Class, Class<Us...>> : std::true_type {};
+
+/// Check if T is std::shared_ptr<U> where U can be anything
+template <typename T>
+using is_shared_ptr = is_instantiation<std::shared_ptr, T>;
+
+/// Check if T looks like an input iterator
+template <typename T, typename = void>
+struct is_input_iterator : std::false_type {};
+template <typename T>
+struct is_input_iterator<T,
+                         void_t<decltype(*std::declval<T &>()), decltype(++std::declval<T &>())>>
+    : std::true_type {};
+
+template <typename T>
+using is_function_pointer
+    = bool_constant<std::is_pointer<T>::value
+                    && std::is_function<typename std::remove_pointer<T>::type>::value>;
+
+template <typename F>
+struct strip_function_object {
+    // If you are encountering an
+    // 'error: name followed by "::" must be a class or namespace name'
+    // with the Intel compiler and a noexcept function here,
+    // try to use noexcept(true) instead of plain noexcept.
+    using type = typename remove_class<decltype(&F::operator())>::type;
+};
+
+// Extracts the function signature from a function, function pointer or lambda.
+template <typename Function, typename F = remove_reference_t<Function>>
+using function_signature_t = conditional_t<
+    std::is_function<F>::value,
+    F,
+    typename conditional_t<std::is_pointer<F>::value || std::is_member_pointer<F>::value,
+                           std::remove_pointer<F>,
+                           strip_function_object<F>>::type>;
+
+/// Returns true if the type looks like a lambda: that is, isn't a function, pointer or member
+/// pointer.  Note that this can catch all sorts of other things, too; this is intended to be used
+/// in a place where passing a lambda makes sense.
+template <typename T>
+using is_lambda = satisfies_none_of<remove_reference_t<T>,
+                                    std::is_function,
+                                    std::is_pointer,
+                                    std::is_member_pointer>;
+
+// [workaround(intel)] Internal error on fold expression
+/// Apply a function over each element of a parameter pack
+#if defined(__cpp_fold_expressions) && !defined(__INTEL_COMPILER)
+// Intel compiler produces an internal error on this fold expression (tested with ICC 19.0.2)
+#    define PYBIND11_EXPAND_SIDE_EFFECTS(PATTERN) (((PATTERN), void()), ...)
+#else
+using expand_side_effects = bool[];
+#    define PYBIND11_EXPAND_SIDE_EFFECTS(PATTERN)                                                 \
+        (void) pybind11::detail::expand_side_effects { ((PATTERN), void(), false)..., false }
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+
+#if defined(_MSC_VER)
+#    pragma warning(push)
+#    pragma warning(disable : 4275)
+//     warning C4275: An exported class was derived from a class that wasn't exported.
+//     Can be ignored when derived from a STL class.
+#endif
+/// C++ bindings of builtin Python exceptions
+class PYBIND11_EXPORT_EXCEPTION builtin_exception : public std::runtime_error {
+public:
+    using std::runtime_error::runtime_error;
+    /// Set the error using the Python C API
+    virtual void set_error() const = 0;
+};
+#if defined(_MSC_VER)
+#    pragma warning(pop)
+#endif
+
+#define PYBIND11_RUNTIME_EXCEPTION(name, type)                                                    \
+    class PYBIND11_EXPORT_EXCEPTION name : public builtin_exception {                             \
+    public:                                                                                       \
+        using builtin_exception::builtin_exception;                                               \
+        name() : name("") {}                                                                      \
+        void set_error() const override { PyErr_SetString(type, what()); }                        \
+    };
+
+PYBIND11_RUNTIME_EXCEPTION(stop_iteration, PyExc_StopIteration)
+PYBIND11_RUNTIME_EXCEPTION(index_error, PyExc_IndexError)
+PYBIND11_RUNTIME_EXCEPTION(key_error, PyExc_KeyError)
+PYBIND11_RUNTIME_EXCEPTION(value_error, PyExc_ValueError)
+PYBIND11_RUNTIME_EXCEPTION(type_error, PyExc_TypeError)
+PYBIND11_RUNTIME_EXCEPTION(buffer_error, PyExc_BufferError)
+PYBIND11_RUNTIME_EXCEPTION(import_error, PyExc_ImportError)
+PYBIND11_RUNTIME_EXCEPTION(attribute_error, PyExc_AttributeError)
+PYBIND11_RUNTIME_EXCEPTION(cast_error, PyExc_RuntimeError) /// Thrown when pybind11::cast or
+                                                           /// handle::call fail due to a type
+                                                           /// casting error
+PYBIND11_RUNTIME_EXCEPTION(reference_cast_error, PyExc_RuntimeError) /// Used internally
+
+[[noreturn]] PYBIND11_NOINLINE void pybind11_fail(const char *reason) {
+    assert(!PyErr_Occurred());
+    throw std::runtime_error(reason);
+}
+[[noreturn]] PYBIND11_NOINLINE void pybind11_fail(const std::string &reason) {
+    assert(!PyErr_Occurred());
+    throw std::runtime_error(reason);
+}
+
+template <typename T, typename SFINAE = void>
+struct format_descriptor {};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+// Returns the index of the given type in the type char array below, and in the list in numpy.h
+// The order here is: bool; 8 ints ((signed,unsigned)x(8,16,32,64)bits); float,double,long double;
+// complex float,double,long double.  Note that the long double types only participate when long
+// double is actually longer than double (it isn't under MSVC).
+// NB: not only the string below but also complex.h and numpy.h rely on this order.
+template <typename T, typename SFINAE = void>
+struct is_fmt_numeric {
+    static constexpr bool value = false;
+};
+template <typename T>
+struct is_fmt_numeric<T, enable_if_t<std::is_arithmetic<T>::value>> {
+    static constexpr bool value = true;
+    static constexpr int index
+        = std::is_same<T, bool>::value
+              ? 0
+              : 1
+                    + (std::is_integral<T>::value
+                           ? detail::log2(sizeof(T)) * 2 + std::is_unsigned<T>::value
+                           : 8
+                                 + (std::is_same<T, double>::value        ? 1
+                                    : std::is_same<T, long double>::value ? 2
+                                                                          : 0));
+};
+PYBIND11_NAMESPACE_END(detail)
+
+template <typename T>
+struct format_descriptor<T, detail::enable_if_t<std::is_arithmetic<T>::value>> {
+    static constexpr const char c = "?bBhHiIqQfdg"[detail::is_fmt_numeric<T>::index];
+    static constexpr const char value[2] = {c, '\0'};
+    static std::string format() { return std::string(1, c); }
+};
+
+#if !defined(PYBIND11_CPP17)
+
+template <typename T>
+constexpr const char
+    format_descriptor<T, detail::enable_if_t<std::is_arithmetic<T>::value>>::value[2];
+
+#endif
+
+/// RAII wrapper that temporarily clears any Python error state
+struct error_scope {
+    PyObject *type, *value, *trace;
+    error_scope() { PyErr_Fetch(&type, &value, &trace); }
+    error_scope(const error_scope &) = delete;
+    error_scope &operator=(const error_scope &) = delete;
+    ~error_scope() { PyErr_Restore(type, value, trace); }
+};
+
+/// Dummy destructor wrapper that can be used to expose classes with a private destructor
+struct nodelete {
+    template <typename T>
+    void operator()(T *) {}
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <typename... Args>
+struct overload_cast_impl {
+    template <typename Return>
+    constexpr auto operator()(Return (*pf)(Args...)) const noexcept -> decltype(pf) {
+        return pf;
+    }
+
+    template <typename Return, typename Class>
+    constexpr auto operator()(Return (Class::*pmf)(Args...), std::false_type = {}) const noexcept
+        -> decltype(pmf) {
+        return pmf;
+    }
+
+    template <typename Return, typename Class>
+    constexpr auto operator()(Return (Class::*pmf)(Args...) const, std::true_type) const noexcept
+        -> decltype(pmf) {
+        return pmf;
+    }
+};
+PYBIND11_NAMESPACE_END(detail)
+
+// overload_cast requires variable templates: C++14
+#if defined(PYBIND11_CPP14)
+#    define PYBIND11_OVERLOAD_CAST 1
+/// Syntax sugar for resolving overloaded function pointers:
+///  - regular: static_cast<Return (Class::*)(Arg0, Arg1, Arg2)>(&Class::func)
+///  - sweet:   overload_cast<Arg0, Arg1, Arg2>(&Class::func)
+template <typename... Args>
+#    if (defined(_MSC_VER) && _MSC_VER < 1920) /* MSVC 2017 */                                    \
+        || (defined(__clang__) && __clang_major__ == 5)
+static constexpr detail::overload_cast_impl<Args...> overload_cast = {};
+#    else
+static constexpr detail::overload_cast_impl<Args...> overload_cast;
+#    endif
+#endif
+
+/// Const member function selector for overload_cast
+///  - regular: static_cast<Return (Class::*)(Arg) const>(&Class::func)
+///  - sweet:   overload_cast<Arg>(&Class::func, const_)
+static constexpr auto const_ = std::true_type{};
+
+#if !defined(PYBIND11_CPP14) // no overload_cast: providing something that static_assert-fails:
+template <typename... Args>
+struct overload_cast {
+    static_assert(detail::deferred_t<std::false_type, Args...>::value,
+                  "pybind11::overload_cast<...> requires compiling in C++14 mode");
+};
+#endif // overload_cast
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Adaptor for converting arbitrary container arguments into a vector; implicitly convertible from
+// any standard container (or C-style array) supporting std::begin/std::end, any singleton
+// arithmetic type (if T is arithmetic), or explicitly constructible from an iterator pair.
+template <typename T>
+class any_container {
+    std::vector<T> v;
+
+public:
+    any_container() = default;
+
+    // Can construct from a pair of iterators
+    template <typename It, typename = enable_if_t<is_input_iterator<It>::value>>
+    any_container(It first, It last) : v(first, last) {}
+
+    // Implicit conversion constructor from any arbitrary container type
+    // with values convertible to T
+    template <typename Container,
+              typename = enable_if_t<
+                  std::is_convertible<decltype(*std::begin(std::declval<const Container &>())),
+                                      T>::value>>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    any_container(const Container &c) : any_container(std::begin(c), std::end(c)) {}
+
+    // initializer_list's aren't deducible, so don't get matched by the above template;
+    // we need this to explicitly allow implicit conversion from one:
+    template <typename TIn, typename = enable_if_t<std::is_convertible<TIn, T>::value>>
+    any_container(const std::initializer_list<TIn> &c) : any_container(c.begin(), c.end()) {}
+
+    // Avoid copying if given an rvalue vector of the correct type.
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    any_container(std::vector<T> &&v) : v(std::move(v)) {}
+
+    // Moves the vector out of an rvalue any_container
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator std::vector<T> &&() && { return std::move(v); }
+
+    // Dereferencing obtains a reference to the underlying vector
+    std::vector<T> &operator*() { return v; }
+    const std::vector<T> &operator*() const { return v; }
+
+    // -> lets you call methods on the underlying vector
+    std::vector<T> *operator->() { return &v; }
+    const std::vector<T> *operator->() const { return &v; }
+};
+
+// Forward-declaration; see detail/class.h
+std::string get_fully_qualified_tp_name(PyTypeObject *);
+
+template <typename T>
+inline static std::shared_ptr<T>
+try_get_shared_from_this(std::enable_shared_from_this<T> *holder_value_ptr) {
+// Pre C++17, this code path exploits undefined behavior, but is known to work on many platforms.
+// Use at your own risk!
+// See also https://en.cppreference.com/w/cpp/memory/enable_shared_from_this, and in particular
+// the `std::shared_ptr<Good> gp1 = not_so_good.getptr();` and `try`-`catch` parts of the example.
+#if defined(__cpp_lib_enable_shared_from_this) && (!defined(_MSC_VER) || _MSC_VER >= 1912)
+    return holder_value_ptr->weak_from_this().lock();
+#else
+    try {
+        return holder_value_ptr->shared_from_this();
+    } catch (const std::bad_weak_ptr &) {
+        return nullptr;
+    }
+#endif
+}
+
+// For silencing "unused" compiler warnings in special situations.
+template <typename... Args>
+#if defined(_MSC_VER) && _MSC_VER < 1920 // MSVC 2017
+constexpr
+#endif
+    inline void
+    silence_unused_warnings(Args &&...) {
+}
+
+// MSVC warning C4100: Unreferenced formal parameter
+#if defined(_MSC_VER) && _MSC_VER <= 1916
+#    define PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(...)                                         \
+        detail::silence_unused_warnings(__VA_ARGS__)
+#else
+#    define PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(...)
+#endif
+
+// GCC -Wunused-but-set-parameter  All GCC versions (as of July 2021).
+#if defined(__GNUG__) && !defined(__clang__) && !defined(__INTEL_COMPILER)
+#    define PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(...)                       \
+        detail::silence_unused_warnings(__VA_ARGS__)
+#else
+#    define PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(...)
+#endif
+
+#if defined(_MSC_VER) // All versions (as of July 2021).
+
+// warning C4127: Conditional expression is constant
+constexpr inline bool silence_msvc_c4127(bool cond) { return cond; }
+
+#    define PYBIND11_SILENCE_MSVC_C4127(...) ::pybind11::detail::silence_msvc_c4127(__VA_ARGS__)
+
+#else
+#    define PYBIND11_SILENCE_MSVC_C4127(...) __VA_ARGS__
+#endif
+
+// Pybind offers detailed error messages by default for all builts that are debug (through the
+// negation of ndebug). This can also be manually enabled by users, for any builds, through
+// defining PYBIND11_DETAILED_ERROR_MESSAGES.
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES) && !defined(NDEBUG)
+#    define PYBIND11_DETAILED_ERROR_MESSAGES
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/descr.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/descr.h
new file mode 100644
index 000000000..e7a5e2c14
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/descr.h
@@ -0,0 +1,158 @@
+/*
+    pybind11/detail/descr.h: Helper type for concatenating type signatures at compile time
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "common.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if !defined(_MSC_VER)
+#    define PYBIND11_DESCR_CONSTEXPR static constexpr
+#else
+#    define PYBIND11_DESCR_CONSTEXPR const
+#endif
+
+/* Concatenate type signatures at compile time */
+template <size_t N, typename... Ts>
+struct descr {
+    char text[N + 1]{'\0'};
+
+    constexpr descr() = default;
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    constexpr descr(char const (&s)[N + 1]) : descr(s, make_index_sequence<N>()) {}
+
+    template <size_t... Is>
+    constexpr descr(char const (&s)[N + 1], index_sequence<Is...>) : text{s[Is]..., '\0'} {}
+
+    template <typename... Chars>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    constexpr descr(char c, Chars... cs) : text{c, static_cast<char>(cs)..., '\0'} {}
+
+    static constexpr std::array<const std::type_info *, sizeof...(Ts) + 1> types() {
+        return {{&typeid(Ts)..., nullptr}};
+    }
+};
+
+template <size_t N1, size_t N2, typename... Ts1, typename... Ts2, size_t... Is1, size_t... Is2>
+constexpr descr<N1 + N2, Ts1..., Ts2...> plus_impl(const descr<N1, Ts1...> &a,
+                                                   const descr<N2, Ts2...> &b,
+                                                   index_sequence<Is1...>,
+                                                   index_sequence<Is2...>) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(b);
+    return {a.text[Is1]..., b.text[Is2]...};
+}
+
+template <size_t N1, size_t N2, typename... Ts1, typename... Ts2>
+constexpr descr<N1 + N2, Ts1..., Ts2...> operator+(const descr<N1, Ts1...> &a,
+                                                   const descr<N2, Ts2...> &b) {
+    return plus_impl(a, b, make_index_sequence<N1>(), make_index_sequence<N2>());
+}
+
+template <size_t N>
+constexpr descr<N - 1> const_name(char const (&text)[N]) {
+    return descr<N - 1>(text);
+}
+constexpr descr<0> const_name(char const (&)[1]) { return {}; }
+
+template <size_t Rem, size_t... Digits>
+struct int_to_str : int_to_str<Rem / 10, Rem % 10, Digits...> {};
+template <size_t... Digits>
+struct int_to_str<0, Digits...> {
+    // WARNING: This only works with C++17 or higher.
+    static constexpr auto digits = descr<sizeof...(Digits)>(('0' + Digits)...);
+};
+
+// Ternary description (like std::conditional)
+template <bool B, size_t N1, size_t N2>
+constexpr enable_if_t<B, descr<N1 - 1>> const_name(char const (&text1)[N1], char const (&)[N2]) {
+    return const_name(text1);
+}
+template <bool B, size_t N1, size_t N2>
+constexpr enable_if_t<!B, descr<N2 - 1>> const_name(char const (&)[N1], char const (&text2)[N2]) {
+    return const_name(text2);
+}
+
+template <bool B, typename T1, typename T2>
+constexpr enable_if_t<B, T1> const_name(const T1 &d, const T2 &) {
+    return d;
+}
+template <bool B, typename T1, typename T2>
+constexpr enable_if_t<!B, T2> const_name(const T1 &, const T2 &d) {
+    return d;
+}
+
+template <size_t Size>
+auto constexpr const_name() -> remove_cv_t<decltype(int_to_str<Size / 10, Size % 10>::digits)> {
+    return int_to_str<Size / 10, Size % 10>::digits;
+}
+
+template <typename Type>
+constexpr descr<1, Type> const_name() {
+    return {'%'};
+}
+
+// If "_" is defined as a macro, py::detail::_ cannot be provided.
+// It is therefore best to use py::detail::const_name universally.
+// This block is for backward compatibility only.
+// (The const_name code is repeated to avoid introducing a "_" #define ourselves.)
+#ifndef _
+#    define PYBIND11_DETAIL_UNDERSCORE_BACKWARD_COMPATIBILITY
+template <size_t N>
+constexpr descr<N - 1> _(char const (&text)[N]) {
+    return const_name<N>(text);
+}
+template <bool B, size_t N1, size_t N2>
+constexpr enable_if_t<B, descr<N1 - 1>> _(char const (&text1)[N1], char const (&text2)[N2]) {
+    return const_name<B, N1, N2>(text1, text2);
+}
+template <bool B, size_t N1, size_t N2>
+constexpr enable_if_t<!B, descr<N2 - 1>> _(char const (&text1)[N1], char const (&text2)[N2]) {
+    return const_name<B, N1, N2>(text1, text2);
+}
+template <bool B, typename T1, typename T2>
+constexpr enable_if_t<B, T1> _(const T1 &d1, const T2 &d2) {
+    return const_name<B, T1, T2>(d1, d2);
+}
+template <bool B, typename T1, typename T2>
+constexpr enable_if_t<!B, T2> _(const T1 &d1, const T2 &d2) {
+    return const_name<B, T1, T2>(d1, d2);
+}
+
+template <size_t Size>
+auto constexpr _() -> remove_cv_t<decltype(int_to_str<Size / 10, Size % 10>::digits)> {
+    return const_name<Size>();
+}
+template <typename Type>
+constexpr descr<1, Type> _() {
+    return const_name<Type>();
+}
+#endif // #ifndef _
+
+constexpr descr<0> concat() { return {}; }
+
+template <size_t N, typename... Ts>
+constexpr descr<N, Ts...> concat(const descr<N, Ts...> &descr) {
+    return descr;
+}
+
+template <size_t N, typename... Ts, typename... Args>
+constexpr auto concat(const descr<N, Ts...> &d, const Args &...args)
+    -> decltype(std::declval<descr<N + 2, Ts...>>() + concat(args...)) {
+    return d + const_name(", ") + concat(args...);
+}
+
+template <size_t N, typename... Ts>
+constexpr descr<N + 2, Ts...> type_descr(const descr<N, Ts...> &descr) {
+    return const_name("{") + descr + const_name("}");
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/init.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/init.h
new file mode 100644
index 000000000..05f4fe54a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/init.h
@@ -0,0 +1,428 @@
+/*
+    pybind11/detail/init.h: init factory function implementation and support code.
+
+    Copyright (c) 2017 Jason Rhinelander <jason@imaginary.ca>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "class.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <>
+class type_caster<value_and_holder> {
+public:
+    bool load(handle h, bool) {
+        value = reinterpret_cast<value_and_holder *>(h.ptr());
+        return true;
+    }
+
+    template <typename>
+    using cast_op_type = value_and_holder &;
+    explicit operator value_and_holder &() { return *value; }
+    static constexpr auto name = const_name<value_and_holder>();
+
+private:
+    value_and_holder *value = nullptr;
+};
+
+PYBIND11_NAMESPACE_BEGIN(initimpl)
+
+inline void no_nullptr(void *ptr) {
+    if (!ptr) {
+        throw type_error("pybind11::init(): factory function returned nullptr");
+    }
+}
+
+// Implementing functions for all forms of py::init<...> and py::init(...)
+template <typename Class>
+using Cpp = typename Class::type;
+template <typename Class>
+using Alias = typename Class::type_alias;
+template <typename Class>
+using Holder = typename Class::holder_type;
+
+template <typename Class>
+using is_alias_constructible = std::is_constructible<Alias<Class>, Cpp<Class> &&>;
+
+// Takes a Cpp pointer and returns true if it actually is a polymorphic Alias instance.
+template <typename Class, enable_if_t<Class::has_alias, int> = 0>
+bool is_alias(Cpp<Class> *ptr) {
+    return dynamic_cast<Alias<Class> *>(ptr) != nullptr;
+}
+// Failing fallback version of the above for a no-alias class (always returns false)
+template <typename /*Class*/>
+constexpr bool is_alias(void *) {
+    return false;
+}
+
+// Constructs and returns a new object; if the given arguments don't map to a constructor, we fall
+// back to brace aggregate initiailization so that for aggregate initialization can be used with
+// py::init, e.g.  `py::init<int, int>` to initialize a `struct T { int a; int b; }`.  For
+// non-aggregate types, we need to use an ordinary T(...) constructor (invoking as `T{...}` usually
+// works, but will not do the expected thing when `T` has an `initializer_list<T>` constructor).
+template <typename Class,
+          typename... Args,
+          detail::enable_if_t<std::is_constructible<Class, Args...>::value, int> = 0>
+inline Class *construct_or_initialize(Args &&...args) {
+    return new Class(std::forward<Args>(args)...);
+}
+template <typename Class,
+          typename... Args,
+          detail::enable_if_t<!std::is_constructible<Class, Args...>::value, int> = 0>
+inline Class *construct_or_initialize(Args &&...args) {
+    return new Class{std::forward<Args>(args)...};
+}
+
+// Attempts to constructs an alias using a `Alias(Cpp &&)` constructor.  This allows types with
+// an alias to provide only a single Cpp factory function as long as the Alias can be
+// constructed from an rvalue reference of the base Cpp type.  This means that Alias classes
+// can, when appropriate, simply define a `Alias(Cpp &&)` constructor rather than needing to
+// inherit all the base class constructors.
+template <typename Class>
+void construct_alias_from_cpp(std::true_type /*is_alias_constructible*/,
+                              value_and_holder &v_h,
+                              Cpp<Class> &&base) {
+    v_h.value_ptr() = new Alias<Class>(std::move(base));
+}
+template <typename Class>
+[[noreturn]] void construct_alias_from_cpp(std::false_type /*!is_alias_constructible*/,
+                                           value_and_holder &,
+                                           Cpp<Class> &&) {
+    throw type_error("pybind11::init(): unable to convert returned instance to required "
+                     "alias class: no `Alias<Class>(Class &&)` constructor available");
+}
+
+// Error-generating fallback for factories that don't match one of the below construction
+// mechanisms.
+template <typename Class>
+void construct(...) {
+    static_assert(!std::is_same<Class, Class>::value /* always false */,
+                  "pybind11::init(): init function must return a compatible pointer, "
+                  "holder, or value");
+}
+
+// Pointer return v1: the factory function returns a class pointer for a registered class.
+// If we don't need an alias (because this class doesn't have one, or because the final type is
+// inherited on the Python side) we can simply take over ownership.  Otherwise we need to try to
+// construct an Alias from the returned base instance.
+template <typename Class>
+void construct(value_and_holder &v_h, Cpp<Class> *ptr, bool need_alias) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(need_alias);
+    no_nullptr(ptr);
+    if (PYBIND11_SILENCE_MSVC_C4127(Class::has_alias) && need_alias && !is_alias<Class>(ptr)) {
+        // We're going to try to construct an alias by moving the cpp type.  Whether or not
+        // that succeeds, we still need to destroy the original cpp pointer (either the
+        // moved away leftover, if the alias construction works, or the value itself if we
+        // throw an error), but we can't just call `delete ptr`: it might have a special
+        // deleter, or might be shared_from_this.  So we construct a holder around it as if
+        // it was a normal instance, then steal the holder away into a local variable; thus
+        // the holder and destruction happens when we leave the C++ scope, and the holder
+        // class gets to handle the destruction however it likes.
+        v_h.value_ptr() = ptr;
+        v_h.set_instance_registered(true);          // To prevent init_instance from registering it
+        v_h.type->init_instance(v_h.inst, nullptr); // Set up the holder
+        Holder<Class> temp_holder(std::move(v_h.holder<Holder<Class>>())); // Steal the holder
+        v_h.type->dealloc(v_h); // Destroys the moved-out holder remains, resets value ptr to null
+        v_h.set_instance_registered(false);
+
+        construct_alias_from_cpp<Class>(is_alias_constructible<Class>{}, v_h, std::move(*ptr));
+    } else {
+        // Otherwise the type isn't inherited, so we don't need an Alias
+        v_h.value_ptr() = ptr;
+    }
+}
+
+// Pointer return v2: a factory that always returns an alias instance ptr.  We simply take over
+// ownership of the pointer.
+template <typename Class, enable_if_t<Class::has_alias, int> = 0>
+void construct(value_and_holder &v_h, Alias<Class> *alias_ptr, bool) {
+    no_nullptr(alias_ptr);
+    v_h.value_ptr() = static_cast<Cpp<Class> *>(alias_ptr);
+}
+
+// Holder return: copy its pointer, and move or copy the returned holder into the new instance's
+// holder.  This also handles types like std::shared_ptr<T> and std::unique_ptr<T> where T is a
+// derived type (through those holder's implicit conversion from derived class holder
+// constructors).
+template <typename Class>
+void construct(value_and_holder &v_h, Holder<Class> holder, bool need_alias) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(need_alias);
+    auto *ptr = holder_helper<Holder<Class>>::get(holder);
+    no_nullptr(ptr);
+    // If we need an alias, check that the held pointer is actually an alias instance
+    if (PYBIND11_SILENCE_MSVC_C4127(Class::has_alias) && need_alias && !is_alias<Class>(ptr)) {
+        throw type_error("pybind11::init(): construction failed: returned holder-wrapped instance "
+                         "is not an alias instance");
+    }
+
+    v_h.value_ptr() = ptr;
+    v_h.type->init_instance(v_h.inst, &holder);
+}
+
+// return-by-value version 1: returning a cpp class by value.  If the class has an alias and an
+// alias is required the alias must have an `Alias(Cpp &&)` constructor so that we can construct
+// the alias from the base when needed (i.e. because of Python-side inheritance).  When we don't
+// need it, we simply move-construct the cpp value into a new instance.
+template <typename Class>
+void construct(value_and_holder &v_h, Cpp<Class> &&result, bool need_alias) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(need_alias);
+    static_assert(std::is_move_constructible<Cpp<Class>>::value,
+                  "pybind11::init() return-by-value factory function requires a movable class");
+    if (PYBIND11_SILENCE_MSVC_C4127(Class::has_alias) && need_alias) {
+        construct_alias_from_cpp<Class>(is_alias_constructible<Class>{}, v_h, std::move(result));
+    } else {
+        v_h.value_ptr() = new Cpp<Class>(std::move(result));
+    }
+}
+
+// return-by-value version 2: returning a value of the alias type itself.  We move-construct an
+// Alias instance (even if no the python-side inheritance is involved).  The is intended for
+// cases where Alias initialization is always desired.
+template <typename Class>
+void construct(value_and_holder &v_h, Alias<Class> &&result, bool) {
+    static_assert(
+        std::is_move_constructible<Alias<Class>>::value,
+        "pybind11::init() return-by-alias-value factory function requires a movable alias class");
+    v_h.value_ptr() = new Alias<Class>(std::move(result));
+}
+
+// Implementing class for py::init<...>()
+template <typename... Args>
+struct constructor {
+    template <typename Class, typename... Extra, enable_if_t<!Class::has_alias, int> = 0>
+    static void execute(Class &cl, const Extra &...extra) {
+        cl.def(
+            "__init__",
+            [](value_and_holder &v_h, Args... args) {
+                v_h.value_ptr() = construct_or_initialize<Cpp<Class>>(std::forward<Args>(args)...);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+
+    template <typename Class,
+              typename... Extra,
+              enable_if_t<Class::has_alias && std::is_constructible<Cpp<Class>, Args...>::value,
+                          int> = 0>
+    static void execute(Class &cl, const Extra &...extra) {
+        cl.def(
+            "__init__",
+            [](value_and_holder &v_h, Args... args) {
+                if (Py_TYPE(v_h.inst) == v_h.type->type) {
+                    v_h.value_ptr()
+                        = construct_or_initialize<Cpp<Class>>(std::forward<Args>(args)...);
+                } else {
+                    v_h.value_ptr()
+                        = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
+                }
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+
+    template <typename Class,
+              typename... Extra,
+              enable_if_t<Class::has_alias && !std::is_constructible<Cpp<Class>, Args...>::value,
+                          int> = 0>
+    static void execute(Class &cl, const Extra &...extra) {
+        cl.def(
+            "__init__",
+            [](value_and_holder &v_h, Args... args) {
+                v_h.value_ptr()
+                    = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+// Implementing class for py::init_alias<...>()
+template <typename... Args>
+struct alias_constructor {
+    template <typename Class,
+              typename... Extra,
+              enable_if_t<Class::has_alias && std::is_constructible<Alias<Class>, Args...>::value,
+                          int> = 0>
+    static void execute(Class &cl, const Extra &...extra) {
+        cl.def(
+            "__init__",
+            [](value_and_holder &v_h, Args... args) {
+                v_h.value_ptr()
+                    = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+// Implementation class for py::init(Func) and py::init(Func, AliasFunc)
+template <typename CFunc,
+          typename AFunc = void_type (*)(),
+          typename = function_signature_t<CFunc>,
+          typename = function_signature_t<AFunc>>
+struct factory;
+
+// Specialization for py::init(Func)
+template <typename Func, typename Return, typename... Args>
+struct factory<Func, void_type (*)(), Return(Args...)> {
+    remove_reference_t<Func> class_factory;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    factory(Func &&f) : class_factory(std::forward<Func>(f)) {}
+
+    // The given class either has no alias or has no separate alias factory;
+    // this always constructs the class itself.  If the class is registered with an alias
+    // type and an alias instance is needed (i.e. because the final type is a Python class
+    // inheriting from the C++ type) the returned value needs to either already be an alias
+    // instance, or the alias needs to be constructible from a `Class &&` argument.
+    template <typename Class, typename... Extra>
+    void execute(Class &cl, const Extra &...extra) && {
+#if defined(PYBIND11_CPP14)
+        cl.def(
+            "__init__",
+            [func = std::move(class_factory)]
+#else
+        auto &func = class_factory;
+        cl.def(
+            "__init__",
+            [func]
+#endif
+            (value_and_holder &v_h, Args... args) {
+                construct<Class>(
+                    v_h, func(std::forward<Args>(args)...), Py_TYPE(v_h.inst) != v_h.type->type);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+// Specialization for py::init(Func, AliasFunc)
+template <typename CFunc,
+          typename AFunc,
+          typename CReturn,
+          typename... CArgs,
+          typename AReturn,
+          typename... AArgs>
+struct factory<CFunc, AFunc, CReturn(CArgs...), AReturn(AArgs...)> {
+    static_assert(sizeof...(CArgs) == sizeof...(AArgs),
+                  "pybind11::init(class_factory, alias_factory): class and alias factories "
+                  "must have identical argument signatures");
+    static_assert(all_of<std::is_same<CArgs, AArgs>...>::value,
+                  "pybind11::init(class_factory, alias_factory): class and alias factories "
+                  "must have identical argument signatures");
+
+    remove_reference_t<CFunc> class_factory;
+    remove_reference_t<AFunc> alias_factory;
+
+    factory(CFunc &&c, AFunc &&a)
+        : class_factory(std::forward<CFunc>(c)), alias_factory(std::forward<AFunc>(a)) {}
+
+    // The class factory is called when the `self` type passed to `__init__` is the direct
+    // class (i.e. not inherited), the alias factory when `self` is a Python-side subtype.
+    template <typename Class, typename... Extra>
+    void execute(Class &cl, const Extra &...extra) && {
+        static_assert(Class::has_alias,
+                      "The two-argument version of `py::init()` can "
+                      "only be used if the class has an alias");
+#if defined(PYBIND11_CPP14)
+        cl.def(
+            "__init__",
+            [class_func = std::move(class_factory), alias_func = std::move(alias_factory)]
+#else
+        auto &class_func = class_factory;
+        auto &alias_func = alias_factory;
+        cl.def(
+            "__init__",
+            [class_func, alias_func]
+#endif
+            (value_and_holder &v_h, CArgs... args) {
+                if (Py_TYPE(v_h.inst) == v_h.type->type) {
+                    // If the instance type equals the registered type we don't have inheritance,
+                    // so don't need the alias and can construct using the class function:
+                    construct<Class>(v_h, class_func(std::forward<CArgs>(args)...), false);
+                } else {
+                    construct<Class>(v_h, alias_func(std::forward<CArgs>(args)...), true);
+                }
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+/// Set just the C++ state. Same as `__init__`.
+template <typename Class, typename T>
+void setstate(value_and_holder &v_h, T &&result, bool need_alias) {
+    construct<Class>(v_h, std::forward<T>(result), need_alias);
+}
+
+/// Set both the C++ and Python states
+template <typename Class,
+          typename T,
+          typename O,
+          enable_if_t<std::is_convertible<O, handle>::value, int> = 0>
+void setstate(value_and_holder &v_h, std::pair<T, O> &&result, bool need_alias) {
+    construct<Class>(v_h, std::move(result.first), need_alias);
+    auto d = handle(result.second);
+    if (PyDict_Check(d.ptr()) && PyDict_Size(d.ptr()) == 0) {
+        // Skipping setattr below, to not force use of py::dynamic_attr() for Class unnecessarily.
+        // See PR #2972 for details.
+        return;
+    }
+    setattr((PyObject *) v_h.inst, "__dict__", d);
+}
+
+/// Implementation for py::pickle(GetState, SetState)
+template <typename Get,
+          typename Set,
+          typename = function_signature_t<Get>,
+          typename = function_signature_t<Set>>
+struct pickle_factory;
+
+template <typename Get,
+          typename Set,
+          typename RetState,
+          typename Self,
+          typename NewInstance,
+          typename ArgState>
+struct pickle_factory<Get, Set, RetState(Self), NewInstance(ArgState)> {
+    static_assert(std::is_same<intrinsic_t<RetState>, intrinsic_t<ArgState>>::value,
+                  "The type returned by `__getstate__` must be the same "
+                  "as the argument accepted by `__setstate__`");
+
+    remove_reference_t<Get> get;
+    remove_reference_t<Set> set;
+
+    pickle_factory(Get get, Set set) : get(std::forward<Get>(get)), set(std::forward<Set>(set)) {}
+
+    template <typename Class, typename... Extra>
+    void execute(Class &cl, const Extra &...extra) && {
+        cl.def("__getstate__", std::move(get));
+
+#if defined(PYBIND11_CPP14)
+        cl.def(
+            "__setstate__",
+            [func = std::move(set)]
+#else
+        auto &func = set;
+        cl.def(
+            "__setstate__",
+            [func]
+#endif
+            (value_and_holder &v_h, ArgState state) {
+                setstate<Class>(
+                    v_h, func(std::forward<ArgState>(state)), Py_TYPE(v_h.inst) != v_h.type->type);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+PYBIND11_NAMESPACE_END(initimpl)
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/internals.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/internals.h
new file mode 100644
index 000000000..6ca5e1482
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/internals.h
@@ -0,0 +1,562 @@
+/*
+    pybind11/detail/internals.h: Internal data structure and related functions
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "../pytypes.h"
+
+#include <exception>
+
+/// Tracks the `internals` and `type_info` ABI version independent of the main library version.
+///
+/// Some portions of the code use an ABI that is conditional depending on this
+/// version number.  That allows ABI-breaking changes to be "pre-implemented".
+/// Once the default version number is incremented, the conditional logic that
+/// no longer applies can be removed.  Additionally, users that need not
+/// maintain ABI compatibility can increase the version number in order to take
+/// advantage of any functionality/efficiency improvements that depend on the
+/// newer ABI.
+///
+/// WARNING: If you choose to manually increase the ABI version, note that
+/// pybind11 may not be tested as thoroughly with a non-default ABI version, and
+/// further ABI-incompatible changes may be made before the ABI is officially
+/// changed to the new version.
+#ifndef PYBIND11_INTERNALS_VERSION
+#    define PYBIND11_INTERNALS_VERSION 4
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+using ExceptionTranslator = void (*)(std::exception_ptr);
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Forward declarations
+inline PyTypeObject *make_static_property_type();
+inline PyTypeObject *make_default_metaclass();
+inline PyObject *make_object_base_type(PyTypeObject *metaclass);
+
+// The old Python Thread Local Storage (TLS) API is deprecated in Python 3.7 in favor of the new
+// Thread Specific Storage (TSS) API.
+#if PY_VERSION_HEX >= 0x03070000
+// Avoid unnecessary allocation of `Py_tss_t`, since we cannot use
+// `Py_LIMITED_API` anyway.
+#    if PYBIND11_INTERNALS_VERSION > 4
+#        define PYBIND11_TLS_KEY_REF Py_tss_t &
+#        ifdef __GNUC__
+// Clang on macOS warns due to `Py_tss_NEEDS_INIT` not specifying an initializer
+// for every field.
+#            define PYBIND11_TLS_KEY_INIT(var)                                                    \
+                _Pragma("GCC diagnostic push")                                         /**/       \
+                    _Pragma("GCC diagnostic ignored \"-Wmissing-field-initializers\"") /**/       \
+                    Py_tss_t var                                                                  \
+                    = Py_tss_NEEDS_INIT;                                                          \
+                _Pragma("GCC diagnostic pop")
+#        else
+#            define PYBIND11_TLS_KEY_INIT(var) Py_tss_t var = Py_tss_NEEDS_INIT;
+#        endif
+#        define PYBIND11_TLS_KEY_CREATE(var) (PyThread_tss_create(&(var)) == 0)
+#        define PYBIND11_TLS_GET_VALUE(key) PyThread_tss_get(&(key))
+#        define PYBIND11_TLS_REPLACE_VALUE(key, value) PyThread_tss_set(&(key), (value))
+#        define PYBIND11_TLS_DELETE_VALUE(key) PyThread_tss_set(&(key), nullptr)
+#        define PYBIND11_TLS_FREE(key) PyThread_tss_delete(&(key))
+#    else
+#        define PYBIND11_TLS_KEY_REF Py_tss_t *
+#        define PYBIND11_TLS_KEY_INIT(var) Py_tss_t *var = nullptr;
+#        define PYBIND11_TLS_KEY_CREATE(var)                                                      \
+            (((var) = PyThread_tss_alloc()) != nullptr && (PyThread_tss_create((var)) == 0))
+#        define PYBIND11_TLS_GET_VALUE(key) PyThread_tss_get((key))
+#        define PYBIND11_TLS_REPLACE_VALUE(key, value) PyThread_tss_set((key), (value))
+#        define PYBIND11_TLS_DELETE_VALUE(key) PyThread_tss_set((key), nullptr)
+#        define PYBIND11_TLS_FREE(key) PyThread_tss_free(key)
+#    endif
+#else
+// Usually an int but a long on Cygwin64 with Python 3.x
+#    define PYBIND11_TLS_KEY_REF decltype(PyThread_create_key())
+#    define PYBIND11_TLS_KEY_INIT(var) PYBIND11_TLS_KEY_REF var = 0;
+#    define PYBIND11_TLS_KEY_CREATE(var) (((var) = PyThread_create_key()) != -1)
+#    define PYBIND11_TLS_GET_VALUE(key) PyThread_get_key_value((key))
+#    if defined(PYPY_VERSION)
+// On CPython < 3.4 and on PyPy, `PyThread_set_key_value` strangely does not set
+// the value if it has already been set.  Instead, it must first be deleted and
+// then set again.
+inline void tls_replace_value(PYBIND11_TLS_KEY_REF key, void *value) {
+    PyThread_delete_key_value(key);
+    PyThread_set_key_value(key, value);
+}
+#        define PYBIND11_TLS_DELETE_VALUE(key) PyThread_delete_key_value(key)
+#        define PYBIND11_TLS_REPLACE_VALUE(key, value)                                            \
+            ::pybind11::detail::tls_replace_value((key), (value))
+#    else
+#        define PYBIND11_TLS_DELETE_VALUE(key) PyThread_set_key_value((key), nullptr)
+#        define PYBIND11_TLS_REPLACE_VALUE(key, value) PyThread_set_key_value((key), (value))
+#    endif
+#    define PYBIND11_TLS_FREE(key) (void) key
+#endif
+
+// Python loads modules by default with dlopen with the RTLD_LOCAL flag; under libc++ and possibly
+// other STLs, this means `typeid(A)` from one module won't equal `typeid(A)` from another module
+// even when `A` is the same, non-hidden-visibility type (e.g. from a common include).  Under
+// libstdc++, this doesn't happen: equality and the type_index hash are based on the type name,
+// which works.  If not under a known-good stl, provide our own name-based hash and equality
+// functions that use the type name.
+#if defined(__GLIBCXX__)
+inline bool same_type(const std::type_info &lhs, const std::type_info &rhs) { return lhs == rhs; }
+using type_hash = std::hash<std::type_index>;
+using type_equal_to = std::equal_to<std::type_index>;
+#else
+inline bool same_type(const std::type_info &lhs, const std::type_info &rhs) {
+    return lhs.name() == rhs.name() || std::strcmp(lhs.name(), rhs.name()) == 0;
+}
+
+struct type_hash {
+    size_t operator()(const std::type_index &t) const {
+        size_t hash = 5381;
+        const char *ptr = t.name();
+        while (auto c = static_cast<unsigned char>(*ptr++)) {
+            hash = (hash * 33) ^ c;
+        }
+        return hash;
+    }
+};
+
+struct type_equal_to {
+    bool operator()(const std::type_index &lhs, const std::type_index &rhs) const {
+        return lhs.name() == rhs.name() || std::strcmp(lhs.name(), rhs.name()) == 0;
+    }
+};
+#endif
+
+template <typename value_type>
+using type_map = std::unordered_map<std::type_index, value_type, type_hash, type_equal_to>;
+
+struct override_hash {
+    inline size_t operator()(const std::pair<const PyObject *, const char *> &v) const {
+        size_t value = std::hash<const void *>()(v.first);
+        value ^= std::hash<const void *>()(v.second) + 0x9e3779b9 + (value << 6) + (value >> 2);
+        return value;
+    }
+};
+
+/// Internal data structure used to track registered instances and types.
+/// Whenever binary incompatible changes are made to this structure,
+/// `PYBIND11_INTERNALS_VERSION` must be incremented.
+struct internals {
+    // std::type_index -> pybind11's type information
+    type_map<type_info *> registered_types_cpp;
+    // PyTypeObject* -> base type_info(s)
+    std::unordered_map<PyTypeObject *, std::vector<type_info *>> registered_types_py;
+    std::unordered_multimap<const void *, instance *> registered_instances; // void * -> instance*
+    std::unordered_set<std::pair<const PyObject *, const char *>, override_hash>
+        inactive_override_cache;
+    type_map<std::vector<bool (*)(PyObject *, void *&)>> direct_conversions;
+    std::unordered_map<const PyObject *, std::vector<PyObject *>> patients;
+    std::forward_list<ExceptionTranslator> registered_exception_translators;
+    std::unordered_map<std::string, void *> shared_data; // Custom data to be shared across
+                                                         // extensions
+#if PYBIND11_INTERNALS_VERSION == 4
+    std::vector<PyObject *> unused_loader_patient_stack_remove_at_v5;
+#endif
+    std::forward_list<std::string> static_strings; // Stores the std::strings backing
+                                                   // detail::c_str()
+    PyTypeObject *static_property_type;
+    PyTypeObject *default_metaclass;
+    PyObject *instance_base;
+#if defined(WITH_THREAD)
+    PYBIND11_TLS_KEY_INIT(tstate)
+#    if PYBIND11_INTERNALS_VERSION > 4
+    PYBIND11_TLS_KEY_INIT(loader_life_support_tls_key)
+#    endif // PYBIND11_INTERNALS_VERSION > 4
+    PyInterpreterState *istate = nullptr;
+    ~internals() {
+#    if PYBIND11_INTERNALS_VERSION > 4
+        PYBIND11_TLS_FREE(loader_life_support_tls_key);
+#    endif // PYBIND11_INTERNALS_VERSION > 4
+
+        // This destructor is called *after* Py_Finalize() in finalize_interpreter().
+        // That *SHOULD BE* fine. The following details what happens when PyThread_tss_free is
+        // called. PYBIND11_TLS_FREE is PyThread_tss_free on python 3.7+. On older python, it does
+        // nothing. PyThread_tss_free calls PyThread_tss_delete and PyMem_RawFree.
+        // PyThread_tss_delete just calls TlsFree (on Windows) or pthread_key_delete (on *NIX).
+        // Neither of those have anything to do with CPython internals. PyMem_RawFree *requires*
+        // that the `tstate` be allocated with the CPython allocator.
+        PYBIND11_TLS_FREE(tstate);
+    }
+#endif
+};
+
+/// Additional type information which does not fit into the PyTypeObject.
+/// Changes to this struct also require bumping `PYBIND11_INTERNALS_VERSION`.
+struct type_info {
+    PyTypeObject *type;
+    const std::type_info *cpptype;
+    size_t type_size, type_align, holder_size_in_ptrs;
+    void *(*operator_new)(size_t);
+    void (*init_instance)(instance *, const void *);
+    void (*dealloc)(value_and_holder &v_h);
+    std::vector<PyObject *(*) (PyObject *, PyTypeObject *)> implicit_conversions;
+    std::vector<std::pair<const std::type_info *, void *(*) (void *)>> implicit_casts;
+    std::vector<bool (*)(PyObject *, void *&)> *direct_conversions;
+    buffer_info *(*get_buffer)(PyObject *, void *) = nullptr;
+    void *get_buffer_data = nullptr;
+    void *(*module_local_load)(PyObject *, const type_info *) = nullptr;
+    /* A simple type never occurs as a (direct or indirect) parent
+     * of a class that makes use of multiple inheritance.
+     * A type can be simple even if it has non-simple ancestors as long as it has no descendants.
+     */
+    bool simple_type : 1;
+    /* True if there is no multiple inheritance in this type's inheritance tree */
+    bool simple_ancestors : 1;
+    /* for base vs derived holder_type checks */
+    bool default_holder : 1;
+    /* true if this is a type registered with py::module_local */
+    bool module_local : 1;
+};
+
+/// On MSVC, debug and release builds are not ABI-compatible!
+#if defined(_MSC_VER) && defined(_DEBUG)
+#    define PYBIND11_BUILD_TYPE "_debug"
+#else
+#    define PYBIND11_BUILD_TYPE ""
+#endif
+
+/// Let's assume that different compilers are ABI-incompatible.
+/// A user can manually set this string if they know their
+/// compiler is compatible.
+#ifndef PYBIND11_COMPILER_TYPE
+#    if defined(_MSC_VER)
+#        define PYBIND11_COMPILER_TYPE "_msvc"
+#    elif defined(__INTEL_COMPILER)
+#        define PYBIND11_COMPILER_TYPE "_icc"
+#    elif defined(__clang__)
+#        define PYBIND11_COMPILER_TYPE "_clang"
+#    elif defined(__PGI)
+#        define PYBIND11_COMPILER_TYPE "_pgi"
+#    elif defined(__MINGW32__)
+#        define PYBIND11_COMPILER_TYPE "_mingw"
+#    elif defined(__CYGWIN__)
+#        define PYBIND11_COMPILER_TYPE "_gcc_cygwin"
+#    elif defined(__GNUC__)
+#        define PYBIND11_COMPILER_TYPE "_gcc"
+#    else
+#        define PYBIND11_COMPILER_TYPE "_unknown"
+#    endif
+#endif
+
+/// Also standard libs
+#ifndef PYBIND11_STDLIB
+#    if defined(_LIBCPP_VERSION)
+#        define PYBIND11_STDLIB "_libcpp"
+#    elif defined(__GLIBCXX__) || defined(__GLIBCPP__)
+#        define PYBIND11_STDLIB "_libstdcpp"
+#    else
+#        define PYBIND11_STDLIB ""
+#    endif
+#endif
+
+/// On Linux/OSX, changes in __GXX_ABI_VERSION__ indicate ABI incompatibility.
+#ifndef PYBIND11_BUILD_ABI
+#    if defined(__GXX_ABI_VERSION)
+#        define PYBIND11_BUILD_ABI "_cxxabi" PYBIND11_TOSTRING(__GXX_ABI_VERSION)
+#    else
+#        define PYBIND11_BUILD_ABI ""
+#    endif
+#endif
+
+#ifndef PYBIND11_INTERNALS_KIND
+#    if defined(WITH_THREAD)
+#        define PYBIND11_INTERNALS_KIND ""
+#    else
+#        define PYBIND11_INTERNALS_KIND "_without_thread"
+#    endif
+#endif
+
+#define PYBIND11_INTERNALS_ID                                                                     \
+    "__pybind11_internals_v" PYBIND11_TOSTRING(PYBIND11_INTERNALS_VERSION)                        \
+        PYBIND11_INTERNALS_KIND PYBIND11_COMPILER_TYPE PYBIND11_STDLIB PYBIND11_BUILD_ABI         \
+            PYBIND11_BUILD_TYPE "__"
+
+#define PYBIND11_MODULE_LOCAL_ID                                                                  \
+    "__pybind11_module_local_v" PYBIND11_TOSTRING(PYBIND11_INTERNALS_VERSION)                     \
+        PYBIND11_INTERNALS_KIND PYBIND11_COMPILER_TYPE PYBIND11_STDLIB PYBIND11_BUILD_ABI         \
+            PYBIND11_BUILD_TYPE "__"
+
+/// Each module locally stores a pointer to the `internals` data. The data
+/// itself is shared among modules with the same `PYBIND11_INTERNALS_ID`.
+inline internals **&get_internals_pp() {
+    static internals **internals_pp = nullptr;
+    return internals_pp;
+}
+
+// forward decl
+inline void translate_exception(std::exception_ptr);
+
+template <class T,
+          enable_if_t<std::is_same<std::nested_exception, remove_cvref_t<T>>::value, int> = 0>
+bool handle_nested_exception(const T &exc, const std::exception_ptr &p) {
+    std::exception_ptr nested = exc.nested_ptr();
+    if (nested != nullptr && nested != p) {
+        translate_exception(nested);
+        return true;
+    }
+    return false;
+}
+
+template <class T,
+          enable_if_t<!std::is_same<std::nested_exception, remove_cvref_t<T>>::value, int> = 0>
+bool handle_nested_exception(const T &exc, const std::exception_ptr &p) {
+    if (const auto *nep = dynamic_cast<const std::nested_exception *>(std::addressof(exc))) {
+        return handle_nested_exception(*nep, p);
+    }
+    return false;
+}
+
+inline bool raise_err(PyObject *exc_type, const char *msg) {
+    if (PyErr_Occurred()) {
+        raise_from(exc_type, msg);
+        return true;
+    }
+    PyErr_SetString(exc_type, msg);
+    return false;
+}
+
+inline void translate_exception(std::exception_ptr p) {
+    if (!p) {
+        return;
+    }
+    try {
+        std::rethrow_exception(p);
+    } catch (error_already_set &e) {
+        handle_nested_exception(e, p);
+        e.restore();
+        return;
+    } catch (const builtin_exception &e) {
+        // Could not use template since it's an abstract class.
+        if (const auto *nep = dynamic_cast<const std::nested_exception *>(std::addressof(e))) {
+            handle_nested_exception(*nep, p);
+        }
+        e.set_error();
+        return;
+    } catch (const std::bad_alloc &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_MemoryError, e.what());
+        return;
+    } catch (const std::domain_error &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_ValueError, e.what());
+        return;
+    } catch (const std::invalid_argument &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_ValueError, e.what());
+        return;
+    } catch (const std::length_error &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_ValueError, e.what());
+        return;
+    } catch (const std::out_of_range &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_IndexError, e.what());
+        return;
+    } catch (const std::range_error &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_ValueError, e.what());
+        return;
+    } catch (const std::overflow_error &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_OverflowError, e.what());
+        return;
+    } catch (const std::exception &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_RuntimeError, e.what());
+        return;
+    } catch (const std::nested_exception &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_RuntimeError, "Caught an unknown nested exception!");
+        return;
+    } catch (...) {
+        raise_err(PyExc_RuntimeError, "Caught an unknown exception!");
+        return;
+    }
+}
+
+#if !defined(__GLIBCXX__)
+inline void translate_local_exception(std::exception_ptr p) {
+    try {
+        if (p) {
+            std::rethrow_exception(p);
+        }
+    } catch (error_already_set &e) {
+        e.restore();
+        return;
+    } catch (const builtin_exception &e) {
+        e.set_error();
+        return;
+    }
+}
+#endif
+
+/// Return a reference to the current `internals` data
+PYBIND11_NOINLINE internals &get_internals() {
+    auto **&internals_pp = get_internals_pp();
+    if (internals_pp && *internals_pp) {
+        return **internals_pp;
+    }
+
+    // Ensure that the GIL is held since we will need to make Python calls.
+    // Cannot use py::gil_scoped_acquire here since that constructor calls get_internals.
+    struct gil_scoped_acquire_local {
+        gil_scoped_acquire_local() : state(PyGILState_Ensure()) {}
+        ~gil_scoped_acquire_local() { PyGILState_Release(state); }
+        const PyGILState_STATE state;
+    } gil;
+    error_scope err_scope;
+
+    PYBIND11_STR_TYPE id(PYBIND11_INTERNALS_ID);
+    auto builtins = handle(PyEval_GetBuiltins());
+    if (builtins.contains(id) && isinstance<capsule>(builtins[id])) {
+        internals_pp = static_cast<internals **>(capsule(builtins[id]));
+
+        // We loaded builtins through python's builtins, which means that our `error_already_set`
+        // and `builtin_exception` may be different local classes than the ones set up in the
+        // initial exception translator, below, so add another for our local exception classes.
+        //
+        // libstdc++ doesn't require this (types there are identified only by name)
+        // libc++ with CPython doesn't require this (types are explicitly exported)
+        // libc++ with PyPy still need it, awaiting further investigation
+#if !defined(__GLIBCXX__)
+        (*internals_pp)->registered_exception_translators.push_front(&translate_local_exception);
+#endif
+    } else {
+        if (!internals_pp) {
+            internals_pp = new internals *();
+        }
+        auto *&internals_ptr = *internals_pp;
+        internals_ptr = new internals();
+#if defined(WITH_THREAD)
+
+#    if PY_VERSION_HEX < 0x03090000
+        PyEval_InitThreads();
+#    endif
+        PyThreadState *tstate = PyThreadState_Get();
+        if (!PYBIND11_TLS_KEY_CREATE(internals_ptr->tstate)) {
+            pybind11_fail("get_internals: could not successfully initialize the tstate TSS key!");
+        }
+        PYBIND11_TLS_REPLACE_VALUE(internals_ptr->tstate, tstate);
+
+#    if PYBIND11_INTERNALS_VERSION > 4
+        if (!PYBIND11_TLS_KEY_CREATE(internals_ptr->loader_life_support_tls_key)) {
+            pybind11_fail("get_internals: could not successfully initialize the "
+                          "loader_life_support TSS key!");
+        }
+#    endif
+        internals_ptr->istate = tstate->interp;
+#endif
+        builtins[id] = capsule(internals_pp);
+        internals_ptr->registered_exception_translators.push_front(&translate_exception);
+        internals_ptr->static_property_type = make_static_property_type();
+        internals_ptr->default_metaclass = make_default_metaclass();
+        internals_ptr->instance_base = make_object_base_type(internals_ptr->default_metaclass);
+    }
+    return **internals_pp;
+}
+
+// the internals struct (above) is shared between all the modules. local_internals are only
+// for a single module. Any changes made to internals may require an update to
+// PYBIND11_INTERNALS_VERSION, breaking backwards compatibility. local_internals is, by design,
+// restricted to a single module. Whether a module has local internals or not should not
+// impact any other modules, because the only things accessing the local internals is the
+// module that contains them.
+struct local_internals {
+    type_map<type_info *> registered_types_cpp;
+    std::forward_list<ExceptionTranslator> registered_exception_translators;
+#if defined(WITH_THREAD) && PYBIND11_INTERNALS_VERSION == 4
+
+    // For ABI compatibility, we can't store the loader_life_support TLS key in
+    // the `internals` struct directly.  Instead, we store it in `shared_data` and
+    // cache a copy in `local_internals`.  If we allocated a separate TLS key for
+    // each instance of `local_internals`, we could end up allocating hundreds of
+    // TLS keys if hundreds of different pybind11 modules are loaded (which is a
+    // plausible number).
+    PYBIND11_TLS_KEY_INIT(loader_life_support_tls_key)
+
+    // Holds the shared TLS key for the loader_life_support stack.
+    struct shared_loader_life_support_data {
+        PYBIND11_TLS_KEY_INIT(loader_life_support_tls_key)
+        shared_loader_life_support_data() {
+            if (!PYBIND11_TLS_KEY_CREATE(loader_life_support_tls_key)) {
+                pybind11_fail("local_internals: could not successfully initialize the "
+                              "loader_life_support TLS key!");
+            }
+        }
+        // We can't help but leak the TLS key, because Python never unloads extension modules.
+    };
+
+    local_internals() {
+        auto &internals = get_internals();
+        // Get or create the `loader_life_support_stack_key`.
+        auto &ptr = internals.shared_data["_life_support"];
+        if (!ptr) {
+            ptr = new shared_loader_life_support_data;
+        }
+        loader_life_support_tls_key
+            = static_cast<shared_loader_life_support_data *>(ptr)->loader_life_support_tls_key;
+    }
+#endif //  defined(WITH_THREAD) && PYBIND11_INTERNALS_VERSION == 4
+};
+
+/// Works like `get_internals`, but for things which are locally registered.
+inline local_internals &get_local_internals() {
+    static local_internals locals;
+    return locals;
+}
+
+/// Constructs a std::string with the given arguments, stores it in `internals`, and returns its
+/// `c_str()`.  Such strings objects have a long storage duration -- the internal strings are only
+/// cleared when the program exits or after interpreter shutdown (when embedding), and so are
+/// suitable for c-style strings needed by Python internals (such as PyTypeObject's tp_name).
+template <typename... Args>
+const char *c_str(Args &&...args) {
+    auto &strings = get_internals().static_strings;
+    strings.emplace_front(std::forward<Args>(args)...);
+    return strings.front().c_str();
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Returns a named pointer that is shared among all extension modules (using the same
+/// pybind11 version) running in the current interpreter. Names starting with underscores
+/// are reserved for internal usage. Returns `nullptr` if no matching entry was found.
+PYBIND11_NOINLINE void *get_shared_data(const std::string &name) {
+    auto &internals = detail::get_internals();
+    auto it = internals.shared_data.find(name);
+    return it != internals.shared_data.end() ? it->second : nullptr;
+}
+
+/// Set the shared data that can be later recovered by `get_shared_data()`.
+PYBIND11_NOINLINE void *set_shared_data(const std::string &name, void *data) {
+    detail::get_internals().shared_data[name] = data;
+    return data;
+}
+
+/// Returns a typed reference to a shared data entry (by using `get_shared_data()`) if
+/// such entry exists. Otherwise, a new object of default-constructible type `T` is
+/// added to the shared data under the given name and a reference to it is returned.
+template <typename T>
+T &get_or_create_shared_data(const std::string &name) {
+    auto &internals = detail::get_internals();
+    auto it = internals.shared_data.find(name);
+    T *ptr = (T *) (it != internals.shared_data.end() ? it->second : nullptr);
+    if (!ptr) {
+        ptr = new T();
+        internals.shared_data[name] = ptr;
+    }
+    return *ptr;
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/type_caster_base.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/type_caster_base.h
new file mode 100644
index 000000000..21f69c289
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/type_caster_base.h
@@ -0,0 +1,1010 @@
+/*
+    pybind11/detail/type_caster_base.h (originally first part of pybind11/cast.h)
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "../pytypes.h"
+#include "common.h"
+#include "descr.h"
+#include "internals.h"
+#include "typeid.h"
+
+#include <cstdint>
+#include <iterator>
+#include <new>
+#include <string>
+#include <type_traits>
+#include <typeindex>
+#include <typeinfo>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// A life support system for temporary objects created by `type_caster::load()`.
+/// Adding a patient will keep it alive up until the enclosing function returns.
+class loader_life_support {
+private:
+    loader_life_support *parent = nullptr;
+    std::unordered_set<PyObject *> keep_alive;
+
+#if defined(WITH_THREAD)
+    // Store stack pointer in thread-local storage.
+    static PYBIND11_TLS_KEY_REF get_stack_tls_key() {
+#    if PYBIND11_INTERNALS_VERSION == 4
+        return get_local_internals().loader_life_support_tls_key;
+#    else
+        return get_internals().loader_life_support_tls_key;
+#    endif
+    }
+    static loader_life_support *get_stack_top() {
+        return static_cast<loader_life_support *>(PYBIND11_TLS_GET_VALUE(get_stack_tls_key()));
+    }
+    static void set_stack_top(loader_life_support *value) {
+        PYBIND11_TLS_REPLACE_VALUE(get_stack_tls_key(), value);
+    }
+#else
+    // Use single global variable for stack.
+    static loader_life_support **get_stack_pp() {
+        static loader_life_support *global_stack = nullptr;
+        return global_stack;
+    }
+    static loader_life_support *get_stack_top() { return *get_stack_pp(); }
+    static void set_stack_top(loader_life_support *value) { *get_stack_pp() = value; }
+#endif
+
+public:
+    /// A new patient frame is created when a function is entered
+    loader_life_support() : parent{get_stack_top()} { set_stack_top(this); }
+
+    /// ... and destroyed after it returns
+    ~loader_life_support() {
+        if (get_stack_top() != this) {
+            pybind11_fail("loader_life_support: internal error");
+        }
+        set_stack_top(parent);
+        for (auto *item : keep_alive) {
+            Py_DECREF(item);
+        }
+    }
+
+    /// This can only be used inside a pybind11-bound function, either by `argument_loader`
+    /// at argument preparation time or by `py::cast()` at execution time.
+    PYBIND11_NOINLINE static void add_patient(handle h) {
+        loader_life_support *frame = get_stack_top();
+        if (!frame) {
+            // NOTE: It would be nice to include the stack frames here, as this indicates
+            // use of pybind11::cast<> outside the normal call framework, finding such
+            // a location is challenging. Developers could consider printing out
+            // stack frame addresses here using something like __builtin_frame_address(0)
+            throw cast_error("When called outside a bound function, py::cast() cannot "
+                             "do Python -> C++ conversions which require the creation "
+                             "of temporary values");
+        }
+
+        if (frame->keep_alive.insert(h.ptr()).second) {
+            Py_INCREF(h.ptr());
+        }
+    }
+};
+
+// Gets the cache entry for the given type, creating it if necessary.  The return value is the pair
+// returned by emplace, i.e. an iterator for the entry and a bool set to `true` if the entry was
+// just created.
+inline std::pair<decltype(internals::registered_types_py)::iterator, bool>
+all_type_info_get_cache(PyTypeObject *type);
+
+// Populates a just-created cache entry.
+PYBIND11_NOINLINE void all_type_info_populate(PyTypeObject *t, std::vector<type_info *> &bases) {
+    std::vector<PyTypeObject *> check;
+    for (handle parent : reinterpret_borrow<tuple>(t->tp_bases)) {
+        check.push_back((PyTypeObject *) parent.ptr());
+    }
+
+    auto const &type_dict = get_internals().registered_types_py;
+    for (size_t i = 0; i < check.size(); i++) {
+        auto *type = check[i];
+        // Ignore Python2 old-style class super type:
+        if (!PyType_Check((PyObject *) type)) {
+            continue;
+        }
+
+        // Check `type` in the current set of registered python types:
+        auto it = type_dict.find(type);
+        if (it != type_dict.end()) {
+            // We found a cache entry for it, so it's either pybind-registered or has pre-computed
+            // pybind bases, but we have to make sure we haven't already seen the type(s) before:
+            // we want to follow Python/virtual C++ rules that there should only be one instance of
+            // a common base.
+            for (auto *tinfo : it->second) {
+                // NB: Could use a second set here, rather than doing a linear search, but since
+                // having a large number of immediate pybind11-registered types seems fairly
+                // unlikely, that probably isn't worthwhile.
+                bool found = false;
+                for (auto *known : bases) {
+                    if (known == tinfo) {
+                        found = true;
+                        break;
+                    }
+                }
+                if (!found) {
+                    bases.push_back(tinfo);
+                }
+            }
+        } else if (type->tp_bases) {
+            // It's some python type, so keep follow its bases classes to look for one or more
+            // registered types
+            if (i + 1 == check.size()) {
+                // When we're at the end, we can pop off the current element to avoid growing
+                // `check` when adding just one base (which is typical--i.e. when there is no
+                // multiple inheritance)
+                check.pop_back();
+                i--;
+            }
+            for (handle parent : reinterpret_borrow<tuple>(type->tp_bases)) {
+                check.push_back((PyTypeObject *) parent.ptr());
+            }
+        }
+    }
+}
+
+/**
+ * Extracts vector of type_info pointers of pybind-registered roots of the given Python type.  Will
+ * be just 1 pybind type for the Python type of a pybind-registered class, or for any Python-side
+ * derived class that uses single inheritance.  Will contain as many types as required for a Python
+ * class that uses multiple inheritance to inherit (directly or indirectly) from multiple
+ * pybind-registered classes.  Will be empty if neither the type nor any base classes are
+ * pybind-registered.
+ *
+ * The value is cached for the lifetime of the Python type.
+ */
+inline const std::vector<detail::type_info *> &all_type_info(PyTypeObject *type) {
+    auto ins = all_type_info_get_cache(type);
+    if (ins.second) {
+        // New cache entry: populate it
+        all_type_info_populate(type, ins.first->second);
+    }
+
+    return ins.first->second;
+}
+
+/**
+ * Gets a single pybind11 type info for a python type.  Returns nullptr if neither the type nor any
+ * ancestors are pybind11-registered.  Throws an exception if there are multiple bases--use
+ * `all_type_info` instead if you want to support multiple bases.
+ */
+PYBIND11_NOINLINE detail::type_info *get_type_info(PyTypeObject *type) {
+    const auto &bases = all_type_info(type);
+    if (bases.empty()) {
+        return nullptr;
+    }
+    if (bases.size() > 1) {
+        pybind11_fail(
+            "pybind11::detail::get_type_info: type has multiple pybind11-registered bases");
+    }
+    return bases.front();
+}
+
+inline detail::type_info *get_local_type_info(const std::type_index &tp) {
+    auto &locals = get_local_internals().registered_types_cpp;
+    auto it = locals.find(tp);
+    if (it != locals.end()) {
+        return it->second;
+    }
+    return nullptr;
+}
+
+inline detail::type_info *get_global_type_info(const std::type_index &tp) {
+    auto &types = get_internals().registered_types_cpp;
+    auto it = types.find(tp);
+    if (it != types.end()) {
+        return it->second;
+    }
+    return nullptr;
+}
+
+/// Return the type info for a given C++ type; on lookup failure can either throw or return
+/// nullptr.
+PYBIND11_NOINLINE detail::type_info *get_type_info(const std::type_index &tp,
+                                                   bool throw_if_missing = false) {
+    if (auto *ltype = get_local_type_info(tp)) {
+        return ltype;
+    }
+    if (auto *gtype = get_global_type_info(tp)) {
+        return gtype;
+    }
+
+    if (throw_if_missing) {
+        std::string tname = tp.name();
+        detail::clean_type_id(tname);
+        pybind11_fail("pybind11::detail::get_type_info: unable to find type info for \""
+                      + std::move(tname) + '"');
+    }
+    return nullptr;
+}
+
+PYBIND11_NOINLINE handle get_type_handle(const std::type_info &tp, bool throw_if_missing) {
+    detail::type_info *type_info = get_type_info(tp, throw_if_missing);
+    return handle(type_info ? ((PyObject *) type_info->type) : nullptr);
+}
+
+// Searches the inheritance graph for a registered Python instance, using all_type_info().
+PYBIND11_NOINLINE handle find_registered_python_instance(void *src,
+                                                         const detail::type_info *tinfo) {
+    auto it_instances = get_internals().registered_instances.equal_range(src);
+    for (auto it_i = it_instances.first; it_i != it_instances.second; ++it_i) {
+        for (auto *instance_type : detail::all_type_info(Py_TYPE(it_i->second))) {
+            if (instance_type && same_type(*instance_type->cpptype, *tinfo->cpptype)) {
+                return handle((PyObject *) it_i->second).inc_ref();
+            }
+        }
+    }
+    return handle();
+}
+
+struct value_and_holder {
+    instance *inst = nullptr;
+    size_t index = 0u;
+    const detail::type_info *type = nullptr;
+    void **vh = nullptr;
+
+    // Main constructor for a found value/holder:
+    value_and_holder(instance *i, const detail::type_info *type, size_t vpos, size_t index)
+        : inst{i}, index{index}, type{type}, vh{inst->simple_layout
+                                                    ? inst->simple_value_holder
+                                                    : &inst->nonsimple.values_and_holders[vpos]} {}
+
+    // Default constructor (used to signal a value-and-holder not found by get_value_and_holder())
+    value_and_holder() = default;
+
+    // Used for past-the-end iterator
+    explicit value_and_holder(size_t index) : index{index} {}
+
+    template <typename V = void>
+    V *&value_ptr() const {
+        return reinterpret_cast<V *&>(vh[0]);
+    }
+    // True if this `value_and_holder` has a non-null value pointer
+    explicit operator bool() const { return value_ptr() != nullptr; }
+
+    template <typename H>
+    H &holder() const {
+        return reinterpret_cast<H &>(vh[1]);
+    }
+    bool holder_constructed() const {
+        return inst->simple_layout
+                   ? inst->simple_holder_constructed
+                   : (inst->nonsimple.status[index] & instance::status_holder_constructed) != 0u;
+    }
+    // NOLINTNEXTLINE(readability-make-member-function-const)
+    void set_holder_constructed(bool v = true) {
+        if (inst->simple_layout) {
+            inst->simple_holder_constructed = v;
+        } else if (v) {
+            inst->nonsimple.status[index] |= instance::status_holder_constructed;
+        } else {
+            inst->nonsimple.status[index] &= (std::uint8_t) ~instance::status_holder_constructed;
+        }
+    }
+    bool instance_registered() const {
+        return inst->simple_layout
+                   ? inst->simple_instance_registered
+                   : ((inst->nonsimple.status[index] & instance::status_instance_registered) != 0);
+    }
+    // NOLINTNEXTLINE(readability-make-member-function-const)
+    void set_instance_registered(bool v = true) {
+        if (inst->simple_layout) {
+            inst->simple_instance_registered = v;
+        } else if (v) {
+            inst->nonsimple.status[index] |= instance::status_instance_registered;
+        } else {
+            inst->nonsimple.status[index] &= (std::uint8_t) ~instance::status_instance_registered;
+        }
+    }
+};
+
+// Container for accessing and iterating over an instance's values/holders
+struct values_and_holders {
+private:
+    instance *inst;
+    using type_vec = std::vector<detail::type_info *>;
+    const type_vec &tinfo;
+
+public:
+    explicit values_and_holders(instance *inst)
+        : inst{inst}, tinfo(all_type_info(Py_TYPE(inst))) {}
+
+    struct iterator {
+    private:
+        instance *inst = nullptr;
+        const type_vec *types = nullptr;
+        value_and_holder curr;
+        friend struct values_and_holders;
+        iterator(instance *inst, const type_vec *tinfo)
+            : inst{inst}, types{tinfo},
+              curr(inst /* instance */,
+                   types->empty() ? nullptr : (*types)[0] /* type info */,
+                   0, /* vpos: (non-simple types only): the first vptr comes first */
+                   0 /* index */) {}
+        // Past-the-end iterator:
+        explicit iterator(size_t end) : curr(end) {}
+
+    public:
+        bool operator==(const iterator &other) const { return curr.index == other.curr.index; }
+        bool operator!=(const iterator &other) const { return curr.index != other.curr.index; }
+        iterator &operator++() {
+            if (!inst->simple_layout) {
+                curr.vh += 1 + (*types)[curr.index]->holder_size_in_ptrs;
+            }
+            ++curr.index;
+            curr.type = curr.index < types->size() ? (*types)[curr.index] : nullptr;
+            return *this;
+        }
+        value_and_holder &operator*() { return curr; }
+        value_and_holder *operator->() { return &curr; }
+    };
+
+    iterator begin() { return iterator(inst, &tinfo); }
+    iterator end() { return iterator(tinfo.size()); }
+
+    iterator find(const type_info *find_type) {
+        auto it = begin(), endit = end();
+        while (it != endit && it->type != find_type) {
+            ++it;
+        }
+        return it;
+    }
+
+    size_t size() { return tinfo.size(); }
+};
+
+/**
+ * Extracts C++ value and holder pointer references from an instance (which may contain multiple
+ * values/holders for python-side multiple inheritance) that match the given type.  Throws an error
+ * if the given type (or ValueType, if omitted) is not a pybind11 base of the given instance.  If
+ * `find_type` is omitted (or explicitly specified as nullptr) the first value/holder are returned,
+ * regardless of type (and the resulting .type will be nullptr).
+ *
+ * The returned object should be short-lived: in particular, it must not outlive the called-upon
+ * instance.
+ */
+PYBIND11_NOINLINE value_and_holder
+instance::get_value_and_holder(const type_info *find_type /*= nullptr default in common.h*/,
+                               bool throw_if_missing /*= true in common.h*/) {
+    // Optimize common case:
+    if (!find_type || Py_TYPE(this) == find_type->type) {
+        return value_and_holder(this, find_type, 0, 0);
+    }
+
+    detail::values_and_holders vhs(this);
+    auto it = vhs.find(find_type);
+    if (it != vhs.end()) {
+        return *it;
+    }
+
+    if (!throw_if_missing) {
+        return value_and_holder();
+    }
+
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+    pybind11_fail("pybind11::detail::instance::get_value_and_holder: `"
+                  + get_fully_qualified_tp_name(find_type->type)
+                  + "' is not a pybind11 base of the given `"
+                  + get_fully_qualified_tp_name(Py_TYPE(this)) + "' instance");
+#else
+    pybind11_fail(
+        "pybind11::detail::instance::get_value_and_holder: "
+        "type is not a pybind11 base of the given instance "
+        "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for type details)");
+#endif
+}
+
+PYBIND11_NOINLINE void instance::allocate_layout() {
+    const auto &tinfo = all_type_info(Py_TYPE(this));
+
+    const size_t n_types = tinfo.size();
+
+    if (n_types == 0) {
+        pybind11_fail(
+            "instance allocation failed: new instance has no pybind11-registered base types");
+    }
+
+    simple_layout
+        = n_types == 1 && tinfo.front()->holder_size_in_ptrs <= instance_simple_holder_in_ptrs();
+
+    // Simple path: no python-side multiple inheritance, and a small-enough holder
+    if (simple_layout) {
+        simple_value_holder[0] = nullptr;
+        simple_holder_constructed = false;
+        simple_instance_registered = false;
+    } else { // multiple base types or a too-large holder
+        // Allocate space to hold: [v1*][h1][v2*][h2]...[bb...] where [vN*] is a value pointer,
+        // [hN] is the (uninitialized) holder instance for value N, and [bb...] is a set of bool
+        // values that tracks whether each associated holder has been initialized.  Each [block] is
+        // padded, if necessary, to an integer multiple of sizeof(void *).
+        size_t space = 0;
+        for (auto *t : tinfo) {
+            space += 1;                      // value pointer
+            space += t->holder_size_in_ptrs; // holder instance
+        }
+        size_t flags_at = space;
+        space += size_in_ptrs(n_types); // status bytes (holder_constructed and
+                                        // instance_registered)
+
+        // Allocate space for flags, values, and holders, and initialize it to 0 (flags and values,
+        // in particular, need to be 0).  Use Python's memory allocation
+        // functions: Python is using pymalloc, which is designed to be
+        // efficient for small allocations like the one we're doing here;
+        // for larger allocations they are just wrappers around malloc.
+        // TODO: is this still true for pure Python 3.6?
+        nonsimple.values_and_holders = (void **) PyMem_Calloc(space, sizeof(void *));
+        if (!nonsimple.values_and_holders) {
+            throw std::bad_alloc();
+        }
+        nonsimple.status
+            = reinterpret_cast<std::uint8_t *>(&nonsimple.values_and_holders[flags_at]);
+    }
+    owned = true;
+}
+
+// NOLINTNEXTLINE(readability-make-member-function-const)
+PYBIND11_NOINLINE void instance::deallocate_layout() {
+    if (!simple_layout) {
+        PyMem_Free(nonsimple.values_and_holders);
+    }
+}
+
+PYBIND11_NOINLINE bool isinstance_generic(handle obj, const std::type_info &tp) {
+    handle type = detail::get_type_handle(tp, false);
+    if (!type) {
+        return false;
+    }
+    return isinstance(obj, type);
+}
+
+PYBIND11_NOINLINE handle get_object_handle(const void *ptr, const detail::type_info *type) {
+    auto &instances = get_internals().registered_instances;
+    auto range = instances.equal_range(ptr);
+    for (auto it = range.first; it != range.second; ++it) {
+        for (const auto &vh : values_and_holders(it->second)) {
+            if (vh.type == type) {
+                return handle((PyObject *) it->second);
+            }
+        }
+    }
+    return handle();
+}
+
+inline PyThreadState *get_thread_state_unchecked() {
+#if defined(PYPY_VERSION)
+    return PyThreadState_GET();
+#else
+    return _PyThreadState_UncheckedGet();
+#endif
+}
+
+// Forward declarations
+void keep_alive_impl(handle nurse, handle patient);
+inline PyObject *make_new_instance(PyTypeObject *type);
+
+class type_caster_generic {
+public:
+    PYBIND11_NOINLINE explicit type_caster_generic(const std::type_info &type_info)
+        : typeinfo(get_type_info(type_info)), cpptype(&type_info) {}
+
+    explicit type_caster_generic(const type_info *typeinfo)
+        : typeinfo(typeinfo), cpptype(typeinfo ? typeinfo->cpptype : nullptr) {}
+
+    bool load(handle src, bool convert) { return load_impl<type_caster_generic>(src, convert); }
+
+    PYBIND11_NOINLINE static handle cast(const void *_src,
+                                         return_value_policy policy,
+                                         handle parent,
+                                         const detail::type_info *tinfo,
+                                         void *(*copy_constructor)(const void *),
+                                         void *(*move_constructor)(const void *),
+                                         const void *existing_holder = nullptr) {
+        if (!tinfo) { // no type info: error will be set already
+            return handle();
+        }
+
+        void *src = const_cast<void *>(_src);
+        if (src == nullptr) {
+            return none().release();
+        }
+
+        if (handle registered_inst = find_registered_python_instance(src, tinfo)) {
+            return registered_inst;
+        }
+
+        auto inst = reinterpret_steal<object>(make_new_instance(tinfo->type));
+        auto *wrapper = reinterpret_cast<instance *>(inst.ptr());
+        wrapper->owned = false;
+        void *&valueptr = values_and_holders(wrapper).begin()->value_ptr();
+
+        switch (policy) {
+            case return_value_policy::automatic:
+            case return_value_policy::take_ownership:
+                valueptr = src;
+                wrapper->owned = true;
+                break;
+
+            case return_value_policy::automatic_reference:
+            case return_value_policy::reference:
+                valueptr = src;
+                wrapper->owned = false;
+                break;
+
+            case return_value_policy::copy:
+                if (copy_constructor) {
+                    valueptr = copy_constructor(src);
+                } else {
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                    std::string type_name(tinfo->cpptype->name());
+                    detail::clean_type_id(type_name);
+                    throw cast_error("return_value_policy = copy, but type " + type_name
+                                     + " is non-copyable!");
+#else
+                    throw cast_error("return_value_policy = copy, but type is "
+                                     "non-copyable! (#define PYBIND11_DETAILED_ERROR_MESSAGES or "
+                                     "compile in debug mode for details)");
+#endif
+                }
+                wrapper->owned = true;
+                break;
+
+            case return_value_policy::move:
+                if (move_constructor) {
+                    valueptr = move_constructor(src);
+                } else if (copy_constructor) {
+                    valueptr = copy_constructor(src);
+                } else {
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                    std::string type_name(tinfo->cpptype->name());
+                    detail::clean_type_id(type_name);
+                    throw cast_error("return_value_policy = move, but type " + type_name
+                                     + " is neither movable nor copyable!");
+#else
+                    throw cast_error("return_value_policy = move, but type is neither "
+                                     "movable nor copyable! "
+                                     "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in "
+                                     "debug mode for details)");
+#endif
+                }
+                wrapper->owned = true;
+                break;
+
+            case return_value_policy::reference_internal:
+                valueptr = src;
+                wrapper->owned = false;
+                keep_alive_impl(inst, parent);
+                break;
+
+            default:
+                throw cast_error("unhandled return_value_policy: should not happen!");
+        }
+
+        tinfo->init_instance(wrapper, existing_holder);
+
+        return inst.release();
+    }
+
+    // Base methods for generic caster; there are overridden in copyable_holder_caster
+    void load_value(value_and_holder &&v_h) {
+        auto *&vptr = v_h.value_ptr();
+        // Lazy allocation for unallocated values:
+        if (vptr == nullptr) {
+            const auto *type = v_h.type ? v_h.type : typeinfo;
+            if (type->operator_new) {
+                vptr = type->operator_new(type->type_size);
+            } else {
+#if defined(__cpp_aligned_new) && (!defined(_MSC_VER) || _MSC_VER >= 1912)
+                if (type->type_align > __STDCPP_DEFAULT_NEW_ALIGNMENT__) {
+                    vptr = ::operator new(type->type_size, std::align_val_t(type->type_align));
+                } else {
+                    vptr = ::operator new(type->type_size);
+                }
+#else
+                vptr = ::operator new(type->type_size);
+#endif
+            }
+        }
+        value = vptr;
+    }
+    bool try_implicit_casts(handle src, bool convert) {
+        for (const auto &cast : typeinfo->implicit_casts) {
+            type_caster_generic sub_caster(*cast.first);
+            if (sub_caster.load(src, convert)) {
+                value = cast.second(sub_caster.value);
+                return true;
+            }
+        }
+        return false;
+    }
+    bool try_direct_conversions(handle src) {
+        for (auto &converter : *typeinfo->direct_conversions) {
+            if (converter(src.ptr(), value)) {
+                return true;
+            }
+        }
+        return false;
+    }
+    void check_holder_compat() {}
+
+    PYBIND11_NOINLINE static void *local_load(PyObject *src, const type_info *ti) {
+        auto caster = type_caster_generic(ti);
+        if (caster.load(src, false)) {
+            return caster.value;
+        }
+        return nullptr;
+    }
+
+    /// Try to load with foreign typeinfo, if available. Used when there is no
+    /// native typeinfo, or when the native one wasn't able to produce a value.
+    PYBIND11_NOINLINE bool try_load_foreign_module_local(handle src) {
+        constexpr auto *local_key = PYBIND11_MODULE_LOCAL_ID;
+        const auto pytype = type::handle_of(src);
+        if (!hasattr(pytype, local_key)) {
+            return false;
+        }
+
+        type_info *foreign_typeinfo = reinterpret_borrow<capsule>(getattr(pytype, local_key));
+        // Only consider this foreign loader if actually foreign and is a loader of the correct cpp
+        // type
+        if (foreign_typeinfo->module_local_load == &local_load
+            || (cpptype && !same_type(*cpptype, *foreign_typeinfo->cpptype))) {
+            return false;
+        }
+
+        if (auto *result = foreign_typeinfo->module_local_load(src.ptr(), foreign_typeinfo)) {
+            value = result;
+            return true;
+        }
+        return false;
+    }
+
+    // Implementation of `load`; this takes the type of `this` so that it can dispatch the relevant
+    // bits of code between here and copyable_holder_caster where the two classes need different
+    // logic (without having to resort to virtual inheritance).
+    template <typename ThisT>
+    PYBIND11_NOINLINE bool load_impl(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (!typeinfo) {
+            return try_load_foreign_module_local(src);
+        }
+
+        auto &this_ = static_cast<ThisT &>(*this);
+        this_.check_holder_compat();
+
+        PyTypeObject *srctype = Py_TYPE(src.ptr());
+
+        // Case 1: If src is an exact type match for the target type then we can reinterpret_cast
+        // the instance's value pointer to the target type:
+        if (srctype == typeinfo->type) {
+            this_.load_value(reinterpret_cast<instance *>(src.ptr())->get_value_and_holder());
+            return true;
+        }
+        // Case 2: We have a derived class
+        if (PyType_IsSubtype(srctype, typeinfo->type)) {
+            const auto &bases = all_type_info(srctype);
+            bool no_cpp_mi = typeinfo->simple_type;
+
+            // Case 2a: the python type is a Python-inherited derived class that inherits from just
+            // one simple (no MI) pybind11 class, or is an exact match, so the C++ instance is of
+            // the right type and we can use reinterpret_cast.
+            // (This is essentially the same as case 2b, but because not using multiple inheritance
+            // is extremely common, we handle it specially to avoid the loop iterator and type
+            // pointer lookup overhead)
+            if (bases.size() == 1 && (no_cpp_mi || bases.front()->type == typeinfo->type)) {
+                this_.load_value(reinterpret_cast<instance *>(src.ptr())->get_value_and_holder());
+                return true;
+            }
+            // Case 2b: the python type inherits from multiple C++ bases.  Check the bases to see
+            // if we can find an exact match (or, for a simple C++ type, an inherited match); if
+            // so, we can safely reinterpret_cast to the relevant pointer.
+            if (bases.size() > 1) {
+                for (auto *base : bases) {
+                    if (no_cpp_mi ? PyType_IsSubtype(base->type, typeinfo->type)
+                                  : base->type == typeinfo->type) {
+                        this_.load_value(
+                            reinterpret_cast<instance *>(src.ptr())->get_value_and_holder(base));
+                        return true;
+                    }
+                }
+            }
+
+            // Case 2c: C++ multiple inheritance is involved and we couldn't find an exact type
+            // match in the registered bases, above, so try implicit casting (needed for proper C++
+            // casting when MI is involved).
+            if (this_.try_implicit_casts(src, convert)) {
+                return true;
+            }
+        }
+
+        // Perform an implicit conversion
+        if (convert) {
+            for (const auto &converter : typeinfo->implicit_conversions) {
+                auto temp = reinterpret_steal<object>(converter(src.ptr(), typeinfo->type));
+                if (load_impl<ThisT>(temp, false)) {
+                    loader_life_support::add_patient(temp);
+                    return true;
+                }
+            }
+            if (this_.try_direct_conversions(src)) {
+                return true;
+            }
+        }
+
+        // Failed to match local typeinfo. Try again with global.
+        if (typeinfo->module_local) {
+            if (auto *gtype = get_global_type_info(*typeinfo->cpptype)) {
+                typeinfo = gtype;
+                return load(src, false);
+            }
+        }
+
+        // Global typeinfo has precedence over foreign module_local
+        if (try_load_foreign_module_local(src)) {
+            return true;
+        }
+
+        // Custom converters didn't take None, now we convert None to nullptr.
+        if (src.is_none()) {
+            // Defer accepting None to other overloads (if we aren't in convert mode):
+            if (!convert) {
+                return false;
+            }
+            value = nullptr;
+            return true;
+        }
+
+        return false;
+    }
+
+    // Called to do type lookup and wrap the pointer and type in a pair when a dynamic_cast
+    // isn't needed or can't be used.  If the type is unknown, sets the error and returns a pair
+    // with .second = nullptr.  (p.first = nullptr is not an error: it becomes None).
+    PYBIND11_NOINLINE static std::pair<const void *, const type_info *>
+    src_and_type(const void *src,
+                 const std::type_info &cast_type,
+                 const std::type_info *rtti_type = nullptr) {
+        if (auto *tpi = get_type_info(cast_type)) {
+            return {src, const_cast<const type_info *>(tpi)};
+        }
+
+        // Not found, set error:
+        std::string tname = rtti_type ? rtti_type->name() : cast_type.name();
+        detail::clean_type_id(tname);
+        std::string msg = "Unregistered type : " + tname;
+        PyErr_SetString(PyExc_TypeError, msg.c_str());
+        return {nullptr, nullptr};
+    }
+
+    const type_info *typeinfo = nullptr;
+    const std::type_info *cpptype = nullptr;
+    void *value = nullptr;
+};
+
+/**
+ * Determine suitable casting operator for pointer-or-lvalue-casting type casters.  The type caster
+ * needs to provide `operator T*()` and `operator T&()` operators.
+ *
+ * If the type supports moving the value away via an `operator T&&() &&` method, it should use
+ * `movable_cast_op_type` instead.
+ */
+template <typename T>
+using cast_op_type = conditional_t<std::is_pointer<remove_reference_t<T>>::value,
+                                   typename std::add_pointer<intrinsic_t<T>>::type,
+                                   typename std::add_lvalue_reference<intrinsic_t<T>>::type>;
+
+/**
+ * Determine suitable casting operator for a type caster with a movable value.  Such a type caster
+ * needs to provide `operator T*()`, `operator T&()`, and `operator T&&() &&`.  The latter will be
+ * called in appropriate contexts where the value can be moved rather than copied.
+ *
+ * These operator are automatically provided when using the PYBIND11_TYPE_CASTER macro.
+ */
+template <typename T>
+using movable_cast_op_type
+    = conditional_t<std::is_pointer<typename std::remove_reference<T>::type>::value,
+                    typename std::add_pointer<intrinsic_t<T>>::type,
+                    conditional_t<std::is_rvalue_reference<T>::value,
+                                  typename std::add_rvalue_reference<intrinsic_t<T>>::type,
+                                  typename std::add_lvalue_reference<intrinsic_t<T>>::type>>;
+
+// std::is_copy_constructible isn't quite enough: it lets std::vector<T> (and similar) through when
+// T is non-copyable, but code containing such a copy constructor fails to actually compile.
+template <typename T, typename SFINAE = void>
+struct is_copy_constructible : std::is_copy_constructible<T> {};
+
+// Specialization for types that appear to be copy constructible but also look like stl containers
+// (we specifically check for: has `value_type` and `reference` with `reference = value_type&`): if
+// so, copy constructability depends on whether the value_type is copy constructible.
+template <typename Container>
+struct is_copy_constructible<
+    Container,
+    enable_if_t<
+        all_of<std::is_copy_constructible<Container>,
+               std::is_same<typename Container::value_type &, typename Container::reference>,
+               // Avoid infinite recursion
+               negation<std::is_same<Container, typename Container::value_type>>>::value>>
+    : is_copy_constructible<typename Container::value_type> {};
+
+// Likewise for std::pair
+// (after C++17 it is mandatory that the copy constructor not exist when the two types aren't
+// themselves copy constructible, but this can not be relied upon when T1 or T2 are themselves
+// containers).
+template <typename T1, typename T2>
+struct is_copy_constructible<std::pair<T1, T2>>
+    : all_of<is_copy_constructible<T1>, is_copy_constructible<T2>> {};
+
+// The same problems arise with std::is_copy_assignable, so we use the same workaround.
+template <typename T, typename SFINAE = void>
+struct is_copy_assignable : std::is_copy_assignable<T> {};
+template <typename Container>
+struct is_copy_assignable<Container,
+                          enable_if_t<all_of<std::is_copy_assignable<Container>,
+                                             std::is_same<typename Container::value_type &,
+                                                          typename Container::reference>>::value>>
+    : is_copy_assignable<typename Container::value_type> {};
+template <typename T1, typename T2>
+struct is_copy_assignable<std::pair<T1, T2>>
+    : all_of<is_copy_assignable<T1>, is_copy_assignable<T2>> {};
+
+PYBIND11_NAMESPACE_END(detail)
+
+// polymorphic_type_hook<itype>::get(src, tinfo) determines whether the object pointed
+// to by `src` actually is an instance of some class derived from `itype`.
+// If so, it sets `tinfo` to point to the std::type_info representing that derived
+// type, and returns a pointer to the start of the most-derived object of that type
+// (in which `src` is a subobject; this will be the same address as `src` in most
+// single inheritance cases). If not, or if `src` is nullptr, it simply returns `src`
+// and leaves `tinfo` at its default value of nullptr.
+//
+// The default polymorphic_type_hook just returns src. A specialization for polymorphic
+// types determines the runtime type of the passed object and adjusts the this-pointer
+// appropriately via dynamic_cast<void*>. This is what enables a C++ Animal* to appear
+// to Python as a Dog (if Dog inherits from Animal, Animal is polymorphic, Dog is
+// registered with pybind11, and this Animal is in fact a Dog).
+//
+// You may specialize polymorphic_type_hook yourself for types that want to appear
+// polymorphic to Python but do not use C++ RTTI. (This is a not uncommon pattern
+// in performance-sensitive applications, used most notably in LLVM.)
+//
+// polymorphic_type_hook_base allows users to specialize polymorphic_type_hook with
+// std::enable_if. User provided specializations will always have higher priority than
+// the default implementation and specialization provided in polymorphic_type_hook_base.
+template <typename itype, typename SFINAE = void>
+struct polymorphic_type_hook_base {
+    static const void *get(const itype *src, const std::type_info *&) { return src; }
+};
+template <typename itype>
+struct polymorphic_type_hook_base<itype, detail::enable_if_t<std::is_polymorphic<itype>::value>> {
+    static const void *get(const itype *src, const std::type_info *&type) {
+        type = src ? &typeid(*src) : nullptr;
+        return dynamic_cast<const void *>(src);
+    }
+};
+template <typename itype, typename SFINAE = void>
+struct polymorphic_type_hook : public polymorphic_type_hook_base<itype> {};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Generic type caster for objects stored on the heap
+template <typename type>
+class type_caster_base : public type_caster_generic {
+    using itype = intrinsic_t<type>;
+
+public:
+    static constexpr auto name = const_name<type>();
+
+    type_caster_base() : type_caster_base(typeid(type)) {}
+    explicit type_caster_base(const std::type_info &info) : type_caster_generic(info) {}
+
+    static handle cast(const itype &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+
+    static handle cast(itype &&src, return_value_policy, handle parent) {
+        return cast(&src, return_value_policy::move, parent);
+    }
+
+    // Returns a (pointer, type_info) pair taking care of necessary type lookup for a
+    // polymorphic type (using RTTI by default, but can be overridden by specializing
+    // polymorphic_type_hook). If the instance isn't derived, returns the base version.
+    static std::pair<const void *, const type_info *> src_and_type(const itype *src) {
+        const auto &cast_type = typeid(itype);
+        const std::type_info *instance_type = nullptr;
+        const void *vsrc = polymorphic_type_hook<itype>::get(src, instance_type);
+        if (instance_type && !same_type(cast_type, *instance_type)) {
+            // This is a base pointer to a derived type. If the derived type is registered
+            // with pybind11, we want to make the full derived object available.
+            // In the typical case where itype is polymorphic, we get the correct
+            // derived pointer (which may be != base pointer) by a dynamic_cast to
+            // most derived type. If itype is not polymorphic, we won't get here
+            // except via a user-provided specialization of polymorphic_type_hook,
+            // and the user has promised that no this-pointer adjustment is
+            // required in that case, so it's OK to use static_cast.
+            if (const auto *tpi = get_type_info(*instance_type)) {
+                return {vsrc, tpi};
+            }
+        }
+        // Otherwise we have either a nullptr, an `itype` pointer, or an unknown derived pointer,
+        // so don't do a cast
+        return type_caster_generic::src_and_type(src, cast_type, instance_type);
+    }
+
+    static handle cast(const itype *src, return_value_policy policy, handle parent) {
+        auto st = src_and_type(src);
+        return type_caster_generic::cast(st.first,
+                                         policy,
+                                         parent,
+                                         st.second,
+                                         make_copy_constructor(src),
+                                         make_move_constructor(src));
+    }
+
+    static handle cast_holder(const itype *src, const void *holder) {
+        auto st = src_and_type(src);
+        return type_caster_generic::cast(st.first,
+                                         return_value_policy::take_ownership,
+                                         {},
+                                         st.second,
+                                         nullptr,
+                                         nullptr,
+                                         holder);
+    }
+
+    template <typename T>
+    using cast_op_type = detail::cast_op_type<T>;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator itype *() { return (type *) value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator itype &() {
+        if (!value) {
+            throw reference_cast_error();
+        }
+        return *((itype *) value);
+    }
+
+protected:
+    using Constructor = void *(*) (const void *);
+
+    /* Only enabled when the types are {copy,move}-constructible *and* when the type
+       does not have a private operator new implementation. A comma operator is used in the
+       decltype argument to apply SFINAE to the public copy/move constructors.*/
+    template <typename T, typename = enable_if_t<is_copy_constructible<T>::value>>
+    static auto make_copy_constructor(const T *)
+        -> decltype(new T(std::declval<const T>()), Constructor{}) {
+        return [](const void *arg) -> void * { return new T(*reinterpret_cast<const T *>(arg)); };
+    }
+
+    template <typename T, typename = enable_if_t<std::is_move_constructible<T>::value>>
+    static auto make_move_constructor(const T *)
+        -> decltype(new T(std::declval<T &&>()), Constructor{}) {
+        return [](const void *arg) -> void * {
+            return new T(std::move(*const_cast<T *>(reinterpret_cast<const T *>(arg))));
+        };
+    }
+
+    static Constructor make_copy_constructor(...) { return nullptr; }
+    static Constructor make_move_constructor(...) { return nullptr; }
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/typeid.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/typeid.h
new file mode 100644
index 000000000..a67b52135
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/detail/typeid.h
@@ -0,0 +1,65 @@
+/*
+    pybind11/detail/typeid.h: Compiler-independent access to type identifiers
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include <cstdio>
+#include <cstdlib>
+
+#if defined(__GNUG__)
+#    include <cxxabi.h>
+#endif
+
+#include "common.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Erase all occurrences of a substring
+inline void erase_all(std::string &string, const std::string &search) {
+    for (size_t pos = 0;;) {
+        pos = string.find(search, pos);
+        if (pos == std::string::npos) {
+            break;
+        }
+        string.erase(pos, search.length());
+    }
+}
+
+PYBIND11_NOINLINE void clean_type_id(std::string &name) {
+#if defined(__GNUG__)
+    int status = 0;
+    std::unique_ptr<char, void (*)(void *)> res{
+        abi::__cxa_demangle(name.c_str(), nullptr, nullptr, &status), std::free};
+    if (status == 0) {
+        name = res.get();
+    }
+#else
+    detail::erase_all(name, "class ");
+    detail::erase_all(name, "struct ");
+    detail::erase_all(name, "enum ");
+#endif
+    detail::erase_all(name, "pybind11::");
+}
+
+inline std::string clean_type_id(const char *typeid_name) {
+    std::string name(typeid_name);
+    detail::clean_type_id(name);
+    return name;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Return a string representation of a C++ type
+template <typename T>
+static std::string type_id() {
+    return detail::clean_type_id(typeid(T).name());
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/eigen.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/eigen.h
new file mode 100644
index 000000000..4d221b3d7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/eigen.h
@@ -0,0 +1,708 @@
+/*
+    pybind11/eigen.h: Transparent conversion for dense and sparse Eigen matrices
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+/* HINT: To suppress warnings originating from the Eigen headers, use -isystem.
+   See also:
+       https://stackoverflow.com/questions/2579576/i-dir-vs-isystem-dir
+       https://stackoverflow.com/questions/1741816/isystem-for-ms-visual-studio-c-compiler
+*/
+
+#include "numpy.h"
+
+// The C4127 suppression was introduced for Eigen 3.4.0. In theory we could
+// make it version specific, or even remove it later, but considering that
+// 1. C4127 is generally far more distracting than useful for modern template code, and
+// 2. we definitely want to ignore any MSVC warnings originating from Eigen code,
+//    it is probably best to keep this around indefinitely.
+#if defined(_MSC_VER)
+#    pragma warning(push)
+#    pragma warning(disable : 4127) // C4127: conditional expression is constant
+#    pragma warning(disable : 5054) // https://github.com/pybind/pybind11/pull/3741
+//       C5054: operator '&': deprecated between enumerations of different types
+#endif
+
+#include <Eigen/Core>
+#include <Eigen/SparseCore>
+
+#if defined(_MSC_VER)
+#    pragma warning(pop)
+#endif
+
+// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
+// move constructors that break things.  We could detect this an explicitly copy, but an extra copy
+// of matrices seems highly undesirable.
+static_assert(EIGEN_VERSION_AT_LEAST(3, 2, 7),
+              "Eigen support in pybind11 requires Eigen >= 3.2.7");
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
+using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>;
+template <typename MatrixType>
+using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>;
+template <typename MatrixType>
+using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>;
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if EIGEN_VERSION_AT_LEAST(3, 3, 0)
+using EigenIndex = Eigen::Index;
+template <typename Scalar, int Flags, typename StorageIndex>
+using EigenMapSparseMatrix = Eigen::Map<Eigen::SparseMatrix<Scalar, Flags, StorageIndex>>;
+#else
+using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
+template <typename Scalar, int Flags, typename StorageIndex>
+using EigenMapSparseMatrix = Eigen::MappedSparseMatrix<Scalar, Flags, StorageIndex>;
+#endif
+
+// Matches Eigen::Map, Eigen::Ref, blocks, etc:
+template <typename T>
+using is_eigen_dense_map = all_of<is_template_base_of<Eigen::DenseBase, T>,
+                                  std::is_base_of<Eigen::MapBase<T, Eigen::ReadOnlyAccessors>, T>>;
+template <typename T>
+using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>;
+template <typename T>
+using is_eigen_dense_plain
+    = all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>;
+template <typename T>
+using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>;
+// Test for objects inheriting from EigenBase<Derived> that aren't captured by the above.  This
+// basically covers anything that can be assigned to a dense matrix but that don't have a typical
+// matrix data layout that can be copied from their .data().  For example, DiagonalMatrix and
+// SelfAdjointView fall into this category.
+template <typename T>
+using is_eigen_other
+    = all_of<is_template_base_of<Eigen::EigenBase, T>,
+             negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>>>;
+
+// Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
+template <bool EigenRowMajor>
+struct EigenConformable {
+    bool conformable = false;
+    EigenIndex rows = 0, cols = 0;
+    EigenDStride stride{0, 0};    // Only valid if negativestrides is false!
+    bool negativestrides = false; // If true, do not use stride!
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    EigenConformable(bool fits = false) : conformable{fits} {}
+    // Matrix type:
+    EigenConformable(EigenIndex r, EigenIndex c, EigenIndex rstride, EigenIndex cstride)
+        : conformable{true}, rows{r}, cols{c},
+          // TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity.
+          // http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
+          stride{EigenRowMajor ? (rstride > 0 ? rstride : 0)
+                               : (cstride > 0 ? cstride : 0) /* outer stride */,
+                 EigenRowMajor ? (cstride > 0 ? cstride : 0)
+                               : (rstride > 0 ? rstride : 0) /* inner stride */},
+          negativestrides{rstride < 0 || cstride < 0} {}
+    // Vector type:
+    EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
+        : EigenConformable(r, c, r == 1 ? c * stride : stride, c == 1 ? r : r * stride) {}
+
+    template <typename props>
+    bool stride_compatible() const {
+        // To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
+        // matching strides, or a dimension size of 1 (in which case the stride value is
+        // irrelevant). Alternatively, if any dimension size is 0, the strides are not relevant
+        // (and numpy ≥ 1.23 sets the strides to 0 in that case, so we need to check explicitly).
+        if (negativestrides) {
+            return false;
+        }
+        if (rows == 0 || cols == 0) {
+            return true;
+        }
+        return (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner()
+                || (EigenRowMajor ? cols : rows) == 1)
+               && (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer()
+                   || (EigenRowMajor ? rows : cols) == 1);
+    }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator bool() const { return conformable; }
+};
+
+template <typename Type>
+struct eigen_extract_stride {
+    using type = Type;
+};
+template <typename PlainObjectType, int MapOptions, typename StrideType>
+struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> {
+    using type = StrideType;
+};
+template <typename PlainObjectType, int Options, typename StrideType>
+struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> {
+    using type = StrideType;
+};
+
+// Helper struct for extracting information from an Eigen type
+template <typename Type_>
+struct EigenProps {
+    using Type = Type_;
+    using Scalar = typename Type::Scalar;
+    using StrideType = typename eigen_extract_stride<Type>::type;
+    static constexpr EigenIndex rows = Type::RowsAtCompileTime, cols = Type::ColsAtCompileTime,
+                                size = Type::SizeAtCompileTime;
+    static constexpr bool row_major = Type::IsRowMajor,
+                          vector
+                          = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
+        fixed_rows = rows != Eigen::Dynamic, fixed_cols = cols != Eigen::Dynamic,
+                          fixed = size != Eigen::Dynamic, // Fully-fixed size
+        dynamic = !fixed_rows && !fixed_cols;             // Fully-dynamic size
+
+    template <EigenIndex i, EigenIndex ifzero>
+    using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>;
+    static constexpr EigenIndex inner_stride
+        = if_zero<StrideType::InnerStrideAtCompileTime, 1>::value,
+        outer_stride = if_zero < StrideType::OuterStrideAtCompileTime,
+        vector      ? size
+        : row_major ? cols
+                    : rows > ::value;
+    static constexpr bool dynamic_stride
+        = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
+    static constexpr bool requires_row_major
+        = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
+    static constexpr bool requires_col_major
+        = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
+
+    // Takes an input array and determines whether we can make it fit into the Eigen type.  If
+    // the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
+    // (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
+    static EigenConformable<row_major> conformable(const array &a) {
+        const auto dims = a.ndim();
+        if (dims < 1 || dims > 2) {
+            return false;
+        }
+
+        if (dims == 2) { // Matrix type: require exact match (or dynamic)
+
+            EigenIndex np_rows = a.shape(0), np_cols = a.shape(1),
+                       np_rstride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)),
+                       np_cstride = a.strides(1) / static_cast<ssize_t>(sizeof(Scalar));
+            if ((PYBIND11_SILENCE_MSVC_C4127(fixed_rows) && np_rows != rows)
+                || (PYBIND11_SILENCE_MSVC_C4127(fixed_cols) && np_cols != cols)) {
+                return false;
+            }
+
+            return {np_rows, np_cols, np_rstride, np_cstride};
+        }
+
+        // Otherwise we're storing an n-vector.  Only one of the strides will be used, but
+        // whichever is used, we want the (single) numpy stride value.
+        const EigenIndex n = a.shape(0),
+                         stride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar));
+
+        if (vector) { // Eigen type is a compile-time vector
+            if (PYBIND11_SILENCE_MSVC_C4127(fixed) && size != n) {
+                return false; // Vector size mismatch
+            }
+            return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
+        }
+        if (fixed) {
+            // The type has a fixed size, but is not a vector: abort
+            return false;
+        }
+        if (fixed_cols) {
+            // Since this isn't a vector, cols must be != 1.  We allow this only if it exactly
+            // equals the number of elements (rows is Dynamic, and so 1 row is allowed).
+            if (cols != n) {
+                return false;
+            }
+            return {1, n, stride};
+        } // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
+        if (PYBIND11_SILENCE_MSVC_C4127(fixed_rows) && rows != n) {
+            return false;
+        }
+        return {n, 1, stride};
+    }
+
+    static constexpr bool show_writeable
+        = is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value;
+    static constexpr bool show_order = is_eigen_dense_map<Type>::value;
+    static constexpr bool show_c_contiguous = show_order && requires_row_major;
+    static constexpr bool show_f_contiguous
+        = !show_c_contiguous && show_order && requires_col_major;
+
+    static constexpr auto descriptor
+        = const_name("numpy.ndarray[") + npy_format_descriptor<Scalar>::name + const_name("[")
+          + const_name<fixed_rows>(const_name<(size_t) rows>(), const_name("m")) + const_name(", ")
+          + const_name<fixed_cols>(const_name<(size_t) cols>(), const_name("n")) + const_name("]")
+          +
+          // For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to
+          // be satisfied: writeable=True (for a mutable reference), and, depending on the map's
+          // stride options, possibly f_contiguous or c_contiguous.  We include them in the
+          // descriptor output to provide some hint as to why a TypeError is occurring (otherwise
+          // it can be confusing to see that a function accepts a 'numpy.ndarray[float64[3,2]]' and
+          // an error message that you *gave* a numpy.ndarray of the right type and dimensions.
+          const_name<show_writeable>(", flags.writeable", "")
+          + const_name<show_c_contiguous>(", flags.c_contiguous", "")
+          + const_name<show_f_contiguous>(", flags.f_contiguous", "") + const_name("]");
+};
+
+// Casts an Eigen type to numpy array.  If given a base, the numpy array references the src data,
+// otherwise it'll make a copy.  writeable lets you turn off the writeable flag for the array.
+template <typename props>
+handle
+eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
+    constexpr ssize_t elem_size = sizeof(typename props::Scalar);
+    array a;
+    if (props::vector) {
+        a = array({src.size()}, {elem_size * src.innerStride()}, src.data(), base);
+    } else {
+        a = array({src.rows(), src.cols()},
+                  {elem_size * src.rowStride(), elem_size * src.colStride()},
+                  src.data(),
+                  base);
+    }
+
+    if (!writeable) {
+        array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+    }
+
+    return a.release();
+}
+
+// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
+// reference the Eigen object's data with `base` as the python-registered base class (if omitted,
+// the base will be set to None, and lifetime management is up to the caller).  The numpy array is
+// non-writeable if the given type is const.
+template <typename props, typename Type>
+handle eigen_ref_array(Type &src, handle parent = none()) {
+    // none here is to get past array's should-we-copy detection, which currently always
+    // copies when there is no base.  Setting the base to None should be harmless.
+    return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value);
+}
+
+// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a
+// numpy array that references the encapsulated data with a python-side reference to the capsule to
+// tie its destruction to that of any dependent python objects.  Const-ness is determined by
+// whether or not the Type of the pointer given is const.
+template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>>
+handle eigen_encapsulate(Type *src) {
+    capsule base(src, [](void *o) { delete static_cast<Type *>(o); });
+    return eigen_ref_array<props>(*src, base);
+}
+
+// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
+// types.
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> {
+    using Scalar = typename Type::Scalar;
+    using props = EigenProps<Type>;
+
+    bool load(handle src, bool convert) {
+        // If we're in no-convert mode, only load if given an array of the correct type
+        if (!convert && !isinstance<array_t<Scalar>>(src)) {
+            return false;
+        }
+
+        // Coerce into an array, but don't do type conversion yet; the copy below handles it.
+        auto buf = array::ensure(src);
+
+        if (!buf) {
+            return false;
+        }
+
+        auto dims = buf.ndim();
+        if (dims < 1 || dims > 2) {
+            return false;
+        }
+
+        auto fits = props::conformable(buf);
+        if (!fits) {
+            return false;
+        }
+
+        // Allocate the new type, then build a numpy reference into it
+        value = Type(fits.rows, fits.cols);
+        auto ref = reinterpret_steal<array>(eigen_ref_array<props>(value));
+        if (dims == 1) {
+            ref = ref.squeeze();
+        } else if (ref.ndim() == 1) {
+            buf = buf.squeeze();
+        }
+
+        int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
+
+        if (result < 0) { // Copy failed!
+            PyErr_Clear();
+            return false;
+        }
+
+        return true;
+    }
+
+private:
+    // Cast implementation
+    template <typename CType>
+    static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
+        switch (policy) {
+            case return_value_policy::take_ownership:
+            case return_value_policy::automatic:
+                return eigen_encapsulate<props>(src);
+            case return_value_policy::move:
+                return eigen_encapsulate<props>(new CType(std::move(*src)));
+            case return_value_policy::copy:
+                return eigen_array_cast<props>(*src);
+            case return_value_policy::reference:
+            case return_value_policy::automatic_reference:
+                return eigen_ref_array<props>(*src);
+            case return_value_policy::reference_internal:
+                return eigen_ref_array<props>(*src, parent);
+            default:
+                throw cast_error("unhandled return_value_policy: should not happen!");
+        };
+    }
+
+public:
+    // Normal returned non-reference, non-const value:
+    static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+    // If you return a non-reference const, we mark the numpy array readonly:
+    static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+    // lvalue reference return; default (automatic) becomes copy
+    static handle cast(Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+    // const lvalue reference return; default (automatic) becomes copy
+    static handle cast(const Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+    // non-const pointer return
+    static handle cast(Type *src, return_value_policy policy, handle parent) {
+        return cast_impl(src, policy, parent);
+    }
+    // const pointer return
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        return cast_impl(src, policy, parent);
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type *() { return &value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &() { return value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &&() && { return std::move(value); }
+    template <typename T>
+    using cast_op_type = movable_cast_op_type<T>;
+
+private:
+    Type value;
+};
+
+// Base class for casting reference/map/block/etc. objects back to python.
+template <typename MapType>
+struct eigen_map_caster {
+private:
+    using props = EigenProps<MapType>;
+
+public:
+    // Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
+    // to stay around), but we'll allow it under the assumption that you know what you're doing
+    // (and have an appropriate keep_alive in place).  We return a numpy array pointing directly at
+    // the ref's data (The numpy array ends up read-only if the ref was to a const matrix type.)
+    // Note that this means you need to ensure you don't destroy the object in some other way (e.g.
+    // with an appropriate keep_alive, or with a reference to a statically allocated matrix).
+    static handle cast(const MapType &src, return_value_policy policy, handle parent) {
+        switch (policy) {
+            case return_value_policy::copy:
+                return eigen_array_cast<props>(src);
+            case return_value_policy::reference_internal:
+                return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value);
+            case return_value_policy::reference:
+            case return_value_policy::automatic:
+            case return_value_policy::automatic_reference:
+                return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value);
+            default:
+                // move, take_ownership don't make any sense for a ref/map:
+                pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
+        }
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
+    // types but not bound arguments).  We still provide them (with an explicitly delete) so that
+    // you end up here if you try anyway.
+    bool load(handle, bool) = delete;
+    operator MapType() = delete;
+    template <typename>
+    using cast_op_type = MapType;
+};
+
+// We can return any map-like object (but can only load Refs, specialized next):
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>> : eigen_map_caster<Type> {};
+
+// Loader for Ref<...> arguments.  See the documentation for info on how to make this work without
+// copying (it requires some extra effort in many cases).
+template <typename PlainObjectType, typename StrideType>
+struct type_caster<
+    Eigen::Ref<PlainObjectType, 0, StrideType>,
+    enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value>>
+    : public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> {
+private:
+    using Type = Eigen::Ref<PlainObjectType, 0, StrideType>;
+    using props = EigenProps<Type>;
+    using Scalar = typename props::Scalar;
+    using MapType = Eigen::Map<PlainObjectType, 0, StrideType>;
+    using Array
+        = array_t<Scalar,
+                  array::forcecast
+                      | ((props::row_major ? props::inner_stride : props::outer_stride) == 1
+                             ? array::c_style
+                         : (props::row_major ? props::outer_stride : props::inner_stride) == 1
+                             ? array::f_style
+                             : 0)>;
+    static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value;
+    // Delay construction (these have no default constructor)
+    std::unique_ptr<MapType> map;
+    std::unique_ptr<Type> ref;
+    // Our array.  When possible, this is just a numpy array pointing to the source data, but
+    // sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an
+    // incompatible layout, or is an array of a type that needs to be converted).  Using a numpy
+    // temporary (rather than an Eigen temporary) saves an extra copy when we need both type
+    // conversion and storage order conversion.  (Note that we refuse to use this temporary copy
+    // when loading an argument for a Ref<M> with M non-const, i.e. a read-write reference).
+    Array copy_or_ref;
+
+public:
+    bool load(handle src, bool convert) {
+        // First check whether what we have is already an array of the right type.  If not, we
+        // can't avoid a copy (because the copy is also going to do type conversion).
+        bool need_copy = !isinstance<Array>(src);
+
+        EigenConformable<props::row_major> fits;
+        if (!need_copy) {
+            // We don't need a converting copy, but we also need to check whether the strides are
+            // compatible with the Ref's stride requirements
+            auto aref = reinterpret_borrow<Array>(src);
+
+            if (aref && (!need_writeable || aref.writeable())) {
+                fits = props::conformable(aref);
+                if (!fits) {
+                    return false; // Incompatible dimensions
+                }
+                if (!fits.template stride_compatible<props>()) {
+                    need_copy = true;
+                } else {
+                    copy_or_ref = std::move(aref);
+                }
+            } else {
+                need_copy = true;
+            }
+        }
+
+        if (need_copy) {
+            // We need to copy: If we need a mutable reference, or we're not supposed to convert
+            // (either because we're in the no-convert overload pass, or because we're explicitly
+            // instructed not to copy (via `py::arg().noconvert()`) we have to fail loading.
+            if (!convert || need_writeable) {
+                return false;
+            }
+
+            Array copy = Array::ensure(src);
+            if (!copy) {
+                return false;
+            }
+            fits = props::conformable(copy);
+            if (!fits || !fits.template stride_compatible<props>()) {
+                return false;
+            }
+            copy_or_ref = std::move(copy);
+            loader_life_support::add_patient(copy_or_ref);
+        }
+
+        ref.reset();
+        map.reset(new MapType(data(copy_or_ref),
+                              fits.rows,
+                              fits.cols,
+                              make_stride(fits.stride.outer(), fits.stride.inner())));
+        ref.reset(new Type(*map));
+
+        return true;
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type *() { return ref.get(); }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &() { return *ref; }
+    template <typename _T>
+    using cast_op_type = pybind11::detail::cast_op_type<_T>;
+
+private:
+    template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0>
+    Scalar *data(Array &a) {
+        return a.mutable_data();
+    }
+
+    template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0>
+    const Scalar *data(Array &a) {
+        return a.data();
+    }
+
+    // Attempt to figure out a constructor of `Stride` that will work.
+    // If both strides are fixed, use a default constructor:
+    template <typename S>
+    using stride_ctor_default = bool_constant<S::InnerStrideAtCompileTime != Eigen::Dynamic
+                                              && S::OuterStrideAtCompileTime != Eigen::Dynamic
+                                              && std::is_default_constructible<S>::value>;
+    // Otherwise, if there is a two-index constructor, assume it is (outer,inner) like
+    // Eigen::Stride, and use it:
+    template <typename S>
+    using stride_ctor_dual
+        = bool_constant<!stride_ctor_default<S>::value
+                        && std::is_constructible<S, EigenIndex, EigenIndex>::value>;
+    // Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use
+    // it (passing whichever stride is dynamic).
+    template <typename S>
+    using stride_ctor_outer
+        = bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
+                        && S::OuterStrideAtCompileTime == Eigen::Dynamic
+                        && S::InnerStrideAtCompileTime != Eigen::Dynamic
+                        && std::is_constructible<S, EigenIndex>::value>;
+    template <typename S>
+    using stride_ctor_inner
+        = bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
+                        && S::InnerStrideAtCompileTime == Eigen::Dynamic
+                        && S::OuterStrideAtCompileTime != Eigen::Dynamic
+                        && std::is_constructible<S, EigenIndex>::value>;
+
+    template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0>
+    static S make_stride(EigenIndex, EigenIndex) {
+        return S();
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0>
+    static S make_stride(EigenIndex outer, EigenIndex inner) {
+        return S(outer, inner);
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0>
+    static S make_stride(EigenIndex outer, EigenIndex) {
+        return S(outer);
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0>
+    static S make_stride(EigenIndex, EigenIndex inner) {
+        return S(inner);
+    }
+};
+
+// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not
+// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout).
+// load() is not supported, but we can cast them into the python domain by first copying to a
+// regular Eigen::Matrix, then casting that.
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_other<Type>::value>> {
+protected:
+    using Matrix
+        = Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>;
+    using props = EigenProps<Matrix>;
+
+public:
+    static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
+        handle h = eigen_encapsulate<props>(new Matrix(src));
+        return h;
+    }
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        return cast(*src, policy, parent);
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
+    // types but not bound arguments).  We still provide them (with an explicitly delete) so that
+    // you end up here if you try anyway.
+    bool load(handle, bool) = delete;
+    operator Type() = delete;
+    template <typename>
+    using cast_op_type = Type;
+};
+
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> {
+    using Scalar = typename Type::Scalar;
+    using StorageIndex = remove_reference_t<decltype(*std::declval<Type>().outerIndexPtr())>;
+    using Index = typename Type::Index;
+    static constexpr bool rowMajor = Type::IsRowMajor;
+
+    bool load(handle src, bool) {
+        if (!src) {
+            return false;
+        }
+
+        auto obj = reinterpret_borrow<object>(src);
+        object sparse_module = module_::import("scipy.sparse");
+        object matrix_type = sparse_module.attr(rowMajor ? "csr_matrix" : "csc_matrix");
+
+        if (!type::handle_of(obj).is(matrix_type)) {
+            try {
+                obj = matrix_type(obj);
+            } catch (const error_already_set &) {
+                return false;
+            }
+        }
+
+        auto values = array_t<Scalar>((object) obj.attr("data"));
+        auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices"));
+        auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr"));
+        auto shape = pybind11::tuple((pybind11::object) obj.attr("shape"));
+        auto nnz = obj.attr("nnz").cast<Index>();
+
+        if (!values || !innerIndices || !outerIndices) {
+            return false;
+        }
+
+        value = EigenMapSparseMatrix<Scalar,
+                                     Type::Flags &(Eigen::RowMajor | Eigen::ColMajor),
+                                     StorageIndex>(shape[0].cast<Index>(),
+                                                   shape[1].cast<Index>(),
+                                                   std::move(nnz),
+                                                   outerIndices.mutable_data(),
+                                                   innerIndices.mutable_data(),
+                                                   values.mutable_data());
+
+        return true;
+    }
+
+    static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
+        const_cast<Type &>(src).makeCompressed();
+
+        object matrix_type
+            = module_::import("scipy.sparse").attr(rowMajor ? "csr_matrix" : "csc_matrix");
+
+        array data(src.nonZeros(), src.valuePtr());
+        array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr());
+        array innerIndices(src.nonZeros(), src.innerIndexPtr());
+
+        return matrix_type(pybind11::make_tuple(
+                               std::move(data), std::move(innerIndices), std::move(outerIndices)),
+                           pybind11::make_tuple(src.rows(), src.cols()))
+            .release();
+    }
+
+    PYBIND11_TYPE_CASTER(Type,
+                         const_name<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[",
+                                                             "scipy.sparse.csc_matrix[")
+                             + npy_format_descriptor<Scalar>::name + const_name("]"));
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/embed.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/embed.h
new file mode 100644
index 000000000..d6999cd77
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/embed.h
@@ -0,0 +1,277 @@
+/*
+    pybind11/embed.h: Support for embedding the interpreter
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+#include "eval.h"
+
+#include <memory>
+#include <vector>
+
+#if defined(PYPY_VERSION)
+#    error Embedding the interpreter is not supported with PyPy
+#endif
+
+#define PYBIND11_EMBEDDED_MODULE_IMPL(name)                                                       \
+    extern "C" PyObject *pybind11_init_impl_##name();                                             \
+    extern "C" PyObject *pybind11_init_impl_##name() { return pybind11_init_wrapper_##name(); }
+
+/** \rst
+    Add a new module to the table of builtins for the interpreter. Must be
+    defined in global scope. The first macro parameter is the name of the
+    module (without quotes). The second parameter is the variable which will
+    be used as the interface to add functions and classes to the module.
+
+    .. code-block:: cpp
+
+        PYBIND11_EMBEDDED_MODULE(example, m) {
+            // ... initialize functions and classes here
+            m.def("foo", []() {
+                return "Hello, World!";
+            });
+        }
+ \endrst */
+#define PYBIND11_EMBEDDED_MODULE(name, variable)                                                  \
+    static ::pybind11::module_::module_def PYBIND11_CONCAT(pybind11_module_def_, name);           \
+    static void PYBIND11_CONCAT(pybind11_init_, name)(::pybind11::module_ &);                     \
+    static PyObject PYBIND11_CONCAT(*pybind11_init_wrapper_, name)() {                            \
+        auto m = ::pybind11::module_::create_extension_module(                                    \
+            PYBIND11_TOSTRING(name), nullptr, &PYBIND11_CONCAT(pybind11_module_def_, name));      \
+        try {                                                                                     \
+            PYBIND11_CONCAT(pybind11_init_, name)(m);                                             \
+            return m.ptr();                                                                       \
+        }                                                                                         \
+        PYBIND11_CATCH_INIT_EXCEPTIONS                                                            \
+    }                                                                                             \
+    PYBIND11_EMBEDDED_MODULE_IMPL(name)                                                           \
+    ::pybind11::detail::embedded_module PYBIND11_CONCAT(pybind11_module_, name)(                  \
+        PYBIND11_TOSTRING(name), PYBIND11_CONCAT(pybind11_init_impl_, name));                     \
+    void PYBIND11_CONCAT(pybind11_init_, name)(::pybind11::module_                                \
+                                               & variable) // NOLINT(bugprone-macro-parentheses)
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Python 2.7/3.x compatible version of `PyImport_AppendInittab` and error checks.
+struct embedded_module {
+    using init_t = PyObject *(*) ();
+    embedded_module(const char *name, init_t init) {
+        if (Py_IsInitialized() != 0) {
+            pybind11_fail("Can't add new modules after the interpreter has been initialized");
+        }
+
+        auto result = PyImport_AppendInittab(name, init);
+        if (result == -1) {
+            pybind11_fail("Insufficient memory to add a new module");
+        }
+    }
+};
+
+struct wide_char_arg_deleter {
+    void operator()(wchar_t *ptr) const {
+        // API docs: https://docs.python.org/3/c-api/sys.html#c.Py_DecodeLocale
+        PyMem_RawFree(ptr);
+    }
+};
+
+inline wchar_t *widen_chars(const char *safe_arg) {
+    wchar_t *widened_arg = Py_DecodeLocale(safe_arg, nullptr);
+    return widened_arg;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/** \rst
+    Initialize the Python interpreter. No other pybind11 or CPython API functions can be
+    called before this is done; with the exception of `PYBIND11_EMBEDDED_MODULE`. The
+    optional `init_signal_handlers` parameter can be used to skip the registration of
+    signal handlers (see the `Python documentation`_ for details). Calling this function
+    again after the interpreter has already been initialized is a fatal error.
+
+    If initializing the Python interpreter fails, then the program is terminated.  (This
+    is controlled by the CPython runtime and is an exception to pybind11's normal behavior
+    of throwing exceptions on errors.)
+
+    The remaining optional parameters, `argc`, `argv`, and `add_program_dir_to_path` are
+    used to populate ``sys.argv`` and ``sys.path``.
+    See the |PySys_SetArgvEx documentation|_ for details.
+
+    .. _Python documentation: https://docs.python.org/3/c-api/init.html#c.Py_InitializeEx
+    .. |PySys_SetArgvEx documentation| replace:: ``PySys_SetArgvEx`` documentation
+    .. _PySys_SetArgvEx documentation: https://docs.python.org/3/c-api/init.html#c.PySys_SetArgvEx
+ \endrst */
+inline void initialize_interpreter(bool init_signal_handlers = true,
+                                   int argc = 0,
+                                   const char *const *argv = nullptr,
+                                   bool add_program_dir_to_path = true) {
+    if (Py_IsInitialized() != 0) {
+        pybind11_fail("The interpreter is already running");
+    }
+
+#if PY_VERSION_HEX < 0x030B0000
+
+    Py_InitializeEx(init_signal_handlers ? 1 : 0);
+
+    // Before it was special-cased in python 3.8, passing an empty or null argv
+    // caused a segfault, so we have to reimplement the special case ourselves.
+    bool special_case = (argv == nullptr || argc <= 0);
+
+    const char *const empty_argv[]{"\0"};
+    const char *const *safe_argv = special_case ? empty_argv : argv;
+    if (special_case) {
+        argc = 1;
+    }
+
+    auto argv_size = static_cast<size_t>(argc);
+    // SetArgv* on python 3 takes wchar_t, so we have to convert.
+    std::unique_ptr<wchar_t *[]> widened_argv(new wchar_t *[argv_size]);
+    std::vector<std::unique_ptr<wchar_t[], detail::wide_char_arg_deleter>> widened_argv_entries;
+    widened_argv_entries.reserve(argv_size);
+    for (size_t ii = 0; ii < argv_size; ++ii) {
+        widened_argv_entries.emplace_back(detail::widen_chars(safe_argv[ii]));
+        if (!widened_argv_entries.back()) {
+            // A null here indicates a character-encoding failure or the python
+            // interpreter out of memory. Give up.
+            return;
+        }
+        widened_argv[ii] = widened_argv_entries.back().get();
+    }
+
+    auto *pysys_argv = widened_argv.get();
+
+    PySys_SetArgvEx(argc, pysys_argv, static_cast<int>(add_program_dir_to_path));
+#else
+    PyConfig config;
+    PyConfig_InitIsolatedConfig(&config);
+    config.install_signal_handlers = init_signal_handlers ? 1 : 0;
+
+    PyStatus status = PyConfig_SetBytesArgv(&config, argc, const_cast<char *const *>(argv));
+    if (PyStatus_Exception(status)) {
+        // A failure here indicates a character-encoding failure or the python
+        // interpreter out of memory. Give up.
+        PyConfig_Clear(&config);
+        throw std::runtime_error(PyStatus_IsError(status) ? status.err_msg
+                                                          : "Failed to prepare CPython");
+    }
+    status = Py_InitializeFromConfig(&config);
+    PyConfig_Clear(&config);
+    if (PyStatus_Exception(status)) {
+        throw std::runtime_error(PyStatus_IsError(status) ? status.err_msg
+                                                          : "Failed to init CPython");
+    }
+    if (add_program_dir_to_path) {
+        PyRun_SimpleString("import sys, os.path; "
+                           "sys.path.insert(0, "
+                           "os.path.abspath(os.path.dirname(sys.argv[0])) "
+                           "if sys.argv and os.path.exists(sys.argv[0]) else '')");
+    }
+#endif
+}
+
+/** \rst
+    Shut down the Python interpreter. No pybind11 or CPython API functions can be called
+    after this. In addition, pybind11 objects must not outlive the interpreter:
+
+    .. code-block:: cpp
+
+        { // BAD
+            py::initialize_interpreter();
+            auto hello = py::str("Hello, World!");
+            py::finalize_interpreter();
+        } // <-- BOOM, hello's destructor is called after interpreter shutdown
+
+        { // GOOD
+            py::initialize_interpreter();
+            { // scoped
+                auto hello = py::str("Hello, World!");
+            } // <-- OK, hello is cleaned up properly
+            py::finalize_interpreter();
+        }
+
+        { // BETTER
+            py::scoped_interpreter guard{};
+            auto hello = py::str("Hello, World!");
+        }
+
+    .. warning::
+
+        The interpreter can be restarted by calling `initialize_interpreter` again.
+        Modules created using pybind11 can be safely re-initialized. However, Python
+        itself cannot completely unload binary extension modules and there are several
+        caveats with regard to interpreter restarting. All the details can be found
+        in the CPython documentation. In short, not all interpreter memory may be
+        freed, either due to reference cycles or user-created global data.
+
+ \endrst */
+inline void finalize_interpreter() {
+    handle builtins(PyEval_GetBuiltins());
+    const char *id = PYBIND11_INTERNALS_ID;
+
+    // Get the internals pointer (without creating it if it doesn't exist).  It's possible for the
+    // internals to be created during Py_Finalize() (e.g. if a py::capsule calls `get_internals()`
+    // during destruction), so we get the pointer-pointer here and check it after Py_Finalize().
+    detail::internals **internals_ptr_ptr = detail::get_internals_pp();
+    // It could also be stashed in builtins, so look there too:
+    if (builtins.contains(id) && isinstance<capsule>(builtins[id])) {
+        internals_ptr_ptr = capsule(builtins[id]);
+    }
+    // Local internals contains data managed by the current interpreter, so we must clear them to
+    // avoid undefined behaviors when initializing another interpreter
+    detail::get_local_internals().registered_types_cpp.clear();
+    detail::get_local_internals().registered_exception_translators.clear();
+
+    Py_Finalize();
+
+    if (internals_ptr_ptr) {
+        delete *internals_ptr_ptr;
+        *internals_ptr_ptr = nullptr;
+    }
+}
+
+/** \rst
+    Scope guard version of `initialize_interpreter` and `finalize_interpreter`.
+    This a move-only guard and only a single instance can exist.
+
+    See `initialize_interpreter` for a discussion of its constructor arguments.
+
+    .. code-block:: cpp
+
+        #include <pybind11/embed.h>
+
+        int main() {
+            py::scoped_interpreter guard{};
+            py::print(Hello, World!);
+        } // <-- interpreter shutdown
+ \endrst */
+class scoped_interpreter {
+public:
+    explicit scoped_interpreter(bool init_signal_handlers = true,
+                                int argc = 0,
+                                const char *const *argv = nullptr,
+                                bool add_program_dir_to_path = true) {
+        initialize_interpreter(init_signal_handlers, argc, argv, add_program_dir_to_path);
+    }
+
+    scoped_interpreter(const scoped_interpreter &) = delete;
+    scoped_interpreter(scoped_interpreter &&other) noexcept { other.is_valid = false; }
+    scoped_interpreter &operator=(const scoped_interpreter &) = delete;
+    scoped_interpreter &operator=(scoped_interpreter &&) = delete;
+
+    ~scoped_interpreter() {
+        if (is_valid) {
+            finalize_interpreter();
+        }
+    }
+
+private:
+    bool is_valid = true;
+};
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/eval.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/eval.h
new file mode 100644
index 000000000..bd5f981f5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/eval.h
@@ -0,0 +1,156 @@
+/*
+    pybind11/eval.h: Support for evaluating Python expressions and statements
+    from strings and files
+
+    Copyright (c) 2016 Klemens Morgenstern <klemens.morgenstern@ed-chemnitz.de> and
+                       Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <utility>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline void ensure_builtins_in_globals(object &global) {
+#if defined(PYPY_VERSION) || PY_VERSION_HEX < 0x03080000
+    // Running exec and eval adds `builtins` module under `__builtins__` key to
+    // globals if not yet present.  Python 3.8 made PyRun_String behave
+    // similarly. Let's also do that for older versions, for consistency. This
+    // was missing from PyPy3.8 7.3.7.
+    if (!global.contains("__builtins__"))
+        global["__builtins__"] = module_::import(PYBIND11_BUILTINS_MODULE);
+#else
+    (void) global;
+#endif
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+enum eval_mode {
+    /// Evaluate a string containing an isolated expression
+    eval_expr,
+
+    /// Evaluate a string containing a single statement. Returns \c none
+    eval_single_statement,
+
+    /// Evaluate a string containing a sequence of statement. Returns \c none
+    eval_statements
+};
+
+template <eval_mode mode = eval_expr>
+object eval(const str &expr, object global = globals(), object local = object()) {
+    if (!local) {
+        local = global;
+    }
+
+    detail::ensure_builtins_in_globals(global);
+
+    /* PyRun_String does not accept a PyObject / encoding specifier,
+       this seems to be the only alternative */
+    std::string buffer = "# -*- coding: utf-8 -*-\n" + (std::string) expr;
+
+    int start = 0;
+    switch (mode) {
+        case eval_expr:
+            start = Py_eval_input;
+            break;
+        case eval_single_statement:
+            start = Py_single_input;
+            break;
+        case eval_statements:
+            start = Py_file_input;
+            break;
+        default:
+            pybind11_fail("invalid evaluation mode");
+    }
+
+    PyObject *result = PyRun_String(buffer.c_str(), start, global.ptr(), local.ptr());
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<object>(result);
+}
+
+template <eval_mode mode = eval_expr, size_t N>
+object eval(const char (&s)[N], object global = globals(), object local = object()) {
+    /* Support raw string literals by removing common leading whitespace */
+    auto expr = (s[0] == '\n') ? str(module_::import("textwrap").attr("dedent")(s)) : str(s);
+    return eval<mode>(expr, std::move(global), std::move(local));
+}
+
+inline void exec(const str &expr, object global = globals(), object local = object()) {
+    eval<eval_statements>(expr, std::move(global), std::move(local));
+}
+
+template <size_t N>
+void exec(const char (&s)[N], object global = globals(), object local = object()) {
+    eval<eval_statements>(s, std::move(global), std::move(local));
+}
+
+#if defined(PYPY_VERSION)
+template <eval_mode mode = eval_statements>
+object eval_file(str, object, object) {
+    pybind11_fail("eval_file not supported in PyPy3. Use eval");
+}
+template <eval_mode mode = eval_statements>
+object eval_file(str, object) {
+    pybind11_fail("eval_file not supported in PyPy3. Use eval");
+}
+template <eval_mode mode = eval_statements>
+object eval_file(str) {
+    pybind11_fail("eval_file not supported in PyPy3. Use eval");
+}
+#else
+template <eval_mode mode = eval_statements>
+object eval_file(str fname, object global = globals(), object local = object()) {
+    if (!local) {
+        local = global;
+    }
+
+    detail::ensure_builtins_in_globals(global);
+
+    int start = 0;
+    switch (mode) {
+        case eval_expr:
+            start = Py_eval_input;
+            break;
+        case eval_single_statement:
+            start = Py_single_input;
+            break;
+        case eval_statements:
+            start = Py_file_input;
+            break;
+        default:
+            pybind11_fail("invalid evaluation mode");
+    }
+
+    int closeFile = 1;
+    std::string fname_str = (std::string) fname;
+    FILE *f = _Py_fopen_obj(fname.ptr(), "r");
+    if (!f) {
+        PyErr_Clear();
+        pybind11_fail("File \"" + fname_str + "\" could not be opened!");
+    }
+
+    if (!global.contains("__file__")) {
+        global["__file__"] = std::move(fname);
+    }
+
+    PyObject *result
+        = PyRun_FileEx(f, fname_str.c_str(), start, global.ptr(), local.ptr(), closeFile);
+
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<object>(result);
+}
+#endif
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/functional.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/functional.h
new file mode 100644
index 000000000..4034990d8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/functional.h
@@ -0,0 +1,130 @@
+/*
+    pybind11/functional.h: std::function<> support
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <functional>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename Return, typename... Args>
+struct type_caster<std::function<Return(Args...)>> {
+    using type = std::function<Return(Args...)>;
+    using retval_type = conditional_t<std::is_same<Return, void>::value, void_type, Return>;
+    using function_type = Return (*)(Args...);
+
+public:
+    bool load(handle src, bool convert) {
+        if (src.is_none()) {
+            // Defer accepting None to other overloads (if we aren't in convert mode):
+            if (!convert) {
+                return false;
+            }
+            return true;
+        }
+
+        if (!isinstance<function>(src)) {
+            return false;
+        }
+
+        auto func = reinterpret_borrow<function>(src);
+
+        /*
+           When passing a C++ function as an argument to another C++
+           function via Python, every function call would normally involve
+           a full C++ -> Python -> C++ roundtrip, which can be prohibitive.
+           Here, we try to at least detect the case where the function is
+           stateless (i.e. function pointer or lambda function without
+           captured variables), in which case the roundtrip can be avoided.
+         */
+        if (auto cfunc = func.cpp_function()) {
+            auto *cfunc_self = PyCFunction_GET_SELF(cfunc.ptr());
+            if (isinstance<capsule>(cfunc_self)) {
+                auto c = reinterpret_borrow<capsule>(cfunc_self);
+                auto *rec = (function_record *) c;
+
+                while (rec != nullptr) {
+                    if (rec->is_stateless
+                        && same_type(typeid(function_type),
+                                     *reinterpret_cast<const std::type_info *>(rec->data[1]))) {
+                        struct capture {
+                            function_type f;
+                        };
+                        value = ((capture *) &rec->data)->f;
+                        return true;
+                    }
+                    rec = rec->next;
+                }
+            }
+            // PYPY segfaults here when passing builtin function like sum.
+            // Raising an fail exception here works to prevent the segfault, but only on gcc.
+            // See PR #1413 for full details
+        }
+
+        // ensure GIL is held during functor destruction
+        struct func_handle {
+            function f;
+#if !(defined(_MSC_VER) && _MSC_VER == 1916 && defined(PYBIND11_CPP17))
+            // This triggers a syntax error under very special conditions (very weird indeed).
+            explicit
+#endif
+                func_handle(function &&f_) noexcept
+                : f(std::move(f_)) {
+            }
+            func_handle(const func_handle &f_) { operator=(f_); }
+            func_handle &operator=(const func_handle &f_) {
+                gil_scoped_acquire acq;
+                f = f_.f;
+                return *this;
+            }
+            ~func_handle() {
+                gil_scoped_acquire acq;
+                function kill_f(std::move(f));
+            }
+        };
+
+        // to emulate 'move initialization capture' in C++11
+        struct func_wrapper {
+            func_handle hfunc;
+            explicit func_wrapper(func_handle &&hf) noexcept : hfunc(std::move(hf)) {}
+            Return operator()(Args... args) const {
+                gil_scoped_acquire acq;
+                // casts the returned object as a rvalue to the return type
+                return hfunc.f(std::forward<Args>(args)...).template cast<Return>();
+            }
+        };
+
+        value = func_wrapper(func_handle(std::move(func)));
+        return true;
+    }
+
+    template <typename Func>
+    static handle cast(Func &&f_, return_value_policy policy, handle /* parent */) {
+        if (!f_) {
+            return none().inc_ref();
+        }
+
+        auto result = f_.template target<function_type>();
+        if (result) {
+            return cpp_function(*result, policy).release();
+        }
+        return cpp_function(std::forward<Func>(f_), policy).release();
+    }
+
+    PYBIND11_TYPE_CASTER(type,
+                         const_name("Callable[[") + concat(make_caster<Args>::name...)
+                             + const_name("], ") + make_caster<retval_type>::name
+                             + const_name("]"));
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/gil.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/gil.h
new file mode 100644
index 000000000..a0b5de151
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/gil.h
@@ -0,0 +1,202 @@
+/*
+    pybind11/gil.h: RAII helpers for managing the GIL
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "detail/internals.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// forward declarations
+PyThreadState *get_thread_state_unchecked();
+
+PYBIND11_NAMESPACE_END(detail)
+
+#if defined(WITH_THREAD) && !defined(PYPY_VERSION)
+
+/* The functions below essentially reproduce the PyGILState_* API using a RAII
+ * pattern, but there are a few important differences:
+ *
+ * 1. When acquiring the GIL from an non-main thread during the finalization
+ *    phase, the GILState API blindly terminates the calling thread, which
+ *    is often not what is wanted. This API does not do this.
+ *
+ * 2. The gil_scoped_release function can optionally cut the relationship
+ *    of a PyThreadState and its associated thread, which allows moving it to
+ *    another thread (this is a fairly rare/advanced use case).
+ *
+ * 3. The reference count of an acquired thread state can be controlled. This
+ *    can be handy to prevent cases where callbacks issued from an external
+ *    thread would otherwise constantly construct and destroy thread state data
+ *    structures.
+ *
+ * See the Python bindings of NanoGUI (http://github.com/wjakob/nanogui) for an
+ * example which uses features 2 and 3 to migrate the Python thread of
+ * execution to another thread (to run the event loop on the original thread,
+ * in this case).
+ */
+
+class gil_scoped_acquire {
+public:
+    PYBIND11_NOINLINE gil_scoped_acquire() {
+        auto &internals = detail::get_internals();
+        tstate = (PyThreadState *) PYBIND11_TLS_GET_VALUE(internals.tstate);
+
+        if (!tstate) {
+            /* Check if the GIL was acquired using the PyGILState_* API instead (e.g. if
+               calling from a Python thread). Since we use a different key, this ensures
+               we don't create a new thread state and deadlock in PyEval_AcquireThread
+               below. Note we don't save this state with internals.tstate, since we don't
+               create it we would fail to clear it (its reference count should be > 0). */
+            tstate = PyGILState_GetThisThreadState();
+        }
+
+        if (!tstate) {
+            tstate = PyThreadState_New(internals.istate);
+#    if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            if (!tstate) {
+                pybind11_fail("scoped_acquire: could not create thread state!");
+            }
+#    endif
+            tstate->gilstate_counter = 0;
+            PYBIND11_TLS_REPLACE_VALUE(internals.tstate, tstate);
+        } else {
+            release = detail::get_thread_state_unchecked() != tstate;
+        }
+
+        if (release) {
+            PyEval_AcquireThread(tstate);
+        }
+
+        inc_ref();
+    }
+
+    void inc_ref() { ++tstate->gilstate_counter; }
+
+    PYBIND11_NOINLINE void dec_ref() {
+        --tstate->gilstate_counter;
+#    if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+        if (detail::get_thread_state_unchecked() != tstate) {
+            pybind11_fail("scoped_acquire::dec_ref(): thread state must be current!");
+        }
+        if (tstate->gilstate_counter < 0) {
+            pybind11_fail("scoped_acquire::dec_ref(): reference count underflow!");
+        }
+#    endif
+        if (tstate->gilstate_counter == 0) {
+#    if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            if (!release) {
+                pybind11_fail("scoped_acquire::dec_ref(): internal error!");
+            }
+#    endif
+            PyThreadState_Clear(tstate);
+            if (active) {
+                PyThreadState_DeleteCurrent();
+            }
+            PYBIND11_TLS_DELETE_VALUE(detail::get_internals().tstate);
+            release = false;
+        }
+    }
+
+    /// This method will disable the PyThreadState_DeleteCurrent call and the
+    /// GIL won't be acquired. This method should be used if the interpreter
+    /// could be shutting down when this is called, as thread deletion is not
+    /// allowed during shutdown. Check _Py_IsFinalizing() on Python 3.7+, and
+    /// protect subsequent code.
+    PYBIND11_NOINLINE void disarm() { active = false; }
+
+    PYBIND11_NOINLINE ~gil_scoped_acquire() {
+        dec_ref();
+        if (release) {
+            PyEval_SaveThread();
+        }
+    }
+
+private:
+    PyThreadState *tstate = nullptr;
+    bool release = true;
+    bool active = true;
+};
+
+class gil_scoped_release {
+public:
+    explicit gil_scoped_release(bool disassoc = false) : disassoc(disassoc) {
+        // `get_internals()` must be called here unconditionally in order to initialize
+        // `internals.tstate` for subsequent `gil_scoped_acquire` calls. Otherwise, an
+        // initialization race could occur as multiple threads try `gil_scoped_acquire`.
+        auto &internals = detail::get_internals();
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        tstate = PyEval_SaveThread();
+        if (disassoc) {
+            // Python >= 3.7 can remove this, it's an int before 3.7
+            // NOLINTNEXTLINE(readability-qualified-auto)
+            auto key = internals.tstate;
+            PYBIND11_TLS_DELETE_VALUE(key);
+        }
+    }
+
+    /// This method will disable the PyThreadState_DeleteCurrent call and the
+    /// GIL won't be acquired. This method should be used if the interpreter
+    /// could be shutting down when this is called, as thread deletion is not
+    /// allowed during shutdown. Check _Py_IsFinalizing() on Python 3.7+, and
+    /// protect subsequent code.
+    PYBIND11_NOINLINE void disarm() { active = false; }
+
+    ~gil_scoped_release() {
+        if (!tstate) {
+            return;
+        }
+        // `PyEval_RestoreThread()` should not be called if runtime is finalizing
+        if (active) {
+            PyEval_RestoreThread(tstate);
+        }
+        if (disassoc) {
+            // Python >= 3.7 can remove this, it's an int before 3.7
+            // NOLINTNEXTLINE(readability-qualified-auto)
+            auto key = detail::get_internals().tstate;
+            PYBIND11_TLS_REPLACE_VALUE(key, tstate);
+        }
+    }
+
+private:
+    PyThreadState *tstate;
+    bool disassoc;
+    bool active = true;
+};
+#elif defined(PYPY_VERSION)
+class gil_scoped_acquire {
+    PyGILState_STATE state;
+
+public:
+    gil_scoped_acquire() { state = PyGILState_Ensure(); }
+    ~gil_scoped_acquire() { PyGILState_Release(state); }
+    void disarm() {}
+};
+
+class gil_scoped_release {
+    PyThreadState *state;
+
+public:
+    gil_scoped_release() { state = PyEval_SaveThread(); }
+    ~gil_scoped_release() { PyEval_RestoreThread(state); }
+    void disarm() {}
+};
+#else
+class gil_scoped_acquire {
+    void disarm() {}
+};
+class gil_scoped_release {
+    void disarm() {}
+};
+#endif
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/iostream.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/iostream.h
new file mode 100644
index 000000000..1878089e3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/iostream.h
@@ -0,0 +1,265 @@
+/*
+    pybind11/iostream.h -- Tools to assist with redirecting cout and cerr to Python
+
+    Copyright (c) 2017 Henry F. Schreiner
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+
+    WARNING: The implementation in this file is NOT thread safe. Multiple
+    threads writing to a redirected ostream concurrently cause data races
+    and potentially buffer overflows. Therefore it is currently a requirement
+    that all (possibly) concurrent redirected ostream writes are protected by
+    a mutex.
+    #HelpAppreciated: Work on iostream.h thread safety.
+    For more background see the discussions under
+    https://github.com/pybind/pybind11/pull/2982 and
+    https://github.com/pybind/pybind11/pull/2995.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <algorithm>
+#include <cstring>
+#include <iostream>
+#include <iterator>
+#include <memory>
+#include <ostream>
+#include <streambuf>
+#include <string>
+#include <utility>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Buffer that writes to Python instead of C++
+class pythonbuf : public std::streambuf {
+private:
+    using traits_type = std::streambuf::traits_type;
+
+    const size_t buf_size;
+    std::unique_ptr<char[]> d_buffer;
+    object pywrite;
+    object pyflush;
+
+    int overflow(int c) override {
+        if (!traits_type::eq_int_type(c, traits_type::eof())) {
+            *pptr() = traits_type::to_char_type(c);
+            pbump(1);
+        }
+        return sync() == 0 ? traits_type::not_eof(c) : traits_type::eof();
+    }
+
+    // Computes how many bytes at the end of the buffer are part of an
+    // incomplete sequence of UTF-8 bytes.
+    // Precondition: pbase() < pptr()
+    size_t utf8_remainder() const {
+        const auto rbase = std::reverse_iterator<char *>(pbase());
+        const auto rpptr = std::reverse_iterator<char *>(pptr());
+        auto is_ascii = [](char c) { return (static_cast<unsigned char>(c) & 0x80) == 0x00; };
+        auto is_leading = [](char c) { return (static_cast<unsigned char>(c) & 0xC0) == 0xC0; };
+        auto is_leading_2b = [](char c) { return static_cast<unsigned char>(c) <= 0xDF; };
+        auto is_leading_3b = [](char c) { return static_cast<unsigned char>(c) <= 0xEF; };
+        // If the last character is ASCII, there are no incomplete code points
+        if (is_ascii(*rpptr)) {
+            return 0;
+        }
+        // Otherwise, work back from the end of the buffer and find the first
+        // UTF-8 leading byte
+        const auto rpend = rbase - rpptr >= 3 ? rpptr + 3 : rbase;
+        const auto leading = std::find_if(rpptr, rpend, is_leading);
+        if (leading == rbase) {
+            return 0;
+        }
+        const auto dist = static_cast<size_t>(leading - rpptr);
+        size_t remainder = 0;
+
+        if (dist == 0) {
+            remainder = 1; // 1-byte code point is impossible
+        } else if (dist == 1) {
+            remainder = is_leading_2b(*leading) ? 0 : dist + 1;
+        } else if (dist == 2) {
+            remainder = is_leading_3b(*leading) ? 0 : dist + 1;
+        }
+        // else if (dist >= 3), at least 4 bytes before encountering an UTF-8
+        // leading byte, either no remainder or invalid UTF-8.
+        // Invalid UTF-8 will cause an exception later when converting
+        // to a Python string, so that's not handled here.
+        return remainder;
+    }
+
+    // This function must be non-virtual to be called in a destructor.
+    int _sync() {
+        if (pbase() != pptr()) { // If buffer is not empty
+            gil_scoped_acquire tmp;
+            // This subtraction cannot be negative, so dropping the sign.
+            auto size = static_cast<size_t>(pptr() - pbase());
+            size_t remainder = utf8_remainder();
+
+            if (size > remainder) {
+                str line(pbase(), size - remainder);
+                pywrite(std::move(line));
+                pyflush();
+            }
+
+            // Copy the remainder at the end of the buffer to the beginning:
+            if (remainder > 0) {
+                std::memmove(pbase(), pptr() - remainder, remainder);
+            }
+            setp(pbase(), epptr());
+            pbump(static_cast<int>(remainder));
+        }
+        return 0;
+    }
+
+    int sync() override { return _sync(); }
+
+public:
+    explicit pythonbuf(const object &pyostream, size_t buffer_size = 1024)
+        : buf_size(buffer_size), d_buffer(new char[buf_size]), pywrite(pyostream.attr("write")),
+          pyflush(pyostream.attr("flush")) {
+        setp(d_buffer.get(), d_buffer.get() + buf_size - 1);
+    }
+
+    pythonbuf(pythonbuf &&) = default;
+
+    /// Sync before destroy
+    ~pythonbuf() override { _sync(); }
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+/** \rst
+    This a move-only guard that redirects output.
+
+    .. code-block:: cpp
+
+        #include <pybind11/iostream.h>
+
+        ...
+
+        {
+            py::scoped_ostream_redirect output;
+            std::cout << "Hello, World!"; // Python stdout
+        } // <-- return std::cout to normal
+
+    You can explicitly pass the c++ stream and the python object,
+    for example to guard stderr instead.
+
+    .. code-block:: cpp
+
+        {
+            py::scoped_ostream_redirect output{
+                std::cerr, py::module::import("sys").attr("stderr")};
+            std::cout << "Hello, World!";
+        }
+ \endrst */
+class scoped_ostream_redirect {
+protected:
+    std::streambuf *old;
+    std::ostream &costream;
+    detail::pythonbuf buffer;
+
+public:
+    explicit scoped_ostream_redirect(std::ostream &costream = std::cout,
+                                     const object &pyostream
+                                     = module_::import("sys").attr("stdout"))
+        : costream(costream), buffer(pyostream) {
+        old = costream.rdbuf(&buffer);
+    }
+
+    ~scoped_ostream_redirect() { costream.rdbuf(old); }
+
+    scoped_ostream_redirect(const scoped_ostream_redirect &) = delete;
+    scoped_ostream_redirect(scoped_ostream_redirect &&other) = default;
+    scoped_ostream_redirect &operator=(const scoped_ostream_redirect &) = delete;
+    scoped_ostream_redirect &operator=(scoped_ostream_redirect &&) = delete;
+};
+
+/** \rst
+    Like `scoped_ostream_redirect`, but redirects cerr by default. This class
+    is provided primary to make ``py::call_guard`` easier to make.
+
+    .. code-block:: cpp
+
+     m.def("noisy_func", &noisy_func,
+           py::call_guard<scoped_ostream_redirect,
+                          scoped_estream_redirect>());
+
+\endrst */
+class scoped_estream_redirect : public scoped_ostream_redirect {
+public:
+    explicit scoped_estream_redirect(std::ostream &costream = std::cerr,
+                                     const object &pyostream
+                                     = module_::import("sys").attr("stderr"))
+        : scoped_ostream_redirect(costream, pyostream) {}
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Class to redirect output as a context manager. C++ backend.
+class OstreamRedirect {
+    bool do_stdout_;
+    bool do_stderr_;
+    std::unique_ptr<scoped_ostream_redirect> redirect_stdout;
+    std::unique_ptr<scoped_estream_redirect> redirect_stderr;
+
+public:
+    explicit OstreamRedirect(bool do_stdout = true, bool do_stderr = true)
+        : do_stdout_(do_stdout), do_stderr_(do_stderr) {}
+
+    void enter() {
+        if (do_stdout_) {
+            redirect_stdout.reset(new scoped_ostream_redirect());
+        }
+        if (do_stderr_) {
+            redirect_stderr.reset(new scoped_estream_redirect());
+        }
+    }
+
+    void exit() {
+        redirect_stdout.reset();
+        redirect_stderr.reset();
+    }
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+/** \rst
+    This is a helper function to add a C++ redirect context manager to Python
+    instead of using a C++ guard. To use it, add the following to your binding code:
+
+    .. code-block:: cpp
+
+        #include <pybind11/iostream.h>
+
+        ...
+
+        py::add_ostream_redirect(m, "ostream_redirect");
+
+    You now have a Python context manager that redirects your output:
+
+    .. code-block:: python
+
+        with m.ostream_redirect():
+            m.print_to_cout_function()
+
+    This manager can optionally be told which streams to operate on:
+
+    .. code-block:: python
+
+        with m.ostream_redirect(stdout=true, stderr=true):
+            m.noisy_function_with_error_printing()
+
+ \endrst */
+inline class_<detail::OstreamRedirect>
+add_ostream_redirect(module_ m, const std::string &name = "ostream_redirect") {
+    return class_<detail::OstreamRedirect>(std::move(m), name.c_str(), module_local())
+        .def(init<bool, bool>(), arg("stdout") = true, arg("stderr") = true)
+        .def("__enter__", &detail::OstreamRedirect::enter)
+        .def("__exit__", [](detail::OstreamRedirect &self_, const args &) { self_.exit(); });
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/numpy.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/numpy.h
new file mode 100644
index 000000000..0291b02d0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/numpy.h
@@ -0,0 +1,1984 @@
+/*
+    pybind11/numpy.h: Basic NumPy support, vectorize() wrapper
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+#include "complex.h"
+
+#include <algorithm>
+#include <array>
+#include <cstdint>
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <numeric>
+#include <sstream>
+#include <string>
+#include <type_traits>
+#include <typeindex>
+#include <utility>
+#include <vector>
+
+/* This will be true on all flat address space platforms and allows us to reduce the
+   whole npy_intp / ssize_t / Py_intptr_t business down to just ssize_t for all size
+   and dimension types (e.g. shape, strides, indexing), instead of inflicting this
+   upon the library user. */
+static_assert(sizeof(::pybind11::ssize_t) == sizeof(Py_intptr_t), "ssize_t != Py_intptr_t");
+static_assert(std::is_signed<Py_intptr_t>::value, "Py_intptr_t must be signed");
+// We now can reinterpret_cast between py::ssize_t and Py_intptr_t (MSVC + PyPy cares)
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+class array; // Forward declaration
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <>
+struct handle_type_name<array> {
+    static constexpr auto name = const_name("numpy.ndarray");
+};
+
+template <typename type, typename SFINAE = void>
+struct npy_format_descriptor;
+
+struct PyArrayDescr_Proxy {
+    PyObject_HEAD
+    PyObject *typeobj;
+    char kind;
+    char type;
+    char byteorder;
+    char flags;
+    int type_num;
+    int elsize;
+    int alignment;
+    char *subarray;
+    PyObject *fields;
+    PyObject *names;
+};
+
+struct PyArray_Proxy {
+    PyObject_HEAD
+    char *data;
+    int nd;
+    ssize_t *dimensions;
+    ssize_t *strides;
+    PyObject *base;
+    PyObject *descr;
+    int flags;
+};
+
+struct PyVoidScalarObject_Proxy {
+    PyObject_VAR_HEAD char *obval;
+    PyArrayDescr_Proxy *descr;
+    int flags;
+    PyObject *base;
+};
+
+struct numpy_type_info {
+    PyObject *dtype_ptr;
+    std::string format_str;
+};
+
+struct numpy_internals {
+    std::unordered_map<std::type_index, numpy_type_info> registered_dtypes;
+
+    numpy_type_info *get_type_info(const std::type_info &tinfo, bool throw_if_missing = true) {
+        auto it = registered_dtypes.find(std::type_index(tinfo));
+        if (it != registered_dtypes.end()) {
+            return &(it->second);
+        }
+        if (throw_if_missing) {
+            pybind11_fail(std::string("NumPy type info missing for ") + tinfo.name());
+        }
+        return nullptr;
+    }
+
+    template <typename T>
+    numpy_type_info *get_type_info(bool throw_if_missing = true) {
+        return get_type_info(typeid(typename std::remove_cv<T>::type), throw_if_missing);
+    }
+};
+
+PYBIND11_NOINLINE void load_numpy_internals(numpy_internals *&ptr) {
+    ptr = &get_or_create_shared_data<numpy_internals>("_numpy_internals");
+}
+
+inline numpy_internals &get_numpy_internals() {
+    static numpy_internals *ptr = nullptr;
+    if (!ptr) {
+        load_numpy_internals(ptr);
+    }
+    return *ptr;
+}
+
+template <typename T>
+struct same_size {
+    template <typename U>
+    using as = bool_constant<sizeof(T) == sizeof(U)>;
+};
+
+template <typename Concrete>
+constexpr int platform_lookup() {
+    return -1;
+}
+
+// Lookup a type according to its size, and return a value corresponding to the NumPy typenum.
+template <typename Concrete, typename T, typename... Ts, typename... Ints>
+constexpr int platform_lookup(int I, Ints... Is) {
+    return sizeof(Concrete) == sizeof(T) ? I : platform_lookup<Concrete, Ts...>(Is...);
+}
+
+struct npy_api {
+    enum constants {
+        NPY_ARRAY_C_CONTIGUOUS_ = 0x0001,
+        NPY_ARRAY_F_CONTIGUOUS_ = 0x0002,
+        NPY_ARRAY_OWNDATA_ = 0x0004,
+        NPY_ARRAY_FORCECAST_ = 0x0010,
+        NPY_ARRAY_ENSUREARRAY_ = 0x0040,
+        NPY_ARRAY_ALIGNED_ = 0x0100,
+        NPY_ARRAY_WRITEABLE_ = 0x0400,
+        NPY_BOOL_ = 0,
+        NPY_BYTE_,
+        NPY_UBYTE_,
+        NPY_SHORT_,
+        NPY_USHORT_,
+        NPY_INT_,
+        NPY_UINT_,
+        NPY_LONG_,
+        NPY_ULONG_,
+        NPY_LONGLONG_,
+        NPY_ULONGLONG_,
+        NPY_FLOAT_,
+        NPY_DOUBLE_,
+        NPY_LONGDOUBLE_,
+        NPY_CFLOAT_,
+        NPY_CDOUBLE_,
+        NPY_CLONGDOUBLE_,
+        NPY_OBJECT_ = 17,
+        NPY_STRING_,
+        NPY_UNICODE_,
+        NPY_VOID_,
+        // Platform-dependent normalization
+        NPY_INT8_ = NPY_BYTE_,
+        NPY_UINT8_ = NPY_UBYTE_,
+        NPY_INT16_ = NPY_SHORT_,
+        NPY_UINT16_ = NPY_USHORT_,
+        // `npy_common.h` defines the integer aliases. In order, it checks:
+        // NPY_BITSOF_LONG, NPY_BITSOF_LONGLONG, NPY_BITSOF_INT, NPY_BITSOF_SHORT, NPY_BITSOF_CHAR
+        // and assigns the alias to the first matching size, so we should check in this order.
+        NPY_INT32_
+        = platform_lookup<std::int32_t, long, int, short>(NPY_LONG_, NPY_INT_, NPY_SHORT_),
+        NPY_UINT32_ = platform_lookup<std::uint32_t, unsigned long, unsigned int, unsigned short>(
+            NPY_ULONG_, NPY_UINT_, NPY_USHORT_),
+        NPY_INT64_
+        = platform_lookup<std::int64_t, long, long long, int>(NPY_LONG_, NPY_LONGLONG_, NPY_INT_),
+        NPY_UINT64_
+        = platform_lookup<std::uint64_t, unsigned long, unsigned long long, unsigned int>(
+            NPY_ULONG_, NPY_ULONGLONG_, NPY_UINT_),
+    };
+
+    struct PyArray_Dims {
+        Py_intptr_t *ptr;
+        int len;
+    };
+
+    static npy_api &get() {
+        static npy_api api = lookup();
+        return api;
+    }
+
+    bool PyArray_Check_(PyObject *obj) const {
+        return PyObject_TypeCheck(obj, PyArray_Type_) != 0;
+    }
+    bool PyArrayDescr_Check_(PyObject *obj) const {
+        return PyObject_TypeCheck(obj, PyArrayDescr_Type_) != 0;
+    }
+
+    unsigned int (*PyArray_GetNDArrayCFeatureVersion_)();
+    PyObject *(*PyArray_DescrFromType_)(int);
+    PyObject *(*PyArray_NewFromDescr_)(PyTypeObject *,
+                                       PyObject *,
+                                       int,
+                                       Py_intptr_t const *,
+                                       Py_intptr_t const *,
+                                       void *,
+                                       int,
+                                       PyObject *);
+    // Unused. Not removed because that affects ABI of the class.
+    PyObject *(*PyArray_DescrNewFromType_)(int);
+    int (*PyArray_CopyInto_)(PyObject *, PyObject *);
+    PyObject *(*PyArray_NewCopy_)(PyObject *, int);
+    PyTypeObject *PyArray_Type_;
+    PyTypeObject *PyVoidArrType_Type_;
+    PyTypeObject *PyArrayDescr_Type_;
+    PyObject *(*PyArray_DescrFromScalar_)(PyObject *);
+    PyObject *(*PyArray_FromAny_)(PyObject *, PyObject *, int, int, int, PyObject *);
+    int (*PyArray_DescrConverter_)(PyObject *, PyObject **);
+    bool (*PyArray_EquivTypes_)(PyObject *, PyObject *);
+    int (*PyArray_GetArrayParamsFromObject_)(PyObject *,
+                                             PyObject *,
+                                             unsigned char,
+                                             PyObject **,
+                                             int *,
+                                             Py_intptr_t *,
+                                             PyObject **,
+                                             PyObject *);
+    PyObject *(*PyArray_Squeeze_)(PyObject *);
+    // Unused. Not removed because that affects ABI of the class.
+    int (*PyArray_SetBaseObject_)(PyObject *, PyObject *);
+    PyObject *(*PyArray_Resize_)(PyObject *, PyArray_Dims *, int, int);
+    PyObject *(*PyArray_Newshape_)(PyObject *, PyArray_Dims *, int);
+    PyObject *(*PyArray_View_)(PyObject *, PyObject *, PyObject *);
+
+private:
+    enum functions {
+        API_PyArray_GetNDArrayCFeatureVersion = 211,
+        API_PyArray_Type = 2,
+        API_PyArrayDescr_Type = 3,
+        API_PyVoidArrType_Type = 39,
+        API_PyArray_DescrFromType = 45,
+        API_PyArray_DescrFromScalar = 57,
+        API_PyArray_FromAny = 69,
+        API_PyArray_Resize = 80,
+        API_PyArray_CopyInto = 82,
+        API_PyArray_NewCopy = 85,
+        API_PyArray_NewFromDescr = 94,
+        API_PyArray_DescrNewFromType = 96,
+        API_PyArray_Newshape = 135,
+        API_PyArray_Squeeze = 136,
+        API_PyArray_View = 137,
+        API_PyArray_DescrConverter = 174,
+        API_PyArray_EquivTypes = 182,
+        API_PyArray_GetArrayParamsFromObject = 278,
+        API_PyArray_SetBaseObject = 282
+    };
+
+    static npy_api lookup() {
+        module_ m = module_::import("numpy.core.multiarray");
+        auto c = m.attr("_ARRAY_API");
+        void **api_ptr = (void **) PyCapsule_GetPointer(c.ptr(), nullptr);
+        npy_api api;
+#define DECL_NPY_API(Func) api.Func##_ = (decltype(api.Func##_)) api_ptr[API_##Func];
+        DECL_NPY_API(PyArray_GetNDArrayCFeatureVersion);
+        if (api.PyArray_GetNDArrayCFeatureVersion_() < 0x7) {
+            pybind11_fail("pybind11 numpy support requires numpy >= 1.7.0");
+        }
+        DECL_NPY_API(PyArray_Type);
+        DECL_NPY_API(PyVoidArrType_Type);
+        DECL_NPY_API(PyArrayDescr_Type);
+        DECL_NPY_API(PyArray_DescrFromType);
+        DECL_NPY_API(PyArray_DescrFromScalar);
+        DECL_NPY_API(PyArray_FromAny);
+        DECL_NPY_API(PyArray_Resize);
+        DECL_NPY_API(PyArray_CopyInto);
+        DECL_NPY_API(PyArray_NewCopy);
+        DECL_NPY_API(PyArray_NewFromDescr);
+        DECL_NPY_API(PyArray_DescrNewFromType);
+        DECL_NPY_API(PyArray_Newshape);
+        DECL_NPY_API(PyArray_Squeeze);
+        DECL_NPY_API(PyArray_View);
+        DECL_NPY_API(PyArray_DescrConverter);
+        DECL_NPY_API(PyArray_EquivTypes);
+        DECL_NPY_API(PyArray_GetArrayParamsFromObject);
+        DECL_NPY_API(PyArray_SetBaseObject);
+
+#undef DECL_NPY_API
+        return api;
+    }
+};
+
+inline PyArray_Proxy *array_proxy(void *ptr) { return reinterpret_cast<PyArray_Proxy *>(ptr); }
+
+inline const PyArray_Proxy *array_proxy(const void *ptr) {
+    return reinterpret_cast<const PyArray_Proxy *>(ptr);
+}
+
+inline PyArrayDescr_Proxy *array_descriptor_proxy(PyObject *ptr) {
+    return reinterpret_cast<PyArrayDescr_Proxy *>(ptr);
+}
+
+inline const PyArrayDescr_Proxy *array_descriptor_proxy(const PyObject *ptr) {
+    return reinterpret_cast<const PyArrayDescr_Proxy *>(ptr);
+}
+
+inline bool check_flags(const void *ptr, int flag) {
+    return (flag == (array_proxy(ptr)->flags & flag));
+}
+
+template <typename T>
+struct is_std_array : std::false_type {};
+template <typename T, size_t N>
+struct is_std_array<std::array<T, N>> : std::true_type {};
+template <typename T>
+struct is_complex : std::false_type {};
+template <typename T>
+struct is_complex<std::complex<T>> : std::true_type {};
+
+template <typename T>
+struct array_info_scalar {
+    using type = T;
+    static constexpr bool is_array = false;
+    static constexpr bool is_empty = false;
+    static constexpr auto extents = const_name("");
+    static void append_extents(list & /* shape */) {}
+};
+// Computes underlying type and a comma-separated list of extents for array
+// types (any mix of std::array and built-in arrays). An array of char is
+// treated as scalar because it gets special handling.
+template <typename T>
+struct array_info : array_info_scalar<T> {};
+template <typename T, size_t N>
+struct array_info<std::array<T, N>> {
+    using type = typename array_info<T>::type;
+    static constexpr bool is_array = true;
+    static constexpr bool is_empty = (N == 0) || array_info<T>::is_empty;
+    static constexpr size_t extent = N;
+
+    // appends the extents to shape
+    static void append_extents(list &shape) {
+        shape.append(N);
+        array_info<T>::append_extents(shape);
+    }
+
+    static constexpr auto extents = const_name<array_info<T>::is_array>(
+        concat(const_name<N>(), array_info<T>::extents), const_name<N>());
+};
+// For numpy we have special handling for arrays of characters, so we don't include
+// the size in the array extents.
+template <size_t N>
+struct array_info<char[N]> : array_info_scalar<char[N]> {};
+template <size_t N>
+struct array_info<std::array<char, N>> : array_info_scalar<std::array<char, N>> {};
+template <typename T, size_t N>
+struct array_info<T[N]> : array_info<std::array<T, N>> {};
+template <typename T>
+using remove_all_extents_t = typename array_info<T>::type;
+
+template <typename T>
+using is_pod_struct
+    = all_of<std::is_standard_layout<T>, // since we're accessing directly in memory
+                                         // we need a standard layout type
+#if defined(__GLIBCXX__)                                                                          \
+    && (__GLIBCXX__ < 20150422 || __GLIBCXX__ == 20150426 || __GLIBCXX__ == 20150623              \
+        || __GLIBCXX__ == 20150626 || __GLIBCXX__ == 20160803)
+             // libstdc++ < 5 (including versions 4.8.5, 4.9.3 and 4.9.4 which were released after
+             // 5) don't implement is_trivially_copyable, so approximate it
+             std::is_trivially_destructible<T>,
+             satisfies_any_of<T, std::has_trivial_copy_constructor, std::has_trivial_copy_assign>,
+#else
+             std::is_trivially_copyable<T>,
+#endif
+             satisfies_none_of<T,
+                               std::is_reference,
+                               std::is_array,
+                               is_std_array,
+                               std::is_arithmetic,
+                               is_complex,
+                               std::is_enum>>;
+
+// Replacement for std::is_pod (deprecated in C++20)
+template <typename T>
+using is_pod = all_of<std::is_standard_layout<T>, std::is_trivial<T>>;
+
+template <ssize_t Dim = 0, typename Strides>
+ssize_t byte_offset_unsafe(const Strides &) {
+    return 0;
+}
+template <ssize_t Dim = 0, typename Strides, typename... Ix>
+ssize_t byte_offset_unsafe(const Strides &strides, ssize_t i, Ix... index) {
+    return i * strides[Dim] + byte_offset_unsafe<Dim + 1>(strides, index...);
+}
+
+/**
+ * Proxy class providing unsafe, unchecked const access to array data.  This is constructed through
+ * the `unchecked<T, N>()` method of `array` or the `unchecked<N>()` method of `array_t<T>`. `Dims`
+ * will be -1 for dimensions determined at runtime.
+ */
+template <typename T, ssize_t Dims>
+class unchecked_reference {
+protected:
+    static constexpr bool Dynamic = Dims < 0;
+    const unsigned char *data_;
+    // Storing the shape & strides in local variables (i.e. these arrays) allows the compiler to
+    // make large performance gains on big, nested loops, but requires compile-time dimensions
+    conditional_t<Dynamic, const ssize_t *, std::array<ssize_t, (size_t) Dims>> shape_, strides_;
+    const ssize_t dims_;
+
+    friend class pybind11::array;
+    // Constructor for compile-time dimensions:
+    template <bool Dyn = Dynamic>
+    unchecked_reference(const void *data,
+                        const ssize_t *shape,
+                        const ssize_t *strides,
+                        enable_if_t<!Dyn, ssize_t>)
+        : data_{reinterpret_cast<const unsigned char *>(data)}, dims_{Dims} {
+        for (size_t i = 0; i < (size_t) dims_; i++) {
+            shape_[i] = shape[i];
+            strides_[i] = strides[i];
+        }
+    }
+    // Constructor for runtime dimensions:
+    template <bool Dyn = Dynamic>
+    unchecked_reference(const void *data,
+                        const ssize_t *shape,
+                        const ssize_t *strides,
+                        enable_if_t<Dyn, ssize_t> dims)
+        : data_{reinterpret_cast<const unsigned char *>(data)}, shape_{shape}, strides_{strides},
+          dims_{dims} {}
+
+public:
+    /**
+     * Unchecked const reference access to data at the given indices.  For a compile-time known
+     * number of dimensions, this requires the correct number of arguments; for run-time
+     * dimensionality, this is not checked (and so is up to the caller to use safely).
+     */
+    template <typename... Ix>
+    const T &operator()(Ix... index) const {
+        static_assert(ssize_t{sizeof...(Ix)} == Dims || Dynamic,
+                      "Invalid number of indices for unchecked array reference");
+        return *reinterpret_cast<const T *>(data_
+                                            + byte_offset_unsafe(strides_, ssize_t(index)...));
+    }
+    /**
+     * Unchecked const reference access to data; this operator only participates if the reference
+     * is to a 1-dimensional array.  When present, this is exactly equivalent to `obj(index)`.
+     */
+    template <ssize_t D = Dims, typename = enable_if_t<D == 1 || Dynamic>>
+    const T &operator[](ssize_t index) const {
+        return operator()(index);
+    }
+
+    /// Pointer access to the data at the given indices.
+    template <typename... Ix>
+    const T *data(Ix... ix) const {
+        return &operator()(ssize_t(ix)...);
+    }
+
+    /// Returns the item size, i.e. sizeof(T)
+    constexpr static ssize_t itemsize() { return sizeof(T); }
+
+    /// Returns the shape (i.e. size) of dimension `dim`
+    ssize_t shape(ssize_t dim) const { return shape_[(size_t) dim]; }
+
+    /// Returns the number of dimensions of the array
+    ssize_t ndim() const { return dims_; }
+
+    /// Returns the total number of elements in the referenced array, i.e. the product of the
+    /// shapes
+    template <bool Dyn = Dynamic>
+    enable_if_t<!Dyn, ssize_t> size() const {
+        return std::accumulate(
+            shape_.begin(), shape_.end(), (ssize_t) 1, std::multiplies<ssize_t>());
+    }
+    template <bool Dyn = Dynamic>
+    enable_if_t<Dyn, ssize_t> size() const {
+        return std::accumulate(shape_, shape_ + ndim(), (ssize_t) 1, std::multiplies<ssize_t>());
+    }
+
+    /// Returns the total number of bytes used by the referenced data.  Note that the actual span
+    /// in memory may be larger if the referenced array has non-contiguous strides (e.g. for a
+    /// slice).
+    ssize_t nbytes() const { return size() * itemsize(); }
+};
+
+template <typename T, ssize_t Dims>
+class unchecked_mutable_reference : public unchecked_reference<T, Dims> {
+    friend class pybind11::array;
+    using ConstBase = unchecked_reference<T, Dims>;
+    using ConstBase::ConstBase;
+    using ConstBase::Dynamic;
+
+public:
+    // Bring in const-qualified versions from base class
+    using ConstBase::operator();
+    using ConstBase::operator[];
+
+    /// Mutable, unchecked access to data at the given indices.
+    template <typename... Ix>
+    T &operator()(Ix... index) {
+        static_assert(ssize_t{sizeof...(Ix)} == Dims || Dynamic,
+                      "Invalid number of indices for unchecked array reference");
+        return const_cast<T &>(ConstBase::operator()(index...));
+    }
+    /**
+     * Mutable, unchecked access data at the given index; this operator only participates if the
+     * reference is to a 1-dimensional array (or has runtime dimensions).  When present, this is
+     * exactly equivalent to `obj(index)`.
+     */
+    template <ssize_t D = Dims, typename = enable_if_t<D == 1 || Dynamic>>
+    T &operator[](ssize_t index) {
+        return operator()(index);
+    }
+
+    /// Mutable pointer access to the data at the given indices.
+    template <typename... Ix>
+    T *mutable_data(Ix... ix) {
+        return &operator()(ssize_t(ix)...);
+    }
+};
+
+template <typename T, ssize_t Dim>
+struct type_caster<unchecked_reference<T, Dim>> {
+    static_assert(Dim == 0 && Dim > 0 /* always fail */,
+                  "unchecked array proxy object is not castable");
+};
+template <typename T, ssize_t Dim>
+struct type_caster<unchecked_mutable_reference<T, Dim>>
+    : type_caster<unchecked_reference<T, Dim>> {};
+
+PYBIND11_NAMESPACE_END(detail)
+
+class dtype : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(dtype, object, detail::npy_api::get().PyArrayDescr_Check_);
+
+    explicit dtype(const buffer_info &info) {
+        dtype descr(_dtype_from_pep3118()(pybind11::str(info.format)));
+        // If info.itemsize == 0, use the value calculated from the format string
+        m_ptr = descr.strip_padding(info.itemsize != 0 ? info.itemsize : descr.itemsize())
+                    .release()
+                    .ptr();
+    }
+
+    explicit dtype(const pybind11::str &format) : dtype(from_args(format)) {}
+
+    explicit dtype(const std::string &format) : dtype(pybind11::str(format)) {}
+
+    explicit dtype(const char *format) : dtype(pybind11::str(format)) {}
+
+    dtype(list names, list formats, list offsets, ssize_t itemsize) {
+        dict args;
+        args["names"] = std::move(names);
+        args["formats"] = std::move(formats);
+        args["offsets"] = std::move(offsets);
+        args["itemsize"] = pybind11::int_(itemsize);
+        m_ptr = from_args(args).release().ptr();
+    }
+
+    explicit dtype(int typenum)
+        : object(detail::npy_api::get().PyArray_DescrFromType_(typenum), stolen_t{}) {
+        if (m_ptr == nullptr) {
+            throw error_already_set();
+        }
+    }
+
+    /// This is essentially the same as calling numpy.dtype(args) in Python.
+    static dtype from_args(const object &args) {
+        PyObject *ptr = nullptr;
+        if ((detail::npy_api::get().PyArray_DescrConverter_(args.ptr(), &ptr) == 0) || !ptr) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<dtype>(ptr);
+    }
+
+    /// Return dtype associated with a C++ type.
+    template <typename T>
+    static dtype of() {
+        return detail::npy_format_descriptor<typename std::remove_cv<T>::type>::dtype();
+    }
+
+    /// Size of the data type in bytes.
+    ssize_t itemsize() const { return detail::array_descriptor_proxy(m_ptr)->elsize; }
+
+    /// Returns true for structured data types.
+    bool has_fields() const { return detail::array_descriptor_proxy(m_ptr)->names != nullptr; }
+
+    /// Single-character code for dtype's kind.
+    /// For example, floating point types are 'f' and integral types are 'i'.
+    char kind() const { return detail::array_descriptor_proxy(m_ptr)->kind; }
+
+    /// Single-character for dtype's type.
+    /// For example, ``float`` is 'f', ``double`` 'd', ``int`` 'i', and ``long`` 'l'.
+    char char_() const {
+        // Note: The signature, `dtype::char_` follows the naming of NumPy's
+        // public Python API (i.e., ``dtype.char``), rather than its internal
+        // C API (``PyArray_Descr::type``).
+        return detail::array_descriptor_proxy(m_ptr)->type;
+    }
+
+    /// type number of dtype.
+    int num() const {
+        // Note: The signature, `dtype::num` follows the naming of NumPy's public
+        // Python API (i.e., ``dtype.num``), rather than its internal
+        // C API (``PyArray_Descr::type_num``).
+        return detail::array_descriptor_proxy(m_ptr)->type_num;
+    }
+
+    /// Single character for byteorder
+    char byteorder() const { return detail::array_descriptor_proxy(m_ptr)->byteorder; }
+
+    /// Alignment of the data type
+    int alignment() const { return detail::array_descriptor_proxy(m_ptr)->alignment; }
+
+    /// Flags for the array descriptor
+    char flags() const { return detail::array_descriptor_proxy(m_ptr)->flags; }
+
+private:
+    static object _dtype_from_pep3118() {
+        static PyObject *obj = module_::import("numpy.core._internal")
+                                   .attr("_dtype_from_pep3118")
+                                   .cast<object>()
+                                   .release()
+                                   .ptr();
+        return reinterpret_borrow<object>(obj);
+    }
+
+    dtype strip_padding(ssize_t itemsize) {
+        // Recursively strip all void fields with empty names that are generated for
+        // padding fields (as of NumPy v1.11).
+        if (!has_fields()) {
+            return *this;
+        }
+
+        struct field_descr {
+            pybind11::str name;
+            object format;
+            pybind11::int_ offset;
+            field_descr(pybind11::str &&name, object &&format, pybind11::int_ &&offset)
+                : name{std::move(name)}, format{std::move(format)}, offset{std::move(offset)} {};
+        };
+        auto field_dict = attr("fields").cast<dict>();
+        std::vector<field_descr> field_descriptors;
+        field_descriptors.reserve(field_dict.size());
+
+        for (auto field : field_dict.attr("items")()) {
+            auto spec = field.cast<tuple>();
+            auto name = spec[0].cast<pybind11::str>();
+            auto spec_fo = spec[1].cast<tuple>();
+            auto format = spec_fo[0].cast<dtype>();
+            auto offset = spec_fo[1].cast<pybind11::int_>();
+            if ((len(name) == 0u) && format.kind() == 'V') {
+                continue;
+            }
+            field_descriptors.emplace_back(
+                std::move(name), format.strip_padding(format.itemsize()), std::move(offset));
+        }
+
+        std::sort(field_descriptors.begin(),
+                  field_descriptors.end(),
+                  [](const field_descr &a, const field_descr &b) {
+                      return a.offset.cast<int>() < b.offset.cast<int>();
+                  });
+
+        list names, formats, offsets;
+        for (auto &descr : field_descriptors) {
+            names.append(std::move(descr.name));
+            formats.append(std::move(descr.format));
+            offsets.append(std::move(descr.offset));
+        }
+        return dtype(std::move(names), std::move(formats), std::move(offsets), itemsize);
+    }
+};
+
+class array : public buffer {
+public:
+    PYBIND11_OBJECT_CVT(array, buffer, detail::npy_api::get().PyArray_Check_, raw_array)
+
+    enum {
+        c_style = detail::npy_api::NPY_ARRAY_C_CONTIGUOUS_,
+        f_style = detail::npy_api::NPY_ARRAY_F_CONTIGUOUS_,
+        forcecast = detail::npy_api::NPY_ARRAY_FORCECAST_
+    };
+
+    array() : array(0, static_cast<const double *>(nullptr)) {}
+
+    using ShapeContainer = detail::any_container<ssize_t>;
+    using StridesContainer = detail::any_container<ssize_t>;
+
+    // Constructs an array taking shape/strides from arbitrary container types
+    array(const pybind11::dtype &dt,
+          ShapeContainer shape,
+          StridesContainer strides,
+          const void *ptr = nullptr,
+          handle base = handle()) {
+
+        if (strides->empty()) {
+            *strides = detail::c_strides(*shape, dt.itemsize());
+        }
+
+        auto ndim = shape->size();
+        if (ndim != strides->size()) {
+            pybind11_fail("NumPy: shape ndim doesn't match strides ndim");
+        }
+        auto descr = dt;
+
+        int flags = 0;
+        if (base && ptr) {
+            if (isinstance<array>(base)) {
+                /* Copy flags from base (except ownership bit) */
+                flags = reinterpret_borrow<array>(base).flags()
+                        & ~detail::npy_api::NPY_ARRAY_OWNDATA_;
+            } else {
+                /* Writable by default, easy to downgrade later on if needed */
+                flags = detail::npy_api::NPY_ARRAY_WRITEABLE_;
+            }
+        }
+
+        auto &api = detail::npy_api::get();
+        auto tmp = reinterpret_steal<object>(api.PyArray_NewFromDescr_(
+            api.PyArray_Type_,
+            descr.release().ptr(),
+            (int) ndim,
+            // Use reinterpret_cast for PyPy on Windows (remove if fixed, checked on 7.3.1)
+            reinterpret_cast<Py_intptr_t *>(shape->data()),
+            reinterpret_cast<Py_intptr_t *>(strides->data()),
+            const_cast<void *>(ptr),
+            flags,
+            nullptr));
+        if (!tmp) {
+            throw error_already_set();
+        }
+        if (ptr) {
+            if (base) {
+                api.PyArray_SetBaseObject_(tmp.ptr(), base.inc_ref().ptr());
+            } else {
+                tmp = reinterpret_steal<object>(
+                    api.PyArray_NewCopy_(tmp.ptr(), -1 /* any order */));
+            }
+        }
+        m_ptr = tmp.release().ptr();
+    }
+
+    array(const pybind11::dtype &dt,
+          ShapeContainer shape,
+          const void *ptr = nullptr,
+          handle base = handle())
+        : array(dt, std::move(shape), {}, ptr, base) {}
+
+    template <typename T,
+              typename
+              = detail::enable_if_t<std::is_integral<T>::value && !std::is_same<bool, T>::value>>
+    array(const pybind11::dtype &dt, T count, const void *ptr = nullptr, handle base = handle())
+        : array(dt, {{count}}, ptr, base) {}
+
+    template <typename T>
+    array(ShapeContainer shape, StridesContainer strides, const T *ptr, handle base = handle())
+        : array(pybind11::dtype::of<T>(), std::move(shape), std::move(strides), ptr, base) {}
+
+    template <typename T>
+    array(ShapeContainer shape, const T *ptr, handle base = handle())
+        : array(std::move(shape), {}, ptr, base) {}
+
+    template <typename T>
+    explicit array(ssize_t count, const T *ptr, handle base = handle())
+        : array({count}, {}, ptr, base) {}
+
+    explicit array(const buffer_info &info, handle base = handle())
+        : array(pybind11::dtype(info), info.shape, info.strides, info.ptr, base) {}
+
+    /// Array descriptor (dtype)
+    pybind11::dtype dtype() const {
+        return reinterpret_borrow<pybind11::dtype>(detail::array_proxy(m_ptr)->descr);
+    }
+
+    /// Total number of elements
+    ssize_t size() const {
+        return std::accumulate(shape(), shape() + ndim(), (ssize_t) 1, std::multiplies<ssize_t>());
+    }
+
+    /// Byte size of a single element
+    ssize_t itemsize() const {
+        return detail::array_descriptor_proxy(detail::array_proxy(m_ptr)->descr)->elsize;
+    }
+
+    /// Total number of bytes
+    ssize_t nbytes() const { return size() * itemsize(); }
+
+    /// Number of dimensions
+    ssize_t ndim() const { return detail::array_proxy(m_ptr)->nd; }
+
+    /// Base object
+    object base() const { return reinterpret_borrow<object>(detail::array_proxy(m_ptr)->base); }
+
+    /// Dimensions of the array
+    const ssize_t *shape() const { return detail::array_proxy(m_ptr)->dimensions; }
+
+    /// Dimension along a given axis
+    ssize_t shape(ssize_t dim) const {
+        if (dim >= ndim()) {
+            fail_dim_check(dim, "invalid axis");
+        }
+        return shape()[dim];
+    }
+
+    /// Strides of the array
+    const ssize_t *strides() const { return detail::array_proxy(m_ptr)->strides; }
+
+    /// Stride along a given axis
+    ssize_t strides(ssize_t dim) const {
+        if (dim >= ndim()) {
+            fail_dim_check(dim, "invalid axis");
+        }
+        return strides()[dim];
+    }
+
+    /// Return the NumPy array flags
+    int flags() const { return detail::array_proxy(m_ptr)->flags; }
+
+    /// If set, the array is writeable (otherwise the buffer is read-only)
+    bool writeable() const {
+        return detail::check_flags(m_ptr, detail::npy_api::NPY_ARRAY_WRITEABLE_);
+    }
+
+    /// If set, the array owns the data (will be freed when the array is deleted)
+    bool owndata() const {
+        return detail::check_flags(m_ptr, detail::npy_api::NPY_ARRAY_OWNDATA_);
+    }
+
+    /// Pointer to the contained data. If index is not provided, points to the
+    /// beginning of the buffer. May throw if the index would lead to out of bounds access.
+    template <typename... Ix>
+    const void *data(Ix... index) const {
+        return static_cast<const void *>(detail::array_proxy(m_ptr)->data + offset_at(index...));
+    }
+
+    /// Mutable pointer to the contained data. If index is not provided, points to the
+    /// beginning of the buffer. May throw if the index would lead to out of bounds access.
+    /// May throw if the array is not writeable.
+    template <typename... Ix>
+    void *mutable_data(Ix... index) {
+        check_writeable();
+        return static_cast<void *>(detail::array_proxy(m_ptr)->data + offset_at(index...));
+    }
+
+    /// Byte offset from beginning of the array to a given index (full or partial).
+    /// May throw if the index would lead to out of bounds access.
+    template <typename... Ix>
+    ssize_t offset_at(Ix... index) const {
+        if ((ssize_t) sizeof...(index) > ndim()) {
+            fail_dim_check(sizeof...(index), "too many indices for an array");
+        }
+        return byte_offset(ssize_t(index)...);
+    }
+
+    ssize_t offset_at() const { return 0; }
+
+    /// Item count from beginning of the array to a given index (full or partial).
+    /// May throw if the index would lead to out of bounds access.
+    template <typename... Ix>
+    ssize_t index_at(Ix... index) const {
+        return offset_at(index...) / itemsize();
+    }
+
+    /**
+     * Returns a proxy object that provides access to the array's data without bounds or
+     * dimensionality checking.  Will throw if the array is missing the `writeable` flag.  Use with
+     * care: the array must not be destroyed or reshaped for the duration of the returned object,
+     * and the caller must take care not to access invalid dimensions or dimension indices.
+     */
+    template <typename T, ssize_t Dims = -1>
+    detail::unchecked_mutable_reference<T, Dims> mutable_unchecked() & {
+        if (PYBIND11_SILENCE_MSVC_C4127(Dims >= 0) && ndim() != Dims) {
+            throw std::domain_error("array has incorrect number of dimensions: "
+                                    + std::to_string(ndim()) + "; expected "
+                                    + std::to_string(Dims));
+        }
+        return detail::unchecked_mutable_reference<T, Dims>(
+            mutable_data(), shape(), strides(), ndim());
+    }
+
+    /**
+     * Returns a proxy object that provides const access to the array's data without bounds or
+     * dimensionality checking.  Unlike `mutable_unchecked()`, this does not require that the
+     * underlying array have the `writable` flag.  Use with care: the array must not be destroyed
+     * or reshaped for the duration of the returned object, and the caller must take care not to
+     * access invalid dimensions or dimension indices.
+     */
+    template <typename T, ssize_t Dims = -1>
+    detail::unchecked_reference<T, Dims> unchecked() const & {
+        if (PYBIND11_SILENCE_MSVC_C4127(Dims >= 0) && ndim() != Dims) {
+            throw std::domain_error("array has incorrect number of dimensions: "
+                                    + std::to_string(ndim()) + "; expected "
+                                    + std::to_string(Dims));
+        }
+        return detail::unchecked_reference<T, Dims>(data(), shape(), strides(), ndim());
+    }
+
+    /// Return a new view with all of the dimensions of length 1 removed
+    array squeeze() {
+        auto &api = detail::npy_api::get();
+        return reinterpret_steal<array>(api.PyArray_Squeeze_(m_ptr));
+    }
+
+    /// Resize array to given shape
+    /// If refcheck is true and more that one reference exist to this array
+    /// then resize will succeed only if it makes a reshape, i.e. original size doesn't change
+    void resize(ShapeContainer new_shape, bool refcheck = true) {
+        detail::npy_api::PyArray_Dims d
+            = {// Use reinterpret_cast for PyPy on Windows (remove if fixed, checked on 7.3.1)
+               reinterpret_cast<Py_intptr_t *>(new_shape->data()),
+               int(new_shape->size())};
+        // try to resize, set ordering param to -1 cause it's not used anyway
+        auto new_array = reinterpret_steal<object>(
+            detail::npy_api::get().PyArray_Resize_(m_ptr, &d, int(refcheck), -1));
+        if (!new_array) {
+            throw error_already_set();
+        }
+        if (isinstance<array>(new_array)) {
+            *this = std::move(new_array);
+        }
+    }
+
+    /// Optional `order` parameter omitted, to be added as needed.
+    array reshape(ShapeContainer new_shape) {
+        detail::npy_api::PyArray_Dims d
+            = {reinterpret_cast<Py_intptr_t *>(new_shape->data()), int(new_shape->size())};
+        auto new_array
+            = reinterpret_steal<array>(detail::npy_api::get().PyArray_Newshape_(m_ptr, &d, 0));
+        if (!new_array) {
+            throw error_already_set();
+        }
+        return new_array;
+    }
+
+    /// Create a view of an array in a different data type.
+    /// This function may fundamentally reinterpret the data in the array.
+    /// It is the responsibility of the caller to ensure that this is safe.
+    /// Only supports the `dtype` argument, the `type` argument is omitted,
+    /// to be added as needed.
+    array view(const std::string &dtype) {
+        auto &api = detail::npy_api::get();
+        auto new_view = reinterpret_steal<array>(api.PyArray_View_(
+            m_ptr, dtype::from_args(pybind11::str(dtype)).release().ptr(), nullptr));
+        if (!new_view) {
+            throw error_already_set();
+        }
+        return new_view;
+    }
+
+    /// Ensure that the argument is a NumPy array
+    /// In case of an error, nullptr is returned and the Python error is cleared.
+    static array ensure(handle h, int ExtraFlags = 0) {
+        auto result = reinterpret_steal<array>(raw_array(h.ptr(), ExtraFlags));
+        if (!result) {
+            PyErr_Clear();
+        }
+        return result;
+    }
+
+protected:
+    template <typename, typename>
+    friend struct detail::npy_format_descriptor;
+
+    void fail_dim_check(ssize_t dim, const std::string &msg) const {
+        throw index_error(msg + ": " + std::to_string(dim) + " (ndim = " + std::to_string(ndim())
+                          + ')');
+    }
+
+    template <typename... Ix>
+    ssize_t byte_offset(Ix... index) const {
+        check_dimensions(index...);
+        return detail::byte_offset_unsafe(strides(), ssize_t(index)...);
+    }
+
+    void check_writeable() const {
+        if (!writeable()) {
+            throw std::domain_error("array is not writeable");
+        }
+    }
+
+    template <typename... Ix>
+    void check_dimensions(Ix... index) const {
+        check_dimensions_impl(ssize_t(0), shape(), ssize_t(index)...);
+    }
+
+    void check_dimensions_impl(ssize_t, const ssize_t *) const {}
+
+    template <typename... Ix>
+    void check_dimensions_impl(ssize_t axis, const ssize_t *shape, ssize_t i, Ix... index) const {
+        if (i >= *shape) {
+            throw index_error(std::string("index ") + std::to_string(i)
+                              + " is out of bounds for axis " + std::to_string(axis)
+                              + " with size " + std::to_string(*shape));
+        }
+        check_dimensions_impl(axis + 1, shape + 1, index...);
+    }
+
+    /// Create array from any object -- always returns a new reference
+    static PyObject *raw_array(PyObject *ptr, int ExtraFlags = 0) {
+        if (ptr == nullptr) {
+            PyErr_SetString(PyExc_ValueError, "cannot create a pybind11::array from a nullptr");
+            return nullptr;
+        }
+        return detail::npy_api::get().PyArray_FromAny_(
+            ptr, nullptr, 0, 0, detail::npy_api::NPY_ARRAY_ENSUREARRAY_ | ExtraFlags, nullptr);
+    }
+};
+
+template <typename T, int ExtraFlags = array::forcecast>
+class array_t : public array {
+private:
+    struct private_ctor {};
+    // Delegating constructor needed when both moving and accessing in the same constructor
+    array_t(private_ctor,
+            ShapeContainer &&shape,
+            StridesContainer &&strides,
+            const T *ptr,
+            handle base)
+        : array(std::move(shape), std::move(strides), ptr, base) {}
+
+public:
+    static_assert(!detail::array_info<T>::is_array, "Array types cannot be used with array_t");
+
+    using value_type = T;
+
+    array_t() : array(0, static_cast<const T *>(nullptr)) {}
+    array_t(handle h, borrowed_t) : array(h, borrowed_t{}) {}
+    array_t(handle h, stolen_t) : array(h, stolen_t{}) {}
+
+    PYBIND11_DEPRECATED("Use array_t<T>::ensure() instead")
+    array_t(handle h, bool is_borrowed) : array(raw_array_t(h.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            PyErr_Clear();
+        }
+        if (!is_borrowed) {
+            Py_XDECREF(h.ptr());
+        }
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    array_t(const object &o) : array(raw_array_t(o.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    explicit array_t(const buffer_info &info, handle base = handle()) : array(info, base) {}
+
+    array_t(ShapeContainer shape,
+            StridesContainer strides,
+            const T *ptr = nullptr,
+            handle base = handle())
+        : array(std::move(shape), std::move(strides), ptr, base) {}
+
+    explicit array_t(ShapeContainer shape, const T *ptr = nullptr, handle base = handle())
+        : array_t(private_ctor{},
+                  std::move(shape),
+                  (ExtraFlags & f_style) != 0 ? detail::f_strides(*shape, itemsize())
+                                              : detail::c_strides(*shape, itemsize()),
+                  ptr,
+                  base) {}
+
+    explicit array_t(ssize_t count, const T *ptr = nullptr, handle base = handle())
+        : array({count}, {}, ptr, base) {}
+
+    constexpr ssize_t itemsize() const { return sizeof(T); }
+
+    template <typename... Ix>
+    ssize_t index_at(Ix... index) const {
+        return offset_at(index...) / itemsize();
+    }
+
+    template <typename... Ix>
+    const T *data(Ix... index) const {
+        return static_cast<const T *>(array::data(index...));
+    }
+
+    template <typename... Ix>
+    T *mutable_data(Ix... index) {
+        return static_cast<T *>(array::mutable_data(index...));
+    }
+
+    // Reference to element at a given index
+    template <typename... Ix>
+    const T &at(Ix... index) const {
+        if ((ssize_t) sizeof...(index) != ndim()) {
+            fail_dim_check(sizeof...(index), "index dimension mismatch");
+        }
+        return *(static_cast<const T *>(array::data())
+                 + byte_offset(ssize_t(index)...) / itemsize());
+    }
+
+    // Mutable reference to element at a given index
+    template <typename... Ix>
+    T &mutable_at(Ix... index) {
+        if ((ssize_t) sizeof...(index) != ndim()) {
+            fail_dim_check(sizeof...(index), "index dimension mismatch");
+        }
+        return *(static_cast<T *>(array::mutable_data())
+                 + byte_offset(ssize_t(index)...) / itemsize());
+    }
+
+    /**
+     * Returns a proxy object that provides access to the array's data without bounds or
+     * dimensionality checking.  Will throw if the array is missing the `writeable` flag.  Use with
+     * care: the array must not be destroyed or reshaped for the duration of the returned object,
+     * and the caller must take care not to access invalid dimensions or dimension indices.
+     */
+    template <ssize_t Dims = -1>
+    detail::unchecked_mutable_reference<T, Dims> mutable_unchecked() & {
+        return array::mutable_unchecked<T, Dims>();
+    }
+
+    /**
+     * Returns a proxy object that provides const access to the array's data without bounds or
+     * dimensionality checking.  Unlike `unchecked()`, this does not require that the underlying
+     * array have the `writable` flag.  Use with care: the array must not be destroyed or reshaped
+     * for the duration of the returned object, and the caller must take care not to access invalid
+     * dimensions or dimension indices.
+     */
+    template <ssize_t Dims = -1>
+    detail::unchecked_reference<T, Dims> unchecked() const & {
+        return array::unchecked<T, Dims>();
+    }
+
+    /// Ensure that the argument is a NumPy array of the correct dtype (and if not, try to convert
+    /// it).  In case of an error, nullptr is returned and the Python error is cleared.
+    static array_t ensure(handle h) {
+        auto result = reinterpret_steal<array_t>(raw_array_t(h.ptr()));
+        if (!result) {
+            PyErr_Clear();
+        }
+        return result;
+    }
+
+    static bool check_(handle h) {
+        const auto &api = detail::npy_api::get();
+        return api.PyArray_Check_(h.ptr())
+               && api.PyArray_EquivTypes_(detail::array_proxy(h.ptr())->descr,
+                                          dtype::of<T>().ptr())
+               && detail::check_flags(h.ptr(), ExtraFlags & (array::c_style | array::f_style));
+    }
+
+protected:
+    /// Create array from any object -- always returns a new reference
+    static PyObject *raw_array_t(PyObject *ptr) {
+        if (ptr == nullptr) {
+            PyErr_SetString(PyExc_ValueError, "cannot create a pybind11::array_t from a nullptr");
+            return nullptr;
+        }
+        return detail::npy_api::get().PyArray_FromAny_(ptr,
+                                                       dtype::of<T>().release().ptr(),
+                                                       0,
+                                                       0,
+                                                       detail::npy_api::NPY_ARRAY_ENSUREARRAY_
+                                                           | ExtraFlags,
+                                                       nullptr);
+    }
+};
+
+template <typename T>
+struct format_descriptor<T, detail::enable_if_t<detail::is_pod_struct<T>::value>> {
+    static std::string format() {
+        return detail::npy_format_descriptor<typename std::remove_cv<T>::type>::format();
+    }
+};
+
+template <size_t N>
+struct format_descriptor<char[N]> {
+    static std::string format() { return std::to_string(N) + 's'; }
+};
+template <size_t N>
+struct format_descriptor<std::array<char, N>> {
+    static std::string format() { return std::to_string(N) + 's'; }
+};
+
+template <typename T>
+struct format_descriptor<T, detail::enable_if_t<std::is_enum<T>::value>> {
+    static std::string format() {
+        return format_descriptor<
+            typename std::remove_cv<typename std::underlying_type<T>::type>::type>::format();
+    }
+};
+
+template <typename T>
+struct format_descriptor<T, detail::enable_if_t<detail::array_info<T>::is_array>> {
+    static std::string format() {
+        using namespace detail;
+        static constexpr auto extents = const_name("(") + array_info<T>::extents + const_name(")");
+        return extents.text + format_descriptor<remove_all_extents_t<T>>::format();
+    }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <typename T, int ExtraFlags>
+struct pyobject_caster<array_t<T, ExtraFlags>> {
+    using type = array_t<T, ExtraFlags>;
+
+    bool load(handle src, bool convert) {
+        if (!convert && !type::check_(src)) {
+            return false;
+        }
+        value = type::ensure(src);
+        return static_cast<bool>(value);
+    }
+
+    static handle cast(const handle &src, return_value_policy /* policy */, handle /* parent */) {
+        return src.inc_ref();
+    }
+    PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name);
+};
+
+template <typename T>
+struct compare_buffer_info<T, detail::enable_if_t<detail::is_pod_struct<T>::value>> {
+    static bool compare(const buffer_info &b) {
+        return npy_api::get().PyArray_EquivTypes_(dtype::of<T>().ptr(), dtype(b).ptr());
+    }
+};
+
+template <typename T, typename = void>
+struct npy_format_descriptor_name;
+
+template <typename T>
+struct npy_format_descriptor_name<T, enable_if_t<std::is_integral<T>::value>> {
+    static constexpr auto name = const_name<std::is_same<T, bool>::value>(
+        const_name("bool"),
+        const_name<std::is_signed<T>::value>("numpy.int", "numpy.uint")
+            + const_name<sizeof(T) * 8>());
+};
+
+template <typename T>
+struct npy_format_descriptor_name<T, enable_if_t<std::is_floating_point<T>::value>> {
+    static constexpr auto name = const_name < std::is_same<T, float>::value
+                                 || std::is_same<T, const float>::value
+                                 || std::is_same<T, double>::value
+                                 || std::is_same<T, const double>::value
+                                        > (const_name("numpy.float") + const_name<sizeof(T) * 8>(),
+                                           const_name("numpy.longdouble"));
+};
+
+template <typename T>
+struct npy_format_descriptor_name<T, enable_if_t<is_complex<T>::value>> {
+    static constexpr auto name = const_name < std::is_same<typename T::value_type, float>::value
+                                 || std::is_same<typename T::value_type, const float>::value
+                                 || std::is_same<typename T::value_type, double>::value
+                                 || std::is_same<typename T::value_type, const double>::value
+                                        > (const_name("numpy.complex")
+                                               + const_name<sizeof(typename T::value_type) * 16>(),
+                                           const_name("numpy.longcomplex"));
+};
+
+template <typename T>
+struct npy_format_descriptor<
+    T,
+    enable_if_t<satisfies_any_of<T, std::is_arithmetic, is_complex>::value>>
+    : npy_format_descriptor_name<T> {
+private:
+    // NB: the order here must match the one in common.h
+    constexpr static const int values[15] = {npy_api::NPY_BOOL_,
+                                             npy_api::NPY_BYTE_,
+                                             npy_api::NPY_UBYTE_,
+                                             npy_api::NPY_INT16_,
+                                             npy_api::NPY_UINT16_,
+                                             npy_api::NPY_INT32_,
+                                             npy_api::NPY_UINT32_,
+                                             npy_api::NPY_INT64_,
+                                             npy_api::NPY_UINT64_,
+                                             npy_api::NPY_FLOAT_,
+                                             npy_api::NPY_DOUBLE_,
+                                             npy_api::NPY_LONGDOUBLE_,
+                                             npy_api::NPY_CFLOAT_,
+                                             npy_api::NPY_CDOUBLE_,
+                                             npy_api::NPY_CLONGDOUBLE_};
+
+public:
+    static constexpr int value = values[detail::is_fmt_numeric<T>::index];
+
+    static pybind11::dtype dtype() {
+        if (auto *ptr = npy_api::get().PyArray_DescrFromType_(value)) {
+            return reinterpret_steal<pybind11::dtype>(ptr);
+        }
+        pybind11_fail("Unsupported buffer format!");
+    }
+};
+
+#define PYBIND11_DECL_CHAR_FMT                                                                    \
+    static constexpr auto name = const_name("S") + const_name<N>();                               \
+    static pybind11::dtype dtype() {                                                              \
+        return pybind11::dtype(std::string("S") + std::to_string(N));                             \
+    }
+template <size_t N>
+struct npy_format_descriptor<char[N]> {
+    PYBIND11_DECL_CHAR_FMT
+};
+template <size_t N>
+struct npy_format_descriptor<std::array<char, N>> {
+    PYBIND11_DECL_CHAR_FMT
+};
+#undef PYBIND11_DECL_CHAR_FMT
+
+template <typename T>
+struct npy_format_descriptor<T, enable_if_t<array_info<T>::is_array>> {
+private:
+    using base_descr = npy_format_descriptor<typename array_info<T>::type>;
+
+public:
+    static_assert(!array_info<T>::is_empty, "Zero-sized arrays are not supported");
+
+    static constexpr auto name
+        = const_name("(") + array_info<T>::extents + const_name(")") + base_descr::name;
+    static pybind11::dtype dtype() {
+        list shape;
+        array_info<T>::append_extents(shape);
+        return pybind11::dtype::from_args(
+            pybind11::make_tuple(base_descr::dtype(), std::move(shape)));
+    }
+};
+
+template <typename T>
+struct npy_format_descriptor<T, enable_if_t<std::is_enum<T>::value>> {
+private:
+    using base_descr = npy_format_descriptor<typename std::underlying_type<T>::type>;
+
+public:
+    static constexpr auto name = base_descr::name;
+    static pybind11::dtype dtype() { return base_descr::dtype(); }
+};
+
+struct field_descriptor {
+    const char *name;
+    ssize_t offset;
+    ssize_t size;
+    std::string format;
+    dtype descr;
+};
+
+PYBIND11_NOINLINE void register_structured_dtype(any_container<field_descriptor> fields,
+                                                 const std::type_info &tinfo,
+                                                 ssize_t itemsize,
+                                                 bool (*direct_converter)(PyObject *, void *&)) {
+
+    auto &numpy_internals = get_numpy_internals();
+    if (numpy_internals.get_type_info(tinfo, false)) {
+        pybind11_fail("NumPy: dtype is already registered");
+    }
+
+    // Use ordered fields because order matters as of NumPy 1.14:
+    // https://docs.scipy.org/doc/numpy/release.html#multiple-field-indexing-assignment-of-structured-arrays
+    std::vector<field_descriptor> ordered_fields(std::move(fields));
+    std::sort(
+        ordered_fields.begin(),
+        ordered_fields.end(),
+        [](const field_descriptor &a, const field_descriptor &b) { return a.offset < b.offset; });
+
+    list names, formats, offsets;
+    for (auto &field : ordered_fields) {
+        if (!field.descr) {
+            pybind11_fail(std::string("NumPy: unsupported field dtype: `") + field.name + "` @ "
+                          + tinfo.name());
+        }
+        names.append(pybind11::str(field.name));
+        formats.append(field.descr);
+        offsets.append(pybind11::int_(field.offset));
+    }
+    auto *dtype_ptr
+        = pybind11::dtype(std::move(names), std::move(formats), std::move(offsets), itemsize)
+              .release()
+              .ptr();
+
+    // There is an existing bug in NumPy (as of v1.11): trailing bytes are
+    // not encoded explicitly into the format string. This will supposedly
+    // get fixed in v1.12; for further details, see these:
+    // - https://github.com/numpy/numpy/issues/7797
+    // - https://github.com/numpy/numpy/pull/7798
+    // Because of this, we won't use numpy's logic to generate buffer format
+    // strings and will just do it ourselves.
+    ssize_t offset = 0;
+    std::ostringstream oss;
+    // mark the structure as unaligned with '^', because numpy and C++ don't
+    // always agree about alignment (particularly for complex), and we're
+    // explicitly listing all our padding. This depends on none of the fields
+    // overriding the endianness. Putting the ^ in front of individual fields
+    // isn't guaranteed to work due to https://github.com/numpy/numpy/issues/9049
+    oss << "^T{";
+    for (auto &field : ordered_fields) {
+        if (field.offset > offset) {
+            oss << (field.offset - offset) << 'x';
+        }
+        oss << field.format << ':' << field.name << ':';
+        offset = field.offset + field.size;
+    }
+    if (itemsize > offset) {
+        oss << (itemsize - offset) << 'x';
+    }
+    oss << '}';
+    auto format_str = oss.str();
+
+    // Sanity check: verify that NumPy properly parses our buffer format string
+    auto &api = npy_api::get();
+    auto arr = array(buffer_info(nullptr, itemsize, format_str, 1));
+    if (!api.PyArray_EquivTypes_(dtype_ptr, arr.dtype().ptr())) {
+        pybind11_fail("NumPy: invalid buffer descriptor!");
+    }
+
+    auto tindex = std::type_index(tinfo);
+    numpy_internals.registered_dtypes[tindex] = {dtype_ptr, std::move(format_str)};
+    get_internals().direct_conversions[tindex].push_back(direct_converter);
+}
+
+template <typename T, typename SFINAE>
+struct npy_format_descriptor {
+    static_assert(is_pod_struct<T>::value,
+                  "Attempt to use a non-POD or unimplemented POD type as a numpy dtype");
+
+    static constexpr auto name = make_caster<T>::name;
+
+    static pybind11::dtype dtype() { return reinterpret_borrow<pybind11::dtype>(dtype_ptr()); }
+
+    static std::string format() {
+        static auto format_str = get_numpy_internals().get_type_info<T>(true)->format_str;
+        return format_str;
+    }
+
+    static void register_dtype(any_container<field_descriptor> fields) {
+        register_structured_dtype(std::move(fields),
+                                  typeid(typename std::remove_cv<T>::type),
+                                  sizeof(T),
+                                  &direct_converter);
+    }
+
+private:
+    static PyObject *dtype_ptr() {
+        static PyObject *ptr = get_numpy_internals().get_type_info<T>(true)->dtype_ptr;
+        return ptr;
+    }
+
+    static bool direct_converter(PyObject *obj, void *&value) {
+        auto &api = npy_api::get();
+        if (!PyObject_TypeCheck(obj, api.PyVoidArrType_Type_)) {
+            return false;
+        }
+        if (auto descr = reinterpret_steal<object>(api.PyArray_DescrFromScalar_(obj))) {
+            if (api.PyArray_EquivTypes_(dtype_ptr(), descr.ptr())) {
+                value = ((PyVoidScalarObject_Proxy *) obj)->obval;
+                return true;
+            }
+        }
+        return false;
+    }
+};
+
+#ifdef __CLION_IDE__ // replace heavy macro with dummy code for the IDE (doesn't affect code)
+#    define PYBIND11_NUMPY_DTYPE(Type, ...) ((void) 0)
+#    define PYBIND11_NUMPY_DTYPE_EX(Type, ...) ((void) 0)
+#else
+
+#    define PYBIND11_FIELD_DESCRIPTOR_EX(T, Field, Name)                                          \
+        ::pybind11::detail::field_descriptor {                                                    \
+            Name, offsetof(T, Field), sizeof(decltype(std::declval<T>().Field)),                  \
+                ::pybind11::format_descriptor<decltype(std::declval<T>().Field)>::format(),       \
+                ::pybind11::detail::npy_format_descriptor<                                        \
+                    decltype(std::declval<T>().Field)>::dtype()                                   \
+        }
+
+// Extract name, offset and format descriptor for a struct field
+#    define PYBIND11_FIELD_DESCRIPTOR(T, Field) PYBIND11_FIELD_DESCRIPTOR_EX(T, Field, #    Field)
+
+// The main idea of this macro is borrowed from https://github.com/swansontec/map-macro
+// (C) William Swanson, Paul Fultz
+#    define PYBIND11_EVAL0(...) __VA_ARGS__
+#    define PYBIND11_EVAL1(...) PYBIND11_EVAL0(PYBIND11_EVAL0(PYBIND11_EVAL0(__VA_ARGS__)))
+#    define PYBIND11_EVAL2(...) PYBIND11_EVAL1(PYBIND11_EVAL1(PYBIND11_EVAL1(__VA_ARGS__)))
+#    define PYBIND11_EVAL3(...) PYBIND11_EVAL2(PYBIND11_EVAL2(PYBIND11_EVAL2(__VA_ARGS__)))
+#    define PYBIND11_EVAL4(...) PYBIND11_EVAL3(PYBIND11_EVAL3(PYBIND11_EVAL3(__VA_ARGS__)))
+#    define PYBIND11_EVAL(...) PYBIND11_EVAL4(PYBIND11_EVAL4(PYBIND11_EVAL4(__VA_ARGS__)))
+#    define PYBIND11_MAP_END(...)
+#    define PYBIND11_MAP_OUT
+#    define PYBIND11_MAP_COMMA ,
+#    define PYBIND11_MAP_GET_END() 0, PYBIND11_MAP_END
+#    define PYBIND11_MAP_NEXT0(test, next, ...) next PYBIND11_MAP_OUT
+#    define PYBIND11_MAP_NEXT1(test, next) PYBIND11_MAP_NEXT0(test, next, 0)
+#    define PYBIND11_MAP_NEXT(test, next) PYBIND11_MAP_NEXT1(PYBIND11_MAP_GET_END test, next)
+#    if defined(_MSC_VER)                                                                         \
+        && !defined(__clang__) // MSVC is not as eager to expand macros, hence this workaround
+#        define PYBIND11_MAP_LIST_NEXT1(test, next)                                               \
+            PYBIND11_EVAL0(PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0))
+#    else
+#        define PYBIND11_MAP_LIST_NEXT1(test, next)                                               \
+            PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0)
+#    endif
+#    define PYBIND11_MAP_LIST_NEXT(test, next)                                                    \
+        PYBIND11_MAP_LIST_NEXT1(PYBIND11_MAP_GET_END test, next)
+#    define PYBIND11_MAP_LIST0(f, t, x, peek, ...)                                                \
+        f(t, x) PYBIND11_MAP_LIST_NEXT(peek, PYBIND11_MAP_LIST1)(f, t, peek, __VA_ARGS__)
+#    define PYBIND11_MAP_LIST1(f, t, x, peek, ...)                                                \
+        f(t, x) PYBIND11_MAP_LIST_NEXT(peek, PYBIND11_MAP_LIST0)(f, t, peek, __VA_ARGS__)
+// PYBIND11_MAP_LIST(f, t, a1, a2, ...) expands to f(t, a1), f(t, a2), ...
+#    define PYBIND11_MAP_LIST(f, t, ...)                                                          \
+        PYBIND11_EVAL(PYBIND11_MAP_LIST1(f, t, __VA_ARGS__, (), 0))
+
+#    define PYBIND11_NUMPY_DTYPE(Type, ...)                                                       \
+        ::pybind11::detail::npy_format_descriptor<Type>::register_dtype(                          \
+            ::std::vector<::pybind11::detail::field_descriptor>{                                  \
+                PYBIND11_MAP_LIST(PYBIND11_FIELD_DESCRIPTOR, Type, __VA_ARGS__)})
+
+#    if defined(_MSC_VER) && !defined(__clang__)
+#        define PYBIND11_MAP2_LIST_NEXT1(test, next)                                              \
+            PYBIND11_EVAL0(PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0))
+#    else
+#        define PYBIND11_MAP2_LIST_NEXT1(test, next)                                              \
+            PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0)
+#    endif
+#    define PYBIND11_MAP2_LIST_NEXT(test, next)                                                   \
+        PYBIND11_MAP2_LIST_NEXT1(PYBIND11_MAP_GET_END test, next)
+#    define PYBIND11_MAP2_LIST0(f, t, x1, x2, peek, ...)                                          \
+        f(t, x1, x2) PYBIND11_MAP2_LIST_NEXT(peek, PYBIND11_MAP2_LIST1)(f, t, peek, __VA_ARGS__)
+#    define PYBIND11_MAP2_LIST1(f, t, x1, x2, peek, ...)                                          \
+        f(t, x1, x2) PYBIND11_MAP2_LIST_NEXT(peek, PYBIND11_MAP2_LIST0)(f, t, peek, __VA_ARGS__)
+// PYBIND11_MAP2_LIST(f, t, a1, a2, ...) expands to f(t, a1, a2), f(t, a3, a4), ...
+#    define PYBIND11_MAP2_LIST(f, t, ...)                                                         \
+        PYBIND11_EVAL(PYBIND11_MAP2_LIST1(f, t, __VA_ARGS__, (), 0))
+
+#    define PYBIND11_NUMPY_DTYPE_EX(Type, ...)                                                    \
+        ::pybind11::detail::npy_format_descriptor<Type>::register_dtype(                          \
+            ::std::vector<::pybind11::detail::field_descriptor>{                                  \
+                PYBIND11_MAP2_LIST(PYBIND11_FIELD_DESCRIPTOR_EX, Type, __VA_ARGS__)})
+
+#endif // __CLION_IDE__
+
+class common_iterator {
+public:
+    using container_type = std::vector<ssize_t>;
+    using value_type = container_type::value_type;
+    using size_type = container_type::size_type;
+
+    common_iterator() : m_strides() {}
+
+    common_iterator(void *ptr, const container_type &strides, const container_type &shape)
+        : p_ptr(reinterpret_cast<char *>(ptr)), m_strides(strides.size()) {
+        m_strides.back() = static_cast<value_type>(strides.back());
+        for (size_type i = m_strides.size() - 1; i != 0; --i) {
+            size_type j = i - 1;
+            auto s = static_cast<value_type>(shape[i]);
+            m_strides[j] = strides[j] + m_strides[i] - strides[i] * s;
+        }
+    }
+
+    void increment(size_type dim) { p_ptr += m_strides[dim]; }
+
+    void *data() const { return p_ptr; }
+
+private:
+    char *p_ptr{nullptr};
+    container_type m_strides;
+};
+
+template <size_t N>
+class multi_array_iterator {
+public:
+    using container_type = std::vector<ssize_t>;
+
+    multi_array_iterator(const std::array<buffer_info, N> &buffers, const container_type &shape)
+        : m_shape(shape.size()), m_index(shape.size(), 0), m_common_iterator() {
+
+        // Manual copy to avoid conversion warning if using std::copy
+        for (size_t i = 0; i < shape.size(); ++i) {
+            m_shape[i] = shape[i];
+        }
+
+        container_type strides(shape.size());
+        for (size_t i = 0; i < N; ++i) {
+            init_common_iterator(buffers[i], shape, m_common_iterator[i], strides);
+        }
+    }
+
+    multi_array_iterator &operator++() {
+        for (size_t j = m_index.size(); j != 0; --j) {
+            size_t i = j - 1;
+            if (++m_index[i] != m_shape[i]) {
+                increment_common_iterator(i);
+                break;
+            }
+            m_index[i] = 0;
+        }
+        return *this;
+    }
+
+    template <size_t K, class T = void>
+    T *data() const {
+        return reinterpret_cast<T *>(m_common_iterator[K].data());
+    }
+
+private:
+    using common_iter = common_iterator;
+
+    void init_common_iterator(const buffer_info &buffer,
+                              const container_type &shape,
+                              common_iter &iterator,
+                              container_type &strides) {
+        auto buffer_shape_iter = buffer.shape.rbegin();
+        auto buffer_strides_iter = buffer.strides.rbegin();
+        auto shape_iter = shape.rbegin();
+        auto strides_iter = strides.rbegin();
+
+        while (buffer_shape_iter != buffer.shape.rend()) {
+            if (*shape_iter == *buffer_shape_iter) {
+                *strides_iter = *buffer_strides_iter;
+            } else {
+                *strides_iter = 0;
+            }
+
+            ++buffer_shape_iter;
+            ++buffer_strides_iter;
+            ++shape_iter;
+            ++strides_iter;
+        }
+
+        std::fill(strides_iter, strides.rend(), 0);
+        iterator = common_iter(buffer.ptr, strides, shape);
+    }
+
+    void increment_common_iterator(size_t dim) {
+        for (auto &iter : m_common_iterator) {
+            iter.increment(dim);
+        }
+    }
+
+    container_type m_shape;
+    container_type m_index;
+    std::array<common_iter, N> m_common_iterator;
+};
+
+enum class broadcast_trivial { non_trivial, c_trivial, f_trivial };
+
+// Populates the shape and number of dimensions for the set of buffers.  Returns a
+// broadcast_trivial enum value indicating whether the broadcast is "trivial"--that is, has each
+// buffer being either a singleton or a full-size, C-contiguous (`c_trivial`) or Fortran-contiguous
+// (`f_trivial`) storage buffer; returns `non_trivial` otherwise.
+template <size_t N>
+broadcast_trivial
+broadcast(const std::array<buffer_info, N> &buffers, ssize_t &ndim, std::vector<ssize_t> &shape) {
+    ndim = std::accumulate(
+        buffers.begin(), buffers.end(), ssize_t(0), [](ssize_t res, const buffer_info &buf) {
+            return std::max(res, buf.ndim);
+        });
+
+    shape.clear();
+    shape.resize((size_t) ndim, 1);
+
+    // Figure out the output size, and make sure all input arrays conform (i.e. are either size 1
+    // or the full size).
+    for (size_t i = 0; i < N; ++i) {
+        auto res_iter = shape.rbegin();
+        auto end = buffers[i].shape.rend();
+        for (auto shape_iter = buffers[i].shape.rbegin(); shape_iter != end;
+             ++shape_iter, ++res_iter) {
+            const auto &dim_size_in = *shape_iter;
+            auto &dim_size_out = *res_iter;
+
+            // Each input dimension can either be 1 or `n`, but `n` values must match across
+            // buffers
+            if (dim_size_out == 1) {
+                dim_size_out = dim_size_in;
+            } else if (dim_size_in != 1 && dim_size_in != dim_size_out) {
+                pybind11_fail("pybind11::vectorize: incompatible size/dimension of inputs!");
+            }
+        }
+    }
+
+    bool trivial_broadcast_c = true;
+    bool trivial_broadcast_f = true;
+    for (size_t i = 0; i < N && (trivial_broadcast_c || trivial_broadcast_f); ++i) {
+        if (buffers[i].size == 1) {
+            continue;
+        }
+
+        // Require the same number of dimensions:
+        if (buffers[i].ndim != ndim) {
+            return broadcast_trivial::non_trivial;
+        }
+
+        // Require all dimensions be full-size:
+        if (!std::equal(buffers[i].shape.cbegin(), buffers[i].shape.cend(), shape.cbegin())) {
+            return broadcast_trivial::non_trivial;
+        }
+
+        // Check for C contiguity (but only if previous inputs were also C contiguous)
+        if (trivial_broadcast_c) {
+            ssize_t expect_stride = buffers[i].itemsize;
+            auto end = buffers[i].shape.crend();
+            for (auto shape_iter = buffers[i].shape.crbegin(),
+                      stride_iter = buffers[i].strides.crbegin();
+                 trivial_broadcast_c && shape_iter != end;
+                 ++shape_iter, ++stride_iter) {
+                if (expect_stride == *stride_iter) {
+                    expect_stride *= *shape_iter;
+                } else {
+                    trivial_broadcast_c = false;
+                }
+            }
+        }
+
+        // Check for Fortran contiguity (if previous inputs were also F contiguous)
+        if (trivial_broadcast_f) {
+            ssize_t expect_stride = buffers[i].itemsize;
+            auto end = buffers[i].shape.cend();
+            for (auto shape_iter = buffers[i].shape.cbegin(),
+                      stride_iter = buffers[i].strides.cbegin();
+                 trivial_broadcast_f && shape_iter != end;
+                 ++shape_iter, ++stride_iter) {
+                if (expect_stride == *stride_iter) {
+                    expect_stride *= *shape_iter;
+                } else {
+                    trivial_broadcast_f = false;
+                }
+            }
+        }
+    }
+
+    return trivial_broadcast_c   ? broadcast_trivial::c_trivial
+           : trivial_broadcast_f ? broadcast_trivial::f_trivial
+                                 : broadcast_trivial::non_trivial;
+}
+
+template <typename T>
+struct vectorize_arg {
+    static_assert(!std::is_rvalue_reference<T>::value,
+                  "Functions with rvalue reference arguments cannot be vectorized");
+    // The wrapped function gets called with this type:
+    using call_type = remove_reference_t<T>;
+    // Is this a vectorized argument?
+    static constexpr bool vectorize
+        = satisfies_any_of<call_type, std::is_arithmetic, is_complex, is_pod>::value
+          && satisfies_none_of<call_type,
+                               std::is_pointer,
+                               std::is_array,
+                               is_std_array,
+                               std::is_enum>::value
+          && (!std::is_reference<T>::value
+              || (std::is_lvalue_reference<T>::value && std::is_const<call_type>::value));
+    // Accept this type: an array for vectorized types, otherwise the type as-is:
+    using type = conditional_t<vectorize, array_t<remove_cv_t<call_type>, array::forcecast>, T>;
+};
+
+// py::vectorize when a return type is present
+template <typename Func, typename Return, typename... Args>
+struct vectorize_returned_array {
+    using Type = array_t<Return>;
+
+    static Type create(broadcast_trivial trivial, const std::vector<ssize_t> &shape) {
+        if (trivial == broadcast_trivial::f_trivial) {
+            return array_t<Return, array::f_style>(shape);
+        }
+        return array_t<Return>(shape);
+    }
+
+    static Return *mutable_data(Type &array) { return array.mutable_data(); }
+
+    static Return call(Func &f, Args &...args) { return f(args...); }
+
+    static void call(Return *out, size_t i, Func &f, Args &...args) { out[i] = f(args...); }
+};
+
+// py::vectorize when a return type is not present
+template <typename Func, typename... Args>
+struct vectorize_returned_array<Func, void, Args...> {
+    using Type = none;
+
+    static Type create(broadcast_trivial, const std::vector<ssize_t> &) { return none(); }
+
+    static void *mutable_data(Type &) { return nullptr; }
+
+    static detail::void_type call(Func &f, Args &...args) {
+        f(args...);
+        return {};
+    }
+
+    static void call(void *, size_t, Func &f, Args &...args) { f(args...); }
+};
+
+template <typename Func, typename Return, typename... Args>
+struct vectorize_helper {
+
+// NVCC for some reason breaks if NVectorized is private
+#ifdef __CUDACC__
+public:
+#else
+private:
+#endif
+
+    static constexpr size_t N = sizeof...(Args);
+    static constexpr size_t NVectorized = constexpr_sum(vectorize_arg<Args>::vectorize...);
+    static_assert(
+        NVectorized >= 1,
+        "pybind11::vectorize(...) requires a function with at least one vectorizable argument");
+
+public:
+    template <typename T,
+              // SFINAE to prevent shadowing the copy constructor.
+              typename = detail::enable_if_t<
+                  !std::is_same<vectorize_helper, typename std::decay<T>::type>::value>>
+    explicit vectorize_helper(T &&f) : f(std::forward<T>(f)) {}
+
+    object operator()(typename vectorize_arg<Args>::type... args) {
+        return run(args...,
+                   make_index_sequence<N>(),
+                   select_indices<vectorize_arg<Args>::vectorize...>(),
+                   make_index_sequence<NVectorized>());
+    }
+
+private:
+    remove_reference_t<Func> f;
+
+    // Internal compiler error in MSVC 19.16.27025.1 (Visual Studio 2017 15.9.4), when compiling
+    // with "/permissive-" flag when arg_call_types is manually inlined.
+    using arg_call_types = std::tuple<typename vectorize_arg<Args>::call_type...>;
+    template <size_t Index>
+    using param_n_t = typename std::tuple_element<Index, arg_call_types>::type;
+
+    using returned_array = vectorize_returned_array<Func, Return, Args...>;
+
+    // Runs a vectorized function given arguments tuple and three index sequences:
+    //     - Index is the full set of 0 ... (N-1) argument indices;
+    //     - VIndex is the subset of argument indices with vectorized parameters, letting us access
+    //       vectorized arguments (anything not in this sequence is passed through)
+    //     - BIndex is a incremental sequence (beginning at 0) of the same size as VIndex, so that
+    //       we can store vectorized buffer_infos in an array (argument VIndex has its buffer at
+    //       index BIndex in the array).
+    template <size_t... Index, size_t... VIndex, size_t... BIndex>
+    object run(typename vectorize_arg<Args>::type &...args,
+               index_sequence<Index...> i_seq,
+               index_sequence<VIndex...> vi_seq,
+               index_sequence<BIndex...> bi_seq) {
+
+        // Pointers to values the function was called with; the vectorized ones set here will start
+        // out as array_t<T> pointers, but they will be changed them to T pointers before we make
+        // call the wrapped function.  Non-vectorized pointers are left as-is.
+        std::array<void *, N> params{{&args...}};
+
+        // The array of `buffer_info`s of vectorized arguments:
+        std::array<buffer_info, NVectorized> buffers{
+            {reinterpret_cast<array *>(params[VIndex])->request()...}};
+
+        /* Determine dimensions parameters of output array */
+        ssize_t nd = 0;
+        std::vector<ssize_t> shape(0);
+        auto trivial = broadcast(buffers, nd, shape);
+        auto ndim = (size_t) nd;
+
+        size_t size
+            = std::accumulate(shape.begin(), shape.end(), (size_t) 1, std::multiplies<size_t>());
+
+        // If all arguments are 0-dimension arrays (i.e. single values) return a plain value (i.e.
+        // not wrapped in an array).
+        if (size == 1 && ndim == 0) {
+            PYBIND11_EXPAND_SIDE_EFFECTS(params[VIndex] = buffers[BIndex].ptr);
+            return cast(
+                returned_array::call(f, *reinterpret_cast<param_n_t<Index> *>(params[Index])...));
+        }
+
+        auto result = returned_array::create(trivial, shape);
+
+        if (size == 0) {
+            return std::move(result);
+        }
+
+        /* Call the function */
+        auto *mutable_data = returned_array::mutable_data(result);
+        if (trivial == broadcast_trivial::non_trivial) {
+            apply_broadcast(buffers, params, mutable_data, size, shape, i_seq, vi_seq, bi_seq);
+        } else {
+            apply_trivial(buffers, params, mutable_data, size, i_seq, vi_seq, bi_seq);
+        }
+
+        return std::move(result);
+    }
+
+    template <size_t... Index, size_t... VIndex, size_t... BIndex>
+    void apply_trivial(std::array<buffer_info, NVectorized> &buffers,
+                       std::array<void *, N> &params,
+                       Return *out,
+                       size_t size,
+                       index_sequence<Index...>,
+                       index_sequence<VIndex...>,
+                       index_sequence<BIndex...>) {
+
+        // Initialize an array of mutable byte references and sizes with references set to the
+        // appropriate pointer in `params`; as we iterate, we'll increment each pointer by its size
+        // (except for singletons, which get an increment of 0).
+        std::array<std::pair<unsigned char *&, const size_t>, NVectorized> vecparams{
+            {std::pair<unsigned char *&, const size_t>(
+                reinterpret_cast<unsigned char *&>(params[VIndex] = buffers[BIndex].ptr),
+                buffers[BIndex].size == 1 ? 0 : sizeof(param_n_t<VIndex>))...}};
+
+        for (size_t i = 0; i < size; ++i) {
+            returned_array::call(
+                out, i, f, *reinterpret_cast<param_n_t<Index> *>(params[Index])...);
+            for (auto &x : vecparams) {
+                x.first += x.second;
+            }
+        }
+    }
+
+    template <size_t... Index, size_t... VIndex, size_t... BIndex>
+    void apply_broadcast(std::array<buffer_info, NVectorized> &buffers,
+                         std::array<void *, N> &params,
+                         Return *out,
+                         size_t size,
+                         const std::vector<ssize_t> &output_shape,
+                         index_sequence<Index...>,
+                         index_sequence<VIndex...>,
+                         index_sequence<BIndex...>) {
+
+        multi_array_iterator<NVectorized> input_iter(buffers, output_shape);
+
+        for (size_t i = 0; i < size; ++i, ++input_iter) {
+            PYBIND11_EXPAND_SIDE_EFFECTS((params[VIndex] = input_iter.template data<BIndex>()));
+            returned_array::call(
+                out, i, f, *reinterpret_cast<param_n_t<Index> *>(std::get<Index>(params))...);
+        }
+    }
+};
+
+template <typename Func, typename Return, typename... Args>
+vectorize_helper<Func, Return, Args...> vectorize_extractor(const Func &f, Return (*)(Args...)) {
+    return detail::vectorize_helper<Func, Return, Args...>(f);
+}
+
+template <typename T, int Flags>
+struct handle_type_name<array_t<T, Flags>> {
+    static constexpr auto name
+        = const_name("numpy.ndarray[") + npy_format_descriptor<T>::name + const_name("]");
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+// Vanilla pointer vectorizer:
+template <typename Return, typename... Args>
+detail::vectorize_helper<Return (*)(Args...), Return, Args...> vectorize(Return (*f)(Args...)) {
+    return detail::vectorize_helper<Return (*)(Args...), Return, Args...>(f);
+}
+
+// lambda vectorizer:
+template <typename Func, detail::enable_if_t<detail::is_lambda<Func>::value, int> = 0>
+auto vectorize(Func &&f)
+    -> decltype(detail::vectorize_extractor(std::forward<Func>(f),
+                                            (detail::function_signature_t<Func> *) nullptr)) {
+    return detail::vectorize_extractor(std::forward<Func>(f),
+                                       (detail::function_signature_t<Func> *) nullptr);
+}
+
+// Vectorize a class method (non-const):
+template <typename Return,
+          typename Class,
+          typename... Args,
+          typename Helper = detail::vectorize_helper<
+              decltype(std::mem_fn(std::declval<Return (Class::*)(Args...)>())),
+              Return,
+              Class *,
+              Args...>>
+Helper vectorize(Return (Class::*f)(Args...)) {
+    return Helper(std::mem_fn(f));
+}
+
+// Vectorize a class method (const):
+template <typename Return,
+          typename Class,
+          typename... Args,
+          typename Helper = detail::vectorize_helper<
+              decltype(std::mem_fn(std::declval<Return (Class::*)(Args...) const>())),
+              Return,
+              const Class *,
+              Args...>>
+Helper vectorize(Return (Class::*f)(Args...) const) {
+    return Helper(std::mem_fn(f));
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/operators.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/operators.h
new file mode 100644
index 000000000..a0c3b78d6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/operators.h
@@ -0,0 +1,201 @@
+/*
+    pybind11/operator.h: Metatemplates for operator overloading
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Enumeration with all supported operator types
+enum op_id : int {
+    op_add,
+    op_sub,
+    op_mul,
+    op_div,
+    op_mod,
+    op_divmod,
+    op_pow,
+    op_lshift,
+    op_rshift,
+    op_and,
+    op_xor,
+    op_or,
+    op_neg,
+    op_pos,
+    op_abs,
+    op_invert,
+    op_int,
+    op_long,
+    op_float,
+    op_str,
+    op_cmp,
+    op_gt,
+    op_ge,
+    op_lt,
+    op_le,
+    op_eq,
+    op_ne,
+    op_iadd,
+    op_isub,
+    op_imul,
+    op_idiv,
+    op_imod,
+    op_ilshift,
+    op_irshift,
+    op_iand,
+    op_ixor,
+    op_ior,
+    op_complex,
+    op_bool,
+    op_nonzero,
+    op_repr,
+    op_truediv,
+    op_itruediv,
+    op_hash
+};
+
+enum op_type : int {
+    op_l, /* base type on left */
+    op_r, /* base type on right */
+    op_u  /* unary operator */
+};
+
+struct self_t {};
+static const self_t self = self_t();
+
+/// Type for an unused type slot
+struct undefined_t {};
+
+/// Don't warn about an unused variable
+inline self_t __self() { return self; }
+
+/// base template of operator implementations
+template <op_id, op_type, typename B, typename L, typename R>
+struct op_impl {};
+
+/// Operator implementation generator
+template <op_id id, op_type ot, typename L, typename R>
+struct op_ {
+    template <typename Class, typename... Extra>
+    void execute(Class &cl, const Extra &...extra) const {
+        using Base = typename Class::type;
+        using L_type = conditional_t<std::is_same<L, self_t>::value, Base, L>;
+        using R_type = conditional_t<std::is_same<R, self_t>::value, Base, R>;
+        using op = op_impl<id, ot, Base, L_type, R_type>;
+        cl.def(op::name(), &op::execute, is_operator(), extra...);
+    }
+    template <typename Class, typename... Extra>
+    void execute_cast(Class &cl, const Extra &...extra) const {
+        using Base = typename Class::type;
+        using L_type = conditional_t<std::is_same<L, self_t>::value, Base, L>;
+        using R_type = conditional_t<std::is_same<R, self_t>::value, Base, R>;
+        using op = op_impl<id, ot, Base, L_type, R_type>;
+        cl.def(op::name(), &op::execute_cast, is_operator(), extra...);
+    }
+};
+
+#define PYBIND11_BINARY_OPERATOR(id, rid, op, expr)                                               \
+    template <typename B, typename L, typename R>                                                 \
+    struct op_impl<op_##id, op_l, B, L, R> {                                                      \
+        static char const *name() { return "__" #id "__"; }                                       \
+        static auto execute(const L &l, const R &r) -> decltype(expr) { return (expr); }          \
+        static B execute_cast(const L &l, const R &r) { return B(expr); }                         \
+    };                                                                                            \
+    template <typename B, typename L, typename R>                                                 \
+    struct op_impl<op_##id, op_r, B, L, R> {                                                      \
+        static char const *name() { return "__" #rid "__"; }                                      \
+        static auto execute(const R &r, const L &l) -> decltype(expr) { return (expr); }          \
+        static B execute_cast(const R &r, const L &l) { return B(expr); }                         \
+    };                                                                                            \
+    inline op_<op_##id, op_l, self_t, self_t> op(const self_t &, const self_t &) {                \
+        return op_<op_##id, op_l, self_t, self_t>();                                              \
+    }                                                                                             \
+    template <typename T>                                                                         \
+    op_<op_##id, op_l, self_t, T> op(const self_t &, const T &) {                                 \
+        return op_<op_##id, op_l, self_t, T>();                                                   \
+    }                                                                                             \
+    template <typename T>                                                                         \
+    op_<op_##id, op_r, T, self_t> op(const T &, const self_t &) {                                 \
+        return op_<op_##id, op_r, T, self_t>();                                                   \
+    }
+
+#define PYBIND11_INPLACE_OPERATOR(id, op, expr)                                                   \
+    template <typename B, typename L, typename R>                                                 \
+    struct op_impl<op_##id, op_l, B, L, R> {                                                      \
+        static char const *name() { return "__" #id "__"; }                                       \
+        static auto execute(L &l, const R &r) -> decltype(expr) { return expr; }                  \
+        static B execute_cast(L &l, const R &r) { return B(expr); }                               \
+    };                                                                                            \
+    template <typename T>                                                                         \
+    op_<op_##id, op_l, self_t, T> op(const self_t &, const T &) {                                 \
+        return op_<op_##id, op_l, self_t, T>();                                                   \
+    }
+
+#define PYBIND11_UNARY_OPERATOR(id, op, expr)                                                     \
+    template <typename B, typename L>                                                             \
+    struct op_impl<op_##id, op_u, B, L, undefined_t> {                                            \
+        static char const *name() { return "__" #id "__"; }                                       \
+        static auto execute(const L &l) -> decltype(expr) { return expr; }                        \
+        static B execute_cast(const L &l) { return B(expr); }                                     \
+    };                                                                                            \
+    inline op_<op_##id, op_u, self_t, undefined_t> op(const self_t &) {                           \
+        return op_<op_##id, op_u, self_t, undefined_t>();                                         \
+    }
+
+PYBIND11_BINARY_OPERATOR(sub, rsub, operator-, l - r)
+PYBIND11_BINARY_OPERATOR(add, radd, operator+, l + r)
+PYBIND11_BINARY_OPERATOR(mul, rmul, operator*, l *r)
+PYBIND11_BINARY_OPERATOR(truediv, rtruediv, operator/, l / r)
+PYBIND11_BINARY_OPERATOR(mod, rmod, operator%, l % r)
+PYBIND11_BINARY_OPERATOR(lshift, rlshift, operator<<, l << r)
+PYBIND11_BINARY_OPERATOR(rshift, rrshift, operator>>, l >> r)
+PYBIND11_BINARY_OPERATOR(and, rand, operator&, l &r)
+PYBIND11_BINARY_OPERATOR(xor, rxor, operator^, l ^ r)
+PYBIND11_BINARY_OPERATOR(eq, eq, operator==, l == r)
+PYBIND11_BINARY_OPERATOR(ne, ne, operator!=, l != r)
+PYBIND11_BINARY_OPERATOR(or, ror, operator|, l | r)
+PYBIND11_BINARY_OPERATOR(gt, lt, operator>, l > r)
+PYBIND11_BINARY_OPERATOR(ge, le, operator>=, l >= r)
+PYBIND11_BINARY_OPERATOR(lt, gt, operator<, l < r)
+PYBIND11_BINARY_OPERATOR(le, ge, operator<=, l <= r)
+// PYBIND11_BINARY_OPERATOR(pow,       rpow,         pow,          std::pow(l,  r))
+PYBIND11_INPLACE_OPERATOR(iadd, operator+=, l += r)
+PYBIND11_INPLACE_OPERATOR(isub, operator-=, l -= r)
+PYBIND11_INPLACE_OPERATOR(imul, operator*=, l *= r)
+PYBIND11_INPLACE_OPERATOR(itruediv, operator/=, l /= r)
+PYBIND11_INPLACE_OPERATOR(imod, operator%=, l %= r)
+PYBIND11_INPLACE_OPERATOR(ilshift, operator<<=, l <<= r)
+PYBIND11_INPLACE_OPERATOR(irshift, operator>>=, l >>= r)
+PYBIND11_INPLACE_OPERATOR(iand, operator&=, l &= r)
+PYBIND11_INPLACE_OPERATOR(ixor, operator^=, l ^= r)
+PYBIND11_INPLACE_OPERATOR(ior, operator|=, l |= r)
+PYBIND11_UNARY_OPERATOR(neg, operator-, -l)
+PYBIND11_UNARY_OPERATOR(pos, operator+, +l)
+// WARNING: This usage of `abs` should only be done for existing STL overloads.
+// Adding overloads directly in to the `std::` namespace is advised against:
+// https://en.cppreference.com/w/cpp/language/extending_std
+PYBIND11_UNARY_OPERATOR(abs, abs, std::abs(l))
+PYBIND11_UNARY_OPERATOR(hash, hash, std::hash<L>()(l))
+PYBIND11_UNARY_OPERATOR(invert, operator~, (~l))
+PYBIND11_UNARY_OPERATOR(bool, operator!, !!l)
+PYBIND11_UNARY_OPERATOR(int, int_, (int) l)
+PYBIND11_UNARY_OPERATOR(float, float_, (double) l)
+
+#undef PYBIND11_BINARY_OPERATOR
+#undef PYBIND11_INPLACE_OPERATOR
+#undef PYBIND11_UNARY_OPERATOR
+PYBIND11_NAMESPACE_END(detail)
+
+using detail::self;
+// Add named operators so that they are accessible via `py::`.
+using detail::hash;
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/options.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/options.h
new file mode 100644
index 000000000..1e493bdcc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/options.h
@@ -0,0 +1,76 @@
+/*
+    pybind11/options.h: global settings that are configurable at runtime.
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+class options {
+public:
+    // Default RAII constructor, which leaves settings as they currently are.
+    options() : previous_state(global_state()) {}
+
+    // Class is non-copyable.
+    options(const options &) = delete;
+    options &operator=(const options &) = delete;
+
+    // Destructor, which restores settings that were in effect before.
+    ~options() { global_state() = previous_state; }
+
+    // Setter methods (affect the global state):
+
+    options &disable_user_defined_docstrings() & {
+        global_state().show_user_defined_docstrings = false;
+        return *this;
+    }
+
+    options &enable_user_defined_docstrings() & {
+        global_state().show_user_defined_docstrings = true;
+        return *this;
+    }
+
+    options &disable_function_signatures() & {
+        global_state().show_function_signatures = false;
+        return *this;
+    }
+
+    options &enable_function_signatures() & {
+        global_state().show_function_signatures = true;
+        return *this;
+    }
+
+    // Getter methods (return the global state):
+
+    static bool show_user_defined_docstrings() {
+        return global_state().show_user_defined_docstrings;
+    }
+
+    static bool show_function_signatures() { return global_state().show_function_signatures; }
+
+    // This type is not meant to be allocated on the heap.
+    void *operator new(size_t) = delete;
+
+private:
+    struct state {
+        bool show_user_defined_docstrings = true; //< Include user-supplied texts in docstrings.
+        bool show_function_signatures = true;     //< Include auto-generated function signatures
+                                                  //  in docstrings.
+    };
+
+    static state &global_state() {
+        static state instance;
+        return instance;
+    }
+
+    state previous_state;
+};
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/pybind11.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/pybind11.h
new file mode 100644
index 000000000..d61dcd5c7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/pybind11.h
@@ -0,0 +1,2864 @@
+/*
+    pybind11/pybind11.h: Main header file of the C++11 python
+    binding generator library
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/class.h"
+#include "detail/init.h"
+#include "attr.h"
+#include "gil.h"
+#include "options.h"
+
+#include <cstdlib>
+#include <cstring>
+#include <memory>
+#include <new>
+#include <string>
+#include <utility>
+#include <vector>
+
+#if defined(__cpp_lib_launder) && !(defined(_MSC_VER) && (_MSC_VER < 1914))
+#    define PYBIND11_STD_LAUNDER std::launder
+#    define PYBIND11_HAS_STD_LAUNDER 1
+#else
+#    define PYBIND11_STD_LAUNDER
+#    define PYBIND11_HAS_STD_LAUNDER 0
+#endif
+#if defined(__GNUG__) && !defined(__clang__)
+#    include <cxxabi.h>
+#endif
+
+/* https://stackoverflow.com/questions/46798456/handling-gccs-noexcept-type-warning
+   This warning is about ABI compatibility, not code health.
+   It is only actually needed in a couple places, but apparently GCC 7 "generates this warning if
+   and only if the first template instantiation ... involves noexcept" [stackoverflow], therefore
+   it could get triggered from seemingly random places, depending on user code.
+   No other GCC version generates this warning.
+ */
+#if defined(__GNUC__) && __GNUC__ == 7
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wnoexcept-type"
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Apply all the extensions translators from a list
+// Return true if one of the translators completed without raising an exception
+// itself. Return of false indicates that if there are other translators
+// available, they should be tried.
+inline bool apply_exception_translators(std::forward_list<ExceptionTranslator> &translators) {
+    auto last_exception = std::current_exception();
+
+    for (auto &translator : translators) {
+        try {
+            translator(last_exception);
+            return true;
+        } catch (...) {
+            last_exception = std::current_exception();
+        }
+    }
+    return false;
+}
+
+#if defined(_MSC_VER)
+#    define PYBIND11_COMPAT_STRDUP _strdup
+#else
+#    define PYBIND11_COMPAT_STRDUP strdup
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Wraps an arbitrary C++ function/method/lambda function/.. into a callable Python object
+class cpp_function : public function {
+public:
+    cpp_function() = default;
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(std::nullptr_t) {}
+
+    /// Construct a cpp_function from a vanilla function pointer
+    template <typename Return, typename... Args, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (*f)(Args...), const Extra &...extra) {
+        initialize(f, f, extra...);
+    }
+
+    /// Construct a cpp_function from a lambda function (possibly with internal state)
+    template <typename Func,
+              typename... Extra,
+              typename = detail::enable_if_t<detail::is_lambda<Func>::value>>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Func &&f, const Extra &...extra) {
+        initialize(
+            std::forward<Func>(f), (detail::function_signature_t<Func> *) nullptr, extra...);
+    }
+
+    /// Construct a cpp_function from a class method (non-const, no ref-qualifier)
+    template <typename Return, typename Class, typename... Arg, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (Class::*f)(Arg...), const Extra &...extra) {
+        initialize(
+            [f](Class *c, Arg... args) -> Return { return (c->*f)(std::forward<Arg>(args)...); },
+            (Return(*)(Class *, Arg...)) nullptr,
+            extra...);
+    }
+
+    /// Construct a cpp_function from a class method (non-const, lvalue ref-qualifier)
+    /// A copy of the overload for non-const functions without explicit ref-qualifier
+    /// but with an added `&`.
+    template <typename Return, typename Class, typename... Arg, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (Class::*f)(Arg...) &, const Extra &...extra) {
+        initialize(
+            [f](Class *c, Arg... args) -> Return { return (c->*f)(std::forward<Arg>(args)...); },
+            (Return(*)(Class *, Arg...)) nullptr,
+            extra...);
+    }
+
+    /// Construct a cpp_function from a class method (const, no ref-qualifier)
+    template <typename Return, typename Class, typename... Arg, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (Class::*f)(Arg...) const, const Extra &...extra) {
+        initialize([f](const Class *c,
+                       Arg... args) -> Return { return (c->*f)(std::forward<Arg>(args)...); },
+                   (Return(*)(const Class *, Arg...)) nullptr,
+                   extra...);
+    }
+
+    /// Construct a cpp_function from a class method (const, lvalue ref-qualifier)
+    /// A copy of the overload for const functions without explicit ref-qualifier
+    /// but with an added `&`.
+    template <typename Return, typename Class, typename... Arg, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (Class::*f)(Arg...) const &, const Extra &...extra) {
+        initialize([f](const Class *c,
+                       Arg... args) -> Return { return (c->*f)(std::forward<Arg>(args)...); },
+                   (Return(*)(const Class *, Arg...)) nullptr,
+                   extra...);
+    }
+
+    /// Return the function name
+    object name() const { return attr("__name__"); }
+
+protected:
+    struct InitializingFunctionRecordDeleter {
+        // `destruct(function_record, false)`: `initialize_generic` copies strings and
+        // takes care of cleaning up in case of exceptions. So pass `false` to `free_strings`.
+        void operator()(detail::function_record *rec) { destruct(rec, false); }
+    };
+    using unique_function_record
+        = std::unique_ptr<detail::function_record, InitializingFunctionRecordDeleter>;
+
+    /// Space optimization: don't inline this frequently instantiated fragment
+    PYBIND11_NOINLINE unique_function_record make_function_record() {
+        return unique_function_record(new detail::function_record());
+    }
+
+    /// Special internal constructor for functors, lambda functions, etc.
+    template <typename Func, typename Return, typename... Args, typename... Extra>
+    void initialize(Func &&f, Return (*)(Args...), const Extra &...extra) {
+        using namespace detail;
+        struct capture {
+            remove_reference_t<Func> f;
+        };
+
+        /* Store the function including any extra state it might have (e.g. a lambda capture
+         * object) */
+        // The unique_ptr makes sure nothing is leaked in case of an exception.
+        auto unique_rec = make_function_record();
+        auto *rec = unique_rec.get();
+
+        /* Store the capture object directly in the function record if there is enough space */
+        if (PYBIND11_SILENCE_MSVC_C4127(sizeof(capture) <= sizeof(rec->data))) {
+            /* Without these pragmas, GCC warns that there might not be
+               enough space to use the placement new operator. However, the
+               'if' statement above ensures that this is the case. */
+#if defined(__GNUG__) && __GNUC__ >= 6 && !defined(__clang__) && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wplacement-new"
+#endif
+            new ((capture *) &rec->data) capture{std::forward<Func>(f)};
+#if defined(__GNUG__) && __GNUC__ >= 6 && !defined(__clang__) && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic pop
+#endif
+#if defined(__GNUG__) && !PYBIND11_HAS_STD_LAUNDER && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wstrict-aliasing"
+#endif
+            // UB without std::launder, but without breaking ABI and/or
+            // a significant refactoring it's "impossible" to solve.
+            if (!std::is_trivially_destructible<capture>::value) {
+                rec->free_data = [](function_record *r) {
+                    auto data = PYBIND11_STD_LAUNDER((capture *) &r->data);
+                    (void) data;
+                    data->~capture();
+                };
+            }
+#if defined(__GNUG__) && !PYBIND11_HAS_STD_LAUNDER && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic pop
+#endif
+        } else {
+            rec->data[0] = new capture{std::forward<Func>(f)};
+            rec->free_data = [](function_record *r) { delete ((capture *) r->data[0]); };
+        }
+
+        /* Type casters for the function arguments and return value */
+        using cast_in = argument_loader<Args...>;
+        using cast_out
+            = make_caster<conditional_t<std::is_void<Return>::value, void_type, Return>>;
+
+        static_assert(
+            expected_num_args<Extra...>(
+                sizeof...(Args), cast_in::args_pos >= 0, cast_in::has_kwargs),
+            "The number of argument annotations does not match the number of function arguments");
+
+        /* Dispatch code which converts function arguments and performs the actual function call */
+        rec->impl = [](function_call &call) -> handle {
+            cast_in args_converter;
+
+            /* Try to cast the function arguments into the C++ domain */
+            if (!args_converter.load_args(call)) {
+                return PYBIND11_TRY_NEXT_OVERLOAD;
+            }
+
+            /* Invoke call policy pre-call hook */
+            process_attributes<Extra...>::precall(call);
+
+            /* Get a pointer to the capture object */
+            const auto *data = (sizeof(capture) <= sizeof(call.func.data) ? &call.func.data
+                                                                          : call.func.data[0]);
+            auto *cap = const_cast<capture *>(reinterpret_cast<const capture *>(data));
+
+            /* Override policy for rvalues -- usually to enforce rvp::move on an rvalue */
+            return_value_policy policy
+                = return_value_policy_override<Return>::policy(call.func.policy);
+
+            /* Function scope guard -- defaults to the compile-to-nothing `void_type` */
+            using Guard = extract_guard_t<Extra...>;
+
+            /* Perform the function call */
+            handle result
+                = cast_out::cast(std::move(args_converter).template call<Return, Guard>(cap->f),
+                                 policy,
+                                 call.parent);
+
+            /* Invoke call policy post-call hook */
+            process_attributes<Extra...>::postcall(call, result);
+
+            return result;
+        };
+
+        rec->nargs_pos = cast_in::args_pos >= 0
+                             ? static_cast<std::uint16_t>(cast_in::args_pos)
+                             : sizeof...(Args) - cast_in::has_kwargs; // Will get reduced more if
+                                                                      // we have a kw_only
+        rec->has_args = cast_in::args_pos >= 0;
+        rec->has_kwargs = cast_in::has_kwargs;
+
+        /* Process any user-provided function attributes */
+        process_attributes<Extra...>::init(extra..., rec);
+
+        {
+            constexpr bool has_kw_only_args = any_of<std::is_same<kw_only, Extra>...>::value,
+                           has_pos_only_args = any_of<std::is_same<pos_only, Extra>...>::value,
+                           has_arg_annotations = any_of<is_keyword<Extra>...>::value;
+            static_assert(has_arg_annotations || !has_kw_only_args,
+                          "py::kw_only requires the use of argument annotations");
+            static_assert(has_arg_annotations || !has_pos_only_args,
+                          "py::pos_only requires the use of argument annotations (for docstrings "
+                          "and aligning the annotations to the argument)");
+
+            static_assert(constexpr_sum(is_kw_only<Extra>::value...) <= 1,
+                          "py::kw_only may be specified only once");
+            static_assert(constexpr_sum(is_pos_only<Extra>::value...) <= 1,
+                          "py::pos_only may be specified only once");
+            constexpr auto kw_only_pos = constexpr_first<is_kw_only, Extra...>();
+            constexpr auto pos_only_pos = constexpr_first<is_pos_only, Extra...>();
+            static_assert(!(has_kw_only_args && has_pos_only_args) || pos_only_pos < kw_only_pos,
+                          "py::pos_only must come before py::kw_only");
+        }
+
+        /* Generate a readable signature describing the function's arguments and return
+           value types */
+        static constexpr auto signature
+            = const_name("(") + cast_in::arg_names + const_name(") -> ") + cast_out::name;
+        PYBIND11_DESCR_CONSTEXPR auto types = decltype(signature)::types();
+
+        /* Register the function with Python from generic (non-templated) code */
+        // Pass on the ownership over the `unique_rec` to `initialize_generic`. `rec` stays valid.
+        initialize_generic(std::move(unique_rec), signature.text, types.data(), sizeof...(Args));
+
+        /* Stash some additional information used by an important optimization in 'functional.h' */
+        using FunctionType = Return (*)(Args...);
+        constexpr bool is_function_ptr
+            = std::is_convertible<Func, FunctionType>::value && sizeof(capture) == sizeof(void *);
+        if (is_function_ptr) {
+            rec->is_stateless = true;
+            rec->data[1]
+                = const_cast<void *>(reinterpret_cast<const void *>(&typeid(FunctionType)));
+        }
+    }
+
+    // Utility class that keeps track of all duplicated strings, and cleans them up in its
+    // destructor, unless they are released. Basically a RAII-solution to deal with exceptions
+    // along the way.
+    class strdup_guard {
+    public:
+        strdup_guard() = default;
+        strdup_guard(const strdup_guard &) = delete;
+        strdup_guard &operator=(const strdup_guard &) = delete;
+
+        ~strdup_guard() {
+            for (auto *s : strings) {
+                std::free(s);
+            }
+        }
+        char *operator()(const char *s) {
+            auto *t = PYBIND11_COMPAT_STRDUP(s);
+            strings.push_back(t);
+            return t;
+        }
+        void release() { strings.clear(); }
+
+    private:
+        std::vector<char *> strings;
+    };
+
+    /// Register a function call with Python (generic non-templated code goes here)
+    void initialize_generic(unique_function_record &&unique_rec,
+                            const char *text,
+                            const std::type_info *const *types,
+                            size_t args) {
+        // Do NOT receive `unique_rec` by value. If this function fails to move out the unique_ptr,
+        // we do not want this to destruct the pointer. `initialize` (the caller) still relies on
+        // the pointee being alive after this call. Only move out if a `capsule` is going to keep
+        // it alive.
+        auto *rec = unique_rec.get();
+
+        // Keep track of strdup'ed strings, and clean them up as long as the function's capsule
+        // has not taken ownership yet (when `unique_rec.release()` is called).
+        // Note: This cannot easily be fixed by a `unique_ptr` with custom deleter, because the
+        // strings are only referenced before strdup'ing. So only *after* the following block could
+        // `destruct` safely be called, but even then, `repr` could still throw in the middle of
+        // copying all strings.
+        strdup_guard guarded_strdup;
+
+        /* Create copies of all referenced C-style strings */
+        rec->name = guarded_strdup(rec->name ? rec->name : "");
+        if (rec->doc) {
+            rec->doc = guarded_strdup(rec->doc);
+        }
+        for (auto &a : rec->args) {
+            if (a.name) {
+                a.name = guarded_strdup(a.name);
+            }
+            if (a.descr) {
+                a.descr = guarded_strdup(a.descr);
+            } else if (a.value) {
+                a.descr = guarded_strdup(repr(a.value).cast<std::string>().c_str());
+            }
+        }
+
+        rec->is_constructor = (std::strcmp(rec->name, "__init__") == 0)
+                              || (std::strcmp(rec->name, "__setstate__") == 0);
+
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES) && !defined(PYBIND11_DISABLE_NEW_STYLE_INIT_WARNING)
+        if (rec->is_constructor && !rec->is_new_style_constructor) {
+            const auto class_name
+                = detail::get_fully_qualified_tp_name((PyTypeObject *) rec->scope.ptr());
+            const auto func_name = std::string(rec->name);
+            PyErr_WarnEx(PyExc_FutureWarning,
+                         ("pybind11-bound class '" + class_name
+                          + "' is using an old-style "
+                            "placement-new '"
+                          + func_name
+                          + "' which has been deprecated. See "
+                            "the upgrade guide in pybind11's docs. This message is only visible "
+                            "when compiled in debug mode.")
+                             .c_str(),
+                         0);
+        }
+#endif
+
+        /* Generate a proper function signature */
+        std::string signature;
+        size_t type_index = 0, arg_index = 0;
+        bool is_starred = false;
+        for (const auto *pc = text; *pc != '\0'; ++pc) {
+            const auto c = *pc;
+
+            if (c == '{') {
+                // Write arg name for everything except *args and **kwargs.
+                is_starred = *(pc + 1) == '*';
+                if (is_starred) {
+                    continue;
+                }
+                // Separator for keyword-only arguments, placed before the kw
+                // arguments start (unless we are already putting an *args)
+                if (!rec->has_args && arg_index == rec->nargs_pos) {
+                    signature += "*, ";
+                }
+                if (arg_index < rec->args.size() && rec->args[arg_index].name) {
+                    signature += rec->args[arg_index].name;
+                } else if (arg_index == 0 && rec->is_method) {
+                    signature += "self";
+                } else {
+                    signature += "arg" + std::to_string(arg_index - (rec->is_method ? 1 : 0));
+                }
+                signature += ": ";
+            } else if (c == '}') {
+                // Write default value if available.
+                if (!is_starred && arg_index < rec->args.size() && rec->args[arg_index].descr) {
+                    signature += " = ";
+                    signature += rec->args[arg_index].descr;
+                }
+                // Separator for positional-only arguments (placed after the
+                // argument, rather than before like *
+                if (rec->nargs_pos_only > 0 && (arg_index + 1) == rec->nargs_pos_only) {
+                    signature += ", /";
+                }
+                if (!is_starred) {
+                    arg_index++;
+                }
+            } else if (c == '%') {
+                const std::type_info *t = types[type_index++];
+                if (!t) {
+                    pybind11_fail("Internal error while parsing type signature (1)");
+                }
+                if (auto *tinfo = detail::get_type_info(*t)) {
+                    handle th((PyObject *) tinfo->type);
+                    signature += th.attr("__module__").cast<std::string>() + "."
+                                 + th.attr("__qualname__").cast<std::string>();
+                } else if (rec->is_new_style_constructor && arg_index == 0) {
+                    // A new-style `__init__` takes `self` as `value_and_holder`.
+                    // Rewrite it to the proper class type.
+                    signature += rec->scope.attr("__module__").cast<std::string>() + "."
+                                 + rec->scope.attr("__qualname__").cast<std::string>();
+                } else {
+                    std::string tname(t->name());
+                    detail::clean_type_id(tname);
+                    signature += tname;
+                }
+            } else {
+                signature += c;
+            }
+        }
+
+        if (arg_index != args - rec->has_args - rec->has_kwargs || types[type_index] != nullptr) {
+            pybind11_fail("Internal error while parsing type signature (2)");
+        }
+
+        rec->signature = guarded_strdup(signature.c_str());
+        rec->args.shrink_to_fit();
+        rec->nargs = (std::uint16_t) args;
+
+        if (rec->sibling && PYBIND11_INSTANCE_METHOD_CHECK(rec->sibling.ptr())) {
+            rec->sibling = PYBIND11_INSTANCE_METHOD_GET_FUNCTION(rec->sibling.ptr());
+        }
+
+        detail::function_record *chain = nullptr, *chain_start = rec;
+        if (rec->sibling) {
+            if (PyCFunction_Check(rec->sibling.ptr())) {
+                auto *self = PyCFunction_GET_SELF(rec->sibling.ptr());
+                capsule rec_capsule = isinstance<capsule>(self) ? reinterpret_borrow<capsule>(self)
+                                                                : capsule(self);
+                chain = (detail::function_record *) rec_capsule;
+                /* Never append a method to an overload chain of a parent class;
+                   instead, hide the parent's overloads in this case */
+                if (!chain->scope.is(rec->scope)) {
+                    chain = nullptr;
+                }
+            }
+            // Don't trigger for things like the default __init__, which are wrapper_descriptors
+            // that we are intentionally replacing
+            else if (!rec->sibling.is_none() && rec->name[0] != '_') {
+                pybind11_fail("Cannot overload existing non-function object \""
+                              + std::string(rec->name) + "\" with a function of the same name");
+            }
+        }
+
+        if (!chain) {
+            /* No existing overload was found, create a new function object */
+            rec->def = new PyMethodDef();
+            std::memset(rec->def, 0, sizeof(PyMethodDef));
+            rec->def->ml_name = rec->name;
+            rec->def->ml_meth
+                = reinterpret_cast<PyCFunction>(reinterpret_cast<void (*)()>(dispatcher));
+            rec->def->ml_flags = METH_VARARGS | METH_KEYWORDS;
+
+            capsule rec_capsule(unique_rec.release(),
+                                [](void *ptr) { destruct((detail::function_record *) ptr); });
+            guarded_strdup.release();
+
+            object scope_module;
+            if (rec->scope) {
+                if (hasattr(rec->scope, "__module__")) {
+                    scope_module = rec->scope.attr("__module__");
+                } else if (hasattr(rec->scope, "__name__")) {
+                    scope_module = rec->scope.attr("__name__");
+                }
+            }
+
+            m_ptr = PyCFunction_NewEx(rec->def, rec_capsule.ptr(), scope_module.ptr());
+            if (!m_ptr) {
+                pybind11_fail("cpp_function::cpp_function(): Could not allocate function object");
+            }
+        } else {
+            /* Append at the beginning or end of the overload chain */
+            m_ptr = rec->sibling.ptr();
+            inc_ref();
+            if (chain->is_method != rec->is_method) {
+                pybind11_fail(
+                    "overloading a method with both static and instance methods is not supported; "
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                    "#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for more "
+                    "details"
+#else
+                    "error while attempting to bind "
+                    + std::string(rec->is_method ? "instance" : "static") + " method "
+                    + std::string(pybind11::str(rec->scope.attr("__name__"))) + "."
+                    + std::string(rec->name) + signature
+#endif
+                );
+            }
+
+            if (rec->prepend) {
+                // Beginning of chain; we need to replace the capsule's current head-of-the-chain
+                // pointer with this one, then make this one point to the previous head of the
+                // chain.
+                chain_start = rec;
+                rec->next = chain;
+                auto rec_capsule
+                    = reinterpret_borrow<capsule>(((PyCFunctionObject *) m_ptr)->m_self);
+                rec_capsule.set_pointer(unique_rec.release());
+                guarded_strdup.release();
+            } else {
+                // Or end of chain (normal behavior)
+                chain_start = chain;
+                while (chain->next) {
+                    chain = chain->next;
+                }
+                chain->next = unique_rec.release();
+                guarded_strdup.release();
+            }
+        }
+
+        std::string signatures;
+        int index = 0;
+        /* Create a nice pydoc rec including all signatures and
+           docstrings of the functions in the overload chain */
+        if (chain && options::show_function_signatures()) {
+            // First a generic signature
+            signatures += rec->name;
+            signatures += "(*args, **kwargs)\n";
+            signatures += "Overloaded function.\n\n";
+        }
+        // Then specific overload signatures
+        bool first_user_def = true;
+        for (auto *it = chain_start; it != nullptr; it = it->next) {
+            if (options::show_function_signatures()) {
+                if (index > 0) {
+                    signatures += '\n';
+                }
+                if (chain) {
+                    signatures += std::to_string(++index) + ". ";
+                }
+                signatures += rec->name;
+                signatures += it->signature;
+                signatures += '\n';
+            }
+            if (it->doc && it->doc[0] != '\0' && options::show_user_defined_docstrings()) {
+                // If we're appending another docstring, and aren't printing function signatures,
+                // we need to append a newline first:
+                if (!options::show_function_signatures()) {
+                    if (first_user_def) {
+                        first_user_def = false;
+                    } else {
+                        signatures += '\n';
+                    }
+                }
+                if (options::show_function_signatures()) {
+                    signatures += '\n';
+                }
+                signatures += it->doc;
+                if (options::show_function_signatures()) {
+                    signatures += '\n';
+                }
+            }
+        }
+
+        /* Install docstring */
+        auto *func = (PyCFunctionObject *) m_ptr;
+        std::free(const_cast<char *>(func->m_ml->ml_doc));
+        // Install docstring if it's non-empty (when at least one option is enabled)
+        func->m_ml->ml_doc
+            = signatures.empty() ? nullptr : PYBIND11_COMPAT_STRDUP(signatures.c_str());
+
+        if (rec->is_method) {
+            m_ptr = PYBIND11_INSTANCE_METHOD_NEW(m_ptr, rec->scope.ptr());
+            if (!m_ptr) {
+                pybind11_fail(
+                    "cpp_function::cpp_function(): Could not allocate instance method object");
+            }
+            Py_DECREF(func);
+        }
+    }
+
+    /// When a cpp_function is GCed, release any memory allocated by pybind11
+    static void destruct(detail::function_record *rec, bool free_strings = true) {
+// If on Python 3.9, check the interpreter "MICRO" (patch) version.
+// If this is running on 3.9.0, we have to work around a bug.
+#if !defined(PYPY_VERSION) && PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 9
+        static bool is_zero = Py_GetVersion()[4] == '0';
+#endif
+
+        while (rec) {
+            detail::function_record *next = rec->next;
+            if (rec->free_data) {
+                rec->free_data(rec);
+            }
+            // During initialization, these strings might not have been copied yet,
+            // so they cannot be freed. Once the function has been created, they can.
+            // Check `make_function_record` for more details.
+            if (free_strings) {
+                std::free((char *) rec->name);
+                std::free((char *) rec->doc);
+                std::free((char *) rec->signature);
+                for (auto &arg : rec->args) {
+                    std::free(const_cast<char *>(arg.name));
+                    std::free(const_cast<char *>(arg.descr));
+                }
+            }
+            for (auto &arg : rec->args) {
+                arg.value.dec_ref();
+            }
+            if (rec->def) {
+                std::free(const_cast<char *>(rec->def->ml_doc));
+// Python 3.9.0 decref's these in the wrong order; rec->def
+// If loaded on 3.9.0, let these leak (use Python 3.9.1 at runtime to fix)
+// See https://github.com/python/cpython/pull/22670
+#if !defined(PYPY_VERSION) && PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 9
+                if (!is_zero) {
+                    delete rec->def;
+                }
+#else
+                delete rec->def;
+#endif
+            }
+            delete rec;
+            rec = next;
+        }
+    }
+
+    /// Main dispatch logic for calls to functions bound using pybind11
+    static PyObject *dispatcher(PyObject *self, PyObject *args_in, PyObject *kwargs_in) {
+        using namespace detail;
+
+        /* Iterator over the list of potentially admissible overloads */
+        const function_record *overloads = (function_record *) PyCapsule_GetPointer(self, nullptr),
+                              *it = overloads;
+
+        /* Need to know how many arguments + keyword arguments there are to pick the right
+           overload */
+        const auto n_args_in = (size_t) PyTuple_GET_SIZE(args_in);
+
+        handle parent = n_args_in > 0 ? PyTuple_GET_ITEM(args_in, 0) : nullptr,
+               result = PYBIND11_TRY_NEXT_OVERLOAD;
+
+        auto self_value_and_holder = value_and_holder();
+        if (overloads->is_constructor) {
+            if (!parent
+                || !PyObject_TypeCheck(parent.ptr(), (PyTypeObject *) overloads->scope.ptr())) {
+                PyErr_SetString(
+                    PyExc_TypeError,
+                    "__init__(self, ...) called with invalid or missing `self` argument");
+                return nullptr;
+            }
+
+            auto *const tinfo = get_type_info((PyTypeObject *) overloads->scope.ptr());
+            auto *const pi = reinterpret_cast<instance *>(parent.ptr());
+            self_value_and_holder = pi->get_value_and_holder(tinfo, true);
+
+            // If this value is already registered it must mean __init__ is invoked multiple times;
+            // we really can't support that in C++, so just ignore the second __init__.
+            if (self_value_and_holder.instance_registered()) {
+                return none().release().ptr();
+            }
+        }
+
+        try {
+            // We do this in two passes: in the first pass, we load arguments with `convert=false`;
+            // in the second, we allow conversion (except for arguments with an explicit
+            // py::arg().noconvert()).  This lets us prefer calls without conversion, with
+            // conversion as a fallback.
+            std::vector<function_call> second_pass;
+
+            // However, if there are no overloads, we can just skip the no-convert pass entirely
+            const bool overloaded = it != nullptr && it->next != nullptr;
+
+            for (; it != nullptr; it = it->next) {
+
+                /* For each overload:
+                   1. Copy all positional arguments we were given, also checking to make sure that
+                      named positional arguments weren't *also* specified via kwarg.
+                   2. If we weren't given enough, try to make up the omitted ones by checking
+                      whether they were provided by a kwarg matching the `py::arg("name")` name. If
+                      so, use it (and remove it from kwargs); if not, see if the function binding
+                      provided a default that we can use.
+                   3. Ensure that either all keyword arguments were "consumed", or that the
+                   function takes a kwargs argument to accept unconsumed kwargs.
+                   4. Any positional arguments still left get put into a tuple (for args), and any
+                      leftover kwargs get put into a dict.
+                   5. Pack everything into a vector; if we have py::args or py::kwargs, they are an
+                      extra tuple or dict at the end of the positional arguments.
+                   6. Call the function call dispatcher (function_record::impl)
+
+                   If one of these fail, move on to the next overload and keep trying until we get
+                   a result other than PYBIND11_TRY_NEXT_OVERLOAD.
+                 */
+
+                const function_record &func = *it;
+                size_t num_args = func.nargs; // Number of positional arguments that we need
+                if (func.has_args) {
+                    --num_args; // (but don't count py::args
+                }
+                if (func.has_kwargs) {
+                    --num_args; //  or py::kwargs)
+                }
+                size_t pos_args = func.nargs_pos;
+
+                if (!func.has_args && n_args_in > pos_args) {
+                    continue; // Too many positional arguments for this overload
+                }
+
+                if (n_args_in < pos_args && func.args.size() < pos_args) {
+                    continue; // Not enough positional arguments given, and not enough defaults to
+                              // fill in the blanks
+                }
+
+                function_call call(func, parent);
+
+                // Protect std::min with parentheses
+                size_t args_to_copy = (std::min)(pos_args, n_args_in);
+                size_t args_copied = 0;
+
+                // 0. Inject new-style `self` argument
+                if (func.is_new_style_constructor) {
+                    // The `value` may have been preallocated by an old-style `__init__`
+                    // if it was a preceding candidate for overload resolution.
+                    if (self_value_and_holder) {
+                        self_value_and_holder.type->dealloc(self_value_and_holder);
+                    }
+
+                    call.init_self = PyTuple_GET_ITEM(args_in, 0);
+                    call.args.emplace_back(reinterpret_cast<PyObject *>(&self_value_and_holder));
+                    call.args_convert.push_back(false);
+                    ++args_copied;
+                }
+
+                // 1. Copy any position arguments given.
+                bool bad_arg = false;
+                for (; args_copied < args_to_copy; ++args_copied) {
+                    const argument_record *arg_rec
+                        = args_copied < func.args.size() ? &func.args[args_copied] : nullptr;
+                    if (kwargs_in && arg_rec && arg_rec->name
+                        && dict_getitemstring(kwargs_in, arg_rec->name)) {
+                        bad_arg = true;
+                        break;
+                    }
+
+                    handle arg(PyTuple_GET_ITEM(args_in, args_copied));
+                    if (arg_rec && !arg_rec->none && arg.is_none()) {
+                        bad_arg = true;
+                        break;
+                    }
+                    call.args.push_back(arg);
+                    call.args_convert.push_back(arg_rec ? arg_rec->convert : true);
+                }
+                if (bad_arg) {
+                    continue; // Maybe it was meant for another overload (issue #688)
+                }
+
+                // Keep track of how many position args we copied out in case we need to come back
+                // to copy the rest into a py::args argument.
+                size_t positional_args_copied = args_copied;
+
+                // We'll need to copy this if we steal some kwargs for defaults
+                dict kwargs = reinterpret_borrow<dict>(kwargs_in);
+
+                // 1.5. Fill in any missing pos_only args from defaults if they exist
+                if (args_copied < func.nargs_pos_only) {
+                    for (; args_copied < func.nargs_pos_only; ++args_copied) {
+                        const auto &arg_rec = func.args[args_copied];
+                        handle value;
+
+                        if (arg_rec.value) {
+                            value = arg_rec.value;
+                        }
+                        if (value) {
+                            call.args.push_back(value);
+                            call.args_convert.push_back(arg_rec.convert);
+                        } else {
+                            break;
+                        }
+                    }
+
+                    if (args_copied < func.nargs_pos_only) {
+                        continue; // Not enough defaults to fill the positional arguments
+                    }
+                }
+
+                // 2. Check kwargs and, failing that, defaults that may help complete the list
+                if (args_copied < num_args) {
+                    bool copied_kwargs = false;
+
+                    for (; args_copied < num_args; ++args_copied) {
+                        const auto &arg_rec = func.args[args_copied];
+
+                        handle value;
+                        if (kwargs_in && arg_rec.name) {
+                            value = dict_getitemstring(kwargs.ptr(), arg_rec.name);
+                        }
+
+                        if (value) {
+                            // Consume a kwargs value
+                            if (!copied_kwargs) {
+                                kwargs = reinterpret_steal<dict>(PyDict_Copy(kwargs.ptr()));
+                                copied_kwargs = true;
+                            }
+                            if (PyDict_DelItemString(kwargs.ptr(), arg_rec.name) == -1) {
+                                throw error_already_set();
+                            }
+                        } else if (arg_rec.value) {
+                            value = arg_rec.value;
+                        }
+
+                        if (!arg_rec.none && value.is_none()) {
+                            break;
+                        }
+
+                        if (value) {
+                            // If we're at the py::args index then first insert a stub for it to be
+                            // replaced later
+                            if (func.has_args && call.args.size() == func.nargs_pos) {
+                                call.args.push_back(none());
+                            }
+
+                            call.args.push_back(value);
+                            call.args_convert.push_back(arg_rec.convert);
+                        } else {
+                            break;
+                        }
+                    }
+
+                    if (args_copied < num_args) {
+                        continue; // Not enough arguments, defaults, or kwargs to fill the
+                                  // positional arguments
+                    }
+                }
+
+                // 3. Check everything was consumed (unless we have a kwargs arg)
+                if (kwargs && !kwargs.empty() && !func.has_kwargs) {
+                    continue; // Unconsumed kwargs, but no py::kwargs argument to accept them
+                }
+
+                // 4a. If we have a py::args argument, create a new tuple with leftovers
+                if (func.has_args) {
+                    tuple extra_args;
+                    if (args_to_copy == 0) {
+                        // We didn't copy out any position arguments from the args_in tuple, so we
+                        // can reuse it directly without copying:
+                        extra_args = reinterpret_borrow<tuple>(args_in);
+                    } else if (positional_args_copied >= n_args_in) {
+                        extra_args = tuple(0);
+                    } else {
+                        size_t args_size = n_args_in - positional_args_copied;
+                        extra_args = tuple(args_size);
+                        for (size_t i = 0; i < args_size; ++i) {
+                            extra_args[i] = PyTuple_GET_ITEM(args_in, positional_args_copied + i);
+                        }
+                    }
+                    if (call.args.size() <= func.nargs_pos) {
+                        call.args.push_back(extra_args);
+                    } else {
+                        call.args[func.nargs_pos] = extra_args;
+                    }
+                    call.args_convert.push_back(false);
+                    call.args_ref = std::move(extra_args);
+                }
+
+                // 4b. If we have a py::kwargs, pass on any remaining kwargs
+                if (func.has_kwargs) {
+                    if (!kwargs.ptr()) {
+                        kwargs = dict(); // If we didn't get one, send an empty one
+                    }
+                    call.args.push_back(kwargs);
+                    call.args_convert.push_back(false);
+                    call.kwargs_ref = std::move(kwargs);
+                }
+
+// 5. Put everything in a vector.  Not technically step 5, we've been building it
+// in `call.args` all along.
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                if (call.args.size() != func.nargs || call.args_convert.size() != func.nargs) {
+                    pybind11_fail("Internal error: function call dispatcher inserted wrong number "
+                                  "of arguments!");
+                }
+#endif
+
+                std::vector<bool> second_pass_convert;
+                if (overloaded) {
+                    // We're in the first no-convert pass, so swap out the conversion flags for a
+                    // set of all-false flags.  If the call fails, we'll swap the flags back in for
+                    // the conversion-allowed call below.
+                    second_pass_convert.resize(func.nargs, false);
+                    call.args_convert.swap(second_pass_convert);
+                }
+
+                // 6. Call the function.
+                try {
+                    loader_life_support guard{};
+                    result = func.impl(call);
+                } catch (reference_cast_error &) {
+                    result = PYBIND11_TRY_NEXT_OVERLOAD;
+                }
+
+                if (result.ptr() != PYBIND11_TRY_NEXT_OVERLOAD) {
+                    break;
+                }
+
+                if (overloaded) {
+                    // The (overloaded) call failed; if the call has at least one argument that
+                    // permits conversion (i.e. it hasn't been explicitly specified `.noconvert()`)
+                    // then add this call to the list of second pass overloads to try.
+                    for (size_t i = func.is_method ? 1 : 0; i < pos_args; i++) {
+                        if (second_pass_convert[i]) {
+                            // Found one: swap the converting flags back in and store the call for
+                            // the second pass.
+                            call.args_convert.swap(second_pass_convert);
+                            second_pass.push_back(std::move(call));
+                            break;
+                        }
+                    }
+                }
+            }
+
+            if (overloaded && !second_pass.empty() && result.ptr() == PYBIND11_TRY_NEXT_OVERLOAD) {
+                // The no-conversion pass finished without success, try again with conversion
+                // allowed
+                for (auto &call : second_pass) {
+                    try {
+                        loader_life_support guard{};
+                        result = call.func.impl(call);
+                    } catch (reference_cast_error &) {
+                        result = PYBIND11_TRY_NEXT_OVERLOAD;
+                    }
+
+                    if (result.ptr() != PYBIND11_TRY_NEXT_OVERLOAD) {
+                        // The error reporting logic below expects 'it' to be valid, as it would be
+                        // if we'd encountered this failure in the first-pass loop.
+                        if (!result) {
+                            it = &call.func;
+                        }
+                        break;
+                    }
+                }
+            }
+        } catch (error_already_set &e) {
+            e.restore();
+            return nullptr;
+#ifdef __GLIBCXX__
+        } catch (abi::__forced_unwind &) {
+            throw;
+#endif
+        } catch (...) {
+            /* When an exception is caught, give each registered exception
+               translator a chance to translate it to a Python exception. First
+               all module-local translators will be tried in reverse order of
+               registration. If none of the module-locale translators handle
+               the exception (or there are no module-locale translators) then
+               the global translators will be tried, also in reverse order of
+               registration.
+
+               A translator may choose to do one of the following:
+
+                - catch the exception and call PyErr_SetString or PyErr_SetObject
+                  to set a standard (or custom) Python exception, or
+                - do nothing and let the exception fall through to the next translator, or
+                - delegate translation to the next translator by throwing a new type of exception.
+             */
+
+            auto &local_exception_translators
+                = get_local_internals().registered_exception_translators;
+            if (detail::apply_exception_translators(local_exception_translators)) {
+                return nullptr;
+            }
+            auto &exception_translators = get_internals().registered_exception_translators;
+            if (detail::apply_exception_translators(exception_translators)) {
+                return nullptr;
+            }
+
+            PyErr_SetString(PyExc_SystemError,
+                            "Exception escaped from default exception translator!");
+            return nullptr;
+        }
+
+        auto append_note_if_missing_header_is_suspected = [](std::string &msg) {
+            if (msg.find("std::") != std::string::npos) {
+                msg += "\n\n"
+                       "Did you forget to `#include <pybind11/stl.h>`? Or <pybind11/complex.h>,\n"
+                       "<pybind11/functional.h>, <pybind11/chrono.h>, etc. Some automatic\n"
+                       "conversions are optional and require extra headers to be included\n"
+                       "when compiling your pybind11 module.";
+            }
+        };
+
+        if (result.ptr() == PYBIND11_TRY_NEXT_OVERLOAD) {
+            if (overloads->is_operator) {
+                return handle(Py_NotImplemented).inc_ref().ptr();
+            }
+
+            std::string msg = std::string(overloads->name) + "(): incompatible "
+                              + std::string(overloads->is_constructor ? "constructor" : "function")
+                              + " arguments. The following argument types are supported:\n";
+
+            int ctr = 0;
+            for (const function_record *it2 = overloads; it2 != nullptr; it2 = it2->next) {
+                msg += "    " + std::to_string(++ctr) + ". ";
+
+                bool wrote_sig = false;
+                if (overloads->is_constructor) {
+                    // For a constructor, rewrite `(self: Object, arg0, ...) -> NoneType` as
+                    // `Object(arg0, ...)`
+                    std::string sig = it2->signature;
+                    size_t start = sig.find('(') + 7; // skip "(self: "
+                    if (start < sig.size()) {
+                        // End at the , for the next argument
+                        size_t end = sig.find(", "), next = end + 2;
+                        size_t ret = sig.rfind(" -> ");
+                        // Or the ), if there is no comma:
+                        if (end >= sig.size()) {
+                            next = end = sig.find(')');
+                        }
+                        if (start < end && next < sig.size()) {
+                            msg.append(sig, start, end - start);
+                            msg += '(';
+                            msg.append(sig, next, ret - next);
+                            wrote_sig = true;
+                        }
+                    }
+                }
+                if (!wrote_sig) {
+                    msg += it2->signature;
+                }
+
+                msg += '\n';
+            }
+            msg += "\nInvoked with: ";
+            auto args_ = reinterpret_borrow<tuple>(args_in);
+            bool some_args = false;
+            for (size_t ti = overloads->is_constructor ? 1 : 0; ti < args_.size(); ++ti) {
+                if (!some_args) {
+                    some_args = true;
+                } else {
+                    msg += ", ";
+                }
+                try {
+                    msg += pybind11::repr(args_[ti]);
+                } catch (const error_already_set &) {
+                    msg += "<repr raised Error>";
+                }
+            }
+            if (kwargs_in) {
+                auto kwargs = reinterpret_borrow<dict>(kwargs_in);
+                if (!kwargs.empty()) {
+                    if (some_args) {
+                        msg += "; ";
+                    }
+                    msg += "kwargs: ";
+                    bool first = true;
+                    for (auto kwarg : kwargs) {
+                        if (first) {
+                            first = false;
+                        } else {
+                            msg += ", ";
+                        }
+                        msg += pybind11::str("{}=").format(kwarg.first);
+                        try {
+                            msg += pybind11::repr(kwarg.second);
+                        } catch (const error_already_set &) {
+                            msg += "<repr raised Error>";
+                        }
+                    }
+                }
+            }
+
+            append_note_if_missing_header_is_suspected(msg);
+            // Attach additional error info to the exception if supported
+            if (PyErr_Occurred()) {
+                // #HelpAppreciated: unit test coverage for this branch.
+                raise_from(PyExc_TypeError, msg.c_str());
+                return nullptr;
+            }
+            PyErr_SetString(PyExc_TypeError, msg.c_str());
+            return nullptr;
+        }
+        if (!result) {
+            std::string msg = "Unable to convert function return value to a "
+                              "Python type! The signature was\n\t";
+            msg += it->signature;
+            append_note_if_missing_header_is_suspected(msg);
+            // Attach additional error info to the exception if supported
+            if (PyErr_Occurred()) {
+                raise_from(PyExc_TypeError, msg.c_str());
+                return nullptr;
+            }
+            PyErr_SetString(PyExc_TypeError, msg.c_str());
+            return nullptr;
+        }
+        if (overloads->is_constructor && !self_value_and_holder.holder_constructed()) {
+            auto *pi = reinterpret_cast<instance *>(parent.ptr());
+            self_value_and_holder.type->init_instance(pi, nullptr);
+        }
+        return result.ptr();
+    }
+};
+
+/// Wrapper for Python extension modules
+class module_ : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(module_, object, PyModule_Check)
+
+    /// Create a new top-level Python module with the given name and docstring
+    PYBIND11_DEPRECATED("Use PYBIND11_MODULE or module_::create_extension_module instead")
+    explicit module_(const char *name, const char *doc = nullptr) {
+        *this = create_extension_module(name, doc, new PyModuleDef());
+    }
+
+    /** \rst
+        Create Python binding for a new function within the module scope. ``Func``
+        can be a plain C++ function, a function pointer, or a lambda function. For
+        details on the ``Extra&& ... extra`` argument, see section :ref:`extras`.
+    \endrst */
+    template <typename Func, typename... Extra>
+    module_ &def(const char *name_, Func &&f, const Extra &...extra) {
+        cpp_function func(std::forward<Func>(f),
+                          name(name_),
+                          scope(*this),
+                          sibling(getattr(*this, name_, none())),
+                          extra...);
+        // NB: allow overwriting here because cpp_function sets up a chain with the intention of
+        // overwriting (and has already checked internally that it isn't overwriting
+        // non-functions).
+        add_object(name_, func, true /* overwrite */);
+        return *this;
+    }
+
+    /** \rst
+        Create and return a new Python submodule with the given name and docstring.
+        This also works recursively, i.e.
+
+        .. code-block:: cpp
+
+            py::module_ m("example", "pybind11 example plugin");
+            py::module_ m2 = m.def_submodule("sub", "A submodule of 'example'");
+            py::module_ m3 = m2.def_submodule("subsub", "A submodule of 'example.sub'");
+    \endrst */
+    module_ def_submodule(const char *name, const char *doc = nullptr) {
+        const char *this_name = PyModule_GetName(m_ptr);
+        if (this_name == nullptr) {
+            throw error_already_set();
+        }
+        std::string full_name = std::string(this_name) + '.' + name;
+        handle submodule = PyImport_AddModule(full_name.c_str());
+        if (!submodule) {
+            throw error_already_set();
+        }
+        auto result = reinterpret_borrow<module_>(submodule);
+        if (doc && options::show_user_defined_docstrings()) {
+            result.attr("__doc__") = pybind11::str(doc);
+        }
+        attr(name) = result;
+        return result;
+    }
+
+    /// Import and return a module or throws `error_already_set`.
+    static module_ import(const char *name) {
+        PyObject *obj = PyImport_ImportModule(name);
+        if (!obj) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<module_>(obj);
+    }
+
+    /// Reload the module or throws `error_already_set`.
+    void reload() {
+        PyObject *obj = PyImport_ReloadModule(ptr());
+        if (!obj) {
+            throw error_already_set();
+        }
+        *this = reinterpret_steal<module_>(obj);
+    }
+
+    /** \rst
+        Adds an object to the module using the given name.  Throws if an object with the given name
+        already exists.
+
+        ``overwrite`` should almost always be false: attempting to overwrite objects that pybind11
+        has established will, in most cases, break things.
+    \endrst */
+    PYBIND11_NOINLINE void add_object(const char *name, handle obj, bool overwrite = false) {
+        if (!overwrite && hasattr(*this, name)) {
+            pybind11_fail(
+                "Error during initialization: multiple incompatible definitions with name \""
+                + std::string(name) + "\"");
+        }
+
+        PyModule_AddObject(ptr(), name, obj.inc_ref().ptr() /* steals a reference */);
+    }
+
+    using module_def = PyModuleDef; // TODO: Can this be removed (it was needed only for Python 2)?
+
+    /** \rst
+        Create a new top-level module that can be used as the main module of a C extension.
+
+        ``def`` should point to a statically allocated module_def.
+    \endrst */
+    static module_ create_extension_module(const char *name, const char *doc, module_def *def) {
+        // module_def is PyModuleDef
+        // Placement new (not an allocation).
+        def = new (def)
+            PyModuleDef{/* m_base */ PyModuleDef_HEAD_INIT,
+                        /* m_name */ name,
+                        /* m_doc */ options::show_user_defined_docstrings() ? doc : nullptr,
+                        /* m_size */ -1,
+                        /* m_methods */ nullptr,
+                        /* m_slots */ nullptr,
+                        /* m_traverse */ nullptr,
+                        /* m_clear */ nullptr,
+                        /* m_free */ nullptr};
+        auto *m = PyModule_Create(def);
+        if (m == nullptr) {
+            if (PyErr_Occurred()) {
+                throw error_already_set();
+            }
+            pybind11_fail("Internal error in module_::create_extension_module()");
+        }
+        // TODO: Should be reinterpret_steal for Python 3, but Python also steals it again when
+        //       returned from PyInit_...
+        //       For Python 2, reinterpret_borrow was correct.
+        return reinterpret_borrow<module_>(m);
+    }
+};
+
+// When inside a namespace (or anywhere as long as it's not the first item on a line),
+// C++20 allows "module" to be used. This is provided for backward compatibility, and for
+// simplicity, if someone wants to use py::module for example, that is perfectly safe.
+using module = module_;
+
+/// \ingroup python_builtins
+/// Return a dictionary representing the global variables in the current execution frame,
+/// or ``__main__.__dict__`` if there is no frame (usually when the interpreter is embedded).
+inline dict globals() {
+    PyObject *p = PyEval_GetGlobals();
+    return reinterpret_borrow<dict>(p ? p : module_::import("__main__").attr("__dict__").ptr());
+}
+
+template <typename... Args, typename = detail::enable_if_t<args_are_all_keyword_or_ds<Args...>()>>
+PYBIND11_DEPRECATED("make_simple_namespace should be replaced with "
+                    "py::module_::import(\"types\").attr(\"SimpleNamespace\") ")
+object make_simple_namespace(Args &&...args_) {
+    return module_::import("types").attr("SimpleNamespace")(std::forward<Args>(args_)...);
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+/// Generic support for creating new Python heap types
+class generic_type : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(generic_type, object, PyType_Check)
+protected:
+    void initialize(const type_record &rec) {
+        if (rec.scope && hasattr(rec.scope, "__dict__")
+            && rec.scope.attr("__dict__").contains(rec.name)) {
+            pybind11_fail("generic_type: cannot initialize type \"" + std::string(rec.name)
+                          + "\": an object with that name is already defined");
+        }
+
+        if ((rec.module_local ? get_local_type_info(*rec.type) : get_global_type_info(*rec.type))
+            != nullptr) {
+            pybind11_fail("generic_type: type \"" + std::string(rec.name)
+                          + "\" is already registered!");
+        }
+
+        m_ptr = make_new_python_type(rec);
+
+        /* Register supplemental type information in C++ dict */
+        auto *tinfo = new detail::type_info();
+        tinfo->type = (PyTypeObject *) m_ptr;
+        tinfo->cpptype = rec.type;
+        tinfo->type_size = rec.type_size;
+        tinfo->type_align = rec.type_align;
+        tinfo->operator_new = rec.operator_new;
+        tinfo->holder_size_in_ptrs = size_in_ptrs(rec.holder_size);
+        tinfo->init_instance = rec.init_instance;
+        tinfo->dealloc = rec.dealloc;
+        tinfo->simple_type = true;
+        tinfo->simple_ancestors = true;
+        tinfo->default_holder = rec.default_holder;
+        tinfo->module_local = rec.module_local;
+
+        auto &internals = get_internals();
+        auto tindex = std::type_index(*rec.type);
+        tinfo->direct_conversions = &internals.direct_conversions[tindex];
+        if (rec.module_local) {
+            get_local_internals().registered_types_cpp[tindex] = tinfo;
+        } else {
+            internals.registered_types_cpp[tindex] = tinfo;
+        }
+        internals.registered_types_py[(PyTypeObject *) m_ptr] = {tinfo};
+
+        if (rec.bases.size() > 1 || rec.multiple_inheritance) {
+            mark_parents_nonsimple(tinfo->type);
+            tinfo->simple_ancestors = false;
+        } else if (rec.bases.size() == 1) {
+            auto *parent_tinfo = get_type_info((PyTypeObject *) rec.bases[0].ptr());
+            assert(parent_tinfo != nullptr);
+            bool parent_simple_ancestors = parent_tinfo->simple_ancestors;
+            tinfo->simple_ancestors = parent_simple_ancestors;
+            // The parent can no longer be a simple type if it has MI and has a child
+            parent_tinfo->simple_type = parent_tinfo->simple_type && parent_simple_ancestors;
+        }
+
+        if (rec.module_local) {
+            // Stash the local typeinfo and loader so that external modules can access it.
+            tinfo->module_local_load = &type_caster_generic::local_load;
+            setattr(m_ptr, PYBIND11_MODULE_LOCAL_ID, capsule(tinfo));
+        }
+    }
+
+    /// Helper function which tags all parents of a type using mult. inheritance
+    void mark_parents_nonsimple(PyTypeObject *value) {
+        auto t = reinterpret_borrow<tuple>(value->tp_bases);
+        for (handle h : t) {
+            auto *tinfo2 = get_type_info((PyTypeObject *) h.ptr());
+            if (tinfo2) {
+                tinfo2->simple_type = false;
+            }
+            mark_parents_nonsimple((PyTypeObject *) h.ptr());
+        }
+    }
+
+    void install_buffer_funcs(buffer_info *(*get_buffer)(PyObject *, void *),
+                              void *get_buffer_data) {
+        auto *type = (PyHeapTypeObject *) m_ptr;
+        auto *tinfo = detail::get_type_info(&type->ht_type);
+
+        if (!type->ht_type.tp_as_buffer) {
+            pybind11_fail("To be able to register buffer protocol support for the type '"
+                          + get_fully_qualified_tp_name(tinfo->type)
+                          + "' the associated class<>(..) invocation must "
+                            "include the pybind11::buffer_protocol() annotation!");
+        }
+
+        tinfo->get_buffer = get_buffer;
+        tinfo->get_buffer_data = get_buffer_data;
+    }
+
+    // rec_func must be set for either fget or fset.
+    void def_property_static_impl(const char *name,
+                                  handle fget,
+                                  handle fset,
+                                  detail::function_record *rec_func) {
+        const auto is_static = (rec_func != nullptr) && !(rec_func->is_method && rec_func->scope);
+        const auto has_doc = (rec_func != nullptr) && (rec_func->doc != nullptr)
+                             && pybind11::options::show_user_defined_docstrings();
+        auto property = handle(
+            (PyObject *) (is_static ? get_internals().static_property_type : &PyProperty_Type));
+        attr(name) = property(fget.ptr() ? fget : none(),
+                              fset.ptr() ? fset : none(),
+                              /*deleter*/ none(),
+                              pybind11::str(has_doc ? rec_func->doc : ""));
+    }
+};
+
+/// Set the pointer to operator new if it exists. The cast is needed because it can be overloaded.
+template <typename T,
+          typename = void_t<decltype(static_cast<void *(*) (size_t)>(T::operator new))>>
+void set_operator_new(type_record *r) {
+    r->operator_new = &T::operator new;
+}
+
+template <typename>
+void set_operator_new(...) {}
+
+template <typename T, typename SFINAE = void>
+struct has_operator_delete : std::false_type {};
+template <typename T>
+struct has_operator_delete<T, void_t<decltype(static_cast<void (*)(void *)>(T::operator delete))>>
+    : std::true_type {};
+template <typename T, typename SFINAE = void>
+struct has_operator_delete_size : std::false_type {};
+template <typename T>
+struct has_operator_delete_size<
+    T,
+    void_t<decltype(static_cast<void (*)(void *, size_t)>(T::operator delete))>> : std::true_type {
+};
+/// Call class-specific delete if it exists or global otherwise. Can also be an overload set.
+template <typename T, enable_if_t<has_operator_delete<T>::value, int> = 0>
+void call_operator_delete(T *p, size_t, size_t) {
+    T::operator delete(p);
+}
+template <
+    typename T,
+    enable_if_t<!has_operator_delete<T>::value && has_operator_delete_size<T>::value, int> = 0>
+void call_operator_delete(T *p, size_t s, size_t) {
+    T::operator delete(p, s);
+}
+
+inline void call_operator_delete(void *p, size_t s, size_t a) {
+    (void) s;
+    (void) a;
+#if defined(__cpp_aligned_new) && (!defined(_MSC_VER) || _MSC_VER >= 1912)
+    if (a > __STDCPP_DEFAULT_NEW_ALIGNMENT__) {
+#    ifdef __cpp_sized_deallocation
+        ::operator delete(p, s, std::align_val_t(a));
+#    else
+        ::operator delete(p, std::align_val_t(a));
+#    endif
+        return;
+    }
+#endif
+#ifdef __cpp_sized_deallocation
+    ::operator delete(p, s);
+#else
+    ::operator delete(p);
+#endif
+}
+
+inline void add_class_method(object &cls, const char *name_, const cpp_function &cf) {
+    cls.attr(cf.name()) = cf;
+    if (std::strcmp(name_, "__eq__") == 0 && !cls.attr("__dict__").contains("__hash__")) {
+        cls.attr("__hash__") = none();
+    }
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Given a pointer to a member function, cast it to its `Derived` version.
+/// Forward everything else unchanged.
+template <typename /*Derived*/, typename F>
+auto method_adaptor(F &&f) -> decltype(std::forward<F>(f)) {
+    return std::forward<F>(f);
+}
+
+template <typename Derived, typename Return, typename Class, typename... Args>
+auto method_adaptor(Return (Class::*pmf)(Args...)) -> Return (Derived::*)(Args...) {
+    static_assert(
+        detail::is_accessible_base_of<Class, Derived>::value,
+        "Cannot bind an inaccessible base class method; use a lambda definition instead");
+    return pmf;
+}
+
+template <typename Derived, typename Return, typename Class, typename... Args>
+auto method_adaptor(Return (Class::*pmf)(Args...) const) -> Return (Derived::*)(Args...) const {
+    static_assert(
+        detail::is_accessible_base_of<Class, Derived>::value,
+        "Cannot bind an inaccessible base class method; use a lambda definition instead");
+    return pmf;
+}
+
+template <typename type_, typename... options>
+class class_ : public detail::generic_type {
+    template <typename T>
+    using is_holder = detail::is_holder_type<type_, T>;
+    template <typename T>
+    using is_subtype = detail::is_strict_base_of<type_, T>;
+    template <typename T>
+    using is_base = detail::is_strict_base_of<T, type_>;
+    // struct instead of using here to help MSVC:
+    template <typename T>
+    struct is_valid_class_option : detail::any_of<is_holder<T>, is_subtype<T>, is_base<T>> {};
+
+public:
+    using type = type_;
+    using type_alias = detail::exactly_one_t<is_subtype, void, options...>;
+    constexpr static bool has_alias = !std::is_void<type_alias>::value;
+    using holder_type = detail::exactly_one_t<is_holder, std::unique_ptr<type>, options...>;
+
+    static_assert(detail::all_of<is_valid_class_option<options>...>::value,
+                  "Unknown/invalid class_ template parameters provided");
+
+    static_assert(!has_alias || std::is_polymorphic<type>::value,
+                  "Cannot use an alias class with a non-polymorphic type");
+
+    PYBIND11_OBJECT(class_, generic_type, PyType_Check)
+
+    template <typename... Extra>
+    class_(handle scope, const char *name, const Extra &...extra) {
+        using namespace detail;
+
+        // MI can only be specified via class_ template options, not constructor parameters
+        static_assert(
+            none_of<is_pyobject<Extra>...>::value || // no base class arguments, or:
+                (constexpr_sum(is_pyobject<Extra>::value...) == 1 && // Exactly one base
+                 constexpr_sum(is_base<options>::value...) == 0 &&   // no template option bases
+                 // no multiple_inheritance attr
+                 none_of<std::is_same<multiple_inheritance, Extra>...>::value),
+            "Error: multiple inheritance bases must be specified via class_ template options");
+
+        type_record record;
+        record.scope = scope;
+        record.name = name;
+        record.type = &typeid(type);
+        record.type_size = sizeof(conditional_t<has_alias, type_alias, type>);
+        record.type_align = alignof(conditional_t<has_alias, type_alias, type> &);
+        record.holder_size = sizeof(holder_type);
+        record.init_instance = init_instance;
+        record.dealloc = dealloc;
+        record.default_holder = detail::is_instantiation<std::unique_ptr, holder_type>::value;
+
+        set_operator_new<type>(&record);
+
+        /* Register base classes specified via template arguments to class_, if any */
+        PYBIND11_EXPAND_SIDE_EFFECTS(add_base<options>(record));
+
+        /* Process optional arguments, if any */
+        process_attributes<Extra...>::init(extra..., &record);
+
+        generic_type::initialize(record);
+
+        if (has_alias) {
+            auto &instances = record.module_local ? get_local_internals().registered_types_cpp
+                                                  : get_internals().registered_types_cpp;
+            instances[std::type_index(typeid(type_alias))]
+                = instances[std::type_index(typeid(type))];
+        }
+    }
+
+    template <typename Base, detail::enable_if_t<is_base<Base>::value, int> = 0>
+    static void add_base(detail::type_record &rec) {
+        rec.add_base(typeid(Base), [](void *src) -> void * {
+            return static_cast<Base *>(reinterpret_cast<type *>(src));
+        });
+    }
+
+    template <typename Base, detail::enable_if_t<!is_base<Base>::value, int> = 0>
+    static void add_base(detail::type_record &) {}
+
+    template <typename Func, typename... Extra>
+    class_ &def(const char *name_, Func &&f, const Extra &...extra) {
+        cpp_function cf(method_adaptor<type>(std::forward<Func>(f)),
+                        name(name_),
+                        is_method(*this),
+                        sibling(getattr(*this, name_, none())),
+                        extra...);
+        add_class_method(*this, name_, cf);
+        return *this;
+    }
+
+    template <typename Func, typename... Extra>
+    class_ &def_static(const char *name_, Func &&f, const Extra &...extra) {
+        static_assert(!std::is_member_function_pointer<Func>::value,
+                      "def_static(...) called with a non-static member function pointer");
+        cpp_function cf(std::forward<Func>(f),
+                        name(name_),
+                        scope(*this),
+                        sibling(getattr(*this, name_, none())),
+                        extra...);
+        auto cf_name = cf.name();
+        attr(std::move(cf_name)) = staticmethod(std::move(cf));
+        return *this;
+    }
+
+    template <detail::op_id id, detail::op_type ot, typename L, typename R, typename... Extra>
+    class_ &def(const detail::op_<id, ot, L, R> &op, const Extra &...extra) {
+        op.execute(*this, extra...);
+        return *this;
+    }
+
+    template <detail::op_id id, detail::op_type ot, typename L, typename R, typename... Extra>
+    class_ &def_cast(const detail::op_<id, ot, L, R> &op, const Extra &...extra) {
+        op.execute_cast(*this, extra...);
+        return *this;
+    }
+
+    template <typename... Args, typename... Extra>
+    class_ &def(const detail::initimpl::constructor<Args...> &init, const Extra &...extra) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(init);
+        init.execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename... Args, typename... Extra>
+    class_ &def(const detail::initimpl::alias_constructor<Args...> &init, const Extra &...extra) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(init);
+        init.execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename... Args, typename... Extra>
+    class_ &def(detail::initimpl::factory<Args...> &&init, const Extra &...extra) {
+        std::move(init).execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename... Args, typename... Extra>
+    class_ &def(detail::initimpl::pickle_factory<Args...> &&pf, const Extra &...extra) {
+        std::move(pf).execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename Func>
+    class_ &def_buffer(Func &&func) {
+        struct capture {
+            Func func;
+        };
+        auto *ptr = new capture{std::forward<Func>(func)};
+        install_buffer_funcs(
+            [](PyObject *obj, void *ptr) -> buffer_info * {
+                detail::make_caster<type> caster;
+                if (!caster.load(obj, false)) {
+                    return nullptr;
+                }
+                return new buffer_info(((capture *) ptr)->func(std::move(caster)));
+            },
+            ptr);
+        weakref(m_ptr, cpp_function([ptr](handle wr) {
+                    delete ptr;
+                    wr.dec_ref();
+                }))
+            .release();
+        return *this;
+    }
+
+    template <typename Return, typename Class, typename... Args>
+    class_ &def_buffer(Return (Class::*func)(Args...)) {
+        return def_buffer([func](type &obj) { return (obj.*func)(); });
+    }
+
+    template <typename Return, typename Class, typename... Args>
+    class_ &def_buffer(Return (Class::*func)(Args...) const) {
+        return def_buffer([func](const type &obj) { return (obj.*func)(); });
+    }
+
+    template <typename C, typename D, typename... Extra>
+    class_ &def_readwrite(const char *name, D C::*pm, const Extra &...extra) {
+        static_assert(std::is_same<C, type>::value || std::is_base_of<C, type>::value,
+                      "def_readwrite() requires a class member (or base class member)");
+        cpp_function fget([pm](const type &c) -> const D & { return c.*pm; }, is_method(*this)),
+            fset([pm](type &c, const D &value) { c.*pm = value; }, is_method(*this));
+        def_property(name, fget, fset, return_value_policy::reference_internal, extra...);
+        return *this;
+    }
+
+    template <typename C, typename D, typename... Extra>
+    class_ &def_readonly(const char *name, const D C::*pm, const Extra &...extra) {
+        static_assert(std::is_same<C, type>::value || std::is_base_of<C, type>::value,
+                      "def_readonly() requires a class member (or base class member)");
+        cpp_function fget([pm](const type &c) -> const D & { return c.*pm; }, is_method(*this));
+        def_property_readonly(name, fget, return_value_policy::reference_internal, extra...);
+        return *this;
+    }
+
+    template <typename D, typename... Extra>
+    class_ &def_readwrite_static(const char *name, D *pm, const Extra &...extra) {
+        cpp_function fget([pm](const object &) -> const D & { return *pm; }, scope(*this)),
+            fset([pm](const object &, const D &value) { *pm = value; }, scope(*this));
+        def_property_static(name, fget, fset, return_value_policy::reference, extra...);
+        return *this;
+    }
+
+    template <typename D, typename... Extra>
+    class_ &def_readonly_static(const char *name, const D *pm, const Extra &...extra) {
+        cpp_function fget([pm](const object &) -> const D & { return *pm; }, scope(*this));
+        def_property_readonly_static(name, fget, return_value_policy::reference, extra...);
+        return *this;
+    }
+
+    /// Uses return_value_policy::reference_internal by default
+    template <typename Getter, typename... Extra>
+    class_ &def_property_readonly(const char *name, const Getter &fget, const Extra &...extra) {
+        return def_property_readonly(name,
+                                     cpp_function(method_adaptor<type>(fget)),
+                                     return_value_policy::reference_internal,
+                                     extra...);
+    }
+
+    /// Uses cpp_function's return_value_policy by default
+    template <typename... Extra>
+    class_ &
+    def_property_readonly(const char *name, const cpp_function &fget, const Extra &...extra) {
+        return def_property(name, fget, nullptr, extra...);
+    }
+
+    /// Uses return_value_policy::reference by default
+    template <typename Getter, typename... Extra>
+    class_ &
+    def_property_readonly_static(const char *name, const Getter &fget, const Extra &...extra) {
+        return def_property_readonly_static(
+            name, cpp_function(fget), return_value_policy::reference, extra...);
+    }
+
+    /// Uses cpp_function's return_value_policy by default
+    template <typename... Extra>
+    class_ &def_property_readonly_static(const char *name,
+                                         const cpp_function &fget,
+                                         const Extra &...extra) {
+        return def_property_static(name, fget, nullptr, extra...);
+    }
+
+    /// Uses return_value_policy::reference_internal by default
+    template <typename Getter, typename Setter, typename... Extra>
+    class_ &
+    def_property(const char *name, const Getter &fget, const Setter &fset, const Extra &...extra) {
+        return def_property(name, fget, cpp_function(method_adaptor<type>(fset)), extra...);
+    }
+    template <typename Getter, typename... Extra>
+    class_ &def_property(const char *name,
+                         const Getter &fget,
+                         const cpp_function &fset,
+                         const Extra &...extra) {
+        return def_property(name,
+                            cpp_function(method_adaptor<type>(fget)),
+                            fset,
+                            return_value_policy::reference_internal,
+                            extra...);
+    }
+
+    /// Uses cpp_function's return_value_policy by default
+    template <typename... Extra>
+    class_ &def_property(const char *name,
+                         const cpp_function &fget,
+                         const cpp_function &fset,
+                         const Extra &...extra) {
+        return def_property_static(name, fget, fset, is_method(*this), extra...);
+    }
+
+    /// Uses return_value_policy::reference by default
+    template <typename Getter, typename... Extra>
+    class_ &def_property_static(const char *name,
+                                const Getter &fget,
+                                const cpp_function &fset,
+                                const Extra &...extra) {
+        return def_property_static(
+            name, cpp_function(fget), fset, return_value_policy::reference, extra...);
+    }
+
+    /// Uses cpp_function's return_value_policy by default
+    template <typename... Extra>
+    class_ &def_property_static(const char *name,
+                                const cpp_function &fget,
+                                const cpp_function &fset,
+                                const Extra &...extra) {
+        static_assert(0 == detail::constexpr_sum(std::is_base_of<arg, Extra>::value...),
+                      "Argument annotations are not allowed for properties");
+        auto rec_fget = get_function_record(fget), rec_fset = get_function_record(fset);
+        auto *rec_active = rec_fget;
+        if (rec_fget) {
+            char *doc_prev = rec_fget->doc; /* 'extra' field may include a property-specific
+                                               documentation string */
+            detail::process_attributes<Extra...>::init(extra..., rec_fget);
+            if (rec_fget->doc && rec_fget->doc != doc_prev) {
+                std::free(doc_prev);
+                rec_fget->doc = PYBIND11_COMPAT_STRDUP(rec_fget->doc);
+            }
+        }
+        if (rec_fset) {
+            char *doc_prev = rec_fset->doc;
+            detail::process_attributes<Extra...>::init(extra..., rec_fset);
+            if (rec_fset->doc && rec_fset->doc != doc_prev) {
+                std::free(doc_prev);
+                rec_fset->doc = PYBIND11_COMPAT_STRDUP(rec_fset->doc);
+            }
+            if (!rec_active) {
+                rec_active = rec_fset;
+            }
+        }
+        def_property_static_impl(name, fget, fset, rec_active);
+        return *this;
+    }
+
+private:
+    /// Initialize holder object, variant 1: object derives from enable_shared_from_this
+    template <typename T>
+    static void init_holder(detail::instance *inst,
+                            detail::value_and_holder &v_h,
+                            const holder_type * /* unused */,
+                            const std::enable_shared_from_this<T> * /* dummy */) {
+
+        auto sh = std::dynamic_pointer_cast<typename holder_type::element_type>(
+            detail::try_get_shared_from_this(v_h.value_ptr<type>()));
+        if (sh) {
+            new (std::addressof(v_h.holder<holder_type>())) holder_type(std::move(sh));
+            v_h.set_holder_constructed();
+        }
+
+        if (!v_h.holder_constructed() && inst->owned) {
+            new (std::addressof(v_h.holder<holder_type>())) holder_type(v_h.value_ptr<type>());
+            v_h.set_holder_constructed();
+        }
+    }
+
+    static void init_holder_from_existing(const detail::value_and_holder &v_h,
+                                          const holder_type *holder_ptr,
+                                          std::true_type /*is_copy_constructible*/) {
+        new (std::addressof(v_h.holder<holder_type>()))
+            holder_type(*reinterpret_cast<const holder_type *>(holder_ptr));
+    }
+
+    static void init_holder_from_existing(const detail::value_and_holder &v_h,
+                                          const holder_type *holder_ptr,
+                                          std::false_type /*is_copy_constructible*/) {
+        new (std::addressof(v_h.holder<holder_type>()))
+            holder_type(std::move(*const_cast<holder_type *>(holder_ptr)));
+    }
+
+    /// Initialize holder object, variant 2: try to construct from existing holder object, if
+    /// possible
+    static void init_holder(detail::instance *inst,
+                            detail::value_and_holder &v_h,
+                            const holder_type *holder_ptr,
+                            const void * /* dummy -- not enable_shared_from_this<T>) */) {
+        if (holder_ptr) {
+            init_holder_from_existing(v_h, holder_ptr, std::is_copy_constructible<holder_type>());
+            v_h.set_holder_constructed();
+        } else if (PYBIND11_SILENCE_MSVC_C4127(detail::always_construct_holder<holder_type>::value)
+                   || inst->owned) {
+            new (std::addressof(v_h.holder<holder_type>())) holder_type(v_h.value_ptr<type>());
+            v_h.set_holder_constructed();
+        }
+    }
+
+    /// Performs instance initialization including constructing a holder and registering the known
+    /// instance.  Should be called as soon as the `type` value_ptr is set for an instance.  Takes
+    /// an optional pointer to an existing holder to use; if not specified and the instance is
+    /// `.owned`, a new holder will be constructed to manage the value pointer.
+    static void init_instance(detail::instance *inst, const void *holder_ptr) {
+        auto v_h = inst->get_value_and_holder(detail::get_type_info(typeid(type)));
+        if (!v_h.instance_registered()) {
+            register_instance(inst, v_h.value_ptr(), v_h.type);
+            v_h.set_instance_registered();
+        }
+        init_holder(inst, v_h, (const holder_type *) holder_ptr, v_h.value_ptr<type>());
+    }
+
+    /// Deallocates an instance; via holder, if constructed; otherwise via operator delete.
+    static void dealloc(detail::value_and_holder &v_h) {
+        // We could be deallocating because we are cleaning up after a Python exception.
+        // If so, the Python error indicator will be set. We need to clear that before
+        // running the destructor, in case the destructor code calls more Python.
+        // If we don't, the Python API will exit with an exception, and pybind11 will
+        // throw error_already_set from the C++ destructor which is forbidden and triggers
+        // std::terminate().
+        error_scope scope;
+        if (v_h.holder_constructed()) {
+            v_h.holder<holder_type>().~holder_type();
+            v_h.set_holder_constructed(false);
+        } else {
+            detail::call_operator_delete(
+                v_h.value_ptr<type>(), v_h.type->type_size, v_h.type->type_align);
+        }
+        v_h.value_ptr() = nullptr;
+    }
+
+    static detail::function_record *get_function_record(handle h) {
+        h = detail::get_function(h);
+        return h ? (detail::function_record *) reinterpret_borrow<capsule>(
+                   PyCFunction_GET_SELF(h.ptr()))
+                 : nullptr;
+    }
+};
+
+/// Binds an existing constructor taking arguments Args...
+template <typename... Args>
+detail::initimpl::constructor<Args...> init() {
+    return {};
+}
+/// Like `init<Args...>()`, but the instance is always constructed through the alias class (even
+/// when not inheriting on the Python side).
+template <typename... Args>
+detail::initimpl::alias_constructor<Args...> init_alias() {
+    return {};
+}
+
+/// Binds a factory function as a constructor
+template <typename Func, typename Ret = detail::initimpl::factory<Func>>
+Ret init(Func &&f) {
+    return {std::forward<Func>(f)};
+}
+
+/// Dual-argument factory function: the first function is called when no alias is needed, the
+/// second when an alias is needed (i.e. due to python-side inheritance).  Arguments must be
+/// identical.
+template <typename CFunc, typename AFunc, typename Ret = detail::initimpl::factory<CFunc, AFunc>>
+Ret init(CFunc &&c, AFunc &&a) {
+    return {std::forward<CFunc>(c), std::forward<AFunc>(a)};
+}
+
+/// Binds pickling functions `__getstate__` and `__setstate__` and ensures that the type
+/// returned by `__getstate__` is the same as the argument accepted by `__setstate__`.
+template <typename GetState, typename SetState>
+detail::initimpl::pickle_factory<GetState, SetState> pickle(GetState &&g, SetState &&s) {
+    return {std::forward<GetState>(g), std::forward<SetState>(s)};
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline str enum_name(handle arg) {
+    dict entries = arg.get_type().attr("__entries");
+    for (auto kv : entries) {
+        if (handle(kv.second[int_(0)]).equal(arg)) {
+            return pybind11::str(kv.first);
+        }
+    }
+    return "???";
+}
+
+struct enum_base {
+    enum_base(const handle &base, const handle &parent) : m_base(base), m_parent(parent) {}
+
+    PYBIND11_NOINLINE void init(bool is_arithmetic, bool is_convertible) {
+        m_base.attr("__entries") = dict();
+        auto property = handle((PyObject *) &PyProperty_Type);
+        auto static_property = handle((PyObject *) get_internals().static_property_type);
+
+        m_base.attr("__repr__") = cpp_function(
+            [](const object &arg) -> str {
+                handle type = type::handle_of(arg);
+                object type_name = type.attr("__name__");
+                return pybind11::str("<{}.{}: {}>")
+                    .format(std::move(type_name), enum_name(arg), int_(arg));
+            },
+            name("__repr__"),
+            is_method(m_base));
+
+        m_base.attr("name") = property(cpp_function(&enum_name, name("name"), is_method(m_base)));
+
+        m_base.attr("__str__") = cpp_function(
+            [](handle arg) -> str {
+                object type_name = type::handle_of(arg).attr("__name__");
+                return pybind11::str("{}.{}").format(std::move(type_name), enum_name(arg));
+            },
+            name("name"),
+            is_method(m_base));
+
+        m_base.attr("__doc__") = static_property(
+            cpp_function(
+                [](handle arg) -> std::string {
+                    std::string docstring;
+                    dict entries = arg.attr("__entries");
+                    if (((PyTypeObject *) arg.ptr())->tp_doc) {
+                        docstring += std::string(((PyTypeObject *) arg.ptr())->tp_doc) + "\n\n";
+                    }
+                    docstring += "Members:";
+                    for (auto kv : entries) {
+                        auto key = std::string(pybind11::str(kv.first));
+                        auto comment = kv.second[int_(1)];
+                        docstring += "\n\n  " + key;
+                        if (!comment.is_none()) {
+                            docstring += " : " + (std::string) pybind11::str(comment);
+                        }
+                    }
+                    return docstring;
+                },
+                name("__doc__")),
+            none(),
+            none(),
+            "");
+
+        m_base.attr("__members__") = static_property(cpp_function(
+                                                         [](handle arg) -> dict {
+                                                             dict entries = arg.attr("__entries"),
+                                                                  m;
+                                                             for (auto kv : entries) {
+                                                                 m[kv.first] = kv.second[int_(0)];
+                                                             }
+                                                             return m;
+                                                         },
+                                                         name("__members__")),
+                                                     none(),
+                                                     none(),
+                                                     "");
+
+#define PYBIND11_ENUM_OP_STRICT(op, expr, strict_behavior)                                        \
+    m_base.attr(op) = cpp_function(                                                               \
+        [](const object &a, const object &b) {                                                    \
+            if (!type::handle_of(a).is(type::handle_of(b)))                                       \
+                strict_behavior; /* NOLINT(bugprone-macro-parentheses) */                         \
+            return expr;                                                                          \
+        },                                                                                        \
+        name(op),                                                                                 \
+        is_method(m_base),                                                                        \
+        arg("other"))
+
+#define PYBIND11_ENUM_OP_CONV(op, expr)                                                           \
+    m_base.attr(op) = cpp_function(                                                               \
+        [](const object &a_, const object &b_) {                                                  \
+            int_ a(a_), b(b_);                                                                    \
+            return expr;                                                                          \
+        },                                                                                        \
+        name(op),                                                                                 \
+        is_method(m_base),                                                                        \
+        arg("other"))
+
+#define PYBIND11_ENUM_OP_CONV_LHS(op, expr)                                                       \
+    m_base.attr(op) = cpp_function(                                                               \
+        [](const object &a_, const object &b) {                                                   \
+            int_ a(a_);                                                                           \
+            return expr;                                                                          \
+        },                                                                                        \
+        name(op),                                                                                 \
+        is_method(m_base),                                                                        \
+        arg("other"))
+
+        if (is_convertible) {
+            PYBIND11_ENUM_OP_CONV_LHS("__eq__", !b.is_none() && a.equal(b));
+            PYBIND11_ENUM_OP_CONV_LHS("__ne__", b.is_none() || !a.equal(b));
+
+            if (is_arithmetic) {
+                PYBIND11_ENUM_OP_CONV("__lt__", a < b);
+                PYBIND11_ENUM_OP_CONV("__gt__", a > b);
+                PYBIND11_ENUM_OP_CONV("__le__", a <= b);
+                PYBIND11_ENUM_OP_CONV("__ge__", a >= b);
+                PYBIND11_ENUM_OP_CONV("__and__", a & b);
+                PYBIND11_ENUM_OP_CONV("__rand__", a & b);
+                PYBIND11_ENUM_OP_CONV("__or__", a | b);
+                PYBIND11_ENUM_OP_CONV("__ror__", a | b);
+                PYBIND11_ENUM_OP_CONV("__xor__", a ^ b);
+                PYBIND11_ENUM_OP_CONV("__rxor__", a ^ b);
+                m_base.attr("__invert__")
+                    = cpp_function([](const object &arg) { return ~(int_(arg)); },
+                                   name("__invert__"),
+                                   is_method(m_base));
+            }
+        } else {
+            PYBIND11_ENUM_OP_STRICT("__eq__", int_(a).equal(int_(b)), return false);
+            PYBIND11_ENUM_OP_STRICT("__ne__", !int_(a).equal(int_(b)), return true);
+
+            if (is_arithmetic) {
+#define PYBIND11_THROW throw type_error("Expected an enumeration of matching type!");
+                PYBIND11_ENUM_OP_STRICT("__lt__", int_(a) < int_(b), PYBIND11_THROW);
+                PYBIND11_ENUM_OP_STRICT("__gt__", int_(a) > int_(b), PYBIND11_THROW);
+                PYBIND11_ENUM_OP_STRICT("__le__", int_(a) <= int_(b), PYBIND11_THROW);
+                PYBIND11_ENUM_OP_STRICT("__ge__", int_(a) >= int_(b), PYBIND11_THROW);
+#undef PYBIND11_THROW
+            }
+        }
+
+#undef PYBIND11_ENUM_OP_CONV_LHS
+#undef PYBIND11_ENUM_OP_CONV
+#undef PYBIND11_ENUM_OP_STRICT
+
+        m_base.attr("__getstate__") = cpp_function(
+            [](const object &arg) { return int_(arg); }, name("__getstate__"), is_method(m_base));
+
+        m_base.attr("__hash__") = cpp_function(
+            [](const object &arg) { return int_(arg); }, name("__hash__"), is_method(m_base));
+    }
+
+    PYBIND11_NOINLINE void value(char const *name_, object value, const char *doc = nullptr) {
+        dict entries = m_base.attr("__entries");
+        str name(name_);
+        if (entries.contains(name)) {
+            std::string type_name = (std::string) str(m_base.attr("__name__"));
+            throw value_error(std::move(type_name) + ": element \"" + std::string(name_)
+                              + "\" already exists!");
+        }
+
+        entries[name] = std::make_pair(value, doc);
+        m_base.attr(std::move(name)) = std::move(value);
+    }
+
+    PYBIND11_NOINLINE void export_values() {
+        dict entries = m_base.attr("__entries");
+        for (auto kv : entries) {
+            m_parent.attr(kv.first) = kv.second[int_(0)];
+        }
+    }
+
+    handle m_base;
+    handle m_parent;
+};
+
+template <bool is_signed, size_t length>
+struct equivalent_integer {};
+template <>
+struct equivalent_integer<true, 1> {
+    using type = int8_t;
+};
+template <>
+struct equivalent_integer<false, 1> {
+    using type = uint8_t;
+};
+template <>
+struct equivalent_integer<true, 2> {
+    using type = int16_t;
+};
+template <>
+struct equivalent_integer<false, 2> {
+    using type = uint16_t;
+};
+template <>
+struct equivalent_integer<true, 4> {
+    using type = int32_t;
+};
+template <>
+struct equivalent_integer<false, 4> {
+    using type = uint32_t;
+};
+template <>
+struct equivalent_integer<true, 8> {
+    using type = int64_t;
+};
+template <>
+struct equivalent_integer<false, 8> {
+    using type = uint64_t;
+};
+
+template <typename IntLike>
+using equivalent_integer_t =
+    typename equivalent_integer<std::is_signed<IntLike>::value, sizeof(IntLike)>::type;
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Binds C++ enumerations and enumeration classes to Python
+template <typename Type>
+class enum_ : public class_<Type> {
+public:
+    using Base = class_<Type>;
+    using Base::attr;
+    using Base::def;
+    using Base::def_property_readonly;
+    using Base::def_property_readonly_static;
+    using Underlying = typename std::underlying_type<Type>::type;
+    // Scalar is the integer representation of underlying type
+    using Scalar = detail::conditional_t<detail::any_of<detail::is_std_char_type<Underlying>,
+                                                        std::is_same<Underlying, bool>>::value,
+                                         detail::equivalent_integer_t<Underlying>,
+                                         Underlying>;
+
+    template <typename... Extra>
+    enum_(const handle &scope, const char *name, const Extra &...extra)
+        : class_<Type>(scope, name, extra...), m_base(*this, scope) {
+        constexpr bool is_arithmetic = detail::any_of<std::is_same<arithmetic, Extra>...>::value;
+        constexpr bool is_convertible = std::is_convertible<Type, Underlying>::value;
+        m_base.init(is_arithmetic, is_convertible);
+
+        def(init([](Scalar i) { return static_cast<Type>(i); }), arg("value"));
+        def_property_readonly("value", [](Type value) { return (Scalar) value; });
+        def("__int__", [](Type value) { return (Scalar) value; });
+        def("__index__", [](Type value) { return (Scalar) value; });
+        attr("__setstate__") = cpp_function(
+            [](detail::value_and_holder &v_h, Scalar arg) {
+                detail::initimpl::setstate<Base>(
+                    v_h, static_cast<Type>(arg), Py_TYPE(v_h.inst) != v_h.type->type);
+            },
+            detail::is_new_style_constructor(),
+            pybind11::name("__setstate__"),
+            is_method(*this),
+            arg("state"));
+    }
+
+    /// Export enumeration entries into the parent scope
+    enum_ &export_values() {
+        m_base.export_values();
+        return *this;
+    }
+
+    /// Add an enumeration entry
+    enum_ &value(char const *name, Type value, const char *doc = nullptr) {
+        m_base.value(name, pybind11::cast(value, return_value_policy::copy), doc);
+        return *this;
+    }
+
+private:
+    detail::enum_base m_base;
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+PYBIND11_NOINLINE void keep_alive_impl(handle nurse, handle patient) {
+    if (!nurse || !patient) {
+        pybind11_fail("Could not activate keep_alive!");
+    }
+
+    if (patient.is_none() || nurse.is_none()) {
+        return; /* Nothing to keep alive or nothing to be kept alive by */
+    }
+
+    auto tinfo = all_type_info(Py_TYPE(nurse.ptr()));
+    if (!tinfo.empty()) {
+        /* It's a pybind-registered type, so we can store the patient in the
+         * internal list. */
+        add_patient(nurse.ptr(), patient.ptr());
+    } else {
+        /* Fall back to clever approach based on weak references taken from
+         * Boost.Python. This is not used for pybind-registered types because
+         * the objects can be destroyed out-of-order in a GC pass. */
+        cpp_function disable_lifesupport([patient](handle weakref) {
+            patient.dec_ref();
+            weakref.dec_ref();
+        });
+
+        weakref wr(nurse, disable_lifesupport);
+
+        patient.inc_ref(); /* reference patient and leak the weak reference */
+        (void) wr.release();
+    }
+}
+
+PYBIND11_NOINLINE void
+keep_alive_impl(size_t Nurse, size_t Patient, function_call &call, handle ret) {
+    auto get_arg = [&](size_t n) {
+        if (n == 0) {
+            return ret;
+        }
+        if (n == 1 && call.init_self) {
+            return call.init_self;
+        }
+        if (n <= call.args.size()) {
+            return call.args[n - 1];
+        }
+        return handle();
+    };
+
+    keep_alive_impl(get_arg(Nurse), get_arg(Patient));
+}
+
+inline std::pair<decltype(internals::registered_types_py)::iterator, bool>
+all_type_info_get_cache(PyTypeObject *type) {
+    auto res = get_internals()
+                   .registered_types_py
+#ifdef __cpp_lib_unordered_map_try_emplace
+                   .try_emplace(type);
+#else
+                   .emplace(type, std::vector<detail::type_info *>());
+#endif
+    if (res.second) {
+        // New cache entry created; set up a weak reference to automatically remove it if the type
+        // gets destroyed:
+        weakref((PyObject *) type, cpp_function([type](handle wr) {
+                    get_internals().registered_types_py.erase(type);
+
+                    // TODO consolidate the erasure code in pybind11_meta_dealloc() in class.h
+                    auto &cache = get_internals().inactive_override_cache;
+                    for (auto it = cache.begin(), last = cache.end(); it != last;) {
+                        if (it->first == reinterpret_cast<PyObject *>(type)) {
+                            it = cache.erase(it);
+                        } else {
+                            ++it;
+                        }
+                    }
+
+                    wr.dec_ref();
+                }))
+            .release();
+    }
+
+    return res;
+}
+
+/* There are a large number of apparently unused template arguments because
+ * each combination requires a separate py::class_ registration.
+ */
+template <typename Access,
+          return_value_policy Policy,
+          typename Iterator,
+          typename Sentinel,
+          typename ValueType,
+          typename... Extra>
+struct iterator_state {
+    Iterator it;
+    Sentinel end;
+    bool first_or_done;
+};
+
+// Note: these helpers take the iterator by non-const reference because some
+// iterators in the wild can't be dereferenced when const. The & after Iterator
+// is required for MSVC < 16.9. SFINAE cannot be reused for result_type due to
+// bugs in ICC, NVCC, and PGI compilers. See PR #3293.
+template <typename Iterator, typename SFINAE = decltype(*std::declval<Iterator &>())>
+struct iterator_access {
+    using result_type = decltype(*std::declval<Iterator &>());
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    result_type operator()(Iterator &it) const { return *it; }
+};
+
+template <typename Iterator, typename SFINAE = decltype((*std::declval<Iterator &>()).first)>
+class iterator_key_access {
+private:
+    using pair_type = decltype(*std::declval<Iterator &>());
+
+public:
+    /* If either the pair itself or the element of the pair is a reference, we
+     * want to return a reference, otherwise a value. When the decltype
+     * expression is parenthesized it is based on the value category of the
+     * expression; otherwise it is the declared type of the pair member.
+     * The use of declval<pair_type> in the second branch rather than directly
+     * using *std::declval<Iterator &>() is a workaround for nvcc
+     * (it's not used in the first branch because going via decltype and back
+     * through declval does not perfectly preserve references).
+     */
+    using result_type
+        = conditional_t<std::is_reference<decltype(*std::declval<Iterator &>())>::value,
+                        decltype(((*std::declval<Iterator &>()).first)),
+                        decltype(std::declval<pair_type>().first)>;
+    result_type operator()(Iterator &it) const { return (*it).first; }
+};
+
+template <typename Iterator, typename SFINAE = decltype((*std::declval<Iterator &>()).second)>
+class iterator_value_access {
+private:
+    using pair_type = decltype(*std::declval<Iterator &>());
+
+public:
+    using result_type
+        = conditional_t<std::is_reference<decltype(*std::declval<Iterator &>())>::value,
+                        decltype(((*std::declval<Iterator &>()).second)),
+                        decltype(std::declval<pair_type>().second)>;
+    result_type operator()(Iterator &it) const { return (*it).second; }
+};
+
+template <typename Access,
+          return_value_policy Policy,
+          typename Iterator,
+          typename Sentinel,
+          typename ValueType,
+          typename... Extra>
+iterator make_iterator_impl(Iterator &&first, Sentinel &&last, Extra &&...extra) {
+    using state = detail::iterator_state<Access, Policy, Iterator, Sentinel, ValueType, Extra...>;
+    // TODO: state captures only the types of Extra, not the values
+
+    if (!detail::get_type_info(typeid(state), false)) {
+        class_<state>(handle(), "iterator", pybind11::module_local())
+            .def("__iter__", [](state &s) -> state & { return s; })
+            .def(
+                "__next__",
+                [](state &s) -> ValueType {
+                    if (!s.first_or_done) {
+                        ++s.it;
+                    } else {
+                        s.first_or_done = false;
+                    }
+                    if (s.it == s.end) {
+                        s.first_or_done = true;
+                        throw stop_iteration();
+                    }
+                    return Access()(s.it);
+                    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+                },
+                std::forward<Extra>(extra)...,
+                Policy);
+    }
+
+    return cast(state{std::forward<Iterator>(first), std::forward<Sentinel>(last), true});
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Makes a python iterator from a first and past-the-end C++ InputIterator.
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Iterator,
+          typename Sentinel,
+          typename ValueType = typename detail::iterator_access<Iterator>::result_type,
+          typename... Extra>
+iterator make_iterator(Iterator &&first, Sentinel &&last, Extra &&...extra) {
+    return detail::make_iterator_impl<detail::iterator_access<Iterator>,
+                                      Policy,
+                                      Iterator,
+                                      Sentinel,
+                                      ValueType,
+                                      Extra...>(std::forward<Iterator>(first),
+                                                std::forward<Sentinel>(last),
+                                                std::forward<Extra>(extra)...);
+}
+
+/// Makes a python iterator over the keys (`.first`) of a iterator over pairs from a
+/// first and past-the-end InputIterator.
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Iterator,
+          typename Sentinel,
+          typename KeyType = typename detail::iterator_key_access<Iterator>::result_type,
+          typename... Extra>
+iterator make_key_iterator(Iterator &&first, Sentinel &&last, Extra &&...extra) {
+    return detail::make_iterator_impl<detail::iterator_key_access<Iterator>,
+                                      Policy,
+                                      Iterator,
+                                      Sentinel,
+                                      KeyType,
+                                      Extra...>(std::forward<Iterator>(first),
+                                                std::forward<Sentinel>(last),
+                                                std::forward<Extra>(extra)...);
+}
+
+/// Makes a python iterator over the values (`.second`) of a iterator over pairs from a
+/// first and past-the-end InputIterator.
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Iterator,
+          typename Sentinel,
+          typename ValueType = typename detail::iterator_value_access<Iterator>::result_type,
+          typename... Extra>
+iterator make_value_iterator(Iterator &&first, Sentinel &&last, Extra &&...extra) {
+    return detail::make_iterator_impl<detail::iterator_value_access<Iterator>,
+                                      Policy,
+                                      Iterator,
+                                      Sentinel,
+                                      ValueType,
+                                      Extra...>(std::forward<Iterator>(first),
+                                                std::forward<Sentinel>(last),
+                                                std::forward<Extra>(extra)...);
+}
+
+/// Makes an iterator over values of an stl container or other container supporting
+/// `std::begin()`/`std::end()`
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Type,
+          typename... Extra>
+iterator make_iterator(Type &value, Extra &&...extra) {
+    return make_iterator<Policy>(
+        std::begin(value), std::end(value), std::forward<Extra>(extra)...);
+}
+
+/// Makes an iterator over the keys (`.first`) of a stl map-like container supporting
+/// `std::begin()`/`std::end()`
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Type,
+          typename... Extra>
+iterator make_key_iterator(Type &value, Extra &&...extra) {
+    return make_key_iterator<Policy>(
+        std::begin(value), std::end(value), std::forward<Extra>(extra)...);
+}
+
+/// Makes an iterator over the values (`.second`) of a stl map-like container supporting
+/// `std::begin()`/`std::end()`
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Type,
+          typename... Extra>
+iterator make_value_iterator(Type &value, Extra &&...extra) {
+    return make_value_iterator<Policy>(
+        std::begin(value), std::end(value), std::forward<Extra>(extra)...);
+}
+
+template <typename InputType, typename OutputType>
+void implicitly_convertible() {
+    struct set_flag {
+        bool &flag;
+        explicit set_flag(bool &flag_) : flag(flag_) { flag_ = true; }
+        ~set_flag() { flag = false; }
+    };
+    auto implicit_caster = [](PyObject *obj, PyTypeObject *type) -> PyObject * {
+        static bool currently_used = false;
+        if (currently_used) { // implicit conversions are non-reentrant
+            return nullptr;
+        }
+        set_flag flag_helper(currently_used);
+        if (!detail::make_caster<InputType>().load(obj, false)) {
+            return nullptr;
+        }
+        tuple args(1);
+        args[0] = obj;
+        PyObject *result = PyObject_Call((PyObject *) type, args.ptr(), nullptr);
+        if (result == nullptr) {
+            PyErr_Clear();
+        }
+        return result;
+    };
+
+    if (auto *tinfo = detail::get_type_info(typeid(OutputType))) {
+        tinfo->implicit_conversions.emplace_back(std::move(implicit_caster));
+    } else {
+        pybind11_fail("implicitly_convertible: Unable to find type " + type_id<OutputType>());
+    }
+}
+
+inline void register_exception_translator(ExceptionTranslator &&translator) {
+    detail::get_internals().registered_exception_translators.push_front(
+        std::forward<ExceptionTranslator>(translator));
+}
+
+/**
+ * Add a new module-local exception translator. Locally registered functions
+ * will be tried before any globally registered exception translators, which
+ * will only be invoked if the module-local handlers do not deal with
+ * the exception.
+ */
+inline void register_local_exception_translator(ExceptionTranslator &&translator) {
+    detail::get_local_internals().registered_exception_translators.push_front(
+        std::forward<ExceptionTranslator>(translator));
+}
+
+/**
+ * Wrapper to generate a new Python exception type.
+ *
+ * This should only be used with PyErr_SetString for now.
+ * It is not (yet) possible to use as a py::base.
+ * Template type argument is reserved for future use.
+ */
+template <typename type>
+class exception : public object {
+public:
+    exception() = default;
+    exception(handle scope, const char *name, handle base = PyExc_Exception) {
+        std::string full_name
+            = scope.attr("__name__").cast<std::string>() + std::string(".") + name;
+        m_ptr = PyErr_NewException(const_cast<char *>(full_name.c_str()), base.ptr(), nullptr);
+        if (hasattr(scope, "__dict__") && scope.attr("__dict__").contains(name)) {
+            pybind11_fail("Error during initialization: multiple incompatible "
+                          "definitions with name \""
+                          + std::string(name) + "\"");
+        }
+        scope.attr(name) = *this;
+    }
+
+    // Sets the current python exception to this exception object with the given message
+    void operator()(const char *message) { PyErr_SetString(m_ptr, message); }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+// Returns a reference to a function-local static exception object used in the simple
+// register_exception approach below.  (It would be simpler to have the static local variable
+// directly in register_exception, but that makes clang <3.5 segfault - issue #1349).
+template <typename CppException>
+exception<CppException> &get_exception_object() {
+    static exception<CppException> ex;
+    return ex;
+}
+
+// Helper function for register_exception and register_local_exception
+template <typename CppException>
+exception<CppException> &
+register_exception_impl(handle scope, const char *name, handle base, bool isLocal) {
+    auto &ex = detail::get_exception_object<CppException>();
+    if (!ex) {
+        ex = exception<CppException>(scope, name, base);
+    }
+
+    auto register_func
+        = isLocal ? &register_local_exception_translator : &register_exception_translator;
+
+    register_func([](std::exception_ptr p) {
+        if (!p) {
+            return;
+        }
+        try {
+            std::rethrow_exception(p);
+        } catch (const CppException &e) {
+            detail::get_exception_object<CppException>()(e.what());
+        }
+    });
+    return ex;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/**
+ * Registers a Python exception in `m` of the given `name` and installs a translator to
+ * translate the C++ exception to the created Python exception using the what() method.
+ * This is intended for simple exception translations; for more complex translation, register the
+ * exception object and translator directly.
+ */
+template <typename CppException>
+exception<CppException> &
+register_exception(handle scope, const char *name, handle base = PyExc_Exception) {
+    return detail::register_exception_impl<CppException>(scope, name, base, false /* isLocal */);
+}
+
+/**
+ * Registers a Python exception in `m` of the given `name` and installs a translator to
+ * translate the C++ exception to the created Python exception using the what() method.
+ * This translator will only be used for exceptions that are thrown in this module and will be
+ * tried before global exception translators, including those registered with register_exception.
+ * This is intended for simple exception translations; for more complex translation, register the
+ * exception object and translator directly.
+ */
+template <typename CppException>
+exception<CppException> &
+register_local_exception(handle scope, const char *name, handle base = PyExc_Exception) {
+    return detail::register_exception_impl<CppException>(scope, name, base, true /* isLocal */);
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+PYBIND11_NOINLINE void print(const tuple &args, const dict &kwargs) {
+    auto strings = tuple(args.size());
+    for (size_t i = 0; i < args.size(); ++i) {
+        strings[i] = str(args[i]);
+    }
+    auto sep = kwargs.contains("sep") ? kwargs["sep"] : str(" ");
+    auto line = sep.attr("join")(std::move(strings));
+
+    object file;
+    if (kwargs.contains("file")) {
+        file = kwargs["file"].cast<object>();
+    } else {
+        try {
+            file = module_::import("sys").attr("stdout");
+        } catch (const error_already_set &) {
+            /* If print() is called from code that is executed as
+               part of garbage collection during interpreter shutdown,
+               importing 'sys' can fail. Give up rather than crashing the
+               interpreter in this case. */
+            return;
+        }
+    }
+
+    auto write = file.attr("write");
+    write(std::move(line));
+    write(kwargs.contains("end") ? kwargs["end"] : str("\n"));
+
+    if (kwargs.contains("flush") && kwargs["flush"].cast<bool>()) {
+        file.attr("flush")();
+    }
+}
+PYBIND11_NAMESPACE_END(detail)
+
+template <return_value_policy policy = return_value_policy::automatic_reference, typename... Args>
+void print(Args &&...args) {
+    auto c = detail::collect_arguments<policy>(std::forward<Args>(args)...);
+    detail::print(c.args(), c.kwargs());
+}
+
+inline void
+error_already_set::m_fetched_error_deleter(detail::error_fetch_and_normalize *raw_ptr) {
+    gil_scoped_acquire gil;
+    error_scope scope;
+    delete raw_ptr;
+}
+
+inline const char *error_already_set::what() const noexcept {
+    gil_scoped_acquire gil;
+    error_scope scope;
+    return m_fetched_error->error_string().c_str();
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline function
+get_type_override(const void *this_ptr, const type_info *this_type, const char *name) {
+    handle self = get_object_handle(this_ptr, this_type);
+    if (!self) {
+        return function();
+    }
+    handle type = type::handle_of(self);
+    auto key = std::make_pair(type.ptr(), name);
+
+    /* Cache functions that aren't overridden in Python to avoid
+       many costly Python dictionary lookups below */
+    auto &cache = get_internals().inactive_override_cache;
+    if (cache.find(key) != cache.end()) {
+        return function();
+    }
+
+    function override = getattr(self, name, function());
+    if (override.is_cpp_function()) {
+        cache.insert(std::move(key));
+        return function();
+    }
+
+    /* Don't call dispatch code if invoked from overridden function.
+       Unfortunately this doesn't work on PyPy. */
+#if !defined(PYPY_VERSION)
+#    if PY_VERSION_HEX >= 0x03090000
+    PyFrameObject *frame = PyThreadState_GetFrame(PyThreadState_Get());
+    if (frame != nullptr) {
+        PyCodeObject *f_code = PyFrame_GetCode(frame);
+        // f_code is guaranteed to not be NULL
+        if ((std::string) str(f_code->co_name) == name && f_code->co_argcount > 0) {
+            PyObject *locals = PyEval_GetLocals();
+            if (locals != nullptr) {
+                PyObject *co_varnames = PyObject_GetAttrString((PyObject *) f_code, "co_varnames");
+                PyObject *self_arg = PyTuple_GET_ITEM(co_varnames, 0);
+                Py_DECREF(co_varnames);
+                PyObject *self_caller = dict_getitem(locals, self_arg);
+                if (self_caller == self.ptr()) {
+                    Py_DECREF(f_code);
+                    Py_DECREF(frame);
+                    return function();
+                }
+            }
+        }
+        Py_DECREF(f_code);
+        Py_DECREF(frame);
+    }
+#    else
+    PyFrameObject *frame = PyThreadState_Get()->frame;
+    if (frame != nullptr && (std::string) str(frame->f_code->co_name) == name
+        && frame->f_code->co_argcount > 0) {
+        PyFrame_FastToLocals(frame);
+        PyObject *self_caller
+            = dict_getitem(frame->f_locals, PyTuple_GET_ITEM(frame->f_code->co_varnames, 0));
+        if (self_caller == self.ptr()) {
+            return function();
+        }
+    }
+#    endif
+
+#else
+    /* PyPy currently doesn't provide a detailed cpyext emulation of
+       frame objects, so we have to emulate this using Python. This
+       is going to be slow..*/
+    dict d;
+    d["self"] = self;
+    d["name"] = pybind11::str(name);
+    PyObject *result
+        = PyRun_String("import inspect\n"
+                       "frame = inspect.currentframe()\n"
+                       "if frame is not None:\n"
+                       "    frame = frame.f_back\n"
+                       "    if frame is not None and str(frame.f_code.co_name) == name and "
+                       "frame.f_code.co_argcount > 0:\n"
+                       "        self_caller = frame.f_locals[frame.f_code.co_varnames[0]]\n"
+                       "        if self_caller == self:\n"
+                       "            self = None\n",
+                       Py_file_input,
+                       d.ptr(),
+                       d.ptr());
+    if (result == nullptr)
+        throw error_already_set();
+    Py_DECREF(result);
+    if (d["self"].is_none())
+        return function();
+#endif
+
+    return override;
+}
+PYBIND11_NAMESPACE_END(detail)
+
+/** \rst
+  Try to retrieve a python method by the provided name from the instance pointed to by the
+  this_ptr.
+
+  :this_ptr: The pointer to the object the overridden method should be retrieved for. This should
+             be the first non-trampoline class encountered in the inheritance chain.
+  :name: The name of the overridden Python method to retrieve.
+  :return: The Python method by this name from the object or an empty function wrapper.
+ \endrst */
+template <class T>
+function get_override(const T *this_ptr, const char *name) {
+    auto *tinfo = detail::get_type_info(typeid(T));
+    return tinfo ? detail::get_type_override(this_ptr, tinfo, name) : function();
+}
+
+#define PYBIND11_OVERRIDE_IMPL(ret_type, cname, name, ...)                                        \
+    do {                                                                                          \
+        pybind11::gil_scoped_acquire gil;                                                         \
+        pybind11::function override                                                               \
+            = pybind11::get_override(static_cast<const cname *>(this), name);                     \
+        if (override) {                                                                           \
+            auto o = override(__VA_ARGS__);                                                       \
+            if (pybind11::detail::cast_is_temporary_value_reference<ret_type>::value) {           \
+                static pybind11::detail::override_caster_t<ret_type> caster;                      \
+                return pybind11::detail::cast_ref<ret_type>(std::move(o), caster);                \
+            }                                                                                     \
+            return pybind11::detail::cast_safe<ret_type>(std::move(o));                           \
+        }                                                                                         \
+    } while (false)
+
+/** \rst
+    Macro to populate the virtual method in the trampoline class. This macro tries to look up a
+    method named 'fn' from the Python side, deals with the :ref:`gil` and necessary argument
+    conversions to call this method and return the appropriate type.
+    See :ref:`overriding_virtuals` for more information. This macro should be used when the method
+    name in C is not the same as the method name in Python. For example with `__str__`.
+
+    .. code-block:: cpp
+
+      std::string toString() override {
+        PYBIND11_OVERRIDE_NAME(
+            std::string, // Return type (ret_type)
+            Animal,      // Parent class (cname)
+            "__str__",   // Name of method in Python (name)
+            toString,    // Name of function in C++ (fn)
+        );
+      }
+\endrst */
+#define PYBIND11_OVERRIDE_NAME(ret_type, cname, name, fn, ...)                                    \
+    do {                                                                                          \
+        PYBIND11_OVERRIDE_IMPL(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, __VA_ARGS__); \
+        return cname::fn(__VA_ARGS__);                                                            \
+    } while (false)
+
+/** \rst
+    Macro for pure virtual functions, this function is identical to
+    :c:macro:`PYBIND11_OVERRIDE_NAME`, except that it throws if no override can be found.
+\endrst */
+#define PYBIND11_OVERRIDE_PURE_NAME(ret_type, cname, name, fn, ...)                               \
+    do {                                                                                          \
+        PYBIND11_OVERRIDE_IMPL(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, __VA_ARGS__); \
+        pybind11::pybind11_fail(                                                                  \
+            "Tried to call pure virtual function \"" PYBIND11_STRINGIFY(cname) "::" name "\"");   \
+    } while (false)
+
+/** \rst
+    Macro to populate the virtual method in the trampoline class. This macro tries to look up the
+    method from the Python side, deals with the :ref:`gil` and necessary argument conversions to
+    call this method and return the appropriate type. This macro should be used if the method name
+    in C and in Python are identical.
+    See :ref:`overriding_virtuals` for more information.
+
+    .. code-block:: cpp
+
+      class PyAnimal : public Animal {
+      public:
+          // Inherit the constructors
+          using Animal::Animal;
+
+          // Trampoline (need one for each virtual function)
+          std::string go(int n_times) override {
+              PYBIND11_OVERRIDE_PURE(
+                  std::string, // Return type (ret_type)
+                  Animal,      // Parent class (cname)
+                  go,          // Name of function in C++ (must match Python name) (fn)
+                  n_times      // Argument(s) (...)
+              );
+          }
+      };
+\endrst */
+#define PYBIND11_OVERRIDE(ret_type, cname, fn, ...)                                               \
+    PYBIND11_OVERRIDE_NAME(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), #fn, fn, __VA_ARGS__)
+
+/** \rst
+    Macro for pure virtual functions, this function is identical to :c:macro:`PYBIND11_OVERRIDE`,
+    except that it throws if no override can be found.
+\endrst */
+#define PYBIND11_OVERRIDE_PURE(ret_type, cname, fn, ...)                                          \
+    PYBIND11_OVERRIDE_PURE_NAME(                                                                  \
+        PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), #fn, fn, __VA_ARGS__)
+
+// Deprecated versions
+
+PYBIND11_DEPRECATED("get_type_overload has been deprecated")
+inline function
+get_type_overload(const void *this_ptr, const detail::type_info *this_type, const char *name) {
+    return detail::get_type_override(this_ptr, this_type, name);
+}
+
+template <class T>
+inline function get_overload(const T *this_ptr, const char *name) {
+    return get_override(this_ptr, name);
+}
+
+#define PYBIND11_OVERLOAD_INT(ret_type, cname, name, ...)                                         \
+    PYBIND11_OVERRIDE_IMPL(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, __VA_ARGS__)
+#define PYBIND11_OVERLOAD_NAME(ret_type, cname, name, fn, ...)                                    \
+    PYBIND11_OVERRIDE_NAME(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, fn, __VA_ARGS__)
+#define PYBIND11_OVERLOAD_PURE_NAME(ret_type, cname, name, fn, ...)                               \
+    PYBIND11_OVERRIDE_PURE_NAME(                                                                  \
+        PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, fn, __VA_ARGS__);
+#define PYBIND11_OVERLOAD(ret_type, cname, fn, ...)                                               \
+    PYBIND11_OVERRIDE(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), fn, __VA_ARGS__)
+#define PYBIND11_OVERLOAD_PURE(ret_type, cname, fn, ...)                                          \
+    PYBIND11_OVERRIDE_PURE(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), fn, __VA_ARGS__);
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
+
+#if defined(__GNUC__) && __GNUC__ == 7
+#    pragma GCC diagnostic pop // -Wnoexcept-type
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/pytypes.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/pytypes.h
new file mode 100644
index 000000000..339b0961e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/pytypes.h
@@ -0,0 +1,2392 @@
+/*
+    pybind11/pytypes.h: Convenience wrapper classes for basic Python types
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "buffer_info.h"
+
+#include <assert.h>
+#include <cstddef>
+#include <exception>
+#include <frameobject.h>
+#include <iterator>
+#include <memory>
+#include <string>
+#include <type_traits>
+#include <typeinfo>
+#include <utility>
+
+#if defined(PYBIND11_HAS_OPTIONAL)
+#    include <optional>
+#endif
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+#    include <string_view>
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+/* A few forward declarations */
+class handle;
+class object;
+class str;
+class iterator;
+class type;
+struct arg;
+struct arg_v;
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+class args_proxy;
+bool isinstance_generic(handle obj, const std::type_info &tp);
+
+// Accessor forward declarations
+template <typename Policy>
+class accessor;
+namespace accessor_policies {
+struct obj_attr;
+struct str_attr;
+struct generic_item;
+struct sequence_item;
+struct list_item;
+struct tuple_item;
+} // namespace accessor_policies
+using obj_attr_accessor = accessor<accessor_policies::obj_attr>;
+using str_attr_accessor = accessor<accessor_policies::str_attr>;
+using item_accessor = accessor<accessor_policies::generic_item>;
+using sequence_accessor = accessor<accessor_policies::sequence_item>;
+using list_accessor = accessor<accessor_policies::list_item>;
+using tuple_accessor = accessor<accessor_policies::tuple_item>;
+
+/// Tag and check to identify a class which implements the Python object API
+class pyobject_tag {};
+template <typename T>
+using is_pyobject = std::is_base_of<pyobject_tag, remove_reference_t<T>>;
+
+/** \rst
+    A mixin class which adds common functions to `handle`, `object` and various accessors.
+    The only requirement for `Derived` is to implement ``PyObject *Derived::ptr() const``.
+\endrst */
+template <typename Derived>
+class object_api : public pyobject_tag {
+    const Derived &derived() const { return static_cast<const Derived &>(*this); }
+
+public:
+    /** \rst
+        Return an iterator equivalent to calling ``iter()`` in Python. The object
+        must be a collection which supports the iteration protocol.
+    \endrst */
+    iterator begin() const;
+    /// Return a sentinel which ends iteration.
+    iterator end() const;
+
+    /** \rst
+        Return an internal functor to invoke the object's sequence protocol. Casting
+        the returned ``detail::item_accessor`` instance to a `handle` or `object`
+        subclass causes a corresponding call to ``__getitem__``. Assigning a `handle`
+        or `object` subclass causes a call to ``__setitem__``.
+    \endrst */
+    item_accessor operator[](handle key) const;
+    /// See above (the only difference is that the key's reference is stolen)
+    item_accessor operator[](object &&key) const;
+    /// See above (the only difference is that the key is provided as a string literal)
+    item_accessor operator[](const char *key) const;
+
+    /** \rst
+        Return an internal functor to access the object's attributes. Casting the
+        returned ``detail::obj_attr_accessor`` instance to a `handle` or `object`
+        subclass causes a corresponding call to ``getattr``. Assigning a `handle`
+        or `object` subclass causes a call to ``setattr``.
+    \endrst */
+    obj_attr_accessor attr(handle key) const;
+    /// See above (the only difference is that the key's reference is stolen)
+    obj_attr_accessor attr(object &&key) const;
+    /// See above (the only difference is that the key is provided as a string literal)
+    str_attr_accessor attr(const char *key) const;
+
+    /** \rst
+        Matches * unpacking in Python, e.g. to unpack arguments out of a ``tuple``
+        or ``list`` for a function call. Applying another * to the result yields
+        ** unpacking, e.g. to unpack a dict as function keyword arguments.
+        See :ref:`calling_python_functions`.
+    \endrst */
+    args_proxy operator*() const;
+
+    /// Check if the given item is contained within this object, i.e. ``item in obj``.
+    template <typename T>
+    bool contains(T &&item) const;
+
+    /** \rst
+        Assuming the Python object is a function or implements the ``__call__``
+        protocol, ``operator()`` invokes the underlying function, passing an
+        arbitrary set of parameters. The result is returned as a `object` and
+        may need to be converted back into a Python object using `handle::cast()`.
+
+        When some of the arguments cannot be converted to Python objects, the
+        function will throw a `cast_error` exception. When the Python function
+        call fails, a `error_already_set` exception is thrown.
+    \endrst */
+    template <return_value_policy policy = return_value_policy::automatic_reference,
+              typename... Args>
+    object operator()(Args &&...args) const;
+    template <return_value_policy policy = return_value_policy::automatic_reference,
+              typename... Args>
+    PYBIND11_DEPRECATED("call(...) was deprecated in favor of operator()(...)")
+    object call(Args &&...args) const;
+
+    /// Equivalent to ``obj is other`` in Python.
+    bool is(object_api const &other) const { return derived().ptr() == other.derived().ptr(); }
+    /// Equivalent to ``obj is None`` in Python.
+    bool is_none() const { return derived().ptr() == Py_None; }
+    /// Equivalent to obj == other in Python
+    bool equal(object_api const &other) const { return rich_compare(other, Py_EQ); }
+    bool not_equal(object_api const &other) const { return rich_compare(other, Py_NE); }
+    bool operator<(object_api const &other) const { return rich_compare(other, Py_LT); }
+    bool operator<=(object_api const &other) const { return rich_compare(other, Py_LE); }
+    bool operator>(object_api const &other) const { return rich_compare(other, Py_GT); }
+    bool operator>=(object_api const &other) const { return rich_compare(other, Py_GE); }
+
+    object operator-() const;
+    object operator~() const;
+    object operator+(object_api const &other) const;
+    object operator+=(object_api const &other) const;
+    object operator-(object_api const &other) const;
+    object operator-=(object_api const &other) const;
+    object operator*(object_api const &other) const;
+    object operator*=(object_api const &other) const;
+    object operator/(object_api const &other) const;
+    object operator/=(object_api const &other) const;
+    object operator|(object_api const &other) const;
+    object operator|=(object_api const &other) const;
+    object operator&(object_api const &other) const;
+    object operator&=(object_api const &other) const;
+    object operator^(object_api const &other) const;
+    object operator^=(object_api const &other) const;
+    object operator<<(object_api const &other) const;
+    object operator<<=(object_api const &other) const;
+    object operator>>(object_api const &other) const;
+    object operator>>=(object_api const &other) const;
+
+    PYBIND11_DEPRECATED("Use py::str(obj) instead")
+    pybind11::str str() const;
+
+    /// Get or set the object's docstring, i.e. ``obj.__doc__``.
+    str_attr_accessor doc() const;
+
+    /// Return the object's current reference count
+    int ref_count() const { return static_cast<int>(Py_REFCNT(derived().ptr())); }
+
+    // TODO PYBIND11_DEPRECATED(
+    //     "Call py::type::handle_of(h) or py::type::of(h) instead of h.get_type()")
+    handle get_type() const;
+
+private:
+    bool rich_compare(object_api const &other, int value) const;
+};
+
+template <typename T>
+using is_pyobj_ptr_or_nullptr_t = detail::any_of<std::is_same<T, PyObject *>,
+                                                 std::is_same<T, PyObject *const>,
+                                                 std::is_same<T, std::nullptr_t>>;
+
+PYBIND11_NAMESPACE_END(detail)
+
+#if !defined(PYBIND11_HANDLE_REF_DEBUG) && !defined(NDEBUG)
+#    define PYBIND11_HANDLE_REF_DEBUG
+#endif
+
+/** \rst
+    Holds a reference to a Python object (no reference counting)
+
+    The `handle` class is a thin wrapper around an arbitrary Python object (i.e. a
+    ``PyObject *`` in Python's C API). It does not perform any automatic reference
+    counting and merely provides a basic C++ interface to various Python API functions.
+
+    .. seealso::
+        The `object` class inherits from `handle` and adds automatic reference
+        counting features.
+\endrst */
+class handle : public detail::object_api<handle> {
+public:
+    /// The default constructor creates a handle with a ``nullptr``-valued pointer
+    handle() = default;
+
+    /// Enable implicit conversion from ``PyObject *`` and ``nullptr``.
+    /// Not using ``handle(PyObject *ptr)`` to avoid implicit conversion from ``0``.
+    template <typename T,
+              detail::enable_if_t<detail::is_pyobj_ptr_or_nullptr_t<T>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    handle(T ptr) : m_ptr(ptr) {}
+
+    /// Enable implicit conversion through ``T::operator PyObject *()``.
+    template <
+        typename T,
+        detail::enable_if_t<detail::all_of<detail::none_of<std::is_base_of<handle, T>,
+                                                           detail::is_pyobj_ptr_or_nullptr_t<T>>,
+                                           std::is_convertible<T, PyObject *>>::value,
+                            int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    handle(T &obj) : m_ptr(obj) {}
+
+    /// Return the underlying ``PyObject *`` pointer
+    PyObject *ptr() const { return m_ptr; }
+    PyObject *&ptr() { return m_ptr; }
+
+    /** \rst
+        Manually increase the reference count of the Python object. Usually, it is
+        preferable to use the `object` class which derives from `handle` and calls
+        this function automatically. Returns a reference to itself.
+    \endrst */
+    const handle &inc_ref() const & {
+#ifdef PYBIND11_HANDLE_REF_DEBUG
+        inc_ref_counter(1);
+#endif
+        Py_XINCREF(m_ptr);
+        return *this;
+    }
+
+    /** \rst
+        Manually decrease the reference count of the Python object. Usually, it is
+        preferable to use the `object` class which derives from `handle` and calls
+        this function automatically. Returns a reference to itself.
+    \endrst */
+    const handle &dec_ref() const & {
+        Py_XDECREF(m_ptr);
+        return *this;
+    }
+
+    /** \rst
+        Attempt to cast the Python object into the given C++ type. A `cast_error`
+        will be throw upon failure.
+    \endrst */
+    template <typename T>
+    T cast() const;
+    /// Return ``true`` when the `handle` wraps a valid Python object
+    explicit operator bool() const { return m_ptr != nullptr; }
+    /** \rst
+        Deprecated: Check that the underlying pointers are the same.
+        Equivalent to ``obj1 is obj2`` in Python.
+    \endrst */
+    PYBIND11_DEPRECATED("Use obj1.is(obj2) instead")
+    bool operator==(const handle &h) const { return m_ptr == h.m_ptr; }
+    PYBIND11_DEPRECATED("Use !obj1.is(obj2) instead")
+    bool operator!=(const handle &h) const { return m_ptr != h.m_ptr; }
+    PYBIND11_DEPRECATED("Use handle::operator bool() instead")
+    bool check() const { return m_ptr != nullptr; }
+
+protected:
+    PyObject *m_ptr = nullptr;
+
+#ifdef PYBIND11_HANDLE_REF_DEBUG
+private:
+    static std::size_t inc_ref_counter(std::size_t add) {
+        thread_local std::size_t counter = 0;
+        counter += add;
+        return counter;
+    }
+
+public:
+    static std::size_t inc_ref_counter() { return inc_ref_counter(0); }
+#endif
+};
+
+/** \rst
+    Holds a reference to a Python object (with reference counting)
+
+    Like `handle`, the `object` class is a thin wrapper around an arbitrary Python
+    object (i.e. a ``PyObject *`` in Python's C API). In contrast to `handle`, it
+    optionally increases the object's reference count upon construction, and it
+    *always* decreases the reference count when the `object` instance goes out of
+    scope and is destructed. When using `object` instances consistently, it is much
+    easier to get reference counting right at the first attempt.
+\endrst */
+class object : public handle {
+public:
+    object() = default;
+    PYBIND11_DEPRECATED("Use reinterpret_borrow<object>() or reinterpret_steal<object>()")
+    object(handle h, bool is_borrowed) : handle(h) {
+        if (is_borrowed) {
+            inc_ref();
+        }
+    }
+    /// Copy constructor; always increases the reference count
+    object(const object &o) : handle(o) { inc_ref(); }
+    /// Move constructor; steals the object from ``other`` and preserves its reference count
+    object(object &&other) noexcept : handle(other) { other.m_ptr = nullptr; }
+    /// Destructor; automatically calls `handle::dec_ref()`
+    ~object() { dec_ref(); }
+
+    /** \rst
+        Resets the internal pointer to ``nullptr`` without decreasing the
+        object's reference count. The function returns a raw handle to the original
+        Python object.
+    \endrst */
+    handle release() {
+        PyObject *tmp = m_ptr;
+        m_ptr = nullptr;
+        return handle(tmp);
+    }
+
+    object &operator=(const object &other) {
+        other.inc_ref();
+        // Use temporary variable to ensure `*this` remains valid while
+        // `Py_XDECREF` executes, in case `*this` is accessible from Python.
+        handle temp(m_ptr);
+        m_ptr = other.m_ptr;
+        temp.dec_ref();
+        return *this;
+    }
+
+    object &operator=(object &&other) noexcept {
+        if (this != &other) {
+            handle temp(m_ptr);
+            m_ptr = other.m_ptr;
+            other.m_ptr = nullptr;
+            temp.dec_ref();
+        }
+        return *this;
+    }
+
+    // Calling cast() on an object lvalue just copies (via handle::cast)
+    template <typename T>
+    T cast() const &;
+    // Calling on an object rvalue does a move, if needed and/or possible
+    template <typename T>
+    T cast() &&;
+
+protected:
+    // Tags for choosing constructors from raw PyObject *
+    struct borrowed_t {};
+    struct stolen_t {};
+
+    /// @cond BROKEN
+    template <typename T>
+    friend T reinterpret_borrow(handle);
+    template <typename T>
+    friend T reinterpret_steal(handle);
+    /// @endcond
+
+public:
+    // Only accessible from derived classes and the reinterpret_* functions
+    object(handle h, borrowed_t) : handle(h) { inc_ref(); }
+    object(handle h, stolen_t) : handle(h) {}
+};
+
+/** \rst
+    Declare that a `handle` or ``PyObject *`` is a certain type and borrow the reference.
+    The target type ``T`` must be `object` or one of its derived classes. The function
+    doesn't do any conversions or checks. It's up to the user to make sure that the
+    target type is correct.
+
+    .. code-block:: cpp
+
+        PyObject *p = PyList_GetItem(obj, index);
+        py::object o = reinterpret_borrow<py::object>(p);
+        // or
+        py::tuple t = reinterpret_borrow<py::tuple>(p); // <-- `p` must be already be a `tuple`
+\endrst */
+template <typename T>
+T reinterpret_borrow(handle h) {
+    return {h, object::borrowed_t{}};
+}
+
+/** \rst
+    Like `reinterpret_borrow`, but steals the reference.
+
+     .. code-block:: cpp
+
+        PyObject *p = PyObject_Str(obj);
+        py::str s = reinterpret_steal<py::str>(p); // <-- `p` must be already be a `str`
+\endrst */
+template <typename T>
+T reinterpret_steal(handle h) {
+    return {h, object::stolen_t{}};
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Equivalent to obj.__class__.__name__ (or obj.__name__ if obj is a class).
+inline const char *obj_class_name(PyObject *obj) {
+    if (Py_TYPE(obj) == &PyType_Type) {
+        return reinterpret_cast<PyTypeObject *>(obj)->tp_name;
+    }
+    return Py_TYPE(obj)->tp_name;
+}
+
+std::string error_string();
+
+struct error_fetch_and_normalize {
+    // Immediate normalization is long-established behavior (starting with
+    // https://github.com/pybind/pybind11/commit/135ba8deafb8bf64a15b24d1513899eb600e2011
+    // from Sep 2016) and safest. Normalization could be deferred, but this could mask
+    // errors elsewhere, the performance gain is very minor in typical situations
+    // (usually the dominant bottleneck is EH unwinding), and the implementation here
+    // would be more complex.
+    explicit error_fetch_and_normalize(const char *called) {
+        PyErr_Fetch(&m_type.ptr(), &m_value.ptr(), &m_trace.ptr());
+        if (!m_type) {
+            pybind11_fail("Internal error: " + std::string(called)
+                          + " called while "
+                            "Python error indicator not set.");
+        }
+        const char *exc_type_name_orig = detail::obj_class_name(m_type.ptr());
+        if (exc_type_name_orig == nullptr) {
+            pybind11_fail("Internal error: " + std::string(called)
+                          + " failed to obtain the name "
+                            "of the original active exception type.");
+        }
+        m_lazy_error_string = exc_type_name_orig;
+        // PyErr_NormalizeException() may change the exception type if there are cascading
+        // failures. This can potentially be extremely confusing.
+        PyErr_NormalizeException(&m_type.ptr(), &m_value.ptr(), &m_trace.ptr());
+        if (m_type.ptr() == nullptr) {
+            pybind11_fail("Internal error: " + std::string(called)
+                          + " failed to normalize the "
+                            "active exception.");
+        }
+        const char *exc_type_name_norm = detail::obj_class_name(m_type.ptr());
+        if (exc_type_name_orig == nullptr) {
+            pybind11_fail("Internal error: " + std::string(called)
+                          + " failed to obtain the name "
+                            "of the normalized active exception type.");
+        }
+        if (exc_type_name_norm != m_lazy_error_string) {
+            std::string msg = std::string(called)
+                              + ": MISMATCH of original and normalized "
+                                "active exception types: ";
+            msg += "ORIGINAL ";
+            msg += m_lazy_error_string;
+            msg += " REPLACED BY ";
+            msg += exc_type_name_norm;
+            msg += ": " + format_value_and_trace();
+            pybind11_fail(msg);
+        }
+    }
+
+    error_fetch_and_normalize(const error_fetch_and_normalize &) = delete;
+    error_fetch_and_normalize(error_fetch_and_normalize &&) = delete;
+
+    std::string format_value_and_trace() const {
+        std::string result;
+        std::string message_error_string;
+        if (m_value) {
+            auto value_str = reinterpret_steal<object>(PyObject_Str(m_value.ptr()));
+            if (!value_str) {
+                message_error_string = detail::error_string();
+                result = "<MESSAGE UNAVAILABLE DUE TO ANOTHER EXCEPTION>";
+            } else {
+                result = value_str.cast<std::string>();
+            }
+        } else {
+            result = "<MESSAGE UNAVAILABLE>";
+        }
+        if (result.empty()) {
+            result = "<EMPTY MESSAGE>";
+        }
+
+        bool have_trace = false;
+        if (m_trace) {
+#if !defined(PYPY_VERSION)
+            auto *tb = reinterpret_cast<PyTracebackObject *>(m_trace.ptr());
+
+            // Get the deepest trace possible.
+            while (tb->tb_next) {
+                tb = tb->tb_next;
+            }
+
+            PyFrameObject *frame = tb->tb_frame;
+            Py_XINCREF(frame);
+            result += "\n\nAt:\n";
+            while (frame) {
+#    if PY_VERSION_HEX >= 0x030900B1
+                PyCodeObject *f_code = PyFrame_GetCode(frame);
+#    else
+                PyCodeObject *f_code = frame->f_code;
+                Py_INCREF(f_code);
+#    endif
+                int lineno = PyFrame_GetLineNumber(frame);
+                result += "  ";
+                result += handle(f_code->co_filename).cast<std::string>();
+                result += '(';
+                result += std::to_string(lineno);
+                result += "): ";
+                result += handle(f_code->co_name).cast<std::string>();
+                result += '\n';
+                Py_DECREF(f_code);
+#    if PY_VERSION_HEX >= 0x030900B1
+                auto *b_frame = PyFrame_GetBack(frame);
+#    else
+                auto *b_frame = frame->f_back;
+                Py_XINCREF(b_frame);
+#    endif
+                Py_DECREF(frame);
+                frame = b_frame;
+            }
+
+            have_trace = true;
+#endif //! defined(PYPY_VERSION)
+        }
+
+        if (!message_error_string.empty()) {
+            if (!have_trace) {
+                result += '\n';
+            }
+            result += "\nMESSAGE UNAVAILABLE DUE TO EXCEPTION: " + message_error_string;
+        }
+
+        return result;
+    }
+
+    std::string const &error_string() const {
+        if (!m_lazy_error_string_completed) {
+            m_lazy_error_string += ": " + format_value_and_trace();
+            m_lazy_error_string_completed = true;
+        }
+        return m_lazy_error_string;
+    }
+
+    void restore() {
+        if (m_restore_called) {
+            pybind11_fail("Internal error: pybind11::detail::error_fetch_and_normalize::restore() "
+                          "called a second time. ORIGINAL ERROR: "
+                          + error_string());
+        }
+        PyErr_Restore(m_type.inc_ref().ptr(), m_value.inc_ref().ptr(), m_trace.inc_ref().ptr());
+        m_restore_called = true;
+    }
+
+    bool matches(handle exc) const {
+        return (PyErr_GivenExceptionMatches(m_type.ptr(), exc.ptr()) != 0);
+    }
+
+    // Not protecting these for simplicity.
+    object m_type, m_value, m_trace;
+
+private:
+    // Only protecting invariants.
+    mutable std::string m_lazy_error_string;
+    mutable bool m_lazy_error_string_completed = false;
+    mutable bool m_restore_called = false;
+};
+
+inline std::string error_string() {
+    return error_fetch_and_normalize("pybind11::detail::error_string").error_string();
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+#if defined(_MSC_VER)
+#    pragma warning(push)
+#    pragma warning(disable : 4275 4251)
+//     warning C4275: An exported class was derived from a class that wasn't exported.
+//     Can be ignored when derived from a STL class.
+#endif
+/// Fetch and hold an error which was already set in Python.  An instance of this is typically
+/// thrown to propagate python-side errors back through C++ which can either be caught manually or
+/// else falls back to the function dispatcher (which then raises the captured error back to
+/// python).
+class PYBIND11_EXPORT_EXCEPTION error_already_set : public std::exception {
+public:
+    /// Fetches the current Python exception (using PyErr_Fetch()), which will clear the
+    /// current Python error indicator.
+    error_already_set()
+        : m_fetched_error{new detail::error_fetch_and_normalize("pybind11::error_already_set"),
+                          m_fetched_error_deleter} {}
+
+    /// The what() result is built lazily on demand.
+    /// WARNING: This member function needs to acquire the Python GIL. This can lead to
+    ///          crashes (undefined behavior) if the Python interpreter is finalizing.
+    const char *what() const noexcept override;
+
+    /// Restores the currently-held Python error (which will clear the Python error indicator first
+    /// if already set).
+    /// NOTE: This member function will always restore the normalized exception, which may or may
+    ///       not be the original Python exception.
+    /// WARNING: The GIL must be held when this member function is called!
+    void restore() { m_fetched_error->restore(); }
+
+    /// If it is impossible to raise the currently-held error, such as in a destructor, we can
+    /// write it out using Python's unraisable hook (`sys.unraisablehook`). The error context
+    /// should be some object whose `repr()` helps identify the location of the error. Python
+    /// already knows the type and value of the error, so there is no need to repeat that.
+    void discard_as_unraisable(object err_context) {
+        restore();
+        PyErr_WriteUnraisable(err_context.ptr());
+    }
+    /// An alternate version of `discard_as_unraisable()`, where a string provides information on
+    /// the location of the error. For example, `__func__` could be helpful.
+    /// WARNING: The GIL must be held when this member function is called!
+    void discard_as_unraisable(const char *err_context) {
+        discard_as_unraisable(reinterpret_steal<object>(PYBIND11_FROM_STRING(err_context)));
+    }
+
+    // Does nothing; provided for backwards compatibility.
+    PYBIND11_DEPRECATED("Use of error_already_set.clear() is deprecated")
+    void clear() {}
+
+    /// Check if the currently trapped error type matches the given Python exception class (or a
+    /// subclass thereof).  May also be passed a tuple to search for any exception class matches in
+    /// the given tuple.
+    bool matches(handle exc) const { return m_fetched_error->matches(exc); }
+
+    const object &type() const { return m_fetched_error->m_type; }
+    const object &value() const { return m_fetched_error->m_value; }
+    const object &trace() const { return m_fetched_error->m_trace; }
+
+private:
+    std::shared_ptr<detail::error_fetch_and_normalize> m_fetched_error;
+
+    /// WARNING: This custom deleter needs to acquire the Python GIL. This can lead to
+    ///          crashes (undefined behavior) if the Python interpreter is finalizing.
+    static void m_fetched_error_deleter(detail::error_fetch_and_normalize *raw_ptr);
+};
+#if defined(_MSC_VER)
+#    pragma warning(pop)
+#endif
+
+/// Replaces the current Python error indicator with the chosen error, performing a
+/// 'raise from' to indicate that the chosen error was caused by the original error.
+inline void raise_from(PyObject *type, const char *message) {
+    // Based on _PyErr_FormatVFromCause:
+    // https://github.com/python/cpython/blob/467ab194fc6189d9f7310c89937c51abeac56839/Python/errors.c#L405
+    // See https://github.com/pybind/pybind11/pull/2112 for details.
+    PyObject *exc = nullptr, *val = nullptr, *val2 = nullptr, *tb = nullptr;
+
+    assert(PyErr_Occurred());
+    PyErr_Fetch(&exc, &val, &tb);
+    PyErr_NormalizeException(&exc, &val, &tb);
+    if (tb != nullptr) {
+        PyException_SetTraceback(val, tb);
+        Py_DECREF(tb);
+    }
+    Py_DECREF(exc);
+    assert(!PyErr_Occurred());
+
+    PyErr_SetString(type, message);
+
+    PyErr_Fetch(&exc, &val2, &tb);
+    PyErr_NormalizeException(&exc, &val2, &tb);
+    Py_INCREF(val);
+    PyException_SetCause(val2, val);
+    PyException_SetContext(val2, val);
+    PyErr_Restore(exc, val2, tb);
+}
+
+/// Sets the current Python error indicator with the chosen error, performing a 'raise from'
+/// from the error contained in error_already_set to indicate that the chosen error was
+/// caused by the original error.
+inline void raise_from(error_already_set &err, PyObject *type, const char *message) {
+    err.restore();
+    raise_from(type, message);
+}
+
+/** \defgroup python_builtins const_name
+    Unless stated otherwise, the following C++ functions behave the same
+    as their Python counterparts.
+ */
+
+/** \ingroup python_builtins
+    \rst
+    Return true if ``obj`` is an instance of ``T``. Type ``T`` must be a subclass of
+    `object` or a class which was exposed to Python as ``py::class_<T>``.
+\endrst */
+template <typename T, detail::enable_if_t<std::is_base_of<object, T>::value, int> = 0>
+bool isinstance(handle obj) {
+    return T::check_(obj);
+}
+
+template <typename T, detail::enable_if_t<!std::is_base_of<object, T>::value, int> = 0>
+bool isinstance(handle obj) {
+    return detail::isinstance_generic(obj, typeid(T));
+}
+
+template <>
+inline bool isinstance<handle>(handle) = delete;
+template <>
+inline bool isinstance<object>(handle obj) {
+    return obj.ptr() != nullptr;
+}
+
+/// \ingroup python_builtins
+/// Return true if ``obj`` is an instance of the ``type``.
+inline bool isinstance(handle obj, handle type) {
+    const auto result = PyObject_IsInstance(obj.ptr(), type.ptr());
+    if (result == -1) {
+        throw error_already_set();
+    }
+    return result != 0;
+}
+
+/// \addtogroup python_builtins
+/// @{
+inline bool hasattr(handle obj, handle name) {
+    return PyObject_HasAttr(obj.ptr(), name.ptr()) == 1;
+}
+
+inline bool hasattr(handle obj, const char *name) {
+    return PyObject_HasAttrString(obj.ptr(), name) == 1;
+}
+
+inline void delattr(handle obj, handle name) {
+    if (PyObject_DelAttr(obj.ptr(), name.ptr()) != 0) {
+        throw error_already_set();
+    }
+}
+
+inline void delattr(handle obj, const char *name) {
+    if (PyObject_DelAttrString(obj.ptr(), name) != 0) {
+        throw error_already_set();
+    }
+}
+
+inline object getattr(handle obj, handle name) {
+    PyObject *result = PyObject_GetAttr(obj.ptr(), name.ptr());
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<object>(result);
+}
+
+inline object getattr(handle obj, const char *name) {
+    PyObject *result = PyObject_GetAttrString(obj.ptr(), name);
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<object>(result);
+}
+
+inline object getattr(handle obj, handle name, handle default_) {
+    if (PyObject *result = PyObject_GetAttr(obj.ptr(), name.ptr())) {
+        return reinterpret_steal<object>(result);
+    }
+    PyErr_Clear();
+    return reinterpret_borrow<object>(default_);
+}
+
+inline object getattr(handle obj, const char *name, handle default_) {
+    if (PyObject *result = PyObject_GetAttrString(obj.ptr(), name)) {
+        return reinterpret_steal<object>(result);
+    }
+    PyErr_Clear();
+    return reinterpret_borrow<object>(default_);
+}
+
+inline void setattr(handle obj, handle name, handle value) {
+    if (PyObject_SetAttr(obj.ptr(), name.ptr(), value.ptr()) != 0) {
+        throw error_already_set();
+    }
+}
+
+inline void setattr(handle obj, const char *name, handle value) {
+    if (PyObject_SetAttrString(obj.ptr(), name, value.ptr()) != 0) {
+        throw error_already_set();
+    }
+}
+
+inline ssize_t hash(handle obj) {
+    auto h = PyObject_Hash(obj.ptr());
+    if (h == -1) {
+        throw error_already_set();
+    }
+    return h;
+}
+
+/// @} python_builtins
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+inline handle get_function(handle value) {
+    if (value) {
+        if (PyInstanceMethod_Check(value.ptr())) {
+            value = PyInstanceMethod_GET_FUNCTION(value.ptr());
+        } else if (PyMethod_Check(value.ptr())) {
+            value = PyMethod_GET_FUNCTION(value.ptr());
+        }
+    }
+    return value;
+}
+
+// Reimplementation of python's dict helper functions to ensure that exceptions
+// aren't swallowed (see #2862)
+
+// copied from cpython _PyDict_GetItemStringWithError
+inline PyObject *dict_getitemstring(PyObject *v, const char *key) {
+    PyObject *kv = nullptr, *rv = nullptr;
+    kv = PyUnicode_FromString(key);
+    if (kv == nullptr) {
+        throw error_already_set();
+    }
+
+    rv = PyDict_GetItemWithError(v, kv);
+    Py_DECREF(kv);
+    if (rv == nullptr && PyErr_Occurred()) {
+        throw error_already_set();
+    }
+    return rv;
+}
+
+inline PyObject *dict_getitem(PyObject *v, PyObject *key) {
+    PyObject *rv = PyDict_GetItemWithError(v, key);
+    if (rv == nullptr && PyErr_Occurred()) {
+        throw error_already_set();
+    }
+    return rv;
+}
+
+// Helper aliases/functions to support implicit casting of values given to python
+// accessors/methods. When given a pyobject, this simply returns the pyobject as-is; for other C++
+// type, the value goes through pybind11::cast(obj) to convert it to an `object`.
+template <typename T, enable_if_t<is_pyobject<T>::value, int> = 0>
+auto object_or_cast(T &&o) -> decltype(std::forward<T>(o)) {
+    return std::forward<T>(o);
+}
+// The following casting version is implemented in cast.h:
+template <typename T, enable_if_t<!is_pyobject<T>::value, int> = 0>
+object object_or_cast(T &&o);
+// Match a PyObject*, which we want to convert directly to handle via its converting constructor
+inline handle object_or_cast(PyObject *ptr) { return ptr; }
+
+#if defined(_MSC_VER) && _MSC_VER < 1920
+#    pragma warning(push)
+#    pragma warning(disable : 4522) // warning C4522: multiple assignment operators specified
+#endif
+template <typename Policy>
+class accessor : public object_api<accessor<Policy>> {
+    using key_type = typename Policy::key_type;
+
+public:
+    accessor(handle obj, key_type key) : obj(obj), key(std::move(key)) {}
+    accessor(const accessor &) = default;
+    accessor(accessor &&) noexcept = default;
+
+    // accessor overload required to override default assignment operator (templates are not
+    // allowed to replace default compiler-generated assignments).
+    void operator=(const accessor &a) && { std::move(*this).operator=(handle(a)); }
+    void operator=(const accessor &a) & { operator=(handle(a)); }
+
+    template <typename T>
+    void operator=(T &&value) && {
+        Policy::set(obj, key, object_or_cast(std::forward<T>(value)));
+    }
+    template <typename T>
+    void operator=(T &&value) & {
+        get_cache() = ensure_object(object_or_cast(std::forward<T>(value)));
+    }
+
+    template <typename T = Policy>
+    PYBIND11_DEPRECATED(
+        "Use of obj.attr(...) as bool is deprecated in favor of pybind11::hasattr(obj, ...)")
+    explicit
+    operator enable_if_t<std::is_same<T, accessor_policies::str_attr>::value
+                             || std::is_same<T, accessor_policies::obj_attr>::value,
+                         bool>() const {
+        return hasattr(obj, key);
+    }
+    template <typename T = Policy>
+    PYBIND11_DEPRECATED("Use of obj[key] as bool is deprecated in favor of obj.contains(key)")
+    explicit
+    operator enable_if_t<std::is_same<T, accessor_policies::generic_item>::value, bool>() const {
+        return obj.contains(key);
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator object() const { return get_cache(); }
+    PyObject *ptr() const { return get_cache().ptr(); }
+    template <typename T>
+    T cast() const {
+        return get_cache().template cast<T>();
+    }
+
+private:
+    static object ensure_object(object &&o) { return std::move(o); }
+    static object ensure_object(handle h) { return reinterpret_borrow<object>(h); }
+
+    object &get_cache() const {
+        if (!cache) {
+            cache = Policy::get(obj, key);
+        }
+        return cache;
+    }
+
+private:
+    handle obj;
+    key_type key;
+    mutable object cache;
+};
+#if defined(_MSC_VER) && _MSC_VER < 1920
+#    pragma warning(pop)
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(accessor_policies)
+struct obj_attr {
+    using key_type = object;
+    static object get(handle obj, handle key) { return getattr(obj, key); }
+    static void set(handle obj, handle key, handle val) { setattr(obj, key, val); }
+};
+
+struct str_attr {
+    using key_type = const char *;
+    static object get(handle obj, const char *key) { return getattr(obj, key); }
+    static void set(handle obj, const char *key, handle val) { setattr(obj, key, val); }
+};
+
+struct generic_item {
+    using key_type = object;
+
+    static object get(handle obj, handle key) {
+        PyObject *result = PyObject_GetItem(obj.ptr(), key.ptr());
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<object>(result);
+    }
+
+    static void set(handle obj, handle key, handle val) {
+        if (PyObject_SetItem(obj.ptr(), key.ptr(), val.ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+
+struct sequence_item {
+    using key_type = size_t;
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static object get(handle obj, const IdxType &index) {
+        PyObject *result = PySequence_GetItem(obj.ptr(), ssize_t_cast(index));
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<object>(result);
+    }
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static void set(handle obj, const IdxType &index, handle val) {
+        // PySequence_SetItem does not steal a reference to 'val'
+        if (PySequence_SetItem(obj.ptr(), ssize_t_cast(index), val.ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+
+struct list_item {
+    using key_type = size_t;
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static object get(handle obj, const IdxType &index) {
+        PyObject *result = PyList_GetItem(obj.ptr(), ssize_t_cast(index));
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_borrow<object>(result);
+    }
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static void set(handle obj, const IdxType &index, handle val) {
+        // PyList_SetItem steals a reference to 'val'
+        if (PyList_SetItem(obj.ptr(), ssize_t_cast(index), val.inc_ref().ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+
+struct tuple_item {
+    using key_type = size_t;
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static object get(handle obj, const IdxType &index) {
+        PyObject *result = PyTuple_GetItem(obj.ptr(), ssize_t_cast(index));
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_borrow<object>(result);
+    }
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static void set(handle obj, const IdxType &index, handle val) {
+        // PyTuple_SetItem steals a reference to 'val'
+        if (PyTuple_SetItem(obj.ptr(), ssize_t_cast(index), val.inc_ref().ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+PYBIND11_NAMESPACE_END(accessor_policies)
+
+/// STL iterator template used for tuple, list, sequence and dict
+template <typename Policy>
+class generic_iterator : public Policy {
+    using It = generic_iterator;
+
+public:
+    using difference_type = ssize_t;
+    using iterator_category = typename Policy::iterator_category;
+    using value_type = typename Policy::value_type;
+    using reference = typename Policy::reference;
+    using pointer = typename Policy::pointer;
+
+    generic_iterator() = default;
+    generic_iterator(handle seq, ssize_t index) : Policy(seq, index) {}
+
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference operator*() const { return Policy::dereference(); }
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference operator[](difference_type n) const { return *(*this + n); }
+    pointer operator->() const { return **this; }
+
+    It &operator++() {
+        Policy::increment();
+        return *this;
+    }
+    It operator++(int) {
+        auto copy = *this;
+        Policy::increment();
+        return copy;
+    }
+    It &operator--() {
+        Policy::decrement();
+        return *this;
+    }
+    It operator--(int) {
+        auto copy = *this;
+        Policy::decrement();
+        return copy;
+    }
+    It &operator+=(difference_type n) {
+        Policy::advance(n);
+        return *this;
+    }
+    It &operator-=(difference_type n) {
+        Policy::advance(-n);
+        return *this;
+    }
+
+    friend It operator+(const It &a, difference_type n) {
+        auto copy = a;
+        return copy += n;
+    }
+    friend It operator+(difference_type n, const It &b) { return b + n; }
+    friend It operator-(const It &a, difference_type n) {
+        auto copy = a;
+        return copy -= n;
+    }
+    friend difference_type operator-(const It &a, const It &b) { return a.distance_to(b); }
+
+    friend bool operator==(const It &a, const It &b) { return a.equal(b); }
+    friend bool operator!=(const It &a, const It &b) { return !(a == b); }
+    friend bool operator<(const It &a, const It &b) { return b - a > 0; }
+    friend bool operator>(const It &a, const It &b) { return b < a; }
+    friend bool operator>=(const It &a, const It &b) { return !(a < b); }
+    friend bool operator<=(const It &a, const It &b) { return !(a > b); }
+};
+
+PYBIND11_NAMESPACE_BEGIN(iterator_policies)
+/// Quick proxy class needed to implement ``operator->`` for iterators which can't return pointers
+template <typename T>
+struct arrow_proxy {
+    T value;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    arrow_proxy(T &&value) noexcept : value(std::move(value)) {}
+    T *operator->() const { return &value; }
+};
+
+/// Lightweight iterator policy using just a simple pointer: see ``PySequence_Fast_ITEMS``
+class sequence_fast_readonly {
+protected:
+    using iterator_category = std::random_access_iterator_tag;
+    using value_type = handle;
+    using reference = const handle; // PR #3263
+    using pointer = arrow_proxy<const handle>;
+
+    sequence_fast_readonly(handle obj, ssize_t n) : ptr(PySequence_Fast_ITEMS(obj.ptr()) + n) {}
+
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference dereference() const { return *ptr; }
+    void increment() { ++ptr; }
+    void decrement() { --ptr; }
+    void advance(ssize_t n) { ptr += n; }
+    bool equal(const sequence_fast_readonly &b) const { return ptr == b.ptr; }
+    ssize_t distance_to(const sequence_fast_readonly &b) const { return ptr - b.ptr; }
+
+private:
+    PyObject **ptr;
+};
+
+/// Full read and write access using the sequence protocol: see ``detail::sequence_accessor``
+class sequence_slow_readwrite {
+protected:
+    using iterator_category = std::random_access_iterator_tag;
+    using value_type = object;
+    using reference = sequence_accessor;
+    using pointer = arrow_proxy<const sequence_accessor>;
+
+    sequence_slow_readwrite(handle obj, ssize_t index) : obj(obj), index(index) {}
+
+    reference dereference() const { return {obj, static_cast<size_t>(index)}; }
+    void increment() { ++index; }
+    void decrement() { --index; }
+    void advance(ssize_t n) { index += n; }
+    bool equal(const sequence_slow_readwrite &b) const { return index == b.index; }
+    ssize_t distance_to(const sequence_slow_readwrite &b) const { return index - b.index; }
+
+private:
+    handle obj;
+    ssize_t index;
+};
+
+/// Python's dictionary protocol permits this to be a forward iterator
+class dict_readonly {
+protected:
+    using iterator_category = std::forward_iterator_tag;
+    using value_type = std::pair<handle, handle>;
+    using reference = const value_type; // PR #3263
+    using pointer = arrow_proxy<const value_type>;
+
+    dict_readonly() = default;
+    dict_readonly(handle obj, ssize_t pos) : obj(obj), pos(pos) { increment(); }
+
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference dereference() const { return {key, value}; }
+    void increment() {
+        if (PyDict_Next(obj.ptr(), &pos, &key, &value) == 0) {
+            pos = -1;
+        }
+    }
+    bool equal(const dict_readonly &b) const { return pos == b.pos; }
+
+private:
+    handle obj;
+    PyObject *key = nullptr, *value = nullptr;
+    ssize_t pos = -1;
+};
+PYBIND11_NAMESPACE_END(iterator_policies)
+
+#if !defined(PYPY_VERSION)
+using tuple_iterator = generic_iterator<iterator_policies::sequence_fast_readonly>;
+using list_iterator = generic_iterator<iterator_policies::sequence_fast_readonly>;
+#else
+using tuple_iterator = generic_iterator<iterator_policies::sequence_slow_readwrite>;
+using list_iterator = generic_iterator<iterator_policies::sequence_slow_readwrite>;
+#endif
+
+using sequence_iterator = generic_iterator<iterator_policies::sequence_slow_readwrite>;
+using dict_iterator = generic_iterator<iterator_policies::dict_readonly>;
+
+inline bool PyIterable_Check(PyObject *obj) {
+    PyObject *iter = PyObject_GetIter(obj);
+    if (iter) {
+        Py_DECREF(iter);
+        return true;
+    }
+    PyErr_Clear();
+    return false;
+}
+
+inline bool PyNone_Check(PyObject *o) { return o == Py_None; }
+inline bool PyEllipsis_Check(PyObject *o) { return o == Py_Ellipsis; }
+
+#ifdef PYBIND11_STR_LEGACY_PERMISSIVE
+inline bool PyUnicode_Check_Permissive(PyObject *o) {
+    return PyUnicode_Check(o) || PYBIND11_BYTES_CHECK(o);
+}
+#    define PYBIND11_STR_CHECK_FUN detail::PyUnicode_Check_Permissive
+#else
+#    define PYBIND11_STR_CHECK_FUN PyUnicode_Check
+#endif
+
+inline bool PyStaticMethod_Check(PyObject *o) { return o->ob_type == &PyStaticMethod_Type; }
+
+class kwargs_proxy : public handle {
+public:
+    explicit kwargs_proxy(handle h) : handle(h) {}
+};
+
+class args_proxy : public handle {
+public:
+    explicit args_proxy(handle h) : handle(h) {}
+    kwargs_proxy operator*() const { return kwargs_proxy(*this); }
+};
+
+/// Python argument categories (using PEP 448 terms)
+template <typename T>
+using is_keyword = std::is_base_of<arg, T>;
+template <typename T>
+using is_s_unpacking = std::is_same<args_proxy, T>; // * unpacking
+template <typename T>
+using is_ds_unpacking = std::is_same<kwargs_proxy, T>; // ** unpacking
+template <typename T>
+using is_positional = satisfies_none_of<T, is_keyword, is_s_unpacking, is_ds_unpacking>;
+template <typename T>
+using is_keyword_or_ds = satisfies_any_of<T, is_keyword, is_ds_unpacking>;
+
+// Call argument collector forward declarations
+template <return_value_policy policy = return_value_policy::automatic_reference>
+class simple_collector;
+template <return_value_policy policy = return_value_policy::automatic_reference>
+class unpacking_collector;
+
+PYBIND11_NAMESPACE_END(detail)
+
+// TODO: After the deprecated constructors are removed, this macro can be simplified by
+//       inheriting ctors: `using Parent::Parent`. It's not an option right now because
+//       the `using` statement triggers the parent deprecation warning even if the ctor
+//       isn't even used.
+#define PYBIND11_OBJECT_COMMON(Name, Parent, CheckFun)                                            \
+public:                                                                                           \
+    PYBIND11_DEPRECATED("Use reinterpret_borrow<" #Name ">() or reinterpret_steal<" #Name ">()")  \
+    Name(handle h, bool is_borrowed)                                                              \
+        : Parent(is_borrowed ? Parent(h, borrowed_t{}) : Parent(h, stolen_t{})) {}                \
+    Name(handle h, borrowed_t) : Parent(h, borrowed_t{}) {}                                       \
+    Name(handle h, stolen_t) : Parent(h, stolen_t{}) {}                                           \
+    PYBIND11_DEPRECATED("Use py::isinstance<py::python_type>(obj) instead")                       \
+    bool check() const { return m_ptr != nullptr && (CheckFun(m_ptr) != 0); }                     \
+    static bool check_(handle h) { return h.ptr() != nullptr && CheckFun(h.ptr()); }              \
+    template <typename Policy_> /* NOLINTNEXTLINE(google-explicit-constructor) */                 \
+    Name(const ::pybind11::detail::accessor<Policy_> &a) : Name(object(a)) {}
+
+#define PYBIND11_OBJECT_CVT(Name, Parent, CheckFun, ConvertFun)                                   \
+    PYBIND11_OBJECT_COMMON(Name, Parent, CheckFun)                                                \
+    /* This is deliberately not 'explicit' to allow implicit conversion from object: */           \
+    /* NOLINTNEXTLINE(google-explicit-constructor) */                                             \
+    Name(const object &o)                                                                         \
+        : Parent(check_(o) ? o.inc_ref().ptr() : ConvertFun(o.ptr()), stolen_t{}) {               \
+        if (!m_ptr)                                                                               \
+            throw ::pybind11::error_already_set();                                                \
+    }                                                                                             \
+    /* NOLINTNEXTLINE(google-explicit-constructor) */                                             \
+    Name(object &&o) : Parent(check_(o) ? o.release().ptr() : ConvertFun(o.ptr()), stolen_t{}) {  \
+        if (!m_ptr)                                                                               \
+            throw ::pybind11::error_already_set();                                                \
+    }
+
+#define PYBIND11_OBJECT_CVT_DEFAULT(Name, Parent, CheckFun, ConvertFun)                           \
+    PYBIND11_OBJECT_CVT(Name, Parent, CheckFun, ConvertFun)                                       \
+    Name() : Parent() {}
+
+#define PYBIND11_OBJECT_CHECK_FAILED(Name, o_ptr)                                                 \
+    ::pybind11::type_error("Object of type '"                                                     \
+                           + ::pybind11::detail::get_fully_qualified_tp_name(Py_TYPE(o_ptr))      \
+                           + "' is not an instance of '" #Name "'")
+
+#define PYBIND11_OBJECT(Name, Parent, CheckFun)                                                   \
+    PYBIND11_OBJECT_COMMON(Name, Parent, CheckFun)                                                \
+    /* This is deliberately not 'explicit' to allow implicit conversion from object: */           \
+    /* NOLINTNEXTLINE(google-explicit-constructor) */                                             \
+    Name(const object &o) : Parent(o) {                                                           \
+        if (m_ptr && !check_(m_ptr))                                                              \
+            throw PYBIND11_OBJECT_CHECK_FAILED(Name, m_ptr);                                      \
+    }                                                                                             \
+    /* NOLINTNEXTLINE(google-explicit-constructor) */                                             \
+    Name(object &&o) : Parent(std::move(o)) {                                                     \
+        if (m_ptr && !check_(m_ptr))                                                              \
+            throw PYBIND11_OBJECT_CHECK_FAILED(Name, m_ptr);                                      \
+    }
+
+#define PYBIND11_OBJECT_DEFAULT(Name, Parent, CheckFun)                                           \
+    PYBIND11_OBJECT(Name, Parent, CheckFun)                                                       \
+    Name() : Parent() {}
+
+/// \addtogroup pytypes
+/// @{
+
+/** \rst
+    Wraps a Python iterator so that it can also be used as a C++ input iterator
+
+    Caveat: copying an iterator does not (and cannot) clone the internal
+    state of the Python iterable. This also applies to the post-increment
+    operator. This iterator should only be used to retrieve the current
+    value using ``operator*()``.
+\endrst */
+class iterator : public object {
+public:
+    using iterator_category = std::input_iterator_tag;
+    using difference_type = ssize_t;
+    using value_type = handle;
+    using reference = const handle; // PR #3263
+    using pointer = const handle *;
+
+    PYBIND11_OBJECT_DEFAULT(iterator, object, PyIter_Check)
+
+    iterator &operator++() {
+        advance();
+        return *this;
+    }
+
+    iterator operator++(int) {
+        auto rv = *this;
+        advance();
+        return rv;
+    }
+
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference operator*() const {
+        if (m_ptr && !value.ptr()) {
+            auto &self = const_cast<iterator &>(*this);
+            self.advance();
+        }
+        return value;
+    }
+
+    pointer operator->() const {
+        operator*();
+        return &value;
+    }
+
+    /** \rst
+         The value which marks the end of the iteration. ``it == iterator::sentinel()``
+         is equivalent to catching ``StopIteration`` in Python.
+
+         .. code-block:: cpp
+
+             void foo(py::iterator it) {
+                 while (it != py::iterator::sentinel()) {
+                    // use `*it`
+                    ++it;
+                 }
+             }
+    \endrst */
+    static iterator sentinel() { return {}; }
+
+    friend bool operator==(const iterator &a, const iterator &b) { return a->ptr() == b->ptr(); }
+    friend bool operator!=(const iterator &a, const iterator &b) { return a->ptr() != b->ptr(); }
+
+private:
+    void advance() {
+        value = reinterpret_steal<object>(PyIter_Next(m_ptr));
+        if (PyErr_Occurred()) {
+            throw error_already_set();
+        }
+    }
+
+private:
+    object value = {};
+};
+
+class type : public object {
+public:
+    PYBIND11_OBJECT(type, object, PyType_Check)
+
+    /// Return a type handle from a handle or an object
+    static handle handle_of(handle h) { return handle((PyObject *) Py_TYPE(h.ptr())); }
+
+    /// Return a type object from a handle or an object
+    static type of(handle h) { return type(type::handle_of(h), borrowed_t{}); }
+
+    // Defined in pybind11/cast.h
+    /// Convert C++ type to handle if previously registered. Does not convert
+    /// standard types, like int, float. etc. yet.
+    /// See https://github.com/pybind/pybind11/issues/2486
+    template <typename T>
+    static handle handle_of();
+
+    /// Convert C++ type to type if previously registered. Does not convert
+    /// standard types, like int, float. etc. yet.
+    /// See https://github.com/pybind/pybind11/issues/2486
+    template <typename T>
+    static type of() {
+        return type(type::handle_of<T>(), borrowed_t{});
+    }
+};
+
+class iterable : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(iterable, object, detail::PyIterable_Check)
+};
+
+class bytes;
+
+class str : public object {
+public:
+    PYBIND11_OBJECT_CVT(str, object, PYBIND11_STR_CHECK_FUN, raw_str)
+
+    template <typename SzType, detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    str(const char *c, const SzType &n)
+        : object(PyUnicode_FromStringAndSize(c, ssize_t_cast(n)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate string object!");
+        }
+    }
+
+    // 'explicit' is explicitly omitted from the following constructors to allow implicit
+    // conversion to py::str from C++ string-like objects
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    str(const char *c = "") : object(PyUnicode_FromString(c), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate string object!");
+        }
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    str(const std::string &s) : str(s.data(), s.size()) {}
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+    // enable_if is needed to avoid "ambiguous conversion" errors (see PR #3521).
+    template <typename T, detail::enable_if_t<std::is_same<T, std::string_view>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    str(T s) : str(s.data(), s.size()) {}
+
+#    ifdef PYBIND11_HAS_U8STRING
+    // reinterpret_cast here is safe (C++20 guarantees char8_t has the same size/alignment as char)
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    str(std::u8string_view s) : str(reinterpret_cast<const char *>(s.data()), s.size()) {}
+#    endif
+
+#endif
+
+    explicit str(const bytes &b);
+
+    /** \rst
+        Return a string representation of the object. This is analogous to
+        the ``str()`` function in Python.
+    \endrst */
+    explicit str(handle h) : object(raw_str(h.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator std::string() const {
+        object temp = *this;
+        if (PyUnicode_Check(m_ptr)) {
+            temp = reinterpret_steal<object>(PyUnicode_AsUTF8String(m_ptr));
+            if (!temp) {
+                throw error_already_set();
+            }
+        }
+        char *buffer = nullptr;
+        ssize_t length = 0;
+        if (PyBytes_AsStringAndSize(temp.ptr(), &buffer, &length) != 0) {
+            throw error_already_set();
+        }
+        return std::string(buffer, (size_t) length);
+    }
+
+    template <typename... Args>
+    str format(Args &&...args) const {
+        return attr("format")(std::forward<Args>(args)...);
+    }
+
+private:
+    /// Return string representation -- always returns a new reference, even if already a str
+    static PyObject *raw_str(PyObject *op) {
+        PyObject *str_value = PyObject_Str(op);
+        return str_value;
+    }
+};
+/// @} pytypes
+
+inline namespace literals {
+/** \rst
+    String literal version of `str`
+ \endrst */
+inline str operator"" _s(const char *s, size_t size) { return {s, size}; }
+} // namespace literals
+
+/// \addtogroup pytypes
+/// @{
+class bytes : public object {
+public:
+    PYBIND11_OBJECT(bytes, object, PYBIND11_BYTES_CHECK)
+
+    // Allow implicit conversion:
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    bytes(const char *c = "") : object(PYBIND11_BYTES_FROM_STRING(c), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate bytes object!");
+        }
+    }
+
+    template <typename SzType, detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    bytes(const char *c, const SzType &n)
+        : object(PYBIND11_BYTES_FROM_STRING_AND_SIZE(c, ssize_t_cast(n)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate bytes object!");
+        }
+    }
+
+    // Allow implicit conversion:
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    bytes(const std::string &s) : bytes(s.data(), s.size()) {}
+
+    explicit bytes(const pybind11::str &s);
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator std::string() const { return string_op<std::string>(); }
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+    // enable_if is needed to avoid "ambiguous conversion" errors (see PR #3521).
+    template <typename T, detail::enable_if_t<std::is_same<T, std::string_view>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    bytes(T s) : bytes(s.data(), s.size()) {}
+
+    // Obtain a string view that views the current `bytes` buffer value.  Note that this is only
+    // valid so long as the `bytes` instance remains alive and so generally should not outlive the
+    // lifetime of the `bytes` instance.
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator std::string_view() const { return string_op<std::string_view>(); }
+#endif
+private:
+    template <typename T>
+    T string_op() const {
+        char *buffer = nullptr;
+        ssize_t length = 0;
+        if (PyBytes_AsStringAndSize(m_ptr, &buffer, &length) != 0) {
+            throw error_already_set();
+        }
+        return {buffer, static_cast<size_t>(length)};
+    }
+};
+// Note: breathe >= 4.17.0 will fail to build docs if the below two constructors
+// are included in the doxygen group; close here and reopen after as a workaround
+/// @} pytypes
+
+inline bytes::bytes(const pybind11::str &s) {
+    object temp = s;
+    if (PyUnicode_Check(s.ptr())) {
+        temp = reinterpret_steal<object>(PyUnicode_AsUTF8String(s.ptr()));
+        if (!temp) {
+            throw error_already_set();
+        }
+    }
+    char *buffer = nullptr;
+    ssize_t length = 0;
+    if (PyBytes_AsStringAndSize(temp.ptr(), &buffer, &length) != 0) {
+        throw error_already_set();
+    }
+    auto obj = reinterpret_steal<object>(PYBIND11_BYTES_FROM_STRING_AND_SIZE(buffer, length));
+    if (!obj) {
+        pybind11_fail("Could not allocate bytes object!");
+    }
+    m_ptr = obj.release().ptr();
+}
+
+inline str::str(const bytes &b) {
+    char *buffer = nullptr;
+    ssize_t length = 0;
+    if (PyBytes_AsStringAndSize(b.ptr(), &buffer, &length) != 0) {
+        throw error_already_set();
+    }
+    auto obj = reinterpret_steal<object>(PyUnicode_FromStringAndSize(buffer, length));
+    if (!obj) {
+        pybind11_fail("Could not allocate string object!");
+    }
+    m_ptr = obj.release().ptr();
+}
+
+/// \addtogroup pytypes
+/// @{
+class bytearray : public object {
+public:
+    PYBIND11_OBJECT_CVT(bytearray, object, PyByteArray_Check, PyByteArray_FromObject)
+
+    template <typename SzType, detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    bytearray(const char *c, const SzType &n)
+        : object(PyByteArray_FromStringAndSize(c, ssize_t_cast(n)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate bytearray object!");
+        }
+    }
+
+    bytearray() : bytearray("", 0) {}
+
+    explicit bytearray(const std::string &s) : bytearray(s.data(), s.size()) {}
+
+    size_t size() const { return static_cast<size_t>(PyByteArray_Size(m_ptr)); }
+
+    explicit operator std::string() const {
+        char *buffer = PyByteArray_AS_STRING(m_ptr);
+        ssize_t size = PyByteArray_GET_SIZE(m_ptr);
+        return std::string(buffer, static_cast<size_t>(size));
+    }
+};
+// Note: breathe >= 4.17.0 will fail to build docs if the below two constructors
+// are included in the doxygen group; close here and reopen after as a workaround
+/// @} pytypes
+
+/// \addtogroup pytypes
+/// @{
+class none : public object {
+public:
+    PYBIND11_OBJECT(none, object, detail::PyNone_Check)
+    none() : object(Py_None, borrowed_t{}) {}
+};
+
+class ellipsis : public object {
+public:
+    PYBIND11_OBJECT(ellipsis, object, detail::PyEllipsis_Check)
+    ellipsis() : object(Py_Ellipsis, borrowed_t{}) {}
+};
+
+class bool_ : public object {
+public:
+    PYBIND11_OBJECT_CVT(bool_, object, PyBool_Check, raw_bool)
+    bool_() : object(Py_False, borrowed_t{}) {}
+    // Allow implicit conversion from and to `bool`:
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    bool_(bool value) : object(value ? Py_True : Py_False, borrowed_t{}) {}
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator bool() const { return (m_ptr != nullptr) && PyLong_AsLong(m_ptr) != 0; }
+
+private:
+    /// Return the truth value of an object -- always returns a new reference
+    static PyObject *raw_bool(PyObject *op) {
+        const auto value = PyObject_IsTrue(op);
+        if (value == -1) {
+            return nullptr;
+        }
+        return handle(value != 0 ? Py_True : Py_False).inc_ref().ptr();
+    }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+// Converts a value to the given unsigned type.  If an error occurs, you get back (Unsigned) -1;
+// otherwise you get back the unsigned long or unsigned long long value cast to (Unsigned).
+// (The distinction is critically important when casting a returned -1 error value to some other
+// unsigned type: (A)-1 != (B)-1 when A and B are unsigned types of different sizes).
+template <typename Unsigned>
+Unsigned as_unsigned(PyObject *o) {
+    if (PYBIND11_SILENCE_MSVC_C4127(sizeof(Unsigned) <= sizeof(unsigned long))) {
+        unsigned long v = PyLong_AsUnsignedLong(o);
+        return v == (unsigned long) -1 && PyErr_Occurred() ? (Unsigned) -1 : (Unsigned) v;
+    }
+    unsigned long long v = PyLong_AsUnsignedLongLong(o);
+    return v == (unsigned long long) -1 && PyErr_Occurred() ? (Unsigned) -1 : (Unsigned) v;
+}
+PYBIND11_NAMESPACE_END(detail)
+
+class int_ : public object {
+public:
+    PYBIND11_OBJECT_CVT(int_, object, PYBIND11_LONG_CHECK, PyNumber_Long)
+    int_() : object(PyLong_FromLong(0), stolen_t{}) {}
+    // Allow implicit conversion from C++ integral types:
+    template <typename T, detail::enable_if_t<std::is_integral<T>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    int_(T value) {
+        if (PYBIND11_SILENCE_MSVC_C4127(sizeof(T) <= sizeof(long))) {
+            if (std::is_signed<T>::value) {
+                m_ptr = PyLong_FromLong((long) value);
+            } else {
+                m_ptr = PyLong_FromUnsignedLong((unsigned long) value);
+            }
+        } else {
+            if (std::is_signed<T>::value) {
+                m_ptr = PyLong_FromLongLong((long long) value);
+            } else {
+                m_ptr = PyLong_FromUnsignedLongLong((unsigned long long) value);
+            }
+        }
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate int object!");
+        }
+    }
+
+    template <typename T, detail::enable_if_t<std::is_integral<T>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator T() const {
+        return std::is_unsigned<T>::value  ? detail::as_unsigned<T>(m_ptr)
+               : sizeof(T) <= sizeof(long) ? (T) PyLong_AsLong(m_ptr)
+                                           : (T) PYBIND11_LONG_AS_LONGLONG(m_ptr);
+    }
+};
+
+class float_ : public object {
+public:
+    PYBIND11_OBJECT_CVT(float_, object, PyFloat_Check, PyNumber_Float)
+    // Allow implicit conversion from float/double:
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    float_(float value) : object(PyFloat_FromDouble((double) value), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate float object!");
+        }
+    }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    float_(double value = .0) : object(PyFloat_FromDouble((double) value), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate float object!");
+        }
+    }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator float() const { return (float) PyFloat_AsDouble(m_ptr); }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator double() const { return (double) PyFloat_AsDouble(m_ptr); }
+};
+
+class weakref : public object {
+public:
+    PYBIND11_OBJECT_CVT_DEFAULT(weakref, object, PyWeakref_Check, raw_weakref)
+    explicit weakref(handle obj, handle callback = {})
+        : object(PyWeakref_NewRef(obj.ptr(), callback.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            if (PyErr_Occurred()) {
+                throw error_already_set();
+            }
+            pybind11_fail("Could not allocate weak reference!");
+        }
+    }
+
+private:
+    static PyObject *raw_weakref(PyObject *o) { return PyWeakref_NewRef(o, nullptr); }
+};
+
+class slice : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(slice, object, PySlice_Check)
+    slice(handle start, handle stop, handle step)
+        : object(PySlice_New(start.ptr(), stop.ptr(), step.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate slice object!");
+        }
+    }
+
+#ifdef PYBIND11_HAS_OPTIONAL
+    slice(std::optional<ssize_t> start, std::optional<ssize_t> stop, std::optional<ssize_t> step)
+        : slice(index_to_object(start), index_to_object(stop), index_to_object(step)) {}
+#else
+    slice(ssize_t start_, ssize_t stop_, ssize_t step_)
+        : slice(int_(start_), int_(stop_), int_(step_)) {}
+#endif
+
+    bool
+    compute(size_t length, size_t *start, size_t *stop, size_t *step, size_t *slicelength) const {
+        return PySlice_GetIndicesEx((PYBIND11_SLICE_OBJECT *) m_ptr,
+                                    (ssize_t) length,
+                                    (ssize_t *) start,
+                                    (ssize_t *) stop,
+                                    (ssize_t *) step,
+                                    (ssize_t *) slicelength)
+               == 0;
+    }
+    bool compute(
+        ssize_t length, ssize_t *start, ssize_t *stop, ssize_t *step, ssize_t *slicelength) const {
+        return PySlice_GetIndicesEx(
+                   (PYBIND11_SLICE_OBJECT *) m_ptr, length, start, stop, step, slicelength)
+               == 0;
+    }
+
+private:
+    template <typename T>
+    static object index_to_object(T index) {
+        return index ? object(int_(*index)) : object(none());
+    }
+};
+
+class capsule : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(capsule, object, PyCapsule_CheckExact)
+    PYBIND11_DEPRECATED("Use reinterpret_borrow<capsule>() or reinterpret_steal<capsule>()")
+    capsule(PyObject *ptr, bool is_borrowed)
+        : object(is_borrowed ? object(ptr, borrowed_t{}) : object(ptr, stolen_t{})) {}
+
+    explicit capsule(const void *value,
+                     const char *name = nullptr,
+                     void (*destructor)(PyObject *) = nullptr)
+        : object(PyCapsule_New(const_cast<void *>(value), name, destructor), stolen_t{}) {
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    PYBIND11_DEPRECATED("Please pass a destructor that takes a void pointer as input")
+    capsule(const void *value, void (*destruct)(PyObject *))
+        : object(PyCapsule_New(const_cast<void *>(value), nullptr, destruct), stolen_t{}) {
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    capsule(const void *value, void (*destructor)(void *)) {
+        m_ptr = PyCapsule_New(const_cast<void *>(value), nullptr, [](PyObject *o) {
+            // guard if destructor called while err indicator is set
+            error_scope error_guard;
+            auto destructor = reinterpret_cast<void (*)(void *)>(PyCapsule_GetContext(o));
+            if (destructor == nullptr) {
+                if (PyErr_Occurred()) {
+                    throw error_already_set();
+                }
+                pybind11_fail("Unable to get capsule context");
+            }
+            const char *name = get_name_in_error_scope(o);
+            void *ptr = PyCapsule_GetPointer(o, name);
+            if (ptr == nullptr) {
+                throw error_already_set();
+            }
+            destructor(ptr);
+        });
+
+        if (!m_ptr || PyCapsule_SetContext(m_ptr, (void *) destructor) != 0) {
+            throw error_already_set();
+        }
+    }
+
+    explicit capsule(void (*destructor)()) {
+        m_ptr = PyCapsule_New(reinterpret_cast<void *>(destructor), nullptr, [](PyObject *o) {
+            const char *name = get_name_in_error_scope(o);
+            auto destructor = reinterpret_cast<void (*)()>(PyCapsule_GetPointer(o, name));
+            if (destructor == nullptr) {
+                throw error_already_set();
+            }
+            destructor();
+        });
+
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    template <typename T>
+    operator T *() const { // NOLINT(google-explicit-constructor)
+        return get_pointer<T>();
+    }
+
+    /// Get the pointer the capsule holds.
+    template <typename T = void>
+    T *get_pointer() const {
+        const auto *name = this->name();
+        T *result = static_cast<T *>(PyCapsule_GetPointer(m_ptr, name));
+        if (!result) {
+            throw error_already_set();
+        }
+        return result;
+    }
+
+    /// Replaces a capsule's pointer *without* calling the destructor on the existing one.
+    void set_pointer(const void *value) {
+        if (PyCapsule_SetPointer(m_ptr, const_cast<void *>(value)) != 0) {
+            throw error_already_set();
+        }
+    }
+
+    const char *name() const {
+        const char *name = PyCapsule_GetName(m_ptr);
+        if ((name == nullptr) && PyErr_Occurred()) {
+            throw error_already_set();
+        }
+        return name;
+    }
+
+    /// Replaces a capsule's name *without* calling the destructor on the existing one.
+    void set_name(const char *new_name) {
+        if (PyCapsule_SetName(m_ptr, new_name) != 0) {
+            throw error_already_set();
+        }
+    }
+
+private:
+    static const char *get_name_in_error_scope(PyObject *o) {
+        error_scope error_guard;
+
+        const char *name = PyCapsule_GetName(o);
+        if ((name == nullptr) && PyErr_Occurred()) {
+            // write out and consume error raised by call to PyCapsule_GetName
+            PyErr_WriteUnraisable(o);
+        }
+
+        return name;
+    }
+};
+
+class tuple : public object {
+public:
+    PYBIND11_OBJECT_CVT(tuple, object, PyTuple_Check, PySequence_Tuple)
+    template <typename SzType = ssize_t,
+              detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    // Some compilers generate link errors when using `const SzType &` here:
+    explicit tuple(SzType size = 0) : object(PyTuple_New(ssize_t_cast(size)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate tuple object!");
+        }
+    }
+    size_t size() const { return (size_t) PyTuple_Size(m_ptr); }
+    bool empty() const { return size() == 0; }
+    detail::tuple_accessor operator[](size_t index) const { return {*this, index}; }
+    template <typename T, detail::enable_if_t<detail::is_pyobject<T>::value, int> = 0>
+    detail::item_accessor operator[](T &&o) const {
+        return object::operator[](std::forward<T>(o));
+    }
+    detail::tuple_iterator begin() const { return {*this, 0}; }
+    detail::tuple_iterator end() const { return {*this, PyTuple_GET_SIZE(m_ptr)}; }
+};
+
+// We need to put this into a separate function because the Intel compiler
+// fails to compile enable_if_t<all_of<is_keyword_or_ds<Args>...>::value> part below
+// (tested with ICC 2021.1 Beta 20200827).
+template <typename... Args>
+constexpr bool args_are_all_keyword_or_ds() {
+    return detail::all_of<detail::is_keyword_or_ds<Args>...>::value;
+}
+
+class dict : public object {
+public:
+    PYBIND11_OBJECT_CVT(dict, object, PyDict_Check, raw_dict)
+    dict() : object(PyDict_New(), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate dict object!");
+        }
+    }
+    template <typename... Args,
+              typename = detail::enable_if_t<args_are_all_keyword_or_ds<Args...>()>,
+              // MSVC workaround: it can't compile an out-of-line definition, so defer the
+              // collector
+              typename collector = detail::deferred_t<detail::unpacking_collector<>, Args...>>
+    explicit dict(Args &&...args) : dict(collector(std::forward<Args>(args)...).kwargs()) {}
+
+    size_t size() const { return (size_t) PyDict_Size(m_ptr); }
+    bool empty() const { return size() == 0; }
+    detail::dict_iterator begin() const { return {*this, 0}; }
+    detail::dict_iterator end() const { return {}; }
+    void clear() /* py-non-const */ { PyDict_Clear(ptr()); }
+    template <typename T>
+    bool contains(T &&key) const {
+        return PyDict_Contains(m_ptr, detail::object_or_cast(std::forward<T>(key)).ptr()) == 1;
+    }
+
+private:
+    /// Call the `dict` Python type -- always returns a new reference
+    static PyObject *raw_dict(PyObject *op) {
+        if (PyDict_Check(op)) {
+            return handle(op).inc_ref().ptr();
+        }
+        return PyObject_CallFunctionObjArgs((PyObject *) &PyDict_Type, op, nullptr);
+    }
+};
+
+class sequence : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(sequence, object, PySequence_Check)
+    size_t size() const {
+        ssize_t result = PySequence_Size(m_ptr);
+        if (result == -1) {
+            throw error_already_set();
+        }
+        return (size_t) result;
+    }
+    bool empty() const { return size() == 0; }
+    detail::sequence_accessor operator[](size_t index) const { return {*this, index}; }
+    template <typename T, detail::enable_if_t<detail::is_pyobject<T>::value, int> = 0>
+    detail::item_accessor operator[](T &&o) const {
+        return object::operator[](std::forward<T>(o));
+    }
+    detail::sequence_iterator begin() const { return {*this, 0}; }
+    detail::sequence_iterator end() const { return {*this, PySequence_Size(m_ptr)}; }
+};
+
+class list : public object {
+public:
+    PYBIND11_OBJECT_CVT(list, object, PyList_Check, PySequence_List)
+    template <typename SzType = ssize_t,
+              detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    // Some compilers generate link errors when using `const SzType &` here:
+    explicit list(SzType size = 0) : object(PyList_New(ssize_t_cast(size)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate list object!");
+        }
+    }
+    size_t size() const { return (size_t) PyList_Size(m_ptr); }
+    bool empty() const { return size() == 0; }
+    detail::list_accessor operator[](size_t index) const { return {*this, index}; }
+    template <typename T, detail::enable_if_t<detail::is_pyobject<T>::value, int> = 0>
+    detail::item_accessor operator[](T &&o) const {
+        return object::operator[](std::forward<T>(o));
+    }
+    detail::list_iterator begin() const { return {*this, 0}; }
+    detail::list_iterator end() const { return {*this, PyList_GET_SIZE(m_ptr)}; }
+    template <typename T>
+    void append(T &&val) /* py-non-const */ {
+        PyList_Append(m_ptr, detail::object_or_cast(std::forward<T>(val)).ptr());
+    }
+    template <typename IdxType,
+              typename ValType,
+              detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    void insert(const IdxType &index, ValType &&val) /* py-non-const */ {
+        PyList_Insert(
+            m_ptr, ssize_t_cast(index), detail::object_or_cast(std::forward<ValType>(val)).ptr());
+    }
+};
+
+class args : public tuple {
+    PYBIND11_OBJECT_DEFAULT(args, tuple, PyTuple_Check)
+};
+class kwargs : public dict {
+    PYBIND11_OBJECT_DEFAULT(kwargs, dict, PyDict_Check)
+};
+
+class anyset : public object {
+public:
+    PYBIND11_OBJECT(anyset, object, PyAnySet_Check)
+    size_t size() const { return static_cast<size_t>(PySet_Size(m_ptr)); }
+    bool empty() const { return size() == 0; }
+    template <typename T>
+    bool contains(T &&val) const {
+        return PySet_Contains(m_ptr, detail::object_or_cast(std::forward<T>(val)).ptr()) == 1;
+    }
+};
+
+class set : public anyset {
+public:
+    PYBIND11_OBJECT_CVT(set, anyset, PySet_Check, PySet_New)
+    set() : anyset(PySet_New(nullptr), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate set object!");
+        }
+    }
+    template <typename T>
+    bool add(T &&val) /* py-non-const */ {
+        return PySet_Add(m_ptr, detail::object_or_cast(std::forward<T>(val)).ptr()) == 0;
+    }
+    void clear() /* py-non-const */ { PySet_Clear(m_ptr); }
+};
+
+class frozenset : public anyset {
+public:
+    PYBIND11_OBJECT_CVT(frozenset, anyset, PyFrozenSet_Check, PyFrozenSet_New)
+};
+
+class function : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(function, object, PyCallable_Check)
+    handle cpp_function() const {
+        handle fun = detail::get_function(m_ptr);
+        if (fun && PyCFunction_Check(fun.ptr())) {
+            return fun;
+        }
+        return handle();
+    }
+    bool is_cpp_function() const { return (bool) cpp_function(); }
+};
+
+class staticmethod : public object {
+public:
+    PYBIND11_OBJECT_CVT(staticmethod, object, detail::PyStaticMethod_Check, PyStaticMethod_New)
+};
+
+class buffer : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(buffer, object, PyObject_CheckBuffer)
+
+    buffer_info request(bool writable = false) const {
+        int flags = PyBUF_STRIDES | PyBUF_FORMAT;
+        if (writable) {
+            flags |= PyBUF_WRITABLE;
+        }
+        auto *view = new Py_buffer();
+        if (PyObject_GetBuffer(m_ptr, view, flags) != 0) {
+            delete view;
+            throw error_already_set();
+        }
+        return buffer_info(view);
+    }
+};
+
+class memoryview : public object {
+public:
+    PYBIND11_OBJECT_CVT(memoryview, object, PyMemoryView_Check, PyMemoryView_FromObject)
+
+    /** \rst
+        Creates ``memoryview`` from ``buffer_info``.
+
+        ``buffer_info`` must be created from ``buffer::request()``. Otherwise
+        throws an exception.
+
+        For creating a ``memoryview`` from objects that support buffer protocol,
+        use ``memoryview(const object& obj)`` instead of this constructor.
+     \endrst */
+    explicit memoryview(const buffer_info &info) {
+        if (!info.view()) {
+            pybind11_fail("Prohibited to create memoryview without Py_buffer");
+        }
+        // Note: PyMemoryView_FromBuffer never increments obj reference.
+        m_ptr = (info.view()->obj) ? PyMemoryView_FromObject(info.view()->obj)
+                                   : PyMemoryView_FromBuffer(info.view());
+        if (!m_ptr) {
+            pybind11_fail("Unable to create memoryview from buffer descriptor");
+        }
+    }
+
+    /** \rst
+        Creates ``memoryview`` from static buffer.
+
+        This method is meant for providing a ``memoryview`` for C/C++ buffer not
+        managed by Python. The caller is responsible for managing the lifetime
+        of ``ptr`` and ``format``, which MUST outlive the memoryview constructed
+        here.
+
+        See also: Python C API documentation for `PyMemoryView_FromBuffer`_.
+
+        .. _PyMemoryView_FromBuffer:
+           https://docs.python.org/c-api/memoryview.html#c.PyMemoryView_FromBuffer
+
+        :param ptr: Pointer to the buffer.
+        :param itemsize: Byte size of an element.
+        :param format: Pointer to the null-terminated format string. For
+            homogeneous Buffers, this should be set to
+            ``format_descriptor<T>::value``.
+        :param shape: Shape of the tensor (1 entry per dimension).
+        :param strides: Number of bytes between adjacent entries (for each
+            per dimension).
+        :param readonly: Flag to indicate if the underlying storage may be
+            written to.
+     \endrst */
+    static memoryview from_buffer(void *ptr,
+                                  ssize_t itemsize,
+                                  const char *format,
+                                  detail::any_container<ssize_t> shape,
+                                  detail::any_container<ssize_t> strides,
+                                  bool readonly = false);
+
+    static memoryview from_buffer(const void *ptr,
+                                  ssize_t itemsize,
+                                  const char *format,
+                                  detail::any_container<ssize_t> shape,
+                                  detail::any_container<ssize_t> strides) {
+        return memoryview::from_buffer(
+            const_cast<void *>(ptr), itemsize, format, std::move(shape), std::move(strides), true);
+    }
+
+    template <typename T>
+    static memoryview from_buffer(T *ptr,
+                                  detail::any_container<ssize_t> shape,
+                                  detail::any_container<ssize_t> strides,
+                                  bool readonly = false) {
+        return memoryview::from_buffer(reinterpret_cast<void *>(ptr),
+                                       sizeof(T),
+                                       format_descriptor<T>::value,
+                                       std::move(shape),
+                                       std::move(strides),
+                                       readonly);
+    }
+
+    template <typename T>
+    static memoryview from_buffer(const T *ptr,
+                                  detail::any_container<ssize_t> shape,
+                                  detail::any_container<ssize_t> strides) {
+        return memoryview::from_buffer(
+            const_cast<T *>(ptr), std::move(shape), std::move(strides), true);
+    }
+
+    /** \rst
+        Creates ``memoryview`` from static memory.
+
+        This method is meant for providing a ``memoryview`` for C/C++ buffer not
+        managed by Python. The caller is responsible for managing the lifetime
+        of ``mem``, which MUST outlive the memoryview constructed here.
+
+        See also: Python C API documentation for `PyMemoryView_FromBuffer`_.
+
+        .. _PyMemoryView_FromMemory:
+           https://docs.python.org/c-api/memoryview.html#c.PyMemoryView_FromMemory
+     \endrst */
+    static memoryview from_memory(void *mem, ssize_t size, bool readonly = false) {
+        PyObject *ptr = PyMemoryView_FromMemory(
+            reinterpret_cast<char *>(mem), size, (readonly) ? PyBUF_READ : PyBUF_WRITE);
+        if (!ptr) {
+            pybind11_fail("Could not allocate memoryview object!");
+        }
+        return memoryview(object(ptr, stolen_t{}));
+    }
+
+    static memoryview from_memory(const void *mem, ssize_t size) {
+        return memoryview::from_memory(const_cast<void *>(mem), size, true);
+    }
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+    static memoryview from_memory(std::string_view mem) {
+        return from_memory(const_cast<char *>(mem.data()), static_cast<ssize_t>(mem.size()), true);
+    }
+#endif
+};
+
+/// @cond DUPLICATE
+inline memoryview memoryview::from_buffer(void *ptr,
+                                          ssize_t itemsize,
+                                          const char *format,
+                                          detail::any_container<ssize_t> shape,
+                                          detail::any_container<ssize_t> strides,
+                                          bool readonly) {
+    size_t ndim = shape->size();
+    if (ndim != strides->size()) {
+        pybind11_fail("memoryview: shape length doesn't match strides length");
+    }
+    ssize_t size = ndim != 0u ? 1 : 0;
+    for (size_t i = 0; i < ndim; ++i) {
+        size *= (*shape)[i];
+    }
+    Py_buffer view;
+    view.buf = ptr;
+    view.obj = nullptr;
+    view.len = size * itemsize;
+    view.readonly = static_cast<int>(readonly);
+    view.itemsize = itemsize;
+    view.format = const_cast<char *>(format);
+    view.ndim = static_cast<int>(ndim);
+    view.shape = shape->data();
+    view.strides = strides->data();
+    view.suboffsets = nullptr;
+    view.internal = nullptr;
+    PyObject *obj = PyMemoryView_FromBuffer(&view);
+    if (!obj) {
+        throw error_already_set();
+    }
+    return memoryview(object(obj, stolen_t{}));
+}
+/// @endcond
+/// @} pytypes
+
+/// \addtogroup python_builtins
+/// @{
+
+/// Get the length of a Python object.
+inline size_t len(handle h) {
+    ssize_t result = PyObject_Length(h.ptr());
+    if (result < 0) {
+        throw error_already_set();
+    }
+    return (size_t) result;
+}
+
+/// Get the length hint of a Python object.
+/// Returns 0 when this cannot be determined.
+inline size_t len_hint(handle h) {
+    ssize_t result = PyObject_LengthHint(h.ptr(), 0);
+    if (result < 0) {
+        // Sometimes a length can't be determined at all (eg generators)
+        // In which case simply return 0
+        PyErr_Clear();
+        return 0;
+    }
+    return (size_t) result;
+}
+
+inline str repr(handle h) {
+    PyObject *str_value = PyObject_Repr(h.ptr());
+    if (!str_value) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<str>(str_value);
+}
+
+inline iterator iter(handle obj) {
+    PyObject *result = PyObject_GetIter(obj.ptr());
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<iterator>(result);
+}
+/// @} python_builtins
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <typename D>
+iterator object_api<D>::begin() const {
+    return iter(derived());
+}
+template <typename D>
+iterator object_api<D>::end() const {
+    return iterator::sentinel();
+}
+template <typename D>
+item_accessor object_api<D>::operator[](handle key) const {
+    return {derived(), reinterpret_borrow<object>(key)};
+}
+template <typename D>
+item_accessor object_api<D>::operator[](object &&key) const {
+    return {derived(), std::move(key)};
+}
+template <typename D>
+item_accessor object_api<D>::operator[](const char *key) const {
+    return {derived(), pybind11::str(key)};
+}
+template <typename D>
+obj_attr_accessor object_api<D>::attr(handle key) const {
+    return {derived(), reinterpret_borrow<object>(key)};
+}
+template <typename D>
+obj_attr_accessor object_api<D>::attr(object &&key) const {
+    return {derived(), std::move(key)};
+}
+template <typename D>
+str_attr_accessor object_api<D>::attr(const char *key) const {
+    return {derived(), key};
+}
+template <typename D>
+args_proxy object_api<D>::operator*() const {
+    return args_proxy(derived().ptr());
+}
+template <typename D>
+template <typename T>
+bool object_api<D>::contains(T &&item) const {
+    return attr("__contains__")(std::forward<T>(item)).template cast<bool>();
+}
+
+template <typename D>
+pybind11::str object_api<D>::str() const {
+    return pybind11::str(derived());
+}
+
+template <typename D>
+str_attr_accessor object_api<D>::doc() const {
+    return attr("__doc__");
+}
+
+template <typename D>
+handle object_api<D>::get_type() const {
+    return type::handle_of(derived());
+}
+
+template <typename D>
+bool object_api<D>::rich_compare(object_api const &other, int value) const {
+    int rv = PyObject_RichCompareBool(derived().ptr(), other.derived().ptr(), value);
+    if (rv == -1) {
+        throw error_already_set();
+    }
+    return rv == 1;
+}
+
+#define PYBIND11_MATH_OPERATOR_UNARY(op, fn)                                                      \
+    template <typename D>                                                                         \
+    object object_api<D>::op() const {                                                            \
+        object result = reinterpret_steal<object>(fn(derived().ptr()));                           \
+        if (!result.ptr())                                                                        \
+            throw error_already_set();                                                            \
+        return result;                                                                            \
+    }
+
+#define PYBIND11_MATH_OPERATOR_BINARY(op, fn)                                                     \
+    template <typename D>                                                                         \
+    object object_api<D>::op(object_api const &other) const {                                     \
+        object result = reinterpret_steal<object>(fn(derived().ptr(), other.derived().ptr()));    \
+        if (!result.ptr())                                                                        \
+            throw error_already_set();                                                            \
+        return result;                                                                            \
+    }
+
+PYBIND11_MATH_OPERATOR_UNARY(operator~, PyNumber_Invert)
+PYBIND11_MATH_OPERATOR_UNARY(operator-, PyNumber_Negative)
+PYBIND11_MATH_OPERATOR_BINARY(operator+, PyNumber_Add)
+PYBIND11_MATH_OPERATOR_BINARY(operator+=, PyNumber_InPlaceAdd)
+PYBIND11_MATH_OPERATOR_BINARY(operator-, PyNumber_Subtract)
+PYBIND11_MATH_OPERATOR_BINARY(operator-=, PyNumber_InPlaceSubtract)
+PYBIND11_MATH_OPERATOR_BINARY(operator*, PyNumber_Multiply)
+PYBIND11_MATH_OPERATOR_BINARY(operator*=, PyNumber_InPlaceMultiply)
+PYBIND11_MATH_OPERATOR_BINARY(operator/, PyNumber_TrueDivide)
+PYBIND11_MATH_OPERATOR_BINARY(operator/=, PyNumber_InPlaceTrueDivide)
+PYBIND11_MATH_OPERATOR_BINARY(operator|, PyNumber_Or)
+PYBIND11_MATH_OPERATOR_BINARY(operator|=, PyNumber_InPlaceOr)
+PYBIND11_MATH_OPERATOR_BINARY(operator&, PyNumber_And)
+PYBIND11_MATH_OPERATOR_BINARY(operator&=, PyNumber_InPlaceAnd)
+PYBIND11_MATH_OPERATOR_BINARY(operator^, PyNumber_Xor)
+PYBIND11_MATH_OPERATOR_BINARY(operator^=, PyNumber_InPlaceXor)
+PYBIND11_MATH_OPERATOR_BINARY(operator<<, PyNumber_Lshift)
+PYBIND11_MATH_OPERATOR_BINARY(operator<<=, PyNumber_InPlaceLshift)
+PYBIND11_MATH_OPERATOR_BINARY(operator>>, PyNumber_Rshift)
+PYBIND11_MATH_OPERATOR_BINARY(operator>>=, PyNumber_InPlaceRshift)
+
+#undef PYBIND11_MATH_OPERATOR_UNARY
+#undef PYBIND11_MATH_OPERATOR_BINARY
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl.h
new file mode 100644
index 000000000..ab30ecac0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl.h
@@ -0,0 +1,425 @@
+/*
+    pybind11/stl.h: Transparent conversion for STL data types
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+#include "detail/common.h"
+
+#include <deque>
+#include <list>
+#include <map>
+#include <ostream>
+#include <set>
+#include <unordered_map>
+#include <unordered_set>
+#include <valarray>
+
+// See `detail/common.h` for implementation of these guards.
+#if defined(PYBIND11_HAS_OPTIONAL)
+#    include <optional>
+#elif defined(PYBIND11_HAS_EXP_OPTIONAL)
+#    include <experimental/optional>
+#endif
+
+#if defined(PYBIND11_HAS_VARIANT)
+#    include <variant>
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Extracts an const lvalue reference or rvalue reference for U based on the type of T (e.g. for
+/// forwarding a container element).  Typically used indirect via forwarded_type(), below.
+template <typename T, typename U>
+using forwarded_type = conditional_t<std::is_lvalue_reference<T>::value,
+                                     remove_reference_t<U> &,
+                                     remove_reference_t<U> &&>;
+
+/// Forwards a value U as rvalue or lvalue according to whether T is rvalue or lvalue; typically
+/// used for forwarding a container's elements.
+template <typename T, typename U>
+forwarded_type<T, U> forward_like(U &&u) {
+    return std::forward<detail::forwarded_type<T, U>>(std::forward<U>(u));
+}
+
+template <typename Type, typename Key>
+struct set_caster {
+    using type = Type;
+    using key_conv = make_caster<Key>;
+
+    bool load(handle src, bool convert) {
+        if (!isinstance<anyset>(src)) {
+            return false;
+        }
+        auto s = reinterpret_borrow<anyset>(src);
+        value.clear();
+        for (auto entry : s) {
+            key_conv conv;
+            if (!conv.load(entry, convert)) {
+                return false;
+            }
+            value.insert(cast_op<Key &&>(std::move(conv)));
+        }
+        return true;
+    }
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        if (!std::is_lvalue_reference<T>::value) {
+            policy = return_value_policy_override<Key>::policy(policy);
+        }
+        pybind11::set s;
+        for (auto &&value : src) {
+            auto value_ = reinterpret_steal<object>(
+                key_conv::cast(forward_like<T>(value), policy, parent));
+            if (!value_ || !s.add(std::move(value_))) {
+                return handle();
+            }
+        }
+        return s.release();
+    }
+
+    PYBIND11_TYPE_CASTER(type, const_name("Set[") + key_conv::name + const_name("]"));
+};
+
+template <typename Type, typename Key, typename Value>
+struct map_caster {
+    using key_conv = make_caster<Key>;
+    using value_conv = make_caster<Value>;
+
+    bool load(handle src, bool convert) {
+        if (!isinstance<dict>(src)) {
+            return false;
+        }
+        auto d = reinterpret_borrow<dict>(src);
+        value.clear();
+        for (auto it : d) {
+            key_conv kconv;
+            value_conv vconv;
+            if (!kconv.load(it.first.ptr(), convert) || !vconv.load(it.second.ptr(), convert)) {
+                return false;
+            }
+            value.emplace(cast_op<Key &&>(std::move(kconv)), cast_op<Value &&>(std::move(vconv)));
+        }
+        return true;
+    }
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        dict d;
+        return_value_policy policy_key = policy;
+        return_value_policy policy_value = policy;
+        if (!std::is_lvalue_reference<T>::value) {
+            policy_key = return_value_policy_override<Key>::policy(policy_key);
+            policy_value = return_value_policy_override<Value>::policy(policy_value);
+        }
+        for (auto &&kv : src) {
+            auto key = reinterpret_steal<object>(
+                key_conv::cast(forward_like<T>(kv.first), policy_key, parent));
+            auto value = reinterpret_steal<object>(
+                value_conv::cast(forward_like<T>(kv.second), policy_value, parent));
+            if (!key || !value) {
+                return handle();
+            }
+            d[std::move(key)] = std::move(value);
+        }
+        return d.release();
+    }
+
+    PYBIND11_TYPE_CASTER(Type,
+                         const_name("Dict[") + key_conv::name + const_name(", ") + value_conv::name
+                             + const_name("]"));
+};
+
+template <typename Type, typename Value>
+struct list_caster {
+    using value_conv = make_caster<Value>;
+
+    bool load(handle src, bool convert) {
+        if (!isinstance<sequence>(src) || isinstance<bytes>(src) || isinstance<str>(src)) {
+            return false;
+        }
+        auto s = reinterpret_borrow<sequence>(src);
+        value.clear();
+        reserve_maybe(s, &value);
+        for (auto it : s) {
+            value_conv conv;
+            if (!conv.load(it, convert)) {
+                return false;
+            }
+            value.push_back(cast_op<Value &&>(std::move(conv)));
+        }
+        return true;
+    }
+
+private:
+    template <
+        typename T = Type,
+        enable_if_t<std::is_same<decltype(std::declval<T>().reserve(0)), void>::value, int> = 0>
+    void reserve_maybe(const sequence &s, Type *) {
+        value.reserve(s.size());
+    }
+    void reserve_maybe(const sequence &, void *) {}
+
+public:
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        if (!std::is_lvalue_reference<T>::value) {
+            policy = return_value_policy_override<Value>::policy(policy);
+        }
+        list l(src.size());
+        ssize_t index = 0;
+        for (auto &&value : src) {
+            auto value_ = reinterpret_steal<object>(
+                value_conv::cast(forward_like<T>(value), policy, parent));
+            if (!value_) {
+                return handle();
+            }
+            PyList_SET_ITEM(l.ptr(), index++, value_.release().ptr()); // steals a reference
+        }
+        return l.release();
+    }
+
+    PYBIND11_TYPE_CASTER(Type, const_name("List[") + value_conv::name + const_name("]"));
+};
+
+template <typename Type, typename Alloc>
+struct type_caster<std::vector<Type, Alloc>> : list_caster<std::vector<Type, Alloc>, Type> {};
+
+template <typename Type, typename Alloc>
+struct type_caster<std::deque<Type, Alloc>> : list_caster<std::deque<Type, Alloc>, Type> {};
+
+template <typename Type, typename Alloc>
+struct type_caster<std::list<Type, Alloc>> : list_caster<std::list<Type, Alloc>, Type> {};
+
+template <typename ArrayType, typename Value, bool Resizable, size_t Size = 0>
+struct array_caster {
+    using value_conv = make_caster<Value>;
+
+private:
+    template <bool R = Resizable>
+    bool require_size(enable_if_t<R, size_t> size) {
+        if (value.size() != size) {
+            value.resize(size);
+        }
+        return true;
+    }
+    template <bool R = Resizable>
+    bool require_size(enable_if_t<!R, size_t> size) {
+        return size == Size;
+    }
+
+public:
+    bool load(handle src, bool convert) {
+        if (!isinstance<sequence>(src)) {
+            return false;
+        }
+        auto l = reinterpret_borrow<sequence>(src);
+        if (!require_size(l.size())) {
+            return false;
+        }
+        size_t ctr = 0;
+        for (auto it : l) {
+            value_conv conv;
+            if (!conv.load(it, convert)) {
+                return false;
+            }
+            value[ctr++] = cast_op<Value &&>(std::move(conv));
+        }
+        return true;
+    }
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        list l(src.size());
+        ssize_t index = 0;
+        for (auto &&value : src) {
+            auto value_ = reinterpret_steal<object>(
+                value_conv::cast(forward_like<T>(value), policy, parent));
+            if (!value_) {
+                return handle();
+            }
+            PyList_SET_ITEM(l.ptr(), index++, value_.release().ptr()); // steals a reference
+        }
+        return l.release();
+    }
+
+    PYBIND11_TYPE_CASTER(ArrayType,
+                         const_name("List[") + value_conv::name
+                             + const_name<Resizable>(const_name(""),
+                                                     const_name("[") + const_name<Size>()
+                                                         + const_name("]"))
+                             + const_name("]"));
+};
+
+template <typename Type, size_t Size>
+struct type_caster<std::array<Type, Size>>
+    : array_caster<std::array<Type, Size>, Type, false, Size> {};
+
+template <typename Type>
+struct type_caster<std::valarray<Type>> : array_caster<std::valarray<Type>, Type, true> {};
+
+template <typename Key, typename Compare, typename Alloc>
+struct type_caster<std::set<Key, Compare, Alloc>>
+    : set_caster<std::set<Key, Compare, Alloc>, Key> {};
+
+template <typename Key, typename Hash, typename Equal, typename Alloc>
+struct type_caster<std::unordered_set<Key, Hash, Equal, Alloc>>
+    : set_caster<std::unordered_set<Key, Hash, Equal, Alloc>, Key> {};
+
+template <typename Key, typename Value, typename Compare, typename Alloc>
+struct type_caster<std::map<Key, Value, Compare, Alloc>>
+    : map_caster<std::map<Key, Value, Compare, Alloc>, Key, Value> {};
+
+template <typename Key, typename Value, typename Hash, typename Equal, typename Alloc>
+struct type_caster<std::unordered_map<Key, Value, Hash, Equal, Alloc>>
+    : map_caster<std::unordered_map<Key, Value, Hash, Equal, Alloc>, Key, Value> {};
+
+// This type caster is intended to be used for std::optional and std::experimental::optional
+template <typename Type, typename Value = typename Type::value_type>
+struct optional_caster {
+    using value_conv = make_caster<Value>;
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        if (!src) {
+            return none().inc_ref();
+        }
+        if (!std::is_lvalue_reference<T>::value) {
+            policy = return_value_policy_override<Value>::policy(policy);
+        }
+        return value_conv::cast(*std::forward<T>(src), policy, parent);
+    }
+
+    bool load(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (src.is_none()) {
+            return true; // default-constructed value is already empty
+        }
+        value_conv inner_caster;
+        if (!inner_caster.load(src, convert)) {
+            return false;
+        }
+
+        value.emplace(cast_op<Value &&>(std::move(inner_caster)));
+        return true;
+    }
+
+    PYBIND11_TYPE_CASTER(Type, const_name("Optional[") + value_conv::name + const_name("]"));
+};
+
+#if defined(PYBIND11_HAS_OPTIONAL)
+template <typename T>
+struct type_caster<std::optional<T>> : public optional_caster<std::optional<T>> {};
+
+template <>
+struct type_caster<std::nullopt_t> : public void_caster<std::nullopt_t> {};
+#endif
+
+#if defined(PYBIND11_HAS_EXP_OPTIONAL)
+template <typename T>
+struct type_caster<std::experimental::optional<T>>
+    : public optional_caster<std::experimental::optional<T>> {};
+
+template <>
+struct type_caster<std::experimental::nullopt_t>
+    : public void_caster<std::experimental::nullopt_t> {};
+#endif
+
+/// Visit a variant and cast any found type to Python
+struct variant_caster_visitor {
+    return_value_policy policy;
+    handle parent;
+
+    using result_type = handle; // required by boost::variant in C++11
+
+    template <typename T>
+    result_type operator()(T &&src) const {
+        return make_caster<T>::cast(std::forward<T>(src), policy, parent);
+    }
+};
+
+/// Helper class which abstracts away variant's `visit` function. `std::variant` and similar
+/// `namespace::variant` types which provide a `namespace::visit()` function are handled here
+/// automatically using argument-dependent lookup. Users can provide specializations for other
+/// variant-like classes, e.g. `boost::variant` and `boost::apply_visitor`.
+template <template <typename...> class Variant>
+struct visit_helper {
+    template <typename... Args>
+    static auto call(Args &&...args) -> decltype(visit(std::forward<Args>(args)...)) {
+        return visit(std::forward<Args>(args)...);
+    }
+};
+
+/// Generic variant caster
+template <typename Variant>
+struct variant_caster;
+
+template <template <typename...> class V, typename... Ts>
+struct variant_caster<V<Ts...>> {
+    static_assert(sizeof...(Ts) > 0, "Variant must consist of at least one alternative.");
+
+    template <typename U, typename... Us>
+    bool load_alternative(handle src, bool convert, type_list<U, Us...>) {
+        auto caster = make_caster<U>();
+        if (caster.load(src, convert)) {
+            value = cast_op<U>(std::move(caster));
+            return true;
+        }
+        return load_alternative(src, convert, type_list<Us...>{});
+    }
+
+    bool load_alternative(handle, bool, type_list<>) { return false; }
+
+    bool load(handle src, bool convert) {
+        // Do a first pass without conversions to improve constructor resolution.
+        // E.g. `py::int_(1).cast<variant<double, int>>()` needs to fill the `int`
+        // slot of the variant. Without two-pass loading `double` would be filled
+        // because it appears first and a conversion is possible.
+        if (convert && load_alternative(src, false, type_list<Ts...>{})) {
+            return true;
+        }
+        return load_alternative(src, convert, type_list<Ts...>{});
+    }
+
+    template <typename Variant>
+    static handle cast(Variant &&src, return_value_policy policy, handle parent) {
+        return visit_helper<V>::call(variant_caster_visitor{policy, parent},
+                                     std::forward<Variant>(src));
+    }
+
+    using Type = V<Ts...>;
+    PYBIND11_TYPE_CASTER(Type,
+                         const_name("Union[") + detail::concat(make_caster<Ts>::name...)
+                             + const_name("]"));
+};
+
+#if defined(PYBIND11_HAS_VARIANT)
+template <typename... Ts>
+struct type_caster<std::variant<Ts...>> : variant_caster<std::variant<Ts...>> {};
+
+template <>
+struct type_caster<std::monostate> : public void_caster<std::monostate> {};
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+
+inline std::ostream &operator<<(std::ostream &os, const handle &obj) {
+#ifdef PYBIND11_HAS_STRING_VIEW
+    os << str(obj).cast<std::string_view>();
+#else
+    os << (std::string) str(obj);
+#endif
+    return os;
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl/filesystem.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl/filesystem.h
new file mode 100644
index 000000000..e26f42177
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl/filesystem.h
@@ -0,0 +1,116 @@
+// Copyright (c) 2021 The Pybind Development Team.
+// All rights reserved. Use of this source code is governed by a
+// BSD-style license that can be found in the LICENSE file.
+
+#pragma once
+
+#include "../pybind11.h"
+#include "../detail/common.h"
+#include "../detail/descr.h"
+#include "../cast.h"
+#include "../pytypes.h"
+
+#include <string>
+
+#ifdef __has_include
+#    if defined(PYBIND11_CPP17)
+#        if __has_include(<filesystem>) && \
+          PY_VERSION_HEX >= 0x03060000
+#            include <filesystem>
+#            define PYBIND11_HAS_FILESYSTEM 1
+#        elif __has_include(<experimental/filesystem>)
+#            include <experimental/filesystem>
+#            define PYBIND11_HAS_EXPERIMENTAL_FILESYSTEM 1
+#        endif
+#    endif
+#endif
+
+#if !defined(PYBIND11_HAS_FILESYSTEM) && !defined(PYBIND11_HAS_EXPERIMENTAL_FILESYSTEM)           \
+    && !defined(PYBIND11_HAS_FILESYSTEM_IS_OPTIONAL)
+#    error                                                                                        \
+        "Neither #include <filesystem> nor #include <experimental/filesystem is available. (Use -DPYBIND11_HAS_FILESYSTEM_IS_OPTIONAL to ignore.)"
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if defined(PYBIND11_HAS_FILESYSTEM) || defined(PYBIND11_HAS_EXPERIMENTAL_FILESYSTEM)
+template <typename T>
+struct path_caster {
+
+private:
+    static PyObject *unicode_from_fs_native(const std::string &w) {
+#    if !defined(PYPY_VERSION)
+        return PyUnicode_DecodeFSDefaultAndSize(w.c_str(), ssize_t(w.size()));
+#    else
+        // PyPy mistakenly declares the first parameter as non-const.
+        return PyUnicode_DecodeFSDefaultAndSize(const_cast<char *>(w.c_str()), ssize_t(w.size()));
+#    endif
+    }
+
+    static PyObject *unicode_from_fs_native(const std::wstring &w) {
+        return PyUnicode_FromWideChar(w.c_str(), ssize_t(w.size()));
+    }
+
+public:
+    static handle cast(const T &path, return_value_policy, handle) {
+        if (auto py_str = unicode_from_fs_native(path.native())) {
+            return module_::import("pathlib")
+                .attr("Path")(reinterpret_steal<object>(py_str))
+                .release();
+        }
+        return nullptr;
+    }
+
+    bool load(handle handle, bool) {
+        // PyUnicode_FSConverter and PyUnicode_FSDecoder normally take care of
+        // calling PyOS_FSPath themselves, but that's broken on PyPy (PyPy
+        // issue #3168) so we do it ourselves instead.
+        PyObject *buf = PyOS_FSPath(handle.ptr());
+        if (!buf) {
+            PyErr_Clear();
+            return false;
+        }
+        PyObject *native = nullptr;
+        if constexpr (std::is_same_v<typename T::value_type, char>) {
+            if (PyUnicode_FSConverter(buf, &native) != 0) {
+                if (auto *c_str = PyBytes_AsString(native)) {
+                    // AsString returns a pointer to the internal buffer, which
+                    // must not be free'd.
+                    value = c_str;
+                }
+            }
+        } else if constexpr (std::is_same_v<typename T::value_type, wchar_t>) {
+            if (PyUnicode_FSDecoder(buf, &native) != 0) {
+                if (auto *c_str = PyUnicode_AsWideCharString(native, nullptr)) {
+                    // AsWideCharString returns a new string that must be free'd.
+                    value = c_str; // Copies the string.
+                    PyMem_Free(c_str);
+                }
+            }
+        }
+        Py_XDECREF(native);
+        Py_DECREF(buf);
+        if (PyErr_Occurred()) {
+            PyErr_Clear();
+            return false;
+        }
+        return true;
+    }
+
+    PYBIND11_TYPE_CASTER(T, const_name("os.PathLike"));
+};
+
+#endif // PYBIND11_HAS_FILESYSTEM || defined(PYBIND11_HAS_EXPERIMENTAL_FILESYSTEM)
+
+#if defined(PYBIND11_HAS_FILESYSTEM)
+template <>
+struct type_caster<std::filesystem::path> : public path_caster<std::filesystem::path> {};
+#elif defined(PYBIND11_HAS_EXPERIMENTAL_FILESYSTEM)
+template <>
+struct type_caster<std::experimental::filesystem::path>
+    : public path_caster<std::experimental::filesystem::path> {};
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl_bind.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl_bind.h
new file mode 100644
index 000000000..22a29b476
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/include/pybind11/stl_bind.h
@@ -0,0 +1,785 @@
+/*
+    pybind11/std_bind.h: Binding generators for STL data types
+
+    Copyright (c) 2016 Sergey Lyskov and Wenzel Jakob
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "operators.h"
+
+#include <algorithm>
+#include <sstream>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/* SFINAE helper class used by 'is_comparable */
+template <typename T>
+struct container_traits {
+    template <typename T2>
+    static std::true_type
+    test_comparable(decltype(std::declval<const T2 &>() == std::declval<const T2 &>()) *);
+    template <typename T2>
+    static std::false_type test_comparable(...);
+    template <typename T2>
+    static std::true_type test_value(typename T2::value_type *);
+    template <typename T2>
+    static std::false_type test_value(...);
+    template <typename T2>
+    static std::true_type test_pair(typename T2::first_type *, typename T2::second_type *);
+    template <typename T2>
+    static std::false_type test_pair(...);
+
+    static constexpr const bool is_comparable
+        = std::is_same<std::true_type, decltype(test_comparable<T>(nullptr))>::value;
+    static constexpr const bool is_pair
+        = std::is_same<std::true_type, decltype(test_pair<T>(nullptr, nullptr))>::value;
+    static constexpr const bool is_vector
+        = std::is_same<std::true_type, decltype(test_value<T>(nullptr))>::value;
+    static constexpr const bool is_element = !is_pair && !is_vector;
+};
+
+/* Default: is_comparable -> std::false_type */
+template <typename T, typename SFINAE = void>
+struct is_comparable : std::false_type {};
+
+/* For non-map data structures, check whether operator== can be instantiated */
+template <typename T>
+struct is_comparable<
+    T,
+    enable_if_t<container_traits<T>::is_element && container_traits<T>::is_comparable>>
+    : std::true_type {};
+
+/* For a vector/map data structure, recursively check the value type
+   (which is std::pair for maps) */
+template <typename T>
+struct is_comparable<T, enable_if_t<container_traits<T>::is_vector>> {
+    static constexpr const bool value = is_comparable<typename T::value_type>::value;
+};
+
+/* For pairs, recursively check the two data types */
+template <typename T>
+struct is_comparable<T, enable_if_t<container_traits<T>::is_pair>> {
+    static constexpr const bool value = is_comparable<typename T::first_type>::value
+                                        && is_comparable<typename T::second_type>::value;
+};
+
+/* Fallback functions */
+template <typename, typename, typename... Args>
+void vector_if_copy_constructible(const Args &...) {}
+template <typename, typename, typename... Args>
+void vector_if_equal_operator(const Args &...) {}
+template <typename, typename, typename... Args>
+void vector_if_insertion_operator(const Args &...) {}
+template <typename, typename, typename... Args>
+void vector_modifiers(const Args &...) {}
+
+template <typename Vector, typename Class_>
+void vector_if_copy_constructible(enable_if_t<is_copy_constructible<Vector>::value, Class_> &cl) {
+    cl.def(init<const Vector &>(), "Copy constructor");
+}
+
+template <typename Vector, typename Class_>
+void vector_if_equal_operator(enable_if_t<is_comparable<Vector>::value, Class_> &cl) {
+    using T = typename Vector::value_type;
+
+    cl.def(self == self);
+    cl.def(self != self);
+
+    cl.def(
+        "count",
+        [](const Vector &v, const T &x) { return std::count(v.begin(), v.end(), x); },
+        arg("x"),
+        "Return the number of times ``x`` appears in the list");
+
+    cl.def(
+        "remove",
+        [](Vector &v, const T &x) {
+            auto p = std::find(v.begin(), v.end(), x);
+            if (p != v.end()) {
+                v.erase(p);
+            } else {
+                throw value_error();
+            }
+        },
+        arg("x"),
+        "Remove the first item from the list whose value is x. "
+        "It is an error if there is no such item.");
+
+    cl.def(
+        "__contains__",
+        [](const Vector &v, const T &x) { return std::find(v.begin(), v.end(), x) != v.end(); },
+        arg("x"),
+        "Return true the container contains ``x``");
+}
+
+// Vector modifiers -- requires a copyable vector_type:
+// (Technically, some of these (pop and __delitem__) don't actually require copyability, but it
+// seems silly to allow deletion but not insertion, so include them here too.)
+template <typename Vector, typename Class_>
+void vector_modifiers(
+    enable_if_t<is_copy_constructible<typename Vector::value_type>::value, Class_> &cl) {
+    using T = typename Vector::value_type;
+    using SizeType = typename Vector::size_type;
+    using DiffType = typename Vector::difference_type;
+
+    auto wrap_i = [](DiffType i, SizeType n) {
+        if (i < 0) {
+            i += n;
+        }
+        if (i < 0 || (SizeType) i >= n) {
+            throw index_error();
+        }
+        return i;
+    };
+
+    cl.def(
+        "append",
+        [](Vector &v, const T &value) { v.push_back(value); },
+        arg("x"),
+        "Add an item to the end of the list");
+
+    cl.def(init([](const iterable &it) {
+        auto v = std::unique_ptr<Vector>(new Vector());
+        v->reserve(len_hint(it));
+        for (handle h : it) {
+            v->push_back(h.cast<T>());
+        }
+        return v.release();
+    }));
+
+    cl.def(
+        "clear", [](Vector &v) { v.clear(); }, "Clear the contents");
+
+    cl.def(
+        "extend",
+        [](Vector &v, const Vector &src) { v.insert(v.end(), src.begin(), src.end()); },
+        arg("L"),
+        "Extend the list by appending all the items in the given list");
+
+    cl.def(
+        "extend",
+        [](Vector &v, const iterable &it) {
+            const size_t old_size = v.size();
+            v.reserve(old_size + len_hint(it));
+            try {
+                for (handle h : it) {
+                    v.push_back(h.cast<T>());
+                }
+            } catch (const cast_error &) {
+                v.erase(v.begin() + static_cast<typename Vector::difference_type>(old_size),
+                        v.end());
+                try {
+                    v.shrink_to_fit();
+                } catch (const std::exception &) {
+                    // Do nothing
+                }
+                throw;
+            }
+        },
+        arg("L"),
+        "Extend the list by appending all the items in the given list");
+
+    cl.def(
+        "insert",
+        [](Vector &v, DiffType i, const T &x) {
+            // Can't use wrap_i; i == v.size() is OK
+            if (i < 0) {
+                i += v.size();
+            }
+            if (i < 0 || (SizeType) i > v.size()) {
+                throw index_error();
+            }
+            v.insert(v.begin() + i, x);
+        },
+        arg("i"),
+        arg("x"),
+        "Insert an item at a given position.");
+
+    cl.def(
+        "pop",
+        [](Vector &v) {
+            if (v.empty()) {
+                throw index_error();
+            }
+            T t = std::move(v.back());
+            v.pop_back();
+            return t;
+        },
+        "Remove and return the last item");
+
+    cl.def(
+        "pop",
+        [wrap_i](Vector &v, DiffType i) {
+            i = wrap_i(i, v.size());
+            T t = std::move(v[(SizeType) i]);
+            v.erase(std::next(v.begin(), i));
+            return t;
+        },
+        arg("i"),
+        "Remove and return the item at index ``i``");
+
+    cl.def("__setitem__", [wrap_i](Vector &v, DiffType i, const T &t) {
+        i = wrap_i(i, v.size());
+        v[(SizeType) i] = t;
+    });
+
+    /// Slicing protocol
+    cl.def(
+        "__getitem__",
+        [](const Vector &v, const slice &slice) -> Vector * {
+            size_t start = 0, stop = 0, step = 0, slicelength = 0;
+
+            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength)) {
+                throw error_already_set();
+            }
+
+            auto *seq = new Vector();
+            seq->reserve((size_t) slicelength);
+
+            for (size_t i = 0; i < slicelength; ++i) {
+                seq->push_back(v[start]);
+                start += step;
+            }
+            return seq;
+        },
+        arg("s"),
+        "Retrieve list elements using a slice object");
+
+    cl.def(
+        "__setitem__",
+        [](Vector &v, const slice &slice, const Vector &value) {
+            size_t start = 0, stop = 0, step = 0, slicelength = 0;
+            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength)) {
+                throw error_already_set();
+            }
+
+            if (slicelength != value.size()) {
+                throw std::runtime_error(
+                    "Left and right hand size of slice assignment have different sizes!");
+            }
+
+            for (size_t i = 0; i < slicelength; ++i) {
+                v[start] = value[i];
+                start += step;
+            }
+        },
+        "Assign list elements using a slice object");
+
+    cl.def(
+        "__delitem__",
+        [wrap_i](Vector &v, DiffType i) {
+            i = wrap_i(i, v.size());
+            v.erase(v.begin() + i);
+        },
+        "Delete the list elements at index ``i``");
+
+    cl.def(
+        "__delitem__",
+        [](Vector &v, const slice &slice) {
+            size_t start = 0, stop = 0, step = 0, slicelength = 0;
+
+            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength)) {
+                throw error_already_set();
+            }
+
+            if (step == 1 && false) {
+                v.erase(v.begin() + (DiffType) start, v.begin() + DiffType(start + slicelength));
+            } else {
+                for (size_t i = 0; i < slicelength; ++i) {
+                    v.erase(v.begin() + DiffType(start));
+                    start += step - 1;
+                }
+            }
+        },
+        "Delete list elements using a slice object");
+}
+
+// If the type has an operator[] that doesn't return a reference (most notably std::vector<bool>),
+// we have to access by copying; otherwise we return by reference.
+template <typename Vector>
+using vector_needs_copy
+    = negation<std::is_same<decltype(std::declval<Vector>()[typename Vector::size_type()]),
+                            typename Vector::value_type &>>;
+
+// The usual case: access and iterate by reference
+template <typename Vector, typename Class_>
+void vector_accessor(enable_if_t<!vector_needs_copy<Vector>::value, Class_> &cl) {
+    using T = typename Vector::value_type;
+    using SizeType = typename Vector::size_type;
+    using DiffType = typename Vector::difference_type;
+    using ItType = typename Vector::iterator;
+
+    auto wrap_i = [](DiffType i, SizeType n) {
+        if (i < 0) {
+            i += n;
+        }
+        if (i < 0 || (SizeType) i >= n) {
+            throw index_error();
+        }
+        return i;
+    };
+
+    cl.def(
+        "__getitem__",
+        [wrap_i](Vector &v, DiffType i) -> T & {
+            i = wrap_i(i, v.size());
+            return v[(SizeType) i];
+        },
+        return_value_policy::reference_internal // ref + keepalive
+    );
+
+    cl.def(
+        "__iter__",
+        [](Vector &v) {
+            return make_iterator<return_value_policy::reference_internal, ItType, ItType, T &>(
+                v.begin(), v.end());
+        },
+        keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
+    );
+}
+
+// The case for special objects, like std::vector<bool>, that have to be returned-by-copy:
+template <typename Vector, typename Class_>
+void vector_accessor(enable_if_t<vector_needs_copy<Vector>::value, Class_> &cl) {
+    using T = typename Vector::value_type;
+    using SizeType = typename Vector::size_type;
+    using DiffType = typename Vector::difference_type;
+    using ItType = typename Vector::iterator;
+    cl.def("__getitem__", [](const Vector &v, DiffType i) -> T {
+        if (i < 0 && (i += v.size()) < 0) {
+            throw index_error();
+        }
+        if ((SizeType) i >= v.size()) {
+            throw index_error();
+        }
+        return v[(SizeType) i];
+    });
+
+    cl.def(
+        "__iter__",
+        [](Vector &v) {
+            return make_iterator<return_value_policy::copy, ItType, ItType, T>(v.begin(), v.end());
+        },
+        keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
+    );
+}
+
+template <typename Vector, typename Class_>
+auto vector_if_insertion_operator(Class_ &cl, std::string const &name)
+    -> decltype(std::declval<std::ostream &>() << std::declval<typename Vector::value_type>(),
+                void()) {
+    using size_type = typename Vector::size_type;
+
+    cl.def(
+        "__repr__",
+        [name](Vector &v) {
+            std::ostringstream s;
+            s << name << '[';
+            for (size_type i = 0; i < v.size(); ++i) {
+                s << v[i];
+                if (i != v.size() - 1) {
+                    s << ", ";
+                }
+            }
+            s << ']';
+            return s.str();
+        },
+        "Return the canonical string representation of this list.");
+}
+
+// Provide the buffer interface for vectors if we have data() and we have a format for it
+// GCC seems to have "void std::vector<bool>::data()" - doing SFINAE on the existence of data()
+// is insufficient, we need to check it returns an appropriate pointer
+template <typename Vector, typename = void>
+struct vector_has_data_and_format : std::false_type {};
+template <typename Vector>
+struct vector_has_data_and_format<
+    Vector,
+    enable_if_t<std::is_same<decltype(format_descriptor<typename Vector::value_type>::format(),
+                                      std::declval<Vector>().data()),
+                             typename Vector::value_type *>::value>> : std::true_type {};
+
+// [workaround(intel)] Separate function required here
+// Workaround as the Intel compiler does not compile the enable_if_t part below
+// (tested with icc (ICC) 2021.1 Beta 20200827)
+template <typename... Args>
+constexpr bool args_any_are_buffer() {
+    return detail::any_of<std::is_same<Args, buffer_protocol>...>::value;
+}
+
+// [workaround(intel)] Separate function required here
+// [workaround(msvc)] Can't use constexpr bool in return type
+
+// Add the buffer interface to a vector
+template <typename Vector, typename Class_, typename... Args>
+void vector_buffer_impl(Class_ &cl, std::true_type) {
+    using T = typename Vector::value_type;
+
+    static_assert(vector_has_data_and_format<Vector>::value,
+                  "There is not an appropriate format descriptor for this vector");
+
+    // numpy.h declares this for arbitrary types, but it may raise an exception and crash hard
+    // at runtime if PYBIND11_NUMPY_DTYPE hasn't been called, so check here
+    format_descriptor<T>::format();
+
+    cl.def_buffer([](Vector &v) -> buffer_info {
+        return buffer_info(v.data(),
+                           static_cast<ssize_t>(sizeof(T)),
+                           format_descriptor<T>::format(),
+                           1,
+                           {v.size()},
+                           {sizeof(T)});
+    });
+
+    cl.def(init([](const buffer &buf) {
+        auto info = buf.request();
+        if (info.ndim != 1 || info.strides[0] % static_cast<ssize_t>(sizeof(T))) {
+            throw type_error("Only valid 1D buffers can be copied to a vector");
+        }
+        if (!detail::compare_buffer_info<T>::compare(info)
+            || (ssize_t) sizeof(T) != info.itemsize) {
+            throw type_error("Format mismatch (Python: " + info.format
+                             + " C++: " + format_descriptor<T>::format() + ")");
+        }
+
+        T *p = static_cast<T *>(info.ptr);
+        ssize_t step = info.strides[0] / static_cast<ssize_t>(sizeof(T));
+        T *end = p + info.shape[0] * step;
+        if (step == 1) {
+            return Vector(p, end);
+        }
+        Vector vec;
+        vec.reserve((size_t) info.shape[0]);
+        for (; p != end; p += step) {
+            vec.push_back(*p);
+        }
+        return vec;
+    }));
+
+    return;
+}
+
+template <typename Vector, typename Class_, typename... Args>
+void vector_buffer_impl(Class_ &, std::false_type) {}
+
+template <typename Vector, typename Class_, typename... Args>
+void vector_buffer(Class_ &cl) {
+    vector_buffer_impl<Vector, Class_, Args...>(
+        cl, detail::any_of<std::is_same<Args, buffer_protocol>...>{});
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+//
+// std::vector
+//
+template <typename Vector, typename holder_type = std::unique_ptr<Vector>, typename... Args>
+class_<Vector, holder_type> bind_vector(handle scope, std::string const &name, Args &&...args) {
+    using Class_ = class_<Vector, holder_type>;
+
+    // If the value_type is unregistered (e.g. a converting type) or is itself registered
+    // module-local then make the vector binding module-local as well:
+    using vtype = typename Vector::value_type;
+    auto *vtype_info = detail::get_type_info(typeid(vtype));
+    bool local = !vtype_info || vtype_info->module_local;
+
+    Class_ cl(scope, name.c_str(), pybind11::module_local(local), std::forward<Args>(args)...);
+
+    // Declare the buffer interface if a buffer_protocol() is passed in
+    detail::vector_buffer<Vector, Class_, Args...>(cl);
+
+    cl.def(init<>());
+
+    // Register copy constructor (if possible)
+    detail::vector_if_copy_constructible<Vector, Class_>(cl);
+
+    // Register comparison-related operators and functions (if possible)
+    detail::vector_if_equal_operator<Vector, Class_>(cl);
+
+    // Register stream insertion operator (if possible)
+    detail::vector_if_insertion_operator<Vector, Class_>(cl, name);
+
+    // Modifiers require copyable vector value type
+    detail::vector_modifiers<Vector, Class_>(cl);
+
+    // Accessor and iterator; return by value if copyable, otherwise we return by ref + keep-alive
+    detail::vector_accessor<Vector, Class_>(cl);
+
+    cl.def(
+        "__bool__",
+        [](const Vector &v) -> bool { return !v.empty(); },
+        "Check whether the list is nonempty");
+
+    cl.def("__len__", &Vector::size);
+
+#if 0
+    // C++ style functions deprecated, leaving it here as an example
+    cl.def(init<size_type>());
+
+    cl.def("resize",
+         (void (Vector::*) (size_type count)) & Vector::resize,
+         "changes the number of elements stored");
+
+    cl.def("erase",
+        [](Vector &v, SizeType i) {
+        if (i >= v.size())
+            throw index_error();
+        v.erase(v.begin() + i);
+    }, "erases element at index ``i``");
+
+    cl.def("empty",         &Vector::empty,         "checks whether the container is empty");
+    cl.def("size",          &Vector::size,          "returns the number of elements");
+    cl.def("push_back", (void (Vector::*)(const T&)) &Vector::push_back, "adds an element to the end");
+    cl.def("pop_back",                               &Vector::pop_back, "removes the last element");
+
+    cl.def("max_size",      &Vector::max_size,      "returns the maximum possible number of elements");
+    cl.def("reserve",       &Vector::reserve,       "reserves storage");
+    cl.def("capacity",      &Vector::capacity,      "returns the number of elements that can be held in currently allocated storage");
+    cl.def("shrink_to_fit", &Vector::shrink_to_fit, "reduces memory usage by freeing unused memory");
+
+    cl.def("clear", &Vector::clear, "clears the contents");
+    cl.def("swap",   &Vector::swap, "swaps the contents");
+
+    cl.def("front", [](Vector &v) {
+        if (v.size()) return v.front();
+        else throw index_error();
+    }, "access the first element");
+
+    cl.def("back", [](Vector &v) {
+        if (v.size()) return v.back();
+        else throw index_error();
+    }, "access the last element ");
+
+#endif
+
+    return cl;
+}
+
+//
+// std::map, std::unordered_map
+//
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/* Fallback functions */
+template <typename, typename, typename... Args>
+void map_if_insertion_operator(const Args &...) {}
+template <typename, typename, typename... Args>
+void map_assignment(const Args &...) {}
+
+// Map assignment when copy-assignable: just copy the value
+template <typename Map, typename Class_>
+void map_assignment(
+    enable_if_t<is_copy_assignable<typename Map::mapped_type>::value, Class_> &cl) {
+    using KeyType = typename Map::key_type;
+    using MappedType = typename Map::mapped_type;
+
+    cl.def("__setitem__", [](Map &m, const KeyType &k, const MappedType &v) {
+        auto it = m.find(k);
+        if (it != m.end()) {
+            it->second = v;
+        } else {
+            m.emplace(k, v);
+        }
+    });
+}
+
+// Not copy-assignable, but still copy-constructible: we can update the value by erasing and
+// reinserting
+template <typename Map, typename Class_>
+void map_assignment(enable_if_t<!is_copy_assignable<typename Map::mapped_type>::value
+                                    && is_copy_constructible<typename Map::mapped_type>::value,
+                                Class_> &cl) {
+    using KeyType = typename Map::key_type;
+    using MappedType = typename Map::mapped_type;
+
+    cl.def("__setitem__", [](Map &m, const KeyType &k, const MappedType &v) {
+        // We can't use m[k] = v; because value type might not be default constructable
+        auto r = m.emplace(k, v);
+        if (!r.second) {
+            // value type is not copy assignable so the only way to insert it is to erase it
+            // first...
+            m.erase(r.first);
+            m.emplace(k, v);
+        }
+    });
+}
+
+template <typename Map, typename Class_>
+auto map_if_insertion_operator(Class_ &cl, std::string const &name)
+    -> decltype(std::declval<std::ostream &>() << std::declval<typename Map::key_type>()
+                                               << std::declval<typename Map::mapped_type>(),
+                void()) {
+
+    cl.def(
+        "__repr__",
+        [name](Map &m) {
+            std::ostringstream s;
+            s << name << '{';
+            bool f = false;
+            for (auto const &kv : m) {
+                if (f) {
+                    s << ", ";
+                }
+                s << kv.first << ": " << kv.second;
+                f = true;
+            }
+            s << '}';
+            return s.str();
+        },
+        "Return the canonical string representation of this map.");
+}
+
+template <typename Map>
+struct keys_view {
+    Map &map;
+};
+
+template <typename Map>
+struct values_view {
+    Map &map;
+};
+
+template <typename Map>
+struct items_view {
+    Map &map;
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+template <typename Map, typename holder_type = std::unique_ptr<Map>, typename... Args>
+class_<Map, holder_type> bind_map(handle scope, const std::string &name, Args &&...args) {
+    using KeyType = typename Map::key_type;
+    using MappedType = typename Map::mapped_type;
+    using KeysView = detail::keys_view<Map>;
+    using ValuesView = detail::values_view<Map>;
+    using ItemsView = detail::items_view<Map>;
+    using Class_ = class_<Map, holder_type>;
+
+    // If either type is a non-module-local bound type then make the map binding non-local as well;
+    // otherwise (e.g. both types are either module-local or converting) the map will be
+    // module-local.
+    auto *tinfo = detail::get_type_info(typeid(MappedType));
+    bool local = !tinfo || tinfo->module_local;
+    if (local) {
+        tinfo = detail::get_type_info(typeid(KeyType));
+        local = !tinfo || tinfo->module_local;
+    }
+
+    Class_ cl(scope, name.c_str(), pybind11::module_local(local), std::forward<Args>(args)...);
+    class_<KeysView> keys_view(
+        scope, ("KeysView[" + name + "]").c_str(), pybind11::module_local(local));
+    class_<ValuesView> values_view(
+        scope, ("ValuesView[" + name + "]").c_str(), pybind11::module_local(local));
+    class_<ItemsView> items_view(
+        scope, ("ItemsView[" + name + "]").c_str(), pybind11::module_local(local));
+
+    cl.def(init<>());
+
+    // Register stream insertion operator (if possible)
+    detail::map_if_insertion_operator<Map, Class_>(cl, name);
+
+    cl.def(
+        "__bool__",
+        [](const Map &m) -> bool { return !m.empty(); },
+        "Check whether the map is nonempty");
+
+    cl.def(
+        "__iter__",
+        [](Map &m) { return make_key_iterator(m.begin(), m.end()); },
+        keep_alive<0, 1>() /* Essential: keep map alive while iterator exists */
+    );
+
+    cl.def(
+        "keys",
+        [](Map &m) { return KeysView{m}; },
+        keep_alive<0, 1>() /* Essential: keep map alive while view exists */
+    );
+
+    cl.def(
+        "values",
+        [](Map &m) { return ValuesView{m}; },
+        keep_alive<0, 1>() /* Essential: keep map alive while view exists */
+    );
+
+    cl.def(
+        "items",
+        [](Map &m) { return ItemsView{m}; },
+        keep_alive<0, 1>() /* Essential: keep map alive while view exists */
+    );
+
+    cl.def(
+        "__getitem__",
+        [](Map &m, const KeyType &k) -> MappedType & {
+            auto it = m.find(k);
+            if (it == m.end()) {
+                throw key_error();
+            }
+            return it->second;
+        },
+        return_value_policy::reference_internal // ref + keepalive
+    );
+
+    cl.def("__contains__", [](Map &m, const KeyType &k) -> bool {
+        auto it = m.find(k);
+        if (it == m.end()) {
+            return false;
+        }
+        return true;
+    });
+    // Fallback for when the object is not of the key type
+    cl.def("__contains__", [](Map &, const object &) -> bool { return false; });
+
+    // Assignment provided only if the type is copyable
+    detail::map_assignment<Map, Class_>(cl);
+
+    cl.def("__delitem__", [](Map &m, const KeyType &k) {
+        auto it = m.find(k);
+        if (it == m.end()) {
+            throw key_error();
+        }
+        m.erase(it);
+    });
+
+    cl.def("__len__", &Map::size);
+
+    keys_view.def("__len__", [](KeysView &view) { return view.map.size(); });
+    keys_view.def(
+        "__iter__",
+        [](KeysView &view) { return make_key_iterator(view.map.begin(), view.map.end()); },
+        keep_alive<0, 1>() /* Essential: keep view alive while iterator exists */
+    );
+    keys_view.def("__contains__", [](KeysView &view, const KeyType &k) -> bool {
+        auto it = view.map.find(k);
+        if (it == view.map.end()) {
+            return false;
+        }
+        return true;
+    });
+    // Fallback for when the object is not of the key type
+    keys_view.def("__contains__", [](KeysView &, const object &) -> bool { return false; });
+
+    values_view.def("__len__", [](ValuesView &view) { return view.map.size(); });
+    values_view.def(
+        "__iter__",
+        [](ValuesView &view) { return make_value_iterator(view.map.begin(), view.map.end()); },
+        keep_alive<0, 1>() /* Essential: keep view alive while iterator exists */
+    );
+
+    items_view.def("__len__", [](ItemsView &view) { return view.map.size(); });
+    items_view.def(
+        "__iter__",
+        [](ItemsView &view) { return make_iterator(view.map.begin(), view.map.end()); },
+        keep_alive<0, 1>() /* Essential: keep view alive while iterator exists */
+    );
+
+    return cl;
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/noxfile.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/noxfile.py
new file mode 100644
index 000000000..fe36d70fe
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/noxfile.py
@@ -0,0 +1,97 @@
+import os
+
+import nox
+
+nox.needs_version = ">=2022.1.7"
+nox.options.sessions = ["lint", "tests", "tests_packaging"]
+
+PYTHON_VERISONS = ["3.6", "3.7", "3.8", "3.9", "3.10", "3.11", "pypy3.7", "pypy3.8"]
+
+if os.environ.get("CI", None):
+    nox.options.error_on_missing_interpreters = True
+
+
+@nox.session(reuse_venv=True)
+def lint(session: nox.Session) -> None:
+    """
+    Lint the codebase (except for clang-format/tidy).
+    """
+    session.install("pre-commit")
+    session.run("pre-commit", "run", "-a")
+
+
+@nox.session(python=PYTHON_VERISONS)
+def tests(session: nox.Session) -> None:
+    """
+    Run the tests (requires a compiler).
+    """
+    tmpdir = session.create_tmp()
+    session.install("cmake")
+    session.install("-r", "tests/requirements.txt")
+    session.run(
+        "cmake",
+        "-S.",
+        f"-B{tmpdir}",
+        "-DPYBIND11_WERROR=ON",
+        "-DDOWNLOAD_CATCH=ON",
+        "-DDOWNLOAD_EIGEN=ON",
+        *session.posargs,
+    )
+    session.run("cmake", "--build", tmpdir)
+    session.run("cmake", "--build", tmpdir, "--config=Release", "--target", "check")
+
+
+@nox.session
+def tests_packaging(session: nox.Session) -> None:
+    """
+    Run the packaging tests.
+    """
+
+    session.install("-r", "tests/requirements.txt", "--prefer-binary")
+    session.run("pytest", "tests/extra_python_package")
+
+
+@nox.session(reuse_venv=True)
+def docs(session: nox.Session) -> None:
+    """
+    Build the docs. Pass "serve" to serve.
+    """
+
+    session.install("-r", "docs/requirements.txt")
+    session.chdir("docs")
+
+    if "pdf" in session.posargs:
+        session.run("sphinx-build", "-M", "latexpdf", ".", "_build")
+        return
+
+    session.run("sphinx-build", "-M", "html", ".", "_build")
+
+    if "serve" in session.posargs:
+        session.log("Launching docs at http://localhost:8000/ - use Ctrl-C to quit")
+        session.run("python", "-m", "http.server", "8000", "-d", "_build/html")
+    elif session.posargs:
+        session.error("Unsupported argument to docs")
+
+
+@nox.session(reuse_venv=True)
+def make_changelog(session: nox.Session) -> None:
+    """
+    Inspect the closed issues and make entries for a changelog.
+    """
+    session.install("ghapi", "rich")
+    session.run("python", "tools/make_changelog.py")
+
+
+@nox.session(reuse_venv=True)
+def build(session: nox.Session) -> None:
+    """
+    Build SDists and wheels.
+    """
+
+    session.install("build")
+    session.log("Building normal files")
+    session.run("python", "-m", "build", *session.posargs)
+    session.log("Building pybind11-global files (PYBIND11_GLOBAL_SDIST=1)")
+    session.run(
+        "python", "-m", "build", *session.posargs, env={"PYBIND11_GLOBAL_SDIST": "1"}
+    )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/__init__.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/__init__.py
new file mode 100644
index 000000000..a1be96981
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/__init__.py
@@ -0,0 +1,16 @@
+import sys
+
+if sys.version_info < (3, 6):
+    msg = "pybind11 does not support Python < 3.6. 2.9 was the last release supporting Python 2.7 and 3.5."
+    raise ImportError(msg)
+
+
+from ._version import __version__, version_info
+from .commands import get_cmake_dir, get_include
+
+__all__ = (
+    "version_info",
+    "__version__",
+    "get_include",
+    "get_cmake_dir",
+)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/__main__.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/__main__.py
new file mode 100644
index 000000000..22fc4471e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/__main__.py
@@ -0,0 +1,49 @@
+# pylint: disable=missing-function-docstring
+
+import argparse
+import sys
+import sysconfig
+
+from .commands import get_cmake_dir, get_include
+
+
+def print_includes() -> None:
+    dirs = [
+        sysconfig.get_path("include"),
+        sysconfig.get_path("platinclude"),
+        get_include(),
+    ]
+
+    # Make unique but preserve order
+    unique_dirs = []
+    for d in dirs:
+        if d and d not in unique_dirs:
+            unique_dirs.append(d)
+
+    print(" ".join("-I" + d for d in unique_dirs))
+
+
+def main() -> None:
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--includes",
+        action="store_true",
+        help="Include flags for both pybind11 and Python headers.",
+    )
+    parser.add_argument(
+        "--cmakedir",
+        action="store_true",
+        help="Print the CMake module directory, ideal for setting -Dpybind11_ROOT in CMake.",
+    )
+    args = parser.parse_args()
+    if not sys.argv[1:]:
+        parser.print_help()
+    if args.includes:
+        print_includes()
+    if args.cmakedir:
+        print(get_cmake_dir())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/_version.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/_version.py
new file mode 100644
index 000000000..b78d5963d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/_version.py
@@ -0,0 +1,12 @@
+from typing import Union
+
+
+def _to_int(s: str) -> Union[int, str]:
+    try:
+        return int(s)
+    except ValueError:
+        return s
+
+
+__version__ = "2.10.0"
+version_info = tuple(_to_int(s) for s in __version__.split("."))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/commands.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/commands.py
new file mode 100644
index 000000000..a29c8ca61
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/commands.py
@@ -0,0 +1,25 @@
+import os
+
+DIR = os.path.abspath(os.path.dirname(__file__))
+
+
+def get_include(user: bool = False) -> str:  # pylint: disable=unused-argument
+    """
+    Return the path to the pybind11 include directory. The historical "user"
+    argument is unused, and may be removed.
+    """
+    installed_path = os.path.join(DIR, "include")
+    source_path = os.path.join(os.path.dirname(DIR), "include")
+    return installed_path if os.path.exists(installed_path) else source_path
+
+
+def get_cmake_dir() -> str:
+    """
+    Return the path to the pybind11 CMake module directory.
+    """
+    cmake_installed_path = os.path.join(DIR, "share", "cmake", "pybind11")
+    if os.path.exists(cmake_installed_path):
+        return cmake_installed_path
+
+    msg = "pybind11 not installed, installation required to access the CMake files"
+    raise ImportError(msg)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/py.typed b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/py.typed
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/setup_helpers.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/setup_helpers.py
new file mode 100644
index 000000000..1fd04b915
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pybind11/setup_helpers.py
@@ -0,0 +1,504 @@
+"""
+This module provides helpers for C++11+ projects using pybind11.
+
+LICENSE:
+
+Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>, All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+# IMPORTANT: If you change this file in the pybind11 repo, also review
+# setup_helpers.pyi for matching changes.
+#
+# If you copy this file in, you don't
+# need the .pyi file; it's just an interface file for static type checkers.
+
+import contextlib
+import os
+import platform
+import shlex
+import shutil
+import sys
+import sysconfig
+import tempfile
+import threading
+import warnings
+from functools import lru_cache
+from pathlib import Path
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    TypeVar,
+    Union,
+)
+
+try:
+    from setuptools import Extension as _Extension
+    from setuptools.command.build_ext import build_ext as _build_ext
+except ImportError:
+    from distutils.command.build_ext import build_ext as _build_ext
+    from distutils.extension import Extension as _Extension
+
+import distutils.ccompiler
+import distutils.errors
+
+WIN = sys.platform.startswith("win32") and "mingw" not in sysconfig.get_platform()
+MACOS = sys.platform.startswith("darwin")
+STD_TMPL = "/std:c++{}" if WIN else "-std=c++{}"
+
+
+# It is recommended to use PEP 518 builds if using this module. However, this
+# file explicitly supports being copied into a user's project directory
+# standalone, and pulling pybind11 with the deprecated setup_requires feature.
+# If you copy the file, remember to add it to your MANIFEST.in, and add the current
+# directory into your path if it sits beside your setup.py.
+
+
+class Pybind11Extension(_Extension):  # type: ignore[misc]
+    """
+    Build a C++11+ Extension module with pybind11. This automatically adds the
+    recommended flags when you init the extension and assumes C++ sources - you
+    can further modify the options yourself.
+
+    The customizations are:
+
+    * ``/EHsc`` and ``/bigobj`` on Windows
+    * ``stdlib=libc++`` on macOS
+    * ``visibility=hidden`` and ``-g0`` on Unix
+
+    Finally, you can set ``cxx_std`` via constructor or afterwards to enable
+    flags for C++ std, and a few extra helper flags related to the C++ standard
+    level. It is _highly_ recommended you either set this, or use the provided
+    ``build_ext``, which will search for the highest supported extension for
+    you if the ``cxx_std`` property is not set. Do not set the ``cxx_std``
+    property more than once, as flags are added when you set it. Set the
+    property to None to disable the addition of C++ standard flags.
+
+    If you want to add pybind11 headers manually, for example for an exact
+    git checkout, then set ``include_pybind11=False``.
+    """
+
+    # flags are prepended, so that they can be further overridden, e.g. by
+    # ``extra_compile_args=["-g"]``.
+
+    def _add_cflags(self, flags: List[str]) -> None:
+        self.extra_compile_args[:0] = flags
+
+    def _add_ldflags(self, flags: List[str]) -> None:
+        self.extra_link_args[:0] = flags
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+
+        self._cxx_level = 0
+        cxx_std = kwargs.pop("cxx_std", 0)
+
+        if "language" not in kwargs:
+            kwargs["language"] = "c++"
+
+        include_pybind11 = kwargs.pop("include_pybind11", True)
+
+        super().__init__(*args, **kwargs)
+
+        # Include the installed package pybind11 headers
+        if include_pybind11:
+            # If using setup_requires, this fails the first time - that's okay
+            try:
+                import pybind11
+
+                pyinc = pybind11.get_include()
+
+                if pyinc not in self.include_dirs:
+                    self.include_dirs.append(pyinc)
+            except ModuleNotFoundError:
+                pass
+
+        self.cxx_std = cxx_std
+
+        cflags = []
+        ldflags = []
+        if WIN:
+            cflags += ["/EHsc", "/bigobj"]
+        else:
+            cflags += ["-fvisibility=hidden"]
+            env_cflags = os.environ.get("CFLAGS", "")
+            env_cppflags = os.environ.get("CPPFLAGS", "")
+            c_cpp_flags = shlex.split(env_cflags) + shlex.split(env_cppflags)
+            if not any(opt.startswith("-g") for opt in c_cpp_flags):
+                cflags += ["-g0"]
+            if MACOS:
+                cflags += ["-stdlib=libc++"]
+                ldflags += ["-stdlib=libc++"]
+        self._add_cflags(cflags)
+        self._add_ldflags(ldflags)
+
+    @property
+    def cxx_std(self) -> int:
+        """
+        The CXX standard level. If set, will add the required flags. If left at
+        0, it will trigger an automatic search when pybind11's build_ext is
+        used. If None, will have no effect.  Besides just the flags, this may
+        add a macos-min 10.9 or 10.14 flag if MACOSX_DEPLOYMENT_TARGET is
+        unset.
+        """
+        return self._cxx_level
+
+    @cxx_std.setter
+    def cxx_std(self, level: int) -> None:
+
+        if self._cxx_level:
+            warnings.warn("You cannot safely change the cxx_level after setting it!")
+
+        # MSVC 2015 Update 3 and later only have 14 (and later 17) modes, so
+        # force a valid flag here.
+        if WIN and level == 11:
+            level = 14
+
+        self._cxx_level = level
+
+        if not level:
+            return
+
+        cflags = [STD_TMPL.format(level)]
+        ldflags = []
+
+        if MACOS and "MACOSX_DEPLOYMENT_TARGET" not in os.environ:
+            # C++17 requires a higher min version of macOS. An earlier version
+            # (10.12 or 10.13) can be set manually via environment variable if
+            # you are careful in your feature usage, but 10.14 is the safest
+            # setting for general use. However, never set higher than the
+            # current macOS version!
+            current_macos = tuple(int(x) for x in platform.mac_ver()[0].split(".")[:2])
+            desired_macos = (10, 9) if level < 17 else (10, 14)
+            macos_string = ".".join(str(x) for x in min(current_macos, desired_macos))
+            macosx_min = f"-mmacosx-version-min={macos_string}"
+            cflags += [macosx_min]
+            ldflags += [macosx_min]
+
+        self._add_cflags(cflags)
+        self._add_ldflags(ldflags)
+
+
+# Just in case someone clever tries to multithread
+tmp_chdir_lock = threading.Lock()
+
+
+@contextlib.contextmanager
+def tmp_chdir() -> Iterator[str]:
+    "Prepare and enter a temporary directory, cleanup when done"
+
+    # Threadsafe
+    with tmp_chdir_lock:
+        olddir = os.getcwd()
+        try:
+            tmpdir = tempfile.mkdtemp()
+            os.chdir(tmpdir)
+            yield tmpdir
+        finally:
+            os.chdir(olddir)
+            shutil.rmtree(tmpdir)
+
+
+# cf http://bugs.python.org/issue26689
+def has_flag(compiler: Any, flag: str) -> bool:
+    """
+    Return the flag if a flag name is supported on the
+    specified compiler, otherwise None (can be used as a boolean).
+    If multiple flags are passed, return the first that matches.
+    """
+
+    with tmp_chdir():
+        fname = Path("flagcheck.cpp")
+        # Don't trigger -Wunused-parameter.
+        fname.write_text("int main (int, char **) { return 0; }", encoding="utf-8")
+
+        try:
+            compiler.compile([str(fname)], extra_postargs=[flag])
+        except distutils.errors.CompileError:
+            return False
+        return True
+
+
+# Every call will cache the result
+cpp_flag_cache = None
+
+
+@lru_cache()
+def auto_cpp_level(compiler: Any) -> Union[str, int]:
+    """
+    Return the max supported C++ std level (17, 14, or 11). Returns latest on Windows.
+    """
+
+    if WIN:
+        return "latest"
+
+    levels = [17, 14, 11]
+
+    for level in levels:
+        if has_flag(compiler, STD_TMPL.format(level)):
+            return level
+
+    msg = "Unsupported compiler -- at least C++11 support is needed!"
+    raise RuntimeError(msg)
+
+
+class build_ext(_build_ext):  # type: ignore[misc] # noqa: N801
+    """
+    Customized build_ext that allows an auto-search for the highest supported
+    C++ level for Pybind11Extension. This is only needed for the auto-search
+    for now, and is completely optional otherwise.
+    """
+
+    def build_extensions(self) -> None:
+        """
+        Build extensions, injecting C++ std for Pybind11Extension if needed.
+        """
+
+        for ext in self.extensions:
+            if hasattr(ext, "_cxx_level") and ext._cxx_level == 0:
+                ext.cxx_std = auto_cpp_level(self.compiler)
+
+        super().build_extensions()
+
+
+def intree_extensions(
+    paths: Iterable[str], package_dir: Optional[Dict[str, str]] = None
+) -> List[Pybind11Extension]:
+    """
+    Generate Pybind11Extensions from source files directly located in a Python
+    source tree.
+
+    ``package_dir`` behaves as in ``setuptools.setup``.  If unset, the Python
+    package root parent is determined as the first parent directory that does
+    not contain an ``__init__.py`` file.
+    """
+    exts = []
+
+    if package_dir is None:
+        for path in paths:
+            parent, _ = os.path.split(path)
+            while os.path.exists(os.path.join(parent, "__init__.py")):
+                parent, _ = os.path.split(parent)
+            relname, _ = os.path.splitext(os.path.relpath(path, parent))
+            qualified_name = relname.replace(os.path.sep, ".")
+            exts.append(Pybind11Extension(qualified_name, [path]))
+        return exts
+
+    for path in paths:
+        for prefix, parent in package_dir.items():
+            if path.startswith(parent):
+                relname, _ = os.path.splitext(os.path.relpath(path, parent))
+                qualified_name = relname.replace(os.path.sep, ".")
+                if prefix:
+                    qualified_name = prefix + "." + qualified_name
+                exts.append(Pybind11Extension(qualified_name, [path]))
+                break
+        else:
+            msg = (
+                f"path {path} is not a child of any of the directories listed "
+                f"in 'package_dir' ({package_dir})"
+            )
+            raise ValueError(msg)
+
+    return exts
+
+
+def naive_recompile(obj: str, src: str) -> bool:
+    """
+    This will recompile only if the source file changes. It does not check
+    header files, so a more advanced function or Ccache is better if you have
+    editable header files in your package.
+    """
+    return os.stat(obj).st_mtime < os.stat(src).st_mtime
+
+
+def no_recompile(obg: str, src: str) -> bool:  # pylint: disable=unused-argument
+    """
+    This is the safest but slowest choice (and is the default) - will always
+    recompile sources.
+    """
+    return True
+
+
+S = TypeVar("S", bound="ParallelCompile")
+
+CCompilerMethod = Callable[
+    [
+        distutils.ccompiler.CCompiler,
+        List[str],
+        Optional[str],
+        Optional[Union[Tuple[str], Tuple[str, Optional[str]]]],
+        Optional[List[str]],
+        bool,
+        Optional[List[str]],
+        Optional[List[str]],
+        Optional[List[str]],
+    ],
+    List[str],
+]
+
+
+# Optional parallel compile utility
+# inspired by: http://stackoverflow.com/questions/11013851/speeding-up-build-process-with-distutils
+# and: https://github.com/tbenthompson/cppimport/blob/stable/cppimport/build_module.py
+# and NumPy's parallel distutils module:
+#              https://github.com/numpy/numpy/blob/master/numpy/distutils/ccompiler.py
+class ParallelCompile:
+    """
+    Make a parallel compile function. Inspired by
+    numpy.distutils.ccompiler.CCompiler.compile and cppimport.
+
+    This takes several arguments that allow you to customize the compile
+    function created:
+
+    envvar:
+        Set an environment variable to control the compilation threads, like
+        NPY_NUM_BUILD_JOBS
+    default:
+        0 will automatically multithread, or 1 will only multithread if the
+        envvar is set.
+    max:
+        The limit for automatic multithreading if non-zero
+    needs_recompile:
+        A function of (obj, src) that returns True when recompile is needed.  No
+        effect in isolated mode; use ccache instead, see
+        https://github.com/matplotlib/matplotlib/issues/1507/
+
+    To use::
+
+        ParallelCompile("NPY_NUM_BUILD_JOBS").install()
+
+    or::
+
+        with ParallelCompile("NPY_NUM_BUILD_JOBS"):
+            setup(...)
+
+    By default, this assumes all files need to be recompiled. A smarter
+    function can be provided via needs_recompile.  If the output has not yet
+    been generated, the compile will always run, and this function is not
+    called.
+    """
+
+    __slots__ = ("envvar", "default", "max", "_old", "needs_recompile")
+
+    def __init__(
+        self,
+        envvar: Optional[str] = None,
+        default: int = 0,
+        max: int = 0,  # pylint: disable=redefined-builtin
+        needs_recompile: Callable[[str, str], bool] = no_recompile,
+    ) -> None:
+        self.envvar = envvar
+        self.default = default
+        self.max = max
+        self.needs_recompile = needs_recompile
+        self._old: List[CCompilerMethod] = []
+
+    def function(self) -> CCompilerMethod:
+        """
+        Builds a function object usable as distutils.ccompiler.CCompiler.compile.
+        """
+
+        def compile_function(
+            compiler: distutils.ccompiler.CCompiler,
+            sources: List[str],
+            output_dir: Optional[str] = None,
+            macros: Optional[Union[Tuple[str], Tuple[str, Optional[str]]]] = None,
+            include_dirs: Optional[List[str]] = None,
+            debug: bool = False,
+            extra_preargs: Optional[List[str]] = None,
+            extra_postargs: Optional[List[str]] = None,
+            depends: Optional[List[str]] = None,
+        ) -> Any:
+
+            # These lines are directly from distutils.ccompiler.CCompiler
+            macros, objects, extra_postargs, pp_opts, build = compiler._setup_compile(  # type: ignore[attr-defined]
+                output_dir, macros, include_dirs, sources, depends, extra_postargs
+            )
+            cc_args = compiler._get_cc_args(pp_opts, debug, extra_preargs)  # type: ignore[attr-defined]
+
+            # The number of threads; start with default.
+            threads = self.default
+
+            # Determine the number of compilation threads, unless set by an environment variable.
+            if self.envvar is not None:
+                threads = int(os.environ.get(self.envvar, self.default))
+
+            def _single_compile(obj: Any) -> None:
+                try:
+                    src, ext = build[obj]
+                except KeyError:
+                    return
+
+                if not os.path.exists(obj) or self.needs_recompile(obj, src):
+                    compiler._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)  # type: ignore[attr-defined]
+
+            try:
+                # Importing .synchronize checks for platforms that have some multiprocessing
+                # capabilities but lack semaphores, such as AWS Lambda and Android Termux.
+                import multiprocessing.synchronize
+                from multiprocessing.pool import ThreadPool
+            except ImportError:
+                threads = 1
+
+            if threads == 0:
+                try:
+                    threads = multiprocessing.cpu_count()
+                    threads = self.max if self.max and self.max < threads else threads
+                except NotImplementedError:
+                    threads = 1
+
+            if threads > 1:
+                with ThreadPool(threads) as pool:
+                    for _ in pool.imap_unordered(_single_compile, objects):
+                        pass
+            else:
+                for ob in objects:
+                    _single_compile(ob)
+
+            return objects
+
+        return compile_function
+
+    def install(self: S) -> S:
+        """
+        Installs the compile function into distutils.ccompiler.CCompiler.compile.
+        """
+        distutils.ccompiler.CCompiler.compile = self.function()  # type: ignore[assignment]
+        return self
+
+    def __enter__(self: S) -> S:
+        self._old.append(distutils.ccompiler.CCompiler.compile)
+        return self.install()
+
+    def __exit__(self, *args: Any) -> None:
+        distutils.ccompiler.CCompiler.compile = self._old.pop()  # type: ignore[assignment]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pyproject.toml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pyproject.toml
new file mode 100644
index 000000000..3ba1b4b22
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/pyproject.toml
@@ -0,0 +1,61 @@
+[build-system]
+requires = ["setuptools>=42", "cmake>=3.18", "ninja"]
+build-backend = "setuptools.build_meta"
+
+[tool.check-manifest]
+ignore = [
+    "tests/**",
+    "docs/**",
+    "tools/**",
+    "include/**",
+    ".*",
+    "pybind11/include/**",
+    "pybind11/share/**",
+    "CMakeLists.txt",
+    "noxfile.py",
+]
+
+[tool.isort]
+# Needs the compiled .so modules and env.py from tests
+known_first_party = "env,pybind11_cross_module_tests,pybind11_tests,"
+# For black compatibility
+profile = "black"
+
+[tool.mypy]
+files = ["pybind11"]
+python_version = "3.6"
+strict = true
+show_error_codes = true
+enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"]
+warn_unreachable = true
+
+[[tool.mypy.overrides]]
+module = ["ghapi.*", "setuptools.*"]
+ignore_missing_imports = true
+
+
+[tool.pytest.ini_options]
+minversion = "6.0"
+addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"]
+xfail_strict = true
+filterwarnings = ["error"]
+log_cli_level = "info"
+testpaths = [
+    "tests",
+]
+timeout=300
+
+
+[tool.pylint]
+master.py-version = "3.6"
+reports.output-format = "colorized"
+messages_control.disable = [
+  "design",
+  "fixme",
+  "imports",
+  "line-too-long",
+  "imports",
+  "invalid-name",
+  "protected-access",
+  "missing-module-docstring",
+]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/setup.cfg b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/setup.cfg
new file mode 100644
index 000000000..36fbfed4f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/setup.cfg
@@ -0,0 +1,50 @@
+[metadata]
+long_description = file: README.rst
+long_description_content_type = text/x-rst
+description = Seamless operability between C++11 and Python
+author = Wenzel Jakob
+author_email = wenzel.jakob@epfl.ch
+url = https://github.com/pybind/pybind11
+license = BSD
+
+classifiers =
+    Development Status :: 5 - Production/Stable
+    Intended Audience :: Developers
+    Topic :: Software Development :: Libraries :: Python Modules
+    Topic :: Utilities
+    Programming Language :: C++
+    Programming Language :: Python :: 3 :: Only
+    Programming Language :: Python :: 3.6
+    Programming Language :: Python :: 3.7
+    Programming Language :: Python :: 3.8
+    Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
+    Programming Language :: Python :: 3.11
+    License :: OSI Approved :: BSD License
+    Programming Language :: Python :: Implementation :: PyPy
+    Programming Language :: Python :: Implementation :: CPython
+    Programming Language :: C++
+    Topic :: Software Development :: Libraries :: Python Modules
+
+keywords =
+    C++11
+    Python bindings
+
+project_urls =
+    Documentation = https://pybind11.readthedocs.io/
+    Bug Tracker = https://github.com/pybind/pybind11/issues
+    Discussions = https://github.com/pybind/pybind11/discussions
+    Changelog = https://pybind11.readthedocs.io/en/latest/changelog.html
+    Chat = https://gitter.im/pybind/Lobby
+
+[options]
+python_requires = >=3.6
+zip_safe = False
+
+
+[flake8]
+max-line-length = 120
+show_source = True
+exclude = .git, __pycache__, build, dist, docs, tools, venv
+extend-ignore = E203, E722, B950
+extend-select = B9
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/setup.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/setup.py
new file mode 100644
index 000000000..a149755bf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/setup.py
@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+
+# Setup script for PyPI; use CMakeFile.txt to build extension modules
+
+import contextlib
+import os
+import re
+import shutil
+import string
+import subprocess
+import sys
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import Dict, Iterator, List, Union
+
+import setuptools.command.sdist
+
+DIR = Path(__file__).parent.absolute()
+VERSION_REGEX = re.compile(
+    r"^\s*#\s*define\s+PYBIND11_VERSION_([A-Z]+)\s+(.*)$", re.MULTILINE
+)
+VERSION_FILE = Path("pybind11/_version.py")
+COMMON_FILE = Path("include/pybind11/detail/common.h")
+
+
+def build_expected_version_hex(matches: Dict[str, str]) -> str:
+    patch_level_serial = matches["PATCH"]
+    serial = None
+    major = int(matches["MAJOR"])
+    minor = int(matches["MINOR"])
+    flds = patch_level_serial.split(".")
+    if flds:
+        patch = int(flds[0])
+        if len(flds) == 1:
+            level = "0"
+            serial = 0
+        elif len(flds) == 2:
+            level_serial = flds[1]
+            for level in ("a", "b", "c", "dev"):
+                if level_serial.startswith(level):
+                    serial = int(level_serial[len(level) :])
+                    break
+    if serial is None:
+        msg = f'Invalid PYBIND11_VERSION_PATCH: "{patch_level_serial}"'
+        raise RuntimeError(msg)
+    version_hex_str = f"{major:02x}{minor:02x}{patch:02x}{level[:1]}{serial:x}"
+    return f"0x{version_hex_str.upper()}"
+
+
+# PYBIND11_GLOBAL_SDIST will build a different sdist, with the python-headers
+# files, and the sys.prefix files (CMake and headers).
+
+global_sdist = os.environ.get("PYBIND11_GLOBAL_SDIST", False)
+
+setup_py = Path(
+    "tools/setup_global.py.in" if global_sdist else "tools/setup_main.py.in"
+)
+extra_cmd = 'cmdclass["sdist"] = SDist\n'
+
+to_src = (
+    (Path("pyproject.toml"), Path("tools/pyproject.toml")),
+    (Path("setup.py"), setup_py),
+)
+
+
+# Read the listed version
+loc: Dict[str, str] = {}
+code = compile(VERSION_FILE.read_text(encoding="utf-8"), "pybind11/_version.py", "exec")
+exec(code, loc)
+version = loc["__version__"]
+
+# Verify that the version matches the one in C++
+matches = dict(VERSION_REGEX.findall(COMMON_FILE.read_text(encoding="utf8")))
+cpp_version = "{MAJOR}.{MINOR}.{PATCH}".format(**matches)
+if version != cpp_version:
+    msg = f"Python version {version} does not match C++ version {cpp_version}!"
+    raise RuntimeError(msg)
+
+version_hex = matches.get("HEX", "MISSING")
+exp_version_hex = build_expected_version_hex(matches)
+if version_hex != exp_version_hex:
+    msg = f"PYBIND11_VERSION_HEX {version_hex} does not match expected value {exp_version_hex}!"
+    raise RuntimeError(msg)
+
+
+# TODO: use literals & overload (typing extensions or Python 3.8)
+def get_and_replace(
+    filename: Path, binary: bool = False, **opts: str
+) -> Union[bytes, str]:
+    if binary:
+        contents = filename.read_bytes()
+        return string.Template(contents.decode()).substitute(opts).encode()
+
+    return string.Template(filename.read_text()).substitute(opts)
+
+
+# Use our input files instead when making the SDist (and anything that depends
+# on it, like a wheel)
+class SDist(setuptools.command.sdist.sdist):  # type: ignore[misc]
+    def make_release_tree(self, base_dir: str, files: List[str]) -> None:
+        super().make_release_tree(base_dir, files)
+
+        for to, src in to_src:
+            txt = get_and_replace(src, binary=True, version=version, extra_cmd="")
+
+            dest = Path(base_dir) / to
+
+            # This is normally linked, so unlink before writing!
+            dest.unlink()
+            dest.write_bytes(txt)  # type: ignore[arg-type]
+
+
+# Remove the CMake install directory when done
+@contextlib.contextmanager
+def remove_output(*sources: str) -> Iterator[None]:
+    try:
+        yield
+    finally:
+        for src in sources:
+            shutil.rmtree(src)
+
+
+with remove_output("pybind11/include", "pybind11/share"):
+    # Generate the files if they are not present.
+    with TemporaryDirectory() as tmpdir:
+        cmd = ["cmake", "-S", ".", "-B", tmpdir] + [
+            "-DCMAKE_INSTALL_PREFIX=pybind11",
+            "-DBUILD_TESTING=OFF",
+            "-DPYBIND11_NOPYTHON=ON",
+        ]
+        if "CMAKE_ARGS" in os.environ:
+            fcommand = [
+                c
+                for c in os.environ["CMAKE_ARGS"].split()
+                if "DCMAKE_INSTALL_PREFIX" not in c
+            ]
+            cmd += fcommand
+        subprocess.run(cmd, check=True, cwd=DIR, stdout=sys.stdout, stderr=sys.stderr)
+        subprocess.run(
+            ["cmake", "--install", tmpdir],
+            check=True,
+            cwd=DIR,
+            stdout=sys.stdout,
+            stderr=sys.stderr,
+        )
+
+    txt = get_and_replace(setup_py, version=version, extra_cmd=extra_cmd)
+    code = compile(txt, setup_py, "exec")
+    exec(code, {"SDist": SDist})
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/CMakeLists.txt
new file mode 100644
index 000000000..7296cd1b8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/CMakeLists.txt
@@ -0,0 +1,558 @@
+# CMakeLists.txt -- Build system for the pybind11 test suite
+#
+# Copyright (c) 2015 Wenzel Jakob <wenzel@inf.ethz.ch>
+#
+# All rights reserved. Use of this source code is governed by a
+# BSD-style license that can be found in the LICENSE file.
+
+cmake_minimum_required(VERSION 3.4)
+
+# The `cmake_minimum_required(VERSION 3.4...3.18)` syntax does not work with
+# some versions of VS that have a patched CMake 3.11. This forces us to emulate
+# the behavior using the following workaround:
+if(${CMAKE_VERSION} VERSION_LESS 3.21)
+  cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION})
+else()
+  cmake_policy(VERSION 3.21)
+endif()
+
+# Only needed for CMake < 3.5 support
+include(CMakeParseArguments)
+
+# Filter out items; print an optional message if any items filtered. This ignores extensions.
+#
+# Usage:
+#   pybind11_filter_tests(LISTNAME file1.cpp file2.cpp ... MESSAGE "")
+#
+macro(pybind11_filter_tests LISTNAME)
+  cmake_parse_arguments(ARG "" "MESSAGE" "" ${ARGN})
+  set(PYBIND11_FILTER_TESTS_FOUND OFF)
+  # Make a list of the test without any extensions, for easier filtering.
+  set(_TMP_ACTUAL_LIST "${${LISTNAME}};") # enforce ';' at the end to allow matching last item.
+  string(REGEX REPLACE "\\.[^.;]*;" ";" LIST_WITHOUT_EXTENSIONS "${_TMP_ACTUAL_LIST}")
+  foreach(filename IN LISTS ARG_UNPARSED_ARGUMENTS)
+    string(REGEX REPLACE "\\.[^.]*$" "" filename_no_ext ${filename})
+    # Search in the list without extensions.
+    list(FIND LIST_WITHOUT_EXTENSIONS ${filename_no_ext} _FILE_FOUND)
+    if(_FILE_FOUND GREATER -1)
+      list(REMOVE_AT ${LISTNAME} ${_FILE_FOUND}) # And remove from the list with extensions.
+      list(REMOVE_AT LIST_WITHOUT_EXTENSIONS ${_FILE_FOUND}
+      )# And our search list, to ensure it is in sync.
+      set(PYBIND11_FILTER_TESTS_FOUND ON)
+    endif()
+  endforeach()
+  if(PYBIND11_FILTER_TESTS_FOUND AND ARG_MESSAGE)
+    message(STATUS "${ARG_MESSAGE}")
+  endif()
+endmacro()
+
+macro(possibly_uninitialized)
+  foreach(VARNAME ${ARGN})
+    if(NOT DEFINED "${VARNAME}")
+      set("${VARNAME}" "")
+    endif()
+  endforeach()
+endmacro()
+
+# Function to add additional targets if any of the provided tests are found.
+# Needles; Specifies the test names to look for.
+# Additions; Specifies the additional test targets to add when any of the needles are found.
+macro(tests_extra_targets needles additions)
+  # Add the index for this relation to the index extra targets map.
+  list(LENGTH PYBIND11_TEST_EXTRA_TARGETS PYBIND11_TEST_EXTRA_TARGETS_LEN)
+  list(APPEND PYBIND11_TEST_EXTRA_TARGETS ${PYBIND11_TEST_EXTRA_TARGETS_LEN})
+  # Add the test names to look for, and the associated test target additions.
+  set(PYBIND11_TEST_EXTRA_TARGETS_NEEDLES_${PYBIND11_TEST_EXTRA_TARGETS_LEN} ${needles})
+  set(PYBIND11_TEST_EXTRA_TARGETS_ADDITION_${PYBIND11_TEST_EXTRA_TARGETS_LEN} ${additions})
+endmacro()
+
+# New Python support
+if(DEFINED Python_EXECUTABLE)
+  set(PYTHON_EXECUTABLE "${Python_EXECUTABLE}")
+  set(PYTHON_VERSION "${Python_VERSION}")
+endif()
+
+# There's no harm in including a project in a project
+project(pybind11_tests CXX)
+
+# Access FindCatch and more
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/../tools")
+
+option(PYBIND11_WERROR "Report all warnings as errors" OFF)
+option(DOWNLOAD_EIGEN "Download EIGEN (requires CMake 3.11+)" OFF)
+option(PYBIND11_CUDA_TESTS "Enable building CUDA tests (requires CMake 3.12+)" OFF)
+set(PYBIND11_TEST_OVERRIDE
+    ""
+    CACHE STRING "Tests from ;-separated list of *.cpp files will be built instead of all tests")
+set(PYBIND11_TEST_FILTER
+    ""
+    CACHE STRING "Tests from ;-separated list of *.cpp files will be removed from all tests")
+
+if(CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_SOURCE_DIR)
+  # We're being loaded directly, i.e. not via add_subdirectory, so make this
+  # work as its own project and load the pybind11Config to get the tools we need
+  find_package(pybind11 REQUIRED CONFIG)
+endif()
+
+if(NOT CMAKE_BUILD_TYPE AND NOT DEFINED CMAKE_CONFIGURATION_TYPES)
+  message(STATUS "Setting tests build type to MinSizeRel as none was specified")
+  set(CMAKE_BUILD_TYPE
+      MinSizeRel
+      CACHE STRING "Choose the type of build." FORCE)
+  set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel"
+                                               "RelWithDebInfo")
+endif()
+
+if(PYBIND11_CUDA_TESTS)
+  enable_language(CUDA)
+  if(DEFINED CMAKE_CXX_STANDARD)
+    set(CMAKE_CUDA_STANDARD ${CMAKE_CXX_STANDARD})
+  endif()
+  set(CMAKE_CUDA_STANDARD_REQUIRED ON)
+endif()
+
+# Full set of test files (you can override these; see below, overrides ignore extension)
+# Any test that has no extension is both .py and .cpp, so 'foo' will add 'foo.cpp' and 'foo.py'.
+# Any test that has an extension is exclusively that and handled as such.
+set(PYBIND11_TEST_FILES
+    test_async
+    test_buffers
+    test_builtin_casters
+    test_call_policies
+    test_callbacks
+    test_chrono
+    test_class
+    test_const_name
+    test_constants_and_functions
+    test_copy_move
+    test_custom_type_casters
+    test_custom_type_setup
+    test_docstring_options
+    test_eigen
+    test_enum
+    test_eval
+    test_exceptions
+    test_factory_constructors
+    test_gil_scoped
+    test_iostream
+    test_kwargs_and_defaults
+    test_local_bindings
+    test_methods_and_attributes
+    test_modules
+    test_multiple_inheritance
+    test_numpy_array
+    test_numpy_dtypes
+    test_numpy_vectorize
+    test_opaque_types
+    test_operator_overloading
+    test_pickling
+    test_pytypes
+    test_sequences_and_iterators
+    test_smart_ptr
+    test_stl
+    test_stl_binders
+    test_tagbased_polymorphic
+    test_thread
+    test_union
+    test_virtual_functions)
+
+# Invoking cmake with something like:
+#     cmake -DPYBIND11_TEST_OVERRIDE="test_callbacks.cpp;test_pickling.cpp" ..
+# lets you override the tests that get compiled and run.  You can restore to all tests with:
+#     cmake -DPYBIND11_TEST_OVERRIDE= ..
+if(PYBIND11_TEST_OVERRIDE)
+  # Instead of doing a direct override here, we iterate over the overrides without extension and
+  # match them against entries from the PYBIND11_TEST_FILES, anything that not matches goes into the filter list.
+  string(REGEX REPLACE "\\.[^.;]*;" ";" TEST_OVERRIDE_NO_EXT "${PYBIND11_TEST_OVERRIDE};")
+  string(REGEX REPLACE "\\.[^.;]*;" ";" TEST_FILES_NO_EXT "${PYBIND11_TEST_FILES};")
+  # This allows the override to be done with extensions, preserving backwards compatibility.
+  foreach(test_name ${TEST_FILES_NO_EXT})
+    if(NOT ${test_name} IN_LIST TEST_OVERRIDE_NO_EXT
+    )# If not in the whitelist, add to be filtered out.
+      list(APPEND PYBIND11_TEST_FILTER ${test_name})
+    endif()
+  endforeach()
+endif()
+
+# You can also filter tests:
+if(PYBIND11_TEST_FILTER)
+  pybind11_filter_tests(PYBIND11_TEST_FILES ${PYBIND11_TEST_FILTER})
+endif()
+
+# Skip tests for CUDA check:
+# /pybind11/tests/test_constants_and_functions.cpp(125):
+#   error: incompatible exception specifications
+if(PYBIND11_CUDA_TESTS)
+  pybind11_filter_tests(
+    PYBIND11_TEST_FILES test_constants_and_functions.cpp MESSAGE
+    "Skipping test_constants_and_functions due to incompatible exception specifications")
+endif()
+
+# Now that the test filtering is complete, we need to split the list into the test for PYTEST
+# and the list for the cpp targets.
+set(PYBIND11_CPPTEST_FILES "")
+set(PYBIND11_PYTEST_FILES "")
+
+foreach(test_name ${PYBIND11_TEST_FILES})
+  if(test_name MATCHES "\\.py$") # Ends in .py, purely python test.
+    list(APPEND PYBIND11_PYTEST_FILES ${test_name})
+  elseif(test_name MATCHES "\\.cpp$") # Ends in .cpp, purely cpp test.
+    list(APPEND PYBIND11_CPPTEST_FILES ${test_name})
+  elseif(NOT test_name MATCHES "\\.") # No extension specified, assume both, add extension.
+    list(APPEND PYBIND11_PYTEST_FILES ${test_name}.py)
+    list(APPEND PYBIND11_CPPTEST_FILES ${test_name}.cpp)
+  else()
+    message(WARNING "Unhanded test extension in test: ${test_name}")
+  endif()
+endforeach()
+set(PYBIND11_TEST_FILES ${PYBIND11_CPPTEST_FILES})
+list(SORT PYBIND11_PYTEST_FILES)
+
+# Contains the set of test files that require pybind11_cross_module_tests to be
+# built; if none of these are built (i.e. because TEST_OVERRIDE is used and
+# doesn't include them) the second module doesn't get built.
+tests_extra_targets("test_exceptions.py;test_local_bindings.py;test_stl.py;test_stl_binders.py"
+                    "pybind11_cross_module_tests")
+
+# And add additional targets for other tests.
+tests_extra_targets("test_exceptions.py" "cross_module_interleaved_error_already_set")
+tests_extra_targets("test_gil_scoped.py" "cross_module_gil_utils")
+
+set(PYBIND11_EIGEN_REPO
+    "https://gitlab.com/libeigen/eigen.git"
+    CACHE STRING "Eigen repository to use for tests")
+# Always use a hash for reconfigure speed and security reasons
+# Include the version number for pretty printing (keep in sync)
+set(PYBIND11_EIGEN_VERSION_AND_HASH
+    "3.4.0;929bc0e191d0927b1735b9a1ddc0e8b77e3a25ec"
+    CACHE STRING "Eigen version to use for tests, format: VERSION;HASH")
+
+list(GET PYBIND11_EIGEN_VERSION_AND_HASH 0 PYBIND11_EIGEN_VERSION_STRING)
+list(GET PYBIND11_EIGEN_VERSION_AND_HASH 1 PYBIND11_EIGEN_VERSION_HASH)
+
+# Check if Eigen is available; if not, remove from PYBIND11_TEST_FILES (but
+# keep it in PYBIND11_PYTEST_FILES, so that we get the "eigen is not installed"
+# skip message).
+list(FIND PYBIND11_TEST_FILES test_eigen.cpp PYBIND11_TEST_FILES_EIGEN_I)
+if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
+  # Try loading via newer Eigen's Eigen3Config first (bypassing tools/FindEigen3.cmake).
+  # Eigen 3.3.1+ exports a cmake 3.0+ target for handling dependency requirements, but also
+  # produces a fatal error if loaded from a pre-3.0 cmake.
+  if(DOWNLOAD_EIGEN)
+    if(CMAKE_VERSION VERSION_LESS 3.11)
+      message(FATAL_ERROR "CMake 3.11+ required when using DOWNLOAD_EIGEN")
+    endif()
+
+    include(FetchContent)
+    FetchContent_Declare(
+      eigen
+      GIT_REPOSITORY "${PYBIND11_EIGEN_REPO}"
+      GIT_TAG "${PYBIND11_EIGEN_VERSION_HASH}")
+
+    FetchContent_GetProperties(eigen)
+    if(NOT eigen_POPULATED)
+      message(
+        STATUS
+          "Downloading Eigen ${PYBIND11_EIGEN_VERSION_STRING} (${PYBIND11_EIGEN_VERSION_HASH}) from ${PYBIND11_EIGEN_REPO}"
+      )
+      FetchContent_Populate(eigen)
+    endif()
+
+    set(EIGEN3_INCLUDE_DIR ${eigen_SOURCE_DIR})
+    set(EIGEN3_FOUND TRUE)
+    # When getting locally, the version is not visible from a superprojet,
+    # so just force it.
+    set(EIGEN3_VERSION "${PYBIND11_EIGEN_VERSION_STRING}")
+
+  else()
+    find_package(Eigen3 3.2.7 QUIET CONFIG)
+
+    if(NOT EIGEN3_FOUND)
+      # Couldn't load via target, so fall back to allowing module mode finding, which will pick up
+      # tools/FindEigen3.cmake
+      find_package(Eigen3 3.2.7 QUIET)
+    endif()
+  endif()
+
+  if(EIGEN3_FOUND)
+    if(NOT TARGET Eigen3::Eigen)
+      add_library(Eigen3::Eigen IMPORTED INTERFACE)
+      set_property(TARGET Eigen3::Eigen PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+                                                 "${EIGEN3_INCLUDE_DIR}")
+    endif()
+
+    # Eigen 3.3.1+ cmake sets EIGEN3_VERSION_STRING (and hard codes the version when installed
+    # rather than looking it up in the cmake script); older versions, and the
+    # tools/FindEigen3.cmake, set EIGEN3_VERSION instead.
+    if(NOT EIGEN3_VERSION AND EIGEN3_VERSION_STRING)
+      set(EIGEN3_VERSION ${EIGEN3_VERSION_STRING})
+    endif()
+    message(STATUS "Building tests with Eigen v${EIGEN3_VERSION}")
+  else()
+    list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
+    message(
+      STATUS "Building tests WITHOUT Eigen, use -DDOWNLOAD_EIGEN=ON on CMake 3.11+ to download")
+  endif()
+endif()
+
+# Optional dependency for some tests (boost::variant is only supported with version >= 1.56)
+find_package(Boost 1.56)
+
+if(Boost_FOUND)
+  if(NOT TARGET Boost::headers)
+    add_library(Boost::headers IMPORTED INTERFACE)
+    if(TARGET Boost::boost)
+      # Classic FindBoost
+      set_property(TARGET Boost::boost PROPERTY INTERFACE_LINK_LIBRARIES Boost::boost)
+    else()
+      # Very old FindBoost, or newer Boost than CMake in older CMakes
+      set_property(TARGET Boost::headers PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+                                                  ${Boost_INCLUDE_DIRS})
+    endif()
+  endif()
+endif()
+
+# Check if we need to add -lstdc++fs or -lc++fs or nothing
+if(DEFINED CMAKE_CXX_STANDARD AND CMAKE_CXX_STANDARD LESS 17)
+  set(STD_FS_NO_LIB_NEEDED TRUE)
+elseif(MSVC)
+  set(STD_FS_NO_LIB_NEEDED TRUE)
+else()
+  file(
+    WRITE ${CMAKE_CURRENT_BINARY_DIR}/main.cpp
+    "#include <filesystem>\nint main(int argc, char ** argv) {\n  std::filesystem::path p(argv[0]);\n  return p.string().length();\n}"
+  )
+  try_compile(
+    STD_FS_NO_LIB_NEEDED ${CMAKE_CURRENT_BINARY_DIR}
+    SOURCES ${CMAKE_CURRENT_BINARY_DIR}/main.cpp
+    COMPILE_DEFINITIONS -std=c++17)
+  try_compile(
+    STD_FS_NEEDS_STDCXXFS ${CMAKE_CURRENT_BINARY_DIR}
+    SOURCES ${CMAKE_CURRENT_BINARY_DIR}/main.cpp
+    COMPILE_DEFINITIONS -std=c++17
+    LINK_LIBRARIES stdc++fs)
+  try_compile(
+    STD_FS_NEEDS_CXXFS ${CMAKE_CURRENT_BINARY_DIR}
+    SOURCES ${CMAKE_CURRENT_BINARY_DIR}/main.cpp
+    COMPILE_DEFINITIONS -std=c++17
+    LINK_LIBRARIES c++fs)
+endif()
+
+if(${STD_FS_NEEDS_STDCXXFS})
+  set(STD_FS_LIB stdc++fs)
+elseif(${STD_FS_NEEDS_CXXFS})
+  set(STD_FS_LIB c++fs)
+elseif(${STD_FS_NO_LIB_NEEDED})
+  set(STD_FS_LIB "")
+else()
+  message(WARNING "Unknown C++17 compiler - not passing -lstdc++fs")
+  set(STD_FS_LIB "")
+endif()
+
+# Compile with compiler warnings turned on
+function(pybind11_enable_warnings target_name)
+  if(MSVC)
+    target_compile_options(${target_name} PRIVATE /W4 /wd4189)
+  elseif(CMAKE_CXX_COMPILER_ID MATCHES "(GNU|Intel|Clang)" AND NOT PYBIND11_CUDA_TESTS)
+    target_compile_options(
+      ${target_name}
+      PRIVATE -Wall
+              -Wextra
+              -Wconversion
+              -Wcast-qual
+              -Wdeprecated
+              -Wundef
+              -Wnon-virtual-dtor)
+  endif()
+
+  if(PYBIND11_WERROR)
+    if(MSVC)
+      target_compile_options(${target_name} PRIVATE /WX)
+    elseif(PYBIND11_CUDA_TESTS)
+      target_compile_options(${target_name} PRIVATE "SHELL:-Werror all-warnings")
+    elseif(CMAKE_CXX_COMPILER_ID MATCHES "(GNU|Clang|IntelLLVM)")
+      target_compile_options(${target_name} PRIVATE -Werror)
+    elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
+      if(CMAKE_CXX_STANDARD EQUAL 17) # See PR #3570
+        target_compile_options(${target_name} PRIVATE -Wno-conversion)
+      endif()
+      target_compile_options(
+        ${target_name}
+        PRIVATE
+          -Werror-all
+          # "Inlining inhibited by limit max-size", "Inlining inhibited by limit max-total-size"
+          -diag-disable 11074,11076)
+    endif()
+  endif()
+endfunction()
+
+set(test_targets pybind11_tests)
+
+# Check if any tests need extra targets by iterating through the mappings registered.
+foreach(i ${PYBIND11_TEST_EXTRA_TARGETS})
+  foreach(needle ${PYBIND11_TEST_EXTRA_TARGETS_NEEDLES_${i}})
+    if(needle IN_LIST PYBIND11_PYTEST_FILES)
+      # Add all the additional targets to the test list. List join in newer cmake.
+      foreach(extra_target ${PYBIND11_TEST_EXTRA_TARGETS_ADDITION_${i}})
+        list(APPEND test_targets ${extra_target})
+      endforeach()
+      break() # Breaks out of the needle search, continues with the next mapping.
+    endif()
+  endforeach()
+endforeach()
+
+# Support CUDA testing by forcing the target file to compile with NVCC
+if(PYBIND11_CUDA_TESTS)
+  set_property(SOURCE ${PYBIND11_TEST_FILES} PROPERTY LANGUAGE CUDA)
+endif()
+
+foreach(target ${test_targets})
+  set(test_files ${PYBIND11_TEST_FILES})
+  if(NOT "${target}" STREQUAL "pybind11_tests")
+    set(test_files "")
+  endif()
+
+  # Support CUDA testing by forcing the target file to compile with NVCC
+  if(PYBIND11_CUDA_TESTS)
+    set_property(SOURCE ${target}.cpp PROPERTY LANGUAGE CUDA)
+  endif()
+
+  # Create the binding library
+  pybind11_add_module(${target} THIN_LTO ${target}.cpp ${test_files} ${PYBIND11_HEADERS})
+  pybind11_enable_warnings(${target})
+
+  if(NOT CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_CURRENT_BINARY_DIR)
+    get_property(
+      suffix
+      TARGET ${target}
+      PROPERTY SUFFIX)
+    set(source_output "${CMAKE_CURRENT_SOURCE_DIR}/${target}${suffix}")
+    if(suffix AND EXISTS "${source_output}")
+      message(WARNING "Output file also in source directory; "
+                      "please remove to avoid confusion: ${source_output}")
+    endif()
+  endif()
+
+  if(MSVC)
+    target_compile_options(${target} PRIVATE /utf-8)
+  endif()
+
+  if(EIGEN3_FOUND)
+    target_link_libraries(${target} PRIVATE Eigen3::Eigen)
+    target_compile_definitions(${target} PRIVATE -DPYBIND11_TEST_EIGEN)
+  endif()
+
+  if(Boost_FOUND)
+    target_link_libraries(${target} PRIVATE Boost::headers)
+    target_compile_definitions(${target} PRIVATE -DPYBIND11_TEST_BOOST)
+  endif()
+
+  target_link_libraries(${target} PRIVATE ${STD_FS_LIB})
+
+  # Always write the output file directly into the 'tests' directory (even on MSVC)
+  if(NOT CMAKE_LIBRARY_OUTPUT_DIRECTORY)
+    set_target_properties(${target} PROPERTIES LIBRARY_OUTPUT_DIRECTORY
+                                               "${CMAKE_CURRENT_BINARY_DIR}")
+
+    if(DEFINED CMAKE_CONFIGURATION_TYPES)
+      foreach(config ${CMAKE_CONFIGURATION_TYPES})
+        string(TOUPPER ${config} config)
+        set_target_properties(${target} PROPERTIES LIBRARY_OUTPUT_DIRECTORY_${config}
+                                                   "${CMAKE_CURRENT_BINARY_DIR}")
+      endforeach()
+    endif()
+  endif()
+endforeach()
+
+# Provide nice organisation in IDEs
+if(NOT CMAKE_VERSION VERSION_LESS 3.8)
+  source_group(
+    TREE "${CMAKE_CURRENT_SOURCE_DIR}/../include"
+    PREFIX "Header Files"
+    FILES ${PYBIND11_HEADERS})
+endif()
+
+# Make sure pytest is found or produce a warning
+pybind11_find_import(pytest VERSION 3.1)
+
+if(NOT CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_CURRENT_BINARY_DIR)
+  # This is not used later in the build, so it's okay to regenerate each time.
+  configure_file("${CMAKE_CURRENT_SOURCE_DIR}/pytest.ini" "${CMAKE_CURRENT_BINARY_DIR}/pytest.ini"
+                 COPYONLY)
+  file(APPEND "${CMAKE_CURRENT_BINARY_DIR}/pytest.ini"
+       "\ntestpaths = \"${CMAKE_CURRENT_SOURCE_DIR}\"")
+
+endif()
+
+# cmake 3.12 added list(transform <list> prepend
+# but we can't use it yet
+string(REPLACE "test_" "${CMAKE_CURRENT_SOURCE_DIR}/test_" PYBIND11_ABS_PYTEST_FILES
+               "${PYBIND11_PYTEST_FILES}")
+
+set(PYBIND11_TEST_PREFIX_COMMAND
+    ""
+    CACHE STRING "Put this before pytest, use for checkers and such")
+
+# A single command to compile and run the tests
+add_custom_target(
+  pytest
+  COMMAND ${PYBIND11_TEST_PREFIX_COMMAND} ${PYTHON_EXECUTABLE} -m pytest
+          ${PYBIND11_ABS_PYTEST_FILES}
+  DEPENDS ${test_targets}
+  WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}"
+  USES_TERMINAL)
+
+if(PYBIND11_TEST_OVERRIDE)
+  add_custom_command(
+    TARGET pytest
+    POST_BUILD
+    COMMAND ${CMAKE_COMMAND} -E echo
+            "Note: not all tests run: -DPYBIND11_TEST_OVERRIDE is in effect")
+endif()
+
+# cmake-format: off
+add_custom_target(
+  memcheck
+  COMMAND
+    PYTHONMALLOC=malloc
+    valgrind
+    --leak-check=full
+    --show-leak-kinds=definite,indirect
+    --errors-for-leak-kinds=definite,indirect
+    --error-exitcode=1
+    --read-var-info=yes
+    --track-origins=yes
+    --suppressions="${CMAKE_CURRENT_SOURCE_DIR}/valgrind-python.supp"
+    --suppressions="${CMAKE_CURRENT_SOURCE_DIR}/valgrind-numpy-scipy.supp"
+    --gen-suppressions=all
+    ${PYTHON_EXECUTABLE} -m pytest ${PYBIND11_ABS_PYTEST_FILES}
+  DEPENDS ${test_targets}
+  WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}"
+  USES_TERMINAL)
+# cmake-format: on
+
+# Add a check target to run all the tests, starting with pytest (we add dependencies to this below)
+add_custom_target(check DEPENDS pytest)
+
+# The remaining tests only apply when being built as part of the pybind11 project, but not if the
+# tests are being built independently.
+if(CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_SOURCE_DIR)
+  return()
+endif()
+
+# Add a post-build comment to show the primary test suite .so size and, if a previous size, compare it:
+add_custom_command(
+  TARGET pybind11_tests
+  POST_BUILD
+  COMMAND
+    ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/../tools/libsize.py
+    $<TARGET_FILE:pybind11_tests>
+    ${CMAKE_CURRENT_BINARY_DIR}/sosize-$<TARGET_FILE_NAME:pybind11_tests>.txt)
+
+if(NOT PYBIND11_CUDA_TESTS)
+  # Test embedding the interpreter. Provides the `cpptest` target.
+  add_subdirectory(test_embed)
+
+  # Test CMake build using functions and targets from subdirectory or installed location
+  add_subdirectory(test_cmake_build)
+endif()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/conftest.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/conftest.py
new file mode 100644
index 000000000..02ce263af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/conftest.py
@@ -0,0 +1,213 @@
+"""pytest configuration
+
+Extends output capture as needed by pybind11: ignore constructors, optional unordered lines.
+Adds docstring and exceptions message sanitizers.
+"""
+
+import contextlib
+import difflib
+import gc
+import re
+import textwrap
+
+import pytest
+
+# Early diagnostic for failed imports
+import pybind11_tests
+
+_long_marker = re.compile(r"([0-9])L")
+_hexadecimal = re.compile(r"0x[0-9a-fA-F]+")
+
+# Avoid collecting Python3 only files
+collect_ignore = []
+
+
+def _strip_and_dedent(s):
+    """For triple-quote strings"""
+    return textwrap.dedent(s.lstrip("\n").rstrip())
+
+
+def _split_and_sort(s):
+    """For output which does not require specific line order"""
+    return sorted(_strip_and_dedent(s).splitlines())
+
+
+def _make_explanation(a, b):
+    """Explanation for a failed assert -- the a and b arguments are List[str]"""
+    return ["--- actual / +++ expected"] + [
+        line.strip("\n") for line in difflib.ndiff(a, b)
+    ]
+
+
+class Output:
+    """Basic output post-processing and comparison"""
+
+    def __init__(self, string):
+        self.string = string
+        self.explanation = []
+
+    def __str__(self):
+        return self.string
+
+    def __eq__(self, other):
+        # Ignore constructor/destructor output which is prefixed with "###"
+        a = [
+            line
+            for line in self.string.strip().splitlines()
+            if not line.startswith("###")
+        ]
+        b = _strip_and_dedent(other).splitlines()
+        if a == b:
+            return True
+        else:
+            self.explanation = _make_explanation(a, b)
+            return False
+
+
+class Unordered(Output):
+    """Custom comparison for output without strict line ordering"""
+
+    def __eq__(self, other):
+        a = _split_and_sort(self.string)
+        b = _split_and_sort(other)
+        if a == b:
+            return True
+        else:
+            self.explanation = _make_explanation(a, b)
+            return False
+
+
+class Capture:
+    def __init__(self, capfd):
+        self.capfd = capfd
+        self.out = ""
+        self.err = ""
+
+    def __enter__(self):
+        self.capfd.readouterr()
+        return self
+
+    def __exit__(self, *args):
+        self.out, self.err = self.capfd.readouterr()
+
+    def __eq__(self, other):
+        a = Output(self.out)
+        b = other
+        if a == b:
+            return True
+        else:
+            self.explanation = a.explanation
+            return False
+
+    def __str__(self):
+        return self.out
+
+    def __contains__(self, item):
+        return item in self.out
+
+    @property
+    def unordered(self):
+        return Unordered(self.out)
+
+    @property
+    def stderr(self):
+        return Output(self.err)
+
+
+@pytest.fixture
+def capture(capsys):
+    """Extended `capsys` with context manager and custom equality operators"""
+    return Capture(capsys)
+
+
+class SanitizedString:
+    def __init__(self, sanitizer):
+        self.sanitizer = sanitizer
+        self.string = ""
+        self.explanation = []
+
+    def __call__(self, thing):
+        self.string = self.sanitizer(thing)
+        return self
+
+    def __eq__(self, other):
+        a = self.string
+        b = _strip_and_dedent(other)
+        if a == b:
+            return True
+        else:
+            self.explanation = _make_explanation(a.splitlines(), b.splitlines())
+            return False
+
+
+def _sanitize_general(s):
+    s = s.strip()
+    s = s.replace("pybind11_tests.", "m.")
+    s = _long_marker.sub(r"\1", s)
+    return s
+
+
+def _sanitize_docstring(thing):
+    s = thing.__doc__
+    s = _sanitize_general(s)
+    return s
+
+
+@pytest.fixture
+def doc():
+    """Sanitize docstrings and add custom failure explanation"""
+    return SanitizedString(_sanitize_docstring)
+
+
+def _sanitize_message(thing):
+    s = str(thing)
+    s = _sanitize_general(s)
+    s = _hexadecimal.sub("0", s)
+    return s
+
+
+@pytest.fixture
+def msg():
+    """Sanitize messages and add custom failure explanation"""
+    return SanitizedString(_sanitize_message)
+
+
+# noinspection PyUnusedLocal
+def pytest_assertrepr_compare(op, left, right):
+    """Hook to insert custom failure explanation"""
+    if hasattr(left, "explanation"):
+        return left.explanation
+
+
+@contextlib.contextmanager
+def suppress(exception):
+    """Suppress the desired exception"""
+    try:
+        yield
+    except exception:
+        pass
+
+
+def gc_collect():
+    """Run the garbage collector twice (needed when running
+    reference counting tests with PyPy)"""
+    gc.collect()
+    gc.collect()
+
+
+def pytest_configure():
+    pytest.suppress = suppress
+    pytest.gc_collect = gc_collect
+
+
+def pytest_report_header(config):
+    del config  # Unused.
+    assert (
+        pybind11_tests.compiler_info is not None
+    ), "Please update pybind11_tests.cpp if this assert fails."
+    return (
+        "C++ Info:"
+        f" {pybind11_tests.compiler_info}"
+        f" {pybind11_tests.cpp_std}"
+        f" {pybind11_tests.PYBIND11_INTERNALS_ID}"
+    )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/constructor_stats.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/constructor_stats.h
new file mode 100644
index 000000000..a3835c21e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/constructor_stats.h
@@ -0,0 +1,322 @@
+#pragma once
+/*
+    tests/constructor_stats.h -- framework for printing and tracking object
+    instance lifetimes in example/test code.
+
+    Copyright (c) 2016 Jason Rhinelander <jason@imaginary.ca>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+
+This header provides a few useful tools for writing examples or tests that want to check and/or
+display object instance lifetimes.  It requires that you include this header and add the following
+function calls to constructors:
+
+    class MyClass {
+        MyClass() { ...; print_default_created(this); }
+        ~MyClass() { ...; print_destroyed(this); }
+        MyClass(const MyClass &c) { ...; print_copy_created(this); }
+        MyClass(MyClass &&c) { ...; print_move_created(this); }
+        MyClass(int a, int b) { ...; print_created(this, a, b); }
+        MyClass &operator=(const MyClass &c) { ...; print_copy_assigned(this); }
+        MyClass &operator=(MyClass &&c) { ...; print_move_assigned(this); }
+
+        ...
+    }
+
+You can find various examples of these in several of the existing testing .cpp files.  (Of course
+you don't need to add any of the above constructors/operators that you don't actually have, except
+for the destructor).
+
+Each of these will print an appropriate message such as:
+
+    ### MyClass @ 0x2801910 created via default constructor
+    ### MyClass @ 0x27fa780 created 100 200
+    ### MyClass @ 0x2801910 destroyed
+    ### MyClass @ 0x27fa780 destroyed
+
+You can also include extra arguments (such as the 100, 200 in the output above, coming from the
+value constructor) for all of the above methods which will be included in the output.
+
+For testing, each of these also keeps track the created instances and allows you to check how many
+of the various constructors have been invoked from the Python side via code such as:
+
+    from pybind11_tests import ConstructorStats
+    cstats = ConstructorStats.get(MyClass)
+    print(cstats.alive())
+    print(cstats.default_constructions)
+
+Note that `.alive()` should usually be the first thing you call as it invokes Python's garbage
+collector to actually destroy objects that aren't yet referenced.
+
+For everything except copy and move constructors and destructors, any extra values given to the
+print_...() function is stored in a class-specific values list which you can retrieve and inspect
+from the ConstructorStats instance `.values()` method.
+
+In some cases, when you need to track instances of a C++ class not registered with pybind11, you
+need to add a function returning the ConstructorStats for the C++ class; this can be done with:
+
+    m.def("get_special_cstats", &ConstructorStats::get<SpecialClass>,
+py::return_value_policy::reference)
+
+Finally, you can suppress the output messages, but keep the constructor tracking (for
+inspection/testing in python) by using the functions with `print_` replaced with `track_` (e.g.
+`track_copy_created(this)`).
+
+*/
+
+#include "pybind11_tests.h"
+
+#include <list>
+#include <sstream>
+#include <typeindex>
+#include <unordered_map>
+
+class ConstructorStats {
+protected:
+    std::unordered_map<void *, int> _instances; // Need a map rather than set because members can
+                                                // shared address with parents
+    std::list<std::string> _values;             // Used to track values
+                                                // (e.g. of value constructors)
+public:
+    int default_constructions = 0;
+    int copy_constructions = 0;
+    int move_constructions = 0;
+    int copy_assignments = 0;
+    int move_assignments = 0;
+
+    void copy_created(void *inst) {
+        created(inst);
+        copy_constructions++;
+    }
+
+    void move_created(void *inst) {
+        created(inst);
+        move_constructions++;
+    }
+
+    void default_created(void *inst) {
+        created(inst);
+        default_constructions++;
+    }
+
+    void created(void *inst) { ++_instances[inst]; }
+
+    void destroyed(void *inst) {
+        if (--_instances[inst] < 0) {
+            throw std::runtime_error("cstats.destroyed() called with unknown "
+                                     "instance; potential double-destruction "
+                                     "or a missing cstats.created()");
+        }
+    }
+
+    static void gc() {
+        // Force garbage collection to ensure any pending destructors are invoked:
+#if defined(PYPY_VERSION)
+        PyObject *globals = PyEval_GetGlobals();
+        PyObject *result = PyRun_String("import gc\n"
+                                        "for i in range(2):"
+                                        "    gc.collect()\n",
+                                        Py_file_input,
+                                        globals,
+                                        globals);
+        if (result == nullptr)
+            throw py::error_already_set();
+        Py_DECREF(result);
+#else
+        py::module_::import("gc").attr("collect")();
+#endif
+    }
+
+    int alive() {
+        gc();
+        int total = 0;
+        for (const auto &p : _instances) {
+            if (p.second > 0) {
+                total += p.second;
+            }
+        }
+        return total;
+    }
+
+    void value() {} // Recursion terminator
+    // Takes one or more values, converts them to strings, then stores them.
+    template <typename T, typename... Tmore>
+    void value(const T &v, Tmore &&...args) {
+        std::ostringstream oss;
+        oss << v;
+        _values.push_back(oss.str());
+        value(std::forward<Tmore>(args)...);
+    }
+
+    // Move out stored values
+    py::list values() {
+        py::list l;
+        for (const auto &v : _values) {
+            l.append(py::cast(v));
+        }
+        _values.clear();
+        return l;
+    }
+
+    // Gets constructor stats from a C++ type index
+    static ConstructorStats &get(std::type_index type) {
+        static std::unordered_map<std::type_index, ConstructorStats> all_cstats;
+        return all_cstats[type];
+    }
+
+    // Gets constructor stats from a C++ type
+    template <typename T>
+    static ConstructorStats &get() {
+#if defined(PYPY_VERSION)
+        gc();
+#endif
+        return get(typeid(T));
+    }
+
+    // Gets constructor stats from a Python class
+    static ConstructorStats &get(py::object class_) {
+        auto &internals = py::detail::get_internals();
+        const std::type_index *t1 = nullptr, *t2 = nullptr;
+        try {
+            auto *type_info
+                = internals.registered_types_py.at((PyTypeObject *) class_.ptr()).at(0);
+            for (auto &p : internals.registered_types_cpp) {
+                if (p.second == type_info) {
+                    if (t1) {
+                        t2 = &p.first;
+                        break;
+                    }
+                    t1 = &p.first;
+                }
+            }
+        } catch (const std::out_of_range &) {
+        }
+        if (!t1) {
+            throw std::runtime_error("Unknown class passed to ConstructorStats::get()");
+        }
+        auto &cs1 = get(*t1);
+        // If we have both a t1 and t2 match, one is probably the trampoline class; return
+        // whichever has more constructions (typically one or the other will be 0)
+        if (t2) {
+            auto &cs2 = get(*t2);
+            int cs1_total = cs1.default_constructions + cs1.copy_constructions
+                            + cs1.move_constructions + (int) cs1._values.size();
+            int cs2_total = cs2.default_constructions + cs2.copy_constructions
+                            + cs2.move_constructions + (int) cs2._values.size();
+            if (cs2_total > cs1_total) {
+                return cs2;
+            }
+        }
+        return cs1;
+    }
+};
+
+// To track construction/destruction, you need to call these methods from the various
+// constructors/operators.  The ones that take extra values record the given values in the
+// constructor stats values for later inspection.
+template <class T>
+void track_copy_created(T *inst) {
+    ConstructorStats::get<T>().copy_created(inst);
+}
+template <class T>
+void track_move_created(T *inst) {
+    ConstructorStats::get<T>().move_created(inst);
+}
+template <class T, typename... Values>
+void track_copy_assigned(T *, Values &&...values) {
+    auto &cst = ConstructorStats::get<T>();
+    cst.copy_assignments++;
+    cst.value(std::forward<Values>(values)...);
+}
+template <class T, typename... Values>
+void track_move_assigned(T *, Values &&...values) {
+    auto &cst = ConstructorStats::get<T>();
+    cst.move_assignments++;
+    cst.value(std::forward<Values>(values)...);
+}
+template <class T, typename... Values>
+void track_default_created(T *inst, Values &&...values) {
+    auto &cst = ConstructorStats::get<T>();
+    cst.default_created(inst);
+    cst.value(std::forward<Values>(values)...);
+}
+template <class T, typename... Values>
+void track_created(T *inst, Values &&...values) {
+    auto &cst = ConstructorStats::get<T>();
+    cst.created(inst);
+    cst.value(std::forward<Values>(values)...);
+}
+template <class T, typename... Values>
+void track_destroyed(T *inst) {
+    ConstructorStats::get<T>().destroyed(inst);
+}
+template <class T, typename... Values>
+void track_values(T *, Values &&...values) {
+    ConstructorStats::get<T>().value(std::forward<Values>(values)...);
+}
+
+/// Don't cast pointers to Python, print them as strings
+inline const char *format_ptrs(const char *p) { return p; }
+template <typename T>
+py::str format_ptrs(T *p) {
+    return "{:#x}"_s.format(reinterpret_cast<std::uintptr_t>(p));
+}
+template <typename T>
+auto format_ptrs(T &&x) -> decltype(std::forward<T>(x)) {
+    return std::forward<T>(x);
+}
+
+template <class T, typename... Output>
+void print_constr_details(T *inst, const std::string &action, Output &&...output) {
+    py::print("###",
+              py::type_id<T>(),
+              "@",
+              format_ptrs(inst),
+              action,
+              format_ptrs(std::forward<Output>(output))...);
+}
+
+// Verbose versions of the above:
+template <class T, typename... Values>
+void print_copy_created(T *inst,
+                        Values &&...values) { // NB: this prints, but doesn't store, given values
+    print_constr_details(inst, "created via copy constructor", values...);
+    track_copy_created(inst);
+}
+template <class T, typename... Values>
+void print_move_created(T *inst,
+                        Values &&...values) { // NB: this prints, but doesn't store, given values
+    print_constr_details(inst, "created via move constructor", values...);
+    track_move_created(inst);
+}
+template <class T, typename... Values>
+void print_copy_assigned(T *inst, Values &&...values) {
+    print_constr_details(inst, "assigned via copy assignment", values...);
+    track_copy_assigned(inst, values...);
+}
+template <class T, typename... Values>
+void print_move_assigned(T *inst, Values &&...values) {
+    print_constr_details(inst, "assigned via move assignment", values...);
+    track_move_assigned(inst, values...);
+}
+template <class T, typename... Values>
+void print_default_created(T *inst, Values &&...values) {
+    print_constr_details(inst, "created via default constructor", values...);
+    track_default_created(inst, values...);
+}
+template <class T, typename... Values>
+void print_created(T *inst, Values &&...values) {
+    print_constr_details(inst, "created", values...);
+    track_created(inst, values...);
+}
+template <class T, typename... Values>
+void print_destroyed(T *inst, Values &&...values) { // Prints but doesn't store given values
+    print_constr_details(inst, "destroyed", values...);
+    track_destroyed(inst);
+}
+template <class T, typename... Values>
+void print_values(T *inst, Values &&...values) {
+    print_constr_details(inst, ":", values...);
+    track_values(inst, values...);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/cross_module_gil_utils.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/cross_module_gil_utils.cpp
new file mode 100644
index 000000000..1436c35d6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/cross_module_gil_utils.cpp
@@ -0,0 +1,45 @@
+/*
+    tests/cross_module_gil_utils.cpp -- tools for acquiring GIL from a different module
+
+    Copyright (c) 2019 Google LLC
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+#include <pybind11/pybind11.h>
+
+#include <cstdint>
+
+// This file mimics a DSO that makes pybind11 calls but does not define a
+// PYBIND11_MODULE. The purpose is to test that such a DSO can create a
+// py::gil_scoped_acquire when the running thread is in a GIL-released state.
+//
+// Note that we define a Python module here for convenience, but in general
+// this need not be the case. The typical scenario would be a DSO that implements
+// shared logic used internally by multiple pybind11 modules.
+
+namespace {
+
+namespace py = pybind11;
+void gil_acquire() { py::gil_scoped_acquire gil; }
+
+constexpr char kModuleName[] = "cross_module_gil_utils";
+
+struct PyModuleDef moduledef = {
+    PyModuleDef_HEAD_INIT, kModuleName, nullptr, 0, nullptr, nullptr, nullptr, nullptr, nullptr};
+
+} // namespace
+
+extern "C" PYBIND11_EXPORT PyObject *PyInit_cross_module_gil_utils() {
+
+    PyObject *m = PyModule_Create(&moduledef);
+
+    if (m != nullptr) {
+        static_assert(sizeof(&gil_acquire) == sizeof(void *),
+                      "Function pointer must have the same size as void*");
+        PyModule_AddObject(
+            m, "gil_acquire_funcaddr", PyLong_FromVoidPtr(reinterpret_cast<void *>(&gil_acquire)));
+    }
+
+    return m;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/cross_module_interleaved_error_already_set.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/cross_module_interleaved_error_already_set.cpp
new file mode 100644
index 000000000..fdd9939e4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/cross_module_interleaved_error_already_set.cpp
@@ -0,0 +1,51 @@
+/*
+    Copyright (c) 2022 Google LLC
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/pybind11.h>
+
+// This file mimics a DSO that makes pybind11 calls but does not define a PYBIND11_MODULE,
+// so that the first call of cross_module_error_already_set() triggers the first call of
+// pybind11::detail::get_internals().
+
+namespace {
+
+namespace py = pybind11;
+
+void interleaved_error_already_set() {
+    PyErr_SetString(PyExc_RuntimeError, "1st error.");
+    try {
+        throw py::error_already_set();
+    } catch (const py::error_already_set &) {
+        // The 2nd error could be conditional in a real application.
+        PyErr_SetString(PyExc_RuntimeError, "2nd error.");
+    } // Here the 1st error is destroyed before the 2nd error is fetched.
+    // The error_already_set dtor triggers a pybind11::detail::get_internals()
+    // call via pybind11::gil_scoped_acquire.
+    if (PyErr_Occurred()) {
+        throw py::error_already_set();
+    }
+}
+
+constexpr char kModuleName[] = "cross_module_interleaved_error_already_set";
+
+struct PyModuleDef moduledef = {
+    PyModuleDef_HEAD_INIT, kModuleName, nullptr, 0, nullptr, nullptr, nullptr, nullptr, nullptr};
+
+} // namespace
+
+extern "C" PYBIND11_EXPORT PyObject *PyInit_cross_module_interleaved_error_already_set() {
+    PyObject *m = PyModule_Create(&moduledef);
+    if (m != nullptr) {
+        static_assert(sizeof(&interleaved_error_already_set) == sizeof(void *),
+                      "Function pointer must have the same size as void *");
+        PyModule_AddObject(
+            m,
+            "funcaddr",
+            PyLong_FromVoidPtr(reinterpret_cast<void *>(&interleaved_error_already_set)));
+    }
+    return m;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/env.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/env.py
new file mode 100644
index 000000000..0345df65d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/env.py
@@ -0,0 +1,28 @@
+import platform
+import sys
+
+import pytest
+
+LINUX = sys.platform.startswith("linux")
+MACOS = sys.platform.startswith("darwin")
+WIN = sys.platform.startswith("win32") or sys.platform.startswith("cygwin")
+
+CPYTHON = platform.python_implementation() == "CPython"
+PYPY = platform.python_implementation() == "PyPy"
+
+
+def deprecated_call():
+    """
+    pytest.deprecated_call() seems broken in pytest<3.9.x; concretely, it
+    doesn't work on CPython 3.8.0 with pytest==3.3.2 on Ubuntu 18.04 (#2922).
+
+    This is a narrowed reimplementation of the following PR :(
+    https://github.com/pytest-dev/pytest/pull/4104
+    """
+    # TODO: Remove this when testing requires pytest>=3.9.
+    pieces = pytest.__version__.split(".")
+    pytest_major_minor = (int(pieces[0]), int(pieces[1]))
+    if pytest_major_minor < (3, 9):
+        return pytest.warns((DeprecationWarning, PendingDeprecationWarning))
+    else:
+        return pytest.deprecated_call()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_python_package/pytest.ini b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_python_package/pytest.ini
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_python_package/test_files.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_python_package/test_files.py
new file mode 100644
index 000000000..ba16b5224
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_python_package/test_files.py
@@ -0,0 +1,274 @@
+import contextlib
+import os
+import string
+import subprocess
+import sys
+import tarfile
+import zipfile
+
+# These tests must be run explicitly
+# They require CMake 3.15+ (--install)
+
+DIR = os.path.abspath(os.path.dirname(__file__))
+MAIN_DIR = os.path.dirname(os.path.dirname(DIR))
+
+
+main_headers = {
+    "include/pybind11/attr.h",
+    "include/pybind11/buffer_info.h",
+    "include/pybind11/cast.h",
+    "include/pybind11/chrono.h",
+    "include/pybind11/common.h",
+    "include/pybind11/complex.h",
+    "include/pybind11/eigen.h",
+    "include/pybind11/embed.h",
+    "include/pybind11/eval.h",
+    "include/pybind11/functional.h",
+    "include/pybind11/gil.h",
+    "include/pybind11/iostream.h",
+    "include/pybind11/numpy.h",
+    "include/pybind11/operators.h",
+    "include/pybind11/options.h",
+    "include/pybind11/pybind11.h",
+    "include/pybind11/pytypes.h",
+    "include/pybind11/stl.h",
+    "include/pybind11/stl_bind.h",
+}
+
+detail_headers = {
+    "include/pybind11/detail/class.h",
+    "include/pybind11/detail/common.h",
+    "include/pybind11/detail/descr.h",
+    "include/pybind11/detail/init.h",
+    "include/pybind11/detail/internals.h",
+    "include/pybind11/detail/type_caster_base.h",
+    "include/pybind11/detail/typeid.h",
+}
+
+stl_headers = {
+    "include/pybind11/stl/filesystem.h",
+}
+
+cmake_files = {
+    "share/cmake/pybind11/FindPythonLibsNew.cmake",
+    "share/cmake/pybind11/pybind11Common.cmake",
+    "share/cmake/pybind11/pybind11Config.cmake",
+    "share/cmake/pybind11/pybind11ConfigVersion.cmake",
+    "share/cmake/pybind11/pybind11NewTools.cmake",
+    "share/cmake/pybind11/pybind11Targets.cmake",
+    "share/cmake/pybind11/pybind11Tools.cmake",
+}
+
+py_files = {
+    "__init__.py",
+    "__main__.py",
+    "_version.py",
+    "commands.py",
+    "py.typed",
+    "setup_helpers.py",
+}
+
+headers = main_headers | detail_headers | stl_headers
+src_files = headers | cmake_files
+all_files = src_files | py_files
+
+
+sdist_files = {
+    "pybind11",
+    "pybind11/include",
+    "pybind11/include/pybind11",
+    "pybind11/include/pybind11/detail",
+    "pybind11/include/pybind11/stl",
+    "pybind11/share",
+    "pybind11/share/cmake",
+    "pybind11/share/cmake/pybind11",
+    "pyproject.toml",
+    "setup.cfg",
+    "setup.py",
+    "LICENSE",
+    "MANIFEST.in",
+    "README.rst",
+    "PKG-INFO",
+}
+
+local_sdist_files = {
+    ".egg-info",
+    ".egg-info/PKG-INFO",
+    ".egg-info/SOURCES.txt",
+    ".egg-info/dependency_links.txt",
+    ".egg-info/not-zip-safe",
+    ".egg-info/top_level.txt",
+}
+
+
+def test_build_sdist(monkeypatch, tmpdir):
+
+    monkeypatch.chdir(MAIN_DIR)
+
+    out = subprocess.check_output(
+        [
+            sys.executable,
+            "-m",
+            "build",
+            "--sdist",
+            "--outdir",
+            str(tmpdir),
+        ]
+    )
+    if hasattr(out, "decode"):
+        out = out.decode()
+
+    (sdist,) = tmpdir.visit("*.tar.gz")
+
+    with tarfile.open(str(sdist), "r:gz") as tar:
+        start = tar.getnames()[0] + "/"
+        version = start[9:-1]
+        simpler = {n.split("/", 1)[-1] for n in tar.getnames()[1:]}
+
+        with contextlib.closing(
+            tar.extractfile(tar.getmember(start + "setup.py"))
+        ) as f:
+            setup_py = f.read()
+
+        with contextlib.closing(
+            tar.extractfile(tar.getmember(start + "pyproject.toml"))
+        ) as f:
+            pyproject_toml = f.read()
+
+        with contextlib.closing(
+            tar.extractfile(
+                tar.getmember(
+                    start + "pybind11/share/cmake/pybind11/pybind11Config.cmake"
+                )
+            )
+        ) as f:
+            contents = f.read().decode("utf8")
+        assert 'set(pybind11_INCLUDE_DIR "${PACKAGE_PREFIX_DIR}/include")' in contents
+
+    files = {f"pybind11/{n}" for n in all_files}
+    files |= sdist_files
+    files |= {f"pybind11{n}" for n in local_sdist_files}
+    files.add("pybind11.egg-info/entry_points.txt")
+    files.add("pybind11.egg-info/requires.txt")
+    assert simpler == files
+
+    with open(os.path.join(MAIN_DIR, "tools", "setup_main.py.in"), "rb") as f:
+        contents = (
+            string.Template(f.read().decode())
+            .substitute(version=version, extra_cmd="")
+            .encode()
+        )
+    assert setup_py == contents
+
+    with open(os.path.join(MAIN_DIR, "tools", "pyproject.toml"), "rb") as f:
+        contents = f.read()
+    assert pyproject_toml == contents
+
+
+def test_build_global_dist(monkeypatch, tmpdir):
+
+    monkeypatch.chdir(MAIN_DIR)
+    monkeypatch.setenv("PYBIND11_GLOBAL_SDIST", "1")
+    out = subprocess.check_output(
+        [
+            sys.executable,
+            "-m",
+            "build",
+            "--sdist",
+            "--outdir",
+            str(tmpdir),
+        ]
+    )
+
+    if hasattr(out, "decode"):
+        out = out.decode()
+
+    (sdist,) = tmpdir.visit("*.tar.gz")
+
+    with tarfile.open(str(sdist), "r:gz") as tar:
+        start = tar.getnames()[0] + "/"
+        version = start[16:-1]
+        simpler = {n.split("/", 1)[-1] for n in tar.getnames()[1:]}
+
+        with contextlib.closing(
+            tar.extractfile(tar.getmember(start + "setup.py"))
+        ) as f:
+            setup_py = f.read()
+
+        with contextlib.closing(
+            tar.extractfile(tar.getmember(start + "pyproject.toml"))
+        ) as f:
+            pyproject_toml = f.read()
+
+    files = {f"pybind11/{n}" for n in all_files}
+    files |= sdist_files
+    files |= {f"pybind11_global{n}" for n in local_sdist_files}
+    assert simpler == files
+
+    with open(os.path.join(MAIN_DIR, "tools", "setup_global.py.in"), "rb") as f:
+        contents = (
+            string.Template(f.read().decode())
+            .substitute(version=version, extra_cmd="")
+            .encode()
+        )
+        assert setup_py == contents
+
+    with open(os.path.join(MAIN_DIR, "tools", "pyproject.toml"), "rb") as f:
+        contents = f.read()
+        assert pyproject_toml == contents
+
+
+def tests_build_wheel(monkeypatch, tmpdir):
+    monkeypatch.chdir(MAIN_DIR)
+
+    subprocess.check_output(
+        [sys.executable, "-m", "pip", "wheel", ".", "-w", str(tmpdir)]
+    )
+
+    (wheel,) = tmpdir.visit("*.whl")
+
+    files = {f"pybind11/{n}" for n in all_files}
+    files |= {
+        "dist-info/LICENSE",
+        "dist-info/METADATA",
+        "dist-info/RECORD",
+        "dist-info/WHEEL",
+        "dist-info/entry_points.txt",
+        "dist-info/top_level.txt",
+    }
+
+    with zipfile.ZipFile(str(wheel)) as z:
+        names = z.namelist()
+
+    trimmed = {n for n in names if "dist-info" not in n}
+    trimmed |= {f"dist-info/{n.split('/', 1)[-1]}" for n in names if "dist-info" in n}
+    assert files == trimmed
+
+
+def tests_build_global_wheel(monkeypatch, tmpdir):
+    monkeypatch.chdir(MAIN_DIR)
+    monkeypatch.setenv("PYBIND11_GLOBAL_SDIST", "1")
+
+    subprocess.check_output(
+        [sys.executable, "-m", "pip", "wheel", ".", "-w", str(tmpdir)]
+    )
+
+    (wheel,) = tmpdir.visit("*.whl")
+
+    files = {f"data/data/{n}" for n in src_files}
+    files |= {f"data/headers/{n[8:]}" for n in headers}
+    files |= {
+        "dist-info/LICENSE",
+        "dist-info/METADATA",
+        "dist-info/WHEEL",
+        "dist-info/top_level.txt",
+        "dist-info/RECORD",
+    }
+
+    with zipfile.ZipFile(str(wheel)) as z:
+        names = z.namelist()
+
+    beginning = names[0].split("/", 1)[0].rsplit(".", 1)[0]
+    trimmed = {n[len(beginning) + 1 :] for n in names}
+
+    assert files == trimmed
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_setuptools/pytest.ini b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_setuptools/pytest.ini
new file mode 100644
index 000000000..e69de29bb
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_setuptools/test_setuphelper.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_setuptools/test_setuphelper.py
new file mode 100644
index 000000000..d5d3093bf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/extra_setuptools/test_setuphelper.py
@@ -0,0 +1,151 @@
+import os
+import subprocess
+import sys
+from textwrap import dedent
+
+import pytest
+
+DIR = os.path.abspath(os.path.dirname(__file__))
+MAIN_DIR = os.path.dirname(os.path.dirname(DIR))
+WIN = sys.platform.startswith("win32") or sys.platform.startswith("cygwin")
+
+
+@pytest.mark.parametrize("parallel", [False, True])
+@pytest.mark.parametrize("std", [11, 0])
+def test_simple_setup_py(monkeypatch, tmpdir, parallel, std):
+    monkeypatch.chdir(tmpdir)
+    monkeypatch.syspath_prepend(MAIN_DIR)
+
+    (tmpdir / "setup.py").write_text(
+        dedent(
+            f"""\
+            import sys
+            sys.path.append({MAIN_DIR!r})
+
+            from setuptools import setup, Extension
+            from pybind11.setup_helpers import build_ext, Pybind11Extension
+
+            std = {std}
+
+            ext_modules = [
+                Pybind11Extension(
+                    "simple_setup",
+                    sorted(["main.cpp"]),
+                    cxx_std=std,
+                ),
+            ]
+
+            cmdclass = dict()
+            if std == 0:
+                cmdclass["build_ext"] = build_ext
+
+
+            parallel = {parallel}
+            if parallel:
+                from pybind11.setup_helpers import ParallelCompile
+                ParallelCompile().install()
+
+            setup(
+                name="simple_setup_package",
+                cmdclass=cmdclass,
+                ext_modules=ext_modules,
+            )
+            """
+        ),
+        encoding="ascii",
+    )
+
+    (tmpdir / "main.cpp").write_text(
+        dedent(
+            """\
+            #include <pybind11/pybind11.h>
+
+            int f(int x) {
+                return x * 3;
+            }
+            PYBIND11_MODULE(simple_setup, m) {
+                m.def("f", &f);
+            }
+            """
+        ),
+        encoding="ascii",
+    )
+
+    out = subprocess.check_output(
+        [sys.executable, "setup.py", "build_ext", "--inplace"],
+    )
+    if not WIN:
+        assert b"-g0" in out
+    out = subprocess.check_output(
+        [sys.executable, "setup.py", "build_ext", "--inplace", "--force"],
+        env=dict(os.environ, CFLAGS="-g"),
+    )
+    if not WIN:
+        assert b"-g0" not in out
+
+    # Debug helper printout, normally hidden
+    print(out)
+    for item in tmpdir.listdir():
+        print(item.basename)
+
+    assert (
+        len([f for f in tmpdir.listdir() if f.basename.startswith("simple_setup")]) == 1
+    )
+    assert len(list(tmpdir.listdir())) == 4  # two files + output + build_dir
+
+    (tmpdir / "test.py").write_text(
+        dedent(
+            """\
+            import simple_setup
+            assert simple_setup.f(3) == 9
+            """
+        ),
+        encoding="ascii",
+    )
+
+    subprocess.check_call(
+        [sys.executable, "test.py"], stdout=sys.stdout, stderr=sys.stderr
+    )
+
+
+def test_intree_extensions(monkeypatch, tmpdir):
+    monkeypatch.syspath_prepend(MAIN_DIR)
+
+    from pybind11.setup_helpers import intree_extensions
+
+    monkeypatch.chdir(tmpdir)
+    root = tmpdir
+    root.ensure_dir()
+    subdir = root / "dir"
+    subdir.ensure_dir()
+    src = subdir / "ext.cpp"
+    src.ensure()
+    relpath = src.relto(tmpdir)
+    (ext,) = intree_extensions([relpath])
+    assert ext.name == "ext"
+    subdir.ensure("__init__.py")
+    (ext,) = intree_extensions([relpath])
+    assert ext.name == "dir.ext"
+
+
+def test_intree_extensions_package_dir(monkeypatch, tmpdir):
+    monkeypatch.syspath_prepend(MAIN_DIR)
+
+    from pybind11.setup_helpers import intree_extensions
+
+    monkeypatch.chdir(tmpdir)
+    root = tmpdir / "src"
+    root.ensure_dir()
+    subdir = root / "dir"
+    subdir.ensure_dir()
+    src = subdir / "ext.cpp"
+    src.ensure()
+    (ext,) = intree_extensions([src.relto(tmpdir)], package_dir={"": "src"})
+    assert ext.name == "dir.ext"
+    (ext,) = intree_extensions([src.relto(tmpdir)], package_dir={"foo": "src"})
+    assert ext.name == "foo.dir.ext"
+    subdir.ensure("__init__.py")
+    (ext,) = intree_extensions([src.relto(tmpdir)], package_dir={"": "src"})
+    assert ext.name == "dir.ext"
+    (ext,) = intree_extensions([src.relto(tmpdir)], package_dir={"foo": "src"})
+    assert ext.name == "foo.dir.ext"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/local_bindings.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/local_bindings.h
new file mode 100644
index 000000000..01d278535
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/local_bindings.h
@@ -0,0 +1,92 @@
+#pragma once
+#include "pybind11_tests.h"
+
+#include <utility>
+
+/// Simple class used to test py::local:
+template <int>
+class LocalBase {
+public:
+    explicit LocalBase(int i) : i(i) {}
+    int i = -1;
+};
+
+/// Registered with py::module_local in both main and secondary modules:
+using LocalType = LocalBase<0>;
+/// Registered without py::module_local in both modules:
+using NonLocalType = LocalBase<1>;
+/// A second non-local type (for stl_bind tests):
+using NonLocal2 = LocalBase<2>;
+/// Tests within-module, different-compilation-unit local definition conflict:
+using LocalExternal = LocalBase<3>;
+/// Mixed: registered local first, then global
+using MixedLocalGlobal = LocalBase<4>;
+/// Mixed: global first, then local
+using MixedGlobalLocal = LocalBase<5>;
+
+/// Registered with py::module_local only in the secondary module:
+using ExternalType1 = LocalBase<6>;
+using ExternalType2 = LocalBase<7>;
+
+using LocalVec = std::vector<LocalType>;
+using LocalVec2 = std::vector<NonLocal2>;
+using LocalMap = std::unordered_map<std::string, LocalType>;
+using NonLocalVec = std::vector<NonLocalType>;
+using NonLocalVec2 = std::vector<NonLocal2>;
+using NonLocalMap = std::unordered_map<std::string, NonLocalType>;
+using NonLocalMap2 = std::unordered_map<std::string, uint8_t>;
+
+// Exception that will be caught via the module local translator.
+class LocalException : public std::exception {
+public:
+    explicit LocalException(const char *m) : message{m} {}
+    const char *what() const noexcept override { return message.c_str(); }
+
+private:
+    std::string message = "";
+};
+
+// Exception that will be registered with register_local_exception_translator
+class LocalSimpleException : public std::exception {
+public:
+    explicit LocalSimpleException(const char *m) : message{m} {}
+    const char *what() const noexcept override { return message.c_str(); }
+
+private:
+    std::string message = "";
+};
+
+PYBIND11_MAKE_OPAQUE(LocalVec);
+PYBIND11_MAKE_OPAQUE(LocalVec2);
+PYBIND11_MAKE_OPAQUE(LocalMap);
+PYBIND11_MAKE_OPAQUE(NonLocalVec);
+// PYBIND11_MAKE_OPAQUE(NonLocalVec2); // same type as LocalVec2
+PYBIND11_MAKE_OPAQUE(NonLocalMap);
+PYBIND11_MAKE_OPAQUE(NonLocalMap2);
+
+// Simple bindings (used with the above):
+template <typename T, int Adjust = 0, typename... Args>
+py::class_<T> bind_local(Args &&...args) {
+    return py::class_<T>(std::forward<Args>(args)...).def(py::init<int>()).def("get", [](T &i) {
+        return i.i + Adjust;
+    });
+};
+
+// Simulate a foreign library base class (to match the example in the docs):
+namespace pets {
+class Pet {
+public:
+    explicit Pet(std::string name) : name_(std::move(name)) {}
+    std::string name_;
+    const std::string &name() const { return name_; }
+};
+} // namespace pets
+
+struct MixGL {
+    int i;
+    explicit MixGL(int i) : i{i} {}
+};
+struct MixGL2 {
+    int i;
+    explicit MixGL2(int i) : i{i} {}
+};
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/object.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/object.h
new file mode 100644
index 000000000..564dd4a7a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/object.h
@@ -0,0 +1,205 @@
+#if !defined(__OBJECT_H)
+#    define __OBJECT_H
+
+#    include "constructor_stats.h"
+
+#    include <atomic>
+
+/// Reference counted object base class
+class Object {
+public:
+    /// Default constructor
+    Object() { print_default_created(this); }
+
+    /// Copy constructor
+    Object(const Object &) : m_refCount(0) { print_copy_created(this); }
+
+    /// Return the current reference count
+    int getRefCount() const { return m_refCount; };
+
+    /// Increase the object's reference count by one
+    void incRef() const { ++m_refCount; }
+
+    /** \brief Decrease the reference count of
+     * the object and possibly deallocate it.
+     *
+     * The object will automatically be deallocated once
+     * the reference count reaches zero.
+     */
+    void decRef(bool dealloc = true) const {
+        --m_refCount;
+        if (m_refCount == 0 && dealloc) {
+            delete this;
+        } else if (m_refCount < 0) {
+            throw std::runtime_error("Internal error: reference count < 0!");
+        }
+    }
+
+    virtual std::string toString() const = 0;
+
+protected:
+    /** \brief Virtual protected deconstructor.
+     * (Will only be called by \ref ref)
+     */
+    virtual ~Object() { print_destroyed(this); }
+
+private:
+    mutable std::atomic<int> m_refCount{0};
+};
+
+// Tag class used to track constructions of ref objects.  When we track constructors, below, we
+// track and print out the actual class (e.g. ref<MyObject>), and *also* add a fake tracker for
+// ref_tag.  This lets us check that the total number of ref<Anything> constructors/destructors is
+// correct without having to check each individual ref<Whatever> type individually.
+class ref_tag {};
+
+/**
+ * \brief Reference counting helper
+ *
+ * The \a ref refeference template is a simple wrapper to store a
+ * pointer to an object. It takes care of increasing and decreasing
+ * the reference count of the object. When the last reference goes
+ * out of scope, the associated object will be deallocated.
+ *
+ * \ingroup libcore
+ */
+template <typename T>
+class ref {
+public:
+    /// Create a nullptr reference
+    ref() : m_ptr(nullptr) {
+        print_default_created(this);
+        track_default_created((ref_tag *) this);
+    }
+
+    /// Construct a reference from a pointer
+    explicit ref(T *ptr) : m_ptr(ptr) {
+        if (m_ptr) {
+            ((Object *) m_ptr)->incRef();
+        }
+
+        print_created(this, "from pointer", m_ptr);
+        track_created((ref_tag *) this, "from pointer");
+    }
+
+    /// Copy constructor
+    ref(const ref &r) : m_ptr(r.m_ptr) {
+        if (m_ptr) {
+            ((Object *) m_ptr)->incRef();
+        }
+
+        print_copy_created(this, "with pointer", m_ptr);
+        track_copy_created((ref_tag *) this);
+    }
+
+    /// Move constructor
+    ref(ref &&r) noexcept : m_ptr(r.m_ptr) {
+        r.m_ptr = nullptr;
+
+        print_move_created(this, "with pointer", m_ptr);
+        track_move_created((ref_tag *) this);
+    }
+
+    /// Destroy this reference
+    ~ref() {
+        if (m_ptr) {
+            ((Object *) m_ptr)->decRef();
+        }
+
+        print_destroyed(this);
+        track_destroyed((ref_tag *) this);
+    }
+
+    /// Move another reference into the current one
+    ref &operator=(ref &&r) noexcept {
+        print_move_assigned(this, "pointer", r.m_ptr);
+        track_move_assigned((ref_tag *) this);
+
+        if (*this == r) {
+            return *this;
+        }
+        if (m_ptr) {
+            ((Object *) m_ptr)->decRef();
+        }
+        m_ptr = r.m_ptr;
+        r.m_ptr = nullptr;
+        return *this;
+    }
+
+    /// Overwrite this reference with another reference
+    ref &operator=(const ref &r) {
+        if (this == &r) {
+            return *this;
+        }
+        print_copy_assigned(this, "pointer", r.m_ptr);
+        track_copy_assigned((ref_tag *) this);
+
+        if (m_ptr == r.m_ptr) {
+            return *this;
+        }
+        if (m_ptr) {
+            ((Object *) m_ptr)->decRef();
+        }
+        m_ptr = r.m_ptr;
+        if (m_ptr) {
+            ((Object *) m_ptr)->incRef();
+        }
+        return *this;
+    }
+
+    /// Overwrite this reference with a pointer to another object
+    ref &operator=(T *ptr) {
+        print_values(this, "assigned pointer");
+        track_values((ref_tag *) this, "assigned pointer");
+
+        if (m_ptr == ptr) {
+            return *this;
+        }
+        if (m_ptr) {
+            ((Object *) m_ptr)->decRef();
+        }
+        m_ptr = ptr;
+        if (m_ptr) {
+            ((Object *) m_ptr)->incRef();
+        }
+        return *this;
+    }
+
+    /// Compare this reference with another reference
+    bool operator==(const ref &r) const { return m_ptr == r.m_ptr; }
+
+    /// Compare this reference with another reference
+    bool operator!=(const ref &r) const { return m_ptr != r.m_ptr; }
+
+    /// Compare this reference with a pointer
+    bool operator==(const T *ptr) const { return m_ptr == ptr; }
+
+    /// Compare this reference with a pointer
+    bool operator!=(const T *ptr) const { return m_ptr != ptr; }
+
+    /// Access the object referenced by this reference
+    T *operator->() { return m_ptr; }
+
+    /// Access the object referenced by this reference
+    const T *operator->() const { return m_ptr; }
+
+    /// Return a C++ reference to the referenced object
+    T &operator*() { return *m_ptr; }
+
+    /// Return a const C++ reference to the referenced object
+    const T &operator*() const { return *m_ptr; }
+
+    /// Return a pointer to the referenced object
+    explicit operator T *() { return m_ptr; }
+
+    /// Return a const pointer to the referenced object
+    T *get_ptr() { return m_ptr; }
+
+    /// Return a pointer to the referenced object
+    const T *get_ptr() const { return m_ptr; }
+
+private:
+    T *m_ptr;
+};
+
+#endif /* __OBJECT_H */
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_cross_module_tests.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_cross_module_tests.cpp
new file mode 100644
index 000000000..9379f3f25
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_cross_module_tests.cpp
@@ -0,0 +1,149 @@
+/*
+    tests/pybind11_cross_module_tests.cpp -- contains tests that require multiple modules
+
+    Copyright (c) 2017 Jason Rhinelander <jason@imaginary.ca>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/stl_bind.h>
+
+#include "local_bindings.h"
+#include "pybind11_tests.h"
+#include "test_exceptions.h"
+
+#include <numeric>
+#include <utility>
+
+PYBIND11_MODULE(pybind11_cross_module_tests, m) {
+    m.doc() = "pybind11 cross-module test module";
+
+    // test_local_bindings.py tests:
+    //
+    // Definitions here are tested by importing both this module and the
+    // relevant pybind11_tests submodule from a test_whatever.py
+
+    // test_load_external
+    bind_local<ExternalType1>(m, "ExternalType1", py::module_local());
+    bind_local<ExternalType2>(m, "ExternalType2", py::module_local());
+
+    // test_exceptions.py
+    py::register_local_exception<LocalSimpleException>(m, "LocalSimpleException");
+    m.def("raise_runtime_error", []() {
+        PyErr_SetString(PyExc_RuntimeError, "My runtime error");
+        throw py::error_already_set();
+    });
+    m.def("raise_value_error", []() {
+        PyErr_SetString(PyExc_ValueError, "My value error");
+        throw py::error_already_set();
+    });
+    m.def("throw_pybind_value_error", []() { throw py::value_error("pybind11 value error"); });
+    m.def("throw_pybind_type_error", []() { throw py::type_error("pybind11 type error"); });
+    m.def("throw_stop_iteration", []() { throw py::stop_iteration(); });
+    m.def("throw_local_error", []() { throw LocalException("just local"); });
+    m.def("throw_local_simple_error", []() { throw LocalSimpleException("external mod"); });
+    py::register_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) {
+                std::rethrow_exception(p);
+            }
+        } catch (const shared_exception &e) {
+            PyErr_SetString(PyExc_KeyError, e.what());
+        }
+    });
+
+    // translate the local exception into a key error but only in this module
+    py::register_local_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) {
+                std::rethrow_exception(p);
+            }
+        } catch (const LocalException &e) {
+            PyErr_SetString(PyExc_KeyError, e.what());
+        }
+    });
+
+    // test_local_bindings.py
+    // Local to both:
+    bind_local<LocalType, 1>(m, "LocalType", py::module_local()).def("get2", [](LocalType &t) {
+        return t.i + 2;
+    });
+
+    // Can only be called with our python type:
+    m.def("local_value", [](LocalType &l) { return l.i; });
+
+    // test_nonlocal_failure
+    // This registration will fail (global registration when LocalFail is already registered
+    // globally in the main test module):
+    m.def("register_nonlocal", [m]() { bind_local<NonLocalType, 0>(m, "NonLocalType"); });
+
+    // test_stl_bind_local
+    // stl_bind.h binders defaults to py::module_local if the types are local or converting:
+    py::bind_vector<LocalVec>(m, "LocalVec");
+    py::bind_map<LocalMap>(m, "LocalMap");
+
+    // test_stl_bind_global
+    // and global if the type (or one of the types, for the map) is global (so these will fail,
+    // assuming pybind11_tests is already loaded):
+    m.def("register_nonlocal_vec", [m]() { py::bind_vector<NonLocalVec>(m, "NonLocalVec"); });
+    m.def("register_nonlocal_map", [m]() { py::bind_map<NonLocalMap>(m, "NonLocalMap"); });
+    // The default can, however, be overridden to global using `py::module_local()` or
+    // `py::module_local(false)`.
+    // Explicitly made local:
+    py::bind_vector<NonLocalVec2>(m, "NonLocalVec2", py::module_local());
+    // Explicitly made global (and so will fail to bind):
+    m.def("register_nonlocal_map2",
+          [m]() { py::bind_map<NonLocalMap2>(m, "NonLocalMap2", py::module_local(false)); });
+
+    // test_mixed_local_global
+    // We try this both with the global type registered first and vice versa (the order shouldn't
+    // matter).
+    m.def("register_mixed_global_local",
+          [m]() { bind_local<MixedGlobalLocal, 200>(m, "MixedGlobalLocal", py::module_local()); });
+    m.def("register_mixed_local_global", [m]() {
+        bind_local<MixedLocalGlobal, 2000>(m, "MixedLocalGlobal", py::module_local(false));
+    });
+    m.def("get_mixed_gl", [](int i) { return MixedGlobalLocal(i); });
+    m.def("get_mixed_lg", [](int i) { return MixedLocalGlobal(i); });
+
+    // test_internal_locals_differ
+    m.def("local_cpp_types_addr",
+          []() { return (uintptr_t) &py::detail::get_local_internals().registered_types_cpp; });
+
+    // test_stl_caster_vs_stl_bind
+    py::bind_vector<std::vector<int>>(m, "VectorInt");
+
+    m.def("load_vector_via_binding",
+          [](std::vector<int> &v) { return std::accumulate(v.begin(), v.end(), 0); });
+
+    // test_cross_module_calls
+    m.def("return_self", [](LocalVec *v) { return v; });
+    m.def("return_copy", [](const LocalVec &v) { return LocalVec(v); });
+
+    class Dog : public pets::Pet {
+    public:
+        explicit Dog(std::string name) : Pet(std::move(name)) {}
+    };
+    py::class_<pets::Pet>(m, "Pet", py::module_local()).def("name", &pets::Pet::name);
+    // Binding for local extending class:
+    py::class_<Dog, pets::Pet>(m, "Dog").def(py::init<std::string>());
+    m.def("pet_name", [](pets::Pet &p) { return p.name(); });
+
+    py::class_<MixGL>(m, "MixGL", py::module_local()).def(py::init<int>());
+    m.def("get_gl_value", [](MixGL &o) { return o.i + 100; });
+
+    py::class_<MixGL2>(m, "MixGL2", py::module_local()).def(py::init<int>());
+
+    // test_vector_bool
+    // We can't test both stl.h and stl_bind.h conversions of `std::vector<bool>` within
+    // the same module (it would be an ODR violation). Therefore `bind_vector` of `bool`
+    // is defined here and tested in `test_stl_binders.py`.
+    py::bind_vector<std::vector<bool>>(m, "VectorBool");
+
+    // test_missing_header_message
+    // The main module already includes stl.h, but we need to test the error message
+    // which appears when this header is missing.
+    m.def("missing_header_arg", [](const std::vector<float> &) {});
+    m.def("missing_header_return", []() { return std::vector<float>(); });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_tests.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_tests.cpp
new file mode 100644
index 000000000..aa3095594
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_tests.cpp
@@ -0,0 +1,117 @@
+/*
+    tests/pybind11_tests.cpp -- pybind example plugin
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+#include "constructor_stats.h"
+
+#include <functional>
+#include <list>
+
+/*
+For testing purposes, we define a static global variable here in a function that each individual
+test .cpp calls with its initialization lambda.  It's convenient here because we can just not
+compile some test files to disable/ignore some of the test code.
+
+It is NOT recommended as a way to use pybind11 in practice, however: the initialization order will
+be essentially random, which is okay for our test scripts (there are no dependencies between the
+individual pybind11 test .cpp files), but most likely not what you want when using pybind11
+productively.
+
+Instead, see the "How can I reduce the build time?" question in the "Frequently asked questions"
+section of the documentation for good practice on splitting binding code over multiple files.
+*/
+std::list<std::function<void(py::module_ &)>> &initializers() {
+    static std::list<std::function<void(py::module_ &)>> inits;
+    return inits;
+}
+
+test_initializer::test_initializer(Initializer init) { initializers().emplace_back(init); }
+
+test_initializer::test_initializer(const char *submodule_name, Initializer init) {
+    initializers().emplace_back([=](py::module_ &parent) {
+        auto m = parent.def_submodule(submodule_name);
+        init(m);
+    });
+}
+
+void bind_ConstructorStats(py::module_ &m) {
+    py::class_<ConstructorStats>(m, "ConstructorStats")
+        .def("alive", &ConstructorStats::alive)
+        .def("values", &ConstructorStats::values)
+        .def_readwrite("default_constructions", &ConstructorStats::default_constructions)
+        .def_readwrite("copy_assignments", &ConstructorStats::copy_assignments)
+        .def_readwrite("move_assignments", &ConstructorStats::move_assignments)
+        .def_readwrite("copy_constructions", &ConstructorStats::copy_constructions)
+        .def_readwrite("move_constructions", &ConstructorStats::move_constructions)
+        .def_static("get",
+                    (ConstructorStats & (*) (py::object)) & ConstructorStats::get,
+                    py::return_value_policy::reference_internal)
+
+        // Not exactly ConstructorStats, but related: expose the internal pybind number of
+        // registered instances to allow instance cleanup checks (invokes a GC first)
+        .def_static("detail_reg_inst", []() {
+            ConstructorStats::gc();
+            return py::detail::get_internals().registered_instances.size();
+        });
+}
+
+const char *cpp_std() {
+    return
+#if defined(PYBIND11_CPP20)
+        "C++20";
+#elif defined(PYBIND11_CPP17)
+        "C++17";
+#elif defined(PYBIND11_CPP14)
+        "C++14";
+#else
+        "C++11";
+#endif
+}
+
+PYBIND11_MODULE(pybind11_tests, m) {
+    m.doc() = "pybind11 test module";
+
+    // Intentionally kept minimal to not create a maintenance chore
+    // ("just enough" to be conclusive).
+#if defined(_MSC_FULL_VER)
+    m.attr("compiler_info") = "MSVC " PYBIND11_TOSTRING(_MSC_FULL_VER);
+#elif defined(__VERSION__)
+    m.attr("compiler_info") = __VERSION__;
+#else
+    m.attr("compiler_info") = py::none();
+#endif
+    m.attr("cpp_std") = cpp_std();
+    m.attr("PYBIND11_INTERNALS_ID") = PYBIND11_INTERNALS_ID;
+
+    bind_ConstructorStats(m);
+
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+    m.attr("detailed_error_messages_enabled") = true;
+#else
+    m.attr("detailed_error_messages_enabled") = false;
+#endif
+
+    py::class_<UserType>(m, "UserType", "A `py::class_` type for testing")
+        .def(py::init<>())
+        .def(py::init<int>())
+        .def("get_value", &UserType::value, "Get value using a method")
+        .def("set_value", &UserType::set, "Set value using a method")
+        .def_property("value", &UserType::value, &UserType::set, "Get/set value using a property")
+        .def("__repr__", [](const UserType &u) { return "UserType({})"_s.format(u.value()); });
+
+    py::class_<IncType, UserType>(m, "IncType")
+        .def(py::init<>())
+        .def(py::init<int>())
+        .def("__repr__", [](const IncType &u) { return "IncType({})"_s.format(u.value()); });
+
+    for (const auto &initializer : initializers()) {
+        initializer(m);
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_tests.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_tests.h
new file mode 100644
index 000000000..a7c00c2f9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pybind11_tests.h
@@ -0,0 +1,85 @@
+#pragma once
+
+#include <pybind11/eval.h>
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+using namespace pybind11::literals;
+
+class test_initializer {
+    using Initializer = void (*)(py::module_ &);
+
+public:
+    explicit test_initializer(Initializer init);
+    test_initializer(const char *submodule_name, Initializer init);
+};
+
+#define TEST_SUBMODULE(name, variable)                                                            \
+    void test_submodule_##name(py::module_ &);                                                    \
+    test_initializer name(#name, test_submodule_##name);                                          \
+    void test_submodule_##name(py::module_ &(variable))
+
+/// Dummy type which is not exported anywhere -- something to trigger a conversion error
+struct UnregisteredType {};
+
+/// A user-defined type which is exported and can be used by any test
+class UserType {
+public:
+    UserType() = default;
+    explicit UserType(int i) : i(i) {}
+
+    int value() const { return i; }
+    void set(int set) { i = set; }
+
+private:
+    int i = -1;
+};
+
+/// Like UserType, but increments `value` on copy for quick reference vs. copy tests
+class IncType : public UserType {
+public:
+    using UserType::UserType;
+    IncType() = default;
+    IncType(const IncType &other) : IncType(other.value() + 1) {}
+    IncType(IncType &&) = delete;
+    IncType &operator=(const IncType &) = delete;
+    IncType &operator=(IncType &&) = delete;
+};
+
+/// A simple union for basic testing
+union IntFloat {
+    int i;
+    float f;
+};
+
+/// Custom cast-only type that casts to a string "rvalue" or "lvalue" depending on the cast
+/// context. Used to test recursive casters (e.g. std::tuple, stl containers).
+struct RValueCaster {};
+PYBIND11_NAMESPACE_BEGIN(pybind11)
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <>
+class type_caster<RValueCaster> {
+public:
+    PYBIND11_TYPE_CASTER(RValueCaster, const_name("RValueCaster"));
+    static handle cast(RValueCaster &&, return_value_policy, handle) {
+        return py::str("rvalue").release();
+    }
+    static handle cast(const RValueCaster &, return_value_policy, handle) {
+        return py::str("lvalue").release();
+    }
+};
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(pybind11)
+
+template <typename F>
+void ignoreOldStyleInitWarnings(F &&body) {
+    py::exec(R"(
+    message = "pybind11-bound class '.+' is using an old-style placement-new '(?:__init__|__setstate__)' which has been deprecated"
+
+    import warnings
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", message=message, category=FutureWarning)
+        body()
+    )",
+             py::dict(py::arg("body") = py::cpp_function(body)));
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pytest.ini b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pytest.ini
new file mode 100644
index 000000000..792ba361f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/pytest.ini
@@ -0,0 +1,22 @@
+[pytest]
+minversion = 3.10
+norecursedirs = test_* extra_*
+xfail_strict = True
+addopts =
+    # show summary of tests
+    -ra
+    # capture only Python print and C++ py::print, but not C output (low-level Python errors)
+    --capture=sys
+    # Show local info when a failure occurs
+    --showlocals
+log_cli_level = info
+filterwarnings =
+    # make warnings into errors but ignore certain third-party extension issues
+    error
+    # somehow, some DeprecationWarnings do not get turned into errors
+    always::DeprecationWarning
+    # importing scipy submodules on some version of Python
+    ignore::ImportWarning
+    # bogus numpy ABI warning (see numpy/#432)
+    ignore:.*numpy.dtype size changed.*:RuntimeWarning
+    ignore:.*numpy.ufunc size changed.*:RuntimeWarning
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/requirements.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/requirements.txt
new file mode 100644
index 000000000..04aafa8cf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/requirements.txt
@@ -0,0 +1,9 @@
+build==0.8.0
+numpy==1.21.5; platform_python_implementation=="PyPy" and sys_platform=="linux" and python_version=="3.7"
+numpy==1.19.3; platform_python_implementation!="PyPy" and python_version=="3.6"
+numpy==1.21.5; platform_python_implementation!="PyPy" and python_version>="3.7" and python_version<"3.10"
+numpy==1.22.2; platform_python_implementation!="PyPy" and python_version>="3.10" and python_version<"3.11"
+pytest==7.0.0
+pytest-timeout
+scipy==1.5.4; platform_python_implementation!="PyPy" and python_version<"3.10"
+scipy==1.8.0; platform_python_implementation!="PyPy" and python_version=="3.10"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_async.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_async.cpp
new file mode 100644
index 000000000..a5d722465
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_async.cpp
@@ -0,0 +1,25 @@
+/*
+    tests/test_async.cpp -- __await__ support
+
+    Copyright (c) 2019 Google Inc.
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+TEST_SUBMODULE(async_module, m) {
+    struct DoesNotSupportAsync {};
+    py::class_<DoesNotSupportAsync>(m, "DoesNotSupportAsync").def(py::init<>());
+    struct SupportsAsync {};
+    py::class_<SupportsAsync>(m, "SupportsAsync")
+        .def(py::init<>())
+        .def("__await__", [](const SupportsAsync &self) -> py::object {
+            static_cast<void>(self);
+            py::object loop = py::module_::import("asyncio.events").attr("get_event_loop")();
+            py::object f = loop.attr("create_future")();
+            f.attr("set_result")(5);
+            return f.attr("__await__")();
+        });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_async.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_async.py
new file mode 100644
index 000000000..b9ff9514d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_async.py
@@ -0,0 +1,24 @@
+import pytest
+
+asyncio = pytest.importorskip("asyncio")
+m = pytest.importorskip("pybind11_tests.async_module")
+
+
+@pytest.fixture
+def event_loop():
+    loop = asyncio.new_event_loop()
+    yield loop
+    loop.close()
+
+
+async def get_await_result(x):
+    return await x
+
+
+def test_await(event_loop):
+    assert 5 == event_loop.run_until_complete(get_await_result(m.SupportsAsync()))
+
+
+def test_await_missing(event_loop):
+    with pytest.raises(TypeError):
+        event_loop.run_until_complete(get_await_result(m.DoesNotSupportAsync()))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_buffers.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_buffers.cpp
new file mode 100644
index 000000000..6b6e8cba7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_buffers.cpp
@@ -0,0 +1,224 @@
+/*
+    tests/test_buffers.cpp -- supporting Pythons' buffer protocol
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/stl.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+TEST_SUBMODULE(buffers, m) {
+    // test_from_python / test_to_python:
+    class Matrix {
+    public:
+        Matrix(py::ssize_t rows, py::ssize_t cols) : m_rows(rows), m_cols(cols) {
+            print_created(this, std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
+            // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+            m_data = new float[(size_t) (rows * cols)];
+            memset(m_data, 0, sizeof(float) * (size_t) (rows * cols));
+        }
+
+        Matrix(const Matrix &s) : m_rows(s.m_rows), m_cols(s.m_cols) {
+            print_copy_created(this,
+                               std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
+            // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+            m_data = new float[(size_t) (m_rows * m_cols)];
+            memcpy(m_data, s.m_data, sizeof(float) * (size_t) (m_rows * m_cols));
+        }
+
+        Matrix(Matrix &&s) noexcept : m_rows(s.m_rows), m_cols(s.m_cols), m_data(s.m_data) {
+            print_move_created(this);
+            s.m_rows = 0;
+            s.m_cols = 0;
+            s.m_data = nullptr;
+        }
+
+        ~Matrix() {
+            print_destroyed(this,
+                            std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
+            delete[] m_data;
+        }
+
+        Matrix &operator=(const Matrix &s) {
+            if (this == &s) {
+                return *this;
+            }
+            print_copy_assigned(this,
+                                std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
+            delete[] m_data;
+            m_rows = s.m_rows;
+            m_cols = s.m_cols;
+            m_data = new float[(size_t) (m_rows * m_cols)];
+            memcpy(m_data, s.m_data, sizeof(float) * (size_t) (m_rows * m_cols));
+            return *this;
+        }
+
+        Matrix &operator=(Matrix &&s) noexcept {
+            print_move_assigned(this,
+                                std::to_string(m_rows) + "x" + std::to_string(m_cols) + " matrix");
+            if (&s != this) {
+                delete[] m_data;
+                m_rows = s.m_rows;
+                m_cols = s.m_cols;
+                m_data = s.m_data;
+                s.m_rows = 0;
+                s.m_cols = 0;
+                s.m_data = nullptr;
+            }
+            return *this;
+        }
+
+        float operator()(py::ssize_t i, py::ssize_t j) const {
+            return m_data[(size_t) (i * m_cols + j)];
+        }
+
+        float &operator()(py::ssize_t i, py::ssize_t j) {
+            return m_data[(size_t) (i * m_cols + j)];
+        }
+
+        float *data() { return m_data; }
+
+        py::ssize_t rows() const { return m_rows; }
+        py::ssize_t cols() const { return m_cols; }
+
+    private:
+        py::ssize_t m_rows;
+        py::ssize_t m_cols;
+        float *m_data;
+    };
+    py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
+        .def(py::init<py::ssize_t, py::ssize_t>())
+        /// Construct from a buffer
+        .def(py::init([](const py::buffer &b) {
+            py::buffer_info info = b.request();
+            if (info.format != py::format_descriptor<float>::format() || info.ndim != 2) {
+                throw std::runtime_error("Incompatible buffer format!");
+            }
+
+            auto *v = new Matrix(info.shape[0], info.shape[1]);
+            memcpy(v->data(), info.ptr, sizeof(float) * (size_t) (v->rows() * v->cols()));
+            return v;
+        }))
+
+        .def("rows", &Matrix::rows)
+        .def("cols", &Matrix::cols)
+
+        /// Bare bones interface
+        .def("__getitem__",
+             [](const Matrix &m, std::pair<py::ssize_t, py::ssize_t> i) {
+                 if (i.first >= m.rows() || i.second >= m.cols()) {
+                     throw py::index_error();
+                 }
+                 return m(i.first, i.second);
+             })
+        .def("__setitem__",
+             [](Matrix &m, std::pair<py::ssize_t, py::ssize_t> i, float v) {
+                 if (i.first >= m.rows() || i.second >= m.cols()) {
+                     throw py::index_error();
+                 }
+                 m(i.first, i.second) = v;
+             })
+        /// Provide buffer access
+        .def_buffer([](Matrix &m) -> py::buffer_info {
+            return py::buffer_info(
+                m.data(),                          /* Pointer to buffer */
+                {m.rows(), m.cols()},              /* Buffer dimensions */
+                {sizeof(float) * size_t(m.cols()), /* Strides (in bytes) for each index */
+                 sizeof(float)});
+        });
+
+    // test_inherited_protocol
+    class SquareMatrix : public Matrix {
+    public:
+        explicit SquareMatrix(py::ssize_t n) : Matrix(n, n) {}
+    };
+    // Derived classes inherit the buffer protocol and the buffer access function
+    py::class_<SquareMatrix, Matrix>(m, "SquareMatrix").def(py::init<py::ssize_t>());
+
+    // test_pointer_to_member_fn
+    // Tests that passing a pointer to member to the base class works in
+    // the derived class.
+    struct Buffer {
+        int32_t value = 0;
+
+        py::buffer_info get_buffer_info() {
+            return py::buffer_info(
+                &value, sizeof(value), py::format_descriptor<int32_t>::format(), 1);
+        }
+    };
+    py::class_<Buffer>(m, "Buffer", py::buffer_protocol())
+        .def(py::init<>())
+        .def_readwrite("value", &Buffer::value)
+        .def_buffer(&Buffer::get_buffer_info);
+
+    class ConstBuffer {
+        std::unique_ptr<int32_t> value;
+
+    public:
+        int32_t get_value() const { return *value; }
+        void set_value(int32_t v) { *value = v; }
+
+        py::buffer_info get_buffer_info() const {
+            return py::buffer_info(
+                value.get(), sizeof(*value), py::format_descriptor<int32_t>::format(), 1);
+        }
+
+        ConstBuffer() : value(new int32_t{0}) {}
+    };
+    py::class_<ConstBuffer>(m, "ConstBuffer", py::buffer_protocol())
+        .def(py::init<>())
+        .def_property("value", &ConstBuffer::get_value, &ConstBuffer::set_value)
+        .def_buffer(&ConstBuffer::get_buffer_info);
+
+    struct DerivedBuffer : public Buffer {};
+    py::class_<DerivedBuffer>(m, "DerivedBuffer", py::buffer_protocol())
+        .def(py::init<>())
+        .def_readwrite("value", (int32_t DerivedBuffer::*) &DerivedBuffer::value)
+        .def_buffer(&DerivedBuffer::get_buffer_info);
+
+    struct BufferReadOnly {
+        const uint8_t value = 0;
+        explicit BufferReadOnly(uint8_t value) : value(value) {}
+
+        py::buffer_info get_buffer_info() { return py::buffer_info(&value, 1); }
+    };
+    py::class_<BufferReadOnly>(m, "BufferReadOnly", py::buffer_protocol())
+        .def(py::init<uint8_t>())
+        .def_buffer(&BufferReadOnly::get_buffer_info);
+
+    struct BufferReadOnlySelect {
+        uint8_t value = 0;
+        bool readonly = false;
+
+        py::buffer_info get_buffer_info() { return py::buffer_info(&value, 1, readonly); }
+    };
+    py::class_<BufferReadOnlySelect>(m, "BufferReadOnlySelect", py::buffer_protocol())
+        .def(py::init<>())
+        .def_readwrite("value", &BufferReadOnlySelect::value)
+        .def_readwrite("readonly", &BufferReadOnlySelect::readonly)
+        .def_buffer(&BufferReadOnlySelect::get_buffer_info);
+
+    // Expose buffer_info for testing.
+    py::class_<py::buffer_info>(m, "buffer_info")
+        .def(py::init<>())
+        .def_readonly("itemsize", &py::buffer_info::itemsize)
+        .def_readonly("size", &py::buffer_info::size)
+        .def_readonly("format", &py::buffer_info::format)
+        .def_readonly("ndim", &py::buffer_info::ndim)
+        .def_readonly("shape", &py::buffer_info::shape)
+        .def_readonly("strides", &py::buffer_info::strides)
+        .def_readonly("readonly", &py::buffer_info::readonly)
+        .def("__repr__", [](py::handle self) {
+            return py::str("itemsize={0.itemsize!r}, size={0.size!r}, format={0.format!r}, "
+                           "ndim={0.ndim!r}, shape={0.shape!r}, strides={0.strides!r}, "
+                           "readonly={0.readonly!r}")
+                .format(self);
+        });
+
+    m.def("get_buffer_info", [](const py::buffer &buffer) { return buffer.request(); });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_buffers.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_buffers.py
new file mode 100644
index 000000000..8354b68cd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_buffers.py
@@ -0,0 +1,163 @@
+import ctypes
+import io
+import struct
+
+import pytest
+
+import env
+from pybind11_tests import ConstructorStats
+from pybind11_tests import buffers as m
+
+np = pytest.importorskip("numpy")
+
+
+def test_from_python():
+    with pytest.raises(RuntimeError) as excinfo:
+        m.Matrix(np.array([1, 2, 3]))  # trying to assign a 1D array
+    assert str(excinfo.value) == "Incompatible buffer format!"
+
+    m3 = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
+    m4 = m.Matrix(m3)
+
+    for i in range(m4.rows()):
+        for j in range(m4.cols()):
+            assert m3[i, j] == m4[i, j]
+
+    cstats = ConstructorStats.get(m.Matrix)
+    assert cstats.alive() == 1
+    del m3, m4
+    assert cstats.alive() == 0
+    assert cstats.values() == ["2x3 matrix"]
+    assert cstats.copy_constructions == 0
+    # assert cstats.move_constructions >= 0  # Don't invoke any
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+
+# https://foss.heptapod.net/pypy/pypy/-/issues/2444
+# TODO: fix on recent PyPy
+@pytest.mark.xfail(
+    env.PYPY, reason="PyPy 7.3.7 doesn't clear this anymore", strict=False
+)
+def test_to_python():
+    mat = m.Matrix(5, 4)
+    assert memoryview(mat).shape == (5, 4)
+
+    assert mat[2, 3] == 0
+    mat[2, 3] = 4.0
+    mat[3, 2] = 7.0
+    assert mat[2, 3] == 4
+    assert mat[3, 2] == 7
+    assert struct.unpack_from("f", mat, (3 * 4 + 2) * 4) == (7,)
+    assert struct.unpack_from("f", mat, (2 * 4 + 3) * 4) == (4,)
+
+    mat2 = np.array(mat, copy=False)
+    assert mat2.shape == (5, 4)
+    assert abs(mat2).sum() == 11
+    assert mat2[2, 3] == 4 and mat2[3, 2] == 7
+    mat2[2, 3] = 5
+    assert mat2[2, 3] == 5
+
+    cstats = ConstructorStats.get(m.Matrix)
+    assert cstats.alive() == 1
+    del mat
+    pytest.gc_collect()
+    assert cstats.alive() == 1
+    del mat2  # holds a mat reference
+    pytest.gc_collect()
+    assert cstats.alive() == 0
+    assert cstats.values() == ["5x4 matrix"]
+    assert cstats.copy_constructions == 0
+    # assert cstats.move_constructions >= 0  # Don't invoke any
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+
+def test_inherited_protocol():
+    """SquareMatrix is derived from Matrix and inherits the buffer protocol"""
+
+    matrix = m.SquareMatrix(5)
+    assert memoryview(matrix).shape == (5, 5)
+    assert np.asarray(matrix).shape == (5, 5)
+
+
+def test_pointer_to_member_fn():
+    for cls in [m.Buffer, m.ConstBuffer, m.DerivedBuffer]:
+        buf = cls()
+        buf.value = 0x12345678
+        value = struct.unpack("i", bytearray(buf))[0]
+        assert value == 0x12345678
+
+
+def test_readonly_buffer():
+    buf = m.BufferReadOnly(0x64)
+    view = memoryview(buf)
+    assert view[0] == 0x64
+    assert view.readonly
+    with pytest.raises(TypeError):
+        view[0] = 0
+
+
+def test_selective_readonly_buffer():
+    buf = m.BufferReadOnlySelect()
+
+    memoryview(buf)[0] = 0x64
+    assert buf.value == 0x64
+
+    io.BytesIO(b"A").readinto(buf)
+    assert buf.value == ord(b"A")
+
+    buf.readonly = True
+    with pytest.raises(TypeError):
+        memoryview(buf)[0] = 0
+    with pytest.raises(TypeError):
+        io.BytesIO(b"1").readinto(buf)
+
+
+def test_ctypes_array_1d():
+    char1d = (ctypes.c_char * 10)()
+    int1d = (ctypes.c_int * 15)()
+    long1d = (ctypes.c_long * 7)()
+
+    for carray in (char1d, int1d, long1d):
+        info = m.get_buffer_info(carray)
+        assert info.itemsize == ctypes.sizeof(carray._type_)
+        assert info.size == len(carray)
+        assert info.ndim == 1
+        assert info.shape == [info.size]
+        assert info.strides == [info.itemsize]
+        assert not info.readonly
+
+
+def test_ctypes_array_2d():
+    char2d = ((ctypes.c_char * 10) * 4)()
+    int2d = ((ctypes.c_int * 15) * 3)()
+    long2d = ((ctypes.c_long * 7) * 2)()
+
+    for carray in (char2d, int2d, long2d):
+        info = m.get_buffer_info(carray)
+        assert info.itemsize == ctypes.sizeof(carray[0]._type_)
+        assert info.size == len(carray) * len(carray[0])
+        assert info.ndim == 2
+        assert info.shape == [len(carray), len(carray[0])]
+        assert info.strides == [info.itemsize * len(carray[0]), info.itemsize]
+        assert not info.readonly
+
+
+def test_ctypes_from_buffer():
+    test_pystr = b"0123456789"
+    for pyarray in (test_pystr, bytearray(test_pystr)):
+        pyinfo = m.get_buffer_info(pyarray)
+
+        if pyinfo.readonly:
+            cbytes = (ctypes.c_char * len(pyarray)).from_buffer_copy(pyarray)
+            cinfo = m.get_buffer_info(cbytes)
+        else:
+            cbytes = (ctypes.c_char * len(pyarray)).from_buffer(pyarray)
+            cinfo = m.get_buffer_info(cbytes)
+
+        assert cinfo.size == pyinfo.size
+        assert cinfo.ndim == pyinfo.ndim
+        assert cinfo.shape == pyinfo.shape
+        assert cinfo.strides == pyinfo.strides
+        assert not cinfo.readonly
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_builtin_casters.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_builtin_casters.cpp
new file mode 100644
index 000000000..6529f47d0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_builtin_casters.cpp
@@ -0,0 +1,382 @@
+/*
+    tests/test_builtin_casters.cpp -- Casters available without any additional headers
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/complex.h>
+
+#include "pybind11_tests.h"
+
+struct ConstRefCasted {
+    int tag;
+};
+
+PYBIND11_NAMESPACE_BEGIN(pybind11)
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <>
+class type_caster<ConstRefCasted> {
+public:
+    static constexpr auto name = const_name<ConstRefCasted>();
+
+    // Input is unimportant, a new value will always be constructed based on the
+    // cast operator.
+    bool load(handle, bool) { return true; }
+
+    explicit operator ConstRefCasted &&() {
+        value = {1};
+        // NOLINTNEXTLINE(performance-move-const-arg)
+        return std::move(value);
+    }
+    explicit operator ConstRefCasted &() {
+        value = {2};
+        return value;
+    }
+    explicit operator ConstRefCasted *() {
+        value = {3};
+        return &value;
+    }
+
+    explicit operator const ConstRefCasted &() {
+        value = {4};
+        return value;
+    }
+    explicit operator const ConstRefCasted *() {
+        value = {5};
+        return &value;
+    }
+
+    // custom cast_op to explicitly propagate types to the conversion operators.
+    template <typename T_>
+    using cast_op_type =
+        /// const
+        conditional_t<
+            std::is_same<remove_reference_t<T_>, const ConstRefCasted *>::value,
+            const ConstRefCasted *,
+            conditional_t<
+                std::is_same<T_, const ConstRefCasted &>::value,
+                const ConstRefCasted &,
+                /// non-const
+                conditional_t<std::is_same<remove_reference_t<T_>, ConstRefCasted *>::value,
+                              ConstRefCasted *,
+                              conditional_t<std::is_same<T_, ConstRefCasted &>::value,
+                                            ConstRefCasted &,
+                                            /* else */ ConstRefCasted &&>>>>;
+
+private:
+    ConstRefCasted value = {0};
+};
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(pybind11)
+
+TEST_SUBMODULE(builtin_casters, m) {
+    // test_simple_string
+    m.def("string_roundtrip", [](const char *s) { return s; });
+
+    // test_unicode_conversion
+    // Some test characters in utf16 and utf32 encodings.  The last one (the 𝐀) contains a null
+    // byte
+    char32_t a32 = 0x61 /*a*/, z32 = 0x7a /*z*/, ib32 = 0x203d /*‽*/, cake32 = 0x1f382 /*🎂*/,
+             mathbfA32 = 0x1d400 /*𝐀*/;
+    char16_t b16 = 0x62 /*b*/, z16 = 0x7a, ib16 = 0x203d, cake16_1 = 0xd83c, cake16_2 = 0xdf82,
+             mathbfA16_1 = 0xd835, mathbfA16_2 = 0xdc00;
+    std::wstring wstr;
+    wstr.push_back(0x61);   // a
+    wstr.push_back(0x2e18); // ⸘
+    if (PYBIND11_SILENCE_MSVC_C4127(sizeof(wchar_t) == 2)) {
+        wstr.push_back(mathbfA16_1);
+        wstr.push_back(mathbfA16_2);
+    } // 𝐀, utf16
+    else {
+        wstr.push_back((wchar_t) mathbfA32);
+    }                     // 𝐀, utf32
+    wstr.push_back(0x7a); // z
+
+    m.def("good_utf8_string", []() {
+        return std::string((const char *) u8"Say utf8\u203d \U0001f382 \U0001d400");
+    }); // Say utf8‽ 🎂 𝐀
+    m.def("good_utf16_string", [=]() {
+        return std::u16string({b16, ib16, cake16_1, cake16_2, mathbfA16_1, mathbfA16_2, z16});
+    }); // b‽🎂𝐀z
+    m.def("good_utf32_string", [=]() {
+        return std::u32string({a32, mathbfA32, cake32, ib32, z32});
+    });                                                 // a𝐀🎂‽z
+    m.def("good_wchar_string", [=]() { return wstr; }); // a‽𝐀z
+    m.def("bad_utf8_string", []() {
+        return std::string("abc\xd0"
+                           "def");
+    });
+    m.def("bad_utf16_string", [=]() { return std::u16string({b16, char16_t(0xd800), z16}); });
+    // Under Python 2.7, invalid unicode UTF-32 characters didn't appear to trigger
+    // UnicodeDecodeError
+    m.def("bad_utf32_string", [=]() { return std::u32string({a32, char32_t(0xd800), z32}); });
+    if (PYBIND11_SILENCE_MSVC_C4127(sizeof(wchar_t) == 2)) {
+        m.def("bad_wchar_string", [=]() {
+            return std::wstring({wchar_t(0x61), wchar_t(0xd800)});
+        });
+    }
+    m.def("u8_Z", []() -> char { return 'Z'; });
+    m.def("u8_eacute", []() -> char { return '\xe9'; });
+    m.def("u16_ibang", [=]() -> char16_t { return ib16; });
+    m.def("u32_mathbfA", [=]() -> char32_t { return mathbfA32; });
+    m.def("wchar_heart", []() -> wchar_t { return 0x2665; });
+
+    // test_single_char_arguments
+    m.attr("wchar_size") = py::cast(sizeof(wchar_t));
+    m.def("ord_char", [](char c) -> int { return static_cast<unsigned char>(c); });
+    m.def("ord_char_lv", [](char &c) -> int { return static_cast<unsigned char>(c); });
+    m.def("ord_char16", [](char16_t c) -> uint16_t { return c; });
+    m.def("ord_char16_lv", [](char16_t &c) -> uint16_t { return c; });
+    m.def("ord_char32", [](char32_t c) -> uint32_t { return c; });
+    m.def("ord_wchar", [](wchar_t c) -> int { return c; });
+
+    // test_bytes_to_string
+    m.def("strlen", [](char *s) { return strlen(s); });
+    m.def("string_length", [](const std::string &s) { return s.length(); });
+
+#ifdef PYBIND11_HAS_U8STRING
+    m.attr("has_u8string") = true;
+    m.def("good_utf8_u8string", []() {
+        return std::u8string(u8"Say utf8\u203d \U0001f382 \U0001d400");
+    }); // Say utf8‽ 🎂 𝐀
+    m.def("bad_utf8_u8string", []() {
+        return std::u8string((const char8_t *) "abc\xd0"
+                                               "def");
+    });
+
+    m.def("u8_char8_Z", []() -> char8_t { return u8'Z'; });
+
+    // test_single_char_arguments
+    m.def("ord_char8", [](char8_t c) -> int { return static_cast<unsigned char>(c); });
+    m.def("ord_char8_lv", [](char8_t &c) -> int { return static_cast<unsigned char>(c); });
+#endif
+
+    // test_string_view
+#ifdef PYBIND11_HAS_STRING_VIEW
+    m.attr("has_string_view") = true;
+    m.def("string_view_print", [](std::string_view s) { py::print(s, s.size()); });
+    m.def("string_view16_print", [](std::u16string_view s) { py::print(s, s.size()); });
+    m.def("string_view32_print", [](std::u32string_view s) { py::print(s, s.size()); });
+    m.def("string_view_chars", [](std::string_view s) {
+        py::list l;
+        for (auto c : s) {
+            l.append((std::uint8_t) c);
+        }
+        return l;
+    });
+    m.def("string_view16_chars", [](std::u16string_view s) {
+        py::list l;
+        for (auto c : s) {
+            l.append((int) c);
+        }
+        return l;
+    });
+    m.def("string_view32_chars", [](std::u32string_view s) {
+        py::list l;
+        for (auto c : s) {
+            l.append((int) c);
+        }
+        return l;
+    });
+    m.def("string_view_return",
+          []() { return std::string_view((const char *) u8"utf8 secret \U0001f382"); });
+    m.def("string_view16_return",
+          []() { return std::u16string_view(u"utf16 secret \U0001f382"); });
+    m.def("string_view32_return",
+          []() { return std::u32string_view(U"utf32 secret \U0001f382"); });
+
+    // The inner lambdas here are to also test implicit conversion
+    using namespace std::literals;
+    m.def("string_view_bytes",
+          []() { return [](py::bytes b) { return b; }("abc \x80\x80 def"sv); });
+    m.def("string_view_str",
+          []() { return [](py::str s) { return s; }("abc \342\200\275 def"sv); });
+    m.def("string_view_from_bytes",
+          [](const py::bytes &b) { return [](std::string_view s) { return s; }(b); });
+    m.def("string_view_memoryview", []() {
+        static constexpr auto val = "Have some \360\237\216\202"sv;
+        return py::memoryview::from_memory(val);
+    });
+
+#    ifdef PYBIND11_HAS_U8STRING
+    m.def("string_view8_print", [](std::u8string_view s) { py::print(s, s.size()); });
+    m.def("string_view8_chars", [](std::u8string_view s) {
+        py::list l;
+        for (auto c : s)
+            l.append((std::uint8_t) c);
+        return l;
+    });
+    m.def("string_view8_return", []() { return std::u8string_view(u8"utf8 secret \U0001f382"); });
+    m.def("string_view8_str", []() { return py::str{std::u8string_view{u8"abc ‽ def"}}; });
+#    endif
+
+    struct TypeWithBothOperatorStringAndStringView {
+        // NOLINTNEXTLINE(google-explicit-constructor)
+        operator std::string() const { return "success"; }
+        // NOLINTNEXTLINE(google-explicit-constructor)
+        operator std::string_view() const { return "failure"; }
+    };
+    m.def("bytes_from_type_with_both_operator_string_and_string_view",
+          []() { return py::bytes(TypeWithBothOperatorStringAndStringView()); });
+    m.def("str_from_type_with_both_operator_string_and_string_view",
+          []() { return py::str(TypeWithBothOperatorStringAndStringView()); });
+#endif
+
+    // test_integer_casting
+    m.def("i32_str", [](std::int32_t v) { return std::to_string(v); });
+    m.def("u32_str", [](std::uint32_t v) { return std::to_string(v); });
+    m.def("i64_str", [](std::int64_t v) { return std::to_string(v); });
+    m.def("u64_str", [](std::uint64_t v) { return std::to_string(v); });
+
+    // test_int_convert
+    m.def("int_passthrough", [](int arg) { return arg; });
+    m.def(
+        "int_passthrough_noconvert", [](int arg) { return arg; }, py::arg{}.noconvert());
+
+    // test_tuple
+    m.def(
+        "pair_passthrough",
+        [](const std::pair<bool, std::string> &input) {
+            return std::make_pair(input.second, input.first);
+        },
+        "Return a pair in reversed order");
+    m.def(
+        "tuple_passthrough",
+        [](std::tuple<bool, std::string, int> input) {
+            return std::make_tuple(std::get<2>(input), std::get<1>(input), std::get<0>(input));
+        },
+        "Return a triple in reversed order");
+    m.def("empty_tuple", []() { return std::tuple<>(); });
+    static std::pair<RValueCaster, RValueCaster> lvpair;
+    static std::tuple<RValueCaster, RValueCaster, RValueCaster> lvtuple;
+    static std::pair<RValueCaster, std::tuple<RValueCaster, std::pair<RValueCaster, RValueCaster>>>
+        lvnested;
+    m.def("rvalue_pair", []() { return std::make_pair(RValueCaster{}, RValueCaster{}); });
+    m.def("lvalue_pair", []() -> const decltype(lvpair) & { return lvpair; });
+    m.def("rvalue_tuple",
+          []() { return std::make_tuple(RValueCaster{}, RValueCaster{}, RValueCaster{}); });
+    m.def("lvalue_tuple", []() -> const decltype(lvtuple) & { return lvtuple; });
+    m.def("rvalue_nested", []() {
+        return std::make_pair(
+            RValueCaster{},
+            std::make_tuple(RValueCaster{}, std::make_pair(RValueCaster{}, RValueCaster{})));
+    });
+    m.def("lvalue_nested", []() -> const decltype(lvnested) & { return lvnested; });
+
+    static std::pair<int, std::string> int_string_pair{2, "items"};
+    m.def(
+        "int_string_pair", []() { return &int_string_pair; }, py::return_value_policy::reference);
+
+    // test_builtins_cast_return_none
+    m.def("return_none_string", []() -> std::string * { return nullptr; });
+    m.def("return_none_char", []() -> const char * { return nullptr; });
+    m.def("return_none_bool", []() -> bool * { return nullptr; });
+    m.def("return_none_int", []() -> int * { return nullptr; });
+    m.def("return_none_float", []() -> float * { return nullptr; });
+    m.def("return_none_pair", []() -> std::pair<int, int> * { return nullptr; });
+
+    // test_none_deferred
+    m.def("defer_none_cstring", [](char *) { return false; });
+    m.def("defer_none_cstring", [](const py::none &) { return true; });
+    m.def("defer_none_custom", [](UserType *) { return false; });
+    m.def("defer_none_custom", [](const py::none &) { return true; });
+    m.def("nodefer_none_void", [](void *) { return true; });
+    m.def("nodefer_none_void", [](const py::none &) { return false; });
+
+    // test_void_caster
+    m.def("load_nullptr_t", [](std::nullptr_t) {}); // not useful, but it should still compile
+    m.def("cast_nullptr_t", []() { return std::nullptr_t{}; });
+
+    // [workaround(intel)] ICC 20/21 breaks with py::arg().stuff, using py::arg{}.stuff works.
+
+    // test_bool_caster
+    m.def("bool_passthrough", [](bool arg) { return arg; });
+    m.def(
+        "bool_passthrough_noconvert", [](bool arg) { return arg; }, py::arg{}.noconvert());
+
+    // TODO: This should be disabled and fixed in future Intel compilers
+#if !defined(__INTEL_COMPILER)
+    // Test "bool_passthrough_noconvert" again, but using () instead of {} to construct py::arg
+    // When compiled with the Intel compiler, this results in segmentation faults when importing
+    // the module. Tested with icc (ICC) 2021.1 Beta 20200827, this should be tested again when
+    // a newer version of icc is available.
+    m.def(
+        "bool_passthrough_noconvert2", [](bool arg) { return arg; }, py::arg().noconvert());
+#endif
+
+    // test_reference_wrapper
+    m.def("refwrap_builtin", [](std::reference_wrapper<int> p) { return 10 * p.get(); });
+    m.def("refwrap_usertype", [](std::reference_wrapper<UserType> p) { return p.get().value(); });
+    m.def("refwrap_usertype_const",
+          [](std::reference_wrapper<const UserType> p) { return p.get().value(); });
+
+    m.def("refwrap_lvalue", []() -> std::reference_wrapper<UserType> {
+        static UserType x(1);
+        return std::ref(x);
+    });
+    m.def("refwrap_lvalue_const", []() -> std::reference_wrapper<const UserType> {
+        static UserType x(1);
+        return std::cref(x);
+    });
+
+    // Not currently supported (std::pair caster has return-by-value cast operator);
+    // triggers static_assert failure.
+    // m.def("refwrap_pair", [](std::reference_wrapper<std::pair<int, int>>) { });
+
+    m.def(
+        "refwrap_list",
+        [](bool copy) {
+            static IncType x1(1), x2(2);
+            py::list l;
+            for (const auto &f : {std::ref(x1), std::ref(x2)}) {
+                l.append(py::cast(
+                    f, copy ? py::return_value_policy::copy : py::return_value_policy::reference));
+            }
+            return l;
+        },
+        "copy"_a);
+
+    m.def("refwrap_iiw", [](const IncType &w) { return w.value(); });
+    m.def("refwrap_call_iiw", [](IncType &w, const py::function &f) {
+        py::list l;
+        l.append(f(std::ref(w)));
+        l.append(f(std::cref(w)));
+        IncType x(w.value());
+        l.append(f(std::ref(x)));
+        IncType y(w.value());
+        auto r3 = std::ref(y);
+        l.append(f(r3));
+        return l;
+    });
+
+    // test_complex
+    m.def("complex_cast", [](float x) { return "{}"_s.format(x); });
+    m.def("complex_cast",
+          [](std::complex<float> x) { return "({}, {})"_s.format(x.real(), x.imag()); });
+
+    // test int vs. long (Python 2)
+    m.def("int_cast", []() { return (int) 42; });
+    m.def("long_cast", []() { return (long) 42; });
+    m.def("longlong_cast", []() { return ULLONG_MAX; });
+
+    /// test void* cast operator
+    m.def("test_void_caster", []() -> bool {
+        void *v = (void *) 0xabcd;
+        py::object o = py::cast(v);
+        return py::cast<void *>(o) == v;
+    });
+
+    // Tests const/non-const propagation in cast_op.
+    m.def("takes", [](ConstRefCasted x) { return x.tag; });
+    m.def("takes_move", [](ConstRefCasted &&x) { return x.tag; });
+    m.def("takes_ptr", [](ConstRefCasted *x) { return x->tag; });
+    m.def("takes_ref", [](ConstRefCasted &x) { return x.tag; });
+    m.def("takes_ref_wrap", [](std::reference_wrapper<ConstRefCasted> x) { return x.get().tag; });
+    m.def("takes_const_ptr", [](const ConstRefCasted *x) { return x->tag; });
+    m.def("takes_const_ref", [](const ConstRefCasted &x) { return x.tag; });
+    m.def("takes_const_ref_wrap",
+          [](std::reference_wrapper<const ConstRefCasted> x) { return x.get().tag; });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_builtin_casters.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_builtin_casters.py
new file mode 100644
index 000000000..d38ae6802
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_builtin_casters.py
@@ -0,0 +1,526 @@
+import sys
+
+import pytest
+
+import env
+from pybind11_tests import IncType, UserType
+from pybind11_tests import builtin_casters as m
+
+
+def test_simple_string():
+    assert m.string_roundtrip("const char *") == "const char *"
+
+
+def test_unicode_conversion():
+    """Tests unicode conversion and error reporting."""
+    assert m.good_utf8_string() == "Say utf8‽ 🎂 𝐀"
+    assert m.good_utf16_string() == "b‽🎂𝐀z"
+    assert m.good_utf32_string() == "a𝐀🎂‽z"
+    assert m.good_wchar_string() == "a⸘𝐀z"
+    if hasattr(m, "has_u8string"):
+        assert m.good_utf8_u8string() == "Say utf8‽ 🎂 𝐀"
+
+    with pytest.raises(UnicodeDecodeError):
+        m.bad_utf8_string()
+
+    with pytest.raises(UnicodeDecodeError):
+        m.bad_utf16_string()
+
+    # These are provided only if they actually fail (they don't when 32-bit)
+    if hasattr(m, "bad_utf32_string"):
+        with pytest.raises(UnicodeDecodeError):
+            m.bad_utf32_string()
+    if hasattr(m, "bad_wchar_string"):
+        with pytest.raises(UnicodeDecodeError):
+            m.bad_wchar_string()
+    if hasattr(m, "has_u8string"):
+        with pytest.raises(UnicodeDecodeError):
+            m.bad_utf8_u8string()
+
+    assert m.u8_Z() == "Z"
+    assert m.u8_eacute() == "é"
+    assert m.u16_ibang() == "‽"
+    assert m.u32_mathbfA() == "𝐀"
+    assert m.wchar_heart() == "♥"
+    if hasattr(m, "has_u8string"):
+        assert m.u8_char8_Z() == "Z"
+
+
+def test_single_char_arguments():
+    """Tests failures for passing invalid inputs to char-accepting functions"""
+
+    def toobig_message(r):
+        return f"Character code point not in range({r:#x})"
+
+    toolong_message = "Expected a character, but multi-character string found"
+
+    assert m.ord_char("a") == 0x61  # simple ASCII
+    assert m.ord_char_lv("b") == 0x62
+    assert (
+        m.ord_char("é") == 0xE9
+    )  # requires 2 bytes in utf-8, but can be stuffed in a char
+    with pytest.raises(ValueError) as excinfo:
+        assert m.ord_char("Ā") == 0x100  # requires 2 bytes, doesn't fit in a char
+    assert str(excinfo.value) == toobig_message(0x100)
+    with pytest.raises(ValueError) as excinfo:
+        assert m.ord_char("ab")
+    assert str(excinfo.value) == toolong_message
+
+    assert m.ord_char16("a") == 0x61
+    assert m.ord_char16("é") == 0xE9
+    assert m.ord_char16_lv("ê") == 0xEA
+    assert m.ord_char16("Ā") == 0x100
+    assert m.ord_char16("‽") == 0x203D
+    assert m.ord_char16("♥") == 0x2665
+    assert m.ord_char16_lv("♡") == 0x2661
+    with pytest.raises(ValueError) as excinfo:
+        assert m.ord_char16("🎂") == 0x1F382  # requires surrogate pair
+    assert str(excinfo.value) == toobig_message(0x10000)
+    with pytest.raises(ValueError) as excinfo:
+        assert m.ord_char16("aa")
+    assert str(excinfo.value) == toolong_message
+
+    assert m.ord_char32("a") == 0x61
+    assert m.ord_char32("é") == 0xE9
+    assert m.ord_char32("Ā") == 0x100
+    assert m.ord_char32("‽") == 0x203D
+    assert m.ord_char32("♥") == 0x2665
+    assert m.ord_char32("🎂") == 0x1F382
+    with pytest.raises(ValueError) as excinfo:
+        assert m.ord_char32("aa")
+    assert str(excinfo.value) == toolong_message
+
+    assert m.ord_wchar("a") == 0x61
+    assert m.ord_wchar("é") == 0xE9
+    assert m.ord_wchar("Ā") == 0x100
+    assert m.ord_wchar("‽") == 0x203D
+    assert m.ord_wchar("♥") == 0x2665
+    if m.wchar_size == 2:
+        with pytest.raises(ValueError) as excinfo:
+            assert m.ord_wchar("🎂") == 0x1F382  # requires surrogate pair
+        assert str(excinfo.value) == toobig_message(0x10000)
+    else:
+        assert m.ord_wchar("🎂") == 0x1F382
+    with pytest.raises(ValueError) as excinfo:
+        assert m.ord_wchar("aa")
+    assert str(excinfo.value) == toolong_message
+
+    if hasattr(m, "has_u8string"):
+        assert m.ord_char8("a") == 0x61  # simple ASCII
+        assert m.ord_char8_lv("b") == 0x62
+        assert (
+            m.ord_char8("é") == 0xE9
+        )  # requires 2 bytes in utf-8, but can be stuffed in a char
+        with pytest.raises(ValueError) as excinfo:
+            assert m.ord_char8("Ā") == 0x100  # requires 2 bytes, doesn't fit in a char
+        assert str(excinfo.value) == toobig_message(0x100)
+        with pytest.raises(ValueError) as excinfo:
+            assert m.ord_char8("ab")
+        assert str(excinfo.value) == toolong_message
+
+
+def test_bytes_to_string():
+    """Tests the ability to pass bytes to C++ string-accepting functions.  Note that this is
+    one-way: the only way to return bytes to Python is via the pybind11::bytes class."""
+    # Issue #816
+
+    assert m.strlen(b"hi") == 2
+    assert m.string_length(b"world") == 5
+    assert m.string_length("a\x00b".encode()) == 3
+    assert m.strlen("a\x00b".encode()) == 1  # C-string limitation
+
+    # passing in a utf8 encoded string should work
+    assert m.string_length("💩".encode()) == 4
+
+
+def test_bytearray_to_string():
+    """Tests the ability to pass bytearray to C++ string-accepting functions"""
+    assert m.string_length(bytearray(b"Hi")) == 2
+    assert m.strlen(bytearray(b"bytearray")) == 9
+    assert m.string_length(bytearray()) == 0
+    assert m.string_length(bytearray("🦜", "utf-8", "strict")) == 4
+    assert m.string_length(bytearray(b"\x80")) == 1
+
+
+@pytest.mark.skipif(not hasattr(m, "has_string_view"), reason="no <string_view>")
+def test_string_view(capture):
+    """Tests support for C++17 string_view arguments and return values"""
+    assert m.string_view_chars("Hi") == [72, 105]
+    assert m.string_view_chars("Hi 🎂") == [72, 105, 32, 0xF0, 0x9F, 0x8E, 0x82]
+    assert m.string_view16_chars("Hi 🎂") == [72, 105, 32, 0xD83C, 0xDF82]
+    assert m.string_view32_chars("Hi 🎂") == [72, 105, 32, 127874]
+    if hasattr(m, "has_u8string"):
+        assert m.string_view8_chars("Hi") == [72, 105]
+        assert m.string_view8_chars("Hi 🎂") == [72, 105, 32, 0xF0, 0x9F, 0x8E, 0x82]
+
+    assert m.string_view_return() == "utf8 secret 🎂"
+    assert m.string_view16_return() == "utf16 secret 🎂"
+    assert m.string_view32_return() == "utf32 secret 🎂"
+    if hasattr(m, "has_u8string"):
+        assert m.string_view8_return() == "utf8 secret 🎂"
+
+    with capture:
+        m.string_view_print("Hi")
+        m.string_view_print("utf8 🎂")
+        m.string_view16_print("utf16 🎂")
+        m.string_view32_print("utf32 🎂")
+    assert (
+        capture
+        == """
+        Hi 2
+        utf8 🎂 9
+        utf16 🎂 8
+        utf32 🎂 7
+    """
+    )
+    if hasattr(m, "has_u8string"):
+        with capture:
+            m.string_view8_print("Hi")
+            m.string_view8_print("utf8 🎂")
+        assert (
+            capture
+            == """
+            Hi 2
+            utf8 🎂 9
+        """
+        )
+
+    with capture:
+        m.string_view_print("Hi, ascii")
+        m.string_view_print("Hi, utf8 🎂")
+        m.string_view16_print("Hi, utf16 🎂")
+        m.string_view32_print("Hi, utf32 🎂")
+    assert (
+        capture
+        == """
+        Hi, ascii 9
+        Hi, utf8 🎂 13
+        Hi, utf16 🎂 12
+        Hi, utf32 🎂 11
+    """
+    )
+    if hasattr(m, "has_u8string"):
+        with capture:
+            m.string_view8_print("Hi, ascii")
+            m.string_view8_print("Hi, utf8 🎂")
+        assert (
+            capture
+            == """
+            Hi, ascii 9
+            Hi, utf8 🎂 13
+        """
+        )
+
+    assert m.string_view_bytes() == b"abc \x80\x80 def"
+    assert m.string_view_str() == "abc ‽ def"
+    assert m.string_view_from_bytes("abc ‽ def".encode()) == "abc ‽ def"
+    if hasattr(m, "has_u8string"):
+        assert m.string_view8_str() == "abc ‽ def"
+    assert m.string_view_memoryview() == "Have some 🎂".encode()
+
+    assert m.bytes_from_type_with_both_operator_string_and_string_view() == b"success"
+    assert m.str_from_type_with_both_operator_string_and_string_view() == "success"
+
+
+def test_integer_casting():
+    """Issue #929 - out-of-range integer values shouldn't be accepted"""
+    assert m.i32_str(-1) == "-1"
+    assert m.i64_str(-1) == "-1"
+    assert m.i32_str(2000000000) == "2000000000"
+    assert m.u32_str(2000000000) == "2000000000"
+    assert m.i64_str(-999999999999) == "-999999999999"
+    assert m.u64_str(999999999999) == "999999999999"
+
+    with pytest.raises(TypeError) as excinfo:
+        m.u32_str(-1)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.u64_str(-1)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.i32_str(-3000000000)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.i32_str(3000000000)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+
+def test_int_convert():
+    class Int:
+        def __int__(self):
+            return 42
+
+    class NotInt:
+        pass
+
+    class Float:
+        def __float__(self):
+            return 41.99999
+
+    class Index:
+        def __index__(self):
+            return 42
+
+    class IntAndIndex:
+        def __int__(self):
+            return 42
+
+        def __index__(self):
+            return 0
+
+    class RaisingTypeErrorOnIndex:
+        def __index__(self):
+            raise TypeError
+
+        def __int__(self):
+            return 42
+
+    class RaisingValueErrorOnIndex:
+        def __index__(self):
+            raise ValueError
+
+        def __int__(self):
+            return 42
+
+    convert, noconvert = m.int_passthrough, m.int_passthrough_noconvert
+
+    def requires_conversion(v):
+        pytest.raises(TypeError, noconvert, v)
+
+    def cant_convert(v):
+        pytest.raises(TypeError, convert, v)
+
+    assert convert(7) == 7
+    assert noconvert(7) == 7
+    cant_convert(3.14159)
+    # TODO: Avoid DeprecationWarning in `PyLong_AsLong` (and similar)
+    # TODO: PyPy 3.8 does not behave like CPython 3.8 here yet (7.3.7)
+    if (3, 8) <= sys.version_info < (3, 10) and env.CPYTHON:
+        with env.deprecated_call():
+            assert convert(Int()) == 42
+    else:
+        assert convert(Int()) == 42
+    requires_conversion(Int())
+    cant_convert(NotInt())
+    cant_convert(Float())
+
+    # Before Python 3.8, `PyLong_AsLong` does not pick up on `obj.__index__`,
+    # but pybind11 "backports" this behavior.
+    assert convert(Index()) == 42
+    assert noconvert(Index()) == 42
+    assert convert(IntAndIndex()) == 0  # Fishy; `int(DoubleThought)` == 42
+    assert noconvert(IntAndIndex()) == 0
+    assert convert(RaisingTypeErrorOnIndex()) == 42
+    requires_conversion(RaisingTypeErrorOnIndex())
+    assert convert(RaisingValueErrorOnIndex()) == 42
+    requires_conversion(RaisingValueErrorOnIndex())
+
+
+def test_numpy_int_convert():
+    np = pytest.importorskip("numpy")
+
+    convert, noconvert = m.int_passthrough, m.int_passthrough_noconvert
+
+    def require_implicit(v):
+        pytest.raises(TypeError, noconvert, v)
+
+    # `np.intc` is an alias that corresponds to a C++ `int`
+    assert convert(np.intc(42)) == 42
+    assert noconvert(np.intc(42)) == 42
+
+    # The implicit conversion from np.float32 is undesirable but currently accepted.
+    # TODO: Avoid DeprecationWarning in `PyLong_AsLong` (and similar)
+    # TODO: PyPy 3.8 does not behave like CPython 3.8 here yet (7.3.7)
+    # https://github.com/pybind/pybind11/issues/3408
+    if (3, 8) <= sys.version_info < (3, 10) and env.CPYTHON:
+        with env.deprecated_call():
+            assert convert(np.float32(3.14159)) == 3
+    else:
+        assert convert(np.float32(3.14159)) == 3
+    require_implicit(np.float32(3.14159))
+
+
+def test_tuple(doc):
+    """std::pair <-> tuple & std::tuple <-> tuple"""
+    assert m.pair_passthrough((True, "test")) == ("test", True)
+    assert m.tuple_passthrough((True, "test", 5)) == (5, "test", True)
+    # Any sequence can be cast to a std::pair or std::tuple
+    assert m.pair_passthrough([True, "test"]) == ("test", True)
+    assert m.tuple_passthrough([True, "test", 5]) == (5, "test", True)
+    assert m.empty_tuple() == ()
+
+    assert (
+        doc(m.pair_passthrough)
+        == """
+        pair_passthrough(arg0: Tuple[bool, str]) -> Tuple[str, bool]
+
+        Return a pair in reversed order
+    """
+    )
+    assert (
+        doc(m.tuple_passthrough)
+        == """
+        tuple_passthrough(arg0: Tuple[bool, str, int]) -> Tuple[int, str, bool]
+
+        Return a triple in reversed order
+    """
+    )
+
+    assert m.rvalue_pair() == ("rvalue", "rvalue")
+    assert m.lvalue_pair() == ("lvalue", "lvalue")
+    assert m.rvalue_tuple() == ("rvalue", "rvalue", "rvalue")
+    assert m.lvalue_tuple() == ("lvalue", "lvalue", "lvalue")
+    assert m.rvalue_nested() == ("rvalue", ("rvalue", ("rvalue", "rvalue")))
+    assert m.lvalue_nested() == ("lvalue", ("lvalue", ("lvalue", "lvalue")))
+
+    assert m.int_string_pair() == (2, "items")
+
+
+def test_builtins_cast_return_none():
+    """Casters produced with PYBIND11_TYPE_CASTER() should convert nullptr to None"""
+    assert m.return_none_string() is None
+    assert m.return_none_char() is None
+    assert m.return_none_bool() is None
+    assert m.return_none_int() is None
+    assert m.return_none_float() is None
+    assert m.return_none_pair() is None
+
+
+def test_none_deferred():
+    """None passed as various argument types should defer to other overloads"""
+    assert not m.defer_none_cstring("abc")
+    assert m.defer_none_cstring(None)
+    assert not m.defer_none_custom(UserType())
+    assert m.defer_none_custom(None)
+    assert m.nodefer_none_void(None)
+
+
+def test_void_caster():
+    assert m.load_nullptr_t(None) is None
+    assert m.cast_nullptr_t() is None
+
+
+def test_reference_wrapper():
+    """std::reference_wrapper for builtin and user types"""
+    assert m.refwrap_builtin(42) == 420
+    assert m.refwrap_usertype(UserType(42)) == 42
+    assert m.refwrap_usertype_const(UserType(42)) == 42
+
+    with pytest.raises(TypeError) as excinfo:
+        m.refwrap_builtin(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.refwrap_usertype(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    assert m.refwrap_lvalue().value == 1
+    assert m.refwrap_lvalue_const().value == 1
+
+    a1 = m.refwrap_list(copy=True)
+    a2 = m.refwrap_list(copy=True)
+    assert [x.value for x in a1] == [2, 3]
+    assert [x.value for x in a2] == [2, 3]
+    assert not a1[0] is a2[0] and not a1[1] is a2[1]
+
+    b1 = m.refwrap_list(copy=False)
+    b2 = m.refwrap_list(copy=False)
+    assert [x.value for x in b1] == [1, 2]
+    assert [x.value for x in b2] == [1, 2]
+    assert b1[0] is b2[0] and b1[1] is b2[1]
+
+    assert m.refwrap_iiw(IncType(5)) == 5
+    assert m.refwrap_call_iiw(IncType(10), m.refwrap_iiw) == [10, 10, 10, 10]
+
+
+def test_complex_cast():
+    """std::complex casts"""
+    assert m.complex_cast(1) == "1.0"
+    assert m.complex_cast(2j) == "(0.0, 2.0)"
+
+
+def test_bool_caster():
+    """Test bool caster implicit conversions."""
+    convert, noconvert = m.bool_passthrough, m.bool_passthrough_noconvert
+
+    def require_implicit(v):
+        pytest.raises(TypeError, noconvert, v)
+
+    def cant_convert(v):
+        pytest.raises(TypeError, convert, v)
+
+    # straight up bool
+    assert convert(True) is True
+    assert convert(False) is False
+    assert noconvert(True) is True
+    assert noconvert(False) is False
+
+    # None requires implicit conversion
+    require_implicit(None)
+    assert convert(None) is False
+
+    class A:
+        def __init__(self, x):
+            self.x = x
+
+        def __nonzero__(self):
+            return self.x
+
+        def __bool__(self):
+            return self.x
+
+    class B:
+        pass
+
+    # Arbitrary objects are not accepted
+    cant_convert(object())
+    cant_convert(B())
+
+    # Objects with __nonzero__ / __bool__ defined can be converted
+    require_implicit(A(True))
+    assert convert(A(True)) is True
+    assert convert(A(False)) is False
+
+
+def test_numpy_bool():
+    np = pytest.importorskip("numpy")
+
+    convert, noconvert = m.bool_passthrough, m.bool_passthrough_noconvert
+
+    def cant_convert(v):
+        pytest.raises(TypeError, convert, v)
+
+    # np.bool_ is not considered implicit
+    assert convert(np.bool_(True)) is True
+    assert convert(np.bool_(False)) is False
+    assert noconvert(np.bool_(True)) is True
+    assert noconvert(np.bool_(False)) is False
+    cant_convert(np.zeros(2, dtype="int"))
+
+
+def test_int_long():
+    assert isinstance(m.int_cast(), int)
+    assert isinstance(m.long_cast(), int)
+    assert isinstance(m.longlong_cast(), int)
+
+
+def test_void_caster_2():
+    assert m.test_void_caster()
+
+
+def test_const_ref_caster():
+    """Verifies that const-ref is propagated through type_caster cast_op.
+    The returned ConstRefCasted type is a minimal type that is constructed to
+    reference the casting mode used.
+    """
+    x = False
+    assert m.takes(x) == 1
+    assert m.takes_move(x) == 1
+
+    assert m.takes_ptr(x) == 3
+    assert m.takes_ref(x) == 2
+    assert m.takes_ref_wrap(x) == 2
+
+    assert m.takes_const_ptr(x) == 5
+    assert m.takes_const_ref(x) == 4
+    assert m.takes_const_ref_wrap(x) == 4
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_call_policies.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_call_policies.cpp
new file mode 100644
index 000000000..d177008cf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_call_policies.cpp
@@ -0,0 +1,115 @@
+/*
+    tests/test_call_policies.cpp -- keep_alive and call_guard
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+struct CustomGuard {
+    static bool enabled;
+
+    CustomGuard() { enabled = true; }
+    ~CustomGuard() { enabled = false; }
+
+    static const char *report_status() { return enabled ? "guarded" : "unguarded"; }
+};
+bool CustomGuard::enabled = false;
+
+struct DependentGuard {
+    static bool enabled;
+
+    DependentGuard() { enabled = CustomGuard::enabled; }
+    ~DependentGuard() { enabled = false; }
+
+    static const char *report_status() { return enabled ? "guarded" : "unguarded"; }
+};
+bool DependentGuard::enabled = false;
+
+TEST_SUBMODULE(call_policies, m) {
+    // Parent/Child are used in:
+    // test_keep_alive_argument, test_keep_alive_return_value, test_alive_gc_derived,
+    // test_alive_gc_multi_derived, test_return_none, test_keep_alive_constructor
+    class Child {
+    public:
+        Child() { py::print("Allocating child."); }
+        Child(const Child &) = default;
+        Child(Child &&) = default;
+        ~Child() { py::print("Releasing child."); }
+    };
+    py::class_<Child>(m, "Child").def(py::init<>());
+
+    class Parent {
+    public:
+        Parent() { py::print("Allocating parent."); }
+        Parent(const Parent &parent) = default;
+        ~Parent() { py::print("Releasing parent."); }
+        void addChild(Child *) {}
+        Child *returnChild() { return new Child(); }
+        Child *returnNullChild() { return nullptr; }
+        static Child *staticFunction(Parent *) { return new Child(); }
+    };
+    py::class_<Parent>(m, "Parent")
+        .def(py::init<>())
+        .def(py::init([](Child *) { return new Parent(); }), py::keep_alive<1, 2>())
+        .def("addChild", &Parent::addChild)
+        .def("addChildKeepAlive", &Parent::addChild, py::keep_alive<1, 2>())
+        .def("returnChild", &Parent::returnChild)
+        .def("returnChildKeepAlive", &Parent::returnChild, py::keep_alive<1, 0>())
+        .def("returnNullChildKeepAliveChild", &Parent::returnNullChild, py::keep_alive<1, 0>())
+        .def("returnNullChildKeepAliveParent", &Parent::returnNullChild, py::keep_alive<0, 1>())
+        .def_static("staticFunction", &Parent::staticFunction, py::keep_alive<1, 0>());
+
+    m.def(
+        "free_function", [](Parent *, Child *) {}, py::keep_alive<1, 2>());
+    m.def(
+        "invalid_arg_index", [] {}, py::keep_alive<0, 1>());
+
+#if !defined(PYPY_VERSION)
+    // test_alive_gc
+    class ParentGC : public Parent {
+    public:
+        using Parent::Parent;
+    };
+    py::class_<ParentGC, Parent>(m, "ParentGC", py::dynamic_attr()).def(py::init<>());
+#endif
+
+    // test_call_guard
+    m.def("unguarded_call", &CustomGuard::report_status);
+    m.def("guarded_call", &CustomGuard::report_status, py::call_guard<CustomGuard>());
+
+    m.def(
+        "multiple_guards_correct_order",
+        []() {
+            return CustomGuard::report_status() + std::string(" & ")
+                   + DependentGuard::report_status();
+        },
+        py::call_guard<CustomGuard, DependentGuard>());
+
+    m.def(
+        "multiple_guards_wrong_order",
+        []() {
+            return DependentGuard::report_status() + std::string(" & ")
+                   + CustomGuard::report_status();
+        },
+        py::call_guard<DependentGuard, CustomGuard>());
+
+#if defined(WITH_THREAD) && !defined(PYPY_VERSION)
+    // `py::call_guard<py::gil_scoped_release>()` should work in PyPy as well,
+    // but it's unclear how to test it without `PyGILState_GetThisThreadState`.
+    auto report_gil_status = []() {
+        auto is_gil_held = false;
+        if (auto *tstate = py::detail::get_thread_state_unchecked()) {
+            is_gil_held = (tstate == PyGILState_GetThisThreadState());
+        }
+
+        return is_gil_held ? "GIL held" : "GIL released";
+    };
+
+    m.def("with_gil", report_gil_status);
+    m.def("without_gil", report_gil_status, py::call_guard<py::gil_scoped_release>());
+#endif
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_call_policies.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_call_policies.py
new file mode 100644
index 000000000..616056412
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_call_policies.py
@@ -0,0 +1,247 @@
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import ConstructorStats
+from pybind11_tests import call_policies as m
+
+
+@pytest.mark.xfail("env.PYPY", reason="sometimes comes out 1 off on PyPy", strict=False)
+def test_keep_alive_argument(capture):
+    n_inst = ConstructorStats.detail_reg_inst()
+    with capture:
+        p = m.Parent()
+    assert capture == "Allocating parent."
+    with capture:
+        p.addChild(m.Child())
+        assert ConstructorStats.detail_reg_inst() == n_inst + 1
+    assert (
+        capture
+        == """
+        Allocating child.
+        Releasing child.
+    """
+    )
+    with capture:
+        del p
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert capture == "Releasing parent."
+
+    with capture:
+        p = m.Parent()
+    assert capture == "Allocating parent."
+    with capture:
+        p.addChildKeepAlive(m.Child())
+        assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    assert capture == "Allocating child."
+    with capture:
+        del p
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert (
+        capture
+        == """
+        Releasing parent.
+        Releasing child.
+    """
+    )
+
+    p = m.Parent()
+    c = m.Child()
+    assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    m.free_function(p, c)
+    del c
+    assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    del p
+    assert ConstructorStats.detail_reg_inst() == n_inst
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.invalid_arg_index()
+    assert str(excinfo.value) == "Could not activate keep_alive!"
+
+
+def test_keep_alive_return_value(capture):
+    n_inst = ConstructorStats.detail_reg_inst()
+    with capture:
+        p = m.Parent()
+    assert capture == "Allocating parent."
+    with capture:
+        p.returnChild()
+        assert ConstructorStats.detail_reg_inst() == n_inst + 1
+    assert (
+        capture
+        == """
+        Allocating child.
+        Releasing child.
+    """
+    )
+    with capture:
+        del p
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert capture == "Releasing parent."
+
+    with capture:
+        p = m.Parent()
+    assert capture == "Allocating parent."
+    with capture:
+        p.returnChildKeepAlive()
+        assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    assert capture == "Allocating child."
+    with capture:
+        del p
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert (
+        capture
+        == """
+        Releasing parent.
+        Releasing child.
+    """
+    )
+
+    p = m.Parent()
+    assert ConstructorStats.detail_reg_inst() == n_inst + 1
+    with capture:
+        m.Parent.staticFunction(p)
+        assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    assert capture == "Allocating child."
+    with capture:
+        del p
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert (
+        capture
+        == """
+        Releasing parent.
+        Releasing child.
+    """
+    )
+
+
+# https://foss.heptapod.net/pypy/pypy/-/issues/2447
+@pytest.mark.xfail("env.PYPY", reason="_PyObject_GetDictPtr is unimplemented")
+def test_alive_gc(capture):
+    n_inst = ConstructorStats.detail_reg_inst()
+    p = m.ParentGC()
+    p.addChildKeepAlive(m.Child())
+    assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    lst = [p]
+    lst.append(lst)  # creates a circular reference
+    with capture:
+        del p, lst
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert (
+        capture
+        == """
+        Releasing parent.
+        Releasing child.
+    """
+    )
+
+
+def test_alive_gc_derived(capture):
+    class Derived(m.Parent):
+        pass
+
+    n_inst = ConstructorStats.detail_reg_inst()
+    p = Derived()
+    p.addChildKeepAlive(m.Child())
+    assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    lst = [p]
+    lst.append(lst)  # creates a circular reference
+    with capture:
+        del p, lst
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert (
+        capture
+        == """
+        Releasing parent.
+        Releasing child.
+    """
+    )
+
+
+def test_alive_gc_multi_derived(capture):
+    class Derived(m.Parent, m.Child):
+        def __init__(self):
+            m.Parent.__init__(self)
+            m.Child.__init__(self)
+
+    n_inst = ConstructorStats.detail_reg_inst()
+    p = Derived()
+    p.addChildKeepAlive(m.Child())
+    # +3 rather than +2 because Derived corresponds to two registered instances
+    assert ConstructorStats.detail_reg_inst() == n_inst + 3
+    lst = [p]
+    lst.append(lst)  # creates a circular reference
+    with capture:
+        del p, lst
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert (
+        capture
+        == """
+        Releasing parent.
+        Releasing child.
+        Releasing child.
+    """
+    )
+
+
+def test_return_none(capture):
+    n_inst = ConstructorStats.detail_reg_inst()
+    with capture:
+        p = m.Parent()
+    assert capture == "Allocating parent."
+    with capture:
+        p.returnNullChildKeepAliveChild()
+        assert ConstructorStats.detail_reg_inst() == n_inst + 1
+    assert capture == ""
+    with capture:
+        del p
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert capture == "Releasing parent."
+
+    with capture:
+        p = m.Parent()
+    assert capture == "Allocating parent."
+    with capture:
+        p.returnNullChildKeepAliveParent()
+        assert ConstructorStats.detail_reg_inst() == n_inst + 1
+    assert capture == ""
+    with capture:
+        del p
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert capture == "Releasing parent."
+
+
+def test_keep_alive_constructor(capture):
+    n_inst = ConstructorStats.detail_reg_inst()
+
+    with capture:
+        p = m.Parent(m.Child())
+        assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    assert (
+        capture
+        == """
+        Allocating child.
+        Allocating parent.
+    """
+    )
+    with capture:
+        del p
+        assert ConstructorStats.detail_reg_inst() == n_inst
+    assert (
+        capture
+        == """
+        Releasing parent.
+        Releasing child.
+    """
+    )
+
+
+def test_call_guard():
+    assert m.unguarded_call() == "unguarded"
+    assert m.guarded_call() == "guarded"
+
+    assert m.multiple_guards_correct_order() == "guarded & guarded"
+    assert m.multiple_guards_wrong_order() == "unguarded & guarded"
+
+    if hasattr(m, "with_gil"):
+        assert m.with_gil() == "GIL held"
+        assert m.without_gil() == "GIL released"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_callbacks.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_callbacks.cpp
new file mode 100644
index 000000000..92b8053de
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_callbacks.cpp
@@ -0,0 +1,243 @@
+/*
+    tests/test_callbacks.cpp -- callbacks
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/functional.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#include <thread>
+
+int dummy_function(int i) { return i + 1; }
+
+TEST_SUBMODULE(callbacks, m) {
+    // test_callbacks, test_function_signatures
+    m.def("test_callback1", [](const py::object &func) { return func(); });
+    m.def("test_callback2", [](const py::object &func) { return func("Hello", 'x', true, 5); });
+    m.def("test_callback3", [](const std::function<int(int)> &func) {
+        return "func(43) = " + std::to_string(func(43));
+    });
+    m.def("test_callback4",
+          []() -> std::function<int(int)> { return [](int i) { return i + 1; }; });
+    m.def("test_callback5",
+          []() { return py::cpp_function([](int i) { return i + 1; }, py::arg("number")); });
+
+    // test_keyword_args_and_generalized_unpacking
+    m.def("test_tuple_unpacking", [](const py::function &f) {
+        auto t1 = py::make_tuple(2, 3);
+        auto t2 = py::make_tuple(5, 6);
+        return f("positional", 1, *t1, 4, *t2);
+    });
+
+    m.def("test_dict_unpacking", [](const py::function &f) {
+        auto d1 = py::dict("key"_a = "value", "a"_a = 1);
+        auto d2 = py::dict();
+        auto d3 = py::dict("b"_a = 2);
+        return f("positional", 1, **d1, **d2, **d3);
+    });
+
+    m.def("test_keyword_args", [](const py::function &f) { return f("x"_a = 10, "y"_a = 20); });
+
+    m.def("test_unpacking_and_keywords1", [](const py::function &f) {
+        auto args = py::make_tuple(2);
+        auto kwargs = py::dict("d"_a = 4);
+        return f(1, *args, "c"_a = 3, **kwargs);
+    });
+
+    m.def("test_unpacking_and_keywords2", [](const py::function &f) {
+        auto kwargs1 = py::dict("a"_a = 1);
+        auto kwargs2 = py::dict("c"_a = 3, "d"_a = 4);
+        return f("positional",
+                 *py::make_tuple(1),
+                 2,
+                 *py::make_tuple(3, 4),
+                 5,
+                 "key"_a = "value",
+                 **kwargs1,
+                 "b"_a = 2,
+                 **kwargs2,
+                 "e"_a = 5);
+    });
+
+    m.def("test_unpacking_error1", [](const py::function &f) {
+        auto kwargs = py::dict("x"_a = 3);
+        return f("x"_a = 1, "y"_a = 2, **kwargs); // duplicate ** after keyword
+    });
+
+    m.def("test_unpacking_error2", [](const py::function &f) {
+        auto kwargs = py::dict("x"_a = 3);
+        return f(**kwargs, "x"_a = 1); // duplicate keyword after **
+    });
+
+    m.def("test_arg_conversion_error1",
+          [](const py::function &f) { f(234, UnregisteredType(), "kw"_a = 567); });
+
+    m.def("test_arg_conversion_error2", [](const py::function &f) {
+        f(234, "expected_name"_a = UnregisteredType(), "kw"_a = 567);
+    });
+
+    // test_lambda_closure_cleanup
+    struct Payload {
+        Payload() { print_default_created(this); }
+        ~Payload() { print_destroyed(this); }
+        Payload(const Payload &) { print_copy_created(this); }
+        Payload(Payload &&) noexcept { print_move_created(this); }
+    };
+    // Export the payload constructor statistics for testing purposes:
+    m.def("payload_cstats", &ConstructorStats::get<Payload>);
+    m.def("test_lambda_closure_cleanup", []() -> std::function<void()> {
+        Payload p;
+
+        // In this situation, `Func` in the implementation of
+        // `cpp_function::initialize` is NOT trivially destructible.
+        return [p]() {
+            /* p should be cleaned up when the returned function is garbage collected */
+            (void) p;
+        };
+    });
+
+    class CppCallable {
+    public:
+        CppCallable() { track_default_created(this); }
+        ~CppCallable() { track_destroyed(this); }
+        CppCallable(const CppCallable &) { track_copy_created(this); }
+        CppCallable(CppCallable &&) noexcept { track_move_created(this); }
+        void operator()() {}
+    };
+
+    m.def("test_cpp_callable_cleanup", []() {
+        // Related issue: https://github.com/pybind/pybind11/issues/3228
+        // Related PR: https://github.com/pybind/pybind11/pull/3229
+        py::list alive_counts;
+        ConstructorStats &stat = ConstructorStats::get<CppCallable>();
+        alive_counts.append(stat.alive());
+        {
+            CppCallable cpp_callable;
+            alive_counts.append(stat.alive());
+            {
+                // In this situation, `Func` in the implementation of
+                // `cpp_function::initialize` IS trivially destructible,
+                // only `capture` is not.
+                py::cpp_function py_func(cpp_callable);
+                py::detail::silence_unused_warnings(py_func);
+                alive_counts.append(stat.alive());
+            }
+            alive_counts.append(stat.alive());
+            {
+                py::cpp_function py_func(std::move(cpp_callable));
+                py::detail::silence_unused_warnings(py_func);
+                alive_counts.append(stat.alive());
+            }
+            alive_counts.append(stat.alive());
+        }
+        alive_counts.append(stat.alive());
+        return alive_counts;
+    });
+
+    // test_cpp_function_roundtrip
+    /* Test if passing a function pointer from C++ -> Python -> C++ yields the original pointer */
+    m.def("dummy_function", &dummy_function);
+    m.def("dummy_function_overloaded", [](int i, int j) { return i + j; });
+    m.def("dummy_function_overloaded", &dummy_function);
+    m.def("dummy_function2", [](int i, int j) { return i + j; });
+    m.def(
+        "roundtrip",
+        [](std::function<int(int)> f, bool expect_none = false) {
+            if (expect_none && f) {
+                throw std::runtime_error("Expected None to be converted to empty std::function");
+            }
+            return f;
+        },
+        py::arg("f"),
+        py::arg("expect_none") = false);
+    m.def("test_dummy_function", [](const std::function<int(int)> &f) -> std::string {
+        using fn_type = int (*)(int);
+        const auto *result = f.target<fn_type>();
+        if (!result) {
+            auto r = f(1);
+            return "can't convert to function pointer: eval(1) = " + std::to_string(r);
+        }
+        if (*result == dummy_function) {
+            auto r = (*result)(1);
+            return "matches dummy_function: eval(1) = " + std::to_string(r);
+        }
+        return "argument does NOT match dummy_function. This should never happen!";
+    });
+
+    class AbstractBase {
+    public:
+        // [workaround(intel)] = default does not work here
+        // Defaulting this destructor results in linking errors with the Intel compiler
+        // (in Debug builds only, tested with icpc (ICC) 2021.1 Beta 20200827)
+        virtual ~AbstractBase() {} // NOLINT(modernize-use-equals-default)
+        virtual unsigned int func() = 0;
+    };
+    m.def("func_accepting_func_accepting_base",
+          [](const std::function<double(AbstractBase &)> &) {});
+
+    struct MovableObject {
+        bool valid = true;
+
+        MovableObject() = default;
+        MovableObject(const MovableObject &) = default;
+        MovableObject &operator=(const MovableObject &) = default;
+        MovableObject(MovableObject &&o) noexcept : valid(o.valid) { o.valid = false; }
+        MovableObject &operator=(MovableObject &&o) noexcept {
+            valid = o.valid;
+            o.valid = false;
+            return *this;
+        }
+    };
+    py::class_<MovableObject>(m, "MovableObject");
+
+    // test_movable_object
+    m.def("callback_with_movable", [](const std::function<void(MovableObject &)> &f) {
+        auto x = MovableObject();
+        f(x);           // lvalue reference shouldn't move out object
+        return x.valid; // must still return `true`
+    });
+
+    // test_bound_method_callback
+    struct CppBoundMethodTest {};
+    py::class_<CppBoundMethodTest>(m, "CppBoundMethodTest")
+        .def(py::init<>())
+        .def("triple", [](CppBoundMethodTest &, int val) { return 3 * val; });
+
+    // This checks that builtin functions can be passed as callbacks
+    // rather than throwing RuntimeError due to trying to extract as capsule
+    m.def("test_sum_builtin",
+          [](const std::function<double(py::iterable)> &sum_builtin, const py::iterable &i) {
+              return sum_builtin(i);
+          });
+
+    // test async Python callbacks
+    using callback_f = std::function<void(int)>;
+    m.def("test_async_callback", [](const callback_f &f, const py::list &work) {
+        // make detached thread that calls `f` with piece of work after a little delay
+        auto start_f = [f](int j) {
+            auto invoke_f = [f, j] {
+                std::this_thread::sleep_for(std::chrono::milliseconds(50));
+                f(j);
+            };
+            auto t = std::thread(std::move(invoke_f));
+            t.detach();
+        };
+
+        // spawn worker threads
+        for (auto i : work) {
+            start_f(py::cast<int>(i));
+        }
+    });
+
+    m.def("callback_num_times", [](const py::function &f, std::size_t num) {
+        for (std::size_t i = 0; i < num; i++) {
+            f();
+        }
+    });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_callbacks.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_callbacks.py
new file mode 100644
index 000000000..0b1047bbf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_callbacks.py
@@ -0,0 +1,195 @@
+import time
+from threading import Thread
+
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import callbacks as m
+
+
+def test_callbacks():
+    from functools import partial
+
+    def func1():
+        return "func1"
+
+    def func2(a, b, c, d):
+        return "func2", a, b, c, d
+
+    def func3(a):
+        return f"func3({a})"
+
+    assert m.test_callback1(func1) == "func1"
+    assert m.test_callback2(func2) == ("func2", "Hello", "x", True, 5)
+    assert m.test_callback1(partial(func2, 1, 2, 3, 4)) == ("func2", 1, 2, 3, 4)
+    assert m.test_callback1(partial(func3, "partial")) == "func3(partial)"
+    assert m.test_callback3(lambda i: i + 1) == "func(43) = 44"
+
+    f = m.test_callback4()
+    assert f(43) == 44
+    f = m.test_callback5()
+    assert f(number=43) == 44
+
+
+def test_bound_method_callback():
+    # Bound Python method:
+    class MyClass:
+        def double(self, val):
+            return 2 * val
+
+    z = MyClass()
+    assert m.test_callback3(z.double) == "func(43) = 86"
+
+    z = m.CppBoundMethodTest()
+    assert m.test_callback3(z.triple) == "func(43) = 129"
+
+
+def test_keyword_args_and_generalized_unpacking():
+    def f(*args, **kwargs):
+        return args, kwargs
+
+    assert m.test_tuple_unpacking(f) == (("positional", 1, 2, 3, 4, 5, 6), {})
+    assert m.test_dict_unpacking(f) == (
+        ("positional", 1),
+        {"key": "value", "a": 1, "b": 2},
+    )
+    assert m.test_keyword_args(f) == ((), {"x": 10, "y": 20})
+    assert m.test_unpacking_and_keywords1(f) == ((1, 2), {"c": 3, "d": 4})
+    assert m.test_unpacking_and_keywords2(f) == (
+        ("positional", 1, 2, 3, 4, 5),
+        {"key": "value", "a": 1, "b": 2, "c": 3, "d": 4, "e": 5},
+    )
+
+    with pytest.raises(TypeError) as excinfo:
+        m.test_unpacking_error1(f)
+    assert "Got multiple values for keyword argument" in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.test_unpacking_error2(f)
+    assert "Got multiple values for keyword argument" in str(excinfo.value)
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.test_arg_conversion_error1(f)
+    assert "Unable to convert call argument" in str(excinfo.value)
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.test_arg_conversion_error2(f)
+    assert "Unable to convert call argument" in str(excinfo.value)
+
+
+def test_lambda_closure_cleanup():
+    m.test_lambda_closure_cleanup()
+    cstats = m.payload_cstats()
+    assert cstats.alive() == 0
+    assert cstats.copy_constructions == 1
+    assert cstats.move_constructions >= 1
+
+
+def test_cpp_callable_cleanup():
+    alive_counts = m.test_cpp_callable_cleanup()
+    assert alive_counts == [0, 1, 2, 1, 2, 1, 0]
+
+
+def test_cpp_function_roundtrip():
+    """Test if passing a function pointer from C++ -> Python -> C++ yields the original pointer"""
+
+    assert (
+        m.test_dummy_function(m.dummy_function) == "matches dummy_function: eval(1) = 2"
+    )
+    assert (
+        m.test_dummy_function(m.roundtrip(m.dummy_function))
+        == "matches dummy_function: eval(1) = 2"
+    )
+    assert (
+        m.test_dummy_function(m.dummy_function_overloaded)
+        == "matches dummy_function: eval(1) = 2"
+    )
+    assert m.roundtrip(None, expect_none=True) is None
+    assert (
+        m.test_dummy_function(lambda x: x + 2)
+        == "can't convert to function pointer: eval(1) = 3"
+    )
+
+    with pytest.raises(TypeError) as excinfo:
+        m.test_dummy_function(m.dummy_function2)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.test_dummy_function(lambda x, y: x + y)
+    assert any(
+        s in str(excinfo.value)
+        for s in ("missing 1 required positional argument", "takes exactly 2 arguments")
+    )
+
+
+def test_function_signatures(doc):
+    assert doc(m.test_callback3) == "test_callback3(arg0: Callable[[int], int]) -> str"
+    assert doc(m.test_callback4) == "test_callback4() -> Callable[[int], int]"
+
+
+def test_movable_object():
+    assert m.callback_with_movable(lambda _: None) is True
+
+
+@pytest.mark.skipif(
+    "env.PYPY",
+    reason="PyPy segfaults on here. See discussion on #1413.",
+)
+def test_python_builtins():
+    """Test if python builtins like sum() can be used as callbacks"""
+    assert m.test_sum_builtin(sum, [1, 2, 3]) == 6
+    assert m.test_sum_builtin(sum, []) == 0
+
+
+def test_async_callbacks():
+    # serves as state for async callback
+    class Item:
+        def __init__(self, value):
+            self.value = value
+
+    res = []
+
+    # generate stateful lambda that will store result in `res`
+    def gen_f():
+        s = Item(3)
+        return lambda j: res.append(s.value + j)
+
+    # do some work async
+    work = [1, 2, 3, 4]
+    m.test_async_callback(gen_f(), work)
+    # wait until work is done
+    from time import sleep
+
+    sleep(0.5)
+    assert sum(res) == sum(x + 3 for x in work)
+
+
+def test_async_async_callbacks():
+    t = Thread(target=test_async_callbacks)
+    t.start()
+    t.join()
+
+
+def test_callback_num_times():
+    # Super-simple micro-benchmarking related to PR #2919.
+    # Example runtimes (Intel Xeon 2.2GHz, fully optimized):
+    #   num_millions  1, repeats  2:  0.1 secs
+    #   num_millions 20, repeats 10: 11.5 secs
+    one_million = 1000000
+    num_millions = 1  # Try 20 for actual micro-benchmarking.
+    repeats = 2  # Try 10.
+    rates = []
+    for rep in range(repeats):
+        t0 = time.time()
+        m.callback_num_times(lambda: None, num_millions * one_million)
+        td = time.time() - t0
+        rate = num_millions / td if td else 0
+        rates.append(rate)
+        if not rep:
+            print()
+        print(
+            f"callback_num_times: {num_millions:d} million / {td:.3f} seconds = {rate:.3f} million / second"
+        )
+    if len(rates) > 1:
+        print("Min    Mean   Max")
+        print(f"{min(rates):6.3f} {sum(rates) / len(rates):6.3f} {max(rates):6.3f}")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_chrono.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_chrono.cpp
new file mode 100644
index 000000000..8be0ffd18
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_chrono.cpp
@@ -0,0 +1,81 @@
+/*
+    tests/test_chrono.cpp -- test conversions to/from std::chrono types
+
+    Copyright (c) 2016 Trent Houliston <trent@houliston.me> and
+                       Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/chrono.h>
+
+#include "pybind11_tests.h"
+
+#include <chrono>
+
+struct different_resolutions {
+    using time_point_h = std::chrono::time_point<std::chrono::system_clock, std::chrono::hours>;
+    using time_point_m = std::chrono::time_point<std::chrono::system_clock, std::chrono::minutes>;
+    using time_point_s = std::chrono::time_point<std::chrono::system_clock, std::chrono::seconds>;
+    using time_point_ms
+        = std::chrono::time_point<std::chrono::system_clock, std::chrono::milliseconds>;
+    using time_point_us
+        = std::chrono::time_point<std::chrono::system_clock, std::chrono::microseconds>;
+    time_point_h timestamp_h;
+    time_point_m timestamp_m;
+    time_point_s timestamp_s;
+    time_point_ms timestamp_ms;
+    time_point_us timestamp_us;
+};
+
+TEST_SUBMODULE(chrono, m) {
+    using system_time = std::chrono::system_clock::time_point;
+    using steady_time = std::chrono::steady_clock::time_point;
+
+    using timespan = std::chrono::duration<int64_t, std::nano>;
+    using timestamp = std::chrono::time_point<std::chrono::system_clock, timespan>;
+
+    // test_chrono_system_clock
+    // Return the current time off the wall clock
+    m.def("test_chrono1", []() { return std::chrono::system_clock::now(); });
+
+    // test_chrono_system_clock_roundtrip
+    // Round trip the passed in system clock time
+    m.def("test_chrono2", [](system_time t) { return t; });
+
+    // test_chrono_duration_roundtrip
+    // Round trip the passed in duration
+    m.def("test_chrono3", [](std::chrono::system_clock::duration d) { return d; });
+
+    // test_chrono_duration_subtraction_equivalence
+    // Difference between two passed in time_points
+    m.def("test_chrono4", [](system_time a, system_time b) { return a - b; });
+
+    // test_chrono_steady_clock
+    // Return the current time off the steady_clock
+    m.def("test_chrono5", []() { return std::chrono::steady_clock::now(); });
+
+    // test_chrono_steady_clock_roundtrip
+    // Round trip a steady clock timepoint
+    m.def("test_chrono6", [](steady_time t) { return t; });
+
+    // test_floating_point_duration
+    // Roundtrip a duration in microseconds from a float argument
+    m.def("test_chrono7", [](std::chrono::microseconds t) { return t; });
+    // Float durations (issue #719)
+    m.def("test_chrono_float_diff",
+          [](std::chrono::duration<float> a, std::chrono::duration<float> b) { return a - b; });
+
+    m.def("test_nano_timepoint",
+          [](timestamp start, timespan delta) -> timestamp { return start + delta; });
+
+    // Test different resolutions
+    py::class_<different_resolutions>(m, "different_resolutions")
+        .def(py::init<>())
+        .def_readwrite("timestamp_h", &different_resolutions::timestamp_h)
+        .def_readwrite("timestamp_m", &different_resolutions::timestamp_m)
+        .def_readwrite("timestamp_s", &different_resolutions::timestamp_s)
+        .def_readwrite("timestamp_ms", &different_resolutions::timestamp_ms)
+        .def_readwrite("timestamp_us", &different_resolutions::timestamp_us);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_chrono.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_chrono.py
new file mode 100644
index 000000000..7f47b37a2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_chrono.py
@@ -0,0 +1,209 @@
+import datetime
+
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import chrono as m
+
+
+def test_chrono_system_clock():
+
+    # Get the time from both c++ and datetime
+    date0 = datetime.datetime.today()
+    date1 = m.test_chrono1()
+    date2 = datetime.datetime.today()
+
+    # The returned value should be a datetime
+    assert isinstance(date1, datetime.datetime)
+
+    # The numbers should vary by a very small amount (time it took to execute)
+    diff_python = abs(date2 - date0)
+    diff = abs(date1 - date2)
+
+    # There should never be a days difference
+    assert diff.days == 0
+
+    # Since datetime.datetime.today() calls time.time(), and on some platforms
+    # that has 1 second accuracy, we compare this way
+    assert diff.seconds <= diff_python.seconds
+
+
+def test_chrono_system_clock_roundtrip():
+    date1 = datetime.datetime.today()
+
+    # Roundtrip the time
+    date2 = m.test_chrono2(date1)
+
+    # The returned value should be a datetime
+    assert isinstance(date2, datetime.datetime)
+
+    # They should be identical (no information lost on roundtrip)
+    diff = abs(date1 - date2)
+    assert diff == datetime.timedelta(0)
+
+
+def test_chrono_system_clock_roundtrip_date():
+    date1 = datetime.date.today()
+
+    # Roundtrip the time
+    datetime2 = m.test_chrono2(date1)
+    date2 = datetime2.date()
+    time2 = datetime2.time()
+
+    # The returned value should be a datetime
+    assert isinstance(datetime2, datetime.datetime)
+    assert isinstance(date2, datetime.date)
+    assert isinstance(time2, datetime.time)
+
+    # They should be identical (no information lost on roundtrip)
+    diff = abs(date1 - date2)
+    assert diff.days == 0
+    assert diff.seconds == 0
+    assert diff.microseconds == 0
+
+    # Year, Month & Day should be the same after the round trip
+    assert date1 == date2
+
+    # There should be no time information
+    assert time2.hour == 0
+    assert time2.minute == 0
+    assert time2.second == 0
+    assert time2.microsecond == 0
+
+
+SKIP_TZ_ENV_ON_WIN = pytest.mark.skipif(
+    "env.WIN", reason="TZ environment variable only supported on POSIX"
+)
+
+
+@pytest.mark.parametrize(
+    "time1",
+    [
+        datetime.datetime.today().time(),
+        datetime.time(0, 0, 0),
+        datetime.time(0, 0, 0, 1),
+        datetime.time(0, 28, 45, 109827),
+        datetime.time(0, 59, 59, 999999),
+        datetime.time(1, 0, 0),
+        datetime.time(5, 59, 59, 0),
+        datetime.time(5, 59, 59, 1),
+    ],
+)
+@pytest.mark.parametrize(
+    "tz",
+    [
+        None,
+        pytest.param("Europe/Brussels", marks=SKIP_TZ_ENV_ON_WIN),
+        pytest.param("Asia/Pyongyang", marks=SKIP_TZ_ENV_ON_WIN),
+        pytest.param("America/New_York", marks=SKIP_TZ_ENV_ON_WIN),
+    ],
+)
+def test_chrono_system_clock_roundtrip_time(time1, tz, monkeypatch):
+    if tz is not None:
+        monkeypatch.setenv("TZ", f"/usr/share/zoneinfo/{tz}")
+
+    # Roundtrip the time
+    datetime2 = m.test_chrono2(time1)
+    date2 = datetime2.date()
+    time2 = datetime2.time()
+
+    # The returned value should be a datetime
+    assert isinstance(datetime2, datetime.datetime)
+    assert isinstance(date2, datetime.date)
+    assert isinstance(time2, datetime.time)
+
+    # Hour, Minute, Second & Microsecond should be the same after the round trip
+    assert time1 == time2
+
+    # There should be no date information (i.e. date = python base date)
+    assert date2.year == 1970
+    assert date2.month == 1
+    assert date2.day == 1
+
+
+def test_chrono_duration_roundtrip():
+
+    # Get the difference between two times (a timedelta)
+    date1 = datetime.datetime.today()
+    date2 = datetime.datetime.today()
+    diff = date2 - date1
+
+    # Make sure this is a timedelta
+    assert isinstance(diff, datetime.timedelta)
+
+    cpp_diff = m.test_chrono3(diff)
+
+    assert cpp_diff == diff
+
+    # Negative timedelta roundtrip
+    diff = datetime.timedelta(microseconds=-1)
+    cpp_diff = m.test_chrono3(diff)
+
+    assert cpp_diff == diff
+
+
+def test_chrono_duration_subtraction_equivalence():
+
+    date1 = datetime.datetime.today()
+    date2 = datetime.datetime.today()
+
+    diff = date2 - date1
+    cpp_diff = m.test_chrono4(date2, date1)
+
+    assert cpp_diff == diff
+
+
+def test_chrono_duration_subtraction_equivalence_date():
+
+    date1 = datetime.date.today()
+    date2 = datetime.date.today()
+
+    diff = date2 - date1
+    cpp_diff = m.test_chrono4(date2, date1)
+
+    assert cpp_diff == diff
+
+
+def test_chrono_steady_clock():
+    time1 = m.test_chrono5()
+    assert isinstance(time1, datetime.timedelta)
+
+
+def test_chrono_steady_clock_roundtrip():
+    time1 = datetime.timedelta(days=10, seconds=10, microseconds=100)
+    time2 = m.test_chrono6(time1)
+
+    assert isinstance(time2, datetime.timedelta)
+
+    # They should be identical (no information lost on roundtrip)
+    assert time1 == time2
+
+
+def test_floating_point_duration():
+    # Test using a floating point number in seconds
+    time = m.test_chrono7(35.525123)
+
+    assert isinstance(time, datetime.timedelta)
+
+    assert time.seconds == 35
+    assert 525122 <= time.microseconds <= 525123
+
+    diff = m.test_chrono_float_diff(43.789012, 1.123456)
+    assert diff.seconds == 42
+    assert 665556 <= diff.microseconds <= 665557
+
+
+def test_nano_timepoint():
+    time = datetime.datetime.now()
+    time1 = m.test_nano_timepoint(time, datetime.timedelta(seconds=60))
+    assert time1 == time + datetime.timedelta(seconds=60)
+
+
+def test_chrono_different_resolutions():
+    resolutions = m.different_resolutions()
+    time = datetime.datetime.now()
+    resolutions.timestamp_h = time
+    resolutions.timestamp_m = time
+    resolutions.timestamp_s = time
+    resolutions.timestamp_ms = time
+    resolutions.timestamp_us = time
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_class.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_class.cpp
new file mode 100644
index 000000000..c8b8071dc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_class.cpp
@@ -0,0 +1,619 @@
+/*
+    tests/test_class.cpp -- test py::class_ definitions and basic functionality
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#if defined(__INTEL_COMPILER) && __cplusplus >= 201703L
+// Intel compiler requires a separate header file to support aligned new operators
+// and does not set the __cpp_aligned_new feature macro.
+// This header needs to be included before pybind11.
+#    include <aligned_new>
+#endif
+
+#include <pybind11/stl.h>
+
+#include "constructor_stats.h"
+#include "local_bindings.h"
+#include "pybind11_tests.h"
+
+#include <utility>
+
+#if defined(_MSC_VER)
+#    pragma warning(disable : 4324)
+//     warning C4324: structure was padded due to alignment specifier
+#endif
+
+// test_brace_initialization
+struct NoBraceInitialization {
+    explicit NoBraceInitialization(std::vector<int> v) : vec{std::move(v)} {}
+    template <typename T>
+    NoBraceInitialization(std::initializer_list<T> l) : vec(l) {}
+
+    std::vector<int> vec;
+};
+
+TEST_SUBMODULE(class_, m) {
+    // test_instance
+    struct NoConstructor {
+        NoConstructor() = default;
+        NoConstructor(const NoConstructor &) = default;
+        NoConstructor(NoConstructor &&) = default;
+        static NoConstructor *new_instance() {
+            auto *ptr = new NoConstructor();
+            print_created(ptr, "via new_instance");
+            return ptr;
+        }
+        ~NoConstructor() { print_destroyed(this); }
+    };
+    struct NoConstructorNew {
+        NoConstructorNew() = default;
+        NoConstructorNew(const NoConstructorNew &) = default;
+        NoConstructorNew(NoConstructorNew &&) = default;
+        static NoConstructorNew *new_instance() {
+            auto *ptr = new NoConstructorNew();
+            print_created(ptr, "via new_instance");
+            return ptr;
+        }
+        ~NoConstructorNew() { print_destroyed(this); }
+    };
+
+    py::class_<NoConstructor>(m, "NoConstructor")
+        .def_static("new_instance", &NoConstructor::new_instance, "Return an instance");
+
+    py::class_<NoConstructorNew>(m, "NoConstructorNew")
+        .def(py::init([](const NoConstructorNew &self) { return self; })) // Need a NOOP __init__
+        .def_static("__new__",
+                    [](const py::object &) { return NoConstructorNew::new_instance(); });
+
+    // test_inheritance
+    class Pet {
+    public:
+        Pet(const std::string &name, const std::string &species)
+            : m_name(name), m_species(species) {}
+        std::string name() const { return m_name; }
+        std::string species() const { return m_species; }
+
+    private:
+        std::string m_name;
+        std::string m_species;
+    };
+
+    class Dog : public Pet {
+    public:
+        explicit Dog(const std::string &name) : Pet(name, "dog") {}
+        std::string bark() const { return "Woof!"; }
+    };
+
+    class Rabbit : public Pet {
+    public:
+        explicit Rabbit(const std::string &name) : Pet(name, "parrot") {}
+    };
+
+    class Hamster : public Pet {
+    public:
+        explicit Hamster(const std::string &name) : Pet(name, "rodent") {}
+    };
+
+    class Chimera : public Pet {
+        Chimera() : Pet("Kimmy", "chimera") {}
+    };
+
+    py::class_<Pet> pet_class(m, "Pet");
+    pet_class.def(py::init<std::string, std::string>())
+        .def("name", &Pet::name)
+        .def("species", &Pet::species);
+
+    /* One way of declaring a subclass relationship: reference parent's class_ object */
+    py::class_<Dog>(m, "Dog", pet_class).def(py::init<std::string>());
+
+    /* Another way of declaring a subclass relationship: reference parent's C++ type */
+    py::class_<Rabbit, Pet>(m, "Rabbit").def(py::init<std::string>());
+
+    /* And another: list parent in class template arguments */
+    py::class_<Hamster, Pet>(m, "Hamster").def(py::init<std::string>());
+
+    /* Constructors are not inherited by default */
+    py::class_<Chimera, Pet>(m, "Chimera");
+
+    m.def("pet_name_species",
+          [](const Pet &pet) { return pet.name() + " is a " + pet.species(); });
+    m.def("dog_bark", [](const Dog &dog) { return dog.bark(); });
+
+    // test_automatic_upcasting
+    struct BaseClass {
+        BaseClass() = default;
+        BaseClass(const BaseClass &) = default;
+        BaseClass(BaseClass &&) = default;
+        virtual ~BaseClass() = default;
+    };
+    struct DerivedClass1 : BaseClass {};
+    struct DerivedClass2 : BaseClass {};
+
+    py::class_<BaseClass>(m, "BaseClass").def(py::init<>());
+    py::class_<DerivedClass1>(m, "DerivedClass1").def(py::init<>());
+    py::class_<DerivedClass2>(m, "DerivedClass2").def(py::init<>());
+
+    m.def("return_class_1", []() -> BaseClass * { return new DerivedClass1(); });
+    m.def("return_class_2", []() -> BaseClass * { return new DerivedClass2(); });
+    m.def("return_class_n", [](int n) -> BaseClass * {
+        if (n == 1) {
+            return new DerivedClass1();
+        }
+        if (n == 2) {
+            return new DerivedClass2();
+        }
+        return new BaseClass();
+    });
+    m.def("return_none", []() -> BaseClass * { return nullptr; });
+
+    // test_isinstance
+    m.def("check_instances", [](const py::list &l) {
+        return py::make_tuple(py::isinstance<py::tuple>(l[0]),
+                              py::isinstance<py::dict>(l[1]),
+                              py::isinstance<Pet>(l[2]),
+                              py::isinstance<Pet>(l[3]),
+                              py::isinstance<Dog>(l[4]),
+                              py::isinstance<Rabbit>(l[5]),
+                              py::isinstance<UnregisteredType>(l[6]));
+    });
+
+    struct Invalid {};
+
+    // test_type
+    m.def("check_type", [](int category) {
+        // Currently not supported (via a fail at compile time)
+        // See https://github.com/pybind/pybind11/issues/2486
+        // if (category == 2)
+        //     return py::type::of<int>();
+        if (category == 1) {
+            return py::type::of<DerivedClass1>();
+        }
+        return py::type::of<Invalid>();
+    });
+
+    m.def("get_type_of", [](py::object ob) { return py::type::of(std::move(ob)); });
+
+    m.def("get_type_classic", [](py::handle h) { return h.get_type(); });
+
+    m.def("as_type", [](const py::object &ob) { return py::type(ob); });
+
+    // test_mismatched_holder
+    struct MismatchBase1 {};
+    struct MismatchDerived1 : MismatchBase1 {};
+
+    struct MismatchBase2 {};
+    struct MismatchDerived2 : MismatchBase2 {};
+
+    m.def("mismatched_holder_1", []() {
+        auto mod = py::module_::import("__main__");
+        py::class_<MismatchBase1, std::shared_ptr<MismatchBase1>>(mod, "MismatchBase1");
+        py::class_<MismatchDerived1, MismatchBase1>(mod, "MismatchDerived1");
+    });
+    m.def("mismatched_holder_2", []() {
+        auto mod = py::module_::import("__main__");
+        py::class_<MismatchBase2>(mod, "MismatchBase2");
+        py::class_<MismatchDerived2, std::shared_ptr<MismatchDerived2>, MismatchBase2>(
+            mod, "MismatchDerived2");
+    });
+
+    // test_override_static
+    // #511: problem with inheritance + overwritten def_static
+    struct MyBase {
+        static std::unique_ptr<MyBase> make() { return std::unique_ptr<MyBase>(new MyBase()); }
+    };
+
+    struct MyDerived : MyBase {
+        static std::unique_ptr<MyDerived> make() {
+            return std::unique_ptr<MyDerived>(new MyDerived());
+        }
+    };
+
+    py::class_<MyBase>(m, "MyBase").def_static("make", &MyBase::make);
+
+    py::class_<MyDerived, MyBase>(m, "MyDerived")
+        .def_static("make", &MyDerived::make)
+        .def_static("make2", &MyDerived::make);
+
+    // test_implicit_conversion_life_support
+    struct ConvertibleFromUserType {
+        int i;
+
+        explicit ConvertibleFromUserType(UserType u) : i(u.value()) {}
+    };
+
+    py::class_<ConvertibleFromUserType>(m, "AcceptsUserType").def(py::init<UserType>());
+    py::implicitly_convertible<UserType, ConvertibleFromUserType>();
+
+    m.def("implicitly_convert_argument", [](const ConvertibleFromUserType &r) { return r.i; });
+    m.def("implicitly_convert_variable", [](const py::object &o) {
+        // `o` is `UserType` and `r` is a reference to a temporary created by implicit
+        // conversion. This is valid when called inside a bound function because the temp
+        // object is attached to the same life support system as the arguments.
+        const auto &r = o.cast<const ConvertibleFromUserType &>();
+        return r.i;
+    });
+    m.add_object("implicitly_convert_variable_fail", [&] {
+        auto f = [](PyObject *, PyObject *args) -> PyObject * {
+            auto o = py::reinterpret_borrow<py::tuple>(args)[0];
+            try { // It should fail here because there is no life support.
+                o.cast<const ConvertibleFromUserType &>();
+            } catch (const py::cast_error &e) {
+                return py::str(e.what()).release().ptr();
+            }
+            return py::str().release().ptr();
+        };
+
+        auto *def = new PyMethodDef{"f", f, METH_VARARGS, nullptr};
+        py::capsule def_capsule(def,
+                                [](void *ptr) { delete reinterpret_cast<PyMethodDef *>(ptr); });
+        return py::reinterpret_steal<py::object>(
+            PyCFunction_NewEx(def, def_capsule.ptr(), m.ptr()));
+    }());
+
+    // test_operator_new_delete
+    struct HasOpNewDel {
+        std::uint64_t i;
+        static void *operator new(size_t s) {
+            py::print("A new", s);
+            return ::operator new(s);
+        }
+        static void *operator new(size_t s, void *ptr) {
+            py::print("A placement-new", s);
+            return ptr;
+        }
+        static void operator delete(void *p) {
+            py::print("A delete");
+            return ::operator delete(p);
+        }
+    };
+    struct HasOpNewDelSize {
+        std::uint32_t i;
+        static void *operator new(size_t s) {
+            py::print("B new", s);
+            return ::operator new(s);
+        }
+        static void *operator new(size_t s, void *ptr) {
+            py::print("B placement-new", s);
+            return ptr;
+        }
+        static void operator delete(void *p, size_t s) {
+            py::print("B delete", s);
+            return ::operator delete(p);
+        }
+    };
+    struct AliasedHasOpNewDelSize {
+        std::uint64_t i;
+        static void *operator new(size_t s) {
+            py::print("C new", s);
+            return ::operator new(s);
+        }
+        static void *operator new(size_t s, void *ptr) {
+            py::print("C placement-new", s);
+            return ptr;
+        }
+        static void operator delete(void *p, size_t s) {
+            py::print("C delete", s);
+            return ::operator delete(p);
+        }
+        virtual ~AliasedHasOpNewDelSize() = default;
+        AliasedHasOpNewDelSize() = default;
+        AliasedHasOpNewDelSize(const AliasedHasOpNewDelSize &) = delete;
+    };
+    struct PyAliasedHasOpNewDelSize : AliasedHasOpNewDelSize {
+        PyAliasedHasOpNewDelSize() = default;
+        explicit PyAliasedHasOpNewDelSize(int) {}
+        std::uint64_t j;
+    };
+    struct HasOpNewDelBoth {
+        std::uint32_t i[8];
+        static void *operator new(size_t s) {
+            py::print("D new", s);
+            return ::operator new(s);
+        }
+        static void *operator new(size_t s, void *ptr) {
+            py::print("D placement-new", s);
+            return ptr;
+        }
+        static void operator delete(void *p) {
+            py::print("D delete");
+            return ::operator delete(p);
+        }
+        static void operator delete(void *p, size_t s) {
+            py::print("D wrong delete", s);
+            return ::operator delete(p);
+        }
+    };
+    py::class_<HasOpNewDel>(m, "HasOpNewDel").def(py::init<>());
+    py::class_<HasOpNewDelSize>(m, "HasOpNewDelSize").def(py::init<>());
+    py::class_<HasOpNewDelBoth>(m, "HasOpNewDelBoth").def(py::init<>());
+    py::class_<AliasedHasOpNewDelSize, PyAliasedHasOpNewDelSize> aliased(m,
+                                                                         "AliasedHasOpNewDelSize");
+    aliased.def(py::init<>());
+    aliased.attr("size_noalias") = py::int_(sizeof(AliasedHasOpNewDelSize));
+    aliased.attr("size_alias") = py::int_(sizeof(PyAliasedHasOpNewDelSize));
+
+    // This test is actually part of test_local_bindings (test_duplicate_local), but we need a
+    // definition in a different compilation unit within the same module:
+    bind_local<LocalExternal, 17>(m, "LocalExternal", py::module_local());
+
+    // test_bind_protected_functions
+    class ProtectedA {
+    protected:
+        int foo() const { return value; }
+
+    private:
+        int value = 42;
+    };
+
+    class PublicistA : public ProtectedA {
+    public:
+        using ProtectedA::foo;
+    };
+
+    py::class_<ProtectedA>(m, "ProtectedA").def(py::init<>()).def("foo", &PublicistA::foo);
+
+    class ProtectedB {
+    public:
+        virtual ~ProtectedB() = default;
+        ProtectedB() = default;
+        ProtectedB(const ProtectedB &) = delete;
+
+    protected:
+        virtual int foo() const { return value; }
+
+    private:
+        int value = 42;
+    };
+
+    class TrampolineB : public ProtectedB {
+    public:
+        int foo() const override { PYBIND11_OVERRIDE(int, ProtectedB, foo, ); }
+    };
+
+    class PublicistB : public ProtectedB {
+    public:
+        // [workaround(intel)] = default does not work here
+        // Removing or defaulting this destructor results in linking errors with the Intel compiler
+        // (in Debug builds only, tested with icpc (ICC) 2021.1 Beta 20200827)
+        ~PublicistB() override{}; // NOLINT(modernize-use-equals-default)
+        using ProtectedB::foo;
+    };
+
+    py::class_<ProtectedB, TrampolineB>(m, "ProtectedB")
+        .def(py::init<>())
+        .def("foo", &PublicistB::foo);
+
+    // test_brace_initialization
+    struct BraceInitialization {
+        int field1;
+        std::string field2;
+    };
+
+    py::class_<BraceInitialization>(m, "BraceInitialization")
+        .def(py::init<int, const std::string &>())
+        .def_readwrite("field1", &BraceInitialization::field1)
+        .def_readwrite("field2", &BraceInitialization::field2);
+    // We *don't* want to construct using braces when the given constructor argument maps to a
+    // constructor, because brace initialization could go to the wrong place (in particular when
+    // there is also an `initializer_list<T>`-accept constructor):
+    py::class_<NoBraceInitialization>(m, "NoBraceInitialization")
+        .def(py::init<std::vector<int>>())
+        .def_readonly("vec", &NoBraceInitialization::vec);
+
+    // test_reentrant_implicit_conversion_failure
+    // #1035: issue with runaway reentrant implicit conversion
+    struct BogusImplicitConversion {
+        BogusImplicitConversion(const BogusImplicitConversion &) = default;
+    };
+
+    py::class_<BogusImplicitConversion>(m, "BogusImplicitConversion")
+        .def(py::init<const BogusImplicitConversion &>());
+
+    py::implicitly_convertible<int, BogusImplicitConversion>();
+
+    // test_qualname
+    // #1166: nested class docstring doesn't show nested name
+    // Also related: tests that __qualname__ is set properly
+    struct NestBase {};
+    struct Nested {};
+    py::class_<NestBase> base(m, "NestBase");
+    base.def(py::init<>());
+    py::class_<Nested>(base, "Nested")
+        .def(py::init<>())
+        .def("fn", [](Nested &, int, NestBase &, Nested &) {})
+        .def(
+            "fa", [](Nested &, int, NestBase &, Nested &) {}, "a"_a, "b"_a, "c"_a);
+    base.def("g", [](NestBase &, Nested &) {});
+    base.def("h", []() { return NestBase(); });
+
+    // test_error_after_conversion
+    // The second-pass path through dispatcher() previously didn't
+    // remember which overload was used, and would crash trying to
+    // generate a useful error message
+
+    struct NotRegistered {};
+    struct StringWrapper {
+        std::string str;
+    };
+    m.def("test_error_after_conversions", [](int) {});
+    m.def("test_error_after_conversions",
+          [](const StringWrapper &) -> NotRegistered { return {}; });
+    py::class_<StringWrapper>(m, "StringWrapper").def(py::init<std::string>());
+    py::implicitly_convertible<std::string, StringWrapper>();
+
+#if defined(PYBIND11_CPP17)
+    struct alignas(1024) Aligned {
+        std::uintptr_t ptr() const { return (uintptr_t) this; }
+    };
+    py::class_<Aligned>(m, "Aligned").def(py::init<>()).def("ptr", &Aligned::ptr);
+#endif
+
+    // test_final
+    struct IsFinal final {};
+    py::class_<IsFinal>(m, "IsFinal", py::is_final());
+
+    // test_non_final_final
+    struct IsNonFinalFinal {};
+    py::class_<IsNonFinalFinal>(m, "IsNonFinalFinal", py::is_final());
+
+    // test_exception_rvalue_abort
+    struct PyPrintDestructor {
+        PyPrintDestructor() = default;
+        ~PyPrintDestructor() { py::print("Print from destructor"); }
+        void throw_something() { throw std::runtime_error("error"); }
+    };
+    py::class_<PyPrintDestructor>(m, "PyPrintDestructor")
+        .def(py::init<>())
+        .def("throw_something", &PyPrintDestructor::throw_something);
+
+    // test_multiple_instances_with_same_pointer
+    struct SamePointer {};
+    static SamePointer samePointer;
+    py::class_<SamePointer, std::unique_ptr<SamePointer, py::nodelete>>(m, "SamePointer")
+        .def(py::init([]() { return &samePointer; }));
+
+    struct Empty {};
+    py::class_<Empty>(m, "Empty").def(py::init<>());
+
+    // test_base_and_derived_nested_scope
+    struct BaseWithNested {
+        struct Nested {};
+    };
+
+    struct DerivedWithNested : BaseWithNested {
+        struct Nested {};
+    };
+
+    py::class_<BaseWithNested> baseWithNested_class(m, "BaseWithNested");
+    py::class_<DerivedWithNested, BaseWithNested> derivedWithNested_class(m, "DerivedWithNested");
+    py::class_<BaseWithNested::Nested>(baseWithNested_class, "Nested")
+        .def_static("get_name", []() { return "BaseWithNested::Nested"; });
+    py::class_<DerivedWithNested::Nested>(derivedWithNested_class, "Nested")
+        .def_static("get_name", []() { return "DerivedWithNested::Nested"; });
+
+    // test_register_duplicate_class
+    struct Duplicate {};
+    struct OtherDuplicate {};
+    struct DuplicateNested {};
+    struct OtherDuplicateNested {};
+
+    m.def("register_duplicate_class_name", [](const py::module_ &m) {
+        py::class_<Duplicate>(m, "Duplicate");
+        py::class_<OtherDuplicate>(m, "Duplicate");
+    });
+    m.def("register_duplicate_class_type", [](const py::module_ &m) {
+        py::class_<OtherDuplicate>(m, "OtherDuplicate");
+        py::class_<OtherDuplicate>(m, "YetAnotherDuplicate");
+    });
+    m.def("register_duplicate_nested_class_name", [](const py::object &gt) {
+        py::class_<DuplicateNested>(gt, "DuplicateNested");
+        py::class_<OtherDuplicateNested>(gt, "DuplicateNested");
+    });
+    m.def("register_duplicate_nested_class_type", [](const py::object &gt) {
+        py::class_<OtherDuplicateNested>(gt, "OtherDuplicateNested");
+        py::class_<OtherDuplicateNested>(gt, "YetAnotherDuplicateNested");
+    });
+}
+
+template <int N>
+class BreaksBase {
+public:
+    virtual ~BreaksBase() = default;
+    BreaksBase() = default;
+    BreaksBase(const BreaksBase &) = delete;
+};
+template <int N>
+class BreaksTramp : public BreaksBase<N> {};
+// These should all compile just fine:
+using DoesntBreak1 = py::class_<BreaksBase<1>, std::unique_ptr<BreaksBase<1>>, BreaksTramp<1>>;
+using DoesntBreak2 = py::class_<BreaksBase<2>, BreaksTramp<2>, std::unique_ptr<BreaksBase<2>>>;
+using DoesntBreak3 = py::class_<BreaksBase<3>, std::unique_ptr<BreaksBase<3>>>;
+using DoesntBreak4 = py::class_<BreaksBase<4>, BreaksTramp<4>>;
+using DoesntBreak5 = py::class_<BreaksBase<5>>;
+using DoesntBreak6 = py::class_<BreaksBase<6>, std::shared_ptr<BreaksBase<6>>, BreaksTramp<6>>;
+using DoesntBreak7 = py::class_<BreaksBase<7>, BreaksTramp<7>, std::shared_ptr<BreaksBase<7>>>;
+using DoesntBreak8 = py::class_<BreaksBase<8>, std::shared_ptr<BreaksBase<8>>>;
+#define CHECK_BASE(N)                                                                             \
+    static_assert(std::is_same<typename DoesntBreak##N::type, BreaksBase<(N)>>::value,            \
+                  "DoesntBreak" #N " has wrong type!")
+CHECK_BASE(1);
+CHECK_BASE(2);
+CHECK_BASE(3);
+CHECK_BASE(4);
+CHECK_BASE(5);
+CHECK_BASE(6);
+CHECK_BASE(7);
+CHECK_BASE(8);
+#define CHECK_ALIAS(N)                                                                            \
+    static_assert(                                                                                \
+        DoesntBreak##N::has_alias                                                                 \
+            && std::is_same<typename DoesntBreak##N::type_alias, BreaksTramp<(N)>>::value,        \
+        "DoesntBreak" #N " has wrong type_alias!")
+#define CHECK_NOALIAS(N)                                                                          \
+    static_assert(!DoesntBreak##N::has_alias                                                      \
+                      && std::is_void<typename DoesntBreak##N::type_alias>::value,                \
+                  "DoesntBreak" #N " has type alias, but shouldn't!")
+CHECK_ALIAS(1);
+CHECK_ALIAS(2);
+CHECK_NOALIAS(3);
+CHECK_ALIAS(4);
+CHECK_NOALIAS(5);
+CHECK_ALIAS(6);
+CHECK_ALIAS(7);
+CHECK_NOALIAS(8);
+#define CHECK_HOLDER(N, TYPE)                                                                     \
+    static_assert(std::is_same<typename DoesntBreak##N::holder_type,                              \
+                               std::TYPE##_ptr<BreaksBase<(N)>>>::value,                          \
+                  "DoesntBreak" #N " has wrong holder_type!")
+CHECK_HOLDER(1, unique);
+CHECK_HOLDER(2, unique);
+CHECK_HOLDER(3, unique);
+CHECK_HOLDER(4, unique);
+CHECK_HOLDER(5, unique);
+CHECK_HOLDER(6, shared);
+CHECK_HOLDER(7, shared);
+CHECK_HOLDER(8, shared);
+
+// There's no nice way to test that these fail because they fail to compile; leave them here,
+// though, so that they can be manually tested by uncommenting them (and seeing that compilation
+// failures occurs).
+
+// We have to actually look into the type: the typedef alone isn't enough to instantiate the type:
+#define CHECK_BROKEN(N)                                                                           \
+    static_assert(std::is_same<typename Breaks##N::type, BreaksBase<-(N)>>::value,                \
+                  "Breaks1 has wrong type!");
+
+#ifdef PYBIND11_NEVER_DEFINED_EVER
+// Two holder classes:
+typedef py::
+    class_<BreaksBase<-1>, std::unique_ptr<BreaksBase<-1>>, std::unique_ptr<BreaksBase<-1>>>
+        Breaks1;
+CHECK_BROKEN(1);
+// Two aliases:
+typedef py::class_<BreaksBase<-2>, BreaksTramp<-2>, BreaksTramp<-2>> Breaks2;
+CHECK_BROKEN(2);
+// Holder + 2 aliases
+typedef py::
+    class_<BreaksBase<-3>, std::unique_ptr<BreaksBase<-3>>, BreaksTramp<-3>, BreaksTramp<-3>>
+        Breaks3;
+CHECK_BROKEN(3);
+// Alias + 2 holders
+typedef py::class_<BreaksBase<-4>,
+                   std::unique_ptr<BreaksBase<-4>>,
+                   BreaksTramp<-4>,
+                   std::shared_ptr<BreaksBase<-4>>>
+    Breaks4;
+CHECK_BROKEN(4);
+// Invalid option (not a subclass or holder)
+typedef py::class_<BreaksBase<-5>, BreaksTramp<-4>> Breaks5;
+CHECK_BROKEN(5);
+// Invalid option: multiple inheritance not supported:
+template <>
+struct BreaksBase<-8> : BreaksBase<-6>, BreaksBase<-7> {};
+typedef py::class_<BreaksBase<-8>, BreaksBase<-6>, BreaksBase<-7>> Breaks8;
+CHECK_BROKEN(8);
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_class.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_class.py
new file mode 100644
index 000000000..ff9196f0f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_class.py
@@ -0,0 +1,471 @@
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import ConstructorStats, UserType
+from pybind11_tests import class_ as m
+
+
+def test_repr():
+    assert "pybind11_type" in repr(type(UserType))
+    assert "UserType" in repr(UserType)
+
+
+def test_instance(msg):
+    with pytest.raises(TypeError) as excinfo:
+        m.NoConstructor()
+    assert msg(excinfo.value) == "m.class_.NoConstructor: No constructor defined!"
+
+    instance = m.NoConstructor.new_instance()
+
+    cstats = ConstructorStats.get(m.NoConstructor)
+    assert cstats.alive() == 1
+    del instance
+    assert cstats.alive() == 0
+
+
+def test_instance_new(msg):
+    instance = m.NoConstructorNew()  # .__new__(m.NoConstructor.__class__)
+    cstats = ConstructorStats.get(m.NoConstructorNew)
+    assert cstats.alive() == 1
+    del instance
+    assert cstats.alive() == 0
+
+
+def test_type():
+    assert m.check_type(1) == m.DerivedClass1
+    with pytest.raises(RuntimeError) as execinfo:
+        m.check_type(0)
+
+    assert "pybind11::detail::get_type_info: unable to find type info" in str(
+        execinfo.value
+    )
+    assert "Invalid" in str(execinfo.value)
+
+    # Currently not supported
+    # See https://github.com/pybind/pybind11/issues/2486
+    # assert m.check_type(2) == int
+
+
+def test_type_of_py():
+    assert m.get_type_of(1) == int
+    assert m.get_type_of(m.DerivedClass1()) == m.DerivedClass1
+    assert m.get_type_of(int) == type
+
+
+def test_type_of_classic():
+    assert m.get_type_classic(1) == int
+    assert m.get_type_classic(m.DerivedClass1()) == m.DerivedClass1
+    assert m.get_type_classic(int) == type
+
+
+def test_type_of_py_nodelete():
+    # If the above test deleted the class, this will segfault
+    assert m.get_type_of(m.DerivedClass1()) == m.DerivedClass1
+
+
+def test_as_type_py():
+    assert m.as_type(int) == int
+
+    with pytest.raises(TypeError):
+        assert m.as_type(1) == int
+
+    with pytest.raises(TypeError):
+        assert m.as_type(m.DerivedClass1()) == m.DerivedClass1
+
+
+def test_docstrings(doc):
+    assert doc(UserType) == "A `py::class_` type for testing"
+    assert UserType.__name__ == "UserType"
+    assert UserType.__module__ == "pybind11_tests"
+    assert UserType.get_value.__name__ == "get_value"
+    assert UserType.get_value.__module__ == "pybind11_tests"
+
+    assert (
+        doc(UserType.get_value)
+        == """
+        get_value(self: m.UserType) -> int
+
+        Get value using a method
+    """
+    )
+    assert doc(UserType.value) == "Get/set value using a property"
+
+    assert (
+        doc(m.NoConstructor.new_instance)
+        == """
+        new_instance() -> m.class_.NoConstructor
+
+        Return an instance
+    """
+    )
+
+
+def test_qualname(doc):
+    """Tests that a properly qualified name is set in __qualname__ and that
+    generated docstrings properly use it and the module name"""
+    assert m.NestBase.__qualname__ == "NestBase"
+    assert m.NestBase.Nested.__qualname__ == "NestBase.Nested"
+
+    assert (
+        doc(m.NestBase.__init__)
+        == """
+        __init__(self: m.class_.NestBase) -> None
+    """
+    )
+    assert (
+        doc(m.NestBase.g)
+        == """
+        g(self: m.class_.NestBase, arg0: m.class_.NestBase.Nested) -> None
+    """
+    )
+    assert (
+        doc(m.NestBase.Nested.__init__)
+        == """
+        __init__(self: m.class_.NestBase.Nested) -> None
+    """
+    )
+    assert (
+        doc(m.NestBase.Nested.fn)
+        == """
+        fn(self: m.class_.NestBase.Nested, arg0: int, arg1: m.class_.NestBase, arg2: m.class_.NestBase.Nested) -> None
+    """
+    )
+    assert (
+        doc(m.NestBase.Nested.fa)
+        == """
+        fa(self: m.class_.NestBase.Nested, a: int, b: m.class_.NestBase, c: m.class_.NestBase.Nested) -> None
+    """
+    )
+    assert m.NestBase.__module__ == "pybind11_tests.class_"
+    assert m.NestBase.Nested.__module__ == "pybind11_tests.class_"
+
+
+def test_inheritance(msg):
+    roger = m.Rabbit("Rabbit")
+    assert roger.name() + " is a " + roger.species() == "Rabbit is a parrot"
+    assert m.pet_name_species(roger) == "Rabbit is a parrot"
+
+    polly = m.Pet("Polly", "parrot")
+    assert polly.name() + " is a " + polly.species() == "Polly is a parrot"
+    assert m.pet_name_species(polly) == "Polly is a parrot"
+
+    molly = m.Dog("Molly")
+    assert molly.name() + " is a " + molly.species() == "Molly is a dog"
+    assert m.pet_name_species(molly) == "Molly is a dog"
+
+    fred = m.Hamster("Fred")
+    assert fred.name() + " is a " + fred.species() == "Fred is a rodent"
+
+    assert m.dog_bark(molly) == "Woof!"
+
+    with pytest.raises(TypeError) as excinfo:
+        m.dog_bark(polly)
+    assert (
+        msg(excinfo.value)
+        == """
+        dog_bark(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: m.class_.Dog) -> str
+
+        Invoked with: <m.class_.Pet object at 0>
+    """
+    )
+
+    with pytest.raises(TypeError) as excinfo:
+        m.Chimera("lion", "goat")
+    assert "No constructor defined!" in str(excinfo.value)
+
+
+def test_inheritance_init(msg):
+
+    # Single base
+    class Python(m.Pet):
+        def __init__(self):
+            pass
+
+    with pytest.raises(TypeError) as exc_info:
+        Python()
+    expected = "m.class_.Pet.__init__() must be called when overriding __init__"
+    assert msg(exc_info.value) == expected
+
+    # Multiple bases
+    class RabbitHamster(m.Rabbit, m.Hamster):
+        def __init__(self):
+            m.Rabbit.__init__(self, "RabbitHamster")
+
+    with pytest.raises(TypeError) as exc_info:
+        RabbitHamster()
+    expected = "m.class_.Hamster.__init__() must be called when overriding __init__"
+    assert msg(exc_info.value) == expected
+
+
+def test_automatic_upcasting():
+    assert type(m.return_class_1()).__name__ == "DerivedClass1"
+    assert type(m.return_class_2()).__name__ == "DerivedClass2"
+    assert type(m.return_none()).__name__ == "NoneType"
+    # Repeat these a few times in a random order to ensure no invalid caching is applied
+    assert type(m.return_class_n(1)).__name__ == "DerivedClass1"
+    assert type(m.return_class_n(2)).__name__ == "DerivedClass2"
+    assert type(m.return_class_n(0)).__name__ == "BaseClass"
+    assert type(m.return_class_n(2)).__name__ == "DerivedClass2"
+    assert type(m.return_class_n(2)).__name__ == "DerivedClass2"
+    assert type(m.return_class_n(0)).__name__ == "BaseClass"
+    assert type(m.return_class_n(1)).__name__ == "DerivedClass1"
+
+
+def test_isinstance():
+    objects = [tuple(), dict(), m.Pet("Polly", "parrot")] + [m.Dog("Molly")] * 4
+    expected = (True, True, True, True, True, False, False)
+    assert m.check_instances(objects) == expected
+
+
+def test_mismatched_holder():
+    import re
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.mismatched_holder_1()
+    assert re.match(
+        'generic_type: type ".*MismatchDerived1" does not have a non-default '
+        'holder type while its base ".*MismatchBase1" does',
+        str(excinfo.value),
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.mismatched_holder_2()
+    assert re.match(
+        'generic_type: type ".*MismatchDerived2" has a non-default holder type '
+        'while its base ".*MismatchBase2" does not',
+        str(excinfo.value),
+    )
+
+
+def test_override_static():
+    """#511: problem with inheritance + overwritten def_static"""
+    b = m.MyBase.make()
+    d1 = m.MyDerived.make2()
+    d2 = m.MyDerived.make()
+
+    assert isinstance(b, m.MyBase)
+    assert isinstance(d1, m.MyDerived)
+    assert isinstance(d2, m.MyDerived)
+
+
+def test_implicit_conversion_life_support():
+    """Ensure the lifetime of temporary objects created for implicit conversions"""
+    assert m.implicitly_convert_argument(UserType(5)) == 5
+    assert m.implicitly_convert_variable(UserType(5)) == 5
+
+    assert "outside a bound function" in m.implicitly_convert_variable_fail(UserType(5))
+
+
+def test_operator_new_delete(capture):
+    """Tests that class-specific operator new/delete functions are invoked"""
+
+    class SubAliased(m.AliasedHasOpNewDelSize):
+        pass
+
+    with capture:
+        a = m.HasOpNewDel()
+        b = m.HasOpNewDelSize()
+        d = m.HasOpNewDelBoth()
+    assert (
+        capture
+        == """
+        A new 8
+        B new 4
+        D new 32
+    """
+    )
+    sz_alias = str(m.AliasedHasOpNewDelSize.size_alias)
+    sz_noalias = str(m.AliasedHasOpNewDelSize.size_noalias)
+    with capture:
+        c = m.AliasedHasOpNewDelSize()
+        c2 = SubAliased()
+    assert capture == ("C new " + sz_noalias + "\n" + "C new " + sz_alias + "\n")
+
+    with capture:
+        del a
+        pytest.gc_collect()
+        del b
+        pytest.gc_collect()
+        del d
+        pytest.gc_collect()
+    assert (
+        capture
+        == """
+        A delete
+        B delete 4
+        D delete
+    """
+    )
+
+    with capture:
+        del c
+        pytest.gc_collect()
+        del c2
+        pytest.gc_collect()
+    assert capture == ("C delete " + sz_noalias + "\n" + "C delete " + sz_alias + "\n")
+
+
+def test_bind_protected_functions():
+    """Expose protected member functions to Python using a helper class"""
+    a = m.ProtectedA()
+    assert a.foo() == 42
+
+    b = m.ProtectedB()
+    assert b.foo() == 42
+
+    class C(m.ProtectedB):
+        def __init__(self):
+            m.ProtectedB.__init__(self)
+
+        def foo(self):
+            return 0
+
+    c = C()
+    assert c.foo() == 0
+
+
+def test_brace_initialization():
+    """Tests that simple POD classes can be constructed using C++11 brace initialization"""
+    a = m.BraceInitialization(123, "test")
+    assert a.field1 == 123
+    assert a.field2 == "test"
+
+    # Tests that a non-simple class doesn't get brace initialization (if the
+    # class defines an initializer_list constructor, in particular, it would
+    # win over the expected constructor).
+    b = m.NoBraceInitialization([123, 456])
+    assert b.vec == [123, 456]
+
+
+@pytest.mark.xfail("env.PYPY")
+def test_class_refcount():
+    """Instances must correctly increase/decrease the reference count of their types (#1029)"""
+    from sys import getrefcount
+
+    class PyDog(m.Dog):
+        pass
+
+    for cls in m.Dog, PyDog:
+        refcount_1 = getrefcount(cls)
+        molly = [cls("Molly") for _ in range(10)]
+        refcount_2 = getrefcount(cls)
+
+        del molly
+        pytest.gc_collect()
+        refcount_3 = getrefcount(cls)
+
+        assert refcount_1 == refcount_3
+        assert refcount_2 > refcount_1
+
+
+def test_reentrant_implicit_conversion_failure(msg):
+    # ensure that there is no runaway reentrant implicit conversion (#1035)
+    with pytest.raises(TypeError) as excinfo:
+        m.BogusImplicitConversion(0)
+    assert (
+        msg(excinfo.value)
+        == """
+        __init__(): incompatible constructor arguments. The following argument types are supported:
+            1. m.class_.BogusImplicitConversion(arg0: m.class_.BogusImplicitConversion)
+
+        Invoked with: 0
+    """
+    )
+
+
+def test_error_after_conversions():
+    with pytest.raises(TypeError) as exc_info:
+        m.test_error_after_conversions("hello")
+    assert str(exc_info.value).startswith(
+        "Unable to convert function return value to a Python type!"
+    )
+
+
+def test_aligned():
+    if hasattr(m, "Aligned"):
+        p = m.Aligned().ptr()
+        assert p % 1024 == 0
+
+
+# https://foss.heptapod.net/pypy/pypy/-/issues/2742
+@pytest.mark.xfail("env.PYPY")
+def test_final():
+    with pytest.raises(TypeError) as exc_info:
+
+        class PyFinalChild(m.IsFinal):
+            pass
+
+    assert str(exc_info.value).endswith("is not an acceptable base type")
+
+
+# https://foss.heptapod.net/pypy/pypy/-/issues/2742
+@pytest.mark.xfail("env.PYPY")
+def test_non_final_final():
+    with pytest.raises(TypeError) as exc_info:
+
+        class PyNonFinalFinalChild(m.IsNonFinalFinal):
+            pass
+
+    assert str(exc_info.value).endswith("is not an acceptable base type")
+
+
+# https://github.com/pybind/pybind11/issues/1878
+def test_exception_rvalue_abort():
+    with pytest.raises(RuntimeError):
+        m.PyPrintDestructor().throw_something()
+
+
+# https://github.com/pybind/pybind11/issues/1568
+def test_multiple_instances_with_same_pointer(capture):
+    n = 100
+    instances = [m.SamePointer() for _ in range(n)]
+    for i in range(n):
+        # We need to reuse the same allocated memory for with a different type,
+        # to ensure the bug in `deregister_instance_impl` is detected. Otherwise
+        # `Py_TYPE(self) == Py_TYPE(it->second)` will still succeed, even though
+        # the `instance` is already deleted.
+        instances[i] = m.Empty()
+    # No assert: if this does not trigger the error
+    #   pybind11_fail("pybind11_object_dealloc(): Tried to deallocate unregistered instance!");
+    # and just completes without crashing, we're good.
+
+
+# https://github.com/pybind/pybind11/issues/1624
+def test_base_and_derived_nested_scope():
+    assert issubclass(m.DerivedWithNested, m.BaseWithNested)
+    assert m.BaseWithNested.Nested != m.DerivedWithNested.Nested
+    assert m.BaseWithNested.Nested.get_name() == "BaseWithNested::Nested"
+    assert m.DerivedWithNested.Nested.get_name() == "DerivedWithNested::Nested"
+
+
+def test_register_duplicate_class():
+    import types
+
+    module_scope = types.ModuleType("module_scope")
+    with pytest.raises(RuntimeError) as exc_info:
+        m.register_duplicate_class_name(module_scope)
+    expected = (
+        'generic_type: cannot initialize type "Duplicate": '
+        "an object with that name is already defined"
+    )
+    assert str(exc_info.value) == expected
+    with pytest.raises(RuntimeError) as exc_info:
+        m.register_duplicate_class_type(module_scope)
+    expected = 'generic_type: type "YetAnotherDuplicate" is already registered!'
+    assert str(exc_info.value) == expected
+
+    class ClassScope:
+        pass
+
+    with pytest.raises(RuntimeError) as exc_info:
+        m.register_duplicate_nested_class_name(ClassScope)
+    expected = (
+        'generic_type: cannot initialize type "DuplicateNested": '
+        "an object with that name is already defined"
+    )
+    assert str(exc_info.value) == expected
+    with pytest.raises(RuntimeError) as exc_info:
+        m.register_duplicate_nested_class_type(ClassScope)
+    expected = 'generic_type: type "YetAnotherDuplicateNested" is already registered!'
+    assert str(exc_info.value) == expected
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/CMakeLists.txt
new file mode 100644
index 000000000..8bfaa386a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/CMakeLists.txt
@@ -0,0 +1,84 @@
+# Built-in in CMake 3.5+
+include(CMakeParseArguments)
+
+add_custom_target(test_cmake_build)
+
+function(pybind11_add_build_test name)
+  cmake_parse_arguments(ARG "INSTALL" "" "" ${ARGN})
+
+  set(build_options "-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}")
+
+  if(PYBIND11_FINDPYTHON)
+    list(APPEND build_options "-DPYBIND11_FINDPYTHON=${PYBIND11_FINDPYTHON}")
+
+    if(DEFINED Python_ROOT_DIR)
+      list(APPEND build_options "-DPython_ROOT_DIR=${Python_ROOT_DIR}")
+    endif()
+
+    list(APPEND build_options "-DPython_EXECUTABLE=${Python_EXECUTABLE}")
+  else()
+    list(APPEND build_options "-DPYTHON_EXECUTABLE=${PYTHON_EXECUTABLE}")
+  endif()
+
+  if(DEFINED CMAKE_CXX_STANDARD)
+    list(APPEND build_options "-DCMAKE_CXX_STANDARD=${CMAKE_CXX_STANDARD}")
+  endif()
+
+  if(NOT ARG_INSTALL)
+    list(APPEND build_options "-Dpybind11_SOURCE_DIR=${pybind11_SOURCE_DIR}")
+  else()
+    list(APPEND build_options "-DCMAKE_PREFIX_PATH=${pybind11_BINARY_DIR}/mock_install")
+  endif()
+
+  add_custom_target(
+    test_build_${name}
+    ${CMAKE_CTEST_COMMAND}
+    --build-and-test
+    "${CMAKE_CURRENT_SOURCE_DIR}/${name}"
+    "${CMAKE_CURRENT_BINARY_DIR}/${name}"
+    --build-config
+    Release
+    --build-noclean
+    --build-generator
+    ${CMAKE_GENERATOR}
+    $<$<BOOL:${CMAKE_GENERATOR_PLATFORM}>:--build-generator-platform>
+    ${CMAKE_GENERATOR_PLATFORM}
+    --build-makeprogram
+    ${CMAKE_MAKE_PROGRAM}
+    --build-target
+    check_${name}
+    --build-options
+    ${build_options})
+  if(ARG_INSTALL)
+    add_dependencies(test_build_${name} mock_install)
+  endif()
+  add_dependencies(test_cmake_build test_build_${name})
+endfunction()
+
+possibly_uninitialized(PYTHON_MODULE_EXTENSION Python_INTERPRETER_ID)
+
+pybind11_add_build_test(subdirectory_function)
+pybind11_add_build_test(subdirectory_target)
+if("${PYTHON_MODULE_EXTENSION}" MATCHES "pypy" OR "${Python_INTERPRETER_ID}" STREQUAL "PyPy")
+  message(STATUS "Skipping embed test on PyPy")
+else()
+  pybind11_add_build_test(subdirectory_embed)
+endif()
+
+if(PYBIND11_INSTALL)
+  add_custom_target(
+    mock_install ${CMAKE_COMMAND} "-DCMAKE_INSTALL_PREFIX=${pybind11_BINARY_DIR}/mock_install" -P
+                 "${pybind11_BINARY_DIR}/cmake_install.cmake")
+
+  pybind11_add_build_test(installed_function INSTALL)
+  pybind11_add_build_test(installed_target INSTALL)
+  if(NOT ("${PYTHON_MODULE_EXTENSION}" MATCHES "pypy" OR "${Python_INTERPRETER_ID}" STREQUAL "PyPy"
+         ))
+    pybind11_add_build_test(installed_embed INSTALL)
+  endif()
+endif()
+
+add_dependencies(check test_cmake_build)
+
+add_subdirectory(subdirectory_target EXCLUDE_FROM_ALL)
+add_subdirectory(subdirectory_embed EXCLUDE_FROM_ALL)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/embed.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/embed.cpp
new file mode 100644
index 000000000..30bc4f1e1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/embed.cpp
@@ -0,0 +1,23 @@
+#include <pybind11/embed.h>
+namespace py = pybind11;
+
+PYBIND11_EMBEDDED_MODULE(test_cmake_build, m) {
+    m.def("add", [](int i, int j) { return i + j; });
+}
+
+int main(int argc, char *argv[]) {
+    if (argc != 2) {
+        throw std::runtime_error("Expected test.py file as the first argument");
+    }
+    auto *test_py_file = argv[1];
+
+    py::scoped_interpreter guard{};
+
+    auto m = py::module_::import("test_cmake_build");
+    if (m.attr("add")(1, 2).cast<int>() != 3) {
+        throw std::runtime_error("embed.cpp failed");
+    }
+
+    py::module_::import("sys").attr("argv") = py::make_tuple("test.py", "embed.cpp");
+    py::eval_file(test_py_file, py::globals());
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_embed/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_embed/CMakeLists.txt
new file mode 100644
index 000000000..f7d693998
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_embed/CMakeLists.txt
@@ -0,0 +1,28 @@
+cmake_minimum_required(VERSION 3.4)
+
+# The `cmake_minimum_required(VERSION 3.4...3.18)` syntax does not work with
+# some versions of VS that have a patched CMake 3.11. This forces us to emulate
+# the behavior using the following workaround:
+if(${CMAKE_VERSION} VERSION_LESS 3.18)
+  cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION})
+else()
+  cmake_policy(VERSION 3.18)
+endif()
+
+project(test_installed_embed CXX)
+
+find_package(pybind11 CONFIG REQUIRED)
+message(STATUS "Found pybind11 v${pybind11_VERSION}: ${pybind11_INCLUDE_DIRS}")
+
+add_executable(test_installed_embed ../embed.cpp)
+target_link_libraries(test_installed_embed PRIVATE pybind11::embed)
+set_target_properties(test_installed_embed PROPERTIES OUTPUT_NAME test_cmake_build)
+
+# Do not treat includes from IMPORTED target as SYSTEM (Python headers in pybind11::embed).
+# This may be needed to resolve header conflicts, e.g. between Python release and debug headers.
+set_target_properties(test_installed_embed PROPERTIES NO_SYSTEM_FROM_IMPORTED ON)
+
+add_custom_target(
+  check_installed_embed
+  $<TARGET_FILE:test_installed_embed> ${PROJECT_SOURCE_DIR}/../test.py
+  DEPENDS test_installed_embed)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_function/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_function/CMakeLists.txt
new file mode 100644
index 000000000..d7ca4db55
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_function/CMakeLists.txt
@@ -0,0 +1,39 @@
+cmake_minimum_required(VERSION 3.4)
+project(test_installed_module CXX)
+
+# The `cmake_minimum_required(VERSION 3.4...3.18)` syntax does not work with
+# some versions of VS that have a patched CMake 3.11. This forces us to emulate
+# the behavior using the following workaround:
+if(${CMAKE_VERSION} VERSION_LESS 3.18)
+  cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION})
+else()
+  cmake_policy(VERSION 3.18)
+endif()
+
+project(test_installed_function CXX)
+
+find_package(pybind11 CONFIG REQUIRED)
+message(
+  STATUS "Found pybind11 v${pybind11_VERSION} ${pybind11_VERSION_TYPE}: ${pybind11_INCLUDE_DIRS}")
+
+pybind11_add_module(test_installed_function SHARED NO_EXTRAS ../main.cpp)
+set_target_properties(test_installed_function PROPERTIES OUTPUT_NAME test_cmake_build)
+
+if(DEFINED Python_EXECUTABLE)
+  set(_Python_EXECUTABLE "${Python_EXECUTABLE}")
+elseif(DEFINED PYTHON_EXECUTABLE)
+  set(_Python_EXECUTABLE "${PYTHON_EXECUTABLE}")
+else()
+  message(FATAL_ERROR "No Python executable defined (should not be possible at this stage)")
+endif()
+
+add_custom_target(
+  check_installed_function
+  ${CMAKE_COMMAND}
+  -E
+  env
+  PYTHONPATH=$<TARGET_FILE_DIR:test_installed_function>
+  ${_Python_EXECUTABLE}
+  ${PROJECT_SOURCE_DIR}/../test.py
+  ${PROJECT_NAME}
+  DEPENDS test_installed_function)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_target/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_target/CMakeLists.txt
new file mode 100644
index 000000000..bc5e101f1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/installed_target/CMakeLists.txt
@@ -0,0 +1,46 @@
+cmake_minimum_required(VERSION 3.4)
+
+# The `cmake_minimum_required(VERSION 3.4...3.18)` syntax does not work with
+# some versions of VS that have a patched CMake 3.11. This forces us to emulate
+# the behavior using the following workaround:
+if(${CMAKE_VERSION} VERSION_LESS 3.18)
+  cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION})
+else()
+  cmake_policy(VERSION 3.18)
+endif()
+
+project(test_installed_target CXX)
+
+find_package(pybind11 CONFIG REQUIRED)
+message(STATUS "Found pybind11 v${pybind11_VERSION}: ${pybind11_INCLUDE_DIRS}")
+
+add_library(test_installed_target MODULE ../main.cpp)
+
+target_link_libraries(test_installed_target PRIVATE pybind11::module)
+set_target_properties(test_installed_target PROPERTIES OUTPUT_NAME test_cmake_build)
+
+# Make sure result is, for example, test_installed_target.so, not libtest_installed_target.dylib
+pybind11_extension(test_installed_target)
+
+# Do not treat includes from IMPORTED target as SYSTEM (Python headers in pybind11::module).
+# This may be needed to resolve header conflicts, e.g. between Python release and debug headers.
+set_target_properties(test_installed_target PROPERTIES NO_SYSTEM_FROM_IMPORTED ON)
+
+if(DEFINED Python_EXECUTABLE)
+  set(_Python_EXECUTABLE "${Python_EXECUTABLE}")
+elseif(DEFINED PYTHON_EXECUTABLE)
+  set(_Python_EXECUTABLE "${PYTHON_EXECUTABLE}")
+else()
+  message(FATAL_ERROR "No Python executable defined (should not be possible at this stage)")
+endif()
+
+add_custom_target(
+  check_installed_target
+  ${CMAKE_COMMAND}
+  -E
+  env
+  PYTHONPATH=$<TARGET_FILE_DIR:test_installed_target>
+  ${_Python_EXECUTABLE}
+  ${PROJECT_SOURCE_DIR}/../test.py
+  ${PROJECT_NAME}
+  DEPENDS test_installed_target)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/main.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/main.cpp
new file mode 100644
index 000000000..e30f2c4b9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/main.cpp
@@ -0,0 +1,6 @@
+#include <pybind11/pybind11.h>
+namespace py = pybind11;
+
+PYBIND11_MODULE(test_cmake_build, m) {
+    m.def("add", [](int i, int j) { return i + j; });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_embed/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_embed/CMakeLists.txt
new file mode 100644
index 000000000..58cdd7cfd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_embed/CMakeLists.txt
@@ -0,0 +1,41 @@
+cmake_minimum_required(VERSION 3.4)
+
+# The `cmake_minimum_required(VERSION 3.4...3.18)` syntax does not work with
+# some versions of VS that have a patched CMake 3.11. This forces us to emulate
+# the behavior using the following workaround:
+if(${CMAKE_VERSION} VERSION_LESS 3.18)
+  cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION})
+else()
+  cmake_policy(VERSION 3.18)
+endif()
+
+project(test_subdirectory_embed CXX)
+
+set(PYBIND11_INSTALL
+    ON
+    CACHE BOOL "")
+set(PYBIND11_EXPORT_NAME test_export)
+
+add_subdirectory("${pybind11_SOURCE_DIR}" pybind11)
+
+# Test basic target functionality
+add_executable(test_subdirectory_embed ../embed.cpp)
+target_link_libraries(test_subdirectory_embed PRIVATE pybind11::embed)
+set_target_properties(test_subdirectory_embed PROPERTIES OUTPUT_NAME test_cmake_build)
+
+add_custom_target(
+  check_subdirectory_embed
+  $<TARGET_FILE:test_subdirectory_embed> "${PROJECT_SOURCE_DIR}/../test.py"
+  DEPENDS test_subdirectory_embed)
+
+# Test custom export group -- PYBIND11_EXPORT_NAME
+add_library(test_embed_lib ../embed.cpp)
+target_link_libraries(test_embed_lib PRIVATE pybind11::embed)
+
+install(
+  TARGETS test_embed_lib
+  EXPORT test_export
+  ARCHIVE DESTINATION bin
+  LIBRARY DESTINATION lib
+  RUNTIME DESTINATION lib)
+install(EXPORT test_export DESTINATION lib/cmake/test_export/test_export-Targets.cmake)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_function/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_function/CMakeLists.txt
new file mode 100644
index 000000000..01557c439
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_function/CMakeLists.txt
@@ -0,0 +1,35 @@
+cmake_minimum_required(VERSION 3.4)
+
+# The `cmake_minimum_required(VERSION 3.4...3.18)` syntax does not work with
+# some versions of VS that have a patched CMake 3.11. This forces us to emulate
+# the behavior using the following workaround:
+if(${CMAKE_VERSION} VERSION_LESS 3.18)
+  cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION})
+else()
+  cmake_policy(VERSION 3.18)
+endif()
+
+project(test_subdirectory_function CXX)
+
+add_subdirectory("${pybind11_SOURCE_DIR}" pybind11)
+pybind11_add_module(test_subdirectory_function ../main.cpp)
+set_target_properties(test_subdirectory_function PROPERTIES OUTPUT_NAME test_cmake_build)
+
+if(DEFINED Python_EXECUTABLE)
+  set(_Python_EXECUTABLE "${Python_EXECUTABLE}")
+elseif(DEFINED PYTHON_EXECUTABLE)
+  set(_Python_EXECUTABLE "${PYTHON_EXECUTABLE}")
+else()
+  message(FATAL_ERROR "No Python executable defined (should not be possible at this stage)")
+endif()
+
+add_custom_target(
+  check_subdirectory_function
+  ${CMAKE_COMMAND}
+  -E
+  env
+  PYTHONPATH=$<TARGET_FILE_DIR:test_subdirectory_function>
+  ${_Python_EXECUTABLE}
+  ${PROJECT_SOURCE_DIR}/../test.py
+  ${PROJECT_NAME}
+  DEPENDS test_subdirectory_function)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_target/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_target/CMakeLists.txt
new file mode 100644
index 000000000..ba82fdee2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/subdirectory_target/CMakeLists.txt
@@ -0,0 +1,41 @@
+cmake_minimum_required(VERSION 3.4)
+
+# The `cmake_minimum_required(VERSION 3.4...3.18)` syntax does not work with
+# some versions of VS that have a patched CMake 3.11. This forces us to emulate
+# the behavior using the following workaround:
+if(${CMAKE_VERSION} VERSION_LESS 3.18)
+  cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION})
+else()
+  cmake_policy(VERSION 3.18)
+endif()
+
+project(test_subdirectory_target CXX)
+
+add_subdirectory("${pybind11_SOURCE_DIR}" pybind11)
+
+add_library(test_subdirectory_target MODULE ../main.cpp)
+set_target_properties(test_subdirectory_target PROPERTIES OUTPUT_NAME test_cmake_build)
+
+target_link_libraries(test_subdirectory_target PRIVATE pybind11::module)
+
+# Make sure result is, for example, test_installed_target.so, not libtest_installed_target.dylib
+pybind11_extension(test_subdirectory_target)
+
+if(DEFINED Python_EXECUTABLE)
+  set(_Python_EXECUTABLE "${Python_EXECUTABLE}")
+elseif(DEFINED PYTHON_EXECUTABLE)
+  set(_Python_EXECUTABLE "${PYTHON_EXECUTABLE}")
+else()
+  message(FATAL_ERROR "No Python executable defined (should not be possible at this stage)")
+endif()
+
+add_custom_target(
+  check_subdirectory_target
+  ${CMAKE_COMMAND}
+  -E
+  env
+  PYTHONPATH=$<TARGET_FILE_DIR:test_subdirectory_target>
+  ${_Python_EXECUTABLE}
+  ${PROJECT_SOURCE_DIR}/../test.py
+  ${PROJECT_NAME}
+  DEPENDS test_subdirectory_target)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/test.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/test.py
new file mode 100644
index 000000000..807fd43b4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_cmake_build/test.py
@@ -0,0 +1,8 @@
+import sys
+
+import test_cmake_build
+
+assert isinstance(__file__, str)  # Test this is properly set
+
+assert test_cmake_build.add(1, 2) == 3
+print(f"{sys.argv[1]} imports, runs, and adds: 1 + 2 = 3")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_const_name.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_const_name.cpp
new file mode 100644
index 000000000..2ad01e682
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_const_name.cpp
@@ -0,0 +1,55 @@
+// Copyright (c) 2021 The Pybind Development Team.
+// All rights reserved. Use of this source code is governed by a
+// BSD-style license that can be found in the LICENSE file.
+
+#include "pybind11_tests.h"
+
+// IUT = Implementation Under Test
+#define CONST_NAME_TESTS(TEST_FUNC, IUT)                                                          \
+    std::string TEST_FUNC(int selector) {                                                         \
+        switch (selector) {                                                                       \
+            case 0:                                                                               \
+                return IUT("").text;                                                              \
+            case 1:                                                                               \
+                return IUT("A").text;                                                             \
+            case 2:                                                                               \
+                return IUT("Bd").text;                                                            \
+            case 3:                                                                               \
+                return IUT("Cef").text;                                                           \
+            case 4:                                                                               \
+                return IUT<int>().text; /*NOLINT(bugprone-macro-parentheses)*/                    \
+            case 5:                                                                               \
+                return IUT<std::string>().text; /*NOLINT(bugprone-macro-parentheses)*/            \
+            case 6:                                                                               \
+                return IUT<true>("T1", "T2").text; /*NOLINT(bugprone-macro-parentheses)*/         \
+            case 7:                                                                               \
+                return IUT<false>("U1", "U2").text; /*NOLINT(bugprone-macro-parentheses)*/        \
+            case 8:                                                                               \
+                /*NOLINTNEXTLINE(bugprone-macro-parentheses)*/                                    \
+                return IUT<true>(IUT("D1"), IUT("D2")).text;                                      \
+            case 9:                                                                               \
+                /*NOLINTNEXTLINE(bugprone-macro-parentheses)*/                                    \
+                return IUT<false>(IUT("E1"), IUT("E2")).text;                                     \
+            case 10:                                                                              \
+                return IUT("KeepAtEnd").text;                                                     \
+            default:                                                                              \
+                break;                                                                            \
+        }                                                                                         \
+        throw std::runtime_error("Invalid selector value.");                                      \
+    }
+
+CONST_NAME_TESTS(const_name_tests, py::detail::const_name)
+
+#ifdef PYBIND11_DETAIL_UNDERSCORE_BACKWARD_COMPATIBILITY
+CONST_NAME_TESTS(underscore_tests, py::detail::_)
+#endif
+
+TEST_SUBMODULE(const_name, m) {
+    m.def("const_name_tests", const_name_tests);
+
+#if defined(PYBIND11_DETAIL_UNDERSCORE_BACKWARD_COMPATIBILITY)
+    m.def("underscore_tests", underscore_tests);
+#else
+    m.attr("underscore_tests") = "PYBIND11_DETAIL_UNDERSCORE_BACKWARD_COMPATIBILITY not defined.";
+#endif
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_const_name.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_const_name.py
new file mode 100644
index 000000000..10b0caee2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_const_name.py
@@ -0,0 +1,29 @@
+import pytest
+
+from pybind11_tests import const_name as m
+
+
+@pytest.mark.parametrize("func", (m.const_name_tests, m.underscore_tests))
+@pytest.mark.parametrize(
+    "selector, expected",
+    enumerate(
+        (
+            "",
+            "A",
+            "Bd",
+            "Cef",
+            "%",
+            "%",
+            "T1",
+            "U2",
+            "D1",
+            "E2",
+            "KeepAtEnd",
+        )
+    ),
+)
+def test_const_name(func, selector, expected):
+    if isinstance(func, str):
+        pytest.skip(func)
+    text = func(selector)
+    assert text == expected
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_constants_and_functions.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_constants_and_functions.cpp
new file mode 100644
index 000000000..1918a429c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_constants_and_functions.cpp
@@ -0,0 +1,159 @@
+/*
+    tests/test_constants_and_functions.cpp -- global constants and functions, enumerations, raw
+    byte strings
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+enum MyEnum { EFirstEntry = 1, ESecondEntry };
+
+std::string test_function1() { return "test_function()"; }
+
+std::string test_function2(MyEnum k) { return "test_function(enum=" + std::to_string(k) + ")"; }
+
+std::string test_function3(int i) { return "test_function(" + std::to_string(i) + ")"; }
+
+py::str test_function4() { return "test_function()"; }
+py::str test_function4(char *) { return "test_function(char *)"; }
+py::str test_function4(int, float) { return "test_function(int, float)"; }
+py::str test_function4(float, int) { return "test_function(float, int)"; }
+
+py::bytes return_bytes() {
+    const char *data = "\x01\x00\x02\x00";
+    return std::string(data, 4);
+}
+
+std::string print_bytes(const py::bytes &bytes) {
+    std::string ret = "bytes[";
+    const auto value = static_cast<std::string>(bytes);
+    for (char c : value) {
+        ret += std::to_string(static_cast<int>(c)) + ' ';
+    }
+    ret.back() = ']';
+    return ret;
+}
+
+// Test that we properly handle C++17 exception specifiers (which are part of the function
+// signature in C++17).  These should all still work before C++17, but don't affect the function
+// signature.
+namespace test_exc_sp {
+// [workaround(intel)] Unable to use noexcept instead of noexcept(true)
+// Make the f1 test basically the same as the f2 test in C++17 mode for the Intel compiler as
+// it fails to compile with a plain noexcept (tested with icc (ICC) 2021.1 Beta 20200827).
+#if defined(__INTEL_COMPILER) && defined(PYBIND11_CPP17)
+int f1(int x) noexcept(true) { return x + 1; }
+#else
+int f1(int x) noexcept { return x + 1; }
+#endif
+int f2(int x) noexcept(true) { return x + 2; }
+int f3(int x) noexcept(false) { return x + 3; }
+#if defined(__GNUG__) && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wdeprecated"
+#endif
+// NOLINTNEXTLINE(modernize-use-noexcept)
+int f4(int x) throw() { return x + 4; } // Deprecated equivalent to noexcept(true)
+#if defined(__GNUG__) && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic pop
+#endif
+struct C {
+    int m1(int x) noexcept { return x - 1; }
+    int m2(int x) const noexcept { return x - 2; }
+    int m3(int x) noexcept(true) { return x - 3; }
+    int m4(int x) const noexcept(true) { return x - 4; }
+    int m5(int x) noexcept(false) { return x - 5; }
+    int m6(int x) const noexcept(false) { return x - 6; }
+#if defined(__GNUG__) && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wdeprecated"
+#endif
+    // NOLINTNEXTLINE(modernize-use-noexcept)
+    int m7(int x) throw() { return x - 7; }
+    // NOLINTNEXTLINE(modernize-use-noexcept)
+    int m8(int x) const throw() { return x - 8; }
+#if defined(__GNUG__) && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic pop
+#endif
+};
+} // namespace test_exc_sp
+
+TEST_SUBMODULE(constants_and_functions, m) {
+    // test_constants
+    m.attr("some_constant") = py::int_(14);
+
+    // test_function_overloading
+    m.def("test_function", &test_function1);
+    m.def("test_function", &test_function2);
+    m.def("test_function", &test_function3);
+
+#if defined(PYBIND11_OVERLOAD_CAST)
+    m.def("test_function", py::overload_cast<>(&test_function4));
+    m.def("test_function", py::overload_cast<char *>(&test_function4));
+    m.def("test_function", py::overload_cast<int, float>(&test_function4));
+    m.def("test_function", py::overload_cast<float, int>(&test_function4));
+#else
+    m.def("test_function", static_cast<py::str (*)()>(&test_function4));
+    m.def("test_function", static_cast<py::str (*)(char *)>(&test_function4));
+    m.def("test_function", static_cast<py::str (*)(int, float)>(&test_function4));
+    m.def("test_function", static_cast<py::str (*)(float, int)>(&test_function4));
+#endif
+
+    py::enum_<MyEnum>(m, "MyEnum")
+        .value("EFirstEntry", EFirstEntry)
+        .value("ESecondEntry", ESecondEntry)
+        .export_values();
+
+    // test_bytes
+    m.def("return_bytes", &return_bytes);
+    m.def("print_bytes", &print_bytes);
+
+    // test_exception_specifiers
+    using namespace test_exc_sp;
+    py::class_<C>(m, "C")
+        .def(py::init<>())
+        .def("m1", &C::m1)
+        .def("m2", &C::m2)
+        .def("m3", &C::m3)
+        .def("m4", &C::m4)
+        .def("m5", &C::m5)
+        .def("m6", &C::m6)
+        .def("m7", &C::m7)
+        .def("m8", &C::m8);
+    m.def("f1", f1);
+    m.def("f2", f2);
+#if defined(__INTEL_COMPILER)
+#    pragma warning push
+#    pragma warning disable 878 // incompatible exception specifications
+#endif
+    m.def("f3", f3);
+#if defined(__INTEL_COMPILER)
+#    pragma warning pop
+#endif
+    m.def("f4", f4);
+
+    // test_function_record_leaks
+    m.def("register_large_capture_with_invalid_arguments", [](py::module_ m) {
+        // This should always be enough to trigger the alternative branch
+        // where `sizeof(capture) > sizeof(rec->data)`
+        uint64_t capture[10] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+#if defined(__GNUC__) && __GNUC__ == 4 // CentOS7
+        py::detail::silence_unused_warnings(capture);
+#endif
+        m.def(
+            "should_raise", [capture](int) { return capture[9] + 33; }, py::kw_only(), py::arg());
+    });
+    m.def("register_with_raising_repr", [](py::module_ m, const py::object &default_value) {
+        m.def(
+            "should_raise",
+            [](int, int, const py::object &) { return 42; },
+            "some docstring",
+            py::arg_v("x", 42),
+            py::arg_v("y", 42, "<the answer>"),
+            py::arg_v("z", default_value));
+    });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_constants_and_functions.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_constants_and_functions.py
new file mode 100644
index 000000000..5da0b84b8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_constants_and_functions.py
@@ -0,0 +1,52 @@
+import pytest
+
+m = pytest.importorskip("pybind11_tests.constants_and_functions")
+
+
+def test_constants():
+    assert m.some_constant == 14
+
+
+def test_function_overloading():
+    assert m.test_function() == "test_function()"
+    assert m.test_function(7) == "test_function(7)"
+    assert m.test_function(m.MyEnum.EFirstEntry) == "test_function(enum=1)"
+    assert m.test_function(m.MyEnum.ESecondEntry) == "test_function(enum=2)"
+
+    assert m.test_function() == "test_function()"
+    assert m.test_function("abcd") == "test_function(char *)"
+    assert m.test_function(1, 1.0) == "test_function(int, float)"
+    assert m.test_function(1, 1.0) == "test_function(int, float)"
+    assert m.test_function(2.0, 2) == "test_function(float, int)"
+
+
+def test_bytes():
+    assert m.print_bytes(m.return_bytes()) == "bytes[1 0 2 0]"
+
+
+def test_exception_specifiers():
+    c = m.C()
+    assert c.m1(2) == 1
+    assert c.m2(3) == 1
+    assert c.m3(5) == 2
+    assert c.m4(7) == 3
+    assert c.m5(10) == 5
+    assert c.m6(14) == 8
+    assert c.m7(20) == 13
+    assert c.m8(29) == 21
+
+    assert m.f1(33) == 34
+    assert m.f2(53) == 55
+    assert m.f3(86) == 89
+    assert m.f4(140) == 144
+
+
+def test_function_record_leaks():
+    class RaisingRepr:
+        def __repr__(self):
+            raise RuntimeError("Surprise!")
+
+    with pytest.raises(RuntimeError):
+        m.register_large_capture_with_invalid_arguments(m)
+    with pytest.raises(RuntimeError):
+        m.register_with_raising_repr(m, RaisingRepr())
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_copy_move.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_copy_move.cpp
new file mode 100644
index 000000000..28c244564
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_copy_move.cpp
@@ -0,0 +1,295 @@
+/*
+    tests/test_copy_move_policies.cpp -- 'copy' and 'move' return value policies
+                                         and related tests
+
+    Copyright (c) 2016 Ben North <ben@redfrontdoor.org>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/stl.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+template <typename derived>
+struct empty {
+    static const derived &get_one() { return instance_; }
+    static derived instance_;
+};
+
+struct lacking_copy_ctor : public empty<lacking_copy_ctor> {
+    lacking_copy_ctor() = default;
+    lacking_copy_ctor(const lacking_copy_ctor &other) = delete;
+};
+
+template <>
+lacking_copy_ctor empty<lacking_copy_ctor>::instance_ = {};
+
+struct lacking_move_ctor : public empty<lacking_move_ctor> {
+    lacking_move_ctor() = default;
+    lacking_move_ctor(const lacking_move_ctor &other) = delete;
+    lacking_move_ctor(lacking_move_ctor &&other) = delete;
+};
+
+template <>
+lacking_move_ctor empty<lacking_move_ctor>::instance_ = {};
+
+/* Custom type caster move/copy test classes */
+class MoveOnlyInt {
+public:
+    MoveOnlyInt() { print_default_created(this); }
+    explicit MoveOnlyInt(int v) : value{v} { print_created(this, value); }
+    MoveOnlyInt(MoveOnlyInt &&m) noexcept {
+        print_move_created(this, m.value);
+        std::swap(value, m.value);
+    }
+    MoveOnlyInt &operator=(MoveOnlyInt &&m) noexcept {
+        print_move_assigned(this, m.value);
+        std::swap(value, m.value);
+        return *this;
+    }
+    MoveOnlyInt(const MoveOnlyInt &) = delete;
+    MoveOnlyInt &operator=(const MoveOnlyInt &) = delete;
+    ~MoveOnlyInt() { print_destroyed(this); }
+
+    int value;
+};
+class MoveOrCopyInt {
+public:
+    MoveOrCopyInt() { print_default_created(this); }
+    explicit MoveOrCopyInt(int v) : value{v} { print_created(this, value); }
+    MoveOrCopyInt(MoveOrCopyInt &&m) noexcept {
+        print_move_created(this, m.value);
+        std::swap(value, m.value);
+    }
+    MoveOrCopyInt &operator=(MoveOrCopyInt &&m) noexcept {
+        print_move_assigned(this, m.value);
+        std::swap(value, m.value);
+        return *this;
+    }
+    MoveOrCopyInt(const MoveOrCopyInt &c) {
+        print_copy_created(this, c.value);
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        value = c.value;
+    }
+    MoveOrCopyInt &operator=(const MoveOrCopyInt &c) {
+        print_copy_assigned(this, c.value);
+        value = c.value;
+        return *this;
+    }
+    ~MoveOrCopyInt() { print_destroyed(this); }
+
+    int value;
+};
+class CopyOnlyInt {
+public:
+    CopyOnlyInt() { print_default_created(this); }
+    explicit CopyOnlyInt(int v) : value{v} { print_created(this, value); }
+    CopyOnlyInt(const CopyOnlyInt &c) {
+        print_copy_created(this, c.value);
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        value = c.value;
+    }
+    CopyOnlyInt &operator=(const CopyOnlyInt &c) {
+        print_copy_assigned(this, c.value);
+        value = c.value;
+        return *this;
+    }
+    ~CopyOnlyInt() { print_destroyed(this); }
+
+    int value;
+};
+PYBIND11_NAMESPACE_BEGIN(pybind11)
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <>
+struct type_caster<MoveOnlyInt> {
+    PYBIND11_TYPE_CASTER(MoveOnlyInt, const_name("MoveOnlyInt"));
+    bool load(handle src, bool) {
+        value = MoveOnlyInt(src.cast<int>());
+        return true;
+    }
+    static handle cast(const MoveOnlyInt &m, return_value_policy r, handle p) {
+        return pybind11::cast(m.value, r, p);
+    }
+};
+
+template <>
+struct type_caster<MoveOrCopyInt> {
+    PYBIND11_TYPE_CASTER(MoveOrCopyInt, const_name("MoveOrCopyInt"));
+    bool load(handle src, bool) {
+        value = MoveOrCopyInt(src.cast<int>());
+        return true;
+    }
+    static handle cast(const MoveOrCopyInt &m, return_value_policy r, handle p) {
+        return pybind11::cast(m.value, r, p);
+    }
+};
+
+template <>
+struct type_caster<CopyOnlyInt> {
+protected:
+    CopyOnlyInt value;
+
+public:
+    static constexpr auto name = const_name("CopyOnlyInt");
+    bool load(handle src, bool) {
+        value = CopyOnlyInt(src.cast<int>());
+        return true;
+    }
+    static handle cast(const CopyOnlyInt &m, return_value_policy r, handle p) {
+        return pybind11::cast(m.value, r, p);
+    }
+    static handle cast(const CopyOnlyInt *src, return_value_policy policy, handle parent) {
+        if (!src) {
+            return none().release();
+        }
+        return cast(*src, policy, parent);
+    }
+    explicit operator CopyOnlyInt *() { return &value; }
+    explicit operator CopyOnlyInt &() { return value; }
+    template <typename T>
+    using cast_op_type = pybind11::detail::cast_op_type<T>;
+};
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(pybind11)
+
+TEST_SUBMODULE(copy_move_policies, m) {
+    // test_lacking_copy_ctor
+    py::class_<lacking_copy_ctor>(m, "lacking_copy_ctor")
+        .def_static("get_one", &lacking_copy_ctor::get_one, py::return_value_policy::copy);
+    // test_lacking_move_ctor
+    py::class_<lacking_move_ctor>(m, "lacking_move_ctor")
+        .def_static("get_one", &lacking_move_ctor::get_one, py::return_value_policy::move);
+
+    // test_move_and_copy_casts
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
+    m.def("move_and_copy_casts", [](const py::object &o) {
+        int r = 0;
+        r += py::cast<MoveOrCopyInt>(o).value; /* moves */
+        r += py::cast<MoveOnlyInt>(o).value;   /* moves */
+        r += py::cast<CopyOnlyInt>(o).value;   /* copies */
+        auto m1(py::cast<MoveOrCopyInt>(o));   /* moves */
+        auto m2(py::cast<MoveOnlyInt>(o));     /* moves */
+        auto m3(py::cast<CopyOnlyInt>(o));     /* copies */
+        r += m1.value + m2.value + m3.value;
+
+        return r;
+    });
+
+    // test_move_and_copy_loads
+    m.def("move_only", [](MoveOnlyInt m) { return m.value; });
+    // Changing this breaks the existing test: needs careful review.
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
+    m.def("move_or_copy", [](MoveOrCopyInt m) { return m.value; });
+    // Changing this breaks the existing test: needs careful review.
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
+    m.def("copy_only", [](CopyOnlyInt m) { return m.value; });
+    m.def("move_pair",
+          [](std::pair<MoveOnlyInt, MoveOrCopyInt> p) { return p.first.value + p.second.value; });
+    m.def("move_tuple", [](std::tuple<MoveOnlyInt, MoveOrCopyInt, MoveOnlyInt> t) {
+        return std::get<0>(t).value + std::get<1>(t).value + std::get<2>(t).value;
+    });
+    m.def("copy_tuple", [](std::tuple<CopyOnlyInt, CopyOnlyInt> t) {
+        return std::get<0>(t).value + std::get<1>(t).value;
+    });
+    m.def("move_copy_nested",
+          [](std::pair<MoveOnlyInt,
+                       std::pair<std::tuple<MoveOrCopyInt, CopyOnlyInt, std::tuple<MoveOnlyInt>>,
+                                 MoveOrCopyInt>> x) {
+              return x.first.value + std::get<0>(x.second.first).value
+                     + std::get<1>(x.second.first).value
+                     + std::get<0>(std::get<2>(x.second.first)).value + x.second.second.value;
+          });
+    m.def("move_and_copy_cstats", []() {
+        ConstructorStats::gc();
+        // Reset counts to 0 so that previous tests don't affect later ones:
+        auto &mc = ConstructorStats::get<MoveOrCopyInt>();
+        mc.move_assignments = mc.move_constructions = mc.copy_assignments = mc.copy_constructions
+            = 0;
+        auto &mo = ConstructorStats::get<MoveOnlyInt>();
+        mo.move_assignments = mo.move_constructions = mo.copy_assignments = mo.copy_constructions
+            = 0;
+        auto &co = ConstructorStats::get<CopyOnlyInt>();
+        co.move_assignments = co.move_constructions = co.copy_assignments = co.copy_constructions
+            = 0;
+        py::dict d;
+        d["MoveOrCopyInt"] = py::cast(mc, py::return_value_policy::reference);
+        d["MoveOnlyInt"] = py::cast(mo, py::return_value_policy::reference);
+        d["CopyOnlyInt"] = py::cast(co, py::return_value_policy::reference);
+        return d;
+    });
+#ifdef PYBIND11_HAS_OPTIONAL
+    // test_move_and_copy_load_optional
+    m.attr("has_optional") = true;
+    m.def("move_optional", [](std::optional<MoveOnlyInt> o) { return o->value; });
+    m.def("move_or_copy_optional", [](std::optional<MoveOrCopyInt> o) { return o->value; });
+    m.def("copy_optional", [](std::optional<CopyOnlyInt> o) { return o->value; });
+    m.def("move_optional_tuple",
+          [](std::optional<std::tuple<MoveOrCopyInt, MoveOnlyInt, CopyOnlyInt>> x) {
+              return std::get<0>(*x).value + std::get<1>(*x).value + std::get<2>(*x).value;
+          });
+#else
+    m.attr("has_optional") = false;
+#endif
+
+    // #70 compilation issue if operator new is not public - simple body added
+    // but not needed on most compilers; MSVC and nvcc don't like a local
+    // struct not having a method defined when declared, since it can not be
+    // added later.
+    struct PrivateOpNew {
+        int value = 1;
+
+    private:
+        void *operator new(size_t bytes) {
+            void *ptr = std::malloc(bytes);
+            if (ptr) {
+                return ptr;
+            }
+            throw std::bad_alloc{};
+        }
+    };
+    py::class_<PrivateOpNew>(m, "PrivateOpNew").def_readonly("value", &PrivateOpNew::value);
+    m.def("private_op_new_value", []() { return PrivateOpNew(); });
+    m.def(
+        "private_op_new_reference",
+        []() -> const PrivateOpNew & {
+            static PrivateOpNew x{};
+            return x;
+        },
+        py::return_value_policy::reference);
+
+    // test_move_fallback
+    // #389: rvp::move should fall-through to copy on non-movable objects
+    struct MoveIssue1 {
+        int v;
+        explicit MoveIssue1(int v) : v{v} {}
+        MoveIssue1(const MoveIssue1 &c) = default;
+        MoveIssue1(MoveIssue1 &&) = delete;
+    };
+    py::class_<MoveIssue1>(m, "MoveIssue1")
+        .def(py::init<int>())
+        .def_readwrite("value", &MoveIssue1::v);
+
+    struct MoveIssue2 {
+        int v;
+        explicit MoveIssue2(int v) : v{v} {}
+        MoveIssue2(MoveIssue2 &&) = default;
+    };
+    py::class_<MoveIssue2>(m, "MoveIssue2")
+        .def(py::init<int>())
+        .def_readwrite("value", &MoveIssue2::v);
+
+    // #2742: Don't expect ownership of raw pointer to `new`ed object to be transferred with
+    // `py::return_value_policy::move`
+    m.def(
+        "get_moveissue1",
+        [](int i) { return std::unique_ptr<MoveIssue1>(new MoveIssue1(i)); },
+        py::return_value_policy::move);
+    m.def(
+        "get_moveissue2", [](int i) { return MoveIssue2(i); }, py::return_value_policy::move);
+
+    // Make sure that cast from pytype rvalue to other pytype works
+    m.def("get_pytype_rvalue_castissue", [](double i) { return py::float_(i).cast<py::int_>(); });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_copy_move.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_copy_move.py
new file mode 100644
index 000000000..9fef08933
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_copy_move.py
@@ -0,0 +1,132 @@
+import pytest
+
+from pybind11_tests import copy_move_policies as m
+
+
+def test_lacking_copy_ctor():
+    with pytest.raises(RuntimeError) as excinfo:
+        m.lacking_copy_ctor.get_one()
+    assert "is non-copyable!" in str(excinfo.value)
+
+
+def test_lacking_move_ctor():
+    with pytest.raises(RuntimeError) as excinfo:
+        m.lacking_move_ctor.get_one()
+    assert "is neither movable nor copyable!" in str(excinfo.value)
+
+
+def test_move_and_copy_casts():
+    """Cast some values in C++ via custom type casters and count the number of moves/copies."""
+
+    cstats = m.move_and_copy_cstats()
+    c_m, c_mc, c_c = (
+        cstats["MoveOnlyInt"],
+        cstats["MoveOrCopyInt"],
+        cstats["CopyOnlyInt"],
+    )
+
+    # The type move constructions/assignments below each get incremented: the move assignment comes
+    # from the type_caster load; the move construction happens when extracting that via a cast or
+    # loading into an argument.
+    assert m.move_and_copy_casts(3) == 18
+    assert c_m.copy_assignments + c_m.copy_constructions == 0
+    assert c_m.move_assignments == 2
+    assert c_m.move_constructions >= 2
+    assert c_mc.alive() == 0
+    assert c_mc.copy_assignments + c_mc.copy_constructions == 0
+    assert c_mc.move_assignments == 2
+    assert c_mc.move_constructions >= 2
+    assert c_c.alive() == 0
+    assert c_c.copy_assignments == 2
+    assert c_c.copy_constructions >= 2
+    assert c_m.alive() + c_mc.alive() + c_c.alive() == 0
+
+
+def test_move_and_copy_loads():
+    """Call some functions that load arguments via custom type casters and count the number of
+    moves/copies."""
+
+    cstats = m.move_and_copy_cstats()
+    c_m, c_mc, c_c = (
+        cstats["MoveOnlyInt"],
+        cstats["MoveOrCopyInt"],
+        cstats["CopyOnlyInt"],
+    )
+
+    assert m.move_only(10) == 10  # 1 move, c_m
+    assert m.move_or_copy(11) == 11  # 1 move, c_mc
+    assert m.copy_only(12) == 12  # 1 copy, c_c
+    assert m.move_pair((13, 14)) == 27  # 1 c_m move, 1 c_mc move
+    assert m.move_tuple((15, 16, 17)) == 48  # 2 c_m moves, 1 c_mc move
+    assert m.copy_tuple((18, 19)) == 37  # 2 c_c copies
+    # Direct constructions: 2 c_m moves, 2 c_mc moves, 1 c_c copy
+    # Extra moves/copies when moving pairs/tuples: 3 c_m, 3 c_mc, 2 c_c
+    assert m.move_copy_nested((1, ((2, 3, (4,)), 5))) == 15
+
+    assert c_m.copy_assignments + c_m.copy_constructions == 0
+    assert c_m.move_assignments == 6
+    assert c_m.move_constructions == 9
+    assert c_mc.copy_assignments + c_mc.copy_constructions == 0
+    assert c_mc.move_assignments == 5
+    assert c_mc.move_constructions == 8
+    assert c_c.copy_assignments == 4
+    assert c_c.copy_constructions == 6
+    assert c_m.alive() + c_mc.alive() + c_c.alive() == 0
+
+
+@pytest.mark.skipif(not m.has_optional, reason="no <optional>")
+def test_move_and_copy_load_optional():
+    """Tests move/copy loads of std::optional arguments"""
+
+    cstats = m.move_and_copy_cstats()
+    c_m, c_mc, c_c = (
+        cstats["MoveOnlyInt"],
+        cstats["MoveOrCopyInt"],
+        cstats["CopyOnlyInt"],
+    )
+
+    # The extra move/copy constructions below come from the std::optional move (which has to move
+    # its arguments):
+    assert m.move_optional(10) == 10  # c_m: 1 move assign, 2 move construct
+    assert m.move_or_copy_optional(11) == 11  # c_mc: 1 move assign, 2 move construct
+    assert m.copy_optional(12) == 12  # c_c: 1 copy assign, 2 copy construct
+    # 1 move assign + move construct moves each of c_m, c_mc, 1 c_c copy
+    # +1 move/copy construct each from moving the tuple
+    # +1 move/copy construct each from moving the optional (which moves the tuple again)
+    assert m.move_optional_tuple((3, 4, 5)) == 12
+
+    assert c_m.copy_assignments + c_m.copy_constructions == 0
+    assert c_m.move_assignments == 2
+    assert c_m.move_constructions == 5
+    assert c_mc.copy_assignments + c_mc.copy_constructions == 0
+    assert c_mc.move_assignments == 2
+    assert c_mc.move_constructions == 5
+    assert c_c.copy_assignments == 2
+    assert c_c.copy_constructions == 5
+    assert c_m.alive() + c_mc.alive() + c_c.alive() == 0
+
+
+def test_private_op_new():
+    """An object with a private `operator new` cannot be returned by value"""
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.private_op_new_value()
+    assert "is neither movable nor copyable" in str(excinfo.value)
+
+    assert m.private_op_new_reference().value == 1
+
+
+def test_move_fallback():
+    """#389: rvp::move should fall-through to copy on non-movable objects"""
+
+    m1 = m.get_moveissue1(1)
+    assert m1.value == 1
+    m2 = m.get_moveissue2(2)
+    assert m2.value == 2
+
+
+def test_pytype_rvalue_cast():
+    """Make sure that cast from pytype rvalue to other pytype works"""
+
+    value = m.get_pytype_rvalue_castissue(1.0)
+    assert value == 1
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_casters.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_casters.cpp
new file mode 100644
index 000000000..25540e368
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_casters.cpp
@@ -0,0 +1,209 @@
+/*
+    tests/test_custom_type_casters.cpp -- tests type_caster<T>
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+// py::arg/py::arg_v testing: these arguments just record their argument when invoked
+class ArgInspector1 {
+public:
+    std::string arg = "(default arg inspector 1)";
+};
+class ArgInspector2 {
+public:
+    std::string arg = "(default arg inspector 2)";
+};
+class ArgAlwaysConverts {};
+
+namespace pybind11 {
+namespace detail {
+template <>
+struct type_caster<ArgInspector1> {
+public:
+    // Classic
+#ifdef PYBIND11_DETAIL_UNDERSCORE_BACKWARD_COMPATIBILITY
+    PYBIND11_TYPE_CASTER(ArgInspector1, _("ArgInspector1"));
+#else
+    PYBIND11_TYPE_CASTER(ArgInspector1, const_name("ArgInspector1"));
+#endif
+
+    bool load(handle src, bool convert) {
+        value.arg = "loading ArgInspector1 argument " + std::string(convert ? "WITH" : "WITHOUT")
+                    + " conversion allowed.  "
+                      "Argument value = "
+                    + (std::string) str(src);
+        return true;
+    }
+
+    static handle cast(const ArgInspector1 &src, return_value_policy, handle) {
+        return str(src.arg).release();
+    }
+};
+template <>
+struct type_caster<ArgInspector2> {
+public:
+    PYBIND11_TYPE_CASTER(ArgInspector2, const_name("ArgInspector2"));
+
+    bool load(handle src, bool convert) {
+        value.arg = "loading ArgInspector2 argument " + std::string(convert ? "WITH" : "WITHOUT")
+                    + " conversion allowed.  "
+                      "Argument value = "
+                    + (std::string) str(src);
+        return true;
+    }
+
+    static handle cast(const ArgInspector2 &src, return_value_policy, handle) {
+        return str(src.arg).release();
+    }
+};
+template <>
+struct type_caster<ArgAlwaysConverts> {
+public:
+    PYBIND11_TYPE_CASTER(ArgAlwaysConverts, const_name("ArgAlwaysConverts"));
+
+    bool load(handle, bool convert) { return convert; }
+
+    static handle cast(const ArgAlwaysConverts &, return_value_policy, handle) {
+        return py::none().release();
+    }
+};
+} // namespace detail
+} // namespace pybind11
+
+// test_custom_caster_destruction
+class DestructionTester {
+public:
+    DestructionTester() { print_default_created(this); }
+    ~DestructionTester() { print_destroyed(this); }
+    DestructionTester(const DestructionTester &) { print_copy_created(this); }
+    DestructionTester(DestructionTester &&) noexcept { print_move_created(this); }
+    DestructionTester &operator=(const DestructionTester &) {
+        print_copy_assigned(this);
+        return *this;
+    }
+    DestructionTester &operator=(DestructionTester &&) noexcept {
+        print_move_assigned(this);
+        return *this;
+    }
+};
+namespace pybind11 {
+namespace detail {
+template <>
+struct type_caster<DestructionTester> {
+    PYBIND11_TYPE_CASTER(DestructionTester, const_name("DestructionTester"));
+    bool load(handle, bool) { return true; }
+
+    static handle cast(const DestructionTester &, return_value_policy, handle) {
+        return py::bool_(true).release();
+    }
+};
+} // namespace detail
+} // namespace pybind11
+
+// Define type caster outside of `pybind11::detail` and then alias it.
+namespace other_lib {
+struct MyType {};
+// Corrupt `py` shorthand alias for surrounding context.
+namespace py {}
+// Corrupt unqualified relative `pybind11` namespace.
+namespace pybind11 {}
+// Correct alias.
+namespace py_ = ::pybind11;
+// Define caster. This is effectively no-op, we only ensure it compiles and we
+// don't have any symbol collision when using macro mixin.
+struct my_caster {
+    PYBIND11_TYPE_CASTER(MyType, py_::detail::const_name("MyType"));
+    bool load(py_::handle, bool) { return true; }
+
+    static py_::handle cast(const MyType &, py_::return_value_policy, py_::handle) {
+        return py_::bool_(true).release();
+    }
+};
+} // namespace other_lib
+// Effectively "alias" it into correct namespace (via inheritance).
+namespace pybind11 {
+namespace detail {
+template <>
+struct type_caster<other_lib::MyType> : public other_lib::my_caster {};
+} // namespace detail
+} // namespace pybind11
+
+TEST_SUBMODULE(custom_type_casters, m) {
+    // test_custom_type_casters
+
+    // test_noconvert_args
+    //
+    // Test converting.  The ArgAlwaysConverts is just there to make the first no-conversion pass
+    // fail so that our call always ends up happening via the second dispatch (the one that allows
+    // some conversion).
+    class ArgInspector {
+    public:
+        ArgInspector1 f(ArgInspector1 a, ArgAlwaysConverts) { return a; }
+        std::string g(const ArgInspector1 &a,
+                      const ArgInspector1 &b,
+                      int c,
+                      ArgInspector2 *d,
+                      ArgAlwaysConverts) {
+            return a.arg + "\n" + b.arg + "\n" + std::to_string(c) + "\n" + d->arg;
+        }
+        static ArgInspector2 h(ArgInspector2 a, ArgAlwaysConverts) { return a; }
+    };
+    // [workaround(intel)] ICC 20/21 breaks with py::arg().stuff, using py::arg{}.stuff works.
+    py::class_<ArgInspector>(m, "ArgInspector")
+        .def(py::init<>())
+        .def("f", &ArgInspector::f, py::arg(), py::arg() = ArgAlwaysConverts())
+        .def("g",
+             &ArgInspector::g,
+             "a"_a.noconvert(),
+             "b"_a,
+             "c"_a.noconvert() = 13,
+             "d"_a = ArgInspector2(),
+             py::arg() = ArgAlwaysConverts())
+        .def_static("h", &ArgInspector::h, py::arg{}.noconvert(), py::arg() = ArgAlwaysConverts());
+    m.def(
+        "arg_inspect_func",
+        [](const ArgInspector2 &a, const ArgInspector1 &b, ArgAlwaysConverts) {
+            return a.arg + "\n" + b.arg;
+        },
+        py::arg{}.noconvert(false),
+        py::arg_v(nullptr, ArgInspector1()).noconvert(true),
+        py::arg() = ArgAlwaysConverts());
+
+    m.def(
+        "floats_preferred", [](double f) { return 0.5 * f; }, "f"_a);
+    m.def(
+        "floats_only", [](double f) { return 0.5 * f; }, "f"_a.noconvert());
+    m.def(
+        "ints_preferred", [](int i) { return i / 2; }, "i"_a);
+    m.def(
+        "ints_only", [](int i) { return i / 2; }, "i"_a.noconvert());
+
+    // test_custom_caster_destruction
+    // Test that `take_ownership` works on types with a custom type caster when given a pointer
+
+    // default policy: don't take ownership:
+    m.def("custom_caster_no_destroy", []() {
+        static auto *dt = new DestructionTester();
+        return dt;
+    });
+
+    m.def(
+        "custom_caster_destroy",
+        []() { return new DestructionTester(); },
+        py::return_value_policy::take_ownership); // Takes ownership: destroy when finished
+    m.def(
+        "custom_caster_destroy_const",
+        []() -> const DestructionTester * { return new DestructionTester(); },
+        py::return_value_policy::take_ownership); // Likewise (const doesn't inhibit destruction)
+    m.def("destruction_tester_cstats",
+          &ConstructorStats::get<DestructionTester>,
+          py::return_value_policy::reference);
+
+    m.def("other_lib_type", [](other_lib::MyType x) { return x; });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_casters.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_casters.py
new file mode 100644
index 000000000..adfa6cf86
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_casters.py
@@ -0,0 +1,120 @@
+import pytest
+
+from pybind11_tests import custom_type_casters as m
+
+
+def test_noconvert_args(msg):
+    a = m.ArgInspector()
+    assert (
+        msg(a.f("hi"))
+        == """
+        loading ArgInspector1 argument WITH conversion allowed.  Argument value = hi
+    """
+    )
+    assert (
+        msg(a.g("this is a", "this is b"))
+        == """
+        loading ArgInspector1 argument WITHOUT conversion allowed.  Argument value = this is a
+        loading ArgInspector1 argument WITH conversion allowed.  Argument value = this is b
+        13
+        loading ArgInspector2 argument WITH conversion allowed.  Argument value = (default arg inspector 2)
+    """
+    )
+    assert (
+        msg(a.g("this is a", "this is b", 42))
+        == """
+        loading ArgInspector1 argument WITHOUT conversion allowed.  Argument value = this is a
+        loading ArgInspector1 argument WITH conversion allowed.  Argument value = this is b
+        42
+        loading ArgInspector2 argument WITH conversion allowed.  Argument value = (default arg inspector 2)
+    """
+    )
+    assert (
+        msg(a.g("this is a", "this is b", 42, "this is d"))
+        == """
+        loading ArgInspector1 argument WITHOUT conversion allowed.  Argument value = this is a
+        loading ArgInspector1 argument WITH conversion allowed.  Argument value = this is b
+        42
+        loading ArgInspector2 argument WITH conversion allowed.  Argument value = this is d
+    """
+    )
+    assert (
+        a.h("arg 1")
+        == "loading ArgInspector2 argument WITHOUT conversion allowed.  Argument value = arg 1"
+    )
+    assert (
+        msg(m.arg_inspect_func("A1", "A2"))
+        == """
+        loading ArgInspector2 argument WITH conversion allowed.  Argument value = A1
+        loading ArgInspector1 argument WITHOUT conversion allowed.  Argument value = A2
+    """
+    )
+
+    assert m.floats_preferred(4) == 2.0
+    assert m.floats_only(4.0) == 2.0
+    with pytest.raises(TypeError) as excinfo:
+        m.floats_only(4)
+    assert (
+        msg(excinfo.value)
+        == """
+        floats_only(): incompatible function arguments. The following argument types are supported:
+            1. (f: float) -> float
+
+        Invoked with: 4
+    """
+    )
+
+    assert m.ints_preferred(4) == 2
+    assert m.ints_preferred(True) == 0
+    with pytest.raises(TypeError) as excinfo:
+        m.ints_preferred(4.0)
+    assert (
+        msg(excinfo.value)
+        == """
+        ints_preferred(): incompatible function arguments. The following argument types are supported:
+            1. (i: int) -> int
+
+        Invoked with: 4.0
+    """
+    )
+
+    assert m.ints_only(4) == 2
+    with pytest.raises(TypeError) as excinfo:
+        m.ints_only(4.0)
+    assert (
+        msg(excinfo.value)
+        == """
+        ints_only(): incompatible function arguments. The following argument types are supported:
+            1. (i: int) -> int
+
+        Invoked with: 4.0
+    """
+    )
+
+
+def test_custom_caster_destruction():
+    """Tests that returning a pointer to a type that gets converted with a custom type caster gets
+    destroyed when the function has py::return_value_policy::take_ownership policy applied."""
+
+    cstats = m.destruction_tester_cstats()
+    # This one *doesn't* have take_ownership: the pointer should be used but not destroyed:
+    z = m.custom_caster_no_destroy()
+    assert cstats.alive() == 1 and cstats.default_constructions == 1
+    assert z
+
+    # take_ownership applied: this constructs a new object, casts it, then destroys it:
+    z = m.custom_caster_destroy()
+    assert z
+    assert cstats.default_constructions == 2
+
+    # Same, but with a const pointer return (which should *not* inhibit destruction):
+    z = m.custom_caster_destroy_const()
+    assert z
+    assert cstats.default_constructions == 3
+
+    # Make sure we still only have the original object (from ..._no_destroy()) alive:
+    assert cstats.alive() == 1
+
+
+def test_custom_caster_other_lib():
+    assert m.other_lib_type(True)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_setup.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_setup.cpp
new file mode 100644
index 000000000..42fae05d5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_setup.cpp
@@ -0,0 +1,41 @@
+/*
+    tests/test_custom_type_setup.cpp -- Tests `pybind11::custom_type_setup`
+
+    Copyright (c) Google LLC
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/pybind11.h>
+
+#include "pybind11_tests.h"
+
+namespace py = pybind11;
+
+namespace {
+
+struct OwnsPythonObjects {
+    py::object value = py::none();
+};
+} // namespace
+
+TEST_SUBMODULE(custom_type_setup, m) {
+    py::class_<OwnsPythonObjects> cls(
+        m, "OwnsPythonObjects", py::custom_type_setup([](PyHeapTypeObject *heap_type) {
+            auto *type = &heap_type->ht_type;
+            type->tp_flags |= Py_TPFLAGS_HAVE_GC;
+            type->tp_traverse = [](PyObject *self_base, visitproc visit, void *arg) {
+                auto &self = py::cast<OwnsPythonObjects &>(py::handle(self_base));
+                Py_VISIT(self.value.ptr());
+                return 0;
+            };
+            type->tp_clear = [](PyObject *self_base) {
+                auto &self = py::cast<OwnsPythonObjects &>(py::handle(self_base));
+                self.value = py::none();
+                return 0;
+            };
+        }));
+    cls.def(py::init<>());
+    cls.def_readwrite("value", &OwnsPythonObjects::value);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_setup.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_setup.py
new file mode 100644
index 000000000..19b44c9de
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_custom_type_setup.py
@@ -0,0 +1,48 @@
+import gc
+import weakref
+
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import custom_type_setup as m
+
+
+@pytest.fixture
+def gc_tester():
+    """Tests that an object is garbage collected.
+
+    Assumes that any unreferenced objects are fully collected after calling
+    `gc.collect()`.  That is true on CPython, but does not appear to reliably
+    hold on PyPy.
+    """
+
+    weak_refs = []
+
+    def add_ref(obj):
+        # PyPy does not support `gc.is_tracked`.
+        if hasattr(gc, "is_tracked"):
+            assert gc.is_tracked(obj)
+        weak_refs.append(weakref.ref(obj))
+
+    yield add_ref
+
+    gc.collect()
+    for ref in weak_refs:
+        assert ref() is None
+
+
+# PyPy does not seem to reliably garbage collect.
+@pytest.mark.skipif("env.PYPY")
+def test_self_cycle(gc_tester):
+    obj = m.OwnsPythonObjects()
+    obj.value = obj
+    gc_tester(obj)
+
+
+# PyPy does not seem to reliably garbage collect.
+@pytest.mark.skipif("env.PYPY")
+def test_indirect_cycle(gc_tester):
+    obj = m.OwnsPythonObjects()
+    obj_list = [obj]
+    obj.value = obj_list
+    gc_tester(obj)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_docstring_options.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_docstring_options.cpp
new file mode 100644
index 000000000..4d44f4e20
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_docstring_options.cpp
@@ -0,0 +1,88 @@
+/*
+    tests/test_docstring_options.cpp -- generation of docstrings and signatures
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+TEST_SUBMODULE(docstring_options, m) {
+    // test_docstring_options
+    {
+        py::options options;
+        options.disable_function_signatures();
+
+        m.def(
+            "test_function1", [](int, int) {}, py::arg("a"), py::arg("b"));
+        m.def(
+            "test_function2", [](int, int) {}, py::arg("a"), py::arg("b"), "A custom docstring");
+
+        m.def(
+            "test_overloaded1", [](int) {}, py::arg("i"), "Overload docstring");
+        m.def(
+            "test_overloaded1", [](double) {}, py::arg("d"));
+
+        m.def(
+            "test_overloaded2", [](int) {}, py::arg("i"), "overload docstring 1");
+        m.def(
+            "test_overloaded2", [](double) {}, py::arg("d"), "overload docstring 2");
+
+        m.def(
+            "test_overloaded3", [](int) {}, py::arg("i"));
+        m.def(
+            "test_overloaded3", [](double) {}, py::arg("d"), "Overload docstr");
+
+        options.enable_function_signatures();
+
+        m.def(
+            "test_function3", [](int, int) {}, py::arg("a"), py::arg("b"));
+        m.def(
+            "test_function4", [](int, int) {}, py::arg("a"), py::arg("b"), "A custom docstring");
+
+        options.disable_function_signatures().disable_user_defined_docstrings();
+
+        m.def(
+            "test_function5", [](int, int) {}, py::arg("a"), py::arg("b"), "A custom docstring");
+
+        {
+            py::options nested_options;
+            nested_options.enable_user_defined_docstrings();
+            m.def(
+                "test_function6",
+                [](int, int) {},
+                py::arg("a"),
+                py::arg("b"),
+                "A custom docstring");
+        }
+    }
+
+    m.def(
+        "test_function7", [](int, int) {}, py::arg("a"), py::arg("b"), "A custom docstring");
+
+    {
+        py::options options;
+        options.disable_user_defined_docstrings();
+        options.disable_function_signatures();
+
+        m.def("test_function8", []() {});
+    }
+
+    {
+        py::options options;
+        options.disable_user_defined_docstrings();
+
+        struct DocstringTestFoo {
+            int value;
+            void setValue(int v) { value = v; }
+            int getValue() const { return value; }
+        };
+        py::class_<DocstringTestFoo>(m, "DocstringTestFoo", "This is a class docstring")
+            .def_property("value_prop",
+                          &DocstringTestFoo::getValue,
+                          &DocstringTestFoo::setValue,
+                          "This is a property docstring");
+    }
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_docstring_options.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_docstring_options.py
new file mode 100644
index 000000000..fcd16b89f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_docstring_options.py
@@ -0,0 +1,41 @@
+from pybind11_tests import docstring_options as m
+
+
+def test_docstring_options():
+    # options.disable_function_signatures()
+    assert not m.test_function1.__doc__
+
+    assert m.test_function2.__doc__ == "A custom docstring"
+
+    # docstring specified on just the first overload definition:
+    assert m.test_overloaded1.__doc__ == "Overload docstring"
+
+    # docstring on both overloads:
+    assert m.test_overloaded2.__doc__ == "overload docstring 1\noverload docstring 2"
+
+    # docstring on only second overload:
+    assert m.test_overloaded3.__doc__ == "Overload docstr"
+
+    # options.enable_function_signatures()
+    assert m.test_function3.__doc__.startswith("test_function3(a: int, b: int) -> None")
+
+    assert m.test_function4.__doc__.startswith("test_function4(a: int, b: int) -> None")
+    assert m.test_function4.__doc__.endswith("A custom docstring\n")
+
+    # options.disable_function_signatures()
+    # options.disable_user_defined_docstrings()
+    assert not m.test_function5.__doc__
+
+    # nested options.enable_user_defined_docstrings()
+    assert m.test_function6.__doc__ == "A custom docstring"
+
+    # RAII destructor
+    assert m.test_function7.__doc__.startswith("test_function7(a: int, b: int) -> None")
+    assert m.test_function7.__doc__.endswith("A custom docstring\n")
+
+    # when all options are disabled, no docstring (instead of an empty one) should be generated
+    assert m.test_function8.__doc__ is None
+
+    # Suppression of user-defined docstrings for non-function objects
+    assert not m.DocstringTestFoo.__doc__
+    assert not m.DocstringTestFoo.value_prop.__doc__
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eigen.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eigen.cpp
new file mode 100644
index 000000000..591dacc62
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eigen.cpp
@@ -0,0 +1,401 @@
+/*
+    tests/eigen.cpp -- automatic conversion of Eigen types
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/eigen.h>
+#include <pybind11/stl.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#if defined(_MSC_VER)
+#    pragma warning(disable : 4996) // C4996: std::unary_negation is deprecated
+#endif
+
+#include <Eigen/Cholesky>
+
+using MatrixXdR = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+
+// Sets/resets a testing reference matrix to have values of 10*r + c, where r and c are the
+// (1-based) row/column number.
+template <typename M>
+void reset_ref(M &x) {
+    for (int i = 0; i < x.rows(); i++) {
+        for (int j = 0; j < x.cols(); j++) {
+            x(i, j) = 11 + 10 * i + j;
+        }
+    }
+}
+
+// Returns a static, column-major matrix
+Eigen::MatrixXd &get_cm() {
+    static Eigen::MatrixXd *x;
+    if (!x) {
+        x = new Eigen::MatrixXd(3, 3);
+        reset_ref(*x);
+    }
+    return *x;
+}
+// Likewise, but row-major
+MatrixXdR &get_rm() {
+    static MatrixXdR *x;
+    if (!x) {
+        x = new MatrixXdR(3, 3);
+        reset_ref(*x);
+    }
+    return *x;
+}
+// Resets the values of the static matrices returned by get_cm()/get_rm()
+void reset_refs() {
+    reset_ref(get_cm());
+    reset_ref(get_rm());
+}
+
+// Returns element 2,1 from a matrix (used to test copy/nocopy)
+double get_elem(const Eigen::Ref<const Eigen::MatrixXd> &m) { return m(2, 1); };
+
+// Returns a matrix with 10*r + 100*c added to each matrix element (to help test that the matrix
+// reference is referencing rows/columns correctly).
+template <typename MatrixArgType>
+Eigen::MatrixXd adjust_matrix(MatrixArgType m) {
+    Eigen::MatrixXd ret(m);
+    for (int c = 0; c < m.cols(); c++) {
+        for (int r = 0; r < m.rows(); r++) {
+            ret(r, c) += 10 * r + 100 * c; // NOLINT(clang-analyzer-core.uninitialized.Assign)
+        }
+    }
+    return ret;
+}
+
+struct CustomOperatorNew {
+    CustomOperatorNew() = default;
+
+    Eigen::Matrix4d a = Eigen::Matrix4d::Zero();
+    Eigen::Matrix4d b = Eigen::Matrix4d::Identity();
+
+    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
+};
+
+TEST_SUBMODULE(eigen, m) {
+    using FixedMatrixR = Eigen::Matrix<float, 5, 6, Eigen::RowMajor>;
+    using FixedMatrixC = Eigen::Matrix<float, 5, 6>;
+    using DenseMatrixR = Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+    using DenseMatrixC = Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic>;
+    using FourRowMatrixC = Eigen::Matrix<float, 4, Eigen::Dynamic>;
+    using FourColMatrixC = Eigen::Matrix<float, Eigen::Dynamic, 4>;
+    using FourRowMatrixR = Eigen::Matrix<float, 4, Eigen::Dynamic>;
+    using FourColMatrixR = Eigen::Matrix<float, Eigen::Dynamic, 4>;
+    using SparseMatrixR = Eigen::SparseMatrix<float, Eigen::RowMajor>;
+    using SparseMatrixC = Eigen::SparseMatrix<float>;
+
+    // various tests
+    m.def("double_col", [](const Eigen::VectorXf &x) -> Eigen::VectorXf { return 2.0f * x; });
+    m.def("double_row",
+          [](const Eigen::RowVectorXf &x) -> Eigen::RowVectorXf { return 2.0f * x; });
+    m.def("double_complex",
+          [](const Eigen::VectorXcf &x) -> Eigen::VectorXcf { return 2.0f * x; });
+    m.def("double_threec", [](py::EigenDRef<Eigen::Vector3f> x) { x *= 2; });
+    m.def("double_threer", [](py::EigenDRef<Eigen::RowVector3f> x) { x *= 2; });
+    m.def("double_mat_cm", [](const Eigen::MatrixXf &x) -> Eigen::MatrixXf { return 2.0f * x; });
+    m.def("double_mat_rm", [](const DenseMatrixR &x) -> DenseMatrixR { return 2.0f * x; });
+
+    // test_eigen_ref_to_python
+    // Different ways of passing via Eigen::Ref; the first and second are the Eigen-recommended
+    m.def("cholesky1",
+          [](const Eigen::Ref<MatrixXdR> &x) -> Eigen::MatrixXd { return x.llt().matrixL(); });
+    m.def("cholesky2", [](const Eigen::Ref<const MatrixXdR> &x) -> Eigen::MatrixXd {
+        return x.llt().matrixL();
+    });
+    m.def("cholesky3",
+          [](const Eigen::Ref<MatrixXdR> &x) -> Eigen::MatrixXd { return x.llt().matrixL(); });
+    m.def("cholesky4", [](const Eigen::Ref<const MatrixXdR> &x) -> Eigen::MatrixXd {
+        return x.llt().matrixL();
+    });
+
+    // test_eigen_ref_mutators
+    // Mutators: these add some value to the given element using Eigen, but Eigen should be mapping
+    // into the numpy array data and so the result should show up there.  There are three versions:
+    // one that works on a contiguous-row matrix (numpy's default), one for a contiguous-column
+    // matrix, and one for any matrix.
+    auto add_rm = [](Eigen::Ref<MatrixXdR> x, int r, int c, double v) { x(r, c) += v; };
+    auto add_cm = [](Eigen::Ref<Eigen::MatrixXd> x, int r, int c, double v) { x(r, c) += v; };
+
+    // Mutators (Eigen maps into numpy variables):
+    m.def("add_rm", add_rm); // Only takes row-contiguous
+    m.def("add_cm", add_cm); // Only takes column-contiguous
+    // Overloaded versions that will accept either row or column contiguous:
+    m.def("add1", add_rm);
+    m.def("add1", add_cm);
+    m.def("add2", add_cm);
+    m.def("add2", add_rm);
+    // This one accepts a matrix of any stride:
+    m.def("add_any",
+          [](py::EigenDRef<Eigen::MatrixXd> x, int r, int c, double v) { x(r, c) += v; });
+
+    // Return mutable references (numpy maps into eigen variables)
+    m.def("get_cm_ref", []() { return Eigen::Ref<Eigen::MatrixXd>(get_cm()); });
+    m.def("get_rm_ref", []() { return Eigen::Ref<MatrixXdR>(get_rm()); });
+    // The same references, but non-mutable (numpy maps into eigen variables, but is !writeable)
+    m.def("get_cm_const_ref", []() { return Eigen::Ref<const Eigen::MatrixXd>(get_cm()); });
+    m.def("get_rm_const_ref", []() { return Eigen::Ref<const MatrixXdR>(get_rm()); });
+
+    m.def("reset_refs", reset_refs); // Restores get_{cm,rm}_ref to original values
+
+    // Increments and returns ref to (same) matrix
+    m.def(
+        "incr_matrix",
+        [](Eigen::Ref<Eigen::MatrixXd> m, double v) {
+            m += Eigen::MatrixXd::Constant(m.rows(), m.cols(), v);
+            return m;
+        },
+        py::return_value_policy::reference);
+
+    // Same, but accepts a matrix of any strides
+    m.def(
+        "incr_matrix_any",
+        [](py::EigenDRef<Eigen::MatrixXd> m, double v) {
+            m += Eigen::MatrixXd::Constant(m.rows(), m.cols(), v);
+            return m;
+        },
+        py::return_value_policy::reference);
+
+    // Returns an eigen slice of even rows
+    m.def(
+        "even_rows",
+        [](py::EigenDRef<Eigen::MatrixXd> m) {
+            return py::EigenDMap<Eigen::MatrixXd>(
+                m.data(),
+                (m.rows() + 1) / 2,
+                m.cols(),
+                py::EigenDStride(m.outerStride(), 2 * m.innerStride()));
+        },
+        py::return_value_policy::reference);
+
+    // Returns an eigen slice of even columns
+    m.def(
+        "even_cols",
+        [](py::EigenDRef<Eigen::MatrixXd> m) {
+            return py::EigenDMap<Eigen::MatrixXd>(
+                m.data(),
+                m.rows(),
+                (m.cols() + 1) / 2,
+                py::EigenDStride(2 * m.outerStride(), m.innerStride()));
+        },
+        py::return_value_policy::reference);
+
+    // Returns diagonals: a vector-like object with an inner stride != 1
+    m.def("diagonal", [](const Eigen::Ref<const Eigen::MatrixXd> &x) { return x.diagonal(); });
+    m.def("diagonal_1",
+          [](const Eigen::Ref<const Eigen::MatrixXd> &x) { return x.diagonal<1>(); });
+    m.def("diagonal_n",
+          [](const Eigen::Ref<const Eigen::MatrixXd> &x, int index) { return x.diagonal(index); });
+
+    // Return a block of a matrix (gives non-standard strides)
+    m.def("block",
+          [](const Eigen::Ref<const Eigen::MatrixXd> &x,
+             int start_row,
+             int start_col,
+             int block_rows,
+             int block_cols) { return x.block(start_row, start_col, block_rows, block_cols); });
+
+    // test_eigen_return_references, test_eigen_keepalive
+    // return value referencing/copying tests:
+    class ReturnTester {
+        Eigen::MatrixXd mat = create();
+
+    public:
+        ReturnTester() { print_created(this); }
+        ~ReturnTester() { print_destroyed(this); }
+        static Eigen::MatrixXd create() { return Eigen::MatrixXd::Ones(10, 10); }
+        // NOLINTNEXTLINE(readability-const-return-type)
+        static const Eigen::MatrixXd createConst() { return Eigen::MatrixXd::Ones(10, 10); }
+        Eigen::MatrixXd &get() { return mat; }
+        Eigen::MatrixXd *getPtr() { return &mat; }
+        const Eigen::MatrixXd &view() { return mat; }
+        const Eigen::MatrixXd *viewPtr() { return &mat; }
+        Eigen::Ref<Eigen::MatrixXd> ref() { return mat; }
+        Eigen::Ref<const Eigen::MatrixXd> refConst() { return mat; }
+        Eigen::Block<Eigen::MatrixXd> block(int r, int c, int nrow, int ncol) {
+            return mat.block(r, c, nrow, ncol);
+        }
+        Eigen::Block<const Eigen::MatrixXd> blockConst(int r, int c, int nrow, int ncol) const {
+            return mat.block(r, c, nrow, ncol);
+        }
+        py::EigenDMap<Eigen::Matrix2d> corners() {
+            return py::EigenDMap<Eigen::Matrix2d>(
+                mat.data(),
+                py::EigenDStride(mat.outerStride() * (mat.outerSize() - 1),
+                                 mat.innerStride() * (mat.innerSize() - 1)));
+        }
+        py::EigenDMap<const Eigen::Matrix2d> cornersConst() const {
+            return py::EigenDMap<const Eigen::Matrix2d>(
+                mat.data(),
+                py::EigenDStride(mat.outerStride() * (mat.outerSize() - 1),
+                                 mat.innerStride() * (mat.innerSize() - 1)));
+        }
+    };
+    using rvp = py::return_value_policy;
+    py::class_<ReturnTester>(m, "ReturnTester")
+        .def(py::init<>())
+        .def_static("create", &ReturnTester::create)
+        .def_static("create_const", &ReturnTester::createConst)
+        .def("get", &ReturnTester::get, rvp::reference_internal)
+        .def("get_ptr", &ReturnTester::getPtr, rvp::reference_internal)
+        .def("view", &ReturnTester::view, rvp::reference_internal)
+        .def("view_ptr", &ReturnTester::view, rvp::reference_internal)
+        .def("copy_get", &ReturnTester::get)       // Default rvp: copy
+        .def("copy_view", &ReturnTester::view)     //         "
+        .def("ref", &ReturnTester::ref)            // Default for Ref is to reference
+        .def("ref_const", &ReturnTester::refConst) // Likewise, but const
+        .def("ref_safe", &ReturnTester::ref, rvp::reference_internal)
+        .def("ref_const_safe", &ReturnTester::refConst, rvp::reference_internal)
+        .def("copy_ref", &ReturnTester::ref, rvp::copy)
+        .def("copy_ref_const", &ReturnTester::refConst, rvp::copy)
+        .def("block", &ReturnTester::block)
+        .def("block_safe", &ReturnTester::block, rvp::reference_internal)
+        .def("block_const", &ReturnTester::blockConst, rvp::reference_internal)
+        .def("copy_block", &ReturnTester::block, rvp::copy)
+        .def("corners", &ReturnTester::corners, rvp::reference_internal)
+        .def("corners_const", &ReturnTester::cornersConst, rvp::reference_internal);
+
+    // test_special_matrix_objects
+    // Returns a DiagonalMatrix with diagonal (1,2,3,...)
+    m.def("incr_diag", [](int k) {
+        Eigen::DiagonalMatrix<int, Eigen::Dynamic> m(k);
+        for (int i = 0; i < k; i++) {
+            m.diagonal()[i] = i + 1;
+        }
+        return m;
+    });
+
+    // Returns a SelfAdjointView referencing the lower triangle of m
+    m.def("symmetric_lower",
+          [](const Eigen::MatrixXi &m) { return m.selfadjointView<Eigen::Lower>(); });
+    // Returns a SelfAdjointView referencing the lower triangle of m
+    m.def("symmetric_upper",
+          [](const Eigen::MatrixXi &m) { return m.selfadjointView<Eigen::Upper>(); });
+
+    // Test matrix for various functions below.
+    Eigen::MatrixXf mat(5, 6);
+    mat << 0, 3, 0, 0, 0, 11, 22, 0, 0, 0, 17, 11, 7, 5, 0, 1, 0, 11, 0, 0, 0, 0, 0, 11, 0, 0, 14,
+        0, 8, 11;
+
+    // test_fixed, and various other tests
+    m.def("fixed_r", [mat]() -> FixedMatrixR { return FixedMatrixR(mat); });
+    // Our Eigen does a hack which respects constness through the numpy writeable flag.
+    // Therefore, the const return actually affects this type despite being an rvalue.
+    // NOLINTNEXTLINE(readability-const-return-type)
+    m.def("fixed_r_const", [mat]() -> const FixedMatrixR { return FixedMatrixR(mat); });
+    m.def("fixed_c", [mat]() -> FixedMatrixC { return FixedMatrixC(mat); });
+    m.def("fixed_copy_r", [](const FixedMatrixR &m) -> FixedMatrixR { return m; });
+    m.def("fixed_copy_c", [](const FixedMatrixC &m) -> FixedMatrixC { return m; });
+    // test_mutator_descriptors
+    m.def("fixed_mutator_r", [](const Eigen::Ref<FixedMatrixR> &) {});
+    m.def("fixed_mutator_c", [](const Eigen::Ref<FixedMatrixC> &) {});
+    m.def("fixed_mutator_a", [](const py::EigenDRef<FixedMatrixC> &) {});
+    // test_dense
+    m.def("dense_r", [mat]() -> DenseMatrixR { return DenseMatrixR(mat); });
+    m.def("dense_c", [mat]() -> DenseMatrixC { return DenseMatrixC(mat); });
+    m.def("dense_copy_r", [](const DenseMatrixR &m) -> DenseMatrixR { return m; });
+    m.def("dense_copy_c", [](const DenseMatrixC &m) -> DenseMatrixC { return m; });
+    // test_sparse, test_sparse_signature
+    m.def("sparse_r", [mat]() -> SparseMatrixR {
+        // NOLINTNEXTLINE(clang-analyzer-core.uninitialized.UndefReturn)
+        return Eigen::SparseView<Eigen::MatrixXf>(mat);
+    });
+    m.def("sparse_c",
+          [mat]() -> SparseMatrixC { return Eigen::SparseView<Eigen::MatrixXf>(mat); });
+    m.def("sparse_copy_r", [](const SparseMatrixR &m) -> SparseMatrixR { return m; });
+    m.def("sparse_copy_c", [](const SparseMatrixC &m) -> SparseMatrixC { return m; });
+    // test_partially_fixed
+    m.def("partial_copy_four_rm_r", [](const FourRowMatrixR &m) -> FourRowMatrixR { return m; });
+    m.def("partial_copy_four_rm_c", [](const FourColMatrixR &m) -> FourColMatrixR { return m; });
+    m.def("partial_copy_four_cm_r", [](const FourRowMatrixC &m) -> FourRowMatrixC { return m; });
+    m.def("partial_copy_four_cm_c", [](const FourColMatrixC &m) -> FourColMatrixC { return m; });
+
+    // test_cpp_casting
+    // Test that we can cast a numpy object to a Eigen::MatrixXd explicitly
+    m.def("cpp_copy", [](py::handle m) { return m.cast<Eigen::MatrixXd>()(1, 0); });
+    m.def("cpp_ref_c", [](py::handle m) { return m.cast<Eigen::Ref<Eigen::MatrixXd>>()(1, 0); });
+    m.def("cpp_ref_r", [](py::handle m) { return m.cast<Eigen::Ref<MatrixXdR>>()(1, 0); });
+    m.def("cpp_ref_any",
+          [](py::handle m) { return m.cast<py::EigenDRef<Eigen::MatrixXd>>()(1, 0); });
+
+    // [workaround(intel)] ICC 20/21 breaks with py::arg().stuff, using py::arg{}.stuff works.
+
+    // test_nocopy_wrapper
+    // Test that we can prevent copying into an argument that would normally copy: First a version
+    // that would allow copying (if types or strides don't match) for comparison:
+    m.def("get_elem", &get_elem);
+    // Now this alternative that calls the tells pybind to fail rather than copy:
+    m.def(
+        "get_elem_nocopy",
+        [](const Eigen::Ref<const Eigen::MatrixXd> &m) -> double { return get_elem(m); },
+        py::arg{}.noconvert());
+    // Also test a row-major-only no-copy const ref:
+    m.def(
+        "get_elem_rm_nocopy",
+        [](Eigen::Ref<const Eigen::Matrix<long, -1, -1, Eigen::RowMajor>> &m) -> long {
+            return m(2, 1);
+        },
+        py::arg{}.noconvert());
+
+    // test_issue738, test_zero_length
+    // Issue #738: 1×N or N×1 2D matrices were neither accepted nor properly copied with an
+    // incompatible stride value on the length-1 dimension--but that should be allowed (without
+    // requiring a copy!) because the stride value can be safely ignored on a size-1 dimension.
+    // Similarly, 0×N or N×0 matrices were not accepted--again, these should be allowed since
+    // they contain no data. This particularly affects numpy ≥ 1.23, which sets the strides to
+    // 0 if any dimension size is 0.
+    m.def("iss738_f1",
+          &adjust_matrix<const Eigen::Ref<const Eigen::MatrixXd> &>,
+          py::arg{}.noconvert());
+    m.def("iss738_f2",
+          &adjust_matrix<const Eigen::Ref<const Eigen::Matrix<double, -1, -1, Eigen::RowMajor>> &>,
+          py::arg{}.noconvert());
+
+    // test_issue1105
+    // Issue #1105: when converting from a numpy two-dimensional (Nx1) or (1xN) value into a dense
+    // eigen Vector or RowVector, the argument would fail to load because the numpy copy would
+    // fail: numpy won't broadcast a Nx1 into a 1-dimensional vector.
+    m.def("iss1105_col", [](const Eigen::VectorXd &) { return true; });
+    m.def("iss1105_row", [](const Eigen::RowVectorXd &) { return true; });
+
+    // test_named_arguments
+    // Make sure named arguments are working properly:
+    m.def(
+        "matrix_multiply",
+        [](const py::EigenDRef<const Eigen::MatrixXd> &A,
+           const py::EigenDRef<const Eigen::MatrixXd> &B) -> Eigen::MatrixXd {
+            if (A.cols() != B.rows()) {
+                throw std::domain_error("Nonconformable matrices!");
+            }
+            return A * B;
+        },
+        py::arg("A"),
+        py::arg("B"));
+
+    // test_custom_operator_new
+    py::class_<CustomOperatorNew>(m, "CustomOperatorNew")
+        .def(py::init<>())
+        .def_readonly("a", &CustomOperatorNew::a)
+        .def_readonly("b", &CustomOperatorNew::b);
+
+    // test_eigen_ref_life_support
+    // In case of a failure (the caster's temp array does not live long enough), creating
+    // a new array (np.ones(10)) increases the chances that the temp array will be garbage
+    // collected and/or that its memory will be overridden with different values.
+    m.def("get_elem_direct", [](const Eigen::Ref<const Eigen::VectorXd> &v) {
+        py::module_::import("numpy").attr("ones")(10);
+        return v(5);
+    });
+    m.def("get_elem_indirect", [](std::vector<Eigen::Ref<const Eigen::VectorXd>> v) {
+        py::module_::import("numpy").attr("ones")(10);
+        return v[0](5);
+    });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eigen.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eigen.py
new file mode 100644
index 000000000..a1c114aed
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eigen.py
@@ -0,0 +1,775 @@
+import pytest
+
+from pybind11_tests import ConstructorStats
+
+np = pytest.importorskip("numpy")
+m = pytest.importorskip("pybind11_tests.eigen")
+
+
+ref = np.array(
+    [
+        [0.0, 3, 0, 0, 0, 11],
+        [22, 0, 0, 0, 17, 11],
+        [7, 5, 0, 1, 0, 11],
+        [0, 0, 0, 0, 0, 11],
+        [0, 0, 14, 0, 8, 11],
+    ]
+)
+
+
+def assert_equal_ref(mat):
+    np.testing.assert_array_equal(mat, ref)
+
+
+def assert_sparse_equal_ref(sparse_mat):
+    assert_equal_ref(sparse_mat.toarray())
+
+
+def test_fixed():
+    assert_equal_ref(m.fixed_c())
+    assert_equal_ref(m.fixed_r())
+    assert_equal_ref(m.fixed_copy_r(m.fixed_r()))
+    assert_equal_ref(m.fixed_copy_c(m.fixed_c()))
+    assert_equal_ref(m.fixed_copy_r(m.fixed_c()))
+    assert_equal_ref(m.fixed_copy_c(m.fixed_r()))
+
+
+def test_dense():
+    assert_equal_ref(m.dense_r())
+    assert_equal_ref(m.dense_c())
+    assert_equal_ref(m.dense_copy_r(m.dense_r()))
+    assert_equal_ref(m.dense_copy_c(m.dense_c()))
+    assert_equal_ref(m.dense_copy_r(m.dense_c()))
+    assert_equal_ref(m.dense_copy_c(m.dense_r()))
+
+
+def test_partially_fixed():
+    ref2 = np.array([[0.0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]])
+    np.testing.assert_array_equal(m.partial_copy_four_rm_r(ref2), ref2)
+    np.testing.assert_array_equal(m.partial_copy_four_rm_c(ref2), ref2)
+    np.testing.assert_array_equal(m.partial_copy_four_rm_r(ref2[:, 1]), ref2[:, [1]])
+    np.testing.assert_array_equal(m.partial_copy_four_rm_c(ref2[0, :]), ref2[[0], :])
+    np.testing.assert_array_equal(
+        m.partial_copy_four_rm_r(ref2[:, (0, 2)]), ref2[:, (0, 2)]
+    )
+    np.testing.assert_array_equal(
+        m.partial_copy_four_rm_c(ref2[(3, 1, 2), :]), ref2[(3, 1, 2), :]
+    )
+
+    np.testing.assert_array_equal(m.partial_copy_four_cm_r(ref2), ref2)
+    np.testing.assert_array_equal(m.partial_copy_four_cm_c(ref2), ref2)
+    np.testing.assert_array_equal(m.partial_copy_four_cm_r(ref2[:, 1]), ref2[:, [1]])
+    np.testing.assert_array_equal(m.partial_copy_four_cm_c(ref2[0, :]), ref2[[0], :])
+    np.testing.assert_array_equal(
+        m.partial_copy_four_cm_r(ref2[:, (0, 2)]), ref2[:, (0, 2)]
+    )
+    np.testing.assert_array_equal(
+        m.partial_copy_four_cm_c(ref2[(3, 1, 2), :]), ref2[(3, 1, 2), :]
+    )
+
+    # TypeError should be raise for a shape mismatch
+    functions = [
+        m.partial_copy_four_rm_r,
+        m.partial_copy_four_rm_c,
+        m.partial_copy_four_cm_r,
+        m.partial_copy_four_cm_c,
+    ]
+    matrix_with_wrong_shape = [[1, 2], [3, 4]]
+    for f in functions:
+        with pytest.raises(TypeError) as excinfo:
+            f(matrix_with_wrong_shape)
+        assert "incompatible function arguments" in str(excinfo.value)
+
+
+def test_mutator_descriptors():
+    zr = np.arange(30, dtype="float32").reshape(5, 6)  # row-major
+    zc = zr.reshape(6, 5).transpose()  # column-major
+
+    m.fixed_mutator_r(zr)
+    m.fixed_mutator_c(zc)
+    m.fixed_mutator_a(zr)
+    m.fixed_mutator_a(zc)
+    with pytest.raises(TypeError) as excinfo:
+        m.fixed_mutator_r(zc)
+    assert (
+        "(arg0: numpy.ndarray[numpy.float32[5, 6],"
+        " flags.writeable, flags.c_contiguous]) -> None" in str(excinfo.value)
+    )
+    with pytest.raises(TypeError) as excinfo:
+        m.fixed_mutator_c(zr)
+    assert (
+        "(arg0: numpy.ndarray[numpy.float32[5, 6],"
+        " flags.writeable, flags.f_contiguous]) -> None" in str(excinfo.value)
+    )
+    with pytest.raises(TypeError) as excinfo:
+        m.fixed_mutator_a(np.array([[1, 2], [3, 4]], dtype="float32"))
+    assert "(arg0: numpy.ndarray[numpy.float32[5, 6], flags.writeable]) -> None" in str(
+        excinfo.value
+    )
+    zr.flags.writeable = False
+    with pytest.raises(TypeError):
+        m.fixed_mutator_r(zr)
+    with pytest.raises(TypeError):
+        m.fixed_mutator_a(zr)
+
+
+def test_cpp_casting():
+    assert m.cpp_copy(m.fixed_r()) == 22.0
+    assert m.cpp_copy(m.fixed_c()) == 22.0
+    z = np.array([[5.0, 6], [7, 8]])
+    assert m.cpp_copy(z) == 7.0
+    assert m.cpp_copy(m.get_cm_ref()) == 21.0
+    assert m.cpp_copy(m.get_rm_ref()) == 21.0
+    assert m.cpp_ref_c(m.get_cm_ref()) == 21.0
+    assert m.cpp_ref_r(m.get_rm_ref()) == 21.0
+    with pytest.raises(RuntimeError) as excinfo:
+        # Can't reference m.fixed_c: it contains floats, m.cpp_ref_any wants doubles
+        m.cpp_ref_any(m.fixed_c())
+    assert "Unable to cast Python instance" in str(excinfo.value)
+    with pytest.raises(RuntimeError) as excinfo:
+        # Can't reference m.fixed_r: it contains floats, m.cpp_ref_any wants doubles
+        m.cpp_ref_any(m.fixed_r())
+    assert "Unable to cast Python instance" in str(excinfo.value)
+    assert m.cpp_ref_any(m.ReturnTester.create()) == 1.0
+
+    assert m.cpp_ref_any(m.get_cm_ref()) == 21.0
+    assert m.cpp_ref_any(m.get_cm_ref()) == 21.0
+
+
+def test_pass_readonly_array():
+    z = np.full((5, 6), 42.0)
+    z.flags.writeable = False
+    np.testing.assert_array_equal(z, m.fixed_copy_r(z))
+    np.testing.assert_array_equal(m.fixed_r_const(), m.fixed_r())
+    assert not m.fixed_r_const().flags.writeable
+    np.testing.assert_array_equal(m.fixed_copy_r(m.fixed_r_const()), m.fixed_r_const())
+
+
+def test_nonunit_stride_from_python():
+    counting_mat = np.arange(9.0, dtype=np.float32).reshape((3, 3))
+    second_row = counting_mat[1, :]
+    second_col = counting_mat[:, 1]
+    np.testing.assert_array_equal(m.double_row(second_row), 2.0 * second_row)
+    np.testing.assert_array_equal(m.double_col(second_row), 2.0 * second_row)
+    np.testing.assert_array_equal(m.double_complex(second_row), 2.0 * second_row)
+    np.testing.assert_array_equal(m.double_row(second_col), 2.0 * second_col)
+    np.testing.assert_array_equal(m.double_col(second_col), 2.0 * second_col)
+    np.testing.assert_array_equal(m.double_complex(second_col), 2.0 * second_col)
+
+    counting_3d = np.arange(27.0, dtype=np.float32).reshape((3, 3, 3))
+    slices = [counting_3d[0, :, :], counting_3d[:, 0, :], counting_3d[:, :, 0]]
+    for ref_mat in slices:
+        np.testing.assert_array_equal(m.double_mat_cm(ref_mat), 2.0 * ref_mat)
+        np.testing.assert_array_equal(m.double_mat_rm(ref_mat), 2.0 * ref_mat)
+
+    # Mutator:
+    m.double_threer(second_row)
+    m.double_threec(second_col)
+    np.testing.assert_array_equal(counting_mat, [[0.0, 2, 2], [6, 16, 10], [6, 14, 8]])
+
+
+def test_negative_stride_from_python(msg):
+    """Eigen doesn't support (as of yet) negative strides. When a function takes an Eigen matrix by
+    copy or const reference, we can pass a numpy array that has negative strides.  Otherwise, an
+    exception will be thrown as Eigen will not be able to map the numpy array."""
+
+    counting_mat = np.arange(9.0, dtype=np.float32).reshape((3, 3))
+    counting_mat = counting_mat[::-1, ::-1]
+    second_row = counting_mat[1, :]
+    second_col = counting_mat[:, 1]
+    np.testing.assert_array_equal(m.double_row(second_row), 2.0 * second_row)
+    np.testing.assert_array_equal(m.double_col(second_row), 2.0 * second_row)
+    np.testing.assert_array_equal(m.double_complex(second_row), 2.0 * second_row)
+    np.testing.assert_array_equal(m.double_row(second_col), 2.0 * second_col)
+    np.testing.assert_array_equal(m.double_col(second_col), 2.0 * second_col)
+    np.testing.assert_array_equal(m.double_complex(second_col), 2.0 * second_col)
+
+    counting_3d = np.arange(27.0, dtype=np.float32).reshape((3, 3, 3))
+    counting_3d = counting_3d[::-1, ::-1, ::-1]
+    slices = [counting_3d[0, :, :], counting_3d[:, 0, :], counting_3d[:, :, 0]]
+    for ref_mat in slices:
+        np.testing.assert_array_equal(m.double_mat_cm(ref_mat), 2.0 * ref_mat)
+        np.testing.assert_array_equal(m.double_mat_rm(ref_mat), 2.0 * ref_mat)
+
+    # Mutator:
+    with pytest.raises(TypeError) as excinfo:
+        m.double_threer(second_row)
+    assert (
+        msg(excinfo.value)
+        == """
+        double_threer(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: numpy.ndarray[numpy.float32[1, 3], flags.writeable]) -> None
+
+        Invoked with: """
+        + repr(np.array([5.0, 4.0, 3.0], dtype="float32"))
+    )
+
+    with pytest.raises(TypeError) as excinfo:
+        m.double_threec(second_col)
+    assert (
+        msg(excinfo.value)
+        == """
+        double_threec(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: numpy.ndarray[numpy.float32[3, 1], flags.writeable]) -> None
+
+        Invoked with: """
+        + repr(np.array([7.0, 4.0, 1.0], dtype="float32"))
+    )
+
+
+def test_nonunit_stride_to_python():
+    assert np.all(m.diagonal(ref) == ref.diagonal())
+    assert np.all(m.diagonal_1(ref) == ref.diagonal(1))
+    for i in range(-5, 7):
+        assert np.all(m.diagonal_n(ref, i) == ref.diagonal(i)), f"m.diagonal_n({i})"
+
+    assert np.all(m.block(ref, 2, 1, 3, 3) == ref[2:5, 1:4])
+    assert np.all(m.block(ref, 1, 4, 4, 2) == ref[1:, 4:])
+    assert np.all(m.block(ref, 1, 4, 3, 2) == ref[1:4, 4:])
+
+
+def test_eigen_ref_to_python():
+    chols = [m.cholesky1, m.cholesky2, m.cholesky3, m.cholesky4]
+    for i, chol in enumerate(chols, start=1):
+        mymat = chol(np.array([[1.0, 2, 4], [2, 13, 23], [4, 23, 77]]))
+        assert np.all(
+            mymat == np.array([[1, 0, 0], [2, 3, 0], [4, 5, 6]])
+        ), f"cholesky{i}"
+
+
+def assign_both(a1, a2, r, c, v):
+    a1[r, c] = v
+    a2[r, c] = v
+
+
+def array_copy_but_one(a, r, c, v):
+    z = np.array(a, copy=True)
+    z[r, c] = v
+    return z
+
+
+def test_eigen_return_references():
+    """Tests various ways of returning references and non-referencing copies"""
+
+    master = np.ones((10, 10))
+    a = m.ReturnTester()
+    a_get1 = a.get()
+    assert not a_get1.flags.owndata and a_get1.flags.writeable
+    assign_both(a_get1, master, 3, 3, 5)
+    a_get2 = a.get_ptr()
+    assert not a_get2.flags.owndata and a_get2.flags.writeable
+    assign_both(a_get1, master, 2, 3, 6)
+
+    a_view1 = a.view()
+    assert not a_view1.flags.owndata and not a_view1.flags.writeable
+    with pytest.raises(ValueError):
+        a_view1[2, 3] = 4
+    a_view2 = a.view_ptr()
+    assert not a_view2.flags.owndata and not a_view2.flags.writeable
+    with pytest.raises(ValueError):
+        a_view2[2, 3] = 4
+
+    a_copy1 = a.copy_get()
+    assert a_copy1.flags.owndata and a_copy1.flags.writeable
+    np.testing.assert_array_equal(a_copy1, master)
+    a_copy1[7, 7] = -44  # Shouldn't affect anything else
+    c1want = array_copy_but_one(master, 7, 7, -44)
+    a_copy2 = a.copy_view()
+    assert a_copy2.flags.owndata and a_copy2.flags.writeable
+    np.testing.assert_array_equal(a_copy2, master)
+    a_copy2[4, 4] = -22  # Shouldn't affect anything else
+    c2want = array_copy_but_one(master, 4, 4, -22)
+
+    a_ref1 = a.ref()
+    assert not a_ref1.flags.owndata and a_ref1.flags.writeable
+    assign_both(a_ref1, master, 1, 1, 15)
+    a_ref2 = a.ref_const()
+    assert not a_ref2.flags.owndata and not a_ref2.flags.writeable
+    with pytest.raises(ValueError):
+        a_ref2[5, 5] = 33
+    a_ref3 = a.ref_safe()
+    assert not a_ref3.flags.owndata and a_ref3.flags.writeable
+    assign_both(a_ref3, master, 0, 7, 99)
+    a_ref4 = a.ref_const_safe()
+    assert not a_ref4.flags.owndata and not a_ref4.flags.writeable
+    with pytest.raises(ValueError):
+        a_ref4[7, 0] = 987654321
+
+    a_copy3 = a.copy_ref()
+    assert a_copy3.flags.owndata and a_copy3.flags.writeable
+    np.testing.assert_array_equal(a_copy3, master)
+    a_copy3[8, 1] = 11
+    c3want = array_copy_but_one(master, 8, 1, 11)
+    a_copy4 = a.copy_ref_const()
+    assert a_copy4.flags.owndata and a_copy4.flags.writeable
+    np.testing.assert_array_equal(a_copy4, master)
+    a_copy4[8, 4] = 88
+    c4want = array_copy_but_one(master, 8, 4, 88)
+
+    a_block1 = a.block(3, 3, 2, 2)
+    assert not a_block1.flags.owndata and a_block1.flags.writeable
+    a_block1[0, 0] = 55
+    master[3, 3] = 55
+    a_block2 = a.block_safe(2, 2, 3, 2)
+    assert not a_block2.flags.owndata and a_block2.flags.writeable
+    a_block2[2, 1] = -123
+    master[4, 3] = -123
+    a_block3 = a.block_const(6, 7, 4, 3)
+    assert not a_block3.flags.owndata and not a_block3.flags.writeable
+    with pytest.raises(ValueError):
+        a_block3[2, 2] = -44444
+
+    a_copy5 = a.copy_block(2, 2, 2, 3)
+    assert a_copy5.flags.owndata and a_copy5.flags.writeable
+    np.testing.assert_array_equal(a_copy5, master[2:4, 2:5])
+    a_copy5[1, 1] = 777
+    c5want = array_copy_but_one(master[2:4, 2:5], 1, 1, 777)
+
+    a_corn1 = a.corners()
+    assert not a_corn1.flags.owndata and a_corn1.flags.writeable
+    a_corn1 *= 50
+    a_corn1[1, 1] = 999
+    master[0, 0] = 50
+    master[0, 9] = 50
+    master[9, 0] = 50
+    master[9, 9] = 999
+    a_corn2 = a.corners_const()
+    assert not a_corn2.flags.owndata and not a_corn2.flags.writeable
+    with pytest.raises(ValueError):
+        a_corn2[1, 0] = 51
+
+    # All of the changes made all the way along should be visible everywhere
+    # now (except for the copies, of course)
+    np.testing.assert_array_equal(a_get1, master)
+    np.testing.assert_array_equal(a_get2, master)
+    np.testing.assert_array_equal(a_view1, master)
+    np.testing.assert_array_equal(a_view2, master)
+    np.testing.assert_array_equal(a_ref1, master)
+    np.testing.assert_array_equal(a_ref2, master)
+    np.testing.assert_array_equal(a_ref3, master)
+    np.testing.assert_array_equal(a_ref4, master)
+    np.testing.assert_array_equal(a_block1, master[3:5, 3:5])
+    np.testing.assert_array_equal(a_block2, master[2:5, 2:4])
+    np.testing.assert_array_equal(a_block3, master[6:10, 7:10])
+    np.testing.assert_array_equal(
+        a_corn1, master[0 :: master.shape[0] - 1, 0 :: master.shape[1] - 1]
+    )
+    np.testing.assert_array_equal(
+        a_corn2, master[0 :: master.shape[0] - 1, 0 :: master.shape[1] - 1]
+    )
+
+    np.testing.assert_array_equal(a_copy1, c1want)
+    np.testing.assert_array_equal(a_copy2, c2want)
+    np.testing.assert_array_equal(a_copy3, c3want)
+    np.testing.assert_array_equal(a_copy4, c4want)
+    np.testing.assert_array_equal(a_copy5, c5want)
+
+
+def assert_keeps_alive(cl, method, *args):
+    cstats = ConstructorStats.get(cl)
+    start_with = cstats.alive()
+    a = cl()
+    assert cstats.alive() == start_with + 1
+    z = method(a, *args)
+    assert cstats.alive() == start_with + 1
+    del a
+    # Here's the keep alive in action:
+    assert cstats.alive() == start_with + 1
+    del z
+    # Keep alive should have expired:
+    assert cstats.alive() == start_with
+
+
+def test_eigen_keepalive():
+    a = m.ReturnTester()
+    cstats = ConstructorStats.get(m.ReturnTester)
+    assert cstats.alive() == 1
+    unsafe = [a.ref(), a.ref_const(), a.block(1, 2, 3, 4)]
+    copies = [
+        a.copy_get(),
+        a.copy_view(),
+        a.copy_ref(),
+        a.copy_ref_const(),
+        a.copy_block(4, 3, 2, 1),
+    ]
+    del a
+    assert cstats.alive() == 0
+    del unsafe
+    del copies
+
+    for meth in [
+        m.ReturnTester.get,
+        m.ReturnTester.get_ptr,
+        m.ReturnTester.view,
+        m.ReturnTester.view_ptr,
+        m.ReturnTester.ref_safe,
+        m.ReturnTester.ref_const_safe,
+        m.ReturnTester.corners,
+        m.ReturnTester.corners_const,
+    ]:
+        assert_keeps_alive(m.ReturnTester, meth)
+
+    for meth in [m.ReturnTester.block_safe, m.ReturnTester.block_const]:
+        assert_keeps_alive(m.ReturnTester, meth, 4, 3, 2, 1)
+
+
+def test_eigen_ref_mutators():
+    """Tests Eigen's ability to mutate numpy values"""
+
+    orig = np.array([[1.0, 2, 3], [4, 5, 6], [7, 8, 9]])
+    zr = np.array(orig)
+    zc = np.array(orig, order="F")
+    m.add_rm(zr, 1, 0, 100)
+    assert np.all(zr == np.array([[1.0, 2, 3], [104, 5, 6], [7, 8, 9]]))
+    m.add_cm(zc, 1, 0, 200)
+    assert np.all(zc == np.array([[1.0, 2, 3], [204, 5, 6], [7, 8, 9]]))
+
+    m.add_any(zr, 1, 0, 20)
+    assert np.all(zr == np.array([[1.0, 2, 3], [124, 5, 6], [7, 8, 9]]))
+    m.add_any(zc, 1, 0, 10)
+    assert np.all(zc == np.array([[1.0, 2, 3], [214, 5, 6], [7, 8, 9]]))
+
+    # Can't reference a col-major array with a row-major Ref, and vice versa:
+    with pytest.raises(TypeError):
+        m.add_rm(zc, 1, 0, 1)
+    with pytest.raises(TypeError):
+        m.add_cm(zr, 1, 0, 1)
+
+    # Overloads:
+    m.add1(zr, 1, 0, -100)
+    m.add2(zr, 1, 0, -20)
+    assert np.all(zr == orig)
+    m.add1(zc, 1, 0, -200)
+    m.add2(zc, 1, 0, -10)
+    assert np.all(zc == orig)
+
+    # a non-contiguous slice (this won't work on either the row- or
+    # column-contiguous refs, but should work for the any)
+    cornersr = zr[0::2, 0::2]
+    cornersc = zc[0::2, 0::2]
+
+    assert np.all(cornersr == np.array([[1.0, 3], [7, 9]]))
+    assert np.all(cornersc == np.array([[1.0, 3], [7, 9]]))
+
+    with pytest.raises(TypeError):
+        m.add_rm(cornersr, 0, 1, 25)
+    with pytest.raises(TypeError):
+        m.add_cm(cornersr, 0, 1, 25)
+    with pytest.raises(TypeError):
+        m.add_rm(cornersc, 0, 1, 25)
+    with pytest.raises(TypeError):
+        m.add_cm(cornersc, 0, 1, 25)
+    m.add_any(cornersr, 0, 1, 25)
+    m.add_any(cornersc, 0, 1, 44)
+    assert np.all(zr == np.array([[1.0, 2, 28], [4, 5, 6], [7, 8, 9]]))
+    assert np.all(zc == np.array([[1.0, 2, 47], [4, 5, 6], [7, 8, 9]]))
+
+    # You shouldn't be allowed to pass a non-writeable array to a mutating Eigen method:
+    zro = zr[0:4, 0:4]
+    zro.flags.writeable = False
+    with pytest.raises(TypeError):
+        m.add_rm(zro, 0, 0, 0)
+    with pytest.raises(TypeError):
+        m.add_any(zro, 0, 0, 0)
+    with pytest.raises(TypeError):
+        m.add1(zro, 0, 0, 0)
+    with pytest.raises(TypeError):
+        m.add2(zro, 0, 0, 0)
+
+    # integer array shouldn't be passable to a double-matrix-accepting mutating func:
+    zi = np.array([[1, 2], [3, 4]])
+    with pytest.raises(TypeError):
+        m.add_rm(zi)
+
+
+def test_numpy_ref_mutators():
+    """Tests numpy mutating Eigen matrices (for returned Eigen::Ref<...>s)"""
+
+    m.reset_refs()  # In case another test already changed it
+
+    zc = m.get_cm_ref()
+    zcro = m.get_cm_const_ref()
+    zr = m.get_rm_ref()
+    zrro = m.get_rm_const_ref()
+
+    assert [zc[1, 2], zcro[1, 2], zr[1, 2], zrro[1, 2]] == [23] * 4
+
+    assert not zc.flags.owndata and zc.flags.writeable
+    assert not zr.flags.owndata and zr.flags.writeable
+    assert not zcro.flags.owndata and not zcro.flags.writeable
+    assert not zrro.flags.owndata and not zrro.flags.writeable
+
+    zc[1, 2] = 99
+    expect = np.array([[11.0, 12, 13], [21, 22, 99], [31, 32, 33]])
+    # We should have just changed zc, of course, but also zcro and the original eigen matrix
+    assert np.all(zc == expect)
+    assert np.all(zcro == expect)
+    assert np.all(m.get_cm_ref() == expect)
+
+    zr[1, 2] = 99
+    assert np.all(zr == expect)
+    assert np.all(zrro == expect)
+    assert np.all(m.get_rm_ref() == expect)
+
+    # Make sure the readonly ones are numpy-readonly:
+    with pytest.raises(ValueError):
+        zcro[1, 2] = 6
+    with pytest.raises(ValueError):
+        zrro[1, 2] = 6
+
+    # We should be able to explicitly copy like this (and since we're copying,
+    # the const should drop away)
+    y1 = np.array(m.get_cm_const_ref())
+
+    assert y1.flags.owndata and y1.flags.writeable
+    # We should get copies of the eigen data, which was modified above:
+    assert y1[1, 2] == 99
+    y1[1, 2] += 12
+    assert y1[1, 2] == 111
+    assert zc[1, 2] == 99  # Make sure we aren't referencing the original
+
+
+def test_both_ref_mutators():
+    """Tests a complex chain of nested eigen/numpy references"""
+
+    m.reset_refs()  # In case another test already changed it
+
+    z = m.get_cm_ref()  # numpy -> eigen
+    z[0, 2] -= 3
+    z2 = m.incr_matrix(z, 1)  # numpy -> eigen -> numpy -> eigen
+    z2[1, 1] += 6
+    z3 = m.incr_matrix(z, 2)  # (numpy -> eigen)^3
+    z3[2, 2] += -5
+    z4 = m.incr_matrix(z, 3)  # (numpy -> eigen)^4
+    z4[1, 1] -= 1
+    z5 = m.incr_matrix(z, 4)  # (numpy -> eigen)^5
+    z5[0, 0] = 0
+    assert np.all(z == z2)
+    assert np.all(z == z3)
+    assert np.all(z == z4)
+    assert np.all(z == z5)
+    expect = np.array([[0.0, 22, 20], [31, 37, 33], [41, 42, 38]])
+    assert np.all(z == expect)
+
+    y = np.array(range(100), dtype="float64").reshape(10, 10)
+    y2 = m.incr_matrix_any(y, 10)  # np -> eigen -> np
+    y3 = m.incr_matrix_any(
+        y2[0::2, 0::2], -33
+    )  # np -> eigen -> np slice -> np -> eigen -> np
+    y4 = m.even_rows(y3)  # numpy -> eigen slice -> (... y3)
+    y5 = m.even_cols(y4)  # numpy -> eigen slice -> (... y4)
+    y6 = m.incr_matrix_any(y5, 1000)  # numpy -> eigen -> (... y5)
+
+    # Apply same mutations using just numpy:
+    yexpect = np.array(range(100), dtype="float64").reshape(10, 10)
+    yexpect += 10
+    yexpect[0::2, 0::2] -= 33
+    yexpect[0::4, 0::4] += 1000
+    assert np.all(y6 == yexpect[0::4, 0::4])
+    assert np.all(y5 == yexpect[0::4, 0::4])
+    assert np.all(y4 == yexpect[0::4, 0::2])
+    assert np.all(y3 == yexpect[0::2, 0::2])
+    assert np.all(y2 == yexpect)
+    assert np.all(y == yexpect)
+
+
+def test_nocopy_wrapper():
+    # get_elem requires a column-contiguous matrix reference, but should be
+    # callable with other types of matrix (via copying):
+    int_matrix_colmajor = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], order="F")
+    dbl_matrix_colmajor = np.array(
+        int_matrix_colmajor, dtype="double", order="F", copy=True
+    )
+    int_matrix_rowmajor = np.array(int_matrix_colmajor, order="C", copy=True)
+    dbl_matrix_rowmajor = np.array(
+        int_matrix_rowmajor, dtype="double", order="C", copy=True
+    )
+
+    # All should be callable via get_elem:
+    assert m.get_elem(int_matrix_colmajor) == 8
+    assert m.get_elem(dbl_matrix_colmajor) == 8
+    assert m.get_elem(int_matrix_rowmajor) == 8
+    assert m.get_elem(dbl_matrix_rowmajor) == 8
+
+    # All but the second should fail with m.get_elem_nocopy:
+    with pytest.raises(TypeError) as excinfo:
+        m.get_elem_nocopy(int_matrix_colmajor)
+    assert "get_elem_nocopy(): incompatible function arguments." in str(
+        excinfo.value
+    ) and ", flags.f_contiguous" in str(excinfo.value)
+    assert m.get_elem_nocopy(dbl_matrix_colmajor) == 8
+    with pytest.raises(TypeError) as excinfo:
+        m.get_elem_nocopy(int_matrix_rowmajor)
+    assert "get_elem_nocopy(): incompatible function arguments." in str(
+        excinfo.value
+    ) and ", flags.f_contiguous" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.get_elem_nocopy(dbl_matrix_rowmajor)
+    assert "get_elem_nocopy(): incompatible function arguments." in str(
+        excinfo.value
+    ) and ", flags.f_contiguous" in str(excinfo.value)
+
+    # For the row-major test, we take a long matrix in row-major, so only the third is allowed:
+    with pytest.raises(TypeError) as excinfo:
+        m.get_elem_rm_nocopy(int_matrix_colmajor)
+    assert "get_elem_rm_nocopy(): incompatible function arguments." in str(
+        excinfo.value
+    ) and ", flags.c_contiguous" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.get_elem_rm_nocopy(dbl_matrix_colmajor)
+    assert "get_elem_rm_nocopy(): incompatible function arguments." in str(
+        excinfo.value
+    ) and ", flags.c_contiguous" in str(excinfo.value)
+    assert m.get_elem_rm_nocopy(int_matrix_rowmajor) == 8
+    with pytest.raises(TypeError) as excinfo:
+        m.get_elem_rm_nocopy(dbl_matrix_rowmajor)
+    assert "get_elem_rm_nocopy(): incompatible function arguments." in str(
+        excinfo.value
+    ) and ", flags.c_contiguous" in str(excinfo.value)
+
+
+def test_eigen_ref_life_support():
+    """Ensure the lifetime of temporary arrays created by the `Ref` caster
+
+    The `Ref` caster sometimes creates a copy which needs to stay alive. This needs to
+    happen both for directs casts (just the array) or indirectly (e.g. list of arrays).
+    """
+
+    a = np.full(shape=10, fill_value=8, dtype=np.int8)
+    assert m.get_elem_direct(a) == 8
+
+    list_of_a = [a]
+    assert m.get_elem_indirect(list_of_a) == 8
+
+
+def test_special_matrix_objects():
+    assert np.all(m.incr_diag(7) == np.diag([1.0, 2, 3, 4, 5, 6, 7]))
+
+    asymm = np.array([[1.0, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]])
+    symm_lower = np.array(asymm)
+    symm_upper = np.array(asymm)
+    for i in range(4):
+        for j in range(i + 1, 4):
+            symm_lower[i, j] = symm_lower[j, i]
+            symm_upper[j, i] = symm_upper[i, j]
+
+    assert np.all(m.symmetric_lower(asymm) == symm_lower)
+    assert np.all(m.symmetric_upper(asymm) == symm_upper)
+
+
+def test_dense_signature(doc):
+    assert (
+        doc(m.double_col)
+        == """
+        double_col(arg0: numpy.ndarray[numpy.float32[m, 1]]) -> numpy.ndarray[numpy.float32[m, 1]]
+    """
+    )
+    assert (
+        doc(m.double_row)
+        == """
+        double_row(arg0: numpy.ndarray[numpy.float32[1, n]]) -> numpy.ndarray[numpy.float32[1, n]]
+    """
+    )
+    assert doc(m.double_complex) == (
+        """
+        double_complex(arg0: numpy.ndarray[numpy.complex64[m, 1]])"""
+        """ -> numpy.ndarray[numpy.complex64[m, 1]]
+    """
+    )
+    assert doc(m.double_mat_rm) == (
+        """
+        double_mat_rm(arg0: numpy.ndarray[numpy.float32[m, n]])"""
+        """ -> numpy.ndarray[numpy.float32[m, n]]
+    """
+    )
+
+
+def test_named_arguments():
+    a = np.array([[1.0, 2], [3, 4], [5, 6]])
+    b = np.ones((2, 1))
+
+    assert np.all(m.matrix_multiply(a, b) == np.array([[3.0], [7], [11]]))
+    assert np.all(m.matrix_multiply(A=a, B=b) == np.array([[3.0], [7], [11]]))
+    assert np.all(m.matrix_multiply(B=b, A=a) == np.array([[3.0], [7], [11]]))
+
+    with pytest.raises(ValueError) as excinfo:
+        m.matrix_multiply(b, a)
+    assert str(excinfo.value) == "Nonconformable matrices!"
+
+    with pytest.raises(ValueError) as excinfo:
+        m.matrix_multiply(A=b, B=a)
+    assert str(excinfo.value) == "Nonconformable matrices!"
+
+    with pytest.raises(ValueError) as excinfo:
+        m.matrix_multiply(B=a, A=b)
+    assert str(excinfo.value) == "Nonconformable matrices!"
+
+
+def test_sparse():
+    pytest.importorskip("scipy")
+    assert_sparse_equal_ref(m.sparse_r())
+    assert_sparse_equal_ref(m.sparse_c())
+    assert_sparse_equal_ref(m.sparse_copy_r(m.sparse_r()))
+    assert_sparse_equal_ref(m.sparse_copy_c(m.sparse_c()))
+    assert_sparse_equal_ref(m.sparse_copy_r(m.sparse_c()))
+    assert_sparse_equal_ref(m.sparse_copy_c(m.sparse_r()))
+
+
+def test_sparse_signature(doc):
+    pytest.importorskip("scipy")
+    assert (
+        doc(m.sparse_copy_r)
+        == """
+        sparse_copy_r(arg0: scipy.sparse.csr_matrix[numpy.float32]) -> scipy.sparse.csr_matrix[numpy.float32]
+    """
+    )
+    assert (
+        doc(m.sparse_copy_c)
+        == """
+        sparse_copy_c(arg0: scipy.sparse.csc_matrix[numpy.float32]) -> scipy.sparse.csc_matrix[numpy.float32]
+    """
+    )
+
+
+def test_issue738():
+    """Ignore strides on a length-1 dimension (even if they would be incompatible length > 1)"""
+    assert np.all(m.iss738_f1(np.array([[1.0, 2, 3]])) == np.array([[1.0, 102, 203]]))
+    assert np.all(
+        m.iss738_f1(np.array([[1.0], [2], [3]])) == np.array([[1.0], [12], [23]])
+    )
+
+    assert np.all(m.iss738_f2(np.array([[1.0, 2, 3]])) == np.array([[1.0, 102, 203]]))
+    assert np.all(
+        m.iss738_f2(np.array([[1.0], [2], [3]])) == np.array([[1.0], [12], [23]])
+    )
+
+
+@pytest.mark.parametrize("func", [m.iss738_f1, m.iss738_f2])
+@pytest.mark.parametrize("sizes", [(0, 2), (2, 0)])
+def test_zero_length(func, sizes):
+    """Ignore strides on a length-0 dimension (even if they would be incompatible length > 1)"""
+    assert np.all(func(np.zeros(sizes)) == np.zeros(sizes))
+
+
+def test_issue1105():
+    """Issue 1105: 1xN or Nx1 input arrays weren't accepted for eigen
+    compile-time row vectors or column vector"""
+    assert m.iss1105_row(np.ones((1, 7)))
+    assert m.iss1105_col(np.ones((7, 1)))
+
+    # These should still fail (incompatible dimensions):
+    with pytest.raises(TypeError) as excinfo:
+        m.iss1105_row(np.ones((7, 1)))
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.iss1105_col(np.ones((1, 7)))
+    assert "incompatible function arguments" in str(excinfo.value)
+
+
+def test_custom_operator_new():
+    """Using Eigen types as member variables requires a class-specific
+    operator new with proper alignment"""
+
+    o = m.CustomOperatorNew()
+    np.testing.assert_allclose(o.a, 0.0)
+    np.testing.assert_allclose(o.b.diagonal(), 1.0)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/CMakeLists.txt b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/CMakeLists.txt
new file mode 100644
index 000000000..09a369399
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/CMakeLists.txt
@@ -0,0 +1,47 @@
+possibly_uninitialized(PYTHON_MODULE_EXTENSION Python_INTERPRETER_ID)
+
+if("${PYTHON_MODULE_EXTENSION}" MATCHES "pypy" OR "${Python_INTERPRETER_ID}" STREQUAL "PyPy")
+  message(STATUS "Skipping embed test on PyPy")
+  add_custom_target(cpptest) # Dummy target on PyPy. Embedding is not supported.
+  set(_suppress_unused_variable_warning "${DOWNLOAD_CATCH}")
+  return()
+endif()
+
+find_package(Catch 2.13.9)
+
+if(CATCH_FOUND)
+  message(STATUS "Building interpreter tests using Catch v${CATCH_VERSION}")
+else()
+  message(STATUS "Catch not detected. Interpreter tests will be skipped. Install Catch headers"
+                 " manually or use `cmake -DDOWNLOAD_CATCH=ON` to fetch them automatically.")
+  return()
+endif()
+
+find_package(Threads REQUIRED)
+
+add_executable(test_embed catch.cpp test_interpreter.cpp)
+pybind11_enable_warnings(test_embed)
+
+target_link_libraries(test_embed PRIVATE pybind11::embed Catch2::Catch2 Threads::Threads)
+
+if(NOT CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_CURRENT_BINARY_DIR)
+  file(COPY test_interpreter.py test_trampoline.py DESTINATION "${CMAKE_CURRENT_BINARY_DIR}")
+endif()
+
+add_custom_target(
+  cpptest
+  COMMAND "$<TARGET_FILE:test_embed>"
+  DEPENDS test_embed
+  WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}")
+
+pybind11_add_module(external_module THIN_LTO external_module.cpp)
+set_target_properties(external_module PROPERTIES LIBRARY_OUTPUT_DIRECTORY
+                                                 "${CMAKE_CURRENT_BINARY_DIR}")
+foreach(config ${CMAKE_CONFIGURATION_TYPES})
+  string(TOUPPER ${config} config)
+  set_target_properties(external_module PROPERTIES LIBRARY_OUTPUT_DIRECTORY_${config}
+                                                   "${CMAKE_CURRENT_BINARY_DIR}")
+endforeach()
+add_dependencies(cpptest external_module)
+
+add_dependencies(check cpptest)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/catch.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/catch.cpp
new file mode 100644
index 000000000..96d2e3f92
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/catch.cpp
@@ -0,0 +1,27 @@
+// The Catch implementation is compiled here. This is a standalone
+// translation unit to avoid recompiling it for every test change.
+
+#include <pybind11/embed.h>
+
+#ifdef _MSC_VER
+// Silence MSVC C++17 deprecation warning from Catch regarding std::uncaught_exceptions (up to
+// catch 2.0.1; this should be fixed in the next catch release after 2.0.1).
+#    pragma warning(disable : 4996)
+#endif
+
+// Catch uses _ internally, which breaks gettext style defines
+#ifdef _
+#    undef _
+#endif
+
+#define CATCH_CONFIG_RUNNER
+#include <catch.hpp>
+
+namespace py = pybind11;
+
+int main(int argc, char *argv[]) {
+    py::scoped_interpreter guard{};
+    auto result = Catch::Session().run(argc, argv);
+
+    return result < 0xff ? result : 0xff;
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/external_module.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/external_module.cpp
new file mode 100644
index 000000000..5c482fe06
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/external_module.cpp
@@ -0,0 +1,20 @@
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+/* Simple test module/test class to check that the referenced internals data of external pybind11
+ * modules aren't preserved over a finalize/initialize.
+ */
+
+PYBIND11_MODULE(external_module, m) {
+    class A {
+    public:
+        explicit A(int value) : v{value} {};
+        int v;
+    };
+
+    py::class_<A>(m, "A").def(py::init<int>()).def_readwrite("value", &A::v);
+
+    m.def("internals_at",
+          []() { return reinterpret_cast<uintptr_t>(&py::detail::get_internals()); });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_interpreter.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_interpreter.cpp
new file mode 100644
index 000000000..1c45457a0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_interpreter.cpp
@@ -0,0 +1,395 @@
+#include <pybind11/embed.h>
+
+#ifdef _MSC_VER
+// Silence MSVC C++17 deprecation warning from Catch regarding std::uncaught_exceptions (up to
+// catch 2.0.1; this should be fixed in the next catch release after 2.0.1).
+#    pragma warning(disable : 4996)
+#endif
+
+#include <catch.hpp>
+#include <cstdlib>
+#include <fstream>
+#include <functional>
+#include <thread>
+#include <utility>
+
+namespace py = pybind11;
+using namespace py::literals;
+
+class Widget {
+public:
+    explicit Widget(std::string message) : message(std::move(message)) {}
+    virtual ~Widget() = default;
+
+    std::string the_message() const { return message; }
+    virtual int the_answer() const = 0;
+    virtual std::string argv0() const = 0;
+
+private:
+    std::string message;
+};
+
+class PyWidget final : public Widget {
+    using Widget::Widget;
+
+    int the_answer() const override { PYBIND11_OVERRIDE_PURE(int, Widget, the_answer); }
+    std::string argv0() const override { PYBIND11_OVERRIDE_PURE(std::string, Widget, argv0); }
+};
+
+class test_override_cache_helper {
+
+public:
+    virtual int func() { return 0; }
+
+    test_override_cache_helper() = default;
+    virtual ~test_override_cache_helper() = default;
+    // Non-copyable
+    test_override_cache_helper &operator=(test_override_cache_helper const &Right) = delete;
+    test_override_cache_helper(test_override_cache_helper const &Copy) = delete;
+};
+
+class test_override_cache_helper_trampoline : public test_override_cache_helper {
+    int func() override { PYBIND11_OVERRIDE(int, test_override_cache_helper, func); }
+};
+
+PYBIND11_EMBEDDED_MODULE(widget_module, m) {
+    py::class_<Widget, PyWidget>(m, "Widget")
+        .def(py::init<std::string>())
+        .def_property_readonly("the_message", &Widget::the_message);
+
+    m.def("add", [](int i, int j) { return i + j; });
+}
+
+PYBIND11_EMBEDDED_MODULE(trampoline_module, m) {
+    py::class_<test_override_cache_helper,
+               test_override_cache_helper_trampoline,
+               std::shared_ptr<test_override_cache_helper>>(m, "test_override_cache_helper")
+        .def(py::init_alias<>())
+        .def("func", &test_override_cache_helper::func);
+}
+
+PYBIND11_EMBEDDED_MODULE(throw_exception, ) { throw std::runtime_error("C++ Error"); }
+
+PYBIND11_EMBEDDED_MODULE(throw_error_already_set, ) {
+    auto d = py::dict();
+    d["missing"].cast<py::object>();
+}
+
+TEST_CASE("Pass classes and data between modules defined in C++ and Python") {
+    auto module_ = py::module_::import("test_interpreter");
+    REQUIRE(py::hasattr(module_, "DerivedWidget"));
+
+    auto locals = py::dict("hello"_a = "Hello, World!", "x"_a = 5, **module_.attr("__dict__"));
+    py::exec(R"(
+        widget = DerivedWidget("{} - {}".format(hello, x))
+        message = widget.the_message
+    )",
+             py::globals(),
+             locals);
+    REQUIRE(locals["message"].cast<std::string>() == "Hello, World! - 5");
+
+    auto py_widget = module_.attr("DerivedWidget")("The question");
+    auto message = py_widget.attr("the_message");
+    REQUIRE(message.cast<std::string>() == "The question");
+
+    const auto &cpp_widget = py_widget.cast<const Widget &>();
+    REQUIRE(cpp_widget.the_answer() == 42);
+}
+
+TEST_CASE("Override cache") {
+    auto module_ = py::module_::import("test_trampoline");
+    REQUIRE(py::hasattr(module_, "func"));
+    REQUIRE(py::hasattr(module_, "func2"));
+
+    auto locals = py::dict(**module_.attr("__dict__"));
+
+    int i = 0;
+    for (; i < 1500; ++i) {
+        std::shared_ptr<test_override_cache_helper> p_obj;
+        std::shared_ptr<test_override_cache_helper> p_obj2;
+
+        py::object loc_inst = locals["func"]();
+        p_obj = py::cast<std::shared_ptr<test_override_cache_helper>>(loc_inst);
+
+        int ret = p_obj->func();
+
+        REQUIRE(ret == 42);
+
+        loc_inst = locals["func2"]();
+
+        p_obj2 = py::cast<std::shared_ptr<test_override_cache_helper>>(loc_inst);
+
+        p_obj2->func();
+    }
+}
+
+TEST_CASE("Import error handling") {
+    REQUIRE_NOTHROW(py::module_::import("widget_module"));
+    REQUIRE_THROWS_WITH(py::module_::import("throw_exception"), "ImportError: C++ Error");
+    REQUIRE_THROWS_WITH(py::module_::import("throw_error_already_set"),
+                        Catch::Contains("ImportError: initialization failed"));
+
+    auto locals = py::dict("is_keyerror"_a = false, "message"_a = "not set");
+    py::exec(R"(
+        try:
+            import throw_error_already_set
+        except ImportError as e:
+            is_keyerror = type(e.__cause__) == KeyError
+            message = str(e.__cause__)
+    )",
+             py::globals(),
+             locals);
+    REQUIRE(locals["is_keyerror"].cast<bool>() == true);
+    REQUIRE(locals["message"].cast<std::string>() == "'missing'");
+}
+
+TEST_CASE("There can be only one interpreter") {
+    static_assert(std::is_move_constructible<py::scoped_interpreter>::value, "");
+    static_assert(!std::is_move_assignable<py::scoped_interpreter>::value, "");
+    static_assert(!std::is_copy_constructible<py::scoped_interpreter>::value, "");
+    static_assert(!std::is_copy_assignable<py::scoped_interpreter>::value, "");
+
+    REQUIRE_THROWS_WITH(py::initialize_interpreter(), "The interpreter is already running");
+    REQUIRE_THROWS_WITH(py::scoped_interpreter(), "The interpreter is already running");
+
+    py::finalize_interpreter();
+    REQUIRE_NOTHROW(py::scoped_interpreter());
+    {
+        auto pyi1 = py::scoped_interpreter();
+        auto pyi2 = std::move(pyi1);
+    }
+    py::initialize_interpreter();
+}
+
+bool has_pybind11_internals_builtin() {
+    auto builtins = py::handle(PyEval_GetBuiltins());
+    return builtins.contains(PYBIND11_INTERNALS_ID);
+};
+
+bool has_pybind11_internals_static() {
+    auto **&ipp = py::detail::get_internals_pp();
+    return (ipp != nullptr) && (*ipp != nullptr);
+}
+
+TEST_CASE("Restart the interpreter") {
+    // Verify pre-restart state.
+    REQUIRE(py::module_::import("widget_module").attr("add")(1, 2).cast<int>() == 3);
+    REQUIRE(has_pybind11_internals_builtin());
+    REQUIRE(has_pybind11_internals_static());
+    REQUIRE(py::module_::import("external_module").attr("A")(123).attr("value").cast<int>()
+            == 123);
+
+    // local and foreign module internals should point to the same internals:
+    REQUIRE(reinterpret_cast<uintptr_t>(*py::detail::get_internals_pp())
+            == py::module_::import("external_module").attr("internals_at")().cast<uintptr_t>());
+
+    // Restart the interpreter.
+    py::finalize_interpreter();
+    REQUIRE(Py_IsInitialized() == 0);
+
+    py::initialize_interpreter();
+    REQUIRE(Py_IsInitialized() == 1);
+
+    // Internals are deleted after a restart.
+    REQUIRE_FALSE(has_pybind11_internals_builtin());
+    REQUIRE_FALSE(has_pybind11_internals_static());
+    pybind11::detail::get_internals();
+    REQUIRE(has_pybind11_internals_builtin());
+    REQUIRE(has_pybind11_internals_static());
+    REQUIRE(reinterpret_cast<uintptr_t>(*py::detail::get_internals_pp())
+            == py::module_::import("external_module").attr("internals_at")().cast<uintptr_t>());
+
+    // Make sure that an interpreter with no get_internals() created until finalize still gets the
+    // internals destroyed
+    py::finalize_interpreter();
+    py::initialize_interpreter();
+    bool ran = false;
+    py::module_::import("__main__").attr("internals_destroy_test")
+        = py::capsule(&ran, [](void *ran) {
+              py::detail::get_internals();
+              *static_cast<bool *>(ran) = true;
+          });
+    REQUIRE_FALSE(has_pybind11_internals_builtin());
+    REQUIRE_FALSE(has_pybind11_internals_static());
+    REQUIRE_FALSE(ran);
+    py::finalize_interpreter();
+    REQUIRE(ran);
+    py::initialize_interpreter();
+    REQUIRE_FALSE(has_pybind11_internals_builtin());
+    REQUIRE_FALSE(has_pybind11_internals_static());
+
+    // C++ modules can be reloaded.
+    auto cpp_module = py::module_::import("widget_module");
+    REQUIRE(cpp_module.attr("add")(1, 2).cast<int>() == 3);
+
+    // C++ type information is reloaded and can be used in python modules.
+    auto py_module = py::module_::import("test_interpreter");
+    auto py_widget = py_module.attr("DerivedWidget")("Hello after restart");
+    REQUIRE(py_widget.attr("the_message").cast<std::string>() == "Hello after restart");
+}
+
+TEST_CASE("Subinterpreter") {
+    // Add tags to the modules in the main interpreter and test the basics.
+    py::module_::import("__main__").attr("main_tag") = "main interpreter";
+    {
+        auto m = py::module_::import("widget_module");
+        m.attr("extension_module_tag") = "added to module in main interpreter";
+
+        REQUIRE(m.attr("add")(1, 2).cast<int>() == 3);
+    }
+    REQUIRE(has_pybind11_internals_builtin());
+    REQUIRE(has_pybind11_internals_static());
+
+    /// Create and switch to a subinterpreter.
+    auto *main_tstate = PyThreadState_Get();
+    auto *sub_tstate = Py_NewInterpreter();
+
+    // Subinterpreters get their own copy of builtins. detail::get_internals() still
+    // works by returning from the static variable, i.e. all interpreters share a single
+    // global pybind11::internals;
+    REQUIRE_FALSE(has_pybind11_internals_builtin());
+    REQUIRE(has_pybind11_internals_static());
+
+    // Modules tags should be gone.
+    REQUIRE_FALSE(py::hasattr(py::module_::import("__main__"), "tag"));
+    {
+        auto m = py::module_::import("widget_module");
+        REQUIRE_FALSE(py::hasattr(m, "extension_module_tag"));
+
+        // Function bindings should still work.
+        REQUIRE(m.attr("add")(1, 2).cast<int>() == 3);
+    }
+
+    // Restore main interpreter.
+    Py_EndInterpreter(sub_tstate);
+    PyThreadState_Swap(main_tstate);
+
+    REQUIRE(py::hasattr(py::module_::import("__main__"), "main_tag"));
+    REQUIRE(py::hasattr(py::module_::import("widget_module"), "extension_module_tag"));
+}
+
+TEST_CASE("Execution frame") {
+    // When the interpreter is embedded, there is no execution frame, but `py::exec`
+    // should still function by using reasonable globals: `__main__.__dict__`.
+    py::exec("var = dict(number=42)");
+    REQUIRE(py::globals()["var"]["number"].cast<int>() == 42);
+}
+
+TEST_CASE("Threads") {
+    // Restart interpreter to ensure threads are not initialized
+    py::finalize_interpreter();
+    py::initialize_interpreter();
+    REQUIRE_FALSE(has_pybind11_internals_static());
+
+    constexpr auto num_threads = 10;
+    auto locals = py::dict("count"_a = 0);
+
+    {
+        py::gil_scoped_release gil_release{};
+        REQUIRE(has_pybind11_internals_static());
+
+        auto threads = std::vector<std::thread>();
+        for (auto i = 0; i < num_threads; ++i) {
+            threads.emplace_back([&]() {
+                py::gil_scoped_acquire gil{};
+                locals["count"] = locals["count"].cast<int>() + 1;
+            });
+        }
+
+        for (auto &thread : threads) {
+            thread.join();
+        }
+    }
+
+    REQUIRE(locals["count"].cast<int>() == num_threads);
+}
+
+// Scope exit utility https://stackoverflow.com/a/36644501/7255855
+struct scope_exit {
+    std::function<void()> f_;
+    explicit scope_exit(std::function<void()> f) noexcept : f_(std::move(f)) {}
+    ~scope_exit() {
+        if (f_) {
+            f_();
+        }
+    }
+};
+
+TEST_CASE("Reload module from file") {
+    // Disable generation of cached bytecode (.pyc files) for this test, otherwise
+    // Python might pick up an old version from the cache instead of the new versions
+    // of the .py files generated below
+    auto sys = py::module_::import("sys");
+    bool dont_write_bytecode = sys.attr("dont_write_bytecode").cast<bool>();
+    sys.attr("dont_write_bytecode") = true;
+    // Reset the value at scope exit
+    scope_exit reset_dont_write_bytecode(
+        [&]() { sys.attr("dont_write_bytecode") = dont_write_bytecode; });
+
+    std::string module_name = "test_module_reload";
+    std::string module_file = module_name + ".py";
+
+    // Create the module .py file
+    std::ofstream test_module(module_file);
+    test_module << "def test():\n";
+    test_module << "    return 1\n";
+    test_module.close();
+    // Delete the file at scope exit
+    scope_exit delete_module_file([&]() { std::remove(module_file.c_str()); });
+
+    // Import the module from file
+    auto module_ = py::module_::import(module_name.c_str());
+    int result = module_.attr("test")().cast<int>();
+    REQUIRE(result == 1);
+
+    // Update the module .py file with a small change
+    test_module.open(module_file);
+    test_module << "def test():\n";
+    test_module << "    return 2\n";
+    test_module.close();
+
+    // Reload the module
+    module_.reload();
+    result = module_.attr("test")().cast<int>();
+    REQUIRE(result == 2);
+}
+
+TEST_CASE("sys.argv gets initialized properly") {
+    py::finalize_interpreter();
+    {
+        py::scoped_interpreter default_scope;
+        auto module = py::module::import("test_interpreter");
+        auto py_widget = module.attr("DerivedWidget")("The question");
+        const auto &cpp_widget = py_widget.cast<const Widget &>();
+        REQUIRE(cpp_widget.argv0().empty());
+    }
+
+    {
+        char *argv[] = {strdup("a.out")};
+        py::scoped_interpreter argv_scope(true, 1, argv);
+        std::free(argv[0]);
+        auto module = py::module::import("test_interpreter");
+        auto py_widget = module.attr("DerivedWidget")("The question");
+        const auto &cpp_widget = py_widget.cast<const Widget &>();
+        REQUIRE(cpp_widget.argv0() == "a.out");
+    }
+    py::initialize_interpreter();
+}
+
+TEST_CASE("make_iterator can be called before then after finalizing an interpreter") {
+    // Reproduction of issue #2101 (https://github.com/pybind/pybind11/issues/2101)
+    py::finalize_interpreter();
+
+    std::vector<int> container;
+    {
+        pybind11::scoped_interpreter g;
+        auto iter = pybind11::make_iterator(container.begin(), container.end());
+    }
+
+    REQUIRE_NOTHROW([&]() {
+        pybind11::scoped_interpreter g;
+        auto iter = pybind11::make_iterator(container.begin(), container.end());
+    }());
+
+    py::initialize_interpreter();
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_interpreter.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_interpreter.py
new file mode 100644
index 000000000..f27944972
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_interpreter.py
@@ -0,0 +1,14 @@
+import sys
+
+from widget_module import Widget
+
+
+class DerivedWidget(Widget):
+    def __init__(self, message):
+        super().__init__(message)
+
+    def the_answer(self):
+        return 42
+
+    def argv0(self):
+        return sys.argv[0]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_trampoline.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_trampoline.py
new file mode 100644
index 000000000..8e14e8ef0
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_embed/test_trampoline.py
@@ -0,0 +1,16 @@
+import trampoline_module
+
+
+def func():
+    class Test(trampoline_module.test_override_cache_helper):
+        def func(self):
+            return 42
+
+    return Test()
+
+
+def func2():
+    class Test(trampoline_module.test_override_cache_helper):
+        pass
+
+    return Test()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_enum.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_enum.cpp
new file mode 100644
index 000000000..2597b275e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_enum.cpp
@@ -0,0 +1,133 @@
+/*
+    tests/test_enums.cpp -- enumerations
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+TEST_SUBMODULE(enums, m) {
+    // test_unscoped_enum
+    enum UnscopedEnum { EOne = 1, ETwo, EThree };
+    py::enum_<UnscopedEnum>(m, "UnscopedEnum", py::arithmetic(), "An unscoped enumeration")
+        .value("EOne", EOne, "Docstring for EOne")
+        .value("ETwo", ETwo, "Docstring for ETwo")
+        .value("EThree", EThree, "Docstring for EThree")
+        .export_values();
+
+    // test_scoped_enum
+    enum class ScopedEnum { Two = 2, Three };
+    py::enum_<ScopedEnum>(m, "ScopedEnum", py::arithmetic())
+        .value("Two", ScopedEnum::Two)
+        .value("Three", ScopedEnum::Three);
+
+    m.def("test_scoped_enum", [](ScopedEnum z) {
+        return "ScopedEnum::" + std::string(z == ScopedEnum::Two ? "Two" : "Three");
+    });
+
+    // test_binary_operators
+    enum Flags { Read = 4, Write = 2, Execute = 1 };
+    py::enum_<Flags>(m, "Flags", py::arithmetic())
+        .value("Read", Flags::Read)
+        .value("Write", Flags::Write)
+        .value("Execute", Flags::Execute)
+        .export_values();
+
+    // test_implicit_conversion
+    class ClassWithUnscopedEnum {
+    public:
+        enum EMode { EFirstMode = 1, ESecondMode };
+
+        static EMode test_function(EMode mode) { return mode; }
+    };
+    py::class_<ClassWithUnscopedEnum> exenum_class(m, "ClassWithUnscopedEnum");
+    exenum_class.def_static("test_function", &ClassWithUnscopedEnum::test_function);
+    py::enum_<ClassWithUnscopedEnum::EMode>(exenum_class, "EMode")
+        .value("EFirstMode", ClassWithUnscopedEnum::EFirstMode)
+        .value("ESecondMode", ClassWithUnscopedEnum::ESecondMode)
+        .export_values();
+
+    // test_enum_to_int
+    m.def("test_enum_to_int", [](int) {});
+    m.def("test_enum_to_uint", [](uint32_t) {});
+    m.def("test_enum_to_long_long", [](long long) {});
+
+    // test_duplicate_enum_name
+    enum SimpleEnum { ONE, TWO, THREE };
+
+    m.def("register_bad_enum", [m]() {
+        py::enum_<SimpleEnum>(m, "SimpleEnum")
+            .value("ONE", SimpleEnum::ONE) // NOTE: all value function calls are called with the
+                                           // same first parameter value
+            .value("ONE", SimpleEnum::TWO)
+            .value("ONE", SimpleEnum::THREE)
+            .export_values();
+    });
+
+    // test_enum_scalar
+    enum UnscopedUCharEnum : unsigned char {};
+    enum class ScopedShortEnum : short {};
+    enum class ScopedLongEnum : long {};
+    enum UnscopedUInt64Enum : std::uint64_t {};
+    static_assert(
+        py::detail::all_of<
+            std::is_same<py::enum_<UnscopedUCharEnum>::Scalar, unsigned char>,
+            std::is_same<py::enum_<ScopedShortEnum>::Scalar, short>,
+            std::is_same<py::enum_<ScopedLongEnum>::Scalar, long>,
+            std::is_same<py::enum_<UnscopedUInt64Enum>::Scalar, std::uint64_t>>::value,
+        "Error during the deduction of enum's scalar type with normal integer underlying");
+
+    // test_enum_scalar_with_char_underlying
+    enum class ScopedCharEnum : char { Zero, Positive };
+    enum class ScopedWCharEnum : wchar_t { Zero, Positive };
+    enum class ScopedChar32Enum : char32_t { Zero, Positive };
+    enum class ScopedChar16Enum : char16_t { Zero, Positive };
+
+    // test the scalar of char type enums according to chapter 'Character types'
+    // from https://en.cppreference.com/w/cpp/language/types
+    static_assert(
+        py::detail::any_of<
+            std::is_same<py::enum_<ScopedCharEnum>::Scalar, signed char>,  // e.g. gcc on x86
+            std::is_same<py::enum_<ScopedCharEnum>::Scalar, unsigned char> // e.g. arm linux
+            >::value,
+        "char should be cast to either signed char or unsigned char");
+    static_assert(sizeof(py::enum_<ScopedWCharEnum>::Scalar) == 2
+                      || sizeof(py::enum_<ScopedWCharEnum>::Scalar) == 4,
+                  "wchar_t should be either 16 bits (Windows) or 32 (everywhere else)");
+    static_assert(
+        py::detail::all_of<
+            std::is_same<py::enum_<ScopedChar32Enum>::Scalar, std::uint_least32_t>,
+            std::is_same<py::enum_<ScopedChar16Enum>::Scalar, std::uint_least16_t>>::value,
+        "char32_t, char16_t (and char8_t)'s size, signedness, and alignment is determined");
+#if defined(PYBIND11_HAS_U8STRING)
+    enum class ScopedChar8Enum : char8_t { Zero, Positive };
+    static_assert(std::is_same<py::enum_<ScopedChar8Enum>::Scalar, unsigned char>::value);
+#endif
+
+    // test_char_underlying_enum
+    py::enum_<ScopedCharEnum>(m, "ScopedCharEnum")
+        .value("Zero", ScopedCharEnum::Zero)
+        .value("Positive", ScopedCharEnum::Positive);
+    py::enum_<ScopedWCharEnum>(m, "ScopedWCharEnum")
+        .value("Zero", ScopedWCharEnum::Zero)
+        .value("Positive", ScopedWCharEnum::Positive);
+    py::enum_<ScopedChar32Enum>(m, "ScopedChar32Enum")
+        .value("Zero", ScopedChar32Enum::Zero)
+        .value("Positive", ScopedChar32Enum::Positive);
+    py::enum_<ScopedChar16Enum>(m, "ScopedChar16Enum")
+        .value("Zero", ScopedChar16Enum::Zero)
+        .value("Positive", ScopedChar16Enum::Positive);
+
+    // test_bool_underlying_enum
+    enum class ScopedBoolEnum : bool { FALSE, TRUE };
+
+    // bool is unsigned (std::is_signed returns false) and 1-byte long, so represented with u8
+    static_assert(std::is_same<py::enum_<ScopedBoolEnum>::Scalar, std::uint8_t>::value, "");
+
+    py::enum_<ScopedBoolEnum>(m, "ScopedBoolEnum")
+        .value("FALSE", ScopedBoolEnum::FALSE)
+        .value("TRUE", ScopedBoolEnum::TRUE);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_enum.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_enum.py
new file mode 100644
index 000000000..f14a72398
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_enum.py
@@ -0,0 +1,264 @@
+import pytest
+
+from pybind11_tests import enums as m
+
+
+def test_unscoped_enum():
+    assert str(m.UnscopedEnum.EOne) == "UnscopedEnum.EOne"
+    assert str(m.UnscopedEnum.ETwo) == "UnscopedEnum.ETwo"
+    assert str(m.EOne) == "UnscopedEnum.EOne"
+    assert repr(m.UnscopedEnum.EOne) == "<UnscopedEnum.EOne: 1>"
+    assert repr(m.UnscopedEnum.ETwo) == "<UnscopedEnum.ETwo: 2>"
+    assert repr(m.EOne) == "<UnscopedEnum.EOne: 1>"
+
+    # name property
+    assert m.UnscopedEnum.EOne.name == "EOne"
+    assert m.UnscopedEnum.EOne.value == 1
+    assert m.UnscopedEnum.ETwo.name == "ETwo"
+    assert m.UnscopedEnum.ETwo.value == 2
+    assert m.EOne is m.UnscopedEnum.EOne
+    # name, value readonly
+    with pytest.raises(AttributeError):
+        m.UnscopedEnum.EOne.name = ""
+    with pytest.raises(AttributeError):
+        m.UnscopedEnum.EOne.value = 10
+    # name, value returns a copy
+    # TODO: Neither the name nor value tests actually check against aliasing.
+    # Use a mutable type that has reference semantics.
+    nonaliased_name = m.UnscopedEnum.EOne.name
+    nonaliased_name = "bar"  # noqa: F841
+    assert m.UnscopedEnum.EOne.name == "EOne"
+    nonaliased_value = m.UnscopedEnum.EOne.value
+    nonaliased_value = 10  # noqa: F841
+    assert m.UnscopedEnum.EOne.value == 1
+
+    # __members__ property
+    assert m.UnscopedEnum.__members__ == {
+        "EOne": m.UnscopedEnum.EOne,
+        "ETwo": m.UnscopedEnum.ETwo,
+        "EThree": m.UnscopedEnum.EThree,
+    }
+    # __members__ readonly
+    with pytest.raises(AttributeError):
+        m.UnscopedEnum.__members__ = {}
+    # __members__ returns a copy
+    nonaliased_members = m.UnscopedEnum.__members__
+    nonaliased_members["bar"] = "baz"
+    assert m.UnscopedEnum.__members__ == {
+        "EOne": m.UnscopedEnum.EOne,
+        "ETwo": m.UnscopedEnum.ETwo,
+        "EThree": m.UnscopedEnum.EThree,
+    }
+
+    for docstring_line in """An unscoped enumeration
+
+Members:
+
+  EOne : Docstring for EOne
+
+  ETwo : Docstring for ETwo
+
+  EThree : Docstring for EThree""".split(
+        "\n"
+    ):
+        assert docstring_line in m.UnscopedEnum.__doc__
+
+    # Unscoped enums will accept ==/!= int comparisons
+    y = m.UnscopedEnum.ETwo
+    assert y == 2
+    assert 2 == y
+    assert y != 3
+    assert 3 != y
+    # Compare with None
+    assert y != None  # noqa: E711
+    assert not (y == None)  # noqa: E711
+    # Compare with an object
+    assert y != object()
+    assert not (y == object())
+    # Compare with string
+    assert y != "2"
+    assert "2" != y
+    assert not ("2" == y)
+    assert not (y == "2")
+
+    with pytest.raises(TypeError):
+        y < object()  # noqa: B015
+
+    with pytest.raises(TypeError):
+        y <= object()  # noqa: B015
+
+    with pytest.raises(TypeError):
+        y > object()  # noqa: B015
+
+    with pytest.raises(TypeError):
+        y >= object()  # noqa: B015
+
+    with pytest.raises(TypeError):
+        y | object()
+
+    with pytest.raises(TypeError):
+        y & object()
+
+    with pytest.raises(TypeError):
+        y ^ object()
+
+    assert int(m.UnscopedEnum.ETwo) == 2
+    assert str(m.UnscopedEnum(2)) == "UnscopedEnum.ETwo"
+
+    # order
+    assert m.UnscopedEnum.EOne < m.UnscopedEnum.ETwo
+    assert m.UnscopedEnum.EOne < 2
+    assert m.UnscopedEnum.ETwo > m.UnscopedEnum.EOne
+    assert m.UnscopedEnum.ETwo > 1
+    assert m.UnscopedEnum.ETwo <= 2
+    assert m.UnscopedEnum.ETwo >= 2
+    assert m.UnscopedEnum.EOne <= m.UnscopedEnum.ETwo
+    assert m.UnscopedEnum.EOne <= 2
+    assert m.UnscopedEnum.ETwo >= m.UnscopedEnum.EOne
+    assert m.UnscopedEnum.ETwo >= 1
+    assert not (m.UnscopedEnum.ETwo < m.UnscopedEnum.EOne)
+    assert not (2 < m.UnscopedEnum.EOne)
+
+    # arithmetic
+    assert m.UnscopedEnum.EOne & m.UnscopedEnum.EThree == m.UnscopedEnum.EOne
+    assert m.UnscopedEnum.EOne | m.UnscopedEnum.ETwo == m.UnscopedEnum.EThree
+    assert m.UnscopedEnum.EOne ^ m.UnscopedEnum.EThree == m.UnscopedEnum.ETwo
+
+
+def test_scoped_enum():
+    assert m.test_scoped_enum(m.ScopedEnum.Three) == "ScopedEnum::Three"
+    z = m.ScopedEnum.Two
+    assert m.test_scoped_enum(z) == "ScopedEnum::Two"
+
+    # Scoped enums will *NOT* accept ==/!= int comparisons (Will always return False)
+    assert not z == 3
+    assert not 3 == z
+    assert z != 3
+    assert 3 != z
+    # Compare with None
+    assert z != None  # noqa: E711
+    assert not (z == None)  # noqa: E711
+    # Compare with an object
+    assert z != object()
+    assert not (z == object())
+    # Scoped enums will *NOT* accept >, <, >= and <= int comparisons (Will throw exceptions)
+    with pytest.raises(TypeError):
+        z > 3  # noqa: B015
+    with pytest.raises(TypeError):
+        z < 3  # noqa: B015
+    with pytest.raises(TypeError):
+        z >= 3  # noqa: B015
+    with pytest.raises(TypeError):
+        z <= 3  # noqa: B015
+
+    # order
+    assert m.ScopedEnum.Two < m.ScopedEnum.Three
+    assert m.ScopedEnum.Three > m.ScopedEnum.Two
+    assert m.ScopedEnum.Two <= m.ScopedEnum.Three
+    assert m.ScopedEnum.Two <= m.ScopedEnum.Two
+    assert m.ScopedEnum.Two >= m.ScopedEnum.Two
+    assert m.ScopedEnum.Three >= m.ScopedEnum.Two
+
+
+def test_implicit_conversion():
+    assert str(m.ClassWithUnscopedEnum.EMode.EFirstMode) == "EMode.EFirstMode"
+    assert str(m.ClassWithUnscopedEnum.EFirstMode) == "EMode.EFirstMode"
+    assert repr(m.ClassWithUnscopedEnum.EMode.EFirstMode) == "<EMode.EFirstMode: 1>"
+    assert repr(m.ClassWithUnscopedEnum.EFirstMode) == "<EMode.EFirstMode: 1>"
+
+    f = m.ClassWithUnscopedEnum.test_function
+    first = m.ClassWithUnscopedEnum.EFirstMode
+    second = m.ClassWithUnscopedEnum.ESecondMode
+
+    assert f(first) == 1
+
+    assert f(first) == f(first)
+    assert not f(first) != f(first)
+
+    assert f(first) != f(second)
+    assert not f(first) == f(second)
+
+    assert f(first) == int(f(first))
+    assert not f(first) != int(f(first))
+
+    assert f(first) != int(f(second))
+    assert not f(first) == int(f(second))
+
+    # noinspection PyDictCreation
+    x = {f(first): 1, f(second): 2}
+    x[f(first)] = 3
+    x[f(second)] = 4
+    # Hashing test
+    assert repr(x) == "{<EMode.EFirstMode: 1>: 3, <EMode.ESecondMode: 2>: 4}"
+
+
+def test_binary_operators():
+    assert int(m.Flags.Read) == 4
+    assert int(m.Flags.Write) == 2
+    assert int(m.Flags.Execute) == 1
+    assert int(m.Flags.Read | m.Flags.Write | m.Flags.Execute) == 7
+    assert int(m.Flags.Read | m.Flags.Write) == 6
+    assert int(m.Flags.Read | m.Flags.Execute) == 5
+    assert int(m.Flags.Write | m.Flags.Execute) == 3
+    assert int(m.Flags.Write | 1) == 3
+    assert ~m.Flags.Write == -3
+
+    state = m.Flags.Read | m.Flags.Write
+    assert (state & m.Flags.Read) != 0
+    assert (state & m.Flags.Write) != 0
+    assert (state & m.Flags.Execute) == 0
+    assert (state & 1) == 0
+
+    state2 = ~state
+    assert state2 == -7
+    assert int(state ^ state2) == -1
+
+
+def test_enum_to_int():
+    m.test_enum_to_int(m.Flags.Read)
+    m.test_enum_to_int(m.ClassWithUnscopedEnum.EMode.EFirstMode)
+    m.test_enum_to_int(m.ScopedCharEnum.Positive)
+    m.test_enum_to_int(m.ScopedBoolEnum.TRUE)
+    m.test_enum_to_uint(m.Flags.Read)
+    m.test_enum_to_uint(m.ClassWithUnscopedEnum.EMode.EFirstMode)
+    m.test_enum_to_uint(m.ScopedCharEnum.Positive)
+    m.test_enum_to_uint(m.ScopedBoolEnum.TRUE)
+    m.test_enum_to_long_long(m.Flags.Read)
+    m.test_enum_to_long_long(m.ClassWithUnscopedEnum.EMode.EFirstMode)
+    m.test_enum_to_long_long(m.ScopedCharEnum.Positive)
+    m.test_enum_to_long_long(m.ScopedBoolEnum.TRUE)
+
+
+def test_duplicate_enum_name():
+    with pytest.raises(ValueError) as excinfo:
+        m.register_bad_enum()
+    assert str(excinfo.value) == 'SimpleEnum: element "ONE" already exists!'
+
+
+def test_char_underlying_enum():  # Issue #1331/PR #1334:
+    assert type(m.ScopedCharEnum.Positive.__int__()) is int
+    assert int(m.ScopedChar16Enum.Zero) == 0
+    assert hash(m.ScopedChar32Enum.Positive) == 1
+    assert type(m.ScopedCharEnum.Positive.__getstate__()) is int
+    assert m.ScopedWCharEnum(1) == m.ScopedWCharEnum.Positive
+    with pytest.raises(TypeError):
+        # Even if the underlying type is char, only an int can be used to construct the enum:
+        m.ScopedCharEnum("0")
+
+
+def test_bool_underlying_enum():
+    assert type(m.ScopedBoolEnum.TRUE.__int__()) is int
+    assert int(m.ScopedBoolEnum.FALSE) == 0
+    assert hash(m.ScopedBoolEnum.TRUE) == 1
+    assert type(m.ScopedBoolEnum.TRUE.__getstate__()) is int
+    assert m.ScopedBoolEnum(1) == m.ScopedBoolEnum.TRUE
+    # Enum could construct with a bool
+    # (bool is a strict subclass of int, and False will be converted to 0)
+    assert m.ScopedBoolEnum(False) == m.ScopedBoolEnum.FALSE
+
+
+def test_docstring_signatures():
+    for enum_type in [m.ScopedEnum, m.UnscopedEnum]:
+        for attr in enum_type.__dict__.values():
+            # Issue #2623/PR #2637: Add argument names to enum_ methods
+            assert "arg0" not in (attr.__doc__ or "")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval.cpp
new file mode 100644
index 000000000..cd2903f0a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval.cpp
@@ -0,0 +1,118 @@
+/*
+    tests/test_eval.cpp -- Usage of eval() and eval_file()
+
+    Copyright (c) 2016 Klemens D. Morgenstern
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/eval.h>
+
+#include "pybind11_tests.h"
+
+#include <utility>
+
+TEST_SUBMODULE(eval_, m) {
+    // test_evals
+
+    auto global = py::dict(py::module_::import("__main__").attr("__dict__"));
+
+    m.def("test_eval_statements", [global]() {
+        auto local = py::dict();
+        local["call_test"] = py::cpp_function([&]() -> int { return 42; });
+
+        // Regular string literal
+        py::exec("message = 'Hello World!'\n"
+                 "x = call_test()",
+                 global,
+                 local);
+
+        // Multi-line raw string literal
+        py::exec(R"(
+            if x == 42:
+                print(message)
+            else:
+                raise RuntimeError
+            )",
+                 global,
+                 local);
+        auto x = local["x"].cast<int>();
+
+        return x == 42;
+    });
+
+    m.def("test_eval", [global]() {
+        auto local = py::dict();
+        local["x"] = py::int_(42);
+        auto x = py::eval("x", global, local);
+        return x.cast<int>() == 42;
+    });
+
+    m.def("test_eval_single_statement", []() {
+        auto local = py::dict();
+        local["call_test"] = py::cpp_function([&]() -> int { return 42; });
+
+        auto result = py::eval<py::eval_single_statement>("x = call_test()", py::dict(), local);
+        auto x = local["x"].cast<int>();
+        return result.is_none() && x == 42;
+    });
+
+    m.def("test_eval_file", [global](py::str filename) {
+        auto local = py::dict();
+        local["y"] = py::int_(43);
+
+        int val_out = 0;
+        local["call_test2"] = py::cpp_function([&](int value) { val_out = value; });
+
+        auto result = py::eval_file(std::move(filename), global, local);
+        return val_out == 43 && result.is_none();
+    });
+
+    m.def("test_eval_failure", []() {
+        try {
+            py::eval("nonsense code ...");
+        } catch (py::error_already_set &) {
+            return true;
+        }
+        return false;
+    });
+
+    m.def("test_eval_file_failure", []() {
+        try {
+            py::eval_file("non-existing file");
+        } catch (std::exception &) {
+            return true;
+        }
+        return false;
+    });
+
+    // test_eval_empty_globals
+    m.def("eval_empty_globals", [](py::object global) {
+        if (global.is_none()) {
+            global = py::dict();
+        }
+        auto int_class = py::eval("isinstance(42, int)", global);
+        return global;
+    });
+
+    // test_eval_closure
+    m.def("test_eval_closure", []() {
+        py::dict global;
+        global["closure_value"] = 42;
+        py::dict local;
+        local["closure_value"] = 0;
+        py::exec(R"(
+            local_value = closure_value
+
+            def func_global():
+                return closure_value
+
+            def func_local():
+                return local_value
+            )",
+                 global,
+                 local);
+        return std::make_pair(global, local);
+    });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval.py
new file mode 100644
index 000000000..51b6b796b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval.py
@@ -0,0 +1,50 @@
+import os
+
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import eval_ as m
+
+
+def test_evals(capture):
+    with capture:
+        assert m.test_eval_statements()
+    assert capture == "Hello World!"
+
+    assert m.test_eval()
+    assert m.test_eval_single_statement()
+
+    assert m.test_eval_failure()
+
+
+@pytest.mark.xfail("env.PYPY", raises=RuntimeError)
+def test_eval_file():
+    filename = os.path.join(os.path.dirname(__file__), "test_eval_call.py")
+    assert m.test_eval_file(filename)
+
+    assert m.test_eval_file_failure()
+
+
+def test_eval_empty_globals():
+    assert "__builtins__" in m.eval_empty_globals(None)
+
+    g = {}
+    assert "__builtins__" in m.eval_empty_globals(g)
+    assert "__builtins__" in g
+
+
+def test_eval_closure():
+    global_, local = m.test_eval_closure()
+
+    assert global_["closure_value"] == 42
+    assert local["closure_value"] == 0
+
+    assert "local_value" not in global_
+    assert local["local_value"] == 0
+
+    assert "func_global" not in global_
+    assert local["func_global"]() == 42
+
+    assert "func_local" not in global_
+    with pytest.raises(NameError):
+        local["func_local"]()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval_call.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval_call.py
new file mode 100644
index 000000000..fd1da2a5c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_eval_call.py
@@ -0,0 +1,4 @@
+# This file is called from 'test_eval.py'
+
+if "call_test2" in locals():
+    call_test2(y)  # noqa: F821 undefined name
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.cpp
new file mode 100644
index 000000000..3ec999d1d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.cpp
@@ -0,0 +1,337 @@
+/*
+    tests/test_custom-exceptions.cpp -- exception translation
+
+    Copyright (c) 2016 Pim Schellart <P.Schellart@princeton.edu>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+#include "test_exceptions.h"
+
+#include "local_bindings.h"
+#include "pybind11_tests.h"
+
+#include <exception>
+#include <stdexcept>
+#include <utility>
+
+// A type that should be raised as an exception in Python
+class MyException : public std::exception {
+public:
+    explicit MyException(const char *m) : message{m} {}
+    const char *what() const noexcept override { return message.c_str(); }
+
+private:
+    std::string message = "";
+};
+
+// A type that should be translated to a standard Python exception
+class MyException2 : public std::exception {
+public:
+    explicit MyException2(const char *m) : message{m} {}
+    const char *what() const noexcept override { return message.c_str(); }
+
+private:
+    std::string message = "";
+};
+
+// A type that is not derived from std::exception (and is thus unknown)
+class MyException3 {
+public:
+    explicit MyException3(const char *m) : message{m} {}
+    virtual const char *what() const noexcept { return message.c_str(); }
+    // Rule of 5 BEGIN: to preempt compiler warnings.
+    MyException3(const MyException3 &) = default;
+    MyException3(MyException3 &&) = default;
+    MyException3 &operator=(const MyException3 &) = default;
+    MyException3 &operator=(MyException3 &&) = default;
+    virtual ~MyException3() = default;
+    // Rule of 5 END.
+private:
+    std::string message = "";
+};
+
+// A type that should be translated to MyException
+// and delegated to its exception translator
+class MyException4 : public std::exception {
+public:
+    explicit MyException4(const char *m) : message{m} {}
+    const char *what() const noexcept override { return message.c_str(); }
+
+private:
+    std::string message = "";
+};
+
+// Like the above, but declared via the helper function
+class MyException5 : public std::logic_error {
+public:
+    explicit MyException5(const std::string &what) : std::logic_error(what) {}
+};
+
+// Inherits from MyException5
+class MyException5_1 : public MyException5 {
+    using MyException5::MyException5;
+};
+
+// Exception that will be caught via the module local translator.
+class MyException6 : public std::exception {
+public:
+    explicit MyException6(const char *m) : message{m} {}
+    const char *what() const noexcept override { return message.c_str(); }
+
+private:
+    std::string message = "";
+};
+
+struct PythonCallInDestructor {
+    explicit PythonCallInDestructor(const py::dict &d) : d(d) {}
+    ~PythonCallInDestructor() { d["good"] = true; }
+
+    py::dict d;
+};
+
+struct PythonAlreadySetInDestructor {
+    explicit PythonAlreadySetInDestructor(const py::str &s) : s(s) {}
+    ~PythonAlreadySetInDestructor() {
+        py::dict foo;
+        try {
+            // Assign to a py::object to force read access of nonexistent dict entry
+            py::object o = foo["bar"];
+        } catch (py::error_already_set &ex) {
+            ex.discard_as_unraisable(s);
+        }
+    }
+
+    py::str s;
+};
+
+std::string error_already_set_what(const py::object &exc_type, const py::object &exc_value) {
+    PyErr_SetObject(exc_type.ptr(), exc_value.ptr());
+    return py::error_already_set().what();
+}
+
+TEST_SUBMODULE(exceptions, m) {
+    m.def("throw_std_exception",
+          []() { throw std::runtime_error("This exception was intentionally thrown."); });
+
+    // make a new custom exception and use it as a translation target
+    static py::exception<MyException> ex(m, "MyException");
+    py::register_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) {
+                std::rethrow_exception(p);
+            }
+        } catch (const MyException &e) {
+            // Set MyException as the active python error
+            ex(e.what());
+        }
+    });
+
+    // register new translator for MyException2
+    // no need to store anything here because this type will
+    // never by visible from Python
+    py::register_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) {
+                std::rethrow_exception(p);
+            }
+        } catch (const MyException2 &e) {
+            // Translate this exception to a standard RuntimeError
+            PyErr_SetString(PyExc_RuntimeError, e.what());
+        }
+    });
+
+    // register new translator for MyException4
+    // which will catch it and delegate to the previously registered
+    // translator for MyException by throwing a new exception
+    py::register_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) {
+                std::rethrow_exception(p);
+            }
+        } catch (const MyException4 &e) {
+            throw MyException(e.what());
+        }
+    });
+
+    // A simple exception translation:
+    auto ex5 = py::register_exception<MyException5>(m, "MyException5");
+    // A slightly more complicated one that declares MyException5_1 as a subclass of MyException5
+    py::register_exception<MyException5_1>(m, "MyException5_1", ex5.ptr());
+
+    // py::register_local_exception<LocalSimpleException>(m, "LocalSimpleException")
+
+    py::register_local_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) {
+                std::rethrow_exception(p);
+            }
+        } catch (const MyException6 &e) {
+            PyErr_SetString(PyExc_RuntimeError, e.what());
+        }
+    });
+
+    m.def("throws1", []() { throw MyException("this error should go to a custom type"); });
+    m.def("throws2",
+          []() { throw MyException2("this error should go to a standard Python exception"); });
+    m.def("throws3", []() { throw MyException3("this error cannot be translated"); });
+    m.def("throws4", []() { throw MyException4("this error is rethrown"); });
+    m.def("throws5",
+          []() { throw MyException5("this is a helper-defined translated exception"); });
+    m.def("throws5_1", []() { throw MyException5_1("MyException5 subclass"); });
+    m.def("throws6", []() { throw MyException6("MyException6 only handled in this module"); });
+    m.def("throws_logic_error", []() {
+        throw std::logic_error("this error should fall through to the standard handler");
+    });
+    m.def("throws_overflow_error", []() { throw std::overflow_error(""); });
+    m.def("throws_local_error", []() { throw LocalException("never caught"); });
+    m.def("throws_local_simple_error", []() { throw LocalSimpleException("this mod"); });
+    m.def("exception_matches", []() {
+        py::dict foo;
+        try {
+            // Assign to a py::object to force read access of nonexistent dict entry
+            py::object o = foo["bar"];
+        } catch (py::error_already_set &ex) {
+            if (!ex.matches(PyExc_KeyError)) {
+                throw;
+            }
+            return true;
+        }
+        return false;
+    });
+    m.def("exception_matches_base", []() {
+        py::dict foo;
+        try {
+            // Assign to a py::object to force read access of nonexistent dict entry
+            py::object o = foo["bar"];
+        } catch (py::error_already_set &ex) {
+            if (!ex.matches(PyExc_Exception)) {
+                throw;
+            }
+            return true;
+        }
+        return false;
+    });
+    m.def("modulenotfound_exception_matches_base", []() {
+        try {
+            // On Python >= 3.6, this raises a ModuleNotFoundError, a subclass of ImportError
+            py::module_::import("nonexistent");
+        } catch (py::error_already_set &ex) {
+            if (!ex.matches(PyExc_ImportError)) {
+                throw;
+            }
+            return true;
+        }
+        return false;
+    });
+
+    m.def("throw_already_set", [](bool err) {
+        if (err) {
+            PyErr_SetString(PyExc_ValueError, "foo");
+        }
+        try {
+            throw py::error_already_set();
+        } catch (const std::runtime_error &e) {
+            if ((err && e.what() != std::string("ValueError: foo"))
+                || (!err
+                    && e.what()
+                           != std::string("Internal error: pybind11::error_already_set called "
+                                          "while Python error indicator not set."))) {
+                PyErr_Clear();
+                throw std::runtime_error("error message mismatch");
+            }
+        }
+        PyErr_Clear();
+        if (err) {
+            PyErr_SetString(PyExc_ValueError, "foo");
+        }
+        throw py::error_already_set();
+    });
+
+    m.def("python_call_in_destructor", [](const py::dict &d) {
+        bool retval = false;
+        try {
+            PythonCallInDestructor set_dict_in_destructor(d);
+            PyErr_SetString(PyExc_ValueError, "foo");
+            throw py::error_already_set();
+        } catch (const py::error_already_set &) {
+            retval = true;
+        }
+        return retval;
+    });
+
+    m.def("python_alreadyset_in_destructor", [](const py::str &s) {
+        PythonAlreadySetInDestructor alreadyset_in_destructor(s);
+        return true;
+    });
+
+    // test_nested_throws
+    m.def("try_catch",
+          [m](const py::object &exc_type, const py::function &f, const py::args &args) {
+              try {
+                  f(*args);
+              } catch (py::error_already_set &ex) {
+                  if (ex.matches(exc_type)) {
+                      py::print(ex.what());
+                  } else {
+                      // Simply `throw;` also works and is better, but using `throw ex;`
+                      // here to cover that situation (as observed in the wild).
+                      throw ex; // Invokes the copy ctor.
+                  }
+              }
+          });
+
+    // Test repr that cannot be displayed
+    m.def("simple_bool_passthrough", [](bool x) { return x; });
+
+    m.def("throw_should_be_translated_to_key_error", []() { throw shared_exception(); });
+
+    m.def("raise_from", []() {
+        PyErr_SetString(PyExc_ValueError, "inner");
+        py::raise_from(PyExc_ValueError, "outer");
+        throw py::error_already_set();
+    });
+
+    m.def("raise_from_already_set", []() {
+        try {
+            PyErr_SetString(PyExc_ValueError, "inner");
+            throw py::error_already_set();
+        } catch (py::error_already_set &e) {
+            py::raise_from(e, PyExc_ValueError, "outer");
+            throw py::error_already_set();
+        }
+    });
+
+    m.def("throw_nested_exception", []() {
+        try {
+            throw std::runtime_error("Inner Exception");
+        } catch (const std::runtime_error &) {
+            std::throw_with_nested(std::runtime_error("Outer Exception"));
+        }
+    });
+
+    m.def("error_already_set_what", [](const py::object &exc_type, const py::object &exc_value) {
+        PyErr_SetObject(exc_type.ptr(), exc_value.ptr());
+        std::string what = py::error_already_set().what();
+        bool py_err_set_after_what = (PyErr_Occurred() != nullptr);
+        PyErr_Clear();
+        return py::make_tuple(std::move(what), py_err_set_after_what);
+    });
+
+    m.def("test_cross_module_interleaved_error_already_set", []() {
+        auto cm = py::module_::import("cross_module_interleaved_error_already_set");
+        auto interleaved_error_already_set
+            = reinterpret_cast<void (*)()>(PyLong_AsVoidPtr(cm.attr("funcaddr").ptr()));
+        interleaved_error_already_set();
+    });
+
+    m.def("test_error_already_set_double_restore", [](bool dry_run) {
+        PyErr_SetString(PyExc_ValueError, "Random error.");
+        py::error_already_set e;
+        e.restore();
+        PyErr_Clear();
+        if (!dry_run) {
+            e.restore();
+        }
+    });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.h b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.h
new file mode 100644
index 000000000..03684b89f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.h
@@ -0,0 +1,13 @@
+#pragma once
+#include "pybind11_tests.h"
+
+#include <stdexcept>
+
+// shared exceptions for cross_module_tests
+
+class PYBIND11_EXPORT_EXCEPTION shared_exception : public pybind11::builtin_exception {
+public:
+    using builtin_exception::builtin_exception;
+    explicit shared_exception() : shared_exception("") {}
+    void set_error() const override { PyErr_SetString(PyExc_RuntimeError, what()); }
+};
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.py
new file mode 100644
index 000000000..a5984a142
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_exceptions.py
@@ -0,0 +1,362 @@
+import sys
+
+import pytest
+
+import env
+import pybind11_cross_module_tests as cm
+from pybind11_tests import exceptions as m
+
+
+def test_std_exception(msg):
+    with pytest.raises(RuntimeError) as excinfo:
+        m.throw_std_exception()
+    assert msg(excinfo.value) == "This exception was intentionally thrown."
+
+
+def test_error_already_set(msg):
+    with pytest.raises(RuntimeError) as excinfo:
+        m.throw_already_set(False)
+    assert (
+        msg(excinfo.value)
+        == "Internal error: pybind11::error_already_set called while Python error indicator not set."
+    )
+
+    with pytest.raises(ValueError) as excinfo:
+        m.throw_already_set(True)
+    assert msg(excinfo.value) == "foo"
+
+
+def test_raise_from(msg):
+    with pytest.raises(ValueError) as excinfo:
+        m.raise_from()
+    assert msg(excinfo.value) == "outer"
+    assert msg(excinfo.value.__cause__) == "inner"
+
+
+def test_raise_from_already_set(msg):
+    with pytest.raises(ValueError) as excinfo:
+        m.raise_from_already_set()
+    assert msg(excinfo.value) == "outer"
+    assert msg(excinfo.value.__cause__) == "inner"
+
+
+def test_cross_module_exceptions(msg):
+    with pytest.raises(RuntimeError) as excinfo:
+        cm.raise_runtime_error()
+    assert str(excinfo.value) == "My runtime error"
+
+    with pytest.raises(ValueError) as excinfo:
+        cm.raise_value_error()
+    assert str(excinfo.value) == "My value error"
+
+    with pytest.raises(ValueError) as excinfo:
+        cm.throw_pybind_value_error()
+    assert str(excinfo.value) == "pybind11 value error"
+
+    with pytest.raises(TypeError) as excinfo:
+        cm.throw_pybind_type_error()
+    assert str(excinfo.value) == "pybind11 type error"
+
+    with pytest.raises(StopIteration) as excinfo:
+        cm.throw_stop_iteration()
+
+    with pytest.raises(cm.LocalSimpleException) as excinfo:
+        cm.throw_local_simple_error()
+    assert msg(excinfo.value) == "external mod"
+
+    with pytest.raises(KeyError) as excinfo:
+        cm.throw_local_error()
+    # KeyError is a repr of the key, so it has an extra set of quotes
+    assert str(excinfo.value) == "'just local'"
+
+
+# TODO: FIXME
+@pytest.mark.xfail(
+    "env.PYPY and env.MACOS",
+    raises=RuntimeError,
+    reason="Expected failure with PyPy and libc++ (Issue #2847 & PR #2999)",
+)
+def test_cross_module_exception_translator():
+    with pytest.raises(KeyError):
+        # translator registered in cross_module_tests
+        m.throw_should_be_translated_to_key_error()
+
+
+def test_python_call_in_catch():
+    d = {}
+    assert m.python_call_in_destructor(d) is True
+    assert d["good"] is True
+
+
+def ignore_pytest_unraisable_warning(f):
+    unraisable = "PytestUnraisableExceptionWarning"
+    if hasattr(pytest, unraisable):  # Python >= 3.8 and pytest >= 6
+        dec = pytest.mark.filterwarnings(f"ignore::pytest.{unraisable}")
+        return dec(f)
+    else:
+        return f
+
+
+# TODO: find out why this fails on PyPy, https://foss.heptapod.net/pypy/pypy/-/issues/3583
+@pytest.mark.xfail(env.PYPY, reason="Failure on PyPy 3.8 (7.3.7)", strict=False)
+@ignore_pytest_unraisable_warning
+def test_python_alreadyset_in_destructor(monkeypatch, capsys):
+    hooked = False
+    triggered = False
+
+    if hasattr(sys, "unraisablehook"):  # Python 3.8+
+        hooked = True
+        # Don't take `sys.unraisablehook`, as that's overwritten by pytest
+        default_hook = sys.__unraisablehook__
+
+        def hook(unraisable_hook_args):
+            exc_type, exc_value, exc_tb, err_msg, obj = unraisable_hook_args
+            if obj == "already_set demo":
+                nonlocal triggered
+                triggered = True
+            default_hook(unraisable_hook_args)
+            return
+
+        # Use monkeypatch so pytest can apply and remove the patch as appropriate
+        monkeypatch.setattr(sys, "unraisablehook", hook)
+
+    assert m.python_alreadyset_in_destructor("already_set demo") is True
+    if hooked:
+        assert triggered is True
+
+    _, captured_stderr = capsys.readouterr()
+    assert captured_stderr.startswith("Exception ignored in: 'already_set demo'")
+    assert captured_stderr.rstrip().endswith("KeyError: 'bar'")
+
+
+def test_exception_matches():
+    assert m.exception_matches()
+    assert m.exception_matches_base()
+    assert m.modulenotfound_exception_matches_base()
+
+
+def test_custom(msg):
+    # Can we catch a MyException?
+    with pytest.raises(m.MyException) as excinfo:
+        m.throws1()
+    assert msg(excinfo.value) == "this error should go to a custom type"
+
+    # Can we translate to standard Python exceptions?
+    with pytest.raises(RuntimeError) as excinfo:
+        m.throws2()
+    assert msg(excinfo.value) == "this error should go to a standard Python exception"
+
+    # Can we handle unknown exceptions?
+    with pytest.raises(RuntimeError) as excinfo:
+        m.throws3()
+    assert msg(excinfo.value) == "Caught an unknown exception!"
+
+    # Can we delegate to another handler by rethrowing?
+    with pytest.raises(m.MyException) as excinfo:
+        m.throws4()
+    assert msg(excinfo.value) == "this error is rethrown"
+
+    # Can we fall-through to the default handler?
+    with pytest.raises(RuntimeError) as excinfo:
+        m.throws_logic_error()
+    assert (
+        msg(excinfo.value) == "this error should fall through to the standard handler"
+    )
+
+    # OverFlow error translation.
+    with pytest.raises(OverflowError) as excinfo:
+        m.throws_overflow_error()
+
+    # Can we handle a helper-declared exception?
+    with pytest.raises(m.MyException5) as excinfo:
+        m.throws5()
+    assert msg(excinfo.value) == "this is a helper-defined translated exception"
+
+    # Exception subclassing:
+    with pytest.raises(m.MyException5) as excinfo:
+        m.throws5_1()
+    assert msg(excinfo.value) == "MyException5 subclass"
+    assert isinstance(excinfo.value, m.MyException5_1)
+
+    with pytest.raises(m.MyException5_1) as excinfo:
+        m.throws5_1()
+    assert msg(excinfo.value) == "MyException5 subclass"
+
+    with pytest.raises(m.MyException5) as excinfo:
+        try:
+            m.throws5()
+        except m.MyException5_1:
+            raise RuntimeError("Exception error: caught child from parent")
+    assert msg(excinfo.value) == "this is a helper-defined translated exception"
+
+
+def test_nested_throws(capture):
+    """Tests nested (e.g. C++ -> Python -> C++) exception handling"""
+
+    def throw_myex():
+        raise m.MyException("nested error")
+
+    def throw_myex5():
+        raise m.MyException5("nested error 5")
+
+    # In the comments below, the exception is caught in the first step, thrown in the last step
+
+    # C++ -> Python
+    with capture:
+        m.try_catch(m.MyException5, throw_myex5)
+    assert str(capture).startswith("MyException5: nested error 5")
+
+    # Python -> C++ -> Python
+    with pytest.raises(m.MyException) as excinfo:
+        m.try_catch(m.MyException5, throw_myex)
+    assert str(excinfo.value) == "nested error"
+
+    def pycatch(exctype, f, *args):
+        try:
+            f(*args)
+        except m.MyException as e:
+            print(e)
+
+    # C++ -> Python -> C++ -> Python
+    with capture:
+        m.try_catch(
+            m.MyException5,
+            pycatch,
+            m.MyException,
+            m.try_catch,
+            m.MyException,
+            throw_myex5,
+        )
+    assert str(capture).startswith("MyException5: nested error 5")
+
+    # C++ -> Python -> C++
+    with capture:
+        m.try_catch(m.MyException, pycatch, m.MyException5, m.throws4)
+    assert capture == "this error is rethrown"
+
+    # Python -> C++ -> Python -> C++
+    with pytest.raises(m.MyException5) as excinfo:
+        m.try_catch(m.MyException, pycatch, m.MyException, m.throws5)
+    assert str(excinfo.value) == "this is a helper-defined translated exception"
+
+
+def test_throw_nested_exception():
+    with pytest.raises(RuntimeError) as excinfo:
+        m.throw_nested_exception()
+    assert str(excinfo.value) == "Outer Exception"
+    assert str(excinfo.value.__cause__) == "Inner Exception"
+
+
+# This can often happen if you wrap a pybind11 class in a Python wrapper
+def test_invalid_repr():
+    class MyRepr:
+        def __repr__(self):
+            raise AttributeError("Example error")
+
+    with pytest.raises(TypeError):
+        m.simple_bool_passthrough(MyRepr())
+
+
+def test_local_translator(msg):
+    """Tests that a local translator works and that the local translator from
+    the cross module is not applied"""
+    with pytest.raises(RuntimeError) as excinfo:
+        m.throws6()
+    assert msg(excinfo.value) == "MyException6 only handled in this module"
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.throws_local_error()
+    assert not isinstance(excinfo.value, KeyError)
+    assert msg(excinfo.value) == "never caught"
+
+    with pytest.raises(Exception) as excinfo:
+        m.throws_local_simple_error()
+    assert not isinstance(excinfo.value, cm.LocalSimpleException)
+    assert msg(excinfo.value) == "this mod"
+
+
+class FlakyException(Exception):
+    def __init__(self, failure_point):
+        if failure_point == "failure_point_init":
+            raise ValueError("triggered_failure_point_init")
+        self.failure_point = failure_point
+
+    def __str__(self):
+        if self.failure_point == "failure_point_str":
+            raise ValueError("triggered_failure_point_str")
+        return "FlakyException.__str__"
+
+
+@pytest.mark.parametrize(
+    "exc_type, exc_value, expected_what",
+    (
+        (ValueError, "plain_str", "ValueError: plain_str"),
+        (ValueError, ("tuple_elem",), "ValueError: tuple_elem"),
+        (FlakyException, ("happy",), "FlakyException: FlakyException.__str__"),
+    ),
+)
+def test_error_already_set_what_with_happy_exceptions(
+    exc_type, exc_value, expected_what
+):
+    what, py_err_set_after_what = m.error_already_set_what(exc_type, exc_value)
+    assert not py_err_set_after_what
+    assert what == expected_what
+
+
+@pytest.mark.skipif("env.PYPY", reason="PyErr_NormalizeException Segmentation fault")
+def test_flaky_exception_failure_point_init():
+    with pytest.raises(RuntimeError) as excinfo:
+        m.error_already_set_what(FlakyException, ("failure_point_init",))
+    lines = str(excinfo.value).splitlines()
+    # PyErr_NormalizeException replaces the original FlakyException with ValueError:
+    assert lines[:3] == [
+        "pybind11::error_already_set: MISMATCH of original and normalized active exception types:"
+        " ORIGINAL FlakyException REPLACED BY ValueError: triggered_failure_point_init",
+        "",
+        "At:",
+    ]
+    # Checking the first two lines of the traceback as formatted in error_string():
+    assert "test_exceptions.py(" in lines[3]
+    assert lines[3].endswith("): __init__")
+    assert lines[4].endswith("): test_flaky_exception_failure_point_init")
+
+
+def test_flaky_exception_failure_point_str():
+    what, py_err_set_after_what = m.error_already_set_what(
+        FlakyException, ("failure_point_str",)
+    )
+    assert not py_err_set_after_what
+    lines = what.splitlines()
+    if env.PYPY and len(lines) == 3:
+        n = 3  # Traceback is missing.
+    else:
+        n = 5
+    assert (
+        lines[:n]
+        == [
+            "FlakyException: <MESSAGE UNAVAILABLE DUE TO ANOTHER EXCEPTION>",
+            "",
+            "MESSAGE UNAVAILABLE DUE TO EXCEPTION: ValueError: triggered_failure_point_str",
+            "",
+            "At:",
+        ][:n]
+    )
+
+
+def test_cross_module_interleaved_error_already_set():
+    with pytest.raises(RuntimeError) as excinfo:
+        m.test_cross_module_interleaved_error_already_set()
+    assert str(excinfo.value) in (
+        "2nd error.",  # Almost all platforms.
+        "RuntimeError: 2nd error.",  # Some PyPy builds (seen under macOS).
+    )
+
+
+def test_error_already_set_double_restore():
+    m.test_error_already_set_double_restore(True)  # dry_run
+    with pytest.raises(RuntimeError) as excinfo:
+        m.test_error_already_set_double_restore(False)
+    assert str(excinfo.value) == (
+        "Internal error: pybind11::detail::error_fetch_and_normalize::restore()"
+        " called a second time. ORIGINAL ERROR: ValueError: Random error."
+    )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_factory_constructors.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_factory_constructors.cpp
new file mode 100644
index 000000000..a387cd2e7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_factory_constructors.cpp
@@ -0,0 +1,430 @@
+/*
+    tests/test_factory_constructors.cpp -- tests construction from a factory function
+                                           via py::init_factory()
+
+    Copyright (c) 2017 Jason Rhinelander <jason@imaginary.ca>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#include <cmath>
+#include <new>
+#include <utility>
+
+// Classes for testing python construction via C++ factory function:
+// Not publicly constructible, copyable, or movable:
+class TestFactory1 {
+    friend class TestFactoryHelper;
+    TestFactory1() : value("(empty)") { print_default_created(this); }
+    explicit TestFactory1(int v) : value(std::to_string(v)) { print_created(this, value); }
+    explicit TestFactory1(std::string v) : value(std::move(v)) { print_created(this, value); }
+
+public:
+    std::string value;
+    TestFactory1(TestFactory1 &&) = delete;
+    TestFactory1(const TestFactory1 &) = delete;
+    TestFactory1 &operator=(TestFactory1 &&) = delete;
+    TestFactory1 &operator=(const TestFactory1 &) = delete;
+    ~TestFactory1() { print_destroyed(this); }
+};
+// Non-public construction, but moveable:
+class TestFactory2 {
+    friend class TestFactoryHelper;
+    TestFactory2() : value("(empty2)") { print_default_created(this); }
+    explicit TestFactory2(int v) : value(std::to_string(v)) { print_created(this, value); }
+    explicit TestFactory2(std::string v) : value(std::move(v)) { print_created(this, value); }
+
+public:
+    TestFactory2(TestFactory2 &&m) noexcept : value{std::move(m.value)} {
+        print_move_created(this);
+    }
+    TestFactory2 &operator=(TestFactory2 &&m) noexcept {
+        value = std::move(m.value);
+        print_move_assigned(this);
+        return *this;
+    }
+    std::string value;
+    ~TestFactory2() { print_destroyed(this); }
+};
+// Mixed direct/factory construction:
+class TestFactory3 {
+protected:
+    friend class TestFactoryHelper;
+    TestFactory3() : value("(empty3)") { print_default_created(this); }
+    explicit TestFactory3(int v) : value(std::to_string(v)) { print_created(this, value); }
+
+public:
+    explicit TestFactory3(std::string v) : value(std::move(v)) { print_created(this, value); }
+    TestFactory3(TestFactory3 &&m) noexcept : value{std::move(m.value)} {
+        print_move_created(this);
+    }
+    TestFactory3 &operator=(TestFactory3 &&m) noexcept {
+        value = std::move(m.value);
+        print_move_assigned(this);
+        return *this;
+    }
+    std::string value;
+    virtual ~TestFactory3() { print_destroyed(this); }
+};
+// Inheritance test
+class TestFactory4 : public TestFactory3 {
+public:
+    TestFactory4() : TestFactory3() { print_default_created(this); }
+    explicit TestFactory4(int v) : TestFactory3(v) { print_created(this, v); }
+    ~TestFactory4() override { print_destroyed(this); }
+};
+// Another class for an invalid downcast test
+class TestFactory5 : public TestFactory3 {
+public:
+    explicit TestFactory5(int i) : TestFactory3(i) { print_created(this, i); }
+    ~TestFactory5() override { print_destroyed(this); }
+};
+
+class TestFactory6 {
+protected:
+    int value;
+    bool alias = false;
+
+public:
+    explicit TestFactory6(int i) : value{i} { print_created(this, i); }
+    TestFactory6(TestFactory6 &&f) noexcept {
+        print_move_created(this);
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        value = f.value;
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        alias = f.alias;
+    }
+    TestFactory6(const TestFactory6 &f) {
+        print_copy_created(this);
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        value = f.value;
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        alias = f.alias;
+    }
+    virtual ~TestFactory6() { print_destroyed(this); }
+    virtual int get() { return value; }
+    bool has_alias() const { return alias; }
+};
+class PyTF6 : public TestFactory6 {
+public:
+    // Special constructor that allows the factory to construct a PyTF6 from a TestFactory6 only
+    // when an alias is needed:
+    explicit PyTF6(TestFactory6 &&base) : TestFactory6(std::move(base)) {
+        alias = true;
+        print_created(this, "move", value);
+    }
+    explicit PyTF6(int i) : TestFactory6(i) {
+        alias = true;
+        print_created(this, i);
+    }
+    PyTF6(PyTF6 &&f) noexcept : TestFactory6(std::move(f)) { print_move_created(this); }
+    PyTF6(const PyTF6 &f) : TestFactory6(f) { print_copy_created(this); }
+    explicit PyTF6(std::string s) : TestFactory6((int) s.size()) {
+        alias = true;
+        print_created(this, s);
+    }
+    ~PyTF6() override { print_destroyed(this); }
+    int get() override { PYBIND11_OVERRIDE(int, TestFactory6, get, /*no args*/); }
+};
+
+class TestFactory7 {
+protected:
+    int value;
+    bool alias = false;
+
+public:
+    explicit TestFactory7(int i) : value{i} { print_created(this, i); }
+    TestFactory7(TestFactory7 &&f) noexcept {
+        print_move_created(this);
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        value = f.value;
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        alias = f.alias;
+    }
+    TestFactory7(const TestFactory7 &f) {
+        print_copy_created(this);
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        value = f.value;
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        alias = f.alias;
+    }
+    virtual ~TestFactory7() { print_destroyed(this); }
+    virtual int get() { return value; }
+    bool has_alias() const { return alias; }
+};
+class PyTF7 : public TestFactory7 {
+public:
+    explicit PyTF7(int i) : TestFactory7(i) {
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        alias = true;
+        print_created(this, i);
+    }
+    PyTF7(PyTF7 &&f) noexcept : TestFactory7(std::move(f)) { print_move_created(this); }
+    PyTF7(const PyTF7 &f) : TestFactory7(f) { print_copy_created(this); }
+    ~PyTF7() override { print_destroyed(this); }
+    int get() override { PYBIND11_OVERRIDE(int, TestFactory7, get, /*no args*/); }
+};
+
+class TestFactoryHelper {
+public:
+    // Non-movable, non-copyable type:
+    // Return via pointer:
+    static TestFactory1 *construct1() { return new TestFactory1(); }
+    // Holder:
+    static std::unique_ptr<TestFactory1> construct1(int a) {
+        return std::unique_ptr<TestFactory1>(new TestFactory1(a));
+    }
+    // pointer again
+    static TestFactory1 *construct1_string(std::string a) {
+        return new TestFactory1(std::move(a));
+    }
+
+    // Moveable type:
+    // pointer:
+    static TestFactory2 *construct2() { return new TestFactory2(); }
+    // holder:
+    static std::unique_ptr<TestFactory2> construct2(int a) {
+        return std::unique_ptr<TestFactory2>(new TestFactory2(a));
+    }
+    // by value moving:
+    static TestFactory2 construct2(std::string a) { return TestFactory2(std::move(a)); }
+
+    // shared_ptr holder type:
+    // pointer:
+    static TestFactory3 *construct3() { return new TestFactory3(); }
+    // holder:
+    static std::shared_ptr<TestFactory3> construct3(int a) {
+        return std::shared_ptr<TestFactory3>(new TestFactory3(a));
+    }
+};
+
+TEST_SUBMODULE(factory_constructors, m) {
+
+    // Define various trivial types to allow simpler overload resolution:
+    py::module_ m_tag = m.def_submodule("tag");
+#define MAKE_TAG_TYPE(Name)                                                                       \
+    struct Name##_tag {};                                                                         \
+    py::class_<Name##_tag>(m_tag, #Name "_tag").def(py::init<>());                                \
+    m_tag.attr(#Name) = py::cast(Name##_tag{})
+    MAKE_TAG_TYPE(pointer);
+    MAKE_TAG_TYPE(unique_ptr);
+    MAKE_TAG_TYPE(move);
+    MAKE_TAG_TYPE(shared_ptr);
+    MAKE_TAG_TYPE(derived);
+    MAKE_TAG_TYPE(TF4);
+    MAKE_TAG_TYPE(TF5);
+    MAKE_TAG_TYPE(null_ptr);
+    MAKE_TAG_TYPE(null_unique_ptr);
+    MAKE_TAG_TYPE(null_shared_ptr);
+    MAKE_TAG_TYPE(base);
+    MAKE_TAG_TYPE(invalid_base);
+    MAKE_TAG_TYPE(alias);
+    MAKE_TAG_TYPE(unaliasable);
+    MAKE_TAG_TYPE(mixed);
+
+    // test_init_factory_basic, test_bad_type
+    py::class_<TestFactory1>(m, "TestFactory1")
+        .def(py::init([](unique_ptr_tag, int v) { return TestFactoryHelper::construct1(v); }))
+        .def(py::init(&TestFactoryHelper::construct1_string)) // raw function pointer
+        .def(py::init([](pointer_tag) { return TestFactoryHelper::construct1(); }))
+        .def(py::init(
+            [](py::handle, int v, py::handle) { return TestFactoryHelper::construct1(v); }))
+        .def_readwrite("value", &TestFactory1::value);
+    py::class_<TestFactory2>(m, "TestFactory2")
+        .def(py::init([](pointer_tag, int v) { return TestFactoryHelper::construct2(v); }))
+        .def(py::init([](unique_ptr_tag, std::string v) {
+            return TestFactoryHelper::construct2(std::move(v));
+        }))
+        .def(py::init([](move_tag) { return TestFactoryHelper::construct2(); }))
+        .def_readwrite("value", &TestFactory2::value);
+
+    // Stateful & reused:
+    int c = 1;
+    auto c4a = [c](pointer_tag, TF4_tag, int a) {
+        (void) c;
+        return new TestFactory4(a);
+    };
+
+    // test_init_factory_basic, test_init_factory_casting
+    py::class_<TestFactory3, std::shared_ptr<TestFactory3>> pyTestFactory3(m, "TestFactory3");
+    pyTestFactory3
+        .def(py::init([](pointer_tag, int v) { return TestFactoryHelper::construct3(v); }))
+        .def(py::init([](shared_ptr_tag) { return TestFactoryHelper::construct3(); }));
+    ignoreOldStyleInitWarnings([&pyTestFactory3]() {
+        pyTestFactory3.def("__init__", [](TestFactory3 &self, std::string v) {
+            new (&self) TestFactory3(std::move(v));
+        }); // placement-new ctor
+    });
+    pyTestFactory3
+        // factories returning a derived type:
+        .def(py::init(c4a)) // derived ptr
+        .def(py::init([](pointer_tag, TF5_tag, int a) { return new TestFactory5(a); }))
+        // derived shared ptr:
+        .def(py::init(
+            [](shared_ptr_tag, TF4_tag, int a) { return std::make_shared<TestFactory4>(a); }))
+        .def(py::init(
+            [](shared_ptr_tag, TF5_tag, int a) { return std::make_shared<TestFactory5>(a); }))
+
+        // Returns nullptr:
+        .def(py::init([](null_ptr_tag) { return (TestFactory3 *) nullptr; }))
+        .def(py::init([](null_unique_ptr_tag) { return std::unique_ptr<TestFactory3>(); }))
+        .def(py::init([](null_shared_ptr_tag) { return std::shared_ptr<TestFactory3>(); }))
+
+        .def_readwrite("value", &TestFactory3::value);
+
+    // test_init_factory_casting
+    py::class_<TestFactory4, TestFactory3, std::shared_ptr<TestFactory4>>(m, "TestFactory4")
+        .def(py::init(c4a)) // pointer
+        ;
+
+    // Doesn't need to be registered, but registering makes getting ConstructorStats easier:
+    py::class_<TestFactory5, TestFactory3, std::shared_ptr<TestFactory5>>(m, "TestFactory5");
+
+    // test_init_factory_alias
+    // Alias testing
+    py::class_<TestFactory6, PyTF6>(m, "TestFactory6")
+        .def(py::init([](base_tag, int i) { return TestFactory6(i); }))
+        .def(py::init([](alias_tag, int i) { return PyTF6(i); }))
+        .def(py::init([](alias_tag, std::string s) { return PyTF6(std::move(s)); }))
+        .def(py::init([](alias_tag, pointer_tag, int i) { return new PyTF6(i); }))
+        .def(py::init([](base_tag, pointer_tag, int i) { return new TestFactory6(i); }))
+        .def(py::init(
+            [](base_tag, alias_tag, pointer_tag, int i) { return (TestFactory6 *) new PyTF6(i); }))
+
+        .def("get", &TestFactory6::get)
+        .def("has_alias", &TestFactory6::has_alias)
+
+        .def_static(
+            "get_cstats", &ConstructorStats::get<TestFactory6>, py::return_value_policy::reference)
+        .def_static(
+            "get_alias_cstats", &ConstructorStats::get<PyTF6>, py::return_value_policy::reference);
+
+    // test_init_factory_dual
+    // Separate alias constructor testing
+    py::class_<TestFactory7, PyTF7, std::shared_ptr<TestFactory7>>(m, "TestFactory7")
+        .def(py::init([](int i) { return TestFactory7(i); }, [](int i) { return PyTF7(i); }))
+        .def(py::init([](pointer_tag, int i) { return new TestFactory7(i); },
+                      [](pointer_tag, int i) { return new PyTF7(i); }))
+        .def(py::init([](mixed_tag, int i) { return new TestFactory7(i); },
+                      [](mixed_tag, int i) { return PyTF7(i); }))
+        .def(py::init([](mixed_tag, const std::string &s) { return TestFactory7((int) s.size()); },
+                      [](mixed_tag, const std::string &s) { return new PyTF7((int) s.size()); }))
+        .def(py::init([](base_tag, pointer_tag, int i) { return new TestFactory7(i); },
+                      [](base_tag, pointer_tag, int i) { return (TestFactory7 *) new PyTF7(i); }))
+        .def(py::init([](alias_tag, pointer_tag, int i) { return new PyTF7(i); },
+                      [](alias_tag, pointer_tag, int i) { return new PyTF7(10 * i); }))
+        .def(py::init(
+            [](shared_ptr_tag, base_tag, int i) { return std::make_shared<TestFactory7>(i); },
+            [](shared_ptr_tag, base_tag, int i) {
+                auto *p = new PyTF7(i);
+                return std::shared_ptr<TestFactory7>(p);
+            }))
+        .def(py::init([](shared_ptr_tag,
+                         invalid_base_tag,
+                         int i) { return std::make_shared<TestFactory7>(i); },
+                      [](shared_ptr_tag, invalid_base_tag, int i) {
+                          return std::make_shared<TestFactory7>(i);
+                      })) // <-- invalid alias factory
+
+        .def("get", &TestFactory7::get)
+        .def("has_alias", &TestFactory7::has_alias)
+
+        .def_static(
+            "get_cstats", &ConstructorStats::get<TestFactory7>, py::return_value_policy::reference)
+        .def_static(
+            "get_alias_cstats", &ConstructorStats::get<PyTF7>, py::return_value_policy::reference);
+
+    // test_placement_new_alternative
+    // Class with a custom new operator but *without* a placement new operator (issue #948)
+    class NoPlacementNew {
+    public:
+        explicit NoPlacementNew(int i) : i(i) {}
+        static void *operator new(std::size_t s) {
+            auto *p = ::operator new(s);
+            py::print("operator new called, returning", reinterpret_cast<uintptr_t>(p));
+            return p;
+        }
+        static void operator delete(void *p) {
+            py::print("operator delete called on", reinterpret_cast<uintptr_t>(p));
+            ::operator delete(p);
+        }
+        int i;
+    };
+    // As of 2.2, `py::init<args>` no longer requires placement new
+    py::class_<NoPlacementNew>(m, "NoPlacementNew")
+        .def(py::init<int>())
+        .def(py::init([]() { return new NoPlacementNew(100); }))
+        .def_readwrite("i", &NoPlacementNew::i);
+
+    // test_reallocations
+    // Class that has verbose operator_new/operator_delete calls
+    struct NoisyAlloc {
+        NoisyAlloc(const NoisyAlloc &) = default;
+        explicit NoisyAlloc(int i) { py::print(py::str("NoisyAlloc(int {})").format(i)); }
+        explicit NoisyAlloc(double d) { py::print(py::str("NoisyAlloc(double {})").format(d)); }
+        ~NoisyAlloc() { py::print("~NoisyAlloc()"); }
+
+        static void *operator new(size_t s) {
+            py::print("noisy new");
+            return ::operator new(s);
+        }
+        static void *operator new(size_t, void *p) {
+            py::print("noisy placement new");
+            return p;
+        }
+        static void operator delete(void *p, size_t) {
+            py::print("noisy delete");
+            ::operator delete(p);
+        }
+        static void operator delete(void *, void *) { py::print("noisy placement delete"); }
+    };
+
+    py::class_<NoisyAlloc> pyNoisyAlloc(m, "NoisyAlloc");
+    // Since these overloads have the same number of arguments, the dispatcher will try each of
+    // them until the arguments convert.  Thus we can get a pre-allocation here when passing a
+    // single non-integer:
+    ignoreOldStyleInitWarnings([&pyNoisyAlloc]() {
+        pyNoisyAlloc.def("__init__", [](NoisyAlloc *a, int i) {
+            new (a) NoisyAlloc(i);
+        }); // Regular constructor, runs first, requires preallocation
+    });
+
+    pyNoisyAlloc.def(py::init([](double d) { return new NoisyAlloc(d); }));
+
+    // The two-argument version: first the factory pointer overload.
+    pyNoisyAlloc.def(py::init([](int i, int) { return new NoisyAlloc(i); }));
+    // Return-by-value:
+    pyNoisyAlloc.def(py::init([](double d, int) { return NoisyAlloc(d); }));
+    // Old-style placement new init; requires preallocation
+    ignoreOldStyleInitWarnings([&pyNoisyAlloc]() {
+        pyNoisyAlloc.def("__init__",
+                         [](NoisyAlloc &a, double d, double) { new (&a) NoisyAlloc(d); });
+    });
+    // Requires deallocation of previous overload preallocated value:
+    pyNoisyAlloc.def(py::init([](int i, double) { return new NoisyAlloc(i); }));
+    // Regular again: requires yet another preallocation
+    ignoreOldStyleInitWarnings([&pyNoisyAlloc]() {
+        pyNoisyAlloc.def(
+            "__init__", [](NoisyAlloc &a, int i, const std::string &) { new (&a) NoisyAlloc(i); });
+    });
+
+    // static_assert testing (the following def's should all fail with appropriate compilation
+    // errors):
+#if 0
+    struct BadF1Base {};
+    struct BadF1 : BadF1Base {};
+    struct PyBadF1 : BadF1 {};
+    py::class_<BadF1, PyBadF1, std::shared_ptr<BadF1>> bf1(m, "BadF1");
+    // wrapped factory function must return a compatible pointer, holder, or value
+    bf1.def(py::init([]() { return 3; }));
+    // incompatible factory function pointer return type
+    bf1.def(py::init([]() { static int three = 3; return &three; }));
+    // incompatible factory function std::shared_ptr<T> return type: cannot convert shared_ptr<T> to holder
+    // (non-polymorphic base)
+    bf1.def(py::init([]() { return std::shared_ptr<BadF1Base>(new BadF1()); }));
+#endif
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_factory_constructors.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_factory_constructors.py
new file mode 100644
index 000000000..120a587c4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_factory_constructors.py
@@ -0,0 +1,516 @@
+import re
+
+import pytest
+
+from pybind11_tests import ConstructorStats
+from pybind11_tests import factory_constructors as m
+from pybind11_tests.factory_constructors import tag
+
+
+def test_init_factory_basic():
+    """Tests py::init_factory() wrapper around various ways of returning the object"""
+
+    cstats = [
+        ConstructorStats.get(c)
+        for c in [m.TestFactory1, m.TestFactory2, m.TestFactory3]
+    ]
+    cstats[0].alive()  # force gc
+    n_inst = ConstructorStats.detail_reg_inst()
+
+    x1 = m.TestFactory1(tag.unique_ptr, 3)
+    assert x1.value == "3"
+    y1 = m.TestFactory1(tag.pointer)
+    assert y1.value == "(empty)"
+    z1 = m.TestFactory1("hi!")
+    assert z1.value == "hi!"
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 3
+
+    x2 = m.TestFactory2(tag.move)
+    assert x2.value == "(empty2)"
+    y2 = m.TestFactory2(tag.pointer, 7)
+    assert y2.value == "7"
+    z2 = m.TestFactory2(tag.unique_ptr, "hi again")
+    assert z2.value == "hi again"
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 6
+
+    x3 = m.TestFactory3(tag.shared_ptr)
+    assert x3.value == "(empty3)"
+    y3 = m.TestFactory3(tag.pointer, 42)
+    assert y3.value == "42"
+    z3 = m.TestFactory3("bye")
+    assert z3.value == "bye"
+
+    for null_ptr_kind in [tag.null_ptr, tag.null_unique_ptr, tag.null_shared_ptr]:
+        with pytest.raises(TypeError) as excinfo:
+            m.TestFactory3(null_ptr_kind)
+        assert (
+            str(excinfo.value) == "pybind11::init(): factory function returned nullptr"
+        )
+
+    assert [i.alive() for i in cstats] == [3, 3, 3]
+    assert ConstructorStats.detail_reg_inst() == n_inst + 9
+
+    del x1, y2, y3, z3
+    assert [i.alive() for i in cstats] == [2, 2, 1]
+    assert ConstructorStats.detail_reg_inst() == n_inst + 5
+    del x2, x3, y1, z1, z2
+    assert [i.alive() for i in cstats] == [0, 0, 0]
+    assert ConstructorStats.detail_reg_inst() == n_inst
+
+    assert [i.values() for i in cstats] == [
+        ["3", "hi!"],
+        ["7", "hi again"],
+        ["42", "bye"],
+    ]
+    assert [i.default_constructions for i in cstats] == [1, 1, 1]
+
+
+def test_init_factory_signature(msg):
+    with pytest.raises(TypeError) as excinfo:
+        m.TestFactory1("invalid", "constructor", "arguments")
+    assert (
+        msg(excinfo.value)
+        == """
+        __init__(): incompatible constructor arguments. The following argument types are supported:
+            1. m.factory_constructors.TestFactory1(arg0: m.factory_constructors.tag.unique_ptr_tag, arg1: int)
+            2. m.factory_constructors.TestFactory1(arg0: str)
+            3. m.factory_constructors.TestFactory1(arg0: m.factory_constructors.tag.pointer_tag)
+            4. m.factory_constructors.TestFactory1(arg0: handle, arg1: int, arg2: handle)
+
+        Invoked with: 'invalid', 'constructor', 'arguments'
+    """
+    )
+
+    assert (
+        msg(m.TestFactory1.__init__.__doc__)
+        == """
+        __init__(*args, **kwargs)
+        Overloaded function.
+
+        1. __init__(self: m.factory_constructors.TestFactory1, arg0: m.factory_constructors.tag.unique_ptr_tag, arg1: int) -> None
+
+        2. __init__(self: m.factory_constructors.TestFactory1, arg0: str) -> None
+
+        3. __init__(self: m.factory_constructors.TestFactory1, arg0: m.factory_constructors.tag.pointer_tag) -> None
+
+        4. __init__(self: m.factory_constructors.TestFactory1, arg0: handle, arg1: int, arg2: handle) -> None
+    """  # noqa: E501 line too long
+    )
+
+
+def test_init_factory_casting():
+    """Tests py::init_factory() wrapper with various upcasting and downcasting returns"""
+
+    cstats = [
+        ConstructorStats.get(c)
+        for c in [m.TestFactory3, m.TestFactory4, m.TestFactory5]
+    ]
+    cstats[0].alive()  # force gc
+    n_inst = ConstructorStats.detail_reg_inst()
+
+    # Construction from derived references:
+    a = m.TestFactory3(tag.pointer, tag.TF4, 4)
+    assert a.value == "4"
+    b = m.TestFactory3(tag.shared_ptr, tag.TF4, 5)
+    assert b.value == "5"
+    c = m.TestFactory3(tag.pointer, tag.TF5, 6)
+    assert c.value == "6"
+    d = m.TestFactory3(tag.shared_ptr, tag.TF5, 7)
+    assert d.value == "7"
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 4
+
+    # Shared a lambda with TF3:
+    e = m.TestFactory4(tag.pointer, tag.TF4, 8)
+    assert e.value == "8"
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 5
+    assert [i.alive() for i in cstats] == [5, 3, 2]
+
+    del a
+    assert [i.alive() for i in cstats] == [4, 2, 2]
+    assert ConstructorStats.detail_reg_inst() == n_inst + 4
+
+    del b, c, e
+    assert [i.alive() for i in cstats] == [1, 0, 1]
+    assert ConstructorStats.detail_reg_inst() == n_inst + 1
+
+    del d
+    assert [i.alive() for i in cstats] == [0, 0, 0]
+    assert ConstructorStats.detail_reg_inst() == n_inst
+
+    assert [i.values() for i in cstats] == [
+        ["4", "5", "6", "7", "8"],
+        ["4", "5", "8"],
+        ["6", "7"],
+    ]
+
+
+def test_init_factory_alias():
+    """Tests py::init_factory() wrapper with value conversions and alias types"""
+
+    cstats = [m.TestFactory6.get_cstats(), m.TestFactory6.get_alias_cstats()]
+    cstats[0].alive()  # force gc
+    n_inst = ConstructorStats.detail_reg_inst()
+
+    a = m.TestFactory6(tag.base, 1)
+    assert a.get() == 1
+    assert not a.has_alias()
+    b = m.TestFactory6(tag.alias, "hi there")
+    assert b.get() == 8
+    assert b.has_alias()
+    c = m.TestFactory6(tag.alias, 3)
+    assert c.get() == 3
+    assert c.has_alias()
+    d = m.TestFactory6(tag.alias, tag.pointer, 4)
+    assert d.get() == 4
+    assert d.has_alias()
+    e = m.TestFactory6(tag.base, tag.pointer, 5)
+    assert e.get() == 5
+    assert not e.has_alias()
+    f = m.TestFactory6(tag.base, tag.alias, tag.pointer, 6)
+    assert f.get() == 6
+    assert f.has_alias()
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 6
+    assert [i.alive() for i in cstats] == [6, 4]
+
+    del a, b, e
+    assert [i.alive() for i in cstats] == [3, 3]
+    assert ConstructorStats.detail_reg_inst() == n_inst + 3
+    del f, c, d
+    assert [i.alive() for i in cstats] == [0, 0]
+    assert ConstructorStats.detail_reg_inst() == n_inst
+
+    class MyTest(m.TestFactory6):
+        def __init__(self, *args):
+            m.TestFactory6.__init__(self, *args)
+
+        def get(self):
+            return -5 + m.TestFactory6.get(self)
+
+    # Return Class by value, moved into new alias:
+    z = MyTest(tag.base, 123)
+    assert z.get() == 118
+    assert z.has_alias()
+
+    # Return alias by value, moved into new alias:
+    y = MyTest(tag.alias, "why hello!")
+    assert y.get() == 5
+    assert y.has_alias()
+
+    # Return Class by pointer, moved into new alias then original destroyed:
+    x = MyTest(tag.base, tag.pointer, 47)
+    assert x.get() == 42
+    assert x.has_alias()
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 3
+    assert [i.alive() for i in cstats] == [3, 3]
+    del x, y, z
+    assert [i.alive() for i in cstats] == [0, 0]
+    assert ConstructorStats.detail_reg_inst() == n_inst
+
+    assert [i.values() for i in cstats] == [
+        ["1", "8", "3", "4", "5", "6", "123", "10", "47"],
+        ["hi there", "3", "4", "6", "move", "123", "why hello!", "move", "47"],
+    ]
+
+
+def test_init_factory_dual():
+    """Tests init factory functions with dual main/alias factory functions"""
+    from pybind11_tests.factory_constructors import TestFactory7
+
+    cstats = [TestFactory7.get_cstats(), TestFactory7.get_alias_cstats()]
+    cstats[0].alive()  # force gc
+    n_inst = ConstructorStats.detail_reg_inst()
+
+    class PythFactory7(TestFactory7):
+        def get(self):
+            return 100 + TestFactory7.get(self)
+
+    a1 = TestFactory7(1)
+    a2 = PythFactory7(2)
+    assert a1.get() == 1
+    assert a2.get() == 102
+    assert not a1.has_alias()
+    assert a2.has_alias()
+
+    b1 = TestFactory7(tag.pointer, 3)
+    b2 = PythFactory7(tag.pointer, 4)
+    assert b1.get() == 3
+    assert b2.get() == 104
+    assert not b1.has_alias()
+    assert b2.has_alias()
+
+    c1 = TestFactory7(tag.mixed, 5)
+    c2 = PythFactory7(tag.mixed, 6)
+    assert c1.get() == 5
+    assert c2.get() == 106
+    assert not c1.has_alias()
+    assert c2.has_alias()
+
+    d1 = TestFactory7(tag.base, tag.pointer, 7)
+    d2 = PythFactory7(tag.base, tag.pointer, 8)
+    assert d1.get() == 7
+    assert d2.get() == 108
+    assert not d1.has_alias()
+    assert d2.has_alias()
+
+    # Both return an alias; the second multiplies the value by 10:
+    e1 = TestFactory7(tag.alias, tag.pointer, 9)
+    e2 = PythFactory7(tag.alias, tag.pointer, 10)
+    assert e1.get() == 9
+    assert e2.get() == 200
+    assert e1.has_alias()
+    assert e2.has_alias()
+
+    f1 = TestFactory7(tag.shared_ptr, tag.base, 11)
+    f2 = PythFactory7(tag.shared_ptr, tag.base, 12)
+    assert f1.get() == 11
+    assert f2.get() == 112
+    assert not f1.has_alias()
+    assert f2.has_alias()
+
+    g1 = TestFactory7(tag.shared_ptr, tag.invalid_base, 13)
+    assert g1.get() == 13
+    assert not g1.has_alias()
+    with pytest.raises(TypeError) as excinfo:
+        PythFactory7(tag.shared_ptr, tag.invalid_base, 14)
+    assert (
+        str(excinfo.value)
+        == "pybind11::init(): construction failed: returned holder-wrapped instance is not an "
+        "alias instance"
+    )
+
+    assert [i.alive() for i in cstats] == [13, 7]
+    assert ConstructorStats.detail_reg_inst() == n_inst + 13
+
+    del a1, a2, b1, d1, e1, e2
+    assert [i.alive() for i in cstats] == [7, 4]
+    assert ConstructorStats.detail_reg_inst() == n_inst + 7
+    del b2, c1, c2, d2, f1, f2, g1
+    assert [i.alive() for i in cstats] == [0, 0]
+    assert ConstructorStats.detail_reg_inst() == n_inst
+
+    assert [i.values() for i in cstats] == [
+        ["1", "2", "3", "4", "5", "6", "7", "8", "9", "100", "11", "12", "13", "14"],
+        ["2", "4", "6", "8", "9", "100", "12"],
+    ]
+
+
+def test_no_placement_new(capture):
+    """Prior to 2.2, `py::init<...>` relied on the type supporting placement
+    new; this tests a class without placement new support."""
+    with capture:
+        a = m.NoPlacementNew(123)
+
+    found = re.search(r"^operator new called, returning (\d+)\n$", str(capture))
+    assert found
+    assert a.i == 123
+    with capture:
+        del a
+        pytest.gc_collect()
+    assert capture == "operator delete called on " + found.group(1)
+
+    with capture:
+        b = m.NoPlacementNew()
+
+    found = re.search(r"^operator new called, returning (\d+)\n$", str(capture))
+    assert found
+    assert b.i == 100
+    with capture:
+        del b
+        pytest.gc_collect()
+    assert capture == "operator delete called on " + found.group(1)
+
+
+def test_multiple_inheritance():
+    class MITest(m.TestFactory1, m.TestFactory2):
+        def __init__(self):
+            m.TestFactory1.__init__(self, tag.unique_ptr, 33)
+            m.TestFactory2.__init__(self, tag.move)
+
+    a = MITest()
+    assert m.TestFactory1.value.fget(a) == "33"
+    assert m.TestFactory2.value.fget(a) == "(empty2)"
+
+
+def create_and_destroy(*args):
+    a = m.NoisyAlloc(*args)
+    print("---")
+    del a
+    pytest.gc_collect()
+
+
+def strip_comments(s):
+    return re.sub(r"\s+#.*", "", s)
+
+
+def test_reallocation_a(capture, msg):
+    """When the constructor is overloaded, previous overloads can require a preallocated value.
+    This test makes sure that such preallocated values only happen when they might be necessary,
+    and that they are deallocated properly."""
+
+    pytest.gc_collect()
+
+    with capture:
+        create_and_destroy(1)
+    assert (
+        msg(capture)
+        == """
+        noisy new
+        noisy placement new
+        NoisyAlloc(int 1)
+        ---
+        ~NoisyAlloc()
+        noisy delete
+    """
+    )
+
+
+def test_reallocation_b(capture, msg):
+    with capture:
+        create_and_destroy(1.5)
+    assert msg(capture) == strip_comments(
+        """
+        noisy new               # allocation required to attempt first overload
+        noisy delete            # have to dealloc before considering factory init overload
+        noisy new               # pointer factory calling "new", part 1: allocation
+        NoisyAlloc(double 1.5)  # ... part two, invoking constructor
+        ---
+        ~NoisyAlloc()  # Destructor
+        noisy delete   # operator delete
+    """
+    )
+
+
+def test_reallocation_c(capture, msg):
+    with capture:
+        create_and_destroy(2, 3)
+    assert msg(capture) == strip_comments(
+        """
+        noisy new          # pointer factory calling "new", allocation
+        NoisyAlloc(int 2)  # constructor
+        ---
+        ~NoisyAlloc()  # Destructor
+        noisy delete   # operator delete
+    """
+    )
+
+
+def test_reallocation_d(capture, msg):
+    with capture:
+        create_and_destroy(2.5, 3)
+    assert msg(capture) == strip_comments(
+        """
+        NoisyAlloc(double 2.5)  # construction (local func variable: operator_new not called)
+        noisy new               # return-by-value "new" part 1: allocation
+        ~NoisyAlloc()           # moved-away local func variable destruction
+        ---
+        ~NoisyAlloc()  # Destructor
+        noisy delete   # operator delete
+    """
+    )
+
+
+def test_reallocation_e(capture, msg):
+    with capture:
+        create_and_destroy(3.5, 4.5)
+    assert msg(capture) == strip_comments(
+        """
+        noisy new               # preallocation needed before invoking placement-new overload
+        noisy placement new     # Placement new
+        NoisyAlloc(double 3.5)  # construction
+        ---
+        ~NoisyAlloc()  # Destructor
+        noisy delete   # operator delete
+    """
+    )
+
+
+def test_reallocation_f(capture, msg):
+    with capture:
+        create_and_destroy(4, 0.5)
+    assert msg(capture) == strip_comments(
+        """
+        noisy new          # preallocation needed before invoking placement-new overload
+        noisy delete       # deallocation of preallocated storage
+        noisy new          # Factory pointer allocation
+        NoisyAlloc(int 4)  # factory pointer construction
+        ---
+        ~NoisyAlloc()  # Destructor
+        noisy delete   # operator delete
+    """
+    )
+
+
+def test_reallocation_g(capture, msg):
+    with capture:
+        create_and_destroy(5, "hi")
+    assert msg(capture) == strip_comments(
+        """
+        noisy new            # preallocation needed before invoking first placement new
+        noisy delete         # delete before considering new-style constructor
+        noisy new            # preallocation for second placement new
+        noisy placement new  # Placement new in the second placement new overload
+        NoisyAlloc(int 5)    # construction
+        ---
+        ~NoisyAlloc()  # Destructor
+        noisy delete   # operator delete
+    """
+    )
+
+
+def test_invalid_self():
+    """Tests invocation of the pybind-registered base class with an invalid `self` argument."""
+
+    class NotPybindDerived:
+        pass
+
+    # Attempts to initialize with an invalid type passed as `self`:
+    class BrokenTF1(m.TestFactory1):
+        def __init__(self, bad):
+            if bad == 1:
+                a = m.TestFactory2(tag.pointer, 1)
+                m.TestFactory1.__init__(a, tag.pointer)
+            elif bad == 2:
+                a = NotPybindDerived()
+                m.TestFactory1.__init__(a, tag.pointer)
+
+    # Same as above, but for a class with an alias:
+    class BrokenTF6(m.TestFactory6):
+        def __init__(self, bad):
+            if bad == 0:
+                m.TestFactory6.__init__()
+            elif bad == 1:
+                a = m.TestFactory2(tag.pointer, 1)
+                m.TestFactory6.__init__(a, tag.base, 1)
+            elif bad == 2:
+                a = m.TestFactory2(tag.pointer, 1)
+                m.TestFactory6.__init__(a, tag.alias, 1)
+            elif bad == 3:
+                m.TestFactory6.__init__(
+                    NotPybindDerived.__new__(NotPybindDerived), tag.base, 1
+                )
+            elif bad == 4:
+                m.TestFactory6.__init__(
+                    NotPybindDerived.__new__(NotPybindDerived), tag.alias, 1
+                )
+
+    for arg in (1, 2):
+        with pytest.raises(TypeError) as excinfo:
+            BrokenTF1(arg)
+        assert (
+            str(excinfo.value)
+            == "__init__(self, ...) called with invalid or missing `self` argument"
+        )
+
+    for arg in (0, 1, 2, 3, 4):
+        with pytest.raises(TypeError) as excinfo:
+            BrokenTF6(arg)
+        assert (
+            str(excinfo.value)
+            == "__init__(self, ...) called with invalid or missing `self` argument"
+        )
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_gil_scoped.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_gil_scoped.cpp
new file mode 100644
index 000000000..97efdc161
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_gil_scoped.cpp
@@ -0,0 +1,47 @@
+/*
+    tests/test_gil_scoped.cpp -- acquire and release gil
+
+    Copyright (c) 2017 Borja Zarco (Google LLC) <bzarco@google.com>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/functional.h>
+
+#include "pybind11_tests.h"
+
+class VirtClass {
+public:
+    virtual ~VirtClass() = default;
+    VirtClass() = default;
+    VirtClass(const VirtClass &) = delete;
+    virtual void virtual_func() {}
+    virtual void pure_virtual_func() = 0;
+};
+
+class PyVirtClass : public VirtClass {
+    void virtual_func() override { PYBIND11_OVERRIDE(void, VirtClass, virtual_func, ); }
+    void pure_virtual_func() override {
+        PYBIND11_OVERRIDE_PURE(void, VirtClass, pure_virtual_func, );
+    }
+};
+
+TEST_SUBMODULE(gil_scoped, m) {
+    py::class_<VirtClass, PyVirtClass>(m, "VirtClass")
+        .def(py::init<>())
+        .def("virtual_func", &VirtClass::virtual_func)
+        .def("pure_virtual_func", &VirtClass::pure_virtual_func);
+
+    m.def("test_callback_py_obj", [](py::object &func) { func(); });
+    m.def("test_callback_std_func", [](const std::function<void()> &func) { func(); });
+    m.def("test_callback_virtual_func", [](VirtClass &virt) { virt.virtual_func(); });
+    m.def("test_callback_pure_virtual_func", [](VirtClass &virt) { virt.pure_virtual_func(); });
+    m.def("test_cross_module_gil", []() {
+        auto cm = py::module_::import("cross_module_gil_utils");
+        auto gil_acquire = reinterpret_cast<void (*)()>(
+            PyLong_AsVoidPtr(cm.attr("gil_acquire_funcaddr").ptr()));
+        py::gil_scoped_release gil_release;
+        gil_acquire();
+    });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_gil_scoped.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_gil_scoped.py
new file mode 100644
index 000000000..52374b0cc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_gil_scoped.py
@@ -0,0 +1,93 @@
+import multiprocessing
+import threading
+
+from pybind11_tests import gil_scoped as m
+
+
+def _run_in_process(target, *args, **kwargs):
+    """Runs target in process and returns its exitcode after 10s (None if still alive)."""
+    process = multiprocessing.Process(target=target, args=args, kwargs=kwargs)
+    process.daemon = True
+    try:
+        process.start()
+        # Do not need to wait much, 10s should be more than enough.
+        process.join(timeout=10)
+        return process.exitcode
+    finally:
+        if process.is_alive():
+            process.terminate()
+
+
+def _python_to_cpp_to_python():
+    """Calls different C++ functions that come back to Python."""
+
+    class ExtendedVirtClass(m.VirtClass):
+        def virtual_func(self):
+            pass
+
+        def pure_virtual_func(self):
+            pass
+
+    extended = ExtendedVirtClass()
+    m.test_callback_py_obj(lambda: None)
+    m.test_callback_std_func(lambda: None)
+    m.test_callback_virtual_func(extended)
+    m.test_callback_pure_virtual_func(extended)
+
+
+def _python_to_cpp_to_python_from_threads(num_threads, parallel=False):
+    """Calls different C++ functions that come back to Python, from Python threads."""
+    threads = []
+    for _ in range(num_threads):
+        thread = threading.Thread(target=_python_to_cpp_to_python)
+        thread.daemon = True
+        thread.start()
+        if parallel:
+            threads.append(thread)
+        else:
+            thread.join()
+    for thread in threads:
+        thread.join()
+
+
+# TODO: FIXME, sometimes returns -11 (segfault) instead of 0 on macOS Python 3.9
+def test_python_to_cpp_to_python_from_thread():
+    """Makes sure there is no GIL deadlock when running in a thread.
+
+    It runs in a separate process to be able to stop and assert if it deadlocks.
+    """
+    assert _run_in_process(_python_to_cpp_to_python_from_threads, 1) == 0
+
+
+# TODO: FIXME on macOS Python 3.9
+def test_python_to_cpp_to_python_from_thread_multiple_parallel():
+    """Makes sure there is no GIL deadlock when running in a thread multiple times in parallel.
+
+    It runs in a separate process to be able to stop and assert if it deadlocks.
+    """
+    assert _run_in_process(_python_to_cpp_to_python_from_threads, 8, parallel=True) == 0
+
+
+# TODO: FIXME on macOS Python 3.9
+def test_python_to_cpp_to_python_from_thread_multiple_sequential():
+    """Makes sure there is no GIL deadlock when running in a thread multiple times sequentially.
+
+    It runs in a separate process to be able to stop and assert if it deadlocks.
+    """
+    assert (
+        _run_in_process(_python_to_cpp_to_python_from_threads, 8, parallel=False) == 0
+    )
+
+
+# TODO: FIXME on macOS Python 3.9
+def test_python_to_cpp_to_python_from_process():
+    """Makes sure there is no GIL deadlock when using processes.
+
+    This test is for completion, but it was never an issue.
+    """
+    assert _run_in_process(_python_to_cpp_to_python) == 0
+
+
+def test_cross_module_gil():
+    """Makes sure that the GIL can be acquired by another module from a GIL-released state."""
+    m.test_cross_module_gil()  # Should not raise a SIGSEGV
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_iostream.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_iostream.cpp
new file mode 100644
index 000000000..421eaa2dd
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_iostream.cpp
@@ -0,0 +1,126 @@
+/*
+    tests/test_iostream.cpp -- Usage of scoped_output_redirect
+
+    Copyright (c) 2017 Henry F. Schreiner
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/iostream.h>
+
+#include "pybind11_tests.h"
+
+#include <atomic>
+#include <iostream>
+#include <mutex>
+#include <string>
+#include <thread>
+
+void noisy_function(const std::string &msg, bool flush) {
+
+    std::cout << msg;
+    if (flush) {
+        std::cout << std::flush;
+    }
+}
+
+void noisy_funct_dual(const std::string &msg, const std::string &emsg) {
+    std::cout << msg;
+    std::cerr << emsg;
+}
+
+// object to manage C++ thread
+// simply repeatedly write to std::cerr until stopped
+// redirect is called at some point to test the safety of scoped_estream_redirect
+struct TestThread {
+    TestThread() : stop_{false} {
+        auto thread_f = [this] {
+            static std::mutex cout_mutex;
+            while (!stop_) {
+                {
+                    // #HelpAppreciated: Work on iostream.h thread safety.
+                    // Without this lock, the clang ThreadSanitizer (tsan) reliably reports a
+                    // data race, and this test is predictably flakey on Windows.
+                    // For more background see the discussion under
+                    // https://github.com/pybind/pybind11/pull/2982 and
+                    // https://github.com/pybind/pybind11/pull/2995.
+                    const std::lock_guard<std::mutex> lock(cout_mutex);
+                    std::cout << "x" << std::flush;
+                }
+                std::this_thread::sleep_for(std::chrono::microseconds(50));
+            }
+        };
+        t_ = new std::thread(std::move(thread_f));
+    }
+
+    ~TestThread() { delete t_; }
+
+    void stop() { stop_ = true; }
+
+    void join() const {
+        py::gil_scoped_release gil_lock;
+        t_->join();
+    }
+
+    void sleep() {
+        py::gil_scoped_release gil_lock;
+        std::this_thread::sleep_for(std::chrono::milliseconds(50));
+    }
+
+    std::thread *t_{nullptr};
+    std::atomic<bool> stop_;
+};
+
+TEST_SUBMODULE(iostream, m) {
+
+    add_ostream_redirect(m);
+
+    // test_evals
+
+    m.def("captured_output_default", [](const std::string &msg) {
+        py::scoped_ostream_redirect redir;
+        std::cout << msg << std::flush;
+    });
+
+    m.def("captured_output", [](const std::string &msg) {
+        py::scoped_ostream_redirect redir(std::cout, py::module_::import("sys").attr("stdout"));
+        std::cout << msg << std::flush;
+    });
+
+    m.def("guard_output",
+          &noisy_function,
+          py::call_guard<py::scoped_ostream_redirect>(),
+          py::arg("msg"),
+          py::arg("flush") = true);
+
+    m.def("captured_err", [](const std::string &msg) {
+        py::scoped_ostream_redirect redir(std::cerr, py::module_::import("sys").attr("stderr"));
+        std::cerr << msg << std::flush;
+    });
+
+    m.def("noisy_function", &noisy_function, py::arg("msg"), py::arg("flush") = true);
+
+    m.def("dual_guard",
+          &noisy_funct_dual,
+          py::call_guard<py::scoped_ostream_redirect, py::scoped_estream_redirect>(),
+          py::arg("msg"),
+          py::arg("emsg"));
+
+    m.def("raw_output", [](const std::string &msg) { std::cout << msg << std::flush; });
+
+    m.def("raw_err", [](const std::string &msg) { std::cerr << msg << std::flush; });
+
+    m.def("captured_dual", [](const std::string &msg, const std::string &emsg) {
+        py::scoped_ostream_redirect redirout(std::cout, py::module_::import("sys").attr("stdout"));
+        py::scoped_ostream_redirect redirerr(std::cerr, py::module_::import("sys").attr("stderr"));
+        std::cout << msg << std::flush;
+        std::cerr << emsg << std::flush;
+    });
+
+    py::class_<TestThread>(m, "TestThread")
+        .def(py::init<>())
+        .def("stop", &TestThread::stop)
+        .def("join", &TestThread::join)
+        .def("sleep", &TestThread::sleep);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_iostream.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_iostream.py
new file mode 100644
index 000000000..5bbdf6955
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_iostream.py
@@ -0,0 +1,295 @@
+from contextlib import redirect_stderr, redirect_stdout
+from io import StringIO
+
+from pybind11_tests import iostream as m
+
+
+def test_captured(capsys):
+    msg = "I've been redirected to Python, I hope!"
+    m.captured_output(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+    m.captured_err(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == ""
+    assert stderr == msg
+
+
+def test_captured_large_string(capsys):
+    # Make this bigger than the buffer used on the C++ side: 1024 chars
+    msg = "I've been redirected to Python, I hope!"
+    msg = msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_2byte_offset0(capsys):
+    msg = "\u07FF"
+    msg = "" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_2byte_offset1(capsys):
+    msg = "\u07FF"
+    msg = "1" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_3byte_offset0(capsys):
+    msg = "\uFFFF"
+    msg = "" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_3byte_offset1(capsys):
+    msg = "\uFFFF"
+    msg = "1" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_3byte_offset2(capsys):
+    msg = "\uFFFF"
+    msg = "12" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_4byte_offset0(capsys):
+    msg = "\U0010FFFF"
+    msg = "" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_4byte_offset1(capsys):
+    msg = "\U0010FFFF"
+    msg = "1" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_4byte_offset2(capsys):
+    msg = "\U0010FFFF"
+    msg = "12" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_captured_utf8_4byte_offset3(capsys):
+    msg = "\U0010FFFF"
+    msg = "123" + msg * (1024 // len(msg) + 1)
+
+    m.captured_output_default(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_guard_capture(capsys):
+    msg = "I've been redirected to Python, I hope!"
+    m.guard_output(msg)
+    stdout, stderr = capsys.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+
+
+def test_series_captured(capture):
+    with capture:
+        m.captured_output("a")
+        m.captured_output("b")
+    assert capture == "ab"
+
+
+def test_flush(capfd):
+    msg = "(not flushed)"
+    msg2 = "(flushed)"
+
+    with m.ostream_redirect():
+        m.noisy_function(msg, flush=False)
+        stdout, stderr = capfd.readouterr()
+        assert stdout == ""
+
+        m.noisy_function(msg2, flush=True)
+        stdout, stderr = capfd.readouterr()
+        assert stdout == msg + msg2
+
+        m.noisy_function(msg, flush=False)
+
+    stdout, stderr = capfd.readouterr()
+    assert stdout == msg
+
+
+def test_not_captured(capfd):
+    msg = "Something that should not show up in log"
+    stream = StringIO()
+    with redirect_stdout(stream):
+        m.raw_output(msg)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+    assert stream.getvalue() == ""
+
+    stream = StringIO()
+    with redirect_stdout(stream):
+        m.captured_output(msg)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == ""
+    assert stderr == ""
+    assert stream.getvalue() == msg
+
+
+def test_err(capfd):
+    msg = "Something that should not show up in log"
+    stream = StringIO()
+    with redirect_stderr(stream):
+        m.raw_err(msg)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == ""
+    assert stderr == msg
+    assert stream.getvalue() == ""
+
+    stream = StringIO()
+    with redirect_stderr(stream):
+        m.captured_err(msg)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == ""
+    assert stderr == ""
+    assert stream.getvalue() == msg
+
+
+def test_multi_captured(capfd):
+    stream = StringIO()
+    with redirect_stdout(stream):
+        m.captured_output("a")
+        m.raw_output("b")
+        m.captured_output("c")
+        m.raw_output("d")
+    stdout, stderr = capfd.readouterr()
+    assert stdout == "bd"
+    assert stream.getvalue() == "ac"
+
+
+def test_dual(capsys):
+    m.captured_dual("a", "b")
+    stdout, stderr = capsys.readouterr()
+    assert stdout == "a"
+    assert stderr == "b"
+
+
+def test_redirect(capfd):
+    msg = "Should not be in log!"
+    stream = StringIO()
+    with redirect_stdout(stream):
+        m.raw_output(msg)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == msg
+    assert stream.getvalue() == ""
+
+    stream = StringIO()
+    with redirect_stdout(stream):
+        with m.ostream_redirect():
+            m.raw_output(msg)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == ""
+    assert stream.getvalue() == msg
+
+    stream = StringIO()
+    with redirect_stdout(stream):
+        m.raw_output(msg)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == msg
+    assert stream.getvalue() == ""
+
+
+def test_redirect_err(capfd):
+    msg = "StdOut"
+    msg2 = "StdErr"
+
+    stream = StringIO()
+    with redirect_stderr(stream):
+        with m.ostream_redirect(stdout=False):
+            m.raw_output(msg)
+            m.raw_err(msg2)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == msg
+    assert stderr == ""
+    assert stream.getvalue() == msg2
+
+
+def test_redirect_both(capfd):
+    msg = "StdOut"
+    msg2 = "StdErr"
+
+    stream = StringIO()
+    stream2 = StringIO()
+    with redirect_stdout(stream):
+        with redirect_stderr(stream2):
+            with m.ostream_redirect():
+                m.raw_output(msg)
+                m.raw_err(msg2)
+    stdout, stderr = capfd.readouterr()
+    assert stdout == ""
+    assert stderr == ""
+    assert stream.getvalue() == msg
+    assert stream2.getvalue() == msg2
+
+
+def test_threading():
+    with m.ostream_redirect(stdout=True, stderr=False):
+        # start some threads
+        threads = []
+
+        # start some threads
+        for _j in range(20):
+            threads.append(m.TestThread())
+
+        # give the threads some time to fail
+        threads[0].sleep()
+
+        # stop all the threads
+        for t in threads:
+            t.stop()
+
+        for t in threads:
+            t.join()
+
+        # if a thread segfaults, we don't get here
+        assert True
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_kwargs_and_defaults.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_kwargs_and_defaults.cpp
new file mode 100644
index 000000000..7418afefb
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_kwargs_and_defaults.cpp
@@ -0,0 +1,273 @@
+/*
+    tests/test_kwargs_and_defaults.cpp -- keyword arguments and default values
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/stl.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#include <utility>
+
+TEST_SUBMODULE(kwargs_and_defaults, m) {
+    auto kw_func
+        = [](int x, int y) { return "x=" + std::to_string(x) + ", y=" + std::to_string(y); };
+
+    // test_named_arguments
+    m.def("kw_func0", kw_func);
+    m.def("kw_func1", kw_func, py::arg("x"), py::arg("y"));
+    m.def("kw_func2", kw_func, py::arg("x") = 100, py::arg("y") = 200);
+    m.def(
+        "kw_func3", [](const char *) {}, py::arg("data") = std::string("Hello world!"));
+
+    /* A fancier default argument */
+    std::vector<int> list{{13, 17}};
+    m.def(
+        "kw_func4",
+        [](const std::vector<int> &entries) {
+            std::string ret = "{";
+            for (int i : entries) {
+                ret += std::to_string(i) + " ";
+            }
+            ret.back() = '}';
+            return ret;
+        },
+        py::arg("myList") = list);
+
+    m.def("kw_func_udl", kw_func, "x"_a, "y"_a = 300);
+    m.def("kw_func_udl_z", kw_func, "x"_a, "y"_a = 0);
+
+    // test_args_and_kwargs
+    m.def("args_function", [](py::args args) -> py::tuple { return std::move(args); });
+    m.def("args_kwargs_function", [](const py::args &args, const py::kwargs &kwargs) {
+        return py::make_tuple(args, kwargs);
+    });
+
+    // test_mixed_args_and_kwargs
+    m.def("mixed_plus_args",
+          [](int i, double j, const py::args &args) { return py::make_tuple(i, j, args); });
+    m.def("mixed_plus_kwargs",
+          [](int i, double j, const py::kwargs &kwargs) { return py::make_tuple(i, j, kwargs); });
+    auto mixed_plus_both = [](int i, double j, const py::args &args, const py::kwargs &kwargs) {
+        return py::make_tuple(i, j, args, kwargs);
+    };
+    m.def("mixed_plus_args_kwargs", mixed_plus_both);
+
+    m.def("mixed_plus_args_kwargs_defaults",
+          mixed_plus_both,
+          py::arg("i") = 1,
+          py::arg("j") = 3.14159);
+
+    m.def(
+        "args_kwonly",
+        [](int i, double j, const py::args &args, int z) { return py::make_tuple(i, j, args, z); },
+        "i"_a,
+        "j"_a,
+        "z"_a);
+    m.def(
+        "args_kwonly_kwargs",
+        [](int i, double j, const py::args &args, int z, const py::kwargs &kwargs) {
+            return py::make_tuple(i, j, args, z, kwargs);
+        },
+        "i"_a,
+        "j"_a,
+        py::kw_only{},
+        "z"_a);
+    m.def(
+        "args_kwonly_kwargs_defaults",
+        [](int i, double j, const py::args &args, int z, const py::kwargs &kwargs) {
+            return py::make_tuple(i, j, args, z, kwargs);
+        },
+        "i"_a = 1,
+        "j"_a = 3.14159,
+        "z"_a = 42);
+    m.def(
+        "args_kwonly_full_monty",
+        [](int h, int i, double j, const py::args &args, int z, const py::kwargs &kwargs) {
+            return py::make_tuple(h, i, j, args, z, kwargs);
+        },
+        py::arg() = 1,
+        py::arg() = 2,
+        py::pos_only{},
+        "j"_a = 3.14159,
+        "z"_a = 42);
+
+// test_args_refcount
+// PyPy needs a garbage collection to get the reference count values to match CPython's behaviour
+#ifdef PYPY_VERSION
+#    define GC_IF_NEEDED ConstructorStats::gc()
+#else
+#    define GC_IF_NEEDED
+#endif
+    m.def("arg_refcount_h", [](py::handle h) {
+        GC_IF_NEEDED;
+        return h.ref_count();
+    });
+    m.def("arg_refcount_h", [](py::handle h, py::handle, py::handle) {
+        GC_IF_NEEDED;
+        return h.ref_count();
+    });
+    m.def("arg_refcount_o", [](const py::object &o) {
+        GC_IF_NEEDED;
+        return o.ref_count();
+    });
+    m.def("args_refcount", [](py::args a) {
+        GC_IF_NEEDED;
+        py::tuple t(a.size());
+        for (size_t i = 0; i < a.size(); i++) {
+            // Use raw Python API here to avoid an extra, intermediate incref on the tuple item:
+            t[i] = (int) Py_REFCNT(PyTuple_GET_ITEM(a.ptr(), static_cast<py::ssize_t>(i)));
+        }
+        return t;
+    });
+    m.def("mixed_args_refcount", [](const py::object &o, py::args a) {
+        GC_IF_NEEDED;
+        py::tuple t(a.size() + 1);
+        t[0] = o.ref_count();
+        for (size_t i = 0; i < a.size(); i++) {
+            // Use raw Python API here to avoid an extra, intermediate incref on the tuple item:
+            t[i + 1] = (int) Py_REFCNT(PyTuple_GET_ITEM(a.ptr(), static_cast<py::ssize_t>(i)));
+        }
+        return t;
+    });
+
+    // pybind11 won't allow these to be bound: args and kwargs, if present, must be at the end.
+    // Uncomment these to test that the static_assert is indeed working:
+    //    m.def("bad_args1", [](py::args, int) {});
+    //    m.def("bad_args2", [](py::kwargs, int) {});
+    //    m.def("bad_args3", [](py::kwargs, py::args) {});
+    //    m.def("bad_args4", [](py::args, int, py::kwargs) {});
+    //    m.def("bad_args5", [](py::args, py::kwargs, int) {});
+    //    m.def("bad_args6", [](py::args, py::args) {});
+    //    m.def("bad_args7", [](py::kwargs, py::kwargs) {});
+
+    // test_keyword_only_args
+    m.def(
+        "kw_only_all",
+        [](int i, int j) { return py::make_tuple(i, j); },
+        py::kw_only(),
+        py::arg("i"),
+        py::arg("j"));
+    m.def(
+        "kw_only_some",
+        [](int i, int j, int k) { return py::make_tuple(i, j, k); },
+        py::arg(),
+        py::kw_only(),
+        py::arg("j"),
+        py::arg("k"));
+    m.def(
+        "kw_only_with_defaults",
+        [](int i, int j, int k, int z) { return py::make_tuple(i, j, k, z); },
+        py::arg() = 3,
+        "j"_a = 4,
+        py::kw_only(),
+        "k"_a = 5,
+        "z"_a);
+    m.def(
+        "kw_only_mixed",
+        [](int i, int j) { return py::make_tuple(i, j); },
+        "i"_a,
+        py::kw_only(),
+        "j"_a);
+    m.def(
+        "kw_only_plus_more",
+        [](int i, int j, int k, const py::kwargs &kwargs) {
+            return py::make_tuple(i, j, k, kwargs);
+        },
+        py::arg() /* positional */,
+        py::arg("j") = -1 /* both */,
+        py::kw_only(),
+        py::arg("k") /* kw-only */);
+
+    m.def("register_invalid_kw_only", [](py::module_ m) {
+        m.def(
+            "bad_kw_only",
+            [](int i, int j) { return py::make_tuple(i, j); },
+            py::kw_only(),
+            py::arg() /* invalid unnamed argument */,
+            "j"_a);
+    });
+
+    // test_positional_only_args
+    m.def(
+        "pos_only_all",
+        [](int i, int j) { return py::make_tuple(i, j); },
+        py::arg("i"),
+        py::arg("j"),
+        py::pos_only());
+    m.def(
+        "pos_only_mix",
+        [](int i, int j) { return py::make_tuple(i, j); },
+        py::arg("i"),
+        py::pos_only(),
+        py::arg("j"));
+    m.def(
+        "pos_kw_only_mix",
+        [](int i, int j, int k) { return py::make_tuple(i, j, k); },
+        py::arg("i"),
+        py::pos_only(),
+        py::arg("j"),
+        py::kw_only(),
+        py::arg("k"));
+    m.def(
+        "pos_only_def_mix",
+        [](int i, int j, int k) { return py::make_tuple(i, j, k); },
+        py::arg("i"),
+        py::arg("j") = 2,
+        py::pos_only(),
+        py::arg("k") = 3);
+
+    // These should fail to compile:
+#ifdef PYBIND11_NEVER_DEFINED_EVER
+    // argument annotations are required when using kw_only
+    m.def(
+        "bad_kw_only1", [](int) {}, py::kw_only());
+    // can't specify both `py::kw_only` and a `py::args` argument
+    m.def(
+        "bad_kw_only2", [](int i, py::args) {}, py::kw_only(), "i"_a);
+#endif
+
+    // test_function_signatures (along with most of the above)
+    struct KWClass {
+        void foo(int, float) {}
+    };
+    py::class_<KWClass>(m, "KWClass")
+        .def("foo0", &KWClass::foo)
+        .def("foo1", &KWClass::foo, "x"_a, "y"_a);
+
+    // Make sure a class (not an instance) can be used as a default argument.
+    // The return value doesn't matter, only that the module is importable.
+    m.def(
+        "class_default_argument",
+        [](py::object a) { return py::repr(std::move(a)); },
+        "a"_a = py::module_::import("decimal").attr("Decimal"));
+
+    // Initial implementation of kw_only was broken when used on a method/constructor before any
+    // other arguments
+    // https://github.com/pybind/pybind11/pull/3402#issuecomment-963341987
+
+    struct first_arg_kw_only {};
+    py::class_<first_arg_kw_only>(m, "first_arg_kw_only")
+        .def(py::init([](int) { return first_arg_kw_only(); }),
+             py::kw_only(), // This being before any args was broken
+             py::arg("i") = 0)
+        .def(
+            "method",
+            [](first_arg_kw_only &, int, int) {},
+            py::kw_only(), // and likewise here
+            py::arg("i") = 1,
+            py::arg("j") = 2)
+        // Closely related: pos_only marker didn't show up properly when it was before any other
+        // arguments (although that is fairly useless in practice).
+        .def(
+            "pos_only",
+            [](first_arg_kw_only &, int, int) {},
+            py::pos_only{},
+            py::arg("i"),
+            py::arg("j"));
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_kwargs_and_defaults.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_kwargs_and_defaults.py
new file mode 100644
index 000000000..ab7017886
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_kwargs_and_defaults.py
@@ -0,0 +1,390 @@
+import pytest
+
+from pybind11_tests import kwargs_and_defaults as m
+
+
+def test_function_signatures(doc):
+    assert doc(m.kw_func0) == "kw_func0(arg0: int, arg1: int) -> str"
+    assert doc(m.kw_func1) == "kw_func1(x: int, y: int) -> str"
+    assert doc(m.kw_func2) == "kw_func2(x: int = 100, y: int = 200) -> str"
+    assert doc(m.kw_func3) == "kw_func3(data: str = 'Hello world!') -> None"
+    assert doc(m.kw_func4) == "kw_func4(myList: List[int] = [13, 17]) -> str"
+    assert doc(m.kw_func_udl) == "kw_func_udl(x: int, y: int = 300) -> str"
+    assert doc(m.kw_func_udl_z) == "kw_func_udl_z(x: int, y: int = 0) -> str"
+    assert doc(m.args_function) == "args_function(*args) -> tuple"
+    assert (
+        doc(m.args_kwargs_function) == "args_kwargs_function(*args, **kwargs) -> tuple"
+    )
+    assert (
+        doc(m.KWClass.foo0)
+        == "foo0(self: m.kwargs_and_defaults.KWClass, arg0: int, arg1: float) -> None"
+    )
+    assert (
+        doc(m.KWClass.foo1)
+        == "foo1(self: m.kwargs_and_defaults.KWClass, x: int, y: float) -> None"
+    )
+
+
+def test_named_arguments(msg):
+    assert m.kw_func0(5, 10) == "x=5, y=10"
+
+    assert m.kw_func1(5, 10) == "x=5, y=10"
+    assert m.kw_func1(5, y=10) == "x=5, y=10"
+    assert m.kw_func1(y=10, x=5) == "x=5, y=10"
+
+    assert m.kw_func2() == "x=100, y=200"
+    assert m.kw_func2(5) == "x=5, y=200"
+    assert m.kw_func2(x=5) == "x=5, y=200"
+    assert m.kw_func2(y=10) == "x=100, y=10"
+    assert m.kw_func2(5, 10) == "x=5, y=10"
+    assert m.kw_func2(x=5, y=10) == "x=5, y=10"
+
+    with pytest.raises(TypeError) as excinfo:
+        # noinspection PyArgumentList
+        m.kw_func2(x=5, y=10, z=12)
+    assert excinfo.match(
+        r"(?s)^kw_func2\(\): incompatible.*Invoked with: kwargs: ((x=5|y=10|z=12)(, |$))"
+        + "{3}$"
+    )
+
+    assert m.kw_func4() == "{13 17}"
+    assert m.kw_func4(myList=[1, 2, 3]) == "{1 2 3}"
+
+    assert m.kw_func_udl(x=5, y=10) == "x=5, y=10"
+    assert m.kw_func_udl_z(x=5) == "x=5, y=0"
+
+
+def test_arg_and_kwargs():
+    args = "arg1_value", "arg2_value", 3
+    assert m.args_function(*args) == args
+
+    args = "a1", "a2"
+    kwargs = dict(arg3="a3", arg4=4)
+    assert m.args_kwargs_function(*args, **kwargs) == (args, kwargs)
+
+
+def test_mixed_args_and_kwargs(msg):
+    mpa = m.mixed_plus_args
+    mpk = m.mixed_plus_kwargs
+    mpak = m.mixed_plus_args_kwargs
+    mpakd = m.mixed_plus_args_kwargs_defaults
+
+    assert mpa(1, 2.5, 4, 99.5, None) == (1, 2.5, (4, 99.5, None))
+    assert mpa(1, 2.5) == (1, 2.5, ())
+    with pytest.raises(TypeError) as excinfo:
+        assert mpa(1)
+    assert (
+        msg(excinfo.value)
+        == """
+        mixed_plus_args(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: int, arg1: float, *args) -> tuple
+
+        Invoked with: 1
+    """
+    )
+    with pytest.raises(TypeError) as excinfo:
+        assert mpa()
+    assert (
+        msg(excinfo.value)
+        == """
+        mixed_plus_args(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: int, arg1: float, *args) -> tuple
+
+        Invoked with:
+    """
+    )
+
+    assert mpk(-2, 3.5, pi=3.14159, e=2.71828) == (
+        -2,
+        3.5,
+        {"e": 2.71828, "pi": 3.14159},
+    )
+    assert mpak(7, 7.7, 7.77, 7.777, 7.7777, minusseven=-7) == (
+        7,
+        7.7,
+        (7.77, 7.777, 7.7777),
+        {"minusseven": -7},
+    )
+    assert mpakd() == (1, 3.14159, (), {})
+    assert mpakd(3) == (3, 3.14159, (), {})
+    assert mpakd(j=2.71828) == (1, 2.71828, (), {})
+    assert mpakd(k=42) == (1, 3.14159, (), {"k": 42})
+    assert mpakd(1, 1, 2, 3, 5, 8, then=13, followedby=21) == (
+        1,
+        1,
+        (2, 3, 5, 8),
+        {"then": 13, "followedby": 21},
+    )
+    # Arguments specified both positionally and via kwargs should fail:
+    with pytest.raises(TypeError) as excinfo:
+        assert mpakd(1, i=1)
+    assert (
+        msg(excinfo.value)
+        == """
+        mixed_plus_args_kwargs_defaults(): incompatible function arguments. The following argument types are supported:
+            1. (i: int = 1, j: float = 3.14159, *args, **kwargs) -> tuple
+
+        Invoked with: 1; kwargs: i=1
+    """
+    )
+    with pytest.raises(TypeError) as excinfo:
+        assert mpakd(1, 2, j=1)
+    assert (
+        msg(excinfo.value)
+        == """
+        mixed_plus_args_kwargs_defaults(): incompatible function arguments. The following argument types are supported:
+            1. (i: int = 1, j: float = 3.14159, *args, **kwargs) -> tuple
+
+        Invoked with: 1, 2; kwargs: j=1
+    """
+    )
+
+    # Arguments after a py::args are automatically keyword-only (pybind 2.9+)
+    assert m.args_kwonly(2, 2.5, z=22) == (2, 2.5, (), 22)
+    assert m.args_kwonly(2, 2.5, "a", "b", "c", z=22) == (2, 2.5, ("a", "b", "c"), 22)
+    assert m.args_kwonly(z=22, i=4, j=16) == (4, 16, (), 22)
+
+    with pytest.raises(TypeError) as excinfo:
+        assert m.args_kwonly(2, 2.5, 22)  # missing z= keyword
+    assert (
+        msg(excinfo.value)
+        == """
+        args_kwonly(): incompatible function arguments. The following argument types are supported:
+            1. (i: int, j: float, *args, z: int) -> tuple
+
+        Invoked with: 2, 2.5, 22
+    """
+    )
+
+    assert m.args_kwonly_kwargs(i=1, k=4, j=10, z=-1, y=9) == (
+        1,
+        10,
+        (),
+        -1,
+        {"k": 4, "y": 9},
+    )
+    assert m.args_kwonly_kwargs(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, z=11, y=12) == (
+        1,
+        2,
+        (3, 4, 5, 6, 7, 8, 9, 10),
+        11,
+        {"y": 12},
+    )
+    assert (
+        m.args_kwonly_kwargs.__doc__
+        == "args_kwonly_kwargs(i: int, j: float, *args, z: int, **kwargs) -> tuple\n"
+    )
+
+    assert (
+        m.args_kwonly_kwargs_defaults.__doc__
+        == "args_kwonly_kwargs_defaults(i: int = 1, j: float = 3.14159, *args, z: int = 42, **kwargs) -> tuple\n"  # noqa: E501 line too long
+    )
+    assert m.args_kwonly_kwargs_defaults() == (1, 3.14159, (), 42, {})
+    assert m.args_kwonly_kwargs_defaults(2) == (2, 3.14159, (), 42, {})
+    assert m.args_kwonly_kwargs_defaults(z=-99) == (1, 3.14159, (), -99, {})
+    assert m.args_kwonly_kwargs_defaults(5, 6, 7, 8) == (5, 6, (7, 8), 42, {})
+    assert m.args_kwonly_kwargs_defaults(5, 6, 7, m=8) == (5, 6, (7,), 42, {"m": 8})
+    assert m.args_kwonly_kwargs_defaults(5, 6, 7, m=8, z=9) == (5, 6, (7,), 9, {"m": 8})
+
+
+def test_keyword_only_args(msg):
+    assert m.kw_only_all(i=1, j=2) == (1, 2)
+    assert m.kw_only_all(j=1, i=2) == (2, 1)
+
+    with pytest.raises(TypeError) as excinfo:
+        assert m.kw_only_all(i=1) == (1,)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        assert m.kw_only_all(1, 2) == (1, 2)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    assert m.kw_only_some(1, k=3, j=2) == (1, 2, 3)
+
+    assert m.kw_only_with_defaults(z=8) == (3, 4, 5, 8)
+    assert m.kw_only_with_defaults(2, z=8) == (2, 4, 5, 8)
+    assert m.kw_only_with_defaults(2, j=7, k=8, z=9) == (2, 7, 8, 9)
+    assert m.kw_only_with_defaults(2, 7, z=9, k=8) == (2, 7, 8, 9)
+
+    assert m.kw_only_mixed(1, j=2) == (1, 2)
+    assert m.kw_only_mixed(j=2, i=3) == (3, 2)
+    assert m.kw_only_mixed(i=2, j=3) == (2, 3)
+
+    assert m.kw_only_plus_more(4, 5, k=6, extra=7) == (4, 5, 6, {"extra": 7})
+    assert m.kw_only_plus_more(3, k=5, j=4, extra=6) == (3, 4, 5, {"extra": 6})
+    assert m.kw_only_plus_more(2, k=3, extra=4) == (2, -1, 3, {"extra": 4})
+
+    with pytest.raises(TypeError) as excinfo:
+        assert m.kw_only_mixed(i=1) == (1,)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.register_invalid_kw_only(m)
+    assert (
+        msg(excinfo.value)
+        == """
+        arg(): cannot specify an unnamed argument after a kw_only() annotation or args() argument
+    """
+    )
+
+    # https://github.com/pybind/pybind11/pull/3402#issuecomment-963341987
+    x = m.first_arg_kw_only(i=1)
+    x.method()
+    x.method(i=1, j=2)
+    assert (
+        m.first_arg_kw_only.__init__.__doc__
+        == "__init__(self: pybind11_tests.kwargs_and_defaults.first_arg_kw_only, *, i: int = 0) -> None\n"  # noqa: E501 line too long
+    )
+    assert (
+        m.first_arg_kw_only.method.__doc__
+        == "method(self: pybind11_tests.kwargs_and_defaults.first_arg_kw_only, *, i: int = 1, j: int = 2) -> None\n"  # noqa: E501 line too long
+    )
+
+
+def test_positional_only_args(msg):
+    assert m.pos_only_all(1, 2) == (1, 2)
+    assert m.pos_only_all(2, 1) == (2, 1)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.pos_only_all(i=1, j=2)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    assert m.pos_only_mix(1, 2) == (1, 2)
+    assert m.pos_only_mix(2, j=1) == (2, 1)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.pos_only_mix(i=1, j=2)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    assert m.pos_kw_only_mix(1, 2, k=3) == (1, 2, 3)
+    assert m.pos_kw_only_mix(1, j=2, k=3) == (1, 2, 3)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.pos_kw_only_mix(i=1, j=2, k=3)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.pos_kw_only_mix(1, 2, 3)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.pos_only_def_mix()
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    assert m.pos_only_def_mix(1) == (1, 2, 3)
+    assert m.pos_only_def_mix(1, 4) == (1, 4, 3)
+    assert m.pos_only_def_mix(1, 4, 7) == (1, 4, 7)
+    assert m.pos_only_def_mix(1, 4, k=7) == (1, 4, 7)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.pos_only_def_mix(1, j=4)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    # Mix it with args and kwargs:
+    assert (
+        m.args_kwonly_full_monty.__doc__
+        == "args_kwonly_full_monty(arg0: int = 1, arg1: int = 2, /, j: float = 3.14159, *args, z: int = 42, **kwargs) -> tuple\n"  # noqa: E501 line too long
+    )
+    assert m.args_kwonly_full_monty() == (1, 2, 3.14159, (), 42, {})
+    assert m.args_kwonly_full_monty(8) == (8, 2, 3.14159, (), 42, {})
+    assert m.args_kwonly_full_monty(8, 9) == (8, 9, 3.14159, (), 42, {})
+    assert m.args_kwonly_full_monty(8, 9, 10) == (8, 9, 10.0, (), 42, {})
+    assert m.args_kwonly_full_monty(3, 4, 5, 6, 7, m=8, z=9) == (
+        3,
+        4,
+        5.0,
+        (
+            6,
+            7,
+        ),
+        9,
+        {"m": 8},
+    )
+    assert m.args_kwonly_full_monty(3, 4, 5, 6, 7, m=8, z=9) == (
+        3,
+        4,
+        5.0,
+        (
+            6,
+            7,
+        ),
+        9,
+        {"m": 8},
+    )
+    assert m.args_kwonly_full_monty(5, j=7, m=8, z=9) == (5, 2, 7.0, (), 9, {"m": 8})
+    assert m.args_kwonly_full_monty(i=5, j=7, m=8, z=9) == (
+        1,
+        2,
+        7.0,
+        (),
+        9,
+        {"i": 5, "m": 8},
+    )
+
+    # pos_only at the beginning of the argument list was "broken" in how it was displayed (though
+    # this is fairly useless in practice).  Related to:
+    # https://github.com/pybind/pybind11/pull/3402#issuecomment-963341987
+    assert (
+        m.first_arg_kw_only.pos_only.__doc__
+        == "pos_only(self: pybind11_tests.kwargs_and_defaults.first_arg_kw_only, /, i: int, j: int) -> None\n"  # noqa: E501 line too long
+    )
+
+
+def test_signatures():
+    assert "kw_only_all(*, i: int, j: int) -> tuple\n" == m.kw_only_all.__doc__
+    assert "kw_only_mixed(i: int, *, j: int) -> tuple\n" == m.kw_only_mixed.__doc__
+    assert "pos_only_all(i: int, j: int, /) -> tuple\n" == m.pos_only_all.__doc__
+    assert "pos_only_mix(i: int, /, j: int) -> tuple\n" == m.pos_only_mix.__doc__
+    assert (
+        "pos_kw_only_mix(i: int, /, j: int, *, k: int) -> tuple\n"
+        == m.pos_kw_only_mix.__doc__
+    )
+
+
+def test_args_refcount():
+    """Issue/PR #1216 - py::args elements get double-inc_ref()ed when combined with regular
+    arguments"""
+    refcount = m.arg_refcount_h
+
+    myval = 54321
+    expected = refcount(myval)
+    assert m.arg_refcount_h(myval) == expected
+    assert m.arg_refcount_o(myval) == expected + 1
+    assert m.arg_refcount_h(myval) == expected
+    assert refcount(myval) == expected
+
+    assert m.mixed_plus_args(1, 2.0, "a", myval) == (1, 2.0, ("a", myval))
+    assert refcount(myval) == expected
+
+    assert m.mixed_plus_kwargs(3, 4.0, a=1, b=myval) == (3, 4.0, {"a": 1, "b": myval})
+    assert refcount(myval) == expected
+
+    assert m.args_function(-1, myval) == (-1, myval)
+    assert refcount(myval) == expected
+
+    assert m.mixed_plus_args_kwargs(5, 6.0, myval, a=myval) == (
+        5,
+        6.0,
+        (myval,),
+        {"a": myval},
+    )
+    assert refcount(myval) == expected
+
+    assert m.args_kwargs_function(7, 8, myval, a=1, b=myval) == (
+        (7, 8, myval),
+        {"a": 1, "b": myval},
+    )
+    assert refcount(myval) == expected
+
+    exp3 = refcount(myval, myval, myval)
+    assert m.args_refcount(myval, myval, myval) == (exp3, exp3, exp3)
+    assert refcount(myval) == expected
+
+    # This function takes the first arg as a `py::object` and the rest as a `py::args`.  Unlike the
+    # previous case, when we have both positional and `py::args` we need to construct a new tuple
+    # for the `py::args`; in the previous case, we could simply inc_ref and pass on Python's input
+    # tuple without having to inc_ref the individual elements, but here we can't, hence the extra
+    # refs.
+    assert m.mixed_args_refcount(myval, myval, myval) == (exp3 + 3, exp3 + 3, exp3 + 3)
+
+    assert m.class_default_argument() == "<class 'decimal.Decimal'>"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_local_bindings.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_local_bindings.cpp
new file mode 100644
index 000000000..137367744
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_local_bindings.cpp
@@ -0,0 +1,106 @@
+/*
+    tests/test_local_bindings.cpp -- tests the py::module_local class feature which makes a class
+                                     binding local to the module in which it is defined.
+
+    Copyright (c) 2017 Jason Rhinelander <jason@imaginary.ca>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+#include "local_bindings.h"
+#include "pybind11_tests.h"
+
+#include <numeric>
+#include <utility>
+
+TEST_SUBMODULE(local_bindings, m) {
+    // test_load_external
+    m.def("load_external1", [](ExternalType1 &e) { return e.i; });
+    m.def("load_external2", [](ExternalType2 &e) { return e.i; });
+
+    // test_local_bindings
+    // Register a class with py::module_local:
+    bind_local<LocalType, -1>(m, "LocalType", py::module_local()).def("get3", [](LocalType &t) {
+        return t.i + 3;
+    });
+
+    m.def("local_value", [](LocalType &l) { return l.i; });
+
+    // test_nonlocal_failure
+    // The main pybind11 test module is loaded first, so this registration will succeed (the second
+    // one, in pybind11_cross_module_tests.cpp, is designed to fail):
+    bind_local<NonLocalType, 0>(m, "NonLocalType")
+        .def(py::init<int>())
+        .def("get", [](LocalType &i) { return i.i; });
+
+    // test_duplicate_local
+    // py::module_local declarations should be visible across compilation units that get linked
+    // together; this tries to register a duplicate local.  It depends on a definition in
+    // test_class.cpp and should raise a runtime error from the duplicate definition attempt.  If
+    // test_class isn't available it *also* throws a runtime error (with "test_class not enabled"
+    // as value).
+    m.def("register_local_external", [m]() {
+        auto main = py::module_::import("pybind11_tests");
+        if (py::hasattr(main, "class_")) {
+            bind_local<LocalExternal, 7>(m, "LocalExternal", py::module_local());
+        } else {
+            throw std::runtime_error("test_class not enabled");
+        }
+    });
+
+    // test_stl_bind_local
+    // stl_bind.h binders defaults to py::module_local if the types are local or converting:
+    py::bind_vector<LocalVec>(m, "LocalVec");
+    py::bind_map<LocalMap>(m, "LocalMap");
+    // and global if the type (or one of the types, for the map) is global:
+    py::bind_vector<NonLocalVec>(m, "NonLocalVec");
+    py::bind_map<NonLocalMap>(m, "NonLocalMap");
+
+    // test_stl_bind_global
+    // They can, however, be overridden to global using `py::module_local(false)`:
+    bind_local<NonLocal2, 10>(m, "NonLocal2");
+    py::bind_vector<LocalVec2>(m, "LocalVec2", py::module_local());
+    py::bind_map<NonLocalMap2>(m, "NonLocalMap2", py::module_local(false));
+
+    // test_mixed_local_global
+    // We try this both with the global type registered first and vice versa (the order shouldn't
+    // matter).
+    m.def("register_mixed_global", [m]() {
+        bind_local<MixedGlobalLocal, 100>(m, "MixedGlobalLocal", py::module_local(false));
+    });
+    m.def("register_mixed_local", [m]() {
+        bind_local<MixedLocalGlobal, 1000>(m, "MixedLocalGlobal", py::module_local());
+    });
+    m.def("get_mixed_gl", [](int i) { return MixedGlobalLocal(i); });
+    m.def("get_mixed_lg", [](int i) { return MixedLocalGlobal(i); });
+
+    // test_internal_locals_differ
+    m.def("local_cpp_types_addr",
+          []() { return (uintptr_t) &py::detail::get_local_internals().registered_types_cpp; });
+
+    // test_stl_caster_vs_stl_bind
+    m.def("load_vector_via_caster",
+          [](std::vector<int> v) { return std::accumulate(v.begin(), v.end(), 0); });
+
+    // test_cross_module_calls
+    m.def("return_self", [](LocalVec *v) { return v; });
+    m.def("return_copy", [](const LocalVec &v) { return LocalVec(v); });
+
+    class Cat : public pets::Pet {
+    public:
+        explicit Cat(std::string name) : Pet(std::move(name)) {}
+    };
+    py::class_<pets::Pet>(m, "Pet", py::module_local()).def("get_name", &pets::Pet::name);
+    // Binding for local extending class:
+    py::class_<Cat, pets::Pet>(m, "Cat").def(py::init<std::string>());
+    m.def("pet_name", [](pets::Pet &p) { return p.name(); });
+
+    py::class_<MixGL>(m, "MixGL").def(py::init<int>());
+    m.def("get_gl_value", [](MixGL &o) { return o.i + 10; });
+
+    py::class_<MixGL2>(m, "MixGL2").def(py::init<int>());
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_local_bindings.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_local_bindings.py
new file mode 100644
index 000000000..654d96d49
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_local_bindings.py
@@ -0,0 +1,256 @@
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import local_bindings as m
+
+
+def test_load_external():
+    """Load a `py::module_local` type that's only registered in an external module"""
+    import pybind11_cross_module_tests as cm
+
+    assert m.load_external1(cm.ExternalType1(11)) == 11
+    assert m.load_external2(cm.ExternalType2(22)) == 22
+
+    with pytest.raises(TypeError) as excinfo:
+        assert m.load_external2(cm.ExternalType1(21)) == 21
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        assert m.load_external1(cm.ExternalType2(12)) == 12
+    assert "incompatible function arguments" in str(excinfo.value)
+
+
+def test_local_bindings():
+    """Tests that duplicate `py::module_local` class bindings work across modules"""
+
+    # Make sure we can load the second module with the conflicting (but local) definition:
+    import pybind11_cross_module_tests as cm
+
+    i1 = m.LocalType(5)
+    assert i1.get() == 4
+    assert i1.get3() == 8
+
+    i2 = cm.LocalType(10)
+    assert i2.get() == 11
+    assert i2.get2() == 12
+
+    assert not hasattr(i1, "get2")
+    assert not hasattr(i2, "get3")
+
+    # Loading within the local module
+    assert m.local_value(i1) == 5
+    assert cm.local_value(i2) == 10
+
+    # Cross-module loading works as well (on failure, the type loader looks for
+    # external module-local converters):
+    assert m.local_value(i2) == 10
+    assert cm.local_value(i1) == 5
+
+
+def test_nonlocal_failure():
+    """Tests that attempting to register a non-local type in multiple modules fails"""
+    import pybind11_cross_module_tests as cm
+
+    with pytest.raises(RuntimeError) as excinfo:
+        cm.register_nonlocal()
+    assert (
+        str(excinfo.value) == 'generic_type: type "NonLocalType" is already registered!'
+    )
+
+
+def test_duplicate_local():
+    """Tests expected failure when registering a class twice with py::local in the same module"""
+    with pytest.raises(RuntimeError) as excinfo:
+        m.register_local_external()
+    import pybind11_tests
+
+    assert str(excinfo.value) == (
+        'generic_type: type "LocalExternal" is already registered!'
+        if hasattr(pybind11_tests, "class_")
+        else "test_class not enabled"
+    )
+
+
+def test_stl_bind_local():
+    import pybind11_cross_module_tests as cm
+
+    v1, v2 = m.LocalVec(), cm.LocalVec()
+    v1.append(m.LocalType(1))
+    v1.append(m.LocalType(2))
+    v2.append(cm.LocalType(1))
+    v2.append(cm.LocalType(2))
+
+    # Cross module value loading:
+    v1.append(cm.LocalType(3))
+    v2.append(m.LocalType(3))
+
+    assert [i.get() for i in v1] == [0, 1, 2]
+    assert [i.get() for i in v2] == [2, 3, 4]
+
+    v3, v4 = m.NonLocalVec(), cm.NonLocalVec2()
+    v3.append(m.NonLocalType(1))
+    v3.append(m.NonLocalType(2))
+    v4.append(m.NonLocal2(3))
+    v4.append(m.NonLocal2(4))
+
+    assert [i.get() for i in v3] == [1, 2]
+    assert [i.get() for i in v4] == [13, 14]
+
+    d1, d2 = m.LocalMap(), cm.LocalMap()
+    d1["a"] = v1[0]
+    d1["b"] = v1[1]
+    d2["c"] = v2[0]
+    d2["d"] = v2[1]
+    assert {i: d1[i].get() for i in d1} == {"a": 0, "b": 1}
+    assert {i: d2[i].get() for i in d2} == {"c": 2, "d": 3}
+
+
+def test_stl_bind_global():
+    import pybind11_cross_module_tests as cm
+
+    with pytest.raises(RuntimeError) as excinfo:
+        cm.register_nonlocal_map()
+    assert (
+        str(excinfo.value) == 'generic_type: type "NonLocalMap" is already registered!'
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        cm.register_nonlocal_vec()
+    assert (
+        str(excinfo.value) == 'generic_type: type "NonLocalVec" is already registered!'
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        cm.register_nonlocal_map2()
+    assert (
+        str(excinfo.value) == 'generic_type: type "NonLocalMap2" is already registered!'
+    )
+
+
+def test_mixed_local_global():
+    """Local types take precedence over globally registered types: a module with a `module_local`
+    type can be registered even if the type is already registered globally.  With the module,
+    casting will go to the local type; outside the module casting goes to the global type."""
+    import pybind11_cross_module_tests as cm
+
+    m.register_mixed_global()
+    m.register_mixed_local()
+
+    a = []
+    a.append(m.MixedGlobalLocal(1))
+    a.append(m.MixedLocalGlobal(2))
+    a.append(m.get_mixed_gl(3))
+    a.append(m.get_mixed_lg(4))
+
+    assert [x.get() for x in a] == [101, 1002, 103, 1004]
+
+    cm.register_mixed_global_local()
+    cm.register_mixed_local_global()
+    a.append(m.MixedGlobalLocal(5))
+    a.append(m.MixedLocalGlobal(6))
+    a.append(cm.MixedGlobalLocal(7))
+    a.append(cm.MixedLocalGlobal(8))
+    a.append(m.get_mixed_gl(9))
+    a.append(m.get_mixed_lg(10))
+    a.append(cm.get_mixed_gl(11))
+    a.append(cm.get_mixed_lg(12))
+
+    assert [x.get() for x in a] == [
+        101,
+        1002,
+        103,
+        1004,
+        105,
+        1006,
+        207,
+        2008,
+        109,
+        1010,
+        211,
+        2012,
+    ]
+
+
+def test_internal_locals_differ():
+    """Makes sure the internal local type map differs across the two modules"""
+    import pybind11_cross_module_tests as cm
+
+    assert m.local_cpp_types_addr() != cm.local_cpp_types_addr()
+
+
+@pytest.mark.xfail("env.PYPY and sys.pypy_version_info < (7, 3, 2)")
+def test_stl_caster_vs_stl_bind(msg):
+    """One module uses a generic vector caster from `<pybind11/stl.h>` while the other
+    exports `std::vector<int>` via `py:bind_vector` and `py::module_local`"""
+    import pybind11_cross_module_tests as cm
+
+    v1 = cm.VectorInt([1, 2, 3])
+    assert m.load_vector_via_caster(v1) == 6
+    assert cm.load_vector_via_binding(v1) == 6
+
+    v2 = [1, 2, 3]
+    assert m.load_vector_via_caster(v2) == 6
+    with pytest.raises(TypeError) as excinfo:
+        cm.load_vector_via_binding(v2)
+    assert (
+        msg(excinfo.value)
+        == """
+    load_vector_via_binding(): incompatible function arguments. The following argument types are supported:
+        1. (arg0: pybind11_cross_module_tests.VectorInt) -> int
+
+    Invoked with: [1, 2, 3]
+    """
+    )
+
+
+def test_cross_module_calls():
+    import pybind11_cross_module_tests as cm
+
+    v1 = m.LocalVec()
+    v1.append(m.LocalType(1))
+    v2 = cm.LocalVec()
+    v2.append(cm.LocalType(2))
+
+    # Returning the self pointer should get picked up as returning an existing
+    # instance (even when that instance is of a foreign, non-local type).
+    assert m.return_self(v1) is v1
+    assert cm.return_self(v2) is v2
+    assert m.return_self(v2) is v2
+    assert cm.return_self(v1) is v1
+
+    assert m.LocalVec is not cm.LocalVec
+    # Returning a copy, on the other hand, always goes to the local type,
+    # regardless of where the source type came from.
+    assert type(m.return_copy(v1)) is m.LocalVec
+    assert type(m.return_copy(v2)) is m.LocalVec
+    assert type(cm.return_copy(v1)) is cm.LocalVec
+    assert type(cm.return_copy(v2)) is cm.LocalVec
+
+    # Test the example given in the documentation (which also tests inheritance casting):
+    mycat = m.Cat("Fluffy")
+    mydog = cm.Dog("Rover")
+    assert mycat.get_name() == "Fluffy"
+    assert mydog.name() == "Rover"
+    assert m.Cat.__base__.__name__ == "Pet"
+    assert cm.Dog.__base__.__name__ == "Pet"
+    assert m.Cat.__base__ is not cm.Dog.__base__
+    assert m.pet_name(mycat) == "Fluffy"
+    assert m.pet_name(mydog) == "Rover"
+    assert cm.pet_name(mycat) == "Fluffy"
+    assert cm.pet_name(mydog) == "Rover"
+
+    assert m.MixGL is not cm.MixGL
+    a = m.MixGL(1)
+    b = cm.MixGL(2)
+    assert m.get_gl_value(a) == 11
+    assert m.get_gl_value(b) == 12
+    assert cm.get_gl_value(a) == 101
+    assert cm.get_gl_value(b) == 102
+
+    c, d = m.MixGL2(3), cm.MixGL2(4)
+    with pytest.raises(TypeError) as excinfo:
+        m.get_gl_value(c)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.get_gl_value(d)
+    assert "incompatible function arguments" in str(excinfo.value)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_methods_and_attributes.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_methods_and_attributes.cpp
new file mode 100644
index 000000000..815dd5e98
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_methods_and_attributes.cpp
@@ -0,0 +1,459 @@
+/*
+    tests/test_methods_and_attributes.cpp -- constructors, deconstructors, attribute access,
+    __str__, argument and return value conventions
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#if !defined(PYBIND11_OVERLOAD_CAST)
+template <typename... Args>
+using overload_cast_ = pybind11::detail::overload_cast_impl<Args...>;
+#endif
+
+class ExampleMandA {
+public:
+    ExampleMandA() { print_default_created(this); }
+    explicit ExampleMandA(int value) : value(value) { print_created(this, value); }
+    ExampleMandA(const ExampleMandA &e) : value(e.value) { print_copy_created(this); }
+    explicit ExampleMandA(std::string &&) {}
+    ExampleMandA(ExampleMandA &&e) noexcept : value(e.value) { print_move_created(this); }
+    ~ExampleMandA() { print_destroyed(this); }
+
+    std::string toString() const { return "ExampleMandA[value=" + std::to_string(value) + "]"; }
+
+    void operator=(const ExampleMandA &e) {
+        print_copy_assigned(this);
+        value = e.value;
+    }
+    void operator=(ExampleMandA &&e) noexcept {
+        print_move_assigned(this);
+        value = e.value;
+    }
+
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
+    void add1(ExampleMandA other) { value += other.value; }         // passing by value
+    void add2(ExampleMandA &other) { value += other.value; }        // passing by reference
+    void add3(const ExampleMandA &other) { value += other.value; }  // passing by const reference
+    void add4(ExampleMandA *other) { value += other->value; }       // passing by pointer
+    void add5(const ExampleMandA *other) { value += other->value; } // passing by const pointer
+
+    void add6(int other) { value += other; }        // passing by value
+    void add7(int &other) { value += other; }       // passing by reference
+    void add8(const int &other) { value += other; } // passing by const reference
+    // NOLINTNEXTLINE(readability-non-const-parameter) Deliberately non-const for testing
+    void add9(int *other) { value += *other; }        // passing by pointer
+    void add10(const int *other) { value += *other; } // passing by const pointer
+
+    void consume_str(std::string &&) {}
+
+    ExampleMandA self1() { return *this; }              // return by value
+    ExampleMandA &self2() { return *this; }             // return by reference
+    const ExampleMandA &self3() const { return *this; } // return by const reference
+    ExampleMandA *self4() { return this; }              // return by pointer
+    const ExampleMandA *self5() const { return this; }  // return by const pointer
+
+    int internal1() const { return value; }        // return by value
+    int &internal2() { return value; }             // return by reference
+    const int &internal3() const { return value; } // return by const reference
+    int *internal4() { return &value; }            // return by pointer
+    const int *internal5() { return &value; }      // return by const pointer
+
+    py::str overloaded() { return "()"; }
+    py::str overloaded(int) { return "(int)"; }
+    py::str overloaded(int, float) { return "(int, float)"; }
+    py::str overloaded(float, int) { return "(float, int)"; }
+    py::str overloaded(int, int) { return "(int, int)"; }
+    py::str overloaded(float, float) { return "(float, float)"; }
+    py::str overloaded(int) const { return "(int) const"; }
+    py::str overloaded(int, float) const { return "(int, float) const"; }
+    py::str overloaded(float, int) const { return "(float, int) const"; }
+    py::str overloaded(int, int) const { return "(int, int) const"; }
+    py::str overloaded(float, float) const { return "(float, float) const"; }
+
+    static py::str overloaded(float) { return "static float"; }
+
+    int value = 0;
+};
+
+struct TestProperties {
+    int value = 1;
+    static int static_value;
+
+    int get() const { return value; }
+    void set(int v) { value = v; }
+
+    static int static_get() { return static_value; }
+    static void static_set(int v) { static_value = v; }
+};
+int TestProperties::static_value = 1;
+
+struct TestPropertiesOverride : TestProperties {
+    int value = 99;
+    static int static_value;
+};
+int TestPropertiesOverride::static_value = 99;
+
+struct TestPropRVP {
+    UserType v1{1};
+    UserType v2{1};
+    static UserType sv1;
+    static UserType sv2;
+
+    const UserType &get1() const { return v1; }
+    const UserType &get2() const { return v2; }
+    UserType get_rvalue() const { return v2; }
+    void set1(int v) { v1.set(v); }
+    void set2(int v) { v2.set(v); }
+};
+UserType TestPropRVP::sv1(1);
+UserType TestPropRVP::sv2(1);
+
+// Test None-allowed py::arg argument policy
+class NoneTester {
+public:
+    int answer = 42;
+};
+int none1(const NoneTester &obj) { return obj.answer; }
+int none2(NoneTester *obj) { return obj ? obj->answer : -1; }
+int none3(std::shared_ptr<NoneTester> &obj) { return obj ? obj->answer : -1; }
+int none4(std::shared_ptr<NoneTester> *obj) { return obj && *obj ? (*obj)->answer : -1; }
+int none5(const std::shared_ptr<NoneTester> &obj) { return obj ? obj->answer : -1; }
+
+// Issue #2778: implicit casting from None to object (not pointer)
+class NoneCastTester {
+public:
+    int answer = -1;
+    NoneCastTester() = default;
+    explicit NoneCastTester(int v) : answer(v) {}
+};
+
+struct StrIssue {
+    int val = -1;
+
+    StrIssue() = default;
+    explicit StrIssue(int i) : val{i} {}
+};
+
+// Issues #854, #910: incompatible function args when member function/pointer is in unregistered
+// base class
+class UnregisteredBase {
+public:
+    void do_nothing() const {}
+    void increase_value() {
+        rw_value++;
+        ro_value += 0.25;
+    }
+    void set_int(int v) { rw_value = v; }
+    int get_int() const { return rw_value; }
+    double get_double() const { return ro_value; }
+    int rw_value = 42;
+    double ro_value = 1.25;
+};
+class RegisteredDerived : public UnregisteredBase {
+public:
+    using UnregisteredBase::UnregisteredBase;
+    double sum() const { return rw_value + ro_value; }
+};
+
+// Test explicit lvalue ref-qualification
+struct RefQualified {
+    int value = 0;
+
+    void refQualified(int other) & { value += other; }
+    int constRefQualified(int other) const & { return value + other; }
+};
+
+// Test rvalue ref param
+struct RValueRefParam {
+    std::size_t func1(std::string &&s) { return s.size(); }
+    std::size_t func2(std::string &&s) const { return s.size(); }
+    std::size_t func3(std::string &&s) & { return s.size(); }
+    std::size_t func4(std::string &&s) const & { return s.size(); }
+};
+
+TEST_SUBMODULE(methods_and_attributes, m) {
+    // test_methods_and_attributes
+    py::class_<ExampleMandA> emna(m, "ExampleMandA");
+    emna.def(py::init<>())
+        .def(py::init<int>())
+        .def(py::init<std::string &&>())
+        .def(py::init<const ExampleMandA &>())
+        .def("add1", &ExampleMandA::add1)
+        .def("add2", &ExampleMandA::add2)
+        .def("add3", &ExampleMandA::add3)
+        .def("add4", &ExampleMandA::add4)
+        .def("add5", &ExampleMandA::add5)
+        .def("add6", &ExampleMandA::add6)
+        .def("add7", &ExampleMandA::add7)
+        .def("add8", &ExampleMandA::add8)
+        .def("add9", &ExampleMandA::add9)
+        .def("add10", &ExampleMandA::add10)
+        .def("consume_str", &ExampleMandA::consume_str)
+        .def("self1", &ExampleMandA::self1)
+        .def("self2", &ExampleMandA::self2)
+        .def("self3", &ExampleMandA::self3)
+        .def("self4", &ExampleMandA::self4)
+        .def("self5", &ExampleMandA::self5)
+        .def("internal1", &ExampleMandA::internal1)
+        .def("internal2", &ExampleMandA::internal2)
+        .def("internal3", &ExampleMandA::internal3)
+        .def("internal4", &ExampleMandA::internal4)
+        .def("internal5", &ExampleMandA::internal5)
+#if defined(PYBIND11_OVERLOAD_CAST)
+        .def("overloaded", py::overload_cast<>(&ExampleMandA::overloaded))
+        .def("overloaded", py::overload_cast<int>(&ExampleMandA::overloaded))
+        .def("overloaded", py::overload_cast<int, float>(&ExampleMandA::overloaded))
+        .def("overloaded", py::overload_cast<float, int>(&ExampleMandA::overloaded))
+        .def("overloaded", py::overload_cast<int, int>(&ExampleMandA::overloaded))
+        .def("overloaded", py::overload_cast<float, float>(&ExampleMandA::overloaded))
+        .def("overloaded_float", py::overload_cast<float, float>(&ExampleMandA::overloaded))
+        .def("overloaded_const", py::overload_cast<int>(&ExampleMandA::overloaded, py::const_))
+        .def("overloaded_const",
+             py::overload_cast<int, float>(&ExampleMandA::overloaded, py::const_))
+        .def("overloaded_const",
+             py::overload_cast<float, int>(&ExampleMandA::overloaded, py::const_))
+        .def("overloaded_const",
+             py::overload_cast<int, int>(&ExampleMandA::overloaded, py::const_))
+        .def("overloaded_const",
+             py::overload_cast<float, float>(&ExampleMandA::overloaded, py::const_))
+#else
+        // Use both the traditional static_cast method and the C++11 compatible overload_cast_
+        .def("overloaded", overload_cast_<>()(&ExampleMandA::overloaded))
+        .def("overloaded", overload_cast_<int>()(&ExampleMandA::overloaded))
+        .def("overloaded", overload_cast_<int,   float>()(&ExampleMandA::overloaded))
+        .def("overloaded", static_cast<py::str (ExampleMandA::*)(float,   int)>(&ExampleMandA::overloaded))
+        .def("overloaded", static_cast<py::str (ExampleMandA::*)(int,     int)>(&ExampleMandA::overloaded))
+        .def("overloaded", static_cast<py::str (ExampleMandA::*)(float, float)>(&ExampleMandA::overloaded))
+        .def("overloaded_float", overload_cast_<float, float>()(&ExampleMandA::overloaded))
+        .def("overloaded_const", overload_cast_<int         >()(&ExampleMandA::overloaded, py::const_))
+        .def("overloaded_const", overload_cast_<int,   float>()(&ExampleMandA::overloaded, py::const_))
+        .def("overloaded_const", static_cast<py::str (ExampleMandA::*)(float,   int) const>(&ExampleMandA::overloaded))
+        .def("overloaded_const", static_cast<py::str (ExampleMandA::*)(int,     int) const>(&ExampleMandA::overloaded))
+        .def("overloaded_const", static_cast<py::str (ExampleMandA::*)(float, float) const>(&ExampleMandA::overloaded))
+#endif
+        // test_no_mixed_overloads
+        // Raise error if trying to mix static/non-static overloads on the same name:
+        .def_static("add_mixed_overloads1",
+                    []() {
+                        auto emna = py::reinterpret_borrow<py::class_<ExampleMandA>>(
+                            py::module_::import("pybind11_tests.methods_and_attributes")
+                                .attr("ExampleMandA"));
+                        emna.def("overload_mixed1",
+                                 static_cast<py::str (ExampleMandA::*)(int, int)>(
+                                     &ExampleMandA::overloaded))
+                            .def_static(
+                                "overload_mixed1",
+                                static_cast<py::str (*)(float)>(&ExampleMandA::overloaded));
+                    })
+        .def_static("add_mixed_overloads2",
+                    []() {
+                        auto emna = py::reinterpret_borrow<py::class_<ExampleMandA>>(
+                            py::module_::import("pybind11_tests.methods_and_attributes")
+                                .attr("ExampleMandA"));
+                        emna.def_static("overload_mixed2",
+                                        static_cast<py::str (*)(float)>(&ExampleMandA::overloaded))
+                            .def("overload_mixed2",
+                                 static_cast<py::str (ExampleMandA::*)(int, int)>(
+                                     &ExampleMandA::overloaded));
+                    })
+        .def("__str__", &ExampleMandA::toString)
+        .def_readwrite("value", &ExampleMandA::value);
+
+    // test_copy_method
+    // Issue #443: can't call copied methods in Python 3
+    emna.attr("add2b") = emna.attr("add2");
+
+    // test_properties, test_static_properties, test_static_cls
+    py::class_<TestProperties>(m, "TestProperties")
+        .def(py::init<>())
+        .def_readonly("def_readonly", &TestProperties::value)
+        .def_readwrite("def_readwrite", &TestProperties::value)
+        .def_property("def_writeonly", nullptr, [](TestProperties &s, int v) { s.value = v; })
+        .def_property("def_property_writeonly", nullptr, &TestProperties::set)
+        .def_property_readonly("def_property_readonly", &TestProperties::get)
+        .def_property("def_property", &TestProperties::get, &TestProperties::set)
+        .def_property("def_property_impossible", nullptr, nullptr)
+        .def_readonly_static("def_readonly_static", &TestProperties::static_value)
+        .def_readwrite_static("def_readwrite_static", &TestProperties::static_value)
+        .def_property_static("def_writeonly_static",
+                             nullptr,
+                             [](const py::object &, int v) { TestProperties::static_value = v; })
+        .def_property_readonly_static(
+            "def_property_readonly_static",
+            [](const py::object &) { return TestProperties::static_get(); })
+        .def_property_static(
+            "def_property_writeonly_static",
+            nullptr,
+            [](const py::object &, int v) { return TestProperties::static_set(v); })
+        .def_property_static(
+            "def_property_static",
+            [](const py::object &) { return TestProperties::static_get(); },
+            [](const py::object &, int v) { TestProperties::static_set(v); })
+        .def_property_static(
+            "static_cls",
+            [](py::object cls) { return cls; },
+            [](const py::object &cls, const py::function &f) { f(cls); });
+
+    py::class_<TestPropertiesOverride, TestProperties>(m, "TestPropertiesOverride")
+        .def(py::init<>())
+        .def_readonly("def_readonly", &TestPropertiesOverride::value)
+        .def_readonly_static("def_readonly_static", &TestPropertiesOverride::static_value);
+
+    auto static_get1 = [](const py::object &) -> const UserType & { return TestPropRVP::sv1; };
+    auto static_get2 = [](const py::object &) -> const UserType & { return TestPropRVP::sv2; };
+    auto static_set1 = [](const py::object &, int v) { TestPropRVP::sv1.set(v); };
+    auto static_set2 = [](const py::object &, int v) { TestPropRVP::sv2.set(v); };
+    auto rvp_copy = py::return_value_policy::copy;
+
+    // test_property_return_value_policies
+    py::class_<TestPropRVP>(m, "TestPropRVP")
+        .def(py::init<>())
+        .def_property_readonly("ro_ref", &TestPropRVP::get1)
+        .def_property_readonly("ro_copy", &TestPropRVP::get2, rvp_copy)
+        .def_property_readonly("ro_func", py::cpp_function(&TestPropRVP::get2, rvp_copy))
+        .def_property("rw_ref", &TestPropRVP::get1, &TestPropRVP::set1)
+        .def_property("rw_copy", &TestPropRVP::get2, &TestPropRVP::set2, rvp_copy)
+        .def_property(
+            "rw_func", py::cpp_function(&TestPropRVP::get2, rvp_copy), &TestPropRVP::set2)
+        .def_property_readonly_static("static_ro_ref", static_get1)
+        .def_property_readonly_static("static_ro_copy", static_get2, rvp_copy)
+        .def_property_readonly_static("static_ro_func", py::cpp_function(static_get2, rvp_copy))
+        .def_property_static("static_rw_ref", static_get1, static_set1)
+        .def_property_static("static_rw_copy", static_get2, static_set2, rvp_copy)
+        .def_property_static(
+            "static_rw_func", py::cpp_function(static_get2, rvp_copy), static_set2)
+        // test_property_rvalue_policy
+        .def_property_readonly("rvalue", &TestPropRVP::get_rvalue)
+        .def_property_readonly_static("static_rvalue",
+                                      [](const py::object &) { return UserType(1); });
+
+    // test_metaclass_override
+    struct MetaclassOverride {};
+    py::class_<MetaclassOverride>(m, "MetaclassOverride", py::metaclass((PyObject *) &PyType_Type))
+        .def_property_readonly_static("readonly", [](const py::object &) { return 1; });
+
+    // test_overload_ordering
+    m.def("overload_order", [](const std::string &) { return 1; });
+    m.def("overload_order", [](const std::string &) { return 2; });
+    m.def("overload_order", [](int) { return 3; });
+    m.def(
+        "overload_order", [](int) { return 4; }, py::prepend{});
+
+#if !defined(PYPY_VERSION)
+    // test_dynamic_attributes
+    class DynamicClass {
+    public:
+        DynamicClass() { print_default_created(this); }
+        DynamicClass(const DynamicClass &) = delete;
+        ~DynamicClass() { print_destroyed(this); }
+    };
+    py::class_<DynamicClass>(m, "DynamicClass", py::dynamic_attr()).def(py::init());
+
+    class CppDerivedDynamicClass : public DynamicClass {};
+    py::class_<CppDerivedDynamicClass, DynamicClass>(m, "CppDerivedDynamicClass").def(py::init());
+#endif
+
+    // test_bad_arg_default
+    // Issue/PR #648: bad arg default debugging output
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+    m.attr("detailed_error_messages_enabled") = true;
+#else
+    m.attr("detailed_error_messages_enabled") = false;
+#endif
+    m.def("bad_arg_def_named", [] {
+        auto m = py::module_::import("pybind11_tests");
+        m.def(
+            "should_fail",
+            [](int, UnregisteredType) {},
+            py::arg(),
+            py::arg("a") = UnregisteredType());
+    });
+    m.def("bad_arg_def_unnamed", [] {
+        auto m = py::module_::import("pybind11_tests");
+        m.def(
+            "should_fail",
+            [](int, UnregisteredType) {},
+            py::arg(),
+            py::arg() = UnregisteredType());
+    });
+
+    // [workaround(intel)] ICC 20/21 breaks with py::arg().stuff, using py::arg{}.stuff works.
+
+    // test_accepts_none
+    py::class_<NoneTester, std::shared_ptr<NoneTester>>(m, "NoneTester").def(py::init<>());
+    m.def("no_none1", &none1, py::arg{}.none(false));
+    m.def("no_none2", &none2, py::arg{}.none(false));
+    m.def("no_none3", &none3, py::arg{}.none(false));
+    m.def("no_none4", &none4, py::arg{}.none(false));
+    m.def("no_none5", &none5, py::arg{}.none(false));
+    m.def("ok_none1", &none1);
+    m.def("ok_none2", &none2, py::arg{}.none(true));
+    m.def("ok_none3", &none3);
+    m.def("ok_none4", &none4, py::arg{}.none(true));
+    m.def("ok_none5", &none5);
+
+    m.def("no_none_kwarg", &none2, "a"_a.none(false));
+    m.def("no_none_kwarg_kw_only", &none2, py::kw_only(), "a"_a.none(false));
+
+    // test_casts_none
+    // Issue #2778: implicit casting from None to object (not pointer)
+    py::class_<NoneCastTester>(m, "NoneCastTester")
+        .def(py::init<>())
+        .def(py::init<int>())
+        .def(py::init([](py::none const &) { return NoneCastTester{}; }));
+    py::implicitly_convertible<py::none, NoneCastTester>();
+    m.def("ok_obj_or_none", [](NoneCastTester const &foo) { return foo.answer; });
+
+    // test_str_issue
+    // Issue #283: __str__ called on uninitialized instance when constructor arguments invalid
+    py::class_<StrIssue>(m, "StrIssue")
+        .def(py::init<int>())
+        .def(py::init<>())
+        .def("__str__",
+             [](const StrIssue &si) { return "StrIssue[" + std::to_string(si.val) + "]"; });
+
+    // test_unregistered_base_implementations
+    //
+    // Issues #854/910: incompatible function args when member function/pointer is in unregistered
+    // base class The methods and member pointers below actually resolve to members/pointers in
+    // UnregisteredBase; before this test/fix they would be registered via lambda with a first
+    // argument of an unregistered type, and thus uncallable.
+    py::class_<RegisteredDerived>(m, "RegisteredDerived")
+        .def(py::init<>())
+        .def("do_nothing", &RegisteredDerived::do_nothing)
+        .def("increase_value", &RegisteredDerived::increase_value)
+        .def_readwrite("rw_value", &RegisteredDerived::rw_value)
+        .def_readonly("ro_value", &RegisteredDerived::ro_value)
+        // Uncommenting the next line should trigger a static_assert:
+        // .def_readwrite("fails", &UserType::value)
+        // Uncommenting the next line should trigger a static_assert:
+        // .def_readonly("fails", &UserType::value)
+        .def_property("rw_value_prop", &RegisteredDerived::get_int, &RegisteredDerived::set_int)
+        .def_property_readonly("ro_value_prop", &RegisteredDerived::get_double)
+        // This one is in the registered class:
+        .def("sum", &RegisteredDerived::sum);
+
+    using Adapted
+        = decltype(py::method_adaptor<RegisteredDerived>(&RegisteredDerived::do_nothing));
+    static_assert(std::is_same<Adapted, void (RegisteredDerived::*)() const>::value, "");
+
+    // test_methods_and_attributes
+    py::class_<RefQualified>(m, "RefQualified")
+        .def(py::init<>())
+        .def_readonly("value", &RefQualified::value)
+        .def("refQualified", &RefQualified::refQualified)
+        .def("constRefQualified", &RefQualified::constRefQualified);
+
+    py::class_<RValueRefParam>(m, "RValueRefParam")
+        .def(py::init<>())
+        .def("func1", &RValueRefParam::func1)
+        .def("func2", &RValueRefParam::func2)
+        .def("func3", &RValueRefParam::func3)
+        .def("func4", &RValueRefParam::func4);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_methods_and_attributes.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_methods_and_attributes.py
new file mode 100644
index 000000000..0a2ae1239
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_methods_and_attributes.py
@@ -0,0 +1,527 @@
+import sys
+
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import ConstructorStats
+from pybind11_tests import methods_and_attributes as m
+
+NO_GETTER_MSG = (
+    "unreadable attribute" if sys.version_info < (3, 11) else "object has no getter"
+)
+NO_SETTER_MSG = (
+    "can't set attribute" if sys.version_info < (3, 11) else "object has no setter"
+)
+NO_DELETER_MSG = (
+    "can't delete attribute" if sys.version_info < (3, 11) else "object has no deleter"
+)
+
+
+def test_methods_and_attributes():
+    instance1 = m.ExampleMandA()
+    instance2 = m.ExampleMandA(32)
+
+    instance1.add1(instance2)
+    instance1.add2(instance2)
+    instance1.add3(instance2)
+    instance1.add4(instance2)
+    instance1.add5(instance2)
+    instance1.add6(32)
+    instance1.add7(32)
+    instance1.add8(32)
+    instance1.add9(32)
+    instance1.add10(32)
+
+    assert str(instance1) == "ExampleMandA[value=320]"
+    assert str(instance2) == "ExampleMandA[value=32]"
+    assert str(instance1.self1()) == "ExampleMandA[value=320]"
+    assert str(instance1.self2()) == "ExampleMandA[value=320]"
+    assert str(instance1.self3()) == "ExampleMandA[value=320]"
+    assert str(instance1.self4()) == "ExampleMandA[value=320]"
+    assert str(instance1.self5()) == "ExampleMandA[value=320]"
+
+    assert instance1.internal1() == 320
+    assert instance1.internal2() == 320
+    assert instance1.internal3() == 320
+    assert instance1.internal4() == 320
+    assert instance1.internal5() == 320
+
+    assert instance1.overloaded() == "()"
+    assert instance1.overloaded(0) == "(int)"
+    assert instance1.overloaded(1, 1.0) == "(int, float)"
+    assert instance1.overloaded(2.0, 2) == "(float, int)"
+    assert instance1.overloaded(3, 3) == "(int, int)"
+    assert instance1.overloaded(4.0, 4.0) == "(float, float)"
+    assert instance1.overloaded_const(-3) == "(int) const"
+    assert instance1.overloaded_const(5, 5.0) == "(int, float) const"
+    assert instance1.overloaded_const(6.0, 6) == "(float, int) const"
+    assert instance1.overloaded_const(7, 7) == "(int, int) const"
+    assert instance1.overloaded_const(8.0, 8.0) == "(float, float) const"
+    assert instance1.overloaded_float(1, 1) == "(float, float)"
+    assert instance1.overloaded_float(1, 1.0) == "(float, float)"
+    assert instance1.overloaded_float(1.0, 1) == "(float, float)"
+    assert instance1.overloaded_float(1.0, 1.0) == "(float, float)"
+
+    assert instance1.value == 320
+    instance1.value = 100
+    assert str(instance1) == "ExampleMandA[value=100]"
+
+    cstats = ConstructorStats.get(m.ExampleMandA)
+    assert cstats.alive() == 2
+    del instance1, instance2
+    assert cstats.alive() == 0
+    assert cstats.values() == ["32"]
+    assert cstats.default_constructions == 1
+    assert cstats.copy_constructions == 2
+    assert cstats.move_constructions >= 2
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+
+def test_copy_method():
+    """Issue #443: calling copied methods fails in Python 3"""
+
+    m.ExampleMandA.add2c = m.ExampleMandA.add2
+    m.ExampleMandA.add2d = m.ExampleMandA.add2b
+    a = m.ExampleMandA(123)
+    assert a.value == 123
+    a.add2(m.ExampleMandA(-100))
+    assert a.value == 23
+    a.add2b(m.ExampleMandA(20))
+    assert a.value == 43
+    a.add2c(m.ExampleMandA(6))
+    assert a.value == 49
+    a.add2d(m.ExampleMandA(-7))
+    assert a.value == 42
+
+
+def test_properties():
+    instance = m.TestProperties()
+
+    assert instance.def_readonly == 1
+    with pytest.raises(AttributeError):
+        instance.def_readonly = 2
+
+    instance.def_readwrite = 2
+    assert instance.def_readwrite == 2
+
+    assert instance.def_property_readonly == 2
+    with pytest.raises(AttributeError):
+        instance.def_property_readonly = 3
+
+    instance.def_property = 3
+    assert instance.def_property == 3
+
+    with pytest.raises(AttributeError) as excinfo:
+        dummy = instance.def_property_writeonly  # unused var
+    assert NO_GETTER_MSG in str(excinfo.value)
+
+    instance.def_property_writeonly = 4
+    assert instance.def_property_readonly == 4
+
+    with pytest.raises(AttributeError) as excinfo:
+        dummy = instance.def_property_impossible  # noqa: F841 unused var
+    assert NO_GETTER_MSG in str(excinfo.value)
+
+    with pytest.raises(AttributeError) as excinfo:
+        instance.def_property_impossible = 5
+    assert NO_SETTER_MSG in str(excinfo.value)
+
+
+def test_static_properties():
+    assert m.TestProperties.def_readonly_static == 1
+    with pytest.raises(AttributeError) as excinfo:
+        m.TestProperties.def_readonly_static = 2
+    assert NO_SETTER_MSG in str(excinfo.value)
+
+    m.TestProperties.def_readwrite_static = 2
+    assert m.TestProperties.def_readwrite_static == 2
+
+    with pytest.raises(AttributeError) as excinfo:
+        dummy = m.TestProperties.def_writeonly_static  # unused var
+    assert NO_GETTER_MSG in str(excinfo.value)
+
+    m.TestProperties.def_writeonly_static = 3
+    assert m.TestProperties.def_readonly_static == 3
+
+    assert m.TestProperties.def_property_readonly_static == 3
+    with pytest.raises(AttributeError) as excinfo:
+        m.TestProperties.def_property_readonly_static = 99
+    assert NO_SETTER_MSG in str(excinfo.value)
+
+    m.TestProperties.def_property_static = 4
+    assert m.TestProperties.def_property_static == 4
+
+    with pytest.raises(AttributeError) as excinfo:
+        dummy = m.TestProperties.def_property_writeonly_static
+    assert NO_GETTER_MSG in str(excinfo.value)
+
+    m.TestProperties.def_property_writeonly_static = 5
+    assert m.TestProperties.def_property_static == 5
+
+    # Static property read and write via instance
+    instance = m.TestProperties()
+
+    m.TestProperties.def_readwrite_static = 0
+    assert m.TestProperties.def_readwrite_static == 0
+    assert instance.def_readwrite_static == 0
+
+    instance.def_readwrite_static = 2
+    assert m.TestProperties.def_readwrite_static == 2
+    assert instance.def_readwrite_static == 2
+
+    with pytest.raises(AttributeError) as excinfo:
+        dummy = instance.def_property_writeonly_static  # noqa: F841 unused var
+    assert NO_GETTER_MSG in str(excinfo.value)
+
+    instance.def_property_writeonly_static = 4
+    assert instance.def_property_static == 4
+
+    # It should be possible to override properties in derived classes
+    assert m.TestPropertiesOverride().def_readonly == 99
+    assert m.TestPropertiesOverride.def_readonly_static == 99
+
+    # Only static attributes can be deleted
+    del m.TestPropertiesOverride.def_readonly_static
+    assert (
+        hasattr(m.TestPropertiesOverride, "def_readonly_static")
+        and m.TestPropertiesOverride.def_readonly_static
+        is m.TestProperties.def_readonly_static
+    )
+    assert "def_readonly_static" not in m.TestPropertiesOverride.__dict__
+    properties_override = m.TestPropertiesOverride()
+    with pytest.raises(AttributeError) as excinfo:
+        del properties_override.def_readonly
+    assert NO_DELETER_MSG in str(excinfo.value)
+
+
+def test_static_cls():
+    """Static property getter and setters expect the type object as the their only argument"""
+
+    instance = m.TestProperties()
+    assert m.TestProperties.static_cls is m.TestProperties
+    assert instance.static_cls is m.TestProperties
+
+    def check_self(self):
+        assert self is m.TestProperties
+
+    m.TestProperties.static_cls = check_self
+    instance.static_cls = check_self
+
+
+def test_metaclass_override():
+    """Overriding pybind11's default metaclass changes the behavior of `static_property`"""
+
+    assert type(m.ExampleMandA).__name__ == "pybind11_type"
+    assert type(m.MetaclassOverride).__name__ == "type"
+
+    assert m.MetaclassOverride.readonly == 1
+    assert (
+        type(m.MetaclassOverride.__dict__["readonly"]).__name__
+        == "pybind11_static_property"
+    )
+
+    # Regular `type` replaces the property instead of calling `__set__()`
+    m.MetaclassOverride.readonly = 2
+    assert m.MetaclassOverride.readonly == 2
+    assert isinstance(m.MetaclassOverride.__dict__["readonly"], int)
+
+
+def test_no_mixed_overloads():
+    from pybind11_tests import detailed_error_messages_enabled
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.ExampleMandA.add_mixed_overloads1()
+    assert str(
+        excinfo.value
+    ) == "overloading a method with both static and instance methods is not supported; " + (
+        "#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for more details"
+        if not detailed_error_messages_enabled
+        else "error while attempting to bind static method ExampleMandA.overload_mixed1"
+        "(arg0: float) -> str"
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.ExampleMandA.add_mixed_overloads2()
+    assert str(
+        excinfo.value
+    ) == "overloading a method with both static and instance methods is not supported; " + (
+        "#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for more details"
+        if not detailed_error_messages_enabled
+        else "error while attempting to bind instance method ExampleMandA.overload_mixed2"
+        "(self: pybind11_tests.methods_and_attributes.ExampleMandA, arg0: int, arg1: int)"
+        " -> str"
+    )
+
+
+@pytest.mark.parametrize("access", ["ro", "rw", "static_ro", "static_rw"])
+def test_property_return_value_policies(access):
+    if not access.startswith("static"):
+        obj = m.TestPropRVP()
+    else:
+        obj = m.TestPropRVP
+
+    ref = getattr(obj, access + "_ref")
+    assert ref.value == 1
+    ref.value = 2
+    assert getattr(obj, access + "_ref").value == 2
+    ref.value = 1  # restore original value for static properties
+
+    copy = getattr(obj, access + "_copy")
+    assert copy.value == 1
+    copy.value = 2
+    assert getattr(obj, access + "_copy").value == 1
+
+    copy = getattr(obj, access + "_func")
+    assert copy.value == 1
+    copy.value = 2
+    assert getattr(obj, access + "_func").value == 1
+
+
+def test_property_rvalue_policy():
+    """When returning an rvalue, the return value policy is automatically changed from
+    `reference(_internal)` to `move`. The following would not work otherwise."""
+
+    instance = m.TestPropRVP()
+    o = instance.rvalue
+    assert o.value == 1
+
+    os = m.TestPropRVP.static_rvalue
+    assert os.value == 1
+
+
+# https://foss.heptapod.net/pypy/pypy/-/issues/2447
+@pytest.mark.xfail("env.PYPY")
+def test_dynamic_attributes():
+    instance = m.DynamicClass()
+    assert not hasattr(instance, "foo")
+    assert "foo" not in dir(instance)
+
+    # Dynamically add attribute
+    instance.foo = 42
+    assert hasattr(instance, "foo")
+    assert instance.foo == 42
+    assert "foo" in dir(instance)
+
+    # __dict__ should be accessible and replaceable
+    assert "foo" in instance.__dict__
+    instance.__dict__ = {"bar": True}
+    assert not hasattr(instance, "foo")
+    assert hasattr(instance, "bar")
+
+    with pytest.raises(TypeError) as excinfo:
+        instance.__dict__ = []
+    assert str(excinfo.value) == "__dict__ must be set to a dictionary, not a 'list'"
+
+    cstats = ConstructorStats.get(m.DynamicClass)
+    assert cstats.alive() == 1
+    del instance
+    assert cstats.alive() == 0
+
+    # Derived classes should work as well
+    class PythonDerivedDynamicClass(m.DynamicClass):
+        pass
+
+    for cls in m.CppDerivedDynamicClass, PythonDerivedDynamicClass:
+        derived = cls()
+        derived.foobar = 100
+        assert derived.foobar == 100
+
+        assert cstats.alive() == 1
+        del derived
+        assert cstats.alive() == 0
+
+
+# https://foss.heptapod.net/pypy/pypy/-/issues/2447
+@pytest.mark.xfail("env.PYPY")
+def test_cyclic_gc():
+    # One object references itself
+    instance = m.DynamicClass()
+    instance.circular_reference = instance
+
+    cstats = ConstructorStats.get(m.DynamicClass)
+    assert cstats.alive() == 1
+    del instance
+    assert cstats.alive() == 0
+
+    # Two object reference each other
+    i1 = m.DynamicClass()
+    i2 = m.DynamicClass()
+    i1.cycle = i2
+    i2.cycle = i1
+
+    assert cstats.alive() == 2
+    del i1, i2
+    assert cstats.alive() == 0
+
+
+def test_bad_arg_default(msg):
+    from pybind11_tests import detailed_error_messages_enabled
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.bad_arg_def_named()
+    assert msg(excinfo.value) == (
+        "arg(): could not convert default argument 'a: UnregisteredType' in function "
+        "'should_fail' into a Python object (type not registered yet?)"
+        if detailed_error_messages_enabled
+        else "arg(): could not convert default argument into a Python object (type not registered "
+        "yet?). #define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for more information."
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.bad_arg_def_unnamed()
+    assert msg(excinfo.value) == (
+        "arg(): could not convert default argument 'UnregisteredType' in function "
+        "'should_fail' into a Python object (type not registered yet?)"
+        if detailed_error_messages_enabled
+        else "arg(): could not convert default argument into a Python object (type not registered "
+        "yet?). #define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for more information."
+    )
+
+
+def test_accepts_none(msg):
+    a = m.NoneTester()
+    assert m.no_none1(a) == 42
+    assert m.no_none2(a) == 42
+    assert m.no_none3(a) == 42
+    assert m.no_none4(a) == 42
+    assert m.no_none5(a) == 42
+    assert m.ok_none1(a) == 42
+    assert m.ok_none2(a) == 42
+    assert m.ok_none3(a) == 42
+    assert m.ok_none4(a) == 42
+    assert m.ok_none5(a) == 42
+
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none1(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none2(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none3(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none4(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none5(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    # The first one still raises because you can't pass None as a lvalue reference arg:
+    with pytest.raises(TypeError) as excinfo:
+        assert m.ok_none1(None) == -1
+    assert (
+        msg(excinfo.value)
+        == """
+        ok_none1(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: m.methods_and_attributes.NoneTester) -> int
+
+        Invoked with: None
+    """
+    )
+
+    # The rest take the argument as pointer or holder, and accept None:
+    assert m.ok_none2(None) == -1
+    assert m.ok_none3(None) == -1
+    assert m.ok_none4(None) == -1
+    assert m.ok_none5(None) == -1
+
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none_kwarg(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none_kwarg(a=None)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none_kwarg_kw_only(None)
+    assert "incompatible function arguments" in str(excinfo.value)
+    with pytest.raises(TypeError) as excinfo:
+        m.no_none_kwarg_kw_only(a=None)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+
+def test_casts_none():
+    """#2778: implicit casting from None to object (not pointer)"""
+    a = m.NoneCastTester()
+    assert m.ok_obj_or_none(a) == -1
+    a = m.NoneCastTester(4)
+    assert m.ok_obj_or_none(a) == 4
+    a = m.NoneCastTester(None)
+    assert m.ok_obj_or_none(a) == -1
+    assert m.ok_obj_or_none(None) == -1
+
+
+def test_str_issue(msg):
+    """#283: __str__ called on uninitialized instance when constructor arguments invalid"""
+
+    assert str(m.StrIssue(3)) == "StrIssue[3]"
+
+    with pytest.raises(TypeError) as excinfo:
+        str(m.StrIssue("no", "such", "constructor"))
+    assert (
+        msg(excinfo.value)
+        == """
+        __init__(): incompatible constructor arguments. The following argument types are supported:
+            1. m.methods_and_attributes.StrIssue(arg0: int)
+            2. m.methods_and_attributes.StrIssue()
+
+        Invoked with: 'no', 'such', 'constructor'
+    """
+    )
+
+
+def test_unregistered_base_implementations():
+    a = m.RegisteredDerived()
+    a.do_nothing()
+    assert a.rw_value == 42
+    assert a.ro_value == 1.25
+    a.rw_value += 5
+    assert a.sum() == 48.25
+    a.increase_value()
+    assert a.rw_value == 48
+    assert a.ro_value == 1.5
+    assert a.sum() == 49.5
+    assert a.rw_value_prop == 48
+    a.rw_value_prop += 1
+    assert a.rw_value_prop == 49
+    a.increase_value()
+    assert a.ro_value_prop == 1.75
+
+
+def test_ref_qualified():
+    """Tests that explicit lvalue ref-qualified methods can be called just like their
+    non ref-qualified counterparts."""
+
+    r = m.RefQualified()
+    assert r.value == 0
+    r.refQualified(17)
+    assert r.value == 17
+    assert r.constRefQualified(23) == 40
+
+
+def test_overload_ordering():
+    "Check to see if the normal overload order (first defined) and prepend overload order works"
+    assert m.overload_order("string") == 1
+    assert m.overload_order(0) == 4
+
+    assert "1. overload_order(arg0: int) -> int" in m.overload_order.__doc__
+    assert "2. overload_order(arg0: str) -> int" in m.overload_order.__doc__
+    assert "3. overload_order(arg0: str) -> int" in m.overload_order.__doc__
+    assert "4. overload_order(arg0: int) -> int" in m.overload_order.__doc__
+
+    with pytest.raises(TypeError) as err:
+        m.overload_order(1.1)
+
+    assert "1. (arg0: int) -> int" in str(err.value)
+    assert "2. (arg0: str) -> int" in str(err.value)
+    assert "3. (arg0: str) -> int" in str(err.value)
+    assert "4. (arg0: int) -> int" in str(err.value)
+
+
+def test_rvalue_ref_param():
+    r = m.RValueRefParam()
+    assert r.func1("123") == 3
+    assert r.func2("1234") == 4
+    assert r.func3("12345") == 5
+    assert r.func4("123456") == 6
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_modules.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_modules.cpp
new file mode 100644
index 000000000..18a7ec74c
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_modules.cpp
@@ -0,0 +1,125 @@
+/*
+    tests/test_modules.cpp -- nested modules, importing modules, and
+                            internal references
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+TEST_SUBMODULE(modules, m) {
+    // test_nested_modules
+    // This is intentionally "py::module" to verify it still can be used in place of "py::module_"
+    py::module m_sub = m.def_submodule("subsubmodule");
+    m_sub.def("submodule_func", []() { return "submodule_func()"; });
+
+    // test_reference_internal
+    class A {
+    public:
+        explicit A(int v) : v(v) { print_created(this, v); }
+        ~A() { print_destroyed(this); }
+        A(const A &) { print_copy_created(this); }
+        A &operator=(const A &copy) {
+            print_copy_assigned(this);
+            v = copy.v;
+            return *this;
+        }
+        std::string toString() const { return "A[" + std::to_string(v) + "]"; }
+
+    private:
+        int v;
+    };
+    py::class_<A>(m_sub, "A").def(py::init<int>()).def("__repr__", &A::toString);
+
+    class B {
+    public:
+        B() { print_default_created(this); }
+        ~B() { print_destroyed(this); }
+        B(const B &) { print_copy_created(this); }
+        B &operator=(const B &copy) {
+            print_copy_assigned(this);
+            a1 = copy.a1;
+            a2 = copy.a2;
+            return *this;
+        }
+        A &get_a1() { return a1; }
+        A &get_a2() { return a2; }
+
+        A a1{1};
+        A a2{2};
+    };
+    py::class_<B>(m_sub, "B")
+        .def(py::init<>())
+        .def("get_a1",
+             &B::get_a1,
+             "Return the internal A 1",
+             py::return_value_policy::reference_internal)
+        .def("get_a2",
+             &B::get_a2,
+             "Return the internal A 2",
+             py::return_value_policy::reference_internal)
+        .def_readwrite("a1", &B::a1) // def_readonly uses an internal
+                                     // reference return policy by default
+        .def_readwrite("a2", &B::a2);
+
+    // This is intentionally "py::module" to verify it still can be used in place of "py::module_"
+    m.attr("OD") = py::module::import("collections").attr("OrderedDict");
+
+    // test_duplicate_registration
+    // Registering two things with the same name
+    m.def("duplicate_registration", []() {
+        class Dupe1 {};
+        class Dupe2 {};
+        class Dupe3 {};
+        class DupeException {};
+
+        // Go ahead and leak, until we have a non-leaking py::module_ constructor
+        auto dm
+            = py::module_::create_extension_module("dummy", nullptr, new py::module_::module_def);
+        auto failures = py::list();
+
+        py::class_<Dupe1>(dm, "Dupe1");
+        py::class_<Dupe2>(dm, "Dupe2");
+        dm.def("dupe1_factory", []() { return Dupe1(); });
+        py::exception<DupeException>(dm, "DupeException");
+
+        try {
+            py::class_<Dupe1>(dm, "Dupe1");
+            failures.append("Dupe1 class");
+        } catch (std::runtime_error &) {
+        }
+        try {
+            dm.def("Dupe1", []() { return Dupe1(); });
+            failures.append("Dupe1 function");
+        } catch (std::runtime_error &) {
+        }
+        try {
+            py::class_<Dupe3>(dm, "dupe1_factory");
+            failures.append("dupe1_factory");
+        } catch (std::runtime_error &) {
+        }
+        try {
+            py::exception<Dupe3>(dm, "Dupe2");
+            failures.append("Dupe2");
+        } catch (std::runtime_error &) {
+        }
+        try {
+            dm.def("DupeException", []() { return 30; });
+            failures.append("DupeException1");
+        } catch (std::runtime_error &) {
+        }
+        try {
+            py::class_<DupeException>(dm, "DupeException");
+            failures.append("DupeException2");
+        } catch (std::runtime_error &) {
+        }
+
+        return failures;
+    });
+
+    m.def("def_submodule", [](py::module_ m, const char *name) { return m.def_submodule(name); });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_modules.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_modules.py
new file mode 100644
index 000000000..e11d68e78
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_modules.py
@@ -0,0 +1,121 @@
+import pytest
+
+import env
+from pybind11_tests import ConstructorStats
+from pybind11_tests import modules as m
+from pybind11_tests.modules import subsubmodule as ms
+
+
+def test_nested_modules():
+    import pybind11_tests
+
+    assert pybind11_tests.__name__ == "pybind11_tests"
+    assert pybind11_tests.modules.__name__ == "pybind11_tests.modules"
+    assert (
+        pybind11_tests.modules.subsubmodule.__name__
+        == "pybind11_tests.modules.subsubmodule"
+    )
+    assert m.__name__ == "pybind11_tests.modules"
+    assert ms.__name__ == "pybind11_tests.modules.subsubmodule"
+
+    assert ms.submodule_func() == "submodule_func()"
+
+
+def test_reference_internal():
+    b = ms.B()
+    assert str(b.get_a1()) == "A[1]"
+    assert str(b.a1) == "A[1]"
+    assert str(b.get_a2()) == "A[2]"
+    assert str(b.a2) == "A[2]"
+
+    b.a1 = ms.A(42)
+    b.a2 = ms.A(43)
+    assert str(b.get_a1()) == "A[42]"
+    assert str(b.a1) == "A[42]"
+    assert str(b.get_a2()) == "A[43]"
+    assert str(b.a2) == "A[43]"
+
+    astats, bstats = ConstructorStats.get(ms.A), ConstructorStats.get(ms.B)
+    assert astats.alive() == 2
+    assert bstats.alive() == 1
+    del b
+    assert astats.alive() == 0
+    assert bstats.alive() == 0
+    assert astats.values() == ["1", "2", "42", "43"]
+    assert bstats.values() == []
+    assert astats.default_constructions == 0
+    assert bstats.default_constructions == 1
+    assert astats.copy_constructions == 0
+    assert bstats.copy_constructions == 0
+    # assert astats.move_constructions >= 0  # Don't invoke any
+    # assert bstats.move_constructions >= 0  # Don't invoke any
+    assert astats.copy_assignments == 2
+    assert bstats.copy_assignments == 0
+    assert astats.move_assignments == 0
+    assert bstats.move_assignments == 0
+
+
+def test_importing():
+    from collections import OrderedDict
+
+    from pybind11_tests.modules import OD
+
+    assert OD is OrderedDict
+    assert str(OD([(1, "a"), (2, "b")])) == "OrderedDict([(1, 'a'), (2, 'b')])"
+
+
+def test_pydoc():
+    """Pydoc needs to be able to provide help() for everything inside a pybind11 module"""
+    import pydoc
+
+    import pybind11_tests
+
+    assert pybind11_tests.__name__ == "pybind11_tests"
+    assert pybind11_tests.__doc__ == "pybind11 test module"
+    assert pydoc.text.docmodule(pybind11_tests)
+
+
+def test_duplicate_registration():
+    """Registering two things with the same name"""
+
+    assert m.duplicate_registration() == []
+
+
+def test_builtin_key_type():
+    """Test that all the keys in the builtin modules have type str.
+
+    Previous versions of pybind11 would add a unicode key in python 2.
+    """
+    if hasattr(__builtins__, "keys"):
+        keys = __builtins__.keys()
+    else:  # this is to make pypy happy since builtins is different there.
+        keys = __builtins__.__dict__.keys()
+
+    assert {type(k) for k in keys} == {str}
+
+
+@pytest.mark.xfail("env.PYPY", reason="PyModule_GetName()")
+def test_def_submodule_failures():
+    sm = m.def_submodule(m, b"ScratchSubModuleName")  # Using bytes to show it works.
+    assert sm.__name__ == m.__name__ + "." + "ScratchSubModuleName"
+    malformed_utf8 = b"\x80"
+    if env.PYPY:
+        # It is not worth the effort finding a trigger for a failure when running with PyPy.
+        pytest.skip("Sufficiently exercised on platforms other than PyPy.")
+    else:
+        # Meant to trigger PyModule_GetName() failure:
+        sm_name_orig = sm.__name__
+        sm.__name__ = malformed_utf8
+        try:
+            with pytest.raises(Exception):
+                # Seen with Python 3.9: SystemError: nameless module
+                # But we do not want to exercise the internals of PyModule_GetName(), which could
+                # change in future versions of Python, but a bad __name__ is very likely to cause
+                # some kind of failure indefinitely.
+                m.def_submodule(sm, b"SubSubModuleName")
+        finally:
+            # Clean up to ensure nothing gets upset by a module with an invalid __name__.
+            sm.__name__ = sm_name_orig  # Purely precautionary.
+    # Meant to trigger PyImport_AddModule() failure:
+    with pytest.raises(UnicodeDecodeError):
+        m.def_submodule(sm, malformed_utf8)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_multiple_inheritance.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_multiple_inheritance.cpp
new file mode 100644
index 000000000..5916ae901
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_multiple_inheritance.cpp
@@ -0,0 +1,341 @@
+/*
+    tests/test_multiple_inheritance.cpp -- multiple inheritance,
+    implicit MI casts
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+namespace {
+
+// Many bases for testing that multiple inheritance from many classes (i.e. requiring extra
+// space for holder constructed flags) works.
+template <int N>
+struct BaseN {
+    explicit BaseN(int i) : i(i) {}
+    int i;
+};
+
+// test_mi_static_properties
+struct Vanilla {
+    std::string vanilla() { return "Vanilla"; };
+};
+struct WithStatic1 {
+    static std::string static_func1() { return "WithStatic1"; };
+    static int static_value1;
+};
+struct WithStatic2 {
+    static std::string static_func2() { return "WithStatic2"; };
+    static int static_value2;
+};
+struct VanillaStaticMix1 : Vanilla, WithStatic1, WithStatic2 {
+    static std::string static_func() { return "VanillaStaticMix1"; }
+    static int static_value;
+};
+struct VanillaStaticMix2 : WithStatic1, Vanilla, WithStatic2 {
+    static std::string static_func() { return "VanillaStaticMix2"; }
+    static int static_value;
+};
+int WithStatic1::static_value1 = 1;
+int WithStatic2::static_value2 = 2;
+int VanillaStaticMix1::static_value = 12;
+int VanillaStaticMix2::static_value = 12;
+
+// test_multiple_inheritance_virtbase
+struct Base1a {
+    explicit Base1a(int i) : i(i) {}
+    int foo() const { return i; }
+    int i;
+};
+struct Base2a {
+    explicit Base2a(int i) : i(i) {}
+    int bar() const { return i; }
+    int i;
+};
+struct Base12a : Base1a, Base2a {
+    Base12a(int i, int j) : Base1a(i), Base2a(j) {}
+};
+
+// test_mi_unaligned_base
+// test_mi_base_return
+struct I801B1 {
+    int a = 1;
+    I801B1() = default;
+    I801B1(const I801B1 &) = default;
+    virtual ~I801B1() = default;
+};
+struct I801B2 {
+    int b = 2;
+    I801B2() = default;
+    I801B2(const I801B2 &) = default;
+    virtual ~I801B2() = default;
+};
+struct I801C : I801B1, I801B2 {};
+struct I801D : I801C {}; // Indirect MI
+
+} // namespace
+
+TEST_SUBMODULE(multiple_inheritance, m) {
+    // Please do not interleave `struct` and `class` definitions with bindings code,
+    // but implement `struct`s and `class`es in the anonymous namespace above.
+    // This helps keeping the smart_holder branch in sync with master.
+
+    // test_multiple_inheritance_mix1
+    // test_multiple_inheritance_mix2
+    struct Base1 {
+        explicit Base1(int i) : i(i) {}
+        int foo() const { return i; }
+        int i;
+    };
+    py::class_<Base1> b1(m, "Base1");
+    b1.def(py::init<int>()).def("foo", &Base1::foo);
+
+    struct Base2 {
+        explicit Base2(int i) : i(i) {}
+        int bar() const { return i; }
+        int i;
+    };
+    py::class_<Base2> b2(m, "Base2");
+    b2.def(py::init<int>()).def("bar", &Base2::bar);
+
+    // test_multiple_inheritance_cpp
+    struct Base12 : Base1, Base2 {
+        Base12(int i, int j) : Base1(i), Base2(j) {}
+    };
+    struct MIType : Base12 {
+        MIType(int i, int j) : Base12(i, j) {}
+    };
+    py::class_<Base12, Base1, Base2>(m, "Base12");
+    py::class_<MIType, Base12>(m, "MIType").def(py::init<int, int>());
+
+    // test_multiple_inheritance_python_many_bases
+#define PYBIND11_BASEN(N)                                                                         \
+    py::class_<BaseN<(N)>>(m, "BaseN" #N).def(py::init<int>()).def("f" #N, [](BaseN<N> &b) {      \
+        return b.i + (N);                                                                         \
+    })
+    PYBIND11_BASEN(1);
+    PYBIND11_BASEN(2);
+    PYBIND11_BASEN(3);
+    PYBIND11_BASEN(4);
+    PYBIND11_BASEN(5);
+    PYBIND11_BASEN(6);
+    PYBIND11_BASEN(7);
+    PYBIND11_BASEN(8);
+    PYBIND11_BASEN(9);
+    PYBIND11_BASEN(10);
+    PYBIND11_BASEN(11);
+    PYBIND11_BASEN(12);
+    PYBIND11_BASEN(13);
+    PYBIND11_BASEN(14);
+    PYBIND11_BASEN(15);
+    PYBIND11_BASEN(16);
+    PYBIND11_BASEN(17);
+
+    // Uncommenting this should result in a compile time failure (MI can only be specified via
+    // template parameters because pybind has to know the types involved; see discussion in #742
+    // for details).
+    //    struct Base12v2 : Base1, Base2 {
+    //        Base12v2(int i, int j) : Base1(i), Base2(j) { }
+    //    };
+    //    py::class_<Base12v2>(m, "Base12v2", b1, b2)
+    //        .def(py::init<int, int>());
+
+    // test_multiple_inheritance_virtbase
+    // Test the case where not all base classes are specified, and where pybind11 requires the
+    // py::multiple_inheritance flag to perform proper casting between types.
+    py::class_<Base1a, std::shared_ptr<Base1a>>(m, "Base1a")
+        .def(py::init<int>())
+        .def("foo", &Base1a::foo);
+
+    py::class_<Base2a, std::shared_ptr<Base2a>>(m, "Base2a")
+        .def(py::init<int>())
+        .def("bar", &Base2a::bar);
+
+    py::class_<Base12a, /* Base1 missing */ Base2a, std::shared_ptr<Base12a>>(
+        m, "Base12a", py::multiple_inheritance())
+        .def(py::init<int, int>());
+
+    m.def("bar_base2a", [](Base2a *b) { return b->bar(); });
+    m.def("bar_base2a_sharedptr", [](const std::shared_ptr<Base2a> &b) { return b->bar(); });
+
+    // test_mi_unaligned_base
+    // test_mi_base_return
+    // Issue #801: invalid casting to derived type with MI bases
+    // Unregistered classes:
+    struct I801B3 {
+        int c = 3;
+        virtual ~I801B3() = default;
+    };
+    struct I801E : I801B3, I801D {};
+
+    py::class_<I801B1, std::shared_ptr<I801B1>>(m, "I801B1")
+        .def(py::init<>())
+        .def_readonly("a", &I801B1::a);
+    py::class_<I801B2, std::shared_ptr<I801B2>>(m, "I801B2")
+        .def(py::init<>())
+        .def_readonly("b", &I801B2::b);
+    py::class_<I801C, I801B1, I801B2, std::shared_ptr<I801C>>(m, "I801C").def(py::init<>());
+    py::class_<I801D, I801C, std::shared_ptr<I801D>>(m, "I801D").def(py::init<>());
+
+    // Two separate issues here: first, we want to recognize a pointer to a base type as being a
+    // known instance even when the pointer value is unequal (i.e. due to a non-first
+    // multiple-inheritance base class):
+    m.def("i801b1_c", [](I801C *c) { return static_cast<I801B1 *>(c); });
+    m.def("i801b2_c", [](I801C *c) { return static_cast<I801B2 *>(c); });
+    m.def("i801b1_d", [](I801D *d) { return static_cast<I801B1 *>(d); });
+    m.def("i801b2_d", [](I801D *d) { return static_cast<I801B2 *>(d); });
+
+    // Second, when returned a base class pointer to a derived instance, we cannot assume that the
+    // pointer is `reinterpret_cast`able to the derived pointer because, like above, the base class
+    // pointer could be offset.
+    m.def("i801c_b1", []() -> I801B1 * { return new I801C(); });
+    m.def("i801c_b2", []() -> I801B2 * { return new I801C(); });
+    m.def("i801d_b1", []() -> I801B1 * { return new I801D(); });
+    m.def("i801d_b2", []() -> I801B2 * { return new I801D(); });
+
+    // Return a base class pointer to a pybind-registered type when the actual derived type
+    // isn't pybind-registered (and uses multiple-inheritance to offset the pybind base)
+    m.def("i801e_c", []() -> I801C * { return new I801E(); });
+    m.def("i801e_b2", []() -> I801B2 * { return new I801E(); });
+
+    // test_mi_static_properties
+    py::class_<Vanilla>(m, "Vanilla").def(py::init<>()).def("vanilla", &Vanilla::vanilla);
+
+    py::class_<WithStatic1>(m, "WithStatic1")
+        .def(py::init<>())
+        .def_static("static_func1", &WithStatic1::static_func1)
+        .def_readwrite_static("static_value1", &WithStatic1::static_value1);
+
+    py::class_<WithStatic2>(m, "WithStatic2")
+        .def(py::init<>())
+        .def_static("static_func2", &WithStatic2::static_func2)
+        .def_readwrite_static("static_value2", &WithStatic2::static_value2);
+
+    py::class_<VanillaStaticMix1, Vanilla, WithStatic1, WithStatic2>(m, "VanillaStaticMix1")
+        .def(py::init<>())
+        .def_static("static_func", &VanillaStaticMix1::static_func)
+        .def_readwrite_static("static_value", &VanillaStaticMix1::static_value);
+
+    py::class_<VanillaStaticMix2, WithStatic1, Vanilla, WithStatic2>(m, "VanillaStaticMix2")
+        .def(py::init<>())
+        .def_static("static_func", &VanillaStaticMix2::static_func)
+        .def_readwrite_static("static_value", &VanillaStaticMix2::static_value);
+
+    struct WithDict {};
+    struct VanillaDictMix1 : Vanilla, WithDict {};
+    struct VanillaDictMix2 : WithDict, Vanilla {};
+    py::class_<WithDict>(m, "WithDict", py::dynamic_attr()).def(py::init<>());
+    py::class_<VanillaDictMix1, Vanilla, WithDict>(m, "VanillaDictMix1").def(py::init<>());
+    py::class_<VanillaDictMix2, WithDict, Vanilla>(m, "VanillaDictMix2").def(py::init<>());
+
+    // test_diamond_inheritance
+    // Issue #959: segfault when constructing diamond inheritance instance
+    // All of these have int members so that there will be various unequal pointers involved.
+    struct B {
+        int b;
+        B() = default;
+        B(const B &) = default;
+        virtual ~B() = default;
+    };
+    struct C0 : public virtual B {
+        int c0;
+    };
+    struct C1 : public virtual B {
+        int c1;
+    };
+    struct D : public C0, public C1 {
+        int d;
+    };
+    py::class_<B>(m, "B").def("b", [](B *self) { return self; });
+    py::class_<C0, B>(m, "C0").def("c0", [](C0 *self) { return self; });
+    py::class_<C1, B>(m, "C1").def("c1", [](C1 *self) { return self; });
+    py::class_<D, C0, C1>(m, "D").def(py::init<>());
+
+    // test_pr3635_diamond_*
+    // - functions are get_{base}_{var}, return {var}
+    struct MVB {
+        MVB() = default;
+        MVB(const MVB &) = default;
+        virtual ~MVB() = default;
+
+        int b = 1;
+        int get_b_b() const { return b; }
+    };
+    struct MVC : virtual MVB {
+        int c = 2;
+        int get_c_b() const { return b; }
+        int get_c_c() const { return c; }
+    };
+    struct MVD0 : virtual MVC {
+        int d0 = 3;
+        int get_d0_b() const { return b; }
+        int get_d0_c() const { return c; }
+        int get_d0_d0() const { return d0; }
+    };
+    struct MVD1 : virtual MVC {
+        int d1 = 4;
+        int get_d1_b() const { return b; }
+        int get_d1_c() const { return c; }
+        int get_d1_d1() const { return d1; }
+    };
+    struct MVE : virtual MVD0, virtual MVD1 {
+        int e = 5;
+        int get_e_b() const { return b; }
+        int get_e_c() const { return c; }
+        int get_e_d0() const { return d0; }
+        int get_e_d1() const { return d1; }
+        int get_e_e() const { return e; }
+    };
+    struct MVF : virtual MVE {
+        int f = 6;
+        int get_f_b() const { return b; }
+        int get_f_c() const { return c; }
+        int get_f_d0() const { return d0; }
+        int get_f_d1() const { return d1; }
+        int get_f_e() const { return e; }
+        int get_f_f() const { return f; }
+    };
+    py::class_<MVB>(m, "MVB")
+        .def(py::init<>())
+        .def("get_b_b", &MVB::get_b_b)
+        .def_readwrite("b", &MVB::b);
+    py::class_<MVC, MVB>(m, "MVC")
+        .def(py::init<>())
+        .def("get_c_b", &MVC::get_c_b)
+        .def("get_c_c", &MVC::get_c_c)
+        .def_readwrite("c", &MVC::c);
+    py::class_<MVD0, MVC>(m, "MVD0")
+        .def(py::init<>())
+        .def("get_d0_b", &MVD0::get_d0_b)
+        .def("get_d0_c", &MVD0::get_d0_c)
+        .def("get_d0_d0", &MVD0::get_d0_d0)
+        .def_readwrite("d0", &MVD0::d0);
+    py::class_<MVD1, MVC>(m, "MVD1")
+        .def(py::init<>())
+        .def("get_d1_b", &MVD1::get_d1_b)
+        .def("get_d1_c", &MVD1::get_d1_c)
+        .def("get_d1_d1", &MVD1::get_d1_d1)
+        .def_readwrite("d1", &MVD1::d1);
+    py::class_<MVE, MVD0, MVD1>(m, "MVE")
+        .def(py::init<>())
+        .def("get_e_b", &MVE::get_e_b)
+        .def("get_e_c", &MVE::get_e_c)
+        .def("get_e_d0", &MVE::get_e_d0)
+        .def("get_e_d1", &MVE::get_e_d1)
+        .def("get_e_e", &MVE::get_e_e)
+        .def_readwrite("e", &MVE::e);
+    py::class_<MVF, MVE>(m, "MVF")
+        .def(py::init<>())
+        .def("get_f_b", &MVF::get_f_b)
+        .def("get_f_c", &MVF::get_f_c)
+        .def("get_f_d0", &MVF::get_f_d0)
+        .def("get_f_d1", &MVF::get_f_d1)
+        .def("get_f_e", &MVF::get_f_e)
+        .def("get_f_f", &MVF::get_f_f)
+        .def_readwrite("f", &MVF::f);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_multiple_inheritance.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_multiple_inheritance.py
new file mode 100644
index 000000000..3a1d88d71
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_multiple_inheritance.py
@@ -0,0 +1,493 @@
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import ConstructorStats
+from pybind11_tests import multiple_inheritance as m
+
+
+def test_multiple_inheritance_cpp():
+    mt = m.MIType(3, 4)
+
+    assert mt.foo() == 3
+    assert mt.bar() == 4
+
+
+@pytest.mark.xfail("env.PYPY")
+def test_multiple_inheritance_mix1():
+    class Base1:
+        def __init__(self, i):
+            self.i = i
+
+        def foo(self):
+            return self.i
+
+    class MITypePy(Base1, m.Base2):
+        def __init__(self, i, j):
+            Base1.__init__(self, i)
+            m.Base2.__init__(self, j)
+
+    mt = MITypePy(3, 4)
+
+    assert mt.foo() == 3
+    assert mt.bar() == 4
+
+
+def test_multiple_inheritance_mix2():
+    class Base2:
+        def __init__(self, i):
+            self.i = i
+
+        def bar(self):
+            return self.i
+
+    class MITypePy(m.Base1, Base2):
+        def __init__(self, i, j):
+            m.Base1.__init__(self, i)
+            Base2.__init__(self, j)
+
+    mt = MITypePy(3, 4)
+
+    assert mt.foo() == 3
+    assert mt.bar() == 4
+
+
+@pytest.mark.xfail("env.PYPY")
+def test_multiple_inheritance_python():
+    class MI1(m.Base1, m.Base2):
+        def __init__(self, i, j):
+            m.Base1.__init__(self, i)
+            m.Base2.__init__(self, j)
+
+    class B1:
+        def v(self):
+            return 1
+
+    class MI2(B1, m.Base1, m.Base2):
+        def __init__(self, i, j):
+            B1.__init__(self)
+            m.Base1.__init__(self, i)
+            m.Base2.__init__(self, j)
+
+    class MI3(MI2):
+        def __init__(self, i, j):
+            MI2.__init__(self, i, j)
+
+    class MI4(MI3, m.Base2):
+        def __init__(self, i, j):
+            MI3.__init__(self, i, j)
+            # This should be ignored (Base2 is already initialized via MI2):
+            m.Base2.__init__(self, i + 100)
+
+    class MI5(m.Base2, B1, m.Base1):
+        def __init__(self, i, j):
+            B1.__init__(self)
+            m.Base1.__init__(self, i)
+            m.Base2.__init__(self, j)
+
+    class MI6(m.Base2, B1):
+        def __init__(self, i):
+            m.Base2.__init__(self, i)
+            B1.__init__(self)
+
+    class B2(B1):
+        def v(self):
+            return 2
+
+    class B3:
+        def v(self):
+            return 3
+
+    class B4(B3, B2):
+        def v(self):
+            return 4
+
+    class MI7(B4, MI6):
+        def __init__(self, i):
+            B4.__init__(self)
+            MI6.__init__(self, i)
+
+    class MI8(MI6, B3):
+        def __init__(self, i):
+            MI6.__init__(self, i)
+            B3.__init__(self)
+
+    class MI8b(B3, MI6):
+        def __init__(self, i):
+            B3.__init__(self)
+            MI6.__init__(self, i)
+
+    mi1 = MI1(1, 2)
+    assert mi1.foo() == 1
+    assert mi1.bar() == 2
+
+    mi2 = MI2(3, 4)
+    assert mi2.v() == 1
+    assert mi2.foo() == 3
+    assert mi2.bar() == 4
+
+    mi3 = MI3(5, 6)
+    assert mi3.v() == 1
+    assert mi3.foo() == 5
+    assert mi3.bar() == 6
+
+    mi4 = MI4(7, 8)
+    assert mi4.v() == 1
+    assert mi4.foo() == 7
+    assert mi4.bar() == 8
+
+    mi5 = MI5(10, 11)
+    assert mi5.v() == 1
+    assert mi5.foo() == 10
+    assert mi5.bar() == 11
+
+    mi6 = MI6(12)
+    assert mi6.v() == 1
+    assert mi6.bar() == 12
+
+    mi7 = MI7(13)
+    assert mi7.v() == 4
+    assert mi7.bar() == 13
+
+    mi8 = MI8(14)
+    assert mi8.v() == 1
+    assert mi8.bar() == 14
+
+    mi8b = MI8b(15)
+    assert mi8b.v() == 3
+    assert mi8b.bar() == 15
+
+
+def test_multiple_inheritance_python_many_bases():
+    class MIMany14(m.BaseN1, m.BaseN2, m.BaseN3, m.BaseN4):
+        def __init__(self):
+            m.BaseN1.__init__(self, 1)
+            m.BaseN2.__init__(self, 2)
+            m.BaseN3.__init__(self, 3)
+            m.BaseN4.__init__(self, 4)
+
+    class MIMany58(m.BaseN5, m.BaseN6, m.BaseN7, m.BaseN8):
+        def __init__(self):
+            m.BaseN5.__init__(self, 5)
+            m.BaseN6.__init__(self, 6)
+            m.BaseN7.__init__(self, 7)
+            m.BaseN8.__init__(self, 8)
+
+    class MIMany916(
+        m.BaseN9,
+        m.BaseN10,
+        m.BaseN11,
+        m.BaseN12,
+        m.BaseN13,
+        m.BaseN14,
+        m.BaseN15,
+        m.BaseN16,
+    ):
+        def __init__(self):
+            m.BaseN9.__init__(self, 9)
+            m.BaseN10.__init__(self, 10)
+            m.BaseN11.__init__(self, 11)
+            m.BaseN12.__init__(self, 12)
+            m.BaseN13.__init__(self, 13)
+            m.BaseN14.__init__(self, 14)
+            m.BaseN15.__init__(self, 15)
+            m.BaseN16.__init__(self, 16)
+
+    class MIMany19(MIMany14, MIMany58, m.BaseN9):
+        def __init__(self):
+            MIMany14.__init__(self)
+            MIMany58.__init__(self)
+            m.BaseN9.__init__(self, 9)
+
+    class MIMany117(MIMany14, MIMany58, MIMany916, m.BaseN17):
+        def __init__(self):
+            MIMany14.__init__(self)
+            MIMany58.__init__(self)
+            MIMany916.__init__(self)
+            m.BaseN17.__init__(self, 17)
+
+    # Inherits from 4 registered C++ classes: can fit in one pointer on any modern arch:
+    a = MIMany14()
+    for i in range(1, 4):
+        assert getattr(a, "f" + str(i))() == 2 * i
+
+    # Inherits from 8: requires 1/2 pointers worth of holder flags on 32/64-bit arch:
+    b = MIMany916()
+    for i in range(9, 16):
+        assert getattr(b, "f" + str(i))() == 2 * i
+
+    # Inherits from 9: requires >= 2 pointers worth of holder flags
+    c = MIMany19()
+    for i in range(1, 9):
+        assert getattr(c, "f" + str(i))() == 2 * i
+
+    # Inherits from 17: requires >= 3 pointers worth of holder flags
+    d = MIMany117()
+    for i in range(1, 17):
+        assert getattr(d, "f" + str(i))() == 2 * i
+
+
+def test_multiple_inheritance_virtbase():
+    class MITypePy(m.Base12a):
+        def __init__(self, i, j):
+            m.Base12a.__init__(self, i, j)
+
+    mt = MITypePy(3, 4)
+    assert mt.bar() == 4
+    assert m.bar_base2a(mt) == 4
+    assert m.bar_base2a_sharedptr(mt) == 4
+
+
+def test_mi_static_properties():
+    """Mixing bases with and without static properties should be possible
+    and the result should be independent of base definition order"""
+
+    for d in (m.VanillaStaticMix1(), m.VanillaStaticMix2()):
+        assert d.vanilla() == "Vanilla"
+        assert d.static_func1() == "WithStatic1"
+        assert d.static_func2() == "WithStatic2"
+        assert d.static_func() == d.__class__.__name__
+
+        m.WithStatic1.static_value1 = 1
+        m.WithStatic2.static_value2 = 2
+        assert d.static_value1 == 1
+        assert d.static_value2 == 2
+        assert d.static_value == 12
+
+        d.static_value1 = 0
+        assert d.static_value1 == 0
+        d.static_value2 = 0
+        assert d.static_value2 == 0
+        d.static_value = 0
+        assert d.static_value == 0
+
+
+# Requires PyPy 6+
+def test_mi_dynamic_attributes():
+    """Mixing bases with and without dynamic attribute support"""
+
+    for d in (m.VanillaDictMix1(), m.VanillaDictMix2()):
+        d.dynamic = 1
+        assert d.dynamic == 1
+
+
+def test_mi_unaligned_base():
+    """Returning an offset (non-first MI) base class pointer should recognize the instance"""
+
+    n_inst = ConstructorStats.detail_reg_inst()
+
+    c = m.I801C()
+    d = m.I801D()
+    # + 4 below because we have the two instances, and each instance has offset base I801B2
+    assert ConstructorStats.detail_reg_inst() == n_inst + 4
+    b1c = m.i801b1_c(c)
+    assert b1c is c
+    b2c = m.i801b2_c(c)
+    assert b2c is c
+    b1d = m.i801b1_d(d)
+    assert b1d is d
+    b2d = m.i801b2_d(d)
+    assert b2d is d
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 4  # no extra instances
+    del c, b1c, b2c
+    assert ConstructorStats.detail_reg_inst() == n_inst + 2
+    del d, b1d, b2d
+    assert ConstructorStats.detail_reg_inst() == n_inst
+
+
+def test_mi_base_return():
+    """Tests returning an offset (non-first MI) base class pointer to a derived instance"""
+
+    n_inst = ConstructorStats.detail_reg_inst()
+
+    c1 = m.i801c_b1()
+    assert type(c1) is m.I801C
+    assert c1.a == 1
+    assert c1.b == 2
+
+    d1 = m.i801d_b1()
+    assert type(d1) is m.I801D
+    assert d1.a == 1
+    assert d1.b == 2
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 4
+
+    c2 = m.i801c_b2()
+    assert type(c2) is m.I801C
+    assert c2.a == 1
+    assert c2.b == 2
+
+    d2 = m.i801d_b2()
+    assert type(d2) is m.I801D
+    assert d2.a == 1
+    assert d2.b == 2
+
+    assert ConstructorStats.detail_reg_inst() == n_inst + 8
+
+    del c2
+    assert ConstructorStats.detail_reg_inst() == n_inst + 6
+    del c1, d1, d2
+    assert ConstructorStats.detail_reg_inst() == n_inst
+
+    # Returning an unregistered derived type with a registered base; we won't
+    # pick up the derived type, obviously, but should still work (as an object
+    # of whatever type was returned).
+    e1 = m.i801e_c()
+    assert type(e1) is m.I801C
+    assert e1.a == 1
+    assert e1.b == 2
+
+    e2 = m.i801e_b2()
+    assert type(e2) is m.I801B2
+    assert e2.b == 2
+
+
+def test_diamond_inheritance():
+    """Tests that diamond inheritance works as expected (issue #959)"""
+
+    # Issue #959: this shouldn't segfault:
+    d = m.D()
+
+    # Make sure all the various distinct pointers are all recognized as registered instances:
+    assert d is d.c0()
+    assert d is d.c1()
+    assert d is d.b()
+    assert d is d.c0().b()
+    assert d is d.c1().b()
+    assert d is d.c0().c1().b().c0().b()
+
+
+def test_pr3635_diamond_b():
+    o = m.MVB()
+    assert o.b == 1
+
+    assert o.get_b_b() == 1
+
+
+def test_pr3635_diamond_c():
+    o = m.MVC()
+    assert o.b == 1
+    assert o.c == 2
+
+    assert o.get_b_b() == 1
+    assert o.get_c_b() == 1
+
+    assert o.get_c_c() == 2
+
+
+def test_pr3635_diamond_d0():
+    o = m.MVD0()
+    assert o.b == 1
+    assert o.c == 2
+    assert o.d0 == 3
+
+    assert o.get_b_b() == 1
+    assert o.get_c_b() == 1
+    assert o.get_d0_b() == 1
+
+    assert o.get_c_c() == 2
+    assert o.get_d0_c() == 2
+
+    assert o.get_d0_d0() == 3
+
+
+def test_pr3635_diamond_d1():
+    o = m.MVD1()
+    assert o.b == 1
+    assert o.c == 2
+    assert o.d1 == 4
+
+    assert o.get_b_b() == 1
+    assert o.get_c_b() == 1
+    assert o.get_d1_b() == 1
+
+    assert o.get_c_c() == 2
+    assert o.get_d1_c() == 2
+
+    assert o.get_d1_d1() == 4
+
+
+def test_pr3635_diamond_e():
+    o = m.MVE()
+    assert o.b == 1
+    assert o.c == 2
+    assert o.d0 == 3
+    assert o.d1 == 4
+    assert o.e == 5
+
+    assert o.get_b_b() == 1
+    assert o.get_c_b() == 1
+    assert o.get_d0_b() == 1
+    assert o.get_d1_b() == 1
+    assert o.get_e_b() == 1
+
+    assert o.get_c_c() == 2
+    assert o.get_d0_c() == 2
+    assert o.get_d1_c() == 2
+    assert o.get_e_c() == 2
+
+    assert o.get_d0_d0() == 3
+    assert o.get_e_d0() == 3
+
+    assert o.get_d1_d1() == 4
+    assert o.get_e_d1() == 4
+
+    assert o.get_e_e() == 5
+
+
+def test_pr3635_diamond_f():
+    o = m.MVF()
+    assert o.b == 1
+    assert o.c == 2
+    assert o.d0 == 3
+    assert o.d1 == 4
+    assert o.e == 5
+    assert o.f == 6
+
+    assert o.get_b_b() == 1
+    assert o.get_c_b() == 1
+    assert o.get_d0_b() == 1
+    assert o.get_d1_b() == 1
+    assert o.get_e_b() == 1
+    assert o.get_f_b() == 1
+
+    assert o.get_c_c() == 2
+    assert o.get_d0_c() == 2
+    assert o.get_d1_c() == 2
+    assert o.get_e_c() == 2
+    assert o.get_f_c() == 2
+
+    assert o.get_d0_d0() == 3
+    assert o.get_e_d0() == 3
+    assert o.get_f_d0() == 3
+
+    assert o.get_d1_d1() == 4
+    assert o.get_e_d1() == 4
+    assert o.get_f_d1() == 4
+
+    assert o.get_e_e() == 5
+    assert o.get_f_e() == 5
+
+    assert o.get_f_f() == 6
+
+
+def test_python_inherit_from_mi():
+    """Tests extending a Python class from a single inheritor of a MI class"""
+
+    class PyMVF(m.MVF):
+        g = 7
+
+        def get_g_g(self):
+            return self.g
+
+    o = PyMVF()
+
+    assert o.b == 1
+    assert o.c == 2
+    assert o.d0 == 3
+    assert o.d1 == 4
+    assert o.e == 5
+    assert o.f == 6
+    assert o.g == 7
+
+    assert o.get_g_g() == 7
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_array.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_array.cpp
new file mode 100644
index 000000000..69ddbe1ef
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_array.cpp
@@ -0,0 +1,524 @@
+/*
+    tests/test_numpy_array.cpp -- test core array functionality
+
+    Copyright (c) 2016 Ivan Smirnov <i.s.smirnov@gmail.com>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/numpy.h>
+#include <pybind11/stl.h>
+
+#include "pybind11_tests.h"
+
+#include <cstdint>
+#include <utility>
+
+// Size / dtype checks.
+struct DtypeCheck {
+    py::dtype numpy{};
+    py::dtype pybind11{};
+};
+
+template <typename T>
+DtypeCheck get_dtype_check(const char *name) {
+    py::module_ np = py::module_::import("numpy");
+    DtypeCheck check{};
+    check.numpy = np.attr("dtype")(np.attr(name));
+    check.pybind11 = py::dtype::of<T>();
+    return check;
+}
+
+std::vector<DtypeCheck> get_concrete_dtype_checks() {
+    return {// Normalization
+            get_dtype_check<std::int8_t>("int8"),
+            get_dtype_check<std::uint8_t>("uint8"),
+            get_dtype_check<std::int16_t>("int16"),
+            get_dtype_check<std::uint16_t>("uint16"),
+            get_dtype_check<std::int32_t>("int32"),
+            get_dtype_check<std::uint32_t>("uint32"),
+            get_dtype_check<std::int64_t>("int64"),
+            get_dtype_check<std::uint64_t>("uint64")};
+}
+
+struct DtypeSizeCheck {
+    std::string name{};
+    int size_cpp{};
+    int size_numpy{};
+    // For debugging.
+    py::dtype dtype{};
+};
+
+template <typename T>
+DtypeSizeCheck get_dtype_size_check() {
+    DtypeSizeCheck check{};
+    check.name = py::type_id<T>();
+    check.size_cpp = sizeof(T);
+    check.dtype = py::dtype::of<T>();
+    check.size_numpy = check.dtype.attr("itemsize").template cast<int>();
+    return check;
+}
+
+std::vector<DtypeSizeCheck> get_platform_dtype_size_checks() {
+    return {
+        get_dtype_size_check<short>(),
+        get_dtype_size_check<unsigned short>(),
+        get_dtype_size_check<int>(),
+        get_dtype_size_check<unsigned int>(),
+        get_dtype_size_check<long>(),
+        get_dtype_size_check<unsigned long>(),
+        get_dtype_size_check<long long>(),
+        get_dtype_size_check<unsigned long long>(),
+    };
+}
+
+// Arrays.
+using arr = py::array;
+using arr_t = py::array_t<uint16_t, 0>;
+static_assert(std::is_same<arr_t::value_type, uint16_t>::value, "");
+
+template <typename... Ix>
+arr data(const arr &a, Ix... index) {
+    return arr(a.nbytes() - a.offset_at(index...), (const uint8_t *) a.data(index...));
+}
+
+template <typename... Ix>
+arr data_t(const arr_t &a, Ix... index) {
+    return arr(a.size() - a.index_at(index...), a.data(index...));
+}
+
+template <typename... Ix>
+arr &mutate_data(arr &a, Ix... index) {
+    auto *ptr = (uint8_t *) a.mutable_data(index...);
+    for (py::ssize_t i = 0; i < a.nbytes() - a.offset_at(index...); i++) {
+        ptr[i] = (uint8_t) (ptr[i] * 2);
+    }
+    return a;
+}
+
+template <typename... Ix>
+arr_t &mutate_data_t(arr_t &a, Ix... index) {
+    auto ptr = a.mutable_data(index...);
+    for (py::ssize_t i = 0; i < a.size() - a.index_at(index...); i++) {
+        ptr[i]++;
+    }
+    return a;
+}
+
+template <typename... Ix>
+py::ssize_t index_at(const arr &a, Ix... idx) {
+    return a.index_at(idx...);
+}
+template <typename... Ix>
+py::ssize_t index_at_t(const arr_t &a, Ix... idx) {
+    return a.index_at(idx...);
+}
+template <typename... Ix>
+py::ssize_t offset_at(const arr &a, Ix... idx) {
+    return a.offset_at(idx...);
+}
+template <typename... Ix>
+py::ssize_t offset_at_t(const arr_t &a, Ix... idx) {
+    return a.offset_at(idx...);
+}
+template <typename... Ix>
+py::ssize_t at_t(const arr_t &a, Ix... idx) {
+    return a.at(idx...);
+}
+template <typename... Ix>
+arr_t &mutate_at_t(arr_t &a, Ix... idx) {
+    a.mutable_at(idx...)++;
+    return a;
+}
+
+#define def_index_fn(name, type)                                                                  \
+    sm.def(#name, [](type a) { return name(a); });                                                \
+    sm.def(#name, [](type a, int i) { return name(a, i); });                                      \
+    sm.def(#name, [](type a, int i, int j) { return name(a, i, j); });                            \
+    sm.def(#name, [](type a, int i, int j, int k) { return name(a, i, j, k); });
+
+template <typename T, typename T2>
+py::handle auxiliaries(T &&r, T2 &&r2) {
+    if (r.ndim() != 2) {
+        throw std::domain_error("error: ndim != 2");
+    }
+    py::list l;
+    l.append(*r.data(0, 0));
+    l.append(*r2.mutable_data(0, 0));
+    l.append(r.data(0, 1) == r2.mutable_data(0, 1));
+    l.append(r.ndim());
+    l.append(r.itemsize());
+    l.append(r.shape(0));
+    l.append(r.shape(1));
+    l.append(r.size());
+    l.append(r.nbytes());
+    return l.release();
+}
+
+// note: declaration at local scope would create a dangling reference!
+static int data_i = 42;
+
+TEST_SUBMODULE(numpy_array, sm) {
+    try {
+        py::module_::import("numpy");
+    } catch (const py::error_already_set &) {
+        return;
+    }
+
+    // test_dtypes
+    py::class_<DtypeCheck>(sm, "DtypeCheck")
+        .def_readonly("numpy", &DtypeCheck::numpy)
+        .def_readonly("pybind11", &DtypeCheck::pybind11)
+        .def("__repr__", [](const DtypeCheck &self) {
+            return py::str("<DtypeCheck numpy={} pybind11={}>").format(self.numpy, self.pybind11);
+        });
+    sm.def("get_concrete_dtype_checks", &get_concrete_dtype_checks);
+
+    py::class_<DtypeSizeCheck>(sm, "DtypeSizeCheck")
+        .def_readonly("name", &DtypeSizeCheck::name)
+        .def_readonly("size_cpp", &DtypeSizeCheck::size_cpp)
+        .def_readonly("size_numpy", &DtypeSizeCheck::size_numpy)
+        .def("__repr__", [](const DtypeSizeCheck &self) {
+            return py::str("<DtypeSizeCheck name='{}' size_cpp={} size_numpy={} dtype={}>")
+                .format(self.name, self.size_cpp, self.size_numpy, self.dtype);
+        });
+    sm.def("get_platform_dtype_size_checks", &get_platform_dtype_size_checks);
+
+    // test_array_attributes
+    sm.def("ndim", [](const arr &a) { return a.ndim(); });
+    sm.def("shape", [](const arr &a) { return arr(a.ndim(), a.shape()); });
+    sm.def("shape", [](const arr &a, py::ssize_t dim) { return a.shape(dim); });
+    sm.def("strides", [](const arr &a) { return arr(a.ndim(), a.strides()); });
+    sm.def("strides", [](const arr &a, py::ssize_t dim) { return a.strides(dim); });
+    sm.def("writeable", [](const arr &a) { return a.writeable(); });
+    sm.def("size", [](const arr &a) { return a.size(); });
+    sm.def("itemsize", [](const arr &a) { return a.itemsize(); });
+    sm.def("nbytes", [](const arr &a) { return a.nbytes(); });
+    sm.def("owndata", [](const arr &a) { return a.owndata(); });
+
+    // test_index_offset
+    def_index_fn(index_at, const arr &);
+    def_index_fn(index_at_t, const arr_t &);
+    def_index_fn(offset_at, const arr &);
+    def_index_fn(offset_at_t, const arr_t &);
+    // test_data
+    def_index_fn(data, const arr &);
+    def_index_fn(data_t, const arr_t &);
+    // test_mutate_data, test_mutate_readonly
+    def_index_fn(mutate_data, arr &);
+    def_index_fn(mutate_data_t, arr_t &);
+    def_index_fn(at_t, const arr_t &);
+    def_index_fn(mutate_at_t, arr_t &);
+
+    // test_make_c_f_array
+    sm.def("make_f_array", [] { return py::array_t<float>({2, 2}, {4, 8}); });
+    sm.def("make_c_array", [] { return py::array_t<float>({2, 2}, {8, 4}); });
+
+    // test_empty_shaped_array
+    sm.def("make_empty_shaped_array", [] { return py::array(py::dtype("f"), {}, {}); });
+    // test numpy scalars (empty shape, ndim==0)
+    sm.def("scalar_int", []() { return py::array(py::dtype("i"), {}, {}, &data_i); });
+
+    // test_wrap
+    sm.def("wrap", [](const py::array &a) {
+        return py::array(a.dtype(),
+                         {a.shape(), a.shape() + a.ndim()},
+                         {a.strides(), a.strides() + a.ndim()},
+                         a.data(),
+                         a);
+    });
+
+    // test_numpy_view
+    struct ArrayClass {
+        int data[2] = {1, 2};
+        ArrayClass() { py::print("ArrayClass()"); }
+        ~ArrayClass() { py::print("~ArrayClass()"); }
+    };
+    py::class_<ArrayClass>(sm, "ArrayClass")
+        .def(py::init<>())
+        .def("numpy_view", [](py::object &obj) {
+            py::print("ArrayClass::numpy_view()");
+            auto &a = obj.cast<ArrayClass &>();
+            return py::array_t<int>({2}, {4}, a.data, obj);
+        });
+
+    // test_cast_numpy_int64_to_uint64
+    sm.def("function_taking_uint64", [](uint64_t) {});
+
+    // test_isinstance
+    sm.def("isinstance_untyped", [](py::object yes, py::object no) {
+        return py::isinstance<py::array>(std::move(yes))
+               && !py::isinstance<py::array>(std::move(no));
+    });
+    sm.def("isinstance_typed", [](const py::object &o) {
+        return py::isinstance<py::array_t<double>>(o) && !py::isinstance<py::array_t<int>>(o);
+    });
+
+    // test_constructors
+    sm.def("default_constructors", []() {
+        return py::dict("array"_a = py::array(),
+                        "array_t<int32>"_a = py::array_t<std::int32_t>(),
+                        "array_t<double>"_a = py::array_t<double>());
+    });
+    sm.def("converting_constructors", [](const py::object &o) {
+        return py::dict("array"_a = py::array(o),
+                        "array_t<int32>"_a = py::array_t<std::int32_t>(o),
+                        "array_t<double>"_a = py::array_t<double>(o));
+    });
+
+    // test_overload_resolution
+    sm.def("overloaded", [](const py::array_t<double> &) { return "double"; });
+    sm.def("overloaded", [](const py::array_t<float> &) { return "float"; });
+    sm.def("overloaded", [](const py::array_t<int> &) { return "int"; });
+    sm.def("overloaded", [](const py::array_t<unsigned short> &) { return "unsigned short"; });
+    sm.def("overloaded", [](const py::array_t<long long> &) { return "long long"; });
+    sm.def("overloaded",
+           [](const py::array_t<std::complex<double>> &) { return "double complex"; });
+    sm.def("overloaded", [](const py::array_t<std::complex<float>> &) { return "float complex"; });
+
+    sm.def("overloaded2",
+           [](const py::array_t<std::complex<double>> &) { return "double complex"; });
+    sm.def("overloaded2", [](const py::array_t<double> &) { return "double"; });
+    sm.def("overloaded2",
+           [](const py::array_t<std::complex<float>> &) { return "float complex"; });
+    sm.def("overloaded2", [](const py::array_t<float> &) { return "float"; });
+
+    // [workaround(intel)] ICC 20/21 breaks with py::arg().stuff, using py::arg{}.stuff works.
+
+    // Only accept the exact types:
+    sm.def(
+        "overloaded3", [](const py::array_t<int> &) { return "int"; }, py::arg{}.noconvert());
+    sm.def(
+        "overloaded3",
+        [](const py::array_t<double> &) { return "double"; },
+        py::arg{}.noconvert());
+
+    // Make sure we don't do unsafe coercion (e.g. float to int) when not using forcecast, but
+    // rather that float gets converted via the safe (conversion to double) overload:
+    sm.def("overloaded4", [](const py::array_t<long long, 0> &) { return "long long"; });
+    sm.def("overloaded4", [](const py::array_t<double, 0> &) { return "double"; });
+
+    // But we do allow conversion to int if forcecast is enabled (but only if no overload matches
+    // without conversion)
+    sm.def("overloaded5", [](const py::array_t<unsigned int> &) { return "unsigned int"; });
+    sm.def("overloaded5", [](const py::array_t<double> &) { return "double"; });
+
+    // test_greedy_string_overload
+    // Issue 685: ndarray shouldn't go to std::string overload
+    sm.def("issue685", [](const std::string &) { return "string"; });
+    sm.def("issue685", [](const py::array &) { return "array"; });
+    sm.def("issue685", [](const py::object &) { return "other"; });
+
+    // test_array_unchecked_fixed_dims
+    sm.def(
+        "proxy_add2",
+        [](py::array_t<double> a, double v) {
+            auto r = a.mutable_unchecked<2>();
+            for (py::ssize_t i = 0; i < r.shape(0); i++) {
+                for (py::ssize_t j = 0; j < r.shape(1); j++) {
+                    r(i, j) += v;
+                }
+            }
+        },
+        py::arg{}.noconvert(),
+        py::arg());
+
+    sm.def("proxy_init3", [](double start) {
+        py::array_t<double, py::array::c_style> a({3, 3, 3});
+        auto r = a.mutable_unchecked<3>();
+        for (py::ssize_t i = 0; i < r.shape(0); i++) {
+            for (py::ssize_t j = 0; j < r.shape(1); j++) {
+                for (py::ssize_t k = 0; k < r.shape(2); k++) {
+                    r(i, j, k) = start++;
+                }
+            }
+        }
+        return a;
+    });
+    sm.def("proxy_init3F", [](double start) {
+        py::array_t<double, py::array::f_style> a({3, 3, 3});
+        auto r = a.mutable_unchecked<3>();
+        for (py::ssize_t k = 0; k < r.shape(2); k++) {
+            for (py::ssize_t j = 0; j < r.shape(1); j++) {
+                for (py::ssize_t i = 0; i < r.shape(0); i++) {
+                    r(i, j, k) = start++;
+                }
+            }
+        }
+        return a;
+    });
+    sm.def("proxy_squared_L2_norm", [](const py::array_t<double> &a) {
+        auto r = a.unchecked<1>();
+        double sumsq = 0;
+        for (py::ssize_t i = 0; i < r.shape(0); i++) {
+            sumsq += r[i] * r(i); // Either notation works for a 1D array
+        }
+        return sumsq;
+    });
+
+    sm.def("proxy_auxiliaries2", [](py::array_t<double> a) {
+        auto r = a.unchecked<2>();
+        auto r2 = a.mutable_unchecked<2>();
+        return auxiliaries(r, r2);
+    });
+
+    sm.def("proxy_auxiliaries1_const_ref", [](py::array_t<double> a) {
+        const auto &r = a.unchecked<1>();
+        const auto &r2 = a.mutable_unchecked<1>();
+        return r(0) == r2(0) && r[0] == r2[0];
+    });
+
+    sm.def("proxy_auxiliaries2_const_ref", [](py::array_t<double> a) {
+        const auto &r = a.unchecked<2>();
+        const auto &r2 = a.mutable_unchecked<2>();
+        return r(0, 0) == r2(0, 0);
+    });
+
+    // test_array_unchecked_dyn_dims
+    // Same as the above, but without a compile-time dimensions specification:
+    sm.def(
+        "proxy_add2_dyn",
+        [](py::array_t<double> a, double v) {
+            auto r = a.mutable_unchecked();
+            if (r.ndim() != 2) {
+                throw std::domain_error("error: ndim != 2");
+            }
+            for (py::ssize_t i = 0; i < r.shape(0); i++) {
+                for (py::ssize_t j = 0; j < r.shape(1); j++) {
+                    r(i, j) += v;
+                }
+            }
+        },
+        py::arg{}.noconvert(),
+        py::arg());
+    sm.def("proxy_init3_dyn", [](double start) {
+        py::array_t<double, py::array::c_style> a({3, 3, 3});
+        auto r = a.mutable_unchecked();
+        if (r.ndim() != 3) {
+            throw std::domain_error("error: ndim != 3");
+        }
+        for (py::ssize_t i = 0; i < r.shape(0); i++) {
+            for (py::ssize_t j = 0; j < r.shape(1); j++) {
+                for (py::ssize_t k = 0; k < r.shape(2); k++) {
+                    r(i, j, k) = start++;
+                }
+            }
+        }
+        return a;
+    });
+    sm.def("proxy_auxiliaries2_dyn", [](py::array_t<double> a) {
+        return auxiliaries(a.unchecked(), a.mutable_unchecked());
+    });
+
+    sm.def("array_auxiliaries2", [](py::array_t<double> a) { return auxiliaries(a, a); });
+
+    // test_array_failures
+    // Issue #785: Uninformative "Unknown internal error" exception when constructing array from
+    // empty object:
+    sm.def("array_fail_test", []() { return py::array(py::object()); });
+    sm.def("array_t_fail_test", []() { return py::array_t<double>(py::object()); });
+    // Make sure the error from numpy is being passed through:
+    sm.def("array_fail_test_negative_size", []() {
+        int c = 0;
+        return py::array(-1, &c);
+    });
+
+    // test_initializer_list
+    // Issue (unnumbered; reported in #788): regression: initializer lists can be ambiguous
+    sm.def("array_initializer_list1", []() { return py::array_t<float>(1); });
+    // { 1 } also works for the above, but clang warns about it
+    sm.def("array_initializer_list2", []() { return py::array_t<float>({1, 2}); });
+    sm.def("array_initializer_list3", []() { return py::array_t<float>({1, 2, 3}); });
+    sm.def("array_initializer_list4", []() { return py::array_t<float>({1, 2, 3, 4}); });
+
+    // test_array_resize
+    // reshape array to 2D without changing size
+    sm.def("array_reshape2", [](py::array_t<double> a) {
+        const auto dim_sz = (py::ssize_t) std::sqrt(a.size());
+        if (dim_sz * dim_sz != a.size()) {
+            throw std::domain_error(
+                "array_reshape2: input array total size is not a squared integer");
+        }
+        a.resize({dim_sz, dim_sz});
+    });
+
+    // resize to 3D array with each dimension = N
+    sm.def("array_resize3", [](py::array_t<double> a, size_t N, bool refcheck) {
+        a.resize({N, N, N}, refcheck);
+    });
+
+    // test_array_create_and_resize
+    // return 2D array with Nrows = Ncols = N
+    sm.def("create_and_resize", [](size_t N) {
+        py::array_t<double> a;
+        a.resize({N, N});
+        std::fill(a.mutable_data(), a.mutable_data() + a.size(), 42.);
+        return a;
+    });
+
+    sm.def("array_view",
+           [](py::array_t<uint8_t> a, const std::string &dtype) { return a.view(dtype); });
+
+    sm.def("reshape_initializer_list", [](py::array_t<int> a, size_t N, size_t M, size_t O) {
+        return a.reshape({N, M, O});
+    });
+    sm.def("reshape_tuple", [](py::array_t<int> a, const std::vector<int> &new_shape) {
+        return a.reshape(new_shape);
+    });
+
+    sm.def("index_using_ellipsis",
+           [](const py::array &a) { return a[py::make_tuple(0, py::ellipsis(), 0)]; });
+
+    // test_argument_conversions
+    sm.def(
+        "accept_double", [](const py::array_t<double, 0> &) {}, py::arg("a"));
+    sm.def(
+        "accept_double_forcecast",
+        [](const py::array_t<double, py::array::forcecast> &) {},
+        py::arg("a"));
+    sm.def(
+        "accept_double_c_style",
+        [](const py::array_t<double, py::array::c_style> &) {},
+        py::arg("a"));
+    sm.def(
+        "accept_double_c_style_forcecast",
+        [](const py::array_t<double, py::array::forcecast | py::array::c_style> &) {},
+        py::arg("a"));
+    sm.def(
+        "accept_double_f_style",
+        [](const py::array_t<double, py::array::f_style> &) {},
+        py::arg("a"));
+    sm.def(
+        "accept_double_f_style_forcecast",
+        [](const py::array_t<double, py::array::forcecast | py::array::f_style> &) {},
+        py::arg("a"));
+    sm.def(
+        "accept_double_noconvert", [](const py::array_t<double, 0> &) {}, "a"_a.noconvert());
+    sm.def(
+        "accept_double_forcecast_noconvert",
+        [](const py::array_t<double, py::array::forcecast> &) {},
+        "a"_a.noconvert());
+    sm.def(
+        "accept_double_c_style_noconvert",
+        [](const py::array_t<double, py::array::c_style> &) {},
+        "a"_a.noconvert());
+    sm.def(
+        "accept_double_c_style_forcecast_noconvert",
+        [](const py::array_t<double, py::array::forcecast | py::array::c_style> &) {},
+        "a"_a.noconvert());
+    sm.def(
+        "accept_double_f_style_noconvert",
+        [](const py::array_t<double, py::array::f_style> &) {},
+        "a"_a.noconvert());
+    sm.def(
+        "accept_double_f_style_forcecast_noconvert",
+        [](const py::array_t<double, py::array::forcecast | py::array::f_style> &) {},
+        "a"_a.noconvert());
+
+    // Check that types returns correct npy format descriptor
+    sm.def("test_fmt_desc_float", [](const py::array_t<float> &) {});
+    sm.def("test_fmt_desc_double", [](const py::array_t<double> &) {});
+    sm.def("test_fmt_desc_const_float", [](const py::array_t<const float> &) {});
+    sm.def("test_fmt_desc_const_double", [](const py::array_t<const double> &) {});
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_array.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_array.py
new file mode 100644
index 000000000..504963b16
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_array.py
@@ -0,0 +1,587 @@
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import numpy_array as m
+
+np = pytest.importorskip("numpy")
+
+
+def test_dtypes():
+    # See issue #1328.
+    # - Platform-dependent sizes.
+    for size_check in m.get_platform_dtype_size_checks():
+        print(size_check)
+        assert size_check.size_cpp == size_check.size_numpy, size_check
+    # - Concrete sizes.
+    for check in m.get_concrete_dtype_checks():
+        print(check)
+        assert check.numpy == check.pybind11, check
+        if check.numpy.num != check.pybind11.num:
+            print(
+                f"NOTE: typenum mismatch for {check}: {check.numpy.num} != {check.pybind11.num}"
+            )
+
+
+@pytest.fixture(scope="function")
+def arr():
+    return np.array([[1, 2, 3], [4, 5, 6]], "=u2")
+
+
+def test_array_attributes():
+    a = np.array(0, "f8")
+    assert m.ndim(a) == 0
+    assert all(m.shape(a) == [])
+    assert all(m.strides(a) == [])
+    with pytest.raises(IndexError) as excinfo:
+        m.shape(a, 0)
+    assert str(excinfo.value) == "invalid axis: 0 (ndim = 0)"
+    with pytest.raises(IndexError) as excinfo:
+        m.strides(a, 0)
+    assert str(excinfo.value) == "invalid axis: 0 (ndim = 0)"
+    assert m.writeable(a)
+    assert m.size(a) == 1
+    assert m.itemsize(a) == 8
+    assert m.nbytes(a) == 8
+    assert m.owndata(a)
+
+    a = np.array([[1, 2, 3], [4, 5, 6]], "u2").view()
+    a.flags.writeable = False
+    assert m.ndim(a) == 2
+    assert all(m.shape(a) == [2, 3])
+    assert m.shape(a, 0) == 2
+    assert m.shape(a, 1) == 3
+    assert all(m.strides(a) == [6, 2])
+    assert m.strides(a, 0) == 6
+    assert m.strides(a, 1) == 2
+    with pytest.raises(IndexError) as excinfo:
+        m.shape(a, 2)
+    assert str(excinfo.value) == "invalid axis: 2 (ndim = 2)"
+    with pytest.raises(IndexError) as excinfo:
+        m.strides(a, 2)
+    assert str(excinfo.value) == "invalid axis: 2 (ndim = 2)"
+    assert not m.writeable(a)
+    assert m.size(a) == 6
+    assert m.itemsize(a) == 2
+    assert m.nbytes(a) == 12
+    assert not m.owndata(a)
+
+
+@pytest.mark.parametrize(
+    "args, ret", [([], 0), ([0], 0), ([1], 3), ([0, 1], 1), ([1, 2], 5)]
+)
+def test_index_offset(arr, args, ret):
+    assert m.index_at(arr, *args) == ret
+    assert m.index_at_t(arr, *args) == ret
+    assert m.offset_at(arr, *args) == ret * arr.dtype.itemsize
+    assert m.offset_at_t(arr, *args) == ret * arr.dtype.itemsize
+
+
+def test_dim_check_fail(arr):
+    for func in (
+        m.index_at,
+        m.index_at_t,
+        m.offset_at,
+        m.offset_at_t,
+        m.data,
+        m.data_t,
+        m.mutate_data,
+        m.mutate_data_t,
+    ):
+        with pytest.raises(IndexError) as excinfo:
+            func(arr, 1, 2, 3)
+        assert str(excinfo.value) == "too many indices for an array: 3 (ndim = 2)"
+
+
+@pytest.mark.parametrize(
+    "args, ret",
+    [
+        ([], [1, 2, 3, 4, 5, 6]),
+        ([1], [4, 5, 6]),
+        ([0, 1], [2, 3, 4, 5, 6]),
+        ([1, 2], [6]),
+    ],
+)
+def test_data(arr, args, ret):
+    from sys import byteorder
+
+    assert all(m.data_t(arr, *args) == ret)
+    assert all(m.data(arr, *args)[(0 if byteorder == "little" else 1) :: 2] == ret)
+    assert all(m.data(arr, *args)[(1 if byteorder == "little" else 0) :: 2] == 0)
+
+
+@pytest.mark.parametrize("dim", [0, 1, 3])
+def test_at_fail(arr, dim):
+    for func in m.at_t, m.mutate_at_t:
+        with pytest.raises(IndexError) as excinfo:
+            func(arr, *([0] * dim))
+        assert str(excinfo.value) == f"index dimension mismatch: {dim} (ndim = 2)"
+
+
+def test_at(arr):
+    assert m.at_t(arr, 0, 2) == 3
+    assert m.at_t(arr, 1, 0) == 4
+
+    assert all(m.mutate_at_t(arr, 0, 2).ravel() == [1, 2, 4, 4, 5, 6])
+    assert all(m.mutate_at_t(arr, 1, 0).ravel() == [1, 2, 4, 5, 5, 6])
+
+
+def test_mutate_readonly(arr):
+    arr.flags.writeable = False
+    for func, args in (
+        (m.mutate_data, ()),
+        (m.mutate_data_t, ()),
+        (m.mutate_at_t, (0, 0)),
+    ):
+        with pytest.raises(ValueError) as excinfo:
+            func(arr, *args)
+        assert str(excinfo.value) == "array is not writeable"
+
+
+def test_mutate_data(arr):
+    assert all(m.mutate_data(arr).ravel() == [2, 4, 6, 8, 10, 12])
+    assert all(m.mutate_data(arr).ravel() == [4, 8, 12, 16, 20, 24])
+    assert all(m.mutate_data(arr, 1).ravel() == [4, 8, 12, 32, 40, 48])
+    assert all(m.mutate_data(arr, 0, 1).ravel() == [4, 16, 24, 64, 80, 96])
+    assert all(m.mutate_data(arr, 1, 2).ravel() == [4, 16, 24, 64, 80, 192])
+
+    assert all(m.mutate_data_t(arr).ravel() == [5, 17, 25, 65, 81, 193])
+    assert all(m.mutate_data_t(arr).ravel() == [6, 18, 26, 66, 82, 194])
+    assert all(m.mutate_data_t(arr, 1).ravel() == [6, 18, 26, 67, 83, 195])
+    assert all(m.mutate_data_t(arr, 0, 1).ravel() == [6, 19, 27, 68, 84, 196])
+    assert all(m.mutate_data_t(arr, 1, 2).ravel() == [6, 19, 27, 68, 84, 197])
+
+
+def test_bounds_check(arr):
+    for func in (
+        m.index_at,
+        m.index_at_t,
+        m.data,
+        m.data_t,
+        m.mutate_data,
+        m.mutate_data_t,
+        m.at_t,
+        m.mutate_at_t,
+    ):
+        with pytest.raises(IndexError) as excinfo:
+            func(arr, 2, 0)
+        assert str(excinfo.value) == "index 2 is out of bounds for axis 0 with size 2"
+        with pytest.raises(IndexError) as excinfo:
+            func(arr, 0, 4)
+        assert str(excinfo.value) == "index 4 is out of bounds for axis 1 with size 3"
+
+
+def test_make_c_f_array():
+    assert m.make_c_array().flags.c_contiguous
+    assert not m.make_c_array().flags.f_contiguous
+    assert m.make_f_array().flags.f_contiguous
+    assert not m.make_f_array().flags.c_contiguous
+
+
+def test_make_empty_shaped_array():
+    m.make_empty_shaped_array()
+
+    # empty shape means numpy scalar, PEP 3118
+    assert m.scalar_int().ndim == 0
+    assert m.scalar_int().shape == ()
+    assert m.scalar_int() == 42
+
+
+def test_wrap():
+    def assert_references(a, b, base=None):
+        if base is None:
+            base = a
+        assert a is not b
+        assert a.__array_interface__["data"][0] == b.__array_interface__["data"][0]
+        assert a.shape == b.shape
+        assert a.strides == b.strides
+        assert a.flags.c_contiguous == b.flags.c_contiguous
+        assert a.flags.f_contiguous == b.flags.f_contiguous
+        assert a.flags.writeable == b.flags.writeable
+        assert a.flags.aligned == b.flags.aligned
+        # 1.13 supported Python 3.6
+        if tuple(int(x) for x in np.__version__.split(".")[:2]) >= (1, 14):
+            assert a.flags.writebackifcopy == b.flags.writebackifcopy
+        else:
+            assert a.flags.updateifcopy == b.flags.updateifcopy
+        assert np.all(a == b)
+        assert not b.flags.owndata
+        assert b.base is base
+        if a.flags.writeable and a.ndim == 2:
+            a[0, 0] = 1234
+            assert b[0, 0] == 1234
+
+    a1 = np.array([1, 2], dtype=np.int16)
+    assert a1.flags.owndata and a1.base is None
+    a2 = m.wrap(a1)
+    assert_references(a1, a2)
+
+    a1 = np.array([[1, 2], [3, 4]], dtype=np.float32, order="F")
+    assert a1.flags.owndata and a1.base is None
+    a2 = m.wrap(a1)
+    assert_references(a1, a2)
+
+    a1 = np.array([[1, 2], [3, 4]], dtype=np.float32, order="C")
+    a1.flags.writeable = False
+    a2 = m.wrap(a1)
+    assert_references(a1, a2)
+
+    a1 = np.random.random((4, 4, 4))
+    a2 = m.wrap(a1)
+    assert_references(a1, a2)
+
+    a1t = a1.transpose()
+    a2 = m.wrap(a1t)
+    assert_references(a1t, a2, a1)
+
+    a1d = a1.diagonal()
+    a2 = m.wrap(a1d)
+    assert_references(a1d, a2, a1)
+
+    a1m = a1[::-1, ::-1, ::-1]
+    a2 = m.wrap(a1m)
+    assert_references(a1m, a2, a1)
+
+
+def test_numpy_view(capture):
+    with capture:
+        ac = m.ArrayClass()
+        ac_view_1 = ac.numpy_view()
+        ac_view_2 = ac.numpy_view()
+        assert np.all(ac_view_1 == np.array([1, 2], dtype=np.int32))
+        del ac
+        pytest.gc_collect()
+    assert (
+        capture
+        == """
+        ArrayClass()
+        ArrayClass::numpy_view()
+        ArrayClass::numpy_view()
+    """
+    )
+    ac_view_1[0] = 4
+    ac_view_1[1] = 3
+    assert ac_view_2[0] == 4
+    assert ac_view_2[1] == 3
+    with capture:
+        del ac_view_1
+        del ac_view_2
+        pytest.gc_collect()
+        pytest.gc_collect()
+    assert (
+        capture
+        == """
+        ~ArrayClass()
+    """
+    )
+
+
+def test_cast_numpy_int64_to_uint64():
+    m.function_taking_uint64(123)
+    m.function_taking_uint64(np.uint64(123))
+
+
+def test_isinstance():
+    assert m.isinstance_untyped(np.array([1, 2, 3]), "not an array")
+    assert m.isinstance_typed(np.array([1.0, 2.0, 3.0]))
+
+
+def test_constructors():
+    defaults = m.default_constructors()
+    for a in defaults.values():
+        assert a.size == 0
+    assert defaults["array"].dtype == np.array([]).dtype
+    assert defaults["array_t<int32>"].dtype == np.int32
+    assert defaults["array_t<double>"].dtype == np.float64
+
+    results = m.converting_constructors([1, 2, 3])
+    for a in results.values():
+        np.testing.assert_array_equal(a, [1, 2, 3])
+    assert results["array"].dtype == np.int_
+    assert results["array_t<int32>"].dtype == np.int32
+    assert results["array_t<double>"].dtype == np.float64
+
+
+def test_overload_resolution(msg):
+    # Exact overload matches:
+    assert m.overloaded(np.array([1], dtype="float64")) == "double"
+    assert m.overloaded(np.array([1], dtype="float32")) == "float"
+    assert m.overloaded(np.array([1], dtype="ushort")) == "unsigned short"
+    assert m.overloaded(np.array([1], dtype="intc")) == "int"
+    assert m.overloaded(np.array([1], dtype="longlong")) == "long long"
+    assert m.overloaded(np.array([1], dtype="complex")) == "double complex"
+    assert m.overloaded(np.array([1], dtype="csingle")) == "float complex"
+
+    # No exact match, should call first convertible version:
+    assert m.overloaded(np.array([1], dtype="uint8")) == "double"
+
+    with pytest.raises(TypeError) as excinfo:
+        m.overloaded("not an array")
+    assert (
+        msg(excinfo.value)
+        == """
+        overloaded(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: numpy.ndarray[numpy.float64]) -> str
+            2. (arg0: numpy.ndarray[numpy.float32]) -> str
+            3. (arg0: numpy.ndarray[numpy.int32]) -> str
+            4. (arg0: numpy.ndarray[numpy.uint16]) -> str
+            5. (arg0: numpy.ndarray[numpy.int64]) -> str
+            6. (arg0: numpy.ndarray[numpy.complex128]) -> str
+            7. (arg0: numpy.ndarray[numpy.complex64]) -> str
+
+        Invoked with: 'not an array'
+    """
+    )
+
+    assert m.overloaded2(np.array([1], dtype="float64")) == "double"
+    assert m.overloaded2(np.array([1], dtype="float32")) == "float"
+    assert m.overloaded2(np.array([1], dtype="complex64")) == "float complex"
+    assert m.overloaded2(np.array([1], dtype="complex128")) == "double complex"
+    assert m.overloaded2(np.array([1], dtype="float32")) == "float"
+
+    assert m.overloaded3(np.array([1], dtype="float64")) == "double"
+    assert m.overloaded3(np.array([1], dtype="intc")) == "int"
+    expected_exc = """
+        overloaded3(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: numpy.ndarray[numpy.int32]) -> str
+            2. (arg0: numpy.ndarray[numpy.float64]) -> str
+
+        Invoked with: """
+
+    with pytest.raises(TypeError) as excinfo:
+        m.overloaded3(np.array([1], dtype="uintc"))
+    assert msg(excinfo.value) == expected_exc + repr(np.array([1], dtype="uint32"))
+    with pytest.raises(TypeError) as excinfo:
+        m.overloaded3(np.array([1], dtype="float32"))
+    assert msg(excinfo.value) == expected_exc + repr(np.array([1.0], dtype="float32"))
+    with pytest.raises(TypeError) as excinfo:
+        m.overloaded3(np.array([1], dtype="complex"))
+    assert msg(excinfo.value) == expected_exc + repr(np.array([1.0 + 0.0j]))
+
+    # Exact matches:
+    assert m.overloaded4(np.array([1], dtype="double")) == "double"
+    assert m.overloaded4(np.array([1], dtype="longlong")) == "long long"
+    # Non-exact matches requiring conversion.  Since float to integer isn't a
+    # save conversion, it should go to the double overload, but short can go to
+    # either (and so should end up on the first-registered, the long long).
+    assert m.overloaded4(np.array([1], dtype="float32")) == "double"
+    assert m.overloaded4(np.array([1], dtype="short")) == "long long"
+
+    assert m.overloaded5(np.array([1], dtype="double")) == "double"
+    assert m.overloaded5(np.array([1], dtype="uintc")) == "unsigned int"
+    assert m.overloaded5(np.array([1], dtype="float32")) == "unsigned int"
+
+
+def test_greedy_string_overload():
+    """Tests fix for #685 - ndarray shouldn't go to std::string overload"""
+
+    assert m.issue685("abc") == "string"
+    assert m.issue685(np.array([97, 98, 99], dtype="b")) == "array"
+    assert m.issue685(123) == "other"
+
+
+def test_array_unchecked_fixed_dims(msg):
+    z1 = np.array([[1, 2], [3, 4]], dtype="float64")
+    m.proxy_add2(z1, 10)
+    assert np.all(z1 == [[11, 12], [13, 14]])
+
+    with pytest.raises(ValueError) as excinfo:
+        m.proxy_add2(np.array([1.0, 2, 3]), 5.0)
+    assert (
+        msg(excinfo.value) == "array has incorrect number of dimensions: 1; expected 2"
+    )
+
+    expect_c = np.ndarray(shape=(3, 3, 3), buffer=np.array(range(3, 30)), dtype="int")
+    assert np.all(m.proxy_init3(3.0) == expect_c)
+    expect_f = np.transpose(expect_c)
+    assert np.all(m.proxy_init3F(3.0) == expect_f)
+
+    assert m.proxy_squared_L2_norm(np.array(range(6))) == 55
+    assert m.proxy_squared_L2_norm(np.array(range(6), dtype="float64")) == 55
+
+    assert m.proxy_auxiliaries2(z1) == [11, 11, True, 2, 8, 2, 2, 4, 32]
+    assert m.proxy_auxiliaries2(z1) == m.array_auxiliaries2(z1)
+
+    assert m.proxy_auxiliaries1_const_ref(z1[0, :])
+    assert m.proxy_auxiliaries2_const_ref(z1)
+
+
+def test_array_unchecked_dyn_dims():
+    z1 = np.array([[1, 2], [3, 4]], dtype="float64")
+    m.proxy_add2_dyn(z1, 10)
+    assert np.all(z1 == [[11, 12], [13, 14]])
+
+    expect_c = np.ndarray(shape=(3, 3, 3), buffer=np.array(range(3, 30)), dtype="int")
+    assert np.all(m.proxy_init3_dyn(3.0) == expect_c)
+
+    assert m.proxy_auxiliaries2_dyn(z1) == [11, 11, True, 2, 8, 2, 2, 4, 32]
+    assert m.proxy_auxiliaries2_dyn(z1) == m.array_auxiliaries2(z1)
+
+
+def test_array_failure():
+    with pytest.raises(ValueError) as excinfo:
+        m.array_fail_test()
+    assert str(excinfo.value) == "cannot create a pybind11::array from a nullptr"
+
+    with pytest.raises(ValueError) as excinfo:
+        m.array_t_fail_test()
+    assert str(excinfo.value) == "cannot create a pybind11::array_t from a nullptr"
+
+    with pytest.raises(ValueError) as excinfo:
+        m.array_fail_test_negative_size()
+    assert str(excinfo.value) == "negative dimensions are not allowed"
+
+
+def test_initializer_list():
+    assert m.array_initializer_list1().shape == (1,)
+    assert m.array_initializer_list2().shape == (1, 2)
+    assert m.array_initializer_list3().shape == (1, 2, 3)
+    assert m.array_initializer_list4().shape == (1, 2, 3, 4)
+
+
+def test_array_resize():
+    a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype="float64")
+    m.array_reshape2(a)
+    assert a.size == 9
+    assert np.all(a == [[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+    # total size change should succced with refcheck off
+    m.array_resize3(a, 4, False)
+    assert a.size == 64
+    # ... and fail with refcheck on
+    try:
+        m.array_resize3(a, 3, True)
+    except ValueError as e:
+        assert str(e).startswith("cannot resize an array")
+    # transposed array doesn't own data
+    b = a.transpose()
+    try:
+        m.array_resize3(b, 3, False)
+    except ValueError as e:
+        assert str(e).startswith("cannot resize this array: it does not own its data")
+    # ... but reshape should be fine
+    m.array_reshape2(b)
+    assert b.shape == (8, 8)
+
+
+@pytest.mark.xfail("env.PYPY")
+def test_array_create_and_resize():
+    a = m.create_and_resize(2)
+    assert a.size == 4
+    assert np.all(a == 42.0)
+
+
+def test_array_view():
+    a = np.ones(100 * 4).astype("uint8")
+    a_float_view = m.array_view(a, "float32")
+    assert a_float_view.shape == (100 * 1,)  # 1 / 4 bytes = 8 / 32
+
+    a_int16_view = m.array_view(a, "int16")  # 1 / 2 bytes = 16 / 32
+    assert a_int16_view.shape == (100 * 2,)
+
+
+def test_array_view_invalid():
+    a = np.ones(100 * 4).astype("uint8")
+    with pytest.raises(TypeError):
+        m.array_view(a, "deadly_dtype")
+
+
+def test_reshape_initializer_list():
+    a = np.arange(2 * 7 * 3) + 1
+    x = m.reshape_initializer_list(a, 2, 7, 3)
+    assert x.shape == (2, 7, 3)
+    assert list(x[1][4]) == [34, 35, 36]
+    with pytest.raises(ValueError) as excinfo:
+        m.reshape_initializer_list(a, 1, 7, 3)
+    assert str(excinfo.value) == "cannot reshape array of size 42 into shape (1,7,3)"
+
+
+def test_reshape_tuple():
+    a = np.arange(3 * 7 * 2) + 1
+    x = m.reshape_tuple(a, (3, 7, 2))
+    assert x.shape == (3, 7, 2)
+    assert list(x[1][4]) == [23, 24]
+    y = m.reshape_tuple(x, (x.size,))
+    assert y.shape == (42,)
+    with pytest.raises(ValueError) as excinfo:
+        m.reshape_tuple(a, (3, 7, 1))
+    assert str(excinfo.value) == "cannot reshape array of size 42 into shape (3,7,1)"
+    with pytest.raises(ValueError) as excinfo:
+        m.reshape_tuple(a, ())
+    assert str(excinfo.value) == "cannot reshape array of size 42 into shape ()"
+
+
+def test_index_using_ellipsis():
+    a = m.index_using_ellipsis(np.zeros((5, 6, 7)))
+    assert a.shape == (6,)
+
+
+@pytest.mark.parametrize(
+    "test_func",
+    [
+        m.test_fmt_desc_float,
+        m.test_fmt_desc_double,
+        m.test_fmt_desc_const_float,
+        m.test_fmt_desc_const_double,
+    ],
+)
+def test_format_descriptors_for_floating_point_types(test_func):
+    assert "numpy.ndarray[numpy.float" in test_func.__doc__
+
+
+@pytest.mark.parametrize("forcecast", [False, True])
+@pytest.mark.parametrize("contiguity", [None, "C", "F"])
+@pytest.mark.parametrize("noconvert", [False, True])
+@pytest.mark.filterwarnings(
+    "ignore:Casting complex values to real discards the imaginary part:numpy.ComplexWarning"
+)
+def test_argument_conversions(forcecast, contiguity, noconvert):
+    function_name = "accept_double"
+    if contiguity == "C":
+        function_name += "_c_style"
+    elif contiguity == "F":
+        function_name += "_f_style"
+    if forcecast:
+        function_name += "_forcecast"
+    if noconvert:
+        function_name += "_noconvert"
+    function = getattr(m, function_name)
+
+    for dtype in [np.dtype("float32"), np.dtype("float64"), np.dtype("complex128")]:
+        for order in ["C", "F"]:
+            for shape in [(2, 2), (1, 3, 1, 1), (1, 1, 1), (0,)]:
+                if not noconvert:
+                    # If noconvert is not passed, only complex128 needs to be truncated and
+                    # "cannot be safely obtained". So without `forcecast`, the argument shouldn't
+                    # be accepted.
+                    should_raise = dtype.name == "complex128" and not forcecast
+                else:
+                    # If noconvert is passed, only float64 and the matching order is accepted.
+                    # If at most one dimension has a size greater than 1, the array is also
+                    # trivially contiguous.
+                    trivially_contiguous = sum(1 for d in shape if d > 1) <= 1
+                    should_raise = dtype.name != "float64" or (
+                        contiguity is not None
+                        and contiguity != order
+                        and not trivially_contiguous
+                    )
+
+                array = np.zeros(shape, dtype=dtype, order=order)
+                if not should_raise:
+                    function(array)
+                else:
+                    with pytest.raises(
+                        TypeError, match="incompatible function arguments"
+                    ):
+                        function(array)
+
+
+@pytest.mark.xfail("env.PYPY")
+def test_dtype_refcount_leak():
+    from sys import getrefcount
+
+    dtype = np.dtype(np.float_)
+    a = np.array([1], dtype=dtype)
+    before = getrefcount(dtype)
+    m.ndim(a)
+    after = getrefcount(dtype)
+    assert after == before
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_dtypes.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_dtypes.cpp
new file mode 100644
index 000000000..6654f9ed8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_dtypes.cpp
@@ -0,0 +1,614 @@
+/*
+  tests/test_numpy_dtypes.cpp -- Structured and compound NumPy dtypes
+
+  Copyright (c) 2016 Ivan Smirnov
+
+  All rights reserved. Use of this source code is governed by a
+  BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/numpy.h>
+
+#include "pybind11_tests.h"
+
+#ifdef __GNUC__
+#    define PYBIND11_PACKED(cls) cls __attribute__((__packed__))
+#else
+#    define PYBIND11_PACKED(cls) __pragma(pack(push, 1)) cls __pragma(pack(pop))
+#endif
+
+namespace py = pybind11;
+
+struct SimpleStruct {
+    bool bool_;
+    uint32_t uint_;
+    float float_;
+    long double ldbl_;
+};
+
+std::ostream &operator<<(std::ostream &os, const SimpleStruct &v) {
+    return os << "s:" << v.bool_ << "," << v.uint_ << "," << v.float_ << "," << v.ldbl_;
+}
+
+struct SimpleStructReordered {
+    bool bool_;
+    float float_;
+    uint32_t uint_;
+    long double ldbl_;
+};
+
+PYBIND11_PACKED(struct PackedStruct {
+    bool bool_;
+    uint32_t uint_;
+    float float_;
+    long double ldbl_;
+});
+
+std::ostream &operator<<(std::ostream &os, const PackedStruct &v) {
+    return os << "p:" << v.bool_ << "," << v.uint_ << "," << v.float_ << "," << v.ldbl_;
+}
+
+PYBIND11_PACKED(struct NestedStruct {
+    SimpleStruct a;
+    PackedStruct b;
+});
+
+std::ostream &operator<<(std::ostream &os, const NestedStruct &v) {
+    return os << "n:a=" << v.a << ";b=" << v.b;
+}
+
+struct PartialStruct {
+    bool bool_;
+    uint32_t uint_;
+    float float_;
+    uint64_t dummy2;
+    long double ldbl_;
+};
+
+struct PartialNestedStruct {
+    uint64_t dummy1;
+    PartialStruct a;
+    uint64_t dummy2;
+};
+
+struct UnboundStruct {};
+
+struct StringStruct {
+    char a[3];
+    std::array<char, 3> b;
+};
+
+struct ComplexStruct {
+    std::complex<float> cflt;
+    std::complex<double> cdbl;
+};
+
+std::ostream &operator<<(std::ostream &os, const ComplexStruct &v) {
+    return os << "c:" << v.cflt << "," << v.cdbl;
+}
+
+struct ArrayStruct {
+    char a[3][4];
+    int32_t b[2];
+    std::array<uint8_t, 3> c;
+    std::array<float, 2> d[4];
+};
+
+PYBIND11_PACKED(struct StructWithUglyNames {
+    int8_t __x__;
+    uint64_t __y__;
+});
+
+enum class E1 : int64_t { A = -1, B = 1 };
+enum E2 : uint8_t { X = 1, Y = 2 };
+
+PYBIND11_PACKED(struct EnumStruct {
+    E1 e1;
+    E2 e2;
+});
+
+std::ostream &operator<<(std::ostream &os, const StringStruct &v) {
+    os << "a='";
+    for (size_t i = 0; i < 3 && (v.a[i] != 0); i++) {
+        os << v.a[i];
+    }
+    os << "',b='";
+    for (size_t i = 0; i < 3 && (v.b[i] != 0); i++) {
+        os << v.b[i];
+    }
+    return os << "'";
+}
+
+std::ostream &operator<<(std::ostream &os, const ArrayStruct &v) {
+    os << "a={";
+    for (int i = 0; i < 3; i++) {
+        if (i > 0) {
+            os << ',';
+        }
+        os << '{';
+        for (int j = 0; j < 3; j++) {
+            os << v.a[i][j] << ',';
+        }
+        os << v.a[i][3] << '}';
+    }
+    os << "},b={" << v.b[0] << ',' << v.b[1];
+    os << "},c={" << int(v.c[0]) << ',' << int(v.c[1]) << ',' << int(v.c[2]);
+    os << "},d={";
+    for (int i = 0; i < 4; i++) {
+        if (i > 0) {
+            os << ',';
+        }
+        os << '{' << v.d[i][0] << ',' << v.d[i][1] << '}';
+    }
+    return os << '}';
+}
+
+std::ostream &operator<<(std::ostream &os, const EnumStruct &v) {
+    return os << "e1=" << (v.e1 == E1::A ? "A" : "B") << ",e2=" << (v.e2 == E2::X ? "X" : "Y");
+}
+
+template <typename T>
+py::array mkarray_via_buffer(size_t n) {
+    return py::array(py::buffer_info(
+        nullptr, sizeof(T), py::format_descriptor<T>::format(), 1, {n}, {sizeof(T)}));
+}
+
+#define SET_TEST_VALS(s, i)                                                                       \
+    do {                                                                                          \
+        (s).bool_ = (i) % 2 != 0;                                                                 \
+        (s).uint_ = (uint32_t) (i);                                                               \
+        (s).float_ = (float) (i) *1.5f;                                                           \
+        (s).ldbl_ = (long double) (i) * -2.5L;                                                    \
+    } while (0)
+
+template <typename S>
+py::array_t<S, 0> create_recarray(size_t n) {
+    auto arr = mkarray_via_buffer<S>(n);
+    auto req = arr.request();
+    auto *ptr = static_cast<S *>(req.ptr);
+    for (size_t i = 0; i < n; i++) {
+        SET_TEST_VALS(ptr[i], i);
+    }
+    return arr;
+}
+
+template <typename S>
+py::list print_recarray(py::array_t<S, 0> arr) {
+    const auto req = arr.request();
+    auto *const ptr = static_cast<S *>(req.ptr);
+    auto l = py::list();
+    for (py::ssize_t i = 0; i < req.size; i++) {
+        std::stringstream ss;
+        ss << ptr[i];
+        l.append(py::str(ss.str()));
+    }
+    return l;
+}
+
+py::array_t<int32_t, 0> test_array_ctors(int i) {
+    using arr_t = py::array_t<int32_t, 0>;
+
+    std::vector<int32_t> data{1, 2, 3, 4, 5, 6};
+    std::vector<py::ssize_t> shape{3, 2};
+    std::vector<py::ssize_t> strides{8, 4};
+
+    auto *ptr = data.data();
+    auto *vptr = (void *) ptr;
+    auto dtype = py::dtype("int32");
+
+    py::buffer_info buf_ndim1(vptr, 4, "i", 6);
+    py::buffer_info buf_ndim1_null(nullptr, 4, "i", 6);
+    py::buffer_info buf_ndim2(vptr, 4, "i", 2, shape, strides);
+    py::buffer_info buf_ndim2_null(nullptr, 4, "i", 2, shape, strides);
+
+    auto fill = [](py::array arr) {
+        auto req = arr.request();
+        for (int i = 0; i < 6; i++) {
+            ((int32_t *) req.ptr)[i] = i + 1;
+        }
+        return arr;
+    };
+
+    switch (i) {
+        // shape: (3, 2)
+        case 10:
+            return arr_t(shape, strides, ptr);
+        case 11:
+            return py::array(shape, strides, ptr);
+        case 12:
+            return py::array(dtype, shape, strides, vptr);
+        case 13:
+            return arr_t(shape, ptr);
+        case 14:
+            return py::array(shape, ptr);
+        case 15:
+            return py::array(dtype, shape, vptr);
+        case 16:
+            return arr_t(buf_ndim2);
+        case 17:
+            return py::array(buf_ndim2);
+        // shape: (3, 2) - post-fill
+        case 20:
+            return fill(arr_t(shape, strides));
+        case 21:
+            return py::array(shape, strides, ptr); // can't have nullptr due to templated ctor
+        case 22:
+            return fill(py::array(dtype, shape, strides));
+        case 23:
+            return fill(arr_t(shape));
+        case 24:
+            return py::array(shape, ptr); // can't have nullptr due to templated ctor
+        case 25:
+            return fill(py::array(dtype, shape));
+        case 26:
+            return fill(arr_t(buf_ndim2_null));
+        case 27:
+            return fill(py::array(buf_ndim2_null));
+        // shape: (6, )
+        case 30:
+            return arr_t(6, ptr);
+        case 31:
+            return py::array(6, ptr);
+        case 32:
+            return py::array(dtype, 6, vptr);
+        case 33:
+            return arr_t(buf_ndim1);
+        case 34:
+            return py::array(buf_ndim1);
+        // shape: (6, )
+        case 40:
+            return fill(arr_t(6));
+        case 41:
+            return py::array(6, ptr); // can't have nullptr due to templated ctor
+        case 42:
+            return fill(py::array(dtype, 6));
+        case 43:
+            return fill(arr_t(buf_ndim1_null));
+        case 44:
+            return fill(py::array(buf_ndim1_null));
+    }
+    return arr_t();
+}
+
+py::list test_dtype_ctors() {
+    py::list list;
+    list.append(py::dtype("int32"));
+    list.append(py::dtype(std::string("float64")));
+    list.append(py::dtype::from_args(py::str("bool")));
+    py::list names, offsets, formats;
+    py::dict dict;
+    names.append(py::str("a"));
+    names.append(py::str("b"));
+    dict["names"] = names;
+    offsets.append(py::int_(1));
+    offsets.append(py::int_(10));
+    dict["offsets"] = offsets;
+    formats.append(py::dtype("int32"));
+    formats.append(py::dtype("float64"));
+    dict["formats"] = formats;
+    dict["itemsize"] = py::int_(20);
+    list.append(py::dtype::from_args(dict));
+    list.append(py::dtype(names, formats, offsets, 20));
+    list.append(py::dtype(py::buffer_info((void *) nullptr, sizeof(unsigned int), "I", 1)));
+    list.append(py::dtype(py::buffer_info((void *) nullptr, 0, "T{i:a:f:b:}", 1)));
+    list.append(py::dtype(py::detail::npy_api::NPY_DOUBLE_));
+    return list;
+}
+
+struct A {};
+struct B {};
+
+TEST_SUBMODULE(numpy_dtypes, m) {
+    try {
+        py::module_::import("numpy");
+    } catch (const py::error_already_set &) {
+        return;
+    }
+
+    // typeinfo may be registered before the dtype descriptor for scalar casts to work...
+    py::class_<SimpleStruct>(m, "SimpleStruct")
+        // Explicit construct to ensure zero-valued initialization.
+        .def(py::init([]() { return SimpleStruct(); }))
+        .def_readwrite("bool_", &SimpleStruct::bool_)
+        .def_readwrite("uint_", &SimpleStruct::uint_)
+        .def_readwrite("float_", &SimpleStruct::float_)
+        .def_readwrite("ldbl_", &SimpleStruct::ldbl_)
+        .def("astuple",
+             [](const SimpleStruct &self) {
+                 return py::make_tuple(self.bool_, self.uint_, self.float_, self.ldbl_);
+             })
+        .def_static("fromtuple", [](const py::tuple &tup) {
+            if (py::len(tup) != 4) {
+                throw py::cast_error("Invalid size");
+            }
+            return SimpleStruct{tup[0].cast<bool>(),
+                                tup[1].cast<uint32_t>(),
+                                tup[2].cast<float>(),
+                                tup[3].cast<long double>()};
+        });
+
+    PYBIND11_NUMPY_DTYPE(SimpleStruct, bool_, uint_, float_, ldbl_);
+    PYBIND11_NUMPY_DTYPE(SimpleStructReordered, bool_, uint_, float_, ldbl_);
+    PYBIND11_NUMPY_DTYPE(PackedStruct, bool_, uint_, float_, ldbl_);
+    PYBIND11_NUMPY_DTYPE(NestedStruct, a, b);
+    PYBIND11_NUMPY_DTYPE(PartialStruct, bool_, uint_, float_, ldbl_);
+    PYBIND11_NUMPY_DTYPE(PartialNestedStruct, a);
+    PYBIND11_NUMPY_DTYPE(StringStruct, a, b);
+    PYBIND11_NUMPY_DTYPE(ArrayStruct, a, b, c, d);
+    PYBIND11_NUMPY_DTYPE(EnumStruct, e1, e2);
+    PYBIND11_NUMPY_DTYPE(ComplexStruct, cflt, cdbl);
+
+    // ... or after
+    py::class_<PackedStruct>(m, "PackedStruct");
+
+    PYBIND11_NUMPY_DTYPE_EX(StructWithUglyNames, __x__, "x", __y__, "y");
+
+#ifdef PYBIND11_NEVER_DEFINED_EVER
+    // If enabled, this should produce a static_assert failure telling the user that the struct
+    // is not a POD type
+    struct NotPOD {
+        std::string v;
+        NotPOD() : v("hi"){};
+    };
+    PYBIND11_NUMPY_DTYPE(NotPOD, v);
+#endif
+
+    // Check that dtypes can be registered programmatically, both from
+    // initializer lists of field descriptors and from other containers.
+    py::detail::npy_format_descriptor<A>::register_dtype({});
+    py::detail::npy_format_descriptor<B>::register_dtype(
+        std::vector<py::detail::field_descriptor>{});
+
+    // test_recarray, test_scalar_conversion
+    m.def("create_rec_simple", &create_recarray<SimpleStruct>);
+    m.def("create_rec_packed", &create_recarray<PackedStruct>);
+    m.def("create_rec_nested", [](size_t n) { // test_signature
+        py::array_t<NestedStruct, 0> arr = mkarray_via_buffer<NestedStruct>(n);
+        auto req = arr.request();
+        auto *ptr = static_cast<NestedStruct *>(req.ptr);
+        for (size_t i = 0; i < n; i++) {
+            SET_TEST_VALS(ptr[i].a, i);
+            SET_TEST_VALS(ptr[i].b, i + 1);
+        }
+        return arr;
+    });
+    m.def("create_rec_partial", &create_recarray<PartialStruct>);
+    m.def("create_rec_partial_nested", [](size_t n) {
+        py::array_t<PartialNestedStruct, 0> arr = mkarray_via_buffer<PartialNestedStruct>(n);
+        auto req = arr.request();
+        auto *ptr = static_cast<PartialNestedStruct *>(req.ptr);
+        for (size_t i = 0; i < n; i++) {
+            SET_TEST_VALS(ptr[i].a, i);
+        }
+        return arr;
+    });
+    m.def("print_rec_simple", &print_recarray<SimpleStruct>);
+    m.def("print_rec_packed", &print_recarray<PackedStruct>);
+    m.def("print_rec_nested", &print_recarray<NestedStruct>);
+
+    // test_format_descriptors
+    m.def("get_format_unbound", []() { return py::format_descriptor<UnboundStruct>::format(); });
+    m.def("print_format_descriptors", []() {
+        py::list l;
+        for (const auto &fmt : {py::format_descriptor<SimpleStruct>::format(),
+                                py::format_descriptor<PackedStruct>::format(),
+                                py::format_descriptor<NestedStruct>::format(),
+                                py::format_descriptor<PartialStruct>::format(),
+                                py::format_descriptor<PartialNestedStruct>::format(),
+                                py::format_descriptor<StringStruct>::format(),
+                                py::format_descriptor<ArrayStruct>::format(),
+                                py::format_descriptor<EnumStruct>::format(),
+                                py::format_descriptor<ComplexStruct>::format()}) {
+            l.append(py::cast(fmt));
+        }
+        return l;
+    });
+
+    // test_dtype
+    std::vector<const char *> dtype_names{
+        "byte",    "short",   "intc",        "int_",  "longlong",   "ubyte",       "ushort",
+        "uintc",   "uint",    "ulonglong",   "half",  "single",     "double",      "longdouble",
+        "csingle", "cdouble", "clongdouble", "bool_", "datetime64", "timedelta64", "object_"};
+
+    m.def("print_dtypes", []() {
+        py::list l;
+        for (const py::handle &d : {py::dtype::of<SimpleStruct>(),
+                                    py::dtype::of<PackedStruct>(),
+                                    py::dtype::of<NestedStruct>(),
+                                    py::dtype::of<PartialStruct>(),
+                                    py::dtype::of<PartialNestedStruct>(),
+                                    py::dtype::of<StringStruct>(),
+                                    py::dtype::of<ArrayStruct>(),
+                                    py::dtype::of<EnumStruct>(),
+                                    py::dtype::of<StructWithUglyNames>(),
+                                    py::dtype::of<ComplexStruct>()}) {
+            l.append(py::str(d));
+        }
+        return l;
+    });
+    m.def("test_dtype_ctors", &test_dtype_ctors);
+    m.def("test_dtype_kind", [dtype_names]() {
+        py::list list;
+        for (const auto &dt_name : dtype_names) {
+            list.append(py::dtype(dt_name).kind());
+        }
+        return list;
+    });
+    m.def("test_dtype_char_", [dtype_names]() {
+        py::list list;
+        for (const auto &dt_name : dtype_names) {
+            list.append(py::dtype(dt_name).char_());
+        }
+        return list;
+    });
+    m.def("test_dtype_num", [dtype_names]() {
+        py::list list;
+        for (const auto &dt_name : dtype_names) {
+            list.append(py::dtype(dt_name).num());
+        }
+        return list;
+    });
+    m.def("test_dtype_byteorder", [dtype_names]() {
+        py::list list;
+        for (const auto &dt_name : dtype_names) {
+            list.append(py::dtype(dt_name).byteorder());
+        }
+        return list;
+    });
+    m.def("test_dtype_alignment", [dtype_names]() {
+        py::list list;
+        for (const auto &dt_name : dtype_names) {
+            list.append(py::dtype(dt_name).alignment());
+        }
+        return list;
+    });
+    m.def("test_dtype_flags", [dtype_names]() {
+        py::list list;
+        for (const auto &dt_name : dtype_names) {
+            list.append(py::dtype(dt_name).flags());
+        }
+        return list;
+    });
+    m.def("test_dtype_methods", []() {
+        py::list list;
+        auto dt1 = py::dtype::of<int32_t>();
+        auto dt2 = py::dtype::of<SimpleStruct>();
+        list.append(dt1);
+        list.append(dt2);
+        list.append(py::bool_(dt1.has_fields()));
+        list.append(py::bool_(dt2.has_fields()));
+        list.append(py::int_(dt1.itemsize()));
+        list.append(py::int_(dt2.itemsize()));
+        return list;
+    });
+    struct TrailingPaddingStruct {
+        int32_t a;
+        char b;
+    };
+    PYBIND11_NUMPY_DTYPE(TrailingPaddingStruct, a, b);
+    m.def("trailing_padding_dtype", []() { return py::dtype::of<TrailingPaddingStruct>(); });
+
+    // test_string_array
+    m.def("create_string_array", [](bool non_empty) {
+        py::array_t<StringStruct, 0> arr = mkarray_via_buffer<StringStruct>(non_empty ? 4 : 0);
+        if (non_empty) {
+            auto req = arr.request();
+            auto *ptr = static_cast<StringStruct *>(req.ptr);
+            for (py::ssize_t i = 0; i < req.size * req.itemsize; i++) {
+                static_cast<char *>(req.ptr)[i] = 0;
+            }
+            ptr[1].a[0] = 'a';
+            ptr[1].b[0] = 'a';
+            ptr[2].a[0] = 'a';
+            ptr[2].b[0] = 'a';
+            ptr[3].a[0] = 'a';
+            ptr[3].b[0] = 'a';
+
+            ptr[2].a[1] = 'b';
+            ptr[2].b[1] = 'b';
+            ptr[3].a[1] = 'b';
+            ptr[3].b[1] = 'b';
+
+            ptr[3].a[2] = 'c';
+            ptr[3].b[2] = 'c';
+        }
+        return arr;
+    });
+    m.def("print_string_array", &print_recarray<StringStruct>);
+
+    // test_array_array
+    m.def("create_array_array", [](size_t n) {
+        py::array_t<ArrayStruct, 0> arr = mkarray_via_buffer<ArrayStruct>(n);
+        auto *ptr = (ArrayStruct *) arr.mutable_data();
+        for (size_t i = 0; i < n; i++) {
+            for (size_t j = 0; j < 3; j++) {
+                for (size_t k = 0; k < 4; k++) {
+                    ptr[i].a[j][k] = char('A' + (i * 100 + j * 10 + k) % 26);
+                }
+            }
+            for (size_t j = 0; j < 2; j++) {
+                ptr[i].b[j] = int32_t(i * 1000 + j);
+            }
+            for (size_t j = 0; j < 3; j++) {
+                ptr[i].c[j] = uint8_t(i * 10 + j);
+            }
+            for (size_t j = 0; j < 4; j++) {
+                for (size_t k = 0; k < 2; k++) {
+                    ptr[i].d[j][k] = float(i) * 100.0f + float(j) * 10.0f + float(k);
+                }
+            }
+        }
+        return arr;
+    });
+    m.def("print_array_array", &print_recarray<ArrayStruct>);
+
+    // test_enum_array
+    m.def("create_enum_array", [](size_t n) {
+        py::array_t<EnumStruct, 0> arr = mkarray_via_buffer<EnumStruct>(n);
+        auto *ptr = (EnumStruct *) arr.mutable_data();
+        for (size_t i = 0; i < n; i++) {
+            ptr[i].e1 = static_cast<E1>(-1 + ((int) i % 2) * 2);
+            ptr[i].e2 = static_cast<E2>(1 + (i % 2));
+        }
+        return arr;
+    });
+    m.def("print_enum_array", &print_recarray<EnumStruct>);
+
+    // test_complex_array
+    m.def("create_complex_array", [](size_t n) {
+        py::array_t<ComplexStruct, 0> arr = mkarray_via_buffer<ComplexStruct>(n);
+        auto *ptr = (ComplexStruct *) arr.mutable_data();
+        for (size_t i = 0; i < n; i++) {
+            ptr[i].cflt.real(float(i));
+            ptr[i].cflt.imag(float(i) + 0.25f);
+            ptr[i].cdbl.real(double(i) + 0.5);
+            ptr[i].cdbl.imag(double(i) + 0.75);
+        }
+        return arr;
+    });
+    m.def("print_complex_array", &print_recarray<ComplexStruct>);
+
+    // test_array_constructors
+    m.def("test_array_ctors", &test_array_ctors);
+
+    // test_compare_buffer_info
+    struct CompareStruct {
+        bool x;
+        uint32_t y;
+        float z;
+    };
+    PYBIND11_NUMPY_DTYPE(CompareStruct, x, y, z);
+    m.def("compare_buffer_info", []() {
+        py::list list;
+        list.append(py::bool_(py::detail::compare_buffer_info<float>::compare(
+            py::buffer_info(nullptr, sizeof(float), "f", 1))));
+        list.append(py::bool_(py::detail::compare_buffer_info<unsigned>::compare(
+            py::buffer_info(nullptr, sizeof(int), "I", 1))));
+        list.append(py::bool_(py::detail::compare_buffer_info<long>::compare(
+            py::buffer_info(nullptr, sizeof(long), "l", 1))));
+        list.append(py::bool_(py::detail::compare_buffer_info<long>::compare(
+            py::buffer_info(nullptr, sizeof(long), sizeof(long) == sizeof(int) ? "i" : "q", 1))));
+        list.append(py::bool_(py::detail::compare_buffer_info<CompareStruct>::compare(
+            py::buffer_info(nullptr, sizeof(CompareStruct), "T{?:x:3xI:y:f:z:}", 1))));
+        return list;
+    });
+    m.def("buffer_to_dtype", [](py::buffer &buf) { return py::dtype(buf.request()); });
+
+    // test_scalar_conversion
+    auto f_simple = [](SimpleStruct s) { return s.uint_ * 10; };
+    m.def("f_simple", f_simple);
+    m.def("f_packed", [](PackedStruct s) { return s.uint_ * 10; });
+    m.def("f_nested", [](NestedStruct s) { return s.a.uint_ * 10; });
+
+    // test_vectorize
+    m.def("f_simple_vectorized", py::vectorize(f_simple));
+    auto f_simple_pass_thru = [](SimpleStruct s) { return s; };
+    m.def("f_simple_pass_thru_vectorized", py::vectorize(f_simple_pass_thru));
+
+    // test_register_dtype
+    m.def("register_dtype",
+          []() { PYBIND11_NUMPY_DTYPE(SimpleStruct, bool_, uint_, float_, ldbl_); });
+
+    // test_str_leak
+    m.def("dtype_wrapper", [](const py::object &d) { return py::dtype::from_args(d); });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_dtypes.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_dtypes.py
new file mode 100644
index 000000000..fcfd587b1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_dtypes.py
@@ -0,0 +1,446 @@
+import re
+
+import pytest
+
+import env  # noqa: F401
+from pybind11_tests import numpy_dtypes as m
+
+np = pytest.importorskip("numpy")
+
+
+@pytest.fixture(scope="module")
+def simple_dtype():
+    ld = np.dtype("longdouble")
+    return np.dtype(
+        {
+            "names": ["bool_", "uint_", "float_", "ldbl_"],
+            "formats": ["?", "u4", "f4", f"f{ld.itemsize}"],
+            "offsets": [0, 4, 8, (16 if ld.alignment > 4 else 12)],
+        }
+    )
+
+
+@pytest.fixture(scope="module")
+def packed_dtype():
+    return np.dtype([("bool_", "?"), ("uint_", "u4"), ("float_", "f4"), ("ldbl_", "g")])
+
+
+def dt_fmt():
+    from sys import byteorder
+
+    e = "<" if byteorder == "little" else ">"
+    return (
+        "{{'names':['bool_','uint_','float_','ldbl_'],"
+        "'formats':['?','" + e + "u4','" + e + "f4','" + e + "f{}'],"
+        "'offsets':[0,4,8,{}],'itemsize':{}}}"
+    )
+
+
+def simple_dtype_fmt():
+    ld = np.dtype("longdouble")
+    simple_ld_off = 12 + 4 * (ld.alignment > 4)
+    return dt_fmt().format(ld.itemsize, simple_ld_off, simple_ld_off + ld.itemsize)
+
+
+def packed_dtype_fmt():
+    from sys import byteorder
+
+    return "[('bool_','?'),('uint_','{e}u4'),('float_','{e}f4'),('ldbl_','{e}f{}')]".format(
+        np.dtype("longdouble").itemsize, e="<" if byteorder == "little" else ">"
+    )
+
+
+def partial_ld_offset():
+    return (
+        12
+        + 4 * (np.dtype("uint64").alignment > 4)
+        + 8
+        + 8 * (np.dtype("longdouble").alignment > 8)
+    )
+
+
+def partial_dtype_fmt():
+    ld = np.dtype("longdouble")
+    partial_ld_off = partial_ld_offset()
+    partial_size = partial_ld_off + ld.itemsize
+    partial_end_padding = partial_size % np.dtype("uint64").alignment
+    return dt_fmt().format(
+        ld.itemsize, partial_ld_off, partial_size + partial_end_padding
+    )
+
+
+def partial_nested_fmt():
+    ld = np.dtype("longdouble")
+    partial_nested_off = 8 + 8 * (ld.alignment > 8)
+    partial_ld_off = partial_ld_offset()
+    partial_size = partial_ld_off + ld.itemsize
+    partial_end_padding = partial_size % np.dtype("uint64").alignment
+    partial_nested_size = partial_nested_off * 2 + partial_size + partial_end_padding
+    return "{{'names':['a'],'formats':[{}],'offsets':[{}],'itemsize':{}}}".format(
+        partial_dtype_fmt(), partial_nested_off, partial_nested_size
+    )
+
+
+def assert_equal(actual, expected_data, expected_dtype):
+    np.testing.assert_equal(actual, np.array(expected_data, dtype=expected_dtype))
+
+
+def test_format_descriptors():
+    with pytest.raises(RuntimeError) as excinfo:
+        m.get_format_unbound()
+    assert re.match(
+        "^NumPy type info missing for .*UnboundStruct.*$", str(excinfo.value)
+    )
+
+    ld = np.dtype("longdouble")
+    ldbl_fmt = ("4x" if ld.alignment > 4 else "") + ld.char
+    ss_fmt = "^T{?:bool_:3xI:uint_:f:float_:" + ldbl_fmt + ":ldbl_:}"
+    dbl = np.dtype("double")
+    end_padding = ld.itemsize % np.dtype("uint64").alignment
+    partial_fmt = (
+        "^T{?:bool_:3xI:uint_:f:float_:"
+        + str(4 * (dbl.alignment > 4) + dbl.itemsize + 8 * (ld.alignment > 8))
+        + "xg:ldbl_:"
+        + (str(end_padding) + "x}" if end_padding > 0 else "}")
+    )
+    nested_extra = str(max(8, ld.alignment))
+    assert m.print_format_descriptors() == [
+        ss_fmt,
+        "^T{?:bool_:I:uint_:f:float_:g:ldbl_:}",
+        "^T{" + ss_fmt + ":a:^T{?:bool_:I:uint_:f:float_:g:ldbl_:}:b:}",
+        partial_fmt,
+        "^T{" + nested_extra + "x" + partial_fmt + ":a:" + nested_extra + "x}",
+        "^T{3s:a:3s:b:}",
+        "^T{(3)4s:a:(2)i:b:(3)B:c:1x(4, 2)f:d:}",
+        "^T{q:e1:B:e2:}",
+        "^T{Zf:cflt:Zd:cdbl:}",
+    ]
+
+
+def test_dtype(simple_dtype):
+    from sys import byteorder
+
+    e = "<" if byteorder == "little" else ">"
+
+    assert [x.replace(" ", "") for x in m.print_dtypes()] == [
+        simple_dtype_fmt(),
+        packed_dtype_fmt(),
+        f"[('a',{simple_dtype_fmt()}),('b',{packed_dtype_fmt()})]",
+        partial_dtype_fmt(),
+        partial_nested_fmt(),
+        "[('a','S3'),('b','S3')]",
+        (
+            "{{'names':['a','b','c','d'],"
+            + "'formats':[('S4',(3,)),('"
+            + e
+            + "i4',(2,)),('u1',(3,)),('"
+            + e
+            + "f4',(4,2))],"
+            + "'offsets':[0,12,20,24],'itemsize':56}}"
+        ).format(e=e),
+        "[('e1','" + e + "i8'),('e2','u1')]",
+        "[('x','i1'),('y','" + e + "u8')]",
+        "[('cflt','" + e + "c8'),('cdbl','" + e + "c16')]",
+    ]
+
+    d1 = np.dtype(
+        {
+            "names": ["a", "b"],
+            "formats": ["int32", "float64"],
+            "offsets": [1, 10],
+            "itemsize": 20,
+        }
+    )
+    d2 = np.dtype([("a", "i4"), ("b", "f4")])
+    assert m.test_dtype_ctors() == [
+        np.dtype("int32"),
+        np.dtype("float64"),
+        np.dtype("bool"),
+        d1,
+        d1,
+        np.dtype("uint32"),
+        d2,
+        np.dtype("d"),
+    ]
+
+    assert m.test_dtype_methods() == [
+        np.dtype("int32"),
+        simple_dtype,
+        False,
+        True,
+        np.dtype("int32").itemsize,
+        simple_dtype.itemsize,
+    ]
+
+    assert m.trailing_padding_dtype() == m.buffer_to_dtype(
+        np.zeros(1, m.trailing_padding_dtype())
+    )
+
+    expected_chars = "bhilqBHILQefdgFDG?MmO"
+    assert m.test_dtype_kind() == list("iiiiiuuuuuffffcccbMmO")
+    assert m.test_dtype_char_() == list(expected_chars)
+    assert m.test_dtype_num() == [np.dtype(ch).num for ch in expected_chars]
+    assert m.test_dtype_byteorder() == [np.dtype(ch).byteorder for ch in expected_chars]
+    assert m.test_dtype_alignment() == [np.dtype(ch).alignment for ch in expected_chars]
+    assert m.test_dtype_flags() == [chr(np.dtype(ch).flags) for ch in expected_chars]
+
+
+def test_recarray(simple_dtype, packed_dtype):
+    elements = [(False, 0, 0.0, -0.0), (True, 1, 1.5, -2.5), (False, 2, 3.0, -5.0)]
+
+    for func, dtype in [
+        (m.create_rec_simple, simple_dtype),
+        (m.create_rec_packed, packed_dtype),
+    ]:
+        arr = func(0)
+        assert arr.dtype == dtype
+        assert_equal(arr, [], simple_dtype)
+        assert_equal(arr, [], packed_dtype)
+
+        arr = func(3)
+        assert arr.dtype == dtype
+        assert_equal(arr, elements, simple_dtype)
+        assert_equal(arr, elements, packed_dtype)
+
+        # Show what recarray's look like in NumPy.
+        assert type(arr[0]) == np.void
+        assert type(arr[0].item()) == tuple
+
+        if dtype == simple_dtype:
+            assert m.print_rec_simple(arr) == [
+                "s:0,0,0,-0",
+                "s:1,1,1.5,-2.5",
+                "s:0,2,3,-5",
+            ]
+        else:
+            assert m.print_rec_packed(arr) == [
+                "p:0,0,0,-0",
+                "p:1,1,1.5,-2.5",
+                "p:0,2,3,-5",
+            ]
+
+    nested_dtype = np.dtype([("a", simple_dtype), ("b", packed_dtype)])
+
+    arr = m.create_rec_nested(0)
+    assert arr.dtype == nested_dtype
+    assert_equal(arr, [], nested_dtype)
+
+    arr = m.create_rec_nested(3)
+    assert arr.dtype == nested_dtype
+    assert_equal(
+        arr,
+        [
+            ((False, 0, 0.0, -0.0), (True, 1, 1.5, -2.5)),
+            ((True, 1, 1.5, -2.5), (False, 2, 3.0, -5.0)),
+            ((False, 2, 3.0, -5.0), (True, 3, 4.5, -7.5)),
+        ],
+        nested_dtype,
+    )
+    assert m.print_rec_nested(arr) == [
+        "n:a=s:0,0,0,-0;b=p:1,1,1.5,-2.5",
+        "n:a=s:1,1,1.5,-2.5;b=p:0,2,3,-5",
+        "n:a=s:0,2,3,-5;b=p:1,3,4.5,-7.5",
+    ]
+
+    arr = m.create_rec_partial(3)
+    assert str(arr.dtype).replace(" ", "") == partial_dtype_fmt()
+    partial_dtype = arr.dtype
+    assert "" not in arr.dtype.fields
+    assert partial_dtype.itemsize > simple_dtype.itemsize
+    assert_equal(arr, elements, simple_dtype)
+    assert_equal(arr, elements, packed_dtype)
+
+    arr = m.create_rec_partial_nested(3)
+    assert str(arr.dtype).replace(" ", "") == partial_nested_fmt()
+    assert "" not in arr.dtype.fields
+    assert "" not in arr.dtype.fields["a"][0].fields
+    assert arr.dtype.itemsize > partial_dtype.itemsize
+    np.testing.assert_equal(arr["a"], m.create_rec_partial(3))
+
+
+def test_array_constructors():
+    data = np.arange(1, 7, dtype="int32")
+    for i in range(8):
+        np.testing.assert_array_equal(m.test_array_ctors(10 + i), data.reshape((3, 2)))
+        np.testing.assert_array_equal(m.test_array_ctors(20 + i), data.reshape((3, 2)))
+    for i in range(5):
+        np.testing.assert_array_equal(m.test_array_ctors(30 + i), data)
+        np.testing.assert_array_equal(m.test_array_ctors(40 + i), data)
+
+
+def test_string_array():
+    arr = m.create_string_array(True)
+    assert str(arr.dtype) == "[('a', 'S3'), ('b', 'S3')]"
+    assert m.print_string_array(arr) == [
+        "a='',b=''",
+        "a='a',b='a'",
+        "a='ab',b='ab'",
+        "a='abc',b='abc'",
+    ]
+    dtype = arr.dtype
+    assert arr["a"].tolist() == [b"", b"a", b"ab", b"abc"]
+    assert arr["b"].tolist() == [b"", b"a", b"ab", b"abc"]
+    arr = m.create_string_array(False)
+    assert dtype == arr.dtype
+
+
+def test_array_array():
+    from sys import byteorder
+
+    e = "<" if byteorder == "little" else ">"
+
+    arr = m.create_array_array(3)
+    assert str(arr.dtype).replace(" ", "") == (
+        "{{'names':['a','b','c','d'],"
+        + "'formats':[('S4',(3,)),('"
+        + e
+        + "i4',(2,)),('u1',(3,)),('{e}f4',(4,2))],"
+        + "'offsets':[0,12,20,24],'itemsize':56}}"
+    ).format(e=e)
+    assert m.print_array_array(arr) == [
+        "a={{A,B,C,D},{K,L,M,N},{U,V,W,X}},b={0,1},"
+        + "c={0,1,2},d={{0,1},{10,11},{20,21},{30,31}}",
+        "a={{W,X,Y,Z},{G,H,I,J},{Q,R,S,T}},b={1000,1001},"
+        + "c={10,11,12},d={{100,101},{110,111},{120,121},{130,131}}",
+        "a={{S,T,U,V},{C,D,E,F},{M,N,O,P}},b={2000,2001},"
+        + "c={20,21,22},d={{200,201},{210,211},{220,221},{230,231}}",
+    ]
+    assert arr["a"].tolist() == [
+        [b"ABCD", b"KLMN", b"UVWX"],
+        [b"WXYZ", b"GHIJ", b"QRST"],
+        [b"STUV", b"CDEF", b"MNOP"],
+    ]
+    assert arr["b"].tolist() == [[0, 1], [1000, 1001], [2000, 2001]]
+    assert m.create_array_array(0).dtype == arr.dtype
+
+
+def test_enum_array():
+    from sys import byteorder
+
+    e = "<" if byteorder == "little" else ">"
+
+    arr = m.create_enum_array(3)
+    dtype = arr.dtype
+    assert dtype == np.dtype([("e1", e + "i8"), ("e2", "u1")])
+    assert m.print_enum_array(arr) == ["e1=A,e2=X", "e1=B,e2=Y", "e1=A,e2=X"]
+    assert arr["e1"].tolist() == [-1, 1, -1]
+    assert arr["e2"].tolist() == [1, 2, 1]
+    assert m.create_enum_array(0).dtype == dtype
+
+
+def test_complex_array():
+    from sys import byteorder
+
+    e = "<" if byteorder == "little" else ">"
+
+    arr = m.create_complex_array(3)
+    dtype = arr.dtype
+    assert dtype == np.dtype([("cflt", e + "c8"), ("cdbl", e + "c16")])
+    assert m.print_complex_array(arr) == [
+        "c:(0,0.25),(0.5,0.75)",
+        "c:(1,1.25),(1.5,1.75)",
+        "c:(2,2.25),(2.5,2.75)",
+    ]
+    assert arr["cflt"].tolist() == [0.0 + 0.25j, 1.0 + 1.25j, 2.0 + 2.25j]
+    assert arr["cdbl"].tolist() == [0.5 + 0.75j, 1.5 + 1.75j, 2.5 + 2.75j]
+    assert m.create_complex_array(0).dtype == dtype
+
+
+def test_signature(doc):
+    assert (
+        doc(m.create_rec_nested)
+        == "create_rec_nested(arg0: int) -> numpy.ndarray[NestedStruct]"
+    )
+
+
+def test_scalar_conversion():
+    n = 3
+    arrays = [
+        m.create_rec_simple(n),
+        m.create_rec_packed(n),
+        m.create_rec_nested(n),
+        m.create_enum_array(n),
+    ]
+    funcs = [m.f_simple, m.f_packed, m.f_nested]
+
+    for i, func in enumerate(funcs):
+        for j, arr in enumerate(arrays):
+            if i == j and i < 2:
+                assert [func(arr[k]) for k in range(n)] == [k * 10 for k in range(n)]
+            else:
+                with pytest.raises(TypeError) as excinfo:
+                    func(arr[0])
+                assert "incompatible function arguments" in str(excinfo.value)
+
+
+def test_vectorize():
+    n = 3
+    array = m.create_rec_simple(n)
+    values = m.f_simple_vectorized(array)
+    np.testing.assert_array_equal(values, [0, 10, 20])
+    array_2 = m.f_simple_pass_thru_vectorized(array)
+    np.testing.assert_array_equal(array, array_2)
+
+
+def test_cls_and_dtype_conversion(simple_dtype):
+    s = m.SimpleStruct()
+    assert s.astuple() == (False, 0, 0.0, 0.0)
+    assert m.SimpleStruct.fromtuple(s.astuple()).astuple() == s.astuple()
+
+    s.uint_ = 2
+    assert m.f_simple(s) == 20
+
+    # Try as recarray of shape==(1,).
+    s_recarray = np.array([(False, 2, 0.0, 0.0)], dtype=simple_dtype)
+    # Show that this will work for vectorized case.
+    np.testing.assert_array_equal(m.f_simple_vectorized(s_recarray), [20])
+
+    # Show as a scalar that inherits from np.generic.
+    s_scalar = s_recarray[0]
+    assert isinstance(s_scalar, np.void)
+    assert m.f_simple(s_scalar) == 20
+
+    # Show that an *array* scalar (np.ndarray.shape == ()) does not convert.
+    # More specifically, conversion to SimpleStruct is not implicit.
+    s_recarray_scalar = s_recarray.reshape(())
+    assert isinstance(s_recarray_scalar, np.ndarray)
+    assert s_recarray_scalar.dtype == simple_dtype
+    with pytest.raises(TypeError) as excinfo:
+        m.f_simple(s_recarray_scalar)
+    assert "incompatible function arguments" in str(excinfo.value)
+    # Explicitly convert to m.SimpleStruct.
+    assert m.f_simple(m.SimpleStruct.fromtuple(s_recarray_scalar.item())) == 20
+
+    # Show that an array of dtype=object does *not* convert.
+    s_array_object = np.array([s])
+    assert s_array_object.dtype == object
+    with pytest.raises(TypeError) as excinfo:
+        m.f_simple_vectorized(s_array_object)
+    assert "incompatible function arguments" in str(excinfo.value)
+    # Explicitly convert to `np.array(..., dtype=simple_dtype)`
+    s_array = np.array([s.astuple()], dtype=simple_dtype)
+    np.testing.assert_array_equal(m.f_simple_vectorized(s_array), [20])
+
+
+def test_register_dtype():
+    with pytest.raises(RuntimeError) as excinfo:
+        m.register_dtype()
+    assert "dtype is already registered" in str(excinfo.value)
+
+
+@pytest.mark.xfail("env.PYPY")
+def test_str_leak():
+    from sys import getrefcount
+
+    fmt = "f4"
+    pytest.gc_collect()
+    start = getrefcount(fmt)
+    d = m.dtype_wrapper(fmt)
+    assert d is np.dtype("f4")
+    del d
+    pytest.gc_collect()
+    assert getrefcount(fmt) == start
+
+
+def test_compare_buffer_info():
+    assert all(m.compare_buffer_info())
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_vectorize.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_vectorize.cpp
new file mode 100644
index 000000000..dcc4c6ac2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_vectorize.cpp
@@ -0,0 +1,107 @@
+/*
+    tests/test_numpy_vectorize.cpp -- auto-vectorize functions over NumPy array
+    arguments
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/numpy.h>
+
+#include "pybind11_tests.h"
+
+#include <utility>
+
+double my_func(int x, float y, double z) {
+    py::print("my_func(x:int={}, y:float={:.0f}, z:float={:.0f})"_s.format(x, y, z));
+    return (float) x * y * z;
+}
+
+TEST_SUBMODULE(numpy_vectorize, m) {
+    try {
+        py::module_::import("numpy");
+    } catch (const py::error_already_set &) {
+        return;
+    }
+
+    // test_vectorize, test_docs, test_array_collapse
+    // Vectorize all arguments of a function (though non-vector arguments are also allowed)
+    m.def("vectorized_func", py::vectorize(my_func));
+
+    // Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the
+    // vectorization)
+    m.def("vectorized_func2", [](py::array_t<int> x, py::array_t<float> y, float z) {
+        return py::vectorize([z](int x, float y) { return my_func(x, y, z); })(std::move(x),
+                                                                               std::move(y));
+    });
+
+    // Vectorize a complex-valued function
+    m.def("vectorized_func3",
+          py::vectorize([](std::complex<double> c) { return c * std::complex<double>(2.f); }));
+
+    // test_type_selection
+    // NumPy function which only accepts specific data types
+    // A lot of these no lints could be replaced with const refs, and probably should at some
+    // point.
+    m.def("selective_func",
+          [](const py::array_t<int, py::array::c_style> &) { return "Int branch taken."; });
+    m.def("selective_func",
+          [](const py::array_t<float, py::array::c_style> &) { return "Float branch taken."; });
+    m.def("selective_func", [](const py::array_t<std::complex<float>, py::array::c_style> &) {
+        return "Complex float branch taken.";
+    });
+
+    // test_passthrough_arguments
+    // Passthrough test: references and non-pod types should be automatically passed through (in
+    // the function definition below, only `b`, `d`, and `g` are vectorized):
+    struct NonPODClass {
+        explicit NonPODClass(int v) : value{v} {}
+        int value;
+    };
+    py::class_<NonPODClass>(m, "NonPODClass")
+        .def(py::init<int>())
+        .def_readwrite("value", &NonPODClass::value);
+    m.def("vec_passthrough",
+          py::vectorize([](const double *a,
+                           double b,
+                           // Changing this broke things
+                           // NOLINTNEXTLINE(performance-unnecessary-value-param)
+                           py::array_t<double> c,
+                           const int &d,
+                           int &e,
+                           NonPODClass f,
+                           const double g) { return *a + b + c.at(0) + d + e + f.value + g; }));
+
+    // test_method_vectorization
+    struct VectorizeTestClass {
+        explicit VectorizeTestClass(int v) : value{v} {};
+        float method(int x, float y) const { return y + (float) (x + value); }
+        int value = 0;
+    };
+    py::class_<VectorizeTestClass> vtc(m, "VectorizeTestClass");
+    vtc.def(py::init<int>()).def_readwrite("value", &VectorizeTestClass::value);
+
+    // Automatic vectorizing of methods
+    vtc.def("method", py::vectorize(&VectorizeTestClass::method));
+
+    // test_trivial_broadcasting
+    // Internal optimization test for whether the input is trivially broadcastable:
+    py::enum_<py::detail::broadcast_trivial>(m, "trivial")
+        .value("f_trivial", py::detail::broadcast_trivial::f_trivial)
+        .value("c_trivial", py::detail::broadcast_trivial::c_trivial)
+        .value("non_trivial", py::detail::broadcast_trivial::non_trivial);
+    m.def("vectorized_is_trivial",
+          [](const py::array_t<int, py::array::forcecast> &arg1,
+             const py::array_t<float, py::array::forcecast> &arg2,
+             const py::array_t<double, py::array::forcecast> &arg3) {
+              py::ssize_t ndim = 0;
+              std::vector<py::ssize_t> shape;
+              std::array<py::buffer_info, 3> buffers{
+                  {arg1.request(), arg2.request(), arg3.request()}};
+              return py::detail::broadcast(buffers, ndim, shape);
+          });
+
+    m.def("add_to", py::vectorize([](NonPODClass &x, int a) { x.value += a; }));
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_vectorize.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_vectorize.py
new file mode 100644
index 000000000..7e8c015c4
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_numpy_vectorize.py
@@ -0,0 +1,266 @@
+import pytest
+
+from pybind11_tests import numpy_vectorize as m
+
+np = pytest.importorskip("numpy")
+
+
+def test_vectorize(capture):
+    assert np.isclose(m.vectorized_func3(np.array(3 + 7j)), [6 + 14j])
+
+    for f in [m.vectorized_func, m.vectorized_func2]:
+        with capture:
+            assert np.isclose(f(1, 2, 3), 6)
+        assert capture == "my_func(x:int=1, y:float=2, z:float=3)"
+        with capture:
+            assert np.isclose(f(np.array(1), np.array(2), 3), 6)
+        assert capture == "my_func(x:int=1, y:float=2, z:float=3)"
+        with capture:
+            assert np.allclose(f(np.array([1, 3]), np.array([2, 4]), 3), [6, 36])
+        assert (
+            capture
+            == """
+            my_func(x:int=1, y:float=2, z:float=3)
+            my_func(x:int=3, y:float=4, z:float=3)
+        """
+        )
+        with capture:
+            a = np.array([[1, 2], [3, 4]], order="F")
+            b = np.array([[10, 20], [30, 40]], order="F")
+            c = 3
+            result = f(a, b, c)
+            assert np.allclose(result, a * b * c)
+            assert result.flags.f_contiguous
+        # All inputs are F order and full or singletons, so we the result is in col-major order:
+        assert (
+            capture
+            == """
+            my_func(x:int=1, y:float=10, z:float=3)
+            my_func(x:int=3, y:float=30, z:float=3)
+            my_func(x:int=2, y:float=20, z:float=3)
+            my_func(x:int=4, y:float=40, z:float=3)
+        """
+        )
+        with capture:
+            a, b, c = (
+                np.array([[1, 3, 5], [7, 9, 11]]),
+                np.array([[2, 4, 6], [8, 10, 12]]),
+                3,
+            )
+            assert np.allclose(f(a, b, c), a * b * c)
+        assert (
+            capture
+            == """
+            my_func(x:int=1, y:float=2, z:float=3)
+            my_func(x:int=3, y:float=4, z:float=3)
+            my_func(x:int=5, y:float=6, z:float=3)
+            my_func(x:int=7, y:float=8, z:float=3)
+            my_func(x:int=9, y:float=10, z:float=3)
+            my_func(x:int=11, y:float=12, z:float=3)
+        """
+        )
+        with capture:
+            a, b, c = np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2
+            assert np.allclose(f(a, b, c), a * b * c)
+        assert (
+            capture
+            == """
+            my_func(x:int=1, y:float=2, z:float=2)
+            my_func(x:int=2, y:float=3, z:float=2)
+            my_func(x:int=3, y:float=4, z:float=2)
+            my_func(x:int=4, y:float=2, z:float=2)
+            my_func(x:int=5, y:float=3, z:float=2)
+            my_func(x:int=6, y:float=4, z:float=2)
+        """
+        )
+        with capture:
+            a, b, c = np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2
+            assert np.allclose(f(a, b, c), a * b * c)
+        assert (
+            capture
+            == """
+            my_func(x:int=1, y:float=2, z:float=2)
+            my_func(x:int=2, y:float=2, z:float=2)
+            my_func(x:int=3, y:float=2, z:float=2)
+            my_func(x:int=4, y:float=3, z:float=2)
+            my_func(x:int=5, y:float=3, z:float=2)
+            my_func(x:int=6, y:float=3, z:float=2)
+        """
+        )
+        with capture:
+            a, b, c = (
+                np.array([[1, 2, 3], [4, 5, 6]], order="F"),
+                np.array([[2], [3]]),
+                2,
+            )
+            assert np.allclose(f(a, b, c), a * b * c)
+        assert (
+            capture
+            == """
+            my_func(x:int=1, y:float=2, z:float=2)
+            my_func(x:int=2, y:float=2, z:float=2)
+            my_func(x:int=3, y:float=2, z:float=2)
+            my_func(x:int=4, y:float=3, z:float=2)
+            my_func(x:int=5, y:float=3, z:float=2)
+            my_func(x:int=6, y:float=3, z:float=2)
+        """
+        )
+        with capture:
+            a, b, c = np.array([[1, 2, 3], [4, 5, 6]])[::, ::2], np.array([[2], [3]]), 2
+            assert np.allclose(f(a, b, c), a * b * c)
+        assert (
+            capture
+            == """
+            my_func(x:int=1, y:float=2, z:float=2)
+            my_func(x:int=3, y:float=2, z:float=2)
+            my_func(x:int=4, y:float=3, z:float=2)
+            my_func(x:int=6, y:float=3, z:float=2)
+        """
+        )
+        with capture:
+            a, b, c = (
+                np.array([[1, 2, 3], [4, 5, 6]], order="F")[::, ::2],
+                np.array([[2], [3]]),
+                2,
+            )
+            assert np.allclose(f(a, b, c), a * b * c)
+        assert (
+            capture
+            == """
+            my_func(x:int=1, y:float=2, z:float=2)
+            my_func(x:int=3, y:float=2, z:float=2)
+            my_func(x:int=4, y:float=3, z:float=2)
+            my_func(x:int=6, y:float=3, z:float=2)
+        """
+        )
+
+
+def test_type_selection():
+    assert m.selective_func(np.array([1], dtype=np.int32)) == "Int branch taken."
+    assert m.selective_func(np.array([1.0], dtype=np.float32)) == "Float branch taken."
+    assert (
+        m.selective_func(np.array([1.0j], dtype=np.complex64))
+        == "Complex float branch taken."
+    )
+
+
+def test_docs(doc):
+    assert (
+        doc(m.vectorized_func)
+        == """
+        vectorized_func(arg0: numpy.ndarray[numpy.int32], arg1: numpy.ndarray[numpy.float32], arg2: numpy.ndarray[numpy.float64]) -> object
+    """  # noqa: E501 line too long
+    )
+
+
+def test_trivial_broadcasting():
+    trivial, vectorized_is_trivial = m.trivial, m.vectorized_is_trivial
+
+    assert vectorized_is_trivial(1, 2, 3) == trivial.c_trivial
+    assert vectorized_is_trivial(np.array(1), np.array(2), 3) == trivial.c_trivial
+    assert (
+        vectorized_is_trivial(np.array([1, 3]), np.array([2, 4]), 3)
+        == trivial.c_trivial
+    )
+    assert trivial.c_trivial == vectorized_is_trivial(
+        np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3
+    )
+    assert (
+        vectorized_is_trivial(np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2)
+        == trivial.non_trivial
+    )
+    assert (
+        vectorized_is_trivial(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2)
+        == trivial.non_trivial
+    )
+    z1 = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype="int32")
+    z2 = np.array(z1, dtype="float32")
+    z3 = np.array(z1, dtype="float64")
+    assert vectorized_is_trivial(z1, z2, z3) == trivial.c_trivial
+    assert vectorized_is_trivial(1, z2, z3) == trivial.c_trivial
+    assert vectorized_is_trivial(z1, 1, z3) == trivial.c_trivial
+    assert vectorized_is_trivial(z1, z2, 1) == trivial.c_trivial
+    assert vectorized_is_trivial(z1[::2, ::2], 1, 1) == trivial.non_trivial
+    assert vectorized_is_trivial(1, 1, z1[::2, ::2]) == trivial.c_trivial
+    assert vectorized_is_trivial(1, 1, z3[::2, ::2]) == trivial.non_trivial
+    assert vectorized_is_trivial(z1, 1, z3[1::4, 1::4]) == trivial.c_trivial
+
+    y1 = np.array(z1, order="F")
+    y2 = np.array(y1)
+    y3 = np.array(y1)
+    assert vectorized_is_trivial(y1, y2, y3) == trivial.f_trivial
+    assert vectorized_is_trivial(y1, 1, 1) == trivial.f_trivial
+    assert vectorized_is_trivial(1, y2, 1) == trivial.f_trivial
+    assert vectorized_is_trivial(1, 1, y3) == trivial.f_trivial
+    assert vectorized_is_trivial(y1, z2, 1) == trivial.non_trivial
+    assert vectorized_is_trivial(z1[1::4, 1::4], y2, 1) == trivial.f_trivial
+    assert vectorized_is_trivial(y1[1::4, 1::4], z2, 1) == trivial.c_trivial
+
+    assert m.vectorized_func(z1, z2, z3).flags.c_contiguous
+    assert m.vectorized_func(y1, y2, y3).flags.f_contiguous
+    assert m.vectorized_func(z1, 1, 1).flags.c_contiguous
+    assert m.vectorized_func(1, y2, 1).flags.f_contiguous
+    assert m.vectorized_func(z1[1::4, 1::4], y2, 1).flags.f_contiguous
+    assert m.vectorized_func(y1[1::4, 1::4], z2, 1).flags.c_contiguous
+
+
+def test_passthrough_arguments(doc):
+    assert doc(m.vec_passthrough) == (
+        "vec_passthrough("
+        + ", ".join(
+            [
+                "arg0: float",
+                "arg1: numpy.ndarray[numpy.float64]",
+                "arg2: numpy.ndarray[numpy.float64]",
+                "arg3: numpy.ndarray[numpy.int32]",
+                "arg4: int",
+                "arg5: m.numpy_vectorize.NonPODClass",
+                "arg6: numpy.ndarray[numpy.float64]",
+            ]
+        )
+        + ") -> object"
+    )
+
+    b = np.array([[10, 20, 30]], dtype="float64")
+    c = np.array([100, 200])  # NOT a vectorized argument
+    d = np.array([[1000], [2000], [3000]], dtype="int")
+    g = np.array([[1000000, 2000000, 3000000]], dtype="int")  # requires casting
+    assert np.all(
+        m.vec_passthrough(1, b, c, d, 10000, m.NonPODClass(100000), g)
+        == np.array(
+            [
+                [1111111, 2111121, 3111131],
+                [1112111, 2112121, 3112131],
+                [1113111, 2113121, 3113131],
+            ]
+        )
+    )
+
+
+def test_method_vectorization():
+    o = m.VectorizeTestClass(3)
+    x = np.array([1, 2], dtype="int")
+    y = np.array([[10], [20]], dtype="float32")
+    assert np.all(o.method(x, y) == [[14, 15], [24, 25]])
+
+
+def test_array_collapse():
+    assert not isinstance(m.vectorized_func(1, 2, 3), np.ndarray)
+    assert not isinstance(m.vectorized_func(np.array(1), 2, 3), np.ndarray)
+    z = m.vectorized_func([1], 2, 3)
+    assert isinstance(z, np.ndarray)
+    assert z.shape == (1,)
+    z = m.vectorized_func(1, [[[2]]], 3)
+    assert isinstance(z, np.ndarray)
+    assert z.shape == (1, 1, 1)
+
+
+def test_vectorized_noreturn():
+    x = m.NonPODClass(0)
+    assert x.value == 0
+    m.add_to(x, [1, 2, 3, 4])
+    assert x.value == 10
+    m.add_to(x, 1)
+    assert x.value == 11
+    m.add_to(x, [[1, 1], [2, 3]])
+    assert x.value == 18
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_opaque_types.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_opaque_types.cpp
new file mode 100644
index 000000000..0386dba03
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_opaque_types.cpp
@@ -0,0 +1,77 @@
+/*
+    tests/test_opaque_types.cpp -- opaque types, passing void pointers
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/stl.h>
+
+#include "pybind11_tests.h"
+
+#include <vector>
+
+// IMPORTANT: Disable internal pybind11 translation mechanisms for STL data structures
+//
+// This also deliberately doesn't use the below StringList type alias to test
+// that MAKE_OPAQUE can handle a type containing a `,`.  (The `std::allocator`
+// bit is just the default `std::vector` allocator).
+PYBIND11_MAKE_OPAQUE(std::vector<std::string, std::allocator<std::string>>);
+
+using StringList = std::vector<std::string, std::allocator<std::string>>;
+
+TEST_SUBMODULE(opaque_types, m) {
+    // test_string_list
+    py::class_<StringList>(m, "StringList")
+        .def(py::init<>())
+        .def("pop_back", &StringList::pop_back)
+        /* There are multiple versions of push_back(), etc. Select the right ones. */
+        .def("push_back", (void(StringList::*)(const std::string &)) & StringList::push_back)
+        .def("back", (std::string & (StringList::*) ()) & StringList::back)
+        .def("__len__", [](const StringList &v) { return v.size(); })
+        .def(
+            "__iter__",
+            [](StringList &v) { return py::make_iterator(v.begin(), v.end()); },
+            py::keep_alive<0, 1>());
+
+    class ClassWithSTLVecProperty {
+    public:
+        StringList stringList;
+    };
+    py::class_<ClassWithSTLVecProperty>(m, "ClassWithSTLVecProperty")
+        .def(py::init<>())
+        .def_readwrite("stringList", &ClassWithSTLVecProperty::stringList);
+
+    m.def("print_opaque_list", [](const StringList &l) {
+        std::string ret = "Opaque list: [";
+        bool first = true;
+        for (const auto &entry : l) {
+            if (!first) {
+                ret += ", ";
+            }
+            ret += entry;
+            first = false;
+        }
+        return ret + "]";
+    });
+
+    // test_pointers
+    m.def("return_void_ptr", []() { return (void *) 0x1234; });
+    m.def("get_void_ptr_value", [](void *ptr) { return reinterpret_cast<std::intptr_t>(ptr); });
+    m.def("return_null_str", []() { return (char *) nullptr; });
+    m.def("get_null_str_value", [](char *ptr) { return reinterpret_cast<std::intptr_t>(ptr); });
+
+    m.def("return_unique_ptr", []() -> std::unique_ptr<StringList> {
+        auto *result = new StringList();
+        result->push_back("some value");
+        return std::unique_ptr<StringList>(result);
+    });
+
+    // test unions
+    py::class_<IntFloat>(m, "IntFloat")
+        .def(py::init<>())
+        .def_readwrite("i", &IntFloat::i)
+        .def_readwrite("f", &IntFloat::f);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_opaque_types.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_opaque_types.py
new file mode 100644
index 000000000..5d4f2a1bf
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_opaque_types.py
@@ -0,0 +1,58 @@
+import pytest
+
+from pybind11_tests import ConstructorStats, UserType
+from pybind11_tests import opaque_types as m
+
+
+def test_string_list():
+    lst = m.StringList()
+    lst.push_back("Element 1")
+    lst.push_back("Element 2")
+    assert m.print_opaque_list(lst) == "Opaque list: [Element 1, Element 2]"
+    assert lst.back() == "Element 2"
+
+    for i, k in enumerate(lst, start=1):
+        assert k == f"Element {i}"
+    lst.pop_back()
+    assert m.print_opaque_list(lst) == "Opaque list: [Element 1]"
+
+    cvp = m.ClassWithSTLVecProperty()
+    assert m.print_opaque_list(cvp.stringList) == "Opaque list: []"
+
+    cvp.stringList = lst
+    cvp.stringList.push_back("Element 3")
+    assert m.print_opaque_list(cvp.stringList) == "Opaque list: [Element 1, Element 3]"
+
+
+def test_pointers(msg):
+    living_before = ConstructorStats.get(UserType).alive()
+    assert m.get_void_ptr_value(m.return_void_ptr()) == 0x1234
+    assert m.get_void_ptr_value(UserType())  # Should also work for other C++ types
+    assert ConstructorStats.get(UserType).alive() == living_before
+
+    with pytest.raises(TypeError) as excinfo:
+        m.get_void_ptr_value([1, 2, 3])  # This should not work
+    assert (
+        msg(excinfo.value)
+        == """
+        get_void_ptr_value(): incompatible function arguments. The following argument types are supported:
+            1. (arg0: capsule) -> int
+
+        Invoked with: [1, 2, 3]
+    """
+    )
+
+    assert m.return_null_str() is None
+    assert m.get_null_str_value(m.return_null_str()) is not None
+
+    ptr = m.return_unique_ptr()
+    assert "StringList" in repr(ptr)
+    assert m.print_opaque_list(ptr) == "Opaque list: [some value]"
+
+
+def test_unions():
+    int_float_union = m.IntFloat()
+    int_float_union.i = 42
+    assert int_float_union.i == 42
+    int_float_union.f = 3.0
+    assert int_float_union.f == 3.0
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_operator_overloading.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_operator_overloading.cpp
new file mode 100644
index 000000000..a4b895a89
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_operator_overloading.cpp
@@ -0,0 +1,288 @@
+/*
+    tests/test_operator_overloading.cpp -- operator overloading
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/operators.h>
+#include <pybind11/stl.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#include <functional>
+
+class Vector2 {
+public:
+    Vector2(float x, float y) : x(x), y(y) { print_created(this, toString()); }
+    Vector2(const Vector2 &v) : x(v.x), y(v.y) { print_copy_created(this); }
+    Vector2(Vector2 &&v) noexcept : x(v.x), y(v.y) {
+        print_move_created(this);
+        v.x = v.y = 0;
+    }
+    Vector2 &operator=(const Vector2 &v) {
+        x = v.x;
+        y = v.y;
+        print_copy_assigned(this);
+        return *this;
+    }
+    Vector2 &operator=(Vector2 &&v) noexcept {
+        x = v.x;
+        y = v.y;
+        v.x = v.y = 0;
+        print_move_assigned(this);
+        return *this;
+    }
+    ~Vector2() { print_destroyed(this); }
+
+    std::string toString() const {
+        return "[" + std::to_string(x) + ", " + std::to_string(y) + "]";
+    }
+
+    Vector2 operator-() const { return Vector2(-x, -y); }
+    Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); }
+    Vector2 operator-(const Vector2 &v) const { return Vector2(x - v.x, y - v.y); }
+    Vector2 operator-(float value) const { return Vector2(x - value, y - value); }
+    Vector2 operator+(float value) const { return Vector2(x + value, y + value); }
+    Vector2 operator*(float value) const { return Vector2(x * value, y * value); }
+    Vector2 operator/(float value) const { return Vector2(x / value, y / value); }
+    Vector2 operator*(const Vector2 &v) const { return Vector2(x * v.x, y * v.y); }
+    Vector2 operator/(const Vector2 &v) const { return Vector2(x / v.x, y / v.y); }
+    Vector2 &operator+=(const Vector2 &v) {
+        x += v.x;
+        y += v.y;
+        return *this;
+    }
+    Vector2 &operator-=(const Vector2 &v) {
+        x -= v.x;
+        y -= v.y;
+        return *this;
+    }
+    Vector2 &operator*=(float v) {
+        x *= v;
+        y *= v;
+        return *this;
+    }
+    Vector2 &operator/=(float v) {
+        x /= v;
+        y /= v;
+        return *this;
+    }
+    Vector2 &operator*=(const Vector2 &v) {
+        x *= v.x;
+        y *= v.y;
+        return *this;
+    }
+    Vector2 &operator/=(const Vector2 &v) {
+        x /= v.x;
+        y /= v.y;
+        return *this;
+    }
+
+    friend Vector2 operator+(float f, const Vector2 &v) { return Vector2(f + v.x, f + v.y); }
+    friend Vector2 operator-(float f, const Vector2 &v) { return Vector2(f - v.x, f - v.y); }
+    friend Vector2 operator*(float f, const Vector2 &v) { return Vector2(f * v.x, f * v.y); }
+    friend Vector2 operator/(float f, const Vector2 &v) { return Vector2(f / v.x, f / v.y); }
+
+    bool operator==(const Vector2 &v) const { return x == v.x && y == v.y; }
+    bool operator!=(const Vector2 &v) const { return x != v.x || y != v.y; }
+
+private:
+    float x, y;
+};
+
+class C1 {};
+class C2 {};
+
+int operator+(const C1 &, const C1 &) { return 11; }
+int operator+(const C2 &, const C2 &) { return 22; }
+int operator+(const C2 &, const C1 &) { return 21; }
+int operator+(const C1 &, const C2 &) { return 12; }
+
+struct HashMe {
+    std::string member;
+};
+
+bool operator==(const HashMe &lhs, const HashMe &rhs) { return lhs.member == rhs.member; }
+
+// Note: Specializing explicit within `namespace std { ... }` is done due to a
+// bug in GCC<7. If you are supporting compilers later than this, consider
+// specializing `using template<> struct std::hash<...>` in the global
+// namespace instead, per this recommendation:
+// https://en.cppreference.com/w/cpp/language/extending_std#Adding_template_specializations
+namespace std {
+template <>
+struct hash<Vector2> {
+    // Not a good hash function, but easy to test
+    size_t operator()(const Vector2 &) { return 4; }
+};
+
+// HashMe has a hash function in C++ but no `__hash__` for Python.
+template <>
+struct hash<HashMe> {
+    std::size_t operator()(const HashMe &selector) const {
+        return std::hash<std::string>()(selector.member);
+    }
+};
+} // namespace std
+
+// Not a good abs function, but easy to test.
+std::string abs(const Vector2 &) { return "abs(Vector2)"; }
+
+// MSVC & Intel warns about unknown pragmas, and warnings are errors.
+#if !defined(_MSC_VER) && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic push
+// clang 7.0.0 and Apple LLVM 10.0.1 introduce `-Wself-assign-overloaded` to
+// `-Wall`, which is used here for overloading (e.g. `py::self += py::self `).
+// Here, we suppress the warning using `#pragma diagnostic`.
+// Taken from: https://github.com/RobotLocomotion/drake/commit/aaf84b46
+// TODO(eric): This could be resolved using a function / functor (e.g. `py::self()`).
+#    if defined(__APPLE__) && defined(__clang__)
+#        if (__clang_major__ >= 10)
+#            pragma GCC diagnostic ignored "-Wself-assign-overloaded"
+#        endif
+#    elif defined(__clang__)
+#        if (__clang_major__ >= 7)
+#            pragma GCC diagnostic ignored "-Wself-assign-overloaded"
+#        endif
+#    endif
+#endif
+
+TEST_SUBMODULE(operators, m) {
+
+    // test_operator_overloading
+    py::class_<Vector2>(m, "Vector2")
+        .def(py::init<float, float>())
+        .def(py::self + py::self)
+        .def(py::self + float())
+        .def(py::self - py::self)
+        .def(py::self - float())
+        .def(py::self * float())
+        .def(py::self / float())
+        .def(py::self * py::self)
+        .def(py::self / py::self)
+        .def(py::self += py::self)
+        .def(py::self -= py::self)
+        .def(py::self *= float())
+        .def(py::self /= float())
+        .def(py::self *= py::self)
+        .def(py::self /= py::self)
+        .def(float() + py::self)
+        .def(float() - py::self)
+        .def(float() * py::self)
+        .def(float() / py::self)
+        .def(-py::self)
+        .def("__str__", &Vector2::toString)
+        .def("__repr__", &Vector2::toString)
+        .def(py::self == py::self)
+        .def(py::self != py::self)
+        .def(py::hash(py::self))
+        // N.B. See warning about usage of `py::detail::abs(py::self)` in
+        // `operators.h`.
+        .def("__abs__", [](const Vector2 &v) { return abs(v); });
+
+    m.attr("Vector") = m.attr("Vector2");
+
+    // test_operators_notimplemented
+    // #393: need to return NotSupported to ensure correct arithmetic operator behavior
+    py::class_<C1>(m, "C1").def(py::init<>()).def(py::self + py::self);
+
+    py::class_<C2>(m, "C2")
+        .def(py::init<>())
+        .def(py::self + py::self)
+        .def("__add__", [](const C2 &c2, const C1 &c1) { return c2 + c1; })
+        .def("__radd__", [](const C2 &c2, const C1 &c1) { return c1 + c2; });
+
+    // test_nested
+    // #328: first member in a class can't be used in operators
+    struct NestABase {
+        int value = -2;
+    };
+    py::class_<NestABase>(m, "NestABase")
+        .def(py::init<>())
+        .def_readwrite("value", &NestABase::value);
+
+    struct NestA : NestABase {
+        int value = 3;
+        NestA &operator+=(int i) {
+            value += i;
+            return *this;
+        }
+    };
+    py::class_<NestA>(m, "NestA")
+        .def(py::init<>())
+        .def(py::self += int())
+        .def(
+            "as_base",
+            [](NestA &a) -> NestABase & { return (NestABase &) a; },
+            py::return_value_policy::reference_internal);
+    m.def("get_NestA", [](const NestA &a) { return a.value; });
+
+    struct NestB {
+        NestA a;
+        int value = 4;
+        NestB &operator-=(int i) {
+            value -= i;
+            return *this;
+        }
+    };
+    py::class_<NestB>(m, "NestB")
+        .def(py::init<>())
+        .def(py::self -= int())
+        .def_readwrite("a", &NestB::a);
+    m.def("get_NestB", [](const NestB &b) { return b.value; });
+
+    struct NestC {
+        NestB b;
+        int value = 5;
+        NestC &operator*=(int i) {
+            value *= i;
+            return *this;
+        }
+    };
+    py::class_<NestC>(m, "NestC")
+        .def(py::init<>())
+        .def(py::self *= int())
+        .def_readwrite("b", &NestC::b);
+    m.def("get_NestC", [](const NestC &c) { return c.value; });
+
+    // test_overriding_eq_reset_hash
+    // #2191 Overriding __eq__ should set __hash__ to None
+    struct Comparable {
+        int value;
+        bool operator==(const Comparable &rhs) const { return value == rhs.value; }
+    };
+
+    struct Hashable : Comparable {
+        explicit Hashable(int value) : Comparable{value} {};
+        size_t hash() const { return static_cast<size_t>(value); }
+    };
+
+    struct Hashable2 : Hashable {
+        using Hashable::Hashable;
+    };
+
+    py::class_<Comparable>(m, "Comparable").def(py::init<int>()).def(py::self == py::self);
+
+    py::class_<Hashable>(m, "Hashable")
+        .def(py::init<int>())
+        .def(py::self == py::self)
+        .def("__hash__", &Hashable::hash);
+
+    // define __hash__ before __eq__
+    py::class_<Hashable2>(m, "Hashable2")
+        .def("__hash__", &Hashable::hash)
+        .def(py::init<int>())
+        .def(py::self == py::self);
+
+    // define __eq__ but not __hash__
+    py::class_<HashMe>(m, "HashMe").def(py::self == py::self);
+
+    m.def("get_unhashable_HashMe_set", []() { return std::unordered_set<HashMe>{{"one"}}; });
+}
+#if !defined(_MSC_VER) && !defined(__INTEL_COMPILER)
+#    pragma GCC diagnostic pop
+#endif
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_operator_overloading.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_operator_overloading.py
new file mode 100644
index 000000000..b228da3cc
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_operator_overloading.py
@@ -0,0 +1,152 @@
+import pytest
+
+from pybind11_tests import ConstructorStats
+from pybind11_tests import operators as m
+
+
+def test_operator_overloading():
+    v1 = m.Vector2(1, 2)
+    v2 = m.Vector(3, -1)
+    v3 = m.Vector2(1, 2)  # Same value as v1, but different instance.
+    assert v1 is not v3
+
+    assert str(v1) == "[1.000000, 2.000000]"
+    assert str(v2) == "[3.000000, -1.000000]"
+
+    assert str(-v2) == "[-3.000000, 1.000000]"
+
+    assert str(v1 + v2) == "[4.000000, 1.000000]"
+    assert str(v1 - v2) == "[-2.000000, 3.000000]"
+    assert str(v1 - 8) == "[-7.000000, -6.000000]"
+    assert str(v1 + 8) == "[9.000000, 10.000000]"
+    assert str(v1 * 8) == "[8.000000, 16.000000]"
+    assert str(v1 / 8) == "[0.125000, 0.250000]"
+    assert str(8 - v1) == "[7.000000, 6.000000]"
+    assert str(8 + v1) == "[9.000000, 10.000000]"
+    assert str(8 * v1) == "[8.000000, 16.000000]"
+    assert str(8 / v1) == "[8.000000, 4.000000]"
+    assert str(v1 * v2) == "[3.000000, -2.000000]"
+    assert str(v2 / v1) == "[3.000000, -0.500000]"
+
+    assert v1 == v3
+    assert v1 != v2
+    assert hash(v1) == 4
+    # TODO(eric.cousineau): Make this work.
+    # assert abs(v1) == "abs(Vector2)"
+
+    v1 += 2 * v2
+    assert str(v1) == "[7.000000, 0.000000]"
+    v1 -= v2
+    assert str(v1) == "[4.000000, 1.000000]"
+    v1 *= 2
+    assert str(v1) == "[8.000000, 2.000000]"
+    v1 /= 16
+    assert str(v1) == "[0.500000, 0.125000]"
+    v1 *= v2
+    assert str(v1) == "[1.500000, -0.125000]"
+    v2 /= v1
+    assert str(v2) == "[2.000000, 8.000000]"
+
+    cstats = ConstructorStats.get(m.Vector2)
+    assert cstats.alive() == 3
+    del v1
+    assert cstats.alive() == 2
+    del v2
+    assert cstats.alive() == 1
+    del v3
+    assert cstats.alive() == 0
+    assert cstats.values() == [
+        "[1.000000, 2.000000]",
+        "[3.000000, -1.000000]",
+        "[1.000000, 2.000000]",
+        "[-3.000000, 1.000000]",
+        "[4.000000, 1.000000]",
+        "[-2.000000, 3.000000]",
+        "[-7.000000, -6.000000]",
+        "[9.000000, 10.000000]",
+        "[8.000000, 16.000000]",
+        "[0.125000, 0.250000]",
+        "[7.000000, 6.000000]",
+        "[9.000000, 10.000000]",
+        "[8.000000, 16.000000]",
+        "[8.000000, 4.000000]",
+        "[3.000000, -2.000000]",
+        "[3.000000, -0.500000]",
+        "[6.000000, -2.000000]",
+    ]
+    assert cstats.default_constructions == 0
+    assert cstats.copy_constructions == 0
+    assert cstats.move_constructions >= 10
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+
+def test_operators_notimplemented():
+    """#393: need to return NotSupported to ensure correct arithmetic operator behavior"""
+
+    c1, c2 = m.C1(), m.C2()
+    assert c1 + c1 == 11
+    assert c2 + c2 == 22
+    assert c2 + c1 == 21
+    assert c1 + c2 == 12
+
+
+def test_nested():
+    """#328: first member in a class can't be used in operators"""
+
+    a = m.NestA()
+    b = m.NestB()
+    c = m.NestC()
+
+    a += 10
+    assert m.get_NestA(a) == 13
+    b.a += 100
+    assert m.get_NestA(b.a) == 103
+    c.b.a += 1000
+    assert m.get_NestA(c.b.a) == 1003
+    b -= 1
+    assert m.get_NestB(b) == 3
+    c.b -= 3
+    assert m.get_NestB(c.b) == 1
+    c *= 7
+    assert m.get_NestC(c) == 35
+
+    abase = a.as_base()
+    assert abase.value == -2
+    a.as_base().value += 44
+    assert abase.value == 42
+    assert c.b.a.as_base().value == -2
+    c.b.a.as_base().value += 44
+    assert c.b.a.as_base().value == 42
+
+    del c
+    pytest.gc_collect()
+    del a  # Shouldn't delete while abase is still alive
+    pytest.gc_collect()
+
+    assert abase.value == 42
+    del abase, b
+    pytest.gc_collect()
+
+
+def test_overriding_eq_reset_hash():
+
+    assert m.Comparable(15) is not m.Comparable(15)
+    assert m.Comparable(15) == m.Comparable(15)
+
+    with pytest.raises(TypeError) as excinfo:
+        hash(m.Comparable(15))
+    assert str(excinfo.value).startswith("unhashable type:")
+
+    for hashable in (m.Hashable, m.Hashable2):
+        assert hashable(15) is not hashable(15)
+        assert hashable(15) == hashable(15)
+
+        assert hash(hashable(15)) == 15
+        assert hash(hashable(15)) == hash(hashable(15))
+
+
+def test_return_set_of_unhashable():
+    with pytest.raises(TypeError) as excinfo:
+        m.get_unhashable_HashMe_set()
+    assert str(excinfo.value.__cause__).startswith("unhashable type:")
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pickling.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pickling.cpp
new file mode 100644
index 000000000..e154bc483
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pickling.cpp
@@ -0,0 +1,194 @@
+/*
+    tests/test_pickling.cpp -- pickle support
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+    Copyright (c) 2021 The Pybind Development Team.
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+#include <memory>
+#include <stdexcept>
+#include <utility>
+
+namespace exercise_trampoline {
+
+struct SimpleBase {
+    int num = 0;
+    virtual ~SimpleBase() = default;
+
+    // For compatibility with old clang versions:
+    SimpleBase() = default;
+    SimpleBase(const SimpleBase &) = default;
+};
+
+struct SimpleBaseTrampoline : SimpleBase {};
+
+struct SimpleCppDerived : SimpleBase {};
+
+void wrap(py::module m) {
+    py::class_<SimpleBase, SimpleBaseTrampoline>(m, "SimpleBase")
+        .def(py::init<>())
+        .def_readwrite("num", &SimpleBase::num)
+        .def(py::pickle(
+            [](const py::object &self) {
+                py::dict d;
+                if (py::hasattr(self, "__dict__")) {
+                    d = self.attr("__dict__");
+                }
+                return py::make_tuple(self.attr("num"), d);
+            },
+            [](const py::tuple &t) {
+                if (t.size() != 2) {
+                    throw std::runtime_error("Invalid state!");
+                }
+                auto cpp_state = std::unique_ptr<SimpleBase>(new SimpleBaseTrampoline);
+                cpp_state->num = t[0].cast<int>();
+                auto py_state = t[1].cast<py::dict>();
+                return std::make_pair(std::move(cpp_state), py_state);
+            }));
+
+    m.def("make_SimpleCppDerivedAsBase",
+          []() { return std::unique_ptr<SimpleBase>(new SimpleCppDerived); });
+    m.def("check_dynamic_cast_SimpleCppDerived", [](const SimpleBase *base_ptr) {
+        return dynamic_cast<const SimpleCppDerived *>(base_ptr) != nullptr;
+    });
+}
+
+} // namespace exercise_trampoline
+
+TEST_SUBMODULE(pickling, m) {
+    m.def("simple_callable", []() { return 20220426; });
+
+    // test_roundtrip
+    class Pickleable {
+    public:
+        explicit Pickleable(const std::string &value) : m_value(value) {}
+        const std::string &value() const { return m_value; }
+
+        void setExtra1(int extra1) { m_extra1 = extra1; }
+        void setExtra2(int extra2) { m_extra2 = extra2; }
+        int extra1() const { return m_extra1; }
+        int extra2() const { return m_extra2; }
+
+    private:
+        std::string m_value;
+        int m_extra1 = 0;
+        int m_extra2 = 0;
+    };
+
+    class PickleableNew : public Pickleable {
+    public:
+        using Pickleable::Pickleable;
+    };
+
+    py::class_<Pickleable> pyPickleable(m, "Pickleable");
+    pyPickleable.def(py::init<std::string>())
+        .def("value", &Pickleable::value)
+        .def("extra1", &Pickleable::extra1)
+        .def("extra2", &Pickleable::extra2)
+        .def("setExtra1", &Pickleable::setExtra1)
+        .def("setExtra2", &Pickleable::setExtra2)
+        // For details on the methods below, refer to
+        // http://docs.python.org/3/library/pickle.html#pickling-class-instances
+        .def("__getstate__", [](const Pickleable &p) {
+            /* Return a tuple that fully encodes the state of the object */
+            return py::make_tuple(p.value(), p.extra1(), p.extra2());
+        });
+    ignoreOldStyleInitWarnings([&pyPickleable]() {
+        pyPickleable.def("__setstate__", [](Pickleable &p, const py::tuple &t) {
+            if (t.size() != 3) {
+                throw std::runtime_error("Invalid state!");
+            }
+            /* Invoke the constructor (need to use in-place version) */
+            new (&p) Pickleable(t[0].cast<std::string>());
+
+            /* Assign any additional state */
+            p.setExtra1(t[1].cast<int>());
+            p.setExtra2(t[2].cast<int>());
+        });
+    });
+
+    py::class_<PickleableNew, Pickleable>(m, "PickleableNew")
+        .def(py::init<std::string>())
+        .def(py::pickle(
+            [](const PickleableNew &p) {
+                return py::make_tuple(p.value(), p.extra1(), p.extra2());
+            },
+            [](const py::tuple &t) {
+                if (t.size() != 3) {
+                    throw std::runtime_error("Invalid state!");
+                }
+                auto p = PickleableNew(t[0].cast<std::string>());
+
+                p.setExtra1(t[1].cast<int>());
+                p.setExtra2(t[2].cast<int>());
+                return p;
+            }));
+
+#if !defined(PYPY_VERSION)
+    // test_roundtrip_with_dict
+    class PickleableWithDict {
+    public:
+        explicit PickleableWithDict(const std::string &value) : value(value) {}
+
+        std::string value;
+        int extra;
+    };
+
+    class PickleableWithDictNew : public PickleableWithDict {
+    public:
+        using PickleableWithDict::PickleableWithDict;
+    };
+
+    py::class_<PickleableWithDict> pyPickleableWithDict(
+        m, "PickleableWithDict", py::dynamic_attr());
+    pyPickleableWithDict.def(py::init<std::string>())
+        .def_readwrite("value", &PickleableWithDict::value)
+        .def_readwrite("extra", &PickleableWithDict::extra)
+        .def("__getstate__", [](const py::object &self) {
+            /* Also include __dict__ in state */
+            return py::make_tuple(self.attr("value"), self.attr("extra"), self.attr("__dict__"));
+        });
+    ignoreOldStyleInitWarnings([&pyPickleableWithDict]() {
+        pyPickleableWithDict.def("__setstate__", [](const py::object &self, const py::tuple &t) {
+            if (t.size() != 3) {
+                throw std::runtime_error("Invalid state!");
+            }
+            /* Cast and construct */
+            auto &p = self.cast<PickleableWithDict &>();
+            new (&p) PickleableWithDict(t[0].cast<std::string>());
+
+            /* Assign C++ state */
+            p.extra = t[1].cast<int>();
+
+            /* Assign Python state */
+            self.attr("__dict__") = t[2];
+        });
+    });
+
+    py::class_<PickleableWithDictNew, PickleableWithDict>(m, "PickleableWithDictNew")
+        .def(py::init<std::string>())
+        .def(py::pickle(
+            [](const py::object &self) {
+                return py::make_tuple(
+                    self.attr("value"), self.attr("extra"), self.attr("__dict__"));
+            },
+            [](const py::tuple &t) {
+                if (t.size() != 3) {
+                    throw std::runtime_error("Invalid state!");
+                }
+
+                auto cpp_state = PickleableWithDictNew(t[0].cast<std::string>());
+                cpp_state.extra = t[1].cast<int>();
+
+                auto py_state = t[2].cast<py::dict>();
+                return std::make_pair(cpp_state, py_state);
+            }));
+#endif
+
+    exercise_trampoline::wrap(m);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pickling.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pickling.py
new file mode 100644
index 000000000..12361a661
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pickling.py
@@ -0,0 +1,93 @@
+import pickle
+import re
+
+import pytest
+
+import env
+from pybind11_tests import pickling as m
+
+
+def test_pickle_simple_callable():
+    assert m.simple_callable() == 20220426
+    if env.PYPY:
+        serialized = pickle.dumps(m.simple_callable)
+        deserialized = pickle.loads(serialized)
+        assert deserialized() == 20220426
+    else:
+        # To document broken behavior: currently it fails universally with
+        # all C Python versions.
+        with pytest.raises(TypeError) as excinfo:
+            pickle.dumps(m.simple_callable)
+        assert re.search("can.*t pickle .*PyCapsule.* object", str(excinfo.value))
+
+
+@pytest.mark.parametrize("cls_name", ["Pickleable", "PickleableNew"])
+def test_roundtrip(cls_name):
+    cls = getattr(m, cls_name)
+    p = cls("test_value")
+    p.setExtra1(15)
+    p.setExtra2(48)
+
+    data = pickle.dumps(p, 2)  # Must use pickle protocol >= 2
+    p2 = pickle.loads(data)
+    assert p2.value() == p.value()
+    assert p2.extra1() == p.extra1()
+    assert p2.extra2() == p.extra2()
+
+
+@pytest.mark.xfail("env.PYPY")
+@pytest.mark.parametrize("cls_name", ["PickleableWithDict", "PickleableWithDictNew"])
+def test_roundtrip_with_dict(cls_name):
+    cls = getattr(m, cls_name)
+    p = cls("test_value")
+    p.extra = 15
+    p.dynamic = "Attribute"
+
+    data = pickle.dumps(p, pickle.HIGHEST_PROTOCOL)
+    p2 = pickle.loads(data)
+    assert p2.value == p.value
+    assert p2.extra == p.extra
+    assert p2.dynamic == p.dynamic
+
+
+def test_enum_pickle():
+    from pybind11_tests import enums as e
+
+    data = pickle.dumps(e.EOne, 2)
+    assert e.EOne == pickle.loads(data)
+
+
+#
+# exercise_trampoline
+#
+class SimplePyDerived(m.SimpleBase):
+    pass
+
+
+def test_roundtrip_simple_py_derived():
+    p = SimplePyDerived()
+    p.num = 202
+    p.stored_in_dict = 303
+    data = pickle.dumps(p, pickle.HIGHEST_PROTOCOL)
+    p2 = pickle.loads(data)
+    assert isinstance(p2, SimplePyDerived)
+    assert p2.num == 202
+    assert p2.stored_in_dict == 303
+
+
+def test_roundtrip_simple_cpp_derived():
+    p = m.make_SimpleCppDerivedAsBase()
+    assert m.check_dynamic_cast_SimpleCppDerived(p)
+    p.num = 404
+    if not env.PYPY:
+        # To ensure that this unit test is not accidentally invalidated.
+        with pytest.raises(AttributeError):
+            # Mimics the `setstate` C++ implementation.
+            setattr(p, "__dict__", {})  # noqa: B010
+    data = pickle.dumps(p, pickle.HIGHEST_PROTOCOL)
+    p2 = pickle.loads(data)
+    assert isinstance(p2, m.SimpleBase)
+    assert p2.num == 404
+    # Issue #3062: pickleable base C++ classes can incur object slicing
+    #              if derived typeid is not registered with pybind11
+    assert not m.check_dynamic_cast_SimpleCppDerived(p2)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pytypes.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pytypes.cpp
new file mode 100644
index 000000000..f532e2608
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pytypes.cpp
@@ -0,0 +1,759 @@
+/*
+    tests/test_pytypes.cpp -- Python type casters
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+#include <utility>
+
+namespace external {
+namespace detail {
+bool check(PyObject *o) { return PyFloat_Check(o) != 0; }
+
+PyObject *conv(PyObject *o) {
+    PyObject *ret = nullptr;
+    if (PyLong_Check(o)) {
+        double v = PyLong_AsDouble(o);
+        if (!(v == -1.0 && PyErr_Occurred())) {
+            ret = PyFloat_FromDouble(v);
+        }
+    } else {
+        PyErr_SetString(PyExc_TypeError, "Unexpected type");
+    }
+    return ret;
+}
+
+PyObject *default_constructed() { return PyFloat_FromDouble(0.0); }
+} // namespace detail
+class float_ : public py::object {
+    PYBIND11_OBJECT_CVT(float_, py::object, external::detail::check, external::detail::conv)
+
+    float_() : py::object(external::detail::default_constructed(), stolen_t{}) {}
+
+    double get_value() const { return PyFloat_AsDouble(this->ptr()); }
+};
+} // namespace external
+
+namespace implicit_conversion_from_0_to_handle {
+// Uncomment to trigger compiler error. Note: Before PR #4008 this used to compile successfully.
+// void expected_to_trigger_compiler_error() { py::handle(0); }
+} // namespace implicit_conversion_from_0_to_handle
+
+// Used to validate systematically that PR #4008 does/did NOT change the behavior.
+void pure_compile_tests_for_handle_from_PyObject_pointers() {
+    {
+        PyObject *ptr = Py_None;
+        py::handle{ptr};
+    }
+    {
+        PyObject *const ptr = Py_None;
+        py::handle{ptr};
+    }
+    // Uncomment to trigger compiler errors.
+    // PyObject const *               ptr = Py_None; py::handle{ptr};
+    // PyObject const *const          ptr = Py_None; py::handle{ptr};
+    // PyObject volatile *            ptr = Py_None; py::handle{ptr};
+    // PyObject volatile *const       ptr = Py_None; py::handle{ptr};
+    // PyObject const volatile *      ptr = Py_None; py::handle{ptr};
+    // PyObject const volatile *const ptr = Py_None; py::handle{ptr};
+}
+
+namespace handle_from_move_only_type_with_operator_PyObject {
+
+// Reduced from
+// https://github.com/pytorch/pytorch/blob/279634f384662b7c3a9f8bf7ccc3a6afd2f05657/torch/csrc/utils/object_ptr.h
+struct operator_ncnst {
+    operator_ncnst() = default;
+    operator_ncnst(operator_ncnst &&) = default;
+    operator PyObject *() /* */ { return Py_None; } // NOLINT(google-explicit-constructor)
+};
+
+struct operator_const {
+    operator_const() = default;
+    operator_const(operator_const &&) = default;
+    operator PyObject *() const { return Py_None; } // NOLINT(google-explicit-constructor)
+};
+
+bool from_ncnst() {
+    operator_ncnst obj;
+    auto h = py::handle(obj);  // Critical part of test: does this compile?
+    return h.ptr() == Py_None; // Just something.
+}
+
+bool from_const() {
+    operator_const obj;
+    auto h = py::handle(obj);  // Critical part of test: does this compile?
+    return h.ptr() == Py_None; // Just something.
+}
+
+void m_defs(py::module_ &m) {
+    m.def("handle_from_move_only_type_with_operator_PyObject_ncnst", from_ncnst);
+    m.def("handle_from_move_only_type_with_operator_PyObject_const", from_const);
+}
+
+} // namespace handle_from_move_only_type_with_operator_PyObject
+
+TEST_SUBMODULE(pytypes, m) {
+    handle_from_move_only_type_with_operator_PyObject::m_defs(m);
+
+    // test_bool
+    m.def("get_bool", [] { return py::bool_(false); });
+    // test_int
+    m.def("get_int", [] { return py::int_(0); });
+    // test_iterator
+    m.def("get_iterator", [] { return py::iterator(); });
+    // test_iterable
+    m.def("get_iterable", [] { return py::iterable(); });
+    // test_float
+    m.def("get_float", [] { return py::float_(0.0f); });
+    // test_list
+    m.def("list_no_args", []() { return py::list{}; });
+    m.def("list_ssize_t", []() { return py::list{(py::ssize_t) 0}; });
+    m.def("list_size_t", []() { return py::list{(py::size_t) 0}; });
+    m.def("list_insert_ssize_t", [](py::list *l) { return l->insert((py::ssize_t) 1, 83); });
+    m.def("list_insert_size_t", [](py::list *l) { return l->insert((py::size_t) 3, 57); });
+    m.def("get_list", []() {
+        py::list list;
+        list.append("value");
+        py::print("Entry at position 0:", list[0]);
+        list[0] = py::str("overwritten");
+        list.insert(0, "inserted-0");
+        list.insert(2, "inserted-2");
+        return list;
+    });
+    m.def("print_list", [](const py::list &list) {
+        int index = 0;
+        for (auto item : list) {
+            py::print("list item {}: {}"_s.format(index++, item));
+        }
+    });
+    // test_none
+    m.def("get_none", [] { return py::none(); });
+    m.def("print_none", [](const py::none &none) { py::print("none: {}"_s.format(none)); });
+
+    // test_set, test_frozenset
+    m.def("get_set", []() {
+        py::set set;
+        set.add(py::str("key1"));
+        set.add("key2");
+        set.add(std::string("key3"));
+        return set;
+    });
+    m.def("get_frozenset", []() {
+        py::set set;
+        set.add(py::str("key1"));
+        set.add("key2");
+        set.add(std::string("key3"));
+        return py::frozenset(set);
+    });
+    m.def("print_anyset", [](const py::anyset &set) {
+        for (auto item : set) {
+            py::print("key:", item);
+        }
+    });
+    m.def("anyset_size", [](const py::anyset &set) { return set.size(); });
+    m.def("anyset_empty", [](const py::anyset &set) { return set.empty(); });
+    m.def("anyset_contains",
+          [](const py::anyset &set, const py::object &key) { return set.contains(key); });
+    m.def("anyset_contains",
+          [](const py::anyset &set, const char *key) { return set.contains(key); });
+    m.def("set_add", [](py::set &set, const py::object &key) { set.add(key); });
+    m.def("set_clear", [](py::set &set) { set.clear(); });
+
+    // test_dict
+    m.def("get_dict", []() { return py::dict("key"_a = "value"); });
+    m.def("print_dict", [](const py::dict &dict) {
+        for (auto item : dict) {
+            py::print("key: {}, value={}"_s.format(item.first, item.second));
+        }
+    });
+    m.def("dict_keyword_constructor", []() {
+        auto d1 = py::dict("x"_a = 1, "y"_a = 2);
+        auto d2 = py::dict("z"_a = 3, **d1);
+        return d2;
+    });
+    m.def("dict_contains",
+          [](const py::dict &dict, py::object val) { return dict.contains(val); });
+    m.def("dict_contains",
+          [](const py::dict &dict, const char *val) { return dict.contains(val); });
+
+    // test_tuple
+    m.def("tuple_no_args", []() { return py::tuple{}; });
+    m.def("tuple_ssize_t", []() { return py::tuple{(py::ssize_t) 0}; });
+    m.def("tuple_size_t", []() { return py::tuple{(py::size_t) 0}; });
+    m.def("get_tuple", []() { return py::make_tuple(42, py::none(), "spam"); });
+
+    // test_simple_namespace
+    m.def("get_simple_namespace", []() {
+        auto ns = py::module_::import("types").attr("SimpleNamespace")(
+            "attr"_a = 42, "x"_a = "foo", "wrong"_a = 1);
+        py::delattr(ns, "wrong");
+        py::setattr(ns, "right", py::int_(2));
+        return ns;
+    });
+
+    // test_str
+    m.def("str_from_char_ssize_t", []() { return py::str{"red", (py::ssize_t) 3}; });
+    m.def("str_from_char_size_t", []() { return py::str{"blue", (py::size_t) 4}; });
+    m.def("str_from_string", []() { return py::str(std::string("baz")); });
+    m.def("str_from_bytes", []() { return py::str(py::bytes("boo", 3)); });
+    m.def("str_from_object", [](const py::object &obj) { return py::str(obj); });
+    m.def("repr_from_object", [](const py::object &obj) { return py::repr(obj); });
+    m.def("str_from_handle", [](py::handle h) { return py::str(h); });
+    m.def("str_from_string_from_str",
+          [](const py::str &obj) { return py::str(static_cast<std::string>(obj)); });
+
+    m.def("str_format", []() {
+        auto s1 = "{} + {} = {}"_s.format(1, 2, 3);
+        auto s2 = "{a} + {b} = {c}"_s.format("a"_a = 1, "b"_a = 2, "c"_a = 3);
+        return py::make_tuple(s1, s2);
+    });
+
+    // test_bytes
+    m.def("bytes_from_char_ssize_t", []() { return py::bytes{"green", (py::ssize_t) 5}; });
+    m.def("bytes_from_char_size_t", []() { return py::bytes{"purple", (py::size_t) 6}; });
+    m.def("bytes_from_string", []() { return py::bytes(std::string("foo")); });
+    m.def("bytes_from_str", []() { return py::bytes(py::str("bar", 3)); });
+
+    // test bytearray
+    m.def("bytearray_from_char_ssize_t", []() { return py::bytearray{"$%", (py::ssize_t) 2}; });
+    m.def("bytearray_from_char_size_t", []() { return py::bytearray{"@$!", (py::size_t) 3}; });
+    m.def("bytearray_from_string", []() { return py::bytearray(std::string("foo")); });
+    m.def("bytearray_size", []() { return py::bytearray("foo").size(); });
+
+    // test_capsule
+    m.def("return_capsule_with_destructor", []() {
+        py::print("creating capsule");
+        return py::capsule([]() { py::print("destructing capsule"); });
+    });
+
+    m.def("return_renamed_capsule_with_destructor", []() {
+        py::print("creating capsule");
+        auto cap = py::capsule([]() { py::print("destructing capsule"); });
+        static const char *capsule_name = "test_name1";
+        py::print("renaming capsule");
+        cap.set_name(capsule_name);
+        return cap;
+    });
+
+    m.def("return_capsule_with_destructor_2", []() {
+        py::print("creating capsule");
+        return py::capsule((void *) 1234, [](void *ptr) {
+            py::print("destructing capsule: {}"_s.format((size_t) ptr));
+        });
+    });
+
+    m.def("return_renamed_capsule_with_destructor_2", []() {
+        py::print("creating capsule");
+        auto cap = py::capsule((void *) 1234, [](void *ptr) {
+            py::print("destructing capsule: {}"_s.format((size_t) ptr));
+        });
+        static const char *capsule_name = "test_name2";
+        py::print("renaming capsule");
+        cap.set_name(capsule_name);
+        return cap;
+    });
+
+    m.def("return_capsule_with_name_and_destructor", []() {
+        auto capsule = py::capsule((void *) 12345, "pointer type description", [](PyObject *ptr) {
+            if (ptr) {
+                const auto *name = PyCapsule_GetName(ptr);
+                py::print("destructing capsule ({}, '{}')"_s.format(
+                    (size_t) PyCapsule_GetPointer(ptr, name), name));
+            }
+        });
+
+        capsule.set_pointer((void *) 1234);
+
+        // Using get_pointer<T>()
+        void *contents1 = static_cast<void *>(capsule);
+        void *contents2 = capsule.get_pointer();
+        void *contents3 = capsule.get_pointer<void>();
+
+        auto result1 = reinterpret_cast<size_t>(contents1);
+        auto result2 = reinterpret_cast<size_t>(contents2);
+        auto result3 = reinterpret_cast<size_t>(contents3);
+
+        py::print(
+            "created capsule ({}, '{}')"_s.format(result1 & result2 & result3, capsule.name()));
+        return capsule;
+    });
+
+    // test_accessors
+    m.def("accessor_api", [](const py::object &o) {
+        auto d = py::dict();
+
+        d["basic_attr"] = o.attr("basic_attr");
+
+        auto l = py::list();
+        for (auto item : o.attr("begin_end")) {
+            l.append(item);
+        }
+        d["begin_end"] = l;
+
+        d["operator[object]"] = o.attr("d")["operator[object]"_s];
+        d["operator[char *]"] = o.attr("d")["operator[char *]"];
+
+        d["attr(object)"] = o.attr("sub").attr("attr_obj");
+        d["attr(char *)"] = o.attr("sub").attr("attr_char");
+        try {
+            o.attr("sub").attr("missing").ptr();
+        } catch (const py::error_already_set &) {
+            d["missing_attr_ptr"] = "raised"_s;
+        }
+        try {
+            o.attr("missing").attr("doesn't matter");
+        } catch (const py::error_already_set &) {
+            d["missing_attr_chain"] = "raised"_s;
+        }
+
+        d["is_none"] = o.attr("basic_attr").is_none();
+
+        d["operator()"] = o.attr("func")(1);
+        d["operator*"] = o.attr("func")(*o.attr("begin_end"));
+
+        // Test implicit conversion
+        py::list implicit_list = o.attr("begin_end");
+        d["implicit_list"] = implicit_list;
+        py::dict implicit_dict = o.attr("__dict__");
+        d["implicit_dict"] = implicit_dict;
+
+        return d;
+    });
+
+    m.def("tuple_accessor", [](const py::tuple &existing_t) {
+        try {
+            existing_t[0] = 1;
+        } catch (const py::error_already_set &) {
+            // --> Python system error
+            // Only new tuples (refcount == 1) are mutable
+            auto new_t = py::tuple(3);
+            for (size_t i = 0; i < new_t.size(); ++i) {
+                new_t[i] = i;
+            }
+            return new_t;
+        }
+        return py::tuple();
+    });
+
+    m.def("accessor_assignment", []() {
+        auto l = py::list(1);
+        l[0] = 0;
+
+        auto d = py::dict();
+        d["get"] = l[0];
+        auto var = l[0];
+        d["deferred_get"] = var;
+        l[0] = 1;
+        d["set"] = l[0];
+        var = 99; // this assignment should not overwrite l[0]
+        d["deferred_set"] = l[0];
+        d["var"] = var;
+
+        return d;
+    });
+
+    m.def("accessor_moves", []() { // See PR #3970
+        py::list return_list;
+#ifdef PYBIND11_HANDLE_REF_DEBUG
+        py::int_ py_int_0(0);
+        py::int_ py_int_42(42);
+        py::str py_str_count("count");
+
+        auto tup = py::make_tuple(0);
+
+        py::sequence seq(tup);
+
+        py::list lst;
+        lst.append(0);
+
+#    define PYBIND11_LOCAL_DEF(...)                                                               \
+        {                                                                                         \
+            std::size_t inc_refs = py::handle::inc_ref_counter();                                 \
+            __VA_ARGS__;                                                                          \
+            inc_refs = py::handle::inc_ref_counter() - inc_refs;                                  \
+            return_list.append(inc_refs);                                                         \
+        }
+
+        PYBIND11_LOCAL_DEF(tup[py_int_0])    // l-value (to have a control)
+        PYBIND11_LOCAL_DEF(tup[py::int_(0)]) // r-value
+
+        PYBIND11_LOCAL_DEF(tup.attr(py_str_count))     // l-value
+        PYBIND11_LOCAL_DEF(tup.attr(py::str("count"))) // r-value
+
+        PYBIND11_LOCAL_DEF(seq[py_int_0])    // l-value
+        PYBIND11_LOCAL_DEF(seq[py::int_(0)]) // r-value
+
+        PYBIND11_LOCAL_DEF(seq.attr(py_str_count))     // l-value
+        PYBIND11_LOCAL_DEF(seq.attr(py::str("count"))) // r-value
+
+        PYBIND11_LOCAL_DEF(lst[py_int_0])    // l-value
+        PYBIND11_LOCAL_DEF(lst[py::int_(0)]) // r-value
+
+        PYBIND11_LOCAL_DEF(lst.attr(py_str_count))     // l-value
+        PYBIND11_LOCAL_DEF(lst.attr(py::str("count"))) // r-value
+
+        auto lst_acc = lst[py::int_(0)];
+        lst_acc = py::int_(42);                    // Detaches lst_acc from lst.
+        PYBIND11_LOCAL_DEF(lst_acc = py_int_42)    // l-value
+        PYBIND11_LOCAL_DEF(lst_acc = py::int_(42)) // r-value
+#    undef PYBIND11_LOCAL_DEF
+#endif
+        return return_list;
+    });
+
+    // test_constructors
+    m.def("default_constructors", []() {
+        return py::dict("bytes"_a = py::bytes(),
+                        "bytearray"_a = py::bytearray(),
+                        "str"_a = py::str(),
+                        "bool"_a = py::bool_(),
+                        "int"_a = py::int_(),
+                        "float"_a = py::float_(),
+                        "tuple"_a = py::tuple(),
+                        "list"_a = py::list(),
+                        "dict"_a = py::dict(),
+                        "set"_a = py::set());
+    });
+
+    m.def("converting_constructors", [](const py::dict &d) {
+        return py::dict("bytes"_a = py::bytes(d["bytes"]),
+                        "bytearray"_a = py::bytearray(d["bytearray"]),
+                        "str"_a = py::str(d["str"]),
+                        "bool"_a = py::bool_(d["bool"]),
+                        "int"_a = py::int_(d["int"]),
+                        "float"_a = py::float_(d["float"]),
+                        "tuple"_a = py::tuple(d["tuple"]),
+                        "list"_a = py::list(d["list"]),
+                        "dict"_a = py::dict(d["dict"]),
+                        "set"_a = py::set(d["set"]),
+                        "frozenset"_a = py::frozenset(d["frozenset"]),
+                        "memoryview"_a = py::memoryview(d["memoryview"]));
+    });
+
+    m.def("cast_functions", [](const py::dict &d) {
+        // When converting between Python types, obj.cast<T>() should be the same as T(obj)
+        return py::dict("bytes"_a = d["bytes"].cast<py::bytes>(),
+                        "bytearray"_a = d["bytearray"].cast<py::bytearray>(),
+                        "str"_a = d["str"].cast<py::str>(),
+                        "bool"_a = d["bool"].cast<py::bool_>(),
+                        "int"_a = d["int"].cast<py::int_>(),
+                        "float"_a = d["float"].cast<py::float_>(),
+                        "tuple"_a = d["tuple"].cast<py::tuple>(),
+                        "list"_a = d["list"].cast<py::list>(),
+                        "dict"_a = d["dict"].cast<py::dict>(),
+                        "set"_a = d["set"].cast<py::set>(),
+                        "frozenset"_a = d["frozenset"].cast<py::frozenset>(),
+                        "memoryview"_a = d["memoryview"].cast<py::memoryview>());
+    });
+
+    m.def("convert_to_pybind11_str", [](const py::object &o) { return py::str(o); });
+
+    m.def("nonconverting_constructor",
+          [](const std::string &type, py::object value, bool move) -> py::object {
+              if (type == "bytes") {
+                  return move ? py::bytes(std::move(value)) : py::bytes(value);
+              }
+              if (type == "none") {
+                  return move ? py::none(std::move(value)) : py::none(value);
+              }
+              if (type == "ellipsis") {
+                  return move ? py::ellipsis(std::move(value)) : py::ellipsis(value);
+              }
+              if (type == "type") {
+                  return move ? py::type(std::move(value)) : py::type(value);
+              }
+              throw std::runtime_error("Invalid type");
+          });
+
+    m.def("get_implicit_casting", []() {
+        py::dict d;
+        d["char*_i1"] = "abc";
+        const char *c2 = "abc";
+        d["char*_i2"] = c2;
+        d["char*_e"] = py::cast(c2);
+        d["char*_p"] = py::str(c2);
+
+        d["int_i1"] = 42;
+        int i = 42;
+        d["int_i2"] = i;
+        i++;
+        d["int_e"] = py::cast(i);
+        i++;
+        d["int_p"] = py::int_(i);
+
+        d["str_i1"] = std::string("str");
+        std::string s2("str1");
+        d["str_i2"] = s2;
+        s2[3] = '2';
+        d["str_e"] = py::cast(s2);
+        s2[3] = '3';
+        d["str_p"] = py::str(s2);
+
+        py::list l(2);
+        l[0] = 3;
+        l[1] = py::cast(6);
+        l.append(9);
+        l.append(py::cast(12));
+        l.append(py::int_(15));
+
+        return py::dict("d"_a = d, "l"_a = l);
+    });
+
+    // test_print
+    m.def("print_function", []() {
+        py::print("Hello, World!");
+        py::print(1, 2.0, "three", true, std::string("-- multiple args"));
+        auto args = py::make_tuple("and", "a", "custom", "separator");
+        py::print("*args", *args, "sep"_a = "-");
+        py::print("no new line here", "end"_a = " -- ");
+        py::print("next print");
+
+        auto py_stderr = py::module_::import("sys").attr("stderr");
+        py::print("this goes to stderr", "file"_a = py_stderr);
+
+        py::print("flush", "flush"_a = true);
+
+        py::print(
+            "{a} + {b} = {c}"_s.format("a"_a = "py::print", "b"_a = "str.format", "c"_a = "this"));
+    });
+
+    m.def("print_failure", []() { py::print(42, UnregisteredType()); });
+
+    m.def("hash_function", [](py::object obj) { return py::hash(std::move(obj)); });
+
+    m.def("test_number_protocol", [](const py::object &a, const py::object &b) {
+        py::list l;
+        l.append(a.equal(b));
+        l.append(a.not_equal(b));
+        l.append(a < b);
+        l.append(a <= b);
+        l.append(a > b);
+        l.append(a >= b);
+        l.append(a + b);
+        l.append(a - b);
+        l.append(a * b);
+        l.append(a / b);
+        l.append(a | b);
+        l.append(a & b);
+        l.append(a ^ b);
+        l.append(a >> b);
+        l.append(a << b);
+        return l;
+    });
+
+    m.def("test_list_slicing", [](const py::list &a) { return a[py::slice(0, -1, 2)]; });
+
+    // See #2361
+    m.def("issue2361_str_implicit_copy_none", []() {
+        py::str is_this_none = py::none();
+        return is_this_none;
+    });
+    m.def("issue2361_dict_implicit_copy_none", []() {
+        py::dict is_this_none = py::none();
+        return is_this_none;
+    });
+
+    m.def("test_memoryview_object", [](const py::buffer &b) { return py::memoryview(b); });
+
+    m.def("test_memoryview_buffer_info",
+          [](const py::buffer &b) { return py::memoryview(b.request()); });
+
+    m.def("test_memoryview_from_buffer", [](bool is_unsigned) {
+        static const int16_t si16[] = {3, 1, 4, 1, 5};
+        static const uint16_t ui16[] = {2, 7, 1, 8};
+        if (is_unsigned) {
+            return py::memoryview::from_buffer(ui16, {4}, {sizeof(uint16_t)});
+        }
+        return py::memoryview::from_buffer(si16, {5}, {sizeof(int16_t)});
+    });
+
+    m.def("test_memoryview_from_buffer_nativeformat", []() {
+        static const char *format = "@i";
+        static const int32_t arr[] = {4, 7, 5};
+        return py::memoryview::from_buffer(arr, sizeof(int32_t), format, {3}, {sizeof(int32_t)});
+    });
+
+    m.def("test_memoryview_from_buffer_empty_shape", []() {
+        static const char *buf = "";
+        return py::memoryview::from_buffer(buf, 1, "B", {}, {});
+    });
+
+    m.def("test_memoryview_from_buffer_invalid_strides", []() {
+        static const char *buf = "\x02\x03\x04";
+        return py::memoryview::from_buffer(buf, 1, "B", {3}, {});
+    });
+
+    m.def("test_memoryview_from_buffer_nullptr", []() {
+        return py::memoryview::from_buffer(static_cast<void *>(nullptr), 1, "B", {}, {});
+    });
+
+    m.def("test_memoryview_from_memory", []() {
+        const char *buf = "\xff\xe1\xab\x37";
+        return py::memoryview::from_memory(buf, static_cast<py::ssize_t>(strlen(buf)));
+    });
+
+    // test_builtin_functions
+    m.def("get_len", [](py::handle h) { return py::len(h); });
+
+#ifdef PYBIND11_STR_LEGACY_PERMISSIVE
+    m.attr("PYBIND11_STR_LEGACY_PERMISSIVE") = true;
+#endif
+
+    m.def("isinstance_pybind11_bytes",
+          [](py::object o) { return py::isinstance<py::bytes>(std::move(o)); });
+    m.def("isinstance_pybind11_str",
+          [](py::object o) { return py::isinstance<py::str>(std::move(o)); });
+
+    m.def("pass_to_pybind11_bytes", [](py::bytes b) { return py::len(std::move(b)); });
+    m.def("pass_to_pybind11_str", [](py::str s) { return py::len(std::move(s)); });
+    m.def("pass_to_std_string", [](const std::string &s) { return s.size(); });
+
+    // test_weakref
+    m.def("weakref_from_handle", [](py::handle h) { return py::weakref(h); });
+    m.def("weakref_from_handle_and_function",
+          [](py::handle h, py::function f) { return py::weakref(h, std::move(f)); });
+    m.def("weakref_from_object", [](const py::object &o) { return py::weakref(o); });
+    m.def("weakref_from_object_and_function",
+          [](py::object o, py::function f) { return py::weakref(std::move(o), std::move(f)); });
+
+// See PR #3263 for background (https://github.com/pybind/pybind11/pull/3263):
+// pytypes.h could be changed to enforce the "most correct" user code below, by removing
+// `const` from iterator `reference` using type aliases, but that will break existing
+// user code.
+#if (defined(__APPLE__) && defined(__clang__)) || defined(PYPY_VERSION)
+// This is "most correct" and enforced on these platforms.
+#    define PYBIND11_AUTO_IT auto it
+#else
+// This works on many platforms and is (unfortunately) reflective of existing user code.
+// NOLINTNEXTLINE(bugprone-macro-parentheses)
+#    define PYBIND11_AUTO_IT auto &it
+#endif
+
+    m.def("tuple_iterator", []() {
+        auto tup = py::make_tuple(5, 7);
+        int tup_sum = 0;
+        for (PYBIND11_AUTO_IT : tup) {
+            tup_sum += it.cast<int>();
+        }
+        return tup_sum;
+    });
+
+    m.def("dict_iterator", []() {
+        py::dict dct;
+        dct[py::int_(3)] = 5;
+        dct[py::int_(7)] = 11;
+        int kv_sum = 0;
+        for (PYBIND11_AUTO_IT : dct) {
+            kv_sum += it.first.cast<int>() * 100 + it.second.cast<int>();
+        }
+        return kv_sum;
+    });
+
+    m.def("passed_iterator", [](const py::iterator &py_it) {
+        int elem_sum = 0;
+        for (PYBIND11_AUTO_IT : py_it) {
+            elem_sum += it.cast<int>();
+        }
+        return elem_sum;
+    });
+
+#undef PYBIND11_AUTO_IT
+
+    // Tests below this line are for pybind11 IMPLEMENTATION DETAILS:
+
+    m.def("sequence_item_get_ssize_t", [](const py::object &o) {
+        return py::detail::accessor_policies::sequence_item::get(o, (py::ssize_t) 1);
+    });
+    m.def("sequence_item_set_ssize_t", [](const py::object &o) {
+        auto s = py::str{"peppa", 5};
+        py::detail::accessor_policies::sequence_item::set(o, (py::ssize_t) 1, s);
+    });
+    m.def("sequence_item_get_size_t", [](const py::object &o) {
+        return py::detail::accessor_policies::sequence_item::get(o, (py::size_t) 2);
+    });
+    m.def("sequence_item_set_size_t", [](const py::object &o) {
+        auto s = py::str{"george", 6};
+        py::detail::accessor_policies::sequence_item::set(o, (py::size_t) 2, s);
+    });
+    m.def("list_item_get_ssize_t", [](const py::object &o) {
+        return py::detail::accessor_policies::list_item::get(o, (py::ssize_t) 3);
+    });
+    m.def("list_item_set_ssize_t", [](const py::object &o) {
+        auto s = py::str{"rebecca", 7};
+        py::detail::accessor_policies::list_item::set(o, (py::ssize_t) 3, s);
+    });
+    m.def("list_item_get_size_t", [](const py::object &o) {
+        return py::detail::accessor_policies::list_item::get(o, (py::size_t) 4);
+    });
+    m.def("list_item_set_size_t", [](const py::object &o) {
+        auto s = py::str{"richard", 7};
+        py::detail::accessor_policies::list_item::set(o, (py::size_t) 4, s);
+    });
+    m.def("tuple_item_get_ssize_t", [](const py::object &o) {
+        return py::detail::accessor_policies::tuple_item::get(o, (py::ssize_t) 5);
+    });
+    m.def("tuple_item_set_ssize_t", []() {
+        auto s0 = py::str{"emely", 5};
+        auto s1 = py::str{"edmond", 6};
+        auto o = py::tuple{2};
+        py::detail::accessor_policies::tuple_item::set(o, (py::ssize_t) 0, s0);
+        py::detail::accessor_policies::tuple_item::set(o, (py::ssize_t) 1, s1);
+        return o;
+    });
+    m.def("tuple_item_get_size_t", [](const py::object &o) {
+        return py::detail::accessor_policies::tuple_item::get(o, (py::size_t) 6);
+    });
+    m.def("tuple_item_set_size_t", []() {
+        auto s0 = py::str{"candy", 5};
+        auto s1 = py::str{"cat", 3};
+        auto o = py::tuple{2};
+        py::detail::accessor_policies::tuple_item::set(o, (py::size_t) 1, s1);
+        py::detail::accessor_policies::tuple_item::set(o, (py::size_t) 0, s0);
+        return o;
+    });
+
+    m.def("square_float_", [](const external::float_ &x) -> double {
+        double v = x.get_value();
+        return v * v;
+    });
+
+    m.def("tuple_rvalue_getter", [](const py::tuple &tup) {
+        // tests accessing tuple object with rvalue int
+        for (size_t i = 0; i < tup.size(); i++) {
+            auto o = py::handle(tup[py::int_(i)]);
+            if (!o) {
+                throw py::value_error("tuple is malformed");
+            }
+        }
+        return tup;
+    });
+    m.def("list_rvalue_getter", [](const py::list &l) {
+        // tests accessing list with rvalue int
+        for (size_t i = 0; i < l.size(); i++) {
+            auto o = py::handle(l[py::int_(i)]);
+            if (!o) {
+                throw py::value_error("list is malformed");
+            }
+        }
+        return l;
+    });
+    m.def("populate_dict_rvalue", [](int population) {
+        auto d = py::dict();
+        for (int i = 0; i < population; i++) {
+            d[py::int_(i)] = py::int_(i);
+        }
+        return d;
+    });
+    m.def("populate_obj_str_attrs", [](py::object &o, int population) {
+        for (int i = 0; i < population; i++) {
+            o.attr(py::str(py::int_(i))) = py::str(py::int_(i));
+        }
+        return o;
+    });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pytypes.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pytypes.py
new file mode 100644
index 000000000..7a0a8b4ab
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_pytypes.py
@@ -0,0 +1,741 @@
+import contextlib
+import sys
+import types
+
+import pytest
+
+import env
+from pybind11_tests import detailed_error_messages_enabled
+from pybind11_tests import pytypes as m
+
+
+def test_handle_from_move_only_type_with_operator_PyObject():  # noqa: N802
+    assert m.handle_from_move_only_type_with_operator_PyObject_ncnst()
+    assert m.handle_from_move_only_type_with_operator_PyObject_const()
+
+
+def test_bool(doc):
+    assert doc(m.get_bool) == "get_bool() -> bool"
+
+
+def test_int(doc):
+    assert doc(m.get_int) == "get_int() -> int"
+
+
+def test_iterator(doc):
+    assert doc(m.get_iterator) == "get_iterator() -> Iterator"
+
+
+def test_iterable(doc):
+    assert doc(m.get_iterable) == "get_iterable() -> Iterable"
+
+
+def test_float(doc):
+    assert doc(m.get_float) == "get_float() -> float"
+
+
+def test_list(capture, doc):
+    assert m.list_no_args() == []
+    assert m.list_ssize_t() == []
+    assert m.list_size_t() == []
+    lins = [1, 2]
+    m.list_insert_ssize_t(lins)
+    assert lins == [1, 83, 2]
+    m.list_insert_size_t(lins)
+    assert lins == [1, 83, 2, 57]
+
+    with capture:
+        lst = m.get_list()
+        assert lst == ["inserted-0", "overwritten", "inserted-2"]
+
+        lst.append("value2")
+        m.print_list(lst)
+    assert (
+        capture.unordered
+        == """
+        Entry at position 0: value
+        list item 0: inserted-0
+        list item 1: overwritten
+        list item 2: inserted-2
+        list item 3: value2
+    """
+    )
+
+    assert doc(m.get_list) == "get_list() -> list"
+    assert doc(m.print_list) == "print_list(arg0: list) -> None"
+
+
+def test_none(capture, doc):
+    assert doc(m.get_none) == "get_none() -> None"
+    assert doc(m.print_none) == "print_none(arg0: None) -> None"
+
+
+def test_set(capture, doc):
+    s = m.get_set()
+    assert isinstance(s, set)
+    assert s == {"key1", "key2", "key3"}
+
+    s.add("key4")
+    with capture:
+        m.print_anyset(s)
+    assert (
+        capture.unordered
+        == """
+        key: key1
+        key: key2
+        key: key3
+        key: key4
+    """
+    )
+
+    m.set_add(s, "key5")
+    assert m.anyset_size(s) == 5
+
+    m.set_clear(s)
+    assert m.anyset_empty(s)
+
+    assert not m.anyset_contains(set(), 42)
+    assert m.anyset_contains({42}, 42)
+    assert m.anyset_contains({"foo"}, "foo")
+
+    assert doc(m.get_set) == "get_set() -> set"
+    assert doc(m.print_anyset) == "print_anyset(arg0: anyset) -> None"
+
+
+def test_frozenset(capture, doc):
+    s = m.get_frozenset()
+    assert isinstance(s, frozenset)
+    assert s == frozenset({"key1", "key2", "key3"})
+
+    with capture:
+        m.print_anyset(s)
+    assert (
+        capture.unordered
+        == """
+        key: key1
+        key: key2
+        key: key3
+    """
+    )
+    assert m.anyset_size(s) == 3
+    assert not m.anyset_empty(s)
+
+    assert not m.anyset_contains(frozenset(), 42)
+    assert m.anyset_contains(frozenset({42}), 42)
+    assert m.anyset_contains(frozenset({"foo"}), "foo")
+
+    assert doc(m.get_frozenset) == "get_frozenset() -> frozenset"
+
+
+def test_dict(capture, doc):
+    d = m.get_dict()
+    assert d == {"key": "value"}
+
+    with capture:
+        d["key2"] = "value2"
+        m.print_dict(d)
+    assert (
+        capture.unordered
+        == """
+        key: key, value=value
+        key: key2, value=value2
+    """
+    )
+
+    assert not m.dict_contains({}, 42)
+    assert m.dict_contains({42: None}, 42)
+    assert m.dict_contains({"foo": None}, "foo")
+
+    assert doc(m.get_dict) == "get_dict() -> dict"
+    assert doc(m.print_dict) == "print_dict(arg0: dict) -> None"
+
+    assert m.dict_keyword_constructor() == {"x": 1, "y": 2, "z": 3}
+
+
+def test_tuple():
+    assert m.tuple_no_args() == ()
+    assert m.tuple_ssize_t() == ()
+    assert m.tuple_size_t() == ()
+    assert m.get_tuple() == (42, None, "spam")
+
+
+def test_simple_namespace():
+    ns = m.get_simple_namespace()
+    assert ns.attr == 42
+    assert ns.x == "foo"
+    assert ns.right == 2
+    assert not hasattr(ns, "wrong")
+
+
+def test_str(doc):
+    assert m.str_from_char_ssize_t().encode().decode() == "red"
+    assert m.str_from_char_size_t().encode().decode() == "blue"
+    assert m.str_from_string().encode().decode() == "baz"
+    assert m.str_from_bytes().encode().decode() == "boo"
+
+    assert doc(m.str_from_bytes) == "str_from_bytes() -> str"
+
+    class A:
+        def __str__(self):
+            return "this is a str"
+
+        def __repr__(self):
+            return "this is a repr"
+
+    assert m.str_from_object(A()) == "this is a str"
+    assert m.repr_from_object(A()) == "this is a repr"
+    assert m.str_from_handle(A()) == "this is a str"
+
+    s1, s2 = m.str_format()
+    assert s1 == "1 + 2 = 3"
+    assert s1 == s2
+
+    malformed_utf8 = b"\x80"
+    if hasattr(m, "PYBIND11_STR_LEGACY_PERMISSIVE"):
+        assert m.str_from_object(malformed_utf8) is malformed_utf8
+    else:
+        assert m.str_from_object(malformed_utf8) == "b'\\x80'"
+    assert m.str_from_handle(malformed_utf8) == "b'\\x80'"
+
+    assert m.str_from_string_from_str("this is a str") == "this is a str"
+    ucs_surrogates_str = "\udcc3"
+    with pytest.raises(UnicodeEncodeError):
+        m.str_from_string_from_str(ucs_surrogates_str)
+
+
+def test_bytes(doc):
+    assert m.bytes_from_char_ssize_t().decode() == "green"
+    assert m.bytes_from_char_size_t().decode() == "purple"
+    assert m.bytes_from_string().decode() == "foo"
+    assert m.bytes_from_str().decode() == "bar"
+
+    assert doc(m.bytes_from_str) == "bytes_from_str() -> bytes"
+
+
+def test_bytearray(doc):
+    assert m.bytearray_from_char_ssize_t().decode() == "$%"
+    assert m.bytearray_from_char_size_t().decode() == "@$!"
+    assert m.bytearray_from_string().decode() == "foo"
+    assert m.bytearray_size() == len("foo")
+
+
+def test_capsule(capture):
+    pytest.gc_collect()
+    with capture:
+        a = m.return_capsule_with_destructor()
+        del a
+        pytest.gc_collect()
+    assert (
+        capture.unordered
+        == """
+        creating capsule
+        destructing capsule
+    """
+    )
+
+    with capture:
+        a = m.return_renamed_capsule_with_destructor()
+        del a
+        pytest.gc_collect()
+    assert (
+        capture.unordered
+        == """
+        creating capsule
+        renaming capsule
+        destructing capsule
+    """
+    )
+
+    with capture:
+        a = m.return_capsule_with_destructor_2()
+        del a
+        pytest.gc_collect()
+    assert (
+        capture.unordered
+        == """
+        creating capsule
+        destructing capsule: 1234
+    """
+    )
+
+    with capture:
+        a = m.return_renamed_capsule_with_destructor_2()
+        del a
+        pytest.gc_collect()
+    assert (
+        capture.unordered
+        == """
+        creating capsule
+        renaming capsule
+        destructing capsule: 1234
+    """
+    )
+
+    with capture:
+        a = m.return_capsule_with_name_and_destructor()
+        del a
+        pytest.gc_collect()
+    assert (
+        capture.unordered
+        == """
+        created capsule (1234, 'pointer type description')
+        destructing capsule (1234, 'pointer type description')
+    """
+    )
+
+
+def test_accessors():
+    class SubTestObject:
+        attr_obj = 1
+        attr_char = 2
+
+    class TestObject:
+        basic_attr = 1
+        begin_end = [1, 2, 3]
+        d = {"operator[object]": 1, "operator[char *]": 2}
+        sub = SubTestObject()
+
+        def func(self, x, *args):
+            return self.basic_attr + x + sum(args)
+
+    d = m.accessor_api(TestObject())
+    assert d["basic_attr"] == 1
+    assert d["begin_end"] == [1, 2, 3]
+    assert d["operator[object]"] == 1
+    assert d["operator[char *]"] == 2
+    assert d["attr(object)"] == 1
+    assert d["attr(char *)"] == 2
+    assert d["missing_attr_ptr"] == "raised"
+    assert d["missing_attr_chain"] == "raised"
+    assert d["is_none"] is False
+    assert d["operator()"] == 2
+    assert d["operator*"] == 7
+    assert d["implicit_list"] == [1, 2, 3]
+    assert all(x in TestObject.__dict__ for x in d["implicit_dict"])
+
+    assert m.tuple_accessor(tuple()) == (0, 1, 2)
+
+    d = m.accessor_assignment()
+    assert d["get"] == 0
+    assert d["deferred_get"] == 0
+    assert d["set"] == 1
+    assert d["deferred_set"] == 1
+    assert d["var"] == 99
+
+
+def test_accessor_moves():
+    inc_refs = m.accessor_moves()
+    if inc_refs:
+        assert inc_refs == [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]
+    else:
+        pytest.skip("Not defined: PYBIND11_HANDLE_REF_DEBUG")
+
+
+def test_constructors():
+    """C++ default and converting constructors are equivalent to type calls in Python"""
+    types = [bytes, bytearray, str, bool, int, float, tuple, list, dict, set]
+    expected = {t.__name__: t() for t in types}
+    assert m.default_constructors() == expected
+
+    data = {
+        bytes: b"41",  # Currently no supported or working conversions.
+        bytearray: bytearray(b"41"),
+        str: 42,
+        bool: "Not empty",
+        int: "42",
+        float: "+1e3",
+        tuple: range(3),
+        list: range(3),
+        dict: [("two", 2), ("one", 1), ("three", 3)],
+        set: [4, 4, 5, 6, 6, 6],
+        frozenset: [4, 4, 5, 6, 6, 6],
+        memoryview: b"abc",
+    }
+    inputs = {k.__name__: v for k, v in data.items()}
+    expected = {k.__name__: k(v) for k, v in data.items()}
+
+    assert m.converting_constructors(inputs) == expected
+    assert m.cast_functions(inputs) == expected
+
+    # Converting constructors and cast functions should just reference rather
+    # than copy when no conversion is needed:
+    noconv1 = m.converting_constructors(expected)
+    for k in noconv1:
+        assert noconv1[k] is expected[k]
+
+    noconv2 = m.cast_functions(expected)
+    for k in noconv2:
+        assert noconv2[k] is expected[k]
+
+
+def test_non_converting_constructors():
+    non_converting_test_cases = [
+        ("bytes", range(10)),
+        ("none", 42),
+        ("ellipsis", 42),
+        ("type", 42),
+    ]
+    for t, v in non_converting_test_cases:
+        for move in [True, False]:
+            with pytest.raises(TypeError) as excinfo:
+                m.nonconverting_constructor(t, v, move)
+            expected_error = (
+                f"Object of type '{type(v).__name__}' is not an instance of '{t}'"
+            )
+            assert str(excinfo.value) == expected_error
+
+
+def test_pybind11_str_raw_str():
+    # specifically to exercise pybind11::str::raw_str
+    cvt = m.convert_to_pybind11_str
+    assert cvt("Str") == "Str"
+    assert cvt(b"Bytes") == "b'Bytes'"
+    assert cvt(None) == "None"
+    assert cvt(False) == "False"
+    assert cvt(True) == "True"
+    assert cvt(42) == "42"
+    assert cvt(2**65) == "36893488147419103232"
+    assert cvt(-1.50) == "-1.5"
+    assert cvt(()) == "()"
+    assert cvt((18,)) == "(18,)"
+    assert cvt([]) == "[]"
+    assert cvt([28]) == "[28]"
+    assert cvt({}) == "{}"
+    assert cvt({3: 4}) == "{3: 4}"
+    assert cvt(set()) == "set()"
+    assert cvt({3, 3}) == "{3}"
+
+    valid_orig = "DZ"
+    valid_utf8 = valid_orig.encode("utf-8")
+    valid_cvt = cvt(valid_utf8)
+    if hasattr(m, "PYBIND11_STR_LEGACY_PERMISSIVE"):
+        assert valid_cvt is valid_utf8
+    else:
+        assert type(valid_cvt) is str
+        assert valid_cvt == "b'\\xc7\\xb1'"
+
+    malformed_utf8 = b"\x80"
+    if hasattr(m, "PYBIND11_STR_LEGACY_PERMISSIVE"):
+        assert cvt(malformed_utf8) is malformed_utf8
+    else:
+        malformed_cvt = cvt(malformed_utf8)
+        assert type(malformed_cvt) is str
+        assert malformed_cvt == "b'\\x80'"
+
+
+def test_implicit_casting():
+    """Tests implicit casting when assigning or appending to dicts and lists."""
+    z = m.get_implicit_casting()
+    assert z["d"] == {
+        "char*_i1": "abc",
+        "char*_i2": "abc",
+        "char*_e": "abc",
+        "char*_p": "abc",
+        "str_i1": "str",
+        "str_i2": "str1",
+        "str_e": "str2",
+        "str_p": "str3",
+        "int_i1": 42,
+        "int_i2": 42,
+        "int_e": 43,
+        "int_p": 44,
+    }
+    assert z["l"] == [3, 6, 9, 12, 15]
+
+
+def test_print(capture):
+    with capture:
+        m.print_function()
+    assert (
+        capture
+        == """
+        Hello, World!
+        1 2.0 three True -- multiple args
+        *args-and-a-custom-separator
+        no new line here -- next print
+        flush
+        py::print + str.format = this
+    """
+    )
+    assert capture.stderr == "this goes to stderr"
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.print_failure()
+    assert str(excinfo.value) == "Unable to convert call argument " + (
+        "'1' of type 'UnregisteredType' to Python object"
+        if detailed_error_messages_enabled
+        else "to Python object (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)"
+    )
+
+
+def test_hash():
+    class Hashable:
+        def __init__(self, value):
+            self.value = value
+
+        def __hash__(self):
+            return self.value
+
+    class Unhashable:
+        __hash__ = None
+
+    assert m.hash_function(Hashable(42)) == 42
+    with pytest.raises(TypeError):
+        m.hash_function(Unhashable())
+
+
+def test_number_protocol():
+    for a, b in [(1, 1), (3, 5)]:
+        li = [
+            a == b,
+            a != b,
+            a < b,
+            a <= b,
+            a > b,
+            a >= b,
+            a + b,
+            a - b,
+            a * b,
+            a / b,
+            a | b,
+            a & b,
+            a ^ b,
+            a >> b,
+            a << b,
+        ]
+        assert m.test_number_protocol(a, b) == li
+
+
+def test_list_slicing():
+    li = list(range(100))
+    assert li[::2] == m.test_list_slicing(li)
+
+
+def test_issue2361():
+    # See issue #2361
+    assert m.issue2361_str_implicit_copy_none() == "None"
+    with pytest.raises(TypeError) as excinfo:
+        assert m.issue2361_dict_implicit_copy_none()
+    assert "NoneType" in str(excinfo.value)
+    assert "iterable" in str(excinfo.value)
+
+
+@pytest.mark.parametrize(
+    "method, args, fmt, expected_view",
+    [
+        (m.test_memoryview_object, (b"red",), "B", b"red"),
+        (m.test_memoryview_buffer_info, (b"green",), "B", b"green"),
+        (m.test_memoryview_from_buffer, (False,), "h", [3, 1, 4, 1, 5]),
+        (m.test_memoryview_from_buffer, (True,), "H", [2, 7, 1, 8]),
+        (m.test_memoryview_from_buffer_nativeformat, (), "@i", [4, 7, 5]),
+    ],
+)
+def test_memoryview(method, args, fmt, expected_view):
+    view = method(*args)
+    assert isinstance(view, memoryview)
+    assert view.format == fmt
+    assert list(view) == list(expected_view)
+
+
+@pytest.mark.xfail("env.PYPY", reason="getrefcount is not available")
+@pytest.mark.parametrize(
+    "method",
+    [
+        m.test_memoryview_object,
+        m.test_memoryview_buffer_info,
+    ],
+)
+def test_memoryview_refcount(method):
+    buf = b"\x0a\x0b\x0c\x0d"
+    ref_before = sys.getrefcount(buf)
+    view = method(buf)
+    ref_after = sys.getrefcount(buf)
+    assert ref_before < ref_after
+    assert list(view) == list(buf)
+
+
+def test_memoryview_from_buffer_empty_shape():
+    view = m.test_memoryview_from_buffer_empty_shape()
+    assert isinstance(view, memoryview)
+    assert view.format == "B"
+    assert bytes(view) == b""
+
+
+def test_test_memoryview_from_buffer_invalid_strides():
+    with pytest.raises(RuntimeError):
+        m.test_memoryview_from_buffer_invalid_strides()
+
+
+def test_test_memoryview_from_buffer_nullptr():
+    with pytest.raises(ValueError):
+        m.test_memoryview_from_buffer_nullptr()
+
+
+def test_memoryview_from_memory():
+    view = m.test_memoryview_from_memory()
+    assert isinstance(view, memoryview)
+    assert view.format == "B"
+    assert bytes(view) == b"\xff\xe1\xab\x37"
+
+
+def test_builtin_functions():
+    assert m.get_len([i for i in range(42)]) == 42
+    with pytest.raises(TypeError) as exc_info:
+        m.get_len(i for i in range(42))
+    assert str(exc_info.value) in [
+        "object of type 'generator' has no len()",
+        "'generator' has no length",
+    ]  # PyPy
+
+
+def test_isinstance_string_types():
+    assert m.isinstance_pybind11_bytes(b"")
+    assert not m.isinstance_pybind11_bytes("")
+
+    assert m.isinstance_pybind11_str("")
+    if hasattr(m, "PYBIND11_STR_LEGACY_PERMISSIVE"):
+        assert m.isinstance_pybind11_str(b"")
+    else:
+        assert not m.isinstance_pybind11_str(b"")
+
+
+def test_pass_bytes_or_unicode_to_string_types():
+    assert m.pass_to_pybind11_bytes(b"Bytes") == 5
+    with pytest.raises(TypeError):
+        m.pass_to_pybind11_bytes("Str")
+
+    if hasattr(m, "PYBIND11_STR_LEGACY_PERMISSIVE"):
+        assert m.pass_to_pybind11_str(b"Bytes") == 5
+    else:
+        with pytest.raises(TypeError):
+            m.pass_to_pybind11_str(b"Bytes")
+    assert m.pass_to_pybind11_str("Str") == 3
+
+    assert m.pass_to_std_string(b"Bytes") == 5
+    assert m.pass_to_std_string("Str") == 3
+
+    malformed_utf8 = b"\x80"
+    if hasattr(m, "PYBIND11_STR_LEGACY_PERMISSIVE"):
+        assert m.pass_to_pybind11_str(malformed_utf8) == 1
+    else:
+        with pytest.raises(TypeError):
+            m.pass_to_pybind11_str(malformed_utf8)
+
+
+@pytest.mark.parametrize(
+    "create_weakref, create_weakref_with_callback",
+    [
+        (m.weakref_from_handle, m.weakref_from_handle_and_function),
+        (m.weakref_from_object, m.weakref_from_object_and_function),
+    ],
+)
+def test_weakref(create_weakref, create_weakref_with_callback):
+    from weakref import getweakrefcount
+
+    # Apparently, you cannot weakly reference an object()
+    class WeaklyReferenced:
+        pass
+
+    callback_called = False
+
+    def callback(wr):
+        nonlocal callback_called
+        callback_called = True
+
+    obj = WeaklyReferenced()
+    assert getweakrefcount(obj) == 0
+    wr = create_weakref(obj)
+    assert getweakrefcount(obj) == 1
+
+    obj = WeaklyReferenced()
+    assert getweakrefcount(obj) == 0
+    wr = create_weakref_with_callback(obj, callback)  # noqa: F841
+    assert getweakrefcount(obj) == 1
+    assert not callback_called
+    del obj
+    pytest.gc_collect()
+    assert callback_called
+
+
+@pytest.mark.parametrize(
+    "create_weakref, has_callback",
+    [
+        (m.weakref_from_handle, False),
+        (m.weakref_from_object, False),
+        (m.weakref_from_handle_and_function, True),
+        (m.weakref_from_object_and_function, True),
+    ],
+)
+def test_weakref_err(create_weakref, has_callback):
+    class C:
+        __slots__ = []
+
+    def callback(_):
+        pass
+
+    ob = C()
+    # Should raise TypeError on CPython
+    with pytest.raises(TypeError) if not env.PYPY else contextlib.nullcontext():
+        if has_callback:
+            _ = create_weakref(ob, callback)
+        else:
+            _ = create_weakref(ob)
+
+
+def test_cpp_iterators():
+    assert m.tuple_iterator() == 12
+    assert m.dict_iterator() == 305 + 711
+    assert m.passed_iterator(iter((-7, 3))) == -4
+
+
+def test_implementation_details():
+    lst = [39, 43, 92, 49, 22, 29, 93, 98, 26, 57, 8]
+    tup = tuple(lst)
+    assert m.sequence_item_get_ssize_t(lst) == 43
+    assert m.sequence_item_set_ssize_t(lst) is None
+    assert lst[1] == "peppa"
+    assert m.sequence_item_get_size_t(lst) == 92
+    assert m.sequence_item_set_size_t(lst) is None
+    assert lst[2] == "george"
+    assert m.list_item_get_ssize_t(lst) == 49
+    assert m.list_item_set_ssize_t(lst) is None
+    assert lst[3] == "rebecca"
+    assert m.list_item_get_size_t(lst) == 22
+    assert m.list_item_set_size_t(lst) is None
+    assert lst[4] == "richard"
+    assert m.tuple_item_get_ssize_t(tup) == 29
+    assert m.tuple_item_set_ssize_t() == ("emely", "edmond")
+    assert m.tuple_item_get_size_t(tup) == 93
+    assert m.tuple_item_set_size_t() == ("candy", "cat")
+
+
+def test_external_float_():
+    r1 = m.square_float_(2.0)
+    assert r1 == 4.0
+
+
+def test_tuple_rvalue_getter():
+    pop = 1000
+    tup = tuple(range(pop))
+    m.tuple_rvalue_getter(tup)
+
+
+def test_list_rvalue_getter():
+    pop = 1000
+    my_list = list(range(pop))
+    m.list_rvalue_getter(my_list)
+
+
+def test_populate_dict_rvalue():
+    pop = 1000
+    my_dict = {i: i for i in range(pop)}
+    assert m.populate_dict_rvalue(pop) == my_dict
+
+
+def test_populate_obj_str_attrs():
+    pop = 1000
+    o = types.SimpleNamespace(**{str(i): i for i in range(pop)})
+    new_o = m.populate_obj_str_attrs(o, pop)
+    new_attrs = {k: v for k, v in new_o.__dict__.items() if not k.startswith("_")}
+    assert all(isinstance(v, str) for v in new_attrs.values())
+    assert len(new_attrs) == pop
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_sequences_and_iterators.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_sequences_and_iterators.cpp
new file mode 100644
index 000000000..b867f49a2
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_sequences_and_iterators.cpp
@@ -0,0 +1,562 @@
+/*
+    tests/test_sequences_and_iterators.cpp -- supporting Pythons' sequence protocol, iterators,
+    etc.
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/operators.h>
+#include <pybind11/stl.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#include <algorithm>
+#include <utility>
+#include <vector>
+
+#ifdef PYBIND11_HAS_OPTIONAL
+#    include <optional>
+#endif // PYBIND11_HAS_OPTIONAL
+
+template <typename T>
+class NonZeroIterator {
+    const T *ptr_;
+
+public:
+    explicit NonZeroIterator(const T *ptr) : ptr_(ptr) {}
+    const T &operator*() const { return *ptr_; }
+    NonZeroIterator &operator++() {
+        ++ptr_;
+        return *this;
+    }
+};
+
+class NonZeroSentinel {};
+
+template <typename A, typename B>
+bool operator==(const NonZeroIterator<std::pair<A, B>> &it, const NonZeroSentinel &) {
+    return !(*it).first || !(*it).second;
+}
+
+/* Iterator where dereferencing returns prvalues instead of references. */
+template <typename T>
+class NonRefIterator {
+    const T *ptr_;
+
+public:
+    explicit NonRefIterator(const T *ptr) : ptr_(ptr) {}
+    T operator*() const { return T(*ptr_); }
+    NonRefIterator &operator++() {
+        ++ptr_;
+        return *this;
+    }
+    bool operator==(const NonRefIterator &other) const { return ptr_ == other.ptr_; }
+};
+
+class NonCopyableInt {
+public:
+    explicit NonCopyableInt(int value) : value_(value) {}
+    NonCopyableInt(const NonCopyableInt &) = delete;
+    NonCopyableInt(NonCopyableInt &&other) noexcept : value_(other.value_) {
+        other.value_ = -1; // detect when an unwanted move occurs
+    }
+    NonCopyableInt &operator=(const NonCopyableInt &) = delete;
+    NonCopyableInt &operator=(NonCopyableInt &&other) noexcept {
+        value_ = other.value_;
+        other.value_ = -1; // detect when an unwanted move occurs
+        return *this;
+    }
+    int get() const { return value_; }
+    void set(int value) { value_ = value; }
+    ~NonCopyableInt() = default;
+
+private:
+    int value_;
+};
+using NonCopyableIntPair = std::pair<NonCopyableInt, NonCopyableInt>;
+PYBIND11_MAKE_OPAQUE(std::vector<NonCopyableInt>);
+PYBIND11_MAKE_OPAQUE(std::vector<NonCopyableIntPair>);
+
+template <typename PythonType>
+py::list test_random_access_iterator(PythonType x) {
+    if (x.size() < 5) {
+        throw py::value_error("Please provide at least 5 elements for testing.");
+    }
+
+    auto checks = py::list();
+    auto assert_equal = [&checks](py::handle a, py::handle b) {
+        auto result = PyObject_RichCompareBool(a.ptr(), b.ptr(), Py_EQ);
+        if (result == -1) {
+            throw py::error_already_set();
+        }
+        checks.append(result != 0);
+    };
+
+    auto it = x.begin();
+    assert_equal(x[0], *it);
+    assert_equal(x[0], it[0]);
+    assert_equal(x[1], it[1]);
+
+    assert_equal(x[1], *(++it));
+    assert_equal(x[1], *(it++));
+    assert_equal(x[2], *it);
+    assert_equal(x[3], *(it += 1));
+    assert_equal(x[2], *(--it));
+    assert_equal(x[2], *(it--));
+    assert_equal(x[1], *it);
+    assert_equal(x[0], *(it -= 1));
+
+    assert_equal(it->attr("real"), x[0].attr("real"));
+    assert_equal((it + 1)->attr("real"), x[1].attr("real"));
+
+    assert_equal(x[1], *(it + 1));
+    assert_equal(x[1], *(1 + it));
+    it += 3;
+    assert_equal(x[1], *(it - 2));
+
+    checks.append(static_cast<std::size_t>(x.end() - x.begin()) == x.size());
+    checks.append((x.begin() + static_cast<std::ptrdiff_t>(x.size())) == x.end());
+    checks.append(x.begin() < x.end());
+
+    return checks;
+}
+
+TEST_SUBMODULE(sequences_and_iterators, m) {
+    // test_sliceable
+    class Sliceable {
+    public:
+        explicit Sliceable(int n) : size(n) {}
+        int start, stop, step;
+        int size;
+    };
+    py::class_<Sliceable>(m, "Sliceable")
+        .def(py::init<int>())
+        .def("__getitem__", [](const Sliceable &s, const py::slice &slice) {
+            py::ssize_t start = 0, stop = 0, step = 0, slicelength = 0;
+            if (!slice.compute(s.size, &start, &stop, &step, &slicelength)) {
+                throw py::error_already_set();
+            }
+            int istart = static_cast<int>(start);
+            int istop = static_cast<int>(stop);
+            int istep = static_cast<int>(step);
+            return std::make_tuple(istart, istop, istep);
+        });
+
+    m.def("make_forward_slice_size_t", []() { return py::slice(0, -1, 1); });
+    m.def("make_reversed_slice_object",
+          []() { return py::slice(py::none(), py::none(), py::int_(-1)); });
+#ifdef PYBIND11_HAS_OPTIONAL
+    m.attr("has_optional") = true;
+    m.def("make_reversed_slice_size_t_optional_verbose",
+          []() { return py::slice(std::nullopt, std::nullopt, -1); });
+    // Warning: The following spelling may still compile if optional<> is not present and give
+    // wrong answers. Please use with caution.
+    m.def("make_reversed_slice_size_t_optional", []() { return py::slice({}, {}, -1); });
+#else
+    m.attr("has_optional") = false;
+#endif
+
+    // test_sequence
+    class Sequence {
+    public:
+        explicit Sequence(size_t size) : m_size(size) {
+            print_created(this, "of size", m_size);
+            // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+            m_data = new float[size];
+            memset(m_data, 0, sizeof(float) * size);
+        }
+        explicit Sequence(const std::vector<float> &value) : m_size(value.size()) {
+            print_created(this, "of size", m_size, "from std::vector");
+            // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+            m_data = new float[m_size];
+            memcpy(m_data, &value[0], sizeof(float) * m_size);
+        }
+        Sequence(const Sequence &s) : m_size(s.m_size) {
+            print_copy_created(this);
+            // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+            m_data = new float[m_size];
+            memcpy(m_data, s.m_data, sizeof(float) * m_size);
+        }
+        Sequence(Sequence &&s) noexcept : m_size(s.m_size), m_data(s.m_data) {
+            print_move_created(this);
+            s.m_size = 0;
+            s.m_data = nullptr;
+        }
+
+        ~Sequence() {
+            print_destroyed(this);
+            delete[] m_data;
+        }
+
+        Sequence &operator=(const Sequence &s) {
+            if (&s != this) {
+                delete[] m_data;
+                m_size = s.m_size;
+                m_data = new float[m_size];
+                memcpy(m_data, s.m_data, sizeof(float) * m_size);
+            }
+            print_copy_assigned(this);
+            return *this;
+        }
+
+        Sequence &operator=(Sequence &&s) noexcept {
+            if (&s != this) {
+                delete[] m_data;
+                m_size = s.m_size;
+                m_data = s.m_data;
+                s.m_size = 0;
+                s.m_data = nullptr;
+            }
+            print_move_assigned(this);
+            return *this;
+        }
+
+        bool operator==(const Sequence &s) const {
+            if (m_size != s.size()) {
+                return false;
+            }
+            for (size_t i = 0; i < m_size; ++i) {
+                if (m_data[i] != s[i]) {
+                    return false;
+                }
+            }
+            return true;
+        }
+        bool operator!=(const Sequence &s) const { return !operator==(s); }
+
+        float operator[](size_t index) const { return m_data[index]; }
+        float &operator[](size_t index) { return m_data[index]; }
+
+        bool contains(float v) const {
+            for (size_t i = 0; i < m_size; ++i) {
+                if (v == m_data[i]) {
+                    return true;
+                }
+            }
+            return false;
+        }
+
+        Sequence reversed() const {
+            Sequence result(m_size);
+            for (size_t i = 0; i < m_size; ++i) {
+                result[m_size - i - 1] = m_data[i];
+            }
+            return result;
+        }
+
+        size_t size() const { return m_size; }
+
+        const float *begin() const { return m_data; }
+        const float *end() const { return m_data + m_size; }
+
+    private:
+        size_t m_size;
+        float *m_data;
+    };
+    py::class_<Sequence>(m, "Sequence")
+        .def(py::init<size_t>())
+        .def(py::init<const std::vector<float> &>())
+        /// Bare bones interface
+        .def("__getitem__",
+             [](const Sequence &s, size_t i) {
+                 if (i >= s.size()) {
+                     throw py::index_error();
+                 }
+                 return s[i];
+             })
+        .def("__setitem__",
+             [](Sequence &s, size_t i, float v) {
+                 if (i >= s.size()) {
+                     throw py::index_error();
+                 }
+                 s[i] = v;
+             })
+        .def("__len__", &Sequence::size)
+        /// Optional sequence protocol operations
+        .def(
+            "__iter__",
+            [](const Sequence &s) { return py::make_iterator(s.begin(), s.end()); },
+            py::keep_alive<0, 1>() /* Essential: keep object alive while iterator exists */)
+        .def("__contains__", [](const Sequence &s, float v) { return s.contains(v); })
+        .def("__reversed__", [](const Sequence &s) -> Sequence { return s.reversed(); })
+        /// Slicing protocol (optional)
+        .def("__getitem__",
+             [](const Sequence &s, const py::slice &slice) -> Sequence * {
+                 size_t start = 0, stop = 0, step = 0, slicelength = 0;
+                 if (!slice.compute(s.size(), &start, &stop, &step, &slicelength)) {
+                     throw py::error_already_set();
+                 }
+                 auto *seq = new Sequence(slicelength);
+                 for (size_t i = 0; i < slicelength; ++i) {
+                     (*seq)[i] = s[start];
+                     start += step;
+                 }
+                 return seq;
+             })
+        .def("__setitem__",
+             [](Sequence &s, const py::slice &slice, const Sequence &value) {
+                 size_t start = 0, stop = 0, step = 0, slicelength = 0;
+                 if (!slice.compute(s.size(), &start, &stop, &step, &slicelength)) {
+                     throw py::error_already_set();
+                 }
+                 if (slicelength != value.size()) {
+                     throw std::runtime_error(
+                         "Left and right hand size of slice assignment have different sizes!");
+                 }
+                 for (size_t i = 0; i < slicelength; ++i) {
+                     s[start] = value[i];
+                     start += step;
+                 }
+             })
+        /// Comparisons
+        .def(py::self == py::self)
+        .def(py::self != py::self)
+        // Could also define py::self + py::self for concatenation, etc.
+        ;
+
+    // test_map_iterator
+    // Interface of a map-like object that isn't (directly) an unordered_map, but provides some
+    // basic map-like functionality.
+    class StringMap {
+    public:
+        StringMap() = default;
+        explicit StringMap(std::unordered_map<std::string, std::string> init)
+            : map(std::move(init)) {}
+
+        void set(const std::string &key, std::string val) { map[key] = std::move(val); }
+        std::string get(const std::string &key) const { return map.at(key); }
+        size_t size() const { return map.size(); }
+
+    private:
+        std::unordered_map<std::string, std::string> map;
+
+    public:
+        decltype(map.cbegin()) begin() const { return map.cbegin(); }
+        decltype(map.cend()) end() const { return map.cend(); }
+    };
+    py::class_<StringMap>(m, "StringMap")
+        .def(py::init<>())
+        .def(py::init<std::unordered_map<std::string, std::string>>())
+        .def("__getitem__",
+             [](const StringMap &map, const std::string &key) {
+                 try {
+                     return map.get(key);
+                 } catch (const std::out_of_range &) {
+                     throw py::key_error("key '" + key + "' does not exist");
+                 }
+             })
+        .def("__setitem__", &StringMap::set)
+        .def("__len__", &StringMap::size)
+        .def(
+            "__iter__",
+            [](const StringMap &map) { return py::make_key_iterator(map.begin(), map.end()); },
+            py::keep_alive<0, 1>())
+        .def(
+            "items",
+            [](const StringMap &map) { return py::make_iterator(map.begin(), map.end()); },
+            py::keep_alive<0, 1>())
+        .def(
+            "values",
+            [](const StringMap &map) { return py::make_value_iterator(map.begin(), map.end()); },
+            py::keep_alive<0, 1>());
+
+    // test_generalized_iterators
+    class IntPairs {
+    public:
+        explicit IntPairs(std::vector<std::pair<int, int>> data) : data_(std::move(data)) {}
+        const std::pair<int, int> *begin() const { return data_.data(); }
+        // .end() only required for py::make_iterator(self) overload
+        const std::pair<int, int> *end() const { return data_.data() + data_.size(); }
+
+    private:
+        std::vector<std::pair<int, int>> data_;
+    };
+    py::class_<IntPairs>(m, "IntPairs")
+        .def(py::init<std::vector<std::pair<int, int>>>())
+        .def(
+            "nonzero",
+            [](const IntPairs &s) {
+                return py::make_iterator(NonZeroIterator<std::pair<int, int>>(s.begin()),
+                                         NonZeroSentinel());
+            },
+            py::keep_alive<0, 1>())
+        .def(
+            "nonzero_keys",
+            [](const IntPairs &s) {
+                return py::make_key_iterator(NonZeroIterator<std::pair<int, int>>(s.begin()),
+                                             NonZeroSentinel());
+            },
+            py::keep_alive<0, 1>())
+        .def(
+            "nonzero_values",
+            [](const IntPairs &s) {
+                return py::make_value_iterator(NonZeroIterator<std::pair<int, int>>(s.begin()),
+                                               NonZeroSentinel());
+            },
+            py::keep_alive<0, 1>())
+
+        // test iterator that returns values instead of references
+        .def(
+            "nonref",
+            [](const IntPairs &s) {
+                return py::make_iterator(NonRefIterator<std::pair<int, int>>(s.begin()),
+                                         NonRefIterator<std::pair<int, int>>(s.end()));
+            },
+            py::keep_alive<0, 1>())
+        .def(
+            "nonref_keys",
+            [](const IntPairs &s) {
+                return py::make_key_iterator(NonRefIterator<std::pair<int, int>>(s.begin()),
+                                             NonRefIterator<std::pair<int, int>>(s.end()));
+            },
+            py::keep_alive<0, 1>())
+        .def(
+            "nonref_values",
+            [](const IntPairs &s) {
+                return py::make_value_iterator(NonRefIterator<std::pair<int, int>>(s.begin()),
+                                               NonRefIterator<std::pair<int, int>>(s.end()));
+            },
+            py::keep_alive<0, 1>())
+
+        // test single-argument make_iterator
+        .def(
+            "simple_iterator",
+            [](IntPairs &self) { return py::make_iterator(self); },
+            py::keep_alive<0, 1>())
+        .def(
+            "simple_keys",
+            [](IntPairs &self) { return py::make_key_iterator(self); },
+            py::keep_alive<0, 1>())
+        .def(
+            "simple_values",
+            [](IntPairs &self) { return py::make_value_iterator(self); },
+            py::keep_alive<0, 1>())
+
+        // Test iterator with an Extra (doesn't do anything useful, so not used
+        // at runtime, but tests need to be able to compile with the correct
+        // overload. See PR #3293.
+        .def(
+            "_make_iterator_extras",
+            [](IntPairs &self) { return py::make_iterator(self, py::call_guard<int>()); },
+            py::keep_alive<0, 1>())
+        .def(
+            "_make_key_extras",
+            [](IntPairs &self) { return py::make_key_iterator(self, py::call_guard<int>()); },
+            py::keep_alive<0, 1>())
+        .def(
+            "_make_value_extras",
+            [](IntPairs &self) { return py::make_value_iterator(self, py::call_guard<int>()); },
+            py::keep_alive<0, 1>());
+
+    // test_iterator_referencing
+    py::class_<NonCopyableInt>(m, "NonCopyableInt")
+        .def(py::init<int>())
+        .def("set", &NonCopyableInt::set)
+        .def("__int__", &NonCopyableInt::get);
+    py::class_<std::vector<NonCopyableInt>>(m, "VectorNonCopyableInt")
+        .def(py::init<>())
+        .def("append",
+             [](std::vector<NonCopyableInt> &vec, int value) { vec.emplace_back(value); })
+        .def("__iter__", [](std::vector<NonCopyableInt> &vec) {
+            return py::make_iterator(vec.begin(), vec.end());
+        });
+    py::class_<std::vector<NonCopyableIntPair>>(m, "VectorNonCopyableIntPair")
+        .def(py::init<>())
+        .def("append",
+             [](std::vector<NonCopyableIntPair> &vec, const std::pair<int, int> &value) {
+                 vec.emplace_back(NonCopyableInt(value.first), NonCopyableInt(value.second));
+             })
+        .def("keys",
+             [](std::vector<NonCopyableIntPair> &vec) {
+                 return py::make_key_iterator(vec.begin(), vec.end());
+             })
+        .def("values", [](std::vector<NonCopyableIntPair> &vec) {
+            return py::make_value_iterator(vec.begin(), vec.end());
+        });
+
+#if 0
+    // Obsolete: special data structure for exposing custom iterator types to python
+    // kept here for illustrative purposes because there might be some use cases which
+    // are not covered by the much simpler py::make_iterator
+
+    struct PySequenceIterator {
+        PySequenceIterator(const Sequence &seq, py::object ref) : seq(seq), ref(ref) { }
+
+        float next() {
+            if (index == seq.size())
+                throw py::stop_iteration();
+            return seq[index++];
+        }
+
+        const Sequence &seq;
+        py::object ref; // keep a reference
+        size_t index = 0;
+    };
+
+    py::class_<PySequenceIterator>(seq, "Iterator")
+        .def("__iter__", [](PySequenceIterator &it) -> PySequenceIterator& { return it; })
+        .def("__next__", &PySequenceIterator::next);
+
+    On the actual Sequence object, the iterator would be constructed as follows:
+    .def("__iter__", [](py::object s) { return PySequenceIterator(s.cast<const Sequence &>(), s); })
+#endif
+
+    // test_python_iterator_in_cpp
+    m.def("object_to_list", [](const py::object &o) {
+        auto l = py::list();
+        for (auto item : o) {
+            l.append(item);
+        }
+        return l;
+    });
+
+    m.def("iterator_to_list", [](py::iterator it) {
+        auto l = py::list();
+        while (it != py::iterator::sentinel()) {
+            l.append(*it);
+            ++it;
+        }
+        return l;
+    });
+
+    // test_sequence_length: check that Python sequences can be converted to py::sequence.
+    m.def("sequence_length", [](const py::sequence &seq) { return seq.size(); });
+
+    // Make sure that py::iterator works with std algorithms
+    m.def("count_none", [](const py::object &o) {
+        return std::count_if(o.begin(), o.end(), [](py::handle h) { return h.is_none(); });
+    });
+
+    m.def("find_none", [](const py::object &o) {
+        auto it = std::find_if(o.begin(), o.end(), [](py::handle h) { return h.is_none(); });
+        return it->is_none();
+    });
+
+    m.def("count_nonzeros", [](const py::dict &d) {
+        return std::count_if(d.begin(), d.end(), [](std::pair<py::handle, py::handle> p) {
+            return p.second.cast<int>() != 0;
+        });
+    });
+
+    m.def("tuple_iterator", &test_random_access_iterator<py::tuple>);
+    m.def("list_iterator", &test_random_access_iterator<py::list>);
+    m.def("sequence_iterator", &test_random_access_iterator<py::sequence>);
+
+    // test_iterator_passthrough
+    // #181: iterator passthrough did not compile
+    m.def("iterator_passthrough", [](py::iterator s) -> py::iterator {
+        return py::make_iterator(std::begin(s), std::end(s));
+    });
+
+    // test_iterator_rvp
+    // #388: Can't make iterators via make_iterator() with different r/v policies
+    static std::vector<int> list = {1, 2, 3};
+    m.def("make_iterator_1",
+          []() { return py::make_iterator<py::return_value_policy::copy>(list); });
+    m.def("make_iterator_2",
+          []() { return py::make_iterator<py::return_value_policy::automatic>(list); });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_sequences_and_iterators.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_sequences_and_iterators.py
new file mode 100644
index 000000000..062e3b3d3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_sequences_and_iterators.py
@@ -0,0 +1,243 @@
+import pytest
+from pytest import approx
+
+from pybind11_tests import ConstructorStats
+from pybind11_tests import sequences_and_iterators as m
+
+
+def test_slice_constructors():
+    assert m.make_forward_slice_size_t() == slice(0, -1, 1)
+    assert m.make_reversed_slice_object() == slice(None, None, -1)
+
+
+@pytest.mark.skipif(not m.has_optional, reason="no <optional>")
+def test_slice_constructors_explicit_optional():
+    assert m.make_reversed_slice_size_t_optional() == slice(None, None, -1)
+    assert m.make_reversed_slice_size_t_optional_verbose() == slice(None, None, -1)
+
+
+def test_generalized_iterators():
+    assert list(m.IntPairs([(1, 2), (3, 4), (0, 5)]).nonzero()) == [(1, 2), (3, 4)]
+    assert list(m.IntPairs([(1, 2), (2, 0), (0, 3), (4, 5)]).nonzero()) == [(1, 2)]
+    assert list(m.IntPairs([(0, 3), (1, 2), (3, 4)]).nonzero()) == []
+
+    assert list(m.IntPairs([(1, 2), (3, 4), (0, 5)]).nonzero_keys()) == [1, 3]
+    assert list(m.IntPairs([(1, 2), (2, 0), (0, 3), (4, 5)]).nonzero_keys()) == [1]
+    assert list(m.IntPairs([(0, 3), (1, 2), (3, 4)]).nonzero_keys()) == []
+
+    assert list(m.IntPairs([(1, 2), (3, 4), (0, 5)]).nonzero_values()) == [2, 4]
+    assert list(m.IntPairs([(1, 2), (2, 0), (0, 3), (4, 5)]).nonzero_values()) == [2]
+    assert list(m.IntPairs([(0, 3), (1, 2), (3, 4)]).nonzero_values()) == []
+
+    # __next__ must continue to raise StopIteration
+    it = m.IntPairs([(0, 0)]).nonzero()
+    for _ in range(3):
+        with pytest.raises(StopIteration):
+            next(it)
+
+    it = m.IntPairs([(0, 0)]).nonzero_keys()
+    for _ in range(3):
+        with pytest.raises(StopIteration):
+            next(it)
+
+
+def test_nonref_iterators():
+    pairs = m.IntPairs([(1, 2), (3, 4), (0, 5)])
+    assert list(pairs.nonref()) == [(1, 2), (3, 4), (0, 5)]
+    assert list(pairs.nonref_keys()) == [1, 3, 0]
+    assert list(pairs.nonref_values()) == [2, 4, 5]
+
+
+def test_generalized_iterators_simple():
+    assert list(m.IntPairs([(1, 2), (3, 4), (0, 5)]).simple_iterator()) == [
+        (1, 2),
+        (3, 4),
+        (0, 5),
+    ]
+    assert list(m.IntPairs([(1, 2), (3, 4), (0, 5)]).simple_keys()) == [1, 3, 0]
+    assert list(m.IntPairs([(1, 2), (3, 4), (0, 5)]).simple_values()) == [2, 4, 5]
+
+
+def test_iterator_referencing():
+    """Test that iterators reference rather than copy their referents."""
+    vec = m.VectorNonCopyableInt()
+    vec.append(3)
+    vec.append(5)
+    assert [int(x) for x in vec] == [3, 5]
+    # Increment everything to make sure the referents can be mutated
+    for x in vec:
+        x.set(int(x) + 1)
+    assert [int(x) for x in vec] == [4, 6]
+
+    vec = m.VectorNonCopyableIntPair()
+    vec.append([3, 4])
+    vec.append([5, 7])
+    assert [int(x) for x in vec.keys()] == [3, 5]
+    assert [int(x) for x in vec.values()] == [4, 7]
+    for x in vec.keys():
+        x.set(int(x) + 1)
+    for x in vec.values():
+        x.set(int(x) + 10)
+    assert [int(x) for x in vec.keys()] == [4, 6]
+    assert [int(x) for x in vec.values()] == [14, 17]
+
+
+def test_sliceable():
+    sliceable = m.Sliceable(100)
+    assert sliceable[::] == (0, 100, 1)
+    assert sliceable[10::] == (10, 100, 1)
+    assert sliceable[:10:] == (0, 10, 1)
+    assert sliceable[::10] == (0, 100, 10)
+    assert sliceable[-10::] == (90, 100, 1)
+    assert sliceable[:-10:] == (0, 90, 1)
+    assert sliceable[::-10] == (99, -1, -10)
+    assert sliceable[50:60:1] == (50, 60, 1)
+    assert sliceable[50:60:-1] == (50, 60, -1)
+
+
+def test_sequence():
+    cstats = ConstructorStats.get(m.Sequence)
+
+    s = m.Sequence(5)
+    assert cstats.values() == ["of size", "5"]
+
+    assert "Sequence" in repr(s)
+    assert len(s) == 5
+    assert s[0] == 0 and s[3] == 0
+    assert 12.34 not in s
+    s[0], s[3] = 12.34, 56.78
+    assert 12.34 in s
+    assert s[0] == approx(12.34, rel=1e-05)
+    assert s[3] == approx(56.78, rel=1e-05)
+
+    rev = reversed(s)
+    assert cstats.values() == ["of size", "5"]
+
+    rev2 = s[::-1]
+    assert cstats.values() == ["of size", "5"]
+
+    it = iter(m.Sequence(0))
+    for _ in range(3):  # __next__ must continue to raise StopIteration
+        with pytest.raises(StopIteration):
+            next(it)
+    assert cstats.values() == ["of size", "0"]
+
+    expected = [0, 56.78, 0, 0, 12.34]
+    assert rev == approx(expected, rel=1e-05)
+    assert rev2 == approx(expected, rel=1e-05)
+    assert rev == rev2
+
+    rev[0::2] = m.Sequence([2.0, 2.0, 2.0])
+    assert cstats.values() == ["of size", "3", "from std::vector"]
+
+    assert rev == approx([2, 56.78, 2, 0, 2], rel=1e-05)
+
+    assert cstats.alive() == 4
+    del it
+    assert cstats.alive() == 3
+    del s
+    assert cstats.alive() == 2
+    del rev
+    assert cstats.alive() == 1
+    del rev2
+    assert cstats.alive() == 0
+
+    assert cstats.values() == []
+    assert cstats.default_constructions == 0
+    assert cstats.copy_constructions == 0
+    assert cstats.move_constructions >= 1
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+
+def test_sequence_length():
+    """#2076: Exception raised by len(arg) should be propagated"""
+
+    class BadLen(RuntimeError):
+        pass
+
+    class SequenceLike:
+        def __getitem__(self, i):
+            return None
+
+        def __len__(self):
+            raise BadLen()
+
+    with pytest.raises(BadLen):
+        m.sequence_length(SequenceLike())
+
+    assert m.sequence_length([1, 2, 3]) == 3
+    assert m.sequence_length("hello") == 5
+
+
+def test_map_iterator():
+    sm = m.StringMap({"hi": "bye", "black": "white"})
+    assert sm["hi"] == "bye"
+    assert len(sm) == 2
+    assert sm["black"] == "white"
+
+    with pytest.raises(KeyError):
+        assert sm["orange"]
+    sm["orange"] = "banana"
+    assert sm["orange"] == "banana"
+
+    expected = {"hi": "bye", "black": "white", "orange": "banana"}
+    for k in sm:
+        assert sm[k] == expected[k]
+    for k, v in sm.items():
+        assert v == expected[k]
+    assert list(sm.values()) == [expected[k] for k in sm]
+
+    it = iter(m.StringMap({}))
+    for _ in range(3):  # __next__ must continue to raise StopIteration
+        with pytest.raises(StopIteration):
+            next(it)
+
+
+def test_python_iterator_in_cpp():
+    t = (1, 2, 3)
+    assert m.object_to_list(t) == [1, 2, 3]
+    assert m.object_to_list(iter(t)) == [1, 2, 3]
+    assert m.iterator_to_list(iter(t)) == [1, 2, 3]
+
+    with pytest.raises(TypeError) as excinfo:
+        m.object_to_list(1)
+    assert "object is not iterable" in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        m.iterator_to_list(1)
+    assert "incompatible function arguments" in str(excinfo.value)
+
+    def bad_next_call():
+        raise RuntimeError("py::iterator::advance() should propagate errors")
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.iterator_to_list(iter(bad_next_call, None))
+    assert str(excinfo.value) == "py::iterator::advance() should propagate errors"
+
+    lst = [1, None, 0, None]
+    assert m.count_none(lst) == 2
+    assert m.find_none(lst) is True
+    assert m.count_nonzeros({"a": 0, "b": 1, "c": 2}) == 2
+
+    r = range(5)
+    assert all(m.tuple_iterator(tuple(r)))
+    assert all(m.list_iterator(list(r)))
+    assert all(m.sequence_iterator(r))
+
+
+def test_iterator_passthrough():
+    """#181: iterator passthrough did not compile"""
+    from pybind11_tests.sequences_and_iterators import iterator_passthrough
+
+    values = [3, 5, 7, 9, 11, 13, 15]
+    assert list(iterator_passthrough(iter(values))) == values
+
+
+def test_iterator_rvp():
+    """#388: Can't make iterators via make_iterator() with different r/v policies"""
+    import pybind11_tests.sequences_and_iterators as m
+
+    assert list(m.make_iterator_1()) == [1, 2, 3]
+    assert list(m.make_iterator_2()) == [1, 2, 3]
+    assert not isinstance(m.make_iterator_1(), type(m.make_iterator_2()))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_smart_ptr.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_smart_ptr.cpp
new file mode 100644
index 000000000..2c23a9a19
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_smart_ptr.cpp
@@ -0,0 +1,470 @@
+/*
+    tests/test_smart_ptr.cpp -- binding classes with custom reference counting,
+    implicit conversions between types
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "object.h"
+#include "pybind11_tests.h"
+
+namespace {
+
+// This is just a wrapper around unique_ptr, but with extra fields to deliberately bloat up the
+// holder size to trigger the non-simple-layout internal instance layout for single inheritance
+// with large holder type:
+template <typename T>
+class huge_unique_ptr {
+    std::unique_ptr<T> ptr;
+    uint64_t padding[10];
+
+public:
+    explicit huge_unique_ptr(T *p) : ptr(p) {}
+    T *get() { return ptr.get(); }
+};
+
+// Simple custom holder that works like unique_ptr
+template <typename T>
+class custom_unique_ptr {
+    std::unique_ptr<T> impl;
+
+public:
+    explicit custom_unique_ptr(T *p) : impl(p) {}
+    T *get() const { return impl.get(); }
+    T *release_ptr() { return impl.release(); }
+};
+
+// Simple custom holder that works like shared_ptr and has operator& overload
+// To obtain address of an instance of this holder pybind should use std::addressof
+// Attempt to get address via operator& may leads to segmentation fault
+template <typename T>
+class shared_ptr_with_addressof_operator {
+    std::shared_ptr<T> impl;
+
+public:
+    shared_ptr_with_addressof_operator() = default;
+    explicit shared_ptr_with_addressof_operator(T *p) : impl(p) {}
+    T *get() const { return impl.get(); }
+    T **operator&() { throw std::logic_error("Call of overloaded operator& is not expected"); }
+};
+
+// Simple custom holder that works like unique_ptr and has operator& overload
+// To obtain address of an instance of this holder pybind should use std::addressof
+// Attempt to get address via operator& may leads to segmentation fault
+template <typename T>
+class unique_ptr_with_addressof_operator {
+    std::unique_ptr<T> impl;
+
+public:
+    unique_ptr_with_addressof_operator() = default;
+    explicit unique_ptr_with_addressof_operator(T *p) : impl(p) {}
+    T *get() const { return impl.get(); }
+    T *release_ptr() { return impl.release(); }
+    T **operator&() { throw std::logic_error("Call of overloaded operator& is not expected"); }
+};
+
+// Custom object with builtin reference counting (see 'object.h' for the implementation)
+class MyObject1 : public Object {
+public:
+    explicit MyObject1(int value) : value(value) { print_created(this, toString()); }
+    std::string toString() const override { return "MyObject1[" + std::to_string(value) + "]"; }
+
+protected:
+    ~MyObject1() override { print_destroyed(this); }
+
+private:
+    int value;
+};
+
+// Object managed by a std::shared_ptr<>
+class MyObject2 {
+public:
+    MyObject2(const MyObject2 &) = default;
+    explicit MyObject2(int value) : value(value) { print_created(this, toString()); }
+    std::string toString() const { return "MyObject2[" + std::to_string(value) + "]"; }
+    virtual ~MyObject2() { print_destroyed(this); }
+
+private:
+    int value;
+};
+
+// Object managed by a std::shared_ptr<>, additionally derives from std::enable_shared_from_this<>
+class MyObject3 : public std::enable_shared_from_this<MyObject3> {
+public:
+    MyObject3(const MyObject3 &) = default;
+    explicit MyObject3(int value) : value(value) { print_created(this, toString()); }
+    std::string toString() const { return "MyObject3[" + std::to_string(value) + "]"; }
+    virtual ~MyObject3() { print_destroyed(this); }
+
+private:
+    int value;
+};
+
+// test_unique_nodelete
+// Object with a private destructor
+class MyObject4;
+std::unordered_set<MyObject4 *> myobject4_instances;
+class MyObject4 {
+public:
+    explicit MyObject4(int value) : value{value} {
+        print_created(this);
+        myobject4_instances.insert(this);
+    }
+    int value;
+
+    static void cleanupAllInstances() {
+        auto tmp = std::move(myobject4_instances);
+        myobject4_instances.clear();
+        for (auto *o : tmp) {
+            delete o;
+        }
+    }
+
+private:
+    ~MyObject4() {
+        myobject4_instances.erase(this);
+        print_destroyed(this);
+    }
+};
+
+// test_unique_deleter
+// Object with std::unique_ptr<T, D> where D is not matching the base class
+// Object with a protected destructor
+class MyObject4a;
+std::unordered_set<MyObject4a *> myobject4a_instances;
+class MyObject4a {
+public:
+    explicit MyObject4a(int i) : value{i} {
+        print_created(this);
+        myobject4a_instances.insert(this);
+    };
+    int value;
+
+    static void cleanupAllInstances() {
+        auto tmp = std::move(myobject4a_instances);
+        myobject4a_instances.clear();
+        for (auto *o : tmp) {
+            delete o;
+        }
+    }
+
+protected:
+    virtual ~MyObject4a() {
+        myobject4a_instances.erase(this);
+        print_destroyed(this);
+    }
+};
+
+// Object derived but with public destructor and no Deleter in default holder
+class MyObject4b : public MyObject4a {
+public:
+    explicit MyObject4b(int i) : MyObject4a(i) { print_created(this); }
+    ~MyObject4b() override { print_destroyed(this); }
+};
+
+// test_large_holder
+class MyObject5 { // managed by huge_unique_ptr
+public:
+    explicit MyObject5(int value) : value{value} { print_created(this); }
+    ~MyObject5() { print_destroyed(this); }
+    int value;
+};
+
+// test_shared_ptr_and_references
+struct SharedPtrRef {
+    struct A {
+        A() { print_created(this); }
+        A(const A &) { print_copy_created(this); }
+        A(A &&) noexcept { print_move_created(this); }
+        ~A() { print_destroyed(this); }
+    };
+
+    A value = {};
+    std::shared_ptr<A> shared = std::make_shared<A>();
+};
+
+// test_shared_ptr_from_this_and_references
+struct SharedFromThisRef {
+    struct B : std::enable_shared_from_this<B> {
+        B() { print_created(this); }
+        // NOLINTNEXTLINE(bugprone-copy-constructor-init)
+        B(const B &) : std::enable_shared_from_this<B>() { print_copy_created(this); }
+        B(B &&) noexcept : std::enable_shared_from_this<B>() { print_move_created(this); }
+        ~B() { print_destroyed(this); }
+    };
+
+    B value = {};
+    std::shared_ptr<B> shared = std::make_shared<B>();
+};
+
+// Issue #865: shared_from_this doesn't work with virtual inheritance
+struct SharedFromThisVBase : std::enable_shared_from_this<SharedFromThisVBase> {
+    SharedFromThisVBase() = default;
+    SharedFromThisVBase(const SharedFromThisVBase &) = default;
+    virtual ~SharedFromThisVBase() = default;
+};
+struct SharedFromThisVirt : virtual SharedFromThisVBase {};
+
+// test_move_only_holder
+struct C {
+    C() { print_created(this); }
+    ~C() { print_destroyed(this); }
+};
+
+// test_holder_with_addressof_operator
+struct TypeForHolderWithAddressOf {
+    TypeForHolderWithAddressOf() { print_created(this); }
+    TypeForHolderWithAddressOf(const TypeForHolderWithAddressOf &) { print_copy_created(this); }
+    TypeForHolderWithAddressOf(TypeForHolderWithAddressOf &&) noexcept {
+        print_move_created(this);
+    }
+    ~TypeForHolderWithAddressOf() { print_destroyed(this); }
+    std::string toString() const {
+        return "TypeForHolderWithAddressOf[" + std::to_string(value) + "]";
+    }
+    int value = 42;
+};
+
+// test_move_only_holder_with_addressof_operator
+struct TypeForMoveOnlyHolderWithAddressOf {
+    explicit TypeForMoveOnlyHolderWithAddressOf(int value) : value{value} { print_created(this); }
+    ~TypeForMoveOnlyHolderWithAddressOf() { print_destroyed(this); }
+    std::string toString() const {
+        return "MoveOnlyHolderWithAddressOf[" + std::to_string(value) + "]";
+    }
+    int value;
+};
+
+// test_smart_ptr_from_default
+struct HeldByDefaultHolder {};
+
+// test_shared_ptr_gc
+// #187: issue involving std::shared_ptr<> return value policy & garbage collection
+struct ElementBase {
+    virtual ~ElementBase() = default; /* Force creation of virtual table */
+    ElementBase() = default;
+    ElementBase(const ElementBase &) = delete;
+};
+
+struct ElementA : ElementBase {
+    explicit ElementA(int v) : v(v) {}
+    int value() const { return v; }
+    int v;
+};
+
+struct ElementList {
+    void add(const std::shared_ptr<ElementBase> &e) { l.push_back(e); }
+    std::vector<std::shared_ptr<ElementBase>> l;
+};
+
+} // namespace
+
+// ref<T> is a wrapper for 'Object' which uses intrusive reference counting
+// It is always possible to construct a ref<T> from an Object* pointer without
+// possible inconsistencies, hence the 'true' argument at the end.
+// Make pybind11 aware of the non-standard getter member function
+namespace pybind11 {
+namespace detail {
+template <typename T>
+struct holder_helper<ref<T>> {
+    static const T *get(const ref<T> &p) { return p.get_ptr(); }
+};
+} // namespace detail
+} // namespace pybind11
+
+// Make pybind aware of the ref-counted wrapper type (s):
+PYBIND11_DECLARE_HOLDER_TYPE(T, ref<T>, true);
+// The following is not required anymore for std::shared_ptr, but it should compile without error:
+PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>);
+PYBIND11_DECLARE_HOLDER_TYPE(T, huge_unique_ptr<T>);
+PYBIND11_DECLARE_HOLDER_TYPE(T, custom_unique_ptr<T>);
+PYBIND11_DECLARE_HOLDER_TYPE(T, shared_ptr_with_addressof_operator<T>);
+PYBIND11_DECLARE_HOLDER_TYPE(T, unique_ptr_with_addressof_operator<T>);
+
+TEST_SUBMODULE(smart_ptr, m) {
+    // Please do not interleave `struct` and `class` definitions with bindings code,
+    // but implement `struct`s and `class`es in the anonymous namespace above.
+    // This helps keeping the smart_holder branch in sync with master.
+
+    // test_smart_ptr
+
+    // Object implementation in `object.h`
+    py::class_<Object, ref<Object>> obj(m, "Object");
+    obj.def("getRefCount", &Object::getRefCount);
+
+    py::class_<MyObject1, ref<MyObject1>>(m, "MyObject1", obj).def(py::init<int>());
+    py::implicitly_convertible<py::int_, MyObject1>();
+
+    m.def("make_object_1", []() -> Object * { return new MyObject1(1); });
+    m.def("make_object_2", []() -> ref<Object> { return ref<Object>(new MyObject1(2)); });
+    m.def("make_myobject1_1", []() -> MyObject1 * { return new MyObject1(4); });
+    m.def("make_myobject1_2", []() -> ref<MyObject1> { return ref<MyObject1>(new MyObject1(5)); });
+    m.def("print_object_1", [](const Object *obj) { py::print(obj->toString()); });
+    m.def("print_object_2", [](ref<Object> obj) { py::print(obj->toString()); });
+    m.def("print_object_3", [](const ref<Object> &obj) { py::print(obj->toString()); });
+    m.def("print_object_4", [](const ref<Object> *obj) { py::print((*obj)->toString()); });
+    m.def("print_myobject1_1", [](const MyObject1 *obj) { py::print(obj->toString()); });
+    m.def("print_myobject1_2", [](ref<MyObject1> obj) { py::print(obj->toString()); });
+    m.def("print_myobject1_3", [](const ref<MyObject1> &obj) { py::print(obj->toString()); });
+    m.def("print_myobject1_4", [](const ref<MyObject1> *obj) { py::print((*obj)->toString()); });
+
+    // Expose constructor stats for the ref type
+    m.def("cstats_ref", &ConstructorStats::get<ref_tag>);
+
+    py::class_<MyObject2, std::shared_ptr<MyObject2>>(m, "MyObject2").def(py::init<int>());
+    m.def("make_myobject2_1", []() { return new MyObject2(6); });
+    m.def("make_myobject2_2", []() { return std::make_shared<MyObject2>(7); });
+    m.def("print_myobject2_1", [](const MyObject2 *obj) { py::print(obj->toString()); });
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
+    m.def("print_myobject2_2", [](std::shared_ptr<MyObject2> obj) { py::print(obj->toString()); });
+    m.def("print_myobject2_3",
+          [](const std::shared_ptr<MyObject2> &obj) { py::print(obj->toString()); });
+    m.def("print_myobject2_4",
+          [](const std::shared_ptr<MyObject2> *obj) { py::print((*obj)->toString()); });
+
+    py::class_<MyObject3, std::shared_ptr<MyObject3>>(m, "MyObject3").def(py::init<int>());
+    m.def("make_myobject3_1", []() { return new MyObject3(8); });
+    m.def("make_myobject3_2", []() { return std::make_shared<MyObject3>(9); });
+    m.def("print_myobject3_1", [](const MyObject3 *obj) { py::print(obj->toString()); });
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
+    m.def("print_myobject3_2", [](std::shared_ptr<MyObject3> obj) { py::print(obj->toString()); });
+    m.def("print_myobject3_3",
+          [](const std::shared_ptr<MyObject3> &obj) { py::print(obj->toString()); });
+    m.def("print_myobject3_4",
+          [](const std::shared_ptr<MyObject3> *obj) { py::print((*obj)->toString()); });
+
+    // test_smart_ptr_refcounting
+    m.def("test_object1_refcounting", []() {
+        auto o = ref<MyObject1>(new MyObject1(0));
+        bool good = o->getRefCount() == 1;
+        py::object o2 = py::cast(o, py::return_value_policy::reference);
+        // always request (partial) ownership for objects with intrusive
+        // reference counting even when using the 'reference' RVP
+        good &= o->getRefCount() == 2;
+        return good;
+    });
+
+    // test_unique_nodelete
+    py::class_<MyObject4, std::unique_ptr<MyObject4, py::nodelete>>(m, "MyObject4")
+        .def(py::init<int>())
+        .def_readwrite("value", &MyObject4::value)
+        .def_static("cleanup_all_instances", &MyObject4::cleanupAllInstances);
+
+    // test_unique_deleter
+    py::class_<MyObject4a, std::unique_ptr<MyObject4a, py::nodelete>>(m, "MyObject4a")
+        .def(py::init<int>())
+        .def_readwrite("value", &MyObject4a::value)
+        .def_static("cleanup_all_instances", &MyObject4a::cleanupAllInstances);
+
+    py::class_<MyObject4b, MyObject4a, std::unique_ptr<MyObject4b>>(m, "MyObject4b")
+        .def(py::init<int>());
+
+    // test_large_holder
+    py::class_<MyObject5, huge_unique_ptr<MyObject5>>(m, "MyObject5")
+        .def(py::init<int>())
+        .def_readwrite("value", &MyObject5::value);
+
+    // test_shared_ptr_and_references
+    using A = SharedPtrRef::A;
+    py::class_<A, std::shared_ptr<A>>(m, "A");
+    py::class_<SharedPtrRef, std::unique_ptr<SharedPtrRef>>(m, "SharedPtrRef")
+        .def(py::init<>())
+        .def_readonly("ref", &SharedPtrRef::value)
+        .def_property_readonly(
+            "copy", [](const SharedPtrRef &s) { return s.value; }, py::return_value_policy::copy)
+        .def_readonly("holder_ref", &SharedPtrRef::shared)
+        .def_property_readonly(
+            "holder_copy",
+            [](const SharedPtrRef &s) { return s.shared; },
+            py::return_value_policy::copy)
+        .def("set_ref", [](SharedPtrRef &, const A &) { return true; })
+        // NOLINTNEXTLINE(performance-unnecessary-value-param)
+        .def("set_holder", [](SharedPtrRef &, std::shared_ptr<A>) { return true; });
+
+    // test_shared_ptr_from_this_and_references
+    using B = SharedFromThisRef::B;
+    py::class_<B, std::shared_ptr<B>>(m, "B");
+    py::class_<SharedFromThisRef, std::unique_ptr<SharedFromThisRef>>(m, "SharedFromThisRef")
+        .def(py::init<>())
+        .def_readonly("bad_wp", &SharedFromThisRef::value)
+        .def_property_readonly("ref",
+                               [](const SharedFromThisRef &s) -> const B & { return *s.shared; })
+        .def_property_readonly(
+            "copy",
+            [](const SharedFromThisRef &s) { return s.value; },
+            py::return_value_policy::copy)
+        .def_readonly("holder_ref", &SharedFromThisRef::shared)
+        .def_property_readonly(
+            "holder_copy",
+            [](const SharedFromThisRef &s) { return s.shared; },
+            py::return_value_policy::copy)
+        .def("set_ref", [](SharedFromThisRef &, const B &) { return true; })
+        // NOLINTNEXTLINE(performance-unnecessary-value-param)
+        .def("set_holder", [](SharedFromThisRef &, std::shared_ptr<B>) { return true; });
+
+    // Issue #865: shared_from_this doesn't work with virtual inheritance
+    static std::shared_ptr<SharedFromThisVirt> sft(new SharedFromThisVirt());
+    py::class_<SharedFromThisVirt, std::shared_ptr<SharedFromThisVirt>>(m, "SharedFromThisVirt")
+        .def_static("get", []() { return sft.get(); });
+
+    // test_move_only_holder
+    py::class_<C, custom_unique_ptr<C>>(m, "TypeWithMoveOnlyHolder")
+        .def_static("make", []() { return custom_unique_ptr<C>(new C); })
+        .def_static("make_as_object", []() { return py::cast(custom_unique_ptr<C>(new C)); });
+
+    // test_holder_with_addressof_operator
+    using HolderWithAddressOf = shared_ptr_with_addressof_operator<TypeForHolderWithAddressOf>;
+    py::class_<TypeForHolderWithAddressOf, HolderWithAddressOf>(m, "TypeForHolderWithAddressOf")
+        .def_static("make", []() { return HolderWithAddressOf(new TypeForHolderWithAddressOf); })
+        .def("get", [](const HolderWithAddressOf &self) { return self.get(); })
+        .def("print_object_1",
+             [](const TypeForHolderWithAddressOf *obj) { py::print(obj->toString()); })
+        // NOLINTNEXTLINE(performance-unnecessary-value-param)
+        .def("print_object_2", [](HolderWithAddressOf obj) { py::print(obj.get()->toString()); })
+        .def("print_object_3",
+             [](const HolderWithAddressOf &obj) { py::print(obj.get()->toString()); })
+        .def("print_object_4",
+             [](const HolderWithAddressOf *obj) { py::print((*obj).get()->toString()); });
+
+    // test_move_only_holder_with_addressof_operator
+    using MoveOnlyHolderWithAddressOf
+        = unique_ptr_with_addressof_operator<TypeForMoveOnlyHolderWithAddressOf>;
+    py::class_<TypeForMoveOnlyHolderWithAddressOf, MoveOnlyHolderWithAddressOf>(
+        m, "TypeForMoveOnlyHolderWithAddressOf")
+        .def_static("make",
+                    []() {
+                        return MoveOnlyHolderWithAddressOf(
+                            new TypeForMoveOnlyHolderWithAddressOf(0));
+                    })
+        .def_readwrite("value", &TypeForMoveOnlyHolderWithAddressOf::value)
+        .def("print_object",
+             [](const TypeForMoveOnlyHolderWithAddressOf *obj) { py::print(obj->toString()); });
+
+    // test_smart_ptr_from_default
+    py::class_<HeldByDefaultHolder, std::unique_ptr<HeldByDefaultHolder>>(m, "HeldByDefaultHolder")
+        .def(py::init<>())
+        // NOLINTNEXTLINE(performance-unnecessary-value-param)
+        .def_static("load_shared_ptr", [](std::shared_ptr<HeldByDefaultHolder>) {});
+
+    // test_shared_ptr_gc
+    // #187: issue involving std::shared_ptr<> return value policy & garbage collection
+    py::class_<ElementBase, std::shared_ptr<ElementBase>>(m, "ElementBase");
+
+    py::class_<ElementA, ElementBase, std::shared_ptr<ElementA>>(m, "ElementA")
+        .def(py::init<int>())
+        .def("value", &ElementA::value);
+
+    py::class_<ElementList, std::shared_ptr<ElementList>>(m, "ElementList")
+        .def(py::init<>())
+        .def("add", &ElementList::add)
+        .def("get", [](ElementList &el) {
+            py::list list;
+            for (auto &e : el.l) {
+                list.append(py::cast(e));
+            }
+            return list;
+        });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_smart_ptr.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_smart_ptr.py
new file mode 100644
index 000000000..2f204e01b
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_smart_ptr.py
@@ -0,0 +1,315 @@
+import pytest
+
+m = pytest.importorskip("pybind11_tests.smart_ptr")
+from pybind11_tests import ConstructorStats  # noqa: E402
+
+
+def test_smart_ptr(capture):
+    # Object1
+    for i, o in enumerate(
+        [m.make_object_1(), m.make_object_2(), m.MyObject1(3)], start=1
+    ):
+        assert o.getRefCount() == 1
+        with capture:
+            m.print_object_1(o)
+            m.print_object_2(o)
+            m.print_object_3(o)
+            m.print_object_4(o)
+        assert capture == f"MyObject1[{i}]\n" * 4
+
+    for i, o in enumerate(
+        [m.make_myobject1_1(), m.make_myobject1_2(), m.MyObject1(6), 7], start=4
+    ):
+        print(o)
+        with capture:
+            if not isinstance(o, int):
+                m.print_object_1(o)
+                m.print_object_2(o)
+                m.print_object_3(o)
+                m.print_object_4(o)
+            m.print_myobject1_1(o)
+            m.print_myobject1_2(o)
+            m.print_myobject1_3(o)
+            m.print_myobject1_4(o)
+
+        times = 4 if isinstance(o, int) else 8
+        assert capture == f"MyObject1[{i}]\n" * times
+
+    cstats = ConstructorStats.get(m.MyObject1)
+    assert cstats.alive() == 0
+    expected_values = [f"MyObject1[{i}]" for i in range(1, 7)] + ["MyObject1[7]"] * 4
+    assert cstats.values() == expected_values
+    assert cstats.default_constructions == 0
+    assert cstats.copy_constructions == 0
+    # assert cstats.move_constructions >= 0 # Doesn't invoke any
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+    # Object2
+    for i, o in zip(
+        [8, 6, 7], [m.MyObject2(8), m.make_myobject2_1(), m.make_myobject2_2()]
+    ):
+        print(o)
+        with capture:
+            m.print_myobject2_1(o)
+            m.print_myobject2_2(o)
+            m.print_myobject2_3(o)
+            m.print_myobject2_4(o)
+        assert capture == f"MyObject2[{i}]\n" * 4
+
+    cstats = ConstructorStats.get(m.MyObject2)
+    assert cstats.alive() == 1
+    o = None
+    assert cstats.alive() == 0
+    assert cstats.values() == ["MyObject2[8]", "MyObject2[6]", "MyObject2[7]"]
+    assert cstats.default_constructions == 0
+    assert cstats.copy_constructions == 0
+    # assert cstats.move_constructions >= 0 # Doesn't invoke any
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+    # Object3
+    for i, o in zip(
+        [9, 8, 9], [m.MyObject3(9), m.make_myobject3_1(), m.make_myobject3_2()]
+    ):
+        print(o)
+        with capture:
+            m.print_myobject3_1(o)
+            m.print_myobject3_2(o)
+            m.print_myobject3_3(o)
+            m.print_myobject3_4(o)
+        assert capture == f"MyObject3[{i}]\n" * 4
+
+    cstats = ConstructorStats.get(m.MyObject3)
+    assert cstats.alive() == 1
+    o = None
+    assert cstats.alive() == 0
+    assert cstats.values() == ["MyObject3[9]", "MyObject3[8]", "MyObject3[9]"]
+    assert cstats.default_constructions == 0
+    assert cstats.copy_constructions == 0
+    # assert cstats.move_constructions >= 0 # Doesn't invoke any
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+    # Object
+    cstats = ConstructorStats.get(m.Object)
+    assert cstats.alive() == 0
+    assert cstats.values() == []
+    assert cstats.default_constructions == 10
+    assert cstats.copy_constructions == 0
+    # assert cstats.move_constructions >= 0 # Doesn't invoke any
+    assert cstats.copy_assignments == 0
+    assert cstats.move_assignments == 0
+
+    # ref<>
+    cstats = m.cstats_ref()
+    assert cstats.alive() == 0
+    assert cstats.values() == ["from pointer"] * 10
+    assert cstats.default_constructions == 30
+    assert cstats.copy_constructions == 12
+    # assert cstats.move_constructions >= 0 # Doesn't invoke any
+    assert cstats.copy_assignments == 30
+    assert cstats.move_assignments == 0
+
+
+def test_smart_ptr_refcounting():
+    assert m.test_object1_refcounting()
+
+
+def test_unique_nodelete():
+    o = m.MyObject4(23)
+    assert o.value == 23
+    cstats = ConstructorStats.get(m.MyObject4)
+    assert cstats.alive() == 1
+    del o
+    assert cstats.alive() == 1
+    m.MyObject4.cleanup_all_instances()
+    assert cstats.alive() == 0
+
+
+def test_unique_nodelete4a():
+    o = m.MyObject4a(23)
+    assert o.value == 23
+    cstats = ConstructorStats.get(m.MyObject4a)
+    assert cstats.alive() == 1
+    del o
+    assert cstats.alive() == 1
+    m.MyObject4a.cleanup_all_instances()
+    assert cstats.alive() == 0
+
+
+def test_unique_deleter():
+    m.MyObject4a(0)
+    o = m.MyObject4b(23)
+    assert o.value == 23
+    cstats4a = ConstructorStats.get(m.MyObject4a)
+    assert cstats4a.alive() == 2
+    cstats4b = ConstructorStats.get(m.MyObject4b)
+    assert cstats4b.alive() == 1
+    del o
+    assert cstats4a.alive() == 1  # Should now only be one leftover
+    assert cstats4b.alive() == 0  # Should be deleted
+    m.MyObject4a.cleanup_all_instances()
+    assert cstats4a.alive() == 0
+    assert cstats4b.alive() == 0
+
+
+def test_large_holder():
+    o = m.MyObject5(5)
+    assert o.value == 5
+    cstats = ConstructorStats.get(m.MyObject5)
+    assert cstats.alive() == 1
+    del o
+    assert cstats.alive() == 0
+
+
+def test_shared_ptr_and_references():
+    s = m.SharedPtrRef()
+    stats = ConstructorStats.get(m.A)
+    assert stats.alive() == 2
+
+    ref = s.ref  # init_holder_helper(holder_ptr=false, owned=false)
+    assert stats.alive() == 2
+    assert s.set_ref(ref)
+    with pytest.raises(RuntimeError) as excinfo:
+        assert s.set_holder(ref)
+    assert "Unable to cast from non-held to held instance" in str(excinfo.value)
+
+    copy = s.copy  # init_holder_helper(holder_ptr=false, owned=true)
+    assert stats.alive() == 3
+    assert s.set_ref(copy)
+    assert s.set_holder(copy)
+
+    holder_ref = s.holder_ref  # init_holder_helper(holder_ptr=true, owned=false)
+    assert stats.alive() == 3
+    assert s.set_ref(holder_ref)
+    assert s.set_holder(holder_ref)
+
+    holder_copy = s.holder_copy  # init_holder_helper(holder_ptr=true, owned=true)
+    assert stats.alive() == 3
+    assert s.set_ref(holder_copy)
+    assert s.set_holder(holder_copy)
+
+    del ref, copy, holder_ref, holder_copy, s
+    assert stats.alive() == 0
+
+
+def test_shared_ptr_from_this_and_references():
+    s = m.SharedFromThisRef()
+    stats = ConstructorStats.get(m.B)
+    assert stats.alive() == 2
+
+    ref = s.ref  # init_holder_helper(holder_ptr=false, owned=false, bad_wp=false)
+    assert stats.alive() == 2
+    assert s.set_ref(ref)
+    assert s.set_holder(
+        ref
+    )  # std::enable_shared_from_this can create a holder from a reference
+
+    bad_wp = s.bad_wp  # init_holder_helper(holder_ptr=false, owned=false, bad_wp=true)
+    assert stats.alive() == 2
+    assert s.set_ref(bad_wp)
+    with pytest.raises(RuntimeError) as excinfo:
+        assert s.set_holder(bad_wp)
+    assert "Unable to cast from non-held to held instance" in str(excinfo.value)
+
+    copy = s.copy  # init_holder_helper(holder_ptr=false, owned=true, bad_wp=false)
+    assert stats.alive() == 3
+    assert s.set_ref(copy)
+    assert s.set_holder(copy)
+
+    holder_ref = (
+        s.holder_ref
+    )  # init_holder_helper(holder_ptr=true, owned=false, bad_wp=false)
+    assert stats.alive() == 3
+    assert s.set_ref(holder_ref)
+    assert s.set_holder(holder_ref)
+
+    holder_copy = (
+        s.holder_copy
+    )  # init_holder_helper(holder_ptr=true, owned=true, bad_wp=false)
+    assert stats.alive() == 3
+    assert s.set_ref(holder_copy)
+    assert s.set_holder(holder_copy)
+
+    del ref, bad_wp, copy, holder_ref, holder_copy, s
+    assert stats.alive() == 0
+
+    z = m.SharedFromThisVirt.get()
+    y = m.SharedFromThisVirt.get()
+    assert y is z
+
+
+def test_move_only_holder():
+    a = m.TypeWithMoveOnlyHolder.make()
+    b = m.TypeWithMoveOnlyHolder.make_as_object()
+    stats = ConstructorStats.get(m.TypeWithMoveOnlyHolder)
+    assert stats.alive() == 2
+    del b
+    assert stats.alive() == 1
+    del a
+    assert stats.alive() == 0
+
+
+def test_holder_with_addressof_operator():
+    # this test must not throw exception from c++
+    a = m.TypeForHolderWithAddressOf.make()
+    a.print_object_1()
+    a.print_object_2()
+    a.print_object_3()
+    a.print_object_4()
+
+    stats = ConstructorStats.get(m.TypeForHolderWithAddressOf)
+    assert stats.alive() == 1
+
+    np = m.TypeForHolderWithAddressOf.make()
+    assert stats.alive() == 2
+    del a
+    assert stats.alive() == 1
+    del np
+    assert stats.alive() == 0
+
+    b = m.TypeForHolderWithAddressOf.make()
+    c = b
+    assert b.get() is c.get()
+    assert stats.alive() == 1
+
+    del b
+    assert stats.alive() == 1
+
+    del c
+    assert stats.alive() == 0
+
+
+def test_move_only_holder_with_addressof_operator():
+    a = m.TypeForMoveOnlyHolderWithAddressOf.make()
+    a.print_object()
+
+    stats = ConstructorStats.get(m.TypeForMoveOnlyHolderWithAddressOf)
+    assert stats.alive() == 1
+
+    a.value = 42
+    assert a.value == 42
+
+    del a
+    assert stats.alive() == 0
+
+
+def test_smart_ptr_from_default():
+    instance = m.HeldByDefaultHolder()
+    with pytest.raises(RuntimeError) as excinfo:
+        m.HeldByDefaultHolder.load_shared_ptr(instance)
+    assert (
+        "Unable to load a custom holder type from a "
+        "default-holder instance" in str(excinfo.value)
+    )
+
+
+def test_shared_ptr_gc():
+    """#187: issue involving std::shared_ptr<> return value policy & garbage collection"""
+    el = m.ElementList()
+    for i in range(10):
+        el.add(m.ElementA(i))
+    pytest.gc_collect()
+    for i, v in enumerate(el.get()):
+        assert i == v.value()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl.cpp
new file mode 100644
index 000000000..38d32fda9
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl.cpp
@@ -0,0 +1,545 @@
+/*
+    tests/test_stl.cpp -- STL type casters
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/stl.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#ifndef PYBIND11_HAS_FILESYSTEM_IS_OPTIONAL
+#    define PYBIND11_HAS_FILESYSTEM_IS_OPTIONAL
+#endif
+#include <pybind11/stl/filesystem.h>
+
+#include <string>
+#include <vector>
+
+#if defined(PYBIND11_TEST_BOOST)
+#    include <boost/optional.hpp>
+
+namespace pybind11 {
+namespace detail {
+template <typename T>
+struct type_caster<boost::optional<T>> : optional_caster<boost::optional<T>> {};
+
+template <>
+struct type_caster<boost::none_t> : void_caster<boost::none_t> {};
+} // namespace detail
+} // namespace pybind11
+#endif
+
+// Test with `std::variant` in C++17 mode, or with `boost::variant` in C++11/14
+#if defined(PYBIND11_HAS_VARIANT)
+using std::variant;
+#    define PYBIND11_TEST_VARIANT 1
+#elif defined(PYBIND11_TEST_BOOST)
+#    include <boost/variant.hpp>
+#    define PYBIND11_TEST_VARIANT 1
+using boost::variant;
+
+namespace pybind11 {
+namespace detail {
+template <typename... Ts>
+struct type_caster<boost::variant<Ts...>> : variant_caster<boost::variant<Ts...>> {};
+
+template <>
+struct visit_helper<boost::variant> {
+    template <typename... Args>
+    static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) {
+        return boost::apply_visitor(args...);
+    }
+};
+} // namespace detail
+} // namespace pybind11
+#endif
+
+PYBIND11_MAKE_OPAQUE(std::vector<std::string, std::allocator<std::string>>);
+
+/// Issue #528: templated constructor
+struct TplCtorClass {
+    template <typename T>
+    explicit TplCtorClass(const T &) {}
+    bool operator==(const TplCtorClass &) const { return true; }
+};
+
+namespace std {
+template <>
+struct hash<TplCtorClass> {
+    size_t operator()(const TplCtorClass &) const { return 0; }
+};
+} // namespace std
+
+template <template <typename> class OptionalImpl, typename T>
+struct OptionalHolder {
+    // NOLINTNEXTLINE(modernize-use-equals-default): breaks GCC 4.8
+    OptionalHolder(){};
+    bool member_initialized() const { return member && member->initialized; }
+    OptionalImpl<T> member = T{};
+};
+
+enum class EnumType {
+    kSet = 42,
+    kUnset = 85,
+};
+
+// This is used to test that return-by-ref and return-by-copy policies are
+// handled properly for optional types. This is a regression test for a dangling
+// reference issue. The issue seemed to require the enum value type to
+// reproduce - it didn't seem to happen if the value type is just an integer.
+template <template <typename> class OptionalImpl>
+class OptionalProperties {
+public:
+    using OptionalEnumValue = OptionalImpl<EnumType>;
+
+    OptionalProperties() : value(EnumType::kSet) {}
+    ~OptionalProperties() {
+        // Reset value to detect use-after-destruction.
+        // This is set to a specific value rather than nullopt to ensure that
+        // the memory that contains the value gets re-written.
+        value = EnumType::kUnset;
+    }
+
+    OptionalEnumValue &access_by_ref() { return value; }
+    OptionalEnumValue access_by_copy() { return value; }
+
+private:
+    OptionalEnumValue value;
+};
+
+// This type mimics aspects of boost::optional from old versions of Boost,
+// which exposed a dangling reference bug in Pybind11. Recent versions of
+// boost::optional, as well as libstdc++'s std::optional, don't seem to be
+// affected by the same issue. This is meant to be a minimal implementation
+// required to reproduce the issue, not fully standard-compliant.
+// See issue #3330 for more details.
+template <typename T>
+class ReferenceSensitiveOptional {
+public:
+    using value_type = T;
+
+    ReferenceSensitiveOptional() = default;
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    ReferenceSensitiveOptional(const T &value) : storage{value} {}
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    ReferenceSensitiveOptional(T &&value) : storage{std::move(value)} {}
+    ReferenceSensitiveOptional &operator=(const T &value) {
+        storage = {value};
+        return *this;
+    }
+    ReferenceSensitiveOptional &operator=(T &&value) {
+        storage = {std::move(value)};
+        return *this;
+    }
+
+    template <typename... Args>
+    T &emplace(Args &&...args) {
+        storage.clear();
+        storage.emplace_back(std::forward<Args>(args)...);
+        return storage.back();
+    }
+
+    const T &value() const noexcept {
+        assert(!storage.empty());
+        return storage[0];
+    }
+
+    const T &operator*() const noexcept { return value(); }
+
+    const T *operator->() const noexcept { return &value(); }
+
+    explicit operator bool() const noexcept { return !storage.empty(); }
+
+private:
+    std::vector<T> storage;
+};
+
+namespace pybind11 {
+namespace detail {
+template <typename T>
+struct type_caster<ReferenceSensitiveOptional<T>>
+    : optional_caster<ReferenceSensitiveOptional<T>> {};
+} // namespace detail
+} // namespace pybind11
+
+TEST_SUBMODULE(stl, m) {
+    // test_vector
+    m.def("cast_vector", []() { return std::vector<int>{1}; });
+    m.def("load_vector", [](const std::vector<int> &v) { return v.at(0) == 1 && v.at(1) == 2; });
+    // `std::vector<bool>` is special because it returns proxy objects instead of references
+    m.def("cast_bool_vector", []() { return std::vector<bool>{true, false}; });
+    m.def("load_bool_vector",
+          [](const std::vector<bool> &v) { return v.at(0) == true && v.at(1) == false; });
+    // Unnumbered regression (caused by #936): pointers to stl containers aren't castable
+    static std::vector<RValueCaster> lvv{2};
+    m.def(
+        "cast_ptr_vector", []() { return &lvv; }, py::return_value_policy::reference);
+
+    // test_deque
+    m.def("cast_deque", []() { return std::deque<int>{1}; });
+    m.def("load_deque", [](const std::deque<int> &v) { return v.at(0) == 1 && v.at(1) == 2; });
+
+    // test_array
+    m.def("cast_array", []() { return std::array<int, 2>{{1, 2}}; });
+    m.def("load_array", [](const std::array<int, 2> &a) { return a[0] == 1 && a[1] == 2; });
+
+    // test_valarray
+    m.def("cast_valarray", []() { return std::valarray<int>{1, 4, 9}; });
+    m.def("load_valarray", [](const std::valarray<int> &v) {
+        return v.size() == 3 && v[0] == 1 && v[1] == 4 && v[2] == 9;
+    });
+
+    // test_map
+    m.def("cast_map", []() { return std::map<std::string, std::string>{{"key", "value"}}; });
+    m.def("load_map", [](const std::map<std::string, std::string> &map) {
+        return map.at("key") == "value" && map.at("key2") == "value2";
+    });
+
+    // test_set
+    m.def("cast_set", []() { return std::set<std::string>{"key1", "key2"}; });
+    m.def("load_set", [](const std::set<std::string> &set) {
+        return (set.count("key1") != 0u) && (set.count("key2") != 0u) && (set.count("key3") != 0u);
+    });
+
+    // test_recursive_casting
+    m.def("cast_rv_vector", []() { return std::vector<RValueCaster>{2}; });
+    m.def("cast_rv_array", []() { return std::array<RValueCaster, 3>(); });
+    // NB: map and set keys are `const`, so while we technically do move them (as `const Type &&`),
+    // casters don't typically do anything with that, which means they fall to the `const Type &`
+    // caster.
+    m.def("cast_rv_map", []() {
+        return std::unordered_map<std::string, RValueCaster>{{"a", RValueCaster{}}};
+    });
+    m.def("cast_rv_nested", []() {
+        std::vector<std::array<std::list<std::unordered_map<std::string, RValueCaster>>, 2>> v;
+        v.emplace_back();           // add an array
+        v.back()[0].emplace_back(); // add a map to the array
+        v.back()[0].back().emplace("b", RValueCaster{});
+        v.back()[0].back().emplace("c", RValueCaster{});
+        v.back()[1].emplace_back(); // add a map to the array
+        v.back()[1].back().emplace("a", RValueCaster{});
+        return v;
+    });
+    static std::array<RValueCaster, 2> lva;
+    static std::unordered_map<std::string, RValueCaster> lvm{{"a", RValueCaster{}},
+                                                             {"b", RValueCaster{}}};
+    static std::unordered_map<std::string, std::vector<std::list<std::array<RValueCaster, 2>>>>
+        lvn;
+    lvn["a"].emplace_back();        // add a list
+    lvn["a"].back().emplace_back(); // add an array
+    lvn["a"].emplace_back();        // another list
+    lvn["a"].back().emplace_back(); // add an array
+    lvn["b"].emplace_back();        // add a list
+    lvn["b"].back().emplace_back(); // add an array
+    lvn["b"].back().emplace_back(); // add another array
+    m.def("cast_lv_vector", []() -> const decltype(lvv) & { return lvv; });
+    m.def("cast_lv_array", []() -> const decltype(lva) & { return lva; });
+    m.def("cast_lv_map", []() -> const decltype(lvm) & { return lvm; });
+    m.def("cast_lv_nested", []() -> const decltype(lvn) & { return lvn; });
+    // #853:
+    m.def("cast_unique_ptr_vector", []() {
+        std::vector<std::unique_ptr<UserType>> v;
+        v.emplace_back(new UserType{7});
+        v.emplace_back(new UserType{42});
+        return v;
+    });
+
+    pybind11::enum_<EnumType>(m, "EnumType")
+        .value("kSet", EnumType::kSet)
+        .value("kUnset", EnumType::kUnset);
+
+    // test_move_out_container
+    struct MoveOutContainer {
+        struct Value {
+            int value;
+        };
+        std::list<Value> move_list() const { return {{0}, {1}, {2}}; }
+    };
+    py::class_<MoveOutContainer::Value>(m, "MoveOutContainerValue")
+        .def_readonly("value", &MoveOutContainer::Value::value);
+    py::class_<MoveOutContainer>(m, "MoveOutContainer")
+        .def(py::init<>())
+        .def_property_readonly("move_list", &MoveOutContainer::move_list);
+
+    // Class that can be move- and copy-constructed, but not assigned
+    struct NoAssign {
+        int value;
+
+        explicit NoAssign(int value = 0) : value(value) {}
+        NoAssign(const NoAssign &) = default;
+        NoAssign(NoAssign &&) = default;
+
+        NoAssign &operator=(const NoAssign &) = delete;
+        NoAssign &operator=(NoAssign &&) = delete;
+    };
+    py::class_<NoAssign>(m, "NoAssign", "Class with no C++ assignment operators")
+        .def(py::init<>())
+        .def(py::init<int>());
+
+    struct MoveOutDetector {
+        MoveOutDetector() = default;
+        MoveOutDetector(const MoveOutDetector &) = default;
+        MoveOutDetector(MoveOutDetector &&other) noexcept : initialized(other.initialized) {
+            // steal underlying resource
+            other.initialized = false;
+        }
+        bool initialized = true;
+    };
+    py::class_<MoveOutDetector>(m, "MoveOutDetector", "Class with move tracking")
+        .def(py::init<>())
+        .def_readonly("initialized", &MoveOutDetector::initialized);
+
+#ifdef PYBIND11_HAS_OPTIONAL
+    // test_optional
+    m.attr("has_optional") = true;
+
+    using opt_int = std::optional<int>;
+    using opt_no_assign = std::optional<NoAssign>;
+    m.def("double_or_zero", [](const opt_int &x) -> int { return x.value_or(0) * 2; });
+    m.def("half_or_none", [](int x) -> opt_int { return x != 0 ? opt_int(x / 2) : opt_int(); });
+    m.def(
+        "test_nullopt",
+        [](opt_int x) { return x.value_or(42); },
+        py::arg_v("x", std::nullopt, "None"));
+    m.def(
+        "test_no_assign",
+        [](const opt_no_assign &x) { return x ? x->value : 42; },
+        py::arg_v("x", std::nullopt, "None"));
+
+    m.def("nodefer_none_optional", [](std::optional<int>) { return true; });
+    m.def("nodefer_none_optional", [](const py::none &) { return false; });
+
+    using opt_holder = OptionalHolder<std::optional, MoveOutDetector>;
+    py::class_<opt_holder>(m, "OptionalHolder", "Class with optional member")
+        .def(py::init<>())
+        .def_readonly("member", &opt_holder::member)
+        .def("member_initialized", &opt_holder::member_initialized);
+
+    using opt_props = OptionalProperties<std::optional>;
+    pybind11::class_<opt_props>(m, "OptionalProperties")
+        .def(pybind11::init<>())
+        .def_property_readonly("access_by_ref", &opt_props::access_by_ref)
+        .def_property_readonly("access_by_copy", &opt_props::access_by_copy);
+#endif
+
+#ifdef PYBIND11_HAS_EXP_OPTIONAL
+    // test_exp_optional
+    m.attr("has_exp_optional") = true;
+
+    using exp_opt_int = std::experimental::optional<int>;
+    using exp_opt_no_assign = std::experimental::optional<NoAssign>;
+    m.def("double_or_zero_exp", [](const exp_opt_int &x) -> int { return x.value_or(0) * 2; });
+    m.def("half_or_none_exp",
+          [](int x) -> exp_opt_int { return x ? exp_opt_int(x / 2) : exp_opt_int(); });
+    m.def(
+        "test_nullopt_exp",
+        [](exp_opt_int x) { return x.value_or(42); },
+        py::arg_v("x", std::experimental::nullopt, "None"));
+    m.def(
+        "test_no_assign_exp",
+        [](const exp_opt_no_assign &x) { return x ? x->value : 42; },
+        py::arg_v("x", std::experimental::nullopt, "None"));
+
+    using opt_exp_holder = OptionalHolder<std::experimental::optional, MoveOutDetector>;
+    py::class_<opt_exp_holder>(m, "OptionalExpHolder", "Class with optional member")
+        .def(py::init<>())
+        .def_readonly("member", &opt_exp_holder::member)
+        .def("member_initialized", &opt_exp_holder::member_initialized);
+
+    using opt_exp_props = OptionalProperties<std::experimental::optional>;
+    pybind11::class_<opt_exp_props>(m, "OptionalExpProperties")
+        .def(pybind11::init<>())
+        .def_property_readonly("access_by_ref", &opt_exp_props::access_by_ref)
+        .def_property_readonly("access_by_copy", &opt_exp_props::access_by_copy);
+#endif
+
+#if defined(PYBIND11_TEST_BOOST)
+    // test_boost_optional
+    m.attr("has_boost_optional") = true;
+
+    using boost_opt_int = boost::optional<int>;
+    using boost_opt_no_assign = boost::optional<NoAssign>;
+    m.def("double_or_zero_boost", [](const boost_opt_int &x) -> int { return x.value_or(0) * 2; });
+    m.def("half_or_none_boost",
+          [](int x) -> boost_opt_int { return x != 0 ? boost_opt_int(x / 2) : boost_opt_int(); });
+    m.def(
+        "test_nullopt_boost",
+        [](boost_opt_int x) { return x.value_or(42); },
+        py::arg_v("x", boost::none, "None"));
+    m.def(
+        "test_no_assign_boost",
+        [](const boost_opt_no_assign &x) { return x ? x->value : 42; },
+        py::arg_v("x", boost::none, "None"));
+
+    using opt_boost_holder = OptionalHolder<boost::optional, MoveOutDetector>;
+    py::class_<opt_boost_holder>(m, "OptionalBoostHolder", "Class with optional member")
+        .def(py::init<>())
+        .def_readonly("member", &opt_boost_holder::member)
+        .def("member_initialized", &opt_boost_holder::member_initialized);
+
+    using opt_boost_props = OptionalProperties<boost::optional>;
+    pybind11::class_<opt_boost_props>(m, "OptionalBoostProperties")
+        .def(pybind11::init<>())
+        .def_property_readonly("access_by_ref", &opt_boost_props::access_by_ref)
+        .def_property_readonly("access_by_copy", &opt_boost_props::access_by_copy);
+#endif
+
+    // test_refsensitive_optional
+    using refsensitive_opt_int = ReferenceSensitiveOptional<int>;
+    using refsensitive_opt_no_assign = ReferenceSensitiveOptional<NoAssign>;
+    m.def("double_or_zero_refsensitive",
+          [](const refsensitive_opt_int &x) -> int { return (x ? x.value() : 0) * 2; });
+    m.def("half_or_none_refsensitive", [](int x) -> refsensitive_opt_int {
+        return x != 0 ? refsensitive_opt_int(x / 2) : refsensitive_opt_int();
+    });
+    m.def(
+        "test_nullopt_refsensitive",
+        // NOLINTNEXTLINE(performance-unnecessary-value-param)
+        [](refsensitive_opt_int x) { return x ? x.value() : 42; },
+        py::arg_v("x", refsensitive_opt_int(), "None"));
+    m.def(
+        "test_no_assign_refsensitive",
+        [](const refsensitive_opt_no_assign &x) { return x ? x->value : 42; },
+        py::arg_v("x", refsensitive_opt_no_assign(), "None"));
+
+    using opt_refsensitive_holder = OptionalHolder<ReferenceSensitiveOptional, MoveOutDetector>;
+    py::class_<opt_refsensitive_holder>(
+        m, "OptionalRefSensitiveHolder", "Class with optional member")
+        .def(py::init<>())
+        .def_readonly("member", &opt_refsensitive_holder::member)
+        .def("member_initialized", &opt_refsensitive_holder::member_initialized);
+
+    using opt_refsensitive_props = OptionalProperties<ReferenceSensitiveOptional>;
+    pybind11::class_<opt_refsensitive_props>(m, "OptionalRefSensitiveProperties")
+        .def(pybind11::init<>())
+        .def_property_readonly("access_by_ref", &opt_refsensitive_props::access_by_ref)
+        .def_property_readonly("access_by_copy", &opt_refsensitive_props::access_by_copy);
+
+#ifdef PYBIND11_HAS_FILESYSTEM
+    // test_fs_path
+    m.attr("has_filesystem") = true;
+    m.def("parent_path", [](const std::filesystem::path &p) { return p.parent_path(); });
+#endif
+
+#ifdef PYBIND11_TEST_VARIANT
+    static_assert(std::is_same<py::detail::variant_caster_visitor::result_type, py::handle>::value,
+                  "visitor::result_type is required by boost::variant in C++11 mode");
+
+    struct visitor {
+        using result_type = const char *;
+
+        result_type operator()(int) { return "int"; }
+        result_type operator()(const std::string &) { return "std::string"; }
+        result_type operator()(double) { return "double"; }
+        result_type operator()(std::nullptr_t) { return "std::nullptr_t"; }
+#    if defined(PYBIND11_HAS_VARIANT)
+        result_type operator()(std::monostate) { return "std::monostate"; }
+#    endif
+    };
+
+    // test_variant
+    m.def("load_variant", [](const variant<int, std::string, double, std::nullptr_t> &v) {
+        return py::detail::visit_helper<variant>::call(visitor(), v);
+    });
+    m.def("load_variant_2pass", [](variant<double, int> v) {
+        return py::detail::visit_helper<variant>::call(visitor(), v);
+    });
+    m.def("cast_variant", []() {
+        using V = variant<int, std::string>;
+        return py::make_tuple(V(5), V("Hello"));
+    });
+
+#    if defined(PYBIND11_HAS_VARIANT)
+    // std::monostate tests.
+    m.def("load_monostate_variant",
+          [](const variant<std::monostate, int, std::string> &v) -> const char * {
+              return py::detail::visit_helper<variant>::call(visitor(), v);
+          });
+    m.def("cast_monostate_variant", []() {
+        using V = variant<std::monostate, int, std::string>;
+        return py::make_tuple(V{}, V(5), V("Hello"));
+    });
+#    endif
+#endif
+
+    // #528: templated constructor
+    // (no python tests: the test here is that this compiles)
+    m.def("tpl_ctor_vector", [](std::vector<TplCtorClass> &) {});
+    m.def("tpl_ctor_map", [](std::unordered_map<TplCtorClass, TplCtorClass> &) {});
+    m.def("tpl_ctor_set", [](std::unordered_set<TplCtorClass> &) {});
+#if defined(PYBIND11_HAS_OPTIONAL)
+    m.def("tpl_constr_optional", [](std::optional<TplCtorClass> &) {});
+#endif
+#if defined(PYBIND11_HAS_EXP_OPTIONAL)
+    m.def("tpl_constr_optional_exp", [](std::experimental::optional<TplCtorClass> &) {});
+#endif
+#if defined(PYBIND11_TEST_BOOST)
+    m.def("tpl_constr_optional_boost", [](boost::optional<TplCtorClass> &) {});
+#endif
+
+    // test_vec_of_reference_wrapper
+    // #171: Can't return STL structures containing reference wrapper
+    m.def("return_vec_of_reference_wrapper", [](std::reference_wrapper<UserType> p4) {
+        static UserType p1{1}, p2{2}, p3{3};
+        return std::vector<std::reference_wrapper<UserType>>{
+            std::ref(p1), std::ref(p2), std::ref(p3), p4};
+    });
+
+    // test_stl_pass_by_pointer
+    m.def(
+        "stl_pass_by_pointer", [](std::vector<int> *v) { return *v; }, "v"_a = nullptr);
+
+    // #1258: pybind11/stl.h converts string to vector<string>
+    m.def("func_with_string_or_vector_string_arg_overload",
+          [](const std::vector<std::string> &) { return 1; });
+    m.def("func_with_string_or_vector_string_arg_overload",
+          [](const std::list<std::string> &) { return 2; });
+    m.def("func_with_string_or_vector_string_arg_overload", [](const std::string &) { return 3; });
+
+    class Placeholder {
+    public:
+        Placeholder() { print_created(this); }
+        Placeholder(const Placeholder &) = delete;
+        ~Placeholder() { print_destroyed(this); }
+    };
+    py::class_<Placeholder>(m, "Placeholder");
+
+    /// test_stl_vector_ownership
+    m.def(
+        "test_stl_ownership",
+        []() {
+            std::vector<Placeholder *> result;
+            result.push_back(new Placeholder());
+            return result;
+        },
+        py::return_value_policy::take_ownership);
+
+    m.def("array_cast_sequence", [](std::array<int, 3> x) { return x; });
+
+    /// test_issue_1561
+    struct Issue1561Inner {
+        std::string data;
+    };
+    struct Issue1561Outer {
+        std::vector<Issue1561Inner> list;
+    };
+
+    py::class_<Issue1561Inner>(m, "Issue1561Inner")
+        .def(py::init<std::string>())
+        .def_readwrite("data", &Issue1561Inner::data);
+
+    py::class_<Issue1561Outer>(m, "Issue1561Outer")
+        .def(py::init<>())
+        .def_readwrite("list", &Issue1561Outer::list);
+
+    m.def(
+        "return_vector_bool_raw_ptr",
+        []() { return new std::vector<bool>(4513); },
+        // Without explicitly specifying `take_ownership`, this function leaks.
+        py::return_value_policy::take_ownership);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl.py
new file mode 100644
index 000000000..d30c38211
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl.py
@@ -0,0 +1,377 @@
+import pytest
+
+from pybind11_tests import ConstructorStats, UserType
+from pybind11_tests import stl as m
+
+
+def test_vector(doc):
+    """std::vector <-> list"""
+    lst = m.cast_vector()
+    assert lst == [1]
+    lst.append(2)
+    assert m.load_vector(lst)
+    assert m.load_vector(tuple(lst))
+
+    assert m.cast_bool_vector() == [True, False]
+    assert m.load_bool_vector([True, False])
+    assert m.load_bool_vector(tuple([True, False]))
+
+    assert doc(m.cast_vector) == "cast_vector() -> List[int]"
+    assert doc(m.load_vector) == "load_vector(arg0: List[int]) -> bool"
+
+    # Test regression caused by 936: pointers to stl containers weren't castable
+    assert m.cast_ptr_vector() == ["lvalue", "lvalue"]
+
+
+def test_deque(doc):
+    """std::deque <-> list"""
+    lst = m.cast_deque()
+    assert lst == [1]
+    lst.append(2)
+    assert m.load_deque(lst)
+    assert m.load_deque(tuple(lst))
+
+
+def test_array(doc):
+    """std::array <-> list"""
+    lst = m.cast_array()
+    assert lst == [1, 2]
+    assert m.load_array(lst)
+    assert m.load_array(tuple(lst))
+
+    assert doc(m.cast_array) == "cast_array() -> List[int[2]]"
+    assert doc(m.load_array) == "load_array(arg0: List[int[2]]) -> bool"
+
+
+def test_valarray(doc):
+    """std::valarray <-> list"""
+    lst = m.cast_valarray()
+    assert lst == [1, 4, 9]
+    assert m.load_valarray(lst)
+    assert m.load_valarray(tuple(lst))
+
+    assert doc(m.cast_valarray) == "cast_valarray() -> List[int]"
+    assert doc(m.load_valarray) == "load_valarray(arg0: List[int]) -> bool"
+
+
+def test_map(doc):
+    """std::map <-> dict"""
+    d = m.cast_map()
+    assert d == {"key": "value"}
+    assert "key" in d
+    d["key2"] = "value2"
+    assert "key2" in d
+    assert m.load_map(d)
+
+    assert doc(m.cast_map) == "cast_map() -> Dict[str, str]"
+    assert doc(m.load_map) == "load_map(arg0: Dict[str, str]) -> bool"
+
+
+def test_set(doc):
+    """std::set <-> set"""
+    s = m.cast_set()
+    assert s == {"key1", "key2"}
+    s.add("key3")
+    assert m.load_set(s)
+    assert m.load_set(frozenset(s))
+
+    assert doc(m.cast_set) == "cast_set() -> Set[str]"
+    assert doc(m.load_set) == "load_set(arg0: Set[str]) -> bool"
+
+
+def test_recursive_casting():
+    """Tests that stl casters preserve lvalue/rvalue context for container values"""
+    assert m.cast_rv_vector() == ["rvalue", "rvalue"]
+    assert m.cast_lv_vector() == ["lvalue", "lvalue"]
+    assert m.cast_rv_array() == ["rvalue", "rvalue", "rvalue"]
+    assert m.cast_lv_array() == ["lvalue", "lvalue"]
+    assert m.cast_rv_map() == {"a": "rvalue"}
+    assert m.cast_lv_map() == {"a": "lvalue", "b": "lvalue"}
+    assert m.cast_rv_nested() == [[[{"b": "rvalue", "c": "rvalue"}], [{"a": "rvalue"}]]]
+    assert m.cast_lv_nested() == {
+        "a": [[["lvalue", "lvalue"]], [["lvalue", "lvalue"]]],
+        "b": [[["lvalue", "lvalue"], ["lvalue", "lvalue"]]],
+    }
+
+    # Issue #853 test case:
+    z = m.cast_unique_ptr_vector()
+    assert z[0].value == 7 and z[1].value == 42
+
+
+def test_move_out_container():
+    """Properties use the `reference_internal` policy by default. If the underlying function
+    returns an rvalue, the policy is automatically changed to `move` to avoid referencing
+    a temporary. In case the return value is a container of user-defined types, the policy
+    also needs to be applied to the elements, not just the container."""
+    c = m.MoveOutContainer()
+    moved_out_list = c.move_list
+    assert [x.value for x in moved_out_list] == [0, 1, 2]
+
+
+@pytest.mark.skipif(not hasattr(m, "has_optional"), reason="no <optional>")
+def test_optional():
+    assert m.double_or_zero(None) == 0
+    assert m.double_or_zero(42) == 84
+    pytest.raises(TypeError, m.double_or_zero, "foo")
+
+    assert m.half_or_none(0) is None
+    assert m.half_or_none(42) == 21
+    pytest.raises(TypeError, m.half_or_none, "foo")
+
+    assert m.test_nullopt() == 42
+    assert m.test_nullopt(None) == 42
+    assert m.test_nullopt(42) == 42
+    assert m.test_nullopt(43) == 43
+
+    assert m.test_no_assign() == 42
+    assert m.test_no_assign(None) == 42
+    assert m.test_no_assign(m.NoAssign(43)) == 43
+    pytest.raises(TypeError, m.test_no_assign, 43)
+
+    assert m.nodefer_none_optional(None)
+
+    holder = m.OptionalHolder()
+    mvalue = holder.member
+    assert mvalue.initialized
+    assert holder.member_initialized()
+
+    props = m.OptionalProperties()
+    assert int(props.access_by_ref) == 42
+    assert int(props.access_by_copy) == 42
+
+
+@pytest.mark.skipif(
+    not hasattr(m, "has_exp_optional"), reason="no <experimental/optional>"
+)
+def test_exp_optional():
+    assert m.double_or_zero_exp(None) == 0
+    assert m.double_or_zero_exp(42) == 84
+    pytest.raises(TypeError, m.double_or_zero_exp, "foo")
+
+    assert m.half_or_none_exp(0) is None
+    assert m.half_or_none_exp(42) == 21
+    pytest.raises(TypeError, m.half_or_none_exp, "foo")
+
+    assert m.test_nullopt_exp() == 42
+    assert m.test_nullopt_exp(None) == 42
+    assert m.test_nullopt_exp(42) == 42
+    assert m.test_nullopt_exp(43) == 43
+
+    assert m.test_no_assign_exp() == 42
+    assert m.test_no_assign_exp(None) == 42
+    assert m.test_no_assign_exp(m.NoAssign(43)) == 43
+    pytest.raises(TypeError, m.test_no_assign_exp, 43)
+
+    holder = m.OptionalExpHolder()
+    mvalue = holder.member
+    assert mvalue.initialized
+    assert holder.member_initialized()
+
+    props = m.OptionalExpProperties()
+    assert int(props.access_by_ref) == 42
+    assert int(props.access_by_copy) == 42
+
+
+@pytest.mark.skipif(not hasattr(m, "has_boost_optional"), reason="no <boost/optional>")
+def test_boost_optional():
+    assert m.double_or_zero_boost(None) == 0
+    assert m.double_or_zero_boost(42) == 84
+    pytest.raises(TypeError, m.double_or_zero_boost, "foo")
+
+    assert m.half_or_none_boost(0) is None
+    assert m.half_or_none_boost(42) == 21
+    pytest.raises(TypeError, m.half_or_none_boost, "foo")
+
+    assert m.test_nullopt_boost() == 42
+    assert m.test_nullopt_boost(None) == 42
+    assert m.test_nullopt_boost(42) == 42
+    assert m.test_nullopt_boost(43) == 43
+
+    assert m.test_no_assign_boost() == 42
+    assert m.test_no_assign_boost(None) == 42
+    assert m.test_no_assign_boost(m.NoAssign(43)) == 43
+    pytest.raises(TypeError, m.test_no_assign_boost, 43)
+
+    holder = m.OptionalBoostHolder()
+    mvalue = holder.member
+    assert mvalue.initialized
+    assert holder.member_initialized()
+
+    props = m.OptionalBoostProperties()
+    assert int(props.access_by_ref) == 42
+    assert int(props.access_by_copy) == 42
+
+
+def test_reference_sensitive_optional():
+    assert m.double_or_zero_refsensitive(None) == 0
+    assert m.double_or_zero_refsensitive(42) == 84
+    pytest.raises(TypeError, m.double_or_zero_refsensitive, "foo")
+
+    assert m.half_or_none_refsensitive(0) is None
+    assert m.half_or_none_refsensitive(42) == 21
+    pytest.raises(TypeError, m.half_or_none_refsensitive, "foo")
+
+    assert m.test_nullopt_refsensitive() == 42
+    assert m.test_nullopt_refsensitive(None) == 42
+    assert m.test_nullopt_refsensitive(42) == 42
+    assert m.test_nullopt_refsensitive(43) == 43
+
+    assert m.test_no_assign_refsensitive() == 42
+    assert m.test_no_assign_refsensitive(None) == 42
+    assert m.test_no_assign_refsensitive(m.NoAssign(43)) == 43
+    pytest.raises(TypeError, m.test_no_assign_refsensitive, 43)
+
+    holder = m.OptionalRefSensitiveHolder()
+    mvalue = holder.member
+    assert mvalue.initialized
+    assert holder.member_initialized()
+
+    props = m.OptionalRefSensitiveProperties()
+    assert int(props.access_by_ref) == 42
+    assert int(props.access_by_copy) == 42
+
+
+@pytest.mark.skipif(not hasattr(m, "has_filesystem"), reason="no <filesystem>")
+def test_fs_path():
+    from pathlib import Path
+
+    class PseudoStrPath:
+        def __fspath__(self):
+            return "foo/bar"
+
+    class PseudoBytesPath:
+        def __fspath__(self):
+            return b"foo/bar"
+
+    assert m.parent_path(Path("foo/bar")) == Path("foo")
+    assert m.parent_path("foo/bar") == Path("foo")
+    assert m.parent_path(b"foo/bar") == Path("foo")
+    assert m.parent_path(PseudoStrPath()) == Path("foo")
+    assert m.parent_path(PseudoBytesPath()) == Path("foo")
+
+
+@pytest.mark.skipif(not hasattr(m, "load_variant"), reason="no <variant>")
+def test_variant(doc):
+    assert m.load_variant(1) == "int"
+    assert m.load_variant("1") == "std::string"
+    assert m.load_variant(1.0) == "double"
+    assert m.load_variant(None) == "std::nullptr_t"
+
+    assert m.load_variant_2pass(1) == "int"
+    assert m.load_variant_2pass(1.0) == "double"
+
+    assert m.cast_variant() == (5, "Hello")
+
+    assert (
+        doc(m.load_variant) == "load_variant(arg0: Union[int, str, float, None]) -> str"
+    )
+
+
+@pytest.mark.skipif(
+    not hasattr(m, "load_monostate_variant"), reason="no std::monostate"
+)
+def test_variant_monostate(doc):
+    assert m.load_monostate_variant(None) == "std::monostate"
+    assert m.load_monostate_variant(1) == "int"
+    assert m.load_monostate_variant("1") == "std::string"
+
+    assert m.cast_monostate_variant() == (None, 5, "Hello")
+
+    assert (
+        doc(m.load_monostate_variant)
+        == "load_monostate_variant(arg0: Union[None, int, str]) -> str"
+    )
+
+
+def test_vec_of_reference_wrapper():
+    """#171: Can't return reference wrappers (or STL structures containing them)"""
+    assert (
+        str(m.return_vec_of_reference_wrapper(UserType(4)))
+        == "[UserType(1), UserType(2), UserType(3), UserType(4)]"
+    )
+
+
+def test_stl_pass_by_pointer(msg):
+    """Passing nullptr or None to an STL container pointer is not expected to work"""
+    with pytest.raises(TypeError) as excinfo:
+        m.stl_pass_by_pointer()  # default value is `nullptr`
+    assert (
+        msg(excinfo.value)
+        == """
+        stl_pass_by_pointer(): incompatible function arguments. The following argument types are supported:
+            1. (v: List[int] = None) -> List[int]
+
+        Invoked with:
+    """
+    )
+
+    with pytest.raises(TypeError) as excinfo:
+        m.stl_pass_by_pointer(None)
+    assert (
+        msg(excinfo.value)
+        == """
+        stl_pass_by_pointer(): incompatible function arguments. The following argument types are supported:
+            1. (v: List[int] = None) -> List[int]
+
+        Invoked with: None
+    """
+    )
+
+    assert m.stl_pass_by_pointer([1, 2, 3]) == [1, 2, 3]
+
+
+def test_missing_header_message():
+    """Trying convert `list` to a `std::vector`, or vice versa, without including
+    <pybind11/stl.h> should result in a helpful suggestion in the error message"""
+    import pybind11_cross_module_tests as cm
+
+    expected_message = (
+        "Did you forget to `#include <pybind11/stl.h>`? Or <pybind11/complex.h>,\n"
+        "<pybind11/functional.h>, <pybind11/chrono.h>, etc. Some automatic\n"
+        "conversions are optional and require extra headers to be included\n"
+        "when compiling your pybind11 module."
+    )
+
+    with pytest.raises(TypeError) as excinfo:
+        cm.missing_header_arg([1.0, 2.0, 3.0])
+    assert expected_message in str(excinfo.value)
+
+    with pytest.raises(TypeError) as excinfo:
+        cm.missing_header_return()
+    assert expected_message in str(excinfo.value)
+
+
+def test_function_with_string_and_vector_string_arg():
+    """Check if a string is NOT implicitly converted to a list, which was the
+    behavior before fix of issue #1258"""
+    assert m.func_with_string_or_vector_string_arg_overload(("A", "B")) == 2
+    assert m.func_with_string_or_vector_string_arg_overload(["A", "B"]) == 2
+    assert m.func_with_string_or_vector_string_arg_overload("A") == 3
+
+
+def test_stl_ownership():
+    cstats = ConstructorStats.get(m.Placeholder)
+    assert cstats.alive() == 0
+    r = m.test_stl_ownership()
+    assert len(r) == 1
+    del r
+    assert cstats.alive() == 0
+
+
+def test_array_cast_sequence():
+    assert m.array_cast_sequence((1, 2, 3)) == [1, 2, 3]
+
+
+def test_issue_1561():
+    """check fix for issue #1561"""
+    bar = m.Issue1561Outer()
+    bar.list = [m.Issue1561Inner("bar")]
+    bar.list
+    assert bar.list[0].data == "bar"
+
+
+def test_return_vector_bool_raw_ptr():
+    # Add `while True:` for manual leak checking.
+    v = m.return_vector_bool_raw_ptr()
+    assert isinstance(v, list)
+    assert len(v) == 4513
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl_binders.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl_binders.cpp
new file mode 100644
index 000000000..ca9630bd1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl_binders.cpp
@@ -0,0 +1,152 @@
+/*
+    tests/test_stl_binders.cpp -- Usage of stl_binders functions
+
+    Copyright (c) 2016 Sergey Lyskov
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/numpy.h>
+#include <pybind11/stl_bind.h>
+
+#include "pybind11_tests.h"
+
+#include <deque>
+#include <map>
+#include <unordered_map>
+
+class El {
+public:
+    El() = delete;
+    explicit El(int v) : a(v) {}
+
+    int a;
+};
+
+std::ostream &operator<<(std::ostream &s, El const &v) {
+    s << "El{" << v.a << '}';
+    return s;
+}
+
+/// Issue #487: binding std::vector<E> with E non-copyable
+class E_nc {
+public:
+    explicit E_nc(int i) : value{i} {}
+    E_nc(const E_nc &) = delete;
+    E_nc &operator=(const E_nc &) = delete;
+    E_nc(E_nc &&) = default;
+    E_nc &operator=(E_nc &&) = default;
+
+    int value;
+};
+
+template <class Container>
+Container *one_to_n(int n) {
+    auto *v = new Container();
+    for (int i = 1; i <= n; i++) {
+        v->emplace_back(i);
+    }
+    return v;
+}
+
+template <class Map>
+Map *times_ten(int n) {
+    auto *m = new Map();
+    for (int i = 1; i <= n; i++) {
+        m->emplace(int(i), E_nc(10 * i));
+    }
+    return m;
+}
+
+template <class NestMap>
+NestMap *times_hundred(int n) {
+    auto *m = new NestMap();
+    for (int i = 1; i <= n; i++) {
+        for (int j = 1; j <= n; j++) {
+            (*m)[i].emplace(int(j * 10), E_nc(100 * j));
+        }
+    }
+    return m;
+}
+
+TEST_SUBMODULE(stl_binders, m) {
+    // test_vector_int
+    py::bind_vector<std::vector<unsigned int>>(m, "VectorInt", py::buffer_protocol());
+
+    // test_vector_custom
+    py::class_<El>(m, "El").def(py::init<int>());
+    py::bind_vector<std::vector<El>>(m, "VectorEl");
+    py::bind_vector<std::vector<std::vector<El>>>(m, "VectorVectorEl");
+
+    // test_map_string_double
+    py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");
+    py::bind_map<std::unordered_map<std::string, double>>(m, "UnorderedMapStringDouble");
+
+    // test_map_string_double_const
+    py::bind_map<std::map<std::string, double const>>(m, "MapStringDoubleConst");
+    py::bind_map<std::unordered_map<std::string, double const>>(m,
+                                                                "UnorderedMapStringDoubleConst");
+
+    py::class_<E_nc>(m, "ENC").def(py::init<int>()).def_readwrite("value", &E_nc::value);
+
+    // test_noncopyable_containers
+    py::bind_vector<std::vector<E_nc>>(m, "VectorENC");
+    m.def("get_vnc", &one_to_n<std::vector<E_nc>>);
+    py::bind_vector<std::deque<E_nc>>(m, "DequeENC");
+    m.def("get_dnc", &one_to_n<std::deque<E_nc>>);
+    py::bind_map<std::map<int, E_nc>>(m, "MapENC");
+    m.def("get_mnc", &times_ten<std::map<int, E_nc>>);
+    py::bind_map<std::unordered_map<int, E_nc>>(m, "UmapENC");
+    m.def("get_umnc", &times_ten<std::unordered_map<int, E_nc>>);
+    // Issue #1885: binding nested std::map<X, Container<E>> with E non-copyable
+    py::bind_map<std::map<int, std::vector<E_nc>>>(m, "MapVecENC");
+    m.def("get_nvnc", [](int n) {
+        auto *m = new std::map<int, std::vector<E_nc>>();
+        for (int i = 1; i <= n; i++) {
+            for (int j = 1; j <= n; j++) {
+                (*m)[i].emplace_back(j);
+            }
+        }
+        return m;
+    });
+    py::bind_map<std::map<int, std::map<int, E_nc>>>(m, "MapMapENC");
+    m.def("get_nmnc", &times_hundred<std::map<int, std::map<int, E_nc>>>);
+    py::bind_map<std::unordered_map<int, std::unordered_map<int, E_nc>>>(m, "UmapUmapENC");
+    m.def("get_numnc", &times_hundred<std::unordered_map<int, std::unordered_map<int, E_nc>>>);
+
+    // test_vector_buffer
+    py::bind_vector<std::vector<unsigned char>>(m, "VectorUChar", py::buffer_protocol());
+    // no dtype declared for this version:
+    struct VUndeclStruct {
+        bool w;
+        uint32_t x;
+        double y;
+        bool z;
+    };
+    m.def("create_undeclstruct", [m]() mutable {
+        py::bind_vector<std::vector<VUndeclStruct>>(
+            m, "VectorUndeclStruct", py::buffer_protocol());
+    });
+
+    // The rest depends on numpy:
+    try {
+        py::module_::import("numpy");
+    } catch (...) {
+        return;
+    }
+
+    // test_vector_buffer_numpy
+    struct VStruct {
+        bool w;
+        uint32_t x;
+        double y;
+        bool z;
+    };
+    PYBIND11_NUMPY_DTYPE(VStruct, w, x, y, z);
+    py::class_<VStruct>(m, "VStruct").def_readwrite("x", &VStruct::x);
+    py::bind_vector<std::vector<VStruct>>(m, "VectorStruct", py::buffer_protocol());
+    m.def("get_vectorstruct", [] {
+        return std::vector<VStruct>{{false, 5, 3.0, true}, {true, 30, -1e4, false}};
+    });
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl_binders.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl_binders.py
new file mode 100644
index 000000000..d5e9ccced
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_stl_binders.py
@@ -0,0 +1,311 @@
+import pytest
+
+from pybind11_tests import stl_binders as m
+
+
+def test_vector_int():
+    v_int = m.VectorInt([0, 0])
+    assert len(v_int) == 2
+    assert bool(v_int) is True
+
+    # test construction from a generator
+    v_int1 = m.VectorInt(x for x in range(5))
+    assert v_int1 == m.VectorInt([0, 1, 2, 3, 4])
+
+    v_int2 = m.VectorInt([0, 0])
+    assert v_int == v_int2
+    v_int2[1] = 1
+    assert v_int != v_int2
+
+    v_int2.append(2)
+    v_int2.insert(0, 1)
+    v_int2.insert(0, 2)
+    v_int2.insert(0, 3)
+    v_int2.insert(6, 3)
+    assert str(v_int2) == "VectorInt[3, 2, 1, 0, 1, 2, 3]"
+    with pytest.raises(IndexError):
+        v_int2.insert(8, 4)
+
+    v_int.append(99)
+    v_int2[2:-2] = v_int
+    assert v_int2 == m.VectorInt([3, 2, 0, 0, 99, 2, 3])
+    del v_int2[1:3]
+    assert v_int2 == m.VectorInt([3, 0, 99, 2, 3])
+    del v_int2[0]
+    assert v_int2 == m.VectorInt([0, 99, 2, 3])
+
+    v_int2.extend(m.VectorInt([4, 5]))
+    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5])
+
+    v_int2.extend([6, 7])
+    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7])
+
+    # test error handling, and that the vector is unchanged
+    with pytest.raises(RuntimeError):
+        v_int2.extend([8, "a"])
+
+    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7])
+
+    # test extending from a generator
+    v_int2.extend(x for x in range(5))
+    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4])
+
+    # test negative indexing
+    assert v_int2[-1] == 4
+
+    # insert with negative index
+    v_int2.insert(-1, 88)
+    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 88, 4])
+
+    # delete negative index
+    del v_int2[-1]
+    assert v_int2 == m.VectorInt([0, 99, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 88])
+
+    v_int2.clear()
+    assert len(v_int2) == 0
+
+
+# Older PyPy's failed here, related to the PyPy's buffer protocol.
+def test_vector_buffer():
+    b = bytearray([1, 2, 3, 4])
+    v = m.VectorUChar(b)
+    assert v[1] == 2
+    v[2] = 5
+    mv = memoryview(v)  # We expose the buffer interface
+    assert mv[2] == 5
+    mv[2] = 6
+    assert v[2] == 6
+
+    mv = memoryview(b)
+    v = m.VectorUChar(mv[::2])
+    assert v[1] == 3
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.create_undeclstruct()  # Undeclared struct contents, no buffer interface
+    assert "NumPy type info missing for " in str(excinfo.value)
+
+
+def test_vector_buffer_numpy():
+    np = pytest.importorskip("numpy")
+    a = np.array([1, 2, 3, 4], dtype=np.int32)
+    with pytest.raises(TypeError):
+        m.VectorInt(a)
+
+    a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], dtype=np.uintc)
+    v = m.VectorInt(a[0, :])
+    assert len(v) == 4
+    assert v[2] == 3
+    ma = np.asarray(v)
+    ma[2] = 5
+    assert v[2] == 5
+
+    v = m.VectorInt(a[:, 1])
+    assert len(v) == 3
+    assert v[2] == 10
+
+    v = m.get_vectorstruct()
+    assert v[0].x == 5
+    ma = np.asarray(v)
+    ma[1]["x"] = 99
+    assert v[1].x == 99
+
+    v = m.VectorStruct(
+        np.zeros(
+            3,
+            dtype=np.dtype(
+                [("w", "bool"), ("x", "I"), ("y", "float64"), ("z", "bool")], align=True
+            ),
+        )
+    )
+    assert len(v) == 3
+
+    b = np.array([1, 2, 3, 4], dtype=np.uint8)
+    v = m.VectorUChar(b[::2])
+    assert v[1] == 3
+
+
+def test_vector_bool():
+    import pybind11_cross_module_tests as cm
+
+    vv_c = cm.VectorBool()
+    for i in range(10):
+        vv_c.append(i % 2 == 0)
+    for i in range(10):
+        assert vv_c[i] == (i % 2 == 0)
+    assert str(vv_c) == "VectorBool[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]"
+
+
+def test_vector_custom():
+    v_a = m.VectorEl()
+    v_a.append(m.El(1))
+    v_a.append(m.El(2))
+    assert str(v_a) == "VectorEl[El{1}, El{2}]"
+
+    vv_a = m.VectorVectorEl()
+    vv_a.append(v_a)
+    vv_b = vv_a[0]
+    assert str(vv_b) == "VectorEl[El{1}, El{2}]"
+
+
+def test_map_string_double():
+    mm = m.MapStringDouble()
+    mm["a"] = 1
+    mm["b"] = 2.5
+
+    assert list(mm) == ["a", "b"]
+    assert str(mm) == "MapStringDouble{a: 1, b: 2.5}"
+    assert "b" in mm
+    assert "c" not in mm
+    assert 123 not in mm
+
+    # Check that keys, values, items are views, not merely iterable
+    keys = mm.keys()
+    values = mm.values()
+    items = mm.items()
+    assert list(keys) == ["a", "b"]
+    assert len(keys) == 2
+    assert "a" in keys
+    assert "c" not in keys
+    assert 123 not in keys
+    assert list(items) == [("a", 1), ("b", 2.5)]
+    assert len(items) == 2
+    assert ("b", 2.5) in items
+    assert "hello" not in items
+    assert ("b", 2.5, None) not in items
+    assert list(values) == [1, 2.5]
+    assert len(values) == 2
+    assert 1 in values
+    assert 2 not in values
+    # Check that views update when the map is updated
+    mm["c"] = -1
+    assert list(keys) == ["a", "b", "c"]
+    assert list(values) == [1, 2.5, -1]
+    assert list(items) == [("a", 1), ("b", 2.5), ("c", -1)]
+
+    um = m.UnorderedMapStringDouble()
+    um["ua"] = 1.1
+    um["ub"] = 2.6
+
+    assert sorted(list(um)) == ["ua", "ub"]
+    assert list(um.keys()) == list(um)
+    assert sorted(list(um.items())) == [("ua", 1.1), ("ub", 2.6)]
+    assert list(zip(um.keys(), um.values())) == list(um.items())
+    assert "UnorderedMapStringDouble" in str(um)
+
+
+def test_map_string_double_const():
+    mc = m.MapStringDoubleConst()
+    mc["a"] = 10
+    mc["b"] = 20.5
+    assert str(mc) == "MapStringDoubleConst{a: 10, b: 20.5}"
+
+    umc = m.UnorderedMapStringDoubleConst()
+    umc["a"] = 11
+    umc["b"] = 21.5
+
+    str(umc)
+
+
+def test_noncopyable_containers():
+    # std::vector
+    vnc = m.get_vnc(5)
+    for i in range(0, 5):
+        assert vnc[i].value == i + 1
+
+    for i, j in enumerate(vnc, start=1):
+        assert j.value == i
+
+    # std::deque
+    dnc = m.get_dnc(5)
+    for i in range(0, 5):
+        assert dnc[i].value == i + 1
+
+    i = 1
+    for j in dnc:
+        assert j.value == i
+        i += 1
+
+    # std::map
+    mnc = m.get_mnc(5)
+    for i in range(1, 6):
+        assert mnc[i].value == 10 * i
+
+    vsum = 0
+    for k, v in mnc.items():
+        assert v.value == 10 * k
+        vsum += v.value
+
+    assert vsum == 150
+
+    # std::unordered_map
+    mnc = m.get_umnc(5)
+    for i in range(1, 6):
+        assert mnc[i].value == 10 * i
+
+    vsum = 0
+    for k, v in mnc.items():
+        assert v.value == 10 * k
+        vsum += v.value
+
+    assert vsum == 150
+
+    # nested std::map<std::vector>
+    nvnc = m.get_nvnc(5)
+    for i in range(1, 6):
+        for j in range(0, 5):
+            assert nvnc[i][j].value == j + 1
+
+    # Note: maps do not have .values()
+    for _, v in nvnc.items():
+        for i, j in enumerate(v, start=1):
+            assert j.value == i
+
+    # nested std::map<std::map>
+    nmnc = m.get_nmnc(5)
+    for i in range(1, 6):
+        for j in range(10, 60, 10):
+            assert nmnc[i][j].value == 10 * j
+
+    vsum = 0
+    for _, v_o in nmnc.items():
+        for k_i, v_i in v_o.items():
+            assert v_i.value == 10 * k_i
+            vsum += v_i.value
+
+    assert vsum == 7500
+
+    # nested std::unordered_map<std::unordered_map>
+    numnc = m.get_numnc(5)
+    for i in range(1, 6):
+        for j in range(10, 60, 10):
+            assert numnc[i][j].value == 10 * j
+
+    vsum = 0
+    for _, v_o in numnc.items():
+        for k_i, v_i in v_o.items():
+            assert v_i.value == 10 * k_i
+            vsum += v_i.value
+
+    assert vsum == 7500
+
+
+def test_map_delitem():
+    mm = m.MapStringDouble()
+    mm["a"] = 1
+    mm["b"] = 2.5
+
+    assert list(mm) == ["a", "b"]
+    assert list(mm.items()) == [("a", 1), ("b", 2.5)]
+    del mm["a"]
+    assert list(mm) == ["b"]
+    assert list(mm.items()) == [("b", 2.5)]
+
+    um = m.UnorderedMapStringDouble()
+    um["ua"] = 1.1
+    um["ub"] = 2.6
+
+    assert sorted(list(um)) == ["ua", "ub"]
+    assert sorted(list(um.items())) == [("ua", 1.1), ("ub", 2.6)]
+    del um["ua"]
+    assert sorted(list(um)) == ["ub"]
+    assert sorted(list(um.items())) == [("ub", 2.6)]
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_tagbased_polymorphic.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_tagbased_polymorphic.cpp
new file mode 100644
index 000000000..2807e86ba
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_tagbased_polymorphic.cpp
@@ -0,0 +1,147 @@
+/*
+    tests/test_tagbased_polymorphic.cpp -- test of polymorphic_type_hook
+
+    Copyright (c) 2018 Hudson River Trading LLC <opensource@hudson-trading.com>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/stl.h>
+
+#include "pybind11_tests.h"
+
+struct Animal {
+    // Make this type also a "standard" polymorphic type, to confirm that
+    // specializing polymorphic_type_hook using enable_if_t still works
+    // (https://github.com/pybind/pybind11/pull/2016/).
+    virtual ~Animal() = default;
+
+    // Enum for tag-based polymorphism.
+    enum class Kind {
+        Unknown = 0,
+        Dog = 100,
+        Labrador,
+        Chihuahua,
+        LastDog = 199,
+        Cat = 200,
+        Panther,
+        LastCat = 299
+    };
+    static const std::type_info *type_of_kind(Kind kind);
+    static std::string name_of_kind(Kind kind);
+
+    const Kind kind;
+    const std::string name;
+
+protected:
+    Animal(const std::string &_name, Kind _kind) : kind(_kind), name(_name) {}
+};
+
+struct Dog : Animal {
+    explicit Dog(const std::string &_name, Kind _kind = Kind::Dog) : Animal(_name, _kind) {}
+    std::string bark() const { return name_of_kind(kind) + " " + name + " goes " + sound; }
+    std::string sound = "WOOF!";
+};
+
+struct Labrador : Dog {
+    explicit Labrador(const std::string &_name, int _excitement = 9001)
+        : Dog(_name, Kind::Labrador), excitement(_excitement) {}
+    int excitement;
+};
+
+struct Chihuahua : Dog {
+    explicit Chihuahua(const std::string &_name) : Dog(_name, Kind::Chihuahua) {
+        sound = "iyiyiyiyiyi";
+    }
+    std::string bark() const { return Dog::bark() + " and runs in circles"; }
+};
+
+struct Cat : Animal {
+    explicit Cat(const std::string &_name, Kind _kind = Kind::Cat) : Animal(_name, _kind) {}
+    std::string purr() const { return "mrowr"; }
+};
+
+struct Panther : Cat {
+    explicit Panther(const std::string &_name) : Cat(_name, Kind::Panther) {}
+    std::string purr() const { return "mrrrRRRRRR"; }
+};
+
+std::vector<std::unique_ptr<Animal>> create_zoo() {
+    std::vector<std::unique_ptr<Animal>> ret;
+    ret.emplace_back(new Labrador("Fido", 15000));
+
+    // simulate some new type of Dog that the Python bindings
+    // haven't been updated for; it should still be considered
+    // a Dog, not just an Animal.
+    ret.emplace_back(new Dog("Ginger", Dog::Kind(150)));
+
+    ret.emplace_back(new Chihuahua("Hertzl"));
+    ret.emplace_back(new Cat("Tiger", Cat::Kind::Cat));
+    ret.emplace_back(new Panther("Leo"));
+    return ret;
+}
+
+const std::type_info *Animal::type_of_kind(Kind kind) {
+    switch (kind) {
+        case Kind::Unknown:
+        case Kind::Dog:
+            break;
+
+        case Kind::Labrador:
+            return &typeid(Labrador);
+        case Kind::Chihuahua:
+            return &typeid(Chihuahua);
+
+        case Kind::LastDog:
+        case Kind::Cat:
+            break;
+        case Kind::Panther:
+            return &typeid(Panther);
+        case Kind::LastCat:
+            break;
+    }
+
+    if (kind >= Kind::Dog && kind <= Kind::LastDog) {
+        return &typeid(Dog);
+    }
+    if (kind >= Kind::Cat && kind <= Kind::LastCat) {
+        return &typeid(Cat);
+    }
+    return nullptr;
+}
+
+std::string Animal::name_of_kind(Kind kind) {
+    std::string raw_name = type_of_kind(kind)->name();
+    py::detail::clean_type_id(raw_name);
+    return raw_name;
+}
+
+namespace pybind11 {
+template <typename itype>
+struct polymorphic_type_hook<itype, detail::enable_if_t<std::is_base_of<Animal, itype>::value>> {
+    static const void *get(const itype *src, const std::type_info *&type) {
+        type = src ? Animal::type_of_kind(src->kind) : nullptr;
+        return src;
+    }
+};
+} // namespace pybind11
+
+TEST_SUBMODULE(tagbased_polymorphic, m) {
+    py::class_<Animal>(m, "Animal").def_readonly("name", &Animal::name);
+    py::class_<Dog, Animal>(m, "Dog")
+        .def(py::init<std::string>())
+        .def_readwrite("sound", &Dog::sound)
+        .def("bark", &Dog::bark);
+    py::class_<Labrador, Dog>(m, "Labrador")
+        .def(py::init<std::string, int>(), "name"_a, "excitement"_a = 9001)
+        .def_readwrite("excitement", &Labrador::excitement);
+    py::class_<Chihuahua, Dog>(m, "Chihuahua")
+        .def(py::init<std::string>())
+        .def("bark", &Chihuahua::bark);
+    py::class_<Cat, Animal>(m, "Cat").def(py::init<std::string>()).def("purr", &Cat::purr);
+    py::class_<Panther, Cat>(m, "Panther")
+        .def(py::init<std::string>())
+        .def("purr", &Panther::purr);
+    m.def("create_zoo", &create_zoo);
+};
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_tagbased_polymorphic.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_tagbased_polymorphic.py
new file mode 100644
index 000000000..84f0ea717
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_tagbased_polymorphic.py
@@ -0,0 +1,28 @@
+from pybind11_tests import tagbased_polymorphic as m
+
+
+def test_downcast():
+    zoo = m.create_zoo()
+    assert [type(animal) for animal in zoo] == [
+        m.Labrador,
+        m.Dog,
+        m.Chihuahua,
+        m.Cat,
+        m.Panther,
+    ]
+    assert [animal.name for animal in zoo] == [
+        "Fido",
+        "Ginger",
+        "Hertzl",
+        "Tiger",
+        "Leo",
+    ]
+    zoo[1].sound = "woooooo"
+    assert [dog.bark() for dog in zoo[:3]] == [
+        "Labrador Fido goes WOOF!",
+        "Dog Ginger goes woooooo",
+        "Chihuahua Hertzl goes iyiyiyiyiyi and runs in circles",
+    ]
+    assert [cat.purr() for cat in zoo[3:]] == ["mrowr", "mrrrRRRRRR"]
+    zoo[0].excitement -= 1000
+    assert zoo[0].excitement == 14000
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_thread.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_thread.cpp
new file mode 100644
index 000000000..1536503b6
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_thread.cpp
@@ -0,0 +1,66 @@
+/*
+    tests/test_thread.cpp -- call pybind11 bound methods in threads
+
+    Copyright (c) 2021 Laramie Leavitt (Google LLC) <lar@google.com>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/cast.h>
+#include <pybind11/pybind11.h>
+
+#include "pybind11_tests.h"
+
+#include <chrono>
+#include <thread>
+
+namespace py = pybind11;
+
+namespace {
+
+struct IntStruct {
+    explicit IntStruct(int v) : value(v){};
+    ~IntStruct() { value = -value; }
+    IntStruct(const IntStruct &) = default;
+    IntStruct &operator=(const IntStruct &) = default;
+
+    int value;
+};
+
+} // namespace
+
+TEST_SUBMODULE(thread, m) {
+
+    py::class_<IntStruct>(m, "IntStruct").def(py::init([](const int i) { return IntStruct(i); }));
+
+    // implicitly_convertible uses loader_life_support when an implicit
+    // conversion is required in order to lifetime extend the reference.
+    //
+    // This test should be run with ASAN for better effectiveness.
+    py::implicitly_convertible<int, IntStruct>();
+
+    m.def("test", [](int expected, const IntStruct &in) {
+        {
+            py::gil_scoped_release release;
+            std::this_thread::sleep_for(std::chrono::milliseconds(5));
+        }
+
+        if (in.value != expected) {
+            throw std::runtime_error("Value changed!!");
+        }
+    });
+
+    m.def(
+        "test_no_gil",
+        [](int expected, const IntStruct &in) {
+            std::this_thread::sleep_for(std::chrono::milliseconds(5));
+            if (in.value != expected) {
+                throw std::runtime_error("Value changed!!");
+            }
+        },
+        py::call_guard<py::gil_scoped_release>());
+
+    // NOTE: std::string_view also uses loader_life_support to ensure that
+    // the string contents remain alive, but that's a C++ 17 feature.
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_thread.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_thread.py
new file mode 100644
index 000000000..e89991f9d
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_thread.py
@@ -0,0 +1,42 @@
+import threading
+
+from pybind11_tests import thread as m
+
+
+class Thread(threading.Thread):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+        self.e = None
+
+    def run(self):
+        try:
+            for i in range(10):
+                self.fn(i, i)
+        except Exception as e:
+            self.e = e
+
+    def join(self):
+        super().join()
+        if self.e:
+            raise self.e
+
+
+def test_implicit_conversion():
+    a = Thread(m.test)
+    b = Thread(m.test)
+    c = Thread(m.test)
+    for x in [a, b, c]:
+        x.start()
+    for x in [c, b, a]:
+        x.join()
+
+
+def test_implicit_conversion_no_gil():
+    a = Thread(m.test_no_gil)
+    b = Thread(m.test_no_gil)
+    c = Thread(m.test_no_gil)
+    for x in [a, b, c]:
+        x.start()
+    for x in [c, b, a]:
+        x.join()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_union.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_union.cpp
new file mode 100644
index 000000000..7b98ea216
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_union.cpp
@@ -0,0 +1,22 @@
+/*
+    tests/test_class.cpp -- test py::class_ definitions and basic functionality
+
+    Copyright (c) 2019 Roland Dreier <roland.dreier@gmail.com>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include "pybind11_tests.h"
+
+TEST_SUBMODULE(union_, m) {
+    union TestUnion {
+        int value_int;
+        unsigned value_uint;
+    };
+
+    py::class_<TestUnion>(m, "TestUnion")
+        .def(py::init<>())
+        .def_readonly("as_int", &TestUnion::value_int)
+        .def_readwrite("as_uint", &TestUnion::value_uint);
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_union.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_union.py
new file mode 100644
index 000000000..e1866e701
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_union.py
@@ -0,0 +1,8 @@
+from pybind11_tests import union_ as m
+
+
+def test_union():
+    instance = m.TestUnion()
+
+    instance.as_uint = 10
+    assert instance.as_int == 10
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_virtual_functions.cpp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_virtual_functions.cpp
new file mode 100644
index 000000000..323aa0d22
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_virtual_functions.cpp
@@ -0,0 +1,591 @@
+/*
+    tests/test_virtual_functions.cpp -- overriding virtual functions from Python
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/functional.h>
+
+#include "constructor_stats.h"
+#include "pybind11_tests.h"
+
+#include <thread>
+
+/* This is an example class that we'll want to be able to extend from Python */
+class ExampleVirt {
+public:
+    explicit ExampleVirt(int state) : state(state) { print_created(this, state); }
+    ExampleVirt(const ExampleVirt &e) : state(e.state) { print_copy_created(this); }
+    ExampleVirt(ExampleVirt &&e) noexcept : state(e.state) {
+        print_move_created(this);
+        e.state = 0;
+    }
+    virtual ~ExampleVirt() { print_destroyed(this); }
+
+    virtual int run(int value) {
+        py::print("Original implementation of "
+                  "ExampleVirt::run(state={}, value={}, str1={}, str2={})"_s.format(
+                      state, value, get_string1(), *get_string2()));
+        return state + value;
+    }
+
+    virtual bool run_bool() = 0;
+    virtual void pure_virtual() = 0;
+
+    // Returning a reference/pointer to a type converted from python (numbers, strings, etc.) is a
+    // bit trickier, because the actual int& or std::string& or whatever only exists temporarily,
+    // so we have to handle it specially in the trampoline class (see below).
+    virtual const std::string &get_string1() { return str1; }
+    virtual const std::string *get_string2() { return &str2; }
+
+private:
+    int state;
+    const std::string str1{"default1"}, str2{"default2"};
+};
+
+/* This is a wrapper class that must be generated */
+class PyExampleVirt : public ExampleVirt {
+public:
+    using ExampleVirt::ExampleVirt; /* Inherit constructors */
+
+    int run(int value) override {
+        /* Generate wrapping code that enables native function overloading */
+        PYBIND11_OVERRIDE(int,         /* Return type */
+                          ExampleVirt, /* Parent class */
+                          run,         /* Name of function */
+                          value        /* Argument(s) */
+        );
+    }
+
+    bool run_bool() override {
+        PYBIND11_OVERRIDE_PURE(bool,        /* Return type */
+                               ExampleVirt, /* Parent class */
+                               run_bool,    /* Name of function */
+                                            /* This function has no arguments. The trailing comma
+                                               in the previous line is needed for some compilers */
+        );
+    }
+
+    void pure_virtual() override {
+        PYBIND11_OVERRIDE_PURE(void,         /* Return type */
+                               ExampleVirt,  /* Parent class */
+                               pure_virtual, /* Name of function */
+                                             /* This function has no arguments. The trailing comma
+                                                in the previous line is needed for some compilers */
+        );
+    }
+
+    // We can return reference types for compatibility with C++ virtual interfaces that do so, but
+    // note they have some significant limitations (see the documentation).
+    const std::string &get_string1() override {
+        PYBIND11_OVERRIDE(const std::string &, /* Return type */
+                          ExampleVirt,         /* Parent class */
+                          get_string1,         /* Name of function */
+                                               /* (no arguments) */
+        );
+    }
+
+    const std::string *get_string2() override {
+        PYBIND11_OVERRIDE(const std::string *, /* Return type */
+                          ExampleVirt,         /* Parent class */
+                          get_string2,         /* Name of function */
+                                               /* (no arguments) */
+        );
+    }
+};
+
+class NonCopyable {
+public:
+    NonCopyable(int a, int b) : value{new int(a * b)} { print_created(this, a, b); }
+    NonCopyable(NonCopyable &&o) noexcept : value{std::move(o.value)} { print_move_created(this); }
+    NonCopyable(const NonCopyable &) = delete;
+    NonCopyable() = delete;
+    void operator=(const NonCopyable &) = delete;
+    void operator=(NonCopyable &&) = delete;
+    std::string get_value() const {
+        if (value) {
+            return std::to_string(*value);
+        }
+        return "(null)";
+    }
+    ~NonCopyable() { print_destroyed(this); }
+
+private:
+    std::unique_ptr<int> value;
+};
+
+// This is like the above, but is both copy and movable.  In effect this means it should get moved
+// when it is not referenced elsewhere, but copied if it is still referenced.
+class Movable {
+public:
+    Movable(int a, int b) : value{a + b} { print_created(this, a, b); }
+    Movable(const Movable &m) : value{m.value} { print_copy_created(this); }
+    Movable(Movable &&m) noexcept : value{m.value} { print_move_created(this); }
+    std::string get_value() const { return std::to_string(value); }
+    ~Movable() { print_destroyed(this); }
+
+private:
+    int value;
+};
+
+class NCVirt {
+public:
+    virtual ~NCVirt() = default;
+    NCVirt() = default;
+    NCVirt(const NCVirt &) = delete;
+    virtual NonCopyable get_noncopyable(int a, int b) { return NonCopyable(a, b); }
+    virtual Movable get_movable(int a, int b) = 0;
+
+    std::string print_nc(int a, int b) { return get_noncopyable(a, b).get_value(); }
+    std::string print_movable(int a, int b) { return get_movable(a, b).get_value(); }
+};
+class NCVirtTrampoline : public NCVirt {
+#if !defined(__INTEL_COMPILER) && !defined(__CUDACC__) && !defined(__PGIC__)
+    NonCopyable get_noncopyable(int a, int b) override {
+        PYBIND11_OVERRIDE(NonCopyable, NCVirt, get_noncopyable, a, b);
+    }
+#endif
+    Movable get_movable(int a, int b) override {
+        PYBIND11_OVERRIDE_PURE(Movable, NCVirt, get_movable, a, b);
+    }
+};
+
+struct Base {
+    virtual std::string dispatch() const = 0;
+    virtual ~Base() = default;
+    Base() = default;
+    Base(const Base &) = delete;
+};
+
+struct DispatchIssue : Base {
+    std::string dispatch() const override {
+        PYBIND11_OVERRIDE_PURE(std::string, Base, dispatch, /* no arguments */);
+    }
+};
+
+// An abstract adder class that uses visitor pattern to add two data
+// objects and send the result to the visitor functor
+struct AdderBase {
+    struct Data {};
+    using DataVisitor = std::function<void(const Data &)>;
+
+    virtual void
+    operator()(const Data &first, const Data &second, const DataVisitor &visitor) const = 0;
+    virtual ~AdderBase() = default;
+    AdderBase() = default;
+    AdderBase(const AdderBase &) = delete;
+};
+
+struct Adder : AdderBase {
+    void
+    operator()(const Data &first, const Data &second, const DataVisitor &visitor) const override {
+        PYBIND11_OVERRIDE_PURE_NAME(
+            void, AdderBase, "__call__", operator(), first, second, visitor);
+    }
+};
+
+static void test_gil() {
+    {
+        py::gil_scoped_acquire lock;
+        py::print("1st lock acquired");
+    }
+
+    {
+        py::gil_scoped_acquire lock;
+        py::print("2nd lock acquired");
+    }
+}
+
+static void test_gil_from_thread() {
+    py::gil_scoped_release release;
+
+    std::thread t(test_gil);
+    t.join();
+}
+
+class test_override_cache_helper {
+
+public:
+    virtual int func() { return 0; }
+
+    test_override_cache_helper() = default;
+    virtual ~test_override_cache_helper() = default;
+    // Non-copyable
+    test_override_cache_helper &operator=(test_override_cache_helper const &Right) = delete;
+    test_override_cache_helper(test_override_cache_helper const &Copy) = delete;
+};
+
+class test_override_cache_helper_trampoline : public test_override_cache_helper {
+    int func() override { PYBIND11_OVERRIDE(int, test_override_cache_helper, func); }
+};
+
+inline int test_override_cache(std::shared_ptr<test_override_cache_helper> const &instance) {
+    return instance->func();
+}
+
+// Forward declaration (so that we can put the main tests here; the inherited virtual approaches
+// are rather long).
+void initialize_inherited_virtuals(py::module_ &m);
+
+TEST_SUBMODULE(virtual_functions, m) {
+    // test_override
+    py::class_<ExampleVirt, PyExampleVirt>(m, "ExampleVirt")
+        .def(py::init<int>())
+        /* Reference original class in function definitions */
+        .def("run", &ExampleVirt::run)
+        .def("run_bool", &ExampleVirt::run_bool)
+        .def("pure_virtual", &ExampleVirt::pure_virtual);
+
+    py::class_<NonCopyable>(m, "NonCopyable").def(py::init<int, int>());
+
+    py::class_<Movable>(m, "Movable").def(py::init<int, int>());
+
+    // test_move_support
+#if !defined(__INTEL_COMPILER) && !defined(__CUDACC__) && !defined(__PGIC__)
+    py::class_<NCVirt, NCVirtTrampoline>(m, "NCVirt")
+        .def(py::init<>())
+        .def("get_noncopyable", &NCVirt::get_noncopyable)
+        .def("get_movable", &NCVirt::get_movable)
+        .def("print_nc", &NCVirt::print_nc)
+        .def("print_movable", &NCVirt::print_movable);
+#endif
+
+    m.def("runExampleVirt", [](ExampleVirt *ex, int value) { return ex->run(value); });
+    m.def("runExampleVirtBool", [](ExampleVirt *ex) { return ex->run_bool(); });
+    m.def("runExampleVirtVirtual", [](ExampleVirt *ex) { ex->pure_virtual(); });
+
+    m.def("cstats_debug", &ConstructorStats::get<ExampleVirt>);
+    initialize_inherited_virtuals(m);
+
+    // test_alias_delay_initialization1
+    // don't invoke Python dispatch classes by default when instantiating C++ classes
+    // that were not extended on the Python side
+    struct A {
+        A() = default;
+        A(const A &) = delete;
+        virtual ~A() = default;
+        virtual void f() { py::print("A.f()"); }
+    };
+
+    struct PyA : A {
+        PyA() { py::print("PyA.PyA()"); }
+        PyA(const PyA &) = delete;
+        ~PyA() override { py::print("PyA.~PyA()"); }
+
+        void f() override {
+            py::print("PyA.f()");
+            // This convolution just gives a `void`, but tests that PYBIND11_TYPE() works to
+            // protect a type containing a ,
+            PYBIND11_OVERRIDE(PYBIND11_TYPE(typename std::enable_if<true, void>::type), A, f);
+        }
+    };
+
+    py::class_<A, PyA>(m, "A").def(py::init<>()).def("f", &A::f);
+
+    m.def("call_f", [](A *a) { a->f(); });
+
+    // test_alias_delay_initialization2
+    // ... unless we explicitly request it, as in this example:
+    struct A2 {
+        A2() = default;
+        A2(const A2 &) = delete;
+        virtual ~A2() = default;
+        virtual void f() { py::print("A2.f()"); }
+    };
+
+    struct PyA2 : A2 {
+        PyA2() { py::print("PyA2.PyA2()"); }
+        PyA2(const PyA2 &) = delete;
+        ~PyA2() override { py::print("PyA2.~PyA2()"); }
+        void f() override {
+            py::print("PyA2.f()");
+            PYBIND11_OVERRIDE(void, A2, f);
+        }
+    };
+
+    py::class_<A2, PyA2>(m, "A2")
+        .def(py::init_alias<>())
+        .def(py::init([](int) { return new PyA2(); }))
+        .def("f", &A2::f);
+
+    m.def("call_f", [](A2 *a2) { a2->f(); });
+
+    // test_dispatch_issue
+    // #159: virtual function dispatch has problems with similar-named functions
+    py::class_<Base, DispatchIssue>(m, "DispatchIssue")
+        .def(py::init<>())
+        .def("dispatch", &Base::dispatch);
+
+    m.def("dispatch_issue_go", [](const Base *b) { return b->dispatch(); });
+
+    // test_recursive_dispatch_issue
+    // #3357: Recursive dispatch fails to find python function override
+    pybind11::class_<AdderBase, Adder>(m, "Adder")
+        .def(pybind11::init<>())
+        .def("__call__", &AdderBase::operator());
+
+    pybind11::class_<AdderBase::Data>(m, "Data").def(pybind11::init<>());
+
+    m.def("add2",
+          [](const AdderBase::Data &first,
+             const AdderBase::Data &second,
+             const AdderBase &adder,
+             const AdderBase::DataVisitor &visitor) { adder(first, second, visitor); });
+
+    m.def("add3",
+          [](const AdderBase::Data &first,
+             const AdderBase::Data &second,
+             const AdderBase::Data &third,
+             const AdderBase &adder,
+             const AdderBase::DataVisitor &visitor) {
+              adder(first, second, [&](const AdderBase::Data &first_plus_second) {
+                  // NOLINTNEXTLINE(readability-suspicious-call-argument)
+                  adder(first_plus_second, third, visitor);
+              });
+          });
+
+    // test_override_ref
+    // #392/397: overriding reference-returning functions
+    class OverrideTest {
+    public:
+        struct A {
+            std::string value = "hi";
+        };
+        std::string v;
+        A a;
+        explicit OverrideTest(const std::string &v) : v{v} {}
+        OverrideTest() = default;
+        OverrideTest(const OverrideTest &) = delete;
+        virtual std::string str_value() { return v; }
+        virtual std::string &str_ref() { return v; }
+        virtual A A_value() { return a; }
+        virtual A &A_ref() { return a; }
+        virtual ~OverrideTest() = default;
+    };
+
+    class PyOverrideTest : public OverrideTest {
+    public:
+        using OverrideTest::OverrideTest;
+        std::string str_value() override {
+            PYBIND11_OVERRIDE(std::string, OverrideTest, str_value);
+        }
+        // Not allowed (enabling the below should hit a static_assert failure): we can't get a
+        // reference to a python numeric value, since we only copy values in the numeric type
+        // caster:
+#ifdef PYBIND11_NEVER_DEFINED_EVER
+        std::string &str_ref() override {
+            PYBIND11_OVERRIDE(std::string &, OverrideTest, str_ref);
+        }
+#endif
+        // But we can work around it like this:
+    private:
+        std::string _tmp;
+        std::string str_ref_helper() { PYBIND11_OVERRIDE(std::string, OverrideTest, str_ref); }
+
+    public:
+        std::string &str_ref() override { return _tmp = str_ref_helper(); }
+
+        A A_value() override { PYBIND11_OVERRIDE(A, OverrideTest, A_value); }
+        A &A_ref() override { PYBIND11_OVERRIDE(A &, OverrideTest, A_ref); }
+    };
+
+    py::class_<OverrideTest::A>(m, "OverrideTest_A")
+        .def_readwrite("value", &OverrideTest::A::value);
+    py::class_<OverrideTest, PyOverrideTest>(m, "OverrideTest")
+        .def(py::init<const std::string &>())
+        .def("str_value", &OverrideTest::str_value)
+#ifdef PYBIND11_NEVER_DEFINED_EVER
+        .def("str_ref", &OverrideTest::str_ref)
+#endif
+        .def("A_value", &OverrideTest::A_value)
+        .def("A_ref", &OverrideTest::A_ref);
+
+    py::class_<test_override_cache_helper,
+               test_override_cache_helper_trampoline,
+               std::shared_ptr<test_override_cache_helper>>(m, "test_override_cache_helper")
+        .def(py::init_alias<>())
+        .def("func", &test_override_cache_helper::func);
+
+    m.def("test_override_cache", test_override_cache);
+}
+
+// Inheriting virtual methods.  We do two versions here: the repeat-everything version and the
+// templated trampoline versions mentioned in docs/advanced.rst.
+//
+// These base classes are exactly the same, but we technically need distinct
+// classes for this example code because we need to be able to bind them
+// properly (pybind11, sensibly, doesn't allow us to bind the same C++ class to
+// multiple python classes).
+class A_Repeat {
+#define A_METHODS                                                                                 \
+public:                                                                                           \
+    virtual int unlucky_number() = 0;                                                             \
+    virtual std::string say_something(unsigned times) {                                           \
+        std::string s = "";                                                                       \
+        for (unsigned i = 0; i < times; ++i)                                                      \
+            s += "hi";                                                                            \
+        return s;                                                                                 \
+    }                                                                                             \
+    std::string say_everything() {                                                                \
+        return say_something(1) + " " + std::to_string(unlucky_number());                         \
+    }
+    A_METHODS
+    A_Repeat() = default;
+    A_Repeat(const A_Repeat &) = delete;
+    virtual ~A_Repeat() = default;
+};
+class B_Repeat : public A_Repeat {
+#define B_METHODS                                                                                 \
+public:                                                                                           \
+    int unlucky_number() override { return 13; }                                                  \
+    std::string say_something(unsigned times) override {                                          \
+        return "B says hi " + std::to_string(times) + " times";                                   \
+    }                                                                                             \
+    virtual double lucky_number() { return 7.0; }
+    B_METHODS
+};
+class C_Repeat : public B_Repeat {
+#define C_METHODS                                                                                 \
+public:                                                                                           \
+    int unlucky_number() override { return 4444; }                                                \
+    double lucky_number() override { return 888; }
+    C_METHODS
+};
+class D_Repeat : public C_Repeat {
+#define D_METHODS // Nothing overridden.
+    D_METHODS
+};
+
+// Base classes for templated inheritance trampolines.  Identical to the repeat-everything version:
+class A_Tpl {
+    A_METHODS;
+    A_Tpl() = default;
+    A_Tpl(const A_Tpl &) = delete;
+    virtual ~A_Tpl() = default;
+};
+class B_Tpl : public A_Tpl {
+    B_METHODS
+};
+class C_Tpl : public B_Tpl {
+    C_METHODS
+};
+class D_Tpl : public C_Tpl {
+    D_METHODS
+};
+
+// Inheritance approach 1: each trampoline gets every virtual method (11 in total)
+class PyA_Repeat : public A_Repeat {
+public:
+    using A_Repeat::A_Repeat;
+    int unlucky_number() override { PYBIND11_OVERRIDE_PURE(int, A_Repeat, unlucky_number, ); }
+    std::string say_something(unsigned times) override {
+        PYBIND11_OVERRIDE(std::string, A_Repeat, say_something, times);
+    }
+};
+class PyB_Repeat : public B_Repeat {
+public:
+    using B_Repeat::B_Repeat;
+    int unlucky_number() override { PYBIND11_OVERRIDE(int, B_Repeat, unlucky_number, ); }
+    std::string say_something(unsigned times) override {
+        PYBIND11_OVERRIDE(std::string, B_Repeat, say_something, times);
+    }
+    double lucky_number() override { PYBIND11_OVERRIDE(double, B_Repeat, lucky_number, ); }
+};
+class PyC_Repeat : public C_Repeat {
+public:
+    using C_Repeat::C_Repeat;
+    int unlucky_number() override { PYBIND11_OVERRIDE(int, C_Repeat, unlucky_number, ); }
+    std::string say_something(unsigned times) override {
+        PYBIND11_OVERRIDE(std::string, C_Repeat, say_something, times);
+    }
+    double lucky_number() override { PYBIND11_OVERRIDE(double, C_Repeat, lucky_number, ); }
+};
+class PyD_Repeat : public D_Repeat {
+public:
+    using D_Repeat::D_Repeat;
+    int unlucky_number() override { PYBIND11_OVERRIDE(int, D_Repeat, unlucky_number, ); }
+    std::string say_something(unsigned times) override {
+        PYBIND11_OVERRIDE(std::string, D_Repeat, say_something, times);
+    }
+    double lucky_number() override { PYBIND11_OVERRIDE(double, D_Repeat, lucky_number, ); }
+};
+
+// Inheritance approach 2: templated trampoline classes.
+//
+// Advantages:
+// - we have only 2 (template) class and 4 method declarations (one per virtual method, plus one
+//   for any override of a pure virtual method), versus 4 classes and 6 methods (MI) or 4 classes
+//   and 11 methods (repeat).
+// - Compared to MI, we also don't have to change the non-trampoline inheritance to virtual, and
+//   can properly inherit constructors.
+//
+// Disadvantage:
+// - the compiler must still generate and compile 14 different methods (more, even, than the 11
+//   required for the repeat approach) instead of the 6 required for MI.  (If there was no pure
+//   method (or no pure method override), the number would drop down to the same 11 as the repeat
+//   approach).
+template <class Base = A_Tpl>
+class PyA_Tpl : public Base {
+public:
+    using Base::Base; // Inherit constructors
+    int unlucky_number() override { PYBIND11_OVERRIDE_PURE(int, Base, unlucky_number, ); }
+    std::string say_something(unsigned times) override {
+        PYBIND11_OVERRIDE(std::string, Base, say_something, times);
+    }
+};
+template <class Base = B_Tpl>
+class PyB_Tpl : public PyA_Tpl<Base> {
+public:
+    using PyA_Tpl<Base>::PyA_Tpl; // Inherit constructors (via PyA_Tpl's inherited constructors)
+    // NOLINTNEXTLINE(bugprone-parent-virtual-call)
+    int unlucky_number() override { PYBIND11_OVERRIDE(int, Base, unlucky_number, ); }
+    double lucky_number() override { PYBIND11_OVERRIDE(double, Base, lucky_number, ); }
+};
+// Since C_Tpl and D_Tpl don't declare any new virtual methods, we don't actually need these
+// (we can use PyB_Tpl<C_Tpl> and PyB_Tpl<D_Tpl> for the trampoline classes instead):
+/*
+template <class Base = C_Tpl> class PyC_Tpl : public PyB_Tpl<Base> {
+public:
+    using PyB_Tpl<Base>::PyB_Tpl;
+};
+template <class Base = D_Tpl> class PyD_Tpl : public PyC_Tpl<Base> {
+public:
+    using PyC_Tpl<Base>::PyC_Tpl;
+};
+*/
+
+void initialize_inherited_virtuals(py::module_ &m) {
+    // test_inherited_virtuals
+
+    // Method 1: repeat
+    py::class_<A_Repeat, PyA_Repeat>(m, "A_Repeat")
+        .def(py::init<>())
+        .def("unlucky_number", &A_Repeat::unlucky_number)
+        .def("say_something", &A_Repeat::say_something)
+        .def("say_everything", &A_Repeat::say_everything);
+    py::class_<B_Repeat, A_Repeat, PyB_Repeat>(m, "B_Repeat")
+        .def(py::init<>())
+        .def("lucky_number", &B_Repeat::lucky_number);
+    py::class_<C_Repeat, B_Repeat, PyC_Repeat>(m, "C_Repeat").def(py::init<>());
+    py::class_<D_Repeat, C_Repeat, PyD_Repeat>(m, "D_Repeat").def(py::init<>());
+
+    // test_
+    // Method 2: Templated trampolines
+    py::class_<A_Tpl, PyA_Tpl<>>(m, "A_Tpl")
+        .def(py::init<>())
+        .def("unlucky_number", &A_Tpl::unlucky_number)
+        .def("say_something", &A_Tpl::say_something)
+        .def("say_everything", &A_Tpl::say_everything);
+    py::class_<B_Tpl, A_Tpl, PyB_Tpl<>>(m, "B_Tpl")
+        .def(py::init<>())
+        .def("lucky_number", &B_Tpl::lucky_number);
+    py::class_<C_Tpl, B_Tpl, PyB_Tpl<C_Tpl>>(m, "C_Tpl").def(py::init<>());
+    py::class_<D_Tpl, C_Tpl, PyB_Tpl<D_Tpl>>(m, "D_Tpl").def(py::init<>());
+
+    // Fix issue #1454 (crash when acquiring/releasing GIL on another thread in Python 2.7)
+    m.def("test_gil", &test_gil);
+    m.def("test_gil_from_thread", &test_gil_from_thread);
+};
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_virtual_functions.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_virtual_functions.py
new file mode 100644
index 000000000..4d00d3690
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/test_virtual_functions.py
@@ -0,0 +1,459 @@
+import pytest
+
+import env  # noqa: F401
+
+m = pytest.importorskip("pybind11_tests.virtual_functions")
+from pybind11_tests import ConstructorStats  # noqa: E402
+
+
+def test_override(capture, msg):
+    class ExtendedExampleVirt(m.ExampleVirt):
+        def __init__(self, state):
+            super().__init__(state + 1)
+            self.data = "Hello world"
+
+        def run(self, value):
+            print(f"ExtendedExampleVirt::run({value}), calling parent..")
+            return super().run(value + 1)
+
+        def run_bool(self):
+            print("ExtendedExampleVirt::run_bool()")
+            return False
+
+        def get_string1(self):
+            return "override1"
+
+        def pure_virtual(self):
+            print(f"ExtendedExampleVirt::pure_virtual(): {self.data}")
+
+    class ExtendedExampleVirt2(ExtendedExampleVirt):
+        def __init__(self, state):
+            super().__init__(state + 1)
+
+        def get_string2(self):
+            return "override2"
+
+    ex12 = m.ExampleVirt(10)
+    with capture:
+        assert m.runExampleVirt(ex12, 20) == 30
+    assert (
+        capture
+        == """
+        Original implementation of ExampleVirt::run(state=10, value=20, str1=default1, str2=default2)
+    """
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        m.runExampleVirtVirtual(ex12)
+    assert (
+        msg(excinfo.value)
+        == 'Tried to call pure virtual function "ExampleVirt::pure_virtual"'
+    )
+
+    ex12p = ExtendedExampleVirt(10)
+    with capture:
+        assert m.runExampleVirt(ex12p, 20) == 32
+    assert (
+        capture
+        == """
+        ExtendedExampleVirt::run(20), calling parent..
+        Original implementation of ExampleVirt::run(state=11, value=21, str1=override1, str2=default2)
+    """
+    )
+    with capture:
+        assert m.runExampleVirtBool(ex12p) is False
+    assert capture == "ExtendedExampleVirt::run_bool()"
+    with capture:
+        m.runExampleVirtVirtual(ex12p)
+    assert capture == "ExtendedExampleVirt::pure_virtual(): Hello world"
+
+    ex12p2 = ExtendedExampleVirt2(15)
+    with capture:
+        assert m.runExampleVirt(ex12p2, 50) == 68
+    assert (
+        capture
+        == """
+        ExtendedExampleVirt::run(50), calling parent..
+        Original implementation of ExampleVirt::run(state=17, value=51, str1=override1, str2=override2)
+    """
+    )
+
+    cstats = ConstructorStats.get(m.ExampleVirt)
+    assert cstats.alive() == 3
+    del ex12, ex12p, ex12p2
+    assert cstats.alive() == 0
+    assert cstats.values() == ["10", "11", "17"]
+    assert cstats.copy_constructions == 0
+    assert cstats.move_constructions >= 0
+
+
+def test_alias_delay_initialization1(capture):
+    """`A` only initializes its trampoline class when we inherit from it
+
+    If we just create and use an A instance directly, the trampoline initialization is
+    bypassed and we only initialize an A() instead (for performance reasons).
+    """
+
+    class B(m.A):
+        def __init__(self):
+            super().__init__()
+
+        def f(self):
+            print("In python f()")
+
+    # C++ version
+    with capture:
+        a = m.A()
+        m.call_f(a)
+        del a
+        pytest.gc_collect()
+    assert capture == "A.f()"
+
+    # Python version
+    with capture:
+        b = B()
+        m.call_f(b)
+        del b
+        pytest.gc_collect()
+    assert (
+        capture
+        == """
+        PyA.PyA()
+        PyA.f()
+        In python f()
+        PyA.~PyA()
+    """
+    )
+
+
+def test_alias_delay_initialization2(capture):
+    """`A2`, unlike the above, is configured to always initialize the alias
+
+    While the extra initialization and extra class layer has small virtual dispatch
+    performance penalty, it also allows us to do more things with the trampoline
+    class such as defining local variables and performing construction/destruction.
+    """
+
+    class B2(m.A2):
+        def __init__(self):
+            super().__init__()
+
+        def f(self):
+            print("In python B2.f()")
+
+    # No python subclass version
+    with capture:
+        a2 = m.A2()
+        m.call_f(a2)
+        del a2
+        pytest.gc_collect()
+        a3 = m.A2(1)
+        m.call_f(a3)
+        del a3
+        pytest.gc_collect()
+    assert (
+        capture
+        == """
+        PyA2.PyA2()
+        PyA2.f()
+        A2.f()
+        PyA2.~PyA2()
+        PyA2.PyA2()
+        PyA2.f()
+        A2.f()
+        PyA2.~PyA2()
+    """
+    )
+
+    # Python subclass version
+    with capture:
+        b2 = B2()
+        m.call_f(b2)
+        del b2
+        pytest.gc_collect()
+    assert (
+        capture
+        == """
+        PyA2.PyA2()
+        PyA2.f()
+        In python B2.f()
+        PyA2.~PyA2()
+    """
+    )
+
+
+# PyPy: Reference count > 1 causes call with noncopyable instance
+# to fail in ncv1.print_nc()
+@pytest.mark.xfail("env.PYPY")
+@pytest.mark.skipif(
+    not hasattr(m, "NCVirt"), reason="NCVirt does not work on Intel/PGI/NVCC compilers"
+)
+def test_move_support():
+    class NCVirtExt(m.NCVirt):
+        def get_noncopyable(self, a, b):
+            # Constructs and returns a new instance:
+            nc = m.NonCopyable(a * a, b * b)
+            return nc
+
+        def get_movable(self, a, b):
+            # Return a referenced copy
+            self.movable = m.Movable(a, b)
+            return self.movable
+
+    class NCVirtExt2(m.NCVirt):
+        def get_noncopyable(self, a, b):
+            # Keep a reference: this is going to throw an exception
+            self.nc = m.NonCopyable(a, b)
+            return self.nc
+
+        def get_movable(self, a, b):
+            # Return a new instance without storing it
+            return m.Movable(a, b)
+
+    ncv1 = NCVirtExt()
+    assert ncv1.print_nc(2, 3) == "36"
+    assert ncv1.print_movable(4, 5) == "9"
+    ncv2 = NCVirtExt2()
+    assert ncv2.print_movable(7, 7) == "14"
+    # Don't check the exception message here because it differs under debug/non-debug mode
+    with pytest.raises(RuntimeError):
+        ncv2.print_nc(9, 9)
+
+    nc_stats = ConstructorStats.get(m.NonCopyable)
+    mv_stats = ConstructorStats.get(m.Movable)
+    assert nc_stats.alive() == 1
+    assert mv_stats.alive() == 1
+    del ncv1, ncv2
+    assert nc_stats.alive() == 0
+    assert mv_stats.alive() == 0
+    assert nc_stats.values() == ["4", "9", "9", "9"]
+    assert mv_stats.values() == ["4", "5", "7", "7"]
+    assert nc_stats.copy_constructions == 0
+    assert mv_stats.copy_constructions == 1
+    assert nc_stats.move_constructions >= 0
+    assert mv_stats.move_constructions >= 0
+
+
+def test_dispatch_issue(msg):
+    """#159: virtual function dispatch has problems with similar-named functions"""
+
+    class PyClass1(m.DispatchIssue):
+        def dispatch(self):
+            return "Yay.."
+
+    class PyClass2(m.DispatchIssue):
+        def dispatch(self):
+            with pytest.raises(RuntimeError) as excinfo:
+                super().dispatch()
+            assert (
+                msg(excinfo.value)
+                == 'Tried to call pure virtual function "Base::dispatch"'
+            )
+
+            return m.dispatch_issue_go(PyClass1())
+
+    b = PyClass2()
+    assert m.dispatch_issue_go(b) == "Yay.."
+
+
+def test_recursive_dispatch_issue(msg):
+    """#3357: Recursive dispatch fails to find python function override"""
+
+    class Data(m.Data):
+        def __init__(self, value):
+            super().__init__()
+            self.value = value
+
+    class Adder(m.Adder):
+        def __call__(self, first, second, visitor):
+            # lambda is a workaround, which adds extra frame to the
+            # current CPython thread. Removing lambda reveals the bug
+            # [https://github.com/pybind/pybind11/issues/3357]
+            (lambda: visitor(Data(first.value + second.value)))()
+
+    class StoreResultVisitor:
+        def __init__(self):
+            self.result = None
+
+        def __call__(self, data):
+            self.result = data.value
+
+    store = StoreResultVisitor()
+
+    m.add2(Data(1), Data(2), Adder(), store)
+    assert store.result == 3
+
+    # without lambda in Adder class, this function fails with
+    # RuntimeError: Tried to call pure virtual function "AdderBase::__call__"
+    m.add3(Data(1), Data(2), Data(3), Adder(), store)
+    assert store.result == 6
+
+
+def test_override_ref():
+    """#392/397: overriding reference-returning functions"""
+    o = m.OverrideTest("asdf")
+
+    # Not allowed (see associated .cpp comment)
+    # i = o.str_ref()
+    # assert o.str_ref() == "asdf"
+    assert o.str_value() == "asdf"
+
+    assert o.A_value().value == "hi"
+    a = o.A_ref()
+    assert a.value == "hi"
+    a.value = "bye"
+    assert a.value == "bye"
+
+
+def test_inherited_virtuals():
+    class AR(m.A_Repeat):
+        def unlucky_number(self):
+            return 99
+
+    class AT(m.A_Tpl):
+        def unlucky_number(self):
+            return 999
+
+    obj = AR()
+    assert obj.say_something(3) == "hihihi"
+    assert obj.unlucky_number() == 99
+    assert obj.say_everything() == "hi 99"
+
+    obj = AT()
+    assert obj.say_something(3) == "hihihi"
+    assert obj.unlucky_number() == 999
+    assert obj.say_everything() == "hi 999"
+
+    for obj in [m.B_Repeat(), m.B_Tpl()]:
+        assert obj.say_something(3) == "B says hi 3 times"
+        assert obj.unlucky_number() == 13
+        assert obj.lucky_number() == 7.0
+        assert obj.say_everything() == "B says hi 1 times 13"
+
+    for obj in [m.C_Repeat(), m.C_Tpl()]:
+        assert obj.say_something(3) == "B says hi 3 times"
+        assert obj.unlucky_number() == 4444
+        assert obj.lucky_number() == 888.0
+        assert obj.say_everything() == "B says hi 1 times 4444"
+
+    class CR(m.C_Repeat):
+        def lucky_number(self):
+            return m.C_Repeat.lucky_number(self) + 1.25
+
+    obj = CR()
+    assert obj.say_something(3) == "B says hi 3 times"
+    assert obj.unlucky_number() == 4444
+    assert obj.lucky_number() == 889.25
+    assert obj.say_everything() == "B says hi 1 times 4444"
+
+    class CT(m.C_Tpl):
+        pass
+
+    obj = CT()
+    assert obj.say_something(3) == "B says hi 3 times"
+    assert obj.unlucky_number() == 4444
+    assert obj.lucky_number() == 888.0
+    assert obj.say_everything() == "B says hi 1 times 4444"
+
+    class CCR(CR):
+        def lucky_number(self):
+            return CR.lucky_number(self) * 10
+
+    obj = CCR()
+    assert obj.say_something(3) == "B says hi 3 times"
+    assert obj.unlucky_number() == 4444
+    assert obj.lucky_number() == 8892.5
+    assert obj.say_everything() == "B says hi 1 times 4444"
+
+    class CCT(CT):
+        def lucky_number(self):
+            return CT.lucky_number(self) * 1000
+
+    obj = CCT()
+    assert obj.say_something(3) == "B says hi 3 times"
+    assert obj.unlucky_number() == 4444
+    assert obj.lucky_number() == 888000.0
+    assert obj.say_everything() == "B says hi 1 times 4444"
+
+    class DR(m.D_Repeat):
+        def unlucky_number(self):
+            return 123
+
+        def lucky_number(self):
+            return 42.0
+
+    for obj in [m.D_Repeat(), m.D_Tpl()]:
+        assert obj.say_something(3) == "B says hi 3 times"
+        assert obj.unlucky_number() == 4444
+        assert obj.lucky_number() == 888.0
+        assert obj.say_everything() == "B says hi 1 times 4444"
+
+    obj = DR()
+    assert obj.say_something(3) == "B says hi 3 times"
+    assert obj.unlucky_number() == 123
+    assert obj.lucky_number() == 42.0
+    assert obj.say_everything() == "B says hi 1 times 123"
+
+    class DT(m.D_Tpl):
+        def say_something(self, times):
+            return "DT says:" + (" quack" * times)
+
+        def unlucky_number(self):
+            return 1234
+
+        def lucky_number(self):
+            return -4.25
+
+    obj = DT()
+    assert obj.say_something(3) == "DT says: quack quack quack"
+    assert obj.unlucky_number() == 1234
+    assert obj.lucky_number() == -4.25
+    assert obj.say_everything() == "DT says: quack 1234"
+
+    class DT2(DT):
+        def say_something(self, times):
+            return "DT2: " + ("QUACK" * times)
+
+        def unlucky_number(self):
+            return -3
+
+    class BT(m.B_Tpl):
+        def say_something(self, times):
+            return "BT" * times
+
+        def unlucky_number(self):
+            return -7
+
+        def lucky_number(self):
+            return -1.375
+
+    obj = BT()
+    assert obj.say_something(3) == "BTBTBT"
+    assert obj.unlucky_number() == -7
+    assert obj.lucky_number() == -1.375
+    assert obj.say_everything() == "BT -7"
+
+
+def test_issue_1454():
+    # Fix issue #1454 (crash when acquiring/releasing GIL on another thread in Python 2.7)
+    m.test_gil()
+    m.test_gil_from_thread()
+
+
+def test_python_override():
+    def func():
+        class Test(m.test_override_cache_helper):
+            def func(self):
+                return 42
+
+        return Test()
+
+    def func2():
+        class Test(m.test_override_cache_helper):
+            pass
+
+        return Test()
+
+    for _ in range(1500):
+        assert m.test_override_cache(func()) == 42
+        assert m.test_override_cache(func2()) == 0
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/valgrind-numpy-scipy.supp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/valgrind-numpy-scipy.supp
new file mode 100644
index 000000000..16db302c1
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/valgrind-numpy-scipy.supp
@@ -0,0 +1,140 @@
+# Valgrind suppression file for NumPy & SciPy errors and leaks in pybind11 tests
+#
+# On updating a dependency, to get a list of "default" leaks in e.g. NumPy, run
+# `PYTHONMALLOC=malloc valgrind --leak-check=full --show-leak-kinds=definite,indirect python3.9-dbg -c "import numpy"`
+# To use these suppression files, add e.g. `--suppressions=valgrind-numpy-scipy.supp`
+
+{
+   Leaks when importing NumPy
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyObject_Malloc
+   fun:_PyObject_GC_Alloc
+   fun:_PyObject_GC_Malloc
+   fun:_PyObject_GC_NewVar
+   fun:tuple_alloc
+   fun:PyTuple_Pack
+   ...
+   fun:__pyx_pymod_exec_*
+}
+
+{
+   Leaks when importing NumPy (bis)
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyObject_Malloc
+   fun:_PyObject_New
+   fun:PyCode_NewWithPosOnlyArgs
+   fun:PyCode_New
+   ...
+   fun:__pyx_pymod_exec_*
+}
+
+{
+   Leaks when importing NumPy (ter)
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyObject_Malloc
+   fun:_PyObject_GC_Alloc
+   fun:_PyObject_GC_Malloc
+   fun:_PyObject_GC_NewVar
+   fun:tuple_alloc
+   fun:_PyTuple_FromArray
+   fun:_PyObject_MakeTpCall
+   fun:_PyObject_VectorcallTstate
+   fun:PyObject_Vectorcall
+   fun:call_function
+   fun:_PyEval_EvalFrameDefault
+   fun:_PyEval_EvalFrame
+   fun:function_code_fastcall
+   fun:_PyFunction_Vectorcall
+}
+
+{
+   Leaks when importing NumPy (quater)
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyObject_Malloc
+   fun:_PyObject_GC_Alloc
+   fun:_PyObject_GC_Malloc
+   fun:_PyObject_GC_NewVar
+   fun:tuple_alloc
+   fun:_PyTuple_FromArray
+   fun:_PyObject_MakeTpCall
+   fun:_PyObject_VectorcallTstate
+   fun:_PyObject_CallFunctionVa
+   fun:PyObject_CallFunction
+   fun:PyImport_Import
+}
+
+{
+   Leaks when importing NumPy (quinquies)
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyObject_Malloc
+   fun:_PyObject_GC_Alloc
+   fun:_PyObject_GC_Malloc
+   fun:_PyObject_GC_NewVar
+   fun:tuple_alloc
+   fun:PyTuple_New
+   fun:r_object
+   fun:r_object
+   fun:r_object
+   fun:r_object
+}
+
+{
+   Leaks when importing NumPy (sexies)
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyObject_Malloc
+   fun:_PyObject_GC_Alloc
+   fun:_PyObject_GC_Malloc
+   fun:_PyObject_GC_NewVar
+   fun:tuple_alloc
+   fun:PyTuple_New
+   fun:dictiter_iternextitem
+   fun:list_extend
+   fun:_PyList_Extend
+   fun:PySequence_List
+}
+
+{
+   Leak when importing scipy.fft
+   Memcheck:Leak
+   fun:_Znwm
+   fun:PyInit_pypocketfft
+   fun:_PyImport_LoadDynamicModuleWithSpec
+   fun:_imp_create_dynamic_impl*
+   fun:_imp_create_dynamic
+   fun:cfunction_vectorcall_FASTCALL
+   fun:PyVectorcall_Call
+   fun:_PyObject_Call
+   fun:PyObject_Call
+   fun:do_call_core
+   fun:_PyEval_EvalFrameDefault
+   fun:_PyEval_EvalFrame
+   fun:_PyEval_EvalCode
+}
+
+{
+   NumPy leaks when spawning a subprocess
+   Memcheck:Leak
+   fun:malloc
+   ...
+   fun:_buffer_get_info
+   fun:array_getbuffer
+   fun:PyObject_GetBuffer
+   fun:__Pyx__GetBufferAndValidate*
+   fun:__pyx_f_5numpy_6random_13bit_generator_12SeedSequence_mix_entropy
+   fun:__pyx_pw_5numpy_6random_13bit_generator_12SeedSequence_1__init__
+   fun:type_call
+   fun:__Pyx__PyObject_CallOneArg
+   fun:__pyx_pw_5numpy_6random_13bit_generator_12BitGenerator_1__init__
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/valgrind-python.supp b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/valgrind-python.supp
new file mode 100644
index 000000000..d77d5e5c5
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tests/valgrind-python.supp
@@ -0,0 +1,117 @@
+# Valgrind suppression file for CPython errors and leaks in pybind11 tests
+
+# Taken verbatim from https://github.com/python/cpython/blob/3.9/Misc/valgrind-python.supp#L266-L272
+{
+   Uninitialised byte(s) false alarm, see bpo-35561
+   Memcheck:Param
+   epoll_ctl(event)
+   fun:epoll_ctl
+   fun:pyepoll_internal_ctl
+}
+
+{
+   Python leaks when spawning a subprocess
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyMem_RawMalloc
+   fun:PyThread_allocate_lock
+   fun:_PyEval_InitState
+   fun:PyInterpreterState_New
+   ...
+   fun:pyinit_core*
+   fun:Py_InitializeFromConfig
+   fun:pymain_init
+   fun:pymain_main
+}
+
+{
+   Python leaks when spawning a subprocess
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:_PyMem_DebugRawAlloc
+   fun:_PyMem_DebugRawMalloc
+   fun:PyMem_RawMalloc
+   fun:PyThread_allocate_lock
+   fun:_PyRuntimeState_Init_impl
+   fun:_PyRuntimeState_Init
+   fun:_PyRuntime_Initialize
+   fun:pymain_init
+   fun:pymain_main
+   fun:Py_BytesMain
+}
+
+{
+   Python leaks when spawning a subprocess
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyMem_RawMalloc
+   fun:PyThread_allocate_lock
+   fun:_PyImport_AcquireLock
+   fun:_imp_acquire_lock_impl*
+   fun:_imp_acquire_lock
+   fun:cfunction_vectorcall_NOARGS
+   fun:_PyObject_VectorcallTstate
+   fun:PyObject_Vectorcall
+   fun:call_function
+   fun:_PyEval_EvalFrameDefault
+   fun:_PyEval_EvalFrame
+   fun:function_code_fastcall
+}
+
+{
+   Python leaks when spawning a subprocess
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyMem_RawMalloc
+   fun:PyThread_allocate_lock
+   fun:newlockobject
+   ...
+   fun:cfunction_vectorcall_NOARGS
+   fun:_PyObject_VectorcallTstate
+   fun:PyObject_Vectorcall
+   fun:call_function
+   fun:_PyEval_EvalFrameDefault
+   fun:_PyEval_EvalFrame
+   fun:function_code_fastcall
+   fun:_PyFunction_Vectorcall
+}
+
+{
+   Python leaks when spawning a subprocess
+   Memcheck:Leak
+   fun:malloc
+   fun:_PyMem_RawMalloc
+   fun:PyMem_RawMalloc
+   fun:PyThread_allocate_lock
+   fun:rlock_new
+   fun:type_call
+   fun:_PyObject_Call
+   fun:PyObject_Call
+   fun:do_call_core
+   fun:_PyEval_EvalFrameDefault
+   fun:_PyEval_EvalFrame
+   fun:_PyEval_EvalCode
+   fun:_PyFunction_Vectorcall
+}
+
+# Not really CPython-specific, see link
+{
+   dlopen leak (https://stackoverflow.com/questions/1542457/memory-leak-reported-by-valgrind-in-dlopen)
+   Memcheck:Leak
+   fun:malloc
+   ...
+   fun:dl_open_worker
+   fun:_dl_catch_exception
+   fun:_dl_open
+   fun:dlopen_doit
+   fun:_dl_catch_exception
+   fun:_dl_catch_error
+   fun:_dlerror_run
+   fun:dlopen@@GLIBC_2.2.5
+   fun:_PyImport_FindSharedFuncptr
+   fun:_PyImport_LoadDynamicModuleWithSpec
+}
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindCatch.cmake b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindCatch.cmake
new file mode 100644
index 000000000..57bba58b3
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindCatch.cmake
@@ -0,0 +1,72 @@
+# - Find the Catch test framework or download it (single header)
+#
+# This is a quick module for internal use. It assumes that Catch is
+# REQUIRED and that a minimum version is provided (not EXACT). If
+# a suitable version isn't found locally, the single header file
+# will be downloaded and placed in the build dir: PROJECT_BINARY_DIR.
+#
+# This code sets the following variables:
+#  CATCH_INCLUDE_DIR      - path to catch.hpp
+#  CATCH_VERSION          - version number
+
+option(DOWNLOAD_CATCH "Download catch2 if not found")
+
+if(NOT Catch_FIND_VERSION)
+  message(FATAL_ERROR "A version number must be specified.")
+elseif(Catch_FIND_REQUIRED)
+  message(FATAL_ERROR "This module assumes Catch is not required.")
+elseif(Catch_FIND_VERSION_EXACT)
+  message(FATAL_ERROR "Exact version numbers are not supported, only minimum.")
+endif()
+
+# Extract the version number from catch.hpp
+function(_get_catch_version)
+  file(
+    STRINGS "${CATCH_INCLUDE_DIR}/catch.hpp" version_line
+    REGEX "Catch v.*"
+    LIMIT_COUNT 1)
+  if(version_line MATCHES "Catch v([0-9]+)\\.([0-9]+)\\.([0-9]+)")
+    set(CATCH_VERSION
+        "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}"
+        PARENT_SCOPE)
+  endif()
+endfunction()
+
+# Download the single-header version of Catch
+function(_download_catch version destination_dir)
+  message(STATUS "Downloading catch v${version}...")
+  set(url https://github.com/philsquared/Catch/releases/download/v${version}/catch.hpp)
+  file(DOWNLOAD ${url} "${destination_dir}/catch.hpp" STATUS status)
+  list(GET status 0 error)
+  if(error)
+    message(FATAL_ERROR "Could not download ${url}")
+  endif()
+  set(CATCH_INCLUDE_DIR
+      "${destination_dir}"
+      CACHE INTERNAL "")
+endfunction()
+
+# Look for catch locally
+find_path(
+  CATCH_INCLUDE_DIR
+  NAMES catch.hpp
+  PATH_SUFFIXES catch2)
+if(CATCH_INCLUDE_DIR)
+  _get_catch_version()
+endif()
+
+# Download the header if it wasn't found or if it's outdated
+if(NOT CATCH_VERSION OR CATCH_VERSION VERSION_LESS ${Catch_FIND_VERSION})
+  if(DOWNLOAD_CATCH)
+    _download_catch(${Catch_FIND_VERSION} "${PROJECT_BINARY_DIR}/catch/")
+    _get_catch_version()
+  else()
+    set(CATCH_FOUND FALSE)
+    return()
+  endif()
+endif()
+
+add_library(Catch2::Catch2 IMPORTED INTERFACE)
+set_property(TARGET Catch2::Catch2 PROPERTY INTERFACE_INCLUDE_DIRECTORIES "${CATCH_INCLUDE_DIR}")
+
+set(CATCH_FOUND TRUE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindEigen3.cmake b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindEigen3.cmake
new file mode 100644
index 000000000..83625d92e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindEigen3.cmake
@@ -0,0 +1,86 @@
+# - Try to find Eigen3 lib
+#
+# This module supports requiring a minimum version, e.g. you can do
+#   find_package(Eigen3 3.1.2)
+# to require version 3.1.2 or newer of Eigen3.
+#
+# Once done this will define
+#
+#  EIGEN3_FOUND - system has eigen lib with correct version
+#  EIGEN3_INCLUDE_DIR - the eigen include directory
+#  EIGEN3_VERSION - eigen version
+
+# Copyright (c) 2006, 2007 Montel Laurent, <montel@kde.org>
+# Copyright (c) 2008, 2009 Gael Guennebaud, <g.gael@free.fr>
+# Copyright (c) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+# Redistribution and use is allowed according to the terms of the 2-clause BSD license.
+
+if(NOT Eigen3_FIND_VERSION)
+  if(NOT Eigen3_FIND_VERSION_MAJOR)
+    set(Eigen3_FIND_VERSION_MAJOR 2)
+  endif(NOT Eigen3_FIND_VERSION_MAJOR)
+  if(NOT Eigen3_FIND_VERSION_MINOR)
+    set(Eigen3_FIND_VERSION_MINOR 91)
+  endif(NOT Eigen3_FIND_VERSION_MINOR)
+  if(NOT Eigen3_FIND_VERSION_PATCH)
+    set(Eigen3_FIND_VERSION_PATCH 0)
+  endif(NOT Eigen3_FIND_VERSION_PATCH)
+
+  set(Eigen3_FIND_VERSION
+      "${Eigen3_FIND_VERSION_MAJOR}.${Eigen3_FIND_VERSION_MINOR}.${Eigen3_FIND_VERSION_PATCH}")
+endif(NOT Eigen3_FIND_VERSION)
+
+macro(_eigen3_check_version)
+  file(READ "${EIGEN3_INCLUDE_DIR}/Eigen/src/Core/util/Macros.h" _eigen3_version_header)
+
+  string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen3_world_version_match
+               "${_eigen3_version_header}")
+  set(EIGEN3_WORLD_VERSION "${CMAKE_MATCH_1}")
+  string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen3_major_version_match
+               "${_eigen3_version_header}")
+  set(EIGEN3_MAJOR_VERSION "${CMAKE_MATCH_1}")
+  string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen3_minor_version_match
+               "${_eigen3_version_header}")
+  set(EIGEN3_MINOR_VERSION "${CMAKE_MATCH_1}")
+
+  set(EIGEN3_VERSION ${EIGEN3_WORLD_VERSION}.${EIGEN3_MAJOR_VERSION}.${EIGEN3_MINOR_VERSION})
+  if(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
+    set(EIGEN3_VERSION_OK FALSE)
+  else(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
+    set(EIGEN3_VERSION_OK TRUE)
+  endif(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
+
+  if(NOT EIGEN3_VERSION_OK)
+
+    message(STATUS "Eigen3 version ${EIGEN3_VERSION} found in ${EIGEN3_INCLUDE_DIR}, "
+                   "but at least version ${Eigen3_FIND_VERSION} is required")
+  endif(NOT EIGEN3_VERSION_OK)
+endmacro(_eigen3_check_version)
+
+if(EIGEN3_INCLUDE_DIR)
+
+  # in cache already
+  _eigen3_check_version()
+  set(EIGEN3_FOUND ${EIGEN3_VERSION_OK})
+
+else(EIGEN3_INCLUDE_DIR)
+  if(NOT DEFINED KDE4_INCLUDE_DIR)
+    set(KDE4_INCLUDE_DIR "")
+  endif()
+
+  find_path(
+    EIGEN3_INCLUDE_DIR
+    NAMES signature_of_eigen3_matrix_library
+    PATHS ${CMAKE_INSTALL_PREFIX}/include ${KDE4_INCLUDE_DIR}
+    PATH_SUFFIXES eigen3 eigen)
+
+  if(EIGEN3_INCLUDE_DIR)
+    _eigen3_check_version()
+  endif(EIGEN3_INCLUDE_DIR)
+
+  include(FindPackageHandleStandardArgs)
+  find_package_handle_standard_args(Eigen3 DEFAULT_MSG EIGEN3_INCLUDE_DIR EIGEN3_VERSION_OK)
+
+  mark_as_advanced(EIGEN3_INCLUDE_DIR)
+
+endif(EIGEN3_INCLUDE_DIR)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindPythonLibsNew.cmake b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindPythonLibsNew.cmake
new file mode 100644
index 000000000..a5a628fd7
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/FindPythonLibsNew.cmake
@@ -0,0 +1,281 @@
+# - Find python libraries
+# This module finds the libraries corresponding to the Python interpreter
+# FindPythonInterp provides.
+# This code sets the following variables:
+#
+#  PYTHONLIBS_FOUND           - have the Python libs been found
+#  PYTHON_PREFIX              - path to the Python installation
+#  PYTHON_LIBRARIES           - path to the python library
+#  PYTHON_INCLUDE_DIRS        - path to where Python.h is found
+#  PYTHON_MODULE_EXTENSION    - lib extension, e.g. '.so' or '.pyd'
+#  PYTHON_MODULE_PREFIX       - lib name prefix: usually an empty string
+#  PYTHON_SITE_PACKAGES       - path to installation site-packages
+#  PYTHON_IS_DEBUG            - whether the Python interpreter is a debug build
+#
+# Thanks to talljimbo for the patch adding the 'LDVERSION' config
+# variable usage.
+
+#=============================================================================
+# Copyright 2001-2009 Kitware, Inc.
+# Copyright 2012 Continuum Analytics, Inc.
+#
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# * Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright
+# notice, this list of conditions and the following disclaimer in the
+# documentation and/or other materials provided with the distribution.
+#
+# * Neither the names of Kitware, Inc., the Insight Software Consortium,
+# nor the names of their contributors may be used to endorse or promote
+# products derived from this software without specific prior written
+# permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#=============================================================================
+
+# Checking for the extension makes sure that `LibsNew` was found and not just `Libs`.
+if(PYTHONLIBS_FOUND AND PYTHON_MODULE_EXTENSION)
+  return()
+endif()
+
+if(PythonLibsNew_FIND_QUIETLY)
+  set(_pythonlibs_quiet QUIET)
+else()
+  set(_pythonlibs_quiet "")
+endif()
+
+if(PythonLibsNew_FIND_REQUIRED)
+  set(_pythonlibs_required REQUIRED)
+endif()
+
+# Check to see if the `python` command is present and from a virtual
+# environment, conda, or GHA activation - if it is, try to use that.
+
+if(NOT DEFINED PYTHON_EXECUTABLE)
+  if(DEFINED ENV{VIRTUAL_ENV})
+    find_program(
+      PYTHON_EXECUTABLE python
+      PATHS "$ENV{VIRTUAL_ENV}" "$ENV{VIRTUAL_ENV}/bin"
+      NO_DEFAULT_PATH)
+  elseif(DEFINED ENV{CONDA_PREFIX})
+    find_program(
+      PYTHON_EXECUTABLE python
+      PATHS "$ENV{CONDA_PREFIX}" "$ENV{CONDA_PREFIX}/bin"
+      NO_DEFAULT_PATH)
+  elseif(DEFINED ENV{pythonLocation})
+    find_program(
+      PYTHON_EXECUTABLE python
+      PATHS "$ENV{pythonLocation}" "$ENV{pythonLocation}/bin"
+      NO_DEFAULT_PATH)
+  endif()
+  if(NOT PYTHON_EXECUTABLE)
+    unset(PYTHON_EXECUTABLE)
+  endif()
+endif()
+
+# Use the Python interpreter to find the libs.
+if(NOT PythonLibsNew_FIND_VERSION)
+  set(PythonLibsNew_FIND_VERSION "3.6")
+endif()
+
+find_package(PythonInterp ${PythonLibsNew_FIND_VERSION} ${_pythonlibs_required}
+             ${_pythonlibs_quiet})
+
+if(NOT PYTHONINTERP_FOUND)
+  set(PYTHONLIBS_FOUND FALSE)
+  set(PythonLibsNew_FOUND FALSE)
+  return()
+endif()
+
+# According to https://stackoverflow.com/questions/646518/python-how-to-detect-debug-interpreter
+# testing whether sys has the gettotalrefcount function is a reliable, cross-platform
+# way to detect a CPython debug interpreter.
+#
+# The library suffix is from the config var LDVERSION sometimes, otherwise
+# VERSION. VERSION will typically be like "2.7" on unix, and "27" on windows.
+execute_process(
+  COMMAND
+    "${PYTHON_EXECUTABLE}" "-c" "
+import sys;import struct;
+import sysconfig as s
+USE_SYSCONFIG = sys.version_info >= (3, 10)
+if not USE_SYSCONFIG:
+    from distutils import sysconfig as ds
+print('.'.join(str(v) for v in sys.version_info));
+print(sys.prefix);
+if USE_SYSCONFIG:
+    scheme = s.get_default_scheme()
+    if scheme == 'posix_local':
+        # Debian's default scheme installs to /usr/local/ but we want to find headers in /usr/
+        scheme = 'posix_prefix'
+    print(s.get_path('platinclude', scheme))
+    print(s.get_path('platlib'))
+    print(s.get_config_var('EXT_SUFFIX') or s.get_config_var('SO'))
+else:
+    print(ds.get_python_inc(plat_specific=True));
+    print(ds.get_python_lib(plat_specific=True));
+    print(ds.get_config_var('EXT_SUFFIX') or ds.get_config_var('SO'));
+print(hasattr(sys, 'gettotalrefcount')+0);
+print(struct.calcsize('@P'));
+print(s.get_config_var('LDVERSION') or s.get_config_var('VERSION'));
+print(s.get_config_var('LIBDIR') or '');
+print(s.get_config_var('MULTIARCH') or '');
+"
+  RESULT_VARIABLE _PYTHON_SUCCESS
+  OUTPUT_VARIABLE _PYTHON_VALUES
+  ERROR_VARIABLE _PYTHON_ERROR_VALUE)
+
+if(NOT _PYTHON_SUCCESS MATCHES 0)
+  if(PythonLibsNew_FIND_REQUIRED)
+    message(FATAL_ERROR "Python config failure:\n${_PYTHON_ERROR_VALUE}")
+  endif()
+  set(PYTHONLIBS_FOUND FALSE)
+  set(PythonLibsNew_FOUND FALSE)
+  return()
+endif()
+
+# Can manually set values when cross-compiling
+macro(_PYBIND11_GET_IF_UNDEF lst index name)
+  if(NOT DEFINED "${name}")
+    list(GET "${lst}" "${index}" "${name}")
+  endif()
+endmacro()
+
+# Convert the process output into a list
+if(WIN32)
+  string(REGEX REPLACE "\\\\" "/" _PYTHON_VALUES ${_PYTHON_VALUES})
+endif()
+string(REGEX REPLACE ";" "\\\\;" _PYTHON_VALUES ${_PYTHON_VALUES})
+string(REGEX REPLACE "\n" ";" _PYTHON_VALUES ${_PYTHON_VALUES})
+_pybind11_get_if_undef(_PYTHON_VALUES 0 _PYTHON_VERSION_LIST)
+_pybind11_get_if_undef(_PYTHON_VALUES 1 PYTHON_PREFIX)
+_pybind11_get_if_undef(_PYTHON_VALUES 2 PYTHON_INCLUDE_DIR)
+_pybind11_get_if_undef(_PYTHON_VALUES 3 PYTHON_SITE_PACKAGES)
+_pybind11_get_if_undef(_PYTHON_VALUES 4 PYTHON_MODULE_EXTENSION)
+_pybind11_get_if_undef(_PYTHON_VALUES 5 PYTHON_IS_DEBUG)
+_pybind11_get_if_undef(_PYTHON_VALUES 6 PYTHON_SIZEOF_VOID_P)
+_pybind11_get_if_undef(_PYTHON_VALUES 7 PYTHON_LIBRARY_SUFFIX)
+_pybind11_get_if_undef(_PYTHON_VALUES 8 PYTHON_LIBDIR)
+_pybind11_get_if_undef(_PYTHON_VALUES 9 PYTHON_MULTIARCH)
+
+# Make sure the Python has the same pointer-size as the chosen compiler
+# Skip if CMAKE_SIZEOF_VOID_P is not defined
+# This should be skipped for (non-Apple) cross-compiles (like EMSCRIPTEN)
+if(NOT CMAKE_CROSSCOMPILING
+   AND CMAKE_SIZEOF_VOID_P
+   AND (NOT "${PYTHON_SIZEOF_VOID_P}" STREQUAL "${CMAKE_SIZEOF_VOID_P}"))
+  if(PythonLibsNew_FIND_REQUIRED)
+    math(EXPR _PYTHON_BITS "${PYTHON_SIZEOF_VOID_P} * 8")
+    math(EXPR _CMAKE_BITS "${CMAKE_SIZEOF_VOID_P} * 8")
+    message(FATAL_ERROR "Python config failure: Python is ${_PYTHON_BITS}-bit, "
+                        "chosen compiler is  ${_CMAKE_BITS}-bit")
+  endif()
+  set(PYTHONLIBS_FOUND FALSE)
+  set(PythonLibsNew_FOUND FALSE)
+  return()
+endif()
+
+# The built-in FindPython didn't always give the version numbers
+string(REGEX REPLACE "\\." ";" _PYTHON_VERSION_LIST ${_PYTHON_VERSION_LIST})
+list(GET _PYTHON_VERSION_LIST 0 PYTHON_VERSION_MAJOR)
+list(GET _PYTHON_VERSION_LIST 1 PYTHON_VERSION_MINOR)
+list(GET _PYTHON_VERSION_LIST 2 PYTHON_VERSION_PATCH)
+set(PYTHON_VERSION "${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}.${PYTHON_VERSION_PATCH}")
+
+# Make sure all directory separators are '/'
+string(REGEX REPLACE "\\\\" "/" PYTHON_PREFIX "${PYTHON_PREFIX}")
+string(REGEX REPLACE "\\\\" "/" PYTHON_INCLUDE_DIR "${PYTHON_INCLUDE_DIR}")
+string(REGEX REPLACE "\\\\" "/" PYTHON_SITE_PACKAGES "${PYTHON_SITE_PACKAGES}")
+
+if(CMAKE_HOST_WIN32)
+  set(PYTHON_LIBRARY "${PYTHON_PREFIX}/libs/python${PYTHON_LIBRARY_SUFFIX}.lib")
+
+  # when run in a venv, PYTHON_PREFIX points to it. But the libraries remain in the
+  # original python installation. They may be found relative to PYTHON_INCLUDE_DIR.
+  if(NOT EXISTS "${PYTHON_LIBRARY}")
+    get_filename_component(_PYTHON_ROOT ${PYTHON_INCLUDE_DIR} DIRECTORY)
+    set(PYTHON_LIBRARY "${_PYTHON_ROOT}/libs/python${PYTHON_LIBRARY_SUFFIX}.lib")
+  endif()
+
+  # if we are in MSYS & MINGW, and we didn't find windows python lib, look for system python lib
+  if(DEFINED ENV{MSYSTEM}
+     AND MINGW
+     AND NOT EXISTS "${PYTHON_LIBRARY}")
+    if(PYTHON_MULTIARCH)
+      set(_PYTHON_LIBS_SEARCH "${PYTHON_LIBDIR}/${PYTHON_MULTIARCH}" "${PYTHON_LIBDIR}")
+    else()
+      set(_PYTHON_LIBS_SEARCH "${PYTHON_LIBDIR}")
+    endif()
+    unset(PYTHON_LIBRARY)
+    find_library(
+      PYTHON_LIBRARY
+      NAMES "python${PYTHON_LIBRARY_SUFFIX}"
+      PATHS ${_PYTHON_LIBS_SEARCH}
+      NO_DEFAULT_PATH)
+  endif()
+
+  # raise an error if the python libs are still not found.
+  if(NOT EXISTS "${PYTHON_LIBRARY}")
+    message(FATAL_ERROR "Python libraries not found")
+  endif()
+
+else()
+  if(PYTHON_MULTIARCH)
+    set(_PYTHON_LIBS_SEARCH "${PYTHON_LIBDIR}/${PYTHON_MULTIARCH}" "${PYTHON_LIBDIR}")
+  else()
+    set(_PYTHON_LIBS_SEARCH "${PYTHON_LIBDIR}")
+  endif()
+  #message(STATUS "Searching for Python libs in ${_PYTHON_LIBS_SEARCH}")
+  # Probably this needs to be more involved. It would be nice if the config
+  # information the python interpreter itself gave us were more complete.
+  find_library(
+    PYTHON_LIBRARY
+    NAMES "python${PYTHON_LIBRARY_SUFFIX}"
+    PATHS ${_PYTHON_LIBS_SEARCH}
+    NO_DEFAULT_PATH)
+
+  # If all else fails, just set the name/version and let the linker figure out the path.
+  if(NOT PYTHON_LIBRARY)
+    set(PYTHON_LIBRARY python${PYTHON_LIBRARY_SUFFIX})
+  endif()
+endif()
+
+mark_as_advanced(PYTHON_LIBRARY PYTHON_INCLUDE_DIR)
+
+# We use PYTHON_INCLUDE_DIR, PYTHON_LIBRARY and PYTHON_DEBUG_LIBRARY for the
+# cache entries because they are meant to specify the location of a single
+# library. We now set the variables listed by the documentation for this
+# module.
+set(PYTHON_INCLUDE_DIRS "${PYTHON_INCLUDE_DIR}")
+set(PYTHON_LIBRARIES "${PYTHON_LIBRARY}")
+if(NOT PYTHON_DEBUG_LIBRARY)
+  set(PYTHON_DEBUG_LIBRARY "")
+endif()
+set(PYTHON_DEBUG_LIBRARIES "${PYTHON_DEBUG_LIBRARY}")
+
+find_package_message(PYTHON "Found PythonLibs: ${PYTHON_LIBRARY}"
+                     "${PYTHON_EXECUTABLE}${PYTHON_VERSION_STRING}")
+
+set(PYTHONLIBS_FOUND TRUE)
+set(PythonLibsNew_FOUND TRUE)
+
+if(NOT PYTHON_MODULE_PREFIX)
+  set(PYTHON_MODULE_PREFIX "")
+endif()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/check-style.sh b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/check-style.sh
new file mode 100644
index 000000000..6d832523a
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/check-style.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+#
+# Script to check include/test code for common pybind11 code style errors.
+#
+# This script currently checks for
+#
+# 1. missing space between keyword and parenthesis, e.g.: for(, if(, while(
+# 2. Missing space between right parenthesis and brace, e.g. 'for (...){'
+# 3. opening brace on its own line. It should always be on the same line as the
+#    if/while/for/do statement.
+#
+# Invoke as: tools/check-style.sh <filenames>
+#
+
+check_style_errors=0
+IFS=$'\n'
+
+
+found="$(grep '\<\(if\|for\|while\|catch\)(\|){' "$@" -rn --color=always)"
+if [ -n "$found" ]; then
+    echo -e '\033[31;01mError: found the following coding style problems:\033[0m'
+    check_style_errors=1
+    echo "${found//^/    /}"
+fi
+
+found="$(awk '
+function prefix(filename, lineno) {
+    return "    \033[35m" filename "\033[36m:\033[32m" lineno "\033[36m:\033[0m"
+}
+function mark(pattern, string) { sub(pattern, "\033[01;31m&\033[0m", string); return string }
+last && /^\s*{/ {
+    print prefix(FILENAME, FNR-1) mark("\\)\\s*$", last)
+    print prefix(FILENAME, FNR)   mark("^\\s*{", $0)
+    last=""
+}
+{ last = /(if|for|while|catch|switch)\s*\(.*\)\s*$/ ? $0 : "" }
+' "$(find include -type f)" "$@")"
+if [ -n "$found" ]; then
+    check_style_errors=1
+    echo -e '\033[31;01mError: braces should occur on the same line as the if/while/.. statement. Found issues in the following files:\033[0m'
+    echo "$found"
+fi
+
+exit $check_style_errors
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/cmake_uninstall.cmake.in b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/cmake_uninstall.cmake.in
new file mode 100644
index 000000000..1e5d2bb87
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/cmake_uninstall.cmake.in
@@ -0,0 +1,23 @@
+# Source: https://gitlab.kitware.com/cmake/community/-/wikis/FAQ#can-i-do-make-uninstall-with-cmake
+
+if(NOT EXISTS "@CMAKE_BINARY_DIR@/install_manifest.txt")
+  message(FATAL_ERROR "Cannot find install manifest: @CMAKE_BINARY_DIR@/install_manifest.txt")
+endif()
+
+file(READ "@CMAKE_BINARY_DIR@/install_manifest.txt" files)
+string(REGEX REPLACE "\n" ";" files "${files}")
+foreach(file ${files})
+  message(STATUS "Uninstalling $ENV{DESTDIR}${file}")
+  if(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}")
+    exec_program(
+      "@CMAKE_COMMAND@" ARGS
+      "-E remove \"$ENV{DESTDIR}${file}\""
+      OUTPUT_VARIABLE rm_out
+      RETURN_VALUE rm_retval)
+    if(NOT "${rm_retval}" STREQUAL 0)
+      message(FATAL_ERROR "Problem when removing $ENV{DESTDIR}${file}")
+    endif()
+  else(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}")
+    message(STATUS "File $ENV{DESTDIR}${file} does not exist.")
+  endif()
+endforeach()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/libsize.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/libsize.py
new file mode 100644
index 000000000..1ac9afbe8
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/libsize.py
@@ -0,0 +1,36 @@
+import os
+import sys
+
+# Internal build script for generating debugging test .so size.
+# Usage:
+#     python libsize.py file.so save.txt -- displays the size of file.so and, if save.txt exists, compares it to the
+#                                           size in it, then overwrites save.txt with the new size for future runs.
+
+if len(sys.argv) != 3:
+    sys.exit("Invalid arguments: usage: python libsize.py file.so save.txt")
+
+lib = sys.argv[1]
+save = sys.argv[2]
+
+if not os.path.exists(lib):
+    sys.exit(f"Error: requested file ({lib}) does not exist")
+
+libsize = os.path.getsize(lib)
+
+print("------", os.path.basename(lib), "file size:", libsize, end="")
+
+if os.path.exists(save):
+    with open(save) as sf:
+        oldsize = int(sf.readline())
+
+    if oldsize > 0:
+        change = libsize - oldsize
+        if change == 0:
+            print(" (no change)")
+        else:
+            print(f" (change of {change:+} bytes = {change / oldsize:+.2%})")
+else:
+    print()
+
+with open(save, "w") as sf:
+    sf.write(str(libsize))
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/make_changelog.py b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/make_changelog.py
new file mode 100644
index 000000000..839040a93
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/make_changelog.py
@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+
+import re
+
+import ghapi.all
+from rich import print
+from rich.syntax import Syntax
+
+ENTRY = re.compile(
+    r"""
+    Suggested \s changelog \s entry:
+    .*
+    ```rst
+    \s*
+    (.*?)
+    \s*
+    ```
+""",
+    re.DOTALL | re.VERBOSE,
+)
+
+print()
+
+
+api = ghapi.all.GhApi(owner="pybind", repo="pybind11")
+
+issues_pages = ghapi.page.paged(
+    api.issues.list_for_repo, labels="needs changelog", state="closed"
+)
+issues = (issue for page in issues_pages for issue in page)
+missing = []
+
+for issue in issues:
+    changelog = ENTRY.findall(issue.body)
+    if changelog:
+        (msg,) = changelog
+        if not msg.startswith("* "):
+            msg = "* " + msg
+        if not msg.endswith("."):
+            msg += "."
+
+        msg += f"\n  `#{issue.number} <{issue.html_url}>`_"
+
+        print(Syntax(msg, "rst", theme="ansi_light", word_wrap=True))
+        print()
+
+    else:
+        missing.append(issue)
+
+if missing:
+    print()
+    print("[blue]" + "-" * 30)
+    print()
+
+    for issue in missing:
+        print(f"[red bold]Missing:[/red bold][red] {issue.title}")
+        print(f"[red]  {issue.html_url}\n")
+
+    print("[bold]Template:\n")
+    msg = "## Suggested changelog entry:\n\n```rst\n\n```"
+    print(Syntax(msg, "md", theme="ansi_light"))
+
+print()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Common.cmake b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Common.cmake
new file mode 100644
index 000000000..e1fb601ac
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Common.cmake
@@ -0,0 +1,385 @@
+#[======================================================[.rst
+
+Adds the following targets::
+
+    pybind11::pybind11 - link to headers and pybind11
+    pybind11::module - Adds module links
+    pybind11::embed - Adds embed links
+    pybind11::lto - Link time optimizations (manual selection)
+    pybind11::thin_lto - Link time optimizations (manual selection)
+    pybind11::python_link_helper - Adds link to Python libraries
+    pybind11::windows_extras - MSVC bigobj and mp for building multithreaded
+    pybind11::opt_size - avoid optimizations that increase code size
+
+Adds the following functions::
+
+    pybind11_strip(target) - strip target after building on linux/macOS
+    pybind11_find_import(module) - See if a module is installed.
+
+#]======================================================]
+
+# CMake 3.10 has an include_guard command, but we can't use that yet
+# include_guard(global) (pre-CMake 3.10)
+if(TARGET pybind11::lto)
+  return()
+endif()
+
+# If we are in subdirectory mode, all IMPORTED targets must be GLOBAL. If we
+# are in CONFIG mode, they should be "normal" targets instead.
+# In CMake 3.11+ you can promote a target to global after you create it,
+# which might be simpler than this check.
+get_property(
+  is_config
+  TARGET pybind11::headers
+  PROPERTY IMPORTED)
+if(NOT is_config)
+  set(optional_global GLOBAL)
+endif()
+
+# If not run in Python mode, we still would like this to at least
+# include pybind11's include directory:
+set(pybind11_INCLUDE_DIRS
+    "${pybind11_INCLUDE_DIR}"
+    CACHE INTERNAL "Include directory for pybind11 (Python not requested)")
+
+# --------------------- Shared targets ----------------------------
+
+# Build an interface library target:
+add_library(pybind11::pybind11 IMPORTED INTERFACE ${optional_global})
+set_property(
+  TARGET pybind11::pybind11
+  APPEND
+  PROPERTY INTERFACE_LINK_LIBRARIES pybind11::headers)
+
+# Build a module target:
+add_library(pybind11::module IMPORTED INTERFACE ${optional_global})
+set_property(
+  TARGET pybind11::module
+  APPEND
+  PROPERTY INTERFACE_LINK_LIBRARIES pybind11::pybind11)
+
+# Build an embed library target:
+add_library(pybind11::embed IMPORTED INTERFACE ${optional_global})
+set_property(
+  TARGET pybind11::embed
+  APPEND
+  PROPERTY INTERFACE_LINK_LIBRARIES pybind11::pybind11)
+
+# --------------------------- link helper ---------------------------
+
+add_library(pybind11::python_link_helper IMPORTED INTERFACE ${optional_global})
+
+if(CMAKE_VERSION VERSION_LESS 3.13)
+  # In CMake 3.11+, you can set INTERFACE properties via the normal methods, and
+  # this would be simpler.
+  set_property(
+    TARGET pybind11::python_link_helper
+    APPEND
+    PROPERTY INTERFACE_LINK_LIBRARIES "$<$<PLATFORM_ID:Darwin>:-undefined dynamic_lookup>")
+else()
+  # link_options was added in 3.13+
+  # This is safer, because you are ensured the deduplication pass in CMake will not consider
+  # these separate and remove one but not the other.
+  set_property(
+    TARGET pybind11::python_link_helper
+    APPEND
+    PROPERTY INTERFACE_LINK_OPTIONS "$<$<PLATFORM_ID:Darwin>:LINKER:-undefined,dynamic_lookup>")
+endif()
+
+# ------------------------ Windows extras -------------------------
+
+add_library(pybind11::windows_extras IMPORTED INTERFACE ${optional_global})
+
+if(MSVC) # That's also clang-cl
+  # /bigobj is needed for bigger binding projects due to the limit to 64k
+  # addressable sections
+  set_property(
+    TARGET pybind11::windows_extras
+    APPEND
+    PROPERTY INTERFACE_COMPILE_OPTIONS $<$<COMPILE_LANGUAGE:CXX>:/bigobj>)
+
+  # /MP enables multithreaded builds (relevant when there are many files) for MSVC
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC") # no Clang no Intel
+    if(CMAKE_VERSION VERSION_LESS 3.11)
+      set_property(
+        TARGET pybind11::windows_extras
+        APPEND
+        PROPERTY INTERFACE_COMPILE_OPTIONS $<$<NOT:$<CONFIG:Debug>>:/MP>)
+    else()
+      # Only set these options for C++ files.  This is important so that, for
+      # instance, projects that include other types of source files like CUDA
+      # .cu files don't get these options propagated to nvcc since that would
+      # cause the build to fail.
+      set_property(
+        TARGET pybind11::windows_extras
+        APPEND
+        PROPERTY INTERFACE_COMPILE_OPTIONS
+                 $<$<NOT:$<CONFIG:Debug>>:$<$<COMPILE_LANGUAGE:CXX>:/MP>>)
+    endif()
+  endif()
+endif()
+
+# ----------------------- Optimize binary size --------------------------
+
+add_library(pybind11::opt_size IMPORTED INTERFACE ${optional_global})
+
+if(MSVC)
+  set(PYBIND11_OPT_SIZE /Os)
+else()
+  set(PYBIND11_OPT_SIZE -Os)
+endif()
+
+set_property(
+  TARGET pybind11::opt_size
+  APPEND
+  PROPERTY INTERFACE_COMPILE_OPTIONS $<$<CONFIG:Release>:${PYBIND11_OPT_SIZE}>
+           $<$<CONFIG:MinSizeRel>:${PYBIND11_OPT_SIZE}>
+           $<$<CONFIG:RelWithDebInfo>:${PYBIND11_OPT_SIZE}>)
+
+# ----------------------- Legacy option --------------------------
+
+# Warn or error if old variable name used
+if(PYBIND11_CPP_STANDARD)
+  string(REGEX MATCH [[..$]] VAL "${PYBIND11_CPP_STANDARD}")
+  if(CMAKE_CXX_STANDARD)
+    if(NOT CMAKE_CXX_STANDARD STREQUAL VAL)
+      message(WARNING "CMAKE_CXX_STANDARD=${CMAKE_CXX_STANDARD} does not match "
+                      "PYBIND11_CPP_STANDARD=${PYBIND11_CPP_STANDARD}, "
+                      "please remove PYBIND11_CPP_STANDARD from your cache")
+    endif()
+  else()
+    set(supported_standards 11 14 17 20)
+    if("${VAL}" IN_LIST supported_standards)
+      message(WARNING "USE -DCMAKE_CXX_STANDARD=${VAL} instead of PYBIND11_CPP_STANDARD")
+      set(CMAKE_CXX_STANDARD
+          ${VAL}
+          CACHE STRING "From PYBIND11_CPP_STANDARD")
+    else()
+      message(FATAL_ERROR "PYBIND11_CPP_STANDARD should be replaced with CMAKE_CXX_STANDARD "
+                          "(last two chars: ${VAL} not understood as a valid CXX std)")
+    endif()
+  endif()
+endif()
+
+# --------------------- Python specifics -------------------------
+
+# Check to see which Python mode we are in, new, old, or no python
+if(PYBIND11_NOPYTHON)
+  set(_pybind11_nopython ON)
+elseif(
+  PYBIND11_FINDPYTHON
+  OR Python_FOUND
+  OR Python2_FOUND
+  OR Python3_FOUND)
+  # New mode
+  include("${CMAKE_CURRENT_LIST_DIR}/pybind11NewTools.cmake")
+
+else()
+
+  # Classic mode
+  include("${CMAKE_CURRENT_LIST_DIR}/pybind11Tools.cmake")
+
+endif()
+
+# --------------------- pybind11_find_import -------------------------------
+
+if(NOT _pybind11_nopython)
+  # Check to see if modules are importable. Use REQUIRED to force an error if
+  # one of the modules is not found. <package_name>_FOUND will be set if the
+  # package was found (underscores replace dashes if present). QUIET will hide
+  # the found message, and VERSION will require a minimum version. A successful
+  # find will cache the result.
+  function(pybind11_find_import PYPI_NAME)
+    # CMake variables need underscores (PyPI doesn't care)
+    string(REPLACE "-" "_" NORM_PYPI_NAME "${PYPI_NAME}")
+
+    # Return if found previously
+    if(${NORM_PYPI_NAME}_FOUND)
+      return()
+    endif()
+
+    set(options "REQUIRED;QUIET")
+    set(oneValueArgs "VERSION")
+    cmake_parse_arguments(ARG "${options}" "${oneValueArgs}" "" ${ARGN})
+
+    if(ARG_REQUIRED)
+      set(status_level FATAL_ERROR)
+    else()
+      set(status_level WARNING)
+    endif()
+
+    execute_process(
+      COMMAND
+        ${${_Python}_EXECUTABLE} -c
+        "from pkg_resources import get_distribution; print(get_distribution('${PYPI_NAME}').version)"
+      RESULT_VARIABLE RESULT_PRESENT
+      OUTPUT_VARIABLE PKG_VERSION
+      ERROR_QUIET)
+
+    string(STRIP "${PKG_VERSION}" PKG_VERSION)
+
+    # If a result is present, this failed
+    if(RESULT_PRESENT)
+      set(${NORM_PYPI_NAME}_FOUND
+          ${NORM_PYPI_NAME}-NOTFOUND
+          CACHE INTERNAL "")
+      # Always warn or error
+      message(
+        ${status_level}
+        "Missing: ${PYPI_NAME} ${ARG_VERSION}\nTry: ${${_Python}_EXECUTABLE} -m pip install ${PYPI_NAME}"
+      )
+    else()
+      if(ARG_VERSION AND PKG_VERSION VERSION_LESS ARG_VERSION)
+        message(
+          ${status_level}
+          "Version incorrect: ${PYPI_NAME} ${PKG_VERSION} found, ${ARG_VERSION} required - try upgrading"
+        )
+      else()
+        set(${NORM_PYPI_NAME}_FOUND
+            YES
+            CACHE INTERNAL "")
+        set(${NORM_PYPI_NAME}_VERSION
+            ${PKG_VERSION}
+            CACHE INTERNAL "")
+      endif()
+      if(NOT ARG_QUIET)
+        message(STATUS "Found ${PYPI_NAME} ${PKG_VERSION}")
+      endif()
+    endif()
+    if(NOT ARG_VERSION OR (NOT PKG_VERSION VERSION_LESS ARG_VERSION))
+      # We have successfully found a good version, cache to avoid calling again.
+    endif()
+  endfunction()
+endif()
+
+# --------------------- LTO -------------------------------
+
+include(CheckCXXCompilerFlag)
+
+# Checks whether the given CXX/linker flags can compile and link a cxx file.
+# cxxflags and linkerflags are lists of flags to use.  The result variable is a
+# unique variable name for each set of flags: the compilation result will be
+# cached base on the result variable.  If the flags work, sets them in
+# cxxflags_out/linkerflags_out internal cache variables (in addition to
+# ${result}).
+function(_pybind11_return_if_cxx_and_linker_flags_work result cxxflags linkerflags cxxflags_out
+         linkerflags_out)
+  set(CMAKE_REQUIRED_LIBRARIES ${linkerflags})
+  check_cxx_compiler_flag("${cxxflags}" ${result})
+  if(${result})
+    set(${cxxflags_out}
+        "${cxxflags}"
+        PARENT_SCOPE)
+    set(${linkerflags_out}
+        "${linkerflags}"
+        PARENT_SCOPE)
+  endif()
+endfunction()
+
+function(_pybind11_generate_lto target prefer_thin_lto)
+  if(MINGW)
+    message(STATUS "${target} disabled (problems with undefined symbols for MinGW for now)")
+    return()
+  endif()
+
+  if(CMAKE_CXX_COMPILER_ID MATCHES "GNU|Clang")
+    set(cxx_append "")
+    set(linker_append "")
+    if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" AND NOT APPLE)
+      # Clang Gold plugin does not support -Os; append -O3 to MinSizeRel builds to override it
+      set(linker_append ";$<$<CONFIG:MinSizeRel>:-O3>")
+    elseif(CMAKE_CXX_COMPILER_ID MATCHES "GNU" AND NOT MINGW)
+      set(cxx_append ";-fno-fat-lto-objects")
+    endif()
+
+    if(CMAKE_SYSTEM_PROCESSOR MATCHES "ppc64le" OR CMAKE_SYSTEM_PROCESSOR MATCHES "mips64")
+      set(NO_FLTO_ARCH TRUE)
+    else()
+      set(NO_FLTO_ARCH FALSE)
+    endif()
+
+    if(CMAKE_CXX_COMPILER_ID MATCHES "Clang"
+       AND prefer_thin_lto
+       AND NOT NO_FLTO_ARCH)
+      _pybind11_return_if_cxx_and_linker_flags_work(
+        HAS_FLTO_THIN "-flto=thin${cxx_append}" "-flto=thin${linker_append}"
+        PYBIND11_LTO_CXX_FLAGS PYBIND11_LTO_LINKER_FLAGS)
+    endif()
+
+    if(NOT HAS_FLTO_THIN AND NOT NO_FLTO_ARCH)
+      _pybind11_return_if_cxx_and_linker_flags_work(
+        HAS_FLTO "-flto${cxx_append}" "-flto${linker_append}" PYBIND11_LTO_CXX_FLAGS
+        PYBIND11_LTO_LINKER_FLAGS)
+    endif()
+  elseif(CMAKE_CXX_COMPILER_ID MATCHES "Intel")
+    # Intel equivalent to LTO is called IPO
+    _pybind11_return_if_cxx_and_linker_flags_work(HAS_INTEL_IPO "-ipo" "-ipo"
+                                                  PYBIND11_LTO_CXX_FLAGS PYBIND11_LTO_LINKER_FLAGS)
+  elseif(MSVC)
+    # cmake only interprets libraries as linker flags when they start with a - (otherwise it
+    # converts /LTCG to \LTCG as if it was a Windows path).  Luckily MSVC supports passing flags
+    # with - instead of /, even if it is a bit non-standard:
+    _pybind11_return_if_cxx_and_linker_flags_work(HAS_MSVC_GL_LTCG "/GL" "-LTCG"
+                                                  PYBIND11_LTO_CXX_FLAGS PYBIND11_LTO_LINKER_FLAGS)
+  endif()
+
+  # Enable LTO flags if found, except for Debug builds
+  if(PYBIND11_LTO_CXX_FLAGS)
+    # CONFIG takes multiple values in CMake 3.19+, until then we have to use OR
+    set(is_debug "$<OR:$<CONFIG:Debug>,$<CONFIG:RelWithDebInfo>>")
+    set(not_debug "$<NOT:${is_debug}>")
+    set(cxx_lang "$<COMPILE_LANGUAGE:CXX>")
+    if(MSVC AND CMAKE_VERSION VERSION_LESS 3.11)
+      set(genex "${not_debug}")
+    else()
+      set(genex "$<AND:${not_debug},${cxx_lang}>")
+    endif()
+    set_property(
+      TARGET ${target}
+      APPEND
+      PROPERTY INTERFACE_COMPILE_OPTIONS "$<${genex}:${PYBIND11_LTO_CXX_FLAGS}>")
+    if(CMAKE_PROJECT_NAME STREQUAL "pybind11")
+      message(STATUS "${target} enabled")
+    endif()
+  else()
+    if(CMAKE_PROJECT_NAME STREQUAL "pybind11")
+      message(STATUS "${target} disabled (not supported by the compiler and/or linker)")
+    endif()
+  endif()
+
+  if(PYBIND11_LTO_LINKER_FLAGS)
+    if(CMAKE_VERSION VERSION_LESS 3.11)
+      set_property(
+        TARGET ${target}
+        APPEND
+        PROPERTY INTERFACE_LINK_LIBRARIES "$<${not_debug}:${PYBIND11_LTO_LINKER_FLAGS}>")
+    else()
+      set_property(
+        TARGET ${target}
+        APPEND
+        PROPERTY INTERFACE_LINK_OPTIONS "$<${not_debug}:${PYBIND11_LTO_LINKER_FLAGS}>")
+    endif()
+  endif()
+endfunction()
+
+add_library(pybind11::lto IMPORTED INTERFACE ${optional_global})
+_pybind11_generate_lto(pybind11::lto FALSE)
+
+add_library(pybind11::thin_lto IMPORTED INTERFACE ${optional_global})
+_pybind11_generate_lto(pybind11::thin_lto TRUE)
+
+# ---------------------- pybind11_strip -----------------------------
+
+function(pybind11_strip target_name)
+  # Strip unnecessary sections of the binary on Linux/macOS
+  if(CMAKE_STRIP)
+    if(APPLE)
+      set(x_opt -x)
+    endif()
+
+    add_custom_command(
+      TARGET ${target_name}
+      POST_BUILD
+      COMMAND ${CMAKE_STRIP} ${x_opt} $<TARGET_FILE:${target_name}>)
+  endif()
+endfunction()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Config.cmake.in b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Config.cmake.in
new file mode 100644
index 000000000..9383e8c67
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Config.cmake.in
@@ -0,0 +1,231 @@
+#[=============================================================================[.rst:
+
+pybind11Config.cmake
+####################
+
+Exported variables
+==================
+
+This module sets the following variables in your project:
+
+``pybind11_FOUND``
+  true if pybind11 and all required components found on the system
+``pybind11_VERSION``
+  pybind11 version in format Major.Minor.Release
+``pybind11_VERSION_TYPE``
+  pybind11 version type (``dev*`` or empty for a release)
+``pybind11_INCLUDE_DIRS``
+  Directories where pybind11 and python headers are located.
+``pybind11_INCLUDE_DIR``
+  Directory where pybind11 headers are located.
+``pybind11_DEFINITIONS``
+  Definitions necessary to use pybind11, namely USING_pybind11.
+``pybind11_LIBRARIES``
+  Compile flags and python libraries (as needed) to link against.
+``pybind11_LIBRARY``
+  Empty.
+
+Available components: None
+
+
+Exported targets
+================
+
+If pybind11 is found, this module defines the following ``IMPORTED``
+interface library targets:
+
+``pybind11::module``
+  for extension modules.
+``pybind11::embed``
+  for embedding the Python interpreter.
+
+Python headers, libraries (as needed by platform), and the C++ standard
+are attached to the target.
+
+Advanced targets are also supplied - these are primary for users building
+complex applications, and they are available in all modes:
+
+``pybind11::headers``
+  Just the pybind11 headers and minimum compile requirements.
+``pybind11::pybind11``
+  Python headers too.
+``pybind11::python_link_helper``
+  Just the "linking" part of ``pybind11:module``, for CMake < 3.15.
+``pybind11::thin_lto``
+  An alternative to ``INTERPROCEDURAL_OPTIMIZATION``.
+``pybind11::lto``
+  An alternative to ``INTERPROCEDURAL_OPTIMIZATION`` (also avoids thin LTO on clang).
+``pybind11::windows_extras``
+  Adds bigobj and mp for MSVC.
+
+Modes
+=====
+
+There are two modes provided; classic, which is built on the old Python
+discovery packages in CMake, or the new FindPython mode, which uses FindPython
+from 3.12+ forward (3.15+ _highly_ recommended).
+
+New FindPython mode
+^^^^^^^^^^^^^^^^^^^
+
+To activate this mode, either call ``find_package(Python COMPONENTS Interpreter Development)``
+before finding this package, or set the ``PYBIND11_FINDPYTHON`` variable to ON. In this mode,
+you can either use the basic targets, or use the FindPython tools:
+
+.. code-block:: cmake
+
+  find_package(Python COMPONENTS Interpreter Development)
+  find_package(pybind11 CONFIG)
+
+  # pybind11 method:
+  pybind11_add_module(MyModule1 src1.cpp)
+
+  # Python method:
+  Python_add_library(MyModule2 src2.cpp)
+  target_link_libraries(MyModule2 pybind11::headers)
+  set_target_properties(MyModule2 PROPERTIES
+                                  INTERPROCEDURAL_OPTIMIZATION ON
+                                  CXX_VISIBILITY_PRESET ON
+                                  VISIBILITY_INLINES_HIDDEN ON)
+
+If you build targets yourself, you may be interested in stripping the output
+for reduced size; this is the one other feature that the helper function gives you.
+
+Classic mode
+^^^^^^^^^^^^
+
+Set PythonLibsNew variables to influence python detection and
+CMAKE_CXX_STANDARD to influence standard setting.
+
+.. code-block:: cmake
+
+  find_package(pybind11 CONFIG REQUIRED)
+
+  # Create an extension module
+  add_library(mylib MODULE main.cpp)
+  target_link_libraries(mylib PUBLIC pybind11::module)
+
+  # Or embed the Python interpreter into an executable
+  add_executable(myexe main.cpp)
+  target_link_libraries(myexe PUBLIC pybind11::embed)
+
+
+Hints
+=====
+
+The following variables can be set to guide the search for this package:
+
+``pybind11_DIR``
+  CMake variable, set to directory containing this Config file.
+``CMAKE_PREFIX_PATH``
+  CMake variable, set to root directory of this package.
+``PATH``
+  Environment variable, set to bin directory of this package.
+``CMAKE_DISABLE_FIND_PACKAGE_pybind11``
+  CMake variable, disables ``find_package(pybind11)`` when not ``REQUIRED``,
+  perhaps to force internal build.
+
+Commands
+========
+
+pybind11_add_module
+^^^^^^^^^^^^^^^^^^^
+
+This module defines the following commands to assist with creating Python modules:
+
+.. code-block:: cmake
+
+  pybind11_add_module(<target>
+    [STATIC|SHARED|MODULE]
+    [THIN_LTO] [OPT_SIZE] [NO_EXTRAS] [WITHOUT_SOABI]
+    <files>...
+    )
+
+Add a module and setup all helpers. You can select the type of the library; the
+default is ``MODULE``. There are several options:
+
+``OPT_SIZE``
+  Optimize for size, even if the ``CMAKE_BUILD_TYPE`` is not ``MinSizeRel``.
+``THIN_LTO``
+  Use thin TLO instead of regular if there's a choice (pybind11's selection
+  is disabled if ``CMAKE_INTERPROCEDURAL_OPTIMIZATIONS`` is set).
+``WITHOUT_SOABI``
+  Disable the SOABI component (``PYBIND11_NEWPYTHON`` mode only).
+``NO_EXTRAS``
+  Disable all extras, exit immediately after making the module.
+
+pybind11_strip
+^^^^^^^^^^^^^^
+
+.. code-block:: cmake
+
+  pybind11_strip(<target>)
+
+Strip a target after building it (linux/macOS), called by ``pybind11_add_module``.
+
+pybind11_extension
+^^^^^^^^^^^^^^^^^^
+
+.. code-block:: cmake
+
+    pybind11_extension(<target>)
+
+Sets the Python extension name correctly for Python on your platform, called by
+``pybind11_add_module``.
+
+pybind11_find_import(module)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: cmake
+
+    pybind11_find_import(<module> [VERSION <number>] [REQUIRED] [QUIET])
+
+See if a module is installed. Use the registered name (the one on PyPI). You
+can specify a ``VERSION``, and you can specify ``REQUIRED`` or ``QUIET``. Only available if
+``NOPYTHON`` mode is not active.  Sets ``module_VERSION`` and ``module_FOUND``. Caches the
+result once a valid install is found.
+
+Suggested usage
+===============
+
+Using ``find_package`` with version info is not recommended except for release versions.
+
+.. code-block:: cmake
+
+  find_package(pybind11 CONFIG)
+  find_package(pybind11 2.9 EXACT CONFIG REQUIRED)
+
+#]=============================================================================]
+@PACKAGE_INIT@
+
+# Location of pybind11/pybind11.h
+# This will be relative unless explicitly set as absolute
+set(pybind11_INCLUDE_DIR "@pybind11_INCLUDEDIR@")
+
+set(pybind11_LIBRARY "")
+set(pybind11_DEFINITIONS USING_pybind11)
+set(pybind11_VERSION_TYPE "@pybind11_VERSION_TYPE@")
+
+check_required_components(pybind11)
+
+if(TARGET pybind11::python_link_helper)
+  # This has already been setup elsewhere, such as with a previous call or
+  # add_subdirectory
+  return()
+endif()
+
+include("${CMAKE_CURRENT_LIST_DIR}/pybind11Targets.cmake")
+
+# Easier to use / remember
+add_library(pybind11::headers IMPORTED INTERFACE)
+set_target_properties(pybind11::headers PROPERTIES INTERFACE_LINK_LIBRARIES
+                                                   pybind11::pybind11_headers)
+
+include("${CMAKE_CURRENT_LIST_DIR}/pybind11Common.cmake")
+
+if(NOT pybind11_FIND_QUIETLY)
+  message(
+    STATUS
+      "Found pybind11: ${pybind11_INCLUDE_DIR} (found version \"${pybind11_VERSION}${pybind11_VERSION_TYPE}\")"
+  )
+endif()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11NewTools.cmake b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11NewTools.cmake
new file mode 100644
index 000000000..abba0fe0e
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11NewTools.cmake
@@ -0,0 +1,254 @@
+# tools/pybind11NewTools.cmake -- Build system for the pybind11 modules
+#
+# Copyright (c) 2020 Wenzel Jakob <wenzel@inf.ethz.ch> and Henry Schreiner
+#
+# All rights reserved. Use of this source code is governed by a
+# BSD-style license that can be found in the LICENSE file.
+
+if(CMAKE_VERSION VERSION_LESS 3.12)
+  message(FATAL_ERROR "You cannot use the new FindPython module with CMake < 3.12")
+endif()
+
+include_guard(GLOBAL)
+
+get_property(
+  is_config
+  TARGET pybind11::headers
+  PROPERTY IMPORTED)
+
+if(pybind11_FIND_QUIETLY)
+  set(_pybind11_quiet QUIET)
+else()
+  set(_pybind11_quiet "")
+endif()
+
+if(NOT Python_FOUND AND NOT Python3_FOUND)
+  if(NOT DEFINED Python_FIND_IMPLEMENTATIONS)
+    set(Python_FIND_IMPLEMENTATIONS CPython PyPy)
+  endif()
+
+  # GitHub Actions like activation
+  if(NOT DEFINED Python_ROOT_DIR AND DEFINED ENV{pythonLocation})
+    set(Python_ROOT_DIR "$ENV{pythonLocation}")
+  endif()
+
+  find_package(Python 3.6 REQUIRED COMPONENTS Interpreter Development ${_pybind11_quiet})
+
+  # If we are in submodule mode, export the Python targets to global targets.
+  # If this behavior is not desired, FindPython _before_ pybind11.
+  if(NOT is_config)
+    set_property(TARGET Python::Python PROPERTY IMPORTED_GLOBAL TRUE)
+    set_property(TARGET Python::Interpreter PROPERTY IMPORTED_GLOBAL TRUE)
+    if(TARGET Python::Module)
+      set_property(TARGET Python::Module PROPERTY IMPORTED_GLOBAL TRUE)
+    endif()
+  endif()
+endif()
+
+if(Python_FOUND)
+  set(_Python
+      Python
+      CACHE INTERNAL "" FORCE)
+elseif(Python3_FOUND)
+  set(_Python
+      Python3
+      CACHE INTERNAL "" FORCE)
+endif()
+
+if(PYBIND11_MASTER_PROJECT)
+  if(${_Python}_INTERPRETER_ID MATCHES "PyPy")
+    message(STATUS "PyPy ${${_Python}_PyPy_VERSION} (Py ${${_Python}_VERSION})")
+  else()
+    message(STATUS "${_Python} ${${_Python}_VERSION}")
+  endif()
+endif()
+
+# If a user finds Python, they may forget to include the Interpreter component
+# and the following two steps require it. It is highly recommended by CMake
+# when finding development libraries anyway, so we will require it.
+if(NOT DEFINED ${_Python}_EXECUTABLE)
+  message(
+    FATAL_ERROR
+      "${_Python} was found without the Interpreter component. Pybind11 requires this component.")
+
+endif()
+
+if(NOT ${_Python}_EXECUTABLE STREQUAL PYBIND11_PYTHON_EXECUTABLE_LAST)
+  # Detect changes to the Python version/binary in subsequent CMake runs, and refresh config if needed
+  unset(PYTHON_IS_DEBUG CACHE)
+  unset(PYTHON_MODULE_EXTENSION CACHE)
+  set(PYBIND11_PYTHON_EXECUTABLE_LAST
+      "${${_Python}_EXECUTABLE}"
+      CACHE INTERNAL "Python executable during the last CMake run")
+endif()
+
+if(NOT DEFINED PYTHON_IS_DEBUG)
+  # Debug check - see https://stackoverflow.com/questions/646518/python-how-to-detect-debug-Interpreter
+  execute_process(
+    COMMAND "${${_Python}_EXECUTABLE}" "-c"
+            "import sys; sys.exit(hasattr(sys, 'gettotalrefcount'))"
+    RESULT_VARIABLE _PYTHON_IS_DEBUG)
+  set(PYTHON_IS_DEBUG
+      "${_PYTHON_IS_DEBUG}"
+      CACHE INTERNAL "Python debug status")
+endif()
+
+# Get the suffix - SO is deprecated, should use EXT_SUFFIX, but this is
+# required for PyPy3 (as of 7.3.1)
+if(NOT DEFINED PYTHON_MODULE_EXTENSION)
+  execute_process(
+    COMMAND
+      "${${_Python}_EXECUTABLE}" "-c"
+      "import sys, importlib; s = importlib.import_module('distutils.sysconfig' if sys.version_info < (3, 10) else 'sysconfig'); print(s.get_config_var('EXT_SUFFIX') or s.get_config_var('SO'))"
+    OUTPUT_VARIABLE _PYTHON_MODULE_EXTENSION
+    ERROR_VARIABLE _PYTHON_MODULE_EXTENSION_ERR
+    OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+  if(_PYTHON_MODULE_EXTENSION STREQUAL "")
+    message(
+      FATAL_ERROR "pybind11 could not query the module file extension, likely the 'distutils'"
+                  "package is not installed. Full error message:\n${_PYTHON_MODULE_EXTENSION_ERR}")
+  endif()
+
+  # This needs to be available for the pybind11_extension function
+  set(PYTHON_MODULE_EXTENSION
+      "${_PYTHON_MODULE_EXTENSION}"
+      CACHE INTERNAL "")
+endif()
+
+# Python debug libraries expose slightly different objects before 3.8
+# https://docs.python.org/3.6/c-api/intro.html#debugging-builds
+# https://stackoverflow.com/questions/39161202/how-to-work-around-missing-pymodule-create2-in-amd64-win-python35-d-lib
+if(PYTHON_IS_DEBUG)
+  set_property(
+    TARGET pybind11::pybind11
+    APPEND
+    PROPERTY INTERFACE_COMPILE_DEFINITIONS Py_DEBUG)
+endif()
+
+# Check on every access - since Python can change - do nothing in that case.
+
+if(DEFINED ${_Python}_INCLUDE_DIRS)
+  # Only add Python for build - must be added during the import for config
+  # since it has to be re-discovered.
+  #
+  # This needs to be a target to be included after the local pybind11
+  # directory, just in case there there is an installed pybind11 sitting
+  # next to Python's includes. It also ensures Python is a SYSTEM library.
+  add_library(pybind11::python_headers INTERFACE IMPORTED)
+  set_property(
+    TARGET pybind11::python_headers PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+                                             "$<BUILD_INTERFACE:${${_Python}_INCLUDE_DIRS}>")
+  set_property(
+    TARGET pybind11::pybind11
+    APPEND
+    PROPERTY INTERFACE_LINK_LIBRARIES pybind11::python_headers)
+  set(pybind11_INCLUDE_DIRS
+      "${pybind11_INCLUDE_DIR}" "${${_Python}_INCLUDE_DIRS}"
+      CACHE INTERNAL "Directories where pybind11 and possibly Python headers are located")
+endif()
+
+# In CMake 3.18+, you can find these separately, so include an if
+if(TARGET ${_Python}::Python)
+  set_property(
+    TARGET pybind11::embed
+    APPEND
+    PROPERTY INTERFACE_LINK_LIBRARIES ${_Python}::Python)
+endif()
+
+# CMake 3.15+ has this
+if(TARGET ${_Python}::Module)
+  set_property(
+    TARGET pybind11::module
+    APPEND
+    PROPERTY INTERFACE_LINK_LIBRARIES ${_Python}::Module)
+else()
+  set_property(
+    TARGET pybind11::module
+    APPEND
+    PROPERTY INTERFACE_LINK_LIBRARIES pybind11::python_link_helper)
+endif()
+
+# WITHOUT_SOABI and WITH_SOABI will disable the custom extension handling used by pybind11.
+# WITH_SOABI is passed on to python_add_library.
+function(pybind11_add_module target_name)
+  cmake_parse_arguments(PARSE_ARGV 1 ARG
+                        "STATIC;SHARED;MODULE;THIN_LTO;OPT_SIZE;NO_EXTRAS;WITHOUT_SOABI" "" "")
+
+  if(ARG_STATIC)
+    set(lib_type STATIC)
+  elseif(ARG_SHARED)
+    set(lib_type SHARED)
+  else()
+    set(lib_type MODULE)
+  endif()
+
+  if("${_Python}" STREQUAL "Python")
+    python_add_library(${target_name} ${lib_type} ${ARG_UNPARSED_ARGUMENTS})
+  elseif("${_Python}" STREQUAL "Python3")
+    python3_add_library(${target_name} ${lib_type} ${ARG_UNPARSED_ARGUMENTS})
+  else()
+    message(FATAL_ERROR "Cannot detect FindPython version: ${_Python}")
+  endif()
+
+  target_link_libraries(${target_name} PRIVATE pybind11::headers)
+
+  if(lib_type STREQUAL "MODULE")
+    target_link_libraries(${target_name} PRIVATE pybind11::module)
+  else()
+    target_link_libraries(${target_name} PRIVATE pybind11::embed)
+  endif()
+
+  if(MSVC)
+    target_link_libraries(${target_name} PRIVATE pybind11::windows_extras)
+  endif()
+
+  # -fvisibility=hidden is required to allow multiple modules compiled against
+  # different pybind versions to work properly, and for some features (e.g.
+  # py::module_local).  We force it on everything inside the `pybind11`
+  # namespace; also turning it on for a pybind module compilation here avoids
+  # potential warnings or issues from having mixed hidden/non-hidden types.
+  if(NOT DEFINED CMAKE_CXX_VISIBILITY_PRESET)
+    set_target_properties(${target_name} PROPERTIES CXX_VISIBILITY_PRESET "hidden")
+  endif()
+
+  if(NOT DEFINED CMAKE_CUDA_VISIBILITY_PRESET)
+    set_target_properties(${target_name} PROPERTIES CUDA_VISIBILITY_PRESET "hidden")
+  endif()
+
+  # If we don't pass a WITH_SOABI or WITHOUT_SOABI, use our own default handling of extensions
+  if(NOT ARG_WITHOUT_SOABI AND NOT "WITH_SOABI" IN_LIST ARG_UNPARSED_ARGUMENTS)
+    pybind11_extension(${target_name})
+  endif()
+
+  if(ARG_NO_EXTRAS)
+    return()
+  endif()
+
+  if(NOT DEFINED CMAKE_INTERPROCEDURAL_OPTIMIZATION)
+    if(ARG_THIN_LTO)
+      target_link_libraries(${target_name} PRIVATE pybind11::thin_lto)
+    else()
+      target_link_libraries(${target_name} PRIVATE pybind11::lto)
+    endif()
+  endif()
+
+  if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo)
+    # Strip unnecessary sections of the binary on Linux/macOS
+    pybind11_strip(${target_name})
+  endif()
+
+  if(MSVC)
+    target_link_libraries(${target_name} PRIVATE pybind11::windows_extras)
+  endif()
+
+  if(ARG_OPT_SIZE)
+    target_link_libraries(${target_name} PRIVATE pybind11::opt_size)
+  endif()
+endfunction()
+
+function(pybind11_extension name)
+  # The extension is precomputed
+  set_target_properties(${name} PROPERTIES PREFIX "" SUFFIX "${PYTHON_MODULE_EXTENSION}")
+
+endfunction()
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Tools.cmake b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Tools.cmake
new file mode 100644
index 000000000..5535e872f
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pybind11Tools.cmake
@@ -0,0 +1,227 @@
+# tools/pybind11Tools.cmake -- Build system for the pybind11 modules
+#
+# Copyright (c) 2020 Wenzel Jakob <wenzel.jakob@epfl.ch>
+#
+# All rights reserved. Use of this source code is governed by a
+# BSD-style license that can be found in the LICENSE file.
+
+# include_guard(global) (pre-CMake 3.10)
+if(TARGET pybind11::python_headers)
+  return()
+endif()
+
+# Built-in in CMake 3.5+
+include(CMakeParseArguments)
+
+if(pybind11_FIND_QUIETLY)
+  set(_pybind11_quiet QUIET)
+else()
+  set(_pybind11_quiet "")
+endif()
+
+# If this is the first run, PYTHON_VERSION can stand in for PYBIND11_PYTHON_VERSION
+if(NOT DEFINED PYBIND11_PYTHON_VERSION AND DEFINED PYTHON_VERSION)
+  message(WARNING "Set PYBIND11_PYTHON_VERSION to search for a specific version, not "
+                  "PYTHON_VERSION (which is an output). Assuming that is what you "
+                  "meant to do and continuing anyway.")
+  set(PYBIND11_PYTHON_VERSION
+      "${PYTHON_VERSION}"
+      CACHE STRING "Python version to use for compiling modules")
+  unset(PYTHON_VERSION)
+  unset(PYTHON_VERSION CACHE)
+elseif(DEFINED PYBIND11_PYTHON_VERSION)
+  # If this is set as a normal variable, promote it
+  set(PYBIND11_PYTHON_VERSION
+      "${PYBIND11_PYTHON_VERSION}"
+      CACHE STRING "Python version to use for compiling modules")
+else()
+  # Make an empty cache variable.
+  set(PYBIND11_PYTHON_VERSION
+      ""
+      CACHE STRING "Python version to use for compiling modules")
+endif()
+
+# A user can set versions manually too
+set(Python_ADDITIONAL_VERSIONS
+    "3.11;3.10;3.9;3.8;3.7;3.6"
+    CACHE INTERNAL "")
+
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}")
+find_package(PythonLibsNew ${PYBIND11_PYTHON_VERSION} MODULE REQUIRED ${_pybind11_quiet})
+list(REMOVE_AT CMAKE_MODULE_PATH -1)
+
+# Makes a normal variable a cached variable
+macro(_PYBIND11_PROMOTE_TO_CACHE NAME)
+  set(_tmp_ptc "${${NAME}}")
+  # CMake 3.21 complains if a cached variable is shadowed by a normal one
+  unset(${NAME})
+  set(${NAME}
+      "${_tmp_ptc}"
+      CACHE INTERNAL "")
+endmacro()
+
+# Cache variables so pybind11_add_module can be used in parent projects
+_pybind11_promote_to_cache(PYTHON_INCLUDE_DIRS)
+_pybind11_promote_to_cache(PYTHON_LIBRARIES)
+_pybind11_promote_to_cache(PYTHON_MODULE_PREFIX)
+_pybind11_promote_to_cache(PYTHON_MODULE_EXTENSION)
+_pybind11_promote_to_cache(PYTHON_VERSION_MAJOR)
+_pybind11_promote_to_cache(PYTHON_VERSION_MINOR)
+_pybind11_promote_to_cache(PYTHON_VERSION)
+_pybind11_promote_to_cache(PYTHON_IS_DEBUG)
+
+if(PYBIND11_MASTER_PROJECT)
+  if(PYTHON_MODULE_EXTENSION MATCHES "pypy")
+    if(NOT DEFINED PYPY_VERSION)
+      execute_process(
+        COMMAND ${PYTHON_EXECUTABLE} -c
+                [=[import sys; sys.stdout.write(".".join(map(str, sys.pypy_version_info[:3])))]=]
+        OUTPUT_VARIABLE pypy_version)
+      set(PYPY_VERSION
+          ${pypy_version}
+          CACHE INTERNAL "")
+    endif()
+    message(STATUS "PYPY ${PYPY_VERSION} (Py ${PYTHON_VERSION})")
+  else()
+    message(STATUS "PYTHON ${PYTHON_VERSION}")
+  endif()
+endif()
+
+# Only add Python for build - must be added during the import for config since
+# it has to be re-discovered.
+#
+# This needs to be an target to it is included after the local pybind11
+# directory, just in case there are multiple versions of pybind11, we want the
+# one we expect.
+add_library(pybind11::python_headers INTERFACE IMPORTED)
+set_property(TARGET pybind11::python_headers PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+                                                      "$<BUILD_INTERFACE:${PYTHON_INCLUDE_DIRS}>")
+set_property(
+  TARGET pybind11::pybind11
+  APPEND
+  PROPERTY INTERFACE_LINK_LIBRARIES pybind11::python_headers)
+
+set(pybind11_INCLUDE_DIRS
+    "${pybind11_INCLUDE_DIR}" "${PYTHON_INCLUDE_DIRS}"
+    CACHE INTERNAL "Directories where pybind11 and possibly Python headers are located")
+
+# Python debug libraries expose slightly different objects before 3.8
+# https://docs.python.org/3.6/c-api/intro.html#debugging-builds
+# https://stackoverflow.com/questions/39161202/how-to-work-around-missing-pymodule-create2-in-amd64-win-python35-d-lib
+if(PYTHON_IS_DEBUG)
+  set_property(
+    TARGET pybind11::pybind11
+    APPEND
+    PROPERTY INTERFACE_COMPILE_DEFINITIONS Py_DEBUG)
+endif()
+
+if(CMAKE_VERSION VERSION_LESS 3.11)
+  set_property(
+    TARGET pybind11::module
+    APPEND
+    PROPERTY
+      INTERFACE_LINK_LIBRARIES
+      pybind11::python_link_helper
+      "$<$<OR:$<PLATFORM_ID:Windows>,$<PLATFORM_ID:Cygwin>>:$<BUILD_INTERFACE:${PYTHON_LIBRARIES}>>"
+  )
+
+  set_property(
+    TARGET pybind11::embed
+    APPEND
+    PROPERTY INTERFACE_LINK_LIBRARIES pybind11::pybind11 $<BUILD_INTERFACE:${PYTHON_LIBRARIES}>)
+else()
+  target_link_libraries(
+    pybind11::module
+    INTERFACE
+      pybind11::python_link_helper
+      "$<$<OR:$<PLATFORM_ID:Windows>,$<PLATFORM_ID:Cygwin>>:$<BUILD_INTERFACE:${PYTHON_LIBRARIES}>>"
+  )
+
+  target_link_libraries(pybind11::embed INTERFACE pybind11::pybind11
+                                                  $<BUILD_INTERFACE:${PYTHON_LIBRARIES}>)
+
+endif()
+
+function(pybind11_extension name)
+  # The prefix and extension are provided by FindPythonLibsNew.cmake
+  set_target_properties(${name} PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}"
+                                           SUFFIX "${PYTHON_MODULE_EXTENSION}")
+endfunction()
+
+# Build a Python extension module:
+# pybind11_add_module(<name> [MODULE | SHARED] [EXCLUDE_FROM_ALL]
+#                     [NO_EXTRAS] [THIN_LTO] [OPT_SIZE] source1 [source2 ...])
+#
+function(pybind11_add_module target_name)
+  set(options "MODULE;SHARED;EXCLUDE_FROM_ALL;NO_EXTRAS;SYSTEM;THIN_LTO;OPT_SIZE")
+  cmake_parse_arguments(ARG "${options}" "" "" ${ARGN})
+
+  if(ARG_MODULE AND ARG_SHARED)
+    message(FATAL_ERROR "Can't be both MODULE and SHARED")
+  elseif(ARG_SHARED)
+    set(lib_type SHARED)
+  else()
+    set(lib_type MODULE)
+  endif()
+
+  if(ARG_EXCLUDE_FROM_ALL)
+    set(exclude_from_all EXCLUDE_FROM_ALL)
+  else()
+    set(exclude_from_all "")
+  endif()
+
+  add_library(${target_name} ${lib_type} ${exclude_from_all} ${ARG_UNPARSED_ARGUMENTS})
+
+  target_link_libraries(${target_name} PRIVATE pybind11::module)
+
+  if(ARG_SYSTEM)
+    message(
+      STATUS
+        "Warning: this does not have an effect - use NO_SYSTEM_FROM_IMPORTED if using imported targets"
+    )
+  endif()
+
+  pybind11_extension(${target_name})
+
+  # -fvisibility=hidden is required to allow multiple modules compiled against
+  # different pybind versions to work properly, and for some features (e.g.
+  # py::module_local).  We force it on everything inside the `pybind11`
+  # namespace; also turning it on for a pybind module compilation here avoids
+  # potential warnings or issues from having mixed hidden/non-hidden types.
+  if(NOT DEFINED CMAKE_CXX_VISIBILITY_PRESET)
+    set_target_properties(${target_name} PROPERTIES CXX_VISIBILITY_PRESET "hidden")
+  endif()
+
+  if(NOT DEFINED CMAKE_CUDA_VISIBILITY_PRESET)
+    set_target_properties(${target_name} PROPERTIES CUDA_VISIBILITY_PRESET "hidden")
+  endif()
+
+  if(ARG_NO_EXTRAS)
+    return()
+  endif()
+
+  if(NOT DEFINED CMAKE_INTERPROCEDURAL_OPTIMIZATION)
+    if(ARG_THIN_LTO)
+      target_link_libraries(${target_name} PRIVATE pybind11::thin_lto)
+    else()
+      target_link_libraries(${target_name} PRIVATE pybind11::lto)
+    endif()
+  endif()
+
+  if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo)
+    pybind11_strip(${target_name})
+  endif()
+
+  if(MSVC)
+    target_link_libraries(${target_name} PRIVATE pybind11::windows_extras)
+  endif()
+
+  if(ARG_OPT_SIZE)
+    target_link_libraries(${target_name} PRIVATE pybind11::opt_size)
+  endif()
+endfunction()
+
+# Provide general way to call common Python commands in "common" file.
+set(_Python
+    PYTHON
+    CACHE INTERNAL "" FORCE)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pyproject.toml b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pyproject.toml
new file mode 100644
index 000000000..8fe2f47af
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/pyproject.toml
@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/setup_global.py.in b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/setup_global.py.in
new file mode 100644
index 000000000..8aa387178
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/setup_global.py.in
@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+
+# Setup script for pybind11-global (in the sdist or in tools/setup_global.py in the repository)
+# This package is targeted for easy use from CMake.
+
+import glob
+import os
+import re
+
+# Setuptools has to be before distutils
+from setuptools import setup
+
+from distutils.command.install_headers import install_headers
+
+class InstallHeadersNested(install_headers):
+    def run(self):
+        headers = self.distribution.headers or []
+        for header in headers:
+            # Remove pybind11/include/
+            short_header = header.split("/", 2)[-1]
+
+            dst = os.path.join(self.install_dir, os.path.dirname(short_header))
+            self.mkpath(dst)
+            (out, _) = self.copy_file(header, dst)
+            self.outfiles.append(out)
+
+
+main_headers = glob.glob("pybind11/include/pybind11/*.h")
+detail_headers = glob.glob("pybind11/include/pybind11/detail/*.h")
+stl_headers = glob.glob("pybind11/include/pybind11/stl/*.h")
+cmake_files = glob.glob("pybind11/share/cmake/pybind11/*.cmake")
+headers = main_headers + detail_headers + stl_headers
+
+cmdclass = {"install_headers": InstallHeadersNested}
+$extra_cmd
+
+# This will _not_ affect installing from wheels,
+# only building wheels or installing from SDist.
+# Primarily intended on Windows, where this is sometimes
+# customized (for example, conda-forge uses Library/)
+base = os.environ.get("PYBIND11_GLOBAL_PREFIX", "")
+
+# Must have a separator
+if base and not base.endswith("/"):
+    base += "/"
+
+setup(
+    name="pybind11_global",
+    version="$version",
+    packages=[],
+    headers=headers,
+    data_files=[
+        (base + "share/cmake/pybind11", cmake_files),
+        (base + "include/pybind11", main_headers),
+        (base + "include/pybind11/detail", detail_headers),
+        (base + "include/pybind11/stl", stl_headers),
+    ],
+    cmdclass=cmdclass,
+)
diff --git a/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/setup_main.py.in b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/setup_main.py.in
new file mode 100644
index 000000000..738d73faa
--- /dev/null
+++ b/cv/3d_detection/pointrcnn-iou/pytorch/spconv/third_party/pybind11/tools/setup_main.py.in
@@ -0,0 +1,40 @@
+#!/usr/bin/env python3
+
+# Setup script (in the sdist or in tools/setup_main.py in the repository)
+
+from setuptools import setup
+
+cmdclass = {}
+$extra_cmd
+
+setup(
+    name="pybind11",
+    version="$version",
+    download_url='https://github.com/pybind/pybind11/tarball/v$version',
+    packages=[
+        "pybind11",
+        "pybind11.include.pybind11",
+        "pybind11.include.pybind11.detail",
+        "pybind11.include.pybind11.stl",
+        "pybind11.share.cmake.pybind11",
+    ],
+    package_data={
+        "pybind11": ["py.typed"],
+        "pybind11.include.pybind11": ["*.h"],
+        "pybind11.include.pybind11.detail": ["*.h"],
+        "pybind11.include.pybind11.stl": ["*.h"],
+        "pybind11.share.cmake.pybind11": ["*.cmake"],
+    },
+    extras_require={
+        "global": ["pybind11_global==$version"]
+        },
+    entry_points={
+        "console_scripts": [
+             "pybind11-config = pybind11.__main__:main",
+        ],
+        "pipx.run": [
+             "pybind11 = pybind11.__main__:main",
+        ]
+    },
+    cmdclass=cmdclass
+)
-- 
Gitee